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decoding_results/fast_beam_search/log-decode-epoch-42-avg-6-beam-4-max-contexts-4-max-states-8-use-averaged-model-2023-03-21-18-33-45

@@ -0,0 +1,46 @@
+2023-03-21 18:33:45,731 INFO [decode.py:482] Decoding started
+2023-03-21 18:33:45,731 INFO [decode.py:488] Device: cuda:0
+2023-03-21 18:33:45,985 INFO [lexicon.py:168] Loading pre-compiled data/lang_char/Linv.pt
+2023-03-21 18:33:46,015 INFO [decode.py:494] {'frame_shift_ms': 10.0, 'allowed_excess_duration_ratio': 0.1, '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': '1.22', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '96c9a2aece2a3a7633da07740e24fa3d96f5498c', 'k2-git-date': 'Thu Nov 10 08:14:02 2022', 'lhotse-version': '1.13.0.dev+git.527d964.clean', 'torch-version': '1.12.1', 'torch-cuda-available': True, 'torch-cuda-version': '11.6', 'python-version': '3.8', 'icefall-git-branch': 'aishell_zipformer', 'icefall-git-sha1': 'd735cdf-dirty', 'icefall-git-date': 'Tue Mar 21 18:08:42 2023', 'icefall-path': '/ceph-data4/yangxiaoyu/softwares/icefall_development/icefall_aishell_zipformer', 'k2-path': '/ceph-data4/yangxiaoyu/softwares/anaconda3/envs/k2_latest/lib/python3.8/site-packages/k2/__init__.py', 'lhotse-path': '/ceph-data4/yangxiaoyu/softwares/lhotse_development/lhotse_random_padding_left/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-1218101249-5d97868c7c-v8ngc', 'IP address': '10.177.77.18'}, 'epoch': 42, 'iter': 0, 'avg': 6, 'use_averaged_model': True, 'exp_dir': PosixPath('pruned_transducer_stateless7/exp'), 'lang_dir': PosixPath('data/lang_char'), 'decoding_method': 'fast_beam_search', 'beam_size': 4, 'beam': 4, 'max_contexts': 4, 'max_states': 8, 'context_size': 1, 'max_sym_per_frame': 1, 'num_encoder_layers': '2,4,3,2,4', 'feedforward_dims': '1024,1024,2048,2048,1024', 'nhead': '8,8,8,8,8', 'encoder_dims': '384,384,384,384,384', 'attention_dims': '192,192,192,192,192', 'encoder_unmasked_dims': '256,256,256,256,256', 'zipformer_downsampling_factors': '1,2,4,8,2', 'cnn_module_kernels': '31,31,31,31,31', 'decoder_dim': 512, 'joiner_dim': 512, 'max_duration': 600, 'bucketing_sampler': True, 'num_buckets': 30, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'manifest_dir': PosixPath('data/fbank'), 'on_the_fly_feats': False, 'res_dir': PosixPath('pruned_transducer_stateless7/exp/fast_beam_search'), 'suffix': 'epoch-42-avg-6-beam-4-max-contexts-4-max-states-8-use-averaged-model', 'blank_id': 0, 'vocab_size': 4336}
+2023-03-21 18:33:46,015 INFO [decode.py:496] About to create model
+2023-03-21 18:33:46,792 INFO [zipformer.py:178] At encoder stack 4, which has downsampling_factor=2, we will combine the outputs of layers 1 and 3, with downsampling_factors=2 and 8.
