upload model, lexicon and log

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log/log-decode-2022-04-08-22-02-12

@@ -0,0 +1,778 @@
+2022-04-08 22:02:12,850 INFO [decode.py:583] Decoding started
+2022-04-08 22:02:12,851 INFO [decode.py:584] {'subsampling_factor': 4, 'vgg_frontend': False, 'use_feat_batchnorm': True, 'feature_dim': 80, 'nhead': 8, 'attention_dim': 512, 'num_decoder_layers': 6, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'env_info': {'k2-version': '1.14', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '6833270cb228aba7bf9681fccd41e2b52f7d984c', 'k2-git-date': 'Wed Mar 16 11:16:05 2022', 'lhotse-version': '1.0.0.dev+git.d917411.clean', 'torch-cuda-available': True, 'torch-cuda-version': '11.1', 'python-version': '3.7', 'icefall-git-branch': 'gigaspeech_recipe', 'icefall-git-sha1': 'c3993a5-dirty', 'icefall-git-date': 'Mon Mar 21 13:49:39 2022', 'icefall-path': '/userhome/user/guanbo/icefall_decode', 'k2-path': '/opt/conda/lib/python3.7/site-packages/k2-1.14.dev20220408+cuda11.1.torch1.10.0-py3.7-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/userhome/user/guanbo/lhotse/lhotse/__init__.py', 'hostname': 'd7b02ab00b70c011ec0a3ee069db84328338-chenx8564-0', 'IP address': '10.9.150.18'}, 'epoch': 18, 'avg': 6, 'method': 'attention-decoder', 'num_paths': 1000, 'nbest_scale': 0.5, 'exp_dir': PosixPath('conformer_ctc/exp_500_8_2'), 'lang_dir': PosixPath('data/lang_bpe_500'), 'lm_dir': PosixPath('data/lm'), 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 20, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 1, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'subset': 'XL', 'lazy_load': True, 'small_dev': False}
+2022-04-08 22:02:13,611 INFO [lexicon.py:176] Loading pre-compiled data/lang_bpe_500/Linv.pt
+2022-04-08 22:02:13,897 INFO [decode.py:594] device: cuda:0
+2022-04-08 22:02:19,463 INFO [decode.py:656] Loading pre-compiled G_4_gram.pt
+2022-04-08 22:02:23,064 INFO [decode.py:692] averaging ['conformer_ctc/exp_500_8_2/epoch-13.pt', 'conformer_ctc/exp_500_8_2/epoch-14.pt', 'conformer_ctc/exp_500_8_2/epoch-15.pt', 'conformer_ctc/exp_500_8_2/epoch-16.pt', 'conformer_ctc/exp_500_8_2/epoch-17.pt', 'conformer_ctc/exp_500_8_2/epoch-18.pt']
+2022-04-08 22:04:17,302 INFO [decode.py:699] Number of model parameters: 109226120
+2022-04-08 22:04:17,303 INFO [asr_datamodule.py:372] About to get dev cuts
+2022-04-08 22:04:21,114 INFO [decode.py:497] batch 0/?, cuts processed until now is 3
+2022-04-08 22:06:56,367 INFO [decode.py:497] batch 100/?, cuts processed until now is 243
+2022-04-08 22:09:33,967 INFO [decode.py:497] batch 200/?, cuts processed until now is 464
+2022-04-08 22:12:05,730 INFO [decode.py:497] batch 300/?, cuts processed until now is 665
+2022-04-08 22:13:23,989 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.93 GiB (GPU 0; 31.75 GiB total capacity; 24.54 GiB already allocated; 3.87 GiB free; 26.53 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:13:23,989 INFO [decode.py:743] num_arcs before pruning: 333034
+2022-04-08 22:13:23,989 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:13:24,010 INFO [decode.py:757] num_arcs after pruning: 7258
+2022-04-08 22:14:38,171 INFO [decode.py:497] batch 400/?, cuts processed until now is 891
+2022-04-08 22:17:05,640 INFO [decode.py:497] batch 500/?, cuts processed until now is 1098
+2022-04-08 22:19:29,901 INFO [decode.py:497] batch 600/?, cuts processed until now is 1363
+2022-04-08 22:20:05,953 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.51 GiB already allocated; 7.07 GiB free; 23.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:20:05,954 INFO [decode.py:743] num_arcs before pruning: 514392
+2022-04-08 22:20:05,954 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:20:05,966 INFO [decode.py:757] num_arcs after pruning: 13888
+2022-04-08 22:22:02,765 INFO [decode.py:497] batch 700/?, cuts processed until now is 1626
+2022-04-08 22:24:05,393 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 14.24 GiB already allocated; 7.07 GiB free; 23.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:24:05,393 INFO [decode.py:743] num_arcs before pruning: 164808
+2022-04-08 22:24:05,393 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:24:05,404 INFO [decode.py:757] num_arcs after pruning: 8771
+2022-04-08 22:24:40,652 INFO [decode.py:497] batch 800/?, cuts processed until now is 1870
+2022-04-08 22:25:03,574 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 14.28 GiB already allocated; 7.07 GiB free; 23.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:25:03,575 INFO [decode.py:743] num_arcs before pruning: 267824
+2022-04-08 22:25:03,575 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:25:03,582 INFO [decode.py:757] num_arcs after pruning: 9250
+2022-04-08 22:27:25,872 INFO [decode.py:497] batch 900/?, cuts processed until now is 2134
+2022-04-08 22:29:45,824 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 14.45 GiB already allocated; 7.06 GiB free; 23.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:29:45,825 INFO [decode.py:743] num_arcs before pruning: 236799
+2022-04-08 22:29:45,825 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:29:45,837 INFO [decode.py:757] num_arcs after pruning: 7885
+2022-04-08 22:30:03,747 INFO [decode.py:497] batch 1000/?, cuts processed until now is 2380
+2022-04-08 22:30:44,532 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-08 22:30:44,532 INFO [decode.py:743] num_arcs before pruning: 632546
+2022-04-08 22:30:44,533 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:30:44,585 INFO [decode.py:757] num_arcs after pruning: 10602
+2022-04-08 22:32:41,978 INFO [decode.py:497] batch 1100/?, cuts processed until now is 2624
+2022-04-08 22:34:54,199 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.67 GiB already allocated; 5.68 GiB free; 24.72 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:34:54,200 INFO [decode.py:743] num_arcs before pruning: 227558
+2022-04-08 22:34:54,200 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:34:54,218 INFO [decode.py:757] num_arcs after pruning: 8505
+2022-04-08 22:35:25,806 INFO [decode.py:497] batch 1200/?, cuts processed until now is 2889
+2022-04-08 22:38:28,827 INFO [decode.py:497] batch 1300/?, cuts processed until now is 3182
+2022-04-08 22:39:35,318 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.65 GiB (GPU 0; 31.75 GiB total capacity; 27.28 GiB already allocated; 1.20 GiB free; 29.19 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:39:35,318 INFO [decode.py:743] num_arcs before pruning: 348294
+2022-04-08 22:39:35,318 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:39:35,324 INFO [decode.py:757] num_arcs after pruning: 4422
+2022-04-08 22:41:48,886 INFO [decode.py:497] batch 1400/?, cuts processed until now is 3491
+2022-04-08 22:42:03,583 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.53 GiB (GPU 0; 31.75 GiB total capacity; 24.43 GiB already allocated; 1.20 GiB free; 29.19 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:42:03,584 INFO [decode.py:743] num_arcs before pruning: 446338
+2022-04-08 22:42:03,584 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:42:03,592 INFO [decode.py:757] num_arcs after pruning: 13422
+2022-04-08 22:44:41,081 INFO [decode.py:497] batch 1500/?, cuts processed until now is 3738
+2022-04-08 22:44:48,819 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 1.94 GiB (GPU 0; 31.75 GiB total capacity; 29.06 GiB already allocated; 231.75 MiB free; 30.17 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:44:48,820 INFO [decode.py:743] num_arcs before pruning: 263598
+2022-04-08 22:44:48,820 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:44:48,833 INFO [decode.py:757] num_arcs after pruning: 7847
+2022-04-08 22:47:10,728 INFO [decode.py:497] batch 1600/?, cuts processed until now is 3970
+2022-04-08 22:47:52,235 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.20 GiB (GPU 0; 31.75 GiB total capacity; 24.71 GiB already allocated; 231.75 MiB free; 30.17 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:47:52,236 INFO [decode.py:743] num_arcs before pruning: 317009
+2022-04-08 22:47:52,236 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:47:52,252 INFO [decode.py:757] num_arcs after pruning: 9354
