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add pretrained model and logs for AMI

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  1. README.md +48 -3
  2. data/lang_bpe_500/bpe.model +3 -0
  3. exp/pretrained.pt +3 -0
  4. exp/tensorboard/events.out.tfevents.1668535898.r8n04.150063.0 +3 -0
  5. log/fast_beam_search/cers-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  6. log/fast_beam_search/cers-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  7. log/fast_beam_search/cers-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  8. log/fast_beam_search/cers-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  9. log/fast_beam_search/cers-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  10. log/fast_beam_search/cers-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  11. log/fast_beam_search/log-decode-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8-2022-11-19-11-56-30 +381 -0
  12. log/fast_beam_search/recogs-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  13. log/fast_beam_search/recogs-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  14. log/fast_beam_search/recogs-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  15. log/fast_beam_search/recogs-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  16. log/fast_beam_search/recogs-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  17. log/fast_beam_search/recogs-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  18. log/fast_beam_search/wer-summary-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +2 -0
  19. log/fast_beam_search/wer-summary-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +2 -0
  20. log/fast_beam_search/wer-summary-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +2 -0
  21. log/fast_beam_search/wer-summary-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +2 -0
  22. log/fast_beam_search/wer-summary-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +2 -0
  23. log/fast_beam_search/wer-summary-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +2 -0
  24. log/fast_beam_search/wers-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  25. log/fast_beam_search/wers-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  26. log/fast_beam_search/wers-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  27. log/fast_beam_search/wers-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  28. log/fast_beam_search/wers-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  29. log/fast_beam_search/wers-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt +0 -0
  30. log/log-train-2022-11-15-13-11-38-0 +0 -0
  31. log/log-train-2022-11-15-13-11-38-1 +0 -0
  32. log/log-train-2022-11-15-13-11-38-2 +0 -0
  33. log/log-train-2022-11-15-13-11-38-3 +0 -0
  34. log/modified_beam_search/cers-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  35. log/modified_beam_search/cers-dev_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  36. log/modified_beam_search/cers-dev_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  37. log/modified_beam_search/cers-test_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  38. log/modified_beam_search/cers-test_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  39. log/modified_beam_search/cers-test_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  40. log/modified_beam_search/log-decode-epoch-99-avg-1-modified_beam_search-beam-size-4-2022-11-19-12-54-02 +346 -0
  41. log/modified_beam_search/recogs-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  42. log/modified_beam_search/recogs-dev_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  43. log/modified_beam_search/recogs-dev_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  44. log/modified_beam_search/recogs-test_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  45. log/modified_beam_search/recogs-test_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  46. log/modified_beam_search/recogs-test_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +0 -0
  47. log/modified_beam_search/wer-summary-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +2 -0
  48. log/modified_beam_search/wer-summary-dev_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +2 -0
  49. log/modified_beam_search/wer-summary-dev_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +2 -0
  50. log/modified_beam_search/wer-summary-test_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt +2 -0
README.md CHANGED
@@ -1,3 +1,48 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # AMI
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+
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+ This is an ASR recipe for the AMI corpus. AMI provides recordings from the speaker's
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+ headset and lapel microphones, and also 2 array microphones containing 8 channels each.
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+ We pool data in the following 4 ways and train a single model on the pooled data:
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+
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+ (i) individual headset microphone (IHM)
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+ (ii) IHM with simulated reverb
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+ (iii) Single distant microphone (SDM)
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+ (iv) GSS-enhanced array microphones
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+
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+ Speed perturbation and MUSAN noise augmentation are additionally performed on the pooled
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+ data. Here are the statistics of the combined training data:
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+
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+ ```python
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+ >>> cuts_train.describe()
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+ Cuts count: 1222053
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+ Total duration (hh:mm:ss): 905:00:28
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+ Speech duration (hh:mm:ss): 905:00:28 (99.9%)
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+ Duration statistics (seconds):
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+ mean 2.7
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+ std 2.8
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+ min 0.0
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+ 25% 0.6
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+ 50% 1.6
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+ 75% 3.8
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+ 99% 12.3
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+ 99.5% 13.9
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+ 99.9% 18.4
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+ max 36.8
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+ ```
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+
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+ **Note:** This recipe additionally uses [GSS](https://github.com/desh2608/gss) for enhancement
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+ of far-field array microphones, but this is optional (see `prepare.sh` for details).
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+
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+ ## Performance Record
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+
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+ ### pruned_transducer_stateless7
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+
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+ The following are decoded using `modified_beam_search`:
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+
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+ | Evaluation set | dev WER | test WER |
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+ |--------------------------|------------|---------|
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+ | IHM | 19.23 | 18.06 |
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+ | SDM | 31.16 | 32.61 |
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+ | MDM (GSS-enhanced) | 22.08 | 23.03 |
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+
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+ See [RESULTS](/egs/ami/ASR/RESULTS.md) for details.
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log/fast_beam_search/cers-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/log-decode-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8-2022-11-19-11-56-30 ADDED
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+ 2022-11-19 11:56:30,475 INFO [decode.py:561] Decoding started
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+ 2022-11-19 11:56:30,476 INFO [decode.py:567] Device: cuda:0
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+ 2022-11-19 11:56:30,484 INFO [decode.py:577] {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 100, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.21', 'k2-build-type': 'Debug', 'k2-with-cuda': True, 'k2-git-sha1': 'f271e82ef30f75fecbae44b163e1244e53def116', 'k2-git-date': 'Fri Oct 28 05:02:16 2022', 'lhotse-version': '1.9.0.dev+git.97bf4b0.dirty', 'torch-version': '1.10.0+cu111', 'torch-cuda-available': True, 'torch-cuda-version': '11.1', 'python-version': '3.8', 'icefall-git-branch': 'ami', 'icefall-git-sha1': 'c2c11ca-clean', 'icefall-git-date': 'Sat Nov 19 10:48:59 2022', 'icefall-path': '/exp/draj/mini_scale_2022/icefall', 'k2-path': '/exp/draj/mini_scale_2022/k2/k2/python/k2/__init__.py', 'lhotse-path': '/exp/draj/mini_scale_2022/lhotse/lhotse/__init__.py', 'hostname': 'r7n01', 'IP address': '10.1.7.1'}, 'epoch': 30, 'iter': 105000, 'avg': 10, 'exp_dir': PosixPath('pruned_transducer_stateless7/exp/v2'), 'lang_dir': PosixPath('data/lang_bpe_500'), 'decoding_method': 'fast_beam_search', 'beam_size': 4, 'beam': 4, 'ngram_lm_scale': 0.01, 'max_contexts': 4, 'max_states': 8, 'context_size': 2, 'max_sym_per_frame': 1, 'num_paths': 200, 'nbest_scale': 0.5, '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, 'manifest_dir': PosixPath('data/manifests'), 'enable_musan': True, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'max_duration': 500, 'max_cuts': None, 'num_buckets': 50, 'on_the_fly_feats': False, 'shuffle': True, 'num_workers': 8, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'ihm_only': False, 'res_dir': PosixPath('pruned_transducer_stateless7/exp/v2/fast_beam_search'), 'suffix': 'iter-105000-avg-10-beam-4-max-contexts-4-max-states-8', 'blank_id': 0, 'unk_id': 2, 'vocab_size': 500}
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+ 2022-11-19 11:56:30,484 INFO [decode.py:579] About to create model
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+ 2022-11-19 11:56:30,977 INFO [zipformer.py:176] 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.