+2023-03-21 18:33:46,896 INFO [decode.py:568] Calculating the averaged model over epoch range from 36 (excluded) to 42
+2023-03-21 18:33:53,521 INFO [decode.py:591] Number of model parameters: 77741923
+2023-03-21 18:33:53,522 INFO [aishell.py:51] About to get test cuts from data/fbank/aishell_cuts_test.jsonl.gz
+2023-03-21 18:33:53,525 INFO [aishell.py:45] About to get valid cuts from data/fbank/aishell_cuts_dev.jsonl.gz
+2023-03-21 18:33:59,812 INFO [decode.py:398] batch 0/?, cuts processed until now is 95
+2023-03-21 18:34:07,903 INFO [zipformer.py:1455] attn_weights_entropy = tensor([3.0248, 3.2528, 2.6991, 3.5256, 3.5019, 3.1906, 3.3530, 3.3513],
+       device='cuda:0'), covar=tensor([0.1796, 0.0752, 0.3624, 0.0732, 0.0305, 0.0309, 0.0494, 0.0420],
+       device='cuda:0'), in_proj_covar=tensor([0.0247, 0.0226, 0.0243, 0.0252, 0.0193, 0.0192, 0.0212, 0.0220],
+       device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0003, 0.0002, 0.0002, 0.0002, 0.0003],
+       device='cuda:0')
+2023-03-21 18:34:08,868 INFO [zipformer.py:1455] attn_weights_entropy = tensor([3.8128, 4.1197, 3.8312, 4.0374, 3.6198, 4.0945, 4.2563, 4.3304],
+       device='cuda:0'), covar=tensor([0.0209, 0.0119, 0.0202, 0.0161, 0.0337, 0.0199, 0.0217, 0.0149],
+       device='cuda:0'), in_proj_covar=tensor([0.0120, 0.0124, 0.0118, 0.0122, 0.0112, 0.0100, 0.0097, 0.0099],
+       device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0002],
+       device='cuda:0')
+2023-03-21 18:34:17,732 INFO [decode.py:398] batch 20/?, cuts processed until now is 2090
+2023-03-21 18:34:33,517 INFO [decode.py:398] batch 40/?, cuts processed until now is 4307
+2023-03-21 18:34:49,549 INFO [decode.py:398] batch 60/?, cuts processed until now is 6579
+2023-03-21 18:34:55,128 INFO [decode.py:412] The transcripts are stored in pruned_transducer_stateless7/exp/fast_beam_search/recogs-test-epoch-42-avg-6-beam-4-max-contexts-4-max-states-8-use-averaged-model.txt
+2023-03-21 18:34:55,495 INFO [utils.py:558] [test-beam_4_max_contexts_4_max_states_8] %WER 4.90% [5130 / 104765, 169 ins, 567 del, 4394 sub ]
+2023-03-21 18:34:55,946 INFO [decode.py:427] Wrote detailed error stats to pruned_transducer_stateless7/exp/fast_beam_search/errs-test-epoch-42-avg-6-beam-4-max-contexts-4-max-states-8-use-averaged-model.txt
+2023-03-21 18:34:55,951 INFO [decode.py:441] 
+For test, CER of different settings are:
+beam_4_max_contexts_4_max_states_8	4.9	best for test
+
+2023-03-21 18:34:57,799 INFO [decode.py:398] batch 0/?, cuts processed until now is 107
+2023-03-21 18:35:13,889 INFO [decode.py:398] batch 20/?, cuts processed until now is 2342
+2023-03-21 18:35:29,299 INFO [decode.py:398] batch 40/?, cuts processed until now is 4714
+2023-03-21 18:35:44,631 INFO [decode.py:398] batch 60/?, cuts processed until now is 7285
+2023-03-21 18:36:00,459 INFO [decode.py:398] batch 80/?, cuts processed until now is 9571
+2023-03-21 18:36:16,401 INFO [decode.py:398] batch 100/?, cuts processed until now is 12026
+2023-03-21 18:36:29,588 INFO [decode.py:398] batch 120/?, cuts processed until now is 14130
+2023-03-21 18:36:31,649 INFO [decode.py:412] The transcripts are stored in pruned_transducer_stateless7/exp/fast_beam_search/recogs-dev-epoch-42-avg-6-beam-4-max-contexts-4-max-states-8-use-averaged-model.txt
+2023-03-21 18:36:32,183 INFO [utils.py:558] [dev-beam_4_max_contexts_4_max_states_8] %WER 4.52% [9284 / 205341, 360 ins, 980 del, 7944 sub ]
+2023-03-21 18:36:33,060 INFO [decode.py:427] Wrote detailed error stats to pruned_transducer_stateless7/exp/fast_beam_search/errs-dev-epoch-42-avg-6-beam-4-max-contexts-4-max-states-8-use-averaged-model.txt
+2023-03-21 18:36:33,061 INFO [decode.py:441] 
+For dev, CER of different settings are:
+beam_4_max_contexts_4_max_states_8	4.52	best for dev
+
+2023-03-21 18:36:33,083 INFO [decode.py:620] Done!