+2022-04-08 22:49:32,370 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.55 GiB (GPU 0; 31.75 GiB total capacity; 24.05 GiB already allocated; 231.75 MiB free; 30.17 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:49:32,371 INFO [decode.py:743] num_arcs before pruning: 136624
+2022-04-08 22:49:32,371 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:49:32,402 INFO [decode.py:757] num_arcs after pruning: 5456
+2022-04-08 22:49:36,398 INFO [decode.py:497] batch 1700/?, cuts processed until now is 4192
+2022-04-08 22:50:50,382 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.56 GiB already allocated; 2.10 GiB free; 28.29 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:50:50,383 INFO [decode.py:743] num_arcs before pruning: 303893
+2022-04-08 22:50:50,383 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:50:50,400 INFO [decode.py:757] num_arcs after pruning: 9312
+2022-04-08 22:52:09,335 INFO [decode.py:497] batch 1800/?, cuts processed until now is 4416
+2022-04-08 22:52:51,744 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.02 GiB (GPU 0; 31.75 GiB total capacity; 26.25 GiB already allocated; 2.10 GiB free; 28.29 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:52:51,745 INFO [decode.py:743] num_arcs before pruning: 379292
+2022-04-08 22:52:51,745 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:52:51,751 INFO [decode.py:757] num_arcs after pruning: 14317
+2022-04-08 22:54:33,478 INFO [decode.py:497] batch 1900/?, cuts processed until now is 4619
+2022-04-08 22:56:34,371 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.32 GiB already allocated; 3.07 GiB free; 27.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:56:34,372 INFO [decode.py:743] num_arcs before pruning: 294097
+2022-04-08 22:56:34,372 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:56:34,389 INFO [decode.py:757] num_arcs after pruning: 5895
+2022-04-08 22:56:47,967 INFO [decode.py:497] batch 2000/?, cuts processed until now is 4816
+2022-04-08 22:58:06,236 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.41 GiB already allocated; 3.06 GiB free; 27.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:58:06,236 INFO [decode.py:743] num_arcs before pruning: 253855
+2022-04-08 22:58:06,236 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:58:06,253 INFO [decode.py:757] num_arcs after pruning: 9191
+2022-04-08 22:58:17,534 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.17 GiB (GPU 0; 31.75 GiB total capacity; 26.06 GiB already allocated; 1.56 GiB free; 28.83 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:58:17,535 INFO [decode.py:743] num_arcs before pruning: 242689
+2022-04-08 22:58:17,535 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:58:17,549 INFO [decode.py:757] num_arcs after pruning: 4733
+2022-04-08 22:58:32,154 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.38 GiB (GPU 0; 31.75 GiB total capacity; 26.65 GiB already allocated; 1.57 GiB free; 28.82 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 22:58:32,155 INFO [decode.py:743] num_arcs before pruning: 288302
+2022-04-08 22:58:32,155 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 22:58:32,164 INFO [decode.py:757] num_arcs after pruning: 5472
+2022-04-08 22:59:15,988 INFO [decode.py:497] batch 2100/?, cuts processed until now is 4981
+2022-04-08 23:00:31,937 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-08 23:00:31,937 INFO [decode.py:743] num_arcs before pruning: 745182
+2022-04-08 23:00:31,937 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 23:00:31,989 INFO [decode.py:757] num_arcs after pruning: 13933
+2022-04-08 23:01:49,408 INFO [decode.py:497] batch 2200/?, cuts processed until now is 5132
+2022-04-08 23:04:08,911 INFO [decode.py:497] batch 2300/?, cuts processed until now is 5273
+2022-04-08 23:06:50,854 INFO [decode.py:497] batch 2400/?, cuts processed until now is 5388
+2022-04-08 23:06:53,493 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-08 23:06:53,493 INFO [decode.py:743] num_arcs before pruning: 203946
+2022-04-08 23:06:53,493 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 23:06:53,545 INFO [decode.py:757] num_arcs after pruning: 7172
+2022-04-08 23:09:08,764 INFO [decode.py:497] batch 2500/?, cuts processed until now is 5488
+2022-04-08 23:10:26,345 INFO [decode.py:841] Caught exception:
+CUDA out of memory. Tried to allocate 5.79 GiB (GPU 0; 31.75 GiB total capacity; 24.31 GiB already allocated; 1.58 GiB free; 28.82 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-08 23:10:26,346 INFO [decode.py:843] num_paths before decreasing: 1000
+2022-04-08 23:10:26,346 INFO [decode.py:852] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-08 23:10:26,346 INFO [decode.py:858] num_paths after decreasing: 500
+2022-04-08 23:11:31,973 INFO [decode.py:497] batch 2600/?, cuts processed until now is 5588
+2022-04-08 23:13:41,208 INFO [decode.py:497] batch 2700/?, cuts processed until now is 5688
+2022-04-08 23:20:49,158 INFO [decode.py:567] 
+For dev, WER of different settings are:
+ngram_lm_scale_0.6_attention_scale_1.5	10.46	best for dev
+ngram_lm_scale_0.6_attention_scale_1.7	10.46
+ngram_lm_scale_0.5_attention_scale_0.9	10.47
+ngram_lm_scale_0.5_attention_scale_1.0	10.47
+ngram_lm_scale_0.5_attention_scale_1.1	10.47
+ngram_lm_scale_0.5_attention_scale_1.2	10.47
+ngram_lm_scale_0.5_attention_scale_1.3	10.47
+ngram_lm_scale_0.5_attention_scale_1.5	10.47
+ngram_lm_scale_0.5_attention_scale_1.7	10.47
+ngram_lm_scale_0.6_attention_scale_1.3	10.47
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+ngram_lm_scale_1.9_attention_scale_1.9	11.18
+ngram_lm_scale_2.0_attention_scale_2.1	11.18
+ngram_lm_scale_1.1_attention_scale_0.5	11.19
+ngram_lm_scale_2.5_attention_scale_3.0	11.19
+ngram_lm_scale_1.7_attention_scale_1.5	11.21
+ngram_lm_scale_2.1_attention_scale_2.2	11.25
+ngram_lm_scale_1.2_attention_scale_0.6	11.26
+ngram_lm_scale_1.5_attention_scale_1.1	11.26
+ngram_lm_scale_2.0_attention_scale_2.0	11.26
+ngram_lm_scale_1.0_attention_scale_0.3	11.29
+ngram_lm_scale_2.3_attention_scale_2.5	11.3
+ngram_lm_scale_2.2_attention_scale_2.3	11.31
+ngram_lm_scale_2.1_attention_scale_2.1	11.32
+ngram_lm_scale_2.0_attention_scale_1.9	11.34
+ngram_lm_scale_1.3_attention_scale_0.7	11.36
+ngram_lm_scale_1.9_attention_scale_1.7	11.37
+ngram_lm_scale_1.5_attention_scale_1.0	11.4
+ngram_lm_scale_2.2_attention_scale_2.2	11.4
+ngram_lm_scale_2.1_attention_scale_2.0	11.41
+ngram_lm_scale_0.9_attention_scale_0.1	11.42
+ngram_lm_scale_1.7_attention_scale_1.3	11.44
+ngram_lm_scale_1.2_attention_scale_0.5	11.45
+ngram_lm_scale_0.9_attention_scale_0.08	11.47
+ngram_lm_scale_2.3_attention_scale_2.3	11.48
+ngram_lm_scale_2.2_attention_scale_2.1	11.51
+ngram_lm_scale_2.1_attention_scale_1.9	11.54
+ngram_lm_scale_1.3_attention_scale_0.6	11.55
+ngram_lm_scale_1.5_attention_scale_0.9	11.56
+ngram_lm_scale_0.9_attention_scale_0.05	11.57
+ngram_lm_scale_2.0_attention_scale_1.7	11.57
+ngram_lm_scale_2.3_attention_scale_2.2	11.58
+ngram_lm_scale_1.1_attention_scale_0.3	11.59
+ngram_lm_scale_1.7_attention_scale_1.2	11.59
+ngram_lm_scale_1.9_attention_scale_1.5	11.63
+ngram_lm_scale_2.2_attention_scale_2.0	11.63
+ngram_lm_scale_2.5_attention_scale_2.5	11.63
+ngram_lm_scale_4.0_attention_scale_5.0	11.67
+ngram_lm_scale_2.3_attention_scale_2.1	11.7
+ngram_lm_scale_0.9_attention_scale_0.01	11.71
+ngram_lm_scale_2.2_attention_scale_1.9	11.73
+ngram_lm_scale_1.3_attention_scale_0.5	11.76
+ngram_lm_scale_1.7_attention_scale_1.1	11.76
+ngram_lm_scale_1.0_attention_scale_0.1	11.78
+ngram_lm_scale_2.1_attention_scale_1.7	11.8
+ngram_lm_scale_2.3_attention_scale_2.0	11.8
+ngram_lm_scale_2.5_attention_scale_2.3	11.83
+ngram_lm_scale_2.0_attention_scale_1.5	11.86
+ngram_lm_scale_1.0_attention_scale_0.08	11.89
+ngram_lm_scale_1.9_attention_scale_1.3	11.93
+ngram_lm_scale_3.0_attention_scale_3.0	11.94
+ngram_lm_scale_1.2_attention_scale_0.3	11.95
+ngram_lm_scale_1.7_attention_scale_1.0	11.95
+ngram_lm_scale_2.3_attention_scale_1.9	11.95
+ngram_lm_scale_2.5_attention_scale_2.2	11.96
+ngram_lm_scale_1.5_attention_scale_0.7	11.98
+ngram_lm_scale_1.0_attention_scale_0.05	12.0
+ngram_lm_scale_2.2_attention_scale_1.7	12.02
+ngram_lm_scale_2.1_attention_scale_1.5	12.09
+ngram_lm_scale_2.5_attention_scale_2.1	12.09
+ngram_lm_scale_1.9_attention_scale_1.2	12.12
+ngram_lm_scale_1.7_attention_scale_0.9	12.16
