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decoding_result/modified_beam_search/log-decode-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model-2023-11-16-14-28-08.txt

@@ -0,0 +1,63 @@
+2023-11-16 14:28:08,614 INFO [decode.py:688] Decoding started
+2023-11-16 14:28:08,614 INFO [decode.py:694] Device: cuda:0
+2023-11-16 14:28:08,622 INFO [decode.py:704] {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '821ebc378e7fb99b8adc81950227963332821e01', 'k2-git-date': 'Wed Jul 19 15:38:25 2023', 'lhotse-version': '1.17.0.dev+git.b3dc9faf.dirty', 'torch-version': '1.11.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.9', 'icefall-git-branch': 'dev/bilingual', 'icefall-git-sha1': '4897f2c0-dirty', 'icefall-git-date': 'Thu Sep 28 11:38:28 2023', 'icefall-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/icefall-1.0-py3.9.egg', 'k2-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/k2-1.24.3.dev20230721+cuda10.2.torch1.11.0-py3.9-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/lhotse-1.17.0.dev0+git.b3dc9faf.dirty-py3.9.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-2-0423201334-6587bbc68d-tn554', 'IP address': '10.177.74.211'}, 'epoch': 34, 'iter': 0, 'avg': 19, 'use_averaged_model': True, 'exp_dir': PosixPath('zipformer/exp-w-tal-csasr'), 'bpe_model': 'data/lang_bbpe_2000/bbpe.model', 'lang_dir': PosixPath('data/lang_bbpe_2000'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 20.0, 'ngram_lm_scale': 0.01, 'max_contexts': 8, 'max_states': 64, 'context_size': 2, 'max_sym_per_frame': 1, 'num_paths': 200, 'nbest_scale': 0.5, 'use_tal_csasr': False, 'use_librispeech': False, 'use_aishell2': False, 'num_encoder_layers': '2,2,3,4,3,2', 'downsampling_factor': '1,2,4,8,4,2', 'feedforward_dim': '512,768,1024,1536,1024,768', 'num_heads': '4,4,4,8,4,4', 'encoder_dim': '192,256,384,512,384,256', 'query_head_dim': '32', 'value_head_dim': '12', 'pos_head_dim': '4', 'pos_dim': 48, 'encoder_unmasked_dim': '192,192,256,256,256,192', 'cnn_module_kernel': '31,31,15,15,15,31', 'decoder_dim': 512, 'joiner_dim': 512, 'causal': False, 'chunk_size': '16,32,64,-1', 'left_context_frames': '64,128,256,-1', 'use_transducer': True, 'use_ctc': False, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'input_strategy': 'PrecomputedFeatures', 'res_dir': PosixPath('zipformer/exp-w-tal-csasr/modified_beam_search'), 'suffix': 'epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model', 'blank_id': 0, 'unk_id': 2, 'vocab_size': 2000}
+2023-11-16 14:28:08,622 INFO [decode.py:706] About to create model
+2023-11-16 14:28:09,216 INFO [decode.py:773] Calculating the averaged model over epoch range from 15 (excluded) to 34
+2023-11-16 14:28:21,516 INFO [decode.py:807] Number of model parameters: 68625511
+2023-11-16 14:28:21,517 INFO [multi_dataset.py:142] About to get multidataset test cuts
+2023-11-16 14:28:21,517 INFO [multi_dataset.py:145] Loading Aishell-2 set in lazy mode
+2023-11-16 14:28:21,517 INFO [multi_dataset.py:157] Loading TAL-CSASR set in lazy mode
+2023-11-16 14:28:22,348 INFO [decode.py:831] Start decoding test set: tal_csasr_test
+2023-11-16 14:28:39,005 INFO [decode.py:585] batch 0/?, cuts processed until now is 27
+2023-11-16 14:29:47,024 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([2.0888, 1.9088, 4.4543, 4.0462], device='cuda:0')
+2023-11-16 14:33:04,626 INFO [decode.py:585] batch 20/?, cuts processed until now is 772
+2023-11-16 14:37:29,256 INFO [decode.py:585] batch 40/?, cuts processed until now is 1576
+2023-11-16 14:40:13,905 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([3.2219, 2.1485, 2.6281, 2.8921, 2.2470, 2.9535, 2.8440, 2.9686],
+       device='cuda:0')
+2023-11-16 14:41:42,369 INFO [decode.py:585] batch 60/?, cuts processed until now is 2563
+2023-11-16 14:44:10,863 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([3.2192, 2.1154, 2.6346, 2.8956, 2.2096, 3.0058, 2.8606, 2.9365],
+       device='cuda:0')