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+ 2022-11-19 11:56:30,993 INFO [decode.py:595] averaging ['pruned_transducer_stateless7/exp/v2/checkpoint-105000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-100000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-95000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-90000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-85000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-80000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-75000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-70000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-65000.pt', 'pruned_transducer_stateless7/exp/v2/checkpoint-60000.pt']
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+ 2022-11-19 11:57:47,988 INFO [decode.py:632] Number of model parameters: 70369391
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+ 2022-11-19 11:57:47,989 INFO [asr_datamodule.py:392] About to get AMI IHM dev cuts
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+ 2022-11-19 11:57:48,019 INFO [asr_datamodule.py:413] About to get AMI IHM test cuts
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+ 2022-11-19 11:57:48,021 INFO [asr_datamodule.py:398] About to get AMI SDM dev cuts
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+ 2022-11-19 11:57:48,023 INFO [asr_datamodule.py:419] About to get AMI SDM test cuts
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+ 2022-11-19 11:57:48,025 INFO [asr_datamodule.py:407] About to get AMI GSS-enhanced dev cuts
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+ 2022-11-19 11:57:48,027 INFO [asr_datamodule.py:428] About to get AMI GSS-enhanced test cuts
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+ 2022-11-19 11:57:50,158 INFO [decode.py:664] Decoding dev_ihm
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+ 2022-11-19 11:57:53,749 INFO [decode.py:456] batch 0/?, cuts processed until now is 72
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+ 2022-11-19 11:57:56,656 INFO [decode.py:456] batch 2/?, cuts processed until now is 537
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+ 2022-11-19 11:57:59,459 INFO [decode.py:456] batch 4/?, cuts processed until now is 689
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+ 2022-11-19 11:59:03,270 INFO [zipformer.py:1411] attn_weights_entropy = tensor([5.1287, 4.7557, 5.0205, 4.9174, 5.1172, 4.5533, 2.8637, 5.2734],
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+ device='cuda:0'), covar=tensor([0.0140, 0.0272, 0.0134, 0.0164, 0.0143, 0.0264, 0.2165, 0.0151],
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+ device='cuda:0'), in_proj_covar=tensor([0.0096, 0.0079, 0.0079, 0.0071, 0.0094, 0.0080, 0.0125, 0.0100],
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+ device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0002, 0.0001, 0.0002, 0.0002, 0.0002, 0.0002],
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+ device='cuda:0')
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+ 2022-11-19 11:59:06,333 INFO [decode.py:456] batch 48/?, cuts processed until now is 5672
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+ 2022-11-19 11:59:09,188 INFO [decode.py:456] batch 50/?, cuts processed until now is 5878
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+ 2022-11-19 11:59:12,535 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.2047, 3.1646, 2.3350, 1.8841, 3.0646, 1.3177, 2.7205, 1.9816],
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+ device='cuda:0'), covar=tensor([0.0669, 0.0176, 0.0795, 0.0913, 0.0189, 0.1283, 0.0412, 0.0708],
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+ device='cuda:0'), in_proj_covar=tensor([0.0113, 0.0095, 0.0106, 0.0107, 0.0092, 0.0114, 0.0091, 0.0105],
50
+ device='cuda:0'), out_proj_covar=tensor([0.0005, 0.0003, 0.0004, 0.0004, 0.0003, 0.0004, 0.0003, 0.0004],
51
+ device='cuda:0')
52
+ 2022-11-19 11:59:13,402 INFO [decode.py:456] batch 54/?, cuts processed until now is 6808
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+ 2022-11-19 11:59:15,807 INFO [decode.py:456] batch 56/?, cuts processed until now is 7117
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+ 2022-11-19 11:59:42,240 INFO [decode.py:456] batch 80/?, cuts processed until now is 11956
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+ 2022-11-19 11:59:43,775 INFO [decode.py:456] batch 82/?, cuts processed until now is 12467
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+ 2022-11-19 11:59:48,114 INFO [decode.py:456] batch 84/?, cuts processed until now is 12586
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+ 2022-11-19 11:59:50,291 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/fast_beam_search/recogs-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
69
+ 2022-11-19 11:59:50,494 INFO [utils.py:531] [dev_ihm-beam_4_max_contexts_4_max_states_8] %WER 19.46% [18471 / 94940, 2582 ins, 4027 del, 11862 sub ]
70
+ 2022-11-19 11:59:51,358 INFO [utils.py:531] [dev_ihm-beam_4_max_contexts_4_max_states_8] %WER 12.39% [45842 / 369873, 10341 ins, 18060 del, 17441 sub ]
71
+ 2022-11-19 11:59:52,357 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/fast_beam_search/wers-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
72
+ 2022-11-19 11:59:52,358 INFO [decode.py:518]
73
+ For dev_ihm, WER/CER of different settings are:
74
+ beam_4_max_contexts_4_max_states_8 19.46 12.39 best for dev_ihm
75
+
76
+ 2022-11-19 11:59:52,363 INFO [decode.py:664] Decoding test_ihm
77
+ 2022-11-19 11:59:55,729 INFO [decode.py:456] batch 0/?, cuts processed until now is 69
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+ 2022-11-19 11:59:58,708 INFO [decode.py:456] batch 2/?, cuts processed until now is 555
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+ 2022-11-19 12:00:01,681 INFO [decode.py:456] batch 4/?, cuts processed until now is 703
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+ 2022-11-19 12:00:04,573 INFO [decode.py:456] batch 6/?, cuts processed until now is 830
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+ 2022-11-19 12:00:07,344 INFO [decode.py:456] batch 8/?, cuts processed until now is 987
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+ 2022-11-19 12:00:11,335 INFO [decode.py:456] batch 10/?, cuts processed until now is 1095
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+ 2022-11-19 12:00:14,147 INFO [decode.py:456] batch 12/?, cuts processed until now is 1267
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+ 2022-11-19 12:00:16,507 INFO [decode.py:456] batch 14/?, cuts processed until now is 1532
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+ 2022-11-19 12:00:18,652 INFO [decode.py:456] batch 16/?, cuts processed until now is 1931
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+ 2022-11-19 12:00:22,624 INFO [decode.py:456] batch 18/?, cuts processed until now is 2055
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+ 2022-11-19 12:00:27,410 INFO [decode.py:456] batch 20/?, cuts processed until now is 2124
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+ 2022-11-19 12:00:32,061 INFO [decode.py:456] batch 24/?, cuts processed until now is 2856
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+ 2022-11-19 12:00:37,754 INFO [decode.py:456] batch 28/?, cuts processed until now is 3278
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+ 2022-11-19 12:00:44,585 INFO [decode.py:456] batch 32/?, cuts processed until now is 3535
