diff --git "a/exp/log/log-train-2022-06-18-10-29-38-3" "b/exp/log/log-train-2022-06-18-10-29-38-3"
new file mode 100644--- /dev/null
+++ "b/exp/log/log-train-2022-06-18-10-29-38-3"
@@ -0,0 +1,2737 @@
+2022-06-18 10:29:38,930 INFO [train.py:963] (3/4) Training started
+2022-06-18 10:29:38,931 INFO [train.py:973] (3/4) Device: cuda:3
+2022-06-18 10:29:39,275 INFO [lexicon.py:176] (3/4) Loading pre-compiled data/lang_char/Linv.pt
+2022-06-18 10:29:39,310 INFO [train.py:985] (3/4) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 1000, 'feature_dim': 80, 'subsampling_factor': 4, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.15.1', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'f8d2dba06c000ffee36aab5b66f24e7c9809f116', 'k2-git-date': 'Thu Apr 21 12:20:34 2022', 'lhotse-version': '1.3.0.dev+missing.version.file', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'pruned-rnnt-aishell', 'icefall-git-sha1': 'd0a5f1d-dirty', 'icefall-git-date': 'Mon Jun 13 20:40:46 2022', 'icefall-path': '/k2-dev/fangjun/open-source/icefall-aishell', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-22/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-jsonl/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-0616225511-78bf4545d8-tv52r', 'IP address': '10.177.77.9'}, 'world_size': 4, 'master_port': 12356, 'tensorboard': True, 'num_epochs': 30, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('pruned_transducer_stateless3/exp-context-size-1'), 'lang_dir': PosixPath('data/lang_char'), 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 6, 'context_size': 1, 'prune_range': 5, 'lm_scale': 0.25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'save_every_n': 4000, 'keep_last_k': 30, 'average_period': 100, 'use_fp16': True, 'datatang_prob': 0.5, 'num_encoder_layers': 12, 'dim_feedforward': 2048, 'nhead': 8, 'encoder_dim': 512, 'decoder_dim': 512, 'joiner_dim': 512, 'max_duration': 200, 'bucketing_sampler': True, 'num_buckets': 30, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'manifest_dir': PosixPath('data/fbank'), 'on_the_fly_feats': False, 'blank_id': 0, 'vocab_size': 4336}
+2022-06-18 10:29:39,310 INFO [train.py:987] (3/4) About to create model
+2022-06-18 10:29:40,141 INFO [train.py:991] (3/4) Number of model parameters: 96983734
+2022-06-18 10:29:45,845 INFO [train.py:1006] (3/4) Using DDP
+2022-06-18 10:29:46,055 INFO [aishell.py:39] (3/4) About to get train cuts from data/fbank/aishell_cuts_train.jsonl.gz
+2022-06-18 10:29:46,062 INFO [aidatatang_200zh.py:39] (3/4) About to get train cuts from data/fbank/aidatatang_cuts_train.jsonl.gz
+2022-06-18 10:29:48,178 INFO [asr_datamodule.py:163] (3/4) Enable MUSAN
+2022-06-18 10:29:48,178 INFO [asr_datamodule.py:175] (3/4) Enable SpecAugment
+2022-06-18 10:29:48,178 INFO [asr_datamodule.py:176] (3/4) Time warp factor: 80
+2022-06-18 10:29:48,179 INFO [asr_datamodule.py:188] (3/4) Num frame mask: 10
+2022-06-18 10:29:48,179 INFO [asr_datamodule.py:201] (3/4) About to create train dataset
+2022-06-18 10:29:48,179 INFO [asr_datamodule.py:229] (3/4) Using DynamicBucketingSampler.
+2022-06-18 10:29:50,838 INFO [asr_datamodule.py:238] (3/4) About to create train dataloader
+2022-06-18 10:29:50,839 INFO [asr_datamodule.py:163] (3/4) Enable MUSAN
+2022-06-18 10:29:50,839 INFO [asr_datamodule.py:175] (3/4) Enable SpecAugment
+2022-06-18 10:29:50,839 INFO [asr_datamodule.py:176] (3/4) Time warp factor: 80
+2022-06-18 10:29:50,840 INFO [asr_datamodule.py:188] (3/4) Num frame mask: 10
+2022-06-18 10:29:50,840 INFO [asr_datamodule.py:201] (3/4) About to create train dataset
+2022-06-18 10:29:50,840 INFO [asr_datamodule.py:229] (3/4) Using DynamicBucketingSampler.
+2022-06-18 10:29:54,246 INFO [asr_datamodule.py:238] (3/4) About to create train dataloader
+2022-06-18 10:29:54,247 INFO [aishell.py:45] (3/4) About to get valid cuts from data/fbank/aishell_cuts_dev.jsonl.gz
+2022-06-18 10:29:54,250 INFO [asr_datamodule.py:251] (3/4) About to create dev dataset
+2022-06-18 10:29:54,905 INFO [asr_datamodule.py:270] (3/4) About to create dev dataloader
+2022-06-18 10:29:54,905 INFO [train.py:1171] (3/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
+2022-06-18 10:31:15,525 INFO [train.py:1081] (3/4) start training from epoch 1
+2022-06-18 10:32:07,270 INFO [train.py:874] (3/4) Epoch 1, batch 50, aishell_loss[loss=0.4671, simple_loss=0.9341, pruned_loss=9.162, over 4885.00 frames.], tot_loss[loss=1.376, simple_loss=2.753, pruned_loss=8.742, over 218350.43 frames.], batch size: 47, aishell_tot_loss[loss=0.7003, simple_loss=1.401, pruned_loss=8.875, over 115949.09 frames.], datatang_tot_loss[loss=2.11, simple_loss=4.219, pruned_loss=8.621, over 116056.44 frames.], batch size: 47, lr: 3.00e-03
+2022-06-18 10:32:38,304 INFO [train.py:874] (3/4) Epoch 1, batch 100, datatang_loss[loss=0.3704, simple_loss=0.7408, pruned_loss=8.372, over 4889.00 frames.], tot_loss[loss=0.8382, simple_loss=1.676, pruned_loss=8.721, over 388056.82 frames.], batch size: 47, aishell_tot_loss[loss=0.558, simple_loss=1.116, pruned_loss=9.018, over 218017.80 frames.], datatang_tot_loss[loss=1.206, simple_loss=2.411, pruned_loss=8.436, over 218434.23 frames.], batch size: 47, lr: 3.00e-03
+2022-06-18 10:33:05,835 INFO [train.py:874] (3/4) Epoch 1, batch 150, aishell_loss[loss=0.3824, simple_loss=0.7648, pruned_loss=9.109, over 4888.00 frames.], tot_loss[loss=0.6428, simple_loss=1.286, pruned_loss=8.75, over 520469.76 frames.], batch size: 42, aishell_tot_loss[loss=0.4977, simple_loss=0.9955, pruned_loss=9.049, over 304920.44 frames.], datatang_tot_loss[loss=0.8767, simple_loss=1.753, pruned_loss=8.458, over 312219.28 frames.], batch size: 42, lr: 3.00e-03
+2022-06-18 10:33:37,031 INFO [train.py:874] (3/4) Epoch 1, batch 200, datatang_loss[loss=0.3434, simple_loss=0.6868, pruned_loss=8.316, over 4920.00 frames.], tot_loss[loss=0.5429, simple_loss=1.086, pruned_loss=8.771, over 623530.69 frames.], batch size: 77, aishell_tot_loss[loss=0.4551, simple_loss=0.9102, pruned_loss=9.104, over 397042.13 frames.], datatang_tot_loss[loss=0.7373, simple_loss=1.475, pruned_loss=8.406, over 379406.11 frames.], batch size: 77, lr: 3.00e-03
+2022-06-18 10:34:09,003 INFO [train.py:874] (3/4) Epoch 1, batch 250, aishell_loss[loss=0.3717, simple_loss=0.7434, pruned_loss=9.113, over 4887.00 frames.], tot_loss[loss=0.4813, simple_loss=0.9625, pruned_loss=8.734, over 704204.52 frames.], batch size: 47, aishell_tot_loss[loss=0.4314, simple_loss=0.8628, pruned_loss=9.083, over 461166.13 frames.], datatang_tot_loss[loss=0.6277, simple_loss=1.255, pruned_loss=8.389, over 456535.31 frames.], batch size: 47, lr: 3.00e-03
+2022-06-18 10:34:36,765 INFO [train.py:874] (3/4) Epoch 1, batch 300, datatang_loss[loss=0.3237, simple_loss=0.6474, pruned_loss=9.118, over 4941.00 frames.], tot_loss[loss=0.4392, simple_loss=0.8784, pruned_loss=8.774, over 766391.24 frames.], batch size: 88, aishell_tot_loss[loss=0.4133, simple_loss=0.8265, pruned_loss=9.064, over 522910.67 frames.], datatang_tot_loss[loss=0.5584, simple_loss=1.117, pruned_loss=8.467, over 518660.36 frames.], batch size: 88, lr: 3.00e-03
+2022-06-18 10:35:07,022 INFO [train.py:874] (3/4) Epoch 1, batch 350, aishell_loss[loss=0.339, simple_loss=0.678, pruned_loss=8.766, over 4963.00 frames.], tot_loss[loss=0.4063, simple_loss=0.8126, pruned_loss=8.817, over 814840.83 frames.], batch size: 64, aishell_tot_loss[loss=0.3997, simple_loss=0.7995, pruned_loss=9.038, over 568911.74 frames.], datatang_tot_loss[loss=0.4998, simple_loss=0.9996, pruned_loss=8.579, over 581893.50 frames.], batch size: 64, lr: 3.00e-03
+2022-06-18 10:35:37,525 INFO [train.py:874] (3/4) Epoch 1, batch 400, aishell_loss[loss=0.2849, simple_loss=0.5697, pruned_loss=8.925, over 4817.00 frames.], tot_loss[loss=0.3823, simple_loss=0.7647, pruned_loss=8.83, over 852523.06 frames.], batch size: 26, aishell_tot_loss[loss=0.3848, simple_loss=0.7696, pruned_loss=9.011, over 617668.25 frames.], datatang_tot_loss[loss=0.4639, simple_loss=0.9277, pruned_loss=8.629, over 629590.02 frames.], batch size: 26, lr: 3.00e-03
+2022-06-18 10:36:05,232 INFO [train.py:874] (3/4) Epoch 1, batch 450, datatang_loss[loss=0.2742, simple_loss=0.5483, pruned_loss=8.838, over 4843.00 frames.], tot_loss[loss=0.3636, simple_loss=0.7272, pruned_loss=8.836, over 882262.60 frames.], batch size: 30, aishell_tot_loss[loss=0.3756, simple_loss=0.7511, pruned_loss=8.987, over 657783.86 frames.], datatang_tot_loss[loss=0.4316, simple_loss=0.8633, pruned_loss=8.668, over 674857.59 frames.], batch size: 30, lr: 2.99e-03
+2022-06-18 10:36:36,121 INFO [train.py:874] (3/4) Epoch 1, batch 500, aishell_loss[loss=0.3285, simple_loss=0.6569, pruned_loss=8.718, over 4933.00 frames.], tot_loss[loss=0.3509, simple_loss=0.7018, pruned_loss=8.858, over 905372.36 frames.], batch size: 32, aishell_tot_loss[loss=0.3665, simple_loss=0.7329, pruned_loss=8.97, over 700648.98 frames.], datatang_tot_loss[loss=0.4112, simple_loss=0.8223, pruned_loss=8.712, over 707583.00 frames.], batch size: 32, lr: 2.99e-03
+2022-06-18 10:37:05,657 INFO [train.py:874] (3/4) Epoch 1, batch 550, aishell_loss[loss=0.3265, simple_loss=0.653, pruned_loss=8.958, over 4966.00 frames.], tot_loss[loss=0.3403, simple_loss=0.6806, pruned_loss=8.863, over 923010.52 frames.], batch size: 44, aishell_tot_loss[loss=0.3587, simple_loss=0.7175, pruned_loss=8.966, over 735312.06 frames.], datatang_tot_loss[loss=0.3932, simple_loss=0.7864, pruned_loss=8.727, over 739084.63 frames.], batch size: 44, lr: 2.99e-03
+2022-06-18 10:37:34,707 INFO [train.py:874] (3/4) Epoch 1, batch 600, datatang_loss[loss=0.2585, simple_loss=0.517, pruned_loss=8.836, over 4941.00 frames.], tot_loss[loss=0.3322, simple_loss=0.6644, pruned_loss=8.892, over 936825.03 frames.], batch size: 37, aishell_tot_loss[loss=0.3535, simple_loss=0.707, pruned_loss=8.958, over 761277.65 frames.], datatang_tot_loss[loss=0.3766, simple_loss=0.7531, pruned_loss=8.782, over 771469.28 frames.], batch size: 37, lr: 2.99e-03
+2022-06-18 10:38:06,410 INFO [train.py:874] (3/4) Epoch 1, batch 650, aishell_loss[loss=0.306, simple_loss=0.612, pruned_loss=8.902, over 4952.00 frames.], tot_loss[loss=0.3258, simple_loss=0.6516, pruned_loss=8.9, over 947261.56 frames.], batch size: 31, aishell_tot_loss[loss=0.3473, simple_loss=0.6946, pruned_loss=8.949, over 790189.53 frames.], datatang_tot_loss[loss=0.3654, simple_loss=0.7309, pruned_loss=8.804, over 793905.33 frames.], batch size: 31, lr: 2.99e-03
+2022-06-18 10:38:34,795 INFO [train.py:874] (3/4) Epoch 1, batch 700, datatang_loss[loss=0.2791, simple_loss=0.5581, pruned_loss=8.992, over 4961.00 frames.], tot_loss[loss=0.3202, simple_loss=0.6403, pruned_loss=8.908, over 956180.41 frames.], batch size: 45, aishell_tot_loss[loss=0.3431, simple_loss=0.6863, pruned_loss=8.945, over 812428.33 frames.], datatang_tot_loss[loss=0.3538, simple_loss=0.7077, pruned_loss=8.825, over 817687.86 frames.], batch size: 45, lr: 2.99e-03
+2022-06-18 10:39:03,392 INFO [train.py:874] (3/4) Epoch 1, batch 750, aishell_loss[loss=0.3226, simple_loss=0.6451, pruned_loss=9.131, over 4870.00 frames.], tot_loss[loss=0.3161, simple_loss=0.6323, pruned_loss=8.925, over 962460.67 frames.], batch size: 36, aishell_tot_loss[loss=0.3403, simple_loss=0.6805, pruned_loss=8.948, over 831803.51 frames.], datatang_tot_loss[loss=0.3439, simple_loss=0.6878, pruned_loss=8.849, over 838191.33 frames.], batch size: 36, lr: 2.98e-03
+2022-06-18 10:39:33,628 INFO [train.py:874] (3/4) Epoch 1, batch 800, datatang_loss[loss=0.2791, simple_loss=0.5582, pruned_loss=9.134, over 4960.00 frames.], tot_loss[loss=0.3113, simple_loss=0.6225, pruned_loss=8.931, over 967983.16 frames.], batch size: 60, aishell_tot_loss[loss=0.337, simple_loss=0.674, pruned_loss=8.939, over 845994.10 frames.], datatang_tot_loss[loss=0.3343, simple_loss=0.6686, pruned_loss=8.874, over 859611.50 frames.], batch size: 60, lr: 2.98e-03
+2022-06-18 10:40:05,990 INFO [train.py:874] (3/4) Epoch 1, batch 850, datatang_loss[loss=0.2811, simple_loss=0.5622, pruned_loss=8.912, over 4888.00 frames.], tot_loss[loss=0.3071, simple_loss=0.6142, pruned_loss=8.935, over 971503.17 frames.], batch size: 52, aishell_tot_loss[loss=0.3342, simple_loss=0.6684, pruned_loss=8.932, over 859679.45 frames.], datatang_tot_loss[loss=0.3258, simple_loss=0.6517, pruned_loss=8.894, over 876501.30 frames.], batch size: 52, lr: 2.98e-03
+2022-06-18 10:40:33,820 INFO [train.py:874] (3/4) Epoch 1, batch 900, datatang_loss[loss=0.2627, simple_loss=0.5255, pruned_loss=8.988, over 4929.00 frames.], tot_loss[loss=0.3034, simple_loss=0.6068, pruned_loss=8.948, over 974753.12 frames.], batch size: 71, aishell_tot_loss[loss=0.33, simple_loss=0.66, pruned_loss=8.93, over 875083.79 frames.], datatang_tot_loss[loss=0.3196, simple_loss=0.6392, pruned_loss=8.917, over 888976.64 frames.], batch size: 71, lr: 2.98e-03
+2022-06-18 10:41:05,022 INFO [train.py:874] (3/4) Epoch 1, batch 950, datatang_loss[loss=0.2849, simple_loss=0.5698, pruned_loss=9.208, over 4912.00 frames.], tot_loss[loss=0.2991, simple_loss=0.5982, pruned_loss=8.967, over 977383.81 frames.], batch size: 98, aishell_tot_loss[loss=0.328, simple_loss=0.6559, pruned_loss=8.938, over 884105.44 frames.], datatang_tot_loss[loss=0.3117, simple_loss=0.6233, pruned_loss=8.937, over 903939.23 frames.], batch size: 98, lr: 2.97e-03
+2022-06-18 10:41:36,999 INFO [train.py:874] (3/4) Epoch 1, batch 1000, datatang_loss[loss=0.2368, simple_loss=0.4737, pruned_loss=8.946, over 4921.00 frames.], tot_loss[loss=0.295, simple_loss=0.5901, pruned_loss=8.976, over 979326.57 frames.], batch size: 25, aishell_tot_loss[loss=0.3233, simple_loss=0.6467, pruned_loss=8.938, over 896153.45 frames.], datatang_tot_loss[loss=0.3063, simple_loss=0.6125, pruned_loss=8.956, over 913580.44 frames.], batch size: 25, lr: 2.97e-03
+2022-06-18 10:41:36,999 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 10:41:53,017 INFO [train.py:914] (3/4) Epoch 1, validation: loss=9.395, simple_loss=0.6242, pruned_loss=9.083, over 1622729.00 frames.
+2022-06-18 10:42:24,234 INFO [train.py:874] (3/4) Epoch 1, batch 1050, aishell_loss[loss=0.2813, simple_loss=0.5627, pruned_loss=9.208, over 4955.00 frames.], tot_loss[loss=0.2911, simple_loss=0.5821, pruned_loss=8.995, over 980710.05 frames.], batch size: 56, aishell_tot_loss[loss=0.3186, simple_loss=0.6371, pruned_loss=8.946, over 907822.61 frames.], datatang_tot_loss[loss=0.3013, simple_loss=0.6026, pruned_loss=8.977, over 921134.87 frames.], batch size: 56, lr: 2.97e-03
+2022-06-18 10:42:52,112 INFO [train.py:874] (3/4) Epoch 1, batch 1100, datatang_loss[loss=0.2755, simple_loss=0.551, pruned_loss=9.398, over 4951.00 frames.], tot_loss[loss=0.2859, simple_loss=0.5719, pruned_loss=9.025, over 981543.42 frames.], batch size: 99, aishell_tot_loss[loss=0.3136, simple_loss=0.6272, pruned_loss=8.954, over 916614.39 frames.], datatang_tot_loss[loss=0.2957, simple_loss=0.5914, pruned_loss=9.01, over 928828.64 frames.], batch size: 99, lr: 2.96e-03
+2022-06-18 10:43:24,163 INFO [train.py:874] (3/4) Epoch 1, batch 1150, aishell_loss[loss=0.2776, simple_loss=0.5551, pruned_loss=8.983, over 4974.00 frames.], tot_loss[loss=0.2822, simple_loss=0.5644, pruned_loss=9.041, over 982254.12 frames.], batch size: 31, aishell_tot_loss[loss=0.3105, simple_loss=0.621, pruned_loss=8.96, over 924026.89 frames.], datatang_tot_loss[loss=0.2902, simple_loss=0.5803, pruned_loss=9.033, over 935898.27 frames.], batch size: 31, lr: 2.96e-03
+2022-06-18 10:43:56,418 INFO [train.py:874] (3/4) Epoch 1, batch 1200, aishell_loss[loss=0.2759, simple_loss=0.5517, pruned_loss=9.086, over 4958.00 frames.], tot_loss[loss=0.2773, simple_loss=0.5545, pruned_loss=9.046, over 983366.97 frames.], batch size: 61, aishell_tot_loss[loss=0.304, simple_loss=0.608, pruned_loss=8.96, over 932277.93 frames.], datatang_tot_loss[loss=0.2864, simple_loss=0.5727, pruned_loss=9.05, over 941286.70 frames.], batch size: 61, lr: 2.96e-03
+2022-06-18 10:44:24,377 INFO [train.py:874] (3/4) Epoch 1, batch 1250, datatang_loss[loss=0.2522, simple_loss=0.5045, pruned_loss=9.323, over 4915.00 frames.], tot_loss[loss=0.2718, simple_loss=0.5435, pruned_loss=9.063, over 983952.55 frames.], batch size: 98, aishell_tot_loss[loss=0.2983, simple_loss=0.5965, pruned_loss=8.962, over 938213.54 frames.], datatang_tot_loss[loss=0.2817, simple_loss=0.5634, pruned_loss=9.077, over 946916.19 frames.], batch size: 98, lr: 2.95e-03
+2022-06-18 10:44:55,600 INFO [train.py:874] (3/4) Epoch 1, batch 1300, datatang_loss[loss=0.2295, simple_loss=0.459, pruned_loss=9.259, over 4959.00 frames.], tot_loss[loss=0.266, simple_loss=0.5319, pruned_loss=9.075, over 984406.00 frames.], batch size: 67, aishell_tot_loss[loss=0.2926, simple_loss=0.5851, pruned_loss=8.966, over 944003.70 frames.], datatang_tot_loss[loss=0.2765, simple_loss=0.553, pruned_loss=9.097, over 951396.16 frames.], batch size: 67, lr: 2.95e-03
+2022-06-18 10:45:26,754 INFO [train.py:874] (3/4) Epoch 1, batch 1350, aishell_loss[loss=0.2482, simple_loss=0.4963, pruned_loss=8.952, over 4933.00 frames.], tot_loss[loss=0.2599, simple_loss=0.5199, pruned_loss=9.08, over 984429.25 frames.], batch size: 45, aishell_tot_loss[loss=0.2862, simple_loss=0.5724, pruned_loss=8.968, over 948931.59 frames.], datatang_tot_loss[loss=0.2715, simple_loss=0.5431, pruned_loss=9.113, over 955212.30 frames.], batch size: 45, lr: 2.95e-03
+2022-06-18 10:45:54,357 INFO [train.py:874] (3/4) Epoch 1, batch 1400, aishell_loss[loss=0.2603, simple_loss=0.5205, pruned_loss=9.026, over 4969.00 frames.], tot_loss[loss=0.2537, simple_loss=0.5075, pruned_loss=9.088, over 984377.15 frames.], batch size: 51, aishell_tot_loss[loss=0.2803, simple_loss=0.5607, pruned_loss=8.97, over 953087.81 frames.], datatang_tot_loss[loss=0.266, simple_loss=0.532, pruned_loss=9.13, over 958608.94 frames.], batch size: 51, lr: 2.94e-03
+2022-06-18 10:46:26,280 INFO [train.py:874] (3/4) Epoch 1, batch 1450, aishell_loss[loss=0.2023, simple_loss=0.4046, pruned_loss=8.904, over 4958.00 frames.], tot_loss[loss=0.2475, simple_loss=0.495, pruned_loss=9.094, over 984901.86 frames.], batch size: 31, aishell_tot_loss[loss=0.2743, simple_loss=0.5485, pruned_loss=8.969, over 957085.69 frames.], datatang_tot_loss[loss=0.2607, simple_loss=0.5214, pruned_loss=9.146, over 961880.21 frames.], batch size: 31, lr: 2.94e-03
+2022-06-18 10:46:58,053 INFO [train.py:874] (3/4) Epoch 1, batch 1500, datatang_loss[loss=0.2123, simple_loss=0.4246, pruned_loss=9.456, over 4941.00 frames.], tot_loss[loss=0.2418, simple_loss=0.4837, pruned_loss=9.096, over 985176.89 frames.], batch size: 88, aishell_tot_loss[loss=0.2687, simple_loss=0.5375, pruned_loss=8.964, over 960271.35 frames.], datatang_tot_loss[loss=0.2553, simple_loss=0.5106, pruned_loss=9.16, over 964915.09 frames.], batch size: 88, lr: 2.94e-03
+2022-06-18 10:47:26,545 INFO [train.py:874] (3/4) Epoch 1, batch 1550, datatang_loss[loss=0.228, simple_loss=0.4559, pruned_loss=9.337, over 4864.00 frames.], tot_loss[loss=0.2365, simple_loss=0.473, pruned_loss=9.096, over 985168.56 frames.], batch size: 30, aishell_tot_loss[loss=0.2636, simple_loss=0.5273, pruned_loss=8.962, over 963052.05 frames.], datatang_tot_loss[loss=0.2499, simple_loss=0.4998, pruned_loss=9.168, over 967395.80 frames.], batch size: 30, lr: 2.93e-03
+2022-06-18 10:47:58,366 INFO [train.py:874] (3/4) Epoch 1, batch 1600, aishell_loss[loss=0.204, simple_loss=0.408, pruned_loss=8.814, over 4983.00 frames.], tot_loss[loss=0.2323, simple_loss=0.4646, pruned_loss=9.104, over 985283.10 frames.], batch size: 27, aishell_tot_loss[loss=0.2594, simple_loss=0.5188, pruned_loss=8.963, over 965368.55 frames.], datatang_tot_loss[loss=0.2449, simple_loss=0.4899, pruned_loss=9.18, over 969839.07 frames.], batch size: 27, lr: 2.93e-03
+2022-06-18 10:48:31,088 INFO [train.py:874] (3/4) Epoch 1, batch 1650, aishell_loss[loss=0.2514, simple_loss=0.5028, pruned_loss=9.073, over 4929.00 frames.], tot_loss[loss=0.2293, simple_loss=0.4587, pruned_loss=9.098, over 985117.00 frames.], batch size: 78, aishell_tot_loss[loss=0.2552, simple_loss=0.5104, pruned_loss=8.959, over 967581.12 frames.], datatang_tot_loss[loss=0.2411, simple_loss=0.4821, pruned_loss=9.188, over 971647.53 frames.], batch size: 78, lr: 2.92e-03
+2022-06-18 10:48:59,352 INFO [train.py:874] (3/4) Epoch 1, batch 1700, aishell_loss[loss=0.2035, simple_loss=0.4071, pruned_loss=8.977, over 4942.00 frames.], tot_loss[loss=0.2241, simple_loss=0.4481, pruned_loss=9.094, over 985185.62 frames.], batch size: 56, aishell_tot_loss[loss=0.25, simple_loss=0.5001, pruned_loss=8.955, over 969689.02 frames.], datatang_tot_loss[loss=0.2361, simple_loss=0.4721, pruned_loss=9.193, over 973274.50 frames.], batch size: 56, lr: 2.92e-03
+2022-06-18 10:49:32,503 INFO [train.py:874] (3/4) Epoch 1, batch 1750, aishell_loss[loss=0.2244, simple_loss=0.4488, pruned_loss=8.847, over 4942.00 frames.], tot_loss[loss=0.2199, simple_loss=0.4398, pruned_loss=9.098, over 985396.64 frames.], batch size: 49, aishell_tot_loss[loss=0.2468, simple_loss=0.4936, pruned_loss=8.951, over 971247.76 frames.], datatang_tot_loss[loss=0.2307, simple_loss=0.4614, pruned_loss=9.2, over 975051.22 frames.], batch size: 49, lr: 2.91e-03
+2022-06-18 10:50:05,295 INFO [train.py:874] (3/4) Epoch 1, batch 1800, datatang_loss[loss=0.1808, simple_loss=0.3616, pruned_loss=9.12, over 4920.00 frames.], tot_loss[loss=0.217, simple_loss=0.4339, pruned_loss=9.097, over 985406.30 frames.], batch size: 47, aishell_tot_loss[loss=0.2432, simple_loss=0.4865, pruned_loss=8.95, over 972958.52 frames.], datatang_tot_loss[loss=0.2267, simple_loss=0.4533, pruned_loss=9.205, over 976235.46 frames.], batch size: 47, lr: 2.91e-03
+2022-06-18 10:50:33,737 INFO [train.py:874] (3/4) Epoch 1, batch 1850, aishell_loss[loss=0.2053, simple_loss=0.4107, pruned_loss=9.01, over 4863.00 frames.], tot_loss[loss=0.2137, simple_loss=0.4273, pruned_loss=9.097, over 985517.52 frames.], batch size: 35, aishell_tot_loss[loss=0.2396, simple_loss=0.4792, pruned_loss=8.948, over 974120.38 frames.], datatang_tot_loss[loss=0.2228, simple_loss=0.4456, pruned_loss=9.207, over 977685.57 frames.], batch size: 35, lr: 2.91e-03
+2022-06-18 10:51:04,568 INFO [train.py:874] (3/4) Epoch 1, batch 1900, aishell_loss[loss=0.206, simple_loss=0.412, pruned_loss=8.869, over 4943.00 frames.], tot_loss[loss=0.2112, simple_loss=0.4225, pruned_loss=9.085, over 985708.24 frames.], batch size: 64, aishell_tot_loss[loss=0.2353, simple_loss=0.4705, pruned_loss=8.94, over 975620.05 frames.], datatang_tot_loss[loss=0.2203, simple_loss=0.4405, pruned_loss=9.208, over 978657.23 frames.], batch size: 64, lr: 2.90e-03
+2022-06-18 10:51:36,268 INFO [train.py:874] (3/4) Epoch 1, batch 1950, datatang_loss[loss=0.1832, simple_loss=0.3664, pruned_loss=9.139, over 4953.00 frames.], tot_loss[loss=0.2085, simple_loss=0.4171, pruned_loss=9.088, over 985678.66 frames.], batch size: 55, aishell_tot_loss[loss=0.2319, simple_loss=0.4639, pruned_loss=8.94, over 976587.77 frames.], datatang_tot_loss[loss=0.217, simple_loss=0.4339, pruned_loss=9.21, over 979651.65 frames.], batch size: 55, lr: 2.90e-03
+2022-06-18 10:52:03,774 INFO [train.py:874] (3/4) Epoch 1, batch 2000, aishell_loss[loss=0.2073, simple_loss=0.4145, pruned_loss=8.914, over 4892.00 frames.], tot_loss[loss=0.2055, simple_loss=0.4111, pruned_loss=9.084, over 985246.99 frames.], batch size: 50, aishell_tot_loss[loss=0.2278, simple_loss=0.4555, pruned_loss=8.938, over 977319.51 frames.], datatang_tot_loss[loss=0.2142, simple_loss=0.4283, pruned_loss=9.212, over 980258.32 frames.], batch size: 50, lr: 2.89e-03
+2022-06-18 10:52:03,775 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 10:52:20,041 INFO [train.py:914] (3/4) Epoch 1, validation: loss=8.987, simple_loss=0.3601, pruned_loss=8.807, over 1622729.00 frames.
+2022-06-18 10:52:48,825 INFO [train.py:874] (3/4) Epoch 1, batch 2050, datatang_loss[loss=0.1832, simple_loss=0.3663, pruned_loss=9.118, over 4961.00 frames.], tot_loss[loss=0.2037, simple_loss=0.4074, pruned_loss=9.081, over 985443.47 frames.], batch size: 45, aishell_tot_loss[loss=0.2249, simple_loss=0.4499, pruned_loss=8.933, over 978192.37 frames.], datatang_tot_loss[loss=0.2116, simple_loss=0.4232, pruned_loss=9.21, over 981063.35 frames.], batch size: 45, lr: 2.89e-03
+2022-06-18 10:53:19,261 INFO [train.py:874] (3/4) Epoch 1, batch 2100, datatang_loss[loss=0.1932, simple_loss=0.3864, pruned_loss=9.222, over 4935.00 frames.], tot_loss[loss=0.2015, simple_loss=0.403, pruned_loss=9.071, over 985562.84 frames.], batch size: 50, aishell_tot_loss[loss=0.2214, simple_loss=0.4427, pruned_loss=8.928, over 979132.94 frames.], datatang_tot_loss[loss=0.2093, simple_loss=0.4187, pruned_loss=9.208, over 981637.07 frames.], batch size: 50, lr: 2.88e-03
+2022-06-18 10:53:51,140 INFO [train.py:874] (3/4) Epoch 1, batch 2150, aishell_loss[loss=0.2064, simple_loss=0.4128, pruned_loss=8.938, over 4881.00 frames.], tot_loss[loss=0.1999, simple_loss=0.3997, pruned_loss=9.061, over 985630.45 frames.], batch size: 35, aishell_tot_loss[loss=0.219, simple_loss=0.438, pruned_loss=8.917, over 979612.41 frames.], datatang_tot_loss[loss=0.2068, simple_loss=0.4136, pruned_loss=9.205, over 982410.31 frames.], batch size: 35, lr: 2.88e-03
+2022-06-18 10:54:18,547 INFO [train.py:874] (3/4) Epoch 1, batch 2200, aishell_loss[loss=0.2065, simple_loss=0.413, pruned_loss=8.927, over 4968.00 frames.], tot_loss[loss=0.1979, simple_loss=0.3958, pruned_loss=9.052, over 985772.54 frames.], batch size: 61, aishell_tot_loss[loss=0.2161, simple_loss=0.4322, pruned_loss=8.912, over 980385.51 frames.], datatang_tot_loss[loss=0.2045, simple_loss=0.4091, pruned_loss=9.2, over 982864.49 frames.], batch size: 61, lr: 2.87e-03
+2022-06-18 10:54:50,355 INFO [train.py:874] (3/4) Epoch 1, batch 2250, aishell_loss[loss=0.1888, simple_loss=0.3775, pruned_loss=8.845, over 4926.00 frames.], tot_loss[loss=0.1963, simple_loss=0.3925, pruned_loss=9.035, over 985585.08 frames.], batch size: 49, aishell_tot_loss[loss=0.212, simple_loss=0.4239, pruned_loss=8.905, over 980954.56 frames.], datatang_tot_loss[loss=0.2036, simple_loss=0.4072, pruned_loss=9.2, over 983159.13 frames.], batch size: 49, lr: 2.86e-03
+2022-06-18 10:55:21,599 INFO [train.py:874] (3/4) Epoch 1, batch 2300, aishell_loss[loss=0.1974, simple_loss=0.3948, pruned_loss=8.937, over 4919.00 frames.], tot_loss[loss=0.1936, simple_loss=0.3872, pruned_loss=9.027, over 985426.90 frames.], batch size: 33, aishell_tot_loss[loss=0.2086, simple_loss=0.4171, pruned_loss=8.896, over 981379.86 frames.], datatang_tot_loss[loss=0.2014, simple_loss=0.4028, pruned_loss=9.197, over 983416.72 frames.], batch size: 33, lr: 2.86e-03
+2022-06-18 10:55:50,112 INFO [train.py:874] (3/4) Epoch 1, batch 2350, aishell_loss[loss=0.1623, simple_loss=0.3247, pruned_loss=8.828, over 4877.00 frames.], tot_loss[loss=0.1913, simple_loss=0.3826, pruned_loss=9.028, over 985377.80 frames.], batch size: 28, aishell_tot_loss[loss=0.2064, simple_loss=0.4128, pruned_loss=8.89, over 981736.12 frames.], datatang_tot_loss[loss=0.1986, simple_loss=0.3973, pruned_loss=9.194, over 983686.04 frames.], batch size: 28, lr: 2.85e-03
+2022-06-18 10:56:21,666 INFO [train.py:874] (3/4) Epoch 1, batch 2400, datatang_loss[loss=0.194, simple_loss=0.388, pruned_loss=9.183, over 4923.00 frames.], tot_loss[loss=0.1889, simple_loss=0.3777, pruned_loss=9.019, over 984855.23 frames.], batch size: 71, aishell_tot_loss[loss=0.2028, simple_loss=0.4056, pruned_loss=8.881, over 981765.87 frames.], datatang_tot_loss[loss=0.1969, simple_loss=0.3938, pruned_loss=9.193, over 983749.94 frames.], batch size: 71, lr: 2.85e-03
+2022-06-18 10:56:52,254 INFO [train.py:874] (3/4) Epoch 1, batch 2450, datatang_loss[loss=0.1795, simple_loss=0.3591, pruned_loss=9.09, over 4883.00 frames.], tot_loss[loss=0.1886, simple_loss=0.3773, pruned_loss=9.016, over 985144.51 frames.], batch size: 39, aishell_tot_loss[loss=0.2007, simple_loss=0.4013, pruned_loss=8.879, over 982523.08 frames.], datatang_tot_loss[loss=0.1961, simple_loss=0.3922, pruned_loss=9.194, over 983823.76 frames.], batch size: 39, lr: 2.84e-03
+2022-06-18 10:57:21,503 INFO [train.py:874] (3/4) Epoch 1, batch 2500, datatang_loss[loss=0.1888, simple_loss=0.3777, pruned_loss=9.153, over 4958.00 frames.], tot_loss[loss=0.1868, simple_loss=0.3736, pruned_loss=9.018, over 985346.74 frames.], batch size: 45, aishell_tot_loss[loss=0.1989, simple_loss=0.3977, pruned_loss=8.876, over 983034.71 frames.], datatang_tot_loss[loss=0.1938, simple_loss=0.3875, pruned_loss=9.189, over 983954.74 frames.], batch size: 45, lr: 2.84e-03
+2022-06-18 10:57:53,548 INFO [train.py:874] (3/4) Epoch 1, batch 2550, aishell_loss[loss=0.2173, simple_loss=0.4346, pruned_loss=8.773, over 4953.00 frames.], tot_loss[loss=0.1854, simple_loss=0.3707, pruned_loss=9.011, over 985122.56 frames.], batch size: 79, aishell_tot_loss[loss=0.1965, simple_loss=0.393, pruned_loss=8.87, over 983241.06 frames.], datatang_tot_loss[loss=0.1922, simple_loss=0.3844, pruned_loss=9.189, over 983963.40 frames.], batch size: 79, lr: 2.83e-03
+2022-06-18 10:58:22,463 INFO [train.py:874] (3/4) Epoch 1, batch 2600, datatang_loss[loss=0.1481, simple_loss=0.2962, pruned_loss=9.018, over 4946.00 frames.], tot_loss[loss=0.1846, simple_loss=0.3692, pruned_loss=9.008, over 984926.13 frames.], batch size: 45, aishell_tot_loss[loss=0.1945, simple_loss=0.3891, pruned_loss=8.863, over 983291.79 frames.], datatang_tot_loss[loss=0.1912, simple_loss=0.3823, pruned_loss=9.191, over 984070.33 frames.], batch size: 45, lr: 2.83e-03
+2022-06-18 10:58:52,629 INFO [train.py:874] (3/4) Epoch 1, batch 2650, datatang_loss[loss=0.176, simple_loss=0.352, pruned_loss=9.159, over 4925.00 frames.], tot_loss[loss=0.1833, simple_loss=0.3666, pruned_loss=9.012, over 985268.17 frames.], batch size: 77, aishell_tot_loss[loss=0.1934, simple_loss=0.3867, pruned_loss=8.858, over 983615.05 frames.], datatang_tot_loss[loss=0.1891, simple_loss=0.3782, pruned_loss=9.188, over 984382.41 frames.], batch size: 77, lr: 2.82e-03
+2022-06-18 10:59:23,891 INFO [train.py:874] (3/4) Epoch 1, batch 2700, aishell_loss[loss=0.1872, simple_loss=0.3744, pruned_loss=8.806, over 4959.00 frames.], tot_loss[loss=0.1821, simple_loss=0.3642, pruned_loss=9.012, over 985449.54 frames.], batch size: 44, aishell_tot_loss[loss=0.1919, simple_loss=0.3839, pruned_loss=8.852, over 983947.20 frames.], datatang_tot_loss[loss=0.1874, simple_loss=0.3748, pruned_loss=9.191, over 984548.34 frames.], batch size: 44, lr: 2.81e-03
+2022-06-18 10:59:52,390 INFO [train.py:874] (3/4) Epoch 1, batch 2750, aishell_loss[loss=0.1899, simple_loss=0.3797, pruned_loss=8.814, over 4911.00 frames.], tot_loss[loss=0.1798, simple_loss=0.3595, pruned_loss=8.996, over 985699.83 frames.], batch size: 41, aishell_tot_loss[loss=0.1894, simple_loss=0.3789, pruned_loss=8.843, over 984297.30 frames.], datatang_tot_loss[loss=0.1856, simple_loss=0.3711, pruned_loss=9.185, over 984747.16 frames.], batch size: 41, lr: 2.81e-03
+2022-06-18 11:00:23,116 INFO [train.py:874] (3/4) Epoch 1, batch 2800, aishell_loss[loss=0.1484, simple_loss=0.2968, pruned_loss=8.584, over 4985.00 frames.], tot_loss[loss=0.1784, simple_loss=0.3568, pruned_loss=8.985, over 985432.66 frames.], batch size: 27, aishell_tot_loss[loss=0.187, simple_loss=0.3739, pruned_loss=8.831, over 984228.77 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.3695, pruned_loss=9.182, over 984807.59 frames.], batch size: 27, lr: 2.80e-03
+2022-06-18 11:00:54,661 INFO [train.py:874] (3/4) Epoch 1, batch 2850, aishell_loss[loss=0.1751, simple_loss=0.3502, pruned_loss=8.829, over 4864.00 frames.], tot_loss[loss=0.1781, simple_loss=0.3562, pruned_loss=8.977, over 985229.95 frames.], batch size: 36, aishell_tot_loss[loss=0.1856, simple_loss=0.3712, pruned_loss=8.822, over 984413.30 frames.], datatang_tot_loss[loss=0.1841, simple_loss=0.3682, pruned_loss=9.184, over 984630.60 frames.], batch size: 36, lr: 2.80e-03
+2022-06-18 11:01:22,334 INFO [train.py:874] (3/4) Epoch 1, batch 2900, datatang_loss[loss=0.1661, simple_loss=0.3322, pruned_loss=9.109, over 4945.00 frames.], tot_loss[loss=0.178, simple_loss=0.356, pruned_loss=8.983, over 985166.16 frames.], batch size: 62, aishell_tot_loss[loss=0.1848, simple_loss=0.3696, pruned_loss=8.814, over 984284.69 frames.], datatang_tot_loss[loss=0.1832, simple_loss=0.3664, pruned_loss=9.19, over 984862.38 frames.], batch size: 62, lr: 2.79e-03
+2022-06-18 11:01:53,491 INFO [train.py:874] (3/4) Epoch 1, batch 2950, datatang_loss[loss=0.1874, simple_loss=0.3748, pruned_loss=8.939, over 4965.00 frames.], tot_loss[loss=0.1769, simple_loss=0.3538, pruned_loss=8.987, over 985558.72 frames.], batch size: 50, aishell_tot_loss[loss=0.1837, simple_loss=0.3675, pruned_loss=8.81, over 984695.58 frames.], datatang_tot_loss[loss=0.1817, simple_loss=0.3634, pruned_loss=9.187, over 985006.59 frames.], batch size: 50, lr: 2.78e-03
+2022-06-18 11:02:24,476 INFO [train.py:874] (3/4) Epoch 1, batch 3000, aishell_loss[loss=9.024, simple_loss=0.3575, pruned_loss=8.846, over 4952.00 frames.], tot_loss[loss=0.2208, simple_loss=0.3527, pruned_loss=8.989, over 985305.91 frames.], batch size: 56, aishell_tot_loss[loss=0.227, simple_loss=0.3651, pruned_loss=8.805, over 984543.81 frames.], datatang_tot_loss[loss=0.181, simple_loss=0.3619, pruned_loss=9.19, over 985034.70 frames.], batch size: 56, lr: 2.78e-03
+2022-06-18 11:02:24,478 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 11:02:40,212 INFO [train.py:914] (3/4) Epoch 1, validation: loss=4.481, simple_loss=0.3323, pruned_loss=4.315, over 1622729.00 frames.
+2022-06-18 11:03:11,408 INFO [train.py:874] (3/4) Epoch 1, batch 3050, aishell_loss[loss=0.2524, simple_loss=0.343, pruned_loss=0.8096, over 4888.00 frames.], tot_loss[loss=0.2427, simple_loss=0.3635, pruned_loss=7.26, over 985298.00 frames.], batch size: 42, aishell_tot_loss[loss=0.2367, simple_loss=0.3705, pruned_loss=7.707, over 984652.22 frames.], datatang_tot_loss[loss=0.1982, simple_loss=0.3656, pruned_loss=8.467, over 985045.83 frames.], batch size: 42, lr: 2.77e-03
+2022-06-18 11:03:41,606 INFO [train.py:874] (3/4) Epoch 1, batch 3100, datatang_loss[loss=0.2429, simple_loss=0.3612, pruned_loss=0.623, over 4902.00 frames.], tot_loss[loss=0.243, simple_loss=0.359, pruned_loss=5.813, over 984916.64 frames.], batch size: 64, aishell_tot_loss[loss=0.2379, simple_loss=0.3679, pruned_loss=7.012, over 984776.05 frames.], datatang_tot_loss[loss=0.204, simple_loss=0.3624, pruned_loss=7.42, over 984628.64 frames.], batch size: 64, lr: 2.77e-03
+2022-06-18 11:04:10,515 INFO [train.py:874] (3/4) Epoch 1, batch 3150, aishell_loss[loss=0.2243, simple_loss=0.3472, pruned_loss=0.5069, over 4945.00 frames.], tot_loss[loss=0.2387, simple_loss=0.3542, pruned_loss=4.646, over 985066.20 frames.], batch size: 56, aishell_tot_loss[loss=0.2361, simple_loss=0.3635, pruned_loss=6.168, over 984736.51 frames.], datatang_tot_loss[loss=0.206, simple_loss=0.3602, pruned_loss=6.699, over 984874.55 frames.], batch size: 56, lr: 2.76e-03
+2022-06-18 11:04:41,755 INFO [train.py:874] (3/4) Epoch 1, batch 3200, datatang_loss[loss=0.1912, simple_loss=0.3148, pruned_loss=0.3378, over 4963.00 frames.], tot_loss[loss=0.233, simple_loss=0.3512, pruned_loss=3.71, over 985221.42 frames.], batch size: 67, aishell_tot_loss[loss=0.2337, simple_loss=0.3609, pruned_loss=5.571, over 984692.52 frames.], datatang_tot_loss[loss=0.207, simple_loss=0.3579, pruned_loss=5.875, over 985135.31 frames.], batch size: 67, lr: 2.75e-03
+2022-06-18 11:05:11,837 INFO [train.py:874] (3/4) Epoch 1, batch 3250, datatang_loss[loss=0.1899, simple_loss=0.3212, pruned_loss=0.293, over 4967.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3491, pruned_loss=2.966, over 985575.77 frames.], batch size: 86, aishell_tot_loss[loss=0.2314, simple_loss=0.3593, pruned_loss=5.051, over 984945.35 frames.], datatang_tot_loss[loss=0.2065, simple_loss=0.3553, pruned_loss=5.125, over 985337.66 frames.], batch size: 86, lr: 2.75e-03
+2022-06-18 11:05:40,207 INFO [train.py:874] (3/4) Epoch 1, batch 3300, datatang_loss[loss=0.1882, simple_loss=0.3217, pruned_loss=0.2732, over 4914.00 frames.], tot_loss[loss=0.2218, simple_loss=0.3469, pruned_loss=2.382, over 985491.58 frames.], batch size: 75, aishell_tot_loss[loss=0.2286, simple_loss=0.357, pruned_loss=4.541, over 984873.83 frames.], datatang_tot_loss[loss=0.2057, simple_loss=0.3533, pruned_loss=4.514, over 985414.27 frames.], batch size: 75, lr: 2.74e-03
+2022-06-18 11:06:11,141 INFO [train.py:874] (3/4) Epoch 1, batch 3350, datatang_loss[loss=0.1817, simple_loss=0.3099, pruned_loss=0.2679, over 4866.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3444, pruned_loss=1.918, over 985365.50 frames.], batch size: 39, aishell_tot_loss[loss=0.225, simple_loss=0.3551, pruned_loss=3.985, over 984959.11 frames.], datatang_tot_loss[loss=0.2042, simple_loss=0.3508, pruned_loss=4.072, over 985277.35 frames.], batch size: 39, lr: 2.73e-03
+2022-06-18 11:06:40,531 INFO [train.py:874] (3/4) Epoch 1, batch 3400, datatang_loss[loss=0.179, simple_loss=0.3098, pruned_loss=0.241, over 4904.00 frames.], tot_loss[loss=0.2111, simple_loss=0.3421, pruned_loss=1.551, over 985125.65 frames.], batch size: 42, aishell_tot_loss[loss=0.2215, simple_loss=0.3533, pruned_loss=3.516, over 984724.53 frames.], datatang_tot_loss[loss=0.2025, simple_loss=0.348, pruned_loss=3.656, over 985331.18 frames.], batch size: 42, lr: 2.73e-03
+2022-06-18 11:07:10,318 INFO [train.py:874] (3/4) Epoch 1, batch 3450, aishell_loss[loss=0.2201, simple_loss=0.3796, pruned_loss=0.3034, over 4865.00 frames.], tot_loss[loss=0.2083, simple_loss=0.3421, pruned_loss=1.268, over 985233.18 frames.], batch size: 38, aishell_tot_loss[loss=0.2176, simple_loss=0.351, pruned_loss=3.074, over 984646.61 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.3483, pruned_loss=3.321, over 985561.72 frames.], batch size: 38, lr: 2.72e-03
+2022-06-18 11:07:41,584 INFO [train.py:874] (3/4) Epoch 1, batch 3500, aishell_loss[loss=0.1876, simple_loss=0.3303, pruned_loss=0.2247, over 4917.00 frames.], tot_loss[loss=0.2042, simple_loss=0.3398, pruned_loss=1.04, over 985023.66 frames.], batch size: 46, aishell_tot_loss[loss=0.214, simple_loss=0.3479, pruned_loss=2.764, over 984526.47 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.3471, pruned_loss=2.933, over 985488.28 frames.], batch size: 46, lr: 2.72e-03
+2022-06-18 11:08:10,122 INFO [train.py:874] (3/4) Epoch 1, batch 3550, aishell_loss[loss=0.2116, simple_loss=0.3684, pruned_loss=0.2737, over 4878.00 frames.], tot_loss[loss=0.2006, simple_loss=0.3378, pruned_loss=0.8596, over 985198.36 frames.], batch size: 35, aishell_tot_loss[loss=0.2107, simple_loss=0.3461, pruned_loss=2.431, over 984615.99 frames.], datatang_tot_loss[loss=0.2003, simple_loss=0.345, pruned_loss=2.651, over 985595.99 frames.], batch size: 35, lr: 2.71e-03
+2022-06-18 11:08:41,149 INFO [train.py:874] (3/4) Epoch 1, batch 3600, datatang_loss[loss=0.1941, simple_loss=0.3408, pruned_loss=0.2371, over 4919.00 frames.], tot_loss[loss=0.1983, simple_loss=0.3372, pruned_loss=0.719, over 985599.75 frames.], batch size: 83, aishell_tot_loss[loss=0.209, simple_loss=0.3454, pruned_loss=2.23, over 984680.96 frames.], datatang_tot_loss[loss=0.1987, simple_loss=0.3433, pruned_loss=2.301, over 985945.39 frames.], batch size: 83, lr: 2.70e-03
+2022-06-18 11:09:13,100 INFO [train.py:874] (3/4) Epoch 1, batch 3650, datatang_loss[loss=0.1657, simple_loss=0.2944, pruned_loss=0.1847, over 4945.00 frames.], tot_loss[loss=0.1955, simple_loss=0.3354, pruned_loss=0.6067, over 985764.00 frames.], batch size: 62, aishell_tot_loss[loss=0.2071, simple_loss=0.3446, pruned_loss=2.038, over 984709.80 frames.], datatang_tot_loss[loss=0.1965, simple_loss=0.3406, pruned_loss=2.009, over 986109.86 frames.], batch size: 62, lr: 2.70e-03
+2022-06-18 11:09:42,021 INFO [train.py:874] (3/4) Epoch 1, batch 3700, aishell_loss[loss=0.1746, simple_loss=0.313, pruned_loss=0.1812, over 4961.00 frames.], tot_loss[loss=0.1948, simple_loss=0.3364, pruned_loss=0.5219, over 985395.71 frames.], batch size: 56, aishell_tot_loss[loss=0.2059, simple_loss=0.345, pruned_loss=1.832, over 984797.45 frames.], datatang_tot_loss[loss=0.1955, simple_loss=0.3395, pruned_loss=1.792, over 985695.41 frames.], batch size: 56, lr: 2.69e-03
+2022-06-18 11:10:11,837 INFO [train.py:874] (3/4) Epoch 1, batch 3750, datatang_loss[loss=0.1676, simple_loss=0.2989, pruned_loss=0.1817, over 4921.00 frames.], tot_loss[loss=0.1927, simple_loss=0.3349, pruned_loss=0.4519, over 985713.76 frames.], batch size: 73, aishell_tot_loss[loss=0.2045, simple_loss=0.3446, pruned_loss=1.676, over 984956.95 frames.], datatang_tot_loss[loss=0.1936, simple_loss=0.3371, pruned_loss=1.571, over 985878.28 frames.], batch size: 73, lr: 2.68e-03
+2022-06-18 11:10:43,271 INFO [train.py:874] (3/4) Epoch 1, batch 3800, datatang_loss[loss=0.1977, simple_loss=0.3494, pruned_loss=0.2305, over 4933.00 frames.], tot_loss[loss=0.1917, simple_loss=0.3344, pruned_loss=0.4001, over 985936.60 frames.], batch size: 79, aishell_tot_loss[loss=0.203, simple_loss=0.3436, pruned_loss=1.532, over 985118.15 frames.], datatang_tot_loss[loss=0.1927, simple_loss=0.3364, pruned_loss=1.387, over 986001.91 frames.], batch size: 79, lr: 2.68e-03
+2022-06-18 11:11:11,671 INFO [train.py:874] (3/4) Epoch 1, batch 3850, datatang_loss[loss=0.1858, simple_loss=0.3269, pruned_loss=0.2235, over 4928.00 frames.], tot_loss[loss=0.1892, simple_loss=0.3317, pruned_loss=0.3544, over 985684.56 frames.], batch size: 69, aishell_tot_loss[loss=0.2, simple_loss=0.3408, pruned_loss=1.361, over 985040.08 frames.], datatang_tot_loss[loss=0.1916, simple_loss=0.335, pruned_loss=1.258, over 985909.81 frames.], batch size: 69, lr: 2.67e-03
+2022-06-18 11:11:41,842 INFO [train.py:874] (3/4) Epoch 1, batch 3900, datatang_loss[loss=0.1783, simple_loss=0.3168, pruned_loss=0.1993, over 4931.00 frames.], tot_loss[loss=0.1881, simple_loss=0.331, pruned_loss=0.3202, over 985701.31 frames.], batch size: 71, aishell_tot_loss[loss=0.1985, simple_loss=0.3398, pruned_loss=1.244, over 985096.28 frames.], datatang_tot_loss[loss=0.1906, simple_loss=0.3338, pruned_loss=1.116, over 985917.38 frames.], batch size: 71, lr: 2.66e-03
+2022-06-18 11:12:10,051 INFO [train.py:874] (3/4) Epoch 1, batch 3950, aishell_loss[loss=0.1911, simple_loss=0.3437, pruned_loss=0.1929, over 4887.00 frames.], tot_loss[loss=0.1877, simple_loss=0.3315, pruned_loss=0.2926, over 985553.43 frames.], batch size: 34, aishell_tot_loss[loss=0.197, simple_loss=0.3394, pruned_loss=1.097, over 985155.56 frames.], datatang_tot_loss[loss=0.19, simple_loss=0.3333, pruned_loss=1.029, over 985766.42 frames.], batch size: 34, lr: 2.66e-03
+2022-06-18 11:12:39,499 INFO [train.py:874] (3/4) Epoch 1, batch 4000, datatang_loss[loss=0.1838, simple_loss=0.3309, pruned_loss=0.1836, over 4956.00 frames.], tot_loss[loss=0.1863, simple_loss=0.3303, pruned_loss=0.2687, over 985591.69 frames.], batch size: 86, aishell_tot_loss[loss=0.1954, simple_loss=0.3387, pruned_loss=0.981, over 985097.00 frames.], datatang_tot_loss[loss=0.1886, simple_loss=0.3316, pruned_loss=0.9375, over 985888.28 frames.], batch size: 86, lr: 2.65e-03
+2022-06-18 11:12:39,500 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 11:13:00,454 INFO [train.py:914] (3/4) Epoch 1, validation: loss=0.2503, simple_loss=0.2882, pruned_loss=0.1062, over 1622729.00 frames.
+2022-06-18 11:13:28,281 INFO [train.py:874] (3/4) Epoch 1, batch 4050, aishell_loss[loss=0.1893, simple_loss=0.341, pruned_loss=0.188, over 4950.00 frames.], tot_loss[loss=0.186, simple_loss=0.3305, pruned_loss=0.2521, over 985894.91 frames.], batch size: 45, aishell_tot_loss[loss=0.1947, simple_loss=0.339, pruned_loss=0.8875, over 985319.11 frames.], datatang_tot_loss[loss=0.1876, simple_loss=0.3302, pruned_loss=0.8504, over 986003.11 frames.], batch size: 45, lr: 2.64e-03
+2022-06-18 11:13:55,349 INFO [train.py:874] (3/4) Epoch 1, batch 4100, aishell_loss[loss=0.1784, simple_loss=0.3248, pruned_loss=0.1599, over 4893.00 frames.], tot_loss[loss=0.1845, simple_loss=0.3286, pruned_loss=0.2367, over 985757.84 frames.], batch size: 50, aishell_tot_loss[loss=0.193, simple_loss=0.3374, pruned_loss=0.8003, over 985138.82 frames.], datatang_tot_loss[loss=0.1866, simple_loss=0.3289, pruned_loss=0.7758, over 986104.74 frames.], batch size: 50, lr: 2.64e-03
+2022-06-18 11:14:25,518 INFO [train.py:874] (3/4) Epoch 1, batch 4150, aishell_loss[loss=0.2122, simple_loss=0.3789, pruned_loss=0.2279, over 4858.00 frames.], tot_loss[loss=0.1848, simple_loss=0.3291, pruned_loss=0.2294, over 985791.89 frames.], batch size: 36, aishell_tot_loss[loss=0.1926, simple_loss=0.3375, pruned_loss=0.736, over 985231.37 frames.], datatang_tot_loss[loss=0.1861, simple_loss=0.3284, pruned_loss=0.7026, over 986086.64 frames.], batch size: 36, lr: 2.63e-03
+2022-06-18 11:14:54,792 INFO [train.py:874] (3/4) Epoch 1, batch 4200, datatang_loss[loss=0.1807, simple_loss=0.3233, pruned_loss=0.191, over 4860.00 frames.], tot_loss[loss=0.1838, simple_loss=0.3281, pruned_loss=0.2187, over 985038.48 frames.], batch size: 39, aishell_tot_loss[loss=0.1912, simple_loss=0.336, pruned_loss=0.6772, over 984515.10 frames.], datatang_tot_loss[loss=0.1855, simple_loss=0.3281, pruned_loss=0.6355, over 986016.84 frames.], batch size: 39, lr: 2.63e-03
+2022-06-18 11:16:16,084 INFO [train.py:874] (3/4) Epoch 2, batch 50, datatang_loss[loss=0.1558, simple_loss=0.2799, pruned_loss=0.1586, over 4844.00 frames.], tot_loss[loss=0.1728, simple_loss=0.3122, pruned_loss=0.167, over 218730.44 frames.], batch size: 30, aishell_tot_loss[loss=0.1742, simple_loss=0.3169, pruned_loss=0.1574, over 120568.75 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.3072, pruned_loss=0.1768, over 111834.78 frames.], batch size: 30, lr: 2.60e-03
+2022-06-18 11:16:44,621 INFO [train.py:874] (3/4) Epoch 2, batch 100, datatang_loss[loss=0.1809, simple_loss=0.325, pruned_loss=0.1835, over 4955.00 frames.], tot_loss[loss=0.174, simple_loss=0.3142, pruned_loss=0.1694, over 389034.25 frames.], batch size: 86, aishell_tot_loss[loss=0.1772, simple_loss=0.3217, pruned_loss=0.1629, over 237780.05 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.3045, pruned_loss=0.1767, over 199252.70 frames.], batch size: 86, lr: 2.59e-03
+2022-06-18 11:17:15,592 INFO [train.py:874] (3/4) Epoch 2, batch 150, datatang_loss[loss=0.1545, simple_loss=0.2829, pruned_loss=0.1312, over 4926.00 frames.], tot_loss[loss=0.1753, simple_loss=0.3166, pruned_loss=0.1706, over 521444.09 frames.], batch size: 79, aishell_tot_loss[loss=0.1781, simple_loss=0.3231, pruned_loss=0.1656, over 342146.08 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.307, pruned_loss=0.1765, over 274483.75 frames.], batch size: 79, lr: 2.58e-03
+2022-06-18 11:17:45,667 INFO [train.py:874] (3/4) Epoch 2, batch 200, aishell_loss[loss=0.1772, simple_loss=0.3219, pruned_loss=0.1628, over 4865.00 frames.], tot_loss[loss=0.1757, simple_loss=0.3174, pruned_loss=0.1698, over 624523.15 frames.], batch size: 36, aishell_tot_loss[loss=0.1776, simple_loss=0.3225, pruned_loss=0.1635, over 423607.05 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.31, pruned_loss=0.1774, over 352114.78 frames.], batch size: 36, lr: 2.58e-03
+2022-06-18 11:18:14,342 INFO [train.py:874] (3/4) Epoch 2, batch 250, datatang_loss[loss=0.1765, simple_loss=0.3161, pruned_loss=0.1842, over 4904.00 frames.], tot_loss[loss=0.1739, simple_loss=0.3147, pruned_loss=0.166, over 704744.74 frames.], batch size: 42, aishell_tot_loss[loss=0.1761, simple_loss=0.3203, pruned_loss=0.1597, over 487455.08 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.3085, pruned_loss=0.1747, over 429553.47 frames.], batch size: 42, lr: 2.57e-03
+2022-06-18 11:18:45,589 INFO [train.py:874] (3/4) Epoch 2, batch 300, datatang_loss[loss=0.1922, simple_loss=0.3473, pruned_loss=0.1858, over 4928.00 frames.], tot_loss[loss=0.1735, simple_loss=0.3142, pruned_loss=0.1646, over 767054.66 frames.], batch size: 94, aishell_tot_loss[loss=0.1758, simple_loss=0.3199, pruned_loss=0.1583, over 552642.96 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.308, pruned_loss=0.1738, over 487701.91 frames.], batch size: 94, lr: 2.57e-03
+2022-06-18 11:19:14,251 INFO [train.py:874] (3/4) Epoch 2, batch 350, aishell_loss[loss=0.1539, simple_loss=0.2848, pruned_loss=0.1152, over 4868.00 frames.], tot_loss[loss=0.1739, simple_loss=0.3146, pruned_loss=0.1657, over 815340.77 frames.], batch size: 36, aishell_tot_loss[loss=0.1753, simple_loss=0.3189, pruned_loss=0.1585, over 610997.15 frames.], datatang_tot_loss[loss=0.1723, simple_loss=0.3096, pruned_loss=0.1754, over 537496.91 frames.], batch size: 36, lr: 2.56e-03
+2022-06-18 11:19:44,672 INFO [train.py:874] (3/4) Epoch 2, batch 400, datatang_loss[loss=0.1763, simple_loss=0.3172, pruned_loss=0.1766, over 4925.00 frames.], tot_loss[loss=0.1738, simple_loss=0.3146, pruned_loss=0.1651, over 852853.28 frames.], batch size: 81, aishell_tot_loss[loss=0.175, simple_loss=0.3186, pruned_loss=0.1573, over 648319.83 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.3103, pruned_loss=0.1746, over 597988.05 frames.], batch size: 81, lr: 2.55e-03
+2022-06-18 11:20:15,823 INFO [train.py:874] (3/4) Epoch 2, batch 450, aishell_loss[loss=0.1742, simple_loss=0.3187, pruned_loss=0.1485, over 4905.00 frames.], tot_loss[loss=0.1722, simple_loss=0.3121, pruned_loss=0.1615, over 882151.89 frames.], batch size: 52, aishell_tot_loss[loss=0.1742, simple_loss=0.3173, pruned_loss=0.1555, over 692212.63 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.3077, pruned_loss=0.1713, over 638603.73 frames.], batch size: 52, lr: 2.55e-03
+2022-06-18 11:20:44,505 INFO [train.py:874] (3/4) Epoch 2, batch 500, datatang_loss[loss=0.1423, simple_loss=0.2587, pruned_loss=0.1297, over 4838.00 frames.], tot_loss[loss=0.1716, simple_loss=0.3112, pruned_loss=0.1597, over 904780.09 frames.], batch size: 30, aishell_tot_loss[loss=0.1736, simple_loss=0.3165, pruned_loss=0.1541, over 730290.46 frames.], datatang_tot_loss[loss=0.1705, simple_loss=0.3071, pruned_loss=0.1698, over 674857.17 frames.], batch size: 30, lr: 2.54e-03
+2022-06-18 11:21:14,603 INFO [train.py:874] (3/4) Epoch 2, batch 550, datatang_loss[loss=0.1575, simple_loss=0.2859, pruned_loss=0.1451, over 4927.00 frames.], tot_loss[loss=0.172, simple_loss=0.312, pruned_loss=0.1599, over 922728.60 frames.], batch size: 50, aishell_tot_loss[loss=0.1732, simple_loss=0.3158, pruned_loss=0.1529, over 759174.06 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.3087, pruned_loss=0.1706, over 712902.16 frames.], batch size: 50, lr: 2.53e-03
+2022-06-18 11:21:45,627 INFO [train.py:874] (3/4) Epoch 2, batch 600, datatang_loss[loss=0.1632, simple_loss=0.2956, pruned_loss=0.1536, over 4914.00 frames.], tot_loss[loss=0.1727, simple_loss=0.3128, pruned_loss=0.1627, over 936355.94 frames.], batch size: 47, aishell_tot_loss[loss=0.1732, simple_loss=0.3153, pruned_loss=0.1552, over 782600.43 frames.], datatang_tot_loss[loss=0.1723, simple_loss=0.3104, pruned_loss=0.1713, over 748492.86 frames.], batch size: 47, lr: 2.53e-03
+2022-06-18 11:22:14,863 INFO [train.py:874] (3/4) Epoch 2, batch 650, aishell_loss[loss=0.1724, simple_loss=0.3111, pruned_loss=0.168, over 4952.00 frames.], tot_loss[loss=0.1725, simple_loss=0.3126, pruned_loss=0.1615, over 947279.35 frames.], batch size: 25, aishell_tot_loss[loss=0.1731, simple_loss=0.3155, pruned_loss=0.154, over 805584.03 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.31, pruned_loss=0.1707, over 777487.25 frames.], batch size: 25, lr: 2.52e-03
+2022-06-18 11:22:45,400 INFO [train.py:874] (3/4) Epoch 2, batch 700, datatang_loss[loss=0.159, simple_loss=0.2899, pruned_loss=0.1408, over 4974.00 frames.], tot_loss[loss=0.1728, simple_loss=0.3135, pruned_loss=0.1606, over 955889.41 frames.], batch size: 45, aishell_tot_loss[loss=0.173, simple_loss=0.3155, pruned_loss=0.1529, over 826764.09 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.3111, pruned_loss=0.1704, over 802144.63 frames.], batch size: 45, lr: 2.51e-03
+2022-06-18 11:23:15,166 INFO [train.py:874] (3/4) Epoch 2, batch 750, aishell_loss[loss=0.1564, simple_loss=0.2885, pruned_loss=0.1212, over 4952.00 frames.], tot_loss[loss=0.1716, simple_loss=0.3117, pruned_loss=0.1574, over 961667.61 frames.], batch size: 40, aishell_tot_loss[loss=0.1724, simple_loss=0.3146, pruned_loss=0.1511, over 846287.87 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.3098, pruned_loss=0.1683, over 821842.53 frames.], batch size: 40, lr: 2.51e-03
+2022-06-18 11:23:44,136 INFO [train.py:874] (3/4) Epoch 2, batch 800, aishell_loss[loss=0.1838, simple_loss=0.338, pruned_loss=0.1486, over 4958.00 frames.], tot_loss[loss=0.1734, simple_loss=0.3145, pruned_loss=0.1617, over 967202.83 frames.], batch size: 61, aishell_tot_loss[loss=0.173, simple_loss=0.3158, pruned_loss=0.1516, over 860327.85 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.3119, pruned_loss=0.1719, over 844110.75 frames.], batch size: 61, lr: 2.50e-03
+2022-06-18 11:24:15,022 INFO [train.py:874] (3/4) Epoch 2, batch 850, datatang_loss[loss=0.1863, simple_loss=0.3339, pruned_loss=0.1934, over 4920.00 frames.], tot_loss[loss=0.173, simple_loss=0.3139, pruned_loss=0.1604, over 971399.16 frames.], batch size: 83, aishell_tot_loss[loss=0.1725, simple_loss=0.3149, pruned_loss=0.1505, over 873585.97 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.3124, pruned_loss=0.1713, over 862590.34 frames.], batch size: 83, lr: 2.50e-03
+2022-06-18 11:24:45,399 INFO [train.py:874] (3/4) Epoch 2, batch 900, aishell_loss[loss=0.191, simple_loss=0.3507, pruned_loss=0.1565, over 4942.00 frames.], tot_loss[loss=0.1724, simple_loss=0.3133, pruned_loss=0.1579, over 974424.78 frames.], batch size: 54, aishell_tot_loss[loss=0.1725, simple_loss=0.3151, pruned_loss=0.1494, over 887163.88 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.3115, pruned_loss=0.1694, over 876508.88 frames.], batch size: 54, lr: 2.49e-03
+2022-06-18 11:25:13,890 INFO [train.py:874] (3/4) Epoch 2, batch 950, aishell_loss[loss=0.1817, simple_loss=0.3308, pruned_loss=0.1636, over 4918.00 frames.], tot_loss[loss=0.1729, simple_loss=0.314, pruned_loss=0.1592, over 976782.01 frames.], batch size: 68, aishell_tot_loss[loss=0.1725, simple_loss=0.315, pruned_loss=0.1502, over 898601.38 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.3124, pruned_loss=0.1696, over 889373.14 frames.], batch size: 68, lr: 2.48e-03
+2022-06-18 11:25:45,489 INFO [train.py:874] (3/4) Epoch 2, batch 1000, datatang_loss[loss=0.2144, simple_loss=0.3817, pruned_loss=0.2351, over 4931.00 frames.], tot_loss[loss=0.1739, simple_loss=0.3159, pruned_loss=0.1599, over 978876.51 frames.], batch size: 108, aishell_tot_loss[loss=0.1731, simple_loss=0.316, pruned_loss=0.1506, over 907642.72 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.3136, pruned_loss=0.1696, over 902177.85 frames.], batch size: 108, lr: 2.48e-03
+2022-06-18 11:25:45,490 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 11:26:02,501 INFO [train.py:914] (3/4) Epoch 2, validation: loss=0.2333, simple_loss=0.2891, pruned_loss=0.08878, over 1622729.00 frames.
+2022-06-18 11:26:32,165 INFO [train.py:874] (3/4) Epoch 2, batch 1050, aishell_loss[loss=0.1639, simple_loss=0.3019, pruned_loss=0.1291, over 4875.00 frames.], tot_loss[loss=0.173, simple_loss=0.3146, pruned_loss=0.1568, over 979909.78 frames.], batch size: 42, aishell_tot_loss[loss=0.1725, simple_loss=0.3152, pruned_loss=0.1487, over 917744.13 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.3132, pruned_loss=0.1683, over 910475.58 frames.], batch size: 42, lr: 2.47e-03
+2022-06-18 11:27:03,448 INFO [train.py:874] (3/4) Epoch 2, batch 1100, datatang_loss[loss=0.1614, simple_loss=0.2933, pruned_loss=0.1477, over 4940.00 frames.], tot_loss[loss=0.1727, simple_loss=0.3141, pruned_loss=0.1558, over 980526.32 frames.], batch size: 79, aishell_tot_loss[loss=0.1722, simple_loss=0.315, pruned_loss=0.1475, over 925051.91 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.3131, pruned_loss=0.1679, over 919369.32 frames.], batch size: 79, lr: 2.46e-03
+2022-06-18 11:27:31,730 INFO [train.py:874] (3/4) Epoch 2, batch 1150, aishell_loss[loss=0.1636, simple_loss=0.3021, pruned_loss=0.1256, over 4943.00 frames.], tot_loss[loss=0.1723, simple_loss=0.3134, pruned_loss=0.1554, over 981687.34 frames.], batch size: 58, aishell_tot_loss[loss=0.1718, simple_loss=0.3142, pruned_loss=0.1466, over 933050.83 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.3131, pruned_loss=0.1682, over 926264.33 frames.], batch size: 58, lr: 2.46e-03
+2022-06-18 11:28:02,584 INFO [train.py:874] (3/4) Epoch 2, batch 1200, aishell_loss[loss=0.1631, simple_loss=0.3017, pruned_loss=0.1222, over 4912.00 frames.], tot_loss[loss=0.1707, simple_loss=0.3109, pruned_loss=0.1527, over 982171.59 frames.], batch size: 41, aishell_tot_loss[loss=0.1707, simple_loss=0.3125, pruned_loss=0.1446, over 938506.77 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.312, pruned_loss=0.1665, over 933651.62 frames.], batch size: 41, lr: 2.45e-03
+2022-06-18 11:28:34,043 INFO [train.py:874] (3/4) Epoch 2, batch 1250, datatang_loss[loss=0.1597, simple_loss=0.2872, pruned_loss=0.1614, over 4953.00 frames.], tot_loss[loss=0.1714, simple_loss=0.3113, pruned_loss=0.1571, over 982760.21 frames.], batch size: 50, aishell_tot_loss[loss=0.1707, simple_loss=0.3119, pruned_loss=0.1469, over 943923.46 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.3126, pruned_loss=0.1682, over 939741.60 frames.], batch size: 50, lr: 2.45e-03
+2022-06-18 11:29:03,429 INFO [train.py:874] (3/4) Epoch 2, batch 1300, aishell_loss[loss=0.1587, simple_loss=0.2941, pruned_loss=0.1161, over 4809.00 frames.], tot_loss[loss=0.1705, simple_loss=0.3102, pruned_loss=0.1543, over 983049.10 frames.], batch size: 26, aishell_tot_loss[loss=0.1707, simple_loss=0.3123, pruned_loss=0.1462, over 947611.38 frames.], datatang_tot_loss[loss=0.1719, simple_loss=0.3108, pruned_loss=0.1653, over 946069.55 frames.], batch size: 26, lr: 2.44e-03
+2022-06-18 11:29:33,677 INFO [train.py:874] (3/4) Epoch 2, batch 1350, datatang_loss[loss=0.1596, simple_loss=0.2917, pruned_loss=0.1377, over 4931.00 frames.], tot_loss[loss=0.1699, simple_loss=0.3095, pruned_loss=0.1514, over 983545.18 frames.], batch size: 71, aishell_tot_loss[loss=0.1706, simple_loss=0.3123, pruned_loss=0.1447, over 952133.59 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.3097, pruned_loss=0.1633, over 950597.23 frames.], batch size: 71, lr: 2.43e-03
+2022-06-18 11:30:05,374 INFO [train.py:874] (3/4) Epoch 2, batch 1400, aishell_loss[loss=0.1696, simple_loss=0.3133, pruned_loss=0.1293, over 4967.00 frames.], tot_loss[loss=0.1699, simple_loss=0.3097, pruned_loss=0.1503, over 984055.97 frames.], batch size: 40, aishell_tot_loss[loss=0.1704, simple_loss=0.3121, pruned_loss=0.1434, over 955958.92 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.3098, pruned_loss=0.1628, over 954889.91 frames.], batch size: 40, lr: 2.43e-03
+2022-06-18 11:30:32,999 INFO [train.py:874] (3/4) Epoch 2, batch 1450, aishell_loss[loss=0.1617, simple_loss=0.2991, pruned_loss=0.1215, over 4882.00 frames.], tot_loss[loss=0.1699, simple_loss=0.3098, pruned_loss=0.1502, over 984319.41 frames.], batch size: 42, aishell_tot_loss[loss=0.1702, simple_loss=0.3118, pruned_loss=0.1427, over 959330.71 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.3099, pruned_loss=0.1629, over 958541.69 frames.], batch size: 42, lr: 2.42e-03
+2022-06-18 11:31:04,403 INFO [train.py:874] (3/4) Epoch 2, batch 1500, aishell_loss[loss=0.1837, simple_loss=0.3348, pruned_loss=0.1631, over 4933.00 frames.], tot_loss[loss=0.1695, simple_loss=0.3093, pruned_loss=0.1488, over 985036.94 frames.], batch size: 54, aishell_tot_loss[loss=0.1699, simple_loss=0.3115, pruned_loss=0.1418, over 962483.89 frames.], datatang_tot_loss[loss=0.1708, simple_loss=0.3094, pruned_loss=0.1614, over 962140.94 frames.], batch size: 54, lr: 2.42e-03
+2022-06-18 11:31:35,327 INFO [train.py:874] (3/4) Epoch 2, batch 1550, datatang_loss[loss=0.1678, simple_loss=0.3074, pruned_loss=0.1413, over 4929.00 frames.], tot_loss[loss=0.1689, simple_loss=0.3082, pruned_loss=0.1473, over 985407.84 frames.], batch size: 88, aishell_tot_loss[loss=0.1699, simple_loss=0.3114, pruned_loss=0.1414, over 965271.30 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.308, pruned_loss=0.1594, over 965097.47 frames.], batch size: 88, lr: 2.41e-03
+2022-06-18 11:32:03,504 INFO [train.py:874] (3/4) Epoch 2, batch 1600, aishell_loss[loss=0.1567, simple_loss=0.29, pruned_loss=0.1173, over 4914.00 frames.], tot_loss[loss=0.1685, simple_loss=0.3078, pruned_loss=0.146, over 985196.72 frames.], batch size: 52, aishell_tot_loss[loss=0.1696, simple_loss=0.3111, pruned_loss=0.1405, over 967530.63 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.3075, pruned_loss=0.1582, over 967377.46 frames.], batch size: 52, lr: 2.40e-03
+2022-06-18 11:32:34,664 INFO [train.py:874] (3/4) Epoch 2, batch 1650, aishell_loss[loss=0.1577, simple_loss=0.2935, pruned_loss=0.1098, over 4966.00 frames.], tot_loss[loss=0.1687, simple_loss=0.3081, pruned_loss=0.1461, over 985494.31 frames.], batch size: 31, aishell_tot_loss[loss=0.1692, simple_loss=0.3104, pruned_loss=0.1395, over 970317.02 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.3081, pruned_loss=0.1591, over 969064.12 frames.], batch size: 31, lr: 2.40e-03
+2022-06-18 11:33:04,839 INFO [train.py:874] (3/4) Epoch 2, batch 1700, aishell_loss[loss=0.1759, simple_loss=0.3273, pruned_loss=0.1223, over 4873.00 frames.], tot_loss[loss=0.1677, simple_loss=0.3066, pruned_loss=0.1438, over 985571.35 frames.], batch size: 42, aishell_tot_loss[loss=0.1685, simple_loss=0.3095, pruned_loss=0.1376, over 972455.60 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.3072, pruned_loss=0.1581, over 970735.88 frames.], batch size: 42, lr: 2.39e-03
+2022-06-18 11:33:33,414 INFO [train.py:874] (3/4) Epoch 2, batch 1750, datatang_loss[loss=0.1619, simple_loss=0.2944, pruned_loss=0.1476, over 4939.00 frames.], tot_loss[loss=0.168, simple_loss=0.3073, pruned_loss=0.1437, over 985738.50 frames.], batch size: 62, aishell_tot_loss[loss=0.168, simple_loss=0.3087, pruned_loss=0.1362, over 973923.44 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.3083, pruned_loss=0.1583, over 972739.09 frames.], batch size: 62, lr: 2.39e-03
+2022-06-18 11:34:05,539 INFO [train.py:874] (3/4) Epoch 2, batch 1800, datatang_loss[loss=0.4035, simple_loss=0.3573, pruned_loss=0.2248, over 4923.00 frames.], tot_loss[loss=0.2003, simple_loss=0.3106, pruned_loss=0.1576, over 986000.05 frames.], batch size: 81, aishell_tot_loss[loss=0.184, simple_loss=0.3096, pruned_loss=0.1447, over 975506.20 frames.], datatang_tot_loss[loss=0.1873, simple_loss=0.3104, pruned_loss=0.1631, over 974370.39 frames.], batch size: 81, lr: 2.38e-03
+2022-06-18 11:34:34,610 INFO [train.py:874] (3/4) Epoch 2, batch 1850, datatang_loss[loss=0.3423, simple_loss=0.3404, pruned_loss=0.1722, over 4931.00 frames.], tot_loss[loss=0.2278, simple_loss=0.313, pruned_loss=0.1589, over 985868.69 frames.], batch size: 94, aishell_tot_loss[loss=0.1997, simple_loss=0.3112, pruned_loss=0.1459, over 976404.82 frames.], datatang_tot_loss[loss=0.2047, simple_loss=0.3117, pruned_loss=0.1643, over 975944.23 frames.], batch size: 94, lr: 2.38e-03
+2022-06-18 11:35:04,207 INFO [train.py:874] (3/4) Epoch 2, batch 1900, datatang_loss[loss=0.34, simple_loss=0.3191, pruned_loss=0.1805, over 4918.00 frames.], tot_loss[loss=0.242, simple_loss=0.3112, pruned_loss=0.1546, over 985906.15 frames.], batch size: 77, aishell_tot_loss[loss=0.21, simple_loss=0.3097, pruned_loss=0.1431, over 977676.57 frames.], datatang_tot_loss[loss=0.2158, simple_loss=0.3115, pruned_loss=0.1638, over 976992.67 frames.], batch size: 77, lr: 2.37e-03
+2022-06-18 11:35:34,946 INFO [train.py:874] (3/4) Epoch 2, batch 1950, aishell_loss[loss=0.2414, simple_loss=0.2841, pruned_loss=0.0993, over 4978.00 frames.], tot_loss[loss=0.2533, simple_loss=0.3104, pruned_loss=0.1512, over 985859.88 frames.], batch size: 51, aishell_tot_loss[loss=0.22, simple_loss=0.3098, pruned_loss=0.1415, over 978482.06 frames.], datatang_tot_loss[loss=0.2249, simple_loss=0.3108, pruned_loss=0.1624, over 978157.80 frames.], batch size: 51, lr: 2.36e-03
+2022-06-18 11:36:03,074 INFO [train.py:874] (3/4) Epoch 2, batch 2000, datatang_loss[loss=0.3058, simple_loss=0.3051, pruned_loss=0.1533, over 4915.00 frames.], tot_loss[loss=0.2633, simple_loss=0.3106, pruned_loss=0.1493, over 985787.88 frames.], batch size: 57, aishell_tot_loss[loss=0.2276, simple_loss=0.3096, pruned_loss=0.1398, over 979412.04 frames.], datatang_tot_loss[loss=0.2353, simple_loss=0.3113, pruned_loss=0.1619, over 978943.33 frames.], batch size: 57, lr: 2.36e-03
+2022-06-18 11:36:03,075 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 11:36:19,300 INFO [train.py:914] (3/4) Epoch 2, validation: loss=0.2142, simple_loss=0.275, pruned_loss=0.07672, over 1622729.00 frames.
+2022-06-18 11:36:49,030 INFO [train.py:874] (3/4) Epoch 2, batch 2050, datatang_loss[loss=0.335, simple_loss=0.3321, pruned_loss=0.1689, over 4914.00 frames.], tot_loss[loss=0.2696, simple_loss=0.3088, pruned_loss=0.1474, over 985557.90 frames.], batch size: 57, aishell_tot_loss[loss=0.2333, simple_loss=0.3079, pruned_loss=0.1378, over 980054.19 frames.], datatang_tot_loss[loss=0.2438, simple_loss=0.3109, pruned_loss=0.1616, over 979610.73 frames.], batch size: 57, lr: 2.35e-03
+2022-06-18 11:37:18,709 INFO [train.py:874] (3/4) Epoch 2, batch 2100, aishell_loss[loss=0.2655, simple_loss=0.2992, pruned_loss=0.1159, over 4968.00 frames.], tot_loss[loss=0.274, simple_loss=0.308, pruned_loss=0.145, over 985954.96 frames.], batch size: 44, aishell_tot_loss[loss=0.239, simple_loss=0.3078, pruned_loss=0.1364, over 980783.82 frames.], datatang_tot_loss[loss=0.2503, simple_loss=0.3102, pruned_loss=0.1601, over 980664.09 frames.], batch size: 44, lr: 2.35e-03
+2022-06-18 11:37:49,807 INFO [train.py:874] (3/4) Epoch 2, batch 2150, aishell_loss[loss=0.2514, simple_loss=0.2997, pruned_loss=0.1016, over 4941.00 frames.], tot_loss[loss=0.2781, simple_loss=0.3077, pruned_loss=0.1437, over 986155.09 frames.], batch size: 45, aishell_tot_loss[loss=0.2433, simple_loss=0.3075, pruned_loss=0.1348, over 981306.86 frames.], datatang_tot_loss[loss=0.2572, simple_loss=0.3099, pruned_loss=0.1594, over 981571.64 frames.], batch size: 45, lr: 2.34e-03
+2022-06-18 11:38:20,251 INFO [train.py:874] (3/4) Epoch 2, batch 2200, datatang_loss[loss=0.3591, simple_loss=0.3336, pruned_loss=0.1923, over 4939.00 frames.], tot_loss[loss=0.2806, simple_loss=0.3068, pruned_loss=0.1423, over 985967.57 frames.], batch size: 37, aishell_tot_loss[loss=0.2475, simple_loss=0.3073, pruned_loss=0.1341, over 981742.66 frames.], datatang_tot_loss[loss=0.262, simple_loss=0.3089, pruned_loss=0.1575, over 982031.26 frames.], batch size: 37, lr: 2.33e-03
+2022-06-18 11:38:49,033 INFO [train.py:874] (3/4) Epoch 2, batch 2250, datatang_loss[loss=0.3148, simple_loss=0.3146, pruned_loss=0.1575, over 4920.00 frames.], tot_loss[loss=0.2862, simple_loss=0.3073, pruned_loss=0.1443, over 985559.26 frames.], batch size: 75, aishell_tot_loss[loss=0.2533, simple_loss=0.3078, pruned_loss=0.1362, over 981836.53 frames.], datatang_tot_loss[loss=0.2677, simple_loss=0.3084, pruned_loss=0.156, over 982462.26 frames.], batch size: 75, lr: 2.33e-03
+2022-06-18 11:39:20,429 INFO [train.py:874] (3/4) Epoch 2, batch 2300, aishell_loss[loss=0.2722, simple_loss=0.3052, pruned_loss=0.1196, over 4965.00 frames.], tot_loss[loss=0.287, simple_loss=0.3073, pruned_loss=0.1425, over 985739.67 frames.], batch size: 44, aishell_tot_loss[loss=0.2572, simple_loss=0.3082, pruned_loss=0.1354, over 982614.38 frames.], datatang_tot_loss[loss=0.2708, simple_loss=0.3078, pruned_loss=0.1545, over 982669.00 frames.], batch size: 44, lr: 2.32e-03
+2022-06-18 11:39:51,153 INFO [train.py:874] (3/4) Epoch 2, batch 2350, aishell_loss[loss=0.2845, simple_loss=0.3099, pruned_loss=0.1295, over 4879.00 frames.], tot_loss[loss=0.2889, simple_loss=0.3083, pruned_loss=0.142, over 985971.72 frames.], batch size: 35, aishell_tot_loss[loss=0.2608, simple_loss=0.309, pruned_loss=0.1351, over 983142.02 frames.], datatang_tot_loss[loss=0.2745, simple_loss=0.3079, pruned_loss=0.1535, over 983118.23 frames.], batch size: 35, lr: 2.32e-03
+2022-06-18 11:40:20,404 INFO [train.py:874] (3/4) Epoch 2, batch 2400, datatang_loss[loss=0.3839, simple_loss=0.3689, pruned_loss=0.1994, over 4935.00 frames.], tot_loss[loss=0.2872, simple_loss=0.3068, pruned_loss=0.1394, over 986131.39 frames.], batch size: 109, aishell_tot_loss[loss=0.2616, simple_loss=0.3081, pruned_loss=0.1333, over 983367.48 frames.], datatang_tot_loss[loss=0.2768, simple_loss=0.3073, pruned_loss=0.1517, over 983726.12 frames.], batch size: 109, lr: 2.31e-03
+2022-06-18 11:40:51,464 INFO [train.py:874] (3/4) Epoch 2, batch 2450, aishell_loss[loss=0.2398, simple_loss=0.2717, pruned_loss=0.104, over 4954.00 frames.], tot_loss[loss=0.2857, simple_loss=0.3056, pruned_loss=0.1372, over 986245.37 frames.], batch size: 27, aishell_tot_loss[loss=0.2628, simple_loss=0.3075, pruned_loss=0.1319, over 983702.89 frames.], datatang_tot_loss[loss=0.278, simple_loss=0.3063, pruned_loss=0.1499, over 984122.38 frames.], batch size: 27, lr: 2.31e-03
+2022-06-18 11:41:22,630 INFO [train.py:874] (3/4) Epoch 2, batch 2500, datatang_loss[loss=0.3529, simple_loss=0.3435, pruned_loss=0.1811, over 4932.00 frames.], tot_loss[loss=0.2882, simple_loss=0.3061, pruned_loss=0.1386, over 985911.14 frames.], batch size: 94, aishell_tot_loss[loss=0.2649, simple_loss=0.3073, pruned_loss=0.1307, over 983726.97 frames.], datatang_tot_loss[loss=0.2826, simple_loss=0.3067, pruned_loss=0.152, over 984304.39 frames.], batch size: 94, lr: 2.30e-03
+2022-06-18 11:41:51,396 INFO [train.py:874] (3/4) Epoch 2, batch 2550, datatang_loss[loss=0.2662, simple_loss=0.2949, pruned_loss=0.1188, over 4922.00 frames.], tot_loss[loss=0.2895, simple_loss=0.3074, pruned_loss=0.1384, over 985620.51 frames.], batch size: 57, aishell_tot_loss[loss=0.2672, simple_loss=0.3079, pruned_loss=0.1304, over 983930.60 frames.], datatang_tot_loss[loss=0.2852, simple_loss=0.3074, pruned_loss=0.1517, over 984254.47 frames.], batch size: 57, lr: 2.30e-03
+2022-06-18 11:42:23,074 INFO [train.py:874] (3/4) Epoch 2, batch 2600, datatang_loss[loss=0.3008, simple_loss=0.3196, pruned_loss=0.141, over 4924.00 frames.], tot_loss[loss=0.2897, simple_loss=0.3078, pruned_loss=0.1379, over 985510.97 frames.], batch size: 94, aishell_tot_loss[loss=0.2692, simple_loss=0.3086, pruned_loss=0.1303, over 983967.82 frames.], datatang_tot_loss[loss=0.2863, simple_loss=0.307, pruned_loss=0.1503, over 984438.76 frames.], batch size: 94, lr: 2.29e-03
+2022-06-18 11:42:52,059 INFO [train.py:874] (3/4) Epoch 2, batch 2650, datatang_loss[loss=0.2594, simple_loss=0.2743, pruned_loss=0.1223, over 4800.00 frames.], tot_loss[loss=0.2895, simple_loss=0.3073, pruned_loss=0.1375, over 985234.77 frames.], batch size: 24, aishell_tot_loss[loss=0.2706, simple_loss=0.308, pruned_loss=0.1299, over 984031.18 frames.], datatang_tot_loss[loss=0.2877, simple_loss=0.307, pruned_loss=0.1499, over 984387.53 frames.], batch size: 24, lr: 2.28e-03
+2022-06-18 11:43:22,510 INFO [train.py:874] (3/4) Epoch 2, batch 2700, aishell_loss[loss=0.3105, simple_loss=0.338, pruned_loss=0.1415, over 4935.00 frames.], tot_loss[loss=0.2878, simple_loss=0.3069, pruned_loss=0.1356, over 985100.26 frames.], batch size: 68, aishell_tot_loss[loss=0.2709, simple_loss=0.3079, pruned_loss=0.1285, over 984055.50 frames.], datatang_tot_loss[loss=0.2883, simple_loss=0.3066, pruned_loss=0.1491, over 984457.41 frames.], batch size: 68, lr: 2.28e-03
+2022-06-18 11:43:52,916 INFO [train.py:874] (3/4) Epoch 2, batch 2750, datatang_loss[loss=0.324, simple_loss=0.3207, pruned_loss=0.1636, over 4946.00 frames.], tot_loss[loss=0.286, simple_loss=0.3058, pruned_loss=0.134, over 985388.96 frames.], batch size: 88, aishell_tot_loss[loss=0.2707, simple_loss=0.3076, pruned_loss=0.1273, over 984238.28 frames.], datatang_tot_loss[loss=0.2882, simple_loss=0.3057, pruned_loss=0.1475, over 984772.35 frames.], batch size: 88, lr: 2.27e-03
+2022-06-18 11:44:21,122 INFO [train.py:874] (3/4) Epoch 2, batch 2800, aishell_loss[loss=0.2962, simple_loss=0.3203, pruned_loss=0.1361, over 4878.00 frames.], tot_loss[loss=0.2858, simple_loss=0.3061, pruned_loss=0.1336, over 985370.93 frames.], batch size: 47, aishell_tot_loss[loss=0.2713, simple_loss=0.308, pruned_loss=0.1265, over 984275.91 frames.], datatang_tot_loss[loss=0.2889, simple_loss=0.3054, pruned_loss=0.1469, over 984930.40 frames.], batch size: 47, lr: 2.27e-03
+2022-06-18 11:44:52,613 INFO [train.py:874] (3/4) Epoch 2, batch 2850, datatang_loss[loss=0.2963, simple_loss=0.3072, pruned_loss=0.1427, over 4923.00 frames.], tot_loss[loss=0.2866, simple_loss=0.3066, pruned_loss=0.1339, over 985781.83 frames.], batch size: 83, aishell_tot_loss[loss=0.2714, simple_loss=0.3076, pruned_loss=0.1258, over 984568.54 frames.], datatang_tot_loss[loss=0.2909, simple_loss=0.3062, pruned_loss=0.1472, over 985249.89 frames.], batch size: 83, lr: 2.26e-03
+2022-06-18 11:45:23,371 INFO [train.py:874] (3/4) Epoch 2, batch 2900, aishell_loss[loss=0.2974, simple_loss=0.3245, pruned_loss=0.1352, over 4925.00 frames.], tot_loss[loss=0.2853, simple_loss=0.3059, pruned_loss=0.1328, over 985708.10 frames.], batch size: 46, aishell_tot_loss[loss=0.2713, simple_loss=0.307, pruned_loss=0.125, over 984659.97 frames.], datatang_tot_loss[loss=0.2911, simple_loss=0.306, pruned_loss=0.1464, over 985282.24 frames.], batch size: 46, lr: 2.26e-03
+2022-06-18 11:45:51,141 INFO [train.py:874] (3/4) Epoch 2, batch 2950, datatang_loss[loss=0.2392, simple_loss=0.2775, pruned_loss=0.1005, over 4914.00 frames.], tot_loss[loss=0.2845, simple_loss=0.3067, pruned_loss=0.1316, over 985845.51 frames.], batch size: 81, aishell_tot_loss[loss=0.2726, simple_loss=0.3081, pruned_loss=0.1247, over 984952.15 frames.], datatang_tot_loss[loss=0.2905, simple_loss=0.3054, pruned_loss=0.1454, over 985310.41 frames.], batch size: 81, lr: 2.25e-03
+2022-06-18 11:46:22,436 INFO [train.py:874] (3/4) Epoch 2, batch 3000, aishell_loss[loss=0.2763, simple_loss=0.3141, pruned_loss=0.1192, over 4873.00 frames.], tot_loss[loss=0.2821, simple_loss=0.3049, pruned_loss=0.1299, over 985572.97 frames.], batch size: 35, aishell_tot_loss[loss=0.2713, simple_loss=0.307, pruned_loss=0.1232, over 985178.80 frames.], datatang_tot_loss[loss=0.2899, simple_loss=0.3048, pruned_loss=0.1442, over 984982.24 frames.], batch size: 35, lr: 2.25e-03
+2022-06-18 11:46:22,437 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 11:46:39,817 INFO [train.py:914] (3/4) Epoch 2, validation: loss=0.2091, simple_loss=0.2742, pruned_loss=0.07205, over 1622729.00 frames.
+2022-06-18 11:47:08,872 INFO [train.py:874] (3/4) Epoch 2, batch 3050, datatang_loss[loss=0.3166, simple_loss=0.3264, pruned_loss=0.1534, over 4903.00 frames.], tot_loss[loss=0.2835, simple_loss=0.3063, pruned_loss=0.1306, over 985551.75 frames.], batch size: 42, aishell_tot_loss[loss=0.2721, simple_loss=0.3075, pruned_loss=0.1231, over 985149.34 frames.], datatang_tot_loss[loss=0.291, simple_loss=0.3054, pruned_loss=0.1443, over 985102.91 frames.], batch size: 42, lr: 2.24e-03
+2022-06-18 11:47:40,910 INFO [train.py:874] (3/4) Epoch 2, batch 3100, aishell_loss[loss=0.2498, simple_loss=0.3032, pruned_loss=0.09818, over 4879.00 frames.], tot_loss[loss=0.2814, simple_loss=0.3056, pruned_loss=0.1288, over 985850.40 frames.], batch size: 42, aishell_tot_loss[loss=0.272, simple_loss=0.3078, pruned_loss=0.1222, over 985280.18 frames.], datatang_tot_loss[loss=0.2898, simple_loss=0.3043, pruned_loss=0.143, over 985398.95 frames.], batch size: 42, lr: 2.24e-03
+2022-06-18 11:48:08,451 INFO [train.py:874] (3/4) Epoch 2, batch 3150, aishell_loss[loss=0.3387, simple_loss=0.3571, pruned_loss=0.1601, over 4957.00 frames.], tot_loss[loss=0.2847, simple_loss=0.3069, pruned_loss=0.1314, over 986066.69 frames.], batch size: 78, aishell_tot_loss[loss=0.2738, simple_loss=0.3086, pruned_loss=0.123, over 985489.09 frames.], datatang_tot_loss[loss=0.2917, simple_loss=0.3046, pruned_loss=0.1441, over 985553.36 frames.], batch size: 78, lr: 2.23e-03
+2022-06-18 11:48:40,408 INFO [train.py:874] (3/4) Epoch 2, batch 3200, datatang_loss[loss=0.2509, simple_loss=0.2737, pruned_loss=0.114, over 4904.00 frames.], tot_loss[loss=0.2804, simple_loss=0.3044, pruned_loss=0.1283, over 985720.99 frames.], batch size: 52, aishell_tot_loss[loss=0.2719, simple_loss=0.3074, pruned_loss=0.1213, over 985202.34 frames.], datatang_tot_loss[loss=0.2896, simple_loss=0.3033, pruned_loss=0.1421, over 985594.53 frames.], batch size: 52, lr: 2.23e-03
+2022-06-18 11:49:11,955 INFO [train.py:874] (3/4) Epoch 2, batch 3250, datatang_loss[loss=0.2632, simple_loss=0.2762, pruned_loss=0.1251, over 4977.00 frames.], tot_loss[loss=0.276, simple_loss=0.3016, pruned_loss=0.1252, over 985547.44 frames.], batch size: 45, aishell_tot_loss[loss=0.2685, simple_loss=0.3051, pruned_loss=0.1188, over 985250.22 frames.], datatang_tot_loss[loss=0.2883, simple_loss=0.3025, pruned_loss=0.1408, over 985442.85 frames.], batch size: 45, lr: 2.22e-03
+2022-06-18 11:49:40,237 INFO [train.py:874] (3/4) Epoch 2, batch 3300, aishell_loss[loss=0.3033, simple_loss=0.3375, pruned_loss=0.1345, over 4870.00 frames.], tot_loss[loss=0.2761, simple_loss=0.3017, pruned_loss=0.1253, over 985535.75 frames.], batch size: 42, aishell_tot_loss[loss=0.268, simple_loss=0.3043, pruned_loss=0.1183, over 985068.13 frames.], datatang_tot_loss[loss=0.2886, simple_loss=0.3029, pruned_loss=0.1405, over 985665.99 frames.], batch size: 42, lr: 2.22e-03
+2022-06-18 11:50:11,331 INFO [train.py:874] (3/4) Epoch 2, batch 3350, datatang_loss[loss=0.2503, simple_loss=0.2799, pruned_loss=0.1104, over 4839.00 frames.], tot_loss[loss=0.2782, simple_loss=0.3033, pruned_loss=0.1266, over 985845.95 frames.], batch size: 30, aishell_tot_loss[loss=0.2684, simple_loss=0.3045, pruned_loss=0.1183, over 985338.77 frames.], datatang_tot_loss[loss=0.2898, simple_loss=0.3039, pruned_loss=0.1408, over 985764.84 frames.], batch size: 30, lr: 2.21e-03
+2022-06-18 11:50:42,291 INFO [train.py:874] (3/4) Epoch 2, batch 3400, aishell_loss[loss=0.2625, simple_loss=0.3096, pruned_loss=0.1076, over 4920.00 frames.], tot_loss[loss=0.2771, simple_loss=0.3029, pruned_loss=0.1257, over 985691.51 frames.], batch size: 52, aishell_tot_loss[loss=0.268, simple_loss=0.3044, pruned_loss=0.1176, over 985202.47 frames.], datatang_tot_loss[loss=0.2891, simple_loss=0.3034, pruned_loss=0.14, over 985810.73 frames.], batch size: 52, lr: 2.21e-03
+2022-06-18 11:51:10,206 INFO [train.py:874] (3/4) Epoch 2, batch 3450, datatang_loss[loss=0.2521, simple_loss=0.2737, pruned_loss=0.1153, over 4959.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3034, pruned_loss=0.1267, over 985706.24 frames.], batch size: 55, aishell_tot_loss[loss=0.2677, simple_loss=0.3042, pruned_loss=0.1173, over 985191.74 frames.], datatang_tot_loss[loss=0.2902, simple_loss=0.3039, pruned_loss=0.1405, over 985868.46 frames.], batch size: 55, lr: 2.20e-03
+2022-06-18 11:51:42,284 INFO [train.py:874] (3/4) Epoch 2, batch 3500, aishell_loss[loss=0.2281, simple_loss=0.2712, pruned_loss=0.09247, over 4945.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3039, pruned_loss=0.1264, over 985628.01 frames.], batch size: 27, aishell_tot_loss[loss=0.2682, simple_loss=0.3046, pruned_loss=0.1174, over 985255.43 frames.], datatang_tot_loss[loss=0.2896, simple_loss=0.3038, pruned_loss=0.1397, over 985765.33 frames.], batch size: 27, lr: 2.20e-03
+2022-06-18 11:52:10,598 INFO [train.py:874] (3/4) Epoch 2, batch 3550, aishell_loss[loss=0.2908, simple_loss=0.3257, pruned_loss=0.128, over 4916.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3038, pruned_loss=0.1255, over 985368.37 frames.], batch size: 46, aishell_tot_loss[loss=0.2679, simple_loss=0.3045, pruned_loss=0.1169, over 984974.43 frames.], datatang_tot_loss[loss=0.2891, simple_loss=0.3037, pruned_loss=0.1391, over 985822.42 frames.], batch size: 46, lr: 2.19e-03
+2022-06-18 11:52:42,109 INFO [train.py:874] (3/4) Epoch 2, batch 3600, aishell_loss[loss=0.2407, simple_loss=0.2678, pruned_loss=0.1068, over 4967.00 frames.], tot_loss[loss=0.2903, simple_loss=0.3076, pruned_loss=0.1365, over 985481.23 frames.], batch size: 25, aishell_tot_loss[loss=0.2699, simple_loss=0.3056, pruned_loss=0.1182, over 985040.51 frames.], datatang_tot_loss[loss=0.3005, simple_loss=0.3067, pruned_loss=0.1487, over 985864.32 frames.], batch size: 25, lr: 2.19e-03
+2022-06-18 11:53:13,286 INFO [train.py:874] (3/4) Epoch 2, batch 3650, aishell_loss[loss=0.2639, simple_loss=0.3052, pruned_loss=0.1114, over 4966.00 frames.], tot_loss[loss=0.283, simple_loss=0.3041, pruned_loss=0.1309, over 985675.08 frames.], batch size: 64, aishell_tot_loss[loss=0.2677, simple_loss=0.3043, pruned_loss=0.1165, over 985370.65 frames.], datatang_tot_loss[loss=0.2965, simple_loss=0.3046, pruned_loss=0.1456, over 985730.10 frames.], batch size: 64, lr: 2.18e-03
+2022-06-18 11:53:42,186 INFO [train.py:874] (3/4) Epoch 2, batch 3700, aishell_loss[loss=0.238, simple_loss=0.2891, pruned_loss=0.09348, over 4919.00 frames.], tot_loss[loss=0.2786, simple_loss=0.3025, pruned_loss=0.1274, over 985519.82 frames.], batch size: 52, aishell_tot_loss[loss=0.2667, simple_loss=0.3039, pruned_loss=0.1156, over 985185.06 frames.], datatang_tot_loss[loss=0.2933, simple_loss=0.3032, pruned_loss=0.143, over 985783.93 frames.], batch size: 52, lr: 2.18e-03
+2022-06-18 11:54:13,067 INFO [train.py:874] (3/4) Epoch 2, batch 3750, datatang_loss[loss=0.2706, simple_loss=0.2956, pruned_loss=0.1228, over 4899.00 frames.], tot_loss[loss=0.2782, simple_loss=0.3028, pruned_loss=0.1269, over 985642.39 frames.], batch size: 47, aishell_tot_loss[loss=0.2662, simple_loss=0.3037, pruned_loss=0.1151, over 985157.70 frames.], datatang_tot_loss[loss=0.2923, simple_loss=0.3034, pruned_loss=0.1417, over 985922.14 frames.], batch size: 47, lr: 2.17e-03
+2022-06-18 11:54:47,936 INFO [train.py:874] (3/4) Epoch 2, batch 3800, datatang_loss[loss=0.2695, simple_loss=0.2983, pruned_loss=0.1204, over 4896.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3022, pruned_loss=0.1255, over 985368.65 frames.], batch size: 52, aishell_tot_loss[loss=0.2652, simple_loss=0.3028, pruned_loss=0.1144, over 985013.65 frames.], datatang_tot_loss[loss=0.2913, simple_loss=0.3035, pruned_loss=0.1405, over 985809.76 frames.], batch size: 52, lr: 2.17e-03
+2022-06-18 11:55:15,871 INFO [train.py:874] (3/4) Epoch 2, batch 3850, datatang_loss[loss=0.314, simple_loss=0.3179, pruned_loss=0.155, over 4960.00 frames.], tot_loss[loss=0.2758, simple_loss=0.3021, pruned_loss=0.1247, over 985259.26 frames.], batch size: 50, aishell_tot_loss[loss=0.2656, simple_loss=0.3033, pruned_loss=0.1145, over 985047.14 frames.], datatang_tot_loss[loss=0.2895, simple_loss=0.3027, pruned_loss=0.139, over 985645.52 frames.], batch size: 50, lr: 2.16e-03
+2022-06-18 11:55:45,629 INFO [train.py:874] (3/4) Epoch 2, batch 3900, datatang_loss[loss=0.2848, simple_loss=0.3106, pruned_loss=0.1295, over 4898.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3025, pruned_loss=0.125, over 985567.78 frames.], batch size: 47, aishell_tot_loss[loss=0.2665, simple_loss=0.3039, pruned_loss=0.1151, over 985061.55 frames.], datatang_tot_loss[loss=0.2882, simple_loss=0.3023, pruned_loss=0.1378, over 985917.54 frames.], batch size: 47, lr: 2.16e-03
+2022-06-18 11:56:14,216 INFO [train.py:874] (3/4) Epoch 2, batch 3950, datatang_loss[loss=0.2699, simple_loss=0.2951, pruned_loss=0.1224, over 4967.00 frames.], tot_loss[loss=0.2755, simple_loss=0.302, pruned_loss=0.1245, over 985337.34 frames.], batch size: 60, aishell_tot_loss[loss=0.2665, simple_loss=0.3038, pruned_loss=0.1151, over 984810.23 frames.], datatang_tot_loss[loss=0.2869, simple_loss=0.3018, pruned_loss=0.1366, over 985914.32 frames.], batch size: 60, lr: 2.15e-03
+2022-06-18 11:56:45,183 INFO [train.py:874] (3/4) Epoch 2, batch 4000, aishell_loss[loss=0.2859, simple_loss=0.325, pruned_loss=0.1234, over 4953.00 frames.], tot_loss[loss=0.2734, simple_loss=0.3006, pruned_loss=0.1231, over 985641.76 frames.], batch size: 40, aishell_tot_loss[loss=0.2657, simple_loss=0.3033, pruned_loss=0.1145, over 984898.07 frames.], datatang_tot_loss[loss=0.2848, simple_loss=0.3007, pruned_loss=0.135, over 986121.31 frames.], batch size: 40, lr: 2.15e-03
+2022-06-18 11:56:45,184 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 11:57:01,107 INFO [train.py:914] (3/4) Epoch 2, validation: loss=0.2024, simple_loss=0.2723, pruned_loss=0.06625, over 1622729.00 frames.
+2022-06-18 11:57:30,565 INFO [train.py:874] (3/4) Epoch 2, batch 4050, datatang_loss[loss=0.2588, simple_loss=0.2872, pruned_loss=0.1152, over 4897.00 frames.], tot_loss[loss=0.2732, simple_loss=0.3003, pruned_loss=0.1231, over 985338.31 frames.], batch size: 59, aishell_tot_loss[loss=0.2668, simple_loss=0.3039, pruned_loss=0.1153, over 984522.46 frames.], datatang_tot_loss[loss=0.2827, simple_loss=0.2994, pruned_loss=0.1335, over 986200.26 frames.], batch size: 59, lr: 2.14e-03
+2022-06-18 11:58:00,751 INFO [train.py:874] (3/4) Epoch 2, batch 4100, aishell_loss[loss=0.2365, simple_loss=0.2919, pruned_loss=0.09053, over 4881.00 frames.], tot_loss[loss=0.2722, simple_loss=0.3, pruned_loss=0.1222, over 984748.18 frames.], batch size: 34, aishell_tot_loss[loss=0.2673, simple_loss=0.3043, pruned_loss=0.1155, over 983941.66 frames.], datatang_tot_loss[loss=0.2805, simple_loss=0.2985, pruned_loss=0.1317, over 986124.56 frames.], batch size: 34, lr: 2.14e-03
+2022-06-18 11:59:09,087 INFO [train.py:874] (3/4) Epoch 3, batch 50, aishell_loss[loss=0.2249, simple_loss=0.2394, pruned_loss=0.1052, over 4780.00 frames.], tot_loss[loss=0.2441, simple_loss=0.2818, pruned_loss=0.1032, over 218687.87 frames.], batch size: 21, aishell_tot_loss[loss=0.2489, simple_loss=0.2923, pruned_loss=0.1028, over 120487.09 frames.], datatang_tot_loss[loss=0.2388, simple_loss=0.2706, pruned_loss=0.1035, over 111865.04 frames.], batch size: 21, lr: 2.09e-03
+2022-06-18 11:59:39,463 INFO [train.py:874] (3/4) Epoch 3, batch 100, aishell_loss[loss=0.3031, simple_loss=0.3286, pruned_loss=0.1388, over 4924.00 frames.], tot_loss[loss=0.2533, simple_loss=0.2879, pruned_loss=0.1094, over 389034.59 frames.], batch size: 68, aishell_tot_loss[loss=0.2598, simple_loss=0.3017, pruned_loss=0.109, over 210945.28 frames.], datatang_tot_loss[loss=0.2462, simple_loss=0.2747, pruned_loss=0.1088, over 226486.48 frames.], batch size: 68, lr: 2.09e-03
+2022-06-18 12:00:11,066 INFO [train.py:874] (3/4) Epoch 3, batch 150, datatang_loss[loss=0.2352, simple_loss=0.2642, pruned_loss=0.1031, over 4903.00 frames.], tot_loss[loss=0.259, simple_loss=0.2886, pruned_loss=0.1146, over 521429.13 frames.], batch size: 64, aishell_tot_loss[loss=0.2675, simple_loss=0.3028, pruned_loss=0.1161, over 274337.52 frames.], datatang_tot_loss[loss=0.2505, simple_loss=0.2774, pruned_loss=0.1118, over 342256.53 frames.], batch size: 64, lr: 2.08e-03
+2022-06-18 12:00:39,869 INFO [train.py:874] (3/4) Epoch 3, batch 200, datatang_loss[loss=0.2635, simple_loss=0.2876, pruned_loss=0.1197, over 4936.00 frames.], tot_loss[loss=0.2588, simple_loss=0.2909, pruned_loss=0.1133, over 624301.43 frames.], batch size: 34, aishell_tot_loss[loss=0.2642, simple_loss=0.3027, pruned_loss=0.1129, over 388837.53 frames.], datatang_tot_loss[loss=0.2517, simple_loss=0.2778, pruned_loss=0.1129, over 388771.41 frames.], batch size: 34, lr: 2.08e-03
+2022-06-18 12:01:10,646 INFO [train.py:874] (3/4) Epoch 3, batch 250, aishell_loss[loss=0.2255, simple_loss=0.271, pruned_loss=0.09002, over 4858.00 frames.], tot_loss[loss=0.2576, simple_loss=0.2905, pruned_loss=0.1123, over 704640.11 frames.], batch size: 28, aishell_tot_loss[loss=0.2597, simple_loss=0.2995, pruned_loss=0.11, over 459216.27 frames.], datatang_tot_loss[loss=0.2546, simple_loss=0.2805, pruned_loss=0.1143, over 459238.29 frames.], batch size: 28, lr: 2.07e-03
+2022-06-18 12:01:42,571 INFO [train.py:874] (3/4) Epoch 3, batch 300, datatang_loss[loss=0.2507, simple_loss=0.2739, pruned_loss=0.1137, over 4951.00 frames.], tot_loss[loss=0.2583, simple_loss=0.292, pruned_loss=0.1123, over 767183.62 frames.], batch size: 55, aishell_tot_loss[loss=0.2575, simple_loss=0.2986, pruned_loss=0.1082, over 530447.41 frames.], datatang_tot_loss[loss=0.2579, simple_loss=0.2834, pruned_loss=0.1162, over 512080.20 frames.], batch size: 55, lr: 2.07e-03
+2022-06-18 12:02:11,431 INFO [train.py:874] (3/4) Epoch 3, batch 350, datatang_loss[loss=0.2644, simple_loss=0.2832, pruned_loss=0.1228, over 4867.00 frames.], tot_loss[loss=0.2607, simple_loss=0.2937, pruned_loss=0.1139, over 815820.72 frames.], batch size: 25, aishell_tot_loss[loss=0.2589, simple_loss=0.2998, pruned_loss=0.109, over 576133.83 frames.], datatang_tot_loss[loss=0.2604, simple_loss=0.2857, pruned_loss=0.1175, over 576177.38 frames.], batch size: 25, lr: 2.06e-03
+2022-06-18 12:02:42,572 INFO [train.py:874] (3/4) Epoch 3, batch 400, aishell_loss[loss=0.2123, simple_loss=0.2391, pruned_loss=0.09271, over 4958.00 frames.], tot_loss[loss=0.2595, simple_loss=0.2928, pruned_loss=0.1131, over 853309.54 frames.], batch size: 21, aishell_tot_loss[loss=0.257, simple_loss=0.2981, pruned_loss=0.1079, over 618737.70 frames.], datatang_tot_loss[loss=0.2609, simple_loss=0.2867, pruned_loss=0.1176, over 629792.07 frames.], batch size: 21, lr: 2.06e-03
+2022-06-18 12:03:13,311 INFO [train.py:874] (3/4) Epoch 3, batch 450, aishell_loss[loss=0.2792, simple_loss=0.3149, pruned_loss=0.1217, over 4906.00 frames.], tot_loss[loss=0.2597, simple_loss=0.293, pruned_loss=0.1132, over 882211.29 frames.], batch size: 41, aishell_tot_loss[loss=0.2562, simple_loss=0.2976, pruned_loss=0.1074, over 667938.63 frames.], datatang_tot_loss[loss=0.2624, simple_loss=0.2874, pruned_loss=0.1186, over 665332.37 frames.], batch size: 41, lr: 2.05e-03
+2022-06-18 12:03:41,600 INFO [train.py:874] (3/4) Epoch 3, batch 500, datatang_loss[loss=0.3244, simple_loss=0.3359, pruned_loss=0.1564, over 4926.00 frames.], tot_loss[loss=0.2618, simple_loss=0.2948, pruned_loss=0.1144, over 905099.33 frames.], batch size: 94, aishell_tot_loss[loss=0.2576, simple_loss=0.2993, pruned_loss=0.108, over 702626.94 frames.], datatang_tot_loss[loss=0.2638, simple_loss=0.2884, pruned_loss=0.1196, over 705760.04 frames.], batch size: 94, lr: 2.05e-03
+2022-06-18 12:04:12,896 INFO [train.py:874] (3/4) Epoch 3, batch 550, aishell_loss[loss=0.2374, simple_loss=0.2869, pruned_loss=0.09392, over 4944.00 frames.], tot_loss[loss=0.262, simple_loss=0.2953, pruned_loss=0.1144, over 923246.69 frames.], batch size: 49, aishell_tot_loss[loss=0.2573, simple_loss=0.2991, pruned_loss=0.1078, over 736194.68 frames.], datatang_tot_loss[loss=0.2647, simple_loss=0.2896, pruned_loss=0.1199, over 738813.11 frames.], batch size: 49, lr: 2.05e-03
+2022-06-18 12:04:44,481 INFO [train.py:874] (3/4) Epoch 3, batch 600, aishell_loss[loss=0.2911, simple_loss=0.315, pruned_loss=0.1336, over 4941.00 frames.], tot_loss[loss=0.2674, simple_loss=0.2974, pruned_loss=0.1187, over 937059.74 frames.], batch size: 49, aishell_tot_loss[loss=0.2597, simple_loss=0.3001, pruned_loss=0.1097, over 760973.38 frames.], datatang_tot_loss[loss=0.2694, simple_loss=0.2919, pruned_loss=0.1234, over 772325.71 frames.], batch size: 49, lr: 2.04e-03
+2022-06-18 12:05:11,896 INFO [train.py:874] (3/4) Epoch 3, batch 650, datatang_loss[loss=0.2879, simple_loss=0.3092, pruned_loss=0.1333, over 4938.00 frames.], tot_loss[loss=0.2653, simple_loss=0.2963, pruned_loss=0.1172, over 947774.53 frames.], batch size: 50, aishell_tot_loss[loss=0.2591, simple_loss=0.2995, pruned_loss=0.1093, over 786329.05 frames.], datatang_tot_loss[loss=0.2682, simple_loss=0.2916, pruned_loss=0.1224, over 798455.05 frames.], batch size: 50, lr: 2.04e-03
+2022-06-18 12:05:43,606 INFO [train.py:874] (3/4) Epoch 3, batch 700, aishell_loss[loss=0.2293, simple_loss=0.2896, pruned_loss=0.08452, over 4936.00 frames.], tot_loss[loss=0.2634, simple_loss=0.2955, pruned_loss=0.1156, over 955544.66 frames.], batch size: 58, aishell_tot_loss[loss=0.2576, simple_loss=0.2989, pruned_loss=0.1082, over 806039.43 frames.], datatang_tot_loss[loss=0.2676, simple_loss=0.2916, pruned_loss=0.1218, over 823313.73 frames.], batch size: 58, lr: 2.03e-03
+2022-06-18 12:06:14,657 INFO [train.py:874] (3/4) Epoch 3, batch 750, aishell_loss[loss=0.2469, simple_loss=0.2953, pruned_loss=0.09929, over 4939.00 frames.], tot_loss[loss=0.2627, simple_loss=0.2958, pruned_loss=0.1148, over 961997.89 frames.], batch size: 49, aishell_tot_loss[loss=0.2559, simple_loss=0.298, pruned_loss=0.1069, over 830117.49 frames.], datatang_tot_loss[loss=0.2689, simple_loss=0.2927, pruned_loss=0.1225, over 839521.39 frames.], batch size: 49, lr: 2.03e-03
+2022-06-18 12:06:42,430 INFO [train.py:874] (3/4) Epoch 3, batch 800, datatang_loss[loss=0.2844, simple_loss=0.3007, pruned_loss=0.134, over 4927.00 frames.], tot_loss[loss=0.2611, simple_loss=0.2949, pruned_loss=0.1137, over 966759.78 frames.], batch size: 71, aishell_tot_loss[loss=0.2544, simple_loss=0.2969, pruned_loss=0.106, over 850639.13 frames.], datatang_tot_loss[loss=0.2688, simple_loss=0.2928, pruned_loss=0.1224, over 854130.49 frames.], batch size: 71, lr: 2.02e-03
+2022-06-18 12:07:14,041 INFO [train.py:874] (3/4) Epoch 3, batch 850, datatang_loss[loss=0.2753, simple_loss=0.2988, pruned_loss=0.126, over 4920.00 frames.], tot_loss[loss=0.2605, simple_loss=0.295, pruned_loss=0.113, over 970979.58 frames.], batch size: 77, aishell_tot_loss[loss=0.2528, simple_loss=0.2962, pruned_loss=0.1047, over 865882.92 frames.], datatang_tot_loss[loss=0.2693, simple_loss=0.2936, pruned_loss=0.1225, over 870290.76 frames.], batch size: 77, lr: 2.02e-03
+2022-06-18 12:07:44,999 INFO [train.py:874] (3/4) Epoch 3, batch 900, aishell_loss[loss=0.1922, simple_loss=0.221, pruned_loss=0.08171, over 4964.00 frames.], tot_loss[loss=0.2599, simple_loss=0.2944, pruned_loss=0.1127, over 973906.85 frames.], batch size: 20, aishell_tot_loss[loss=0.2526, simple_loss=0.2956, pruned_loss=0.1048, over 881140.25 frames.], datatang_tot_loss[loss=0.2692, simple_loss=0.2936, pruned_loss=0.1224, over 882440.25 frames.], batch size: 20, lr: 2.02e-03
+2022-06-18 12:08:13,008 INFO [train.py:874] (3/4) Epoch 3, batch 950, datatang_loss[loss=0.2292, simple_loss=0.2696, pruned_loss=0.09435, over 4938.00 frames.], tot_loss[loss=0.2585, simple_loss=0.2929, pruned_loss=0.112, over 976550.17 frames.], batch size: 79, aishell_tot_loss[loss=0.2512, simple_loss=0.2946, pruned_loss=0.1039, over 891186.44 frames.], datatang_tot_loss[loss=0.2682, simple_loss=0.2928, pruned_loss=0.1218, over 896814.01 frames.], batch size: 79, lr: 2.01e-03
+2022-06-18 12:08:44,434 INFO [train.py:874] (3/4) Epoch 3, batch 1000, datatang_loss[loss=0.2572, simple_loss=0.2886, pruned_loss=0.1129, over 4940.00 frames.], tot_loss[loss=0.2584, simple_loss=0.2936, pruned_loss=0.1116, over 978277.83 frames.], batch size: 69, aishell_tot_loss[loss=0.2514, simple_loss=0.2953, pruned_loss=0.1037, over 902872.64 frames.], datatang_tot_loss[loss=0.2678, simple_loss=0.2928, pruned_loss=0.1214, over 906434.13 frames.], batch size: 69, lr: 2.01e-03
+2022-06-18 12:08:44,435 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 12:09:01,538 INFO [train.py:914] (3/4) Epoch 3, validation: loss=0.1966, simple_loss=0.2677, pruned_loss=0.06274, over 1622729.00 frames.
+2022-06-18 12:09:30,063 INFO [train.py:874] (3/4) Epoch 3, batch 1050, datatang_loss[loss=0.2953, simple_loss=0.3048, pruned_loss=0.1429, over 4971.00 frames.], tot_loss[loss=0.2588, simple_loss=0.294, pruned_loss=0.1118, over 979871.95 frames.], batch size: 55, aishell_tot_loss[loss=0.2515, simple_loss=0.2956, pruned_loss=0.1038, over 912526.78 frames.], datatang_tot_loss[loss=0.2677, simple_loss=0.2927, pruned_loss=0.1213, over 915819.32 frames.], batch size: 55, lr: 2.00e-03
+2022-06-18 12:10:01,743 INFO [train.py:874] (3/4) Epoch 3, batch 1100, aishell_loss[loss=0.2458, simple_loss=0.3007, pruned_loss=0.09542, over 4940.00 frames.], tot_loss[loss=0.2575, simple_loss=0.2939, pruned_loss=0.1106, over 981291.37 frames.], batch size: 54, aishell_tot_loss[loss=0.252, simple_loss=0.2965, pruned_loss=0.1038, over 922862.85 frames.], datatang_tot_loss[loss=0.266, simple_loss=0.2916, pruned_loss=0.1202, over 922501.05 frames.], batch size: 54, lr: 2.00e-03
+2022-06-18 12:10:29,542 INFO [train.py:874] (3/4) Epoch 3, batch 1150, datatang_loss[loss=0.2686, simple_loss=0.2959, pruned_loss=0.1206, over 4922.00 frames.], tot_loss[loss=0.2593, simple_loss=0.2953, pruned_loss=0.1117, over 982297.92 frames.], batch size: 81, aishell_tot_loss[loss=0.2521, simple_loss=0.2967, pruned_loss=0.1037, over 929732.60 frames.], datatang_tot_loss[loss=0.2675, simple_loss=0.293, pruned_loss=0.1211, over 930514.40 frames.], batch size: 81, lr: 2.00e-03
+2022-06-18 12:11:00,755 INFO [train.py:874] (3/4) Epoch 3, batch 1200, aishell_loss[loss=0.2565, simple_loss=0.2951, pruned_loss=0.109, over 4907.00 frames.], tot_loss[loss=0.2609, simple_loss=0.2959, pruned_loss=0.1129, over 982737.74 frames.], batch size: 34, aishell_tot_loss[loss=0.2526, simple_loss=0.2968, pruned_loss=0.1042, over 936628.01 frames.], datatang_tot_loss[loss=0.2687, simple_loss=0.2935, pruned_loss=0.1219, over 936373.80 frames.], batch size: 34, lr: 1.99e-03
+2022-06-18 12:11:32,019 INFO [train.py:874] (3/4) Epoch 3, batch 1250, datatang_loss[loss=0.2202, simple_loss=0.2545, pruned_loss=0.09301, over 4909.00 frames.], tot_loss[loss=0.2598, simple_loss=0.2948, pruned_loss=0.1124, over 983435.72 frames.], batch size: 30, aishell_tot_loss[loss=0.2519, simple_loss=0.2963, pruned_loss=0.1037, over 943107.20 frames.], datatang_tot_loss[loss=0.2688, simple_loss=0.293, pruned_loss=0.1223, over 941506.35 frames.], batch size: 30, lr: 1.99e-03
+2022-06-18 12:11:59,405 INFO [train.py:874] (3/4) Epoch 3, batch 1300, datatang_loss[loss=0.225, simple_loss=0.2642, pruned_loss=0.0929, over 4958.00 frames.], tot_loss[loss=0.2579, simple_loss=0.2939, pruned_loss=0.111, over 983506.19 frames.], batch size: 45, aishell_tot_loss[loss=0.2507, simple_loss=0.2958, pruned_loss=0.1028, over 948049.41 frames.], datatang_tot_loss[loss=0.2679, simple_loss=0.2924, pruned_loss=0.1216, over 946296.40 frames.], batch size: 45, lr: 1.98e-03
+2022-06-18 12:12:30,476 INFO [train.py:874] (3/4) Epoch 3, batch 1350, datatang_loss[loss=0.2344, simple_loss=0.27, pruned_loss=0.0994, over 4925.00 frames.], tot_loss[loss=0.2576, simple_loss=0.294, pruned_loss=0.1106, over 984254.96 frames.], batch size: 77, aishell_tot_loss[loss=0.2506, simple_loss=0.296, pruned_loss=0.1026, over 951847.07 frames.], datatang_tot_loss[loss=0.267, simple_loss=0.2924, pruned_loss=0.1208, over 951867.80 frames.], batch size: 77, lr: 1.98e-03
+2022-06-18 12:13:01,724 INFO [train.py:874] (3/4) Epoch 3, batch 1400, datatang_loss[loss=0.2458, simple_loss=0.2803, pruned_loss=0.1057, over 4921.00 frames.], tot_loss[loss=0.2572, simple_loss=0.2941, pruned_loss=0.1102, over 984591.05 frames.], batch size: 73, aishell_tot_loss[loss=0.2513, simple_loss=0.2964, pruned_loss=0.1031, over 956613.01 frames.], datatang_tot_loss[loss=0.266, simple_loss=0.2919, pruned_loss=0.12, over 955073.96 frames.], batch size: 73, lr: 1.97e-03
+2022-06-18 12:13:29,195 INFO [train.py:874] (3/4) Epoch 3, batch 1450, aishell_loss[loss=0.2649, simple_loss=0.3104, pruned_loss=0.1097, over 4977.00 frames.], tot_loss[loss=0.256, simple_loss=0.2931, pruned_loss=0.1094, over 984589.68 frames.], batch size: 61, aishell_tot_loss[loss=0.2496, simple_loss=0.2951, pruned_loss=0.102, over 960389.07 frames.], datatang_tot_loss[loss=0.2664, simple_loss=0.2921, pruned_loss=0.1204, over 958019.35 frames.], batch size: 61, lr: 1.97e-03
+2022-06-18 12:14:01,060 INFO [train.py:874] (3/4) Epoch 3, batch 1500, datatang_loss[loss=0.247, simple_loss=0.2817, pruned_loss=0.1062, over 4939.00 frames.], tot_loss[loss=0.2587, simple_loss=0.2943, pruned_loss=0.1116, over 984796.22 frames.], batch size: 62, aishell_tot_loss[loss=0.2505, simple_loss=0.2959, pruned_loss=0.1025, over 963434.74 frames.], datatang_tot_loss[loss=0.2681, simple_loss=0.2925, pruned_loss=0.1219, over 961166.79 frames.], batch size: 62, lr: 1.97e-03
+2022-06-18 12:14:30,086 INFO [train.py:874] (3/4) Epoch 3, batch 1550, aishell_loss[loss=0.2365, simple_loss=0.2923, pruned_loss=0.0904, over 4821.00 frames.], tot_loss[loss=0.2571, simple_loss=0.2934, pruned_loss=0.1104, over 984756.20 frames.], batch size: 29, aishell_tot_loss[loss=0.2501, simple_loss=0.2957, pruned_loss=0.1023, over 965741.74 frames.], datatang_tot_loss[loss=0.2665, simple_loss=0.2917, pruned_loss=0.1206, over 964121.21 frames.], batch size: 29, lr: 1.96e-03
+2022-06-18 12:15:00,015 INFO [train.py:874] (3/4) Epoch 3, batch 1600, datatang_loss[loss=0.2685, simple_loss=0.2862, pruned_loss=0.1254, over 4823.00 frames.], tot_loss[loss=0.2568, simple_loss=0.293, pruned_loss=0.1103, over 984495.25 frames.], batch size: 24, aishell_tot_loss[loss=0.2493, simple_loss=0.2951, pruned_loss=0.1018, over 967562.35 frames.], datatang_tot_loss[loss=0.2665, simple_loss=0.2919, pruned_loss=0.1206, over 966716.98 frames.], batch size: 24, lr: 1.96e-03
+2022-06-18 12:15:30,960 INFO [train.py:874] (3/4) Epoch 3, batch 1650, aishell_loss[loss=0.2017, simple_loss=0.2516, pruned_loss=0.07596, over 4818.00 frames.], tot_loss[loss=0.2572, simple_loss=0.2939, pruned_loss=0.1103, over 984453.21 frames.], batch size: 26, aishell_tot_loss[loss=0.2496, simple_loss=0.2956, pruned_loss=0.1018, over 969741.13 frames.], datatang_tot_loss[loss=0.2668, simple_loss=0.2922, pruned_loss=0.1207, over 968563.68 frames.], batch size: 26, lr: 1.96e-03
+2022-06-18 12:16:00,130 INFO [train.py:874] (3/4) Epoch 3, batch 1700, aishell_loss[loss=0.1942, simple_loss=0.2448, pruned_loss=0.07185, over 4950.00 frames.], tot_loss[loss=0.2565, simple_loss=0.2927, pruned_loss=0.1101, over 984816.60 frames.], batch size: 25, aishell_tot_loss[loss=0.2487, simple_loss=0.2948, pruned_loss=0.1013, over 971301.88 frames.], datatang_tot_loss[loss=0.2662, simple_loss=0.2918, pruned_loss=0.1203, over 970995.36 frames.], batch size: 25, lr: 1.95e-03
+2022-06-18 12:16:30,644 INFO [train.py:874] (3/4) Epoch 3, batch 1750, datatang_loss[loss=0.2526, simple_loss=0.2804, pruned_loss=0.1124, over 4967.00 frames.], tot_loss[loss=0.2567, simple_loss=0.2927, pruned_loss=0.1104, over 984598.08 frames.], batch size: 34, aishell_tot_loss[loss=0.2488, simple_loss=0.2947, pruned_loss=0.1014, over 972859.05 frames.], datatang_tot_loss[loss=0.2661, simple_loss=0.2916, pruned_loss=0.1203, over 972427.50 frames.], batch size: 34, lr: 1.95e-03
+2022-06-18 12:17:02,308 INFO [train.py:874] (3/4) Epoch 3, batch 1800, datatang_loss[loss=0.2885, simple_loss=0.3089, pruned_loss=0.134, over 4921.00 frames.], tot_loss[loss=0.2544, simple_loss=0.2913, pruned_loss=0.1087, over 985044.51 frames.], batch size: 57, aishell_tot_loss[loss=0.2478, simple_loss=0.2942, pruned_loss=0.1007, over 974700.88 frames.], datatang_tot_loss[loss=0.2646, simple_loss=0.2905, pruned_loss=0.1194, over 973879.64 frames.], batch size: 57, lr: 1.94e-03
+2022-06-18 12:17:29,507 INFO [train.py:874] (3/4) Epoch 3, batch 1850, datatang_loss[loss=0.2697, simple_loss=0.2914, pruned_loss=0.124, over 4934.00 frames.], tot_loss[loss=0.2543, simple_loss=0.2914, pruned_loss=0.1086, over 985350.97 frames.], batch size: 50, aishell_tot_loss[loss=0.2476, simple_loss=0.2942, pruned_loss=0.1005, over 976092.33 frames.], datatang_tot_loss[loss=0.2646, simple_loss=0.2904, pruned_loss=0.1194, over 975354.83 frames.], batch size: 50, lr: 1.94e-03
+2022-06-18 12:18:00,804 INFO [train.py:874] (3/4) Epoch 3, batch 1900, aishell_loss[loss=0.2481, simple_loss=0.2989, pruned_loss=0.09865, over 4857.00 frames.], tot_loss[loss=0.2543, simple_loss=0.2919, pruned_loss=0.1084, over 985199.50 frames.], batch size: 37, aishell_tot_loss[loss=0.2467, simple_loss=0.2939, pruned_loss=0.09981, over 977056.65 frames.], datatang_tot_loss[loss=0.2651, simple_loss=0.2911, pruned_loss=0.1195, over 976505.70 frames.], batch size: 37, lr: 1.94e-03
+2022-06-18 12:18:32,185 INFO [train.py:874] (3/4) Epoch 3, batch 1950, aishell_loss[loss=0.2026, simple_loss=0.26, pruned_loss=0.07256, over 4977.00 frames.], tot_loss[loss=0.254, simple_loss=0.2921, pruned_loss=0.108, over 985412.33 frames.], batch size: 30, aishell_tot_loss[loss=0.2464, simple_loss=0.2937, pruned_loss=0.09951, over 978317.02 frames.], datatang_tot_loss[loss=0.2648, simple_loss=0.2913, pruned_loss=0.1191, over 977451.70 frames.], batch size: 30, lr: 1.93e-03
+2022-06-18 12:18:59,706 INFO [train.py:874] (3/4) Epoch 3, batch 2000, aishell_loss[loss=0.2172, simple_loss=0.2667, pruned_loss=0.08381, over 4858.00 frames.], tot_loss[loss=0.2549, simple_loss=0.2922, pruned_loss=0.1088, over 985480.36 frames.], batch size: 28, aishell_tot_loss[loss=0.2458, simple_loss=0.2933, pruned_loss=0.09914, over 979251.20 frames.], datatang_tot_loss[loss=0.2659, simple_loss=0.2918, pruned_loss=0.12, over 978356.71 frames.], batch size: 28, lr: 1.93e-03
+2022-06-18 12:18:59,707 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 12:19:15,684 INFO [train.py:914] (3/4) Epoch 3, validation: loss=0.1934, simple_loss=0.2657, pruned_loss=0.0605, over 1622729.00 frames.
+2022-06-18 12:19:43,630 INFO [train.py:874] (3/4) Epoch 3, batch 2050, aishell_loss[loss=0.2521, simple_loss=0.3011, pruned_loss=0.1016, over 4916.00 frames.], tot_loss[loss=0.2561, simple_loss=0.2934, pruned_loss=0.1094, over 985749.86 frames.], batch size: 33, aishell_tot_loss[loss=0.246, simple_loss=0.2937, pruned_loss=0.09908, over 980018.68 frames.], datatang_tot_loss[loss=0.2665, simple_loss=0.2926, pruned_loss=0.1203, over 979455.57 frames.], batch size: 33, lr: 1.92e-03
+2022-06-18 12:20:13,737 INFO [train.py:874] (3/4) Epoch 3, batch 2100, datatang_loss[loss=0.2194, simple_loss=0.2656, pruned_loss=0.0866, over 4922.00 frames.], tot_loss[loss=0.253, simple_loss=0.291, pruned_loss=0.1075, over 985649.74 frames.], batch size: 83, aishell_tot_loss[loss=0.2462, simple_loss=0.2939, pruned_loss=0.09921, over 980542.28 frames.], datatang_tot_loss[loss=0.2626, simple_loss=0.29, pruned_loss=0.1176, over 980232.23 frames.], batch size: 83, lr: 1.92e-03
+2022-06-18 12:20:44,299 INFO [train.py:874] (3/4) Epoch 3, batch 2150, datatang_loss[loss=0.2604, simple_loss=0.2771, pruned_loss=0.1219, over 4897.00 frames.], tot_loss[loss=0.2517, simple_loss=0.2907, pruned_loss=0.1063, over 985764.98 frames.], batch size: 47, aishell_tot_loss[loss=0.2454, simple_loss=0.2936, pruned_loss=0.09856, over 981419.35 frames.], datatang_tot_loss[loss=0.2622, simple_loss=0.2896, pruned_loss=0.1174, over 980705.98 frames.], batch size: 47, lr: 1.92e-03
+2022-06-18 12:21:13,468 INFO [train.py:874] (3/4) Epoch 3, batch 2200, aishell_loss[loss=0.2535, simple_loss=0.31, pruned_loss=0.09845, over 4951.00 frames.], tot_loss[loss=0.2523, simple_loss=0.2911, pruned_loss=0.1068, over 985895.49 frames.], batch size: 61, aishell_tot_loss[loss=0.2452, simple_loss=0.2936, pruned_loss=0.09843, over 981762.02 frames.], datatang_tot_loss[loss=0.2624, simple_loss=0.2898, pruned_loss=0.1175, over 981609.66 frames.], batch size: 61, lr: 1.91e-03
+2022-06-18 12:21:43,281 INFO [train.py:874] (3/4) Epoch 3, batch 2250, aishell_loss[loss=0.2352, simple_loss=0.2928, pruned_loss=0.08882, over 4872.00 frames.], tot_loss[loss=0.2516, simple_loss=0.2917, pruned_loss=0.1058, over 986256.07 frames.], batch size: 42, aishell_tot_loss[loss=0.2453, simple_loss=0.2942, pruned_loss=0.09814, over 982518.83 frames.], datatang_tot_loss[loss=0.2619, simple_loss=0.2895, pruned_loss=0.1171, over 982220.87 frames.], batch size: 42, lr: 1.91e-03
+2022-06-18 12:22:13,534 INFO [train.py:874] (3/4) Epoch 3, batch 2300, datatang_loss[loss=0.25, simple_loss=0.2792, pruned_loss=0.1104, over 4900.00 frames.], tot_loss[loss=0.2533, simple_loss=0.2929, pruned_loss=0.1069, over 985993.43 frames.], batch size: 24, aishell_tot_loss[loss=0.2455, simple_loss=0.2947, pruned_loss=0.09818, over 982817.41 frames.], datatang_tot_loss[loss=0.263, simple_loss=0.2903, pruned_loss=0.1178, over 982564.49 frames.], batch size: 24, lr: 1.91e-03
+2022-06-18 12:22:41,832 INFO [train.py:874] (3/4) Epoch 3, batch 2350, datatang_loss[loss=0.2394, simple_loss=0.2757, pruned_loss=0.1015, over 4943.00 frames.], tot_loss[loss=0.2526, simple_loss=0.2927, pruned_loss=0.1063, over 986225.91 frames.], batch size: 69, aishell_tot_loss[loss=0.245, simple_loss=0.2944, pruned_loss=0.09782, over 983237.17 frames.], datatang_tot_loss[loss=0.2626, simple_loss=0.2904, pruned_loss=0.1174, over 983179.52 frames.], batch size: 69, lr: 1.90e-03
+2022-06-18 12:23:13,329 INFO [train.py:874] (3/4) Epoch 3, batch 2400, datatang_loss[loss=0.2534, simple_loss=0.2892, pruned_loss=0.1087, over 4958.00 frames.], tot_loss[loss=0.2513, simple_loss=0.2923, pruned_loss=0.1051, over 986421.69 frames.], batch size: 86, aishell_tot_loss[loss=0.2441, simple_loss=0.2943, pruned_loss=0.09694, over 983625.83 frames.], datatang_tot_loss[loss=0.2619, simple_loss=0.2902, pruned_loss=0.1168, over 983719.61 frames.], batch size: 86, lr: 1.90e-03
+2022-06-18 12:23:44,066 INFO [train.py:874] (3/4) Epoch 3, batch 2450, datatang_loss[loss=0.258, simple_loss=0.2919, pruned_loss=0.112, over 4897.00 frames.], tot_loss[loss=0.252, simple_loss=0.2921, pruned_loss=0.106, over 986322.42 frames.], batch size: 52, aishell_tot_loss[loss=0.2441, simple_loss=0.2942, pruned_loss=0.09699, over 983856.73 frames.], datatang_tot_loss[loss=0.2619, simple_loss=0.2902, pruned_loss=0.1168, over 984024.78 frames.], batch size: 52, lr: 1.89e-03
+2022-06-18 12:24:13,175 INFO [train.py:874] (3/4) Epoch 3, batch 2500, aishell_loss[loss=0.2419, simple_loss=0.2967, pruned_loss=0.09355, over 4929.00 frames.], tot_loss[loss=0.2516, simple_loss=0.2918, pruned_loss=0.1056, over 986311.18 frames.], batch size: 32, aishell_tot_loss[loss=0.2443, simple_loss=0.2945, pruned_loss=0.09702, over 984340.00 frames.], datatang_tot_loss[loss=0.2608, simple_loss=0.2895, pruned_loss=0.116, over 984092.52 frames.], batch size: 32, lr: 1.89e-03
+2022-06-18 12:24:44,617 INFO [train.py:874] (3/4) Epoch 3, batch 2550, aishell_loss[loss=0.2367, simple_loss=0.2925, pruned_loss=0.09039, over 4877.00 frames.], tot_loss[loss=0.2509, simple_loss=0.2917, pruned_loss=0.1051, over 985809.16 frames.], batch size: 35, aishell_tot_loss[loss=0.2438, simple_loss=0.2942, pruned_loss=0.09669, over 984304.19 frames.], datatang_tot_loss[loss=0.2606, simple_loss=0.2896, pruned_loss=0.1158, over 984092.87 frames.], batch size: 35, lr: 1.89e-03
+2022-06-18 12:25:14,290 INFO [train.py:874] (3/4) Epoch 3, batch 2600, datatang_loss[loss=0.2569, simple_loss=0.2843, pruned_loss=0.1147, over 4917.00 frames.], tot_loss[loss=0.2473, simple_loss=0.2887, pruned_loss=0.103, over 985614.88 frames.], batch size: 52, aishell_tot_loss[loss=0.2414, simple_loss=0.2924, pruned_loss=0.0952, over 984262.91 frames.], datatang_tot_loss[loss=0.2589, simple_loss=0.2882, pruned_loss=0.1148, over 984309.65 frames.], batch size: 52, lr: 1.88e-03
+2022-06-18 12:25:43,689 INFO [train.py:874] (3/4) Epoch 3, batch 2650, datatang_loss[loss=0.2399, simple_loss=0.277, pruned_loss=0.1013, over 4926.00 frames.], tot_loss[loss=0.2481, simple_loss=0.2893, pruned_loss=0.1035, over 985639.02 frames.], batch size: 73, aishell_tot_loss[loss=0.2415, simple_loss=0.2926, pruned_loss=0.09517, over 984432.38 frames.], datatang_tot_loss[loss=0.2589, simple_loss=0.2882, pruned_loss=0.1148, over 984477.46 frames.], batch size: 73, lr: 1.88e-03
+2022-06-18 12:26:15,021 INFO [train.py:874] (3/4) Epoch 3, batch 2700, aishell_loss[loss=0.2463, simple_loss=0.2986, pruned_loss=0.09698, over 4966.00 frames.], tot_loss[loss=0.2493, simple_loss=0.29, pruned_loss=0.1044, over 985781.74 frames.], batch size: 51, aishell_tot_loss[loss=0.2403, simple_loss=0.2914, pruned_loss=0.09462, over 984699.33 frames.], datatang_tot_loss[loss=0.2611, simple_loss=0.2898, pruned_loss=0.1162, over 984640.22 frames.], batch size: 51, lr: 1.88e-03
+2022-06-18 12:26:44,662 INFO [train.py:874] (3/4) Epoch 3, batch 2750, aishell_loss[loss=0.2153, simple_loss=0.2686, pruned_loss=0.08102, over 4940.00 frames.], tot_loss[loss=0.2481, simple_loss=0.2895, pruned_loss=0.1033, over 986028.80 frames.], batch size: 32, aishell_tot_loss[loss=0.2401, simple_loss=0.2914, pruned_loss=0.0944, over 984874.34 frames.], datatang_tot_loss[loss=0.26, simple_loss=0.2891, pruned_loss=0.1154, over 984997.25 frames.], batch size: 32, lr: 1.87e-03
+2022-06-18 12:27:14,057 INFO [train.py:874] (3/4) Epoch 3, batch 2800, aishell_loss[loss=0.3108, simple_loss=0.3298, pruned_loss=0.1459, over 4876.00 frames.], tot_loss[loss=0.2493, simple_loss=0.2901, pruned_loss=0.1042, over 985856.84 frames.], batch size: 35, aishell_tot_loss[loss=0.2406, simple_loss=0.2918, pruned_loss=0.09473, over 984789.98 frames.], datatang_tot_loss[loss=0.2602, simple_loss=0.2891, pruned_loss=0.1156, over 985162.51 frames.], batch size: 35, lr: 1.87e-03
+2022-06-18 12:27:45,794 INFO [train.py:874] (3/4) Epoch 3, batch 2850, aishell_loss[loss=0.2162, simple_loss=0.2706, pruned_loss=0.08084, over 4868.00 frames.], tot_loss[loss=0.2476, simple_loss=0.2892, pruned_loss=0.103, over 986036.97 frames.], batch size: 28, aishell_tot_loss[loss=0.2395, simple_loss=0.2909, pruned_loss=0.09408, over 985108.77 frames.], datatang_tot_loss[loss=0.2598, simple_loss=0.2891, pruned_loss=0.1153, over 985257.74 frames.], batch size: 28, lr: 1.87e-03
+2022-06-18 12:28:14,941 INFO [train.py:874] (3/4) Epoch 3, batch 2900, datatang_loss[loss=0.2686, simple_loss=0.31, pruned_loss=0.1136, over 4937.00 frames.], tot_loss[loss=0.2481, simple_loss=0.2892, pruned_loss=0.1035, over 986042.06 frames.], batch size: 88, aishell_tot_loss[loss=0.2401, simple_loss=0.2912, pruned_loss=0.09444, over 985177.53 frames.], datatang_tot_loss[loss=0.2594, simple_loss=0.2886, pruned_loss=0.1151, over 985406.50 frames.], batch size: 88, lr: 1.86e-03
+2022-06-18 12:28:45,275 INFO [train.py:874] (3/4) Epoch 3, batch 2950, datatang_loss[loss=0.2314, simple_loss=0.2663, pruned_loss=0.09822, over 4949.00 frames.], tot_loss[loss=0.2496, simple_loss=0.2897, pruned_loss=0.1047, over 986137.47 frames.], batch size: 45, aishell_tot_loss[loss=0.2409, simple_loss=0.2912, pruned_loss=0.09526, over 985235.25 frames.], datatang_tot_loss[loss=0.2596, simple_loss=0.2889, pruned_loss=0.1151, over 985627.85 frames.], batch size: 45, lr: 1.86e-03
+2022-06-18 12:29:16,495 INFO [train.py:874] (3/4) Epoch 3, batch 3000, aishell_loss[loss=0.2339, simple_loss=0.2858, pruned_loss=0.09104, over 4927.00 frames.], tot_loss[loss=0.2483, simple_loss=0.2891, pruned_loss=0.1037, over 985879.75 frames.], batch size: 33, aishell_tot_loss[loss=0.2403, simple_loss=0.2909, pruned_loss=0.09488, over 985048.98 frames.], datatang_tot_loss[loss=0.259, simple_loss=0.2885, pruned_loss=0.1147, over 985727.13 frames.], batch size: 33, lr: 1.86e-03
+2022-06-18 12:29:16,496 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 12:29:32,000 INFO [train.py:914] (3/4) Epoch 3, validation: loss=0.1943, simple_loss=0.2686, pruned_loss=0.05999, over 1622729.00 frames.
+2022-06-18 12:30:03,163 INFO [train.py:874] (3/4) Epoch 3, batch 3050, datatang_loss[loss=0.262, simple_loss=0.2853, pruned_loss=0.1193, over 4895.00 frames.], tot_loss[loss=0.245, simple_loss=0.287, pruned_loss=0.1015, over 985682.19 frames.], batch size: 59, aishell_tot_loss[loss=0.238, simple_loss=0.2892, pruned_loss=0.09344, over 984848.23 frames.], datatang_tot_loss[loss=0.2576, simple_loss=0.2878, pruned_loss=0.1136, over 985845.68 frames.], batch size: 59, lr: 1.85e-03
+2022-06-18 12:30:33,677 INFO [train.py:874] (3/4) Epoch 3, batch 3100, datatang_loss[loss=0.2318, simple_loss=0.2726, pruned_loss=0.09547, over 4922.00 frames.], tot_loss[loss=0.2453, simple_loss=0.2874, pruned_loss=0.1016, over 985675.89 frames.], batch size: 71, aishell_tot_loss[loss=0.2381, simple_loss=0.2894, pruned_loss=0.09339, over 984897.75 frames.], datatang_tot_loss[loss=0.257, simple_loss=0.2876, pruned_loss=0.1132, over 985871.30 frames.], batch size: 71, lr: 1.85e-03
+2022-06-18 12:31:02,216 INFO [train.py:874] (3/4) Epoch 3, batch 3150, datatang_loss[loss=0.2583, simple_loss=0.2969, pruned_loss=0.1099, over 4908.00 frames.], tot_loss[loss=0.2452, simple_loss=0.2877, pruned_loss=0.1014, over 985635.79 frames.], batch size: 52, aishell_tot_loss[loss=0.2384, simple_loss=0.29, pruned_loss=0.09341, over 984813.04 frames.], datatang_tot_loss[loss=0.2561, simple_loss=0.2871, pruned_loss=0.1125, over 985980.36 frames.], batch size: 52, lr: 1.85e-03
+2022-06-18 12:31:34,040 INFO [train.py:874] (3/4) Epoch 3, batch 3200, datatang_loss[loss=0.2, simple_loss=0.2451, pruned_loss=0.07744, over 4978.00 frames.], tot_loss[loss=0.2445, simple_loss=0.2872, pruned_loss=0.1009, over 985524.65 frames.], batch size: 37, aishell_tot_loss[loss=0.2378, simple_loss=0.2895, pruned_loss=0.09309, over 984637.71 frames.], datatang_tot_loss[loss=0.2556, simple_loss=0.2869, pruned_loss=0.1122, over 986094.69 frames.], batch size: 37, lr: 1.84e-03
+2022-06-18 12:32:03,742 INFO [train.py:874] (3/4) Epoch 3, batch 3250, aishell_loss[loss=0.2092, simple_loss=0.269, pruned_loss=0.07464, over 4877.00 frames.], tot_loss[loss=0.2444, simple_loss=0.2875, pruned_loss=0.1007, over 985224.10 frames.], batch size: 42, aishell_tot_loss[loss=0.2375, simple_loss=0.2891, pruned_loss=0.09295, over 984498.94 frames.], datatang_tot_loss[loss=0.2559, simple_loss=0.2873, pruned_loss=0.1123, over 986000.98 frames.], batch size: 42, lr: 1.84e-03
+2022-06-18 12:32:33,316 INFO [train.py:874] (3/4) Epoch 3, batch 3300, aishell_loss[loss=0.2655, simple_loss=0.3122, pruned_loss=0.1094, over 4865.00 frames.], tot_loss[loss=0.2472, simple_loss=0.2889, pruned_loss=0.1027, over 985618.77 frames.], batch size: 37, aishell_tot_loss[loss=0.2386, simple_loss=0.2897, pruned_loss=0.09374, over 984936.72 frames.], datatang_tot_loss[loss=0.2568, simple_loss=0.288, pruned_loss=0.1128, over 985948.40 frames.], batch size: 37, lr: 1.84e-03
+2022-06-18 12:33:03,656 INFO [train.py:874] (3/4) Epoch 3, batch 3350, datatang_loss[loss=0.2077, simple_loss=0.2427, pruned_loss=0.08639, over 4981.00 frames.], tot_loss[loss=0.2475, simple_loss=0.29, pruned_loss=0.1025, over 985782.35 frames.], batch size: 45, aishell_tot_loss[loss=0.2389, simple_loss=0.2903, pruned_loss=0.09382, over 985163.40 frames.], datatang_tot_loss[loss=0.2571, simple_loss=0.2886, pruned_loss=0.1128, over 985938.69 frames.], batch size: 45, lr: 1.83e-03
+2022-06-18 12:33:33,273 INFO [train.py:874] (3/4) Epoch 3, batch 3400, aishell_loss[loss=0.2604, simple_loss=0.3096, pruned_loss=0.1056, over 4913.00 frames.], tot_loss[loss=0.2467, simple_loss=0.2896, pruned_loss=0.1019, over 984968.57 frames.], batch size: 52, aishell_tot_loss[loss=0.2387, simple_loss=0.2902, pruned_loss=0.09362, over 984603.04 frames.], datatang_tot_loss[loss=0.2565, simple_loss=0.2884, pruned_loss=0.1123, over 985680.76 frames.], batch size: 52, lr: 1.83e-03
+2022-06-18 12:34:03,676 INFO [train.py:874] (3/4) Epoch 3, batch 3450, datatang_loss[loss=0.2566, simple_loss=0.2852, pruned_loss=0.114, over 4962.00 frames.], tot_loss[loss=0.2455, simple_loss=0.2889, pruned_loss=0.101, over 985098.95 frames.], batch size: 60, aishell_tot_loss[loss=0.2382, simple_loss=0.2901, pruned_loss=0.09312, over 984705.95 frames.], datatang_tot_loss[loss=0.2557, simple_loss=0.2878, pruned_loss=0.1118, over 985692.22 frames.], batch size: 60, lr: 1.83e-03
+2022-06-18 12:34:34,691 INFO [train.py:874] (3/4) Epoch 3, batch 3500, aishell_loss[loss=0.1991, simple_loss=0.2625, pruned_loss=0.0678, over 4930.00 frames.], tot_loss[loss=0.2441, simple_loss=0.2874, pruned_loss=0.1005, over 985311.99 frames.], batch size: 58, aishell_tot_loss[loss=0.2366, simple_loss=0.2885, pruned_loss=0.09232, over 984737.51 frames.], datatang_tot_loss[loss=0.2555, simple_loss=0.2877, pruned_loss=0.1116, over 985856.93 frames.], batch size: 58, lr: 1.82e-03
+2022-06-18 12:35:02,640 INFO [train.py:874] (3/4) Epoch 3, batch 3550, datatang_loss[loss=0.2556, simple_loss=0.2818, pruned_loss=0.1147, over 4938.00 frames.], tot_loss[loss=0.2443, simple_loss=0.287, pruned_loss=0.1008, over 985370.81 frames.], batch size: 50, aishell_tot_loss[loss=0.2363, simple_loss=0.2881, pruned_loss=0.09227, over 984872.48 frames.], datatang_tot_loss[loss=0.2552, simple_loss=0.2875, pruned_loss=0.1114, over 985798.44 frames.], batch size: 50, lr: 1.82e-03
+2022-06-18 12:35:34,159 INFO [train.py:874] (3/4) Epoch 3, batch 3600, aishell_loss[loss=0.2068, simple_loss=0.2746, pruned_loss=0.06946, over 4944.00 frames.], tot_loss[loss=0.2447, simple_loss=0.287, pruned_loss=0.1012, over 985093.10 frames.], batch size: 64, aishell_tot_loss[loss=0.237, simple_loss=0.2883, pruned_loss=0.09283, over 984915.96 frames.], datatang_tot_loss[loss=0.2547, simple_loss=0.287, pruned_loss=0.1111, over 985455.22 frames.], batch size: 64, lr: 1.82e-03
+2022-06-18 12:36:04,052 INFO [train.py:874] (3/4) Epoch 3, batch 3650, datatang_loss[loss=0.2454, simple_loss=0.2854, pruned_loss=0.1027, over 4961.00 frames.], tot_loss[loss=0.2442, simple_loss=0.2863, pruned_loss=0.1011, over 985412.96 frames.], batch size: 67, aishell_tot_loss[loss=0.2372, simple_loss=0.2884, pruned_loss=0.093, over 985095.63 frames.], datatang_tot_loss[loss=0.2532, simple_loss=0.2862, pruned_loss=0.1101, over 985573.96 frames.], batch size: 67, lr: 1.81e-03
+2022-06-18 12:36:39,229 INFO [train.py:874] (3/4) Epoch 3, batch 3700, aishell_loss[loss=0.2236, simple_loss=0.2908, pruned_loss=0.07825, over 4964.00 frames.], tot_loss[loss=0.2448, simple_loss=0.2869, pruned_loss=0.1014, over 985607.84 frames.], batch size: 64, aishell_tot_loss[loss=0.2371, simple_loss=0.2883, pruned_loss=0.09294, over 985270.70 frames.], datatang_tot_loss[loss=0.2534, simple_loss=0.2866, pruned_loss=0.1101, over 985624.49 frames.], batch size: 64, lr: 1.81e-03
+2022-06-18 12:37:08,716 INFO [train.py:874] (3/4) Epoch 3, batch 3750, aishell_loss[loss=0.196, simple_loss=0.2387, pruned_loss=0.07663, over 4810.00 frames.], tot_loss[loss=0.2449, simple_loss=0.2868, pruned_loss=0.1015, over 985592.32 frames.], batch size: 24, aishell_tot_loss[loss=0.2375, simple_loss=0.2886, pruned_loss=0.09318, over 985331.09 frames.], datatang_tot_loss[loss=0.2526, simple_loss=0.2862, pruned_loss=0.1095, over 985593.00 frames.], batch size: 24, lr: 1.81e-03
+2022-06-18 12:37:37,208 INFO [train.py:874] (3/4) Epoch 3, batch 3800, datatang_loss[loss=0.2196, simple_loss=0.2683, pruned_loss=0.08546, over 4914.00 frames.], tot_loss[loss=0.245, simple_loss=0.2869, pruned_loss=0.1016, over 985428.00 frames.], batch size: 25, aishell_tot_loss[loss=0.2373, simple_loss=0.2886, pruned_loss=0.093, over 985157.02 frames.], datatang_tot_loss[loss=0.2527, simple_loss=0.2863, pruned_loss=0.1096, over 985629.29 frames.], batch size: 25, lr: 1.80e-03
+2022-06-18 12:38:06,949 INFO [train.py:874] (3/4) Epoch 3, batch 3850, datatang_loss[loss=0.2587, simple_loss=0.2959, pruned_loss=0.1107, over 4940.00 frames.], tot_loss[loss=0.2446, simple_loss=0.2864, pruned_loss=0.1014, over 985556.82 frames.], batch size: 88, aishell_tot_loss[loss=0.2382, simple_loss=0.2891, pruned_loss=0.09365, over 985184.44 frames.], datatang_tot_loss[loss=0.2509, simple_loss=0.2852, pruned_loss=0.1083, over 985736.93 frames.], batch size: 88, lr: 1.80e-03
+2022-06-18 12:38:36,207 INFO [train.py:874] (3/4) Epoch 3, batch 3900, datatang_loss[loss=0.2575, simple_loss=0.2949, pruned_loss=0.11, over 4921.00 frames.], tot_loss[loss=0.2445, simple_loss=0.2868, pruned_loss=0.1011, over 985825.59 frames.], batch size: 83, aishell_tot_loss[loss=0.238, simple_loss=0.2891, pruned_loss=0.09341, over 985408.41 frames.], datatang_tot_loss[loss=0.251, simple_loss=0.2853, pruned_loss=0.1083, over 985830.78 frames.], batch size: 83, lr: 1.80e-03
+2022-06-18 12:39:04,536 INFO [train.py:874] (3/4) Epoch 3, batch 3950, aishell_loss[loss=0.2344, simple_loss=0.2834, pruned_loss=0.09264, over 4890.00 frames.], tot_loss[loss=0.243, simple_loss=0.286, pruned_loss=0.1, over 985884.58 frames.], batch size: 47, aishell_tot_loss[loss=0.2379, simple_loss=0.2892, pruned_loss=0.09331, over 985536.85 frames.], datatang_tot_loss[loss=0.2492, simple_loss=0.2843, pruned_loss=0.107, over 985801.78 frames.], batch size: 47, lr: 1.79e-03
+2022-06-18 12:39:34,741 INFO [train.py:874] (3/4) Epoch 3, batch 4000, datatang_loss[loss=0.2554, simple_loss=0.2886, pruned_loss=0.111, over 4954.00 frames.], tot_loss[loss=0.2434, simple_loss=0.286, pruned_loss=0.1004, over 985779.72 frames.], batch size: 55, aishell_tot_loss[loss=0.2379, simple_loss=0.2892, pruned_loss=0.09334, over 985257.82 frames.], datatang_tot_loss[loss=0.2493, simple_loss=0.2843, pruned_loss=0.1071, over 986012.20 frames.], batch size: 55, lr: 1.79e-03
+2022-06-18 12:39:34,741 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 12:39:51,131 INFO [train.py:914] (3/4) Epoch 3, validation: loss=0.1881, simple_loss=0.2644, pruned_loss=0.05586, over 1622729.00 frames.
+2022-06-18 12:40:21,178 INFO [train.py:874] (3/4) Epoch 3, batch 4050, datatang_loss[loss=0.2355, simple_loss=0.2725, pruned_loss=0.09923, over 4905.00 frames.], tot_loss[loss=0.2429, simple_loss=0.2856, pruned_loss=0.1001, over 985394.04 frames.], batch size: 30, aishell_tot_loss[loss=0.2372, simple_loss=0.2889, pruned_loss=0.09281, over 984848.60 frames.], datatang_tot_loss[loss=0.2491, simple_loss=0.284, pruned_loss=0.1071, over 986048.85 frames.], batch size: 30, lr: 1.79e-03
+2022-06-18 12:40:49,614 INFO [train.py:874] (3/4) Epoch 3, batch 4100, aishell_loss[loss=0.2307, simple_loss=0.296, pruned_loss=0.08273, over 4867.00 frames.], tot_loss[loss=0.2424, simple_loss=0.2861, pruned_loss=0.09931, over 985374.40 frames.], batch size: 35, aishell_tot_loss[loss=0.2368, simple_loss=0.2891, pruned_loss=0.09223, over 984642.46 frames.], datatang_tot_loss[loss=0.2492, simple_loss=0.2841, pruned_loss=0.1071, over 986256.02 frames.], batch size: 35, lr: 1.78e-03
+2022-06-18 12:42:03,169 INFO [train.py:874] (3/4) Epoch 4, batch 50, aishell_loss[loss=0.2345, simple_loss=0.2912, pruned_loss=0.08886, over 4883.00 frames.], tot_loss[loss=0.228, simple_loss=0.2753, pruned_loss=0.09034, over 218559.91 frames.], batch size: 47, aishell_tot_loss[loss=0.2287, simple_loss=0.2832, pruned_loss=0.08711, over 111742.17 frames.], datatang_tot_loss[loss=0.2272, simple_loss=0.2679, pruned_loss=0.09325, over 120464.51 frames.], batch size: 47, lr: 1.73e-03
+2022-06-18 12:42:33,984 INFO [train.py:874] (3/4) Epoch 4, batch 100, aishell_loss[loss=0.2304, simple_loss=0.2934, pruned_loss=0.0837, over 4977.00 frames.], tot_loss[loss=0.233, simple_loss=0.2808, pruned_loss=0.09259, over 388807.01 frames.], batch size: 44, aishell_tot_loss[loss=0.2352, simple_loss=0.2893, pruned_loss=0.09058, over 230074.44 frames.], datatang_tot_loss[loss=0.2298, simple_loss=0.2706, pruned_loss=0.09454, over 207009.85 frames.], batch size: 44, lr: 1.73e-03
+2022-06-18 12:43:05,300 INFO [train.py:874] (3/4) Epoch 4, batch 150, datatang_loss[loss=0.2268, simple_loss=0.2691, pruned_loss=0.09224, over 4917.00 frames.], tot_loss[loss=0.2312, simple_loss=0.2791, pruned_loss=0.09169, over 521166.10 frames.], batch size: 75, aishell_tot_loss[loss=0.2329, simple_loss=0.288, pruned_loss=0.08889, over 312306.78 frames.], datatang_tot_loss[loss=0.2295, simple_loss=0.2699, pruned_loss=0.09455, over 305662.35 frames.], batch size: 75, lr: 1.72e-03
+2022-06-18 12:43:34,808 INFO [train.py:874] (3/4) Epoch 4, batch 200, datatang_loss[loss=0.1982, simple_loss=0.261, pruned_loss=0.0677, over 4944.00 frames.], tot_loss[loss=0.2337, simple_loss=0.281, pruned_loss=0.09319, over 624149.55 frames.], batch size: 86, aishell_tot_loss[loss=0.2353, simple_loss=0.29, pruned_loss=0.09027, over 385499.64 frames.], datatang_tot_loss[loss=0.2314, simple_loss=0.2715, pruned_loss=0.09569, over 391869.57 frames.], batch size: 86, lr: 1.72e-03
+2022-06-18 12:44:05,329 INFO [train.py:874] (3/4) Epoch 4, batch 250, datatang_loss[loss=0.2416, simple_loss=0.2768, pruned_loss=0.1032, over 4912.00 frames.], tot_loss[loss=0.235, simple_loss=0.2819, pruned_loss=0.09405, over 704229.59 frames.], batch size: 52, aishell_tot_loss[loss=0.2368, simple_loss=0.2914, pruned_loss=0.09117, over 448111.37 frames.], datatang_tot_loss[loss=0.2322, simple_loss=0.2723, pruned_loss=0.09609, over 469607.33 frames.], batch size: 52, lr: 1.72e-03
+2022-06-18 12:44:35,773 INFO [train.py:874] (3/4) Epoch 4, batch 300, aishell_loss[loss=0.1815, simple_loss=0.2395, pruned_loss=0.06177, over 4981.00 frames.], tot_loss[loss=0.2344, simple_loss=0.2812, pruned_loss=0.0938, over 766510.41 frames.], batch size: 30, aishell_tot_loss[loss=0.2341, simple_loss=0.2886, pruned_loss=0.08983, over 515798.07 frames.], datatang_tot_loss[loss=0.2341, simple_loss=0.2737, pruned_loss=0.09721, over 526014.00 frames.], batch size: 30, lr: 1.71e-03
+2022-06-18 12:45:04,622 INFO [train.py:874] (3/4) Epoch 4, batch 350, aishell_loss[loss=0.2442, simple_loss=0.3003, pruned_loss=0.09401, over 4940.00 frames.], tot_loss[loss=0.2339, simple_loss=0.2804, pruned_loss=0.09366, over 815245.93 frames.], batch size: 58, aishell_tot_loss[loss=0.2326, simple_loss=0.2871, pruned_loss=0.08904, over 573103.93 frames.], datatang_tot_loss[loss=0.2349, simple_loss=0.2739, pruned_loss=0.09799, over 578370.83 frames.], batch size: 58, lr: 1.71e-03
+2022-06-18 12:45:35,310 INFO [train.py:874] (3/4) Epoch 4, batch 400, aishell_loss[loss=0.2159, simple_loss=0.2782, pruned_loss=0.07677, over 4947.00 frames.], tot_loss[loss=0.2345, simple_loss=0.2811, pruned_loss=0.09392, over 852853.69 frames.], batch size: 31, aishell_tot_loss[loss=0.2313, simple_loss=0.286, pruned_loss=0.08832, over 637710.45 frames.], datatang_tot_loss[loss=0.2374, simple_loss=0.2751, pruned_loss=0.09986, over 609603.00 frames.], batch size: 31, lr: 1.71e-03
+2022-06-18 12:46:05,241 INFO [train.py:874] (3/4) Epoch 4, batch 450, datatang_loss[loss=0.2421, simple_loss=0.2873, pruned_loss=0.0984, over 4954.00 frames.], tot_loss[loss=0.2337, simple_loss=0.2804, pruned_loss=0.09346, over 882375.68 frames.], batch size: 62, aishell_tot_loss[loss=0.2312, simple_loss=0.286, pruned_loss=0.08818, over 675634.71 frames.], datatang_tot_loss[loss=0.2365, simple_loss=0.2747, pruned_loss=0.0992, over 657254.16 frames.], batch size: 62, lr: 1.71e-03
+2022-06-18 12:46:35,437 INFO [train.py:874] (3/4) Epoch 4, batch 500, datatang_loss[loss=0.3286, simple_loss=0.3481, pruned_loss=0.1546, over 4919.00 frames.], tot_loss[loss=0.2336, simple_loss=0.28, pruned_loss=0.09354, over 905292.86 frames.], batch size: 108, aishell_tot_loss[loss=0.2302, simple_loss=0.2849, pruned_loss=0.08773, over 712310.11 frames.], datatang_tot_loss[loss=0.2374, simple_loss=0.2753, pruned_loss=0.09971, over 695811.48 frames.], batch size: 108, lr: 1.70e-03
+2022-06-18 12:47:05,953 INFO [train.py:874] (3/4) Epoch 4, batch 550, aishell_loss[loss=0.2111, simple_loss=0.2793, pruned_loss=0.07149, over 4910.00 frames.], tot_loss[loss=0.2339, simple_loss=0.2806, pruned_loss=0.09358, over 923133.32 frames.], batch size: 52, aishell_tot_loss[loss=0.2299, simple_loss=0.2849, pruned_loss=0.08742, over 745478.94 frames.], datatang_tot_loss[loss=0.2381, simple_loss=0.276, pruned_loss=0.1001, over 728924.47 frames.], batch size: 52, lr: 1.70e-03
+2022-06-18 12:47:36,207 INFO [train.py:874] (3/4) Epoch 4, batch 600, datatang_loss[loss=0.2417, simple_loss=0.2814, pruned_loss=0.101, over 4950.00 frames.], tot_loss[loss=0.2353, simple_loss=0.2819, pruned_loss=0.09431, over 937310.42 frames.], batch size: 86, aishell_tot_loss[loss=0.2297, simple_loss=0.2849, pruned_loss=0.08728, over 772764.43 frames.], datatang_tot_loss[loss=0.2401, simple_loss=0.2779, pruned_loss=0.1011, over 760558.95 frames.], batch size: 86, lr: 1.70e-03
+2022-06-18 12:48:07,268 INFO [train.py:874] (3/4) Epoch 4, batch 650, aishell_loss[loss=0.2058, simple_loss=0.2735, pruned_loss=0.06902, over 4913.00 frames.], tot_loss[loss=0.2356, simple_loss=0.2822, pruned_loss=0.09444, over 948070.97 frames.], batch size: 52, aishell_tot_loss[loss=0.2306, simple_loss=0.2854, pruned_loss=0.08792, over 798870.24 frames.], datatang_tot_loss[loss=0.2397, simple_loss=0.2779, pruned_loss=0.1008, over 786028.64 frames.], batch size: 52, lr: 1.69e-03
+2022-06-18 12:48:37,414 INFO [train.py:874] (3/4) Epoch 4, batch 700, datatang_loss[loss=0.2611, simple_loss=0.2882, pruned_loss=0.117, over 4925.00 frames.], tot_loss[loss=0.2383, simple_loss=0.2837, pruned_loss=0.09647, over 956784.08 frames.], batch size: 42, aishell_tot_loss[loss=0.2315, simple_loss=0.286, pruned_loss=0.0885, over 817647.11 frames.], datatang_tot_loss[loss=0.242, simple_loss=0.2794, pruned_loss=0.1023, over 813317.64 frames.], batch size: 42, lr: 1.69e-03
+2022-06-18 12:49:07,626 INFO [train.py:874] (3/4) Epoch 4, batch 750, aishell_loss[loss=0.322, simple_loss=0.3612, pruned_loss=0.1414, over 4939.00 frames.], tot_loss[loss=0.2389, simple_loss=0.2842, pruned_loss=0.09679, over 963171.22 frames.], batch size: 79, aishell_tot_loss[loss=0.2321, simple_loss=0.2866, pruned_loss=0.08882, over 831303.29 frames.], datatang_tot_loss[loss=0.2421, simple_loss=0.2801, pruned_loss=0.1021, over 839645.98 frames.], batch size: 79, lr: 1.69e-03
+2022-06-18 12:49:37,726 INFO [train.py:874] (3/4) Epoch 4, batch 800, datatang_loss[loss=0.2355, simple_loss=0.2865, pruned_loss=0.09225, over 4960.00 frames.], tot_loss[loss=0.2375, simple_loss=0.2842, pruned_loss=0.09537, over 968388.86 frames.], batch size: 91, aishell_tot_loss[loss=0.2313, simple_loss=0.2864, pruned_loss=0.08805, over 853605.04 frames.], datatang_tot_loss[loss=0.242, simple_loss=0.2802, pruned_loss=0.1019, over 853068.23 frames.], batch size: 91, lr: 1.69e-03
+2022-06-18 12:50:07,690 INFO [train.py:874] (3/4) Epoch 4, batch 850, aishell_loss[loss=0.1862, simple_loss=0.2575, pruned_loss=0.05748, over 4971.00 frames.], tot_loss[loss=0.2382, simple_loss=0.2852, pruned_loss=0.09565, over 972463.97 frames.], batch size: 51, aishell_tot_loss[loss=0.231, simple_loss=0.2867, pruned_loss=0.08771, over 871026.55 frames.], datatang_tot_loss[loss=0.2436, simple_loss=0.2812, pruned_loss=0.103, over 867041.85 frames.], batch size: 51, lr: 1.68e-03
+2022-06-18 12:50:37,452 INFO [train.py:874] (3/4) Epoch 4, batch 900, aishell_loss[loss=0.2561, simple_loss=0.304, pruned_loss=0.1041, over 4941.00 frames.], tot_loss[loss=0.2378, simple_loss=0.2845, pruned_loss=0.09555, over 975335.37 frames.], batch size: 31, aishell_tot_loss[loss=0.2302, simple_loss=0.2858, pruned_loss=0.08734, over 884900.28 frames.], datatang_tot_loss[loss=0.2442, simple_loss=0.2816, pruned_loss=0.1034, over 880563.88 frames.], batch size: 31, lr: 1.68e-03
+2022-06-18 12:51:08,884 INFO [train.py:874] (3/4) Epoch 4, batch 950, datatang_loss[loss=0.2407, simple_loss=0.2738, pruned_loss=0.1038, over 4892.00 frames.], tot_loss[loss=0.2368, simple_loss=0.2834, pruned_loss=0.09509, over 977692.83 frames.], batch size: 47, aishell_tot_loss[loss=0.2294, simple_loss=0.2849, pruned_loss=0.08693, over 896826.33 frames.], datatang_tot_loss[loss=0.244, simple_loss=0.2814, pruned_loss=0.1033, over 892959.39 frames.], batch size: 47, lr: 1.68e-03
+2022-06-18 12:51:39,354 INFO [train.py:874] (3/4) Epoch 4, batch 1000, datatang_loss[loss=0.243, simple_loss=0.2851, pruned_loss=0.1004, over 4940.00 frames.], tot_loss[loss=0.2348, simple_loss=0.2819, pruned_loss=0.09386, over 979200.15 frames.], batch size: 88, aishell_tot_loss[loss=0.2281, simple_loss=0.2841, pruned_loss=0.08607, over 906444.14 frames.], datatang_tot_loss[loss=0.2429, simple_loss=0.2806, pruned_loss=0.1026, over 904459.02 frames.], batch size: 88, lr: 1.67e-03
+2022-06-18 12:51:39,356 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 12:51:55,328 INFO [train.py:914] (3/4) Epoch 4, validation: loss=0.1866, simple_loss=0.2641, pruned_loss=0.05457, over 1622729.00 frames.
