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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
ngram_lm_scale_0.6_attention_scale_1.9 10.47
ngram_lm_scale_0.6_attention_scale_2.0 10.47
ngram_lm_scale_0.6_attention_scale_2.1 10.47
ngram_lm_scale_0.7_attention_scale_1.9 10.47
ngram_lm_scale_0.7_attention_scale_2.0 10.47
ngram_lm_scale_0.7_attention_scale_2.1 10.47
ngram_lm_scale_0.7_attention_scale_2.2 10.47
ngram_lm_scale_0.5_attention_scale_1.9 10.48
ngram_lm_scale_0.6_attention_scale_1.1 10.48
ngram_lm_scale_0.6_attention_scale_1.2 10.48
ngram_lm_scale_0.6_attention_scale_2.2 10.48
ngram_lm_scale_0.6_attention_scale_2.3 10.48
ngram_lm_scale_0.7_attention_scale_1.5 10.48
ngram_lm_scale_0.7_attention_scale_1.7 10.48
ngram_lm_scale_0.7_attention_scale_2.3 10.48
ngram_lm_scale_0.7_attention_scale_2.5 10.48
ngram_lm_scale_0.9_attention_scale_4.0 10.48
ngram_lm_scale_0.3_attention_scale_1.1 10.49
ngram_lm_scale_0.5_attention_scale_0.6 10.49
ngram_lm_scale_0.5_attention_scale_0.7 10.49
ngram_lm_scale_0.5_attention_scale_2.0 10.49
ngram_lm_scale_0.5_attention_scale_2.1 10.49
ngram_lm_scale_0.5_attention_scale_2.5 10.49
ngram_lm_scale_0.5_attention_scale_3.0 10.49
ngram_lm_scale_0.6_attention_scale_1.0 10.49
ngram_lm_scale_0.6_attention_scale_2.5 10.49
ngram_lm_scale_0.6_attention_scale_3.0 10.49
ngram_lm_scale_0.7_attention_scale_1.3 10.49
ngram_lm_scale_0.7_attention_scale_3.0 10.49
ngram_lm_scale_0.7_attention_scale_4.0 10.49
ngram_lm_scale_0.9_attention_scale_3.0 10.49
ngram_lm_scale_0.9_attention_scale_5.0 10.49
ngram_lm_scale_1.0_attention_scale_4.0 10.49
ngram_lm_scale_1.0_attention_scale_5.0 10.49
ngram_lm_scale_1.1_attention_scale_4.0 10.49
ngram_lm_scale_1.1_attention_scale_5.0 10.49
ngram_lm_scale_1.2_attention_scale_4.0 10.49
ngram_lm_scale_1.2_attention_scale_5.0 10.49
ngram_lm_scale_1.3_attention_scale_5.0 10.49
ngram_lm_scale_1.5_attention_scale_5.0 10.49
ngram_lm_scale_0.3_attention_scale_0.7 10.5
ngram_lm_scale_0.3_attention_scale_0.9 10.5
ngram_lm_scale_0.3_attention_scale_1.0 10.5
ngram_lm_scale_0.3_attention_scale_1.2 10.5
ngram_lm_scale_0.3_attention_scale_1.3 10.5
ngram_lm_scale_0.3_attention_scale_1.5 10.5
ngram_lm_scale_0.5_attention_scale_2.2 10.5
ngram_lm_scale_0.5_attention_scale_2.3 10.5
ngram_lm_scale_0.6_attention_scale_0.7 10.5
ngram_lm_scale_0.6_attention_scale_0.9 10.5
ngram_lm_scale_0.7_attention_scale_1.0 10.5
ngram_lm_scale_0.7_attention_scale_1.1 10.5
ngram_lm_scale_0.7_attention_scale_5.0 10.5
ngram_lm_scale_0.9_attention_scale_2.1 10.5
ngram_lm_scale_1.0_attention_scale_3.0 10.5
ngram_lm_scale_1.3_attention_scale_4.0 10.5
ngram_lm_scale_1.5_attention_scale_4.0 10.5
ngram_lm_scale_0.3_attention_scale_1.7 10.51
ngram_lm_scale_0.3_attention_scale_1.9 10.51
ngram_lm_scale_0.3_attention_scale_2.0 10.51
ngram_lm_scale_0.3_attention_scale_2.1 10.51
ngram_lm_scale_0.3_attention_scale_2.2 10.51
ngram_lm_scale_0.3_attention_scale_2.3 10.51
ngram_lm_scale_0.3_attention_scale_2.5 10.51