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+ 2022-11-19 12:00:50,600 INFO [decode.py:456] batch 36/?, cuts processed until now is 3822
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+ 2022-11-19 12:01:52,764 INFO [decode.py:456] batch 84/?, cuts processed until now is 12110
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+ 2022-11-19 12:01:53,020 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/fast_beam_search/recogs-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
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+ 2022-11-19 12:01:53,199 INFO [utils.py:531] [test_ihm-beam_4_max_contexts_4_max_states_8] %WER 18.35% [16449 / 89659, 1901 ins, 4044 del, 10504 sub ]
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+ 2022-11-19 12:01:53,873 INFO [utils.py:531] [test_ihm-beam_4_max_contexts_4_max_states_8] %WER 11.50% [40727 / 354205, 8552 ins, 17081 del, 15094 sub ]
123
+ 2022-11-19 12:01:54,902 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/fast_beam_search/wers-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
124
+ 2022-11-19 12:01:54,903 INFO [decode.py:518]
125
+ For test_ihm, WER/CER of different settings are:
126
+ beam_4_max_contexts_4_max_states_8 18.35 11.5 best for test_ihm
127
+
128
+ 2022-11-19 12:01:54,908 INFO [decode.py:664] Decoding dev_sdm
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+ 2022-11-19 12:01:58,144 INFO [decode.py:456] batch 0/?, cuts processed until now is 71
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+ 2022-11-19 12:02:00,702 INFO [decode.py:456] batch 2/?, cuts processed until now is 535
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+ 2022-11-19 12:02:03,476 INFO [decode.py:456] batch 4/?, cuts processed until now is 686
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+ 2022-11-19 12:02:06,323 INFO [decode.py:456] batch 6/?, cuts processed until now is 819
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+ 2022-11-19 12:02:07,881 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.0469, 1.5212, 1.9862, 1.1136, 1.9181, 1.8707, 1.4307, 1.7934],
134
+ device='cuda:0'), covar=tensor([0.0691, 0.0744, 0.0420, 0.1360, 0.1466, 0.0986, 0.1064, 0.0307],
135
+ device='cuda:0'), in_proj_covar=tensor([0.0013, 0.0021, 0.0014, 0.0018, 0.0014, 0.0013, 0.0019, 0.0013],
136
+ device='cuda:0'), out_proj_covar=tensor([7.0063e-05, 9.8332e-05, 7.2372e-05, 8.8655e-05, 7.6633e-05, 6.9844e-05,
137
+ 9.3643e-05, 6.9182e-05], device='cuda:0')
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+ 2022-11-19 12:02:09,041 INFO [decode.py:456] batch 8/?, cuts processed until now is 980
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+ 2022-11-19 12:02:13,345 INFO [decode.py:456] batch 10/?, cuts processed until now is 1083
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+ 2022-11-19 12:02:16,238 INFO [decode.py:456] batch 12/?, cuts processed until now is 1257
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+ 2022-11-19 12:02:18,758 INFO [decode.py:456] batch 14/?, cuts processed until now is 1513
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+ 2022-11-19 12:02:20,166 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.0954, 2.5017, 2.0002, 1.5301, 2.4209, 2.7677, 2.6900, 2.8832],
143
+ device='cuda:0'), covar=tensor([0.1359, 0.1360, 0.1853, 0.2378, 0.0760, 0.1000, 0.0585, 0.0894],
144
+ device='cuda:0'), in_proj_covar=tensor([0.0155, 0.0168, 0.0150, 0.0172, 0.0162, 0.0181, 0.0147, 0.0167],
145
+ device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0004, 0.0003, 0.0004, 0.0003, 0.0004],
146
+ device='cuda:0')
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+ 2022-11-19 12:02:21,030 INFO [decode.py:456] batch 16/?, cuts processed until now is 1892
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+ 2022-11-19 12:02:24,579 INFO [decode.py:456] batch 18/?, cuts processed until now is 2020
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+ 2022-11-19 12:03:48,945 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.8918, 4.0775, 4.0075, 2.0122, 3.9736, 4.2795, 4.1332, 4.6819],
183
+ device='cuda:0'), covar=tensor([0.1667, 0.1013, 0.0762, 0.2696, 0.0253, 0.0403, 0.0447, 0.0409],
184
+ device='cuda:0'), in_proj_covar=tensor([0.0155, 0.0168, 0.0150, 0.0172, 0.0162, 0.0181, 0.0147, 0.0167],
185
+ device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0004, 0.0003, 0.0004, 0.0003, 0.0004],
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+ device='cuda:0')
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+ 2022-11-19 12:03:50,431 INFO [decode.py:456] batch 86/?, cuts processed until now is 12601
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+ 2022-11-19 12:03:50,666 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/fast_beam_search/recogs-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
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+ 2022-11-19 12:03:50,920 INFO [utils.py:531] [dev_sdm-beam_4_max_contexts_4_max_states_8] %WER 31.14% [29566 / 94940, 3772 ins, 8463 del, 17331 sub ]
190
+ 2022-11-19 12:03:51,646 INFO [utils.py:531] [dev_sdm-beam_4_max_contexts_4_max_states_8] %WER 22.76% [84184 / 369873, 17170 ins, 36158 del, 30856 sub ]
191
+ 2022-11-19 12:03:52,593 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/fast_beam_search/wers-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
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+ 2022-11-19 12:03:52,594 INFO [decode.py:518]
193
+ For dev_sdm, WER/CER of different settings are:
194
+ beam_4_max_contexts_4_max_states_8 31.14 22.76 best for dev_sdm
195
+
196
+ 2022-11-19 12:03:52,598 INFO [decode.py:664] Decoding test_sdm
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+ 2022-11-19 12:03:56,370 INFO [decode.py:456] batch 0/?, cuts processed until now is 69
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+ 2022-11-19 12:03:59,640 INFO [decode.py:456] batch 2/?, cuts processed until now is 555
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+ 2022-11-19 12:04:22,636 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.4038, 4.1485, 3.0703, 4.0386, 3.3197, 3.0083, 2.3652, 3.5807],
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+ device='cuda:0'), covar=tensor([0.1479, 0.0211, 0.0951, 0.0264, 0.0733, 0.0969, 0.2025, 0.0382],
208
+ device='cuda:0'), in_proj_covar=tensor([0.0147, 0.0124, 0.0146, 0.0129, 0.0160, 0.0156, 0.0151, 0.0141],
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+ device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0003, 0.0004, 0.0004, 0.0004, 0.0003],
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+ device='cuda:0')
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+ 2022-11-19 12:04:23,701 INFO [decode.py:456] batch 18/?, cuts processed until now is 2057
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+ 2022-11-19 12:05:40,109 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.9114, 3.1058, 3.0254, 3.1113, 3.1652, 3.0739, 2.8505, 2.9252],
239
+ device='cuda:0'), covar=tensor([0.0274, 0.0463, 0.0757, 0.0313, 0.0311, 0.0245, 0.0605, 0.0398],
240
+ device='cuda:0'), in_proj_covar=tensor([0.0119, 0.0165, 0.0262, 0.0160, 0.0205, 0.0160, 0.0175, 0.0162],
241
+ device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0003, 0.0002, 0.0003, 0.0002, 0.0002, 0.0002],
242
+ device='cuda:0')
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+ 2022-11-19 12:05:43,161 INFO [decode.py:456] batch 72/?, cuts processed until now is 10711
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+ 2022-11-19 12:05:45,488 INFO [decode.py:456] batch 74/?, cuts processed until now is 10820