decoding_results/greedy_search/log-decode-epoch-42-avg-6-context-1-max-sym-per-frame-1-use-averaged-model-2023-03-21-16-48-27

@@ -0,0 +1,40 @@
+2023-03-21 16:48:27,403 INFO [decode.py:482] Decoding started
+2023-03-21 16:48:27,403 INFO [decode.py:488] Device: cuda:0
+2023-03-21 16:48:27,645 INFO [lexicon.py:168] Loading pre-compiled data/lang_char/Linv.pt
+2023-03-21 16:48:27,670 INFO [decode.py:494] {'frame_shift_ms': 10.0, 'allowed_excess_duration_ratio': 0.1, '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': '1.22', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '96c9a2aece2a3a7633da07740e24fa3d96f5498c', 'k2-git-date': 'Thu Nov 10 08:14:02 2022', 'lhotse-version': '1.13.0.dev+git.527d964.clean', 'torch-version': '1.12.1', 'torch-cuda-available': True, 'torch-cuda-version': '11.6', 'python-version': '3.8', 'icefall-git-branch': 'aishell_zipformer', 'icefall-git-sha1': '8337628-dirty', 'icefall-git-date': 'Mon Mar 20 16:20:05 2023', 'icefall-path': '/ceph-data4/yangxiaoyu/softwares/icefall_development/icefall_aishell_zipformer', 'k2-path': '/ceph-data4/yangxiaoyu/softwares/anaconda3/envs/k2_latest/lib/python3.8/site-packages/k2/__init__.py', 'lhotse-path': '/ceph-data4/yangxiaoyu/softwares/lhotse_development/lhotse_random_padding_left/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-1218101249-5d97868c7c-v8ngc', 'IP address': '10.177.77.18'}, 'epoch': 42, 'iter': 0, 'avg': 6, 'use_averaged_model': True, 'exp_dir': PosixPath('pruned_transducer_stateless7/exp'), 'lang_dir': PosixPath('data/lang_char'), 'decoding_method': 'greedy_search', 'beam_size': 4, 'beam': 4, 'max_contexts': 4, 'max_states': 8, 'context_size': 1, 'max_sym_per_frame': 1, 'num_encoder_layers': '2,4,3,2,4', 'feedforward_dims': '1024,1024,2048,2048,1024', 'nhead': '8,8,8,8,8', 'encoder_dims': '384,384,384,384,384', 'attention_dims': '192,192,192,192,192', 'encoder_unmasked_dims': '256,256,256,256,256', 'zipformer_downsampling_factors': '1,2,4,8,2', 'cnn_module_kernels': '31,31,31,31,31', 'decoder_dim': 512, 'joiner_dim': 512, 'max_duration': 600, 'bucketing_sampler': True, 'num_buckets': 30, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'manifest_dir': PosixPath('data/fbank'), 'on_the_fly_feats': False, 'res_dir': PosixPath('pruned_transducer_stateless7/exp/greedy_search'), 'suffix': 'epoch-42-avg-6-context-1-max-sym-per-frame-1-use-averaged-model', 'blank_id': 0, 'vocab_size': 4336}
+2023-03-21 16:48:27,671 INFO [decode.py:496] About to create model
+2023-03-21 16:48:28,361 INFO [zipformer.py:178] At encoder stack 4, which has downsampling_factor=2, we will combine the outputs of layers 1 and 3, with downsampling_factors=2 and 8.