+2023-11-16 14:46:46,842 INFO [decode.py:585] batch 80/?, cuts processed until now is 3238
+2023-11-16 14:51:45,568 INFO [decode.py:585] batch 100/?, cuts processed until now is 4248
+2023-11-16 14:52:33,159 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([1.8017, 3.0786, 4.3977, 2.9964], device='cuda:0')
+2023-11-16 14:56:44,530 INFO [decode.py:585] batch 120/?, cuts processed until now is 5015
+2023-11-16 15:01:16,944 INFO [decode.py:585] batch 140/?, cuts processed until now is 6099
+2023-11-16 15:05:39,289 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([4.7921, 4.7333, 4.6640, 4.1834], device='cuda:0')
+2023-11-16 15:05:52,961 INFO [decode.py:585] batch 160/?, cuts processed until now is 7227
+2023-11-16 15:10:41,114 INFO [decode.py:585] batch 180/?, cuts processed until now is 8156
+2023-11-16 15:10:54,305 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([5.2312, 5.1842, 5.1210, 4.6343], device='cuda:0')
+2023-11-16 15:15:31,189 INFO [decode.py:585] batch 200/?, cuts processed until now is 9045
+2023-11-16 15:20:11,614 INFO [decode.py:585] batch 220/?, cuts processed until now is 9901
+2023-11-16 15:23:11,079 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([2.6544, 4.2853, 3.4731, 3.3227, 3.6138, 3.9955, 3.3355, 4.2058],
+       device='cuda:0')
+2023-11-16 15:24:51,949 INFO [decode.py:585] batch 240/?, cuts processed until now is 10806
+2023-11-16 15:29:42,114 INFO [decode.py:585] batch 260/?, cuts processed until now is 11684
+2023-11-16 15:34:20,959 INFO [decode.py:585] batch 280/?, cuts processed until now is 12756
+2023-11-16 15:36:38,939 INFO [decode.py:585] batch 300/?, cuts processed until now is 13809
+2023-11-16 15:40:49,986 INFO [decode.py:585] batch 320/?, cuts processed until now is 14719
+2023-11-16 15:42:14,893 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([3.7449, 2.2636, 3.6499, 3.6228], device='cuda:0')
+2023-11-16 15:42:24,703 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-tal_csasr_test-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-16 15:42:25,450 INFO [utils.py:565] [tal_csasr_test-beam_size_4] %WER 6.51% [21824 / 334989, 4249 ins, 4339 del, 13236 sub ]
+2023-11-16 15:42:26,602 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-tal_csasr_test-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-16 15:42:26,606 INFO [decode.py:630] 
+For tal_csasr_test, WER of different settings are:
+beam_size_4	6.51	best for tal_csasr_test
+
+2023-11-16 15:42:26,606 INFO [decode.py:831] Start decoding test set: tal_csasr_dev
+2023-11-16 15:42:42,527 INFO [decode.py:585] batch 0/?, cuts processed until now is 26
+2023-11-16 15:47:41,696 INFO [decode.py:585] batch 20/?, cuts processed until now is 758
+2023-11-16 15:51:59,934 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([3.4485, 2.2654, 2.7497, 2.9992, 2.3958, 3.1589, 2.9635, 3.1356],
+       device='cuda:0')
+2023-11-16 15:52:42,299 INFO [decode.py:585] batch 40/?, cuts processed until now is 1552
+2023-11-16 15:57:27,582 INFO [decode.py:585] batch 60/?, cuts processed until now is 2520
+2023-11-16 15:57:44,135 INFO [zipformer.py:1853] name=None, attn_weights_entropy = tensor([5.3619, 5.1097, 4.8749, 4.9043], device='cuda:0')
+2023-11-16 16:02:26,759 INFO [decode.py:585] batch 80/?, cuts processed until now is 3309
+2023-11-16 16:06:57,381 INFO [decode.py:585] batch 100/?, cuts processed until now is 4451
+2023-11-16 16:09:13,419 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-tal_csasr_dev-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-16 16:09:13,616 INFO [utils.py:565] [tal_csasr_dev-beam_size_4] %WER 6.46% [7357 / 113905, 1458 ins, 1424 del, 4475 sub ]
+2023-11-16 16:09:14,031 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-tal_csasr_dev-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-16 16:09:14,034 INFO [decode.py:630] 
+For tal_csasr_dev, WER of different settings are:
+beam_size_4	6.46	best for tal_csasr_dev
+
+2023-11-16 16:09:14,034 INFO [decode.py:848] Done!