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+ 2022-11-19 12:05:47,140 INFO [decode.py:456] batch 76/?, cuts processed until now is 11076
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+ 2022-11-19 12:05:48,230 INFO [decode.py:456] batch 78/?, cuts processed until now is 11209
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+ 2022-11-19 12:05:49,877 INFO [decode.py:456] batch 80/?, cuts processed until now is 11651
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+ 2022-11-19 12:05:50,055 INFO [zipformer.py:1411] attn_weights_entropy = tensor([1.2445, 1.7691, 1.5410, 1.6569, 1.8904, 2.3912, 1.9761, 1.8214],
249
+ device='cuda:0'), covar=tensor([0.2463, 0.0429, 0.3108, 0.2256, 0.1364, 0.0250, 0.1293, 0.1741],
250
+ device='cuda:0'), in_proj_covar=tensor([0.0089, 0.0080, 0.0079, 0.0089, 0.0065, 0.0055, 0.0065, 0.0077],
251
+ device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0001, 0.0002, 0.0002],
252
+ device='cuda:0')
253
+ 2022-11-19 12:05:52,270 INFO [decode.py:456] batch 82/?, cuts processed until now is 12070
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+ 2022-11-19 12:05:53,724 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/fast_beam_search/recogs-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
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+ 2022-11-19 12:05:53,875 INFO [utils.py:531] [test_sdm-beam_4_max_contexts_4_max_states_8] %WER 32.52% [29153 / 89659, 3384 ins, 9146 del, 16623 sub ]
256
+ 2022-11-19 12:05:54,687 INFO [utils.py:531] [test_sdm-beam_4_max_contexts_4_max_states_8] %WER 23.78% [84225 / 354205, 16274 ins, 38001 del, 29950 sub ]
257
+ 2022-11-19 12:05:55,678 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/fast_beam_search/wers-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
258
+ 2022-11-19 12:05:55,680 INFO [decode.py:518]
259
+ For test_sdm, WER/CER of different settings are:
260
+ beam_4_max_contexts_4_max_states_8 32.52 23.78 best for test_sdm
261
+
262
+ 2022-11-19 12:05:55,684 INFO [decode.py:664] Decoding dev_gss
263
+ 2022-11-19 12:05:58,735 INFO [decode.py:456] batch 0/?, cuts processed until now is 71
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+ 2022-11-19 12:06:01,525 INFO [decode.py:456] batch 2/?, cuts processed until now is 535
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+ 2022-11-19 12:06:04,466 INFO [decode.py:456] batch 4/?, cuts processed until now is 686
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+ 2022-11-19 12:06:07,447 INFO [decode.py:456] batch 6/?, cuts processed until now is 819
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+ 2022-11-19 12:06:10,237 INFO [decode.py:456] batch 8/?, cuts processed until now is 980
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+ 2022-11-19 12:06:15,672 INFO [decode.py:456] batch 10/?, cuts processed until now is 1083
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+ 2022-11-19 12:06:18,556 INFO [decode.py:456] batch 12/?, cuts processed until now is 1257
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+ 2022-11-19 12:06:21,107 INFO [decode.py:456] batch 14/?, cuts processed until now is 1513
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+ 2022-11-19 12:06:23,284 INFO [decode.py:456] batch 16/?, cuts processed until now is 1892
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+ 2022-11-19 12:06:27,032 INFO [decode.py:456] batch 18/?, cuts processed until now is 2020
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+ 2022-11-19 12:06:30,963 INFO [decode.py:456] batch 20/?, cuts processed until now is 2106
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+ 2022-11-19 12:06:33,370 INFO [decode.py:456] batch 22/?, cuts processed until now is 2362
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+ 2022-11-19 12:06:35,629 INFO [decode.py:456] batch 24/?, cuts processed until now is 2807
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+ 2022-11-19 12:06:38,773 INFO [decode.py:456] batch 26/?, cuts processed until now is 2952
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+ 2022-11-19 12:06:41,475 INFO [decode.py:456] batch 28/?, cuts processed until now is 3226
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+ 2022-11-19 12:06:44,385 INFO [decode.py:456] batch 30/?, cuts processed until now is 3381
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+ 2022-11-19 12:06:47,883 INFO [decode.py:456] batch 32/?, cuts processed until now is 3499
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+ 2022-11-19 12:06:50,916 INFO [decode.py:456] batch 34/?, cuts processed until now is 3673
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+ 2022-11-19 12:06:53,965 INFO [decode.py:456] batch 36/?, cuts processed until now is 3797
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+ 2022-11-19 12:06:56,868 INFO [decode.py:456] batch 38/?, cuts processed until now is 3948
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+ 2022-11-19 12:06:59,352 INFO [decode.py:456] batch 40/?, cuts processed until now is 4722
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+ 2022-11-19 12:07:02,038 INFO [decode.py:456] batch 42/?, cuts processed until now is 5007
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+ 2022-11-19 12:07:05,489 INFO [decode.py:456] batch 44/?, cuts processed until now is 5112
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+ 2022-11-19 12:07:08,880 INFO [decode.py:456] batch 46/?, cuts processed until now is 5219
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+ 2022-11-19 12:07:12,726 INFO [decode.py:456] batch 48/?, cuts processed until now is 5636
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+ 2022-11-19 12:07:15,554 INFO [decode.py:456] batch 50/?, cuts processed until now is 5842
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+ 2022-11-19 12:07:17,780 INFO [decode.py:456] batch 52/?, cuts processed until now is 6222
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+ 2022-11-19 12:07:17,973 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.4400, 3.9245, 2.9125, 1.9497, 3.5919, 1.6291, 3.2518, 2.3731],
291
+ device='cuda:0'), covar=tensor([0.1357, 0.0146, 0.0875, 0.2236, 0.0221, 0.1806, 0.0374, 0.1393],
292
+ device='cuda:0'), in_proj_covar=tensor([0.0113, 0.0095, 0.0106, 0.0107, 0.0092, 0.0114, 0.0091, 0.0105],
293
+ device='cuda:0'), out_proj_covar=tensor([0.0005, 0.0003, 0.0004, 0.0004, 0.0003, 0.0004, 0.0003, 0.0004],
294
+ device='cuda:0')
295
+ 2022-11-19 12:07:20,129 INFO [decode.py:456] batch 54/?, cuts processed until now is 6766
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+ 2022-11-19 12:07:22,726 INFO [decode.py:456] batch 56/?, cuts processed until now is 7072
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+ 2022-11-19 12:07:25,257 INFO [decode.py:456] batch 58/?, cuts processed until now is 7518
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+ 2022-11-19 12:07:27,685 INFO [decode.py:456] batch 60/?, cuts processed until now is 8027
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+ 2022-11-19 12:07:29,981 INFO [decode.py:456] batch 62/?, cuts processed until now is 8571
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+ 2022-11-19 12:07:30,119 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.9705, 3.1991, 3.1004, 3.2066, 3.2365, 3.1405, 2.8701, 3.0248],
301
+ device='cuda:0'), covar=tensor([0.0274, 0.0332, 0.0728, 0.0303, 0.0320, 0.0258, 0.0755, 0.0400],
302