+2022-06-18 12:52:26,608 INFO [train.py:874] (3/4) Epoch 4, batch 1050, aishell_loss[loss=0.1972, simple_loss=0.2717, pruned_loss=0.06133, over 4928.00 frames.], tot_loss[loss=0.2331, simple_loss=0.2806, pruned_loss=0.09278, over 980460.53 frames.], batch size: 46, aishell_tot_loss[loss=0.2272, simple_loss=0.2834, pruned_loss=0.08548, over 914525.58 frames.], datatang_tot_loss[loss=0.2415, simple_loss=0.2797, pruned_loss=0.1016, over 915099.75 frames.], batch size: 46, lr: 1.67e-03
+2022-06-18 12:52:56,832 INFO [train.py:874] (3/4) Epoch 4, batch 1100, datatang_loss[loss=0.2676, simple_loss=0.2894, pruned_loss=0.1229, over 4970.00 frames.], tot_loss[loss=0.2321, simple_loss=0.2802, pruned_loss=0.09205, over 981703.43 frames.], batch size: 60, aishell_tot_loss[loss=0.2262, simple_loss=0.2828, pruned_loss=0.08483, over 923279.17 frames.], datatang_tot_loss[loss=0.2411, simple_loss=0.2795, pruned_loss=0.1013, over 923149.42 frames.], batch size: 60, lr: 1.67e-03
+2022-06-18 12:53:26,806 INFO [train.py:874] (3/4) Epoch 4, batch 1150, aishell_loss[loss=0.2543, simple_loss=0.3155, pruned_loss=0.09661, over 4979.00 frames.], tot_loss[loss=0.2326, simple_loss=0.2813, pruned_loss=0.09194, over 982541.79 frames.], batch size: 39, aishell_tot_loss[loss=0.2272, simple_loss=0.284, pruned_loss=0.08515, over 932264.68 frames.], datatang_tot_loss[loss=0.2407, simple_loss=0.2793, pruned_loss=0.1011, over 928778.46 frames.], batch size: 39, lr: 1.67e-03
+2022-06-18 12:53:57,057 INFO [train.py:874] (3/4) Epoch 4, batch 1200, datatang_loss[loss=0.2974, simple_loss=0.3246, pruned_loss=0.1351, over 4966.00 frames.], tot_loss[loss=0.2311, simple_loss=0.2804, pruned_loss=0.0909, over 982765.75 frames.], batch size: 91, aishell_tot_loss[loss=0.2252, simple_loss=0.2827, pruned_loss=0.0839, over 937939.79 frames.], datatang_tot_loss[loss=0.2405, simple_loss=0.2794, pruned_loss=0.1008, over 935572.33 frames.], batch size: 91, lr: 1.66e-03
+2022-06-18 12:54:27,085 INFO [train.py:874] (3/4) Epoch 4, batch 1250, datatang_loss[loss=0.2135, simple_loss=0.2541, pruned_loss=0.08647, over 4941.00 frames.], tot_loss[loss=0.2324, simple_loss=0.2812, pruned_loss=0.0918, over 983536.67 frames.], batch size: 50, aishell_tot_loss[loss=0.2256, simple_loss=0.2828, pruned_loss=0.08423, over 943939.69 frames.], datatang_tot_loss[loss=0.2412, simple_loss=0.28, pruned_loss=0.1012, over 941199.66 frames.], batch size: 50, lr: 1.66e-03
+2022-06-18 12:54:57,648 INFO [train.py:874] (3/4) Epoch 4, batch 1300, datatang_loss[loss=0.252, simple_loss=0.2937, pruned_loss=0.1051, over 4959.00 frames.], tot_loss[loss=0.233, simple_loss=0.2819, pruned_loss=0.09204, over 984613.08 frames.], batch size: 37, aishell_tot_loss[loss=0.225, simple_loss=0.2826, pruned_loss=0.08374, over 950306.15 frames.], datatang_tot_loss[loss=0.2426, simple_loss=0.2808, pruned_loss=0.1022, over 945518.23 frames.], batch size: 37, lr: 1.66e-03
+2022-06-18 12:55:28,257 INFO [train.py:874] (3/4) Epoch 4, batch 1350, aishell_loss[loss=0.2299, simple_loss=0.2864, pruned_loss=0.08672, over 4962.00 frames.], tot_loss[loss=0.234, simple_loss=0.2831, pruned_loss=0.09246, over 985068.27 frames.], batch size: 64, aishell_tot_loss[loss=0.2257, simple_loss=0.2833, pruned_loss=0.08403, over 955303.18 frames.], datatang_tot_loss[loss=0.2434, simple_loss=0.2815, pruned_loss=0.1026, over 949520.07 frames.], batch size: 64, lr: 1.66e-03
+2022-06-18 12:55:57,882 INFO [train.py:874] (3/4) Epoch 4, batch 1400, datatang_loss[loss=0.2071, simple_loss=0.2598, pruned_loss=0.07713, over 4931.00 frames.], tot_loss[loss=0.233, simple_loss=0.2817, pruned_loss=0.09213, over 985301.83 frames.], batch size: 79, aishell_tot_loss[loss=0.2257, simple_loss=0.2835, pruned_loss=0.08395, over 958030.27 frames.], datatang_tot_loss[loss=0.2415, simple_loss=0.28, pruned_loss=0.1015, over 954857.92 frames.], batch size: 79, lr: 1.65e-03
+2022-06-18 12:56:27,928 INFO [train.py:874] (3/4) Epoch 4, batch 1450, datatang_loss[loss=0.2051, simple_loss=0.2627, pruned_loss=0.07376, over 4956.00 frames.], tot_loss[loss=0.2316, simple_loss=0.2812, pruned_loss=0.09105, over 985357.19 frames.], batch size: 67, aishell_tot_loss[loss=0.2248, simple_loss=0.283, pruned_loss=0.0833, over 962067.27 frames.], datatang_tot_loss[loss=0.2413, simple_loss=0.2798, pruned_loss=0.1014, over 957554.12 frames.], batch size: 67, lr: 1.65e-03
+2022-06-18 12:57:00,074 INFO [train.py:874] (3/4) Epoch 4, batch 1500, aishell_loss[loss=0.2128, simple_loss=0.2702, pruned_loss=0.0777, over 4864.00 frames.], tot_loss[loss=0.2315, simple_loss=0.2803, pruned_loss=0.09133, over 985849.07 frames.], batch size: 37, aishell_tot_loss[loss=0.2244, simple_loss=0.2821, pruned_loss=0.08334, over 964866.91 frames.], datatang_tot_loss[loss=0.2411, simple_loss=0.2796, pruned_loss=0.1013, over 961337.43 frames.], batch size: 37, lr: 1.65e-03
+2022-06-18 12:57:28,959 INFO [train.py:874] (3/4) Epoch 4, batch 1550, datatang_loss[loss=0.2229, simple_loss=0.2691, pruned_loss=0.08833, over 4921.00 frames.], tot_loss[loss=0.2307, simple_loss=0.2795, pruned_loss=0.09097, over 985712.30 frames.], batch size: 71, aishell_tot_loss[loss=0.2246, simple_loss=0.282, pruned_loss=0.08366, over 967323.86 frames.], datatang_tot_loss[loss=0.24, simple_loss=0.2787, pruned_loss=0.1006, over 964051.32 frames.], batch size: 71, lr: 1.65e-03
+2022-06-18 12:57:59,886 INFO [train.py:874] (3/4) Epoch 4, batch 1600, datatang_loss[loss=0.2329, simple_loss=0.2687, pruned_loss=0.09853, over 4970.00 frames.], tot_loss[loss=0.2315, simple_loss=0.2797, pruned_loss=0.09166, over 985813.03 frames.], batch size: 55, aishell_tot_loss[loss=0.2247, simple_loss=0.2819, pruned_loss=0.08374, over 968908.90 frames.], datatang_tot_loss[loss=0.2398, simple_loss=0.2788, pruned_loss=0.1004, over 967377.38 frames.], batch size: 55, lr: 1.64e-03
+2022-06-18 12:58:30,932 INFO [train.py:874] (3/4) Epoch 4, batch 1650, datatang_loss[loss=0.2002, simple_loss=0.2494, pruned_loss=0.07544, over 4922.00 frames.], tot_loss[loss=0.2311, simple_loss=0.2797, pruned_loss=0.09124, over 985921.54 frames.], batch size: 42, aishell_tot_loss[loss=0.2241, simple_loss=0.2816, pruned_loss=0.08325, over 970918.35 frames.], datatang_tot_loss[loss=0.2398, simple_loss=0.279, pruned_loss=0.1004, over 969658.04 frames.], batch size: 42, lr: 1.64e-03
+2022-06-18 12:59:01,570 INFO [train.py:874] (3/4) Epoch 4, batch 1700, datatang_loss[loss=0.2082, simple_loss=0.2572, pruned_loss=0.07957, over 4928.00 frames.], tot_loss[loss=0.2309, simple_loss=0.2794, pruned_loss=0.09124, over 985745.35 frames.], batch size: 79, aishell_tot_loss[loss=0.2238, simple_loss=0.2813, pruned_loss=0.0831, over 972427.29 frames.], datatang_tot_loss[loss=0.2395, simple_loss=0.2788, pruned_loss=0.1001, over 971660.41 frames.], batch size: 79, lr: 1.64e-03
+2022-06-18 12:59:32,110 INFO [train.py:874] (3/4) Epoch 4, batch 1750, aishell_loss[loss=0.2035, simple_loss=0.2607, pruned_loss=0.07318, over 4961.00 frames.], tot_loss[loss=0.2305, simple_loss=0.2795, pruned_loss=0.09076, over 985667.83 frames.], batch size: 31, aishell_tot_loss[loss=0.224, simple_loss=0.2819, pruned_loss=0.08309, over 973891.23 frames.], datatang_tot_loss[loss=0.2389, simple_loss=0.2782, pruned_loss=0.09983, over 973335.86 frames.], batch size: 31, lr: 1.63e-03
+2022-06-18 13:00:02,774 INFO [train.py:874] (3/4) Epoch 4, batch 1800, aishell_loss[loss=0.2252, simple_loss=0.2731, pruned_loss=0.08867, over 4953.00 frames.], tot_loss[loss=0.2306, simple_loss=0.2792, pruned_loss=0.09101, over 985132.60 frames.], batch size: 25, aishell_tot_loss[loss=0.224, simple_loss=0.2819, pruned_loss=0.08311, over 975016.84 frames.], datatang_tot_loss[loss=0.2389, simple_loss=0.2779, pruned_loss=0.09996, over 974491.96 frames.], batch size: 25, lr: 1.63e-03
+2022-06-18 13:00:32,654 INFO [train.py:874] (3/4) Epoch 4, batch 1850, aishell_loss[loss=0.2268, simple_loss=0.2879, pruned_loss=0.08286, over 4974.00 frames.], tot_loss[loss=0.2299, simple_loss=0.279, pruned_loss=0.09035, over 984972.36 frames.], batch size: 48, aishell_tot_loss[loss=0.2235, simple_loss=0.2815, pruned_loss=0.08272, over 975959.17 frames.], datatang_tot_loss[loss=0.2385, simple_loss=0.2778, pruned_loss=0.09959, over 975823.99 frames.], batch size: 48, lr: 1.63e-03
+2022-06-18 13:01:02,860 INFO [train.py:874] (3/4) Epoch 4, batch 1900, aishell_loss[loss=0.2096, simple_loss=0.2798, pruned_loss=0.0697, over 4929.00 frames.], tot_loss[loss=0.2329, simple_loss=0.2809, pruned_loss=0.09246, over 985441.26 frames.], batch size: 49, aishell_tot_loss[loss=0.2252, simple_loss=0.2828, pruned_loss=0.08384, over 977239.22 frames.], datatang_tot_loss[loss=0.2397, simple_loss=0.2783, pruned_loss=0.1005, over 977181.19 frames.], batch size: 49, lr: 1.63e-03
+2022-06-18 13:01:34,359 INFO [train.py:874] (3/4) Epoch 4, batch 1950, aishell_loss[loss=0.2356, simple_loss=0.2982, pruned_loss=0.08651, over 4935.00 frames.], tot_loss[loss=0.2346, simple_loss=0.2818, pruned_loss=0.09372, over 985704.97 frames.], batch size: 49, aishell_tot_loss[loss=0.2253, simple_loss=0.2828, pruned_loss=0.08391, over 978401.11 frames.], datatang_tot_loss[loss=0.2417, simple_loss=0.2794, pruned_loss=0.102, over 978239.31 frames.], batch size: 49, lr: 1.62e-03
+2022-06-18 13:02:04,175 INFO [train.py:874] (3/4) Epoch 4, batch 2000, datatang_loss[loss=0.2401, simple_loss=0.2784, pruned_loss=0.1009, over 4954.00 frames.], tot_loss[loss=0.233, simple_loss=0.2809, pruned_loss=0.09254, over 985840.36 frames.], batch size: 86, aishell_tot_loss[loss=0.2251, simple_loss=0.283, pruned_loss=0.08358, over 979313.31 frames.], datatang_tot_loss[loss=0.2401, simple_loss=0.2785, pruned_loss=0.1009, over 979208.14 frames.], batch size: 86, lr: 1.62e-03
+2022-06-18 13:02:04,176 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 13:02:20,120 INFO [train.py:914] (3/4) Epoch 4, validation: loss=0.1875, simple_loss=0.2609, pruned_loss=0.05705, over 1622729.00 frames.
+2022-06-18 13:02:49,798 INFO [train.py:874] (3/4) Epoch 4, batch 2050, aishell_loss[loss=0.2238, simple_loss=0.2829, pruned_loss=0.08241, over 4938.00 frames.], tot_loss[loss=0.2328, simple_loss=0.281, pruned_loss=0.09228, over 986005.89 frames.], batch size: 54, aishell_tot_loss[loss=0.2248, simple_loss=0.2829, pruned_loss=0.08332, over 979994.38 frames.], datatang_tot_loss[loss=0.2402, simple_loss=0.2788, pruned_loss=0.1008, over 980251.76 frames.], batch size: 54, lr: 1.62e-03
+2022-06-18 13:03:20,657 INFO [train.py:874] (3/4) Epoch 4, batch 2100, datatang_loss[loss=0.1811, simple_loss=0.2277, pruned_loss=0.0673, over 4955.00 frames.], tot_loss[loss=0.2311, simple_loss=0.2795, pruned_loss=0.09133, over 986009.02 frames.], batch size: 45, aishell_tot_loss[loss=0.2247, simple_loss=0.2827, pruned_loss=0.08339, over 980783.33 frames.], datatang_tot_loss[loss=0.2383, simple_loss=0.2775, pruned_loss=0.09958, over 980853.10 frames.], batch size: 45, lr: 1.62e-03
+2022-06-18 13:03:50,546 INFO [train.py:874] (3/4) Epoch 4, batch 2150, datatang_loss[loss=0.2274, simple_loss=0.2645, pruned_loss=0.0951, over 4975.00 frames.], tot_loss[loss=0.232, simple_loss=0.2806, pruned_loss=0.09175, over 986408.16 frames.], batch size: 45, aishell_tot_loss[loss=0.2254, simple_loss=0.2834, pruned_loss=0.08374, over 981668.64 frames.], datatang_tot_loss[loss=0.2387, simple_loss=0.2778, pruned_loss=0.09978, over 981612.62 frames.], batch size: 45, lr: 1.61e-03
+2022-06-18 13:04:20,297 INFO [train.py:874] (3/4) Epoch 4, batch 2200, datatang_loss[loss=0.2129, simple_loss=0.2631, pruned_loss=0.08137, over 4893.00 frames.], tot_loss[loss=0.2304, simple_loss=0.2796, pruned_loss=0.09064, over 985906.51 frames.], batch size: 47, aishell_tot_loss[loss=0.2252, simple_loss=0.2831, pruned_loss=0.08367, over 981883.12 frames.], datatang_tot_loss[loss=0.2373, simple_loss=0.2769, pruned_loss=0.09882, over 982004.96 frames.], batch size: 47, lr: 1.61e-03
+2022-06-18 13:04:51,386 INFO [train.py:874] (3/4) Epoch 4, batch 2250, aishell_loss[loss=0.2103, simple_loss=0.2636, pruned_loss=0.07846, over 4905.00 frames.], tot_loss[loss=0.2324, simple_loss=0.2812, pruned_loss=0.09182, over 985750.53 frames.], batch size: 33, aishell_tot_loss[loss=0.2256, simple_loss=0.2834, pruned_loss=0.08392, over 982133.77 frames.], datatang_tot_loss[loss=0.2388, simple_loss=0.2783, pruned_loss=0.09966, over 982525.60 frames.], batch size: 33, lr: 1.61e-03
+2022-06-18 13:05:21,540 INFO [train.py:874] (3/4) Epoch 4, batch 2300, datatang_loss[loss=0.229, simple_loss=0.2797, pruned_loss=0.08918, over 4927.00 frames.], tot_loss[loss=0.2328, simple_loss=0.2815, pruned_loss=0.09199, over 985724.65 frames.], batch size: 77, aishell_tot_loss[loss=0.2252, simple_loss=0.2831, pruned_loss=0.08368, over 982407.45 frames.], datatang_tot_loss[loss=0.2395, simple_loss=0.2789, pruned_loss=0.1, over 983030.75 frames.], batch size: 77, lr: 1.61e-03
+2022-06-18 13:05:52,046 INFO [train.py:874] (3/4) Epoch 4, batch 2350, datatang_loss[loss=0.2615, simple_loss=0.3034, pruned_loss=0.1098, over 4945.00 frames.], tot_loss[loss=0.2323, simple_loss=0.2813, pruned_loss=0.09164, over 985748.13 frames.], batch size: 91, aishell_tot_loss[loss=0.225, simple_loss=0.2834, pruned_loss=0.08328, over 982692.68 frames.], datatang_tot_loss[loss=0.2392, simple_loss=0.2785, pruned_loss=0.09988, over 983470.20 frames.], batch size: 91, lr: 1.60e-03
+2022-06-18 13:06:21,694 INFO [train.py:874] (3/4) Epoch 4, batch 2400, aishell_loss[loss=0.2457, simple_loss=0.2971, pruned_loss=0.09717, over 4934.00 frames.], tot_loss[loss=0.2329, simple_loss=0.2819, pruned_loss=0.09192, over 985387.49 frames.], batch size: 58, aishell_tot_loss[loss=0.2255, simple_loss=0.2837, pruned_loss=0.08363, over 982778.97 frames.], datatang_tot_loss[loss=0.2397, simple_loss=0.2789, pruned_loss=0.1002, over 983653.05 frames.], batch size: 58, lr: 1.60e-03
+2022-06-18 13:06:51,619 INFO [train.py:874] (3/4) Epoch 4, batch 2450, datatang_loss[loss=0.2548, simple_loss=0.2905, pruned_loss=0.1095, over 4914.00 frames.], tot_loss[loss=0.2318, simple_loss=0.2816, pruned_loss=0.09103, over 985237.87 frames.], batch size: 57, aishell_tot_loss[loss=0.2254, simple_loss=0.284, pruned_loss=0.08342, over 983106.69 frames.], datatang_tot_loss[loss=0.2388, simple_loss=0.2782, pruned_loss=0.09969, over 983679.07 frames.], batch size: 57, lr: 1.60e-03
+2022-06-18 13:07:23,772 INFO [train.py:874] (3/4) Epoch 4, batch 2500, datatang_loss[loss=0.2388, simple_loss=0.2772, pruned_loss=0.1002, over 4927.00 frames.], tot_loss[loss=0.2317, simple_loss=0.2812, pruned_loss=0.09113, over 985348.55 frames.], batch size: 73, aishell_tot_loss[loss=0.2249, simple_loss=0.2836, pruned_loss=0.08309, over 983452.77 frames.], datatang_tot_loss[loss=0.2387, simple_loss=0.2785, pruned_loss=0.09939, over 983862.50 frames.], batch size: 73, lr: 1.60e-03
+2022-06-18 13:07:53,555 INFO [train.py:874] (3/4) Epoch 4, batch 2550, datatang_loss[loss=0.3004, simple_loss=0.3258, pruned_loss=0.1375, over 4918.00 frames.], tot_loss[loss=0.2344, simple_loss=0.2827, pruned_loss=0.09306, over 985334.56 frames.], batch size: 98, aishell_tot_loss[loss=0.2266, simple_loss=0.2847, pruned_loss=0.08426, over 983499.02 frames.], datatang_tot_loss[loss=0.2392, simple_loss=0.2792, pruned_loss=0.09958, over 984175.72 frames.], batch size: 98, lr: 1.60e-03
+2022-06-18 13:08:25,266 INFO [train.py:874] (3/4) Epoch 4, batch 2600, aishell_loss[loss=0.2479, simple_loss=0.3087, pruned_loss=0.09357, over 4941.00 frames.], tot_loss[loss=0.2332, simple_loss=0.2817, pruned_loss=0.09237, over 985423.03 frames.], batch size: 49, aishell_tot_loss[loss=0.2268, simple_loss=0.285, pruned_loss=0.08432, over 983763.56 frames.], datatang_tot_loss[loss=0.2377, simple_loss=0.2783, pruned_loss=0.09862, over 984336.01 frames.], batch size: 49, lr: 1.59e-03
+2022-06-18 13:08:55,136 INFO [train.py:874] (3/4) Epoch 4, batch 2650, aishell_loss[loss=0.2527, simple_loss=0.3063, pruned_loss=0.09952, over 4912.00 frames.], tot_loss[loss=0.233, simple_loss=0.2819, pruned_loss=0.09198, over 985618.05 frames.], batch size: 41, aishell_tot_loss[loss=0.2265, simple_loss=0.2847, pruned_loss=0.08415, over 984233.39 frames.], datatang_tot_loss[loss=0.2383, simple_loss=0.2786, pruned_loss=0.09896, over 984393.15 frames.], batch size: 41, lr: 1.59e-03
+2022-06-18 13:09:25,414 INFO [train.py:874] (3/4) Epoch 4, batch 2700, datatang_loss[loss=0.2107, simple_loss=0.2609, pruned_loss=0.0803, over 4919.00 frames.], tot_loss[loss=0.2324, simple_loss=0.2813, pruned_loss=0.09172, over 985427.82 frames.], batch size: 77, aishell_tot_loss[loss=0.2268, simple_loss=0.2849, pruned_loss=0.08433, over 984266.39 frames.], datatang_tot_loss[loss=0.2375, simple_loss=0.2779, pruned_loss=0.0985, over 984461.91 frames.], batch size: 77, lr: 1.59e-03
+2022-06-18 13:09:56,930 INFO [train.py:874] (3/4) Epoch 4, batch 2750, datatang_loss[loss=0.2094, simple_loss=0.2579, pruned_loss=0.08043, over 4983.00 frames.], tot_loss[loss=0.23, simple_loss=0.2792, pruned_loss=0.09039, over 985636.15 frames.], batch size: 31, aishell_tot_loss[loss=0.2254, simple_loss=0.2836, pruned_loss=0.08357, over 984495.77 frames.], datatang_tot_loss[loss=0.2362, simple_loss=0.2771, pruned_loss=0.09763, over 984713.38 frames.], batch size: 31, lr: 1.59e-03
+2022-06-18 13:10:26,892 INFO [train.py:874] (3/4) Epoch 4, batch 2800, datatang_loss[loss=0.2416, simple_loss=0.2826, pruned_loss=0.1003, over 4946.00 frames.], tot_loss[loss=0.2294, simple_loss=0.2789, pruned_loss=0.09001, over 985841.49 frames.], batch size: 42, aishell_tot_loss[loss=0.2249, simple_loss=0.283, pruned_loss=0.08338, over 984911.24 frames.], datatang_tot_loss[loss=0.2357, simple_loss=0.277, pruned_loss=0.09723, over 984750.68 frames.], batch size: 42, lr: 1.58e-03
+2022-06-18 13:10:56,215 INFO [train.py:874] (3/4) Epoch 4, batch 2850, datatang_loss[loss=0.2216, simple_loss=0.2737, pruned_loss=0.08478, over 4931.00 frames.], tot_loss[loss=0.2279, simple_loss=0.2776, pruned_loss=0.08911, over 985851.09 frames.], batch size: 88, aishell_tot_loss[loss=0.2239, simple_loss=0.282, pruned_loss=0.08293, over 984985.74 frames.], datatang_tot_loss[loss=0.2348, simple_loss=0.2764, pruned_loss=0.09659, over 984924.74 frames.], batch size: 88, lr: 1.58e-03
+2022-06-18 13:11:27,495 INFO [train.py:874] (3/4) Epoch 4, batch 2900, aishell_loss[loss=0.2158, simple_loss=0.2758, pruned_loss=0.07792, over 4979.00 frames.], tot_loss[loss=0.2279, simple_loss=0.2779, pruned_loss=0.08893, over 985789.56 frames.], batch size: 30, aishell_tot_loss[loss=0.2243, simple_loss=0.2825, pruned_loss=0.08308, over 985045.34 frames.], datatang_tot_loss[loss=0.2338, simple_loss=0.276, pruned_loss=0.09579, over 985005.99 frames.], batch size: 30, lr: 1.58e-03
+2022-06-18 13:11:56,670 INFO [train.py:874] (3/4) Epoch 4, batch 2950, aishell_loss[loss=0.2386, simple_loss=0.2972, pruned_loss=0.08997, over 4963.00 frames.], tot_loss[loss=0.2301, simple_loss=0.2796, pruned_loss=0.09028, over 985758.42 frames.], batch size: 61, aishell_tot_loss[loss=0.2253, simple_loss=0.2833, pruned_loss=0.08364, over 985114.01 frames.], datatang_tot_loss[loss=0.2346, simple_loss=0.2769, pruned_loss=0.09617, over 985071.66 frames.], batch size: 61, lr: 1.58e-03
+2022-06-18 13:12:25,383 INFO [train.py:874] (3/4) Epoch 4, batch 3000, aishell_loss[loss=0.2693, simple_loss=0.3241, pruned_loss=0.1072, over 4931.00 frames.], tot_loss[loss=0.2302, simple_loss=0.28, pruned_loss=0.09014, over 985725.19 frames.], batch size: 78, aishell_tot_loss[loss=0.2256, simple_loss=0.2837, pruned_loss=0.08378, over 985058.86 frames.], datatang_tot_loss[loss=0.2344, simple_loss=0.2767, pruned_loss=0.09605, over 985244.01 frames.], batch size: 78, lr: 1.57e-03
+2022-06-18 13:12:25,384 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 13:12:41,438 INFO [train.py:914] (3/4) Epoch 4, validation: loss=0.1874, simple_loss=0.266, pruned_loss=0.05438, over 1622729.00 frames.
+2022-06-18 13:13:12,192 INFO [train.py:874] (3/4) Epoch 4, batch 3050, datatang_loss[loss=0.2176, simple_loss=0.265, pruned_loss=0.08513, over 4937.00 frames.], tot_loss[loss=0.2302, simple_loss=0.2798, pruned_loss=0.09034, over 985835.95 frames.], batch size: 69, aishell_tot_loss[loss=0.225, simple_loss=0.2835, pruned_loss=0.08325, over 985144.44 frames.], datatang_tot_loss[loss=0.2351, simple_loss=0.2765, pruned_loss=0.09687, over 985427.88 frames.], batch size: 69, lr: 1.57e-03
+2022-06-18 13:13:42,384 INFO [train.py:874] (3/4) Epoch 4, batch 3100, aishell_loss[loss=0.2323, simple_loss=0.2941, pruned_loss=0.08527, over 4930.00 frames.], tot_loss[loss=0.2304, simple_loss=0.2804, pruned_loss=0.0902, over 986178.71 frames.], batch size: 79, aishell_tot_loss[loss=0.2254, simple_loss=0.2841, pruned_loss=0.08335, over 985342.07 frames.], datatang_tot_loss[loss=0.2351, simple_loss=0.2766, pruned_loss=0.09684, over 985727.14 frames.], batch size: 79, lr: 1.57e-03
+2022-06-18 13:14:11,463 INFO [train.py:874] (3/4) Epoch 4, batch 3150, datatang_loss[loss=0.2107, simple_loss=0.2474, pruned_loss=0.08702, over 4947.00 frames.], tot_loss[loss=0.2288, simple_loss=0.2796, pruned_loss=0.08904, over 986231.02 frames.], batch size: 50, aishell_tot_loss[loss=0.2245, simple_loss=0.2837, pruned_loss=0.08262, over 985573.03 frames.], datatang_tot_loss[loss=0.2346, simple_loss=0.2761, pruned_loss=0.09653, over 985711.16 frames.], batch size: 50, lr: 1.57e-03
+2022-06-18 13:14:41,549 INFO [train.py:874] (3/4) Epoch 4, batch 3200, datatang_loss[loss=0.2597, simple_loss=0.2998, pruned_loss=0.1098, over 4949.00 frames.], tot_loss[loss=0.2284, simple_loss=0.2788, pruned_loss=0.08894, over 986359.99 frames.], batch size: 99, aishell_tot_loss[loss=0.2247, simple_loss=0.2838, pruned_loss=0.08278, over 985718.12 frames.], datatang_tot_loss[loss=0.2334, simple_loss=0.2753, pruned_loss=0.09574, over 985829.36 frames.], batch size: 99, lr: 1.57e-03
+2022-06-18 13:15:12,042 INFO [train.py:874] (3/4) Epoch 4, batch 3250, aishell_loss[loss=0.2463, simple_loss=0.2943, pruned_loss=0.09915, over 4934.00 frames.], tot_loss[loss=0.2271, simple_loss=0.2777, pruned_loss=0.0882, over 986334.71 frames.], batch size: 58, aishell_tot_loss[loss=0.2248, simple_loss=0.2837, pruned_loss=0.083, over 985840.51 frames.], datatang_tot_loss[loss=0.2316, simple_loss=0.2742, pruned_loss=0.09454, over 985823.81 frames.], batch size: 58, lr: 1.56e-03
+2022-06-18 13:15:41,405 INFO [train.py:874] (3/4) Epoch 4, batch 3300, datatang_loss[loss=0.2142, simple_loss=0.2656, pruned_loss=0.08142, over 4926.00 frames.], tot_loss[loss=0.2265, simple_loss=0.2775, pruned_loss=0.08772, over 986389.28 frames.], batch size: 81, aishell_tot_loss[loss=0.2243, simple_loss=0.2835, pruned_loss=0.08252, over 985956.95 frames.], datatang_tot_loss[loss=0.2313, simple_loss=0.2739, pruned_loss=0.09431, over 985873.92 frames.], batch size: 81, lr: 1.56e-03
+2022-06-18 13:16:12,276 INFO [train.py:874] (3/4) Epoch 4, batch 3350, aishell_loss[loss=0.2268, simple_loss=0.2722, pruned_loss=0.0907, over 4955.00 frames.], tot_loss[loss=0.2263, simple_loss=0.2771, pruned_loss=0.08772, over 986282.81 frames.], batch size: 31, aishell_tot_loss[loss=0.2237, simple_loss=0.2828, pruned_loss=0.08235, over 985766.55 frames.], datatang_tot_loss[loss=0.2313, simple_loss=0.2738, pruned_loss=0.09444, over 986059.02 frames.], batch size: 31, lr: 1.56e-03
+2022-06-18 13:16:42,811 INFO [train.py:874] (3/4) Epoch 4, batch 3400, aishell_loss[loss=0.2431, simple_loss=0.3031, pruned_loss=0.09151, over 4943.00 frames.], tot_loss[loss=0.2258, simple_loss=0.2769, pruned_loss=0.08731, over 985877.53 frames.], batch size: 58, aishell_tot_loss[loss=0.2224, simple_loss=0.2817, pruned_loss=0.08154, over 985587.29 frames.], datatang_tot_loss[loss=0.232, simple_loss=0.2743, pruned_loss=0.09486, over 985891.08 frames.], batch size: 58, lr: 1.56e-03
+2022-06-18 13:17:12,150 INFO [train.py:874] (3/4) Epoch 4, batch 3450, aishell_loss[loss=0.2358, simple_loss=0.2897, pruned_loss=0.09092, over 4935.00 frames.], tot_loss[loss=0.2265, simple_loss=0.2779, pruned_loss=0.0876, over 985440.85 frames.], batch size: 49, aishell_tot_loss[loss=0.2232, simple_loss=0.2823, pruned_loss=0.08205, over 985180.02 frames.], datatang_tot_loss[loss=0.2318, simple_loss=0.2743, pruned_loss=0.09462, over 985878.11 frames.], batch size: 49, lr: 1.55e-03
+2022-06-18 13:17:41,287 INFO [train.py:874] (3/4) Epoch 4, batch 3500, datatang_loss[loss=0.2648, simple_loss=0.2636, pruned_loss=0.1329, over 4976.00 frames.], tot_loss[loss=0.2267, simple_loss=0.2783, pruned_loss=0.08755, over 985473.36 frames.], batch size: 40, aishell_tot_loss[loss=0.2226, simple_loss=0.2822, pruned_loss=0.08143, over 985004.86 frames.], datatang_tot_loss[loss=0.2326, simple_loss=0.2745, pruned_loss=0.09536, over 986080.13 frames.], batch size: 40, lr: 1.55e-03
+2022-06-18 13:18:17,440 INFO [train.py:874] (3/4) Epoch 4, batch 3550, datatang_loss[loss=0.2008, simple_loss=0.2559, pruned_loss=0.07279, over 4914.00 frames.], tot_loss[loss=0.2268, simple_loss=0.2779, pruned_loss=0.08783, over 985684.31 frames.], batch size: 81, aishell_tot_loss[loss=0.2224, simple_loss=0.2819, pruned_loss=0.08138, over 984938.81 frames.], datatang_tot_loss[loss=0.2326, simple_loss=0.2745, pruned_loss=0.0954, over 986347.19 frames.], batch size: 81, lr: 1.55e-03
+2022-06-18 13:18:46,022 INFO [train.py:874] (3/4) Epoch 4, batch 3600, aishell_loss[loss=0.1516, simple_loss=0.2073, pruned_loss=0.04792, over 4869.00 frames.], tot_loss[loss=0.2255, simple_loss=0.2768, pruned_loss=0.08706, over 985408.80 frames.], batch size: 21, aishell_tot_loss[loss=0.2211, simple_loss=0.2808, pruned_loss=0.08065, over 984501.69 frames.], datatang_tot_loss[loss=0.2324, simple_loss=0.2745, pruned_loss=0.09519, over 986493.19 frames.], batch size: 21, lr: 1.55e-03
+2022-06-18 13:19:16,744 INFO [train.py:874] (3/4) Epoch 4, batch 3650, aishell_loss[loss=0.2333, simple_loss=0.2997, pruned_loss=0.08348, over 4930.00 frames.], tot_loss[loss=0.226, simple_loss=0.2772, pruned_loss=0.08743, over 985764.62 frames.], batch size: 78, aishell_tot_loss[loss=0.2213, simple_loss=0.2811, pruned_loss=0.08075, over 984510.81 frames.], datatang_tot_loss[loss=0.232, simple_loss=0.2746, pruned_loss=0.09474, over 986761.20 frames.], batch size: 78, lr: 1.54e-03
+2022-06-18 13:19:48,137 INFO [train.py:874] (3/4) Epoch 4, batch 3700, datatang_loss[loss=0.2351, simple_loss=0.2807, pruned_loss=0.0947, over 4922.00 frames.], tot_loss[loss=0.2257, simple_loss=0.2771, pruned_loss=0.08721, over 985723.28 frames.], batch size: 83, aishell_tot_loss[loss=0.2211, simple_loss=0.281, pruned_loss=0.08054, over 984552.38 frames.], datatang_tot_loss[loss=0.2317, simple_loss=0.2744, pruned_loss=0.09453, over 986695.91 frames.], batch size: 83, lr: 1.54e-03
+2022-06-18 13:20:16,831 INFO [train.py:874] (3/4) Epoch 4, batch 3750, datatang_loss[loss=0.2119, simple_loss=0.264, pruned_loss=0.07994, over 4947.00 frames.], tot_loss[loss=0.2261, simple_loss=0.2777, pruned_loss=0.08721, over 985930.93 frames.], batch size: 62, aishell_tot_loss[loss=0.2215, simple_loss=0.2815, pruned_loss=0.08075, over 984547.09 frames.], datatang_tot_loss[loss=0.2314, simple_loss=0.2745, pruned_loss=0.09418, over 986940.89 frames.], batch size: 62, lr: 1.54e-03
+2022-06-18 13:20:47,187 INFO [train.py:874] (3/4) Epoch 4, batch 3800, datatang_loss[loss=0.2249, simple_loss=0.273, pruned_loss=0.08843, over 4919.00 frames.], tot_loss[loss=0.2263, simple_loss=0.2781, pruned_loss=0.0872, over 985985.33 frames.], batch size: 73, aishell_tot_loss[loss=0.2216, simple_loss=0.2817, pruned_loss=0.08073, over 984533.57 frames.], datatang_tot_loss[loss=0.2315, simple_loss=0.2747, pruned_loss=0.09415, over 987054.59 frames.], batch size: 73, lr: 1.54e-03
+2022-06-18 13:21:17,377 INFO [train.py:874] (3/4) Epoch 4, batch 3850, datatang_loss[loss=0.2164, simple_loss=0.264, pruned_loss=0.08444, over 4923.00 frames.], tot_loss[loss=0.2256, simple_loss=0.2779, pruned_loss=0.08666, over 985438.36 frames.], batch size: 73, aishell_tot_loss[loss=0.2222, simple_loss=0.2822, pruned_loss=0.08109, over 984224.45 frames.], datatang_tot_loss[loss=0.23, simple_loss=0.274, pruned_loss=0.09302, over 986804.33 frames.], batch size: 73, lr: 1.54e-03
+2022-06-18 13:21:46,313 INFO [train.py:874] (3/4) Epoch 4, batch 3900, aishell_loss[loss=0.1955, simple_loss=0.2606, pruned_loss=0.06515, over 4916.00 frames.], tot_loss[loss=0.2282, simple_loss=0.2797, pruned_loss=0.08835, over 985692.28 frames.], batch size: 52, aishell_tot_loss[loss=0.2228, simple_loss=0.2825, pruned_loss=0.08158, over 984547.88 frames.], datatang_tot_loss[loss=0.2321, simple_loss=0.2754, pruned_loss=0.09444, over 986783.11 frames.], batch size: 52, lr: 1.53e-03
+2022-06-18 13:22:14,524 INFO [train.py:874] (3/4) Epoch 4, batch 3950, datatang_loss[loss=0.2936, simple_loss=0.3202, pruned_loss=0.1335, over 4962.00 frames.], tot_loss[loss=0.228, simple_loss=0.2799, pruned_loss=0.08807, over 985680.50 frames.], batch size: 67, aishell_tot_loss[loss=0.2225, simple_loss=0.2827, pruned_loss=0.08117, over 984552.14 frames.], datatang_tot_loss[loss=0.2324, simple_loss=0.2755, pruned_loss=0.09461, over 986771.89 frames.], batch size: 67, lr: 1.53e-03
+2022-06-18 13:22:44,239 INFO [train.py:874] (3/4) Epoch 4, batch 4000, aishell_loss[loss=0.232, simple_loss=0.295, pruned_loss=0.08447, over 4980.00 frames.], tot_loss[loss=0.2276, simple_loss=0.2797, pruned_loss=0.08776, over 985665.46 frames.], batch size: 51, aishell_tot_loss[loss=0.2226, simple_loss=0.283, pruned_loss=0.08107, over 984574.58 frames.], datatang_tot_loss[loss=0.2321, simple_loss=0.2752, pruned_loss=0.09444, over 986724.53 frames.], batch size: 51, lr: 1.53e-03
+2022-06-18 13:22:44,240 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 13:23:00,879 INFO [train.py:914] (3/4) Epoch 4, validation: loss=0.1818, simple_loss=0.2607, pruned_loss=0.0515, over 1622729.00 frames.
+2022-06-18 13:23:30,390 INFO [train.py:874] (3/4) Epoch 4, batch 4050, aishell_loss[loss=0.2561, simple_loss=0.3147, pruned_loss=0.09872, over 4928.00 frames.], tot_loss[loss=0.2287, simple_loss=0.2804, pruned_loss=0.08849, over 985701.56 frames.], batch size: 68, aishell_tot_loss[loss=0.2233, simple_loss=0.2835, pruned_loss=0.08152, over 984582.82 frames.], datatang_tot_loss[loss=0.2325, simple_loss=0.2756, pruned_loss=0.09469, over 986749.48 frames.], batch size: 68, lr: 1.53e-03
+2022-06-18 13:24:00,230 INFO [train.py:874] (3/4) Epoch 4, batch 4100, datatang_loss[loss=0.2207, simple_loss=0.2658, pruned_loss=0.08776, over 4974.00 frames.], tot_loss[loss=0.2281, simple_loss=0.2795, pruned_loss=0.08836, over 985334.46 frames.], batch size: 60, aishell_tot_loss[loss=0.2234, simple_loss=0.2837, pruned_loss=0.08158, over 984480.29 frames.], datatang_tot_loss[loss=0.2317, simple_loss=0.275, pruned_loss=0.09419, over 986413.78 frames.], batch size: 60, lr: 1.53e-03
+2022-06-18 13:24:29,189 INFO [train.py:874] (3/4) Epoch 4, batch 4150, aishell_loss[loss=0.2193, simple_loss=0.2732, pruned_loss=0.0827, over 4889.00 frames.], tot_loss[loss=0.2264, simple_loss=0.2782, pruned_loss=0.08728, over 985221.33 frames.], batch size: 34, aishell_tot_loss[loss=0.2228, simple_loss=0.2831, pruned_loss=0.0813, over 984396.76 frames.], datatang_tot_loss[loss=0.2305, simple_loss=0.2742, pruned_loss=0.09341, over 986350.83 frames.], batch size: 34, lr: 1.52e-03
+2022-06-18 13:25:55,788 INFO [train.py:874] (3/4) Epoch 5, batch 50, datatang_loss[loss=0.1949, simple_loss=0.2603, pruned_loss=0.06472, over 4938.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2692, pruned_loss=0.08066, over 218717.60 frames.], batch size: 69, aishell_tot_loss[loss=0.2108, simple_loss=0.2736, pruned_loss=0.07397, over 98537.40 frames.], datatang_tot_loss[loss=0.219, simple_loss=0.2666, pruned_loss=0.08572, over 133534.09 frames.], batch size: 69, lr: 1.47e-03
+2022-06-18 13:26:26,186 INFO [train.py:874] (3/4) Epoch 5, batch 100, aishell_loss[loss=0.2281, simple_loss=0.2919, pruned_loss=0.08215, over 4982.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2672, pruned_loss=0.07809, over 388999.07 frames.], batch size: 48, aishell_tot_loss[loss=0.2132, simple_loss=0.2765, pruned_loss=0.07492, over 195137.82 frames.], datatang_tot_loss[loss=0.211, simple_loss=0.2601, pruned_loss=0.08091, over 241667.07 frames.], batch size: 48, lr: 1.46e-03
+2022-06-18 13:26:56,344 INFO [train.py:874] (3/4) Epoch 5, batch 150, aishell_loss[loss=0.245, simple_loss=0.2949, pruned_loss=0.09751, over 4945.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2697, pruned_loss=0.07964, over 520802.35 frames.], batch size: 45, aishell_tot_loss[loss=0.217, simple_loss=0.2792, pruned_loss=0.07739, over 291255.64 frames.], datatang_tot_loss[loss=0.2122, simple_loss=0.261, pruned_loss=0.08166, over 325985.39 frames.], batch size: 45, lr: 1.46e-03
+2022-06-18 13:27:26,088 INFO [train.py:874] (3/4) Epoch 5, batch 200, aishell_loss[loss=0.1887, simple_loss=0.2673, pruned_loss=0.05511, over 4942.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2696, pruned_loss=0.08023, over 623715.91 frames.], batch size: 45, aishell_tot_loss[loss=0.2167, simple_loss=0.2791, pruned_loss=0.07715, over 372920.62 frames.], datatang_tot_loss[loss=0.2133, simple_loss=0.2607, pruned_loss=0.08298, over 403649.84 frames.], batch size: 45, lr: 1.46e-03
+2022-06-18 13:27:56,655 INFO [train.py:874] (3/4) Epoch 5, batch 250, datatang_loss[loss=0.2605, simple_loss=0.2892, pruned_loss=0.1159, over 4920.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2707, pruned_loss=0.08176, over 703846.69 frames.], batch size: 77, aishell_tot_loss[loss=0.2166, simple_loss=0.2792, pruned_loss=0.07701, over 436743.03 frames.], datatang_tot_loss[loss=0.2165, simple_loss=0.2625, pruned_loss=0.08525, over 479908.17 frames.], batch size: 77, lr: 1.46e-03
+2022-06-18 13:28:27,286 INFO [train.py:874] (3/4) Epoch 5, batch 300, datatang_loss[loss=0.2372, simple_loss=0.2894, pruned_loss=0.09254, over 4931.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2728, pruned_loss=0.08278, over 766413.08 frames.], batch size: 94, aishell_tot_loss[loss=0.218, simple_loss=0.2798, pruned_loss=0.07805, over 503612.35 frames.], datatang_tot_loss[loss=0.2186, simple_loss=0.2649, pruned_loss=0.08613, over 537538.29 frames.], batch size: 94, lr: 1.46e-03
+2022-06-18 13:28:56,595 INFO [train.py:874] (3/4) Epoch 5, batch 350, datatang_loss[loss=0.2047, simple_loss=0.2535, pruned_loss=0.0779, over 4953.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2734, pruned_loss=0.08262, over 815335.66 frames.], batch size: 67, aishell_tot_loss[loss=0.2181, simple_loss=0.2803, pruned_loss=0.07799, over 564809.46 frames.], datatang_tot_loss[loss=0.219, simple_loss=0.2654, pruned_loss=0.08635, over 586488.52 frames.], batch size: 67, lr: 1.45e-03
+2022-06-18 13:29:27,508 INFO [train.py:874] (3/4) Epoch 5, batch 400, aishell_loss[loss=0.2409, simple_loss=0.2917, pruned_loss=0.09504, over 4942.00 frames.], tot_loss[loss=0.2185, simple_loss=0.2728, pruned_loss=0.0821, over 852978.35 frames.], batch size: 64, aishell_tot_loss[loss=0.2171, simple_loss=0.2794, pruned_loss=0.07741, over 604708.14 frames.], datatang_tot_loss[loss=0.2191, simple_loss=0.2661, pruned_loss=0.086, over 642328.62 frames.], batch size: 64, lr: 1.45e-03
+2022-06-18 13:29:57,474 INFO [train.py:874] (3/4) Epoch 5, batch 450, aishell_loss[loss=0.2046, simple_loss=0.2699, pruned_loss=0.06962, over 4964.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2728, pruned_loss=0.08144, over 882424.36 frames.], batch size: 31, aishell_tot_loss[loss=0.2161, simple_loss=0.2786, pruned_loss=0.07683, over 656187.50 frames.], datatang_tot_loss[loss=0.2194, simple_loss=0.2666, pruned_loss=0.08607, over 676732.29 frames.], batch size: 31, lr: 1.45e-03
+2022-06-18 13:30:27,327 INFO [train.py:874] (3/4) Epoch 5, batch 500, datatang_loss[loss=0.1982, simple_loss=0.2544, pruned_loss=0.07101, over 4938.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2726, pruned_loss=0.08109, over 905492.61 frames.], batch size: 79, aishell_tot_loss[loss=0.2156, simple_loss=0.2784, pruned_loss=0.07644, over 690634.38 frames.], datatang_tot_loss[loss=0.2192, simple_loss=0.2668, pruned_loss=0.08581, over 717344.53 frames.], batch size: 79, lr: 1.45e-03
+2022-06-18 13:30:57,350 INFO [train.py:874] (3/4) Epoch 5, batch 550, aishell_loss[loss=0.2013, simple_loss=0.2654, pruned_loss=0.06859, over 4880.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2732, pruned_loss=0.08091, over 923381.70 frames.], batch size: 35, aishell_tot_loss[loss=0.2167, simple_loss=0.2793, pruned_loss=0.07707, over 726803.32 frames.], datatang_tot_loss[loss=0.2184, simple_loss=0.2667, pruned_loss=0.08502, over 747777.49 frames.], batch size: 35, lr: 1.45e-03
+2022-06-18 13:31:28,003 INFO [train.py:874] (3/4) Epoch 5, batch 600, aishell_loss[loss=0.2109, simple_loss=0.278, pruned_loss=0.07189, over 4872.00 frames.], tot_loss[loss=0.217, simple_loss=0.2725, pruned_loss=0.0807, over 937167.67 frames.], batch size: 35, aishell_tot_loss[loss=0.2154, simple_loss=0.2779, pruned_loss=0.07642, over 759322.60 frames.], datatang_tot_loss[loss=0.219, simple_loss=0.2671, pruned_loss=0.08547, over 773915.76 frames.], batch size: 35, lr: 1.44e-03
+2022-06-18 13:31:56,172 INFO [train.py:874] (3/4) Epoch 5, batch 650, aishell_loss[loss=0.1838, simple_loss=0.2543, pruned_loss=0.05664, over 4940.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2733, pruned_loss=0.08078, over 947892.05 frames.], batch size: 54, aishell_tot_loss[loss=0.2153, simple_loss=0.2782, pruned_loss=0.07621, over 788834.15 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2675, pruned_loss=0.08581, over 796116.87 frames.], batch size: 54, lr: 1.44e-03
+2022-06-18 13:32:27,671 INFO [train.py:874] (3/4) Epoch 5, batch 700, aishell_loss[loss=0.1925, simple_loss=0.2673, pruned_loss=0.05881, over 4937.00 frames.], tot_loss[loss=0.217, simple_loss=0.2733, pruned_loss=0.08033, over 956824.35 frames.], batch size: 56, aishell_tot_loss[loss=0.214, simple_loss=0.2774, pruned_loss=0.0753, over 814897.89 frames.], datatang_tot_loss[loss=0.2205, simple_loss=0.2684, pruned_loss=0.08634, over 816208.75 frames.], batch size: 56, lr: 1.44e-03
+2022-06-18 13:32:56,864 INFO [train.py:874] (3/4) Epoch 5, batch 750, datatang_loss[loss=0.2119, simple_loss=0.2629, pruned_loss=0.08038, over 4915.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2755, pruned_loss=0.08153, over 963345.61 frames.], batch size: 64, aishell_tot_loss[loss=0.2147, simple_loss=0.2782, pruned_loss=0.07559, over 835865.97 frames.], datatang_tot_loss[loss=0.2226, simple_loss=0.2703, pruned_loss=0.08746, over 835451.36 frames.], batch size: 64, lr: 1.44e-03
+2022-06-18 13:33:26,672 INFO [train.py:874] (3/4) Epoch 5, batch 800, datatang_loss[loss=0.2376, simple_loss=0.2812, pruned_loss=0.09696, over 4957.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2755, pruned_loss=0.08196, over 968386.16 frames.], batch size: 67, aishell_tot_loss[loss=0.2139, simple_loss=0.2775, pruned_loss=0.07511, over 852088.83 frames.], datatang_tot_loss[loss=0.224, simple_loss=0.2715, pruned_loss=0.0883, over 854651.06 frames.], batch size: 67, lr: 1.44e-03
+2022-06-18 13:33:57,622 INFO [train.py:874] (3/4) Epoch 5, batch 850, aishell_loss[loss=0.203, simple_loss=0.2752, pruned_loss=0.06536, over 4873.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2742, pruned_loss=0.08129, over 972198.31 frames.], batch size: 42, aishell_tot_loss[loss=0.2131, simple_loss=0.2768, pruned_loss=0.07474, over 867119.91 frames.], datatang_tot_loss[loss=0.2234, simple_loss=0.271, pruned_loss=0.08791, over 870760.94 frames.], batch size: 42, lr: 1.43e-03
+2022-06-18 13:34:26,227 INFO [train.py:874] (3/4) Epoch 5, batch 900, aishell_loss[loss=0.2403, simple_loss=0.3043, pruned_loss=0.08821, over 4875.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2748, pruned_loss=0.08191, over 975393.75 frames.], batch size: 35, aishell_tot_loss[loss=0.2139, simple_loss=0.2774, pruned_loss=0.07519, over 880713.55 frames.], datatang_tot_loss[loss=0.2236, simple_loss=0.2711, pruned_loss=0.08809, over 884869.48 frames.], batch size: 35, lr: 1.43e-03
+2022-06-18 13:34:56,245 INFO [train.py:874] (3/4) Epoch 5, batch 950, datatang_loss[loss=0.2812, simple_loss=0.3041, pruned_loss=0.1292, over 4912.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2738, pruned_loss=0.08171, over 977817.10 frames.], batch size: 64, aishell_tot_loss[loss=0.2125, simple_loss=0.2759, pruned_loss=0.07454, over 891964.57 frames.], datatang_tot_loss[loss=0.2242, simple_loss=0.2716, pruned_loss=0.0884, over 897958.57 frames.], batch size: 64, lr: 1.43e-03
+2022-06-18 13:35:27,008 INFO [train.py:874] (3/4) Epoch 5, batch 1000, datatang_loss[loss=0.2039, simple_loss=0.2602, pruned_loss=0.0738, over 4943.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2743, pruned_loss=0.08236, over 979216.18 frames.], batch size: 50, aishell_tot_loss[loss=0.213, simple_loss=0.2761, pruned_loss=0.07493, over 902655.55 frames.], datatang_tot_loss[loss=0.2248, simple_loss=0.272, pruned_loss=0.08878, over 908281.18 frames.], batch size: 50, lr: 1.43e-03
+2022-06-18 13:35:27,009 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 13:35:43,415 INFO [train.py:914] (3/4) Epoch 5, validation: loss=0.1786, simple_loss=0.258, pruned_loss=0.04955, over 1622729.00 frames.
+2022-06-18 13:36:14,008 INFO [train.py:874] (3/4) Epoch 5, batch 1050, aishell_loss[loss=0.1536, simple_loss=0.2103, pruned_loss=0.04851, over 4949.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2739, pruned_loss=0.08295, over 980445.45 frames.], batch size: 21, aishell_tot_loss[loss=0.2123, simple_loss=0.2754, pruned_loss=0.07457, over 910582.91 frames.], datatang_tot_loss[loss=0.2258, simple_loss=0.2724, pruned_loss=0.08959, over 918852.44 frames.], batch size: 21, lr: 1.43e-03
+2022-06-18 13:36:44,180 INFO [train.py:874] (3/4) Epoch 5, batch 1100, datatang_loss[loss=0.2423, simple_loss=0.2914, pruned_loss=0.09655, over 4958.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2739, pruned_loss=0.08249, over 981643.40 frames.], batch size: 55, aishell_tot_loss[loss=0.2127, simple_loss=0.2757, pruned_loss=0.07485, over 920790.09 frames.], datatang_tot_loss[loss=0.2253, simple_loss=0.272, pruned_loss=0.08934, over 925525.27 frames.], batch size: 55, lr: 1.43e-03
+2022-06-18 13:37:13,266 INFO [train.py:874] (3/4) Epoch 5, batch 1150, datatang_loss[loss=0.2153, simple_loss=0.2615, pruned_loss=0.08456, over 4957.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2735, pruned_loss=0.08244, over 982588.16 frames.], batch size: 67, aishell_tot_loss[loss=0.2123, simple_loss=0.2753, pruned_loss=0.07461, over 927567.87 frames.], datatang_tot_loss[loss=0.2254, simple_loss=0.272, pruned_loss=0.08939, over 933445.52 frames.], batch size: 67, lr: 1.42e-03
+2022-06-18 13:37:45,255 INFO [train.py:874] (3/4) Epoch 5, batch 1200, aishell_loss[loss=0.2413, simple_loss=0.2868, pruned_loss=0.09788, over 4947.00 frames.], tot_loss[loss=0.219, simple_loss=0.2735, pruned_loss=0.08224, over 983171.71 frames.], batch size: 56, aishell_tot_loss[loss=0.2124, simple_loss=0.2754, pruned_loss=0.0747, over 934315.80 frames.], datatang_tot_loss[loss=0.2252, simple_loss=0.272, pruned_loss=0.0892, over 939574.32 frames.], batch size: 56, lr: 1.42e-03
+2022-06-18 13:38:15,699 INFO [train.py:874] (3/4) Epoch 5, batch 1250, aishell_loss[loss=0.2392, simple_loss=0.2921, pruned_loss=0.09313, over 4925.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2742, pruned_loss=0.08303, over 983779.32 frames.], batch size: 33, aishell_tot_loss[loss=0.2125, simple_loss=0.2753, pruned_loss=0.07488, over 940512.65 frames.], datatang_tot_loss[loss=0.2264, simple_loss=0.2728, pruned_loss=0.09002, over 944944.79 frames.], batch size: 33, lr: 1.42e-03
+2022-06-18 13:38:44,453 INFO [train.py:874] (3/4) Epoch 5, batch 1300, datatang_loss[loss=0.1879, simple_loss=0.2511, pruned_loss=0.06238, over 4932.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2747, pruned_loss=0.0832, over 983901.65 frames.], batch size: 79, aishell_tot_loss[loss=0.2134, simple_loss=0.2763, pruned_loss=0.07524, over 945752.26 frames.], datatang_tot_loss[loss=0.2262, simple_loss=0.2723, pruned_loss=0.09001, over 949497.08 frames.], batch size: 79, lr: 1.42e-03
+2022-06-18 13:39:15,070 INFO [train.py:874] (3/4) Epoch 5, batch 1350, datatang_loss[loss=0.1933, simple_loss=0.2472, pruned_loss=0.06967, over 4936.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2738, pruned_loss=0.082, over 984350.00 frames.], batch size: 79, aishell_tot_loss[loss=0.2137, simple_loss=0.2768, pruned_loss=0.07525, over 950486.43 frames.], datatang_tot_loss[loss=0.2242, simple_loss=0.2709, pruned_loss=0.08876, over 953774.31 frames.], batch size: 79, lr: 1.42e-03
+2022-06-18 13:39:45,005 INFO [train.py:874] (3/4) Epoch 5, batch 1400, datatang_loss[loss=0.2495, simple_loss=0.2808, pruned_loss=0.1091, over 4944.00 frames.], tot_loss[loss=0.2188, simple_loss=0.273, pruned_loss=0.08227, over 984433.69 frames.], batch size: 91, aishell_tot_loss[loss=0.2124, simple_loss=0.2756, pruned_loss=0.07461, over 954475.21 frames.], datatang_tot_loss[loss=0.2254, simple_loss=0.2713, pruned_loss=0.08972, over 957450.23 frames.], batch size: 91, lr: 1.41e-03
+2022-06-18 13:40:14,850 INFO [train.py:874] (3/4) Epoch 5, batch 1450, aishell_loss[loss=0.216, simple_loss=0.2771, pruned_loss=0.07743, over 4889.00 frames.], tot_loss[loss=0.2187, simple_loss=0.2735, pruned_loss=0.0819, over 984613.35 frames.], batch size: 42, aishell_tot_loss[loss=0.2125, simple_loss=0.2762, pruned_loss=0.07447, over 958201.26 frames.], datatang_tot_loss[loss=0.2251, simple_loss=0.2711, pruned_loss=0.08954, over 960644.01 frames.], batch size: 42, lr: 1.41e-03
+2022-06-18 13:40:45,919 INFO [train.py:874] (3/4) Epoch 5, batch 1500, datatang_loss[loss=0.2353, simple_loss=0.2763, pruned_loss=0.09715, over 4915.00 frames.], tot_loss[loss=0.218, simple_loss=0.2731, pruned_loss=0.08142, over 984926.35 frames.], batch size: 64, aishell_tot_loss[loss=0.2127, simple_loss=0.2764, pruned_loss=0.07453, over 960696.88 frames.], datatang_tot_loss[loss=0.2238, simple_loss=0.2706, pruned_loss=0.08856, over 964349.06 frames.], batch size: 64, lr: 1.41e-03
+2022-06-18 13:41:16,070 INFO [train.py:874] (3/4) Epoch 5, batch 1550, datatang_loss[loss=0.2456, simple_loss=0.2761, pruned_loss=0.1076, over 4921.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2732, pruned_loss=0.08204, over 985185.20 frames.], batch size: 42, aishell_tot_loss[loss=0.2138, simple_loss=0.277, pruned_loss=0.07526, over 963042.78 frames.], datatang_tot_loss[loss=0.2232, simple_loss=0.27, pruned_loss=0.08824, over 967507.55 frames.], batch size: 42, lr: 1.41e-03
+2022-06-18 13:41:45,645 INFO [train.py:874] (3/4) Epoch 5, batch 1600, aishell_loss[loss=0.2151, simple_loss=0.276, pruned_loss=0.07715, over 4928.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2748, pruned_loss=0.08312, over 985419.31 frames.], batch size: 32, aishell_tot_loss[loss=0.2152, simple_loss=0.2779, pruned_loss=0.07624, over 965827.86 frames.], datatang_tot_loss[loss=0.224, simple_loss=0.2708, pruned_loss=0.08859, over 969682.74 frames.], batch size: 32, lr: 1.41e-03
+2022-06-18 13:42:15,556 INFO [train.py:874] (3/4) Epoch 5, batch 1650, datatang_loss[loss=0.2284, simple_loss=0.2655, pruned_loss=0.09559, over 4940.00 frames.], tot_loss[loss=0.2208, simple_loss=0.2747, pruned_loss=0.08343, over 985672.49 frames.], batch size: 34, aishell_tot_loss[loss=0.2151, simple_loss=0.2778, pruned_loss=0.07623, over 967961.41 frames.], datatang_tot_loss[loss=0.2244, simple_loss=0.2709, pruned_loss=0.08889, over 971921.89 frames.], batch size: 34, lr: 1.40e-03
+2022-06-18 13:42:46,186 INFO [train.py:874] (3/4) Epoch 5, batch 1700, aishell_loss[loss=0.21, simple_loss=0.2848, pruned_loss=0.06762, over 4896.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2737, pruned_loss=0.08238, over 985800.07 frames.], batch size: 34, aishell_tot_loss[loss=0.214, simple_loss=0.2771, pruned_loss=0.07546, over 969955.80 frames.], datatang_tot_loss[loss=0.224, simple_loss=0.2705, pruned_loss=0.08869, over 973761.06 frames.], batch size: 34, lr: 1.40e-03
+2022-06-18 13:43:15,708 INFO [train.py:874] (3/4) Epoch 5, batch 1750, datatang_loss[loss=0.2156, simple_loss=0.2644, pruned_loss=0.08339, over 4922.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2725, pruned_loss=0.08092, over 985495.27 frames.], batch size: 83, aishell_tot_loss[loss=0.2139, simple_loss=0.2773, pruned_loss=0.07527, over 971410.67 frames.], datatang_tot_loss[loss=0.222, simple_loss=0.2691, pruned_loss=0.08739, over 975268.37 frames.], batch size: 83, lr: 1.40e-03
+2022-06-18 13:43:46,689 INFO [train.py:874] (3/4) Epoch 5, batch 1800, aishell_loss[loss=0.1789, simple_loss=0.2546, pruned_loss=0.05162, over 4830.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2713, pruned_loss=0.08006, over 985552.15 frames.], batch size: 29, aishell_tot_loss[loss=0.2134, simple_loss=0.2769, pruned_loss=0.07494, over 972611.39 frames.], datatang_tot_loss[loss=0.2205, simple_loss=0.2684, pruned_loss=0.08632, over 976867.86 frames.], batch size: 29, lr: 1.40e-03
+2022-06-18 13:44:17,152 INFO [train.py:874] (3/4) Epoch 5, batch 1850, datatang_loss[loss=0.289, simple_loss=0.3237, pruned_loss=0.1271, over 4934.00 frames.], tot_loss[loss=0.2163, simple_loss=0.2721, pruned_loss=0.08024, over 985712.80 frames.], batch size: 109, aishell_tot_loss[loss=0.2139, simple_loss=0.2774, pruned_loss=0.07518, over 974292.20 frames.], datatang_tot_loss[loss=0.2203, simple_loss=0.2684, pruned_loss=0.08608, over 977887.26 frames.], batch size: 109, lr: 1.40e-03
+2022-06-18 13:44:47,090 INFO [train.py:874] (3/4) Epoch 5, batch 1900, datatang_loss[loss=0.204, simple_loss=0.2622, pruned_loss=0.07288, over 4941.00 frames.], tot_loss[loss=0.2153, simple_loss=0.272, pruned_loss=0.07937, over 985931.71 frames.], batch size: 62, aishell_tot_loss[loss=0.2135, simple_loss=0.2773, pruned_loss=0.07489, over 975859.79 frames.], datatang_tot_loss[loss=0.2195, simple_loss=0.2681, pruned_loss=0.08544, over 978845.68 frames.], batch size: 62, lr: 1.40e-03
+2022-06-18 13:45:17,774 INFO [train.py:874] (3/4) Epoch 5, batch 1950, datatang_loss[loss=0.2151, simple_loss=0.2606, pruned_loss=0.08485, over 4912.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2719, pruned_loss=0.07957, over 986005.63 frames.], batch size: 57, aishell_tot_loss[loss=0.213, simple_loss=0.2771, pruned_loss=0.07439, over 976998.82 frames.], datatang_tot_loss[loss=0.2199, simple_loss=0.2682, pruned_loss=0.08581, over 979792.31 frames.], batch size: 57, lr: 1.39e-03
+2022-06-18 13:45:46,810 INFO [train.py:874] (3/4) Epoch 5, batch 2000, aishell_loss[loss=0.1754, simple_loss=0.2434, pruned_loss=0.05376, over 4948.00 frames.], tot_loss[loss=0.2164, simple_loss=0.2728, pruned_loss=0.08, over 986231.14 frames.], batch size: 56, aishell_tot_loss[loss=0.213, simple_loss=0.2771, pruned_loss=0.07442, over 978237.67 frames.], datatang_tot_loss[loss=0.2206, simple_loss=0.2688, pruned_loss=0.08622, over 980604.97 frames.], batch size: 56, lr: 1.39e-03
+2022-06-18 13:45:46,811 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 13:46:03,087 INFO [train.py:914] (3/4) Epoch 5, validation: loss=0.1805, simple_loss=0.2595, pruned_loss=0.05074, over 1622729.00 frames.
+2022-06-18 13:46:32,408 INFO [train.py:874] (3/4) Epoch 5, batch 2050, datatang_loss[loss=0.2192, simple_loss=0.2692, pruned_loss=0.08462, over 4930.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2729, pruned_loss=0.08003, over 985806.90 frames.], batch size: 34, aishell_tot_loss[loss=0.2126, simple_loss=0.2768, pruned_loss=0.07417, over 978792.80 frames.], datatang_tot_loss[loss=0.2211, simple_loss=0.2692, pruned_loss=0.08645, over 981229.16 frames.], batch size: 34, lr: 1.39e-03
+2022-06-18 13:47:01,884 INFO [train.py:874] (3/4) Epoch 5, batch 2100, datatang_loss[loss=0.1832, simple_loss=0.2386, pruned_loss=0.06397, over 4923.00 frames.], tot_loss[loss=0.2171, simple_loss=0.2738, pruned_loss=0.0802, over 985842.37 frames.], batch size: 73, aishell_tot_loss[loss=0.2127, simple_loss=0.2771, pruned_loss=0.07412, over 979971.32 frames.], datatang_tot_loss[loss=0.2219, simple_loss=0.2696, pruned_loss=0.08706, over 981508.28 frames.], batch size: 73, lr: 1.39e-03
+2022-06-18 13:47:31,731 INFO [train.py:874] (3/4) Epoch 5, batch 2150, aishell_loss[loss=0.2423, simple_loss=0.2979, pruned_loss=0.09339, over 4943.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2742, pruned_loss=0.08034, over 985946.87 frames.], batch size: 54, aishell_tot_loss[loss=0.213, simple_loss=0.2773, pruned_loss=0.07435, over 980828.55 frames.], datatang_tot_loss[loss=0.2221, simple_loss=0.2697, pruned_loss=0.08726, over 982013.78 frames.], batch size: 54, lr: 1.39e-03
+2022-06-18 13:48:01,866 INFO [train.py:874] (3/4) Epoch 5, batch 2200, datatang_loss[loss=0.2821, simple_loss=0.3071, pruned_loss=0.1286, over 4934.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2737, pruned_loss=0.08068, over 985960.32 frames.], batch size: 79, aishell_tot_loss[loss=0.2125, simple_loss=0.277, pruned_loss=0.07399, over 981263.80 frames.], datatang_tot_loss[loss=0.2222, simple_loss=0.2698, pruned_loss=0.08731, over 982603.47 frames.], batch size: 79, lr: 1.39e-03
+2022-06-18 13:48:32,511 INFO [train.py:874] (3/4) Epoch 5, batch 2250, datatang_loss[loss=0.2279, simple_loss=0.2695, pruned_loss=0.09313, over 4933.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2735, pruned_loss=0.08141, over 985985.83 frames.], batch size: 69, aishell_tot_loss[loss=0.2137, simple_loss=0.2776, pruned_loss=0.07493, over 981628.73 frames.], datatang_tot_loss[loss=0.2215, simple_loss=0.2694, pruned_loss=0.08684, over 983173.46 frames.], batch size: 69, lr: 1.38e-03
+2022-06-18 13:49:03,372 INFO [train.py:874] (3/4) Epoch 5, batch 2300, datatang_loss[loss=0.2423, simple_loss=0.2856, pruned_loss=0.09946, over 4929.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2741, pruned_loss=0.08187, over 985957.24 frames.], batch size: 88, aishell_tot_loss[loss=0.2137, simple_loss=0.2775, pruned_loss=0.07495, over 982057.41 frames.], datatang_tot_loss[loss=0.2222, simple_loss=0.2703, pruned_loss=0.08703, over 983497.14 frames.], batch size: 88, lr: 1.38e-03
+2022-06-18 13:49:34,091 INFO [train.py:874] (3/4) Epoch 5, batch 2350, aishell_loss[loss=0.2183, simple_loss=0.2851, pruned_loss=0.07581, over 4946.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2728, pruned_loss=0.08006, over 985812.52 frames.], batch size: 64, aishell_tot_loss[loss=0.2127, simple_loss=0.2768, pruned_loss=0.07431, over 982465.67 frames.], datatang_tot_loss[loss=0.2209, simple_loss=0.2698, pruned_loss=0.08603, over 983696.10 frames.], batch size: 64, lr: 1.38e-03
+2022-06-18 13:50:03,401 INFO [train.py:874] (3/4) Epoch 5, batch 2400, datatang_loss[loss=0.2037, simple_loss=0.2406, pruned_loss=0.08338, over 4896.00 frames.], tot_loss[loss=0.2167, simple_loss=0.273, pruned_loss=0.08017, over 986057.14 frames.], batch size: 52, aishell_tot_loss[loss=0.2121, simple_loss=0.2765, pruned_loss=0.07383, over 983013.76 frames.], datatang_tot_loss[loss=0.2216, simple_loss=0.2701, pruned_loss=0.08651, over 984043.13 frames.], batch size: 52, lr: 1.38e-03
+2022-06-18 13:50:34,624 INFO [train.py:874] (3/4) Epoch 5, batch 2450, aishell_loss[loss=0.214, simple_loss=0.2898, pruned_loss=0.06913, over 4893.00 frames.], tot_loss[loss=0.2165, simple_loss=0.2729, pruned_loss=0.08, over 985775.95 frames.], batch size: 60, aishell_tot_loss[loss=0.2121, simple_loss=0.2768, pruned_loss=0.07369, over 983151.08 frames.], datatang_tot_loss[loss=0.2211, simple_loss=0.2699, pruned_loss=0.08614, over 984180.89 frames.], batch size: 60, lr: 1.38e-03
+2022-06-18 13:51:05,516 INFO [train.py:874] (3/4) Epoch 5, batch 2500, datatang_loss[loss=0.1831, simple_loss=0.2519, pruned_loss=0.05713, over 4916.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2722, pruned_loss=0.07946, over 985379.46 frames.], batch size: 57, aishell_tot_loss[loss=0.2128, simple_loss=0.2772, pruned_loss=0.07417, over 983117.17 frames.], datatang_tot_loss[loss=0.2194, simple_loss=0.2686, pruned_loss=0.08509, over 984298.21 frames.], batch size: 57, lr: 1.38e-03
+2022-06-18 13:51:35,055 INFO [train.py:874] (3/4) Epoch 5, batch 2550, datatang_loss[loss=0.2057, simple_loss=0.2452, pruned_loss=0.0831, over 4919.00 frames.], tot_loss[loss=0.2153, simple_loss=0.2723, pruned_loss=0.0792, over 985247.73 frames.], batch size: 42, aishell_tot_loss[loss=0.2125, simple_loss=0.277, pruned_loss=0.07395, over 983433.03 frames.], datatang_tot_loss[loss=0.2195, simple_loss=0.2685, pruned_loss=0.08529, over 984260.69 frames.], batch size: 42, lr: 1.37e-03
+2022-06-18 13:52:06,176 INFO [train.py:874] (3/4) Epoch 5, batch 2600, datatang_loss[loss=0.242, simple_loss=0.2969, pruned_loss=0.09357, over 4914.00 frames.], tot_loss[loss=0.2138, simple_loss=0.2711, pruned_loss=0.07832, over 985258.31 frames.], batch size: 98, aishell_tot_loss[loss=0.2116, simple_loss=0.2762, pruned_loss=0.07351, over 983642.05 frames.], datatang_tot_loss[loss=0.2187, simple_loss=0.2678, pruned_loss=0.08479, over 984417.47 frames.], batch size: 98, lr: 1.37e-03
+2022-06-18 13:52:35,482 INFO [train.py:874] (3/4) Epoch 5, batch 2650, datatang_loss[loss=0.2256, simple_loss=0.2776, pruned_loss=0.08679, over 4943.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2717, pruned_loss=0.0788, over 985267.10 frames.], batch size: 94, aishell_tot_loss[loss=0.2119, simple_loss=0.2765, pruned_loss=0.07362, over 983753.07 frames.], datatang_tot_loss[loss=0.2191, simple_loss=0.2681, pruned_loss=0.08501, over 984625.33 frames.], batch size: 94, lr: 1.37e-03
+2022-06-18 13:53:06,329 INFO [train.py:874] (3/4) Epoch 5, batch 2700, datatang_loss[loss=0.1838, simple_loss=0.2506, pruned_loss=0.05848, over 4918.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2711, pruned_loss=0.07837, over 985551.91 frames.], batch size: 81, aishell_tot_loss[loss=0.2113, simple_loss=0.276, pruned_loss=0.07333, over 984107.29 frames.], datatang_tot_loss[loss=0.2186, simple_loss=0.2677, pruned_loss=0.08479, over 984843.76 frames.], batch size: 81, lr: 1.37e-03
+2022-06-18 13:53:36,296 INFO [train.py:874] (3/4) Epoch 5, batch 2750, datatang_loss[loss=0.1901, simple_loss=0.2351, pruned_loss=0.07249, over 4978.00 frames.], tot_loss[loss=0.2124, simple_loss=0.27, pruned_loss=0.07739, over 985255.56 frames.], batch size: 40, aishell_tot_loss[loss=0.2109, simple_loss=0.2758, pruned_loss=0.07303, over 984031.12 frames.], datatang_tot_loss[loss=0.2172, simple_loss=0.2666, pruned_loss=0.08392, over 984850.90 frames.], batch size: 40, lr: 1.37e-03
+2022-06-18 13:54:05,738 INFO [train.py:874] (3/4) Epoch 5, batch 2800, datatang_loss[loss=0.2548, simple_loss=0.2953, pruned_loss=0.1072, over 4905.00 frames.], tot_loss[loss=0.2123, simple_loss=0.27, pruned_loss=0.07731, over 985413.53 frames.], batch size: 39, aishell_tot_loss[loss=0.2103, simple_loss=0.2752, pruned_loss=0.07271, over 984365.77 frames.], datatang_tot_loss[loss=0.2174, simple_loss=0.2669, pruned_loss=0.08392, over 984887.35 frames.], batch size: 39, lr: 1.37e-03
+2022-06-18 13:54:37,311 INFO [train.py:874] (3/4) Epoch 5, batch 2850, datatang_loss[loss=0.2158, simple_loss=0.2558, pruned_loss=0.0879, over 4980.00 frames.], tot_loss[loss=0.213, simple_loss=0.2704, pruned_loss=0.07778, over 985413.15 frames.], batch size: 31, aishell_tot_loss[loss=0.2105, simple_loss=0.2757, pruned_loss=0.0727, over 984419.82 frames.], datatang_tot_loss[loss=0.2174, simple_loss=0.2668, pruned_loss=0.08399, over 984993.57 frames.], batch size: 31, lr: 1.36e-03
+2022-06-18 13:55:07,716 INFO [train.py:874] (3/4) Epoch 5, batch 2900, datatang_loss[loss=0.2184, simple_loss=0.2727, pruned_loss=0.08209, over 4916.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2714, pruned_loss=0.07798, over 985738.49 frames.], batch size: 75, aishell_tot_loss[loss=0.2106, simple_loss=0.2759, pruned_loss=0.0727, over 984737.68 frames.], datatang_tot_loss[loss=0.2179, simple_loss=0.2673, pruned_loss=0.08423, over 985195.12 frames.], batch size: 75, lr: 1.36e-03
+2022-06-18 13:55:36,572 INFO [train.py:874] (3/4) Epoch 5, batch 2950, aishell_loss[loss=0.2332, simple_loss=0.2948, pruned_loss=0.08582, over 4957.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2706, pruned_loss=0.07756, over 985678.43 frames.], batch size: 61, aishell_tot_loss[loss=0.2098, simple_loss=0.2751, pruned_loss=0.07222, over 984716.33 frames.], datatang_tot_loss[loss=0.2177, simple_loss=0.2672, pruned_loss=0.08411, over 985342.47 frames.], batch size: 61, lr: 1.36e-03
+2022-06-18 13:56:08,965 INFO [train.py:874] (3/4) Epoch 5, batch 3000, datatang_loss[loss=0.2369, simple_loss=0.2857, pruned_loss=0.09402, over 4956.00 frames.], tot_loss[loss=0.2122, simple_loss=0.2701, pruned_loss=0.07717, over 985502.95 frames.], batch size: 91, aishell_tot_loss[loss=0.2098, simple_loss=0.2749, pruned_loss=0.07236, over 984706.42 frames.], datatang_tot_loss[loss=0.2168, simple_loss=0.2666, pruned_loss=0.0835, over 985332.53 frames.], batch size: 91, lr: 1.36e-03
+2022-06-18 13:56:08,967 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 13:56:26,008 INFO [train.py:914] (3/4) Epoch 5, validation: loss=0.1806, simple_loss=0.2585, pruned_loss=0.05141, over 1622729.00 frames.
+2022-06-18 13:56:55,568 INFO [train.py:874] (3/4) Epoch 5, batch 3050, aishell_loss[loss=0.2115, simple_loss=0.2805, pruned_loss=0.07123, over 4964.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2691, pruned_loss=0.0763, over 985525.92 frames.], batch size: 61, aishell_tot_loss[loss=0.2089, simple_loss=0.2743, pruned_loss=0.07175, over 984967.15 frames.], datatang_tot_loss[loss=0.2161, simple_loss=0.266, pruned_loss=0.08307, over 985207.63 frames.], batch size: 61, lr: 1.36e-03
+2022-06-18 13:57:26,908 INFO [train.py:874] (3/4) Epoch 5, batch 3100, datatang_loss[loss=0.225, simple_loss=0.267, pruned_loss=0.09151, over 4924.00 frames.], tot_loss[loss=0.2115, simple_loss=0.2694, pruned_loss=0.07682, over 985866.19 frames.], batch size: 71, aishell_tot_loss[loss=0.2092, simple_loss=0.2746, pruned_loss=0.07191, over 985175.70 frames.], datatang_tot_loss[loss=0.2159, simple_loss=0.2659, pruned_loss=0.083, over 985447.25 frames.], batch size: 71, lr: 1.36e-03
+2022-06-18 13:57:57,649 INFO [train.py:874] (3/4) Epoch 5, batch 3150, aishell_loss[loss=0.2279, simple_loss=0.2963, pruned_loss=0.07978, over 4869.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2701, pruned_loss=0.07758, over 985711.36 frames.], batch size: 36, aishell_tot_loss[loss=0.2095, simple_loss=0.2749, pruned_loss=0.07211, over 985183.91 frames.], datatang_tot_loss[loss=0.2164, simple_loss=0.2663, pruned_loss=0.0833, over 985413.88 frames.], batch size: 36, lr: 1.35e-03
+2022-06-18 13:58:27,958 INFO [train.py:874] (3/4) Epoch 5, batch 3200, aishell_loss[loss=0.1922, simple_loss=0.2692, pruned_loss=0.05763, over 4914.00 frames.], tot_loss[loss=0.2117, simple_loss=0.2691, pruned_loss=0.07721, over 985865.60 frames.], batch size: 46, aishell_tot_loss[loss=0.2091, simple_loss=0.2747, pruned_loss=0.07178, over 985263.70 frames.], datatang_tot_loss[loss=0.2157, simple_loss=0.2654, pruned_loss=0.08298, over 985576.41 frames.], batch size: 46, lr: 1.35e-03
+2022-06-18 13:58:58,347 INFO [train.py:874] (3/4) Epoch 5, batch 3250, datatang_loss[loss=0.2432, simple_loss=0.2775, pruned_loss=0.1045, over 4925.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2703, pruned_loss=0.07796, over 985622.18 frames.], batch size: 50, aishell_tot_loss[loss=0.2098, simple_loss=0.2754, pruned_loss=0.07211, over 985019.94 frames.], datatang_tot_loss[loss=0.2163, simple_loss=0.2657, pruned_loss=0.08351, over 985655.60 frames.], batch size: 50, lr: 1.35e-03
+2022-06-18 13:59:26,795 INFO [train.py:874] (3/4) Epoch 5, batch 3300, aishell_loss[loss=0.2191, simple_loss=0.2868, pruned_loss=0.07569, over 4934.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2724, pruned_loss=0.07861, over 985471.12 frames.], batch size: 46, aishell_tot_loss[loss=0.2102, simple_loss=0.2759, pruned_loss=0.07224, over 984755.79 frames.], datatang_tot_loss[loss=0.218, simple_loss=0.2671, pruned_loss=0.08448, over 985851.83 frames.], batch size: 46, lr: 1.35e-03
+2022-06-18 13:59:58,785 INFO [train.py:874] (3/4) Epoch 5, batch 3350, datatang_loss[loss=0.1712, simple_loss=0.228, pruned_loss=0.05719, over 4955.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2719, pruned_loss=0.07794, over 985248.26 frames.], batch size: 55, aishell_tot_loss[loss=0.2104, simple_loss=0.2763, pruned_loss=0.07229, over 984660.20 frames.], datatang_tot_loss[loss=0.2169, simple_loss=0.2665, pruned_loss=0.08372, over 985748.28 frames.], batch size: 55, lr: 1.35e-03
+2022-06-18 14:00:33,667 INFO [train.py:874] (3/4) Epoch 5, batch 3400, datatang_loss[loss=0.1806, simple_loss=0.2426, pruned_loss=0.05928, over 4950.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2728, pruned_loss=0.07839, over 985433.83 frames.], batch size: 67, aishell_tot_loss[loss=0.2106, simple_loss=0.2762, pruned_loss=0.07245, over 984874.43 frames.], datatang_tot_loss[loss=0.218, simple_loss=0.2672, pruned_loss=0.0844, over 985768.82 frames.], batch size: 67, lr: 1.35e-03
+2022-06-18 14:01:04,107 INFO [train.py:874] (3/4) Epoch 5, batch 3450, aishell_loss[loss=0.1709, simple_loss=0.2317, pruned_loss=0.05511, over 4982.00 frames.], tot_loss[loss=0.2152, simple_loss=0.2728, pruned_loss=0.0788, over 985454.98 frames.], batch size: 25, aishell_tot_loss[loss=0.2104, simple_loss=0.2759, pruned_loss=0.07242, over 984888.61 frames.], datatang_tot_loss[loss=0.2187, simple_loss=0.268, pruned_loss=0.08471, over 985792.14 frames.], batch size: 25, lr: 1.34e-03
+2022-06-18 14:01:34,036 INFO [train.py:874] (3/4) Epoch 5, batch 3500, aishell_loss[loss=0.2254, simple_loss=0.2934, pruned_loss=0.07865, over 4944.00 frames.], tot_loss[loss=0.2161, simple_loss=0.274, pruned_loss=0.0791, over 985643.50 frames.], batch size: 64, aishell_tot_loss[loss=0.2109, simple_loss=0.2766, pruned_loss=0.07262, over 984987.89 frames.], datatang_tot_loss[loss=0.2192, simple_loss=0.2687, pruned_loss=0.08485, over 985923.92 frames.], batch size: 64, lr: 1.34e-03
+2022-06-18 14:02:04,016 INFO [train.py:874] (3/4) Epoch 5, batch 3550, datatang_loss[loss=0.2602, simple_loss=0.3136, pruned_loss=0.1034, over 4931.00 frames.], tot_loss[loss=0.2156, simple_loss=0.2736, pruned_loss=0.07879, over 985654.08 frames.], batch size: 57, aishell_tot_loss[loss=0.2112, simple_loss=0.2766, pruned_loss=0.07289, over 984986.74 frames.], datatang_tot_loss[loss=0.2188, simple_loss=0.2685, pruned_loss=0.0845, over 985987.73 frames.], batch size: 57, lr: 1.34e-03
+2022-06-18 14:02:33,856 INFO [train.py:874] (3/4) Epoch 5, batch 3600, aishell_loss[loss=0.2045, simple_loss=0.2723, pruned_loss=0.0683, over 4925.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2729, pruned_loss=0.07838, over 985715.02 frames.], batch size: 33, aishell_tot_loss[loss=0.2111, simple_loss=0.2766, pruned_loss=0.07279, over 985142.18 frames.], datatang_tot_loss[loss=0.2182, simple_loss=0.2679, pruned_loss=0.08423, over 985932.27 frames.], batch size: 33, lr: 1.34e-03
+2022-06-18 14:03:03,216 INFO [train.py:874] (3/4) Epoch 5, batch 3650, datatang_loss[loss=0.2175, simple_loss=0.2573, pruned_loss=0.08885, over 4960.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2724, pruned_loss=0.07852, over 985748.05 frames.], batch size: 37, aishell_tot_loss[loss=0.2112, simple_loss=0.2765, pruned_loss=0.07293, over 985136.88 frames.], datatang_tot_loss[loss=0.2179, simple_loss=0.2677, pruned_loss=0.08409, over 985999.00 frames.], batch size: 37, lr: 1.34e-03
+2022-06-18 14:03:33,772 INFO [train.py:874] (3/4) Epoch 5, batch 3700, aishell_loss[loss=0.2111, simple_loss=0.2889, pruned_loss=0.06667, over 4921.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2722, pruned_loss=0.07815, over 985654.54 frames.], batch size: 52, aishell_tot_loss[loss=0.2108, simple_loss=0.2762, pruned_loss=0.07264, over 985261.43 frames.], datatang_tot_loss[loss=0.218, simple_loss=0.2679, pruned_loss=0.08401, over 985817.19 frames.], batch size: 52, lr: 1.34e-03
+2022-06-18 14:04:03,517 INFO [train.py:874] (3/4) Epoch 5, batch 3750, aishell_loss[loss=0.171, simple_loss=0.2453, pruned_loss=0.04835, over 4872.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2718, pruned_loss=0.07774, over 985580.17 frames.], batch size: 28, aishell_tot_loss[loss=0.2105, simple_loss=0.276, pruned_loss=0.07249, over 985131.90 frames.], datatang_tot_loss[loss=0.2176, simple_loss=0.2677, pruned_loss=0.08377, over 985897.46 frames.], batch size: 28, lr: 1.34e-03
+2022-06-18 14:04:33,315 INFO [train.py:874] (3/4) Epoch 5, batch 3800, aishell_loss[loss=0.1986, simple_loss=0.2802, pruned_loss=0.05853, over 4911.00 frames.], tot_loss[loss=0.2124, simple_loss=0.2712, pruned_loss=0.0768, over 985706.80 frames.], batch size: 78, aishell_tot_loss[loss=0.209, simple_loss=0.275, pruned_loss=0.07154, over 985147.47 frames.], datatang_tot_loss[loss=0.2179, simple_loss=0.2678, pruned_loss=0.08395, over 986057.10 frames.], batch size: 78, lr: 1.33e-03
+2022-06-18 14:05:02,193 INFO [train.py:874] (3/4) Epoch 5, batch 3850, datatang_loss[loss=0.216, simple_loss=0.2751, pruned_loss=0.07848, over 4921.00 frames.], tot_loss[loss=0.2114, simple_loss=0.2706, pruned_loss=0.07608, over 985397.57 frames.], batch size: 81, aishell_tot_loss[loss=0.2081, simple_loss=0.2743, pruned_loss=0.07094, over 984830.26 frames.], datatang_tot_loss[loss=0.2177, simple_loss=0.2676, pruned_loss=0.08393, over 986100.99 frames.], batch size: 81, lr: 1.33e-03
+2022-06-18 14:05:32,259 INFO [train.py:874] (3/4) Epoch 5, batch 3900, datatang_loss[loss=0.1867, simple_loss=0.2475, pruned_loss=0.06291, over 4970.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2695, pruned_loss=0.075, over 985430.40 frames.], batch size: 67, aishell_tot_loss[loss=0.2071, simple_loss=0.2738, pruned_loss=0.07025, over 984833.53 frames.], datatang_tot_loss[loss=0.2167, simple_loss=0.2669, pruned_loss=0.08321, over 986116.37 frames.], batch size: 67, lr: 1.33e-03
+2022-06-18 14:06:00,087 INFO [train.py:874] (3/4) Epoch 5, batch 3950, datatang_loss[loss=0.2124, simple_loss=0.2634, pruned_loss=0.0807, over 4927.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2693, pruned_loss=0.07481, over 985723.70 frames.], batch size: 50, aishell_tot_loss[loss=0.2071, simple_loss=0.2739, pruned_loss=0.0701, over 985059.73 frames.], datatang_tot_loss[loss=0.216, simple_loss=0.2662, pruned_loss=0.0829, over 986214.34 frames.], batch size: 50, lr: 1.33e-03
+2022-06-18 14:06:30,700 INFO [train.py:874] (3/4) Epoch 5, batch 4000, aishell_loss[loss=0.1956, simple_loss=0.2588, pruned_loss=0.06624, over 4901.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2691, pruned_loss=0.07533, over 985684.39 frames.], batch size: 34, aishell_tot_loss[loss=0.2075, simple_loss=0.274, pruned_loss=0.07047, over 985042.53 frames.], datatang_tot_loss[loss=0.2155, simple_loss=0.2658, pruned_loss=0.08264, over 986187.91 frames.], batch size: 34, lr: 1.33e-03
+2022-06-18 14:06:30,701 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 14:06:46,840 INFO [train.py:914] (3/4) Epoch 5, validation: loss=0.1787, simple_loss=0.2582, pruned_loss=0.04961, over 1622729.00 frames.
+2022-06-18 14:07:16,342 INFO [train.py:874] (3/4) Epoch 5, batch 4050, aishell_loss[loss=0.2083, simple_loss=0.2738, pruned_loss=0.07145, over 4935.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2714, pruned_loss=0.0782, over 985175.73 frames.], batch size: 54, aishell_tot_loss[loss=0.2093, simple_loss=0.275, pruned_loss=0.07183, over 984886.34 frames.], datatang_tot_loss[loss=0.2172, simple_loss=0.2672, pruned_loss=0.08366, over 985799.09 frames.], batch size: 54, lr: 1.33e-03
+2022-06-18 14:07:45,374 INFO [train.py:874] (3/4) Epoch 5, batch 4100, aishell_loss[loss=0.208, simple_loss=0.27, pruned_loss=0.07302, over 4951.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2713, pruned_loss=0.07875, over 985400.81 frames.], batch size: 45, aishell_tot_loss[loss=0.2097, simple_loss=0.2751, pruned_loss=0.07216, over 984950.70 frames.], datatang_tot_loss[loss=0.2174, simple_loss=0.2672, pruned_loss=0.08377, over 985907.84 frames.], batch size: 45, lr: 1.32e-03
+2022-06-18 14:08:14,136 INFO [train.py:874] (3/4) Epoch 5, batch 4150, aishell_loss[loss=0.2035, simple_loss=0.2809, pruned_loss=0.06307, over 4906.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2724, pruned_loss=0.07932, over 985259.81 frames.], batch size: 41, aishell_tot_loss[loss=0.21, simple_loss=0.2754, pruned_loss=0.07227, over 984665.01 frames.], datatang_tot_loss[loss=0.2184, simple_loss=0.268, pruned_loss=0.0844, over 986039.67 frames.], batch size: 41, lr: 1.32e-03
+2022-06-18 14:09:32,258 INFO [train.py:874] (3/4) Epoch 6, batch 50, datatang_loss[loss=0.1734, simple_loss=0.2276, pruned_loss=0.05964, over 4946.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2579, pruned_loss=0.0665, over 218802.92 frames.], batch size: 50, aishell_tot_loss[loss=0.2025, simple_loss=0.2711, pruned_loss=0.06692, over 120687.13 frames.], datatang_tot_loss[loss=0.1881, simple_loss=0.2439, pruned_loss=0.06614, over 111790.13 frames.], batch size: 50, lr: 1.27e-03
+2022-06-18 14:10:03,305 INFO [train.py:874] (3/4) Epoch 6, batch 100, datatang_loss[loss=0.1827, simple_loss=0.2443, pruned_loss=0.06055, over 4898.00 frames.], tot_loss[loss=0.1974, simple_loss=0.259, pruned_loss=0.06784, over 389260.39 frames.], batch size: 59, aishell_tot_loss[loss=0.2034, simple_loss=0.2718, pruned_loss=0.06746, over 215121.21 frames.], datatang_tot_loss[loss=0.1916, simple_loss=0.2469, pruned_loss=0.06818, over 222638.08 frames.], batch size: 59, lr: 1.27e-03
+2022-06-18 14:10:32,601 INFO [train.py:874] (3/4) Epoch 6, batch 150, datatang_loss[loss=0.1696, simple_loss=0.2246, pruned_loss=0.05728, over 4924.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2581, pruned_loss=0.06781, over 521528.92 frames.], batch size: 73, aishell_tot_loss[loss=0.2017, simple_loss=0.2705, pruned_loss=0.06643, over 288525.80 frames.], datatang_tot_loss[loss=0.1931, simple_loss=0.2482, pruned_loss=0.06902, over 329318.24 frames.], batch size: 73, lr: 1.27e-03
+2022-06-18 14:11:03,773 INFO [train.py:874] (3/4) Epoch 6, batch 200, aishell_loss[loss=0.2062, simple_loss=0.2791, pruned_loss=0.0667, over 4981.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2594, pruned_loss=0.06873, over 624695.90 frames.], batch size: 51, aishell_tot_loss[loss=0.2011, simple_loss=0.2695, pruned_loss=0.06635, over 364474.05 frames.], datatang_tot_loss[loss=0.1959, simple_loss=0.2508, pruned_loss=0.07052, over 412641.45 frames.], batch size: 51, lr: 1.26e-03
+2022-06-18 14:11:33,038 INFO [train.py:874] (3/4) Epoch 6, batch 250, datatang_loss[loss=0.203, simple_loss=0.259, pruned_loss=0.07348, over 4932.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2629, pruned_loss=0.07082, over 704667.73 frames.], batch size: 79, aishell_tot_loss[loss=0.2044, simple_loss=0.2716, pruned_loss=0.06855, over 456301.32 frames.], datatang_tot_loss[loss=0.1985, simple_loss=0.2528, pruned_loss=0.07212, over 462269.55 frames.], batch size: 79, lr: 1.26e-03
+2022-06-18 14:12:03,500 INFO [train.py:874] (3/4) Epoch 6, batch 300, datatang_loss[loss=0.2428, simple_loss=0.2861, pruned_loss=0.09977, over 4940.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2642, pruned_loss=0.07215, over 766896.11 frames.], batch size: 108, aishell_tot_loss[loss=0.204, simple_loss=0.2709, pruned_loss=0.06853, over 513826.94 frames.], datatang_tot_loss[loss=0.2023, simple_loss=0.2561, pruned_loss=0.07431, over 528576.05 frames.], batch size: 108, lr: 1.26e-03
+2022-06-18 14:12:34,600 INFO [train.py:874] (3/4) Epoch 6, batch 350, datatang_loss[loss=0.2219, simple_loss=0.2716, pruned_loss=0.08607, over 4956.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2652, pruned_loss=0.07207, over 815550.76 frames.], batch size: 86, aishell_tot_loss[loss=0.2049, simple_loss=0.2721, pruned_loss=0.06885, over 567316.09 frames.], datatang_tot_loss[loss=0.2025, simple_loss=0.2567, pruned_loss=0.0741, over 584590.70 frames.], batch size: 86, lr: 1.26e-03
+2022-06-18 14:13:03,905 INFO [train.py:874] (3/4) Epoch 6, batch 400, datatang_loss[loss=0.1993, simple_loss=0.2549, pruned_loss=0.0719, over 4928.00 frames.], tot_loss[loss=0.2053, simple_loss=0.266, pruned_loss=0.07227, over 853540.68 frames.], batch size: 73, aishell_tot_loss[loss=0.2051, simple_loss=0.2726, pruned_loss=0.0688, over 622255.83 frames.], datatang_tot_loss[loss=0.2035, simple_loss=0.2574, pruned_loss=0.07474, over 626599.18 frames.], batch size: 73, lr: 1.26e-03
+2022-06-18 14:13:33,671 INFO [train.py:874] (3/4) Epoch 6, batch 450, aishell_loss[loss=0.2172, simple_loss=0.2939, pruned_loss=0.07023, over 4937.00 frames.], tot_loss[loss=0.206, simple_loss=0.2666, pruned_loss=0.07272, over 882816.10 frames.], batch size: 68, aishell_tot_loss[loss=0.2043, simple_loss=0.2718, pruned_loss=0.06839, over 668208.91 frames.], datatang_tot_loss[loss=0.2056, simple_loss=0.2591, pruned_loss=0.07601, over 665749.67 frames.], batch size: 68, lr: 1.26e-03
+2022-06-18 14:14:05,082 INFO [train.py:874] (3/4) Epoch 6, batch 500, datatang_loss[loss=0.2105, simple_loss=0.2523, pruned_loss=0.08437, over 4940.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2678, pruned_loss=0.07368, over 905492.51 frames.], batch size: 34, aishell_tot_loss[loss=0.2041, simple_loss=0.2717, pruned_loss=0.06829, over 709630.55 frames.], datatang_tot_loss[loss=0.2083, simple_loss=0.2611, pruned_loss=0.07776, over 699163.00 frames.], batch size: 34, lr: 1.26e-03
+2022-06-18 14:14:34,063 INFO [train.py:874] (3/4) Epoch 6, batch 550, aishell_loss[loss=0.2067, simple_loss=0.2796, pruned_loss=0.06688, over 4919.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2668, pruned_loss=0.07251, over 923468.22 frames.], batch size: 46, aishell_tot_loss[loss=0.2031, simple_loss=0.2708, pruned_loss=0.0677, over 756325.11 frames.], datatang_tot_loss[loss=0.208, simple_loss=0.2603, pruned_loss=0.07779, over 717482.22 frames.], batch size: 46, lr: 1.25e-03
+2022-06-18 14:15:04,050 INFO [train.py:874] (3/4) Epoch 6, batch 600, datatang_loss[loss=0.1917, simple_loss=0.2533, pruned_loss=0.06504, over 4974.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2674, pruned_loss=0.07317, over 937716.16 frames.], batch size: 60, aishell_tot_loss[loss=0.2025, simple_loss=0.2701, pruned_loss=0.06746, over 782642.15 frames.], datatang_tot_loss[loss=0.21, simple_loss=0.2623, pruned_loss=0.07884, over 750415.08 frames.], batch size: 60, lr: 1.25e-03
+2022-06-18 14:15:34,732 INFO [train.py:874] (3/4) Epoch 6, batch 650, datatang_loss[loss=0.1922, simple_loss=0.2547, pruned_loss=0.06481, over 4913.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2684, pruned_loss=0.07367, over 948244.30 frames.], batch size: 77, aishell_tot_loss[loss=0.2032, simple_loss=0.271, pruned_loss=0.06771, over 805505.34 frames.], datatang_tot_loss[loss=0.2106, simple_loss=0.2629, pruned_loss=0.07916, over 779207.14 frames.], batch size: 77, lr: 1.25e-03
+2022-06-18 14:16:03,562 INFO [train.py:874] (3/4) Epoch 6, batch 700, aishell_loss[loss=0.1953, simple_loss=0.2638, pruned_loss=0.06345, over 4938.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2689, pruned_loss=0.07376, over 956631.75 frames.], batch size: 58, aishell_tot_loss[loss=0.203, simple_loss=0.2711, pruned_loss=0.06744, over 826785.97 frames.], datatang_tot_loss[loss=0.2116, simple_loss=0.2639, pruned_loss=0.07964, over 803554.53 frames.], batch size: 58, lr: 1.25e-03
+2022-06-18 14:16:34,064 INFO [train.py:874] (3/4) Epoch 6, batch 750, aishell_loss[loss=0.2254, simple_loss=0.2959, pruned_loss=0.07748, over 4950.00 frames.], tot_loss[loss=0.2098, simple_loss=0.27, pruned_loss=0.07476, over 963164.80 frames.], batch size: 80, aishell_tot_loss[loss=0.204, simple_loss=0.2722, pruned_loss=0.06787, over 842757.94 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.2648, pruned_loss=0.0802, over 828199.91 frames.], batch size: 80, lr: 1.25e-03
+2022-06-18 14:17:06,198 INFO [train.py:874] (3/4) Epoch 6, batch 800, datatang_loss[loss=0.2233, simple_loss=0.2622, pruned_loss=0.09216, over 4927.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2689, pruned_loss=0.0745, over 967729.74 frames.], batch size: 50, aishell_tot_loss[loss=0.203, simple_loss=0.2714, pruned_loss=0.06734, over 856685.54 frames.], datatang_tot_loss[loss=0.2127, simple_loss=0.2648, pruned_loss=0.08029, over 849406.48 frames.], batch size: 50, lr: 1.25e-03
+2022-06-18 14:17:35,720 INFO [train.py:874] (3/4) Epoch 6, batch 850, datatang_loss[loss=0.1898, simple_loss=0.2441, pruned_loss=0.06776, over 4880.00 frames.], tot_loss[loss=0.2108, simple_loss=0.27, pruned_loss=0.07579, over 971330.07 frames.], batch size: 47, aishell_tot_loss[loss=0.2041, simple_loss=0.2718, pruned_loss=0.06814, over 870385.43 frames.], datatang_tot_loss[loss=0.214, simple_loss=0.266, pruned_loss=0.08107, over 866596.32 frames.], batch size: 47, lr: 1.25e-03
+2022-06-18 14:18:05,665 INFO [train.py:874] (3/4) Epoch 6, batch 900, aishell_loss[loss=0.2158, simple_loss=0.2771, pruned_loss=0.07727, over 4978.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2693, pruned_loss=0.07514, over 974395.52 frames.], batch size: 31, aishell_tot_loss[loss=0.2036, simple_loss=0.2712, pruned_loss=0.06795, over 885376.64 frames.], datatang_tot_loss[loss=0.2139, simple_loss=0.2659, pruned_loss=0.08096, over 879019.01 frames.], batch size: 31, lr: 1.25e-03
+2022-06-18 14:18:36,717 INFO [train.py:874] (3/4) Epoch 6, batch 950, aishell_loss[loss=0.2203, simple_loss=0.2899, pruned_loss=0.07538, over 4954.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2694, pruned_loss=0.07459, over 977110.38 frames.], batch size: 64, aishell_tot_loss[loss=0.2036, simple_loss=0.2716, pruned_loss=0.06784, over 897691.24 frames.], datatang_tot_loss[loss=0.2136, simple_loss=0.2658, pruned_loss=0.08067, over 891281.39 frames.], batch size: 64, lr: 1.24e-03
+2022-06-18 14:19:07,045 INFO [train.py:874] (3/4) Epoch 6, batch 1000, datatang_loss[loss=0.2052, simple_loss=0.2629, pruned_loss=0.07374, over 4929.00 frames.], tot_loss[loss=0.208, simple_loss=0.2687, pruned_loss=0.07361, over 978876.97 frames.], batch size: 83, aishell_tot_loss[loss=0.2029, simple_loss=0.2712, pruned_loss=0.0673, over 907862.77 frames.], datatang_tot_loss[loss=0.213, simple_loss=0.2657, pruned_loss=0.08015, over 902446.88 frames.], batch size: 83, lr: 1.24e-03
+2022-06-18 14:19:07,046 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 14:19:23,422 INFO [train.py:914] (3/4) Epoch 6, validation: loss=0.175, simple_loss=0.2564, pruned_loss=0.04677, over 1622729.00 frames.
+2022-06-18 14:19:52,929 INFO [train.py:874] (3/4) Epoch 6, batch 1050, datatang_loss[loss=0.1761, simple_loss=0.2359, pruned_loss=0.05819, over 4922.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2673, pruned_loss=0.07244, over 980347.79 frames.], batch size: 73, aishell_tot_loss[loss=0.2024, simple_loss=0.2706, pruned_loss=0.06714, over 917710.84 frames.], datatang_tot_loss[loss=0.2114, simple_loss=0.2647, pruned_loss=0.07911, over 911480.60 frames.], batch size: 73, lr: 1.24e-03
+2022-06-18 14:20:23,080 INFO [train.py:874] (3/4) Epoch 6, batch 1100, aishell_loss[loss=0.1864, simple_loss=0.2542, pruned_loss=0.05935, over 4857.00 frames.], tot_loss[loss=0.2105, simple_loss=0.27, pruned_loss=0.07547, over 981561.99 frames.], batch size: 28, aishell_tot_loss[loss=0.2049, simple_loss=0.2723, pruned_loss=0.06869, over 924255.51 frames.], datatang_tot_loss[loss=0.2134, simple_loss=0.2659, pruned_loss=0.08047, over 921808.64 frames.], batch size: 28, lr: 1.24e-03
+2022-06-18 14:20:54,902 INFO [train.py:874] (3/4) Epoch 6, batch 1150, aishell_loss[loss=0.2391, simple_loss=0.3007, pruned_loss=0.08876, over 4942.00 frames.], tot_loss[loss=0.2106, simple_loss=0.2695, pruned_loss=0.07585, over 982309.68 frames.], batch size: 45, aishell_tot_loss[loss=0.2055, simple_loss=0.2723, pruned_loss=0.06932, over 929882.75 frames.], datatang_tot_loss[loss=0.2131, simple_loss=0.2658, pruned_loss=0.08023, over 930791.32 frames.], batch size: 45, lr: 1.24e-03
+2022-06-18 14:21:24,864 INFO [train.py:874] (3/4) Epoch 6, batch 1200, datatang_loss[loss=0.2081, simple_loss=0.2537, pruned_loss=0.08127, over 4946.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2687, pruned_loss=0.07533, over 982828.56 frames.], batch size: 69, aishell_tot_loss[loss=0.2045, simple_loss=0.2715, pruned_loss=0.06881, over 935682.85 frames.], datatang_tot_loss[loss=0.2132, simple_loss=0.2658, pruned_loss=0.08031, over 937772.52 frames.], batch size: 69, lr: 1.24e-03
+2022-06-18 14:21:54,137 INFO [train.py:874] (3/4) Epoch 6, batch 1250, datatang_loss[loss=0.2218, simple_loss=0.2671, pruned_loss=0.08825, over 4952.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2688, pruned_loss=0.07548, over 983142.27 frames.], batch size: 34, aishell_tot_loss[loss=0.2045, simple_loss=0.2714, pruned_loss=0.06874, over 940988.27 frames.], datatang_tot_loss[loss=0.2137, simple_loss=0.266, pruned_loss=0.08067, over 943654.31 frames.], batch size: 34, lr: 1.24e-03
+2022-06-18 14:22:25,464 INFO [train.py:874] (3/4) Epoch 6, batch 1300, aishell_loss[loss=0.1996, simple_loss=0.2636, pruned_loss=0.06782, over 4879.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2689, pruned_loss=0.07506, over 983656.78 frames.], batch size: 35, aishell_tot_loss[loss=0.2049, simple_loss=0.2723, pruned_loss=0.06878, over 946128.10 frames.], datatang_tot_loss[loss=0.2131, simple_loss=0.2655, pruned_loss=0.08035, over 948674.07 frames.], batch size: 35, lr: 1.23e-03
+2022-06-18 14:22:55,180 INFO [train.py:874] (3/4) Epoch 6, batch 1350, aishell_loss[loss=0.2134, simple_loss=0.2727, pruned_loss=0.07711, over 4864.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2683, pruned_loss=0.07496, over 984244.81 frames.], batch size: 37, aishell_tot_loss[loss=0.2049, simple_loss=0.2721, pruned_loss=0.06886, over 950233.40 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.2651, pruned_loss=0.08002, over 953675.83 frames.], batch size: 37, lr: 1.23e-03
+2022-06-18 14:23:24,998 INFO [train.py:874] (3/4) Epoch 6, batch 1400, aishell_loss[loss=0.1711, simple_loss=0.2513, pruned_loss=0.04545, over 4974.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2683, pruned_loss=0.07405, over 984310.20 frames.], batch size: 30, aishell_tot_loss[loss=0.2044, simple_loss=0.2719, pruned_loss=0.06842, over 955079.22 frames.], datatang_tot_loss[loss=0.2125, simple_loss=0.2651, pruned_loss=0.07994, over 956578.01 frames.], batch size: 30, lr: 1.23e-03
+2022-06-18 14:23:56,882 INFO [train.py:874] (3/4) Epoch 6, batch 1450, aishell_loss[loss=0.167, simple_loss=0.2409, pruned_loss=0.04659, over 4880.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2674, pruned_loss=0.07305, over 984504.02 frames.], batch size: 35, aishell_tot_loss[loss=0.204, simple_loss=0.2716, pruned_loss=0.06817, over 958992.83 frames.], datatang_tot_loss[loss=0.2113, simple_loss=0.2642, pruned_loss=0.07923, over 959596.56 frames.], batch size: 35, lr: 1.23e-03
+2022-06-18 14:24:26,619 INFO [train.py:874] (3/4) Epoch 6, batch 1500, aishell_loss[loss=0.2103, simple_loss=0.2789, pruned_loss=0.0709, over 4883.00 frames.], tot_loss[loss=0.207, simple_loss=0.268, pruned_loss=0.07303, over 984856.30 frames.], batch size: 47, aishell_tot_loss[loss=0.2033, simple_loss=0.2714, pruned_loss=0.06757, over 962914.69 frames.], datatang_tot_loss[loss=0.2123, simple_loss=0.2647, pruned_loss=0.07991, over 961985.10 frames.], batch size: 47, lr: 1.23e-03
+2022-06-18 14:24:56,227 INFO [train.py:874] (3/4) Epoch 6, batch 1550, aishell_loss[loss=0.1982, simple_loss=0.2614, pruned_loss=0.06746, over 4921.00 frames.], tot_loss[loss=0.207, simple_loss=0.2686, pruned_loss=0.07273, over 984916.83 frames.], batch size: 33, aishell_tot_loss[loss=0.2031, simple_loss=0.2716, pruned_loss=0.06727, over 965865.40 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2649, pruned_loss=0.08001, over 964355.01 frames.], batch size: 33, lr: 1.23e-03
+2022-06-18 14:25:27,167 INFO [train.py:874] (3/4) Epoch 6, batch 1600, aishell_loss[loss=0.2475, simple_loss=0.3098, pruned_loss=0.09264, over 4937.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2688, pruned_loss=0.07287, over 984836.52 frames.], batch size: 80, aishell_tot_loss[loss=0.2027, simple_loss=0.2711, pruned_loss=0.06721, over 968561.17 frames.], datatang_tot_loss[loss=0.2131, simple_loss=0.2655, pruned_loss=0.08033, over 966198.89 frames.], batch size: 80, lr: 1.23e-03
+2022-06-18 14:25:56,141 INFO [train.py:874] (3/4) Epoch 6, batch 1650, datatang_loss[loss=0.2189, simple_loss=0.2672, pruned_loss=0.0853, over 4922.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2676, pruned_loss=0.07284, over 985116.58 frames.], batch size: 73, aishell_tot_loss[loss=0.2024, simple_loss=0.2706, pruned_loss=0.06709, over 970252.75 frames.], datatang_tot_loss[loss=0.2125, simple_loss=0.265, pruned_loss=0.07996, over 968944.27 frames.], batch size: 73, lr: 1.23e-03
+2022-06-18 14:26:28,056 INFO [train.py:874] (3/4) Epoch 6, batch 1700, datatang_loss[loss=0.207, simple_loss=0.2652, pruned_loss=0.07446, over 4928.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2681, pruned_loss=0.07339, over 985018.71 frames.], batch size: 83, aishell_tot_loss[loss=0.203, simple_loss=0.2712, pruned_loss=0.06735, over 971748.62 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.265, pruned_loss=0.08009, over 971002.43 frames.], batch size: 83, lr: 1.22e-03
+2022-06-18 14:26:57,308 INFO [train.py:874] (3/4) Epoch 6, batch 1750, aishell_loss[loss=0.1752, simple_loss=0.2552, pruned_loss=0.04759, over 4826.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2685, pruned_loss=0.07345, over 985202.21 frames.], batch size: 29, aishell_tot_loss[loss=0.2033, simple_loss=0.2715, pruned_loss=0.06753, over 973187.39 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2651, pruned_loss=0.07983, over 972991.98 frames.], batch size: 29, lr: 1.22e-03
+2022-06-18 14:27:27,500 INFO [train.py:874] (3/4) Epoch 6, batch 1800, aishell_loss[loss=0.2206, simple_loss=0.2918, pruned_loss=0.07471, over 4921.00 frames.], tot_loss[loss=0.208, simple_loss=0.2688, pruned_loss=0.07357, over 985039.07 frames.], batch size: 68, aishell_tot_loss[loss=0.2031, simple_loss=0.2715, pruned_loss=0.06736, over 974351.93 frames.], datatang_tot_loss[loss=0.2128, simple_loss=0.2655, pruned_loss=0.08004, over 974513.29 frames.], batch size: 68, lr: 1.22e-03
+2022-06-18 14:27:59,192 INFO [train.py:874] (3/4) Epoch 6, batch 1850, aishell_loss[loss=0.1818, simple_loss=0.2554, pruned_loss=0.05408, over 4864.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2689, pruned_loss=0.07344, over 985334.34 frames.], batch size: 36, aishell_tot_loss[loss=0.203, simple_loss=0.2714, pruned_loss=0.06733, over 975852.29 frames.], datatang_tot_loss[loss=0.2129, simple_loss=0.2656, pruned_loss=0.08007, over 975834.64 frames.], batch size: 36, lr: 1.22e-03
+2022-06-18 14:28:29,089 INFO [train.py:874] (3/4) Epoch 6, batch 1900, aishell_loss[loss=0.2778, simple_loss=0.3149, pruned_loss=0.1203, over 4866.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2677, pruned_loss=0.07227, over 985251.25 frames.], batch size: 35, aishell_tot_loss[loss=0.2018, simple_loss=0.2702, pruned_loss=0.06668, over 977089.83 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.2655, pruned_loss=0.07987, over 976742.46 frames.], batch size: 35, lr: 1.22e-03
+2022-06-18 14:28:59,064 INFO [train.py:874] (3/4) Epoch 6, batch 1950, aishell_loss[loss=0.1708, simple_loss=0.2497, pruned_loss=0.04593, over 4950.00 frames.], tot_loss[loss=0.207, simple_loss=0.2682, pruned_loss=0.07296, over 985393.98 frames.], batch size: 56, aishell_tot_loss[loss=0.2023, simple_loss=0.2706, pruned_loss=0.06703, over 977928.73 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.2656, pruned_loss=0.07982, over 978023.10 frames.], batch size: 56, lr: 1.22e-03
+2022-06-18 14:29:30,687 INFO [train.py:874] (3/4) Epoch 6, batch 2000, datatang_loss[loss=0.2019, simple_loss=0.2613, pruned_loss=0.07128, over 4956.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2666, pruned_loss=0.07197, over 985145.02 frames.], batch size: 86, aishell_tot_loss[loss=0.2014, simple_loss=0.2695, pruned_loss=0.0666, over 978638.87 frames.], datatang_tot_loss[loss=0.2117, simple_loss=0.265, pruned_loss=0.07914, over 978797.30 frames.], batch size: 86, lr: 1.22e-03
+2022-06-18 14:29:30,688 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 14:29:46,438 INFO [train.py:914] (3/4) Epoch 6, validation: loss=0.1751, simple_loss=0.2557, pruned_loss=0.04723, over 1622729.00 frames.
+2022-06-18 14:30:18,826 INFO [train.py:874] (3/4) Epoch 6, batch 2050, aishell_loss[loss=0.1948, simple_loss=0.2678, pruned_loss=0.06091, over 4934.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2677, pruned_loss=0.07304, over 985430.31 frames.], batch size: 61, aishell_tot_loss[loss=0.2013, simple_loss=0.2694, pruned_loss=0.0666, over 979351.14 frames.], datatang_tot_loss[loss=0.213, simple_loss=0.2662, pruned_loss=0.07991, over 979891.29 frames.], batch size: 61, lr: 1.22e-03
+2022-06-18 14:30:50,090 INFO [train.py:874] (3/4) Epoch 6, batch 2100, datatang_loss[loss=0.2763, simple_loss=0.3001, pruned_loss=0.1262, over 4977.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2681, pruned_loss=0.07349, over 985573.35 frames.], batch size: 31, aishell_tot_loss[loss=0.2021, simple_loss=0.2703, pruned_loss=0.0669, over 980145.07 frames.], datatang_tot_loss[loss=0.2127, simple_loss=0.2658, pruned_loss=0.0798, over 980596.83 frames.], batch size: 31, lr: 1.21e-03
+2022-06-18 14:31:18,759 INFO [train.py:874] (3/4) Epoch 6, batch 2150, datatang_loss[loss=0.1824, simple_loss=0.2399, pruned_loss=0.06246, over 4962.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2675, pruned_loss=0.07253, over 985343.43 frames.], batch size: 67, aishell_tot_loss[loss=0.2016, simple_loss=0.2701, pruned_loss=0.06653, over 980644.68 frames.], datatang_tot_loss[loss=0.2122, simple_loss=0.2653, pruned_loss=0.07953, over 981090.99 frames.], batch size: 67, lr: 1.21e-03
+2022-06-18 14:31:50,720 INFO [train.py:874] (3/4) Epoch 6, batch 2200, aishell_loss[loss=0.2033, simple_loss=0.2716, pruned_loss=0.06753, over 4938.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2688, pruned_loss=0.07299, over 985233.52 frames.], batch size: 32, aishell_tot_loss[loss=0.2022, simple_loss=0.2709, pruned_loss=0.06676, over 981148.40 frames.], datatang_tot_loss[loss=0.2131, simple_loss=0.2656, pruned_loss=0.08028, over 981547.27 frames.], batch size: 32, lr: 1.21e-03
+2022-06-18 14:32:20,737 INFO [train.py:874] (3/4) Epoch 6, batch 2250, datatang_loss[loss=0.1846, simple_loss=0.249, pruned_loss=0.06014, over 4922.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2673, pruned_loss=0.07299, over 985239.02 frames.], batch size: 75, aishell_tot_loss[loss=0.2013, simple_loss=0.2698, pruned_loss=0.06638, over 981579.23 frames.], datatang_tot_loss[loss=0.2131, simple_loss=0.2653, pruned_loss=0.08044, over 982033.54 frames.], batch size: 75, lr: 1.21e-03
+2022-06-18 14:32:50,399 INFO [train.py:874] (3/4) Epoch 6, batch 2300, datatang_loss[loss=0.1818, simple_loss=0.2515, pruned_loss=0.05603, over 4931.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2672, pruned_loss=0.07193, over 985073.22 frames.], batch size: 71, aishell_tot_loss[loss=0.2013, simple_loss=0.2701, pruned_loss=0.06627, over 982016.79 frames.], datatang_tot_loss[loss=0.212, simple_loss=0.2645, pruned_loss=0.07979, over 982232.74 frames.], batch size: 71, lr: 1.21e-03
+2022-06-18 14:33:22,413 INFO [train.py:874] (3/4) Epoch 6, batch 2350, datatang_loss[loss=0.196, simple_loss=0.2571, pruned_loss=0.06746, over 4946.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2683, pruned_loss=0.07202, over 985327.68 frames.], batch size: 62, aishell_tot_loss[loss=0.2013, simple_loss=0.2706, pruned_loss=0.06599, over 982496.60 frames.], datatang_tot_loss[loss=0.2127, simple_loss=0.2652, pruned_loss=0.08008, over 982710.75 frames.], batch size: 62, lr: 1.21e-03
+2022-06-18 14:33:50,411 INFO [train.py:874] (3/4) Epoch 6, batch 2400, aishell_loss[loss=0.1908, simple_loss=0.2597, pruned_loss=0.0609, over 4947.00 frames.], tot_loss[loss=0.205, simple_loss=0.2673, pruned_loss=0.07131, over 985723.70 frames.], batch size: 56, aishell_tot_loss[loss=0.2003, simple_loss=0.27, pruned_loss=0.06536, over 983175.45 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2647, pruned_loss=0.08005, over 983093.17 frames.], batch size: 56, lr: 1.21e-03
+2022-06-18 14:34:22,307 INFO [train.py:874] (3/4) Epoch 6, batch 2450, aishell_loss[loss=0.2282, simple_loss=0.2964, pruned_loss=0.08002, over 4935.00 frames.], tot_loss[loss=0.2049, simple_loss=0.2668, pruned_loss=0.07151, over 985566.39 frames.], batch size: 78, aishell_tot_loss[loss=0.2002, simple_loss=0.2699, pruned_loss=0.06526, over 983330.88 frames.], datatang_tot_loss[loss=0.2122, simple_loss=0.2644, pruned_loss=0.07998, over 983401.18 frames.], batch size: 78, lr: 1.21e-03
+2022-06-18 14:34:52,702 INFO [train.py:874] (3/4) Epoch 6, batch 2500, datatang_loss[loss=0.1981, simple_loss=0.2582, pruned_loss=0.06902, over 4946.00 frames.], tot_loss[loss=0.204, simple_loss=0.2661, pruned_loss=0.07097, over 985864.56 frames.], batch size: 55, aishell_tot_loss[loss=0.1988, simple_loss=0.2689, pruned_loss=0.06432, over 983604.07 frames.], datatang_tot_loss[loss=0.2121, simple_loss=0.2646, pruned_loss=0.07982, over 983962.26 frames.], batch size: 55, lr: 1.20e-03
+2022-06-18 14:35:21,375 INFO [train.py:874] (3/4) Epoch 6, batch 2550, datatang_loss[loss=0.2417, simple_loss=0.2941, pruned_loss=0.09463, over 4923.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2665, pruned_loss=0.07131, over 986063.98 frames.], batch size: 98, aishell_tot_loss[loss=0.1986, simple_loss=0.2687, pruned_loss=0.06428, over 983958.99 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2651, pruned_loss=0.07986, over 984293.73 frames.], batch size: 98, lr: 1.20e-03
+2022-06-18 14:35:53,633 INFO [train.py:874] (3/4) Epoch 6, batch 2600, aishell_loss[loss=0.2243, simple_loss=0.284, pruned_loss=0.08236, over 4925.00 frames.], tot_loss[loss=0.2052, simple_loss=0.2667, pruned_loss=0.07188, over 986181.61 frames.], batch size: 32, aishell_tot_loss[loss=0.1991, simple_loss=0.2689, pruned_loss=0.06472, over 984334.00 frames.], datatang_tot_loss[loss=0.2123, simple_loss=0.265, pruned_loss=0.07981, over 984496.71 frames.], batch size: 32, lr: 1.20e-03
+2022-06-18 14:36:23,577 INFO [train.py:874] (3/4) Epoch 6, batch 2650, aishell_loss[loss=0.2478, simple_loss=0.309, pruned_loss=0.09331, over 4907.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2684, pruned_loss=0.07362, over 986631.44 frames.], batch size: 33, aishell_tot_loss[loss=0.2009, simple_loss=0.2702, pruned_loss=0.06583, over 984948.37 frames.], datatang_tot_loss[loss=0.2134, simple_loss=0.2653, pruned_loss=0.08074, over 984774.61 frames.], batch size: 33, lr: 1.20e-03
+2022-06-18 14:36:53,158 INFO [train.py:874] (3/4) Epoch 6, batch 2700, datatang_loss[loss=0.2142, simple_loss=0.265, pruned_loss=0.08172, over 4865.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2677, pruned_loss=0.07293, over 986452.96 frames.], batch size: 39, aishell_tot_loss[loss=0.2005, simple_loss=0.2699, pruned_loss=0.06555, over 984923.64 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.2651, pruned_loss=0.08008, over 985032.73 frames.], batch size: 39, lr: 1.20e-03
+2022-06-18 14:37:24,211 INFO [train.py:874] (3/4) Epoch 6, batch 2750, datatang_loss[loss=0.191, simple_loss=0.2442, pruned_loss=0.06888, over 4934.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2681, pruned_loss=0.07313, over 986027.39 frames.], batch size: 50, aishell_tot_loss[loss=0.2011, simple_loss=0.2704, pruned_loss=0.06587, over 984882.62 frames.], datatang_tot_loss[loss=0.2125, simple_loss=0.2651, pruned_loss=0.07995, over 984968.91 frames.], batch size: 50, lr: 1.20e-03
+2022-06-18 14:37:54,793 INFO [train.py:874] (3/4) Epoch 6, batch 2800, datatang_loss[loss=0.2008, simple_loss=0.2528, pruned_loss=0.07442, over 4976.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2676, pruned_loss=0.07277, over 986199.57 frames.], batch size: 53, aishell_tot_loss[loss=0.2008, simple_loss=0.2701, pruned_loss=0.06572, over 984943.04 frames.], datatang_tot_loss[loss=0.2121, simple_loss=0.2649, pruned_loss=0.07969, over 985347.44 frames.], batch size: 53, lr: 1.20e-03
+2022-06-18 14:38:23,500 INFO [train.py:874] (3/4) Epoch 6, batch 2850, datatang_loss[loss=0.2643, simple_loss=0.297, pruned_loss=0.1158, over 4927.00 frames.], tot_loss[loss=0.2065, simple_loss=0.268, pruned_loss=0.07245, over 985846.34 frames.], batch size: 64, aishell_tot_loss[loss=0.2008, simple_loss=0.2706, pruned_loss=0.06551, over 984779.24 frames.], datatang_tot_loss[loss=0.212, simple_loss=0.2648, pruned_loss=0.07953, over 985390.94 frames.], batch size: 64, lr: 1.20e-03
+2022-06-18 14:38:55,094 INFO [train.py:874] (3/4) Epoch 6, batch 2900, aishell_loss[loss=0.2042, simple_loss=0.2756, pruned_loss=0.06645, over 4960.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2683, pruned_loss=0.07233, over 985931.27 frames.], batch size: 40, aishell_tot_loss[loss=0.201, simple_loss=0.2708, pruned_loss=0.06564, over 984983.46 frames.], datatang_tot_loss[loss=0.2118, simple_loss=0.2651, pruned_loss=0.07919, over 985455.55 frames.], batch size: 40, lr: 1.19e-03
+2022-06-18 14:39:25,950 INFO [train.py:874] (3/4) Epoch 6, batch 2950, datatang_loss[loss=0.2136, simple_loss=0.2627, pruned_loss=0.08225, over 4957.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2673, pruned_loss=0.07167, over 985947.24 frames.], batch size: 91, aishell_tot_loss[loss=0.2004, simple_loss=0.2703, pruned_loss=0.06526, over 984983.79 frames.], datatang_tot_loss[loss=0.2111, simple_loss=0.2646, pruned_loss=0.07878, over 985630.99 frames.], batch size: 91, lr: 1.19e-03
+2022-06-18 14:39:54,998 INFO [train.py:874] (3/4) Epoch 6, batch 3000, aishell_loss[loss=0.2037, simple_loss=0.2725, pruned_loss=0.06749, over 4934.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2663, pruned_loss=0.0713, over 985658.07 frames.], batch size: 49, aishell_tot_loss[loss=0.2006, simple_loss=0.2705, pruned_loss=0.06539, over 984607.37 frames.], datatang_tot_loss[loss=0.2097, simple_loss=0.2634, pruned_loss=0.07805, over 985841.22 frames.], batch size: 49, lr: 1.19e-03
+2022-06-18 14:39:54,999 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 14:40:11,302 INFO [train.py:914] (3/4) Epoch 6, validation: loss=0.1748, simple_loss=0.2561, pruned_loss=0.04677, over 1622729.00 frames.
+2022-06-18 14:40:41,204 INFO [train.py:874] (3/4) Epoch 6, batch 3050, aishell_loss[loss=0.1977, simple_loss=0.2765, pruned_loss=0.05945, over 4929.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2661, pruned_loss=0.0705, over 985545.71 frames.], batch size: 58, aishell_tot_loss[loss=0.2007, simple_loss=0.2708, pruned_loss=0.06534, over 984668.62 frames.], datatang_tot_loss[loss=0.2087, simple_loss=0.2626, pruned_loss=0.07736, over 985775.10 frames.], batch size: 58, lr: 1.19e-03
+2022-06-18 14:41:11,434 INFO [train.py:874] (3/4) Epoch 6, batch 3100, aishell_loss[loss=0.2407, simple_loss=0.3147, pruned_loss=0.08332, over 4926.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2663, pruned_loss=0.07021, over 985586.58 frames.], batch size: 68, aishell_tot_loss[loss=0.2011, simple_loss=0.2714, pruned_loss=0.06539, over 984653.43 frames.], datatang_tot_loss[loss=0.2079, simple_loss=0.262, pruned_loss=0.07687, over 985920.02 frames.], batch size: 68, lr: 1.19e-03
+2022-06-18 14:41:41,851 INFO [train.py:874] (3/4) Epoch 6, batch 3150, datatang_loss[loss=0.1886, simple_loss=0.245, pruned_loss=0.06608, over 4907.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2654, pruned_loss=0.07021, over 985537.82 frames.], batch size: 52, aishell_tot_loss[loss=0.201, simple_loss=0.271, pruned_loss=0.06549, over 984680.27 frames.], datatang_tot_loss[loss=0.2072, simple_loss=0.2612, pruned_loss=0.07664, over 985928.51 frames.], batch size: 52, lr: 1.19e-03
+2022-06-18 14:42:15,276 INFO [train.py:874] (3/4) Epoch 6, batch 3200, aishell_loss[loss=0.1685, simple_loss=0.2487, pruned_loss=0.04418, over 4940.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2653, pruned_loss=0.06926, over 985786.54 frames.], batch size: 54, aishell_tot_loss[loss=0.2002, simple_loss=0.2709, pruned_loss=0.06479, over 984942.52 frames.], datatang_tot_loss[loss=0.2068, simple_loss=0.2609, pruned_loss=0.07637, over 986000.20 frames.], batch size: 54, lr: 1.19e-03
+2022-06-18 14:42:46,513 INFO [train.py:874] (3/4) Epoch 6, batch 3250, datatang_loss[loss=0.1981, simple_loss=0.2555, pruned_loss=0.07038, over 4922.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2647, pruned_loss=0.06886, over 986104.04 frames.], batch size: 73, aishell_tot_loss[loss=0.1991, simple_loss=0.2701, pruned_loss=0.06409, over 985437.84 frames.], datatang_tot_loss[loss=0.2069, simple_loss=0.2608, pruned_loss=0.07651, over 985928.13 frames.], batch size: 73, lr: 1.19e-03
+2022-06-18 14:43:16,381 INFO [train.py:874] (3/4) Epoch 6, batch 3300, aishell_loss[loss=0.1903, simple_loss=0.2752, pruned_loss=0.05274, over 4884.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2645, pruned_loss=0.06865, over 985783.03 frames.], batch size: 42, aishell_tot_loss[loss=0.1988, simple_loss=0.2698, pruned_loss=0.06385, over 985253.51 frames.], datatang_tot_loss[loss=0.2065, simple_loss=0.2607, pruned_loss=0.0761, over 985846.97 frames.], batch size: 42, lr: 1.18e-03
+2022-06-18 14:43:45,845 INFO [train.py:874] (3/4) Epoch 6, batch 3350, aishell_loss[loss=0.1796, simple_loss=0.2587, pruned_loss=0.05029, over 4882.00 frames.], tot_loss[loss=0.2003, simple_loss=0.264, pruned_loss=0.06834, over 986034.14 frames.], batch size: 42, aishell_tot_loss[loss=0.1983, simple_loss=0.2692, pruned_loss=0.06369, over 985323.79 frames.], datatang_tot_loss[loss=0.206, simple_loss=0.2604, pruned_loss=0.07583, over 986110.42 frames.], batch size: 42, lr: 1.18e-03
+2022-06-18 14:44:17,242 INFO [train.py:874] (3/4) Epoch 6, batch 3400, aishell_loss[loss=0.2098, simple_loss=0.2774, pruned_loss=0.07106, over 4959.00 frames.], tot_loss[loss=0.2012, simple_loss=0.265, pruned_loss=0.06869, over 985784.67 frames.], batch size: 64, aishell_tot_loss[loss=0.1982, simple_loss=0.2691, pruned_loss=0.06362, over 985226.17 frames.], datatang_tot_loss[loss=0.2069, simple_loss=0.2612, pruned_loss=0.0763, over 986027.47 frames.], batch size: 64, lr: 1.18e-03
+2022-06-18 14:44:46,279 INFO [train.py:874] (3/4) Epoch 6, batch 3450, aishell_loss[loss=0.1715, simple_loss=0.2477, pruned_loss=0.04764, over 4936.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2664, pruned_loss=0.06967, over 985864.10 frames.], batch size: 32, aishell_tot_loss[loss=0.1981, simple_loss=0.2691, pruned_loss=0.06352, over 985429.20 frames.], datatang_tot_loss[loss=0.2085, simple_loss=0.2625, pruned_loss=0.07725, over 985949.53 frames.], batch size: 32, lr: 1.18e-03
+2022-06-18 14:45:16,450 INFO [train.py:874] (3/4) Epoch 6, batch 3500, datatang_loss[loss=0.2043, simple_loss=0.2545, pruned_loss=0.07712, over 4935.00 frames.], tot_loss[loss=0.2033, simple_loss=0.267, pruned_loss=0.06977, over 985645.53 frames.], batch size: 69, aishell_tot_loss[loss=0.1981, simple_loss=0.2691, pruned_loss=0.06356, over 985317.16 frames.], datatang_tot_loss[loss=0.209, simple_loss=0.2631, pruned_loss=0.07745, over 985882.38 frames.], batch size: 69, lr: 1.18e-03
+2022-06-18 14:45:47,063 INFO [train.py:874] (3/4) Epoch 6, batch 3550, aishell_loss[loss=0.2235, simple_loss=0.2961, pruned_loss=0.07551, over 4968.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2661, pruned_loss=0.06873, over 985237.57 frames.], batch size: 56, aishell_tot_loss[loss=0.1975, simple_loss=0.2685, pruned_loss=0.06322, over 984987.51 frames.], datatang_tot_loss[loss=0.2084, simple_loss=0.2628, pruned_loss=0.07706, over 985807.98 frames.], batch size: 56, lr: 1.18e-03
+2022-06-18 14:46:15,926 INFO [train.py:874] (3/4) Epoch 6, batch 3600, aishell_loss[loss=0.1998, simple_loss=0.2687, pruned_loss=0.06542, over 4872.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2666, pruned_loss=0.06926, over 985243.01 frames.], batch size: 35, aishell_tot_loss[loss=0.1978, simple_loss=0.2688, pruned_loss=0.06336, over 984876.58 frames.], datatang_tot_loss[loss=0.2087, simple_loss=0.2631, pruned_loss=0.07714, over 985895.27 frames.], batch size: 35, lr: 1.18e-03
+2022-06-18 14:46:46,889 INFO [train.py:874] (3/4) Epoch 6, batch 3650, datatang_loss[loss=0.2242, simple_loss=0.2787, pruned_loss=0.08485, over 4969.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2669, pruned_loss=0.06987, over 985145.19 frames.], batch size: 45, aishell_tot_loss[loss=0.198, simple_loss=0.2692, pruned_loss=0.06342, over 984735.29 frames.], datatang_tot_loss[loss=0.2089, simple_loss=0.2634, pruned_loss=0.07723, over 985875.87 frames.], batch size: 45, lr: 1.18e-03
+2022-06-18 14:47:16,908 INFO [train.py:874] (3/4) Epoch 6, batch 3700, datatang_loss[loss=0.2196, simple_loss=0.2711, pruned_loss=0.08403, over 4932.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2668, pruned_loss=0.06998, over 985363.22 frames.], batch size: 71, aishell_tot_loss[loss=0.1978, simple_loss=0.2689, pruned_loss=0.06339, over 984829.33 frames.], datatang_tot_loss[loss=0.2092, simple_loss=0.2637, pruned_loss=0.07738, over 985980.18 frames.], batch size: 71, lr: 1.18e-03
+2022-06-18 14:47:44,946 INFO [train.py:874] (3/4) Epoch 6, batch 3750, datatang_loss[loss=0.2139, simple_loss=0.2526, pruned_loss=0.0876, over 4946.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2666, pruned_loss=0.07033, over 985303.17 frames.], batch size: 34, aishell_tot_loss[loss=0.1976, simple_loss=0.2686, pruned_loss=0.06332, over 984693.17 frames.], datatang_tot_loss[loss=0.2094, simple_loss=0.2638, pruned_loss=0.0775, over 986022.88 frames.], batch size: 34, lr: 1.17e-03
+2022-06-18 14:48:15,178 INFO [train.py:874] (3/4) Epoch 6, batch 3800, aishell_loss[loss=0.1951, simple_loss=0.2665, pruned_loss=0.06182, over 4884.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2656, pruned_loss=0.06981, over 985515.57 frames.], batch size: 34, aishell_tot_loss[loss=0.197, simple_loss=0.2679, pruned_loss=0.06305, over 984855.75 frames.], datatang_tot_loss[loss=0.2089, simple_loss=0.2636, pruned_loss=0.07708, over 986070.32 frames.], batch size: 34, lr: 1.17e-03
+2022-06-18 14:48:43,871 INFO [train.py:874] (3/4) Epoch 6, batch 3850, datatang_loss[loss=0.188, simple_loss=0.2478, pruned_loss=0.06413, over 4923.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2652, pruned_loss=0.06987, over 985165.43 frames.], batch size: 73, aishell_tot_loss[loss=0.1969, simple_loss=0.2678, pruned_loss=0.06296, over 984606.89 frames.], datatang_tot_loss[loss=0.2087, simple_loss=0.2634, pruned_loss=0.07702, over 985943.65 frames.], batch size: 73, lr: 1.17e-03
+2022-06-18 14:49:12,108 INFO [train.py:874] (3/4) Epoch 6, batch 3900, aishell_loss[loss=0.1993, simple_loss=0.285, pruned_loss=0.05681, over 4967.00 frames.], tot_loss[loss=0.203, simple_loss=0.2657, pruned_loss=0.07018, over 985154.69 frames.], batch size: 44, aishell_tot_loss[loss=0.1978, simple_loss=0.2684, pruned_loss=0.06356, over 984501.77 frames.], datatang_tot_loss[loss=0.2086, simple_loss=0.2632, pruned_loss=0.07697, over 986037.47 frames.], batch size: 44, lr: 1.17e-03
+2022-06-18 14:49:41,322 INFO [train.py:874] (3/4) Epoch 6, batch 3950, datatang_loss[loss=0.2043, simple_loss=0.2598, pruned_loss=0.07437, over 4916.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2651, pruned_loss=0.06953, over 985100.49 frames.], batch size: 75, aishell_tot_loss[loss=0.1974, simple_loss=0.2683, pruned_loss=0.06326, over 984292.91 frames.], datatang_tot_loss[loss=0.2079, simple_loss=0.2627, pruned_loss=0.07656, over 986168.98 frames.], batch size: 75, lr: 1.17e-03
+2022-06-18 14:50:10,179 INFO [train.py:874] (3/4) Epoch 6, batch 4000, aishell_loss[loss=0.1723, simple_loss=0.2419, pruned_loss=0.05131, over 4901.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2641, pruned_loss=0.0685, over 984661.46 frames.], batch size: 34, aishell_tot_loss[loss=0.1965, simple_loss=0.2676, pruned_loss=0.0627, over 983976.52 frames.], datatang_tot_loss[loss=0.2071, simple_loss=0.2623, pruned_loss=0.07597, over 986007.94 frames.], batch size: 34, lr: 1.17e-03
+2022-06-18 14:50:10,180 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 14:50:26,078 INFO [train.py:914] (3/4) Epoch 6, validation: loss=0.1741, simple_loss=0.2559, pruned_loss=0.04619, over 1622729.00 frames.
+2022-06-18 14:51:43,945 INFO [train.py:874] (3/4) Epoch 7, batch 50, aishell_loss[loss=0.1976, simple_loss=0.2725, pruned_loss=0.0614, over 4962.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2608, pruned_loss=0.0644, over 218751.23 frames.], batch size: 61, aishell_tot_loss[loss=0.1952, simple_loss=0.2683, pruned_loss=0.06101, over 116163.34 frames.], datatang_tot_loss[loss=0.1948, simple_loss=0.2537, pruned_loss=0.06795, over 116291.59 frames.], batch size: 61, lr: 1.12e-03
+2022-06-18 14:52:15,060 INFO [train.py:874] (3/4) Epoch 7, batch 100, datatang_loss[loss=0.2638, simple_loss=0.3118, pruned_loss=0.1079, over 4917.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2614, pruned_loss=0.06475, over 388909.75 frames.], batch size: 107, aishell_tot_loss[loss=0.1948, simple_loss=0.268, pruned_loss=0.06082, over 226303.60 frames.], datatang_tot_loss[loss=0.1961, simple_loss=0.2542, pruned_loss=0.06902, over 211034.77 frames.], batch size: 107, lr: 1.12e-03
+2022-06-18 14:52:44,502 INFO [train.py:874] (3/4) Epoch 7, batch 150, aishell_loss[loss=0.2344, simple_loss=0.2899, pruned_loss=0.08952, over 4927.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2616, pruned_loss=0.06498, over 521266.10 frames.], batch size: 52, aishell_tot_loss[loss=0.1947, simple_loss=0.267, pruned_loss=0.06123, over 345318.25 frames.], datatang_tot_loss[loss=0.1972, simple_loss=0.2543, pruned_loss=0.07001, over 270780.55 frames.], batch size: 52, lr: 1.12e-03
+2022-06-18 14:53:13,228 INFO [train.py:874] (3/4) Epoch 7, batch 200, datatang_loss[loss=0.1974, simple_loss=0.2581, pruned_loss=0.06836, over 4955.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2629, pruned_loss=0.06662, over 624484.63 frames.], batch size: 86, aishell_tot_loss[loss=0.1963, simple_loss=0.269, pruned_loss=0.0618, over 406410.00 frames.], datatang_tot_loss[loss=0.1991, simple_loss=0.2557, pruned_loss=0.07129, over 370859.26 frames.], batch size: 86, lr: 1.12e-03
+2022-06-18 14:53:45,407 INFO [train.py:874] (3/4) Epoch 7, batch 250, aishell_loss[loss=0.2097, simple_loss=0.2729, pruned_loss=0.07321, over 4899.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2633, pruned_loss=0.06706, over 704027.85 frames.], batch size: 34, aishell_tot_loss[loss=0.1962, simple_loss=0.2686, pruned_loss=0.06192, over 474260.53 frames.], datatang_tot_loss[loss=0.2004, simple_loss=0.2569, pruned_loss=0.07195, over 443097.96 frames.], batch size: 34, lr: 1.11e-03
+2022-06-18 14:54:14,231 INFO [train.py:874] (3/4) Epoch 7, batch 300, aishell_loss[loss=0.2549, simple_loss=0.3104, pruned_loss=0.09974, over 4877.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2638, pruned_loss=0.06679, over 766020.20 frames.], batch size: 36, aishell_tot_loss[loss=0.198, simple_loss=0.2698, pruned_loss=0.06306, over 545335.53 frames.], datatang_tot_loss[loss=0.1989, simple_loss=0.2563, pruned_loss=0.07078, over 494725.31 frames.], batch size: 36, lr: 1.11e-03
+2022-06-18 14:54:43,199 INFO [train.py:874] (3/4) Epoch 7, batch 350, aishell_loss[loss=0.1535, simple_loss=0.2118, pruned_loss=0.04758, over 4902.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2628, pruned_loss=0.06691, over 814072.29 frames.], batch size: 21, aishell_tot_loss[loss=0.1972, simple_loss=0.2683, pruned_loss=0.0631, over 592749.89 frames.], datatang_tot_loss[loss=0.199, simple_loss=0.2567, pruned_loss=0.07069, over 556708.06 frames.], batch size: 21, lr: 1.11e-03
+2022-06-18 14:55:14,508 INFO [train.py:874] (3/4) Epoch 7, batch 400, aishell_loss[loss=0.2249, simple_loss=0.3023, pruned_loss=0.07373, over 4912.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2623, pruned_loss=0.06744, over 852045.77 frames.], batch size: 80, aishell_tot_loss[loss=0.1961, simple_loss=0.2666, pruned_loss=0.0628, over 646002.31 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2575, pruned_loss=0.0721, over 599400.28 frames.], batch size: 80, lr: 1.11e-03
+2022-06-18 14:55:44,399 INFO [train.py:874] (3/4) Epoch 7, batch 450, datatang_loss[loss=0.225, simple_loss=0.2833, pruned_loss=0.08341, over 4901.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2626, pruned_loss=0.06732, over 881774.50 frames.], batch size: 47, aishell_tot_loss[loss=0.196, simple_loss=0.267, pruned_loss=0.06246, over 678442.60 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2579, pruned_loss=0.07193, over 653386.68 frames.], batch size: 47, lr: 1.11e-03
+2022-06-18 14:56:12,635 INFO [train.py:874] (3/4) Epoch 7, batch 500, aishell_loss[loss=0.1951, simple_loss=0.2665, pruned_loss=0.06189, over 4868.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2619, pruned_loss=0.0669, over 904846.76 frames.], batch size: 36, aishell_tot_loss[loss=0.1953, simple_loss=0.2664, pruned_loss=0.06207, over 715784.17 frames.], datatang_tot_loss[loss=0.2006, simple_loss=0.2575, pruned_loss=0.07182, over 691300.95 frames.], batch size: 36, lr: 1.11e-03
+2022-06-18 14:56:43,879 INFO [train.py:874] (3/4) Epoch 7, batch 550, aishell_loss[loss=0.2139, simple_loss=0.2906, pruned_loss=0.06856, over 4861.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2623, pruned_loss=0.0671, over 922813.31 frames.], batch size: 35, aishell_tot_loss[loss=0.1961, simple_loss=0.2675, pruned_loss=0.06236, over 741443.39 frames.], datatang_tot_loss[loss=0.2002, simple_loss=0.2573, pruned_loss=0.07149, over 732536.24 frames.], batch size: 35, lr: 1.11e-03
+2022-06-18 14:57:13,482 INFO [train.py:874] (3/4) Epoch 7, batch 600, datatang_loss[loss=0.2404, simple_loss=0.2974, pruned_loss=0.09169, over 4925.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2623, pruned_loss=0.06729, over 936533.54 frames.], batch size: 94, aishell_tot_loss[loss=0.1966, simple_loss=0.2681, pruned_loss=0.06258, over 763488.61 frames.], datatang_tot_loss[loss=0.1998, simple_loss=0.2571, pruned_loss=0.07124, over 768884.77 frames.], batch size: 94, lr: 1.11e-03
+2022-06-18 14:57:42,505 INFO [train.py:874] (3/4) Epoch 7, batch 650, aishell_loss[loss=0.2076, simple_loss=0.2731, pruned_loss=0.07099, over 4971.00 frames.], tot_loss[loss=0.2001, simple_loss=0.264, pruned_loss=0.06805, over 947724.01 frames.], batch size: 40, aishell_tot_loss[loss=0.1973, simple_loss=0.2685, pruned_loss=0.06304, over 793611.97 frames.], datatang_tot_loss[loss=0.2014, simple_loss=0.2586, pruned_loss=0.07209, over 790813.00 frames.], batch size: 40, lr: 1.11e-03
+2022-06-18 14:58:14,118 INFO [train.py:874] (3/4) Epoch 7, batch 700, datatang_loss[loss=0.1947, simple_loss=0.2536, pruned_loss=0.06789, over 4921.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2637, pruned_loss=0.0679, over 956089.77 frames.], batch size: 71, aishell_tot_loss[loss=0.1965, simple_loss=0.268, pruned_loss=0.06252, over 815898.55 frames.], datatang_tot_loss[loss=0.202, simple_loss=0.2588, pruned_loss=0.07255, over 814057.12 frames.], batch size: 71, lr: 1.11e-03
+2022-06-18 14:58:45,124 INFO [train.py:874] (3/4) Epoch 7, batch 750, datatang_loss[loss=0.1422, simple_loss=0.202, pruned_loss=0.04121, over 4821.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2634, pruned_loss=0.06794, over 962371.07 frames.], batch size: 30, aishell_tot_loss[loss=0.1964, simple_loss=0.2678, pruned_loss=0.06249, over 834159.91 frames.], datatang_tot_loss[loss=0.202, simple_loss=0.2589, pruned_loss=0.07259, over 835708.63 frames.], batch size: 30, lr: 1.10e-03
+2022-06-18 14:59:13,909 INFO [train.py:874] (3/4) Epoch 7, batch 800, datatang_loss[loss=0.2028, simple_loss=0.266, pruned_loss=0.06981, over 4899.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2634, pruned_loss=0.06811, over 967337.02 frames.], batch size: 52, aishell_tot_loss[loss=0.1953, simple_loss=0.2669, pruned_loss=0.06192, over 851559.42 frames.], datatang_tot_loss[loss=0.2033, simple_loss=0.2598, pruned_loss=0.07343, over 853600.15 frames.], batch size: 52, lr: 1.10e-03
+2022-06-18 14:59:44,760 INFO [train.py:874] (3/4) Epoch 7, batch 850, aishell_loss[loss=0.1977, simple_loss=0.2729, pruned_loss=0.06121, over 4965.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2643, pruned_loss=0.06875, over 971649.55 frames.], batch size: 61, aishell_tot_loss[loss=0.197, simple_loss=0.2684, pruned_loss=0.06276, over 866301.81 frames.], datatang_tot_loss[loss=0.2031, simple_loss=0.2594, pruned_loss=0.07337, over 870421.29 frames.], batch size: 61, lr: 1.10e-03
+2022-06-18 15:00:16,402 INFO [train.py:874] (3/4) Epoch 7, batch 900, datatang_loss[loss=0.1641, simple_loss=0.2312, pruned_loss=0.04852, over 4916.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2636, pruned_loss=0.06829, over 974944.56 frames.], batch size: 75, aishell_tot_loss[loss=0.1963, simple_loss=0.268, pruned_loss=0.06231, over 878710.02 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.2593, pruned_loss=0.07329, over 885766.88 frames.], batch size: 75, lr: 1.10e-03
+2022-06-18 15:00:45,834 INFO [train.py:874] (3/4) Epoch 7, batch 950, aishell_loss[loss=0.1911, simple_loss=0.275, pruned_loss=0.05358, over 4937.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2632, pruned_loss=0.06818, over 977312.57 frames.], batch size: 49, aishell_tot_loss[loss=0.1961, simple_loss=0.2675, pruned_loss=0.06233, over 891036.91 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.2593, pruned_loss=0.07331, over 897803.05 frames.], batch size: 49, lr: 1.10e-03
+2022-06-18 15:01:17,338 INFO [train.py:874] (3/4) Epoch 7, batch 1000, datatang_loss[loss=0.2256, simple_loss=0.2828, pruned_loss=0.08419, over 4972.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2621, pruned_loss=0.0675, over 979144.34 frames.], batch size: 40, aishell_tot_loss[loss=0.1957, simple_loss=0.2672, pruned_loss=0.06204, over 900954.83 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.2585, pruned_loss=0.07271, over 909251.43 frames.], batch size: 40, lr: 1.10e-03
+2022-06-18 15:01:17,338 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 15:01:33,320 INFO [train.py:914] (3/4) Epoch 7, validation: loss=0.1728, simple_loss=0.2548, pruned_loss=0.04541, over 1622729.00 frames.
+2022-06-18 15:02:05,472 INFO [train.py:874] (3/4) Epoch 7, batch 1050, datatang_loss[loss=0.2074, simple_loss=0.2732, pruned_loss=0.07083, over 4876.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2618, pruned_loss=0.06691, over 980495.85 frames.], batch size: 25, aishell_tot_loss[loss=0.1951, simple_loss=0.267, pruned_loss=0.06157, over 909439.36 frames.], datatang_tot_loss[loss=0.2015, simple_loss=0.2583, pruned_loss=0.07236, over 919478.41 frames.], batch size: 25, lr: 1.10e-03
+2022-06-18 15:02:35,733 INFO [train.py:874] (3/4) Epoch 7, batch 1100, datatang_loss[loss=0.1511, simple_loss=0.2204, pruned_loss=0.04087, over 4831.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2626, pruned_loss=0.06629, over 981599.54 frames.], batch size: 30, aishell_tot_loss[loss=0.1944, simple_loss=0.2667, pruned_loss=0.06109, over 920668.21 frames.], datatang_tot_loss[loss=0.2021, simple_loss=0.2591, pruned_loss=0.07258, over 925227.02 frames.], batch size: 30, lr: 1.10e-03
+2022-06-18 15:03:04,481 INFO [train.py:874] (3/4) Epoch 7, batch 1150, aishell_loss[loss=0.195, simple_loss=0.2667, pruned_loss=0.0617, over 4968.00 frames.], tot_loss[loss=0.198, simple_loss=0.2628, pruned_loss=0.06658, over 982505.03 frames.], batch size: 31, aishell_tot_loss[loss=0.1947, simple_loss=0.2669, pruned_loss=0.06123, over 928001.96 frames.], datatang_tot_loss[loss=0.2021, simple_loss=0.2591, pruned_loss=0.07255, over 932640.79 frames.], batch size: 31, lr: 1.10e-03
+2022-06-18 15:03:35,935 INFO [train.py:874] (3/4) Epoch 7, batch 1200, datatang_loss[loss=0.1823, simple_loss=0.2458, pruned_loss=0.05938, over 4975.00 frames.], tot_loss[loss=0.1979, simple_loss=0.263, pruned_loss=0.06646, over 983580.68 frames.], batch size: 60, aishell_tot_loss[loss=0.1952, simple_loss=0.2676, pruned_loss=0.06144, over 935508.16 frames.], datatang_tot_loss[loss=0.2015, simple_loss=0.2585, pruned_loss=0.07221, over 938584.86 frames.], batch size: 60, lr: 1.10e-03
+2022-06-18 15:04:06,622 INFO [train.py:874] (3/4) Epoch 7, batch 1250, aishell_loss[loss=0.1491, simple_loss=0.2306, pruned_loss=0.03378, over 4881.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2635, pruned_loss=0.06633, over 983582.66 frames.], batch size: 28, aishell_tot_loss[loss=0.1956, simple_loss=0.2679, pruned_loss=0.06168, over 942062.57 frames.], datatang_tot_loss[loss=0.2012, simple_loss=0.2585, pruned_loss=0.07199, over 943055.67 frames.], batch size: 28, lr: 1.09e-03
+2022-06-18 15:04:34,753 INFO [train.py:874] (3/4) Epoch 7, batch 1300, aishell_loss[loss=0.2046, simple_loss=0.2822, pruned_loss=0.06346, over 4964.00 frames.], tot_loss[loss=0.1988, simple_loss=0.264, pruned_loss=0.06685, over 984266.55 frames.], batch size: 64, aishell_tot_loss[loss=0.1959, simple_loss=0.2682, pruned_loss=0.06175, over 946981.01 frames.], datatang_tot_loss[loss=0.2017, simple_loss=0.2589, pruned_loss=0.0723, over 948516.37 frames.], batch size: 64, lr: 1.09e-03
+2022-06-18 15:05:04,967 INFO [train.py:874] (3/4) Epoch 7, batch 1350, aishell_loss[loss=0.1919, simple_loss=0.2686, pruned_loss=0.05761, over 4974.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2633, pruned_loss=0.06689, over 984739.36 frames.], batch size: 39, aishell_tot_loss[loss=0.1949, simple_loss=0.2673, pruned_loss=0.06127, over 951703.99 frames.], datatang_tot_loss[loss=0.2024, simple_loss=0.2592, pruned_loss=0.07278, over 952902.60 frames.], batch size: 39, lr: 1.09e-03
+2022-06-18 15:05:36,509 INFO [train.py:874] (3/4) Epoch 7, batch 1400, aishell_loss[loss=0.2103, simple_loss=0.2816, pruned_loss=0.06948, over 4945.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2628, pruned_loss=0.06704, over 984511.01 frames.], batch size: 54, aishell_tot_loss[loss=0.1948, simple_loss=0.2669, pruned_loss=0.06133, over 955231.18 frames.], datatang_tot_loss[loss=0.2024, simple_loss=0.2591, pruned_loss=0.07286, over 956771.64 frames.], batch size: 54, lr: 1.09e-03
+2022-06-18 15:06:05,428 INFO [train.py:874] (3/4) Epoch 7, batch 1450, aishell_loss[loss=0.2135, simple_loss=0.2816, pruned_loss=0.07268, over 4920.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2618, pruned_loss=0.06634, over 984696.57 frames.], batch size: 68, aishell_tot_loss[loss=0.1943, simple_loss=0.2665, pruned_loss=0.06099, over 958493.09 frames.], datatang_tot_loss[loss=0.2016, simple_loss=0.2584, pruned_loss=0.07237, over 960425.29 frames.], batch size: 68, lr: 1.09e-03
+2022-06-18 15:06:36,096 INFO [train.py:874] (3/4) Epoch 7, batch 1500, datatang_loss[loss=0.1887, simple_loss=0.2275, pruned_loss=0.07496, over 4971.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2624, pruned_loss=0.06729, over 985329.10 frames.], batch size: 40, aishell_tot_loss[loss=0.1946, simple_loss=0.2667, pruned_loss=0.06119, over 961387.42 frames.], datatang_tot_loss[loss=0.2023, simple_loss=0.2587, pruned_loss=0.07295, over 964122.79 frames.], batch size: 40, lr: 1.09e-03
+2022-06-18 15:07:07,678 INFO [train.py:874] (3/4) Epoch 7, batch 1550, aishell_loss[loss=0.2196, simple_loss=0.2931, pruned_loss=0.07307, over 4937.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2622, pruned_loss=0.06713, over 985445.45 frames.], batch size: 79, aishell_tot_loss[loss=0.1939, simple_loss=0.266, pruned_loss=0.06087, over 964050.58 frames.], datatang_tot_loss[loss=0.2027, simple_loss=0.2592, pruned_loss=0.07311, over 966899.56 frames.], batch size: 79, lr: 1.09e-03
+2022-06-18 15:07:35,829 INFO [train.py:874] (3/4) Epoch 7, batch 1600, datatang_loss[loss=0.2001, simple_loss=0.2605, pruned_loss=0.06983, over 4951.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2622, pruned_loss=0.06602, over 985583.53 frames.], batch size: 37, aishell_tot_loss[loss=0.1929, simple_loss=0.2656, pruned_loss=0.06007, over 967443.15 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.2593, pruned_loss=0.07326, over 968433.78 frames.], batch size: 37, lr: 1.09e-03
+2022-06-18 15:08:07,461 INFO [train.py:874] (3/4) Epoch 7, batch 1650, datatang_loss[loss=0.1953, simple_loss=0.2563, pruned_loss=0.06715, over 4917.00 frames.], tot_loss[loss=0.198, simple_loss=0.2624, pruned_loss=0.06678, over 985622.31 frames.], batch size: 81, aishell_tot_loss[loss=0.1928, simple_loss=0.2655, pruned_loss=0.06003, over 969124.12 frames.], datatang_tot_loss[loss=0.2034, simple_loss=0.2596, pruned_loss=0.07362, over 970911.96 frames.], batch size: 81, lr: 1.09e-03
+2022-06-18 15:08:38,382 INFO [train.py:874] (3/4) Epoch 7, batch 1700, datatang_loss[loss=0.1707, simple_loss=0.2333, pruned_loss=0.05409, over 4932.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2615, pruned_loss=0.06613, over 985872.46 frames.], batch size: 71, aishell_tot_loss[loss=0.1928, simple_loss=0.2654, pruned_loss=0.06006, over 971202.00 frames.], datatang_tot_loss[loss=0.2021, simple_loss=0.2586, pruned_loss=0.07276, over 972753.35 frames.], batch size: 71, lr: 1.09e-03
+2022-06-18 15:09:08,424 INFO [train.py:874] (3/4) Epoch 7, batch 1750, aishell_loss[loss=0.2023, simple_loss=0.272, pruned_loss=0.06629, over 4953.00 frames.], tot_loss[loss=0.1975, simple_loss=0.262, pruned_loss=0.06648, over 986038.05 frames.], batch size: 40, aishell_tot_loss[loss=0.194, simple_loss=0.2665, pruned_loss=0.06076, over 972680.62 frames.], datatang_tot_loss[loss=0.2012, simple_loss=0.2581, pruned_loss=0.07216, over 974704.56 frames.], batch size: 40, lr: 1.08e-03
+2022-06-18 15:09:38,853 INFO [train.py:874] (3/4) Epoch 7, batch 1800, aishell_loss[loss=0.154, simple_loss=0.222, pruned_loss=0.04305, over 4813.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2617, pruned_loss=0.06601, over 985481.33 frames.], batch size: 26, aishell_tot_loss[loss=0.1939, simple_loss=0.2664, pruned_loss=0.06069, over 973879.98 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.2578, pruned_loss=0.07186, over 975854.95 frames.], batch size: 26, lr: 1.08e-03
+2022-06-18 15:10:08,385 INFO [train.py:874] (3/4) Epoch 7, batch 1850, datatang_loss[loss=0.1692, simple_loss=0.237, pruned_loss=0.05067, over 4952.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2623, pruned_loss=0.06624, over 985721.37 frames.], batch size: 34, aishell_tot_loss[loss=0.1944, simple_loss=0.2668, pruned_loss=0.061, over 975511.82 frames.], datatang_tot_loss[loss=0.2007, simple_loss=0.2579, pruned_loss=0.07179, over 976994.56 frames.], batch size: 34, lr: 1.08e-03
+2022-06-18 15:10:38,777 INFO [train.py:874] (3/4) Epoch 7, batch 1900, aishell_loss[loss=0.1994, simple_loss=0.2707, pruned_loss=0.06408, over 4863.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2625, pruned_loss=0.06648, over 985504.78 frames.], batch size: 35, aishell_tot_loss[loss=0.1942, simple_loss=0.2664, pruned_loss=0.06097, over 976294.05 frames.], datatang_tot_loss[loss=0.2013, simple_loss=0.2585, pruned_loss=0.07208, over 978226.17 frames.], batch size: 35, lr: 1.08e-03
+2022-06-18 15:11:10,050 INFO [train.py:874] (3/4) Epoch 7, batch 1950, aishell_loss[loss=0.192, simple_loss=0.2629, pruned_loss=0.0606, over 4864.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2628, pruned_loss=0.06792, over 985294.88 frames.], batch size: 37, aishell_tot_loss[loss=0.195, simple_loss=0.2664, pruned_loss=0.06175, over 976893.35 frames.], datatang_tot_loss[loss=0.202, simple_loss=0.2591, pruned_loss=0.0725, over 979285.43 frames.], batch size: 37, lr: 1.08e-03
+2022-06-18 15:11:38,576 INFO [train.py:874] (3/4) Epoch 7, batch 2000, aishell_loss[loss=0.1426, simple_loss=0.2027, pruned_loss=0.04124, over 4844.00 frames.], tot_loss[loss=0.1987, simple_loss=0.263, pruned_loss=0.06721, over 985323.01 frames.], batch size: 21, aishell_tot_loss[loss=0.1953, simple_loss=0.2672, pruned_loss=0.06165, over 977936.74 frames.], datatang_tot_loss[loss=0.2013, simple_loss=0.2583, pruned_loss=0.0722, over 980010.84 frames.], batch size: 21, lr: 1.08e-03
+2022-06-18 15:11:38,577 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 15:11:55,041 INFO [train.py:914] (3/4) Epoch 7, validation: loss=0.1748, simple_loss=0.2567, pruned_loss=0.04647, over 1622729.00 frames.
+2022-06-18 15:12:25,175 INFO [train.py:874] (3/4) Epoch 7, batch 2050, aishell_loss[loss=0.178, simple_loss=0.24, pruned_loss=0.05799, over 4851.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2628, pruned_loss=0.06623, over 985351.91 frames.], batch size: 28, aishell_tot_loss[loss=0.1944, simple_loss=0.267, pruned_loss=0.0609, over 978778.60 frames.], datatang_tot_loss[loss=0.2012, simple_loss=0.2584, pruned_loss=0.07205, over 980717.35 frames.], batch size: 28, lr: 1.08e-03
+2022-06-18 15:12:54,838 INFO [train.py:874] (3/4) Epoch 7, batch 2100, aishell_loss[loss=0.1843, simple_loss=0.2648, pruned_loss=0.05189, over 4983.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2631, pruned_loss=0.06587, over 985765.04 frames.], batch size: 51, aishell_tot_loss[loss=0.1945, simple_loss=0.2671, pruned_loss=0.06096, over 980067.62 frames.], datatang_tot_loss[loss=0.2011, simple_loss=0.2583, pruned_loss=0.07191, over 981241.64 frames.], batch size: 51, lr: 1.08e-03
+2022-06-18 15:13:25,751 INFO [train.py:874] (3/4) Epoch 7, batch 2150, datatang_loss[loss=0.1953, simple_loss=0.2635, pruned_loss=0.06354, over 4914.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2634, pruned_loss=0.06545, over 985359.45 frames.], batch size: 42, aishell_tot_loss[loss=0.1941, simple_loss=0.2667, pruned_loss=0.06075, over 980405.72 frames.], datatang_tot_loss[loss=0.2012, simple_loss=0.2589, pruned_loss=0.0718, over 981706.99 frames.], batch size: 42, lr: 1.08e-03
+2022-06-18 15:13:55,587 INFO [train.py:874] (3/4) Epoch 7, batch 2200, datatang_loss[loss=0.2045, simple_loss=0.2677, pruned_loss=0.07064, over 4888.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2627, pruned_loss=0.06586, over 985414.99 frames.], batch size: 37, aishell_tot_loss[loss=0.1942, simple_loss=0.2665, pruned_loss=0.06089, over 980614.65 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2588, pruned_loss=0.07149, over 982516.70 frames.], batch size: 37, lr: 1.08e-03
+2022-06-18 15:14:26,131 INFO [train.py:874] (3/4) Epoch 7, batch 2250, aishell_loss[loss=0.2157, simple_loss=0.2838, pruned_loss=0.07384, over 4874.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2623, pruned_loss=0.06521, over 985169.27 frames.], batch size: 42, aishell_tot_loss[loss=0.194, simple_loss=0.2666, pruned_loss=0.06072, over 980972.33 frames.], datatang_tot_loss[loss=0.2002, simple_loss=0.2583, pruned_loss=0.07103, over 982849.00 frames.], batch size: 42, lr: 1.07e-03
+2022-06-18 15:14:56,319 INFO [train.py:874] (3/4) Epoch 7, batch 2300, aishell_loss[loss=0.2094, simple_loss=0.2903, pruned_loss=0.06423, over 4911.00 frames.], tot_loss[loss=0.1955, simple_loss=0.262, pruned_loss=0.06445, over 985017.20 frames.], batch size: 52, aishell_tot_loss[loss=0.1935, simple_loss=0.2663, pruned_loss=0.06037, over 981415.21 frames.], datatang_tot_loss[loss=0.1998, simple_loss=0.258, pruned_loss=0.07076, over 983078.56 frames.], batch size: 52, lr: 1.07e-03
+2022-06-18 15:15:26,248 INFO [train.py:874] (3/4) Epoch 7, batch 2350, datatang_loss[loss=0.1857, simple_loss=0.2602, pruned_loss=0.05563, over 4954.00 frames.], tot_loss[loss=0.197, simple_loss=0.2631, pruned_loss=0.06543, over 985122.20 frames.], batch size: 86, aishell_tot_loss[loss=0.1936, simple_loss=0.2667, pruned_loss=0.06024, over 981925.83 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2588, pruned_loss=0.07154, over 983321.30 frames.], batch size: 86, lr: 1.07e-03
+2022-06-18 15:15:56,450 INFO [train.py:874] (3/4) Epoch 7, batch 2400, datatang_loss[loss=0.2111, simple_loss=0.268, pruned_loss=0.07706, over 4933.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2624, pruned_loss=0.06507, over 984954.84 frames.], batch size: 77, aishell_tot_loss[loss=0.1929, simple_loss=0.2662, pruned_loss=0.0598, over 982060.81 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.2586, pruned_loss=0.07145, over 983592.18 frames.], batch size: 77, lr: 1.07e-03
+2022-06-18 15:16:27,371 INFO [train.py:874] (3/4) Epoch 7, batch 2450, aishell_loss[loss=0.2026, simple_loss=0.2699, pruned_loss=0.06768, over 4939.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2631, pruned_loss=0.06564, over 984942.17 frames.], batch size: 54, aishell_tot_loss[loss=0.1929, simple_loss=0.2662, pruned_loss=0.05982, over 982302.90 frames.], datatang_tot_loss[loss=0.2016, simple_loss=0.2593, pruned_loss=0.072, over 983839.95 frames.], batch size: 54, lr: 1.07e-03
+2022-06-18 15:16:56,410 INFO [train.py:874] (3/4) Epoch 7, batch 2500, datatang_loss[loss=0.1823, simple_loss=0.2439, pruned_loss=0.06033, over 4919.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2617, pruned_loss=0.06519, over 984803.28 frames.], batch size: 71, aishell_tot_loss[loss=0.1927, simple_loss=0.2656, pruned_loss=0.05993, over 982471.55 frames.], datatang_tot_loss[loss=0.2007, simple_loss=0.2586, pruned_loss=0.07143, over 983979.05 frames.], batch size: 71, lr: 1.07e-03
+2022-06-18 15:17:26,909 INFO [train.py:874] (3/4) Epoch 7, batch 2550, aishell_loss[loss=0.1777, simple_loss=0.2619, pruned_loss=0.04673, over 4916.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2629, pruned_loss=0.06575, over 984964.30 frames.], batch size: 46, aishell_tot_loss[loss=0.1931, simple_loss=0.2658, pruned_loss=0.06021, over 982713.88 frames.], datatang_tot_loss[loss=0.2016, simple_loss=0.2593, pruned_loss=0.07196, over 984308.69 frames.], batch size: 46, lr: 1.07e-03
+2022-06-18 15:17:58,055 INFO [train.py:874] (3/4) Epoch 7, batch 2600, aishell_loss[loss=0.1757, simple_loss=0.2241, pruned_loss=0.06363, over 4823.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2623, pruned_loss=0.06556, over 984899.75 frames.], batch size: 21, aishell_tot_loss[loss=0.1933, simple_loss=0.2658, pruned_loss=0.06039, over 982646.75 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2588, pruned_loss=0.07153, over 984677.19 frames.], batch size: 21, lr: 1.07e-03
+2022-06-18 15:18:28,196 INFO [train.py:874] (3/4) Epoch 7, batch 2650, aishell_loss[loss=0.1732, simple_loss=0.2496, pruned_loss=0.04841, over 4900.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2619, pruned_loss=0.06536, over 984976.57 frames.], batch size: 41, aishell_tot_loss[loss=0.1928, simple_loss=0.2653, pruned_loss=0.06018, over 982740.40 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2589, pruned_loss=0.07148, over 984982.94 frames.], batch size: 41, lr: 1.07e-03
+2022-06-18 15:18:58,579 INFO [train.py:874] (3/4) Epoch 7, batch 2700, aishell_loss[loss=0.2224, simple_loss=0.2873, pruned_loss=0.07873, over 4921.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2617, pruned_loss=0.06559, over 984859.24 frames.], batch size: 52, aishell_tot_loss[loss=0.1923, simple_loss=0.2648, pruned_loss=0.0599, over 982954.18 frames.], datatang_tot_loss[loss=0.2015, simple_loss=0.2591, pruned_loss=0.07196, over 984917.55 frames.], batch size: 52, lr: 1.07e-03
+2022-06-18 15:19:28,875 INFO [train.py:874] (3/4) Epoch 7, batch 2750, aishell_loss[loss=0.1739, simple_loss=0.2549, pruned_loss=0.04646, over 4947.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2605, pruned_loss=0.0644, over 985207.71 frames.], batch size: 54, aishell_tot_loss[loss=0.1911, simple_loss=0.2639, pruned_loss=0.05918, over 983457.94 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.2587, pruned_loss=0.07146, over 985025.53 frames.], batch size: 54, lr: 1.07e-03
+2022-06-18 15:19:58,441 INFO [train.py:874] (3/4) Epoch 7, batch 2800, aishell_loss[loss=0.1811, simple_loss=0.2566, pruned_loss=0.05275, over 4895.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2594, pruned_loss=0.06404, over 985210.15 frames.], batch size: 34, aishell_tot_loss[loss=0.1904, simple_loss=0.2632, pruned_loss=0.05878, over 983544.73 frames.], datatang_tot_loss[loss=0.2001, simple_loss=0.258, pruned_loss=0.07105, over 985147.22 frames.], batch size: 34, lr: 1.06e-03
+2022-06-18 15:20:29,307 INFO [train.py:874] (3/4) Epoch 7, batch 2850, aishell_loss[loss=0.1547, simple_loss=0.2205, pruned_loss=0.04443, over 4796.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2598, pruned_loss=0.06439, over 985112.87 frames.], batch size: 20, aishell_tot_loss[loss=0.1902, simple_loss=0.263, pruned_loss=0.05868, over 983461.97 frames.], datatang_tot_loss[loss=0.2004, simple_loss=0.2584, pruned_loss=0.07127, over 985327.26 frames.], batch size: 20, lr: 1.06e-03
+2022-06-18 15:21:00,435 INFO [train.py:874] (3/4) Epoch 7, batch 2900, aishell_loss[loss=0.2039, simple_loss=0.2716, pruned_loss=0.06807, over 4862.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2605, pruned_loss=0.06514, over 985304.42 frames.], batch size: 37, aishell_tot_loss[loss=0.1908, simple_loss=0.2635, pruned_loss=0.05898, over 983629.19 frames.], datatang_tot_loss[loss=0.2005, simple_loss=0.2585, pruned_loss=0.07122, over 985476.15 frames.], batch size: 37, lr: 1.06e-03
+2022-06-18 15:21:30,614 INFO [train.py:874] (3/4) Epoch 7, batch 2950, aishell_loss[loss=0.2038, simple_loss=0.2701, pruned_loss=0.06869, over 4960.00 frames.], tot_loss[loss=0.1962, simple_loss=0.261, pruned_loss=0.06574, over 985842.04 frames.], batch size: 44, aishell_tot_loss[loss=0.1913, simple_loss=0.2641, pruned_loss=0.0593, over 984073.07 frames.], datatang_tot_loss[loss=0.2005, simple_loss=0.2586, pruned_loss=0.07124, over 985726.61 frames.], batch size: 44, lr: 1.06e-03
+2022-06-18 15:22:01,275 INFO [train.py:874] (3/4) Epoch 7, batch 3000, datatang_loss[loss=0.1861, simple_loss=0.2437, pruned_loss=0.06425, over 4948.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2606, pruned_loss=0.06545, over 985788.96 frames.], batch size: 62, aishell_tot_loss[loss=0.1912, simple_loss=0.2641, pruned_loss=0.05918, over 984284.03 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2581, pruned_loss=0.07088, over 985643.89 frames.], batch size: 62, lr: 1.06e-03
+2022-06-18 15:22:01,276 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 15:22:18,150 INFO [train.py:914] (3/4) Epoch 7, validation: loss=0.1737, simple_loss=0.2558, pruned_loss=0.0458, over 1622729.00 frames.