decoding_result/modified_beam_search/log-decode-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model-2023-11-23-14-36-22.txt

@@ -0,0 +1,11 @@
+2023-11-23 14:36:22,654 INFO [decode.py:688] Decoding started
+2023-11-23 14:36:22,655 INFO [decode.py:694] Device: cuda:0
+2023-11-23 14:36:22,662 INFO [decode.py:704] {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '821ebc378e7fb99b8adc81950227963332821e01', 'k2-git-date': 'Wed Jul 19 15:38:25 2023', 'lhotse-version': '1.17.0.dev+git.b3dc9faf.dirty', 'torch-version': '1.11.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.9', 'icefall-git-branch': 'dev/bilingual', 'icefall-git-sha1': '5074520b-clean', 'icefall-git-date': 'Wed Nov 22 17:26:32 2023', 'icefall-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/icefall-1.0-py3.9.egg', 'k2-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/k2-1.24.3.dev20230721+cuda10.2.torch1.11.0-py3.9-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/lhotse-1.17.0.dev0+git.b3dc9faf.dirty-py3.9.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-2-0423201334-6587bbc68d-tn554', 'IP address': '10.177.74.211'}, 'epoch': 34, 'iter': 0, 'avg': 19, 'use_averaged_model': True, 'exp_dir': PosixPath('zipformer/exp-w-tal-csasr'), 'bpe_model': 'data/lang_bbpe_2000/bbpe.model', 'lang_dir': PosixPath('data/lang_bbpe_2000'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 20.0, 'ngram_lm_scale': 0.01, 'max_contexts': 8, 'max_states': 64, 'context_size': 2, 'max_sym_per_frame': 1, 'num_paths': 200, 'nbest_scale': 0.5, 'use_tal_csasr': False, 'use_librispeech': False, 'use_aishell2': False, 'num_encoder_layers': '2,2,3,4,3,2', 'downsampling_factor': '1,2,4,8,4,2', 'feedforward_dim': '512,768,1024,1536,1024,768', 'num_heads': '4,4,4,8,4,4', 'encoder_dim': '192,256,384,512,384,256', 'query_head_dim': '32', 'value_head_dim': '12', 'pos_head_dim': '4', 'pos_dim': 48, 'encoder_unmasked_dim': '192,192,256,256,256,192', 'cnn_module_kernel': '31,31,15,15,15,31', 'decoder_dim': 512, 'joiner_dim': 512, 'causal': False, 'chunk_size': '16,32,64,-1', 'left_context_frames': '64,128,256,-1', 'use_transducer': True, 'use_ctc': False, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'input_strategy': 'PrecomputedFeatures', 'res_dir': PosixPath('zipformer/exp-w-tal-csasr/modified_beam_search'), 'suffix': 'epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model', 'blank_id': 0, 'unk_id': 2, 'vocab_size': 2000}
+2023-11-23 14:36:22,663 INFO [decode.py:706] About to create model
+2023-11-23 14:36:23,259 INFO [decode.py:773] Calculating the averaged model over epoch range from 15 (excluded) to 34
+2023-11-23 14:36:36,624 INFO [decode.py:807] Number of model parameters: 68625511
+2023-11-23 14:36:36,624 INFO [multi_dataset.py:149] About to get multidataset test cuts
+2023-11-23 14:36:36,624 INFO [multi_dataset.py:166] Loading TAL-CSASR set in lazy mode
+2023-11-23 14:36:37,458 INFO [decode.py:831] Start decoding test set: tal_csasr_test
+2023-11-23 14:36:42,679 INFO [decode.py:585] batch 0/?, cuts processed until now is 27
+2023-11-23 14:38:37,102 INFO [decode.py:585] batch 20/?, cuts processed until now is 772

