diff --git "a/exp/log/log-train-2022-06-18-10-29-38-0" "b/exp/log/log-train-2022-06-18-10-29-38-0"
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+2022-06-18 10:29:38,910 INFO [train.py:963] (0/4) Training started
+2022-06-18 10:29:38,914 INFO [train.py:973] (0/4) Device: cuda:0
+2022-06-18 10:29:39,323 INFO [lexicon.py:176] (0/4) Loading pre-compiled data/lang_char/Linv.pt
+2022-06-18 10:29:39,354 INFO [train.py:985] (0/4) {'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': 1000, 'feature_dim': 80, 'subsampling_factor': 4, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.15.1', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'f8d2dba06c000ffee36aab5b66f24e7c9809f116', 'k2-git-date': 'Thu Apr 21 12:20:34 2022', 'lhotse-version': '1.3.0.dev+missing.version.file', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'pruned-rnnt-aishell', 'icefall-git-sha1': 'd0a5f1d-dirty', 'icefall-git-date': 'Mon Jun 13 20:40:46 2022', 'icefall-path': '/k2-dev/fangjun/open-source/icefall-aishell', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-22/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-jsonl/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-0616225511-78bf4545d8-tv52r', 'IP address': '10.177.77.9'}, 'world_size': 4, 'master_port': 12356, 'tensorboard': True, 'num_epochs': 30, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('pruned_transducer_stateless3/exp-context-size-1'), 'lang_dir': PosixPath('data/lang_char'), 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 6, 'context_size': 1, 'prune_range': 5, 'lm_scale': 0.25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'save_every_n': 4000, 'keep_last_k': 30, 'average_period': 100, 'use_fp16': True, 'datatang_prob': 0.5, 'num_encoder_layers': 12, 'dim_feedforward': 2048, 'nhead': 8, 'encoder_dim': 512, 'decoder_dim': 512, 'joiner_dim': 512, 'max_duration': 200, '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, 'blank_id': 0, 'vocab_size': 4336}
+2022-06-18 10:29:39,354 INFO [train.py:987] (0/4) About to create model
+2022-06-18 10:29:40,114 INFO [train.py:991] (0/4) Number of model parameters: 96983734
+2022-06-18 10:29:45,212 INFO [train.py:1006] (0/4) Using DDP
+2022-06-18 10:29:46,052 INFO [aishell.py:39] (0/4) About to get train cuts from data/fbank/aishell_cuts_train.jsonl.gz
+2022-06-18 10:29:46,054 INFO [aidatatang_200zh.py:39] (0/4) About to get train cuts from data/fbank/aidatatang_cuts_train.jsonl.gz
+2022-06-18 10:29:47,823 INFO [asr_datamodule.py:163] (0/4) Enable MUSAN
+2022-06-18 10:29:47,823 INFO [asr_datamodule.py:175] (0/4) Enable SpecAugment
+2022-06-18 10:29:47,823 INFO [asr_datamodule.py:176] (0/4) Time warp factor: 80
+2022-06-18 10:29:47,824 INFO [asr_datamodule.py:188] (0/4) Num frame mask: 10
+2022-06-18 10:29:47,824 INFO [asr_datamodule.py:201] (0/4) About to create train dataset
+2022-06-18 10:29:47,824 INFO [asr_datamodule.py:229] (0/4) Using DynamicBucketingSampler.
+2022-06-18 10:29:50,119 INFO [asr_datamodule.py:238] (0/4) About to create train dataloader
+2022-06-18 10:29:50,120 INFO [asr_datamodule.py:163] (0/4) Enable MUSAN
+2022-06-18 10:29:50,120 INFO [asr_datamodule.py:175] (0/4) Enable SpecAugment
+2022-06-18 10:29:50,120 INFO [asr_datamodule.py:176] (0/4) Time warp factor: 80
+2022-06-18 10:29:50,120 INFO [asr_datamodule.py:188] (0/4) Num frame mask: 10
+2022-06-18 10:29:50,120 INFO [asr_datamodule.py:201] (0/4) About to create train dataset
+2022-06-18 10:29:50,120 INFO [asr_datamodule.py:229] (0/4) Using DynamicBucketingSampler.
+2022-06-18 10:29:52,844 INFO [asr_datamodule.py:238] (0/4) About to create train dataloader
+2022-06-18 10:29:52,845 INFO [aishell.py:45] (0/4) About to get valid cuts from data/fbank/aishell_cuts_dev.jsonl.gz
+2022-06-18 10:29:52,847 INFO [asr_datamodule.py:251] (0/4) About to create dev dataset
+2022-06-18 10:29:53,343 INFO [asr_datamodule.py:270] (0/4) About to create dev dataloader
+2022-06-18 10:29:53,344 INFO [train.py:1171] (0/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
+2022-06-18 10:31:15,519 INFO [train.py:1081] (0/4) start training from epoch 1
+2022-06-18 10:32:07,200 INFO [train.py:874] (0/4) Epoch 1, batch 50, aishell_loss[loss=0.4794, simple_loss=0.9588, pruned_loss=9.154, over 4886.00 frames.], tot_loss[loss=1.355, simple_loss=2.71, pruned_loss=8.721, over 217779.85 frames.], batch size: 42, aishell_tot_loss[loss=0.6725, simple_loss=1.345, pruned_loss=8.845, over 115578.82 frames.], datatang_tot_loss[loss=2.095, simple_loss=4.19, pruned_loss=8.612, over 115852.70 frames.], batch size: 42, lr: 3.00e-03
+2022-06-18 10:32:38,691 INFO [train.py:874] (0/4) Epoch 1, batch 100, datatang_loss[loss=0.3372, simple_loss=0.6744, pruned_loss=8.406, over 4924.00 frames.], tot_loss[loss=0.8214, simple_loss=1.643, pruned_loss=8.702, over 387742.33 frames.], batch size: 37, aishell_tot_loss[loss=0.5371, simple_loss=1.074, pruned_loss=8.989, over 217634.88 frames.], datatang_tot_loss[loss=1.192, simple_loss=2.383, pruned_loss=8.427, over 218422.86 frames.], batch size: 37, lr: 3.00e-03
+2022-06-18 10:33:05,801 INFO [train.py:874] (0/4) Epoch 1, batch 150, aishell_loss[loss=0.3925, simple_loss=0.785, pruned_loss=9.045, over 4966.00 frames.], tot_loss[loss=0.6309, simple_loss=1.262, pruned_loss=8.726, over 520022.36 frames.], batch size: 61, aishell_tot_loss[loss=0.4828, simple_loss=0.9655, pruned_loss=9.024, over 304537.35 frames.], datatang_tot_loss[loss=0.8662, simple_loss=1.732, pruned_loss=8.438, over 312022.76 frames.], batch size: 61, lr: 3.00e-03
+2022-06-18 10:33:37,399 INFO [train.py:874] (0/4) Epoch 1, batch 200, datatang_loss[loss=0.3291, simple_loss=0.6582, pruned_loss=8.261, over 4912.00 frames.], tot_loss[loss=0.5325, simple_loss=1.065, pruned_loss=8.747, over 623126.40 frames.], batch size: 75, aishell_tot_loss[loss=0.4404, simple_loss=0.8808, pruned_loss=9.073, over 396483.88 frames.], datatang_tot_loss[loss=0.73, simple_loss=1.46, pruned_loss=8.39, over 379401.06 frames.], batch size: 75, lr: 3.00e-03
+2022-06-18 10:34:08,979 INFO [train.py:874] (0/4) Epoch 1, batch 250, aishell_loss[loss=0.3572, simple_loss=0.7144, pruned_loss=9.02, over 4939.00 frames.], tot_loss[loss=0.4734, simple_loss=0.9468, pruned_loss=8.713, over 703746.54 frames.], batch size: 58, aishell_tot_loss[loss=0.4198, simple_loss=0.8396, pruned_loss=9.06, over 460732.02 frames.], datatang_tot_loss[loss=0.6216, simple_loss=1.243, pruned_loss=8.369, over 456331.12 frames.], batch size: 58, lr: 3.00e-03
+2022-06-18 10:34:37,082 INFO [train.py:874] (0/4) Epoch 1, batch 300, datatang_loss[loss=0.3097, simple_loss=0.6193, pruned_loss=8.998, over 4926.00 frames.], tot_loss[loss=0.4325, simple_loss=0.8649, pruned_loss=8.752, over 766287.58 frames.], batch size: 77, aishell_tot_loss[loss=0.4042, simple_loss=0.8083, pruned_loss=9.045, over 522424.16 frames.], datatang_tot_loss[loss=0.5519, simple_loss=1.104, pruned_loss=8.443, over 518851.91 frames.], batch size: 77, lr: 3.00e-03
+2022-06-18 10:35:07,032 INFO [train.py:874] (0/4) Epoch 1, batch 350, aishell_loss[loss=0.3521, simple_loss=0.7043, pruned_loss=8.734, over 4915.00 frames.], tot_loss[loss=0.4014, simple_loss=0.8029, pruned_loss=8.803, over 814973.78 frames.], batch size: 33, aishell_tot_loss[loss=0.3923, simple_loss=0.7846, pruned_loss=9.023, over 568646.81 frames.], datatang_tot_loss[loss=0.4947, simple_loss=0.9894, pruned_loss=8.562, over 582141.82 frames.], batch size: 33, lr: 3.00e-03
+2022-06-18 10:35:37,851 INFO [train.py:874] (0/4) Epoch 1, batch 400, aishell_loss[loss=0.3221, simple_loss=0.6441, pruned_loss=9.054, over 4938.00 frames.], tot_loss[loss=0.3775, simple_loss=0.755, pruned_loss=8.812, over 852555.40 frames.], batch size: 33, aishell_tot_loss[loss=0.3789, simple_loss=0.7578, pruned_loss=8.995, over 617462.62 frames.], datatang_tot_loss[loss=0.4578, simple_loss=0.9156, pruned_loss=8.607, over 629691.84 frames.], batch size: 33, lr: 3.00e-03
+2022-06-18 10:36:05,194 INFO [train.py:874] (0/4) Epoch 1, batch 450, datatang_loss[loss=0.2814, simple_loss=0.5629, pruned_loss=8.929, over 4937.00 frames.], tot_loss[loss=0.3609, simple_loss=0.7218, pruned_loss=8.823, over 882262.86 frames.], batch size: 45, aishell_tot_loss[loss=0.3702, simple_loss=0.7403, pruned_loss=8.973, over 657699.62 frames.], datatang_tot_loss[loss=0.4284, simple_loss=0.8568, pruned_loss=8.653, over 674827.86 frames.], batch size: 45, lr: 2.99e-03
+2022-06-18 10:36:36,411 INFO [train.py:874] (0/4) Epoch 1, batch 500, aishell_loss[loss=0.3529, simple_loss=0.7058, pruned_loss=8.657, over 4953.00 frames.], tot_loss[loss=0.3499, simple_loss=0.6997, pruned_loss=8.849, over 905369.42 frames.], batch size: 78, aishell_tot_loss[loss=0.3632, simple_loss=0.7265, pruned_loss=8.958, over 700931.28 frames.], datatang_tot_loss[loss=0.4087, simple_loss=0.8174, pruned_loss=8.701, over 707194.73 frames.], batch size: 78, lr: 2.99e-03
+2022-06-18 10:37:05,651 INFO [train.py:874] (0/4) Epoch 1, batch 550, aishell_loss[loss=0.3429, simple_loss=0.6859, pruned_loss=8.891, over 4946.00 frames.], tot_loss[loss=0.3402, simple_loss=0.6805, pruned_loss=8.859, over 922920.63 frames.], batch size: 68, aishell_tot_loss[loss=0.3573, simple_loss=0.7145, pruned_loss=8.957, over 735408.03 frames.], datatang_tot_loss[loss=0.3908, simple_loss=0.7816, pruned_loss=8.72, over 738803.98 frames.], batch size: 68, lr: 2.99e-03
+2022-06-18 10:37:35,025 INFO [train.py:874] (0/4) Epoch 1, batch 600, datatang_loss[loss=0.3018, simple_loss=0.6036, pruned_loss=8.925, over 4961.00 frames.], tot_loss[loss=0.3331, simple_loss=0.6662, pruned_loss=8.888, over 936789.06 frames.], batch size: 67, aishell_tot_loss[loss=0.3532, simple_loss=0.7064, pruned_loss=8.949, over 761392.49 frames.], datatang_tot_loss[loss=0.3748, simple_loss=0.7497, pruned_loss=8.775, over 771244.92 frames.], batch size: 67, lr: 2.99e-03
+2022-06-18 10:38:06,369 INFO [train.py:874] (0/4) Epoch 1, batch 650, aishell_loss[loss=0.3392, simple_loss=0.6784, pruned_loss=8.986, over 4886.00 frames.], tot_loss[loss=0.3267, simple_loss=0.6535, pruned_loss=8.893, over 947559.79 frames.], batch size: 42, aishell_tot_loss[loss=0.3476, simple_loss=0.6953, pruned_loss=8.94, over 790717.15 frames.], datatang_tot_loss[loss=0.3636, simple_loss=0.7273, pruned_loss=8.795, over 793596.03 frames.], batch size: 42, lr: 2.99e-03
+2022-06-18 10:38:35,063 INFO [train.py:874] (0/4) Epoch 1, batch 700, datatang_loss[loss=0.2816, simple_loss=0.5632, pruned_loss=9, over 4919.00 frames.], tot_loss[loss=0.3205, simple_loss=0.641, pruned_loss=8.902, over 956235.47 frames.], batch size: 57, aishell_tot_loss[loss=0.3427, simple_loss=0.6854, pruned_loss=8.933, over 812958.60 frames.], datatang_tot_loss[loss=0.3526, simple_loss=0.7051, pruned_loss=8.82, over 817174.39 frames.], batch size: 57, lr: 2.99e-03
+2022-06-18 10:39:03,380 INFO [train.py:874] (0/4) Epoch 1, batch 750, aishell_loss[loss=0.321, simple_loss=0.6419, pruned_loss=9.058, over 4946.00 frames.], tot_loss[loss=0.3157, simple_loss=0.6313, pruned_loss=8.917, over 963017.60 frames.], batch size: 40, aishell_tot_loss[loss=0.3391, simple_loss=0.6781, pruned_loss=8.938, over 832699.85 frames.], datatang_tot_loss[loss=0.3427, simple_loss=0.6853, pruned_loss=8.842, over 837868.01 frames.], batch size: 40, lr: 2.98e-03
+2022-06-18 10:39:33,891 INFO [train.py:874] (0/4) Epoch 1, batch 800, datatang_loss[loss=0.273, simple_loss=0.5459, pruned_loss=9.068, over 4933.00 frames.], tot_loss[loss=0.3093, simple_loss=0.6185, pruned_loss=8.921, over 968017.95 frames.], batch size: 73, aishell_tot_loss[loss=0.3358, simple_loss=0.6716, pruned_loss=8.93, over 846555.87 frames.], datatang_tot_loss[loss=0.3315, simple_loss=0.6629, pruned_loss=8.863, over 859159.74 frames.], batch size: 73, lr: 2.98e-03
+2022-06-18 10:40:05,970 INFO [train.py:874] (0/4) Epoch 1, batch 850, datatang_loss[loss=0.2704, simple_loss=0.5408, pruned_loss=9.013, over 4928.00 frames.], tot_loss[loss=0.3052, simple_loss=0.6103, pruned_loss=8.928, over 971864.40 frames.], batch size: 79, aishell_tot_loss[loss=0.3329, simple_loss=0.6658, pruned_loss=8.928, over 860411.95 frames.], datatang_tot_loss[loss=0.3232, simple_loss=0.6464, pruned_loss=8.882, over 876246.02 frames.], batch size: 79, lr: 2.98e-03
+2022-06-18 10:40:34,178 INFO [train.py:874] (0/4) Epoch 1, batch 900, datatang_loss[loss=0.2844, simple_loss=0.5688, pruned_loss=9.052, over 4957.00 frames.], tot_loss[loss=0.3022, simple_loss=0.6044, pruned_loss=8.94, over 975350.42 frames.], batch size: 55, aishell_tot_loss[loss=0.3294, simple_loss=0.6588, pruned_loss=8.926, over 875906.31 frames.], datatang_tot_loss[loss=0.3171, simple_loss=0.6343, pruned_loss=8.903, over 888920.93 frames.], batch size: 55, lr: 2.98e-03
+2022-06-18 10:41:04,976 INFO [train.py:874] (0/4) Epoch 1, batch 950, datatang_loss[loss=0.2758, simple_loss=0.5516, pruned_loss=9.103, over 4947.00 frames.], tot_loss[loss=0.2971, simple_loss=0.5942, pruned_loss=8.952, over 977539.05 frames.], batch size: 67, aishell_tot_loss[loss=0.3268, simple_loss=0.6536, pruned_loss=8.93, over 884834.97 frames.], datatang_tot_loss[loss=0.309, simple_loss=0.618, pruned_loss=8.921, over 903613.96 frames.], batch size: 67, lr: 2.97e-03
+2022-06-18 10:41:37,346 INFO [train.py:874] (0/4) Epoch 1, batch 1000, datatang_loss[loss=0.2596, simple_loss=0.5191, pruned_loss=9.005, over 4963.00 frames.], tot_loss[loss=0.2938, simple_loss=0.5876, pruned_loss=8.968, over 978988.92 frames.], batch size: 26, aishell_tot_loss[loss=0.3228, simple_loss=0.6456, pruned_loss=8.933, over 896334.31 frames.], datatang_tot_loss[loss=0.3038, simple_loss=0.6076, pruned_loss=8.943, over 913278.40 frames.], batch size: 26, lr: 2.97e-03
+2022-06-18 10:41:37,348 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 10:41:53,017 INFO [train.py:914] (0/4) Epoch 1, validation: loss=9.395, simple_loss=0.6242, pruned_loss=9.083, over 1622729.00 frames.
+2022-06-18 10:42:24,213 INFO [train.py:874] (0/4) Epoch 1, batch 1050, aishell_loss[loss=0.266, simple_loss=0.5319, pruned_loss=9.215, over 4921.00 frames.], tot_loss[loss=0.2896, simple_loss=0.5792, pruned_loss=8.989, over 979918.62 frames.], batch size: 49, aishell_tot_loss[loss=0.3178, simple_loss=0.6357, pruned_loss=8.942, over 907613.68 frames.], datatang_tot_loss[loss=0.2988, simple_loss=0.5976, pruned_loss=8.965, over 920669.19 frames.], batch size: 49, lr: 2.97e-03
+2022-06-18 10:42:52,486 INFO [train.py:874] (0/4) Epoch 1, batch 1100, datatang_loss[loss=0.2663, simple_loss=0.5326, pruned_loss=9.245, over 4933.00 frames.], tot_loss[loss=0.2839, simple_loss=0.5678, pruned_loss=9.013, over 981921.59 frames.], batch size: 73, aishell_tot_loss[loss=0.3125, simple_loss=0.625, pruned_loss=8.949, over 917349.76 frames.], datatang_tot_loss[loss=0.293, simple_loss=0.586, pruned_loss=8.995, over 928539.11 frames.], batch size: 73, lr: 2.96e-03
+2022-06-18 10:43:24,183 INFO [train.py:874] (0/4) Epoch 1, batch 1150, aishell_loss[loss=0.284, simple_loss=0.5679, pruned_loss=8.988, over 4941.00 frames.], tot_loss[loss=0.2791, simple_loss=0.5583, pruned_loss=9.03, over 983158.24 frames.], batch size: 54, aishell_tot_loss[loss=0.308, simple_loss=0.6161, pruned_loss=8.953, over 925131.51 frames.], datatang_tot_loss[loss=0.2876, simple_loss=0.5751, pruned_loss=9.019, over 935866.85 frames.], batch size: 54, lr: 2.96e-03
+2022-06-18 10:43:56,681 INFO [train.py:874] (0/4) Epoch 1, batch 1200, aishell_loss[loss=0.2878, simple_loss=0.5757, pruned_loss=9.078, over 4927.00 frames.], tot_loss[loss=0.2764, simple_loss=0.5527, pruned_loss=9.043, over 983563.08 frames.], batch size: 46, aishell_tot_loss[loss=0.3037, simple_loss=0.6074, pruned_loss=8.96, over 932924.13 frames.], datatang_tot_loss[loss=0.284, simple_loss=0.5679, pruned_loss=9.039, over 941045.54 frames.], batch size: 46, lr: 2.96e-03
+2022-06-18 10:44:24,360 INFO [train.py:874] (0/4) Epoch 1, batch 1250, datatang_loss[loss=0.241, simple_loss=0.482, pruned_loss=9.242, over 4935.00 frames.], tot_loss[loss=0.2717, simple_loss=0.5435, pruned_loss=9.062, over 984230.72 frames.], batch size: 79, aishell_tot_loss[loss=0.2987, simple_loss=0.5975, pruned_loss=8.968, over 939052.89 frames.], datatang_tot_loss[loss=0.2796, simple_loss=0.5592, pruned_loss=9.061, over 946556.92 frames.], batch size: 79, lr: 2.95e-03
+2022-06-18 10:44:55,913 INFO [train.py:874] (0/4) Epoch 1, batch 1300, datatang_loss[loss=0.2622, simple_loss=0.5244, pruned_loss=9.403, over 4955.00 frames.], tot_loss[loss=0.266, simple_loss=0.532, pruned_loss=9.075, over 984825.69 frames.], batch size: 99, aishell_tot_loss[loss=0.2929, simple_loss=0.5857, pruned_loss=8.969, over 944817.82 frames.], datatang_tot_loss[loss=0.2748, simple_loss=0.5496, pruned_loss=9.087, over 951222.04 frames.], batch size: 99, lr: 2.95e-03
+2022-06-18 10:45:26,699 INFO [train.py:874] (0/4) Epoch 1, batch 1350, aishell_loss[loss=0.2417, simple_loss=0.4834, pruned_loss=8.986, over 4887.00 frames.], tot_loss[loss=0.2597, simple_loss=0.5194, pruned_loss=9.08, over 985023.74 frames.], batch size: 50, aishell_tot_loss[loss=0.2864, simple_loss=0.5728, pruned_loss=8.97, over 949977.57 frames.], datatang_tot_loss[loss=0.2698, simple_loss=0.5397, pruned_loss=9.105, over 954992.40 frames.], batch size: 50, lr: 2.95e-03
+2022-06-18 10:45:54,718 INFO [train.py:874] (0/4) Epoch 1, batch 1400, aishell_loss[loss=0.1899, simple_loss=0.3799, pruned_loss=8.896, over 4924.00 frames.], tot_loss[loss=0.2541, simple_loss=0.5083, pruned_loss=9.09, over 985489.17 frames.], batch size: 21, aishell_tot_loss[loss=0.2806, simple_loss=0.5612, pruned_loss=8.972, over 954260.92 frames.], datatang_tot_loss[loss=0.2651, simple_loss=0.5302, pruned_loss=9.124, over 958871.69 frames.], batch size: 21, lr: 2.94e-03
+2022-06-18 10:46:26,251 INFO [train.py:874] (0/4) Epoch 1, batch 1450, aishell_loss[loss=0.238, simple_loss=0.4759, pruned_loss=8.992, over 4905.00 frames.], tot_loss[loss=0.2483, simple_loss=0.4967, pruned_loss=9.095, over 985206.74 frames.], batch size: 52, aishell_tot_loss[loss=0.275, simple_loss=0.5501, pruned_loss=8.973, over 957661.63 frames.], datatang_tot_loss[loss=0.2599, simple_loss=0.5197, pruned_loss=9.138, over 961974.87 frames.], batch size: 52, lr: 2.94e-03
+2022-06-18 10:46:58,307 INFO [train.py:874] (0/4) Epoch 1, batch 1500, datatang_loss[loss=0.2533, simple_loss=0.5066, pruned_loss=9.479, over 4918.00 frames.], tot_loss[loss=0.2438, simple_loss=0.4876, pruned_loss=9.098, over 985037.66 frames.], batch size: 108, aishell_tot_loss[loss=0.2699, simple_loss=0.5398, pruned_loss=8.969, over 960591.65 frames.], datatang_tot_loss[loss=0.2556, simple_loss=0.5112, pruned_loss=9.153, over 964803.76 frames.], batch size: 108, lr: 2.94e-03
+2022-06-18 10:47:26,503 INFO [train.py:874] (0/4) Epoch 1, batch 1550, datatang_loss[loss=0.2233, simple_loss=0.4467, pruned_loss=9.279, over 4928.00 frames.], tot_loss[loss=0.2388, simple_loss=0.4775, pruned_loss=9.098, over 984629.67 frames.], batch size: 79, aishell_tot_loss[loss=0.2654, simple_loss=0.5308, pruned_loss=8.964, over 962893.01 frames.], datatang_tot_loss[loss=0.2503, simple_loss=0.5005, pruned_loss=9.164, over 967291.46 frames.], batch size: 79, lr: 2.93e-03
+2022-06-18 10:47:58,711 INFO [train.py:874] (0/4) Epoch 1, batch 1600, aishell_loss[loss=0.2277, simple_loss=0.4554, pruned_loss=8.926, over 4939.00 frames.], tot_loss[loss=0.2334, simple_loss=0.4668, pruned_loss=9.105, over 984932.25 frames.], batch size: 49, aishell_tot_loss[loss=0.2605, simple_loss=0.5211, pruned_loss=8.965, over 965265.09 frames.], datatang_tot_loss[loss=0.2449, simple_loss=0.4898, pruned_loss=9.176, over 969775.60 frames.], batch size: 49, lr: 2.93e-03
+2022-06-18 10:48:31,037 INFO [train.py:874] (0/4) Epoch 1, batch 1650, aishell_loss[loss=0.2165, simple_loss=0.4331, pruned_loss=9.017, over 4935.00 frames.], tot_loss[loss=0.2298, simple_loss=0.4597, pruned_loss=9.094, over 985275.46 frames.], batch size: 54, aishell_tot_loss[loss=0.2555, simple_loss=0.5111, pruned_loss=8.957, over 967731.48 frames.], datatang_tot_loss[loss=0.2414, simple_loss=0.4828, pruned_loss=9.183, over 971815.48 frames.], batch size: 54, lr: 2.92e-03
+2022-06-18 10:48:59,730 INFO [train.py:874] (0/4) Epoch 1, batch 1700, aishell_loss[loss=0.2102, simple_loss=0.4205, pruned_loss=8.776, over 4833.00 frames.], tot_loss[loss=0.2249, simple_loss=0.4499, pruned_loss=9.09, over 985019.23 frames.], batch size: 29, aishell_tot_loss[loss=0.2499, simple_loss=0.4998, pruned_loss=8.951, over 969210.97 frames.], datatang_tot_loss[loss=0.2373, simple_loss=0.4746, pruned_loss=9.191, over 973733.95 frames.], batch size: 29, lr: 2.92e-03
+2022-06-18 10:49:32,477 INFO [train.py:874] (0/4) Epoch 1, batch 1750, aishell_loss[loss=0.2197, simple_loss=0.4393, pruned_loss=8.903, over 4908.00 frames.], tot_loss[loss=0.2214, simple_loss=0.4428, pruned_loss=9.098, over 984587.01 frames.], batch size: 52, aishell_tot_loss[loss=0.2465, simple_loss=0.493, pruned_loss=8.947, over 970192.85 frames.], datatang_tot_loss[loss=0.2328, simple_loss=0.4656, pruned_loss=9.201, over 975341.21 frames.], batch size: 52, lr: 2.91e-03
+2022-06-18 10:50:05,546 INFO [train.py:874] (0/4) Epoch 1, batch 1800, datatang_loss[loss=0.207, simple_loss=0.4141, pruned_loss=9.254, over 4950.00 frames.], tot_loss[loss=0.217, simple_loss=0.4341, pruned_loss=9.092, over 984545.13 frames.], batch size: 91, aishell_tot_loss[loss=0.2415, simple_loss=0.483, pruned_loss=8.939, over 971860.84 frames.], datatang_tot_loss[loss=0.2288, simple_loss=0.4577, pruned_loss=9.208, over 976430.97 frames.], batch size: 91, lr: 2.91e-03
+2022-06-18 10:50:33,703 INFO [train.py:874] (0/4) Epoch 1, batch 1850, aishell_loss[loss=0.2126, simple_loss=0.4253, pruned_loss=8.921, over 4944.00 frames.], tot_loss[loss=0.2131, simple_loss=0.4262, pruned_loss=9.088, over 984848.15 frames.], batch size: 64, aishell_tot_loss[loss=0.2375, simple_loss=0.4749, pruned_loss=8.935, over 973268.44 frames.], datatang_tot_loss[loss=0.2246, simple_loss=0.4492, pruned_loss=9.208, over 977707.02 frames.], batch size: 64, lr: 2.91e-03
+2022-06-18 10:51:04,883 INFO [train.py:874] (0/4) Epoch 1, batch 1900, aishell_loss[loss=0.1994, simple_loss=0.3987, pruned_loss=8.86, over 4949.00 frames.], tot_loss[loss=0.211, simple_loss=0.422, pruned_loss=9.083, over 985249.86 frames.], batch size: 40, aishell_tot_loss[loss=0.2341, simple_loss=0.4681, pruned_loss=8.934, over 974917.40 frames.], datatang_tot_loss[loss=0.2215, simple_loss=0.4429, pruned_loss=9.208, over 978698.05 frames.], batch size: 40, lr: 2.90e-03
+2022-06-18 10:51:36,222 INFO [train.py:874] (0/4) Epoch 1, batch 1950, datatang_loss[loss=0.1843, simple_loss=0.3686, pruned_loss=9.186, over 4959.00 frames.], tot_loss[loss=0.209, simple_loss=0.4181, pruned_loss=9.083, over 985334.40 frames.], batch size: 45, aishell_tot_loss[loss=0.2315, simple_loss=0.4631, pruned_loss=8.935, over 976288.39 frames.], datatang_tot_loss[loss=0.218, simple_loss=0.436, pruned_loss=9.208, over 979382.14 frames.], batch size: 45, lr: 2.90e-03
+2022-06-18 10:52:04,183 INFO [train.py:874] (0/4) Epoch 1, batch 2000, aishell_loss[loss=0.1853, simple_loss=0.3706, pruned_loss=8.898, over 4939.00 frames.], tot_loss[loss=0.2047, simple_loss=0.4093, pruned_loss=9.075, over 985460.37 frames.], batch size: 54, aishell_tot_loss[loss=0.2267, simple_loss=0.4533, pruned_loss=8.93, over 977599.14 frames.], datatang_tot_loss[loss=0.2145, simple_loss=0.4289, pruned_loss=9.207, over 979974.13 frames.], batch size: 54, lr: 2.89e-03
+2022-06-18 10:52:04,185 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 10:52:20,041 INFO [train.py:914] (0/4) Epoch 1, validation: loss=8.987, simple_loss=0.3601, pruned_loss=8.807, over 1622729.00 frames.
+2022-06-18 10:52:48,751 INFO [train.py:874] (0/4) Epoch 1, batch 2050, datatang_loss[loss=0.1908, simple_loss=0.3816, pruned_loss=9.234, over 4920.00 frames.], tot_loss[loss=0.202, simple_loss=0.404, pruned_loss=9.07, over 985495.54 frames.], batch size: 81, aishell_tot_loss[loss=0.2235, simple_loss=0.4471, pruned_loss=8.924, over 978232.00 frames.], datatang_tot_loss[loss=0.2112, simple_loss=0.4224, pruned_loss=9.203, over 980913.83 frames.], batch size: 81, lr: 2.89e-03
+2022-06-18 10:53:19,699 INFO [train.py:874] (0/4) Epoch 1, batch 2100, datatang_loss[loss=0.1809, simple_loss=0.3617, pruned_loss=9.186, over 4918.00 frames.], tot_loss[loss=0.2002, simple_loss=0.4004, pruned_loss=9.064, over 985833.96 frames.], batch size: 75, aishell_tot_loss[loss=0.2202, simple_loss=0.4405, pruned_loss=8.922, over 979284.64 frames.], datatang_tot_loss[loss=0.2089, simple_loss=0.4179, pruned_loss=9.201, over 981649.84 frames.], batch size: 75, lr: 2.88e-03
+2022-06-18 10:53:51,102 INFO [train.py:874] (0/4) Epoch 1, batch 2150, aishell_loss[loss=0.2146, simple_loss=0.4292, pruned_loss=8.896, over 4963.00 frames.], tot_loss[loss=0.1988, simple_loss=0.3975, pruned_loss=9.059, over 985991.04 frames.], batch size: 39, aishell_tot_loss[loss=0.2179, simple_loss=0.4358, pruned_loss=8.914, over 980000.52 frames.], datatang_tot_loss[loss=0.2064, simple_loss=0.4129, pruned_loss=9.2, over 982320.25 frames.], batch size: 39, lr: 2.88e-03
+2022-06-18 10:54:18,815 INFO [train.py:874] (0/4) Epoch 1, batch 2200, aishell_loss[loss=0.1878, simple_loss=0.3756, pruned_loss=8.918, over 4928.00 frames.], tot_loss[loss=0.1973, simple_loss=0.3947, pruned_loss=9.053, over 985841.80 frames.], batch size: 49, aishell_tot_loss[loss=0.2148, simple_loss=0.4297, pruned_loss=8.909, over 980589.81 frames.], datatang_tot_loss[loss=0.2048, simple_loss=0.4097, pruned_loss=9.2, over 982717.12 frames.], batch size: 49, lr: 2.87e-03
+2022-06-18 10:54:50,317 INFO [train.py:874] (0/4) Epoch 1, batch 2250, aishell_loss[loss=0.1931, simple_loss=0.3862, pruned_loss=8.929, over 4932.00 frames.], tot_loss[loss=0.1962, simple_loss=0.3924, pruned_loss=9.037, over 985810.97 frames.], batch size: 52, aishell_tot_loss[loss=0.2119, simple_loss=0.4239, pruned_loss=8.905, over 981153.63 frames.], datatang_tot_loss[loss=0.2032, simple_loss=0.4064, pruned_loss=9.198, over 983202.43 frames.], batch size: 52, lr: 2.86e-03
+2022-06-18 10:55:21,933 INFO [train.py:874] (0/4) Epoch 1, batch 2300, aishell_loss[loss=0.197, simple_loss=0.394, pruned_loss=8.901, over 4973.00 frames.], tot_loss[loss=0.1938, simple_loss=0.3875, pruned_loss=9.029, over 985835.48 frames.], batch size: 61, aishell_tot_loss[loss=0.2091, simple_loss=0.4181, pruned_loss=8.896, over 981488.29 frames.], datatang_tot_loss[loss=0.2007, simple_loss=0.4015, pruned_loss=9.196, over 983749.72 frames.], batch size: 61, lr: 2.86e-03
+2022-06-18 10:55:50,103 INFO [train.py:874] (0/4) Epoch 1, batch 2350, aishell_loss[loss=0.1822, simple_loss=0.3644, pruned_loss=8.884, over 4969.00 frames.], tot_loss[loss=0.1911, simple_loss=0.3822, pruned_loss=9.028, over 985930.60 frames.], batch size: 44, aishell_tot_loss[loss=0.2066, simple_loss=0.4132, pruned_loss=8.89, over 982063.64 frames.], datatang_tot_loss[loss=0.1979, simple_loss=0.3957, pruned_loss=9.192, over 983986.06 frames.], batch size: 44, lr: 2.85e-03
+2022-06-18 10:56:22,039 INFO [train.py:874] (0/4) Epoch 1, batch 2400, datatang_loss[loss=0.1911, simple_loss=0.3822, pruned_loss=9.164, over 4967.00 frames.], tot_loss[loss=0.1894, simple_loss=0.3788, pruned_loss=9.021, over 986021.53 frames.], batch size: 60, aishell_tot_loss[loss=0.2034, simple_loss=0.4068, pruned_loss=8.882, over 982454.98 frames.], datatang_tot_loss[loss=0.1965, simple_loss=0.393, pruned_loss=9.192, over 984373.84 frames.], batch size: 60, lr: 2.85e-03
+2022-06-18 10:56:52,217 INFO [train.py:874] (0/4) Epoch 1, batch 2450, datatang_loss[loss=0.1649, simple_loss=0.3299, pruned_loss=9.124, over 4925.00 frames.], tot_loss[loss=0.1887, simple_loss=0.3774, pruned_loss=9.016, over 986081.51 frames.], batch size: 73, aishell_tot_loss[loss=0.2012, simple_loss=0.4024, pruned_loss=8.878, over 982940.16 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.3907, pruned_loss=9.192, over 984601.66 frames.], batch size: 73, lr: 2.84e-03
+2022-06-18 10:57:21,760 INFO [train.py:874] (0/4) Epoch 1, batch 2500, datatang_loss[loss=0.1781, simple_loss=0.3563, pruned_loss=9.204, over 4866.00 frames.], tot_loss[loss=0.1866, simple_loss=0.3733, pruned_loss=9.017, over 986026.46 frames.], batch size: 30, aishell_tot_loss[loss=0.199, simple_loss=0.3981, pruned_loss=8.874, over 983307.12 frames.], datatang_tot_loss[loss=0.1932, simple_loss=0.3864, pruned_loss=9.188, over 984683.43 frames.], batch size: 30, lr: 2.84e-03
+2022-06-18 10:57:53,540 INFO [train.py:874] (0/4) Epoch 1, batch 2550, aishell_loss[loss=0.2001, simple_loss=0.4002, pruned_loss=8.876, over 4919.00 frames.], tot_loss[loss=0.1845, simple_loss=0.3691, pruned_loss=9.011, over 986169.87 frames.], batch size: 46, aishell_tot_loss[loss=0.1964, simple_loss=0.3928, pruned_loss=8.87, over 983795.22 frames.], datatang_tot_loss[loss=0.1913, simple_loss=0.3825, pruned_loss=9.188, over 984846.63 frames.], batch size: 46, lr: 2.83e-03
+2022-06-18 10:58:22,777 INFO [train.py:874] (0/4) Epoch 1, batch 2600, datatang_loss[loss=0.1464, simple_loss=0.2929, pruned_loss=9.033, over 4980.00 frames.], tot_loss[loss=0.1832, simple_loss=0.3664, pruned_loss=9.006, over 986400.21 frames.], batch size: 40, aishell_tot_loss[loss=0.194, simple_loss=0.3881, pruned_loss=8.861, over 984041.12 frames.], datatang_tot_loss[loss=0.19, simple_loss=0.3799, pruned_loss=9.189, over 985286.90 frames.], batch size: 40, lr: 2.83e-03
+2022-06-18 10:58:52,576 INFO [train.py:874] (0/4) Epoch 1, batch 2650, datatang_loss[loss=0.188, simple_loss=0.3761, pruned_loss=9.145, over 4918.00 frames.], tot_loss[loss=0.1832, simple_loss=0.3663, pruned_loss=9.013, over 986107.31 frames.], batch size: 83, aishell_tot_loss[loss=0.1933, simple_loss=0.3866, pruned_loss=8.856, over 984119.99 frames.], datatang_tot_loss[loss=0.1887, simple_loss=0.3774, pruned_loss=9.189, over 985273.08 frames.], batch size: 83, lr: 2.82e-03
+2022-06-18 10:59:24,226 INFO [train.py:874] (0/4) Epoch 1, batch 2700, aishell_loss[loss=0.1685, simple_loss=0.3369, pruned_loss=8.773, over 4949.00 frames.], tot_loss[loss=0.1818, simple_loss=0.3636, pruned_loss=9.015, over 985865.73 frames.], batch size: 27, aishell_tot_loss[loss=0.1916, simple_loss=0.3832, pruned_loss=8.85, over 984127.45 frames.], datatang_tot_loss[loss=0.1871, simple_loss=0.3742, pruned_loss=9.194, over 985335.99 frames.], batch size: 27, lr: 2.81e-03
+2022-06-18 10:59:52,385 INFO [train.py:874] (0/4) Epoch 1, batch 2750, aishell_loss[loss=0.1766, simple_loss=0.3531, pruned_loss=8.825, over 4928.00 frames.], tot_loss[loss=0.1811, simple_loss=0.3621, pruned_loss=9.005, over 985872.18 frames.], batch size: 45, aishell_tot_loss[loss=0.1903, simple_loss=0.3806, pruned_loss=8.846, over 984503.62 frames.], datatang_tot_loss[loss=0.1858, simple_loss=0.3717, pruned_loss=9.19, over 985236.47 frames.], batch size: 45, lr: 2.81e-03
+2022-06-18 11:00:23,348 INFO [train.py:874] (0/4) Epoch 1, batch 2800, aishell_loss[loss=0.1935, simple_loss=0.387, pruned_loss=8.81, over 4972.00 frames.], tot_loss[loss=0.1803, simple_loss=0.3606, pruned_loss=8.996, over 986099.91 frames.], batch size: 44, aishell_tot_loss[loss=0.1889, simple_loss=0.3778, pruned_loss=8.84, over 984799.02 frames.], datatang_tot_loss[loss=0.1847, simple_loss=0.3694, pruned_loss=9.185, over 985416.48 frames.], batch size: 44, lr: 2.80e-03
+2022-06-18 11:00:54,637 INFO [train.py:874] (0/4) Epoch 1, batch 2850, aishell_loss[loss=0.1959, simple_loss=0.3918, pruned_loss=8.836, over 4962.00 frames.], tot_loss[loss=0.1784, simple_loss=0.3568, pruned_loss=8.985, over 986038.72 frames.], batch size: 64, aishell_tot_loss[loss=0.1863, simple_loss=0.3727, pruned_loss=8.829, over 984912.82 frames.], datatang_tot_loss[loss=0.1836, simple_loss=0.3673, pruned_loss=9.187, over 985482.74 frames.], batch size: 64, lr: 2.80e-03
+2022-06-18 11:01:22,656 INFO [train.py:874] (0/4) Epoch 1, batch 2900, datatang_loss[loss=0.1807, simple_loss=0.3615, pruned_loss=9.132, over 4954.00 frames.], tot_loss[loss=0.1777, simple_loss=0.3554, pruned_loss=8.989, over 986061.81 frames.], batch size: 45, aishell_tot_loss[loss=0.1856, simple_loss=0.3712, pruned_loss=8.824, over 985096.40 frames.], datatang_tot_loss[loss=0.1821, simple_loss=0.3642, pruned_loss=9.188, over 985511.82 frames.], batch size: 45, lr: 2.79e-03
+2022-06-18 11:01:53,458 INFO [train.py:874] (0/4) Epoch 1, batch 2950, datatang_loss[loss=0.2132, simple_loss=0.4263, pruned_loss=9.079, over 4951.00 frames.], tot_loss[loss=0.1761, simple_loss=0.3522, pruned_loss=8.99, over 985296.52 frames.], batch size: 91, aishell_tot_loss[loss=0.1842, simple_loss=0.3683, pruned_loss=8.817, over 984690.52 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.3609, pruned_loss=9.186, over 985262.27 frames.], batch size: 91, lr: 2.78e-03
+2022-06-18 11:02:24,826 INFO [train.py:874] (0/4) Epoch 1, batch 3000, aishell_loss[loss=8.946, simple_loss=0.3209, pruned_loss=8.786, over 4821.00 frames.], tot_loss[loss=0.2185, simple_loss=0.351, pruned_loss=8.991, over 985193.93 frames.], batch size: 29, aishell_tot_loss[loss=0.226, simple_loss=0.366, pruned_loss=8.813, over 984549.47 frames.], datatang_tot_loss[loss=0.1795, simple_loss=0.359, pruned_loss=9.187, over 985366.78 frames.], batch size: 29, lr: 2.78e-03
+2022-06-18 11:02:24,828 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 11:02:40,211 INFO [train.py:914] (0/4) Epoch 1, validation: loss=4.481, simple_loss=0.3323, pruned_loss=4.315, over 1622729.00 frames.
+2022-06-18 11:03:11,372 INFO [train.py:874] (0/4) Epoch 1, batch 3050, aishell_loss[loss=0.2602, simple_loss=0.3502, pruned_loss=0.851, over 4946.00 frames.], tot_loss[loss=0.2431, simple_loss=0.3651, pruned_loss=7.267, over 985519.57 frames.], batch size: 56, aishell_tot_loss[loss=0.2371, simple_loss=0.3734, pruned_loss=7.714, over 984993.55 frames.], datatang_tot_loss[loss=0.198, simple_loss=0.3641, pruned_loss=8.47, over 985338.86 frames.], batch size: 56, lr: 2.77e-03
+2022-06-18 11:03:41,944 INFO [train.py:874] (0/4) Epoch 1, batch 3100, datatang_loss[loss=0.243, simple_loss=0.3593, pruned_loss=0.6329, over 4969.00 frames.], tot_loss[loss=0.2441, simple_loss=0.3615, pruned_loss=5.816, over 985758.77 frames.], batch size: 45, aishell_tot_loss[loss=0.2381, simple_loss=0.3703, pruned_loss=7.018, over 985130.04 frames.], datatang_tot_loss[loss=0.2047, simple_loss=0.3625, pruned_loss=7.42, over 985521.73 frames.], batch size: 45, lr: 2.77e-03
+2022-06-18 11:04:10,467 INFO [train.py:874] (0/4) Epoch 1, batch 3150, aishell_loss[loss=0.2288, simple_loss=0.3504, pruned_loss=0.5356, over 4959.00 frames.], tot_loss[loss=0.241, simple_loss=0.3585, pruned_loss=4.651, over 985902.91 frames.], batch size: 64, aishell_tot_loss[loss=0.2374, simple_loss=0.3674, pruned_loss=6.174, over 985374.78 frames.], datatang_tot_loss[loss=0.2071, simple_loss=0.361, pruned_loss=6.7, over 985538.85 frames.], batch size: 64, lr: 2.76e-03
+2022-06-18 11:04:42,052 INFO [train.py:874] (0/4) Epoch 1, batch 3200, datatang_loss[loss=0.1818, simple_loss=0.3002, pruned_loss=0.3173, over 4964.00 frames.], tot_loss[loss=0.2344, simple_loss=0.354, pruned_loss=3.715, over 985483.98 frames.], batch size: 60, aishell_tot_loss[loss=0.2352, simple_loss=0.3649, pruned_loss=5.577, over 985079.67 frames.], datatang_tot_loss[loss=0.2072, simple_loss=0.3575, pruned_loss=5.876, over 985492.44 frames.], batch size: 60, lr: 2.75e-03
+2022-06-18 11:05:11,784 INFO [train.py:874] (0/4) Epoch 1, batch 3250, datatang_loss[loss=0.1719, simple_loss=0.2933, pruned_loss=0.2526, over 4923.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3518, pruned_loss=2.973, over 985256.40 frames.], batch size: 71, aishell_tot_loss[loss=0.2327, simple_loss=0.3627, pruned_loss=5.061, over 984668.83 frames.], datatang_tot_loss[loss=0.2073, simple_loss=0.3559, pruned_loss=5.128, over 985681.52 frames.], batch size: 71, lr: 2.75e-03
+2022-06-18 11:05:40,546 INFO [train.py:874] (0/4) Epoch 1, batch 3300, datatang_loss[loss=0.219, simple_loss=0.3779, pruned_loss=0.3006, over 4956.00 frames.], tot_loss[loss=0.2233, simple_loss=0.3496, pruned_loss=2.389, over 984858.54 frames.], batch size: 99, aishell_tot_loss[loss=0.2304, simple_loss=0.3611, pruned_loss=4.553, over 984197.33 frames.], datatang_tot_loss[loss=0.2061, simple_loss=0.3534, pruned_loss=4.517, over 985746.24 frames.], batch size: 99, lr: 2.74e-03
+2022-06-18 11:06:11,163 INFO [train.py:874] (0/4) Epoch 1, batch 3350, datatang_loss[loss=0.1863, simple_loss=0.3243, pruned_loss=0.2418, over 4922.00 frames.], tot_loss[loss=0.2177, simple_loss=0.3469, pruned_loss=1.924, over 984790.63 frames.], batch size: 81, aishell_tot_loss[loss=0.2262, simple_loss=0.3581, pruned_loss=3.995, over 984247.28 frames.], datatang_tot_loss[loss=0.2052, simple_loss=0.3517, pruned_loss=4.076, over 985621.30 frames.], batch size: 81, lr: 2.73e-03
+2022-06-18 11:06:40,824 INFO [train.py:874] (0/4) Epoch 1, batch 3400, datatang_loss[loss=0.2241, simple_loss=0.3864, pruned_loss=0.3089, over 4918.00 frames.], tot_loss[loss=0.2133, simple_loss=0.346, pruned_loss=1.556, over 985114.22 frames.], batch size: 98, aishell_tot_loss[loss=0.223, simple_loss=0.3566, pruned_loss=3.523, over 984501.10 frames.], datatang_tot_loss[loss=0.2041, simple_loss=0.3502, pruned_loss=3.66, over 985699.04 frames.], batch size: 98, lr: 2.73e-03
+2022-06-18 11:07:10,275 INFO [train.py:874] (0/4) Epoch 1, batch 3450, aishell_loss[loss=0.1881, simple_loss=0.3234, pruned_loss=0.2635, over 4987.00 frames.], tot_loss[loss=0.2103, simple_loss=0.3457, pruned_loss=1.272, over 985180.74 frames.], batch size: 30, aishell_tot_loss[loss=0.2193, simple_loss=0.3547, pruned_loss=3.079, over 984675.77 frames.], datatang_tot_loss[loss=0.2042, simple_loss=0.3499, pruned_loss=3.326, over 985612.57 frames.], batch size: 30, lr: 2.72e-03
+2022-06-18 11:07:41,933 INFO [train.py:874] (0/4) Epoch 1, batch 3500, aishell_loss[loss=0.1657, simple_loss=0.2934, pruned_loss=0.1903, over 4924.00 frames.], tot_loss[loss=0.207, simple_loss=0.3445, pruned_loss=1.046, over 985215.01 frames.], batch size: 32, aishell_tot_loss[loss=0.217, simple_loss=0.3537, pruned_loss=2.772, over 984736.53 frames.], datatang_tot_loss[loss=0.2027, simple_loss=0.3482, pruned_loss=2.936, over 985587.40 frames.], batch size: 32, lr: 2.72e-03
+2022-06-18 11:08:10,087 INFO [train.py:874] (0/4) Epoch 1, batch 3550, aishell_loss[loss=0.2108, simple_loss=0.3654, pruned_loss=0.281, over 4908.00 frames.], tot_loss[loss=0.2031, simple_loss=0.3418, pruned_loss=0.8652, over 985270.37 frames.], batch size: 41, aishell_tot_loss[loss=0.2131, simple_loss=0.3506, pruned_loss=2.438, over 984576.26 frames.], datatang_tot_loss[loss=0.2017, simple_loss=0.3469, pruned_loss=2.655, over 985847.21 frames.], batch size: 41, lr: 2.71e-03
+2022-06-18 11:08:41,477 INFO [train.py:874] (0/4) Epoch 1, batch 3600, datatang_loss[loss=0.1762, simple_loss=0.3098, pruned_loss=0.2131, over 4921.00 frames.], tot_loss[loss=0.1999, simple_loss=0.3396, pruned_loss=0.7239, over 985301.32 frames.], batch size: 47, aishell_tot_loss[loss=0.2111, simple_loss=0.3493, pruned_loss=2.237, over 984755.58 frames.], datatang_tot_loss[loss=0.1996, simple_loss=0.3444, pruned_loss=2.306, over 985689.10 frames.], batch size: 47, lr: 2.70e-03
+2022-06-18 11:09:13,092 INFO [train.py:874] (0/4) Epoch 1, batch 3650, datatang_loss[loss=0.1674, simple_loss=0.2994, pruned_loss=0.1774, over 4918.00 frames.], tot_loss[loss=0.1962, simple_loss=0.3363, pruned_loss=0.6093, over 985494.77 frames.], batch size: 81, aishell_tot_loss[loss=0.2088, simple_loss=0.3478, pruned_loss=2.043, over 984953.50 frames.], datatang_tot_loss[loss=0.1969, simple_loss=0.3408, pruned_loss=2.013, over 985698.10 frames.], batch size: 81, lr: 2.70e-03
+2022-06-18 11:09:42,307 INFO [train.py:874] (0/4) Epoch 1, batch 3700, aishell_loss[loss=0.1925, simple_loss=0.3411, pruned_loss=0.2195, over 4982.00 frames.], tot_loss[loss=0.1939, simple_loss=0.3349, pruned_loss=0.5215, over 984827.03 frames.], batch size: 51, aishell_tot_loss[loss=0.2062, simple_loss=0.3459, pruned_loss=1.836, over 984347.99 frames.], datatang_tot_loss[loss=0.1956, simple_loss=0.3393, pruned_loss=1.794, over 985631.95 frames.], batch size: 51, lr: 2.69e-03
+2022-06-18 11:10:11,813 INFO [train.py:874] (0/4) Epoch 1, batch 3750, datatang_loss[loss=0.1878, simple_loss=0.3313, pruned_loss=0.2218, over 4974.00 frames.], tot_loss[loss=0.1918, simple_loss=0.3333, pruned_loss=0.4512, over 985440.83 frames.], batch size: 45, aishell_tot_loss[loss=0.2041, simple_loss=0.3442, pruned_loss=1.679, over 984712.08 frames.], datatang_tot_loss[loss=0.1941, simple_loss=0.3377, pruned_loss=1.572, over 985856.98 frames.], batch size: 45, lr: 2.68e-03
+2022-06-18 11:10:43,619 INFO [train.py:874] (0/4) Epoch 1, batch 3800, datatang_loss[loss=0.2203, simple_loss=0.3861, pruned_loss=0.272, over 4900.00 frames.], tot_loss[loss=0.1913, simple_loss=0.3338, pruned_loss=0.3999, over 985288.81 frames.], batch size: 47, aishell_tot_loss[loss=0.2031, simple_loss=0.3439, pruned_loss=1.534, over 984835.17 frames.], datatang_tot_loss[loss=0.1931, simple_loss=0.3368, pruned_loss=1.388, over 985595.18 frames.], batch size: 47, lr: 2.68e-03
+2022-06-18 11:11:11,667 INFO [train.py:874] (0/4) Epoch 1, batch 3850, datatang_loss[loss=0.1671, simple_loss=0.2944, pruned_loss=0.1994, over 4903.00 frames.], tot_loss[loss=0.1896, simple_loss=0.3323, pruned_loss=0.3552, over 985688.52 frames.], batch size: 52, aishell_tot_loss[loss=0.2008, simple_loss=0.3423, pruned_loss=1.364, over 984983.13 frames.], datatang_tot_loss[loss=0.1919, simple_loss=0.3353, pruned_loss=1.259, over 985906.52 frames.], batch size: 52, lr: 2.67e-03
+2022-06-18 11:11:42,187 INFO [train.py:874] (0/4) Epoch 1, batch 3900, datatang_loss[loss=0.1699, simple_loss=0.3042, pruned_loss=0.1781, over 4930.00 frames.], tot_loss[loss=0.1881, simple_loss=0.3309, pruned_loss=0.3202, over 985397.28 frames.], batch size: 69, aishell_tot_loss[loss=0.1993, simple_loss=0.3414, pruned_loss=1.247, over 984747.65 frames.], datatang_tot_loss[loss=0.1904, simple_loss=0.3333, pruned_loss=1.116, over 985854.38 frames.], batch size: 69, lr: 2.66e-03
+2022-06-18 11:12:10,013 INFO [train.py:874] (0/4) Epoch 1, batch 3950, aishell_loss[loss=0.2117, simple_loss=0.3796, pruned_loss=0.2193, over 4980.00 frames.], tot_loss[loss=0.1867, simple_loss=0.3297, pruned_loss=0.2913, over 985168.52 frames.], batch size: 39, aishell_tot_loss[loss=0.1971, simple_loss=0.3398, pruned_loss=1.099, over 984580.53 frames.], datatang_tot_loss[loss=0.1892, simple_loss=0.3319, pruned_loss=1.028, over 985822.30 frames.], batch size: 39, lr: 2.66e-03
+2022-06-18 11:12:39,789 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-4000.pt
+2022-06-18 11:12:43,854 INFO [train.py:874] (0/4) Epoch 1, batch 4000, datatang_loss[loss=0.1854, simple_loss=0.328, pruned_loss=0.2139, over 4943.00 frames.], tot_loss[loss=0.1859, simple_loss=0.3294, pruned_loss=0.2684, over 985143.72 frames.], batch size: 69, aishell_tot_loss[loss=0.195, simple_loss=0.3381, pruned_loss=0.982, over 984581.13 frames.], datatang_tot_loss[loss=0.1889, simple_loss=0.3318, pruned_loss=0.9388, over 985813.05 frames.], batch size: 69, lr: 2.65e-03
+2022-06-18 11:12:43,856 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 11:13:00,455 INFO [train.py:914] (0/4) Epoch 1, validation: loss=0.2503, simple_loss=0.2882, pruned_loss=0.1062, over 1622729.00 frames.