+ device='cuda:0'), in_proj_covar=tensor([0.0119, 0.0165, 0.0262, 0.0160, 0.0205, 0.0160, 0.0175, 0.0162],
303
+ device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0003, 0.0002, 0.0003, 0.0002, 0.0002, 0.0002],
304
+ device='cuda:0')
305
+ 2022-11-19 12:07:32,538 INFO [decode.py:456] batch 64/?, cuts processed until now is 9115
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+ 2022-11-19 12:07:35,105 INFO [decode.py:456] batch 66/?, cuts processed until now is 9395
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+ 2022-11-19 12:07:37,426 INFO [decode.py:456] batch 68/?, cuts processed until now is 9904
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+ 2022-11-19 12:07:39,791 INFO [decode.py:456] batch 70/?, cuts processed until now is 10413
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+ 2022-11-19 12:07:42,275 INFO [decode.py:456] batch 72/?, cuts processed until now is 11190
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+ 2022-11-19 12:07:44,769 INFO [decode.py:456] batch 74/?, cuts processed until now is 11589
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+ 2022-11-19 12:07:46,693 INFO [decode.py:456] batch 76/?, cuts processed until now is 11699
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+ 2022-11-19 12:07:48,652 INFO [decode.py:456] batch 78/?, cuts processed until now is 11799
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+ 2022-11-19 12:07:50,346 INFO [decode.py:456] batch 80/?, cuts processed until now is 11889
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+ 2022-11-19 12:07:52,025 INFO [decode.py:456] batch 82/?, cuts processed until now is 12461
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+ 2022-11-19 12:07:54,095 INFO [decode.py:456] batch 84/?, cuts processed until now is 12568
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+ 2022-11-19 12:07:59,233 INFO [decode.py:456] batch 86/?, cuts processed until now is 12601
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+ 2022-11-19 12:07:59,490 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/fast_beam_search/recogs-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
318
+ 2022-11-19 12:07:59,799 INFO [utils.py:531] [dev_gss-beam_4_max_contexts_4_max_states_8] %WER 22.45% [21318 / 94940, 2659 ins, 4967 del, 13692 sub ]
319
+ 2022-11-19 12:08:00,685 INFO [utils.py:531] [dev_gss-beam_4_max_contexts_4_max_states_8] %WER 14.81% [54769 / 369873, 11328 ins, 21762 del, 21679 sub ]
320
+ 2022-11-19 12:08:01,838 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/fast_beam_search/wers-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
321
+ 2022-11-19 12:08:01,840 INFO [decode.py:518]
322
+ For dev_gss, WER/CER of different settings are:
323
+ beam_4_max_contexts_4_max_states_8 22.45 14.81 best for dev_gss
324
+
325
+ 2022-11-19 12:08:01,848 INFO [decode.py:664] Decoding test_gss
326
+ 2022-11-19 12:08:04,914 INFO [decode.py:456] batch 0/?, cuts processed until now is 69
327
+ 2022-11-19 12:08:07,716 INFO [decode.py:456] batch 2/?, cuts processed until now is 555
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+ 2022-11-19 12:08:10,492 INFO [decode.py:456] batch 4/?, cuts processed until now is 703
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+ 2022-11-19 12:08:13,414 INFO [decode.py:456] batch 6/?, cuts processed until now is 831
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+ 2022-11-19 12:08:16,011 INFO [decode.py:456] batch 8/?, cuts processed until now is 988
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+ 2022-11-19 12:08:19,693 INFO [decode.py:456] batch 10/?, cuts processed until now is 1096
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+ 2022-11-19 12:08:22,574 INFO [decode.py:456] batch 12/?, cuts processed until now is 1268
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+ 2022-11-19 12:08:24,976 INFO [decode.py:456] batch 14/?, cuts processed until now is 1533
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+ 2022-11-19 12:08:27,615 INFO [decode.py:456] batch 16/?, cuts processed until now is 1932
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+ 2022-11-19 12:08:31,972 INFO [decode.py:456] batch 18/?, cuts processed until now is 2057
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+ 2022-11-19 12:08:36,693 INFO [decode.py:456] batch 20/?, cuts processed until now is 2126
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+ 2022-11-19 12:08:39,094 INFO [decode.py:456] batch 22/?, cuts processed until now is 2390
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+ 2022-11-19 12:08:41,204 INFO [decode.py:456] batch 24/?, cuts processed until now is 2858
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+ 2022-11-19 12:08:44,256 INFO [decode.py:456] batch 26/?, cuts processed until now is 2998
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+ 2022-11-19 12:08:46,938 INFO [decode.py:456] batch 28/?, cuts processed until now is 3280
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+ 2022-11-19 12:08:49,719 INFO [decode.py:456] batch 30/?, cuts processed until now is 3432
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+ 2022-11-19 12:08:54,026 INFO [decode.py:456] batch 32/?, cuts processed until now is 3537
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+ 2022-11-19 12:08:57,029 INFO [decode.py:456] batch 34/?, cuts processed until now is 3709
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+ 2022-11-19 12:09:00,191 INFO [decode.py:456] batch 36/?, cuts processed until now is 3825
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+ 2022-11-19 12:09:03,137 INFO [decode.py:456] batch 38/?, cuts processed until now is 3972
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+ 2022-11-19 12:09:06,356 INFO [decode.py:456] batch 40/?, cuts processed until now is 4410
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+ 2022-11-19 12:09:08,836 INFO [decode.py:456] batch 42/?, cuts processed until now is 5060
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+ 2022-11-19 12:09:11,267 INFO [decode.py:456] batch 44/?, cuts processed until now is 5546
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+ 2022-11-19 12:09:14,175 INFO [decode.py:456] batch 46/?, cuts processed until now is 5687
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+ 2022-11-19 12:09:16,811 INFO [decode.py:456] batch 48/?, cuts processed until now is 5893
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+ 2022-11-19 12:09:19,425 INFO [decode.py:456] batch 50/?, cuts processed until now is 6379
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+ 2022-11-19 12:09:20,784 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.5938, 4.3109, 3.2093, 3.9648, 3.2693, 3.0485, 2.5234, 3.5965],
353
+ device='cuda:0'), covar=tensor([0.1265, 0.0167, 0.0893, 0.0254, 0.0792, 0.0922, 0.1719, 0.0275],
354
+ device='cuda:0'), in_proj_covar=tensor([0.0147, 0.0124, 0.0146, 0.0129, 0.0160, 0.0156, 0.0151, 0.0141],
355
+ device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0003, 0.0004, 0.0004, 0.0004, 0.0003],
356
+ device='cuda:0')
357
+ 2022-11-19 12:09:21,839 INFO [decode.py:456] batch 52/?, cuts processed until now is 6713
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+ 2022-11-19 12:09:24,015 INFO [decode.py:456] batch 54/?, cuts processed until now is 7112
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+ 2022-11-19 12:09:28,198 INFO [decode.py:456] batch 56/?, cuts processed until now is 7298