+ngram_lm_scale_1.0_attention_scale_0.01	12.19
+ngram_lm_scale_2.0_attention_scale_1.3	12.2
+ngram_lm_scale_2.5_attention_scale_2.0	12.22
+ngram_lm_scale_1.5_attention_scale_0.6	12.24
+ngram_lm_scale_2.3_attention_scale_1.7	12.24
+ngram_lm_scale_1.1_attention_scale_0.1	12.27
+ngram_lm_scale_1.9_attention_scale_1.1	12.3
+ngram_lm_scale_4.0_attention_scale_4.0	12.31
+ngram_lm_scale_2.2_attention_scale_1.5	12.32
+ngram_lm_scale_2.5_attention_scale_1.9	12.35
+ngram_lm_scale_1.1_attention_scale_0.08	12.36
+ngram_lm_scale_2.0_attention_scale_1.2	12.37
+ngram_lm_scale_1.3_attention_scale_0.3	12.4
+ngram_lm_scale_2.1_attention_scale_1.3	12.43
+ngram_lm_scale_3.0_attention_scale_2.5	12.46
+ngram_lm_scale_1.1_attention_scale_0.05	12.51
+ngram_lm_scale_1.9_attention_scale_1.0	12.52
+ngram_lm_scale_2.3_attention_scale_1.5	12.53
+ngram_lm_scale_1.5_attention_scale_0.5	12.54
+ngram_lm_scale_2.0_attention_scale_1.1	12.58
+ngram_lm_scale_5.0_attention_scale_5.0	12.62
+ngram_lm_scale_2.1_attention_scale_1.2	12.63
+ngram_lm_scale_2.5_attention_scale_1.7	12.64
+ngram_lm_scale_1.7_attention_scale_0.7	12.68
+ngram_lm_scale_2.2_attention_scale_1.3	12.68
+ngram_lm_scale_1.1_attention_scale_0.01	12.72
+ngram_lm_scale_3.0_attention_scale_2.3	12.72
+ngram_lm_scale_1.9_attention_scale_0.9	12.78
+ngram_lm_scale_1.2_attention_scale_0.1	12.79
+ngram_lm_scale_2.0_attention_scale_1.0	12.82
+ngram_lm_scale_2.1_attention_scale_1.1	12.86
+ngram_lm_scale_3.0_attention_scale_2.2	12.87
+ngram_lm_scale_1.2_attention_scale_0.08	12.88
+ngram_lm_scale_2.2_attention_scale_1.2	12.92
+ngram_lm_scale_2.3_attention_scale_1.3	12.97
+ngram_lm_scale_1.7_attention_scale_0.6	12.98
+ngram_lm_scale_3.0_attention_scale_2.1	13.03
+ngram_lm_scale_2.5_attention_scale_1.5	13.04
+ngram_lm_scale_1.2_attention_scale_0.05	13.05
+ngram_lm_scale_2.0_attention_scale_0.9	13.11
+ngram_lm_scale_2.1_attention_scale_1.0	13.17
+ngram_lm_scale_2.2_attention_scale_1.1	13.2
+ngram_lm_scale_3.0_attention_scale_2.0	13.2
+ngram_lm_scale_2.3_attention_scale_1.2	13.24
+ngram_lm_scale_1.2_attention_scale_0.01	13.27
+ngram_lm_scale_1.3_attention_scale_0.1	13.3
+ngram_lm_scale_1.5_attention_scale_0.3	13.32
+ngram_lm_scale_1.7_attention_scale_0.5	13.33
+ngram_lm_scale_1.3_attention_scale_0.08	13.4
+ngram_lm_scale_4.0_attention_scale_3.0	13.41
+ngram_lm_scale_1.9_attention_scale_0.7	13.42
+ngram_lm_scale_3.0_attention_scale_1.9	13.42
+ngram_lm_scale_2.1_attention_scale_0.9	13.45
+ngram_lm_scale_2.2_attention_scale_1.0	13.46
+ngram_lm_scale_2.3_attention_scale_1.1	13.47
+ngram_lm_scale_2.5_attention_scale_1.3	13.53
+ngram_lm_scale_1.3_attention_scale_0.05	13.56
+ngram_lm_scale_5.0_attention_scale_4.0	13.57
+ngram_lm_scale_2.0_attention_scale_0.7	13.73
+ngram_lm_scale_2.2_attention_scale_0.9	13.74
+ngram_lm_scale_1.9_attention_scale_0.6	13.75
+ngram_lm_scale_2.3_attention_scale_1.0	13.75
+ngram_lm_scale_2.5_attention_scale_1.2	13.78
+ngram_lm_scale_1.3_attention_scale_0.01	13.81
+ngram_lm_scale_3.0_attention_scale_1.7	13.84
+ngram_lm_scale_2.5_attention_scale_1.1	14.05
+ngram_lm_scale_2.1_attention_scale_0.7	14.07
+ngram_lm_scale_2.3_attention_scale_0.9	14.07
+ngram_lm_scale_2.0_attention_scale_0.6	14.1
+ngram_lm_scale_1.9_attention_scale_0.5	14.14
+ngram_lm_scale_1.7_attention_scale_0.3	14.18
+ngram_lm_scale_4.0_attention_scale_2.5	14.2
+ngram_lm_scale_3.0_attention_scale_1.5	14.28
+ngram_lm_scale_1.5_attention_scale_0.1	14.3
+ngram_lm_scale_2.5_attention_scale_1.0	14.35
+ngram_lm_scale_1.5_attention_scale_0.08	14.41
+ngram_lm_scale_2.2_attention_scale_0.7	14.42
+ngram_lm_scale_2.1_attention_scale_0.6	14.47
+ngram_lm_scale_2.0_attention_scale_0.5	14.51
+ngram_lm_scale_4.0_attention_scale_2.3	14.56
+ngram_lm_scale_1.5_attention_scale_0.05	14.57
+ngram_lm_scale_2.5_attention_scale_0.9	14.66
+ngram_lm_scale_2.3_attention_scale_0.7	14.72
+ngram_lm_scale_4.0_attention_scale_2.2	14.75
+ngram_lm_scale_2.2_attention_scale_0.6	14.76
+ngram_lm_scale_3.0_attention_scale_1.3	14.76
+ngram_lm_scale_2.1_attention_scale_0.5	14.8
+ngram_lm_scale_1.5_attention_scale_0.01	14.82
+ngram_lm_scale_5.0_attention_scale_3.0	14.84
+ngram_lm_scale_4.0_attention_scale_2.1	14.9
+ngram_lm_scale_1.9_attention_scale_0.3	14.93
+ngram_lm_scale_3.0_attention_scale_1.2	14.98
+ngram_lm_scale_2.3_attention_scale_0.6	15.04
+ngram_lm_scale_4.0_attention_scale_2.0	15.07
+ngram_lm_scale_2.2_attention_scale_0.5	15.13
+ngram_lm_scale_1.7_attention_scale_0.1	15.2
+ngram_lm_scale_3.0_attention_scale_1.1	15.24
+ngram_lm_scale_4.0_attention_scale_1.9	15.25
+ngram_lm_scale_2.5_attention_scale_0.7	15.26
+ngram_lm_scale_1.7_attention_scale_0.08	15.3
+ngram_lm_scale_2.0_attention_scale_0.3	15.31
+ngram_lm_scale_2.3_attention_scale_0.5	15.41
+ngram_lm_scale_1.7_attention_scale_0.05	15.48
+ngram_lm_scale_3.0_attention_scale_1.0	15.54
+ngram_lm_scale_2.5_attention_scale_0.6	15.59
+ngram_lm_scale_5.0_attention_scale_2.5	15.61
+ngram_lm_scale_2.1_attention_scale_0.3	15.62
+ngram_lm_scale_4.0_attention_scale_1.7	15.66
+ngram_lm_scale_1.7_attention_scale_0.01	15.73
+ngram_lm_scale_3.0_attention_scale_0.9	15.8
+ngram_lm_scale_5.0_attention_scale_2.3	15.9
+ngram_lm_scale_1.9_attention_scale_0.1	15.91
+ngram_lm_scale_2.2_attention_scale_0.3	15.93
+ngram_lm_scale_2.5_attention_scale_0.5	15.96
+ngram_lm_scale_1.9_attention_scale_0.08	16.02
+ngram_lm_scale_4.0_attention_scale_1.5	16.04
+ngram_lm_scale_5.0_attention_scale_2.2	16.04
+ngram_lm_scale_1.9_attention_scale_0.05	16.18
+ngram_lm_scale_5.0_attention_scale_2.1	16.2
+ngram_lm_scale_2.3_attention_scale_0.3	16.21
+ngram_lm_scale_2.0_attention_scale_0.1	16.25
+ngram_lm_scale_3.0_attention_scale_0.7	16.34
+ngram_lm_scale_2.0_attention_scale_0.08	16.35
+ngram_lm_scale_5.0_attention_scale_2.0	16.37
+ngram_lm_scale_1.9_attention_scale_0.01	16.42
+ngram_lm_scale_4.0_attention_scale_1.3	16.45
+ngram_lm_scale_2.0_attention_scale_0.05	16.5
+ngram_lm_scale_5.0_attention_scale_1.9	16.52
+ngram_lm_scale_2.1_attention_scale_0.1	16.55
+ngram_lm_scale_4.0_attention_scale_1.2	16.62
+ngram_lm_scale_2.1_attention_scale_0.08	16.64
+ngram_lm_scale_3.0_attention_scale_0.6	16.64
+ngram_lm_scale_2.5_attention_scale_0.3	16.67
+ngram_lm_scale_2.0_attention_scale_0.01	16.71
+ngram_lm_scale_2.1_attention_scale_0.05	16.77
+ngram_lm_scale_2.2_attention_scale_0.1	16.8
+ngram_lm_scale_5.0_attention_scale_1.7	16.82
+ngram_lm_scale_4.0_attention_scale_1.1	16.84
+ngram_lm_scale_2.2_attention_scale_0.08	16.89
+ngram_lm_scale_3.0_attention_scale_0.5	16.95
+ngram_lm_scale_2.1_attention_scale_0.01	16.99
+ngram_lm_scale_2.2_attention_scale_0.05	17.02
+ngram_lm_scale_2.3_attention_scale_0.1	17.02
+ngram_lm_scale_4.0_attention_scale_1.0	17.07
+ngram_lm_scale_2.3_attention_scale_0.08	17.09
+ngram_lm_scale_5.0_attention_scale_1.5	17.16
+ngram_lm_scale_2.2_attention_scale_0.01	17.18
+ngram_lm_scale_2.3_attention_scale_0.05	17.2
+ngram_lm_scale_4.0_attention_scale_0.9	17.24
+ngram_lm_scale_2.3_attention_scale_0.01	17.38
+ngram_lm_scale_2.5_attention_scale_0.1	17.4
+ngram_lm_scale_5.0_attention_scale_1.3	17.45
+ngram_lm_scale_2.5_attention_scale_0.08	17.47
+ngram_lm_scale_3.0_attention_scale_0.3	17.53
+ngram_lm_scale_2.5_attention_scale_0.05	17.58
+ngram_lm_scale_5.0_attention_scale_1.2	17.63
+ngram_lm_scale_2.5_attention_scale_0.01	17.7
+ngram_lm_scale_4.0_attention_scale_0.7	17.7
+ngram_lm_scale_5.0_attention_scale_1.1	17.8
+ngram_lm_scale_4.0_attention_scale_0.6	17.89
+ngram_lm_scale_5.0_attention_scale_1.0	17.94
+ngram_lm_scale_3.0_attention_scale_0.1	18.09
+ngram_lm_scale_4.0_attention_scale_0.5	18.09
+ngram_lm_scale_5.0_attention_scale_0.9	18.09
+ngram_lm_scale_3.0_attention_scale_0.08	18.14
+ngram_lm_scale_3.0_attention_scale_0.05	18.21
+ngram_lm_scale_3.0_attention_scale_0.01	18.31
+ngram_lm_scale_5.0_attention_scale_0.7	18.41
+ngram_lm_scale_4.0_attention_scale_0.3	18.49
+ngram_lm_scale_5.0_attention_scale_0.6	18.57
+ngram_lm_scale_5.0_attention_scale_0.5	18.71
+ngram_lm_scale_4.0_attention_scale_0.1	18.85
+ngram_lm_scale_4.0_attention_scale_0.08	18.88
+ngram_lm_scale_4.0_attention_scale_0.05	18.95
+ngram_lm_scale_5.0_attention_scale_0.3	19.01
+ngram_lm_scale_4.0_attention_scale_0.01	19.02
+ngram_lm_scale_5.0_attention_scale_0.1	19.3
+ngram_lm_scale_5.0_attention_scale_0.08	19.32
+ngram_lm_scale_5.0_attention_scale_0.05	19.37
+ngram_lm_scale_5.0_attention_scale_0.01	19.43
+
+2022-04-08 23:20:49,165 INFO [decode.py:730] Done!