+2022-06-18 11:13:28,274 INFO [train.py:874] (0/4) Epoch 1, batch 4050, aishell_loss[loss=0.2098, simple_loss=0.3766, pruned_loss=0.2149, over 4872.00 frames.], tot_loss[loss=0.1853, simple_loss=0.3291, pruned_loss=0.252, over 985095.59 frames.], batch size: 37, aishell_tot_loss[loss=0.1937, simple_loss=0.3374, pruned_loss=0.8881, over 984472.41 frames.], datatang_tot_loss[loss=0.1881, simple_loss=0.3309, pruned_loss=0.8523, over 985864.39 frames.], batch size: 37, lr: 2.64e-03
+2022-06-18 11:13:55,732 INFO [train.py:874] (0/4) Epoch 1, batch 4100, aishell_loss[loss=0.1951, simple_loss=0.3509, pruned_loss=0.1961, over 4941.00 frames.], tot_loss[loss=0.1845, simple_loss=0.3286, pruned_loss=0.2371, over 985245.53 frames.], batch size: 56, aishell_tot_loss[loss=0.1923, simple_loss=0.3364, pruned_loss=0.8007, over 984680.02 frames.], datatang_tot_loss[loss=0.1874, simple_loss=0.3301, pruned_loss=0.778, over 985793.39 frames.], batch size: 56, lr: 2.64e-03
+2022-06-18 11:14:25,454 INFO [train.py:874] (0/4) Epoch 1, batch 4150, aishell_loss[loss=0.1657, simple_loss=0.3013, pruned_loss=0.1506, over 4953.00 frames.], tot_loss[loss=0.1848, simple_loss=0.3291, pruned_loss=0.2298, over 985167.15 frames.], batch size: 40, aishell_tot_loss[loss=0.1916, simple_loss=0.3359, pruned_loss=0.7356, over 984709.55 frames.], datatang_tot_loss[loss=0.1871, simple_loss=0.3301, pruned_loss=0.7054, over 985658.31 frames.], batch size: 40, lr: 2.63e-03
+2022-06-18 11:14:55,128 INFO [train.py:874] (0/4) Epoch 1, batch 4200, datatang_loss[loss=0.1751, simple_loss=0.3168, pruned_loss=0.1668, over 4928.00 frames.], tot_loss[loss=0.1834, simple_loss=0.3272, pruned_loss=0.2187, over 985173.64 frames.], batch size: 73, aishell_tot_loss[loss=0.1907, simple_loss=0.3353, pruned_loss=0.6774, over 984595.95 frames.], datatang_tot_loss[loss=0.1855, simple_loss=0.3277, pruned_loss=0.6367, over 985759.19 frames.], batch size: 73, lr: 2.63e-03
+2022-06-18 11:15:11,850 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-1.pt
+2022-06-18 11:16:16,056 INFO [train.py:874] (0/4) Epoch 2, batch 50, datatang_loss[loss=0.1616, simple_loss=0.2917, pruned_loss=0.158, over 4936.00 frames.], tot_loss[loss=0.1707, simple_loss=0.3084, pruned_loss=0.1647, over 218550.77 frames.], batch size: 62, aishell_tot_loss[loss=0.1761, simple_loss=0.3195, pruned_loss=0.1637, over 120240.69 frames.], datatang_tot_loss[loss=0.1649, simple_loss=0.2967, pruned_loss=0.1658, over 111963.52 frames.], batch size: 62, lr: 2.60e-03
+2022-06-18 11:16:44,617 INFO [train.py:874] (0/4) Epoch 2, batch 100, datatang_loss[loss=0.1957, simple_loss=0.3507, pruned_loss=0.203, over 4924.00 frames.], tot_loss[loss=0.1733, simple_loss=0.3129, pruned_loss=0.1685, over 388490.99 frames.], batch size: 98, aishell_tot_loss[loss=0.1773, simple_loss=0.322, pruned_loss=0.1628, over 237130.15 frames.], datatang_tot_loss[loss=0.168, simple_loss=0.3011, pruned_loss=0.1747, over 199344.44 frames.], batch size: 98, lr: 2.59e-03
+2022-06-18 11:17:15,565 INFO [train.py:874] (0/4) Epoch 2, batch 150, datatang_loss[loss=0.1776, simple_loss=0.321, pruned_loss=0.1713, over 4943.00 frames.], tot_loss[loss=0.1754, simple_loss=0.3166, pruned_loss=0.1713, over 520911.70 frames.], batch size: 34, aishell_tot_loss[loss=0.1786, simple_loss=0.3238, pruned_loss=0.1671, over 341624.51 frames.], datatang_tot_loss[loss=0.1704, simple_loss=0.3057, pruned_loss=0.1757, over 274401.69 frames.], batch size: 34, lr: 2.58e-03
+2022-06-18 11:17:45,630 INFO [train.py:874] (0/4) Epoch 2, batch 200, aishell_loss[loss=0.1956, simple_loss=0.3524, pruned_loss=0.1943, over 4934.00 frames.], tot_loss[loss=0.174, simple_loss=0.3143, pruned_loss=0.1681, over 623944.54 frames.], batch size: 58, aishell_tot_loss[loss=0.1762, simple_loss=0.32, pruned_loss=0.1624, over 423045.86 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.3068, pruned_loss=0.1755, over 351962.40 frames.], batch size: 58, lr: 2.58e-03
+2022-06-18 11:18:14,303 INFO [train.py:874] (0/4) Epoch 2, batch 250, datatang_loss[loss=0.1756, simple_loss=0.3153, pruned_loss=0.1795, over 4939.00 frames.], tot_loss[loss=0.1743, simple_loss=0.3151, pruned_loss=0.1673, over 704244.61 frames.], batch size: 69, aishell_tot_loss[loss=0.1762, simple_loss=0.3202, pruned_loss=0.1613, over 486650.86 frames.], datatang_tot_loss[loss=0.1718, simple_loss=0.3086, pruned_loss=0.1748, over 429675.21 frames.], batch size: 69, lr: 2.57e-03
+2022-06-18 11:18:45,564 INFO [train.py:874] (0/4) Epoch 2, batch 300, datatang_loss[loss=0.167, simple_loss=0.3037, pruned_loss=0.1513, over 4907.00 frames.], tot_loss[loss=0.1737, simple_loss=0.3143, pruned_loss=0.1659, over 766606.06 frames.], batch size: 42, aishell_tot_loss[loss=0.1759, simple_loss=0.3198, pruned_loss=0.1599, over 551883.18 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.3077, pruned_loss=0.174, over 487803.51 frames.], batch size: 42, lr: 2.57e-03
+2022-06-18 11:19:14,251 INFO [train.py:874] (0/4) Epoch 2, batch 350, aishell_loss[loss=0.164, simple_loss=0.3021, pruned_loss=0.13, over 4939.00 frames.], tot_loss[loss=0.1738, simple_loss=0.3144, pruned_loss=0.1662, over 815076.94 frames.], batch size: 49, aishell_tot_loss[loss=0.1756, simple_loss=0.3193, pruned_loss=0.1596, over 610395.70 frames.], datatang_tot_loss[loss=0.1716, simple_loss=0.3083, pruned_loss=0.175, over 537615.72 frames.], batch size: 49, lr: 2.56e-03
+2022-06-18 11:19:44,614 INFO [train.py:874] (0/4) Epoch 2, batch 400, datatang_loss[loss=0.1687, simple_loss=0.3069, pruned_loss=0.153, over 4952.00 frames.], tot_loss[loss=0.1734, simple_loss=0.3138, pruned_loss=0.1647, over 852607.84 frames.], batch size: 86, aishell_tot_loss[loss=0.1757, simple_loss=0.3196, pruned_loss=0.1588, over 647631.14 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.3076, pruned_loss=0.1727, over 598181.76 frames.], batch size: 86, lr: 2.55e-03
+2022-06-18 11:20:15,804 INFO [train.py:874] (0/4) Epoch 2, batch 450, aishell_loss[loss=0.1649, simple_loss=0.3034, pruned_loss=0.1319, over 4942.00 frames.], tot_loss[loss=0.1724, simple_loss=0.3124, pruned_loss=0.1621, over 882197.10 frames.], batch size: 64, aishell_tot_loss[loss=0.1746, simple_loss=0.318, pruned_loss=0.1561, over 691949.68 frames.], datatang_tot_loss[loss=0.1707, simple_loss=0.3071, pruned_loss=0.1716, over 638691.49 frames.], batch size: 64, lr: 2.55e-03
+2022-06-18 11:20:44,455 INFO [train.py:874] (0/4) Epoch 2, batch 500, datatang_loss[loss=0.1854, simple_loss=0.3325, pruned_loss=0.1912, over 4930.00 frames.], tot_loss[loss=0.1724, simple_loss=0.3125, pruned_loss=0.1613, over 905134.06 frames.], batch size: 69, aishell_tot_loss[loss=0.1746, simple_loss=0.3181, pruned_loss=0.1557, over 730456.50 frames.], datatang_tot_loss[loss=0.1705, simple_loss=0.3069, pruned_loss=0.1707, over 674868.34 frames.], batch size: 69, lr: 2.54e-03
+2022-06-18 11:21:14,566 INFO [train.py:874] (0/4) Epoch 2, batch 550, datatang_loss[loss=0.1747, simple_loss=0.3158, pruned_loss=0.1681, over 4921.00 frames.], tot_loss[loss=0.173, simple_loss=0.3136, pruned_loss=0.1616, over 923261.60 frames.], batch size: 42, aishell_tot_loss[loss=0.1745, simple_loss=0.318, pruned_loss=0.1552, over 759627.17 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.3086, pruned_loss=0.1709, over 712899.80 frames.], batch size: 42, lr: 2.53e-03
+2022-06-18 11:21:45,598 INFO [train.py:874] (0/4) Epoch 2, batch 600, datatang_loss[loss=0.1554, simple_loss=0.2868, pruned_loss=0.1201, over 4960.00 frames.], tot_loss[loss=0.1731, simple_loss=0.3135, pruned_loss=0.164, over 936774.16 frames.], batch size: 86, aishell_tot_loss[loss=0.1746, simple_loss=0.3176, pruned_loss=0.1578, over 782968.81 frames.], datatang_tot_loss[loss=0.1716, simple_loss=0.3091, pruned_loss=0.1708, over 748529.52 frames.], batch size: 86, lr: 2.53e-03
+2022-06-18 11:22:14,820 INFO [train.py:874] (0/4) Epoch 2, batch 650, aishell_loss[loss=0.1766, simple_loss=0.3219, pruned_loss=0.1563, over 4952.00 frames.], tot_loss[loss=0.1734, simple_loss=0.3141, pruned_loss=0.1638, over 947712.63 frames.], batch size: 40, aishell_tot_loss[loss=0.1739, simple_loss=0.3166, pruned_loss=0.1561, over 806173.03 frames.], datatang_tot_loss[loss=0.1728, simple_loss=0.3111, pruned_loss=0.1722, over 777394.28 frames.], batch size: 40, lr: 2.52e-03
+2022-06-18 11:22:45,353 INFO [train.py:874] (0/4) Epoch 2, batch 700, datatang_loss[loss=0.1688, simple_loss=0.3071, pruned_loss=0.1524, over 4912.00 frames.], tot_loss[loss=0.1733, simple_loss=0.3141, pruned_loss=0.1624, over 956028.50 frames.], batch size: 24, aishell_tot_loss[loss=0.1731, simple_loss=0.3155, pruned_loss=0.1539, over 827011.89 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.3122, pruned_loss=0.1725, over 802109.16 frames.], batch size: 24, lr: 2.51e-03
+2022-06-18 11:23:15,123 INFO [train.py:874] (0/4) Epoch 2, batch 750, aishell_loss[loss=0.1754, simple_loss=0.3237, pruned_loss=0.136, over 4899.00 frames.], tot_loss[loss=0.1732, simple_loss=0.3142, pruned_loss=0.1607, over 962750.08 frames.], batch size: 41, aishell_tot_loss[loss=0.1732, simple_loss=0.3157, pruned_loss=0.1531, over 847181.18 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.3121, pruned_loss=0.1712, over 822175.89 frames.], batch size: 41, lr: 2.51e-03
+2022-06-18 11:23:44,089 INFO [train.py:874] (0/4) Epoch 2, batch 800, aishell_loss[loss=0.1825, simple_loss=0.3341, pruned_loss=0.1549, over 4977.00 frames.], tot_loss[loss=0.1743, simple_loss=0.3159, pruned_loss=0.1638, over 968100.07 frames.], batch size: 51, aishell_tot_loss[loss=0.1734, simple_loss=0.3161, pruned_loss=0.1534, over 861163.97 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.3139, pruned_loss=0.1738, over 844413.70 frames.], batch size: 51, lr: 2.50e-03
+2022-06-18 11:24:15,002 INFO [train.py:874] (0/4) Epoch 2, batch 850, datatang_loss[loss=0.1608, simple_loss=0.2912, pruned_loss=0.1521, over 4858.00 frames.], tot_loss[loss=0.1732, simple_loss=0.3141, pruned_loss=0.1614, over 971418.93 frames.], batch size: 30, aishell_tot_loss[loss=0.1728, simple_loss=0.3152, pruned_loss=0.1521, over 873952.14 frames.], datatang_tot_loss[loss=0.1737, simple_loss=0.313, pruned_loss=0.1722, over 862510.65 frames.], batch size: 30, lr: 2.50e-03
+2022-06-18 11:24:45,390 INFO [train.py:874] (0/4) Epoch 2, batch 900, aishell_loss[loss=0.1923, simple_loss=0.3488, pruned_loss=0.1788, over 4961.00 frames.], tot_loss[loss=0.1727, simple_loss=0.3135, pruned_loss=0.1592, over 974496.26 frames.], batch size: 61, aishell_tot_loss[loss=0.1727, simple_loss=0.3152, pruned_loss=0.1511, over 887601.44 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.3123, pruned_loss=0.1704, over 876392.27 frames.], batch size: 61, lr: 2.49e-03
+2022-06-18 11:25:13,889 INFO [train.py:874] (0/4) Epoch 2, batch 950, aishell_loss[loss=0.1707, simple_loss=0.3127, pruned_loss=0.1439, over 4973.00 frames.], tot_loss[loss=0.1719, simple_loss=0.312, pruned_loss=0.1583, over 976846.01 frames.], batch size: 48, aishell_tot_loss[loss=0.1721, simple_loss=0.3141, pruned_loss=0.1508, over 898983.44 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.3116, pruned_loss=0.1692, over 889294.31 frames.], batch size: 48, lr: 2.48e-03
+2022-06-18 11:25:45,478 INFO [train.py:874] (0/4) Epoch 2, batch 1000, datatang_loss[loss=0.1613, simple_loss=0.2943, pruned_loss=0.1415, over 4970.00 frames.], tot_loss[loss=0.1729, simple_loss=0.3142, pruned_loss=0.1584, over 978609.84 frames.], batch size: 60, aishell_tot_loss[loss=0.1728, simple_loss=0.3155, pruned_loss=0.1506, over 907599.59 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.3125, pruned_loss=0.1688, over 902132.14 frames.], batch size: 60, lr: 2.48e-03
+2022-06-18 11:25:45,481 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 11:26:02,500 INFO [train.py:914] (0/4) Epoch 2, validation: loss=0.2333, simple_loss=0.2891, pruned_loss=0.08878, over 1622729.00 frames.
+2022-06-18 11:26:32,168 INFO [train.py:874] (0/4) Epoch 2, batch 1050, aishell_loss[loss=0.1916, simple_loss=0.3466, pruned_loss=0.1835, over 4908.00 frames.], tot_loss[loss=0.1725, simple_loss=0.3138, pruned_loss=0.156, over 979824.49 frames.], batch size: 52, aishell_tot_loss[loss=0.1727, simple_loss=0.3155, pruned_loss=0.1492, over 917905.37 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.312, pruned_loss=0.1675, over 910359.94 frames.], batch size: 52, lr: 2.47e-03
+2022-06-18 11:27:03,421 INFO [train.py:874] (0/4) Epoch 2, batch 1100, datatang_loss[loss=0.1677, simple_loss=0.3034, pruned_loss=0.1596, over 4927.00 frames.], tot_loss[loss=0.1721, simple_loss=0.3132, pruned_loss=0.1548, over 981634.01 frames.], batch size: 64, aishell_tot_loss[loss=0.1723, simple_loss=0.315, pruned_loss=0.1478, over 925959.13 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.3119, pruned_loss=0.1668, over 919719.99 frames.], batch size: 64, lr: 2.46e-03
+2022-06-18 11:27:31,690 INFO [train.py:874] (0/4) Epoch 2, batch 1150, aishell_loss[loss=0.17, simple_loss=0.3118, pruned_loss=0.1407, over 4937.00 frames.], tot_loss[loss=0.1716, simple_loss=0.3124, pruned_loss=0.1534, over 982384.40 frames.], batch size: 58, aishell_tot_loss[loss=0.172, simple_loss=0.3148, pruned_loss=0.1464, over 933581.91 frames.], datatang_tot_loss[loss=0.1722, simple_loss=0.3111, pruned_loss=0.1663, over 926652.44 frames.], batch size: 58, lr: 2.46e-03
+2022-06-18 11:28:02,550 INFO [train.py:874] (0/4) Epoch 2, batch 1200, aishell_loss[loss=0.1929, simple_loss=0.3523, pruned_loss=0.1678, over 4863.00 frames.], tot_loss[loss=0.1721, simple_loss=0.3135, pruned_loss=0.1533, over 982937.66 frames.], batch size: 37, aishell_tot_loss[loss=0.1722, simple_loss=0.3152, pruned_loss=0.1457, over 939132.70 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.3118, pruned_loss=0.166, over 934065.73 frames.], batch size: 37, lr: 2.45e-03
+2022-06-18 11:28:34,000 INFO [train.py:874] (0/4) Epoch 2, batch 1250, datatang_loss[loss=0.1759, simple_loss=0.316, pruned_loss=0.1788, over 4947.00 frames.], tot_loss[loss=0.1718, simple_loss=0.3124, pruned_loss=0.1565, over 983819.73 frames.], batch size: 45, aishell_tot_loss[loss=0.172, simple_loss=0.3143, pruned_loss=0.1483, over 944484.62 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.3115, pruned_loss=0.1662, over 940574.49 frames.], batch size: 45, lr: 2.45e-03
+2022-06-18 11:29:03,384 INFO [train.py:874] (0/4) Epoch 2, batch 1300, aishell_loss[loss=0.1699, simple_loss=0.3126, pruned_loss=0.1358, over 4930.00 frames.], tot_loss[loss=0.1711, simple_loss=0.3113, pruned_loss=0.1547, over 984157.70 frames.], batch size: 33, aishell_tot_loss[loss=0.1715, simple_loss=0.3136, pruned_loss=0.147, over 948307.79 frames.], datatang_tot_loss[loss=0.1719, simple_loss=0.3109, pruned_loss=0.165, over 946881.85 frames.], batch size: 33, lr: 2.44e-03
+2022-06-18 11:29:33,625 INFO [train.py:874] (0/4) Epoch 2, batch 1350, datatang_loss[loss=0.186, simple_loss=0.3389, pruned_loss=0.1652, over 4943.00 frames.], tot_loss[loss=0.1703, simple_loss=0.3103, pruned_loss=0.1515, over 984708.69 frames.], batch size: 88, aishell_tot_loss[loss=0.1712, simple_loss=0.3132, pruned_loss=0.1456, over 953143.67 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.3099, pruned_loss=0.1627, over 951230.00 frames.], batch size: 88, lr: 2.43e-03
+2022-06-18 11:30:05,331 INFO [train.py:874] (0/4) Epoch 2, batch 1400, aishell_loss[loss=0.1907, simple_loss=0.3472, pruned_loss=0.1714, over 4925.00 frames.], tot_loss[loss=0.1708, simple_loss=0.3114, pruned_loss=0.1516, over 985107.34 frames.], batch size: 41, aishell_tot_loss[loss=0.1714, simple_loss=0.3137, pruned_loss=0.145, over 957002.30 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.3103, pruned_loss=0.1626, over 955461.66 frames.], batch size: 41, lr: 2.43e-03
+2022-06-18 11:30:32,952 INFO [train.py:874] (0/4) Epoch 2, batch 1450, aishell_loss[loss=0.1705, simple_loss=0.3159, pruned_loss=0.1257, over 4935.00 frames.], tot_loss[loss=0.1705, simple_loss=0.3109, pruned_loss=0.1509, over 985267.84 frames.], batch size: 58, aishell_tot_loss[loss=0.1708, simple_loss=0.3129, pruned_loss=0.1437, over 960650.13 frames.], datatang_tot_loss[loss=0.1715, simple_loss=0.3104, pruned_loss=0.1627, over 958774.45 frames.], batch size: 58, lr: 2.42e-03
+2022-06-18 11:31:04,362 INFO [train.py:874] (0/4) Epoch 2, batch 1500, aishell_loss[loss=0.1607, simple_loss=0.2967, pruned_loss=0.1236, over 4961.00 frames.], tot_loss[loss=0.1696, simple_loss=0.3096, pruned_loss=0.1485, over 985580.80 frames.], batch size: 56, aishell_tot_loss[loss=0.1708, simple_loss=0.3131, pruned_loss=0.1427, over 963650.85 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.3086, pruned_loss=0.1604, over 962144.51 frames.], batch size: 56, lr: 2.42e-03
+2022-06-18 11:31:35,279 INFO [train.py:874] (0/4) Epoch 2, batch 1550, datatang_loss[loss=0.1589, simple_loss=0.2898, pruned_loss=0.1397, over 4907.00 frames.], tot_loss[loss=0.169, simple_loss=0.3086, pruned_loss=0.1473, over 985937.73 frames.], batch size: 64, aishell_tot_loss[loss=0.1704, simple_loss=0.3124, pruned_loss=0.1419, over 966308.91 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.3079, pruned_loss=0.1592, over 965213.15 frames.], batch size: 64, lr: 2.41e-03
+2022-06-18 11:32:03,496 INFO [train.py:874] (0/4) Epoch 2, batch 1600, aishell_loss[loss=0.1794, simple_loss=0.3301, pruned_loss=0.1437, over 4949.00 frames.], tot_loss[loss=0.1687, simple_loss=0.3082, pruned_loss=0.1458, over 986002.08 frames.], batch size: 54, aishell_tot_loss[loss=0.1697, simple_loss=0.3113, pruned_loss=0.14, over 968863.25 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.3082, pruned_loss=0.1588, over 967488.90 frames.], batch size: 54, lr: 2.40e-03
+2022-06-18 11:32:34,645 INFO [train.py:874] (0/4) Epoch 2, batch 1650, aishell_loss[loss=0.2021, simple_loss=0.3681, pruned_loss=0.1812, over 4906.00 frames.], tot_loss[loss=0.168, simple_loss=0.3071, pruned_loss=0.1445, over 985734.80 frames.], batch size: 78, aishell_tot_loss[loss=0.1689, simple_loss=0.3101, pruned_loss=0.1383, over 971099.99 frames.], datatang_tot_loss[loss=0.1697, simple_loss=0.3077, pruned_loss=0.1587, over 969109.89 frames.], batch size: 78, lr: 2.40e-03
+2022-06-18 11:33:04,802 INFO [train.py:874] (0/4) Epoch 2, batch 1700, aishell_loss[loss=0.1641, simple_loss=0.3052, pruned_loss=0.1152, over 4894.00 frames.], tot_loss[loss=0.1679, simple_loss=0.3072, pruned_loss=0.1435, over 985586.08 frames.], batch size: 52, aishell_tot_loss[loss=0.1689, simple_loss=0.3103, pruned_loss=0.1373, over 972686.04 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.3072, pruned_loss=0.158, over 971024.72 frames.], batch size: 52, lr: 2.39e-03
+2022-06-18 11:33:33,377 INFO [train.py:874] (0/4) Epoch 2, batch 1750, datatang_loss[loss=0.1617, simple_loss=0.2978, pruned_loss=0.1277, over 4940.00 frames.], tot_loss[loss=0.1679, simple_loss=0.3071, pruned_loss=0.1429, over 985523.56 frames.], batch size: 88, aishell_tot_loss[loss=0.169, simple_loss=0.3107, pruned_loss=0.1369, over 973932.70 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.3064, pruned_loss=0.1567, over 972943.99 frames.], batch size: 88, lr: 2.39e-03
+2022-06-18 11:34:05,527 INFO [train.py:874] (0/4) Epoch 2, batch 1800, datatang_loss[loss=0.3359, simple_loss=0.3109, pruned_loss=0.1805, over 4895.00 frames.], tot_loss[loss=0.1988, simple_loss=0.3101, pruned_loss=0.1563, over 985311.42 frames.], batch size: 52, aishell_tot_loss[loss=0.1846, simple_loss=0.3112, pruned_loss=0.1452, over 975076.44 frames.], datatang_tot_loss[loss=0.1852, simple_loss=0.3086, pruned_loss=0.1612, over 974442.10 frames.], batch size: 52, lr: 2.38e-03
+2022-06-18 11:34:34,631 INFO [train.py:874] (0/4) Epoch 2, batch 1850, datatang_loss[loss=0.3546, simple_loss=0.3335, pruned_loss=0.1879, over 4983.00 frames.], tot_loss[loss=0.225, simple_loss=0.3111, pruned_loss=0.157, over 985547.81 frames.], batch size: 34, aishell_tot_loss[loss=0.1993, simple_loss=0.3118, pruned_loss=0.1458, over 976398.73 frames.], datatang_tot_loss[loss=0.2021, simple_loss=0.3093, pruned_loss=0.1622, over 975851.06 frames.], batch size: 34, lr: 2.38e-03
+2022-06-18 11:35:04,186 INFO [train.py:874] (0/4) Epoch 2, batch 1900, datatang_loss[loss=0.435, simple_loss=0.391, pruned_loss=0.2395, over 4931.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3113, pruned_loss=0.1545, over 985271.36 frames.], batch size: 108, aishell_tot_loss[loss=0.2115, simple_loss=0.3116, pruned_loss=0.1442, over 977375.35 frames.], datatang_tot_loss[loss=0.2139, simple_loss=0.3098, pruned_loss=0.1622, over 976801.97 frames.], batch size: 108, lr: 2.37e-03
+2022-06-18 11:35:34,931 INFO [train.py:874] (0/4) Epoch 2, batch 1950, aishell_loss[loss=0.2962, simple_loss=0.3222, pruned_loss=0.1351, over 4948.00 frames.], tot_loss[loss=0.2528, simple_loss=0.3099, pruned_loss=0.1509, over 985324.87 frames.], batch size: 32, aishell_tot_loss[loss=0.2207, simple_loss=0.3109, pruned_loss=0.1421, over 978412.55 frames.], datatang_tot_loss[loss=0.2232, simple_loss=0.3091, pruned_loss=0.161, over 977747.07 frames.], batch size: 32, lr: 2.36e-03
+2022-06-18 11:36:03,037 INFO [train.py:874] (0/4) Epoch 2, batch 2000, datatang_loss[loss=0.2954, simple_loss=0.2969, pruned_loss=0.147, over 4922.00 frames.], tot_loss[loss=0.2605, simple_loss=0.3084, pruned_loss=0.1476, over 985536.81 frames.], batch size: 77, aishell_tot_loss[loss=0.228, simple_loss=0.3102, pruned_loss=0.1402, over 979197.80 frames.], datatang_tot_loss[loss=0.2315, simple_loss=0.3081, pruned_loss=0.1592, over 978887.53 frames.], batch size: 77, lr: 2.36e-03
+2022-06-18 11:36:03,039 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 11:36:19,300 INFO [train.py:914] (0/4) Epoch 2, validation: loss=0.2142, simple_loss=0.275, pruned_loss=0.07672, over 1622729.00 frames.
+2022-06-18 11:36:48,995 INFO [train.py:874] (0/4) Epoch 2, batch 2050, datatang_loss[loss=0.295, simple_loss=0.2967, pruned_loss=0.1466, over 4939.00 frames.], tot_loss[loss=0.2674, simple_loss=0.3071, pruned_loss=0.146, over 985674.05 frames.], batch size: 62, aishell_tot_loss[loss=0.2344, simple_loss=0.3092, pruned_loss=0.1385, over 979878.93 frames.], datatang_tot_loss[loss=0.2397, simple_loss=0.3075, pruned_loss=0.1587, over 979874.09 frames.], batch size: 62, lr: 2.35e-03
+2022-06-18 11:37:18,671 INFO [train.py:874] (0/4) Epoch 2, batch 2100, aishell_loss[loss=0.284, simple_loss=0.3206, pruned_loss=0.1237, over 4973.00 frames.], tot_loss[loss=0.2735, simple_loss=0.3074, pruned_loss=0.1448, over 985911.53 frames.], batch size: 64, aishell_tot_loss[loss=0.2409, simple_loss=0.3095, pruned_loss=0.1377, over 980707.12 frames.], datatang_tot_loss[loss=0.2469, simple_loss=0.3072, pruned_loss=0.1578, over 980652.21 frames.], batch size: 64, lr: 2.35e-03
+2022-06-18 11:37:49,773 INFO [train.py:874] (0/4) Epoch 2, batch 2150, aishell_loss[loss=0.276, simple_loss=0.302, pruned_loss=0.125, over 4906.00 frames.], tot_loss[loss=0.2779, simple_loss=0.3075, pruned_loss=0.1436, over 985693.47 frames.], batch size: 68, aishell_tot_loss[loss=0.246, simple_loss=0.3095, pruned_loss=0.1368, over 981209.04 frames.], datatang_tot_loss[loss=0.2535, simple_loss=0.3072, pruned_loss=0.1567, over 981146.91 frames.], batch size: 68, lr: 2.34e-03
+2022-06-18 11:38:20,205 INFO [train.py:874] (0/4) Epoch 2, batch 2200, datatang_loss[loss=0.3585, simple_loss=0.3288, pruned_loss=0.1941, over 4945.00 frames.], tot_loss[loss=0.2799, simple_loss=0.3065, pruned_loss=0.1418, over 985713.80 frames.], batch size: 62, aishell_tot_loss[loss=0.2492, simple_loss=0.3084, pruned_loss=0.1355, over 981617.03 frames.], datatang_tot_loss[loss=0.259, simple_loss=0.307, pruned_loss=0.1551, over 981826.67 frames.], batch size: 62, lr: 2.33e-03
+2022-06-18 11:38:48,993 INFO [train.py:874] (0/4) Epoch 2, batch 2250, datatang_loss[loss=0.2555, simple_loss=0.2839, pruned_loss=0.1136, over 4947.00 frames.], tot_loss[loss=0.2884, simple_loss=0.3087, pruned_loss=0.1458, over 985960.36 frames.], batch size: 67, aishell_tot_loss[loss=0.2561, simple_loss=0.3097, pruned_loss=0.1382, over 982138.25 frames.], datatang_tot_loss[loss=0.2668, simple_loss=0.3077, pruned_loss=0.1552, over 982494.08 frames.], batch size: 67, lr: 2.33e-03
+2022-06-18 11:39:20,383 INFO [train.py:874] (0/4) Epoch 2, batch 2300, aishell_loss[loss=0.294, simple_loss=0.326, pruned_loss=0.131, over 4912.00 frames.], tot_loss[loss=0.2886, simple_loss=0.3086, pruned_loss=0.1435, over 985770.61 frames.], batch size: 52, aishell_tot_loss[loss=0.2606, simple_loss=0.3106, pruned_loss=0.1377, over 982370.67 frames.], datatang_tot_loss[loss=0.2691, simple_loss=0.3069, pruned_loss=0.1531, over 982924.79 frames.], batch size: 52, lr: 2.32e-03
+2022-06-18 11:39:51,122 INFO [train.py:874] (0/4) Epoch 2, batch 2350, aishell_loss[loss=0.2843, simple_loss=0.3196, pruned_loss=0.1245, over 4948.00 frames.], tot_loss[loss=0.2895, simple_loss=0.3091, pruned_loss=0.1421, over 986017.99 frames.], batch size: 49, aishell_tot_loss[loss=0.263, simple_loss=0.3107, pruned_loss=0.1366, over 982992.36 frames.], datatang_tot_loss[loss=0.2732, simple_loss=0.3072, pruned_loss=0.1522, over 983302.72 frames.], batch size: 49, lr: 2.32e-03
+2022-06-18 11:40:20,384 INFO [train.py:874] (0/4) Epoch 2, batch 2400, datatang_loss[loss=0.3049, simple_loss=0.3128, pruned_loss=0.1485, over 4877.00 frames.], tot_loss[loss=0.2891, simple_loss=0.3087, pruned_loss=0.1403, over 985763.07 frames.], batch size: 44, aishell_tot_loss[loss=0.2644, simple_loss=0.3105, pruned_loss=0.1351, over 983306.64 frames.], datatang_tot_loss[loss=0.2761, simple_loss=0.3071, pruned_loss=0.151, over 983397.97 frames.], batch size: 44, lr: 2.31e-03
+2022-06-18 11:40:51,460 INFO [train.py:874] (0/4) Epoch 2, batch 2450, aishell_loss[loss=0.2112, simple_loss=0.2324, pruned_loss=0.09499, over 4903.00 frames.], tot_loss[loss=0.2876, simple_loss=0.3072, pruned_loss=0.1384, over 986109.98 frames.], batch size: 21, aishell_tot_loss[loss=0.2654, simple_loss=0.3096, pruned_loss=0.1335, over 983774.20 frames.], datatang_tot_loss[loss=0.278, simple_loss=0.3065, pruned_loss=0.1497, over 983867.23 frames.], batch size: 21, lr: 2.31e-03
+2022-06-18 11:41:22,604 INFO [train.py:874] (0/4) Epoch 2, batch 2500, datatang_loss[loss=0.31, simple_loss=0.3023, pruned_loss=0.1588, over 4918.00 frames.], tot_loss[loss=0.2897, simple_loss=0.307, pruned_loss=0.1395, over 986045.21 frames.], batch size: 64, aishell_tot_loss[loss=0.2672, simple_loss=0.309, pruned_loss=0.1324, over 984009.98 frames.], datatang_tot_loss[loss=0.2823, simple_loss=0.3065, pruned_loss=0.1517, over 984105.57 frames.], batch size: 64, lr: 2.30e-03
+2022-06-18 11:41:51,392 INFO [train.py:874] (0/4) Epoch 2, batch 2550, datatang_loss[loss=0.3308, simple_loss=0.3317, pruned_loss=0.165, over 4960.00 frames.], tot_loss[loss=0.2893, simple_loss=0.3066, pruned_loss=0.1386, over 985827.70 frames.], batch size: 86, aishell_tot_loss[loss=0.269, simple_loss=0.3089, pruned_loss=0.1318, over 984218.40 frames.], datatang_tot_loss[loss=0.2837, simple_loss=0.3059, pruned_loss=0.1508, over 984145.17 frames.], batch size: 86, lr: 2.30e-03
+2022-06-18 11:42:23,035 INFO [train.py:874] (0/4) Epoch 2, batch 2600, datatang_loss[loss=0.2416, simple_loss=0.2716, pruned_loss=0.1058, over 4916.00 frames.], tot_loss[loss=0.2863, simple_loss=0.305, pruned_loss=0.1358, over 985807.39 frames.], batch size: 73, aishell_tot_loss[loss=0.2688, simple_loss=0.3079, pruned_loss=0.1303, over 984265.51 frames.], datatang_tot_loss[loss=0.2836, simple_loss=0.3051, pruned_loss=0.1485, over 984462.38 frames.], batch size: 73, lr: 2.29e-03
+2022-06-18 11:42:52,019 INFO [train.py:874] (0/4) Epoch 2, batch 2650, datatang_loss[loss=0.2915, simple_loss=0.2986, pruned_loss=0.1423, over 4932.00 frames.], tot_loss[loss=0.2858, simple_loss=0.3044, pruned_loss=0.1352, over 985936.92 frames.], batch size: 69, aishell_tot_loss[loss=0.2697, simple_loss=0.307, pruned_loss=0.1296, over 984596.61 frames.], datatang_tot_loss[loss=0.2847, simple_loss=0.305, pruned_loss=0.1479, over 984617.11 frames.], batch size: 69, lr: 2.28e-03
+2022-06-18 11:43:22,498 INFO [train.py:874] (0/4) Epoch 2, batch 2700, aishell_loss[loss=0.2693, simple_loss=0.299, pruned_loss=0.1198, over 4873.00 frames.], tot_loss[loss=0.2848, simple_loss=0.3044, pruned_loss=0.1338, over 985911.03 frames.], batch size: 35, aishell_tot_loss[loss=0.2698, simple_loss=0.3065, pruned_loss=0.1282, over 984529.61 frames.], datatang_tot_loss[loss=0.2858, simple_loss=0.3051, pruned_loss=0.1472, over 984978.99 frames.], batch size: 35, lr: 2.28e-03
+2022-06-18 11:43:52,867 INFO [train.py:874] (0/4) Epoch 2, batch 2750, datatang_loss[loss=0.3209, simple_loss=0.3166, pruned_loss=0.1626, over 4931.00 frames.], tot_loss[loss=0.2858, simple_loss=0.3057, pruned_loss=0.134, over 986051.05 frames.], batch size: 79, aishell_tot_loss[loss=0.2709, simple_loss=0.3071, pruned_loss=0.1279, over 984528.25 frames.], datatang_tot_loss[loss=0.2872, simple_loss=0.3056, pruned_loss=0.1465, over 985384.79 frames.], batch size: 79, lr: 2.27e-03
+2022-06-18 11:44:21,080 INFO [train.py:874] (0/4) Epoch 2, batch 2800, aishell_loss[loss=0.2825, simple_loss=0.3091, pruned_loss=0.128, over 4879.00 frames.], tot_loss[loss=0.2863, simple_loss=0.3064, pruned_loss=0.1339, over 986141.05 frames.], batch size: 47, aishell_tot_loss[loss=0.272, simple_loss=0.3077, pruned_loss=0.1275, over 984601.41 frames.], datatang_tot_loss[loss=0.2881, simple_loss=0.3056, pruned_loss=0.1459, over 985658.64 frames.], batch size: 47, lr: 2.27e-03
+2022-06-18 11:44:52,576 INFO [train.py:874] (0/4) Epoch 2, batch 2850, datatang_loss[loss=0.3144, simple_loss=0.307, pruned_loss=0.1609, over 4890.00 frames.], tot_loss[loss=0.2871, simple_loss=0.3068, pruned_loss=0.1343, over 985968.90 frames.], batch size: 42, aishell_tot_loss[loss=0.2719, simple_loss=0.3073, pruned_loss=0.1265, over 984596.88 frames.], datatang_tot_loss[loss=0.2903, simple_loss=0.3063, pruned_loss=0.1465, over 985715.16 frames.], batch size: 42, lr: 2.26e-03
+2022-06-18 11:45:23,340 INFO [train.py:874] (0/4) Epoch 2, batch 2900, aishell_loss[loss=0.3011, simple_loss=0.3304, pruned_loss=0.1359, over 4971.00 frames.], tot_loss[loss=0.2853, simple_loss=0.3062, pruned_loss=0.1327, over 985701.54 frames.], batch size: 44, aishell_tot_loss[loss=0.2721, simple_loss=0.3071, pruned_loss=0.1258, over 984705.18 frames.], datatang_tot_loss[loss=0.2898, simple_loss=0.3058, pruned_loss=0.1452, over 985526.09 frames.], batch size: 44, lr: 2.26e-03
+2022-06-18 11:45:51,129 INFO [train.py:874] (0/4) Epoch 2, batch 2950, datatang_loss[loss=0.384, simple_loss=0.3735, pruned_loss=0.1972, over 4955.00 frames.], tot_loss[loss=0.2843, simple_loss=0.3062, pruned_loss=0.1315, over 985928.86 frames.], batch size: 108, aishell_tot_loss[loss=0.2716, simple_loss=0.307, pruned_loss=0.1242, over 985144.23 frames.], datatang_tot_loss[loss=0.2908, simple_loss=0.3058, pruned_loss=0.1455, over 985479.44 frames.], batch size: 108, lr: 2.25e-03
+2022-06-18 11:46:22,400 INFO [train.py:874] (0/4) Epoch 2, batch 3000, aishell_loss[loss=0.3412, simple_loss=0.3515, pruned_loss=0.1655, over 4931.00 frames.], tot_loss[loss=0.2833, simple_loss=0.3052, pruned_loss=0.131, over 985796.77 frames.], batch size: 80, aishell_tot_loss[loss=0.2713, simple_loss=0.3065, pruned_loss=0.1235, over 984925.72 frames.], datatang_tot_loss[loss=0.2906, simple_loss=0.3052, pruned_loss=0.1447, over 985697.39 frames.], batch size: 80, lr: 2.25e-03
+2022-06-18 11:46:22,403 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 11:46:39,817 INFO [train.py:914] (0/4) Epoch 2, validation: loss=0.2091, simple_loss=0.2742, pruned_loss=0.07205, over 1622729.00 frames.
+2022-06-18 11:47:08,831 INFO [train.py:874] (0/4) Epoch 2, batch 3050, datatang_loss[loss=0.2726, simple_loss=0.2853, pruned_loss=0.1299, over 4916.00 frames.], tot_loss[loss=0.2813, simple_loss=0.3038, pruned_loss=0.1296, over 986088.76 frames.], batch size: 64, aishell_tot_loss[loss=0.2705, simple_loss=0.3056, pruned_loss=0.1224, over 985285.51 frames.], datatang_tot_loss[loss=0.29, simple_loss=0.3044, pruned_loss=0.1437, over 985763.69 frames.], batch size: 64, lr: 2.24e-03
+2022-06-18 11:47:40,865 INFO [train.py:874] (0/4) Epoch 2, batch 3100, aishell_loss[loss=0.2247, simple_loss=0.2766, pruned_loss=0.08642, over 4880.00 frames.], tot_loss[loss=0.2805, simple_loss=0.304, pruned_loss=0.1286, over 986216.14 frames.], batch size: 36, aishell_tot_loss[loss=0.2706, simple_loss=0.306, pruned_loss=0.1217, over 985467.32 frames.], datatang_tot_loss[loss=0.2897, simple_loss=0.3039, pruned_loss=0.1431, over 985868.76 frames.], batch size: 36, lr: 2.24e-03
+2022-06-18 11:48:08,416 INFO [train.py:874] (0/4) Epoch 2, batch 3150, aishell_loss[loss=0.2403, simple_loss=0.2802, pruned_loss=0.1002, over 4946.00 frames.], tot_loss[loss=0.2817, simple_loss=0.3034, pruned_loss=0.1302, over 985762.42 frames.], batch size: 54, aishell_tot_loss[loss=0.2712, simple_loss=0.3057, pruned_loss=0.122, over 985072.58 frames.], datatang_tot_loss[loss=0.2907, simple_loss=0.3034, pruned_loss=0.1437, over 985917.23 frames.], batch size: 54, lr: 2.23e-03
+2022-06-18 11:48:40,362 INFO [train.py:874] (0/4) Epoch 2, batch 3200, datatang_loss[loss=0.2402, simple_loss=0.2667, pruned_loss=0.1068, over 4925.00 frames.], tot_loss[loss=0.2787, simple_loss=0.3019, pruned_loss=0.1279, over 985677.54 frames.], batch size: 57, aishell_tot_loss[loss=0.2697, simple_loss=0.3046, pruned_loss=0.1206, over 985066.33 frames.], datatang_tot_loss[loss=0.2893, simple_loss=0.3027, pruned_loss=0.1421, over 985891.15 frames.], batch size: 57, lr: 2.23e-03
+2022-06-18 11:49:11,900 INFO [train.py:874] (0/4) Epoch 2, batch 3250, datatang_loss[loss=0.3028, simple_loss=0.3088, pruned_loss=0.1484, over 4926.00 frames.], tot_loss[loss=0.2774, simple_loss=0.3017, pruned_loss=0.1267, over 985318.45 frames.], batch size: 73, aishell_tot_loss[loss=0.2685, simple_loss=0.3039, pruned_loss=0.1193, over 984746.25 frames.], datatang_tot_loss[loss=0.289, simple_loss=0.3026, pruned_loss=0.1415, over 985864.89 frames.], batch size: 73, lr: 2.22e-03
+2022-06-18 11:49:40,186 INFO [train.py:874] (0/4) Epoch 2, batch 3300, aishell_loss[loss=0.2954, simple_loss=0.3276, pruned_loss=0.1316, over 4941.00 frames.], tot_loss[loss=0.2777, simple_loss=0.302, pruned_loss=0.1268, over 984960.16 frames.], batch size: 64, aishell_tot_loss[loss=0.2684, simple_loss=0.3035, pruned_loss=0.119, over 984582.55 frames.], datatang_tot_loss[loss=0.2894, simple_loss=0.303, pruned_loss=0.1412, over 985656.86 frames.], batch size: 64, lr: 2.22e-03
+2022-06-18 11:50:11,289 INFO [train.py:874] (0/4) Epoch 2, batch 3350, datatang_loss[loss=0.2773, simple_loss=0.2915, pruned_loss=0.1315, over 4929.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3024, pruned_loss=0.1272, over 985064.25 frames.], batch size: 73, aishell_tot_loss[loss=0.2689, simple_loss=0.3035, pruned_loss=0.1192, over 984765.90 frames.], datatang_tot_loss[loss=0.2891, simple_loss=0.303, pruned_loss=0.1405, over 985539.00 frames.], batch size: 73, lr: 2.21e-03
+2022-06-18 11:50:42,253 INFO [train.py:874] (0/4) Epoch 2, batch 3400, aishell_loss[loss=0.2539, simple_loss=0.3009, pruned_loss=0.1035, over 4904.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3021, pruned_loss=0.1256, over 985123.17 frames.], batch size: 33, aishell_tot_loss[loss=0.2677, simple_loss=0.3032, pruned_loss=0.118, over 984817.44 frames.], datatang_tot_loss[loss=0.2884, simple_loss=0.3028, pruned_loss=0.1396, over 985525.38 frames.], batch size: 33, lr: 2.21e-03
+2022-06-18 11:51:10,166 INFO [train.py:874] (0/4) Epoch 2, batch 3450, datatang_loss[loss=0.2965, simple_loss=0.3145, pruned_loss=0.1392, over 4949.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3014, pruned_loss=0.125, over 985191.51 frames.], batch size: 91, aishell_tot_loss[loss=0.2667, simple_loss=0.3028, pruned_loss=0.117, over 984631.22 frames.], datatang_tot_loss[loss=0.288, simple_loss=0.3023, pruned_loss=0.1391, over 985763.42 frames.], batch size: 91, lr: 2.20e-03
+2022-06-18 11:51:42,273 INFO [train.py:874] (0/4) Epoch 2, batch 3500, aishell_loss[loss=0.3028, simple_loss=0.336, pruned_loss=0.1348, over 4910.00 frames.], tot_loss[loss=0.2764, simple_loss=0.3023, pruned_loss=0.1252, over 985233.42 frames.], batch size: 41, aishell_tot_loss[loss=0.2679, simple_loss=0.3042, pruned_loss=0.1173, over 984539.81 frames.], datatang_tot_loss[loss=0.2872, simple_loss=0.3016, pruned_loss=0.1384, over 985909.81 frames.], batch size: 41, lr: 2.20e-03
+2022-06-18 11:52:10,599 INFO [train.py:874] (0/4) Epoch 2, batch 3550, aishell_loss[loss=0.252, simple_loss=0.2991, pruned_loss=0.1025, over 4973.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3018, pruned_loss=0.1245, over 985303.27 frames.], batch size: 39, aishell_tot_loss[loss=0.2677, simple_loss=0.3039, pruned_loss=0.117, over 984673.72 frames.], datatang_tot_loss[loss=0.2864, simple_loss=0.3011, pruned_loss=0.1377, over 985861.00 frames.], batch size: 39, lr: 2.19e-03
+2022-06-18 11:52:42,014 INFO [train.py:874] (0/4) Epoch 2, batch 3600, aishell_loss[loss=0.2911, simple_loss=0.3286, pruned_loss=0.1268, over 4974.00 frames.], tot_loss[loss=0.2867, simple_loss=0.3044, pruned_loss=0.1345, over 985621.34 frames.], batch size: 69, aishell_tot_loss[loss=0.2692, simple_loss=0.3045, pruned_loss=0.1181, over 984994.56 frames.], datatang_tot_loss[loss=0.2964, simple_loss=0.3032, pruned_loss=0.1464, over 985889.24 frames.], batch size: 69, lr: 2.19e-03
+2022-06-18 11:53:13,264 INFO [train.py:874] (0/4) Epoch 2, batch 3650, aishell_loss[loss=0.269, simple_loss=0.314, pruned_loss=0.112, over 4963.00 frames.], tot_loss[loss=0.282, simple_loss=0.3027, pruned_loss=0.1307, over 985490.86 frames.], batch size: 61, aishell_tot_loss[loss=0.267, simple_loss=0.3032, pruned_loss=0.1164, over 985108.75 frames.], datatang_tot_loss[loss=0.2948, simple_loss=0.3027, pruned_loss=0.1448, over 985674.27 frames.], batch size: 61, lr: 2.18e-03
+2022-06-18 11:53:42,103 INFO [train.py:874] (0/4) Epoch 2, batch 3700, aishell_loss[loss=0.3261, simple_loss=0.342, pruned_loss=0.1551, over 4952.00 frames.], tot_loss[loss=0.2777, simple_loss=0.301, pruned_loss=0.1272, over 985645.59 frames.], batch size: 78, aishell_tot_loss[loss=0.2663, simple_loss=0.303, pruned_loss=0.1156, over 985242.11 frames.], datatang_tot_loss[loss=0.2914, simple_loss=0.301, pruned_loss=0.1422, over 985733.46 frames.], batch size: 78, lr: 2.18e-03
+2022-06-18 11:54:13,036 INFO [train.py:874] (0/4) Epoch 2, batch 3750, datatang_loss[loss=0.2306, simple_loss=0.2638, pruned_loss=0.09867, over 4944.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3007, pruned_loss=0.126, over 985999.21 frames.], batch size: 50, aishell_tot_loss[loss=0.2657, simple_loss=0.3027, pruned_loss=0.1151, over 985433.17 frames.], datatang_tot_loss[loss=0.2895, simple_loss=0.3008, pruned_loss=0.1402, over 985946.10 frames.], batch size: 50, lr: 2.17e-03
+2022-06-18 11:54:26,500 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-8000.pt
+2022-06-18 11:54:47,895 INFO [train.py:874] (0/4) Epoch 2, batch 3800, datatang_loss[loss=0.2632, simple_loss=0.2875, pruned_loss=0.1194, over 4925.00 frames.], tot_loss[loss=0.2769, simple_loss=0.3019, pruned_loss=0.126, over 986246.98 frames.], batch size: 71, aishell_tot_loss[loss=0.2665, simple_loss=0.3033, pruned_loss=0.1156, over 985712.95 frames.], datatang_tot_loss[loss=0.289, simple_loss=0.3012, pruned_loss=0.1394, over 986002.48 frames.], batch size: 71, lr: 2.17e-03
+2022-06-18 11:55:15,866 INFO [train.py:874] (0/4) Epoch 2, batch 3850, datatang_loss[loss=0.2315, simple_loss=0.2661, pruned_loss=0.0984, over 4876.00 frames.], tot_loss[loss=0.2774, simple_loss=0.3027, pruned_loss=0.126, over 986105.60 frames.], batch size: 24, aishell_tot_loss[loss=0.268, simple_loss=0.3043, pruned_loss=0.1164, over 985757.05 frames.], datatang_tot_loss[loss=0.2879, simple_loss=0.3011, pruned_loss=0.1382, over 985894.66 frames.], batch size: 24, lr: 2.16e-03
+2022-06-18 11:55:45,602 INFO [train.py:874] (0/4) Epoch 2, batch 3900, datatang_loss[loss=0.2398, simple_loss=0.2537, pruned_loss=0.113, over 4973.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3016, pruned_loss=0.1249, over 986163.21 frames.], batch size: 43, aishell_tot_loss[loss=0.2674, simple_loss=0.3037, pruned_loss=0.1161, over 985678.16 frames.], datatang_tot_loss[loss=0.2861, simple_loss=0.3005, pruned_loss=0.1365, over 986091.73 frames.], batch size: 43, lr: 2.16e-03
+2022-06-18 11:56:14,177 INFO [train.py:874] (0/4) Epoch 2, batch 3950, datatang_loss[loss=0.2421, simple_loss=0.2682, pruned_loss=0.1081, over 4972.00 frames.], tot_loss[loss=0.2749, simple_loss=0.3014, pruned_loss=0.1242, over 986039.60 frames.], batch size: 31, aishell_tot_loss[loss=0.2674, simple_loss=0.3039, pruned_loss=0.116, over 985822.20 frames.], datatang_tot_loss[loss=0.2848, simple_loss=0.3001, pruned_loss=0.1354, over 985884.75 frames.], batch size: 31, lr: 2.15e-03
+2022-06-18 11:56:45,168 INFO [train.py:874] (0/4) Epoch 2, batch 4000, aishell_loss[loss=0.2841, simple_loss=0.3248, pruned_loss=0.1217, over 4938.00 frames.], tot_loss[loss=0.2715, simple_loss=0.2989, pruned_loss=0.1221, over 985804.29 frames.], batch size: 56, aishell_tot_loss[loss=0.2659, simple_loss=0.3026, pruned_loss=0.115, over 985640.76 frames.], datatang_tot_loss[loss=0.2822, simple_loss=0.2987, pruned_loss=0.1334, over 985865.89 frames.], batch size: 56, lr: 2.15e-03
+2022-06-18 11:56:45,170 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 11:57:01,103 INFO [train.py:914] (0/4) Epoch 2, validation: loss=0.2024, simple_loss=0.2723, pruned_loss=0.06625, over 1622729.00 frames.
+2022-06-18 11:57:30,531 INFO [train.py:874] (0/4) Epoch 2, batch 4050, datatang_loss[loss=0.23, simple_loss=0.2732, pruned_loss=0.09346, over 4933.00 frames.], tot_loss[loss=0.2708, simple_loss=0.2988, pruned_loss=0.1215, over 985541.51 frames.], batch size: 73, aishell_tot_loss[loss=0.2662, simple_loss=0.3028, pruned_loss=0.1152, over 985389.76 frames.], datatang_tot_loss[loss=0.2802, simple_loss=0.2979, pruned_loss=0.1317, over 985851.48 frames.], batch size: 73, lr: 2.14e-03
+2022-06-18 11:58:00,708 INFO [train.py:874] (0/4) Epoch 2, batch 4100, aishell_loss[loss=0.2574, simple_loss=0.2991, pruned_loss=0.1079, over 4914.00 frames.], tot_loss[loss=0.2719, simple_loss=0.2997, pruned_loss=0.122, over 985171.06 frames.], batch size: 41, aishell_tot_loss[loss=0.2668, simple_loss=0.3034, pruned_loss=0.1154, over 984791.91 frames.], datatang_tot_loss[loss=0.28, simple_loss=0.2981, pruned_loss=0.1313, over 986030.68 frames.], batch size: 41, lr: 2.14e-03
+2022-06-18 11:58:09,788 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-2.pt
+2022-06-18 11:59:09,053 INFO [train.py:874] (0/4) Epoch 3, batch 50, aishell_loss[loss=0.2373, simple_loss=0.2535, pruned_loss=0.1106, over 4978.00 frames.], tot_loss[loss=0.2554, simple_loss=0.2873, pruned_loss=0.1117, over 218289.76 frames.], batch size: 21, aishell_tot_loss[loss=0.2554, simple_loss=0.2924, pruned_loss=0.1092, over 120415.90 frames.], datatang_tot_loss[loss=0.256, simple_loss=0.2824, pruned_loss=0.1148, over 111517.27 frames.], batch size: 21, lr: 2.09e-03
+2022-06-18 11:59:39,422 INFO [train.py:874] (0/4) Epoch 3, batch 100, aishell_loss[loss=0.2741, simple_loss=0.3058, pruned_loss=0.1211, over 4969.00 frames.], tot_loss[loss=0.2577, simple_loss=0.2894, pruned_loss=0.113, over 388392.81 frames.], batch size: 61, aishell_tot_loss[loss=0.2565, simple_loss=0.2956, pruned_loss=0.1087, over 210527.67 frames.], datatang_tot_loss[loss=0.2585, simple_loss=0.2835, pruned_loss=0.1167, over 226180.17 frames.], batch size: 61, lr: 2.09e-03
+2022-06-18 12:00:11,062 INFO [train.py:874] (0/4) Epoch 3, batch 150, datatang_loss[loss=0.2624, simple_loss=0.2916, pruned_loss=0.1165, over 4934.00 frames.], tot_loss[loss=0.2622, simple_loss=0.2895, pruned_loss=0.1174, over 520753.91 frames.], batch size: 88, aishell_tot_loss[loss=0.2653, simple_loss=0.2987, pruned_loss=0.1159, over 273961.48 frames.], datatang_tot_loss[loss=0.2584, simple_loss=0.2823, pruned_loss=0.1173, over 341812.81 frames.], batch size: 88, lr: 2.08e-03
+2022-06-18 12:00:39,816 INFO [train.py:874] (0/4) Epoch 3, batch 200, datatang_loss[loss=0.2134, simple_loss=0.2636, pruned_loss=0.08156, over 4868.00 frames.], tot_loss[loss=0.262, simple_loss=0.2932, pruned_loss=0.1154, over 623976.98 frames.], batch size: 30, aishell_tot_loss[loss=0.2641, simple_loss=0.3016, pruned_loss=0.1132, over 388563.57 frames.], datatang_tot_loss[loss=0.2588, simple_loss=0.2832, pruned_loss=0.1172, over 388539.75 frames.], batch size: 30, lr: 2.08e-03
+2022-06-18 12:01:10,605 INFO [train.py:874] (0/4) Epoch 3, batch 250, aishell_loss[loss=0.2552, simple_loss=0.3029, pruned_loss=0.1038, over 4976.00 frames.], tot_loss[loss=0.2621, simple_loss=0.2944, pruned_loss=0.1149, over 704431.86 frames.], batch size: 61, aishell_tot_loss[loss=0.2617, simple_loss=0.3018, pruned_loss=0.1108, over 459065.72 frames.], datatang_tot_loss[loss=0.2616, simple_loss=0.2852, pruned_loss=0.119, over 458971.61 frames.], batch size: 61, lr: 2.07e-03
+2022-06-18 12:01:42,549 INFO [train.py:874] (0/4) Epoch 3, batch 300, datatang_loss[loss=0.2432, simple_loss=0.2781, pruned_loss=0.1041, over 4967.00 frames.], tot_loss[loss=0.2612, simple_loss=0.2944, pruned_loss=0.114, over 766842.65 frames.], batch size: 60, aishell_tot_loss[loss=0.257, simple_loss=0.2991, pruned_loss=0.1075, over 530346.92 frames.], datatang_tot_loss[loss=0.265, simple_loss=0.2878, pruned_loss=0.1211, over 511610.78 frames.], batch size: 60, lr: 2.07e-03
+2022-06-18 12:02:11,411 INFO [train.py:874] (0/4) Epoch 3, batch 350, datatang_loss[loss=0.3349, simple_loss=0.3393, pruned_loss=0.1652, over 4953.00 frames.], tot_loss[loss=0.262, simple_loss=0.2941, pruned_loss=0.1149, over 815264.86 frames.], batch size: 99, aishell_tot_loss[loss=0.2561, simple_loss=0.2979, pruned_loss=0.1072, over 575568.43 frames.], datatang_tot_loss[loss=0.2669, simple_loss=0.2893, pruned_loss=0.1222, over 575925.95 frames.], batch size: 99, lr: 2.06e-03
+2022-06-18 12:02:42,549 INFO [train.py:874] (0/4) Epoch 3, batch 400, aishell_loss[loss=0.2596, simple_loss=0.3048, pruned_loss=0.1072, over 4945.00 frames.], tot_loss[loss=0.2616, simple_loss=0.2941, pruned_loss=0.1146, over 853161.43 frames.], batch size: 45, aishell_tot_loss[loss=0.2548, simple_loss=0.2972, pruned_loss=0.1062, over 618395.46 frames.], datatang_tot_loss[loss=0.2676, simple_loss=0.2904, pruned_loss=0.1224, over 629678.73 frames.], batch size: 45, lr: 2.06e-03
+2022-06-18 12:03:13,328 INFO [train.py:874] (0/4) Epoch 3, batch 450, aishell_loss[loss=0.3162, simple_loss=0.3563, pruned_loss=0.1381, over 4961.00 frames.], tot_loss[loss=0.2624, simple_loss=0.2951, pruned_loss=0.1148, over 882819.41 frames.], batch size: 40, aishell_tot_loss[loss=0.2553, simple_loss=0.2979, pruned_loss=0.1063, over 668182.33 frames.], datatang_tot_loss[loss=0.2688, simple_loss=0.2911, pruned_loss=0.1232, over 665460.59 frames.], batch size: 40, lr: 2.05e-03
+2022-06-18 12:03:41,568 INFO [train.py:874] (0/4) Epoch 3, batch 500, datatang_loss[loss=0.2577, simple_loss=0.2801, pruned_loss=0.1177, over 4977.00 frames.], tot_loss[loss=0.2622, simple_loss=0.2951, pruned_loss=0.1146, over 905300.60 frames.], batch size: 40, aishell_tot_loss[loss=0.2561, simple_loss=0.2986, pruned_loss=0.1068, over 702399.52 frames.], datatang_tot_loss[loss=0.2676, simple_loss=0.2907, pruned_loss=0.1222, over 705997.96 frames.], batch size: 40, lr: 2.05e-03
+2022-06-18 12:04:12,853 INFO [train.py:874] (0/4) Epoch 3, batch 550, aishell_loss[loss=0.2463, simple_loss=0.2909, pruned_loss=0.1008, over 4921.00 frames.], tot_loss[loss=0.2636, simple_loss=0.2967, pruned_loss=0.1152, over 923413.16 frames.], batch size: 46, aishell_tot_loss[loss=0.2559, simple_loss=0.2985, pruned_loss=0.1067, over 735982.04 frames.], datatang_tot_loss[loss=0.2697, simple_loss=0.2929, pruned_loss=0.1232, over 739051.14 frames.], batch size: 46, lr: 2.05e-03
+2022-06-18 12:04:44,434 INFO [train.py:874] (0/4) Epoch 3, batch 600, aishell_loss[loss=0.2696, simple_loss=0.3089, pruned_loss=0.1152, over 4972.00 frames.], tot_loss[loss=0.2681, simple_loss=0.2986, pruned_loss=0.1188, over 937342.77 frames.], batch size: 39, aishell_tot_loss[loss=0.2576, simple_loss=0.2996, pruned_loss=0.1078, over 760892.52 frames.], datatang_tot_loss[loss=0.2737, simple_loss=0.2948, pruned_loss=0.1263, over 772584.79 frames.], batch size: 39, lr: 2.04e-03
+2022-06-18 12:05:11,863 INFO [train.py:874] (0/4) Epoch 3, batch 650, datatang_loss[loss=0.2227, simple_loss=0.266, pruned_loss=0.08966, over 4930.00 frames.], tot_loss[loss=0.2667, simple_loss=0.2981, pruned_loss=0.1177, over 947947.31 frames.], batch size: 34, aishell_tot_loss[loss=0.2581, simple_loss=0.2998, pruned_loss=0.1082, over 786459.69 frames.], datatang_tot_loss[loss=0.2721, simple_loss=0.2944, pruned_loss=0.125, over 798407.05 frames.], batch size: 34, lr: 2.04e-03
+2022-06-18 12:05:43,568 INFO [train.py:874] (0/4) Epoch 3, batch 700, aishell_loss[loss=0.2736, simple_loss=0.3258, pruned_loss=0.1107, over 4929.00 frames.], tot_loss[loss=0.2657, simple_loss=0.2984, pruned_loss=0.1165, over 956493.78 frames.], batch size: 49, aishell_tot_loss[loss=0.2589, simple_loss=0.3011, pruned_loss=0.1083, over 806344.52 frames.], datatang_tot_loss[loss=0.2703, simple_loss=0.294, pruned_loss=0.1233, over 823949.48 frames.], batch size: 49, lr: 2.03e-03
+2022-06-18 12:06:14,589 INFO [train.py:874] (0/4) Epoch 3, batch 750, aishell_loss[loss=0.2862, simple_loss=0.3207, pruned_loss=0.1258, over 4880.00 frames.], tot_loss[loss=0.2638, simple_loss=0.2976, pruned_loss=0.115, over 963217.77 frames.], batch size: 42, aishell_tot_loss[loss=0.2576, simple_loss=0.3004, pruned_loss=0.1074, over 830811.01 frames.], datatang_tot_loss[loss=0.2699, simple_loss=0.2938, pruned_loss=0.123, over 840177.58 frames.], batch size: 42, lr: 2.03e-03
+2022-06-18 12:06:42,403 INFO [train.py:874] (0/4) Epoch 3, batch 800, datatang_loss[loss=0.2712, simple_loss=0.298, pruned_loss=0.1221, over 4932.00 frames.], tot_loss[loss=0.2612, simple_loss=0.2962, pruned_loss=0.1131, over 967923.52 frames.], batch size: 71, aishell_tot_loss[loss=0.2561, simple_loss=0.2993, pruned_loss=0.1064, over 851712.26 frames.], datatang_tot_loss[loss=0.2685, simple_loss=0.2932, pruned_loss=0.1219, over 854473.61 frames.], batch size: 71, lr: 2.02e-03
+2022-06-18 12:07:14,003 INFO [train.py:874] (0/4) Epoch 3, batch 850, datatang_loss[loss=0.2145, simple_loss=0.2492, pruned_loss=0.08985, over 4958.00 frames.], tot_loss[loss=0.2604, simple_loss=0.2957, pruned_loss=0.1125, over 971670.94 frames.], batch size: 67, aishell_tot_loss[loss=0.2552, simple_loss=0.299, pruned_loss=0.1057, over 866668.69 frames.], datatang_tot_loss[loss=0.2681, simple_loss=0.2929, pruned_loss=0.1216, over 870524.79 frames.], batch size: 67, lr: 2.02e-03
+2022-06-18 12:07:44,971 INFO [train.py:874] (0/4) Epoch 3, batch 900, aishell_loss[loss=0.3093, simple_loss=0.3372, pruned_loss=0.1407, over 4968.00 frames.], tot_loss[loss=0.2614, simple_loss=0.2964, pruned_loss=0.1132, over 974717.32 frames.], batch size: 61, aishell_tot_loss[loss=0.2555, simple_loss=0.299, pruned_loss=0.1059, over 882336.54 frames.], datatang_tot_loss[loss=0.2689, simple_loss=0.2937, pruned_loss=0.1221, over 882390.42 frames.], batch size: 61, lr: 2.02e-03
+2022-06-18 12:08:12,991 INFO [train.py:874] (0/4) Epoch 3, batch 950, datatang_loss[loss=0.261, simple_loss=0.2892, pruned_loss=0.1164, over 4927.00 frames.], tot_loss[loss=0.2619, simple_loss=0.2964, pruned_loss=0.1137, over 977413.90 frames.], batch size: 73, aishell_tot_loss[loss=0.2547, simple_loss=0.2986, pruned_loss=0.1054, over 892325.10 frames.], datatang_tot_loss[loss=0.2697, simple_loss=0.2942, pruned_loss=0.1226, over 896970.04 frames.], batch size: 73, lr: 2.01e-03
+2022-06-18 12:08:44,383 INFO [train.py:874] (0/4) Epoch 3, batch 1000, datatang_loss[loss=0.2471, simple_loss=0.2833, pruned_loss=0.1055, over 4922.00 frames.], tot_loss[loss=0.2611, simple_loss=0.2961, pruned_loss=0.113, over 979159.94 frames.], batch size: 71, aishell_tot_loss[loss=0.2542, simple_loss=0.2981, pruned_loss=0.1052, over 903954.95 frames.], datatang_tot_loss[loss=0.2694, simple_loss=0.2944, pruned_loss=0.1222, over 906716.98 frames.], batch size: 71, lr: 2.01e-03
+2022-06-18 12:08:44,386 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 12:09:01,538 INFO [train.py:914] (0/4) Epoch 3, validation: loss=0.1966, simple_loss=0.2677, pruned_loss=0.06274, over 1622729.00 frames.