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+ 2022-11-19 12:09:30,434 INFO [decode.py:456] batch 58/?, cuts processed until now is 8130
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+ 2022-11-19 12:09:34,783 INFO [decode.py:456] batch 60/?, cuts processed until now is 8273
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+ 2022-11-19 12:09:37,045 INFO [decode.py:456] batch 62/?, cuts processed until now is 8813
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+ 2022-11-19 12:09:39,493 INFO [decode.py:456] batch 64/?, cuts processed until now is 9353
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+ 2022-11-19 12:09:43,457 INFO [decode.py:456] batch 66/?, cuts processed until now is 9500
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+ 2022-11-19 12:09:46,848 INFO [decode.py:456] batch 68/?, cuts processed until now is 9944
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+ 2022-11-19 12:09:49,082 INFO [decode.py:456] batch 70/?, cuts processed until now is 10274
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+ 2022-11-19 12:09:52,092 INFO [decode.py:456] batch 72/?, cuts processed until now is 10711
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+ 2022-11-19 12:09:54,382 INFO [decode.py:456] batch 74/?, cuts processed until now is 10820
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+ 2022-11-19 12:09:56,179 INFO [decode.py:456] batch 76/?, cuts processed until now is 11076
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+ 2022-11-19 12:09:57,351 INFO [decode.py:456] batch 78/?, cuts processed until now is 11209
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+ 2022-11-19 12:09:59,115 INFO [decode.py:456] batch 80/?, cuts processed until now is 11651
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+ 2022-11-19 12:10:01,667 INFO [decode.py:456] batch 82/?, cuts processed until now is 12070
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+ 2022-11-19 12:10:03,209 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/fast_beam_search/recogs-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
374
+ 2022-11-19 12:10:03,388 INFO [utils.py:531] [test_gss-beam_4_max_contexts_4_max_states_8] %WER 23.38% [20960 / 89659, 2089 ins, 5792 del, 13079 sub ]
375
+ 2022-11-19 12:10:04,277 INFO [utils.py:531] [test_gss-beam_4_max_contexts_4_max_states_8] %WER 15.71% [55662 / 354205, 10250 ins, 24389 del, 21023 sub ]
376
+ 2022-11-19 12:10:05,352 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/fast_beam_search/wers-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt
377
+ 2022-11-19 12:10:05,353 INFO [decode.py:518]
378
+ For test_gss, WER/CER of different settings are:
379
+ beam_4_max_contexts_4_max_states_8 23.38 15.71 best for test_gss
380
+
381
+ 2022-11-19 12:10:05,358 INFO [decode.py:681] Done!
log/fast_beam_search/recogs-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/recogs-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/recogs-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/recogs-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/recogs-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/wer-summary-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_4_max_contexts_4_max_states_8 22.45 14.81
log/fast_beam_search/wer-summary-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_4_max_contexts_4_max_states_8 19.46 12.39
log/fast_beam_search/wer-summary-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_4_max_contexts_4_max_states_8 31.14 22.76
log/fast_beam_search/wer-summary-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_4_max_contexts_4_max_states_8 23.38 15.71
log/fast_beam_search/wer-summary-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_4_max_contexts_4_max_states_8 18.35 11.5
log/fast_beam_search/wer-summary-test_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_4_max_contexts_4_max_states_8 32.52 23.78
log/fast_beam_search/wers-dev_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/wers-dev_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/wers-dev_sdm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/wers-test_gss-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/fast_beam_search/wers-test_ihm-beam_4_max_contexts_4_max_states_8-iter-105000-avg-10-beam-4-max-contexts-4-max-states-8.txt ADDED
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log/log-train-2022-11-15-13-11-38-0 ADDED
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log/modified_beam_search/cers-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
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log/modified_beam_search/cers-dev_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
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log/modified_beam_search/cers-dev_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
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log/modified_beam_search/cers-test_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
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log/modified_beam_search/cers-test_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
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log/modified_beam_search/cers-test_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
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log/modified_beam_search/log-decode-epoch-99-avg-1-modified_beam_search-beam-size-4-2022-11-19-12-54-02 ADDED
@@ -0,0 +1,346 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2022-11-19 12:54:02,151 INFO [decode.py:561] Decoding started
2
+ 2022-11-19 12:54:02,152 INFO [decode.py:567] Device: cuda:0
3
+ 2022-11-19 12:54:02,159 INFO [decode.py:577] {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 100, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'warm_step': 2000, 'env_info': {'k2-version': '1.21', 'k2-build-type': 'Debug', 'k2-with-cuda': True, 'k2-git-sha1': 'f271e82ef30f75fecbae44b163e1244e53def116', 'k2-git-date': 'Fri Oct 28 05:02:16 2022', 'lhotse-version': '1.9.0.dev+git.97bf4b0.dirty', 'torch-version': '1.10.0+cu111', 'torch-cuda-available': True, 'torch-cuda-version': '11.1', 'python-version': '3.8', 'icefall-git-branch': 'ami', 'icefall-git-sha1': 'c2c11ca-clean', 'icefall-git-date': 'Sat Nov 19 10:48:59 2022', 'icefall-path': '/exp/draj/mini_scale_2022/icefall', 'k2-path': '/exp/draj/mini_scale_2022/k2/k2/python/k2/__init__.py', 'lhotse-path': '/exp/draj/mini_scale_2022/lhotse/lhotse/__init__.py', 'hostname': 'r2n03', 'IP address': '10.1.2.3'}, 'epoch': 99, 'iter': 0, 'avg': 1, 'exp_dir': PosixPath('pruned_transducer_stateless7/exp/v2'), 'lang_dir': PosixPath('data/lang_bpe_500'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 4, 'ngram_lm_scale': 0.01, 'max_contexts': 4, 'max_states': 8, 'context_size': 2, 'max_sym_per_frame': 1, 'num_paths': 200, 'nbest_scale': 0.5, '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, 'manifest_dir': PosixPath('data/manifests'), 'enable_musan': True, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'max_duration': 500, 'max_cuts': None, 'num_buckets': 50, 'on_the_fly_feats': False, 'shuffle': True, 'num_workers': 8, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'ihm_only': False, 'res_dir': PosixPath('pruned_transducer_stateless7/exp/v2/modified_beam_search'), 'suffix': 'epoch-99-avg-1-modified_beam_search-beam-size-4', 'blank_id': 0, 'unk_id': 2, 'vocab_size': 500}
4
+ 2022-11-19 12:54:02,160 INFO [decode.py:579] About to create model
5
+ 2022-11-19 12:54:02,584 INFO [zipformer.py:176] 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.