log/log-decode-2022-04-09-01-40-41

@@ -0,0 +1,1176 @@
+2022-04-09 01:40:41,909 INFO [decode_test.py:583] Decoding started
+2022-04-09 01:40:41,910 INFO [decode_test.py:584] {'subsampling_factor': 4, 'vgg_frontend': False, 'use_feat_batchnorm': True, 'feature_dim': 80, 'nhead': 8, 'attention_dim': 512, 'num_decoder_layers': 6, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'env_info': {'k2-version': '1.14', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '6833270cb228aba7bf9681fccd41e2b52f7d984c', 'k2-git-date': 'Wed Mar 16 11:16:05 2022', 'lhotse-version': '1.0.0.dev+git.d917411.clean', 'torch-cuda-available': True, 'torch-cuda-version': '11.1', 'python-version': '3.7', 'icefall-git-branch': 'gigaspeech_recipe', 'icefall-git-sha1': 'c3993a5-dirty', 'icefall-git-date': 'Mon Mar 21 13:49:39 2022', 'icefall-path': '/userhome/user/guanbo/icefall_decode', 'k2-path': '/opt/conda/lib/python3.7/site-packages/k2-1.14.dev20220408+cuda11.1.torch1.10.0-py3.7-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/userhome/user/guanbo/lhotse/lhotse/__init__.py', 'hostname': 'c8861f400b70d011ec0a3ee069db84328338-chenx8564-0', 'IP address': '10.9.150.55'}, 'epoch': 18, 'avg': 6, 'method': 'attention-decoder', 'num_paths': 1000, 'nbest_scale': 0.5, 'exp_dir': PosixPath('conformer_ctc/exp_500_8_2'), 'lang_dir': PosixPath('data/lang_bpe_500'), 'lm_dir': PosixPath('data/lm'), 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 20, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 1, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'subset': 'XL', 'lazy_load': True, 'small_dev': False}
+2022-04-09 01:40:42,371 INFO [lexicon.py:176] Loading pre-compiled data/lang_bpe_500/Linv.pt
+2022-04-09 01:40:42,473 INFO [decode_test.py:594] device: cuda:0
+2022-04-09 01:40:46,249 INFO [decode_test.py:656] Loading pre-compiled G_4_gram.pt
+2022-04-09 01:40:47,406 INFO [decode_test.py:692] averaging ['conformer_ctc/exp_500_8_2/epoch-13.pt', 'conformer_ctc/exp_500_8_2/epoch-14.pt', 'conformer_ctc/exp_500_8_2/epoch-15.pt', 'conformer_ctc/exp_500_8_2/epoch-16.pt', 'conformer_ctc/exp_500_8_2/epoch-17.pt', 'conformer_ctc/exp_500_8_2/epoch-18.pt']
+2022-04-09 01:40:53,065 INFO [decode_test.py:699] Number of model parameters: 109226120
+2022-04-09 01:40:53,065 INFO [asr_datamodule.py:381] About to get test cuts
+2022-04-09 01:40:56,361 INFO [decode_test.py:497] batch 0/?, cuts processed until now is 3
+2022-04-09 01:41:24,462 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.93 GiB (GPU 0; 31.75 GiB total capacity; 27.23 GiB already allocated; 1.90 GiB free; 28.49 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:41:24,462 INFO [decode.py:743] num_arcs before pruning: 324363
+2022-04-09 01:41:24,462 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:41:24,473 INFO [decode.py:757] num_arcs after pruning: 7174
+2022-04-09 01:41:40,284 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.67 GiB (GPU 0; 31.75 GiB total capacity; 25.69 GiB already allocated; 2.92 GiB free; 27.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:41:40,285 INFO [decode.py:743] num_arcs before pruning: 368362
+2022-04-09 01:41:40,285 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:41:40,305 INFO [decode.py:757] num_arcs after pruning: 8521
+2022-04-09 01:42:38,727 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.18 GiB (GPU 0; 31.75 GiB total capacity; 26.05 GiB already allocated; 1.42 GiB free; 28.98 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:42:38,727 INFO [decode.py:743] num_arcs before pruning: 432616
+2022-04-09 01:42:38,728 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:42:38,736 INFO [decode.py:757] num_arcs after pruning: 9233
+2022-04-09 01:43:13,573 INFO [decode_test.py:497] batch 100/?, cuts processed until now is 297
+2022-04-09 01:43:48,362 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 25.34 GiB already allocated; 2.20 GiB free; 28.20 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:43:48,363 INFO [decode.py:743] num_arcs before pruning: 319907
+2022-04-09 01:43:48,363 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:43:48,372 INFO [decode.py:757] num_arcs after pruning: 6358
+2022-04-09 01:43:59,713 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.74 GiB (GPU 0; 31.75 GiB total capacity; 27.51 GiB already allocated; 2.19 GiB free; 28.20 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:43:59,713 INFO [decode.py:743] num_arcs before pruning: 313596
+2022-04-09 01:43:59,713 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:43:59,724 INFO [decode.py:757] num_arcs after pruning: 8252
+2022-04-09 01:44:54,463 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 25.25 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:44:54,463 INFO [decode.py:743] num_arcs before pruning: 353355
+2022-04-09 01:44:54,463 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:44:54,485 INFO [decode.py:757] num_arcs after pruning: 7520
+2022-04-09 01:45:20,716 INFO [decode_test.py:497] batch 200/?, cuts processed until now is 570
+2022-04-09 01:47:19,457 INFO [decode_test.py:497] batch 300/?, cuts processed until now is 806
+2022-04-09 01:47:38,292 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.28 GiB (GPU 0; 31.75 GiB total capacity; 26.28 GiB already allocated; 1.48 GiB free; 28.92 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:47:38,293 INFO [decode.py:743] num_arcs before pruning: 596002
+2022-04-09 01:47:38,293 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:47:38,312 INFO [decode.py:757] num_arcs after pruning: 10745
+2022-04-09 01:49:18,493 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-09 01:49:18,494 INFO [decode.py:743] num_arcs before pruning: 398202
+2022-04-09 01:49:18,494 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:49:18,541 INFO [decode.py:757] num_arcs after pruning: 14003
+2022-04-09 01:49:21,800 INFO [decode_test.py:497] batch 400/?, cuts processed until now is 1082
+2022-04-09 01:50:58,700 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.85 GiB (GPU 0; 31.75 GiB total capacity; 25.89 GiB already allocated; 1.48 GiB free; 28.92 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:50:58,701 INFO [decode.py:743] num_arcs before pruning: 398349
+2022-04-09 01:50:58,701 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:50:58,709 INFO [decode.py:757] num_arcs after pruning: 10321
+2022-04-09 01:51:31,627 INFO [decode_test.py:497] batch 500/?, cuts processed until now is 1334
+2022-04-09 01:52:05,232 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.62 GiB already allocated; 1.47 GiB free; 28.93 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:52:05,232 INFO [decode.py:743] num_arcs before pruning: 212665
+2022-04-09 01:52:05,232 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:52:05,241 INFO [decode.py:757] num_arcs after pruning: 6301
+2022-04-09 01:53:29,890 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 1.91 GiB (GPU 0; 31.75 GiB total capacity; 25.66 GiB already allocated; 1.48 GiB free; 28.92 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:53:29,891 INFO [decode.py:743] num_arcs before pruning: 883555
+2022-04-09 01:53:29,891 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:53:29,905 INFO [decode.py:757] num_arcs after pruning: 14819
+2022-04-09 01:53:38,676 INFO [decode_test.py:497] batch 600/?, cuts processed until now is 1651
+2022-04-09 01:54:57,438 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 25.34 GiB already allocated; 1.48 GiB free; 28.92 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:54:57,438 INFO [decode.py:743] num_arcs before pruning: 515795
+2022-04-09 01:54:57,438 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:54:57,447 INFO [decode.py:757] num_arcs after pruning: 10132
+2022-04-09 01:55:28,356 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.46 GiB already allocated; 1.48 GiB free; 28.92 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:55:28,356 INFO [decode.py:743] num_arcs before pruning: 670748
+2022-04-09 01:55:28,356 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:55:28,365 INFO [decode.py:757] num_arcs after pruning: 10497
+2022-04-09 01:55:42,238 INFO [decode_test.py:497] batch 700/?, cuts processed until now is 1956
+2022-04-09 01:57:57,456 INFO [decode_test.py:497] batch 800/?, cuts processed until now is 2238
+2022-04-09 01:58:04,281 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.45 GiB already allocated; 3.07 GiB free; 27.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:58:04,282 INFO [decode.py:743] num_arcs before pruning: 175423
+2022-04-09 01:58:04,282 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:58:04,296 INFO [decode.py:757] num_arcs after pruning: 7926
+2022-04-09 01:59:07,916 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.68 GiB (GPU 0; 31.75 GiB total capacity; 24.40 GiB already allocated; 3.06 GiB free; 27.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 01:59:07,917 INFO [decode.py:743] num_arcs before pruning: 259758
+2022-04-09 01:59:07,917 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 01:59:07,928 INFO [decode.py:757] num_arcs after pruning: 6026
+2022-04-09 02:00:00,623 INFO [decode_test.py:497] batch 900/?, cuts processed until now is 2536
+2022-04-09 02:01:22,959 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.44 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:01:22,959 INFO [decode.py:743] num_arcs before pruning: 749228
+2022-04-09 02:01:22,959 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:01:22,968 INFO [decode.py:757] num_arcs after pruning: 23868
+2022-04-09 02:01:59,449 INFO [decode_test.py:497] batch 1000/?, cuts processed until now is 2824
+2022-04-09 02:03:05,494 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.38 GiB already allocated; 3.06 GiB free; 27.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:03:05,494 INFO [decode.py:743] num_arcs before pruning: 255135
+2022-04-09 02:03:05,494 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:03:05,504 INFO [decode.py:757] num_arcs after pruning: 5955
+2022-04-09 02:03:48,017 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.61 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:03:48,017 INFO [decode.py:743] num_arcs before pruning: 517077
+2022-04-09 02:03:48,017 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:03:48,026 INFO [decode.py:757] num_arcs after pruning: 7695
+2022-04-09 02:04:09,806 INFO [decode_test.py:497] batch 1100/?, cuts processed until now is 3105
+2022-04-09 02:04:31,410 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.34 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:04:31,411 INFO [decode.py:743] num_arcs before pruning: 859561
+2022-04-09 02:04:31,411 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:04:31,422 INFO [decode.py:757] num_arcs after pruning: 13014
+2022-04-09 02:06:11,496 INFO [decode_test.py:497] batch 1200/?, cuts processed until now is 3401
+2022-04-09 02:08:10,727 INFO [decode_test.py:497] batch 1300/?, cuts processed until now is 3730
+2022-04-09 02:10:17,677 INFO [decode_test.py:497] batch 1400/?, cuts processed until now is 4067
+2022-04-09 02:12:13,175 INFO [decode_test.py:497] batch 1500/?, cuts processed until now is 4329
+2022-04-09 02:13:02,842 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.55 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:13:02,843 INFO [decode.py:743] num_arcs before pruning: 475511
+2022-04-09 02:13:02,843 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:13:02,849 INFO [decode.py:757] num_arcs after pruning: 8439
+2022-04-09 02:13:46,588 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.37 GiB (GPU 0; 31.75 GiB total capacity; 26.83 GiB already allocated; 1.45 GiB free; 28.94 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:13:46,588 INFO [decode.py:743] num_arcs before pruning: 595488
+2022-04-09 02:13:46,588 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:13:46,598 INFO [decode.py:757] num_arcs after pruning: 13475
+2022-04-09 02:14:21,206 INFO [decode_test.py:497] batch 1600/?, cuts processed until now is 4598
+2022-04-09 02:16:42,740 INFO [decode_test.py:497] batch 1700/?, cuts processed until now is 4969
+2022-04-09 02:17:13,672 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 25.39 GiB already allocated; 1.45 GiB free; 28.94 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:17:13,673 INFO [decode.py:743] num_arcs before pruning: 615734
+2022-04-09 02:17:13,673 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:17:13,685 INFO [decode.py:757] num_arcs after pruning: 8684
+2022-04-09 02:18:54,514 INFO [decode_test.py:497] batch 1800/?, cuts processed until now is 5260
+2022-04-09 02:18:59,938 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.36 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:18:59,938 INFO [decode.py:743] num_arcs before pruning: 360099
+2022-04-09 02:18:59,938 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:18:59,949 INFO [decode.py:757] num_arcs after pruning: 6898