+2022-06-18 15:22:48,138 INFO [train.py:874] (3/4) Epoch 7, batch 3050, aishell_loss[loss=0.188, simple_loss=0.2664, pruned_loss=0.05484, over 4969.00 frames.], tot_loss[loss=0.196, simple_loss=0.2612, pruned_loss=0.06538, over 985816.88 frames.], batch size: 61, aishell_tot_loss[loss=0.1919, simple_loss=0.2649, pruned_loss=0.05947, over 984388.32 frames.], datatang_tot_loss[loss=0.1996, simple_loss=0.2579, pruned_loss=0.07066, over 985759.74 frames.], batch size: 61, lr: 1.06e-03
+2022-06-18 15:23:18,093 INFO [train.py:874] (3/4) Epoch 7, batch 3100, datatang_loss[loss=0.3048, simple_loss=0.3353, pruned_loss=0.1372, over 4915.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2622, pruned_loss=0.06561, over 985972.06 frames.], batch size: 98, aishell_tot_loss[loss=0.1917, simple_loss=0.265, pruned_loss=0.05924, over 984627.74 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.2587, pruned_loss=0.0714, over 985878.29 frames.], batch size: 98, lr: 1.06e-03
+2022-06-18 15:23:50,894 INFO [train.py:874] (3/4) Epoch 7, batch 3150, aishell_loss[loss=0.1653, simple_loss=0.2403, pruned_loss=0.04518, over 4857.00 frames.], tot_loss[loss=0.1977, simple_loss=0.263, pruned_loss=0.06618, over 985968.00 frames.], batch size: 28, aishell_tot_loss[loss=0.1917, simple_loss=0.2649, pruned_loss=0.05927, over 984768.66 frames.], datatang_tot_loss[loss=0.202, simple_loss=0.2596, pruned_loss=0.07219, over 985933.44 frames.], batch size: 28, lr: 1.06e-03
+2022-06-18 15:24:21,591 INFO [train.py:874] (3/4) Epoch 7, batch 3200, datatang_loss[loss=0.242, simple_loss=0.2953, pruned_loss=0.09435, over 4957.00 frames.], tot_loss[loss=0.199, simple_loss=0.264, pruned_loss=0.06704, over 985742.24 frames.], batch size: 99, aishell_tot_loss[loss=0.1919, simple_loss=0.2651, pruned_loss=0.05932, over 984756.27 frames.], datatang_tot_loss[loss=0.2034, simple_loss=0.2607, pruned_loss=0.07305, over 985836.65 frames.], batch size: 99, lr: 1.06e-03
+2022-06-18 15:24:53,257 INFO [train.py:874] (3/4) Epoch 7, batch 3250, aishell_loss[loss=0.1928, simple_loss=0.2707, pruned_loss=0.05745, over 4970.00 frames.], tot_loss[loss=0.1975, simple_loss=0.2631, pruned_loss=0.06592, over 985459.21 frames.], batch size: 61, aishell_tot_loss[loss=0.191, simple_loss=0.2643, pruned_loss=0.05888, over 984759.67 frames.], datatang_tot_loss[loss=0.2033, simple_loss=0.2607, pruned_loss=0.073, over 985676.96 frames.], batch size: 61, lr: 1.06e-03
+2022-06-18 15:25:21,911 INFO [train.py:874] (3/4) Epoch 7, batch 3300, datatang_loss[loss=0.1837, simple_loss=0.2544, pruned_loss=0.05652, over 4943.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2634, pruned_loss=0.06598, over 985850.62 frames.], batch size: 88, aishell_tot_loss[loss=0.191, simple_loss=0.2646, pruned_loss=0.05872, over 985165.39 frames.], datatang_tot_loss[loss=0.2036, simple_loss=0.2609, pruned_loss=0.07315, over 985745.28 frames.], batch size: 88, lr: 1.05e-03
+2022-06-18 15:25:52,848 INFO [train.py:874] (3/4) Epoch 7, batch 3350, datatang_loss[loss=0.2173, simple_loss=0.2699, pruned_loss=0.08233, over 4916.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2633, pruned_loss=0.06544, over 985818.68 frames.], batch size: 81, aishell_tot_loss[loss=0.1911, simple_loss=0.265, pruned_loss=0.05861, over 985229.60 frames.], datatang_tot_loss[loss=0.2032, simple_loss=0.2604, pruned_loss=0.07303, over 985751.09 frames.], batch size: 81, lr: 1.05e-03
+2022-06-18 15:26:24,301 INFO [train.py:874] (3/4) Epoch 7, batch 3400, aishell_loss[loss=0.1949, simple_loss=0.2689, pruned_loss=0.06044, over 4924.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2635, pruned_loss=0.06644, over 985859.74 frames.], batch size: 41, aishell_tot_loss[loss=0.191, simple_loss=0.2645, pruned_loss=0.05877, over 985230.18 frames.], datatang_tot_loss[loss=0.2044, simple_loss=0.2612, pruned_loss=0.0738, over 985864.68 frames.], batch size: 41, lr: 1.05e-03
+2022-06-18 15:26:53,582 INFO [train.py:874] (3/4) Epoch 7, batch 3450, datatang_loss[loss=0.1731, simple_loss=0.2432, pruned_loss=0.05154, over 4978.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2637, pruned_loss=0.06632, over 986072.79 frames.], batch size: 60, aishell_tot_loss[loss=0.1916, simple_loss=0.2651, pruned_loss=0.05903, over 985304.18 frames.], datatang_tot_loss[loss=0.2041, simple_loss=0.261, pruned_loss=0.07363, over 986108.47 frames.], batch size: 60, lr: 1.05e-03
+2022-06-18 15:27:24,028 INFO [train.py:874] (3/4) Epoch 7, batch 3500, aishell_loss[loss=0.1736, simple_loss=0.231, pruned_loss=0.05808, over 4786.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2632, pruned_loss=0.06599, over 985596.94 frames.], batch size: 24, aishell_tot_loss[loss=0.1925, simple_loss=0.2659, pruned_loss=0.0596, over 984941.46 frames.], datatang_tot_loss[loss=0.2026, simple_loss=0.26, pruned_loss=0.07259, over 986054.15 frames.], batch size: 24, lr: 1.05e-03
+2022-06-18 15:27:53,613 INFO [train.py:874] (3/4) Epoch 7, batch 3550, aishell_loss[loss=0.2083, simple_loss=0.2832, pruned_loss=0.06669, over 4946.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2632, pruned_loss=0.06582, over 985874.86 frames.], batch size: 54, aishell_tot_loss[loss=0.1921, simple_loss=0.2655, pruned_loss=0.05933, over 985402.99 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.2603, pruned_loss=0.07278, over 985924.79 frames.], batch size: 54, lr: 1.05e-03
+2022-06-18 15:28:23,421 INFO [train.py:874] (3/4) Epoch 7, batch 3600, aishell_loss[loss=0.1706, simple_loss=0.2466, pruned_loss=0.0473, over 4883.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2622, pruned_loss=0.06472, over 985325.30 frames.], batch size: 28, aishell_tot_loss[loss=0.1919, simple_loss=0.2653, pruned_loss=0.05919, over 985088.97 frames.], datatang_tot_loss[loss=0.2016, simple_loss=0.2595, pruned_loss=0.07189, over 985716.35 frames.], batch size: 28, lr: 1.05e-03
+2022-06-18 15:28:54,616 INFO [train.py:874] (3/4) Epoch 7, batch 3650, aishell_loss[loss=0.2129, simple_loss=0.2878, pruned_loss=0.06902, over 4945.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2615, pruned_loss=0.06464, over 985608.16 frames.], batch size: 45, aishell_tot_loss[loss=0.1923, simple_loss=0.2653, pruned_loss=0.05966, over 985182.71 frames.], datatang_tot_loss[loss=0.2004, simple_loss=0.2588, pruned_loss=0.071, over 985907.65 frames.], batch size: 45, lr: 1.05e-03
+2022-06-18 15:29:24,190 INFO [train.py:874] (3/4) Epoch 7, batch 3700, aishell_loss[loss=0.1879, simple_loss=0.2614, pruned_loss=0.0572, over 4970.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2613, pruned_loss=0.06478, over 985881.84 frames.], batch size: 39, aishell_tot_loss[loss=0.1914, simple_loss=0.2645, pruned_loss=0.0592, over 985518.81 frames.], datatang_tot_loss[loss=0.2011, simple_loss=0.2592, pruned_loss=0.07153, over 985878.04 frames.], batch size: 39, lr: 1.05e-03
+2022-06-18 15:29:54,981 INFO [train.py:874] (3/4) Epoch 7, batch 3750, aishell_loss[loss=0.2182, simple_loss=0.2868, pruned_loss=0.07481, over 4862.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2616, pruned_loss=0.06439, over 985851.54 frames.], batch size: 35, aishell_tot_loss[loss=0.191, simple_loss=0.2645, pruned_loss=0.05876, over 985587.77 frames.], datatang_tot_loss[loss=0.2012, simple_loss=0.2594, pruned_loss=0.07145, over 985812.52 frames.], batch size: 35, lr: 1.05e-03
+2022-06-18 15:30:24,013 INFO [train.py:874] (3/4) Epoch 7, batch 3800, aishell_loss[loss=0.1996, simple_loss=0.2763, pruned_loss=0.0615, over 4928.00 frames.], tot_loss[loss=0.1949, simple_loss=0.2619, pruned_loss=0.06394, over 985894.96 frames.], batch size: 33, aishell_tot_loss[loss=0.1911, simple_loss=0.2649, pruned_loss=0.05866, over 985412.72 frames.], datatang_tot_loss[loss=0.2006, simple_loss=0.2592, pruned_loss=0.07098, over 986077.56 frames.], batch size: 33, lr: 1.05e-03
+2022-06-18 15:30:52,454 INFO [train.py:874] (3/4) Epoch 7, batch 3850, datatang_loss[loss=0.2802, simple_loss=0.3208, pruned_loss=0.1198, over 4955.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2616, pruned_loss=0.0637, over 985729.84 frames.], batch size: 99, aishell_tot_loss[loss=0.1914, simple_loss=0.265, pruned_loss=0.05891, over 985231.00 frames.], datatang_tot_loss[loss=0.1997, simple_loss=0.2587, pruned_loss=0.07033, over 986124.15 frames.], batch size: 99, lr: 1.05e-03
+2022-06-18 15:31:22,666 INFO [train.py:874] (3/4) Epoch 7, batch 3900, aishell_loss[loss=0.2042, simple_loss=0.2805, pruned_loss=0.0639, over 4932.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2622, pruned_loss=0.06441, over 985748.23 frames.], batch size: 58, aishell_tot_loss[loss=0.1918, simple_loss=0.2655, pruned_loss=0.05904, over 985245.30 frames.], datatang_tot_loss[loss=0.2, simple_loss=0.2589, pruned_loss=0.07055, over 986141.56 frames.], batch size: 58, lr: 1.04e-03
+2022-06-18 15:31:51,750 INFO [train.py:874] (3/4) Epoch 7, batch 3950, aishell_loss[loss=0.1724, simple_loss=0.2455, pruned_loss=0.04965, over 4895.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2616, pruned_loss=0.06398, over 985878.70 frames.], batch size: 34, aishell_tot_loss[loss=0.1918, simple_loss=0.2657, pruned_loss=0.05894, over 985340.57 frames.], datatang_tot_loss[loss=0.1991, simple_loss=0.2581, pruned_loss=0.07011, over 986207.15 frames.], batch size: 34, lr: 1.04e-03
+2022-06-18 15:32:19,181 INFO [train.py:874] (3/4) Epoch 7, batch 4000, aishell_loss[loss=0.1944, simple_loss=0.2729, pruned_loss=0.05793, over 4933.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2621, pruned_loss=0.06371, over 985917.50 frames.], batch size: 49, aishell_tot_loss[loss=0.1923, simple_loss=0.2665, pruned_loss=0.05899, over 985380.28 frames.], datatang_tot_loss[loss=0.1987, simple_loss=0.2574, pruned_loss=0.06997, over 986246.15 frames.], batch size: 49, lr: 1.04e-03
+2022-06-18 15:32:19,182 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 15:32:35,475 INFO [train.py:914] (3/4) Epoch 7, validation: loss=0.1718, simple_loss=0.2543, pruned_loss=0.04468, over 1622729.00 frames.
+2022-06-18 15:33:04,693 INFO [train.py:874] (3/4) Epoch 7, batch 4050, datatang_loss[loss=0.2196, simple_loss=0.278, pruned_loss=0.08063, over 4947.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2603, pruned_loss=0.06324, over 985472.10 frames.], batch size: 86, aishell_tot_loss[loss=0.1911, simple_loss=0.2654, pruned_loss=0.05837, over 984976.53 frames.], datatang_tot_loss[loss=0.1983, simple_loss=0.2568, pruned_loss=0.06987, over 986185.29 frames.], batch size: 86, lr: 1.04e-03
+2022-06-18 15:33:33,299 INFO [train.py:874] (3/4) Epoch 7, batch 4100, aishell_loss[loss=0.2013, simple_loss=0.2701, pruned_loss=0.06625, over 4924.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2599, pruned_loss=0.06289, over 984946.28 frames.], batch size: 41, aishell_tot_loss[loss=0.1909, simple_loss=0.265, pruned_loss=0.05842, over 984430.39 frames.], datatang_tot_loss[loss=0.1975, simple_loss=0.2565, pruned_loss=0.06924, over 986171.26 frames.], batch size: 41, lr: 1.04e-03
+2022-06-18 15:34:37,416 INFO [train.py:874] (3/4) Epoch 8, batch 50, datatang_loss[loss=0.1739, simple_loss=0.2441, pruned_loss=0.05189, over 4858.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2534, pruned_loss=0.05726, over 218452.19 frames.], batch size: 39, aishell_tot_loss[loss=0.1906, simple_loss=0.2666, pruned_loss=0.05731, over 102960.44 frames.], datatang_tot_loss[loss=0.1789, simple_loss=0.2429, pruned_loss=0.05744, over 128967.87 frames.], batch size: 39, lr: 9.97e-04
+2022-06-18 15:35:06,802 INFO [train.py:874] (3/4) Epoch 8, batch 100, aishell_loss[loss=0.2034, simple_loss=0.2811, pruned_loss=0.06282, over 4865.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2568, pruned_loss=0.05826, over 388530.02 frames.], batch size: 35, aishell_tot_loss[loss=0.1893, simple_loss=0.2655, pruned_loss=0.05653, over 229831.47 frames.], datatang_tot_loss[loss=0.1834, simple_loss=0.2464, pruned_loss=0.06015, over 206955.03 frames.], batch size: 35, lr: 9.97e-04
+2022-06-18 15:35:37,043 INFO [train.py:874] (3/4) Epoch 8, batch 150, aishell_loss[loss=0.2025, simple_loss=0.2716, pruned_loss=0.06673, over 4950.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2558, pruned_loss=0.05835, over 520778.80 frames.], batch size: 40, aishell_tot_loss[loss=0.1915, simple_loss=0.2668, pruned_loss=0.05814, over 318793.37 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2438, pruned_loss=0.05847, over 298610.98 frames.], batch size: 40, lr: 9.96e-04
+2022-06-18 15:36:06,979 INFO [train.py:874] (3/4) Epoch 8, batch 200, datatang_loss[loss=0.1817, simple_loss=0.2491, pruned_loss=0.05718, over 4917.00 frames.], tot_loss[loss=0.185, simple_loss=0.2542, pruned_loss=0.05789, over 623626.78 frames.], batch size: 81, aishell_tot_loss[loss=0.1879, simple_loss=0.263, pruned_loss=0.05639, over 399817.36 frames.], datatang_tot_loss[loss=0.182, simple_loss=0.245, pruned_loss=0.05955, over 376735.31 frames.], batch size: 81, lr: 9.95e-04
+2022-06-18 15:36:36,936 INFO [train.py:874] (3/4) Epoch 8, batch 250, aishell_loss[loss=0.1818, simple_loss=0.2582, pruned_loss=0.05273, over 4954.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2545, pruned_loss=0.05816, over 703799.06 frames.], batch size: 31, aishell_tot_loss[loss=0.1884, simple_loss=0.2626, pruned_loss=0.05704, over 463420.43 frames.], datatang_tot_loss[loss=0.1825, simple_loss=0.2464, pruned_loss=0.05928, over 453905.35 frames.], batch size: 31, lr: 9.94e-04
+2022-06-18 15:37:06,826 INFO [train.py:874] (3/4) Epoch 8, batch 300, datatang_loss[loss=0.1884, simple_loss=0.2496, pruned_loss=0.06362, over 4929.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2541, pruned_loss=0.05737, over 766529.03 frames.], batch size: 69, aishell_tot_loss[loss=0.1878, simple_loss=0.2628, pruned_loss=0.0564, over 525029.57 frames.], datatang_tot_loss[loss=0.1815, simple_loss=0.2455, pruned_loss=0.05871, over 516694.20 frames.], batch size: 69, lr: 9.93e-04
+2022-06-18 15:37:37,476 INFO [train.py:874] (3/4) Epoch 8, batch 350, datatang_loss[loss=0.1744, simple_loss=0.2359, pruned_loss=0.05647, over 4922.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2543, pruned_loss=0.05844, over 815114.41 frames.], batch size: 81, aishell_tot_loss[loss=0.1878, simple_loss=0.2626, pruned_loss=0.05654, over 564613.82 frames.], datatang_tot_loss[loss=0.1834, simple_loss=0.247, pruned_loss=0.05995, over 586335.20 frames.], batch size: 81, lr: 9.93e-04
+2022-06-18 15:38:07,286 INFO [train.py:874] (3/4) Epoch 8, batch 400, aishell_loss[loss=0.183, simple_loss=0.2696, pruned_loss=0.04825, over 4928.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2551, pruned_loss=0.059, over 853142.48 frames.], batch size: 46, aishell_tot_loss[loss=0.1871, simple_loss=0.2624, pruned_loss=0.0559, over 610466.24 frames.], datatang_tot_loss[loss=0.1856, simple_loss=0.2484, pruned_loss=0.0614, over 637109.25 frames.], batch size: 46, lr: 9.92e-04
+2022-06-18 15:38:37,499 INFO [train.py:874] (3/4) Epoch 8, batch 450, aishell_loss[loss=0.1765, simple_loss=0.2614, pruned_loss=0.04578, over 4921.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2563, pruned_loss=0.05949, over 882113.83 frames.], batch size: 52, aishell_tot_loss[loss=0.1865, simple_loss=0.2618, pruned_loss=0.0556, over 662528.46 frames.], datatang_tot_loss[loss=0.1878, simple_loss=0.2501, pruned_loss=0.06274, over 670235.03 frames.], batch size: 52, lr: 9.91e-04
+2022-06-18 15:39:07,803 INFO [train.py:874] (3/4) Epoch 8, batch 500, aishell_loss[loss=0.1954, simple_loss=0.2792, pruned_loss=0.05579, over 4955.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2566, pruned_loss=0.05976, over 905227.28 frames.], batch size: 80, aishell_tot_loss[loss=0.187, simple_loss=0.2623, pruned_loss=0.0559, over 697923.16 frames.], datatang_tot_loss[loss=0.188, simple_loss=0.2503, pruned_loss=0.06282, over 710127.44 frames.], batch size: 80, lr: 9.90e-04
+2022-06-18 15:39:37,992 INFO [train.py:874] (3/4) Epoch 8, batch 550, datatang_loss[loss=0.2146, simple_loss=0.2694, pruned_loss=0.07993, over 4902.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2589, pruned_loss=0.0615, over 923027.29 frames.], batch size: 42, aishell_tot_loss[loss=0.1886, simple_loss=0.2637, pruned_loss=0.0568, over 730645.16 frames.], datatang_tot_loss[loss=0.1904, simple_loss=0.2521, pruned_loss=0.06433, over 743660.23 frames.], batch size: 42, lr: 9.89e-04
+2022-06-18 15:40:08,038 INFO [train.py:874] (3/4) Epoch 8, batch 600, aishell_loss[loss=0.2197, simple_loss=0.2777, pruned_loss=0.08078, over 4953.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2588, pruned_loss=0.06241, over 937196.26 frames.], batch size: 40, aishell_tot_loss[loss=0.1889, simple_loss=0.2634, pruned_loss=0.0572, over 753737.77 frames.], datatang_tot_loss[loss=0.1916, simple_loss=0.2529, pruned_loss=0.06509, over 778845.98 frames.], batch size: 40, lr: 9.88e-04
+2022-06-18 15:40:37,342 INFO [train.py:874] (3/4) Epoch 8, batch 650, aishell_loss[loss=0.1872, simple_loss=0.2677, pruned_loss=0.05336, over 4917.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2595, pruned_loss=0.06261, over 948042.67 frames.], batch size: 52, aishell_tot_loss[loss=0.1896, simple_loss=0.2639, pruned_loss=0.05767, over 785254.68 frames.], datatang_tot_loss[loss=0.1922, simple_loss=0.2534, pruned_loss=0.0655, over 799476.09 frames.], batch size: 52, lr: 9.88e-04
+2022-06-18 15:41:07,639 INFO [train.py:874] (3/4) Epoch 8, batch 700, datatang_loss[loss=0.183, simple_loss=0.2416, pruned_loss=0.06215, over 4928.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2589, pruned_loss=0.06228, over 956369.77 frames.], batch size: 71, aishell_tot_loss[loss=0.189, simple_loss=0.2633, pruned_loss=0.05731, over 807870.26 frames.], datatang_tot_loss[loss=0.1924, simple_loss=0.2535, pruned_loss=0.06568, over 822309.73 frames.], batch size: 71, lr: 9.87e-04
+2022-06-18 15:41:37,848 INFO [train.py:874] (3/4) Epoch 8, batch 750, aishell_loss[loss=0.1774, simple_loss=0.2527, pruned_loss=0.05107, over 4935.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2595, pruned_loss=0.06254, over 962703.20 frames.], batch size: 32, aishell_tot_loss[loss=0.1891, simple_loss=0.2636, pruned_loss=0.05736, over 827951.32 frames.], datatang_tot_loss[loss=0.1932, simple_loss=0.2543, pruned_loss=0.06611, over 842168.10 frames.], batch size: 32, lr: 9.86e-04
+2022-06-18 15:42:08,117 INFO [train.py:874] (3/4) Epoch 8, batch 800, datatang_loss[loss=0.1771, simple_loss=0.2479, pruned_loss=0.05311, over 4941.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2596, pruned_loss=0.06279, over 967560.37 frames.], batch size: 34, aishell_tot_loss[loss=0.1887, simple_loss=0.2629, pruned_loss=0.05721, over 845539.07 frames.], datatang_tot_loss[loss=0.1943, simple_loss=0.2552, pruned_loss=0.0667, over 859742.61 frames.], batch size: 34, lr: 9.85e-04
+2022-06-18 15:42:37,805 INFO [train.py:874] (3/4) Epoch 8, batch 850, aishell_loss[loss=0.1722, simple_loss=0.2498, pruned_loss=0.04728, over 4971.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2599, pruned_loss=0.0624, over 971379.08 frames.], batch size: 44, aishell_tot_loss[loss=0.1884, simple_loss=0.2632, pruned_loss=0.05678, over 862357.97 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.2553, pruned_loss=0.06689, over 874103.82 frames.], batch size: 44, lr: 9.84e-04
+2022-06-18 15:43:07,895 INFO [train.py:874] (3/4) Epoch 8, batch 900, datatang_loss[loss=0.2202, simple_loss=0.2822, pruned_loss=0.0791, over 4927.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2594, pruned_loss=0.06199, over 974655.42 frames.], batch size: 98, aishell_tot_loss[loss=0.1883, simple_loss=0.2632, pruned_loss=0.05668, over 877953.30 frames.], datatang_tot_loss[loss=0.1942, simple_loss=0.2549, pruned_loss=0.06677, over 886382.39 frames.], batch size: 98, lr: 9.84e-04
+2022-06-18 15:43:39,083 INFO [train.py:874] (3/4) Epoch 8, batch 950, datatang_loss[loss=0.1902, simple_loss=0.246, pruned_loss=0.06722, over 4970.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2587, pruned_loss=0.06191, over 977439.84 frames.], batch size: 45, aishell_tot_loss[loss=0.1884, simple_loss=0.2633, pruned_loss=0.05675, over 886424.56 frames.], datatang_tot_loss[loss=0.1934, simple_loss=0.2544, pruned_loss=0.06624, over 902131.97 frames.], batch size: 45, lr: 9.83e-04
+2022-06-18 15:44:08,478 INFO [train.py:874] (3/4) Epoch 8, batch 1000, datatang_loss[loss=0.196, simple_loss=0.2617, pruned_loss=0.06512, over 4953.00 frames.], tot_loss[loss=0.1899, simple_loss=0.258, pruned_loss=0.06093, over 979222.13 frames.], batch size: 91, aishell_tot_loss[loss=0.1877, simple_loss=0.2626, pruned_loss=0.05637, over 898888.08 frames.], datatang_tot_loss[loss=0.1928, simple_loss=0.2542, pruned_loss=0.06567, over 911274.44 frames.], batch size: 91, lr: 9.82e-04
+2022-06-18 15:44:08,479 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 15:44:24,593 INFO [train.py:914] (3/4) Epoch 8, validation: loss=0.1735, simple_loss=0.2544, pruned_loss=0.04634, over 1622729.00 frames.
+2022-06-18 15:44:54,419 INFO [train.py:874] (3/4) Epoch 8, batch 1050, datatang_loss[loss=0.2232, simple_loss=0.2893, pruned_loss=0.07853, over 4926.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2576, pruned_loss=0.06011, over 980201.43 frames.], batch size: 98, aishell_tot_loss[loss=0.1868, simple_loss=0.2623, pruned_loss=0.05566, over 908648.93 frames.], datatang_tot_loss[loss=0.1924, simple_loss=0.254, pruned_loss=0.0654, over 920013.10 frames.], batch size: 98, lr: 9.81e-04
+2022-06-18 15:45:25,121 INFO [train.py:874] (3/4) Epoch 8, batch 1100, aishell_loss[loss=0.1756, simple_loss=0.2445, pruned_loss=0.05336, over 4945.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2573, pruned_loss=0.06027, over 981321.34 frames.], batch size: 32, aishell_tot_loss[loss=0.1864, simple_loss=0.2615, pruned_loss=0.05565, over 918485.03 frames.], datatang_tot_loss[loss=0.1927, simple_loss=0.2541, pruned_loss=0.06563, over 927024.36 frames.], batch size: 32, lr: 9.80e-04
+2022-06-18 15:45:54,959 INFO [train.py:874] (3/4) Epoch 8, batch 1150, datatang_loss[loss=0.2174, simple_loss=0.2848, pruned_loss=0.075, over 4928.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2576, pruned_loss=0.06037, over 982414.26 frames.], batch size: 94, aishell_tot_loss[loss=0.1863, simple_loss=0.2617, pruned_loss=0.05545, over 926191.33 frames.], datatang_tot_loss[loss=0.1929, simple_loss=0.2542, pruned_loss=0.0658, over 934244.87 frames.], batch size: 94, lr: 9.80e-04
+2022-06-18 15:46:24,580 INFO [train.py:874] (3/4) Epoch 8, batch 1200, aishell_loss[loss=0.1568, simple_loss=0.2359, pruned_loss=0.03882, over 4983.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2586, pruned_loss=0.06122, over 982921.11 frames.], batch size: 30, aishell_tot_loss[loss=0.1872, simple_loss=0.2621, pruned_loss=0.05613, over 933754.29 frames.], datatang_tot_loss[loss=0.1935, simple_loss=0.2547, pruned_loss=0.06618, over 939624.11 frames.], batch size: 30, lr: 9.79e-04
+2022-06-18 15:46:55,097 INFO [train.py:874] (3/4) Epoch 8, batch 1250, datatang_loss[loss=0.2231, simple_loss=0.2732, pruned_loss=0.08647, over 4949.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2591, pruned_loss=0.06175, over 983424.78 frames.], batch size: 55, aishell_tot_loss[loss=0.1874, simple_loss=0.2622, pruned_loss=0.05628, over 940550.34 frames.], datatang_tot_loss[loss=0.1943, simple_loss=0.2551, pruned_loss=0.06677, over 944332.32 frames.], batch size: 55, lr: 9.78e-04
+2022-06-18 15:47:24,825 INFO [train.py:874] (3/4) Epoch 8, batch 1300, aishell_loss[loss=0.1769, simple_loss=0.2587, pruned_loss=0.0475, over 4952.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2581, pruned_loss=0.06122, over 983644.39 frames.], batch size: 31, aishell_tot_loss[loss=0.1876, simple_loss=0.2623, pruned_loss=0.05648, over 945905.86 frames.], datatang_tot_loss[loss=0.193, simple_loss=0.2538, pruned_loss=0.06608, over 948868.68 frames.], batch size: 31, lr: 9.77e-04
+2022-06-18 15:47:55,300 INFO [train.py:874] (3/4) Epoch 8, batch 1350, datatang_loss[loss=0.1793, simple_loss=0.2411, pruned_loss=0.05872, over 4952.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2598, pruned_loss=0.06242, over 984079.97 frames.], batch size: 69, aishell_tot_loss[loss=0.1877, simple_loss=0.2626, pruned_loss=0.0564, over 950531.70 frames.], datatang_tot_loss[loss=0.1951, simple_loss=0.2554, pruned_loss=0.06738, over 953243.09 frames.], batch size: 69, lr: 9.76e-04
+2022-06-18 15:48:24,919 INFO [train.py:874] (3/4) Epoch 8, batch 1400, aishell_loss[loss=0.1789, simple_loss=0.2619, pruned_loss=0.04801, over 4905.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2598, pruned_loss=0.06174, over 984590.11 frames.], batch size: 41, aishell_tot_loss[loss=0.1871, simple_loss=0.2623, pruned_loss=0.05597, over 955620.73 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.2558, pruned_loss=0.06746, over 956315.10 frames.], batch size: 41, lr: 9.76e-04
+2022-06-18 15:48:55,418 INFO [train.py:874] (3/4) Epoch 8, batch 1450, aishell_loss[loss=0.1815, simple_loss=0.2616, pruned_loss=0.05066, over 4914.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2596, pruned_loss=0.06125, over 984751.52 frames.], batch size: 41, aishell_tot_loss[loss=0.1871, simple_loss=0.2625, pruned_loss=0.05578, over 958891.74 frames.], datatang_tot_loss[loss=0.1949, simple_loss=0.2555, pruned_loss=0.06713, over 959956.11 frames.], batch size: 41, lr: 9.75e-04
+2022-06-18 15:49:25,890 INFO [train.py:874] (3/4) Epoch 8, batch 1500, datatang_loss[loss=0.1918, simple_loss=0.2447, pruned_loss=0.06946, over 4984.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2595, pruned_loss=0.06118, over 984939.28 frames.], batch size: 25, aishell_tot_loss[loss=0.1866, simple_loss=0.2621, pruned_loss=0.05556, over 962298.78 frames.], datatang_tot_loss[loss=0.1953, simple_loss=0.2559, pruned_loss=0.06729, over 962711.32 frames.], batch size: 25, lr: 9.74e-04
+2022-06-18 15:49:55,609 INFO [train.py:874] (3/4) Epoch 8, batch 1550, datatang_loss[loss=0.1651, simple_loss=0.2408, pruned_loss=0.04467, over 4939.00 frames.], tot_loss[loss=0.1912, simple_loss=0.259, pruned_loss=0.06164, over 985187.37 frames.], batch size: 69, aishell_tot_loss[loss=0.1863, simple_loss=0.2619, pruned_loss=0.05537, over 964115.36 frames.], datatang_tot_loss[loss=0.1955, simple_loss=0.256, pruned_loss=0.06749, over 966390.09 frames.], batch size: 69, lr: 9.73e-04
+2022-06-18 15:50:25,913 INFO [train.py:874] (3/4) Epoch 8, batch 1600, datatang_loss[loss=0.1639, simple_loss=0.229, pruned_loss=0.04942, over 4935.00 frames.], tot_loss[loss=0.192, simple_loss=0.2598, pruned_loss=0.06213, over 984849.16 frames.], batch size: 69, aishell_tot_loss[loss=0.1868, simple_loss=0.2622, pruned_loss=0.05572, over 966788.13 frames.], datatang_tot_loss[loss=0.1963, simple_loss=0.2564, pruned_loss=0.06808, over 968107.38 frames.], batch size: 69, lr: 9.73e-04
+2022-06-18 15:50:55,450 INFO [train.py:874] (3/4) Epoch 8, batch 1650, aishell_loss[loss=0.2203, simple_loss=0.2955, pruned_loss=0.07253, over 4935.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2606, pruned_loss=0.06235, over 985122.65 frames.], batch size: 68, aishell_tot_loss[loss=0.1882, simple_loss=0.2634, pruned_loss=0.05651, over 969059.45 frames.], datatang_tot_loss[loss=0.1957, simple_loss=0.2562, pruned_loss=0.06766, over 970248.61 frames.], batch size: 68, lr: 9.72e-04
+2022-06-18 15:51:24,689 INFO [train.py:874] (3/4) Epoch 8, batch 1700, datatang_loss[loss=0.1721, simple_loss=0.2322, pruned_loss=0.05607, over 4951.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2616, pruned_loss=0.06237, over 985514.62 frames.], batch size: 67, aishell_tot_loss[loss=0.1885, simple_loss=0.2641, pruned_loss=0.05651, over 971456.44 frames.], datatang_tot_loss[loss=0.1963, simple_loss=0.2567, pruned_loss=0.06796, over 971929.07 frames.], batch size: 67, lr: 9.71e-04
+2022-06-18 15:51:53,634 INFO [train.py:874] (3/4) Epoch 8, batch 1750, aishell_loss[loss=0.2075, simple_loss=0.2874, pruned_loss=0.0638, over 4924.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2605, pruned_loss=0.0612, over 985502.85 frames.], batch size: 68, aishell_tot_loss[loss=0.1879, simple_loss=0.2636, pruned_loss=0.05616, over 973415.01 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.256, pruned_loss=0.0674, over 973198.60 frames.], batch size: 68, lr: 9.70e-04
+2022-06-18 15:52:24,180 INFO [train.py:874] (3/4) Epoch 8, batch 1800, datatang_loss[loss=0.1737, simple_loss=0.2339, pruned_loss=0.05675, over 4925.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2601, pruned_loss=0.06161, over 985212.60 frames.], batch size: 24, aishell_tot_loss[loss=0.1879, simple_loss=0.2636, pruned_loss=0.0561, over 974193.31 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.256, pruned_loss=0.06736, over 974966.21 frames.], batch size: 24, lr: 9.69e-04
+2022-06-18 15:52:53,952 INFO [train.py:874] (3/4) Epoch 8, batch 1850, datatang_loss[loss=0.204, simple_loss=0.2439, pruned_loss=0.082, over 4894.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2585, pruned_loss=0.0607, over 985476.19 frames.], batch size: 47, aishell_tot_loss[loss=0.187, simple_loss=0.2627, pruned_loss=0.05563, over 975721.37 frames.], datatang_tot_loss[loss=0.1945, simple_loss=0.2551, pruned_loss=0.06697, over 976246.89 frames.], batch size: 47, lr: 9.69e-04
+2022-06-18 15:53:23,157 INFO [train.py:874] (3/4) Epoch 8, batch 1900, aishell_loss[loss=0.1965, simple_loss=0.2719, pruned_loss=0.06056, over 4955.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2585, pruned_loss=0.06125, over 985257.35 frames.], batch size: 64, aishell_tot_loss[loss=0.1872, simple_loss=0.2628, pruned_loss=0.05578, over 976372.00 frames.], datatang_tot_loss[loss=0.1945, simple_loss=0.255, pruned_loss=0.06696, over 977573.50 frames.], batch size: 64, lr: 9.68e-04
+2022-06-18 15:53:54,960 INFO [train.py:874] (3/4) Epoch 8, batch 1950, aishell_loss[loss=0.1895, simple_loss=0.2651, pruned_loss=0.05694, over 4968.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2587, pruned_loss=0.06138, over 985529.99 frames.], batch size: 61, aishell_tot_loss[loss=0.1873, simple_loss=0.2629, pruned_loss=0.05583, over 977282.67 frames.], datatang_tot_loss[loss=0.1944, simple_loss=0.2553, pruned_loss=0.06681, over 978858.35 frames.], batch size: 61, lr: 9.67e-04
+2022-06-18 15:54:24,729 INFO [train.py:874] (3/4) Epoch 8, batch 2000, datatang_loss[loss=0.192, simple_loss=0.2588, pruned_loss=0.06265, over 4929.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2587, pruned_loss=0.06103, over 985324.69 frames.], batch size: 83, aishell_tot_loss[loss=0.1866, simple_loss=0.2624, pruned_loss=0.05542, over 977994.92 frames.], datatang_tot_loss[loss=0.1947, simple_loss=0.2557, pruned_loss=0.0669, over 979704.02 frames.], batch size: 83, lr: 9.66e-04
+2022-06-18 15:54:24,730 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 15:54:40,503 INFO [train.py:914] (3/4) Epoch 8, validation: loss=0.1711, simple_loss=0.254, pruned_loss=0.04413, over 1622729.00 frames.
+2022-06-18 15:55:10,373 INFO [train.py:874] (3/4) Epoch 8, batch 2050, aishell_loss[loss=0.1722, simple_loss=0.2496, pruned_loss=0.04737, over 4904.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2583, pruned_loss=0.06014, over 985103.50 frames.], batch size: 52, aishell_tot_loss[loss=0.1865, simple_loss=0.2624, pruned_loss=0.05527, over 978699.72 frames.], datatang_tot_loss[loss=0.1937, simple_loss=0.2551, pruned_loss=0.06619, over 980312.22 frames.], batch size: 52, lr: 9.66e-04
+2022-06-18 15:55:40,626 INFO [train.py:874] (3/4) Epoch 8, batch 2100, datatang_loss[loss=0.1994, simple_loss=0.2584, pruned_loss=0.0702, over 4914.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2578, pruned_loss=0.06046, over 985138.22 frames.], batch size: 81, aishell_tot_loss[loss=0.1872, simple_loss=0.2627, pruned_loss=0.05578, over 979308.42 frames.], datatang_tot_loss[loss=0.1928, simple_loss=0.2542, pruned_loss=0.06569, over 981032.14 frames.], batch size: 81, lr: 9.65e-04
+2022-06-18 15:56:10,922 INFO [train.py:874] (3/4) Epoch 8, batch 2150, datatang_loss[loss=0.1748, simple_loss=0.2413, pruned_loss=0.05411, over 4911.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2578, pruned_loss=0.0606, over 985073.92 frames.], batch size: 75, aishell_tot_loss[loss=0.1871, simple_loss=0.2628, pruned_loss=0.05573, over 979977.60 frames.], datatang_tot_loss[loss=0.1928, simple_loss=0.2541, pruned_loss=0.06573, over 981475.34 frames.], batch size: 75, lr: 9.64e-04
+2022-06-18 15:56:40,055 INFO [train.py:874] (3/4) Epoch 8, batch 2200, datatang_loss[loss=0.1831, simple_loss=0.2538, pruned_loss=0.05622, over 4878.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2579, pruned_loss=0.06032, over 985290.61 frames.], batch size: 39, aishell_tot_loss[loss=0.1878, simple_loss=0.2634, pruned_loss=0.05606, over 980576.27 frames.], datatang_tot_loss[loss=0.1918, simple_loss=0.2535, pruned_loss=0.06502, over 982121.41 frames.], batch size: 39, lr: 9.63e-04
+2022-06-18 15:57:10,723 INFO [train.py:874] (3/4) Epoch 8, batch 2250, aishell_loss[loss=0.2026, simple_loss=0.2749, pruned_loss=0.0651, over 4966.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2587, pruned_loss=0.06115, over 985592.03 frames.], batch size: 64, aishell_tot_loss[loss=0.1878, simple_loss=0.263, pruned_loss=0.05627, over 981453.81 frames.], datatang_tot_loss[loss=0.193, simple_loss=0.2545, pruned_loss=0.06576, over 982500.32 frames.], batch size: 64, lr: 9.63e-04
+2022-06-18 15:57:40,714 INFO [train.py:874] (3/4) Epoch 8, batch 2300, datatang_loss[loss=0.1859, simple_loss=0.2388, pruned_loss=0.06648, over 4931.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2588, pruned_loss=0.0608, over 985204.35 frames.], batch size: 42, aishell_tot_loss[loss=0.1874, simple_loss=0.2629, pruned_loss=0.05598, over 981627.35 frames.], datatang_tot_loss[loss=0.1931, simple_loss=0.2545, pruned_loss=0.06586, over 982780.24 frames.], batch size: 42, lr: 9.62e-04
+2022-06-18 15:58:10,192 INFO [train.py:874] (3/4) Epoch 8, batch 2350, aishell_loss[loss=0.1906, simple_loss=0.266, pruned_loss=0.0576, over 4939.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2589, pruned_loss=0.06029, over 985378.61 frames.], batch size: 56, aishell_tot_loss[loss=0.1874, simple_loss=0.2629, pruned_loss=0.05594, over 982281.40 frames.], datatang_tot_loss[loss=0.1929, simple_loss=0.2543, pruned_loss=0.06571, over 983055.31 frames.], batch size: 56, lr: 9.61e-04
+2022-06-18 15:58:39,609 INFO [train.py:874] (3/4) Epoch 8, batch 2400, datatang_loss[loss=0.2352, simple_loss=0.2739, pruned_loss=0.09828, over 4957.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2589, pruned_loss=0.06027, over 985496.23 frames.], batch size: 60, aishell_tot_loss[loss=0.1871, simple_loss=0.2628, pruned_loss=0.05572, over 982839.01 frames.], datatang_tot_loss[loss=0.1931, simple_loss=0.2543, pruned_loss=0.06595, over 983275.89 frames.], batch size: 60, lr: 9.60e-04
+2022-06-18 15:59:09,485 INFO [train.py:874] (3/4) Epoch 8, batch 2450, aishell_loss[loss=0.184, simple_loss=0.2675, pruned_loss=0.0502, over 4903.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2592, pruned_loss=0.06052, over 985544.15 frames.], batch size: 68, aishell_tot_loss[loss=0.1871, simple_loss=0.2626, pruned_loss=0.05577, over 983170.53 frames.], datatang_tot_loss[loss=0.1937, simple_loss=0.2546, pruned_loss=0.06637, over 983606.56 frames.], batch size: 68, lr: 9.60e-04
+2022-06-18 15:59:39,502 INFO [train.py:874] (3/4) Epoch 8, batch 2500, aishell_loss[loss=0.192, simple_loss=0.2708, pruned_loss=0.05655, over 4892.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2583, pruned_loss=0.06023, over 985691.36 frames.], batch size: 42, aishell_tot_loss[loss=0.1859, simple_loss=0.2615, pruned_loss=0.05516, over 983043.71 frames.], datatang_tot_loss[loss=0.1939, simple_loss=0.2549, pruned_loss=0.06648, over 984385.69 frames.], batch size: 42, lr: 9.59e-04
+2022-06-18 16:00:09,590 INFO [train.py:874] (3/4) Epoch 8, batch 2550, aishell_loss[loss=0.1803, simple_loss=0.2577, pruned_loss=0.05143, over 4911.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2582, pruned_loss=0.06075, over 986045.93 frames.], batch size: 52, aishell_tot_loss[loss=0.1858, simple_loss=0.2614, pruned_loss=0.0551, over 983489.95 frames.], datatang_tot_loss[loss=0.1941, simple_loss=0.2552, pruned_loss=0.06647, over 984731.09 frames.], batch size: 52, lr: 9.58e-04
+2022-06-18 16:00:41,172 INFO [train.py:874] (3/4) Epoch 8, batch 2600, datatang_loss[loss=0.1705, simple_loss=0.2388, pruned_loss=0.05106, over 4916.00 frames.], tot_loss[loss=0.1885, simple_loss=0.257, pruned_loss=0.06005, over 985983.65 frames.], batch size: 75, aishell_tot_loss[loss=0.1859, simple_loss=0.2614, pruned_loss=0.05516, over 983535.39 frames.], datatang_tot_loss[loss=0.1925, simple_loss=0.254, pruned_loss=0.0655, over 985052.86 frames.], batch size: 75, lr: 9.57e-04
+2022-06-18 16:01:09,776 INFO [train.py:874] (3/4) Epoch 8, batch 2650, aishell_loss[loss=0.1684, simple_loss=0.2475, pruned_loss=0.04465, over 4985.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2574, pruned_loss=0.06076, over 985756.70 frames.], batch size: 30, aishell_tot_loss[loss=0.1862, simple_loss=0.2615, pruned_loss=0.05547, over 983815.14 frames.], datatang_tot_loss[loss=0.1929, simple_loss=0.2542, pruned_loss=0.06584, over 984935.74 frames.], batch size: 30, lr: 9.57e-04
+2022-06-18 16:01:39,863 INFO [train.py:874] (3/4) Epoch 8, batch 2700, datatang_loss[loss=0.1862, simple_loss=0.2553, pruned_loss=0.05856, over 4975.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2575, pruned_loss=0.06093, over 986075.66 frames.], batch size: 60, aishell_tot_loss[loss=0.1858, simple_loss=0.2611, pruned_loss=0.05521, over 984145.57 frames.], datatang_tot_loss[loss=0.1937, simple_loss=0.2545, pruned_loss=0.06645, over 985261.37 frames.], batch size: 60, lr: 9.56e-04
+2022-06-18 16:02:09,380 INFO [train.py:874] (3/4) Epoch 8, batch 2750, datatang_loss[loss=0.1999, simple_loss=0.2621, pruned_loss=0.06885, over 4915.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2583, pruned_loss=0.06121, over 985937.24 frames.], batch size: 81, aishell_tot_loss[loss=0.186, simple_loss=0.2613, pruned_loss=0.05535, over 984312.40 frames.], datatang_tot_loss[loss=0.1942, simple_loss=0.255, pruned_loss=0.06674, over 985277.91 frames.], batch size: 81, lr: 9.55e-04
+2022-06-18 16:02:39,822 INFO [train.py:874] (3/4) Epoch 8, batch 2800, datatang_loss[loss=0.1749, simple_loss=0.2397, pruned_loss=0.05506, over 4958.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2579, pruned_loss=0.06085, over 985608.26 frames.], batch size: 37, aishell_tot_loss[loss=0.1858, simple_loss=0.2612, pruned_loss=0.05521, over 984038.66 frames.], datatang_tot_loss[loss=0.1939, simple_loss=0.2546, pruned_loss=0.06655, over 985486.96 frames.], batch size: 37, lr: 9.54e-04
+2022-06-18 16:03:10,534 INFO [train.py:874] (3/4) Epoch 8, batch 2850, aishell_loss[loss=0.1808, simple_loss=0.2527, pruned_loss=0.0544, over 4937.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2568, pruned_loss=0.05976, over 985854.32 frames.], batch size: 32, aishell_tot_loss[loss=0.1853, simple_loss=0.2609, pruned_loss=0.05485, over 984454.47 frames.], datatang_tot_loss[loss=0.1927, simple_loss=0.2538, pruned_loss=0.06582, over 985545.41 frames.], batch size: 32, lr: 9.54e-04
+2022-06-18 16:03:39,791 INFO [train.py:874] (3/4) Epoch 8, batch 2900, aishell_loss[loss=0.149, simple_loss=0.2179, pruned_loss=0.04005, over 4952.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2564, pruned_loss=0.05947, over 985741.34 frames.], batch size: 25, aishell_tot_loss[loss=0.1851, simple_loss=0.2609, pruned_loss=0.05468, over 984614.82 frames.], datatang_tot_loss[loss=0.192, simple_loss=0.2533, pruned_loss=0.06537, over 985453.57 frames.], batch size: 25, lr: 9.53e-04
+2022-06-18 16:04:10,475 INFO [train.py:874] (3/4) Epoch 8, batch 2950, aishell_loss[loss=0.1677, simple_loss=0.2516, pruned_loss=0.04187, over 4867.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2565, pruned_loss=0.05934, over 985243.60 frames.], batch size: 28, aishell_tot_loss[loss=0.1853, simple_loss=0.2609, pruned_loss=0.05483, over 984336.30 frames.], datatang_tot_loss[loss=0.1917, simple_loss=0.2533, pruned_loss=0.06508, over 985379.40 frames.], batch size: 28, lr: 9.52e-04
+2022-06-18 16:04:40,931 INFO [train.py:874] (3/4) Epoch 8, batch 3000, aishell_loss[loss=0.1543, simple_loss=0.2313, pruned_loss=0.03864, over 4827.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2559, pruned_loss=0.05918, over 984902.80 frames.], batch size: 29, aishell_tot_loss[loss=0.1844, simple_loss=0.26, pruned_loss=0.0544, over 984027.09 frames.], datatang_tot_loss[loss=0.1918, simple_loss=0.2534, pruned_loss=0.06514, over 985411.69 frames.], batch size: 29, lr: 9.52e-04
+2022-06-18 16:04:40,932 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 16:04:57,748 INFO [train.py:914] (3/4) Epoch 8, validation: loss=0.1712, simple_loss=0.2536, pruned_loss=0.04441, over 1622729.00 frames.
+2022-06-18 16:05:32,098 INFO [train.py:874] (3/4) Epoch 8, batch 3050, datatang_loss[loss=0.1667, simple_loss=0.2312, pruned_loss=0.05114, over 4918.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2567, pruned_loss=0.05919, over 985194.14 frames.], batch size: 71, aishell_tot_loss[loss=0.1848, simple_loss=0.2606, pruned_loss=0.05452, over 984399.57 frames.], datatang_tot_loss[loss=0.1915, simple_loss=0.2535, pruned_loss=0.0648, over 985379.91 frames.], batch size: 71, lr: 9.51e-04
+2022-06-18 16:06:03,029 INFO [train.py:874] (3/4) Epoch 8, batch 3100, aishell_loss[loss=0.2104, simple_loss=0.2835, pruned_loss=0.06859, over 4978.00 frames.], tot_loss[loss=0.1882, simple_loss=0.257, pruned_loss=0.05967, over 985703.90 frames.], batch size: 51, aishell_tot_loss[loss=0.1844, simple_loss=0.2603, pruned_loss=0.05427, over 984674.18 frames.], datatang_tot_loss[loss=0.1923, simple_loss=0.2541, pruned_loss=0.06523, over 985684.60 frames.], batch size: 51, lr: 9.50e-04
+2022-06-18 16:06:31,676 INFO [train.py:874] (3/4) Epoch 8, batch 3150, datatang_loss[loss=0.1696, simple_loss=0.2367, pruned_loss=0.05125, over 4932.00 frames.], tot_loss[loss=0.19, simple_loss=0.2586, pruned_loss=0.06067, over 985423.74 frames.], batch size: 79, aishell_tot_loss[loss=0.1849, simple_loss=0.2609, pruned_loss=0.05447, over 984231.51 frames.], datatang_tot_loss[loss=0.1937, simple_loss=0.2552, pruned_loss=0.06613, over 985972.53 frames.], batch size: 79, lr: 9.49e-04
+2022-06-18 16:07:02,077 INFO [train.py:874] (3/4) Epoch 8, batch 3200, datatang_loss[loss=0.1645, simple_loss=0.2262, pruned_loss=0.05136, over 4928.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2568, pruned_loss=0.06028, over 985394.18 frames.], batch size: 73, aishell_tot_loss[loss=0.1844, simple_loss=0.2603, pruned_loss=0.05428, over 984314.30 frames.], datatang_tot_loss[loss=0.1928, simple_loss=0.254, pruned_loss=0.06577, over 985888.63 frames.], batch size: 73, lr: 9.49e-04
+2022-06-18 16:07:32,573 INFO [train.py:874] (3/4) Epoch 8, batch 3250, datatang_loss[loss=0.2019, simple_loss=0.2586, pruned_loss=0.07255, over 4888.00 frames.], tot_loss[loss=0.1884, simple_loss=0.257, pruned_loss=0.05988, over 984968.57 frames.], batch size: 47, aishell_tot_loss[loss=0.1842, simple_loss=0.2603, pruned_loss=0.05401, over 984020.08 frames.], datatang_tot_loss[loss=0.1929, simple_loss=0.2542, pruned_loss=0.0658, over 985823.19 frames.], batch size: 47, lr: 9.48e-04
+2022-06-18 16:08:02,046 INFO [train.py:874] (3/4) Epoch 8, batch 3300, datatang_loss[loss=0.1999, simple_loss=0.2612, pruned_loss=0.06934, over 4943.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2568, pruned_loss=0.05927, over 985136.51 frames.], batch size: 50, aishell_tot_loss[loss=0.1841, simple_loss=0.2604, pruned_loss=0.05389, over 984238.15 frames.], datatang_tot_loss[loss=0.192, simple_loss=0.2537, pruned_loss=0.0652, over 985768.46 frames.], batch size: 50, lr: 9.47e-04
+2022-06-18 16:08:32,933 INFO [train.py:874] (3/4) Epoch 8, batch 3350, aishell_loss[loss=0.1532, simple_loss=0.2343, pruned_loss=0.03609, over 4974.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2572, pruned_loss=0.05989, over 985495.86 frames.], batch size: 30, aishell_tot_loss[loss=0.1844, simple_loss=0.2605, pruned_loss=0.05414, over 984499.02 frames.], datatang_tot_loss[loss=0.1924, simple_loss=0.2542, pruned_loss=0.06528, over 985887.38 frames.], batch size: 30, lr: 9.46e-04
+2022-06-18 16:09:03,009 INFO [train.py:874] (3/4) Epoch 8, batch 3400, aishell_loss[loss=0.1859, simple_loss=0.2595, pruned_loss=0.05612, over 4945.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2569, pruned_loss=0.05995, over 985403.57 frames.], batch size: 31, aishell_tot_loss[loss=0.1845, simple_loss=0.2605, pruned_loss=0.05427, over 984338.69 frames.], datatang_tot_loss[loss=0.1921, simple_loss=0.2538, pruned_loss=0.06515, over 986013.06 frames.], batch size: 31, lr: 9.46e-04
+2022-06-18 16:09:31,677 INFO [train.py:874] (3/4) Epoch 8, batch 3450, datatang_loss[loss=0.198, simple_loss=0.2644, pruned_loss=0.06578, over 4942.00 frames.], tot_loss[loss=0.1881, simple_loss=0.257, pruned_loss=0.05956, over 985235.39 frames.], batch size: 88, aishell_tot_loss[loss=0.1853, simple_loss=0.2612, pruned_loss=0.05468, over 984529.63 frames.], datatang_tot_loss[loss=0.1911, simple_loss=0.2531, pruned_loss=0.06452, over 985737.45 frames.], batch size: 88, lr: 9.45e-04
+2022-06-18 16:10:01,663 INFO [train.py:874] (3/4) Epoch 8, batch 3500, datatang_loss[loss=0.1539, simple_loss=0.2288, pruned_loss=0.03947, over 4953.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2577, pruned_loss=0.06, over 985222.57 frames.], batch size: 67, aishell_tot_loss[loss=0.1857, simple_loss=0.2615, pruned_loss=0.05493, over 984612.27 frames.], datatang_tot_loss[loss=0.1916, simple_loss=0.2534, pruned_loss=0.06487, over 985677.79 frames.], batch size: 67, lr: 9.44e-04
+2022-06-18 16:10:31,560 INFO [train.py:874] (3/4) Epoch 8, batch 3550, datatang_loss[loss=0.1945, simple_loss=0.2474, pruned_loss=0.07083, over 4843.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2566, pruned_loss=0.05903, over 985174.44 frames.], batch size: 25, aishell_tot_loss[loss=0.1853, simple_loss=0.2612, pruned_loss=0.05463, over 984770.75 frames.], datatang_tot_loss[loss=0.1904, simple_loss=0.2526, pruned_loss=0.06412, over 985482.29 frames.], batch size: 25, lr: 9.44e-04
+2022-06-18 16:11:02,161 INFO [train.py:874] (3/4) Epoch 8, batch 3600, datatang_loss[loss=0.2352, simple_loss=0.281, pruned_loss=0.09471, over 4908.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2571, pruned_loss=0.0591, over 985550.66 frames.], batch size: 52, aishell_tot_loss[loss=0.185, simple_loss=0.2615, pruned_loss=0.05429, over 984863.29 frames.], datatang_tot_loss[loss=0.1907, simple_loss=0.253, pruned_loss=0.06421, over 985802.85 frames.], batch size: 52, lr: 9.43e-04
+2022-06-18 16:11:32,863 INFO [train.py:874] (3/4) Epoch 8, batch 3650, datatang_loss[loss=0.1972, simple_loss=0.2628, pruned_loss=0.06579, over 4965.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2563, pruned_loss=0.05841, over 985807.78 frames.], batch size: 45, aishell_tot_loss[loss=0.1848, simple_loss=0.2612, pruned_loss=0.05419, over 984961.81 frames.], datatang_tot_loss[loss=0.1898, simple_loss=0.2524, pruned_loss=0.06353, over 986027.07 frames.], batch size: 45, lr: 9.42e-04
+2022-06-18 16:12:03,750 INFO [train.py:874] (3/4) Epoch 8, batch 3700, aishell_loss[loss=0.1728, simple_loss=0.2562, pruned_loss=0.04468, over 4878.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2551, pruned_loss=0.05801, over 985583.80 frames.], batch size: 35, aishell_tot_loss[loss=0.1844, simple_loss=0.2607, pruned_loss=0.05405, over 984755.34 frames.], datatang_tot_loss[loss=0.1888, simple_loss=0.2517, pruned_loss=0.0629, over 986037.42 frames.], batch size: 35, lr: 9.42e-04
+2022-06-18 16:12:32,620 INFO [train.py:874] (3/4) Epoch 8, batch 3750, datatang_loss[loss=0.1754, simple_loss=0.2422, pruned_loss=0.05432, over 4916.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2553, pruned_loss=0.05793, over 985748.94 frames.], batch size: 77, aishell_tot_loss[loss=0.1838, simple_loss=0.2602, pruned_loss=0.05371, over 984912.11 frames.], datatang_tot_loss[loss=0.1892, simple_loss=0.2521, pruned_loss=0.06317, over 986112.96 frames.], batch size: 77, lr: 9.41e-04
+2022-06-18 16:13:02,590 INFO [train.py:874] (3/4) Epoch 8, batch 3800, datatang_loss[loss=0.1922, simple_loss=0.2543, pruned_loss=0.06509, over 4943.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2558, pruned_loss=0.05819, over 985467.85 frames.], batch size: 69, aishell_tot_loss[loss=0.184, simple_loss=0.2603, pruned_loss=0.05379, over 984702.48 frames.], datatang_tot_loss[loss=0.1894, simple_loss=0.2523, pruned_loss=0.06322, over 986057.66 frames.], batch size: 69, lr: 9.40e-04
+2022-06-18 16:13:31,941 INFO [train.py:874] (3/4) Epoch 8, batch 3850, aishell_loss[loss=0.1948, simple_loss=0.264, pruned_loss=0.06283, over 4956.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2561, pruned_loss=0.05808, over 985269.37 frames.], batch size: 64, aishell_tot_loss[loss=0.1846, simple_loss=0.2609, pruned_loss=0.05418, over 984643.09 frames.], datatang_tot_loss[loss=0.1885, simple_loss=0.2518, pruned_loss=0.0626, over 985908.84 frames.], batch size: 64, lr: 9.39e-04
+2022-06-18 16:14:00,730 INFO [train.py:874] (3/4) Epoch 8, batch 3900, aishell_loss[loss=0.2018, simple_loss=0.2749, pruned_loss=0.06437, over 4862.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2554, pruned_loss=0.05722, over 985075.11 frames.], batch size: 36, aishell_tot_loss[loss=0.1841, simple_loss=0.2606, pruned_loss=0.05383, over 984709.70 frames.], datatang_tot_loss[loss=0.1877, simple_loss=0.2512, pruned_loss=0.06213, over 985655.72 frames.], batch size: 36, lr: 9.39e-04
+2022-06-18 16:14:30,251 INFO [train.py:874] (3/4) Epoch 8, batch 3950, datatang_loss[loss=0.1931, simple_loss=0.2506, pruned_loss=0.06777, over 4962.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2555, pruned_loss=0.05777, over 984992.53 frames.], batch size: 67, aishell_tot_loss[loss=0.185, simple_loss=0.2615, pruned_loss=0.05426, over 984754.19 frames.], datatang_tot_loss[loss=0.1872, simple_loss=0.2506, pruned_loss=0.0619, over 985466.97 frames.], batch size: 67, lr: 9.38e-04
+2022-06-18 16:14:59,736 INFO [train.py:874] (3/4) Epoch 8, batch 4000, datatang_loss[loss=0.1897, simple_loss=0.2491, pruned_loss=0.0652, over 4889.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2547, pruned_loss=0.05737, over 985282.31 frames.], batch size: 52, aishell_tot_loss[loss=0.1846, simple_loss=0.2611, pruned_loss=0.05401, over 984755.05 frames.], datatang_tot_loss[loss=0.1867, simple_loss=0.2501, pruned_loss=0.0616, over 985726.79 frames.], batch size: 52, lr: 9.37e-04
+2022-06-18 16:14:59,737 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 16:15:16,494 INFO [train.py:914] (3/4) Epoch 8, validation: loss=0.1678, simple_loss=0.2518, pruned_loss=0.0419, over 1622729.00 frames.
+2022-06-18 16:15:46,569 INFO [train.py:874] (3/4) Epoch 8, batch 4050, datatang_loss[loss=0.1918, simple_loss=0.255, pruned_loss=0.06429, over 4892.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2561, pruned_loss=0.05869, over 985372.19 frames.], batch size: 47, aishell_tot_loss[loss=0.1857, simple_loss=0.2619, pruned_loss=0.05475, over 984789.67 frames.], datatang_tot_loss[loss=0.1874, simple_loss=0.2506, pruned_loss=0.06211, over 985796.64 frames.], batch size: 47, lr: 9.37e-04
+2022-06-18 16:16:15,617 INFO [train.py:874] (3/4) Epoch 8, batch 4100, datatang_loss[loss=0.1976, simple_loss=0.269, pruned_loss=0.06311, over 4934.00 frames.], tot_loss[loss=0.186, simple_loss=0.2553, pruned_loss=0.0583, over 985313.04 frames.], batch size: 94, aishell_tot_loss[loss=0.1851, simple_loss=0.2614, pruned_loss=0.05437, over 984881.19 frames.], datatang_tot_loss[loss=0.1873, simple_loss=0.2505, pruned_loss=0.06199, over 985640.53 frames.], batch size: 94, lr: 9.36e-04
+2022-06-18 16:16:43,420 INFO [train.py:874] (3/4) Epoch 8, batch 4150, aishell_loss[loss=0.1786, simple_loss=0.265, pruned_loss=0.04615, over 4922.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2559, pruned_loss=0.0586, over 984776.03 frames.], batch size: 41, aishell_tot_loss[loss=0.1857, simple_loss=0.262, pruned_loss=0.05475, over 984356.11 frames.], datatang_tot_loss[loss=0.1872, simple_loss=0.2504, pruned_loss=0.06196, over 985594.38 frames.], batch size: 41, lr: 9.35e-04
+2022-06-18 16:18:01,461 INFO [train.py:874] (3/4) Epoch 9, batch 50, datatang_loss[loss=0.1765, simple_loss=0.245, pruned_loss=0.05396, over 4957.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2548, pruned_loss=0.05497, over 218226.45 frames.], batch size: 91, aishell_tot_loss[loss=0.1849, simple_loss=0.2636, pruned_loss=0.05306, over 132990.56 frames.], datatang_tot_loss[loss=0.1794, simple_loss=0.2433, pruned_loss=0.0577, over 98546.16 frames.], batch size: 91, lr: 8.97e-04
+2022-06-18 16:18:32,063 INFO [train.py:874] (3/4) Epoch 9, batch 100, datatang_loss[loss=0.1435, simple_loss=0.211, pruned_loss=0.03805, over 4972.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2497, pruned_loss=0.05398, over 388212.16 frames.], batch size: 45, aishell_tot_loss[loss=0.1842, simple_loss=0.2619, pruned_loss=0.05326, over 218026.99 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2389, pruned_loss=0.05492, over 218558.54 frames.], batch size: 45, lr: 8.96e-04
+2022-06-18 16:19:01,264 INFO [train.py:874] (3/4) Epoch 9, batch 150, datatang_loss[loss=0.1708, simple_loss=0.2434, pruned_loss=0.04916, over 4935.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2509, pruned_loss=0.05472, over 520578.32 frames.], batch size: 57, aishell_tot_loss[loss=0.1852, simple_loss=0.2635, pruned_loss=0.05345, over 294450.88 frames.], datatang_tot_loss[loss=0.1758, simple_loss=0.2402, pruned_loss=0.05576, over 322526.46 frames.], batch size: 57, lr: 8.96e-04
+2022-06-18 16:19:31,594 INFO [train.py:874] (3/4) Epoch 9, batch 200, datatang_loss[loss=0.2288, simple_loss=0.2869, pruned_loss=0.08538, over 4935.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2519, pruned_loss=0.05423, over 623637.84 frames.], batch size: 111, aishell_tot_loss[loss=0.1837, simple_loss=0.2624, pruned_loss=0.05253, over 396724.94 frames.], datatang_tot_loss[loss=0.1762, simple_loss=0.2403, pruned_loss=0.05604, over 379861.09 frames.], batch size: 111, lr: 8.95e-04
+2022-06-18 16:20:02,142 INFO [train.py:874] (3/4) Epoch 9, batch 250, datatang_loss[loss=0.1494, simple_loss=0.2138, pruned_loss=0.04246, over 4914.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2507, pruned_loss=0.05387, over 703568.78 frames.], batch size: 75, aishell_tot_loss[loss=0.1826, simple_loss=0.2605, pruned_loss=0.05229, over 465651.42 frames.], datatang_tot_loss[loss=0.176, simple_loss=0.2406, pruned_loss=0.05567, over 451303.17 frames.], batch size: 75, lr: 8.94e-04
+2022-06-18 16:20:30,905 INFO [train.py:874] (3/4) Epoch 9, batch 300, datatang_loss[loss=0.156, simple_loss=0.2263, pruned_loss=0.04288, over 4915.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2516, pruned_loss=0.0543, over 766192.78 frames.], batch size: 81, aishell_tot_loss[loss=0.1834, simple_loss=0.2613, pruned_loss=0.05274, over 531376.87 frames.], datatang_tot_loss[loss=0.1764, simple_loss=0.241, pruned_loss=0.05593, over 509681.10 frames.], batch size: 81, lr: 8.94e-04
+2022-06-18 16:21:02,301 INFO [train.py:874] (3/4) Epoch 9, batch 350, aishell_loss[loss=0.1696, simple_loss=0.2487, pruned_loss=0.04525, over 4860.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2525, pruned_loss=0.05489, over 814846.06 frames.], batch size: 28, aishell_tot_loss[loss=0.1836, simple_loss=0.2612, pruned_loss=0.05298, over 588830.32 frames.], datatang_tot_loss[loss=0.1778, simple_loss=0.2423, pruned_loss=0.05664, over 561563.87 frames.], batch size: 28, lr: 8.93e-04
+2022-06-18 16:21:31,298 INFO [train.py:874] (3/4) Epoch 9, batch 400, datatang_loss[loss=0.266, simple_loss=0.3229, pruned_loss=0.1045, over 4945.00 frames.], tot_loss[loss=0.182, simple_loss=0.2541, pruned_loss=0.05495, over 852407.92 frames.], batch size: 37, aishell_tot_loss[loss=0.1839, simple_loss=0.2618, pruned_loss=0.05299, over 648982.42 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2431, pruned_loss=0.05694, over 596239.77 frames.], batch size: 37, lr: 8.92e-04
+2022-06-18 16:22:01,552 INFO [train.py:874] (3/4) Epoch 9, batch 450, datatang_loss[loss=0.1693, simple_loss=0.2455, pruned_loss=0.04656, over 4963.00 frames.], tot_loss[loss=0.183, simple_loss=0.2549, pruned_loss=0.05553, over 881794.00 frames.], batch size: 86, aishell_tot_loss[loss=0.1845, simple_loss=0.2623, pruned_loss=0.05338, over 685487.47 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2447, pruned_loss=0.05731, over 645521.46 frames.], batch size: 86, lr: 8.92e-04
+2022-06-18 16:22:32,073 INFO [train.py:874] (3/4) Epoch 9, batch 500, datatang_loss[loss=0.1661, simple_loss=0.2281, pruned_loss=0.05201, over 4937.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2531, pruned_loss=0.05537, over 904592.27 frames.], batch size: 37, aishell_tot_loss[loss=0.1835, simple_loss=0.2613, pruned_loss=0.05288, over 712530.95 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2444, pruned_loss=0.05745, over 694449.99 frames.], batch size: 37, lr: 8.91e-04
+2022-06-18 16:23:02,725 INFO [train.py:874] (3/4) Epoch 9, batch 550, datatang_loss[loss=0.1453, simple_loss=0.2165, pruned_loss=0.03703, over 4924.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2541, pruned_loss=0.0563, over 922099.76 frames.], batch size: 71, aishell_tot_loss[loss=0.1829, simple_loss=0.2609, pruned_loss=0.05246, over 738056.26 frames.], datatang_tot_loss[loss=0.1824, simple_loss=0.2467, pruned_loss=0.05906, over 735151.64 frames.], batch size: 71, lr: 8.90e-04
+2022-06-18 16:23:32,353 INFO [train.py:874] (3/4) Epoch 9, batch 600, aishell_loss[loss=0.1846, simple_loss=0.2738, pruned_loss=0.0477, over 4939.00 frames.], tot_loss[loss=0.1833, simple_loss=0.254, pruned_loss=0.05627, over 936016.11 frames.], batch size: 45, aishell_tot_loss[loss=0.1826, simple_loss=0.2604, pruned_loss=0.05238, over 769220.64 frames.], datatang_tot_loss[loss=0.1828, simple_loss=0.247, pruned_loss=0.05931, over 762423.78 frames.], batch size: 45, lr: 8.90e-04
+2022-06-18 16:24:02,282 INFO [train.py:874] (3/4) Epoch 9, batch 650, datatang_loss[loss=0.1711, simple_loss=0.2454, pruned_loss=0.0484, over 4898.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2539, pruned_loss=0.05631, over 946765.50 frames.], batch size: 52, aishell_tot_loss[loss=0.1826, simple_loss=0.2602, pruned_loss=0.05252, over 795276.86 frames.], datatang_tot_loss[loss=0.183, simple_loss=0.2471, pruned_loss=0.0594, over 787863.63 frames.], batch size: 52, lr: 8.89e-04
+2022-06-18 16:24:31,751 INFO [train.py:874] (3/4) Epoch 9, batch 700, aishell_loss[loss=0.1809, simple_loss=0.2558, pruned_loss=0.05299, over 4973.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2544, pruned_loss=0.05585, over 955384.43 frames.], batch size: 51, aishell_tot_loss[loss=0.1825, simple_loss=0.2602, pruned_loss=0.0524, over 820179.44 frames.], datatang_tot_loss[loss=0.183, simple_loss=0.2476, pruned_loss=0.05917, over 808536.07 frames.], batch size: 51, lr: 8.88e-04
+2022-06-18 16:25:02,578 INFO [train.py:874] (3/4) Epoch 9, batch 750, aishell_loss[loss=0.1797, simple_loss=0.2645, pruned_loss=0.04743, over 4911.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2541, pruned_loss=0.05544, over 961980.61 frames.], batch size: 41, aishell_tot_loss[loss=0.1817, simple_loss=0.2593, pruned_loss=0.05211, over 843057.55 frames.], datatang_tot_loss[loss=0.1832, simple_loss=0.248, pruned_loss=0.05923, over 825541.90 frames.], batch size: 41, lr: 8.88e-04
+2022-06-18 16:25:33,567 INFO [train.py:874] (3/4) Epoch 9, batch 800, aishell_loss[loss=0.1714, simple_loss=0.2515, pruned_loss=0.04561, over 4975.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2538, pruned_loss=0.05484, over 966980.52 frames.], batch size: 51, aishell_tot_loss[loss=0.1813, simple_loss=0.2594, pruned_loss=0.05158, over 859790.91 frames.], datatang_tot_loss[loss=0.1827, simple_loss=0.2476, pruned_loss=0.05897, over 844184.31 frames.], batch size: 51, lr: 8.87e-04
+2022-06-18 16:26:02,688 INFO [train.py:874] (3/4) Epoch 9, batch 850, aishell_loss[loss=0.1709, simple_loss=0.2469, pruned_loss=0.04744, over 4958.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2544, pruned_loss=0.05562, over 971079.32 frames.], batch size: 30, aishell_tot_loss[loss=0.1814, simple_loss=0.2595, pruned_loss=0.05164, over 873395.24 frames.], datatang_tot_loss[loss=0.1838, simple_loss=0.2484, pruned_loss=0.05961, over 862148.80 frames.], batch size: 30, lr: 8.87e-04
+2022-06-18 16:26:33,114 INFO [train.py:874] (3/4) Epoch 9, batch 900, datatang_loss[loss=0.1918, simple_loss=0.2611, pruned_loss=0.06126, over 4917.00 frames.], tot_loss[loss=0.1832, simple_loss=0.255, pruned_loss=0.05569, over 973851.90 frames.], batch size: 75, aishell_tot_loss[loss=0.1822, simple_loss=0.2598, pruned_loss=0.05229, over 891481.64 frames.], datatang_tot_loss[loss=0.1836, simple_loss=0.2483, pruned_loss=0.05949, over 870484.25 frames.], batch size: 75, lr: 8.86e-04
+2022-06-18 16:27:02,546 INFO [train.py:874] (3/4) Epoch 9, batch 950, aishell_loss[loss=0.1605, simple_loss=0.2368, pruned_loss=0.04204, over 4878.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2551, pruned_loss=0.05619, over 975865.77 frames.], batch size: 28, aishell_tot_loss[loss=0.1818, simple_loss=0.2593, pruned_loss=0.05211, over 904587.53 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.2487, pruned_loss=0.06041, over 880755.46 frames.], batch size: 28, lr: 8.85e-04
+2022-06-18 16:27:32,439 INFO [train.py:874] (3/4) Epoch 9, batch 1000, datatang_loss[loss=0.1728, simple_loss=0.2415, pruned_loss=0.052, over 4958.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2552, pruned_loss=0.0563, over 977922.98 frames.], batch size: 86, aishell_tot_loss[loss=0.182, simple_loss=0.2594, pruned_loss=0.05228, over 914775.78 frames.], datatang_tot_loss[loss=0.1849, simple_loss=0.2489, pruned_loss=0.06047, over 891993.29 frames.], batch size: 86, lr: 8.85e-04
+2022-06-18 16:27:32,440 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 16:27:48,782 INFO [train.py:914] (3/4) Epoch 9, validation: loss=0.1688, simple_loss=0.2514, pruned_loss=0.04309, over 1622729.00 frames.
+2022-06-18 16:28:18,792 INFO [train.py:874] (3/4) Epoch 9, batch 1050, aishell_loss[loss=0.1517, simple_loss=0.2338, pruned_loss=0.03478, over 4860.00 frames.], tot_loss[loss=0.1825, simple_loss=0.254, pruned_loss=0.05546, over 979867.64 frames.], batch size: 28, aishell_tot_loss[loss=0.1814, simple_loss=0.2588, pruned_loss=0.052, over 923458.66 frames.], datatang_tot_loss[loss=0.184, simple_loss=0.2484, pruned_loss=0.05983, over 902844.06 frames.], batch size: 28, lr: 8.84e-04
+2022-06-18 16:28:49,632 INFO [train.py:874] (3/4) Epoch 9, batch 1100, datatang_loss[loss=0.2715, simple_loss=0.3198, pruned_loss=0.1116, over 4916.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2542, pruned_loss=0.05578, over 981016.99 frames.], batch size: 108, aishell_tot_loss[loss=0.1809, simple_loss=0.2582, pruned_loss=0.05178, over 930803.38 frames.], datatang_tot_loss[loss=0.185, simple_loss=0.2493, pruned_loss=0.06032, over 912332.82 frames.], batch size: 108, lr: 8.83e-04
+2022-06-18 16:29:19,153 INFO [train.py:874] (3/4) Epoch 9, batch 1150, datatang_loss[loss=0.2253, simple_loss=0.2878, pruned_loss=0.08142, over 4933.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2532, pruned_loss=0.05546, over 981588.24 frames.], batch size: 109, aishell_tot_loss[loss=0.1798, simple_loss=0.257, pruned_loss=0.05131, over 936850.71 frames.], datatang_tot_loss[loss=0.1851, simple_loss=0.2494, pruned_loss=0.06038, over 920889.84 frames.], batch size: 109, lr: 8.83e-04
+2022-06-18 16:29:49,994 INFO [train.py:874] (3/4) Epoch 9, batch 1200, aishell_loss[loss=0.1473, simple_loss=0.2155, pruned_loss=0.03956, over 4925.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2538, pruned_loss=0.05574, over 982470.69 frames.], batch size: 25, aishell_tot_loss[loss=0.1796, simple_loss=0.257, pruned_loss=0.05112, over 943372.65 frames.], datatang_tot_loss[loss=0.186, simple_loss=0.25, pruned_loss=0.06099, over 927433.17 frames.], batch size: 25, lr: 8.82e-04
+2022-06-18 16:30:20,550 INFO [train.py:874] (3/4) Epoch 9, batch 1250, datatang_loss[loss=0.2164, simple_loss=0.2679, pruned_loss=0.08243, over 4961.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2542, pruned_loss=0.05519, over 983245.65 frames.], batch size: 67, aishell_tot_loss[loss=0.1791, simple_loss=0.2569, pruned_loss=0.05064, over 949893.97 frames.], datatang_tot_loss[loss=0.1863, simple_loss=0.2502, pruned_loss=0.06114, over 932188.54 frames.], batch size: 67, lr: 8.82e-04
+2022-06-18 16:30:49,256 INFO [train.py:874] (3/4) Epoch 9, batch 1300, aishell_loss[loss=0.1921, simple_loss=0.2713, pruned_loss=0.05648, over 4911.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2545, pruned_loss=0.05535, over 983605.76 frames.], batch size: 33, aishell_tot_loss[loss=0.1791, simple_loss=0.2571, pruned_loss=0.05059, over 954399.80 frames.], datatang_tot_loss[loss=0.1867, simple_loss=0.2506, pruned_loss=0.0614, over 937774.14 frames.], batch size: 33, lr: 8.81e-04
+2022-06-18 16:31:19,917 INFO [train.py:874] (3/4) Epoch 9, batch 1350, aishell_loss[loss=0.1746, simple_loss=0.2543, pruned_loss=0.04746, over 4888.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2552, pruned_loss=0.05529, over 984118.04 frames.], batch size: 42, aishell_tot_loss[loss=0.1797, simple_loss=0.2579, pruned_loss=0.05078, over 958312.94 frames.], datatang_tot_loss[loss=0.1864, simple_loss=0.2506, pruned_loss=0.06112, over 943024.28 frames.], batch size: 42, lr: 8.80e-04
+2022-06-18 16:31:50,509 INFO [train.py:874] (3/4) Epoch 9, batch 1400, aishell_loss[loss=0.1632, simple_loss=0.2454, pruned_loss=0.04044, over 4855.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2549, pruned_loss=0.05542, over 984422.52 frames.], batch size: 37, aishell_tot_loss[loss=0.1798, simple_loss=0.2578, pruned_loss=0.0509, over 961555.25 frames.], datatang_tot_loss[loss=0.1864, simple_loss=0.2504, pruned_loss=0.06114, over 947790.84 frames.], batch size: 37, lr: 8.80e-04
+2022-06-18 16:32:21,328 INFO [train.py:874] (3/4) Epoch 9, batch 1450, datatang_loss[loss=0.1526, simple_loss=0.2261, pruned_loss=0.03959, over 4924.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2557, pruned_loss=0.05626, over 984447.95 frames.], batch size: 71, aishell_tot_loss[loss=0.1804, simple_loss=0.2581, pruned_loss=0.0513, over 964140.51 frames.], datatang_tot_loss[loss=0.1872, simple_loss=0.2512, pruned_loss=0.06161, over 952194.63 frames.], batch size: 71, lr: 8.79e-04
+2022-06-18 16:32:52,222 INFO [train.py:874] (3/4) Epoch 9, batch 1500, datatang_loss[loss=0.2832, simple_loss=0.3268, pruned_loss=0.1198, over 4932.00 frames.], tot_loss[loss=0.183, simple_loss=0.2544, pruned_loss=0.05583, over 984534.75 frames.], batch size: 108, aishell_tot_loss[loss=0.1792, simple_loss=0.2569, pruned_loss=0.05082, over 966299.68 frames.], datatang_tot_loss[loss=0.1871, simple_loss=0.2513, pruned_loss=0.0615, over 956371.06 frames.], batch size: 108, lr: 8.78e-04
+2022-06-18 16:33:21,752 INFO [train.py:874] (3/4) Epoch 9, batch 1550, aishell_loss[loss=0.1916, simple_loss=0.2656, pruned_loss=0.05886, over 4981.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2553, pruned_loss=0.05705, over 985013.91 frames.], batch size: 48, aishell_tot_loss[loss=0.18, simple_loss=0.2574, pruned_loss=0.05135, over 968460.11 frames.], datatang_tot_loss[loss=0.188, simple_loss=0.2519, pruned_loss=0.0621, over 960268.09 frames.], batch size: 48, lr: 8.78e-04
+2022-06-18 16:33:52,523 INFO [train.py:874] (3/4) Epoch 9, batch 1600, datatang_loss[loss=0.2408, simple_loss=0.2931, pruned_loss=0.09426, over 4916.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2568, pruned_loss=0.05754, over 984960.45 frames.], batch size: 98, aishell_tot_loss[loss=0.1808, simple_loss=0.2582, pruned_loss=0.05169, over 970514.45 frames.], datatang_tot_loss[loss=0.1889, simple_loss=0.2528, pruned_loss=0.0625, over 962914.25 frames.], batch size: 98, lr: 8.77e-04
+2022-06-18 16:34:23,061 INFO [train.py:874] (3/4) Epoch 9, batch 1650, datatang_loss[loss=0.1561, simple_loss=0.2293, pruned_loss=0.04141, over 4934.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2559, pruned_loss=0.05718, over 985058.76 frames.], batch size: 79, aishell_tot_loss[loss=0.1804, simple_loss=0.258, pruned_loss=0.05139, over 972316.96 frames.], datatang_tot_loss[loss=0.1887, simple_loss=0.2523, pruned_loss=0.06257, over 965496.07 frames.], batch size: 79, lr: 8.77e-04
+2022-06-18 16:34:54,175 INFO [train.py:874] (3/4) Epoch 9, batch 1700, datatang_loss[loss=0.1544, simple_loss=0.2123, pruned_loss=0.04824, over 4907.00 frames.], tot_loss[loss=0.184, simple_loss=0.2546, pruned_loss=0.05664, over 985412.90 frames.], batch size: 26, aishell_tot_loss[loss=0.1807, simple_loss=0.2584, pruned_loss=0.05155, over 973865.25 frames.], datatang_tot_loss[loss=0.1872, simple_loss=0.2508, pruned_loss=0.06175, over 968183.44 frames.], batch size: 26, lr: 8.76e-04
+2022-06-18 16:35:25,130 INFO [train.py:874] (3/4) Epoch 9, batch 1750, aishell_loss[loss=0.1667, simple_loss=0.2493, pruned_loss=0.04208, over 4947.00 frames.], tot_loss[loss=0.184, simple_loss=0.254, pruned_loss=0.05699, over 985617.91 frames.], batch size: 32, aishell_tot_loss[loss=0.1807, simple_loss=0.2584, pruned_loss=0.0515, over 974776.90 frames.], datatang_tot_loss[loss=0.1871, simple_loss=0.2503, pruned_loss=0.06194, over 970990.32 frames.], batch size: 32, lr: 8.75e-04
+2022-06-18 16:35:56,495 INFO [train.py:874] (3/4) Epoch 9, batch 1800, aishell_loss[loss=0.1612, simple_loss=0.2396, pruned_loss=0.04139, over 4974.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2531, pruned_loss=0.05677, over 985887.61 frames.], batch size: 39, aishell_tot_loss[loss=0.1803, simple_loss=0.2577, pruned_loss=0.05144, over 975975.07 frames.], datatang_tot_loss[loss=0.1868, simple_loss=0.2501, pruned_loss=0.06174, over 973114.20 frames.], batch size: 39, lr: 8.75e-04
+2022-06-18 16:36:25,587 INFO [train.py:874] (3/4) Epoch 9, batch 1850, aishell_loss[loss=0.1987, simple_loss=0.2785, pruned_loss=0.05948, over 4862.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2539, pruned_loss=0.05686, over 985418.58 frames.], batch size: 37, aishell_tot_loss[loss=0.1805, simple_loss=0.2576, pruned_loss=0.05172, over 976680.71 frames.], datatang_tot_loss[loss=0.187, simple_loss=0.2507, pruned_loss=0.06168, over 974568.03 frames.], batch size: 37, lr: 8.74e-04
+2022-06-18 16:36:56,417 INFO [train.py:874] (3/4) Epoch 9, batch 1900, datatang_loss[loss=0.2243, simple_loss=0.281, pruned_loss=0.0838, over 4927.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2549, pruned_loss=0.057, over 985815.77 frames.], batch size: 94, aishell_tot_loss[loss=0.1812, simple_loss=0.2583, pruned_loss=0.05207, over 977883.96 frames.], datatang_tot_loss[loss=0.187, simple_loss=0.251, pruned_loss=0.06153, over 976082.07 frames.], batch size: 94, lr: 8.73e-04
+2022-06-18 16:37:28,074 INFO [train.py:874] (3/4) Epoch 9, batch 1950, aishell_loss[loss=0.1828, simple_loss=0.2577, pruned_loss=0.05398, over 4972.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2541, pruned_loss=0.05643, over 985681.59 frames.], batch size: 48, aishell_tot_loss[loss=0.1804, simple_loss=0.2575, pruned_loss=0.05162, over 978704.41 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.251, pruned_loss=0.06139, over 977191.55 frames.], batch size: 48, lr: 8.73e-04
+2022-06-18 16:37:57,071 INFO [train.py:874] (3/4) Epoch 9, batch 2000, datatang_loss[loss=0.207, simple_loss=0.2631, pruned_loss=0.07539, over 4972.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2555, pruned_loss=0.05735, over 986045.28 frames.], batch size: 55, aishell_tot_loss[loss=0.1812, simple_loss=0.2584, pruned_loss=0.05198, over 979618.72 frames.], datatang_tot_loss[loss=0.1878, simple_loss=0.2517, pruned_loss=0.062, over 978484.79 frames.], batch size: 55, lr: 8.72e-04
+2022-06-18 16:37:57,072 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 16:38:13,638 INFO [train.py:914] (3/4) Epoch 9, validation: loss=0.1694, simple_loss=0.2525, pruned_loss=0.04315, over 1622729.00 frames.
+2022-06-18 16:38:43,932 INFO [train.py:874] (3/4) Epoch 9, batch 2050, datatang_loss[loss=0.1959, simple_loss=0.258, pruned_loss=0.06693, over 4925.00 frames.], tot_loss[loss=0.185, simple_loss=0.2555, pruned_loss=0.0572, over 985935.41 frames.], batch size: 77, aishell_tot_loss[loss=0.1813, simple_loss=0.2586, pruned_loss=0.05198, over 980218.41 frames.], datatang_tot_loss[loss=0.1876, simple_loss=0.2515, pruned_loss=0.06183, over 979428.34 frames.], batch size: 77, lr: 8.72e-04
+2022-06-18 16:39:14,835 INFO [train.py:874] (3/4) Epoch 9, batch 2100, aishell_loss[loss=0.1823, simple_loss=0.2563, pruned_loss=0.05408, over 4969.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2557, pruned_loss=0.05743, over 985588.32 frames.], batch size: 44, aishell_tot_loss[loss=0.1812, simple_loss=0.2585, pruned_loss=0.05195, over 980462.58 frames.], datatang_tot_loss[loss=0.188, simple_loss=0.252, pruned_loss=0.06202, over 980261.38 frames.], batch size: 44, lr: 8.71e-04
+2022-06-18 16:39:46,144 INFO [train.py:874] (3/4) Epoch 9, batch 2150, aishell_loss[loss=0.1521, simple_loss=0.2244, pruned_loss=0.0399, over 4883.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2551, pruned_loss=0.05707, over 985566.84 frames.], batch size: 25, aishell_tot_loss[loss=0.1809, simple_loss=0.2581, pruned_loss=0.05182, over 980887.71 frames.], datatang_tot_loss[loss=0.1878, simple_loss=0.2519, pruned_loss=0.06183, over 981057.09 frames.], batch size: 25, lr: 8.70e-04
+2022-06-18 16:40:16,396 INFO [train.py:874] (3/4) Epoch 9, batch 2200, aishell_loss[loss=0.1767, simple_loss=0.2507, pruned_loss=0.05139, over 4968.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2554, pruned_loss=0.05734, over 985791.69 frames.], batch size: 31, aishell_tot_loss[loss=0.1811, simple_loss=0.2585, pruned_loss=0.05187, over 981550.53 frames.], datatang_tot_loss[loss=0.188, simple_loss=0.2519, pruned_loss=0.06206, over 981699.79 frames.], batch size: 31, lr: 8.70e-04
+2022-06-18 16:40:46,546 INFO [train.py:874] (3/4) Epoch 9, batch 2250, datatang_loss[loss=0.2108, simple_loss=0.2752, pruned_loss=0.07322, over 4920.00 frames.], tot_loss[loss=0.186, simple_loss=0.2564, pruned_loss=0.05783, over 986100.81 frames.], batch size: 50, aishell_tot_loss[loss=0.1818, simple_loss=0.2594, pruned_loss=0.05215, over 982192.46 frames.], datatang_tot_loss[loss=0.1884, simple_loss=0.2522, pruned_loss=0.06233, over 982364.85 frames.], batch size: 50, lr: 8.69e-04
+2022-06-18 16:41:17,748 INFO [train.py:874] (3/4) Epoch 9, batch 2300, datatang_loss[loss=0.1622, simple_loss=0.2334, pruned_loss=0.04554, over 4921.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2558, pruned_loss=0.05715, over 985874.61 frames.], batch size: 83, aishell_tot_loss[loss=0.1813, simple_loss=0.2589, pruned_loss=0.05183, over 982628.94 frames.], datatang_tot_loss[loss=0.1883, simple_loss=0.2521, pruned_loss=0.06221, over 982594.10 frames.], batch size: 83, lr: 8.69e-04
+2022-06-18 16:41:48,154 INFO [train.py:874] (3/4) Epoch 9, batch 2350, aishell_loss[loss=0.1961, simple_loss=0.2795, pruned_loss=0.05637, over 4886.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2558, pruned_loss=0.05714, over 985942.24 frames.], batch size: 42, aishell_tot_loss[loss=0.1816, simple_loss=0.2592, pruned_loss=0.052, over 982904.71 frames.], datatang_tot_loss[loss=0.188, simple_loss=0.2519, pruned_loss=0.06207, over 983166.69 frames.], batch size: 42, lr: 8.68e-04
+2022-06-18 16:42:20,339 INFO [train.py:874] (3/4) Epoch 9, batch 2400, aishell_loss[loss=0.1991, simple_loss=0.2782, pruned_loss=0.06004, over 4858.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2558, pruned_loss=0.05724, over 985708.65 frames.], batch size: 37, aishell_tot_loss[loss=0.1823, simple_loss=0.2597, pruned_loss=0.05248, over 983166.24 frames.], datatang_tot_loss[loss=0.1875, simple_loss=0.2515, pruned_loss=0.06173, over 983360.50 frames.], batch size: 37, lr: 8.67e-04
+2022-06-18 16:42:51,881 INFO [train.py:874] (3/4) Epoch 9, batch 2450, datatang_loss[loss=0.1926, simple_loss=0.2489, pruned_loss=0.0681, over 4900.00 frames.], tot_loss[loss=0.185, simple_loss=0.2564, pruned_loss=0.05678, over 985873.78 frames.], batch size: 59, aishell_tot_loss[loss=0.1827, simple_loss=0.2605, pruned_loss=0.05249, over 983667.00 frames.], datatang_tot_loss[loss=0.1871, simple_loss=0.2511, pruned_loss=0.06152, over 983606.60 frames.], batch size: 59, lr: 8.67e-04
+2022-06-18 16:43:22,169 INFO [train.py:874] (3/4) Epoch 9, batch 2500, datatang_loss[loss=0.1947, simple_loss=0.2622, pruned_loss=0.06363, over 4962.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2553, pruned_loss=0.05625, over 985908.24 frames.], batch size: 86, aishell_tot_loss[loss=0.1818, simple_loss=0.2595, pruned_loss=0.05208, over 983834.88 frames.], datatang_tot_loss[loss=0.1868, simple_loss=0.2511, pruned_loss=0.06127, over 984004.29 frames.], batch size: 86, lr: 8.66e-04
+2022-06-18 16:43:51,889 INFO [train.py:874] (3/4) Epoch 9, batch 2550, aishell_loss[loss=0.1899, simple_loss=0.2703, pruned_loss=0.05469, over 4917.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2553, pruned_loss=0.05608, over 986153.69 frames.], batch size: 33, aishell_tot_loss[loss=0.182, simple_loss=0.2599, pruned_loss=0.05203, over 984300.97 frames.], datatang_tot_loss[loss=0.1863, simple_loss=0.2507, pruned_loss=0.06091, over 984274.71 frames.], batch size: 33, lr: 8.66e-04
+2022-06-18 16:44:23,897 INFO [train.py:874] (3/4) Epoch 9, batch 2600, datatang_loss[loss=0.1907, simple_loss=0.2488, pruned_loss=0.0663, over 4875.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2556, pruned_loss=0.05669, over 986046.91 frames.], batch size: 39, aishell_tot_loss[loss=0.1826, simple_loss=0.2602, pruned_loss=0.05246, over 984583.30 frames.], datatang_tot_loss[loss=0.1865, simple_loss=0.2509, pruned_loss=0.06104, over 984332.45 frames.], batch size: 39, lr: 8.65e-04
+2022-06-18 16:44:54,229 INFO [train.py:874] (3/4) Epoch 9, batch 2650, aishell_loss[loss=0.1374, simple_loss=0.1986, pruned_loss=0.0381, over 4714.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2549, pruned_loss=0.05619, over 985584.95 frames.], batch size: 20, aishell_tot_loss[loss=0.1814, simple_loss=0.2592, pruned_loss=0.05183, over 984124.89 frames.], datatang_tot_loss[loss=0.1867, simple_loss=0.2511, pruned_loss=0.06113, over 984685.75 frames.], batch size: 20, lr: 8.64e-04
+2022-06-18 16:45:24,652 INFO [train.py:874] (3/4) Epoch 9, batch 2700, aishell_loss[loss=0.1629, simple_loss=0.2448, pruned_loss=0.04052, over 4865.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2552, pruned_loss=0.05676, over 985616.24 frames.], batch size: 37, aishell_tot_loss[loss=0.1813, simple_loss=0.2593, pruned_loss=0.05163, over 984315.58 frames.], datatang_tot_loss[loss=0.1873, simple_loss=0.2515, pruned_loss=0.06156, over 984794.05 frames.], batch size: 37, lr: 8.64e-04
+2022-06-18 16:45:55,587 INFO [train.py:874] (3/4) Epoch 9, batch 2750, datatang_loss[loss=0.1501, simple_loss=0.2229, pruned_loss=0.0386, over 4923.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2565, pruned_loss=0.0572, over 985492.52 frames.], batch size: 77, aishell_tot_loss[loss=0.1823, simple_loss=0.2604, pruned_loss=0.05211, over 984326.44 frames.], datatang_tot_loss[loss=0.1875, simple_loss=0.2516, pruned_loss=0.06171, over 984913.43 frames.], batch size: 77, lr: 8.63e-04
+2022-06-18 16:46:25,661 INFO [train.py:874] (3/4) Epoch 9, batch 2800, datatang_loss[loss=0.1788, simple_loss=0.2441, pruned_loss=0.05676, over 4967.00 frames.], tot_loss[loss=0.185, simple_loss=0.2561, pruned_loss=0.05691, over 985528.50 frames.], batch size: 55, aishell_tot_loss[loss=0.1824, simple_loss=0.2604, pruned_loss=0.05215, over 984556.13 frames.], datatang_tot_loss[loss=0.1871, simple_loss=0.2513, pruned_loss=0.06146, over 984934.19 frames.], batch size: 55, lr: 8.63e-04
+2022-06-18 16:46:55,392 INFO [train.py:874] (3/4) Epoch 9, batch 2850, datatang_loss[loss=0.2167, simple_loss=0.2687, pruned_loss=0.08234, over 4917.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2577, pruned_loss=0.05774, over 985198.74 frames.], batch size: 47, aishell_tot_loss[loss=0.1827, simple_loss=0.2611, pruned_loss=0.05217, over 984254.03 frames.], datatang_tot_loss[loss=0.1886, simple_loss=0.2523, pruned_loss=0.06242, over 985078.71 frames.], batch size: 47, lr: 8.62e-04
+2022-06-18 16:47:30,689 INFO [train.py:874] (3/4) Epoch 9, batch 2900, datatang_loss[loss=0.1701, simple_loss=0.2402, pruned_loss=0.05005, over 4975.00 frames.], tot_loss[loss=0.185, simple_loss=0.2567, pruned_loss=0.05668, over 984973.32 frames.], batch size: 55, aishell_tot_loss[loss=0.1823, simple_loss=0.2608, pruned_loss=0.05189, over 984006.59 frames.], datatang_tot_loss[loss=0.1876, simple_loss=0.2517, pruned_loss=0.06177, over 985225.16 frames.], batch size: 55, lr: 8.61e-04
+2022-06-18 16:48:02,002 INFO [train.py:874] (3/4) Epoch 9, batch 2950, datatang_loss[loss=0.1734, simple_loss=0.2469, pruned_loss=0.05, over 4929.00 frames.], tot_loss[loss=0.1835, simple_loss=0.255, pruned_loss=0.05602, over 985205.18 frames.], batch size: 77, aishell_tot_loss[loss=0.1822, simple_loss=0.2605, pruned_loss=0.05196, over 984100.88 frames.], datatang_tot_loss[loss=0.186, simple_loss=0.2506, pruned_loss=0.06071, over 985425.13 frames.], batch size: 77, lr: 8.61e-04
+2022-06-18 16:48:32,330 INFO [train.py:874] (3/4) Epoch 9, batch 3000, datatang_loss[loss=0.1539, simple_loss=0.2298, pruned_loss=0.03895, over 4912.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2545, pruned_loss=0.05584, over 985388.77 frames.], batch size: 77, aishell_tot_loss[loss=0.1822, simple_loss=0.2603, pruned_loss=0.05207, over 984330.52 frames.], datatang_tot_loss[loss=0.1855, simple_loss=0.2501, pruned_loss=0.0604, over 985509.73 frames.], batch size: 77, lr: 8.60e-04
+2022-06-18 16:48:32,331 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 16:48:48,782 INFO [train.py:914] (3/4) Epoch 9, validation: loss=0.169, simple_loss=0.2511, pruned_loss=0.04348, over 1622729.00 frames.
+2022-06-18 16:49:18,642 INFO [train.py:874] (3/4) Epoch 9, batch 3050, datatang_loss[loss=0.1905, simple_loss=0.2539, pruned_loss=0.06357, over 4922.00 frames.], tot_loss[loss=0.183, simple_loss=0.2547, pruned_loss=0.05566, over 985637.76 frames.], batch size: 83, aishell_tot_loss[loss=0.1818, simple_loss=0.26, pruned_loss=0.05175, over 984579.55 frames.], datatang_tot_loss[loss=0.1858, simple_loss=0.2503, pruned_loss=0.06059, over 985652.45 frames.], batch size: 83, lr: 8.60e-04
+2022-06-18 16:49:51,962 INFO [train.py:874] (3/4) Epoch 9, batch 3100, datatang_loss[loss=0.1987, simple_loss=0.2517, pruned_loss=0.07282, over 4935.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2548, pruned_loss=0.05579, over 986123.22 frames.], batch size: 79, aishell_tot_loss[loss=0.1818, simple_loss=0.2603, pruned_loss=0.05165, over 984954.42 frames.], datatang_tot_loss[loss=0.1857, simple_loss=0.2501, pruned_loss=0.06064, over 985913.74 frames.], batch size: 79, lr: 8.59e-04
+2022-06-18 16:50:22,262 INFO [train.py:874] (3/4) Epoch 9, batch 3150, datatang_loss[loss=0.1725, simple_loss=0.2441, pruned_loss=0.05044, over 4919.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2544, pruned_loss=0.05566, over 985761.62 frames.], batch size: 77, aishell_tot_loss[loss=0.1818, simple_loss=0.2604, pruned_loss=0.05167, over 984812.46 frames.], datatang_tot_loss[loss=0.1851, simple_loss=0.2496, pruned_loss=0.0603, over 985807.12 frames.], batch size: 77, lr: 8.59e-04
+2022-06-18 16:50:52,630 INFO [train.py:874] (3/4) Epoch 9, batch 3200, datatang_loss[loss=0.2138, simple_loss=0.2813, pruned_loss=0.07317, over 4960.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2541, pruned_loss=0.05557, over 985753.69 frames.], batch size: 99, aishell_tot_loss[loss=0.1811, simple_loss=0.2595, pruned_loss=0.05133, over 984713.43 frames.], datatang_tot_loss[loss=0.1854, simple_loss=0.25, pruned_loss=0.06041, over 986008.36 frames.], batch size: 99, lr: 8.58e-04
+2022-06-18 16:51:25,287 INFO [train.py:874] (3/4) Epoch 9, batch 3250, datatang_loss[loss=0.1584, simple_loss=0.2188, pruned_loss=0.04898, over 4954.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2541, pruned_loss=0.05605, over 985812.17 frames.], batch size: 37, aishell_tot_loss[loss=0.1812, simple_loss=0.2597, pruned_loss=0.05141, over 984694.42 frames.], datatang_tot_loss[loss=0.1855, simple_loss=0.2499, pruned_loss=0.06054, over 986149.00 frames.], batch size: 37, lr: 8.57e-04
+2022-06-18 16:51:55,922 INFO [train.py:874] (3/4) Epoch 9, batch 3300, aishell_loss[loss=0.1865, simple_loss=0.27, pruned_loss=0.05151, over 4978.00 frames.], tot_loss[loss=0.1829, simple_loss=0.254, pruned_loss=0.05593, over 985887.42 frames.], batch size: 51, aishell_tot_loss[loss=0.1815, simple_loss=0.2599, pruned_loss=0.0515, over 984602.70 frames.], datatang_tot_loss[loss=0.185, simple_loss=0.2497, pruned_loss=0.06015, over 986378.44 frames.], batch size: 51, lr: 8.57e-04
+2022-06-18 16:52:26,684 INFO [train.py:874] (3/4) Epoch 9, batch 3350, datatang_loss[loss=0.1611, simple_loss=0.2374, pruned_loss=0.04237, over 4868.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2536, pruned_loss=0.0553, over 986033.80 frames.], batch size: 25, aishell_tot_loss[loss=0.1818, simple_loss=0.2603, pruned_loss=0.05162, over 984919.77 frames.], datatang_tot_loss[loss=0.1838, simple_loss=0.2488, pruned_loss=0.05936, over 986311.17 frames.], batch size: 25, lr: 8.56e-04
+2022-06-18 16:52:58,029 INFO [train.py:874] (3/4) Epoch 9, batch 3400, datatang_loss[loss=0.169, simple_loss=0.2357, pruned_loss=0.05113, over 4910.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2525, pruned_loss=0.05424, over 986110.62 frames.], batch size: 47, aishell_tot_loss[loss=0.1809, simple_loss=0.2596, pruned_loss=0.05111, over 985157.57 frames.], datatang_tot_loss[loss=0.1827, simple_loss=0.2481, pruned_loss=0.05867, over 986236.10 frames.], batch size: 47, lr: 8.56e-04
+2022-06-18 16:53:28,265 INFO [train.py:874] (3/4) Epoch 9, batch 3450, datatang_loss[loss=0.1797, simple_loss=0.2489, pruned_loss=0.05522, over 4929.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2519, pruned_loss=0.05374, over 986133.67 frames.], batch size: 57, aishell_tot_loss[loss=0.1803, simple_loss=0.2591, pruned_loss=0.05073, over 985272.41 frames.], datatang_tot_loss[loss=0.1822, simple_loss=0.2477, pruned_loss=0.0584, over 986252.59 frames.], batch size: 57, lr: 8.55e-04
+2022-06-18 16:53:59,608 INFO [train.py:874] (3/4) Epoch 9, batch 3500, aishell_loss[loss=0.2013, simple_loss=0.2774, pruned_loss=0.06258, over 4968.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2532, pruned_loss=0.05499, over 986154.04 frames.], batch size: 40, aishell_tot_loss[loss=0.1809, simple_loss=0.2596, pruned_loss=0.05107, over 985302.84 frames.], datatang_tot_loss[loss=0.1832, simple_loss=0.2483, pruned_loss=0.05904, over 986313.58 frames.], batch size: 40, lr: 8.55e-04
+2022-06-18 16:54:30,614 INFO [train.py:874] (3/4) Epoch 9, batch 3550, aishell_loss[loss=0.1904, simple_loss=0.2811, pruned_loss=0.04987, over 4907.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2537, pruned_loss=0.05541, over 986299.11 frames.], batch size: 68, aishell_tot_loss[loss=0.1813, simple_loss=0.2601, pruned_loss=0.05125, over 985526.63 frames.], datatang_tot_loss[loss=0.1835, simple_loss=0.2482, pruned_loss=0.0594, over 986332.70 frames.], batch size: 68, lr: 8.54e-04
+2022-06-18 16:55:00,739 INFO [train.py:874] (3/4) Epoch 9, batch 3600, datatang_loss[loss=0.2832, simple_loss=0.3214, pruned_loss=0.1225, over 4958.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2535, pruned_loss=0.05503, over 986139.93 frames.], batch size: 110, aishell_tot_loss[loss=0.1811, simple_loss=0.26, pruned_loss=0.05108, over 985399.34 frames.], datatang_tot_loss[loss=0.1831, simple_loss=0.248, pruned_loss=0.05913, over 986372.50 frames.], batch size: 110, lr: 8.53e-04
+2022-06-18 16:55:30,720 INFO [train.py:874] (3/4) Epoch 9, batch 3650, aishell_loss[loss=0.1984, simple_loss=0.2743, pruned_loss=0.06129, over 4941.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2544, pruned_loss=0.05533, over 986280.84 frames.], batch size: 45, aishell_tot_loss[loss=0.1808, simple_loss=0.2595, pruned_loss=0.05098, over 985664.48 frames.], datatang_tot_loss[loss=0.1842, simple_loss=0.2489, pruned_loss=0.05977, over 986341.44 frames.], batch size: 45, lr: 8.53e-04
+2022-06-18 16:56:03,460 INFO [train.py:874] (3/4) Epoch 9, batch 3700, datatang_loss[loss=0.1985, simple_loss=0.2583, pruned_loss=0.06931, over 4933.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2548, pruned_loss=0.05536, over 985661.82 frames.], batch size: 88, aishell_tot_loss[loss=0.1811, simple_loss=0.2599, pruned_loss=0.05113, over 985153.39 frames.], datatang_tot_loss[loss=0.1842, simple_loss=0.2488, pruned_loss=0.05982, over 986282.39 frames.], batch size: 88, lr: 8.52e-04
+2022-06-18 16:56:32,420 INFO [train.py:874] (3/4) Epoch 9, batch 3750, datatang_loss[loss=0.1985, simple_loss=0.2508, pruned_loss=0.0731, over 4913.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2543, pruned_loss=0.05523, over 985662.08 frames.], batch size: 75, aishell_tot_loss[loss=0.1804, simple_loss=0.2594, pruned_loss=0.05073, over 985113.30 frames.], datatang_tot_loss[loss=0.1845, simple_loss=0.249, pruned_loss=0.06, over 986291.82 frames.], batch size: 75, lr: 8.52e-04
+2022-06-18 16:57:03,118 INFO [train.py:874] (3/4) Epoch 9, batch 3800, aishell_loss[loss=0.1742, simple_loss=0.2604, pruned_loss=0.04395, over 4916.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2545, pruned_loss=0.05583, over 985392.60 frames.], batch size: 46, aishell_tot_loss[loss=0.1808, simple_loss=0.2598, pruned_loss=0.05093, over 984920.64 frames.], datatang_tot_loss[loss=0.1847, simple_loss=0.249, pruned_loss=0.06017, over 986171.92 frames.], batch size: 46, lr: 8.51e-04
+2022-06-18 16:57:32,539 INFO [train.py:874] (3/4) Epoch 9, batch 3850, aishell_loss[loss=0.1862, simple_loss=0.2631, pruned_loss=0.05468, over 4895.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2541, pruned_loss=0.05572, over 985435.13 frames.], batch size: 34, aishell_tot_loss[loss=0.1808, simple_loss=0.2595, pruned_loss=0.05104, over 984703.70 frames.], datatang_tot_loss[loss=0.1845, simple_loss=0.2492, pruned_loss=0.05989, over 986371.99 frames.], batch size: 34, lr: 8.51e-04
+2022-06-18 16:58:01,313 INFO [train.py:874] (3/4) Epoch 9, batch 3900, aishell_loss[loss=0.1821, simple_loss=0.2642, pruned_loss=0.05004, over 4937.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2535, pruned_loss=0.0547, over 985636.74 frames.], batch size: 64, aishell_tot_loss[loss=0.1803, simple_loss=0.2593, pruned_loss=0.05072, over 984847.39 frames.], datatang_tot_loss[loss=0.1836, simple_loss=0.2485, pruned_loss=0.0593, over 986452.34 frames.], batch size: 64, lr: 8.50e-04
+2022-06-18 16:58:29,679 INFO [train.py:874] (3/4) Epoch 9, batch 3950, datatang_loss[loss=0.2045, simple_loss=0.2641, pruned_loss=0.07243, over 4809.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2525, pruned_loss=0.05369, over 985483.87 frames.], batch size: 25, aishell_tot_loss[loss=0.1801, simple_loss=0.2589, pruned_loss=0.05059, over 984790.06 frames.], datatang_tot_loss[loss=0.1821, simple_loss=0.2475, pruned_loss=0.05839, over 986376.80 frames.], batch size: 25, lr: 8.49e-04
+2022-06-18 16:58:57,920 INFO [train.py:874] (3/4) Epoch 9, batch 4000, aishell_loss[loss=0.1802, simple_loss=0.2548, pruned_loss=0.05279, over 4934.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2523, pruned_loss=0.05342, over 985482.67 frames.], batch size: 49, aishell_tot_loss[loss=0.1797, simple_loss=0.2587, pruned_loss=0.05038, over 984722.29 frames.], datatang_tot_loss[loss=0.1818, simple_loss=0.2471, pruned_loss=0.05829, over 986449.29 frames.], batch size: 49, lr: 8.49e-04
+2022-06-18 16:58:57,922 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 16:59:14,871 INFO [train.py:914] (3/4) Epoch 9, validation: loss=0.1682, simple_loss=0.2517, pruned_loss=0.04232, over 1622729.00 frames.
+2022-06-18 16:59:43,975 INFO [train.py:874] (3/4) Epoch 9, batch 4050, datatang_loss[loss=0.1508, simple_loss=0.2089, pruned_loss=0.04641, over 4921.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2517, pruned_loss=0.05365, over 985492.31 frames.], batch size: 37, aishell_tot_loss[loss=0.1794, simple_loss=0.2584, pruned_loss=0.05018, over 984589.17 frames.], datatang_tot_loss[loss=0.1818, simple_loss=0.2469, pruned_loss=0.05832, over 986535.10 frames.], batch size: 37, lr: 8.48e-04
+2022-06-18 17:00:57,202 INFO [train.py:874] (3/4) Epoch 10, batch 50, datatang_loss[loss=0.1434, simple_loss=0.2287, pruned_loss=0.02909, over 4957.00 frames.], tot_loss[loss=0.171, simple_loss=0.2444, pruned_loss=0.04882, over 218887.46 frames.], batch size: 86, aishell_tot_loss[loss=0.1752, simple_loss=0.2546, pruned_loss=0.04785, over 112054.88 frames.], datatang_tot_loss[loss=0.1676, simple_loss=0.2355, pruned_loss=0.04985, over 120509.06 frames.], batch size: 86, lr: 8.15e-04
+2022-06-18 17:01:24,376 INFO [train.py:874] (3/4) Epoch 10, batch 100, aishell_loss[loss=0.1628, simple_loss=0.2473, pruned_loss=0.0391, over 4975.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2461, pruned_loss=0.04926, over 388713.72 frames.], batch size: 56, aishell_tot_loss[loss=0.174, simple_loss=0.2529, pruned_loss=0.04758, over 244958.71 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2369, pruned_loss=0.05158, over 191301.70 frames.], batch size: 56, lr: 8.14e-04
+2022-06-18 17:01:55,766 INFO [train.py:874] (3/4) Epoch 10, batch 150, aishell_loss[loss=0.1688, simple_loss=0.2527, pruned_loss=0.0424, over 4866.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2483, pruned_loss=0.04936, over 521386.80 frames.], batch size: 36, aishell_tot_loss[loss=0.1749, simple_loss=0.2552, pruned_loss=0.04729, over 348550.49 frames.], datatang_tot_loss[loss=0.1715, simple_loss=0.2383, pruned_loss=0.05238, over 267274.53 frames.], batch size: 36, lr: 8.14e-04
+2022-06-18 17:02:27,261 INFO [train.py:874] (3/4) Epoch 10, batch 200, datatang_loss[loss=0.1611, simple_loss=0.2225, pruned_loss=0.04982, over 4927.00 frames.], tot_loss[loss=0.175, simple_loss=0.2492, pruned_loss=0.05042, over 624136.70 frames.], batch size: 71, aishell_tot_loss[loss=0.1762, simple_loss=0.2562, pruned_loss=0.04806, over 431869.39 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2391, pruned_loss=0.05337, over 342194.26 frames.], batch size: 71, lr: 8.13e-04
+2022-06-18 17:02:55,511 INFO [train.py:874] (3/4) Epoch 10, batch 250, datatang_loss[loss=0.1709, simple_loss=0.2377, pruned_loss=0.05207, over 4956.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2499, pruned_loss=0.05078, over 704566.68 frames.], batch size: 67, aishell_tot_loss[loss=0.1772, simple_loss=0.2574, pruned_loss=0.0485, over 499775.82 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2398, pruned_loss=0.05341, over 415178.13 frames.], batch size: 67, lr: 8.13e-04
+2022-06-18 17:03:27,430 INFO [train.py:874] (3/4) Epoch 10, batch 300, aishell_loss[loss=0.1584, simple_loss=0.2366, pruned_loss=0.04006, over 4978.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2504, pruned_loss=0.05146, over 766787.70 frames.], batch size: 27, aishell_tot_loss[loss=0.1779, simple_loss=0.2573, pruned_loss=0.0492, over 556942.28 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2413, pruned_loss=0.05365, over 482324.92 frames.], batch size: 27, lr: 8.12e-04
+2022-06-18 17:03:58,510 INFO [train.py:874] (3/4) Epoch 10, batch 350, datatang_loss[loss=0.1776, simple_loss=0.2458, pruned_loss=0.0547, over 4954.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2508, pruned_loss=0.05208, over 815255.04 frames.], batch size: 67, aishell_tot_loss[loss=0.1767, simple_loss=0.2559, pruned_loss=0.0488, over 605385.91 frames.], datatang_tot_loss[loss=0.177, simple_loss=0.244, pruned_loss=0.05499, over 543910.21 frames.], batch size: 67, lr: 8.12e-04
+2022-06-18 17:04:26,581 INFO [train.py:874] (3/4) Epoch 10, batch 400, datatang_loss[loss=0.1525, simple_loss=0.2241, pruned_loss=0.04043, over 4908.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2521, pruned_loss=0.05338, over 853113.09 frames.], batch size: 59, aishell_tot_loss[loss=0.1788, simple_loss=0.2575, pruned_loss=0.05009, over 651704.47 frames.], datatang_tot_loss[loss=0.1779, simple_loss=0.2444, pruned_loss=0.05572, over 594230.94 frames.], batch size: 59, lr: 8.11e-04
+2022-06-18 17:04:57,249 INFO [train.py:874] (3/4) Epoch 10, batch 450, aishell_loss[loss=0.1907, simple_loss=0.2577, pruned_loss=0.06186, over 4967.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2527, pruned_loss=0.05284, over 882932.14 frames.], batch size: 31, aishell_tot_loss[loss=0.1783, simple_loss=0.2573, pruned_loss=0.0496, over 700098.62 frames.], datatang_tot_loss[loss=0.1786, simple_loss=0.2455, pruned_loss=0.05588, over 629926.41 frames.], batch size: 31, lr: 8.11e-04
+2022-06-18 17:05:26,480 INFO [train.py:874] (3/4) Epoch 10, batch 500, aishell_loss[loss=0.1939, simple_loss=0.2725, pruned_loss=0.05765, over 4965.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2543, pruned_loss=0.05349, over 906148.08 frames.], batch size: 44, aishell_tot_loss[loss=0.1783, simple_loss=0.2577, pruned_loss=0.04949, over 740374.28 frames.], datatang_tot_loss[loss=0.1809, simple_loss=0.2474, pruned_loss=0.05718, over 663913.10 frames.], batch size: 44, lr: 8.10e-04
+2022-06-18 17:05:55,057 INFO [train.py:874] (3/4) Epoch 10, batch 550, datatang_loss[loss=0.1674, simple_loss=0.2504, pruned_loss=0.04218, over 4938.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2549, pruned_loss=0.0537, over 923664.81 frames.], batch size: 88, aishell_tot_loss[loss=0.1788, simple_loss=0.2582, pruned_loss=0.04973, over 774499.72 frames.], datatang_tot_loss[loss=0.1815, simple_loss=0.2479, pruned_loss=0.05754, over 694605.46 frames.], batch size: 88, lr: 8.10e-04
+2022-06-18 17:06:26,471 INFO [train.py:874] (3/4) Epoch 10, batch 600, aishell_loss[loss=0.179, simple_loss=0.2612, pruned_loss=0.04836, over 4915.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2546, pruned_loss=0.05422, over 937374.38 frames.], batch size: 52, aishell_tot_loss[loss=0.1783, simple_loss=0.2576, pruned_loss=0.04955, over 801143.27 frames.], datatang_tot_loss[loss=0.183, simple_loss=0.2487, pruned_loss=0.05861, over 726336.86 frames.], batch size: 52, lr: 8.09e-04
+2022-06-18 17:06:55,511 INFO [train.py:874] (3/4) Epoch 10, batch 650, datatang_loss[loss=0.1745, simple_loss=0.2569, pruned_loss=0.04601, over 4927.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2537, pruned_loss=0.05365, over 948164.37 frames.], batch size: 94, aishell_tot_loss[loss=0.1784, simple_loss=0.2577, pruned_loss=0.04958, over 819603.46 frames.], datatang_tot_loss[loss=0.1817, simple_loss=0.2481, pruned_loss=0.05764, over 761488.90 frames.], batch size: 94, lr: 8.09e-04
+2022-06-18 17:07:24,957 INFO [train.py:874] (3/4) Epoch 10, batch 700, aishell_loss[loss=0.1574, simple_loss=0.2326, pruned_loss=0.04104, over 4798.00 frames.], tot_loss[loss=0.18, simple_loss=0.2532, pruned_loss=0.05336, over 956314.59 frames.], batch size: 24, aishell_tot_loss[loss=0.1775, simple_loss=0.257, pruned_loss=0.04899, over 840461.39 frames.], datatang_tot_loss[loss=0.1822, simple_loss=0.2483, pruned_loss=0.05803, over 785888.49 frames.], batch size: 24, lr: 8.08e-04
+2022-06-18 17:07:56,753 INFO [train.py:874] (3/4) Epoch 10, batch 750, aishell_loss[loss=0.1963, simple_loss=0.273, pruned_loss=0.05982, over 4866.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2536, pruned_loss=0.05415, over 963014.74 frames.], batch size: 35, aishell_tot_loss[loss=0.1777, simple_loss=0.2573, pruned_loss=0.04908, over 855876.01 frames.], datatang_tot_loss[loss=0.1831, simple_loss=0.2488, pruned_loss=0.05874, over 812001.10 frames.], batch size: 35, lr: 8.08e-04
+2022-06-18 17:08:27,700 INFO [train.py:874] (3/4) Epoch 10, batch 800, aishell_loss[loss=0.1689, simple_loss=0.2511, pruned_loss=0.04341, over 4916.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2522, pruned_loss=0.0536, over 967909.44 frames.], batch size: 46, aishell_tot_loss[loss=0.1768, simple_loss=0.2563, pruned_loss=0.04866, over 869496.83 frames.], datatang_tot_loss[loss=0.1827, simple_loss=0.2485, pruned_loss=0.05842, over 834546.25 frames.], batch size: 46, lr: 8.07e-04
+2022-06-18 17:08:57,273 INFO [train.py:874] (3/4) Epoch 10, batch 850, datatang_loss[loss=0.1523, simple_loss=0.2252, pruned_loss=0.03975, over 4946.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2524, pruned_loss=0.05369, over 971728.23 frames.], batch size: 69, aishell_tot_loss[loss=0.1767, simple_loss=0.2565, pruned_loss=0.04847, over 881436.10 frames.], datatang_tot_loss[loss=0.1828, simple_loss=0.2486, pruned_loss=0.05851, over 854388.68 frames.], batch size: 69, lr: 8.07e-04
+2022-06-18 17:09:29,335 INFO [train.py:874] (3/4) Epoch 10, batch 900, aishell_loss[loss=0.2347, simple_loss=0.3027, pruned_loss=0.08338, over 4968.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2517, pruned_loss=0.053, over 974923.27 frames.], batch size: 44, aishell_tot_loss[loss=0.1761, simple_loss=0.2559, pruned_loss=0.04817, over 894187.29 frames.], datatang_tot_loss[loss=0.1823, simple_loss=0.2483, pruned_loss=0.05811, over 869363.89 frames.], batch size: 44, lr: 8.06e-04
+2022-06-18 17:09:58,878 INFO [train.py:874] (3/4) Epoch 10, batch 950, datatang_loss[loss=0.1567, simple_loss=0.2373, pruned_loss=0.03809, over 4924.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2522, pruned_loss=0.05315, over 977191.59 frames.], batch size: 81, aishell_tot_loss[loss=0.1764, simple_loss=0.2562, pruned_loss=0.04828, over 905205.94 frames.], datatang_tot_loss[loss=0.1824, simple_loss=0.2484, pruned_loss=0.0582, over 882595.19 frames.], batch size: 81, lr: 8.05e-04
+2022-06-18 17:10:29,068 INFO [train.py:874] (3/4) Epoch 10, batch 1000, aishell_loss[loss=0.1644, simple_loss=0.236, pruned_loss=0.04642, over 4813.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2527, pruned_loss=0.05356, over 979000.20 frames.], batch size: 26, aishell_tot_loss[loss=0.1773, simple_loss=0.2568, pruned_loss=0.04886, over 913878.22 frames.], datatang_tot_loss[loss=0.1822, simple_loss=0.2485, pruned_loss=0.05798, over 895658.11 frames.], batch size: 26, lr: 8.05e-04
+2022-06-18 17:10:29,069 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 17:10:46,217 INFO [train.py:914] (3/4) Epoch 10, validation: loss=0.1689, simple_loss=0.2533, pruned_loss=0.04224, over 1622729.00 frames.
+2022-06-18 17:11:16,241 INFO [train.py:874] (3/4) Epoch 10, batch 1050, aishell_loss[loss=0.1707, simple_loss=0.2482, pruned_loss=0.04659, over 4873.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2507, pruned_loss=0.05285, over 979934.30 frames.], batch size: 42, aishell_tot_loss[loss=0.1769, simple_loss=0.2563, pruned_loss=0.04878, over 918698.73 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2471, pruned_loss=0.05683, over 909939.01 frames.], batch size: 42, lr: 8.04e-04
+2022-06-18 17:11:46,601 INFO [train.py:874] (3/4) Epoch 10, batch 1100, aishell_loss[loss=0.1781, simple_loss=0.2603, pruned_loss=0.04791, over 4969.00 frames.], tot_loss[loss=0.177, simple_loss=0.2504, pruned_loss=0.05179, over 981178.96 frames.], batch size: 61, aishell_tot_loss[loss=0.1761, simple_loss=0.256, pruned_loss=0.04807, over 927696.08 frames.], datatang_tot_loss[loss=0.1799, simple_loss=0.2467, pruned_loss=0.05651, over 917544.48 frames.], batch size: 61, lr: 8.04e-04
+2022-06-18 17:12:16,124 INFO [train.py:874] (3/4) Epoch 10, batch 1150, datatang_loss[loss=0.1744, simple_loss=0.2492, pruned_loss=0.04977, over 4928.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2515, pruned_loss=0.0523, over 981680.04 frames.], batch size: 42, aishell_tot_loss[loss=0.1763, simple_loss=0.2564, pruned_loss=0.04814, over 934362.93 frames.], datatang_tot_loss[loss=0.1805, simple_loss=0.2472, pruned_loss=0.05689, over 925162.18 frames.], batch size: 42, lr: 8.03e-04
+2022-06-18 17:12:47,090 INFO [train.py:874] (3/4) Epoch 10, batch 1200, aishell_loss[loss=0.2001, simple_loss=0.2805, pruned_loss=0.0599, over 4981.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2515, pruned_loss=0.05292, over 982547.39 frames.], batch size: 39, aishell_tot_loss[loss=0.1769, simple_loss=0.2568, pruned_loss=0.04847, over 939189.67 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2469, pruned_loss=0.05695, over 933653.85 frames.], batch size: 39, lr: 8.03e-04
+2022-06-18 17:13:18,892 INFO [train.py:874] (3/4) Epoch 10, batch 1250, aishell_loss[loss=0.1591, simple_loss=0.224, pruned_loss=0.04707, over 4936.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2506, pruned_loss=0.05231, over 983173.85 frames.], batch size: 25, aishell_tot_loss[loss=0.1765, simple_loss=0.2563, pruned_loss=0.04837, over 944899.13 frames.], datatang_tot_loss[loss=0.1796, simple_loss=0.2462, pruned_loss=0.05646, over 939441.72 frames.], batch size: 25, lr: 8.02e-04
+2022-06-18 17:13:47,332 INFO [train.py:874] (3/4) Epoch 10, batch 1300, datatang_loss[loss=0.1824, simple_loss=0.2527, pruned_loss=0.0561, over 4928.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2508, pruned_loss=0.05247, over 983656.10 frames.], batch size: 79, aishell_tot_loss[loss=0.1767, simple_loss=0.2564, pruned_loss=0.04855, over 949252.56 frames.], datatang_tot_loss[loss=0.1795, simple_loss=0.2463, pruned_loss=0.05633, over 945309.33 frames.], batch size: 79, lr: 8.02e-04
+2022-06-18 17:14:20,346 INFO [train.py:874] (3/4) Epoch 10, batch 1350, datatang_loss[loss=0.1731, simple_loss=0.2353, pruned_loss=0.05543, over 4975.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2497, pruned_loss=0.05202, over 984537.04 frames.], batch size: 45, aishell_tot_loss[loss=0.1764, simple_loss=0.2561, pruned_loss=0.04831, over 951965.53 frames.], datatang_tot_loss[loss=0.1786, simple_loss=0.2458, pruned_loss=0.05568, over 952191.13 frames.], batch size: 45, lr: 8.01e-04
+2022-06-18 17:14:52,560 INFO [train.py:874] (3/4) Epoch 10, batch 1400, datatang_loss[loss=0.1706, simple_loss=0.2379, pruned_loss=0.05167, over 4954.00 frames.], tot_loss[loss=0.1779, simple_loss=0.251, pruned_loss=0.0524, over 984549.87 frames.], batch size: 55, aishell_tot_loss[loss=0.1769, simple_loss=0.2565, pruned_loss=0.04865, over 956555.20 frames.], datatang_tot_loss[loss=0.1792, simple_loss=0.2464, pruned_loss=0.056, over 955238.31 frames.], batch size: 55, lr: 8.01e-04
+2022-06-18 17:15:21,178 INFO [train.py:874] (3/4) Epoch 10, batch 1450, datatang_loss[loss=0.1522, simple_loss=0.2161, pruned_loss=0.04413, over 4909.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2505, pruned_loss=0.05225, over 984761.07 frames.], batch size: 30, aishell_tot_loss[loss=0.1765, simple_loss=0.2563, pruned_loss=0.04836, over 959557.44 frames.], datatang_tot_loss[loss=0.179, simple_loss=0.246, pruned_loss=0.05601, over 959228.38 frames.], batch size: 30, lr: 8.00e-04
+2022-06-18 17:15:52,748 INFO [train.py:874] (3/4) Epoch 10, batch 1500, datatang_loss[loss=0.1733, simple_loss=0.2474, pruned_loss=0.04956, over 4924.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2511, pruned_loss=0.0529, over 984561.56 frames.], batch size: 83, aishell_tot_loss[loss=0.1768, simple_loss=0.2562, pruned_loss=0.04867, over 961872.26 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2466, pruned_loss=0.05636, over 962664.31 frames.], batch size: 83, lr: 8.00e-04
+2022-06-18 17:16:23,765 INFO [train.py:874] (3/4) Epoch 10, batch 1550, datatang_loss[loss=0.1765, simple_loss=0.2465, pruned_loss=0.05329, over 4915.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2519, pruned_loss=0.05293, over 984785.64 frames.], batch size: 94, aishell_tot_loss[loss=0.1771, simple_loss=0.2565, pruned_loss=0.04881, over 965162.79 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2469, pruned_loss=0.05656, over 964866.69 frames.], batch size: 94, lr: 7.99e-04
+2022-06-18 17:16:51,743 INFO [train.py:874] (3/4) Epoch 10, batch 1600, aishell_loss[loss=0.1856, simple_loss=0.2652, pruned_loss=0.05296, over 4928.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2526, pruned_loss=0.05352, over 985196.73 frames.], batch size: 58, aishell_tot_loss[loss=0.1774, simple_loss=0.257, pruned_loss=0.0489, over 967650.09 frames.], datatang_tot_loss[loss=0.1807, simple_loss=0.2472, pruned_loss=0.05714, over 967465.20 frames.], batch size: 58, lr: 7.99e-04
+2022-06-18 17:17:24,174 INFO [train.py:874] (3/4) Epoch 10, batch 1650, datatang_loss[loss=0.1853, simple_loss=0.2461, pruned_loss=0.06227, over 4939.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2515, pruned_loss=0.05319, over 985182.60 frames.], batch size: 45, aishell_tot_loss[loss=0.1769, simple_loss=0.2563, pruned_loss=0.04877, over 969336.79 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.247, pruned_loss=0.05691, over 969906.97 frames.], batch size: 45, lr: 7.98e-04
+2022-06-18 17:17:56,419 INFO [train.py:874] (3/4) Epoch 10, batch 1700, aishell_loss[loss=0.1922, simple_loss=0.2773, pruned_loss=0.05351, over 4937.00 frames.], tot_loss[loss=0.179, simple_loss=0.2517, pruned_loss=0.05311, over 985106.33 frames.], batch size: 54, aishell_tot_loss[loss=0.1776, simple_loss=0.257, pruned_loss=0.04911, over 971035.17 frames.], datatang_tot_loss[loss=0.1798, simple_loss=0.2462, pruned_loss=0.05663, over 971793.12 frames.], batch size: 54, lr: 7.98e-04
+2022-06-18 17:18:24,545 INFO [train.py:874] (3/4) Epoch 10, batch 1750, datatang_loss[loss=0.2195, simple_loss=0.2826, pruned_loss=0.07822, over 4962.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2523, pruned_loss=0.05335, over 985019.61 frames.], batch size: 91, aishell_tot_loss[loss=0.1781, simple_loss=0.2576, pruned_loss=0.04933, over 972698.79 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2465, pruned_loss=0.05677, over 973271.07 frames.], batch size: 91, lr: 7.97e-04
+2022-06-18 17:18:55,259 INFO [train.py:874] (3/4) Epoch 10, batch 1800, datatang_loss[loss=0.1678, simple_loss=0.236, pruned_loss=0.04982, over 4900.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2528, pruned_loss=0.05334, over 985331.38 frames.], batch size: 52, aishell_tot_loss[loss=0.1778, simple_loss=0.2574, pruned_loss=0.0491, over 974298.30 frames.], datatang_tot_loss[loss=0.1806, simple_loss=0.2472, pruned_loss=0.05699, over 974845.03 frames.], batch size: 52, lr: 7.97e-04
+2022-06-18 17:19:27,570 INFO [train.py:874] (3/4) Epoch 10, batch 1850, datatang_loss[loss=0.2129, simple_loss=0.2764, pruned_loss=0.07467, over 4912.00 frames.], tot_loss[loss=0.179, simple_loss=0.2521, pruned_loss=0.05301, over 985192.08 frames.], batch size: 98, aishell_tot_loss[loss=0.1771, simple_loss=0.2568, pruned_loss=0.04869, over 975548.22 frames.], datatang_tot_loss[loss=0.1806, simple_loss=0.2471, pruned_loss=0.0571, over 975983.42 frames.], batch size: 98, lr: 7.96e-04
+2022-06-18 17:19:55,272 INFO [train.py:874] (3/4) Epoch 10, batch 1900, aishell_loss[loss=0.1705, simple_loss=0.2633, pruned_loss=0.03886, over 4913.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2512, pruned_loss=0.05275, over 984748.26 frames.], batch size: 41, aishell_tot_loss[loss=0.1769, simple_loss=0.2567, pruned_loss=0.04858, over 975808.30 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2466, pruned_loss=0.05668, over 977426.94 frames.], batch size: 41, lr: 7.96e-04
+2022-06-18 17:20:27,404 INFO [train.py:874] (3/4) Epoch 10, batch 1950, datatang_loss[loss=0.2268, simple_loss=0.2724, pruned_loss=0.09062, over 4878.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2511, pruned_loss=0.05279, over 984620.97 frames.], batch size: 39, aishell_tot_loss[loss=0.1766, simple_loss=0.2562, pruned_loss=0.04849, over 976811.57 frames.], datatang_tot_loss[loss=0.1803, simple_loss=0.2469, pruned_loss=0.05692, over 978212.25 frames.], batch size: 39, lr: 7.95e-04
+2022-06-18 17:20:59,424 INFO [train.py:874] (3/4) Epoch 10, batch 2000, aishell_loss[loss=0.174, simple_loss=0.2601, pruned_loss=0.04393, over 4974.00 frames.], tot_loss[loss=0.179, simple_loss=0.2517, pruned_loss=0.05317, over 984958.65 frames.], batch size: 61, aishell_tot_loss[loss=0.1774, simple_loss=0.2569, pruned_loss=0.04894, over 977733.11 frames.], datatang_tot_loss[loss=0.1802, simple_loss=0.2467, pruned_loss=0.0569, over 979310.01 frames.], batch size: 61, lr: 7.95e-04
+2022-06-18 17:20:59,425 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 17:21:15,567 INFO [train.py:914] (3/4) Epoch 10, validation: loss=0.1694, simple_loss=0.2523, pruned_loss=0.04323, over 1622729.00 frames.
+2022-06-18 17:21:47,962 INFO [train.py:874] (3/4) Epoch 10, batch 2050, aishell_loss[loss=0.2012, simple_loss=0.2642, pruned_loss=0.06905, over 4881.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2527, pruned_loss=0.05316, over 984960.00 frames.], batch size: 28, aishell_tot_loss[loss=0.1775, simple_loss=0.2572, pruned_loss=0.04893, over 978666.62 frames.], datatang_tot_loss[loss=0.1807, simple_loss=0.2472, pruned_loss=0.05711, over 979917.07 frames.], batch size: 28, lr: 7.94e-04
+2022-06-18 17:22:16,740 INFO [train.py:874] (3/4) Epoch 10, batch 2100, aishell_loss[loss=0.1433, simple_loss=0.229, pruned_loss=0.02877, over 4986.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2519, pruned_loss=0.05288, over 984714.24 frames.], batch size: 30, aishell_tot_loss[loss=0.1769, simple_loss=0.2568, pruned_loss=0.04854, over 979292.62 frames.], datatang_tot_loss[loss=0.1808, simple_loss=0.2469, pruned_loss=0.05734, over 980369.57 frames.], batch size: 30, lr: 7.94e-04
+2022-06-18 17:22:48,300 INFO [train.py:874] (3/4) Epoch 10, batch 2150, aishell_loss[loss=0.176, simple_loss=0.2628, pruned_loss=0.04463, over 4938.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2517, pruned_loss=0.0525, over 985160.69 frames.], batch size: 64, aishell_tot_loss[loss=0.1774, simple_loss=0.2574, pruned_loss=0.04871, over 980066.02 frames.], datatang_tot_loss[loss=0.1798, simple_loss=0.2462, pruned_loss=0.05668, over 981201.60 frames.], batch size: 64, lr: 7.93e-04
+2022-06-18 17:23:19,919 INFO [train.py:874] (3/4) Epoch 10, batch 2200, aishell_loss[loss=0.1839, simple_loss=0.274, pruned_loss=0.04684, over 4867.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2507, pruned_loss=0.05158, over 985364.08 frames.], batch size: 35, aishell_tot_loss[loss=0.1768, simple_loss=0.2571, pruned_loss=0.0483, over 980776.87 frames.], datatang_tot_loss[loss=0.1788, simple_loss=0.2454, pruned_loss=0.05608, over 981781.56 frames.], batch size: 35, lr: 7.93e-04
+2022-06-18 17:23:47,956 INFO [train.py:874] (3/4) Epoch 10, batch 2250, datatang_loss[loss=0.2111, simple_loss=0.2678, pruned_loss=0.07717, over 4952.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2503, pruned_loss=0.05172, over 985644.16 frames.], batch size: 86, aishell_tot_loss[loss=0.1767, simple_loss=0.2566, pruned_loss=0.0484, over 981451.83 frames.], datatang_tot_loss[loss=0.1787, simple_loss=0.2453, pruned_loss=0.05606, over 982369.64 frames.], batch size: 86, lr: 7.92e-04
+2022-06-18 17:24:17,599 INFO [train.py:874] (3/4) Epoch 10, batch 2300, aishell_loss[loss=0.1821, simple_loss=0.2662, pruned_loss=0.04895, over 4874.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2521, pruned_loss=0.05223, over 985376.35 frames.], batch size: 42, aishell_tot_loss[loss=0.1775, simple_loss=0.2574, pruned_loss=0.04876, over 981720.06 frames.], datatang_tot_loss[loss=0.1793, simple_loss=0.2458, pruned_loss=0.05636, over 982727.32 frames.], batch size: 42, lr: 7.92e-04
+2022-06-18 17:24:49,751 INFO [train.py:874] (3/4) Epoch 10, batch 2350, datatang_loss[loss=0.1829, simple_loss=0.2461, pruned_loss=0.05987, over 4971.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2508, pruned_loss=0.05144, over 985458.89 frames.], batch size: 55, aishell_tot_loss[loss=0.176, simple_loss=0.256, pruned_loss=0.04798, over 982068.59 frames.], datatang_tot_loss[loss=0.1793, simple_loss=0.246, pruned_loss=0.05627, over 983217.15 frames.], batch size: 55, lr: 7.91e-04
+2022-06-18 17:25:17,524 INFO [train.py:874] (3/4) Epoch 10, batch 2400, datatang_loss[loss=0.1909, simple_loss=0.256, pruned_loss=0.06292, over 4968.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2501, pruned_loss=0.05179, over 985536.11 frames.], batch size: 37, aishell_tot_loss[loss=0.1761, simple_loss=0.256, pruned_loss=0.0481, over 982648.02 frames.], datatang_tot_loss[loss=0.1789, simple_loss=0.2454, pruned_loss=0.05617, over 983347.07 frames.], batch size: 37, lr: 7.91e-04
+2022-06-18 17:25:49,046 INFO [train.py:874] (3/4) Epoch 10, batch 2450, datatang_loss[loss=0.1783, simple_loss=0.2477, pruned_loss=0.05444, over 4937.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2495, pruned_loss=0.05133, over 985517.70 frames.], batch size: 62, aishell_tot_loss[loss=0.1757, simple_loss=0.2555, pruned_loss=0.04794, over 982657.69 frames.], datatang_tot_loss[loss=0.1783, simple_loss=0.2451, pruned_loss=0.05576, over 983913.62 frames.], batch size: 62, lr: 7.90e-04
+2022-06-18 17:26:19,999 INFO [train.py:874] (3/4) Epoch 10, batch 2500, aishell_loss[loss=0.2023, simple_loss=0.2857, pruned_loss=0.05941, over 4966.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2497, pruned_loss=0.05158, over 985466.96 frames.], batch size: 56, aishell_tot_loss[loss=0.1757, simple_loss=0.2553, pruned_loss=0.04809, over 982853.37 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2452, pruned_loss=0.05588, over 984211.13 frames.], batch size: 56, lr: 7.90e-04
+2022-06-18 17:26:50,272 INFO [train.py:874] (3/4) Epoch 10, batch 2550, datatang_loss[loss=0.1735, simple_loss=0.2355, pruned_loss=0.05572, over 4884.00 frames.], tot_loss[loss=0.1767, simple_loss=0.25, pruned_loss=0.05173, over 985662.20 frames.], batch size: 52, aishell_tot_loss[loss=0.1754, simple_loss=0.2551, pruned_loss=0.04782, over 983322.38 frames.], datatang_tot_loss[loss=0.179, simple_loss=0.2456, pruned_loss=0.05624, over 984401.25 frames.], batch size: 52, lr: 7.89e-04
+2022-06-18 17:27:22,154 INFO [train.py:874] (3/4) Epoch 10, batch 2600, datatang_loss[loss=0.1911, simple_loss=0.2623, pruned_loss=0.05994, over 4789.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2496, pruned_loss=0.05139, over 985373.86 frames.], batch size: 23, aishell_tot_loss[loss=0.1753, simple_loss=0.2549, pruned_loss=0.04783, over 983364.30 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2452, pruned_loss=0.05586, over 984494.08 frames.], batch size: 23, lr: 7.89e-04
+2022-06-18 17:27:53,277 INFO [train.py:874] (3/4) Epoch 10, batch 2650, aishell_loss[loss=0.1945, simple_loss=0.2662, pruned_loss=0.06134, over 4903.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2496, pruned_loss=0.05155, over 985410.94 frames.], batch size: 41, aishell_tot_loss[loss=0.1756, simple_loss=0.2551, pruned_loss=0.04809, over 983489.90 frames.], datatang_tot_loss[loss=0.178, simple_loss=0.245, pruned_loss=0.05553, over 984718.68 frames.], batch size: 41, lr: 7.88e-04
+2022-06-18 17:28:23,931 INFO [train.py:874] (3/4) Epoch 10, batch 2700, datatang_loss[loss=0.1586, simple_loss=0.2302, pruned_loss=0.04348, over 4917.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2496, pruned_loss=0.05197, over 985594.28 frames.], batch size: 75, aishell_tot_loss[loss=0.1761, simple_loss=0.2556, pruned_loss=0.04832, over 983670.05 frames.], datatang_tot_loss[loss=0.1778, simple_loss=0.245, pruned_loss=0.05534, over 984970.69 frames.], batch size: 75, lr: 7.88e-04
+2022-06-18 17:28:56,029 INFO [train.py:874] (3/4) Epoch 10, batch 2750, aishell_loss[loss=0.1893, simple_loss=0.2655, pruned_loss=0.05659, over 4863.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2491, pruned_loss=0.05172, over 985404.98 frames.], batch size: 36, aishell_tot_loss[loss=0.1764, simple_loss=0.2559, pruned_loss=0.04847, over 983922.91 frames.], datatang_tot_loss[loss=0.177, simple_loss=0.2441, pruned_loss=0.05495, over 984838.62 frames.], batch size: 36, lr: 7.87e-04
+2022-06-18 17:29:26,500 INFO [train.py:874] (3/4) Epoch 10, batch 2800, datatang_loss[loss=0.2512, simple_loss=0.3017, pruned_loss=0.1004, over 4980.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2489, pruned_loss=0.05178, over 985718.36 frames.], batch size: 31, aishell_tot_loss[loss=0.1767, simple_loss=0.2556, pruned_loss=0.04886, over 984090.11 frames.], datatang_tot_loss[loss=0.1766, simple_loss=0.2439, pruned_loss=0.05463, over 985251.33 frames.], batch size: 31, lr: 7.87e-04
+2022-06-18 17:30:00,763 INFO [train.py:874] (3/4) Epoch 10, batch 2850, datatang_loss[loss=0.1535, simple_loss=0.2231, pruned_loss=0.04188, over 4943.00 frames.], tot_loss[loss=0.1757, simple_loss=0.249, pruned_loss=0.05123, over 985393.42 frames.], batch size: 62, aishell_tot_loss[loss=0.1758, simple_loss=0.2552, pruned_loss=0.04819, over 984064.46 frames.], datatang_tot_loss[loss=0.1768, simple_loss=0.2442, pruned_loss=0.05475, over 985185.42 frames.], batch size: 62, lr: 7.86e-04
+2022-06-18 17:30:33,010 INFO [train.py:874] (3/4) Epoch 10, batch 2900, datatang_loss[loss=0.1613, simple_loss=0.2333, pruned_loss=0.04465, over 4951.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2505, pruned_loss=0.05251, over 985480.10 frames.], batch size: 91, aishell_tot_loss[loss=0.1768, simple_loss=0.2562, pruned_loss=0.04875, over 984093.56 frames.], datatang_tot_loss[loss=0.1778, simple_loss=0.2447, pruned_loss=0.05544, over 985402.49 frames.], batch size: 91, lr: 7.86e-04
+2022-06-18 17:31:03,407 INFO [train.py:874] (3/4) Epoch 10, batch 2950, aishell_loss[loss=0.1871, simple_loss=0.2631, pruned_loss=0.05559, over 4967.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2511, pruned_loss=0.05222, over 985656.02 frames.], batch size: 44, aishell_tot_loss[loss=0.1768, simple_loss=0.2564, pruned_loss=0.04861, over 984272.05 frames.], datatang_tot_loss[loss=0.178, simple_loss=0.245, pruned_loss=0.05546, over 985606.55 frames.], batch size: 44, lr: 7.85e-04
+2022-06-18 17:31:32,486 INFO [train.py:874] (3/4) Epoch 10, batch 3000, aishell_loss[loss=0.1464, simple_loss=0.2245, pruned_loss=0.03411, over 4973.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2522, pruned_loss=0.05339, over 986085.87 frames.], batch size: 27, aishell_tot_loss[loss=0.1773, simple_loss=0.2569, pruned_loss=0.04882, over 984807.70 frames.], datatang_tot_loss[loss=0.1794, simple_loss=0.2458, pruned_loss=0.05648, over 985694.19 frames.], batch size: 27, lr: 7.85e-04
+2022-06-18 17:31:32,487 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 17:31:49,159 INFO [train.py:914] (3/4) Epoch 10, validation: loss=0.1672, simple_loss=0.2512, pruned_loss=0.04165, over 1622729.00 frames.
+2022-06-18 17:32:18,402 INFO [train.py:874] (3/4) Epoch 10, batch 3050, aishell_loss[loss=0.1652, simple_loss=0.2637, pruned_loss=0.03339, over 4948.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2525, pruned_loss=0.05303, over 986295.91 frames.], batch size: 40, aishell_tot_loss[loss=0.1769, simple_loss=0.2568, pruned_loss=0.0485, over 985019.03 frames.], datatang_tot_loss[loss=0.1798, simple_loss=0.2464, pruned_loss=0.05657, over 985890.37 frames.], batch size: 40, lr: 7.85e-04
+2022-06-18 17:32:48,994 INFO [train.py:874] (3/4) Epoch 10, batch 3100, aishell_loss[loss=0.1686, simple_loss=0.2517, pruned_loss=0.04277, over 4918.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2519, pruned_loss=0.05253, over 986462.58 frames.], batch size: 33, aishell_tot_loss[loss=0.1769, simple_loss=0.257, pruned_loss=0.04837, over 985475.72 frames.], datatang_tot_loss[loss=0.1793, simple_loss=0.2458, pruned_loss=0.05637, over 985805.59 frames.], batch size: 33, lr: 7.84e-04
+2022-06-18 17:33:21,061 INFO [train.py:874] (3/4) Epoch 10, batch 3150, aishell_loss[loss=0.2095, simple_loss=0.2932, pruned_loss=0.06293, over 4912.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2519, pruned_loss=0.05181, over 986133.00 frames.], batch size: 33, aishell_tot_loss[loss=0.1768, simple_loss=0.2573, pruned_loss=0.04814, over 985302.07 frames.], datatang_tot_loss[loss=0.1787, simple_loss=0.2456, pruned_loss=0.05588, over 985822.75 frames.], batch size: 33, lr: 7.84e-04
+2022-06-18 17:33:49,748 INFO [train.py:874] (3/4) Epoch 10, batch 3200, aishell_loss[loss=0.1872, simple_loss=0.2707, pruned_loss=0.05185, over 4866.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2535, pruned_loss=0.05261, over 985836.44 frames.], batch size: 36, aishell_tot_loss[loss=0.1781, simple_loss=0.2587, pruned_loss=0.04878, over 985085.42 frames.], datatang_tot_loss[loss=0.1791, simple_loss=0.246, pruned_loss=0.05608, over 985873.09 frames.], batch size: 36, lr: 7.83e-04
+2022-06-18 17:34:21,007 INFO [train.py:874] (3/4) Epoch 10, batch 3250, datatang_loss[loss=0.1791, simple_loss=0.2441, pruned_loss=0.05704, over 4919.00 frames.], tot_loss[loss=0.1787, simple_loss=0.253, pruned_loss=0.05217, over 985923.37 frames.], batch size: 83, aishell_tot_loss[loss=0.1777, simple_loss=0.2583, pruned_loss=0.04851, over 985096.59 frames.], datatang_tot_loss[loss=0.179, simple_loss=0.2459, pruned_loss=0.05608, over 986060.82 frames.], batch size: 83, lr: 7.83e-04
+2022-06-18 17:34:51,822 INFO [train.py:874] (3/4) Epoch 10, batch 3300, datatang_loss[loss=0.1887, simple_loss=0.2622, pruned_loss=0.05761, over 4915.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2517, pruned_loss=0.05193, over 985899.80 frames.], batch size: 98, aishell_tot_loss[loss=0.1775, simple_loss=0.2582, pruned_loss=0.04844, over 985077.02 frames.], datatang_tot_loss[loss=0.1782, simple_loss=0.245, pruned_loss=0.05572, over 986120.11 frames.], batch size: 98, lr: 7.82e-04
+2022-06-18 17:35:21,953 INFO [train.py:874] (3/4) Epoch 10, batch 3350, aishell_loss[loss=0.1742, simple_loss=0.2542, pruned_loss=0.04708, over 4938.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2509, pruned_loss=0.05146, over 985719.99 frames.], batch size: 45, aishell_tot_loss[loss=0.1773, simple_loss=0.2579, pruned_loss=0.04837, over 985011.86 frames.], datatang_tot_loss[loss=0.1775, simple_loss=0.2444, pruned_loss=0.05532, over 986066.69 frames.], batch size: 45, lr: 7.82e-04
+2022-06-18 17:35:53,690 INFO [train.py:874] (3/4) Epoch 10, batch 3400, aishell_loss[loss=0.2127, simple_loss=0.2861, pruned_loss=0.06961, over 4956.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2515, pruned_loss=0.05158, over 985804.72 frames.], batch size: 61, aishell_tot_loss[loss=0.1776, simple_loss=0.258, pruned_loss=0.04863, over 984999.90 frames.], datatang_tot_loss[loss=0.1775, simple_loss=0.2446, pruned_loss=0.05522, over 986220.58 frames.], batch size: 61, lr: 7.81e-04
+2022-06-18 17:36:23,962 INFO [train.py:874] (3/4) Epoch 10, batch 3450, datatang_loss[loss=0.2123, simple_loss=0.2532, pruned_loss=0.08569, over 4952.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2505, pruned_loss=0.05135, over 985940.60 frames.], batch size: 37, aishell_tot_loss[loss=0.1773, simple_loss=0.2574, pruned_loss=0.04856, over 985176.14 frames.], datatang_tot_loss[loss=0.1771, simple_loss=0.2441, pruned_loss=0.05506, over 986259.23 frames.], batch size: 37, lr: 7.81e-04
+2022-06-18 17:36:53,836 INFO [train.py:874] (3/4) Epoch 10, batch 3500, aishell_loss[loss=0.2181, simple_loss=0.2859, pruned_loss=0.07517, over 4889.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2512, pruned_loss=0.05195, over 985375.98 frames.], batch size: 34, aishell_tot_loss[loss=0.1771, simple_loss=0.2571, pruned_loss=0.04853, over 984829.65 frames.], datatang_tot_loss[loss=0.1782, simple_loss=0.245, pruned_loss=0.05566, over 986073.18 frames.], batch size: 34, lr: 7.80e-04
+2022-06-18 17:37:26,149 INFO [train.py:874] (3/4) Epoch 10, batch 3550, datatang_loss[loss=0.1754, simple_loss=0.2423, pruned_loss=0.0542, over 4929.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2515, pruned_loss=0.05243, over 985426.34 frames.], batch size: 83, aishell_tot_loss[loss=0.177, simple_loss=0.2574, pruned_loss=0.04834, over 984710.94 frames.], datatang_tot_loss[loss=0.1788, simple_loss=0.2453, pruned_loss=0.05622, over 986220.79 frames.], batch size: 83, lr: 7.80e-04
+2022-06-18 17:37:55,081 INFO [train.py:874] (3/4) Epoch 10, batch 3600, datatang_loss[loss=0.1746, simple_loss=0.2391, pruned_loss=0.05499, over 4876.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2503, pruned_loss=0.05155, over 985381.42 frames.], batch size: 39, aishell_tot_loss[loss=0.176, simple_loss=0.2562, pruned_loss=0.04796, over 984694.58 frames.], datatang_tot_loss[loss=0.1783, simple_loss=0.2449, pruned_loss=0.05588, over 986218.78 frames.], batch size: 39, lr: 7.79e-04
+2022-06-18 17:38:26,136 INFO [train.py:874] (3/4) Epoch 10, batch 3650, datatang_loss[loss=0.1761, simple_loss=0.2455, pruned_loss=0.05334, over 4906.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2502, pruned_loss=0.05119, over 985542.88 frames.], batch size: 64, aishell_tot_loss[loss=0.1761, simple_loss=0.2562, pruned_loss=0.04799, over 984824.95 frames.], datatang_tot_loss[loss=0.1778, simple_loss=0.2448, pruned_loss=0.05537, over 986247.27 frames.], batch size: 64, lr: 7.79e-04
+2022-06-18 17:38:57,513 INFO [train.py:874] (3/4) Epoch 10, batch 3700, aishell_loss[loss=0.1794, simple_loss=0.2611, pruned_loss=0.04883, over 4897.00 frames.], tot_loss[loss=0.1772, simple_loss=0.251, pruned_loss=0.05165, over 985214.55 frames.], batch size: 34, aishell_tot_loss[loss=0.1763, simple_loss=0.2566, pruned_loss=0.04802, over 984633.43 frames.], datatang_tot_loss[loss=0.1783, simple_loss=0.2453, pruned_loss=0.0556, over 986080.93 frames.], batch size: 34, lr: 7.78e-04
+2022-06-18 17:39:26,972 INFO [train.py:874] (3/4) Epoch 10, batch 3750, datatang_loss[loss=0.1918, simple_loss=0.2465, pruned_loss=0.06856, over 4922.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2507, pruned_loss=0.05121, over 984781.11 frames.], batch size: 75, aishell_tot_loss[loss=0.1753, simple_loss=0.2557, pruned_loss=0.04746, over 984394.69 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2457, pruned_loss=0.05568, over 985851.44 frames.], batch size: 75, lr: 7.78e-04
+2022-06-18 17:39:57,930 INFO [train.py:874] (3/4) Epoch 10, batch 3800, datatang_loss[loss=0.152, simple_loss=0.2279, pruned_loss=0.03804, over 4914.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2505, pruned_loss=0.05099, over 984682.40 frames.], batch size: 75, aishell_tot_loss[loss=0.1752, simple_loss=0.2559, pruned_loss=0.04725, over 984467.09 frames.], datatang_tot_loss[loss=0.1783, simple_loss=0.2454, pruned_loss=0.05559, over 985598.51 frames.], batch size: 75, lr: 7.77e-04
+2022-06-18 17:40:26,686 INFO [train.py:874] (3/4) Epoch 10, batch 3850, aishell_loss[loss=0.1627, simple_loss=0.2467, pruned_loss=0.03934, over 4984.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2498, pruned_loss=0.05078, over 984863.78 frames.], batch size: 39, aishell_tot_loss[loss=0.1749, simple_loss=0.2556, pruned_loss=0.04712, over 984527.23 frames.], datatang_tot_loss[loss=0.1778, simple_loss=0.2448, pruned_loss=0.05539, over 985624.45 frames.], batch size: 39, lr: 7.77e-04
+2022-06-18 17:40:56,185 INFO [train.py:874] (3/4) Epoch 10, batch 3900, datatang_loss[loss=0.1789, simple_loss=0.2448, pruned_loss=0.05654, over 4930.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2499, pruned_loss=0.05063, over 984522.20 frames.], batch size: 71, aishell_tot_loss[loss=0.175, simple_loss=0.256, pruned_loss=0.04704, over 984172.66 frames.], datatang_tot_loss[loss=0.1773, simple_loss=0.2446, pruned_loss=0.055, over 985539.25 frames.], batch size: 71, lr: 7.76e-04
+2022-06-18 17:41:24,555 INFO [train.py:874] (3/4) Epoch 10, batch 3950, datatang_loss[loss=0.1546, simple_loss=0.2304, pruned_loss=0.03935, over 4956.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2493, pruned_loss=0.05007, over 984341.32 frames.], batch size: 55, aishell_tot_loss[loss=0.1752, simple_loss=0.256, pruned_loss=0.04715, over 983950.37 frames.], datatang_tot_loss[loss=0.1761, simple_loss=0.2438, pruned_loss=0.05425, over 985501.19 frames.], batch size: 55, lr: 7.76e-04
+2022-06-18 17:41:54,503 INFO [train.py:874] (3/4) Epoch 10, batch 4000, aishell_loss[loss=0.1701, simple_loss=0.2483, pruned_loss=0.04597, over 4878.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2484, pruned_loss=0.04946, over 984647.16 frames.], batch size: 47, aishell_tot_loss[loss=0.1747, simple_loss=0.2556, pruned_loss=0.04693, over 984120.19 frames.], datatang_tot_loss[loss=0.1751, simple_loss=0.2429, pruned_loss=0.05372, over 985579.51 frames.], batch size: 47, lr: 7.76e-04
+2022-06-18 17:41:54,505 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 17:42:11,517 INFO [train.py:914] (3/4) Epoch 10, validation: loss=0.169, simple_loss=0.2525, pruned_loss=0.04271, over 1622729.00 frames.
+2022-06-18 17:42:40,806 INFO [train.py:874] (3/4) Epoch 10, batch 4050, aishell_loss[loss=0.2083, simple_loss=0.2815, pruned_loss=0.06752, over 4888.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2496, pruned_loss=0.05001, over 984837.38 frames.], batch size: 50, aishell_tot_loss[loss=0.175, simple_loss=0.2559, pruned_loss=0.04705, over 984333.30 frames.], datatang_tot_loss[loss=0.1758, simple_loss=0.2435, pruned_loss=0.05403, over 985536.66 frames.], batch size: 50, lr: 7.75e-04
+2022-06-18 17:43:07,883 INFO [train.py:874] (3/4) Epoch 10, batch 4100, aishell_loss[loss=0.1549, simple_loss=0.2443, pruned_loss=0.03272, over 4916.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2493, pruned_loss=0.04991, over 984691.01 frames.], batch size: 41, aishell_tot_loss[loss=0.1746, simple_loss=0.2556, pruned_loss=0.04682, over 984237.16 frames.], datatang_tot_loss[loss=0.1758, simple_loss=0.2433, pruned_loss=0.05417, over 985475.85 frames.], batch size: 41, lr: 7.75e-04
+2022-06-18 17:44:17,223 INFO [train.py:874] (3/4) Epoch 11, batch 50, aishell_loss[loss=0.1552, simple_loss=0.25, pruned_loss=0.03014, over 4978.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2471, pruned_loss=0.04754, over 218560.32 frames.], batch size: 51, aishell_tot_loss[loss=0.1733, simple_loss=0.2548, pruned_loss=0.04593, over 137464.78 frames.], datatang_tot_loss[loss=0.1678, simple_loss=0.2356, pruned_loss=0.05005, over 94238.75 frames.], batch size: 51, lr: 7.46e-04
+2022-06-18 17:44:48,052 INFO [train.py:874] (3/4) Epoch 11, batch 100, datatang_loss[loss=0.1517, simple_loss=0.2334, pruned_loss=0.03497, over 4942.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2459, pruned_loss=0.04742, over 388540.13 frames.], batch size: 50, aishell_tot_loss[loss=0.1721, simple_loss=0.2546, pruned_loss=0.04478, over 233522.63 frames.], datatang_tot_loss[loss=0.1686, simple_loss=0.2361, pruned_loss=0.05057, over 203147.27 frames.], batch size: 50, lr: 7.45e-04
+2022-06-18 17:45:16,862 INFO [train.py:874] (3/4) Epoch 11, batch 150, aishell_loss[loss=0.1859, simple_loss=0.2633, pruned_loss=0.05423, over 4910.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2443, pruned_loss=0.04838, over 521012.31 frames.], batch size: 34, aishell_tot_loss[loss=0.1714, simple_loss=0.2532, pruned_loss=0.0448, over 298407.21 frames.], datatang_tot_loss[loss=0.1697, simple_loss=0.2367, pruned_loss=0.0514, over 319221.86 frames.], batch size: 34, lr: 7.45e-04
+2022-06-18 17:45:48,312 INFO [train.py:874] (3/4) Epoch 11, batch 200, datatang_loss[loss=0.1857, simple_loss=0.2537, pruned_loss=0.05886, over 4942.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2438, pruned_loss=0.04774, over 624025.91 frames.], batch size: 88, aishell_tot_loss[loss=0.1714, simple_loss=0.2523, pruned_loss=0.04527, over 385503.84 frames.], datatang_tot_loss[loss=0.1682, simple_loss=0.2361, pruned_loss=0.05016, over 391685.71 frames.], batch size: 88, lr: 7.44e-04
+2022-06-18 17:46:17,727 INFO [train.py:874] (3/4) Epoch 11, batch 250, datatang_loss[loss=0.1729, simple_loss=0.233, pruned_loss=0.05642, over 4964.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2453, pruned_loss=0.04888, over 704131.50 frames.], batch size: 45, aishell_tot_loss[loss=0.1722, simple_loss=0.2521, pruned_loss=0.04619, over 466538.43 frames.], datatang_tot_loss[loss=0.1702, simple_loss=0.2378, pruned_loss=0.05132, over 451128.62 frames.], batch size: 45, lr: 7.44e-04
+2022-06-18 17:46:47,555 INFO [train.py:874] (3/4) Epoch 11, batch 300, datatang_loss[loss=0.1824, simple_loss=0.2554, pruned_loss=0.05474, over 4963.00 frames.], tot_loss[loss=0.172, simple_loss=0.2453, pruned_loss=0.04936, over 766760.33 frames.], batch size: 45, aishell_tot_loss[loss=0.1717, simple_loss=0.2513, pruned_loss=0.04604, over 515973.19 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.2393, pruned_loss=0.05204, over 526068.77 frames.], batch size: 45, lr: 7.43e-04
+2022-06-18 17:47:17,457 INFO [train.py:874] (3/4) Epoch 11, batch 350, aishell_loss[loss=0.1765, simple_loss=0.2628, pruned_loss=0.04509, over 4943.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2463, pruned_loss=0.04881, over 815229.03 frames.], batch size: 58, aishell_tot_loss[loss=0.171, simple_loss=0.2513, pruned_loss=0.04532, over 585380.21 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2401, pruned_loss=0.05237, over 565839.32 frames.], batch size: 58, lr: 7.43e-04
+2022-06-18 17:47:47,238 INFO [train.py:874] (3/4) Epoch 11, batch 400, aishell_loss[loss=0.178, simple_loss=0.2617, pruned_loss=0.04712, over 4953.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2461, pruned_loss=0.04868, over 853147.73 frames.], batch size: 40, aishell_tot_loss[loss=0.1712, simple_loss=0.2517, pruned_loss=0.0454, over 630662.27 frames.], datatang_tot_loss[loss=0.1719, simple_loss=0.2397, pruned_loss=0.05205, over 617354.90 frames.], batch size: 40, lr: 7.42e-04
+2022-06-18 17:48:17,560 INFO [train.py:874] (3/4) Epoch 11, batch 450, datatang_loss[loss=0.2525, simple_loss=0.3017, pruned_loss=0.1017, over 4950.00 frames.], tot_loss[loss=0.1723, simple_loss=0.246, pruned_loss=0.04929, over 882444.88 frames.], batch size: 108, aishell_tot_loss[loss=0.1718, simple_loss=0.2522, pruned_loss=0.04566, over 657944.57 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.2399, pruned_loss=0.05215, over 675061.81 frames.], batch size: 108, lr: 7.42e-04
+2022-06-18 17:48:48,077 INFO [train.py:874] (3/4) Epoch 11, batch 500, datatang_loss[loss=0.1896, simple_loss=0.2569, pruned_loss=0.06113, over 4924.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2461, pruned_loss=0.04919, over 905316.00 frames.], batch size: 81, aishell_tot_loss[loss=0.1714, simple_loss=0.2517, pruned_loss=0.04551, over 696474.00 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.2404, pruned_loss=0.05226, over 711687.68 frames.], batch size: 81, lr: 7.42e-04
+2022-06-18 17:49:17,373 INFO [train.py:874] (3/4) Epoch 11, batch 550, datatang_loss[loss=0.19, simple_loss=0.2536, pruned_loss=0.06323, over 4963.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2472, pruned_loss=0.0492, over 923402.49 frames.], batch size: 67, aishell_tot_loss[loss=0.1712, simple_loss=0.2522, pruned_loss=0.04508, over 730488.55 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2414, pruned_loss=0.05282, over 744290.07 frames.], batch size: 67, lr: 7.41e-04
+2022-06-18 17:49:48,477 INFO [train.py:874] (3/4) Epoch 11, batch 600, datatang_loss[loss=0.1881, simple_loss=0.2567, pruned_loss=0.05979, over 4943.00 frames.], tot_loss[loss=0.1728, simple_loss=0.247, pruned_loss=0.04929, over 937062.49 frames.], batch size: 62, aishell_tot_loss[loss=0.1712, simple_loss=0.2521, pruned_loss=0.04514, over 756092.15 frames.], datatang_tot_loss[loss=0.1736, simple_loss=0.2416, pruned_loss=0.05276, over 776692.16 frames.], batch size: 62, lr: 7.41e-04
+2022-06-18 17:50:18,164 INFO [train.py:874] (3/4) Epoch 11, batch 650, aishell_loss[loss=0.1657, simple_loss=0.2453, pruned_loss=0.0431, over 4920.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2474, pruned_loss=0.04857, over 947830.98 frames.], batch size: 41, aishell_tot_loss[loss=0.1714, simple_loss=0.2526, pruned_loss=0.04515, over 788141.82 frames.], datatang_tot_loss[loss=0.1728, simple_loss=0.2414, pruned_loss=0.05214, over 796618.33 frames.], batch size: 41, lr: 7.40e-04
+2022-06-18 17:50:47,915 INFO [train.py:874] (3/4) Epoch 11, batch 700, aishell_loss[loss=0.1937, simple_loss=0.2758, pruned_loss=0.05581, over 4900.00 frames.], tot_loss[loss=0.1725, simple_loss=0.248, pruned_loss=0.04848, over 956420.45 frames.], batch size: 34, aishell_tot_loss[loss=0.1716, simple_loss=0.2532, pruned_loss=0.04502, over 813412.35 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2415, pruned_loss=0.05222, over 817146.96 frames.], batch size: 34, lr: 7.40e-04
+2022-06-18 17:51:18,387 INFO [train.py:874] (3/4) Epoch 11, batch 750, aishell_loss[loss=0.1991, simple_loss=0.2629, pruned_loss=0.06769, over 4941.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2489, pruned_loss=0.04916, over 963085.36 frames.], batch size: 32, aishell_tot_loss[loss=0.1724, simple_loss=0.2538, pruned_loss=0.0455, over 832977.96 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2422, pruned_loss=0.05245, over 837886.93 frames.], batch size: 32, lr: 7.39e-04
+2022-06-18 17:51:48,666 INFO [train.py:874] (3/4) Epoch 11, batch 800, datatang_loss[loss=0.2352, simple_loss=0.2962, pruned_loss=0.08706, over 4944.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2487, pruned_loss=0.04938, over 968245.62 frames.], batch size: 109, aishell_tot_loss[loss=0.1724, simple_loss=0.2536, pruned_loss=0.04559, over 852366.95 frames.], datatang_tot_loss[loss=0.1739, simple_loss=0.2423, pruned_loss=0.05271, over 854071.76 frames.], batch size: 109, lr: 7.39e-04
+2022-06-18 17:52:17,437 INFO [train.py:874] (3/4) Epoch 11, batch 850, aishell_loss[loss=0.1704, simple_loss=0.2548, pruned_loss=0.04301, over 4963.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2494, pruned_loss=0.04961, over 971823.09 frames.], batch size: 61, aishell_tot_loss[loss=0.173, simple_loss=0.2542, pruned_loss=0.04593, over 869660.51 frames.], datatang_tot_loss[loss=0.1741, simple_loss=0.2426, pruned_loss=0.05279, over 867668.96 frames.], batch size: 61, lr: 7.39e-04
+2022-06-18 17:52:48,423 INFO [train.py:874] (3/4) Epoch 11, batch 900, datatang_loss[loss=0.1734, simple_loss=0.2306, pruned_loss=0.05807, over 4982.00 frames.], tot_loss[loss=0.1736, simple_loss=0.248, pruned_loss=0.04967, over 974978.97 frames.], batch size: 40, aishell_tot_loss[loss=0.1725, simple_loss=0.2534, pruned_loss=0.04579, over 881844.73 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.2422, pruned_loss=0.05296, over 883137.77 frames.], batch size: 40, lr: 7.38e-04
+2022-06-18 17:53:18,606 INFO [train.py:874] (3/4) Epoch 11, batch 950, datatang_loss[loss=0.1727, simple_loss=0.232, pruned_loss=0.05676, over 4948.00 frames.], tot_loss[loss=0.1736, simple_loss=0.248, pruned_loss=0.04964, over 976890.74 frames.], batch size: 67, aishell_tot_loss[loss=0.1729, simple_loss=0.2536, pruned_loss=0.04608, over 893193.29 frames.], datatang_tot_loss[loss=0.1737, simple_loss=0.2421, pruned_loss=0.0527, over 895600.46 frames.], batch size: 67, lr: 7.38e-04
+2022-06-18 17:53:46,816 INFO [train.py:874] (3/4) Epoch 11, batch 1000, aishell_loss[loss=0.1743, simple_loss=0.2517, pruned_loss=0.04847, over 4971.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2485, pruned_loss=0.04989, over 979108.57 frames.], batch size: 51, aishell_tot_loss[loss=0.1726, simple_loss=0.2532, pruned_loss=0.04602, over 906871.11 frames.], datatang_tot_loss[loss=0.1748, simple_loss=0.2428, pruned_loss=0.05335, over 903695.39 frames.], batch size: 51, lr: 7.37e-04
+2022-06-18 17:53:46,817 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 17:54:02,791 INFO [train.py:914] (3/4) Epoch 11, validation: loss=0.1676, simple_loss=0.2521, pruned_loss=0.04158, over 1622729.00 frames.