decoding_result/modified_beam_search/log-decode-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model-2023-11-23-14-50-23.txt

@@ -0,0 +1,115 @@
+2023-11-23 14:50:23,939 INFO [decode.py:688] Decoding started
+2023-11-23 14:50:23,939 INFO [decode.py:694] Device: cuda:0
+2023-11-23 14:50:23,945 INFO [decode.py:704] {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.24.3', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '821ebc378e7fb99b8adc81950227963332821e01', 'k2-git-date': 'Wed Jul 19 15:38:25 2023', 'lhotse-version': '1.17.0.dev+git.b3dc9faf.dirty', 'torch-version': '1.11.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.9', 'icefall-git-branch': 'dev/bilingual', 'icefall-git-sha1': '5074520b-clean', 'icefall-git-date': 'Wed Nov 22 17:26:32 2023', 'icefall-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/icefall-1.0-py3.9.egg', 'k2-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/k2-1.24.3.dev20230721+cuda10.2.torch1.11.0-py3.9-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/lhotse-1.17.0.dev0+git.b3dc9faf.dirty-py3.9.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-2-0423201334-6587bbc68d-tn554', 'IP address': '10.177.74.211'}, 'epoch': 34, 'iter': 0, 'avg': 19, 'use_averaged_model': True, 'exp_dir': PosixPath('zipformer/exp-w-tal-csasr'), 'bpe_model': 'data/lang_bbpe_2000/bbpe.model', 'lang_dir': PosixPath('data/lang_bbpe_2000'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 20.0, 'ngram_lm_scale': 0.01, 'max_contexts': 8, 'max_states': 64, 'context_size': 2, 'max_sym_per_frame': 1, 'num_paths': 200, 'nbest_scale': 0.5, 'use_tal_csasr': False, 'use_librispeech': True, 'use_aishell2': True, 'num_encoder_layers': '2,2,3,4,3,2', 'downsampling_factor': '1,2,4,8,4,2', 'feedforward_dim': '512,768,1024,1536,1024,768', 'num_heads': '4,4,4,8,4,4', 'encoder_dim': '192,256,384,512,384,256', 'query_head_dim': '32', 'value_head_dim': '12', 'pos_head_dim': '4', 'pos_dim': 48, 'encoder_unmasked_dim': '192,192,256,256,256,192', 'cnn_module_kernel': '31,31,15,15,15,31', 'decoder_dim': 512, 'joiner_dim': 512, 'causal': False, 'chunk_size': '16,32,64,-1', 'left_context_frames': '64,128,256,-1', 'use_transducer': True, 'use_ctc': False, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300.0, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'input_strategy': 'PrecomputedFeatures', 'res_dir': PosixPath('zipformer/exp-w-tal-csasr/modified_beam_search'), 'suffix': 'epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model', 'blank_id': 0, 'unk_id': 2, 'vocab_size': 2000}
+2023-11-23 14:50:23,946 INFO [decode.py:706] About to create model
+2023-11-23 14:50:24,637 INFO [decode.py:773] Calculating the averaged model over epoch range from 15 (excluded) to 34
+2023-11-23 14:50:38,836 INFO [decode.py:807] Number of model parameters: 68625511
+2023-11-23 14:50:38,837 INFO [multi_dataset.py:149] About to get multidataset test cuts
+2023-11-23 14:50:38,837 INFO [multi_dataset.py:153] Loading Aishell-2 set in lazy mode
+2023-11-23 14:50:38,861 INFO [multi_dataset.py:232] About to get test-clean cuts
+2023-11-23 14:50:38,866 INFO [multi_dataset.py:239] About to get test-other cuts
+2023-11-23 14:50:38,869 INFO [multi_dataset.py:166] Loading TAL-CSASR set in lazy mode