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+ 2022-11-19 12:54:02,594 INFO [checkpoint.py:112] Loading checkpoint from pruned_transducer_stateless7/exp/v2/epoch-99.pt
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+ 2022-11-19 12:54:08,970 INFO [decode.py:632] Number of model parameters: 70369391
8
+ 2022-11-19 12:54:08,970 INFO [asr_datamodule.py:392] About to get AMI IHM dev cuts
9
+ 2022-11-19 12:54:08,973 INFO [asr_datamodule.py:413] About to get AMI IHM test cuts
10
+ 2022-11-19 12:54:08,974 INFO [asr_datamodule.py:398] About to get AMI SDM dev cuts
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+ 2022-11-19 12:54:08,975 INFO [asr_datamodule.py:419] About to get AMI SDM test cuts
12
+ 2022-11-19 12:54:08,976 INFO [asr_datamodule.py:407] About to get AMI GSS-enhanced dev cuts
13
+ 2022-11-19 12:54:08,977 INFO [asr_datamodule.py:428] About to get AMI GSS-enhanced test cuts
14
+ 2022-11-19 12:54:11,093 INFO [decode.py:664] Decoding dev_ihm
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+ 2022-11-19 12:54:16,741 INFO [decode.py:456] batch 0/?, cuts processed until now is 72
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+ 2022-11-19 12:54:23,033 INFO [decode.py:456] batch 2/?, cuts processed until now is 537
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+ 2022-11-19 12:56:11,959 INFO [decode.py:456] batch 30/?, cuts processed until now is 3401
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+ 2022-11-19 12:56:16,027 INFO [zipformer.py:1411] attn_weights_entropy = tensor([4.6559, 3.0677, 3.6884, 4.5468, 4.6187, 3.6254, 3.2735, 4.6512],
32
+ device='cuda:0'), covar=tensor([0.0500, 0.3337, 0.2018, 0.2960, 0.0827, 0.3194, 0.2184, 0.0447],
33
+ device='cuda:0'), in_proj_covar=tensor([0.0228, 0.0185, 0.0175, 0.0289, 0.0205, 0.0190, 0.0175, 0.0223],
34
+ device='cuda:0'), out_proj_covar=tensor([0.0005, 0.0004, 0.0004, 0.0006, 0.0005, 0.0005, 0.0004, 0.0005],
35
+ device='cuda:0')
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+ 2022-11-19 12:56:19,677 INFO [decode.py:456] batch 32/?, cuts processed until now is 3519
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+ 2022-11-19 12:58:41,725 INFO [decode.py:456] batch 78/?, cuts processed until now is 11843
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+ 2022-11-19 12:58:41,842 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.4958, 3.9087, 3.0948, 3.5424, 2.9032, 2.9524, 2.3525, 3.2757],
61
+ device='cuda:0'), covar=tensor([0.1316, 0.0197, 0.0685, 0.0387, 0.1168, 0.0841, 0.1668, 0.0395],
62
+ device='cuda:0'), in_proj_covar=tensor([0.0147, 0.0124, 0.0146, 0.0129, 0.0160, 0.0156, 0.0151, 0.0141],
63
+ device='cuda:0'), out_proj_covar=tensor([0.0003, 0.0003, 0.0003, 0.0003, 0.0004, 0.0004, 0.0004, 0.0003],
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+ device='cuda:0')
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+ 2022-11-19 12:58:45,493 INFO [decode.py:456] batch 80/?, cuts processed until now is 11956
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+ 2022-11-19 12:58:48,594 INFO [decode.py:456] batch 82/?, cuts processed until now is 12467
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+ 2022-11-19 12:58:55,386 INFO [decode.py:456] batch 84/?, cuts processed until now is 12586
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+ 2022-11-19 12:58:57,593 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/modified_beam_search/recogs-dev_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
69
+ 2022-11-19 12:58:57,753 INFO [utils.py:531] [dev_ihm-beam_size_4] %WER 19.23% [18256 / 94940, 2870 ins, 3323 del, 12063 sub ]
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+ 2022-11-19 12:58:58,486 INFO [utils.py:531] [dev_ihm-beam_size_4] %WER 12.01% [44428 / 369873, 11497 ins, 15114 del, 17817 sub ]
71
+ 2022-11-19 12:58:59,581 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/modified_beam_search/wers-dev_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
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+ 2022-11-19 12:58:59,582 INFO [decode.py:518]
73
+ For dev_ihm, WER/CER of different settings are:
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+ beam_size_4 19.23 12.01 best for dev_ihm
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+
76
+ 2022-11-19 12:58:59,589 INFO [decode.py:664] Decoding test_ihm
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+ 2022-11-19 12:59:05,333 INFO [decode.py:456] batch 0/?, cuts processed until now is 69
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+ 2022-11-19 13:03:37,991 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/modified_beam_search/recogs-test_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
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+ 2022-11-19 13:03:38,127 INFO [utils.py:531] [test_ihm-beam_size_4] %WER 18.06% [16188 / 89659, 2094 ins, 3408 del, 10686 sub ]
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+ 2022-11-19 13:03:38,852 INFO [utils.py:531] [test_ihm-beam_size_4] %WER 11.13% [39423 / 354205, 9472 ins, 14545 del, 15406 sub ]
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+ 2022-11-19 13:03:39,696 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/modified_beam_search/wers-test_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
124
+ 2022-11-19 13:03:39,698 INFO [decode.py:518]
125
+ For test_ihm, WER/CER of different settings are:
126
+ beam_size_4 18.06 11.13 best for test_ihm
127
+
128
+ 2022-11-19 13:03:39,702 INFO [decode.py:664] Decoding dev_sdm
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+ 2022-11-19 13:03:44,715 INFO [decode.py:456] batch 0/?, cuts processed until now is 71
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+ 2022-11-19 13:08:01,920 INFO [decode.py:456] batch 86/?, cuts processed until now is 12601
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+ 2022-11-19 13:08:02,120 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/modified_beam_search/recogs-dev_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
174
+ 2022-11-19 13:08:02,304 INFO [utils.py:531] [dev_sdm-beam_size_4] %WER 31.16% [29584 / 94940, 4923 ins, 6221 del, 18440 sub ]
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+ 2022-11-19 13:08:03,054 INFO [utils.py:531] [dev_sdm-beam_size_4] %WER 22.34% [82613 / 369873, 21552 ins, 27378 del, 33683 sub ]
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+ 2022-11-19 13:08:04,137 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/modified_beam_search/wers-dev_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
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+ 2022-11-19 13:08:04,139 INFO [decode.py:518]
178
+ For dev_sdm, WER/CER of different settings are:
179
+ beam_size_4 31.16 22.34 best for dev_sdm
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+
181
+ 2022-11-19 13:08:04,148 INFO [decode.py:664] Decoding test_sdm
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+ 2022-11-19 13:08:09,082 INFO [decode.py:456] batch 0/?, cuts processed until now is 69
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+ 2022-11-19 13:08:14,972 INFO [decode.py:456] batch 2/?, cuts processed until now is 555
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+ 2022-11-19 13:12:19,170 INFO [decode.py:456] batch 82/?, cuts processed until now is 12070
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+ 2022-11-19 13:12:21,853 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/modified_beam_search/recogs-test_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
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+ 2022-11-19 13:12:22,090 INFO [utils.py:531] [test_sdm-beam_size_4] %WER 32.61% [29235 / 89659, 4435 ins, 7052 del, 17748 sub ]
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+ 2022-11-19 13:12:22,766 INFO [utils.py:531] [test_sdm-beam_size_4] %WER 23.59% [83552 / 354205, 20989 ins, 29709 del, 32854 sub ]