+2022-06-18 12:09:30,024 INFO [train.py:874] (0/4) Epoch 3, batch 1050, datatang_loss[loss=0.2438, simple_loss=0.2739, pruned_loss=0.1068, over 4966.00 frames.], tot_loss[loss=0.2605, simple_loss=0.2956, pruned_loss=0.1127, over 980581.42 frames.], batch size: 67, aishell_tot_loss[loss=0.2529, simple_loss=0.2973, pruned_loss=0.1043, over 913385.25 frames.], datatang_tot_loss[loss=0.2698, simple_loss=0.2946, pruned_loss=0.1225, over 916181.65 frames.], batch size: 67, lr: 2.00e-03
+2022-06-18 12:10:01,703 INFO [train.py:874] (0/4) Epoch 3, batch 1100, aishell_loss[loss=0.2532, simple_loss=0.3039, pruned_loss=0.1012, over 4868.00 frames.], tot_loss[loss=0.2599, simple_loss=0.2957, pruned_loss=0.1121, over 981539.95 frames.], batch size: 36, aishell_tot_loss[loss=0.2534, simple_loss=0.2979, pruned_loss=0.1045, over 923504.00 frames.], datatang_tot_loss[loss=0.2689, simple_loss=0.294, pruned_loss=0.1219, over 922595.19 frames.], batch size: 36, lr: 2.00e-03
+2022-06-18 12:10:29,520 INFO [train.py:874] (0/4) Epoch 3, batch 1150, datatang_loss[loss=0.2665, simple_loss=0.3015, pruned_loss=0.1157, over 4925.00 frames.], tot_loss[loss=0.2597, simple_loss=0.2957, pruned_loss=0.1119, over 982715.17 frames.], batch size: 88, aishell_tot_loss[loss=0.252, simple_loss=0.2971, pruned_loss=0.1034, over 930725.18 frames.], datatang_tot_loss[loss=0.2697, simple_loss=0.2946, pruned_loss=0.1224, over 930399.47 frames.], batch size: 88, lr: 2.00e-03
+2022-06-18 12:11:00,715 INFO [train.py:874] (0/4) Epoch 3, batch 1200, aishell_loss[loss=0.2281, simple_loss=0.2834, pruned_loss=0.08644, over 4948.00 frames.], tot_loss[loss=0.2606, simple_loss=0.2958, pruned_loss=0.1127, over 982887.62 frames.], batch size: 45, aishell_tot_loss[loss=0.2524, simple_loss=0.2967, pruned_loss=0.104, over 937206.22 frames.], datatang_tot_loss[loss=0.2703, simple_loss=0.2952, pruned_loss=0.1227, over 936383.21 frames.], batch size: 45, lr: 1.99e-03
+2022-06-18 12:11:31,968 INFO [train.py:874] (0/4) Epoch 3, batch 1250, datatang_loss[loss=0.2847, simple_loss=0.2913, pruned_loss=0.139, over 4924.00 frames.], tot_loss[loss=0.2606, simple_loss=0.2958, pruned_loss=0.1127, over 983479.24 frames.], batch size: 62, aishell_tot_loss[loss=0.2518, simple_loss=0.2964, pruned_loss=0.1036, over 943709.40 frames.], datatang_tot_loss[loss=0.271, simple_loss=0.2953, pruned_loss=0.1234, over 941335.38 frames.], batch size: 62, lr: 1.99e-03
+2022-06-18 12:11:59,374 INFO [train.py:874] (0/4) Epoch 3, batch 1300, datatang_loss[loss=0.2303, simple_loss=0.2622, pruned_loss=0.09916, over 4918.00 frames.], tot_loss[loss=0.2591, simple_loss=0.2949, pruned_loss=0.1116, over 983959.90 frames.], batch size: 75, aishell_tot_loss[loss=0.2502, simple_loss=0.2957, pruned_loss=0.1023, over 949245.06 frames.], datatang_tot_loss[loss=0.271, simple_loss=0.295, pruned_loss=0.1235, over 945920.13 frames.], batch size: 75, lr: 1.98e-03
+2022-06-18 12:12:30,438 INFO [train.py:874] (0/4) Epoch 3, batch 1350, datatang_loss[loss=0.2471, simple_loss=0.2869, pruned_loss=0.1037, over 4917.00 frames.], tot_loss[loss=0.2588, simple_loss=0.2945, pruned_loss=0.1115, over 984301.82 frames.], batch size: 77, aishell_tot_loss[loss=0.2504, simple_loss=0.296, pruned_loss=0.1024, over 952823.99 frames.], datatang_tot_loss[loss=0.2697, simple_loss=0.2942, pruned_loss=0.1226, over 951328.28 frames.], batch size: 77, lr: 1.98e-03
+2022-06-18 12:13:01,674 INFO [train.py:874] (0/4) Epoch 3, batch 1400, datatang_loss[loss=0.2254, simple_loss=0.2645, pruned_loss=0.09314, over 4908.00 frames.], tot_loss[loss=0.2578, simple_loss=0.2942, pruned_loss=0.1107, over 984312.87 frames.], batch size: 64, aishell_tot_loss[loss=0.2505, simple_loss=0.296, pruned_loss=0.1025, over 957109.69 frames.], datatang_tot_loss[loss=0.2688, simple_loss=0.2938, pruned_loss=0.1219, over 954610.81 frames.], batch size: 64, lr: 1.97e-03
+2022-06-18 12:13:29,152 INFO [train.py:874] (0/4) Epoch 3, batch 1450, aishell_loss[loss=0.2093, simple_loss=0.2656, pruned_loss=0.07657, over 4951.00 frames.], tot_loss[loss=0.2578, simple_loss=0.2946, pruned_loss=0.1105, over 984705.86 frames.], batch size: 56, aishell_tot_loss[loss=0.2496, simple_loss=0.2955, pruned_loss=0.1018, over 961086.94 frames.], datatang_tot_loss[loss=0.2697, simple_loss=0.2944, pruned_loss=0.1225, over 957683.64 frames.], batch size: 56, lr: 1.97e-03
+2022-06-18 12:14:01,022 INFO [train.py:874] (0/4) Epoch 3, batch 1500, datatang_loss[loss=0.2364, simple_loss=0.2778, pruned_loss=0.09746, over 4961.00 frames.], tot_loss[loss=0.2582, simple_loss=0.2939, pruned_loss=0.1112, over 984594.40 frames.], batch size: 60, aishell_tot_loss[loss=0.2486, simple_loss=0.2948, pruned_loss=0.1013, over 963927.84 frames.], datatang_tot_loss[loss=0.2707, simple_loss=0.2944, pruned_loss=0.1235, over 960694.83 frames.], batch size: 60, lr: 1.97e-03
+2022-06-18 12:14:30,079 INFO [train.py:874] (0/4) Epoch 3, batch 1550, aishell_loss[loss=0.2632, simple_loss=0.3069, pruned_loss=0.1097, over 4951.00 frames.], tot_loss[loss=0.2594, simple_loss=0.2952, pruned_loss=0.1118, over 984590.66 frames.], batch size: 56, aishell_tot_loss[loss=0.2491, simple_loss=0.2951, pruned_loss=0.1015, over 966198.65 frames.], datatang_tot_loss[loss=0.2711, simple_loss=0.2952, pruned_loss=0.1235, over 963663.36 frames.], batch size: 56, lr: 1.96e-03
+2022-06-18 12:14:59,941 INFO [train.py:874] (0/4) Epoch 3, batch 1600, datatang_loss[loss=0.2654, simple_loss=0.298, pruned_loss=0.1163, over 4961.00 frames.], tot_loss[loss=0.2581, simple_loss=0.2946, pruned_loss=0.1108, over 985038.49 frames.], batch size: 86, aishell_tot_loss[loss=0.249, simple_loss=0.2954, pruned_loss=0.1013, over 968257.04 frames.], datatang_tot_loss[loss=0.2694, simple_loss=0.2943, pruned_loss=0.1222, over 966739.23 frames.], batch size: 86, lr: 1.96e-03
+2022-06-18 12:15:30,919 INFO [train.py:874] (0/4) Epoch 3, batch 1650, aishell_loss[loss=0.2056, simple_loss=0.2346, pruned_loss=0.08828, over 4928.00 frames.], tot_loss[loss=0.2568, simple_loss=0.2942, pruned_loss=0.1098, over 984501.08 frames.], batch size: 21, aishell_tot_loss[loss=0.2485, simple_loss=0.2952, pruned_loss=0.1009, over 970414.26 frames.], datatang_tot_loss[loss=0.2687, simple_loss=0.294, pruned_loss=0.1217, over 968096.23 frames.], batch size: 21, lr: 1.96e-03
+2022-06-18 12:16:00,111 INFO [train.py:874] (0/4) Epoch 3, batch 1700, aishell_loss[loss=0.2225, simple_loss=0.2646, pruned_loss=0.0902, over 4985.00 frames.], tot_loss[loss=0.2544, simple_loss=0.292, pruned_loss=0.1084, over 985165.75 frames.], batch size: 30, aishell_tot_loss[loss=0.2464, simple_loss=0.2934, pruned_loss=0.09971, over 972028.36 frames.], datatang_tot_loss[loss=0.2673, simple_loss=0.2934, pruned_loss=0.1205, over 970820.37 frames.], batch size: 30, lr: 1.95e-03
+2022-06-18 12:16:30,619 INFO [train.py:874] (0/4) Epoch 3, batch 1750, datatang_loss[loss=0.3088, simple_loss=0.335, pruned_loss=0.1413, over 4907.00 frames.], tot_loss[loss=0.2544, simple_loss=0.2922, pruned_loss=0.1083, over 985078.88 frames.], batch size: 64, aishell_tot_loss[loss=0.2468, simple_loss=0.2939, pruned_loss=0.09984, over 973588.60 frames.], datatang_tot_loss[loss=0.2664, simple_loss=0.2928, pruned_loss=0.12, over 972412.39 frames.], batch size: 64, lr: 1.95e-03
+2022-06-18 12:17:02,301 INFO [train.py:874] (0/4) Epoch 3, batch 1800, datatang_loss[loss=0.2405, simple_loss=0.2704, pruned_loss=0.1053, over 4943.00 frames.], tot_loss[loss=0.2534, simple_loss=0.2915, pruned_loss=0.1076, over 985384.54 frames.], batch size: 50, aishell_tot_loss[loss=0.2462, simple_loss=0.2934, pruned_loss=0.09954, over 975272.27 frames.], datatang_tot_loss[loss=0.2656, simple_loss=0.2923, pruned_loss=0.1195, over 973876.27 frames.], batch size: 50, lr: 1.94e-03
+2022-06-18 12:17:29,487 INFO [train.py:874] (0/4) Epoch 3, batch 1850, datatang_loss[loss=0.2401, simple_loss=0.2653, pruned_loss=0.1075, over 4900.00 frames.], tot_loss[loss=0.2527, simple_loss=0.2909, pruned_loss=0.1072, over 985383.87 frames.], batch size: 42, aishell_tot_loss[loss=0.2457, simple_loss=0.2928, pruned_loss=0.09929, over 976565.89 frames.], datatang_tot_loss[loss=0.2649, simple_loss=0.2919, pruned_loss=0.119, over 975117.94 frames.], batch size: 42, lr: 1.94e-03
+2022-06-18 12:18:00,798 INFO [train.py:874] (0/4) Epoch 3, batch 1900, aishell_loss[loss=0.1569, simple_loss=0.1972, pruned_loss=0.05831, over 4943.00 frames.], tot_loss[loss=0.2521, simple_loss=0.2907, pruned_loss=0.1067, over 985385.54 frames.], batch size: 21, aishell_tot_loss[loss=0.2452, simple_loss=0.2922, pruned_loss=0.09907, over 977523.91 frames.], datatang_tot_loss[loss=0.2642, simple_loss=0.2919, pruned_loss=0.1182, over 976413.43 frames.], batch size: 21, lr: 1.94e-03
+2022-06-18 12:18:32,185 INFO [train.py:874] (0/4) Epoch 3, batch 1950, aishell_loss[loss=0.2453, simple_loss=0.2995, pruned_loss=0.09556, over 4943.00 frames.], tot_loss[loss=0.2521, simple_loss=0.2911, pruned_loss=0.1066, over 985768.17 frames.], batch size: 54, aishell_tot_loss[loss=0.2452, simple_loss=0.2927, pruned_loss=0.09885, over 978750.91 frames.], datatang_tot_loss[loss=0.2637, simple_loss=0.2915, pruned_loss=0.1179, over 977582.22 frames.], batch size: 54, lr: 1.93e-03
+2022-06-18 12:18:59,666 INFO [train.py:874] (0/4) Epoch 3, batch 2000, aishell_loss[loss=0.2491, simple_loss=0.3048, pruned_loss=0.09672, over 4874.00 frames.], tot_loss[loss=0.2537, simple_loss=0.2921, pruned_loss=0.1077, over 985385.26 frames.], batch size: 35, aishell_tot_loss[loss=0.2462, simple_loss=0.2936, pruned_loss=0.0994, over 979332.76 frames.], datatang_tot_loss[loss=0.2638, simple_loss=0.2915, pruned_loss=0.118, over 978384.39 frames.], batch size: 35, lr: 1.93e-03
+2022-06-18 12:18:59,668 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 12:19:15,683 INFO [train.py:914] (0/4) Epoch 3, validation: loss=0.1934, simple_loss=0.2657, pruned_loss=0.0605, over 1622729.00 frames.
+2022-06-18 12:19:43,609 INFO [train.py:874] (0/4) Epoch 3, batch 2050, aishell_loss[loss=0.2436, simple_loss=0.2924, pruned_loss=0.09739, over 4888.00 frames.], tot_loss[loss=0.2551, simple_loss=0.2931, pruned_loss=0.1086, over 985176.07 frames.], batch size: 50, aishell_tot_loss[loss=0.2464, simple_loss=0.2938, pruned_loss=0.09946, over 979654.11 frames.], datatang_tot_loss[loss=0.2645, simple_loss=0.2922, pruned_loss=0.1184, over 979381.60 frames.], batch size: 50, lr: 1.92e-03
+2022-06-18 12:20:13,700 INFO [train.py:874] (0/4) Epoch 3, batch 2100, datatang_loss[loss=0.2633, simple_loss=0.2988, pruned_loss=0.1139, over 4945.00 frames.], tot_loss[loss=0.2529, simple_loss=0.291, pruned_loss=0.1074, over 985085.86 frames.], batch size: 67, aishell_tot_loss[loss=0.2457, simple_loss=0.2933, pruned_loss=0.09908, over 980065.52 frames.], datatang_tot_loss[loss=0.2623, simple_loss=0.2905, pruned_loss=0.1171, over 980213.77 frames.], batch size: 67, lr: 1.92e-03
+2022-06-18 12:20:44,266 INFO [train.py:874] (0/4) Epoch 3, batch 2150, datatang_loss[loss=0.2743, simple_loss=0.2959, pruned_loss=0.1264, over 4922.00 frames.], tot_loss[loss=0.2522, simple_loss=0.2917, pruned_loss=0.1064, over 985146.31 frames.], batch size: 73, aishell_tot_loss[loss=0.2451, simple_loss=0.2934, pruned_loss=0.09841, over 980710.85 frames.], datatang_tot_loss[loss=0.2624, simple_loss=0.2909, pruned_loss=0.117, over 980791.80 frames.], batch size: 73, lr: 1.92e-03
+2022-06-18 12:21:13,463 INFO [train.py:874] (0/4) Epoch 3, batch 2200, aishell_loss[loss=0.2164, simple_loss=0.2736, pruned_loss=0.07963, over 4985.00 frames.], tot_loss[loss=0.2534, simple_loss=0.2919, pruned_loss=0.1075, over 984710.78 frames.], batch size: 30, aishell_tot_loss[loss=0.2441, simple_loss=0.2924, pruned_loss=0.09787, over 980873.54 frames.], datatang_tot_loss[loss=0.2643, simple_loss=0.292, pruned_loss=0.1183, over 981203.46 frames.], batch size: 30, lr: 1.91e-03
+2022-06-18 12:21:43,213 INFO [train.py:874] (0/4) Epoch 3, batch 2250, aishell_loss[loss=0.2579, simple_loss=0.3092, pruned_loss=0.1033, over 4946.00 frames.], tot_loss[loss=0.2519, simple_loss=0.2913, pruned_loss=0.1063, over 985034.09 frames.], batch size: 64, aishell_tot_loss[loss=0.2436, simple_loss=0.2922, pruned_loss=0.09748, over 981581.44 frames.], datatang_tot_loss[loss=0.2638, simple_loss=0.2914, pruned_loss=0.118, over 981712.69 frames.], batch size: 64, lr: 1.91e-03
+2022-06-18 12:22:13,503 INFO [train.py:874] (0/4) Epoch 3, batch 2300, datatang_loss[loss=0.2465, simple_loss=0.2813, pruned_loss=0.1058, over 4917.00 frames.], tot_loss[loss=0.2504, simple_loss=0.2901, pruned_loss=0.1053, over 984992.97 frames.], batch size: 77, aishell_tot_loss[loss=0.2425, simple_loss=0.2915, pruned_loss=0.09675, over 981856.02 frames.], datatang_tot_loss[loss=0.2629, simple_loss=0.2908, pruned_loss=0.1175, over 982189.56 frames.], batch size: 77, lr: 1.91e-03
+2022-06-18 12:22:41,794 INFO [train.py:874] (0/4) Epoch 3, batch 2350, datatang_loss[loss=0.251, simple_loss=0.2933, pruned_loss=0.1043, over 4923.00 frames.], tot_loss[loss=0.2501, simple_loss=0.2904, pruned_loss=0.1049, over 985132.58 frames.], batch size: 73, aishell_tot_loss[loss=0.2424, simple_loss=0.2918, pruned_loss=0.09651, over 982338.93 frames.], datatang_tot_loss[loss=0.2622, simple_loss=0.2905, pruned_loss=0.1169, over 982561.96 frames.], batch size: 73, lr: 1.90e-03
+2022-06-18 12:23:13,299 INFO [train.py:874] (0/4) Epoch 3, batch 2400, datatang_loss[loss=0.2433, simple_loss=0.2803, pruned_loss=0.1031, over 4926.00 frames.], tot_loss[loss=0.2484, simple_loss=0.2896, pruned_loss=0.1036, over 984954.35 frames.], batch size: 83, aishell_tot_loss[loss=0.2416, simple_loss=0.2915, pruned_loss=0.09588, over 982618.70 frames.], datatang_tot_loss[loss=0.2607, simple_loss=0.2897, pruned_loss=0.1158, over 982713.53 frames.], batch size: 83, lr: 1.90e-03
+2022-06-18 12:23:44,005 INFO [train.py:874] (0/4) Epoch 3, batch 2450, datatang_loss[loss=0.2565, simple_loss=0.2757, pruned_loss=0.1186, over 4957.00 frames.], tot_loss[loss=0.2502, simple_loss=0.2906, pruned_loss=0.1049, over 985176.20 frames.], batch size: 34, aishell_tot_loss[loss=0.2425, simple_loss=0.2923, pruned_loss=0.09634, over 982977.68 frames.], datatang_tot_loss[loss=0.2608, simple_loss=0.2899, pruned_loss=0.1158, over 983113.70 frames.], batch size: 34, lr: 1.89e-03
+2022-06-18 12:24:13,130 INFO [train.py:874] (0/4) Epoch 3, batch 2500, aishell_loss[loss=0.2505, simple_loss=0.2936, pruned_loss=0.1037, over 4893.00 frames.], tot_loss[loss=0.2508, simple_loss=0.2907, pruned_loss=0.1054, over 985016.60 frames.], batch size: 28, aishell_tot_loss[loss=0.2429, simple_loss=0.2925, pruned_loss=0.09669, over 982869.49 frames.], datatang_tot_loss[loss=0.2605, simple_loss=0.2897, pruned_loss=0.1156, over 983538.83 frames.], batch size: 28, lr: 1.89e-03
+2022-06-18 12:24:44,572 INFO [train.py:874] (0/4) Epoch 3, batch 2550, aishell_loss[loss=0.244, simple_loss=0.291, pruned_loss=0.09853, over 4984.00 frames.], tot_loss[loss=0.2504, simple_loss=0.2907, pruned_loss=0.1051, over 985393.32 frames.], batch size: 38, aishell_tot_loss[loss=0.2426, simple_loss=0.2924, pruned_loss=0.09642, over 983383.83 frames.], datatang_tot_loss[loss=0.2603, simple_loss=0.2897, pruned_loss=0.1155, over 983852.96 frames.], batch size: 38, lr: 1.89e-03
+2022-06-18 12:25:14,265 INFO [train.py:874] (0/4) Epoch 3, batch 2600, datatang_loss[loss=0.2646, simple_loss=0.2942, pruned_loss=0.1175, over 4933.00 frames.], tot_loss[loss=0.2495, simple_loss=0.2903, pruned_loss=0.1044, over 985474.15 frames.], batch size: 69, aishell_tot_loss[loss=0.2421, simple_loss=0.2921, pruned_loss=0.09602, over 983653.11 frames.], datatang_tot_loss[loss=0.2597, simple_loss=0.2894, pruned_loss=0.115, over 984089.61 frames.], batch size: 69, lr: 1.88e-03
+2022-06-18 12:25:43,667 INFO [train.py:874] (0/4) Epoch 3, batch 2650, datatang_loss[loss=0.225, simple_loss=0.2613, pruned_loss=0.09432, over 4914.00 frames.], tot_loss[loss=0.2481, simple_loss=0.2888, pruned_loss=0.1037, over 985323.88 frames.], batch size: 75, aishell_tot_loss[loss=0.2416, simple_loss=0.2918, pruned_loss=0.09573, over 983664.97 frames.], datatang_tot_loss[loss=0.2582, simple_loss=0.288, pruned_loss=0.1143, over 984294.59 frames.], batch size: 75, lr: 1.88e-03
+2022-06-18 12:26:15,017 INFO [train.py:874] (0/4) Epoch 3, batch 2700, aishell_loss[loss=0.2397, simple_loss=0.2955, pruned_loss=0.09198, over 4964.00 frames.], tot_loss[loss=0.2489, simple_loss=0.2901, pruned_loss=0.1039, over 985674.05 frames.], batch size: 40, aishell_tot_loss[loss=0.2415, simple_loss=0.292, pruned_loss=0.09548, over 984007.07 frames.], datatang_tot_loss[loss=0.259, simple_loss=0.2889, pruned_loss=0.1146, over 984631.69 frames.], batch size: 40, lr: 1.88e-03
+2022-06-18 12:26:44,657 INFO [train.py:874] (0/4) Epoch 3, batch 2750, aishell_loss[loss=0.1951, simple_loss=0.2594, pruned_loss=0.0654, over 4930.00 frames.], tot_loss[loss=0.2478, simple_loss=0.2894, pruned_loss=0.1031, over 985486.73 frames.], batch size: 41, aishell_tot_loss[loss=0.2405, simple_loss=0.2912, pruned_loss=0.09492, over 983979.12 frames.], datatang_tot_loss[loss=0.2589, simple_loss=0.2888, pruned_loss=0.1145, over 984799.85 frames.], batch size: 41, lr: 1.87e-03
+2022-06-18 12:27:14,023 INFO [train.py:874] (0/4) Epoch 3, batch 2800, aishell_loss[loss=0.2668, simple_loss=0.3164, pruned_loss=0.1086, over 4955.00 frames.], tot_loss[loss=0.2482, simple_loss=0.289, pruned_loss=0.1038, over 985783.63 frames.], batch size: 64, aishell_tot_loss[loss=0.24, simple_loss=0.2907, pruned_loss=0.0947, over 984253.70 frames.], datatang_tot_loss[loss=0.2593, simple_loss=0.2887, pruned_loss=0.1149, over 985092.58 frames.], batch size: 64, lr: 1.87e-03
+2022-06-18 12:27:45,744 INFO [train.py:874] (0/4) Epoch 3, batch 2850, aishell_loss[loss=0.1933, simple_loss=0.2469, pruned_loss=0.06991, over 4945.00 frames.], tot_loss[loss=0.2467, simple_loss=0.2883, pruned_loss=0.1026, over 985325.64 frames.], batch size: 25, aishell_tot_loss[loss=0.2387, simple_loss=0.2898, pruned_loss=0.09385, over 983919.83 frames.], datatang_tot_loss[loss=0.259, simple_loss=0.2886, pruned_loss=0.1147, over 985240.08 frames.], batch size: 25, lr: 1.87e-03
+2022-06-18 12:28:14,898 INFO [train.py:874] (0/4) Epoch 3, batch 2900, datatang_loss[loss=0.2835, simple_loss=0.3021, pruned_loss=0.1324, over 4974.00 frames.], tot_loss[loss=0.2462, simple_loss=0.2875, pruned_loss=0.1024, over 985251.57 frames.], batch size: 65, aishell_tot_loss[loss=0.2388, simple_loss=0.2898, pruned_loss=0.0939, over 984103.96 frames.], datatang_tot_loss[loss=0.258, simple_loss=0.2876, pruned_loss=0.1142, over 985175.00 frames.], batch size: 65, lr: 1.86e-03
+2022-06-18 12:28:45,259 INFO [train.py:874] (0/4) Epoch 3, batch 2950, datatang_loss[loss=0.259, simple_loss=0.2884, pruned_loss=0.1148, over 4976.00 frames.], tot_loss[loss=0.2472, simple_loss=0.2883, pruned_loss=0.103, over 985388.44 frames.], batch size: 60, aishell_tot_loss[loss=0.2389, simple_loss=0.2899, pruned_loss=0.09398, over 984186.97 frames.], datatang_tot_loss[loss=0.2582, simple_loss=0.2881, pruned_loss=0.1142, over 985366.19 frames.], batch size: 60, lr: 1.86e-03
+2022-06-18 12:29:16,450 INFO [train.py:874] (0/4) Epoch 3, batch 3000, aishell_loss[loss=0.2364, simple_loss=0.2754, pruned_loss=0.09869, over 4923.00 frames.], tot_loss[loss=0.2458, simple_loss=0.2875, pruned_loss=0.1021, over 985598.38 frames.], batch size: 32, aishell_tot_loss[loss=0.2383, simple_loss=0.2895, pruned_loss=0.09355, over 984500.86 frames.], datatang_tot_loss[loss=0.2573, simple_loss=0.2873, pruned_loss=0.1137, over 985424.62 frames.], batch size: 32, lr: 1.86e-03
+2022-06-18 12:29:16,453 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 12:29:32,000 INFO [train.py:914] (0/4) Epoch 3, validation: loss=0.1943, simple_loss=0.2686, pruned_loss=0.05999, over 1622729.00 frames.
+2022-06-18 12:30:03,145 INFO [train.py:874] (0/4) Epoch 3, batch 3050, datatang_loss[loss=0.2815, simple_loss=0.3036, pruned_loss=0.1297, over 4939.00 frames.], tot_loss[loss=0.2453, simple_loss=0.2878, pruned_loss=0.1014, over 985411.84 frames.], batch size: 69, aishell_tot_loss[loss=0.238, simple_loss=0.2899, pruned_loss=0.09308, over 984375.24 frames.], datatang_tot_loss[loss=0.2568, simple_loss=0.2871, pruned_loss=0.1133, over 985513.31 frames.], batch size: 69, lr: 1.85e-03
+2022-06-18 12:30:33,651 INFO [train.py:874] (0/4) Epoch 3, batch 3100, datatang_loss[loss=0.192, simple_loss=0.2352, pruned_loss=0.07438, over 4900.00 frames.], tot_loss[loss=0.2461, simple_loss=0.288, pruned_loss=0.1021, over 985277.04 frames.], batch size: 52, aishell_tot_loss[loss=0.238, simple_loss=0.2898, pruned_loss=0.09315, over 984329.95 frames.], datatang_tot_loss[loss=0.257, simple_loss=0.2873, pruned_loss=0.1134, over 985519.82 frames.], batch size: 52, lr: 1.85e-03
+2022-06-18 12:31:02,147 INFO [train.py:874] (0/4) Epoch 3, batch 3150, datatang_loss[loss=0.2357, simple_loss=0.2717, pruned_loss=0.09987, over 4925.00 frames.], tot_loss[loss=0.2461, simple_loss=0.2884, pruned_loss=0.1019, over 985528.28 frames.], batch size: 71, aishell_tot_loss[loss=0.2388, simple_loss=0.2906, pruned_loss=0.09346, over 984693.44 frames.], datatang_tot_loss[loss=0.2559, simple_loss=0.2868, pruned_loss=0.1125, over 985500.60 frames.], batch size: 71, lr: 1.85e-03
+2022-06-18 12:31:33,993 INFO [train.py:874] (0/4) Epoch 3, batch 3200, datatang_loss[loss=0.2378, simple_loss=0.2787, pruned_loss=0.09842, over 4922.00 frames.], tot_loss[loss=0.2475, simple_loss=0.2895, pruned_loss=0.1028, over 985182.40 frames.], batch size: 75, aishell_tot_loss[loss=0.2402, simple_loss=0.2918, pruned_loss=0.09432, over 984723.52 frames.], datatang_tot_loss[loss=0.2559, simple_loss=0.2866, pruned_loss=0.1126, over 985215.70 frames.], batch size: 75, lr: 1.84e-03
+2022-06-18 12:32:03,695 INFO [train.py:874] (0/4) Epoch 3, batch 3250, aishell_loss[loss=0.2169, simple_loss=0.2809, pruned_loss=0.07649, over 4911.00 frames.], tot_loss[loss=0.2497, simple_loss=0.2917, pruned_loss=0.1038, over 985278.85 frames.], batch size: 46, aishell_tot_loss[loss=0.2417, simple_loss=0.2931, pruned_loss=0.09514, over 984776.16 frames.], datatang_tot_loss[loss=0.2571, simple_loss=0.2876, pruned_loss=0.1133, over 985345.90 frames.], batch size: 46, lr: 1.84e-03
+2022-06-18 12:32:33,314 INFO [train.py:874] (0/4) Epoch 3, batch 3300, aishell_loss[loss=0.2593, simple_loss=0.3082, pruned_loss=0.1052, over 4928.00 frames.], tot_loss[loss=0.2501, simple_loss=0.2916, pruned_loss=0.1043, over 985661.12 frames.], batch size: 49, aishell_tot_loss[loss=0.2422, simple_loss=0.2934, pruned_loss=0.09555, over 984814.85 frames.], datatang_tot_loss[loss=0.2567, simple_loss=0.2876, pruned_loss=0.1129, over 985755.62 frames.], batch size: 49, lr: 1.84e-03
+2022-06-18 12:33:03,686 INFO [train.py:874] (0/4) Epoch 3, batch 3350, datatang_loss[loss=0.2808, simple_loss=0.3016, pruned_loss=0.13, over 4948.00 frames.], tot_loss[loss=0.2492, simple_loss=0.291, pruned_loss=0.1037, over 985609.15 frames.], batch size: 67, aishell_tot_loss[loss=0.2415, simple_loss=0.2928, pruned_loss=0.09505, over 984799.21 frames.], datatang_tot_loss[loss=0.257, simple_loss=0.2877, pruned_loss=0.1131, over 985822.80 frames.], batch size: 67, lr: 1.83e-03
+2022-06-18 12:33:33,268 INFO [train.py:874] (0/4) Epoch 3, batch 3400, aishell_loss[loss=0.2441, simple_loss=0.2914, pruned_loss=0.09838, over 4870.00 frames.], tot_loss[loss=0.2478, simple_loss=0.2902, pruned_loss=0.1027, over 985526.33 frames.], batch size: 37, aishell_tot_loss[loss=0.2412, simple_loss=0.2927, pruned_loss=0.09488, over 984784.86 frames.], datatang_tot_loss[loss=0.2559, simple_loss=0.2871, pruned_loss=0.1124, over 985837.64 frames.], batch size: 37, lr: 1.83e-03
+2022-06-18 12:34:03,641 INFO [train.py:874] (0/4) Epoch 3, batch 3450, datatang_loss[loss=0.2595, simple_loss=0.2988, pruned_loss=0.1101, over 4962.00 frames.], tot_loss[loss=0.2461, simple_loss=0.2891, pruned_loss=0.1016, over 985492.63 frames.], batch size: 45, aishell_tot_loss[loss=0.2396, simple_loss=0.2916, pruned_loss=0.09382, over 984835.24 frames.], datatang_tot_loss[loss=0.2557, simple_loss=0.2871, pruned_loss=0.1122, over 985811.87 frames.], batch size: 45, lr: 1.83e-03
+2022-06-18 12:34:34,624 INFO [train.py:874] (0/4) Epoch 3, batch 3500, aishell_loss[loss=0.2489, simple_loss=0.3001, pruned_loss=0.09882, over 4970.00 frames.], tot_loss[loss=0.2454, simple_loss=0.2883, pruned_loss=0.1012, over 985631.11 frames.], batch size: 44, aishell_tot_loss[loss=0.2389, simple_loss=0.2911, pruned_loss=0.09337, over 984918.87 frames.], datatang_tot_loss[loss=0.2552, simple_loss=0.2867, pruned_loss=0.1119, over 985917.68 frames.], batch size: 44, lr: 1.82e-03
+2022-06-18 12:35:02,636 INFO [train.py:874] (0/4) Epoch 3, batch 3550, datatang_loss[loss=0.263, simple_loss=0.2965, pruned_loss=0.1148, over 4924.00 frames.], tot_loss[loss=0.2448, simple_loss=0.2878, pruned_loss=0.1008, over 985839.36 frames.], batch size: 62, aishell_tot_loss[loss=0.2386, simple_loss=0.2909, pruned_loss=0.09309, over 985134.57 frames.], datatang_tot_loss[loss=0.2544, simple_loss=0.2863, pruned_loss=0.1112, over 985971.70 frames.], batch size: 62, lr: 1.82e-03
+2022-06-18 12:35:34,109 INFO [train.py:874] (0/4) Epoch 3, batch 3600, aishell_loss[loss=0.2376, simple_loss=0.2897, pruned_loss=0.09273, over 4947.00 frames.], tot_loss[loss=0.2461, simple_loss=0.2886, pruned_loss=0.1018, over 985513.47 frames.], batch size: 45, aishell_tot_loss[loss=0.2389, simple_loss=0.2912, pruned_loss=0.09328, over 984893.58 frames.], datatang_tot_loss[loss=0.2552, simple_loss=0.2866, pruned_loss=0.1119, over 985951.62 frames.], batch size: 45, lr: 1.82e-03
+2022-06-18 12:36:04,024 INFO [train.py:874] (0/4) Epoch 3, batch 3650, datatang_loss[loss=0.1908, simple_loss=0.2468, pruned_loss=0.06739, over 4905.00 frames.], tot_loss[loss=0.2456, simple_loss=0.2879, pruned_loss=0.1016, over 985902.44 frames.], batch size: 47, aishell_tot_loss[loss=0.2379, simple_loss=0.2905, pruned_loss=0.09267, over 984948.68 frames.], datatang_tot_loss[loss=0.2549, simple_loss=0.2866, pruned_loss=0.1116, over 986296.07 frames.], batch size: 47, lr: 1.81e-03
+2022-06-18 12:36:12,011 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-12000.pt
+2022-06-18 12:36:39,221 INFO [train.py:874] (0/4) Epoch 3, batch 3700, aishell_loss[loss=0.2737, simple_loss=0.3253, pruned_loss=0.1111, over 4912.00 frames.], tot_loss[loss=0.2463, simple_loss=0.2882, pruned_loss=0.1022, over 985533.94 frames.], batch size: 79, aishell_tot_loss[loss=0.2381, simple_loss=0.2905, pruned_loss=0.09282, over 984630.76 frames.], datatang_tot_loss[loss=0.2548, simple_loss=0.2868, pruned_loss=0.1115, over 986251.53 frames.], batch size: 79, lr: 1.81e-03
+2022-06-18 12:37:08,716 INFO [train.py:874] (0/4) Epoch 3, batch 3750, aishell_loss[loss=0.2716, simple_loss=0.3162, pruned_loss=0.1135, over 4916.00 frames.], tot_loss[loss=0.2455, simple_loss=0.2873, pruned_loss=0.1019, over 985545.85 frames.], batch size: 78, aishell_tot_loss[loss=0.2385, simple_loss=0.2906, pruned_loss=0.09316, over 984690.03 frames.], datatang_tot_loss[loss=0.2532, simple_loss=0.2857, pruned_loss=0.1104, over 986186.90 frames.], batch size: 78, lr: 1.81e-03
+2022-06-18 12:37:37,181 INFO [train.py:874] (0/4) Epoch 3, batch 3800, datatang_loss[loss=0.2185, simple_loss=0.2539, pruned_loss=0.09158, over 4921.00 frames.], tot_loss[loss=0.2451, simple_loss=0.2872, pruned_loss=0.1015, over 985538.95 frames.], batch size: 57, aishell_tot_loss[loss=0.2384, simple_loss=0.2907, pruned_loss=0.09308, over 984680.27 frames.], datatang_tot_loss[loss=0.2524, simple_loss=0.2854, pruned_loss=0.1097, over 986171.55 frames.], batch size: 57, lr: 1.80e-03
+2022-06-18 12:38:06,928 INFO [train.py:874] (0/4) Epoch 3, batch 3850, datatang_loss[loss=0.2359, simple_loss=0.2776, pruned_loss=0.09707, over 4932.00 frames.], tot_loss[loss=0.2454, simple_loss=0.2869, pruned_loss=0.1019, over 985614.49 frames.], batch size: 79, aishell_tot_loss[loss=0.2386, simple_loss=0.2904, pruned_loss=0.0934, over 985021.04 frames.], datatang_tot_loss[loss=0.2521, simple_loss=0.2854, pruned_loss=0.1094, over 985887.97 frames.], batch size: 79, lr: 1.80e-03
+2022-06-18 12:38:36,189 INFO [train.py:874] (0/4) Epoch 3, batch 3900, datatang_loss[loss=0.2398, simple_loss=0.277, pruned_loss=0.1013, over 4950.00 frames.], tot_loss[loss=0.2447, simple_loss=0.2874, pruned_loss=0.1011, over 985755.37 frames.], batch size: 55, aishell_tot_loss[loss=0.2387, simple_loss=0.291, pruned_loss=0.09317, over 985167.59 frames.], datatang_tot_loss[loss=0.2513, simple_loss=0.285, pruned_loss=0.1088, over 985916.65 frames.], batch size: 55, lr: 1.80e-03
+2022-06-18 12:39:04,526 INFO [train.py:874] (0/4) Epoch 3, batch 3950, aishell_loss[loss=0.2093, simple_loss=0.2722, pruned_loss=0.07313, over 4977.00 frames.], tot_loss[loss=0.245, simple_loss=0.2877, pruned_loss=0.1011, over 985836.55 frames.], batch size: 39, aishell_tot_loss[loss=0.2379, simple_loss=0.2906, pruned_loss=0.0926, over 985315.84 frames.], datatang_tot_loss[loss=0.252, simple_loss=0.2857, pruned_loss=0.1092, over 985883.16 frames.], batch size: 39, lr: 1.79e-03
+2022-06-18 12:39:34,736 INFO [train.py:874] (0/4) Epoch 3, batch 4000, datatang_loss[loss=0.2672, simple_loss=0.3036, pruned_loss=0.1154, over 4915.00 frames.], tot_loss[loss=0.2454, simple_loss=0.2881, pruned_loss=0.1014, over 985590.57 frames.], batch size: 75, aishell_tot_loss[loss=0.2389, simple_loss=0.2916, pruned_loss=0.09313, over 985107.81 frames.], datatang_tot_loss[loss=0.2512, simple_loss=0.2851, pruned_loss=0.1086, over 985868.60 frames.], batch size: 75, lr: 1.79e-03
+2022-06-18 12:39:34,738 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 12:39:51,130 INFO [train.py:914] (0/4) Epoch 3, validation: loss=0.1881, simple_loss=0.2644, pruned_loss=0.05586, over 1622729.00 frames.
+2022-06-18 12:40:21,158 INFO [train.py:874] (0/4) Epoch 3, batch 4050, datatang_loss[loss=0.2234, simple_loss=0.2623, pruned_loss=0.09226, over 4977.00 frames.], tot_loss[loss=0.2451, simple_loss=0.2881, pruned_loss=0.101, over 986021.08 frames.], batch size: 40, aishell_tot_loss[loss=0.2394, simple_loss=0.2924, pruned_loss=0.09323, over 985487.81 frames.], datatang_tot_loss[loss=0.2503, simple_loss=0.2844, pruned_loss=0.1081, over 985951.05 frames.], batch size: 40, lr: 1.79e-03
+2022-06-18 12:40:49,595 INFO [train.py:874] (0/4) Epoch 3, batch 4100, aishell_loss[loss=0.2151, simple_loss=0.2761, pruned_loss=0.07705, over 4860.00 frames.], tot_loss[loss=0.2437, simple_loss=0.2874, pruned_loss=0.09997, over 985344.13 frames.], batch size: 35, aishell_tot_loss[loss=0.2388, simple_loss=0.2917, pruned_loss=0.09294, over 984980.59 frames.], datatang_tot_loss[loss=0.2496, simple_loss=0.2841, pruned_loss=0.1075, over 985822.45 frames.], batch size: 35, lr: 1.78e-03
+2022-06-18 12:41:02,031 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-3.pt
+2022-06-18 12:42:03,121 INFO [train.py:874] (0/4) Epoch 4, batch 50, aishell_loss[loss=0.255, simple_loss=0.304, pruned_loss=0.103, over 4879.00 frames.], tot_loss[loss=0.2241, simple_loss=0.2744, pruned_loss=0.0869, over 218303.94 frames.], batch size: 37, aishell_tot_loss[loss=0.2321, simple_loss=0.2884, pruned_loss=0.0879, over 111645.52 frames.], datatang_tot_loss[loss=0.2166, simple_loss=0.2614, pruned_loss=0.08588, over 120308.35 frames.], batch size: 37, lr: 1.73e-03
+2022-06-18 12:42:33,921 INFO [train.py:874] (0/4) Epoch 4, batch 100, aishell_loss[loss=0.2817, simple_loss=0.3191, pruned_loss=0.1221, over 4882.00 frames.], tot_loss[loss=0.23, simple_loss=0.2802, pruned_loss=0.08994, over 388033.07 frames.], batch size: 47, aishell_tot_loss[loss=0.2368, simple_loss=0.2916, pruned_loss=0.091, over 229482.75 frames.], datatang_tot_loss[loss=0.2215, simple_loss=0.2665, pruned_loss=0.08826, over 206772.31 frames.], batch size: 47, lr: 1.73e-03
+2022-06-18 12:43:05,276 INFO [train.py:874] (0/4) Epoch 4, batch 150, datatang_loss[loss=0.2456, simple_loss=0.2715, pruned_loss=0.1099, over 4909.00 frames.], tot_loss[loss=0.2331, simple_loss=0.2817, pruned_loss=0.0922, over 520593.77 frames.], batch size: 47, aishell_tot_loss[loss=0.2381, simple_loss=0.2932, pruned_loss=0.09153, over 311833.01 frames.], datatang_tot_loss[loss=0.2267, simple_loss=0.2691, pruned_loss=0.09217, over 305434.62 frames.], batch size: 47, lr: 1.72e-03
+2022-06-18 12:43:34,803 INFO [train.py:874] (0/4) Epoch 4, batch 200, datatang_loss[loss=0.2298, simple_loss=0.2726, pruned_loss=0.09348, over 4959.00 frames.], tot_loss[loss=0.2335, simple_loss=0.2806, pruned_loss=0.09322, over 623645.91 frames.], batch size: 86, aishell_tot_loss[loss=0.2356, simple_loss=0.2902, pruned_loss=0.09046, over 384908.45 frames.], datatang_tot_loss[loss=0.2308, simple_loss=0.2711, pruned_loss=0.09524, over 391790.80 frames.], batch size: 86, lr: 1.72e-03
+2022-06-18 12:44:05,296 INFO [train.py:874] (0/4) Epoch 4, batch 250, datatang_loss[loss=0.2479, simple_loss=0.2849, pruned_loss=0.1054, over 4957.00 frames.], tot_loss[loss=0.236, simple_loss=0.2824, pruned_loss=0.09477, over 704189.29 frames.], batch size: 50, aishell_tot_loss[loss=0.2378, simple_loss=0.2922, pruned_loss=0.09171, over 447863.26 frames.], datatang_tot_loss[loss=0.2327, simple_loss=0.2724, pruned_loss=0.0965, over 469633.35 frames.], batch size: 50, lr: 1.72e-03
+2022-06-18 12:44:35,728 INFO [train.py:874] (0/4) Epoch 4, batch 300, aishell_loss[loss=0.2439, simple_loss=0.2884, pruned_loss=0.09973, over 4894.00 frames.], tot_loss[loss=0.2351, simple_loss=0.2816, pruned_loss=0.09429, over 766518.52 frames.], batch size: 28, aishell_tot_loss[loss=0.2361, simple_loss=0.2902, pruned_loss=0.09099, over 515783.22 frames.], datatang_tot_loss[loss=0.2333, simple_loss=0.273, pruned_loss=0.0968, over 525903.31 frames.], batch size: 28, lr: 1.71e-03
+2022-06-18 12:45:04,607 INFO [train.py:874] (0/4) Epoch 4, batch 350, aishell_loss[loss=0.2569, simple_loss=0.3032, pruned_loss=0.1053, over 4905.00 frames.], tot_loss[loss=0.2364, simple_loss=0.2828, pruned_loss=0.09494, over 814879.06 frames.], batch size: 41, aishell_tot_loss[loss=0.236, simple_loss=0.2901, pruned_loss=0.09095, over 572938.38 frames.], datatang_tot_loss[loss=0.2353, simple_loss=0.2746, pruned_loss=0.09799, over 578036.48 frames.], batch size: 41, lr: 1.71e-03
+2022-06-18 12:45:35,292 INFO [train.py:874] (0/4) Epoch 4, batch 400, aishell_loss[loss=0.2243, simple_loss=0.2826, pruned_loss=0.08297, over 4949.00 frames.], tot_loss[loss=0.236, simple_loss=0.2831, pruned_loss=0.09445, over 852860.03 frames.], batch size: 56, aishell_tot_loss[loss=0.2335, simple_loss=0.2886, pruned_loss=0.0892, over 637738.95 frames.], datatang_tot_loss[loss=0.2377, simple_loss=0.2758, pruned_loss=0.09979, over 609447.08 frames.], batch size: 56, lr: 1.71e-03
+2022-06-18 12:46:05,195 INFO [train.py:874] (0/4) Epoch 4, batch 450, datatang_loss[loss=0.2334, simple_loss=0.2733, pruned_loss=0.09676, over 4948.00 frames.], tot_loss[loss=0.2347, simple_loss=0.2815, pruned_loss=0.09396, over 881623.32 frames.], batch size: 50, aishell_tot_loss[loss=0.2311, simple_loss=0.2863, pruned_loss=0.08798, over 675022.70 frames.], datatang_tot_loss[loss=0.2383, simple_loss=0.2763, pruned_loss=0.1002, over 656941.95 frames.], batch size: 50, lr: 1.71e-03
+2022-06-18 12:46:35,410 INFO [train.py:874] (0/4) Epoch 4, batch 500, datatang_loss[loss=0.2107, simple_loss=0.2627, pruned_loss=0.07937, over 4956.00 frames.], tot_loss[loss=0.2358, simple_loss=0.2828, pruned_loss=0.0944, over 904514.45 frames.], batch size: 86, aishell_tot_loss[loss=0.2316, simple_loss=0.2869, pruned_loss=0.08819, over 711152.56 frames.], datatang_tot_loss[loss=0.2393, simple_loss=0.2775, pruned_loss=0.1005, over 695918.96 frames.], batch size: 86, lr: 1.70e-03
+2022-06-18 12:47:05,930 INFO [train.py:874] (0/4) Epoch 4, batch 550, aishell_loss[loss=0.2444, simple_loss=0.3031, pruned_loss=0.09283, over 4919.00 frames.], tot_loss[loss=0.2357, simple_loss=0.2828, pruned_loss=0.09432, over 922691.31 frames.], batch size: 78, aishell_tot_loss[loss=0.2315, simple_loss=0.287, pruned_loss=0.08797, over 744805.28 frames.], datatang_tot_loss[loss=0.2396, simple_loss=0.2777, pruned_loss=0.1007, over 728851.22 frames.], batch size: 78, lr: 1.70e-03
+2022-06-18 12:47:36,166 INFO [train.py:874] (0/4) Epoch 4, batch 600, datatang_loss[loss=0.2486, simple_loss=0.2874, pruned_loss=0.1048, over 4932.00 frames.], tot_loss[loss=0.2352, simple_loss=0.2827, pruned_loss=0.09388, over 936387.49 frames.], batch size: 69, aishell_tot_loss[loss=0.2305, simple_loss=0.2864, pruned_loss=0.08727, over 771849.75 frames.], datatang_tot_loss[loss=0.2399, simple_loss=0.2783, pruned_loss=0.1007, over 760225.28 frames.], batch size: 69, lr: 1.70e-03
+2022-06-18 12:48:07,245 INFO [train.py:874] (0/4) Epoch 4, batch 650, aishell_loss[loss=0.2646, simple_loss=0.3113, pruned_loss=0.1089, over 4910.00 frames.], tot_loss[loss=0.2363, simple_loss=0.2836, pruned_loss=0.09446, over 947035.91 frames.], batch size: 77, aishell_tot_loss[loss=0.2305, simple_loss=0.2863, pruned_loss=0.08736, over 797804.65 frames.], datatang_tot_loss[loss=0.2413, simple_loss=0.2797, pruned_loss=0.1014, over 785649.84 frames.], batch size: 77, lr: 1.69e-03
+2022-06-18 12:48:37,396 INFO [train.py:874] (0/4) Epoch 4, batch 700, datatang_loss[loss=0.2327, simple_loss=0.2734, pruned_loss=0.09605, over 4924.00 frames.], tot_loss[loss=0.2395, simple_loss=0.2846, pruned_loss=0.09726, over 955430.44 frames.], batch size: 73, aishell_tot_loss[loss=0.2317, simple_loss=0.2865, pruned_loss=0.08849, over 816215.28 frames.], datatang_tot_loss[loss=0.2438, simple_loss=0.2809, pruned_loss=0.1034, over 812882.12 frames.], batch size: 73, lr: 1.69e-03
+2022-06-18 12:49:07,584 INFO [train.py:874] (0/4) Epoch 4, batch 750, aishell_loss[loss=0.2665, simple_loss=0.2994, pruned_loss=0.1168, over 4946.00 frames.], tot_loss[loss=0.2402, simple_loss=0.285, pruned_loss=0.09764, over 962425.16 frames.], batch size: 27, aishell_tot_loss[loss=0.2326, simple_loss=0.2873, pruned_loss=0.08895, over 830185.64 frames.], datatang_tot_loss[loss=0.2437, simple_loss=0.2812, pruned_loss=0.1031, over 839394.38 frames.], batch size: 27, lr: 1.69e-03
+2022-06-18 12:49:37,664 INFO [train.py:874] (0/4) Epoch 4, batch 800, datatang_loss[loss=0.2461, simple_loss=0.2926, pruned_loss=0.09983, over 4980.00 frames.], tot_loss[loss=0.2395, simple_loss=0.2858, pruned_loss=0.09662, over 967787.21 frames.], batch size: 37, aishell_tot_loss[loss=0.2323, simple_loss=0.2876, pruned_loss=0.08849, over 852664.76 frames.], datatang_tot_loss[loss=0.2442, simple_loss=0.2819, pruned_loss=0.1033, over 852794.58 frames.], batch size: 37, lr: 1.69e-03
+2022-06-18 12:50:07,650 INFO [train.py:874] (0/4) Epoch 4, batch 850, aishell_loss[loss=0.2114, simple_loss=0.2797, pruned_loss=0.0715, over 4885.00 frames.], tot_loss[loss=0.238, simple_loss=0.2852, pruned_loss=0.09544, over 971697.39 frames.], batch size: 34, aishell_tot_loss[loss=0.2312, simple_loss=0.287, pruned_loss=0.08769, over 869983.80 frames.], datatang_tot_loss[loss=0.2441, simple_loss=0.2819, pruned_loss=0.1031, over 866700.81 frames.], batch size: 34, lr: 1.68e-03
+2022-06-18 12:50:37,411 INFO [train.py:874] (0/4) Epoch 4, batch 900, aishell_loss[loss=0.2553, simple_loss=0.3102, pruned_loss=0.1003, over 4912.00 frames.], tot_loss[loss=0.2394, simple_loss=0.2859, pruned_loss=0.09644, over 974550.25 frames.], batch size: 46, aishell_tot_loss[loss=0.2321, simple_loss=0.2876, pruned_loss=0.08831, over 883844.00 frames.], datatang_tot_loss[loss=0.2449, simple_loss=0.2823, pruned_loss=0.1038, over 880232.45 frames.], batch size: 46, lr: 1.68e-03
+2022-06-18 12:51:08,856 INFO [train.py:874] (0/4) Epoch 4, batch 950, datatang_loss[loss=0.2151, simple_loss=0.2598, pruned_loss=0.08521, over 4925.00 frames.], tot_loss[loss=0.2384, simple_loss=0.285, pruned_loss=0.09585, over 976861.68 frames.], batch size: 81, aishell_tot_loss[loss=0.2324, simple_loss=0.2875, pruned_loss=0.08867, over 895609.63 frames.], datatang_tot_loss[loss=0.2437, simple_loss=0.2816, pruned_loss=0.1028, over 892710.38 frames.], batch size: 81, lr: 1.68e-03
+2022-06-18 12:51:39,318 INFO [train.py:874] (0/4) Epoch 4, batch 1000, datatang_loss[loss=0.248, simple_loss=0.2788, pruned_loss=0.1086, over 4942.00 frames.], tot_loss[loss=0.2387, simple_loss=0.2856, pruned_loss=0.09593, over 978560.72 frames.], batch size: 50, aishell_tot_loss[loss=0.2335, simple_loss=0.2887, pruned_loss=0.08918, over 905671.03 frames.], datatang_tot_loss[loss=0.243, simple_loss=0.2813, pruned_loss=0.1024, over 903943.10 frames.], batch size: 50, lr: 1.67e-03
+2022-06-18 12:51:39,320 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 12:51:55,327 INFO [train.py:914] (0/4) Epoch 4, validation: loss=0.1866, simple_loss=0.2641, pruned_loss=0.05457, over 1622729.00 frames.