+2022-06-18 17:54:32,189 INFO [train.py:874] (3/4) Epoch 11, batch 1050, aishell_loss[loss=0.1867, simple_loss=0.2635, pruned_loss=0.05492, over 4867.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2484, pruned_loss=0.04892, over 980509.14 frames.], batch size: 35, aishell_tot_loss[loss=0.172, simple_loss=0.2529, pruned_loss=0.0456, over 918393.53 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2427, pruned_loss=0.05298, over 910879.85 frames.], batch size: 35, lr: 7.37e-04
+2022-06-18 17:55:02,945 INFO [train.py:874] (3/4) Epoch 11, batch 1100, datatang_loss[loss=0.1618, simple_loss=0.2435, pruned_loss=0.04, over 4923.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2487, pruned_loss=0.04888, over 981854.63 frames.], batch size: 94, aishell_tot_loss[loss=0.1718, simple_loss=0.2528, pruned_loss=0.04544, over 927230.32 frames.], datatang_tot_loss[loss=0.1747, simple_loss=0.2432, pruned_loss=0.05312, over 918881.30 frames.], batch size: 94, lr: 7.36e-04
+2022-06-18 17:55:32,190 INFO [train.py:874] (3/4) Epoch 11, batch 1150, datatang_loss[loss=0.1714, simple_loss=0.232, pruned_loss=0.05547, over 4932.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2478, pruned_loss=0.04895, over 982353.69 frames.], batch size: 50, aishell_tot_loss[loss=0.1718, simple_loss=0.2526, pruned_loss=0.04548, over 932999.75 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2428, pruned_loss=0.05297, over 927595.45 frames.], batch size: 50, lr: 7.36e-04
+2022-06-18 17:56:02,977 INFO [train.py:874] (3/4) Epoch 11, batch 1200, aishell_loss[loss=0.1606, simple_loss=0.2422, pruned_loss=0.03953, over 4942.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2472, pruned_loss=0.04871, over 983099.13 frames.], batch size: 56, aishell_tot_loss[loss=0.1717, simple_loss=0.2526, pruned_loss=0.04545, over 938201.64 frames.], datatang_tot_loss[loss=0.1737, simple_loss=0.2424, pruned_loss=0.05253, over 935506.93 frames.], batch size: 56, lr: 7.36e-04
+2022-06-18 17:56:33,897 INFO [train.py:874] (3/4) Epoch 11, batch 1250, datatang_loss[loss=0.1389, simple_loss=0.2117, pruned_loss=0.03299, over 4959.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2477, pruned_loss=0.04903, over 983936.13 frames.], batch size: 67, aishell_tot_loss[loss=0.172, simple_loss=0.2529, pruned_loss=0.04551, over 943154.86 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.2427, pruned_loss=0.05261, over 942368.48 frames.], batch size: 67, lr: 7.35e-04
+2022-06-18 17:57:03,624 INFO [train.py:874] (3/4) Epoch 11, batch 1300, datatang_loss[loss=0.1641, simple_loss=0.2338, pruned_loss=0.04724, over 4964.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2481, pruned_loss=0.04905, over 984016.32 frames.], batch size: 67, aishell_tot_loss[loss=0.1724, simple_loss=0.2536, pruned_loss=0.04557, over 947778.03 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2426, pruned_loss=0.0525, over 947526.89 frames.], batch size: 67, lr: 7.35e-04
+2022-06-18 17:57:34,451 INFO [train.py:874] (3/4) Epoch 11, batch 1350, datatang_loss[loss=0.1634, simple_loss=0.2384, pruned_loss=0.04423, over 4924.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2478, pruned_loss=0.04918, over 984285.69 frames.], batch size: 77, aishell_tot_loss[loss=0.1723, simple_loss=0.2534, pruned_loss=0.04562, over 951946.78 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2425, pruned_loss=0.05255, over 952200.87 frames.], batch size: 77, lr: 7.34e-04
+2022-06-18 17:58:05,181 INFO [train.py:874] (3/4) Epoch 11, batch 1400, datatang_loss[loss=0.1921, simple_loss=0.2558, pruned_loss=0.06419, over 4961.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2477, pruned_loss=0.04877, over 985140.61 frames.], batch size: 86, aishell_tot_loss[loss=0.1722, simple_loss=0.2533, pruned_loss=0.0456, over 956973.82 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2422, pruned_loss=0.05226, over 955650.07 frames.], batch size: 86, lr: 7.34e-04
+2022-06-18 17:58:33,331 INFO [train.py:874] (3/4) Epoch 11, batch 1450, datatang_loss[loss=0.1636, simple_loss=0.2359, pruned_loss=0.04564, over 4913.00 frames.], tot_loss[loss=0.1728, simple_loss=0.248, pruned_loss=0.04878, over 985223.94 frames.], batch size: 75, aishell_tot_loss[loss=0.1723, simple_loss=0.2534, pruned_loss=0.04555, over 960895.85 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2421, pruned_loss=0.05242, over 958563.13 frames.], batch size: 75, lr: 7.33e-04
+2022-06-18 17:59:04,292 INFO [train.py:874] (3/4) Epoch 11, batch 1500, datatang_loss[loss=0.1622, simple_loss=0.2396, pruned_loss=0.04242, over 4914.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2487, pruned_loss=0.04933, over 985056.49 frames.], batch size: 73, aishell_tot_loss[loss=0.1729, simple_loss=0.2538, pruned_loss=0.04602, over 963583.23 frames.], datatang_tot_loss[loss=0.1737, simple_loss=0.2425, pruned_loss=0.05248, over 961710.95 frames.], batch size: 73, lr: 7.33e-04
+2022-06-18 17:59:35,793 INFO [train.py:874] (3/4) Epoch 11, batch 1550, aishell_loss[loss=0.1611, simple_loss=0.2542, pruned_loss=0.03395, over 4886.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2489, pruned_loss=0.04968, over 984926.30 frames.], batch size: 50, aishell_tot_loss[loss=0.1736, simple_loss=0.2544, pruned_loss=0.0464, over 966007.87 frames.], datatang_tot_loss[loss=0.1736, simple_loss=0.2423, pruned_loss=0.05249, over 964422.36 frames.], batch size: 50, lr: 7.33e-04
+2022-06-18 18:00:05,477 INFO [train.py:874] (3/4) Epoch 11, batch 1600, datatang_loss[loss=0.2274, simple_loss=0.2885, pruned_loss=0.08317, over 4920.00 frames.], tot_loss[loss=0.1746, simple_loss=0.249, pruned_loss=0.05014, over 984946.78 frames.], batch size: 108, aishell_tot_loss[loss=0.1729, simple_loss=0.2537, pruned_loss=0.04604, over 968154.68 frames.], datatang_tot_loss[loss=0.1749, simple_loss=0.2431, pruned_loss=0.05335, over 966940.16 frames.], batch size: 108, lr: 7.32e-04
+2022-06-18 18:00:35,377 INFO [train.py:874] (3/4) Epoch 11, batch 1650, aishell_loss[loss=0.1383, simple_loss=0.222, pruned_loss=0.02736, over 4981.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2491, pruned_loss=0.05065, over 985346.60 frames.], batch size: 30, aishell_tot_loss[loss=0.1729, simple_loss=0.2537, pruned_loss=0.04608, over 970088.03 frames.], datatang_tot_loss[loss=0.1756, simple_loss=0.2436, pruned_loss=0.05381, over 969547.94 frames.], batch size: 30, lr: 7.32e-04
+2022-06-18 18:01:07,031 INFO [train.py:874] (3/4) Epoch 11, batch 1700, aishell_loss[loss=0.1685, simple_loss=0.2432, pruned_loss=0.04684, over 4982.00 frames.], tot_loss[loss=0.1746, simple_loss=0.249, pruned_loss=0.05014, over 985117.40 frames.], batch size: 27, aishell_tot_loss[loss=0.173, simple_loss=0.2542, pruned_loss=0.04594, over 971582.98 frames.], datatang_tot_loss[loss=0.1751, simple_loss=0.2432, pruned_loss=0.05352, over 971476.86 frames.], batch size: 27, lr: 7.31e-04
+2022-06-18 18:01:36,155 INFO [train.py:874] (3/4) Epoch 11, batch 1750, aishell_loss[loss=0.1729, simple_loss=0.2519, pruned_loss=0.04692, over 4884.00 frames.], tot_loss[loss=0.1742, simple_loss=0.249, pruned_loss=0.04975, over 984761.97 frames.], batch size: 34, aishell_tot_loss[loss=0.1735, simple_loss=0.2548, pruned_loss=0.04605, over 972863.43 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2425, pruned_loss=0.05315, over 973028.92 frames.], batch size: 34, lr: 7.31e-04
+2022-06-18 18:02:06,176 INFO [train.py:874] (3/4) Epoch 11, batch 1800, datatang_loss[loss=0.1688, simple_loss=0.2463, pruned_loss=0.04563, over 4929.00 frames.], tot_loss[loss=0.172, simple_loss=0.2474, pruned_loss=0.04835, over 985039.24 frames.], batch size: 83, aishell_tot_loss[loss=0.1725, simple_loss=0.2542, pruned_loss=0.04543, over 974577.34 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2414, pruned_loss=0.05234, over 974388.38 frames.], batch size: 83, lr: 7.30e-04
+2022-06-18 18:02:37,461 INFO [train.py:874] (3/4) Epoch 11, batch 1850, aishell_loss[loss=0.1574, simple_loss=0.2372, pruned_loss=0.03875, over 4965.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2485, pruned_loss=0.04893, over 985331.32 frames.], batch size: 44, aishell_tot_loss[loss=0.1733, simple_loss=0.2548, pruned_loss=0.04586, over 976041.24 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2417, pruned_loss=0.05243, over 975717.40 frames.], batch size: 44, lr: 7.30e-04
+2022-06-18 18:03:06,883 INFO [train.py:874] (3/4) Epoch 11, batch 1900, datatang_loss[loss=0.2067, simple_loss=0.2753, pruned_loss=0.06905, over 4955.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2484, pruned_loss=0.04911, over 985687.04 frames.], batch size: 99, aishell_tot_loss[loss=0.1733, simple_loss=0.2549, pruned_loss=0.04586, over 977029.17 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2418, pruned_loss=0.05241, over 977327.68 frames.], batch size: 99, lr: 7.30e-04
+2022-06-18 18:03:36,077 INFO [train.py:874] (3/4) Epoch 11, batch 1950, datatang_loss[loss=0.1644, simple_loss=0.2401, pruned_loss=0.04437, over 4933.00 frames.], tot_loss[loss=0.1733, simple_loss=0.249, pruned_loss=0.0488, over 985674.65 frames.], batch size: 94, aishell_tot_loss[loss=0.1729, simple_loss=0.2547, pruned_loss=0.04553, over 978143.46 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2424, pruned_loss=0.05256, over 978218.91 frames.], batch size: 94, lr: 7.29e-04
+2022-06-18 18:04:07,336 INFO [train.py:874] (3/4) Epoch 11, batch 2000, aishell_loss[loss=0.1912, simple_loss=0.2844, pruned_loss=0.04903, over 4900.00 frames.], tot_loss[loss=0.174, simple_loss=0.2489, pruned_loss=0.04955, over 985679.42 frames.], batch size: 68, aishell_tot_loss[loss=0.1735, simple_loss=0.2549, pruned_loss=0.046, over 978858.64 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.2424, pruned_loss=0.05281, over 979275.84 frames.], batch size: 68, lr: 7.29e-04
+2022-06-18 18:04:07,339 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 18:04:24,295 INFO [train.py:914] (3/4) Epoch 11, validation: loss=0.167, simple_loss=0.2509, pruned_loss=0.04156, over 1622729.00 frames.
+2022-06-18 18:04:53,811 INFO [train.py:874] (3/4) Epoch 11, batch 2050, aishell_loss[loss=0.1646, simple_loss=0.2505, pruned_loss=0.03937, over 4979.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2503, pruned_loss=0.05065, over 985483.96 frames.], batch size: 39, aishell_tot_loss[loss=0.1745, simple_loss=0.2557, pruned_loss=0.0467, over 979801.35 frames.], datatang_tot_loss[loss=0.1749, simple_loss=0.2429, pruned_loss=0.05345, over 979668.12 frames.], batch size: 39, lr: 7.28e-04
+2022-06-18 18:05:23,762 INFO [train.py:874] (3/4) Epoch 11, batch 2100, aishell_loss[loss=0.1526, simple_loss=0.2399, pruned_loss=0.03266, over 4962.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2493, pruned_loss=0.04978, over 985797.32 frames.], batch size: 56, aishell_tot_loss[loss=0.174, simple_loss=0.2553, pruned_loss=0.04635, over 980922.55 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2421, pruned_loss=0.05322, over 980230.16 frames.], batch size: 56, lr: 7.28e-04
+2022-06-18 18:05:53,459 INFO [train.py:874] (3/4) Epoch 11, batch 2150, datatang_loss[loss=0.1553, simple_loss=0.2257, pruned_loss=0.04251, over 4916.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2494, pruned_loss=0.05018, over 985700.04 frames.], batch size: 77, aishell_tot_loss[loss=0.1745, simple_loss=0.2559, pruned_loss=0.04658, over 981072.06 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2425, pruned_loss=0.05309, over 981206.79 frames.], batch size: 77, lr: 7.28e-04
+2022-06-18 18:06:24,968 INFO [train.py:874] (3/4) Epoch 11, batch 2200, datatang_loss[loss=0.1693, simple_loss=0.2348, pruned_loss=0.05188, over 4928.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2484, pruned_loss=0.04984, over 985834.96 frames.], batch size: 77, aishell_tot_loss[loss=0.1739, simple_loss=0.2554, pruned_loss=0.04623, over 981691.84 frames.], datatang_tot_loss[loss=0.1742, simple_loss=0.2424, pruned_loss=0.05298, over 981804.19 frames.], batch size: 77, lr: 7.27e-04
+2022-06-18 18:06:54,604 INFO [train.py:874] (3/4) Epoch 11, batch 2250, aishell_loss[loss=0.1883, simple_loss=0.2689, pruned_loss=0.05378, over 4958.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2485, pruned_loss=0.04996, over 985800.75 frames.], batch size: 61, aishell_tot_loss[loss=0.1737, simple_loss=0.2551, pruned_loss=0.04619, over 982131.35 frames.], datatang_tot_loss[loss=0.1746, simple_loss=0.2429, pruned_loss=0.05312, over 982291.80 frames.], batch size: 61, lr: 7.27e-04
+2022-06-18 18:07:24,141 INFO [train.py:874] (3/4) Epoch 11, batch 2300, datatang_loss[loss=0.1522, simple_loss=0.2225, pruned_loss=0.04097, over 4860.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2481, pruned_loss=0.04951, over 985264.05 frames.], batch size: 30, aishell_tot_loss[loss=0.1732, simple_loss=0.2544, pruned_loss=0.04599, over 982053.65 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2429, pruned_loss=0.05297, over 982655.28 frames.], batch size: 30, lr: 7.26e-04
+2022-06-18 18:07:55,355 INFO [train.py:874] (3/4) Epoch 11, batch 2350, datatang_loss[loss=0.1856, simple_loss=0.2492, pruned_loss=0.06102, over 4939.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2475, pruned_loss=0.04911, over 985344.38 frames.], batch size: 88, aishell_tot_loss[loss=0.173, simple_loss=0.254, pruned_loss=0.04596, over 982545.17 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2423, pruned_loss=0.05269, over 982945.61 frames.], batch size: 88, lr: 7.26e-04
+2022-06-18 18:08:25,224 INFO [train.py:874] (3/4) Epoch 11, batch 2400, aishell_loss[loss=0.1491, simple_loss=0.2406, pruned_loss=0.02877, over 4976.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2489, pruned_loss=0.04981, over 985512.41 frames.], batch size: 61, aishell_tot_loss[loss=0.1732, simple_loss=0.2543, pruned_loss=0.04603, over 983199.37 frames.], datatang_tot_loss[loss=0.175, simple_loss=0.2432, pruned_loss=0.05341, over 983066.41 frames.], batch size: 61, lr: 7.25e-04
+2022-06-18 18:08:55,164 INFO [train.py:874] (3/4) Epoch 11, batch 2450, datatang_loss[loss=0.1843, simple_loss=0.2593, pruned_loss=0.05471, over 4939.00 frames.], tot_loss[loss=0.1733, simple_loss=0.248, pruned_loss=0.04931, over 985343.40 frames.], batch size: 94, aishell_tot_loss[loss=0.173, simple_loss=0.254, pruned_loss=0.04598, over 983143.00 frames.], datatang_tot_loss[loss=0.1742, simple_loss=0.2426, pruned_loss=0.05291, over 983493.88 frames.], batch size: 94, lr: 7.25e-04
+2022-06-18 18:09:26,618 INFO [train.py:874] (3/4) Epoch 11, batch 2500, datatang_loss[loss=0.1712, simple_loss=0.248, pruned_loss=0.04718, over 4935.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2477, pruned_loss=0.04927, over 985334.55 frames.], batch size: 88, aishell_tot_loss[loss=0.1724, simple_loss=0.2534, pruned_loss=0.04573, over 983180.96 frames.], datatang_tot_loss[loss=0.1745, simple_loss=0.243, pruned_loss=0.05298, over 983908.14 frames.], batch size: 88, lr: 7.25e-04
+2022-06-18 18:09:56,469 INFO [train.py:874] (3/4) Epoch 11, batch 2550, datatang_loss[loss=0.2164, simple_loss=0.2726, pruned_loss=0.08012, over 4960.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2464, pruned_loss=0.04899, over 984976.83 frames.], batch size: 99, aishell_tot_loss[loss=0.1723, simple_loss=0.253, pruned_loss=0.0458, over 983267.48 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2419, pruned_loss=0.05254, over 983853.54 frames.], batch size: 99, lr: 7.24e-04
+2022-06-18 18:10:26,153 INFO [train.py:874] (3/4) Epoch 11, batch 2600, datatang_loss[loss=0.1602, simple_loss=0.2286, pruned_loss=0.04592, over 4919.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2468, pruned_loss=0.04906, over 985645.16 frames.], batch size: 57, aishell_tot_loss[loss=0.1721, simple_loss=0.253, pruned_loss=0.04555, over 983994.01 frames.], datatang_tot_loss[loss=0.1739, simple_loss=0.242, pruned_loss=0.05289, over 984166.31 frames.], batch size: 57, lr: 7.24e-04
+2022-06-18 18:10:56,983 INFO [train.py:874] (3/4) Epoch 11, batch 2650, datatang_loss[loss=0.1535, simple_loss=0.222, pruned_loss=0.04255, over 4925.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2468, pruned_loss=0.04832, over 985723.17 frames.], batch size: 71, aishell_tot_loss[loss=0.1715, simple_loss=0.2528, pruned_loss=0.04515, over 984273.95 frames.], datatang_tot_loss[loss=0.1736, simple_loss=0.2419, pruned_loss=0.05263, over 984342.40 frames.], batch size: 71, lr: 7.23e-04
+2022-06-18 18:11:31,764 INFO [train.py:874] (3/4) Epoch 11, batch 2700, aishell_loss[loss=0.2055, simple_loss=0.2846, pruned_loss=0.06318, over 4909.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2469, pruned_loss=0.04835, over 985538.07 frames.], batch size: 52, aishell_tot_loss[loss=0.1719, simple_loss=0.2532, pruned_loss=0.04534, over 984294.38 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2415, pruned_loss=0.05228, over 984447.59 frames.], batch size: 52, lr: 7.23e-04
+2022-06-18 18:12:01,152 INFO [train.py:874] (3/4) Epoch 11, batch 2750, aishell_loss[loss=0.1665, simple_loss=0.2506, pruned_loss=0.04124, over 4937.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2458, pruned_loss=0.04785, over 985565.44 frames.], batch size: 45, aishell_tot_loss[loss=0.1715, simple_loss=0.2527, pruned_loss=0.04515, over 984141.88 frames.], datatang_tot_loss[loss=0.1722, simple_loss=0.2408, pruned_loss=0.05176, over 984890.56 frames.], batch size: 45, lr: 7.23e-04
+2022-06-18 18:12:33,199 INFO [train.py:874] (3/4) Epoch 11, batch 2800, datatang_loss[loss=0.1665, simple_loss=0.2309, pruned_loss=0.05103, over 4921.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2458, pruned_loss=0.04796, over 985526.45 frames.], batch size: 57, aishell_tot_loss[loss=0.1712, simple_loss=0.2525, pruned_loss=0.04498, over 984252.85 frames.], datatang_tot_loss[loss=0.1723, simple_loss=0.2409, pruned_loss=0.05189, over 984986.68 frames.], batch size: 57, lr: 7.22e-04
+2022-06-18 18:13:03,710 INFO [train.py:874] (3/4) Epoch 11, batch 2850, datatang_loss[loss=0.1636, simple_loss=0.2371, pruned_loss=0.04506, over 4915.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2456, pruned_loss=0.04787, over 985299.25 frames.], batch size: 77, aishell_tot_loss[loss=0.1711, simple_loss=0.2526, pruned_loss=0.04482, over 984400.63 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.2404, pruned_loss=0.0518, over 984809.99 frames.], batch size: 77, lr: 7.22e-04
+2022-06-18 18:13:33,101 INFO [train.py:874] (3/4) Epoch 11, batch 2900, datatang_loss[loss=0.1695, simple_loss=0.2324, pruned_loss=0.05329, over 4972.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2458, pruned_loss=0.04774, over 985245.94 frames.], batch size: 45, aishell_tot_loss[loss=0.171, simple_loss=0.2526, pruned_loss=0.04472, over 984682.12 frames.], datatang_tot_loss[loss=0.1718, simple_loss=0.2403, pruned_loss=0.05162, over 984634.20 frames.], batch size: 45, lr: 7.21e-04
+2022-06-18 18:14:04,203 INFO [train.py:874] (3/4) Epoch 11, batch 2950, datatang_loss[loss=0.1804, simple_loss=0.2464, pruned_loss=0.05717, over 4913.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2456, pruned_loss=0.04793, over 985165.30 frames.], batch size: 75, aishell_tot_loss[loss=0.1706, simple_loss=0.2521, pruned_loss=0.04455, over 984724.33 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.2406, pruned_loss=0.05181, over 984628.51 frames.], batch size: 75, lr: 7.21e-04
+2022-06-18 18:14:33,592 INFO [train.py:874] (3/4) Epoch 11, batch 3000, datatang_loss[loss=0.1813, simple_loss=0.2432, pruned_loss=0.05975, over 4871.00 frames.], tot_loss[loss=0.1731, simple_loss=0.248, pruned_loss=0.04914, over 985331.38 frames.], batch size: 39, aishell_tot_loss[loss=0.1724, simple_loss=0.254, pruned_loss=0.04546, over 984818.22 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.2411, pruned_loss=0.05213, over 984814.98 frames.], batch size: 39, lr: 7.21e-04
+2022-06-18 18:14:33,593 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 18:14:50,210 INFO [train.py:914] (3/4) Epoch 11, validation: loss=0.1662, simple_loss=0.2504, pruned_loss=0.04101, over 1622729.00 frames.
+2022-06-18 18:15:20,411 INFO [train.py:874] (3/4) Epoch 11, batch 3050, aishell_loss[loss=0.1844, simple_loss=0.2553, pruned_loss=0.05676, over 4875.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2479, pruned_loss=0.04911, over 985230.12 frames.], batch size: 35, aishell_tot_loss[loss=0.1726, simple_loss=0.2539, pruned_loss=0.04564, over 984868.46 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.2411, pruned_loss=0.05204, over 984789.66 frames.], batch size: 35, lr: 7.20e-04
+2022-06-18 18:15:50,766 INFO [train.py:874] (3/4) Epoch 11, batch 3100, aishell_loss[loss=0.1838, simple_loss=0.262, pruned_loss=0.05276, over 4883.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2477, pruned_loss=0.04964, over 985262.44 frames.], batch size: 35, aishell_tot_loss[loss=0.173, simple_loss=0.2539, pruned_loss=0.04601, over 984842.22 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2411, pruned_loss=0.05232, over 984949.43 frames.], batch size: 35, lr: 7.20e-04
+2022-06-18 18:16:23,189 INFO [train.py:874] (3/4) Epoch 11, batch 3150, datatang_loss[loss=0.1672, simple_loss=0.2407, pruned_loss=0.04687, over 4920.00 frames.], tot_loss[loss=0.173, simple_loss=0.2477, pruned_loss=0.04916, over 985467.61 frames.], batch size: 64, aishell_tot_loss[loss=0.1725, simple_loss=0.2535, pruned_loss=0.04575, over 985145.39 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2417, pruned_loss=0.05209, over 984940.38 frames.], batch size: 64, lr: 7.19e-04
+2022-06-18 18:16:53,785 INFO [train.py:874] (3/4) Epoch 11, batch 3200, aishell_loss[loss=0.2037, simple_loss=0.2744, pruned_loss=0.06649, over 4929.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2474, pruned_loss=0.04878, over 985215.31 frames.], batch size: 33, aishell_tot_loss[loss=0.1725, simple_loss=0.2533, pruned_loss=0.04584, over 984867.05 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2414, pruned_loss=0.05168, over 985058.49 frames.], batch size: 33, lr: 7.19e-04
+2022-06-18 18:17:23,948 INFO [train.py:874] (3/4) Epoch 11, batch 3250, datatang_loss[loss=0.1536, simple_loss=0.2257, pruned_loss=0.04078, over 4901.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2469, pruned_loss=0.04822, over 985348.97 frames.], batch size: 52, aishell_tot_loss[loss=0.1721, simple_loss=0.2532, pruned_loss=0.04553, over 984855.81 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.241, pruned_loss=0.05147, over 985268.84 frames.], batch size: 52, lr: 7.19e-04
+2022-06-18 18:17:56,645 INFO [train.py:874] (3/4) Epoch 11, batch 3300, aishell_loss[loss=0.1611, simple_loss=0.2483, pruned_loss=0.03689, over 4922.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2476, pruned_loss=0.04852, over 985254.53 frames.], batch size: 33, aishell_tot_loss[loss=0.1726, simple_loss=0.2537, pruned_loss=0.04574, over 984709.74 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.2411, pruned_loss=0.05157, over 985375.62 frames.], batch size: 33, lr: 7.18e-04
+2022-06-18 18:18:26,829 INFO [train.py:874] (3/4) Epoch 11, batch 3350, datatang_loss[loss=0.1879, simple_loss=0.2476, pruned_loss=0.06413, over 4927.00 frames.], tot_loss[loss=0.173, simple_loss=0.2485, pruned_loss=0.04877, over 985165.69 frames.], batch size: 71, aishell_tot_loss[loss=0.1734, simple_loss=0.2543, pruned_loss=0.04619, over 984648.67 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.2413, pruned_loss=0.05142, over 985402.89 frames.], batch size: 71, lr: 7.18e-04
+2022-06-18 18:18:57,208 INFO [train.py:874] (3/4) Epoch 11, batch 3400, aishell_loss[loss=0.1605, simple_loss=0.2421, pruned_loss=0.03946, over 4851.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2485, pruned_loss=0.04932, over 985164.61 frames.], batch size: 36, aishell_tot_loss[loss=0.173, simple_loss=0.254, pruned_loss=0.04602, over 984616.34 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2419, pruned_loss=0.05206, over 985473.35 frames.], batch size: 36, lr: 7.17e-04
+2022-06-18 18:19:29,619 INFO [train.py:874] (3/4) Epoch 11, batch 3450, datatang_loss[loss=0.1722, simple_loss=0.233, pruned_loss=0.05568, over 4898.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2481, pruned_loss=0.04972, over 985332.47 frames.], batch size: 52, aishell_tot_loss[loss=0.1738, simple_loss=0.2545, pruned_loss=0.04658, over 984917.99 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.2414, pruned_loss=0.05192, over 985369.96 frames.], batch size: 52, lr: 7.17e-04
+2022-06-18 18:20:00,375 INFO [train.py:874] (3/4) Epoch 11, batch 3500, datatang_loss[loss=0.1644, simple_loss=0.2477, pruned_loss=0.04055, over 4939.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2479, pruned_loss=0.04928, over 985494.86 frames.], batch size: 50, aishell_tot_loss[loss=0.173, simple_loss=0.2539, pruned_loss=0.04608, over 985055.12 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2419, pruned_loss=0.05205, over 985453.04 frames.], batch size: 50, lr: 7.17e-04
+2022-06-18 18:20:30,463 INFO [train.py:874] (3/4) Epoch 11, batch 3550, aishell_loss[loss=0.1373, simple_loss=0.2022, pruned_loss=0.03623, over 4818.00 frames.], tot_loss[loss=0.172, simple_loss=0.2469, pruned_loss=0.04853, over 985458.68 frames.], batch size: 21, aishell_tot_loss[loss=0.1724, simple_loss=0.2533, pruned_loss=0.04575, over 984964.75 frames.], datatang_tot_loss[loss=0.1723, simple_loss=0.2413, pruned_loss=0.05168, over 985581.50 frames.], batch size: 21, lr: 7.16e-04
+2022-06-18 18:21:01,568 INFO [train.py:874] (3/4) Epoch 11, batch 3600, datatang_loss[loss=0.1691, simple_loss=0.2478, pruned_loss=0.04524, over 4955.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2482, pruned_loss=0.04871, over 985684.67 frames.], batch size: 91, aishell_tot_loss[loss=0.1728, simple_loss=0.2539, pruned_loss=0.04583, over 985023.74 frames.], datatang_tot_loss[loss=0.1728, simple_loss=0.2418, pruned_loss=0.05187, over 985807.00 frames.], batch size: 91, lr: 7.16e-04
+2022-06-18 18:21:30,589 INFO [train.py:874] (3/4) Epoch 11, batch 3650, aishell_loss[loss=0.1813, simple_loss=0.2667, pruned_loss=0.04794, over 4859.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2473, pruned_loss=0.04821, over 985658.18 frames.], batch size: 37, aishell_tot_loss[loss=0.1723, simple_loss=0.2536, pruned_loss=0.04555, over 984952.63 frames.], datatang_tot_loss[loss=0.1722, simple_loss=0.241, pruned_loss=0.0517, over 985940.75 frames.], batch size: 37, lr: 7.15e-04
+2022-06-18 18:22:02,557 INFO [train.py:874] (3/4) Epoch 11, batch 3700, aishell_loss[loss=0.2088, simple_loss=0.2791, pruned_loss=0.06921, over 4906.00 frames.], tot_loss[loss=0.1728, simple_loss=0.248, pruned_loss=0.04885, over 985737.52 frames.], batch size: 33, aishell_tot_loss[loss=0.1731, simple_loss=0.2539, pruned_loss=0.04619, over 984957.21 frames.], datatang_tot_loss[loss=0.1723, simple_loss=0.2413, pruned_loss=0.05166, over 986092.70 frames.], batch size: 33, lr: 7.15e-04
+2022-06-18 18:22:32,577 INFO [train.py:874] (3/4) Epoch 11, batch 3750, aishell_loss[loss=0.1461, simple_loss=0.2241, pruned_loss=0.03408, over 4950.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2479, pruned_loss=0.04855, over 985197.97 frames.], batch size: 27, aishell_tot_loss[loss=0.1725, simple_loss=0.2532, pruned_loss=0.0459, over 984586.88 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.2418, pruned_loss=0.05179, over 985964.84 frames.], batch size: 27, lr: 7.15e-04
+2022-06-18 18:23:02,899 INFO [train.py:874] (3/4) Epoch 11, batch 3800, aishell_loss[loss=0.1221, simple_loss=0.2107, pruned_loss=0.01669, over 4831.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2473, pruned_loss=0.0481, over 985349.61 frames.], batch size: 29, aishell_tot_loss[loss=0.1719, simple_loss=0.2528, pruned_loss=0.0455, over 984485.64 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2417, pruned_loss=0.05158, over 986206.80 frames.], batch size: 29, lr: 7.14e-04
+2022-06-18 18:23:32,308 INFO [train.py:874] (3/4) Epoch 11, batch 3850, datatang_loss[loss=0.1842, simple_loss=0.2454, pruned_loss=0.0615, over 4928.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2467, pruned_loss=0.04773, over 984908.66 frames.], batch size: 71, aishell_tot_loss[loss=0.1712, simple_loss=0.252, pruned_loss=0.04523, over 984062.77 frames.], datatang_tot_loss[loss=0.1722, simple_loss=0.2415, pruned_loss=0.05147, over 986192.28 frames.], batch size: 71, lr: 7.14e-04
+2022-06-18 18:24:01,241 INFO [train.py:874] (3/4) Epoch 11, batch 3900, aishell_loss[loss=0.1741, simple_loss=0.2576, pruned_loss=0.04529, over 4925.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2466, pruned_loss=0.04827, over 985126.97 frames.], batch size: 33, aishell_tot_loss[loss=0.1711, simple_loss=0.2515, pruned_loss=0.04539, over 984198.45 frames.], datatang_tot_loss[loss=0.1728, simple_loss=0.2419, pruned_loss=0.05181, over 986254.76 frames.], batch size: 33, lr: 7.14e-04
+2022-06-18 18:24:28,652 INFO [train.py:874] (3/4) Epoch 11, batch 3950, datatang_loss[loss=0.1608, simple_loss=0.2385, pruned_loss=0.04154, over 4926.00 frames.], tot_loss[loss=0.171, simple_loss=0.2463, pruned_loss=0.04782, over 985161.19 frames.], batch size: 83, aishell_tot_loss[loss=0.1705, simple_loss=0.2512, pruned_loss=0.04492, over 984447.42 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.2417, pruned_loss=0.0519, over 986077.15 frames.], batch size: 83, lr: 7.13e-04
+2022-06-18 18:24:59,169 INFO [train.py:874] (3/4) Epoch 11, batch 4000, aishell_loss[loss=0.1617, simple_loss=0.2439, pruned_loss=0.03976, over 4879.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2467, pruned_loss=0.0484, over 985323.16 frames.], batch size: 47, aishell_tot_loss[loss=0.1707, simple_loss=0.2514, pruned_loss=0.045, over 984445.58 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.242, pruned_loss=0.05219, over 986206.91 frames.], batch size: 47, lr: 7.13e-04
+2022-06-18 18:24:59,170 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 18:25:16,906 INFO [train.py:914] (3/4) Epoch 11, validation: loss=0.1668, simple_loss=0.2498, pruned_loss=0.04186, over 1622729.00 frames.
+2022-06-18 18:25:45,404 INFO [train.py:874] (3/4) Epoch 11, batch 4050, aishell_loss[loss=0.1639, simple_loss=0.2472, pruned_loss=0.04031, over 4964.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2472, pruned_loss=0.04865, over 985153.51 frames.], batch size: 56, aishell_tot_loss[loss=0.1715, simple_loss=0.2522, pruned_loss=0.04545, over 984397.94 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2417, pruned_loss=0.05208, over 986098.72 frames.], batch size: 56, lr: 7.12e-04
+2022-06-18 18:26:14,033 INFO [train.py:874] (3/4) Epoch 11, batch 4100, datatang_loss[loss=0.1765, simple_loss=0.2512, pruned_loss=0.05095, over 4904.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2472, pruned_loss=0.04849, over 984924.14 frames.], batch size: 52, aishell_tot_loss[loss=0.1711, simple_loss=0.2521, pruned_loss=0.0451, over 984154.86 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.2419, pruned_loss=0.0522, over 986089.54 frames.], batch size: 52, lr: 7.12e-04
+2022-06-18 18:27:19,890 INFO [train.py:874] (3/4) Epoch 12, batch 50, datatang_loss[loss=0.158, simple_loss=0.2272, pruned_loss=0.04436, over 4925.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2437, pruned_loss=0.04752, over 218523.27 frames.], batch size: 83, aishell_tot_loss[loss=0.1716, simple_loss=0.2519, pruned_loss=0.04561, over 128933.45 frames.], datatang_tot_loss[loss=0.1664, simple_loss=0.2333, pruned_loss=0.04973, over 103070.69 frames.], batch size: 83, lr: 6.86e-04
+2022-06-18 18:27:51,829 INFO [train.py:874] (3/4) Epoch 12, batch 100, datatang_loss[loss=0.1514, simple_loss=0.2163, pruned_loss=0.04331, over 4922.00 frames.], tot_loss[loss=0.1659, simple_loss=0.242, pruned_loss=0.04494, over 388790.90 frames.], batch size: 57, aishell_tot_loss[loss=0.1689, simple_loss=0.2509, pruned_loss=0.0434, over 233695.71 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.232, pruned_loss=0.04683, over 203253.13 frames.], batch size: 57, lr: 6.86e-04
+2022-06-18 18:28:21,790 INFO [train.py:874] (3/4) Epoch 12, batch 150, datatang_loss[loss=0.1724, simple_loss=0.2407, pruned_loss=0.0521, over 4919.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2415, pruned_loss=0.04592, over 521351.90 frames.], batch size: 81, aishell_tot_loss[loss=0.1692, simple_loss=0.2512, pruned_loss=0.04356, over 302166.87 frames.], datatang_tot_loss[loss=0.1644, simple_loss=0.233, pruned_loss=0.04789, over 315948.44 frames.], batch size: 81, lr: 6.86e-04
+2022-06-18 18:28:52,367 INFO [train.py:874] (3/4) Epoch 12, batch 200, aishell_loss[loss=0.1946, simple_loss=0.271, pruned_loss=0.05908, over 4937.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2411, pruned_loss=0.04503, over 624292.27 frames.], batch size: 49, aishell_tot_loss[loss=0.1694, simple_loss=0.2514, pruned_loss=0.04367, over 385566.76 frames.], datatang_tot_loss[loss=0.1622, simple_loss=0.2316, pruned_loss=0.04639, over 391983.61 frames.], batch size: 49, lr: 6.85e-04
+2022-06-18 18:29:23,947 INFO [train.py:874] (3/4) Epoch 12, batch 250, datatang_loss[loss=0.152, simple_loss=0.2245, pruned_loss=0.03975, over 4928.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2432, pruned_loss=0.04552, over 704066.05 frames.], batch size: 77, aishell_tot_loss[loss=0.1708, simple_loss=0.2528, pruned_loss=0.04441, over 476761.96 frames.], datatang_tot_loss[loss=0.1624, simple_loss=0.2316, pruned_loss=0.0466, over 440414.17 frames.], batch size: 77, lr: 6.85e-04
+2022-06-18 18:29:54,047 INFO [train.py:874] (3/4) Epoch 12, batch 300, aishell_loss[loss=0.195, simple_loss=0.2647, pruned_loss=0.0626, over 4985.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2429, pruned_loss=0.04582, over 766285.93 frames.], batch size: 38, aishell_tot_loss[loss=0.1699, simple_loss=0.2517, pruned_loss=0.04411, over 536430.71 frames.], datatang_tot_loss[loss=0.1639, simple_loss=0.2329, pruned_loss=0.04745, over 504667.89 frames.], batch size: 38, lr: 6.85e-04
+2022-06-18 18:30:23,456 INFO [train.py:874] (3/4) Epoch 12, batch 350, aishell_loss[loss=0.1899, simple_loss=0.2672, pruned_loss=0.05631, over 4868.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2435, pruned_loss=0.04588, over 814794.66 frames.], batch size: 36, aishell_tot_loss[loss=0.1701, simple_loss=0.2521, pruned_loss=0.04409, over 587396.18 frames.], datatang_tot_loss[loss=0.1645, simple_loss=0.2339, pruned_loss=0.04757, over 563278.23 frames.], batch size: 36, lr: 6.84e-04
+2022-06-18 18:30:56,377 INFO [train.py:874] (3/4) Epoch 12, batch 400, datatang_loss[loss=0.1533, simple_loss=0.2272, pruned_loss=0.03973, over 4927.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2442, pruned_loss=0.04666, over 853072.81 frames.], batch size: 57, aishell_tot_loss[loss=0.1695, simple_loss=0.2519, pruned_loss=0.04359, over 623668.31 frames.], datatang_tot_loss[loss=0.167, simple_loss=0.236, pruned_loss=0.04901, over 624395.14 frames.], batch size: 57, lr: 6.84e-04
+2022-06-18 18:31:26,896 INFO [train.py:874] (3/4) Epoch 12, batch 450, aishell_loss[loss=0.183, simple_loss=0.2663, pruned_loss=0.04988, over 4877.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2445, pruned_loss=0.04617, over 882565.96 frames.], batch size: 42, aishell_tot_loss[loss=0.1685, simple_loss=0.2509, pruned_loss=0.04309, over 669412.37 frames.], datatang_tot_loss[loss=0.1677, simple_loss=0.2373, pruned_loss=0.04901, over 663918.73 frames.], batch size: 42, lr: 6.84e-04
+2022-06-18 18:31:57,639 INFO [train.py:874] (3/4) Epoch 12, batch 500, aishell_loss[loss=0.1583, simple_loss=0.2421, pruned_loss=0.03723, over 4868.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2447, pruned_loss=0.04634, over 904848.82 frames.], batch size: 35, aishell_tot_loss[loss=0.1685, simple_loss=0.2507, pruned_loss=0.04318, over 711510.33 frames.], datatang_tot_loss[loss=0.1682, simple_loss=0.2377, pruned_loss=0.04937, over 696174.36 frames.], batch size: 35, lr: 6.83e-04
+2022-06-18 18:32:29,738 INFO [train.py:874] (3/4) Epoch 12, batch 550, aishell_loss[loss=0.2003, simple_loss=0.2767, pruned_loss=0.06193, over 4969.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2451, pruned_loss=0.04699, over 922863.88 frames.], batch size: 44, aishell_tot_loss[loss=0.1693, simple_loss=0.2512, pruned_loss=0.04373, over 740369.76 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2379, pruned_loss=0.04957, over 733944.40 frames.], batch size: 44, lr: 6.83e-04
+2022-06-18 18:33:00,950 INFO [train.py:874] (3/4) Epoch 12, batch 600, aishell_loss[loss=0.1629, simple_loss=0.2497, pruned_loss=0.03803, over 4911.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2448, pruned_loss=0.04681, over 936536.42 frames.], batch size: 46, aishell_tot_loss[loss=0.1697, simple_loss=0.2519, pruned_loss=0.0438, over 762482.17 frames.], datatang_tot_loss[loss=0.168, simple_loss=0.2375, pruned_loss=0.04918, over 770081.68 frames.], batch size: 46, lr: 6.82e-04
+2022-06-18 18:33:32,133 INFO [train.py:874] (3/4) Epoch 12, batch 650, aishell_loss[loss=0.1776, simple_loss=0.2675, pruned_loss=0.04391, over 4888.00 frames.], tot_loss[loss=0.17, simple_loss=0.2455, pruned_loss=0.04724, over 947570.12 frames.], batch size: 50, aishell_tot_loss[loss=0.1696, simple_loss=0.2516, pruned_loss=0.04381, over 787722.32 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.2389, pruned_loss=0.04978, over 796629.07 frames.], batch size: 50, lr: 6.82e-04
+2022-06-18 18:34:02,858 INFO [train.py:874] (3/4) Epoch 12, batch 700, datatang_loss[loss=0.156, simple_loss=0.2378, pruned_loss=0.03713, over 4929.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2456, pruned_loss=0.04687, over 955998.74 frames.], batch size: 81, aishell_tot_loss[loss=0.1696, simple_loss=0.2516, pruned_loss=0.04383, over 817874.06 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2385, pruned_loss=0.04968, over 812079.45 frames.], batch size: 81, lr: 6.82e-04
+2022-06-18 18:34:31,772 INFO [train.py:874] (3/4) Epoch 12, batch 750, datatang_loss[loss=0.1967, simple_loss=0.2565, pruned_loss=0.06847, over 4912.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2446, pruned_loss=0.04647, over 962323.11 frames.], batch size: 77, aishell_tot_loss[loss=0.1684, simple_loss=0.2498, pruned_loss=0.04345, over 839375.33 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2389, pruned_loss=0.04968, over 830380.33 frames.], batch size: 77, lr: 6.81e-04
+2022-06-18 18:35:04,214 INFO [train.py:874] (3/4) Epoch 12, batch 800, aishell_loss[loss=0.159, simple_loss=0.2421, pruned_loss=0.03794, over 4948.00 frames.], tot_loss[loss=0.169, simple_loss=0.2449, pruned_loss=0.04651, over 967719.16 frames.], batch size: 54, aishell_tot_loss[loss=0.1689, simple_loss=0.2503, pruned_loss=0.04371, over 857977.06 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2388, pruned_loss=0.04953, over 847444.63 frames.], batch size: 54, lr: 6.81e-04
+2022-06-18 18:35:34,635 INFO [train.py:874] (3/4) Epoch 12, batch 850, datatang_loss[loss=0.1542, simple_loss=0.2154, pruned_loss=0.04651, over 4901.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2444, pruned_loss=0.0464, over 971710.61 frames.], batch size: 42, aishell_tot_loss[loss=0.1686, simple_loss=0.2501, pruned_loss=0.04361, over 871871.05 frames.], datatang_tot_loss[loss=0.1687, simple_loss=0.2386, pruned_loss=0.04937, over 864972.28 frames.], batch size: 42, lr: 6.81e-04
+2022-06-18 18:36:04,290 INFO [train.py:874] (3/4) Epoch 12, batch 900, aishell_loss[loss=0.1708, simple_loss=0.253, pruned_loss=0.04425, over 4951.00 frames.], tot_loss[loss=0.169, simple_loss=0.2446, pruned_loss=0.04672, over 974474.80 frames.], batch size: 40, aishell_tot_loss[loss=0.1688, simple_loss=0.2503, pruned_loss=0.04363, over 884586.31 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2388, pruned_loss=0.04967, over 879558.63 frames.], batch size: 40, lr: 6.80e-04
+2022-06-18 18:36:36,402 INFO [train.py:874] (3/4) Epoch 12, batch 950, datatang_loss[loss=0.1757, simple_loss=0.2485, pruned_loss=0.0514, over 4919.00 frames.], tot_loss[loss=0.1692, simple_loss=0.245, pruned_loss=0.04667, over 976837.58 frames.], batch size: 73, aishell_tot_loss[loss=0.1686, simple_loss=0.2502, pruned_loss=0.0435, over 897276.69 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.2392, pruned_loss=0.04979, over 891087.73 frames.], batch size: 73, lr: 6.80e-04
+2022-06-18 18:37:06,881 INFO [train.py:874] (3/4) Epoch 12, batch 1000, aishell_loss[loss=0.1651, simple_loss=0.256, pruned_loss=0.03716, over 4933.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2465, pruned_loss=0.04735, over 978879.28 frames.], batch size: 58, aishell_tot_loss[loss=0.1689, simple_loss=0.2506, pruned_loss=0.04361, over 908430.15 frames.], datatang_tot_loss[loss=0.1708, simple_loss=0.2406, pruned_loss=0.05054, over 901504.59 frames.], batch size: 58, lr: 6.79e-04
+2022-06-18 18:37:06,882 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 18:37:23,200 INFO [train.py:914] (3/4) Epoch 12, validation: loss=0.1654, simple_loss=0.2501, pruned_loss=0.04036, over 1622729.00 frames.
+2022-06-18 18:37:54,316 INFO [train.py:874] (3/4) Epoch 12, batch 1050, datatang_loss[loss=0.1573, simple_loss=0.2318, pruned_loss=0.04142, over 4906.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2462, pruned_loss=0.04756, over 980485.32 frames.], batch size: 64, aishell_tot_loss[loss=0.1694, simple_loss=0.2509, pruned_loss=0.0439, over 915687.82 frames.], datatang_tot_loss[loss=0.1705, simple_loss=0.2403, pruned_loss=0.05036, over 913477.57 frames.], batch size: 64, lr: 6.79e-04
+2022-06-18 18:38:26,550 INFO [train.py:874] (3/4) Epoch 12, batch 1100, aishell_loss[loss=0.1679, simple_loss=0.2432, pruned_loss=0.04636, over 4883.00 frames.], tot_loss[loss=0.17, simple_loss=0.2456, pruned_loss=0.0472, over 981886.39 frames.], batch size: 42, aishell_tot_loss[loss=0.1693, simple_loss=0.2509, pruned_loss=0.04383, over 923460.21 frames.], datatang_tot_loss[loss=0.1701, simple_loss=0.2399, pruned_loss=0.05008, over 922720.61 frames.], batch size: 42, lr: 6.79e-04
+2022-06-18 18:38:56,171 INFO [train.py:874] (3/4) Epoch 12, batch 1150, aishell_loss[loss=0.1577, simple_loss=0.2487, pruned_loss=0.03332, over 4969.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2453, pruned_loss=0.04665, over 982750.82 frames.], batch size: 44, aishell_tot_loss[loss=0.1691, simple_loss=0.2509, pruned_loss=0.04364, over 931029.44 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.2397, pruned_loss=0.04974, over 929907.54 frames.], batch size: 44, lr: 6.78e-04
+2022-06-18 18:39:27,876 INFO [train.py:874] (3/4) Epoch 12, batch 1200, aishell_loss[loss=0.1763, simple_loss=0.2541, pruned_loss=0.04923, over 4943.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2463, pruned_loss=0.04748, over 983697.68 frames.], batch size: 32, aishell_tot_loss[loss=0.1691, simple_loss=0.2505, pruned_loss=0.0438, over 938159.42 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.241, pruned_loss=0.0505, over 936084.10 frames.], batch size: 32, lr: 6.78e-04
+2022-06-18 18:39:58,267 INFO [train.py:874] (3/4) Epoch 12, batch 1250, aishell_loss[loss=0.1774, simple_loss=0.2691, pruned_loss=0.04282, over 4894.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2464, pruned_loss=0.04736, over 984341.70 frames.], batch size: 42, aishell_tot_loss[loss=0.1693, simple_loss=0.2508, pruned_loss=0.04386, over 943627.09 frames.], datatang_tot_loss[loss=0.1709, simple_loss=0.241, pruned_loss=0.05035, over 942281.80 frames.], batch size: 42, lr: 6.78e-04
+2022-06-18 18:40:30,444 INFO [train.py:874] (3/4) Epoch 12, batch 1300, aishell_loss[loss=0.1535, simple_loss=0.2341, pruned_loss=0.03638, over 4899.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2459, pruned_loss=0.04687, over 984721.10 frames.], batch size: 28, aishell_tot_loss[loss=0.1692, simple_loss=0.2511, pruned_loss=0.04365, over 948154.02 frames.], datatang_tot_loss[loss=0.1702, simple_loss=0.2404, pruned_loss=0.05001, over 947911.77 frames.], batch size: 28, lr: 6.77e-04
+2022-06-18 18:41:01,696 INFO [train.py:874] (3/4) Epoch 12, batch 1350, datatang_loss[loss=0.2085, simple_loss=0.2661, pruned_loss=0.07546, over 4893.00 frames.], tot_loss[loss=0.17, simple_loss=0.2461, pruned_loss=0.04691, over 984977.49 frames.], batch size: 52, aishell_tot_loss[loss=0.1692, simple_loss=0.2512, pruned_loss=0.04354, over 952225.35 frames.], datatang_tot_loss[loss=0.1704, simple_loss=0.2406, pruned_loss=0.05009, over 952762.53 frames.], batch size: 52, lr: 6.77e-04
+2022-06-18 18:41:32,151 INFO [train.py:874] (3/4) Epoch 12, batch 1400, datatang_loss[loss=0.1798, simple_loss=0.252, pruned_loss=0.05378, over 4946.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2462, pruned_loss=0.04713, over 985404.31 frames.], batch size: 91, aishell_tot_loss[loss=0.1685, simple_loss=0.2506, pruned_loss=0.0432, over 955967.68 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.2415, pruned_loss=0.0506, over 957131.36 frames.], batch size: 91, lr: 6.77e-04
+2022-06-18 18:42:02,822 INFO [train.py:874] (3/4) Epoch 12, batch 1450, datatang_loss[loss=0.151, simple_loss=0.2299, pruned_loss=0.03605, over 4844.00 frames.], tot_loss[loss=0.1706, simple_loss=0.247, pruned_loss=0.04713, over 985312.35 frames.], batch size: 30, aishell_tot_loss[loss=0.1684, simple_loss=0.2506, pruned_loss=0.04315, over 960418.77 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.242, pruned_loss=0.051, over 959385.29 frames.], batch size: 30, lr: 6.76e-04
+2022-06-18 18:42:33,379 INFO [train.py:874] (3/4) Epoch 12, batch 1500, datatang_loss[loss=0.1715, simple_loss=0.2489, pruned_loss=0.04708, over 4841.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2453, pruned_loss=0.04601, over 985040.15 frames.], batch size: 30, aishell_tot_loss[loss=0.167, simple_loss=0.2493, pruned_loss=0.04236, over 963380.17 frames.], datatang_tot_loss[loss=0.1715, simple_loss=0.2416, pruned_loss=0.05066, over 962119.07 frames.], batch size: 30, lr: 6.76e-04
+2022-06-18 18:43:02,328 INFO [train.py:874] (3/4) Epoch 12, batch 1550, aishell_loss[loss=0.2064, simple_loss=0.2805, pruned_loss=0.06613, over 4913.00 frames.], tot_loss[loss=0.1694, simple_loss=0.246, pruned_loss=0.04635, over 985388.55 frames.], batch size: 46, aishell_tot_loss[loss=0.1677, simple_loss=0.2499, pruned_loss=0.04273, over 966422.41 frames.], datatang_tot_loss[loss=0.1715, simple_loss=0.2416, pruned_loss=0.05066, over 964672.16 frames.], batch size: 46, lr: 6.76e-04
+2022-06-18 18:43:34,373 INFO [train.py:874] (3/4) Epoch 12, batch 1600, aishell_loss[loss=0.16, simple_loss=0.2559, pruned_loss=0.03203, over 4970.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2468, pruned_loss=0.04693, over 985100.35 frames.], batch size: 61, aishell_tot_loss[loss=0.1678, simple_loss=0.25, pruned_loss=0.04283, over 968604.04 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2424, pruned_loss=0.05117, over 966851.73 frames.], batch size: 61, lr: 6.75e-04
+2022-06-18 18:44:05,066 INFO [train.py:874] (3/4) Epoch 12, batch 1650, aishell_loss[loss=0.1307, simple_loss=0.1907, pruned_loss=0.03537, over 4878.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2463, pruned_loss=0.04701, over 985035.72 frames.], batch size: 21, aishell_tot_loss[loss=0.1676, simple_loss=0.2494, pruned_loss=0.04283, over 970338.77 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2425, pruned_loss=0.05117, over 969145.17 frames.], batch size: 21, lr: 6.75e-04
+2022-06-18 18:44:35,467 INFO [train.py:874] (3/4) Epoch 12, batch 1700, aishell_loss[loss=0.1773, simple_loss=0.2651, pruned_loss=0.04476, over 4978.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2463, pruned_loss=0.04715, over 985456.28 frames.], batch size: 51, aishell_tot_loss[loss=0.168, simple_loss=0.25, pruned_loss=0.04303, over 972060.73 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.242, pruned_loss=0.05107, over 971465.87 frames.], batch size: 51, lr: 6.74e-04
+2022-06-18 18:45:06,642 INFO [train.py:874] (3/4) Epoch 12, batch 1750, aishell_loss[loss=0.1164, simple_loss=0.1852, pruned_loss=0.02381, over 4979.00 frames.], tot_loss[loss=0.1702, simple_loss=0.246, pruned_loss=0.0472, over 985667.15 frames.], batch size: 21, aishell_tot_loss[loss=0.168, simple_loss=0.25, pruned_loss=0.04298, over 974073.42 frames.], datatang_tot_loss[loss=0.1722, simple_loss=0.2418, pruned_loss=0.05131, over 972877.08 frames.], batch size: 21, lr: 6.74e-04
+2022-06-18 18:45:38,085 INFO [train.py:874] (3/4) Epoch 12, batch 1800, aishell_loss[loss=0.1748, simple_loss=0.2521, pruned_loss=0.04877, over 4921.00 frames.], tot_loss[loss=0.169, simple_loss=0.2444, pruned_loss=0.04677, over 985250.32 frames.], batch size: 33, aishell_tot_loss[loss=0.1671, simple_loss=0.2489, pruned_loss=0.04262, over 974821.51 frames.], datatang_tot_loss[loss=0.1718, simple_loss=0.2412, pruned_loss=0.05115, over 974579.43 frames.], batch size: 33, lr: 6.74e-04
+2022-06-18 18:46:08,195 INFO [train.py:874] (3/4) Epoch 12, batch 1850, aishell_loss[loss=0.2321, simple_loss=0.3064, pruned_loss=0.07893, over 4909.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2449, pruned_loss=0.04642, over 985486.93 frames.], batch size: 79, aishell_tot_loss[loss=0.1674, simple_loss=0.2498, pruned_loss=0.0425, over 976226.64 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.2407, pruned_loss=0.05086, over 975912.05 frames.], batch size: 79, lr: 6.73e-04
+2022-06-18 18:46:38,191 INFO [train.py:874] (3/4) Epoch 12, batch 1900, datatang_loss[loss=0.2084, simple_loss=0.2683, pruned_loss=0.07422, over 4949.00 frames.], tot_loss[loss=0.1687, simple_loss=0.245, pruned_loss=0.04617, over 985409.19 frames.], batch size: 86, aishell_tot_loss[loss=0.167, simple_loss=0.2494, pruned_loss=0.04229, over 977342.16 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.2409, pruned_loss=0.05089, over 976917.76 frames.], batch size: 86, lr: 6.73e-04
+2022-06-18 18:47:08,375 INFO [train.py:874] (3/4) Epoch 12, batch 1950, datatang_loss[loss=0.1613, simple_loss=0.23, pruned_loss=0.04631, over 4920.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2446, pruned_loss=0.0459, over 985269.47 frames.], batch size: 77, aishell_tot_loss[loss=0.1663, simple_loss=0.2489, pruned_loss=0.04189, over 978169.90 frames.], datatang_tot_loss[loss=0.1715, simple_loss=0.2408, pruned_loss=0.05113, over 977877.55 frames.], batch size: 77, lr: 6.73e-04
+2022-06-18 18:47:38,754 INFO [train.py:874] (3/4) Epoch 12, batch 2000, datatang_loss[loss=0.1722, simple_loss=0.2381, pruned_loss=0.05315, over 4923.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2443, pruned_loss=0.04629, over 985574.37 frames.], batch size: 42, aishell_tot_loss[loss=0.1669, simple_loss=0.2493, pruned_loss=0.04223, over 979042.82 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.2402, pruned_loss=0.05088, over 979042.86 frames.], batch size: 42, lr: 6.72e-04
+2022-06-18 18:47:38,755 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 18:47:55,403 INFO [train.py:914] (3/4) Epoch 12, validation: loss=0.1674, simple_loss=0.25, pruned_loss=0.04245, over 1622729.00 frames.
+2022-06-18 18:48:25,438 INFO [train.py:874] (3/4) Epoch 12, batch 2050, datatang_loss[loss=0.1674, simple_loss=0.2393, pruned_loss=0.04776, over 4925.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2448, pruned_loss=0.04634, over 986014.49 frames.], batch size: 77, aishell_tot_loss[loss=0.1671, simple_loss=0.2496, pruned_loss=0.04236, over 979990.06 frames.], datatang_tot_loss[loss=0.1709, simple_loss=0.2404, pruned_loss=0.05072, over 980097.59 frames.], batch size: 77, lr: 6.72e-04
+2022-06-18 18:48:56,132 INFO [train.py:874] (3/4) Epoch 12, batch 2100, aishell_loss[loss=0.1702, simple_loss=0.2398, pruned_loss=0.05032, over 4936.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2457, pruned_loss=0.04691, over 986359.60 frames.], batch size: 32, aishell_tot_loss[loss=0.1681, simple_loss=0.2504, pruned_loss=0.04292, over 981068.45 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.2404, pruned_loss=0.0509, over 980790.43 frames.], batch size: 32, lr: 6.72e-04
+2022-06-18 18:49:26,436 INFO [train.py:874] (3/4) Epoch 12, batch 2150, datatang_loss[loss=0.1864, simple_loss=0.2592, pruned_loss=0.05676, over 4911.00 frames.], tot_loss[loss=0.17, simple_loss=0.2463, pruned_loss=0.04684, over 986200.32 frames.], batch size: 64, aishell_tot_loss[loss=0.1683, simple_loss=0.2504, pruned_loss=0.04307, over 981553.64 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.2409, pruned_loss=0.05075, over 981413.17 frames.], batch size: 64, lr: 6.71e-04
+2022-06-18 18:49:55,770 INFO [train.py:874] (3/4) Epoch 12, batch 2200, datatang_loss[loss=0.1653, simple_loss=0.2408, pruned_loss=0.04495, over 4924.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2459, pruned_loss=0.0468, over 986116.96 frames.], batch size: 71, aishell_tot_loss[loss=0.1685, simple_loss=0.2507, pruned_loss=0.04317, over 981815.02 frames.], datatang_tot_loss[loss=0.1708, simple_loss=0.2405, pruned_loss=0.05049, over 982161.24 frames.], batch size: 71, lr: 6.71e-04
+2022-06-18 18:50:25,881 INFO [train.py:874] (3/4) Epoch 12, batch 2250, datatang_loss[loss=0.1529, simple_loss=0.2314, pruned_loss=0.03723, over 4918.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2454, pruned_loss=0.04667, over 986411.78 frames.], batch size: 81, aishell_tot_loss[loss=0.1684, simple_loss=0.2505, pruned_loss=0.04318, over 982401.47 frames.], datatang_tot_loss[loss=0.1704, simple_loss=0.2404, pruned_loss=0.05026, over 982850.64 frames.], batch size: 81, lr: 6.71e-04
+2022-06-18 18:50:57,156 INFO [train.py:874] (3/4) Epoch 12, batch 2300, datatang_loss[loss=0.1641, simple_loss=0.2361, pruned_loss=0.04604, over 4931.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2457, pruned_loss=0.04698, over 985991.46 frames.], batch size: 79, aishell_tot_loss[loss=0.1689, simple_loss=0.2511, pruned_loss=0.04336, over 982416.85 frames.], datatang_tot_loss[loss=0.1704, simple_loss=0.2403, pruned_loss=0.05026, over 983249.23 frames.], batch size: 79, lr: 6.70e-04
+2022-06-18 18:51:27,022 INFO [train.py:874] (3/4) Epoch 12, batch 2350, datatang_loss[loss=0.1609, simple_loss=0.2322, pruned_loss=0.0448, over 4943.00 frames.], tot_loss[loss=0.169, simple_loss=0.2452, pruned_loss=0.04635, over 985767.38 frames.], batch size: 69, aishell_tot_loss[loss=0.1679, simple_loss=0.2501, pruned_loss=0.04278, over 982680.54 frames.], datatang_tot_loss[loss=0.1706, simple_loss=0.2405, pruned_loss=0.05035, over 983516.49 frames.], batch size: 69, lr: 6.70e-04
+2022-06-18 18:51:58,569 INFO [train.py:874] (3/4) Epoch 12, batch 2400, datatang_loss[loss=0.157, simple_loss=0.2224, pruned_loss=0.04584, over 4875.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2457, pruned_loss=0.04684, over 985487.42 frames.], batch size: 39, aishell_tot_loss[loss=0.1685, simple_loss=0.2508, pruned_loss=0.04314, over 982908.69 frames.], datatang_tot_loss[loss=0.1706, simple_loss=0.2403, pruned_loss=0.05041, over 983614.85 frames.], batch size: 39, lr: 6.70e-04
+2022-06-18 18:52:28,317 INFO [train.py:874] (3/4) Epoch 12, batch 2450, aishell_loss[loss=0.1611, simple_loss=0.2339, pruned_loss=0.04419, over 4904.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2459, pruned_loss=0.04684, over 985580.45 frames.], batch size: 33, aishell_tot_loss[loss=0.1682, simple_loss=0.2505, pruned_loss=0.04294, over 983150.58 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.2408, pruned_loss=0.05074, over 984011.80 frames.], batch size: 33, lr: 6.69e-04
+2022-06-18 18:52:59,341 INFO [train.py:874] (3/4) Epoch 12, batch 2500, datatang_loss[loss=0.1491, simple_loss=0.2186, pruned_loss=0.03985, over 4934.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2467, pruned_loss=0.04774, over 985363.18 frames.], batch size: 62, aishell_tot_loss[loss=0.1686, simple_loss=0.251, pruned_loss=0.04315, over 983101.38 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.2416, pruned_loss=0.05118, over 984259.78 frames.], batch size: 62, lr: 6.69e-04
+2022-06-18 18:53:29,797 INFO [train.py:874] (3/4) Epoch 12, batch 2550, aishell_loss[loss=0.1647, simple_loss=0.257, pruned_loss=0.03622, over 4911.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2461, pruned_loss=0.04718, over 985475.25 frames.], batch size: 52, aishell_tot_loss[loss=0.168, simple_loss=0.2505, pruned_loss=0.04275, over 983322.79 frames.], datatang_tot_loss[loss=0.1719, simple_loss=0.2413, pruned_loss=0.05132, over 984586.65 frames.], batch size: 52, lr: 6.69e-04
+2022-06-18 18:54:06,363 INFO [train.py:874] (3/4) Epoch 12, batch 2600, datatang_loss[loss=0.145, simple_loss=0.2237, pruned_loss=0.03313, over 4926.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2461, pruned_loss=0.04716, over 985277.36 frames.], batch size: 83, aishell_tot_loss[loss=0.1683, simple_loss=0.2509, pruned_loss=0.04283, over 983393.83 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.2412, pruned_loss=0.0511, over 984628.53 frames.], batch size: 83, lr: 6.68e-04
+2022-06-18 18:54:36,804 INFO [train.py:874] (3/4) Epoch 12, batch 2650, datatang_loss[loss=0.1368, simple_loss=0.2069, pruned_loss=0.03329, over 4863.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2456, pruned_loss=0.04702, over 985161.97 frames.], batch size: 30, aishell_tot_loss[loss=0.1687, simple_loss=0.2512, pruned_loss=0.04312, over 983482.36 frames.], datatang_tot_loss[loss=0.1708, simple_loss=0.2403, pruned_loss=0.05067, over 984717.25 frames.], batch size: 30, lr: 6.68e-04
+2022-06-18 18:55:07,948 INFO [train.py:874] (3/4) Epoch 12, batch 2700, datatang_loss[loss=0.1898, simple_loss=0.2475, pruned_loss=0.06599, over 4955.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2447, pruned_loss=0.04629, over 985310.23 frames.], batch size: 91, aishell_tot_loss[loss=0.1675, simple_loss=0.2501, pruned_loss=0.04238, over 983767.15 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.2401, pruned_loss=0.05089, over 984869.13 frames.], batch size: 91, lr: 6.68e-04
+2022-06-18 18:55:38,361 INFO [train.py:874] (3/4) Epoch 12, batch 2750, datatang_loss[loss=0.1871, simple_loss=0.2634, pruned_loss=0.05541, over 4958.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2443, pruned_loss=0.0462, over 985618.40 frames.], batch size: 99, aishell_tot_loss[loss=0.1677, simple_loss=0.2503, pruned_loss=0.04254, over 983905.76 frames.], datatang_tot_loss[loss=0.1702, simple_loss=0.2395, pruned_loss=0.05048, over 985272.53 frames.], batch size: 99, lr: 6.67e-04
+2022-06-18 18:56:09,581 INFO [train.py:874] (3/4) Epoch 12, batch 2800, aishell_loss[loss=0.156, simple_loss=0.2329, pruned_loss=0.03956, over 4929.00 frames.], tot_loss[loss=0.1682, simple_loss=0.244, pruned_loss=0.04622, over 985582.24 frames.], batch size: 58, aishell_tot_loss[loss=0.1682, simple_loss=0.2508, pruned_loss=0.04282, over 984192.77 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.2386, pruned_loss=0.05011, over 985189.88 frames.], batch size: 58, lr: 6.67e-04
+2022-06-18 18:56:40,355 INFO [train.py:874] (3/4) Epoch 12, batch 2850, datatang_loss[loss=0.2006, simple_loss=0.2639, pruned_loss=0.06865, over 4954.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2439, pruned_loss=0.04594, over 985796.22 frames.], batch size: 37, aishell_tot_loss[loss=0.1683, simple_loss=0.251, pruned_loss=0.04278, over 984296.62 frames.], datatang_tot_loss[loss=0.1688, simple_loss=0.2383, pruned_loss=0.04969, over 985499.40 frames.], batch size: 37, lr: 6.66e-04
+2022-06-18 18:57:09,906 INFO [train.py:874] (3/4) Epoch 12, batch 2900, aishell_loss[loss=0.1891, simple_loss=0.2787, pruned_loss=0.04981, over 4968.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2439, pruned_loss=0.04523, over 985999.34 frames.], batch size: 48, aishell_tot_loss[loss=0.1683, simple_loss=0.2515, pruned_loss=0.04261, over 984673.75 frames.], datatang_tot_loss[loss=0.1679, simple_loss=0.2374, pruned_loss=0.04917, over 985570.48 frames.], batch size: 48, lr: 6.66e-04
+2022-06-18 18:57:41,776 INFO [train.py:874] (3/4) Epoch 12, batch 2950, aishell_loss[loss=0.1587, simple_loss=0.2464, pruned_loss=0.03546, over 4977.00 frames.], tot_loss[loss=0.168, simple_loss=0.244, pruned_loss=0.04599, over 986115.43 frames.], batch size: 51, aishell_tot_loss[loss=0.1685, simple_loss=0.2516, pruned_loss=0.04274, over 985077.08 frames.], datatang_tot_loss[loss=0.1683, simple_loss=0.2376, pruned_loss=0.04952, over 985471.84 frames.], batch size: 51, lr: 6.66e-04
+2022-06-18 18:58:12,946 INFO [train.py:874] (3/4) Epoch 12, batch 3000, aishell_loss[loss=0.1574, simple_loss=0.2474, pruned_loss=0.03368, over 4967.00 frames.], tot_loss[loss=0.1691, simple_loss=0.245, pruned_loss=0.04659, over 985864.62 frames.], batch size: 39, aishell_tot_loss[loss=0.169, simple_loss=0.2518, pruned_loss=0.04312, over 984806.89 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2382, pruned_loss=0.04979, over 985656.73 frames.], batch size: 39, lr: 6.65e-04
+2022-06-18 18:58:12,948 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 18:58:29,562 INFO [train.py:914] (3/4) Epoch 12, validation: loss=0.1665, simple_loss=0.2506, pruned_loss=0.04121, over 1622729.00 frames.
+2022-06-18 18:59:00,902 INFO [train.py:874] (3/4) Epoch 12, batch 3050, aishell_loss[loss=0.1567, simple_loss=0.2222, pruned_loss=0.04565, over 4880.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2439, pruned_loss=0.04621, over 985910.47 frames.], batch size: 21, aishell_tot_loss[loss=0.1686, simple_loss=0.2511, pruned_loss=0.04304, over 984943.93 frames.], datatang_tot_loss[loss=0.1684, simple_loss=0.2379, pruned_loss=0.04941, over 985706.10 frames.], batch size: 21, lr: 6.65e-04
+2022-06-18 18:59:32,451 INFO [train.py:874] (3/4) Epoch 12, batch 3100, aishell_loss[loss=0.164, simple_loss=0.2606, pruned_loss=0.03371, over 4956.00 frames.], tot_loss[loss=0.1692, simple_loss=0.245, pruned_loss=0.04667, over 985945.33 frames.], batch size: 44, aishell_tot_loss[loss=0.1691, simple_loss=0.2514, pruned_loss=0.04338, over 985059.84 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2386, pruned_loss=0.0496, over 985774.45 frames.], batch size: 44, lr: 6.65e-04
+2022-06-18 19:00:02,824 INFO [train.py:874] (3/4) Epoch 12, batch 3150, aishell_loss[loss=0.1789, simple_loss=0.2649, pruned_loss=0.0465, over 4976.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2435, pruned_loss=0.04597, over 985986.09 frames.], batch size: 44, aishell_tot_loss[loss=0.1685, simple_loss=0.2508, pruned_loss=0.04314, over 985198.50 frames.], datatang_tot_loss[loss=0.168, simple_loss=0.238, pruned_loss=0.049, over 985801.48 frames.], batch size: 44, lr: 6.64e-04
+2022-06-18 19:00:33,867 INFO [train.py:874] (3/4) Epoch 12, batch 3200, aishell_loss[loss=0.2004, simple_loss=0.2775, pruned_loss=0.06167, over 4939.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2445, pruned_loss=0.04608, over 985808.68 frames.], batch size: 78, aishell_tot_loss[loss=0.1688, simple_loss=0.2512, pruned_loss=0.04321, over 985054.41 frames.], datatang_tot_loss[loss=0.1682, simple_loss=0.2381, pruned_loss=0.0491, over 985880.80 frames.], batch size: 78, lr: 6.64e-04
+2022-06-18 19:01:04,154 INFO [train.py:874] (3/4) Epoch 12, batch 3250, aishell_loss[loss=0.1772, simple_loss=0.2591, pruned_loss=0.04766, over 4921.00 frames.], tot_loss[loss=0.167, simple_loss=0.243, pruned_loss=0.04548, over 985488.03 frames.], batch size: 33, aishell_tot_loss[loss=0.1677, simple_loss=0.2499, pruned_loss=0.04269, over 984705.79 frames.], datatang_tot_loss[loss=0.1679, simple_loss=0.2378, pruned_loss=0.04893, over 985953.14 frames.], batch size: 33, lr: 6.64e-04
+2022-06-18 19:01:35,706 INFO [train.py:874] (3/4) Epoch 12, batch 3300, datatang_loss[loss=0.1812, simple_loss=0.2598, pruned_loss=0.05135, over 4910.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2434, pruned_loss=0.04555, over 985968.85 frames.], batch size: 64, aishell_tot_loss[loss=0.1677, simple_loss=0.25, pruned_loss=0.04271, over 984958.13 frames.], datatang_tot_loss[loss=0.1678, simple_loss=0.2379, pruned_loss=0.04886, over 986251.01 frames.], batch size: 64, lr: 6.63e-04
+2022-06-18 19:02:06,905 INFO [train.py:874] (3/4) Epoch 12, batch 3350, aishell_loss[loss=0.161, simple_loss=0.2464, pruned_loss=0.0378, over 4952.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2439, pruned_loss=0.04563, over 985668.47 frames.], batch size: 31, aishell_tot_loss[loss=0.1674, simple_loss=0.2499, pruned_loss=0.04246, over 984777.35 frames.], datatang_tot_loss[loss=0.1684, simple_loss=0.2381, pruned_loss=0.04932, over 986238.08 frames.], batch size: 31, lr: 6.63e-04
+2022-06-18 19:02:37,689 INFO [train.py:874] (3/4) Epoch 12, batch 3400, datatang_loss[loss=0.1555, simple_loss=0.225, pruned_loss=0.04294, over 4948.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2442, pruned_loss=0.04577, over 985416.28 frames.], batch size: 25, aishell_tot_loss[loss=0.1673, simple_loss=0.2497, pruned_loss=0.04246, over 984476.11 frames.], datatang_tot_loss[loss=0.1688, simple_loss=0.2385, pruned_loss=0.04951, over 986334.28 frames.], batch size: 25, lr: 6.63e-04
+2022-06-18 19:03:08,116 INFO [train.py:874] (3/4) Epoch 12, batch 3450, aishell_loss[loss=0.2218, simple_loss=0.2931, pruned_loss=0.07527, over 4952.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2448, pruned_loss=0.04577, over 985673.11 frames.], batch size: 56, aishell_tot_loss[loss=0.1672, simple_loss=0.2498, pruned_loss=0.04232, over 984704.78 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2389, pruned_loss=0.04964, over 986401.78 frames.], batch size: 56, lr: 6.62e-04
+2022-06-18 19:03:38,690 INFO [train.py:874] (3/4) Epoch 12, batch 3500, aishell_loss[loss=0.1479, simple_loss=0.2399, pruned_loss=0.02797, over 4957.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2453, pruned_loss=0.04624, over 985767.62 frames.], batch size: 64, aishell_tot_loss[loss=0.1675, simple_loss=0.25, pruned_loss=0.04249, over 984747.17 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.2393, pruned_loss=0.04997, over 986517.48 frames.], batch size: 64, lr: 6.62e-04
+2022-06-18 19:04:07,531 INFO [train.py:874] (3/4) Epoch 12, batch 3550, datatang_loss[loss=0.1388, simple_loss=0.2231, pruned_loss=0.02721, over 4918.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2449, pruned_loss=0.04565, over 985835.56 frames.], batch size: 81, aishell_tot_loss[loss=0.1676, simple_loss=0.2503, pruned_loss=0.04247, over 984978.58 frames.], datatang_tot_loss[loss=0.1687, simple_loss=0.2387, pruned_loss=0.04932, over 986389.57 frames.], batch size: 81, lr: 6.62e-04
+2022-06-18 19:04:39,627 INFO [train.py:874] (3/4) Epoch 12, batch 3600, datatang_loss[loss=0.2687, simple_loss=0.3142, pruned_loss=0.1116, over 4912.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2456, pruned_loss=0.0459, over 985931.55 frames.], batch size: 107, aishell_tot_loss[loss=0.1675, simple_loss=0.2505, pruned_loss=0.04228, over 985129.48 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.2391, pruned_loss=0.04983, over 986394.21 frames.], batch size: 107, lr: 6.61e-04
+2022-06-18 19:05:11,087 INFO [train.py:874] (3/4) Epoch 12, batch 3650, aishell_loss[loss=0.1678, simple_loss=0.2464, pruned_loss=0.04461, over 4936.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2446, pruned_loss=0.04586, over 985860.38 frames.], batch size: 45, aishell_tot_loss[loss=0.1671, simple_loss=0.2498, pruned_loss=0.04224, over 985036.47 frames.], datatang_tot_loss[loss=0.1693, simple_loss=0.239, pruned_loss=0.04976, over 986440.59 frames.], batch size: 45, lr: 6.61e-04
+2022-06-18 19:05:40,345 INFO [train.py:874] (3/4) Epoch 12, batch 3700, datatang_loss[loss=0.1865, simple_loss=0.2616, pruned_loss=0.05568, over 4925.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2441, pruned_loss=0.04604, over 985813.12 frames.], batch size: 94, aishell_tot_loss[loss=0.1669, simple_loss=0.2494, pruned_loss=0.04217, over 984916.57 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.239, pruned_loss=0.04988, over 986502.60 frames.], batch size: 94, lr: 6.61e-04
+2022-06-18 19:06:10,990 INFO [train.py:874] (3/4) Epoch 12, batch 3750, aishell_loss[loss=0.1591, simple_loss=0.2431, pruned_loss=0.03756, over 4979.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2447, pruned_loss=0.04578, over 986102.60 frames.], batch size: 48, aishell_tot_loss[loss=0.1675, simple_loss=0.25, pruned_loss=0.04245, over 985365.02 frames.], datatang_tot_loss[loss=0.169, simple_loss=0.2385, pruned_loss=0.04969, over 986450.41 frames.], batch size: 48, lr: 6.60e-04
+2022-06-18 19:06:39,882 INFO [train.py:874] (3/4) Epoch 12, batch 3800, datatang_loss[loss=0.1375, simple_loss=0.2128, pruned_loss=0.03113, over 4918.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2442, pruned_loss=0.04534, over 986048.06 frames.], batch size: 57, aishell_tot_loss[loss=0.1674, simple_loss=0.2501, pruned_loss=0.0424, over 985513.06 frames.], datatang_tot_loss[loss=0.1683, simple_loss=0.238, pruned_loss=0.04925, over 986300.41 frames.], batch size: 57, lr: 6.60e-04
+2022-06-18 19:07:09,957 INFO [train.py:874] (3/4) Epoch 12, batch 3850, aishell_loss[loss=0.161, simple_loss=0.2425, pruned_loss=0.03974, over 4961.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2442, pruned_loss=0.04561, over 985612.15 frames.], batch size: 56, aishell_tot_loss[loss=0.1676, simple_loss=0.25, pruned_loss=0.04258, over 985153.58 frames.], datatang_tot_loss[loss=0.1683, simple_loss=0.2381, pruned_loss=0.04923, over 986230.50 frames.], batch size: 56, lr: 6.60e-04
+2022-06-18 19:07:38,572 INFO [train.py:874] (3/4) Epoch 12, batch 3900, aishell_loss[loss=0.1382, simple_loss=0.2106, pruned_loss=0.0329, over 4940.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2432, pruned_loss=0.04566, over 985485.47 frames.], batch size: 25, aishell_tot_loss[loss=0.1673, simple_loss=0.2496, pruned_loss=0.04244, over 984824.50 frames.], datatang_tot_loss[loss=0.1681, simple_loss=0.2379, pruned_loss=0.04914, over 986375.83 frames.], batch size: 25, lr: 6.59e-04
+2022-06-18 19:08:09,116 INFO [train.py:874] (3/4) Epoch 12, batch 3950, aishell_loss[loss=0.1304, simple_loss=0.1975, pruned_loss=0.03161, over 4898.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2425, pruned_loss=0.0456, over 985416.39 frames.], batch size: 20, aishell_tot_loss[loss=0.1673, simple_loss=0.2494, pruned_loss=0.04261, over 984616.82 frames.], datatang_tot_loss[loss=0.1675, simple_loss=0.2376, pruned_loss=0.04871, over 986444.26 frames.], batch size: 20, lr: 6.59e-04
+2022-06-18 19:08:37,888 INFO [train.py:874] (3/4) Epoch 12, batch 4000, datatang_loss[loss=0.18, simple_loss=0.2573, pruned_loss=0.05139, over 4917.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2441, pruned_loss=0.04607, over 985305.82 frames.], batch size: 57, aishell_tot_loss[loss=0.1679, simple_loss=0.25, pruned_loss=0.04293, over 984666.05 frames.], datatang_tot_loss[loss=0.1681, simple_loss=0.2384, pruned_loss=0.04892, over 986278.98 frames.], batch size: 57, lr: 6.59e-04
+2022-06-18 19:08:37,889 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 19:08:54,416 INFO [train.py:914] (3/4) Epoch 12, validation: loss=0.1659, simple_loss=0.2501, pruned_loss=0.0409, over 1622729.00 frames.
+2022-06-18 19:09:24,455 INFO [train.py:874] (3/4) Epoch 12, batch 4050, datatang_loss[loss=0.1784, simple_loss=0.2512, pruned_loss=0.05283, over 4966.00 frames.], tot_loss[loss=0.168, simple_loss=0.2437, pruned_loss=0.0462, over 985433.10 frames.], batch size: 60, aishell_tot_loss[loss=0.1672, simple_loss=0.2491, pruned_loss=0.04259, over 984607.47 frames.], datatang_tot_loss[loss=0.1688, simple_loss=0.239, pruned_loss=0.0493, over 986396.67 frames.], batch size: 60, lr: 6.58e-04
+2022-06-18 19:09:52,508 INFO [train.py:874] (3/4) Epoch 12, batch 4100, datatang_loss[loss=0.1446, simple_loss=0.2173, pruned_loss=0.03597, over 4909.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2433, pruned_loss=0.04562, over 984974.12 frames.], batch size: 64, aishell_tot_loss[loss=0.1668, simple_loss=0.2487, pruned_loss=0.04243, over 984101.07 frames.], datatang_tot_loss[loss=0.1684, simple_loss=0.2387, pruned_loss=0.04903, over 986447.32 frames.], batch size: 64, lr: 6.58e-04
+2022-06-18 19:11:11,453 INFO [train.py:874] (3/4) Epoch 13, batch 50, datatang_loss[loss=0.1841, simple_loss=0.2484, pruned_loss=0.05995, over 4900.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2385, pruned_loss=0.0442, over 218576.05 frames.], batch size: 47, aishell_tot_loss[loss=0.1669, simple_loss=0.2497, pruned_loss=0.04201, over 107385.82 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.2292, pruned_loss=0.04597, over 124782.51 frames.], batch size: 47, lr: 6.36e-04
+2022-06-18 19:11:41,612 INFO [train.py:874] (3/4) Epoch 13, batch 100, aishell_loss[loss=0.1462, simple_loss=0.232, pruned_loss=0.03026, over 4944.00 frames.], tot_loss[loss=0.162, simple_loss=0.2393, pruned_loss=0.04241, over 389061.78 frames.], batch size: 49, aishell_tot_loss[loss=0.1677, simple_loss=0.251, pruned_loss=0.0422, over 230247.52 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2257, pruned_loss=0.04282, over 207123.07 frames.], batch size: 49, lr: 6.35e-04
+2022-06-18 19:12:12,383 INFO [train.py:874] (3/4) Epoch 13, batch 150, datatang_loss[loss=0.146, simple_loss=0.2185, pruned_loss=0.03675, over 4920.00 frames.], tot_loss[loss=0.1617, simple_loss=0.239, pruned_loss=0.0422, over 521420.30 frames.], batch size: 77, aishell_tot_loss[loss=0.1669, simple_loss=0.2505, pruned_loss=0.04164, over 309124.81 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2276, pruned_loss=0.04297, over 309170.68 frames.], batch size: 77, lr: 6.35e-04
+2022-06-18 19:12:44,612 INFO [train.py:874] (3/4) Epoch 13, batch 200, datatang_loss[loss=0.1309, simple_loss=0.2147, pruned_loss=0.02353, over 4944.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2377, pruned_loss=0.04199, over 624219.19 frames.], batch size: 69, aishell_tot_loss[loss=0.167, simple_loss=0.2506, pruned_loss=0.04175, over 379639.44 frames.], datatang_tot_loss[loss=0.1556, simple_loss=0.2264, pruned_loss=0.04242, over 397761.48 frames.], batch size: 69, lr: 6.35e-04
+2022-06-18 19:13:15,041 INFO [train.py:874] (3/4) Epoch 13, batch 250, datatang_loss[loss=0.1727, simple_loss=0.2461, pruned_loss=0.04968, over 4950.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2384, pruned_loss=0.04263, over 704620.18 frames.], batch size: 91, aishell_tot_loss[loss=0.1667, simple_loss=0.2496, pruned_loss=0.04191, over 445611.86 frames.], datatang_tot_loss[loss=0.1575, simple_loss=0.2285, pruned_loss=0.04321, over 472496.87 frames.], batch size: 91, lr: 6.34e-04
+2022-06-18 19:13:46,378 INFO [train.py:874] (3/4) Epoch 13, batch 300, datatang_loss[loss=0.1575, simple_loss=0.2234, pruned_loss=0.04577, over 4948.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2382, pruned_loss=0.04257, over 766818.94 frames.], batch size: 37, aishell_tot_loss[loss=0.1662, simple_loss=0.2494, pruned_loss=0.04152, over 499323.09 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.229, pruned_loss=0.04349, over 542060.75 frames.], batch size: 37, lr: 6.34e-04
+2022-06-18 19:14:18,051 INFO [train.py:874] (3/4) Epoch 13, batch 350, aishell_loss[loss=0.1648, simple_loss=0.2565, pruned_loss=0.03651, over 4916.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2389, pruned_loss=0.04321, over 815109.63 frames.], batch size: 41, aishell_tot_loss[loss=0.1652, simple_loss=0.2484, pruned_loss=0.041, over 549847.90 frames.], datatang_tot_loss[loss=0.1603, simple_loss=0.231, pruned_loss=0.04479, over 600170.80 frames.], batch size: 41, lr: 6.34e-04
+2022-06-18 19:14:49,304 INFO [train.py:874] (3/4) Epoch 13, batch 400, aishell_loss[loss=0.098, simple_loss=0.1688, pruned_loss=0.0136, over 4807.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2387, pruned_loss=0.04287, over 852995.11 frames.], batch size: 20, aishell_tot_loss[loss=0.1647, simple_loss=0.2482, pruned_loss=0.04061, over 599053.64 frames.], datatang_tot_loss[loss=0.1602, simple_loss=0.2308, pruned_loss=0.04477, over 647558.83 frames.], batch size: 20, lr: 6.33e-04
+2022-06-18 19:15:19,283 INFO [train.py:874] (3/4) Epoch 13, batch 450, aishell_loss[loss=0.1864, simple_loss=0.2777, pruned_loss=0.04756, over 4977.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2383, pruned_loss=0.04328, over 882472.14 frames.], batch size: 39, aishell_tot_loss[loss=0.165, simple_loss=0.248, pruned_loss=0.04095, over 636006.35 frames.], datatang_tot_loss[loss=0.1603, simple_loss=0.2307, pruned_loss=0.04496, over 694763.14 frames.], batch size: 39, lr: 6.33e-04
+2022-06-18 19:15:51,490 INFO [train.py:874] (3/4) Epoch 13, batch 500, aishell_loss[loss=0.144, simple_loss=0.2058, pruned_loss=0.04109, over 4988.00 frames.], tot_loss[loss=0.1638, simple_loss=0.24, pruned_loss=0.04374, over 905387.69 frames.], batch size: 25, aishell_tot_loss[loss=0.1653, simple_loss=0.2481, pruned_loss=0.04124, over 680088.32 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2324, pruned_loss=0.04547, over 726654.52 frames.], batch size: 25, lr: 6.33e-04
+2022-06-18 19:16:22,055 INFO [train.py:874] (3/4) Epoch 13, batch 550, datatang_loss[loss=0.1836, simple_loss=0.2532, pruned_loss=0.057, over 4962.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2405, pruned_loss=0.04392, over 922994.31 frames.], batch size: 91, aishell_tot_loss[loss=0.1653, simple_loss=0.248, pruned_loss=0.04129, over 717191.03 frames.], datatang_tot_loss[loss=0.1622, simple_loss=0.2329, pruned_loss=0.04576, over 756056.84 frames.], batch size: 91, lr: 6.32e-04
+2022-06-18 19:16:51,660 INFO [train.py:874] (3/4) Epoch 13, batch 600, datatang_loss[loss=0.1281, simple_loss=0.2061, pruned_loss=0.02504, over 4859.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2406, pruned_loss=0.04421, over 936349.67 frames.], batch size: 30, aishell_tot_loss[loss=0.1654, simple_loss=0.2476, pruned_loss=0.04163, over 749539.06 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2334, pruned_loss=0.04591, over 781970.80 frames.], batch size: 30, lr: 6.32e-04
+2022-06-18 19:17:23,516 INFO [train.py:874] (3/4) Epoch 13, batch 650, aishell_loss[loss=0.1626, simple_loss=0.2434, pruned_loss=0.04094, over 4942.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2414, pruned_loss=0.04443, over 947393.87 frames.], batch size: 32, aishell_tot_loss[loss=0.165, simple_loss=0.2472, pruned_loss=0.04142, over 781381.35 frames.], datatang_tot_loss[loss=0.1639, simple_loss=0.2345, pruned_loss=0.04664, over 802546.45 frames.], batch size: 32, lr: 6.32e-04
+2022-06-18 19:17:54,591 INFO [train.py:874] (3/4) Epoch 13, batch 700, aishell_loss[loss=0.1502, simple_loss=0.243, pruned_loss=0.02867, over 4978.00 frames.], tot_loss[loss=0.167, simple_loss=0.2434, pruned_loss=0.04535, over 956332.02 frames.], batch size: 48, aishell_tot_loss[loss=0.1656, simple_loss=0.248, pruned_loss=0.04159, over 807582.59 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2362, pruned_loss=0.04779, over 822612.37 frames.], batch size: 48, lr: 6.31e-04
+2022-06-18 19:18:24,410 INFO [train.py:874] (3/4) Epoch 13, batch 750, aishell_loss[loss=0.145, simple_loss=0.2294, pruned_loss=0.03027, over 4931.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2429, pruned_loss=0.04508, over 962704.12 frames.], batch size: 54, aishell_tot_loss[loss=0.1654, simple_loss=0.2477, pruned_loss=0.0416, over 829007.63 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2362, pruned_loss=0.04765, over 841284.66 frames.], batch size: 54, lr: 6.31e-04
+2022-06-18 19:18:56,681 INFO [train.py:874] (3/4) Epoch 13, batch 800, aishell_loss[loss=0.1595, simple_loss=0.2443, pruned_loss=0.03741, over 4958.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2424, pruned_loss=0.04486, over 968007.29 frames.], batch size: 40, aishell_tot_loss[loss=0.1648, simple_loss=0.2472, pruned_loss=0.04117, over 848330.26 frames.], datatang_tot_loss[loss=0.166, simple_loss=0.2362, pruned_loss=0.04793, over 857697.54 frames.], batch size: 40, lr: 6.31e-04
+2022-06-18 19:19:27,055 INFO [train.py:874] (3/4) Epoch 13, batch 850, aishell_loss[loss=0.1481, simple_loss=0.2349, pruned_loss=0.03066, over 4866.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2426, pruned_loss=0.04485, over 971507.97 frames.], batch size: 28, aishell_tot_loss[loss=0.1653, simple_loss=0.2477, pruned_loss=0.0415, over 862085.52 frames.], datatang_tot_loss[loss=0.1657, simple_loss=0.2363, pruned_loss=0.04757, over 874605.17 frames.], batch size: 28, lr: 6.31e-04
+2022-06-18 19:19:57,537 INFO [train.py:874] (3/4) Epoch 13, batch 900, aishell_loss[loss=0.1831, simple_loss=0.2686, pruned_loss=0.04885, over 4976.00 frames.], tot_loss[loss=0.166, simple_loss=0.2428, pruned_loss=0.04454, over 974674.71 frames.], batch size: 39, aishell_tot_loss[loss=0.1656, simple_loss=0.2482, pruned_loss=0.04155, over 877253.99 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2363, pruned_loss=0.04726, over 887164.71 frames.], batch size: 39, lr: 6.30e-04
+2022-06-18 19:20:29,010 INFO [train.py:874] (3/4) Epoch 13, batch 950, aishell_loss[loss=0.1707, simple_loss=0.2515, pruned_loss=0.04494, over 4962.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2435, pruned_loss=0.04445, over 977206.51 frames.], batch size: 31, aishell_tot_loss[loss=0.1659, simple_loss=0.2483, pruned_loss=0.04174, over 893418.61 frames.], datatang_tot_loss[loss=0.1655, simple_loss=0.2367, pruned_loss=0.04718, over 895657.66 frames.], batch size: 31, lr: 6.30e-04
+2022-06-18 19:20:58,058 INFO [train.py:874] (3/4) Epoch 13, batch 1000, aishell_loss[loss=0.1522, simple_loss=0.237, pruned_loss=0.03364, over 4981.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2437, pruned_loss=0.0446, over 979360.57 frames.], batch size: 39, aishell_tot_loss[loss=0.1656, simple_loss=0.2481, pruned_loss=0.04157, over 904755.33 frames.], datatang_tot_loss[loss=0.1662, simple_loss=0.2373, pruned_loss=0.04756, over 906073.44 frames.], batch size: 39, lr: 6.30e-04
+2022-06-18 19:20:58,059 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 19:21:15,125 INFO [train.py:914] (3/4) Epoch 13, validation: loss=0.1646, simple_loss=0.2486, pruned_loss=0.04032, over 1622729.00 frames.
+2022-06-18 19:21:45,839 INFO [train.py:874] (3/4) Epoch 13, batch 1050, datatang_loss[loss=0.1882, simple_loss=0.2625, pruned_loss=0.05699, over 4927.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2437, pruned_loss=0.04485, over 980721.60 frames.], batch size: 83, aishell_tot_loss[loss=0.1657, simple_loss=0.248, pruned_loss=0.04176, over 914838.93 frames.], datatang_tot_loss[loss=0.1665, simple_loss=0.2375, pruned_loss=0.04773, over 914852.85 frames.], batch size: 83, lr: 6.29e-04
+2022-06-18 19:22:16,663 INFO [train.py:874] (3/4) Epoch 13, batch 1100, datatang_loss[loss=0.1363, simple_loss=0.2073, pruned_loss=0.03262, over 4962.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2422, pruned_loss=0.04415, over 981833.13 frames.], batch size: 45, aishell_tot_loss[loss=0.1649, simple_loss=0.2472, pruned_loss=0.0413, over 922684.07 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.237, pruned_loss=0.04738, over 923693.10 frames.], batch size: 45, lr: 6.29e-04
+2022-06-18 19:22:47,010 INFO [train.py:874] (3/4) Epoch 13, batch 1150, aishell_loss[loss=0.144, simple_loss=0.2245, pruned_loss=0.03177, over 4868.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2428, pruned_loss=0.04435, over 982717.36 frames.], batch size: 28, aishell_tot_loss[loss=0.1654, simple_loss=0.2478, pruned_loss=0.04145, over 930827.16 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2369, pruned_loss=0.04749, over 930303.92 frames.], batch size: 28, lr: 6.29e-04
+2022-06-18 19:23:17,746 INFO [train.py:874] (3/4) Epoch 13, batch 1200, datatang_loss[loss=0.1584, simple_loss=0.2299, pruned_loss=0.04348, over 4966.00 frames.], tot_loss[loss=0.167, simple_loss=0.2436, pruned_loss=0.04515, over 983444.97 frames.], batch size: 37, aishell_tot_loss[loss=0.1658, simple_loss=0.2482, pruned_loss=0.04169, over 936876.44 frames.], datatang_tot_loss[loss=0.1669, simple_loss=0.2377, pruned_loss=0.04802, over 937306.43 frames.], batch size: 37, lr: 6.28e-04
+2022-06-18 19:23:48,021 INFO [train.py:874] (3/4) Epoch 13, batch 1250, datatang_loss[loss=0.1605, simple_loss=0.2255, pruned_loss=0.04777, over 4913.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2436, pruned_loss=0.04512, over 984105.41 frames.], batch size: 34, aishell_tot_loss[loss=0.1661, simple_loss=0.2485, pruned_loss=0.04184, over 943136.30 frames.], datatang_tot_loss[loss=0.1666, simple_loss=0.2374, pruned_loss=0.04794, over 942653.92 frames.], batch size: 34, lr: 6.28e-04
+2022-06-18 19:24:19,164 INFO [train.py:874] (3/4) Epoch 13, batch 1300, datatang_loss[loss=0.1685, simple_loss=0.2445, pruned_loss=0.04624, over 4939.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2432, pruned_loss=0.04529, over 984102.45 frames.], batch size: 79, aishell_tot_loss[loss=0.1661, simple_loss=0.2485, pruned_loss=0.04182, over 946903.66 frames.], datatang_tot_loss[loss=0.1668, simple_loss=0.2374, pruned_loss=0.04803, over 948581.59 frames.], batch size: 79, lr: 6.28e-04
+2022-06-18 19:24:49,585 INFO [train.py:874] (3/4) Epoch 13, batch 1350, aishell_loss[loss=0.1512, simple_loss=0.2408, pruned_loss=0.0308, over 4939.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2435, pruned_loss=0.04577, over 984477.47 frames.], batch size: 45, aishell_tot_loss[loss=0.1661, simple_loss=0.2487, pruned_loss=0.0418, over 950714.19 frames.], datatang_tot_loss[loss=0.1674, simple_loss=0.2378, pruned_loss=0.04854, over 953671.75 frames.], batch size: 45, lr: 6.27e-04
+2022-06-18 19:25:19,829 INFO [train.py:874] (3/4) Epoch 13, batch 1400, datatang_loss[loss=0.1678, simple_loss=0.2359, pruned_loss=0.04989, over 4891.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2423, pruned_loss=0.04502, over 984620.16 frames.], batch size: 42, aishell_tot_loss[loss=0.1654, simple_loss=0.248, pruned_loss=0.04143, over 954057.53 frames.], datatang_tot_loss[loss=0.1668, simple_loss=0.2375, pruned_loss=0.0481, over 958008.02 frames.], batch size: 42, lr: 6.27e-04
+2022-06-18 19:25:50,299 INFO [train.py:874] (3/4) Epoch 13, batch 1450, aishell_loss[loss=0.1901, simple_loss=0.2759, pruned_loss=0.05215, over 4965.00 frames.], tot_loss[loss=0.1658, simple_loss=0.242, pruned_loss=0.04476, over 985098.35 frames.], batch size: 56, aishell_tot_loss[loss=0.1655, simple_loss=0.2481, pruned_loss=0.04151, over 957804.30 frames.], datatang_tot_loss[loss=0.1662, simple_loss=0.237, pruned_loss=0.04774, over 961472.54 frames.], batch size: 56, lr: 6.27e-04
+2022-06-18 19:26:20,120 INFO [train.py:874] (3/4) Epoch 13, batch 1500, datatang_loss[loss=0.1609, simple_loss=0.2316, pruned_loss=0.04514, over 4973.00 frames.], tot_loss[loss=0.166, simple_loss=0.2422, pruned_loss=0.04487, over 985152.93 frames.], batch size: 60, aishell_tot_loss[loss=0.1656, simple_loss=0.2481, pruned_loss=0.04161, over 960714.12 frames.], datatang_tot_loss[loss=0.1663, simple_loss=0.2371, pruned_loss=0.04778, over 964598.91 frames.], batch size: 60, lr: 6.27e-04
+2022-06-18 19:26:50,930 INFO [train.py:874] (3/4) Epoch 13, batch 1550, aishell_loss[loss=0.1624, simple_loss=0.2569, pruned_loss=0.03399, over 4862.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2429, pruned_loss=0.04472, over 985106.92 frames.], batch size: 37, aishell_tot_loss[loss=0.1655, simple_loss=0.2481, pruned_loss=0.04144, over 963927.23 frames.], datatang_tot_loss[loss=0.1667, simple_loss=0.2375, pruned_loss=0.04792, over 966673.76 frames.], batch size: 37, lr: 6.26e-04
+2022-06-18 19:27:20,358 INFO [train.py:874] (3/4) Epoch 13, batch 1600, datatang_loss[loss=0.1592, simple_loss=0.2156, pruned_loss=0.05137, over 4940.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2413, pruned_loss=0.04418, over 985000.32 frames.], batch size: 34, aishell_tot_loss[loss=0.1651, simple_loss=0.2474, pruned_loss=0.04136, over 966196.27 frames.], datatang_tot_loss[loss=0.1657, simple_loss=0.2366, pruned_loss=0.04742, over 968956.02 frames.], batch size: 34, lr: 6.26e-04
+2022-06-18 19:27:49,966 INFO [train.py:874] (3/4) Epoch 13, batch 1650, datatang_loss[loss=0.1679, simple_loss=0.2467, pruned_loss=0.04453, over 4923.00 frames.], tot_loss[loss=0.166, simple_loss=0.2424, pruned_loss=0.04478, over 985481.44 frames.], batch size: 83, aishell_tot_loss[loss=0.166, simple_loss=0.2483, pruned_loss=0.04187, over 968661.90 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2366, pruned_loss=0.04752, over 971105.27 frames.], batch size: 83, lr: 6.26e-04
+2022-06-18 19:28:22,057 INFO [train.py:874] (3/4) Epoch 13, batch 1700, aishell_loss[loss=0.1518, simple_loss=0.2423, pruned_loss=0.03062, over 4961.00 frames.], tot_loss[loss=0.1654, simple_loss=0.242, pruned_loss=0.04437, over 985357.23 frames.], batch size: 56, aishell_tot_loss[loss=0.1661, simple_loss=0.2488, pruned_loss=0.04176, over 970393.38 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.236, pruned_loss=0.0471, over 972841.53 frames.], batch size: 56, lr: 6.25e-04
+2022-06-18 19:28:51,162 INFO [train.py:874] (3/4) Epoch 13, batch 1750, datatang_loss[loss=0.137, simple_loss=0.2043, pruned_loss=0.03489, over 4970.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2423, pruned_loss=0.04408, over 985599.13 frames.], batch size: 31, aishell_tot_loss[loss=0.1663, simple_loss=0.2492, pruned_loss=0.04174, over 972519.38 frames.], datatang_tot_loss[loss=0.1647, simple_loss=0.2356, pruned_loss=0.04688, over 974246.68 frames.], batch size: 31, lr: 6.25e-04
+2022-06-18 19:29:20,519 INFO [train.py:874] (3/4) Epoch 13, batch 1800, aishell_loss[loss=0.1549, simple_loss=0.2435, pruned_loss=0.03317, over 4979.00 frames.], tot_loss[loss=0.166, simple_loss=0.2428, pruned_loss=0.04457, over 985639.41 frames.], batch size: 38, aishell_tot_loss[loss=0.1666, simple_loss=0.2493, pruned_loss=0.042, over 974285.86 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2357, pruned_loss=0.04717, over 975413.56 frames.], batch size: 38, lr: 6.25e-04
+2022-06-18 19:29:52,053 INFO [train.py:874] (3/4) Epoch 13, batch 1850, datatang_loss[loss=0.1617, simple_loss=0.2282, pruned_loss=0.04759, over 4925.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2434, pruned_loss=0.04492, over 985310.95 frames.], batch size: 73, aishell_tot_loss[loss=0.1671, simple_loss=0.2497, pruned_loss=0.04223, over 975194.40 frames.], datatang_tot_loss[loss=0.1653, simple_loss=0.2361, pruned_loss=0.04728, over 976695.57 frames.], batch size: 73, lr: 6.24e-04
+2022-06-18 19:30:21,983 INFO [train.py:874] (3/4) Epoch 13, batch 1900, datatang_loss[loss=0.2191, simple_loss=0.277, pruned_loss=0.08059, over 4944.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2442, pruned_loss=0.0457, over 985529.71 frames.], batch size: 110, aishell_tot_loss[loss=0.1678, simple_loss=0.2504, pruned_loss=0.04262, over 976253.46 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2365, pruned_loss=0.04764, over 978044.10 frames.], batch size: 110, lr: 6.24e-04
+2022-06-18 19:30:51,634 INFO [train.py:874] (3/4) Epoch 13, batch 1950, datatang_loss[loss=0.1612, simple_loss=0.2336, pruned_loss=0.04437, over 4927.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2428, pruned_loss=0.04481, over 985543.44 frames.], batch size: 71, aishell_tot_loss[loss=0.1672, simple_loss=0.2499, pruned_loss=0.04222, over 976899.58 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.236, pruned_loss=0.04713, over 979347.80 frames.], batch size: 71, lr: 6.24e-04
+2022-06-18 19:31:23,258 INFO [train.py:874] (3/4) Epoch 13, batch 2000, aishell_loss[loss=0.1818, simple_loss=0.2749, pruned_loss=0.04434, over 4913.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2441, pruned_loss=0.04586, over 985824.43 frames.], batch size: 41, aishell_tot_loss[loss=0.1679, simple_loss=0.2502, pruned_loss=0.04278, over 978457.74 frames.], datatang_tot_loss[loss=0.1662, simple_loss=0.2367, pruned_loss=0.04784, over 979895.95 frames.], batch size: 41, lr: 6.24e-04
+2022-06-18 19:31:23,259 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 19:31:40,588 INFO [train.py:914] (3/4) Epoch 13, validation: loss=0.1662, simple_loss=0.2512, pruned_loss=0.04057, over 1622729.00 frames.
+2022-06-18 19:32:10,617 INFO [train.py:874] (3/4) Epoch 13, batch 2050, aishell_loss[loss=0.1506, simple_loss=0.2388, pruned_loss=0.03115, over 4888.00 frames.], tot_loss[loss=0.1677, simple_loss=0.244, pruned_loss=0.04575, over 985947.31 frames.], batch size: 47, aishell_tot_loss[loss=0.1675, simple_loss=0.2497, pruned_loss=0.04263, over 979398.24 frames.], datatang_tot_loss[loss=0.1666, simple_loss=0.237, pruned_loss=0.04806, over 980665.01 frames.], batch size: 47, lr: 6.23e-04
+2022-06-18 19:32:41,299 INFO [train.py:874] (3/4) Epoch 13, batch 2100, aishell_loss[loss=0.1541, simple_loss=0.2428, pruned_loss=0.03267, over 4965.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2431, pruned_loss=0.04525, over 985942.14 frames.], batch size: 61, aishell_tot_loss[loss=0.1676, simple_loss=0.2497, pruned_loss=0.0427, over 980095.30 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2363, pruned_loss=0.04748, over 981351.21 frames.], batch size: 61, lr: 6.23e-04
+2022-06-18 19:33:09,909 INFO [train.py:874] (3/4) Epoch 13, batch 2150, datatang_loss[loss=0.1484, simple_loss=0.2236, pruned_loss=0.03662, over 4928.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2425, pruned_loss=0.04461, over 985668.08 frames.], batch size: 77, aishell_tot_loss[loss=0.1674, simple_loss=0.2495, pruned_loss=0.04264, over 980597.34 frames.], datatang_tot_loss[loss=0.1648, simple_loss=0.2359, pruned_loss=0.04688, over 981781.84 frames.], batch size: 77, lr: 6.23e-04
+2022-06-18 19:33:41,245 INFO [train.py:874] (3/4) Epoch 13, batch 2200, aishell_loss[loss=0.1876, simple_loss=0.2676, pruned_loss=0.05383, over 4891.00 frames.], tot_loss[loss=0.166, simple_loss=0.2428, pruned_loss=0.04456, over 985591.30 frames.], batch size: 34, aishell_tot_loss[loss=0.1668, simple_loss=0.2491, pruned_loss=0.04221, over 980933.30 frames.], datatang_tot_loss[loss=0.1655, simple_loss=0.2367, pruned_loss=0.04722, over 982433.22 frames.], batch size: 34, lr: 6.22e-04
+2022-06-18 19:34:10,840 INFO [train.py:874] (3/4) Epoch 13, batch 2250, aishell_loss[loss=0.1659, simple_loss=0.2466, pruned_loss=0.04256, over 4960.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2433, pruned_loss=0.04467, over 985768.95 frames.], batch size: 61, aishell_tot_loss[loss=0.1668, simple_loss=0.2492, pruned_loss=0.04222, over 981566.65 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.237, pruned_loss=0.04731, over 982906.38 frames.], batch size: 61, lr: 6.22e-04
+2022-06-18 19:34:40,283 INFO [train.py:874] (3/4) Epoch 13, batch 2300, datatang_loss[loss=0.1557, simple_loss=0.2348, pruned_loss=0.03828, over 4934.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2418, pruned_loss=0.04398, over 985837.76 frames.], batch size: 88, aishell_tot_loss[loss=0.1665, simple_loss=0.2488, pruned_loss=0.04206, over 982149.76 frames.], datatang_tot_loss[loss=0.1647, simple_loss=0.236, pruned_loss=0.04667, over 983215.27 frames.], batch size: 88, lr: 6.22e-04
+2022-06-18 19:35:11,441 INFO [train.py:874] (3/4) Epoch 13, batch 2350, aishell_loss[loss=0.196, simple_loss=0.2675, pruned_loss=0.06224, over 4868.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2428, pruned_loss=0.04435, over 985878.97 frames.], batch size: 37, aishell_tot_loss[loss=0.1664, simple_loss=0.2488, pruned_loss=0.042, over 982736.48 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2365, pruned_loss=0.04715, over 983444.31 frames.], batch size: 37, lr: 6.21e-04
+2022-06-18 19:35:41,120 INFO [train.py:874] (3/4) Epoch 13, batch 2400, datatang_loss[loss=0.1661, simple_loss=0.2379, pruned_loss=0.04717, over 4925.00 frames.], tot_loss[loss=0.166, simple_loss=0.2431, pruned_loss=0.0445, over 985794.31 frames.], batch size: 79, aishell_tot_loss[loss=0.1669, simple_loss=0.2494, pruned_loss=0.04222, over 983028.07 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2364, pruned_loss=0.04698, over 983722.69 frames.], batch size: 79, lr: 6.21e-04
+2022-06-18 19:36:10,624 INFO [train.py:874] (3/4) Epoch 13, batch 2450, datatang_loss[loss=0.1643, simple_loss=0.2342, pruned_loss=0.04716, over 4919.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2438, pruned_loss=0.04471, over 985923.85 frames.], batch size: 83, aishell_tot_loss[loss=0.1667, simple_loss=0.2493, pruned_loss=0.04202, over 983484.01 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2372, pruned_loss=0.04737, over 983971.25 frames.], batch size: 83, lr: 6.21e-04
+2022-06-18 19:36:46,703 INFO [train.py:874] (3/4) Epoch 13, batch 2500, datatang_loss[loss=0.1485, simple_loss=0.2194, pruned_loss=0.03887, over 4859.00 frames.], tot_loss[loss=0.1673, simple_loss=0.245, pruned_loss=0.0448, over 985970.38 frames.], batch size: 39, aishell_tot_loss[loss=0.167, simple_loss=0.2501, pruned_loss=0.04191, over 983779.29 frames.], datatang_tot_loss[loss=0.1665, simple_loss=0.2377, pruned_loss=0.04768, over 984251.54 frames.], batch size: 39, lr: 6.21e-04
+2022-06-18 19:37:16,533 INFO [train.py:874] (3/4) Epoch 13, batch 2550, datatang_loss[loss=0.152, simple_loss=0.2173, pruned_loss=0.04332, over 4863.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2438, pruned_loss=0.04444, over 985998.70 frames.], batch size: 39, aishell_tot_loss[loss=0.1664, simple_loss=0.2497, pruned_loss=0.0416, over 984204.36 frames.], datatang_tot_loss[loss=0.1661, simple_loss=0.237, pruned_loss=0.04761, over 984331.79 frames.], batch size: 39, lr: 6.20e-04
+2022-06-18 19:37:46,620 INFO [train.py:874] (3/4) Epoch 13, batch 2600, datatang_loss[loss=0.1873, simple_loss=0.2551, pruned_loss=0.05969, over 4923.00 frames.], tot_loss[loss=0.166, simple_loss=0.2432, pruned_loss=0.0444, over 986063.23 frames.], batch size: 50, aishell_tot_loss[loss=0.1662, simple_loss=0.2493, pruned_loss=0.04151, over 984398.75 frames.], datatang_tot_loss[loss=0.166, simple_loss=0.2369, pruned_loss=0.04759, over 984623.33 frames.], batch size: 50, lr: 6.20e-04
+2022-06-18 19:38:17,698 INFO [train.py:874] (3/4) Epoch 13, batch 2650, aishell_loss[loss=0.1766, simple_loss=0.2736, pruned_loss=0.03979, over 4972.00 frames.], tot_loss[loss=0.165, simple_loss=0.2427, pruned_loss=0.04366, over 985935.99 frames.], batch size: 39, aishell_tot_loss[loss=0.1655, simple_loss=0.2488, pruned_loss=0.04105, over 984526.24 frames.], datatang_tot_loss[loss=0.1657, simple_loss=0.2368, pruned_loss=0.04728, over 984731.59 frames.], batch size: 39, lr: 6.20e-04
+2022-06-18 19:38:46,929 INFO [train.py:874] (3/4) Epoch 13, batch 2700, datatang_loss[loss=0.1486, simple_loss=0.213, pruned_loss=0.04208, over 4909.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2416, pruned_loss=0.04349, over 985430.07 frames.], batch size: 52, aishell_tot_loss[loss=0.1657, simple_loss=0.2488, pruned_loss=0.04129, over 984325.91 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.236, pruned_loss=0.04664, over 984680.79 frames.], batch size: 52, lr: 6.19e-04
+2022-06-18 19:39:16,985 INFO [train.py:874] (3/4) Epoch 13, batch 2750, datatang_loss[loss=0.1713, simple_loss=0.246, pruned_loss=0.04828, over 4914.00 frames.], tot_loss[loss=0.1642, simple_loss=0.241, pruned_loss=0.04369, over 985468.17 frames.], batch size: 50, aishell_tot_loss[loss=0.165, simple_loss=0.2479, pruned_loss=0.04109, over 984253.54 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2362, pruned_loss=0.04686, over 984997.07 frames.], batch size: 50, lr: 6.19e-04
+2022-06-18 19:39:48,848 INFO [train.py:874] (3/4) Epoch 13, batch 2800, aishell_loss[loss=0.1536, simple_loss=0.237, pruned_loss=0.03512, over 4922.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2405, pruned_loss=0.04344, over 985737.22 frames.], batch size: 41, aishell_tot_loss[loss=0.165, simple_loss=0.2477, pruned_loss=0.0411, over 984503.87 frames.], datatang_tot_loss[loss=0.1643, simple_loss=0.2356, pruned_loss=0.04649, over 985240.35 frames.], batch size: 41, lr: 6.19e-04
+2022-06-18 19:40:18,724 INFO [train.py:874] (3/4) Epoch 13, batch 2850, aishell_loss[loss=0.1549, simple_loss=0.2407, pruned_loss=0.03459, over 4915.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2416, pruned_loss=0.04363, over 986062.66 frames.], batch size: 41, aishell_tot_loss[loss=0.1647, simple_loss=0.2475, pruned_loss=0.04095, over 984908.77 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2365, pruned_loss=0.04691, over 985397.17 frames.], batch size: 41, lr: 6.18e-04
+2022-06-18 19:40:48,106 INFO [train.py:874] (3/4) Epoch 13, batch 2900, datatang_loss[loss=0.1618, simple_loss=0.2265, pruned_loss=0.04858, over 4957.00 frames.], tot_loss[loss=0.1643, simple_loss=0.242, pruned_loss=0.04331, over 986348.80 frames.], batch size: 25, aishell_tot_loss[loss=0.1646, simple_loss=0.2477, pruned_loss=0.04076, over 985323.15 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2365, pruned_loss=0.04677, over 985509.12 frames.], batch size: 25, lr: 6.18e-04
+2022-06-18 19:41:19,791 INFO [train.py:874] (3/4) Epoch 13, batch 2950, aishell_loss[loss=0.1291, simple_loss=0.2093, pruned_loss=0.02444, over 4986.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2424, pruned_loss=0.04435, over 986227.23 frames.], batch size: 25, aishell_tot_loss[loss=0.1656, simple_loss=0.2484, pruned_loss=0.04136, over 985405.75 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2363, pruned_loss=0.04707, over 985502.05 frames.], batch size: 25, lr: 6.18e-04
+2022-06-18 19:41:49,042 INFO [train.py:874] (3/4) Epoch 13, batch 3000, aishell_loss[loss=0.1938, simple_loss=0.2658, pruned_loss=0.06086, over 4891.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2429, pruned_loss=0.04449, over 985762.70 frames.], batch size: 34, aishell_tot_loss[loss=0.1662, simple_loss=0.2491, pruned_loss=0.04165, over 985399.45 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.2363, pruned_loss=0.04692, over 985193.42 frames.], batch size: 34, lr: 6.18e-04
+2022-06-18 19:41:49,043 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 19:42:05,183 INFO [train.py:914] (3/4) Epoch 13, validation: loss=0.1653, simple_loss=0.2497, pruned_loss=0.04043, over 1622729.00 frames.
+2022-06-18 19:42:35,307 INFO [train.py:874] (3/4) Epoch 13, batch 3050, datatang_loss[loss=0.1578, simple_loss=0.2305, pruned_loss=0.04258, over 4952.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2415, pruned_loss=0.04373, over 985631.25 frames.], batch size: 45, aishell_tot_loss[loss=0.1652, simple_loss=0.248, pruned_loss=0.0412, over 985467.23 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.236, pruned_loss=0.04658, over 985109.49 frames.], batch size: 45, lr: 6.17e-04
+2022-06-18 19:43:05,373 INFO [train.py:874] (3/4) Epoch 13, batch 3100, datatang_loss[loss=0.1868, simple_loss=0.2627, pruned_loss=0.05548, over 4969.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2411, pruned_loss=0.04369, over 985641.01 frames.], batch size: 37, aishell_tot_loss[loss=0.1653, simple_loss=0.2481, pruned_loss=0.04131, over 985386.62 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2357, pruned_loss=0.04626, over 985289.18 frames.], batch size: 37, lr: 6.17e-04
+2022-06-18 19:43:36,267 INFO [train.py:874] (3/4) Epoch 13, batch 3150, aishell_loss[loss=0.1486, simple_loss=0.2263, pruned_loss=0.03543, over 4971.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2407, pruned_loss=0.04315, over 985661.92 frames.], batch size: 27, aishell_tot_loss[loss=0.1649, simple_loss=0.2477, pruned_loss=0.041, over 985336.71 frames.], datatang_tot_loss[loss=0.1637, simple_loss=0.2354, pruned_loss=0.046, over 985426.26 frames.], batch size: 27, lr: 6.17e-04
+2022-06-18 19:44:06,021 INFO [train.py:874] (3/4) Epoch 13, batch 3200, aishell_loss[loss=0.1622, simple_loss=0.2509, pruned_loss=0.03676, over 4956.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2402, pruned_loss=0.0431, over 985827.30 frames.], batch size: 31, aishell_tot_loss[loss=0.1646, simple_loss=0.2475, pruned_loss=0.04088, over 985477.75 frames.], datatang_tot_loss[loss=0.1635, simple_loss=0.2351, pruned_loss=0.04588, over 985541.06 frames.], batch size: 31, lr: 6.16e-04
+2022-06-18 19:44:35,549 INFO [train.py:874] (3/4) Epoch 13, batch 3250, datatang_loss[loss=0.1533, simple_loss=0.2367, pruned_loss=0.03495, over 4890.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2399, pruned_loss=0.04255, over 985711.99 frames.], batch size: 52, aishell_tot_loss[loss=0.164, simple_loss=0.2473, pruned_loss=0.0404, over 985382.70 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2348, pruned_loss=0.04572, over 985594.20 frames.], batch size: 52, lr: 6.16e-04
+2022-06-18 19:45:06,296 INFO [train.py:874] (3/4) Epoch 13, batch 3300, datatang_loss[loss=0.1477, simple_loss=0.2217, pruned_loss=0.03685, over 4962.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2404, pruned_loss=0.04256, over 985850.70 frames.], batch size: 55, aishell_tot_loss[loss=0.1642, simple_loss=0.2475, pruned_loss=0.04045, over 985345.10 frames.], datatang_tot_loss[loss=0.1629, simple_loss=0.2346, pruned_loss=0.04562, over 985829.33 frames.], batch size: 55, lr: 6.16e-04
+2022-06-18 19:45:35,753 INFO [train.py:874] (3/4) Epoch 13, batch 3350, datatang_loss[loss=0.1454, simple_loss=0.2231, pruned_loss=0.03385, over 4911.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2403, pruned_loss=0.04273, over 985734.71 frames.], batch size: 52, aishell_tot_loss[loss=0.1639, simple_loss=0.2473, pruned_loss=0.04029, over 985385.58 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2347, pruned_loss=0.04576, over 985724.75 frames.], batch size: 52, lr: 6.16e-04
+2022-06-18 19:46:06,217 INFO [train.py:874] (3/4) Epoch 13, batch 3400, datatang_loss[loss=0.171, simple_loss=0.2422, pruned_loss=0.04989, over 4864.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2406, pruned_loss=0.0428, over 985773.42 frames.], batch size: 30, aishell_tot_loss[loss=0.1639, simple_loss=0.2475, pruned_loss=0.04018, over 985265.29 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2349, pruned_loss=0.04579, over 985922.17 frames.], batch size: 30, lr: 6.15e-04
+2022-06-18 19:46:38,167 INFO [train.py:874] (3/4) Epoch 13, batch 3450, datatang_loss[loss=0.1581, simple_loss=0.2259, pruned_loss=0.04518, over 4967.00 frames.], tot_loss[loss=0.163, simple_loss=0.2407, pruned_loss=0.04268, over 985792.68 frames.], batch size: 25, aishell_tot_loss[loss=0.1643, simple_loss=0.248, pruned_loss=0.04029, over 985249.40 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2345, pruned_loss=0.04545, over 985988.03 frames.], batch size: 25, lr: 6.15e-04
+2022-06-18 19:47:08,012 INFO [train.py:874] (3/4) Epoch 13, batch 3500, datatang_loss[loss=0.1635, simple_loss=0.2367, pruned_loss=0.04516, over 4921.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2403, pruned_loss=0.04244, over 985648.30 frames.], batch size: 73, aishell_tot_loss[loss=0.164, simple_loss=0.2476, pruned_loss=0.04017, over 985026.94 frames.], datatang_tot_loss[loss=0.1624, simple_loss=0.2343, pruned_loss=0.04529, over 986100.17 frames.], batch size: 73, lr: 6.15e-04
+2022-06-18 19:47:37,794 INFO [train.py:874] (3/4) Epoch 13, batch 3550, aishell_loss[loss=0.1529, simple_loss=0.2363, pruned_loss=0.03475, over 4926.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2414, pruned_loss=0.04252, over 985737.28 frames.], batch size: 46, aishell_tot_loss[loss=0.1648, simple_loss=0.2486, pruned_loss=0.04053, over 984935.09 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2341, pruned_loss=0.04499, over 986327.07 frames.], batch size: 46, lr: 6.14e-04
+2022-06-18 19:48:09,695 INFO [train.py:874] (3/4) Epoch 13, batch 3600, datatang_loss[loss=0.1601, simple_loss=0.2277, pruned_loss=0.04626, over 4925.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2421, pruned_loss=0.04307, over 985835.95 frames.], batch size: 73, aishell_tot_loss[loss=0.1651, simple_loss=0.2489, pruned_loss=0.04067, over 984972.60 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2345, pruned_loss=0.04544, over 986443.88 frames.], batch size: 73, lr: 6.14e-04
+2022-06-18 19:48:39,155 INFO [train.py:874] (3/4) Epoch 13, batch 3650, datatang_loss[loss=0.1563, simple_loss=0.2497, pruned_loss=0.0314, over 4931.00 frames.], tot_loss[loss=0.164, simple_loss=0.2423, pruned_loss=0.04285, over 985356.90 frames.], batch size: 94, aishell_tot_loss[loss=0.1647, simple_loss=0.2484, pruned_loss=0.04049, over 984818.15 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2351, pruned_loss=0.04542, over 986120.75 frames.], batch size: 94, lr: 6.14e-04
+2022-06-18 19:49:09,255 INFO [train.py:874] (3/4) Epoch 13, batch 3700, datatang_loss[loss=0.1523, simple_loss=0.2331, pruned_loss=0.03569, over 4848.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2429, pruned_loss=0.04325, over 985169.15 frames.], batch size: 30, aishell_tot_loss[loss=0.1652, simple_loss=0.249, pruned_loss=0.04073, over 984547.29 frames.], datatang_tot_loss[loss=0.1632, simple_loss=0.2353, pruned_loss=0.04555, over 986164.09 frames.], batch size: 30, lr: 6.14e-04
+2022-06-18 19:49:40,858 INFO [train.py:874] (3/4) Epoch 13, batch 3750, aishell_loss[loss=0.1529, simple_loss=0.2331, pruned_loss=0.03639, over 4930.00 frames.], tot_loss[loss=0.1651, simple_loss=0.243, pruned_loss=0.04356, over 985397.49 frames.], batch size: 33, aishell_tot_loss[loss=0.1653, simple_loss=0.2489, pruned_loss=0.04082, over 984895.91 frames.], datatang_tot_loss[loss=0.1637, simple_loss=0.2357, pruned_loss=0.04584, over 986010.83 frames.], batch size: 33, lr: 6.13e-04
+2022-06-18 19:50:08,764 INFO [train.py:874] (3/4) Epoch 13, batch 3800, aishell_loss[loss=0.1612, simple_loss=0.2409, pruned_loss=0.04073, over 4879.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2429, pruned_loss=0.04383, over 985473.81 frames.], batch size: 42, aishell_tot_loss[loss=0.166, simple_loss=0.2494, pruned_loss=0.04128, over 984892.88 frames.], datatang_tot_loss[loss=0.1634, simple_loss=0.2353, pruned_loss=0.04573, over 986088.09 frames.], batch size: 42, lr: 6.13e-04
+2022-06-18 19:50:39,602 INFO [train.py:874] (3/4) Epoch 13, batch 3850, datatang_loss[loss=0.1941, simple_loss=0.2428, pruned_loss=0.0727, over 4929.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2424, pruned_loss=0.04385, over 985062.63 frames.], batch size: 64, aishell_tot_loss[loss=0.1659, simple_loss=0.249, pruned_loss=0.04139, over 984493.29 frames.], datatang_tot_loss[loss=0.1634, simple_loss=0.2352, pruned_loss=0.04576, over 986047.76 frames.], batch size: 64, lr: 6.13e-04
+2022-06-18 19:51:08,468 INFO [train.py:874] (3/4) Epoch 13, batch 3900, aishell_loss[loss=0.1485, simple_loss=0.2351, pruned_loss=0.03101, over 4890.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2423, pruned_loss=0.04352, over 984951.10 frames.], batch size: 34, aishell_tot_loss[loss=0.1657, simple_loss=0.2488, pruned_loss=0.04126, over 984369.56 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2353, pruned_loss=0.04563, over 986000.51 frames.], batch size: 34, lr: 6.12e-04
+2022-06-18 19:51:38,483 INFO [train.py:874] (3/4) Epoch 13, batch 3950, aishell_loss[loss=0.1825, simple_loss=0.265, pruned_loss=0.05002, over 4905.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2415, pruned_loss=0.04305, over 985031.01 frames.], batch size: 41, aishell_tot_loss[loss=0.1655, simple_loss=0.2486, pruned_loss=0.04123, over 984510.05 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2345, pruned_loss=0.04522, over 985914.38 frames.], batch size: 41, lr: 6.12e-04
+2022-06-18 19:52:05,708 INFO [train.py:874] (3/4) Epoch 13, batch 4000, datatang_loss[loss=0.1542, simple_loss=0.2348, pruned_loss=0.03684, over 4927.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2417, pruned_loss=0.0427, over 984975.41 frames.], batch size: 73, aishell_tot_loss[loss=0.1654, simple_loss=0.249, pruned_loss=0.04094, over 984349.21 frames.], datatang_tot_loss[loss=0.1623, simple_loss=0.2343, pruned_loss=0.04512, over 986000.15 frames.], batch size: 73, lr: 6.12e-04
+2022-06-18 19:52:05,709 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 19:52:22,451 INFO [train.py:914] (3/4) Epoch 13, validation: loss=0.1652, simple_loss=0.2502, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-18 19:52:50,052 INFO [train.py:874] (3/4) Epoch 13, batch 4050, datatang_loss[loss=0.1481, simple_loss=0.2227, pruned_loss=0.03672, over 4789.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2422, pruned_loss=0.0433, over 985164.80 frames.], batch size: 24, aishell_tot_loss[loss=0.1658, simple_loss=0.2493, pruned_loss=0.04117, over 984535.86 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2346, pruned_loss=0.04546, over 985971.67 frames.], batch size: 24, lr: 6.12e-04
+2022-06-18 19:53:20,322 INFO [train.py:874] (3/4) Epoch 13, batch 4100, datatang_loss[loss=0.2234, simple_loss=0.2873, pruned_loss=0.07972, over 4956.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2419, pruned_loss=0.04345, over 985509.82 frames.], batch size: 108, aishell_tot_loss[loss=0.1653, simple_loss=0.2484, pruned_loss=0.04108, over 984873.70 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.235, pruned_loss=0.04575, over 985995.02 frames.], batch size: 108, lr: 6.11e-04
+2022-06-18 19:53:48,828 INFO [train.py:874] (3/4) Epoch 13, batch 4150, aishell_loss[loss=0.1753, simple_loss=0.2537, pruned_loss=0.04846, over 4895.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2424, pruned_loss=0.04401, over 985381.51 frames.], batch size: 34, aishell_tot_loss[loss=0.1661, simple_loss=0.2491, pruned_loss=0.04151, over 984628.18 frames.], datatang_tot_loss[loss=0.1634, simple_loss=0.235, pruned_loss=0.04588, over 986116.34 frames.], batch size: 34, lr: 6.11e-04
+2022-06-18 19:55:12,963 INFO [train.py:874] (3/4) Epoch 14, batch 50, aishell_loss[loss=0.1756, simple_loss=0.263, pruned_loss=0.0441, over 4878.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2339, pruned_loss=0.04161, over 218483.23 frames.], batch size: 42, aishell_tot_loss[loss=0.1719, simple_loss=0.2505, pruned_loss=0.0466, over 93605.95 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2225, pruned_loss=0.03824, over 138001.82 frames.], batch size: 42, lr: 5.91e-04
+2022-06-18 19:55:42,442 INFO [train.py:874] (3/4) Epoch 14, batch 100, datatang_loss[loss=0.145, simple_loss=0.2179, pruned_loss=0.03611, over 4977.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2348, pruned_loss=0.04085, over 388491.47 frames.], batch size: 53, aishell_tot_loss[loss=0.166, simple_loss=0.2468, pruned_loss=0.0426, over 194791.91 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2251, pruned_loss=0.03956, over 241438.49 frames.], batch size: 53, lr: 5.91e-04
+2022-06-18 19:56:14,071 INFO [train.py:874] (3/4) Epoch 14, batch 150, datatang_loss[loss=0.1539, simple_loss=0.2277, pruned_loss=0.04, over 4938.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2357, pruned_loss=0.04182, over 520990.72 frames.], batch size: 69, aishell_tot_loss[loss=0.1657, simple_loss=0.2477, pruned_loss=0.04179, over 266538.16 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2267, pruned_loss=0.04184, over 348756.33 frames.], batch size: 69, lr: 5.91e-04
+2022-06-18 19:56:43,565 INFO [train.py:874] (3/4) Epoch 14, batch 200, datatang_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.035, over 4921.00 frames.], tot_loss[loss=0.1602, simple_loss=0.237, pruned_loss=0.04172, over 623763.32 frames.], batch size: 83, aishell_tot_loss[loss=0.1683, simple_loss=0.251, pruned_loss=0.04283, over 354157.59 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2248, pruned_loss=0.04079, over 420940.19 frames.], batch size: 83, lr: 5.90e-04
+2022-06-18 19:57:13,609 INFO [train.py:874] (3/4) Epoch 14, batch 250, aishell_loss[loss=0.1841, simple_loss=0.2656, pruned_loss=0.05128, over 4861.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2381, pruned_loss=0.04163, over 703958.97 frames.], batch size: 37, aishell_tot_loss[loss=0.1669, simple_loss=0.2501, pruned_loss=0.04186, over 439471.76 frames.], datatang_tot_loss[loss=0.1544, simple_loss=0.2259, pruned_loss=0.04143, over 477443.89 frames.], batch size: 37, lr: 5.90e-04
+2022-06-18 19:57:44,742 INFO [train.py:874] (3/4) Epoch 14, batch 300, aishell_loss[loss=0.1849, simple_loss=0.2694, pruned_loss=0.0502, over 4901.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2378, pruned_loss=0.04103, over 766152.26 frames.], batch size: 42, aishell_tot_loss[loss=0.1657, simple_loss=0.2491, pruned_loss=0.04115, over 513048.19 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2257, pruned_loss=0.04123, over 528268.36 frames.], batch size: 42, lr: 5.90e-04
+2022-06-18 19:58:15,405 INFO [train.py:874] (3/4) Epoch 14, batch 350, datatang_loss[loss=0.154, simple_loss=0.2325, pruned_loss=0.03774, over 4920.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2379, pruned_loss=0.04082, over 815145.50 frames.], batch size: 83, aishell_tot_loss[loss=0.1657, simple_loss=0.2494, pruned_loss=0.04102, over 569009.16 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.2259, pruned_loss=0.04097, over 582192.30 frames.], batch size: 83, lr: 5.89e-04
+2022-06-18 19:58:44,827 INFO [train.py:874] (3/4) Epoch 14, batch 400, datatang_loss[loss=0.1656, simple_loss=0.2359, pruned_loss=0.0477, over 4910.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2383, pruned_loss=0.04109, over 852913.60 frames.], batch size: 75, aishell_tot_loss[loss=0.165, simple_loss=0.2491, pruned_loss=0.04042, over 604947.40 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2281, pruned_loss=0.04182, over 641959.84 frames.], batch size: 75, lr: 5.89e-04
+2022-06-18 19:59:15,093 INFO [train.py:874] (3/4) Epoch 14, batch 450, datatang_loss[loss=0.1624, simple_loss=0.2387, pruned_loss=0.04309, over 4928.00 frames.], tot_loss[loss=0.1605, simple_loss=0.239, pruned_loss=0.04099, over 882513.28 frames.], batch size: 83, aishell_tot_loss[loss=0.1649, simple_loss=0.2493, pruned_loss=0.04029, over 649908.17 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2288, pruned_loss=0.0418, over 682500.77 frames.], batch size: 83, lr: 5.89e-04
+2022-06-18 19:59:45,591 INFO [train.py:874] (3/4) Epoch 14, batch 500, datatang_loss[loss=0.1553, simple_loss=0.2222, pruned_loss=0.04418, over 4967.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2393, pruned_loss=0.04121, over 905467.48 frames.], batch size: 67, aishell_tot_loss[loss=0.1653, simple_loss=0.2495, pruned_loss=0.04052, over 692494.35 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2288, pruned_loss=0.04187, over 715511.11 frames.], batch size: 67, lr: 5.89e-04
+2022-06-18 20:00:15,503 INFO [train.py:874] (3/4) Epoch 14, batch 550, datatang_loss[loss=0.1932, simple_loss=0.2463, pruned_loss=0.07008, over 4982.00 frames.], tot_loss[loss=0.1619, simple_loss=0.24, pruned_loss=0.04196, over 923388.64 frames.], batch size: 37, aishell_tot_loss[loss=0.1652, simple_loss=0.2493, pruned_loss=0.04054, over 725663.24 frames.], datatang_tot_loss[loss=0.1578, simple_loss=0.23, pruned_loss=0.0428, over 748736.63 frames.], batch size: 37, lr: 5.88e-04
+2022-06-18 20:00:45,860 INFO [train.py:874] (3/4) Epoch 14, batch 600, datatang_loss[loss=0.1457, simple_loss=0.2235, pruned_loss=0.03399, over 4928.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2407, pruned_loss=0.04198, over 937310.52 frames.], batch size: 79, aishell_tot_loss[loss=0.1652, simple_loss=0.2495, pruned_loss=0.04042, over 758443.11 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2306, pruned_loss=0.04306, over 774793.74 frames.], batch size: 79, lr: 5.88e-04
+2022-06-18 20:01:15,142 INFO [train.py:874] (3/4) Epoch 14, batch 650, aishell_loss[loss=0.1634, simple_loss=0.251, pruned_loss=0.03794, over 4951.00 frames.], tot_loss[loss=0.1638, simple_loss=0.242, pruned_loss=0.04282, over 948174.13 frames.], batch size: 54, aishell_tot_loss[loss=0.1655, simple_loss=0.2498, pruned_loss=0.04053, over 783377.76 frames.], datatang_tot_loss[loss=0.1602, simple_loss=0.2322, pruned_loss=0.04407, over 801454.76 frames.], batch size: 54, lr: 5.88e-04
+2022-06-18 20:01:44,243 INFO [train.py:874] (3/4) Epoch 14, batch 700, datatang_loss[loss=0.133, simple_loss=0.1992, pruned_loss=0.03343, over 4927.00 frames.], tot_loss[loss=0.164, simple_loss=0.2424, pruned_loss=0.04286, over 956608.75 frames.], batch size: 62, aishell_tot_loss[loss=0.1652, simple_loss=0.2498, pruned_loss=0.0403, over 807055.41 frames.], datatang_tot_loss[loss=0.161, simple_loss=0.233, pruned_loss=0.04447, over 823430.05 frames.], batch size: 62, lr: 5.88e-04
+2022-06-18 20:02:13,989 INFO [train.py:874] (3/4) Epoch 14, batch 750, aishell_loss[loss=0.1611, simple_loss=0.2456, pruned_loss=0.0383, over 4908.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2407, pruned_loss=0.04279, over 962940.32 frames.], batch size: 34, aishell_tot_loss[loss=0.1645, simple_loss=0.2486, pruned_loss=0.04022, over 826375.21 frames.], datatang_tot_loss[loss=0.1609, simple_loss=0.2327, pruned_loss=0.04456, over 843973.69 frames.], batch size: 34, lr: 5.87e-04
+2022-06-18 20:02:45,474 INFO [train.py:874] (3/4) Epoch 14, batch 800, datatang_loss[loss=0.1881, simple_loss=0.2576, pruned_loss=0.05927, over 4957.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2413, pruned_loss=0.04297, over 968045.58 frames.], batch size: 86, aishell_tot_loss[loss=0.1651, simple_loss=0.2491, pruned_loss=0.04057, over 842998.67 frames.], datatang_tot_loss[loss=0.161, simple_loss=0.2331, pruned_loss=0.04445, over 862620.74 frames.], batch size: 86, lr: 5.87e-04
+2022-06-18 20:03:15,240 INFO [train.py:874] (3/4) Epoch 14, batch 850, aishell_loss[loss=0.1887, simple_loss=0.2785, pruned_loss=0.04946, over 4938.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2407, pruned_loss=0.04256, over 971799.64 frames.], batch size: 80, aishell_tot_loss[loss=0.1649, simple_loss=0.2485, pruned_loss=0.04071, over 862748.70 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2327, pruned_loss=0.04406, over 874383.39 frames.], batch size: 80, lr: 5.87e-04
+2022-06-18 20:03:44,506 INFO [train.py:874] (3/4) Epoch 14, batch 900, aishell_loss[loss=0.1562, simple_loss=0.244, pruned_loss=0.03423, over 4950.00 frames.], tot_loss[loss=0.1632, simple_loss=0.241, pruned_loss=0.04274, over 974632.72 frames.], batch size: 54, aishell_tot_loss[loss=0.1648, simple_loss=0.2481, pruned_loss=0.04071, over 877703.43 frames.], datatang_tot_loss[loss=0.161, simple_loss=0.2334, pruned_loss=0.04433, over 886798.42 frames.], batch size: 54, lr: 5.86e-04
+2022-06-18 20:04:15,197 INFO [train.py:874] (3/4) Epoch 14, batch 950, datatang_loss[loss=0.1597, simple_loss=0.2286, pruned_loss=0.0454, over 4936.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2415, pruned_loss=0.04307, over 976826.71 frames.], batch size: 62, aishell_tot_loss[loss=0.165, simple_loss=0.2484, pruned_loss=0.04075, over 887950.48 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2341, pruned_loss=0.04464, over 900382.13 frames.], batch size: 62, lr: 5.86e-04
+2022-06-18 20:04:45,287 INFO [train.py:874] (3/4) Epoch 14, batch 1000, datatang_loss[loss=0.1286, simple_loss=0.2151, pruned_loss=0.02107, over 4918.00 frames.], tot_loss[loss=0.1635, simple_loss=0.241, pruned_loss=0.04305, over 978712.41 frames.], batch size: 77, aishell_tot_loss[loss=0.1654, simple_loss=0.2487, pruned_loss=0.041, over 898313.40 frames.], datatang_tot_loss[loss=0.1612, simple_loss=0.2335, pruned_loss=0.04445, over 911379.35 frames.], batch size: 77, lr: 5.86e-04
+2022-06-18 20:04:45,288 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 20:05:01,200 INFO [train.py:914] (3/4) Epoch 14, validation: loss=0.1656, simple_loss=0.2496, pruned_loss=0.04079, over 1622729.00 frames.
+2022-06-18 20:05:31,543 INFO [train.py:874] (3/4) Epoch 14, batch 1050, aishell_loss[loss=0.1064, simple_loss=0.1923, pruned_loss=0.01026, over 4949.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2404, pruned_loss=0.04253, over 979846.35 frames.], batch size: 27, aishell_tot_loss[loss=0.1641, simple_loss=0.2475, pruned_loss=0.04042, over 908499.61 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.234, pruned_loss=0.04459, over 919861.43 frames.], batch size: 27, lr: 5.86e-04
+2022-06-18 20:06:02,004 INFO [train.py:874] (3/4) Epoch 14, batch 1100, aishell_loss[loss=0.1764, simple_loss=0.2546, pruned_loss=0.04914, over 4951.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2401, pruned_loss=0.04245, over 981037.32 frames.], batch size: 56, aishell_tot_loss[loss=0.1642, simple_loss=0.2475, pruned_loss=0.04041, over 915736.60 frames.], datatang_tot_loss[loss=0.1613, simple_loss=0.2339, pruned_loss=0.0444, over 929077.07 frames.], batch size: 56, lr: 5.85e-04
+2022-06-18 20:06:31,928 INFO [train.py:874] (3/4) Epoch 14, batch 1150, datatang_loss[loss=0.1453, simple_loss=0.2245, pruned_loss=0.03308, over 4954.00 frames.], tot_loss[loss=0.163, simple_loss=0.2409, pruned_loss=0.04253, over 982225.41 frames.], batch size: 67, aishell_tot_loss[loss=0.1638, simple_loss=0.2473, pruned_loss=0.04015, over 924569.00 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2346, pruned_loss=0.04479, over 935442.81 frames.], batch size: 67, lr: 5.85e-04
+2022-06-18 20:07:00,211 INFO [train.py:874] (3/4) Epoch 14, batch 1200, datatang_loss[loss=0.1901, simple_loss=0.2641, pruned_loss=0.0581, over 4954.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2412, pruned_loss=0.04227, over 982812.28 frames.], batch size: 99, aishell_tot_loss[loss=0.1635, simple_loss=0.2472, pruned_loss=0.03991, over 932901.12 frames.], datatang_tot_loss[loss=0.1622, simple_loss=0.2348, pruned_loss=0.04486, over 940238.98 frames.], batch size: 99, lr: 5.85e-04
+2022-06-18 20:07:32,196 INFO [train.py:874] (3/4) Epoch 14, batch 1250, aishell_loss[loss=0.1787, simple_loss=0.2668, pruned_loss=0.04529, over 4934.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2414, pruned_loss=0.04237, over 983413.73 frames.], batch size: 49, aishell_tot_loss[loss=0.1638, simple_loss=0.2476, pruned_loss=0.04001, over 938796.09 frames.], datatang_tot_loss[loss=0.1622, simple_loss=0.2347, pruned_loss=0.0448, over 945847.46 frames.], batch size: 49, lr: 5.85e-04
+2022-06-18 20:08:00,559 INFO [train.py:874] (3/4) Epoch 14, batch 1300, aishell_loss[loss=0.1674, simple_loss=0.2516, pruned_loss=0.04163, over 4972.00 frames.], tot_loss[loss=0.163, simple_loss=0.2416, pruned_loss=0.04219, over 983389.91 frames.], batch size: 39, aishell_tot_loss[loss=0.1643, simple_loss=0.2481, pruned_loss=0.04026, over 945397.78 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.234, pruned_loss=0.0445, over 949084.38 frames.], batch size: 39, lr: 5.84e-04
+2022-06-18 20:08:31,688 INFO [train.py:874] (3/4) Epoch 14, batch 1350, aishell_loss[loss=0.1662, simple_loss=0.257, pruned_loss=0.03766, over 4894.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2419, pruned_loss=0.04276, over 983447.02 frames.], batch size: 34, aishell_tot_loss[loss=0.1639, simple_loss=0.2478, pruned_loss=0.04001, over 949075.13 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.235, pruned_loss=0.04526, over 953901.81 frames.], batch size: 34, lr: 5.84e-04
+2022-06-18 20:09:02,355 INFO [train.py:874] (3/4) Epoch 14, batch 1400, aishell_loss[loss=0.1543, simple_loss=0.2314, pruned_loss=0.03855, over 4886.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2418, pruned_loss=0.04285, over 983955.25 frames.], batch size: 34, aishell_tot_loss[loss=0.1638, simple_loss=0.2477, pruned_loss=0.03994, over 954044.26 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2349, pruned_loss=0.04555, over 957076.11 frames.], batch size: 34, lr: 5.84e-04
+2022-06-18 20:09:32,295 INFO [train.py:874] (3/4) Epoch 14, batch 1450, aishell_loss[loss=0.1569, simple_loss=0.242, pruned_loss=0.0359, over 4947.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2413, pruned_loss=0.0426, over 984154.45 frames.], batch size: 54, aishell_tot_loss[loss=0.1635, simple_loss=0.2473, pruned_loss=0.03983, over 957668.08 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2347, pruned_loss=0.04539, over 960356.87 frames.], batch size: 54, lr: 5.84e-04
+2022-06-18 20:10:03,025 INFO [train.py:874] (3/4) Epoch 14, batch 1500, datatang_loss[loss=0.1246, simple_loss=0.1987, pruned_loss=0.02521, over 4916.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2411, pruned_loss=0.04297, over 984208.66 frames.], batch size: 64, aishell_tot_loss[loss=0.1638, simple_loss=0.2477, pruned_loss=0.03995, over 960509.17 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2344, pruned_loss=0.04562, over 963437.34 frames.], batch size: 64, lr: 5.83e-04
+2022-06-18 20:10:33,065 INFO [train.py:874] (3/4) Epoch 14, batch 1550, datatang_loss[loss=0.1589, simple_loss=0.2213, pruned_loss=0.04828, over 4901.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2407, pruned_loss=0.04226, over 984701.95 frames.], batch size: 47, aishell_tot_loss[loss=0.1632, simple_loss=0.2475, pruned_loss=0.03948, over 963464.89 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2342, pruned_loss=0.04538, over 966253.73 frames.], batch size: 47, lr: 5.83e-04
+2022-06-18 20:11:02,441 INFO [train.py:874] (3/4) Epoch 14, batch 1600, aishell_loss[loss=0.165, simple_loss=0.2452, pruned_loss=0.04239, over 4859.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2413, pruned_loss=0.0417, over 984952.65 frames.], batch size: 37, aishell_tot_loss[loss=0.1626, simple_loss=0.247, pruned_loss=0.03915, over 967267.83 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2346, pruned_loss=0.04543, over 967479.65 frames.], batch size: 37, lr: 5.83e-04
+2022-06-18 20:11:33,270 INFO [train.py:874] (3/4) Epoch 14, batch 1650, datatang_loss[loss=0.1479, simple_loss=0.2166, pruned_loss=0.03962, over 4934.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2425, pruned_loss=0.04238, over 984922.29 frames.], batch size: 57, aishell_tot_loss[loss=0.163, simple_loss=0.2472, pruned_loss=0.03935, over 969464.74 frames.], datatang_tot_loss[loss=0.1637, simple_loss=0.2357, pruned_loss=0.0458, over 969383.94 frames.], batch size: 57, lr: 5.83e-04
+2022-06-18 20:12:02,988 INFO [train.py:874] (3/4) Epoch 14, batch 1700, aishell_loss[loss=0.1876, simple_loss=0.2728, pruned_loss=0.05123, over 4947.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2426, pruned_loss=0.04288, over 984980.77 frames.], batch size: 58, aishell_tot_loss[loss=0.163, simple_loss=0.247, pruned_loss=0.03951, over 971269.10 frames.], datatang_tot_loss[loss=0.1643, simple_loss=0.2363, pruned_loss=0.04617, over 971291.47 frames.], batch size: 58, lr: 5.82e-04
+2022-06-18 20:12:32,531 INFO [train.py:874] (3/4) Epoch 14, batch 1750, datatang_loss[loss=0.1569, simple_loss=0.2384, pruned_loss=0.03772, over 4934.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2424, pruned_loss=0.04304, over 985003.56 frames.], batch size: 94, aishell_tot_loss[loss=0.1636, simple_loss=0.2474, pruned_loss=0.03993, over 972936.64 frames.], datatang_tot_loss[loss=0.1639, simple_loss=0.2359, pruned_loss=0.04593, over 972865.78 frames.], batch size: 94, lr: 5.82e-04
+2022-06-18 20:13:04,460 INFO [train.py:874] (3/4) Epoch 14, batch 1800, datatang_loss[loss=0.2317, simple_loss=0.2929, pruned_loss=0.08527, over 4924.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2417, pruned_loss=0.04293, over 984969.63 frames.], batch size: 108, aishell_tot_loss[loss=0.163, simple_loss=0.2469, pruned_loss=0.03957, over 974165.97 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2359, pruned_loss=0.04612, over 974442.64 frames.], batch size: 108, lr: 5.82e-04
+2022-06-18 20:13:34,495 INFO [train.py:874] (3/4) Epoch 14, batch 1850, datatang_loss[loss=0.2002, simple_loss=0.2639, pruned_loss=0.06828, over 4911.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2414, pruned_loss=0.04308, over 985344.17 frames.], batch size: 64, aishell_tot_loss[loss=0.1627, simple_loss=0.2465, pruned_loss=0.03942, over 975179.85 frames.], datatang_tot_loss[loss=0.1644, simple_loss=0.2363, pruned_loss=0.04626, over 976324.32 frames.], batch size: 64, lr: 5.81e-04
+2022-06-18 20:14:03,953 INFO [train.py:874] (3/4) Epoch 14, batch 1900, aishell_loss[loss=0.1756, simple_loss=0.2462, pruned_loss=0.05244, over 4907.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2418, pruned_loss=0.04364, over 985284.60 frames.], batch size: 34, aishell_tot_loss[loss=0.1632, simple_loss=0.2467, pruned_loss=0.03981, over 976189.96 frames.], datatang_tot_loss[loss=0.1648, simple_loss=0.2368, pruned_loss=0.04643, over 977495.19 frames.], batch size: 34, lr: 5.81e-04
+2022-06-18 20:14:35,094 INFO [train.py:874] (3/4) Epoch 14, batch 1950, datatang_loss[loss=0.1644, simple_loss=0.2448, pruned_loss=0.04203, over 4918.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2411, pruned_loss=0.04332, over 985306.31 frames.], batch size: 81, aishell_tot_loss[loss=0.1633, simple_loss=0.2468, pruned_loss=0.03997, over 977343.90 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.236, pruned_loss=0.04609, over 978350.96 frames.], batch size: 81, lr: 5.81e-04
+2022-06-18 20:15:05,465 INFO [train.py:874] (3/4) Epoch 14, batch 2000, aishell_loss[loss=0.1714, simple_loss=0.2566, pruned_loss=0.04313, over 4861.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2412, pruned_loss=0.04269, over 985211.88 frames.], batch size: 37, aishell_tot_loss[loss=0.1627, simple_loss=0.2462, pruned_loss=0.03965, over 978468.84 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2362, pruned_loss=0.04601, over 978903.23 frames.], batch size: 37, lr: 5.81e-04
+2022-06-18 20:15:05,466 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 20:15:22,471 INFO [train.py:914] (3/4) Epoch 14, validation: loss=0.1654, simple_loss=0.2497, pruned_loss=0.04057, over 1622729.00 frames.
+2022-06-18 20:15:52,306 INFO [train.py:874] (3/4) Epoch 14, batch 2050, datatang_loss[loss=0.1848, simple_loss=0.2602, pruned_loss=0.05469, over 4871.00 frames.], tot_loss[loss=0.162, simple_loss=0.2403, pruned_loss=0.04184, over 985153.70 frames.], batch size: 30, aishell_tot_loss[loss=0.1619, simple_loss=0.2456, pruned_loss=0.03915, over 979308.11 frames.], datatang_tot_loss[loss=0.1634, simple_loss=0.2358, pruned_loss=0.04555, over 979545.01 frames.], batch size: 30, lr: 5.80e-04
+2022-06-18 20:16:21,788 INFO [train.py:874] (3/4) Epoch 14, batch 2100, datatang_loss[loss=0.1681, simple_loss=0.242, pruned_loss=0.04711, over 4943.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2402, pruned_loss=0.04202, over 985279.64 frames.], batch size: 88, aishell_tot_loss[loss=0.1622, simple_loss=0.2457, pruned_loss=0.0393, over 979787.94 frames.], datatang_tot_loss[loss=0.1632, simple_loss=0.2356, pruned_loss=0.04542, over 980543.61 frames.], batch size: 88, lr: 5.80e-04
+2022-06-18 20:16:52,424 INFO [train.py:874] (3/4) Epoch 14, batch 2150, datatang_loss[loss=0.1455, simple_loss=0.2199, pruned_loss=0.03557, over 4924.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2407, pruned_loss=0.04171, over 985501.24 frames.], batch size: 81, aishell_tot_loss[loss=0.1623, simple_loss=0.2462, pruned_loss=0.03922, over 980836.08 frames.], datatang_tot_loss[loss=0.1629, simple_loss=0.2352, pruned_loss=0.04532, over 980945.61 frames.], batch size: 81, lr: 5.80e-04
+2022-06-18 20:17:23,116 INFO [train.py:874] (3/4) Epoch 14, batch 2200, datatang_loss[loss=0.1529, simple_loss=0.232, pruned_loss=0.03687, over 4936.00 frames.], tot_loss[loss=0.1625, simple_loss=0.241, pruned_loss=0.04205, over 985863.23 frames.], batch size: 79, aishell_tot_loss[loss=0.163, simple_loss=0.2467, pruned_loss=0.03958, over 981379.93 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.235, pruned_loss=0.04513, over 981879.29 frames.], batch size: 79, lr: 5.80e-04
+2022-06-18 20:17:52,985 INFO [train.py:874] (3/4) Epoch 14, batch 2250, aishell_loss[loss=0.1724, simple_loss=0.2531, pruned_loss=0.04582, over 4882.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2412, pruned_loss=0.04183, over 985583.15 frames.], batch size: 47, aishell_tot_loss[loss=0.1627, simple_loss=0.2468, pruned_loss=0.03928, over 981705.38 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.235, pruned_loss=0.04527, over 982263.76 frames.], batch size: 47, lr: 5.79e-04
+2022-06-18 20:18:24,011 INFO [train.py:874] (3/4) Epoch 14, batch 2300, datatang_loss[loss=0.1533, simple_loss=0.2307, pruned_loss=0.03794, over 4929.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2395, pruned_loss=0.04154, over 985765.07 frames.], batch size: 81, aishell_tot_loss[loss=0.1623, simple_loss=0.2464, pruned_loss=0.03914, over 982082.99 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.234, pruned_loss=0.04479, over 982906.29 frames.], batch size: 81, lr: 5.79e-04
+2022-06-18 20:18:58,992 INFO [train.py:874] (3/4) Epoch 14, batch 2350, datatang_loss[loss=0.1247, simple_loss=0.1948, pruned_loss=0.02725, over 4879.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2397, pruned_loss=0.0413, over 985803.32 frames.], batch size: 47, aishell_tot_loss[loss=0.162, simple_loss=0.2461, pruned_loss=0.03898, over 982499.76 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2341, pruned_loss=0.04475, over 983331.19 frames.], batch size: 47, lr: 5.79e-04
+2022-06-18 20:19:28,646 INFO [train.py:874] (3/4) Epoch 14, batch 2400, aishell_loss[loss=0.1638, simple_loss=0.2478, pruned_loss=0.03984, over 4971.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2399, pruned_loss=0.04153, over 985835.37 frames.], batch size: 44, aishell_tot_loss[loss=0.1622, simple_loss=0.2464, pruned_loss=0.03902, over 982723.99 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2341, pruned_loss=0.04473, over 983794.01 frames.], batch size: 44, lr: 5.79e-04
+2022-06-18 20:19:59,672 INFO [train.py:874] (3/4) Epoch 14, batch 2450, datatang_loss[loss=0.1905, simple_loss=0.2618, pruned_loss=0.05955, over 4927.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2406, pruned_loss=0.04209, over 985905.62 frames.], batch size: 94, aishell_tot_loss[loss=0.1627, simple_loss=0.2469, pruned_loss=0.03923, over 982984.01 frames.], datatang_tot_loss[loss=0.1622, simple_loss=0.2347, pruned_loss=0.0449, over 984190.95 frames.], batch size: 94, lr: 5.78e-04
+2022-06-18 20:20:30,400 INFO [train.py:874] (3/4) Epoch 14, batch 2500, datatang_loss[loss=0.1704, simple_loss=0.2439, pruned_loss=0.04851, over 4963.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2409, pruned_loss=0.04231, over 985825.17 frames.], batch size: 91, aishell_tot_loss[loss=0.1631, simple_loss=0.2472, pruned_loss=0.03948, over 983292.77 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2343, pruned_loss=0.04495, over 984362.85 frames.], batch size: 91, lr: 5.78e-04
+2022-06-18 20:20:59,956 INFO [train.py:874] (3/4) Epoch 14, batch 2550, datatang_loss[loss=0.1557, simple_loss=0.2194, pruned_loss=0.046, over 4938.00 frames.], tot_loss[loss=0.1623, simple_loss=0.24, pruned_loss=0.04227, over 985968.00 frames.], batch size: 50, aishell_tot_loss[loss=0.1631, simple_loss=0.2471, pruned_loss=0.03957, over 983724.21 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.2335, pruned_loss=0.04488, over 984557.84 frames.], batch size: 50, lr: 5.78e-04
+2022-06-18 20:21:29,292 INFO [train.py:874] (3/4) Epoch 14, batch 2600, datatang_loss[loss=0.1757, simple_loss=0.2555, pruned_loss=0.04795, over 4957.00 frames.], tot_loss[loss=0.1621, simple_loss=0.24, pruned_loss=0.04213, over 985939.44 frames.], batch size: 99, aishell_tot_loss[loss=0.1635, simple_loss=0.2474, pruned_loss=0.0398, over 983921.62 frames.], datatang_tot_loss[loss=0.161, simple_loss=0.2332, pruned_loss=0.04444, over 984764.95 frames.], batch size: 99, lr: 5.78e-04
+2022-06-18 20:22:01,035 INFO [train.py:874] (3/4) Epoch 14, batch 2650, datatang_loss[loss=0.1473, simple_loss=0.2286, pruned_loss=0.03295, over 4957.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2393, pruned_loss=0.04165, over 985456.46 frames.], batch size: 91, aishell_tot_loss[loss=0.1632, simple_loss=0.2471, pruned_loss=0.03963, over 983680.01 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2328, pruned_loss=0.04403, over 984873.70 frames.], batch size: 91, lr: 5.77e-04
+2022-06-18 20:22:30,536 INFO [train.py:874] (3/4) Epoch 14, batch 2700, datatang_loss[loss=0.1621, simple_loss=0.2352, pruned_loss=0.04448, over 4955.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2399, pruned_loss=0.04166, over 985811.70 frames.], batch size: 55, aishell_tot_loss[loss=0.1636, simple_loss=0.2477, pruned_loss=0.03972, over 984005.78 frames.], datatang_tot_loss[loss=0.1603, simple_loss=0.2328, pruned_loss=0.04384, over 985176.31 frames.], batch size: 55, lr: 5.77e-04
+2022-06-18 20:22:59,857 INFO [train.py:874] (3/4) Epoch 14, batch 2750, datatang_loss[loss=0.1522, simple_loss=0.2259, pruned_loss=0.03924, over 4937.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2407, pruned_loss=0.04182, over 985882.59 frames.], batch size: 69, aishell_tot_loss[loss=0.1643, simple_loss=0.2484, pruned_loss=0.04011, over 984141.85 frames.], datatang_tot_loss[loss=0.16, simple_loss=0.2326, pruned_loss=0.04364, over 985438.00 frames.], batch size: 69, lr: 5.77e-04
+2022-06-18 20:23:30,618 INFO [train.py:874] (3/4) Epoch 14, batch 2800, aishell_loss[loss=0.1852, simple_loss=0.2684, pruned_loss=0.05098, over 4935.00 frames.], tot_loss[loss=0.162, simple_loss=0.241, pruned_loss=0.04146, over 985901.29 frames.], batch size: 68, aishell_tot_loss[loss=0.1641, simple_loss=0.2486, pruned_loss=0.0398, over 984282.15 frames.], datatang_tot_loss[loss=0.1599, simple_loss=0.2326, pruned_loss=0.04361, over 985590.30 frames.], batch size: 68, lr: 5.77e-04
+2022-06-18 20:24:01,508 INFO [train.py:874] (3/4) Epoch 14, batch 2850, aishell_loss[loss=0.1658, simple_loss=0.255, pruned_loss=0.03831, over 4937.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2407, pruned_loss=0.04146, over 985900.15 frames.], batch size: 45, aishell_tot_loss[loss=0.1637, simple_loss=0.2483, pruned_loss=0.03957, over 984757.58 frames.], datatang_tot_loss[loss=0.1601, simple_loss=0.2326, pruned_loss=0.04378, over 985347.95 frames.], batch size: 45, lr: 5.76e-04
+2022-06-18 20:24:30,594 INFO [train.py:874] (3/4) Epoch 14, batch 2900, aishell_loss[loss=0.173, simple_loss=0.2597, pruned_loss=0.04317, over 4941.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2404, pruned_loss=0.04187, over 985895.54 frames.], batch size: 49, aishell_tot_loss[loss=0.1638, simple_loss=0.2483, pruned_loss=0.03962, over 984894.42 frames.], datatang_tot_loss[loss=0.1603, simple_loss=0.2326, pruned_loss=0.04404, over 985408.80 frames.], batch size: 49, lr: 5.76e-04
+2022-06-18 20:25:01,242 INFO [train.py:874] (3/4) Epoch 14, batch 2950, aishell_loss[loss=0.1497, simple_loss=0.2319, pruned_loss=0.03377, over 4930.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2404, pruned_loss=0.04199, over 985528.29 frames.], batch size: 33, aishell_tot_loss[loss=0.1634, simple_loss=0.2478, pruned_loss=0.03948, over 984725.15 frames.], datatang_tot_loss[loss=0.1608, simple_loss=0.2331, pruned_loss=0.04428, over 985368.85 frames.], batch size: 33, lr: 5.76e-04
+2022-06-18 20:25:31,718 INFO [train.py:874] (3/4) Epoch 14, batch 3000, datatang_loss[loss=0.1615, simple_loss=0.2362, pruned_loss=0.04342, over 4911.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2397, pruned_loss=0.04196, over 985996.57 frames.], batch size: 75, aishell_tot_loss[loss=0.1637, simple_loss=0.2478, pruned_loss=0.03975, over 985074.68 frames.], datatang_tot_loss[loss=0.1603, simple_loss=0.2326, pruned_loss=0.04395, over 985623.64 frames.], batch size: 75, lr: 5.76e-04
+2022-06-18 20:25:31,719 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 20:25:47,766 INFO [train.py:914] (3/4) Epoch 14, validation: loss=0.1656, simple_loss=0.2493, pruned_loss=0.0409, over 1622729.00 frames.
+2022-06-18 20:26:17,879 INFO [train.py:874] (3/4) Epoch 14, batch 3050, aishell_loss[loss=0.1514, simple_loss=0.2387, pruned_loss=0.03207, over 4942.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2398, pruned_loss=0.04176, over 986032.49 frames.], batch size: 54, aishell_tot_loss[loss=0.1638, simple_loss=0.2478, pruned_loss=0.03987, over 985137.99 frames.], datatang_tot_loss[loss=0.16, simple_loss=0.2326, pruned_loss=0.04366, over 985766.64 frames.], batch size: 54, lr: 5.75e-04
+2022-06-18 20:26:48,772 INFO [train.py:874] (3/4) Epoch 14, batch 3100, datatang_loss[loss=0.1343, simple_loss=0.2076, pruned_loss=0.03048, over 4955.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2396, pruned_loss=0.04227, over 986385.47 frames.], batch size: 67, aishell_tot_loss[loss=0.1637, simple_loss=0.2476, pruned_loss=0.03993, over 985247.15 frames.], datatang_tot_loss[loss=0.1605, simple_loss=0.233, pruned_loss=0.044, over 986135.78 frames.], batch size: 67, lr: 5.75e-04
+2022-06-18 20:27:19,577 INFO [train.py:874] (3/4) Epoch 14, batch 3150, aishell_loss[loss=0.1916, simple_loss=0.2655, pruned_loss=0.05879, over 4914.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2389, pruned_loss=0.04261, over 986080.17 frames.], batch size: 41, aishell_tot_loss[loss=0.1634, simple_loss=0.2469, pruned_loss=0.03994, over 985076.32 frames.], datatang_tot_loss[loss=0.1608, simple_loss=0.2329, pruned_loss=0.04435, over 986120.87 frames.], batch size: 41, lr: 5.75e-04
+2022-06-18 20:27:49,695 INFO [train.py:874] (3/4) Epoch 14, batch 3200, aishell_loss[loss=0.1633, simple_loss=0.2281, pruned_loss=0.04923, over 4987.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2385, pruned_loss=0.04252, over 985880.98 frames.], batch size: 25, aishell_tot_loss[loss=0.1635, simple_loss=0.2466, pruned_loss=0.04016, over 985037.70 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2325, pruned_loss=0.04413, over 986050.38 frames.], batch size: 25, lr: 5.75e-04
+2022-06-18 20:28:20,148 INFO [train.py:874] (3/4) Epoch 14, batch 3250, datatang_loss[loss=0.1573, simple_loss=0.2321, pruned_loss=0.04131, over 4964.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2403, pruned_loss=0.04318, over 985816.12 frames.], batch size: 34, aishell_tot_loss[loss=0.1636, simple_loss=0.2468, pruned_loss=0.04015, over 985002.74 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2336, pruned_loss=0.04502, over 986110.22 frames.], batch size: 34, lr: 5.74e-04
+2022-06-18 20:28:49,506 INFO [train.py:874] (3/4) Epoch 14, batch 3300, aishell_loss[loss=0.1665, simple_loss=0.2639, pruned_loss=0.0345, over 4968.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2417, pruned_loss=0.04356, over 985599.13 frames.], batch size: 64, aishell_tot_loss[loss=0.1639, simple_loss=0.2474, pruned_loss=0.04022, over 984866.99 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2342, pruned_loss=0.04559, over 986095.49 frames.], batch size: 64, lr: 5.74e-04
+2022-06-18 20:29:19,745 INFO [train.py:874] (3/4) Epoch 14, batch 3350, datatang_loss[loss=0.1478, simple_loss=0.2262, pruned_loss=0.03475, over 4924.00 frames.], tot_loss[loss=0.1638, simple_loss=0.241, pruned_loss=0.04324, over 985492.51 frames.], batch size: 81, aishell_tot_loss[loss=0.1638, simple_loss=0.2471, pruned_loss=0.04024, over 984934.18 frames.], datatang_tot_loss[loss=0.1624, simple_loss=0.2339, pruned_loss=0.04542, over 985929.85 frames.], batch size: 81, lr: 5.74e-04
+2022-06-18 20:29:50,027 INFO [train.py:874] (3/4) Epoch 14, batch 3400, aishell_loss[loss=0.1602, simple_loss=0.25, pruned_loss=0.03518, over 4938.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2399, pruned_loss=0.04272, over 985272.24 frames.], batch size: 54, aishell_tot_loss[loss=0.1625, simple_loss=0.2458, pruned_loss=0.03965, over 984705.11 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2339, pruned_loss=0.04567, over 985963.25 frames.], batch size: 54, lr: 5.74e-04
+2022-06-18 20:30:18,825 INFO [train.py:874] (3/4) Epoch 14, batch 3450, aishell_loss[loss=0.1554, simple_loss=0.2351, pruned_loss=0.03779, over 4872.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2398, pruned_loss=0.04229, over 985418.11 frames.], batch size: 28, aishell_tot_loss[loss=0.1624, simple_loss=0.2456, pruned_loss=0.03962, over 984639.01 frames.], datatang_tot_loss[loss=0.1623, simple_loss=0.2339, pruned_loss=0.04535, over 986177.85 frames.], batch size: 28, lr: 5.73e-04
+2022-06-18 20:30:50,327 INFO [train.py:874] (3/4) Epoch 14, batch 3500, aishell_loss[loss=0.1648, simple_loss=0.2484, pruned_loss=0.0406, over 4982.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2409, pruned_loss=0.04342, over 985866.47 frames.], batch size: 48, aishell_tot_loss[loss=0.1633, simple_loss=0.2463, pruned_loss=0.04011, over 984950.13 frames.], datatang_tot_loss[loss=0.1632, simple_loss=0.2345, pruned_loss=0.04598, over 986316.57 frames.], batch size: 48, lr: 5.73e-04
+2022-06-18 20:31:19,807 INFO [train.py:874] (3/4) Epoch 14, batch 3550, datatang_loss[loss=0.1644, simple_loss=0.2364, pruned_loss=0.04615, over 4895.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2407, pruned_loss=0.0432, over 985600.37 frames.], batch size: 47, aishell_tot_loss[loss=0.1632, simple_loss=0.2462, pruned_loss=0.04012, over 984859.27 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2345, pruned_loss=0.04578, over 986189.17 frames.], batch size: 47, lr: 5.73e-04
+2022-06-18 20:31:49,317 INFO [train.py:874] (3/4) Epoch 14, batch 3600, aishell_loss[loss=0.1575, simple_loss=0.244, pruned_loss=0.03545, over 4940.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2408, pruned_loss=0.04291, over 985863.22 frames.], batch size: 68, aishell_tot_loss[loss=0.163, simple_loss=0.246, pruned_loss=0.04001, over 985061.46 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2347, pruned_loss=0.04573, over 986299.23 frames.], batch size: 68, lr: 5.73e-04
+2022-06-18 20:32:20,225 INFO [train.py:874] (3/4) Epoch 14, batch 3650, datatang_loss[loss=0.1949, simple_loss=0.2653, pruned_loss=0.0623, over 4949.00 frames.], tot_loss[loss=0.162, simple_loss=0.2397, pruned_loss=0.04214, over 986096.25 frames.], batch size: 108, aishell_tot_loss[loss=0.1622, simple_loss=0.2454, pruned_loss=0.03951, over 985430.36 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2343, pruned_loss=0.04535, over 986207.42 frames.], batch size: 108, lr: 5.72e-04
+2022-06-18 20:32:50,575 INFO [train.py:874] (3/4) Epoch 14, batch 3700, aishell_loss[loss=0.1491, simple_loss=0.2377, pruned_loss=0.03031, over 4967.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2397, pruned_loss=0.04193, over 986304.75 frames.], batch size: 39, aishell_tot_loss[loss=0.1619, simple_loss=0.2452, pruned_loss=0.03924, over 985606.04 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2346, pruned_loss=0.04527, over 986318.82 frames.], batch size: 39, lr: 5.72e-04
+2022-06-18 20:33:20,219 INFO [train.py:874] (3/4) Epoch 14, batch 3750, datatang_loss[loss=0.1588, simple_loss=0.2343, pruned_loss=0.04167, over 4921.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2396, pruned_loss=0.04169, over 985964.71 frames.], batch size: 77, aishell_tot_loss[loss=0.1619, simple_loss=0.2452, pruned_loss=0.03932, over 985607.52 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2343, pruned_loss=0.04496, over 986033.95 frames.], batch size: 77, lr: 5.72e-04
+2022-06-18 20:33:50,752 INFO [train.py:874] (3/4) Epoch 14, batch 3800, datatang_loss[loss=0.1558, simple_loss=0.2294, pruned_loss=0.04109, over 4912.00 frames.], tot_loss[loss=0.161, simple_loss=0.2393, pruned_loss=0.04134, over 985718.06 frames.], batch size: 64, aishell_tot_loss[loss=0.1625, simple_loss=0.2458, pruned_loss=0.03954, over 985397.70 frames.], datatang_tot_loss[loss=0.1609, simple_loss=0.2336, pruned_loss=0.04416, over 986014.23 frames.], batch size: 64, lr: 5.72e-04
+2022-06-18 20:34:19,309 INFO [train.py:874] (3/4) Epoch 14, batch 3850, aishell_loss[loss=0.1524, simple_loss=0.2337, pruned_loss=0.03555, over 4884.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2396, pruned_loss=0.04133, over 985786.87 frames.], batch size: 42, aishell_tot_loss[loss=0.1623, simple_loss=0.2458, pruned_loss=0.03935, over 985436.64 frames.], datatang_tot_loss[loss=0.1611, simple_loss=0.2338, pruned_loss=0.04422, over 986062.05 frames.], batch size: 42, lr: 5.71e-04
+2022-06-18 20:34:49,162 INFO [train.py:874] (3/4) Epoch 14, batch 3900, aishell_loss[loss=0.1808, simple_loss=0.2618, pruned_loss=0.04989, over 4895.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2408, pruned_loss=0.04182, over 985756.23 frames.], batch size: 34, aishell_tot_loss[loss=0.1632, simple_loss=0.2466, pruned_loss=0.03988, over 985357.54 frames.], datatang_tot_loss[loss=0.1612, simple_loss=0.2341, pruned_loss=0.04411, over 986134.16 frames.], batch size: 34, lr: 5.71e-04
+2022-06-18 20:35:17,122 INFO [train.py:874] (3/4) Epoch 14, batch 3950, datatang_loss[loss=0.1908, simple_loss=0.2587, pruned_loss=0.06141, over 4960.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2394, pruned_loss=0.041, over 985783.33 frames.], batch size: 62, aishell_tot_loss[loss=0.1621, simple_loss=0.2454, pruned_loss=0.03938, over 985313.53 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.2336, pruned_loss=0.04377, over 986212.92 frames.], batch size: 62, lr: 5.71e-04
+2022-06-18 20:35:46,884 INFO [train.py:874] (3/4) Epoch 14, batch 4000, datatang_loss[loss=0.1374, simple_loss=0.2148, pruned_loss=0.02999, over 4987.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2392, pruned_loss=0.04117, over 985679.88 frames.], batch size: 31, aishell_tot_loss[loss=0.1619, simple_loss=0.2454, pruned_loss=0.03924, over 985232.54 frames.], datatang_tot_loss[loss=0.1608, simple_loss=0.2337, pruned_loss=0.0439, over 986173.26 frames.], batch size: 31, lr: 5.71e-04
+2022-06-18 20:35:46,885 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 20:36:02,939 INFO [train.py:914] (3/4) Epoch 14, validation: loss=0.1656, simple_loss=0.2513, pruned_loss=0.03999, over 1622729.00 frames.