+2023-03-21 16:48:28,460 INFO [decode.py:568] Calculating the averaged model over epoch range from 36 (excluded) to 42
+2023-03-21 16:48:33,538 INFO [decode.py:591] Number of model parameters: 77741923
+2023-03-21 16:48:33,539 INFO [aishell.py:51] About to get test cuts from data/fbank/aishell_cuts_test.jsonl.gz
+2023-03-21 16:48:33,541 INFO [aishell.py:45] About to get valid cuts from data/fbank/aishell_cuts_dev.jsonl.gz
+2023-03-21 16:48:37,730 INFO [decode.py:398] batch 0/?, cuts processed until now is 95
+2023-03-21 16:48:47,465 INFO [zipformer.py:1455] attn_weights_entropy = tensor([4.1485, 4.4648, 4.1247, 4.3739, 3.9270, 4.4197, 4.6020, 4.6753],
+       device='cuda:0'), covar=tensor([0.0192, 0.0104, 0.0187, 0.0150, 0.0288, 0.0183, 0.0200, 0.0139],
+       device='cuda:0'), in_proj_covar=tensor([0.0120, 0.0124, 0.0118, 0.0122, 0.0112, 0.0100, 0.0097, 0.0099],
+       device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0002],
+       device='cuda:0')
+2023-03-21 16:48:56,058 INFO [zipformer.py:1455] attn_weights_entropy = tensor([2.5963, 2.8684, 2.6166, 2.7795, 2.8613, 2.4702, 2.7993, 2.6541],
+       device='cuda:0'), covar=tensor([0.0984, 0.0509, 0.1008, 0.0947, 0.0580, 0.0780, 0.0669, 0.1205],
+       device='cuda:0'), in_proj_covar=tensor([0.0057, 0.0058, 0.0066, 0.0058, 0.0056, 0.0061, 0.0055, 0.0052],
+       device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0003, 0.0003, 0.0002, 0.0003, 0.0002, 0.0002],
+       device='cuda:0')
+2023-03-21 16:48:56,942 INFO [decode.py:398] batch 50/?, cuts processed until now is 5273
+2023-03-21 16:49:02,930 INFO [decode.py:412] The transcripts are stored in pruned_transducer_stateless7/exp/greedy_search/recogs-test-epoch-42-avg-6-context-1-max-sym-per-frame-1-use-averaged-model.txt
+2023-03-21 16:49:03,302 INFO [utils.py:558] [test-greedy_search] %WER 5.02% [5255 / 104765, 176 ins, 685 del, 4394 sub ]
+2023-03-21 16:49:03,769 INFO [decode.py:427] Wrote detailed error stats to pruned_transducer_stateless7/exp/greedy_search/errs-test-epoch-42-avg-6-context-1-max-sym-per-frame-1-use-averaged-model.txt
+2023-03-21 16:49:03,770 INFO [decode.py:441] 
+For test, CER of different settings are:
+greedy_search	5.02	best for test
+
+2023-03-21 16:49:05,241 INFO [decode.py:398] batch 0/?, cuts processed until now is 107
+2023-03-21 16:49:24,083 INFO [decode.py:398] batch 50/?, cuts processed until now is 6037
+2023-03-21 16:49:42,427 INFO [decode.py:398] batch 100/?, cuts processed until now is 12026
+2023-03-21 16:49:49,481 INFO [decode.py:412] The transcripts are stored in pruned_transducer_stateless7/exp/greedy_search/recogs-dev-epoch-42-avg-6-context-1-max-sym-per-frame-1-use-averaged-model.txt
+2023-03-21 16:49:50,128 INFO [utils.py:558] [dev-greedy_search] %WER 4.61% [9472 / 205341, 368 ins, 1154 del, 7950 sub ]
+2023-03-21 16:49:51,047 INFO [decode.py:427] Wrote detailed error stats to pruned_transducer_stateless7/exp/greedy_search/errs-dev-epoch-42-avg-6-context-1-max-sym-per-frame-1-use-averaged-model.txt
+2023-03-21 16:49:51,048 INFO [decode.py:441] 
+For dev, CER of different settings are:
+greedy_search	4.61	best for dev
+
+2023-03-21 16:49:51,064 INFO [decode.py:620] Done!