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+ 2022-11-19 13:12:23,740 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/modified_beam_search/wers-test_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
228
+ 2022-11-19 13:12:23,742 INFO [decode.py:518]
229
+ For test_sdm, WER/CER of different settings are:
230
+ beam_size_4 32.61 23.59 best for test_sdm
231
+
232
+ 2022-11-19 13:12:23,745 INFO [decode.py:664] Decoding dev_gss
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+ 2022-11-19 13:12:28,501 INFO [decode.py:456] batch 0/?, cuts processed until now is 71
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+ 2022-11-19 13:12:34,360 INFO [decode.py:456] batch 2/?, cuts processed until now is 535
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+ 2022-11-19 13:13:59,106 INFO [decode.py:456] batch 26/?, cuts processed until now is 2952
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+ 2022-11-19 13:14:06,253 INFO [decode.py:456] batch 28/?, cuts processed until now is 3226
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+ 2022-11-19 13:14:13,520 INFO [decode.py:456] batch 30/?, cuts processed until now is 3381
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+ 2022-11-19 13:14:20,833 INFO [decode.py:456] batch 32/?, cuts processed until now is 3499
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+ 2022-11-19 13:14:42,922 INFO [decode.py:456] batch 38/?, cuts processed until now is 3948
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+ 2022-11-19 13:15:08,047 INFO [decode.py:456] batch 46/?, cuts processed until now is 5219
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+ 2022-11-19 13:15:13,106 INFO [decode.py:456] batch 48/?, cuts processed until now is 5636
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+ 2022-11-19 13:15:17,115 INFO [zipformer.py:1411] attn_weights_entropy = tensor([2.1174, 1.9694, 2.7793, 1.8865, 1.6161, 3.0895, 2.4197, 2.1991],
259
+ device='cuda:0'), covar=tensor([0.1009, 0.2240, 0.0841, 0.3189, 0.4134, 0.0713, 0.1611, 0.2062],
260
+ device='cuda:0'), in_proj_covar=tensor([0.0089, 0.0080, 0.0079, 0.0089, 0.0065, 0.0055, 0.0065, 0.0077],
261
+ device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 0.0001, 0.0002, 0.0002],
262
+ device='cuda:0')
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+ 2022-11-19 13:15:20,335 INFO [decode.py:456] batch 50/?, cuts processed until now is 5842
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+ 2022-11-19 13:15:26,492 INFO [decode.py:456] batch 52/?, cuts processed until now is 6222
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+ 2022-11-19 13:15:31,490 INFO [decode.py:456] batch 54/?, cuts processed until now is 6766
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+ 2022-11-19 13:15:38,029 INFO [decode.py:456] batch 56/?, cuts processed until now is 7072
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+ 2022-11-19 13:15:43,996 INFO [decode.py:456] batch 58/?, cuts processed until now is 7518
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+ 2022-11-19 13:16:16,150 INFO [decode.py:456] batch 70/?, cuts processed until now is 10413
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+ 2022-11-19 13:16:19,999 INFO [decode.py:456] batch 72/?, cuts processed until now is 11190
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+ 2022-11-19 13:16:23,041 INFO [decode.py:456] batch 74/?, cuts processed until now is 11589
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+ 2022-11-19 13:16:25,768 INFO [decode.py:456] batch 76/?, cuts processed until now is 11699
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+ 2022-11-19 13:16:29,457 INFO [decode.py:456] batch 78/?, cuts processed until now is 11799
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+ 2022-11-19 13:16:44,150 INFO [decode.py:456] batch 86/?, cuts processed until now is 12601
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+ 2022-11-19 13:16:44,321 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/modified_beam_search/recogs-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
283
+ 2022-11-19 13:16:44,571 INFO [utils.py:531] [dev_gss-beam_size_4] %WER 22.08% [20967 / 94940, 2979 ins, 4055 del, 13933 sub ]
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+ 2022-11-19 13:16:45,272 INFO [utils.py:531] [dev_gss-beam_size_4] %WER 14.33% [53002 / 369873, 12785 ins, 18039 del, 22178 sub ]
285
+ 2022-11-19 13:16:46,164 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/modified_beam_search/wers-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
286
+ 2022-11-19 13:16:46,166 INFO [decode.py:518]
287
+ For dev_gss, WER/CER of different settings are:
288
+ beam_size_4 22.08 14.33 best for dev_gss
289
+
290
+ 2022-11-19 13:16:46,170 INFO [decode.py:664] Decoding test_gss
291
+ 2022-11-19 13:16:51,050 INFO [decode.py:456] batch 0/?, cuts processed until now is 69
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+ 2022-11-19 13:16:56,710 INFO [decode.py:456] batch 2/?, cuts processed until now is 555
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+ 2022-11-19 13:17:11,131 INFO [decode.py:456] batch 6/?, cuts processed until now is 831
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+ 2022-11-19 13:18:19,958 INFO [decode.py:456] batch 26/?, cuts processed until now is 2998
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+ 2022-11-19 13:19:12,188 INFO [zipformer.py:1411] attn_weights_entropy = tensor([4.4848, 4.6789, 4.1788, 4.6840, 4.6719, 3.8186, 4.3463, 4.1185],
314
+ device='cuda:0'), covar=tensor([0.0147, 0.0285, 0.1255, 0.0281, 0.0348, 0.0404, 0.0395, 0.0541],
315
+ device='cuda:0'), in_proj_covar=tensor([0.0119, 0.0165, 0.0262, 0.0160, 0.0205, 0.0160, 0.0175, 0.0162],
316
+ device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0003, 0.0002, 0.0003, 0.0002, 0.0002, 0.0002],
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+ device='cuda:0')
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+ 2022-11-19 13:20:53,853 INFO [decode.py:456] batch 80/?, cuts processed until now is 11651
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+ 2022-11-19 13:20:58,067 INFO [decode.py:456] batch 82/?, cuts processed until now is 12070
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+ 2022-11-19 13:21:01,142 INFO [decode.py:472] The transcripts are stored in pruned_transducer_stateless7/exp/v2/modified_beam_search/recogs-test_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
339
+ 2022-11-19 13:21:01,286 INFO [utils.py:531] [test_gss-beam_size_4] %WER 23.03% [20652 / 89659, 2423 ins, 4743 del, 13486 sub ]
340
+ 2022-11-19 13:21:01,980 INFO [utils.py:531] [test_gss-beam_size_4] %WER 15.22% [53902 / 354205, 11533 ins, 20212 del, 22157 sub ]
341
+ 2022-11-19 13:21:02,980 INFO [decode.py:498] Wrote detailed error stats to pruned_transducer_stateless7/exp/v2/modified_beam_search/wers-test_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt
342
+ 2022-11-19 13:21:02,993 INFO [decode.py:518]
343
+ For test_gss, WER/CER of different settings are:
344
+ beam_size_4 23.03 15.22 best for test_gss
345
+
346
+ 2022-11-19 13:21:02,997 INFO [decode.py:681] Done!
log/modified_beam_search/recogs-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
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log/modified_beam_search/wer-summary-dev_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_size_4 22.08 14.33
log/modified_beam_search/wer-summary-dev_ihm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_size_4 19.23 12.01
log/modified_beam_search/wer-summary-dev_sdm-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_size_4 31.16 22.34
log/modified_beam_search/wer-summary-test_gss-beam_size_4-epoch-99-avg-1-modified_beam_search-beam-size-4.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ settings WER CER
2
+ beam_size_4 23.03 15.22