+2022-06-18 20:36:31,865 INFO [train.py:874] (3/4) Epoch 14, batch 4050, datatang_loss[loss=0.1406, simple_loss=0.2257, pruned_loss=0.0277, over 4911.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2393, pruned_loss=0.04101, over 985302.99 frames.], batch size: 77, aishell_tot_loss[loss=0.162, simple_loss=0.2455, pruned_loss=0.03921, over 984944.64 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2334, pruned_loss=0.04373, over 986074.56 frames.], batch size: 77, lr: 5.70e-04
+2022-06-18 20:36:59,661 INFO [train.py:874] (3/4) Epoch 14, batch 4100, datatang_loss[loss=0.1633, simple_loss=0.2307, pruned_loss=0.04797, over 4957.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2384, pruned_loss=0.04064, over 984777.07 frames.], batch size: 67, aishell_tot_loss[loss=0.1614, simple_loss=0.2451, pruned_loss=0.03882, over 984328.27 frames.], datatang_tot_loss[loss=0.16, simple_loss=0.2329, pruned_loss=0.04355, over 986067.25 frames.], batch size: 67, lr: 5.70e-04
+2022-06-18 20:38:15,691 INFO [train.py:874] (3/4) Epoch 15, batch 50, datatang_loss[loss=0.1505, simple_loss=0.2217, pruned_loss=0.03968, over 4953.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2341, pruned_loss=0.03837, over 219072.72 frames.], batch size: 86, aishell_tot_loss[loss=0.1634, simple_loss=0.2478, pruned_loss=0.03946, over 116575.57 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2203, pruned_loss=0.03718, over 116199.72 frames.], batch size: 86, lr: 5.52e-04
+2022-06-18 20:38:46,668 INFO [train.py:874] (3/4) Epoch 15, batch 100, aishell_loss[loss=0.1622, simple_loss=0.239, pruned_loss=0.04271, over 4822.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2363, pruned_loss=0.03996, over 388829.17 frames.], batch size: 26, aishell_tot_loss[loss=0.164, simple_loss=0.248, pruned_loss=0.04002, over 226228.09 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2232, pruned_loss=0.03968, over 211051.69 frames.], batch size: 26, lr: 5.52e-04
+2022-06-18 20:39:17,035 INFO [train.py:874] (3/4) Epoch 15, batch 150, aishell_loss[loss=0.1689, simple_loss=0.257, pruned_loss=0.04039, over 4856.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2358, pruned_loss=0.03928, over 520968.59 frames.], batch size: 37, aishell_tot_loss[loss=0.1616, simple_loss=0.2458, pruned_loss=0.03867, over 322178.18 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2245, pruned_loss=0.03995, over 295406.20 frames.], batch size: 37, lr: 5.52e-04
+2022-06-18 20:39:45,880 INFO [train.py:874] (3/4) Epoch 15, batch 200, aishell_loss[loss=0.1443, simple_loss=0.2241, pruned_loss=0.03225, over 4875.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2355, pruned_loss=0.03977, over 623820.10 frames.], batch size: 28, aishell_tot_loss[loss=0.1614, simple_loss=0.2453, pruned_loss=0.03876, over 394190.67 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2256, pruned_loss=0.04072, over 382836.43 frames.], batch size: 28, lr: 5.52e-04
+2022-06-18 20:40:16,197 INFO [train.py:874] (3/4) Epoch 15, batch 250, aishell_loss[loss=0.1354, simple_loss=0.2158, pruned_loss=0.02748, over 4968.00 frames.], tot_loss[loss=0.158, simple_loss=0.237, pruned_loss=0.03945, over 703883.44 frames.], batch size: 30, aishell_tot_loss[loss=0.161, simple_loss=0.2451, pruned_loss=0.03848, over 489272.15 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.2264, pruned_loss=0.04074, over 426426.69 frames.], batch size: 30, lr: 5.51e-04
+2022-06-18 20:40:47,503 INFO [train.py:874] (3/4) Epoch 15, batch 300, datatang_loss[loss=0.1352, simple_loss=0.2047, pruned_loss=0.03282, over 4927.00 frames.], tot_loss[loss=0.1578, simple_loss=0.237, pruned_loss=0.0393, over 766456.61 frames.], batch size: 71, aishell_tot_loss[loss=0.1614, simple_loss=0.2456, pruned_loss=0.03857, over 554473.70 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2258, pruned_loss=0.04037, over 484718.57 frames.], batch size: 71, lr: 5.51e-04
+2022-06-18 20:41:16,940 INFO [train.py:874] (3/4) Epoch 15, batch 350, datatang_loss[loss=0.1964, simple_loss=0.2705, pruned_loss=0.06115, over 4866.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2386, pruned_loss=0.04001, over 815281.09 frames.], batch size: 39, aishell_tot_loss[loss=0.1623, simple_loss=0.2466, pruned_loss=0.03898, over 603460.91 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2279, pruned_loss=0.04093, over 546068.27 frames.], batch size: 39, lr: 5.51e-04
+2022-06-18 20:41:47,891 INFO [train.py:874] (3/4) Epoch 15, batch 400, aishell_loss[loss=0.1594, simple_loss=0.2399, pruned_loss=0.03949, over 4863.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2387, pruned_loss=0.04021, over 853358.46 frames.], batch size: 36, aishell_tot_loss[loss=0.1626, simple_loss=0.2469, pruned_loss=0.03919, over 648628.96 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2281, pruned_loss=0.04101, over 598018.56 frames.], batch size: 36, lr: 5.51e-04
+2022-06-18 20:42:19,926 INFO [train.py:874] (3/4) Epoch 15, batch 450, aishell_loss[loss=0.1544, simple_loss=0.2442, pruned_loss=0.03231, over 4957.00 frames.], tot_loss[loss=0.158, simple_loss=0.2372, pruned_loss=0.03946, over 882798.47 frames.], batch size: 64, aishell_tot_loss[loss=0.1618, simple_loss=0.2463, pruned_loss=0.03867, over 679214.70 frames.], datatang_tot_loss[loss=0.1544, simple_loss=0.2278, pruned_loss=0.04046, over 653968.23 frames.], batch size: 64, lr: 5.51e-04
+2022-06-18 20:42:49,067 INFO [train.py:874] (3/4) Epoch 15, batch 500, aishell_loss[loss=0.1585, simple_loss=0.2516, pruned_loss=0.03273, over 4952.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2367, pruned_loss=0.03979, over 905710.29 frames.], batch size: 54, aishell_tot_loss[loss=0.1627, simple_loss=0.2472, pruned_loss=0.03913, over 707092.59 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.2271, pruned_loss=0.04036, over 701787.55 frames.], batch size: 54, lr: 5.50e-04
+2022-06-18 20:43:19,970 INFO [train.py:874] (3/4) Epoch 15, batch 550, aishell_loss[loss=0.1737, simple_loss=0.2659, pruned_loss=0.04072, over 4930.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2361, pruned_loss=0.03978, over 923488.21 frames.], batch size: 49, aishell_tot_loss[loss=0.1626, simple_loss=0.2468, pruned_loss=0.03919, over 735128.44 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.227, pruned_loss=0.04028, over 740054.47 frames.], batch size: 49, lr: 5.50e-04
+2022-06-18 20:43:50,631 INFO [train.py:874] (3/4) Epoch 15, batch 600, aishell_loss[loss=0.183, simple_loss=0.2585, pruned_loss=0.05372, over 4867.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2358, pruned_loss=0.03957, over 937138.12 frames.], batch size: 37, aishell_tot_loss[loss=0.1616, simple_loss=0.2454, pruned_loss=0.03885, over 766666.50 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2275, pruned_loss=0.04036, over 766833.20 frames.], batch size: 37, lr: 5.50e-04
+2022-06-18 20:44:19,817 INFO [train.py:874] (3/4) Epoch 15, batch 650, aishell_loss[loss=0.1519, simple_loss=0.2445, pruned_loss=0.02969, over 4879.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2369, pruned_loss=0.0398, over 947637.22 frames.], batch size: 42, aishell_tot_loss[loss=0.1615, simple_loss=0.2456, pruned_loss=0.03869, over 793193.35 frames.], datatang_tot_loss[loss=0.155, simple_loss=0.2284, pruned_loss=0.04082, over 791615.26 frames.], batch size: 42, lr: 5.50e-04
+2022-06-18 20:44:51,159 INFO [train.py:874] (3/4) Epoch 15, batch 700, aishell_loss[loss=0.1611, simple_loss=0.2344, pruned_loss=0.04388, over 4871.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2356, pruned_loss=0.03953, over 956114.92 frames.], batch size: 37, aishell_tot_loss[loss=0.1602, simple_loss=0.2441, pruned_loss=0.03811, over 811434.29 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2287, pruned_loss=0.04104, over 818868.33 frames.], batch size: 37, lr: 5.49e-04
+2022-06-18 20:45:21,732 INFO [train.py:874] (3/4) Epoch 15, batch 750, datatang_loss[loss=0.1588, simple_loss=0.2276, pruned_loss=0.04496, over 4937.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2359, pruned_loss=0.0395, over 962608.69 frames.], batch size: 62, aishell_tot_loss[loss=0.1594, simple_loss=0.2434, pruned_loss=0.03771, over 833182.02 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2295, pruned_loss=0.04145, over 837271.38 frames.], batch size: 62, lr: 5.49e-04
+2022-06-18 20:45:51,560 INFO [train.py:874] (3/4) Epoch 15, batch 800, datatang_loss[loss=0.1617, simple_loss=0.2459, pruned_loss=0.03871, over 4858.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2358, pruned_loss=0.03958, over 967769.68 frames.], batch size: 39, aishell_tot_loss[loss=0.1589, simple_loss=0.2428, pruned_loss=0.0375, over 847839.89 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2301, pruned_loss=0.04161, over 857921.62 frames.], batch size: 39, lr: 5.49e-04
+2022-06-18 20:46:21,691 INFO [train.py:874] (3/4) Epoch 15, batch 850, datatang_loss[loss=0.1488, simple_loss=0.2061, pruned_loss=0.04572, over 4919.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2364, pruned_loss=0.03964, over 972131.84 frames.], batch size: 47, aishell_tot_loss[loss=0.1595, simple_loss=0.2434, pruned_loss=0.03778, over 863800.05 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2302, pruned_loss=0.0414, over 873624.39 frames.], batch size: 47, lr: 5.49e-04
+2022-06-18 20:46:52,661 INFO [train.py:874] (3/4) Epoch 15, batch 900, aishell_loss[loss=0.1525, simple_loss=0.2398, pruned_loss=0.0326, over 4883.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2372, pruned_loss=0.04004, over 974910.08 frames.], batch size: 50, aishell_tot_loss[loss=0.1601, simple_loss=0.244, pruned_loss=0.03805, over 876330.98 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2305, pruned_loss=0.04156, over 888265.98 frames.], batch size: 50, lr: 5.48e-04
+2022-06-18 20:47:21,785 INFO [train.py:874] (3/4) Epoch 15, batch 950, datatang_loss[loss=0.1725, simple_loss=0.256, pruned_loss=0.04447, over 4959.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2368, pruned_loss=0.04015, over 977161.45 frames.], batch size: 67, aishell_tot_loss[loss=0.1594, simple_loss=0.2434, pruned_loss=0.0377, over 887868.05 frames.], datatang_tot_loss[loss=0.1575, simple_loss=0.2309, pruned_loss=0.042, over 900765.06 frames.], batch size: 67, lr: 5.48e-04
+2022-06-18 20:47:53,043 INFO [train.py:874] (3/4) Epoch 15, batch 1000, aishell_loss[loss=0.1462, simple_loss=0.2264, pruned_loss=0.03301, over 4821.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2376, pruned_loss=0.04, over 979172.13 frames.], batch size: 29, aishell_tot_loss[loss=0.1602, simple_loss=0.2443, pruned_loss=0.03802, over 899782.48 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.2308, pruned_loss=0.04162, over 910566.61 frames.], batch size: 29, lr: 5.48e-04
+2022-06-18 20:47:53,044 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 20:48:10,192 INFO [train.py:914] (3/4) Epoch 15, validation: loss=0.1651, simple_loss=0.2495, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-18 20:48:40,040 INFO [train.py:874] (3/4) Epoch 15, batch 1050, datatang_loss[loss=0.1372, simple_loss=0.2219, pruned_loss=0.02623, over 4928.00 frames.], tot_loss[loss=0.1583, simple_loss=0.237, pruned_loss=0.03976, over 980474.71 frames.], batch size: 83, aishell_tot_loss[loss=0.1597, simple_loss=0.2435, pruned_loss=0.03789, over 908603.38 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.2311, pruned_loss=0.04146, over 920416.76 frames.], batch size: 83, lr: 5.48e-04
+2022-06-18 20:49:11,002 INFO [train.py:874] (3/4) Epoch 15, batch 1100, aishell_loss[loss=0.1784, simple_loss=0.2641, pruned_loss=0.04635, over 4966.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2378, pruned_loss=0.03978, over 981932.01 frames.], batch size: 39, aishell_tot_loss[loss=0.1601, simple_loss=0.2443, pruned_loss=0.03795, over 919097.81 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.2309, pruned_loss=0.04151, over 927181.55 frames.], batch size: 39, lr: 5.48e-04
+2022-06-18 20:49:39,108 INFO [train.py:874] (3/4) Epoch 15, batch 1150, datatang_loss[loss=0.1563, simple_loss=0.2393, pruned_loss=0.03668, over 4918.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2385, pruned_loss=0.0399, over 982387.35 frames.], batch size: 77, aishell_tot_loss[loss=0.1602, simple_loss=0.2445, pruned_loss=0.03793, over 927486.52 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2313, pruned_loss=0.04171, over 933229.77 frames.], batch size: 77, lr: 5.47e-04
+2022-06-18 20:50:10,391 INFO [train.py:874] (3/4) Epoch 15, batch 1200, aishell_loss[loss=0.1302, simple_loss=0.213, pruned_loss=0.02372, over 4915.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2392, pruned_loss=0.04018, over 983066.04 frames.], batch size: 33, aishell_tot_loss[loss=0.1603, simple_loss=0.2447, pruned_loss=0.03793, over 933872.77 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2321, pruned_loss=0.04202, over 939776.15 frames.], batch size: 33, lr: 5.47e-04
+2022-06-18 20:50:40,938 INFO [train.py:874] (3/4) Epoch 15, batch 1250, aishell_loss[loss=0.1488, simple_loss=0.2297, pruned_loss=0.03393, over 4866.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2391, pruned_loss=0.04018, over 983428.28 frames.], batch size: 28, aishell_tot_loss[loss=0.1607, simple_loss=0.2453, pruned_loss=0.03806, over 939576.74 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2317, pruned_loss=0.04191, over 945335.78 frames.], batch size: 28, lr: 5.47e-04
+2022-06-18 20:51:09,586 INFO [train.py:874] (3/4) Epoch 15, batch 1300, aishell_loss[loss=0.1605, simple_loss=0.2512, pruned_loss=0.03492, over 4883.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2384, pruned_loss=0.04008, over 983912.05 frames.], batch size: 35, aishell_tot_loss[loss=0.1602, simple_loss=0.2445, pruned_loss=0.03793, over 945782.16 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2317, pruned_loss=0.04207, over 949393.11 frames.], batch size: 35, lr: 5.47e-04
+2022-06-18 20:51:39,881 INFO [train.py:874] (3/4) Epoch 15, batch 1350, datatang_loss[loss=0.1604, simple_loss=0.2341, pruned_loss=0.04339, over 4920.00 frames.], tot_loss[loss=0.16, simple_loss=0.2385, pruned_loss=0.04075, over 983916.42 frames.], batch size: 25, aishell_tot_loss[loss=0.16, simple_loss=0.2442, pruned_loss=0.03793, over 949222.23 frames.], datatang_tot_loss[loss=0.159, simple_loss=0.2325, pruned_loss=0.04273, over 954406.81 frames.], batch size: 25, lr: 5.46e-04
+2022-06-18 20:52:11,253 INFO [train.py:874] (3/4) Epoch 15, batch 1400, datatang_loss[loss=0.15, simple_loss=0.2275, pruned_loss=0.03629, over 4891.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2377, pruned_loss=0.04025, over 983931.15 frames.], batch size: 42, aishell_tot_loss[loss=0.1597, simple_loss=0.244, pruned_loss=0.03766, over 952691.34 frames.], datatang_tot_loss[loss=0.1585, simple_loss=0.232, pruned_loss=0.04248, over 958456.79 frames.], batch size: 42, lr: 5.46e-04
+2022-06-18 20:52:39,874 INFO [train.py:874] (3/4) Epoch 15, batch 1450, aishell_loss[loss=0.1836, simple_loss=0.2667, pruned_loss=0.0503, over 4861.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2375, pruned_loss=0.03978, over 984270.15 frames.], batch size: 36, aishell_tot_loss[loss=0.1596, simple_loss=0.244, pruned_loss=0.03765, over 956375.54 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2318, pruned_loss=0.04204, over 961809.67 frames.], batch size: 36, lr: 5.46e-04
+2022-06-18 20:53:10,643 INFO [train.py:874] (3/4) Epoch 15, batch 1500, aishell_loss[loss=0.1587, simple_loss=0.2501, pruned_loss=0.0337, over 4931.00 frames.], tot_loss[loss=0.1582, simple_loss=0.237, pruned_loss=0.03965, over 984428.16 frames.], batch size: 52, aishell_tot_loss[loss=0.1598, simple_loss=0.2442, pruned_loss=0.03775, over 959105.26 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2312, pruned_loss=0.04169, over 965063.87 frames.], batch size: 52, lr: 5.46e-04
+2022-06-18 20:53:41,108 INFO [train.py:874] (3/4) Epoch 15, batch 1550, aishell_loss[loss=0.1707, simple_loss=0.2438, pruned_loss=0.04883, over 4904.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2377, pruned_loss=0.04035, over 984850.90 frames.], batch size: 34, aishell_tot_loss[loss=0.1608, simple_loss=0.2449, pruned_loss=0.03833, over 962212.67 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2309, pruned_loss=0.04187, over 967697.66 frames.], batch size: 34, lr: 5.45e-04
+2022-06-18 20:54:09,751 INFO [train.py:874] (3/4) Epoch 15, batch 1600, datatang_loss[loss=0.1915, simple_loss=0.266, pruned_loss=0.05849, over 4937.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2371, pruned_loss=0.03999, over 985119.39 frames.], batch size: 108, aishell_tot_loss[loss=0.1601, simple_loss=0.2442, pruned_loss=0.03803, over 964728.25 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2311, pruned_loss=0.04181, over 970091.97 frames.], batch size: 108, lr: 5.45e-04
+2022-06-18 20:54:39,923 INFO [train.py:874] (3/4) Epoch 15, batch 1650, aishell_loss[loss=0.167, simple_loss=0.2441, pruned_loss=0.04493, over 4834.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2372, pruned_loss=0.03969, over 985354.29 frames.], batch size: 29, aishell_tot_loss[loss=0.1603, simple_loss=0.2446, pruned_loss=0.03803, over 967221.97 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2306, pruned_loss=0.04151, over 972005.15 frames.], batch size: 29, lr: 5.45e-04
+2022-06-18 20:55:11,441 INFO [train.py:874] (3/4) Epoch 15, batch 1700, aishell_loss[loss=0.153, simple_loss=0.2377, pruned_loss=0.0341, over 4920.00 frames.], tot_loss[loss=0.159, simple_loss=0.2373, pruned_loss=0.04032, over 985361.53 frames.], batch size: 41, aishell_tot_loss[loss=0.1607, simple_loss=0.245, pruned_loss=0.03822, over 968560.03 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2308, pruned_loss=0.04184, over 974189.11 frames.], batch size: 41, lr: 5.45e-04
+2022-06-18 20:55:40,701 INFO [train.py:874] (3/4) Epoch 15, batch 1750, aishell_loss[loss=0.1624, simple_loss=0.2519, pruned_loss=0.03643, over 4912.00 frames.], tot_loss[loss=0.1589, simple_loss=0.237, pruned_loss=0.04045, over 985082.43 frames.], batch size: 41, aishell_tot_loss[loss=0.1606, simple_loss=0.2447, pruned_loss=0.03826, over 970272.83 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2309, pruned_loss=0.04196, over 975378.31 frames.], batch size: 41, lr: 5.45e-04
+2022-06-18 20:56:11,402 INFO [train.py:874] (3/4) Epoch 15, batch 1800, datatang_loss[loss=0.1364, simple_loss=0.22, pruned_loss=0.02634, over 4960.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2381, pruned_loss=0.04109, over 984966.51 frames.], batch size: 86, aishell_tot_loss[loss=0.1606, simple_loss=0.2448, pruned_loss=0.03822, over 971824.32 frames.], datatang_tot_loss[loss=0.1587, simple_loss=0.2319, pruned_loss=0.04271, over 976508.14 frames.], batch size: 86, lr: 5.44e-04
+2022-06-18 20:56:41,322 INFO [train.py:874] (3/4) Epoch 15, batch 1850, datatang_loss[loss=0.1654, simple_loss=0.236, pruned_loss=0.04745, over 4936.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2383, pruned_loss=0.04051, over 984790.47 frames.], batch size: 88, aishell_tot_loss[loss=0.1603, simple_loss=0.2449, pruned_loss=0.03788, over 973221.98 frames.], datatang_tot_loss[loss=0.1585, simple_loss=0.2318, pruned_loss=0.04259, over 977484.89 frames.], batch size: 88, lr: 5.44e-04
+2022-06-18 20:57:10,582 INFO [train.py:874] (3/4) Epoch 15, batch 1900, aishell_loss[loss=0.1628, simple_loss=0.2578, pruned_loss=0.03391, over 4968.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2393, pruned_loss=0.04103, over 985084.50 frames.], batch size: 61, aishell_tot_loss[loss=0.1609, simple_loss=0.2454, pruned_loss=0.0382, over 974821.70 frames.], datatang_tot_loss[loss=0.1591, simple_loss=0.2324, pruned_loss=0.04294, over 978447.81 frames.], batch size: 61, lr: 5.44e-04
+2022-06-18 20:57:39,944 INFO [train.py:874] (3/4) Epoch 15, batch 1950, aishell_loss[loss=0.1727, simple_loss=0.2541, pruned_loss=0.04565, over 4880.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2386, pruned_loss=0.04088, over 985016.11 frames.], batch size: 42, aishell_tot_loss[loss=0.1604, simple_loss=0.2448, pruned_loss=0.03801, over 975810.60 frames.], datatang_tot_loss[loss=0.1593, simple_loss=0.2324, pruned_loss=0.0431, over 979370.40 frames.], batch size: 42, lr: 5.44e-04
+2022-06-18 20:58:10,947 INFO [train.py:874] (3/4) Epoch 15, batch 2000, aishell_loss[loss=0.1633, simple_loss=0.2577, pruned_loss=0.0345, over 4933.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2391, pruned_loss=0.04091, over 985423.03 frames.], batch size: 77, aishell_tot_loss[loss=0.1606, simple_loss=0.2452, pruned_loss=0.03796, over 977076.08 frames.], datatang_tot_loss[loss=0.1595, simple_loss=0.2326, pruned_loss=0.04315, over 980249.43 frames.], batch size: 77, lr: 5.43e-04
+2022-06-18 20:58:10,948 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 20:58:27,551 INFO [train.py:914] (3/4) Epoch 15, validation: loss=0.166, simple_loss=0.2499, pruned_loss=0.04104, over 1622729.00 frames.
+2022-06-18 20:58:58,127 INFO [train.py:874] (3/4) Epoch 15, batch 2050, datatang_loss[loss=0.1728, simple_loss=0.245, pruned_loss=0.05024, over 4984.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2395, pruned_loss=0.04079, over 985548.79 frames.], batch size: 40, aishell_tot_loss[loss=0.1612, simple_loss=0.2459, pruned_loss=0.03827, over 978400.69 frames.], datatang_tot_loss[loss=0.1591, simple_loss=0.2324, pruned_loss=0.04284, over 980669.01 frames.], batch size: 40, lr: 5.43e-04
+2022-06-18 20:59:28,245 INFO [train.py:874] (3/4) Epoch 15, batch 2100, datatang_loss[loss=0.1647, simple_loss=0.2235, pruned_loss=0.05292, over 4922.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2383, pruned_loss=0.04036, over 985755.07 frames.], batch size: 34, aishell_tot_loss[loss=0.1604, simple_loss=0.2451, pruned_loss=0.03781, over 979388.75 frames.], datatang_tot_loss[loss=0.1589, simple_loss=0.232, pruned_loss=0.04287, over 981307.72 frames.], batch size: 34, lr: 5.43e-04
+2022-06-18 20:59:58,298 INFO [train.py:874] (3/4) Epoch 15, batch 2150, aishell_loss[loss=0.1616, simple_loss=0.2541, pruned_loss=0.03458, over 4858.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2378, pruned_loss=0.04032, over 986239.40 frames.], batch size: 37, aishell_tot_loss[loss=0.1606, simple_loss=0.2452, pruned_loss=0.03804, over 980282.30 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2316, pruned_loss=0.0425, over 982170.14 frames.], batch size: 37, lr: 5.43e-04
+2022-06-18 21:00:33,836 INFO [train.py:874] (3/4) Epoch 15, batch 2200, aishell_loss[loss=0.1711, simple_loss=0.2645, pruned_loss=0.0388, over 4943.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2384, pruned_loss=0.04067, over 985964.31 frames.], batch size: 64, aishell_tot_loss[loss=0.1604, simple_loss=0.2449, pruned_loss=0.03789, over 980587.48 frames.], datatang_tot_loss[loss=0.1592, simple_loss=0.2324, pruned_loss=0.04303, over 982736.63 frames.], batch size: 64, lr: 5.43e-04
+2022-06-18 21:01:02,781 INFO [train.py:874] (3/4) Epoch 15, batch 2250, aishell_loss[loss=0.1546, simple_loss=0.2352, pruned_loss=0.03698, over 4875.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2379, pruned_loss=0.04032, over 985470.39 frames.], batch size: 35, aishell_tot_loss[loss=0.1598, simple_loss=0.2442, pruned_loss=0.03767, over 980929.78 frames.], datatang_tot_loss[loss=0.1592, simple_loss=0.2323, pruned_loss=0.04304, over 982929.06 frames.], batch size: 35, lr: 5.42e-04
+2022-06-18 21:01:33,989 INFO [train.py:874] (3/4) Epoch 15, batch 2300, datatang_loss[loss=0.1521, simple_loss=0.2218, pruned_loss=0.04124, over 4945.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2382, pruned_loss=0.04033, over 985362.51 frames.], batch size: 69, aishell_tot_loss[loss=0.1599, simple_loss=0.2442, pruned_loss=0.03775, over 981262.05 frames.], datatang_tot_loss[loss=0.1592, simple_loss=0.2326, pruned_loss=0.04292, over 983305.00 frames.], batch size: 69, lr: 5.42e-04
+2022-06-18 21:02:05,493 INFO [train.py:874] (3/4) Epoch 15, batch 2350, aishell_loss[loss=0.1911, simple_loss=0.2675, pruned_loss=0.05731, over 4878.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2384, pruned_loss=0.04048, over 984917.57 frames.], batch size: 35, aishell_tot_loss[loss=0.1605, simple_loss=0.2448, pruned_loss=0.03817, over 981290.94 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2322, pruned_loss=0.04267, over 983532.41 frames.], batch size: 35, lr: 5.42e-04
+2022-06-18 21:02:33,678 INFO [train.py:874] (3/4) Epoch 15, batch 2400, datatang_loss[loss=0.1541, simple_loss=0.2345, pruned_loss=0.0369, over 4919.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2384, pruned_loss=0.0403, over 985262.89 frames.], batch size: 81, aishell_tot_loss[loss=0.1608, simple_loss=0.2449, pruned_loss=0.03833, over 981921.23 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2319, pruned_loss=0.04238, over 983887.73 frames.], batch size: 81, lr: 5.42e-04
+2022-06-18 21:03:04,149 INFO [train.py:874] (3/4) Epoch 15, batch 2450, aishell_loss[loss=0.1655, simple_loss=0.2609, pruned_loss=0.03507, over 4968.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2391, pruned_loss=0.04057, over 985482.76 frames.], batch size: 44, aishell_tot_loss[loss=0.1608, simple_loss=0.2452, pruned_loss=0.0382, over 982332.36 frames.], datatang_tot_loss[loss=0.159, simple_loss=0.2327, pruned_loss=0.04267, over 984222.28 frames.], batch size: 44, lr: 5.41e-04
+2022-06-18 21:03:35,676 INFO [train.py:874] (3/4) Epoch 15, batch 2500, aishell_loss[loss=0.1584, simple_loss=0.2502, pruned_loss=0.03333, over 4942.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2394, pruned_loss=0.04018, over 985709.32 frames.], batch size: 68, aishell_tot_loss[loss=0.1607, simple_loss=0.2454, pruned_loss=0.03804, over 982763.69 frames.], datatang_tot_loss[loss=0.1589, simple_loss=0.2327, pruned_loss=0.04258, over 984587.78 frames.], batch size: 68, lr: 5.41e-04
+2022-06-18 21:04:05,226 INFO [train.py:874] (3/4) Epoch 15, batch 2550, datatang_loss[loss=0.165, simple_loss=0.2334, pruned_loss=0.04835, over 4894.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2388, pruned_loss=0.04012, over 985839.83 frames.], batch size: 59, aishell_tot_loss[loss=0.1606, simple_loss=0.2452, pruned_loss=0.03799, over 983328.81 frames.], datatang_tot_loss[loss=0.1586, simple_loss=0.2321, pruned_loss=0.04256, over 984635.48 frames.], batch size: 59, lr: 5.41e-04
+2022-06-18 21:04:36,482 INFO [train.py:874] (3/4) Epoch 15, batch 2600, datatang_loss[loss=0.1353, simple_loss=0.2125, pruned_loss=0.02899, over 4908.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2392, pruned_loss=0.04045, over 985805.13 frames.], batch size: 75, aishell_tot_loss[loss=0.1608, simple_loss=0.2453, pruned_loss=0.03812, over 983699.57 frames.], datatang_tot_loss[loss=0.1589, simple_loss=0.2324, pruned_loss=0.04273, over 984672.92 frames.], batch size: 75, lr: 5.41e-04
+2022-06-18 21:05:08,113 INFO [train.py:874] (3/4) Epoch 15, batch 2650, datatang_loss[loss=0.1667, simple_loss=0.245, pruned_loss=0.04417, over 4960.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2387, pruned_loss=0.04035, over 985931.27 frames.], batch size: 55, aishell_tot_loss[loss=0.1606, simple_loss=0.2451, pruned_loss=0.03802, over 983861.26 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2322, pruned_loss=0.04273, over 985014.67 frames.], batch size: 55, lr: 5.41e-04
+2022-06-18 21:05:37,308 INFO [train.py:874] (3/4) Epoch 15, batch 2700, datatang_loss[loss=0.138, simple_loss=0.2128, pruned_loss=0.03162, over 4966.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2377, pruned_loss=0.03996, over 985771.55 frames.], batch size: 60, aishell_tot_loss[loss=0.1607, simple_loss=0.2454, pruned_loss=0.038, over 983899.81 frames.], datatang_tot_loss[loss=0.1578, simple_loss=0.2312, pruned_loss=0.04221, over 985117.22 frames.], batch size: 60, lr: 5.40e-04
+2022-06-18 21:06:06,767 INFO [train.py:874] (3/4) Epoch 15, batch 2750, datatang_loss[loss=0.1441, simple_loss=0.2167, pruned_loss=0.03572, over 4931.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2389, pruned_loss=0.0404, over 985648.30 frames.], batch size: 62, aishell_tot_loss[loss=0.161, simple_loss=0.2457, pruned_loss=0.03811, over 984273.43 frames.], datatang_tot_loss[loss=0.1585, simple_loss=0.2318, pruned_loss=0.04261, over 984930.25 frames.], batch size: 62, lr: 5.40e-04
+2022-06-18 21:06:38,024 INFO [train.py:874] (3/4) Epoch 15, batch 2800, datatang_loss[loss=0.14, simple_loss=0.2116, pruned_loss=0.03422, over 4958.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2377, pruned_loss=0.04031, over 985946.42 frames.], batch size: 67, aishell_tot_loss[loss=0.1608, simple_loss=0.2453, pruned_loss=0.03813, over 984685.71 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.2311, pruned_loss=0.04246, over 985071.30 frames.], batch size: 67, lr: 5.40e-04
+2022-06-18 21:07:05,698 INFO [train.py:874] (3/4) Epoch 15, batch 2850, aishell_loss[loss=0.1824, simple_loss=0.2563, pruned_loss=0.05425, over 4937.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2371, pruned_loss=0.0398, over 985788.54 frames.], batch size: 32, aishell_tot_loss[loss=0.1606, simple_loss=0.2452, pruned_loss=0.03802, over 984750.95 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2304, pruned_loss=0.04206, over 985102.14 frames.], batch size: 32, lr: 5.40e-04
+2022-06-18 21:07:37,041 INFO [train.py:874] (3/4) Epoch 15, batch 2900, datatang_loss[loss=0.1486, simple_loss=0.2295, pruned_loss=0.03388, over 4954.00 frames.], tot_loss[loss=0.1581, simple_loss=0.237, pruned_loss=0.03965, over 985839.73 frames.], batch size: 86, aishell_tot_loss[loss=0.1603, simple_loss=0.2451, pruned_loss=0.03773, over 984895.35 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2305, pruned_loss=0.04202, over 985215.74 frames.], batch size: 86, lr: 5.39e-04
+2022-06-18 21:08:07,698 INFO [train.py:874] (3/4) Epoch 15, batch 2950, aishell_loss[loss=0.1586, simple_loss=0.2425, pruned_loss=0.03741, over 4948.00 frames.], tot_loss[loss=0.1577, simple_loss=0.237, pruned_loss=0.03922, over 985994.31 frames.], batch size: 54, aishell_tot_loss[loss=0.1593, simple_loss=0.244, pruned_loss=0.03727, over 985074.99 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.2312, pruned_loss=0.04206, over 985398.82 frames.], batch size: 54, lr: 5.39e-04
+2022-06-18 21:08:37,289 INFO [train.py:874] (3/4) Epoch 15, batch 3000, datatang_loss[loss=0.1397, simple_loss=0.1971, pruned_loss=0.04112, over 4985.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2373, pruned_loss=0.03973, over 986063.75 frames.], batch size: 34, aishell_tot_loss[loss=0.1601, simple_loss=0.2448, pruned_loss=0.03768, over 985087.01 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2308, pruned_loss=0.04203, over 985637.48 frames.], batch size: 34, lr: 5.39e-04
+2022-06-18 21:08:37,290 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 21:08:54,390 INFO [train.py:914] (3/4) Epoch 15, validation: loss=0.1663, simple_loss=0.2504, pruned_loss=0.04115, over 1622729.00 frames.
+2022-06-18 21:09:23,253 INFO [train.py:874] (3/4) Epoch 15, batch 3050, aishell_loss[loss=0.1513, simple_loss=0.2456, pruned_loss=0.02856, over 4935.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2379, pruned_loss=0.03961, over 986041.18 frames.], batch size: 49, aishell_tot_loss[loss=0.1599, simple_loss=0.2447, pruned_loss=0.03752, over 985147.76 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.231, pruned_loss=0.04216, over 985726.43 frames.], batch size: 49, lr: 5.39e-04
+2022-06-18 21:09:54,963 INFO [train.py:874] (3/4) Epoch 15, batch 3100, aishell_loss[loss=0.1597, simple_loss=0.2531, pruned_loss=0.03315, over 4882.00 frames.], tot_loss[loss=0.1586, simple_loss=0.238, pruned_loss=0.03959, over 986109.06 frames.], batch size: 42, aishell_tot_loss[loss=0.1602, simple_loss=0.2453, pruned_loss=0.03758, over 985406.28 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2307, pruned_loss=0.042, over 985675.46 frames.], batch size: 42, lr: 5.39e-04
+2022-06-18 21:10:26,297 INFO [train.py:874] (3/4) Epoch 15, batch 3150, aishell_loss[loss=0.1628, simple_loss=0.2557, pruned_loss=0.03489, over 4947.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2381, pruned_loss=0.03992, over 985760.22 frames.], batch size: 56, aishell_tot_loss[loss=0.1602, simple_loss=0.245, pruned_loss=0.03772, over 985419.49 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.231, pruned_loss=0.04218, over 985439.26 frames.], batch size: 56, lr: 5.38e-04
+2022-06-18 21:10:56,739 INFO [train.py:874] (3/4) Epoch 15, batch 3200, datatang_loss[loss=0.1523, simple_loss=0.2387, pruned_loss=0.03295, over 4950.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2373, pruned_loss=0.03981, over 985719.06 frames.], batch size: 91, aishell_tot_loss[loss=0.1602, simple_loss=0.2448, pruned_loss=0.03782, over 985207.27 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2303, pruned_loss=0.04197, over 985695.91 frames.], batch size: 91, lr: 5.38e-04
+2022-06-18 21:11:27,596 INFO [train.py:874] (3/4) Epoch 15, batch 3250, aishell_loss[loss=0.1539, simple_loss=0.2313, pruned_loss=0.03823, over 4969.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2375, pruned_loss=0.04003, over 985616.42 frames.], batch size: 44, aishell_tot_loss[loss=0.1603, simple_loss=0.2448, pruned_loss=0.03792, over 985143.05 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2307, pruned_loss=0.04199, over 985698.64 frames.], batch size: 44, lr: 5.38e-04
+2022-06-18 21:11:59,189 INFO [train.py:874] (3/4) Epoch 15, batch 3300, datatang_loss[loss=0.162, simple_loss=0.2387, pruned_loss=0.04265, over 4922.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2373, pruned_loss=0.0401, over 985300.76 frames.], batch size: 71, aishell_tot_loss[loss=0.1603, simple_loss=0.2446, pruned_loss=0.03795, over 984848.78 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2308, pruned_loss=0.04198, over 985697.98 frames.], batch size: 71, lr: 5.38e-04
+2022-06-18 21:12:28,873 INFO [train.py:874] (3/4) Epoch 15, batch 3350, aishell_loss[loss=0.1485, simple_loss=0.2316, pruned_loss=0.03266, over 4866.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2364, pruned_loss=0.03969, over 985258.88 frames.], batch size: 28, aishell_tot_loss[loss=0.1601, simple_loss=0.2444, pruned_loss=0.03792, over 984783.06 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.23, pruned_loss=0.0416, over 985726.71 frames.], batch size: 28, lr: 5.37e-04
+2022-06-18 21:12:59,965 INFO [train.py:874] (3/4) Epoch 15, batch 3400, datatang_loss[loss=0.1424, simple_loss=0.2136, pruned_loss=0.0356, over 4915.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2361, pruned_loss=0.03932, over 985544.67 frames.], batch size: 75, aishell_tot_loss[loss=0.1597, simple_loss=0.244, pruned_loss=0.03763, over 984971.21 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2296, pruned_loss=0.04153, over 985863.83 frames.], batch size: 75, lr: 5.37e-04
+2022-06-18 21:13:28,605 INFO [train.py:874] (3/4) Epoch 15, batch 3450, aishell_loss[loss=0.1594, simple_loss=0.2577, pruned_loss=0.03057, over 4974.00 frames.], tot_loss[loss=0.1581, simple_loss=0.237, pruned_loss=0.0396, over 985681.66 frames.], batch size: 48, aishell_tot_loss[loss=0.1598, simple_loss=0.2445, pruned_loss=0.03761, over 985178.59 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2298, pruned_loss=0.04184, over 985823.47 frames.], batch size: 48, lr: 5.37e-04
+2022-06-18 21:13:59,981 INFO [train.py:874] (3/4) Epoch 15, batch 3500, aishell_loss[loss=0.1575, simple_loss=0.2517, pruned_loss=0.0317, over 4946.00 frames.], tot_loss[loss=0.158, simple_loss=0.2369, pruned_loss=0.0396, over 986087.93 frames.], batch size: 64, aishell_tot_loss[loss=0.1601, simple_loss=0.245, pruned_loss=0.03762, over 985301.29 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2295, pruned_loss=0.04169, over 986150.13 frames.], batch size: 64, lr: 5.37e-04
+2022-06-18 21:14:32,189 INFO [train.py:874] (3/4) Epoch 15, batch 3550, aishell_loss[loss=0.1664, simple_loss=0.2523, pruned_loss=0.04018, over 4912.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2371, pruned_loss=0.03927, over 986040.31 frames.], batch size: 41, aishell_tot_loss[loss=0.1603, simple_loss=0.2452, pruned_loss=0.03775, over 985255.95 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.2291, pruned_loss=0.04126, over 986241.23 frames.], batch size: 41, lr: 5.37e-04
+2022-06-18 21:15:01,943 INFO [train.py:874] (3/4) Epoch 15, batch 3600, aishell_loss[loss=0.2035, simple_loss=0.261, pruned_loss=0.07301, over 4941.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2366, pruned_loss=0.03951, over 986179.65 frames.], batch size: 45, aishell_tot_loss[loss=0.1608, simple_loss=0.2455, pruned_loss=0.03808, over 985238.27 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2286, pruned_loss=0.04106, over 986448.65 frames.], batch size: 45, lr: 5.36e-04
+2022-06-18 21:15:34,151 INFO [train.py:874] (3/4) Epoch 15, batch 3650, datatang_loss[loss=0.1386, simple_loss=0.2167, pruned_loss=0.03031, over 4959.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2363, pruned_loss=0.03961, over 986152.54 frames.], batch size: 67, aishell_tot_loss[loss=0.1607, simple_loss=0.2451, pruned_loss=0.0381, over 985323.90 frames.], datatang_tot_loss[loss=0.1555, simple_loss=0.2289, pruned_loss=0.04105, over 986400.01 frames.], batch size: 67, lr: 5.36e-04
+2022-06-18 21:16:05,789 INFO [train.py:874] (3/4) Epoch 15, batch 3700, aishell_loss[loss=0.134, simple_loss=0.2001, pruned_loss=0.03395, over 4905.00 frames.], tot_loss[loss=0.159, simple_loss=0.238, pruned_loss=0.03999, over 985780.31 frames.], batch size: 21, aishell_tot_loss[loss=0.1607, simple_loss=0.2453, pruned_loss=0.03804, over 984973.19 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.23, pruned_loss=0.0416, over 986451.31 frames.], batch size: 21, lr: 5.36e-04
+2022-06-18 21:16:34,908 INFO [train.py:874] (3/4) Epoch 15, batch 3750, datatang_loss[loss=0.1567, simple_loss=0.2349, pruned_loss=0.03926, over 4959.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2371, pruned_loss=0.03978, over 985669.75 frames.], batch size: 91, aishell_tot_loss[loss=0.1608, simple_loss=0.2456, pruned_loss=0.038, over 985062.26 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2291, pruned_loss=0.04141, over 986250.93 frames.], batch size: 91, lr: 5.36e-04
+2022-06-18 21:17:05,073 INFO [train.py:874] (3/4) Epoch 15, batch 3800, aishell_loss[loss=0.174, simple_loss=0.2647, pruned_loss=0.04164, over 4906.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2372, pruned_loss=0.0397, over 985561.75 frames.], batch size: 33, aishell_tot_loss[loss=0.1604, simple_loss=0.2451, pruned_loss=0.03784, over 984992.56 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2294, pruned_loss=0.04155, over 986231.70 frames.], batch size: 33, lr: 5.35e-04
+2022-06-18 21:17:36,803 INFO [train.py:874] (3/4) Epoch 15, batch 3850, aishell_loss[loss=0.1734, simple_loss=0.2532, pruned_loss=0.04678, over 4978.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2375, pruned_loss=0.03932, over 985653.06 frames.], batch size: 51, aishell_tot_loss[loss=0.1599, simple_loss=0.2448, pruned_loss=0.0375, over 985092.54 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2297, pruned_loss=0.04154, over 986239.97 frames.], batch size: 51, lr: 5.35e-04
+2022-06-18 21:18:05,729 INFO [train.py:874] (3/4) Epoch 15, batch 3900, datatang_loss[loss=0.1369, simple_loss=0.2163, pruned_loss=0.02874, over 4906.00 frames.], tot_loss[loss=0.158, simple_loss=0.2372, pruned_loss=0.03936, over 985399.43 frames.], batch size: 64, aishell_tot_loss[loss=0.1599, simple_loss=0.2447, pruned_loss=0.03754, over 985012.86 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2299, pruned_loss=0.04145, over 986018.40 frames.], batch size: 64, lr: 5.35e-04
+2022-06-18 21:18:34,830 INFO [train.py:874] (3/4) Epoch 15, batch 3950, datatang_loss[loss=0.1246, simple_loss=0.2113, pruned_loss=0.01896, over 4912.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2372, pruned_loss=0.0393, over 985325.03 frames.], batch size: 64, aishell_tot_loss[loss=0.1604, simple_loss=0.245, pruned_loss=0.03795, over 984855.90 frames.], datatang_tot_loss[loss=0.1556, simple_loss=0.2292, pruned_loss=0.04104, over 986104.94 frames.], batch size: 64, lr: 5.35e-04
+2022-06-18 21:19:03,512 INFO [train.py:874] (3/4) Epoch 15, batch 4000, datatang_loss[loss=0.1588, simple_loss=0.2233, pruned_loss=0.0471, over 4911.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2377, pruned_loss=0.03937, over 985239.89 frames.], batch size: 57, aishell_tot_loss[loss=0.1607, simple_loss=0.2452, pruned_loss=0.03806, over 984778.10 frames.], datatang_tot_loss[loss=0.1556, simple_loss=0.2293, pruned_loss=0.041, over 986069.83 frames.], batch size: 57, lr: 5.35e-04
+2022-06-18 21:19:03,513 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 21:19:20,714 INFO [train.py:914] (3/4) Epoch 15, validation: loss=0.1641, simple_loss=0.2483, pruned_loss=0.0399, over 1622729.00 frames.
+2022-06-18 21:19:49,378 INFO [train.py:874] (3/4) Epoch 15, batch 4050, datatang_loss[loss=0.1727, simple_loss=0.249, pruned_loss=0.04819, over 4929.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2382, pruned_loss=0.0393, over 985165.52 frames.], batch size: 94, aishell_tot_loss[loss=0.161, simple_loss=0.2457, pruned_loss=0.03817, over 984521.72 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2293, pruned_loss=0.04081, over 986203.85 frames.], batch size: 94, lr: 5.34e-04
+2022-06-18 21:20:18,335 INFO [train.py:874] (3/4) Epoch 15, batch 4100, aishell_loss[loss=0.1431, simple_loss=0.2333, pruned_loss=0.0265, over 4959.00 frames.], tot_loss[loss=0.1591, simple_loss=0.239, pruned_loss=0.0396, over 985283.12 frames.], batch size: 64, aishell_tot_loss[loss=0.1608, simple_loss=0.2455, pruned_loss=0.0381, over 984649.40 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2302, pruned_loss=0.04122, over 986184.37 frames.], batch size: 64, lr: 5.34e-04
+2022-06-18 21:20:47,971 INFO [train.py:874] (3/4) Epoch 15, batch 4150, datatang_loss[loss=0.1773, simple_loss=0.2504, pruned_loss=0.0521, over 4954.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2384, pruned_loss=0.04, over 985527.77 frames.], batch size: 91, aishell_tot_loss[loss=0.1604, simple_loss=0.245, pruned_loss=0.0379, over 984905.95 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2306, pruned_loss=0.0418, over 986151.55 frames.], batch size: 91, lr: 5.34e-04
+2022-06-18 21:22:22,413 INFO [train.py:874] (3/4) Epoch 16, batch 50, aishell_loss[loss=0.1547, simple_loss=0.2389, pruned_loss=0.03521, over 4947.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2312, pruned_loss=0.03662, over 218683.45 frames.], batch size: 45, aishell_tot_loss[loss=0.1559, simple_loss=0.243, pruned_loss=0.03436, over 111674.65 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2206, pruned_loss=0.03871, over 120668.47 frames.], batch size: 45, lr: 5.18e-04
+2022-06-18 21:22:53,235 INFO [train.py:874] (3/4) Epoch 16, batch 100, aishell_loss[loss=0.1652, simple_loss=0.2519, pruned_loss=0.03932, over 4936.00 frames.], tot_loss[loss=0.153, simple_loss=0.233, pruned_loss=0.03652, over 388735.04 frames.], batch size: 49, aishell_tot_loss[loss=0.1593, simple_loss=0.2464, pruned_loss=0.0361, over 202675.31 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2213, pruned_loss=0.03681, over 234223.02 frames.], batch size: 49, lr: 5.18e-04
+2022-06-18 21:23:22,640 INFO [train.py:874] (3/4) Epoch 16, batch 150, aishell_loss[loss=0.1461, simple_loss=0.2268, pruned_loss=0.0327, over 4983.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2318, pruned_loss=0.03655, over 521295.76 frames.], batch size: 27, aishell_tot_loss[loss=0.1589, simple_loss=0.245, pruned_loss=0.03634, over 281020.04 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2211, pruned_loss=0.03665, over 336008.65 frames.], batch size: 27, lr: 5.18e-04
+2022-06-18 21:23:53,729 INFO [train.py:874] (3/4) Epoch 16, batch 200, aishell_loss[loss=0.1414, simple_loss=0.2337, pruned_loss=0.02458, over 4921.00 frames.], tot_loss[loss=0.1528, simple_loss=0.232, pruned_loss=0.03676, over 624093.55 frames.], batch size: 41, aishell_tot_loss[loss=0.1599, simple_loss=0.246, pruned_loss=0.03694, over 351423.30 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.221, pruned_loss=0.03657, over 423727.59 frames.], batch size: 41, lr: 5.17e-04
+2022-06-18 21:24:24,734 INFO [train.py:874] (3/4) Epoch 16, batch 250, aishell_loss[loss=0.1635, simple_loss=0.2521, pruned_loss=0.03743, over 4897.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2333, pruned_loss=0.03677, over 704079.82 frames.], batch size: 34, aishell_tot_loss[loss=0.159, simple_loss=0.245, pruned_loss=0.03653, over 439662.21 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2221, pruned_loss=0.037, over 477462.55 frames.], batch size: 34, lr: 5.17e-04
+2022-06-18 21:24:55,274 INFO [train.py:874] (3/4) Epoch 16, batch 300, datatang_loss[loss=0.1654, simple_loss=0.2317, pruned_loss=0.04954, over 4922.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2331, pruned_loss=0.03651, over 766638.99 frames.], batch size: 81, aishell_tot_loss[loss=0.1584, simple_loss=0.2441, pruned_loss=0.03638, over 508822.08 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2221, pruned_loss=0.03672, over 532897.40 frames.], batch size: 81, lr: 5.17e-04
+2022-06-18 21:25:25,782 INFO [train.py:874] (3/4) Epoch 16, batch 350, datatang_loss[loss=0.1629, simple_loss=0.2345, pruned_loss=0.04569, over 4928.00 frames.], tot_loss[loss=0.1547, simple_loss=0.234, pruned_loss=0.03771, over 815212.39 frames.], batch size: 73, aishell_tot_loss[loss=0.1584, simple_loss=0.2438, pruned_loss=0.03643, over 554170.21 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2244, pruned_loss=0.03838, over 596262.15 frames.], batch size: 73, lr: 5.17e-04
+2022-06-18 21:25:56,626 INFO [train.py:874] (3/4) Epoch 16, batch 400, datatang_loss[loss=0.1333, simple_loss=0.202, pruned_loss=0.0323, over 4924.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2339, pruned_loss=0.03814, over 852877.52 frames.], batch size: 77, aishell_tot_loss[loss=0.1584, simple_loss=0.2433, pruned_loss=0.03672, over 605077.37 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2248, pruned_loss=0.03884, over 641903.87 frames.], batch size: 77, lr: 5.17e-04
+2022-06-18 21:26:25,974 INFO [train.py:874] (3/4) Epoch 16, batch 450, datatang_loss[loss=0.15, simple_loss=0.2284, pruned_loss=0.03583, over 4923.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2351, pruned_loss=0.03852, over 882538.51 frames.], batch size: 77, aishell_tot_loss[loss=0.1598, simple_loss=0.2445, pruned_loss=0.03761, over 658324.60 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2248, pruned_loss=0.0387, over 674818.94 frames.], batch size: 77, lr: 5.16e-04
+2022-06-18 21:26:54,484 INFO [train.py:874] (3/4) Epoch 16, batch 500, datatang_loss[loss=0.1652, simple_loss=0.2375, pruned_loss=0.0465, over 4909.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2358, pruned_loss=0.03848, over 905259.32 frames.], batch size: 52, aishell_tot_loss[loss=0.1596, simple_loss=0.2439, pruned_loss=0.03758, over 710906.90 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2252, pruned_loss=0.03886, over 697271.42 frames.], batch size: 52, lr: 5.16e-04
+2022-06-18 21:27:26,333 INFO [train.py:874] (3/4) Epoch 16, batch 550, aishell_loss[loss=0.118, simple_loss=0.1993, pruned_loss=0.01839, over 4939.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2353, pruned_loss=0.03831, over 922938.91 frames.], batch size: 25, aishell_tot_loss[loss=0.1595, simple_loss=0.2438, pruned_loss=0.03762, over 743133.71 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.225, pruned_loss=0.03868, over 731224.56 frames.], batch size: 25, lr: 5.16e-04
+2022-06-18 21:27:56,282 INFO [train.py:874] (3/4) Epoch 16, batch 600, aishell_loss[loss=0.1621, simple_loss=0.2498, pruned_loss=0.03722, over 4925.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2359, pruned_loss=0.03882, over 937211.92 frames.], batch size: 49, aishell_tot_loss[loss=0.1591, simple_loss=0.2435, pruned_loss=0.03738, over 771722.49 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2263, pruned_loss=0.03963, over 761542.79 frames.], batch size: 49, lr: 5.16e-04
+2022-06-18 21:28:26,906 INFO [train.py:874] (3/4) Epoch 16, batch 650, datatang_loss[loss=0.1583, simple_loss=0.2322, pruned_loss=0.04226, over 4921.00 frames.], tot_loss[loss=0.158, simple_loss=0.2371, pruned_loss=0.03949, over 947584.97 frames.], batch size: 81, aishell_tot_loss[loss=0.1592, simple_loss=0.2436, pruned_loss=0.03741, over 793661.52 frames.], datatang_tot_loss[loss=0.1547, simple_loss=0.2283, pruned_loss=0.04052, over 790887.44 frames.], batch size: 81, lr: 5.16e-04
+2022-06-18 21:28:57,716 INFO [train.py:874] (3/4) Epoch 16, batch 700, aishell_loss[loss=0.131, simple_loss=0.2173, pruned_loss=0.02235, over 4873.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2374, pruned_loss=0.04008, over 956264.29 frames.], batch size: 28, aishell_tot_loss[loss=0.1593, simple_loss=0.2437, pruned_loss=0.03748, over 812892.68 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2295, pruned_loss=0.0413, over 817442.86 frames.], batch size: 28, lr: 5.15e-04
+2022-06-18 21:29:27,988 INFO [train.py:874] (3/4) Epoch 16, batch 750, aishell_loss[loss=0.1748, simple_loss=0.2585, pruned_loss=0.04551, over 4880.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2371, pruned_loss=0.0404, over 962623.36 frames.], batch size: 34, aishell_tot_loss[loss=0.1598, simple_loss=0.2439, pruned_loss=0.03783, over 829246.34 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2294, pruned_loss=0.0414, over 840911.62 frames.], batch size: 34, lr: 5.15e-04
+2022-06-18 21:29:58,399 INFO [train.py:874] (3/4) Epoch 16, batch 800, aishell_loss[loss=0.1389, simple_loss=0.2235, pruned_loss=0.02718, over 4831.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2372, pruned_loss=0.04025, over 967785.17 frames.], batch size: 29, aishell_tot_loss[loss=0.1597, simple_loss=0.244, pruned_loss=0.03772, over 847719.54 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2296, pruned_loss=0.04152, over 857975.27 frames.], batch size: 29, lr: 5.15e-04
+2022-06-18 21:30:29,010 INFO [train.py:874] (3/4) Epoch 16, batch 850, aishell_loss[loss=0.16, simple_loss=0.2402, pruned_loss=0.03993, over 4881.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2366, pruned_loss=0.03992, over 971421.51 frames.], batch size: 34, aishell_tot_loss[loss=0.159, simple_loss=0.2431, pruned_loss=0.03742, over 862421.05 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.23, pruned_loss=0.04156, over 874120.13 frames.], batch size: 34, lr: 5.15e-04
+2022-06-18 21:30:59,007 INFO [train.py:874] (3/4) Epoch 16, batch 900, aishell_loss[loss=0.1596, simple_loss=0.2397, pruned_loss=0.0397, over 4964.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2358, pruned_loss=0.03987, over 974694.30 frames.], batch size: 31, aishell_tot_loss[loss=0.1588, simple_loss=0.2428, pruned_loss=0.03733, over 874398.53 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2297, pruned_loss=0.04157, over 889604.46 frames.], batch size: 31, lr: 5.15e-04
+2022-06-18 21:31:29,485 INFO [train.py:874] (3/4) Epoch 16, batch 950, datatang_loss[loss=0.1299, simple_loss=0.2094, pruned_loss=0.02521, over 4900.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2352, pruned_loss=0.03986, over 976641.68 frames.], batch size: 52, aishell_tot_loss[loss=0.1585, simple_loss=0.2423, pruned_loss=0.03733, over 885083.69 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2296, pruned_loss=0.04159, over 902506.42 frames.], batch size: 52, lr: 5.14e-04
+2022-06-18 21:31:59,363 INFO [train.py:874] (3/4) Epoch 16, batch 1000, aishell_loss[loss=0.1565, simple_loss=0.2427, pruned_loss=0.03514, over 4974.00 frames.], tot_loss[loss=0.1584, simple_loss=0.237, pruned_loss=0.03993, over 978951.07 frames.], batch size: 51, aishell_tot_loss[loss=0.1592, simple_loss=0.2434, pruned_loss=0.03755, over 900675.29 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.23, pruned_loss=0.04175, over 909366.18 frames.], batch size: 51, lr: 5.14e-04
+2022-06-18 21:31:59,365 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 21:32:15,097 INFO [train.py:914] (3/4) Epoch 16, validation: loss=0.1652, simple_loss=0.2489, pruned_loss=0.04072, over 1622729.00 frames.
+2022-06-18 21:32:45,830 INFO [train.py:874] (3/4) Epoch 16, batch 1050, aishell_loss[loss=0.1145, simple_loss=0.1882, pruned_loss=0.02036, over 4988.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2368, pruned_loss=0.0398, over 980760.75 frames.], batch size: 25, aishell_tot_loss[loss=0.1592, simple_loss=0.2433, pruned_loss=0.03755, over 909755.24 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2302, pruned_loss=0.04161, over 919501.54 frames.], batch size: 25, lr: 5.14e-04
+2022-06-18 21:33:17,643 INFO [train.py:874] (3/4) Epoch 16, batch 1100, aishell_loss[loss=0.1208, simple_loss=0.2016, pruned_loss=0.01999, over 4964.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2365, pruned_loss=0.03939, over 981915.30 frames.], batch size: 27, aishell_tot_loss[loss=0.1594, simple_loss=0.2436, pruned_loss=0.03765, over 917824.24 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2297, pruned_loss=0.04106, over 928077.73 frames.], batch size: 27, lr: 5.14e-04
+2022-06-18 21:33:46,496 INFO [train.py:874] (3/4) Epoch 16, batch 1150, aishell_loss[loss=0.1785, simple_loss=0.2583, pruned_loss=0.04933, over 4978.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2359, pruned_loss=0.03912, over 982553.39 frames.], batch size: 39, aishell_tot_loss[loss=0.1589, simple_loss=0.2429, pruned_loss=0.0375, over 925913.76 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.2297, pruned_loss=0.04094, over 934588.62 frames.], batch size: 39, lr: 5.14e-04
+2022-06-18 21:34:16,226 INFO [train.py:874] (3/4) Epoch 16, batch 1200, aishell_loss[loss=0.1857, simple_loss=0.2693, pruned_loss=0.051, over 4924.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2364, pruned_loss=0.03927, over 983220.75 frames.], batch size: 52, aishell_tot_loss[loss=0.1594, simple_loss=0.2431, pruned_loss=0.03785, over 933685.92 frames.], datatang_tot_loss[loss=0.1556, simple_loss=0.2297, pruned_loss=0.04076, over 939946.18 frames.], batch size: 52, lr: 5.13e-04
+2022-06-18 21:34:46,053 INFO [train.py:874] (3/4) Epoch 16, batch 1250, aishell_loss[loss=0.1608, simple_loss=0.2479, pruned_loss=0.03684, over 4977.00 frames.], tot_loss[loss=0.1563, simple_loss=0.236, pruned_loss=0.03826, over 983603.82 frames.], batch size: 51, aishell_tot_loss[loss=0.1582, simple_loss=0.2422, pruned_loss=0.03714, over 941076.27 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2298, pruned_loss=0.04044, over 944020.20 frames.], batch size: 51, lr: 5.13e-04
+2022-06-18 21:35:16,598 INFO [train.py:874] (3/4) Epoch 16, batch 1300, datatang_loss[loss=0.1719, simple_loss=0.2455, pruned_loss=0.04913, over 4958.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2368, pruned_loss=0.039, over 984415.16 frames.], batch size: 99, aishell_tot_loss[loss=0.1584, simple_loss=0.2426, pruned_loss=0.03708, over 945958.55 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2305, pruned_loss=0.04115, over 949613.16 frames.], batch size: 99, lr: 5.13e-04
+2022-06-18 21:35:48,269 INFO [train.py:874] (3/4) Epoch 16, batch 1350, datatang_loss[loss=0.1732, simple_loss=0.2454, pruned_loss=0.05049, over 4933.00 frames.], tot_loss[loss=0.158, simple_loss=0.2374, pruned_loss=0.0393, over 984793.44 frames.], batch size: 64, aishell_tot_loss[loss=0.1582, simple_loss=0.2425, pruned_loss=0.03692, over 950921.12 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2311, pruned_loss=0.04166, over 953711.56 frames.], batch size: 64, lr: 5.13e-04
+2022-06-18 21:36:18,300 INFO [train.py:874] (3/4) Epoch 16, batch 1400, aishell_loss[loss=0.1687, simple_loss=0.2518, pruned_loss=0.04276, over 4943.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2378, pruned_loss=0.03969, over 985280.98 frames.], batch size: 45, aishell_tot_loss[loss=0.1588, simple_loss=0.2431, pruned_loss=0.03727, over 954518.34 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2313, pruned_loss=0.04164, over 958215.93 frames.], batch size: 45, lr: 5.13e-04
+2022-06-18 21:36:47,833 INFO [train.py:874] (3/4) Epoch 16, batch 1450, datatang_loss[loss=0.1405, simple_loss=0.2211, pruned_loss=0.02997, over 4950.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2385, pruned_loss=0.03988, over 985302.17 frames.], batch size: 86, aishell_tot_loss[loss=0.159, simple_loss=0.2434, pruned_loss=0.03731, over 958059.23 frames.], datatang_tot_loss[loss=0.1578, simple_loss=0.2318, pruned_loss=0.04189, over 961510.35 frames.], batch size: 86, lr: 5.12e-04
+2022-06-18 21:37:19,348 INFO [train.py:874] (3/4) Epoch 16, batch 1500, datatang_loss[loss=0.1496, simple_loss=0.2278, pruned_loss=0.03566, over 4971.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2383, pruned_loss=0.03975, over 985798.68 frames.], batch size: 40, aishell_tot_loss[loss=0.159, simple_loss=0.2435, pruned_loss=0.0373, over 961748.91 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2318, pruned_loss=0.04186, over 964376.24 frames.], batch size: 40, lr: 5.12e-04
+2022-06-18 21:37:48,689 INFO [train.py:874] (3/4) Epoch 16, batch 1550, datatang_loss[loss=0.2022, simple_loss=0.2683, pruned_loss=0.06804, over 4926.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2386, pruned_loss=0.03986, over 985734.34 frames.], batch size: 94, aishell_tot_loss[loss=0.1591, simple_loss=0.2435, pruned_loss=0.03736, over 964582.16 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2322, pruned_loss=0.04197, over 966839.07 frames.], batch size: 94, lr: 5.12e-04
+2022-06-18 21:38:18,609 INFO [train.py:874] (3/4) Epoch 16, batch 1600, datatang_loss[loss=0.1444, simple_loss=0.2279, pruned_loss=0.0304, over 4920.00 frames.], tot_loss[loss=0.158, simple_loss=0.2375, pruned_loss=0.03923, over 985471.13 frames.], batch size: 83, aishell_tot_loss[loss=0.1588, simple_loss=0.2432, pruned_loss=0.03724, over 966597.79 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2315, pruned_loss=0.04144, over 969246.26 frames.], batch size: 83, lr: 5.12e-04
+2022-06-18 21:38:49,745 INFO [train.py:874] (3/4) Epoch 16, batch 1650, aishell_loss[loss=0.1831, simple_loss=0.2643, pruned_loss=0.05089, over 4934.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2361, pruned_loss=0.03869, over 985354.40 frames.], batch size: 49, aishell_tot_loss[loss=0.1586, simple_loss=0.243, pruned_loss=0.03709, over 968334.07 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2304, pruned_loss=0.04088, over 971437.88 frames.], batch size: 49, lr: 5.12e-04
+2022-06-18 21:39:20,232 INFO [train.py:874] (3/4) Epoch 16, batch 1700, aishell_loss[loss=0.1504, simple_loss=0.2416, pruned_loss=0.02959, over 4946.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2361, pruned_loss=0.03871, over 985460.37 frames.], batch size: 56, aishell_tot_loss[loss=0.1587, simple_loss=0.2432, pruned_loss=0.03707, over 970226.88 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2303, pruned_loss=0.04082, over 973282.16 frames.], batch size: 56, lr: 5.11e-04
+2022-06-18 21:39:48,472 INFO [train.py:874] (3/4) Epoch 16, batch 1750, datatang_loss[loss=0.147, simple_loss=0.2261, pruned_loss=0.0339, over 4927.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2362, pruned_loss=0.0388, over 985254.24 frames.], batch size: 64, aishell_tot_loss[loss=0.1584, simple_loss=0.2427, pruned_loss=0.03703, over 971816.29 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2307, pruned_loss=0.04089, over 974698.83 frames.], batch size: 64, lr: 5.11e-04
+2022-06-18 21:40:19,054 INFO [train.py:874] (3/4) Epoch 16, batch 1800, aishell_loss[loss=0.151, simple_loss=0.237, pruned_loss=0.03246, over 4864.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2365, pruned_loss=0.03851, over 985237.47 frames.], batch size: 36, aishell_tot_loss[loss=0.1586, simple_loss=0.243, pruned_loss=0.03708, over 973614.51 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2303, pruned_loss=0.0406, over 975761.64 frames.], batch size: 36, lr: 5.11e-04
+2022-06-18 21:40:47,584 INFO [train.py:874] (3/4) Epoch 16, batch 1850, aishell_loss[loss=0.1704, simple_loss=0.2543, pruned_loss=0.04329, over 4947.00 frames.], tot_loss[loss=0.157, simple_loss=0.2364, pruned_loss=0.03881, over 985456.35 frames.], batch size: 58, aishell_tot_loss[loss=0.1588, simple_loss=0.2431, pruned_loss=0.03719, over 974983.19 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2301, pruned_loss=0.04069, over 977095.81 frames.], batch size: 58, lr: 5.11e-04
+2022-06-18 21:41:17,948 INFO [train.py:874] (3/4) Epoch 16, batch 1900, datatang_loss[loss=0.1662, simple_loss=0.2337, pruned_loss=0.04938, over 4952.00 frames.], tot_loss[loss=0.158, simple_loss=0.2375, pruned_loss=0.03928, over 985549.72 frames.], batch size: 34, aishell_tot_loss[loss=0.1587, simple_loss=0.2435, pruned_loss=0.03699, over 976456.32 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2306, pruned_loss=0.0414, over 977977.99 frames.], batch size: 34, lr: 5.11e-04
+2022-06-18 21:41:47,579 INFO [train.py:874] (3/4) Epoch 16, batch 1950, aishell_loss[loss=0.1655, simple_loss=0.2457, pruned_loss=0.04264, over 4906.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2373, pruned_loss=0.0391, over 985762.01 frames.], batch size: 34, aishell_tot_loss[loss=0.1589, simple_loss=0.2436, pruned_loss=0.03706, over 977470.46 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2304, pruned_loss=0.04114, over 979141.81 frames.], batch size: 34, lr: 5.10e-04
+2022-06-18 21:42:22,194 INFO [train.py:874] (3/4) Epoch 16, batch 2000, datatang_loss[loss=0.1673, simple_loss=0.2388, pruned_loss=0.04792, over 4945.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2383, pruned_loss=0.03967, over 985728.08 frames.], batch size: 37, aishell_tot_loss[loss=0.1592, simple_loss=0.244, pruned_loss=0.03723, over 978450.28 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2311, pruned_loss=0.04162, over 979883.19 frames.], batch size: 37, lr: 5.10e-04
+2022-06-18 21:42:22,195 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 21:42:38,236 INFO [train.py:914] (3/4) Epoch 16, validation: loss=0.1651, simple_loss=0.2493, pruned_loss=0.04046, over 1622729.00 frames.
+2022-06-18 21:43:07,844 INFO [train.py:874] (3/4) Epoch 16, batch 2050, datatang_loss[loss=0.1376, simple_loss=0.2198, pruned_loss=0.02769, over 4960.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2377, pruned_loss=0.03951, over 985683.40 frames.], batch size: 67, aishell_tot_loss[loss=0.1589, simple_loss=0.2436, pruned_loss=0.03704, over 979159.50 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2313, pruned_loss=0.04161, over 980653.26 frames.], batch size: 67, lr: 5.10e-04
+2022-06-18 21:43:37,789 INFO [train.py:874] (3/4) Epoch 16, batch 2100, aishell_loss[loss=0.1561, simple_loss=0.2379, pruned_loss=0.03717, over 4977.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2374, pruned_loss=0.03892, over 985706.31 frames.], batch size: 27, aishell_tot_loss[loss=0.1588, simple_loss=0.2437, pruned_loss=0.03694, over 979770.89 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.2308, pruned_loss=0.04119, over 981445.51 frames.], batch size: 27, lr: 5.10e-04
+2022-06-18 21:44:09,098 INFO [train.py:874] (3/4) Epoch 16, batch 2150, aishell_loss[loss=0.1839, simple_loss=0.2575, pruned_loss=0.05515, over 4865.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2377, pruned_loss=0.03884, over 985684.04 frames.], batch size: 35, aishell_tot_loss[loss=0.1591, simple_loss=0.244, pruned_loss=0.03713, over 980407.83 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2308, pruned_loss=0.04087, over 981987.40 frames.], batch size: 35, lr: 5.10e-04
+2022-06-18 21:44:39,283 INFO [train.py:874] (3/4) Epoch 16, batch 2200, datatang_loss[loss=0.1835, simple_loss=0.2666, pruned_loss=0.05015, over 4946.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2373, pruned_loss=0.03877, over 985246.07 frames.], batch size: 31, aishell_tot_loss[loss=0.1581, simple_loss=0.2432, pruned_loss=0.03655, over 980674.26 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2313, pruned_loss=0.0413, over 982305.32 frames.], batch size: 31, lr: 5.09e-04
+2022-06-18 21:45:09,224 INFO [train.py:874] (3/4) Epoch 16, batch 2250, datatang_loss[loss=0.1479, simple_loss=0.2268, pruned_loss=0.03447, over 4958.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2372, pruned_loss=0.03889, over 985789.02 frames.], batch size: 34, aishell_tot_loss[loss=0.1586, simple_loss=0.2437, pruned_loss=0.03678, over 981467.14 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2309, pruned_loss=0.0411, over 982966.53 frames.], batch size: 34, lr: 5.09e-04
+2022-06-18 21:45:40,322 INFO [train.py:874] (3/4) Epoch 16, batch 2300, aishell_loss[loss=0.179, simple_loss=0.2617, pruned_loss=0.04811, over 4957.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2372, pruned_loss=0.03855, over 986153.75 frames.], batch size: 80, aishell_tot_loss[loss=0.1589, simple_loss=0.244, pruned_loss=0.0369, over 981967.63 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2305, pruned_loss=0.04062, over 983693.86 frames.], batch size: 80, lr: 5.09e-04
+2022-06-18 21:46:10,858 INFO [train.py:874] (3/4) Epoch 16, batch 2350, aishell_loss[loss=0.1529, simple_loss=0.2374, pruned_loss=0.03425, over 4918.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2376, pruned_loss=0.03875, over 986054.81 frames.], batch size: 68, aishell_tot_loss[loss=0.159, simple_loss=0.2441, pruned_loss=0.03695, over 982347.59 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2307, pruned_loss=0.04073, over 983997.70 frames.], batch size: 68, lr: 5.09e-04
+2022-06-18 21:46:41,023 INFO [train.py:874] (3/4) Epoch 16, batch 2400, datatang_loss[loss=0.141, simple_loss=0.2202, pruned_loss=0.03084, over 4944.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2373, pruned_loss=0.03899, over 986020.36 frames.], batch size: 69, aishell_tot_loss[loss=0.1591, simple_loss=0.2441, pruned_loss=0.03702, over 982669.52 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2308, pruned_loss=0.04084, over 984279.57 frames.], batch size: 69, lr: 5.09e-04
+2022-06-18 21:47:10,453 INFO [train.py:874] (3/4) Epoch 16, batch 2450, datatang_loss[loss=0.1971, simple_loss=0.2596, pruned_loss=0.06735, over 4946.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2392, pruned_loss=0.0393, over 986023.47 frames.], batch size: 37, aishell_tot_loss[loss=0.1597, simple_loss=0.2448, pruned_loss=0.03729, over 983119.90 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2312, pruned_loss=0.04107, over 984506.29 frames.], batch size: 37, lr: 5.08e-04
+2022-06-18 21:47:41,051 INFO [train.py:874] (3/4) Epoch 16, batch 2500, datatang_loss[loss=0.1439, simple_loss=0.2186, pruned_loss=0.03454, over 4924.00 frames.], tot_loss[loss=0.1585, simple_loss=0.239, pruned_loss=0.03903, over 985873.32 frames.], batch size: 81, aishell_tot_loss[loss=0.1595, simple_loss=0.2448, pruned_loss=0.03711, over 983342.55 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2313, pruned_loss=0.041, over 984645.79 frames.], batch size: 81, lr: 5.08e-04
+2022-06-18 21:48:11,450 INFO [train.py:874] (3/4) Epoch 16, batch 2550, datatang_loss[loss=0.1582, simple_loss=0.2333, pruned_loss=0.04158, over 4940.00 frames.], tot_loss[loss=0.1587, simple_loss=0.239, pruned_loss=0.03923, over 985751.62 frames.], batch size: 69, aishell_tot_loss[loss=0.1598, simple_loss=0.2448, pruned_loss=0.03738, over 983509.75 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.2312, pruned_loss=0.04102, over 984834.80 frames.], batch size: 69, lr: 5.08e-04
+2022-06-18 21:48:41,705 INFO [train.py:874] (3/4) Epoch 16, batch 2600, datatang_loss[loss=0.1577, simple_loss=0.2304, pruned_loss=0.04248, over 4954.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2379, pruned_loss=0.03865, over 985746.23 frames.], batch size: 62, aishell_tot_loss[loss=0.1589, simple_loss=0.2442, pruned_loss=0.03684, over 983679.57 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.231, pruned_loss=0.04088, over 985005.45 frames.], batch size: 62, lr: 5.08e-04
+2022-06-18 21:49:12,469 INFO [train.py:874] (3/4) Epoch 16, batch 2650, datatang_loss[loss=0.1446, simple_loss=0.214, pruned_loss=0.03759, over 4882.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2381, pruned_loss=0.03921, over 985892.06 frames.], batch size: 39, aishell_tot_loss[loss=0.159, simple_loss=0.2441, pruned_loss=0.03688, over 984120.94 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2315, pruned_loss=0.04135, over 985048.85 frames.], batch size: 39, lr: 5.08e-04
+2022-06-18 21:49:42,520 INFO [train.py:874] (3/4) Epoch 16, batch 2700, aishell_loss[loss=0.1567, simple_loss=0.2464, pruned_loss=0.0335, over 4959.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2377, pruned_loss=0.03842, over 985813.90 frames.], batch size: 44, aishell_tot_loss[loss=0.1583, simple_loss=0.2437, pruned_loss=0.03647, over 984331.18 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.2312, pruned_loss=0.04104, over 985088.17 frames.], batch size: 44, lr: 5.07e-04
+2022-06-18 21:50:12,515 INFO [train.py:874] (3/4) Epoch 16, batch 2750, datatang_loss[loss=0.1593, simple_loss=0.2391, pruned_loss=0.03976, over 4961.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2371, pruned_loss=0.0386, over 985932.23 frames.], batch size: 45, aishell_tot_loss[loss=0.1581, simple_loss=0.2435, pruned_loss=0.03635, over 984556.50 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.231, pruned_loss=0.0412, over 985239.70 frames.], batch size: 45, lr: 5.07e-04
+2022-06-18 21:50:43,018 INFO [train.py:874] (3/4) Epoch 16, batch 2800, datatang_loss[loss=0.164, simple_loss=0.2327, pruned_loss=0.04761, over 4895.00 frames.], tot_loss[loss=0.156, simple_loss=0.2359, pruned_loss=0.03804, over 985705.25 frames.], batch size: 52, aishell_tot_loss[loss=0.1576, simple_loss=0.2429, pruned_loss=0.03614, over 984600.18 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2305, pruned_loss=0.04071, over 985195.42 frames.], batch size: 52, lr: 5.07e-04
+2022-06-18 21:51:13,720 INFO [train.py:874] (3/4) Epoch 16, batch 2850, aishell_loss[loss=0.1475, simple_loss=0.2353, pruned_loss=0.02988, over 4896.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2359, pruned_loss=0.03855, over 985861.44 frames.], batch size: 34, aishell_tot_loss[loss=0.1577, simple_loss=0.2429, pruned_loss=0.03631, over 984692.05 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2305, pruned_loss=0.04099, over 985456.00 frames.], batch size: 34, lr: 5.07e-04
+2022-06-18 21:51:43,148 INFO [train.py:874] (3/4) Epoch 16, batch 2900, datatang_loss[loss=0.1649, simple_loss=0.2246, pruned_loss=0.05259, over 4904.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2356, pruned_loss=0.03849, over 985939.08 frames.], batch size: 42, aishell_tot_loss[loss=0.157, simple_loss=0.2419, pruned_loss=0.03603, over 985077.70 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.2308, pruned_loss=0.04122, over 985346.06 frames.], batch size: 42, lr: 5.07e-04
+2022-06-18 21:52:12,508 INFO [train.py:874] (3/4) Epoch 16, batch 2950, datatang_loss[loss=0.1672, simple_loss=0.2415, pruned_loss=0.0465, over 4918.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2343, pruned_loss=0.03806, over 985758.51 frames.], batch size: 83, aishell_tot_loss[loss=0.1564, simple_loss=0.2413, pruned_loss=0.03573, over 984862.10 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2301, pruned_loss=0.04083, over 985514.44 frames.], batch size: 83, lr: 5.06e-04
+2022-06-18 21:52:43,991 INFO [train.py:874] (3/4) Epoch 16, batch 3000, aishell_loss[loss=0.157, simple_loss=0.2444, pruned_loss=0.03483, over 4878.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2349, pruned_loss=0.03809, over 985579.37 frames.], batch size: 35, aishell_tot_loss[loss=0.1566, simple_loss=0.2416, pruned_loss=0.03582, over 984849.79 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.23, pruned_loss=0.04079, over 985483.37 frames.], batch size: 35, lr: 5.06e-04
+2022-06-18 21:52:43,992 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 21:53:00,710 INFO [train.py:914] (3/4) Epoch 16, validation: loss=0.1647, simple_loss=0.249, pruned_loss=0.04025, over 1622729.00 frames.
+2022-06-18 21:53:29,693 INFO [train.py:874] (3/4) Epoch 16, batch 3050, datatang_loss[loss=0.1422, simple_loss=0.2224, pruned_loss=0.03099, over 4900.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2355, pruned_loss=0.03777, over 985755.15 frames.], batch size: 64, aishell_tot_loss[loss=0.1564, simple_loss=0.2416, pruned_loss=0.03555, over 985010.74 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.23, pruned_loss=0.04082, over 985623.83 frames.], batch size: 64, lr: 5.06e-04
+2022-06-18 21:53:59,470 INFO [train.py:874] (3/4) Epoch 16, batch 3100, datatang_loss[loss=0.1615, simple_loss=0.2128, pruned_loss=0.05508, over 4916.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2362, pruned_loss=0.03831, over 985672.52 frames.], batch size: 42, aishell_tot_loss[loss=0.1561, simple_loss=0.2414, pruned_loss=0.03542, over 985068.16 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2307, pruned_loss=0.04155, over 985592.09 frames.], batch size: 42, lr: 5.06e-04
+2022-06-18 21:54:29,148 INFO [train.py:874] (3/4) Epoch 16, batch 3150, aishell_loss[loss=0.1643, simple_loss=0.2517, pruned_loss=0.03843, over 4937.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2362, pruned_loss=0.03802, over 985751.83 frames.], batch size: 54, aishell_tot_loss[loss=0.1559, simple_loss=0.2414, pruned_loss=0.03521, over 984920.57 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2307, pruned_loss=0.04143, over 985906.28 frames.], batch size: 54, lr: 5.06e-04
+2022-06-18 21:55:00,848 INFO [train.py:874] (3/4) Epoch 16, batch 3200, aishell_loss[loss=0.1468, simple_loss=0.2268, pruned_loss=0.03343, over 4918.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2369, pruned_loss=0.03824, over 985627.87 frames.], batch size: 52, aishell_tot_loss[loss=0.1561, simple_loss=0.2415, pruned_loss=0.03535, over 985085.35 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2311, pruned_loss=0.04155, over 985684.43 frames.], batch size: 52, lr: 5.05e-04
+2022-06-18 21:55:30,471 INFO [train.py:874] (3/4) Epoch 16, batch 3250, datatang_loss[loss=0.1555, simple_loss=0.227, pruned_loss=0.04207, over 4948.00 frames.], tot_loss[loss=0.157, simple_loss=0.2367, pruned_loss=0.03862, over 985526.06 frames.], batch size: 34, aishell_tot_loss[loss=0.1557, simple_loss=0.2411, pruned_loss=0.03517, over 984690.10 frames.], datatang_tot_loss[loss=0.1578, simple_loss=0.2318, pruned_loss=0.04195, over 986001.63 frames.], batch size: 34, lr: 5.05e-04
+2022-06-18 21:55:59,859 INFO [train.py:874] (3/4) Epoch 16, batch 3300, datatang_loss[loss=0.147, simple_loss=0.2267, pruned_loss=0.03367, over 4945.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2361, pruned_loss=0.03856, over 985326.93 frames.], batch size: 62, aishell_tot_loss[loss=0.1561, simple_loss=0.2412, pruned_loss=0.03548, over 984598.21 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2312, pruned_loss=0.04146, over 985903.21 frames.], batch size: 62, lr: 5.05e-04
+2022-06-18 21:56:30,811 INFO [train.py:874] (3/4) Epoch 16, batch 3350, datatang_loss[loss=0.1737, simple_loss=0.243, pruned_loss=0.05219, over 4872.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2361, pruned_loss=0.03833, over 985364.58 frames.], batch size: 24, aishell_tot_loss[loss=0.1561, simple_loss=0.2409, pruned_loss=0.03568, over 984895.43 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2313, pruned_loss=0.04124, over 985705.17 frames.], batch size: 24, lr: 5.05e-04
+2022-06-18 21:57:00,676 INFO [train.py:874] (3/4) Epoch 16, batch 3400, aishell_loss[loss=0.1572, simple_loss=0.2498, pruned_loss=0.0323, over 4940.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2358, pruned_loss=0.03832, over 985479.31 frames.], batch size: 64, aishell_tot_loss[loss=0.1561, simple_loss=0.2409, pruned_loss=0.03566, over 984946.14 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2311, pruned_loss=0.04115, over 985792.36 frames.], batch size: 64, lr: 5.05e-04
+2022-06-18 21:57:30,377 INFO [train.py:874] (3/4) Epoch 16, batch 3450, aishell_loss[loss=0.1917, simple_loss=0.2707, pruned_loss=0.05637, over 4932.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2365, pruned_loss=0.03849, over 985611.10 frames.], batch size: 68, aishell_tot_loss[loss=0.1568, simple_loss=0.2416, pruned_loss=0.036, over 985152.62 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2308, pruned_loss=0.0411, over 985771.44 frames.], batch size: 68, lr: 5.05e-04
+2022-06-18 21:58:01,271 INFO [train.py:874] (3/4) Epoch 16, batch 3500, aishell_loss[loss=0.17, simple_loss=0.263, pruned_loss=0.03846, over 4925.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2371, pruned_loss=0.03868, over 985421.96 frames.], batch size: 46, aishell_tot_loss[loss=0.1571, simple_loss=0.2423, pruned_loss=0.03591, over 985069.71 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2308, pruned_loss=0.04132, over 985671.97 frames.], batch size: 46, lr: 5.04e-04
+2022-06-18 21:58:30,746 INFO [train.py:874] (3/4) Epoch 16, batch 3550, aishell_loss[loss=0.1658, simple_loss=0.2492, pruned_loss=0.0412, over 4914.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2372, pruned_loss=0.03884, over 985266.96 frames.], batch size: 41, aishell_tot_loss[loss=0.1574, simple_loss=0.2428, pruned_loss=0.03601, over 984973.28 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2305, pruned_loss=0.04145, over 985602.28 frames.], batch size: 41, lr: 5.04e-04
+2022-06-18 21:59:01,918 INFO [train.py:874] (3/4) Epoch 16, batch 3600, datatang_loss[loss=0.1552, simple_loss=0.2302, pruned_loss=0.04006, over 4955.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2369, pruned_loss=0.0387, over 985029.07 frames.], batch size: 86, aishell_tot_loss[loss=0.1578, simple_loss=0.2429, pruned_loss=0.03634, over 984684.27 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2301, pruned_loss=0.04105, over 985638.17 frames.], batch size: 86, lr: 5.04e-04
+2022-06-18 21:59:31,587 INFO [train.py:874] (3/4) Epoch 16, batch 3650, aishell_loss[loss=0.1531, simple_loss=0.2369, pruned_loss=0.03469, over 4971.00 frames.], tot_loss[loss=0.1569, simple_loss=0.237, pruned_loss=0.03842, over 985518.94 frames.], batch size: 44, aishell_tot_loss[loss=0.1575, simple_loss=0.2427, pruned_loss=0.03613, over 985092.40 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2303, pruned_loss=0.041, over 985726.20 frames.], batch size: 44, lr: 5.04e-04
+2022-06-18 22:00:02,495 INFO [train.py:874] (3/4) Epoch 16, batch 3700, datatang_loss[loss=0.1451, simple_loss=0.2136, pruned_loss=0.0383, over 4932.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2373, pruned_loss=0.03886, over 985342.64 frames.], batch size: 42, aishell_tot_loss[loss=0.1575, simple_loss=0.243, pruned_loss=0.03605, over 985148.79 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2307, pruned_loss=0.04142, over 985510.27 frames.], batch size: 42, lr: 5.04e-04
+2022-06-18 22:00:32,977 INFO [train.py:874] (3/4) Epoch 16, batch 3750, aishell_loss[loss=0.1618, simple_loss=0.2478, pruned_loss=0.03796, over 4942.00 frames.], tot_loss[loss=0.157, simple_loss=0.2368, pruned_loss=0.03863, over 985649.17 frames.], batch size: 54, aishell_tot_loss[loss=0.1572, simple_loss=0.2426, pruned_loss=0.03587, over 985313.28 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2306, pruned_loss=0.0414, over 985675.15 frames.], batch size: 54, lr: 5.03e-04
+2022-06-18 22:01:02,991 INFO [train.py:874] (3/4) Epoch 16, batch 3800, datatang_loss[loss=0.1445, simple_loss=0.2189, pruned_loss=0.03507, over 4912.00 frames.], tot_loss[loss=0.157, simple_loss=0.2364, pruned_loss=0.0388, over 985563.83 frames.], batch size: 64, aishell_tot_loss[loss=0.1564, simple_loss=0.2418, pruned_loss=0.03544, over 985088.13 frames.], datatang_tot_loss[loss=0.1575, simple_loss=0.2311, pruned_loss=0.04195, over 985843.94 frames.], batch size: 64, lr: 5.03e-04
+2022-06-18 22:01:31,726 INFO [train.py:874] (3/4) Epoch 16, batch 3850, datatang_loss[loss=0.1413, simple_loss=0.2224, pruned_loss=0.03006, over 4935.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2362, pruned_loss=0.03839, over 985312.46 frames.], batch size: 69, aishell_tot_loss[loss=0.1564, simple_loss=0.2418, pruned_loss=0.03545, over 984776.00 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.2309, pruned_loss=0.04156, over 985911.23 frames.], batch size: 69, lr: 5.03e-04
+2022-06-18 22:02:00,007 INFO [train.py:874] (3/4) Epoch 16, batch 3900, datatang_loss[loss=0.1265, simple_loss=0.2031, pruned_loss=0.02495, over 4920.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2362, pruned_loss=0.03856, over 985720.35 frames.], batch size: 75, aishell_tot_loss[loss=0.1567, simple_loss=0.2419, pruned_loss=0.03573, over 985002.21 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2308, pruned_loss=0.04143, over 986114.68 frames.], batch size: 75, lr: 5.03e-04
+2022-06-18 22:02:29,061 INFO [train.py:874] (3/4) Epoch 16, batch 3950, aishell_loss[loss=0.1525, simple_loss=0.2429, pruned_loss=0.03106, over 4859.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2359, pruned_loss=0.03825, over 985710.69 frames.], batch size: 35, aishell_tot_loss[loss=0.1567, simple_loss=0.2419, pruned_loss=0.03575, over 985148.89 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2305, pruned_loss=0.04111, over 986014.40 frames.], batch size: 35, lr: 5.03e-04
+2022-06-18 22:02:58,129 INFO [train.py:874] (3/4) Epoch 16, batch 4000, aishell_loss[loss=0.1785, simple_loss=0.2587, pruned_loss=0.04921, over 4931.00 frames.], tot_loss[loss=0.1559, simple_loss=0.236, pruned_loss=0.03793, over 985582.42 frames.], batch size: 58, aishell_tot_loss[loss=0.1569, simple_loss=0.2423, pruned_loss=0.03577, over 985077.83 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.23, pruned_loss=0.04073, over 985978.67 frames.], batch size: 58, lr: 5.02e-04
+2022-06-18 22:02:58,130 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 22:03:14,116 INFO [train.py:914] (3/4) Epoch 16, validation: loss=0.164, simple_loss=0.2482, pruned_loss=0.03992, over 1622729.00 frames.
+2022-06-18 22:03:42,710 INFO [train.py:874] (3/4) Epoch 16, batch 4050, datatang_loss[loss=0.1539, simple_loss=0.2316, pruned_loss=0.03806, over 4906.00 frames.], tot_loss[loss=0.1563, simple_loss=0.236, pruned_loss=0.03832, over 985558.18 frames.], batch size: 24, aishell_tot_loss[loss=0.1575, simple_loss=0.2427, pruned_loss=0.03615, over 985124.51 frames.], datatang_tot_loss[loss=0.1555, simple_loss=0.2299, pruned_loss=0.04057, over 985914.34 frames.], batch size: 24, lr: 5.02e-04
+2022-06-18 22:04:12,040 INFO [train.py:874] (3/4) Epoch 16, batch 4100, aishell_loss[loss=0.17, simple_loss=0.2546, pruned_loss=0.0427, over 4892.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2351, pruned_loss=0.03798, over 985707.60 frames.], batch size: 34, aishell_tot_loss[loss=0.157, simple_loss=0.2421, pruned_loss=0.03596, over 985099.58 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2294, pruned_loss=0.04043, over 986109.19 frames.], batch size: 34, lr: 5.02e-04
+2022-06-18 22:05:35,116 INFO [train.py:874] (3/4) Epoch 17, batch 50, datatang_loss[loss=0.1457, simple_loss=0.2197, pruned_loss=0.03587, over 4918.00 frames.], tot_loss[loss=0.1547, simple_loss=0.234, pruned_loss=0.0377, over 218624.14 frames.], batch size: 81, aishell_tot_loss[loss=0.1595, simple_loss=0.2433, pruned_loss=0.0378, over 129112.17 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.223, pruned_loss=0.03789, over 102999.63 frames.], batch size: 81, lr: 4.88e-04
+2022-06-18 22:06:05,306 INFO [train.py:874] (3/4) Epoch 17, batch 100, aishell_loss[loss=0.1383, simple_loss=0.2138, pruned_loss=0.0314, over 4832.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2308, pruned_loss=0.03618, over 388302.28 frames.], batch size: 24, aishell_tot_loss[loss=0.1583, simple_loss=0.2421, pruned_loss=0.03719, over 222069.83 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.22, pruned_loss=0.03549, over 214649.55 frames.], batch size: 24, lr: 4.88e-04
+2022-06-18 22:06:34,666 INFO [train.py:874] (3/4) Epoch 17, batch 150, datatang_loss[loss=0.1668, simple_loss=0.2488, pruned_loss=0.04245, over 4950.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2327, pruned_loss=0.03739, over 520974.55 frames.], batch size: 50, aishell_tot_loss[loss=0.1608, simple_loss=0.2453, pruned_loss=0.03817, over 302060.86 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2209, pruned_loss=0.03655, over 315621.43 frames.], batch size: 50, lr: 4.87e-04
+2022-06-18 22:07:06,334 INFO [train.py:874] (3/4) Epoch 17, batch 200, datatang_loss[loss=0.1308, simple_loss=0.2102, pruned_loss=0.02568, over 4960.00 frames.], tot_loss[loss=0.1515, simple_loss=0.231, pruned_loss=0.03603, over 624364.54 frames.], batch size: 67, aishell_tot_loss[loss=0.1584, simple_loss=0.2433, pruned_loss=0.03678, over 379716.15 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.22, pruned_loss=0.03566, over 397717.86 frames.], batch size: 67, lr: 4.87e-04
+2022-06-18 22:07:35,648 INFO [train.py:874] (3/4) Epoch 17, batch 250, datatang_loss[loss=0.124, simple_loss=0.1915, pruned_loss=0.02826, over 4849.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2302, pruned_loss=0.03603, over 703756.73 frames.], batch size: 25, aishell_tot_loss[loss=0.1577, simple_loss=0.2423, pruned_loss=0.03649, over 447821.05 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2197, pruned_loss=0.03584, over 469394.26 frames.], batch size: 25, lr: 4.87e-04
+2022-06-18 22:08:05,805 INFO [train.py:874] (3/4) Epoch 17, batch 300, aishell_loss[loss=0.1218, simple_loss=0.2021, pruned_loss=0.02074, over 4979.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2314, pruned_loss=0.0365, over 766611.52 frames.], batch size: 27, aishell_tot_loss[loss=0.1564, simple_loss=0.241, pruned_loss=0.03583, over 509052.05 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2226, pruned_loss=0.03708, over 532598.90 frames.], batch size: 27, lr: 4.87e-04
+2022-06-18 22:08:37,004 INFO [train.py:874] (3/4) Epoch 17, batch 350, aishell_loss[loss=0.1327, simple_loss=0.2236, pruned_loss=0.02092, over 4978.00 frames.], tot_loss[loss=0.154, simple_loss=0.2334, pruned_loss=0.03731, over 815380.97 frames.], batch size: 51, aishell_tot_loss[loss=0.1567, simple_loss=0.2417, pruned_loss=0.03583, over 563168.90 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.225, pruned_loss=0.03833, over 588061.99 frames.], batch size: 51, lr: 4.87e-04
+2022-06-18 22:09:07,092 INFO [train.py:874] (3/4) Epoch 17, batch 400, datatang_loss[loss=0.1359, simple_loss=0.2183, pruned_loss=0.02675, over 4937.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2342, pruned_loss=0.0376, over 853036.25 frames.], batch size: 88, aishell_tot_loss[loss=0.1577, simple_loss=0.2429, pruned_loss=0.03629, over 618196.39 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2246, pruned_loss=0.03841, over 629785.58 frames.], batch size: 88, lr: 4.87e-04
+2022-06-18 22:09:36,464 INFO [train.py:874] (3/4) Epoch 17, batch 450, datatang_loss[loss=0.1651, simple_loss=0.2512, pruned_loss=0.0395, over 4926.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2352, pruned_loss=0.03778, over 882287.88 frames.], batch size: 98, aishell_tot_loss[loss=0.1578, simple_loss=0.2431, pruned_loss=0.03624, over 659624.68 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.226, pruned_loss=0.03878, over 673327.71 frames.], batch size: 98, lr: 4.86e-04
+2022-06-18 22:10:07,646 INFO [train.py:874] (3/4) Epoch 17, batch 500, datatang_loss[loss=0.1261, simple_loss=0.1987, pruned_loss=0.02671, over 4942.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2346, pruned_loss=0.03779, over 904958.95 frames.], batch size: 50, aishell_tot_loss[loss=0.1575, simple_loss=0.2427, pruned_loss=0.03617, over 702114.90 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2256, pruned_loss=0.03901, over 705883.71 frames.], batch size: 50, lr: 4.86e-04
+2022-06-18 22:10:36,802 INFO [train.py:874] (3/4) Epoch 17, batch 550, aishell_loss[loss=0.1381, simple_loss=0.2261, pruned_loss=0.0251, over 4941.00 frames.], tot_loss[loss=0.1559, simple_loss=0.235, pruned_loss=0.03837, over 922896.65 frames.], batch size: 54, aishell_tot_loss[loss=0.1578, simple_loss=0.2429, pruned_loss=0.03631, over 729151.27 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2265, pruned_loss=0.03961, over 745001.99 frames.], batch size: 54, lr: 4.86e-04
+2022-06-18 22:11:06,767 INFO [train.py:874] (3/4) Epoch 17, batch 600, datatang_loss[loss=0.1418, simple_loss=0.2283, pruned_loss=0.02763, over 4929.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2348, pruned_loss=0.03875, over 936502.27 frames.], batch size: 83, aishell_tot_loss[loss=0.1581, simple_loss=0.243, pruned_loss=0.03655, over 751102.29 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.227, pruned_loss=0.03983, over 780526.87 frames.], batch size: 83, lr: 4.86e-04
+2022-06-18 22:11:38,262 INFO [train.py:874] (3/4) Epoch 17, batch 650, aishell_loss[loss=0.1504, simple_loss=0.2384, pruned_loss=0.03121, over 4958.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2352, pruned_loss=0.03852, over 947604.58 frames.], batch size: 44, aishell_tot_loss[loss=0.1585, simple_loss=0.2434, pruned_loss=0.03681, over 780885.23 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2269, pruned_loss=0.03948, over 802999.83 frames.], batch size: 44, lr: 4.86e-04
+2022-06-18 22:12:08,254 INFO [train.py:874] (3/4) Epoch 17, batch 700, datatang_loss[loss=0.1716, simple_loss=0.2381, pruned_loss=0.05259, over 4912.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2351, pruned_loss=0.03835, over 955819.92 frames.], batch size: 64, aishell_tot_loss[loss=0.1578, simple_loss=0.2425, pruned_loss=0.03659, over 805759.86 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2276, pruned_loss=0.03961, over 823632.89 frames.], batch size: 64, lr: 4.86e-04
+2022-06-18 22:12:37,724 INFO [train.py:874] (3/4) Epoch 17, batch 750, aishell_loss[loss=0.1431, simple_loss=0.2238, pruned_loss=0.03116, over 4923.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2344, pruned_loss=0.03791, over 962189.82 frames.], batch size: 46, aishell_tot_loss[loss=0.157, simple_loss=0.2415, pruned_loss=0.03622, over 827419.56 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2276, pruned_loss=0.03951, over 842068.95 frames.], batch size: 46, lr: 4.85e-04
+2022-06-18 22:13:08,741 INFO [train.py:874] (3/4) Epoch 17, batch 800, aishell_loss[loss=0.1773, simple_loss=0.2547, pruned_loss=0.04999, over 4904.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2345, pruned_loss=0.0377, over 967227.91 frames.], batch size: 34, aishell_tot_loss[loss=0.1569, simple_loss=0.2414, pruned_loss=0.0362, over 846606.74 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2277, pruned_loss=0.03932, over 858340.66 frames.], batch size: 34, lr: 4.85e-04
+2022-06-18 22:13:38,527 INFO [train.py:874] (3/4) Epoch 17, batch 850, aishell_loss[loss=0.1538, simple_loss=0.2384, pruned_loss=0.0346, over 4881.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2341, pruned_loss=0.03728, over 971271.99 frames.], batch size: 42, aishell_tot_loss[loss=0.1569, simple_loss=0.2417, pruned_loss=0.03607, over 863653.58 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2269, pruned_loss=0.03896, over 872685.47 frames.], batch size: 42, lr: 4.85e-04
+2022-06-18 22:14:08,899 INFO [train.py:874] (3/4) Epoch 17, batch 900, datatang_loss[loss=0.1535, simple_loss=0.2319, pruned_loss=0.03751, over 4931.00 frames.], tot_loss[loss=0.1541, simple_loss=0.234, pruned_loss=0.0371, over 974513.94 frames.], batch size: 94, aishell_tot_loss[loss=0.1564, simple_loss=0.2412, pruned_loss=0.03585, over 878421.56 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2272, pruned_loss=0.03894, over 885699.86 frames.], batch size: 94, lr: 4.85e-04
+2022-06-18 22:14:39,887 INFO [train.py:874] (3/4) Epoch 17, batch 950, datatang_loss[loss=0.1254, simple_loss=0.1941, pruned_loss=0.02831, over 4827.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2352, pruned_loss=0.03762, over 976752.70 frames.], batch size: 30, aishell_tot_loss[loss=0.1567, simple_loss=0.2415, pruned_loss=0.03595, over 890370.29 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2282, pruned_loss=0.03935, over 897869.53 frames.], batch size: 30, lr: 4.85e-04
+2022-06-18 22:15:10,116 INFO [train.py:874] (3/4) Epoch 17, batch 1000, datatang_loss[loss=0.1445, simple_loss=0.231, pruned_loss=0.02902, over 4941.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2348, pruned_loss=0.03742, over 978345.27 frames.], batch size: 88, aishell_tot_loss[loss=0.1565, simple_loss=0.2411, pruned_loss=0.03596, over 900860.63 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2284, pruned_loss=0.03909, over 908516.78 frames.], batch size: 88, lr: 4.84e-04
+2022-06-18 22:15:10,117 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 22:15:27,678 INFO [train.py:914] (3/4) Epoch 17, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-18 22:15:57,382 INFO [train.py:874] (3/4) Epoch 17, batch 1050, aishell_loss[loss=0.1703, simple_loss=0.2613, pruned_loss=0.03969, over 4930.00 frames.], tot_loss[loss=0.1545, simple_loss=0.235, pruned_loss=0.037, over 979349.59 frames.], batch size: 68, aishell_tot_loss[loss=0.1565, simple_loss=0.2412, pruned_loss=0.03584, over 913267.55 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2279, pruned_loss=0.03886, over 914692.08 frames.], batch size: 68, lr: 4.84e-04
+2022-06-18 22:16:27,453 INFO [train.py:874] (3/4) Epoch 17, batch 1100, aishell_loss[loss=0.162, simple_loss=0.2461, pruned_loss=0.03895, over 4954.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2347, pruned_loss=0.03696, over 980816.03 frames.], batch size: 56, aishell_tot_loss[loss=0.1564, simple_loss=0.2411, pruned_loss=0.03578, over 923118.04 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2275, pruned_loss=0.03882, over 921794.95 frames.], batch size: 56, lr: 4.84e-04
+2022-06-18 22:16:58,186 INFO [train.py:874] (3/4) Epoch 17, batch 1150, datatang_loss[loss=0.1432, simple_loss=0.2259, pruned_loss=0.03022, over 4895.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2336, pruned_loss=0.03701, over 981848.31 frames.], batch size: 47, aishell_tot_loss[loss=0.1556, simple_loss=0.2405, pruned_loss=0.03538, over 928249.29 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2275, pruned_loss=0.03907, over 931464.40 frames.], batch size: 47, lr: 4.84e-04
+2022-06-18 22:17:27,504 INFO [train.py:874] (3/4) Epoch 17, batch 1200, aishell_loss[loss=0.1311, simple_loss=0.2102, pruned_loss=0.02598, over 4973.00 frames.], tot_loss[loss=0.154, simple_loss=0.2337, pruned_loss=0.03709, over 982610.76 frames.], batch size: 25, aishell_tot_loss[loss=0.1555, simple_loss=0.2402, pruned_loss=0.03538, over 936181.28 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2276, pruned_loss=0.0392, over 936623.77 frames.], batch size: 25, lr: 4.84e-04
+2022-06-18 22:17:58,172 INFO [train.py:874] (3/4) Epoch 17, batch 1250, datatang_loss[loss=0.1388, simple_loss=0.2195, pruned_loss=0.02906, over 4855.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2333, pruned_loss=0.03721, over 982871.69 frames.], batch size: 25, aishell_tot_loss[loss=0.1554, simple_loss=0.2398, pruned_loss=0.03552, over 941064.04 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2275, pruned_loss=0.03908, over 942891.28 frames.], batch size: 25, lr: 4.84e-04
+2022-06-18 22:18:27,933 INFO [train.py:874] (3/4) Epoch 17, batch 1300, aishell_loss[loss=0.1701, simple_loss=0.2494, pruned_loss=0.04544, over 4921.00 frames.], tot_loss[loss=0.1544, simple_loss=0.234, pruned_loss=0.03744, over 982967.83 frames.], batch size: 68, aishell_tot_loss[loss=0.1564, simple_loss=0.2406, pruned_loss=0.03607, over 945954.55 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2274, pruned_loss=0.03877, over 947748.13 frames.], batch size: 68, lr: 4.83e-04
+2022-06-18 22:18:58,677 INFO [train.py:874] (3/4) Epoch 17, batch 1350, aishell_loss[loss=0.1344, simple_loss=0.2213, pruned_loss=0.02373, over 4989.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2339, pruned_loss=0.03687, over 983454.21 frames.], batch size: 27, aishell_tot_loss[loss=0.1558, simple_loss=0.2404, pruned_loss=0.03564, over 950989.94 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2273, pruned_loss=0.03861, over 951733.89 frames.], batch size: 27, lr: 4.83e-04
+2022-06-18 22:19:29,819 INFO [train.py:874] (3/4) Epoch 17, batch 1400, aishell_loss[loss=0.1319, simple_loss=0.2107, pruned_loss=0.02661, over 4977.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2333, pruned_loss=0.0365, over 983812.84 frames.], batch size: 25, aishell_tot_loss[loss=0.1553, simple_loss=0.2399, pruned_loss=0.0353, over 954817.69 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2272, pruned_loss=0.03846, over 955836.30 frames.], batch size: 25, lr: 4.83e-04
+2022-06-18 22:19:58,866 INFO [train.py:874] (3/4) Epoch 17, batch 1450, aishell_loss[loss=0.1648, simple_loss=0.2501, pruned_loss=0.0397, over 4941.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2337, pruned_loss=0.03694, over 984151.23 frames.], batch size: 54, aishell_tot_loss[loss=0.155, simple_loss=0.2397, pruned_loss=0.03512, over 958263.81 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2278, pruned_loss=0.039, over 959457.25 frames.], batch size: 54, lr: 4.83e-04
+2022-06-18 22:20:29,730 INFO [train.py:874] (3/4) Epoch 17, batch 1500, datatang_loss[loss=0.178, simple_loss=0.249, pruned_loss=0.05346, over 4956.00 frames.], tot_loss[loss=0.1547, simple_loss=0.234, pruned_loss=0.03765, over 984231.05 frames.], batch size: 55, aishell_tot_loss[loss=0.1557, simple_loss=0.2402, pruned_loss=0.03554, over 960766.48 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2278, pruned_loss=0.03925, over 962955.54 frames.], batch size: 55, lr: 4.83e-04
+2022-06-18 22:20:59,710 INFO [train.py:874] (3/4) Epoch 17, batch 1550, datatang_loss[loss=0.1524, simple_loss=0.222, pruned_loss=0.04143, over 4958.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2345, pruned_loss=0.03766, over 984479.50 frames.], batch size: 55, aishell_tot_loss[loss=0.1555, simple_loss=0.2403, pruned_loss=0.03535, over 963743.03 frames.], datatang_tot_loss[loss=0.1536, simple_loss=0.2281, pruned_loss=0.03955, over 965514.05 frames.], batch size: 55, lr: 4.82e-04
+2022-06-18 22:21:29,260 INFO [train.py:874] (3/4) Epoch 17, batch 1600, aishell_loss[loss=0.1749, simple_loss=0.2639, pruned_loss=0.04295, over 4919.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2342, pruned_loss=0.03774, over 984843.29 frames.], batch size: 41, aishell_tot_loss[loss=0.1556, simple_loss=0.2405, pruned_loss=0.03535, over 965784.13 frames.], datatang_tot_loss[loss=0.1536, simple_loss=0.228, pruned_loss=0.03958, over 968483.31 frames.], batch size: 41, lr: 4.82e-04
+2022-06-18 22:21:59,642 INFO [train.py:874] (3/4) Epoch 17, batch 1650, aishell_loss[loss=0.1659, simple_loss=0.2576, pruned_loss=0.03709, over 4945.00 frames.], tot_loss[loss=0.1541, simple_loss=0.233, pruned_loss=0.03759, over 984487.42 frames.], batch size: 64, aishell_tot_loss[loss=0.155, simple_loss=0.2395, pruned_loss=0.03529, over 967767.63 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2277, pruned_loss=0.03958, over 970321.80 frames.], batch size: 64, lr: 4.82e-04
+2022-06-18 22:22:30,221 INFO [train.py:874] (3/4) Epoch 17, batch 1700, datatang_loss[loss=0.1646, simple_loss=0.2413, pruned_loss=0.04397, over 4958.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2344, pruned_loss=0.0376, over 984847.83 frames.], batch size: 67, aishell_tot_loss[loss=0.1553, simple_loss=0.2402, pruned_loss=0.03523, over 970093.48 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2282, pruned_loss=0.03969, over 972032.48 frames.], batch size: 67, lr: 4.82e-04
+2022-06-18 22:22:59,535 INFO [train.py:874] (3/4) Epoch 17, batch 1750, datatang_loss[loss=0.1749, simple_loss=0.2521, pruned_loss=0.04887, over 4933.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2336, pruned_loss=0.03761, over 985388.25 frames.], batch size: 94, aishell_tot_loss[loss=0.1551, simple_loss=0.2399, pruned_loss=0.03514, over 971443.90 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.228, pruned_loss=0.03965, over 974394.39 frames.], batch size: 94, lr: 4.82e-04
+2022-06-18 22:23:31,103 INFO [train.py:874] (3/4) Epoch 17, batch 1800, datatang_loss[loss=0.1475, simple_loss=0.2337, pruned_loss=0.03061, over 4838.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2346, pruned_loss=0.03846, over 985351.19 frames.], batch size: 30, aishell_tot_loss[loss=0.1561, simple_loss=0.2408, pruned_loss=0.03569, over 972742.64 frames.], datatang_tot_loss[loss=0.1542, simple_loss=0.2285, pruned_loss=0.03997, over 975945.34 frames.], batch size: 30, lr: 4.82e-04
+2022-06-18 22:24:01,683 INFO [train.py:874] (3/4) Epoch 17, batch 1850, aishell_loss[loss=0.153, simple_loss=0.2366, pruned_loss=0.03471, over 4983.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2355, pruned_loss=0.03847, over 985241.71 frames.], batch size: 38, aishell_tot_loss[loss=0.1568, simple_loss=0.2417, pruned_loss=0.036, over 974077.68 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2286, pruned_loss=0.03982, over 977085.22 frames.], batch size: 38, lr: 4.81e-04
+2022-06-18 22:24:35,565 INFO [train.py:874] (3/4) Epoch 17, batch 1900, aishell_loss[loss=0.1546, simple_loss=0.2467, pruned_loss=0.03128, over 4943.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2357, pruned_loss=0.03862, over 985399.99 frames.], batch size: 56, aishell_tot_loss[loss=0.1572, simple_loss=0.2421, pruned_loss=0.03617, over 975335.78 frames.], datatang_tot_loss[loss=0.1542, simple_loss=0.2287, pruned_loss=0.03988, over 978208.62 frames.], batch size: 56, lr: 4.81e-04
+2022-06-18 22:25:06,063 INFO [train.py:874] (3/4) Epoch 17, batch 1950, datatang_loss[loss=0.2087, simple_loss=0.2712, pruned_loss=0.07314, over 4924.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2353, pruned_loss=0.03824, over 985228.97 frames.], batch size: 98, aishell_tot_loss[loss=0.157, simple_loss=0.2421, pruned_loss=0.03599, over 976332.45 frames.], datatang_tot_loss[loss=0.154, simple_loss=0.2285, pruned_loss=0.03974, over 979023.51 frames.], batch size: 98, lr: 4.81e-04
+2022-06-18 22:25:36,696 INFO [train.py:874] (3/4) Epoch 17, batch 2000, datatang_loss[loss=0.142, simple_loss=0.2284, pruned_loss=0.02777, over 4918.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2344, pruned_loss=0.03789, over 985657.44 frames.], batch size: 83, aishell_tot_loss[loss=0.157, simple_loss=0.2421, pruned_loss=0.03597, over 977493.48 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2276, pruned_loss=0.03946, over 980073.90 frames.], batch size: 83, lr: 4.81e-04
+2022-06-18 22:25:36,697 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 22:25:53,691 INFO [train.py:914] (3/4) Epoch 17, validation: loss=0.1656, simple_loss=0.2494, pruned_loss=0.04087, over 1622729.00 frames.
+2022-06-18 22:26:23,342 INFO [train.py:874] (3/4) Epoch 17, batch 2050, aishell_loss[loss=0.1673, simple_loss=0.2521, pruned_loss=0.04122, over 4919.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2348, pruned_loss=0.03791, over 985265.75 frames.], batch size: 41, aishell_tot_loss[loss=0.1568, simple_loss=0.2419, pruned_loss=0.03586, over 978169.57 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.228, pruned_loss=0.03968, over 980626.40 frames.], batch size: 41, lr: 4.81e-04
+2022-06-18 22:26:54,519 INFO [train.py:874] (3/4) Epoch 17, batch 2100, aishell_loss[loss=0.1735, simple_loss=0.2603, pruned_loss=0.04341, over 4907.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2353, pruned_loss=0.03802, over 985358.04 frames.], batch size: 41, aishell_tot_loss[loss=0.1568, simple_loss=0.242, pruned_loss=0.03579, over 979025.37 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2284, pruned_loss=0.03991, over 981249.68 frames.], batch size: 41, lr: 4.81e-04
+2022-06-18 22:27:23,969 INFO [train.py:874] (3/4) Epoch 17, batch 2150, datatang_loss[loss=0.1722, simple_loss=0.2473, pruned_loss=0.04851, over 4842.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2352, pruned_loss=0.03752, over 985345.84 frames.], batch size: 25, aishell_tot_loss[loss=0.1564, simple_loss=0.2417, pruned_loss=0.03552, over 979724.95 frames.], datatang_tot_loss[loss=0.154, simple_loss=0.2285, pruned_loss=0.0397, over 981786.80 frames.], batch size: 25, lr: 4.80e-04
+2022-06-18 22:27:55,189 INFO [train.py:874] (3/4) Epoch 17, batch 2200, datatang_loss[loss=0.161, simple_loss=0.2512, pruned_loss=0.03543, over 4949.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2354, pruned_loss=0.03751, over 985364.51 frames.], batch size: 37, aishell_tot_loss[loss=0.1563, simple_loss=0.2416, pruned_loss=0.03546, over 980186.21 frames.], datatang_tot_loss[loss=0.1542, simple_loss=0.2288, pruned_loss=0.03977, over 982465.41 frames.], batch size: 37, lr: 4.80e-04
+2022-06-18 22:28:25,184 INFO [train.py:874] (3/4) Epoch 17, batch 2250, datatang_loss[loss=0.1553, simple_loss=0.2362, pruned_loss=0.0372, over 4928.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2355, pruned_loss=0.03738, over 985596.23 frames.], batch size: 94, aishell_tot_loss[loss=0.1566, simple_loss=0.2418, pruned_loss=0.03569, over 981007.84 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.2284, pruned_loss=0.03945, over 982876.91 frames.], batch size: 94, lr: 4.80e-04
+2022-06-18 22:28:55,892 INFO [train.py:874] (3/4) Epoch 17, batch 2300, aishell_loss[loss=0.1529, simple_loss=0.2439, pruned_loss=0.03089, over 4945.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2352, pruned_loss=0.03714, over 985292.28 frames.], batch size: 54, aishell_tot_loss[loss=0.1561, simple_loss=0.2415, pruned_loss=0.03532, over 981134.67 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2284, pruned_loss=0.03959, over 983324.55 frames.], batch size: 54, lr: 4.80e-04
+2022-06-18 22:29:26,659 INFO [train.py:874] (3/4) Epoch 17, batch 2350, aishell_loss[loss=0.1356, simple_loss=0.214, pruned_loss=0.02866, over 4965.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2353, pruned_loss=0.03691, over 985468.60 frames.], batch size: 27, aishell_tot_loss[loss=0.1561, simple_loss=0.2414, pruned_loss=0.03539, over 981685.86 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2284, pruned_loss=0.0393, over 983706.30 frames.], batch size: 27, lr: 4.80e-04
+2022-06-18 22:29:55,941 INFO [train.py:874] (3/4) Epoch 17, batch 2400, datatang_loss[loss=0.1497, simple_loss=0.223, pruned_loss=0.03825, over 4930.00 frames.], tot_loss[loss=0.1534, simple_loss=0.234, pruned_loss=0.0364, over 985729.37 frames.], batch size: 57, aishell_tot_loss[loss=0.1555, simple_loss=0.2406, pruned_loss=0.03514, over 982282.69 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2278, pruned_loss=0.03889, over 984036.66 frames.], batch size: 57, lr: 4.79e-04
+2022-06-18 22:30:25,220 INFO [train.py:874] (3/4) Epoch 17, batch 2450, aishell_loss[loss=0.1767, simple_loss=0.2598, pruned_loss=0.04675, over 4864.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2351, pruned_loss=0.03701, over 985689.73 frames.], batch size: 37, aishell_tot_loss[loss=0.1558, simple_loss=0.2408, pruned_loss=0.03534, over 982527.34 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2286, pruned_loss=0.03924, over 984365.09 frames.], batch size: 37, lr: 4.79e-04
+2022-06-18 22:30:56,092 INFO [train.py:874] (3/4) Epoch 17, batch 2500, aishell_loss[loss=0.1599, simple_loss=0.2458, pruned_loss=0.03702, over 4869.00 frames.], tot_loss[loss=0.155, simple_loss=0.2353, pruned_loss=0.03728, over 985403.40 frames.], batch size: 35, aishell_tot_loss[loss=0.1562, simple_loss=0.2413, pruned_loss=0.03551, over 982564.91 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2284, pruned_loss=0.03931, over 984559.96 frames.], batch size: 35, lr: 4.79e-04
+2022-06-18 22:31:26,749 INFO [train.py:874] (3/4) Epoch 17, batch 2550, aishell_loss[loss=0.1679, simple_loss=0.2518, pruned_loss=0.042, over 4935.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2344, pruned_loss=0.03699, over 985677.97 frames.], batch size: 49, aishell_tot_loss[loss=0.1554, simple_loss=0.2405, pruned_loss=0.0351, over 983135.55 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2282, pruned_loss=0.03942, over 984724.99 frames.], batch size: 49, lr: 4.79e-04
+2022-06-18 22:31:56,391 INFO [train.py:874] (3/4) Epoch 17, batch 2600, aishell_loss[loss=0.1541, simple_loss=0.2475, pruned_loss=0.03033, over 4913.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2344, pruned_loss=0.03667, over 985180.59 frames.], batch size: 52, aishell_tot_loss[loss=0.1554, simple_loss=0.2407, pruned_loss=0.03506, over 983068.07 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2281, pruned_loss=0.03907, over 984689.62 frames.], batch size: 52, lr: 4.79e-04
+2022-06-18 22:32:27,332 INFO [train.py:874] (3/4) Epoch 17, batch 2650, aishell_loss[loss=0.1517, simple_loss=0.2378, pruned_loss=0.0328, over 4913.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2343, pruned_loss=0.03664, over 985262.09 frames.], batch size: 46, aishell_tot_loss[loss=0.1556, simple_loss=0.241, pruned_loss=0.0351, over 983328.99 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2278, pruned_loss=0.03889, over 984809.61 frames.], batch size: 46, lr: 4.79e-04
+2022-06-18 22:32:58,310 INFO [train.py:874] (3/4) Epoch 17, batch 2700, datatang_loss[loss=0.1607, simple_loss=0.2367, pruned_loss=0.04233, over 4989.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2347, pruned_loss=0.0369, over 985399.54 frames.], batch size: 25, aishell_tot_loss[loss=0.1559, simple_loss=0.2413, pruned_loss=0.0352, over 983497.14 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2281, pruned_loss=0.0389, over 985038.50 frames.], batch size: 25, lr: 4.78e-04
+2022-06-18 22:33:27,940 INFO [train.py:874] (3/4) Epoch 17, batch 2750, datatang_loss[loss=0.1486, simple_loss=0.2225, pruned_loss=0.03734, over 4930.00 frames.], tot_loss[loss=0.154, simple_loss=0.2344, pruned_loss=0.03679, over 985611.94 frames.], batch size: 50, aishell_tot_loss[loss=0.1559, simple_loss=0.2415, pruned_loss=0.03512, over 983845.06 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2276, pruned_loss=0.03882, over 985172.55 frames.], batch size: 50, lr: 4.78e-04
+2022-06-18 22:33:57,733 INFO [train.py:874] (3/4) Epoch 17, batch 2800, aishell_loss[loss=0.1629, simple_loss=0.2438, pruned_loss=0.04104, over 4918.00 frames.], tot_loss[loss=0.154, simple_loss=0.2344, pruned_loss=0.0368, over 985954.24 frames.], batch size: 33, aishell_tot_loss[loss=0.156, simple_loss=0.2415, pruned_loss=0.03529, over 984332.41 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2273, pruned_loss=0.03867, over 985330.04 frames.], batch size: 33, lr: 4.78e-04
+2022-06-18 22:34:28,504 INFO [train.py:874] (3/4) Epoch 17, batch 2850, aishell_loss[loss=0.159, simple_loss=0.2406, pruned_loss=0.03873, over 4949.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2349, pruned_loss=0.03717, over 985653.96 frames.], batch size: 31, aishell_tot_loss[loss=0.156, simple_loss=0.2414, pruned_loss=0.03529, over 984077.81 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2277, pruned_loss=0.03907, over 985541.54 frames.], batch size: 31, lr: 4.78e-04
+2022-06-18 22:34:59,017 INFO [train.py:874] (3/4) Epoch 17, batch 2900, datatang_loss[loss=0.1613, simple_loss=0.2348, pruned_loss=0.04389, over 4961.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2346, pruned_loss=0.03681, over 985925.09 frames.], batch size: 45, aishell_tot_loss[loss=0.1555, simple_loss=0.241, pruned_loss=0.03496, over 984372.99 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2276, pruned_loss=0.03909, over 985758.83 frames.], batch size: 45, lr: 4.78e-04
+2022-06-18 22:35:28,977 INFO [train.py:874] (3/4) Epoch 17, batch 2950, aishell_loss[loss=0.1486, simple_loss=0.2351, pruned_loss=0.03099, over 4865.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2344, pruned_loss=0.03697, over 985484.64 frames.], batch size: 37, aishell_tot_loss[loss=0.1554, simple_loss=0.2408, pruned_loss=0.03503, over 984202.30 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2278, pruned_loss=0.03913, over 985674.65 frames.], batch size: 37, lr: 4.78e-04
+2022-06-18 22:35:58,633 INFO [train.py:874] (3/4) Epoch 17, batch 3000, datatang_loss[loss=0.17, simple_loss=0.256, pruned_loss=0.04204, over 4907.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2342, pruned_loss=0.03702, over 985128.44 frames.], batch size: 47, aishell_tot_loss[loss=0.1559, simple_loss=0.2412, pruned_loss=0.03534, over 984036.17 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2274, pruned_loss=0.03879, over 985565.12 frames.], batch size: 47, lr: 4.77e-04
+2022-06-18 22:35:58,634 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 22:36:15,621 INFO [train.py:914] (3/4) Epoch 17, validation: loss=0.1675, simple_loss=0.2537, pruned_loss=0.04061, over 1622729.00 frames.
+2022-06-18 22:36:45,565 INFO [train.py:874] (3/4) Epoch 17, batch 3050, aishell_loss[loss=0.1688, simple_loss=0.2525, pruned_loss=0.04252, over 4880.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2333, pruned_loss=0.03698, over 985104.78 frames.], batch size: 47, aishell_tot_loss[loss=0.1552, simple_loss=0.2403, pruned_loss=0.03508, over 984100.86 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.2275, pruned_loss=0.03891, over 985521.62 frames.], batch size: 47, lr: 4.77e-04
+2022-06-18 22:37:16,162 INFO [train.py:874] (3/4) Epoch 17, batch 3100, datatang_loss[loss=0.171, simple_loss=0.2402, pruned_loss=0.05091, over 4954.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2334, pruned_loss=0.03713, over 985234.06 frames.], batch size: 37, aishell_tot_loss[loss=0.1551, simple_loss=0.24, pruned_loss=0.03508, over 984026.57 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2281, pruned_loss=0.03896, over 985758.34 frames.], batch size: 37, lr: 4.77e-04
+2022-06-18 22:37:46,695 INFO [train.py:874] (3/4) Epoch 17, batch 3150, aishell_loss[loss=0.1677, simple_loss=0.2564, pruned_loss=0.03953, over 4919.00 frames.], tot_loss[loss=0.1533, simple_loss=0.233, pruned_loss=0.0368, over 985554.34 frames.], batch size: 41, aishell_tot_loss[loss=0.1549, simple_loss=0.2399, pruned_loss=0.03495, over 984390.49 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2277, pruned_loss=0.03877, over 985807.20 frames.], batch size: 41, lr: 4.77e-04
+2022-06-18 22:38:17,521 INFO [train.py:874] (3/4) Epoch 17, batch 3200, aishell_loss[loss=0.149, simple_loss=0.2484, pruned_loss=0.02478, over 4922.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2334, pruned_loss=0.03647, over 985668.03 frames.], batch size: 41, aishell_tot_loss[loss=0.1553, simple_loss=0.2404, pruned_loss=0.03512, over 984672.70 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2271, pruned_loss=0.0383, over 985784.46 frames.], batch size: 41, lr: 4.77e-04
+2022-06-18 22:38:47,541 INFO [train.py:874] (3/4) Epoch 17, batch 3250, aishell_loss[loss=0.1177, simple_loss=0.1867, pruned_loss=0.02433, over 4854.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2325, pruned_loss=0.0367, over 985743.99 frames.], batch size: 20, aishell_tot_loss[loss=0.1549, simple_loss=0.2399, pruned_loss=0.035, over 984621.83 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.227, pruned_loss=0.03849, over 985974.69 frames.], batch size: 20, lr: 4.77e-04
+2022-06-18 22:39:17,042 INFO [train.py:874] (3/4) Epoch 17, batch 3300, aishell_loss[loss=0.1675, simple_loss=0.2488, pruned_loss=0.04312, over 4904.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2323, pruned_loss=0.03636, over 985575.54 frames.], batch size: 34, aishell_tot_loss[loss=0.1543, simple_loss=0.2394, pruned_loss=0.0346, over 984725.87 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2271, pruned_loss=0.03852, over 985794.80 frames.], batch size: 34, lr: 4.76e-04
+2022-06-18 22:39:47,771 INFO [train.py:874] (3/4) Epoch 17, batch 3350, aishell_loss[loss=0.1302, simple_loss=0.2106, pruned_loss=0.02489, over 4960.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2326, pruned_loss=0.03651, over 985909.76 frames.], batch size: 27, aishell_tot_loss[loss=0.154, simple_loss=0.2392, pruned_loss=0.03446, over 985143.82 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2275, pruned_loss=0.03879, over 985794.03 frames.], batch size: 27, lr: 4.76e-04
+2022-06-18 22:40:18,468 INFO [train.py:874] (3/4) Epoch 17, batch 3400, datatang_loss[loss=0.1491, simple_loss=0.229, pruned_loss=0.03457, over 4961.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2331, pruned_loss=0.03654, over 986010.56 frames.], batch size: 67, aishell_tot_loss[loss=0.1541, simple_loss=0.2393, pruned_loss=0.03447, over 985192.06 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2275, pruned_loss=0.03882, over 985959.07 frames.], batch size: 67, lr: 4.76e-04
+2022-06-18 22:40:47,890 INFO [train.py:874] (3/4) Epoch 17, batch 3450, aishell_loss[loss=0.129, simple_loss=0.2124, pruned_loss=0.02283, over 4887.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2324, pruned_loss=0.03641, over 985691.94 frames.], batch size: 28, aishell_tot_loss[loss=0.1535, simple_loss=0.2387, pruned_loss=0.03421, over 985164.18 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2274, pruned_loss=0.03888, over 985759.95 frames.], batch size: 28, lr: 4.76e-04
+2022-06-18 22:41:19,019 INFO [train.py:874] (3/4) Epoch 17, batch 3500, aishell_loss[loss=0.1729, simple_loss=0.2585, pruned_loss=0.0437, over 4933.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2327, pruned_loss=0.03642, over 985602.17 frames.], batch size: 32, aishell_tot_loss[loss=0.1541, simple_loss=0.2392, pruned_loss=0.03448, over 985002.62 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2271, pruned_loss=0.03861, over 985893.13 frames.], batch size: 32, lr: 4.76e-04
+2022-06-18 22:41:50,029 INFO [train.py:874] (3/4) Epoch 17, batch 3550, datatang_loss[loss=0.1121, simple_loss=0.1811, pruned_loss=0.0216, over 4959.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2323, pruned_loss=0.03659, over 985613.43 frames.], batch size: 45, aishell_tot_loss[loss=0.1544, simple_loss=0.2394, pruned_loss=0.03471, over 985026.29 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2266, pruned_loss=0.03845, over 985904.52 frames.], batch size: 45, lr: 4.76e-04
+2022-06-18 22:42:20,095 INFO [train.py:874] (3/4) Epoch 17, batch 3600, datatang_loss[loss=0.135, simple_loss=0.2146, pruned_loss=0.02775, over 4934.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2333, pruned_loss=0.03669, over 985866.57 frames.], batch size: 79, aishell_tot_loss[loss=0.1553, simple_loss=0.2403, pruned_loss=0.03516, over 985375.71 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2263, pruned_loss=0.03822, over 985879.61 frames.], batch size: 79, lr: 4.75e-04
+2022-06-18 22:42:49,393 INFO [train.py:874] (3/4) Epoch 17, batch 3650, aishell_loss[loss=0.1936, simple_loss=0.2771, pruned_loss=0.05511, over 4971.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2341, pruned_loss=0.03689, over 985821.52 frames.], batch size: 44, aishell_tot_loss[loss=0.1556, simple_loss=0.2408, pruned_loss=0.03515, over 985518.85 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2265, pruned_loss=0.03844, over 985741.81 frames.], batch size: 44, lr: 4.75e-04
+2022-06-18 22:43:21,336 INFO [train.py:874] (3/4) Epoch 17, batch 3700, aishell_loss[loss=0.1594, simple_loss=0.2544, pruned_loss=0.03219, over 4941.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2344, pruned_loss=0.03693, over 985930.26 frames.], batch size: 68, aishell_tot_loss[loss=0.1549, simple_loss=0.2404, pruned_loss=0.03472, over 985689.99 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2273, pruned_loss=0.03891, over 985749.77 frames.], batch size: 68, lr: 4.75e-04
+2022-06-18 22:43:51,508 INFO [train.py:874] (3/4) Epoch 17, batch 3750, aishell_loss[loss=0.1798, simple_loss=0.266, pruned_loss=0.04674, over 4882.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2335, pruned_loss=0.03681, over 985422.63 frames.], batch size: 50, aishell_tot_loss[loss=0.1543, simple_loss=0.2396, pruned_loss=0.03449, over 985120.72 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.2275, pruned_loss=0.03896, over 985835.64 frames.], batch size: 50, lr: 4.75e-04
+2022-06-18 22:44:21,511 INFO [train.py:874] (3/4) Epoch 17, batch 3800, aishell_loss[loss=0.1118, simple_loss=0.177, pruned_loss=0.02334, over 4890.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2331, pruned_loss=0.03663, over 985229.87 frames.], batch size: 21, aishell_tot_loss[loss=0.1539, simple_loss=0.239, pruned_loss=0.03433, over 984840.44 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.2276, pruned_loss=0.03895, over 985899.88 frames.], batch size: 21, lr: 4.75e-04
+2022-06-18 22:44:50,861 INFO [train.py:874] (3/4) Epoch 17, batch 3850, aishell_loss[loss=0.1575, simple_loss=0.24, pruned_loss=0.03751, over 4870.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2335, pruned_loss=0.03641, over 985188.88 frames.], batch size: 42, aishell_tot_loss[loss=0.1544, simple_loss=0.2396, pruned_loss=0.03459, over 984811.53 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2272, pruned_loss=0.03855, over 985883.87 frames.], batch size: 42, lr: 4.75e-04
+2022-06-18 22:45:20,685 INFO [train.py:874] (3/4) Epoch 17, batch 3900, aishell_loss[loss=0.1474, simple_loss=0.2255, pruned_loss=0.03472, over 4917.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2332, pruned_loss=0.03668, over 985134.73 frames.], batch size: 32, aishell_tot_loss[loss=0.1546, simple_loss=0.2396, pruned_loss=0.03479, over 984806.36 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2269, pruned_loss=0.03858, over 985783.12 frames.], batch size: 32, lr: 4.74e-04
+2022-06-18 22:45:49,983 INFO [train.py:874] (3/4) Epoch 17, batch 3950, aishell_loss[loss=0.1668, simple_loss=0.2497, pruned_loss=0.04194, over 4890.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2344, pruned_loss=0.03721, over 985265.09 frames.], batch size: 34, aishell_tot_loss[loss=0.1554, simple_loss=0.2404, pruned_loss=0.03521, over 984822.56 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2274, pruned_loss=0.03875, over 985874.33 frames.], batch size: 34, lr: 4.74e-04
+2022-06-18 22:46:19,532 INFO [train.py:874] (3/4) Epoch 17, batch 4000, datatang_loss[loss=0.156, simple_loss=0.2283, pruned_loss=0.04184, over 4929.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2342, pruned_loss=0.03708, over 984754.97 frames.], batch size: 79, aishell_tot_loss[loss=0.1557, simple_loss=0.2407, pruned_loss=0.0353, over 984395.36 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.227, pruned_loss=0.03857, over 985746.61 frames.], batch size: 79, lr: 4.74e-04
+2022-06-18 22:46:19,533 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 22:46:35,710 INFO [train.py:914] (3/4) Epoch 17, validation: loss=0.1673, simple_loss=0.2516, pruned_loss=0.04154, over 1622729.00 frames.
+2022-06-18 22:47:04,918 INFO [train.py:874] (3/4) Epoch 17, batch 4050, datatang_loss[loss=0.1705, simple_loss=0.2566, pruned_loss=0.04223, over 4956.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2329, pruned_loss=0.03635, over 984754.77 frames.], batch size: 99, aishell_tot_loss[loss=0.1554, simple_loss=0.2405, pruned_loss=0.0352, over 984309.31 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2262, pruned_loss=0.03786, over 985720.61 frames.], batch size: 99, lr: 4.74e-04
+2022-06-18 22:47:35,532 INFO [train.py:874] (3/4) Epoch 17, batch 4100, aishell_loss[loss=0.1654, simple_loss=0.2419, pruned_loss=0.04445, over 4940.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2332, pruned_loss=0.03653, over 984995.21 frames.], batch size: 31, aishell_tot_loss[loss=0.1551, simple_loss=0.2402, pruned_loss=0.03494, over 984397.06 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2269, pruned_loss=0.03818, over 985793.80 frames.], batch size: 31, lr: 4.74e-04
+2022-06-18 22:48:03,152 INFO [train.py:874] (3/4) Epoch 17, batch 4150, aishell_loss[loss=0.1622, simple_loss=0.2454, pruned_loss=0.03948, over 4879.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2326, pruned_loss=0.03614, over 985130.44 frames.], batch size: 34, aishell_tot_loss[loss=0.155, simple_loss=0.2401, pruned_loss=0.0349, over 984382.70 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2264, pruned_loss=0.0378, over 985945.20 frames.], batch size: 34, lr: 4.73e-04
+2022-06-18 22:49:24,030 INFO [train.py:874] (3/4) Epoch 18, batch 50, aishell_loss[loss=0.1431, simple_loss=0.211, pruned_loss=0.03765, over 4738.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2253, pruned_loss=0.03405, over 218344.31 frames.], batch size: 20, aishell_tot_loss[loss=0.1565, simple_loss=0.2382, pruned_loss=0.03738, over 129018.69 frames.], datatang_tot_loss[loss=0.134, simple_loss=0.2086, pruned_loss=0.02964, over 102796.86 frames.], batch size: 20, lr: 4.61e-04
+2022-06-18 22:49:54,865 INFO [train.py:874] (3/4) Epoch 18, batch 100, datatang_loss[loss=0.1267, simple_loss=0.2022, pruned_loss=0.02558, over 4909.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2278, pruned_loss=0.03442, over 388180.22 frames.], batch size: 64, aishell_tot_loss[loss=0.1569, simple_loss=0.241, pruned_loss=0.03642, over 221996.24 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2143, pruned_loss=0.03228, over 214563.54 frames.], batch size: 64, lr: 4.61e-04
+2022-06-18 22:50:25,819 INFO [train.py:874] (3/4) Epoch 18, batch 150, datatang_loss[loss=0.1524, simple_loss=0.2246, pruned_loss=0.04004, over 4923.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2282, pruned_loss=0.03434, over 520226.85 frames.], batch size: 94, aishell_tot_loss[loss=0.1564, simple_loss=0.2412, pruned_loss=0.03584, over 325092.62 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2135, pruned_loss=0.03258, over 291368.47 frames.], batch size: 94, lr: 4.60e-04
+2022-06-18 22:50:54,386 INFO [train.py:874] (3/4) Epoch 18, batch 200, aishell_loss[loss=0.1647, simple_loss=0.2486, pruned_loss=0.04035, over 4902.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2294, pruned_loss=0.03439, over 623229.55 frames.], batch size: 41, aishell_tot_loss[loss=0.1567, simple_loss=0.2419, pruned_loss=0.03578, over 414346.88 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2139, pruned_loss=0.03267, over 360660.29 frames.], batch size: 41, lr: 4.60e-04
+2022-06-18 22:51:24,610 INFO [train.py:874] (3/4) Epoch 18, batch 250, aishell_loss[loss=0.1704, simple_loss=0.2552, pruned_loss=0.0428, over 4903.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2305, pruned_loss=0.03551, over 703784.94 frames.], batch size: 34, aishell_tot_loss[loss=0.1566, simple_loss=0.2417, pruned_loss=0.03579, over 479215.32 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2172, pruned_loss=0.03467, over 437155.37 frames.], batch size: 34, lr: 4.60e-04
+2022-06-18 22:51:56,110 INFO [train.py:874] (3/4) Epoch 18, batch 300, datatang_loss[loss=0.1569, simple_loss=0.2373, pruned_loss=0.03825, over 4920.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2295, pruned_loss=0.03558, over 766664.97 frames.], batch size: 57, aishell_tot_loss[loss=0.156, simple_loss=0.2411, pruned_loss=0.03548, over 527848.16 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2177, pruned_loss=0.03526, over 513828.38 frames.], batch size: 57, lr: 4.60e-04
+2022-06-18 22:52:24,122 INFO [train.py:874] (3/4) Epoch 18, batch 350, datatang_loss[loss=0.1778, simple_loss=0.2479, pruned_loss=0.05384, over 4933.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2299, pruned_loss=0.03535, over 814965.12 frames.], batch size: 108, aishell_tot_loss[loss=0.1551, simple_loss=0.24, pruned_loss=0.0351, over 595278.26 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2183, pruned_loss=0.03538, over 554686.88 frames.], batch size: 108, lr: 4.60e-04
+2022-06-18 22:52:55,588 INFO [train.py:874] (3/4) Epoch 18, batch 400, datatang_loss[loss=0.1307, simple_loss=0.1966, pruned_loss=0.03243, over 4888.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2295, pruned_loss=0.03553, over 852795.81 frames.], batch size: 47, aishell_tot_loss[loss=0.1549, simple_loss=0.24, pruned_loss=0.03488, over 632489.92 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2189, pruned_loss=0.03592, over 614854.58 frames.], batch size: 47, lr: 4.60e-04
+2022-06-18 22:53:26,700 INFO [train.py:874] (3/4) Epoch 18, batch 450, datatang_loss[loss=0.1351, simple_loss=0.2154, pruned_loss=0.02735, over 4925.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2304, pruned_loss=0.03551, over 882307.28 frames.], batch size: 42, aishell_tot_loss[loss=0.1557, simple_loss=0.2411, pruned_loss=0.03514, over 675675.88 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2189, pruned_loss=0.0357, over 656908.78 frames.], batch size: 42, lr: 4.59e-04
+2022-06-18 22:53:54,571 INFO [train.py:874] (3/4) Epoch 18, batch 500, datatang_loss[loss=0.1359, simple_loss=0.2064, pruned_loss=0.03268, over 4926.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2304, pruned_loss=0.03512, over 905110.59 frames.], batch size: 23, aishell_tot_loss[loss=0.1552, simple_loss=0.2409, pruned_loss=0.03477, over 712079.56 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2193, pruned_loss=0.03556, over 695615.10 frames.], batch size: 23, lr: 4.59e-04
+2022-06-18 22:54:26,298 INFO [train.py:874] (3/4) Epoch 18, batch 550, datatang_loss[loss=0.1426, simple_loss=0.2166, pruned_loss=0.03428, over 4955.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2307, pruned_loss=0.03495, over 922897.94 frames.], batch size: 60, aishell_tot_loss[loss=0.1543, simple_loss=0.2399, pruned_loss=0.03435, over 746759.69 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2205, pruned_loss=0.03577, over 727060.54 frames.], batch size: 60, lr: 4.59e-04
+2022-06-18 22:54:57,133 INFO [train.py:874] (3/4) Epoch 18, batch 600, datatang_loss[loss=0.1588, simple_loss=0.2268, pruned_loss=0.04541, over 4963.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2326, pruned_loss=0.03615, over 937223.68 frames.], batch size: 67, aishell_tot_loss[loss=0.1551, simple_loss=0.2408, pruned_loss=0.0347, over 769621.52 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2228, pruned_loss=0.03693, over 763513.43 frames.], batch size: 67, lr: 4.59e-04
+2022-06-18 22:55:24,802 INFO [train.py:874] (3/4) Epoch 18, batch 650, datatang_loss[loss=0.1481, simple_loss=0.2279, pruned_loss=0.03414, over 4923.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2332, pruned_loss=0.03633, over 947896.90 frames.], batch size: 57, aishell_tot_loss[loss=0.1552, simple_loss=0.2408, pruned_loss=0.03484, over 795863.32 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2238, pruned_loss=0.03713, over 788786.44 frames.], batch size: 57, lr: 4.59e-04
+2022-06-18 22:55:56,353 INFO [train.py:874] (3/4) Epoch 18, batch 700, datatang_loss[loss=0.1231, simple_loss=0.2096, pruned_loss=0.01829, over 4919.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2336, pruned_loss=0.03609, over 955923.53 frames.], batch size: 77, aishell_tot_loss[loss=0.1551, simple_loss=0.2408, pruned_loss=0.03473, over 821323.39 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2242, pruned_loss=0.03711, over 808319.23 frames.], batch size: 77, lr: 4.59e-04
+2022-06-18 22:56:25,877 INFO [train.py:874] (3/4) Epoch 18, batch 750, aishell_loss[loss=0.1522, simple_loss=0.2406, pruned_loss=0.03187, over 4951.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2338, pruned_loss=0.03624, over 962272.29 frames.], batch size: 45, aishell_tot_loss[loss=0.155, simple_loss=0.2407, pruned_loss=0.0347, over 841172.42 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2248, pruned_loss=0.03739, over 828396.12 frames.], batch size: 45, lr: 4.58e-04
+2022-06-18 22:56:55,827 INFO [train.py:874] (3/4) Epoch 18, batch 800, aishell_loss[loss=0.1292, simple_loss=0.2171, pruned_loss=0.02069, over 4911.00 frames.], tot_loss[loss=0.153, simple_loss=0.2334, pruned_loss=0.03635, over 967576.71 frames.], batch size: 52, aishell_tot_loss[loss=0.1548, simple_loss=0.2404, pruned_loss=0.03457, over 856264.17 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2251, pruned_loss=0.03767, over 849103.11 frames.], batch size: 52, lr: 4.58e-04
+2022-06-18 22:57:27,133 INFO [train.py:874] (3/4) Epoch 18, batch 850, aishell_loss[loss=0.1074, simple_loss=0.1738, pruned_loss=0.02055, over 4834.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2325, pruned_loss=0.03587, over 971530.43 frames.], batch size: 20, aishell_tot_loss[loss=0.154, simple_loss=0.2395, pruned_loss=0.03422, over 871875.58 frames.], datatang_tot_loss[loss=0.15, simple_loss=0.225, pruned_loss=0.03753, over 864712.38 frames.], batch size: 20, lr: 4.58e-04
+2022-06-18 22:57:56,163 INFO [train.py:874] (3/4) Epoch 18, batch 900, datatang_loss[loss=0.1406, simple_loss=0.2186, pruned_loss=0.03128, over 4919.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2319, pruned_loss=0.03579, over 974302.33 frames.], batch size: 81, aishell_tot_loss[loss=0.154, simple_loss=0.2395, pruned_loss=0.03418, over 884060.21 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2245, pruned_loss=0.0374, over 879841.62 frames.], batch size: 81, lr: 4.58e-04
+2022-06-18 22:58:25,747 INFO [train.py:874] (3/4) Epoch 18, batch 950, datatang_loss[loss=0.1366, simple_loss=0.1995, pruned_loss=0.03684, over 4958.00 frames.], tot_loss[loss=0.152, simple_loss=0.2317, pruned_loss=0.03612, over 976926.81 frames.], batch size: 37, aishell_tot_loss[loss=0.1544, simple_loss=0.2398, pruned_loss=0.03453, over 892804.73 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2246, pruned_loss=0.03731, over 895654.03 frames.], batch size: 37, lr: 4.58e-04
+2022-06-18 22:58:57,160 INFO [train.py:874] (3/4) Epoch 18, batch 1000, aishell_loss[loss=0.1588, simple_loss=0.2402, pruned_loss=0.03872, over 4939.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2322, pruned_loss=0.0358, over 978766.33 frames.], batch size: 45, aishell_tot_loss[loss=0.1538, simple_loss=0.2392, pruned_loss=0.03414, over 906089.30 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2252, pruned_loss=0.03748, over 903798.35 frames.], batch size: 45, lr: 4.58e-04
+2022-06-18 22:58:57,161 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 22:59:14,040 INFO [train.py:914] (3/4) Epoch 18, validation: loss=0.1636, simple_loss=0.2479, pruned_loss=0.03969, over 1622729.00 frames.
+2022-06-18 22:59:44,491 INFO [train.py:874] (3/4) Epoch 18, batch 1050, datatang_loss[loss=0.1237, simple_loss=0.201, pruned_loss=0.02318, over 4915.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2317, pruned_loss=0.03566, over 979992.11 frames.], batch size: 30, aishell_tot_loss[loss=0.1536, simple_loss=0.239, pruned_loss=0.03409, over 914533.59 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.225, pruned_loss=0.03734, over 914046.45 frames.], batch size: 30, lr: 4.58e-04
+2022-06-18 23:00:16,749 INFO [train.py:874] (3/4) Epoch 18, batch 1100, datatang_loss[loss=0.1759, simple_loss=0.2518, pruned_loss=0.05, over 4938.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2325, pruned_loss=0.03604, over 981144.27 frames.], batch size: 94, aishell_tot_loss[loss=0.1539, simple_loss=0.2393, pruned_loss=0.0342, over 922122.25 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2256, pruned_loss=0.03762, over 923152.07 frames.], batch size: 94, lr: 4.57e-04
+2022-06-18 23:00:44,256 INFO [train.py:874] (3/4) Epoch 18, batch 1150, datatang_loss[loss=0.2017, simple_loss=0.2756, pruned_loss=0.06389, over 4934.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2323, pruned_loss=0.03656, over 982239.16 frames.], batch size: 109, aishell_tot_loss[loss=0.1535, simple_loss=0.2388, pruned_loss=0.03413, over 928765.71 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2261, pruned_loss=0.03823, over 931417.22 frames.], batch size: 109, lr: 4.57e-04
+2022-06-18 23:01:15,695 INFO [train.py:874] (3/4) Epoch 18, batch 1200, aishell_loss[loss=0.1982, simple_loss=0.2738, pruned_loss=0.06127, over 4937.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2331, pruned_loss=0.03657, over 983209.78 frames.], batch size: 58, aishell_tot_loss[loss=0.1536, simple_loss=0.239, pruned_loss=0.0341, over 936003.29 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2267, pruned_loss=0.03841, over 937513.08 frames.], batch size: 58, lr: 4.57e-04
+2022-06-18 23:01:47,533 INFO [train.py:874] (3/4) Epoch 18, batch 1250, datatang_loss[loss=0.1673, simple_loss=0.2374, pruned_loss=0.04858, over 4954.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2339, pruned_loss=0.0368, over 983885.70 frames.], batch size: 86, aishell_tot_loss[loss=0.1542, simple_loss=0.2397, pruned_loss=0.03433, over 942010.03 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2268, pruned_loss=0.03852, over 943158.60 frames.], batch size: 86, lr: 4.57e-04
+2022-06-18 23:02:16,004 INFO [train.py:874] (3/4) Epoch 18, batch 1300, datatang_loss[loss=0.1395, simple_loss=0.2203, pruned_loss=0.02934, over 4914.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2337, pruned_loss=0.03658, over 984171.16 frames.], batch size: 81, aishell_tot_loss[loss=0.1545, simple_loss=0.2403, pruned_loss=0.03434, over 946010.13 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2265, pruned_loss=0.03827, over 949142.27 frames.], batch size: 81, lr: 4.57e-04
+2022-06-18 23:02:45,806 INFO [train.py:874] (3/4) Epoch 18, batch 1350, aishell_loss[loss=0.1518, simple_loss=0.2376, pruned_loss=0.03299, over 4968.00 frames.], tot_loss[loss=0.1528, simple_loss=0.233, pruned_loss=0.03634, over 984245.26 frames.], batch size: 44, aishell_tot_loss[loss=0.1543, simple_loss=0.2399, pruned_loss=0.03438, over 950921.63 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.226, pruned_loss=0.03808, over 952942.00 frames.], batch size: 44, lr: 4.57e-04
+2022-06-18 23:03:17,865 INFO [train.py:874] (3/4) Epoch 18, batch 1400, datatang_loss[loss=0.1485, simple_loss=0.2275, pruned_loss=0.03475, over 4973.00 frames.], tot_loss[loss=0.153, simple_loss=0.233, pruned_loss=0.03651, over 984655.33 frames.], batch size: 60, aishell_tot_loss[loss=0.1547, simple_loss=0.24, pruned_loss=0.03472, over 954828.92 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.226, pruned_loss=0.03794, over 957060.07 frames.], batch size: 60, lr: 4.56e-04
+2022-06-18 23:03:45,991 INFO [train.py:874] (3/4) Epoch 18, batch 1450, aishell_loss[loss=0.1676, simple_loss=0.2671, pruned_loss=0.0341, over 4943.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2332, pruned_loss=0.03649, over 984760.10 frames.], batch size: 54, aishell_tot_loss[loss=0.1545, simple_loss=0.24, pruned_loss=0.03454, over 958330.88 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2262, pruned_loss=0.03812, over 960427.45 frames.], batch size: 54, lr: 4.56e-04
+2022-06-18 23:04:16,741 INFO [train.py:874] (3/4) Epoch 18, batch 1500, datatang_loss[loss=0.1556, simple_loss=0.2413, pruned_loss=0.0349, over 4914.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2332, pruned_loss=0.03633, over 984745.25 frames.], batch size: 52, aishell_tot_loss[loss=0.1543, simple_loss=0.24, pruned_loss=0.03433, over 961342.23 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2262, pruned_loss=0.03821, over 963391.35 frames.], batch size: 52, lr: 4.56e-04
+2022-06-18 23:04:45,959 INFO [train.py:874] (3/4) Epoch 18, batch 1550, aishell_loss[loss=0.157, simple_loss=0.2437, pruned_loss=0.0351, over 4934.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2337, pruned_loss=0.03653, over 984613.76 frames.], batch size: 58, aishell_tot_loss[loss=0.1553, simple_loss=0.2408, pruned_loss=0.03488, over 964522.73 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2255, pruned_loss=0.03797, over 965372.29 frames.], batch size: 58, lr: 4.56e-04
+2022-06-18 23:05:15,915 INFO [train.py:874] (3/4) Epoch 18, batch 1600, datatang_loss[loss=0.1663, simple_loss=0.2506, pruned_loss=0.04098, over 4915.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2336, pruned_loss=0.0363, over 984730.46 frames.], batch size: 98, aishell_tot_loss[loss=0.1548, simple_loss=0.2402, pruned_loss=0.03464, over 967131.64 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2261, pruned_loss=0.038, over 967537.32 frames.], batch size: 98, lr: 4.56e-04
+2022-06-18 23:05:47,575 INFO [train.py:874] (3/4) Epoch 18, batch 1650, datatang_loss[loss=0.1501, simple_loss=0.2375, pruned_loss=0.03139, over 4925.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2338, pruned_loss=0.03675, over 984747.88 frames.], batch size: 94, aishell_tot_loss[loss=0.155, simple_loss=0.2405, pruned_loss=0.03476, over 968692.29 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2263, pruned_loss=0.03831, over 970077.73 frames.], batch size: 94, lr: 4.56e-04
+2022-06-18 23:06:22,729 INFO [train.py:874] (3/4) Epoch 18, batch 1700, datatang_loss[loss=0.1711, simple_loss=0.2336, pruned_loss=0.0543, over 4871.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2339, pruned_loss=0.03714, over 984836.03 frames.], batch size: 44, aishell_tot_loss[loss=0.1552, simple_loss=0.2406, pruned_loss=0.03492, over 970528.19 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2265, pruned_loss=0.03861, over 971961.76 frames.], batch size: 44, lr: 4.55e-04
+2022-06-18 23:06:51,297 INFO [train.py:874] (3/4) Epoch 18, batch 1750, aishell_loss[loss=0.1515, simple_loss=0.2435, pruned_loss=0.02975, over 4954.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2341, pruned_loss=0.0373, over 985011.02 frames.], batch size: 40, aishell_tot_loss[loss=0.1552, simple_loss=0.2407, pruned_loss=0.0349, over 972309.53 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2266, pruned_loss=0.0389, over 973568.51 frames.], batch size: 40, lr: 4.55e-04
+2022-06-18 23:07:22,832 INFO [train.py:874] (3/4) Epoch 18, batch 1800, datatang_loss[loss=0.1435, simple_loss=0.2328, pruned_loss=0.02715, over 4957.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2336, pruned_loss=0.037, over 985304.95 frames.], batch size: 67, aishell_tot_loss[loss=0.1554, simple_loss=0.2409, pruned_loss=0.03492, over 973716.78 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2262, pruned_loss=0.03858, over 975270.96 frames.], batch size: 67, lr: 4.55e-04
+2022-06-18 23:07:53,221 INFO [train.py:874] (3/4) Epoch 18, batch 1850, aishell_loss[loss=0.1847, simple_loss=0.2702, pruned_loss=0.04958, over 4940.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2343, pruned_loss=0.03705, over 985694.80 frames.], batch size: 79, aishell_tot_loss[loss=0.1554, simple_loss=0.2412, pruned_loss=0.03485, over 975340.84 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2266, pruned_loss=0.03879, over 976612.60 frames.], batch size: 79, lr: 4.55e-04
+2022-06-18 23:08:22,169 INFO [train.py:874] (3/4) Epoch 18, batch 1900, aishell_loss[loss=0.1541, simple_loss=0.2449, pruned_loss=0.03164, over 4959.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2349, pruned_loss=0.03711, over 985856.30 frames.], batch size: 40, aishell_tot_loss[loss=0.1553, simple_loss=0.2411, pruned_loss=0.03476, over 976632.40 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.2272, pruned_loss=0.03906, over 977809.58 frames.], batch size: 40, lr: 4.55e-04
+2022-06-18 23:08:53,187 INFO [train.py:874] (3/4) Epoch 18, batch 1950, datatang_loss[loss=0.1238, simple_loss=0.2036, pruned_loss=0.02201, over 4915.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2334, pruned_loss=0.0369, over 985890.76 frames.], batch size: 75, aishell_tot_loss[loss=0.1556, simple_loss=0.2413, pruned_loss=0.0349, over 977370.25 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2261, pruned_loss=0.0386, over 979084.86 frames.], batch size: 75, lr: 4.55e-04
+2022-06-18 23:09:24,296 INFO [train.py:874] (3/4) Epoch 18, batch 2000, datatang_loss[loss=0.1599, simple_loss=0.2323, pruned_loss=0.04378, over 4964.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2335, pruned_loss=0.03698, over 985753.01 frames.], batch size: 67, aishell_tot_loss[loss=0.1556, simple_loss=0.2413, pruned_loss=0.03493, over 978131.52 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2263, pruned_loss=0.03867, over 979969.72 frames.], batch size: 67, lr: 4.55e-04
+2022-06-18 23:09:24,297 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 23:09:40,069 INFO [train.py:914] (3/4) Epoch 18, validation: loss=0.1648, simple_loss=0.2489, pruned_loss=0.0403, over 1622729.00 frames.
+2022-06-18 23:10:11,567 INFO [train.py:874] (3/4) Epoch 18, batch 2050, aishell_loss[loss=0.1637, simple_loss=0.2598, pruned_loss=0.03376, over 4865.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2332, pruned_loss=0.03661, over 985899.52 frames.], batch size: 36, aishell_tot_loss[loss=0.1556, simple_loss=0.2414, pruned_loss=0.03492, over 979023.85 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2258, pruned_loss=0.03832, over 980820.02 frames.], batch size: 36, lr: 4.54e-04
+2022-06-18 23:10:39,574 INFO [train.py:874] (3/4) Epoch 18, batch 2100, aishell_loss[loss=0.15, simple_loss=0.232, pruned_loss=0.03399, over 4937.00 frames.], tot_loss[loss=0.1531, simple_loss=0.233, pruned_loss=0.0366, over 985419.11 frames.], batch size: 32, aishell_tot_loss[loss=0.1552, simple_loss=0.2408, pruned_loss=0.03478, over 979560.02 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.226, pruned_loss=0.03846, over 981191.88 frames.], batch size: 32, lr: 4.54e-04
+2022-06-18 23:11:11,074 INFO [train.py:874] (3/4) Epoch 18, batch 2150, datatang_loss[loss=0.1413, simple_loss=0.2133, pruned_loss=0.03469, over 4883.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2326, pruned_loss=0.0364, over 985051.39 frames.], batch size: 47, aishell_tot_loss[loss=0.1547, simple_loss=0.2401, pruned_loss=0.03466, over 979937.24 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2261, pruned_loss=0.03838, over 981619.85 frames.], batch size: 47, lr: 4.54e-04
+2022-06-18 23:11:42,839 INFO [train.py:874] (3/4) Epoch 18, batch 2200, aishell_loss[loss=0.1664, simple_loss=0.2514, pruned_loss=0.04074, over 4947.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2338, pruned_loss=0.03668, over 985169.29 frames.], batch size: 56, aishell_tot_loss[loss=0.1552, simple_loss=0.2407, pruned_loss=0.03486, over 980439.83 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2268, pruned_loss=0.03844, over 982236.82 frames.], batch size: 56, lr: 4.54e-04
+2022-06-18 23:12:10,531 INFO [train.py:874] (3/4) Epoch 18, batch 2250, datatang_loss[loss=0.1994, simple_loss=0.2793, pruned_loss=0.05977, over 4923.00 frames.], tot_loss[loss=0.1539, simple_loss=0.234, pruned_loss=0.03687, over 985371.22 frames.], batch size: 108, aishell_tot_loss[loss=0.1554, simple_loss=0.2409, pruned_loss=0.03493, over 981088.46 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.227, pruned_loss=0.03851, over 982683.36 frames.], batch size: 108, lr: 4.54e-04
+2022-06-18 23:12:42,780 INFO [train.py:874] (3/4) Epoch 18, batch 2300, datatang_loss[loss=0.1429, simple_loss=0.2186, pruned_loss=0.03353, over 4923.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2333, pruned_loss=0.03704, over 985377.91 frames.], batch size: 77, aishell_tot_loss[loss=0.1554, simple_loss=0.2406, pruned_loss=0.03507, over 981546.59 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2266, pruned_loss=0.03851, over 983035.26 frames.], batch size: 77, lr: 4.54e-04
+2022-06-18 23:13:13,536 INFO [train.py:874] (3/4) Epoch 18, batch 2350, aishell_loss[loss=0.1405, simple_loss=0.2329, pruned_loss=0.02406, over 4897.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2336, pruned_loss=0.03686, over 985075.71 frames.], batch size: 60, aishell_tot_loss[loss=0.1552, simple_loss=0.2408, pruned_loss=0.03481, over 982008.52 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2265, pruned_loss=0.03867, over 982980.58 frames.], batch size: 60, lr: 4.53e-04
+2022-06-18 23:13:42,708 INFO [train.py:874] (3/4) Epoch 18, batch 2400, datatang_loss[loss=0.1487, simple_loss=0.2245, pruned_loss=0.03643, over 4926.00 frames.], tot_loss[loss=0.153, simple_loss=0.2334, pruned_loss=0.03637, over 985114.56 frames.], batch size: 71, aishell_tot_loss[loss=0.1549, simple_loss=0.2407, pruned_loss=0.03451, over 982374.35 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2264, pruned_loss=0.03847, over 983248.77 frames.], batch size: 71, lr: 4.53e-04
+2022-06-18 23:14:13,894 INFO [train.py:874] (3/4) Epoch 18, batch 2450, aishell_loss[loss=0.1604, simple_loss=0.2501, pruned_loss=0.03536, over 4967.00 frames.], tot_loss[loss=0.1531, simple_loss=0.234, pruned_loss=0.03615, over 985144.65 frames.], batch size: 44, aishell_tot_loss[loss=0.1549, simple_loss=0.2406, pruned_loss=0.03461, over 982744.01 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2267, pruned_loss=0.03823, over 983467.95 frames.], batch size: 44, lr: 4.53e-04
+2022-06-18 23:14:43,684 INFO [train.py:874] (3/4) Epoch 18, batch 2500, aishell_loss[loss=0.1534, simple_loss=0.2417, pruned_loss=0.03256, over 4935.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2343, pruned_loss=0.03601, over 985084.92 frames.], batch size: 56, aishell_tot_loss[loss=0.155, simple_loss=0.2408, pruned_loss=0.03463, over 982890.67 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2265, pruned_loss=0.0381, over 983775.19 frames.], batch size: 56, lr: 4.53e-04
+2022-06-18 23:15:12,810 INFO [train.py:874] (3/4) Epoch 18, batch 2550, datatang_loss[loss=0.1619, simple_loss=0.2312, pruned_loss=0.04628, over 4935.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2343, pruned_loss=0.03601, over 985413.01 frames.], batch size: 88, aishell_tot_loss[loss=0.1549, simple_loss=0.2406, pruned_loss=0.03455, over 983429.76 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2266, pruned_loss=0.03815, over 984018.63 frames.], batch size: 88, lr: 4.53e-04
+2022-06-18 23:15:44,521 INFO [train.py:874] (3/4) Epoch 18, batch 2600, aishell_loss[loss=0.1637, simple_loss=0.2494, pruned_loss=0.03901, over 4877.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2337, pruned_loss=0.03571, over 985162.97 frames.], batch size: 35, aishell_tot_loss[loss=0.1548, simple_loss=0.2407, pruned_loss=0.03442, over 983438.52 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2259, pruned_loss=0.03787, over 984150.76 frames.], batch size: 35, lr: 4.53e-04
+2022-06-18 23:16:12,087 INFO [train.py:874] (3/4) Epoch 18, batch 2650, aishell_loss[loss=0.1683, simple_loss=0.2597, pruned_loss=0.03841, over 4903.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2327, pruned_loss=0.03536, over 985698.79 frames.], batch size: 68, aishell_tot_loss[loss=0.1542, simple_loss=0.2402, pruned_loss=0.03413, over 984061.32 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2255, pruned_loss=0.03766, over 984416.05 frames.], batch size: 68, lr: 4.52e-04
+2022-06-18 23:16:42,976 INFO [train.py:874] (3/4) Epoch 18, batch 2700, aishell_loss[loss=0.1294, simple_loss=0.1955, pruned_loss=0.03164, over 4917.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2331, pruned_loss=0.03569, over 985648.93 frames.], batch size: 25, aishell_tot_loss[loss=0.1545, simple_loss=0.2401, pruned_loss=0.03439, over 984187.80 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.226, pruned_loss=0.03762, over 984579.07 frames.], batch size: 25, lr: 4.52e-04
+2022-06-18 23:17:14,071 INFO [train.py:874] (3/4) Epoch 18, batch 2750, aishell_loss[loss=0.1399, simple_loss=0.2304, pruned_loss=0.02468, over 4940.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2334, pruned_loss=0.03612, over 985638.53 frames.], batch size: 49, aishell_tot_loss[loss=0.155, simple_loss=0.2405, pruned_loss=0.03476, over 984306.45 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2263, pruned_loss=0.03757, over 984746.36 frames.], batch size: 49, lr: 4.52e-04
+2022-06-18 23:17:42,098 INFO [train.py:874] (3/4) Epoch 18, batch 2800, aishell_loss[loss=0.121, simple_loss=0.2085, pruned_loss=0.01677, over 4895.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2347, pruned_loss=0.03675, over 985834.21 frames.], batch size: 28, aishell_tot_loss[loss=0.1557, simple_loss=0.2414, pruned_loss=0.035, over 984505.94 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2267, pruned_loss=0.03797, over 985025.08 frames.], batch size: 28, lr: 4.52e-04
+2022-06-18 23:18:13,846 INFO [train.py:874] (3/4) Epoch 18, batch 2850, aishell_loss[loss=0.1466, simple_loss=0.2367, pruned_loss=0.02831, over 4982.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2348, pruned_loss=0.03672, over 985573.18 frames.], batch size: 44, aishell_tot_loss[loss=0.1548, simple_loss=0.2408, pruned_loss=0.03446, over 984405.05 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2276, pruned_loss=0.03852, over 985094.22 frames.], batch size: 44, lr: 4.52e-04
+2022-06-18 23:18:44,578 INFO [train.py:874] (3/4) Epoch 18, batch 2900, aishell_loss[loss=0.1645, simple_loss=0.2581, pruned_loss=0.03542, over 4971.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2338, pruned_loss=0.03627, over 985589.60 frames.], batch size: 68, aishell_tot_loss[loss=0.1547, simple_loss=0.2406, pruned_loss=0.03435, over 984611.71 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2269, pruned_loss=0.03817, over 985086.15 frames.], batch size: 68, lr: 4.52e-04
+2022-06-18 23:19:14,177 INFO [train.py:874] (3/4) Epoch 18, batch 2950, datatang_loss[loss=0.1427, simple_loss=0.2204, pruned_loss=0.03252, over 4866.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2333, pruned_loss=0.03624, over 985362.84 frames.], batch size: 39, aishell_tot_loss[loss=0.1552, simple_loss=0.2411, pruned_loss=0.03467, over 984345.68 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2264, pruned_loss=0.03771, over 985258.87 frames.], batch size: 39, lr: 4.52e-04
+2022-06-18 23:19:45,411 INFO [train.py:874] (3/4) Epoch 18, batch 3000, aishell_loss[loss=0.166, simple_loss=0.2462, pruned_loss=0.04289, over 4943.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2323, pruned_loss=0.03607, over 985143.05 frames.], batch size: 45, aishell_tot_loss[loss=0.1547, simple_loss=0.2403, pruned_loss=0.03454, over 984119.35 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2264, pruned_loss=0.0376, over 985366.95 frames.], batch size: 45, lr: 4.51e-04
+2022-06-18 23:19:45,412 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 23:20:02,476 INFO [train.py:914] (3/4) Epoch 18, validation: loss=0.165, simple_loss=0.2484, pruned_loss=0.04079, over 1622729.00 frames.
+2022-06-18 23:20:31,623 INFO [train.py:874] (3/4) Epoch 18, batch 3050, datatang_loss[loss=0.1465, simple_loss=0.2244, pruned_loss=0.03435, over 4917.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2327, pruned_loss=0.03601, over 985178.84 frames.], batch size: 64, aishell_tot_loss[loss=0.1546, simple_loss=0.2402, pruned_loss=0.03449, over 984224.11 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2266, pruned_loss=0.0376, over 985402.54 frames.], batch size: 64, lr: 4.51e-04
+2022-06-18 23:21:04,196 INFO [train.py:874] (3/4) Epoch 18, batch 3100, datatang_loss[loss=0.1504, simple_loss=0.2251, pruned_loss=0.0378, over 4926.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2313, pruned_loss=0.03521, over 984910.30 frames.], batch size: 71, aishell_tot_loss[loss=0.1537, simple_loss=0.2393, pruned_loss=0.03406, over 983796.10 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.226, pruned_loss=0.03719, over 985609.67 frames.], batch size: 71, lr: 4.51e-04
+2022-06-18 23:21:34,443 INFO [train.py:874] (3/4) Epoch 18, batch 3150, datatang_loss[loss=0.1991, simple_loss=0.2672, pruned_loss=0.06546, over 4952.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2321, pruned_loss=0.03583, over 985062.36 frames.], batch size: 109, aishell_tot_loss[loss=0.1536, simple_loss=0.2391, pruned_loss=0.03408, over 984116.78 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2266, pruned_loss=0.03776, over 985501.45 frames.], batch size: 109, lr: 4.51e-04
+2022-06-18 23:22:05,235 INFO [train.py:874] (3/4) Epoch 18, batch 3200, datatang_loss[loss=0.1443, simple_loss=0.2253, pruned_loss=0.03167, over 4926.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2324, pruned_loss=0.03631, over 985201.57 frames.], batch size: 57, aishell_tot_loss[loss=0.1538, simple_loss=0.2395, pruned_loss=0.03406, over 984282.40 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2267, pruned_loss=0.03817, over 985496.08 frames.], batch size: 57, lr: 4.51e-04
+2022-06-18 23:22:37,323 INFO [train.py:874] (3/4) Epoch 18, batch 3250, datatang_loss[loss=0.1522, simple_loss=0.2159, pruned_loss=0.04428, over 4912.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2323, pruned_loss=0.03618, over 985363.38 frames.], batch size: 47, aishell_tot_loss[loss=0.1538, simple_loss=0.2397, pruned_loss=0.03392, over 984570.68 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2264, pruned_loss=0.0382, over 985439.82 frames.], batch size: 47, lr: 4.51e-04
+2022-06-18 23:23:06,623 INFO [train.py:874] (3/4) Epoch 18, batch 3300, aishell_loss[loss=0.1511, simple_loss=0.2449, pruned_loss=0.02869, over 4908.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2328, pruned_loss=0.03642, over 985560.20 frames.], batch size: 41, aishell_tot_loss[loss=0.1535, simple_loss=0.2393, pruned_loss=0.03384, over 984903.89 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.227, pruned_loss=0.03856, over 985399.34 frames.], batch size: 41, lr: 4.50e-04
+2022-06-18 23:23:37,982 INFO [train.py:874] (3/4) Epoch 18, batch 3350, datatang_loss[loss=0.1687, simple_loss=0.2334, pruned_loss=0.05196, over 4913.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2331, pruned_loss=0.0366, over 985491.63 frames.], batch size: 37, aishell_tot_loss[loss=0.1536, simple_loss=0.2395, pruned_loss=0.03383, over 984957.20 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2272, pruned_loss=0.03875, over 985367.78 frames.], batch size: 37, lr: 4.50e-04
+2022-06-18 23:24:09,536 INFO [train.py:874] (3/4) Epoch 18, batch 3400, datatang_loss[loss=0.1403, simple_loss=0.2161, pruned_loss=0.03221, over 4935.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2324, pruned_loss=0.03643, over 985097.57 frames.], batch size: 50, aishell_tot_loss[loss=0.1531, simple_loss=0.2391, pruned_loss=0.03353, over 984639.89 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2272, pruned_loss=0.03882, over 985348.76 frames.], batch size: 50, lr: 4.50e-04
+2022-06-18 23:24:39,102 INFO [train.py:874] (3/4) Epoch 18, batch 3450, datatang_loss[loss=0.1437, simple_loss=0.2238, pruned_loss=0.03182, over 4936.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2325, pruned_loss=0.03655, over 985255.75 frames.], batch size: 94, aishell_tot_loss[loss=0.1536, simple_loss=0.2395, pruned_loss=0.03383, over 984616.17 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.227, pruned_loss=0.03863, over 985563.14 frames.], batch size: 94, lr: 4.50e-04
+2022-06-18 23:25:10,263 INFO [train.py:874] (3/4) Epoch 18, batch 3500, aishell_loss[loss=0.1626, simple_loss=0.2476, pruned_loss=0.03884, over 4879.00 frames.], tot_loss[loss=0.1534, simple_loss=0.233, pruned_loss=0.03687, over 985483.46 frames.], batch size: 35, aishell_tot_loss[loss=0.1542, simple_loss=0.24, pruned_loss=0.03415, over 984628.80 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.227, pruned_loss=0.03865, over 985822.92 frames.], batch size: 35, lr: 4.50e-04
+2022-06-18 23:25:41,333 INFO [train.py:874] (3/4) Epoch 18, batch 3550, aishell_loss[loss=0.1619, simple_loss=0.2522, pruned_loss=0.03584, over 4869.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2329, pruned_loss=0.03669, over 985400.74 frames.], batch size: 36, aishell_tot_loss[loss=0.1544, simple_loss=0.2404, pruned_loss=0.03427, over 984665.33 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2265, pruned_loss=0.03843, over 985745.44 frames.], batch size: 36, lr: 4.50e-04
+2022-06-18 23:26:10,931 INFO [train.py:874] (3/4) Epoch 18, batch 3600, aishell_loss[loss=0.1466, simple_loss=0.2321, pruned_loss=0.03057, over 4985.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2336, pruned_loss=0.03686, over 985476.44 frames.], batch size: 39, aishell_tot_loss[loss=0.1546, simple_loss=0.2404, pruned_loss=0.03445, over 984883.92 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.227, pruned_loss=0.03854, over 985657.05 frames.], batch size: 39, lr: 4.50e-04
+2022-06-18 23:26:42,483 INFO [train.py:874] (3/4) Epoch 18, batch 3650, aishell_loss[loss=0.1642, simple_loss=0.2457, pruned_loss=0.04135, over 4915.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2331, pruned_loss=0.0366, over 985567.99 frames.], batch size: 46, aishell_tot_loss[loss=0.155, simple_loss=0.2407, pruned_loss=0.0347, over 984962.27 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2262, pruned_loss=0.03808, over 985724.93 frames.], batch size: 46, lr: 4.49e-04
+2022-06-18 23:27:14,822 INFO [train.py:874] (3/4) Epoch 18, batch 3700, aishell_loss[loss=0.1115, simple_loss=0.186, pruned_loss=0.01851, over 4834.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2327, pruned_loss=0.03652, over 985305.17 frames.], batch size: 20, aishell_tot_loss[loss=0.1547, simple_loss=0.2402, pruned_loss=0.03458, over 984658.72 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2261, pruned_loss=0.03817, over 985819.53 frames.], batch size: 20, lr: 4.49e-04
+2022-06-18 23:27:43,247 INFO [train.py:874] (3/4) Epoch 18, batch 3750, aishell_loss[loss=0.164, simple_loss=0.2493, pruned_loss=0.03933, over 4948.00 frames.], tot_loss[loss=0.153, simple_loss=0.2329, pruned_loss=0.03654, over 985331.42 frames.], batch size: 45, aishell_tot_loss[loss=0.1548, simple_loss=0.2403, pruned_loss=0.03461, over 984634.11 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.226, pruned_loss=0.03824, over 985904.19 frames.], batch size: 45, lr: 4.49e-04
+2022-06-18 23:28:15,735 INFO [train.py:874] (3/4) Epoch 18, batch 3800, datatang_loss[loss=0.1413, simple_loss=0.2192, pruned_loss=0.0317, over 4921.00 frames.], tot_loss[loss=0.153, simple_loss=0.2334, pruned_loss=0.03629, over 985581.94 frames.], batch size: 75, aishell_tot_loss[loss=0.1547, simple_loss=0.2404, pruned_loss=0.03449, over 984834.42 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2259, pruned_loss=0.03823, over 986017.32 frames.], batch size: 75, lr: 4.49e-04
+2022-06-18 23:28:45,442 INFO [train.py:874] (3/4) Epoch 18, batch 3850, datatang_loss[loss=0.1397, simple_loss=0.2221, pruned_loss=0.02864, over 4841.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2344, pruned_loss=0.03645, over 985444.74 frames.], batch size: 30, aishell_tot_loss[loss=0.1546, simple_loss=0.2406, pruned_loss=0.03432, over 984694.40 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2264, pruned_loss=0.03864, over 986088.80 frames.], batch size: 30, lr: 4.49e-04
+2022-06-18 23:29:15,522 INFO [train.py:874] (3/4) Epoch 18, batch 3900, datatang_loss[loss=0.1407, simple_loss=0.2226, pruned_loss=0.02941, over 4973.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2333, pruned_loss=0.0361, over 985284.58 frames.], batch size: 60, aishell_tot_loss[loss=0.1546, simple_loss=0.2405, pruned_loss=0.03439, over 984526.49 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2256, pruned_loss=0.03822, over 986113.77 frames.], batch size: 60, lr: 4.49e-04
+2022-06-18 23:29:45,436 INFO [train.py:874] (3/4) Epoch 18, batch 3950, datatang_loss[loss=0.1563, simple_loss=0.2251, pruned_loss=0.04372, over 4959.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2338, pruned_loss=0.03633, over 985257.08 frames.], batch size: 26, aishell_tot_loss[loss=0.1546, simple_loss=0.2406, pruned_loss=0.03432, over 984386.74 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2263, pruned_loss=0.03841, over 986173.61 frames.], batch size: 26, lr: 4.49e-04
+2022-06-18 23:30:15,553 INFO [train.py:874] (3/4) Epoch 18, batch 4000, aishell_loss[loss=0.1602, simple_loss=0.2446, pruned_loss=0.03791, over 4953.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2333, pruned_loss=0.03598, over 985499.60 frames.], batch size: 56, aishell_tot_loss[loss=0.1543, simple_loss=0.2403, pruned_loss=0.03417, over 984555.38 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2263, pruned_loss=0.03812, over 986229.32 frames.], batch size: 56, lr: 4.48e-04
+2022-06-18 23:30:15,554 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 23:30:33,178 INFO [train.py:914] (3/4) Epoch 18, validation: loss=0.1643, simple_loss=0.249, pruned_loss=0.03975, over 1622729.00 frames.