ngram_lm_scale_0.3_attention_scale_3.0 10.51
ngram_lm_scale_0.3_attention_scale_4.0 10.51
ngram_lm_scale_0.5_attention_scale_0.5 10.51
ngram_lm_scale_0.5_attention_scale_4.0 10.51
ngram_lm_scale_0.5_attention_scale_5.0 10.51
ngram_lm_scale_0.6_attention_scale_4.0 10.51
ngram_lm_scale_0.6_attention_scale_5.0 10.51
ngram_lm_scale_0.7_attention_scale_1.2 10.51
ngram_lm_scale_0.9_attention_scale_2.0 10.51
ngram_lm_scale_0.9_attention_scale_2.2 10.51
ngram_lm_scale_0.9_attention_scale_2.3 10.51
ngram_lm_scale_0.9_attention_scale_2.5 10.51
ngram_lm_scale_1.0_attention_scale_2.2 10.51
ngram_lm_scale_1.0_attention_scale_2.3 10.51
ngram_lm_scale_1.0_attention_scale_2.5 10.51
ngram_lm_scale_1.1_attention_scale_2.5 10.51
ngram_lm_scale_1.2_attention_scale_3.0 10.51
ngram_lm_scale_1.7_attention_scale_5.0 10.51
ngram_lm_scale_0.05_attention_scale_2.5 10.52
ngram_lm_scale_0.05_attention_scale_3.0 10.52
ngram_lm_scale_0.08_attention_scale_2.5 10.52
ngram_lm_scale_0.08_attention_scale_4.0 10.52
ngram_lm_scale_0.08_attention_scale_5.0 10.52
ngram_lm_scale_0.1_attention_scale_2.5 10.52
ngram_lm_scale_0.1_attention_scale_3.0 10.52
ngram_lm_scale_0.1_attention_scale_4.0 10.52
ngram_lm_scale_0.1_attention_scale_5.0 10.52
ngram_lm_scale_0.3_attention_scale_0.5 10.52
ngram_lm_scale_0.3_attention_scale_0.6 10.52
ngram_lm_scale_0.3_attention_scale_5.0 10.52
ngram_lm_scale_0.6_attention_scale_0.6 10.52
ngram_lm_scale_0.7_attention_scale_0.9 10.52
ngram_lm_scale_0.9_attention_scale_1.7 10.52
ngram_lm_scale_0.9_attention_scale_1.9 10.52
ngram_lm_scale_1.0_attention_scale_2.0 10.52
ngram_lm_scale_1.0_attention_scale_2.1 10.52
ngram_lm_scale_1.1_attention_scale_2.3 10.52
ngram_lm_scale_1.1_attention_scale_3.0 10.52
ngram_lm_scale_1.9_attention_scale_5.0 10.52
ngram_lm_scale_0.01_attention_scale_2.5 10.53
ngram_lm_scale_0.01_attention_scale_3.0 10.53
ngram_lm_scale_0.01_attention_scale_4.0 10.53
ngram_lm_scale_0.01_attention_scale_5.0 10.53
ngram_lm_scale_0.05_attention_scale_1.9 10.53
ngram_lm_scale_0.05_attention_scale_2.1 10.53
ngram_lm_scale_0.05_attention_scale_2.3 10.53
ngram_lm_scale_0.05_attention_scale_4.0 10.53
ngram_lm_scale_0.05_attention_scale_5.0 10.53
ngram_lm_scale_0.08_attention_scale_1.9 10.53
ngram_lm_scale_0.08_attention_scale_2.1 10.53
ngram_lm_scale_0.08_attention_scale_2.2 10.53
ngram_lm_scale_0.08_attention_scale_2.3 10.53
ngram_lm_scale_0.08_attention_scale_3.0 10.53
ngram_lm_scale_0.1_attention_scale_2.2 10.53
ngram_lm_scale_0.1_attention_scale_2.3 10.53
ngram_lm_scale_0.3_attention_scale_0.3 10.53
ngram_lm_scale_0.9_attention_scale_1.5 10.53
ngram_lm_scale_1.0_attention_scale_1.9 10.53
ngram_lm_scale_1.1_attention_scale_2.1 10.53
ngram_lm_scale_1.1_attention_scale_2.2 10.53
ngram_lm_scale_1.2_attention_scale_2.5 10.53
ngram_lm_scale_1.3_attention_scale_3.0 10.53
ngram_lm_scale_1.7_attention_scale_4.0 10.53
ngram_lm_scale_2.0_attention_scale_5.0 10.53
ngram_lm_scale_0.01_attention_scale_2.2 10.54
ngram_lm_scale_0.01_attention_scale_2.3 10.54