+2022-06-18 12:52:26,585 INFO [train.py:874] (0/4) Epoch 4, batch 1050, aishell_loss[loss=0.2251, simple_loss=0.286, pruned_loss=0.08205, over 4930.00 frames.], tot_loss[loss=0.2364, simple_loss=0.2842, pruned_loss=0.09434, over 980222.74 frames.], batch size: 49, aishell_tot_loss[loss=0.2319, simple_loss=0.2879, pruned_loss=0.08799, over 914081.74 frames.], datatang_tot_loss[loss=0.242, simple_loss=0.2807, pruned_loss=0.1016, over 914672.61 frames.], batch size: 49, lr: 1.67e-03
+2022-06-18 12:52:56,825 INFO [train.py:874] (0/4) Epoch 4, batch 1100, datatang_loss[loss=0.2312, simple_loss=0.2787, pruned_loss=0.09183, over 4905.00 frames.], tot_loss[loss=0.2372, simple_loss=0.2846, pruned_loss=0.09491, over 981614.44 frames.], batch size: 30, aishell_tot_loss[loss=0.2315, simple_loss=0.2875, pruned_loss=0.0878, over 922785.30 frames.], datatang_tot_loss[loss=0.2432, simple_loss=0.2816, pruned_loss=0.1024, over 922961.21 frames.], batch size: 30, lr: 1.67e-03
+2022-06-18 12:53:26,761 INFO [train.py:874] (0/4) Epoch 4, batch 1150, aishell_loss[loss=0.2407, simple_loss=0.2882, pruned_loss=0.09662, over 4977.00 frames.], tot_loss[loss=0.2368, simple_loss=0.2841, pruned_loss=0.09473, over 982290.30 frames.], batch size: 44, aishell_tot_loss[loss=0.2306, simple_loss=0.2864, pruned_loss=0.08739, over 931665.54 frames.], datatang_tot_loss[loss=0.2441, simple_loss=0.2821, pruned_loss=0.103, over 928581.13 frames.], batch size: 44, lr: 1.67e-03
+2022-06-18 12:53:57,026 INFO [train.py:874] (0/4) Epoch 4, batch 1200, datatang_loss[loss=0.2263, simple_loss=0.2741, pruned_loss=0.08927, over 4916.00 frames.], tot_loss[loss=0.2366, simple_loss=0.2845, pruned_loss=0.09437, over 983028.02 frames.], batch size: 30, aishell_tot_loss[loss=0.2301, simple_loss=0.2864, pruned_loss=0.08686, over 937920.52 frames.], datatang_tot_loss[loss=0.2442, simple_loss=0.2824, pruned_loss=0.103, over 935406.78 frames.], batch size: 30, lr: 1.66e-03
+2022-06-18 12:54:27,045 INFO [train.py:874] (0/4) Epoch 4, batch 1250, datatang_loss[loss=0.2493, simple_loss=0.2935, pruned_loss=0.1026, over 4944.00 frames.], tot_loss[loss=0.2368, simple_loss=0.285, pruned_loss=0.09435, over 983348.01 frames.], batch size: 69, aishell_tot_loss[loss=0.2295, simple_loss=0.2863, pruned_loss=0.08632, over 943910.69 frames.], datatang_tot_loss[loss=0.245, simple_loss=0.283, pruned_loss=0.1035, over 940653.44 frames.], batch size: 69, lr: 1.66e-03
+2022-06-18 12:54:57,600 INFO [train.py:874] (0/4) Epoch 4, batch 1300, datatang_loss[loss=0.2297, simple_loss=0.2755, pruned_loss=0.09191, over 4937.00 frames.], tot_loss[loss=0.2365, simple_loss=0.2849, pruned_loss=0.09398, over 983932.65 frames.], batch size: 88, aishell_tot_loss[loss=0.2295, simple_loss=0.2866, pruned_loss=0.08618, over 949993.80 frames.], datatang_tot_loss[loss=0.2449, simple_loss=0.2826, pruned_loss=0.1036, over 944735.26 frames.], batch size: 88, lr: 1.66e-03
+2022-06-18 12:55:28,214 INFO [train.py:874] (0/4) Epoch 4, batch 1350, aishell_loss[loss=0.2299, simple_loss=0.2845, pruned_loss=0.0877, over 4884.00 frames.], tot_loss[loss=0.2347, simple_loss=0.2834, pruned_loss=0.09297, over 984377.80 frames.], batch size: 47, aishell_tot_loss[loss=0.2286, simple_loss=0.2857, pruned_loss=0.08574, over 954996.05 frames.], datatang_tot_loss[loss=0.2441, simple_loss=0.2819, pruned_loss=0.1031, over 948688.65 frames.], batch size: 47, lr: 1.66e-03
+2022-06-18 12:55:57,855 INFO [train.py:874] (0/4) Epoch 4, batch 1400, datatang_loss[loss=0.2305, simple_loss=0.2715, pruned_loss=0.09476, over 4946.00 frames.], tot_loss[loss=0.2343, simple_loss=0.2829, pruned_loss=0.09289, over 984567.58 frames.], batch size: 62, aishell_tot_loss[loss=0.2283, simple_loss=0.2854, pruned_loss=0.08559, over 957709.25 frames.], datatang_tot_loss[loss=0.2431, simple_loss=0.2816, pruned_loss=0.1023, over 953988.09 frames.], batch size: 62, lr: 1.65e-03
+2022-06-18 12:56:27,847 INFO [train.py:874] (0/4) Epoch 4, batch 1450, datatang_loss[loss=0.2137, simple_loss=0.2579, pruned_loss=0.08477, over 4960.00 frames.], tot_loss[loss=0.2333, simple_loss=0.2823, pruned_loss=0.09212, over 984458.43 frames.], batch size: 67, aishell_tot_loss[loss=0.2269, simple_loss=0.2842, pruned_loss=0.08475, over 961691.11 frames.], datatang_tot_loss[loss=0.2435, simple_loss=0.2818, pruned_loss=0.1026, over 956499.50 frames.], batch size: 67, lr: 1.65e-03
+2022-06-18 12:57:00,070 INFO [train.py:874] (0/4) Epoch 4, batch 1500, aishell_loss[loss=0.2456, simple_loss=0.2966, pruned_loss=0.09728, over 4949.00 frames.], tot_loss[loss=0.2325, simple_loss=0.2818, pruned_loss=0.0916, over 984846.04 frames.], batch size: 80, aishell_tot_loss[loss=0.2269, simple_loss=0.2844, pruned_loss=0.08467, over 964293.22 frames.], datatang_tot_loss[loss=0.2422, simple_loss=0.2809, pruned_loss=0.1017, over 960321.01 frames.], batch size: 80, lr: 1.65e-03
+2022-06-18 12:57:28,931 INFO [train.py:874] (0/4) Epoch 4, batch 1550, datatang_loss[loss=0.2549, simple_loss=0.292, pruned_loss=0.1089, over 4934.00 frames.], tot_loss[loss=0.2327, simple_loss=0.2816, pruned_loss=0.09194, over 985067.38 frames.], batch size: 69, aishell_tot_loss[loss=0.2271, simple_loss=0.2842, pruned_loss=0.08502, over 966925.75 frames.], datatang_tot_loss[loss=0.2419, simple_loss=0.2807, pruned_loss=0.1016, over 963187.81 frames.], batch size: 69, lr: 1.65e-03
+2022-06-18 12:57:59,844 INFO [train.py:874] (0/4) Epoch 4, batch 1600, datatang_loss[loss=0.193, simple_loss=0.2414, pruned_loss=0.07229, over 4850.00 frames.], tot_loss[loss=0.2331, simple_loss=0.2813, pruned_loss=0.09248, over 985168.80 frames.], batch size: 30, aishell_tot_loss[loss=0.2271, simple_loss=0.284, pruned_loss=0.08509, over 968449.05 frames.], datatang_tot_loss[loss=0.2414, simple_loss=0.2804, pruned_loss=0.1012, over 966586.81 frames.], batch size: 30, lr: 1.64e-03
+2022-06-18 12:58:30,895 INFO [train.py:874] (0/4) Epoch 4, batch 1650, datatang_loss[loss=0.2426, simple_loss=0.2942, pruned_loss=0.09551, over 4937.00 frames.], tot_loss[loss=0.2324, simple_loss=0.2811, pruned_loss=0.09186, over 985339.20 frames.], batch size: 94, aishell_tot_loss[loss=0.226, simple_loss=0.2833, pruned_loss=0.08432, over 970385.23 frames.], datatang_tot_loss[loss=0.2416, simple_loss=0.2808, pruned_loss=0.1012, over 969007.29 frames.], batch size: 94, lr: 1.64e-03
+2022-06-18 12:59:01,537 INFO [train.py:874] (0/4) Epoch 4, batch 1700, datatang_loss[loss=0.3221, simple_loss=0.339, pruned_loss=0.1527, over 4942.00 frames.], tot_loss[loss=0.2331, simple_loss=0.2812, pruned_loss=0.0925, over 985143.73 frames.], batch size: 109, aishell_tot_loss[loss=0.2254, simple_loss=0.2829, pruned_loss=0.08396, over 971509.98 frames.], datatang_tot_loss[loss=0.2423, simple_loss=0.2812, pruned_loss=0.1018, over 971375.69 frames.], batch size: 109, lr: 1.64e-03
+2022-06-18 12:59:32,075 INFO [train.py:874] (0/4) Epoch 4, batch 1750, aishell_loss[loss=0.2127, simple_loss=0.2633, pruned_loss=0.08108, over 4930.00 frames.], tot_loss[loss=0.2336, simple_loss=0.282, pruned_loss=0.09263, over 985215.26 frames.], batch size: 33, aishell_tot_loss[loss=0.2257, simple_loss=0.2834, pruned_loss=0.084, over 973277.09 frames.], datatang_tot_loss[loss=0.2427, simple_loss=0.2812, pruned_loss=0.1021, over 972916.13 frames.], batch size: 33, lr: 1.63e-03
+2022-06-18 13:00:02,765 INFO [train.py:874] (0/4) Epoch 4, batch 1800, aishell_loss[loss=0.2139, simple_loss=0.2834, pruned_loss=0.07215, over 4959.00 frames.], tot_loss[loss=0.2338, simple_loss=0.2824, pruned_loss=0.09261, over 985455.36 frames.], batch size: 61, aishell_tot_loss[loss=0.2258, simple_loss=0.2837, pruned_loss=0.08396, over 974862.84 frames.], datatang_tot_loss[loss=0.2427, simple_loss=0.2813, pruned_loss=0.102, over 974433.07 frames.], batch size: 61, lr: 1.63e-03
+2022-06-18 13:00:32,616 INFO [train.py:874] (0/4) Epoch 4, batch 1850, aishell_loss[loss=0.2222, simple_loss=0.2901, pruned_loss=0.07718, over 4970.00 frames.], tot_loss[loss=0.2311, simple_loss=0.2804, pruned_loss=0.09093, over 985343.06 frames.], batch size: 44, aishell_tot_loss[loss=0.2248, simple_loss=0.2829, pruned_loss=0.08339, over 975987.12 frames.], datatang_tot_loss[loss=0.2407, simple_loss=0.28, pruned_loss=0.1008, over 975724.28 frames.], batch size: 44, lr: 1.63e-03
+2022-06-18 13:01:02,818 INFO [train.py:874] (0/4) Epoch 4, batch 1900, aishell_loss[loss=0.2012, simple_loss=0.264, pruned_loss=0.0692, over 4922.00 frames.], tot_loss[loss=0.2329, simple_loss=0.2811, pruned_loss=0.09239, over 985517.44 frames.], batch size: 52, aishell_tot_loss[loss=0.2259, simple_loss=0.2835, pruned_loss=0.08416, over 977153.73 frames.], datatang_tot_loss[loss=0.2412, simple_loss=0.2799, pruned_loss=0.1013, over 976989.88 frames.], batch size: 52, lr: 1.63e-03
+2022-06-18 13:01:34,319 INFO [train.py:874] (0/4) Epoch 4, batch 1950, aishell_loss[loss=0.216, simple_loss=0.2813, pruned_loss=0.0753, over 4911.00 frames.], tot_loss[loss=0.2351, simple_loss=0.2824, pruned_loss=0.09384, over 985521.00 frames.], batch size: 68, aishell_tot_loss[loss=0.2269, simple_loss=0.2846, pruned_loss=0.08458, over 978171.36 frames.], datatang_tot_loss[loss=0.2424, simple_loss=0.28, pruned_loss=0.1024, over 977968.79 frames.], batch size: 68, lr: 1.62e-03
+2022-06-18 13:02:04,138 INFO [train.py:874] (0/4) Epoch 4, batch 2000, datatang_loss[loss=0.2355, simple_loss=0.2791, pruned_loss=0.09591, over 4927.00 frames.], tot_loss[loss=0.2346, simple_loss=0.2822, pruned_loss=0.09349, over 985347.49 frames.], batch size: 83, aishell_tot_loss[loss=0.2271, simple_loss=0.2848, pruned_loss=0.08466, over 978622.59 frames.], datatang_tot_loss[loss=0.2416, simple_loss=0.2797, pruned_loss=0.1017, over 979092.04 frames.], batch size: 83, lr: 1.62e-03
+2022-06-18 13:02:04,141 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 13:02:20,119 INFO [train.py:914] (0/4) Epoch 4, validation: loss=0.1875, simple_loss=0.2609, pruned_loss=0.05705, over 1622729.00 frames.
+2022-06-18 13:02:49,816 INFO [train.py:874] (0/4) Epoch 4, batch 2050, aishell_loss[loss=0.2133, simple_loss=0.282, pruned_loss=0.07227, over 4969.00 frames.], tot_loss[loss=0.2338, simple_loss=0.2819, pruned_loss=0.09287, over 985809.61 frames.], batch size: 31, aishell_tot_loss[loss=0.2263, simple_loss=0.2843, pruned_loss=0.08413, over 979534.53 frames.], datatang_tot_loss[loss=0.2415, simple_loss=0.28, pruned_loss=0.1015, over 980181.91 frames.], batch size: 31, lr: 1.62e-03
+2022-06-18 13:03:20,618 INFO [train.py:874] (0/4) Epoch 4, batch 2100, datatang_loss[loss=0.2103, simple_loss=0.2582, pruned_loss=0.08122, over 4928.00 frames.], tot_loss[loss=0.2339, simple_loss=0.2822, pruned_loss=0.0928, over 985725.68 frames.], batch size: 57, aishell_tot_loss[loss=0.2257, simple_loss=0.2837, pruned_loss=0.0838, over 980017.83 frames.], datatang_tot_loss[loss=0.242, simple_loss=0.2808, pruned_loss=0.1016, over 981014.98 frames.], batch size: 57, lr: 1.62e-03
+2022-06-18 13:03:50,533 INFO [train.py:874] (0/4) Epoch 4, batch 2150, datatang_loss[loss=0.256, simple_loss=0.2915, pruned_loss=0.1102, over 4911.00 frames.], tot_loss[loss=0.2335, simple_loss=0.2822, pruned_loss=0.09245, over 985731.17 frames.], batch size: 64, aishell_tot_loss[loss=0.2249, simple_loss=0.2832, pruned_loss=0.08327, over 980766.62 frames.], datatang_tot_loss[loss=0.2426, simple_loss=0.2813, pruned_loss=0.1019, over 981503.22 frames.], batch size: 64, lr: 1.61e-03
+2022-06-18 13:04:20,260 INFO [train.py:874] (0/4) Epoch 4, batch 2200, datatang_loss[loss=0.3795, simple_loss=0.3754, pruned_loss=0.1918, over 4956.00 frames.], tot_loss[loss=0.2337, simple_loss=0.2823, pruned_loss=0.09262, over 985550.78 frames.], batch size: 109, aishell_tot_loss[loss=0.2254, simple_loss=0.2834, pruned_loss=0.08376, over 981089.06 frames.], datatang_tot_loss[loss=0.2423, simple_loss=0.2811, pruned_loss=0.1018, over 982088.32 frames.], batch size: 109, lr: 1.61e-03
+2022-06-18 13:04:51,366 INFO [train.py:874] (0/4) Epoch 4, batch 2250, aishell_loss[loss=0.2335, simple_loss=0.2971, pruned_loss=0.08495, over 4944.00 frames.], tot_loss[loss=0.2314, simple_loss=0.281, pruned_loss=0.09096, over 985638.91 frames.], batch size: 49, aishell_tot_loss[loss=0.2251, simple_loss=0.2835, pruned_loss=0.08335, over 981627.02 frames.], datatang_tot_loss[loss=0.2402, simple_loss=0.2796, pruned_loss=0.1004, over 982582.20 frames.], batch size: 49, lr: 1.61e-03
+2022-06-18 13:05:21,495 INFO [train.py:874] (0/4) Epoch 4, batch 2300, datatang_loss[loss=0.1899, simple_loss=0.2409, pruned_loss=0.06949, over 4920.00 frames.], tot_loss[loss=0.2338, simple_loss=0.2829, pruned_loss=0.09233, over 985715.35 frames.], batch size: 26, aishell_tot_loss[loss=0.2266, simple_loss=0.2849, pruned_loss=0.08414, over 982107.09 frames.], datatang_tot_loss[loss=0.2408, simple_loss=0.2801, pruned_loss=0.1008, over 983013.93 frames.], batch size: 26, lr: 1.61e-03
+2022-06-18 13:05:52,002 INFO [train.py:874] (0/4) Epoch 4, batch 2350, datatang_loss[loss=0.256, simple_loss=0.2783, pruned_loss=0.1169, over 4948.00 frames.], tot_loss[loss=0.2334, simple_loss=0.2824, pruned_loss=0.09215, over 985521.04 frames.], batch size: 34, aishell_tot_loss[loss=0.2264, simple_loss=0.2846, pruned_loss=0.08406, over 982356.76 frames.], datatang_tot_loss[loss=0.2404, simple_loss=0.28, pruned_loss=0.1004, over 983281.67 frames.], batch size: 34, lr: 1.60e-03
+2022-06-18 13:06:21,649 INFO [train.py:874] (0/4) Epoch 4, batch 2400, aishell_loss[loss=0.1988, simple_loss=0.235, pruned_loss=0.08127, over 4810.00 frames.], tot_loss[loss=0.2326, simple_loss=0.2815, pruned_loss=0.09184, over 985771.24 frames.], batch size: 20, aishell_tot_loss[loss=0.2263, simple_loss=0.284, pruned_loss=0.08436, over 982966.69 frames.], datatang_tot_loss[loss=0.24, simple_loss=0.2797, pruned_loss=0.1001, over 983602.83 frames.], batch size: 20, lr: 1.60e-03
+2022-06-18 13:06:51,595 INFO [train.py:874] (0/4) Epoch 4, batch 2450, datatang_loss[loss=0.1962, simple_loss=0.2499, pruned_loss=0.07124, over 4928.00 frames.], tot_loss[loss=0.2306, simple_loss=0.2802, pruned_loss=0.09052, over 985696.65 frames.], batch size: 81, aishell_tot_loss[loss=0.2242, simple_loss=0.2823, pruned_loss=0.08304, over 983042.14 frames.], datatang_tot_loss[loss=0.2401, simple_loss=0.2798, pruned_loss=0.1002, over 984054.60 frames.], batch size: 81, lr: 1.60e-03
+2022-06-18 13:07:23,700 INFO [train.py:874] (0/4) Epoch 4, batch 2500, datatang_loss[loss=0.273, simple_loss=0.3084, pruned_loss=0.1188, over 4937.00 frames.], tot_loss[loss=0.2317, simple_loss=0.2809, pruned_loss=0.09124, over 986215.27 frames.], batch size: 88, aishell_tot_loss[loss=0.2246, simple_loss=0.2827, pruned_loss=0.08323, over 983528.78 frames.], datatang_tot_loss[loss=0.2398, simple_loss=0.28, pruned_loss=0.09984, over 984585.40 frames.], batch size: 88, lr: 1.60e-03
+2022-06-18 13:07:53,516 INFO [train.py:874] (0/4) Epoch 4, batch 2550, datatang_loss[loss=0.2317, simple_loss=0.275, pruned_loss=0.09418, over 4961.00 frames.], tot_loss[loss=0.2308, simple_loss=0.2797, pruned_loss=0.09096, over 985605.81 frames.], batch size: 86, aishell_tot_loss[loss=0.2256, simple_loss=0.2832, pruned_loss=0.08405, over 983368.80 frames.], datatang_tot_loss[loss=0.2372, simple_loss=0.2783, pruned_loss=0.09803, over 984560.21 frames.], batch size: 86, lr: 1.60e-03
+2022-06-18 13:08:25,262 INFO [train.py:874] (0/4) Epoch 4, batch 2600, aishell_loss[loss=0.2412, simple_loss=0.3007, pruned_loss=0.09082, over 4877.00 frames.], tot_loss[loss=0.2299, simple_loss=0.279, pruned_loss=0.09046, over 985881.55 frames.], batch size: 34, aishell_tot_loss[loss=0.2258, simple_loss=0.2835, pruned_loss=0.08402, over 983709.93 frames.], datatang_tot_loss[loss=0.2357, simple_loss=0.2771, pruned_loss=0.09714, over 984872.31 frames.], batch size: 34, lr: 1.59e-03
+2022-06-18 13:08:55,094 INFO [train.py:874] (0/4) Epoch 4, batch 2650, aishell_loss[loss=0.3138, simple_loss=0.3649, pruned_loss=0.1314, over 4880.00 frames.], tot_loss[loss=0.2304, simple_loss=0.2796, pruned_loss=0.09058, over 985701.48 frames.], batch size: 42, aishell_tot_loss[loss=0.227, simple_loss=0.2845, pruned_loss=0.08477, over 984009.15 frames.], datatang_tot_loss[loss=0.2348, simple_loss=0.2762, pruned_loss=0.09673, over 984779.70 frames.], batch size: 42, lr: 1.59e-03
+2022-06-18 13:09:25,294 INFO [train.py:874] (0/4) Epoch 4, batch 2700, datatang_loss[loss=0.2261, simple_loss=0.2704, pruned_loss=0.09088, over 4919.00 frames.], tot_loss[loss=0.231, simple_loss=0.2798, pruned_loss=0.09117, over 985921.21 frames.], batch size: 81, aishell_tot_loss[loss=0.2271, simple_loss=0.2843, pruned_loss=0.08496, over 984329.75 frames.], datatang_tot_loss[loss=0.2353, simple_loss=0.2765, pruned_loss=0.09706, over 984987.01 frames.], batch size: 81, lr: 1.59e-03
+2022-06-18 13:09:56,899 INFO [train.py:874] (0/4) Epoch 4, batch 2750, datatang_loss[loss=0.2427, simple_loss=0.2734, pruned_loss=0.1059, over 4882.00 frames.], tot_loss[loss=0.231, simple_loss=0.2798, pruned_loss=0.09107, over 985767.43 frames.], batch size: 26, aishell_tot_loss[loss=0.2272, simple_loss=0.2846, pruned_loss=0.08485, over 984404.53 frames.], datatang_tot_loss[loss=0.235, simple_loss=0.2763, pruned_loss=0.09687, over 985039.92 frames.], batch size: 26, lr: 1.59e-03
+2022-06-18 13:10:26,845 INFO [train.py:874] (0/4) Epoch 4, batch 2800, datatang_loss[loss=0.2302, simple_loss=0.2766, pruned_loss=0.09191, over 4942.00 frames.], tot_loss[loss=0.2308, simple_loss=0.2796, pruned_loss=0.09102, over 985429.82 frames.], batch size: 62, aishell_tot_loss[loss=0.2273, simple_loss=0.2848, pruned_loss=0.08492, over 984245.78 frames.], datatang_tot_loss[loss=0.2346, simple_loss=0.2759, pruned_loss=0.09671, over 985073.09 frames.], batch size: 62, lr: 1.58e-03
+2022-06-18 13:10:56,179 INFO [train.py:874] (0/4) Epoch 4, batch 2850, datatang_loss[loss=0.2351, simple_loss=0.2764, pruned_loss=0.09684, over 4956.00 frames.], tot_loss[loss=0.2292, simple_loss=0.2784, pruned_loss=0.09, over 985590.77 frames.], batch size: 67, aishell_tot_loss[loss=0.2263, simple_loss=0.2839, pruned_loss=0.08432, over 984469.96 frames.], datatang_tot_loss[loss=0.2339, simple_loss=0.2753, pruned_loss=0.0962, over 985208.65 frames.], batch size: 67, lr: 1.58e-03
+2022-06-18 13:11:27,466 INFO [train.py:874] (0/4) Epoch 4, batch 2900, aishell_loss[loss=0.2103, simple_loss=0.2696, pruned_loss=0.07545, over 4872.00 frames.], tot_loss[loss=0.2289, simple_loss=0.2786, pruned_loss=0.08962, over 985803.11 frames.], batch size: 35, aishell_tot_loss[loss=0.2262, simple_loss=0.2842, pruned_loss=0.08408, over 984695.45 frames.], datatang_tot_loss[loss=0.2333, simple_loss=0.2752, pruned_loss=0.09568, over 985375.23 frames.], batch size: 35, lr: 1.58e-03
+2022-06-18 13:11:56,666 INFO [train.py:874] (0/4) Epoch 4, batch 2950, aishell_loss[loss=0.2526, simple_loss=0.3114, pruned_loss=0.0969, over 4971.00 frames.], tot_loss[loss=0.2297, simple_loss=0.2794, pruned_loss=0.09004, over 985652.99 frames.], batch size: 44, aishell_tot_loss[loss=0.2271, simple_loss=0.2851, pruned_loss=0.08454, over 984675.43 frames.], datatang_tot_loss[loss=0.2329, simple_loss=0.2752, pruned_loss=0.09532, over 985394.43 frames.], batch size: 44, lr: 1.58e-03
+2022-06-18 13:12:25,370 INFO [train.py:874] (0/4) Epoch 4, batch 3000, aishell_loss[loss=0.2099, simple_loss=0.2751, pruned_loss=0.07232, over 4967.00 frames.], tot_loss[loss=0.2285, simple_loss=0.2788, pruned_loss=0.08911, over 985433.48 frames.], batch size: 51, aishell_tot_loss[loss=0.2264, simple_loss=0.2847, pruned_loss=0.08403, over 984713.35 frames.], datatang_tot_loss[loss=0.2323, simple_loss=0.2747, pruned_loss=0.09496, over 985271.39 frames.], batch size: 51, lr: 1.57e-03
+2022-06-18 13:12:25,372 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 13:12:41,439 INFO [train.py:914] (0/4) Epoch 4, validation: loss=0.1874, simple_loss=0.266, pruned_loss=0.05438, over 1622729.00 frames.
+2022-06-18 13:13:12,187 INFO [train.py:874] (0/4) Epoch 4, batch 3050, datatang_loss[loss=0.2291, simple_loss=0.2617, pruned_loss=0.09821, over 4950.00 frames.], tot_loss[loss=0.2294, simple_loss=0.2792, pruned_loss=0.08982, over 985566.31 frames.], batch size: 37, aishell_tot_loss[loss=0.226, simple_loss=0.2847, pruned_loss=0.08364, over 984836.48 frames.], datatang_tot_loss[loss=0.2334, simple_loss=0.2748, pruned_loss=0.09599, over 985383.66 frames.], batch size: 37, lr: 1.57e-03
+2022-06-18 13:13:42,310 INFO [train.py:874] (0/4) Epoch 4, batch 3100, aishell_loss[loss=0.1843, simple_loss=0.261, pruned_loss=0.05382, over 4976.00 frames.], tot_loss[loss=0.2289, simple_loss=0.2792, pruned_loss=0.08931, over 985443.03 frames.], batch size: 30, aishell_tot_loss[loss=0.226, simple_loss=0.2847, pruned_loss=0.08366, over 984898.72 frames.], datatang_tot_loss[loss=0.2329, simple_loss=0.2746, pruned_loss=0.09562, over 985304.28 frames.], batch size: 30, lr: 1.57e-03
+2022-06-18 13:14:11,474 INFO [train.py:874] (0/4) Epoch 4, batch 3150, datatang_loss[loss=0.2525, simple_loss=0.2976, pruned_loss=0.1037, over 4965.00 frames.], tot_loss[loss=0.2277, simple_loss=0.279, pruned_loss=0.08823, over 985695.86 frames.], batch size: 55, aishell_tot_loss[loss=0.2248, simple_loss=0.2842, pruned_loss=0.08269, over 985095.09 frames.], datatang_tot_loss[loss=0.2328, simple_loss=0.2746, pruned_loss=0.09546, over 985459.13 frames.], batch size: 55, lr: 1.57e-03
+2022-06-18 13:14:41,508 INFO [train.py:874] (0/4) Epoch 4, batch 3200, datatang_loss[loss=0.2164, simple_loss=0.2628, pruned_loss=0.08504, over 4907.00 frames.], tot_loss[loss=0.2264, simple_loss=0.2771, pruned_loss=0.08785, over 985853.75 frames.], batch size: 75, aishell_tot_loss[loss=0.2234, simple_loss=0.2827, pruned_loss=0.08208, over 985089.33 frames.], datatang_tot_loss[loss=0.2322, simple_loss=0.2743, pruned_loss=0.0951, over 985718.02 frames.], batch size: 75, lr: 1.57e-03
+2022-06-18 13:15:11,999 INFO [train.py:874] (0/4) Epoch 4, batch 3250, aishell_loss[loss=0.2467, simple_loss=0.2932, pruned_loss=0.1, over 4983.00 frames.], tot_loss[loss=0.2287, simple_loss=0.2793, pruned_loss=0.08912, over 985971.20 frames.], batch size: 48, aishell_tot_loss[loss=0.2243, simple_loss=0.2834, pruned_loss=0.08257, over 985053.85 frames.], datatang_tot_loss[loss=0.2335, simple_loss=0.2756, pruned_loss=0.09564, over 985971.60 frames.], batch size: 48, lr: 1.56e-03
+2022-06-18 13:15:41,369 INFO [train.py:874] (0/4) Epoch 4, batch 3300, datatang_loss[loss=0.2377, simple_loss=0.273, pruned_loss=0.1012, over 4934.00 frames.], tot_loss[loss=0.2285, simple_loss=0.2787, pruned_loss=0.08913, over 986031.82 frames.], batch size: 55, aishell_tot_loss[loss=0.2238, simple_loss=0.2828, pruned_loss=0.08243, over 985196.59 frames.], datatang_tot_loss[loss=0.2336, simple_loss=0.2756, pruned_loss=0.09577, over 985992.18 frames.], batch size: 55, lr: 1.56e-03
+2022-06-18 13:16:12,232 INFO [train.py:874] (0/4) Epoch 4, batch 3350, aishell_loss[loss=0.2318, simple_loss=0.2935, pruned_loss=0.08503, over 4968.00 frames.], tot_loss[loss=0.2294, simple_loss=0.2801, pruned_loss=0.08934, over 985432.99 frames.], batch size: 61, aishell_tot_loss[loss=0.2246, simple_loss=0.2839, pruned_loss=0.08266, over 984751.02 frames.], datatang_tot_loss[loss=0.2338, simple_loss=0.2758, pruned_loss=0.09586, over 985912.32 frames.], batch size: 61, lr: 1.56e-03
+2022-06-18 13:16:42,779 INFO [train.py:874] (0/4) Epoch 4, batch 3400, aishell_loss[loss=0.2211, simple_loss=0.2923, pruned_loss=0.07497, over 4910.00 frames.], tot_loss[loss=0.2284, simple_loss=0.2792, pruned_loss=0.08876, over 985399.67 frames.], batch size: 41, aishell_tot_loss[loss=0.2239, simple_loss=0.2835, pruned_loss=0.08219, over 984781.85 frames.], datatang_tot_loss[loss=0.2336, simple_loss=0.2752, pruned_loss=0.09597, over 985875.69 frames.], batch size: 41, lr: 1.56e-03
+2022-06-18 13:17:12,116 INFO [train.py:874] (0/4) Epoch 4, batch 3450, aishell_loss[loss=0.2435, simple_loss=0.3051, pruned_loss=0.09097, over 4956.00 frames.], tot_loss[loss=0.2275, simple_loss=0.2793, pruned_loss=0.08785, over 985437.84 frames.], batch size: 64, aishell_tot_loss[loss=0.2236, simple_loss=0.2835, pruned_loss=0.08181, over 984946.56 frames.], datatang_tot_loss[loss=0.2331, simple_loss=0.2752, pruned_loss=0.09548, over 985775.49 frames.], batch size: 64, lr: 1.55e-03
+2022-06-18 13:17:41,246 INFO [train.py:874] (0/4) Epoch 4, batch 3500, datatang_loss[loss=0.2724, simple_loss=0.2784, pruned_loss=0.1332, over 4893.00 frames.], tot_loss[loss=0.227, simple_loss=0.2789, pruned_loss=0.08752, over 985137.47 frames.], batch size: 52, aishell_tot_loss[loss=0.2228, simple_loss=0.2827, pruned_loss=0.08139, over 984820.00 frames.], datatang_tot_loss[loss=0.2334, simple_loss=0.2752, pruned_loss=0.09573, over 985635.76 frames.], batch size: 52, lr: 1.55e-03
+2022-06-18 13:18:10,529 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-16000.pt
+2022-06-18 13:18:17,392 INFO [train.py:874] (0/4) Epoch 4, batch 3550, datatang_loss[loss=0.225, simple_loss=0.2727, pruned_loss=0.0886, over 4902.00 frames.], tot_loss[loss=0.2266, simple_loss=0.2782, pruned_loss=0.08747, over 985134.05 frames.], batch size: 47, aishell_tot_loss[loss=0.2226, simple_loss=0.2828, pruned_loss=0.08122, over 984904.97 frames.], datatang_tot_loss[loss=0.2328, simple_loss=0.2746, pruned_loss=0.09547, over 985512.73 frames.], batch size: 47, lr: 1.55e-03
+2022-06-18 13:18:45,982 INFO [train.py:874] (0/4) Epoch 4, batch 3600, aishell_loss[loss=0.1971, simple_loss=0.264, pruned_loss=0.0651, over 4982.00 frames.], tot_loss[loss=0.2269, simple_loss=0.2784, pruned_loss=0.08774, over 985449.01 frames.], batch size: 30, aishell_tot_loss[loss=0.2226, simple_loss=0.2826, pruned_loss=0.08125, over 985138.55 frames.], datatang_tot_loss[loss=0.2329, simple_loss=0.2748, pruned_loss=0.09551, over 985598.44 frames.], batch size: 30, lr: 1.55e-03
+2022-06-18 13:19:16,706 INFO [train.py:874] (0/4) Epoch 4, batch 3650, aishell_loss[loss=0.2558, simple_loss=0.3018, pruned_loss=0.1049, over 4947.00 frames.], tot_loss[loss=0.229, simple_loss=0.2796, pruned_loss=0.08922, over 985642.54 frames.], batch size: 54, aishell_tot_loss[loss=0.2233, simple_loss=0.2834, pruned_loss=0.08164, over 985376.05 frames.], datatang_tot_loss[loss=0.2338, simple_loss=0.2755, pruned_loss=0.09601, over 985574.30 frames.], batch size: 54, lr: 1.54e-03
+2022-06-18 13:19:48,113 INFO [train.py:874] (0/4) Epoch 4, batch 3700, datatang_loss[loss=0.2268, simple_loss=0.2724, pruned_loss=0.09057, over 4968.00 frames.], tot_loss[loss=0.2273, simple_loss=0.2785, pruned_loss=0.08803, over 985127.85 frames.], batch size: 60, aishell_tot_loss[loss=0.2222, simple_loss=0.2827, pruned_loss=0.08085, over 984926.54 frames.], datatang_tot_loss[loss=0.2331, simple_loss=0.2751, pruned_loss=0.09553, over 985501.53 frames.], batch size: 60, lr: 1.54e-03
+2022-06-18 13:20:16,794 INFO [train.py:874] (0/4) Epoch 4, batch 3750, datatang_loss[loss=0.239, simple_loss=0.2822, pruned_loss=0.09791, over 4930.00 frames.], tot_loss[loss=0.2258, simple_loss=0.2776, pruned_loss=0.08701, over 985290.02 frames.], batch size: 57, aishell_tot_loss[loss=0.2211, simple_loss=0.2818, pruned_loss=0.08018, over 984890.24 frames.], datatang_tot_loss[loss=0.2326, simple_loss=0.2751, pruned_loss=0.09503, over 985688.27 frames.], batch size: 57, lr: 1.54e-03
+2022-06-18 13:20:47,170 INFO [train.py:874] (0/4) Epoch 4, batch 3800, datatang_loss[loss=0.2001, simple_loss=0.2595, pruned_loss=0.0704, over 4915.00 frames.], tot_loss[loss=0.2242, simple_loss=0.2765, pruned_loss=0.08591, over 985683.61 frames.], batch size: 75, aishell_tot_loss[loss=0.2204, simple_loss=0.2812, pruned_loss=0.07978, over 985161.92 frames.], datatang_tot_loss[loss=0.2313, simple_loss=0.2743, pruned_loss=0.09413, over 985831.02 frames.], batch size: 75, lr: 1.54e-03
+2022-06-18 13:21:17,336 INFO [train.py:874] (0/4) Epoch 4, batch 3850, datatang_loss[loss=0.2051, simple_loss=0.2581, pruned_loss=0.07606, over 4920.00 frames.], tot_loss[loss=0.2249, simple_loss=0.2768, pruned_loss=0.08643, over 985694.70 frames.], batch size: 75, aishell_tot_loss[loss=0.2214, simple_loss=0.2819, pruned_loss=0.08044, over 985285.42 frames.], datatang_tot_loss[loss=0.2305, simple_loss=0.2737, pruned_loss=0.09364, over 985736.70 frames.], batch size: 75, lr: 1.54e-03
+2022-06-18 13:21:46,276 INFO [train.py:874] (0/4) Epoch 4, batch 3900, aishell_loss[loss=0.2298, simple_loss=0.276, pruned_loss=0.09181, over 4934.00 frames.], tot_loss[loss=0.2261, simple_loss=0.2779, pruned_loss=0.0872, over 985869.21 frames.], batch size: 45, aishell_tot_loss[loss=0.2226, simple_loss=0.2825, pruned_loss=0.08133, over 985371.98 frames.], datatang_tot_loss[loss=0.2305, simple_loss=0.2739, pruned_loss=0.09362, over 985897.43 frames.], batch size: 45, lr: 1.53e-03
+2022-06-18 13:22:14,508 INFO [train.py:874] (0/4) Epoch 4, batch 3950, datatang_loss[loss=0.2335, simple_loss=0.2846, pruned_loss=0.09123, over 4924.00 frames.], tot_loss[loss=0.2264, simple_loss=0.2788, pruned_loss=0.08701, over 985912.45 frames.], batch size: 81, aishell_tot_loss[loss=0.2227, simple_loss=0.283, pruned_loss=0.08117, over 985550.71 frames.], datatang_tot_loss[loss=0.2306, simple_loss=0.2742, pruned_loss=0.09351, over 985825.18 frames.], batch size: 81, lr: 1.53e-03
+2022-06-18 13:22:44,204 INFO [train.py:874] (0/4) Epoch 4, batch 4000, aishell_loss[loss=0.2216, simple_loss=0.2894, pruned_loss=0.07687, over 4886.00 frames.], tot_loss[loss=0.2269, simple_loss=0.2795, pruned_loss=0.08713, over 985906.35 frames.], batch size: 42, aishell_tot_loss[loss=0.2221, simple_loss=0.2828, pruned_loss=0.08072, over 985581.85 frames.], datatang_tot_loss[loss=0.2316, simple_loss=0.2751, pruned_loss=0.09398, over 985851.49 frames.], batch size: 42, lr: 1.53e-03
+2022-06-18 13:22:44,207 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 13:23:00,878 INFO [train.py:914] (0/4) Epoch 4, validation: loss=0.1818, simple_loss=0.2607, pruned_loss=0.0515, over 1622729.00 frames.
+2022-06-18 13:23:30,353 INFO [train.py:874] (0/4) Epoch 4, batch 4050, aishell_loss[loss=0.2604, simple_loss=0.3117, pruned_loss=0.1046, over 4946.00 frames.], tot_loss[loss=0.227, simple_loss=0.2799, pruned_loss=0.0871, over 986002.25 frames.], batch size: 64, aishell_tot_loss[loss=0.2218, simple_loss=0.2828, pruned_loss=0.08038, over 985680.56 frames.], datatang_tot_loss[loss=0.232, simple_loss=0.2757, pruned_loss=0.0942, over 985900.73 frames.], batch size: 64, lr: 1.53e-03
+2022-06-18 13:24:00,193 INFO [train.py:874] (0/4) Epoch 4, batch 4100, datatang_loss[loss=0.2342, simple_loss=0.268, pruned_loss=0.1001, over 4961.00 frames.], tot_loss[loss=0.2271, simple_loss=0.2793, pruned_loss=0.08744, over 985648.94 frames.], batch size: 45, aishell_tot_loss[loss=0.2212, simple_loss=0.2823, pruned_loss=0.08002, over 985338.93 frames.], datatang_tot_loss[loss=0.2324, simple_loss=0.2759, pruned_loss=0.09442, over 985908.34 frames.], batch size: 45, lr: 1.53e-03
+2022-06-18 13:24:29,183 INFO [train.py:874] (0/4) Epoch 4, batch 4150, aishell_loss[loss=0.2164, simple_loss=0.2662, pruned_loss=0.08333, over 4866.00 frames.], tot_loss[loss=0.2256, simple_loss=0.2779, pruned_loss=0.08671, over 985304.47 frames.], batch size: 28, aishell_tot_loss[loss=0.2207, simple_loss=0.2817, pruned_loss=0.07989, over 984915.49 frames.], datatang_tot_loss[loss=0.2313, simple_loss=0.275, pruned_loss=0.09379, over 985959.44 frames.], batch size: 28, lr: 1.52e-03
+2022-06-18 13:24:56,095 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-4.pt
+2022-06-18 13:25:55,759 INFO [train.py:874] (0/4) Epoch 5, batch 50, datatang_loss[loss=0.1637, simple_loss=0.217, pruned_loss=0.05523, over 4954.00 frames.], tot_loss[loss=0.2113, simple_loss=0.2648, pruned_loss=0.07891, over 218162.69 frames.], batch size: 37, aishell_tot_loss[loss=0.2131, simple_loss=0.2711, pruned_loss=0.07753, over 98206.44 frames.], datatang_tot_loss[loss=0.2104, simple_loss=0.2607, pruned_loss=0.08007, over 133246.00 frames.], batch size: 37, lr: 1.47e-03
+2022-06-18 13:26:26,152 INFO [train.py:874] (0/4) Epoch 5, batch 100, aishell_loss[loss=0.2082, simple_loss=0.2669, pruned_loss=0.07476, over 4940.00 frames.], tot_loss[loss=0.21, simple_loss=0.2662, pruned_loss=0.07693, over 387999.35 frames.], batch size: 45, aishell_tot_loss[loss=0.2175, simple_loss=0.2789, pruned_loss=0.07806, over 194391.11 frames.], datatang_tot_loss[loss=0.2041, simple_loss=0.2559, pruned_loss=0.07619, over 241280.96 frames.], batch size: 45, lr: 1.46e-03
+2022-06-18 13:26:56,297 INFO [train.py:874] (0/4) Epoch 5, batch 150, aishell_loss[loss=0.2752, simple_loss=0.3257, pruned_loss=0.1123, over 4978.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2678, pruned_loss=0.07795, over 520225.35 frames.], batch size: 39, aishell_tot_loss[loss=0.2196, simple_loss=0.2813, pruned_loss=0.07896, over 290749.28 frames.], datatang_tot_loss[loss=0.2046, simple_loss=0.2552, pruned_loss=0.07696, over 325706.33 frames.], batch size: 39, lr: 1.46e-03
+2022-06-18 13:27:26,067 INFO [train.py:874] (0/4) Epoch 5, batch 200, aishell_loss[loss=0.2328, simple_loss=0.2996, pruned_loss=0.08297, over 4955.00 frames.], tot_loss[loss=0.2118, simple_loss=0.2676, pruned_loss=0.078, over 623069.53 frames.], batch size: 64, aishell_tot_loss[loss=0.2181, simple_loss=0.2801, pruned_loss=0.07803, over 372327.05 frames.], datatang_tot_loss[loss=0.2056, simple_loss=0.2555, pruned_loss=0.07786, over 403329.81 frames.], batch size: 64, lr: 1.46e-03
+2022-06-18 13:27:56,609 INFO [train.py:874] (0/4) Epoch 5, batch 250, datatang_loss[loss=0.2485, simple_loss=0.2929, pruned_loss=0.102, over 4956.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2694, pruned_loss=0.07991, over 703340.79 frames.], batch size: 86, aishell_tot_loss[loss=0.2183, simple_loss=0.2804, pruned_loss=0.07807, over 436382.00 frames.], datatang_tot_loss[loss=0.21, simple_loss=0.2585, pruned_loss=0.08074, over 479453.81 frames.], batch size: 86, lr: 1.46e-03
+2022-06-18 13:28:27,267 INFO [train.py:874] (0/4) Epoch 5, batch 300, datatang_loss[loss=0.206, simple_loss=0.2434, pruned_loss=0.08431, over 4903.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2698, pruned_loss=0.08058, over 765459.62 frames.], batch size: 42, aishell_tot_loss[loss=0.219, simple_loss=0.2802, pruned_loss=0.07891, over 503049.34 frames.], datatang_tot_loss[loss=0.2107, simple_loss=0.2591, pruned_loss=0.08117, over 536793.03 frames.], batch size: 42, lr: 1.46e-03
+2022-06-18 13:28:56,556 INFO [train.py:874] (0/4) Epoch 5, batch 350, datatang_loss[loss=0.2503, simple_loss=0.295, pruned_loss=0.1028, over 4956.00 frames.], tot_loss[loss=0.219, simple_loss=0.2731, pruned_loss=0.08241, over 814413.65 frames.], batch size: 86, aishell_tot_loss[loss=0.2195, simple_loss=0.2813, pruned_loss=0.07887, over 564122.20 frames.], datatang_tot_loss[loss=0.2154, simple_loss=0.2627, pruned_loss=0.08409, over 585822.05 frames.], batch size: 86, lr: 1.45e-03
+2022-06-18 13:29:27,462 INFO [train.py:874] (0/4) Epoch 5, batch 400, aishell_loss[loss=0.2125, simple_loss=0.2683, pruned_loss=0.07841, over 4976.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2722, pruned_loss=0.08231, over 852603.62 frames.], batch size: 48, aishell_tot_loss[loss=0.2184, simple_loss=0.2804, pruned_loss=0.07821, over 604750.28 frames.], datatang_tot_loss[loss=0.2164, simple_loss=0.2633, pruned_loss=0.08472, over 641488.48 frames.], batch size: 48, lr: 1.45e-03
+2022-06-18 13:29:57,470 INFO [train.py:874] (0/4) Epoch 5, batch 450, aishell_loss[loss=0.1921, simple_loss=0.2616, pruned_loss=0.06128, over 4947.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2739, pruned_loss=0.08252, over 882402.45 frames.], batch size: 54, aishell_tot_loss[loss=0.2187, simple_loss=0.2809, pruned_loss=0.07819, over 656417.98 frames.], datatang_tot_loss[loss=0.218, simple_loss=0.2648, pruned_loss=0.0856, over 676066.53 frames.], batch size: 54, lr: 1.45e-03
+2022-06-18 13:30:27,313 INFO [train.py:874] (0/4) Epoch 5, batch 500, datatang_loss[loss=0.211, simple_loss=0.257, pruned_loss=0.08246, over 4910.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2731, pruned_loss=0.08238, over 904921.54 frames.], batch size: 64, aishell_tot_loss[loss=0.2172, simple_loss=0.2794, pruned_loss=0.07746, over 690563.25 frames.], datatang_tot_loss[loss=0.219, simple_loss=0.2658, pruned_loss=0.08611, over 716440.51 frames.], batch size: 64, lr: 1.45e-03
+2022-06-18 13:30:57,353 INFO [train.py:874] (0/4) Epoch 5, batch 550, aishell_loss[loss=0.198, simple_loss=0.2515, pruned_loss=0.07228, over 4954.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2734, pruned_loss=0.08269, over 922894.90 frames.], batch size: 31, aishell_tot_loss[loss=0.2168, simple_loss=0.2787, pruned_loss=0.07742, over 726569.39 frames.], datatang_tot_loss[loss=0.2203, simple_loss=0.2671, pruned_loss=0.08679, over 747089.17 frames.], batch size: 31, lr: 1.45e-03
+2022-06-18 13:31:27,951 INFO [train.py:874] (0/4) Epoch 5, batch 600, aishell_loss[loss=0.2213, simple_loss=0.2868, pruned_loss=0.07787, over 4955.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2738, pruned_loss=0.08242, over 936753.79 frames.], batch size: 64, aishell_tot_loss[loss=0.2158, simple_loss=0.2781, pruned_loss=0.07679, over 759120.31 frames.], datatang_tot_loss[loss=0.2215, simple_loss=0.2682, pruned_loss=0.08739, over 773264.16 frames.], batch size: 64, lr: 1.44e-03
+2022-06-18 13:31:56,164 INFO [train.py:874] (0/4) Epoch 5, batch 650, aishell_loss[loss=0.2239, simple_loss=0.2831, pruned_loss=0.08231, over 4919.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2736, pruned_loss=0.08199, over 947808.10 frames.], batch size: 33, aishell_tot_loss[loss=0.2153, simple_loss=0.2777, pruned_loss=0.07645, over 788838.42 frames.], datatang_tot_loss[loss=0.2216, simple_loss=0.2683, pruned_loss=0.08745, over 795613.54 frames.], batch size: 33, lr: 1.44e-03
+2022-06-18 13:32:27,679 INFO [train.py:874] (0/4) Epoch 5, batch 700, aishell_loss[loss=0.2183, simple_loss=0.2794, pruned_loss=0.0786, over 4933.00 frames.], tot_loss[loss=0.2186, simple_loss=0.2738, pruned_loss=0.0817, over 956294.78 frames.], batch size: 33, aishell_tot_loss[loss=0.215, simple_loss=0.2777, pruned_loss=0.07619, over 814586.07 frames.], datatang_tot_loss[loss=0.2219, simple_loss=0.2686, pruned_loss=0.08758, over 815593.44 frames.], batch size: 33, lr: 1.44e-03
+2022-06-18 13:32:56,846 INFO [train.py:874] (0/4) Epoch 5, batch 750, datatang_loss[loss=0.1974, simple_loss=0.2539, pruned_loss=0.07048, over 4914.00 frames.], tot_loss[loss=0.2186, simple_loss=0.274, pruned_loss=0.08164, over 963003.57 frames.], batch size: 64, aishell_tot_loss[loss=0.2144, simple_loss=0.2774, pruned_loss=0.07573, over 835788.28 frames.], datatang_tot_loss[loss=0.2226, simple_loss=0.2692, pruned_loss=0.08801, over 834781.96 frames.], batch size: 64, lr: 1.44e-03
+2022-06-18 13:33:26,627 INFO [train.py:874] (0/4) Epoch 5, batch 800, datatang_loss[loss=0.2491, simple_loss=0.2954, pruned_loss=0.1014, over 4951.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2744, pruned_loss=0.08227, over 968172.39 frames.], batch size: 99, aishell_tot_loss[loss=0.2146, simple_loss=0.2775, pruned_loss=0.07581, over 852069.16 frames.], datatang_tot_loss[loss=0.2234, simple_loss=0.2699, pruned_loss=0.08845, over 854072.74 frames.], batch size: 99, lr: 1.44e-03
+2022-06-18 13:33:57,569 INFO [train.py:874] (0/4) Epoch 5, batch 850, aishell_loss[loss=0.2088, simple_loss=0.2726, pruned_loss=0.07252, over 4938.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2749, pruned_loss=0.0824, over 971880.07 frames.], batch size: 45, aishell_tot_loss[loss=0.2151, simple_loss=0.278, pruned_loss=0.07606, over 867206.97 frames.], datatang_tot_loss[loss=0.2234, simple_loss=0.2701, pruned_loss=0.08834, over 869987.70 frames.], batch size: 45, lr: 1.43e-03
+2022-06-18 13:34:26,193 INFO [train.py:874] (0/4) Epoch 5, batch 900, aishell_loss[loss=0.1775, simple_loss=0.2531, pruned_loss=0.05102, over 4928.00 frames.], tot_loss[loss=0.219, simple_loss=0.2742, pruned_loss=0.08188, over 975079.04 frames.], batch size: 41, aishell_tot_loss[loss=0.2145, simple_loss=0.2775, pruned_loss=0.07575, over 880659.54 frames.], datatang_tot_loss[loss=0.2231, simple_loss=0.2702, pruned_loss=0.08806, over 884271.67 frames.], batch size: 41, lr: 1.43e-03
+2022-06-18 13:34:56,205 INFO [train.py:874] (0/4) Epoch 5, batch 950, datatang_loss[loss=0.2122, simple_loss=0.2593, pruned_loss=0.08254, over 4928.00 frames.], tot_loss[loss=0.2187, simple_loss=0.274, pruned_loss=0.08177, over 977616.75 frames.], batch size: 77, aishell_tot_loss[loss=0.214, simple_loss=0.2772, pruned_loss=0.07543, over 892263.73 frames.], datatang_tot_loss[loss=0.2233, simple_loss=0.2704, pruned_loss=0.08813, over 897138.17 frames.], batch size: 77, lr: 1.43e-03
+2022-06-18 13:35:26,996 INFO [train.py:874] (0/4) Epoch 5, batch 1000, datatang_loss[loss=0.2592, simple_loss=0.2968, pruned_loss=0.1108, over 4878.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2743, pruned_loss=0.08236, over 979609.99 frames.], batch size: 39, aishell_tot_loss[loss=0.2139, simple_loss=0.277, pruned_loss=0.07538, over 903542.09 frames.], datatang_tot_loss[loss=0.2244, simple_loss=0.271, pruned_loss=0.08885, over 907510.86 frames.], batch size: 39, lr: 1.43e-03
+2022-06-18 13:35:26,999 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 13:35:43,412 INFO [train.py:914] (0/4) Epoch 5, validation: loss=0.1786, simple_loss=0.258, pruned_loss=0.04955, over 1622729.00 frames.
+2022-06-18 13:36:13,975 INFO [train.py:874] (0/4) Epoch 5, batch 1050, aishell_loss[loss=0.1952, simple_loss=0.2627, pruned_loss=0.06388, over 4984.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2756, pruned_loss=0.08328, over 980876.34 frames.], batch size: 30, aishell_tot_loss[loss=0.2147, simple_loss=0.2779, pruned_loss=0.07576, over 911499.70 frames.], datatang_tot_loss[loss=0.2252, simple_loss=0.2718, pruned_loss=0.08927, over 918217.71 frames.], batch size: 30, lr: 1.43e-03
+2022-06-18 13:36:44,160 INFO [train.py:874] (0/4) Epoch 5, batch 1100, datatang_loss[loss=0.2685, simple_loss=0.3168, pruned_loss=0.1101, over 4926.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2753, pruned_loss=0.08265, over 981682.73 frames.], batch size: 98, aishell_tot_loss[loss=0.2148, simple_loss=0.2778, pruned_loss=0.07592, over 921367.18 frames.], datatang_tot_loss[loss=0.2246, simple_loss=0.2716, pruned_loss=0.08885, over 924835.13 frames.], batch size: 98, lr: 1.43e-03
+2022-06-18 13:37:13,242 INFO [train.py:874] (0/4) Epoch 5, batch 1150, datatang_loss[loss=0.188, simple_loss=0.2473, pruned_loss=0.06432, over 4935.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2746, pruned_loss=0.08212, over 982634.88 frames.], batch size: 69, aishell_tot_loss[loss=0.2144, simple_loss=0.2775, pruned_loss=0.07562, over 928215.85 frames.], datatang_tot_loss[loss=0.2241, simple_loss=0.2714, pruned_loss=0.08842, over 932736.33 frames.], batch size: 69, lr: 1.42e-03
+2022-06-18 13:37:45,216 INFO [train.py:874] (0/4) Epoch 5, batch 1200, aishell_loss[loss=0.2206, simple_loss=0.2765, pruned_loss=0.08235, over 4872.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2743, pruned_loss=0.08193, over 983055.44 frames.], batch size: 28, aishell_tot_loss[loss=0.2146, simple_loss=0.2776, pruned_loss=0.07582, over 934673.08 frames.], datatang_tot_loss[loss=0.2235, simple_loss=0.271, pruned_loss=0.088, over 939025.94 frames.], batch size: 28, lr: 1.42e-03
+2022-06-18 13:38:15,695 INFO [train.py:874] (0/4) Epoch 5, batch 1250, aishell_loss[loss=0.1966, simple_loss=0.2658, pruned_loss=0.06364, over 4889.00 frames.], tot_loss[loss=0.2177, simple_loss=0.273, pruned_loss=0.08124, over 983845.72 frames.], batch size: 47, aishell_tot_loss[loss=0.2139, simple_loss=0.2771, pruned_loss=0.07541, over 941186.40 frames.], datatang_tot_loss[loss=0.2227, simple_loss=0.27, pruned_loss=0.08769, over 944257.27 frames.], batch size: 47, lr: 1.42e-03
+2022-06-18 13:38:44,451 INFO [train.py:874] (0/4) Epoch 5, batch 1300, datatang_loss[loss=0.2544, simple_loss=0.2914, pruned_loss=0.1087, over 4904.00 frames.], tot_loss[loss=0.2181, simple_loss=0.2735, pruned_loss=0.08134, over 983822.22 frames.], batch size: 52, aishell_tot_loss[loss=0.214, simple_loss=0.2772, pruned_loss=0.07538, over 945992.02 frames.], datatang_tot_loss[loss=0.2229, simple_loss=0.2702, pruned_loss=0.08777, over 949106.93 frames.], batch size: 52, lr: 1.42e-03
+2022-06-18 13:39:15,004 INFO [train.py:874] (0/4) Epoch 5, batch 1350, datatang_loss[loss=0.2663, simple_loss=0.2947, pruned_loss=0.119, over 4938.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2737, pruned_loss=0.08138, over 984538.06 frames.], batch size: 69, aishell_tot_loss[loss=0.2135, simple_loss=0.2771, pruned_loss=0.07497, over 950634.15 frames.], datatang_tot_loss[loss=0.2233, simple_loss=0.2705, pruned_loss=0.08809, over 953768.88 frames.], batch size: 69, lr: 1.42e-03
+2022-06-18 13:39:44,961 INFO [train.py:874] (0/4) Epoch 5, batch 1400, datatang_loss[loss=0.1986, simple_loss=0.2437, pruned_loss=0.0768, over 4940.00 frames.], tot_loss[loss=0.2185, simple_loss=0.274, pruned_loss=0.08152, over 984733.16 frames.], batch size: 69, aishell_tot_loss[loss=0.2144, simple_loss=0.2781, pruned_loss=0.07541, over 954704.05 frames.], datatang_tot_loss[loss=0.2227, simple_loss=0.2699, pruned_loss=0.08778, over 957525.56 frames.], batch size: 69, lr: 1.41e-03
+2022-06-18 13:40:14,813 INFO [train.py:874] (0/4) Epoch 5, batch 1450, aishell_loss[loss=0.2166, simple_loss=0.2835, pruned_loss=0.07487, over 4912.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2734, pruned_loss=0.08153, over 984832.48 frames.], batch size: 52, aishell_tot_loss[loss=0.2144, simple_loss=0.278, pruned_loss=0.07539, over 958388.04 frames.], datatang_tot_loss[loss=0.2225, simple_loss=0.2692, pruned_loss=0.08783, over 960718.87 frames.], batch size: 52, lr: 1.41e-03
+2022-06-18 13:40:45,875 INFO [train.py:874] (0/4) Epoch 5, batch 1500, datatang_loss[loss=0.2131, simple_loss=0.2671, pruned_loss=0.07951, over 4957.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2727, pruned_loss=0.08083, over 985147.90 frames.], batch size: 67, aishell_tot_loss[loss=0.2133, simple_loss=0.2773, pruned_loss=0.07464, over 960965.72 frames.], datatang_tot_loss[loss=0.2222, simple_loss=0.2694, pruned_loss=0.0875, over 964354.43 frames.], batch size: 67, lr: 1.41e-03
+2022-06-18 13:41:16,035 INFO [train.py:874] (0/4) Epoch 5, batch 1550, datatang_loss[loss=0.1991, simple_loss=0.2486, pruned_loss=0.07478, over 4874.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2729, pruned_loss=0.08146, over 984978.50 frames.], batch size: 39, aishell_tot_loss[loss=0.2142, simple_loss=0.2777, pruned_loss=0.07531, over 963299.63 frames.], datatang_tot_loss[loss=0.2218, simple_loss=0.269, pruned_loss=0.08723, over 967085.32 frames.], batch size: 39, lr: 1.41e-03
+2022-06-18 13:41:45,622 INFO [train.py:874] (0/4) Epoch 5, batch 1600, aishell_loss[loss=0.2131, simple_loss=0.2848, pruned_loss=0.07076, over 4967.00 frames.], tot_loss[loss=0.2182, simple_loss=0.2733, pruned_loss=0.08158, over 985313.79 frames.], batch size: 61, aishell_tot_loss[loss=0.2148, simple_loss=0.2779, pruned_loss=0.07584, over 966096.74 frames.], datatang_tot_loss[loss=0.2216, simple_loss=0.2692, pruned_loss=0.08702, over 969331.04 frames.], batch size: 61, lr: 1.41e-03
+2022-06-18 13:42:15,550 INFO [train.py:874] (0/4) Epoch 5, batch 1650, datatang_loss[loss=0.2041, simple_loss=0.2392, pruned_loss=0.08446, over 4964.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2738, pruned_loss=0.08202, over 985254.69 frames.], batch size: 45, aishell_tot_loss[loss=0.2149, simple_loss=0.2781, pruned_loss=0.07587, over 967801.87 frames.], datatang_tot_loss[loss=0.2221, simple_loss=0.2695, pruned_loss=0.08729, over 971650.77 frames.], batch size: 45, lr: 1.40e-03
+2022-06-18 13:42:46,145 INFO [train.py:874] (0/4) Epoch 5, batch 1700, aishell_loss[loss=0.2037, simple_loss=0.2748, pruned_loss=0.06632, over 4897.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2742, pruned_loss=0.08167, over 985147.42 frames.], batch size: 34, aishell_tot_loss[loss=0.2147, simple_loss=0.2782, pruned_loss=0.07558, over 969640.05 frames.], datatang_tot_loss[loss=0.2223, simple_loss=0.2699, pruned_loss=0.0873, over 973349.21 frames.], batch size: 34, lr: 1.40e-03
+2022-06-18 13:43:15,674 INFO [train.py:874] (0/4) Epoch 5, batch 1750, datatang_loss[loss=0.2369, simple_loss=0.2827, pruned_loss=0.09552, over 4926.00 frames.], tot_loss[loss=0.2184, simple_loss=0.2742, pruned_loss=0.08135, over 985037.22 frames.], batch size: 94, aishell_tot_loss[loss=0.2142, simple_loss=0.278, pruned_loss=0.07522, over 971206.34 frames.], datatang_tot_loss[loss=0.2224, simple_loss=0.2701, pruned_loss=0.08735, over 974882.26 frames.], batch size: 94, lr: 1.40e-03
+2022-06-18 13:43:46,684 INFO [train.py:874] (0/4) Epoch 5, batch 1800, aishell_loss[loss=0.1513, simple_loss=0.2026, pruned_loss=0.05004, over 4867.00 frames.], tot_loss[loss=0.2176, simple_loss=0.273, pruned_loss=0.08113, over 985069.80 frames.], batch size: 21, aishell_tot_loss[loss=0.2139, simple_loss=0.2776, pruned_loss=0.07506, over 972413.81 frames.], datatang_tot_loss[loss=0.2217, simple_loss=0.2696, pruned_loss=0.0869, over 976426.84 frames.], batch size: 21, lr: 1.40e-03
+2022-06-18 13:44:17,138 INFO [train.py:874] (0/4) Epoch 5, batch 1850, datatang_loss[loss=0.2005, simple_loss=0.251, pruned_loss=0.07502, over 4954.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2733, pruned_loss=0.08121, over 985300.90 frames.], batch size: 31, aishell_tot_loss[loss=0.2135, simple_loss=0.2774, pruned_loss=0.07481, over 973873.16 frames.], datatang_tot_loss[loss=0.2223, simple_loss=0.27, pruned_loss=0.08725, over 977707.44 frames.], batch size: 31, lr: 1.40e-03
+2022-06-18 13:44:47,051 INFO [train.py:874] (0/4) Epoch 5, batch 1900, datatang_loss[loss=0.2323, simple_loss=0.2754, pruned_loss=0.09459, over 4888.00 frames.], tot_loss[loss=0.2194, simple_loss=0.2746, pruned_loss=0.08208, over 985568.15 frames.], batch size: 52, aishell_tot_loss[loss=0.214, simple_loss=0.2777, pruned_loss=0.0752, over 975498.89 frames.], datatang_tot_loss[loss=0.2233, simple_loss=0.2709, pruned_loss=0.0879, over 978648.10 frames.], batch size: 52, lr: 1.40e-03
+2022-06-18 13:45:17,710 INFO [train.py:874] (0/4) Epoch 5, batch 1950, datatang_loss[loss=0.1812, simple_loss=0.2409, pruned_loss=0.06069, over 4933.00 frames.], tot_loss[loss=0.2195, simple_loss=0.2744, pruned_loss=0.08225, over 985413.28 frames.], batch size: 69, aishell_tot_loss[loss=0.2137, simple_loss=0.2775, pruned_loss=0.07493, over 976506.34 frames.], datatang_tot_loss[loss=0.2238, simple_loss=0.271, pruned_loss=0.08832, over 979460.68 frames.], batch size: 69, lr: 1.39e-03
+2022-06-18 13:45:46,802 INFO [train.py:874] (0/4) Epoch 5, batch 2000, aishell_loss[loss=0.1754, simple_loss=0.2477, pruned_loss=0.05155, over 4830.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2758, pruned_loss=0.08296, over 984869.36 frames.], batch size: 29, aishell_tot_loss[loss=0.2139, simple_loss=0.2777, pruned_loss=0.07511, over 977235.40 frames.], datatang_tot_loss[loss=0.2254, simple_loss=0.2724, pruned_loss=0.08919, over 979957.55 frames.], batch size: 29, lr: 1.39e-03
+2022-06-18 13:45:46,805 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 13:46:03,087 INFO [train.py:914] (0/4) Epoch 5, validation: loss=0.1805, simple_loss=0.2595, pruned_loss=0.05074, over 1622729.00 frames.