decoding_results/modified_beam_search/log-decode-epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model-2023-03-21-18-41-51

@@ -0,0 +1,61 @@
+2023-03-21 18:41:51,183 INFO [decode.py:482] Decoding started
+2023-03-21 18:41:51,183 INFO [decode.py:488] Device: cuda:0
+2023-03-21 18:41:51,433 INFO [lexicon.py:168] Loading pre-compiled data/lang_char/Linv.pt
+2023-03-21 18:41:51,460 INFO [decode.py:494] {'frame_shift_ms': 10.0, 'allowed_excess_duration_ratio': 0.1, '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': '1.22', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '96c9a2aece2a3a7633da07740e24fa3d96f5498c', 'k2-git-date': 'Thu Nov 10 08:14:02 2022', 'lhotse-version': '1.13.0.dev+git.527d964.clean', 'torch-version': '1.12.1', 'torch-cuda-available': True, 'torch-cuda-version': '11.6', 'python-version': '3.8', 'icefall-git-branch': 'aishell_zipformer', 'icefall-git-sha1': 'd735cdf-dirty', 'icefall-git-date': 'Tue Mar 21 18:08:42 2023', 'icefall-path': '/ceph-data4/yangxiaoyu/softwares/icefall_development/icefall_aishell_zipformer', 'k2-path': '/ceph-data4/yangxiaoyu/softwares/anaconda3/envs/k2_latest/lib/python3.8/site-packages/k2/__init__.py', 'lhotse-path': '/ceph-data4/yangxiaoyu/softwares/lhotse_development/lhotse_random_padding_left/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-1218101249-5d97868c7c-v8ngc', 'IP address': '10.177.77.18'}, 'epoch': 42, 'iter': 0, 'avg': 6, 'use_averaged_model': True, 'exp_dir': PosixPath('pruned_transducer_stateless7/exp'), 'lang_dir': PosixPath('data/lang_char'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 4, 'max_contexts': 4, 'max_states': 8, 'context_size': 1, 'max_sym_per_frame': 1, 'num_encoder_layers': '2,4,3,2,4', 'feedforward_dims': '1024,1024,2048,2048,1024', 'nhead': '8,8,8,8,8', 'encoder_dims': '384,384,384,384,384', 'attention_dims': '192,192,192,192,192', 'encoder_unmasked_dims': '256,256,256,256,256', 'zipformer_downsampling_factors': '1,2,4,8,2', 'cnn_module_kernels': '31,31,31,31,31', 'decoder_dim': 512, 'joiner_dim': 512, 'max_duration': 600, 'bucketing_sampler': True, 'num_buckets': 30, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'manifest_dir': PosixPath('data/fbank'), 'on_the_fly_feats': False, 'res_dir': PosixPath('pruned_transducer_stateless7/exp/modified_beam_search'), 'suffix': 'epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model', 'blank_id': 0, 'vocab_size': 4336}
+2023-03-21 18:41:51,461 INFO [decode.py:496] About to create model
+2023-03-21 18:41:52,139 INFO [zipformer.py:178] At encoder stack 4, which has downsampling_factor=2, we will combine the outputs of layers 1 and 3, with downsampling_factors=2 and 8.
+2023-03-21 18:41:52,238 INFO [decode.py:568] Calculating the averaged model over epoch range from 36 (excluded) to 42
+2023-03-21 18:41:58,022 INFO [decode.py:591] Number of model parameters: 77741923
+2023-03-21 18:41:58,022 INFO [aishell.py:51] About to get test cuts from data/fbank/aishell_cuts_test.jsonl.gz
+2023-03-21 18:41:58,025 INFO [aishell.py:45] About to get valid cuts from data/fbank/aishell_cuts_dev.jsonl.gz
+2023-03-21 18:42:07,013 INFO [decode.py:398] batch 0/?, cuts processed until now is 95
+2023-03-21 18:43:28,973 INFO [decode.py:398] batch 20/?, cuts processed until now is 2090
+2023-03-21 18:44:11,191 INFO [zipformer.py:1455] attn_weights_entropy = tensor([2.3754, 2.5692, 2.1182, 2.7716, 2.6796, 2.5163, 2.4200, 2.5013],
+       device='cuda:0'), covar=tensor([0.2012, 0.1195, 0.4092, 0.0579, 0.0289, 0.0206, 0.0343, 0.0367],
+       device='cuda:0'), in_proj_covar=tensor([0.0247, 0.0226, 0.0243, 0.0252, 0.0193, 0.0192, 0.0212, 0.0220],
+       device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0003, 0.0002, 0.0002, 0.0002, 0.0003],
+       device='cuda:0')
+2023-03-21 18:44:51,904 INFO [decode.py:398] batch 40/?, cuts processed until now is 4307
+2023-03-21 18:46:13,644 INFO [decode.py:398] batch 60/?, cuts processed until now is 6579