+2022-06-18 23:31:01,780 INFO [train.py:874] (3/4) Epoch 18, batch 4050, aishell_loss[loss=0.1599, simple_loss=0.2471, pruned_loss=0.03639, over 4973.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2331, pruned_loss=0.03595, over 985541.10 frames.], batch size: 51, aishell_tot_loss[loss=0.1546, simple_loss=0.2408, pruned_loss=0.03424, over 984693.80 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2255, pruned_loss=0.03797, over 986143.44 frames.], batch size: 51, lr: 4.48e-04
+2022-06-18 23:31:31,913 INFO [train.py:874] (3/4) Epoch 18, batch 4100, datatang_loss[loss=0.146, simple_loss=0.2299, pruned_loss=0.03108, over 4981.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2328, pruned_loss=0.03574, over 985172.18 frames.], batch size: 34, aishell_tot_loss[loss=0.1545, simple_loss=0.2406, pruned_loss=0.03418, over 984441.81 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2257, pruned_loss=0.0377, over 986005.09 frames.], batch size: 34, lr: 4.48e-04
+2022-06-18 23:32:01,327 INFO [train.py:874] (3/4) Epoch 18, batch 4150, aishell_loss[loss=0.1406, simple_loss=0.2345, pruned_loss=0.02332, over 4862.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2321, pruned_loss=0.03557, over 985065.75 frames.], batch size: 35, aishell_tot_loss[loss=0.1546, simple_loss=0.2406, pruned_loss=0.03426, over 984205.91 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2249, pruned_loss=0.03737, over 986113.28 frames.], batch size: 35, lr: 4.48e-04
+2022-06-18 23:32:31,829 INFO [train.py:874] (3/4) Epoch 18, batch 4200, datatang_loss[loss=0.1794, simple_loss=0.2376, pruned_loss=0.06058, over 4896.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2332, pruned_loss=0.03589, over 985020.92 frames.], batch size: 52, aishell_tot_loss[loss=0.1555, simple_loss=0.2418, pruned_loss=0.03457, over 984118.83 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2247, pruned_loss=0.03735, over 986137.19 frames.], batch size: 52, lr: 4.48e-04
+2022-06-18 23:33:39,382 INFO [train.py:874] (3/4) Epoch 19, batch 50, aishell_loss[loss=0.1492, simple_loss=0.2399, pruned_loss=0.02926, over 4944.00 frames.], tot_loss[loss=0.15, simple_loss=0.2308, pruned_loss=0.0346, over 218166.01 frames.], batch size: 54, aishell_tot_loss[loss=0.1553, simple_loss=0.2434, pruned_loss=0.03361, over 111372.78 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2194, pruned_loss=0.03553, over 120416.55 frames.], batch size: 54, lr: 4.36e-04
+2022-06-18 23:34:10,886 INFO [train.py:874] (3/4) Epoch 19, batch 100, aishell_loss[loss=0.1614, simple_loss=0.2443, pruned_loss=0.03921, over 4946.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2301, pruned_loss=0.03412, over 388606.16 frames.], batch size: 45, aishell_tot_loss[loss=0.157, simple_loss=0.2438, pruned_loss=0.03513, over 214434.16 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2168, pruned_loss=0.03312, over 222540.86 frames.], batch size: 45, lr: 4.36e-04
+2022-06-18 23:34:43,851 INFO [train.py:874] (3/4) Epoch 19, batch 150, datatang_loss[loss=0.1438, simple_loss=0.2257, pruned_loss=0.03089, over 4920.00 frames.], tot_loss[loss=0.147, simple_loss=0.2273, pruned_loss=0.03336, over 520989.40 frames.], batch size: 64, aishell_tot_loss[loss=0.1546, simple_loss=0.2407, pruned_loss=0.03424, over 298304.65 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2153, pruned_loss=0.03268, over 319266.44 frames.], batch size: 64, lr: 4.36e-04
+2022-06-18 23:35:13,743 INFO [train.py:874] (3/4) Epoch 19, batch 200, aishell_loss[loss=0.1643, simple_loss=0.2426, pruned_loss=0.04298, over 4974.00 frames.], tot_loss[loss=0.147, simple_loss=0.227, pruned_loss=0.03351, over 623816.02 frames.], batch size: 51, aishell_tot_loss[loss=0.1539, simple_loss=0.2396, pruned_loss=0.03413, over 369846.70 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2163, pruned_loss=0.03301, over 406549.13 frames.], batch size: 51, lr: 4.36e-04
+2022-06-18 23:35:44,945 INFO [train.py:874] (3/4) Epoch 19, batch 250, datatang_loss[loss=0.1876, simple_loss=0.2574, pruned_loss=0.05888, over 4956.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2282, pruned_loss=0.03328, over 703878.81 frames.], batch size: 109, aishell_tot_loss[loss=0.1539, simple_loss=0.2402, pruned_loss=0.03378, over 444790.25 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2168, pruned_loss=0.03289, over 472336.22 frames.], batch size: 109, lr: 4.36e-04
+2022-06-18 23:36:17,281 INFO [train.py:874] (3/4) Epoch 19, batch 300, aishell_loss[loss=0.1632, simple_loss=0.2509, pruned_loss=0.03774, over 4917.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2286, pruned_loss=0.03356, over 765887.90 frames.], batch size: 33, aishell_tot_loss[loss=0.1536, simple_loss=0.2398, pruned_loss=0.03366, over 505810.72 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2178, pruned_loss=0.0334, over 534860.14 frames.], batch size: 33, lr: 4.36e-04
+2022-06-18 23:36:46,774 INFO [train.py:874] (3/4) Epoch 19, batch 350, aishell_loss[loss=0.1765, simple_loss=0.2565, pruned_loss=0.04823, over 4876.00 frames.], tot_loss[loss=0.149, simple_loss=0.2299, pruned_loss=0.03403, over 814767.08 frames.], batch size: 35, aishell_tot_loss[loss=0.1542, simple_loss=0.2404, pruned_loss=0.03397, over 564563.05 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2189, pruned_loss=0.03383, over 586008.41 frames.], batch size: 35, lr: 4.35e-04
+2022-06-18 23:37:18,136 INFO [train.py:874] (3/4) Epoch 19, batch 400, aishell_loss[loss=0.1718, simple_loss=0.262, pruned_loss=0.04077, over 4964.00 frames.], tot_loss[loss=0.1494, simple_loss=0.23, pruned_loss=0.03438, over 852721.56 frames.], batch size: 64, aishell_tot_loss[loss=0.1549, simple_loss=0.2412, pruned_loss=0.03433, over 610257.15 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2187, pruned_loss=0.03402, over 636802.91 frames.], batch size: 64, lr: 4.35e-04
+2022-06-18 23:37:49,271 INFO [train.py:874] (3/4) Epoch 19, batch 450, datatang_loss[loss=0.1448, simple_loss=0.2225, pruned_loss=0.0336, over 4923.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2297, pruned_loss=0.03402, over 882472.01 frames.], batch size: 81, aishell_tot_loss[loss=0.1548, simple_loss=0.2409, pruned_loss=0.0343, over 656448.79 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2184, pruned_loss=0.03361, over 676301.24 frames.], batch size: 81, lr: 4.35e-04
+2022-06-18 23:38:17,554 INFO [train.py:874] (3/4) Epoch 19, batch 500, aishell_loss[loss=0.1562, simple_loss=0.2421, pruned_loss=0.03512, over 4857.00 frames.], tot_loss[loss=0.1491, simple_loss=0.23, pruned_loss=0.03408, over 905270.17 frames.], batch size: 37, aishell_tot_loss[loss=0.1538, simple_loss=0.2399, pruned_loss=0.03382, over 695027.49 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2197, pruned_loss=0.03422, over 712858.39 frames.], batch size: 37, lr: 4.35e-04
+2022-06-18 23:38:49,461 INFO [train.py:874] (3/4) Epoch 19, batch 550, aishell_loss[loss=0.1575, simple_loss=0.2403, pruned_loss=0.03735, over 4878.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2302, pruned_loss=0.03421, over 922956.39 frames.], batch size: 35, aishell_tot_loss[loss=0.1534, simple_loss=0.2396, pruned_loss=0.03357, over 729033.88 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2203, pruned_loss=0.03465, over 745089.42 frames.], batch size: 35, lr: 4.35e-04
+2022-06-18 23:39:21,398 INFO [train.py:874] (3/4) Epoch 19, batch 600, aishell_loss[loss=0.1918, simple_loss=0.2752, pruned_loss=0.0542, over 4969.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2308, pruned_loss=0.03486, over 936782.68 frames.], batch size: 64, aishell_tot_loss[loss=0.1536, simple_loss=0.2395, pruned_loss=0.03381, over 760222.22 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2211, pruned_loss=0.03524, over 772448.10 frames.], batch size: 64, lr: 4.35e-04
+2022-06-18 23:39:49,988 INFO [train.py:874] (3/4) Epoch 19, batch 650, aishell_loss[loss=0.1714, simple_loss=0.2485, pruned_loss=0.0472, over 4936.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2307, pruned_loss=0.03493, over 947665.65 frames.], batch size: 49, aishell_tot_loss[loss=0.1534, simple_loss=0.2395, pruned_loss=0.03368, over 785816.97 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2211, pruned_loss=0.03552, over 798521.45 frames.], batch size: 49, lr: 4.35e-04
+2022-06-18 23:40:22,402 INFO [train.py:874] (3/4) Epoch 19, batch 700, datatang_loss[loss=0.1414, simple_loss=0.216, pruned_loss=0.03342, over 4928.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2312, pruned_loss=0.03504, over 956193.59 frames.], batch size: 83, aishell_tot_loss[loss=0.153, simple_loss=0.2391, pruned_loss=0.03342, over 808712.45 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2224, pruned_loss=0.03597, over 821259.10 frames.], batch size: 83, lr: 4.34e-04
+2022-06-18 23:40:54,901 INFO [train.py:874] (3/4) Epoch 19, batch 750, aishell_loss[loss=0.1698, simple_loss=0.2525, pruned_loss=0.04358, over 4935.00 frames.], tot_loss[loss=0.152, simple_loss=0.2325, pruned_loss=0.03573, over 962793.97 frames.], batch size: 32, aishell_tot_loss[loss=0.1531, simple_loss=0.2393, pruned_loss=0.03346, over 828647.49 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.224, pruned_loss=0.03685, over 841534.09 frames.], batch size: 32, lr: 4.34e-04
+2022-06-18 23:41:23,225 INFO [train.py:874] (3/4) Epoch 19, batch 800, aishell_loss[loss=0.1747, simple_loss=0.2443, pruned_loss=0.05259, over 4918.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2324, pruned_loss=0.03542, over 968011.12 frames.], batch size: 33, aishell_tot_loss[loss=0.1533, simple_loss=0.2397, pruned_loss=0.03351, over 850288.42 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2234, pruned_loss=0.03664, over 855677.33 frames.], batch size: 33, lr: 4.34e-04
+2022-06-18 23:41:55,372 INFO [train.py:874] (3/4) Epoch 19, batch 850, aishell_loss[loss=0.1531, simple_loss=0.2334, pruned_loss=0.03639, over 4926.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2327, pruned_loss=0.03592, over 971800.44 frames.], batch size: 32, aishell_tot_loss[loss=0.1535, simple_loss=0.2396, pruned_loss=0.03369, over 866260.73 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2241, pruned_loss=0.03713, over 870815.67 frames.], batch size: 32, lr: 4.34e-04
+2022-06-18 23:42:25,163 INFO [train.py:874] (3/4) Epoch 19, batch 900, aishell_loss[loss=0.1451, simple_loss=0.2334, pruned_loss=0.02842, over 4905.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2329, pruned_loss=0.03593, over 974570.95 frames.], batch size: 52, aishell_tot_loss[loss=0.1536, simple_loss=0.2396, pruned_loss=0.03378, over 880731.25 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2245, pruned_loss=0.03722, over 883638.60 frames.], batch size: 52, lr: 4.34e-04
+2022-06-18 23:42:56,511 INFO [train.py:874] (3/4) Epoch 19, batch 950, aishell_loss[loss=0.1631, simple_loss=0.2547, pruned_loss=0.03577, over 4934.00 frames.], tot_loss[loss=0.152, simple_loss=0.2324, pruned_loss=0.03585, over 976856.91 frames.], batch size: 49, aishell_tot_loss[loss=0.1534, simple_loss=0.2393, pruned_loss=0.03376, over 891924.20 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2245, pruned_loss=0.0372, over 896580.91 frames.], batch size: 49, lr: 4.34e-04
+2022-06-18 23:43:29,230 INFO [train.py:874] (3/4) Epoch 19, batch 1000, aishell_loss[loss=0.1693, simple_loss=0.2567, pruned_loss=0.04088, over 4946.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2317, pruned_loss=0.03554, over 978499.11 frames.], batch size: 64, aishell_tot_loss[loss=0.1536, simple_loss=0.2396, pruned_loss=0.03382, over 900158.50 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.224, pruned_loss=0.03679, over 909338.58 frames.], batch size: 64, lr: 4.34e-04
+2022-06-18 23:43:29,232 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 23:43:46,290 INFO [train.py:914] (3/4) Epoch 19, validation: loss=0.1644, simple_loss=0.2486, pruned_loss=0.04009, over 1622729.00 frames.
+2022-06-18 23:44:16,874 INFO [train.py:874] (3/4) Epoch 19, batch 1050, datatang_loss[loss=0.152, simple_loss=0.2257, pruned_loss=0.03916, over 4956.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2321, pruned_loss=0.03581, over 980012.47 frames.], batch size: 60, aishell_tot_loss[loss=0.1538, simple_loss=0.24, pruned_loss=0.03386, over 909143.60 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2242, pruned_loss=0.03706, over 919240.36 frames.], batch size: 60, lr: 4.33e-04
+2022-06-18 23:44:49,147 INFO [train.py:874] (3/4) Epoch 19, batch 1100, aishell_loss[loss=0.1326, simple_loss=0.2262, pruned_loss=0.01946, over 4976.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2313, pruned_loss=0.03526, over 980970.63 frames.], batch size: 51, aishell_tot_loss[loss=0.1528, simple_loss=0.239, pruned_loss=0.03336, over 916608.48 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2246, pruned_loss=0.03698, over 928143.43 frames.], batch size: 51, lr: 4.33e-04
+2022-06-18 23:45:17,840 INFO [train.py:874] (3/4) Epoch 19, batch 1150, datatang_loss[loss=0.1387, simple_loss=0.2119, pruned_loss=0.0328, over 4925.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2319, pruned_loss=0.0354, over 982147.47 frames.], batch size: 83, aishell_tot_loss[loss=0.1525, simple_loss=0.2389, pruned_loss=0.03308, over 923847.26 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2255, pruned_loss=0.03741, over 935799.79 frames.], batch size: 83, lr: 4.33e-04
+2022-06-18 23:45:50,890 INFO [train.py:874] (3/4) Epoch 19, batch 1200, aishell_loss[loss=0.1817, simple_loss=0.275, pruned_loss=0.04424, over 4965.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2323, pruned_loss=0.03542, over 982628.52 frames.], batch size: 40, aishell_tot_loss[loss=0.153, simple_loss=0.2393, pruned_loss=0.03338, over 931195.16 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2254, pruned_loss=0.0372, over 941332.11 frames.], batch size: 40, lr: 4.33e-04
+2022-06-18 23:46:23,248 INFO [train.py:874] (3/4) Epoch 19, batch 1250, aishell_loss[loss=0.1615, simple_loss=0.2409, pruned_loss=0.04101, over 4933.00 frames.], tot_loss[loss=0.1516, simple_loss=0.232, pruned_loss=0.0356, over 983486.81 frames.], batch size: 33, aishell_tot_loss[loss=0.1527, simple_loss=0.2387, pruned_loss=0.03337, over 938147.99 frames.], datatang_tot_loss[loss=0.1503, simple_loss=0.2256, pruned_loss=0.03747, over 946288.55 frames.], batch size: 33, lr: 4.33e-04
+2022-06-18 23:46:52,203 INFO [train.py:874] (3/4) Epoch 19, batch 1300, aishell_loss[loss=0.151, simple_loss=0.2438, pruned_loss=0.02904, over 4957.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2326, pruned_loss=0.03639, over 984213.11 frames.], batch size: 64, aishell_tot_loss[loss=0.153, simple_loss=0.2391, pruned_loss=0.03346, over 942648.76 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2261, pruned_loss=0.03816, over 952024.81 frames.], batch size: 64, lr: 4.33e-04
+2022-06-18 23:47:23,399 INFO [train.py:874] (3/4) Epoch 19, batch 1350, aishell_loss[loss=0.1733, simple_loss=0.2568, pruned_loss=0.04491, over 4958.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2327, pruned_loss=0.03598, over 984430.05 frames.], batch size: 40, aishell_tot_loss[loss=0.1532, simple_loss=0.2394, pruned_loss=0.03352, over 948060.46 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2258, pruned_loss=0.03788, over 955633.78 frames.], batch size: 40, lr: 4.33e-04
+2022-06-18 23:47:56,193 INFO [train.py:874] (3/4) Epoch 19, batch 1400, aishell_loss[loss=0.1845, simple_loss=0.261, pruned_loss=0.054, over 4873.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2322, pruned_loss=0.03577, over 984871.82 frames.], batch size: 42, aishell_tot_loss[loss=0.1536, simple_loss=0.2398, pruned_loss=0.03366, over 951099.85 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2253, pruned_loss=0.03745, over 960451.23 frames.], batch size: 42, lr: 4.32e-04
+2022-06-18 23:48:24,844 INFO [train.py:874] (3/4) Epoch 19, batch 1450, datatang_loss[loss=0.1559, simple_loss=0.2194, pruned_loss=0.04614, over 4803.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2317, pruned_loss=0.03557, over 985112.59 frames.], batch size: 24, aishell_tot_loss[loss=0.1535, simple_loss=0.2396, pruned_loss=0.03371, over 955413.01 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2247, pruned_loss=0.03727, over 963331.74 frames.], batch size: 24, lr: 4.32e-04
+2022-06-18 23:49:02,575 INFO [train.py:874] (3/4) Epoch 19, batch 1500, datatang_loss[loss=0.1477, simple_loss=0.2179, pruned_loss=0.03878, over 4923.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2322, pruned_loss=0.03555, over 985128.18 frames.], batch size: 71, aishell_tot_loss[loss=0.1538, simple_loss=0.2399, pruned_loss=0.03385, over 959141.52 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2247, pruned_loss=0.03717, over 965751.17 frames.], batch size: 71, lr: 4.32e-04
+2022-06-18 23:49:33,576 INFO [train.py:874] (3/4) Epoch 19, batch 1550, aishell_loss[loss=0.1471, simple_loss=0.2319, pruned_loss=0.03116, over 4937.00 frames.], tot_loss[loss=0.1522, simple_loss=0.233, pruned_loss=0.03575, over 985518.56 frames.], batch size: 45, aishell_tot_loss[loss=0.1544, simple_loss=0.2405, pruned_loss=0.03416, over 963011.18 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2246, pruned_loss=0.03715, over 967767.14 frames.], batch size: 45, lr: 4.32e-04
+2022-06-18 23:50:04,170 INFO [train.py:874] (3/4) Epoch 19, batch 1600, aishell_loss[loss=0.1513, simple_loss=0.2329, pruned_loss=0.03485, over 4909.00 frames.], tot_loss[loss=0.152, simple_loss=0.2327, pruned_loss=0.03561, over 985405.09 frames.], batch size: 46, aishell_tot_loss[loss=0.1546, simple_loss=0.2408, pruned_loss=0.03423, over 965547.96 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.2242, pruned_loss=0.03695, over 969854.60 frames.], batch size: 46, lr: 4.32e-04
+2022-06-18 23:50:37,493 INFO [train.py:874] (3/4) Epoch 19, batch 1650, aishell_loss[loss=0.1561, simple_loss=0.2476, pruned_loss=0.03228, over 4980.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2335, pruned_loss=0.03569, over 985768.11 frames.], batch size: 38, aishell_tot_loss[loss=0.1543, simple_loss=0.2404, pruned_loss=0.03411, over 968786.78 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2251, pruned_loss=0.03722, over 971251.38 frames.], batch size: 38, lr: 4.32e-04
+2022-06-18 23:51:09,512 INFO [train.py:874] (3/4) Epoch 19, batch 1700, datatang_loss[loss=0.1468, simple_loss=0.232, pruned_loss=0.03079, over 4921.00 frames.], tot_loss[loss=0.1533, simple_loss=0.234, pruned_loss=0.03627, over 985889.36 frames.], batch size: 75, aishell_tot_loss[loss=0.1545, simple_loss=0.2406, pruned_loss=0.03416, over 970529.13 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.226, pruned_loss=0.03773, over 973269.08 frames.], batch size: 75, lr: 4.32e-04
+2022-06-18 23:51:39,523 INFO [train.py:874] (3/4) Epoch 19, batch 1750, datatang_loss[loss=0.1547, simple_loss=0.2343, pruned_loss=0.03759, over 4960.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2341, pruned_loss=0.03646, over 985526.70 frames.], batch size: 91, aishell_tot_loss[loss=0.1549, simple_loss=0.2411, pruned_loss=0.03433, over 971691.18 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2261, pruned_loss=0.03777, over 974969.66 frames.], batch size: 91, lr: 4.31e-04
+2022-06-18 23:52:12,630 INFO [train.py:874] (3/4) Epoch 19, batch 1800, aishell_loss[loss=0.1489, simple_loss=0.245, pruned_loss=0.02642, over 4871.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2341, pruned_loss=0.0365, over 985762.71 frames.], batch size: 42, aishell_tot_loss[loss=0.1552, simple_loss=0.2411, pruned_loss=0.03466, over 973648.39 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.226, pruned_loss=0.0376, over 976160.15 frames.], batch size: 42, lr: 4.31e-04
+2022-06-18 23:52:41,210 INFO [train.py:874] (3/4) Epoch 19, batch 1850, datatang_loss[loss=0.1733, simple_loss=0.2397, pruned_loss=0.05343, over 4942.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2341, pruned_loss=0.03618, over 985914.58 frames.], batch size: 69, aishell_tot_loss[loss=0.155, simple_loss=0.241, pruned_loss=0.03446, over 975569.73 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2258, pruned_loss=0.03765, over 977021.65 frames.], batch size: 69, lr: 4.31e-04
+2022-06-18 23:53:12,999 INFO [train.py:874] (3/4) Epoch 19, batch 1900, aishell_loss[loss=0.1435, simple_loss=0.2337, pruned_loss=0.02662, over 4960.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2345, pruned_loss=0.03607, over 985992.07 frames.], batch size: 61, aishell_tot_loss[loss=0.1551, simple_loss=0.2413, pruned_loss=0.0345, over 976864.29 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.226, pruned_loss=0.03754, over 978084.07 frames.], batch size: 61, lr: 4.31e-04
+2022-06-18 23:53:45,833 INFO [train.py:874] (3/4) Epoch 19, batch 1950, datatang_loss[loss=0.1426, simple_loss=0.2221, pruned_loss=0.03161, over 4933.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2338, pruned_loss=0.03553, over 986070.37 frames.], batch size: 37, aishell_tot_loss[loss=0.1545, simple_loss=0.2407, pruned_loss=0.03413, over 977850.60 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2261, pruned_loss=0.03737, over 979180.61 frames.], batch size: 37, lr: 4.31e-04
+2022-06-18 23:54:14,605 INFO [train.py:874] (3/4) Epoch 19, batch 2000, datatang_loss[loss=0.1519, simple_loss=0.2242, pruned_loss=0.0398, over 4938.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2324, pruned_loss=0.03519, over 985827.42 frames.], batch size: 42, aishell_tot_loss[loss=0.153, simple_loss=0.2392, pruned_loss=0.03343, over 978648.57 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2261, pruned_loss=0.03765, over 979918.94 frames.], batch size: 42, lr: 4.31e-04
+2022-06-18 23:54:14,607 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-18 23:54:31,136 INFO [train.py:914] (3/4) Epoch 19, validation: loss=0.1648, simple_loss=0.2482, pruned_loss=0.04066, over 1622729.00 frames.
+2022-06-18 23:55:03,652 INFO [train.py:874] (3/4) Epoch 19, batch 2050, datatang_loss[loss=0.1423, simple_loss=0.2219, pruned_loss=0.03134, over 4924.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2322, pruned_loss=0.03513, over 986154.82 frames.], batch size: 81, aishell_tot_loss[loss=0.1527, simple_loss=0.2386, pruned_loss=0.03336, over 979774.26 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2263, pruned_loss=0.03763, over 980686.13 frames.], batch size: 81, lr: 4.31e-04
+2022-06-18 23:55:34,146 INFO [train.py:874] (3/4) Epoch 19, batch 2100, datatang_loss[loss=0.1288, simple_loss=0.2107, pruned_loss=0.02346, over 4933.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2319, pruned_loss=0.035, over 986373.62 frames.], batch size: 79, aishell_tot_loss[loss=0.1524, simple_loss=0.2381, pruned_loss=0.03331, over 980458.99 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2265, pruned_loss=0.03737, over 981604.83 frames.], batch size: 79, lr: 4.30e-04
+2022-06-18 23:56:07,366 INFO [train.py:874] (3/4) Epoch 19, batch 2150, aishell_loss[loss=0.2158, simple_loss=0.2982, pruned_loss=0.06669, over 4871.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2303, pruned_loss=0.03458, over 985620.13 frames.], batch size: 35, aishell_tot_loss[loss=0.1522, simple_loss=0.2379, pruned_loss=0.03322, over 980650.85 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2252, pruned_loss=0.03686, over 981850.55 frames.], batch size: 35, lr: 4.30e-04
+2022-06-18 23:56:40,117 INFO [train.py:874] (3/4) Epoch 19, batch 2200, datatang_loss[loss=0.1416, simple_loss=0.2161, pruned_loss=0.03352, over 4972.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2303, pruned_loss=0.03505, over 985506.99 frames.], batch size: 60, aishell_tot_loss[loss=0.1523, simple_loss=0.2376, pruned_loss=0.03349, over 981161.93 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2253, pruned_loss=0.03697, over 982237.32 frames.], batch size: 60, lr: 4.30e-04
+2022-06-18 23:57:09,692 INFO [train.py:874] (3/4) Epoch 19, batch 2250, aishell_loss[loss=0.1531, simple_loss=0.2341, pruned_loss=0.03605, over 4935.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2309, pruned_loss=0.03544, over 985690.52 frames.], batch size: 32, aishell_tot_loss[loss=0.1528, simple_loss=0.2381, pruned_loss=0.03368, over 981781.60 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2252, pruned_loss=0.0371, over 982713.38 frames.], batch size: 32, lr: 4.30e-04
+2022-06-18 23:57:42,533 INFO [train.py:874] (3/4) Epoch 19, batch 2300, datatang_loss[loss=0.1517, simple_loss=0.2298, pruned_loss=0.03677, over 4936.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2313, pruned_loss=0.03515, over 985713.50 frames.], batch size: 69, aishell_tot_loss[loss=0.1525, simple_loss=0.238, pruned_loss=0.03346, over 982375.02 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2255, pruned_loss=0.03707, over 982952.41 frames.], batch size: 69, lr: 4.30e-04
+2022-06-18 23:58:14,312 INFO [train.py:874] (3/4) Epoch 19, batch 2350, aishell_loss[loss=0.1665, simple_loss=0.2565, pruned_loss=0.0383, over 4951.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2311, pruned_loss=0.03483, over 985572.23 frames.], batch size: 61, aishell_tot_loss[loss=0.1523, simple_loss=0.2378, pruned_loss=0.03341, over 982600.96 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2251, pruned_loss=0.03682, over 983302.50 frames.], batch size: 61, lr: 4.30e-04
+2022-06-18 23:58:44,262 INFO [train.py:874] (3/4) Epoch 19, batch 2400, datatang_loss[loss=0.1892, simple_loss=0.2653, pruned_loss=0.05656, over 4957.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2328, pruned_loss=0.03572, over 985601.35 frames.], batch size: 109, aishell_tot_loss[loss=0.1532, simple_loss=0.2388, pruned_loss=0.03379, over 982864.14 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2259, pruned_loss=0.03727, over 983675.77 frames.], batch size: 109, lr: 4.30e-04
+2022-06-18 23:59:16,967 INFO [train.py:874] (3/4) Epoch 19, batch 2450, datatang_loss[loss=0.1378, simple_loss=0.2202, pruned_loss=0.02775, over 4915.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2328, pruned_loss=0.03574, over 985373.29 frames.], batch size: 75, aishell_tot_loss[loss=0.1531, simple_loss=0.2388, pruned_loss=0.03368, over 982939.91 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2262, pruned_loss=0.03741, over 983902.43 frames.], batch size: 75, lr: 4.30e-04
+2022-06-18 23:59:48,746 INFO [train.py:874] (3/4) Epoch 19, batch 2500, datatang_loss[loss=0.1461, simple_loss=0.2234, pruned_loss=0.03436, over 4964.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2332, pruned_loss=0.03571, over 985450.56 frames.], batch size: 60, aishell_tot_loss[loss=0.1534, simple_loss=0.239, pruned_loss=0.03387, over 983086.05 frames.], datatang_tot_loss[loss=0.1503, simple_loss=0.2261, pruned_loss=0.03729, over 984321.60 frames.], batch size: 60, lr: 4.29e-04
+2022-06-19 00:00:18,860 INFO [train.py:874] (3/4) Epoch 19, batch 2550, aishell_loss[loss=0.1684, simple_loss=0.2451, pruned_loss=0.04586, over 4868.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2324, pruned_loss=0.03533, over 984805.83 frames.], batch size: 36, aishell_tot_loss[loss=0.153, simple_loss=0.2387, pruned_loss=0.03364, over 983139.53 frames.], datatang_tot_loss[loss=0.15, simple_loss=0.2257, pruned_loss=0.03714, over 984008.53 frames.], batch size: 36, lr: 4.29e-04
+2022-06-19 00:00:52,807 INFO [train.py:874] (3/4) Epoch 19, batch 2600, datatang_loss[loss=0.1482, simple_loss=0.2216, pruned_loss=0.03743, over 4977.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2325, pruned_loss=0.03564, over 985393.73 frames.], batch size: 65, aishell_tot_loss[loss=0.1537, simple_loss=0.2394, pruned_loss=0.03396, over 983574.21 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2254, pruned_loss=0.0371, over 984471.65 frames.], batch size: 65, lr: 4.29e-04
+2022-06-19 00:01:22,220 INFO [train.py:874] (3/4) Epoch 19, batch 2650, aishell_loss[loss=0.1731, simple_loss=0.2585, pruned_loss=0.04385, over 4881.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2318, pruned_loss=0.03539, over 985379.12 frames.], batch size: 42, aishell_tot_loss[loss=0.1532, simple_loss=0.239, pruned_loss=0.03373, over 983762.31 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2251, pruned_loss=0.03708, over 984586.09 frames.], batch size: 42, lr: 4.29e-04
+2022-06-19 00:01:54,141 INFO [train.py:874] (3/4) Epoch 19, batch 2700, datatang_loss[loss=0.1608, simple_loss=0.2365, pruned_loss=0.04258, over 4951.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2321, pruned_loss=0.0352, over 985116.37 frames.], batch size: 67, aishell_tot_loss[loss=0.1533, simple_loss=0.2392, pruned_loss=0.03371, over 983753.71 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2253, pruned_loss=0.03687, over 984581.24 frames.], batch size: 67, lr: 4.29e-04
+2022-06-19 00:02:27,706 INFO [train.py:874] (3/4) Epoch 19, batch 2750, aishell_loss[loss=0.1498, simple_loss=0.2427, pruned_loss=0.02846, over 4914.00 frames.], tot_loss[loss=0.15, simple_loss=0.2308, pruned_loss=0.03465, over 985296.33 frames.], batch size: 41, aishell_tot_loss[loss=0.153, simple_loss=0.2388, pruned_loss=0.03359, over 983828.17 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2247, pruned_loss=0.03629, over 984894.38 frames.], batch size: 41, lr: 4.29e-04
+2022-06-19 00:03:00,503 INFO [train.py:874] (3/4) Epoch 19, batch 2800, aishell_loss[loss=0.1526, simple_loss=0.2388, pruned_loss=0.03314, over 4979.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2305, pruned_loss=0.03488, over 985751.38 frames.], batch size: 38, aishell_tot_loss[loss=0.1534, simple_loss=0.239, pruned_loss=0.03389, over 984198.69 frames.], datatang_tot_loss[loss=0.1483, simple_loss=0.2244, pruned_loss=0.03608, over 985188.88 frames.], batch size: 38, lr: 4.29e-04
+2022-06-19 00:03:29,877 INFO [train.py:874] (3/4) Epoch 19, batch 2850, aishell_loss[loss=0.1562, simple_loss=0.2447, pruned_loss=0.03387, over 4900.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2312, pruned_loss=0.03488, over 985483.89 frames.], batch size: 60, aishell_tot_loss[loss=0.1533, simple_loss=0.239, pruned_loss=0.03382, over 984122.30 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2244, pruned_loss=0.03619, over 985274.09 frames.], batch size: 60, lr: 4.28e-04
+2022-06-19 00:04:03,089 INFO [train.py:874] (3/4) Epoch 19, batch 2900, datatang_loss[loss=0.1553, simple_loss=0.2284, pruned_loss=0.0411, over 4925.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2322, pruned_loss=0.0351, over 985589.97 frames.], batch size: 57, aishell_tot_loss[loss=0.1532, simple_loss=0.239, pruned_loss=0.03369, over 984374.29 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2251, pruned_loss=0.0366, over 985334.53 frames.], batch size: 57, lr: 4.28e-04
+2022-06-19 00:04:31,814 INFO [train.py:874] (3/4) Epoch 19, batch 2950, datatang_loss[loss=0.111, simple_loss=0.19, pruned_loss=0.01604, over 4957.00 frames.], tot_loss[loss=0.151, simple_loss=0.2317, pruned_loss=0.03511, over 985461.70 frames.], batch size: 55, aishell_tot_loss[loss=0.1528, simple_loss=0.2385, pruned_loss=0.0336, over 984377.37 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2252, pruned_loss=0.03666, over 985359.64 frames.], batch size: 55, lr: 4.28e-04
+2022-06-19 00:05:05,084 INFO [train.py:874] (3/4) Epoch 19, batch 3000, aishell_loss[loss=0.1495, simple_loss=0.2475, pruned_loss=0.02579, over 4950.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2317, pruned_loss=0.03475, over 985622.31 frames.], batch size: 64, aishell_tot_loss[loss=0.1524, simple_loss=0.2382, pruned_loss=0.03325, over 984431.75 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2253, pruned_loss=0.03666, over 985620.71 frames.], batch size: 64, lr: 4.28e-04
+2022-06-19 00:05:05,085 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 00:05:22,959 INFO [train.py:914] (3/4) Epoch 19, validation: loss=0.1643, simple_loss=0.2485, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 00:05:51,999 INFO [train.py:874] (3/4) Epoch 19, batch 3050, aishell_loss[loss=0.1674, simple_loss=0.2498, pruned_loss=0.04249, over 4949.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2332, pruned_loss=0.03499, over 985653.22 frames.], batch size: 56, aishell_tot_loss[loss=0.1533, simple_loss=0.2396, pruned_loss=0.03356, over 984676.02 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2251, pruned_loss=0.03665, over 985584.42 frames.], batch size: 56, lr: 4.28e-04
+2022-06-19 00:06:24,311 INFO [train.py:874] (3/4) Epoch 19, batch 3100, datatang_loss[loss=0.1312, simple_loss=0.2106, pruned_loss=0.02587, over 4950.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2324, pruned_loss=0.03487, over 985403.62 frames.], batch size: 86, aishell_tot_loss[loss=0.1531, simple_loss=0.2391, pruned_loss=0.0336, over 984399.30 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2248, pruned_loss=0.03649, over 985738.28 frames.], batch size: 86, lr: 4.28e-04
+2022-06-19 00:06:52,851 INFO [train.py:874] (3/4) Epoch 19, batch 3150, aishell_loss[loss=0.1738, simple_loss=0.2608, pruned_loss=0.04336, over 4950.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2321, pruned_loss=0.03482, over 985417.55 frames.], batch size: 56, aishell_tot_loss[loss=0.1533, simple_loss=0.2393, pruned_loss=0.03363, over 984524.33 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2246, pruned_loss=0.03634, over 985697.17 frames.], batch size: 56, lr: 4.28e-04
+2022-06-19 00:07:26,237 INFO [train.py:874] (3/4) Epoch 19, batch 3200, aishell_loss[loss=0.1564, simple_loss=0.2395, pruned_loss=0.03661, over 4977.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2318, pruned_loss=0.0352, over 985251.77 frames.], batch size: 48, aishell_tot_loss[loss=0.1529, simple_loss=0.2388, pruned_loss=0.03354, over 984556.52 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.225, pruned_loss=0.03676, over 985547.79 frames.], batch size: 48, lr: 4.27e-04
+2022-06-19 00:07:58,229 INFO [train.py:874] (3/4) Epoch 19, batch 3250, aishell_loss[loss=0.1428, simple_loss=0.2371, pruned_loss=0.02422, over 4972.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2317, pruned_loss=0.03534, over 985370.68 frames.], batch size: 44, aishell_tot_loss[loss=0.1531, simple_loss=0.2388, pruned_loss=0.03374, over 984714.50 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.2246, pruned_loss=0.03677, over 985571.11 frames.], batch size: 44, lr: 4.27e-04
+2022-06-19 00:08:26,545 INFO [train.py:874] (3/4) Epoch 19, batch 3300, aishell_loss[loss=0.1562, simple_loss=0.2409, pruned_loss=0.03578, over 4917.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2311, pruned_loss=0.03477, over 985310.83 frames.], batch size: 46, aishell_tot_loss[loss=0.1532, simple_loss=0.239, pruned_loss=0.03367, over 984587.28 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2238, pruned_loss=0.03625, over 985686.38 frames.], batch size: 46, lr: 4.27e-04
+2022-06-19 00:08:58,832 INFO [train.py:874] (3/4) Epoch 19, batch 3350, aishell_loss[loss=0.1536, simple_loss=0.2353, pruned_loss=0.03593, over 4881.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2319, pruned_loss=0.03493, over 985325.67 frames.], batch size: 34, aishell_tot_loss[loss=0.1535, simple_loss=0.2395, pruned_loss=0.03374, over 984586.77 frames.], datatang_tot_loss[loss=0.1483, simple_loss=0.2239, pruned_loss=0.0363, over 985749.67 frames.], batch size: 34, lr: 4.27e-04
+2022-06-19 00:09:31,478 INFO [train.py:874] (3/4) Epoch 19, batch 3400, aishell_loss[loss=0.1757, simple_loss=0.2617, pruned_loss=0.04485, over 4862.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2315, pruned_loss=0.03511, over 985523.95 frames.], batch size: 35, aishell_tot_loss[loss=0.1533, simple_loss=0.2392, pruned_loss=0.0337, over 984680.11 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.224, pruned_loss=0.0365, over 985891.54 frames.], batch size: 35, lr: 4.27e-04
+2022-06-19 00:10:00,011 INFO [train.py:874] (3/4) Epoch 19, batch 3450, datatang_loss[loss=0.1548, simple_loss=0.2207, pruned_loss=0.0445, over 4904.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2312, pruned_loss=0.03497, over 985671.19 frames.], batch size: 47, aishell_tot_loss[loss=0.1531, simple_loss=0.2389, pruned_loss=0.0336, over 984986.55 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.224, pruned_loss=0.03643, over 985794.56 frames.], batch size: 47, lr: 4.27e-04
+2022-06-19 00:10:33,120 INFO [train.py:874] (3/4) Epoch 19, batch 3500, aishell_loss[loss=0.1203, simple_loss=0.2056, pruned_loss=0.01755, over 4819.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2307, pruned_loss=0.03459, over 985792.50 frames.], batch size: 26, aishell_tot_loss[loss=0.153, simple_loss=0.2388, pruned_loss=0.03357, over 985238.54 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2234, pruned_loss=0.03609, over 985751.53 frames.], batch size: 26, lr: 4.27e-04
+2022-06-19 00:11:03,953 INFO [train.py:874] (3/4) Epoch 19, batch 3550, aishell_loss[loss=0.1535, simple_loss=0.234, pruned_loss=0.03649, over 4889.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2319, pruned_loss=0.0355, over 985258.88 frames.], batch size: 34, aishell_tot_loss[loss=0.1534, simple_loss=0.2391, pruned_loss=0.03383, over 985042.02 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2243, pruned_loss=0.0367, over 985453.36 frames.], batch size: 34, lr: 4.26e-04
+2022-06-19 00:11:33,811 INFO [train.py:874] (3/4) Epoch 19, batch 3600, aishell_loss[loss=0.1644, simple_loss=0.256, pruned_loss=0.03642, over 4862.00 frames.], tot_loss[loss=0.152, simple_loss=0.232, pruned_loss=0.03603, over 985523.32 frames.], batch size: 37, aishell_tot_loss[loss=0.1538, simple_loss=0.2395, pruned_loss=0.03406, over 985233.47 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2244, pruned_loss=0.03703, over 985548.65 frames.], batch size: 37, lr: 4.26e-04
+2022-06-19 00:12:05,725 INFO [train.py:874] (3/4) Epoch 19, batch 3650, aishell_loss[loss=0.1623, simple_loss=0.2545, pruned_loss=0.035, over 4932.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2321, pruned_loss=0.03568, over 985297.10 frames.], batch size: 49, aishell_tot_loss[loss=0.1537, simple_loss=0.2393, pruned_loss=0.03405, over 985125.17 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.2246, pruned_loss=0.03681, over 985446.72 frames.], batch size: 49, lr: 4.26e-04
+2022-06-19 00:12:38,565 INFO [train.py:874] (3/4) Epoch 19, batch 3700, datatang_loss[loss=0.1399, simple_loss=0.2262, pruned_loss=0.02683, over 4943.00 frames.], tot_loss[loss=0.1518, simple_loss=0.232, pruned_loss=0.03575, over 985386.40 frames.], batch size: 50, aishell_tot_loss[loss=0.1538, simple_loss=0.2393, pruned_loss=0.03415, over 984964.68 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2249, pruned_loss=0.03677, over 985712.54 frames.], batch size: 50, lr: 4.26e-04
+2022-06-19 00:13:06,482 INFO [train.py:874] (3/4) Epoch 19, batch 3750, datatang_loss[loss=0.1377, simple_loss=0.2072, pruned_loss=0.03407, over 4917.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2315, pruned_loss=0.0349, over 985592.30 frames.], batch size: 73, aishell_tot_loss[loss=0.1531, simple_loss=0.2388, pruned_loss=0.03374, over 985030.47 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2247, pruned_loss=0.03636, over 985885.99 frames.], batch size: 73, lr: 4.26e-04
+2022-06-19 00:13:39,457 INFO [train.py:874] (3/4) Epoch 19, batch 3800, aishell_loss[loss=0.1643, simple_loss=0.25, pruned_loss=0.03932, over 4897.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2315, pruned_loss=0.03454, over 985392.23 frames.], batch size: 34, aishell_tot_loss[loss=0.153, simple_loss=0.2387, pruned_loss=0.03361, over 985034.14 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2245, pruned_loss=0.03611, over 985703.27 frames.], batch size: 34, lr: 4.26e-04
+2022-06-19 00:14:10,398 INFO [train.py:874] (3/4) Epoch 19, batch 3850, aishell_loss[loss=0.1664, simple_loss=0.2482, pruned_loss=0.04233, over 4974.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2312, pruned_loss=0.03491, over 985411.48 frames.], batch size: 51, aishell_tot_loss[loss=0.153, simple_loss=0.2386, pruned_loss=0.03364, over 985014.70 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2245, pruned_loss=0.03631, over 985724.50 frames.], batch size: 51, lr: 4.26e-04
+2022-06-19 00:14:40,657 INFO [train.py:874] (3/4) Epoch 19, batch 3900, aishell_loss[loss=0.1596, simple_loss=0.2378, pruned_loss=0.04071, over 4972.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2313, pruned_loss=0.0351, over 985765.33 frames.], batch size: 48, aishell_tot_loss[loss=0.1536, simple_loss=0.2392, pruned_loss=0.03397, over 985217.83 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2241, pruned_loss=0.03614, over 985887.64 frames.], batch size: 48, lr: 4.26e-04
+2022-06-19 00:15:09,710 INFO [train.py:874] (3/4) Epoch 19, batch 3950, aishell_loss[loss=0.1534, simple_loss=0.2383, pruned_loss=0.03428, over 4958.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2317, pruned_loss=0.0352, over 985774.86 frames.], batch size: 31, aishell_tot_loss[loss=0.1538, simple_loss=0.2394, pruned_loss=0.0341, over 985116.93 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.224, pruned_loss=0.03612, over 986061.41 frames.], batch size: 31, lr: 4.25e-04
+2022-06-19 00:15:40,667 INFO [train.py:874] (3/4) Epoch 19, batch 4000, aishell_loss[loss=0.1524, simple_loss=0.2422, pruned_loss=0.03134, over 4907.00 frames.], tot_loss[loss=0.151, simple_loss=0.2315, pruned_loss=0.03522, over 985696.30 frames.], batch size: 52, aishell_tot_loss[loss=0.1536, simple_loss=0.2391, pruned_loss=0.03408, over 985019.81 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.224, pruned_loss=0.0362, over 986139.07 frames.], batch size: 52, lr: 4.25e-04
+2022-06-19 00:15:40,668 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 00:15:58,333 INFO [train.py:914] (3/4) Epoch 19, validation: loss=0.165, simple_loss=0.2492, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 00:16:27,777 INFO [train.py:874] (3/4) Epoch 19, batch 4050, aishell_loss[loss=0.1837, simple_loss=0.2636, pruned_loss=0.05196, over 4964.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2315, pruned_loss=0.03517, over 985278.49 frames.], batch size: 61, aishell_tot_loss[loss=0.1538, simple_loss=0.2393, pruned_loss=0.0342, over 984738.56 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2239, pruned_loss=0.03604, over 985991.34 frames.], batch size: 61, lr: 4.25e-04
+2022-06-19 00:16:58,291 INFO [train.py:874] (3/4) Epoch 19, batch 4100, datatang_loss[loss=0.1498, simple_loss=0.2292, pruned_loss=0.03525, over 4866.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2314, pruned_loss=0.03522, over 985345.82 frames.], batch size: 39, aishell_tot_loss[loss=0.1533, simple_loss=0.2389, pruned_loss=0.03386, over 984838.68 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2243, pruned_loss=0.03641, over 985944.12 frames.], batch size: 39, lr: 4.25e-04
+2022-06-19 00:17:27,303 INFO [train.py:874] (3/4) Epoch 19, batch 4150, aishell_loss[loss=0.1542, simple_loss=0.2432, pruned_loss=0.03263, over 4965.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2317, pruned_loss=0.0348, over 985433.72 frames.], batch size: 44, aishell_tot_loss[loss=0.1533, simple_loss=0.2392, pruned_loss=0.03374, over 985081.49 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2239, pruned_loss=0.03614, over 985793.16 frames.], batch size: 44, lr: 4.25e-04
+2022-06-19 00:19:01,294 INFO [train.py:874] (3/4) Epoch 20, batch 50, datatang_loss[loss=0.1332, simple_loss=0.2127, pruned_loss=0.02686, over 4867.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2234, pruned_loss=0.03137, over 218656.54 frames.], batch size: 39, aishell_tot_loss[loss=0.1487, simple_loss=0.2346, pruned_loss=0.03141, over 89526.76 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2164, pruned_loss=0.03138, over 142027.58 frames.], batch size: 39, lr: 4.14e-04
+2022-06-19 00:19:31,461 INFO [train.py:874] (3/4) Epoch 20, batch 100, aishell_loss[loss=0.2127, simple_loss=0.2864, pruned_loss=0.0695, over 4955.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2304, pruned_loss=0.03451, over 388724.68 frames.], batch size: 40, aishell_tot_loss[loss=0.156, simple_loss=0.2412, pruned_loss=0.03542, over 214590.14 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2188, pruned_loss=0.03317, over 222585.75 frames.], batch size: 40, lr: 4.14e-04
+2022-06-19 00:20:03,714 INFO [train.py:874] (3/4) Epoch 20, batch 150, datatang_loss[loss=0.1223, simple_loss=0.1978, pruned_loss=0.02345, over 4863.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2297, pruned_loss=0.0339, over 521412.77 frames.], batch size: 30, aishell_tot_loss[loss=0.1558, simple_loss=0.2411, pruned_loss=0.03525, over 312525.75 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2173, pruned_loss=0.03237, over 305728.27 frames.], batch size: 30, lr: 4.14e-04
+2022-06-19 00:20:32,903 INFO [train.py:874] (3/4) Epoch 20, batch 200, datatang_loss[loss=0.1293, simple_loss=0.2076, pruned_loss=0.02554, over 4942.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2278, pruned_loss=0.03304, over 624618.55 frames.], batch size: 62, aishell_tot_loss[loss=0.1555, simple_loss=0.2411, pruned_loss=0.03501, over 391947.19 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2144, pruned_loss=0.0312, over 385970.23 frames.], batch size: 62, lr: 4.14e-04
+2022-06-19 00:21:05,364 INFO [train.py:874] (3/4) Epoch 20, batch 250, aishell_loss[loss=0.1403, simple_loss=0.2277, pruned_loss=0.02638, over 4887.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2287, pruned_loss=0.03325, over 704458.85 frames.], batch size: 34, aishell_tot_loss[loss=0.1552, simple_loss=0.2413, pruned_loss=0.03456, over 464376.51 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2152, pruned_loss=0.03194, over 453866.26 frames.], batch size: 34, lr: 4.14e-04
+2022-06-19 00:21:37,004 INFO [train.py:874] (3/4) Epoch 20, batch 300, datatang_loss[loss=0.1288, simple_loss=0.2104, pruned_loss=0.02358, over 4919.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2275, pruned_loss=0.0328, over 766962.31 frames.], batch size: 73, aishell_tot_loss[loss=0.1539, simple_loss=0.2397, pruned_loss=0.03407, over 521196.73 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2156, pruned_loss=0.03177, over 521299.29 frames.], batch size: 73, lr: 4.14e-04
+2022-06-19 00:22:05,513 INFO [train.py:874] (3/4) Epoch 20, batch 350, aishell_loss[loss=0.1189, simple_loss=0.1893, pruned_loss=0.02423, over 4832.00 frames.], tot_loss[loss=0.147, simple_loss=0.2285, pruned_loss=0.03274, over 815361.85 frames.], batch size: 21, aishell_tot_loss[loss=0.1529, simple_loss=0.239, pruned_loss=0.03337, over 581967.16 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2172, pruned_loss=0.03227, over 569743.16 frames.], batch size: 21, lr: 4.14e-04
+2022-06-19 00:22:38,819 INFO [train.py:874] (3/4) Epoch 20, batch 400, datatang_loss[loss=0.1399, simple_loss=0.2182, pruned_loss=0.03075, over 4953.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2275, pruned_loss=0.03317, over 853145.84 frames.], batch size: 37, aishell_tot_loss[loss=0.1516, simple_loss=0.2375, pruned_loss=0.03288, over 616369.80 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2185, pruned_loss=0.03335, over 631809.91 frames.], batch size: 37, lr: 4.13e-04
+2022-06-19 00:23:11,577 INFO [train.py:874] (3/4) Epoch 20, batch 450, aishell_loss[loss=0.1517, simple_loss=0.2387, pruned_loss=0.03231, over 4945.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2287, pruned_loss=0.03345, over 882686.10 frames.], batch size: 54, aishell_tot_loss[loss=0.1525, simple_loss=0.2383, pruned_loss=0.03332, over 661624.50 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2192, pruned_loss=0.03332, over 671960.61 frames.], batch size: 54, lr: 4.13e-04
+2022-06-19 00:23:40,932 INFO [train.py:874] (3/4) Epoch 20, batch 500, datatang_loss[loss=0.134, simple_loss=0.2111, pruned_loss=0.02846, over 4941.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2287, pruned_loss=0.03306, over 905291.24 frames.], batch size: 69, aishell_tot_loss[loss=0.1523, simple_loss=0.2383, pruned_loss=0.03313, over 696652.47 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2195, pruned_loss=0.03304, over 711666.71 frames.], batch size: 69, lr: 4.13e-04
+2022-06-19 00:24:13,585 INFO [train.py:874] (3/4) Epoch 20, batch 550, aishell_loss[loss=0.136, simple_loss=0.2232, pruned_loss=0.02445, over 4977.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2277, pruned_loss=0.03284, over 922978.13 frames.], batch size: 27, aishell_tot_loss[loss=0.151, simple_loss=0.2366, pruned_loss=0.03266, over 729313.14 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2199, pruned_loss=0.03321, over 745106.16 frames.], batch size: 27, lr: 4.13e-04
+2022-06-19 00:24:45,828 INFO [train.py:874] (3/4) Epoch 20, batch 600, aishell_loss[loss=0.1592, simple_loss=0.2409, pruned_loss=0.03871, over 4873.00 frames.], tot_loss[loss=0.1478, simple_loss=0.229, pruned_loss=0.03324, over 936515.23 frames.], batch size: 36, aishell_tot_loss[loss=0.1516, simple_loss=0.2374, pruned_loss=0.03293, over 761408.25 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2205, pruned_loss=0.03344, over 771291.66 frames.], batch size: 36, lr: 4.13e-04
+2022-06-19 00:25:14,758 INFO [train.py:874] (3/4) Epoch 20, batch 650, datatang_loss[loss=0.1354, simple_loss=0.2097, pruned_loss=0.0305, over 4960.00 frames.], tot_loss[loss=0.148, simple_loss=0.2296, pruned_loss=0.03313, over 947529.45 frames.], batch size: 45, aishell_tot_loss[loss=0.1516, simple_loss=0.2377, pruned_loss=0.0328, over 789877.04 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2208, pruned_loss=0.03345, over 794674.99 frames.], batch size: 45, lr: 4.13e-04
+2022-06-19 00:25:47,590 INFO [train.py:874] (3/4) Epoch 20, batch 700, datatang_loss[loss=0.1397, simple_loss=0.2153, pruned_loss=0.03204, over 4925.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2298, pruned_loss=0.03369, over 955937.92 frames.], batch size: 73, aishell_tot_loss[loss=0.1514, simple_loss=0.2372, pruned_loss=0.03283, over 812890.06 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2216, pruned_loss=0.0341, over 817158.55 frames.], batch size: 73, lr: 4.13e-04
+2022-06-19 00:26:18,458 INFO [train.py:874] (3/4) Epoch 20, batch 750, datatang_loss[loss=0.1507, simple_loss=0.2247, pruned_loss=0.03841, over 4911.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2306, pruned_loss=0.03416, over 962344.35 frames.], batch size: 75, aishell_tot_loss[loss=0.1522, simple_loss=0.2377, pruned_loss=0.03339, over 839146.46 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.2215, pruned_loss=0.03424, over 830808.75 frames.], batch size: 75, lr: 4.13e-04
+2022-06-19 00:26:48,916 INFO [train.py:874] (3/4) Epoch 20, batch 800, aishell_loss[loss=0.161, simple_loss=0.2517, pruned_loss=0.03511, over 4920.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2307, pruned_loss=0.03453, over 967345.17 frames.], batch size: 41, aishell_tot_loss[loss=0.1519, simple_loss=0.2374, pruned_loss=0.03317, over 855703.76 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2223, pruned_loss=0.035, over 849590.11 frames.], batch size: 41, lr: 4.12e-04
+2022-06-19 00:27:20,468 INFO [train.py:874] (3/4) Epoch 20, batch 850, aishell_loss[loss=0.1399, simple_loss=0.2349, pruned_loss=0.02244, over 4972.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2311, pruned_loss=0.03451, over 971403.92 frames.], batch size: 48, aishell_tot_loss[loss=0.1521, simple_loss=0.2379, pruned_loss=0.03312, over 873366.99 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2222, pruned_loss=0.03518, over 863096.86 frames.], batch size: 48, lr: 4.12e-04
+2022-06-19 00:27:50,547 INFO [train.py:874] (3/4) Epoch 20, batch 900, aishell_loss[loss=0.151, simple_loss=0.232, pruned_loss=0.03498, over 4872.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2305, pruned_loss=0.03441, over 973959.61 frames.], batch size: 37, aishell_tot_loss[loss=0.1521, simple_loss=0.2378, pruned_loss=0.0332, over 883840.29 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2223, pruned_loss=0.03504, over 879809.71 frames.], batch size: 37, lr: 4.12e-04
+2022-06-19 00:28:20,693 INFO [train.py:874] (3/4) Epoch 20, batch 950, aishell_loss[loss=0.1489, simple_loss=0.2328, pruned_loss=0.03245, over 4973.00 frames.], tot_loss[loss=0.151, simple_loss=0.2316, pruned_loss=0.03521, over 976606.12 frames.], batch size: 31, aishell_tot_loss[loss=0.152, simple_loss=0.2378, pruned_loss=0.03311, over 896206.19 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2236, pruned_loss=0.03613, over 891949.91 frames.], batch size: 31, lr: 4.12e-04
+2022-06-19 00:28:53,582 INFO [train.py:874] (3/4) Epoch 20, batch 1000, aishell_loss[loss=0.1724, simple_loss=0.26, pruned_loss=0.04239, over 4943.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2313, pruned_loss=0.03522, over 978630.00 frames.], batch size: 45, aishell_tot_loss[loss=0.1527, simple_loss=0.2384, pruned_loss=0.03349, over 905502.17 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.223, pruned_loss=0.03585, over 904282.10 frames.], batch size: 45, lr: 4.12e-04
+2022-06-19 00:28:53,583 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 00:29:10,061 INFO [train.py:914] (3/4) Epoch 20, validation: loss=0.1639, simple_loss=0.248, pruned_loss=0.0399, over 1622729.00 frames.
+2022-06-19 00:29:43,703 INFO [train.py:874] (3/4) Epoch 20, batch 1050, aishell_loss[loss=0.1526, simple_loss=0.2371, pruned_loss=0.03406, over 4980.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2309, pruned_loss=0.03489, over 980035.39 frames.], batch size: 48, aishell_tot_loss[loss=0.1521, simple_loss=0.238, pruned_loss=0.03314, over 914406.26 frames.], datatang_tot_loss[loss=0.1476, simple_loss=0.2234, pruned_loss=0.03596, over 914236.20 frames.], batch size: 48, lr: 4.12e-04
+2022-06-19 00:30:16,607 INFO [train.py:874] (3/4) Epoch 20, batch 1100, aishell_loss[loss=0.1462, simple_loss=0.2355, pruned_loss=0.0284, over 4931.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2311, pruned_loss=0.035, over 981194.18 frames.], batch size: 32, aishell_tot_loss[loss=0.1516, simple_loss=0.2373, pruned_loss=0.03296, over 922615.49 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2243, pruned_loss=0.03636, over 922745.58 frames.], batch size: 32, lr: 4.12e-04
+2022-06-19 00:30:45,820 INFO [train.py:874] (3/4) Epoch 20, batch 1150, aishell_loss[loss=0.1755, simple_loss=0.2481, pruned_loss=0.05139, over 4938.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2312, pruned_loss=0.03478, over 982449.64 frames.], batch size: 32, aishell_tot_loss[loss=0.152, simple_loss=0.2378, pruned_loss=0.03308, over 929647.74 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2242, pruned_loss=0.03603, over 930832.18 frames.], batch size: 32, lr: 4.11e-04
+2022-06-19 00:31:19,200 INFO [train.py:874] (3/4) Epoch 20, batch 1200, aishell_loss[loss=0.1609, simple_loss=0.2523, pruned_loss=0.03476, over 4972.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2309, pruned_loss=0.03464, over 983090.56 frames.], batch size: 61, aishell_tot_loss[loss=0.152, simple_loss=0.2378, pruned_loss=0.03309, over 935422.55 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2241, pruned_loss=0.03587, over 938011.70 frames.], batch size: 61, lr: 4.11e-04
+2022-06-19 00:31:51,449 INFO [train.py:874] (3/4) Epoch 20, batch 1250, datatang_loss[loss=0.1456, simple_loss=0.2213, pruned_loss=0.03497, over 4921.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2306, pruned_loss=0.03427, over 983850.57 frames.], batch size: 73, aishell_tot_loss[loss=0.1519, simple_loss=0.238, pruned_loss=0.0329, over 941205.97 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2236, pruned_loss=0.03567, over 943957.21 frames.], batch size: 73, lr: 4.11e-04
+2022-06-19 00:32:25,052 INFO [train.py:874] (3/4) Epoch 20, batch 1300, aishell_loss[loss=0.1654, simple_loss=0.2541, pruned_loss=0.03832, over 4934.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2314, pruned_loss=0.03473, over 983881.53 frames.], batch size: 49, aishell_tot_loss[loss=0.1518, simple_loss=0.2378, pruned_loss=0.03287, over 945892.25 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2247, pruned_loss=0.03619, over 949023.10 frames.], batch size: 49, lr: 4.11e-04
+2022-06-19 00:32:57,755 INFO [train.py:874] (3/4) Epoch 20, batch 1350, aishell_loss[loss=0.2298, simple_loss=0.3136, pruned_loss=0.07302, over 4974.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2317, pruned_loss=0.03457, over 984223.43 frames.], batch size: 61, aishell_tot_loss[loss=0.1523, simple_loss=0.2384, pruned_loss=0.03303, over 950635.24 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2244, pruned_loss=0.03593, over 953253.40 frames.], batch size: 61, lr: 4.11e-04
+2022-06-19 00:33:29,251 INFO [train.py:874] (3/4) Epoch 20, batch 1400, datatang_loss[loss=0.1344, simple_loss=0.2163, pruned_loss=0.02626, over 4936.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2312, pruned_loss=0.03431, over 984384.28 frames.], batch size: 79, aishell_tot_loss[loss=0.152, simple_loss=0.2381, pruned_loss=0.03294, over 954664.98 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2241, pruned_loss=0.0358, over 957024.74 frames.], batch size: 79, lr: 4.11e-04
+2022-06-19 00:33:59,695 INFO [train.py:874] (3/4) Epoch 20, batch 1450, datatang_loss[loss=0.147, simple_loss=0.2315, pruned_loss=0.03121, over 4921.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2317, pruned_loss=0.03442, over 984222.32 frames.], batch size: 75, aishell_tot_loss[loss=0.1523, simple_loss=0.2381, pruned_loss=0.03326, over 958872.62 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2241, pruned_loss=0.03569, over 959430.14 frames.], batch size: 75, lr: 4.11e-04
+2022-06-19 00:34:33,159 INFO [train.py:874] (3/4) Epoch 20, batch 1500, aishell_loss[loss=0.1654, simple_loss=0.2507, pruned_loss=0.04, over 4900.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2313, pruned_loss=0.03443, over 984291.56 frames.], batch size: 34, aishell_tot_loss[loss=0.1519, simple_loss=0.2376, pruned_loss=0.03307, over 961747.21 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2242, pruned_loss=0.03585, over 962529.11 frames.], batch size: 34, lr: 4.11e-04
+2022-06-19 00:35:03,394 INFO [train.py:874] (3/4) Epoch 20, batch 1550, datatang_loss[loss=0.195, simple_loss=0.2646, pruned_loss=0.06272, over 4949.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2314, pruned_loss=0.03493, over 984604.44 frames.], batch size: 108, aishell_tot_loss[loss=0.1524, simple_loss=0.2383, pruned_loss=0.03326, over 963555.23 frames.], datatang_tot_loss[loss=0.1483, simple_loss=0.2244, pruned_loss=0.03606, over 966197.29 frames.], batch size: 108, lr: 4.10e-04
+2022-06-19 00:35:34,721 INFO [train.py:874] (3/4) Epoch 20, batch 1600, datatang_loss[loss=0.1417, simple_loss=0.212, pruned_loss=0.03569, over 4959.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2323, pruned_loss=0.03496, over 985279.53 frames.], batch size: 60, aishell_tot_loss[loss=0.1527, simple_loss=0.2389, pruned_loss=0.03328, over 967074.91 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2245, pruned_loss=0.03619, over 968064.70 frames.], batch size: 60, lr: 4.10e-04
+2022-06-19 00:36:07,095 INFO [train.py:874] (3/4) Epoch 20, batch 1650, datatang_loss[loss=0.1462, simple_loss=0.2222, pruned_loss=0.03506, over 4922.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2325, pruned_loss=0.03504, over 985425.76 frames.], batch size: 83, aishell_tot_loss[loss=0.1527, simple_loss=0.2389, pruned_loss=0.03328, over 969227.00 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.225, pruned_loss=0.0363, over 970234.09 frames.], batch size: 83, lr: 4.10e-04
+2022-06-19 00:36:36,981 INFO [train.py:874] (3/4) Epoch 20, batch 1700, aishell_loss[loss=0.1703, simple_loss=0.2604, pruned_loss=0.04006, over 4889.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2314, pruned_loss=0.03459, over 985354.30 frames.], batch size: 42, aishell_tot_loss[loss=0.1521, simple_loss=0.2385, pruned_loss=0.03285, over 970863.25 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2244, pruned_loss=0.03629, over 972221.84 frames.], batch size: 42, lr: 4.10e-04
+2022-06-19 00:37:08,939 INFO [train.py:874] (3/4) Epoch 20, batch 1750, aishell_loss[loss=0.1643, simple_loss=0.2438, pruned_loss=0.0424, over 4936.00 frames.], tot_loss[loss=0.15, simple_loss=0.2308, pruned_loss=0.03455, over 985467.50 frames.], batch size: 33, aishell_tot_loss[loss=0.1521, simple_loss=0.2383, pruned_loss=0.03296, over 972418.98 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2242, pruned_loss=0.03612, over 974032.99 frames.], batch size: 33, lr: 4.10e-04
+2022-06-19 00:37:42,157 INFO [train.py:874] (3/4) Epoch 20, batch 1800, aishell_loss[loss=0.1684, simple_loss=0.2584, pruned_loss=0.03916, over 4913.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2307, pruned_loss=0.03449, over 985153.61 frames.], batch size: 78, aishell_tot_loss[loss=0.1515, simple_loss=0.2375, pruned_loss=0.03275, over 973858.46 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2244, pruned_loss=0.03635, over 975167.96 frames.], batch size: 78, lr: 4.10e-04
+2022-06-19 00:38:12,157 INFO [train.py:874] (3/4) Epoch 20, batch 1850, aishell_loss[loss=0.1494, simple_loss=0.2321, pruned_loss=0.03333, over 4879.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2306, pruned_loss=0.03451, over 985646.75 frames.], batch size: 28, aishell_tot_loss[loss=0.1511, simple_loss=0.237, pruned_loss=0.03264, over 975673.26 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2246, pruned_loss=0.03655, over 976393.00 frames.], batch size: 28, lr: 4.10e-04
+2022-06-19 00:38:43,893 INFO [train.py:874] (3/4) Epoch 20, batch 1900, aishell_loss[loss=0.1457, simple_loss=0.2538, pruned_loss=0.01883, over 4949.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2311, pruned_loss=0.03462, over 985305.07 frames.], batch size: 64, aishell_tot_loss[loss=0.1517, simple_loss=0.2379, pruned_loss=0.03276, over 976394.31 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2244, pruned_loss=0.03645, over 977540.85 frames.], batch size: 64, lr: 4.10e-04
+2022-06-19 00:39:16,035 INFO [train.py:874] (3/4) Epoch 20, batch 1950, datatang_loss[loss=0.1711, simple_loss=0.25, pruned_loss=0.04613, over 4953.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2315, pruned_loss=0.03489, over 985109.59 frames.], batch size: 45, aishell_tot_loss[loss=0.1515, simple_loss=0.2376, pruned_loss=0.03267, over 977287.72 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2249, pruned_loss=0.03688, over 978408.09 frames.], batch size: 45, lr: 4.09e-04
+2022-06-19 00:39:45,797 INFO [train.py:874] (3/4) Epoch 20, batch 2000, aishell_loss[loss=0.1403, simple_loss=0.231, pruned_loss=0.02483, over 4959.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2311, pruned_loss=0.0346, over 985166.47 frames.], batch size: 40, aishell_tot_loss[loss=0.1516, simple_loss=0.2378, pruned_loss=0.03272, over 978273.48 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2244, pruned_loss=0.03656, over 979190.94 frames.], batch size: 40, lr: 4.09e-04
+2022-06-19 00:39:45,798 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 00:40:02,387 INFO [train.py:914] (3/4) Epoch 20, validation: loss=0.1645, simple_loss=0.2482, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 00:40:32,715 INFO [train.py:874] (3/4) Epoch 20, batch 2050, datatang_loss[loss=0.1596, simple_loss=0.2405, pruned_loss=0.03931, over 4923.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2315, pruned_loss=0.03481, over 985003.24 frames.], batch size: 83, aishell_tot_loss[loss=0.1517, simple_loss=0.2379, pruned_loss=0.03277, over 978946.82 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2245, pruned_loss=0.03677, over 979861.80 frames.], batch size: 83, lr: 4.09e-04
+2022-06-19 00:41:04,535 INFO [train.py:874] (3/4) Epoch 20, batch 2100, datatang_loss[loss=0.1446, simple_loss=0.2157, pruned_loss=0.03674, over 4931.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2316, pruned_loss=0.03462, over 985201.71 frames.], batch size: 77, aishell_tot_loss[loss=0.1518, simple_loss=0.2383, pruned_loss=0.03267, over 979504.26 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2246, pruned_loss=0.03657, over 980804.83 frames.], batch size: 77, lr: 4.09e-04
+2022-06-19 00:41:37,745 INFO [train.py:874] (3/4) Epoch 20, batch 2150, aishell_loss[loss=0.1391, simple_loss=0.2317, pruned_loss=0.02323, over 4959.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2306, pruned_loss=0.03445, over 985428.05 frames.], batch size: 31, aishell_tot_loss[loss=0.1517, simple_loss=0.238, pruned_loss=0.03265, over 980039.63 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2243, pruned_loss=0.03633, over 981668.39 frames.], batch size: 31, lr: 4.09e-04
+2022-06-19 00:42:09,233 INFO [train.py:874] (3/4) Epoch 20, batch 2200, aishell_loss[loss=0.1728, simple_loss=0.2579, pruned_loss=0.04385, over 4942.00 frames.], tot_loss[loss=0.15, simple_loss=0.2307, pruned_loss=0.03462, over 985383.49 frames.], batch size: 56, aishell_tot_loss[loss=0.1524, simple_loss=0.2388, pruned_loss=0.03296, over 980448.87 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2236, pruned_loss=0.03614, over 982284.44 frames.], batch size: 56, lr: 4.09e-04
+2022-06-19 00:42:40,398 INFO [train.py:874] (3/4) Epoch 20, batch 2250, datatang_loss[loss=0.1428, simple_loss=0.2194, pruned_loss=0.03305, over 4917.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2306, pruned_loss=0.03507, over 985449.05 frames.], batch size: 81, aishell_tot_loss[loss=0.1526, simple_loss=0.2388, pruned_loss=0.0332, over 980986.62 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2239, pruned_loss=0.0363, over 982721.02 frames.], batch size: 81, lr: 4.09e-04
+2022-06-19 00:43:13,281 INFO [train.py:874] (3/4) Epoch 20, batch 2300, aishell_loss[loss=0.1097, simple_loss=0.1812, pruned_loss=0.01905, over 4812.00 frames.], tot_loss[loss=0.15, simple_loss=0.2308, pruned_loss=0.03458, over 985281.85 frames.], batch size: 24, aishell_tot_loss[loss=0.1517, simple_loss=0.2382, pruned_loss=0.03265, over 981291.19 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2242, pruned_loss=0.0365, over 983117.03 frames.], batch size: 24, lr: 4.09e-04
+2022-06-19 00:43:44,406 INFO [train.py:874] (3/4) Epoch 20, batch 2350, datatang_loss[loss=0.1661, simple_loss=0.2463, pruned_loss=0.04294, over 4952.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2308, pruned_loss=0.03446, over 985234.00 frames.], batch size: 69, aishell_tot_loss[loss=0.1516, simple_loss=0.238, pruned_loss=0.03256, over 981413.26 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2244, pruned_loss=0.03642, over 983644.91 frames.], batch size: 69, lr: 4.08e-04
+2022-06-19 00:44:15,738 INFO [train.py:874] (3/4) Epoch 20, batch 2400, datatang_loss[loss=0.151, simple_loss=0.236, pruned_loss=0.03304, over 4926.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2302, pruned_loss=0.03425, over 985542.07 frames.], batch size: 94, aishell_tot_loss[loss=0.1517, simple_loss=0.2379, pruned_loss=0.03278, over 982150.24 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2239, pruned_loss=0.03592, over 983858.55 frames.], batch size: 94, lr: 4.08e-04
+2022-06-19 00:44:49,192 INFO [train.py:874] (3/4) Epoch 20, batch 2450, aishell_loss[loss=0.1764, simple_loss=0.2624, pruned_loss=0.04517, over 4944.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2303, pruned_loss=0.03449, over 985991.14 frames.], batch size: 58, aishell_tot_loss[loss=0.1512, simple_loss=0.2375, pruned_loss=0.0325, over 982677.37 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2245, pruned_loss=0.03638, over 984386.15 frames.], batch size: 58, lr: 4.08e-04
+2022-06-19 00:45:21,556 INFO [train.py:874] (3/4) Epoch 20, batch 2500, datatang_loss[loss=0.146, simple_loss=0.2272, pruned_loss=0.03241, over 4949.00 frames.], tot_loss[loss=0.1494, simple_loss=0.23, pruned_loss=0.03437, over 985799.23 frames.], batch size: 91, aishell_tot_loss[loss=0.151, simple_loss=0.2369, pruned_loss=0.03257, over 982903.92 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2248, pruned_loss=0.03613, over 984507.00 frames.], batch size: 91, lr: 4.08e-04
+2022-06-19 00:45:51,673 INFO [train.py:874] (3/4) Epoch 20, batch 2550, datatang_loss[loss=0.147, simple_loss=0.2172, pruned_loss=0.03837, over 4876.00 frames.], tot_loss[loss=0.149, simple_loss=0.2293, pruned_loss=0.03438, over 985561.04 frames.], batch size: 39, aishell_tot_loss[loss=0.1518, simple_loss=0.2376, pruned_loss=0.033, over 983101.96 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2235, pruned_loss=0.03565, over 984529.34 frames.], batch size: 39, lr: 4.08e-04
+2022-06-19 00:46:25,381 INFO [train.py:874] (3/4) Epoch 20, batch 2600, datatang_loss[loss=0.1519, simple_loss=0.2174, pruned_loss=0.04322, over 4917.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2295, pruned_loss=0.03446, over 985483.78 frames.], batch size: 42, aishell_tot_loss[loss=0.1519, simple_loss=0.2381, pruned_loss=0.0328, over 983481.71 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2231, pruned_loss=0.0359, over 984458.05 frames.], batch size: 42, lr: 4.08e-04
+2022-06-19 00:46:57,831 INFO [train.py:874] (3/4) Epoch 20, batch 2650, aishell_loss[loss=0.1764, simple_loss=0.2588, pruned_loss=0.04704, over 4926.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2302, pruned_loss=0.03463, over 985685.69 frames.], batch size: 46, aishell_tot_loss[loss=0.1521, simple_loss=0.2382, pruned_loss=0.03298, over 983811.41 frames.], datatang_tot_loss[loss=0.1477, simple_loss=0.2234, pruned_loss=0.03595, over 984700.27 frames.], batch size: 46, lr: 4.08e-04
+2022-06-19 00:47:28,230 INFO [train.py:874] (3/4) Epoch 20, batch 2700, aishell_loss[loss=0.1554, simple_loss=0.2481, pruned_loss=0.03136, over 4983.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2296, pruned_loss=0.03399, over 985858.65 frames.], batch size: 39, aishell_tot_loss[loss=0.1515, simple_loss=0.238, pruned_loss=0.03249, over 984306.31 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2231, pruned_loss=0.03576, over 984704.55 frames.], batch size: 39, lr: 4.08e-04
+2022-06-19 00:48:02,058 INFO [train.py:874] (3/4) Epoch 20, batch 2750, datatang_loss[loss=0.2239, simple_loss=0.286, pruned_loss=0.08084, over 4947.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2291, pruned_loss=0.03405, over 985895.55 frames.], batch size: 108, aishell_tot_loss[loss=0.1512, simple_loss=0.2373, pruned_loss=0.03256, over 984546.79 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2231, pruned_loss=0.03568, over 984812.91 frames.], batch size: 108, lr: 4.07e-04
+2022-06-19 00:48:34,553 INFO [train.py:874] (3/4) Epoch 20, batch 2800, aishell_loss[loss=0.1589, simple_loss=0.2576, pruned_loss=0.03014, over 4910.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2292, pruned_loss=0.03409, over 985628.31 frames.], batch size: 68, aishell_tot_loss[loss=0.1515, simple_loss=0.2376, pruned_loss=0.03272, over 984710.07 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2227, pruned_loss=0.03554, over 984665.53 frames.], batch size: 68, lr: 4.07e-04
+2022-06-19 00:49:04,829 INFO [train.py:874] (3/4) Epoch 20, batch 2850, aishell_loss[loss=0.1515, simple_loss=0.2366, pruned_loss=0.03317, over 4882.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2284, pruned_loss=0.0336, over 985781.61 frames.], batch size: 50, aishell_tot_loss[loss=0.1511, simple_loss=0.2373, pruned_loss=0.03248, over 984721.90 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2221, pruned_loss=0.03522, over 985038.84 frames.], batch size: 50, lr: 4.07e-04
+2022-06-19 00:49:38,268 INFO [train.py:874] (3/4) Epoch 20, batch 2900, aishell_loss[loss=0.1518, simple_loss=0.2368, pruned_loss=0.03335, over 4878.00 frames.], tot_loss[loss=0.148, simple_loss=0.2285, pruned_loss=0.03377, over 985592.19 frames.], batch size: 42, aishell_tot_loss[loss=0.151, simple_loss=0.237, pruned_loss=0.03248, over 984612.22 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.222, pruned_loss=0.03536, over 985194.33 frames.], batch size: 42, lr: 4.07e-04
+2022-06-19 00:50:09,800 INFO [train.py:874] (3/4) Epoch 20, batch 2950, datatang_loss[loss=0.157, simple_loss=0.2377, pruned_loss=0.03815, over 4962.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2294, pruned_loss=0.03385, over 985784.58 frames.], batch size: 99, aishell_tot_loss[loss=0.1513, simple_loss=0.2376, pruned_loss=0.03252, over 984722.16 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2224, pruned_loss=0.03534, over 985434.82 frames.], batch size: 99, lr: 4.07e-04
+2022-06-19 00:50:39,737 INFO [train.py:874] (3/4) Epoch 20, batch 3000, datatang_loss[loss=0.1351, simple_loss=0.2216, pruned_loss=0.02428, over 4936.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2286, pruned_loss=0.0336, over 985592.85 frames.], batch size: 88, aishell_tot_loss[loss=0.1509, simple_loss=0.2369, pruned_loss=0.03244, over 984790.21 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2222, pruned_loss=0.03514, over 985355.13 frames.], batch size: 88, lr: 4.07e-04
+2022-06-19 00:50:39,738 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 00:50:56,987 INFO [train.py:914] (3/4) Epoch 20, validation: loss=0.164, simple_loss=0.2487, pruned_loss=0.03966, over 1622729.00 frames.
+2022-06-19 00:51:26,493 INFO [train.py:874] (3/4) Epoch 20, batch 3050, datatang_loss[loss=0.127, simple_loss=0.1972, pruned_loss=0.02834, over 4922.00 frames.], tot_loss[loss=0.1474, simple_loss=0.228, pruned_loss=0.03339, over 985380.64 frames.], batch size: 50, aishell_tot_loss[loss=0.1508, simple_loss=0.2367, pruned_loss=0.03244, over 984575.82 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2217, pruned_loss=0.03484, over 985441.10 frames.], batch size: 50, lr: 4.07e-04
+2022-06-19 00:51:58,862 INFO [train.py:874] (3/4) Epoch 20, batch 3100, datatang_loss[loss=0.1421, simple_loss=0.2192, pruned_loss=0.03246, over 4943.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2284, pruned_loss=0.03359, over 985229.85 frames.], batch size: 69, aishell_tot_loss[loss=0.1507, simple_loss=0.2364, pruned_loss=0.03246, over 984260.42 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2224, pruned_loss=0.03496, over 985659.49 frames.], batch size: 69, lr: 4.07e-04
+2022-06-19 00:52:31,258 INFO [train.py:874] (3/4) Epoch 20, batch 3150, datatang_loss[loss=0.1288, simple_loss=0.213, pruned_loss=0.02236, over 4918.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2282, pruned_loss=0.03352, over 985330.90 frames.], batch size: 57, aishell_tot_loss[loss=0.151, simple_loss=0.2367, pruned_loss=0.03267, over 984486.63 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2219, pruned_loss=0.0346, over 985580.84 frames.], batch size: 57, lr: 4.06e-04
+2022-06-19 00:53:02,594 INFO [train.py:874] (3/4) Epoch 20, batch 3200, datatang_loss[loss=0.1191, simple_loss=0.1927, pruned_loss=0.02275, over 4948.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2284, pruned_loss=0.03296, over 985686.69 frames.], batch size: 62, aishell_tot_loss[loss=0.1501, simple_loss=0.236, pruned_loss=0.03205, over 984896.27 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.222, pruned_loss=0.03464, over 985650.31 frames.], batch size: 62, lr: 4.06e-04
+2022-06-19 00:53:34,161 INFO [train.py:874] (3/4) Epoch 20, batch 3250, datatang_loss[loss=0.1808, simple_loss=0.248, pruned_loss=0.05674, over 4921.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2288, pruned_loss=0.0328, over 985484.01 frames.], batch size: 42, aishell_tot_loss[loss=0.1501, simple_loss=0.2364, pruned_loss=0.03192, over 984806.45 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.222, pruned_loss=0.03452, over 985627.75 frames.], batch size: 42, lr: 4.06e-04
+2022-06-19 00:54:06,348 INFO [train.py:874] (3/4) Epoch 20, batch 3300, datatang_loss[loss=0.1788, simple_loss=0.2516, pruned_loss=0.05295, over 4940.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2292, pruned_loss=0.03324, over 985805.89 frames.], batch size: 88, aishell_tot_loss[loss=0.1502, simple_loss=0.2364, pruned_loss=0.03199, over 984957.73 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2225, pruned_loss=0.0348, over 985876.58 frames.], batch size: 88, lr: 4.06e-04
+2022-06-19 00:54:36,933 INFO [train.py:874] (3/4) Epoch 20, batch 3350, datatang_loss[loss=0.1351, simple_loss=0.2149, pruned_loss=0.02762, over 4928.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2284, pruned_loss=0.033, over 985635.18 frames.], batch size: 83, aishell_tot_loss[loss=0.1499, simple_loss=0.236, pruned_loss=0.03193, over 984836.59 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.222, pruned_loss=0.03457, over 985905.48 frames.], batch size: 83, lr: 4.06e-04
+2022-06-19 00:55:10,722 INFO [train.py:874] (3/4) Epoch 20, batch 3400, datatang_loss[loss=0.1514, simple_loss=0.2321, pruned_loss=0.03541, over 4953.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2302, pruned_loss=0.03327, over 985665.83 frames.], batch size: 67, aishell_tot_loss[loss=0.1503, simple_loss=0.2368, pruned_loss=0.03187, over 984821.71 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2229, pruned_loss=0.03487, over 986009.28 frames.], batch size: 67, lr: 4.06e-04
+2022-06-19 00:55:42,692 INFO [train.py:874] (3/4) Epoch 20, batch 3450, datatang_loss[loss=0.1268, simple_loss=0.2079, pruned_loss=0.02284, over 4947.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2299, pruned_loss=0.03372, over 985226.55 frames.], batch size: 69, aishell_tot_loss[loss=0.151, simple_loss=0.2374, pruned_loss=0.03234, over 984548.02 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2225, pruned_loss=0.03479, over 985840.98 frames.], batch size: 69, lr: 4.06e-04
+2022-06-19 00:56:12,543 INFO [train.py:874] (3/4) Epoch 20, batch 3500, datatang_loss[loss=0.1347, simple_loss=0.2032, pruned_loss=0.03316, over 4915.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2304, pruned_loss=0.03393, over 985394.17 frames.], batch size: 52, aishell_tot_loss[loss=0.1514, simple_loss=0.2377, pruned_loss=0.03252, over 984699.86 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2227, pruned_loss=0.03489, over 985885.64 frames.], batch size: 52, lr: 4.06e-04
+2022-06-19 00:56:46,208 INFO [train.py:874] (3/4) Epoch 20, batch 3550, aishell_loss[loss=0.1603, simple_loss=0.2528, pruned_loss=0.03389, over 4950.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2298, pruned_loss=0.03403, over 985249.07 frames.], batch size: 56, aishell_tot_loss[loss=0.151, simple_loss=0.2373, pruned_loss=0.03232, over 984520.41 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2228, pruned_loss=0.0352, over 985901.87 frames.], batch size: 56, lr: 4.05e-04
+2022-06-19 00:57:18,986 INFO [train.py:874] (3/4) Epoch 20, batch 3600, aishell_loss[loss=0.1689, simple_loss=0.2554, pruned_loss=0.04117, over 4953.00 frames.], tot_loss[loss=0.15, simple_loss=0.2309, pruned_loss=0.03452, over 985213.40 frames.], batch size: 40, aishell_tot_loss[loss=0.1518, simple_loss=0.2381, pruned_loss=0.03273, over 984531.91 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2234, pruned_loss=0.03531, over 985814.97 frames.], batch size: 40, lr: 4.05e-04
+2022-06-19 00:57:49,560 INFO [train.py:874] (3/4) Epoch 20, batch 3650, aishell_loss[loss=0.1467, simple_loss=0.2438, pruned_loss=0.02486, over 4899.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2302, pruned_loss=0.0341, over 985101.82 frames.], batch size: 63, aishell_tot_loss[loss=0.1517, simple_loss=0.2379, pruned_loss=0.03269, over 984540.50 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2232, pruned_loss=0.03499, over 985682.47 frames.], batch size: 63, lr: 4.05e-04
+2022-06-19 00:58:21,962 INFO [train.py:874] (3/4) Epoch 20, batch 3700, aishell_loss[loss=0.1408, simple_loss=0.2317, pruned_loss=0.02493, over 4909.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2299, pruned_loss=0.03363, over 985194.06 frames.], batch size: 52, aishell_tot_loss[loss=0.1517, simple_loss=0.2382, pruned_loss=0.03258, over 984659.12 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2226, pruned_loss=0.03468, over 985649.21 frames.], batch size: 52, lr: 4.05e-04
+2022-06-19 00:58:52,962 INFO [train.py:874] (3/4) Epoch 20, batch 3750, aishell_loss[loss=0.1376, simple_loss=0.233, pruned_loss=0.02112, over 4960.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2296, pruned_loss=0.03338, over 985214.10 frames.], batch size: 61, aishell_tot_loss[loss=0.1517, simple_loss=0.2384, pruned_loss=0.03251, over 984540.69 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2221, pruned_loss=0.03446, over 985776.69 frames.], batch size: 61, lr: 4.05e-04
+2022-06-19 00:59:23,373 INFO [train.py:874] (3/4) Epoch 20, batch 3800, aishell_loss[loss=0.1588, simple_loss=0.2518, pruned_loss=0.03295, over 4907.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2287, pruned_loss=0.03291, over 985308.61 frames.], batch size: 41, aishell_tot_loss[loss=0.1515, simple_loss=0.238, pruned_loss=0.03247, over 984571.72 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2212, pruned_loss=0.03399, over 985890.20 frames.], batch size: 41, lr: 4.05e-04
+2022-06-19 00:59:56,378 INFO [train.py:874] (3/4) Epoch 20, batch 3850, aishell_loss[loss=0.17, simple_loss=0.2571, pruned_loss=0.0415, over 4913.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2285, pruned_loss=0.03332, over 985334.89 frames.], batch size: 41, aishell_tot_loss[loss=0.1519, simple_loss=0.2384, pruned_loss=0.03266, over 984524.27 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.221, pruned_loss=0.0341, over 985928.63 frames.], batch size: 41, lr: 4.05e-04
+2022-06-19 01:00:26,628 INFO [train.py:874] (3/4) Epoch 20, batch 3900, aishell_loss[loss=0.129, simple_loss=0.223, pruned_loss=0.01746, over 4968.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2288, pruned_loss=0.03377, over 985536.26 frames.], batch size: 30, aishell_tot_loss[loss=0.1522, simple_loss=0.2387, pruned_loss=0.03285, over 984803.94 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2209, pruned_loss=0.03439, over 985893.04 frames.], batch size: 30, lr: 4.05e-04
+2022-06-19 01:00:57,386 INFO [train.py:874] (3/4) Epoch 20, batch 3950, aishell_loss[loss=0.1394, simple_loss=0.2291, pruned_loss=0.02484, over 4961.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2291, pruned_loss=0.03341, over 985961.69 frames.], batch size: 61, aishell_tot_loss[loss=0.1525, simple_loss=0.239, pruned_loss=0.03297, over 985238.82 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2207, pruned_loss=0.03393, over 985939.40 frames.], batch size: 61, lr: 4.04e-04
+2022-06-19 01:01:28,338 INFO [train.py:874] (3/4) Epoch 20, batch 4000, datatang_loss[loss=0.1455, simple_loss=0.2273, pruned_loss=0.03186, over 4913.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2286, pruned_loss=0.03326, over 985854.52 frames.], batch size: 75, aishell_tot_loss[loss=0.1524, simple_loss=0.2387, pruned_loss=0.03308, over 985277.34 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2207, pruned_loss=0.03365, over 985868.15 frames.], batch size: 75, lr: 4.04e-04
+2022-06-19 01:01:28,340 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 01:01:44,968 INFO [train.py:914] (3/4) Epoch 20, validation: loss=0.1658, simple_loss=0.2493, pruned_loss=0.04118, over 1622729.00 frames.
+2022-06-19 01:02:16,073 INFO [train.py:874] (3/4) Epoch 20, batch 4050, aishell_loss[loss=0.148, simple_loss=0.2419, pruned_loss=0.02699, over 4867.00 frames.], tot_loss[loss=0.1472, simple_loss=0.228, pruned_loss=0.03316, over 985812.81 frames.], batch size: 35, aishell_tot_loss[loss=0.152, simple_loss=0.2382, pruned_loss=0.03286, over 985165.05 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2205, pruned_loss=0.03372, over 986000.30 frames.], batch size: 35, lr: 4.04e-04
+2022-06-19 01:02:45,538 INFO [train.py:874] (3/4) Epoch 20, batch 4100, aishell_loss[loss=0.1405, simple_loss=0.2341, pruned_loss=0.02347, over 4924.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2287, pruned_loss=0.03351, over 985768.08 frames.], batch size: 68, aishell_tot_loss[loss=0.1525, simple_loss=0.2386, pruned_loss=0.03322, over 985200.11 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2202, pruned_loss=0.03371, over 985982.17 frames.], batch size: 68, lr: 4.04e-04
+2022-06-19 01:03:16,360 INFO [train.py:874] (3/4) Epoch 20, batch 4150, datatang_loss[loss=0.1304, simple_loss=0.2113, pruned_loss=0.02476, over 4944.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2278, pruned_loss=0.03296, over 985771.74 frames.], batch size: 57, aishell_tot_loss[loss=0.1522, simple_loss=0.2382, pruned_loss=0.03308, over 985198.05 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2195, pruned_loss=0.03328, over 986027.02 frames.], batch size: 57, lr: 4.04e-04
+2022-06-19 01:03:46,116 INFO [train.py:874] (3/4) Epoch 20, batch 4200, aishell_loss[loss=0.1601, simple_loss=0.2396, pruned_loss=0.04028, over 4864.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2271, pruned_loss=0.03287, over 985637.40 frames.], batch size: 35, aishell_tot_loss[loss=0.152, simple_loss=0.238, pruned_loss=0.03304, over 985216.52 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.219, pruned_loss=0.03315, over 985898.71 frames.], batch size: 35, lr: 4.04e-04
+2022-06-19 01:04:16,283 INFO [train.py:874] (3/4) Epoch 20, batch 4250, aishell_loss[loss=0.1695, simple_loss=0.2543, pruned_loss=0.04234, over 4901.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2281, pruned_loss=0.03304, over 985542.21 frames.], batch size: 34, aishell_tot_loss[loss=0.1521, simple_loss=0.2382, pruned_loss=0.03294, over 984939.54 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2195, pruned_loss=0.03339, over 986096.10 frames.], batch size: 34, lr: 4.04e-04
+2022-06-19 01:05:47,395 INFO [train.py:874] (3/4) Epoch 21, batch 50, datatang_loss[loss=0.1276, simple_loss=0.1981, pruned_loss=0.02855, over 4965.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2233, pruned_loss=0.03119, over 218510.48 frames.], batch size: 55, aishell_tot_loss[loss=0.1514, simple_loss=0.2378, pruned_loss=0.03252, over 93993.56 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2136, pruned_loss=0.03033, over 137646.25 frames.], batch size: 55, lr: 3.94e-04
+2022-06-19 01:06:17,279 INFO [train.py:874] (3/4) Epoch 21, batch 100, aishell_loss[loss=0.1436, simple_loss=0.2205, pruned_loss=0.03338, over 4937.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2249, pruned_loss=0.03114, over 388734.56 frames.], batch size: 49, aishell_tot_loss[loss=0.1518, simple_loss=0.2394, pruned_loss=0.03208, over 199196.26 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2125, pruned_loss=0.03033, over 237508.57 frames.], batch size: 49, lr: 3.94e-04
+2022-06-19 01:06:49,613 INFO [train.py:874] (3/4) Epoch 21, batch 150, aishell_loss[loss=0.1673, simple_loss=0.2568, pruned_loss=0.03886, over 4937.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2261, pruned_loss=0.03248, over 521285.40 frames.], batch size: 45, aishell_tot_loss[loss=0.1536, simple_loss=0.24, pruned_loss=0.03357, over 298922.72 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2124, pruned_loss=0.0312, over 319020.66 frames.], batch size: 45, lr: 3.94e-04
+2022-06-19 01:07:21,610 INFO [train.py:874] (3/4) Epoch 21, batch 200, datatang_loss[loss=0.1334, simple_loss=0.2097, pruned_loss=0.02852, over 4941.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2278, pruned_loss=0.03294, over 624244.09 frames.], batch size: 69, aishell_tot_loss[loss=0.153, simple_loss=0.2393, pruned_loss=0.03331, over 391661.30 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2146, pruned_loss=0.03222, over 385833.96 frames.], batch size: 69, lr: 3.94e-04
+2022-06-19 01:07:51,004 INFO [train.py:874] (3/4) Epoch 21, batch 250, datatang_loss[loss=0.1182, simple_loss=0.2046, pruned_loss=0.0159, over 4924.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2279, pruned_loss=0.03289, over 704246.19 frames.], batch size: 83, aishell_tot_loss[loss=0.1532, simple_loss=0.2389, pruned_loss=0.03377, over 476969.38 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2141, pruned_loss=0.03166, over 440366.21 frames.], batch size: 83, lr: 3.94e-04
+2022-06-19 01:08:22,411 INFO [train.py:874] (3/4) Epoch 21, batch 300, datatang_loss[loss=0.1416, simple_loss=0.2183, pruned_loss=0.03245, over 4913.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2271, pruned_loss=0.0328, over 766680.79 frames.], batch size: 52, aishell_tot_loss[loss=0.152, simple_loss=0.2371, pruned_loss=0.03346, over 547958.97 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2144, pruned_loss=0.03184, over 492463.05 frames.], batch size: 52, lr: 3.94e-04
+2022-06-19 01:08:53,958 INFO [train.py:874] (3/4) Epoch 21, batch 350, aishell_loss[loss=0.1675, simple_loss=0.2591, pruned_loss=0.038, over 4886.00 frames.], tot_loss[loss=0.1461, simple_loss=0.227, pruned_loss=0.03261, over 814465.10 frames.], batch size: 47, aishell_tot_loss[loss=0.1518, simple_loss=0.237, pruned_loss=0.03331, over 600857.01 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2147, pruned_loss=0.03173, over 548116.70 frames.], batch size: 47, lr: 3.93e-04
+2022-06-19 01:09:24,494 INFO [train.py:874] (3/4) Epoch 21, batch 400, datatang_loss[loss=0.1723, simple_loss=0.2342, pruned_loss=0.05515, over 4923.00 frames.], tot_loss[loss=0.1465, simple_loss=0.227, pruned_loss=0.03298, over 852332.53 frames.], batch size: 81, aishell_tot_loss[loss=0.1511, simple_loss=0.236, pruned_loss=0.03308, over 649534.40 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2158, pruned_loss=0.03251, over 595708.73 frames.], batch size: 81, lr: 3.93e-04
+2022-06-19 01:09:56,176 INFO [train.py:874] (3/4) Epoch 21, batch 450, datatang_loss[loss=0.1627, simple_loss=0.2224, pruned_loss=0.05152, over 4971.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2273, pruned_loss=0.03303, over 881856.78 frames.], batch size: 45, aishell_tot_loss[loss=0.151, simple_loss=0.2366, pruned_loss=0.03276, over 685917.91 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2165, pruned_loss=0.03299, over 645249.00 frames.], batch size: 45, lr: 3.93e-04
+2022-06-19 01:10:28,254 INFO [train.py:874] (3/4) Epoch 21, batch 500, aishell_loss[loss=0.1511, simple_loss=0.237, pruned_loss=0.03256, over 4922.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2277, pruned_loss=0.03344, over 905197.57 frames.], batch size: 41, aishell_tot_loss[loss=0.1516, simple_loss=0.2372, pruned_loss=0.03305, over 717584.78 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2172, pruned_loss=0.03332, over 689821.97 frames.], batch size: 41, lr: 3.93e-04
+2022-06-19 01:10:58,451 INFO [train.py:874] (3/4) Epoch 21, batch 550, aishell_loss[loss=0.1544, simple_loss=0.2289, pruned_loss=0.03997, over 4868.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2273, pruned_loss=0.03326, over 922965.01 frames.], batch size: 36, aishell_tot_loss[loss=0.151, simple_loss=0.2364, pruned_loss=0.03282, over 747827.26 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2175, pruned_loss=0.0334, over 726040.54 frames.], batch size: 36, lr: 3.93e-04
+2022-06-19 01:11:30,272 INFO [train.py:874] (3/4) Epoch 21, batch 600, datatang_loss[loss=0.18, simple_loss=0.2496, pruned_loss=0.05523, over 4959.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2282, pruned_loss=0.03373, over 936798.75 frames.], batch size: 99, aishell_tot_loss[loss=0.1514, simple_loss=0.2369, pruned_loss=0.03301, over 775774.39 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2183, pruned_loss=0.03381, over 756607.50 frames.], batch size: 99, lr: 3.93e-04
+2022-06-19 01:12:03,212 INFO [train.py:874] (3/4) Epoch 21, batch 650, datatang_loss[loss=0.1488, simple_loss=0.2199, pruned_loss=0.0389, over 4926.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2276, pruned_loss=0.03398, over 947646.76 frames.], batch size: 83, aishell_tot_loss[loss=0.1513, simple_loss=0.2365, pruned_loss=0.03301, over 793791.50 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2189, pruned_loss=0.03417, over 790648.91 frames.], batch size: 83, lr: 3.93e-04
+2022-06-19 01:12:34,586 INFO [train.py:874] (3/4) Epoch 21, batch 700, datatang_loss[loss=0.142, simple_loss=0.2191, pruned_loss=0.03247, over 4952.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2267, pruned_loss=0.03347, over 956447.53 frames.], batch size: 55, aishell_tot_loss[loss=0.1504, simple_loss=0.2357, pruned_loss=0.03258, over 814903.63 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2187, pruned_loss=0.03403, over 815511.87 frames.], batch size: 55, lr: 3.93e-04
+2022-06-19 01:13:05,879 INFO [train.py:874] (3/4) Epoch 21, batch 750, aishell_loss[loss=0.194, simple_loss=0.2674, pruned_loss=0.06032, over 4955.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2274, pruned_loss=0.03362, over 963218.07 frames.], batch size: 31, aishell_tot_loss[loss=0.1506, simple_loss=0.2359, pruned_loss=0.03264, over 835305.54 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2193, pruned_loss=0.0342, over 835569.67 frames.], batch size: 31, lr: 3.93e-04
+2022-06-19 01:13:36,487 INFO [train.py:874] (3/4) Epoch 21, batch 800, datatang_loss[loss=0.2046, simple_loss=0.2703, pruned_loss=0.06949, over 4933.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2282, pruned_loss=0.03355, over 968081.68 frames.], batch size: 109, aishell_tot_loss[loss=0.1507, simple_loss=0.2365, pruned_loss=0.03249, over 853387.16 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2195, pruned_loss=0.03432, over 852761.23 frames.], batch size: 109, lr: 3.92e-04
+2022-06-19 01:14:08,679 INFO [train.py:874] (3/4) Epoch 21, batch 850, aishell_loss[loss=0.1686, simple_loss=0.2512, pruned_loss=0.04303, over 4873.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2287, pruned_loss=0.03359, over 971817.89 frames.], batch size: 42, aishell_tot_loss[loss=0.1508, simple_loss=0.2366, pruned_loss=0.03247, over 868938.01 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2201, pruned_loss=0.03444, over 868282.45 frames.], batch size: 42, lr: 3.92e-04
+2022-06-19 01:14:40,462 INFO [train.py:874] (3/4) Epoch 21, batch 900, aishell_loss[loss=0.1269, simple_loss=0.2185, pruned_loss=0.01763, over 4967.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2291, pruned_loss=0.03365, over 975209.25 frames.], batch size: 44, aishell_tot_loss[loss=0.1507, simple_loss=0.2367, pruned_loss=0.0324, over 883434.88 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2205, pruned_loss=0.03462, over 881700.69 frames.], batch size: 44, lr: 3.92e-04
+2022-06-19 01:15:10,954 INFO [train.py:874] (3/4) Epoch 21, batch 950, datatang_loss[loss=0.1471, simple_loss=0.231, pruned_loss=0.03161, over 4929.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2302, pruned_loss=0.03381, over 977648.49 frames.], batch size: 79, aishell_tot_loss[loss=0.1511, simple_loss=0.237, pruned_loss=0.03261, over 899068.64 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2209, pruned_loss=0.03473, over 890267.00 frames.], batch size: 79, lr: 3.92e-04
+2022-06-19 01:15:49,515 INFO [train.py:874] (3/4) Epoch 21, batch 1000, aishell_loss[loss=0.159, simple_loss=0.2471, pruned_loss=0.03543, over 4879.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2291, pruned_loss=0.03338, over 979605.41 frames.], batch size: 47, aishell_tot_loss[loss=0.1506, simple_loss=0.2364, pruned_loss=0.03238, over 907699.00 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2208, pruned_loss=0.03444, over 903381.10 frames.], batch size: 47, lr: 3.92e-04
+2022-06-19 01:15:49,516 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 01:16:06,687 INFO [train.py:914] (3/4) Epoch 21, validation: loss=0.1651, simple_loss=0.2487, pruned_loss=0.04071, over 1622729.00 frames.
+2022-06-19 01:16:39,126 INFO [train.py:874] (3/4) Epoch 21, batch 1050, datatang_loss[loss=0.1604, simple_loss=0.2329, pruned_loss=0.04396, over 4922.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2296, pruned_loss=0.03357, over 980814.02 frames.], batch size: 37, aishell_tot_loss[loss=0.1501, simple_loss=0.2357, pruned_loss=0.03222, over 918031.49 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2219, pruned_loss=0.03486, over 911678.07 frames.], batch size: 37, lr: 3.92e-04
+2022-06-19 01:17:12,028 INFO [train.py:874] (3/4) Epoch 21, batch 1100, datatang_loss[loss=0.1858, simple_loss=0.2604, pruned_loss=0.05561, over 4944.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2301, pruned_loss=0.03318, over 981739.64 frames.], batch size: 107, aishell_tot_loss[loss=0.1495, simple_loss=0.2356, pruned_loss=0.03171, over 927690.08 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2224, pruned_loss=0.03503, over 918312.62 frames.], batch size: 107, lr: 3.92e-04
+2022-06-19 01:17:43,377 INFO [train.py:874] (3/4) Epoch 21, batch 1150, datatang_loss[loss=0.1513, simple_loss=0.2256, pruned_loss=0.03847, over 4949.00 frames.], tot_loss[loss=0.149, simple_loss=0.231, pruned_loss=0.03347, over 982132.83 frames.], batch size: 62, aishell_tot_loss[loss=0.15, simple_loss=0.2364, pruned_loss=0.03185, over 934512.21 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2229, pruned_loss=0.03519, over 925689.98 frames.], batch size: 62, lr: 3.92e-04
+2022-06-19 01:18:16,615 INFO [train.py:874] (3/4) Epoch 21, batch 1200, datatang_loss[loss=0.1499, simple_loss=0.2234, pruned_loss=0.0382, over 4921.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2313, pruned_loss=0.03378, over 983032.45 frames.], batch size: 75, aishell_tot_loss[loss=0.1505, simple_loss=0.2367, pruned_loss=0.03215, over 941220.91 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2231, pruned_loss=0.03526, over 932064.30 frames.], batch size: 75, lr: 3.91e-04
+2022-06-19 01:18:47,859 INFO [train.py:874] (3/4) Epoch 21, batch 1250, aishell_loss[loss=0.1541, simple_loss=0.2401, pruned_loss=0.03405, over 4955.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2305, pruned_loss=0.03352, over 983418.17 frames.], batch size: 56, aishell_tot_loss[loss=0.1495, simple_loss=0.2356, pruned_loss=0.03169, over 947291.17 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2232, pruned_loss=0.03554, over 937045.73 frames.], batch size: 56, lr: 3.91e-04
+2022-06-19 01:19:18,420 INFO [train.py:874] (3/4) Epoch 21, batch 1300, aishell_loss[loss=0.1348, simple_loss=0.2147, pruned_loss=0.02745, over 4812.00 frames.], tot_loss[loss=0.1482, simple_loss=0.23, pruned_loss=0.03321, over 983614.25 frames.], batch size: 26, aishell_tot_loss[loss=0.1494, simple_loss=0.2355, pruned_loss=0.03164, over 951892.36 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2228, pruned_loss=0.03525, over 942285.77 frames.], batch size: 26, lr: 3.91e-04
+2022-06-19 01:19:51,553 INFO [train.py:874] (3/4) Epoch 21, batch 1350, aishell_loss[loss=0.1605, simple_loss=0.2499, pruned_loss=0.03551, over 4949.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2306, pruned_loss=0.03362, over 983941.67 frames.], batch size: 54, aishell_tot_loss[loss=0.15, simple_loss=0.2362, pruned_loss=0.03193, over 955549.64 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.223, pruned_loss=0.03534, over 947619.47 frames.], batch size: 54, lr: 3.91e-04
+2022-06-19 01:20:24,008 INFO [train.py:874] (3/4) Epoch 21, batch 1400, datatang_loss[loss=0.1374, simple_loss=0.2204, pruned_loss=0.02723, over 4931.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2298, pruned_loss=0.03349, over 984499.57 frames.], batch size: 83, aishell_tot_loss[loss=0.1498, simple_loss=0.2359, pruned_loss=0.03187, over 958984.99 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2228, pruned_loss=0.03524, over 952428.31 frames.], batch size: 83, lr: 3.91e-04
+2022-06-19 01:20:54,661 INFO [train.py:874] (3/4) Epoch 21, batch 1450, datatang_loss[loss=0.1273, simple_loss=0.2046, pruned_loss=0.02504, over 4971.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2293, pruned_loss=0.03302, over 984790.34 frames.], batch size: 26, aishell_tot_loss[loss=0.1499, simple_loss=0.2361, pruned_loss=0.03189, over 962111.66 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2222, pruned_loss=0.03471, over 956391.61 frames.], batch size: 26, lr: 3.91e-04
+2022-06-19 01:21:28,294 INFO [train.py:874] (3/4) Epoch 21, batch 1500, datatang_loss[loss=0.1556, simple_loss=0.2402, pruned_loss=0.03551, over 4927.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2285, pruned_loss=0.03289, over 984818.70 frames.], batch size: 94, aishell_tot_loss[loss=0.1494, simple_loss=0.2354, pruned_loss=0.03167, over 964214.60 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2222, pruned_loss=0.03469, over 960405.82 frames.], batch size: 94, lr: 3.91e-04
+2022-06-19 01:21:57,789 INFO [train.py:874] (3/4) Epoch 21, batch 1550, aishell_loss[loss=0.1237, simple_loss=0.2089, pruned_loss=0.0193, over 4888.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2282, pruned_loss=0.03275, over 985149.13 frames.], batch size: 28, aishell_tot_loss[loss=0.1489, simple_loss=0.2348, pruned_loss=0.03145, over 966984.35 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2223, pruned_loss=0.03473, over 963260.70 frames.], batch size: 28, lr: 3.91e-04
+2022-06-19 01:22:31,082 INFO [train.py:874] (3/4) Epoch 21, batch 1600, datatang_loss[loss=0.1677, simple_loss=0.2593, pruned_loss=0.03803, over 4937.00 frames.], tot_loss[loss=0.1475, simple_loss=0.229, pruned_loss=0.03303, over 985140.05 frames.], batch size: 94, aishell_tot_loss[loss=0.1491, simple_loss=0.2352, pruned_loss=0.03153, over 969484.48 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2225, pruned_loss=0.03495, over 965411.89 frames.], batch size: 94, lr: 3.91e-04
+2022-06-19 01:23:03,007 INFO [train.py:874] (3/4) Epoch 21, batch 1650, datatang_loss[loss=0.1246, simple_loss=0.203, pruned_loss=0.02306, over 4919.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2298, pruned_loss=0.03357, over 984890.03 frames.], batch size: 73, aishell_tot_loss[loss=0.1501, simple_loss=0.2362, pruned_loss=0.03199, over 970979.13 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2224, pruned_loss=0.03499, over 967846.69 frames.], batch size: 73, lr: 3.90e-04
+2022-06-19 01:23:32,773 INFO [train.py:874] (3/4) Epoch 21, batch 1700, aishell_loss[loss=0.127, simple_loss=0.2097, pruned_loss=0.02217, over 4903.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2296, pruned_loss=0.03341, over 985123.87 frames.], batch size: 28, aishell_tot_loss[loss=0.1499, simple_loss=0.236, pruned_loss=0.03186, over 972409.80 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2227, pruned_loss=0.03492, over 970348.84 frames.], batch size: 28, lr: 3.90e-04
+2022-06-19 01:24:06,713 INFO [train.py:874] (3/4) Epoch 21, batch 1750, aishell_loss[loss=0.1739, simple_loss=0.258, pruned_loss=0.04483, over 4898.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2294, pruned_loss=0.03366, over 985120.12 frames.], batch size: 34, aishell_tot_loss[loss=0.1498, simple_loss=0.236, pruned_loss=0.03186, over 973466.71 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2229, pruned_loss=0.03511, over 972556.86 frames.], batch size: 34, lr: 3.90e-04
+2022-06-19 01:24:40,323 INFO [train.py:874] (3/4) Epoch 21, batch 1800, datatang_loss[loss=0.155, simple_loss=0.2292, pruned_loss=0.04042, over 4904.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2301, pruned_loss=0.03361, over 985130.85 frames.], batch size: 52, aishell_tot_loss[loss=0.1501, simple_loss=0.2363, pruned_loss=0.03198, over 974960.23 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2231, pruned_loss=0.03504, over 973900.71 frames.], batch size: 52, lr: 3.90e-04
+2022-06-19 01:25:09,471 INFO [train.py:874] (3/4) Epoch 21, batch 1850, datatang_loss[loss=0.1519, simple_loss=0.2253, pruned_loss=0.03927, over 4916.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2305, pruned_loss=0.03396, over 985291.50 frames.], batch size: 52, aishell_tot_loss[loss=0.1507, simple_loss=0.2368, pruned_loss=0.03228, over 976396.44 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2231, pruned_loss=0.03517, over 975143.09 frames.], batch size: 52, lr: 3.90e-04
+2022-06-19 01:25:41,933 INFO [train.py:874] (3/4) Epoch 21, batch 1900, aishell_loss[loss=0.166, simple_loss=0.2483, pruned_loss=0.04179, over 4935.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2308, pruned_loss=0.03415, over 985542.03 frames.], batch size: 58, aishell_tot_loss[loss=0.1513, simple_loss=0.237, pruned_loss=0.03274, over 977807.28 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2229, pruned_loss=0.03503, over 976208.09 frames.], batch size: 58, lr: 3.90e-04
+2022-06-19 01:26:13,800 INFO [train.py:874] (3/4) Epoch 21, batch 1950, datatang_loss[loss=0.1274, simple_loss=0.209, pruned_loss=0.02292, over 4928.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2305, pruned_loss=0.0341, over 985618.55 frames.], batch size: 77, aishell_tot_loss[loss=0.1509, simple_loss=0.2366, pruned_loss=0.03259, over 978549.51 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2233, pruned_loss=0.03518, over 977590.18 frames.], batch size: 77, lr: 3.90e-04
+2022-06-19 01:26:45,073 INFO [train.py:874] (3/4) Epoch 21, batch 2000, aishell_loss[loss=0.1699, simple_loss=0.2613, pruned_loss=0.03932, over 4877.00 frames.], tot_loss[loss=0.1498, simple_loss=0.231, pruned_loss=0.03428, over 985419.41 frames.], batch size: 42, aishell_tot_loss[loss=0.1508, simple_loss=0.2366, pruned_loss=0.03248, over 979302.16 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2238, pruned_loss=0.03561, over 978389.47 frames.], batch size: 42, lr: 3.90e-04
+2022-06-19 01:26:45,074 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 01:27:02,719 INFO [train.py:914] (3/4) Epoch 21, validation: loss=0.164, simple_loss=0.2481, pruned_loss=0.03994, over 1622729.00 frames.
+2022-06-19 01:27:32,223 INFO [train.py:874] (3/4) Epoch 21, batch 2050, aishell_loss[loss=0.1428, simple_loss=0.2254, pruned_loss=0.03008, over 4961.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2303, pruned_loss=0.03447, over 985655.38 frames.], batch size: 40, aishell_tot_loss[loss=0.1504, simple_loss=0.2361, pruned_loss=0.03232, over 979890.31 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2239, pruned_loss=0.03595, over 979591.48 frames.], batch size: 40, lr: 3.90e-04
+2022-06-19 01:28:06,114 INFO [train.py:874] (3/4) Epoch 21, batch 2100, aishell_loss[loss=0.1544, simple_loss=0.244, pruned_loss=0.03234, over 4924.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2302, pruned_loss=0.03432, over 985471.81 frames.], batch size: 58, aishell_tot_loss[loss=0.1502, simple_loss=0.236, pruned_loss=0.03227, over 980296.62 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.224, pruned_loss=0.03588, over 980369.23 frames.], batch size: 58, lr: 3.89e-04
+2022-06-19 01:28:39,739 INFO [train.py:874] (3/4) Epoch 21, batch 2150, aishell_loss[loss=0.1651, simple_loss=0.252, pruned_loss=0.03906, over 4977.00 frames.], tot_loss[loss=0.149, simple_loss=0.2296, pruned_loss=0.03419, over 985326.94 frames.], batch size: 44, aishell_tot_loss[loss=0.1502, simple_loss=0.2357, pruned_loss=0.03232, over 980887.11 frames.], datatang_tot_loss[loss=0.1476, simple_loss=0.2237, pruned_loss=0.03573, over 980822.10 frames.], batch size: 44, lr: 3.89e-04
+2022-06-19 01:29:09,164 INFO [train.py:874] (3/4) Epoch 21, batch 2200, aishell_loss[loss=0.1662, simple_loss=0.2539, pruned_loss=0.03919, over 4943.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2298, pruned_loss=0.03441, over 985442.32 frames.], batch size: 45, aishell_tot_loss[loss=0.1505, simple_loss=0.236, pruned_loss=0.03249, over 981295.85 frames.], datatang_tot_loss[loss=0.1477, simple_loss=0.2238, pruned_loss=0.03581, over 981578.25 frames.], batch size: 45, lr: 3.89e-04
+2022-06-19 01:29:41,158 INFO [train.py:874] (3/4) Epoch 21, batch 2250, datatang_loss[loss=0.1445, simple_loss=0.2217, pruned_loss=0.03364, over 4916.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2293, pruned_loss=0.03394, over 985859.84 frames.], batch size: 75, aishell_tot_loss[loss=0.1503, simple_loss=0.2356, pruned_loss=0.03249, over 982164.65 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2235, pruned_loss=0.0354, over 982103.47 frames.], batch size: 75, lr: 3.89e-04
+2022-06-19 01:30:15,211 INFO [train.py:874] (3/4) Epoch 21, batch 2300, aishell_loss[loss=0.1501, simple_loss=0.2349, pruned_loss=0.03269, over 4985.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2298, pruned_loss=0.03452, over 985777.05 frames.], batch size: 38, aishell_tot_loss[loss=0.1505, simple_loss=0.2357, pruned_loss=0.0326, over 982564.56 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.224, pruned_loss=0.03589, over 982491.17 frames.], batch size: 38, lr: 3.89e-04
+2022-06-19 01:30:45,620 INFO [train.py:874] (3/4) Epoch 21, batch 2350, aishell_loss[loss=0.1479, simple_loss=0.2269, pruned_loss=0.03445, over 4919.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2291, pruned_loss=0.03406, over 985462.82 frames.], batch size: 33, aishell_tot_loss[loss=0.1507, simple_loss=0.2359, pruned_loss=0.03276, over 982559.91 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2232, pruned_loss=0.03532, over 982917.43 frames.], batch size: 33, lr: 3.89e-04
+2022-06-19 01:31:19,371 INFO [train.py:874] (3/4) Epoch 21, batch 2400, datatang_loss[loss=0.1383, simple_loss=0.2215, pruned_loss=0.02754, over 4921.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2293, pruned_loss=0.03347, over 985394.30 frames.], batch size: 83, aishell_tot_loss[loss=0.1498, simple_loss=0.2354, pruned_loss=0.03207, over 982848.35 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2236, pruned_loss=0.03543, over 983202.70 frames.], batch size: 83, lr: 3.89e-04
+2022-06-19 01:31:50,759 INFO [train.py:874] (3/4) Epoch 21, batch 2450, datatang_loss[loss=0.1503, simple_loss=0.2207, pruned_loss=0.03996, over 4873.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2285, pruned_loss=0.03339, over 985555.80 frames.], batch size: 39, aishell_tot_loss[loss=0.1498, simple_loss=0.235, pruned_loss=0.03223, over 983027.74 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2233, pruned_loss=0.03506, over 983765.70 frames.], batch size: 39, lr: 3.89e-04
+2022-06-19 01:32:21,770 INFO [train.py:874] (3/4) Epoch 21, batch 2500, aishell_loss[loss=0.1506, simple_loss=0.2395, pruned_loss=0.0308, over 4899.00 frames.], tot_loss[loss=0.1478, simple_loss=0.229, pruned_loss=0.03327, over 985874.99 frames.], batch size: 34, aishell_tot_loss[loss=0.1501, simple_loss=0.2355, pruned_loss=0.03233, over 983469.96 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2228, pruned_loss=0.03485, over 984200.77 frames.], batch size: 34, lr: 3.89e-04
+2022-06-19 01:32:56,181 INFO [train.py:874] (3/4) Epoch 21, batch 2550, aishell_loss[loss=0.1454, simple_loss=0.2344, pruned_loss=0.02819, over 4892.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2285, pruned_loss=0.03305, over 985416.81 frames.], batch size: 42, aishell_tot_loss[loss=0.15, simple_loss=0.2355, pruned_loss=0.03225, over 983287.09 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2225, pruned_loss=0.03459, over 984363.09 frames.], batch size: 42, lr: 3.88e-04
+2022-06-19 01:33:29,099 INFO [train.py:874] (3/4) Epoch 21, batch 2600, datatang_loss[loss=0.1493, simple_loss=0.2259, pruned_loss=0.03631, over 4921.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2286, pruned_loss=0.03301, over 985527.53 frames.], batch size: 75, aishell_tot_loss[loss=0.1501, simple_loss=0.2355, pruned_loss=0.03236, over 983484.07 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2225, pruned_loss=0.03436, over 984663.62 frames.], batch size: 75, lr: 3.88e-04
+2022-06-19 01:33:59,984 INFO [train.py:874] (3/4) Epoch 21, batch 2650, aishell_loss[loss=0.1645, simple_loss=0.2519, pruned_loss=0.03855, over 4966.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2286, pruned_loss=0.03284, over 985660.23 frames.], batch size: 79, aishell_tot_loss[loss=0.1496, simple_loss=0.2352, pruned_loss=0.032, over 984001.91 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2227, pruned_loss=0.03445, over 984631.38 frames.], batch size: 79, lr: 3.88e-04
+2022-06-19 01:34:33,297 INFO [train.py:874] (3/4) Epoch 21, batch 2700, aishell_loss[loss=0.132, simple_loss=0.22, pruned_loss=0.02202, over 4857.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2284, pruned_loss=0.03357, over 985078.83 frames.], batch size: 28, aishell_tot_loss[loss=0.149, simple_loss=0.2347, pruned_loss=0.03169, over 983604.69 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2232, pruned_loss=0.03537, over 984713.00 frames.], batch size: 28, lr: 3.88e-04
+2022-06-19 01:35:05,929 INFO [train.py:874] (3/4) Epoch 21, batch 2750, aishell_loss[loss=0.1524, simple_loss=0.2353, pruned_loss=0.03479, over 4939.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2295, pruned_loss=0.0338, over 985657.62 frames.], batch size: 49, aishell_tot_loss[loss=0.1495, simple_loss=0.2356, pruned_loss=0.03175, over 984225.15 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2235, pruned_loss=0.03552, over 984910.06 frames.], batch size: 49, lr: 3.88e-04
+2022-06-19 01:35:36,821 INFO [train.py:874] (3/4) Epoch 21, batch 2800, aishell_loss[loss=0.1585, simple_loss=0.2463, pruned_loss=0.03534, over 4921.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2302, pruned_loss=0.03427, over 985941.83 frames.], batch size: 78, aishell_tot_loss[loss=0.1504, simple_loss=0.2363, pruned_loss=0.03223, over 984470.39 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2235, pruned_loss=0.03558, over 985226.61 frames.], batch size: 78, lr: 3.88e-04
+2022-06-19 01:36:09,430 INFO [train.py:874] (3/4) Epoch 21, batch 2850, datatang_loss[loss=0.1648, simple_loss=0.2323, pruned_loss=0.04867, over 4930.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2298, pruned_loss=0.03378, over 985568.07 frames.], batch size: 88, aishell_tot_loss[loss=0.1499, simple_loss=0.2359, pruned_loss=0.03194, over 984450.27 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2232, pruned_loss=0.03552, over 985120.97 frames.], batch size: 88, lr: 3.88e-04
+2022-06-19 01:36:39,719 INFO [train.py:874] (3/4) Epoch 21, batch 2900, datatang_loss[loss=0.1381, simple_loss=0.215, pruned_loss=0.03059, over 4950.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2296, pruned_loss=0.03345, over 985671.35 frames.], batch size: 62, aishell_tot_loss[loss=0.1502, simple_loss=0.2363, pruned_loss=0.03202, over 984556.72 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2228, pruned_loss=0.03506, over 985288.96 frames.], batch size: 62, lr: 3.88e-04
+2022-06-19 01:37:10,805 INFO [train.py:874] (3/4) Epoch 21, batch 2950, datatang_loss[loss=0.1449, simple_loss=0.2189, pruned_loss=0.03546, over 4913.00 frames.], tot_loss[loss=0.148, simple_loss=0.2298, pruned_loss=0.03314, over 985640.93 frames.], batch size: 81, aishell_tot_loss[loss=0.1501, simple_loss=0.2365, pruned_loss=0.03186, over 984657.41 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2225, pruned_loss=0.03494, over 985350.01 frames.], batch size: 81, lr: 3.87e-04
+2022-06-19 01:37:43,745 INFO [train.py:874] (3/4) Epoch 21, batch 3000, aishell_loss[loss=0.141, simple_loss=0.2313, pruned_loss=0.02531, over 4885.00 frames.], tot_loss[loss=0.1492, simple_loss=0.231, pruned_loss=0.03372, over 985532.15 frames.], batch size: 42, aishell_tot_loss[loss=0.1507, simple_loss=0.2372, pruned_loss=0.03211, over 984644.67 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2231, pruned_loss=0.03525, over 985401.56 frames.], batch size: 42, lr: 3.87e-04
+2022-06-19 01:37:43,746 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 01:38:00,375 INFO [train.py:914] (3/4) Epoch 21, validation: loss=0.1648, simple_loss=0.2487, pruned_loss=0.04045, over 1622729.00 frames.
+2022-06-19 01:38:34,205 INFO [train.py:874] (3/4) Epoch 21, batch 3050, aishell_loss[loss=0.1521, simple_loss=0.2383, pruned_loss=0.03292, over 4972.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2299, pruned_loss=0.03327, over 985266.11 frames.], batch size: 61, aishell_tot_loss[loss=0.15, simple_loss=0.2362, pruned_loss=0.03195, over 984381.16 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2229, pruned_loss=0.03501, over 985542.06 frames.], batch size: 61, lr: 3.87e-04
+2022-06-19 01:39:07,213 INFO [train.py:874] (3/4) Epoch 21, batch 3100, aishell_loss[loss=0.1669, simple_loss=0.2544, pruned_loss=0.0397, over 4929.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2309, pruned_loss=0.03384, over 985176.52 frames.], batch size: 49, aishell_tot_loss[loss=0.1504, simple_loss=0.2367, pruned_loss=0.0321, over 984357.82 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2236, pruned_loss=0.0354, over 985532.84 frames.], batch size: 49, lr: 3.87e-04
+2022-06-19 01:39:37,336 INFO [train.py:874] (3/4) Epoch 21, batch 3150, aishell_loss[loss=0.1824, simple_loss=0.2696, pruned_loss=0.04763, over 4860.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2309, pruned_loss=0.03437, over 985565.97 frames.], batch size: 36, aishell_tot_loss[loss=0.1509, simple_loss=0.237, pruned_loss=0.03238, over 984629.56 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2236, pruned_loss=0.03562, over 985712.01 frames.], batch size: 36, lr: 3.87e-04
+2022-06-19 01:40:10,738 INFO [train.py:874] (3/4) Epoch 21, batch 3200, aishell_loss[loss=0.1447, simple_loss=0.2337, pruned_loss=0.0278, over 4830.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2312, pruned_loss=0.03408, over 985440.58 frames.], batch size: 29, aishell_tot_loss[loss=0.1508, simple_loss=0.237, pruned_loss=0.0323, over 984625.60 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2236, pruned_loss=0.0356, over 985730.54 frames.], batch size: 29, lr: 3.87e-04
+2022-06-19 01:40:43,359 INFO [train.py:874] (3/4) Epoch 21, batch 3250, datatang_loss[loss=0.1403, simple_loss=0.2178, pruned_loss=0.03139, over 4950.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2307, pruned_loss=0.03438, over 985813.44 frames.], batch size: 62, aishell_tot_loss[loss=0.1506, simple_loss=0.2368, pruned_loss=0.03223, over 984707.49 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2239, pruned_loss=0.03589, over 986061.23 frames.], batch size: 62, lr: 3.87e-04
+2022-06-19 01:41:14,482 INFO [train.py:874] (3/4) Epoch 21, batch 3300, datatang_loss[loss=0.1302, simple_loss=0.2068, pruned_loss=0.02676, over 4929.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2305, pruned_loss=0.03426, over 985787.95 frames.], batch size: 79, aishell_tot_loss[loss=0.1505, simple_loss=0.2363, pruned_loss=0.03229, over 984741.81 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2242, pruned_loss=0.03582, over 986123.17 frames.], batch size: 79, lr: 3.87e-04
+2022-06-19 01:41:47,813 INFO [train.py:874] (3/4) Epoch 21, batch 3350, datatang_loss[loss=0.1396, simple_loss=0.2133, pruned_loss=0.03298, over 4905.00 frames.], tot_loss[loss=0.1486, simple_loss=0.23, pruned_loss=0.03361, over 985993.27 frames.], batch size: 47, aishell_tot_loss[loss=0.1501, simple_loss=0.2361, pruned_loss=0.03205, over 985030.88 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2237, pruned_loss=0.0355, over 986170.98 frames.], batch size: 47, lr: 3.87e-04
+2022-06-19 01:42:19,530 INFO [train.py:874] (3/4) Epoch 21, batch 3400, datatang_loss[loss=0.1446, simple_loss=0.2136, pruned_loss=0.03774, over 4935.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2306, pruned_loss=0.03383, over 986312.02 frames.], batch size: 45, aishell_tot_loss[loss=0.1506, simple_loss=0.2364, pruned_loss=0.03241, over 985370.12 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2238, pruned_loss=0.03541, over 986289.69 frames.], batch size: 45, lr: 3.86e-04
+2022-06-19 01:42:51,720 INFO [train.py:874] (3/4) Epoch 21, batch 3450, datatang_loss[loss=0.1675, simple_loss=0.2443, pruned_loss=0.04536, over 4945.00 frames.], tot_loss[loss=0.1493, simple_loss=0.231, pruned_loss=0.03382, over 985888.04 frames.], batch size: 109, aishell_tot_loss[loss=0.1513, simple_loss=0.2373, pruned_loss=0.03266, over 985413.34 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2235, pruned_loss=0.0351, over 985892.20 frames.], batch size: 109, lr: 3.86e-04
+2022-06-19 01:43:25,951 INFO [train.py:874] (3/4) Epoch 21, batch 3500, datatang_loss[loss=0.1433, simple_loss=0.2235, pruned_loss=0.03158, over 4932.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2308, pruned_loss=0.03397, over 985911.14 frames.], batch size: 79, aishell_tot_loss[loss=0.1509, simple_loss=0.237, pruned_loss=0.03241, over 985640.06 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2238, pruned_loss=0.03546, over 985749.01 frames.], batch size: 79, lr: 3.86e-04
+2022-06-19 01:43:56,550 INFO [train.py:874] (3/4) Epoch 21, batch 3550, datatang_loss[loss=0.1407, simple_loss=0.2116, pruned_loss=0.03496, over 4914.00 frames.], tot_loss[loss=0.149, simple_loss=0.2302, pruned_loss=0.03384, over 985709.18 frames.], batch size: 75, aishell_tot_loss[loss=0.1512, simple_loss=0.237, pruned_loss=0.03263, over 985658.20 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2234, pruned_loss=0.0351, over 985576.93 frames.], batch size: 75, lr: 3.86e-04
+2022-06-19 01:44:29,760 INFO [train.py:874] (3/4) Epoch 21, batch 3600, aishell_loss[loss=0.1467, simple_loss=0.2327, pruned_loss=0.03031, over 4966.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2303, pruned_loss=0.03399, over 985816.24 frames.], batch size: 61, aishell_tot_loss[loss=0.1511, simple_loss=0.2372, pruned_loss=0.03249, over 985789.50 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2234, pruned_loss=0.03537, over 985581.15 frames.], batch size: 61, lr: 3.86e-04
+2022-06-19 01:45:03,520 INFO [train.py:874] (3/4) Epoch 21, batch 3650, datatang_loss[loss=0.1455, simple_loss=0.2208, pruned_loss=0.03512, over 4937.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2296, pruned_loss=0.03352, over 985506.09 frames.], batch size: 25, aishell_tot_loss[loss=0.151, simple_loss=0.2373, pruned_loss=0.03231, over 985499.76 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2227, pruned_loss=0.03502, over 985569.92 frames.], batch size: 25, lr: 3.86e-04
+2022-06-19 01:45:34,440 INFO [train.py:874] (3/4) Epoch 21, batch 3700, datatang_loss[loss=0.1317, simple_loss=0.2128, pruned_loss=0.02528, over 4920.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2297, pruned_loss=0.03337, over 985703.95 frames.], batch size: 75, aishell_tot_loss[loss=0.1509, simple_loss=0.2374, pruned_loss=0.03222, over 985542.01 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2227, pruned_loss=0.03491, over 985719.65 frames.], batch size: 75, lr: 3.86e-04
+2022-06-19 01:46:07,256 INFO [train.py:874] (3/4) Epoch 21, batch 3750, aishell_loss[loss=0.1468, simple_loss=0.2333, pruned_loss=0.03012, over 4970.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2293, pruned_loss=0.03327, over 985750.40 frames.], batch size: 40, aishell_tot_loss[loss=0.1506, simple_loss=0.2371, pruned_loss=0.03209, over 985658.56 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2225, pruned_loss=0.03487, over 985677.05 frames.], batch size: 40, lr: 3.86e-04
+2022-06-19 01:46:36,137 INFO [train.py:874] (3/4) Epoch 21, batch 3800, aishell_loss[loss=0.1232, simple_loss=0.1899, pruned_loss=0.02827, over 4894.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2298, pruned_loss=0.03295, over 985578.96 frames.], batch size: 21, aishell_tot_loss[loss=0.1497, simple_loss=0.2363, pruned_loss=0.03157, over 985348.57 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2232, pruned_loss=0.03515, over 985840.81 frames.], batch size: 21, lr: 3.86e-04
+2022-06-19 01:47:09,332 INFO [train.py:874] (3/4) Epoch 21, batch 3850, datatang_loss[loss=0.1699, simple_loss=0.2532, pruned_loss=0.04325, over 4894.00 frames.], tot_loss[loss=0.1482, simple_loss=0.23, pruned_loss=0.03319, over 985621.50 frames.], batch size: 52, aishell_tot_loss[loss=0.151, simple_loss=0.2375, pruned_loss=0.03225, over 985290.98 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2225, pruned_loss=0.03461, over 985916.32 frames.], batch size: 52, lr: 3.85e-04
+2022-06-19 01:47:38,027 INFO [train.py:874] (3/4) Epoch 21, batch 3900, datatang_loss[loss=0.1309, simple_loss=0.206, pruned_loss=0.02788, over 4988.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2301, pruned_loss=0.03317, over 985405.86 frames.], batch size: 31, aishell_tot_loss[loss=0.1504, simple_loss=0.2369, pruned_loss=0.03197, over 984952.05 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2227, pruned_loss=0.03487, over 986039.26 frames.], batch size: 31, lr: 3.85e-04
+2022-06-19 01:48:11,155 INFO [train.py:874] (3/4) Epoch 21, batch 3950, datatang_loss[loss=0.1322, simple_loss=0.2085, pruned_loss=0.02795, over 4945.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2299, pruned_loss=0.03364, over 985689.75 frames.], batch size: 67, aishell_tot_loss[loss=0.1507, simple_loss=0.237, pruned_loss=0.03218, over 985232.71 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2226, pruned_loss=0.03506, over 986029.89 frames.], batch size: 67, lr: 3.85e-04
+2022-06-19 01:48:40,055 INFO [train.py:874] (3/4) Epoch 21, batch 4000, aishell_loss[loss=0.1456, simple_loss=0.2343, pruned_loss=0.02845, over 4901.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2298, pruned_loss=0.03336, over 985830.38 frames.], batch size: 41, aishell_tot_loss[loss=0.1508, simple_loss=0.2371, pruned_loss=0.03224, over 985277.52 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2222, pruned_loss=0.03474, over 986171.70 frames.], batch size: 41, lr: 3.85e-04
+2022-06-19 01:48:40,056 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 01:48:57,403 INFO [train.py:914] (3/4) Epoch 21, validation: loss=0.1643, simple_loss=0.2479, pruned_loss=0.04039, over 1622729.00 frames.
+2022-06-19 01:49:25,788 INFO [train.py:874] (3/4) Epoch 21, batch 4050, datatang_loss[loss=0.145, simple_loss=0.2164, pruned_loss=0.03675, over 4912.00 frames.], tot_loss[loss=0.148, simple_loss=0.2298, pruned_loss=0.03312, over 985595.79 frames.], batch size: 64, aishell_tot_loss[loss=0.1508, simple_loss=0.2372, pruned_loss=0.0322, over 985114.64 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.222, pruned_loss=0.0345, over 986117.50 frames.], batch size: 64, lr: 3.85e-04
+2022-06-19 01:50:39,876 INFO [train.py:874] (3/4) Epoch 22, batch 50, datatang_loss[loss=0.1235, simple_loss=0.2061, pruned_loss=0.02045, over 4964.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2228, pruned_loss=0.02948, over 218546.12 frames.], batch size: 60, aishell_tot_loss[loss=0.1483, simple_loss=0.2341, pruned_loss=0.03125, over 120335.02 frames.], datatang_tot_loss[loss=0.133, simple_loss=0.2107, pruned_loss=0.02761, over 111855.62 frames.], batch size: 60, lr: 3.76e-04
+2022-06-19 01:51:13,496 INFO [train.py:874] (3/4) Epoch 22, batch 100, datatang_loss[loss=0.1341, simple_loss=0.2093, pruned_loss=0.02944, over 4891.00 frames.], tot_loss[loss=0.1435, simple_loss=0.225, pruned_loss=0.03094, over 388225.49 frames.], batch size: 47, aishell_tot_loss[loss=0.1492, simple_loss=0.2345, pruned_loss=0.03198, over 225738.84 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2147, pruned_loss=0.02976, over 210829.38 frames.], batch size: 47, lr: 3.76e-04
+2022-06-19 01:51:45,217 INFO [train.py:874] (3/4) Epoch 22, batch 150, aishell_loss[loss=0.1547, simple_loss=0.2419, pruned_loss=0.0337, over 4951.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2258, pruned_loss=0.03175, over 521188.79 frames.], batch size: 31, aishell_tot_loss[loss=0.1489, simple_loss=0.2345, pruned_loss=0.03162, over 312126.17 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2166, pruned_loss=0.03163, over 305798.36 frames.], batch size: 31, lr: 3.76e-04
+2022-06-19 01:52:17,271 INFO [train.py:874] (3/4) Epoch 22, batch 200, aishell_loss[loss=0.1588, simple_loss=0.2464, pruned_loss=0.03553, over 4879.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2269, pruned_loss=0.03169, over 624198.43 frames.], batch size: 42, aishell_tot_loss[loss=0.1492, simple_loss=0.2355, pruned_loss=0.03142, over 391528.82 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2176, pruned_loss=0.03184, over 385856.62 frames.], batch size: 42, lr: 3.76e-04
+2022-06-19 01:52:51,014 INFO [train.py:874] (3/4) Epoch 22, batch 250, aishell_loss[loss=0.1231, simple_loss=0.2121, pruned_loss=0.01707, over 4974.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2276, pruned_loss=0.03181, over 704385.75 frames.], batch size: 39, aishell_tot_loss[loss=0.1496, simple_loss=0.2356, pruned_loss=0.03183, over 477109.25 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2178, pruned_loss=0.03167, over 440324.53 frames.], batch size: 39, lr: 3.76e-04
+2022-06-19 01:53:21,247 INFO [train.py:874] (3/4) Epoch 22, batch 300, datatang_loss[loss=0.1435, simple_loss=0.2221, pruned_loss=0.0324, over 4959.00 frames.], tot_loss[loss=0.146, simple_loss=0.2277, pruned_loss=0.03218, over 766607.27 frames.], batch size: 91, aishell_tot_loss[loss=0.15, simple_loss=0.2358, pruned_loss=0.03204, over 541276.19 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2178, pruned_loss=0.03208, over 499764.93 frames.], batch size: 91, lr: 3.76e-04
+2022-06-19 01:53:54,164 INFO [train.py:874] (3/4) Epoch 22, batch 350, datatang_loss[loss=0.1457, simple_loss=0.2222, pruned_loss=0.03458, over 4945.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2283, pruned_loss=0.03253, over 815039.92 frames.], batch size: 69, aishell_tot_loss[loss=0.15, simple_loss=0.2358, pruned_loss=0.03204, over 593405.84 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2191, pruned_loss=0.03266, over 557080.46 frames.], batch size: 69, lr: 3.76e-04
+2022-06-19 01:54:25,671 INFO [train.py:874] (3/4) Epoch 22, batch 400, aishell_loss[loss=0.1543, simple_loss=0.2408, pruned_loss=0.03388, over 4893.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2274, pruned_loss=0.03242, over 852629.46 frames.], batch size: 34, aishell_tot_loss[loss=0.1496, simple_loss=0.2353, pruned_loss=0.03191, over 636067.41 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2188, pruned_loss=0.03268, over 611137.97 frames.], batch size: 34, lr: 3.76e-04
+2022-06-19 01:54:58,627 INFO [train.py:874] (3/4) Epoch 22, batch 450, datatang_loss[loss=0.243, simple_loss=0.3003, pruned_loss=0.09286, over 4903.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2273, pruned_loss=0.03266, over 882258.70 frames.], batch size: 47, aishell_tot_loss[loss=0.1494, simple_loss=0.2351, pruned_loss=0.03183, over 669367.41 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2195, pruned_loss=0.03311, over 663626.91 frames.], batch size: 47, lr: 3.76e-04
+2022-06-19 01:55:29,525 INFO [train.py:874] (3/4) Epoch 22, batch 500, datatang_loss[loss=0.1419, simple_loss=0.2306, pruned_loss=0.02662, over 4840.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2268, pruned_loss=0.03252, over 904889.46 frames.], batch size: 24, aishell_tot_loss[loss=0.1489, simple_loss=0.2344, pruned_loss=0.03169, over 706468.03 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2196, pruned_loss=0.03311, over 701406.94 frames.], batch size: 24, lr: 3.75e-04
+2022-06-19 01:56:02,095 INFO [train.py:874] (3/4) Epoch 22, batch 550, datatang_loss[loss=0.1776, simple_loss=0.2494, pruned_loss=0.05295, over 4946.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2274, pruned_loss=0.03253, over 923124.32 frames.], batch size: 55, aishell_tot_loss[loss=0.1489, simple_loss=0.2344, pruned_loss=0.03166, over 736967.05 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2205, pruned_loss=0.03316, over 737632.25 frames.], batch size: 55, lr: 3.75e-04
+2022-06-19 01:56:34,641 INFO [train.py:874] (3/4) Epoch 22, batch 600, aishell_loss[loss=0.162, simple_loss=0.2616, pruned_loss=0.03127, over 4975.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2276, pruned_loss=0.03294, over 937123.74 frames.], batch size: 79, aishell_tot_loss[loss=0.1494, simple_loss=0.2348, pruned_loss=0.03205, over 764325.40 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2205, pruned_loss=0.03333, over 768890.22 frames.], batch size: 79, lr: 3.75e-04
+2022-06-19 01:57:06,373 INFO [train.py:874] (3/4) Epoch 22, batch 650, aishell_loss[loss=0.1536, simple_loss=0.2435, pruned_loss=0.03178, over 4944.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2273, pruned_loss=0.03247, over 947970.76 frames.], batch size: 54, aishell_tot_loss[loss=0.1498, simple_loss=0.2357, pruned_loss=0.03192, over 788426.80 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2194, pruned_loss=0.03293, over 796420.96 frames.], batch size: 54, lr: 3.75e-04
+2022-06-19 01:57:38,740 INFO [train.py:874] (3/4) Epoch 22, batch 700, aishell_loss[loss=0.1371, simple_loss=0.2276, pruned_loss=0.02336, over 4851.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2273, pruned_loss=0.03272, over 956336.98 frames.], batch size: 36, aishell_tot_loss[loss=0.1495, simple_loss=0.2353, pruned_loss=0.03182, over 811750.82 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2198, pruned_loss=0.03336, over 818639.74 frames.], batch size: 36, lr: 3.75e-04
+2022-06-19 01:58:11,462 INFO [train.py:874] (3/4) Epoch 22, batch 750, datatang_loss[loss=0.1495, simple_loss=0.2113, pruned_loss=0.04385, over 4912.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2285, pruned_loss=0.0329, over 962664.80 frames.], batch size: 42, aishell_tot_loss[loss=0.1498, simple_loss=0.2359, pruned_loss=0.03189, over 833378.42 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2204, pruned_loss=0.03354, over 837017.30 frames.], batch size: 42, lr: 3.75e-04
+2022-06-19 01:58:43,791 INFO [train.py:874] (3/4) Epoch 22, batch 800, datatang_loss[loss=0.1634, simple_loss=0.2276, pruned_loss=0.04961, over 4934.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2288, pruned_loss=0.03309, over 967488.64 frames.], batch size: 34, aishell_tot_loss[loss=0.1498, simple_loss=0.2359, pruned_loss=0.03183, over 849165.13 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2211, pruned_loss=0.03385, over 856318.47 frames.], batch size: 34, lr: 3.75e-04
+2022-06-19 01:59:15,048 INFO [train.py:874] (3/4) Epoch 22, batch 850, aishell_loss[loss=0.1502, simple_loss=0.2338, pruned_loss=0.03327, over 4970.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2284, pruned_loss=0.03263, over 971300.31 frames.], batch size: 61, aishell_tot_loss[loss=0.1491, simple_loss=0.2355, pruned_loss=0.03134, over 865523.66 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.221, pruned_loss=0.03386, over 871074.23 frames.], batch size: 61, lr: 3.75e-04
+2022-06-19 01:59:46,536 INFO [train.py:874] (3/4) Epoch 22, batch 900, aishell_loss[loss=0.1393, simple_loss=0.229, pruned_loss=0.02484, over 4975.00 frames.], tot_loss[loss=0.147, simple_loss=0.2287, pruned_loss=0.03263, over 974557.63 frames.], batch size: 39, aishell_tot_loss[loss=0.1489, simple_loss=0.2355, pruned_loss=0.03111, over 878650.01 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2215, pruned_loss=0.03406, over 885624.01 frames.], batch size: 39, lr: 3.75e-04
+2022-06-19 02:00:24,085 INFO [train.py:874] (3/4) Epoch 22, batch 950, aishell_loss[loss=0.1638, simple_loss=0.244, pruned_loss=0.04177, over 4867.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2289, pruned_loss=0.03295, over 976923.12 frames.], batch size: 36, aishell_tot_loss[loss=0.1497, simple_loss=0.236, pruned_loss=0.0317, over 890897.96 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2212, pruned_loss=0.03383, over 897674.76 frames.], batch size: 36, lr: 3.74e-04
+2022-06-19 02:00:55,159 INFO [train.py:874] (3/4) Epoch 22, batch 1000, aishell_loss[loss=0.138, simple_loss=0.2293, pruned_loss=0.02335, over 4967.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2288, pruned_loss=0.03285, over 978409.45 frames.], batch size: 31, aishell_tot_loss[loss=0.1496, simple_loss=0.2359, pruned_loss=0.0317, over 902952.04 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2211, pruned_loss=0.03379, over 906741.99 frames.], batch size: 31, lr: 3.74e-04
+2022-06-19 02:00:55,161 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 02:01:12,462 INFO [train.py:914] (3/4) Epoch 22, validation: loss=0.1645, simple_loss=0.2481, pruned_loss=0.04044, over 1622729.00 frames.
+2022-06-19 02:01:46,882 INFO [train.py:874] (3/4) Epoch 22, batch 1050, datatang_loss[loss=0.1385, simple_loss=0.2232, pruned_loss=0.02688, over 4930.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2284, pruned_loss=0.03298, over 980066.35 frames.], batch size: 79, aishell_tot_loss[loss=0.1496, simple_loss=0.2359, pruned_loss=0.0316, over 907144.27 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2216, pruned_loss=0.0339, over 921023.95 frames.], batch size: 79, lr: 3.74e-04
+2022-06-19 02:02:18,000 INFO [train.py:874] (3/4) Epoch 22, batch 1100, datatang_loss[loss=0.1129, simple_loss=0.1906, pruned_loss=0.0176, over 4925.00 frames.], tot_loss[loss=0.1467, simple_loss=0.228, pruned_loss=0.0327, over 981215.93 frames.], batch size: 73, aishell_tot_loss[loss=0.1498, simple_loss=0.2363, pruned_loss=0.03169, over 917060.70 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2207, pruned_loss=0.03359, over 927989.92 frames.], batch size: 73, lr: 3.74e-04
+2022-06-19 02:02:49,877 INFO [train.py:874] (3/4) Epoch 22, batch 1150, datatang_loss[loss=0.1366, simple_loss=0.2063, pruned_loss=0.03349, over 4912.00 frames.], tot_loss[loss=0.1468, simple_loss=0.228, pruned_loss=0.03283, over 982125.05 frames.], batch size: 75, aishell_tot_loss[loss=0.1502, simple_loss=0.2367, pruned_loss=0.03191, over 924977.06 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2203, pruned_loss=0.03351, over 934808.86 frames.], batch size: 75, lr: 3.74e-04
+2022-06-19 02:03:20,693 INFO [train.py:874] (3/4) Epoch 22, batch 1200, aishell_loss[loss=0.157, simple_loss=0.2454, pruned_loss=0.03426, over 4936.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2292, pruned_loss=0.03286, over 982824.11 frames.], batch size: 49, aishell_tot_loss[loss=0.1505, simple_loss=0.237, pruned_loss=0.03205, over 933109.54 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2209, pruned_loss=0.03347, over 939877.84 frames.], batch size: 49, lr: 3.74e-04
+2022-06-19 02:03:54,538 INFO [train.py:874] (3/4) Epoch 22, batch 1250, datatang_loss[loss=0.1562, simple_loss=0.2417, pruned_loss=0.03531, over 4905.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2308, pruned_loss=0.03326, over 983320.22 frames.], batch size: 98, aishell_tot_loss[loss=0.1509, simple_loss=0.2375, pruned_loss=0.03214, over 941353.76 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2214, pruned_loss=0.03396, over 943258.13 frames.], batch size: 98, lr: 3.74e-04
+2022-06-19 02:04:26,533 INFO [train.py:874] (3/4) Epoch 22, batch 1300, datatang_loss[loss=0.1385, simple_loss=0.2088, pruned_loss=0.03413, over 4851.00 frames.], tot_loss[loss=0.148, simple_loss=0.2299, pruned_loss=0.03306, over 983944.12 frames.], batch size: 25, aishell_tot_loss[loss=0.1504, simple_loss=0.2369, pruned_loss=0.032, over 947238.36 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2211, pruned_loss=0.03395, over 947704.36 frames.], batch size: 25, lr: 3.74e-04
+2022-06-19 02:04:58,095 INFO [train.py:874] (3/4) Epoch 22, batch 1350, datatang_loss[loss=0.1409, simple_loss=0.2161, pruned_loss=0.03281, over 4922.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2296, pruned_loss=0.03276, over 984489.47 frames.], batch size: 81, aishell_tot_loss[loss=0.1507, simple_loss=0.2374, pruned_loss=0.03203, over 952325.38 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2204, pruned_loss=0.03362, over 951796.15 frames.], batch size: 81, lr: 3.74e-04
+2022-06-19 02:05:30,734 INFO [train.py:874] (3/4) Epoch 22, batch 1400, datatang_loss[loss=0.1345, simple_loss=0.213, pruned_loss=0.02803, over 4923.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2287, pruned_loss=0.0325, over 984752.88 frames.], batch size: 75, aishell_tot_loss[loss=0.1501, simple_loss=0.2365, pruned_loss=0.03186, over 956219.81 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2204, pruned_loss=0.03349, over 955820.38 frames.], batch size: 75, lr: 3.73e-04
+2022-06-19 02:06:03,270 INFO [train.py:874] (3/4) Epoch 22, batch 1450, aishell_loss[loss=0.1522, simple_loss=0.2269, pruned_loss=0.03874, over 4931.00 frames.], tot_loss[loss=0.147, simple_loss=0.229, pruned_loss=0.03245, over 985304.68 frames.], batch size: 32, aishell_tot_loss[loss=0.15, simple_loss=0.2364, pruned_loss=0.03183, over 960602.01 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2205, pruned_loss=0.03345, over 958749.18 frames.], batch size: 32, lr: 3.73e-04
+2022-06-19 02:06:34,681 INFO [train.py:874] (3/4) Epoch 22, batch 1500, aishell_loss[loss=0.1642, simple_loss=0.2391, pruned_loss=0.04463, over 4895.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2277, pruned_loss=0.03225, over 985574.67 frames.], batch size: 34, aishell_tot_loss[loss=0.1495, simple_loss=0.2356, pruned_loss=0.03171, over 963503.25 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2202, pruned_loss=0.03331, over 962177.31 frames.], batch size: 34, lr: 3.73e-04
+2022-06-19 02:07:06,480 INFO [train.py:874] (3/4) Epoch 22, batch 1550, datatang_loss[loss=0.1214, simple_loss=0.2036, pruned_loss=0.01963, over 4918.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2273, pruned_loss=0.03217, over 985363.26 frames.], batch size: 77, aishell_tot_loss[loss=0.1494, simple_loss=0.2357, pruned_loss=0.03156, over 966016.67 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2195, pruned_loss=0.03332, over 964782.86 frames.], batch size: 77, lr: 3.73e-04
+2022-06-19 02:07:39,262 INFO [train.py:874] (3/4) Epoch 22, batch 1600, datatang_loss[loss=0.1401, simple_loss=0.2108, pruned_loss=0.03467, over 4878.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2275, pruned_loss=0.03208, over 985308.14 frames.], batch size: 36, aishell_tot_loss[loss=0.1491, simple_loss=0.2354, pruned_loss=0.03137, over 968779.27 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2195, pruned_loss=0.03339, over 966612.13 frames.], batch size: 36, lr: 3.73e-04
+2022-06-19 02:08:12,809 INFO [train.py:874] (3/4) Epoch 22, batch 1650, aishell_loss[loss=0.1635, simple_loss=0.2511, pruned_loss=0.03797, over 4945.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2268, pruned_loss=0.03209, over 985180.64 frames.], batch size: 80, aishell_tot_loss[loss=0.1491, simple_loss=0.2352, pruned_loss=0.03153, over 970477.31 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2192, pruned_loss=0.03314, over 968952.66 frames.], batch size: 80, lr: 3.73e-04
+2022-06-19 02:08:44,604 INFO [train.py:874] (3/4) Epoch 22, batch 1700, datatang_loss[loss=0.1447, simple_loss=0.2234, pruned_loss=0.03297, over 4922.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2276, pruned_loss=0.03244, over 985189.63 frames.], batch size: 73, aishell_tot_loss[loss=0.1489, simple_loss=0.2349, pruned_loss=0.03141, over 972359.17 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2199, pruned_loss=0.03361, over 970661.94 frames.], batch size: 73, lr: 3.73e-04
+2022-06-19 02:09:16,560 INFO [train.py:874] (3/4) Epoch 22, batch 1750, datatang_loss[loss=0.1241, simple_loss=0.2071, pruned_loss=0.02052, over 4927.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2271, pruned_loss=0.03226, over 985377.02 frames.], batch size: 77, aishell_tot_loss[loss=0.1485, simple_loss=0.2343, pruned_loss=0.03132, over 973949.41 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.22, pruned_loss=0.03349, over 972482.08 frames.], batch size: 77, lr: 3.73e-04
+2022-06-19 02:09:50,824 INFO [train.py:874] (3/4) Epoch 22, batch 1800, aishell_loss[loss=0.1486, simple_loss=0.2381, pruned_loss=0.02954, over 4918.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2278, pruned_loss=0.03254, over 985348.67 frames.], batch size: 46, aishell_tot_loss[loss=0.1491, simple_loss=0.235, pruned_loss=0.03157, over 974868.72 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2204, pruned_loss=0.03346, over 974418.30 frames.], batch size: 46, lr: 3.73e-04
+2022-06-19 02:10:22,952 INFO [train.py:874] (3/4) Epoch 22, batch 1850, datatang_loss[loss=0.1521, simple_loss=0.232, pruned_loss=0.03609, over 4897.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2283, pruned_loss=0.03291, over 985510.48 frames.], batch size: 52, aishell_tot_loss[loss=0.1491, simple_loss=0.2351, pruned_loss=0.0315, over 976292.73 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2208, pruned_loss=0.03391, over 975675.73 frames.], batch size: 52, lr: 3.73e-04
+2022-06-19 02:10:56,207 INFO [train.py:874] (3/4) Epoch 22, batch 1900, datatang_loss[loss=0.1434, simple_loss=0.2225, pruned_loss=0.03221, over 4952.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2282, pruned_loss=0.03282, over 985821.36 frames.], batch size: 67, aishell_tot_loss[loss=0.1491, simple_loss=0.2351, pruned_loss=0.03151, over 977540.42 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2208, pruned_loss=0.03381, over 976997.12 frames.], batch size: 67, lr: 3.72e-04
+2022-06-19 02:11:27,964 INFO [train.py:874] (3/4) Epoch 22, batch 1950, aishell_loss[loss=0.1697, simple_loss=0.2584, pruned_loss=0.04047, over 4956.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2277, pruned_loss=0.03236, over 985867.60 frames.], batch size: 78, aishell_tot_loss[loss=0.1488, simple_loss=0.2348, pruned_loss=0.03139, over 978577.06 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2206, pruned_loss=0.03348, over 978024.86 frames.], batch size: 78, lr: 3.72e-04
+2022-06-19 02:12:00,364 INFO [train.py:874] (3/4) Epoch 22, batch 2000, datatang_loss[loss=0.1681, simple_loss=0.2303, pruned_loss=0.05293, over 4857.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2282, pruned_loss=0.03252, over 985467.14 frames.], batch size: 33, aishell_tot_loss[loss=0.1487, simple_loss=0.235, pruned_loss=0.03125, over 979071.78 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2209, pruned_loss=0.03377, over 978889.04 frames.], batch size: 33, lr: 3.72e-04
+2022-06-19 02:12:00,365 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 02:12:18,453 INFO [train.py:914] (3/4) Epoch 22, validation: loss=0.165, simple_loss=0.2489, pruned_loss=0.04058, over 1622729.00 frames.
+2022-06-19 02:12:50,785 INFO [train.py:874] (3/4) Epoch 22, batch 2050, aishell_loss[loss=0.1698, simple_loss=0.2572, pruned_loss=0.04117, over 4940.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2282, pruned_loss=0.03222, over 985460.78 frames.], batch size: 33, aishell_tot_loss[loss=0.1486, simple_loss=0.235, pruned_loss=0.03107, over 979582.51 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.221, pruned_loss=0.03359, over 979896.11 frames.], batch size: 33, lr: 3.72e-04
+2022-06-19 02:13:23,368 INFO [train.py:874] (3/4) Epoch 22, batch 2100, aishell_loss[loss=0.1725, simple_loss=0.2469, pruned_loss=0.04907, over 4888.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2287, pruned_loss=0.03237, over 985355.53 frames.], batch size: 42, aishell_tot_loss[loss=0.1497, simple_loss=0.2361, pruned_loss=0.03162, over 980113.46 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2204, pruned_loss=0.03317, over 980606.27 frames.], batch size: 42, lr: 3.72e-04
+2022-06-19 02:13:54,878 INFO [train.py:874] (3/4) Epoch 22, batch 2150, datatang_loss[loss=0.1469, simple_loss=0.2147, pruned_loss=0.03953, over 4962.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2284, pruned_loss=0.03242, over 985460.50 frames.], batch size: 86, aishell_tot_loss[loss=0.1493, simple_loss=0.2356, pruned_loss=0.03147, over 980555.18 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2208, pruned_loss=0.03333, over 981422.55 frames.], batch size: 86, lr: 3.72e-04
+2022-06-19 02:14:25,694 INFO [train.py:874] (3/4) Epoch 22, batch 2200, aishell_loss[loss=0.125, simple_loss=0.2079, pruned_loss=0.02107, over 4973.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2277, pruned_loss=0.03188, over 985256.94 frames.], batch size: 27, aishell_tot_loss[loss=0.1488, simple_loss=0.2353, pruned_loss=0.03118, over 980816.98 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2204, pruned_loss=0.03305, over 981998.76 frames.], batch size: 27, lr: 3.72e-04
+2022-06-19 02:14:59,147 INFO [train.py:874] (3/4) Epoch 22, batch 2250, datatang_loss[loss=0.1422, simple_loss=0.212, pruned_loss=0.03623, over 4918.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2286, pruned_loss=0.03236, over 985227.04 frames.], batch size: 42, aishell_tot_loss[loss=0.1495, simple_loss=0.2358, pruned_loss=0.03163, over 981414.39 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2207, pruned_loss=0.03309, over 982280.88 frames.], batch size: 42, lr: 3.72e-04
+2022-06-19 02:15:30,345 INFO [train.py:874] (3/4) Epoch 22, batch 2300, datatang_loss[loss=0.1396, simple_loss=0.2102, pruned_loss=0.03454, over 4940.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2282, pruned_loss=0.03265, over 985320.27 frames.], batch size: 62, aishell_tot_loss[loss=0.1492, simple_loss=0.2354, pruned_loss=0.03147, over 981966.86 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2207, pruned_loss=0.03353, over 982621.53 frames.], batch size: 62, lr: 3.72e-04
+2022-06-19 02:16:02,709 INFO [train.py:874] (3/4) Epoch 22, batch 2350, aishell_loss[loss=0.1575, simple_loss=0.2444, pruned_loss=0.03532, over 4861.00 frames.], tot_loss[loss=0.1463, simple_loss=0.228, pruned_loss=0.03233, over 985079.77 frames.], batch size: 37, aishell_tot_loss[loss=0.1489, simple_loss=0.2351, pruned_loss=0.0314, over 982180.02 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2206, pruned_loss=0.03332, over 982893.46 frames.], batch size: 37, lr: 3.72e-04
+2022-06-19 02:16:33,562 INFO [train.py:874] (3/4) Epoch 22, batch 2400, datatang_loss[loss=0.1294, simple_loss=0.1975, pruned_loss=0.0307, over 4959.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2275, pruned_loss=0.03231, over 985084.94 frames.], batch size: 45, aishell_tot_loss[loss=0.1484, simple_loss=0.2344, pruned_loss=0.03122, over 982378.01 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2207, pruned_loss=0.03351, over 983303.75 frames.], batch size: 45, lr: 3.71e-04
+2022-06-19 02:17:04,652 INFO [train.py:874] (3/4) Epoch 22, batch 2450, datatang_loss[loss=0.1485, simple_loss=0.2133, pruned_loss=0.0418, over 4940.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2271, pruned_loss=0.03206, over 985246.97 frames.], batch size: 37, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.03091, over 982807.47 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2207, pruned_loss=0.03353, over 983558.21 frames.], batch size: 37, lr: 3.71e-04
+2022-06-19 02:17:37,792 INFO [train.py:874] (3/4) Epoch 22, batch 2500, aishell_loss[loss=0.1444, simple_loss=0.2285, pruned_loss=0.03015, over 4948.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2285, pruned_loss=0.0324, over 985514.60 frames.], batch size: 45, aishell_tot_loss[loss=0.1483, simple_loss=0.2345, pruned_loss=0.03104, over 983254.34 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2215, pruned_loss=0.03374, over 983878.49 frames.], batch size: 45, lr: 3.71e-04
+2022-06-19 02:18:09,196 INFO [train.py:874] (3/4) Epoch 22, batch 2550, aishell_loss[loss=0.1642, simple_loss=0.2504, pruned_loss=0.03901, over 4906.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2286, pruned_loss=0.03259, over 985046.90 frames.], batch size: 52, aishell_tot_loss[loss=0.1481, simple_loss=0.234, pruned_loss=0.03106, over 983118.13 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.2221, pruned_loss=0.03395, over 983973.41 frames.], batch size: 52, lr: 3.71e-04
+2022-06-19 02:18:41,369 INFO [train.py:874] (3/4) Epoch 22, batch 2600, aishell_loss[loss=0.1431, simple_loss=0.2411, pruned_loss=0.02259, over 4896.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2293, pruned_loss=0.03271, over 984993.40 frames.], batch size: 60, aishell_tot_loss[loss=0.149, simple_loss=0.2352, pruned_loss=0.03139, over 983550.65 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2217, pruned_loss=0.03376, over 983827.48 frames.], batch size: 60, lr: 3.71e-04
+2022-06-19 02:19:14,463 INFO [train.py:874] (3/4) Epoch 22, batch 2650, datatang_loss[loss=0.1533, simple_loss=0.2347, pruned_loss=0.03596, over 4953.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2288, pruned_loss=0.03235, over 985126.58 frames.], batch size: 91, aishell_tot_loss[loss=0.1488, simple_loss=0.235, pruned_loss=0.03126, over 983716.40 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2213, pruned_loss=0.03362, over 984116.28 frames.], batch size: 91, lr: 3.71e-04
+2022-06-19 02:19:44,880 INFO [train.py:874] (3/4) Epoch 22, batch 2700, aishell_loss[loss=0.1142, simple_loss=0.2028, pruned_loss=0.01275, over 4959.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2286, pruned_loss=0.03218, over 985152.39 frames.], batch size: 27, aishell_tot_loss[loss=0.148, simple_loss=0.2342, pruned_loss=0.03089, over 984001.04 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2217, pruned_loss=0.03386, over 984149.30 frames.], batch size: 27, lr: 3.71e-04
+2022-06-19 02:20:16,483 INFO [train.py:874] (3/4) Epoch 22, batch 2750, datatang_loss[loss=0.1419, simple_loss=0.2267, pruned_loss=0.02855, over 4953.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2297, pruned_loss=0.03225, over 985331.73 frames.], batch size: 86, aishell_tot_loss[loss=0.1484, simple_loss=0.2349, pruned_loss=0.03093, over 984122.27 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.222, pruned_loss=0.03393, over 984467.21 frames.], batch size: 86, lr: 3.71e-04
+2022-06-19 02:20:48,528 INFO [train.py:874] (3/4) Epoch 22, batch 2800, aishell_loss[loss=0.149, simple_loss=0.2384, pruned_loss=0.02981, over 4935.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2293, pruned_loss=0.03246, over 984949.46 frames.], batch size: 54, aishell_tot_loss[loss=0.1483, simple_loss=0.2348, pruned_loss=0.03091, over 984126.96 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2218, pruned_loss=0.03421, over 984310.30 frames.], batch size: 54, lr: 3.71e-04
+2022-06-19 02:21:19,615 INFO [train.py:874] (3/4) Epoch 22, batch 2850, aishell_loss[loss=0.1545, simple_loss=0.2436, pruned_loss=0.0327, over 4959.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2287, pruned_loss=0.03215, over 984471.95 frames.], batch size: 61, aishell_tot_loss[loss=0.1484, simple_loss=0.2347, pruned_loss=0.03108, over 983809.11 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2213, pruned_loss=0.0337, over 984297.79 frames.], batch size: 61, lr: 3.70e-04
+2022-06-19 02:21:52,087 INFO [train.py:874] (3/4) Epoch 22, batch 2900, aishell_loss[loss=0.166, simple_loss=0.2459, pruned_loss=0.04306, over 4944.00 frames.], tot_loss[loss=0.146, simple_loss=0.2276, pruned_loss=0.03225, over 984459.67 frames.], batch size: 49, aishell_tot_loss[loss=0.1481, simple_loss=0.2342, pruned_loss=0.031, over 983916.59 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2209, pruned_loss=0.03378, over 984272.84 frames.], batch size: 49, lr: 3.70e-04
+2022-06-19 02:22:24,894 INFO [train.py:874] (3/4) Epoch 22, batch 2950, aishell_loss[loss=0.162, simple_loss=0.2555, pruned_loss=0.03425, over 4971.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2272, pruned_loss=0.03197, over 984784.05 frames.], batch size: 44, aishell_tot_loss[loss=0.148, simple_loss=0.2343, pruned_loss=0.0309, over 984064.22 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2205, pruned_loss=0.03352, over 984551.89 frames.], batch size: 44, lr: 3.70e-04
+2022-06-19 02:22:56,284 INFO [train.py:874] (3/4) Epoch 22, batch 3000, datatang_loss[loss=0.1212, simple_loss=0.203, pruned_loss=0.01969, over 4921.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2276, pruned_loss=0.03207, over 984966.98 frames.], batch size: 73, aishell_tot_loss[loss=0.1479, simple_loss=0.2342, pruned_loss=0.03085, over 984026.96 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2208, pruned_loss=0.03368, over 984926.10 frames.], batch size: 73, lr: 3.70e-04
+2022-06-19 02:22:56,285 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 02:23:14,260 INFO [train.py:914] (3/4) Epoch 22, validation: loss=0.1638, simple_loss=0.2477, pruned_loss=0.03991, over 1622729.00 frames.
+2022-06-19 02:23:45,751 INFO [train.py:874] (3/4) Epoch 22, batch 3050, datatang_loss[loss=0.1357, simple_loss=0.2014, pruned_loss=0.035, over 4864.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2272, pruned_loss=0.03207, over 985351.02 frames.], batch size: 36, aishell_tot_loss[loss=0.1473, simple_loss=0.2335, pruned_loss=0.03055, over 984463.37 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2212, pruned_loss=0.03393, over 984994.94 frames.], batch size: 36, lr: 3.70e-04
+2022-06-19 02:24:17,478 INFO [train.py:874] (3/4) Epoch 22, batch 3100, aishell_loss[loss=0.1516, simple_loss=0.2356, pruned_loss=0.03378, over 4910.00 frames.], tot_loss[loss=0.146, simple_loss=0.2281, pruned_loss=0.03193, over 985177.02 frames.], batch size: 41, aishell_tot_loss[loss=0.1476, simple_loss=0.2343, pruned_loss=0.03042, over 984542.59 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2215, pruned_loss=0.03382, over 984879.47 frames.], batch size: 41, lr: 3.70e-04
+2022-06-19 02:24:49,592 INFO [train.py:874] (3/4) Epoch 22, batch 3150, aishell_loss[loss=0.1503, simple_loss=0.2434, pruned_loss=0.0286, over 4917.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2291, pruned_loss=0.03204, over 985494.98 frames.], batch size: 52, aishell_tot_loss[loss=0.1478, simple_loss=0.2347, pruned_loss=0.03044, over 984810.68 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2217, pruned_loss=0.03398, over 985083.07 frames.], batch size: 52, lr: 3.70e-04
+2022-06-19 02:25:22,897 INFO [train.py:874] (3/4) Epoch 22, batch 3200, datatang_loss[loss=0.1369, simple_loss=0.219, pruned_loss=0.02745, over 4940.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2278, pruned_loss=0.03163, over 985363.99 frames.], batch size: 88, aishell_tot_loss[loss=0.1474, simple_loss=0.2342, pruned_loss=0.03033, over 984828.43 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2213, pruned_loss=0.03356, over 985047.52 frames.], batch size: 88, lr: 3.70e-04
+2022-06-19 02:25:55,790 INFO [train.py:874] (3/4) Epoch 22, batch 3250, datatang_loss[loss=0.125, simple_loss=0.2015, pruned_loss=0.02424, over 4950.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2281, pruned_loss=0.03224, over 985276.80 frames.], batch size: 25, aishell_tot_loss[loss=0.1478, simple_loss=0.2343, pruned_loss=0.03061, over 984623.58 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2216, pruned_loss=0.03383, over 985246.28 frames.], batch size: 25, lr: 3.70e-04
+2022-06-19 02:26:28,559 INFO [train.py:874] (3/4) Epoch 22, batch 3300, datatang_loss[loss=0.1339, simple_loss=0.2168, pruned_loss=0.02545, over 4945.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2283, pruned_loss=0.03222, over 984862.23 frames.], batch size: 88, aishell_tot_loss[loss=0.1475, simple_loss=0.2339, pruned_loss=0.03052, over 984310.80 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.222, pruned_loss=0.03395, over 985214.70 frames.], batch size: 88, lr: 3.70e-04
+2022-06-19 02:27:02,252 INFO [train.py:874] (3/4) Epoch 22, batch 3350, aishell_loss[loss=0.1437, simple_loss=0.2338, pruned_loss=0.02679, over 4935.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2276, pruned_loss=0.03189, over 985083.30 frames.], batch size: 49, aishell_tot_loss[loss=0.1471, simple_loss=0.2336, pruned_loss=0.03034, over 984538.94 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2215, pruned_loss=0.03385, over 985264.44 frames.], batch size: 49, lr: 3.69e-04
+2022-06-19 02:27:34,708 INFO [train.py:874] (3/4) Epoch 22, batch 3400, aishell_loss[loss=0.1257, simple_loss=0.2131, pruned_loss=0.01914, over 4982.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2275, pruned_loss=0.03165, over 985651.79 frames.], batch size: 30, aishell_tot_loss[loss=0.1472, simple_loss=0.2338, pruned_loss=0.03031, over 984821.06 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2211, pruned_loss=0.03358, over 985636.63 frames.], batch size: 30, lr: 3.69e-04
+2022-06-19 02:28:08,712 INFO [train.py:874] (3/4) Epoch 22, batch 3450, aishell_loss[loss=0.1579, simple_loss=0.2517, pruned_loss=0.03209, over 4983.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2273, pruned_loss=0.03199, over 986164.35 frames.], batch size: 43, aishell_tot_loss[loss=0.1471, simple_loss=0.2338, pruned_loss=0.03016, over 985198.03 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2215, pruned_loss=0.03387, over 985876.73 frames.], batch size: 43, lr: 3.69e-04
+2022-06-19 02:28:40,703 INFO [train.py:874] (3/4) Epoch 22, batch 3500, aishell_loss[loss=0.1326, simple_loss=0.2087, pruned_loss=0.02821, over 4951.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2271, pruned_loss=0.03198, over 985879.30 frames.], batch size: 27, aishell_tot_loss[loss=0.1475, simple_loss=0.2342, pruned_loss=0.03036, over 985118.92 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2211, pruned_loss=0.03358, over 985789.41 frames.], batch size: 27, lr: 3.69e-04
+2022-06-19 02:29:12,279 INFO [train.py:874] (3/4) Epoch 22, batch 3550, aishell_loss[loss=0.1462, simple_loss=0.2278, pruned_loss=0.0323, over 4913.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2281, pruned_loss=0.0325, over 985346.52 frames.], batch size: 33, aishell_tot_loss[loss=0.1479, simple_loss=0.2347, pruned_loss=0.03062, over 984833.71 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2214, pruned_loss=0.03391, over 985619.15 frames.], batch size: 33, lr: 3.69e-04
+2022-06-19 02:29:44,468 INFO [train.py:874] (3/4) Epoch 22, batch 3600, datatang_loss[loss=0.1397, simple_loss=0.2105, pruned_loss=0.0344, over 4895.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2279, pruned_loss=0.03271, over 985529.82 frames.], batch size: 47, aishell_tot_loss[loss=0.1483, simple_loss=0.2348, pruned_loss=0.03085, over 985049.83 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2211, pruned_loss=0.03394, over 985625.18 frames.], batch size: 47, lr: 3.69e-04
+2022-06-19 02:30:17,441 INFO [train.py:874] (3/4) Epoch 22, batch 3650, datatang_loss[loss=0.1535, simple_loss=0.228, pruned_loss=0.03954, over 4925.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2279, pruned_loss=0.03231, over 985585.49 frames.], batch size: 83, aishell_tot_loss[loss=0.148, simple_loss=0.2349, pruned_loss=0.03056, over 985077.62 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2211, pruned_loss=0.03387, over 985706.74 frames.], batch size: 83, lr: 3.69e-04
+2022-06-19 02:30:48,837 INFO [train.py:874] (3/4) Epoch 22, batch 3700, datatang_loss[loss=0.1314, simple_loss=0.2115, pruned_loss=0.02564, over 4872.00 frames.], tot_loss[loss=0.1461, simple_loss=0.228, pruned_loss=0.03216, over 985383.30 frames.], batch size: 30, aishell_tot_loss[loss=0.1482, simple_loss=0.235, pruned_loss=0.03073, over 984873.49 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2211, pruned_loss=0.03356, over 985741.00 frames.], batch size: 30, lr: 3.69e-04
+2022-06-19 02:31:19,504 INFO [train.py:874] (3/4) Epoch 22, batch 3750, datatang_loss[loss=0.1308, simple_loss=0.2002, pruned_loss=0.03072, over 4874.00 frames.], tot_loss[loss=0.146, simple_loss=0.2283, pruned_loss=0.03187, over 985294.61 frames.], batch size: 39, aishell_tot_loss[loss=0.1476, simple_loss=0.2346, pruned_loss=0.03034, over 985139.23 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2215, pruned_loss=0.03372, over 985420.31 frames.], batch size: 39, lr: 3.69e-04
+2022-06-19 02:31:49,526 INFO [train.py:874] (3/4) Epoch 22, batch 3800, aishell_loss[loss=0.1561, simple_loss=0.2331, pruned_loss=0.03956, over 4881.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2281, pruned_loss=0.03209, over 985419.55 frames.], batch size: 47, aishell_tot_loss[loss=0.1477, simple_loss=0.2344, pruned_loss=0.03046, over 985107.54 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2215, pruned_loss=0.03377, over 985599.79 frames.], batch size: 47, lr: 3.69e-04
+2022-06-19 02:32:20,356 INFO [train.py:874] (3/4) Epoch 22, batch 3850, datatang_loss[loss=0.154, simple_loss=0.2312, pruned_loss=0.0384, over 4969.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2282, pruned_loss=0.03245, over 985280.81 frames.], batch size: 60, aishell_tot_loss[loss=0.1482, simple_loss=0.2349, pruned_loss=0.03073, over 984974.28 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2214, pruned_loss=0.03383, over 985588.81 frames.], batch size: 60, lr: 3.68e-04
+2022-06-19 02:32:51,413 INFO [train.py:874] (3/4) Epoch 22, batch 3900, aishell_loss[loss=0.1434, simple_loss=0.2351, pruned_loss=0.02583, over 4874.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2269, pruned_loss=0.03219, over 985065.84 frames.], batch size: 36, aishell_tot_loss[loss=0.148, simple_loss=0.2346, pruned_loss=0.03069, over 984632.62 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2208, pruned_loss=0.03351, over 985682.69 frames.], batch size: 36, lr: 3.68e-04
+2022-06-19 02:33:21,742 INFO [train.py:874] (3/4) Epoch 22, batch 3950, aishell_loss[loss=0.1542, simple_loss=0.2372, pruned_loss=0.03559, over 4930.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2276, pruned_loss=0.03239, over 984821.78 frames.], batch size: 58, aishell_tot_loss[loss=0.1488, simple_loss=0.2353, pruned_loss=0.0311, over 984293.73 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2201, pruned_loss=0.0334, over 985771.52 frames.], batch size: 58, lr: 3.68e-04
+2022-06-19 02:33:51,317 INFO [train.py:874] (3/4) Epoch 22, batch 4000, aishell_loss[loss=0.1528, simple_loss=0.2381, pruned_loss=0.03377, over 4979.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2271, pruned_loss=0.0318, over 985173.12 frames.], batch size: 44, aishell_tot_loss[loss=0.1486, simple_loss=0.2353, pruned_loss=0.03092, over 984470.59 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2197, pruned_loss=0.03298, over 985890.78 frames.], batch size: 44, lr: 3.68e-04
+2022-06-19 02:33:51,318 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 02:34:08,827 INFO [train.py:914] (3/4) Epoch 22, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04074, over 1622729.00 frames.