+2022-04-09 02:19:48,186 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 6.00 GiB (GPU 0; 31.75 GiB total capacity; 27.15 GiB already allocated; 967.75 MiB free; 29.45 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:19:48,186 INFO [decode.py:743] num_arcs before pruning: 168720
+2022-04-09 02:19:48,186 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:19:48,201 INFO [decode.py:757] num_arcs after pruning: 5346
+2022-04-09 02:20:52,049 INFO [decode_test.py:497] batch 1900/?, cuts processed until now is 5585
+2022-04-09 02:22:12,107 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.45 GiB already allocated; 973.75 MiB free; 29.44 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:22:12,107 INFO [decode.py:743] num_arcs before pruning: 1151735
+2022-04-09 02:22:12,107 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:22:12,120 INFO [decode.py:757] num_arcs after pruning: 8335
+2022-04-09 02:23:01,497 INFO [decode_test.py:497] batch 2000/?, cuts processed until now is 5902
+2022-04-09 02:25:26,356 INFO [decode_test.py:497] batch 2100/?, cuts processed until now is 6219
+2022-04-09 02:25:56,466 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.34 GiB already allocated; 973.75 MiB free; 29.44 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:25:56,467 INFO [decode.py:743] num_arcs before pruning: 612804
+2022-04-09 02:25:56,467 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:25:56,477 INFO [decode.py:757] num_arcs after pruning: 10853
+2022-04-09 02:27:26,441 INFO [decode_test.py:497] batch 2200/?, cuts processed until now is 6480
+2022-04-09 02:29:28,073 INFO [decode_test.py:497] batch 2300/?, cuts processed until now is 6768
+2022-04-09 02:31:41,553 INFO [decode_test.py:497] batch 2400/?, cuts processed until now is 7120
+2022-04-09 02:31:55,632 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.42 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:31:55,632 INFO [decode.py:743] num_arcs before pruning: 411490
+2022-04-09 02:31:55,632 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:31:55,638 INFO [decode.py:757] num_arcs after pruning: 8626
+2022-04-09 02:33:22,034 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.42 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:33:22,034 INFO [decode.py:743] num_arcs before pruning: 625728
+2022-04-09 02:33:22,035 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:33:22,043 INFO [decode.py:757] num_arcs after pruning: 9502
+2022-04-09 02:33:37,663 INFO [decode_test.py:497] batch 2500/?, cuts processed until now is 7387
+2022-04-09 02:34:18,300 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.51 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:34:18,301 INFO [decode.py:743] num_arcs before pruning: 1015956
+2022-04-09 02:34:18,301 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:34:18,314 INFO [decode.py:757] num_arcs after pruning: 14404
+2022-04-09 02:34:20,220 INFO [decode.py:841] Caught exception:
+CUDA out of memory. Tried to allocate 5.58 GiB (GPU 0; 31.75 GiB total capacity; 24.87 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:34:20,221 INFO [decode.py:843] num_paths before decreasing: 1000
+2022-04-09 02:34:20,221 INFO [decode.py:852] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:34:20,221 INFO [decode.py:858] num_paths after decreasing: 500
+2022-04-09 02:34:40,089 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.38 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:34:40,089 INFO [decode.py:743] num_arcs before pruning: 570686
+2022-04-09 02:34:40,089 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:34:40,098 INFO [decode.py:757] num_arcs after pruning: 9182
+2022-04-09 02:35:50,624 INFO [decode_test.py:497] batch 2600/?, cuts processed until now is 7764
+2022-04-09 02:36:44,519 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.61 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:36:44,519 INFO [decode.py:743] num_arcs before pruning: 1066267
+2022-04-09 02:36:44,519 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:36:44,530 INFO [decode.py:757] num_arcs after pruning: 6963
+2022-04-09 02:38:18,717 INFO [decode_test.py:497] batch 2700/?, cuts processed until now is 8078
+2022-04-09 02:40:07,021 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.42 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:40:07,022 INFO [decode.py:743] num_arcs before pruning: 1023667
+2022-04-09 02:40:07,022 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:40:07,034 INFO [decode.py:757] num_arcs after pruning: 13090
+2022-04-09 02:40:25,184 INFO [decode_test.py:497] batch 2800/?, cuts processed until now is 8444
+2022-04-09 02:41:27,080 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.32 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:41:27,080 INFO [decode.py:743] num_arcs before pruning: 739744
+2022-04-09 02:41:27,080 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:41:27,093 INFO [decode.py:757] num_arcs after pruning: 9791
+2022-04-09 02:42:44,319 INFO [decode_test.py:497] batch 2900/?, cuts processed until now is 8765
+2022-04-09 02:42:44,656 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.73 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:42:44,656 INFO [decode.py:743] num_arcs before pruning: 666168
+2022-04-09 02:42:44,656 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:42:44,665 INFO [decode.py:757] num_arcs after pruning: 17223
+2022-04-09 02:43:05,748 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.60 GiB (GPU 0; 31.75 GiB total capacity; 26.18 GiB already allocated; 1.14 GiB free; 29.26 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:43:05,748 INFO [decode.py:743] num_arcs before pruning: 188729
+2022-04-09 02:43:05,748 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:43:05,762 INFO [decode.py:757] num_arcs after pruning: 8688
+2022-04-09 02:44:54,469 INFO [decode_test.py:497] batch 3000/?, cuts processed until now is 9050
+2022-04-09 02:46:55,167 INFO [decode_test.py:497] batch 3100/?, cuts processed until now is 9296
+2022-04-09 02:47:28,418 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 20.00 GiB already allocated; 3.07 GiB free; 27.33 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:47:28,419 INFO [decode.py:743] num_arcs before pruning: 160153
+2022-04-09 02:47:28,419 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:47:28,448 INFO [decode.py:757] num_arcs after pruning: 7778
+2022-04-09 02:49:21,448 INFO [decode_test.py:497] batch 3200/?, cuts processed until now is 9652
+2022-04-09 02:50:17,558 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 6.13 GiB (GPU 0; 31.75 GiB total capacity; 27.60 GiB already allocated; 895.75 MiB free; 29.52 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:50:17,558 INFO [decode.py:743] num_arcs before pruning: 388116
+2022-04-09 02:50:17,559 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:50:17,565 INFO [decode.py:757] num_arcs after pruning: 10555
+2022-04-09 02:51:30,675 INFO [decode_test.py:497] batch 3300/?, cuts processed until now is 10071
+2022-04-09 02:53:49,565 INFO [decode_test.py:497] batch 3400/?, cuts processed until now is 10342
+2022-04-09 02:55:49,392 INFO [decode_test.py:497] batch 3500/?, cuts processed until now is 10642
+2022-04-09 02:58:07,518 INFO [decode_test.py:497] batch 3600/?, cuts processed until now is 10951
+2022-04-09 02:58:16,360 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.29 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 02:58:16,361 INFO [decode.py:743] num_arcs before pruning: 396714
+2022-04-09 02:58:16,361 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 02:58:16,374 INFO [decode.py:757] num_arcs after pruning: 9543
+2022-04-09 03:00:00,485 INFO [decode_test.py:497] batch 3700/?, cuts processed until now is 11231
+2022-04-09 03:00:17,600 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.45 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:00:17,601 INFO [decode.py:743] num_arcs before pruning: 854366
+2022-04-09 03:00:17,601 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:00:17,612 INFO [decode.py:757] num_arcs after pruning: 10487
+2022-04-09 03:00:20,098 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.68 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:00:20,098 INFO [decode.py:743] num_arcs before pruning: 442824
+2022-04-09 03:00:20,098 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:00:20,108 INFO [decode.py:757] num_arcs after pruning: 5265
+2022-04-09 03:02:00,114 INFO [decode_test.py:497] batch 3800/?, cuts processed until now is 11509
+2022-04-09 03:02:11,570 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.19 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:02:11,571 INFO [decode.py:743] num_arcs before pruning: 285638
+2022-04-09 03:02:11,571 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:02:11,579 INFO [decode.py:757] num_arcs after pruning: 5903
+2022-04-09 03:04:02,757 INFO [decode_test.py:497] batch 3900/?, cuts processed until now is 11774
+2022-04-09 03:05:19,989 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.73 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:05:19,990 INFO [decode.py:743] num_arcs before pruning: 637327
+2022-04-09 03:05:19,990 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:05:19,999 INFO [decode.py:757] num_arcs after pruning: 6357
+2022-04-09 03:06:01,953 INFO [decode_test.py:497] batch 4000/?, cuts processed until now is 12045
+2022-04-09 03:07:49,854 INFO [decode_test.py:497] batch 4100/?, cuts processed until now is 12300
+2022-04-09 03:09:15,137 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.45 GiB already allocated; 3.08 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:09:15,138 INFO [decode.py:743] num_arcs before pruning: 507733
+2022-04-09 03:09:15,138 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:09:15,148 INFO [decode.py:757] num_arcs after pruning: 4196
+2022-04-09 03:09:47,397 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.86 GiB (GPU 0; 31.75 GiB total capacity; 27.78 GiB already allocated; 925.75 MiB free; 29.49 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:09:47,397 INFO [decode.py:743] num_arcs before pruning: 514118
+2022-04-09 03:09:47,397 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:09:47,407 INFO [decode.py:757] num_arcs after pruning: 7168
+2022-04-09 03:10:00,013 INFO [decode_test.py:497] batch 4200/?, cuts processed until now is 12580
+2022-04-09 03:10:33,411 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.80 GiB (GPU 0; 31.75 GiB total capacity; 27.70 GiB already allocated; 925.75 MiB free; 29.49 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:10:33,411 INFO [decode.py:743] num_arcs before pruning: 374935
+2022-04-09 03:10:33,411 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:10:33,418 INFO [decode.py:757] num_arcs after pruning: 10023
+2022-04-09 03:12:04,333 INFO [decode_test.py:497] batch 4300/?, cuts processed until now is 12807
+2022-04-09 03:14:06,889 INFO [decode_test.py:497] batch 4400/?, cuts processed until now is 13050
+2022-04-09 03:14:34,787 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.47 GiB already allocated; 925.75 MiB free; 29.49 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:14:34,788 INFO [decode.py:743] num_arcs before pruning: 767465
+2022-04-09 03:14:34,788 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:14:34,797 INFO [decode.py:757] num_arcs after pruning: 19151
+2022-04-09 03:15:08,864 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-09 03:15:08,864 INFO [decode.py:743] num_arcs before pruning: 123833
+2022-04-09 03:15:08,864 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:15:08,913 INFO [decode.py:757] num_arcs after pruning: 4150
+2022-04-09 03:15:34,899 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 25.64 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:15:34,899 INFO [decode.py:743] num_arcs before pruning: 444800
+2022-04-09 03:15:34,899 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:15:34,908 INFO [decode.py:757] num_arcs after pruning: 11839
+2022-04-09 03:16:08,462 INFO [decode_test.py:497] batch 4500/?, cuts processed until now is 13295
+2022-04-09 03:17:56,946 INFO [decode_test.py:497] batch 4600/?, cuts processed until now is 13593
+2022-04-09 03:18:16,099 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.53 GiB (GPU 0; 31.75 GiB total capacity; 26.53 GiB already allocated; 1.12 GiB free; 29.28 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:18:16,099 INFO [decode.py:743] num_arcs before pruning: 350609
+2022-04-09 03:18:16,100 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:18:16,105 INFO [decode.py:757] num_arcs after pruning: 9262
+2022-04-09 03:19:57,230 INFO [decode_test.py:497] batch 4700/?, cuts processed until now is 13858
+2022-04-09 03:20:19,775 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.87 GiB (GPU 0; 31.75 GiB total capacity; 25.78 GiB already allocated; 1.12 GiB free; 29.28 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:20:19,775 INFO [decode.py:743] num_arcs before pruning: 375071