+2023-03-21 18:46:34,521 INFO [decode.py:412] The transcripts are stored in pruned_transducer_stateless7/exp/modified_beam_search/recogs-test-epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-03-21 18:46:34,897 INFO [utils.py:558] [test-beam_size_4] %WER 4.81% [5036 / 104765, 296 ins, 345 del, 4395 sub ]
+2023-03-21 18:46:35,354 INFO [decode.py:427] Wrote detailed error stats to pruned_transducer_stateless7/exp/modified_beam_search/errs-test-epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-03-21 18:46:35,355 INFO [decode.py:441] 
+For test, CER of different settings are:
+beam_size_4	4.81	best for test
+
+2023-03-21 18:46:40,760 INFO [decode.py:398] batch 0/?, cuts processed until now is 107
+2023-03-21 18:47:36,154 INFO [zipformer.py:1455] attn_weights_entropy = tensor([3.3676, 2.4324, 2.5787, 3.6028, 1.9502, 3.4498, 1.5245, 3.4007],
+       device='cuda:0'), covar=tensor([0.0189, 0.1538, 0.1914, 0.0206, 0.4477, 0.0261, 0.1290, 0.0412],
+       device='cuda:0'), in_proj_covar=tensor([0.0200, 0.0242, 0.0256, 0.0203, 0.0247, 0.0211, 0.0222, 0.0222],
+       device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003, 0.0003],
+       device='cuda:0')
+2023-03-21 18:48:04,140 INFO [decode.py:398] batch 20/?, cuts processed until now is 2342
+2023-03-21 18:49:29,869 INFO [decode.py:398] batch 40/?, cuts processed until now is 4714
+2023-03-21 18:50:53,548 INFO [decode.py:398] batch 60/?, cuts processed until now is 7285
+2023-03-21 18:50:58,152 INFO [zipformer.py:1455] attn_weights_entropy = tensor([3.1326, 2.9183, 3.3644, 3.0881, 3.4080, 3.2231, 2.7680, 3.1645],
+       device='cuda:0'), covar=tensor([0.1478, 0.0702, 0.0917, 0.1508, 0.0632, 0.0890, 0.2270, 0.1686],
+       device='cuda:0'), in_proj_covar=tensor([0.0050, 0.0067, 0.0050, 0.0050, 0.0049, 0.0048, 0.0068, 0.0050],
+       device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0004, 0.0003, 0.0003, 0.0003, 0.0003, 0.0004, 0.0003],
+       device='cuda:0')
+2023-03-21 18:51:24,190 INFO [zipformer.py:1455] attn_weights_entropy = tensor([3.0803, 3.2704, 2.5419, 3.6931, 2.7154, 3.0610, 1.7244, 2.8929],
+       device='cuda:0'), covar=tensor([0.0358, 0.0707, 0.2734, 0.0471, 0.0382, 0.0595, 0.4120, 0.1639],
+       device='cuda:0'), in_proj_covar=tensor([0.0272, 0.0253, 0.0276, 0.0264, 0.0266, 0.0260, 0.0230, 0.0254],
+       device='cuda:0'), out_proj_covar=tensor([0.0004, 0.0003, 0.0004, 0.0003, 0.0004, 0.0003, 0.0003, 0.0003],
+       device='cuda:0')
+2023-03-21 18:52:18,309 INFO [decode.py:398] batch 80/?, cuts processed until now is 9571
+2023-03-21 18:53:38,385 INFO [decode.py:398] batch 100/?, cuts processed until now is 12026
+2023-03-21 18:54:38,777 INFO [zipformer.py:1455] attn_weights_entropy = tensor([2.1870, 2.3851, 1.9720, 2.6331, 2.4814, 2.3893, 2.2158, 2.2789],
+       device='cuda:0'), covar=tensor([0.2080, 0.1264, 0.4227, 0.0516, 0.0238, 0.0213, 0.0338, 0.0339],
+       device='cuda:0'), in_proj_covar=tensor([0.0247, 0.0226, 0.0243, 0.0252, 0.0193, 0.0192, 0.0212, 0.0220],
+       device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0003, 0.0002, 0.0002, 0.0002, 0.0003],
+       device='cuda:0')
+2023-03-21 18:54:44,275 INFO [decode.py:398] batch 120/?, cuts processed until now is 14130
+2023-03-21 18:54:51,400 INFO [decode.py:412] The transcripts are stored in pruned_transducer_stateless7/exp/modified_beam_search/recogs-dev-epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-03-21 18:54:52,019 INFO [utils.py:558] [dev-beam_size_4] %WER 4.40% [9033 / 205341, 517 ins, 550 del, 7966 sub ]
+2023-03-21 18:54:52,892 INFO [decode.py:427] Wrote detailed error stats to pruned_transducer_stateless7/exp/modified_beam_search/errs-dev-epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model.txt
+2023-03-21 18:54:52,892 INFO [decode.py:441] 
+For dev, CER of different settings are:
+beam_size_4	4.4	best for dev
+
+2023-03-21 18:54:52,909 INFO [decode.py:620] Done!