+2022-06-18 13:46:32,371 INFO [train.py:874] (0/4) Epoch 5, batch 2050, datatang_loss[loss=0.2056, simple_loss=0.2537, pruned_loss=0.07876, over 4955.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2749, pruned_loss=0.08185, over 984552.87 frames.], batch size: 67, aishell_tot_loss[loss=0.2133, simple_loss=0.2773, pruned_loss=0.07468, over 977724.73 frames.], datatang_tot_loss[loss=0.2246, simple_loss=0.2719, pruned_loss=0.08867, over 980611.27 frames.], batch size: 67, lr: 1.39e-03
+2022-06-18 13:47:01,878 INFO [train.py:874] (0/4) Epoch 5, batch 2100, datatang_loss[loss=0.1818, simple_loss=0.2335, pruned_loss=0.06505, over 4933.00 frames.], tot_loss[loss=0.2191, simple_loss=0.2753, pruned_loss=0.0815, over 984926.33 frames.], batch size: 25, aishell_tot_loss[loss=0.2133, simple_loss=0.2774, pruned_loss=0.07462, over 979069.29 frames.], datatang_tot_loss[loss=0.2249, simple_loss=0.2721, pruned_loss=0.08885, over 981011.81 frames.], batch size: 25, lr: 1.39e-03
+2022-06-18 13:47:31,712 INFO [train.py:874] (0/4) Epoch 5, batch 2150, aishell_loss[loss=0.1771, simple_loss=0.2431, pruned_loss=0.05556, over 4854.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2747, pruned_loss=0.08059, over 984878.94 frames.], batch size: 28, aishell_tot_loss[loss=0.213, simple_loss=0.2771, pruned_loss=0.0744, over 979817.83 frames.], datatang_tot_loss[loss=0.2243, simple_loss=0.2717, pruned_loss=0.08845, over 981408.25 frames.], batch size: 28, lr: 1.39e-03
+2022-06-18 13:48:01,810 INFO [train.py:874] (0/4) Epoch 5, batch 2200, datatang_loss[loss=0.2596, simple_loss=0.2943, pruned_loss=0.1125, over 4965.00 frames.], tot_loss[loss=0.218, simple_loss=0.274, pruned_loss=0.08099, over 985050.64 frames.], batch size: 91, aishell_tot_loss[loss=0.2131, simple_loss=0.2773, pruned_loss=0.07449, over 980270.43 frames.], datatang_tot_loss[loss=0.2237, simple_loss=0.2713, pruned_loss=0.08808, over 982074.20 frames.], batch size: 91, lr: 1.39e-03
+2022-06-18 13:48:32,482 INFO [train.py:874] (0/4) Epoch 5, batch 2250, datatang_loss[loss=0.2243, simple_loss=0.2754, pruned_loss=0.08658, over 4953.00 frames.], tot_loss[loss=0.2213, simple_loss=0.2754, pruned_loss=0.08355, over 985114.31 frames.], batch size: 86, aishell_tot_loss[loss=0.2147, simple_loss=0.2779, pruned_loss=0.07571, over 980686.03 frames.], datatang_tot_loss[loss=0.2253, simple_loss=0.2722, pruned_loss=0.08918, over 982582.24 frames.], batch size: 86, lr: 1.38e-03
+2022-06-18 13:49:03,295 INFO [train.py:874] (0/4) Epoch 5, batch 2300, datatang_loss[loss=0.2384, simple_loss=0.2806, pruned_loss=0.09813, over 4949.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2756, pruned_loss=0.08333, over 985300.40 frames.], batch size: 62, aishell_tot_loss[loss=0.2148, simple_loss=0.2781, pruned_loss=0.07573, over 981212.11 frames.], datatang_tot_loss[loss=0.225, simple_loss=0.2724, pruned_loss=0.08875, over 983009.11 frames.], batch size: 62, lr: 1.38e-03
+2022-06-18 13:49:34,052 INFO [train.py:874] (0/4) Epoch 5, batch 2350, aishell_loss[loss=0.2041, simple_loss=0.2705, pruned_loss=0.06882, over 4868.00 frames.], tot_loss[loss=0.2205, simple_loss=0.2759, pruned_loss=0.08255, over 985258.61 frames.], batch size: 35, aishell_tot_loss[loss=0.215, simple_loss=0.2787, pruned_loss=0.07561, over 981559.09 frames.], datatang_tot_loss[loss=0.2244, simple_loss=0.2722, pruned_loss=0.08832, over 983386.21 frames.], batch size: 35, lr: 1.38e-03
+2022-06-18 13:50:03,384 INFO [train.py:874] (0/4) Epoch 5, batch 2400, datatang_loss[loss=0.1634, simple_loss=0.2082, pruned_loss=0.05935, over 4837.00 frames.], tot_loss[loss=0.2197, simple_loss=0.2755, pruned_loss=0.08195, over 985030.21 frames.], batch size: 33, aishell_tot_loss[loss=0.2145, simple_loss=0.2788, pruned_loss=0.07514, over 981906.44 frames.], datatang_tot_loss[loss=0.2242, simple_loss=0.2719, pruned_loss=0.08825, over 983451.70 frames.], batch size: 33, lr: 1.38e-03
+2022-06-18 13:50:34,590 INFO [train.py:874] (0/4) Epoch 5, batch 2450, aishell_loss[loss=0.1985, simple_loss=0.273, pruned_loss=0.062, over 4894.00 frames.], tot_loss[loss=0.2193, simple_loss=0.2746, pruned_loss=0.08199, over 985053.98 frames.], batch size: 50, aishell_tot_loss[loss=0.2141, simple_loss=0.2781, pruned_loss=0.07498, over 982075.61 frames.], datatang_tot_loss[loss=0.2242, simple_loss=0.2718, pruned_loss=0.08824, over 983814.99 frames.], batch size: 50, lr: 1.38e-03
+2022-06-18 13:51:05,475 INFO [train.py:874] (0/4) Epoch 5, batch 2500, datatang_loss[loss=0.2226, simple_loss=0.2561, pruned_loss=0.0945, over 4930.00 frames.], tot_loss[loss=0.219, simple_loss=0.2745, pruned_loss=0.08171, over 985249.99 frames.], batch size: 62, aishell_tot_loss[loss=0.2135, simple_loss=0.2779, pruned_loss=0.07459, over 982520.81 frames.], datatang_tot_loss[loss=0.2244, simple_loss=0.2719, pruned_loss=0.08845, over 984076.34 frames.], batch size: 62, lr: 1.38e-03
+2022-06-18 13:51:35,048 INFO [train.py:874] (0/4) Epoch 5, batch 2550, datatang_loss[loss=0.2075, simple_loss=0.2681, pruned_loss=0.0735, over 4980.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2737, pruned_loss=0.08073, over 984975.86 frames.], batch size: 40, aishell_tot_loss[loss=0.2129, simple_loss=0.2771, pruned_loss=0.07434, over 982558.59 frames.], datatang_tot_loss[loss=0.2238, simple_loss=0.2716, pruned_loss=0.08802, over 984238.41 frames.], batch size: 40, lr: 1.37e-03
+2022-06-18 13:52:06,132 INFO [train.py:874] (0/4) Epoch 5, batch 2600, datatang_loss[loss=0.2029, simple_loss=0.2568, pruned_loss=0.07452, over 4904.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2725, pruned_loss=0.07925, over 985052.09 frames.], batch size: 64, aishell_tot_loss[loss=0.2119, simple_loss=0.2765, pruned_loss=0.07366, over 982953.35 frames.], datatang_tot_loss[loss=0.2225, simple_loss=0.2707, pruned_loss=0.08712, over 984303.13 frames.], batch size: 64, lr: 1.37e-03
+2022-06-18 13:52:35,437 INFO [train.py:874] (0/4) Epoch 5, batch 2650, datatang_loss[loss=0.2086, simple_loss=0.2631, pruned_loss=0.07705, over 4927.00 frames.], tot_loss[loss=0.2164, simple_loss=0.273, pruned_loss=0.07989, over 985065.50 frames.], batch size: 81, aishell_tot_loss[loss=0.2115, simple_loss=0.2761, pruned_loss=0.07342, over 982961.27 frames.], datatang_tot_loss[loss=0.2235, simple_loss=0.2714, pruned_loss=0.08777, over 984643.56 frames.], batch size: 81, lr: 1.37e-03
+2022-06-18 13:53:06,282 INFO [train.py:874] (0/4) Epoch 5, batch 2700, datatang_loss[loss=0.2205, simple_loss=0.2715, pruned_loss=0.08476, over 4950.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2723, pruned_loss=0.07921, over 985143.97 frames.], batch size: 55, aishell_tot_loss[loss=0.2112, simple_loss=0.276, pruned_loss=0.07322, over 983240.02 frames.], datatang_tot_loss[loss=0.2224, simple_loss=0.2706, pruned_loss=0.08712, over 984755.76 frames.], batch size: 55, lr: 1.37e-03
+2022-06-18 13:53:36,276 INFO [train.py:874] (0/4) Epoch 5, batch 2750, datatang_loss[loss=0.2103, simple_loss=0.2547, pruned_loss=0.08298, over 4952.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2723, pruned_loss=0.07936, over 985655.66 frames.], batch size: 50, aishell_tot_loss[loss=0.2119, simple_loss=0.2769, pruned_loss=0.07343, over 983618.13 frames.], datatang_tot_loss[loss=0.2216, simple_loss=0.2694, pruned_loss=0.08685, over 985188.78 frames.], batch size: 50, lr: 1.37e-03
+2022-06-18 13:54:05,686 INFO [train.py:874] (0/4) Epoch 5, batch 2800, datatang_loss[loss=0.2511, simple_loss=0.2895, pruned_loss=0.1064, over 4953.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2723, pruned_loss=0.07964, over 985821.01 frames.], batch size: 67, aishell_tot_loss[loss=0.2125, simple_loss=0.277, pruned_loss=0.07402, over 983876.65 frames.], datatang_tot_loss[loss=0.2209, simple_loss=0.2691, pruned_loss=0.0864, over 985385.49 frames.], batch size: 67, lr: 1.37e-03
+2022-06-18 13:54:37,280 INFO [train.py:874] (0/4) Epoch 5, batch 2850, datatang_loss[loss=0.1887, simple_loss=0.2337, pruned_loss=0.07188, over 4979.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2719, pruned_loss=0.07981, over 985854.01 frames.], batch size: 40, aishell_tot_loss[loss=0.2122, simple_loss=0.2767, pruned_loss=0.07386, over 983997.52 frames.], datatang_tot_loss[loss=0.2209, simple_loss=0.269, pruned_loss=0.08637, over 985535.10 frames.], batch size: 40, lr: 1.36e-03
+2022-06-18 13:55:07,711 INFO [train.py:874] (0/4) Epoch 5, batch 2900, datatang_loss[loss=0.2365, simple_loss=0.2757, pruned_loss=0.09863, over 4935.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2729, pruned_loss=0.08049, over 985267.82 frames.], batch size: 37, aishell_tot_loss[loss=0.2119, simple_loss=0.2763, pruned_loss=0.07374, over 983647.30 frames.], datatang_tot_loss[loss=0.2224, simple_loss=0.2701, pruned_loss=0.08731, over 985543.32 frames.], batch size: 37, lr: 1.36e-03
+2022-06-18 13:55:36,550 INFO [train.py:874] (0/4) Epoch 5, batch 2950, aishell_loss[loss=0.2143, simple_loss=0.2915, pruned_loss=0.06859, over 4922.00 frames.], tot_loss[loss=0.2169, simple_loss=0.2734, pruned_loss=0.08017, over 985319.28 frames.], batch size: 46, aishell_tot_loss[loss=0.2114, simple_loss=0.2762, pruned_loss=0.07333, over 983768.80 frames.], datatang_tot_loss[loss=0.2227, simple_loss=0.2706, pruned_loss=0.08735, over 985625.23 frames.], batch size: 46, lr: 1.36e-03
+2022-06-18 13:56:08,922 INFO [train.py:874] (0/4) Epoch 5, batch 3000, datatang_loss[loss=0.2124, simple_loss=0.2631, pruned_loss=0.08085, over 4915.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2717, pruned_loss=0.07879, over 985360.20 frames.], batch size: 42, aishell_tot_loss[loss=0.2103, simple_loss=0.2754, pruned_loss=0.07256, over 983945.45 frames.], datatang_tot_loss[loss=0.2215, simple_loss=0.2696, pruned_loss=0.08671, over 985659.41 frames.], batch size: 42, lr: 1.36e-03
+2022-06-18 13:56:08,925 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 13:56:26,007 INFO [train.py:914] (0/4) Epoch 5, validation: loss=0.1806, simple_loss=0.2585, pruned_loss=0.05141, over 1622729.00 frames.
+2022-06-18 13:56:55,549 INFO [train.py:874] (0/4) Epoch 5, batch 3050, aishell_loss[loss=0.2235, simple_loss=0.2923, pruned_loss=0.07739, over 4944.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2719, pruned_loss=0.07888, over 985457.80 frames.], batch size: 45, aishell_tot_loss[loss=0.2104, simple_loss=0.2756, pruned_loss=0.07261, over 984143.30 frames.], datatang_tot_loss[loss=0.2213, simple_loss=0.2694, pruned_loss=0.08663, over 985692.66 frames.], batch size: 45, lr: 1.36e-03
+2022-06-18 13:57:26,861 INFO [train.py:874] (0/4) Epoch 5, batch 3100, datatang_loss[loss=0.2076, simple_loss=0.2612, pruned_loss=0.07695, over 4912.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2719, pruned_loss=0.07896, over 985353.91 frames.], batch size: 75, aishell_tot_loss[loss=0.2102, simple_loss=0.2755, pruned_loss=0.07247, over 984079.91 frames.], datatang_tot_loss[loss=0.2212, simple_loss=0.2695, pruned_loss=0.08646, over 985747.16 frames.], batch size: 75, lr: 1.36e-03
+2022-06-18 13:57:57,632 INFO [train.py:874] (0/4) Epoch 5, batch 3150, aishell_loss[loss=0.2045, simple_loss=0.2817, pruned_loss=0.06362, over 4936.00 frames.], tot_loss[loss=0.214, simple_loss=0.2711, pruned_loss=0.0784, over 985155.52 frames.], batch size: 49, aishell_tot_loss[loss=0.2096, simple_loss=0.2751, pruned_loss=0.0721, over 983892.96 frames.], datatang_tot_loss[loss=0.2204, simple_loss=0.269, pruned_loss=0.0859, over 985792.63 frames.], batch size: 49, lr: 1.35e-03
+2022-06-18 13:58:27,915 INFO [train.py:874] (0/4) Epoch 5, batch 3200, aishell_loss[loss=0.2094, simple_loss=0.2775, pruned_loss=0.07059, over 4938.00 frames.], tot_loss[loss=0.2152, simple_loss=0.272, pruned_loss=0.0792, over 985064.77 frames.], batch size: 32, aishell_tot_loss[loss=0.2103, simple_loss=0.2755, pruned_loss=0.07251, over 983747.59 frames.], datatang_tot_loss[loss=0.2205, simple_loss=0.2694, pruned_loss=0.08586, over 985864.65 frames.], batch size: 32, lr: 1.35e-03
+2022-06-18 13:58:58,335 INFO [train.py:874] (0/4) Epoch 5, batch 3250, datatang_loss[loss=0.2298, simple_loss=0.2746, pruned_loss=0.09253, over 4936.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2723, pruned_loss=0.0785, over 985028.37 frames.], batch size: 88, aishell_tot_loss[loss=0.2102, simple_loss=0.276, pruned_loss=0.07224, over 983785.00 frames.], datatang_tot_loss[loss=0.22, simple_loss=0.269, pruned_loss=0.08551, over 985865.63 frames.], batch size: 88, lr: 1.35e-03
+2022-06-18 13:59:26,745 INFO [train.py:874] (0/4) Epoch 5, batch 3300, aishell_loss[loss=0.2037, simple_loss=0.2658, pruned_loss=0.07082, over 4952.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2731, pruned_loss=0.07855, over 985316.64 frames.], batch size: 31, aishell_tot_loss[loss=0.2107, simple_loss=0.2764, pruned_loss=0.07248, over 984186.60 frames.], datatang_tot_loss[loss=0.2203, simple_loss=0.2692, pruned_loss=0.0857, over 985892.94 frames.], batch size: 31, lr: 1.35e-03
+2022-06-18 13:59:58,743 INFO [train.py:874] (0/4) Epoch 5, batch 3350, datatang_loss[loss=0.2177, simple_loss=0.268, pruned_loss=0.08371, over 4981.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2715, pruned_loss=0.07778, over 985305.49 frames.], batch size: 31, aishell_tot_loss[loss=0.21, simple_loss=0.2757, pruned_loss=0.07217, over 984115.62 frames.], datatang_tot_loss[loss=0.2192, simple_loss=0.2683, pruned_loss=0.085, over 985996.34 frames.], batch size: 31, lr: 1.35e-03
+2022-06-18 14:00:00,862 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-20000.pt
+2022-06-18 14:00:33,632 INFO [train.py:874] (0/4) Epoch 5, batch 3400, datatang_loss[loss=0.2397, simple_loss=0.2849, pruned_loss=0.09724, over 4902.00 frames.], tot_loss[loss=0.2133, simple_loss=0.2717, pruned_loss=0.07751, over 984968.33 frames.], batch size: 59, aishell_tot_loss[loss=0.2094, simple_loss=0.2749, pruned_loss=0.07189, over 984021.56 frames.], datatang_tot_loss[loss=0.2197, simple_loss=0.2689, pruned_loss=0.08522, over 985847.47 frames.], batch size: 59, lr: 1.35e-03
+2022-06-18 14:01:04,097 INFO [train.py:874] (0/4) Epoch 5, batch 3450, aishell_loss[loss=0.1997, simple_loss=0.262, pruned_loss=0.06871, over 4923.00 frames.], tot_loss[loss=0.214, simple_loss=0.272, pruned_loss=0.07797, over 985361.31 frames.], batch size: 33, aishell_tot_loss[loss=0.2096, simple_loss=0.2753, pruned_loss=0.07197, over 984330.20 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2689, pruned_loss=0.08514, over 985946.96 frames.], batch size: 33, lr: 1.34e-03
+2022-06-18 14:01:33,995 INFO [train.py:874] (0/4) Epoch 5, batch 3500, aishell_loss[loss=0.1964, simple_loss=0.2506, pruned_loss=0.07113, over 4969.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2714, pruned_loss=0.07771, over 985577.05 frames.], batch size: 27, aishell_tot_loss[loss=0.2095, simple_loss=0.2751, pruned_loss=0.07202, over 984563.30 frames.], datatang_tot_loss[loss=0.2189, simple_loss=0.2685, pruned_loss=0.08464, over 985986.83 frames.], batch size: 27, lr: 1.34e-03
+2022-06-18 14:02:04,009 INFO [train.py:874] (0/4) Epoch 5, batch 3550, datatang_loss[loss=0.2153, simple_loss=0.2703, pruned_loss=0.08014, over 4921.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2711, pruned_loss=0.07759, over 985367.42 frames.], batch size: 71, aishell_tot_loss[loss=0.2093, simple_loss=0.2748, pruned_loss=0.07187, over 984486.20 frames.], datatang_tot_loss[loss=0.2189, simple_loss=0.2683, pruned_loss=0.08473, over 985941.74 frames.], batch size: 71, lr: 1.34e-03
+2022-06-18 14:02:33,813 INFO [train.py:874] (0/4) Epoch 5, batch 3600, aishell_loss[loss=0.203, simple_loss=0.2712, pruned_loss=0.0674, over 4933.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2724, pruned_loss=0.07809, over 985187.11 frames.], batch size: 49, aishell_tot_loss[loss=0.2103, simple_loss=0.2758, pruned_loss=0.07235, over 984432.89 frames.], datatang_tot_loss[loss=0.2189, simple_loss=0.2685, pruned_loss=0.0847, over 985853.48 frames.], batch size: 49, lr: 1.34e-03
+2022-06-18 14:03:03,178 INFO [train.py:874] (0/4) Epoch 5, batch 3650, datatang_loss[loss=0.2043, simple_loss=0.2653, pruned_loss=0.07165, over 4938.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2726, pruned_loss=0.07847, over 984936.59 frames.], batch size: 69, aishell_tot_loss[loss=0.2102, simple_loss=0.2757, pruned_loss=0.07235, over 984381.94 frames.], datatang_tot_loss[loss=0.2193, simple_loss=0.2689, pruned_loss=0.08483, over 985632.20 frames.], batch size: 69, lr: 1.34e-03
+2022-06-18 14:03:33,725 INFO [train.py:874] (0/4) Epoch 5, batch 3700, aishell_loss[loss=0.2204, simple_loss=0.2805, pruned_loss=0.0801, over 4939.00 frames.], tot_loss[loss=0.2144, simple_loss=0.2726, pruned_loss=0.07808, over 985163.99 frames.], batch size: 49, aishell_tot_loss[loss=0.2106, simple_loss=0.2761, pruned_loss=0.07255, over 984575.56 frames.], datatang_tot_loss[loss=0.2184, simple_loss=0.2684, pruned_loss=0.08416, over 985662.72 frames.], batch size: 49, lr: 1.34e-03
+2022-06-18 14:04:03,518 INFO [train.py:874] (0/4) Epoch 5, batch 3750, aishell_loss[loss=0.1826, simple_loss=0.2486, pruned_loss=0.05827, over 4891.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2718, pruned_loss=0.07753, over 985448.32 frames.], batch size: 34, aishell_tot_loss[loss=0.2098, simple_loss=0.2752, pruned_loss=0.0722, over 984654.25 frames.], datatang_tot_loss[loss=0.2183, simple_loss=0.2687, pruned_loss=0.08394, over 985907.50 frames.], batch size: 34, lr: 1.34e-03
+2022-06-18 14:04:33,268 INFO [train.py:874] (0/4) Epoch 5, batch 3800, aishell_loss[loss=0.2189, simple_loss=0.2982, pruned_loss=0.06979, over 4957.00 frames.], tot_loss[loss=0.2145, simple_loss=0.2728, pruned_loss=0.07807, over 985565.53 frames.], batch size: 44, aishell_tot_loss[loss=0.2095, simple_loss=0.275, pruned_loss=0.07205, over 984882.51 frames.], datatang_tot_loss[loss=0.2198, simple_loss=0.2698, pruned_loss=0.0849, over 985867.96 frames.], batch size: 44, lr: 1.33e-03
+2022-06-18 14:05:02,182 INFO [train.py:874] (0/4) Epoch 5, batch 3850, datatang_loss[loss=0.1816, simple_loss=0.2329, pruned_loss=0.0651, over 4841.00 frames.], tot_loss[loss=0.2132, simple_loss=0.2716, pruned_loss=0.07741, over 985151.21 frames.], batch size: 30, aishell_tot_loss[loss=0.2088, simple_loss=0.2741, pruned_loss=0.07174, over 984680.14 frames.], datatang_tot_loss[loss=0.2194, simple_loss=0.2692, pruned_loss=0.08475, over 985708.99 frames.], batch size: 30, lr: 1.33e-03
+2022-06-18 14:05:32,249 INFO [train.py:874] (0/4) Epoch 5, batch 3900, datatang_loss[loss=0.1679, simple_loss=0.2292, pruned_loss=0.05328, over 4957.00 frames.], tot_loss[loss=0.2128, simple_loss=0.2718, pruned_loss=0.07688, over 985238.27 frames.], batch size: 55, aishell_tot_loss[loss=0.2095, simple_loss=0.2752, pruned_loss=0.07194, over 984728.12 frames.], datatang_tot_loss[loss=0.218, simple_loss=0.2684, pruned_loss=0.08385, over 985755.37 frames.], batch size: 55, lr: 1.33e-03
+2022-06-18 14:06:00,093 INFO [train.py:874] (0/4) Epoch 5, batch 3950, datatang_loss[loss=0.194, simple_loss=0.249, pruned_loss=0.06953, over 4877.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2716, pruned_loss=0.07706, over 985244.45 frames.], batch size: 39, aishell_tot_loss[loss=0.2085, simple_loss=0.2741, pruned_loss=0.07145, over 984767.79 frames.], datatang_tot_loss[loss=0.219, simple_loss=0.2691, pruned_loss=0.08443, over 985738.73 frames.], batch size: 39, lr: 1.33e-03
+2022-06-18 14:06:30,656 INFO [train.py:874] (0/4) Epoch 5, batch 4000, aishell_loss[loss=0.1588, simple_loss=0.2147, pruned_loss=0.05143, over 4815.00 frames.], tot_loss[loss=0.2135, simple_loss=0.2718, pruned_loss=0.07755, over 985331.80 frames.], batch size: 21, aishell_tot_loss[loss=0.208, simple_loss=0.2735, pruned_loss=0.07126, over 984835.07 frames.], datatang_tot_loss[loss=0.2199, simple_loss=0.27, pruned_loss=0.08489, over 985771.01 frames.], batch size: 21, lr: 1.33e-03
+2022-06-18 14:06:30,658 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 14:06:46,839 INFO [train.py:914] (0/4) Epoch 5, validation: loss=0.1787, simple_loss=0.2582, pruned_loss=0.04961, over 1622729.00 frames.
+2022-06-18 14:07:16,323 INFO [train.py:874] (0/4) Epoch 5, batch 4050, aishell_loss[loss=0.1904, simple_loss=0.2632, pruned_loss=0.05876, over 4864.00 frames.], tot_loss[loss=0.2146, simple_loss=0.272, pruned_loss=0.07862, over 985551.16 frames.], batch size: 35, aishell_tot_loss[loss=0.2089, simple_loss=0.2739, pruned_loss=0.07193, over 984826.32 frames.], datatang_tot_loss[loss=0.2197, simple_loss=0.2698, pruned_loss=0.08484, over 985988.34 frames.], batch size: 35, lr: 1.33e-03
+2022-06-18 14:07:45,345 INFO [train.py:874] (0/4) Epoch 5, batch 4100, aishell_loss[loss=0.2264, simple_loss=0.2875, pruned_loss=0.08267, over 4955.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2724, pruned_loss=0.0787, over 985403.33 frames.], batch size: 40, aishell_tot_loss[loss=0.2089, simple_loss=0.2741, pruned_loss=0.07178, over 984495.74 frames.], datatang_tot_loss[loss=0.2199, simple_loss=0.2701, pruned_loss=0.08487, over 986165.55 frames.], batch size: 40, lr: 1.32e-03
+2022-06-18 14:08:14,093 INFO [train.py:874] (0/4) Epoch 5, batch 4150, aishell_loss[loss=0.1882, simple_loss=0.2626, pruned_loss=0.05694, over 4893.00 frames.], tot_loss[loss=0.2142, simple_loss=0.272, pruned_loss=0.0782, over 985055.29 frames.], batch size: 34, aishell_tot_loss[loss=0.2086, simple_loss=0.2741, pruned_loss=0.0715, over 984178.97 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2699, pruned_loss=0.08469, over 986133.34 frames.], batch size: 34, lr: 1.32e-03
+2022-06-18 14:08:33,354 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-5.pt
+2022-06-18 14:09:32,219 INFO [train.py:874] (0/4) Epoch 6, batch 50, datatang_loss[loss=0.2553, simple_loss=0.2988, pruned_loss=0.1059, over 4925.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2606, pruned_loss=0.06762, over 218218.64 frames.], batch size: 94, aishell_tot_loss[loss=0.202, simple_loss=0.271, pruned_loss=0.06647, over 120174.56 frames.], datatang_tot_loss[loss=0.1933, simple_loss=0.2491, pruned_loss=0.06874, over 111666.54 frames.], batch size: 94, lr: 1.27e-03
+2022-06-18 14:10:03,277 INFO [train.py:874] (0/4) Epoch 6, batch 100, datatang_loss[loss=0.1797, simple_loss=0.2465, pruned_loss=0.0564, over 4910.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2618, pruned_loss=0.07017, over 387987.33 frames.], batch size: 64, aishell_tot_loss[loss=0.2038, simple_loss=0.2722, pruned_loss=0.06766, over 214163.94 frames.], datatang_tot_loss[loss=0.1982, simple_loss=0.2518, pruned_loss=0.07233, over 222161.24 frames.], batch size: 64, lr: 1.27e-03
+2022-06-18 14:10:32,596 INFO [train.py:874] (0/4) Epoch 6, batch 150, datatang_loss[loss=0.1696, simple_loss=0.2331, pruned_loss=0.05305, over 4915.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2626, pruned_loss=0.07029, over 521011.44 frames.], batch size: 77, aishell_tot_loss[loss=0.2038, simple_loss=0.2726, pruned_loss=0.0675, over 288072.68 frames.], datatang_tot_loss[loss=0.1995, simple_loss=0.2541, pruned_loss=0.07242, over 329003.34 frames.], batch size: 77, lr: 1.27e-03
+2022-06-18 14:11:03,732 INFO [train.py:874] (0/4) Epoch 6, batch 200, aishell_loss[loss=0.1989, simple_loss=0.2644, pruned_loss=0.06667, over 4949.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2623, pruned_loss=0.07062, over 624475.39 frames.], batch size: 31, aishell_tot_loss[loss=0.2034, simple_loss=0.2721, pruned_loss=0.06736, over 364097.61 frames.], datatang_tot_loss[loss=0.2003, simple_loss=0.2542, pruned_loss=0.07319, over 412555.41 frames.], batch size: 31, lr: 1.26e-03
+2022-06-18 14:11:32,996 INFO [train.py:874] (0/4) Epoch 6, batch 250, datatang_loss[loss=0.1498, simple_loss=0.2158, pruned_loss=0.04189, over 4947.00 frames.], tot_loss[loss=0.2044, simple_loss=0.265, pruned_loss=0.07189, over 704447.60 frames.], batch size: 31, aishell_tot_loss[loss=0.2066, simple_loss=0.274, pruned_loss=0.06958, over 456158.16 frames.], datatang_tot_loss[loss=0.2011, simple_loss=0.2549, pruned_loss=0.07361, over 461946.99 frames.], batch size: 31, lr: 1.26e-03
+2022-06-18 14:12:03,453 INFO [train.py:874] (0/4) Epoch 6, batch 300, datatang_loss[loss=0.1986, simple_loss=0.2536, pruned_loss=0.0718, over 4965.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2641, pruned_loss=0.07177, over 766717.26 frames.], batch size: 67, aishell_tot_loss[loss=0.2054, simple_loss=0.2729, pruned_loss=0.069, over 513629.05 frames.], datatang_tot_loss[loss=0.2017, simple_loss=0.2551, pruned_loss=0.07408, over 528333.34 frames.], batch size: 67, lr: 1.26e-03
+2022-06-18 14:12:34,549 INFO [train.py:874] (0/4) Epoch 6, batch 350, datatang_loss[loss=0.206, simple_loss=0.25, pruned_loss=0.08104, over 4954.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2651, pruned_loss=0.07255, over 815395.78 frames.], batch size: 55, aishell_tot_loss[loss=0.2059, simple_loss=0.2732, pruned_loss=0.0693, over 567130.28 frames.], datatang_tot_loss[loss=0.2033, simple_loss=0.2565, pruned_loss=0.07502, over 584365.12 frames.], batch size: 55, lr: 1.26e-03
+2022-06-18 14:13:03,873 INFO [train.py:874] (0/4) Epoch 6, batch 400, datatang_loss[loss=0.2206, simple_loss=0.2748, pruned_loss=0.08323, over 4915.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2652, pruned_loss=0.07247, over 853079.84 frames.], batch size: 77, aishell_tot_loss[loss=0.2047, simple_loss=0.2721, pruned_loss=0.06864, over 621876.67 frames.], datatang_tot_loss[loss=0.2045, simple_loss=0.2575, pruned_loss=0.07576, over 626269.36 frames.], batch size: 77, lr: 1.26e-03
+2022-06-18 14:13:33,620 INFO [train.py:874] (0/4) Epoch 6, batch 450, aishell_loss[loss=0.2065, simple_loss=0.2783, pruned_loss=0.06741, over 4963.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2656, pruned_loss=0.07203, over 882908.44 frames.], batch size: 44, aishell_tot_loss[loss=0.2044, simple_loss=0.2723, pruned_loss=0.06822, over 668252.15 frames.], datatang_tot_loss[loss=0.2047, simple_loss=0.2579, pruned_loss=0.07578, over 665541.59 frames.], batch size: 44, lr: 1.26e-03
+2022-06-18 14:14:05,035 INFO [train.py:874] (0/4) Epoch 6, batch 500, datatang_loss[loss=0.2169, simple_loss=0.2722, pruned_loss=0.08075, over 4950.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2681, pruned_loss=0.0735, over 905460.72 frames.], batch size: 91, aishell_tot_loss[loss=0.2051, simple_loss=0.2729, pruned_loss=0.06866, over 709719.09 frames.], datatang_tot_loss[loss=0.2078, simple_loss=0.2605, pruned_loss=0.0775, over 698822.01 frames.], batch size: 91, lr: 1.26e-03
+2022-06-18 14:14:34,030 INFO [train.py:874] (0/4) Epoch 6, batch 550, aishell_loss[loss=0.2034, simple_loss=0.271, pruned_loss=0.06788, over 4959.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2694, pruned_loss=0.07317, over 923546.79 frames.], batch size: 31, aishell_tot_loss[loss=0.205, simple_loss=0.2734, pruned_loss=0.06828, over 756742.28 frames.], datatang_tot_loss[loss=0.209, simple_loss=0.2613, pruned_loss=0.07832, over 716924.89 frames.], batch size: 31, lr: 1.25e-03
+2022-06-18 14:15:04,054 INFO [train.py:874] (0/4) Epoch 6, batch 600, datatang_loss[loss=0.1902, simple_loss=0.2448, pruned_loss=0.06786, over 4917.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2694, pruned_loss=0.07346, over 937437.17 frames.], batch size: 64, aishell_tot_loss[loss=0.2046, simple_loss=0.273, pruned_loss=0.06804, over 782772.30 frames.], datatang_tot_loss[loss=0.21, simple_loss=0.2623, pruned_loss=0.07883, over 749804.68 frames.], batch size: 64, lr: 1.25e-03
+2022-06-18 14:15:34,712 INFO [train.py:874] (0/4) Epoch 6, batch 650, datatang_loss[loss=0.1907, simple_loss=0.239, pruned_loss=0.07125, over 4953.00 frames.], tot_loss[loss=0.207, simple_loss=0.2683, pruned_loss=0.07285, over 947738.44 frames.], batch size: 67, aishell_tot_loss[loss=0.2037, simple_loss=0.2723, pruned_loss=0.06752, over 805259.71 frames.], datatang_tot_loss[loss=0.2096, simple_loss=0.2622, pruned_loss=0.07852, over 778714.84 frames.], batch size: 67, lr: 1.25e-03
+2022-06-18 14:16:03,557 INFO [train.py:874] (0/4) Epoch 6, batch 700, aishell_loss[loss=0.1974, simple_loss=0.2717, pruned_loss=0.06155, over 4897.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2681, pruned_loss=0.07308, over 955927.57 frames.], batch size: 60, aishell_tot_loss[loss=0.2038, simple_loss=0.2724, pruned_loss=0.06762, over 826448.83 frames.], datatang_tot_loss[loss=0.2099, simple_loss=0.2623, pruned_loss=0.07872, over 802934.07 frames.], batch size: 60, lr: 1.25e-03
+2022-06-18 14:16:34,006 INFO [train.py:874] (0/4) Epoch 6, batch 750, aishell_loss[loss=0.1881, simple_loss=0.2604, pruned_loss=0.05796, over 4908.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2682, pruned_loss=0.07351, over 962602.77 frames.], batch size: 52, aishell_tot_loss[loss=0.2038, simple_loss=0.2724, pruned_loss=0.06764, over 842308.20 frames.], datatang_tot_loss[loss=0.2104, simple_loss=0.2629, pruned_loss=0.07897, over 827785.19 frames.], batch size: 52, lr: 1.25e-03
+2022-06-18 14:17:06,169 INFO [train.py:874] (0/4) Epoch 6, batch 800, datatang_loss[loss=0.2076, simple_loss=0.2513, pruned_loss=0.08201, over 4927.00 frames.], tot_loss[loss=0.208, simple_loss=0.2678, pruned_loss=0.07411, over 967342.25 frames.], batch size: 64, aishell_tot_loss[loss=0.2032, simple_loss=0.2716, pruned_loss=0.06742, over 856534.34 frames.], datatang_tot_loss[loss=0.2115, simple_loss=0.2636, pruned_loss=0.07972, over 848848.50 frames.], batch size: 64, lr: 1.25e-03
+2022-06-18 14:17:35,731 INFO [train.py:874] (0/4) Epoch 6, batch 850, datatang_loss[loss=0.2949, simple_loss=0.3253, pruned_loss=0.1323, over 4948.00 frames.], tot_loss[loss=0.2102, simple_loss=0.2697, pruned_loss=0.07531, over 971734.09 frames.], batch size: 109, aishell_tot_loss[loss=0.2045, simple_loss=0.2726, pruned_loss=0.06816, over 870806.18 frames.], datatang_tot_loss[loss=0.2129, simple_loss=0.265, pruned_loss=0.08041, over 866302.80 frames.], batch size: 109, lr: 1.25e-03
+2022-06-18 14:18:05,630 INFO [train.py:874] (0/4) Epoch 6, batch 900, aishell_loss[loss=0.2343, simple_loss=0.298, pruned_loss=0.08525, over 4943.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2707, pruned_loss=0.07544, over 974650.80 frames.], batch size: 58, aishell_tot_loss[loss=0.2053, simple_loss=0.2733, pruned_loss=0.06859, over 885588.44 frames.], datatang_tot_loss[loss=0.2133, simple_loss=0.2655, pruned_loss=0.08058, over 878843.45 frames.], batch size: 58, lr: 1.25e-03
+2022-06-18 14:18:36,667 INFO [train.py:874] (0/4) Epoch 6, batch 950, aishell_loss[loss=0.1773, simple_loss=0.2556, pruned_loss=0.04947, over 4883.00 frames.], tot_loss[loss=0.209, simple_loss=0.2694, pruned_loss=0.07434, over 977227.90 frames.], batch size: 35, aishell_tot_loss[loss=0.2046, simple_loss=0.2728, pruned_loss=0.0682, over 897859.65 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2648, pruned_loss=0.08, over 891047.60 frames.], batch size: 35, lr: 1.24e-03
+2022-06-18 14:19:06,991 INFO [train.py:874] (0/4) Epoch 6, batch 1000, datatang_loss[loss=0.1761, simple_loss=0.2399, pruned_loss=0.05613, over 4924.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2683, pruned_loss=0.07303, over 978994.21 frames.], batch size: 81, aishell_tot_loss[loss=0.2039, simple_loss=0.2724, pruned_loss=0.06775, over 908143.27 frames.], datatang_tot_loss[loss=0.2111, simple_loss=0.2641, pruned_loss=0.079, over 902138.26 frames.], batch size: 81, lr: 1.24e-03
+2022-06-18 14:19:06,994 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 14:19:23,422 INFO [train.py:914] (0/4) Epoch 6, validation: loss=0.175, simple_loss=0.2564, pruned_loss=0.04677, over 1622729.00 frames.
+2022-06-18 14:19:52,889 INFO [train.py:874] (0/4) Epoch 6, batch 1050, datatang_loss[loss=0.1964, simple_loss=0.2482, pruned_loss=0.07228, over 4961.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2671, pruned_loss=0.07225, over 980026.95 frames.], batch size: 37, aishell_tot_loss[loss=0.2035, simple_loss=0.2719, pruned_loss=0.06755, over 917684.81 frames.], datatang_tot_loss[loss=0.2099, simple_loss=0.2632, pruned_loss=0.07836, over 911041.80 frames.], batch size: 37, lr: 1.24e-03
+2022-06-18 14:20:23,049 INFO [train.py:874] (0/4) Epoch 6, batch 1100, aishell_loss[loss=0.2428, simple_loss=0.298, pruned_loss=0.09377, over 4858.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2699, pruned_loss=0.07494, over 981202.29 frames.], batch size: 36, aishell_tot_loss[loss=0.2059, simple_loss=0.2736, pruned_loss=0.06907, over 924059.20 frames.], datatang_tot_loss[loss=0.2117, simple_loss=0.2646, pruned_loss=0.07941, over 921508.86 frames.], batch size: 36, lr: 1.24e-03
+2022-06-18 14:20:54,783 INFO [train.py:874] (0/4) Epoch 6, batch 1150, aishell_loss[loss=0.1857, simple_loss=0.2536, pruned_loss=0.05887, over 4962.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2698, pruned_loss=0.07631, over 981879.85 frames.], batch size: 31, aishell_tot_loss[loss=0.2069, simple_loss=0.2737, pruned_loss=0.07007, over 929578.55 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2649, pruned_loss=0.07992, over 930484.49 frames.], batch size: 31, lr: 1.24e-03
+2022-06-18 14:21:24,814 INFO [train.py:874] (0/4) Epoch 6, batch 1200, datatang_loss[loss=0.2028, simple_loss=0.2633, pruned_loss=0.07119, over 4973.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2688, pruned_loss=0.07476, over 982911.98 frames.], batch size: 37, aishell_tot_loss[loss=0.2062, simple_loss=0.2736, pruned_loss=0.06937, over 935813.10 frames.], datatang_tot_loss[loss=0.2112, simple_loss=0.2642, pruned_loss=0.07914, over 937553.25 frames.], batch size: 37, lr: 1.24e-03
+2022-06-18 14:21:54,087 INFO [train.py:874] (0/4) Epoch 6, batch 1250, datatang_loss[loss=0.1891, simple_loss=0.2499, pruned_loss=0.06416, over 4911.00 frames.], tot_loss[loss=0.2095, simple_loss=0.269, pruned_loss=0.07496, over 983278.31 frames.], batch size: 75, aishell_tot_loss[loss=0.2054, simple_loss=0.273, pruned_loss=0.06895, over 941157.71 frames.], datatang_tot_loss[loss=0.2123, simple_loss=0.265, pruned_loss=0.07983, over 943461.66 frames.], batch size: 75, lr: 1.24e-03
+2022-06-18 14:22:25,456 INFO [train.py:874] (0/4) Epoch 6, batch 1300, aishell_loss[loss=0.1899, simple_loss=0.2685, pruned_loss=0.0556, over 4954.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2684, pruned_loss=0.07425, over 983967.88 frames.], batch size: 44, aishell_tot_loss[loss=0.2045, simple_loss=0.2723, pruned_loss=0.06833, over 946245.80 frames.], datatang_tot_loss[loss=0.2122, simple_loss=0.265, pruned_loss=0.07975, over 948723.52 frames.], batch size: 44, lr: 1.23e-03
+2022-06-18 14:22:55,165 INFO [train.py:874] (0/4) Epoch 6, batch 1350, aishell_loss[loss=0.2167, simple_loss=0.2838, pruned_loss=0.07475, over 4932.00 frames.], tot_loss[loss=0.209, simple_loss=0.2686, pruned_loss=0.07467, over 984267.19 frames.], batch size: 68, aishell_tot_loss[loss=0.2047, simple_loss=0.2724, pruned_loss=0.0685, over 949818.13 frames.], datatang_tot_loss[loss=0.2125, simple_loss=0.2653, pruned_loss=0.07987, over 953985.19 frames.], batch size: 68, lr: 1.23e-03
+2022-06-18 14:23:24,945 INFO [train.py:874] (0/4) Epoch 6, batch 1400, aishell_loss[loss=0.1823, simple_loss=0.2461, pruned_loss=0.0592, over 4926.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2684, pruned_loss=0.07345, over 984156.66 frames.], batch size: 32, aishell_tot_loss[loss=0.2036, simple_loss=0.2719, pruned_loss=0.06763, over 954700.25 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2653, pruned_loss=0.0798, over 956678.77 frames.], batch size: 32, lr: 1.23e-03
+2022-06-18 14:23:56,916 INFO [train.py:874] (0/4) Epoch 6, batch 1450, aishell_loss[loss=0.1793, simple_loss=0.2525, pruned_loss=0.05306, over 4898.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2686, pruned_loss=0.07327, over 984323.01 frames.], batch size: 28, aishell_tot_loss[loss=0.2031, simple_loss=0.2715, pruned_loss=0.06731, over 958448.85 frames.], datatang_tot_loss[loss=0.2129, simple_loss=0.2657, pruned_loss=0.0801, over 959827.34 frames.], batch size: 28, lr: 1.23e-03
+2022-06-18 14:24:26,584 INFO [train.py:874] (0/4) Epoch 6, batch 1500, aishell_loss[loss=0.2123, simple_loss=0.2871, pruned_loss=0.06871, over 4858.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2691, pruned_loss=0.07376, over 983939.72 frames.], batch size: 37, aishell_tot_loss[loss=0.2027, simple_loss=0.2711, pruned_loss=0.06713, over 961676.74 frames.], datatang_tot_loss[loss=0.2143, simple_loss=0.2665, pruned_loss=0.08102, over 962152.91 frames.], batch size: 37, lr: 1.23e-03
+2022-06-18 14:24:56,217 INFO [train.py:874] (0/4) Epoch 6, batch 1550, aishell_loss[loss=0.2081, simple_loss=0.2752, pruned_loss=0.07048, over 4864.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2699, pruned_loss=0.07385, over 984228.00 frames.], batch size: 37, aishell_tot_loss[loss=0.2032, simple_loss=0.2717, pruned_loss=0.06733, over 965020.68 frames.], datatang_tot_loss[loss=0.2146, simple_loss=0.2667, pruned_loss=0.08118, over 964309.19 frames.], batch size: 37, lr: 1.23e-03
+2022-06-18 14:25:27,128 INFO [train.py:874] (0/4) Epoch 6, batch 1600, aishell_loss[loss=0.2202, simple_loss=0.288, pruned_loss=0.07619, over 4935.00 frames.], tot_loss[loss=0.2081, simple_loss=0.27, pruned_loss=0.07308, over 984422.35 frames.], batch size: 56, aishell_tot_loss[loss=0.2035, simple_loss=0.2724, pruned_loss=0.06725, over 967764.86 frames.], datatang_tot_loss[loss=0.2138, simple_loss=0.2661, pruned_loss=0.08072, over 966342.06 frames.], batch size: 56, lr: 1.23e-03
+2022-06-18 14:25:56,106 INFO [train.py:874] (0/4) Epoch 6, batch 1650, datatang_loss[loss=0.2084, simple_loss=0.2575, pruned_loss=0.07961, over 4965.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2696, pruned_loss=0.07324, over 984597.18 frames.], batch size: 45, aishell_tot_loss[loss=0.2037, simple_loss=0.2728, pruned_loss=0.06726, over 969567.33 frames.], datatang_tot_loss[loss=0.2132, simple_loss=0.2656, pruned_loss=0.0804, over 968846.13 frames.], batch size: 45, lr: 1.23e-03
+2022-06-18 14:26:28,015 INFO [train.py:874] (0/4) Epoch 6, batch 1700, datatang_loss[loss=0.2288, simple_loss=0.2846, pruned_loss=0.08647, over 4952.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2688, pruned_loss=0.07289, over 984954.92 frames.], batch size: 91, aishell_tot_loss[loss=0.2028, simple_loss=0.272, pruned_loss=0.0668, over 971319.95 frames.], datatang_tot_loss[loss=0.2131, simple_loss=0.2657, pruned_loss=0.08027, over 971105.88 frames.], batch size: 91, lr: 1.22e-03
+2022-06-18 14:26:57,264 INFO [train.py:874] (0/4) Epoch 6, batch 1750, aishell_loss[loss=0.1662, simple_loss=0.2374, pruned_loss=0.04755, over 4948.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2686, pruned_loss=0.07324, over 985341.22 frames.], batch size: 27, aishell_tot_loss[loss=0.2032, simple_loss=0.2721, pruned_loss=0.06713, over 972968.37 frames.], datatang_tot_loss[loss=0.2128, simple_loss=0.2653, pruned_loss=0.08011, over 973090.31 frames.], batch size: 27, lr: 1.22e-03
+2022-06-18 14:27:27,451 INFO [train.py:874] (0/4) Epoch 6, batch 1800, aishell_loss[loss=0.2296, simple_loss=0.2966, pruned_loss=0.0813, over 4866.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2701, pruned_loss=0.07445, over 985253.74 frames.], batch size: 36, aishell_tot_loss[loss=0.205, simple_loss=0.2738, pruned_loss=0.06809, over 974411.50 frames.], datatang_tot_loss[loss=0.213, simple_loss=0.2653, pruned_loss=0.08029, over 974461.41 frames.], batch size: 36, lr: 1.22e-03
+2022-06-18 14:27:59,154 INFO [train.py:874] (0/4) Epoch 6, batch 1850, aishell_loss[loss=0.1936, simple_loss=0.2668, pruned_loss=0.06022, over 4962.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2697, pruned_loss=0.07454, over 985252.53 frames.], batch size: 40, aishell_tot_loss[loss=0.2046, simple_loss=0.2731, pruned_loss=0.06807, over 975770.89 frames.], datatang_tot_loss[loss=0.2134, simple_loss=0.2656, pruned_loss=0.08065, over 975647.32 frames.], batch size: 40, lr: 1.22e-03
+2022-06-18 14:28:29,049 INFO [train.py:874] (0/4) Epoch 6, batch 1900, aishell_loss[loss=0.2616, simple_loss=0.3221, pruned_loss=0.1006, over 4957.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2704, pruned_loss=0.0742, over 985155.88 frames.], batch size: 64, aishell_tot_loss[loss=0.2047, simple_loss=0.2735, pruned_loss=0.06798, over 977226.70 frames.], datatang_tot_loss[loss=0.2137, simple_loss=0.2657, pruned_loss=0.08087, over 976307.56 frames.], batch size: 64, lr: 1.22e-03
+2022-06-18 14:28:59,013 INFO [train.py:874] (0/4) Epoch 6, batch 1950, aishell_loss[loss=0.2109, simple_loss=0.2717, pruned_loss=0.07506, over 4937.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2694, pruned_loss=0.07418, over 985010.49 frames.], batch size: 58, aishell_tot_loss[loss=0.2044, simple_loss=0.2728, pruned_loss=0.06795, over 977803.64 frames.], datatang_tot_loss[loss=0.2135, simple_loss=0.2656, pruned_loss=0.08066, over 977569.79 frames.], batch size: 58, lr: 1.22e-03
+2022-06-18 14:29:30,643 INFO [train.py:874] (0/4) Epoch 6, batch 2000, datatang_loss[loss=0.208, simple_loss=0.2664, pruned_loss=0.07478, over 4939.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2689, pruned_loss=0.07344, over 985312.89 frames.], batch size: 62, aishell_tot_loss[loss=0.2042, simple_loss=0.2729, pruned_loss=0.06778, over 978735.67 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.265, pruned_loss=0.0801, over 978672.03 frames.], batch size: 62, lr: 1.22e-03
+2022-06-18 14:29:30,646 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 14:29:46,437 INFO [train.py:914] (0/4) Epoch 6, validation: loss=0.1751, simple_loss=0.2557, pruned_loss=0.04723, over 1622729.00 frames.