+2023-11-23 14:50:40,839 INFO [decode.py:831] Start decoding test set: tal_csasr_test
+2023-11-23 14:50:47,655 INFO [decode.py:585] batch 0/?, cuts processed until now is 27
+2023-11-23 14:52:41,880 INFO [decode.py:585] batch 20/?, cuts processed until now is 772
+2023-11-23 14:54:37,247 INFO [decode.py:585] batch 40/?, cuts processed until now is 1576
+2023-11-23 14:56:24,732 INFO [decode.py:585] batch 60/?, cuts processed until now is 2563
+2023-11-23 14:58:21,755 INFO [decode.py:585] batch 80/?, cuts processed until now is 3238
+2023-11-23 14:59:56,940 INFO [decode.py:585] batch 100/?, cuts processed until now is 4248
+2023-11-23 15:01:12,391 INFO [decode.py:585] batch 120/?, cuts processed until now is 5015
+2023-11-23 15:02:39,036 INFO [decode.py:585] batch 140/?, cuts processed until now is 6099
+2023-11-23 15:03:07,760 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([5.1211, 3.3950, 4.9162, 4.6900], device='cuda:0')
+2023-11-23 15:04:13,112 INFO [decode.py:585] batch 160/?, cuts processed until now is 7227
+2023-11-23 15:06:04,684 INFO [decode.py:585] batch 180/?, cuts processed until now is 8156
+2023-11-23 15:07:55,973 INFO [decode.py:585] batch 200/?, cuts processed until now is 9045
+2023-11-23 15:08:07,138 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([3.7384, 4.1458, 3.2149, 3.6539, 3.8415, 2.6047, 2.3104, 3.9797],
+       device='cuda:0')
+2023-11-23 15:09:16,729 INFO [decode.py:585] batch 220/?, cuts processed until now is 9901
+2023-11-23 15:09:28,900 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([2.3074, 2.0681, 1.8565, 2.1119, 1.8037, 2.1030, 2.1433, 2.1016],
+       device='cuda:0')
+2023-11-23 15:10:19,234 INFO [decode.py:585] batch 240/?, cuts processed until now is 10806
+2023-11-23 15:11:21,139 INFO [decode.py:585] batch 260/?, cuts processed until now is 11684
+2023-11-23 15:11:55,321 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([2.9946, 3.3716, 2.7272, 3.1053, 3.1575, 1.9481, 1.8491, 3.1446],
+       device='cuda:0')
+2023-11-23 15:12:31,457 INFO [decode.py:585] batch 280/?, cuts processed until now is 12756
+2023-11-23 15:13:57,828 INFO [decode.py:585] batch 300/?, cuts processed until now is 13809
+2023-11-23 15:15:25,146 INFO [decode.py:585] batch 320/?, cuts processed until now is 14719
+2023-11-23 15:16:01,331 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-tal_csasr_test-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:16:02,961 INFO [utils.py:565] [tal_csasr_test-beam_size_4] %WER 6.51% [21824 / 334989, 4249 ins, 4339 del, 13236 sub ]
+2023-11-23 15:16:04,660 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-tal_csasr_test-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:16:04,674 INFO [decode.py:630] 
+For tal_csasr_test, WER of different settings are:
+beam_size_4	6.51	best for tal_csasr_test
+
+2023-11-23 15:16:04,675 INFO [decode.py:831] Start decoding test set: tal_csasr_dev
+2023-11-23 15:16:11,197 INFO [decode.py:585] batch 0/?, cuts processed until now is 26
+2023-11-23 15:17:31,306 INFO [decode.py:585] batch 20/?, cuts processed until now is 758
+2023-11-23 15:18:33,023 INFO [decode.py:585] batch 40/?, cuts processed until now is 1552
+2023-11-23 15:19:31,523 INFO [decode.py:585] batch 60/?, cuts processed until now is 2520