decoding_results/modified_beam_search/log-decode-epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model-2023-03-21-18-45-02

@@ -0,0 +1,7 @@
+2023-03-21 18:45:02,018 INFO [decode.py:482] Decoding started
+2023-03-21 18:45:02,018 INFO [decode.py:488] Device: cuda:0
+2023-03-21 18:45:02,231 INFO [lexicon.py:168] Loading pre-compiled data/lang_char/Linv.pt
+2023-03-21 18:45:02,253 INFO [decode.py:494] {'frame_shift_ms': 10.0, 'allowed_excess_duration_ratio': 0.1, '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': '1.22', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '96c9a2aece2a3a7633da07740e24fa3d96f5498c', 'k2-git-date': 'Thu Nov 10 08:14:02 2022', 'lhotse-version': '1.13.0.dev+git.527d964.clean', 'torch-version': '1.12.1', 'torch-cuda-available': True, 'torch-cuda-version': '11.6', 'python-version': '3.8', 'icefall-git-branch': 'aishell_zipformer', 'icefall-git-sha1': 'd735cdf-dirty', 'icefall-git-date': 'Tue Mar 21 18:08:42 2023', 'icefall-path': '/ceph-data4/yangxiaoyu/softwares/icefall_development/icefall_aishell_zipformer', 'k2-path': '/ceph-data4/yangxiaoyu/softwares/anaconda3/envs/k2_latest/lib/python3.8/site-packages/k2/__init__.py', 'lhotse-path': '/ceph-data4/yangxiaoyu/softwares/lhotse_development/lhotse_random_padding_left/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-1-1220091118-57c4d55446-mlpzc', 'IP address': '10.177.22.19'}, 'epoch': 42, 'iter': 0, 'avg': 6, 'use_averaged_model': True, 'exp_dir': PosixPath('pruned_transducer_stateless7/exp'), 'lang_dir': PosixPath('data/lang_char'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 4, 'max_contexts': 4, 'max_states': 8, 'context_size': 1, 'max_sym_per_frame': 1, 'num_encoder_layers': '2,4,3,2,4', 'feedforward_dims': '1024,1024,2048,2048,1024', 'nhead': '8,8,8,8,8', 'encoder_dims': '384,384,384,384,384', 'attention_dims': '192,192,192,192,192', 'encoder_unmasked_dims': '256,256,256,256,256', 'zipformer_downsampling_factors': '1,2,4,8,2', 'cnn_module_kernels': '31,31,31,31,31', 'decoder_dim': 512, 'joiner_dim': 512, 'max_duration': 600, 'bucketing_sampler': True, 'num_buckets': 30, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'manifest_dir': PosixPath('data/fbank'), 'on_the_fly_feats': False, 'res_dir': PosixPath('pruned_transducer_stateless7/exp/modified_beam_search'), 'suffix': 'epoch-42-avg-6-modified_beam_search-beam-size-4-use-averaged-model', 'blank_id': 0, 'vocab_size': 4336}
+2023-03-21 18:45:02,253 INFO [decode.py:496] About to create model
+2023-03-21 18:45:02,843 INFO [zipformer.py:178] At encoder stack 4, which has downsampling_factor=2, we will combine the outputs of layers 1 and 3, with downsampling_factors=2 and 8.
+2023-03-21 18:45:02,925 INFO [decode.py:568] Calculating the averaged model over epoch range from 36 (excluded) to 42

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+BAC009S0764W0121     甚至  出现  交易  几乎  停滞  的  情况
+BAC009S0764W0122     一二  线  城市  虽然  也  处于  调整  中
+BAC009S0764W0123     但  因为  聚集  了  过多  公共  资源
+