+2022-06-18 14:30:18,807 INFO [train.py:874] (0/4) Epoch 6, batch 2050, aishell_loss[loss=0.1818, simple_loss=0.2539, pruned_loss=0.05483, over 4921.00 frames.], tot_loss[loss=0.207, simple_loss=0.2681, pruned_loss=0.07294, over 985467.67 frames.], batch size: 33, aishell_tot_loss[loss=0.2036, simple_loss=0.2726, pruned_loss=0.06734, over 979439.32 frames.], datatang_tot_loss[loss=0.212, simple_loss=0.2646, pruned_loss=0.07973, over 979688.70 frames.], batch size: 33, lr: 1.22e-03
+2022-06-18 14:30:50,050 INFO [train.py:874] (0/4) Epoch 6, batch 2100, datatang_loss[loss=0.2147, simple_loss=0.2716, pruned_loss=0.07895, over 4929.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2684, pruned_loss=0.07386, over 985496.22 frames.], batch size: 64, aishell_tot_loss[loss=0.2038, simple_loss=0.2724, pruned_loss=0.0676, over 980021.38 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.2651, pruned_loss=0.08007, over 980545.99 frames.], batch size: 64, lr: 1.21e-03
+2022-06-18 14:31:18,772 INFO [train.py:874] (0/4) Epoch 6, batch 2150, datatang_loss[loss=0.1917, simple_loss=0.254, pruned_loss=0.06469, over 4925.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2682, pruned_loss=0.0727, over 985515.91 frames.], batch size: 83, aishell_tot_loss[loss=0.2024, simple_loss=0.2716, pruned_loss=0.06666, over 980788.16 frames.], datatang_tot_loss[loss=0.2128, simple_loss=0.2655, pruned_loss=0.08011, over 981049.98 frames.], batch size: 83, lr: 1.21e-03
+2022-06-18 14:31:50,701 INFO [train.py:874] (0/4) Epoch 6, batch 2200, aishell_loss[loss=0.1991, simple_loss=0.2639, pruned_loss=0.06718, over 4916.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2698, pruned_loss=0.07368, over 985775.40 frames.], batch size: 33, aishell_tot_loss[loss=0.2023, simple_loss=0.2718, pruned_loss=0.06645, over 981628.57 frames.], datatang_tot_loss[loss=0.2152, simple_loss=0.2666, pruned_loss=0.0819, over 981576.43 frames.], batch size: 33, lr: 1.21e-03
+2022-06-18 14:32:20,685 INFO [train.py:874] (0/4) Epoch 6, batch 2250, datatang_loss[loss=0.1559, simple_loss=0.2107, pruned_loss=0.0506, over 4868.00 frames.], tot_loss[loss=0.206, simple_loss=0.2676, pruned_loss=0.07221, over 985898.16 frames.], batch size: 25, aishell_tot_loss[loss=0.2018, simple_loss=0.2715, pruned_loss=0.06604, over 982250.42 frames.], datatang_tot_loss[loss=0.2128, simple_loss=0.2647, pruned_loss=0.08048, over 982080.28 frames.], batch size: 25, lr: 1.21e-03
+2022-06-18 14:32:50,358 INFO [train.py:874] (0/4) Epoch 6, batch 2300, datatang_loss[loss=0.1802, simple_loss=0.2419, pruned_loss=0.05923, over 4906.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2669, pruned_loss=0.07097, over 985436.82 frames.], batch size: 64, aishell_tot_loss[loss=0.2008, simple_loss=0.2707, pruned_loss=0.06546, over 982454.28 frames.], datatang_tot_loss[loss=0.2123, simple_loss=0.2645, pruned_loss=0.08, over 982271.21 frames.], batch size: 64, lr: 1.21e-03
+2022-06-18 14:33:22,407 INFO [train.py:874] (0/4) Epoch 6, batch 2350, datatang_loss[loss=0.1794, simple_loss=0.2393, pruned_loss=0.05981, over 4919.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2667, pruned_loss=0.07095, over 985651.94 frames.], batch size: 77, aishell_tot_loss[loss=0.2006, simple_loss=0.2704, pruned_loss=0.06535, over 982878.71 frames.], datatang_tot_loss[loss=0.212, simple_loss=0.2643, pruned_loss=0.07985, over 982795.21 frames.], batch size: 77, lr: 1.21e-03
+2022-06-18 14:33:50,370 INFO [train.py:874] (0/4) Epoch 6, batch 2400, aishell_loss[loss=0.2118, simple_loss=0.2741, pruned_loss=0.07481, over 4932.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2675, pruned_loss=0.07132, over 985457.29 frames.], batch size: 32, aishell_tot_loss[loss=0.2004, simple_loss=0.2701, pruned_loss=0.06534, over 983152.30 frames.], datatang_tot_loss[loss=0.2127, simple_loss=0.2651, pruned_loss=0.08018, over 982973.97 frames.], batch size: 32, lr: 1.21e-03
+2022-06-18 14:34:22,270 INFO [train.py:874] (0/4) Epoch 6, batch 2450, aishell_loss[loss=0.2256, simple_loss=0.2851, pruned_loss=0.08307, over 4876.00 frames.], tot_loss[loss=0.205, simple_loss=0.2672, pruned_loss=0.07144, over 985072.54 frames.], batch size: 42, aishell_tot_loss[loss=0.2005, simple_loss=0.2702, pruned_loss=0.06537, over 983118.80 frames.], datatang_tot_loss[loss=0.2122, simple_loss=0.2647, pruned_loss=0.07987, over 983157.44 frames.], batch size: 42, lr: 1.21e-03
+2022-06-18 14:34:52,670 INFO [train.py:874] (0/4) Epoch 6, batch 2500, datatang_loss[loss=0.188, simple_loss=0.2449, pruned_loss=0.06553, over 4960.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2657, pruned_loss=0.07057, over 985127.24 frames.], batch size: 60, aishell_tot_loss[loss=0.1992, simple_loss=0.2693, pruned_loss=0.06454, over 983364.30 frames.], datatang_tot_loss[loss=0.2112, simple_loss=0.2641, pruned_loss=0.07922, over 983416.48 frames.], batch size: 60, lr: 1.20e-03
+2022-06-18 14:35:21,336 INFO [train.py:874] (0/4) Epoch 6, batch 2550, datatang_loss[loss=0.2262, simple_loss=0.2835, pruned_loss=0.08445, over 4930.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2657, pruned_loss=0.07092, over 985115.81 frames.], batch size: 81, aishell_tot_loss[loss=0.199, simple_loss=0.2688, pruned_loss=0.06459, over 983298.64 frames.], datatang_tot_loss[loss=0.2113, simple_loss=0.2643, pruned_loss=0.07916, over 983867.88 frames.], batch size: 81, lr: 1.20e-03
+2022-06-18 14:35:53,587 INFO [train.py:874] (0/4) Epoch 6, batch 2600, aishell_loss[loss=0.2131, simple_loss=0.2733, pruned_loss=0.07645, over 4925.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2659, pruned_loss=0.07125, over 985395.82 frames.], batch size: 33, aishell_tot_loss[loss=0.1994, simple_loss=0.269, pruned_loss=0.06489, over 983752.06 frames.], datatang_tot_loss[loss=0.211, simple_loss=0.2642, pruned_loss=0.07894, over 984076.28 frames.], batch size: 33, lr: 1.20e-03
+2022-06-18 14:36:23,546 INFO [train.py:874] (0/4) Epoch 6, batch 2650, aishell_loss[loss=0.1902, simple_loss=0.2659, pruned_loss=0.05728, over 4862.00 frames.], tot_loss[loss=0.2058, simple_loss=0.267, pruned_loss=0.07232, over 985060.96 frames.], batch size: 37, aishell_tot_loss[loss=0.2002, simple_loss=0.2697, pruned_loss=0.06532, over 983598.45 frames.], datatang_tot_loss[loss=0.212, simple_loss=0.2644, pruned_loss=0.0798, over 984228.22 frames.], batch size: 37, lr: 1.20e-03
+2022-06-18 14:36:53,119 INFO [train.py:874] (0/4) Epoch 6, batch 2700, datatang_loss[loss=0.1952, simple_loss=0.2627, pruned_loss=0.06389, over 4952.00 frames.], tot_loss[loss=0.2053, simple_loss=0.2666, pruned_loss=0.07202, over 984932.73 frames.], batch size: 86, aishell_tot_loss[loss=0.1992, simple_loss=0.269, pruned_loss=0.06475, over 983540.43 frames.], datatang_tot_loss[loss=0.2121, simple_loss=0.2648, pruned_loss=0.0797, over 984408.29 frames.], batch size: 86, lr: 1.20e-03
+2022-06-18 14:37:24,162 INFO [train.py:874] (0/4) Epoch 6, batch 2750, datatang_loss[loss=0.1992, simple_loss=0.26, pruned_loss=0.06923, over 4962.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2674, pruned_loss=0.07259, over 985240.30 frames.], batch size: 37, aishell_tot_loss[loss=0.2, simple_loss=0.2693, pruned_loss=0.06537, over 984022.15 frames.], datatang_tot_loss[loss=0.2122, simple_loss=0.2652, pruned_loss=0.07961, over 984485.71 frames.], batch size: 37, lr: 1.20e-03
+2022-06-18 14:37:54,764 INFO [train.py:874] (0/4) Epoch 6, batch 2800, datatang_loss[loss=0.2122, simple_loss=0.2551, pruned_loss=0.08466, over 4902.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2669, pruned_loss=0.07214, over 985181.73 frames.], batch size: 42, aishell_tot_loss[loss=0.2002, simple_loss=0.2696, pruned_loss=0.0654, over 983791.08 frames.], datatang_tot_loss[loss=0.2113, simple_loss=0.2644, pruned_loss=0.07904, over 984896.13 frames.], batch size: 42, lr: 1.20e-03
+2022-06-18 14:38:23,483 INFO [train.py:874] (0/4) Epoch 6, batch 2850, datatang_loss[loss=0.2324, simple_loss=0.2739, pruned_loss=0.09545, over 4922.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2676, pruned_loss=0.07246, over 985500.03 frames.], batch size: 73, aishell_tot_loss[loss=0.2015, simple_loss=0.2709, pruned_loss=0.06608, over 984218.10 frames.], datatang_tot_loss[loss=0.2106, simple_loss=0.2639, pruned_loss=0.07866, over 984998.79 frames.], batch size: 73, lr: 1.20e-03
+2022-06-18 14:38:55,051 INFO [train.py:874] (0/4) Epoch 6, batch 2900, aishell_loss[loss=0.2129, simple_loss=0.2829, pruned_loss=0.07142, over 4977.00 frames.], tot_loss[loss=0.206, simple_loss=0.2676, pruned_loss=0.07221, over 985687.61 frames.], batch size: 44, aishell_tot_loss[loss=0.2019, simple_loss=0.2713, pruned_loss=0.06621, over 984405.92 frames.], datatang_tot_loss[loss=0.2099, simple_loss=0.2635, pruned_loss=0.07818, over 985206.14 frames.], batch size: 44, lr: 1.19e-03
+2022-06-18 14:39:25,919 INFO [train.py:874] (0/4) Epoch 6, batch 2950, datatang_loss[loss=0.194, simple_loss=0.2515, pruned_loss=0.06825, over 4863.00 frames.], tot_loss[loss=0.2057, simple_loss=0.2672, pruned_loss=0.07208, over 985754.80 frames.], batch size: 30, aishell_tot_loss[loss=0.2016, simple_loss=0.2708, pruned_loss=0.06616, over 984610.13 frames.], datatang_tot_loss[loss=0.2099, simple_loss=0.2637, pruned_loss=0.07803, over 985285.74 frames.], batch size: 30, lr: 1.19e-03
+2022-06-18 14:39:54,953 INFO [train.py:874] (0/4) Epoch 6, batch 3000, aishell_loss[loss=0.2001, simple_loss=0.2745, pruned_loss=0.06288, over 4948.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2661, pruned_loss=0.07091, over 985857.23 frames.], batch size: 54, aishell_tot_loss[loss=0.2016, simple_loss=0.2711, pruned_loss=0.06605, over 985018.13 frames.], datatang_tot_loss[loss=0.2079, simple_loss=0.2622, pruned_loss=0.07678, over 985170.67 frames.], batch size: 54, lr: 1.19e-03
+2022-06-18 14:39:54,955 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 14:40:11,301 INFO [train.py:914] (0/4) Epoch 6, validation: loss=0.1748, simple_loss=0.2561, pruned_loss=0.04677, over 1622729.00 frames.
+2022-06-18 14:40:41,181 INFO [train.py:874] (0/4) Epoch 6, batch 3050, aishell_loss[loss=0.2084, simple_loss=0.2738, pruned_loss=0.0715, over 4943.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2659, pruned_loss=0.07056, over 985775.60 frames.], batch size: 32, aishell_tot_loss[loss=0.2013, simple_loss=0.2708, pruned_loss=0.06587, over 985002.33 frames.], datatang_tot_loss[loss=0.2076, simple_loss=0.2619, pruned_loss=0.07664, over 985274.08 frames.], batch size: 32, lr: 1.19e-03
+2022-06-18 14:41:11,400 INFO [train.py:874] (0/4) Epoch 6, batch 3100, aishell_loss[loss=0.1968, simple_loss=0.2704, pruned_loss=0.06164, over 4915.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2659, pruned_loss=0.0709, over 985434.02 frames.], batch size: 46, aishell_tot_loss[loss=0.201, simple_loss=0.2703, pruned_loss=0.06584, over 984841.02 frames.], datatang_tot_loss[loss=0.2081, simple_loss=0.2623, pruned_loss=0.07693, over 985223.58 frames.], batch size: 46, lr: 1.19e-03
+2022-06-18 14:41:41,823 INFO [train.py:874] (0/4) Epoch 6, batch 3150, datatang_loss[loss=0.1986, simple_loss=0.2525, pruned_loss=0.07232, over 4914.00 frames.], tot_loss[loss=0.2048, simple_loss=0.2669, pruned_loss=0.07134, over 985424.18 frames.], batch size: 75, aishell_tot_loss[loss=0.2014, simple_loss=0.2709, pruned_loss=0.06593, over 985047.68 frames.], datatang_tot_loss[loss=0.2087, simple_loss=0.2627, pruned_loss=0.0773, over 985107.20 frames.], batch size: 75, lr: 1.19e-03
+2022-06-18 14:41:55,706 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-24000.pt
+2022-06-18 14:42:15,239 INFO [train.py:874] (0/4) Epoch 6, batch 3200, aishell_loss[loss=0.2379, simple_loss=0.3103, pruned_loss=0.08275, over 4927.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2677, pruned_loss=0.07168, over 985690.54 frames.], batch size: 46, aishell_tot_loss[loss=0.2013, simple_loss=0.2708, pruned_loss=0.06585, over 985213.95 frames.], datatang_tot_loss[loss=0.2096, simple_loss=0.2634, pruned_loss=0.0779, over 985306.61 frames.], batch size: 46, lr: 1.19e-03
+2022-06-18 14:42:46,473 INFO [train.py:874] (0/4) Epoch 6, batch 3250, datatang_loss[loss=0.2604, simple_loss=0.3095, pruned_loss=0.1056, over 4941.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2668, pruned_loss=0.07097, over 985712.71 frames.], batch size: 109, aishell_tot_loss[loss=0.2004, simple_loss=0.27, pruned_loss=0.06536, over 985124.49 frames.], datatang_tot_loss[loss=0.2094, simple_loss=0.2633, pruned_loss=0.07774, over 985512.85 frames.], batch size: 109, lr: 1.19e-03
+2022-06-18 14:43:16,339 INFO [train.py:874] (0/4) Epoch 6, batch 3300, aishell_loss[loss=0.2062, simple_loss=0.2744, pruned_loss=0.069, over 4968.00 frames.], tot_loss[loss=0.204, simple_loss=0.2663, pruned_loss=0.0709, over 985863.11 frames.], batch size: 61, aishell_tot_loss[loss=0.1997, simple_loss=0.2693, pruned_loss=0.06509, over 985223.81 frames.], datatang_tot_loss[loss=0.2095, simple_loss=0.2635, pruned_loss=0.07771, over 985638.30 frames.], batch size: 61, lr: 1.18e-03
+2022-06-18 14:43:45,785 INFO [train.py:874] (0/4) Epoch 6, batch 3350, aishell_loss[loss=0.182, simple_loss=0.2397, pruned_loss=0.06211, over 4755.00 frames.], tot_loss[loss=0.2021, simple_loss=0.265, pruned_loss=0.06965, over 985353.18 frames.], batch size: 21, aishell_tot_loss[loss=0.1989, simple_loss=0.2686, pruned_loss=0.06461, over 984704.67 frames.], datatang_tot_loss[loss=0.2084, simple_loss=0.2627, pruned_loss=0.07699, over 985734.70 frames.], batch size: 21, lr: 1.18e-03
+2022-06-18 14:44:17,237 INFO [train.py:874] (0/4) Epoch 6, batch 3400, aishell_loss[loss=0.1808, simple_loss=0.2593, pruned_loss=0.05117, over 4967.00 frames.], tot_loss[loss=0.2028, simple_loss=0.266, pruned_loss=0.06978, over 985231.44 frames.], batch size: 44, aishell_tot_loss[loss=0.1986, simple_loss=0.2685, pruned_loss=0.06431, over 984743.17 frames.], datatang_tot_loss[loss=0.2093, simple_loss=0.2636, pruned_loss=0.0775, over 985613.38 frames.], batch size: 44, lr: 1.18e-03
+2022-06-18 14:44:46,245 INFO [train.py:874] (0/4) Epoch 6, batch 3450, aishell_loss[loss=0.1898, simple_loss=0.273, pruned_loss=0.05328, over 4981.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2659, pruned_loss=0.07012, over 985719.86 frames.], batch size: 39, aishell_tot_loss[loss=0.1983, simple_loss=0.2684, pruned_loss=0.06414, over 985178.73 frames.], datatang_tot_loss[loss=0.2097, simple_loss=0.2636, pruned_loss=0.07789, over 985724.14 frames.], batch size: 39, lr: 1.18e-03
+2022-06-18 14:45:16,429 INFO [train.py:874] (0/4) Epoch 6, batch 3500, datatang_loss[loss=0.2344, simple_loss=0.2865, pruned_loss=0.09111, over 4810.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2671, pruned_loss=0.07024, over 985380.80 frames.], batch size: 24, aishell_tot_loss[loss=0.1997, simple_loss=0.27, pruned_loss=0.06466, over 985089.30 frames.], datatang_tot_loss[loss=0.2091, simple_loss=0.263, pruned_loss=0.07757, over 985540.52 frames.], batch size: 24, lr: 1.18e-03
+2022-06-18 14:45:47,070 INFO [train.py:874] (0/4) Epoch 6, batch 3550, aishell_loss[loss=0.2025, simple_loss=0.2658, pruned_loss=0.06957, over 4960.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2675, pruned_loss=0.06962, over 985264.60 frames.], batch size: 31, aishell_tot_loss[loss=0.1996, simple_loss=0.2702, pruned_loss=0.0645, over 985099.14 frames.], datatang_tot_loss[loss=0.2089, simple_loss=0.2631, pruned_loss=0.07733, over 985436.64 frames.], batch size: 31, lr: 1.18e-03
+2022-06-18 14:46:15,901 INFO [train.py:874] (0/4) Epoch 6, batch 3600, aishell_loss[loss=0.2258, simple_loss=0.2906, pruned_loss=0.08052, over 4895.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2668, pruned_loss=0.06991, over 985638.89 frames.], batch size: 60, aishell_tot_loss[loss=0.1986, simple_loss=0.2691, pruned_loss=0.06401, over 985314.15 frames.], datatang_tot_loss[loss=0.2097, simple_loss=0.2637, pruned_loss=0.07788, over 985623.81 frames.], batch size: 60, lr: 1.18e-03
+2022-06-18 14:46:46,857 INFO [train.py:874] (0/4) Epoch 6, batch 3650, datatang_loss[loss=0.2129, simple_loss=0.2637, pruned_loss=0.08106, over 4907.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2663, pruned_loss=0.07009, over 985234.27 frames.], batch size: 52, aishell_tot_loss[loss=0.1985, simple_loss=0.2692, pruned_loss=0.06397, over 985053.48 frames.], datatang_tot_loss[loss=0.2093, simple_loss=0.2634, pruned_loss=0.07764, over 985500.28 frames.], batch size: 52, lr: 1.18e-03
+2022-06-18 14:47:16,891 INFO [train.py:874] (0/4) Epoch 6, batch 3700, datatang_loss[loss=0.2427, simple_loss=0.2972, pruned_loss=0.09413, over 4955.00 frames.], tot_loss[loss=0.203, simple_loss=0.2663, pruned_loss=0.06989, over 985422.68 frames.], batch size: 99, aishell_tot_loss[loss=0.1986, simple_loss=0.2692, pruned_loss=0.06399, over 985221.46 frames.], datatang_tot_loss[loss=0.209, simple_loss=0.2633, pruned_loss=0.07729, over 985530.91 frames.], batch size: 99, lr: 1.18e-03
+2022-06-18 14:47:44,897 INFO [train.py:874] (0/4) Epoch 6, batch 3750, datatang_loss[loss=0.1788, simple_loss=0.2383, pruned_loss=0.05964, over 4919.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2667, pruned_loss=0.07105, over 985571.03 frames.], batch size: 83, aishell_tot_loss[loss=0.1988, simple_loss=0.2692, pruned_loss=0.06415, over 985516.16 frames.], datatang_tot_loss[loss=0.2099, simple_loss=0.2638, pruned_loss=0.07801, over 985400.95 frames.], batch size: 83, lr: 1.17e-03
+2022-06-18 14:48:15,172 INFO [train.py:874] (0/4) Epoch 6, batch 3800, aishell_loss[loss=0.2019, simple_loss=0.278, pruned_loss=0.06293, over 4932.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2661, pruned_loss=0.07037, over 985716.97 frames.], batch size: 52, aishell_tot_loss[loss=0.199, simple_loss=0.2694, pruned_loss=0.06435, over 985457.38 frames.], datatang_tot_loss[loss=0.2085, simple_loss=0.2631, pruned_loss=0.07696, over 985640.41 frames.], batch size: 52, lr: 1.17e-03
+2022-06-18 14:48:43,815 INFO [train.py:874] (0/4) Epoch 6, batch 3850, datatang_loss[loss=0.2425, simple_loss=0.2909, pruned_loss=0.09702, over 4912.00 frames.], tot_loss[loss=0.204, simple_loss=0.2664, pruned_loss=0.07084, over 985543.24 frames.], batch size: 42, aishell_tot_loss[loss=0.1991, simple_loss=0.2694, pruned_loss=0.06438, over 985090.64 frames.], datatang_tot_loss[loss=0.2089, simple_loss=0.2634, pruned_loss=0.07721, over 985855.01 frames.], batch size: 42, lr: 1.17e-03
+2022-06-18 14:49:12,061 INFO [train.py:874] (0/4) Epoch 6, batch 3900, aishell_loss[loss=0.1602, simple_loss=0.2118, pruned_loss=0.05424, over 4793.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2664, pruned_loss=0.07113, over 985327.92 frames.], batch size: 20, aishell_tot_loss[loss=0.1993, simple_loss=0.2691, pruned_loss=0.06474, over 984797.36 frames.], datatang_tot_loss[loss=0.2092, simple_loss=0.2636, pruned_loss=0.07737, over 985951.47 frames.], batch size: 20, lr: 1.17e-03
+2022-06-18 14:49:41,277 INFO [train.py:874] (0/4) Epoch 6, batch 3950, datatang_loss[loss=0.1852, simple_loss=0.2494, pruned_loss=0.06045, over 4946.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2653, pruned_loss=0.07015, over 985104.16 frames.], batch size: 69, aishell_tot_loss[loss=0.198, simple_loss=0.2683, pruned_loss=0.06389, over 984641.61 frames.], datatang_tot_loss[loss=0.2088, simple_loss=0.2633, pruned_loss=0.07717, over 985854.93 frames.], batch size: 69, lr: 1.17e-03
+2022-06-18 14:50:10,157 INFO [train.py:874] (0/4) Epoch 6, batch 4000, aishell_loss[loss=0.1851, simple_loss=0.2593, pruned_loss=0.05545, over 4888.00 frames.], tot_loss[loss=0.2014, simple_loss=0.2639, pruned_loss=0.06942, over 985314.48 frames.], batch size: 34, aishell_tot_loss[loss=0.1967, simple_loss=0.2667, pruned_loss=0.06332, over 984785.63 frames.], datatang_tot_loss[loss=0.2085, simple_loss=0.2632, pruned_loss=0.0769, over 985918.36 frames.], batch size: 34, lr: 1.17e-03
+2022-06-18 14:50:10,159 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 14:50:26,076 INFO [train.py:914] (0/4) Epoch 6, validation: loss=0.1741, simple_loss=0.2559, pruned_loss=0.04619, over 1622729.00 frames.
+2022-06-18 14:50:44,255 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-6.pt
+2022-06-18 14:51:43,915 INFO [train.py:874] (0/4) Epoch 7, batch 50, aishell_loss[loss=0.1821, simple_loss=0.2563, pruned_loss=0.05401, over 4907.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2568, pruned_loss=0.06307, over 218113.44 frames.], batch size: 34, aishell_tot_loss[loss=0.1893, simple_loss=0.2597, pruned_loss=0.05943, over 115702.90 frames.], datatang_tot_loss[loss=0.1939, simple_loss=0.2542, pruned_loss=0.06685, over 116056.18 frames.], batch size: 34, lr: 1.12e-03
+2022-06-18 14:52:15,041 INFO [train.py:874] (0/4) Epoch 7, batch 100, datatang_loss[loss=0.18, simple_loss=0.2525, pruned_loss=0.05378, over 4923.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2579, pruned_loss=0.06301, over 388663.43 frames.], batch size: 81, aishell_tot_loss[loss=0.1911, simple_loss=0.2635, pruned_loss=0.05936, over 225982.89 frames.], datatang_tot_loss[loss=0.1928, simple_loss=0.2517, pruned_loss=0.06698, over 211012.11 frames.], batch size: 81, lr: 1.12e-03
+2022-06-18 14:52:44,467 INFO [train.py:874] (0/4) Epoch 7, batch 150, aishell_loss[loss=0.2101, simple_loss=0.283, pruned_loss=0.0686, over 4910.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2598, pruned_loss=0.06275, over 520893.25 frames.], batch size: 68, aishell_tot_loss[loss=0.1923, simple_loss=0.2648, pruned_loss=0.05993, over 345010.68 frames.], datatang_tot_loss[loss=0.193, simple_loss=0.2525, pruned_loss=0.06673, over 270591.33 frames.], batch size: 68, lr: 1.12e-03
+2022-06-18 14:53:13,180 INFO [train.py:874] (0/4) Epoch 7, batch 200, datatang_loss[loss=0.1897, simple_loss=0.2512, pruned_loss=0.06406, over 4976.00 frames.], tot_loss[loss=0.194, simple_loss=0.2605, pruned_loss=0.0637, over 623727.59 frames.], batch size: 40, aishell_tot_loss[loss=0.1929, simple_loss=0.2661, pruned_loss=0.05981, over 405935.83 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.2537, pruned_loss=0.06779, over 370388.91 frames.], batch size: 40, lr: 1.12e-03
+2022-06-18 14:53:45,403 INFO [train.py:874] (0/4) Epoch 7, batch 250, aishell_loss[loss=0.1779, simple_loss=0.2491, pruned_loss=0.05338, over 4940.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2613, pruned_loss=0.065, over 704383.74 frames.], batch size: 32, aishell_tot_loss[loss=0.1936, simple_loss=0.2663, pruned_loss=0.06047, over 474455.33 frames.], datatang_tot_loss[loss=0.1969, simple_loss=0.2553, pruned_loss=0.06929, over 443063.18 frames.], batch size: 32, lr: 1.11e-03
+2022-06-18 14:54:14,193 INFO [train.py:874] (0/4) Epoch 7, batch 300, aishell_loss[loss=0.2177, simple_loss=0.2815, pruned_loss=0.07692, over 4958.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2618, pruned_loss=0.06546, over 766785.51 frames.], batch size: 64, aishell_tot_loss[loss=0.195, simple_loss=0.2668, pruned_loss=0.06162, over 545829.64 frames.], datatang_tot_loss[loss=0.1968, simple_loss=0.2552, pruned_loss=0.06922, over 494868.64 frames.], batch size: 64, lr: 1.11e-03
+2022-06-18 14:54:43,151 INFO [train.py:874] (0/4) Epoch 7, batch 350, aishell_loss[loss=0.2, simple_loss=0.274, pruned_loss=0.06298, over 4922.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2632, pruned_loss=0.06672, over 814884.03 frames.], batch size: 46, aishell_tot_loss[loss=0.1971, simple_loss=0.2684, pruned_loss=0.06287, over 593262.17 frames.], datatang_tot_loss[loss=0.1978, simple_loss=0.2561, pruned_loss=0.06971, over 557018.96 frames.], batch size: 46, lr: 1.11e-03
+2022-06-18 14:55:14,502 INFO [train.py:874] (0/4) Epoch 7, batch 400, aishell_loss[loss=0.2083, simple_loss=0.2746, pruned_loss=0.07102, over 4971.00 frames.], tot_loss[loss=0.199, simple_loss=0.2639, pruned_loss=0.06711, over 852982.22 frames.], batch size: 30, aishell_tot_loss[loss=0.197, simple_loss=0.2687, pruned_loss=0.06271, over 646492.99 frames.], datatang_tot_loss[loss=0.1994, simple_loss=0.2569, pruned_loss=0.07091, over 599944.01 frames.], batch size: 30, lr: 1.11e-03
+2022-06-18 14:55:44,347 INFO [train.py:874] (0/4) Epoch 7, batch 450, datatang_loss[loss=0.202, simple_loss=0.2593, pruned_loss=0.07241, over 4949.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2643, pruned_loss=0.06752, over 882189.75 frames.], batch size: 45, aishell_tot_loss[loss=0.1977, simple_loss=0.2696, pruned_loss=0.06291, over 678684.78 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2574, pruned_loss=0.07113, over 653742.82 frames.], batch size: 45, lr: 1.11e-03
+2022-06-18 14:56:12,595 INFO [train.py:874] (0/4) Epoch 7, batch 500, aishell_loss[loss=0.2079, simple_loss=0.2882, pruned_loss=0.06376, over 4879.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2645, pruned_loss=0.06725, over 904998.80 frames.], batch size: 42, aishell_tot_loss[loss=0.1971, simple_loss=0.2691, pruned_loss=0.0626, over 715944.73 frames.], datatang_tot_loss[loss=0.2005, simple_loss=0.2584, pruned_loss=0.0713, over 691492.69 frames.], batch size: 42, lr: 1.11e-03
+2022-06-18 14:56:43,875 INFO [train.py:874] (0/4) Epoch 7, batch 550, aishell_loss[loss=0.1622, simple_loss=0.2467, pruned_loss=0.03885, over 4982.00 frames.], tot_loss[loss=0.1979, simple_loss=0.263, pruned_loss=0.06644, over 923027.18 frames.], batch size: 27, aishell_tot_loss[loss=0.1959, simple_loss=0.2677, pruned_loss=0.06198, over 741529.01 frames.], datatang_tot_loss[loss=0.1998, simple_loss=0.2584, pruned_loss=0.07065, over 732847.12 frames.], batch size: 27, lr: 1.11e-03
+2022-06-18 14:57:13,434 INFO [train.py:874] (0/4) Epoch 7, batch 600, datatang_loss[loss=0.1837, simple_loss=0.2529, pruned_loss=0.05723, over 4928.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2632, pruned_loss=0.06675, over 937053.12 frames.], batch size: 42, aishell_tot_loss[loss=0.1963, simple_loss=0.2683, pruned_loss=0.06214, over 763764.42 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2584, pruned_loss=0.07065, over 769319.24 frames.], batch size: 42, lr: 1.11e-03
+2022-06-18 14:57:42,477 INFO [train.py:874] (0/4) Epoch 7, batch 650, aishell_loss[loss=0.1851, simple_loss=0.2685, pruned_loss=0.0508, over 4943.00 frames.], tot_loss[loss=0.198, simple_loss=0.2633, pruned_loss=0.06639, over 947579.29 frames.], batch size: 54, aishell_tot_loss[loss=0.1961, simple_loss=0.2684, pruned_loss=0.06196, over 793617.89 frames.], datatang_tot_loss[loss=0.1998, simple_loss=0.2582, pruned_loss=0.07074, over 790832.14 frames.], batch size: 54, lr: 1.11e-03
+2022-06-18 14:58:14,072 INFO [train.py:874] (0/4) Epoch 7, batch 700, datatang_loss[loss=0.2003, simple_loss=0.2656, pruned_loss=0.06747, over 4958.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2629, pruned_loss=0.06672, over 955843.48 frames.], batch size: 86, aishell_tot_loss[loss=0.1954, simple_loss=0.2675, pruned_loss=0.06165, over 816255.57 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.2586, pruned_loss=0.07148, over 813585.68 frames.], batch size: 86, lr: 1.11e-03
+2022-06-18 14:58:45,052 INFO [train.py:874] (0/4) Epoch 7, batch 750, datatang_loss[loss=0.2179, simple_loss=0.2624, pruned_loss=0.08671, over 4908.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2633, pruned_loss=0.0674, over 962406.22 frames.], batch size: 47, aishell_tot_loss[loss=0.1954, simple_loss=0.2676, pruned_loss=0.06166, over 834552.22 frames.], datatang_tot_loss[loss=0.2017, simple_loss=0.2591, pruned_loss=0.07214, over 835452.71 frames.], batch size: 47, lr: 1.10e-03
+2022-06-18 14:59:13,883 INFO [train.py:874] (0/4) Epoch 7, batch 800, datatang_loss[loss=0.1866, simple_loss=0.2405, pruned_loss=0.06636, over 4887.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2636, pruned_loss=0.06771, over 967727.36 frames.], batch size: 47, aishell_tot_loss[loss=0.1961, simple_loss=0.268, pruned_loss=0.06213, over 852388.12 frames.], datatang_tot_loss[loss=0.2017, simple_loss=0.259, pruned_loss=0.07217, over 853279.72 frames.], batch size: 47, lr: 1.10e-03
+2022-06-18 14:59:44,734 INFO [train.py:874] (0/4) Epoch 7, batch 850, aishell_loss[loss=0.2241, simple_loss=0.2953, pruned_loss=0.07647, over 4958.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2633, pruned_loss=0.06752, over 971795.76 frames.], batch size: 64, aishell_tot_loss[loss=0.1959, simple_loss=0.2678, pruned_loss=0.062, over 866950.24 frames.], datatang_tot_loss[loss=0.2016, simple_loss=0.259, pruned_loss=0.07208, over 870083.52 frames.], batch size: 64, lr: 1.10e-03
+2022-06-18 15:00:16,349 INFO [train.py:874] (0/4) Epoch 7, batch 900, datatang_loss[loss=0.21, simple_loss=0.2564, pruned_loss=0.08177, over 4905.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2629, pruned_loss=0.06716, over 974817.65 frames.], batch size: 47, aishell_tot_loss[loss=0.1952, simple_loss=0.2673, pruned_loss=0.06152, over 879391.29 frames.], datatang_tot_loss[loss=0.2016, simple_loss=0.259, pruned_loss=0.07207, over 885115.58 frames.], batch size: 47, lr: 1.10e-03
+2022-06-18 15:00:45,814 INFO [train.py:874] (0/4) Epoch 7, batch 950, aishell_loss[loss=0.2385, simple_loss=0.3022, pruned_loss=0.08735, over 4975.00 frames.], tot_loss[loss=0.1997, simple_loss=0.2634, pruned_loss=0.068, over 976876.38 frames.], batch size: 51, aishell_tot_loss[loss=0.1961, simple_loss=0.2676, pruned_loss=0.06229, over 891763.29 frames.], datatang_tot_loss[loss=0.202, simple_loss=0.2592, pruned_loss=0.07237, over 896738.87 frames.], batch size: 51, lr: 1.10e-03
+2022-06-18 15:01:17,334 INFO [train.py:874] (0/4) Epoch 7, batch 1000, datatang_loss[loss=0.182, simple_loss=0.2485, pruned_loss=0.05772, over 4916.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2635, pruned_loss=0.0675, over 978657.90 frames.], batch size: 77, aishell_tot_loss[loss=0.1962, simple_loss=0.2683, pruned_loss=0.06206, over 901566.48 frames.], datatang_tot_loss[loss=0.2014, simple_loss=0.2588, pruned_loss=0.07202, over 908215.17 frames.], batch size: 77, lr: 1.10e-03
+2022-06-18 15:01:17,337 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 15:01:33,320 INFO [train.py:914] (0/4) Epoch 7, validation: loss=0.1728, simple_loss=0.2548, pruned_loss=0.04541, over 1622729.00 frames.
+2022-06-18 15:02:05,422 INFO [train.py:874] (0/4) Epoch 7, batch 1050, datatang_loss[loss=0.2473, simple_loss=0.3127, pruned_loss=0.09089, over 4961.00 frames.], tot_loss[loss=0.2005, simple_loss=0.2643, pruned_loss=0.06837, over 980179.21 frames.], batch size: 37, aishell_tot_loss[loss=0.196, simple_loss=0.2678, pruned_loss=0.06211, over 909809.24 frames.], datatang_tot_loss[loss=0.2031, simple_loss=0.2604, pruned_loss=0.07293, over 918826.05 frames.], batch size: 37, lr: 1.10e-03
+2022-06-18 15:02:35,722 INFO [train.py:874] (0/4) Epoch 7, batch 1100, datatang_loss[loss=0.2001, simple_loss=0.2514, pruned_loss=0.07436, over 4850.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2647, pruned_loss=0.06757, over 981589.01 frames.], batch size: 25, aishell_tot_loss[loss=0.1966, simple_loss=0.2687, pruned_loss=0.06223, over 921280.60 frames.], datatang_tot_loss[loss=0.2024, simple_loss=0.2598, pruned_loss=0.0725, over 924565.77 frames.], batch size: 25, lr: 1.10e-03
+2022-06-18 15:03:04,433 INFO [train.py:874] (0/4) Epoch 7, batch 1150, aishell_loss[loss=0.1879, simple_loss=0.2668, pruned_loss=0.05449, over 4938.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2626, pruned_loss=0.06655, over 982599.81 frames.], batch size: 58, aishell_tot_loss[loss=0.1954, simple_loss=0.2674, pruned_loss=0.06174, over 928528.84 frames.], datatang_tot_loss[loss=0.2013, simple_loss=0.2589, pruned_loss=0.07183, over 932200.45 frames.], batch size: 58, lr: 1.10e-03
+2022-06-18 15:03:35,928 INFO [train.py:874] (0/4) Epoch 7, batch 1200, datatang_loss[loss=0.1928, simple_loss=0.247, pruned_loss=0.06924, over 4882.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2625, pruned_loss=0.06621, over 983165.38 frames.], batch size: 47, aishell_tot_loss[loss=0.1953, simple_loss=0.2672, pruned_loss=0.06165, over 935544.51 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2587, pruned_loss=0.07153, over 938098.90 frames.], batch size: 47, lr: 1.10e-03
+2022-06-18 15:04:06,584 INFO [train.py:874] (0/4) Epoch 7, batch 1250, aishell_loss[loss=0.1955, simple_loss=0.272, pruned_loss=0.05947, over 4964.00 frames.], tot_loss[loss=0.1967, simple_loss=0.262, pruned_loss=0.06572, over 983470.82 frames.], batch size: 51, aishell_tot_loss[loss=0.1942, simple_loss=0.266, pruned_loss=0.0612, over 942319.40 frames.], datatang_tot_loss[loss=0.201, simple_loss=0.259, pruned_loss=0.07156, over 942583.39 frames.], batch size: 51, lr: 1.09e-03
+2022-06-18 15:04:34,738 INFO [train.py:874] (0/4) Epoch 7, batch 1300, aishell_loss[loss=0.2024, simple_loss=0.278, pruned_loss=0.06338, over 4881.00 frames.], tot_loss[loss=0.197, simple_loss=0.262, pruned_loss=0.06603, over 983942.37 frames.], batch size: 34, aishell_tot_loss[loss=0.1943, simple_loss=0.266, pruned_loss=0.06125, over 947228.54 frames.], datatang_tot_loss[loss=0.2011, simple_loss=0.2589, pruned_loss=0.07163, over 947832.62 frames.], batch size: 34, lr: 1.09e-03
+2022-06-18 15:05:04,913 INFO [train.py:874] (0/4) Epoch 7, batch 1350, aishell_loss[loss=0.183, simple_loss=0.2662, pruned_loss=0.04991, over 4971.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2632, pruned_loss=0.06718, over 984209.04 frames.], batch size: 39, aishell_tot_loss[loss=0.1942, simple_loss=0.266, pruned_loss=0.06124, over 951674.14 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.2602, pruned_loss=0.07277, over 952247.06 frames.], batch size: 39, lr: 1.09e-03
+2022-06-18 15:05:36,471 INFO [train.py:874] (0/4) Epoch 7, batch 1400, aishell_loss[loss=0.1986, simple_loss=0.2708, pruned_loss=0.06325, over 4955.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2634, pruned_loss=0.06701, over 984508.22 frames.], batch size: 56, aishell_tot_loss[loss=0.1946, simple_loss=0.2664, pruned_loss=0.06144, over 955499.27 frames.], datatang_tot_loss[loss=0.2025, simple_loss=0.26, pruned_loss=0.07246, over 956313.70 frames.], batch size: 56, lr: 1.09e-03
+2022-06-18 15:06:05,397 INFO [train.py:874] (0/4) Epoch 7, batch 1450, aishell_loss[loss=0.1992, simple_loss=0.275, pruned_loss=0.06171, over 4926.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2626, pruned_loss=0.06658, over 984892.29 frames.], batch size: 58, aishell_tot_loss[loss=0.1942, simple_loss=0.2661, pruned_loss=0.06113, over 959033.95 frames.], datatang_tot_loss[loss=0.202, simple_loss=0.2594, pruned_loss=0.07225, over 959921.60 frames.], batch size: 58, lr: 1.09e-03
+2022-06-18 15:06:36,066 INFO [train.py:874] (0/4) Epoch 7, batch 1500, datatang_loss[loss=0.2089, simple_loss=0.2729, pruned_loss=0.07241, over 4975.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2627, pruned_loss=0.06681, over 985182.88 frames.], batch size: 55, aishell_tot_loss[loss=0.1944, simple_loss=0.2662, pruned_loss=0.06135, over 961880.45 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.2596, pruned_loss=0.07213, over 963358.94 frames.], batch size: 55, lr: 1.09e-03
+2022-06-18 15:07:07,638 INFO [train.py:874] (0/4) Epoch 7, batch 1550, aishell_loss[loss=0.1883, simple_loss=0.2575, pruned_loss=0.05952, over 4982.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2622, pruned_loss=0.06626, over 985804.54 frames.], batch size: 30, aishell_tot_loss[loss=0.1946, simple_loss=0.2663, pruned_loss=0.06143, over 965130.67 frames.], datatang_tot_loss[loss=0.2009, simple_loss=0.2589, pruned_loss=0.07147, over 966094.87 frames.], batch size: 30, lr: 1.09e-03
+2022-06-18 15:07:35,785 INFO [train.py:874] (0/4) Epoch 7, batch 1600, datatang_loss[loss=0.2674, simple_loss=0.3138, pruned_loss=0.1105, over 4945.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2616, pruned_loss=0.06534, over 985644.00 frames.], batch size: 108, aishell_tot_loss[loss=0.1936, simple_loss=0.2655, pruned_loss=0.06084, over 968210.37 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.2587, pruned_loss=0.07142, over 967615.82 frames.], batch size: 108, lr: 1.09e-03
+2022-06-18 15:08:07,453 INFO [train.py:874] (0/4) Epoch 7, batch 1650, datatang_loss[loss=0.1835, simple_loss=0.2528, pruned_loss=0.05713, over 4937.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2615, pruned_loss=0.06558, over 985884.68 frames.], batch size: 62, aishell_tot_loss[loss=0.1935, simple_loss=0.2654, pruned_loss=0.06083, over 969912.84 frames.], datatang_tot_loss[loss=0.2006, simple_loss=0.2587, pruned_loss=0.07125, over 970358.59 frames.], batch size: 62, lr: 1.09e-03
+2022-06-18 15:08:38,351 INFO [train.py:874] (0/4) Epoch 7, batch 1700, datatang_loss[loss=0.1779, simple_loss=0.2389, pruned_loss=0.05849, over 4950.00 frames.], tot_loss[loss=0.1961, simple_loss=0.261, pruned_loss=0.06554, over 985883.27 frames.], batch size: 69, aishell_tot_loss[loss=0.1928, simple_loss=0.2649, pruned_loss=0.06032, over 971532.70 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.2586, pruned_loss=0.07151, over 972424.83 frames.], batch size: 69, lr: 1.09e-03
+2022-06-18 15:09:08,393 INFO [train.py:874] (0/4) Epoch 7, batch 1750, aishell_loss[loss=0.2294, simple_loss=0.2917, pruned_loss=0.08359, over 4974.00 frames.], tot_loss[loss=0.1977, simple_loss=0.262, pruned_loss=0.06666, over 985658.47 frames.], batch size: 51, aishell_tot_loss[loss=0.1944, simple_loss=0.2663, pruned_loss=0.06128, over 972741.46 frames.], datatang_tot_loss[loss=0.2006, simple_loss=0.2583, pruned_loss=0.07147, over 974234.72 frames.], batch size: 51, lr: 1.08e-03
+2022-06-18 15:09:38,819 INFO [train.py:874] (0/4) Epoch 7, batch 1800, aishell_loss[loss=0.2211, simple_loss=0.2977, pruned_loss=0.0722, over 4950.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2617, pruned_loss=0.06589, over 985529.59 frames.], batch size: 64, aishell_tot_loss[loss=0.1941, simple_loss=0.2662, pruned_loss=0.06097, over 974241.54 frames.], datatang_tot_loss[loss=0.2, simple_loss=0.2577, pruned_loss=0.07112, over 975485.49 frames.], batch size: 64, lr: 1.08e-03
+2022-06-18 15:10:08,341 INFO [train.py:874] (0/4) Epoch 7, batch 1850, datatang_loss[loss=0.1709, simple_loss=0.2438, pruned_loss=0.04906, over 4953.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2634, pruned_loss=0.06703, over 985659.20 frames.], batch size: 42, aishell_tot_loss[loss=0.1948, simple_loss=0.267, pruned_loss=0.06127, over 975759.43 frames.], datatang_tot_loss[loss=0.2013, simple_loss=0.2587, pruned_loss=0.07201, over 976622.49 frames.], batch size: 42, lr: 1.08e-03
+2022-06-18 15:10:38,790 INFO [train.py:874] (0/4) Epoch 7, batch 1900, aishell_loss[loss=0.1694, simple_loss=0.2514, pruned_loss=0.04372, over 4947.00 frames.], tot_loss[loss=0.1979, simple_loss=0.263, pruned_loss=0.06644, over 985192.38 frames.], batch size: 56, aishell_tot_loss[loss=0.1948, simple_loss=0.267, pruned_loss=0.0613, over 976555.09 frames.], datatang_tot_loss[loss=0.2006, simple_loss=0.2583, pruned_loss=0.07145, over 977569.37 frames.], batch size: 56, lr: 1.08e-03
+2022-06-18 15:11:09,995 INFO [train.py:874] (0/4) Epoch 7, batch 1950, aishell_loss[loss=0.226, simple_loss=0.2948, pruned_loss=0.07856, over 4884.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2627, pruned_loss=0.06782, over 985602.51 frames.], batch size: 50, aishell_tot_loss[loss=0.1963, simple_loss=0.2675, pruned_loss=0.06254, over 977484.28 frames.], datatang_tot_loss[loss=0.2004, simple_loss=0.2579, pruned_loss=0.0714, over 978957.02 frames.], batch size: 50, lr: 1.08e-03
+2022-06-18 15:11:38,574 INFO [train.py:874] (0/4) Epoch 7, batch 2000, aishell_loss[loss=0.1984, simple_loss=0.2749, pruned_loss=0.06099, over 4942.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2627, pruned_loss=0.06694, over 985149.49 frames.], batch size: 56, aishell_tot_loss[loss=0.1958, simple_loss=0.2676, pruned_loss=0.06203, over 978149.72 frames.], datatang_tot_loss[loss=0.2001, simple_loss=0.2577, pruned_loss=0.07132, over 979574.53 frames.], batch size: 56, lr: 1.08e-03
+2022-06-18 15:11:38,577 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 15:11:55,040 INFO [train.py:914] (0/4) Epoch 7, validation: loss=0.1748, simple_loss=0.2567, pruned_loss=0.04647, over 1622729.00 frames.
+2022-06-18 15:12:25,173 INFO [train.py:874] (0/4) Epoch 7, batch 2050, aishell_loss[loss=0.2223, simple_loss=0.2894, pruned_loss=0.07757, over 4910.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2633, pruned_loss=0.0669, over 985390.65 frames.], batch size: 33, aishell_tot_loss[loss=0.1964, simple_loss=0.2682, pruned_loss=0.06233, over 979166.00 frames.], datatang_tot_loss[loss=0.2, simple_loss=0.2576, pruned_loss=0.07113, over 980305.31 frames.], batch size: 33, lr: 1.08e-03
+2022-06-18 15:12:54,801 INFO [train.py:874] (0/4) Epoch 7, batch 2100, aishell_loss[loss=0.1912, simple_loss=0.2649, pruned_loss=0.05874, over 4986.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2631, pruned_loss=0.06618, over 985010.65 frames.], batch size: 39, aishell_tot_loss[loss=0.1961, simple_loss=0.268, pruned_loss=0.06209, over 979772.78 frames.], datatang_tot_loss[loss=0.1996, simple_loss=0.2573, pruned_loss=0.07092, over 980656.90 frames.], batch size: 39, lr: 1.08e-03
+2022-06-18 15:13:25,725 INFO [train.py:874] (0/4) Epoch 7, batch 2150, datatang_loss[loss=0.1975, simple_loss=0.2642, pruned_loss=0.0654, over 4985.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2632, pruned_loss=0.06548, over 985123.55 frames.], batch size: 40, aishell_tot_loss[loss=0.1956, simple_loss=0.2678, pruned_loss=0.06168, over 980507.79 frames.], datatang_tot_loss[loss=0.1995, simple_loss=0.2575, pruned_loss=0.07074, over 981190.23 frames.], batch size: 40, lr: 1.08e-03
+2022-06-18 15:13:55,546 INFO [train.py:874] (0/4) Epoch 7, batch 2200, datatang_loss[loss=0.1772, simple_loss=0.249, pruned_loss=0.05271, over 4927.00 frames.], tot_loss[loss=0.1973, simple_loss=0.263, pruned_loss=0.06585, over 985478.66 frames.], batch size: 77, aishell_tot_loss[loss=0.196, simple_loss=0.2685, pruned_loss=0.06178, over 980943.58 frames.], datatang_tot_loss[loss=0.199, simple_loss=0.2571, pruned_loss=0.07047, over 982102.36 frames.], batch size: 77, lr: 1.08e-03
+2022-06-18 15:14:26,107 INFO [train.py:874] (0/4) Epoch 7, batch 2250, aishell_loss[loss=0.2004, simple_loss=0.2754, pruned_loss=0.06271, over 4910.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2622, pruned_loss=0.06528, over 985085.26 frames.], batch size: 52, aishell_tot_loss[loss=0.195, simple_loss=0.2675, pruned_loss=0.0613, over 981140.85 frames.], datatang_tot_loss[loss=0.199, simple_loss=0.2572, pruned_loss=0.0704, over 982418.46 frames.], batch size: 52, lr: 1.07e-03
+2022-06-18 15:14:56,345 INFO [train.py:874] (0/4) Epoch 7, batch 2300, aishell_loss[loss=0.2129, simple_loss=0.2818, pruned_loss=0.07207, over 4978.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2621, pruned_loss=0.06482, over 984967.24 frames.], batch size: 48, aishell_tot_loss[loss=0.1944, simple_loss=0.267, pruned_loss=0.06096, over 981402.16 frames.], datatang_tot_loss[loss=0.199, simple_loss=0.2572, pruned_loss=0.07041, over 982844.47 frames.], batch size: 48, lr: 1.07e-03
+2022-06-18 15:15:26,201 INFO [train.py:874] (0/4) Epoch 7, batch 2350, datatang_loss[loss=0.1891, simple_loss=0.2559, pruned_loss=0.0611, over 4894.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2625, pruned_loss=0.0654, over 984957.32 frames.], batch size: 47, aishell_tot_loss[loss=0.1946, simple_loss=0.267, pruned_loss=0.06114, over 981787.28 frames.], datatang_tot_loss[loss=0.1994, simple_loss=0.2577, pruned_loss=0.07053, over 983106.70 frames.], batch size: 47, lr: 1.07e-03
+2022-06-18 15:15:56,418 INFO [train.py:874] (0/4) Epoch 7, batch 2400, datatang_loss[loss=0.1562, simple_loss=0.2173, pruned_loss=0.04759, over 4981.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2624, pruned_loss=0.06525, over 985324.98 frames.], batch size: 53, aishell_tot_loss[loss=0.1948, simple_loss=0.2672, pruned_loss=0.06116, over 982367.77 frames.], datatang_tot_loss[loss=0.199, simple_loss=0.2576, pruned_loss=0.07023, over 983496.08 frames.], batch size: 53, lr: 1.07e-03
+2022-06-18 15:16:27,325 INFO [train.py:874] (0/4) Epoch 7, batch 2450, aishell_loss[loss=0.1445, simple_loss=0.2247, pruned_loss=0.03215, over 4874.00 frames.], tot_loss[loss=0.196, simple_loss=0.2622, pruned_loss=0.06493, over 985541.42 frames.], batch size: 28, aishell_tot_loss[loss=0.1948, simple_loss=0.2673, pruned_loss=0.06118, over 982971.37 frames.], datatang_tot_loss[loss=0.1985, simple_loss=0.2572, pruned_loss=0.06989, over 983709.05 frames.], batch size: 28, lr: 1.07e-03
+2022-06-18 15:16:56,372 INFO [train.py:874] (0/4) Epoch 7, batch 2500, datatang_loss[loss=0.1881, simple_loss=0.2513, pruned_loss=0.06248, over 4931.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2627, pruned_loss=0.06525, over 985915.83 frames.], batch size: 71, aishell_tot_loss[loss=0.195, simple_loss=0.2675, pruned_loss=0.06124, over 983506.67 frames.], datatang_tot_loss[loss=0.1988, simple_loss=0.2575, pruned_loss=0.07008, over 984085.91 frames.], batch size: 71, lr: 1.07e-03
+2022-06-18 15:17:26,855 INFO [train.py:874] (0/4) Epoch 7, batch 2550, aishell_loss[loss=0.2046, simple_loss=0.2723, pruned_loss=0.06844, over 4924.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2623, pruned_loss=0.06496, over 986073.69 frames.], batch size: 49, aishell_tot_loss[loss=0.1944, simple_loss=0.2668, pruned_loss=0.06095, over 984052.11 frames.], datatang_tot_loss[loss=0.199, simple_loss=0.2575, pruned_loss=0.07022, over 984226.10 frames.], batch size: 49, lr: 1.07e-03
+2022-06-18 15:17:58,013 INFO [train.py:874] (0/4) Epoch 7, batch 2600, aishell_loss[loss=0.1758, simple_loss=0.2513, pruned_loss=0.05015, over 4874.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2622, pruned_loss=0.06519, over 985725.97 frames.], batch size: 36, aishell_tot_loss[loss=0.1943, simple_loss=0.2669, pruned_loss=0.06086, over 984007.74 frames.], datatang_tot_loss[loss=0.1991, simple_loss=0.2574, pruned_loss=0.07044, over 984361.15 frames.], batch size: 36, lr: 1.07e-03
+2022-06-18 15:18:28,166 INFO [train.py:874] (0/4) Epoch 7, batch 2650, aishell_loss[loss=0.1859, simple_loss=0.2494, pruned_loss=0.0612, over 4921.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2613, pruned_loss=0.06501, over 985403.31 frames.], batch size: 33, aishell_tot_loss[loss=0.1941, simple_loss=0.2663, pruned_loss=0.06089, over 983854.39 frames.], datatang_tot_loss[loss=0.1986, simple_loss=0.257, pruned_loss=0.07016, over 984534.59 frames.], batch size: 33, lr: 1.07e-03
+2022-06-18 15:18:58,540 INFO [train.py:874] (0/4) Epoch 7, batch 2700, aishell_loss[loss=0.2038, simple_loss=0.268, pruned_loss=0.06982, over 4955.00 frames.], tot_loss[loss=0.196, simple_loss=0.2617, pruned_loss=0.06516, over 985521.76 frames.], batch size: 56, aishell_tot_loss[loss=0.1939, simple_loss=0.2665, pruned_loss=0.06063, over 984215.75 frames.], datatang_tot_loss[loss=0.199, simple_loss=0.257, pruned_loss=0.0705, over 984584.40 frames.], batch size: 56, lr: 1.07e-03
+2022-06-18 15:19:28,844 INFO [train.py:874] (0/4) Epoch 7, batch 2750, aishell_loss[loss=0.2028, simple_loss=0.2775, pruned_loss=0.06401, over 4865.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2619, pruned_loss=0.06489, over 985371.25 frames.], batch size: 36, aishell_tot_loss[loss=0.1928, simple_loss=0.2661, pruned_loss=0.05972, over 984176.14 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2576, pruned_loss=0.07111, over 984740.35 frames.], batch size: 36, lr: 1.07e-03
+2022-06-18 15:19:58,434 INFO [train.py:874] (0/4) Epoch 7, batch 2800, aishell_loss[loss=0.204, simple_loss=0.2818, pruned_loss=0.06307, over 4957.00 frames.], tot_loss[loss=0.1946, simple_loss=0.261, pruned_loss=0.06415, over 985571.74 frames.], batch size: 40, aishell_tot_loss[loss=0.192, simple_loss=0.2653, pruned_loss=0.05934, over 984432.39 frames.], datatang_tot_loss[loss=0.1991, simple_loss=0.2575, pruned_loss=0.07041, over 984885.55 frames.], batch size: 40, lr: 1.06e-03
+2022-06-18 15:20:29,256 INFO [train.py:874] (0/4) Epoch 7, batch 2850, aishell_loss[loss=0.2161, simple_loss=0.2898, pruned_loss=0.07121, over 4878.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2617, pruned_loss=0.06497, over 985623.24 frames.], batch size: 35, aishell_tot_loss[loss=0.1924, simple_loss=0.2655, pruned_loss=0.05964, over 984565.43 frames.], datatang_tot_loss[loss=0.1998, simple_loss=0.258, pruned_loss=0.07075, over 985032.46 frames.], batch size: 35, lr: 1.06e-03
+2022-06-18 15:21:00,379 INFO [train.py:874] (0/4) Epoch 7, batch 2900, aishell_loss[loss=0.1982, simple_loss=0.278, pruned_loss=0.05916, over 4968.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2603, pruned_loss=0.06459, over 985395.48 frames.], batch size: 51, aishell_tot_loss[loss=0.1924, simple_loss=0.2655, pruned_loss=0.05967, over 984716.00 frames.], datatang_tot_loss[loss=0.1983, simple_loss=0.2568, pruned_loss=0.06992, over 984822.80 frames.], batch size: 51, lr: 1.06e-03
+2022-06-18 15:21:30,576 INFO [train.py:874] (0/4) Epoch 7, batch 2950, aishell_loss[loss=0.2022, simple_loss=0.2661, pruned_loss=0.06914, over 4877.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2618, pruned_loss=0.06591, over 985480.58 frames.], batch size: 34, aishell_tot_loss[loss=0.1927, simple_loss=0.2657, pruned_loss=0.05983, over 984765.99 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2581, pruned_loss=0.07083, over 985004.34 frames.], batch size: 34, lr: 1.06e-03
+2022-06-18 15:22:01,254 INFO [train.py:874] (0/4) Epoch 7, batch 3000, datatang_loss[loss=0.1648, simple_loss=0.2268, pruned_loss=0.05135, over 4930.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2609, pruned_loss=0.0654, over 985403.26 frames.], batch size: 79, aishell_tot_loss[loss=0.1934, simple_loss=0.2663, pruned_loss=0.06023, over 984810.56 frames.], datatang_tot_loss[loss=0.1982, simple_loss=0.2567, pruned_loss=0.06978, over 985017.22 frames.], batch size: 79, lr: 1.06e-03
+2022-06-18 15:22:01,257 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 15:22:18,149 INFO [train.py:914] (0/4) Epoch 7, validation: loss=0.1737, simple_loss=0.2558, pruned_loss=0.0458, over 1622729.00 frames.