+2023-11-23 15:19:41,188 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([2.0551, 1.8339, 4.1879, 3.8974], device='cuda:0')
+2023-11-23 15:20:33,159 INFO [decode.py:585] batch 80/?, cuts processed until now is 3309
+2023-11-23 15:20:44,950 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([4.4425, 3.8237, 3.7115, 3.7420], device='cuda:0')
+2023-11-23 15:21:30,933 INFO [decode.py:585] batch 100/?, cuts processed until now is 4451
+2023-11-23 15:22:04,016 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-tal_csasr_dev-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:22:04,209 INFO [utils.py:565] [tal_csasr_dev-beam_size_4] %WER 6.46% [7357 / 113905, 1458 ins, 1424 del, 4475 sub ]
+2023-11-23 15:22:04,616 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-tal_csasr_dev-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:22:04,619 INFO [decode.py:630] 
+For tal_csasr_dev, WER of different settings are:
+beam_size_4	6.46	best for tal_csasr_dev
+
+2023-11-23 15:22:04,620 INFO [decode.py:831] Start decoding test set: aishell-2_test
+2023-11-23 15:22:08,723 INFO [decode.py:585] batch 0/?, cuts processed until now is 83
+2023-11-23 15:23:04,310 INFO [decode.py:585] batch 20/?, cuts processed until now is 1822
+2023-11-23 15:23:29,710 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([2.9362, 2.8307, 2.7671, 2.8998, 2.8828, 1.8621, 1.7937, 2.7947],
+       device='cuda:0')
+2023-11-23 15:24:00,632 INFO [decode.py:585] batch 40/?, cuts processed until now is 3782
+2023-11-23 15:24:26,708 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([3.5121, 3.2281, 3.1362, 3.3672], device='cuda:0')
+2023-11-23 15:24:33,455 INFO [decode.py:585] batch 60/?, cuts processed until now is 5000
+2023-11-23 15:24:33,660 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-aishell-2_test-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:24:33,770 INFO [utils.py:565] [aishell-2_test-beam_size_4] %WER 6.60% [3268 / 49532, 79 ins, 244 del, 2945 sub ]
+2023-11-23 15:24:33,960 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-aishell-2_test-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:24:33,964 INFO [decode.py:630] 
+For aishell-2_test, WER of different settings are:
+beam_size_4	6.6	best for aishell-2_test
+
+2023-11-23 15:24:33,964 INFO [decode.py:831] Start decoding test set: aishell-2_dev
+2023-11-23 15:24:37,407 INFO [decode.py:585] batch 0/?, cuts processed until now is 81
+2023-11-23 15:25:31,329 INFO [decode.py:585] batch 20/?, cuts processed until now is 1916
+2023-11-23 15:25:48,467 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-aishell-2_dev-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:25:48,519 INFO [utils.py:565] [aishell-2_dev-beam_size_4] %WER 6.18% [1534 / 24802, 38 ins, 118 del, 1378 sub ]
+2023-11-23 15:25:48,619 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-aishell-2_dev-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:25:48,623 INFO [decode.py:630] 
+For aishell-2_dev, WER of different settings are:
+beam_size_4	6.18	best for aishell-2_dev
+
+2023-11-23 15:25:48,623 INFO [decode.py:831] Start decoding test set: librispeech_test_clean
+2023-11-23 15:25:52,697 INFO [decode.py:585] batch 0/?, cuts processed until now is 21
+2023-11-23 15:26:29,681 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([2.5800, 4.4167, 3.4376, 3.5429, 3.5702, 4.1270, 3.2604, 4.3152],