+2022-06-19 02:34:38,040 INFO [train.py:874] (3/4) Epoch 22, batch 4050, aishell_loss[loss=0.1465, simple_loss=0.236, pruned_loss=0.02855, over 4916.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2275, pruned_loss=0.03174, over 984871.59 frames.], batch size: 41, aishell_tot_loss[loss=0.1489, simple_loss=0.2357, pruned_loss=0.03109, over 984263.27 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2194, pruned_loss=0.03272, over 985800.18 frames.], batch size: 41, lr: 3.68e-04
+2022-06-19 02:36:04,912 INFO [train.py:874] (3/4) Epoch 23, batch 50, datatang_loss[loss=0.1479, simple_loss=0.2294, pruned_loss=0.03319, over 4951.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2246, pruned_loss=0.03187, over 218480.18 frames.], batch size: 91, aishell_tot_loss[loss=0.1506, simple_loss=0.2375, pruned_loss=0.03186, over 107379.32 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2139, pruned_loss=0.03207, over 124663.69 frames.], batch size: 91, lr: 3.60e-04
+2022-06-19 02:36:35,979 INFO [train.py:874] (3/4) Epoch 23, batch 100, datatang_loss[loss=0.1542, simple_loss=0.2262, pruned_loss=0.04109, over 4915.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2244, pruned_loss=0.03062, over 388523.38 frames.], batch size: 34, aishell_tot_loss[loss=0.1467, simple_loss=0.2336, pruned_loss=0.02993, over 210613.17 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2161, pruned_loss=0.03147, over 226249.30 frames.], batch size: 34, lr: 3.60e-04
+2022-06-19 02:37:08,886 INFO [train.py:874] (3/4) Epoch 23, batch 150, aishell_loss[loss=0.1371, simple_loss=0.2406, pruned_loss=0.01682, over 4912.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2239, pruned_loss=0.03029, over 520656.81 frames.], batch size: 41, aishell_tot_loss[loss=0.1471, simple_loss=0.2339, pruned_loss=0.03015, over 280831.48 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2161, pruned_loss=0.03057, over 335475.18 frames.], batch size: 41, lr: 3.60e-04
+2022-06-19 02:37:40,906 INFO [train.py:874] (3/4) Epoch 23, batch 200, aishell_loss[loss=0.1175, simple_loss=0.2106, pruned_loss=0.01219, over 4915.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2265, pruned_loss=0.03034, over 623648.13 frames.], batch size: 52, aishell_tot_loss[loss=0.1478, simple_loss=0.2355, pruned_loss=0.03007, over 388353.52 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2167, pruned_loss=0.0308, over 388406.72 frames.], batch size: 52, lr: 3.60e-04
+2022-06-19 02:38:12,823 INFO [train.py:874] (3/4) Epoch 23, batch 250, aishell_loss[loss=0.1703, simple_loss=0.2562, pruned_loss=0.0422, over 4943.00 frames.], tot_loss[loss=0.1436, simple_loss=0.226, pruned_loss=0.03055, over 703674.13 frames.], batch size: 40, aishell_tot_loss[loss=0.1471, simple_loss=0.2343, pruned_loss=0.03, over 458559.61 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2172, pruned_loss=0.03113, over 458631.80 frames.], batch size: 40, lr: 3.60e-04
+2022-06-19 02:38:44,893 INFO [train.py:874] (3/4) Epoch 23, batch 300, datatang_loss[loss=0.1343, simple_loss=0.2193, pruned_loss=0.02469, over 4965.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2269, pruned_loss=0.03121, over 765787.14 frames.], batch size: 67, aishell_tot_loss[loss=0.1485, simple_loss=0.2355, pruned_loss=0.0307, over 517862.37 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2175, pruned_loss=0.03141, over 523042.05 frames.], batch size: 67, lr: 3.60e-04
+2022-06-19 02:39:17,433 INFO [train.py:874] (3/4) Epoch 23, batch 350, aishell_loss[loss=0.1526, simple_loss=0.2437, pruned_loss=0.03077, over 4926.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2262, pruned_loss=0.03066, over 814190.07 frames.], batch size: 68, aishell_tot_loss[loss=0.1474, simple_loss=0.2342, pruned_loss=0.0303, over 576959.18 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2177, pruned_loss=0.03105, over 573180.11 frames.], batch size: 68, lr: 3.59e-04
+2022-06-19 02:39:48,723 INFO [train.py:874] (3/4) Epoch 23, batch 400, datatang_loss[loss=0.174, simple_loss=0.2452, pruned_loss=0.05142, over 4959.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2258, pruned_loss=0.03114, over 852200.97 frames.], batch size: 91, aishell_tot_loss[loss=0.1475, simple_loss=0.2341, pruned_loss=0.03043, over 617624.58 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.218, pruned_loss=0.0316, over 629147.40 frames.], batch size: 91, lr: 3.59e-04
+2022-06-19 02:40:20,294 INFO [train.py:874] (3/4) Epoch 23, batch 450, datatang_loss[loss=0.1487, simple_loss=0.2175, pruned_loss=0.03995, over 4899.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2277, pruned_loss=0.03157, over 881622.89 frames.], batch size: 52, aishell_tot_loss[loss=0.1487, simple_loss=0.2356, pruned_loss=0.03085, over 668980.80 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2185, pruned_loss=0.03185, over 663007.79 frames.], batch size: 52, lr: 3.59e-04
+2022-06-19 02:40:53,003 INFO [train.py:874] (3/4) Epoch 23, batch 500, datatang_loss[loss=0.1903, simple_loss=0.2562, pruned_loss=0.06218, over 4948.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2267, pruned_loss=0.03135, over 904620.92 frames.], batch size: 110, aishell_tot_loss[loss=0.1472, simple_loss=0.2339, pruned_loss=0.0303, over 710274.79 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2188, pruned_loss=0.03216, over 696800.75 frames.], batch size: 110, lr: 3.59e-04
+2022-06-19 02:41:25,266 INFO [train.py:874] (3/4) Epoch 23, batch 550, datatang_loss[loss=0.1714, simple_loss=0.2497, pruned_loss=0.04658, over 4918.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2271, pruned_loss=0.0315, over 922757.43 frames.], batch size: 98, aishell_tot_loss[loss=0.1473, simple_loss=0.234, pruned_loss=0.03032, over 743961.26 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2192, pruned_loss=0.03241, over 729692.12 frames.], batch size: 98, lr: 3.59e-04
+2022-06-19 02:41:56,294 INFO [train.py:874] (3/4) Epoch 23, batch 600, aishell_loss[loss=0.1596, simple_loss=0.242, pruned_loss=0.03856, over 4862.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2274, pruned_loss=0.03158, over 936797.43 frames.], batch size: 36, aishell_tot_loss[loss=0.1478, simple_loss=0.2346, pruned_loss=0.03048, over 772608.75 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2191, pruned_loss=0.03237, over 759773.78 frames.], batch size: 36, lr: 3.59e-04
+2022-06-19 02:42:28,281 INFO [train.py:874] (3/4) Epoch 23, batch 650, datatang_loss[loss=0.1493, simple_loss=0.2299, pruned_loss=0.03437, over 4925.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2271, pruned_loss=0.03123, over 948060.33 frames.], batch size: 94, aishell_tot_loss[loss=0.1474, simple_loss=0.2342, pruned_loss=0.03031, over 799732.24 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2192, pruned_loss=0.03217, over 784675.00 frames.], batch size: 94, lr: 3.59e-04
+2022-06-19 02:42:59,764 INFO [train.py:874] (3/4) Epoch 23, batch 700, aishell_loss[loss=0.1282, simple_loss=0.2126, pruned_loss=0.02186, over 4883.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2267, pruned_loss=0.03114, over 956505.48 frames.], batch size: 28, aishell_tot_loss[loss=0.147, simple_loss=0.2339, pruned_loss=0.03007, over 820919.29 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2192, pruned_loss=0.03229, over 809238.51 frames.], batch size: 28, lr: 3.59e-04
+2022-06-19 02:43:32,133 INFO [train.py:874] (3/4) Epoch 23, batch 750, datatang_loss[loss=0.1441, simple_loss=0.2197, pruned_loss=0.03425, over 4940.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2266, pruned_loss=0.03095, over 962766.71 frames.], batch size: 69, aishell_tot_loss[loss=0.1467, simple_loss=0.2338, pruned_loss=0.02978, over 838699.62 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2192, pruned_loss=0.03231, over 831520.77 frames.], batch size: 69, lr: 3.59e-04
+2022-06-19 02:44:03,181 INFO [train.py:874] (3/4) Epoch 23, batch 800, aishell_loss[loss=0.1474, simple_loss=0.2343, pruned_loss=0.03025, over 4961.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2266, pruned_loss=0.03052, over 967998.18 frames.], batch size: 64, aishell_tot_loss[loss=0.1465, simple_loss=0.234, pruned_loss=0.02955, over 856119.58 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2191, pruned_loss=0.03201, over 849718.43 frames.], batch size: 64, lr: 3.59e-04
+2022-06-19 02:44:41,842 INFO [train.py:874] (3/4) Epoch 23, batch 850, aishell_loss[loss=0.1571, simple_loss=0.2392, pruned_loss=0.03746, over 4879.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2271, pruned_loss=0.03108, over 971501.49 frames.], batch size: 42, aishell_tot_loss[loss=0.1473, simple_loss=0.2345, pruned_loss=0.03009, over 873851.67 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2189, pruned_loss=0.03215, over 862607.69 frames.], batch size: 42, lr: 3.58e-04
+2022-06-19 02:45:13,110 INFO [train.py:874] (3/4) Epoch 23, batch 900, datatang_loss[loss=0.1278, simple_loss=0.2091, pruned_loss=0.0232, over 4945.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2266, pruned_loss=0.03115, over 974383.33 frames.], batch size: 55, aishell_tot_loss[loss=0.1472, simple_loss=0.2343, pruned_loss=0.03007, over 886759.59 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2186, pruned_loss=0.03222, over 877094.42 frames.], batch size: 55, lr: 3.58e-04
+2022-06-19 02:45:44,669 INFO [train.py:874] (3/4) Epoch 23, batch 950, datatang_loss[loss=0.1462, simple_loss=0.2125, pruned_loss=0.03996, over 4955.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2272, pruned_loss=0.03134, over 976805.61 frames.], batch size: 50, aishell_tot_loss[loss=0.1469, simple_loss=0.2337, pruned_loss=0.03004, over 900391.15 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2196, pruned_loss=0.03254, over 887551.10 frames.], batch size: 50, lr: 3.58e-04
+2022-06-19 02:46:17,142 INFO [train.py:874] (3/4) Epoch 23, batch 1000, datatang_loss[loss=0.1427, simple_loss=0.2164, pruned_loss=0.03447, over 4880.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2275, pruned_loss=0.03151, over 979000.16 frames.], batch size: 44, aishell_tot_loss[loss=0.1471, simple_loss=0.2339, pruned_loss=0.03019, over 911420.24 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2198, pruned_loss=0.03261, over 898254.09 frames.], batch size: 44, lr: 3.58e-04
+2022-06-19 02:46:17,143 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 02:46:34,401 INFO [train.py:914] (3/4) Epoch 23, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04077, over 1622729.00 frames.
+2022-06-19 02:47:05,352 INFO [train.py:874] (3/4) Epoch 23, batch 1050, datatang_loss[loss=0.1238, simple_loss=0.1942, pruned_loss=0.02665, over 4920.00 frames.], tot_loss[loss=0.1452, simple_loss=0.227, pruned_loss=0.03166, over 980197.36 frames.], batch size: 75, aishell_tot_loss[loss=0.1469, simple_loss=0.2336, pruned_loss=0.03007, over 919044.26 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2198, pruned_loss=0.03289, over 909516.83 frames.], batch size: 75, lr: 3.58e-04
+2022-06-19 02:47:36,049 INFO [train.py:874] (3/4) Epoch 23, batch 1100, datatang_loss[loss=0.1242, simple_loss=0.2083, pruned_loss=0.02002, over 4939.00 frames.], tot_loss[loss=0.145, simple_loss=0.227, pruned_loss=0.03147, over 981575.49 frames.], batch size: 62, aishell_tot_loss[loss=0.1474, simple_loss=0.234, pruned_loss=0.03046, over 926108.91 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2198, pruned_loss=0.0323, over 919566.42 frames.], batch size: 62, lr: 3.58e-04
+2022-06-19 02:48:07,622 INFO [train.py:874] (3/4) Epoch 23, batch 1150, aishell_loss[loss=0.1599, simple_loss=0.2531, pruned_loss=0.03329, over 4866.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2274, pruned_loss=0.03147, over 982402.02 frames.], batch size: 43, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03062, over 933329.09 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.22, pruned_loss=0.03214, over 927034.22 frames.], batch size: 43, lr: 3.58e-04
+2022-06-19 02:48:40,956 INFO [train.py:874] (3/4) Epoch 23, batch 1200, datatang_loss[loss=0.1276, simple_loss=0.2095, pruned_loss=0.02287, over 4925.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2258, pruned_loss=0.03123, over 983035.37 frames.], batch size: 79, aishell_tot_loss[loss=0.1466, simple_loss=0.2327, pruned_loss=0.03025, over 938944.13 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2198, pruned_loss=0.03224, over 934452.52 frames.], batch size: 79, lr: 3.58e-04
+2022-06-19 02:49:12,912 INFO [train.py:874] (3/4) Epoch 23, batch 1250, datatang_loss[loss=0.1434, simple_loss=0.2238, pruned_loss=0.03146, over 4925.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2258, pruned_loss=0.03118, over 983342.59 frames.], batch size: 83, aishell_tot_loss[loss=0.1463, simple_loss=0.2322, pruned_loss=0.03015, over 944717.88 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.22, pruned_loss=0.03227, over 939872.64 frames.], batch size: 83, lr: 3.58e-04
+2022-06-19 02:49:43,411 INFO [train.py:874] (3/4) Epoch 23, batch 1300, aishell_loss[loss=0.1693, simple_loss=0.2564, pruned_loss=0.04112, over 4888.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2271, pruned_loss=0.03168, over 983482.50 frames.], batch size: 42, aishell_tot_loss[loss=0.1467, simple_loss=0.2327, pruned_loss=0.03029, over 950119.99 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2206, pruned_loss=0.0327, over 944163.05 frames.], batch size: 42, lr: 3.58e-04
+2022-06-19 02:50:15,904 INFO [train.py:874] (3/4) Epoch 23, batch 1350, aishell_loss[loss=0.162, simple_loss=0.2535, pruned_loss=0.03521, over 4915.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2286, pruned_loss=0.03252, over 983785.56 frames.], batch size: 41, aishell_tot_loss[loss=0.1471, simple_loss=0.2332, pruned_loss=0.03045, over 953210.83 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2221, pruned_loss=0.03345, over 950072.42 frames.], batch size: 41, lr: 3.57e-04
+2022-06-19 02:50:47,443 INFO [train.py:874] (3/4) Epoch 23, batch 1400, datatang_loss[loss=0.1382, simple_loss=0.2153, pruned_loss=0.03055, over 4923.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2286, pruned_loss=0.03294, over 984020.12 frames.], batch size: 42, aishell_tot_loss[loss=0.1469, simple_loss=0.2328, pruned_loss=0.03048, over 957041.36 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2227, pruned_loss=0.03402, over 954049.84 frames.], batch size: 42, lr: 3.57e-04
+2022-06-19 02:51:19,403 INFO [train.py:874] (3/4) Epoch 23, batch 1450, aishell_loss[loss=0.1436, simple_loss=0.2348, pruned_loss=0.02618, over 4885.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2286, pruned_loss=0.03287, over 984354.14 frames.], batch size: 42, aishell_tot_loss[loss=0.1472, simple_loss=0.2332, pruned_loss=0.03061, over 959963.04 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2225, pruned_loss=0.03398, over 958200.96 frames.], batch size: 42, lr: 3.57e-04
+2022-06-19 02:51:53,386 INFO [train.py:874] (3/4) Epoch 23, batch 1500, aishell_loss[loss=0.1254, simple_loss=0.208, pruned_loss=0.02145, over 4967.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2275, pruned_loss=0.03213, over 984717.99 frames.], batch size: 25, aishell_tot_loss[loss=0.1472, simple_loss=0.2332, pruned_loss=0.03054, over 962907.72 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2216, pruned_loss=0.03337, over 961599.57 frames.], batch size: 25, lr: 3.57e-04
+2022-06-19 02:52:25,023 INFO [train.py:874] (3/4) Epoch 23, batch 1550, aishell_loss[loss=0.1499, simple_loss=0.2377, pruned_loss=0.03101, over 4899.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2286, pruned_loss=0.03235, over 984853.16 frames.], batch size: 34, aishell_tot_loss[loss=0.148, simple_loss=0.2341, pruned_loss=0.03091, over 966158.06 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2214, pruned_loss=0.03335, over 963723.54 frames.], batch size: 34, lr: 3.57e-04
+2022-06-19 02:52:56,571 INFO [train.py:874] (3/4) Epoch 23, batch 1600, datatang_loss[loss=0.1396, simple_loss=0.2071, pruned_loss=0.03605, over 4984.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2283, pruned_loss=0.03302, over 984863.34 frames.], batch size: 37, aishell_tot_loss[loss=0.1479, simple_loss=0.2337, pruned_loss=0.03107, over 967948.01 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2219, pruned_loss=0.03389, over 966660.64 frames.], batch size: 37, lr: 3.57e-04
+2022-06-19 02:53:27,775 INFO [train.py:874] (3/4) Epoch 23, batch 1650, datatang_loss[loss=0.1348, simple_loss=0.2186, pruned_loss=0.02551, over 4952.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2279, pruned_loss=0.03264, over 984717.41 frames.], batch size: 62, aishell_tot_loss[loss=0.1476, simple_loss=0.2334, pruned_loss=0.03092, over 969952.34 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2218, pruned_loss=0.03381, over 968625.55 frames.], batch size: 62, lr: 3.57e-04
+2022-06-19 02:54:00,731 INFO [train.py:874] (3/4) Epoch 23, batch 1700, aishell_loss[loss=0.1523, simple_loss=0.2465, pruned_loss=0.02907, over 4953.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2288, pruned_loss=0.03267, over 984731.62 frames.], batch size: 68, aishell_tot_loss[loss=0.1478, simple_loss=0.2339, pruned_loss=0.03087, over 971837.86 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2221, pruned_loss=0.03401, over 970372.18 frames.], batch size: 68, lr: 3.57e-04
+2022-06-19 02:54:33,053 INFO [train.py:874] (3/4) Epoch 23, batch 1750, aishell_loss[loss=0.1361, simple_loss=0.2305, pruned_loss=0.02087, over 4952.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2287, pruned_loss=0.03247, over 984886.25 frames.], batch size: 45, aishell_tot_loss[loss=0.1476, simple_loss=0.2339, pruned_loss=0.03061, over 973096.23 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2223, pruned_loss=0.03409, over 972501.68 frames.], batch size: 45, lr: 3.57e-04
+2022-06-19 02:55:05,110 INFO [train.py:874] (3/4) Epoch 23, batch 1800, aishell_loss[loss=0.1301, simple_loss=0.2267, pruned_loss=0.01673, over 4941.00 frames.], tot_loss[loss=0.146, simple_loss=0.2281, pruned_loss=0.03202, over 985315.67 frames.], batch size: 56, aishell_tot_loss[loss=0.1471, simple_loss=0.2337, pruned_loss=0.03021, over 974705.37 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.222, pruned_loss=0.03399, over 974203.62 frames.], batch size: 56, lr: 3.57e-04
+2022-06-19 02:55:38,754 INFO [train.py:874] (3/4) Epoch 23, batch 1850, datatang_loss[loss=0.1129, simple_loss=0.1887, pruned_loss=0.01857, over 4957.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2265, pruned_loss=0.03114, over 985163.35 frames.], batch size: 67, aishell_tot_loss[loss=0.1464, simple_loss=0.233, pruned_loss=0.02985, over 975693.78 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.221, pruned_loss=0.03342, over 975613.77 frames.], batch size: 67, lr: 3.57e-04
+2022-06-19 02:56:09,977 INFO [train.py:874] (3/4) Epoch 23, batch 1900, aishell_loss[loss=0.1573, simple_loss=0.2403, pruned_loss=0.03714, over 4943.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2266, pruned_loss=0.03145, over 985688.06 frames.], batch size: 64, aishell_tot_loss[loss=0.1464, simple_loss=0.233, pruned_loss=0.02989, over 977105.18 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.221, pruned_loss=0.0336, over 976987.63 frames.], batch size: 64, lr: 3.56e-04
+2022-06-19 02:56:41,494 INFO [train.py:874] (3/4) Epoch 23, batch 1950, datatang_loss[loss=0.1312, simple_loss=0.224, pruned_loss=0.01921, over 4929.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2263, pruned_loss=0.03147, over 985464.28 frames.], batch size: 79, aishell_tot_loss[loss=0.1467, simple_loss=0.2333, pruned_loss=0.0301, over 977491.35 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2206, pruned_loss=0.03325, over 978369.15 frames.], batch size: 79, lr: 3.56e-04
+2022-06-19 02:57:14,393 INFO [train.py:874] (3/4) Epoch 23, batch 2000, datatang_loss[loss=0.1422, simple_loss=0.2154, pruned_loss=0.0345, over 4898.00 frames.], tot_loss[loss=0.1445, simple_loss=0.226, pruned_loss=0.03144, over 985632.09 frames.], batch size: 42, aishell_tot_loss[loss=0.1468, simple_loss=0.2335, pruned_loss=0.03008, over 978205.44 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2204, pruned_loss=0.0331, over 979588.76 frames.], batch size: 42, lr: 3.56e-04
+2022-06-19 02:57:14,394 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 02:57:32,302 INFO [train.py:914] (3/4) Epoch 23, validation: loss=0.1648, simple_loss=0.2485, pruned_loss=0.04055, over 1622729.00 frames.
+2022-06-19 02:58:03,768 INFO [train.py:874] (3/4) Epoch 23, batch 2050, datatang_loss[loss=0.1589, simple_loss=0.2411, pruned_loss=0.03831, over 4950.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2258, pruned_loss=0.03141, over 985283.26 frames.], batch size: 86, aishell_tot_loss[loss=0.1469, simple_loss=0.2333, pruned_loss=0.03023, over 978713.08 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.22, pruned_loss=0.03289, over 980308.81 frames.], batch size: 86, lr: 3.56e-04
+2022-06-19 02:58:35,303 INFO [train.py:874] (3/4) Epoch 23, batch 2100, aishell_loss[loss=0.1411, simple_loss=0.23, pruned_loss=0.02608, over 4969.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2249, pruned_loss=0.03087, over 985061.00 frames.], batch size: 61, aishell_tot_loss[loss=0.1464, simple_loss=0.2327, pruned_loss=0.02999, over 979216.64 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2195, pruned_loss=0.0325, over 980920.09 frames.], batch size: 61, lr: 3.56e-04
+2022-06-19 02:59:08,021 INFO [train.py:874] (3/4) Epoch 23, batch 2150, datatang_loss[loss=0.1474, simple_loss=0.2276, pruned_loss=0.03358, over 4921.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2252, pruned_loss=0.03112, over 985249.26 frames.], batch size: 75, aishell_tot_loss[loss=0.1468, simple_loss=0.2331, pruned_loss=0.03031, over 979728.48 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2196, pruned_loss=0.0323, over 981747.15 frames.], batch size: 75, lr: 3.56e-04
+2022-06-19 02:59:40,134 INFO [train.py:874] (3/4) Epoch 23, batch 2200, datatang_loss[loss=0.1426, simple_loss=0.219, pruned_loss=0.03311, over 4944.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2258, pruned_loss=0.03141, over 985039.31 frames.], batch size: 67, aishell_tot_loss[loss=0.1471, simple_loss=0.2334, pruned_loss=0.03046, over 980275.37 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2196, pruned_loss=0.03243, over 982044.64 frames.], batch size: 67, lr: 3.56e-04
+2022-06-19 03:00:11,269 INFO [train.py:874] (3/4) Epoch 23, batch 2250, datatang_loss[loss=0.1412, simple_loss=0.215, pruned_loss=0.03363, over 4941.00 frames.], tot_loss[loss=0.1446, simple_loss=0.226, pruned_loss=0.0316, over 984980.90 frames.], batch size: 62, aishell_tot_loss[loss=0.1474, simple_loss=0.2337, pruned_loss=0.03053, over 980647.25 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2197, pruned_loss=0.03252, over 982482.78 frames.], batch size: 62, lr: 3.56e-04
+2022-06-19 03:00:43,987 INFO [train.py:874] (3/4) Epoch 23, batch 2300, aishell_loss[loss=0.164, simple_loss=0.2572, pruned_loss=0.03545, over 4985.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2265, pruned_loss=0.03163, over 985275.17 frames.], batch size: 44, aishell_tot_loss[loss=0.1481, simple_loss=0.2346, pruned_loss=0.03078, over 981331.14 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2192, pruned_loss=0.03229, over 982916.41 frames.], batch size: 44, lr: 3.56e-04
+2022-06-19 03:01:16,174 INFO [train.py:874] (3/4) Epoch 23, batch 2350, aishell_loss[loss=0.1479, simple_loss=0.2382, pruned_loss=0.02876, over 4890.00 frames.], tot_loss[loss=0.144, simple_loss=0.2257, pruned_loss=0.03116, over 985024.59 frames.], batch size: 34, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03067, over 981392.49 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2189, pruned_loss=0.0319, over 983332.05 frames.], batch size: 34, lr: 3.56e-04
+2022-06-19 03:01:47,623 INFO [train.py:874] (3/4) Epoch 23, batch 2400, aishell_loss[loss=0.1668, simple_loss=0.2574, pruned_loss=0.03805, over 4923.00 frames.], tot_loss[loss=0.145, simple_loss=0.2265, pruned_loss=0.03181, over 985237.01 frames.], batch size: 32, aishell_tot_loss[loss=0.1483, simple_loss=0.2346, pruned_loss=0.03103, over 981714.71 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2194, pruned_loss=0.03217, over 983808.91 frames.], batch size: 32, lr: 3.55e-04
+2022-06-19 03:02:18,552 INFO [train.py:874] (3/4) Epoch 23, batch 2450, aishell_loss[loss=0.1554, simple_loss=0.2368, pruned_loss=0.03695, over 4916.00 frames.], tot_loss[loss=0.1451, simple_loss=0.227, pruned_loss=0.03161, over 985120.69 frames.], batch size: 46, aishell_tot_loss[loss=0.1482, simple_loss=0.2346, pruned_loss=0.03085, over 981998.40 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2194, pruned_loss=0.03223, over 984029.10 frames.], batch size: 46, lr: 3.55e-04
+2022-06-19 03:02:51,578 INFO [train.py:874] (3/4) Epoch 23, batch 2500, datatang_loss[loss=0.1623, simple_loss=0.2362, pruned_loss=0.04419, over 4915.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2265, pruned_loss=0.03149, over 985471.04 frames.], batch size: 64, aishell_tot_loss[loss=0.1483, simple_loss=0.2347, pruned_loss=0.03096, over 982319.90 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2192, pruned_loss=0.03198, over 984525.59 frames.], batch size: 64, lr: 3.55e-04
+2022-06-19 03:03:23,744 INFO [train.py:874] (3/4) Epoch 23, batch 2550, datatang_loss[loss=0.1554, simple_loss=0.2334, pruned_loss=0.03866, over 4949.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2268, pruned_loss=0.03196, over 985339.20 frames.], batch size: 99, aishell_tot_loss[loss=0.1485, simple_loss=0.2349, pruned_loss=0.03109, over 982462.62 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2194, pruned_loss=0.03235, over 984695.38 frames.], batch size: 99, lr: 3.55e-04
+2022-06-19 03:03:55,775 INFO [train.py:874] (3/4) Epoch 23, batch 2600, aishell_loss[loss=0.1652, simple_loss=0.2567, pruned_loss=0.03686, over 4974.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2268, pruned_loss=0.03196, over 985572.34 frames.], batch size: 44, aishell_tot_loss[loss=0.1487, simple_loss=0.235, pruned_loss=0.0312, over 982831.88 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2194, pruned_loss=0.03227, over 984956.65 frames.], batch size: 44, lr: 3.55e-04
+2022-06-19 03:04:28,553 INFO [train.py:874] (3/4) Epoch 23, batch 2650, datatang_loss[loss=0.1104, simple_loss=0.186, pruned_loss=0.01743, over 4841.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2267, pruned_loss=0.03132, over 985463.67 frames.], batch size: 24, aishell_tot_loss[loss=0.1482, simple_loss=0.2347, pruned_loss=0.03091, over 983142.72 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2192, pruned_loss=0.03194, over 984961.45 frames.], batch size: 24, lr: 3.55e-04
+2022-06-19 03:05:00,048 INFO [train.py:874] (3/4) Epoch 23, batch 2700, aishell_loss[loss=0.1748, simple_loss=0.2588, pruned_loss=0.04541, over 4924.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2268, pruned_loss=0.03119, over 985654.65 frames.], batch size: 41, aishell_tot_loss[loss=0.1482, simple_loss=0.2349, pruned_loss=0.03071, over 983518.16 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.219, pruned_loss=0.03197, over 985117.20 frames.], batch size: 41, lr: 3.55e-04
+2022-06-19 03:05:31,355 INFO [train.py:874] (3/4) Epoch 23, batch 2750, aishell_loss[loss=0.1709, simple_loss=0.2559, pruned_loss=0.04293, over 4938.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2259, pruned_loss=0.03088, over 985477.77 frames.], batch size: 58, aishell_tot_loss[loss=0.148, simple_loss=0.2347, pruned_loss=0.03059, over 983862.42 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2182, pruned_loss=0.03172, over 984895.30 frames.], batch size: 58, lr: 3.55e-04
+2022-06-19 03:06:03,763 INFO [train.py:874] (3/4) Epoch 23, batch 2800, aishell_loss[loss=0.1548, simple_loss=0.2572, pruned_loss=0.02625, over 4914.00 frames.], tot_loss[loss=0.1436, simple_loss=0.226, pruned_loss=0.03058, over 985503.73 frames.], batch size: 79, aishell_tot_loss[loss=0.148, simple_loss=0.235, pruned_loss=0.03052, over 984126.92 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.218, pruned_loss=0.03142, over 984906.12 frames.], batch size: 79, lr: 3.55e-04
+2022-06-19 03:06:35,775 INFO [train.py:874] (3/4) Epoch 23, batch 2850, aishell_loss[loss=0.119, simple_loss=0.2028, pruned_loss=0.01764, over 4891.00 frames.], tot_loss[loss=0.144, simple_loss=0.2263, pruned_loss=0.0309, over 985298.47 frames.], batch size: 28, aishell_tot_loss[loss=0.1478, simple_loss=0.2347, pruned_loss=0.03044, over 984123.82 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2184, pruned_loss=0.03177, over 984914.19 frames.], batch size: 28, lr: 3.55e-04
+2022-06-19 03:07:06,111 INFO [train.py:874] (3/4) Epoch 23, batch 2900, aishell_loss[loss=0.1409, simple_loss=0.226, pruned_loss=0.0279, over 4861.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2258, pruned_loss=0.03121, over 985131.14 frames.], batch size: 36, aishell_tot_loss[loss=0.1478, simple_loss=0.2347, pruned_loss=0.03047, over 984130.69 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2179, pruned_loss=0.03201, over 984916.80 frames.], batch size: 36, lr: 3.55e-04
+2022-06-19 03:07:38,894 INFO [train.py:874] (3/4) Epoch 23, batch 2950, datatang_loss[loss=0.147, simple_loss=0.2175, pruned_loss=0.03821, over 4968.00 frames.], tot_loss[loss=0.144, simple_loss=0.2256, pruned_loss=0.03124, over 985375.20 frames.], batch size: 67, aishell_tot_loss[loss=0.1473, simple_loss=0.2342, pruned_loss=0.03025, over 984261.10 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.218, pruned_loss=0.03228, over 985198.00 frames.], batch size: 67, lr: 3.54e-04
+2022-06-19 03:08:11,545 INFO [train.py:874] (3/4) Epoch 23, batch 3000, aishell_loss[loss=0.1533, simple_loss=0.2433, pruned_loss=0.03163, over 4907.00 frames.], tot_loss[loss=0.1451, simple_loss=0.227, pruned_loss=0.03165, over 985783.15 frames.], batch size: 52, aishell_tot_loss[loss=0.1476, simple_loss=0.2346, pruned_loss=0.03035, over 984644.13 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2188, pruned_loss=0.03261, over 985404.24 frames.], batch size: 52, lr: 3.54e-04
+2022-06-19 03:08:11,546 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 03:08:29,178 INFO [train.py:914] (3/4) Epoch 23, validation: loss=0.1651, simple_loss=0.2488, pruned_loss=0.04074, over 1622729.00 frames.
+2022-06-19 03:09:00,992 INFO [train.py:874] (3/4) Epoch 23, batch 3050, datatang_loss[loss=0.1406, simple_loss=0.2103, pruned_loss=0.03544, over 4921.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2275, pruned_loss=0.03145, over 985515.17 frames.], batch size: 34, aishell_tot_loss[loss=0.1477, simple_loss=0.2347, pruned_loss=0.03037, over 984541.37 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2188, pruned_loss=0.03246, over 985426.31 frames.], batch size: 34, lr: 3.54e-04
+2022-06-19 03:09:32,360 INFO [train.py:874] (3/4) Epoch 23, batch 3100, datatang_loss[loss=0.1216, simple_loss=0.1979, pruned_loss=0.02261, over 4911.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2285, pruned_loss=0.03148, over 985643.10 frames.], batch size: 52, aishell_tot_loss[loss=0.1485, simple_loss=0.2357, pruned_loss=0.03067, over 984844.63 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2185, pruned_loss=0.03223, over 985396.37 frames.], batch size: 52, lr: 3.54e-04
+2022-06-19 03:10:04,612 INFO [train.py:874] (3/4) Epoch 23, batch 3150, datatang_loss[loss=0.1409, simple_loss=0.2089, pruned_loss=0.03639, over 4965.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2276, pruned_loss=0.03132, over 985689.76 frames.], batch size: 45, aishell_tot_loss[loss=0.1489, simple_loss=0.236, pruned_loss=0.0309, over 985019.63 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2179, pruned_loss=0.03184, over 985387.44 frames.], batch size: 45, lr: 3.54e-04
+2022-06-19 03:10:38,055 INFO [train.py:874] (3/4) Epoch 23, batch 3200, datatang_loss[loss=0.1246, simple_loss=0.2106, pruned_loss=0.01927, over 4925.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2276, pruned_loss=0.03156, over 985706.96 frames.], batch size: 71, aishell_tot_loss[loss=0.1488, simple_loss=0.2356, pruned_loss=0.03094, over 985164.53 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2183, pruned_loss=0.03203, over 985375.51 frames.], batch size: 71, lr: 3.54e-04
+2022-06-19 03:11:09,326 INFO [train.py:874] (3/4) Epoch 23, batch 3250, datatang_loss[loss=0.1894, simple_loss=0.2623, pruned_loss=0.05828, over 4959.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2277, pruned_loss=0.03164, over 985797.15 frames.], batch size: 99, aishell_tot_loss[loss=0.1488, simple_loss=0.2357, pruned_loss=0.031, over 985427.59 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2183, pruned_loss=0.03209, over 985310.54 frames.], batch size: 99, lr: 3.54e-04
+2022-06-19 03:11:41,554 INFO [train.py:874] (3/4) Epoch 23, batch 3300, aishell_loss[loss=0.167, simple_loss=0.2526, pruned_loss=0.04069, over 4849.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2273, pruned_loss=0.03141, over 985741.36 frames.], batch size: 36, aishell_tot_loss[loss=0.1487, simple_loss=0.2356, pruned_loss=0.03089, over 985153.70 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2179, pruned_loss=0.03198, over 985620.73 frames.], batch size: 36, lr: 3.54e-04
+2022-06-19 03:12:14,936 INFO [train.py:874] (3/4) Epoch 23, batch 3350, aishell_loss[loss=0.1289, simple_loss=0.221, pruned_loss=0.01837, over 4980.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2266, pruned_loss=0.03099, over 985626.62 frames.], batch size: 30, aishell_tot_loss[loss=0.1483, simple_loss=0.2352, pruned_loss=0.03064, over 985392.96 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2176, pruned_loss=0.03176, over 985361.57 frames.], batch size: 30, lr: 3.54e-04
+2022-06-19 03:12:46,784 INFO [train.py:874] (3/4) Epoch 23, batch 3400, datatang_loss[loss=0.1361, simple_loss=0.2136, pruned_loss=0.02925, over 4977.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2268, pruned_loss=0.03132, over 985728.13 frames.], batch size: 31, aishell_tot_loss[loss=0.1481, simple_loss=0.2351, pruned_loss=0.03058, over 985350.49 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2182, pruned_loss=0.03213, over 985561.83 frames.], batch size: 31, lr: 3.54e-04
+2022-06-19 03:13:18,850 INFO [train.py:874] (3/4) Epoch 23, batch 3450, aishell_loss[loss=0.1284, simple_loss=0.2082, pruned_loss=0.02431, over 4971.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2255, pruned_loss=0.031, over 985477.33 frames.], batch size: 27, aishell_tot_loss[loss=0.1477, simple_loss=0.2344, pruned_loss=0.03046, over 985084.51 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2178, pruned_loss=0.03186, over 985616.98 frames.], batch size: 27, lr: 3.54e-04
+2022-06-19 03:13:52,071 INFO [train.py:874] (3/4) Epoch 23, batch 3500, datatang_loss[loss=0.1537, simple_loss=0.233, pruned_loss=0.03726, over 4873.00 frames.], tot_loss[loss=0.1433, simple_loss=0.225, pruned_loss=0.03084, over 985303.20 frames.], batch size: 39, aishell_tot_loss[loss=0.1472, simple_loss=0.2339, pruned_loss=0.03027, over 984853.33 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.218, pruned_loss=0.03183, over 985689.88 frames.], batch size: 39, lr: 3.53e-04
+2022-06-19 03:14:23,689 INFO [train.py:874] (3/4) Epoch 23, batch 3550, datatang_loss[loss=0.1498, simple_loss=0.2287, pruned_loss=0.03544, over 4960.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2253, pruned_loss=0.03068, over 985584.60 frames.], batch size: 34, aishell_tot_loss[loss=0.1474, simple_loss=0.2344, pruned_loss=0.03027, over 984894.59 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.218, pruned_loss=0.0316, over 985941.01 frames.], batch size: 34, lr: 3.53e-04
+2022-06-19 03:14:55,523 INFO [train.py:874] (3/4) Epoch 23, batch 3600, aishell_loss[loss=0.1757, simple_loss=0.2662, pruned_loss=0.04257, over 4959.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2252, pruned_loss=0.03125, over 985499.76 frames.], batch size: 64, aishell_tot_loss[loss=0.1477, simple_loss=0.2343, pruned_loss=0.03048, over 984883.50 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2181, pruned_loss=0.03189, over 985881.46 frames.], batch size: 64, lr: 3.53e-04
+2022-06-19 03:15:29,884 INFO [train.py:874] (3/4) Epoch 23, batch 3650, aishell_loss[loss=0.142, simple_loss=0.2353, pruned_loss=0.02434, over 4967.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2254, pruned_loss=0.03115, over 985398.36 frames.], batch size: 51, aishell_tot_loss[loss=0.148, simple_loss=0.2349, pruned_loss=0.03054, over 984833.70 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2177, pruned_loss=0.03172, over 985843.21 frames.], batch size: 51, lr: 3.53e-04
+2022-06-19 03:16:01,440 INFO [train.py:874] (3/4) Epoch 23, batch 3700, datatang_loss[loss=0.135, simple_loss=0.208, pruned_loss=0.03093, over 4968.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2253, pruned_loss=0.03123, over 985896.66 frames.], batch size: 45, aishell_tot_loss[loss=0.1481, simple_loss=0.235, pruned_loss=0.03062, over 985011.90 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2179, pruned_loss=0.03171, over 986160.55 frames.], batch size: 45, lr: 3.53e-04
+2022-06-19 03:16:34,294 INFO [train.py:874] (3/4) Epoch 23, batch 3750, datatang_loss[loss=0.1342, simple_loss=0.2148, pruned_loss=0.02676, over 4931.00 frames.], tot_loss[loss=0.1444, simple_loss=0.226, pruned_loss=0.0314, over 985847.74 frames.], batch size: 79, aishell_tot_loss[loss=0.1482, simple_loss=0.235, pruned_loss=0.03071, over 985129.36 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2182, pruned_loss=0.03184, over 986070.52 frames.], batch size: 79, lr: 3.53e-04
+2022-06-19 03:17:06,343 INFO [train.py:874] (3/4) Epoch 23, batch 3800, datatang_loss[loss=0.1411, simple_loss=0.2285, pruned_loss=0.0268, over 4922.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2253, pruned_loss=0.03094, over 986224.92 frames.], batch size: 98, aishell_tot_loss[loss=0.1475, simple_loss=0.2341, pruned_loss=0.03042, over 985440.41 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.218, pruned_loss=0.03168, over 986226.42 frames.], batch size: 98, lr: 3.53e-04
+2022-06-19 03:17:37,374 INFO [train.py:874] (3/4) Epoch 23, batch 3850, aishell_loss[loss=0.1674, simple_loss=0.2549, pruned_loss=0.03991, over 4921.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2261, pruned_loss=0.03088, over 986319.99 frames.], batch size: 68, aishell_tot_loss[loss=0.1479, simple_loss=0.2347, pruned_loss=0.03052, over 985605.60 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2177, pruned_loss=0.0315, over 986267.22 frames.], batch size: 68, lr: 3.53e-04
+2022-06-19 03:18:07,550 INFO [train.py:874] (3/4) Epoch 23, batch 3900, aishell_loss[loss=0.1514, simple_loss=0.2446, pruned_loss=0.02905, over 4921.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2267, pruned_loss=0.03146, over 985924.95 frames.], batch size: 52, aishell_tot_loss[loss=0.1481, simple_loss=0.2349, pruned_loss=0.03068, over 985488.12 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2182, pruned_loss=0.03192, over 986052.13 frames.], batch size: 52, lr: 3.53e-04
+2022-06-19 03:18:38,104 INFO [train.py:874] (3/4) Epoch 23, batch 3950, datatang_loss[loss=0.1312, simple_loss=0.2162, pruned_loss=0.02312, over 4963.00 frames.], tot_loss[loss=0.145, simple_loss=0.2272, pruned_loss=0.03142, over 986131.68 frames.], batch size: 60, aishell_tot_loss[loss=0.148, simple_loss=0.2346, pruned_loss=0.03068, over 985615.18 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2186, pruned_loss=0.03196, over 986197.51 frames.], batch size: 60, lr: 3.53e-04
+2022-06-19 03:19:10,330 INFO [train.py:874] (3/4) Epoch 23, batch 4000, aishell_loss[loss=0.158, simple_loss=0.2393, pruned_loss=0.03842, over 4881.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2261, pruned_loss=0.03121, over 985595.80 frames.], batch size: 35, aishell_tot_loss[loss=0.1476, simple_loss=0.2341, pruned_loss=0.03054, over 985380.29 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2181, pruned_loss=0.03192, over 985930.40 frames.], batch size: 35, lr: 3.52e-04
+2022-06-19 03:19:10,331 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 03:19:27,335 INFO [train.py:914] (3/4) Epoch 23, validation: loss=0.166, simple_loss=0.2494, pruned_loss=0.04127, over 1622729.00 frames.
+2022-06-19 03:19:59,999 INFO [train.py:874] (3/4) Epoch 23, batch 4050, datatang_loss[loss=0.14, simple_loss=0.2173, pruned_loss=0.03138, over 4927.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2254, pruned_loss=0.03098, over 985451.05 frames.], batch size: 77, aishell_tot_loss[loss=0.1476, simple_loss=0.2343, pruned_loss=0.0305, over 985290.89 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2179, pruned_loss=0.03166, over 985826.08 frames.], batch size: 77, lr: 3.52e-04
+2022-06-19 03:20:30,744 INFO [train.py:874] (3/4) Epoch 23, batch 4100, datatang_loss[loss=0.1122, simple_loss=0.1872, pruned_loss=0.01855, over 4909.00 frames.], tot_loss[loss=0.1445, simple_loss=0.226, pruned_loss=0.03147, over 985230.00 frames.], batch size: 64, aishell_tot_loss[loss=0.1474, simple_loss=0.234, pruned_loss=0.03036, over 985172.74 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2189, pruned_loss=0.03231, over 985688.87 frames.], batch size: 64, lr: 3.52e-04
+2022-06-19 03:21:01,213 INFO [train.py:874] (3/4) Epoch 23, batch 4150, datatang_loss[loss=0.1444, simple_loss=0.2227, pruned_loss=0.03307, over 4904.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2266, pruned_loss=0.03152, over 985387.71 frames.], batch size: 59, aishell_tot_loss[loss=0.1475, simple_loss=0.2341, pruned_loss=0.03041, over 985334.93 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.219, pruned_loss=0.03235, over 985651.85 frames.], batch size: 59, lr: 3.52e-04
+2022-06-19 03:22:22,991 INFO [train.py:874] (3/4) Epoch 24, batch 50, aishell_loss[loss=0.1337, simple_loss=0.2271, pruned_loss=0.02014, over 4964.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2245, pruned_loss=0.02864, over 218388.84 frames.], batch size: 61, aishell_tot_loss[loss=0.1504, simple_loss=0.2386, pruned_loss=0.03108, over 124606.32 frames.], datatang_tot_loss[loss=0.1298, simple_loss=0.208, pruned_loss=0.0258, over 107351.59 frames.], batch size: 61, lr: 3.45e-04
+2022-06-19 03:22:55,101 INFO [train.py:874] (3/4) Epoch 24, batch 100, datatang_loss[loss=0.1912, simple_loss=0.2663, pruned_loss=0.05811, over 4920.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2243, pruned_loss=0.02949, over 388020.28 frames.], batch size: 109, aishell_tot_loss[loss=0.1482, simple_loss=0.236, pruned_loss=0.03015, over 236989.71 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2097, pruned_loss=0.02847, over 198951.44 frames.], batch size: 109, lr: 3.45e-04
+2022-06-19 03:23:26,211 INFO [train.py:874] (3/4) Epoch 24, batch 150, aishell_loss[loss=0.1495, simple_loss=0.2416, pruned_loss=0.02868, over 4972.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2232, pruned_loss=0.02894, over 520211.16 frames.], batch size: 61, aishell_tot_loss[loss=0.146, simple_loss=0.2329, pruned_loss=0.02951, over 335042.26 frames.], datatang_tot_loss[loss=0.1339, simple_loss=0.2114, pruned_loss=0.02826, over 280766.22 frames.], batch size: 61, lr: 3.45e-04
+2022-06-19 03:23:59,966 INFO [train.py:874] (3/4) Epoch 24, batch 200, datatang_loss[loss=0.1547, simple_loss=0.224, pruned_loss=0.04265, over 4868.00 frames.], tot_loss[loss=0.14, simple_loss=0.2219, pruned_loss=0.02904, over 623246.78 frames.], batch size: 39, aishell_tot_loss[loss=0.1454, simple_loss=0.2318, pruned_loss=0.02946, over 408453.89 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2112, pruned_loss=0.02853, over 367065.20 frames.], batch size: 39, lr: 3.45e-04
+2022-06-19 03:24:28,479 INFO [train.py:874] (3/4) Epoch 24, batch 250, aishell_loss[loss=0.1368, simple_loss=0.2261, pruned_loss=0.02378, over 4962.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2221, pruned_loss=0.029, over 703923.42 frames.], batch size: 51, aishell_tot_loss[loss=0.1451, simple_loss=0.2319, pruned_loss=0.0291, over 484328.85 frames.], datatang_tot_loss[loss=0.1343, simple_loss=0.2109, pruned_loss=0.02885, over 431709.11 frames.], batch size: 51, lr: 3.44e-04
+2022-06-19 03:25:02,129 INFO [train.py:874] (3/4) Epoch 24, batch 300, datatang_loss[loss=0.1385, simple_loss=0.2146, pruned_loss=0.03119, over 4927.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2225, pruned_loss=0.02993, over 766454.45 frames.], batch size: 71, aishell_tot_loss[loss=0.1468, simple_loss=0.2336, pruned_loss=0.03001, over 527741.78 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2114, pruned_loss=0.02943, over 513726.83 frames.], batch size: 71, lr: 3.44e-04
+2022-06-19 03:25:34,630 INFO [train.py:874] (3/4) Epoch 24, batch 350, aishell_loss[loss=0.1419, simple_loss=0.2311, pruned_loss=0.02634, over 4980.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2227, pruned_loss=0.02989, over 815116.06 frames.], batch size: 38, aishell_tot_loss[loss=0.1469, simple_loss=0.2334, pruned_loss=0.03023, over 583762.77 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2117, pruned_loss=0.02923, over 567215.73 frames.], batch size: 38, lr: 3.44e-04
+2022-06-19 03:26:05,137 INFO [train.py:874] (3/4) Epoch 24, batch 400, datatang_loss[loss=0.1264, simple_loss=0.2068, pruned_loss=0.02302, over 4958.00 frames.], tot_loss[loss=0.141, simple_loss=0.2221, pruned_loss=0.02996, over 852777.84 frames.], batch size: 67, aishell_tot_loss[loss=0.1466, simple_loss=0.2326, pruned_loss=0.03029, over 623804.53 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2123, pruned_loss=0.02935, over 623783.70 frames.], batch size: 67, lr: 3.44e-04
+2022-06-19 03:26:39,177 INFO [train.py:874] (3/4) Epoch 24, batch 450, datatang_loss[loss=0.1665, simple_loss=0.2315, pruned_loss=0.0507, over 4963.00 frames.], tot_loss[loss=0.142, simple_loss=0.2233, pruned_loss=0.03033, over 882264.94 frames.], batch size: 45, aishell_tot_loss[loss=0.1462, simple_loss=0.2327, pruned_loss=0.02981, over 655140.32 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2148, pruned_loss=0.03038, over 677324.28 frames.], batch size: 45, lr: 3.44e-04
+2022-06-19 03:27:12,130 INFO [train.py:874] (3/4) Epoch 24, batch 500, aishell_loss[loss=0.1222, simple_loss=0.1774, pruned_loss=0.03355, over 4916.00 frames.], tot_loss[loss=0.142, simple_loss=0.2231, pruned_loss=0.03044, over 905316.07 frames.], batch size: 20, aishell_tot_loss[loss=0.1452, simple_loss=0.231, pruned_loss=0.02968, over 699979.47 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2154, pruned_loss=0.03075, over 708145.02 frames.], batch size: 20, lr: 3.44e-04
+2022-06-19 03:27:40,983 INFO [train.py:874] (3/4) Epoch 24, batch 550, datatang_loss[loss=0.131, simple_loss=0.2091, pruned_loss=0.02647, over 4948.00 frames.], tot_loss[loss=0.1421, simple_loss=0.223, pruned_loss=0.03054, over 922942.20 frames.], batch size: 62, aishell_tot_loss[loss=0.1451, simple_loss=0.2309, pruned_loss=0.02961, over 734580.90 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2154, pruned_loss=0.03101, over 739717.80 frames.], batch size: 62, lr: 3.44e-04
+2022-06-19 03:28:15,557 INFO [train.py:874] (3/4) Epoch 24, batch 600, datatang_loss[loss=0.1221, simple_loss=0.2011, pruned_loss=0.02152, over 4931.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2245, pruned_loss=0.03096, over 936387.70 frames.], batch size: 42, aishell_tot_loss[loss=0.1462, simple_loss=0.2321, pruned_loss=0.03012, over 764070.16 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.216, pruned_loss=0.0311, over 768276.78 frames.], batch size: 42, lr: 3.44e-04
+2022-06-19 03:28:47,988 INFO [train.py:874] (3/4) Epoch 24, batch 650, aishell_loss[loss=0.1489, simple_loss=0.2433, pruned_loss=0.02726, over 4906.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2255, pruned_loss=0.03088, over 947052.57 frames.], batch size: 60, aishell_tot_loss[loss=0.1459, simple_loss=0.2319, pruned_loss=0.02998, over 796708.89 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2171, pruned_loss=0.0313, over 786954.06 frames.], batch size: 60, lr: 3.44e-04
+2022-06-19 03:29:24,115 INFO [train.py:874] (3/4) Epoch 24, batch 700, aishell_loss[loss=0.1449, simple_loss=0.2399, pruned_loss=0.025, over 4922.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2267, pruned_loss=0.03119, over 955562.04 frames.], batch size: 33, aishell_tot_loss[loss=0.1463, simple_loss=0.2327, pruned_loss=0.02996, over 819705.14 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2179, pruned_loss=0.03179, over 809517.55 frames.], batch size: 33, lr: 3.44e-04
+2022-06-19 03:29:56,728 INFO [train.py:874] (3/4) Epoch 24, batch 750, aishell_loss[loss=0.1672, simple_loss=0.2562, pruned_loss=0.0391, over 4977.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2254, pruned_loss=0.03102, over 962317.15 frames.], batch size: 39, aishell_tot_loss[loss=0.1454, simple_loss=0.2316, pruned_loss=0.02962, over 835435.35 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2181, pruned_loss=0.03196, over 834309.78 frames.], batch size: 39, lr: 3.44e-04
+2022-06-19 03:30:29,858 INFO [train.py:874] (3/4) Epoch 24, batch 800, datatang_loss[loss=0.1253, simple_loss=0.2087, pruned_loss=0.02096, over 4916.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2278, pruned_loss=0.03137, over 967598.47 frames.], batch size: 77, aishell_tot_loss[loss=0.147, simple_loss=0.2338, pruned_loss=0.03013, over 856558.47 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2186, pruned_loss=0.03199, over 848703.10 frames.], batch size: 77, lr: 3.44e-04
+2022-06-19 03:30:59,448 INFO [train.py:874] (3/4) Epoch 24, batch 850, aishell_loss[loss=0.1321, simple_loss=0.2298, pruned_loss=0.01721, over 4968.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2272, pruned_loss=0.03127, over 971630.92 frames.], batch size: 51, aishell_tot_loss[loss=0.1464, simple_loss=0.2332, pruned_loss=0.02982, over 871112.51 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.219, pruned_loss=0.03227, over 865531.97 frames.], batch size: 51, lr: 3.43e-04
+2022-06-19 03:31:31,313 INFO [train.py:874] (3/4) Epoch 24, batch 900, aishell_loss[loss=0.1626, simple_loss=0.2601, pruned_loss=0.03255, over 4934.00 frames.], tot_loss[loss=0.145, simple_loss=0.2271, pruned_loss=0.03144, over 974774.19 frames.], batch size: 58, aishell_tot_loss[loss=0.1466, simple_loss=0.2333, pruned_loss=0.03, over 885568.74 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.219, pruned_loss=0.03237, over 878690.52 frames.], batch size: 58, lr: 3.43e-04
+2022-06-19 03:31:59,214 INFO [train.py:874] (3/4) Epoch 24, batch 950, aishell_loss[loss=0.1587, simple_loss=0.2432, pruned_loss=0.03713, over 4943.00 frames.], tot_loss[loss=0.145, simple_loss=0.2274, pruned_loss=0.03126, over 977150.17 frames.], batch size: 54, aishell_tot_loss[loss=0.1469, simple_loss=0.2338, pruned_loss=0.03002, over 897765.75 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.219, pruned_loss=0.03222, over 890815.45 frames.], batch size: 54, lr: 3.43e-04
+2022-06-19 03:32:28,612 INFO [train.py:874] (3/4) Epoch 24, batch 1000, datatang_loss[loss=0.1167, simple_loss=0.1919, pruned_loss=0.02074, over 4863.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2268, pruned_loss=0.03117, over 978736.32 frames.], batch size: 30, aishell_tot_loss[loss=0.1465, simple_loss=0.2333, pruned_loss=0.02988, over 907947.27 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2191, pruned_loss=0.03229, over 901832.94 frames.], batch size: 30, lr: 3.43e-04
+2022-06-19 03:32:28,613 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 03:32:45,107 INFO [train.py:914] (3/4) Epoch 24, validation: loss=0.1641, simple_loss=0.2485, pruned_loss=0.03987, over 1622729.00 frames.
+2022-06-19 03:33:11,290 INFO [train.py:874] (3/4) Epoch 24, batch 1050, aishell_loss[loss=0.1396, simple_loss=0.236, pruned_loss=0.02164, over 4952.00 frames.], tot_loss[loss=0.1448, simple_loss=0.227, pruned_loss=0.03133, over 979808.23 frames.], batch size: 40, aishell_tot_loss[loss=0.1464, simple_loss=0.2329, pruned_loss=0.02989, over 918595.93 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2195, pruned_loss=0.03254, over 909513.22 frames.], batch size: 40, lr: 3.43e-04
+2022-06-19 03:33:42,081 INFO [train.py:874] (3/4) Epoch 24, batch 1100, aishell_loss[loss=0.1375, simple_loss=0.2232, pruned_loss=0.02593, over 4942.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2262, pruned_loss=0.03123, over 981325.20 frames.], batch size: 32, aishell_tot_loss[loss=0.146, simple_loss=0.2322, pruned_loss=0.02988, over 926672.65 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2195, pruned_loss=0.03248, over 918524.80 frames.], batch size: 32, lr: 3.43e-04
+2022-06-19 03:34:11,137 INFO [train.py:874] (3/4) Epoch 24, batch 1150, aishell_loss[loss=0.1515, simple_loss=0.2399, pruned_loss=0.03155, over 4936.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2266, pruned_loss=0.031, over 982134.75 frames.], batch size: 56, aishell_tot_loss[loss=0.1459, simple_loss=0.2323, pruned_loss=0.02972, over 934369.30 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2196, pruned_loss=0.03244, over 925364.71 frames.], batch size: 56, lr: 3.43e-04
+2022-06-19 03:34:38,371 INFO [train.py:874] (3/4) Epoch 24, batch 1200, datatang_loss[loss=0.1211, simple_loss=0.2012, pruned_loss=0.02051, over 4827.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2263, pruned_loss=0.03138, over 982698.32 frames.], batch size: 25, aishell_tot_loss[loss=0.1455, simple_loss=0.2318, pruned_loss=0.02955, over 939309.04 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2201, pruned_loss=0.03296, over 933465.86 frames.], batch size: 25, lr: 3.43e-04
+2022-06-19 03:35:08,068 INFO [train.py:874] (3/4) Epoch 24, batch 1250, datatang_loss[loss=0.1338, simple_loss=0.2171, pruned_loss=0.02529, over 4941.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2269, pruned_loss=0.03172, over 983519.34 frames.], batch size: 88, aishell_tot_loss[loss=0.1453, simple_loss=0.2318, pruned_loss=0.02945, over 943653.50 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2212, pruned_loss=0.03336, over 941012.90 frames.], batch size: 88, lr: 3.43e-04
+2022-06-19 03:35:37,368 INFO [train.py:874] (3/4) Epoch 24, batch 1300, datatang_loss[loss=0.1284, simple_loss=0.2092, pruned_loss=0.02378, over 4968.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2273, pruned_loss=0.03174, over 984404.99 frames.], batch size: 60, aishell_tot_loss[loss=0.1454, simple_loss=0.2319, pruned_loss=0.02944, over 948568.60 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2218, pruned_loss=0.03346, over 946753.59 frames.], batch size: 60, lr: 3.43e-04
+2022-06-19 03:36:04,189 INFO [train.py:874] (3/4) Epoch 24, batch 1350, aishell_loss[loss=0.1316, simple_loss=0.21, pruned_loss=0.02656, over 4966.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2284, pruned_loss=0.03212, over 984835.25 frames.], batch size: 31, aishell_tot_loss[loss=0.146, simple_loss=0.2324, pruned_loss=0.02982, over 953451.82 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2224, pruned_loss=0.03365, over 950930.27 frames.], batch size: 31, lr: 3.43e-04
+2022-06-19 03:36:34,130 INFO [train.py:874] (3/4) Epoch 24, batch 1400, aishell_loss[loss=0.1801, simple_loss=0.2735, pruned_loss=0.04333, over 4866.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2276, pruned_loss=0.03161, over 985071.17 frames.], batch size: 35, aishell_tot_loss[loss=0.1463, simple_loss=0.2329, pruned_loss=0.02989, over 956980.41 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2214, pruned_loss=0.03308, over 955362.52 frames.], batch size: 35, lr: 3.42e-04
+2022-06-19 03:37:01,590 INFO [train.py:874] (3/4) Epoch 24, batch 1450, datatang_loss[loss=0.1229, simple_loss=0.2037, pruned_loss=0.02111, over 4924.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2267, pruned_loss=0.03174, over 985114.20 frames.], batch size: 77, aishell_tot_loss[loss=0.1459, simple_loss=0.232, pruned_loss=0.02987, over 960140.79 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2214, pruned_loss=0.03329, over 959046.78 frames.], batch size: 77, lr: 3.42e-04
+2022-06-19 03:37:30,964 INFO [train.py:874] (3/4) Epoch 24, batch 1500, aishell_loss[loss=0.1099, simple_loss=0.178, pruned_loss=0.0209, over 4793.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2264, pruned_loss=0.0315, over 984937.00 frames.], batch size: 20, aishell_tot_loss[loss=0.1453, simple_loss=0.2315, pruned_loss=0.02957, over 962469.60 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2217, pruned_loss=0.03332, over 962552.81 frames.], batch size: 20, lr: 3.42e-04
+2022-06-19 03:38:00,694 INFO [train.py:874] (3/4) Epoch 24, batch 1550, datatang_loss[loss=0.1368, simple_loss=0.2166, pruned_loss=0.02846, over 4934.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2257, pruned_loss=0.03153, over 984872.92 frames.], batch size: 71, aishell_tot_loss[loss=0.1452, simple_loss=0.2312, pruned_loss=0.02958, over 964884.16 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2214, pruned_loss=0.03334, over 965353.18 frames.], batch size: 71, lr: 3.42e-04
+2022-06-19 03:38:29,172 INFO [train.py:874] (3/4) Epoch 24, batch 1600, datatang_loss[loss=0.1319, simple_loss=0.2093, pruned_loss=0.02721, over 4921.00 frames.], tot_loss[loss=0.144, simple_loss=0.2258, pruned_loss=0.03106, over 984934.59 frames.], batch size: 79, aishell_tot_loss[loss=0.1452, simple_loss=0.2315, pruned_loss=0.02946, over 967026.54 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2211, pruned_loss=0.03295, over 967911.79 frames.], batch size: 79, lr: 3.42e-04
+2022-06-19 03:38:56,963 INFO [train.py:874] (3/4) Epoch 24, batch 1650, aishell_loss[loss=0.1165, simple_loss=0.2059, pruned_loss=0.01354, over 4988.00 frames.], tot_loss[loss=0.144, simple_loss=0.2258, pruned_loss=0.03108, over 984897.04 frames.], batch size: 30, aishell_tot_loss[loss=0.1454, simple_loss=0.2316, pruned_loss=0.02962, over 969091.29 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2209, pruned_loss=0.0328, over 969939.56 frames.], batch size: 30, lr: 3.42e-04
+2022-06-19 03:39:27,583 INFO [train.py:874] (3/4) Epoch 24, batch 1700, datatang_loss[loss=0.1214, simple_loss=0.2041, pruned_loss=0.01932, over 4920.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2254, pruned_loss=0.03171, over 985114.55 frames.], batch size: 75, aishell_tot_loss[loss=0.1455, simple_loss=0.2318, pruned_loss=0.02967, over 970452.37 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2205, pruned_loss=0.03326, over 972373.37 frames.], batch size: 75, lr: 3.42e-04
+2022-06-19 03:39:57,080 INFO [train.py:874] (3/4) Epoch 24, batch 1750, aishell_loss[loss=0.1481, simple_loss=0.2326, pruned_loss=0.03183, over 4881.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2251, pruned_loss=0.03173, over 985598.56 frames.], batch size: 42, aishell_tot_loss[loss=0.1461, simple_loss=0.2322, pruned_loss=0.02999, over 971831.74 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2201, pruned_loss=0.0329, over 974638.04 frames.], batch size: 42, lr: 3.42e-04
+2022-06-19 03:40:24,417 INFO [train.py:874] (3/4) Epoch 24, batch 1800, datatang_loss[loss=0.1463, simple_loss=0.2219, pruned_loss=0.03534, over 4936.00 frames.], tot_loss[loss=0.1443, simple_loss=0.225, pruned_loss=0.03176, over 985615.76 frames.], batch size: 83, aishell_tot_loss[loss=0.1464, simple_loss=0.2323, pruned_loss=0.0302, over 972970.76 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2198, pruned_loss=0.03273, over 976373.40 frames.], batch size: 83, lr: 3.42e-04
+2022-06-19 03:40:54,934 INFO [train.py:874] (3/4) Epoch 24, batch 1850, datatang_loss[loss=0.1326, simple_loss=0.2097, pruned_loss=0.02775, over 4924.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2244, pruned_loss=0.03162, over 985885.59 frames.], batch size: 83, aishell_tot_loss[loss=0.1456, simple_loss=0.2312, pruned_loss=0.02999, over 974555.64 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.22, pruned_loss=0.03284, over 977647.16 frames.], batch size: 83, lr: 3.42e-04
+2022-06-19 03:41:25,567 INFO [train.py:874] (3/4) Epoch 24, batch 1900, datatang_loss[loss=0.1166, simple_loss=0.1946, pruned_loss=0.01931, over 4925.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2243, pruned_loss=0.03106, over 985783.06 frames.], batch size: 73, aishell_tot_loss[loss=0.1455, simple_loss=0.2314, pruned_loss=0.0298, over 976000.95 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2193, pruned_loss=0.03252, over 978431.17 frames.], batch size: 73, lr: 3.42e-04
+2022-06-19 03:41:52,666 INFO [train.py:874] (3/4) Epoch 24, batch 1950, datatang_loss[loss=0.1309, simple_loss=0.2146, pruned_loss=0.02357, over 4950.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2234, pruned_loss=0.03067, over 986022.07 frames.], batch size: 67, aishell_tot_loss[loss=0.1452, simple_loss=0.2314, pruned_loss=0.02953, over 976968.38 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2185, pruned_loss=0.03227, over 979646.26 frames.], batch size: 67, lr: 3.41e-04
+2022-06-19 03:42:22,527 INFO [train.py:874] (3/4) Epoch 24, batch 2000, datatang_loss[loss=0.1357, simple_loss=0.2039, pruned_loss=0.03373, over 4973.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2236, pruned_loss=0.03056, over 985869.57 frames.], batch size: 60, aishell_tot_loss[loss=0.1451, simple_loss=0.2313, pruned_loss=0.02944, over 977638.89 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2186, pruned_loss=0.03214, over 980599.04 frames.], batch size: 60, lr: 3.41e-04
+2022-06-19 03:42:22,528 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 03:42:39,281 INFO [train.py:914] (3/4) Epoch 24, validation: loss=0.1651, simple_loss=0.2488, pruned_loss=0.04067, over 1622729.00 frames.
+2022-06-19 03:43:08,913 INFO [train.py:874] (3/4) Epoch 24, batch 2050, aishell_loss[loss=0.1598, simple_loss=0.2548, pruned_loss=0.03239, over 4904.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2258, pruned_loss=0.03167, over 985736.45 frames.], batch size: 68, aishell_tot_loss[loss=0.1467, simple_loss=0.2328, pruned_loss=0.03033, over 978634.74 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.219, pruned_loss=0.03241, over 981079.65 frames.], batch size: 68, lr: 3.41e-04
+2022-06-19 03:43:38,486 INFO [train.py:874] (3/4) Epoch 24, batch 2100, datatang_loss[loss=0.1489, simple_loss=0.2091, pruned_loss=0.04439, over 4900.00 frames.], tot_loss[loss=0.145, simple_loss=0.2262, pruned_loss=0.03192, over 985325.44 frames.], batch size: 47, aishell_tot_loss[loss=0.1467, simple_loss=0.2329, pruned_loss=0.03028, over 979110.91 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2195, pruned_loss=0.03276, over 981518.83 frames.], batch size: 47, lr: 3.41e-04
+2022-06-19 03:44:05,040 INFO [train.py:874] (3/4) Epoch 24, batch 2150, aishell_loss[loss=0.1478, simple_loss=0.2347, pruned_loss=0.03041, over 4977.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2264, pruned_loss=0.03185, over 985209.06 frames.], batch size: 51, aishell_tot_loss[loss=0.1471, simple_loss=0.2335, pruned_loss=0.03031, over 979473.76 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2192, pruned_loss=0.03272, over 982180.40 frames.], batch size: 51, lr: 3.41e-04
+2022-06-19 03:44:35,456 INFO [train.py:874] (3/4) Epoch 24, batch 2200, aishell_loss[loss=0.1355, simple_loss=0.2282, pruned_loss=0.0214, over 4868.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2274, pruned_loss=0.03211, over 985212.48 frames.], batch size: 36, aishell_tot_loss[loss=0.1475, simple_loss=0.234, pruned_loss=0.03051, over 980235.55 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2196, pruned_loss=0.0329, over 982496.29 frames.], batch size: 36, lr: 3.41e-04
+2022-06-19 03:45:05,778 INFO [train.py:874] (3/4) Epoch 24, batch 2250, datatang_loss[loss=0.1365, simple_loss=0.2032, pruned_loss=0.03492, over 4934.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2274, pruned_loss=0.03206, over 985178.01 frames.], batch size: 50, aishell_tot_loss[loss=0.1476, simple_loss=0.2341, pruned_loss=0.03058, over 980713.31 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2198, pruned_loss=0.03283, over 982877.05 frames.], batch size: 50, lr: 3.41e-04
+2022-06-19 03:45:33,378 INFO [train.py:874] (3/4) Epoch 24, batch 2300, aishell_loss[loss=0.1602, simple_loss=0.2489, pruned_loss=0.03574, over 4969.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2262, pruned_loss=0.03154, over 984984.27 frames.], batch size: 39, aishell_tot_loss[loss=0.1476, simple_loss=0.2342, pruned_loss=0.0305, over 980879.21 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2189, pruned_loss=0.03238, over 983243.99 frames.], batch size: 39, lr: 3.41e-04
+2022-06-19 03:46:03,325 INFO [train.py:874] (3/4) Epoch 24, batch 2350, datatang_loss[loss=0.1433, simple_loss=0.2323, pruned_loss=0.02711, over 4964.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2272, pruned_loss=0.03179, over 985294.47 frames.], batch size: 91, aishell_tot_loss[loss=0.148, simple_loss=0.2346, pruned_loss=0.03071, over 981390.17 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2194, pruned_loss=0.03245, over 983749.48 frames.], batch size: 91, lr: 3.41e-04
+2022-06-19 03:46:33,454 INFO [train.py:874] (3/4) Epoch 24, batch 2400, datatang_loss[loss=0.1322, simple_loss=0.2228, pruned_loss=0.02085, over 4913.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2273, pruned_loss=0.03155, over 985467.21 frames.], batch size: 98, aishell_tot_loss[loss=0.1479, simple_loss=0.2347, pruned_loss=0.03055, over 982096.72 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2196, pruned_loss=0.03238, over 983843.41 frames.], batch size: 98, lr: 3.41e-04
+2022-06-19 03:46:59,470 INFO [train.py:874] (3/4) Epoch 24, batch 2450, datatang_loss[loss=0.1387, simple_loss=0.2024, pruned_loss=0.03755, over 4858.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2275, pruned_loss=0.03167, over 985418.54 frames.], batch size: 23, aishell_tot_loss[loss=0.1482, simple_loss=0.2351, pruned_loss=0.0306, over 982624.79 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2195, pruned_loss=0.03248, over 983856.56 frames.], batch size: 23, lr: 3.41e-04
+2022-06-19 03:47:29,052 INFO [train.py:874] (3/4) Epoch 24, batch 2500, aishell_loss[loss=0.1537, simple_loss=0.2387, pruned_loss=0.03436, over 4976.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2268, pruned_loss=0.03141, over 985739.65 frames.], batch size: 61, aishell_tot_loss[loss=0.1477, simple_loss=0.2346, pruned_loss=0.03037, over 983234.63 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2194, pruned_loss=0.03244, over 984108.18 frames.], batch size: 61, lr: 3.41e-04
+2022-06-19 03:47:57,743 INFO [train.py:874] (3/4) Epoch 24, batch 2550, aishell_loss[loss=0.1435, simple_loss=0.2317, pruned_loss=0.02765, over 4869.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2272, pruned_loss=0.03128, over 985607.95 frames.], batch size: 36, aishell_tot_loss[loss=0.1475, simple_loss=0.2346, pruned_loss=0.03018, over 983516.68 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2192, pruned_loss=0.03257, over 984189.90 frames.], batch size: 36, lr: 3.40e-04
+2022-06-19 03:48:24,744 INFO [train.py:874] (3/4) Epoch 24, batch 2600, aishell_loss[loss=0.1433, simple_loss=0.2254, pruned_loss=0.03058, over 4959.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2268, pruned_loss=0.03109, over 985680.16 frames.], batch size: 32, aishell_tot_loss[loss=0.1472, simple_loss=0.2344, pruned_loss=0.02997, over 983904.29 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.219, pruned_loss=0.03262, over 984291.39 frames.], batch size: 32, lr: 3.40e-04
+2022-06-19 03:48:54,381 INFO [train.py:874] (3/4) Epoch 24, batch 2650, aishell_loss[loss=0.1656, simple_loss=0.252, pruned_loss=0.03962, over 4915.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2271, pruned_loss=0.03137, over 985760.64 frames.], batch size: 41, aishell_tot_loss[loss=0.1479, simple_loss=0.2352, pruned_loss=0.03036, over 984068.18 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2186, pruned_loss=0.03248, over 984603.30 frames.], batch size: 41, lr: 3.40e-04
+2022-06-19 03:49:23,896 INFO [train.py:874] (3/4) Epoch 24, batch 2700, datatang_loss[loss=0.1471, simple_loss=0.2333, pruned_loss=0.03044, over 4941.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2266, pruned_loss=0.03137, over 985131.54 frames.], batch size: 88, aishell_tot_loss[loss=0.1476, simple_loss=0.2349, pruned_loss=0.03015, over 983889.50 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2187, pruned_loss=0.03261, over 984426.55 frames.], batch size: 88, lr: 3.40e-04
+2022-06-19 03:49:50,618 INFO [train.py:874] (3/4) Epoch 24, batch 2750, aishell_loss[loss=0.1655, simple_loss=0.2499, pruned_loss=0.04055, over 4864.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2278, pruned_loss=0.03222, over 985298.05 frames.], batch size: 37, aishell_tot_loss[loss=0.1484, simple_loss=0.2353, pruned_loss=0.03075, over 984432.17 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2191, pruned_loss=0.03299, over 984309.16 frames.], batch size: 37, lr: 3.40e-04
+2022-06-19 03:50:20,323 INFO [train.py:874] (3/4) Epoch 24, batch 2800, aishell_loss[loss=0.1313, simple_loss=0.2019, pruned_loss=0.03037, over 4957.00 frames.], tot_loss[loss=0.1444, simple_loss=0.226, pruned_loss=0.03135, over 985868.14 frames.], batch size: 25, aishell_tot_loss[loss=0.1476, simple_loss=0.2344, pruned_loss=0.03038, over 984825.22 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2184, pruned_loss=0.03251, over 984739.02 frames.], batch size: 25, lr: 3.40e-04
+2022-06-19 03:50:47,487 INFO [train.py:874] (3/4) Epoch 24, batch 2850, aishell_loss[loss=0.149, simple_loss=0.2333, pruned_loss=0.03233, over 4933.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2255, pruned_loss=0.03105, over 985798.64 frames.], batch size: 32, aishell_tot_loss[loss=0.1472, simple_loss=0.234, pruned_loss=0.03023, over 984744.22 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2184, pruned_loss=0.03227, over 984988.65 frames.], batch size: 32, lr: 3.40e-04
+2022-06-19 03:51:16,581 INFO [train.py:874] (3/4) Epoch 24, batch 2900, aishell_loss[loss=0.1312, simple_loss=0.2071, pruned_loss=0.0276, over 4809.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2254, pruned_loss=0.03059, over 985651.62 frames.], batch size: 26, aishell_tot_loss[loss=0.1465, simple_loss=0.2334, pruned_loss=0.02982, over 984658.28 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2184, pruned_loss=0.03217, over 985150.09 frames.], batch size: 26, lr: 3.40e-04
+2022-06-19 03:51:46,030 INFO [train.py:874] (3/4) Epoch 24, batch 2950, datatang_loss[loss=0.135, simple_loss=0.2244, pruned_loss=0.0228, over 4912.00 frames.], tot_loss[loss=0.1429, simple_loss=0.225, pruned_loss=0.0304, over 985885.67 frames.], batch size: 71, aishell_tot_loss[loss=0.1459, simple_loss=0.2328, pruned_loss=0.02955, over 984805.08 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2185, pruned_loss=0.03216, over 985423.65 frames.], batch size: 71, lr: 3.40e-04
+2022-06-19 03:52:13,919 INFO [train.py:874] (3/4) Epoch 24, batch 3000, aishell_loss[loss=0.1388, simple_loss=0.2333, pruned_loss=0.02215, over 4945.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2251, pruned_loss=0.03039, over 985555.17 frames.], batch size: 64, aishell_tot_loss[loss=0.1457, simple_loss=0.2328, pruned_loss=0.02934, over 984883.87 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2185, pruned_loss=0.03223, over 985163.04 frames.], batch size: 64, lr: 3.40e-04
+2022-06-19 03:52:13,920 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 03:52:29,518 INFO [train.py:914] (3/4) Epoch 24, validation: loss=0.1647, simple_loss=0.2484, pruned_loss=0.04048, over 1622729.00 frames.
+2022-06-19 03:52:58,021 INFO [train.py:874] (3/4) Epoch 24, batch 3050, aishell_loss[loss=0.1461, simple_loss=0.2311, pruned_loss=0.03055, over 4838.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2253, pruned_loss=0.03044, over 985011.17 frames.], batch size: 28, aishell_tot_loss[loss=0.146, simple_loss=0.2331, pruned_loss=0.02943, over 984517.21 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2183, pruned_loss=0.03214, over 985077.60 frames.], batch size: 28, lr: 3.40e-04
+2022-06-19 03:53:25,004 INFO [train.py:874] (3/4) Epoch 24, batch 3100, aishell_loss[loss=0.1287, simple_loss=0.2119, pruned_loss=0.02273, over 4985.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2258, pruned_loss=0.03029, over 985191.05 frames.], batch size: 27, aishell_tot_loss[loss=0.1457, simple_loss=0.233, pruned_loss=0.02921, over 984622.95 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2189, pruned_loss=0.0321, over 985218.14 frames.], batch size: 27, lr: 3.39e-04
+2022-06-19 03:53:54,436 INFO [train.py:874] (3/4) Epoch 24, batch 3150, aishell_loss[loss=0.1515, simple_loss=0.2505, pruned_loss=0.02624, over 4966.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2262, pruned_loss=0.03055, over 984999.27 frames.], batch size: 44, aishell_tot_loss[loss=0.1456, simple_loss=0.2331, pruned_loss=0.02906, over 984487.94 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.219, pruned_loss=0.03251, over 985220.25 frames.], batch size: 44, lr: 3.39e-04
+2022-06-19 03:54:21,508 INFO [train.py:874] (3/4) Epoch 24, batch 3200, datatang_loss[loss=0.1696, simple_loss=0.2489, pruned_loss=0.04519, over 4911.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2267, pruned_loss=0.03105, over 985238.84 frames.], batch size: 98, aishell_tot_loss[loss=0.146, simple_loss=0.2333, pruned_loss=0.02938, over 984788.59 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.219, pruned_loss=0.03271, over 985210.03 frames.], batch size: 98, lr: 3.39e-04
+2022-06-19 03:54:50,563 INFO [train.py:874] (3/4) Epoch 24, batch 3250, datatang_loss[loss=0.1546, simple_loss=0.2279, pruned_loss=0.04062, over 4953.00 frames.], tot_loss[loss=0.144, simple_loss=0.2264, pruned_loss=0.03079, over 985363.03 frames.], batch size: 55, aishell_tot_loss[loss=0.1455, simple_loss=0.2328, pruned_loss=0.02909, over 984951.18 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2191, pruned_loss=0.03275, over 985248.26 frames.], batch size: 55, lr: 3.39e-04
+2022-06-19 03:55:21,760 INFO [train.py:874] (3/4) Epoch 24, batch 3300, aishell_loss[loss=0.1873, simple_loss=0.2681, pruned_loss=0.05321, over 4899.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2253, pruned_loss=0.03058, over 985447.34 frames.], batch size: 52, aishell_tot_loss[loss=0.1452, simple_loss=0.2324, pruned_loss=0.02896, over 984862.04 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2185, pruned_loss=0.0326, over 985481.38 frames.], batch size: 52, lr: 3.39e-04
+2022-06-19 03:55:47,630 INFO [train.py:874] (3/4) Epoch 24, batch 3350, aishell_loss[loss=0.1542, simple_loss=0.2496, pruned_loss=0.02937, over 4951.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2261, pruned_loss=0.03055, over 985571.82 frames.], batch size: 64, aishell_tot_loss[loss=0.1456, simple_loss=0.233, pruned_loss=0.02909, over 985088.51 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2183, pruned_loss=0.03251, over 985457.87 frames.], batch size: 64, lr: 3.39e-04
+2022-06-19 03:56:18,380 INFO [train.py:874] (3/4) Epoch 24, batch 3400, aishell_loss[loss=0.1084, simple_loss=0.185, pruned_loss=0.01593, over 4955.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2249, pruned_loss=0.03018, over 985374.78 frames.], batch size: 25, aishell_tot_loss[loss=0.145, simple_loss=0.2322, pruned_loss=0.02889, over 984933.21 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2179, pruned_loss=0.03227, over 985485.54 frames.], batch size: 25, lr: 3.39e-04
+2022-06-19 03:56:47,587 INFO [train.py:874] (3/4) Epoch 24, batch 3450, aishell_loss[loss=0.1436, simple_loss=0.2344, pruned_loss=0.02644, over 4962.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2245, pruned_loss=0.03011, over 985333.76 frames.], batch size: 61, aishell_tot_loss[loss=0.1447, simple_loss=0.232, pruned_loss=0.02866, over 984939.12 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2179, pruned_loss=0.03226, over 985446.85 frames.], batch size: 61, lr: 3.39e-04
+2022-06-19 03:57:14,731 INFO [train.py:874] (3/4) Epoch 24, batch 3500, aishell_loss[loss=0.1577, simple_loss=0.2464, pruned_loss=0.0345, over 4969.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2248, pruned_loss=0.03019, over 985477.29 frames.], batch size: 64, aishell_tot_loss[loss=0.1452, simple_loss=0.2325, pruned_loss=0.02891, over 985052.23 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2178, pruned_loss=0.03197, over 985502.61 frames.], batch size: 64, lr: 3.39e-04
+2022-06-19 03:57:44,573 INFO [train.py:874] (3/4) Epoch 24, batch 3550, datatang_loss[loss=0.1376, simple_loss=0.2268, pruned_loss=0.02419, over 4956.00 frames.], tot_loss[loss=0.1429, simple_loss=0.225, pruned_loss=0.03045, over 985374.88 frames.], batch size: 91, aishell_tot_loss[loss=0.1455, simple_loss=0.2328, pruned_loss=0.02912, over 985065.96 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2181, pruned_loss=0.03187, over 985403.08 frames.], batch size: 91, lr: 3.39e-04
+2022-06-19 03:58:14,383 INFO [train.py:874] (3/4) Epoch 24, batch 3600, datatang_loss[loss=0.1848, simple_loss=0.2332, pruned_loss=0.06825, over 4950.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2251, pruned_loss=0.03088, over 985590.04 frames.], batch size: 55, aishell_tot_loss[loss=0.1451, simple_loss=0.2322, pruned_loss=0.02901, over 985288.48 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2188, pruned_loss=0.03237, over 985430.71 frames.], batch size: 55, lr: 3.39e-04
+2022-06-19 03:58:41,456 INFO [train.py:874] (3/4) Epoch 24, batch 3650, aishell_loss[loss=0.1555, simple_loss=0.2359, pruned_loss=0.0376, over 4923.00 frames.], tot_loss[loss=0.144, simple_loss=0.2259, pruned_loss=0.03101, over 985501.73 frames.], batch size: 46, aishell_tot_loss[loss=0.1455, simple_loss=0.2327, pruned_loss=0.0292, over 985105.03 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.219, pruned_loss=0.03237, over 985579.56 frames.], batch size: 46, lr: 3.39e-04
+2022-06-19 03:59:11,574 INFO [train.py:874] (3/4) Epoch 24, batch 3700, aishell_loss[loss=0.138, simple_loss=0.229, pruned_loss=0.02354, over 4962.00 frames.], tot_loss[loss=0.1439, simple_loss=0.226, pruned_loss=0.0309, over 985573.11 frames.], batch size: 44, aishell_tot_loss[loss=0.1455, simple_loss=0.2327, pruned_loss=0.02917, over 985043.04 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2193, pruned_loss=0.0323, over 985734.29 frames.], batch size: 44, lr: 3.38e-04
+2022-06-19 03:59:40,801 INFO [train.py:874] (3/4) Epoch 24, batch 3750, aishell_loss[loss=0.1395, simple_loss=0.2275, pruned_loss=0.02579, over 4912.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2258, pruned_loss=0.03072, over 985471.52 frames.], batch size: 52, aishell_tot_loss[loss=0.1459, simple_loss=0.2329, pruned_loss=0.02945, over 984940.81 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2188, pruned_loss=0.03187, over 985774.10 frames.], batch size: 52, lr: 3.38e-04
+2022-06-19 04:00:08,477 INFO [train.py:874] (3/4) Epoch 24, batch 3800, datatang_loss[loss=0.117, simple_loss=0.2012, pruned_loss=0.01642, over 4934.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2259, pruned_loss=0.03076, over 985569.15 frames.], batch size: 73, aishell_tot_loss[loss=0.1466, simple_loss=0.2337, pruned_loss=0.02974, over 984935.26 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2183, pruned_loss=0.03163, over 985911.77 frames.], batch size: 73, lr: 3.38e-04
+2022-06-19 04:00:38,014 INFO [train.py:874] (3/4) Epoch 24, batch 3850, datatang_loss[loss=0.1347, simple_loss=0.2019, pruned_loss=0.03378, over 4831.00 frames.], tot_loss[loss=0.143, simple_loss=0.2257, pruned_loss=0.03018, over 985772.82 frames.], batch size: 30, aishell_tot_loss[loss=0.1463, simple_loss=0.2335, pruned_loss=0.02952, over 985089.70 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2179, pruned_loss=0.03129, over 986044.61 frames.], batch size: 30, lr: 3.38e-04
+2022-06-19 04:01:05,131 INFO [train.py:874] (3/4) Epoch 24, batch 3900, aishell_loss[loss=0.1681, simple_loss=0.2611, pruned_loss=0.03752, over 4921.00 frames.], tot_loss[loss=0.1425, simple_loss=0.225, pruned_loss=0.02998, over 985680.34 frames.], batch size: 68, aishell_tot_loss[loss=0.1463, simple_loss=0.2335, pruned_loss=0.02949, over 985206.41 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2173, pruned_loss=0.03103, over 985862.30 frames.], batch size: 68, lr: 3.38e-04
+2022-06-19 04:01:33,267 INFO [train.py:874] (3/4) Epoch 24, batch 3950, aishell_loss[loss=0.1303, simple_loss=0.2263, pruned_loss=0.01714, over 4942.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2244, pruned_loss=0.02956, over 985701.40 frames.], batch size: 58, aishell_tot_loss[loss=0.1458, simple_loss=0.2331, pruned_loss=0.02925, over 985202.81 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2172, pruned_loss=0.03074, over 985916.54 frames.], batch size: 58, lr: 3.38e-04
+2022-06-19 04:01:59,980 INFO [train.py:874] (3/4) Epoch 24, batch 4000, aishell_loss[loss=0.1743, simple_loss=0.2628, pruned_loss=0.04294, over 4905.00 frames.], tot_loss[loss=0.1414, simple_loss=0.224, pruned_loss=0.02941, over 985387.78 frames.], batch size: 33, aishell_tot_loss[loss=0.1453, simple_loss=0.2324, pruned_loss=0.02916, over 984989.82 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2171, pruned_loss=0.0306, over 985828.20 frames.], batch size: 33, lr: 3.38e-04
+2022-06-19 04:01:59,981 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 04:02:15,554 INFO [train.py:914] (3/4) Epoch 24, validation: loss=0.1649, simple_loss=0.2488, pruned_loss=0.04056, over 1622729.00 frames.
+2022-06-19 04:02:42,076 INFO [train.py:874] (3/4) Epoch 24, batch 4050, aishell_loss[loss=0.1538, simple_loss=0.236, pruned_loss=0.03576, over 4977.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2244, pruned_loss=0.02971, over 985246.79 frames.], batch size: 39, aishell_tot_loss[loss=0.1455, simple_loss=0.2324, pruned_loss=0.02928, over 984856.93 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2172, pruned_loss=0.03071, over 985799.48 frames.], batch size: 39, lr: 3.38e-04
+2022-06-19 04:03:10,344 INFO [train.py:874] (3/4) Epoch 24, batch 4100, datatang_loss[loss=0.1137, simple_loss=0.1972, pruned_loss=0.0151, over 4916.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2248, pruned_loss=0.02983, over 985199.08 frames.], batch size: 75, aishell_tot_loss[loss=0.146, simple_loss=0.233, pruned_loss=0.02954, over 984769.05 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.217, pruned_loss=0.03052, over 985825.27 frames.], batch size: 75, lr: 3.38e-04
+2022-06-19 04:03:36,868 INFO [train.py:874] (3/4) Epoch 24, batch 4150, aishell_loss[loss=0.153, simple_loss=0.2512, pruned_loss=0.02742, over 4946.00 frames.], tot_loss[loss=0.1424, simple_loss=0.225, pruned_loss=0.02993, over 985284.48 frames.], batch size: 78, aishell_tot_loss[loss=0.1459, simple_loss=0.233, pruned_loss=0.0294, over 984871.86 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2173, pruned_loss=0.0307, over 985760.54 frames.], batch size: 78, lr: 3.38e-04
+2022-06-19 04:04:55,980 INFO [train.py:874] (3/4) Epoch 25, batch 50, datatang_loss[loss=0.1249, simple_loss=0.2036, pruned_loss=0.02303, over 4933.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2223, pruned_loss=0.03019, over 218525.06 frames.], batch size: 69, aishell_tot_loss[loss=0.1467, simple_loss=0.2322, pruned_loss=0.03057, over 141750.42 frames.], datatang_tot_loss[loss=0.1328, simple_loss=0.2064, pruned_loss=0.0296, over 89673.32 frames.], batch size: 69, lr: 3.31e-04
+2022-06-19 04:05:25,171 INFO [train.py:874] (3/4) Epoch 25, batch 100, datatang_loss[loss=0.1217, simple_loss=0.1963, pruned_loss=0.02352, over 4929.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2219, pruned_loss=0.02931, over 388488.93 frames.], batch size: 79, aishell_tot_loss[loss=0.1454, simple_loss=0.2316, pruned_loss=0.02958, over 233509.01 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2108, pruned_loss=0.0291, over 203105.42 frames.], batch size: 79, lr: 3.31e-04
+2022-06-19 04:05:54,551 INFO [train.py:874] (3/4) Epoch 25, batch 150, datatang_loss[loss=0.1276, simple_loss=0.2006, pruned_loss=0.02731, over 4897.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2218, pruned_loss=0.02952, over 520880.26 frames.], batch size: 52, aishell_tot_loss[loss=0.146, simple_loss=0.2323, pruned_loss=0.02987, over 305312.86 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2121, pruned_loss=0.0292, over 312317.42 frames.], batch size: 52, lr: 3.31e-04
+2022-06-19 04:06:21,635 INFO [train.py:874] (3/4) Epoch 25, batch 200, aishell_loss[loss=0.1464, simple_loss=0.2318, pruned_loss=0.03049, over 4942.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2217, pruned_loss=0.02924, over 623839.42 frames.], batch size: 40, aishell_tot_loss[loss=0.1455, simple_loss=0.2317, pruned_loss=0.02967, over 394185.99 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2114, pruned_loss=0.02885, over 382751.22 frames.], batch size: 40, lr: 3.31e-04
+2022-06-19 04:06:50,350 INFO [train.py:874] (3/4) Epoch 25, batch 250, datatang_loss[loss=0.1391, simple_loss=0.2061, pruned_loss=0.03606, over 4920.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2219, pruned_loss=0.02947, over 704255.46 frames.], batch size: 75, aishell_tot_loss[loss=0.1458, simple_loss=0.2318, pruned_loss=0.02992, over 458531.59 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2122, pruned_loss=0.02899, over 459339.15 frames.], batch size: 75, lr: 3.31e-04
+2022-06-19 04:07:19,540 INFO [train.py:874] (3/4) Epoch 25, batch 300, datatang_loss[loss=0.1189, simple_loss=0.1962, pruned_loss=0.02082, over 4904.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2207, pruned_loss=0.02947, over 766606.06 frames.], batch size: 52, aishell_tot_loss[loss=0.1447, simple_loss=0.2304, pruned_loss=0.02955, over 520653.28 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2117, pruned_loss=0.02938, over 521257.52 frames.], batch size: 52, lr: 3.30e-04
+2022-06-19 04:07:46,671 INFO [train.py:874] (3/4) Epoch 25, batch 350, datatang_loss[loss=0.1521, simple_loss=0.2206, pruned_loss=0.04173, over 4951.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2219, pruned_loss=0.0294, over 815691.36 frames.], batch size: 55, aishell_tot_loss[loss=0.1449, simple_loss=0.231, pruned_loss=0.02946, over 583804.29 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2121, pruned_loss=0.02936, over 567983.05 frames.], batch size: 55, lr: 3.30e-04
+2022-06-19 04:08:15,451 INFO [train.py:874] (3/4) Epoch 25, batch 400, datatang_loss[loss=0.1456, simple_loss=0.2212, pruned_loss=0.03497, over 4942.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2219, pruned_loss=0.02936, over 853329.42 frames.], batch size: 62, aishell_tot_loss[loss=0.1454, simple_loss=0.2312, pruned_loss=0.02981, over 625581.05 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2125, pruned_loss=0.02897, over 622799.41 frames.], batch size: 62, lr: 3.30e-04
+2022-06-19 04:08:43,835 INFO [train.py:874] (3/4) Epoch 25, batch 450, datatang_loss[loss=0.1191, simple_loss=0.2027, pruned_loss=0.01779, over 4963.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2228, pruned_loss=0.02951, over 882710.16 frames.], batch size: 55, aishell_tot_loss[loss=0.1457, simple_loss=0.2319, pruned_loss=0.02975, over 658521.98 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2138, pruned_loss=0.02927, over 674845.95 frames.], batch size: 55, lr: 3.30e-04
+2022-06-19 04:09:15,245 INFO [train.py:874] (3/4) Epoch 25, batch 500, aishell_loss[loss=0.1462, simple_loss=0.2292, pruned_loss=0.03157, over 4896.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2224, pruned_loss=0.02949, over 905066.63 frames.], batch size: 34, aishell_tot_loss[loss=0.1451, simple_loss=0.2309, pruned_loss=0.02967, over 698119.43 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.214, pruned_loss=0.02933, over 709963.80 frames.], batch size: 34, lr: 3.30e-04
+2022-06-19 04:09:43,985 INFO [train.py:874] (3/4) Epoch 25, batch 550, aishell_loss[loss=0.2046, simple_loss=0.2969, pruned_loss=0.05616, over 4971.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2238, pruned_loss=0.03018, over 922939.16 frames.], batch size: 44, aishell_tot_loss[loss=0.146, simple_loss=0.2317, pruned_loss=0.03012, over 733233.37 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.215, pruned_loss=0.02978, over 741234.10 frames.], batch size: 44, lr: 3.30e-04
+2022-06-19 04:10:12,940 INFO [train.py:874] (3/4) Epoch 25, batch 600, aishell_loss[loss=0.1462, simple_loss=0.2326, pruned_loss=0.02984, over 4943.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2241, pruned_loss=0.03004, over 936779.49 frames.], batch size: 58, aishell_tot_loss[loss=0.1458, simple_loss=0.2316, pruned_loss=0.03006, over 767345.18 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2152, pruned_loss=0.02973, over 765620.52 frames.], batch size: 58, lr: 3.30e-04
+2022-06-19 04:10:41,782 INFO [train.py:874] (3/4) Epoch 25, batch 650, aishell_loss[loss=0.1633, simple_loss=0.2462, pruned_loss=0.04015, over 4979.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2246, pruned_loss=0.02998, over 947794.61 frames.], batch size: 51, aishell_tot_loss[loss=0.1462, simple_loss=0.2321, pruned_loss=0.03019, over 799894.39 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2148, pruned_loss=0.02954, over 784598.64 frames.], batch size: 51, lr: 3.30e-04
+2022-06-19 04:11:11,135 INFO [train.py:874] (3/4) Epoch 25, batch 700, datatang_loss[loss=0.1435, simple_loss=0.212, pruned_loss=0.03749, over 4931.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2251, pruned_loss=0.03002, over 956450.07 frames.], batch size: 57, aishell_tot_loss[loss=0.1462, simple_loss=0.2322, pruned_loss=0.03007, over 822832.71 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2157, pruned_loss=0.02974, over 807424.19 frames.], batch size: 57, lr: 3.30e-04
+2022-06-19 04:11:37,455 INFO [train.py:874] (3/4) Epoch 25, batch 750, datatang_loss[loss=0.1144, simple_loss=0.1919, pruned_loss=0.01846, over 4844.00 frames.], tot_loss[loss=0.142, simple_loss=0.2244, pruned_loss=0.02983, over 962663.33 frames.], batch size: 30, aishell_tot_loss[loss=0.1457, simple_loss=0.2316, pruned_loss=0.02993, over 843244.17 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2156, pruned_loss=0.0297, over 826757.40 frames.], batch size: 30, lr: 3.30e-04
+2022-06-19 04:12:06,296 INFO [train.py:874] (3/4) Epoch 25, batch 800, datatang_loss[loss=0.1269, simple_loss=0.2107, pruned_loss=0.02155, over 4925.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2239, pruned_loss=0.02978, over 967697.39 frames.], batch size: 83, aishell_tot_loss[loss=0.1453, simple_loss=0.2312, pruned_loss=0.02976, over 858121.79 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2159, pruned_loss=0.0298, over 847489.15 frames.], batch size: 83, lr: 3.30e-04
+2022-06-19 04:12:35,219 INFO [train.py:874] (3/4) Epoch 25, batch 850, datatang_loss[loss=0.1424, simple_loss=0.2256, pruned_loss=0.02963, over 4919.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2236, pruned_loss=0.02972, over 971624.74 frames.], batch size: 73, aishell_tot_loss[loss=0.145, simple_loss=0.2307, pruned_loss=0.02959, over 871820.78 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2163, pruned_loss=0.0299, over 865129.22 frames.], batch size: 73, lr: 3.30e-04
+2022-06-19 04:13:03,065 INFO [train.py:874] (3/4) Epoch 25, batch 900, datatang_loss[loss=0.1355, simple_loss=0.2089, pruned_loss=0.03108, over 4962.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2232, pruned_loss=0.02977, over 974811.68 frames.], batch size: 55, aishell_tot_loss[loss=0.1447, simple_loss=0.2305, pruned_loss=0.02945, over 884119.19 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2162, pruned_loss=0.03008, over 880571.64 frames.], batch size: 55, lr: 3.29e-04
+2022-06-19 04:13:30,847 INFO [train.py:874] (3/4) Epoch 25, batch 950, aishell_loss[loss=0.1553, simple_loss=0.2384, pruned_loss=0.03608, over 4975.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2234, pruned_loss=0.03015, over 976919.79 frames.], batch size: 31, aishell_tot_loss[loss=0.1448, simple_loss=0.2305, pruned_loss=0.02958, over 895401.99 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2165, pruned_loss=0.0304, over 893308.39 frames.], batch size: 31, lr: 3.29e-04
+2022-06-19 04:14:00,703 INFO [train.py:874] (3/4) Epoch 25, batch 1000, datatang_loss[loss=0.1352, simple_loss=0.2168, pruned_loss=0.02683, over 4932.00 frames.], tot_loss[loss=0.1428, simple_loss=0.225, pruned_loss=0.03029, over 978938.66 frames.], batch size: 57, aishell_tot_loss[loss=0.1452, simple_loss=0.2313, pruned_loss=0.0295, over 908521.78 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.217, pruned_loss=0.03073, over 901640.01 frames.], batch size: 57, lr: 3.29e-04
+2022-06-19 04:14:00,704 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 04:14:17,422 INFO [train.py:914] (3/4) Epoch 25, validation: loss=0.1644, simple_loss=0.248, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 04:14:45,776 INFO [train.py:874] (3/4) Epoch 25, batch 1050, datatang_loss[loss=0.1147, simple_loss=0.1978, pruned_loss=0.01581, over 4922.00 frames.], tot_loss[loss=0.1439, simple_loss=0.226, pruned_loss=0.03093, over 979785.06 frames.], batch size: 75, aishell_tot_loss[loss=0.1458, simple_loss=0.232, pruned_loss=0.02983, over 918117.08 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2175, pruned_loss=0.03119, over 910238.84 frames.], batch size: 75, lr: 3.29e-04
+2022-06-19 04:15:14,195 INFO [train.py:874] (3/4) Epoch 25, batch 1100, aishell_loss[loss=0.1418, simple_loss=0.2311, pruned_loss=0.0263, over 4968.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2269, pruned_loss=0.03077, over 981499.03 frames.], batch size: 44, aishell_tot_loss[loss=0.1458, simple_loss=0.2322, pruned_loss=0.02969, over 928498.06 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2182, pruned_loss=0.03131, over 916812.10 frames.], batch size: 44, lr: 3.29e-04
+2022-06-19 04:15:41,606 INFO [train.py:874] (3/4) Epoch 25, batch 1150, aishell_loss[loss=0.1633, simple_loss=0.2486, pruned_loss=0.03899, over 4869.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2266, pruned_loss=0.03059, over 981920.36 frames.], batch size: 36, aishell_tot_loss[loss=0.1451, simple_loss=0.2316, pruned_loss=0.02929, over 934937.91 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2187, pruned_loss=0.03158, over 924646.27 frames.], batch size: 36, lr: 3.29e-04
+2022-06-19 04:16:10,683 INFO [train.py:874] (3/4) Epoch 25, batch 1200, aishell_loss[loss=0.143, simple_loss=0.2336, pruned_loss=0.02622, over 4904.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2268, pruned_loss=0.03077, over 982275.77 frames.], batch size: 41, aishell_tot_loss[loss=0.1455, simple_loss=0.232, pruned_loss=0.02951, over 941256.48 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2187, pruned_loss=0.03161, over 930851.38 frames.], batch size: 41, lr: 3.29e-04
+2022-06-19 04:16:37,449 INFO [train.py:874] (3/4) Epoch 25, batch 1250, datatang_loss[loss=0.1353, simple_loss=0.2076, pruned_loss=0.03148, over 4937.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2259, pruned_loss=0.03066, over 982407.53 frames.], batch size: 79, aishell_tot_loss[loss=0.1453, simple_loss=0.2315, pruned_loss=0.02956, over 946468.70 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2184, pruned_loss=0.03149, over 936571.15 frames.], batch size: 79, lr: 3.29e-04
+2022-06-19 04:17:06,244 INFO [train.py:874] (3/4) Epoch 25, batch 1300, aishell_loss[loss=0.1625, simple_loss=0.249, pruned_loss=0.03801, over 4976.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2258, pruned_loss=0.03098, over 983005.73 frames.], batch size: 44, aishell_tot_loss[loss=0.1456, simple_loss=0.2317, pruned_loss=0.02975, over 950345.22 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2186, pruned_loss=0.03164, over 943083.22 frames.], batch size: 44, lr: 3.29e-04
+2022-06-19 04:17:37,333 INFO [train.py:874] (3/4) Epoch 25, batch 1350, datatang_loss[loss=0.1276, simple_loss=0.2144, pruned_loss=0.02038, over 4927.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2257, pruned_loss=0.03086, over 983845.28 frames.], batch size: 94, aishell_tot_loss[loss=0.1456, simple_loss=0.2318, pruned_loss=0.02972, over 954414.43 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2186, pruned_loss=0.03157, over 948524.54 frames.], batch size: 94, lr: 3.29e-04
+2022-06-19 04:18:04,169 INFO [train.py:874] (3/4) Epoch 25, batch 1400, datatang_loss[loss=0.1336, simple_loss=0.2145, pruned_loss=0.0263, over 4935.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2257, pruned_loss=0.03066, over 983879.14 frames.], batch size: 79, aishell_tot_loss[loss=0.1456, simple_loss=0.2318, pruned_loss=0.02972, over 957322.82 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2188, pruned_loss=0.03138, over 953335.80 frames.], batch size: 79, lr: 3.29e-04
+2022-06-19 04:18:32,341 INFO [train.py:874] (3/4) Epoch 25, batch 1450, datatang_loss[loss=0.1657, simple_loss=0.2428, pruned_loss=0.04432, over 4946.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2256, pruned_loss=0.03063, over 984381.46 frames.], batch size: 109, aishell_tot_loss[loss=0.1453, simple_loss=0.2316, pruned_loss=0.02957, over 960386.16 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2192, pruned_loss=0.0315, over 957559.52 frames.], batch size: 109, lr: 3.29e-04
+2022-06-19 04:19:02,042 INFO [train.py:874] (3/4) Epoch 25, batch 1500, aishell_loss[loss=0.1399, simple_loss=0.2247, pruned_loss=0.02758, over 4839.00 frames.], tot_loss[loss=0.1429, simple_loss=0.225, pruned_loss=0.03035, over 984488.38 frames.], batch size: 28, aishell_tot_loss[loss=0.1448, simple_loss=0.2309, pruned_loss=0.02934, over 962814.24 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2193, pruned_loss=0.03141, over 961252.04 frames.], batch size: 28, lr: 3.28e-04
+2022-06-19 04:19:29,744 INFO [train.py:874] (3/4) Epoch 25, batch 1550, datatang_loss[loss=0.1273, simple_loss=0.2136, pruned_loss=0.02047, over 4938.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2257, pruned_loss=0.03087, over 984614.60 frames.], batch size: 62, aishell_tot_loss[loss=0.1453, simple_loss=0.2317, pruned_loss=0.02949, over 965107.57 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2195, pruned_loss=0.03182, over 964404.16 frames.], batch size: 62, lr: 3.28e-04
+2022-06-19 04:19:59,154 INFO [train.py:874] (3/4) Epoch 25, batch 1600, aishell_loss[loss=0.1381, simple_loss=0.2271, pruned_loss=0.02457, over 4973.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2255, pruned_loss=0.03091, over 984889.79 frames.], batch size: 61, aishell_tot_loss[loss=0.1456, simple_loss=0.2319, pruned_loss=0.02962, over 967743.07 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2188, pruned_loss=0.03179, over 966725.08 frames.], batch size: 61, lr: 3.28e-04
+2022-06-19 04:20:27,794 INFO [train.py:874] (3/4) Epoch 25, batch 1650, aishell_loss[loss=0.16, simple_loss=0.2437, pruned_loss=0.03812, over 4902.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2248, pruned_loss=0.03076, over 984925.61 frames.], batch size: 52, aishell_tot_loss[loss=0.1457, simple_loss=0.2321, pruned_loss=0.02962, over 969715.46 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.218, pruned_loss=0.03166, over 968960.56 frames.], batch size: 52, lr: 3.28e-04
+2022-06-19 04:20:55,483 INFO [train.py:874] (3/4) Epoch 25, batch 1700, datatang_loss[loss=0.1364, simple_loss=0.21, pruned_loss=0.03147, over 4915.00 frames.], tot_loss[loss=0.1432, simple_loss=0.225, pruned_loss=0.03069, over 984959.82 frames.], batch size: 57, aishell_tot_loss[loss=0.1461, simple_loss=0.2326, pruned_loss=0.02977, over 971438.73 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2177, pruned_loss=0.03146, over 970946.42 frames.], batch size: 57, lr: 3.28e-04
+2022-06-19 04:21:24,470 INFO [train.py:874] (3/4) Epoch 25, batch 1750, aishell_loss[loss=0.1256, simple_loss=0.192, pruned_loss=0.02959, over 4805.00 frames.], tot_loss[loss=0.1434, simple_loss=0.225, pruned_loss=0.03093, over 984882.66 frames.], batch size: 20, aishell_tot_loss[loss=0.1457, simple_loss=0.2319, pruned_loss=0.0297, over 972867.44 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2184, pruned_loss=0.03179, over 972677.37 frames.], batch size: 20, lr: 3.28e-04
+2022-06-19 04:21:53,974 INFO [train.py:874] (3/4) Epoch 25, batch 1800, datatang_loss[loss=0.1348, simple_loss=0.2092, pruned_loss=0.03018, over 4927.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2247, pruned_loss=0.03108, over 984830.64 frames.], batch size: 73, aishell_tot_loss[loss=0.1452, simple_loss=0.2315, pruned_loss=0.02947, over 973724.49 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2189, pruned_loss=0.03213, over 974613.16 frames.], batch size: 73, lr: 3.28e-04
+2022-06-19 04:22:21,503 INFO [train.py:874] (3/4) Epoch 25, batch 1850, datatang_loss[loss=0.1282, simple_loss=0.2094, pruned_loss=0.02348, over 4911.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2254, pruned_loss=0.03088, over 985214.08 frames.], batch size: 25, aishell_tot_loss[loss=0.1456, simple_loss=0.2321, pruned_loss=0.02958, over 975504.25 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2187, pruned_loss=0.03195, over 975781.84 frames.], batch size: 25, lr: 3.28e-04
+2022-06-19 04:22:52,307 INFO [train.py:874] (3/4) Epoch 25, batch 1900, datatang_loss[loss=0.1375, simple_loss=0.2161, pruned_loss=0.02944, over 4952.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2256, pruned_loss=0.03087, over 985347.39 frames.], batch size: 55, aishell_tot_loss[loss=0.1459, simple_loss=0.2323, pruned_loss=0.02972, over 976735.85 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2185, pruned_loss=0.03186, over 976972.28 frames.], batch size: 55, lr: 3.28e-04
+2022-06-19 04:23:20,048 INFO [train.py:874] (3/4) Epoch 25, batch 1950, aishell_loss[loss=0.1693, simple_loss=0.2576, pruned_loss=0.04051, over 4913.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2254, pruned_loss=0.03082, over 985600.19 frames.], batch size: 52, aishell_tot_loss[loss=0.146, simple_loss=0.2324, pruned_loss=0.02982, over 978026.18 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2182, pruned_loss=0.03174, over 977946.20 frames.], batch size: 52, lr: 3.28e-04
+2022-06-19 04:23:47,737 INFO [train.py:874] (3/4) Epoch 25, batch 2000, datatang_loss[loss=0.1321, simple_loss=0.2137, pruned_loss=0.0253, over 4926.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2262, pruned_loss=0.03137, over 985450.36 frames.], batch size: 75, aishell_tot_loss[loss=0.1457, simple_loss=0.2323, pruned_loss=0.0296, over 978804.54 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2191, pruned_loss=0.03258, over 978813.47 frames.], batch size: 75, lr: 3.28e-04
+2022-06-19 04:23:47,738 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 04:24:04,382 INFO [train.py:914] (3/4) Epoch 25, validation: loss=0.1644, simple_loss=0.2485, pruned_loss=0.04013, over 1622729.00 frames.
+2022-06-19 04:24:31,270 INFO [train.py:874] (3/4) Epoch 25, batch 2050, aishell_loss[loss=0.112, simple_loss=0.1997, pruned_loss=0.01214, over 4963.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2266, pruned_loss=0.03097, over 985630.52 frames.], batch size: 27, aishell_tot_loss[loss=0.1455, simple_loss=0.2321, pruned_loss=0.0294, over 979960.83 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2192, pruned_loss=0.03253, over 979400.08 frames.], batch size: 27, lr: 3.28e-04
+2022-06-19 04:24:59,622 INFO [train.py:874] (3/4) Epoch 25, batch 2100, aishell_loss[loss=0.1598, simple_loss=0.2507, pruned_loss=0.03441, over 4917.00 frames.], tot_loss[loss=0.144, simple_loss=0.2261, pruned_loss=0.03102, over 985481.36 frames.], batch size: 46, aishell_tot_loss[loss=0.1454, simple_loss=0.2318, pruned_loss=0.02956, over 980303.30 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.219, pruned_loss=0.03247, over 980306.72 frames.], batch size: 46, lr: 3.28e-04
+2022-06-19 04:25:26,940 INFO [train.py:874] (3/4) Epoch 25, batch 2150, datatang_loss[loss=0.1284, simple_loss=0.2103, pruned_loss=0.02322, over 4924.00 frames.], tot_loss[loss=0.145, simple_loss=0.2269, pruned_loss=0.03155, over 985725.80 frames.], batch size: 73, aishell_tot_loss[loss=0.1458, simple_loss=0.2321, pruned_loss=0.02974, over 980861.52 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2199, pruned_loss=0.03281, over 981216.86 frames.], batch size: 73, lr: 3.27e-04
+2022-06-19 04:25:55,456 INFO [train.py:874] (3/4) Epoch 25, batch 2200, datatang_loss[loss=0.1423, simple_loss=0.2244, pruned_loss=0.03011, over 4926.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2261, pruned_loss=0.03112, over 985749.74 frames.], batch size: 79, aishell_tot_loss[loss=0.1457, simple_loss=0.232, pruned_loss=0.02972, over 981116.22 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2194, pruned_loss=0.03242, over 982088.66 frames.], batch size: 79, lr: 3.27e-04
+2022-06-19 04:26:24,830 INFO [train.py:874] (3/4) Epoch 25, batch 2250, aishell_loss[loss=0.1353, simple_loss=0.2207, pruned_loss=0.02491, over 4969.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2254, pruned_loss=0.03083, over 985898.80 frames.], batch size: 30, aishell_tot_loss[loss=0.1455, simple_loss=0.2318, pruned_loss=0.02954, over 981723.59 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2191, pruned_loss=0.03228, over 982603.68 frames.], batch size: 30, lr: 3.27e-04
+2022-06-19 04:26:54,174 INFO [train.py:874] (3/4) Epoch 25, batch 2300, datatang_loss[loss=0.1187, simple_loss=0.1986, pruned_loss=0.01936, over 4962.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2251, pruned_loss=0.03063, over 985618.82 frames.], batch size: 45, aishell_tot_loss[loss=0.1456, simple_loss=0.2318, pruned_loss=0.02965, over 981748.33 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2187, pruned_loss=0.03192, over 983165.52 frames.], batch size: 45, lr: 3.27e-04
+2022-06-19 04:27:21,757 INFO [train.py:874] (3/4) Epoch 25, batch 2350, aishell_loss[loss=0.1434, simple_loss=0.2353, pruned_loss=0.02572, over 4940.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2247, pruned_loss=0.02997, over 985209.26 frames.], batch size: 68, aishell_tot_loss[loss=0.1451, simple_loss=0.2316, pruned_loss=0.02931, over 982071.86 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2186, pruned_loss=0.03152, over 983133.77 frames.], batch size: 68, lr: 3.27e-04
+2022-06-19 04:27:52,159 INFO [train.py:874] (3/4) Epoch 25, batch 2400, aishell_loss[loss=0.1741, simple_loss=0.2547, pruned_loss=0.04679, over 4915.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2255, pruned_loss=0.03076, over 985195.91 frames.], batch size: 41, aishell_tot_loss[loss=0.1457, simple_loss=0.2321, pruned_loss=0.0297, over 982235.20 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2193, pruned_loss=0.03176, over 983535.78 frames.], batch size: 41, lr: 3.27e-04
+2022-06-19 04:28:20,102 INFO [train.py:874] (3/4) Epoch 25, batch 2450, aishell_loss[loss=0.1352, simple_loss=0.2182, pruned_loss=0.02612, over 4947.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2253, pruned_loss=0.03094, over 985435.39 frames.], batch size: 31, aishell_tot_loss[loss=0.1455, simple_loss=0.2319, pruned_loss=0.02961, over 982416.61 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2193, pruned_loss=0.03205, over 984136.37 frames.], batch size: 31, lr: 3.27e-04
+2022-06-19 04:28:48,393 INFO [train.py:874] (3/4) Epoch 25, batch 2500, aishell_loss[loss=0.149, simple_loss=0.2415, pruned_loss=0.0282, over 4948.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2257, pruned_loss=0.03106, over 985743.84 frames.], batch size: 45, aishell_tot_loss[loss=0.1453, simple_loss=0.2317, pruned_loss=0.02947, over 983018.53 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2196, pruned_loss=0.03238, over 984378.69 frames.], batch size: 45, lr: 3.27e-04
+2022-06-19 04:29:17,551 INFO [train.py:874] (3/4) Epoch 25, batch 2550, aishell_loss[loss=0.1557, simple_loss=0.2367, pruned_loss=0.03735, over 4982.00 frames.], tot_loss[loss=0.144, simple_loss=0.2259, pruned_loss=0.0311, over 986013.50 frames.], batch size: 37, aishell_tot_loss[loss=0.1456, simple_loss=0.2317, pruned_loss=0.02977, over 983572.36 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2195, pruned_loss=0.03221, over 984608.54 frames.], batch size: 37, lr: 3.27e-04
+2022-06-19 04:29:45,270 INFO [train.py:874] (3/4) Epoch 25, batch 2600, aishell_loss[loss=0.1482, simple_loss=0.2265, pruned_loss=0.03491, over 4942.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2259, pruned_loss=0.03095, over 985437.82 frames.], batch size: 49, aishell_tot_loss[loss=0.1454, simple_loss=0.2317, pruned_loss=0.02952, over 983148.62 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2196, pruned_loss=0.03234, over 984886.54 frames.], batch size: 49, lr: 3.27e-04
+2022-06-19 04:30:13,913 INFO [train.py:874] (3/4) Epoch 25, batch 2650, aishell_loss[loss=0.1527, simple_loss=0.247, pruned_loss=0.0292, over 4971.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2253, pruned_loss=0.03068, over 985362.92 frames.], batch size: 51, aishell_tot_loss[loss=0.145, simple_loss=0.2316, pruned_loss=0.02924, over 983269.46 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2193, pruned_loss=0.0323, over 984995.66 frames.], batch size: 51, lr: 3.27e-04
+2022-06-19 04:30:43,854 INFO [train.py:874] (3/4) Epoch 25, batch 2700, aishell_loss[loss=0.145, simple_loss=0.2343, pruned_loss=0.02786, over 4889.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2266, pruned_loss=0.03131, over 985588.00 frames.], batch size: 50, aishell_tot_loss[loss=0.1457, simple_loss=0.2324, pruned_loss=0.02949, over 983761.39 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2198, pruned_loss=0.03276, over 985064.99 frames.], batch size: 50, lr: 3.27e-04
+2022-06-19 04:31:12,115 INFO [train.py:874] (3/4) Epoch 25, batch 2750, datatang_loss[loss=0.1355, simple_loss=0.2112, pruned_loss=0.0299, over 4878.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2261, pruned_loss=0.03152, over 985458.24 frames.], batch size: 25, aishell_tot_loss[loss=0.1461, simple_loss=0.2325, pruned_loss=0.0298, over 983837.55 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2193, pruned_loss=0.03273, over 985134.85 frames.], batch size: 25, lr: 3.26e-04
+2022-06-19 04:31:39,178 INFO [train.py:874] (3/4) Epoch 25, batch 2800, aishell_loss[loss=0.1564, simple_loss=0.2447, pruned_loss=0.03401, over 4962.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2264, pruned_loss=0.03123, over 985677.06 frames.], batch size: 39, aishell_tot_loss[loss=0.1462, simple_loss=0.2327, pruned_loss=0.02982, over 984191.55 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2191, pruned_loss=0.03254, over 985267.99 frames.], batch size: 39, lr: 3.26e-04
+2022-06-19 04:32:09,654 INFO [train.py:874] (3/4) Epoch 25, batch 2850, aishell_loss[loss=0.1427, simple_loss=0.2293, pruned_loss=0.02805, over 4922.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2259, pruned_loss=0.03137, over 986018.39 frames.], batch size: 33, aishell_tot_loss[loss=0.1468, simple_loss=0.2333, pruned_loss=0.03014, over 984383.08 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2184, pruned_loss=0.03236, over 985639.32 frames.], batch size: 33, lr: 3.26e-04
+2022-06-19 04:32:36,512 INFO [train.py:874] (3/4) Epoch 25, batch 2900, datatang_loss[loss=0.1563, simple_loss=0.2299, pruned_loss=0.04136, over 4961.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2263, pruned_loss=0.03103, over 985906.07 frames.], batch size: 67, aishell_tot_loss[loss=0.1474, simple_loss=0.234, pruned_loss=0.03035, over 984500.21 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2178, pruned_loss=0.03185, over 985658.58 frames.], batch size: 67, lr: 3.26e-04
+2022-06-19 04:33:06,624 INFO [train.py:874] (3/4) Epoch 25, batch 2950, datatang_loss[loss=0.1466, simple_loss=0.2178, pruned_loss=0.03772, over 4953.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2256, pruned_loss=0.03077, over 985793.85 frames.], batch size: 62, aishell_tot_loss[loss=0.147, simple_loss=0.2336, pruned_loss=0.03022, over 984514.89 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2177, pruned_loss=0.03167, over 985708.77 frames.], batch size: 62, lr: 3.26e-04
+2022-06-19 04:33:34,217 INFO [train.py:874] (3/4) Epoch 25, batch 3000, aishell_loss[loss=0.1597, simple_loss=0.2526, pruned_loss=0.03339, over 4957.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2252, pruned_loss=0.0302, over 985958.95 frames.], batch size: 79, aishell_tot_loss[loss=0.1461, simple_loss=0.2328, pruned_loss=0.02964, over 984854.52 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2176, pruned_loss=0.03162, over 985749.41 frames.], batch size: 79, lr: 3.26e-04
+2022-06-19 04:33:34,218 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 04:33:49,970 INFO [train.py:914] (3/4) Epoch 25, validation: loss=0.1645, simple_loss=0.2487, pruned_loss=0.04017, over 1622729.00 frames.
+2022-06-19 04:34:20,846 INFO [train.py:874] (3/4) Epoch 25, batch 3050, aishell_loss[loss=0.1486, simple_loss=0.2452, pruned_loss=0.02602, over 4929.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2254, pruned_loss=0.03054, over 985357.77 frames.], batch size: 68, aishell_tot_loss[loss=0.1465, simple_loss=0.2332, pruned_loss=0.02993, over 984620.46 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2172, pruned_loss=0.03164, over 985520.24 frames.], batch size: 68, lr: 3.26e-04
+2022-06-19 04:34:49,384 INFO [train.py:874] (3/4) Epoch 25, batch 3100, aishell_loss[loss=0.1362, simple_loss=0.2265, pruned_loss=0.02294, over 4916.00 frames.], tot_loss[loss=0.1434, simple_loss=0.226, pruned_loss=0.03034, over 985355.49 frames.], batch size: 46, aishell_tot_loss[loss=0.1468, simple_loss=0.2337, pruned_loss=0.02991, over 984616.83 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2169, pruned_loss=0.03144, over 985622.51 frames.], batch size: 46, lr: 3.26e-04
+2022-06-19 04:35:19,314 INFO [train.py:874] (3/4) Epoch 25, batch 3150, datatang_loss[loss=0.1207, simple_loss=0.1969, pruned_loss=0.02224, over 4916.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2252, pruned_loss=0.03046, over 985450.13 frames.], batch size: 77, aishell_tot_loss[loss=0.1466, simple_loss=0.2333, pruned_loss=0.02997, over 984594.35 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2166, pruned_loss=0.03144, over 985809.31 frames.], batch size: 77, lr: 3.26e-04
+2022-06-19 04:35:49,029 INFO [train.py:874] (3/4) Epoch 25, batch 3200, datatang_loss[loss=0.1231, simple_loss=0.2042, pruned_loss=0.02096, over 4931.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2253, pruned_loss=0.03017, over 985223.42 frames.], batch size: 71, aishell_tot_loss[loss=0.1465, simple_loss=0.2333, pruned_loss=0.02987, over 984290.11 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2165, pruned_loss=0.03121, over 985947.32 frames.], batch size: 71, lr: 3.26e-04
+2022-06-19 04:36:17,750 INFO [train.py:874] (3/4) Epoch 25, batch 3250, aishell_loss[loss=0.1514, simple_loss=0.2429, pruned_loss=0.02995, over 4876.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2255, pruned_loss=0.0302, over 985306.24 frames.], batch size: 47, aishell_tot_loss[loss=0.1465, simple_loss=0.2334, pruned_loss=0.02984, over 984301.70 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2165, pruned_loss=0.03119, over 986073.54 frames.], batch size: 47, lr: 3.26e-04
+2022-06-19 04:36:47,049 INFO [train.py:874] (3/4) Epoch 25, batch 3300, datatang_loss[loss=0.1691, simple_loss=0.2542, pruned_loss=0.04203, over 4940.00 frames.], tot_loss[loss=0.143, simple_loss=0.2255, pruned_loss=0.03019, over 985426.04 frames.], batch size: 99, aishell_tot_loss[loss=0.1463, simple_loss=0.2331, pruned_loss=0.02969, over 984540.70 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2173, pruned_loss=0.03122, over 985967.79 frames.], batch size: 99, lr: 3.26e-04
+2022-06-19 04:37:14,511 INFO [train.py:874] (3/4) Epoch 25, batch 3350, datatang_loss[loss=0.1339, simple_loss=0.2139, pruned_loss=0.027, over 4917.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2256, pruned_loss=0.02988, over 985376.05 frames.], batch size: 81, aishell_tot_loss[loss=0.146, simple_loss=0.2331, pruned_loss=0.02942, over 984618.83 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2173, pruned_loss=0.0311, over 985886.30 frames.], batch size: 81, lr: 3.26e-04
+2022-06-19 04:37:42,873 INFO [train.py:874] (3/4) Epoch 25, batch 3400, aishell_loss[loss=0.1443, simple_loss=0.2306, pruned_loss=0.02902, over 4955.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2252, pruned_loss=0.02996, over 985700.76 frames.], batch size: 56, aishell_tot_loss[loss=0.1453, simple_loss=0.2321, pruned_loss=0.02931, over 984902.20 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2178, pruned_loss=0.03127, over 986007.55 frames.], batch size: 56, lr: 3.25e-04
+2022-06-19 04:38:11,580 INFO [train.py:874] (3/4) Epoch 25, batch 3450, datatang_loss[loss=0.1588, simple_loss=0.2226, pruned_loss=0.04749, over 4961.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2262, pruned_loss=0.03022, over 985725.03 frames.], batch size: 55, aishell_tot_loss[loss=0.1453, simple_loss=0.2324, pruned_loss=0.02914, over 984962.00 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2183, pruned_loss=0.03167, over 986051.32 frames.], batch size: 55, lr: 3.25e-04
+2022-06-19 04:38:39,606 INFO [train.py:874] (3/4) Epoch 25, batch 3500, aishell_loss[loss=0.1205, simple_loss=0.1896, pruned_loss=0.02568, over 4842.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2259, pruned_loss=0.02972, over 985789.39 frames.], batch size: 21, aishell_tot_loss[loss=0.1451, simple_loss=0.2322, pruned_loss=0.029, over 985182.74 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.218, pruned_loss=0.03134, over 985985.91 frames.], batch size: 21, lr: 3.25e-04
+2022-06-19 04:39:08,261 INFO [train.py:874] (3/4) Epoch 25, batch 3550, datatang_loss[loss=0.1708, simple_loss=0.2517, pruned_loss=0.04501, over 4942.00 frames.], tot_loss[loss=0.1434, simple_loss=0.226, pruned_loss=0.03038, over 985315.68 frames.], batch size: 108, aishell_tot_loss[loss=0.145, simple_loss=0.2317, pruned_loss=0.0291, over 984675.38 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2187, pruned_loss=0.03182, over 986016.40 frames.], batch size: 108, lr: 3.25e-04
+2022-06-19 04:39:38,080 INFO [train.py:874] (3/4) Epoch 25, batch 3600, aishell_loss[loss=0.1444, simple_loss=0.2259, pruned_loss=0.03147, over 4956.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2262, pruned_loss=0.03052, over 985568.14 frames.], batch size: 56, aishell_tot_loss[loss=0.1453, simple_loss=0.2318, pruned_loss=0.02937, over 984868.19 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2185, pruned_loss=0.03176, over 986142.28 frames.], batch size: 56, lr: 3.25e-04
+2022-06-19 04:40:04,741 INFO [train.py:874] (3/4) Epoch 25, batch 3650, aishell_loss[loss=0.1314, simple_loss=0.2208, pruned_loss=0.02095, over 4942.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2261, pruned_loss=0.03061, over 985040.95 frames.], batch size: 58, aishell_tot_loss[loss=0.1448, simple_loss=0.2314, pruned_loss=0.02913, over 984599.94 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2189, pruned_loss=0.03214, over 985893.85 frames.], batch size: 58, lr: 3.25e-04
+2022-06-19 04:40:33,811 INFO [train.py:874] (3/4) Epoch 25, batch 3700, datatang_loss[loss=0.1401, simple_loss=0.227, pruned_loss=0.02663, over 4959.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2261, pruned_loss=0.03067, over 985149.01 frames.], batch size: 91, aishell_tot_loss[loss=0.1451, simple_loss=0.2318, pruned_loss=0.02924, over 984607.01 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2189, pruned_loss=0.03206, over 985918.53 frames.], batch size: 91, lr: 3.25e-04
+2022-06-19 04:41:00,280 INFO [train.py:874] (3/4) Epoch 25, batch 3750, datatang_loss[loss=0.1512, simple_loss=0.225, pruned_loss=0.03869, over 4898.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2264, pruned_loss=0.031, over 985509.66 frames.], batch size: 47, aishell_tot_loss[loss=0.1459, simple_loss=0.2325, pruned_loss=0.02962, over 984777.11 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2187, pruned_loss=0.03206, over 986115.27 frames.], batch size: 47, lr: 3.25e-04
+2022-06-19 04:41:29,113 INFO [train.py:874] (3/4) Epoch 25, batch 3800, datatang_loss[loss=0.1318, simple_loss=0.2152, pruned_loss=0.02425, over 4940.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2267, pruned_loss=0.03092, over 985471.69 frames.], batch size: 69, aishell_tot_loss[loss=0.1461, simple_loss=0.2331, pruned_loss=0.02958, over 984636.83 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2188, pruned_loss=0.03202, over 986212.83 frames.], batch size: 69, lr: 3.25e-04
+2022-06-19 04:41:57,154 INFO [train.py:874] (3/4) Epoch 25, batch 3850, datatang_loss[loss=0.1388, simple_loss=0.2245, pruned_loss=0.02655, over 4932.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2266, pruned_loss=0.03059, over 985285.82 frames.], batch size: 94, aishell_tot_loss[loss=0.1459, simple_loss=0.2329, pruned_loss=0.02943, over 984545.19 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.219, pruned_loss=0.03185, over 986114.37 frames.], batch size: 94, lr: 3.25e-04
+2022-06-19 04:42:25,194 INFO [train.py:874] (3/4) Epoch 25, batch 3900, aishell_loss[loss=0.1445, simple_loss=0.2273, pruned_loss=0.03088, over 4912.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2263, pruned_loss=0.03024, over 985394.80 frames.], batch size: 33, aishell_tot_loss[loss=0.1457, simple_loss=0.2328, pruned_loss=0.02932, over 984587.77 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2188, pruned_loss=0.03161, over 986202.76 frames.], batch size: 33, lr: 3.25e-04
+2022-06-19 04:42:52,859 INFO [train.py:874] (3/4) Epoch 25, batch 3950, aishell_loss[loss=0.122, simple_loss=0.2113, pruned_loss=0.01633, over 4985.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2259, pruned_loss=0.03058, over 985364.84 frames.], batch size: 30, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.02915, over 984696.43 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2189, pruned_loss=0.0321, over 986075.59 frames.], batch size: 30, lr: 3.25e-04
+2022-06-19 04:43:21,325 INFO [train.py:874] (3/4) Epoch 25, batch 4000, aishell_loss[loss=0.1277, simple_loss=0.2135, pruned_loss=0.02095, over 4865.00 frames.], tot_loss[loss=0.1434, simple_loss=0.226, pruned_loss=0.03042, over 985241.57 frames.], batch size: 28, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.02915, over 984476.60 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2191, pruned_loss=0.03195, over 986170.50 frames.], batch size: 28, lr: 3.25e-04
+2022-06-19 04:43:21,326 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 04:43:37,170 INFO [train.py:914] (3/4) Epoch 25, validation: loss=0.1639, simple_loss=0.2478, pruned_loss=0.03995, over 1622729.00 frames.
+2022-06-19 04:44:42,291 INFO [train.py:874] (3/4) Epoch 26, batch 50, aishell_loss[loss=0.1486, simple_loss=0.24, pruned_loss=0.02862, over 4955.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2195, pruned_loss=0.02714, over 218576.50 frames.], batch size: 40, aishell_tot_loss[loss=0.1414, simple_loss=0.2277, pruned_loss=0.02758, over 98196.27 frames.], datatang_tot_loss[loss=0.1335, simple_loss=0.2133, pruned_loss=0.02679, over 133695.96 frames.], batch size: 40, lr: 3.18e-04
+2022-06-19 04:45:12,051 INFO [train.py:874] (3/4) Epoch 26, batch 100, datatang_loss[loss=0.1327, simple_loss=0.2125, pruned_loss=0.02647, over 4956.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2215, pruned_loss=0.02819, over 388850.86 frames.], batch size: 45, aishell_tot_loss[loss=0.1441, simple_loss=0.2309, pruned_loss=0.02866, over 206633.56 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2128, pruned_loss=0.02768, over 230502.02 frames.], batch size: 45, lr: 3.18e-04
+2022-06-19 04:45:40,329 INFO [train.py:874] (3/4) Epoch 26, batch 150, datatang_loss[loss=0.1427, simple_loss=0.2192, pruned_loss=0.03307, over 4914.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2215, pruned_loss=0.02815, over 521194.20 frames.], batch size: 75, aishell_tot_loss[loss=0.1445, simple_loss=0.2324, pruned_loss=0.0283, over 298532.72 frames.], datatang_tot_loss[loss=0.1335, simple_loss=0.2111, pruned_loss=0.02796, over 319344.78 frames.], batch size: 75, lr: 3.18e-04
+2022-06-19 04:46:09,980 INFO [train.py:874] (3/4) Epoch 26, batch 200, datatang_loss[loss=0.1648, simple_loss=0.2408, pruned_loss=0.04438, over 4952.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2216, pruned_loss=0.02872, over 624229.24 frames.], batch size: 91, aishell_tot_loss[loss=0.1436, simple_loss=0.2312, pruned_loss=0.02802, over 370195.68 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2128, pruned_loss=0.02916, over 406750.79 frames.], batch size: 91, lr: 3.18e-04
+2022-06-19 04:46:40,016 INFO [train.py:874] (3/4) Epoch 26, batch 250, datatang_loss[loss=0.1444, simple_loss=0.2261, pruned_loss=0.03135, over 4953.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2211, pruned_loss=0.02864, over 704325.51 frames.], batch size: 91, aishell_tot_loss[loss=0.1432, simple_loss=0.2303, pruned_loss=0.02808, over 423132.50 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2137, pruned_loss=0.02898, over 492667.50 frames.], batch size: 91, lr: 3.18e-04
+2022-06-19 04:47:07,491 INFO [train.py:874] (3/4) Epoch 26, batch 300, aishell_loss[loss=0.1557, simple_loss=0.2482, pruned_loss=0.03161, over 4860.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2233, pruned_loss=0.02956, over 766737.60 frames.], batch size: 36, aishell_tot_loss[loss=0.1448, simple_loss=0.2322, pruned_loss=0.02866, over 481938.83 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2153, pruned_loss=0.02981, over 557208.18 frames.], batch size: 36, lr: 3.18e-04
+2022-06-19 04:47:36,382 INFO [train.py:874] (3/4) Epoch 26, batch 350, aishell_loss[loss=0.1409, simple_loss=0.2306, pruned_loss=0.02565, over 4973.00 frames.], tot_loss[loss=0.142, simple_loss=0.2241, pruned_loss=0.02996, over 815405.02 frames.], batch size: 44, aishell_tot_loss[loss=0.1451, simple_loss=0.2322, pruned_loss=0.02899, over 536735.07 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2164, pruned_loss=0.03017, over 611633.52 frames.], batch size: 44, lr: 3.18e-04
+2022-06-19 04:48:05,367 INFO [train.py:874] (3/4) Epoch 26, batch 400, aishell_loss[loss=0.1656, simple_loss=0.2592, pruned_loss=0.036, over 4906.00 frames.], tot_loss[loss=0.1427, simple_loss=0.225, pruned_loss=0.03025, over 853272.40 frames.], batch size: 68, aishell_tot_loss[loss=0.146, simple_loss=0.2329, pruned_loss=0.02961, over 595607.38 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2166, pruned_loss=0.03017, over 650738.83 frames.], batch size: 68, lr: 3.18e-04
+2022-06-19 04:48:32,771 INFO [train.py:874] (3/4) Epoch 26, batch 450, aishell_loss[loss=0.1421, simple_loss=0.2422, pruned_loss=0.021, over 4934.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2252, pruned_loss=0.03022, over 882468.40 frames.], batch size: 54, aishell_tot_loss[loss=0.1452, simple_loss=0.2322, pruned_loss=0.02911, over 644920.65 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2175, pruned_loss=0.03073, over 687101.43 frames.], batch size: 54, lr: 3.18e-04
+2022-06-19 04:49:06,569 INFO [train.py:874] (3/4) Epoch 26, batch 500, datatang_loss[loss=0.1857, simple_loss=0.2655, pruned_loss=0.05292, over 4945.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2244, pruned_loss=0.03027, over 905560.32 frames.], batch size: 108, aishell_tot_loss[loss=0.1447, simple_loss=0.2318, pruned_loss=0.02883, over 676023.39 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2174, pruned_loss=0.03108, over 730142.70 frames.], batch size: 108, lr: 3.18e-04
+2022-06-19 04:49:36,229 INFO [train.py:874] (3/4) Epoch 26, batch 550, aishell_loss[loss=0.1303, simple_loss=0.2109, pruned_loss=0.02481, over 4872.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2242, pruned_loss=0.02998, over 922723.58 frames.], batch size: 28, aishell_tot_loss[loss=0.1442, simple_loss=0.231, pruned_loss=0.02874, over 718703.51 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2175, pruned_loss=0.03094, over 754464.32 frames.], batch size: 28, lr: 3.17e-04
+2022-06-19 04:50:04,786 INFO [train.py:874] (3/4) Epoch 26, batch 600, datatang_loss[loss=0.1344, simple_loss=0.215, pruned_loss=0.02688, over 4958.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2234, pruned_loss=0.0298, over 936583.84 frames.], batch size: 50, aishell_tot_loss[loss=0.1437, simple_loss=0.2301, pruned_loss=0.02866, over 748845.13 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2173, pruned_loss=0.03081, over 782723.26 frames.], batch size: 50, lr: 3.17e-04
+2022-06-19 04:50:33,446 INFO [train.py:874] (3/4) Epoch 26, batch 650, datatang_loss[loss=0.1343, simple_loss=0.2103, pruned_loss=0.02911, over 4926.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2233, pruned_loss=0.02991, over 947797.31 frames.], batch size: 79, aishell_tot_loss[loss=0.1431, simple_loss=0.2292, pruned_loss=0.02845, over 778087.75 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2178, pruned_loss=0.03119, over 805762.12 frames.], batch size: 79, lr: 3.17e-04
+2022-06-19 04:51:04,506 INFO [train.py:874] (3/4) Epoch 26, batch 700, datatang_loss[loss=0.1116, simple_loss=0.1965, pruned_loss=0.01337, over 4926.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2239, pruned_loss=0.02991, over 956056.73 frames.], batch size: 81, aishell_tot_loss[loss=0.1436, simple_loss=0.2299, pruned_loss=0.02863, over 804136.51 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2178, pruned_loss=0.0311, over 825447.95 frames.], batch size: 81, lr: 3.17e-04
+2022-06-19 04:51:32,319 INFO [train.py:874] (3/4) Epoch 26, batch 750, aishell_loss[loss=0.1408, simple_loss=0.2224, pruned_loss=0.02956, over 4941.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2242, pruned_loss=0.03013, over 962568.10 frames.], batch size: 31, aishell_tot_loss[loss=0.1438, simple_loss=0.2301, pruned_loss=0.0287, over 825588.80 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2179, pruned_loss=0.0313, over 844207.22 frames.], batch size: 31, lr: 3.17e-04
+2022-06-19 04:52:00,219 INFO [train.py:874] (3/4) Epoch 26, batch 800, aishell_loss[loss=0.1369, simple_loss=0.2166, pruned_loss=0.02862, over 4950.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2247, pruned_loss=0.03026, over 967822.41 frames.], batch size: 31, aishell_tot_loss[loss=0.144, simple_loss=0.2305, pruned_loss=0.02875, over 840921.89 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2185, pruned_loss=0.03134, over 864039.44 frames.], batch size: 31, lr: 3.17e-04
+2022-06-19 04:52:30,618 INFO [train.py:874] (3/4) Epoch 26, batch 850, aishell_loss[loss=0.1504, simple_loss=0.2449, pruned_loss=0.02789, over 4967.00 frames.], tot_loss[loss=0.142, simple_loss=0.2243, pruned_loss=0.0298, over 971597.83 frames.], batch size: 40, aishell_tot_loss[loss=0.1431, simple_loss=0.2297, pruned_loss=0.02827, over 860753.56 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2185, pruned_loss=0.03139, over 875838.93 frames.], batch size: 40, lr: 3.17e-04
+2022-06-19 04:52:59,214 INFO [train.py:874] (3/4) Epoch 26, batch 900, aishell_loss[loss=0.1506, simple_loss=0.2409, pruned_loss=0.03018, over 4917.00 frames.], tot_loss[loss=0.142, simple_loss=0.2242, pruned_loss=0.02994, over 974834.22 frames.], batch size: 68, aishell_tot_loss[loss=0.1433, simple_loss=0.2299, pruned_loss=0.02831, over 871995.62 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2186, pruned_loss=0.0314, over 891876.71 frames.], batch size: 68, lr: 3.17e-04
+2022-06-19 04:53:26,730 INFO [train.py:874] (3/4) Epoch 26, batch 950, aishell_loss[loss=0.1427, simple_loss=0.2292, pruned_loss=0.02805, over 4870.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2243, pruned_loss=0.03013, over 977263.63 frames.], batch size: 35, aishell_tot_loss[loss=0.1436, simple_loss=0.2303, pruned_loss=0.02851, over 884193.66 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2185, pruned_loss=0.03141, over 903956.18 frames.], batch size: 35, lr: 3.17e-04
+2022-06-19 04:53:57,640 INFO [train.py:874] (3/4) Epoch 26, batch 1000, datatang_loss[loss=0.1405, simple_loss=0.2193, pruned_loss=0.03083, over 4960.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2241, pruned_loss=0.03041, over 978893.83 frames.], batch size: 67, aishell_tot_loss[loss=0.1441, simple_loss=0.2307, pruned_loss=0.02876, over 893989.82 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.218, pruned_loss=0.03147, over 915108.55 frames.], batch size: 67, lr: 3.17e-04
+2022-06-19 04:53:57,641 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 04:54:14,575 INFO [train.py:914] (3/4) Epoch 26, validation: loss=0.165, simple_loss=0.2487, pruned_loss=0.04059, over 1622729.00 frames.
+2022-06-19 04:54:43,212 INFO [train.py:874] (3/4) Epoch 26, batch 1050, aishell_loss[loss=0.1394, simple_loss=0.2261, pruned_loss=0.02631, over 4878.00 frames.], tot_loss[loss=0.142, simple_loss=0.2237, pruned_loss=0.0301, over 980294.96 frames.], batch size: 36, aishell_tot_loss[loss=0.1442, simple_loss=0.2307, pruned_loss=0.02885, over 904312.25 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2176, pruned_loss=0.03108, over 923680.00 frames.], batch size: 36, lr: 3.17e-04
+2022-06-19 04:55:11,954 INFO [train.py:874] (3/4) Epoch 26, batch 1100, datatang_loss[loss=0.1335, simple_loss=0.2015, pruned_loss=0.03272, over 4964.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2238, pruned_loss=0.0303, over 981445.26 frames.], batch size: 60, aishell_tot_loss[loss=0.1444, simple_loss=0.231, pruned_loss=0.02886, over 911236.40 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2178, pruned_loss=0.03126, over 932904.71 frames.], batch size: 60, lr: 3.17e-04
+2022-06-19 04:55:39,898 INFO [train.py:874] (3/4) Epoch 26, batch 1150, aishell_loss[loss=0.1404, simple_loss=0.2234, pruned_loss=0.02869, over 4865.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2246, pruned_loss=0.03081, over 982130.81 frames.], batch size: 28, aishell_tot_loss[loss=0.1449, simple_loss=0.2314, pruned_loss=0.02917, over 919246.79 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2182, pruned_loss=0.03155, over 939512.05 frames.], batch size: 28, lr: 3.17e-04
+2022-06-19 04:56:09,257 INFO [train.py:874] (3/4) Epoch 26, batch 1200, datatang_loss[loss=0.1619, simple_loss=0.2409, pruned_loss=0.04147, over 4881.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2251, pruned_loss=0.03064, over 982280.30 frames.], batch size: 47, aishell_tot_loss[loss=0.1445, simple_loss=0.2309, pruned_loss=0.02905, over 927556.53 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2189, pruned_loss=0.03164, over 944124.92 frames.], batch size: 47, lr: 3.16e-04
+2022-06-19 04:56:37,393 INFO [train.py:874] (3/4) Epoch 26, batch 1250, datatang_loss[loss=0.1289, simple_loss=0.1997, pruned_loss=0.02906, over 4937.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2249, pruned_loss=0.03022, over 982931.25 frames.], batch size: 50, aishell_tot_loss[loss=0.1443, simple_loss=0.2309, pruned_loss=0.02888, over 936390.52 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2183, pruned_loss=0.03152, over 947428.61 frames.], batch size: 50, lr: 3.16e-04
+2022-06-19 04:57:05,528 INFO [train.py:874] (3/4) Epoch 26, batch 1300, aishell_loss[loss=0.1584, simple_loss=0.2416, pruned_loss=0.03761, over 4928.00 frames.], tot_loss[loss=0.1423, simple_loss=0.224, pruned_loss=0.03029, over 983473.36 frames.], batch size: 33, aishell_tot_loss[loss=0.1441, simple_loss=0.2305, pruned_loss=0.02886, over 941369.35 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2181, pruned_loss=0.03158, over 952479.19 frames.], batch size: 33, lr: 3.16e-04
+2022-06-19 04:57:34,765 INFO [train.py:874] (3/4) Epoch 26, batch 1350, datatang_loss[loss=0.1284, simple_loss=0.222, pruned_loss=0.01737, over 4907.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2244, pruned_loss=0.03009, over 984375.90 frames.], batch size: 25, aishell_tot_loss[loss=0.1444, simple_loss=0.2309, pruned_loss=0.02889, over 946791.77 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2181, pruned_loss=0.03138, over 956624.86 frames.], batch size: 25, lr: 3.16e-04
+2022-06-19 04:58:03,306 INFO [train.py:874] (3/4) Epoch 26, batch 1400, datatang_loss[loss=0.1254, simple_loss=0.2077, pruned_loss=0.02153, over 4942.00 frames.], tot_loss[loss=0.142, simple_loss=0.2243, pruned_loss=0.0298, over 984466.17 frames.], batch size: 62, aishell_tot_loss[loss=0.1444, simple_loss=0.231, pruned_loss=0.02892, over 950872.27 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2179, pruned_loss=0.03099, over 960240.57 frames.], batch size: 62, lr: 3.16e-04
+2022-06-19 04:58:31,207 INFO [train.py:874] (3/4) Epoch 26, batch 1450, aishell_loss[loss=0.1573, simple_loss=0.239, pruned_loss=0.03779, over 4862.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2236, pruned_loss=0.02963, over 984380.88 frames.], batch size: 35, aishell_tot_loss[loss=0.1441, simple_loss=0.2307, pruned_loss=0.02882, over 954189.04 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2176, pruned_loss=0.03083, over 963542.48 frames.], batch size: 35, lr: 3.16e-04
+2022-06-19 04:59:01,379 INFO [train.py:874] (3/4) Epoch 26, batch 1500, aishell_loss[loss=0.1664, simple_loss=0.248, pruned_loss=0.04239, over 4897.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2241, pruned_loss=0.02972, over 984698.54 frames.], batch size: 34, aishell_tot_loss[loss=0.1443, simple_loss=0.2309, pruned_loss=0.0289, over 958286.77 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2176, pruned_loss=0.03081, over 965905.30 frames.], batch size: 34, lr: 3.16e-04
+2022-06-19 04:59:30,332 INFO [train.py:874] (3/4) Epoch 26, batch 1550, aishell_loss[loss=0.1549, simple_loss=0.2517, pruned_loss=0.02898, over 4857.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2253, pruned_loss=0.03014, over 984756.86 frames.], batch size: 36, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02903, over 961690.35 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2179, pruned_loss=0.03114, over 967993.54 frames.], batch size: 36, lr: 3.16e-04
+2022-06-19 04:59:58,550 INFO [train.py:874] (3/4) Epoch 26, batch 1600, datatang_loss[loss=0.1358, simple_loss=0.2128, pruned_loss=0.02939, over 4938.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2248, pruned_loss=0.02995, over 985071.16 frames.], batch size: 79, aishell_tot_loss[loss=0.1445, simple_loss=0.2313, pruned_loss=0.02883, over 964354.44 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2179, pruned_loss=0.03113, over 970326.54 frames.], batch size: 79, lr: 3.16e-04
+2022-06-19 05:00:28,453 INFO [train.py:874] (3/4) Epoch 26, batch 1650, aishell_loss[loss=0.1463, simple_loss=0.2357, pruned_loss=0.02842, over 4874.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2242, pruned_loss=0.03001, over 985072.59 frames.], batch size: 42, aishell_tot_loss[loss=0.1445, simple_loss=0.2315, pruned_loss=0.02876, over 966192.70 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2174, pruned_loss=0.03118, over 972527.25 frames.], batch size: 42, lr: 3.16e-04
+2022-06-19 05:00:57,272 INFO [train.py:874] (3/4) Epoch 26, batch 1700, datatang_loss[loss=0.1333, simple_loss=0.2158, pruned_loss=0.0254, over 4936.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2244, pruned_loss=0.03004, over 985480.52 frames.], batch size: 79, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02901, over 968451.45 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2173, pruned_loss=0.03096, over 974400.70 frames.], batch size: 79, lr: 3.16e-04
+2022-06-19 05:01:26,599 INFO [train.py:874] (3/4) Epoch 26, batch 1750, datatang_loss[loss=0.1313, simple_loss=0.2129, pruned_loss=0.02486, over 4907.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2236, pruned_loss=0.02977, over 985492.31 frames.], batch size: 52, aishell_tot_loss[loss=0.1447, simple_loss=0.2313, pruned_loss=0.02901, over 970424.27 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2169, pruned_loss=0.03067, over 975720.03 frames.], batch size: 52, lr: 3.16e-04
+2022-06-19 05:01:57,146 INFO [train.py:874] (3/4) Epoch 26, batch 1800, aishell_loss[loss=0.1441, simple_loss=0.2378, pruned_loss=0.0252, over 4948.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2243, pruned_loss=0.03032, over 985632.96 frames.], batch size: 40, aishell_tot_loss[loss=0.1454, simple_loss=0.232, pruned_loss=0.02939, over 971903.60 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2171, pruned_loss=0.03082, over 977204.70 frames.], batch size: 40, lr: 3.16e-04
+2022-06-19 05:02:25,553 INFO [train.py:874] (3/4) Epoch 26, batch 1850, datatang_loss[loss=0.1424, simple_loss=0.2277, pruned_loss=0.02858, over 4954.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2235, pruned_loss=0.03013, over 985893.22 frames.], batch size: 91, aishell_tot_loss[loss=0.1454, simple_loss=0.2319, pruned_loss=0.02943, over 973208.40 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2168, pruned_loss=0.03058, over 978622.58 frames.], batch size: 91, lr: 3.16e-04
+2022-06-19 05:02:55,379 INFO [train.py:874] (3/4) Epoch 26, batch 1900, datatang_loss[loss=0.1151, simple_loss=0.1989, pruned_loss=0.01566, over 4929.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2237, pruned_loss=0.02978, over 985739.28 frames.], batch size: 71, aishell_tot_loss[loss=0.1458, simple_loss=0.2326, pruned_loss=0.02955, over 974691.03 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.216, pruned_loss=0.03013, over 979375.40 frames.], batch size: 71, lr: 3.15e-04
+2022-06-19 05:03:25,576 INFO [train.py:874] (3/4) Epoch 26, batch 1950, datatang_loss[loss=0.1038, simple_loss=0.17, pruned_loss=0.01878, over 4933.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2231, pruned_loss=0.02933, over 985469.29 frames.], batch size: 50, aishell_tot_loss[loss=0.146, simple_loss=0.2327, pruned_loss=0.02963, over 975580.01 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2149, pruned_loss=0.02958, over 980291.30 frames.], batch size: 50, lr: 3.15e-04
+2022-06-19 05:03:53,632 INFO [train.py:874] (3/4) Epoch 26, batch 2000, datatang_loss[loss=0.1135, simple_loss=0.1978, pruned_loss=0.01462, over 4968.00 frames.], tot_loss[loss=0.1417, simple_loss=0.224, pruned_loss=0.02963, over 985698.10 frames.], batch size: 45, aishell_tot_loss[loss=0.1466, simple_loss=0.2334, pruned_loss=0.02993, over 977009.48 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.215, pruned_loss=0.02957, over 980924.52 frames.], batch size: 45, lr: 3.15e-04
+2022-06-19 05:03:53,633 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 05:04:09,288 INFO [train.py:914] (3/4) Epoch 26, validation: loss=0.1659, simple_loss=0.2484, pruned_loss=0.04175, over 1622729.00 frames.
+2022-06-19 05:04:38,953 INFO [train.py:874] (3/4) Epoch 26, batch 2050, aishell_loss[loss=0.1478, simple_loss=0.2241, pruned_loss=0.03575, over 4954.00 frames.], tot_loss[loss=0.1416, simple_loss=0.224, pruned_loss=0.02964, over 985724.75 frames.], batch size: 40, aishell_tot_loss[loss=0.1462, simple_loss=0.2329, pruned_loss=0.02971, over 977811.73 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2155, pruned_loss=0.02978, over 981708.50 frames.], batch size: 40, lr: 3.15e-04
+2022-06-19 05:05:07,646 INFO [train.py:874] (3/4) Epoch 26, batch 2100, datatang_loss[loss=0.1814, simple_loss=0.2481, pruned_loss=0.05733, over 4929.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2244, pruned_loss=0.03009, over 985843.41 frames.], batch size: 50, aishell_tot_loss[loss=0.1458, simple_loss=0.2326, pruned_loss=0.02946, over 978857.90 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2161, pruned_loss=0.03045, over 982180.86 frames.], batch size: 50, lr: 3.15e-04
+2022-06-19 05:05:37,221 INFO [train.py:874] (3/4) Epoch 26, batch 2150, aishell_loss[loss=0.1315, simple_loss=0.2205, pruned_loss=0.02124, over 4945.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2253, pruned_loss=0.03044, over 985794.96 frames.], batch size: 64, aishell_tot_loss[loss=0.1458, simple_loss=0.2327, pruned_loss=0.02945, over 979622.49 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2167, pruned_loss=0.03088, over 982682.74 frames.], batch size: 64, lr: 3.15e-04
+2022-06-19 05:06:06,685 INFO [train.py:874] (3/4) Epoch 26, batch 2200, datatang_loss[loss=0.1359, simple_loss=0.2169, pruned_loss=0.02746, over 4926.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2257, pruned_loss=0.03029, over 985650.06 frames.], batch size: 83, aishell_tot_loss[loss=0.1459, simple_loss=0.2332, pruned_loss=0.02933, over 980165.41 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2171, pruned_loss=0.03086, over 983016.85 frames.], batch size: 83, lr: 3.15e-04
+2022-06-19 05:06:34,935 INFO [train.py:874] (3/4) Epoch 26, batch 2250, datatang_loss[loss=0.1536, simple_loss=0.2275, pruned_loss=0.03984, over 4952.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2261, pruned_loss=0.03043, over 985506.93 frames.], batch size: 55, aishell_tot_loss[loss=0.1465, simple_loss=0.2338, pruned_loss=0.0296, over 980867.76 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2166, pruned_loss=0.03082, over 983167.82 frames.], batch size: 55, lr: 3.15e-04
+2022-06-19 05:07:05,059 INFO [train.py:874] (3/4) Epoch 26, batch 2300, datatang_loss[loss=0.1509, simple_loss=0.2275, pruned_loss=0.03719, over 4904.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2249, pruned_loss=0.02975, over 985420.46 frames.], batch size: 52, aishell_tot_loss[loss=0.1459, simple_loss=0.2332, pruned_loss=0.02924, over 981479.32 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2163, pruned_loss=0.03049, over 983258.31 frames.], batch size: 52, lr: 3.15e-04
+2022-06-19 05:07:33,221 INFO [train.py:874] (3/4) Epoch 26, batch 2350, aishell_loss[loss=0.1349, simple_loss=0.2322, pruned_loss=0.01883, over 4939.00 frames.], tot_loss[loss=0.142, simple_loss=0.2246, pruned_loss=0.02973, over 985563.05 frames.], batch size: 56, aishell_tot_loss[loss=0.1457, simple_loss=0.233, pruned_loss=0.02925, over 982191.19 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2163, pruned_loss=0.03043, over 983392.97 frames.], batch size: 56, lr: 3.15e-04
+2022-06-19 05:08:00,967 INFO [train.py:874] (3/4) Epoch 26, batch 2400, datatang_loss[loss=0.1543, simple_loss=0.2129, pruned_loss=0.04785, over 4818.00 frames.], tot_loss[loss=0.1423, simple_loss=0.225, pruned_loss=0.02982, over 985454.22 frames.], batch size: 25, aishell_tot_loss[loss=0.1466, simple_loss=0.2339, pruned_loss=0.02967, over 982375.81 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2159, pruned_loss=0.03008, over 983757.45 frames.], batch size: 25, lr: 3.15e-04
+2022-06-19 05:08:30,387 INFO [train.py:874] (3/4) Epoch 26, batch 2450, aishell_loss[loss=0.1536, simple_loss=0.2408, pruned_loss=0.03321, over 4940.00 frames.], tot_loss[loss=0.1421, simple_loss=0.225, pruned_loss=0.02966, over 985564.20 frames.], batch size: 58, aishell_tot_loss[loss=0.1462, simple_loss=0.2337, pruned_loss=0.02934, over 982753.14 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2163, pruned_loss=0.0302, over 984042.92 frames.], batch size: 58, lr: 3.15e-04
+2022-06-19 05:08:59,242 INFO [train.py:874] (3/4) Epoch 26, batch 2500, aishell_loss[loss=0.1488, simple_loss=0.2389, pruned_loss=0.02939, over 4945.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2249, pruned_loss=0.02988, over 985549.79 frames.], batch size: 45, aishell_tot_loss[loss=0.1456, simple_loss=0.2328, pruned_loss=0.0292, over 982870.99 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2169, pruned_loss=0.03056, over 984427.79 frames.], batch size: 45, lr: 3.15e-04
+2022-06-19 05:09:26,868 INFO [train.py:874] (3/4) Epoch 26, batch 2550, aishell_loss[loss=0.1537, simple_loss=0.242, pruned_loss=0.03269, over 4921.00 frames.], tot_loss[loss=0.1425, simple_loss=0.225, pruned_loss=0.03001, over 985624.18 frames.], batch size: 46, aishell_tot_loss[loss=0.1453, simple_loss=0.2324, pruned_loss=0.02914, over 983375.32 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2171, pruned_loss=0.03082, over 984483.87 frames.], batch size: 46, lr: 3.14e-04
+2022-06-19 05:09:56,396 INFO [train.py:874] (3/4) Epoch 26, batch 2600, datatang_loss[loss=0.1641, simple_loss=0.2222, pruned_loss=0.05304, over 4970.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2247, pruned_loss=0.02992, over 985895.42 frames.], batch size: 34, aishell_tot_loss[loss=0.1458, simple_loss=0.2328, pruned_loss=0.0294, over 983936.24 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2163, pruned_loss=0.03048, over 984626.60 frames.], batch size: 34, lr: 3.14e-04
+2022-06-19 05:10:26,639 INFO [train.py:874] (3/4) Epoch 26, batch 2650, datatang_loss[loss=0.1237, simple_loss=0.2072, pruned_loss=0.02011, over 4938.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2243, pruned_loss=0.02958, over 986003.78 frames.], batch size: 69, aishell_tot_loss[loss=0.1456, simple_loss=0.2327, pruned_loss=0.02919, over 984305.58 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2162, pruned_loss=0.03031, over 984737.81 frames.], batch size: 69, lr: 3.14e-04
+2022-06-19 05:10:56,034 INFO [train.py:874] (3/4) Epoch 26, batch 2700, datatang_loss[loss=0.1483, simple_loss=0.2188, pruned_loss=0.03891, over 4946.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2248, pruned_loss=0.03008, over 986160.34 frames.], batch size: 55, aishell_tot_loss[loss=0.1461, simple_loss=0.2333, pruned_loss=0.02946, over 984569.84 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2163, pruned_loss=0.03055, over 984989.75 frames.], batch size: 55, lr: 3.14e-04
+2022-06-19 05:11:24,570 INFO [train.py:874] (3/4) Epoch 26, batch 2750, aishell_loss[loss=0.1467, simple_loss=0.236, pruned_loss=0.02874, over 4963.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2257, pruned_loss=0.03045, over 985894.59 frames.], batch size: 44, aishell_tot_loss[loss=0.1471, simple_loss=0.2343, pruned_loss=0.02993, over 984650.01 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2158, pruned_loss=0.03053, over 984979.42 frames.], batch size: 44, lr: 3.14e-04
+2022-06-19 05:11:53,316 INFO [train.py:874] (3/4) Epoch 26, batch 2800, aishell_loss[loss=0.1655, simple_loss=0.2581, pruned_loss=0.03646, over 4924.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2255, pruned_loss=0.02996, over 985685.76 frames.], batch size: 79, aishell_tot_loss[loss=0.1464, simple_loss=0.2335, pruned_loss=0.02967, over 984672.23 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2156, pruned_loss=0.03036, over 985006.48 frames.], batch size: 79, lr: 3.14e-04
+2022-06-19 05:12:22,085 INFO [train.py:874] (3/4) Epoch 26, batch 2850, aishell_loss[loss=0.1222, simple_loss=0.2208, pruned_loss=0.01179, over 4916.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2252, pruned_loss=0.0295, over 985651.02 frames.], batch size: 46, aishell_tot_loss[loss=0.1455, simple_loss=0.233, pruned_loss=0.02904, over 984571.49 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2157, pruned_loss=0.03052, over 985278.99 frames.], batch size: 46, lr: 3.14e-04
+2022-06-19 05:12:50,270 INFO [train.py:874] (3/4) Epoch 26, batch 2900, datatang_loss[loss=0.1305, simple_loss=0.2107, pruned_loss=0.02512, over 4925.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2252, pruned_loss=0.02934, over 985528.61 frames.], batch size: 73, aishell_tot_loss[loss=0.1455, simple_loss=0.2329, pruned_loss=0.029, over 984498.26 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2156, pruned_loss=0.03035, over 985414.11 frames.], batch size: 73, lr: 3.14e-04
+2022-06-19 05:13:18,648 INFO [train.py:874] (3/4) Epoch 26, batch 2950, aishell_loss[loss=0.1525, simple_loss=0.2449, pruned_loss=0.02999, over 4973.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2246, pruned_loss=0.02898, over 984948.55 frames.], batch size: 51, aishell_tot_loss[loss=0.1448, simple_loss=0.2323, pruned_loss=0.02868, over 984075.37 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2157, pruned_loss=0.03025, over 985379.51 frames.], batch size: 51, lr: 3.14e-04
+2022-06-19 05:13:48,857 INFO [train.py:874] (3/4) Epoch 26, batch 3000, aishell_loss[loss=0.1521, simple_loss=0.2418, pruned_loss=0.03125, over 4976.00 frames.], tot_loss[loss=0.141, simple_loss=0.2244, pruned_loss=0.0288, over 984915.19 frames.], batch size: 48, aishell_tot_loss[loss=0.1443, simple_loss=0.2316, pruned_loss=0.02853, over 984119.10 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2157, pruned_loss=0.03014, over 985383.75 frames.], batch size: 48, lr: 3.14e-04
+2022-06-19 05:13:48,857 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 05:14:05,914 INFO [train.py:914] (3/4) Epoch 26, validation: loss=0.1644, simple_loss=0.2479, pruned_loss=0.04043, over 1622729.00 frames.
+2022-06-19 05:14:34,730 INFO [train.py:874] (3/4) Epoch 26, batch 3050, aishell_loss[loss=0.1688, simple_loss=0.2634, pruned_loss=0.03708, over 4921.00 frames.], tot_loss[loss=0.1409, simple_loss=0.224, pruned_loss=0.02889, over 985064.47 frames.], batch size: 79, aishell_tot_loss[loss=0.1447, simple_loss=0.2321, pruned_loss=0.02866, over 984281.10 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2156, pruned_loss=0.02992, over 985368.22 frames.], batch size: 79, lr: 3.14e-04
+2022-06-19 05:15:02,367 INFO [train.py:874] (3/4) Epoch 26, batch 3100, datatang_loss[loss=0.1223, simple_loss=0.2075, pruned_loss=0.01859, over 4924.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2233, pruned_loss=0.02906, over 985483.96 frames.], batch size: 73, aishell_tot_loss[loss=0.1446, simple_loss=0.2318, pruned_loss=0.0287, over 984493.99 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2156, pruned_loss=0.02993, over 985625.61 frames.], batch size: 73, lr: 3.14e-04
+2022-06-19 05:15:30,161 INFO [train.py:874] (3/4) Epoch 26, batch 3150, datatang_loss[loss=0.1403, simple_loss=0.2087, pruned_loss=0.03596, over 4945.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2233, pruned_loss=0.0295, over 985223.11 frames.], batch size: 62, aishell_tot_loss[loss=0.1447, simple_loss=0.2318, pruned_loss=0.02882, over 984481.11 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2155, pruned_loss=0.03022, over 985457.71 frames.], batch size: 62, lr: 3.14e-04
+2022-06-19 05:16:00,432 INFO [train.py:874] (3/4) Epoch 26, batch 3200, datatang_loss[loss=0.1462, simple_loss=0.2166, pruned_loss=0.03785, over 4947.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2227, pruned_loss=0.02905, over 985129.86 frames.], batch size: 62, aishell_tot_loss[loss=0.1443, simple_loss=0.2313, pruned_loss=0.02866, over 984499.18 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2152, pruned_loss=0.0299, over 985381.78 frames.], batch size: 62, lr: 3.14e-04
+2022-06-19 05:16:28,125 INFO [train.py:874] (3/4) Epoch 26, batch 3250, aishell_loss[loss=0.1351, simple_loss=0.2317, pruned_loss=0.01924, over 4968.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2231, pruned_loss=0.02892, over 985108.05 frames.], batch size: 61, aishell_tot_loss[loss=0.1443, simple_loss=0.2314, pruned_loss=0.02864, over 984519.72 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2151, pruned_loss=0.02977, over 985394.44 frames.], batch size: 61, lr: 3.13e-04
+2022-06-19 05:16:56,620 INFO [train.py:874] (3/4) Epoch 26, batch 3300, datatang_loss[loss=0.1287, simple_loss=0.2056, pruned_loss=0.02589, over 4882.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2227, pruned_loss=0.0287, over 985394.37 frames.], batch size: 25, aishell_tot_loss[loss=0.1444, simple_loss=0.2315, pruned_loss=0.0286, over 984605.85 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.215, pruned_loss=0.02947, over 985619.51 frames.], batch size: 25, lr: 3.13e-04
+2022-06-19 05:17:26,238 INFO [train.py:874] (3/4) Epoch 26, batch 3350, datatang_loss[loss=0.202, simple_loss=0.2643, pruned_loss=0.0698, over 4919.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2224, pruned_loss=0.02891, over 985300.75 frames.], batch size: 108, aishell_tot_loss[loss=0.1442, simple_loss=0.2312, pruned_loss=0.0286, over 984771.05 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2148, pruned_loss=0.0296, over 985418.27 frames.], batch size: 108, lr: 3.13e-04
+2022-06-19 05:17:54,050 INFO [train.py:874] (3/4) Epoch 26, batch 3400, datatang_loss[loss=0.1242, simple_loss=0.2027, pruned_loss=0.02284, over 4916.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2232, pruned_loss=0.02913, over 985416.37 frames.], batch size: 75, aishell_tot_loss[loss=0.1444, simple_loss=0.2315, pruned_loss=0.02868, over 984995.36 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2148, pruned_loss=0.02973, over 985378.19 frames.], batch size: 75, lr: 3.13e-04
+2022-06-19 05:18:21,630 INFO [train.py:874] (3/4) Epoch 26, batch 3450, aishell_loss[loss=0.1535, simple_loss=0.225, pruned_loss=0.04103, over 4931.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2235, pruned_loss=0.02975, over 985195.12 frames.], batch size: 33, aishell_tot_loss[loss=0.1449, simple_loss=0.2316, pruned_loss=0.02907, over 984592.79 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2152, pruned_loss=0.02997, over 985592.38 frames.], batch size: 33, lr: 3.13e-04
+2022-06-19 05:18:52,425 INFO [train.py:874] (3/4) Epoch 26, batch 3500, datatang_loss[loss=0.1362, simple_loss=0.2106, pruned_loss=0.03093, over 4922.00 frames.], tot_loss[loss=0.142, simple_loss=0.2239, pruned_loss=0.03009, over 985259.49 frames.], batch size: 73, aishell_tot_loss[loss=0.1452, simple_loss=0.232, pruned_loss=0.0292, over 984563.75 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2156, pruned_loss=0.03023, over 985684.11 frames.], batch size: 73, lr: 3.13e-04
+2022-06-19 05:19:22,043 INFO [train.py:874] (3/4) Epoch 26, batch 3550, datatang_loss[loss=0.1615, simple_loss=0.235, pruned_loss=0.04399, over 4916.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2241, pruned_loss=0.02968, over 984828.25 frames.], batch size: 73, aishell_tot_loss[loss=0.1451, simple_loss=0.2321, pruned_loss=0.02904, over 984413.67 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2158, pruned_loss=0.03004, over 985411.45 frames.], batch size: 73, lr: 3.13e-04
+2022-06-19 05:19:50,841 INFO [train.py:874] (3/4) Epoch 26, batch 3600, aishell_loss[loss=0.1447, simple_loss=0.2332, pruned_loss=0.02813, over 4971.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2237, pruned_loss=0.02968, over 984890.27 frames.], batch size: 44, aishell_tot_loss[loss=0.1448, simple_loss=0.2318, pruned_loss=0.02895, over 984314.53 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2155, pruned_loss=0.03019, over 985557.72 frames.], batch size: 44, lr: 3.13e-04
+2022-06-19 05:20:19,580 INFO [train.py:874] (3/4) Epoch 26, batch 3650, aishell_loss[loss=0.1354, simple_loss=0.2171, pruned_loss=0.02682, over 4984.00 frames.], tot_loss[loss=0.1418, simple_loss=0.224, pruned_loss=0.02984, over 984708.19 frames.], batch size: 30, aishell_tot_loss[loss=0.1448, simple_loss=0.2315, pruned_loss=0.02905, over 984095.37 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2157, pruned_loss=0.0303, over 985611.10 frames.], batch size: 30, lr: 3.13e-04
+2022-06-19 05:20:49,064 INFO [train.py:874] (3/4) Epoch 26, batch 3700, datatang_loss[loss=0.1247, simple_loss=0.197, pruned_loss=0.02622, over 4957.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2231, pruned_loss=0.0299, over 985244.32 frames.], batch size: 55, aishell_tot_loss[loss=0.1447, simple_loss=0.2313, pruned_loss=0.02909, over 984238.13 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2155, pruned_loss=0.03031, over 985966.96 frames.], batch size: 55, lr: 3.13e-04
+2022-06-19 05:21:16,727 INFO [train.py:874] (3/4) Epoch 26, batch 3750, aishell_loss[loss=0.1363, simple_loss=0.2172, pruned_loss=0.02764, over 4872.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2227, pruned_loss=0.02941, over 985214.98 frames.], batch size: 28, aishell_tot_loss[loss=0.1446, simple_loss=0.2312, pruned_loss=0.02899, over 984382.08 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2149, pruned_loss=0.02994, over 985833.64 frames.], batch size: 28, lr: 3.13e-04
+2022-06-19 05:21:44,671 INFO [train.py:874] (3/4) Epoch 26, batch 3800, aishell_loss[loss=0.1417, simple_loss=0.2316, pruned_loss=0.02587, over 4941.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2236, pruned_loss=0.02933, over 985349.89 frames.], batch size: 56, aishell_tot_loss[loss=0.1449, simple_loss=0.232, pruned_loss=0.02895, over 984668.98 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.215, pruned_loss=0.0299, over 985710.12 frames.], batch size: 56, lr: 3.13e-04
+2022-06-19 05:22:11,984 INFO [train.py:874] (3/4) Epoch 26, batch 3850, aishell_loss[loss=0.1362, simple_loss=0.2208, pruned_loss=0.02583, over 4971.00 frames.], tot_loss[loss=0.141, simple_loss=0.2237, pruned_loss=0.02912, over 985484.14 frames.], batch size: 39, aishell_tot_loss[loss=0.1448, simple_loss=0.2318, pruned_loss=0.02892, over 984737.02 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.215, pruned_loss=0.0297, over 985833.20 frames.], batch size: 39, lr: 3.13e-04
+2022-06-19 05:22:40,503 INFO [train.py:874] (3/4) Epoch 26, batch 3900, datatang_loss[loss=0.1185, simple_loss=0.1992, pruned_loss=0.01892, over 4919.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2239, pruned_loss=0.02984, over 985426.30 frames.], batch size: 73, aishell_tot_loss[loss=0.1451, simple_loss=0.2318, pruned_loss=0.02919, over 984753.32 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2158, pruned_loss=0.03014, over 985782.47 frames.], batch size: 73, lr: 3.12e-04
+2022-06-19 05:23:06,644 INFO [train.py:874] (3/4) Epoch 26, batch 3950, aishell_loss[loss=0.1504, simple_loss=0.2379, pruned_loss=0.03146, over 4983.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2233, pruned_loss=0.02921, over 985500.71 frames.], batch size: 38, aishell_tot_loss[loss=0.1441, simple_loss=0.2308, pruned_loss=0.02873, over 984816.82 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2159, pruned_loss=0.02998, over 985842.10 frames.], batch size: 38, lr: 3.12e-04
+2022-06-19 05:23:34,149 INFO [train.py:874] (3/4) Epoch 26, batch 4000, aishell_loss[loss=0.1404, simple_loss=0.23, pruned_loss=0.02533, over 4976.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2228, pruned_loss=0.02872, over 985800.93 frames.], batch size: 30, aishell_tot_loss[loss=0.1438, simple_loss=0.2305, pruned_loss=0.02853, over 985075.60 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2159, pruned_loss=0.02962, over 985931.43 frames.], batch size: 30, lr: 3.12e-04
+2022-06-19 05:23:34,151 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 05:23:50,809 INFO [train.py:914] (3/4) Epoch 26, validation: loss=0.1644, simple_loss=0.2484, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-19 05:24:19,243 INFO [train.py:874] (3/4) Epoch 26, batch 4050, aishell_loss[loss=0.105, simple_loss=0.1748, pruned_loss=0.01763, over 4840.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2223, pruned_loss=0.02856, over 985339.37 frames.], batch size: 20, aishell_tot_loss[loss=0.1435, simple_loss=0.2302, pruned_loss=0.02836, over 984860.27 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2153, pruned_loss=0.02954, over 985739.76 frames.], batch size: 20, lr: 3.12e-04
+2022-06-19 05:24:46,163 INFO [train.py:874] (3/4) Epoch 26, batch 4100, datatang_loss[loss=0.1529, simple_loss=0.227, pruned_loss=0.03941, over 4943.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2222, pruned_loss=0.02854, over 985211.89 frames.], batch size: 45, aishell_tot_loss[loss=0.1438, simple_loss=0.2306, pruned_loss=0.02848, over 984540.09 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2148, pruned_loss=0.0293, over 985937.05 frames.], batch size: 45, lr: 3.12e-04
+2022-06-19 05:26:04,700 INFO [train.py:874] (3/4) Epoch 27, batch 50, datatang_loss[loss=0.1169, simple_loss=0.1963, pruned_loss=0.01878, over 4951.00 frames.], tot_loss[loss=0.1371, simple_loss=0.2185, pruned_loss=0.02785, over 218610.03 frames.], batch size: 62, aishell_tot_loss[loss=0.1447, simple_loss=0.2319, pruned_loss=0.0287, over 111723.69 frames.], datatang_tot_loss[loss=0.1301, simple_loss=0.2061, pruned_loss=0.02712, over 120546.97 frames.], batch size: 62, lr: 3.06e-04
+2022-06-19 05:26:34,285 INFO [train.py:874] (3/4) Epoch 27, batch 100, datatang_loss[loss=0.122, simple_loss=0.1837, pruned_loss=0.03012, over 4948.00 frames.], tot_loss[loss=0.137, simple_loss=0.218, pruned_loss=0.02799, over 388437.88 frames.], batch size: 37, aishell_tot_loss[loss=0.1439, simple_loss=0.2299, pruned_loss=0.02889, over 206505.61 frames.], datatang_tot_loss[loss=0.1312, simple_loss=0.2078, pruned_loss=0.02727, over 230203.98 frames.], batch size: 37, lr: 3.06e-04
+2022-06-19 05:27:02,761 INFO [train.py:874] (3/4) Epoch 27, batch 150, aishell_loss[loss=0.1601, simple_loss=0.248, pruned_loss=0.03605, over 4924.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2213, pruned_loss=0.02823, over 520735.99 frames.], batch size: 79, aishell_tot_loss[loss=0.1445, simple_loss=0.2308, pruned_loss=0.02908, over 334928.47 frames.], datatang_tot_loss[loss=0.1313, simple_loss=0.2084, pruned_loss=0.02715, over 281536.08 frames.], batch size: 79, lr: 3.06e-04
+2022-06-19 05:27:29,185 INFO [train.py:874] (3/4) Epoch 27, batch 200, aishell_loss[loss=0.1621, simple_loss=0.2519, pruned_loss=0.03618, over 4961.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2227, pruned_loss=0.02876, over 623586.09 frames.], batch size: 61, aishell_tot_loss[loss=0.1454, simple_loss=0.2321, pruned_loss=0.02934, over 425541.18 frames.], datatang_tot_loss[loss=0.1323, simple_loss=0.209, pruned_loss=0.02779, over 348726.24 frames.], batch size: 61, lr: 3.06e-04
+2022-06-19 05:27:59,043 INFO [train.py:874] (3/4) Epoch 27, batch 250, datatang_loss[loss=0.1384, simple_loss=0.2168, pruned_loss=0.02998, over 4966.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2228, pruned_loss=0.02871, over 703666.26 frames.], batch size: 60, aishell_tot_loss[loss=0.1456, simple_loss=0.2323, pruned_loss=0.0294, over 488776.52 frames.], datatang_tot_loss[loss=0.1329, simple_loss=0.2102, pruned_loss=0.02777, over 426617.02 frames.], batch size: 60, lr: 3.06e-04
+2022-06-19 05:28:28,391 INFO [train.py:874] (3/4) Epoch 27, batch 300, datatang_loss[loss=0.128, simple_loss=0.2019, pruned_loss=0.02701, over 4930.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2227, pruned_loss=0.0288, over 766172.16 frames.], batch size: 64, aishell_tot_loss[loss=0.1453, simple_loss=0.232, pruned_loss=0.02925, over 549351.85 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.211, pruned_loss=0.02811, over 490117.91 frames.], batch size: 64, lr: 3.06e-04
+2022-06-19 05:29:00,146 INFO [train.py:874] (3/4) Epoch 27, batch 350, datatang_loss[loss=0.1592, simple_loss=0.2389, pruned_loss=0.03973, over 4934.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2228, pruned_loss=0.02918, over 814648.94 frames.], batch size: 94, aishell_tot_loss[loss=0.1452, simple_loss=0.232, pruned_loss=0.02918, over 594484.81 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2122, pruned_loss=0.02883, over 555240.66 frames.], batch size: 94, lr: 3.06e-04
+2022-06-19 05:29:29,891 INFO [train.py:874] (3/4) Epoch 27, batch 400, datatang_loss[loss=0.1434, simple_loss=0.2178, pruned_loss=0.03454, over 4928.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2237, pruned_loss=0.02963, over 852565.72 frames.], batch size: 88, aishell_tot_loss[loss=0.1454, simple_loss=0.2322, pruned_loss=0.02927, over 635121.48 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.214, pruned_loss=0.02947, over 611827.27 frames.], batch size: 88, lr: 3.06e-04
+2022-06-19 05:29:59,167 INFO [train.py:874] (3/4) Epoch 27, batch 450, datatang_loss[loss=0.2099, simple_loss=0.2792, pruned_loss=0.07032, over 4920.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2225, pruned_loss=0.02953, over 882041.40 frames.], batch size: 107, aishell_tot_loss[loss=0.1445, simple_loss=0.2309, pruned_loss=0.02904, over 663474.26 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2147, pruned_loss=0.02959, over 669064.82 frames.], batch size: 107, lr: 3.06e-04
+2022-06-19 05:30:26,694 INFO [train.py:874] (3/4) Epoch 27, batch 500, datatang_loss[loss=0.1473, simple_loss=0.2189, pruned_loss=0.03788, over 4961.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2228, pruned_loss=0.02941, over 905105.50 frames.], batch size: 67, aishell_tot_loss[loss=0.1445, simple_loss=0.2311, pruned_loss=0.0289, over 699807.34 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2149, pruned_loss=0.02961, over 708031.57 frames.], batch size: 67, lr: 3.06e-04
+2022-06-19 05:30:56,953 INFO [train.py:874] (3/4) Epoch 27, batch 550, datatang_loss[loss=0.1278, simple_loss=0.2184, pruned_loss=0.0186, over 4956.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2212, pruned_loss=0.02905, over 922544.78 frames.], batch size: 86, aishell_tot_loss[loss=0.1436, simple_loss=0.2297, pruned_loss=0.02874, over 729399.06 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2145, pruned_loss=0.02933, over 744188.06 frames.], batch size: 86, lr: 3.06e-04
+2022-06-19 05:31:26,637 INFO [train.py:874] (3/4) Epoch 27, batch 600, datatang_loss[loss=0.1497, simple_loss=0.2223, pruned_loss=0.0385, over 4947.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2218, pruned_loss=0.02924, over 936366.89 frames.], batch size: 55, aishell_tot_loss[loss=0.1438, simple_loss=0.2297, pruned_loss=0.02895, over 763662.29 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2147, pruned_loss=0.02939, over 768567.48 frames.], batch size: 55, lr: 3.06e-04
+2022-06-19 05:31:54,500 INFO [train.py:874] (3/4) Epoch 27, batch 650, aishell_loss[loss=0.1491, simple_loss=0.2333, pruned_loss=0.03242, over 4948.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2219, pruned_loss=0.02922, over 946897.19 frames.], batch size: 32, aishell_tot_loss[loss=0.1438, simple_loss=0.2298, pruned_loss=0.02886, over 789473.83 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2145, pruned_loss=0.02947, over 794040.93 frames.], batch size: 32, lr: 3.05e-04
+2022-06-19 05:32:23,248 INFO [train.py:874] (3/4) Epoch 27, batch 700, datatang_loss[loss=0.1723, simple_loss=0.2438, pruned_loss=0.05045, over 4923.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2227, pruned_loss=0.02928, over 955727.07 frames.], batch size: 47, aishell_tot_loss[loss=0.1438, simple_loss=0.2297, pruned_loss=0.02898, over 818710.39 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.215, pruned_loss=0.02946, over 810661.33 frames.], batch size: 47, lr: 3.05e-04
+2022-06-19 05:32:53,872 INFO [train.py:874] (3/4) Epoch 27, batch 750, aishell_loss[loss=0.1528, simple_loss=0.2403, pruned_loss=0.03258, over 4918.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2235, pruned_loss=0.02942, over 962354.53 frames.], batch size: 52, aishell_tot_loss[loss=0.1441, simple_loss=0.2302, pruned_loss=0.02899, over 837619.61 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2157, pruned_loss=0.02965, over 832063.96 frames.], batch size: 52, lr: 3.05e-04
+2022-06-19 05:33:22,662 INFO [train.py:874] (3/4) Epoch 27, batch 800, aishell_loss[loss=0.1605, simple_loss=0.2425, pruned_loss=0.03925, over 4883.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2244, pruned_loss=0.0297, over 967488.66 frames.], batch size: 42, aishell_tot_loss[loss=0.1445, simple_loss=0.2309, pruned_loss=0.02904, over 854457.97 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2163, pruned_loss=0.02996, over 850739.35 frames.], batch size: 42, lr: 3.05e-04
+2022-06-19 05:33:51,886 INFO [train.py:874] (3/4) Epoch 27, batch 850, datatang_loss[loss=0.1572, simple_loss=0.2305, pruned_loss=0.04196, over 4917.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2238, pruned_loss=0.0295, over 971737.07 frames.], batch size: 98, aishell_tot_loss[loss=0.144, simple_loss=0.2305, pruned_loss=0.02878, over 870403.62 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2161, pruned_loss=0.03004, over 866353.21 frames.], batch size: 98, lr: 3.05e-04
+2022-06-19 05:34:20,058 INFO [train.py:874] (3/4) Epoch 27, batch 900, datatang_loss[loss=0.1445, simple_loss=0.2322, pruned_loss=0.02842, over 4930.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2224, pruned_loss=0.02915, over 974799.34 frames.], batch size: 94, aishell_tot_loss[loss=0.1435, simple_loss=0.2298, pruned_loss=0.02856, over 882667.40 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2154, pruned_loss=0.02985, over 881706.36 frames.], batch size: 94, lr: 3.05e-04
+2022-06-19 05:34:50,673 INFO [train.py:874] (3/4) Epoch 27, batch 950, datatang_loss[loss=0.1318, simple_loss=0.2175, pruned_loss=0.02305, over 4945.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2231, pruned_loss=0.02958, over 977482.37 frames.], batch size: 88, aishell_tot_loss[loss=0.1443, simple_loss=0.2306, pruned_loss=0.02906, over 893673.47 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2157, pruned_loss=0.02983, over 895354.94 frames.], batch size: 88, lr: 3.05e-04
+2022-06-19 05:35:20,662 INFO [train.py:874] (3/4) Epoch 27, batch 1000, datatang_loss[loss=0.1325, simple_loss=0.2117, pruned_loss=0.02666, over 4943.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2239, pruned_loss=0.02923, over 979616.22 frames.], batch size: 69, aishell_tot_loss[loss=0.1439, simple_loss=0.2304, pruned_loss=0.02866, over 907819.60 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2162, pruned_loss=0.02992, over 902949.69 frames.], batch size: 69, lr: 3.05e-04
+2022-06-19 05:35:20,663 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 05:35:36,380 INFO [train.py:914] (3/4) Epoch 27, validation: loss=0.1644, simple_loss=0.2483, pruned_loss=0.04028, over 1622729.00 frames.