ngram_lm_scale_0.05_attention_scale_1.7 10.54
ngram_lm_scale_0.05_attention_scale_2.0 10.54
ngram_lm_scale_0.05_attention_scale_2.2 10.54
ngram_lm_scale_0.08_attention_scale_1.2 10.54
ngram_lm_scale_0.08_attention_scale_1.3 10.54
ngram_lm_scale_0.08_attention_scale_1.7 10.54
ngram_lm_scale_0.08_attention_scale_2.0 10.54
ngram_lm_scale_0.1_attention_scale_1.5 10.54
ngram_lm_scale_0.1_attention_scale_1.7 10.54
ngram_lm_scale_0.1_attention_scale_1.9 10.54
ngram_lm_scale_0.1_attention_scale_2.0 10.54
ngram_lm_scale_0.1_attention_scale_2.1 10.54
ngram_lm_scale_0.9_attention_scale_1.2 10.54
ngram_lm_scale_1.0_attention_scale_1.7 10.54
ngram_lm_scale_1.2_attention_scale_2.3 10.54
ngram_lm_scale_1.3_attention_scale_2.3 10.54
ngram_lm_scale_1.5_attention_scale_3.0 10.54
ngram_lm_scale_0.01_attention_scale_1.9 10.55
ngram_lm_scale_0.01_attention_scale_2.0 10.55
ngram_lm_scale_0.01_attention_scale_2.1 10.55
ngram_lm_scale_0.05_attention_scale_1.2 10.55
ngram_lm_scale_0.05_attention_scale_1.3 10.55
ngram_lm_scale_0.08_attention_scale_1.1 10.55
ngram_lm_scale_0.08_attention_scale_1.5 10.55
ngram_lm_scale_0.1_attention_scale_1.1 10.55
ngram_lm_scale_0.1_attention_scale_1.2 10.55
ngram_lm_scale_0.1_attention_scale_1.3 10.55
ngram_lm_scale_0.6_attention_scale_0.5 10.55
ngram_lm_scale_0.7_attention_scale_0.7 10.55
ngram_lm_scale_0.9_attention_scale_1.3 10.55
ngram_lm_scale_1.0_attention_scale_1.5 10.55
ngram_lm_scale_1.1_attention_scale_2.0 10.55
ngram_lm_scale_1.2_attention_scale_2.0 10.55
ngram_lm_scale_1.2_attention_scale_2.1 10.55
ngram_lm_scale_1.2_attention_scale_2.2 10.55
ngram_lm_scale_1.3_attention_scale_2.2 10.55
ngram_lm_scale_1.3_attention_scale_2.5 10.55
ngram_lm_scale_2.1_attention_scale_5.0 10.55
ngram_lm_scale_0.01_attention_scale_1.1 10.56
ngram_lm_scale_0.01_attention_scale_1.3 10.56
ngram_lm_scale_0.01_attention_scale_1.7 10.56
ngram_lm_scale_0.05_attention_scale_1.1 10.56
ngram_lm_scale_0.05_attention_scale_1.5 10.56
ngram_lm_scale_0.08_attention_scale_1.0 10.56
ngram_lm_scale_0.1_attention_scale_1.0 10.56
ngram_lm_scale_0.7_attention_scale_0.6 10.56
ngram_lm_scale_0.9_attention_scale_1.1 10.56
ngram_lm_scale_1.0_attention_scale_1.3 10.56
ngram_lm_scale_1.1_attention_scale_1.7 10.56
ngram_lm_scale_1.1_attention_scale_1.9 10.56
ngram_lm_scale_1.2_attention_scale_1.9 10.56
ngram_lm_scale_1.3_attention_scale_2.0 10.56
ngram_lm_scale_1.9_attention_scale_4.0 10.56
ngram_lm_scale_2.2_attention_scale_5.0 10.56
ngram_lm_scale_0.01_attention_scale_1.2 10.57
ngram_lm_scale_0.01_attention_scale_1.5 10.57
ngram_lm_scale_0.05_attention_scale_1.0 10.57
ngram_lm_scale_0.1_attention_scale_0.5 10.57
ngram_lm_scale_0.1_attention_scale_0.7 10.57
ngram_lm_scale_0.1_attention_scale_0.9 10.57
ngram_lm_scale_0.5_attention_scale_0.3 10.57
ngram_lm_scale_0.9_attention_scale_1.0 10.57
ngram_lm_scale_1.1_attention_scale_1.5 10.57
ngram_lm_scale_1.2_attention_scale_1.7 10.57
ngram_lm_scale_1.3_attention_scale_2.1 10.57
ngram_lm_scale_0.01_attention_scale_1.0 10.58