+2022-04-09 03:20:19,775 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:20:19,785 INFO [decode.py:757] num_arcs after pruning: 6365
+2022-04-09 03:21:29,481 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.42 GiB already allocated; 1.12 GiB free; 29.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:21:29,481 INFO [decode.py:743] num_arcs before pruning: 872088
+2022-04-09 03:21:29,481 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:21:29,492 INFO [decode.py:757] num_arcs after pruning: 10043
+2022-04-09 03:22:01,760 INFO [decode_test.py:497] batch 4800/?, cuts processed until now is 14079
+2022-04-09 03:24:10,370 INFO [decode_test.py:497] batch 4900/?, cuts processed until now is 14298
+2022-04-09 03:26:10,811 INFO [decode_test.py:497] batch 5000/?, cuts processed until now is 14515
+2022-04-09 03:27:46,191 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-09 03:27:46,192 INFO [decode.py:743] num_arcs before pruning: 246382
+2022-04-09 03:27:46,192 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:27:46,253 INFO [decode.py:757] num_arcs after pruning: 6775
+2022-04-09 03:28:15,199 INFO [decode_test.py:497] batch 5100/?, cuts processed until now is 14718
+2022-04-09 03:29:19,807 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 6.15 GiB (GPU 0; 31.75 GiB total capacity; 26.67 GiB already allocated; 1.11 GiB free; 29.29 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:29:19,808 INFO [decode.py:743] num_arcs before pruning: 220820
+2022-04-09 03:29:19,808 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:29:19,815 INFO [decode.py:757] num_arcs after pruning: 13482
+2022-04-09 03:30:16,045 INFO [decode_test.py:497] batch 5200/?, cuts processed until now is 14930
+2022-04-09 03:32:12,235 INFO [decode_test.py:497] batch 5300/?, cuts processed until now is 15128
+2022-04-09 03:33:06,358 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-09 03:33:06,359 INFO [decode.py:743] num_arcs before pruning: 190203
+2022-04-09 03:33:06,359 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:33:06,413 INFO [decode.py:757] num_arcs after pruning: 6202
+2022-04-09 03:34:14,862 INFO [decode_test.py:497] batch 5400/?, cuts processed until now is 15327
+2022-04-09 03:36:18,973 INFO [decode_test.py:497] batch 5500/?, cuts processed until now is 15531
+2022-04-09 03:38:18,633 INFO [decode_test.py:497] batch 5600/?, cuts processed until now is 15724
+2022-04-09 03:38:48,490 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.52 GiB already allocated; 3.07 GiB free; 27.32 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:38:48,491 INFO [decode.py:743] num_arcs before pruning: 554330
+2022-04-09 03:38:48,491 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:38:48,500 INFO [decode.py:757] num_arcs after pruning: 10730
+2022-04-09 03:39:51,281 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.83 GiB (GPU 0; 31.75 GiB total capacity; 25.96 GiB already allocated; 1.31 GiB free; 29.08 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:39:51,281 INFO [decode.py:743] num_arcs before pruning: 160031
+2022-04-09 03:39:51,281 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:39:51,288 INFO [decode.py:757] num_arcs after pruning: 4270
+2022-04-09 03:40:28,016 INFO [decode_test.py:497] batch 5700/?, cuts processed until now is 15908
+2022-04-09 03:40:46,608 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.58 GiB (GPU 0; 31.75 GiB total capacity; 27.28 GiB already allocated; 1.32 GiB free; 29.07 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:40:46,608 INFO [decode.py:743] num_arcs before pruning: 406026
+2022-04-09 03:40:46,608 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:40:46,616 INFO [decode.py:757] num_arcs after pruning: 11179
+2022-04-09 03:42:16,464 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.29 GiB (GPU 0; 31.75 GiB total capacity; 26.71 GiB already allocated; 1.32 GiB free; 29.07 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:42:16,464 INFO [decode.py:743] num_arcs before pruning: 639824
+2022-04-09 03:42:16,464 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:42:16,476 INFO [decode.py:757] num_arcs after pruning: 5520
+2022-04-09 03:42:52,683 INFO [decode_test.py:497] batch 5800/?, cuts processed until now is 16094
+2022-04-09 03:44:51,754 INFO [decode_test.py:497] batch 5900/?, cuts processed until now is 16289
+2022-04-09 03:46:52,121 INFO [decode_test.py:497] batch 6000/?, cuts processed until now is 16488
+2022-04-09 03:48:54,739 INFO [decode_test.py:497] batch 6100/?, cuts processed until now is 16661
+2022-04-09 03:49:24,829 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 1.84 GiB (GPU 0; 31.75 GiB total capacity; 28.87 GiB already allocated; 409.75 MiB free; 29.99 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:49:24,830 INFO [decode.py:743] num_arcs before pruning: 443401
+2022-04-09 03:49:24,830 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:49:24,837 INFO [decode.py:757] num_arcs after pruning: 5211
+2022-04-09 03:50:27,492 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.35 GiB already allocated; 2.15 GiB free; 28.24 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:50:27,493 INFO [decode.py:743] num_arcs before pruning: 361598
+2022-04-09 03:50:27,493 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:50:27,507 INFO [decode.py:757] num_arcs after pruning: 8660
+2022-04-09 03:51:02,856 INFO [decode_test.py:497] batch 6200/?, cuts processed until now is 16828
+2022-04-09 03:53:03,912 INFO [decode_test.py:497] batch 6300/?, cuts processed until now is 17002
+2022-04-09 03:55:04,964 INFO [decode_test.py:497] batch 6400/?, cuts processed until now is 17181
+2022-04-09 03:55:08,345 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.89 GiB (GPU 0; 31.75 GiB total capacity; 26.28 GiB already allocated; 2.16 GiB free; 28.24 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:55:08,345 INFO [decode.py:743] num_arcs before pruning: 867262
+2022-04-09 03:55:08,345 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:55:08,356 INFO [decode.py:757] num_arcs after pruning: 6494
+2022-04-09 03:56:03,884 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 1.90 GiB (GPU 0; 31.75 GiB total capacity; 28.97 GiB already allocated; 1.16 GiB free; 29.23 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:56:03,885 INFO [decode.py:743] num_arcs before pruning: 233755
+2022-04-09 03:56:03,885 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:56:03,910 INFO [decode.py:757] num_arcs after pruning: 5823
+2022-04-09 03:57:08,774 INFO [decode_test.py:497] batch 6500/?, cuts processed until now is 17347
+2022-04-09 03:59:01,245 INFO [decode_test.py:497] batch 6600/?, cuts processed until now is 17502
+2022-04-09 03:59:13,147 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.80 GiB (GPU 0; 31.75 GiB total capacity; 26.73 GiB already allocated; 1.17 GiB free; 29.22 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 03:59:13,147 INFO [decode.py:743] num_arcs before pruning: 174004
+2022-04-09 03:59:13,147 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 03:59:13,155 INFO [decode.py:757] num_arcs after pruning: 6857
+2022-04-09 04:00:59,687 INFO [decode_test.py:497] batch 6700/?, cuts processed until now is 17661
+2022-04-09 04:03:01,660 INFO [decode_test.py:497] batch 6800/?, cuts processed until now is 17823
+2022-04-09 04:04:55,219 INFO [decode_test.py:497] batch 6900/?, cuts processed until now is 17997
+2022-04-09 04:07:05,841 INFO [decode_test.py:497] batch 7000/?, cuts processed until now is 18159
+2022-04-09 04:09:04,994 INFO [decode_test.py:497] batch 7100/?, cuts processed until now is 18299
+2022-04-09 04:11:07,439 INFO [decode_test.py:497] batch 7200/?, cuts processed until now is 18432
+2022-04-09 04:13:18,126 INFO [decode_test.py:497] batch 7300/?, cuts processed until now is 18552
+2022-04-09 04:15:23,102 INFO [decode_test.py:497] batch 7400/?, cuts processed until now is 18656
+2022-04-09 04:17:49,550 INFO [decode_test.py:497] batch 7500/?, cuts processed until now is 18798
+2022-04-09 04:19:16,128 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.34 GiB already allocated; 2.12 GiB free; 28.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:19:16,129 INFO [decode.py:743] num_arcs before pruning: 1155990
+2022-04-09 04:19:16,129 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:19:16,143 INFO [decode.py:757] num_arcs after pruning: 9141
+2022-04-09 04:20:19,961 INFO [decode_test.py:497] batch 7600/?, cuts processed until now is 18945
+2022-04-09 04:22:44,642 INFO [decode_test.py:497] batch 7700/?, cuts processed until now is 19084
+2022-04-09 04:23:18,184 INFO [decode.py:841] Caught exception:
+CUDA out of memory. Tried to allocate 1.26 GiB (GPU 0; 31.75 GiB total capacity; 27.36 GiB already allocated; 881.75 MiB free; 29.53 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:23:18,184 INFO [decode.py:843] num_paths before decreasing: 1000
+2022-04-09 04:23:18,184 INFO [decode.py:852] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:23:18,184 INFO [decode.py:858] num_paths after decreasing: 500
+2022-04-09 04:24:52,959 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.53 GiB already allocated; 2.12 GiB free; 28.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:24:52,960 INFO [decode.py:743] num_arcs before pruning: 624026
+2022-04-09 04:24:52,960 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:24:52,972 INFO [decode.py:757] num_arcs after pruning: 10008
+2022-04-09 04:25:07,718 INFO [decode_test.py:497] batch 7800/?, cuts processed until now is 19232
+2022-04-09 04:25:31,876 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.51 GiB already allocated; 2.12 GiB free; 28.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:25:31,876 INFO [decode.py:743] num_arcs before pruning: 688909
+2022-04-09 04:25:31,877 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:25:31,887 INFO [decode.py:757] num_arcs after pruning: 8886
+2022-04-09 04:25:57,970 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 5.04 GiB (GPU 0; 31.75 GiB total capacity; 25.95 GiB already allocated; 2.12 GiB free; 28.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:25:57,971 INFO [decode.py:743] num_arcs before pruning: 891176
+2022-04-09 04:25:57,971 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:25:57,982 INFO [decode.py:757] num_arcs after pruning: 10106
+2022-04-09 04:26:19,609 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 2.63 GiB (GPU 0; 31.75 GiB total capacity; 27.60 GiB already allocated; 327.75 MiB free; 30.07 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:26:19,609 INFO [decode.py:743] num_arcs before pruning: 415376
+2022-04-09 04:26:19,609 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:26:19,620 INFO [decode.py:757] num_arcs after pruning: 7771
+2022-04-09 04:27:33,059 INFO [decode_test.py:497] batch 7900/?, cuts processed until now is 19375
+2022-04-09 04:29:43,649 INFO [decode_test.py:497] batch 8000/?, cuts processed until now is 19510
+2022-04-09 04:30:20,590 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.65 GiB already allocated; 2.12 GiB free; 28.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:30:20,591 INFO [decode.py:743] num_arcs before pruning: 330767
+2022-04-09 04:30:20,591 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:30:20,606 INFO [decode.py:757] num_arcs after pruning: 5820
+2022-04-09 04:31:55,818 INFO [decode_test.py:497] batch 8100/?, cuts processed until now is 19643
+2022-04-09 04:34:11,720 INFO [decode_test.py:497] batch 8200/?, cuts processed until now is 19776
+2022-04-09 04:35:04,147 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 4.49 GiB (GPU 0; 31.75 GiB total capacity; 24.38 GiB already allocated; 2.12 GiB free; 28.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:35:04,147 INFO [decode.py:743] num_arcs before pruning: 533967
+2022-04-09 04:35:04,147 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:35:04,157 INFO [decode.py:757] num_arcs after pruning: 3449
+2022-04-09 04:36:15,595 INFO [decode.py:736] Caught exception:
+CUDA out of memory. Tried to allocate 8.00 GiB (GPU 0; 31.75 GiB total capacity; 19.67 GiB already allocated; 2.12 GiB free; 28.27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
+
+2022-04-09 04:36:15,595 INFO [decode.py:743] num_arcs before pruning: 397138
+2022-04-09 04:36:15,596 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:36:15,605 INFO [decode.py:757] num_arcs after pruning: 6775
+2022-04-09 04:36:31,844 INFO [decode_test.py:497] batch 8300/?, cuts processed until now is 19882
+2022-04-09 04:37:04,130 INFO [decode.py:736] Caught exception:
+
+    Some bad things happened. Please read the above error messages and stack
+    trace. If you are using Python, the following command may be helpful:
+
+      gdb --args python /path/to/your/code.py
+
+    (You can use `gdb` to debug the code. Please consider compiling
+    a debug version of k2.).
