logs/modified_beam_search/log-decode-epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model-2023-06-21-15-31-32

2023-06-21 15:31:32,694 INFO [decode.py:652] Decoding started
2023-06-21 15:31:32,695 INFO [decode.py:658] Device: cuda:0
2023-06-21 15:31:34,575 INFO [lexicon.py:168] Loading pre-compiled data/lang_char/Linv.pt
2023-06-21 15:31:34,749 INFO [decode.py:669] {'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': '1.24.1', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'c51a0b9684442a88ee37f3ce0af686a04b66855b', 'k2-git-date': 'Mon May 1 21:38:03 2023', 'lhotse-version': '1.14.0.dev+git.0f812851.dirty', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'zipformer_wenetspeech', 'icefall-git-sha1': 'a7d0588-dirty', 'icefall-git-date': 'Mon Jun 19 12:12:33 2023', 'icefall-path': '/star-kw/kangwei/code/icefall_wenetspeech', 'k2-path': '/ceph-hw/kangwei/code/k2_release/k2/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-hw/kangwei/dev_tools/anaconda3/envs/rnnt2/lib/python3.8/site-packages/lhotse-1.14.0.dev0+git.0f812851.dirty-py3.8.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-2-0423201334-6587bbc68d-tn554', 'IP address': '10.177.74.211'}, 'epoch': 12, 'iter': 0, 'avg': 4, 'use_averaged_model': True, 'exp_dir': PosixPath('zipformer/exp_L_causal_context_2'), 'lang_dir': PosixPath('data/lang_char'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 20.0, 'ngram_lm_scale': 0.01, 'ilme_scale': 0.2, 'max_contexts': 8, 'max_states': 64, 'context_size': 2, 'max_sym_per_frame': 1, 'num_paths': 200, 'nbest_scale': 0.5, 'blank_penalty': 1.5, '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': True, 'chunk_size': '32', 'left_context_frames': '256', 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 1000, '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': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'training_subset': 'L', 'res_dir': PosixPath('zipformer/exp_L_causal_context_2/modified_beam_search'), 'suffix': 'epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model', 'blank_id': 0, 'vocab_size': 5537}
2023-06-21 15:31:34,749 INFO [decode.py:671] About to create model
2023-06-21 15:31:35,494 INFO [decode.py:738] Calculating the averaged model over epoch range from 8 (excluded) to 12
2023-06-21 15:31:46,019 INFO [decode.py:769] Number of model parameters: 76441818
2023-06-21 15:31:46,020 INFO [asr_datamodule.py:398] About to get dev cuts
2023-06-21 15:31:46,026 INFO [asr_datamodule.py:336] About to create dev dataset
2023-06-21 15:31:46,646 INFO [asr_datamodule.py:354] About to create dev dataloader
2023-06-21 15:31:46,647 INFO [asr_datamodule.py:403] About to get TEST_NET cuts
2023-06-21 15:31:46,649 INFO [asr_datamodule.py:367] About to create test dataset
2023-06-21 15:31:46,704 WARNING [decode.py:778] Exclude cut with ID TEST_NET_Y0000000004_0ub4ZzdHzBc_S00023 from decoding, num_frames : 8.
2023-06-21 15:31:47,266 INFO [asr_datamodule.py:408] About to get TEST_MEETING cuts
2023-06-21 15:31:47,268 INFO [asr_datamodule.py:367] About to create test dataset
2023-06-21 15:31:47,785 INFO [asr_datamodule.py:413] About to get TEST_AISHELL cuts
2023-06-21 15:31:47,788 INFO [asr_datamodule.py:367] About to create test dataset
2023-06-21 15:31:58,920 INFO [decode.py:548] batch 0/?, cuts processed until now is 130
2023-06-21 15:34:48,249 INFO [decode.py:548] batch 20/?, cuts processed until now is 3192
2023-06-21 15:37:36,346 INFO [decode.py:548] batch 40/?, cuts processed until now is 6421
2023-06-21 15:40:22,551 INFO [decode.py:548] batch 60/?, cuts processed until now is 10176
2023-06-21 15:42:36,273 INFO [decode.py:548] batch 80/?, cuts processed until now is 13727
2023-06-21 15:42:43,812 INFO [decode.py:564] The transcripts are stored in zipformer/exp_L_causal_context_2/modified_beam_search/recogs-DEV-beam_size_4_blank_penalty_1.5-epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model.txt
2023-06-21 15:42:44,196 INFO [utils.py:562] [DEV-beam_size_4_blank_penalty_1.5] %WER 7.84% [25916 / 330498, 2903 ins, 9047 del, 13966 sub ]
2023-06-21 15:42:45,241 INFO [decode.py:577] Wrote detailed error stats to zipformer/exp_L_causal_context_2/modified_beam_search/errs-DEV-beam_size_4_blank_penalty_1.5-epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model.txt
2023-06-21 15:42:45,245 INFO [decode.py:593] 
For DEV, WER of different settings are:
beam_size_4_blank_penalty_1.5	7.84	best for DEV