ngram_lm_scale_0.05_attention_scale_0.9 10.58
ngram_lm_scale_0.08_attention_scale_0.7 10.58
ngram_lm_scale_0.08_attention_scale_0.9 10.58
ngram_lm_scale_0.1_attention_scale_0.6 10.58
ngram_lm_scale_0.3_attention_scale_0.1 10.58
ngram_lm_scale_0.9_attention_scale_0.9 10.58
ngram_lm_scale_1.0_attention_scale_1.2 10.58
ngram_lm_scale_1.3_attention_scale_1.9 10.58
ngram_lm_scale_1.5_attention_scale_2.5 10.58
ngram_lm_scale_2.0_attention_scale_4.0 10.58
ngram_lm_scale_0.01_attention_scale_0.9 10.59
ngram_lm_scale_0.08_attention_scale_0.5 10.59
ngram_lm_scale_0.08_attention_scale_0.6 10.59
ngram_lm_scale_0.1_attention_scale_0.3 10.59
ngram_lm_scale_0.3_attention_scale_0.08 10.59
ngram_lm_scale_0.6_attention_scale_0.3 10.59
ngram_lm_scale_0.7_attention_scale_0.5 10.59
ngram_lm_scale_1.7_attention_scale_3.0 10.59
ngram_lm_scale_2.3_attention_scale_5.0 10.59
ngram_lm_scale_0.05_attention_scale_0.6 10.6
ngram_lm_scale_0.05_attention_scale_0.7 10.6
ngram_lm_scale_0.08_attention_scale_0.3 10.6
ngram_lm_scale_0.3_attention_scale_0.05 10.6
ngram_lm_scale_1.0_attention_scale_1.1 10.6
ngram_lm_scale_1.1_attention_scale_1.3 10.6
ngram_lm_scale_1.2_attention_scale_1.5 10.6
ngram_lm_scale_1.5_attention_scale_2.3 10.6
ngram_lm_scale_0.01_attention_scale_0.7 10.61
ngram_lm_scale_1.3_attention_scale_1.7 10.61
ngram_lm_scale_0.01_attention_scale_0.6 10.62
ngram_lm_scale_0.05_attention_scale_0.3 10.62
ngram_lm_scale_0.05_attention_scale_0.5 10.62
ngram_lm_scale_0.1_attention_scale_0.1 10.62
ngram_lm_scale_2.1_attention_scale_4.0 10.62
ngram_lm_scale_0.01_attention_scale_0.5 10.63
ngram_lm_scale_1.0_attention_scale_1.0 10.63
ngram_lm_scale_1.5_attention_scale_2.2 10.63
ngram_lm_scale_2.5_attention_scale_5.0 10.63
ngram_lm_scale_0.08_attention_scale_0.1 10.64
ngram_lm_scale_0.1_attention_scale_0.08 10.64
ngram_lm_scale_0.3_attention_scale_0.01 10.64
ngram_lm_scale_1.1_attention_scale_1.2 10.64
ngram_lm_scale_0.01_attention_scale_0.3 10.65
ngram_lm_scale_0.5_attention_scale_0.1 10.65
ngram_lm_scale_0.7_attention_scale_0.3 10.65
ngram_lm_scale_1.5_attention_scale_2.1 10.65
ngram_lm_scale_0.08_attention_scale_0.08 10.66
ngram_lm_scale_0.1_attention_scale_0.05 10.66
ngram_lm_scale_0.5_attention_scale_0.08 10.66
ngram_lm_scale_0.9_attention_scale_0.7 10.66
ngram_lm_scale_2.2_attention_scale_4.0 10.66
ngram_lm_scale_0.1_attention_scale_0.01 10.67
ngram_lm_scale_1.0_attention_scale_0.9 10.67
ngram_lm_scale_1.1_attention_scale_1.1 10.67
ngram_lm_scale_1.7_attention_scale_2.5 10.67
ngram_lm_scale_0.05_attention_scale_0.1 10.68
ngram_lm_scale_0.5_attention_scale_0.05 10.68
ngram_lm_scale_1.5_attention_scale_2.0 10.68
ngram_lm_scale_0.05_attention_scale_0.08 10.69
ngram_lm_scale_0.08_attention_scale_0.05 10.69
ngram_lm_scale_1.2_attention_scale_1.3 10.69
ngram_lm_scale_1.9_attention_scale_3.0 10.69
ngram_lm_scale_0.08_attention_scale_0.01 10.7
ngram_lm_scale_0.6_attention_scale_0.1 10.7
ngram_lm_scale_1.3_attention_scale_1.5 10.7