+
+    If you are unable to fix it, please open an issue at:
+
+      https://github.com/k2-fsa/k2/issues/new
+    
+
+2022-04-09 04:37:04,130 INFO [decode.py:743] num_arcs before pruning: 456591
+2022-04-09 04:37:04,130 INFO [decode.py:746] This OOM is not an error. You can ignore it. If your model does not converge well, or --max-duration is too large, or the input sound file is difficult to decode, you will meet this exception.
+2022-04-09 04:37:04,180 INFO [decode.py:757] num_arcs after pruning: 5275
+2022-04-09 04:57:33,432 INFO [decode_test.py:567] 
+For test, WER of different settings are:
+ngram_lm_scale_0.3_attention_scale_0.7	10.58	best for test
+ngram_lm_scale_0.5_attention_scale_1.3	10.58
+ngram_lm_scale_0.3_attention_scale_0.5	10.59
+ngram_lm_scale_0.3_attention_scale_0.6	10.59
+ngram_lm_scale_0.3_attention_scale_0.9	10.59
+ngram_lm_scale_0.3_attention_scale_1.0	10.59
+ngram_lm_scale_0.3_attention_scale_1.1	10.59
+ngram_lm_scale_0.3_attention_scale_1.2	10.59
+ngram_lm_scale_0.3_attention_scale_1.3	10.59
+ngram_lm_scale_0.5_attention_scale_1.0	10.59
+ngram_lm_scale_0.5_attention_scale_1.1	10.59
+ngram_lm_scale_0.5_attention_scale_1.2	10.59
+ngram_lm_scale_0.5_attention_scale_1.5	10.59
+ngram_lm_scale_0.5_attention_scale_1.7	10.59
+ngram_lm_scale_0.5_attention_scale_1.9	10.59
+ngram_lm_scale_0.5_attention_scale_2.0	10.59
+ngram_lm_scale_0.5_attention_scale_2.1	10.59
+ngram_lm_scale_0.5_attention_scale_2.2	10.59
+ngram_lm_scale_0.5_attention_scale_2.3	10.59
+ngram_lm_scale_0.6_attention_scale_1.9	10.59
+ngram_lm_scale_0.6_attention_scale_2.0	10.59
+ngram_lm_scale_0.6_attention_scale_2.1	10.59
+ngram_lm_scale_0.6_attention_scale_2.2	10.59
+ngram_lm_scale_0.6_attention_scale_2.3	10.59
+ngram_lm_scale_0.6_attention_scale_2.5	10.59
+ngram_lm_scale_0.3_attention_scale_1.5	10.6
+ngram_lm_scale_0.3_attention_scale_1.7	10.6
+ngram_lm_scale_0.3_attention_scale_1.9	10.6
+ngram_lm_scale_0.3_attention_scale_2.0	10.6
+ngram_lm_scale_0.3_attention_scale_2.1	10.6
+ngram_lm_scale_0.3_attention_scale_2.2	10.6
+ngram_lm_scale_0.3_attention_scale_2.3	10.6
+ngram_lm_scale_0.3_attention_scale_2.5	10.6
+ngram_lm_scale_0.5_attention_scale_0.9	10.6
+ngram_lm_scale_0.5_attention_scale_2.5	10.6
+ngram_lm_scale_0.5_attention_scale_3.0	10.6
+ngram_lm_scale_0.6_attention_scale_1.3	10.6
+ngram_lm_scale_0.6_attention_scale_1.5	10.6
+ngram_lm_scale_0.6_attention_scale_1.7	10.6
+ngram_lm_scale_0.6_attention_scale_3.0	10.6
+ngram_lm_scale_0.3_attention_scale_0.3	10.61
+ngram_lm_scale_0.3_attention_scale_3.0	10.61
+ngram_lm_scale_0.5_attention_scale_4.0	10.61
+ngram_lm_scale_0.5_attention_scale_5.0	10.61
+ngram_lm_scale_0.6_attention_scale_1.2	10.61
+ngram_lm_scale_0.6_attention_scale_4.0	10.61
+ngram_lm_scale_0.6_attention_scale_5.0	10.61
+ngram_lm_scale_0.7_attention_scale_1.7	10.61
+ngram_lm_scale_0.7_attention_scale_1.9	10.61
+ngram_lm_scale_0.7_attention_scale_2.0	10.61
+ngram_lm_scale_0.7_attention_scale_2.1	10.61
+ngram_lm_scale_0.7_attention_scale_2.2	10.61
+ngram_lm_scale_0.7_attention_scale_2.3	10.61
+ngram_lm_scale_0.7_attention_scale_2.5	10.61
+ngram_lm_scale_0.7_attention_scale_3.0	10.61
+ngram_lm_scale_0.7_attention_scale_4.0	10.61
+ngram_lm_scale_0.7_attention_scale_5.0	10.61
+ngram_lm_scale_0.1_attention_scale_1.1	10.62
+ngram_lm_scale_0.3_attention_scale_4.0	10.62
+ngram_lm_scale_0.3_attention_scale_5.0	10.62
+ngram_lm_scale_0.5_attention_scale_0.7	10.62
+ngram_lm_scale_0.6_attention_scale_1.0	10.62
+ngram_lm_scale_0.6_attention_scale_1.1	10.62
+ngram_lm_scale_0.7_attention_scale_1.5	10.62
+ngram_lm_scale_0.9_attention_scale_3.0	10.62
+ngram_lm_scale_0.9_attention_scale_4.0	10.62
+ngram_lm_scale_0.9_attention_scale_5.0	10.62
+ngram_lm_scale_1.0_attention_scale_4.0	10.62
+ngram_lm_scale_1.1_attention_scale_5.0	10.62
+ngram_lm_scale_0.05_attention_scale_1.1	10.63
+ngram_lm_scale_0.05_attention_scale_1.2	10.63
+ngram_lm_scale_0.08_attention_scale_0.9	10.63
+ngram_lm_scale_0.08_attention_scale_1.0	10.63
+ngram_lm_scale_0.08_attention_scale_1.1	10.63
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+ngram_lm_scale_2.0_attention_scale_1.3	12.5
+ngram_lm_scale_2.5_attention_scale_2.0	12.51
+ngram_lm_scale_2.3_attention_scale_1.7	12.54
+ngram_lm_scale_1.5_attention_scale_0.6	12.55
+ngram_lm_scale_1.1_attention_scale_0.1	12.58
+ngram_lm_scale_1.9_attention_scale_1.1	12.62
+ngram_lm_scale_2.2_attention_scale_1.5	12.64
+ngram_lm_scale_1.1_attention_scale_0.08	12.67
+ngram_lm_scale_2.5_attention_scale_1.9	12.67
+ngram_lm_scale_4.0_attention_scale_4.0	12.67
+ngram_lm_scale_1.3_attention_scale_0.3	12.71
+ngram_lm_scale_2.0_attention_scale_1.2	12.71
+ngram_lm_scale_2.1_attention_scale_1.3	12.78
+ngram_lm_scale_3.0_attention_scale_2.5	12.8
+ngram_lm_scale_1.1_attention_scale_0.05	12.81
+ngram_lm_scale_1.9_attention_scale_1.0	12.85
+ngram_lm_scale_1.5_attention_scale_0.5	12.86
+ngram_lm_scale_2.3_attention_scale_1.5	12.91
+ngram_lm_scale_2.0_attention_scale_1.1	12.92
+ngram_lm_scale_1.7_attention_scale_0.7	12.99
+ngram_lm_scale_2.1_attention_scale_1.2	12.99
+ngram_lm_scale_5.0_attention_scale_5.0	13.01
+ngram_lm_scale_1.1_attention_scale_0.01	13.02
+ngram_lm_scale_2.5_attention_scale_1.7	13.02
+ngram_lm_scale_2.2_attention_scale_1.3	13.05
+ngram_lm_scale_3.0_attention_scale_2.3	13.09
+ngram_lm_scale_1.2_attention_scale_0.1	13.1
+ngram_lm_scale_1.9_attention_scale_0.9	13.11
+ngram_lm_scale_2.0_attention_scale_1.0	13.17
+ngram_lm_scale_1.2_attention_scale_0.08	13.2
+ngram_lm_scale_2.1_attention_scale_1.1	13.22
+ngram_lm_scale_3.0_attention_scale_2.2	13.24
+ngram_lm_scale_2.2_attention_scale_1.2	13.28
+ngram_lm_scale_1.7_attention_scale_0.6	13.33
+ngram_lm_scale_2.3_attention_scale_1.3	13.34
+ngram_lm_scale_1.2_attention_scale_0.05	13.36
+ngram_lm_scale_3.0_attention_scale_2.1	13.42
+ngram_lm_scale_2.5_attention_scale_1.5	13.43
+ngram_lm_scale_2.0_attention_scale_0.9	13.48
+ngram_lm_scale_2.1_attention_scale_1.0	13.51
+ngram_lm_scale_2.2_attention_scale_1.1	13.56
+ngram_lm_scale_1.2_attention_scale_0.01	13.6
+ngram_lm_scale_2.3_attention_scale_1.2	13.6
+ngram_lm_scale_3.0_attention_scale_2.0	13.62
+ngram_lm_scale_1.3_attention_scale_0.1	13.65
+ngram_lm_scale_1.5_attention_scale_0.3	13.68
