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

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
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+ngram_lm_scale_2.2_attention_scale_2.5	11.16
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+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!