log/modified_beam_search/log-decode-epoch-30-avg-9-streaming-chunk-size-32-modified_beam_search-beam-size-4-use-averaged-model-2023-04-04-09-26-31

2023-04-04 09:26:31,976 INFO [decode.py:649] Decoding started
2023-04-04 09:26:31,976 INFO [decode.py:655] Device: cuda:0
2023-04-04 09:26:31,978 INFO [decode.py:665] {'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': '1.23.3', 'k2-build-type': 'Debug', 'k2-with-cuda': True, 'k2-git-sha1': '1c9950559223ec24d187f56bc424c3b43904bed3', 'k2-git-date': 'Thu Jan 26 22:00:26 2023', 'lhotse-version': '1.13.0.dev+git.ca98c73.dirty', 'torch-version': '2.0.0+cu117', 'torch-cuda-available': True, 'torch-cuda-version': '11.7', 'python-version': '3.8', 'icefall-git-branch': 'surt', 'icefall-git-sha1': '51e6a8a-dirty', 'icefall-git-date': 'Fri Mar 17 11:23:13 2023', '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': 'r7n04', 'IP address': '10.1.7.4'}, 'epoch': 30, 'iter': 0, 'avg': 9, 'use_averaged_model': True, 'exp_dir': PosixPath('pruned_transducer_stateless7_streaming/exp/v2'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'lang_dir': PosixPath('data/lang_bpe_500'), 'decoding_method': 'modified_beam_search', 'beam_size': 4, 'beam': 20.0, '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': '2,2,2,2,2', 'feedforward_dims': '768,768,768,768,768', 'nhead': '8,8,8,8,8', 'encoder_dims': '256,256,256,256,256', 'attention_dims': '192,192,192,192,192', 'encoder_unmasked_dims': '192,192,192,192,192', 'zipformer_downsampling_factors': '1,2,4,8,2', 'cnn_module_kernels': '31,31,31,31,31', 'decoder_dim': 512, 'joiner_dim': 512, 'short_chunk_size': 50, 'num_left_chunks': 4, 'decode_chunk_len': 32, 'full_libri': True, 'manifest_dir': PosixPath('data/manifests'), 'max_duration': 500, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'input_strategy': 'PrecomputedFeatures', 'res_dir': PosixPath('pruned_transducer_stateless7_streaming/exp/v2/modified_beam_search'), 'suffix': 'epoch-30-avg-9-streaming-chunk-size-32-modified_beam_search-beam-size-4-use-averaged-model', 'blank_id': 0, 'unk_id': 2, 'vocab_size': 500}
2023-04-04 09:26:31,979 INFO [decode.py:667] About to create model
2023-04-04 09:26:32,322 INFO [zipformer.py:405] 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.
2023-04-04 09:26:32,330 INFO [decode.py:738] Calculating the averaged model over epoch range from 21 (excluded) to 30
2023-04-04 09:26:34,928 INFO [decode.py:772] Number of model parameters: 20697573
2023-04-04 09:26:34,928 INFO [asr_datamodule.py:454] About to get test-clean cuts
2023-04-04 09:26:34,931 INFO [asr_datamodule.py:461] About to get test-other cuts
2023-04-04 09:26:42,421 INFO [decode.py:560] batch 0/?, cuts processed until now is 36
2023-04-04 09:28:20,169 INFO [decode.py:560] batch 20/?, cuts processed until now is 1038
2023-04-04 09:29:48,445 INFO [decode.py:560] batch 40/?, cuts processed until now is 2296
2023-04-04 09:30:18,365 INFO [decode.py:574] The transcripts are stored in pruned_transducer_stateless7_streaming/exp/v2/modified_beam_search/recogs-test-clean-epoch-30-avg-9-streaming-chunk-size-32-modified_beam_search-beam-size-4-use-averaged-model.txt
2023-04-04 09:30:18,575 INFO [utils.py:560] [test-clean-beam_size_4] %WER 3.41% [1792 / 52576, 213 ins, 129 del, 1450 sub ]
2023-04-04 09:30:18,740 INFO [decode.py:585] Wrote detailed error stats to pruned_transducer_stateless7_streaming/exp/v2/modified_beam_search/errs-test-clean-epoch-30-avg-9-streaming-chunk-size-32-modified_beam_search-beam-size-4-use-averaged-model.txt
2023-04-04 09:30:18,741 INFO [decode.py:599] 
For test-clean, WER of different settings are:
beam_size_4	3.41	best for test-clean

2023-04-04 09:30:24,057 INFO [decode.py:560] batch 0/?, cuts processed until now is 43
2023-04-04 09:31:28,835 INFO [zipformer.py:2441] attn_weights_entropy = tensor([1.5981, 1.5829, 1.6027, 1.3932, 1.3421, 1.3843, 0.4039, 0.7473],
       device='cuda:0'), covar=tensor([0.0687, 0.0691, 0.0417, 0.0690, 0.1242, 0.0840, 0.1421, 0.1122],
       device='cuda:0'), in_proj_covar=tensor([0.0357, 0.0356, 0.0360, 0.0384, 0.0463, 0.0389, 0.0338, 0.0341],
       device='cuda:0'), out_proj_covar=tensor([0.0002, 0.0002, 0.0002, 0.0002, 0.0003, 0.0002, 0.0002, 0.0002],
       device='cuda:0')
2023-04-04 09:31:56,538 INFO [decode.py:560] batch 20/?, cuts processed until now is 1198
2023-04-04 09:33:26,353 INFO [decode.py:560] batch 40/?, cuts processed until now is 2642
2023-04-04 09:33:50,079 INFO [decode.py:574] The transcripts are stored in pruned_transducer_stateless7_streaming/exp/v2/modified_beam_search/recogs-test-other-epoch-30-avg-9-streaming-chunk-size-32-modified_beam_search-beam-size-4-use-averaged-model.txt
2023-04-04 09:33:50,162 INFO [utils.py:560] [test-other-beam_size_4] %WER 8.94% [4681 / 52343, 512 ins, 424 del, 3745 sub ]
2023-04-04 09:33:50,334 INFO [decode.py:585] Wrote detailed error stats to pruned_transducer_stateless7_streaming/exp/v2/modified_beam_search/errs-test-other-epoch-30-avg-9-streaming-chunk-size-32-modified_beam_search-beam-size-4-use-averaged-model.txt
2023-04-04 09:33:50,334 INFO [decode.py:599] 
For test-other, WER of different settings are:
beam_size_4	8.94	best for test-other

2023-04-04 09:33:50,335 INFO [decode.py:803] Done!