+ngram_lm_scale_1.7_attention_scale_0.5	13.72
+ngram_lm_scale_1.3_attention_scale_0.08	13.76
+ngram_lm_scale_1.9_attention_scale_0.7	13.78
+ngram_lm_scale_3.0_attention_scale_1.9	13.81
+ngram_lm_scale_2.1_attention_scale_0.9	13.82
+ngram_lm_scale_2.2_attention_scale_1.0	13.85
+ngram_lm_scale_4.0_attention_scale_3.0	13.85
+ngram_lm_scale_2.3_attention_scale_1.1	13.89
+ngram_lm_scale_1.3_attention_scale_0.05	13.94
+ngram_lm_scale_2.5_attention_scale_1.3	13.94
+ngram_lm_scale_5.0_attention_scale_4.0	13.97
+ngram_lm_scale_1.9_attention_scale_0.6	14.15
+ngram_lm_scale_2.0_attention_scale_0.7	14.16
+ngram_lm_scale_2.2_attention_scale_0.9	14.17
+ngram_lm_scale_2.3_attention_scale_1.0	14.19
+ngram_lm_scale_1.3_attention_scale_0.01	14.2
+ngram_lm_scale_2.5_attention_scale_1.2	14.2
+ngram_lm_scale_3.0_attention_scale_1.7	14.26
+ngram_lm_scale_2.5_attention_scale_1.1	14.48
+ngram_lm_scale_2.3_attention_scale_0.9	14.5
+ngram_lm_scale_2.1_attention_scale_0.7	14.53
+ngram_lm_scale_2.0_attention_scale_0.6	14.54
+ngram_lm_scale_1.9_attention_scale_0.5	14.57
+ngram_lm_scale_4.0_attention_scale_2.5	14.63
+ngram_lm_scale_1.7_attention_scale_0.3	14.64
+ngram_lm_scale_3.0_attention_scale_1.5	14.71
+ngram_lm_scale_1.5_attention_scale_0.1	14.75
+ngram_lm_scale_2.5_attention_scale_1.0	14.79
+ngram_lm_scale_2.2_attention_scale_0.7	14.86
+ngram_lm_scale_1.5_attention_scale_0.08	14.87
+ngram_lm_scale_2.1_attention_scale_0.6	14.91
+ngram_lm_scale_2.0_attention_scale_0.5	14.95
+ngram_lm_scale_4.0_attention_scale_2.3	14.98
+ngram_lm_scale_1.5_attention_scale_0.05	15.05
+ngram_lm_scale_2.5_attention_scale_0.9	15.12
+ngram_lm_scale_4.0_attention_scale_2.2	15.17
+ngram_lm_scale_2.3_attention_scale_0.7	15.21
+ngram_lm_scale_3.0_attention_scale_1.3	15.22
+ngram_lm_scale_2.2_attention_scale_0.6	15.27
+ngram_lm_scale_1.5_attention_scale_0.01	15.3
+ngram_lm_scale_5.0_attention_scale_3.0	15.32
+ngram_lm_scale_2.1_attention_scale_0.5	15.33
+ngram_lm_scale_4.0_attention_scale_2.1	15.37
+ngram_lm_scale_1.9_attention_scale_0.3	15.5
+ngram_lm_scale_3.0_attention_scale_1.2	15.51
+ngram_lm_scale_4.0_attention_scale_2.0	15.57
+ngram_lm_scale_2.3_attention_scale_0.6	15.61
+ngram_lm_scale_2.2_attention_scale_0.5	15.68
+ngram_lm_scale_1.7_attention_scale_0.1	15.72
+ngram_lm_scale_4.0_attention_scale_1.9	15.79
+ngram_lm_scale_3.0_attention_scale_1.1	15.82
+ngram_lm_scale_1.7_attention_scale_0.08	15.83
+ngram_lm_scale_2.5_attention_scale_0.7	15.85
+ngram_lm_scale_2.0_attention_scale_0.3	15.87
+ngram_lm_scale_2.3_attention_scale_0.5	16.0
+ngram_lm_scale_1.7_attention_scale_0.05	16.01
+ngram_lm_scale_3.0_attention_scale_1.0	16.11
+ngram_lm_scale_5.0_attention_scale_2.5	16.12
+ngram_lm_scale_2.5_attention_scale_0.6	16.19
+ngram_lm_scale_2.1_attention_scale_0.3	16.2
+ngram_lm_scale_4.0_attention_scale_1.7	16.22
+ngram_lm_scale_1.7_attention_scale_0.01	16.23
+ngram_lm_scale_3.0_attention_scale_0.9	16.4
+ngram_lm_scale_5.0_attention_scale_2.3	16.44
+ngram_lm_scale_1.9_attention_scale_0.1	16.5
+ngram_lm_scale_2.2_attention_scale_0.3	16.53
+ngram_lm_scale_2.5_attention_scale_0.5	16.54
+ngram_lm_scale_1.9_attention_scale_0.08	16.6
+ngram_lm_scale_5.0_attention_scale_2.2	16.6
+ngram_lm_scale_4.0_attention_scale_1.5	16.63
+ngram_lm_scale_1.9_attention_scale_0.05	16.74
+ngram_lm_scale_5.0_attention_scale_2.1	16.77
+ngram_lm_scale_2.3_attention_scale_0.3	16.81
+ngram_lm_scale_2.0_attention_scale_0.1	16.83
+ngram_lm_scale_2.0_attention_scale_0.08	16.92
+ngram_lm_scale_5.0_attention_scale_2.0	16.94
+ngram_lm_scale_1.9_attention_scale_0.01	16.95
+ngram_lm_scale_3.0_attention_scale_0.7	16.96
+ngram_lm_scale_2.0_attention_scale_0.05	17.05
+ngram_lm_scale_4.0_attention_scale_1.3	17.05
+ngram_lm_scale_2.1_attention_scale_0.1	17.11
+ngram_lm_scale_5.0_attention_scale_1.9	17.11
+ngram_lm_scale_2.1_attention_scale_0.08	17.21
+ngram_lm_scale_2.0_attention_scale_0.01	17.24
+ngram_lm_scale_3.0_attention_scale_0.6	17.26
+ngram_lm_scale_4.0_attention_scale_1.2	17.27
+ngram_lm_scale_2.5_attention_scale_0.3	17.28
+ngram_lm_scale_2.1_attention_scale_0.05	17.34
+ngram_lm_scale_2.2_attention_scale_0.1	17.38
+ngram_lm_scale_5.0_attention_scale_1.7	17.44
+ngram_lm_scale_2.2_attention_scale_0.08	17.46
+ngram_lm_scale_4.0_attention_scale_1.1	17.5
+ngram_lm_scale_2.1_attention_scale_0.01	17.52
+ngram_lm_scale_3.0_attention_scale_0.5	17.57
+ngram_lm_scale_2.2_attention_scale_0.05	17.59
+ngram_lm_scale_2.3_attention_scale_0.1	17.62
+ngram_lm_scale_2.3_attention_scale_0.08	17.7
+ngram_lm_scale_4.0_attention_scale_1.0	17.72
+ngram_lm_scale_2.2_attention_scale_0.01	17.76
+ngram_lm_scale_5.0_attention_scale_1.5	17.8
+ngram_lm_scale_2.3_attention_scale_0.05	17.82
+ngram_lm_scale_4.0_attention_scale_0.9	17.94
+ngram_lm_scale_2.3_attention_scale_0.01	17.98
+ngram_lm_scale_2.5_attention_scale_0.1	18.03
+ngram_lm_scale_2.5_attention_scale_0.08	18.1
+ngram_lm_scale_5.0_attention_scale_1.3	18.12
+ngram_lm_scale_3.0_attention_scale_0.3	18.17
+ngram_lm_scale_2.5_attention_scale_0.05	18.2
+ngram_lm_scale_5.0_attention_scale_1.2	18.29
+ngram_lm_scale_2.5_attention_scale_0.01	18.33
+ngram_lm_scale_4.0_attention_scale_0.7	18.36
+ngram_lm_scale_5.0_attention_scale_1.1	18.48
+ngram_lm_scale_4.0_attention_scale_0.6	18.58
+ngram_lm_scale_5.0_attention_scale_1.0	18.65
+ngram_lm_scale_3.0_attention_scale_0.1	18.75
+ngram_lm_scale_4.0_attention_scale_0.5	18.79
+ngram_lm_scale_3.0_attention_scale_0.08	18.81
+ngram_lm_scale_5.0_attention_scale_0.9	18.81
+ngram_lm_scale_3.0_attention_scale_0.05	18.89
+ngram_lm_scale_3.0_attention_scale_0.01	18.99
+ngram_lm_scale_5.0_attention_scale_0.7	19.11
+ngram_lm_scale_4.0_attention_scale_0.3	19.18
+ngram_lm_scale_5.0_attention_scale_0.6	19.25
+ngram_lm_scale_5.0_attention_scale_0.5	19.41
+ngram_lm_scale_4.0_attention_scale_0.1	19.57
+ngram_lm_scale_4.0_attention_scale_0.08	19.61
+ngram_lm_scale_4.0_attention_scale_0.05	19.67
+ngram_lm_scale_5.0_attention_scale_0.3	19.71
+ngram_lm_scale_4.0_attention_scale_0.01	19.73
+ngram_lm_scale_5.0_attention_scale_0.1	19.99
+ngram_lm_scale_5.0_attention_scale_0.08	20.01
+ngram_lm_scale_5.0_attention_scale_0.05	20.05
+ngram_lm_scale_5.0_attention_scale_0.01	20.11
+
+2022-04-09 04:57:33,455 INFO [decode_test.py:730] Done!