+2022-06-18 15:22:48,093 INFO [train.py:874] (0/4) Epoch 7, batch 3050, aishell_loss[loss=0.1838, simple_loss=0.2583, pruned_loss=0.05471, over 4868.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2609, pruned_loss=0.06514, over 985570.73 frames.], batch size: 35, aishell_tot_loss[loss=0.1937, simple_loss=0.2665, pruned_loss=0.06038, over 985079.37 frames.], datatang_tot_loss[loss=0.1976, simple_loss=0.2563, pruned_loss=0.06945, over 985034.66 frames.], batch size: 35, lr: 1.06e-03
+2022-06-18 15:23:18,089 INFO [train.py:874] (0/4) Epoch 7, batch 3100, datatang_loss[loss=0.2161, simple_loss=0.2755, pruned_loss=0.07835, over 4921.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2615, pruned_loss=0.06551, over 985524.53 frames.], batch size: 52, aishell_tot_loss[loss=0.1932, simple_loss=0.2661, pruned_loss=0.06013, over 984963.75 frames.], datatang_tot_loss[loss=0.1988, simple_loss=0.2571, pruned_loss=0.07027, over 985225.04 frames.], batch size: 52, lr: 1.06e-03
+2022-06-18 15:23:44,670 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-28000.pt
+2022-06-18 15:23:50,867 INFO [train.py:874] (0/4) Epoch 7, batch 3150, aishell_loss[loss=0.1766, simple_loss=0.2453, pruned_loss=0.05397, over 4878.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2619, pruned_loss=0.06516, over 985466.17 frames.], batch size: 28, aishell_tot_loss[loss=0.1939, simple_loss=0.267, pruned_loss=0.06039, over 984924.30 frames.], datatang_tot_loss[loss=0.1981, simple_loss=0.2565, pruned_loss=0.06983, over 985314.01 frames.], batch size: 28, lr: 1.06e-03
+2022-06-18 15:24:21,584 INFO [train.py:874] (0/4) Epoch 7, batch 3200, datatang_loss[loss=0.1905, simple_loss=0.2634, pruned_loss=0.05883, over 4927.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2623, pruned_loss=0.06529, over 985434.16 frames.], batch size: 94, aishell_tot_loss[loss=0.1937, simple_loss=0.267, pruned_loss=0.06024, over 985166.75 frames.], datatang_tot_loss[loss=0.1986, simple_loss=0.257, pruned_loss=0.07006, over 985129.13 frames.], batch size: 94, lr: 1.06e-03
+2022-06-18 15:24:53,202 INFO [train.py:874] (0/4) Epoch 7, batch 3250, aishell_loss[loss=0.1986, simple_loss=0.2742, pruned_loss=0.06152, over 4965.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2629, pruned_loss=0.0651, over 985422.64 frames.], batch size: 40, aishell_tot_loss[loss=0.1937, simple_loss=0.267, pruned_loss=0.06019, over 985239.80 frames.], datatang_tot_loss[loss=0.1989, simple_loss=0.2573, pruned_loss=0.07027, over 985098.19 frames.], batch size: 40, lr: 1.06e-03
+2022-06-18 15:25:21,868 INFO [train.py:874] (0/4) Epoch 7, batch 3300, datatang_loss[loss=0.2186, simple_loss=0.26, pruned_loss=0.08862, over 4943.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2624, pruned_loss=0.06498, over 985524.06 frames.], batch size: 24, aishell_tot_loss[loss=0.1928, simple_loss=0.2663, pruned_loss=0.05968, over 985187.05 frames.], datatang_tot_loss[loss=0.1994, simple_loss=0.2577, pruned_loss=0.07056, over 985323.38 frames.], batch size: 24, lr: 1.05e-03
+2022-06-18 15:25:52,843 INFO [train.py:874] (0/4) Epoch 7, batch 3350, datatang_loss[loss=0.2016, simple_loss=0.253, pruned_loss=0.07507, over 4924.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2622, pruned_loss=0.06471, over 985131.16 frames.], batch size: 73, aishell_tot_loss[loss=0.1922, simple_loss=0.2657, pruned_loss=0.05938, over 984961.15 frames.], datatang_tot_loss[loss=0.1998, simple_loss=0.258, pruned_loss=0.07083, over 985206.54 frames.], batch size: 73, lr: 1.05e-03
+2022-06-18 15:26:24,279 INFO [train.py:874] (0/4) Epoch 7, batch 3400, aishell_loss[loss=0.1791, simple_loss=0.2572, pruned_loss=0.0505, over 4928.00 frames.], tot_loss[loss=0.1966, simple_loss=0.2627, pruned_loss=0.06527, over 985664.59 frames.], batch size: 46, aishell_tot_loss[loss=0.1923, simple_loss=0.2656, pruned_loss=0.05949, over 985212.39 frames.], datatang_tot_loss[loss=0.2006, simple_loss=0.2587, pruned_loss=0.07119, over 985521.29 frames.], batch size: 46, lr: 1.05e-03
+2022-06-18 15:26:53,559 INFO [train.py:874] (0/4) Epoch 7, batch 3450, datatang_loss[loss=0.179, simple_loss=0.2451, pruned_loss=0.05646, over 4935.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2617, pruned_loss=0.06472, over 985439.29 frames.], batch size: 73, aishell_tot_loss[loss=0.1917, simple_loss=0.2648, pruned_loss=0.05924, over 985170.47 frames.], datatang_tot_loss[loss=0.2003, simple_loss=0.2585, pruned_loss=0.071, over 985391.29 frames.], batch size: 73, lr: 1.05e-03
+2022-06-18 15:27:24,024 INFO [train.py:874] (0/4) Epoch 7, batch 3500, aishell_loss[loss=0.1793, simple_loss=0.253, pruned_loss=0.05283, over 4984.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2621, pruned_loss=0.06479, over 985744.41 frames.], batch size: 30, aishell_tot_loss[loss=0.192, simple_loss=0.2653, pruned_loss=0.05932, over 985365.88 frames.], datatang_tot_loss[loss=0.2001, simple_loss=0.2586, pruned_loss=0.07081, over 985554.76 frames.], batch size: 30, lr: 1.05e-03
+2022-06-18 15:27:53,566 INFO [train.py:874] (0/4) Epoch 7, batch 3550, aishell_loss[loss=0.1973, simple_loss=0.2692, pruned_loss=0.06267, over 4948.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2623, pruned_loss=0.06525, over 985222.44 frames.], batch size: 32, aishell_tot_loss[loss=0.1921, simple_loss=0.2655, pruned_loss=0.0593, over 985173.50 frames.], datatang_tot_loss[loss=0.2006, simple_loss=0.2586, pruned_loss=0.07132, over 985266.56 frames.], batch size: 32, lr: 1.05e-03
+2022-06-18 15:28:23,403 INFO [train.py:874] (0/4) Epoch 7, batch 3600, aishell_loss[loss=0.2277, simple_loss=0.3022, pruned_loss=0.07662, over 4970.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2623, pruned_loss=0.06498, over 985748.67 frames.], batch size: 79, aishell_tot_loss[loss=0.1925, simple_loss=0.2658, pruned_loss=0.05956, over 985571.79 frames.], datatang_tot_loss[loss=0.2001, simple_loss=0.2583, pruned_loss=0.07095, over 985410.41 frames.], batch size: 79, lr: 1.05e-03
+2022-06-18 15:28:54,595 INFO [train.py:874] (0/4) Epoch 7, batch 3650, aishell_loss[loss=0.1907, simple_loss=0.2593, pruned_loss=0.06107, over 4890.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2615, pruned_loss=0.06484, over 985691.43 frames.], batch size: 42, aishell_tot_loss[loss=0.1923, simple_loss=0.2656, pruned_loss=0.0595, over 985557.34 frames.], datatang_tot_loss[loss=0.1995, simple_loss=0.2577, pruned_loss=0.07065, over 985434.57 frames.], batch size: 42, lr: 1.05e-03
+2022-06-18 15:29:24,186 INFO [train.py:874] (0/4) Epoch 7, batch 3700, aishell_loss[loss=0.1888, simple_loss=0.2657, pruned_loss=0.05595, over 4931.00 frames.], tot_loss[loss=0.1964, simple_loss=0.262, pruned_loss=0.06542, over 985234.57 frames.], batch size: 58, aishell_tot_loss[loss=0.1921, simple_loss=0.2654, pruned_loss=0.0594, over 985047.20 frames.], datatang_tot_loss[loss=0.2005, simple_loss=0.2583, pruned_loss=0.07136, over 985512.29 frames.], batch size: 58, lr: 1.05e-03
+2022-06-18 15:29:54,972 INFO [train.py:874] (0/4) Epoch 7, batch 3750, aishell_loss[loss=0.1998, simple_loss=0.2757, pruned_loss=0.062, over 4943.00 frames.], tot_loss[loss=0.196, simple_loss=0.2622, pruned_loss=0.06485, over 985770.40 frames.], batch size: 49, aishell_tot_loss[loss=0.1918, simple_loss=0.2655, pruned_loss=0.05906, over 985402.67 frames.], datatang_tot_loss[loss=0.2004, simple_loss=0.2585, pruned_loss=0.07116, over 985717.78 frames.], batch size: 49, lr: 1.05e-03
+2022-06-18 15:30:23,977 INFO [train.py:874] (0/4) Epoch 7, batch 3800, aishell_loss[loss=0.2, simple_loss=0.2854, pruned_loss=0.05734, over 4978.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2628, pruned_loss=0.0648, over 985400.63 frames.], batch size: 51, aishell_tot_loss[loss=0.1923, simple_loss=0.2661, pruned_loss=0.05924, over 985175.02 frames.], datatang_tot_loss[loss=0.2002, simple_loss=0.2585, pruned_loss=0.07095, over 985603.17 frames.], batch size: 51, lr: 1.05e-03
+2022-06-18 15:30:52,415 INFO [train.py:874] (0/4) Epoch 7, batch 3850, datatang_loss[loss=0.2253, simple_loss=0.2827, pruned_loss=0.08395, over 4965.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2629, pruned_loss=0.06465, over 985456.88 frames.], batch size: 37, aishell_tot_loss[loss=0.1927, simple_loss=0.2666, pruned_loss=0.05942, over 985044.33 frames.], datatang_tot_loss[loss=0.1997, simple_loss=0.2582, pruned_loss=0.07064, over 985790.00 frames.], batch size: 37, lr: 1.05e-03
+2022-06-18 15:31:22,653 INFO [train.py:874] (0/4) Epoch 7, batch 3900, aishell_loss[loss=0.1813, simple_loss=0.2551, pruned_loss=0.05371, over 4925.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2617, pruned_loss=0.06454, over 985050.78 frames.], batch size: 52, aishell_tot_loss[loss=0.1919, simple_loss=0.2658, pruned_loss=0.05899, over 984969.92 frames.], datatang_tot_loss[loss=0.1997, simple_loss=0.258, pruned_loss=0.07069, over 985446.59 frames.], batch size: 52, lr: 1.04e-03
+2022-06-18 15:31:51,713 INFO [train.py:874] (0/4) Epoch 7, batch 3950, aishell_loss[loss=0.198, simple_loss=0.2727, pruned_loss=0.06164, over 4930.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2615, pruned_loss=0.06471, over 984828.56 frames.], batch size: 33, aishell_tot_loss[loss=0.1917, simple_loss=0.2655, pruned_loss=0.05893, over 984711.37 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2582, pruned_loss=0.07086, over 985448.21 frames.], batch size: 33, lr: 1.04e-03
+2022-06-18 15:32:19,177 INFO [train.py:874] (0/4) Epoch 7, batch 4000, aishell_loss[loss=0.1842, simple_loss=0.2583, pruned_loss=0.05507, over 4872.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2613, pruned_loss=0.06394, over 984838.72 frames.], batch size: 37, aishell_tot_loss[loss=0.1915, simple_loss=0.2655, pruned_loss=0.05881, over 984663.05 frames.], datatang_tot_loss[loss=0.1993, simple_loss=0.2576, pruned_loss=0.07046, over 985465.60 frames.], batch size: 37, lr: 1.04e-03
+2022-06-18 15:32:19,180 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 15:32:35,474 INFO [train.py:914] (0/4) Epoch 7, validation: loss=0.1718, simple_loss=0.2543, pruned_loss=0.04468, over 1622729.00 frames.
+2022-06-18 15:33:04,684 INFO [train.py:874] (0/4) Epoch 7, batch 4050, datatang_loss[loss=0.1851, simple_loss=0.2582, pruned_loss=0.05598, over 4932.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2616, pruned_loss=0.06403, over 985070.35 frames.], batch size: 81, aishell_tot_loss[loss=0.191, simple_loss=0.2649, pruned_loss=0.05853, over 984761.51 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2584, pruned_loss=0.07065, over 985558.65 frames.], batch size: 81, lr: 1.04e-03
+2022-06-18 15:33:33,293 INFO [train.py:874] (0/4) Epoch 7, batch 4100, aishell_loss[loss=0.2095, simple_loss=0.2775, pruned_loss=0.07069, over 4917.00 frames.], tot_loss[loss=0.1941, simple_loss=0.261, pruned_loss=0.06363, over 984771.67 frames.], batch size: 41, aishell_tot_loss[loss=0.1904, simple_loss=0.2642, pruned_loss=0.05828, over 984168.38 frames.], datatang_tot_loss[loss=0.1996, simple_loss=0.2584, pruned_loss=0.07036, over 985850.15 frames.], batch size: 41, lr: 1.04e-03
+2022-06-18 15:33:36,782 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-7.pt
+2022-06-18 15:34:37,390 INFO [train.py:874] (0/4) Epoch 8, batch 50, datatang_loss[loss=0.1544, simple_loss=0.225, pruned_loss=0.04186, over 4937.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2535, pruned_loss=0.0584, over 218871.99 frames.], batch size: 62, aishell_tot_loss[loss=0.199, simple_loss=0.273, pruned_loss=0.06256, over 103071.67 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2375, pruned_loss=0.05509, over 129295.14 frames.], batch size: 62, lr: 9.97e-04
+2022-06-18 15:35:06,762 INFO [train.py:874] (0/4) Epoch 8, batch 100, aishell_loss[loss=0.1914, simple_loss=0.2621, pruned_loss=0.06038, over 4933.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2565, pruned_loss=0.05888, over 388805.28 frames.], batch size: 32, aishell_tot_loss[loss=0.193, simple_loss=0.2683, pruned_loss=0.05889, over 230067.06 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2421, pruned_loss=0.05865, over 207047.47 frames.], batch size: 32, lr: 9.97e-04
+2022-06-18 15:35:37,047 INFO [train.py:874] (0/4) Epoch 8, batch 150, aishell_loss[loss=0.2203, simple_loss=0.2877, pruned_loss=0.07649, over 4972.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2571, pruned_loss=0.0599, over 521223.58 frames.], batch size: 39, aishell_tot_loss[loss=0.192, simple_loss=0.267, pruned_loss=0.05848, over 319098.90 frames.], datatang_tot_loss[loss=0.1839, simple_loss=0.2456, pruned_loss=0.06111, over 298820.52 frames.], batch size: 39, lr: 9.96e-04
+2022-06-18 15:36:06,931 INFO [train.py:874] (0/4) Epoch 8, batch 200, datatang_loss[loss=0.2285, simple_loss=0.2847, pruned_loss=0.08618, over 4940.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2558, pruned_loss=0.0598, over 624640.07 frames.], batch size: 45, aishell_tot_loss[loss=0.1901, simple_loss=0.2647, pruned_loss=0.05771, over 401059.30 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.2459, pruned_loss=0.06185, over 376640.25 frames.], batch size: 45, lr: 9.95e-04
+2022-06-18 15:36:36,912 INFO [train.py:874] (0/4) Epoch 8, batch 250, aishell_loss[loss=0.1842, simple_loss=0.2696, pruned_loss=0.04942, over 4884.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2567, pruned_loss=0.06025, over 704638.60 frames.], batch size: 42, aishell_tot_loss[loss=0.1908, simple_loss=0.2651, pruned_loss=0.05822, over 464477.97 frames.], datatang_tot_loss[loss=0.1858, simple_loss=0.2476, pruned_loss=0.06199, over 453941.69 frames.], batch size: 42, lr: 9.94e-04
+2022-06-18 15:37:06,797 INFO [train.py:874] (0/4) Epoch 8, batch 300, datatang_loss[loss=0.1686, simple_loss=0.2322, pruned_loss=0.05253, over 4946.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2567, pruned_loss=0.05956, over 766907.19 frames.], batch size: 45, aishell_tot_loss[loss=0.191, simple_loss=0.2658, pruned_loss=0.05812, over 525855.22 frames.], datatang_tot_loss[loss=0.1845, simple_loss=0.247, pruned_loss=0.06102, over 516537.14 frames.], batch size: 45, lr: 9.93e-04
+2022-06-18 15:37:37,432 INFO [train.py:874] (0/4) Epoch 8, batch 350, datatang_loss[loss=0.2535, simple_loss=0.2994, pruned_loss=0.1037, over 4931.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2576, pruned_loss=0.06106, over 815805.21 frames.], batch size: 108, aishell_tot_loss[loss=0.1915, simple_loss=0.2663, pruned_loss=0.05832, over 565726.47 frames.], datatang_tot_loss[loss=0.1873, simple_loss=0.2489, pruned_loss=0.06282, over 586285.22 frames.], batch size: 108, lr: 9.93e-04
+2022-06-18 15:38:07,247 INFO [train.py:874] (0/4) Epoch 8, batch 400, aishell_loss[loss=0.2034, simple_loss=0.2833, pruned_loss=0.06172, over 4940.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2582, pruned_loss=0.06153, over 853324.62 frames.], batch size: 45, aishell_tot_loss[loss=0.1905, simple_loss=0.2655, pruned_loss=0.0578, over 611314.44 frames.], datatang_tot_loss[loss=0.1895, simple_loss=0.2508, pruned_loss=0.06409, over 636829.37 frames.], batch size: 45, lr: 9.92e-04
+2022-06-18 15:38:37,480 INFO [train.py:874] (0/4) Epoch 8, batch 450, aishell_loss[loss=0.181, simple_loss=0.2678, pruned_loss=0.04714, over 4967.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2582, pruned_loss=0.06083, over 882628.71 frames.], batch size: 51, aishell_tot_loss[loss=0.1897, simple_loss=0.265, pruned_loss=0.05721, over 663558.22 frames.], datatang_tot_loss[loss=0.1895, simple_loss=0.2508, pruned_loss=0.06408, over 670088.66 frames.], batch size: 51, lr: 9.91e-04
+2022-06-18 15:39:07,771 INFO [train.py:874] (0/4) Epoch 8, batch 500, aishell_loss[loss=0.1912, simple_loss=0.2654, pruned_loss=0.05857, over 4937.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2594, pruned_loss=0.06173, over 905682.39 frames.], batch size: 45, aishell_tot_loss[loss=0.1901, simple_loss=0.2655, pruned_loss=0.05738, over 698753.76 frames.], datatang_tot_loss[loss=0.1912, simple_loss=0.2522, pruned_loss=0.06509, over 710139.67 frames.], batch size: 45, lr: 9.90e-04
+2022-06-18 15:39:37,949 INFO [train.py:874] (0/4) Epoch 8, batch 550, datatang_loss[loss=0.2481, simple_loss=0.2969, pruned_loss=0.09963, over 4938.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2596, pruned_loss=0.06251, over 923172.25 frames.], batch size: 108, aishell_tot_loss[loss=0.1903, simple_loss=0.2649, pruned_loss=0.05783, over 731048.66 frames.], datatang_tot_loss[loss=0.1924, simple_loss=0.2533, pruned_loss=0.06578, over 743778.77 frames.], batch size: 108, lr: 9.89e-04
+2022-06-18 15:40:08,001 INFO [train.py:874] (0/4) Epoch 8, batch 600, aishell_loss[loss=0.2034, simple_loss=0.2843, pruned_loss=0.06129, over 4889.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2608, pruned_loss=0.06374, over 937420.11 frames.], batch size: 42, aishell_tot_loss[loss=0.191, simple_loss=0.2655, pruned_loss=0.0582, over 754321.32 frames.], datatang_tot_loss[loss=0.1943, simple_loss=0.2547, pruned_loss=0.06693, over 778861.38 frames.], batch size: 42, lr: 9.88e-04
+2022-06-18 15:40:37,298 INFO [train.py:874] (0/4) Epoch 8, batch 650, aishell_loss[loss=0.1887, simple_loss=0.2669, pruned_loss=0.05532, over 4975.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2607, pruned_loss=0.06286, over 948213.66 frames.], batch size: 44, aishell_tot_loss[loss=0.1902, simple_loss=0.2652, pruned_loss=0.05759, over 785864.26 frames.], datatang_tot_loss[loss=0.1945, simple_loss=0.255, pruned_loss=0.06699, over 799383.23 frames.], batch size: 44, lr: 9.88e-04
+2022-06-18 15:41:07,646 INFO [train.py:874] (0/4) Epoch 8, batch 700, datatang_loss[loss=0.2122, simple_loss=0.2759, pruned_loss=0.0743, over 4935.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2601, pruned_loss=0.06254, over 956481.61 frames.], batch size: 94, aishell_tot_loss[loss=0.1887, simple_loss=0.2639, pruned_loss=0.05672, over 808236.39 frames.], datatang_tot_loss[loss=0.1953, simple_loss=0.2557, pruned_loss=0.0675, over 822373.62 frames.], batch size: 94, lr: 9.87e-04
+2022-06-18 15:41:37,806 INFO [train.py:874] (0/4) Epoch 8, batch 750, aishell_loss[loss=0.2128, simple_loss=0.2879, pruned_loss=0.06883, over 4949.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2596, pruned_loss=0.0624, over 962906.06 frames.], batch size: 49, aishell_tot_loss[loss=0.1877, simple_loss=0.2628, pruned_loss=0.05635, over 828248.79 frames.], datatang_tot_loss[loss=0.1959, simple_loss=0.2563, pruned_loss=0.06775, over 842373.53 frames.], batch size: 49, lr: 9.86e-04
+2022-06-18 15:42:08,061 INFO [train.py:874] (0/4) Epoch 8, batch 800, datatang_loss[loss=0.1922, simple_loss=0.254, pruned_loss=0.06519, over 4943.00 frames.], tot_loss[loss=0.193, simple_loss=0.2607, pruned_loss=0.06265, over 968250.16 frames.], batch size: 55, aishell_tot_loss[loss=0.1879, simple_loss=0.2631, pruned_loss=0.05636, over 846298.48 frames.], datatang_tot_loss[loss=0.1967, simple_loss=0.2574, pruned_loss=0.06805, over 859995.43 frames.], batch size: 55, lr: 9.85e-04
+2022-06-18 15:42:37,767 INFO [train.py:874] (0/4) Epoch 8, batch 850, aishell_loss[loss=0.1838, simple_loss=0.257, pruned_loss=0.05533, over 4955.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2598, pruned_loss=0.06223, over 971823.03 frames.], batch size: 56, aishell_tot_loss[loss=0.1875, simple_loss=0.2626, pruned_loss=0.05616, over 863109.99 frames.], datatang_tot_loss[loss=0.1964, simple_loss=0.2569, pruned_loss=0.06792, over 874133.46 frames.], batch size: 56, lr: 9.84e-04
+2022-06-18 15:43:07,862 INFO [train.py:874] (0/4) Epoch 8, batch 900, datatang_loss[loss=0.1935, simple_loss=0.2399, pruned_loss=0.07354, over 4965.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2604, pruned_loss=0.06207, over 974877.19 frames.], batch size: 45, aishell_tot_loss[loss=0.1879, simple_loss=0.2632, pruned_loss=0.05631, over 878512.46 frames.], datatang_tot_loss[loss=0.1962, simple_loss=0.2569, pruned_loss=0.06773, over 886389.03 frames.], batch size: 45, lr: 9.84e-04
+2022-06-18 15:43:39,064 INFO [train.py:874] (0/4) Epoch 8, batch 950, datatang_loss[loss=0.1925, simple_loss=0.2608, pruned_loss=0.06206, over 4956.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2591, pruned_loss=0.06211, over 977334.28 frames.], batch size: 45, aishell_tot_loss[loss=0.1876, simple_loss=0.2627, pruned_loss=0.05625, over 886791.32 frames.], datatang_tot_loss[loss=0.1955, simple_loss=0.2564, pruned_loss=0.06735, over 902003.36 frames.], batch size: 45, lr: 9.83e-04
+2022-06-18 15:44:08,431 INFO [train.py:874] (0/4) Epoch 8, batch 1000, datatang_loss[loss=0.1943, simple_loss=0.2428, pruned_loss=0.07295, over 4868.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2585, pruned_loss=0.06162, over 978687.31 frames.], batch size: 39, aishell_tot_loss[loss=0.1872, simple_loss=0.2624, pruned_loss=0.05603, over 898624.02 frames.], datatang_tot_loss[loss=0.1951, simple_loss=0.2559, pruned_loss=0.06717, over 911273.34 frames.], batch size: 39, lr: 9.82e-04
+2022-06-18 15:44:08,433 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 15:44:24,592 INFO [train.py:914] (0/4) Epoch 8, validation: loss=0.1735, simple_loss=0.2544, pruned_loss=0.04634, over 1622729.00 frames.
+2022-06-18 15:44:54,415 INFO [train.py:874] (0/4) Epoch 8, batch 1050, datatang_loss[loss=0.1928, simple_loss=0.2552, pruned_loss=0.06516, over 4929.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2583, pruned_loss=0.06148, over 980194.71 frames.], batch size: 50, aishell_tot_loss[loss=0.1872, simple_loss=0.2623, pruned_loss=0.05602, over 908795.37 frames.], datatang_tot_loss[loss=0.1948, simple_loss=0.2555, pruned_loss=0.06703, over 920073.04 frames.], batch size: 50, lr: 9.81e-04
+2022-06-18 15:45:25,068 INFO [train.py:874] (0/4) Epoch 8, batch 1100, aishell_loss[loss=0.2012, simple_loss=0.2728, pruned_loss=0.06476, over 4942.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2592, pruned_loss=0.06161, over 981616.27 frames.], batch size: 58, aishell_tot_loss[loss=0.1876, simple_loss=0.263, pruned_loss=0.05613, over 919026.25 frames.], datatang_tot_loss[loss=0.195, simple_loss=0.2556, pruned_loss=0.0672, over 926975.93 frames.], batch size: 58, lr: 9.80e-04
+2022-06-18 15:45:54,910 INFO [train.py:874] (0/4) Epoch 8, batch 1150, datatang_loss[loss=0.1697, simple_loss=0.238, pruned_loss=0.05074, over 4906.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2599, pruned_loss=0.06235, over 982708.07 frames.], batch size: 64, aishell_tot_loss[loss=0.1876, simple_loss=0.2628, pruned_loss=0.05621, over 926478.98 frames.], datatang_tot_loss[loss=0.1963, simple_loss=0.2567, pruned_loss=0.06792, over 934441.18 frames.], batch size: 64, lr: 9.80e-04
+2022-06-18 15:46:24,543 INFO [train.py:874] (0/4) Epoch 8, batch 1200, aishell_loss[loss=0.1746, simple_loss=0.258, pruned_loss=0.04565, over 4832.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2606, pruned_loss=0.06284, over 983293.28 frames.], batch size: 29, aishell_tot_loss[loss=0.1884, simple_loss=0.2633, pruned_loss=0.05678, over 934095.45 frames.], datatang_tot_loss[loss=0.1966, simple_loss=0.2569, pruned_loss=0.06817, over 939863.71 frames.], batch size: 29, lr: 9.79e-04
+2022-06-18 15:46:55,064 INFO [train.py:874] (0/4) Epoch 8, batch 1250, datatang_loss[loss=0.1475, simple_loss=0.2055, pruned_loss=0.04478, over 4859.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2607, pruned_loss=0.06253, over 983986.59 frames.], batch size: 39, aishell_tot_loss[loss=0.1881, simple_loss=0.2633, pruned_loss=0.05647, over 940929.69 frames.], datatang_tot_loss[loss=0.1968, simple_loss=0.2569, pruned_loss=0.06836, over 944750.05 frames.], batch size: 39, lr: 9.78e-04
+2022-06-18 15:47:24,819 INFO [train.py:874] (0/4) Epoch 8, batch 1300, aishell_loss[loss=0.1663, simple_loss=0.2415, pruned_loss=0.04561, over 4810.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2598, pruned_loss=0.06182, over 984264.67 frames.], batch size: 26, aishell_tot_loss[loss=0.1879, simple_loss=0.2632, pruned_loss=0.05632, over 946170.05 frames.], datatang_tot_loss[loss=0.1959, simple_loss=0.2562, pruned_loss=0.0678, over 949520.76 frames.], batch size: 26, lr: 9.77e-04
+2022-06-18 15:47:55,254 INFO [train.py:874] (0/4) Epoch 8, batch 1350, datatang_loss[loss=0.1764, simple_loss=0.2337, pruned_loss=0.05954, over 4908.00 frames.], tot_loss[loss=0.191, simple_loss=0.2588, pruned_loss=0.06159, over 984747.28 frames.], batch size: 64, aishell_tot_loss[loss=0.1876, simple_loss=0.2628, pruned_loss=0.05617, over 950761.20 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.2555, pruned_loss=0.06762, over 954006.78 frames.], batch size: 64, lr: 9.76e-04
+2022-06-18 15:48:24,915 INFO [train.py:874] (0/4) Epoch 8, batch 1400, aishell_loss[loss=0.1837, simple_loss=0.2644, pruned_loss=0.05147, over 4863.00 frames.], tot_loss[loss=0.191, simple_loss=0.2593, pruned_loss=0.06138, over 984780.35 frames.], batch size: 35, aishell_tot_loss[loss=0.1876, simple_loss=0.2628, pruned_loss=0.05615, over 955259.40 frames.], datatang_tot_loss[loss=0.1955, simple_loss=0.2557, pruned_loss=0.0676, over 957235.99 frames.], batch size: 35, lr: 9.76e-04
+2022-06-18 15:48:55,375 INFO [train.py:874] (0/4) Epoch 8, batch 1450, aishell_loss[loss=0.1839, simple_loss=0.2641, pruned_loss=0.0519, over 4936.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2586, pruned_loss=0.06114, over 985106.44 frames.], batch size: 45, aishell_tot_loss[loss=0.1879, simple_loss=0.2632, pruned_loss=0.05629, over 958845.10 frames.], datatang_tot_loss[loss=0.1944, simple_loss=0.2547, pruned_loss=0.06707, over 960724.20 frames.], batch size: 45, lr: 9.75e-04
+2022-06-18 15:49:25,885 INFO [train.py:874] (0/4) Epoch 8, batch 1500, datatang_loss[loss=0.2423, simple_loss=0.2963, pruned_loss=0.09413, over 4919.00 frames.], tot_loss[loss=0.1895, simple_loss=0.258, pruned_loss=0.06049, over 984977.61 frames.], batch size: 94, aishell_tot_loss[loss=0.1865, simple_loss=0.2619, pruned_loss=0.05555, over 961821.31 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.2551, pruned_loss=0.067, over 963578.62 frames.], batch size: 94, lr: 9.74e-04
+2022-06-18 15:49:55,559 INFO [train.py:874] (0/4) Epoch 8, batch 1550, datatang_loss[loss=0.2029, simple_loss=0.2489, pruned_loss=0.07847, over 4959.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2559, pruned_loss=0.05989, over 985352.39 frames.], batch size: 55, aishell_tot_loss[loss=0.1855, simple_loss=0.261, pruned_loss=0.05501, over 963914.07 frames.], datatang_tot_loss[loss=0.1933, simple_loss=0.2539, pruned_loss=0.06629, over 967051.14 frames.], batch size: 55, lr: 9.73e-04
+2022-06-18 15:50:25,888 INFO [train.py:874] (0/4) Epoch 8, batch 1600, datatang_loss[loss=0.1761, simple_loss=0.2424, pruned_loss=0.05492, over 4969.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2575, pruned_loss=0.06015, over 985457.33 frames.], batch size: 37, aishell_tot_loss[loss=0.1863, simple_loss=0.2618, pruned_loss=0.05537, over 967119.84 frames.], datatang_tot_loss[loss=0.1935, simple_loss=0.2542, pruned_loss=0.06638, over 968711.24 frames.], batch size: 37, lr: 9.73e-04
+2022-06-18 15:50:55,420 INFO [train.py:874] (0/4) Epoch 8, batch 1650, aishell_loss[loss=0.182, simple_loss=0.2656, pruned_loss=0.04923, over 4969.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2576, pruned_loss=0.06032, over 984968.75 frames.], batch size: 56, aishell_tot_loss[loss=0.1865, simple_loss=0.2621, pruned_loss=0.05548, over 969093.33 frames.], datatang_tot_loss[loss=0.1933, simple_loss=0.2539, pruned_loss=0.06633, over 970355.88 frames.], batch size: 56, lr: 9.72e-04
+2022-06-18 15:51:24,655 INFO [train.py:874] (0/4) Epoch 8, batch 1700, datatang_loss[loss=0.1756, simple_loss=0.2417, pruned_loss=0.05476, over 4976.00 frames.], tot_loss[loss=0.188, simple_loss=0.2566, pruned_loss=0.0597, over 984972.76 frames.], batch size: 60, aishell_tot_loss[loss=0.1858, simple_loss=0.2613, pruned_loss=0.05515, over 970878.74 frames.], datatang_tot_loss[loss=0.1927, simple_loss=0.2534, pruned_loss=0.06605, over 972187.56 frames.], batch size: 60, lr: 9.71e-04
+2022-06-18 15:51:53,607 INFO [train.py:874] (0/4) Epoch 8, batch 1750, aishell_loss[loss=0.1916, simple_loss=0.2711, pruned_loss=0.05602, over 4902.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2576, pruned_loss=0.05909, over 985083.38 frames.], batch size: 34, aishell_tot_loss[loss=0.1858, simple_loss=0.2618, pruned_loss=0.05491, over 973038.99 frames.], datatang_tot_loss[loss=0.1924, simple_loss=0.2534, pruned_loss=0.0657, over 973335.78 frames.], batch size: 34, lr: 9.70e-04
+2022-06-18 15:52:24,174 INFO [train.py:874] (0/4) Epoch 8, batch 1800, datatang_loss[loss=0.2012, simple_loss=0.2593, pruned_loss=0.0715, over 4945.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2578, pruned_loss=0.05967, over 985116.19 frames.], batch size: 50, aishell_tot_loss[loss=0.1862, simple_loss=0.2624, pruned_loss=0.05505, over 974003.96 frames.], datatang_tot_loss[loss=0.1922, simple_loss=0.2535, pruned_loss=0.06551, over 975173.12 frames.], batch size: 50, lr: 9.69e-04
+2022-06-18 15:52:53,916 INFO [train.py:874] (0/4) Epoch 8, batch 1850, datatang_loss[loss=0.1987, simple_loss=0.2554, pruned_loss=0.07103, over 4947.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2585, pruned_loss=0.06004, over 985033.50 frames.], batch size: 67, aishell_tot_loss[loss=0.1867, simple_loss=0.2627, pruned_loss=0.05535, over 975202.55 frames.], datatang_tot_loss[loss=0.1925, simple_loss=0.2537, pruned_loss=0.06561, over 976385.36 frames.], batch size: 67, lr: 9.69e-04
+2022-06-18 15:53:23,133 INFO [train.py:874] (0/4) Epoch 8, batch 1900, aishell_loss[loss=0.185, simple_loss=0.2649, pruned_loss=0.05256, over 4970.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2581, pruned_loss=0.06087, over 985762.61 frames.], batch size: 39, aishell_tot_loss[loss=0.187, simple_loss=0.2629, pruned_loss=0.05554, over 976425.72 frames.], datatang_tot_loss[loss=0.1926, simple_loss=0.2534, pruned_loss=0.06592, over 978072.31 frames.], batch size: 39, lr: 9.68e-04
+2022-06-18 15:53:54,914 INFO [train.py:874] (0/4) Epoch 8, batch 1950, aishell_loss[loss=0.1744, simple_loss=0.2595, pruned_loss=0.0447, over 4974.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2582, pruned_loss=0.06078, over 986035.71 frames.], batch size: 61, aishell_tot_loss[loss=0.1868, simple_loss=0.2628, pruned_loss=0.05539, over 977499.40 frames.], datatang_tot_loss[loss=0.1928, simple_loss=0.2539, pruned_loss=0.06584, over 979255.70 frames.], batch size: 61, lr: 9.67e-04
+2022-06-18 15:54:24,746 INFO [train.py:874] (0/4) Epoch 8, batch 2000, datatang_loss[loss=0.1995, simple_loss=0.2675, pruned_loss=0.06569, over 4942.00 frames.], tot_loss[loss=0.192, simple_loss=0.2604, pruned_loss=0.0618, over 986371.36 frames.], batch size: 69, aishell_tot_loss[loss=0.1873, simple_loss=0.2635, pruned_loss=0.05558, over 978623.88 frames.], datatang_tot_loss[loss=0.1945, simple_loss=0.2554, pruned_loss=0.0668, over 980284.77 frames.], batch size: 69, lr: 9.66e-04
+2022-06-18 15:54:24,749 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 15:54:40,502 INFO [train.py:914] (0/4) Epoch 8, validation: loss=0.1711, simple_loss=0.254, pruned_loss=0.04413, over 1622729.00 frames.
+2022-06-18 15:55:10,325 INFO [train.py:874] (0/4) Epoch 8, batch 2050, aishell_loss[loss=0.2068, simple_loss=0.2765, pruned_loss=0.06857, over 4953.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2591, pruned_loss=0.0606, over 986371.36 frames.], batch size: 32, aishell_tot_loss[loss=0.1864, simple_loss=0.2626, pruned_loss=0.05505, over 979428.13 frames.], datatang_tot_loss[loss=0.1938, simple_loss=0.2551, pruned_loss=0.0663, over 981128.20 frames.], batch size: 32, lr: 9.66e-04
+2022-06-18 15:55:40,579 INFO [train.py:874] (0/4) Epoch 8, batch 2100, datatang_loss[loss=0.2293, simple_loss=0.2892, pruned_loss=0.08469, over 4920.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2587, pruned_loss=0.06037, over 986519.37 frames.], batch size: 98, aishell_tot_loss[loss=0.1866, simple_loss=0.2629, pruned_loss=0.05509, over 980121.95 frames.], datatang_tot_loss[loss=0.1931, simple_loss=0.2545, pruned_loss=0.06579, over 981991.58 frames.], batch size: 98, lr: 9.65e-04
+2022-06-18 15:56:10,905 INFO [train.py:874] (0/4) Epoch 8, batch 2150, datatang_loss[loss=0.1888, simple_loss=0.2542, pruned_loss=0.0617, over 4939.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2591, pruned_loss=0.0607, over 986277.52 frames.], batch size: 69, aishell_tot_loss[loss=0.1867, simple_loss=0.263, pruned_loss=0.05516, over 980845.68 frames.], datatang_tot_loss[loss=0.1935, simple_loss=0.255, pruned_loss=0.06602, over 982299.83 frames.], batch size: 69, lr: 9.64e-04
+2022-06-18 15:56:40,019 INFO [train.py:874] (0/4) Epoch 8, batch 2200, datatang_loss[loss=0.1573, simple_loss=0.2198, pruned_loss=0.04738, over 4931.00 frames.], tot_loss[loss=0.191, simple_loss=0.2598, pruned_loss=0.06111, over 986273.02 frames.], batch size: 62, aishell_tot_loss[loss=0.1874, simple_loss=0.2637, pruned_loss=0.05558, over 981357.79 frames.], datatang_tot_loss[loss=0.1936, simple_loss=0.2551, pruned_loss=0.06603, over 982890.23 frames.], batch size: 62, lr: 9.63e-04
+2022-06-18 15:57:10,684 INFO [train.py:874] (0/4) Epoch 8, batch 2250, aishell_loss[loss=0.1748, simple_loss=0.2491, pruned_loss=0.05023, over 4859.00 frames.], tot_loss[loss=0.19, simple_loss=0.259, pruned_loss=0.06052, over 985808.93 frames.], batch size: 36, aishell_tot_loss[loss=0.1874, simple_loss=0.2635, pruned_loss=0.05564, over 981680.98 frames.], datatang_tot_loss[loss=0.1927, simple_loss=0.2545, pruned_loss=0.06551, over 983061.21 frames.], batch size: 36, lr: 9.63e-04
+2022-06-18 15:57:40,689 INFO [train.py:874] (0/4) Epoch 8, batch 2300, datatang_loss[loss=0.2128, simple_loss=0.2714, pruned_loss=0.07706, over 4935.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2586, pruned_loss=0.05999, over 985689.39 frames.], batch size: 79, aishell_tot_loss[loss=0.1868, simple_loss=0.2631, pruned_loss=0.05526, over 982023.30 frames.], datatang_tot_loss[loss=0.1926, simple_loss=0.2543, pruned_loss=0.0655, over 983416.30 frames.], batch size: 79, lr: 9.62e-04
+2022-06-18 15:58:10,147 INFO [train.py:874] (0/4) Epoch 8, batch 2350, aishell_loss[loss=0.1763, simple_loss=0.2594, pruned_loss=0.04657, over 4939.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2578, pruned_loss=0.05906, over 985695.84 frames.], batch size: 45, aishell_tot_loss[loss=0.1859, simple_loss=0.2622, pruned_loss=0.0548, over 982484.05 frames.], datatang_tot_loss[loss=0.1923, simple_loss=0.254, pruned_loss=0.0653, over 983717.80 frames.], batch size: 45, lr: 9.61e-04
+2022-06-18 15:58:39,603 INFO [train.py:874] (0/4) Epoch 8, batch 2400, datatang_loss[loss=0.1618, simple_loss=0.229, pruned_loss=0.04732, over 4975.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2593, pruned_loss=0.05989, over 986031.73 frames.], batch size: 65, aishell_tot_loss[loss=0.186, simple_loss=0.2625, pruned_loss=0.0547, over 983189.59 frames.], datatang_tot_loss[loss=0.1938, simple_loss=0.2551, pruned_loss=0.06625, over 984000.42 frames.], batch size: 65, lr: 9.60e-04
+2022-06-18 15:59:09,432 INFO [train.py:874] (0/4) Epoch 8, batch 2450, aishell_loss[loss=0.1805, simple_loss=0.2594, pruned_loss=0.05077, over 4915.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2606, pruned_loss=0.06035, over 985614.70 frames.], batch size: 46, aishell_tot_loss[loss=0.1872, simple_loss=0.2636, pruned_loss=0.05538, over 983341.63 frames.], datatang_tot_loss[loss=0.194, simple_loss=0.2553, pruned_loss=0.06632, over 983988.52 frames.], batch size: 46, lr: 9.60e-04
+2022-06-18 15:59:39,451 INFO [train.py:874] (0/4) Epoch 8, batch 2500, aishell_loss[loss=0.2006, simple_loss=0.2691, pruned_loss=0.0661, over 4924.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2595, pruned_loss=0.05991, over 985622.90 frames.], batch size: 33, aishell_tot_loss[loss=0.1862, simple_loss=0.2626, pruned_loss=0.0549, over 983352.89 frames.], datatang_tot_loss[loss=0.1939, simple_loss=0.2554, pruned_loss=0.06618, over 984443.82 frames.], batch size: 33, lr: 9.59e-04
+2022-06-18 16:00:09,549 INFO [train.py:874] (0/4) Epoch 8, batch 2550, aishell_loss[loss=0.2121, simple_loss=0.2836, pruned_loss=0.07032, over 4891.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2596, pruned_loss=0.06059, over 985663.63 frames.], batch size: 34, aishell_tot_loss[loss=0.1863, simple_loss=0.2628, pruned_loss=0.0549, over 983490.93 frames.], datatang_tot_loss[loss=0.1942, simple_loss=0.2558, pruned_loss=0.06627, over 984706.79 frames.], batch size: 34, lr: 9.58e-04
+2022-06-18 16:00:41,123 INFO [train.py:874] (0/4) Epoch 8, batch 2600, datatang_loss[loss=0.1891, simple_loss=0.252, pruned_loss=0.06313, over 4919.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2574, pruned_loss=0.05917, over 985541.15 frames.], batch size: 64, aishell_tot_loss[loss=0.1855, simple_loss=0.262, pruned_loss=0.05447, over 983860.93 frames.], datatang_tot_loss[loss=0.1922, simple_loss=0.2544, pruned_loss=0.065, over 984560.39 frames.], batch size: 64, lr: 9.57e-04
+2022-06-18 16:01:09,773 INFO [train.py:874] (0/4) Epoch 8, batch 2650, aishell_loss[loss=0.1594, simple_loss=0.2402, pruned_loss=0.03933, over 4846.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2572, pruned_loss=0.05917, over 985379.13 frames.], batch size: 28, aishell_tot_loss[loss=0.1858, simple_loss=0.2623, pruned_loss=0.0546, over 983957.08 frames.], datatang_tot_loss[loss=0.1915, simple_loss=0.2536, pruned_loss=0.06469, over 984602.66 frames.], batch size: 28, lr: 9.57e-04
+2022-06-18 16:01:39,858 INFO [train.py:874] (0/4) Epoch 8, batch 2700, datatang_loss[loss=0.194, simple_loss=0.2573, pruned_loss=0.06536, over 4937.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2571, pruned_loss=0.05934, over 985344.36 frames.], batch size: 62, aishell_tot_loss[loss=0.1851, simple_loss=0.2616, pruned_loss=0.05428, over 984154.68 frames.], datatang_tot_loss[loss=0.1922, simple_loss=0.2539, pruned_loss=0.06523, over 984629.22 frames.], batch size: 62, lr: 9.56e-04
+2022-06-18 16:02:09,342 INFO [train.py:874] (0/4) Epoch 8, batch 2750, datatang_loss[loss=0.2037, simple_loss=0.2656, pruned_loss=0.07089, over 4928.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2568, pruned_loss=0.05982, over 985225.56 frames.], batch size: 83, aishell_tot_loss[loss=0.1853, simple_loss=0.2613, pruned_loss=0.05465, over 984191.03 frames.], datatang_tot_loss[loss=0.1923, simple_loss=0.2537, pruned_loss=0.06538, over 984679.65 frames.], batch size: 83, lr: 9.55e-04
+2022-06-18 16:02:39,819 INFO [train.py:874] (0/4) Epoch 8, batch 2800, datatang_loss[loss=0.1314, simple_loss=0.2052, pruned_loss=0.02883, over 4897.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2558, pruned_loss=0.05904, over 985675.93 frames.], batch size: 52, aishell_tot_loss[loss=0.1838, simple_loss=0.2602, pruned_loss=0.05371, over 984513.79 frames.], datatang_tot_loss[loss=0.1923, simple_loss=0.2536, pruned_loss=0.06547, over 985020.78 frames.], batch size: 52, lr: 9.54e-04
+2022-06-18 16:03:10,494 INFO [train.py:874] (0/4) Epoch 8, batch 2850, aishell_loss[loss=0.1506, simple_loss=0.2267, pruned_loss=0.03723, over 4979.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2548, pruned_loss=0.05817, over 985926.23 frames.], batch size: 27, aishell_tot_loss[loss=0.1829, simple_loss=0.2595, pruned_loss=0.05316, over 984868.30 frames.], datatang_tot_loss[loss=0.1916, simple_loss=0.2531, pruned_loss=0.06504, over 985129.28 frames.], batch size: 27, lr: 9.54e-04
+2022-06-18 16:03:39,757 INFO [train.py:874] (0/4) Epoch 8, batch 2900, aishell_loss[loss=0.2159, simple_loss=0.2874, pruned_loss=0.07218, over 4896.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2551, pruned_loss=0.05888, over 985418.50 frames.], batch size: 42, aishell_tot_loss[loss=0.1835, simple_loss=0.2596, pruned_loss=0.05366, over 984562.99 frames.], datatang_tot_loss[loss=0.1914, simple_loss=0.253, pruned_loss=0.06493, over 985113.79 frames.], batch size: 42, lr: 9.53e-04
+2022-06-18 16:04:10,431 INFO [train.py:874] (0/4) Epoch 8, batch 2950, aishell_loss[loss=0.2436, simple_loss=0.3103, pruned_loss=0.08844, over 4930.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2556, pruned_loss=0.05885, over 985953.12 frames.], batch size: 78, aishell_tot_loss[loss=0.1837, simple_loss=0.2599, pruned_loss=0.05378, over 984964.80 frames.], datatang_tot_loss[loss=0.1912, simple_loss=0.2528, pruned_loss=0.06479, over 985423.54 frames.], batch size: 78, lr: 9.52e-04
+2022-06-18 16:04:40,903 INFO [train.py:874] (0/4) Epoch 8, batch 3000, aishell_loss[loss=0.1743, simple_loss=0.2537, pruned_loss=0.04748, over 4955.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2554, pruned_loss=0.05879, over 985854.31 frames.], batch size: 31, aishell_tot_loss[loss=0.1833, simple_loss=0.2595, pruned_loss=0.05351, over 984946.40 frames.], datatang_tot_loss[loss=0.1913, simple_loss=0.2529, pruned_loss=0.06485, over 985524.01 frames.], batch size: 31, lr: 9.52e-04
+2022-06-18 16:04:40,906 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 16:04:57,747 INFO [train.py:914] (0/4) Epoch 8, validation: loss=0.1712, simple_loss=0.2536, pruned_loss=0.04441, over 1622729.00 frames.
+2022-06-18 16:05:24,138 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-32000.pt
+2022-06-18 16:05:32,037 INFO [train.py:874] (0/4) Epoch 8, batch 3050, datatang_loss[loss=0.1912, simple_loss=0.2458, pruned_loss=0.06835, over 4965.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2559, pruned_loss=0.05881, over 985871.30 frames.], batch size: 55, aishell_tot_loss[loss=0.1835, simple_loss=0.2599, pruned_loss=0.05359, over 985045.64 frames.], datatang_tot_loss[loss=0.191, simple_loss=0.2529, pruned_loss=0.06457, over 985578.57 frames.], batch size: 55, lr: 9.51e-04
+2022-06-18 16:06:02,992 INFO [train.py:874] (0/4) Epoch 8, batch 3100, aishell_loss[loss=0.1493, simple_loss=0.2305, pruned_loss=0.03402, over 4986.00 frames.], tot_loss[loss=0.1866, simple_loss=0.256, pruned_loss=0.05859, over 986110.26 frames.], batch size: 30, aishell_tot_loss[loss=0.1834, simple_loss=0.26, pruned_loss=0.05337, over 985289.82 frames.], datatang_tot_loss[loss=0.1908, simple_loss=0.2529, pruned_loss=0.06435, over 985706.02 frames.], batch size: 30, lr: 9.50e-04
+2022-06-18 16:06:31,628 INFO [train.py:874] (0/4) Epoch 8, batch 3150, datatang_loss[loss=0.1641, simple_loss=0.2364, pruned_loss=0.04594, over 4927.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2558, pruned_loss=0.0586, over 985749.48 frames.], batch size: 73, aishell_tot_loss[loss=0.1834, simple_loss=0.2599, pruned_loss=0.05344, over 985001.44 frames.], datatang_tot_loss[loss=0.1906, simple_loss=0.2527, pruned_loss=0.06427, over 985746.76 frames.], batch size: 73, lr: 9.49e-04
+2022-06-18 16:07:02,060 INFO [train.py:874] (0/4) Epoch 8, batch 3200, datatang_loss[loss=0.1884, simple_loss=0.2543, pruned_loss=0.06128, over 4955.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2564, pruned_loss=0.05917, over 986096.48 frames.], batch size: 86, aishell_tot_loss[loss=0.1833, simple_loss=0.2599, pruned_loss=0.05339, over 985226.25 frames.], datatang_tot_loss[loss=0.1913, simple_loss=0.2534, pruned_loss=0.06465, over 985966.10 frames.], batch size: 86, lr: 9.49e-04
+2022-06-18 16:07:32,564 INFO [train.py:874] (0/4) Epoch 8, batch 3250, datatang_loss[loss=0.1817, simple_loss=0.2437, pruned_loss=0.05982, over 4916.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2567, pruned_loss=0.05913, over 985954.59 frames.], batch size: 64, aishell_tot_loss[loss=0.1841, simple_loss=0.2608, pruned_loss=0.05376, over 985294.31 frames.], datatang_tot_loss[loss=0.1907, simple_loss=0.2526, pruned_loss=0.0644, over 985878.64 frames.], batch size: 64, lr: 9.48e-04
+2022-06-18 16:08:02,003 INFO [train.py:874] (0/4) Epoch 8, batch 3300, datatang_loss[loss=0.1804, simple_loss=0.2444, pruned_loss=0.05817, over 4946.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2566, pruned_loss=0.05882, over 985849.22 frames.], batch size: 62, aishell_tot_loss[loss=0.184, simple_loss=0.2607, pruned_loss=0.05364, over 985142.67 frames.], datatang_tot_loss[loss=0.1905, simple_loss=0.2526, pruned_loss=0.06415, over 986004.25 frames.], batch size: 62, lr: 9.47e-04
+2022-06-18 16:08:32,969 INFO [train.py:874] (0/4) Epoch 8, batch 3350, aishell_loss[loss=0.1771, simple_loss=0.2562, pruned_loss=0.04904, over 4868.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2566, pruned_loss=0.05965, over 985592.53 frames.], batch size: 35, aishell_tot_loss[loss=0.1847, simple_loss=0.2612, pruned_loss=0.05417, over 984935.92 frames.], datatang_tot_loss[loss=0.1904, simple_loss=0.2523, pruned_loss=0.06425, over 985972.15 frames.], batch size: 35, lr: 9.46e-04
+2022-06-18 16:09:02,966 INFO [train.py:874] (0/4) Epoch 8, batch 3400, aishell_loss[loss=0.1709, simple_loss=0.2476, pruned_loss=0.04708, over 4969.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2569, pruned_loss=0.05975, over 985646.80 frames.], batch size: 61, aishell_tot_loss[loss=0.1841, simple_loss=0.2607, pruned_loss=0.0538, over 985019.94 frames.], datatang_tot_loss[loss=0.1914, simple_loss=0.2532, pruned_loss=0.06477, over 985955.63 frames.], batch size: 61, lr: 9.46e-04
+2022-06-18 16:09:31,651 INFO [train.py:874] (0/4) Epoch 8, batch 3450, datatang_loss[loss=0.1869, simple_loss=0.2531, pruned_loss=0.06034, over 4945.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2561, pruned_loss=0.05908, over 985712.76 frames.], batch size: 69, aishell_tot_loss[loss=0.1831, simple_loss=0.2598, pruned_loss=0.05323, over 985133.83 frames.], datatang_tot_loss[loss=0.1914, simple_loss=0.2531, pruned_loss=0.06484, over 985964.08 frames.], batch size: 69, lr: 9.45e-04
+2022-06-18 16:10:01,615 INFO [train.py:874] (0/4) Epoch 8, batch 3500, datatang_loss[loss=0.1955, simple_loss=0.2524, pruned_loss=0.06932, over 4815.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2569, pruned_loss=0.05921, over 985533.70 frames.], batch size: 24, aishell_tot_loss[loss=0.1842, simple_loss=0.2608, pruned_loss=0.05377, over 985393.09 frames.], datatang_tot_loss[loss=0.191, simple_loss=0.2528, pruned_loss=0.06461, over 985568.47 frames.], batch size: 24, lr: 9.44e-04
+2022-06-18 16:10:31,542 INFO [train.py:874] (0/4) Epoch 8, batch 3550, datatang_loss[loss=0.1591, simple_loss=0.2264, pruned_loss=0.04592, over 4917.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2573, pruned_loss=0.05909, over 985662.16 frames.], batch size: 71, aishell_tot_loss[loss=0.1849, simple_loss=0.2617, pruned_loss=0.054, over 985450.98 frames.], datatang_tot_loss[loss=0.1904, simple_loss=0.2524, pruned_loss=0.0642, over 985659.12 frames.], batch size: 71, lr: 9.44e-04
+2022-06-18 16:11:02,121 INFO [train.py:874] (0/4) Epoch 8, batch 3600, datatang_loss[loss=0.202, simple_loss=0.2528, pruned_loss=0.07567, over 4923.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2576, pruned_loss=0.05955, over 986114.34 frames.], batch size: 57, aishell_tot_loss[loss=0.185, simple_loss=0.262, pruned_loss=0.05401, over 985688.26 frames.], datatang_tot_loss[loss=0.1908, simple_loss=0.2527, pruned_loss=0.06446, over 985908.46 frames.], batch size: 57, lr: 9.43e-04
+2022-06-18 16:11:32,844 INFO [train.py:874] (0/4) Epoch 8, batch 3650, datatang_loss[loss=0.2128, simple_loss=0.2589, pruned_loss=0.08335, over 4913.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2575, pruned_loss=0.05944, over 986140.68 frames.], batch size: 77, aishell_tot_loss[loss=0.1847, simple_loss=0.2619, pruned_loss=0.05369, over 985748.86 frames.], datatang_tot_loss[loss=0.1911, simple_loss=0.2527, pruned_loss=0.06476, over 985953.32 frames.], batch size: 77, lr: 9.42e-04
+2022-06-18 16:12:03,786 INFO [train.py:874] (0/4) Epoch 8, batch 3700, aishell_loss[loss=0.2096, simple_loss=0.2936, pruned_loss=0.06276, over 4853.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2571, pruned_loss=0.05931, over 985836.36 frames.], batch size: 36, aishell_tot_loss[loss=0.1846, simple_loss=0.2618, pruned_loss=0.05372, over 985701.74 frames.], datatang_tot_loss[loss=0.1907, simple_loss=0.2527, pruned_loss=0.06436, over 985749.91 frames.], batch size: 36, lr: 9.42e-04
+2022-06-18 16:12:32,578 INFO [train.py:874] (0/4) Epoch 8, batch 3750, datatang_loss[loss=0.203, simple_loss=0.261, pruned_loss=0.07249, over 4968.00 frames.], tot_loss[loss=0.188, simple_loss=0.2578, pruned_loss=0.05914, over 985862.40 frames.], batch size: 60, aishell_tot_loss[loss=0.1848, simple_loss=0.2622, pruned_loss=0.05371, over 985764.22 frames.], datatang_tot_loss[loss=0.1909, simple_loss=0.2529, pruned_loss=0.0644, over 985742.16 frames.], batch size: 60, lr: 9.41e-04
+2022-06-18 16:13:02,543 INFO [train.py:874] (0/4) Epoch 8, batch 3800, datatang_loss[loss=0.1723, simple_loss=0.2406, pruned_loss=0.052, over 4955.00 frames.], tot_loss[loss=0.1886, simple_loss=0.258, pruned_loss=0.0596, over 985564.69 frames.], batch size: 67, aishell_tot_loss[loss=0.1859, simple_loss=0.2628, pruned_loss=0.05448, over 985369.27 frames.], datatang_tot_loss[loss=0.1904, simple_loss=0.2526, pruned_loss=0.0641, over 985845.78 frames.], batch size: 67, lr: 9.40e-04
+2022-06-18 16:13:31,932 INFO [train.py:874] (0/4) Epoch 8, batch 3850, aishell_loss[loss=0.2173, simple_loss=0.2906, pruned_loss=0.07203, over 4981.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2583, pruned_loss=0.05959, over 985920.85 frames.], batch size: 38, aishell_tot_loss[loss=0.1859, simple_loss=0.2629, pruned_loss=0.05452, over 985662.36 frames.], datatang_tot_loss[loss=0.1906, simple_loss=0.2529, pruned_loss=0.0641, over 985913.90 frames.], batch size: 38, lr: 9.39e-04
+2022-06-18 16:14:00,682 INFO [train.py:874] (0/4) Epoch 8, batch 3900, aishell_loss[loss=0.1912, simple_loss=0.2709, pruned_loss=0.05575, over 4933.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2581, pruned_loss=0.05956, over 985602.96 frames.], batch size: 49, aishell_tot_loss[loss=0.1849, simple_loss=0.2618, pruned_loss=0.05398, over 985395.00 frames.], datatang_tot_loss[loss=0.1918, simple_loss=0.2537, pruned_loss=0.06492, over 985900.80 frames.], batch size: 49, lr: 9.39e-04
+2022-06-18 16:14:30,201 INFO [train.py:874] (0/4) Epoch 8, batch 3950, datatang_loss[loss=0.1661, simple_loss=0.2351, pruned_loss=0.04857, over 4927.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2558, pruned_loss=0.05882, over 985574.35 frames.], batch size: 75, aishell_tot_loss[loss=0.1842, simple_loss=0.2609, pruned_loss=0.05372, over 985384.59 frames.], datatang_tot_loss[loss=0.1903, simple_loss=0.2524, pruned_loss=0.06412, over 985870.07 frames.], batch size: 75, lr: 9.38e-04
+2022-06-18 16:14:59,691 INFO [train.py:874] (0/4) Epoch 8, batch 4000, datatang_loss[loss=0.1684, simple_loss=0.2294, pruned_loss=0.05365, over 4947.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2553, pruned_loss=0.05853, over 985667.26 frames.], batch size: 45, aishell_tot_loss[loss=0.1839, simple_loss=0.2606, pruned_loss=0.05354, over 985433.30 frames.], datatang_tot_loss[loss=0.1899, simple_loss=0.252, pruned_loss=0.06387, over 985897.65 frames.], batch size: 45, lr: 9.37e-04
+2022-06-18 16:14:59,694 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 16:15:16,494 INFO [train.py:914] (0/4) Epoch 8, validation: loss=0.1678, simple_loss=0.2518, pruned_loss=0.0419, over 1622729.00 frames.