+2022-06-19 05:36:07,028 INFO [train.py:874] (3/4) Epoch 27, batch 1050, aishell_loss[loss=0.1481, simple_loss=0.2368, pruned_loss=0.02968, over 4941.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2241, pruned_loss=0.02977, over 981065.62 frames.], batch size: 78, aishell_tot_loss[loss=0.1435, simple_loss=0.2302, pruned_loss=0.0284, over 916748.12 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2167, pruned_loss=0.03075, over 913070.68 frames.], batch size: 78, lr: 3.05e-04
+2022-06-19 05:36:36,813 INFO [train.py:874] (3/4) Epoch 27, batch 1100, aishell_loss[loss=0.1511, simple_loss=0.2338, pruned_loss=0.03419, over 4929.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2236, pruned_loss=0.02945, over 981867.81 frames.], batch size: 33, aishell_tot_loss[loss=0.1434, simple_loss=0.2301, pruned_loss=0.0284, over 925287.15 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2162, pruned_loss=0.03046, over 920893.55 frames.], batch size: 33, lr: 3.05e-04
+2022-06-19 05:37:06,349 INFO [train.py:874] (3/4) Epoch 27, batch 1150, datatang_loss[loss=0.1315, simple_loss=0.2068, pruned_loss=0.02806, over 4981.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2237, pruned_loss=0.02945, over 982621.06 frames.], batch size: 37, aishell_tot_loss[loss=0.1445, simple_loss=0.2311, pruned_loss=0.02894, over 932563.88 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2155, pruned_loss=0.02993, over 928216.41 frames.], batch size: 37, lr: 3.05e-04
+2022-06-19 05:37:36,220 INFO [train.py:874] (3/4) Epoch 27, batch 1200, aishell_loss[loss=0.1287, simple_loss=0.2204, pruned_loss=0.01848, over 4959.00 frames.], tot_loss[loss=0.1417, simple_loss=0.224, pruned_loss=0.02969, over 983112.65 frames.], batch size: 61, aishell_tot_loss[loss=0.1441, simple_loss=0.2308, pruned_loss=0.02871, over 938107.88 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2163, pruned_loss=0.03044, over 935534.97 frames.], batch size: 61, lr: 3.05e-04
+2022-06-19 05:38:04,631 INFO [train.py:874] (3/4) Epoch 27, batch 1250, aishell_loss[loss=0.1404, simple_loss=0.2315, pruned_loss=0.02464, over 4940.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2233, pruned_loss=0.02979, over 983699.02 frames.], batch size: 58, aishell_tot_loss[loss=0.1438, simple_loss=0.2302, pruned_loss=0.02866, over 943163.57 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2164, pruned_loss=0.03062, over 942029.91 frames.], batch size: 58, lr: 3.05e-04
+2022-06-19 05:38:35,004 INFO [train.py:874] (3/4) Epoch 27, batch 1300, aishell_loss[loss=0.1397, simple_loss=0.2412, pruned_loss=0.01909, over 4960.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2235, pruned_loss=0.03014, over 984306.90 frames.], batch size: 40, aishell_tot_loss[loss=0.144, simple_loss=0.2304, pruned_loss=0.0288, over 948090.30 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2165, pruned_loss=0.03091, over 947439.59 frames.], batch size: 40, lr: 3.05e-04
+2022-06-19 05:39:05,115 INFO [train.py:874] (3/4) Epoch 27, batch 1350, datatang_loss[loss=0.126, simple_loss=0.1955, pruned_loss=0.02823, over 4958.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2228, pruned_loss=0.03019, over 984587.91 frames.], batch size: 55, aishell_tot_loss[loss=0.1439, simple_loss=0.2303, pruned_loss=0.02877, over 950868.96 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2166, pruned_loss=0.03096, over 953523.47 frames.], batch size: 55, lr: 3.04e-04
+2022-06-19 05:39:34,210 INFO [train.py:874] (3/4) Epoch 27, batch 1400, datatang_loss[loss=0.1243, simple_loss=0.1896, pruned_loss=0.0295, over 4897.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2225, pruned_loss=0.02995, over 984838.60 frames.], batch size: 52, aishell_tot_loss[loss=0.144, simple_loss=0.2303, pruned_loss=0.02888, over 954794.37 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2161, pruned_loss=0.03068, over 957495.35 frames.], batch size: 52, lr: 3.04e-04
+2022-06-19 05:40:02,660 INFO [train.py:874] (3/4) Epoch 27, batch 1450, datatang_loss[loss=0.1226, simple_loss=0.1995, pruned_loss=0.0228, over 4920.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2229, pruned_loss=0.02984, over 985134.33 frames.], batch size: 77, aishell_tot_loss[loss=0.144, simple_loss=0.2304, pruned_loss=0.02882, over 958119.24 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2164, pruned_loss=0.03065, over 961208.00 frames.], batch size: 77, lr: 3.04e-04
+2022-06-19 05:40:32,276 INFO [train.py:874] (3/4) Epoch 27, batch 1500, aishell_loss[loss=0.1606, simple_loss=0.2398, pruned_loss=0.04073, over 4867.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2242, pruned_loss=0.0302, over 985170.83 frames.], batch size: 35, aishell_tot_loss[loss=0.1449, simple_loss=0.2314, pruned_loss=0.0292, over 961286.97 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.03069, over 964072.12 frames.], batch size: 35, lr: 3.04e-04
+2022-06-19 05:41:00,516 INFO [train.py:874] (3/4) Epoch 27, batch 1550, aishell_loss[loss=0.1598, simple_loss=0.2473, pruned_loss=0.03617, over 4862.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2242, pruned_loss=0.03024, over 985195.81 frames.], batch size: 35, aishell_tot_loss[loss=0.1447, simple_loss=0.2311, pruned_loss=0.02911, over 963786.27 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2171, pruned_loss=0.03086, over 966872.89 frames.], batch size: 35, lr: 3.04e-04
+2022-06-19 05:41:29,487 INFO [train.py:874] (3/4) Epoch 27, batch 1600, aishell_loss[loss=0.1484, simple_loss=0.2371, pruned_loss=0.02986, over 4968.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2236, pruned_loss=0.02965, over 984712.85 frames.], batch size: 44, aishell_tot_loss[loss=0.1438, simple_loss=0.2303, pruned_loss=0.02864, over 966369.90 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2169, pruned_loss=0.0308, over 968529.84 frames.], batch size: 44, lr: 3.04e-04
+2022-06-19 05:41:58,670 INFO [train.py:874] (3/4) Epoch 27, batch 1650, aishell_loss[loss=0.1388, simple_loss=0.2284, pruned_loss=0.02463, over 4921.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2248, pruned_loss=0.03021, over 985298.31 frames.], batch size: 52, aishell_tot_loss[loss=0.145, simple_loss=0.2315, pruned_loss=0.0292, over 969013.61 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.217, pruned_loss=0.03084, over 970571.02 frames.], batch size: 52, lr: 3.04e-04
+2022-06-19 05:42:26,858 INFO [train.py:874] (3/4) Epoch 27, batch 1700, aishell_loss[loss=0.1523, simple_loss=0.2438, pruned_loss=0.03041, over 4946.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2256, pruned_loss=0.03081, over 985766.58 frames.], batch size: 45, aishell_tot_loss[loss=0.1453, simple_loss=0.2317, pruned_loss=0.02941, over 971180.33 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2179, pruned_loss=0.03133, over 972553.21 frames.], batch size: 45, lr: 3.04e-04
+2022-06-19 05:42:56,270 INFO [train.py:874] (3/4) Epoch 27, batch 1750, datatang_loss[loss=0.1371, simple_loss=0.2226, pruned_loss=0.02583, over 4918.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2254, pruned_loss=0.03018, over 985930.59 frames.], batch size: 81, aishell_tot_loss[loss=0.1451, simple_loss=0.2317, pruned_loss=0.02925, over 973210.59 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2177, pruned_loss=0.03093, over 974007.89 frames.], batch size: 81, lr: 3.04e-04
+2022-06-19 05:43:25,983 INFO [train.py:874] (3/4) Epoch 27, batch 1800, aishell_loss[loss=0.1406, simple_loss=0.2338, pruned_loss=0.02373, over 4919.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2254, pruned_loss=0.03035, over 985732.32 frames.], batch size: 41, aishell_tot_loss[loss=0.1454, simple_loss=0.2319, pruned_loss=0.02949, over 974361.12 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2177, pruned_loss=0.0309, over 975542.35 frames.], batch size: 41, lr: 3.04e-04
+2022-06-19 05:43:54,030 INFO [train.py:874] (3/4) Epoch 27, batch 1850, aishell_loss[loss=0.1521, simple_loss=0.2386, pruned_loss=0.03285, over 4945.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2259, pruned_loss=0.03013, over 985686.56 frames.], batch size: 45, aishell_tot_loss[loss=0.1451, simple_loss=0.2319, pruned_loss=0.02922, over 975793.43 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2179, pruned_loss=0.03102, over 976619.08 frames.], batch size: 45, lr: 3.04e-04
+2022-06-19 05:44:24,538 INFO [train.py:874] (3/4) Epoch 27, batch 1900, aishell_loss[loss=0.1577, simple_loss=0.2403, pruned_loss=0.03755, over 4914.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2248, pruned_loss=0.02984, over 986080.95 frames.], batch size: 52, aishell_tot_loss[loss=0.1442, simple_loss=0.2306, pruned_loss=0.02891, over 977202.57 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2182, pruned_loss=0.03103, over 977866.43 frames.], batch size: 52, lr: 3.04e-04
+2022-06-19 05:44:54,851 INFO [train.py:874] (3/4) Epoch 27, batch 1950, aishell_loss[loss=0.1419, simple_loss=0.2332, pruned_loss=0.0253, over 4872.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2242, pruned_loss=0.02935, over 985729.11 frames.], batch size: 28, aishell_tot_loss[loss=0.1437, simple_loss=0.23, pruned_loss=0.02865, over 977957.01 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.218, pruned_loss=0.03077, over 978755.45 frames.], batch size: 28, lr: 3.04e-04
+2022-06-19 05:45:24,380 INFO [train.py:874] (3/4) Epoch 27, batch 2000, datatang_loss[loss=0.1777, simple_loss=0.2416, pruned_loss=0.05685, over 4957.00 frames.], tot_loss[loss=0.141, simple_loss=0.2239, pruned_loss=0.02907, over 985894.88 frames.], batch size: 86, aishell_tot_loss[loss=0.1433, simple_loss=0.23, pruned_loss=0.02827, over 978917.27 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2178, pruned_loss=0.03075, over 979710.08 frames.], batch size: 86, lr: 3.04e-04
+2022-06-19 05:45:24,381 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 05:45:41,440 INFO [train.py:914] (3/4) Epoch 27, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04011, over 1622729.00 frames.
+2022-06-19 05:46:10,070 INFO [train.py:874] (3/4) Epoch 27, batch 2050, aishell_loss[loss=0.1292, simple_loss=0.2075, pruned_loss=0.02546, over 4981.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2234, pruned_loss=0.0291, over 985568.54 frames.], batch size: 27, aishell_tot_loss[loss=0.1434, simple_loss=0.2302, pruned_loss=0.02828, over 979361.24 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2172, pruned_loss=0.03062, over 980458.57 frames.], batch size: 27, lr: 3.04e-04
+2022-06-19 05:46:39,371 INFO [train.py:874] (3/4) Epoch 27, batch 2100, datatang_loss[loss=0.1416, simple_loss=0.2291, pruned_loss=0.02708, over 4961.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2232, pruned_loss=0.02897, over 984940.51 frames.], batch size: 91, aishell_tot_loss[loss=0.1435, simple_loss=0.23, pruned_loss=0.02848, over 979543.56 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2168, pruned_loss=0.03025, over 980964.36 frames.], batch size: 91, lr: 3.03e-04
+2022-06-19 05:47:08,913 INFO [train.py:874] (3/4) Epoch 27, batch 2150, aishell_loss[loss=0.1243, simple_loss=0.2079, pruned_loss=0.02039, over 4974.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2234, pruned_loss=0.02915, over 984889.49 frames.], batch size: 30, aishell_tot_loss[loss=0.1437, simple_loss=0.2303, pruned_loss=0.02858, over 980025.94 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2167, pruned_loss=0.03025, over 981526.36 frames.], batch size: 30, lr: 3.03e-04
+2022-06-19 05:47:37,128 INFO [train.py:874] (3/4) Epoch 27, batch 2200, aishell_loss[loss=0.1586, simple_loss=0.2469, pruned_loss=0.03512, over 4863.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2236, pruned_loss=0.02889, over 985028.93 frames.], batch size: 36, aishell_tot_loss[loss=0.1438, simple_loss=0.2306, pruned_loss=0.02851, over 980616.14 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2165, pruned_loss=0.02999, over 982076.06 frames.], batch size: 36, lr: 3.03e-04
+2022-06-19 05:48:07,809 INFO [train.py:874] (3/4) Epoch 27, batch 2250, aishell_loss[loss=0.1438, simple_loss=0.2308, pruned_loss=0.02843, over 4964.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2234, pruned_loss=0.02856, over 985288.92 frames.], batch size: 40, aishell_tot_loss[loss=0.1436, simple_loss=0.2307, pruned_loss=0.02828, over 981321.27 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2163, pruned_loss=0.02978, over 982497.47 frames.], batch size: 40, lr: 3.03e-04
+2022-06-19 05:48:35,823 INFO [train.py:874] (3/4) Epoch 27, batch 2300, aishell_loss[loss=0.125, simple_loss=0.2047, pruned_loss=0.02267, over 4982.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2238, pruned_loss=0.02863, over 985266.72 frames.], batch size: 25, aishell_tot_loss[loss=0.1441, simple_loss=0.2312, pruned_loss=0.02847, over 981818.83 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2159, pruned_loss=0.02959, over 982797.43 frames.], batch size: 25, lr: 3.03e-04
+2022-06-19 05:49:05,345 INFO [train.py:874] (3/4) Epoch 27, batch 2350, aishell_loss[loss=0.1366, simple_loss=0.2299, pruned_loss=0.02161, over 4949.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2243, pruned_loss=0.02923, over 985102.48 frames.], batch size: 56, aishell_tot_loss[loss=0.1441, simple_loss=0.2311, pruned_loss=0.02851, over 982263.18 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2166, pruned_loss=0.03006, over 982871.98 frames.], batch size: 56, lr: 3.03e-04
+2022-06-19 05:49:34,610 INFO [train.py:874] (3/4) Epoch 27, batch 2400, aishell_loss[loss=0.1426, simple_loss=0.2353, pruned_loss=0.02493, over 4928.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2236, pruned_loss=0.02879, over 985334.88 frames.], batch size: 33, aishell_tot_loss[loss=0.1441, simple_loss=0.2311, pruned_loss=0.02857, over 982831.78 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2158, pruned_loss=0.02953, over 983145.90 frames.], batch size: 33, lr: 3.03e-04
+2022-06-19 05:50:01,645 INFO [train.py:874] (3/4) Epoch 27, batch 2450, datatang_loss[loss=0.115, simple_loss=0.1991, pruned_loss=0.01544, over 4941.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2239, pruned_loss=0.02883, over 985033.22 frames.], batch size: 69, aishell_tot_loss[loss=0.1441, simple_loss=0.231, pruned_loss=0.02863, over 982785.27 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2161, pruned_loss=0.02945, over 983447.53 frames.], batch size: 69, lr: 3.03e-04
+2022-06-19 05:50:31,003 INFO [train.py:874] (3/4) Epoch 27, batch 2500, aishell_loss[loss=0.1232, simple_loss=0.192, pruned_loss=0.02724, over 4877.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2232, pruned_loss=0.02902, over 984948.06 frames.], batch size: 21, aishell_tot_loss[loss=0.1439, simple_loss=0.2304, pruned_loss=0.02868, over 982895.63 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2161, pruned_loss=0.02955, over 983699.20 frames.], batch size: 21, lr: 3.03e-04
+2022-06-19 05:51:00,258 INFO [train.py:874] (3/4) Epoch 27, batch 2550, datatang_loss[loss=0.1195, simple_loss=0.1984, pruned_loss=0.02036, over 4895.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2227, pruned_loss=0.02878, over 984871.48 frames.], batch size: 47, aishell_tot_loss[loss=0.1437, simple_loss=0.2303, pruned_loss=0.02858, over 983253.73 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2157, pruned_loss=0.02936, over 983648.68 frames.], batch size: 47, lr: 3.03e-04
+2022-06-19 05:51:28,173 INFO [train.py:874] (3/4) Epoch 27, batch 2600, aishell_loss[loss=0.1272, simple_loss=0.2152, pruned_loss=0.01961, over 4939.00 frames.], tot_loss[loss=0.1402, simple_loss=0.223, pruned_loss=0.02872, over 985207.00 frames.], batch size: 54, aishell_tot_loss[loss=0.1433, simple_loss=0.2298, pruned_loss=0.02839, over 983680.94 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2159, pruned_loss=0.02948, over 983923.39 frames.], batch size: 54, lr: 3.03e-04
+2022-06-19 05:51:58,812 INFO [train.py:874] (3/4) Epoch 27, batch 2650, aishell_loss[loss=0.1225, simple_loss=0.2172, pruned_loss=0.01388, over 4882.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2227, pruned_loss=0.02879, over 985059.99 frames.], batch size: 50, aishell_tot_loss[loss=0.1437, simple_loss=0.2303, pruned_loss=0.02857, over 983626.17 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2155, pruned_loss=0.02932, over 984155.46 frames.], batch size: 50, lr: 3.03e-04
+2022-06-19 05:52:27,315 INFO [train.py:874] (3/4) Epoch 27, batch 2700, datatang_loss[loss=0.1269, simple_loss=0.2045, pruned_loss=0.02468, over 4908.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2225, pruned_loss=0.02837, over 985577.41 frames.], batch size: 52, aishell_tot_loss[loss=0.1434, simple_loss=0.2303, pruned_loss=0.02826, over 984132.12 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.215, pruned_loss=0.02916, over 984484.73 frames.], batch size: 52, lr: 3.03e-04
+2022-06-19 05:52:55,902 INFO [train.py:874] (3/4) Epoch 27, batch 2750, aishell_loss[loss=0.147, simple_loss=0.231, pruned_loss=0.03149, over 4951.00 frames.], tot_loss[loss=0.14, simple_loss=0.2227, pruned_loss=0.02871, over 985476.94 frames.], batch size: 31, aishell_tot_loss[loss=0.1439, simple_loss=0.231, pruned_loss=0.02843, over 984151.83 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2146, pruned_loss=0.02926, over 984653.73 frames.], batch size: 31, lr: 3.03e-04
+2022-06-19 05:53:25,477 INFO [train.py:874] (3/4) Epoch 27, batch 2800, aishell_loss[loss=0.1298, simple_loss=0.2125, pruned_loss=0.02355, over 4969.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2219, pruned_loss=0.02873, over 985155.79 frames.], batch size: 27, aishell_tot_loss[loss=0.1428, simple_loss=0.2296, pruned_loss=0.02804, over 983873.81 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2151, pruned_loss=0.02963, over 984839.00 frames.], batch size: 27, lr: 3.02e-04
+2022-06-19 05:53:55,472 INFO [train.py:874] (3/4) Epoch 27, batch 2850, datatang_loss[loss=0.1078, simple_loss=0.1897, pruned_loss=0.01294, over 4830.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2211, pruned_loss=0.0287, over 984647.49 frames.], batch size: 30, aishell_tot_loss[loss=0.1422, simple_loss=0.2284, pruned_loss=0.02793, over 983345.70 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2153, pruned_loss=0.02968, over 985028.94 frames.], batch size: 30, lr: 3.02e-04
+2022-06-19 05:54:23,641 INFO [train.py:874] (3/4) Epoch 27, batch 2900, datatang_loss[loss=0.1322, simple_loss=0.2136, pruned_loss=0.02537, over 4921.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2211, pruned_loss=0.02881, over 984728.74 frames.], batch size: 75, aishell_tot_loss[loss=0.1426, simple_loss=0.2289, pruned_loss=0.02821, over 983476.18 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2147, pruned_loss=0.02951, over 985094.29 frames.], batch size: 75, lr: 3.02e-04
+2022-06-19 05:54:53,407 INFO [train.py:874] (3/4) Epoch 27, batch 2950, datatang_loss[loss=0.1802, simple_loss=0.2594, pruned_loss=0.05047, over 4948.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2221, pruned_loss=0.0292, over 985302.84 frames.], batch size: 108, aishell_tot_loss[loss=0.1432, simple_loss=0.2296, pruned_loss=0.02838, over 983970.41 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2151, pruned_loss=0.02971, over 985298.11 frames.], batch size: 108, lr: 3.02e-04
+2022-06-19 05:55:22,539 INFO [train.py:874] (3/4) Epoch 27, batch 3000, aishell_loss[loss=0.1498, simple_loss=0.2438, pruned_loss=0.02788, over 4979.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2223, pruned_loss=0.02908, over 985524.99 frames.], batch size: 51, aishell_tot_loss[loss=0.1434, simple_loss=0.23, pruned_loss=0.02838, over 984038.48 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2152, pruned_loss=0.02961, over 985593.21 frames.], batch size: 51, lr: 3.02e-04
+2022-06-19 05:55:22,539 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 05:55:39,145 INFO [train.py:914] (3/4) Epoch 27, validation: loss=0.1642, simple_loss=0.2486, pruned_loss=0.03985, over 1622729.00 frames.
+2022-06-19 05:56:10,080 INFO [train.py:874] (3/4) Epoch 27, batch 3050, datatang_loss[loss=0.1407, simple_loss=0.2259, pruned_loss=0.02775, over 4935.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2227, pruned_loss=0.02892, over 985574.07 frames.], batch size: 88, aishell_tot_loss[loss=0.1434, simple_loss=0.2302, pruned_loss=0.02833, over 984183.85 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2153, pruned_loss=0.02949, over 985660.19 frames.], batch size: 88, lr: 3.02e-04
+2022-06-19 05:56:38,540 INFO [train.py:874] (3/4) Epoch 27, batch 3100, datatang_loss[loss=0.1547, simple_loss=0.2184, pruned_loss=0.04554, over 4846.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2227, pruned_loss=0.02882, over 985288.82 frames.], batch size: 33, aishell_tot_loss[loss=0.1434, simple_loss=0.2302, pruned_loss=0.02829, over 984347.63 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2154, pruned_loss=0.02942, over 985330.16 frames.], batch size: 33, lr: 3.02e-04
+2022-06-19 05:57:09,325 INFO [train.py:874] (3/4) Epoch 27, batch 3150, datatang_loss[loss=0.1388, simple_loss=0.2124, pruned_loss=0.03262, over 4881.00 frames.], tot_loss[loss=0.14, simple_loss=0.2225, pruned_loss=0.02873, over 985229.25 frames.], batch size: 47, aishell_tot_loss[loss=0.1433, simple_loss=0.2303, pruned_loss=0.02818, over 984621.72 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2153, pruned_loss=0.0294, over 985103.84 frames.], batch size: 47, lr: 3.02e-04
+2022-06-19 05:57:40,487 INFO [train.py:874] (3/4) Epoch 27, batch 3200, datatang_loss[loss=0.1522, simple_loss=0.23, pruned_loss=0.03722, over 4930.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2239, pruned_loss=0.02946, over 985090.53 frames.], batch size: 88, aishell_tot_loss[loss=0.1447, simple_loss=0.2315, pruned_loss=0.02895, over 984512.83 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2154, pruned_loss=0.0294, over 985150.40 frames.], batch size: 88, lr: 3.02e-04
+2022-06-19 05:58:09,161 INFO [train.py:874] (3/4) Epoch 27, batch 3250, aishell_loss[loss=0.1288, simple_loss=0.2205, pruned_loss=0.01858, over 4976.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2237, pruned_loss=0.02889, over 985391.17 frames.], batch size: 51, aishell_tot_loss[loss=0.1439, simple_loss=0.2311, pruned_loss=0.02833, over 984581.51 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2156, pruned_loss=0.02949, over 985469.54 frames.], batch size: 51, lr: 3.02e-04
+2022-06-19 05:58:39,179 INFO [train.py:874] (3/4) Epoch 27, batch 3300, aishell_loss[loss=0.1315, simple_loss=0.2264, pruned_loss=0.01835, over 4963.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2232, pruned_loss=0.02865, over 985750.89 frames.], batch size: 61, aishell_tot_loss[loss=0.1435, simple_loss=0.2308, pruned_loss=0.02813, over 984849.57 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2154, pruned_loss=0.02944, over 985671.15 frames.], batch size: 61, lr: 3.02e-04
+2022-06-19 05:59:09,435 INFO [train.py:874] (3/4) Epoch 27, batch 3350, aishell_loss[loss=0.1329, simple_loss=0.2211, pruned_loss=0.02239, over 4966.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2233, pruned_loss=0.02862, over 985855.77 frames.], batch size: 30, aishell_tot_loss[loss=0.1436, simple_loss=0.2312, pruned_loss=0.028, over 984867.62 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2155, pruned_loss=0.02944, over 985842.59 frames.], batch size: 30, lr: 3.02e-04
+2022-06-19 05:59:36,786 INFO [train.py:874] (3/4) Epoch 27, batch 3400, aishell_loss[loss=0.1328, simple_loss=0.2266, pruned_loss=0.0195, over 4919.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2226, pruned_loss=0.02896, over 985641.32 frames.], batch size: 41, aishell_tot_loss[loss=0.1443, simple_loss=0.2316, pruned_loss=0.02851, over 984779.82 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2148, pruned_loss=0.02925, over 985808.37 frames.], batch size: 41, lr: 3.02e-04
+2022-06-19 06:00:05,985 INFO [train.py:874] (3/4) Epoch 27, batch 3450, aishell_loss[loss=0.1604, simple_loss=0.2503, pruned_loss=0.03522, over 4951.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2227, pruned_loss=0.02905, over 985720.26 frames.], batch size: 78, aishell_tot_loss[loss=0.1441, simple_loss=0.2311, pruned_loss=0.02857, over 985177.70 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2151, pruned_loss=0.02929, over 985593.44 frames.], batch size: 78, lr: 3.02e-04
+2022-06-19 06:00:35,362 INFO [train.py:874] (3/4) Epoch 27, batch 3500, aishell_loss[loss=0.1312, simple_loss=0.2108, pruned_loss=0.02573, over 4860.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2239, pruned_loss=0.0294, over 985437.38 frames.], batch size: 28, aishell_tot_loss[loss=0.1449, simple_loss=0.232, pruned_loss=0.0289, over 984745.07 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2156, pruned_loss=0.02936, over 985794.06 frames.], batch size: 28, lr: 3.02e-04
+2022-06-19 06:01:04,258 INFO [train.py:874] (3/4) Epoch 27, batch 3550, datatang_loss[loss=0.1468, simple_loss=0.2247, pruned_loss=0.03443, over 4942.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2231, pruned_loss=0.0291, over 985719.11 frames.], batch size: 62, aishell_tot_loss[loss=0.1448, simple_loss=0.2319, pruned_loss=0.02883, over 985013.24 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2153, pruned_loss=0.02917, over 985831.60 frames.], batch size: 62, lr: 3.01e-04
+2022-06-19 06:01:33,731 INFO [train.py:874] (3/4) Epoch 27, batch 3600, aishell_loss[loss=0.1596, simple_loss=0.2448, pruned_loss=0.03723, over 4939.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2243, pruned_loss=0.03007, over 985794.17 frames.], batch size: 45, aishell_tot_loss[loss=0.145, simple_loss=0.232, pruned_loss=0.02899, over 984916.10 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2164, pruned_loss=0.03004, over 986065.60 frames.], batch size: 45, lr: 3.01e-04
+2022-06-19 06:02:05,272 INFO [train.py:874] (3/4) Epoch 27, batch 3650, aishell_loss[loss=0.1519, simple_loss=0.2391, pruned_loss=0.03238, over 4863.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2235, pruned_loss=0.02988, over 985741.06 frames.], batch size: 35, aishell_tot_loss[loss=0.1451, simple_loss=0.2321, pruned_loss=0.02901, over 984998.09 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.216, pruned_loss=0.02991, over 985972.54 frames.], batch size: 35, lr: 3.01e-04
+2022-06-19 06:02:32,483 INFO [train.py:874] (3/4) Epoch 27, batch 3700, datatang_loss[loss=0.1456, simple_loss=0.2234, pruned_loss=0.03385, over 4946.00 frames.], tot_loss[loss=0.1418, simple_loss=0.224, pruned_loss=0.02977, over 985391.21 frames.], batch size: 50, aishell_tot_loss[loss=0.1453, simple_loss=0.2324, pruned_loss=0.02912, over 984747.67 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.216, pruned_loss=0.02977, over 985919.64 frames.], batch size: 50, lr: 3.01e-04
+2022-06-19 06:03:03,271 INFO [train.py:874] (3/4) Epoch 27, batch 3750, datatang_loss[loss=0.1433, simple_loss=0.2147, pruned_loss=0.03597, over 4915.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2236, pruned_loss=0.02992, over 985045.96 frames.], batch size: 42, aishell_tot_loss[loss=0.145, simple_loss=0.2321, pruned_loss=0.0289, over 984737.00 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2159, pruned_loss=0.0302, over 985578.06 frames.], batch size: 42, lr: 3.01e-04
+2022-06-19 06:03:32,618 INFO [train.py:874] (3/4) Epoch 27, batch 3800, aishell_loss[loss=0.1487, simple_loss=0.2444, pruned_loss=0.02655, over 4941.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2241, pruned_loss=0.02954, over 985135.98 frames.], batch size: 54, aishell_tot_loss[loss=0.1451, simple_loss=0.2324, pruned_loss=0.02891, over 984785.20 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2159, pruned_loss=0.02988, over 985612.92 frames.], batch size: 54, lr: 3.01e-04
+2022-06-19 06:04:01,612 INFO [train.py:874] (3/4) Epoch 27, batch 3850, datatang_loss[loss=0.1216, simple_loss=0.206, pruned_loss=0.01862, over 4916.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2228, pruned_loss=0.02925, over 984794.55 frames.], batch size: 75, aishell_tot_loss[loss=0.1443, simple_loss=0.2312, pruned_loss=0.02871, over 984343.07 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.216, pruned_loss=0.02979, over 985656.55 frames.], batch size: 75, lr: 3.01e-04
+2022-06-19 06:04:28,994 INFO [train.py:874] (3/4) Epoch 27, batch 3900, aishell_loss[loss=0.1464, simple_loss=0.2473, pruned_loss=0.02272, over 4913.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2241, pruned_loss=0.02928, over 984852.24 frames.], batch size: 41, aishell_tot_loss[loss=0.1445, simple_loss=0.2318, pruned_loss=0.02859, over 984319.56 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.216, pruned_loss=0.02997, over 985746.98 frames.], batch size: 41, lr: 3.01e-04
+2022-06-19 06:04:58,556 INFO [train.py:874] (3/4) Epoch 27, batch 3950, aishell_loss[loss=0.1752, simple_loss=0.255, pruned_loss=0.04772, over 4956.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2233, pruned_loss=0.02909, over 984898.97 frames.], batch size: 40, aishell_tot_loss[loss=0.1443, simple_loss=0.2315, pruned_loss=0.02854, over 984227.41 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2159, pruned_loss=0.02979, over 985819.86 frames.], batch size: 40, lr: 3.01e-04
+2022-06-19 06:05:27,306 INFO [train.py:874] (3/4) Epoch 27, batch 4000, datatang_loss[loss=0.1771, simple_loss=0.2577, pruned_loss=0.0482, over 4956.00 frames.], tot_loss[loss=0.141, simple_loss=0.2234, pruned_loss=0.02933, over 984798.97 frames.], batch size: 109, aishell_tot_loss[loss=0.1444, simple_loss=0.2313, pruned_loss=0.02876, over 984181.51 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2159, pruned_loss=0.02979, over 985745.90 frames.], batch size: 109, lr: 3.01e-04
+2022-06-19 06:05:27,307 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 06:05:43,551 INFO [train.py:914] (3/4) Epoch 27, validation: loss=0.1646, simple_loss=0.249, pruned_loss=0.0401, over 1622729.00 frames.
+2022-06-19 06:06:11,911 INFO [train.py:874] (3/4) Epoch 27, batch 4050, aishell_loss[loss=0.1474, simple_loss=0.2417, pruned_loss=0.02654, over 4954.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2236, pruned_loss=0.02902, over 985476.80 frames.], batch size: 64, aishell_tot_loss[loss=0.1447, simple_loss=0.2318, pruned_loss=0.02877, over 984674.45 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2156, pruned_loss=0.02947, over 985927.72 frames.], batch size: 64, lr: 3.01e-04
+2022-06-19 06:06:38,055 INFO [train.py:874] (3/4) Epoch 27, batch 4100, datatang_loss[loss=0.1284, simple_loss=0.2089, pruned_loss=0.02397, over 4955.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2239, pruned_loss=0.02884, over 985549.10 frames.], batch size: 60, aishell_tot_loss[loss=0.1448, simple_loss=0.2321, pruned_loss=0.02878, over 984788.19 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2154, pruned_loss=0.02925, over 985947.19 frames.], batch size: 60, lr: 3.01e-04
+2022-06-19 06:07:06,631 INFO [train.py:874] (3/4) Epoch 27, batch 4150, aishell_loss[loss=0.1257, simple_loss=0.2175, pruned_loss=0.017, over 4901.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2237, pruned_loss=0.02881, over 985164.88 frames.], batch size: 60, aishell_tot_loss[loss=0.1442, simple_loss=0.2318, pruned_loss=0.02834, over 984522.45 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2158, pruned_loss=0.02958, over 985829.03 frames.], batch size: 60, lr: 3.01e-04
+2022-06-19 06:08:12,734 INFO [train.py:874] (3/4) Epoch 28, batch 50, datatang_loss[loss=0.125, simple_loss=0.2024, pruned_loss=0.02375, over 4916.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2196, pruned_loss=0.02706, over 218434.74 frames.], batch size: 81, aishell_tot_loss[loss=0.14, simple_loss=0.2281, pruned_loss=0.02597, over 120285.25 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.2107, pruned_loss=0.02826, over 111792.90 frames.], batch size: 81, lr: 2.95e-04
+2022-06-19 06:08:39,697 INFO [train.py:874] (3/4) Epoch 28, batch 100, aishell_loss[loss=0.1514, simple_loss=0.2437, pruned_loss=0.02953, over 4878.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2204, pruned_loss=0.02716, over 388503.35 frames.], batch size: 42, aishell_tot_loss[loss=0.143, simple_loss=0.2312, pruned_loss=0.02746, over 229773.69 frames.], datatang_tot_loss[loss=0.1309, simple_loss=0.2081, pruned_loss=0.02682, over 206992.97 frames.], batch size: 42, lr: 2.95e-04
+2022-06-19 06:09:09,745 INFO [train.py:874] (3/4) Epoch 28, batch 150, aishell_loss[loss=0.1451, simple_loss=0.2377, pruned_loss=0.0262, over 4880.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2216, pruned_loss=0.02786, over 520380.16 frames.], batch size: 47, aishell_tot_loss[loss=0.1435, simple_loss=0.2315, pruned_loss=0.02776, over 334952.93 frames.], datatang_tot_loss[loss=0.1321, simple_loss=0.2087, pruned_loss=0.02775, over 281102.88 frames.], batch size: 47, lr: 2.95e-04
+2022-06-19 06:09:42,584 INFO [train.py:874] (3/4) Epoch 28, batch 200, datatang_loss[loss=0.1729, simple_loss=0.2444, pruned_loss=0.05065, over 4920.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2226, pruned_loss=0.02878, over 623528.14 frames.], batch size: 34, aishell_tot_loss[loss=0.1442, simple_loss=0.2316, pruned_loss=0.02839, over 422840.72 frames.], datatang_tot_loss[loss=0.1339, simple_loss=0.2103, pruned_loss=0.02874, over 351643.93 frames.], batch size: 34, lr: 2.95e-04
+2022-06-19 06:10:12,335 INFO [train.py:874] (3/4) Epoch 28, batch 250, datatang_loss[loss=0.1329, simple_loss=0.2099, pruned_loss=0.02792, over 4935.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2213, pruned_loss=0.02812, over 704050.69 frames.], batch size: 79, aishell_tot_loss[loss=0.1434, simple_loss=0.2308, pruned_loss=0.028, over 484270.03 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2103, pruned_loss=0.02818, over 432005.26 frames.], batch size: 79, lr: 2.95e-04
+2022-06-19 06:10:42,409 INFO [train.py:874] (3/4) Epoch 28, batch 300, aishell_loss[loss=0.1405, simple_loss=0.2177, pruned_loss=0.03164, over 4974.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2206, pruned_loss=0.02813, over 766456.39 frames.], batch size: 51, aishell_tot_loss[loss=0.1432, simple_loss=0.2301, pruned_loss=0.02819, over 545519.75 frames.], datatang_tot_loss[loss=0.133, simple_loss=0.2101, pruned_loss=0.02798, over 494751.04 frames.], batch size: 51, lr: 2.95e-04
+2022-06-19 06:11:09,591 INFO [train.py:874] (3/4) Epoch 28, batch 350, datatang_loss[loss=0.1458, simple_loss=0.2151, pruned_loss=0.03823, over 4895.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2199, pruned_loss=0.0279, over 815175.21 frames.], batch size: 52, aishell_tot_loss[loss=0.1427, simple_loss=0.2295, pruned_loss=0.02799, over 587406.59 frames.], datatang_tot_loss[loss=0.1332, simple_loss=0.2106, pruned_loss=0.02787, over 563509.87 frames.], batch size: 52, lr: 2.95e-04
+2022-06-19 06:11:39,742 INFO [train.py:874] (3/4) Epoch 28, batch 400, aishell_loss[loss=0.1457, simple_loss=0.2333, pruned_loss=0.02909, over 4861.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2195, pruned_loss=0.02831, over 852952.43 frames.], batch size: 37, aishell_tot_loss[loss=0.1418, simple_loss=0.228, pruned_loss=0.02785, over 628976.71 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.212, pruned_loss=0.0286, over 618776.95 frames.], batch size: 37, lr: 2.95e-04
+2022-06-19 06:12:10,385 INFO [train.py:874] (3/4) Epoch 28, batch 450, datatang_loss[loss=0.1204, simple_loss=0.1935, pruned_loss=0.02369, over 4918.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2194, pruned_loss=0.028, over 882623.29 frames.], batch size: 75, aishell_tot_loss[loss=0.1425, simple_loss=0.2288, pruned_loss=0.02814, over 666283.85 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.2114, pruned_loss=0.02791, over 667000.24 frames.], batch size: 75, lr: 2.95e-04
+2022-06-19 06:12:37,885 INFO [train.py:874] (3/4) Epoch 28, batch 500, aishell_loss[loss=0.1525, simple_loss=0.2424, pruned_loss=0.03134, over 4938.00 frames.], tot_loss[loss=0.1368, simple_loss=0.2188, pruned_loss=0.02738, over 905572.35 frames.], batch size: 54, aishell_tot_loss[loss=0.142, simple_loss=0.2282, pruned_loss=0.02791, over 708027.90 frames.], datatang_tot_loss[loss=0.1325, simple_loss=0.2104, pruned_loss=0.02726, over 700466.75 frames.], batch size: 54, lr: 2.95e-04
+2022-06-19 06:13:06,547 INFO [train.py:874] (3/4) Epoch 28, batch 550, datatang_loss[loss=0.1349, simple_loss=0.2185, pruned_loss=0.02563, over 4940.00 frames.], tot_loss[loss=0.1376, simple_loss=0.22, pruned_loss=0.02764, over 923196.78 frames.], batch size: 50, aishell_tot_loss[loss=0.1423, simple_loss=0.2288, pruned_loss=0.0279, over 743038.69 frames.], datatang_tot_loss[loss=0.1331, simple_loss=0.211, pruned_loss=0.02754, over 731515.31 frames.], batch size: 50, lr: 2.95e-04
+2022-06-19 06:13:35,839 INFO [train.py:874] (3/4) Epoch 28, batch 600, aishell_loss[loss=0.1738, simple_loss=0.2579, pruned_loss=0.04483, over 4874.00 frames.], tot_loss[loss=0.138, simple_loss=0.2212, pruned_loss=0.02745, over 936790.80 frames.], batch size: 36, aishell_tot_loss[loss=0.1419, simple_loss=0.2287, pruned_loss=0.02755, over 777690.75 frames.], datatang_tot_loss[loss=0.1337, simple_loss=0.2121, pruned_loss=0.02762, over 754596.57 frames.], batch size: 36, lr: 2.95e-04
+2022-06-19 06:14:02,742 INFO [train.py:874] (3/4) Epoch 28, batch 650, aishell_loss[loss=0.1744, simple_loss=0.2556, pruned_loss=0.04656, over 4931.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2226, pruned_loss=0.02822, over 947594.73 frames.], batch size: 32, aishell_tot_loss[loss=0.1424, simple_loss=0.2292, pruned_loss=0.02777, over 802070.57 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2136, pruned_loss=0.02835, over 781884.12 frames.], batch size: 32, lr: 2.94e-04
+2022-06-19 06:14:32,409 INFO [train.py:874] (3/4) Epoch 28, batch 700, aishell_loss[loss=0.167, simple_loss=0.2444, pruned_loss=0.04484, over 4934.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2228, pruned_loss=0.02908, over 955742.00 frames.], batch size: 33, aishell_tot_loss[loss=0.1431, simple_loss=0.2294, pruned_loss=0.02843, over 821684.56 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2141, pruned_loss=0.02877, over 807756.86 frames.], batch size: 33, lr: 2.94e-04
+2022-06-19 06:15:01,769 INFO [train.py:874] (3/4) Epoch 28, batch 750, aishell_loss[loss=0.1353, simple_loss=0.2219, pruned_loss=0.02431, over 4961.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2237, pruned_loss=0.02879, over 962546.59 frames.], batch size: 64, aishell_tot_loss[loss=0.1434, simple_loss=0.2302, pruned_loss=0.02833, over 841068.88 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2148, pruned_loss=0.02864, over 828822.97 frames.], batch size: 64, lr: 2.94e-04
+2022-06-19 06:15:30,197 INFO [train.py:874] (3/4) Epoch 28, batch 800, datatang_loss[loss=0.1326, simple_loss=0.2065, pruned_loss=0.02931, over 4916.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2228, pruned_loss=0.02874, over 967673.01 frames.], batch size: 25, aishell_tot_loss[loss=0.1433, simple_loss=0.2301, pruned_loss=0.02829, over 853407.32 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2148, pruned_loss=0.02871, over 852193.84 frames.], batch size: 25, lr: 2.94e-04
+2022-06-19 06:16:00,401 INFO [train.py:874] (3/4) Epoch 28, batch 850, aishell_loss[loss=0.1404, simple_loss=0.2314, pruned_loss=0.02472, over 4935.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2232, pruned_loss=0.029, over 971616.77 frames.], batch size: 58, aishell_tot_loss[loss=0.1435, simple_loss=0.2302, pruned_loss=0.02842, over 869436.37 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2152, pruned_loss=0.02898, over 867407.43 frames.], batch size: 58, lr: 2.94e-04
+2022-06-19 06:16:30,565 INFO [train.py:874] (3/4) Epoch 28, batch 900, datatang_loss[loss=0.1131, simple_loss=0.1959, pruned_loss=0.01517, over 4931.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2229, pruned_loss=0.02871, over 974695.79 frames.], batch size: 71, aishell_tot_loss[loss=0.143, simple_loss=0.2297, pruned_loss=0.02814, over 882906.92 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2154, pruned_loss=0.029, over 881519.14 frames.], batch size: 71, lr: 2.94e-04
+2022-06-19 06:16:57,989 INFO [train.py:874] (3/4) Epoch 28, batch 950, datatang_loss[loss=0.1503, simple_loss=0.2246, pruned_loss=0.03803, over 4958.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2228, pruned_loss=0.02852, over 977288.30 frames.], batch size: 45, aishell_tot_loss[loss=0.143, simple_loss=0.2297, pruned_loss=0.02811, over 894685.00 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2154, pruned_loss=0.02883, over 894279.32 frames.], batch size: 45, lr: 2.94e-04
+2022-06-19 06:17:29,803 INFO [train.py:874] (3/4) Epoch 28, batch 1000, datatang_loss[loss=0.1258, simple_loss=0.2011, pruned_loss=0.02519, over 4928.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2238, pruned_loss=0.02897, over 979090.46 frames.], batch size: 57, aishell_tot_loss[loss=0.1431, simple_loss=0.2301, pruned_loss=0.02805, over 905693.68 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2161, pruned_loss=0.02943, over 904678.93 frames.], batch size: 57, lr: 2.94e-04
+2022-06-19 06:17:29,804 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 06:17:46,267 INFO [train.py:914] (3/4) Epoch 28, validation: loss=0.1641, simple_loss=0.2478, pruned_loss=0.0402, over 1622729.00 frames.
+2022-06-19 06:18:14,337 INFO [train.py:874] (3/4) Epoch 28, batch 1050, aishell_loss[loss=0.1485, simple_loss=0.2362, pruned_loss=0.0304, over 4861.00 frames.], tot_loss[loss=0.141, simple_loss=0.2238, pruned_loss=0.02908, over 980428.73 frames.], batch size: 35, aishell_tot_loss[loss=0.1429, simple_loss=0.23, pruned_loss=0.0279, over 914967.45 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2165, pruned_loss=0.02976, over 914248.82 frames.], batch size: 35, lr: 2.94e-04
+2022-06-19 06:18:45,868 INFO [train.py:874] (3/4) Epoch 28, batch 1100, aishell_loss[loss=0.1383, simple_loss=0.2278, pruned_loss=0.02439, over 4945.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2232, pruned_loss=0.02906, over 981461.52 frames.], batch size: 54, aishell_tot_loss[loss=0.1429, simple_loss=0.2299, pruned_loss=0.02799, over 922784.01 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2162, pruned_loss=0.02969, over 923041.42 frames.], batch size: 54, lr: 2.94e-04
+2022-06-19 06:19:12,540 INFO [train.py:874] (3/4) Epoch 28, batch 1150, aishell_loss[loss=0.1325, simple_loss=0.2231, pruned_loss=0.02098, over 4907.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2232, pruned_loss=0.02893, over 982521.27 frames.], batch size: 33, aishell_tot_loss[loss=0.1428, simple_loss=0.23, pruned_loss=0.02784, over 930010.33 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2162, pruned_loss=0.02974, over 930725.46 frames.], batch size: 33, lr: 2.94e-04
+2022-06-19 06:19:43,200 INFO [train.py:874] (3/4) Epoch 28, batch 1200, datatang_loss[loss=0.1305, simple_loss=0.1994, pruned_loss=0.0308, over 4900.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2232, pruned_loss=0.02926, over 983002.08 frames.], batch size: 47, aishell_tot_loss[loss=0.1429, simple_loss=0.2297, pruned_loss=0.02803, over 937085.43 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2163, pruned_loss=0.02996, over 936451.14 frames.], batch size: 47, lr: 2.94e-04
+2022-06-19 06:20:13,512 INFO [train.py:874] (3/4) Epoch 28, batch 1250, aishell_loss[loss=0.1479, simple_loss=0.2421, pruned_loss=0.02681, over 4961.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2225, pruned_loss=0.02892, over 983218.58 frames.], batch size: 44, aishell_tot_loss[loss=0.1422, simple_loss=0.2288, pruned_loss=0.02776, over 942789.37 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2164, pruned_loss=0.02993, over 941837.09 frames.], batch size: 44, lr: 2.94e-04
+2022-06-19 06:20:39,908 INFO [train.py:874] (3/4) Epoch 28, batch 1300, datatang_loss[loss=0.1425, simple_loss=0.2139, pruned_loss=0.03559, over 4886.00 frames.], tot_loss[loss=0.1405, simple_loss=0.223, pruned_loss=0.02897, over 983565.33 frames.], batch size: 39, aishell_tot_loss[loss=0.1427, simple_loss=0.2294, pruned_loss=0.02804, over 948383.51 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2161, pruned_loss=0.02976, over 946216.87 frames.], batch size: 39, lr: 2.94e-04
+2022-06-19 06:21:10,140 INFO [train.py:874] (3/4) Epoch 28, batch 1350, datatang_loss[loss=0.1466, simple_loss=0.2259, pruned_loss=0.03363, over 4954.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2224, pruned_loss=0.02875, over 983980.52 frames.], batch size: 86, aishell_tot_loss[loss=0.1422, simple_loss=0.2289, pruned_loss=0.02781, over 952745.15 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.216, pruned_loss=0.02974, over 950853.50 frames.], batch size: 86, lr: 2.94e-04
+2022-06-19 06:21:39,773 INFO [train.py:874] (3/4) Epoch 28, batch 1400, datatang_loss[loss=0.1245, simple_loss=0.2135, pruned_loss=0.01779, over 4924.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2224, pruned_loss=0.02885, over 984155.22 frames.], batch size: 83, aishell_tot_loss[loss=0.1422, simple_loss=0.2289, pruned_loss=0.02772, over 956082.40 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2162, pruned_loss=0.02991, over 955296.06 frames.], batch size: 83, lr: 2.93e-04
+2022-06-19 06:22:07,144 INFO [train.py:874] (3/4) Epoch 28, batch 1450, aishell_loss[loss=0.1298, simple_loss=0.2155, pruned_loss=0.02204, over 4827.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2232, pruned_loss=0.0293, over 984150.91 frames.], batch size: 29, aishell_tot_loss[loss=0.1424, simple_loss=0.2291, pruned_loss=0.02789, over 958867.56 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2169, pruned_loss=0.03022, over 959229.92 frames.], batch size: 29, lr: 2.93e-04
+2022-06-19 06:22:39,124 INFO [train.py:874] (3/4) Epoch 28, batch 1500, datatang_loss[loss=0.1074, simple_loss=0.1928, pruned_loss=0.01101, over 4949.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2227, pruned_loss=0.02897, over 984044.84 frames.], batch size: 67, aishell_tot_loss[loss=0.1422, simple_loss=0.2287, pruned_loss=0.02784, over 961092.63 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2168, pruned_loss=0.02995, over 962828.81 frames.], batch size: 67, lr: 2.93e-04
+2022-06-19 06:23:09,515 INFO [train.py:874] (3/4) Epoch 28, batch 1550, aishell_loss[loss=0.151, simple_loss=0.2309, pruned_loss=0.03559, over 4955.00 frames.], tot_loss[loss=0.1409, simple_loss=0.223, pruned_loss=0.02935, over 984454.29 frames.], batch size: 31, aishell_tot_loss[loss=0.1426, simple_loss=0.2292, pruned_loss=0.02803, over 963783.71 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2169, pruned_loss=0.03017, over 965753.69 frames.], batch size: 31, lr: 2.93e-04
+2022-06-19 06:23:37,908 INFO [train.py:874] (3/4) Epoch 28, batch 1600, datatang_loss[loss=0.1183, simple_loss=0.1922, pruned_loss=0.02224, over 4734.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2226, pruned_loss=0.02896, over 984392.65 frames.], batch size: 24, aishell_tot_loss[loss=0.1425, simple_loss=0.2292, pruned_loss=0.02788, over 966134.54 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2164, pruned_loss=0.02994, over 967971.66 frames.], batch size: 24, lr: 2.93e-04
+2022-06-19 06:24:07,788 INFO [train.py:874] (3/4) Epoch 28, batch 1650, datatang_loss[loss=0.1353, simple_loss=0.2107, pruned_loss=0.03001, over 4946.00 frames.], tot_loss[loss=0.14, simple_loss=0.2223, pruned_loss=0.02883, over 984242.76 frames.], batch size: 50, aishell_tot_loss[loss=0.1424, simple_loss=0.2291, pruned_loss=0.02788, over 968074.67 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2162, pruned_loss=0.02982, over 969962.72 frames.], batch size: 50, lr: 2.93e-04
+2022-06-19 06:24:36,389 INFO [train.py:874] (3/4) Epoch 28, batch 1700, aishell_loss[loss=0.1442, simple_loss=0.2346, pruned_loss=0.02686, over 4881.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2227, pruned_loss=0.02861, over 984578.45 frames.], batch size: 47, aishell_tot_loss[loss=0.1425, simple_loss=0.2293, pruned_loss=0.02783, over 970650.49 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2158, pruned_loss=0.02969, over 971372.27 frames.], batch size: 47, lr: 2.93e-04
+2022-06-19 06:25:04,627 INFO [train.py:874] (3/4) Epoch 28, batch 1750, datatang_loss[loss=0.1384, simple_loss=0.217, pruned_loss=0.02994, over 4925.00 frames.], tot_loss[loss=0.1402, simple_loss=0.223, pruned_loss=0.02872, over 984098.65 frames.], batch size: 57, aishell_tot_loss[loss=0.1425, simple_loss=0.2294, pruned_loss=0.02781, over 971861.46 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2163, pruned_loss=0.02979, over 972849.77 frames.], batch size: 57, lr: 2.93e-04
+2022-06-19 06:25:35,103 INFO [train.py:874] (3/4) Epoch 28, batch 1800, datatang_loss[loss=0.1299, simple_loss=0.2032, pruned_loss=0.02828, over 4967.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2233, pruned_loss=0.02871, over 984236.88 frames.], batch size: 50, aishell_tot_loss[loss=0.1427, simple_loss=0.2295, pruned_loss=0.02797, over 973594.13 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2161, pruned_loss=0.02966, over 974031.49 frames.], batch size: 50, lr: 2.93e-04
+2022-06-19 06:26:02,306 INFO [train.py:874] (3/4) Epoch 28, batch 1850, datatang_loss[loss=0.1325, simple_loss=0.2117, pruned_loss=0.02668, over 4923.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2223, pruned_loss=0.02863, over 984505.77 frames.], batch size: 81, aishell_tot_loss[loss=0.1423, simple_loss=0.2289, pruned_loss=0.0278, over 974917.56 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.216, pruned_loss=0.0297, over 975451.27 frames.], batch size: 81, lr: 2.93e-04
+2022-06-19 06:26:30,992 INFO [train.py:874] (3/4) Epoch 28, batch 1900, aishell_loss[loss=0.1296, simple_loss=0.2209, pruned_loss=0.01912, over 4949.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2223, pruned_loss=0.02843, over 984793.66 frames.], batch size: 58, aishell_tot_loss[loss=0.1421, simple_loss=0.2291, pruned_loss=0.02761, over 976256.35 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2158, pruned_loss=0.02964, over 976607.32 frames.], batch size: 58, lr: 2.93e-04
+2022-06-19 06:27:02,467 INFO [train.py:874] (3/4) Epoch 28, batch 1950, aishell_loss[loss=0.1325, simple_loss=0.216, pruned_loss=0.02448, over 4968.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2218, pruned_loss=0.0284, over 985116.12 frames.], batch size: 30, aishell_tot_loss[loss=0.1421, simple_loss=0.229, pruned_loss=0.02765, over 977326.77 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2155, pruned_loss=0.02952, over 977851.66 frames.], batch size: 30, lr: 2.93e-04
+2022-06-19 06:27:30,209 INFO [train.py:874] (3/4) Epoch 28, batch 2000, datatang_loss[loss=0.1481, simple_loss=0.2157, pruned_loss=0.04028, over 4957.00 frames.], tot_loss[loss=0.14, simple_loss=0.2227, pruned_loss=0.02862, over 985164.32 frames.], batch size: 67, aishell_tot_loss[loss=0.1425, simple_loss=0.2294, pruned_loss=0.02783, over 978227.21 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2157, pruned_loss=0.02954, over 978787.76 frames.], batch size: 67, lr: 2.93e-04
+2022-06-19 06:27:30,210 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 06:27:46,238 INFO [train.py:914] (3/4) Epoch 28, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 06:28:15,610 INFO [train.py:874] (3/4) Epoch 28, batch 2050, datatang_loss[loss=0.126, simple_loss=0.2093, pruned_loss=0.0213, over 4923.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2234, pruned_loss=0.02874, over 985104.97 frames.], batch size: 81, aishell_tot_loss[loss=0.1429, simple_loss=0.23, pruned_loss=0.02788, over 979102.35 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2159, pruned_loss=0.02961, over 979420.90 frames.], batch size: 81, lr: 2.93e-04
+2022-06-19 06:28:43,213 INFO [train.py:874] (3/4) Epoch 28, batch 2100, datatang_loss[loss=0.1374, simple_loss=0.2165, pruned_loss=0.02913, over 4920.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2241, pruned_loss=0.02913, over 985019.02 frames.], batch size: 75, aishell_tot_loss[loss=0.1437, simple_loss=0.2308, pruned_loss=0.02827, over 979772.95 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2158, pruned_loss=0.02966, over 980045.22 frames.], batch size: 75, lr: 2.93e-04
+2022-06-19 06:29:13,540 INFO [train.py:874] (3/4) Epoch 28, batch 2150, datatang_loss[loss=0.1561, simple_loss=0.2303, pruned_loss=0.04093, over 4903.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2242, pruned_loss=0.0292, over 985107.66 frames.], batch size: 64, aishell_tot_loss[loss=0.1436, simple_loss=0.2308, pruned_loss=0.02813, over 980260.38 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2165, pruned_loss=0.02988, over 980843.07 frames.], batch size: 64, lr: 2.93e-04
+2022-06-19 06:29:42,226 INFO [train.py:874] (3/4) Epoch 28, batch 2200, aishell_loss[loss=0.1517, simple_loss=0.2361, pruned_loss=0.0337, over 4934.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2233, pruned_loss=0.02906, over 985115.09 frames.], batch size: 54, aishell_tot_loss[loss=0.1434, simple_loss=0.2305, pruned_loss=0.0281, over 980575.30 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.216, pruned_loss=0.02978, over 981588.25 frames.], batch size: 54, lr: 2.92e-04
+2022-06-19 06:30:10,886 INFO [train.py:874] (3/4) Epoch 28, batch 2250, aishell_loss[loss=0.1327, simple_loss=0.2177, pruned_loss=0.02387, over 4917.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2229, pruned_loss=0.02903, over 985061.42 frames.], batch size: 46, aishell_tot_loss[loss=0.143, simple_loss=0.2301, pruned_loss=0.02794, over 981050.40 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2158, pruned_loss=0.02998, over 982028.63 frames.], batch size: 46, lr: 2.92e-04
+2022-06-19 06:30:41,942 INFO [train.py:874] (3/4) Epoch 28, batch 2300, datatang_loss[loss=0.1423, simple_loss=0.2245, pruned_loss=0.03002, over 4926.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2224, pruned_loss=0.02841, over 984986.08 frames.], batch size: 94, aishell_tot_loss[loss=0.1424, simple_loss=0.2295, pruned_loss=0.02767, over 981334.89 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2158, pruned_loss=0.0296, over 982503.43 frames.], batch size: 94, lr: 2.92e-04
+2022-06-19 06:31:09,130 INFO [train.py:874] (3/4) Epoch 28, batch 2350, datatang_loss[loss=0.122, simple_loss=0.1983, pruned_loss=0.02285, over 4924.00 frames.], tot_loss[loss=0.1396, simple_loss=0.222, pruned_loss=0.02862, over 984951.25 frames.], batch size: 73, aishell_tot_loss[loss=0.1428, simple_loss=0.2296, pruned_loss=0.02794, over 981796.70 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2152, pruned_loss=0.02953, over 982728.80 frames.], batch size: 73, lr: 2.92e-04
+2022-06-19 06:31:38,577 INFO [train.py:874] (3/4) Epoch 28, batch 2400, aishell_loss[loss=0.1385, simple_loss=0.2324, pruned_loss=0.02233, over 4888.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2227, pruned_loss=0.02838, over 985313.77 frames.], batch size: 34, aishell_tot_loss[loss=0.143, simple_loss=0.2302, pruned_loss=0.02788, over 982377.56 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.215, pruned_loss=0.02933, over 983187.37 frames.], batch size: 34, lr: 2.92e-04
+2022-06-19 06:32:08,614 INFO [train.py:874] (3/4) Epoch 28, batch 2450, aishell_loss[loss=0.1267, simple_loss=0.2123, pruned_loss=0.02057, over 4885.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2233, pruned_loss=0.02866, over 985108.41 frames.], batch size: 42, aishell_tot_loss[loss=0.143, simple_loss=0.2303, pruned_loss=0.0279, over 982722.91 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2153, pruned_loss=0.02962, over 983244.65 frames.], batch size: 42, lr: 2.92e-04
+2022-06-19 06:32:35,298 INFO [train.py:874] (3/4) Epoch 28, batch 2500, datatang_loss[loss=0.1431, simple_loss=0.2266, pruned_loss=0.0298, over 4921.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2237, pruned_loss=0.02892, over 985155.65 frames.], batch size: 57, aishell_tot_loss[loss=0.1431, simple_loss=0.2303, pruned_loss=0.02791, over 982878.37 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2159, pruned_loss=0.02987, over 983641.44 frames.], batch size: 57, lr: 2.92e-04
+2022-06-19 06:33:05,168 INFO [train.py:874] (3/4) Epoch 28, batch 2550, aishell_loss[loss=0.165, simple_loss=0.2495, pruned_loss=0.0402, over 4874.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2229, pruned_loss=0.02864, over 984585.46 frames.], batch size: 42, aishell_tot_loss[loss=0.1428, simple_loss=0.2298, pruned_loss=0.02792, over 982781.06 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2156, pruned_loss=0.02958, over 983589.38 frames.], batch size: 42, lr: 2.92e-04
+2022-06-19 06:33:35,664 INFO [train.py:874] (3/4) Epoch 28, batch 2600, datatang_loss[loss=0.115, simple_loss=0.197, pruned_loss=0.01647, over 4926.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2234, pruned_loss=0.02914, over 984959.29 frames.], batch size: 79, aishell_tot_loss[loss=0.1435, simple_loss=0.23, pruned_loss=0.02845, over 983151.46 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2156, pruned_loss=0.02956, over 983961.07 frames.], batch size: 79, lr: 2.92e-04
+2022-06-19 06:34:01,448 INFO [train.py:874] (3/4) Epoch 28, batch 2650, datatang_loss[loss=0.1272, simple_loss=0.2079, pruned_loss=0.02321, over 4922.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2236, pruned_loss=0.02895, over 985123.81 frames.], batch size: 81, aishell_tot_loss[loss=0.1435, simple_loss=0.2304, pruned_loss=0.02828, over 983714.69 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2154, pruned_loss=0.02959, over 983914.60 frames.], batch size: 81, lr: 2.92e-04
+2022-06-19 06:34:31,385 INFO [train.py:874] (3/4) Epoch 28, batch 2700, datatang_loss[loss=0.157, simple_loss=0.2341, pruned_loss=0.03996, over 4918.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2237, pruned_loss=0.02887, over 985232.18 frames.], batch size: 77, aishell_tot_loss[loss=0.1435, simple_loss=0.2307, pruned_loss=0.02814, over 983853.40 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2151, pruned_loss=0.02966, over 984200.14 frames.], batch size: 77, lr: 2.92e-04
+2022-06-19 06:34:59,559 INFO [train.py:874] (3/4) Epoch 28, batch 2750, aishell_loss[loss=0.1666, simple_loss=0.2468, pruned_loss=0.04319, over 4936.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2237, pruned_loss=0.02885, over 985191.17 frames.], batch size: 58, aishell_tot_loss[loss=0.1436, simple_loss=0.2308, pruned_loss=0.02824, over 983886.99 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2149, pruned_loss=0.02955, over 984419.54 frames.], batch size: 58, lr: 2.92e-04
+2022-06-19 06:35:27,854 INFO [train.py:874] (3/4) Epoch 28, batch 2800, aishell_loss[loss=0.1427, simple_loss=0.2375, pruned_loss=0.02396, over 4962.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2244, pruned_loss=0.02936, over 985135.69 frames.], batch size: 40, aishell_tot_loss[loss=0.1443, simple_loss=0.2315, pruned_loss=0.02856, over 984099.04 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2152, pruned_loss=0.02976, over 984384.75 frames.], batch size: 40, lr: 2.92e-04
+2022-06-19 06:35:57,611 INFO [train.py:874] (3/4) Epoch 28, batch 2850, datatang_loss[loss=0.1426, simple_loss=0.2177, pruned_loss=0.03373, over 4898.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2245, pruned_loss=0.02942, over 985132.57 frames.], batch size: 52, aishell_tot_loss[loss=0.1441, simple_loss=0.2313, pruned_loss=0.02848, over 984051.84 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2157, pruned_loss=0.02994, over 984644.92 frames.], batch size: 52, lr: 2.92e-04
+2022-06-19 06:36:25,979 INFO [train.py:874] (3/4) Epoch 28, batch 2900, aishell_loss[loss=0.1467, simple_loss=0.2374, pruned_loss=0.02798, over 4919.00 frames.], tot_loss[loss=0.141, simple_loss=0.2237, pruned_loss=0.0291, over 985373.63 frames.], batch size: 68, aishell_tot_loss[loss=0.1437, simple_loss=0.231, pruned_loss=0.02815, over 984322.35 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2156, pruned_loss=0.02993, over 984810.49 frames.], batch size: 68, lr: 2.92e-04
+2022-06-19 06:36:54,066 INFO [train.py:874] (3/4) Epoch 28, batch 2950, aishell_loss[loss=0.151, simple_loss=0.2425, pruned_loss=0.02976, over 4970.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2236, pruned_loss=0.02944, over 985625.41 frames.], batch size: 61, aishell_tot_loss[loss=0.1439, simple_loss=0.231, pruned_loss=0.02842, over 984508.06 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2156, pruned_loss=0.03004, over 985070.76 frames.], batch size: 61, lr: 2.91e-04
+2022-06-19 06:37:24,549 INFO [train.py:874] (3/4) Epoch 28, batch 3000, aishell_loss[loss=0.1476, simple_loss=0.2283, pruned_loss=0.03344, over 4929.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2232, pruned_loss=0.02925, over 985741.83 frames.], batch size: 33, aishell_tot_loss[loss=0.1441, simple_loss=0.2311, pruned_loss=0.0285, over 984600.42 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2156, pruned_loss=0.02976, over 985272.63 frames.], batch size: 33, lr: 2.91e-04
+2022-06-19 06:37:24,550 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 06:37:41,302 INFO [train.py:914] (3/4) Epoch 28, validation: loss=0.1644, simple_loss=0.2482, pruned_loss=0.04027, over 1622729.00 frames.
+2022-06-19 06:38:10,665 INFO [train.py:874] (3/4) Epoch 28, batch 3050, datatang_loss[loss=0.1697, simple_loss=0.244, pruned_loss=0.04769, over 4917.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2236, pruned_loss=0.02942, over 985739.78 frames.], batch size: 108, aishell_tot_loss[loss=0.1441, simple_loss=0.2312, pruned_loss=0.02855, over 984603.65 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.216, pruned_loss=0.02991, over 985453.69 frames.], batch size: 108, lr: 2.91e-04
+2022-06-19 06:38:41,038 INFO [train.py:874] (3/4) Epoch 28, batch 3100, datatang_loss[loss=0.1274, simple_loss=0.2136, pruned_loss=0.0206, over 4961.00 frames.], tot_loss[loss=0.141, simple_loss=0.2232, pruned_loss=0.02943, over 985582.59 frames.], batch size: 91, aishell_tot_loss[loss=0.1446, simple_loss=0.2314, pruned_loss=0.02886, over 984532.70 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2156, pruned_loss=0.02965, over 985499.17 frames.], batch size: 91, lr: 2.91e-04
+2022-06-19 06:39:07,316 INFO [train.py:874] (3/4) Epoch 28, batch 3150, aishell_loss[loss=0.1483, simple_loss=0.248, pruned_loss=0.02426, over 4962.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2235, pruned_loss=0.029, over 985319.79 frames.], batch size: 79, aishell_tot_loss[loss=0.1446, simple_loss=0.2317, pruned_loss=0.02874, over 984596.58 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2153, pruned_loss=0.02936, over 985310.53 frames.], batch size: 79, lr: 2.91e-04
+2022-06-19 06:39:36,957 INFO [train.py:874] (3/4) Epoch 28, batch 3200, datatang_loss[loss=0.1451, simple_loss=0.2301, pruned_loss=0.02999, over 4960.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2233, pruned_loss=0.02873, over 985321.65 frames.], batch size: 86, aishell_tot_loss[loss=0.144, simple_loss=0.2314, pruned_loss=0.02835, over 984528.49 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2153, pruned_loss=0.02946, over 985474.34 frames.], batch size: 86, lr: 2.91e-04
+2022-06-19 06:40:07,325 INFO [train.py:874] (3/4) Epoch 28, batch 3250, aishell_loss[loss=0.1404, simple_loss=0.2243, pruned_loss=0.02827, over 4922.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2225, pruned_loss=0.02863, over 985100.13 frames.], batch size: 54, aishell_tot_loss[loss=0.1438, simple_loss=0.2309, pruned_loss=0.02835, over 984145.91 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.215, pruned_loss=0.0293, over 985691.41 frames.], batch size: 54, lr: 2.91e-04
+2022-06-19 06:40:34,287 INFO [train.py:874] (3/4) Epoch 28, batch 3300, aishell_loss[loss=0.1174, simple_loss=0.2078, pruned_loss=0.01349, over 4887.00 frames.], tot_loss[loss=0.1393, simple_loss=0.222, pruned_loss=0.02836, over 984919.20 frames.], batch size: 28, aishell_tot_loss[loss=0.1437, simple_loss=0.2306, pruned_loss=0.02838, over 983859.15 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2148, pruned_loss=0.02894, over 985808.89 frames.], batch size: 28, lr: 2.91e-04
+2022-06-19 06:41:04,438 INFO [train.py:874] (3/4) Epoch 28, batch 3350, aishell_loss[loss=0.1163, simple_loss=0.2069, pruned_loss=0.01288, over 4934.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2223, pruned_loss=0.02843, over 984740.36 frames.], batch size: 49, aishell_tot_loss[loss=0.144, simple_loss=0.2308, pruned_loss=0.02862, over 983881.79 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2143, pruned_loss=0.02871, over 985664.74 frames.], batch size: 49, lr: 2.91e-04
+2022-06-19 06:41:34,606 INFO [train.py:874] (3/4) Epoch 28, batch 3400, datatang_loss[loss=0.1541, simple_loss=0.2374, pruned_loss=0.0354, over 4970.00 frames.], tot_loss[loss=0.139, simple_loss=0.2218, pruned_loss=0.02811, over 984565.60 frames.], batch size: 37, aishell_tot_loss[loss=0.1433, simple_loss=0.23, pruned_loss=0.02833, over 983693.72 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2144, pruned_loss=0.0286, over 985671.51 frames.], batch size: 37, lr: 2.91e-04
+2022-06-19 06:42:02,124 INFO [train.py:874] (3/4) Epoch 28, batch 3450, datatang_loss[loss=0.1496, simple_loss=0.223, pruned_loss=0.03804, over 4922.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2223, pruned_loss=0.02818, over 984490.22 frames.], batch size: 81, aishell_tot_loss[loss=0.1435, simple_loss=0.2303, pruned_loss=0.02833, over 983820.83 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2147, pruned_loss=0.02858, over 985431.19 frames.], batch size: 81, lr: 2.91e-04
+2022-06-19 06:42:33,162 INFO [train.py:874] (3/4) Epoch 28, batch 3500, datatang_loss[loss=0.1316, simple_loss=0.216, pruned_loss=0.02365, over 4942.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2229, pruned_loss=0.02827, over 984845.28 frames.], batch size: 88, aishell_tot_loss[loss=0.1437, simple_loss=0.2305, pruned_loss=0.02841, over 984081.47 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2151, pruned_loss=0.02854, over 985521.52 frames.], batch size: 88, lr: 2.91e-04
+2022-06-19 06:43:02,764 INFO [train.py:874] (3/4) Epoch 28, batch 3550, aishell_loss[loss=0.1347, simple_loss=0.218, pruned_loss=0.02568, over 4944.00 frames.], tot_loss[loss=0.14, simple_loss=0.2232, pruned_loss=0.02842, over 985200.10 frames.], batch size: 31, aishell_tot_loss[loss=0.1435, simple_loss=0.2307, pruned_loss=0.02815, over 984318.46 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2153, pruned_loss=0.02891, over 985671.21 frames.], batch size: 31, lr: 2.91e-04
+2022-06-19 06:43:30,726 INFO [train.py:874] (3/4) Epoch 28, batch 3600, aishell_loss[loss=0.1598, simple_loss=0.2546, pruned_loss=0.0325, over 4943.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2235, pruned_loss=0.02896, over 984965.11 frames.], batch size: 80, aishell_tot_loss[loss=0.1442, simple_loss=0.2313, pruned_loss=0.02856, over 984194.24 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2153, pruned_loss=0.02904, over 985581.38 frames.], batch size: 80, lr: 2.91e-04
+2022-06-19 06:44:01,919 INFO [train.py:874] (3/4) Epoch 28, batch 3650, datatang_loss[loss=0.1593, simple_loss=0.2367, pruned_loss=0.04092, over 4922.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2243, pruned_loss=0.02948, over 984879.34 frames.], batch size: 108, aishell_tot_loss[loss=0.1446, simple_loss=0.2316, pruned_loss=0.02881, over 984065.69 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2157, pruned_loss=0.02937, over 985648.99 frames.], batch size: 108, lr: 2.91e-04
+2022-06-19 06:44:30,443 INFO [train.py:874] (3/4) Epoch 28, batch 3700, datatang_loss[loss=0.1435, simple_loss=0.227, pruned_loss=0.02998, over 4958.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2238, pruned_loss=0.02947, over 985405.53 frames.], batch size: 99, aishell_tot_loss[loss=0.1446, simple_loss=0.2313, pruned_loss=0.02895, over 984435.06 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2155, pruned_loss=0.02931, over 985837.61 frames.], batch size: 99, lr: 2.91e-04
+2022-06-19 06:44:59,367 INFO [train.py:874] (3/4) Epoch 28, batch 3750, datatang_loss[loss=0.1205, simple_loss=0.206, pruned_loss=0.01755, over 4926.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2245, pruned_loss=0.02958, over 985936.32 frames.], batch size: 79, aishell_tot_loss[loss=0.1445, simple_loss=0.2314, pruned_loss=0.02878, over 984907.29 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2161, pruned_loss=0.02968, over 986003.35 frames.], batch size: 79, lr: 2.90e-04
+2022-06-19 06:45:28,863 INFO [train.py:874] (3/4) Epoch 28, batch 3800, aishell_loss[loss=0.1323, simple_loss=0.228, pruned_loss=0.0183, over 4946.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2238, pruned_loss=0.02937, over 985676.48 frames.], batch size: 54, aishell_tot_loss[loss=0.1442, simple_loss=0.231, pruned_loss=0.02869, over 984780.90 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2159, pruned_loss=0.02964, over 985949.79 frames.], batch size: 54, lr: 2.90e-04
+2022-06-19 06:45:56,696 INFO [train.py:874] (3/4) Epoch 28, batch 3850, datatang_loss[loss=0.129, simple_loss=0.2049, pruned_loss=0.02653, over 4917.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2229, pruned_loss=0.0288, over 985298.46 frames.], batch size: 75, aishell_tot_loss[loss=0.1435, simple_loss=0.2304, pruned_loss=0.02834, over 984625.33 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2157, pruned_loss=0.02943, over 985799.33 frames.], batch size: 75, lr: 2.90e-04
+2022-06-19 06:46:25,685 INFO [train.py:874] (3/4) Epoch 28, batch 3900, aishell_loss[loss=0.1491, simple_loss=0.2293, pruned_loss=0.03449, over 4862.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2231, pruned_loss=0.02865, over 985479.43 frames.], batch size: 38, aishell_tot_loss[loss=0.1433, simple_loss=0.2304, pruned_loss=0.02813, over 984750.53 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2156, pruned_loss=0.02948, over 985908.04 frames.], batch size: 38, lr: 2.90e-04
+2022-06-19 06:46:53,825 INFO [train.py:874] (3/4) Epoch 28, batch 3950, aishell_loss[loss=0.1463, simple_loss=0.2329, pruned_loss=0.02989, over 4919.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2237, pruned_loss=0.02875, over 985454.57 frames.], batch size: 33, aishell_tot_loss[loss=0.1437, simple_loss=0.2309, pruned_loss=0.02825, over 984727.54 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2156, pruned_loss=0.02944, over 985954.82 frames.], batch size: 33, lr: 2.90e-04
+2022-06-19 06:47:20,493 INFO [train.py:874] (3/4) Epoch 28, batch 4000, aishell_loss[loss=0.1298, simple_loss=0.2226, pruned_loss=0.01851, over 4976.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2236, pruned_loss=0.02868, over 985345.03 frames.], batch size: 51, aishell_tot_loss[loss=0.1439, simple_loss=0.231, pruned_loss=0.02838, over 984631.84 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2152, pruned_loss=0.02922, over 985976.34 frames.], batch size: 51, lr: 2.90e-04
+2022-06-19 06:47:20,494 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 06:47:36,386 INFO [train.py:914] (3/4) Epoch 28, validation: loss=0.1646, simple_loss=0.2484, pruned_loss=0.04036, over 1622729.00 frames.
+2022-06-19 06:48:02,206 INFO [train.py:874] (3/4) Epoch 28, batch 4050, aishell_loss[loss=0.1759, simple_loss=0.2671, pruned_loss=0.0424, over 4923.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2245, pruned_loss=0.02866, over 984851.73 frames.], batch size: 68, aishell_tot_loss[loss=0.1439, simple_loss=0.2312, pruned_loss=0.02833, over 984297.62 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2156, pruned_loss=0.02923, over 985830.02 frames.], batch size: 68, lr: 2.90e-04
+2022-06-19 06:49:07,975 INFO [train.py:874] (3/4) Epoch 29, batch 50, aishell_loss[loss=0.1371, simple_loss=0.2233, pruned_loss=0.02546, over 4945.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2201, pruned_loss=0.02756, over 218670.79 frames.], batch size: 54, aishell_tot_loss[loss=0.1454, simple_loss=0.2318, pruned_loss=0.02951, over 120578.43 frames.], datatang_tot_loss[loss=0.1293, simple_loss=0.2076, pruned_loss=0.02551, over 111767.20 frames.], batch size: 54, lr: 2.85e-04
+2022-06-19 06:49:37,791 INFO [train.py:874] (3/4) Epoch 29, batch 100, datatang_loss[loss=0.1263, simple_loss=0.208, pruned_loss=0.02234, over 4920.00 frames.], tot_loss[loss=0.1361, simple_loss=0.2188, pruned_loss=0.02669, over 388764.42 frames.], batch size: 77, aishell_tot_loss[loss=0.1425, simple_loss=0.2289, pruned_loss=0.02805, over 222407.51 frames.], datatang_tot_loss[loss=0.1296, simple_loss=0.2084, pruned_loss=0.02539, over 214819.03 frames.], batch size: 77, lr: 2.85e-04
+2022-06-19 06:50:11,753 INFO [train.py:874] (3/4) Epoch 29, batch 150, aishell_loss[loss=0.1723, simple_loss=0.2594, pruned_loss=0.0426, over 4922.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2202, pruned_loss=0.0273, over 520920.06 frames.], batch size: 68, aishell_tot_loss[loss=0.1452, simple_loss=0.2316, pruned_loss=0.02934, over 318713.41 frames.], datatang_tot_loss[loss=0.1288, simple_loss=0.2076, pruned_loss=0.02502, over 298886.01 frames.], batch size: 68, lr: 2.85e-04
+2022-06-19 06:50:40,066 INFO [train.py:874] (3/4) Epoch 29, batch 200, datatang_loss[loss=0.1259, simple_loss=0.2135, pruned_loss=0.01918, over 4941.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2212, pruned_loss=0.02783, over 624241.43 frames.], batch size: 45, aishell_tot_loss[loss=0.1444, simple_loss=0.2312, pruned_loss=0.02882, over 406233.95 frames.], datatang_tot_loss[loss=0.1312, simple_loss=0.2094, pruned_loss=0.02648, over 370725.25 frames.], batch size: 45, lr: 2.85e-04
+2022-06-19 06:51:09,835 INFO [train.py:874] (3/4) Epoch 29, batch 250, datatang_loss[loss=0.1327, simple_loss=0.2084, pruned_loss=0.02849, over 4915.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2204, pruned_loss=0.0274, over 704490.03 frames.], batch size: 42, aishell_tot_loss[loss=0.1428, simple_loss=0.2295, pruned_loss=0.02809, over 474345.42 frames.], datatang_tot_loss[loss=0.1318, simple_loss=0.2104, pruned_loss=0.02662, over 443408.93 frames.], batch size: 42, lr: 2.85e-04
+2022-06-19 06:51:37,972 INFO [train.py:874] (3/4) Epoch 29, batch 300, datatang_loss[loss=0.1287, simple_loss=0.2111, pruned_loss=0.02311, over 4924.00 frames.], tot_loss[loss=0.138, simple_loss=0.2211, pruned_loss=0.02739, over 767160.28 frames.], batch size: 73, aishell_tot_loss[loss=0.142, simple_loss=0.2286, pruned_loss=0.02768, over 550431.22 frames.], datatang_tot_loss[loss=0.1328, simple_loss=0.2116, pruned_loss=0.02701, over 490241.14 frames.], batch size: 73, lr: 2.85e-04
+2022-06-19 06:52:07,643 INFO [train.py:874] (3/4) Epoch 29, batch 350, aishell_loss[loss=0.1256, simple_loss=0.21, pruned_loss=0.02058, over 4862.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2211, pruned_loss=0.02755, over 815351.12 frames.], batch size: 28, aishell_tot_loss[loss=0.1409, simple_loss=0.2275, pruned_loss=0.02719, over 610667.60 frames.], datatang_tot_loss[loss=0.1342, simple_loss=0.2126, pruned_loss=0.02785, over 537778.11 frames.], batch size: 28, lr: 2.85e-04
+2022-06-19 06:52:37,150 INFO [train.py:874] (3/4) Epoch 29, batch 400, aishell_loss[loss=0.1313, simple_loss=0.2242, pruned_loss=0.01919, over 4962.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2214, pruned_loss=0.02767, over 853118.46 frames.], batch size: 40, aishell_tot_loss[loss=0.1409, simple_loss=0.2275, pruned_loss=0.02709, over 656503.77 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2132, pruned_loss=0.02817, over 588541.27 frames.], batch size: 40, lr: 2.85e-04
+2022-06-19 06:53:05,442 INFO [train.py:874] (3/4) Epoch 29, batch 450, datatang_loss[loss=0.1429, simple_loss=0.2232, pruned_loss=0.03125, over 4940.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2213, pruned_loss=0.02787, over 882398.95 frames.], batch size: 88, aishell_tot_loss[loss=0.1403, simple_loss=0.2269, pruned_loss=0.0269, over 699495.52 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2138, pruned_loss=0.02869, over 630011.92 frames.], batch size: 88, lr: 2.85e-04
+2022-06-19 06:53:35,520 INFO [train.py:874] (3/4) Epoch 29, batch 500, datatang_loss[loss=0.1421, simple_loss=0.2103, pruned_loss=0.03698, over 4927.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2211, pruned_loss=0.02793, over 905260.20 frames.], batch size: 42, aishell_tot_loss[loss=0.14, simple_loss=0.2265, pruned_loss=0.02679, over 733164.45 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2142, pruned_loss=0.02892, over 671903.18 frames.], batch size: 42, lr: 2.84e-04
+2022-06-19 06:54:05,090 INFO [train.py:874] (3/4) Epoch 29, batch 550, aishell_loss[loss=0.1323, simple_loss=0.2264, pruned_loss=0.01916, over 4939.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2221, pruned_loss=0.02849, over 922902.74 frames.], batch size: 54, aishell_tot_loss[loss=0.1409, simple_loss=0.2274, pruned_loss=0.02719, over 757168.16 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2152, pruned_loss=0.02925, over 715599.12 frames.], batch size: 54, lr: 2.84e-04
+2022-06-19 06:54:33,140 INFO [train.py:874] (3/4) Epoch 29, batch 600, datatang_loss[loss=0.1484, simple_loss=0.2342, pruned_loss=0.03129, over 4928.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2225, pruned_loss=0.02861, over 936384.81 frames.], batch size: 57, aishell_tot_loss[loss=0.1409, simple_loss=0.2272, pruned_loss=0.02729, over 785562.16 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.216, pruned_loss=0.02938, over 745089.93 frames.], batch size: 57, lr: 2.84e-04
+2022-06-19 06:55:02,482 INFO [train.py:874] (3/4) Epoch 29, batch 650, aishell_loss[loss=0.1171, simple_loss=0.1953, pruned_loss=0.01941, over 4881.00 frames.], tot_loss[loss=0.1404, simple_loss=0.223, pruned_loss=0.0289, over 947170.24 frames.], batch size: 28, aishell_tot_loss[loss=0.1416, simple_loss=0.228, pruned_loss=0.02758, over 808973.03 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2162, pruned_loss=0.02955, over 773419.27 frames.], batch size: 28, lr: 2.84e-04
+2022-06-19 06:55:31,665 INFO [train.py:874] (3/4) Epoch 29, batch 700, datatang_loss[loss=0.1236, simple_loss=0.2015, pruned_loss=0.02285, over 4937.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2232, pruned_loss=0.02897, over 956160.39 frames.], batch size: 79, aishell_tot_loss[loss=0.142, simple_loss=0.2285, pruned_loss=0.02772, over 827648.84 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2164, pruned_loss=0.02957, over 801466.98 frames.], batch size: 79, lr: 2.84e-04
+2022-06-19 06:56:00,602 INFO [train.py:874] (3/4) Epoch 29, batch 750, aishell_loss[loss=0.1644, simple_loss=0.2576, pruned_loss=0.03555, over 4972.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2233, pruned_loss=0.02904, over 963258.39 frames.], batch size: 78, aishell_tot_loss[loss=0.1423, simple_loss=0.229, pruned_loss=0.02782, over 847512.50 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2162, pruned_loss=0.02967, over 822370.57 frames.], batch size: 78, lr: 2.84e-04
+2022-06-19 06:56:31,391 INFO [train.py:874] (3/4) Epoch 29, batch 800, aishell_loss[loss=0.1624, simple_loss=0.2537, pruned_loss=0.03549, over 4873.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2227, pruned_loss=0.02882, over 968298.09 frames.], batch size: 42, aishell_tot_loss[loss=0.1419, simple_loss=0.2284, pruned_loss=0.02774, over 865645.50 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.216, pruned_loss=0.02958, over 839473.98 frames.], batch size: 42, lr: 2.84e-04
+2022-06-19 06:57:01,125 INFO [train.py:874] (3/4) Epoch 29, batch 850, aishell_loss[loss=0.1417, simple_loss=0.2295, pruned_loss=0.02694, over 4869.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2226, pruned_loss=0.02883, over 972355.42 frames.], batch size: 37, aishell_tot_loss[loss=0.1421, simple_loss=0.2285, pruned_loss=0.02783, over 880423.01 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2158, pruned_loss=0.02957, over 856164.21 frames.], batch size: 37, lr: 2.84e-04
+2022-06-19 06:57:29,421 INFO [train.py:874] (3/4) Epoch 29, batch 900, aishell_loss[loss=0.1443, simple_loss=0.2284, pruned_loss=0.03011, over 4925.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2221, pruned_loss=0.02867, over 975548.96 frames.], batch size: 33, aishell_tot_loss[loss=0.1423, simple_loss=0.2288, pruned_loss=0.0279, over 892028.89 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2153, pruned_loss=0.02931, over 872660.54 frames.], batch size: 33, lr: 2.84e-04
+2022-06-19 06:57:59,991 INFO [train.py:874] (3/4) Epoch 29, batch 950, aishell_loss[loss=0.1405, simple_loss=0.224, pruned_loss=0.02849, over 4830.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2218, pruned_loss=0.02865, over 977913.85 frames.], batch size: 29, aishell_tot_loss[loss=0.1426, simple_loss=0.2289, pruned_loss=0.02818, over 903070.70 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2147, pruned_loss=0.02903, over 886085.68 frames.], batch size: 29, lr: 2.84e-04
+2022-06-19 06:58:28,360 INFO [train.py:874] (3/4) Epoch 29, batch 1000, aishell_loss[loss=0.1458, simple_loss=0.228, pruned_loss=0.03184, over 4956.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2229, pruned_loss=0.0291, over 979460.27 frames.], batch size: 40, aishell_tot_loss[loss=0.1433, simple_loss=0.2295, pruned_loss=0.02851, over 911375.65 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2155, pruned_loss=0.0292, over 899254.70 frames.], batch size: 40, lr: 2.84e-04
+2022-06-19 06:58:28,361 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 06:58:44,050 INFO [train.py:914] (3/4) Epoch 29, validation: loss=0.165, simple_loss=0.2483, pruned_loss=0.04086, over 1622729.00 frames.
+2022-06-19 06:59:14,575 INFO [train.py:874] (3/4) Epoch 29, batch 1050, aishell_loss[loss=0.1849, simple_loss=0.2652, pruned_loss=0.05226, over 4911.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2225, pruned_loss=0.0289, over 980944.13 frames.], batch size: 68, aishell_tot_loss[loss=0.1432, simple_loss=0.2296, pruned_loss=0.02842, over 919712.75 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2152, pruned_loss=0.02912, over 910019.52 frames.], batch size: 68, lr: 2.84e-04
+2022-06-19 06:59:43,117 INFO [train.py:874] (3/4) Epoch 29, batch 1100, aishell_loss[loss=0.1236, simple_loss=0.1948, pruned_loss=0.02617, over 4921.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2224, pruned_loss=0.02894, over 981729.18 frames.], batch size: 25, aishell_tot_loss[loss=0.1428, simple_loss=0.229, pruned_loss=0.02833, over 926959.95 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2158, pruned_loss=0.02928, over 919225.38 frames.], batch size: 25, lr: 2.84e-04
+2022-06-19 07:00:12,943 INFO [train.py:874] (3/4) Epoch 29, batch 1150, aishell_loss[loss=0.1517, simple_loss=0.2409, pruned_loss=0.03121, over 4974.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2227, pruned_loss=0.02896, over 982766.11 frames.], batch size: 61, aishell_tot_loss[loss=0.1426, simple_loss=0.2289, pruned_loss=0.02813, over 933529.44 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2163, pruned_loss=0.02954, over 927634.05 frames.], batch size: 61, lr: 2.84e-04
+2022-06-19 07:00:42,851 INFO [train.py:874] (3/4) Epoch 29, batch 1200, datatang_loss[loss=0.1244, simple_loss=0.216, pruned_loss=0.01646, over 4936.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2218, pruned_loss=0.02892, over 983327.31 frames.], batch size: 94, aishell_tot_loss[loss=0.1425, simple_loss=0.2288, pruned_loss=0.02812, over 937570.93 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2159, pruned_loss=0.02948, over 936589.25 frames.], batch size: 94, lr: 2.84e-04
+2022-06-19 07:01:11,899 INFO [train.py:874] (3/4) Epoch 29, batch 1250, aishell_loss[loss=0.1428, simple_loss=0.2364, pruned_loss=0.02459, over 4861.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2213, pruned_loss=0.02887, over 983877.03 frames.], batch size: 37, aishell_tot_loss[loss=0.1422, simple_loss=0.2283, pruned_loss=0.02802, over 942618.51 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2159, pruned_loss=0.02952, over 943037.06 frames.], batch size: 37, lr: 2.84e-04
+2022-06-19 07:01:42,658 INFO [train.py:874] (3/4) Epoch 29, batch 1300, datatang_loss[loss=0.1434, simple_loss=0.2196, pruned_loss=0.03363, over 4923.00 frames.], tot_loss[loss=0.14, simple_loss=0.2217, pruned_loss=0.02919, over 984272.75 frames.], batch size: 73, aishell_tot_loss[loss=0.1427, simple_loss=0.2288, pruned_loss=0.02827, over 947435.81 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2156, pruned_loss=0.02962, over 948327.32 frames.], batch size: 73, lr: 2.84e-04
+2022-06-19 07:02:12,786 INFO [train.py:874] (3/4) Epoch 29, batch 1350, aishell_loss[loss=0.1314, simple_loss=0.2203, pruned_loss=0.02124, over 4952.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2216, pruned_loss=0.02903, over 984792.76 frames.], batch size: 56, aishell_tot_loss[loss=0.1426, simple_loss=0.2288, pruned_loss=0.02818, over 951755.10 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2155, pruned_loss=0.02959, over 953130.65 frames.], batch size: 56, lr: 2.83e-04
+2022-06-19 07:02:41,621 INFO [train.py:874] (3/4) Epoch 29, batch 1400, datatang_loss[loss=0.1332, simple_loss=0.2119, pruned_loss=0.02723, over 4924.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2212, pruned_loss=0.02876, over 984992.01 frames.], batch size: 71, aishell_tot_loss[loss=0.1427, simple_loss=0.2288, pruned_loss=0.0283, over 956121.04 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2147, pruned_loss=0.02922, over 956591.94 frames.], batch size: 71, lr: 2.83e-04
+2022-06-19 07:03:11,152 INFO [train.py:874] (3/4) Epoch 29, batch 1450, aishell_loss[loss=0.1801, simple_loss=0.2592, pruned_loss=0.05054, over 4947.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2216, pruned_loss=0.02865, over 984642.58 frames.], batch size: 32, aishell_tot_loss[loss=0.1427, simple_loss=0.229, pruned_loss=0.0282, over 959394.89 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2148, pruned_loss=0.02917, over 959690.49 frames.], batch size: 32, lr: 2.83e-04
+2022-06-19 07:03:40,965 INFO [train.py:874] (3/4) Epoch 29, batch 1500, datatang_loss[loss=0.1553, simple_loss=0.2148, pruned_loss=0.04796, over 4889.00 frames.], tot_loss[loss=0.14, simple_loss=0.2221, pruned_loss=0.02893, over 984755.70 frames.], batch size: 52, aishell_tot_loss[loss=0.1426, simple_loss=0.2292, pruned_loss=0.02802, over 962034.68 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2153, pruned_loss=0.02962, over 963080.92 frames.], batch size: 52, lr: 2.83e-04
+2022-06-19 07:04:10,518 INFO [train.py:874] (3/4) Epoch 29, batch 1550, datatang_loss[loss=0.1397, simple_loss=0.2168, pruned_loss=0.03128, over 4928.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2212, pruned_loss=0.02855, over 984953.23 frames.], batch size: 79, aishell_tot_loss[loss=0.1422, simple_loss=0.2288, pruned_loss=0.02781, over 964522.67 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2147, pruned_loss=0.02946, over 966021.58 frames.], batch size: 79, lr: 2.83e-04
+2022-06-19 07:04:40,814 INFO [train.py:874] (3/4) Epoch 29, batch 1600, aishell_loss[loss=0.1418, simple_loss=0.2281, pruned_loss=0.02773, over 4939.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2206, pruned_loss=0.02824, over 985380.21 frames.], batch size: 32, aishell_tot_loss[loss=0.1422, simple_loss=0.2289, pruned_loss=0.02774, over 966663.86 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2141, pruned_loss=0.02916, over 968936.47 frames.], batch size: 32, lr: 2.83e-04
+2022-06-19 07:05:09,461 INFO [train.py:874] (3/4) Epoch 29, batch 1650, datatang_loss[loss=0.1333, simple_loss=0.2194, pruned_loss=0.02364, over 4942.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2211, pruned_loss=0.02835, over 985394.57 frames.], batch size: 94, aishell_tot_loss[loss=0.1424, simple_loss=0.229, pruned_loss=0.02787, over 968866.44 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2144, pruned_loss=0.02909, over 970876.26 frames.], batch size: 94, lr: 2.83e-04
+2022-06-19 07:05:37,792 INFO [train.py:874] (3/4) Epoch 29, batch 1700, aishell_loss[loss=0.1551, simple_loss=0.2473, pruned_loss=0.03152, over 4949.00 frames.], tot_loss[loss=0.139, simple_loss=0.2214, pruned_loss=0.02836, over 985673.62 frames.], batch size: 45, aishell_tot_loss[loss=0.1423, simple_loss=0.229, pruned_loss=0.02774, over 971134.72 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2144, pruned_loss=0.02919, over 972570.65 frames.], batch size: 45, lr: 2.83e-04
+2022-06-19 07:06:08,742 INFO [train.py:874] (3/4) Epoch 29, batch 1750, datatang_loss[loss=0.1466, simple_loss=0.228, pruned_loss=0.03263, over 4959.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2226, pruned_loss=0.0288, over 985596.01 frames.], batch size: 45, aishell_tot_loss[loss=0.1428, simple_loss=0.2297, pruned_loss=0.02798, over 972844.82 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2149, pruned_loss=0.02942, over 974036.14 frames.], batch size: 45, lr: 2.83e-04
+2022-06-19 07:06:37,264 INFO [train.py:874] (3/4) Epoch 29, batch 1800, datatang_loss[loss=0.1395, simple_loss=0.209, pruned_loss=0.03494, over 4957.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2228, pruned_loss=0.02903, over 985539.30 frames.], batch size: 34, aishell_tot_loss[loss=0.1432, simple_loss=0.2299, pruned_loss=0.02823, over 974128.08 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2151, pruned_loss=0.02944, over 975556.64 frames.], batch size: 34, lr: 2.83e-04
+2022-06-19 07:07:05,217 INFO [train.py:874] (3/4) Epoch 29, batch 1850, datatang_loss[loss=0.124, simple_loss=0.2058, pruned_loss=0.02106, over 4944.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2235, pruned_loss=0.02886, over 985509.73 frames.], batch size: 88, aishell_tot_loss[loss=0.1429, simple_loss=0.2298, pruned_loss=0.02801, over 975725.73 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2155, pruned_loss=0.02957, over 976478.50 frames.], batch size: 88, lr: 2.83e-04
+2022-06-19 07:07:34,108 INFO [train.py:874] (3/4) Epoch 29, batch 1900, aishell_loss[loss=0.1608, simple_loss=0.2457, pruned_loss=0.038, over 4910.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2236, pruned_loss=0.02885, over 985737.13 frames.], batch size: 41, aishell_tot_loss[loss=0.1432, simple_loss=0.2301, pruned_loss=0.02818, over 977408.35 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2151, pruned_loss=0.02948, over 977263.77 frames.], batch size: 41, lr: 2.83e-04
+2022-06-19 07:08:03,024 INFO [train.py:874] (3/4) Epoch 29, batch 1950, aishell_loss[loss=0.1443, simple_loss=0.2476, pruned_loss=0.02049, over 4883.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2239, pruned_loss=0.02885, over 985940.48 frames.], batch size: 42, aishell_tot_loss[loss=0.1431, simple_loss=0.23, pruned_loss=0.02809, over 978517.95 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2155, pruned_loss=0.0296, over 978351.35 frames.], batch size: 42, lr: 2.83e-04
+2022-06-19 07:08:32,536 INFO [train.py:874] (3/4) Epoch 29, batch 2000, aishell_loss[loss=0.1435, simple_loss=0.2311, pruned_loss=0.0279, over 4938.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2246, pruned_loss=0.02906, over 985879.70 frames.], batch size: 54, aishell_tot_loss[loss=0.1434, simple_loss=0.2302, pruned_loss=0.02827, over 979515.78 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2157, pruned_loss=0.02972, over 979046.12 frames.], batch size: 54, lr: 2.83e-04
+2022-06-19 07:08:32,537 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 07:08:49,527 INFO [train.py:914] (3/4) Epoch 29, validation: loss=0.1647, simple_loss=0.2482, pruned_loss=0.04062, over 1622729.00 frames.
+2022-06-19 07:09:18,424 INFO [train.py:874] (3/4) Epoch 29, batch 2050, datatang_loss[loss=0.1493, simple_loss=0.2302, pruned_loss=0.03421, over 4953.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2244, pruned_loss=0.02886, over 985891.56 frames.], batch size: 55, aishell_tot_loss[loss=0.1432, simple_loss=0.2303, pruned_loss=0.02804, over 980274.19 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2159, pruned_loss=0.02973, over 979847.03 frames.], batch size: 55, lr: 2.83e-04
+2022-06-19 07:09:48,727 INFO [train.py:874] (3/4) Epoch 29, batch 2100, datatang_loss[loss=0.1158, simple_loss=0.1923, pruned_loss=0.01964, over 4871.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2234, pruned_loss=0.02867, over 986096.79 frames.], batch size: 39, aishell_tot_loss[loss=0.1431, simple_loss=0.2303, pruned_loss=0.02795, over 980893.78 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2154, pruned_loss=0.02959, over 980829.24 frames.], batch size: 39, lr: 2.83e-04
+2022-06-19 07:10:18,301 INFO [train.py:874] (3/4) Epoch 29, batch 2150, datatang_loss[loss=0.1444, simple_loss=0.2263, pruned_loss=0.03125, over 4916.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2231, pruned_loss=0.02873, over 986214.95 frames.], batch size: 64, aishell_tot_loss[loss=0.1426, simple_loss=0.2294, pruned_loss=0.02786, over 981730.15 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.216, pruned_loss=0.02972, over 981350.82 frames.], batch size: 64, lr: 2.82e-04
+2022-06-19 07:10:47,513 INFO [train.py:874] (3/4) Epoch 29, batch 2200, datatang_loss[loss=0.1445, simple_loss=0.2305, pruned_loss=0.02925, over 4910.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2228, pruned_loss=0.02873, over 986028.84 frames.], batch size: 98, aishell_tot_loss[loss=0.1429, simple_loss=0.2294, pruned_loss=0.02818, over 982100.16 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2158, pruned_loss=0.02942, over 981883.51 frames.], batch size: 98, lr: 2.82e-04
+2022-06-19 07:11:17,507 INFO [train.py:874] (3/4) Epoch 29, batch 2250, datatang_loss[loss=0.1317, simple_loss=0.2108, pruned_loss=0.02629, over 4903.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2231, pruned_loss=0.02863, over 986300.80 frames.], batch size: 47, aishell_tot_loss[loss=0.1431, simple_loss=0.2298, pruned_loss=0.0282, over 982862.75 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2158, pruned_loss=0.0293, over 982369.21 frames.], batch size: 47, lr: 2.82e-04
+2022-06-19 07:11:46,236 INFO [train.py:874] (3/4) Epoch 29, batch 2300, datatang_loss[loss=0.1191, simple_loss=0.1912, pruned_loss=0.02345, over 4924.00 frames.], tot_loss[loss=0.1399, simple_loss=0.222, pruned_loss=0.02888, over 986456.53 frames.], batch size: 64, aishell_tot_loss[loss=0.1435, simple_loss=0.23, pruned_loss=0.02847, over 983291.30 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.215, pruned_loss=0.02924, over 982989.97 frames.], batch size: 64, lr: 2.82e-04
+2022-06-19 07:12:16,291 INFO [train.py:874] (3/4) Epoch 29, batch 2350, datatang_loss[loss=0.1279, simple_loss=0.1932, pruned_loss=0.03132, over 4909.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2225, pruned_loss=0.02939, over 986048.17 frames.], batch size: 47, aishell_tot_loss[loss=0.1438, simple_loss=0.2305, pruned_loss=0.0286, over 983180.13 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2151, pruned_loss=0.02964, over 983431.87 frames.], batch size: 47, lr: 2.82e-04
+2022-06-19 07:12:46,310 INFO [train.py:874] (3/4) Epoch 29, batch 2400, aishell_loss[loss=0.1432, simple_loss=0.2353, pruned_loss=0.02556, over 4949.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2228, pruned_loss=0.02937, over 986194.74 frames.], batch size: 64, aishell_tot_loss[loss=0.1444, simple_loss=0.2311, pruned_loss=0.02885, over 983473.88 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2153, pruned_loss=0.02941, over 983919.00 frames.], batch size: 64, lr: 2.82e-04
+2022-06-19 07:13:14,706 INFO [train.py:874] (3/4) Epoch 29, batch 2450, aishell_loss[loss=0.1571, simple_loss=0.243, pruned_loss=0.03559, over 4928.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2225, pruned_loss=0.02881, over 986086.32 frames.], batch size: 49, aishell_tot_loss[loss=0.1443, simple_loss=0.2312, pruned_loss=0.02872, over 983411.39 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.029, over 984447.92 frames.], batch size: 49, lr: 2.82e-04
+2022-06-19 07:13:44,309 INFO [train.py:874] (3/4) Epoch 29, batch 2500, aishell_loss[loss=0.1276, simple_loss=0.1877, pruned_loss=0.03376, over 4854.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2227, pruned_loss=0.02891, over 985813.04 frames.], batch size: 20, aishell_tot_loss[loss=0.1441, simple_loss=0.2309, pruned_loss=0.02866, over 983368.62 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2149, pruned_loss=0.02914, over 984726.72 frames.], batch size: 20, lr: 2.82e-04
+2022-06-19 07:14:14,340 INFO [train.py:874] (3/4) Epoch 29, batch 2550, aishell_loss[loss=0.1276, simple_loss=0.2218, pruned_loss=0.01672, over 4917.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2242, pruned_loss=0.02915, over 985666.02 frames.], batch size: 52, aishell_tot_loss[loss=0.1445, simple_loss=0.2315, pruned_loss=0.02881, over 983507.40 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2154, pruned_loss=0.02926, over 984887.18 frames.], batch size: 52, lr: 2.82e-04
+2022-06-19 07:14:43,069 INFO [train.py:874] (3/4) Epoch 29, batch 2600, aishell_loss[loss=0.1468, simple_loss=0.2383, pruned_loss=0.02764, over 4946.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2241, pruned_loss=0.02918, over 985922.09 frames.], batch size: 45, aishell_tot_loss[loss=0.1448, simple_loss=0.232, pruned_loss=0.02878, over 983742.20 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2152, pruned_loss=0.02935, over 985248.88 frames.], batch size: 45, lr: 2.82e-04
+2022-06-19 07:15:12,755 INFO [train.py:874] (3/4) Epoch 29, batch 2650, datatang_loss[loss=0.1526, simple_loss=0.2401, pruned_loss=0.03259, over 4959.00 frames.], tot_loss[loss=0.1409, simple_loss=0.224, pruned_loss=0.02887, over 986197.47 frames.], batch size: 99, aishell_tot_loss[loss=0.1445, simple_loss=0.2318, pruned_loss=0.02863, over 984198.52 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2151, pruned_loss=0.02921, over 985436.94 frames.], batch size: 99, lr: 2.82e-04
+2022-06-19 07:15:41,978 INFO [train.py:874] (3/4) Epoch 29, batch 2700, datatang_loss[loss=0.124, simple_loss=0.2053, pruned_loss=0.02139, over 4945.00 frames.], tot_loss[loss=0.1409, simple_loss=0.224, pruned_loss=0.02885, over 986084.90 frames.], batch size: 88, aishell_tot_loss[loss=0.1442, simple_loss=0.2313, pruned_loss=0.02854, over 984454.37 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2152, pruned_loss=0.02929, over 985427.53 frames.], batch size: 88, lr: 2.82e-04
+2022-06-19 07:16:09,401 INFO [train.py:874] (3/4) Epoch 29, batch 2750, aishell_loss[loss=0.1489, simple_loss=0.2459, pruned_loss=0.02591, over 4916.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2236, pruned_loss=0.02847, over 985278.31 frames.], batch size: 78, aishell_tot_loss[loss=0.1437, simple_loss=0.231, pruned_loss=0.02822, over 984094.71 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2151, pruned_loss=0.02919, over 985213.79 frames.], batch size: 78, lr: 2.82e-04
+2022-06-19 07:16:40,492 INFO [train.py:874] (3/4) Epoch 29, batch 2800, datatang_loss[loss=0.131, simple_loss=0.2089, pruned_loss=0.02654, over 4903.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2231, pruned_loss=0.02834, over 985256.75 frames.], batch size: 59, aishell_tot_loss[loss=0.1432, simple_loss=0.2306, pruned_loss=0.02792, over 984000.96 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2155, pruned_loss=0.02927, over 985406.36 frames.], batch size: 59, lr: 2.82e-04
+2022-06-19 07:17:10,575 INFO [train.py:874] (3/4) Epoch 29, batch 2850, aishell_loss[loss=0.1421, simple_loss=0.2223, pruned_loss=0.03093, over 4980.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2233, pruned_loss=0.02852, over 985545.64 frames.], batch size: 39, aishell_tot_loss[loss=0.1431, simple_loss=0.2302, pruned_loss=0.02797, over 984346.94 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.216, pruned_loss=0.02938, over 985504.36 frames.], batch size: 39, lr: 2.82e-04
+2022-06-19 07:17:38,911 INFO [train.py:874] (3/4) Epoch 29, batch 2900, aishell_loss[loss=0.1214, simple_loss=0.2196, pruned_loss=0.01158, over 4918.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2216, pruned_loss=0.02809, over 985653.68 frames.], batch size: 46, aishell_tot_loss[loss=0.1423, simple_loss=0.2291, pruned_loss=0.02775, over 984709.17 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2152, pruned_loss=0.02914, over 985399.86 frames.], batch size: 46, lr: 2.82e-04
+2022-06-19 07:18:09,836 INFO [train.py:874] (3/4) Epoch 29, batch 2950, datatang_loss[loss=0.1318, simple_loss=0.2099, pruned_loss=0.02686, over 4950.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2223, pruned_loss=0.02844, over 985792.44 frames.], batch size: 67, aishell_tot_loss[loss=0.1426, simple_loss=0.2296, pruned_loss=0.02781, over 984973.01 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2154, pruned_loss=0.02935, over 985437.89 frames.], batch size: 67, lr: 2.82e-04
+2022-06-19 07:18:38,438 INFO [train.py:874] (3/4) Epoch 29, batch 3000, datatang_loss[loss=0.102, simple_loss=0.1786, pruned_loss=0.01271, over 4906.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2222, pruned_loss=0.02817, over 985742.17 frames.], batch size: 47, aishell_tot_loss[loss=0.1429, simple_loss=0.2301, pruned_loss=0.02784, over 985297.76 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2146, pruned_loss=0.02901, over 985200.83 frames.], batch size: 47, lr: 2.81e-04
+2022-06-19 07:18:38,439 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 07:18:54,223 INFO [train.py:914] (3/4) Epoch 29, validation: loss=0.165, simple_loss=0.2488, pruned_loss=0.0406, over 1622729.00 frames.
+2022-06-19 07:19:24,212 INFO [train.py:874] (3/4) Epoch 29, batch 3050, datatang_loss[loss=0.121, simple_loss=0.2004, pruned_loss=0.02078, over 4899.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2214, pruned_loss=0.02817, over 985610.40 frames.], batch size: 52, aishell_tot_loss[loss=0.1426, simple_loss=0.2297, pruned_loss=0.02775, over 984999.48 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2145, pruned_loss=0.029, over 985459.30 frames.], batch size: 52, lr: 2.81e-04
+2022-06-19 07:19:51,671 INFO [train.py:874] (3/4) Epoch 29, batch 3100, aishell_loss[loss=0.1387, simple_loss=0.2319, pruned_loss=0.0228, over 4937.00 frames.], tot_loss[loss=0.1394, simple_loss=0.222, pruned_loss=0.02842, over 985785.55 frames.], batch size: 49, aishell_tot_loss[loss=0.1428, simple_loss=0.2298, pruned_loss=0.02791, over 985214.65 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.02906, over 985515.60 frames.], batch size: 49, lr: 2.81e-04
+2022-06-19 07:20:22,511 INFO [train.py:874] (3/4) Epoch 29, batch 3150, datatang_loss[loss=0.1434, simple_loss=0.2167, pruned_loss=0.03509, over 4926.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2212, pruned_loss=0.02821, over 985783.85 frames.], batch size: 77, aishell_tot_loss[loss=0.1426, simple_loss=0.2294, pruned_loss=0.02787, over 985375.11 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2143, pruned_loss=0.02887, over 985460.20 frames.], batch size: 77, lr: 2.81e-04
+2022-06-19 07:20:52,237 INFO [train.py:874] (3/4) Epoch 29, batch 3200, aishell_loss[loss=0.1372, simple_loss=0.2304, pruned_loss=0.02198, over 4883.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2219, pruned_loss=0.0282, over 986203.48 frames.], batch size: 34, aishell_tot_loss[loss=0.1424, simple_loss=0.2295, pruned_loss=0.02767, over 985537.81 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.02903, over 985827.27 frames.], batch size: 34, lr: 2.81e-04
+2022-06-19 07:21:20,850 INFO [train.py:874] (3/4) Epoch 29, batch 3250, aishell_loss[loss=0.1431, simple_loss=0.2327, pruned_loss=0.02678, over 4956.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2212, pruned_loss=0.02804, over 986062.59 frames.], batch size: 56, aishell_tot_loss[loss=0.1418, simple_loss=0.2288, pruned_loss=0.02734, over 985633.99 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2147, pruned_loss=0.02916, over 985712.80 frames.], batch size: 56, lr: 2.81e-04
+2022-06-19 07:21:51,307 INFO [train.py:874] (3/4) Epoch 29, batch 3300, aishell_loss[loss=0.127, simple_loss=0.2174, pruned_loss=0.01832, over 4883.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2219, pruned_loss=0.02841, over 985679.20 frames.], batch size: 47, aishell_tot_loss[loss=0.142, simple_loss=0.2291, pruned_loss=0.0274, over 985353.31 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.215, pruned_loss=0.02946, over 985696.56 frames.], batch size: 47, lr: 2.81e-04
+2022-06-19 07:22:21,004 INFO [train.py:874] (3/4) Epoch 29, batch 3350, datatang_loss[loss=0.1542, simple_loss=0.2273, pruned_loss=0.04057, over 4949.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2216, pruned_loss=0.02839, over 985753.85 frames.], batch size: 45, aishell_tot_loss[loss=0.1414, simple_loss=0.2283, pruned_loss=0.02727, over 985259.00 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2153, pruned_loss=0.02954, over 985912.13 frames.], batch size: 45, lr: 2.81e-04
+2022-06-19 07:22:48,915 INFO [train.py:874] (3/4) Epoch 29, batch 3400, datatang_loss[loss=0.1238, simple_loss=0.2092, pruned_loss=0.0192, over 4923.00 frames.], tot_loss[loss=0.1397, simple_loss=0.222, pruned_loss=0.02867, over 985693.49 frames.], batch size: 81, aishell_tot_loss[loss=0.1419, simple_loss=0.2288, pruned_loss=0.02753, over 985225.63 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2155, pruned_loss=0.02952, over 985901.24 frames.], batch size: 81, lr: 2.81e-04
+2022-06-19 07:23:20,427 INFO [train.py:874] (3/4) Epoch 29, batch 3450, datatang_loss[loss=0.1379, simple_loss=0.218, pruned_loss=0.02887, over 4970.00 frames.], tot_loss[loss=0.14, simple_loss=0.2227, pruned_loss=0.02869, over 985812.74 frames.], batch size: 60, aishell_tot_loss[loss=0.1424, simple_loss=0.2294, pruned_loss=0.02768, over 985427.79 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2154, pruned_loss=0.02945, over 985861.33 frames.], batch size: 60, lr: 2.81e-04
+2022-06-19 07:23:50,348 INFO [train.py:874] (3/4) Epoch 29, batch 3500, aishell_loss[loss=0.1469, simple_loss=0.229, pruned_loss=0.0324, over 4950.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2227, pruned_loss=0.02869, over 985600.94 frames.], batch size: 32, aishell_tot_loss[loss=0.1419, simple_loss=0.229, pruned_loss=0.02741, over 985289.59 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2158, pruned_loss=0.02977, over 985817.44 frames.], batch size: 32, lr: 2.81e-04
+2022-06-19 07:24:19,348 INFO [train.py:874] (3/4) Epoch 29, batch 3550, aishell_loss[loss=0.1352, simple_loss=0.2253, pruned_loss=0.0226, over 4878.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2226, pruned_loss=0.02854, over 985356.74 frames.], batch size: 47, aishell_tot_loss[loss=0.1418, simple_loss=0.2289, pruned_loss=0.02732, over 985199.78 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2157, pruned_loss=0.02975, over 985675.34 frames.], batch size: 47, lr: 2.81e-04
+2022-06-19 07:24:50,032 INFO [train.py:874] (3/4) Epoch 29, batch 3600, datatang_loss[loss=0.1096, simple_loss=0.187, pruned_loss=0.01608, over 4902.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2223, pruned_loss=0.02835, over 985507.89 frames.], batch size: 59, aishell_tot_loss[loss=0.1418, simple_loss=0.229, pruned_loss=0.02728, over 985292.76 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2156, pruned_loss=0.02959, over 985730.87 frames.], batch size: 59, lr: 2.81e-04
+2022-06-19 07:25:18,947 INFO [train.py:874] (3/4) Epoch 29, batch 3650, datatang_loss[loss=0.1262, simple_loss=0.2133, pruned_loss=0.01953, over 4926.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2218, pruned_loss=0.0284, over 985034.65 frames.], batch size: 77, aishell_tot_loss[loss=0.1417, simple_loss=0.2288, pruned_loss=0.0273, over 984964.12 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2154, pruned_loss=0.02957, over 985545.43 frames.], batch size: 77, lr: 2.81e-04
+2022-06-19 07:25:46,590 INFO [train.py:874] (3/4) Epoch 29, batch 3700, aishell_loss[loss=0.1418, simple_loss=0.2284, pruned_loss=0.02757, over 4970.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2222, pruned_loss=0.02851, over 985663.30 frames.], batch size: 44, aishell_tot_loss[loss=0.1424, simple_loss=0.2296, pruned_loss=0.02764, over 985406.33 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2152, pruned_loss=0.02933, over 985723.19 frames.], batch size: 44, lr: 2.81e-04
+2022-06-19 07:26:16,125 INFO [train.py:874] (3/4) Epoch 29, batch 3750, aishell_loss[loss=0.1501, simple_loss=0.2323, pruned_loss=0.03397, over 4906.00 frames.], tot_loss[loss=0.14, simple_loss=0.223, pruned_loss=0.02849, over 985700.27 frames.], batch size: 33, aishell_tot_loss[loss=0.1423, simple_loss=0.2295, pruned_loss=0.02758, over 985442.10 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2155, pruned_loss=0.02945, over 985765.80 frames.], batch size: 33, lr: 2.81e-04
+2022-06-19 07:26:43,590 INFO [train.py:874] (3/4) Epoch 29, batch 3800, datatang_loss[loss=0.1391, simple_loss=0.2212, pruned_loss=0.02849, over 4939.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2227, pruned_loss=0.02841, over 985471.29 frames.], batch size: 94, aishell_tot_loss[loss=0.1428, simple_loss=0.23, pruned_loss=0.0278, over 985241.67 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2148, pruned_loss=0.02914, over 985745.29 frames.], batch size: 94, lr: 2.81e-04
+2022-06-19 07:27:13,073 INFO [train.py:874] (3/4) Epoch 29, batch 3850, aishell_loss[loss=0.1249, simple_loss=0.1914, pruned_loss=0.02923, over 4833.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2225, pruned_loss=0.0283, over 985310.09 frames.], batch size: 21, aishell_tot_loss[loss=0.1429, simple_loss=0.2299, pruned_loss=0.02797, over 985195.14 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2145, pruned_loss=0.02884, over 985641.67 frames.], batch size: 21, lr: 2.80e-04
+2022-06-19 07:27:40,507 INFO [train.py:874] (3/4) Epoch 29, batch 3900, datatang_loss[loss=0.1099, simple_loss=0.191, pruned_loss=0.01437, over 4930.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2229, pruned_loss=0.02865, over 985573.50 frames.], batch size: 79, aishell_tot_loss[loss=0.1436, simple_loss=0.2307, pruned_loss=0.02821, over 985156.58 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2149, pruned_loss=0.02889, over 985912.23 frames.], batch size: 79, lr: 2.80e-04
+2022-06-19 07:28:09,840 INFO [train.py:874] (3/4) Epoch 29, batch 3950, aishell_loss[loss=0.1052, simple_loss=0.1877, pruned_loss=0.0114, over 4982.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2222, pruned_loss=0.02802, over 985604.07 frames.], batch size: 27, aishell_tot_loss[loss=0.1435, simple_loss=0.2309, pruned_loss=0.02804, over 985281.59 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2141, pruned_loss=0.02842, over 985823.98 frames.], batch size: 27, lr: 2.80e-04
+2022-06-19 07:28:37,205 INFO [train.py:874] (3/4) Epoch 29, batch 4000, aishell_loss[loss=0.1544, simple_loss=0.2468, pruned_loss=0.03104, over 4956.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2216, pruned_loss=0.02795, over 985437.50 frames.], batch size: 31, aishell_tot_loss[loss=0.1431, simple_loss=0.2304, pruned_loss=0.02791, over 985090.93 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2142, pruned_loss=0.02841, over 985850.35 frames.], batch size: 31, lr: 2.80e-04
+2022-06-19 07:28:37,206 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 07:28:54,128 INFO [train.py:914] (3/4) Epoch 29, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04075, over 1622729.00 frames.
+2022-06-19 07:29:22,713 INFO [train.py:874] (3/4) Epoch 29, batch 4050, aishell_loss[loss=0.1484, simple_loss=0.239, pruned_loss=0.02887, over 4969.00 frames.], tot_loss[loss=0.139, simple_loss=0.2224, pruned_loss=0.02786, over 985552.02 frames.], batch size: 39, aishell_tot_loss[loss=0.1436, simple_loss=0.2311, pruned_loss=0.02802, over 985173.14 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2138, pruned_loss=0.02817, over 985885.17 frames.], batch size: 39, lr: 2.80e-04
+2022-06-19 07:29:48,245 INFO [train.py:874] (3/4) Epoch 29, batch 4100, aishell_loss[loss=0.1752, simple_loss=0.2549, pruned_loss=0.04772, over 4925.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2228, pruned_loss=0.02815, over 985367.37 frames.], batch size: 41, aishell_tot_loss[loss=0.1441, simple_loss=0.2316, pruned_loss=0.02836, over 984926.99 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2136, pruned_loss=0.02809, over 985961.74 frames.], batch size: 41, lr: 2.80e-04
+2022-06-19 07:30:56,084 INFO [train.py:874] (3/4) Epoch 30, batch 50, datatang_loss[loss=0.1272, simple_loss=0.2124, pruned_loss=0.02101, over 4961.00 frames.], tot_loss[loss=0.1333, simple_loss=0.2162, pruned_loss=0.02521, over 218588.66 frames.], batch size: 91, aishell_tot_loss[loss=0.1417, simple_loss=0.23, pruned_loss=0.02667, over 120232.29 frames.], datatang_tot_loss[loss=0.1244, simple_loss=0.2015, pruned_loss=0.02371, over 112031.66 frames.], batch size: 91, lr: 2.75e-04
+2022-06-19 07:31:24,137 INFO [train.py:874] (3/4) Epoch 30, batch 100, datatang_loss[loss=0.1729, simple_loss=0.2537, pruned_loss=0.04608, over 4941.00 frames.], tot_loss[loss=0.1336, simple_loss=0.215, pruned_loss=0.02609, over 388305.87 frames.], batch size: 109, aishell_tot_loss[loss=0.1384, simple_loss=0.2254, pruned_loss=0.02569, over 218069.23 frames.], datatang_tot_loss[loss=0.1288, simple_loss=0.2049, pruned_loss=0.02637, over 218686.40 frames.], batch size: 109, lr: 2.75e-04
+2022-06-19 07:31:54,685 INFO [train.py:874] (3/4) Epoch 30, batch 150, aishell_loss[loss=0.1533, simple_loss=0.2303, pruned_loss=0.03813, over 4893.00 frames.], tot_loss[loss=0.1363, simple_loss=0.219, pruned_loss=0.02686, over 521140.53 frames.], batch size: 34, aishell_tot_loss[loss=0.1414, simple_loss=0.229, pruned_loss=0.02689, over 328813.36 frames.], datatang_tot_loss[loss=0.1298, simple_loss=0.2064, pruned_loss=0.02662, over 288515.61 frames.], batch size: 34, lr: 2.75e-04
+2022-06-19 07:32:24,885 INFO [train.py:874] (3/4) Epoch 30, batch 200, aishell_loss[loss=0.1328, simple_loss=0.2262, pruned_loss=0.01969, over 4966.00 frames.], tot_loss[loss=0.1366, simple_loss=0.2193, pruned_loss=0.02689, over 624195.09 frames.], batch size: 61, aishell_tot_loss[loss=0.1416, simple_loss=0.2297, pruned_loss=0.02678, over 397353.71 frames.], datatang_tot_loss[loss=0.1308, simple_loss=0.2079, pruned_loss=0.02683, over 379919.92 frames.], batch size: 61, lr: 2.75e-04
+2022-06-19 07:32:53,110 INFO [train.py:874] (3/4) Epoch 30, batch 250, datatang_loss[loss=0.1722, simple_loss=0.246, pruned_loss=0.04922, over 4874.00 frames.], tot_loss[loss=0.1367, simple_loss=0.2194, pruned_loss=0.02696, over 704348.17 frames.], batch size: 39, aishell_tot_loss[loss=0.1417, simple_loss=0.2297, pruned_loss=0.02686, over 461501.01 frames.], datatang_tot_loss[loss=0.1312, simple_loss=0.2087, pruned_loss=0.0269, over 456515.09 frames.], batch size: 39, lr: 2.75e-04
+2022-06-19 07:33:24,747 INFO [train.py:874] (3/4) Epoch 30, batch 300, aishell_loss[loss=0.1149, simple_loss=0.1928, pruned_loss=0.01855, over 4941.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2206, pruned_loss=0.02732, over 766782.98 frames.], batch size: 25, aishell_tot_loss[loss=0.1421, simple_loss=0.2303, pruned_loss=0.02696, over 523273.44 frames.], datatang_tot_loss[loss=0.1324, simple_loss=0.21, pruned_loss=0.0274, over 518863.00 frames.], batch size: 25, lr: 2.75e-04
+2022-06-19 07:33:54,684 INFO [train.py:874] (3/4) Epoch 30, batch 350, aishell_loss[loss=0.154, simple_loss=0.2406, pruned_loss=0.03376, over 4921.00 frames.], tot_loss[loss=0.1389, simple_loss=0.222, pruned_loss=0.02787, over 815174.94 frames.], batch size: 52, aishell_tot_loss[loss=0.1423, simple_loss=0.2302, pruned_loss=0.02717, over 593745.69 frames.], datatang_tot_loss[loss=0.1337, simple_loss=0.211, pruned_loss=0.02813, over 556862.72 frames.], batch size: 52, lr: 2.75e-04
+2022-06-19 07:34:23,758 INFO [train.py:874] (3/4) Epoch 30, batch 400, datatang_loss[loss=0.1847, simple_loss=0.2543, pruned_loss=0.05749, over 4929.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2223, pruned_loss=0.02817, over 852937.94 frames.], batch size: 109, aishell_tot_loss[loss=0.1421, simple_loss=0.2295, pruned_loss=0.02734, over 643177.27 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2125, pruned_loss=0.0285, over 603710.73 frames.], batch size: 109, lr: 2.75e-04
+2022-06-19 07:34:53,352 INFO [train.py:874] (3/4) Epoch 30, batch 450, datatang_loss[loss=0.1343, simple_loss=0.2157, pruned_loss=0.02649, over 4943.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2223, pruned_loss=0.02848, over 882355.82 frames.], batch size: 50, aishell_tot_loss[loss=0.1426, simple_loss=0.2301, pruned_loss=0.02754, over 682036.84 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2125, pruned_loss=0.02882, over 650318.46 frames.], batch size: 50, lr: 2.75e-04
+2022-06-19 07:35:22,830 INFO [train.py:874] (3/4) Epoch 30, batch 500, datatang_loss[loss=0.1452, simple_loss=0.2258, pruned_loss=0.03227, over 4926.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2238, pruned_loss=0.02845, over 905437.22 frames.], batch size: 34, aishell_tot_loss[loss=0.1429, simple_loss=0.2308, pruned_loss=0.02751, over 723078.47 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2137, pruned_loss=0.02897, over 684056.25 frames.], batch size: 34, lr: 2.75e-04
+2022-06-19 07:35:50,051 INFO [train.py:874] (3/4) Epoch 30, batch 550, aishell_loss[loss=0.1437, simple_loss=0.2353, pruned_loss=0.02606, over 4943.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2242, pruned_loss=0.02851, over 923118.95 frames.], batch size: 54, aishell_tot_loss[loss=0.1433, simple_loss=0.2311, pruned_loss=0.02774, over 762915.29 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2134, pruned_loss=0.02895, over 708788.06 frames.], batch size: 54, lr: 2.75e-04
+2022-06-19 07:36:21,128 INFO [train.py:874] (3/4) Epoch 30, batch 600, datatang_loss[loss=0.1741, simple_loss=0.2558, pruned_loss=0.04617, over 4916.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2231, pruned_loss=0.02824, over 937032.53 frames.], batch size: 109, aishell_tot_loss[loss=0.1425, simple_loss=0.2299, pruned_loss=0.02759, over 790126.17 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2137, pruned_loss=0.02878, over 740227.15 frames.], batch size: 109, lr: 2.75e-04
+2022-06-19 07:36:51,317 INFO [train.py:874] (3/4) Epoch 30, batch 650, datatang_loss[loss=0.1536, simple_loss=0.2229, pruned_loss=0.04213, over 4890.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2222, pruned_loss=0.02815, over 947640.07 frames.], batch size: 52, aishell_tot_loss[loss=0.1424, simple_loss=0.2296, pruned_loss=0.02762, over 813023.78 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2133, pruned_loss=0.02864, over 769079.04 frames.], batch size: 52, lr: 2.75e-04
+2022-06-19 07:37:20,510 INFO [train.py:874] (3/4) Epoch 30, batch 700, datatang_loss[loss=0.1415, simple_loss=0.2226, pruned_loss=0.03016, over 4957.00 frames.], tot_loss[loss=0.138, simple_loss=0.2211, pruned_loss=0.02742, over 955736.23 frames.], batch size: 86, aishell_tot_loss[loss=0.1419, simple_loss=0.2292, pruned_loss=0.02726, over 830792.05 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2127, pruned_loss=0.02806, over 797339.21 frames.], batch size: 86, lr: 2.75e-04
+2022-06-19 07:37:50,965 INFO [train.py:874] (3/4) Epoch 30, batch 750, aishell_loss[loss=0.1419, simple_loss=0.2208, pruned_loss=0.03151, over 4913.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2215, pruned_loss=0.02763, over 962524.27 frames.], batch size: 41, aishell_tot_loss[loss=0.1416, simple_loss=0.2289, pruned_loss=0.02713, over 850437.17 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2135, pruned_loss=0.02841, over 817956.07 frames.], batch size: 41, lr: 2.75e-04
+2022-06-19 07:38:21,530 INFO [train.py:874] (3/4) Epoch 30, batch 800, datatang_loss[loss=0.158, simple_loss=0.2334, pruned_loss=0.04131, over 4929.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2215, pruned_loss=0.02756, over 967793.87 frames.], batch size: 42, aishell_tot_loss[loss=0.1417, simple_loss=0.2289, pruned_loss=0.02727, over 869523.34 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2132, pruned_loss=0.02818, over 833901.31 frames.], batch size: 42, lr: 2.75e-04
+2022-06-19 07:38:51,812 INFO [train.py:874] (3/4) Epoch 30, batch 850, datatang_loss[loss=0.1434, simple_loss=0.23, pruned_loss=0.02838, over 4961.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2212, pruned_loss=0.02757, over 971836.97 frames.], batch size: 91, aishell_tot_loss[loss=0.1413, simple_loss=0.2285, pruned_loss=0.02702, over 879790.68 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2138, pruned_loss=0.02835, over 856106.38 frames.], batch size: 91, lr: 2.75e-04
+2022-06-19 07:39:22,127 INFO [train.py:874] (3/4) Epoch 30, batch 900, datatang_loss[loss=0.1448, simple_loss=0.212, pruned_loss=0.03886, over 4957.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2206, pruned_loss=0.02759, over 975183.33 frames.], batch size: 45, aishell_tot_loss[loss=0.1411, simple_loss=0.2284, pruned_loss=0.02689, over 889961.22 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2136, pruned_loss=0.02843, over 874416.38 frames.], batch size: 45, lr: 2.74e-04
+2022-06-19 07:39:50,714 INFO [train.py:874] (3/4) Epoch 30, batch 950, aishell_loss[loss=0.1457, simple_loss=0.2395, pruned_loss=0.02597, over 4953.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2208, pruned_loss=0.02779, over 977596.37 frames.], batch size: 61, aishell_tot_loss[loss=0.1409, simple_loss=0.228, pruned_loss=0.02691, over 902061.14 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2141, pruned_loss=0.02865, over 886637.50 frames.], batch size: 61, lr: 2.74e-04
+2022-06-19 07:40:20,538 INFO [train.py:874] (3/4) Epoch 30, batch 1000, datatang_loss[loss=0.1577, simple_loss=0.2426, pruned_loss=0.03645, over 4916.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2203, pruned_loss=0.02761, over 978859.86 frames.], batch size: 107, aishell_tot_loss[loss=0.1404, simple_loss=0.2272, pruned_loss=0.02674, over 912110.30 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.214, pruned_loss=0.0286, over 897393.49 frames.], batch size: 107, lr: 2.74e-04
+2022-06-19 07:40:20,539 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 07:40:38,847 INFO [train.py:914] (3/4) Epoch 30, validation: loss=0.1647, simple_loss=0.2491, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-19 07:41:06,112 INFO [train.py:874] (3/4) Epoch 30, batch 1050, datatang_loss[loss=0.123, simple_loss=0.2077, pruned_loss=0.01921, over 4922.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2211, pruned_loss=0.02795, over 980352.50 frames.], batch size: 73, aishell_tot_loss[loss=0.1411, simple_loss=0.2278, pruned_loss=0.02714, over 921424.17 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.214, pruned_loss=0.02856, over 906979.03 frames.], batch size: 73, lr: 2.74e-04
+2022-06-19 07:41:34,474 INFO [train.py:874] (3/4) Epoch 30, batch 1100, datatang_loss[loss=0.1317, simple_loss=0.212, pruned_loss=0.0257, over 4958.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2217, pruned_loss=0.02809, over 981692.10 frames.], batch size: 86, aishell_tot_loss[loss=0.1413, simple_loss=0.2281, pruned_loss=0.02724, over 930032.92 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2142, pruned_loss=0.02868, over 915097.47 frames.], batch size: 86, lr: 2.74e-04
+2022-06-19 07:42:04,654 INFO [train.py:874] (3/4) Epoch 30, batch 1150, datatang_loss[loss=0.1417, simple_loss=0.2146, pruned_loss=0.03443, over 4965.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2204, pruned_loss=0.02785, over 982512.39 frames.], batch size: 60, aishell_tot_loss[loss=0.1416, simple_loss=0.2282, pruned_loss=0.02745, over 934492.38 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2134, pruned_loss=0.02822, over 925864.14 frames.], batch size: 60, lr: 2.74e-04
+2022-06-19 07:42:34,013 INFO [train.py:874] (3/4) Epoch 30, batch 1200, datatang_loss[loss=0.1188, simple_loss=0.1972, pruned_loss=0.02022, over 4923.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2195, pruned_loss=0.02793, over 983773.51 frames.], batch size: 73, aishell_tot_loss[loss=0.1411, simple_loss=0.2275, pruned_loss=0.02732, over 939716.44 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2134, pruned_loss=0.02843, over 934455.11 frames.], batch size: 73, lr: 2.74e-04
+2022-06-19 07:43:03,428 INFO [train.py:874] (3/4) Epoch 30, batch 1250, aishell_loss[loss=0.1555, simple_loss=0.2481, pruned_loss=0.03144, over 4914.00 frames.], tot_loss[loss=0.138, simple_loss=0.2204, pruned_loss=0.02776, over 984061.97 frames.], batch size: 46, aishell_tot_loss[loss=0.1417, simple_loss=0.2283, pruned_loss=0.02757, over 946322.65 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2129, pruned_loss=0.02803, over 938937.61 frames.], batch size: 46, lr: 2.74e-04
+2022-06-19 07:43:31,158 INFO [train.py:874] (3/4) Epoch 30, batch 1300, aishell_loss[loss=0.1649, simple_loss=0.2513, pruned_loss=0.03929, over 4856.00 frames.], tot_loss[loss=0.1391, simple_loss=0.222, pruned_loss=0.02806, over 984464.10 frames.], batch size: 37, aishell_tot_loss[loss=0.1422, simple_loss=0.2291, pruned_loss=0.02767, over 952592.41 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.213, pruned_loss=0.02826, over 942431.27 frames.], batch size: 37, lr: 2.74e-04
+2022-06-19 07:44:01,901 INFO [train.py:874] (3/4) Epoch 30, batch 1350, datatang_loss[loss=0.1054, simple_loss=0.186, pruned_loss=0.01244, over 4911.00 frames.], tot_loss[loss=0.139, simple_loss=0.2216, pruned_loss=0.02817, over 984644.98 frames.], batch size: 64, aishell_tot_loss[loss=0.1423, simple_loss=0.2291, pruned_loss=0.02771, over 956197.60 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2129, pruned_loss=0.02837, over 947827.05 frames.], batch size: 64, lr: 2.74e-04
+2022-06-19 07:44:33,766 INFO [train.py:874] (3/4) Epoch 30, batch 1400, aishell_loss[loss=0.1312, simple_loss=0.2106, pruned_loss=0.02592, over 4970.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2221, pruned_loss=0.02825, over 985366.11 frames.], batch size: 27, aishell_tot_loss[loss=0.1427, simple_loss=0.2296, pruned_loss=0.02789, over 961072.88 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2124, pruned_loss=0.02832, over 951043.31 frames.], batch size: 27, lr: 2.74e-04
+2022-06-19 07:45:02,001 INFO [train.py:874] (3/4) Epoch 30, batch 1450, aishell_loss[loss=0.1406, simple_loss=0.2261, pruned_loss=0.02757, over 4960.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2221, pruned_loss=0.02802, over 985303.64 frames.], batch size: 31, aishell_tot_loss[loss=0.1425, simple_loss=0.2295, pruned_loss=0.02777, over 964236.61 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2128, pruned_loss=0.02821, over 954676.14 frames.], batch size: 31, lr: 2.74e-04
+2022-06-19 07:45:32,017 INFO [train.py:874] (3/4) Epoch 30, batch 1500, datatang_loss[loss=0.126, simple_loss=0.202, pruned_loss=0.02504, over 4921.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2222, pruned_loss=0.02768, over 985502.63 frames.], batch size: 75, aishell_tot_loss[loss=0.1425, simple_loss=0.2298, pruned_loss=0.02767, over 966897.35 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2128, pruned_loss=0.02797, over 958267.43 frames.], batch size: 75, lr: 2.74e-04
+2022-06-19 07:46:02,924 INFO [train.py:874] (3/4) Epoch 30, batch 1550, datatang_loss[loss=0.123, simple_loss=0.1926, pruned_loss=0.02673, over 4876.00 frames.], tot_loss[loss=0.139, simple_loss=0.2222, pruned_loss=0.02787, over 985691.89 frames.], batch size: 39, aishell_tot_loss[loss=0.1418, simple_loss=0.2288, pruned_loss=0.02737, over 969117.91 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.214, pruned_loss=0.02842, over 961709.43 frames.], batch size: 39, lr: 2.74e-04
+2022-06-19 07:46:30,648 INFO [train.py:874] (3/4) Epoch 30, batch 1600, datatang_loss[loss=0.1285, simple_loss=0.2074, pruned_loss=0.02478, over 4824.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2216, pruned_loss=0.02795, over 985262.98 frames.], batch size: 30, aishell_tot_loss[loss=0.1421, simple_loss=0.2289, pruned_loss=0.02769, over 970758.78 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2135, pruned_loss=0.0282, over 964433.24 frames.], batch size: 30, lr: 2.74e-04
+2022-06-19 07:47:01,042 INFO [train.py:874] (3/4) Epoch 30, batch 1650, aishell_loss[loss=0.1566, simple_loss=0.239, pruned_loss=0.03712, over 4868.00 frames.], tot_loss[loss=0.1384, simple_loss=0.221, pruned_loss=0.02793, over 985036.19 frames.], batch size: 35, aishell_tot_loss[loss=0.1418, simple_loss=0.2285, pruned_loss=0.02759, over 971665.24 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2137, pruned_loss=0.02826, over 967563.19 frames.], batch size: 35, lr: 2.74e-04
+2022-06-19 07:47:31,309 INFO [train.py:874] (3/4) Epoch 30, batch 1700, datatang_loss[loss=0.1274, simple_loss=0.2078, pruned_loss=0.02353, over 4911.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2206, pruned_loss=0.02787, over 985133.33 frames.], batch size: 64, aishell_tot_loss[loss=0.1415, simple_loss=0.2282, pruned_loss=0.0274, over 973098.25 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2136, pruned_loss=0.0284, over 969842.14 frames.], batch size: 64, lr: 2.74e-04
+2022-06-19 07:47:59,760 INFO [train.py:874] (3/4) Epoch 30, batch 1750, datatang_loss[loss=0.1375, simple_loss=0.2082, pruned_loss=0.03338, over 4882.00 frames.], tot_loss[loss=0.1384, simple_loss=0.221, pruned_loss=0.02788, over 985261.48 frames.], batch size: 42, aishell_tot_loss[loss=0.1419, simple_loss=0.2286, pruned_loss=0.02755, over 974589.92 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2135, pruned_loss=0.02826, over 971706.35 frames.], batch size: 42, lr: 2.74e-04
+2022-06-19 07:48:29,651 INFO [train.py:874] (3/4) Epoch 30, batch 1800, datatang_loss[loss=0.14, simple_loss=0.2187, pruned_loss=0.03062, over 4920.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2218, pruned_loss=0.02822, over 985444.53 frames.], batch size: 75, aishell_tot_loss[loss=0.1417, simple_loss=0.2286, pruned_loss=0.02743, over 975867.14 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2143, pruned_loss=0.02873, over 973494.68 frames.], batch size: 75, lr: 2.73e-04
+2022-06-19 07:49:00,495 INFO [train.py:874] (3/4) Epoch 30, batch 1850, datatang_loss[loss=0.1283, simple_loss=0.2079, pruned_loss=0.02438, over 4920.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2217, pruned_loss=0.02819, over 985338.73 frames.], batch size: 75, aishell_tot_loss[loss=0.1418, simple_loss=0.2286, pruned_loss=0.0275, over 976876.56 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2145, pruned_loss=0.02865, over 974943.79 frames.], batch size: 75, lr: 2.73e-04
+2022-06-19 07:49:28,739 INFO [train.py:874] (3/4) Epoch 30, batch 1900, datatang_loss[loss=0.116, simple_loss=0.1871, pruned_loss=0.0224, over 4851.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2215, pruned_loss=0.0279, over 985440.59 frames.], batch size: 36, aishell_tot_loss[loss=0.1413, simple_loss=0.2282, pruned_loss=0.02723, over 978031.70 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2146, pruned_loss=0.02866, over 976086.77 frames.], batch size: 36, lr: 2.73e-04
+2022-06-19 07:49:58,523 INFO [train.py:874] (3/4) Epoch 30, batch 1950, aishell_loss[loss=0.142, simple_loss=0.2297, pruned_loss=0.02718, over 4967.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2223, pruned_loss=0.02799, over 985389.56 frames.], batch size: 44, aishell_tot_loss[loss=0.1416, simple_loss=0.2286, pruned_loss=0.0273, over 978879.26 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2149, pruned_loss=0.02869, over 977131.27 frames.], batch size: 44, lr: 2.73e-04
+2022-06-19 07:50:27,898 INFO [train.py:874] (3/4) Epoch 30, batch 2000, datatang_loss[loss=0.1439, simple_loss=0.2087, pruned_loss=0.03957, over 4972.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2217, pruned_loss=0.02775, over 985461.08 frames.], batch size: 67, aishell_tot_loss[loss=0.1411, simple_loss=0.228, pruned_loss=0.02714, over 979881.60 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2146, pruned_loss=0.02863, over 977877.23 frames.], batch size: 67, lr: 2.73e-04
+2022-06-19 07:50:27,899 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 07:50:43,695 INFO [train.py:914] (3/4) Epoch 30, validation: loss=0.1643, simple_loss=0.2485, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 07:51:13,786 INFO [train.py:874] (3/4) Epoch 30, batch 2050, datatang_loss[loss=0.1379, simple_loss=0.2196, pruned_loss=0.02811, over 4948.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2214, pruned_loss=0.02746, over 985130.25 frames.], batch size: 62, aishell_tot_loss[loss=0.1413, simple_loss=0.2283, pruned_loss=0.02719, over 980309.42 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.214, pruned_loss=0.02824, over 978642.51 frames.], batch size: 62, lr: 2.73e-04
+2022-06-19 07:51:43,747 INFO [train.py:874] (3/4) Epoch 30, batch 2100, datatang_loss[loss=0.1479, simple_loss=0.2293, pruned_loss=0.03326, over 4921.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2205, pruned_loss=0.02711, over 985171.71 frames.], batch size: 81, aishell_tot_loss[loss=0.1409, simple_loss=0.2278, pruned_loss=0.027, over 980724.78 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2138, pruned_loss=0.02798, over 979603.44 frames.], batch size: 81, lr: 2.73e-04
+2022-06-19 07:52:12,621 INFO [train.py:874] (3/4) Epoch 30, batch 2150, datatang_loss[loss=0.1949, simple_loss=0.2717, pruned_loss=0.05907, over 4935.00 frames.], tot_loss[loss=0.137, simple_loss=0.2197, pruned_loss=0.02716, over 985304.69 frames.], batch size: 109, aishell_tot_loss[loss=0.1407, simple_loss=0.2273, pruned_loss=0.02705, over 981228.04 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2134, pruned_loss=0.02788, over 980407.54 frames.], batch size: 109, lr: 2.73e-04
+2022-06-19 07:52:42,927 INFO [train.py:874] (3/4) Epoch 30, batch 2200, aishell_loss[loss=0.1149, simple_loss=0.1953, pruned_loss=0.01722, over 4960.00 frames.], tot_loss[loss=0.136, simple_loss=0.2186, pruned_loss=0.02672, over 985098.75 frames.], batch size: 27, aishell_tot_loss[loss=0.14, simple_loss=0.2267, pruned_loss=0.02666, over 981562.89 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2128, pruned_loss=0.02772, over 980914.96 frames.], batch size: 27, lr: 2.73e-04
+2022-06-19 07:53:12,740 INFO [train.py:874] (3/4) Epoch 30, batch 2250, aishell_loss[loss=0.132, simple_loss=0.2005, pruned_loss=0.03173, over 4954.00 frames.], tot_loss[loss=0.1364, simple_loss=0.2191, pruned_loss=0.0268, over 985155.47 frames.], batch size: 25, aishell_tot_loss[loss=0.1402, simple_loss=0.2269, pruned_loss=0.02679, over 981984.95 frames.], datatang_tot_loss[loss=0.1339, simple_loss=0.2127, pruned_loss=0.02758, over 981452.81 frames.], batch size: 25, lr: 2.73e-04
+2022-06-19 07:53:44,004 INFO [train.py:874] (3/4) Epoch 30, batch 2300, aishell_loss[loss=0.1423, simple_loss=0.2331, pruned_loss=0.02571, over 4953.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2198, pruned_loss=0.027, over 985380.30 frames.], batch size: 64, aishell_tot_loss[loss=0.1407, simple_loss=0.2276, pruned_loss=0.02689, over 982520.62 frames.], datatang_tot_loss[loss=0.134, simple_loss=0.2129, pruned_loss=0.0276, over 981988.41 frames.], batch size: 64, lr: 2.73e-04
+2022-06-19 07:54:14,776 INFO [train.py:874] (3/4) Epoch 30, batch 2350, aishell_loss[loss=0.1252, simple_loss=0.1972, pruned_loss=0.02655, over 4923.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2201, pruned_loss=0.02761, over 985619.70 frames.], batch size: 25, aishell_tot_loss[loss=0.1408, simple_loss=0.2274, pruned_loss=0.02704, over 982829.46 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2134, pruned_loss=0.02801, over 982664.45 frames.], batch size: 25, lr: 2.73e-04
+2022-06-19 07:54:42,746 INFO [train.py:874] (3/4) Epoch 30, batch 2400, datatang_loss[loss=0.1287, simple_loss=0.206, pruned_loss=0.02573, over 4913.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2203, pruned_loss=0.02768, over 985675.23 frames.], batch size: 75, aishell_tot_loss[loss=0.1406, simple_loss=0.2273, pruned_loss=0.0269, over 983119.18 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2138, pruned_loss=0.02823, over 983116.89 frames.], batch size: 75, lr: 2.73e-04
+2022-06-19 07:55:12,755 INFO [train.py:874] (3/4) Epoch 30, batch 2450, datatang_loss[loss=0.1498, simple_loss=0.2236, pruned_loss=0.03799, over 4946.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2199, pruned_loss=0.0276, over 985404.63 frames.], batch size: 37, aishell_tot_loss[loss=0.1405, simple_loss=0.2273, pruned_loss=0.02687, over 983203.60 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2136, pruned_loss=0.02816, over 983347.55 frames.], batch size: 37, lr: 2.73e-04
+2022-06-19 07:55:43,674 INFO [train.py:874] (3/4) Epoch 30, batch 2500, aishell_loss[loss=0.1565, simple_loss=0.2362, pruned_loss=0.03842, over 4980.00 frames.], tot_loss[loss=0.1375, simple_loss=0.22, pruned_loss=0.02747, over 985518.15 frames.], batch size: 48, aishell_tot_loss[loss=0.1402, simple_loss=0.2269, pruned_loss=0.02677, over 983579.18 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2138, pruned_loss=0.02815, over 983597.37 frames.], batch size: 48, lr: 2.73e-04
+2022-06-19 07:56:12,512 INFO [train.py:874] (3/4) Epoch 30, batch 2550, datatang_loss[loss=0.1397, simple_loss=0.2175, pruned_loss=0.03089, over 4939.00 frames.], tot_loss[loss=0.138, simple_loss=0.2204, pruned_loss=0.02775, over 985783.92 frames.], batch size: 25, aishell_tot_loss[loss=0.1408, simple_loss=0.2272, pruned_loss=0.02717, over 983875.54 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2138, pruned_loss=0.02806, over 984038.10 frames.], batch size: 25, lr: 2.73e-04
+2022-06-19 07:56:42,342 INFO [train.py:874] (3/4) Epoch 30, batch 2600, aishell_loss[loss=0.1594, simple_loss=0.2424, pruned_loss=0.03817, over 4949.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2207, pruned_loss=0.02792, over 985921.30 frames.], batch size: 32, aishell_tot_loss[loss=0.141, simple_loss=0.2274, pruned_loss=0.02727, over 984065.82 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.214, pruned_loss=0.02814, over 984430.86 frames.], batch size: 32, lr: 2.73e-04
+2022-06-19 07:57:13,798 INFO [train.py:874] (3/4) Epoch 30, batch 2650, aishell_loss[loss=0.1554, simple_loss=0.2402, pruned_loss=0.03528, over 4955.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2207, pruned_loss=0.02809, over 985741.40 frames.], batch size: 31, aishell_tot_loss[loss=0.1409, simple_loss=0.2273, pruned_loss=0.02723, over 984015.01 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2143, pruned_loss=0.0284, over 984678.66 frames.], batch size: 31, lr: 2.73e-04
+2022-06-19 07:57:41,439 INFO [train.py:874] (3/4) Epoch 30, batch 2700, datatang_loss[loss=0.1576, simple_loss=0.232, pruned_loss=0.04159, over 4952.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2214, pruned_loss=0.02816, over 985500.42 frames.], batch size: 91, aishell_tot_loss[loss=0.141, simple_loss=0.2276, pruned_loss=0.02722, over 984211.41 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2145, pruned_loss=0.02854, over 984574.10 frames.], batch size: 91, lr: 2.72e-04
+2022-06-19 07:58:11,586 INFO [train.py:874] (3/4) Epoch 30, batch 2750, aishell_loss[loss=0.1336, simple_loss=0.2221, pruned_loss=0.02251, over 4858.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2217, pruned_loss=0.02792, over 984966.15 frames.], batch size: 35, aishell_tot_loss[loss=0.1412, simple_loss=0.228, pruned_loss=0.02719, over 983898.73 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2142, pruned_loss=0.02842, over 984600.15 frames.], batch size: 35, lr: 2.72e-04
+2022-06-19 07:58:40,973 INFO [train.py:874] (3/4) Epoch 30, batch 2800, aishell_loss[loss=0.1405, simple_loss=0.2337, pruned_loss=0.02365, over 4941.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2218, pruned_loss=0.02799, over 985360.35 frames.], batch size: 54, aishell_tot_loss[loss=0.1413, simple_loss=0.2282, pruned_loss=0.02719, over 984279.69 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2142, pruned_loss=0.02853, over 984805.96 frames.], batch size: 54, lr: 2.72e-04
+2022-06-19 07:59:11,162 INFO [train.py:874] (3/4) Epoch 30, batch 2850, datatang_loss[loss=0.1268, simple_loss=0.2014, pruned_loss=0.02605, over 4937.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2212, pruned_loss=0.02806, over 985564.38 frames.], batch size: 62, aishell_tot_loss[loss=0.1414, simple_loss=0.2284, pruned_loss=0.02722, over 984604.96 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2138, pruned_loss=0.02858, over 984881.39 frames.], batch size: 62, lr: 2.72e-04
+2022-06-19 07:59:41,814 INFO [train.py:874] (3/4) Epoch 30, batch 2900, aishell_loss[loss=0.09762, simple_loss=0.1671, pruned_loss=0.01409, over 4824.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2221, pruned_loss=0.02867, over 985757.53 frames.], batch size: 21, aishell_tot_loss[loss=0.1417, simple_loss=0.2286, pruned_loss=0.02742, over 984725.42 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2145, pruned_loss=0.02908, over 985161.08 frames.], batch size: 21, lr: 2.72e-04
+2022-06-19 08:00:11,902 INFO [train.py:874] (3/4) Epoch 30, batch 2950, datatang_loss[loss=0.1249, simple_loss=0.2051, pruned_loss=0.02232, over 4959.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2217, pruned_loss=0.02839, over 985735.78 frames.], batch size: 86, aishell_tot_loss[loss=0.1419, simple_loss=0.2287, pruned_loss=0.02749, over 984923.69 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2143, pruned_loss=0.02879, over 985127.15 frames.], batch size: 86, lr: 2.72e-04
+2022-06-19 08:00:40,836 INFO [train.py:874] (3/4) Epoch 30, batch 3000, datatang_loss[loss=0.132, simple_loss=0.1984, pruned_loss=0.03277, over 4976.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2223, pruned_loss=0.02841, over 985863.46 frames.], batch size: 45, aishell_tot_loss[loss=0.1418, simple_loss=0.2288, pruned_loss=0.0274, over 985052.57 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2147, pruned_loss=0.02898, over 985315.61 frames.], batch size: 45, lr: 2.72e-04
+2022-06-19 08:00:40,837 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 08:00:57,062 INFO [train.py:914] (3/4) Epoch 30, validation: loss=0.165, simple_loss=0.2488, pruned_loss=0.04058, over 1622729.00 frames.
+2022-06-19 08:01:25,996 INFO [train.py:874] (3/4) Epoch 30, batch 3050, datatang_loss[loss=0.1171, simple_loss=0.1976, pruned_loss=0.01829, over 4934.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2221, pruned_loss=0.02809, over 985637.47 frames.], batch size: 57, aishell_tot_loss[loss=0.1415, simple_loss=0.2285, pruned_loss=0.02723, over 984850.85 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2145, pruned_loss=0.02889, over 985455.67 frames.], batch size: 57, lr: 2.72e-04
+2022-06-19 08:01:55,872 INFO [train.py:874] (3/4) Epoch 30, batch 3100, datatang_loss[loss=0.1255, simple_loss=0.2063, pruned_loss=0.02236, over 4921.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2206, pruned_loss=0.02744, over 985768.05 frames.], batch size: 81, aishell_tot_loss[loss=0.1412, simple_loss=0.2283, pruned_loss=0.02705, over 985127.73 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2137, pruned_loss=0.02834, over 985419.86 frames.], batch size: 81, lr: 2.72e-04
+2022-06-19 08:02:25,662 INFO [train.py:874] (3/4) Epoch 30, batch 3150, datatang_loss[loss=0.1382, simple_loss=0.2265, pruned_loss=0.02494, over 4932.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2211, pruned_loss=0.02797, over 985808.11 frames.], batch size: 94, aishell_tot_loss[loss=0.1419, simple_loss=0.2289, pruned_loss=0.02742, over 984986.75 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2139, pruned_loss=0.02847, over 985719.46 frames.], batch size: 94, lr: 2.72e-04
+2022-06-19 08:02:54,303 INFO [train.py:874] (3/4) Epoch 30, batch 3200, datatang_loss[loss=0.14, simple_loss=0.2304, pruned_loss=0.02477, over 4933.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2215, pruned_loss=0.02843, over 985877.43 frames.], batch size: 37, aishell_tot_loss[loss=0.1418, simple_loss=0.2284, pruned_loss=0.02757, over 984942.76 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2144, pruned_loss=0.02881, over 985951.93 frames.], batch size: 37, lr: 2.72e-04
+2022-06-19 08:03:24,513 INFO [train.py:874] (3/4) Epoch 30, batch 3250, datatang_loss[loss=0.1339, simple_loss=0.2148, pruned_loss=0.02643, over 4945.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2216, pruned_loss=0.02847, over 985367.22 frames.], batch size: 69, aishell_tot_loss[loss=0.1417, simple_loss=0.2282, pruned_loss=0.02756, over 984584.38 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2148, pruned_loss=0.02893, over 985889.21 frames.], batch size: 69, lr: 2.72e-04
+2022-06-19 08:03:54,525 INFO [train.py:874] (3/4) Epoch 30, batch 3300, datatang_loss[loss=0.158, simple_loss=0.2201, pruned_loss=0.04794, over 4902.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2208, pruned_loss=0.02831, over 985352.32 frames.], batch size: 42, aishell_tot_loss[loss=0.1414, simple_loss=0.228, pruned_loss=0.02743, over 984465.50 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2144, pruned_loss=0.02893, over 986003.78 frames.], batch size: 42, lr: 2.72e-04
+2022-06-19 08:04:23,853 INFO [train.py:874] (3/4) Epoch 30, batch 3350, aishell_loss[loss=0.1451, simple_loss=0.2287, pruned_loss=0.03074, over 4935.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2213, pruned_loss=0.02821, over 985333.27 frames.], batch size: 33, aishell_tot_loss[loss=0.1414, simple_loss=0.228, pruned_loss=0.02741, over 984628.99 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2145, pruned_loss=0.02892, over 985909.93 frames.], batch size: 33, lr: 2.72e-04
+2022-06-19 08:04:55,176 INFO [train.py:874] (3/4) Epoch 30, batch 3400, aishell_loss[loss=0.1513, simple_loss=0.2374, pruned_loss=0.0326, over 4979.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2218, pruned_loss=0.02852, over 985563.50 frames.], batch size: 48, aishell_tot_loss[loss=0.1415, simple_loss=0.2282, pruned_loss=0.02736, over 984712.47 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2149, pruned_loss=0.02932, over 986095.70 frames.], batch size: 48, lr: 2.72e-04
+2022-06-19 08:05:24,114 INFO [train.py:874] (3/4) Epoch 30, batch 3450, aishell_loss[loss=0.1587, simple_loss=0.2464, pruned_loss=0.03551, over 4941.00 frames.], tot_loss[loss=0.1385, simple_loss=0.221, pruned_loss=0.028, over 985837.42 frames.], batch size: 54, aishell_tot_loss[loss=0.1414, simple_loss=0.228, pruned_loss=0.02735, over 984991.02 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2142, pruned_loss=0.02883, over 986156.11 frames.], batch size: 54, lr: 2.72e-04
+2022-06-19 08:05:54,721 INFO [train.py:874] (3/4) Epoch 30, batch 3500, datatang_loss[loss=0.1399, simple_loss=0.2154, pruned_loss=0.03218, over 4977.00 frames.], tot_loss[loss=0.138, simple_loss=0.2203, pruned_loss=0.02784, over 985593.18 frames.], batch size: 60, aishell_tot_loss[loss=0.141, simple_loss=0.2276, pruned_loss=0.02722, over 984882.86 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.214, pruned_loss=0.02875, over 986020.02 frames.], batch size: 60, lr: 2.72e-04
+2022-06-19 08:06:24,520 INFO [train.py:874] (3/4) Epoch 30, batch 3550, datatang_loss[loss=0.145, simple_loss=0.2178, pruned_loss=0.0361, over 4900.00 frames.], tot_loss[loss=0.1378, simple_loss=0.22, pruned_loss=0.02785, over 985630.67 frames.], batch size: 52, aishell_tot_loss[loss=0.1409, simple_loss=0.2275, pruned_loss=0.02709, over 984821.90 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2139, pruned_loss=0.0288, over 986145.38 frames.], batch size: 52, lr: 2.72e-04
+2022-06-19 08:06:53,801 INFO [train.py:874] (3/4) Epoch 30, batch 3600, datatang_loss[loss=0.1213, simple_loss=0.1986, pruned_loss=0.02196, over 4927.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2205, pruned_loss=0.02816, over 985628.73 frames.], batch size: 71, aishell_tot_loss[loss=0.1408, simple_loss=0.2276, pruned_loss=0.02702, over 984952.97 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.214, pruned_loss=0.02918, over 986056.93 frames.], batch size: 71, lr: 2.71e-04
+2022-06-19 08:07:23,932 INFO [train.py:874] (3/4) Epoch 30, batch 3650, datatang_loss[loss=0.1799, simple_loss=0.2521, pruned_loss=0.05383, over 4918.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2204, pruned_loss=0.02817, over 985560.84 frames.], batch size: 108, aishell_tot_loss[loss=0.1407, simple_loss=0.2275, pruned_loss=0.02691, over 984942.23 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2143, pruned_loss=0.02924, over 985990.88 frames.], batch size: 108, lr: 2.71e-04
+2022-06-19 08:07:54,545 INFO [train.py:874] (3/4) Epoch 30, batch 3700, aishell_loss[loss=0.1453, simple_loss=0.2325, pruned_loss=0.02907, over 4887.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2195, pruned_loss=0.02791, over 985473.60 frames.], batch size: 50, aishell_tot_loss[loss=0.1403, simple_loss=0.227, pruned_loss=0.02678, over 985124.03 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2143, pruned_loss=0.02903, over 985714.28 frames.], batch size: 50, lr: 2.71e-04
+2022-06-19 08:08:23,947 INFO [train.py:874] (3/4) Epoch 30, batch 3750, aishell_loss[loss=0.1301, simple_loss=0.2187, pruned_loss=0.02075, over 4933.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2192, pruned_loss=0.02783, over 985752.17 frames.], batch size: 58, aishell_tot_loss[loss=0.1401, simple_loss=0.2267, pruned_loss=0.0268, over 985292.99 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2141, pruned_loss=0.02892, over 985846.80 frames.], batch size: 58, lr: 2.71e-04
+2022-06-19 08:08:52,779 INFO [train.py:874] (3/4) Epoch 30, batch 3800, datatang_loss[loss=0.1412, simple_loss=0.2171, pruned_loss=0.03266, over 4923.00 frames.], tot_loss[loss=0.139, simple_loss=0.221, pruned_loss=0.0285, over 985384.24 frames.], batch size: 83, aishell_tot_loss[loss=0.1412, simple_loss=0.2277, pruned_loss=0.02731, over 985153.78 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2146, pruned_loss=0.02914, over 985644.76 frames.], batch size: 83, lr: 2.71e-04
+2022-06-19 08:09:20,724 INFO [train.py:874] (3/4) Epoch 30, batch 3850, datatang_loss[loss=0.1292, simple_loss=0.1977, pruned_loss=0.03032, over 4868.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2209, pruned_loss=0.02823, over 985522.31 frames.], batch size: 39, aishell_tot_loss[loss=0.1412, simple_loss=0.2279, pruned_loss=0.02727, over 985341.79 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.214, pruned_loss=0.02898, over 985623.16 frames.], batch size: 39, lr: 2.71e-04
+2022-06-19 08:09:50,462 INFO [train.py:874] (3/4) Epoch 30, batch 3900, aishell_loss[loss=0.1385, simple_loss=0.2284, pruned_loss=0.02431, over 4888.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2221, pruned_loss=0.02806, over 985465.49 frames.], batch size: 50, aishell_tot_loss[loss=0.1419, simple_loss=0.2288, pruned_loss=0.02747, over 985340.87 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2136, pruned_loss=0.02869, over 985591.11 frames.], batch size: 50, lr: 2.71e-04
+2022-06-19 08:10:17,954 INFO [train.py:874] (3/4) Epoch 30, batch 3950, datatang_loss[loss=0.138, simple_loss=0.2163, pruned_loss=0.02979, over 4921.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2225, pruned_loss=0.02851, over 985724.29 frames.], batch size: 83, aishell_tot_loss[loss=0.1427, simple_loss=0.2296, pruned_loss=0.02792, over 985555.37 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2135, pruned_loss=0.02872, over 985663.50 frames.], batch size: 83, lr: 2.71e-04
+2022-06-19 08:10:51,426 INFO [train.py:874] (3/4) Epoch 30, batch 4000, aishell_loss[loss=0.1448, simple_loss=0.2341, pruned_loss=0.02778, over 4898.00 frames.], tot_loss[loss=0.1391, simple_loss=0.222, pruned_loss=0.02812, over 985355.43 frames.], batch size: 28, aishell_tot_loss[loss=0.1419, simple_loss=0.2288, pruned_loss=0.02746, over 985324.54 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2138, pruned_loss=0.02881, over 985528.63 frames.], batch size: 28, lr: 2.71e-04
+2022-06-19 08:10:51,427 INFO [train.py:905] (3/4) Computing validation loss
+2022-06-19 08:11:08,528 INFO [train.py:914] (3/4) Epoch 30, validation: loss=0.1651, simple_loss=0.249, pruned_loss=0.04054, over 1622729.00 frames.
+2022-06-19 08:11:36,716 INFO [train.py:874] (3/4) Epoch 30, batch 4050, aishell_loss[loss=0.1631, simple_loss=0.2469, pruned_loss=0.0396, over 4917.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2217, pruned_loss=0.02821, over 985134.15 frames.], batch size: 41, aishell_tot_loss[loss=0.1421, simple_loss=0.2289, pruned_loss=0.02764, over 985108.89 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2136, pruned_loss=0.02869, over 985503.94 frames.], batch size: 41, lr: 2.71e-04
+2022-06-19 08:11:57,301 INFO [train.py:1125] (3/4) Done!