+       device='cuda:0')
+2023-11-23 15:26:59,342 INFO [decode.py:585] batch 20/?, cuts processed until now is 592
+2023-11-23 15:28:05,140 INFO [decode.py:585] batch 40/?, cuts processed until now is 1216
+2023-11-23 15:29:04,205 INFO [decode.py:585] batch 60/?, cuts processed until now is 2033
+2023-11-23 15:29:54,103 INFO [decode.py:585] batch 80/?, cuts processed until now is 2616
+2023-11-23 15:29:55,512 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-librispeech_test_clean-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:29:55,614 INFO [utils.py:565] [librispeech_test_clean-beam_size_4] %WER 2.41% [1267 / 52576, 149 ins, 92 del, 1026 sub ]
+2023-11-23 15:29:55,870 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-librispeech_test_clean-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:29:55,873 INFO [decode.py:630] 
+For librispeech_test_clean, WER of different settings are:
+beam_size_4	2.41	best for librispeech_test_clean
+
+2023-11-23 15:29:55,873 INFO [decode.py:831] Start decoding test set: librispeech_test_other
+2023-11-23 15:30:00,619 INFO [decode.py:585] batch 0/?, cuts processed until now is 26
+2023-11-23 15:31:22,156 INFO [decode.py:585] batch 20/?, cuts processed until now is 680
+2023-11-23 15:31:39,412 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([4.0456, 3.5721, 5.1830, 4.9239], device='cuda:0')
+2023-11-23 15:32:45,954 INFO [decode.py:585] batch 40/?, cuts processed until now is 1392
+2023-11-23 15:34:07,553 INFO [decode.py:585] batch 60/?, cuts processed until now is 2313
+2023-11-23 15:34:40,222 INFO [zipformer.py:1858] name=None, attn_weights_entropy = tensor([5.1762, 5.0484, 4.7701, 4.4069], device='cuda:0')
+2023-11-23 15:35:05,714 INFO [decode.py:585] batch 80/?, cuts processed until now is 2924
+2023-11-23 15:35:07,753 INFO [decode.py:601] The transcripts are stored in zipformer/exp-w-tal-csasr/modified_beam_search/recogs-librispeech_test_other-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:35:07,866 INFO [utils.py:565] [librispeech_test_other-beam_size_4] %WER 5.57% [2915 / 52343, 305 ins, 231 del, 2379 sub ]
+2023-11-23 15:35:08,083 INFO [decode.py:614] Wrote detailed error stats to zipformer/exp-w-tal-csasr/modified_beam_search/errs-librispeech_test_other-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-11-23 15:35:08,090 INFO [decode.py:630] 
+For librispeech_test_other, WER of different settings are:
+beam_size_4	5.57	best for librispeech_test_other
+
+2023-11-23 15:35:08,091 INFO [decode.py:848] Done!

decoding_result/modified_beam_search/wer-summary-aishell-2_dev-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt

@@ -0,0 +1,2 @@
+settings	WER
+beam_size_4	6.18

decoding_result/modified_beam_search/wer-summary-aishell-2_test-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt

@@ -0,0 +1,2 @@
+settings	WER
+beam_size_4	6.6

decoding_result/modified_beam_search/wer-summary-librispeech_test_clean-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt

@@ -0,0 +1,2 @@
+settings	WER
+beam_size_4	2.41

decoding_result/modified_beam_search/wer-summary-librispeech_test_other-beam_size_4-epoch-34-avg-19-modified_beam_search-beam-size-4-use-averaged-model.txt

@@ -0,0 +1,2 @@
+settings	WER
+beam_size_4	5.57