ngram_lm_scale_2.3_attention_scale_4.0 10.7
ngram_lm_scale_0.05_attention_scale_0.05 10.71
ngram_lm_scale_0.5_attention_scale_0.01 10.71
ngram_lm_scale_0.9_attention_scale_0.6 10.71
ngram_lm_scale_1.1_attention_scale_1.0 10.71
ngram_lm_scale_1.5_attention_scale_1.9 10.71
ngram_lm_scale_0.01_attention_scale_0.1 10.72
ngram_lm_scale_0.01_attention_scale_0.08 10.73
ngram_lm_scale_0.05_attention_scale_0.01 10.73
ngram_lm_scale_0.6_attention_scale_0.08 10.73
ngram_lm_scale_1.2_attention_scale_1.2 10.73
ngram_lm_scale_0.01_attention_scale_0.05 10.75
ngram_lm_scale_0.9_attention_scale_0.5 10.75
ngram_lm_scale_1.0_attention_scale_0.7 10.75
ngram_lm_scale_1.1_attention_scale_0.9 10.75
ngram_lm_scale_1.2_attention_scale_1.1 10.75
ngram_lm_scale_1.3_attention_scale_1.3 10.76
ngram_lm_scale_1.7_attention_scale_2.3 10.76
ngram_lm_scale_2.0_attention_scale_3.0 10.77
ngram_lm_scale_0.6_attention_scale_0.05 10.78
ngram_lm_scale_0.01_attention_scale_0.01 10.79
ngram_lm_scale_1.5_attention_scale_1.7 10.79
ngram_lm_scale_1.7_attention_scale_2.2 10.79
ngram_lm_scale_1.2_attention_scale_1.0 10.8
ngram_lm_scale_1.3_attention_scale_1.2 10.8
ngram_lm_scale_2.5_attention_scale_4.0 10.81
ngram_lm_scale_1.7_attention_scale_2.1 10.82
ngram_lm_scale_1.0_attention_scale_0.6 10.83
ngram_lm_scale_2.1_attention_scale_3.0 10.84
ngram_lm_scale_0.6_attention_scale_0.01 10.85
ngram_lm_scale_1.7_attention_scale_2.0 10.85
ngram_lm_scale_1.9_attention_scale_2.5 10.85
ngram_lm_scale_3.0_attention_scale_5.0 10.86
ngram_lm_scale_1.3_attention_scale_1.1 10.87
ngram_lm_scale_0.7_attention_scale_0.1 10.88
ngram_lm_scale_1.5_attention_scale_1.5 10.88
ngram_lm_scale_1.2_attention_scale_0.9 10.89
ngram_lm_scale_1.7_attention_scale_1.9 10.89
ngram_lm_scale_2.2_attention_scale_3.0 10.9
ngram_lm_scale_1.1_attention_scale_0.7 10.91
ngram_lm_scale_1.9_attention_scale_2.3 10.91
ngram_lm_scale_2.0_attention_scale_2.5 10.91
ngram_lm_scale_0.7_attention_scale_0.08 10.92
ngram_lm_scale_0.7_attention_scale_0.05 10.96
ngram_lm_scale_1.0_attention_scale_0.5 10.96
ngram_lm_scale_1.9_attention_scale_2.2 10.97
ngram_lm_scale_2.3_attention_scale_3.0 10.97
ngram_lm_scale_1.3_attention_scale_1.0 10.99
ngram_lm_scale_1.7_attention_scale_1.7 11.01
ngram_lm_scale_2.1_attention_scale_2.5 11.02
ngram_lm_scale_0.9_attention_scale_0.3 11.03
ngram_lm_scale_1.9_attention_scale_2.1 11.03
ngram_lm_scale_0.7_attention_scale_0.01 11.04
ngram_lm_scale_1.5_attention_scale_1.3 11.04
ngram_lm_scale_2.0_attention_scale_2.3 11.04
ngram_lm_scale_1.1_attention_scale_0.6 11.05
ngram_lm_scale_1.9_attention_scale_2.0 11.1
ngram_lm_scale_2.0_attention_scale_2.2 11.1
ngram_lm_scale_1.3_attention_scale_0.9 11.11
ngram_lm_scale_1.2_attention_scale_0.7 11.14
ngram_lm_scale_1.5_attention_scale_1.2 11.15
ngram_lm_scale_2.2_attention_scale_2.5 11.16
ngram_lm_scale_2.1_attention_scale_2.3 11.17
ngram_lm_scale_3.0_attention_scale_4.0 11.17
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!
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