2023-06-21 15:42:45,588 WARNING [decode.py:778] Exclude cut with ID TEST_NET_Y0000000004_0ub4ZzdHzBc_S00023 from decoding, num_frames : 8.
2023-06-21 15:42:55,450 INFO [decode.py:548] batch 0/?, cuts processed until now is 146
2023-06-21 15:45:32,866 INFO [decode.py:548] batch 20/?, cuts processed until now is 4116
2023-06-21 15:46:07,020 INFO [zipformer.py:1728] name=None, attn_weights_entropy = tensor([1.6174, 1.5967, 1.9845, 2.3811, 1.4972, 1.9646, 1.9814, 2.2427],
       device='cuda:0')
2023-06-21 15:48:16,935 INFO [decode.py:548] batch 40/?, cuts processed until now is 8601
2023-06-21 15:48:33,552 INFO [zipformer.py:1728] name=None, attn_weights_entropy = tensor([2.8828, 2.1374, 1.8402, 1.6450, 1.5009, 1.7714, 1.8731, 1.8959],
       device='cuda:0')
2023-06-21 15:50:59,913 INFO [decode.py:548] batch 60/?, cuts processed until now is 14082
2023-06-21 15:53:45,520 INFO [decode.py:548] batch 80/?, cuts processed until now is 18750
2023-06-21 15:56:14,191 INFO [decode.py:548] batch 100/?, cuts processed until now is 24487
2023-06-21 15:56:33,605 INFO [decode.py:564] The transcripts are stored in zipformer/exp_L_causal_context_2/modified_beam_search/recogs-TEST_NET-beam_size_4_blank_penalty_1.5-epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model.txt
2023-06-21 15:56:34,150 INFO [utils.py:562] [TEST_NET-beam_size_4_blank_penalty_1.5] %WER 8.94% [37184 / 415746, 4584 ins, 7265 del, 25335 sub ]
2023-06-21 15:56:35,598 INFO [decode.py:577] Wrote detailed error stats to zipformer/exp_L_causal_context_2/modified_beam_search/errs-TEST_NET-beam_size_4_blank_penalty_1.5-epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model.txt
2023-06-21 15:56:35,602 INFO [decode.py:593] 
For TEST_NET, WER of different settings are:
beam_size_4_blank_penalty_1.5	8.94	best for TEST_NET

2023-06-21 15:56:45,448 INFO [decode.py:548] batch 0/?, cuts processed until now is 93
2023-06-21 15:59:28,938 INFO [decode.py:548] batch 20/?, cuts processed until now is 2345
2023-06-21 16:02:15,987 INFO [decode.py:548] batch 40/?, cuts processed until now is 4929
2023-06-21 16:03:10,204 INFO [zipformer.py:1728] name=None, attn_weights_entropy = tensor([4.4251, 2.1945, 2.7307, 4.4327], device='cuda:0')
2023-06-21 16:03:57,109 INFO [zipformer.py:1728] name=None, attn_weights_entropy = tensor([2.6528, 2.3295, 2.2063, 2.0467, 2.3025, 2.1849, 2.5591, 2.1965],
       device='cuda:0')
2023-06-21 16:04:31,502 INFO [decode.py:548] batch 60/?, cuts processed until now is 7955
2023-06-21 16:05:03,760 INFO [decode.py:564] The transcripts are stored in zipformer/exp_L_causal_context_2/modified_beam_search/recogs-TEST_MEETING-beam_size_4_blank_penalty_1.5-epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model.txt
2023-06-21 16:05:04,025 INFO [utils.py:562] [TEST_MEETING-beam_size_4_blank_penalty_1.5] %WER 14.92% [32878 / 220385, 3834 ins, 11207 del, 17837 sub ]
2023-06-21 16:05:04,747 INFO [decode.py:577] Wrote detailed error stats to zipformer/exp_L_causal_context_2/modified_beam_search/errs-TEST_MEETING-beam_size_4_blank_penalty_1.5-epoch-12-avg-4-chunk-32-left-context-256-modified_beam_search-beam-size-4-blank-penalty-1.5-use-averaged-model.txt
2023-06-21 16:05:04,750 INFO [decode.py:593] 
For TEST_MEETING, WER of different settings are:
beam_size_4_blank_penalty_1.5	14.92	best for TEST_MEETING

2023-06-21 16:05:04,750 INFO [decode.py:821] Done!