+2022-06-18 16:15:46,524 INFO [train.py:874] (0/4) Epoch 8, batch 4050, datatang_loss[loss=0.1625, simple_loss=0.2366, pruned_loss=0.04417, over 4898.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2552, pruned_loss=0.05873, over 985369.73 frames.], batch size: 64, aishell_tot_loss[loss=0.1847, simple_loss=0.261, pruned_loss=0.05414, over 985175.74 frames.], datatang_tot_loss[loss=0.1891, simple_loss=0.2515, pruned_loss=0.06337, over 985841.56 frames.], batch size: 64, lr: 9.37e-04
+2022-06-18 16:16:15,499 INFO [train.py:874] (0/4) Epoch 8, batch 4100, datatang_loss[loss=0.1796, simple_loss=0.2375, pruned_loss=0.06086, over 4908.00 frames.], tot_loss[loss=0.187, simple_loss=0.2559, pruned_loss=0.05903, over 985154.64 frames.], batch size: 64, aishell_tot_loss[loss=0.1852, simple_loss=0.2616, pruned_loss=0.05436, over 984917.40 frames.], datatang_tot_loss[loss=0.189, simple_loss=0.2515, pruned_loss=0.06328, over 985832.74 frames.], batch size: 64, lr: 9.36e-04
+2022-06-18 16:16:43,404 INFO [train.py:874] (0/4) Epoch 8, batch 4150, aishell_loss[loss=0.1859, simple_loss=0.2743, pruned_loss=0.04871, over 4906.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2551, pruned_loss=0.0587, over 985083.73 frames.], batch size: 41, aishell_tot_loss[loss=0.1851, simple_loss=0.2614, pruned_loss=0.05437, over 984734.02 frames.], datatang_tot_loss[loss=0.1883, simple_loss=0.2507, pruned_loss=0.06297, over 985891.93 frames.], batch size: 41, lr: 9.35e-04
+2022-06-18 16:17:02,171 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-8.pt
+2022-06-18 16:18:01,482 INFO [train.py:874] (0/4) Epoch 9, batch 50, datatang_loss[loss=0.1596, simple_loss=0.2291, pruned_loss=0.045, over 4960.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2578, pruned_loss=0.05749, over 218566.46 frames.], batch size: 45, aishell_tot_loss[loss=0.1859, simple_loss=0.2637, pruned_loss=0.0541, over 133402.03 frames.], datatang_tot_loss[loss=0.188, simple_loss=0.2507, pruned_loss=0.06261, over 98489.05 frames.], batch size: 45, lr: 8.97e-04
+2022-06-18 16:18:32,026 INFO [train.py:874] (0/4) Epoch 9, batch 100, datatang_loss[loss=0.1372, simple_loss=0.2047, pruned_loss=0.03483, over 4921.00 frames.], tot_loss[loss=0.182, simple_loss=0.2533, pruned_loss=0.05537, over 388894.27 frames.], batch size: 42, aishell_tot_loss[loss=0.1862, simple_loss=0.2641, pruned_loss=0.05414, over 218673.43 frames.], datatang_tot_loss[loss=0.179, simple_loss=0.244, pruned_loss=0.05705, over 218684.98 frames.], batch size: 42, lr: 8.96e-04
+2022-06-18 16:19:01,279 INFO [train.py:874] (0/4) Epoch 9, batch 150, datatang_loss[loss=0.1881, simple_loss=0.2481, pruned_loss=0.06404, over 4908.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2498, pruned_loss=0.05438, over 521037.01 frames.], batch size: 64, aishell_tot_loss[loss=0.1839, simple_loss=0.2614, pruned_loss=0.05316, over 295014.47 frames.], datatang_tot_loss[loss=0.1765, simple_loss=0.2411, pruned_loss=0.05593, over 322590.96 frames.], batch size: 64, lr: 8.96e-04
+2022-06-18 16:19:31,626 INFO [train.py:874] (0/4) Epoch 9, batch 200, datatang_loss[loss=0.1688, simple_loss=0.2399, pruned_loss=0.04881, over 4944.00 frames.], tot_loss[loss=0.1799, simple_loss=0.251, pruned_loss=0.05439, over 623777.45 frames.], batch size: 94, aishell_tot_loss[loss=0.1831, simple_loss=0.2605, pruned_loss=0.05291, over 396980.16 frames.], datatang_tot_loss[loss=0.1772, simple_loss=0.2416, pruned_loss=0.0564, over 379933.65 frames.], batch size: 94, lr: 8.95e-04
+2022-06-18 16:20:02,108 INFO [train.py:874] (0/4) Epoch 9, batch 250, datatang_loss[loss=0.1838, simple_loss=0.2435, pruned_loss=0.06208, over 4917.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2508, pruned_loss=0.05383, over 704180.39 frames.], batch size: 64, aishell_tot_loss[loss=0.1825, simple_loss=0.2602, pruned_loss=0.05244, over 466456.58 frames.], datatang_tot_loss[loss=0.1766, simple_loss=0.2416, pruned_loss=0.05581, over 451309.84 frames.], batch size: 64, lr: 8.94e-04
+2022-06-18 16:20:30,901 INFO [train.py:874] (0/4) Epoch 9, batch 300, datatang_loss[loss=0.1674, simple_loss=0.2268, pruned_loss=0.05396, over 4983.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2516, pruned_loss=0.05463, over 766384.54 frames.], batch size: 37, aishell_tot_loss[loss=0.1823, simple_loss=0.2598, pruned_loss=0.05238, over 531827.88 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2428, pruned_loss=0.05707, over 509617.90 frames.], batch size: 37, lr: 8.94e-04
+2022-06-18 16:21:02,268 INFO [train.py:874] (0/4) Epoch 9, batch 350, aishell_loss[loss=0.1907, simple_loss=0.2747, pruned_loss=0.05337, over 4936.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2527, pruned_loss=0.05533, over 815227.55 frames.], batch size: 45, aishell_tot_loss[loss=0.1834, simple_loss=0.2611, pruned_loss=0.05287, over 589459.05 frames.], datatang_tot_loss[loss=0.1792, simple_loss=0.2431, pruned_loss=0.0577, over 561521.92 frames.], batch size: 45, lr: 8.93e-04
+2022-06-18 16:21:31,222 INFO [train.py:874] (0/4) Epoch 9, batch 400, datatang_loss[loss=0.1685, simple_loss=0.2302, pruned_loss=0.05344, over 4906.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2533, pruned_loss=0.05506, over 852783.54 frames.], batch size: 52, aishell_tot_loss[loss=0.1836, simple_loss=0.2615, pruned_loss=0.05284, over 649523.83 frames.], datatang_tot_loss[loss=0.1789, simple_loss=0.2427, pruned_loss=0.05762, over 596287.93 frames.], batch size: 52, lr: 8.92e-04
+2022-06-18 16:22:01,507 INFO [train.py:874] (0/4) Epoch 9, batch 450, datatang_loss[loss=0.1775, simple_loss=0.2351, pruned_loss=0.05991, over 4903.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2536, pruned_loss=0.05595, over 882079.64 frames.], batch size: 64, aishell_tot_loss[loss=0.1835, simple_loss=0.2611, pruned_loss=0.05291, over 685980.88 frames.], datatang_tot_loss[loss=0.1808, simple_loss=0.2442, pruned_loss=0.0587, over 645544.80 frames.], batch size: 64, lr: 8.92e-04
+2022-06-18 16:22:32,032 INFO [train.py:874] (0/4) Epoch 9, batch 500, datatang_loss[loss=0.1796, simple_loss=0.2268, pruned_loss=0.06617, over 4956.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2539, pruned_loss=0.05673, over 905248.70 frames.], batch size: 34, aishell_tot_loss[loss=0.1832, simple_loss=0.2607, pruned_loss=0.05282, over 713217.01 frames.], datatang_tot_loss[loss=0.1826, simple_loss=0.246, pruned_loss=0.05964, over 694691.43 frames.], batch size: 34, lr: 8.91e-04
+2022-06-18 16:23:02,676 INFO [train.py:874] (0/4) Epoch 9, batch 550, datatang_loss[loss=0.1921, simple_loss=0.2547, pruned_loss=0.06473, over 4972.00 frames.], tot_loss[loss=0.184, simple_loss=0.254, pruned_loss=0.05696, over 923313.31 frames.], batch size: 37, aishell_tot_loss[loss=0.1831, simple_loss=0.2608, pruned_loss=0.05269, over 739375.40 frames.], datatang_tot_loss[loss=0.1834, simple_loss=0.2467, pruned_loss=0.06003, over 735398.14 frames.], batch size: 37, lr: 8.90e-04
+2022-06-18 16:23:32,312 INFO [train.py:874] (0/4) Epoch 9, batch 600, aishell_loss[loss=0.1829, simple_loss=0.2672, pruned_loss=0.04933, over 4977.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2541, pruned_loss=0.05649, over 937055.90 frames.], batch size: 61, aishell_tot_loss[loss=0.1828, simple_loss=0.2604, pruned_loss=0.05257, over 770463.74 frames.], datatang_tot_loss[loss=0.1834, simple_loss=0.2472, pruned_loss=0.05984, over 762665.41 frames.], batch size: 61, lr: 8.90e-04
+2022-06-18 16:24:02,267 INFO [train.py:874] (0/4) Epoch 9, batch 650, datatang_loss[loss=0.1708, simple_loss=0.2409, pruned_loss=0.05037, over 4921.00 frames.], tot_loss[loss=0.1845, simple_loss=0.255, pruned_loss=0.05696, over 948183.02 frames.], batch size: 83, aishell_tot_loss[loss=0.1825, simple_loss=0.2601, pruned_loss=0.05243, over 797181.88 frames.], datatang_tot_loss[loss=0.185, simple_loss=0.2486, pruned_loss=0.06072, over 787885.58 frames.], batch size: 83, lr: 8.89e-04
+2022-06-18 16:24:31,728 INFO [train.py:874] (0/4) Epoch 9, batch 700, aishell_loss[loss=0.1424, simple_loss=0.2267, pruned_loss=0.02909, over 4829.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2544, pruned_loss=0.05653, over 956171.46 frames.], batch size: 29, aishell_tot_loss[loss=0.1816, simple_loss=0.2592, pruned_loss=0.05204, over 821379.37 frames.], datatang_tot_loss[loss=0.1852, simple_loss=0.2487, pruned_loss=0.0608, over 808692.64 frames.], batch size: 29, lr: 8.88e-04
+2022-06-18 16:25:02,543 INFO [train.py:874] (0/4) Epoch 9, batch 750, aishell_loss[loss=0.2127, simple_loss=0.2879, pruned_loss=0.0687, over 4919.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2557, pruned_loss=0.05641, over 962248.88 frames.], batch size: 68, aishell_tot_loss[loss=0.1821, simple_loss=0.2601, pruned_loss=0.05203, over 843980.21 frames.], datatang_tot_loss[loss=0.1856, simple_loss=0.2492, pruned_loss=0.06099, over 825424.21 frames.], batch size: 68, lr: 8.88e-04
+2022-06-18 16:25:33,513 INFO [train.py:874] (0/4) Epoch 9, batch 800, aishell_loss[loss=0.1945, simple_loss=0.2677, pruned_loss=0.06065, over 4968.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2554, pruned_loss=0.05608, over 967741.83 frames.], batch size: 39, aishell_tot_loss[loss=0.1816, simple_loss=0.2597, pruned_loss=0.05171, over 860796.37 frames.], datatang_tot_loss[loss=0.1857, simple_loss=0.2496, pruned_loss=0.0609, over 844482.14 frames.], batch size: 39, lr: 8.87e-04
+2022-06-18 16:26:02,691 INFO [train.py:874] (0/4) Epoch 9, batch 850, aishell_loss[loss=0.1891, simple_loss=0.2674, pruned_loss=0.05536, over 4917.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2549, pruned_loss=0.05603, over 971396.04 frames.], batch size: 46, aishell_tot_loss[loss=0.1811, simple_loss=0.2592, pruned_loss=0.05154, over 874004.92 frames.], datatang_tot_loss[loss=0.1857, simple_loss=0.2498, pruned_loss=0.06081, over 862392.77 frames.], batch size: 46, lr: 8.87e-04
+2022-06-18 16:26:33,075 INFO [train.py:874] (0/4) Epoch 9, batch 900, datatang_loss[loss=0.1924, simple_loss=0.2544, pruned_loss=0.06521, over 4954.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2561, pruned_loss=0.05616, over 974387.61 frames.], batch size: 86, aishell_tot_loss[loss=0.1819, simple_loss=0.2598, pruned_loss=0.05204, over 892367.66 frames.], datatang_tot_loss[loss=0.186, simple_loss=0.2501, pruned_loss=0.06095, over 870646.65 frames.], batch size: 86, lr: 8.86e-04
+2022-06-18 16:27:02,539 INFO [train.py:874] (0/4) Epoch 9, batch 950, aishell_loss[loss=0.1692, simple_loss=0.2495, pruned_loss=0.04445, over 4917.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2568, pruned_loss=0.05726, over 977212.97 frames.], batch size: 41, aishell_tot_loss[loss=0.1821, simple_loss=0.2595, pruned_loss=0.05231, over 906093.73 frames.], datatang_tot_loss[loss=0.1877, simple_loss=0.2512, pruned_loss=0.06213, over 881142.34 frames.], batch size: 41, lr: 8.85e-04
+2022-06-18 16:27:32,411 INFO [train.py:874] (0/4) Epoch 9, batch 1000, datatang_loss[loss=0.1848, simple_loss=0.2377, pruned_loss=0.06591, over 4978.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2552, pruned_loss=0.05666, over 979111.36 frames.], batch size: 45, aishell_tot_loss[loss=0.1812, simple_loss=0.2585, pruned_loss=0.05198, over 916432.74 frames.], datatang_tot_loss[loss=0.1873, simple_loss=0.2507, pruned_loss=0.06192, over 892165.27 frames.], batch size: 45, lr: 8.85e-04
+2022-06-18 16:27:32,413 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 16:27:48,781 INFO [train.py:914] (0/4) Epoch 9, validation: loss=0.1688, simple_loss=0.2514, pruned_loss=0.04309, over 1622729.00 frames.
+2022-06-18 16:28:18,805 INFO [train.py:874] (0/4) Epoch 9, batch 1050, aishell_loss[loss=0.1853, simple_loss=0.2543, pruned_loss=0.05814, over 4883.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2558, pruned_loss=0.05674, over 980750.71 frames.], batch size: 47, aishell_tot_loss[loss=0.1806, simple_loss=0.258, pruned_loss=0.05155, over 924932.11 frames.], datatang_tot_loss[loss=0.1884, simple_loss=0.252, pruned_loss=0.06244, over 902925.74 frames.], batch size: 47, lr: 8.84e-04
+2022-06-18 16:28:49,628 INFO [train.py:874] (0/4) Epoch 9, batch 1100, datatang_loss[loss=0.1962, simple_loss=0.266, pruned_loss=0.06322, over 4971.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2562, pruned_loss=0.05664, over 982128.75 frames.], batch size: 45, aishell_tot_loss[loss=0.1808, simple_loss=0.2587, pruned_loss=0.05148, over 932478.62 frames.], datatang_tot_loss[loss=0.1884, simple_loss=0.252, pruned_loss=0.06241, over 912482.80 frames.], batch size: 45, lr: 8.83e-04
+2022-06-18 16:29:19,121 INFO [train.py:874] (0/4) Epoch 9, batch 1150, datatang_loss[loss=0.2183, simple_loss=0.2703, pruned_loss=0.08313, over 4957.00 frames.], tot_loss[loss=0.1847, simple_loss=0.2563, pruned_loss=0.05653, over 983115.60 frames.], batch size: 60, aishell_tot_loss[loss=0.1813, simple_loss=0.2594, pruned_loss=0.05165, over 938771.39 frames.], datatang_tot_loss[loss=0.1879, simple_loss=0.2517, pruned_loss=0.06203, over 921290.06 frames.], batch size: 60, lr: 8.83e-04
+2022-06-18 16:29:49,953 INFO [train.py:874] (0/4) Epoch 9, batch 1200, aishell_loss[loss=0.1904, simple_loss=0.2811, pruned_loss=0.04985, over 4864.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2563, pruned_loss=0.05622, over 983256.00 frames.], batch size: 36, aishell_tot_loss[loss=0.1811, simple_loss=0.2591, pruned_loss=0.05153, over 944887.86 frames.], datatang_tot_loss[loss=0.1879, simple_loss=0.2519, pruned_loss=0.06194, over 927486.08 frames.], batch size: 36, lr: 8.82e-04
+2022-06-18 16:30:20,583 INFO [train.py:874] (0/4) Epoch 9, batch 1250, datatang_loss[loss=0.2437, simple_loss=0.2965, pruned_loss=0.09547, over 4866.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2565, pruned_loss=0.05605, over 983847.56 frames.], batch size: 39, aishell_tot_loss[loss=0.1809, simple_loss=0.259, pruned_loss=0.05141, over 950998.87 frames.], datatang_tot_loss[loss=0.1882, simple_loss=0.2521, pruned_loss=0.06212, over 932417.86 frames.], batch size: 39, lr: 8.82e-04
+2022-06-18 16:30:49,257 INFO [train.py:874] (0/4) Epoch 9, batch 1300, aishell_loss[loss=0.1726, simple_loss=0.2504, pruned_loss=0.04742, over 4945.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2573, pruned_loss=0.05666, over 984479.61 frames.], batch size: 45, aishell_tot_loss[loss=0.1817, simple_loss=0.2597, pruned_loss=0.05186, over 955482.31 frames.], datatang_tot_loss[loss=0.1885, simple_loss=0.2524, pruned_loss=0.06231, over 938289.28 frames.], batch size: 45, lr: 8.81e-04
+2022-06-18 16:31:19,947 INFO [train.py:874] (0/4) Epoch 9, batch 1350, aishell_loss[loss=0.1598, simple_loss=0.2509, pruned_loss=0.03431, over 4928.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2564, pruned_loss=0.05622, over 984430.32 frames.], batch size: 58, aishell_tot_loss[loss=0.181, simple_loss=0.2591, pruned_loss=0.05143, over 959334.72 frames.], datatang_tot_loss[loss=0.1884, simple_loss=0.2522, pruned_loss=0.06234, over 942973.62 frames.], batch size: 58, lr: 8.80e-04
+2022-06-18 16:31:50,462 INFO [train.py:874] (0/4) Epoch 9, batch 1400, aishell_loss[loss=0.1759, simple_loss=0.2517, pruned_loss=0.05009, over 4931.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2562, pruned_loss=0.05643, over 984440.40 frames.], batch size: 32, aishell_tot_loss[loss=0.1815, simple_loss=0.2596, pruned_loss=0.05172, over 962699.52 frames.], datatang_tot_loss[loss=0.188, simple_loss=0.2515, pruned_loss=0.06227, over 947223.32 frames.], batch size: 32, lr: 8.80e-04
+2022-06-18 16:32:21,316 INFO [train.py:874] (0/4) Epoch 9, batch 1450, datatang_loss[loss=0.1811, simple_loss=0.2483, pruned_loss=0.05695, over 4919.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2564, pruned_loss=0.0563, over 984436.93 frames.], batch size: 81, aishell_tot_loss[loss=0.1815, simple_loss=0.2598, pruned_loss=0.05155, over 965122.78 frames.], datatang_tot_loss[loss=0.1881, simple_loss=0.2517, pruned_loss=0.06224, over 951682.52 frames.], batch size: 81, lr: 8.79e-04
+2022-06-18 16:32:52,181 INFO [train.py:874] (0/4) Epoch 9, batch 1500, datatang_loss[loss=0.1858, simple_loss=0.2496, pruned_loss=0.06106, over 4916.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2548, pruned_loss=0.05568, over 984487.05 frames.], batch size: 71, aishell_tot_loss[loss=0.1807, simple_loss=0.2589, pruned_loss=0.0513, over 967199.62 frames.], datatang_tot_loss[loss=0.1872, simple_loss=0.2511, pruned_loss=0.06161, over 955845.49 frames.], batch size: 71, lr: 8.78e-04
+2022-06-18 16:33:21,717 INFO [train.py:874] (0/4) Epoch 9, batch 1550, aishell_loss[loss=0.1342, simple_loss=0.1923, pruned_loss=0.038, over 4874.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2538, pruned_loss=0.05599, over 984553.67 frames.], batch size: 21, aishell_tot_loss[loss=0.1799, simple_loss=0.2577, pruned_loss=0.0511, over 968876.62 frames.], datatang_tot_loss[loss=0.1876, simple_loss=0.2513, pruned_loss=0.06191, over 959759.37 frames.], batch size: 21, lr: 8.78e-04
+2022-06-18 16:33:52,466 INFO [train.py:874] (0/4) Epoch 9, batch 1600, datatang_loss[loss=0.1947, simple_loss=0.2585, pruned_loss=0.0655, over 4926.00 frames.], tot_loss[loss=0.183, simple_loss=0.2539, pruned_loss=0.05601, over 984791.63 frames.], batch size: 42, aishell_tot_loss[loss=0.1798, simple_loss=0.2574, pruned_loss=0.05108, over 971126.19 frames.], datatang_tot_loss[loss=0.1877, simple_loss=0.2514, pruned_loss=0.06197, over 962408.95 frames.], batch size: 42, lr: 8.77e-04
+2022-06-18 16:34:23,056 INFO [train.py:874] (0/4) Epoch 9, batch 1650, datatang_loss[loss=0.1796, simple_loss=0.2496, pruned_loss=0.05486, over 4932.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2544, pruned_loss=0.05619, over 984947.70 frames.], batch size: 69, aishell_tot_loss[loss=0.1804, simple_loss=0.2579, pruned_loss=0.05145, over 972685.22 frames.], datatang_tot_loss[loss=0.1873, simple_loss=0.2513, pruned_loss=0.06169, over 965223.24 frames.], batch size: 69, lr: 8.77e-04
+2022-06-18 16:34:54,131 INFO [train.py:874] (0/4) Epoch 9, batch 1700, datatang_loss[loss=0.1753, simple_loss=0.2444, pruned_loss=0.05315, over 4937.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2545, pruned_loss=0.05657, over 985357.24 frames.], batch size: 88, aishell_tot_loss[loss=0.1801, simple_loss=0.2577, pruned_loss=0.05124, over 974099.88 frames.], datatang_tot_loss[loss=0.1879, simple_loss=0.2516, pruned_loss=0.06211, over 968074.72 frames.], batch size: 88, lr: 8.76e-04
+2022-06-18 16:35:25,092 INFO [train.py:874] (0/4) Epoch 9, batch 1750, aishell_loss[loss=0.1822, simple_loss=0.2574, pruned_loss=0.05352, over 4858.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2546, pruned_loss=0.05692, over 985523.42 frames.], batch size: 36, aishell_tot_loss[loss=0.1811, simple_loss=0.2588, pruned_loss=0.05175, over 975364.04 frames.], datatang_tot_loss[loss=0.1871, simple_loss=0.2508, pruned_loss=0.06168, over 970502.75 frames.], batch size: 36, lr: 8.75e-04
+2022-06-18 16:35:56,456 INFO [train.py:874] (0/4) Epoch 9, batch 1800, aishell_loss[loss=0.1792, simple_loss=0.2655, pruned_loss=0.04639, over 4906.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2546, pruned_loss=0.05696, over 985502.63 frames.], batch size: 52, aishell_tot_loss[loss=0.1818, simple_loss=0.2592, pruned_loss=0.05218, over 976251.40 frames.], datatang_tot_loss[loss=0.1864, simple_loss=0.2504, pruned_loss=0.06126, over 972599.24 frames.], batch size: 52, lr: 8.75e-04
+2022-06-18 16:36:25,539 INFO [train.py:874] (0/4) Epoch 9, batch 1850, aishell_loss[loss=0.1559, simple_loss=0.2492, pruned_loss=0.03125, over 4922.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2555, pruned_loss=0.05682, over 985626.14 frames.], batch size: 41, aishell_tot_loss[loss=0.1818, simple_loss=0.2595, pruned_loss=0.05202, over 977476.94 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.2509, pruned_loss=0.06142, over 974082.03 frames.], batch size: 41, lr: 8.74e-04
+2022-06-18 16:36:56,410 INFO [train.py:874] (0/4) Epoch 9, batch 1900, datatang_loss[loss=0.228, simple_loss=0.2827, pruned_loss=0.0866, over 4962.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2561, pruned_loss=0.05753, over 985729.72 frames.], batch size: 99, aishell_tot_loss[loss=0.182, simple_loss=0.2596, pruned_loss=0.05224, over 978508.28 frames.], datatang_tot_loss[loss=0.1877, simple_loss=0.2515, pruned_loss=0.06201, over 975461.60 frames.], batch size: 99, lr: 8.73e-04
+2022-06-18 16:37:28,047 INFO [train.py:874] (0/4) Epoch 9, batch 1950, aishell_loss[loss=0.1734, simple_loss=0.2523, pruned_loss=0.04724, over 4965.00 frames.], tot_loss[loss=0.184, simple_loss=0.2546, pruned_loss=0.05671, over 985682.43 frames.], batch size: 51, aishell_tot_loss[loss=0.1813, simple_loss=0.259, pruned_loss=0.05183, over 979371.86 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.2506, pruned_loss=0.06161, over 976625.63 frames.], batch size: 51, lr: 8.73e-04
+2022-06-18 16:37:57,021 INFO [train.py:874] (0/4) Epoch 9, batch 2000, datatang_loss[loss=0.2086, simple_loss=0.2643, pruned_loss=0.07646, over 4952.00 frames.], tot_loss[loss=0.183, simple_loss=0.2539, pruned_loss=0.05607, over 985659.80 frames.], batch size: 91, aishell_tot_loss[loss=0.1801, simple_loss=0.258, pruned_loss=0.05106, over 979845.92 frames.], datatang_tot_loss[loss=0.187, simple_loss=0.2507, pruned_loss=0.06168, over 977923.62 frames.], batch size: 91, lr: 8.72e-04
+2022-06-18 16:37:57,027 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 16:38:13,638 INFO [train.py:914] (0/4) Epoch 9, validation: loss=0.1694, simple_loss=0.2525, pruned_loss=0.04315, over 1622729.00 frames.
+2022-06-18 16:38:43,886 INFO [train.py:874] (0/4) Epoch 9, batch 2050, datatang_loss[loss=0.1893, simple_loss=0.2525, pruned_loss=0.06311, over 4968.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2535, pruned_loss=0.05604, over 985551.17 frames.], batch size: 37, aishell_tot_loss[loss=0.1798, simple_loss=0.2578, pruned_loss=0.05092, over 980410.78 frames.], datatang_tot_loss[loss=0.1868, simple_loss=0.2504, pruned_loss=0.0616, over 978850.17 frames.], batch size: 37, lr: 8.72e-04
+2022-06-18 16:39:14,762 INFO [train.py:874] (0/4) Epoch 9, batch 2100, aishell_loss[loss=0.1895, simple_loss=0.2651, pruned_loss=0.05694, over 4941.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2548, pruned_loss=0.05698, over 985835.94 frames.], batch size: 56, aishell_tot_loss[loss=0.1811, simple_loss=0.2589, pruned_loss=0.05166, over 981016.98 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.2506, pruned_loss=0.06162, over 979961.54 frames.], batch size: 56, lr: 8.71e-04
+2022-06-18 16:39:46,126 INFO [train.py:874] (0/4) Epoch 9, batch 2150, aishell_loss[loss=0.2047, simple_loss=0.285, pruned_loss=0.06221, over 4964.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2544, pruned_loss=0.05633, over 985621.58 frames.], batch size: 40, aishell_tot_loss[loss=0.1807, simple_loss=0.2587, pruned_loss=0.05137, over 981398.94 frames.], datatang_tot_loss[loss=0.1865, simple_loss=0.2504, pruned_loss=0.0613, over 980611.93 frames.], batch size: 40, lr: 8.70e-04
+2022-06-18 16:40:16,371 INFO [train.py:874] (0/4) Epoch 9, batch 2200, aishell_loss[loss=0.1621, simple_loss=0.2249, pruned_loss=0.04965, over 4956.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2546, pruned_loss=0.05615, over 985718.40 frames.], batch size: 25, aishell_tot_loss[loss=0.1806, simple_loss=0.2588, pruned_loss=0.0512, over 982069.84 frames.], datatang_tot_loss[loss=0.1864, simple_loss=0.2506, pruned_loss=0.06116, over 981136.40 frames.], batch size: 25, lr: 8.70e-04
+2022-06-18 16:40:46,507 INFO [train.py:874] (0/4) Epoch 9, batch 2250, datatang_loss[loss=0.1891, simple_loss=0.2478, pruned_loss=0.06516, over 4951.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2552, pruned_loss=0.05673, over 985808.86 frames.], batch size: 67, aishell_tot_loss[loss=0.1808, simple_loss=0.2589, pruned_loss=0.05132, over 982359.91 frames.], datatang_tot_loss[loss=0.1872, simple_loss=0.2512, pruned_loss=0.06161, over 981908.95 frames.], batch size: 67, lr: 8.69e-04
+2022-06-18 16:41:17,730 INFO [train.py:874] (0/4) Epoch 9, batch 2300, datatang_loss[loss=0.1539, simple_loss=0.2124, pruned_loss=0.04769, over 4960.00 frames.], tot_loss[loss=0.184, simple_loss=0.255, pruned_loss=0.05647, over 985758.68 frames.], batch size: 37, aishell_tot_loss[loss=0.1813, simple_loss=0.2593, pruned_loss=0.05169, over 982494.29 frames.], datatang_tot_loss[loss=0.1865, simple_loss=0.2506, pruned_loss=0.06117, over 982575.62 frames.], batch size: 37, lr: 8.69e-04
+2022-06-18 16:41:48,085 INFO [train.py:874] (0/4) Epoch 9, batch 2350, aishell_loss[loss=0.1686, simple_loss=0.2599, pruned_loss=0.03869, over 4897.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2544, pruned_loss=0.05599, over 985543.95 frames.], batch size: 50, aishell_tot_loss[loss=0.1805, simple_loss=0.2585, pruned_loss=0.05123, over 982617.90 frames.], datatang_tot_loss[loss=0.1865, simple_loss=0.2507, pruned_loss=0.06114, over 982976.16 frames.], batch size: 50, lr: 8.68e-04
+2022-06-18 16:42:20,332 INFO [train.py:874] (0/4) Epoch 9, batch 2400, aishell_loss[loss=0.1849, simple_loss=0.2695, pruned_loss=0.05014, over 4942.00 frames.], tot_loss[loss=0.1823, simple_loss=0.254, pruned_loss=0.05533, over 985227.17 frames.], batch size: 56, aishell_tot_loss[loss=0.1804, simple_loss=0.2586, pruned_loss=0.0511, over 982893.11 frames.], datatang_tot_loss[loss=0.1856, simple_loss=0.2501, pruned_loss=0.06053, over 983011.84 frames.], batch size: 56, lr: 8.67e-04
+2022-06-18 16:42:51,834 INFO [train.py:874] (0/4) Epoch 9, batch 2450, datatang_loss[loss=0.1919, simple_loss=0.2555, pruned_loss=0.06412, over 4919.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2553, pruned_loss=0.0562, over 985711.67 frames.], batch size: 83, aishell_tot_loss[loss=0.1813, simple_loss=0.2594, pruned_loss=0.05154, over 983499.63 frames.], datatang_tot_loss[loss=0.1864, simple_loss=0.2504, pruned_loss=0.06115, over 983469.25 frames.], batch size: 83, lr: 8.67e-04
+2022-06-18 16:43:22,136 INFO [train.py:874] (0/4) Epoch 9, batch 2500, datatang_loss[loss=0.1633, simple_loss=0.23, pruned_loss=0.04832, over 4836.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2539, pruned_loss=0.05513, over 985569.38 frames.], batch size: 24, aishell_tot_loss[loss=0.1805, simple_loss=0.2588, pruned_loss=0.05112, over 983604.26 frames.], datatang_tot_loss[loss=0.1852, simple_loss=0.2496, pruned_loss=0.06036, over 983716.81 frames.], batch size: 24, lr: 8.66e-04
+2022-06-18 16:43:51,853 INFO [train.py:874] (0/4) Epoch 9, batch 2550, aishell_loss[loss=0.1642, simple_loss=0.257, pruned_loss=0.03572, over 4930.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2539, pruned_loss=0.05512, over 985464.46 frames.], batch size: 46, aishell_tot_loss[loss=0.1802, simple_loss=0.2587, pruned_loss=0.05089, over 983543.45 frames.], datatang_tot_loss[loss=0.1853, simple_loss=0.2499, pruned_loss=0.06034, over 984112.05 frames.], batch size: 46, lr: 8.66e-04
+2022-06-18 16:44:23,862 INFO [train.py:874] (0/4) Epoch 9, batch 2600, datatang_loss[loss=0.1644, simple_loss=0.2314, pruned_loss=0.04871, over 4915.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2539, pruned_loss=0.05535, over 985788.05 frames.], batch size: 25, aishell_tot_loss[loss=0.1804, simple_loss=0.2588, pruned_loss=0.05097, over 983969.61 frames.], datatang_tot_loss[loss=0.1852, simple_loss=0.2497, pruned_loss=0.06034, over 984421.92 frames.], batch size: 25, lr: 8.65e-04
+2022-06-18 16:44:54,225 INFO [train.py:874] (0/4) Epoch 9, batch 2650, aishell_loss[loss=0.2028, simple_loss=0.272, pruned_loss=0.06682, over 4910.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2537, pruned_loss=0.05495, over 985808.88 frames.], batch size: 41, aishell_tot_loss[loss=0.1804, simple_loss=0.2589, pruned_loss=0.05094, over 984150.13 frames.], datatang_tot_loss[loss=0.1846, simple_loss=0.2493, pruned_loss=0.0599, over 984650.88 frames.], batch size: 41, lr: 8.64e-04
+2022-06-18 16:45:24,647 INFO [train.py:874] (0/4) Epoch 9, batch 2700, aishell_loss[loss=0.1685, simple_loss=0.2493, pruned_loss=0.04385, over 4907.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2536, pruned_loss=0.0551, over 985796.00 frames.], batch size: 41, aishell_tot_loss[loss=0.1805, simple_loss=0.2592, pruned_loss=0.05086, over 984309.64 frames.], datatang_tot_loss[loss=0.1843, simple_loss=0.249, pruned_loss=0.05975, over 984806.10 frames.], batch size: 41, lr: 8.64e-04
+2022-06-18 16:45:55,554 INFO [train.py:874] (0/4) Epoch 9, batch 2750, datatang_loss[loss=0.1842, simple_loss=0.2494, pruned_loss=0.05949, over 4921.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2542, pruned_loss=0.05561, over 985622.69 frames.], batch size: 83, aishell_tot_loss[loss=0.1807, simple_loss=0.2594, pruned_loss=0.05103, over 984285.95 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.2492, pruned_loss=0.06018, over 984951.21 frames.], batch size: 83, lr: 8.63e-04
+2022-06-18 16:46:25,599 INFO [train.py:874] (0/4) Epoch 9, batch 2800, datatang_loss[loss=0.1551, simple_loss=0.2321, pruned_loss=0.03907, over 4942.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2541, pruned_loss=0.05523, over 985867.89 frames.], batch size: 88, aishell_tot_loss[loss=0.1804, simple_loss=0.259, pruned_loss=0.05085, over 984678.73 frames.], datatang_tot_loss[loss=0.1847, simple_loss=0.2494, pruned_loss=0.05999, over 985063.64 frames.], batch size: 88, lr: 8.63e-04
+2022-06-18 16:46:55,358 INFO [train.py:874] (0/4) Epoch 9, batch 2850, datatang_loss[loss=0.1977, simple_loss=0.2481, pruned_loss=0.07371, over 4951.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2541, pruned_loss=0.05591, over 985855.07 frames.], batch size: 62, aishell_tot_loss[loss=0.1806, simple_loss=0.259, pruned_loss=0.05109, over 984932.22 frames.], datatang_tot_loss[loss=0.1852, simple_loss=0.2493, pruned_loss=0.0605, over 985042.21 frames.], batch size: 62, lr: 8.62e-04
+2022-06-18 16:47:04,997 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-36000.pt
+2022-06-18 16:47:30,666 INFO [train.py:874] (0/4) Epoch 9, batch 2900, datatang_loss[loss=0.1486, simple_loss=0.2208, pruned_loss=0.03823, over 4872.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2535, pruned_loss=0.05544, over 986036.03 frames.], batch size: 30, aishell_tot_loss[loss=0.1805, simple_loss=0.2586, pruned_loss=0.05114, over 985223.41 frames.], datatang_tot_loss[loss=0.1845, simple_loss=0.249, pruned_loss=0.06006, over 985154.16 frames.], batch size: 30, lr: 8.61e-04
+2022-06-18 16:48:01,970 INFO [train.py:874] (0/4) Epoch 9, batch 2950, datatang_loss[loss=0.1837, simple_loss=0.2518, pruned_loss=0.05781, over 4929.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2532, pruned_loss=0.05508, over 985893.54 frames.], batch size: 83, aishell_tot_loss[loss=0.1799, simple_loss=0.2584, pruned_loss=0.05069, over 985292.68 frames.], datatang_tot_loss[loss=0.1844, simple_loss=0.2491, pruned_loss=0.05983, over 985128.37 frames.], batch size: 83, lr: 8.61e-04
+2022-06-18 16:48:32,283 INFO [train.py:874] (0/4) Epoch 9, batch 3000, datatang_loss[loss=0.1629, simple_loss=0.2454, pruned_loss=0.04021, over 4877.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2526, pruned_loss=0.05457, over 985712.43 frames.], batch size: 30, aishell_tot_loss[loss=0.1793, simple_loss=0.2577, pruned_loss=0.05041, over 985206.19 frames.], datatang_tot_loss[loss=0.1841, simple_loss=0.2489, pruned_loss=0.05959, over 985162.47 frames.], batch size: 30, lr: 8.60e-04
+2022-06-18 16:48:32,286 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 16:48:48,782 INFO [train.py:914] (0/4) Epoch 9, validation: loss=0.169, simple_loss=0.2511, pruned_loss=0.04348, over 1622729.00 frames.
+2022-06-18 16:49:18,617 INFO [train.py:874] (0/4) Epoch 9, batch 3050, datatang_loss[loss=0.1506, simple_loss=0.2278, pruned_loss=0.03667, over 4848.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2524, pruned_loss=0.05407, over 985856.46 frames.], batch size: 24, aishell_tot_loss[loss=0.1791, simple_loss=0.2577, pruned_loss=0.05021, over 985392.56 frames.], datatang_tot_loss[loss=0.1835, simple_loss=0.2484, pruned_loss=0.05931, over 985246.61 frames.], batch size: 24, lr: 8.60e-04
+2022-06-18 16:49:51,930 INFO [train.py:874] (0/4) Epoch 9, batch 3100, datatang_loss[loss=0.2015, simple_loss=0.2703, pruned_loss=0.06635, over 4955.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2527, pruned_loss=0.05447, over 985858.75 frames.], batch size: 37, aishell_tot_loss[loss=0.1792, simple_loss=0.258, pruned_loss=0.05019, over 985179.02 frames.], datatang_tot_loss[loss=0.1837, simple_loss=0.2484, pruned_loss=0.05951, over 985573.72 frames.], batch size: 37, lr: 8.59e-04
+2022-06-18 16:50:22,217 INFO [train.py:874] (0/4) Epoch 9, batch 3150, datatang_loss[loss=0.1582, simple_loss=0.2306, pruned_loss=0.04287, over 4917.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2526, pruned_loss=0.05431, over 985520.61 frames.], batch size: 64, aishell_tot_loss[loss=0.1787, simple_loss=0.2577, pruned_loss=0.04986, over 985043.67 frames.], datatang_tot_loss[loss=0.1838, simple_loss=0.2486, pruned_loss=0.05949, over 985459.72 frames.], batch size: 64, lr: 8.59e-04
+2022-06-18 16:50:52,583 INFO [train.py:874] (0/4) Epoch 9, batch 3200, datatang_loss[loss=0.1871, simple_loss=0.248, pruned_loss=0.06315, over 4940.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2527, pruned_loss=0.05425, over 985491.31 frames.], batch size: 88, aishell_tot_loss[loss=0.1788, simple_loss=0.2578, pruned_loss=0.04992, over 985089.76 frames.], datatang_tot_loss[loss=0.1834, simple_loss=0.2484, pruned_loss=0.05925, over 985436.48 frames.], batch size: 88, lr: 8.58e-04
+2022-06-18 16:51:25,241 INFO [train.py:874] (0/4) Epoch 9, batch 3250, datatang_loss[loss=0.1698, simple_loss=0.244, pruned_loss=0.04783, over 4907.00 frames.], tot_loss[loss=0.1821, simple_loss=0.254, pruned_loss=0.05506, over 985468.47 frames.], batch size: 85, aishell_tot_loss[loss=0.1792, simple_loss=0.2583, pruned_loss=0.05003, over 984907.81 frames.], datatang_tot_loss[loss=0.1844, simple_loss=0.2494, pruned_loss=0.05966, over 985634.46 frames.], batch size: 85, lr: 8.57e-04
+2022-06-18 16:51:55,892 INFO [train.py:874] (0/4) Epoch 9, batch 3300, aishell_loss[loss=0.2016, simple_loss=0.2832, pruned_loss=0.06, over 4868.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2541, pruned_loss=0.05559, over 985553.71 frames.], batch size: 35, aishell_tot_loss[loss=0.1799, simple_loss=0.2589, pruned_loss=0.05044, over 985120.33 frames.], datatang_tot_loss[loss=0.1842, simple_loss=0.2492, pruned_loss=0.05964, over 985555.96 frames.], batch size: 35, lr: 8.57e-04
+2022-06-18 16:52:26,647 INFO [train.py:874] (0/4) Epoch 9, batch 3350, datatang_loss[loss=0.2028, simple_loss=0.2703, pruned_loss=0.06763, over 4939.00 frames.], tot_loss[loss=0.1834, simple_loss=0.255, pruned_loss=0.05593, over 985762.45 frames.], batch size: 50, aishell_tot_loss[loss=0.1798, simple_loss=0.2589, pruned_loss=0.05031, over 985258.76 frames.], datatang_tot_loss[loss=0.1853, simple_loss=0.2502, pruned_loss=0.0602, over 985691.06 frames.], batch size: 50, lr: 8.56e-04
+2022-06-18 16:52:57,989 INFO [train.py:874] (0/4) Epoch 9, batch 3400, datatang_loss[loss=0.1601, simple_loss=0.2258, pruned_loss=0.04719, over 4937.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2544, pruned_loss=0.05527, over 985489.93 frames.], batch size: 79, aishell_tot_loss[loss=0.1799, simple_loss=0.2592, pruned_loss=0.0503, over 984957.89 frames.], datatang_tot_loss[loss=0.1843, simple_loss=0.2494, pruned_loss=0.05963, over 985771.76 frames.], batch size: 79, lr: 8.56e-04
+2022-06-18 16:53:28,255 INFO [train.py:874] (0/4) Epoch 9, batch 3450, datatang_loss[loss=0.1859, simple_loss=0.2396, pruned_loss=0.06612, over 4950.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2534, pruned_loss=0.05484, over 985559.17 frames.], batch size: 55, aishell_tot_loss[loss=0.1794, simple_loss=0.2587, pruned_loss=0.05006, over 984855.83 frames.], datatang_tot_loss[loss=0.1839, simple_loss=0.2488, pruned_loss=0.05953, over 985980.04 frames.], batch size: 55, lr: 8.55e-04
+2022-06-18 16:53:59,608 INFO [train.py:874] (0/4) Epoch 9, batch 3500, aishell_loss[loss=0.1769, simple_loss=0.2491, pruned_loss=0.05234, over 4977.00 frames.], tot_loss[loss=0.1824, simple_loss=0.254, pruned_loss=0.05539, over 985565.16 frames.], batch size: 39, aishell_tot_loss[loss=0.1795, simple_loss=0.2587, pruned_loss=0.05018, over 984866.52 frames.], datatang_tot_loss[loss=0.1846, simple_loss=0.2496, pruned_loss=0.05981, over 985998.89 frames.], batch size: 39, lr: 8.55e-04
+2022-06-18 16:54:30,570 INFO [train.py:874] (0/4) Epoch 9, batch 3550, aishell_loss[loss=0.1604, simple_loss=0.2318, pruned_loss=0.0445, over 4970.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2542, pruned_loss=0.05551, over 985916.68 frames.], batch size: 27, aishell_tot_loss[loss=0.1792, simple_loss=0.2583, pruned_loss=0.05001, over 985020.15 frames.], datatang_tot_loss[loss=0.1853, simple_loss=0.2501, pruned_loss=0.0603, over 986254.04 frames.], batch size: 27, lr: 8.54e-04
+2022-06-18 16:55:00,705 INFO [train.py:874] (0/4) Epoch 9, batch 3600, datatang_loss[loss=0.1733, simple_loss=0.2371, pruned_loss=0.05476, over 4954.00 frames.], tot_loss[loss=0.182, simple_loss=0.2536, pruned_loss=0.05524, over 986191.42 frames.], batch size: 34, aishell_tot_loss[loss=0.179, simple_loss=0.2581, pruned_loss=0.04992, over 985266.31 frames.], datatang_tot_loss[loss=0.1849, simple_loss=0.2496, pruned_loss=0.06013, over 986351.25 frames.], batch size: 34, lr: 8.53e-04
+2022-06-18 16:55:30,667 INFO [train.py:874] (0/4) Epoch 9, batch 3650, aishell_loss[loss=0.185, simple_loss=0.2599, pruned_loss=0.05502, over 4873.00 frames.], tot_loss[loss=0.1809, simple_loss=0.253, pruned_loss=0.0544, over 986146.97 frames.], batch size: 28, aishell_tot_loss[loss=0.1783, simple_loss=0.2575, pruned_loss=0.04956, over 985379.23 frames.], datatang_tot_loss[loss=0.1846, simple_loss=0.2492, pruned_loss=0.05993, over 986303.24 frames.], batch size: 28, lr: 8.53e-04
+2022-06-18 16:56:03,411 INFO [train.py:874] (0/4) Epoch 9, batch 3700, datatang_loss[loss=0.1656, simple_loss=0.2348, pruned_loss=0.04818, over 4971.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2527, pruned_loss=0.05415, over 986387.84 frames.], batch size: 31, aishell_tot_loss[loss=0.1778, simple_loss=0.257, pruned_loss=0.04935, over 985615.20 frames.], datatang_tot_loss[loss=0.1846, simple_loss=0.2492, pruned_loss=0.06004, over 986411.02 frames.], batch size: 31, lr: 8.52e-04
+2022-06-18 16:56:32,381 INFO [train.py:874] (0/4) Epoch 9, batch 3750, datatang_loss[loss=0.1815, simple_loss=0.2543, pruned_loss=0.05429, over 4938.00 frames.], tot_loss[loss=0.1808, simple_loss=0.253, pruned_loss=0.05429, over 986137.75 frames.], batch size: 94, aishell_tot_loss[loss=0.1778, simple_loss=0.257, pruned_loss=0.04928, over 985615.75 frames.], datatang_tot_loss[loss=0.1847, simple_loss=0.2495, pruned_loss=0.06, over 986231.33 frames.], batch size: 94, lr: 8.52e-04
+2022-06-18 16:57:03,082 INFO [train.py:874] (0/4) Epoch 9, batch 3800, aishell_loss[loss=0.1654, simple_loss=0.2387, pruned_loss=0.04605, over 4950.00 frames.], tot_loss[loss=0.1807, simple_loss=0.253, pruned_loss=0.05426, over 986000.51 frames.], batch size: 31, aishell_tot_loss[loss=0.1787, simple_loss=0.258, pruned_loss=0.04969, over 985612.23 frames.], datatang_tot_loss[loss=0.1835, simple_loss=0.2486, pruned_loss=0.05922, over 986123.17 frames.], batch size: 31, lr: 8.51e-04
+2022-06-18 16:57:32,531 INFO [train.py:874] (0/4) Epoch 9, batch 3850, aishell_loss[loss=0.1569, simple_loss=0.2141, pruned_loss=0.04989, over 4855.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2523, pruned_loss=0.054, over 985815.81 frames.], batch size: 21, aishell_tot_loss[loss=0.1792, simple_loss=0.2582, pruned_loss=0.05006, over 985606.50 frames.], datatang_tot_loss[loss=0.1823, simple_loss=0.2478, pruned_loss=0.05841, over 985979.78 frames.], batch size: 21, lr: 8.51e-04
+2022-06-18 16:58:01,281 INFO [train.py:874] (0/4) Epoch 9, batch 3900, aishell_loss[loss=0.1777, simple_loss=0.2619, pruned_loss=0.04672, over 4960.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2524, pruned_loss=0.05356, over 985472.95 frames.], batch size: 40, aishell_tot_loss[loss=0.1792, simple_loss=0.2584, pruned_loss=0.04995, over 985366.38 frames.], datatang_tot_loss[loss=0.1818, simple_loss=0.2474, pruned_loss=0.05814, over 985869.95 frames.], batch size: 40, lr: 8.50e-04
+2022-06-18 16:58:29,654 INFO [train.py:874] (0/4) Epoch 9, batch 3950, datatang_loss[loss=0.179, simple_loss=0.252, pruned_loss=0.05302, over 4941.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2523, pruned_loss=0.05351, over 985518.24 frames.], batch size: 88, aishell_tot_loss[loss=0.1782, simple_loss=0.2574, pruned_loss=0.04955, over 985463.82 frames.], datatang_tot_loss[loss=0.1825, simple_loss=0.248, pruned_loss=0.05852, over 985807.07 frames.], batch size: 88, lr: 8.49e-04
+2022-06-18 16:58:57,893 INFO [train.py:874] (0/4) Epoch 9, batch 4000, aishell_loss[loss=0.2006, simple_loss=0.2758, pruned_loss=0.06272, over 4979.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2529, pruned_loss=0.05344, over 985456.03 frames.], batch size: 51, aishell_tot_loss[loss=0.1788, simple_loss=0.2581, pruned_loss=0.04973, over 985402.90 frames.], datatang_tot_loss[loss=0.1821, simple_loss=0.2476, pruned_loss=0.05833, over 985790.35 frames.], batch size: 51, lr: 8.49e-04
+2022-06-18 16:58:57,896 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 16:59:14,871 INFO [train.py:914] (0/4) Epoch 9, validation: loss=0.1682, simple_loss=0.2517, pruned_loss=0.04232, over 1622729.00 frames.
+2022-06-18 16:59:43,953 INFO [train.py:874] (0/4) Epoch 9, batch 4050, datatang_loss[loss=0.1651, simple_loss=0.2363, pruned_loss=0.04698, over 4865.00 frames.], tot_loss[loss=0.1793, simple_loss=0.252, pruned_loss=0.05327, over 985471.83 frames.], batch size: 30, aishell_tot_loss[loss=0.1788, simple_loss=0.258, pruned_loss=0.04982, over 985264.71 frames.], datatang_tot_loss[loss=0.1812, simple_loss=0.2469, pruned_loss=0.05773, over 985902.18 frames.], batch size: 30, lr: 8.48e-04
+2022-06-18 16:59:53,646 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-9.pt
+2022-06-18 17:00:57,164 INFO [train.py:874] (0/4) Epoch 10, batch 50, datatang_loss[loss=0.1661, simple_loss=0.2399, pruned_loss=0.04619, over 4922.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2434, pruned_loss=0.0482, over 218772.05 frames.], batch size: 42, aishell_tot_loss[loss=0.1776, simple_loss=0.2578, pruned_loss=0.04874, over 111967.40 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2302, pruned_loss=0.04762, over 120481.13 frames.], batch size: 42, lr: 8.15e-04
+2022-06-18 17:01:24,347 INFO [train.py:874] (0/4) Epoch 10, batch 100, aishell_loss[loss=0.1645, simple_loss=0.243, pruned_loss=0.04298, over 4984.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2491, pruned_loss=0.04957, over 389065.82 frames.], batch size: 30, aishell_tot_loss[loss=0.1806, simple_loss=0.2608, pruned_loss=0.05021, over 245301.36 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.2324, pruned_loss=0.04839, over 191328.25 frames.], batch size: 30, lr: 8.14e-04
+2022-06-18 17:01:55,761 INFO [train.py:874] (0/4) Epoch 10, batch 150, aishell_loss[loss=0.1751, simple_loss=0.2444, pruned_loss=0.05293, over 4943.00 frames.], tot_loss[loss=0.176, simple_loss=0.2516, pruned_loss=0.05025, over 521600.19 frames.], batch size: 32, aishell_tot_loss[loss=0.1809, simple_loss=0.2615, pruned_loss=0.05012, over 348928.26 frames.], datatang_tot_loss[loss=0.1681, simple_loss=0.2363, pruned_loss=0.04999, over 267124.04 frames.], batch size: 32, lr: 8.14e-04
+2022-06-18 17:02:27,253 INFO [train.py:874] (0/4) Epoch 10, batch 200, datatang_loss[loss=0.1808, simple_loss=0.243, pruned_loss=0.05935, over 4927.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2512, pruned_loss=0.0507, over 624379.05 frames.], batch size: 73, aishell_tot_loss[loss=0.1795, simple_loss=0.2596, pruned_loss=0.04964, over 432008.22 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.2388, pruned_loss=0.05159, over 342355.12 frames.], batch size: 73, lr: 8.13e-04
+2022-06-18 17:02:55,479 INFO [train.py:874] (0/4) Epoch 10, batch 250, datatang_loss[loss=0.2025, simple_loss=0.2717, pruned_loss=0.06668, over 4961.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2503, pruned_loss=0.05061, over 704460.73 frames.], batch size: 99, aishell_tot_loss[loss=0.1776, simple_loss=0.2575, pruned_loss=0.04882, over 499677.27 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2407, pruned_loss=0.05253, over 415232.53 frames.], batch size: 99, lr: 8.13e-04
+2022-06-18 17:03:27,368 INFO [train.py:874] (0/4) Epoch 10, batch 300, aishell_loss[loss=0.1927, simple_loss=0.2725, pruned_loss=0.05645, over 4944.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2505, pruned_loss=0.05112, over 766648.48 frames.], batch size: 68, aishell_tot_loss[loss=0.1782, simple_loss=0.258, pruned_loss=0.04919, over 556796.63 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.241, pruned_loss=0.05292, over 482373.09 frames.], batch size: 68, lr: 8.12e-04
+2022-06-18 17:03:58,465 INFO [train.py:874] (0/4) Epoch 10, batch 350, datatang_loss[loss=0.1949, simple_loss=0.26, pruned_loss=0.06491, over 4919.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2515, pruned_loss=0.05214, over 815196.81 frames.], batch size: 73, aishell_tot_loss[loss=0.177, simple_loss=0.257, pruned_loss=0.04843, over 605507.88 frames.], datatang_tot_loss[loss=0.1776, simple_loss=0.2443, pruned_loss=0.05542, over 543771.81 frames.], batch size: 73, lr: 8.12e-04
+2022-06-18 17:04:26,544 INFO [train.py:874] (0/4) Epoch 10, batch 400, datatang_loss[loss=0.1776, simple_loss=0.2389, pruned_loss=0.05815, over 4924.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2515, pruned_loss=0.05239, over 852914.16 frames.], batch size: 34, aishell_tot_loss[loss=0.1768, simple_loss=0.257, pruned_loss=0.04829, over 651921.42 frames.], datatang_tot_loss[loss=0.1784, simple_loss=0.2446, pruned_loss=0.0561, over 593827.55 frames.], batch size: 34, lr: 8.11e-04
+2022-06-18 17:04:57,243 INFO [train.py:874] (0/4) Epoch 10, batch 450, aishell_loss[loss=0.1647, simple_loss=0.2552, pruned_loss=0.03713, over 4932.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2521, pruned_loss=0.05218, over 882505.15 frames.], batch size: 64, aishell_tot_loss[loss=0.176, simple_loss=0.2563, pruned_loss=0.04789, over 699889.78 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2461, pruned_loss=0.05664, over 629658.46 frames.], batch size: 64, lr: 8.11e-04
+2022-06-18 17:05:26,438 INFO [train.py:874] (0/4) Epoch 10, batch 500, aishell_loss[loss=0.186, simple_loss=0.2772, pruned_loss=0.0474, over 4956.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2519, pruned_loss=0.05193, over 905020.78 frames.], batch size: 80, aishell_tot_loss[loss=0.1756, simple_loss=0.2556, pruned_loss=0.04774, over 739377.10 frames.], datatang_tot_loss[loss=0.1799, simple_loss=0.2464, pruned_loss=0.05667, over 663652.26 frames.], batch size: 80, lr: 8.10e-04
+2022-06-18 17:05:55,039 INFO [train.py:874] (0/4) Epoch 10, batch 550, datatang_loss[loss=0.1907, simple_loss=0.2564, pruned_loss=0.06251, over 4967.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2519, pruned_loss=0.05215, over 922791.52 frames.], batch size: 45, aishell_tot_loss[loss=0.1757, simple_loss=0.2555, pruned_loss=0.04793, over 773493.86 frames.], datatang_tot_loss[loss=0.1802, simple_loss=0.2465, pruned_loss=0.05695, over 694530.54 frames.], batch size: 45, lr: 8.10e-04
+2022-06-18 17:06:26,462 INFO [train.py:874] (0/4) Epoch 10, batch 600, aishell_loss[loss=0.1556, simple_loss=0.2393, pruned_loss=0.03598, over 4855.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2513, pruned_loss=0.05223, over 936420.93 frames.], batch size: 36, aishell_tot_loss[loss=0.1755, simple_loss=0.2551, pruned_loss=0.04795, over 800158.17 frames.], datatang_tot_loss[loss=0.1803, simple_loss=0.2463, pruned_loss=0.05713, over 726095.08 frames.], batch size: 36, lr: 8.09e-04
+2022-06-18 17:06:55,504 INFO [train.py:874] (0/4) Epoch 10, batch 650, datatang_loss[loss=0.1951, simple_loss=0.2678, pruned_loss=0.0612, over 4959.00 frames.], tot_loss[loss=0.178, simple_loss=0.2509, pruned_loss=0.05254, over 947702.62 frames.], batch size: 99, aishell_tot_loss[loss=0.1758, simple_loss=0.2553, pruned_loss=0.04817, over 819072.94 frames.], datatang_tot_loss[loss=0.1799, simple_loss=0.246, pruned_loss=0.05695, over 761226.71 frames.], batch size: 99, lr: 8.09e-04
+2022-06-18 17:07:24,953 INFO [train.py:874] (0/4) Epoch 10, batch 700, aishell_loss[loss=0.192, simple_loss=0.2696, pruned_loss=0.05717, over 4872.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2511, pruned_loss=0.05252, over 955732.45 frames.], batch size: 37, aishell_tot_loss[loss=0.1757, simple_loss=0.2551, pruned_loss=0.04819, over 839967.22 frames.], datatang_tot_loss[loss=0.1802, simple_loss=0.2464, pruned_loss=0.05702, over 785453.11 frames.], batch size: 37, lr: 8.08e-04
+2022-06-18 17:07:56,723 INFO [train.py:874] (0/4) Epoch 10, batch 750, aishell_loss[loss=0.1784, simple_loss=0.257, pruned_loss=0.04993, over 4925.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2509, pruned_loss=0.05313, over 962214.03 frames.], batch size: 32, aishell_tot_loss[loss=0.1751, simple_loss=0.2543, pruned_loss=0.04798, over 854831.76 frames.], datatang_tot_loss[loss=0.1814, simple_loss=0.2472, pruned_loss=0.05782, over 811845.29 frames.], batch size: 32, lr: 8.08e-04
+2022-06-18 17:08:27,666 INFO [train.py:874] (0/4) Epoch 10, batch 800, aishell_loss[loss=0.1195, simple_loss=0.2009, pruned_loss=0.01906, over 4975.00 frames.], tot_loss[loss=0.1786, simple_loss=0.251, pruned_loss=0.05306, over 967499.27 frames.], batch size: 27, aishell_tot_loss[loss=0.1745, simple_loss=0.2539, pruned_loss=0.0476, over 868578.27 frames.], datatang_tot_loss[loss=0.182, simple_loss=0.248, pruned_loss=0.05801, over 834612.21 frames.], batch size: 27, lr: 8.07e-04
+2022-06-18 17:08:57,273 INFO [train.py:874] (0/4) Epoch 10, batch 850, datatang_loss[loss=0.1633, simple_loss=0.2374, pruned_loss=0.0446, over 4937.00 frames.], tot_loss[loss=0.179, simple_loss=0.2513, pruned_loss=0.05337, over 971705.31 frames.], batch size: 81, aishell_tot_loss[loss=0.1751, simple_loss=0.2543, pruned_loss=0.04795, over 881112.35 frames.], datatang_tot_loss[loss=0.182, simple_loss=0.248, pruned_loss=0.05794, over 854280.61 frames.], batch size: 81, lr: 8.07e-04
+2022-06-18 17:09:29,301 INFO [train.py:874] (0/4) Epoch 10, batch 900, aishell_loss[loss=0.1752, simple_loss=0.2371, pruned_loss=0.0566, over 4972.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2519, pruned_loss=0.05381, over 974823.36 frames.], batch size: 27, aishell_tot_loss[loss=0.1755, simple_loss=0.2545, pruned_loss=0.04825, over 893774.72 frames.], datatang_tot_loss[loss=0.1827, simple_loss=0.2487, pruned_loss=0.05833, over 869313.24 frames.], batch size: 27, lr: 8.06e-04
+2022-06-18 17:09:58,847 INFO [train.py:874] (0/4) Epoch 10, batch 950, datatang_loss[loss=0.2077, simple_loss=0.2719, pruned_loss=0.07171, over 4748.00 frames.], tot_loss[loss=0.1798, simple_loss=0.252, pruned_loss=0.05379, over 976828.65 frames.], batch size: 25, aishell_tot_loss[loss=0.1757, simple_loss=0.2545, pruned_loss=0.04841, over 904836.65 frames.], datatang_tot_loss[loss=0.1827, simple_loss=0.2488, pruned_loss=0.05835, over 882262.62 frames.], batch size: 25, lr: 8.05e-04
+2022-06-18 17:10:29,062 INFO [train.py:874] (0/4) Epoch 10, batch 1000, aishell_loss[loss=0.2011, simple_loss=0.2782, pruned_loss=0.06195, over 4964.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2519, pruned_loss=0.05336, over 979046.40 frames.], batch size: 61, aishell_tot_loss[loss=0.176, simple_loss=0.2551, pruned_loss=0.04842, over 913505.03 frames.], datatang_tot_loss[loss=0.182, simple_loss=0.2482, pruned_loss=0.05789, over 895753.38 frames.], batch size: 61, lr: 8.05e-04
+2022-06-18 17:10:29,065 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 17:10:46,216 INFO [train.py:914] (0/4) Epoch 10, validation: loss=0.1689, simple_loss=0.2533, pruned_loss=0.04224, over 1622729.00 frames.
+2022-06-18 17:11:16,220 INFO [train.py:874] (0/4) Epoch 10, batch 1050, aishell_loss[loss=0.1616, simple_loss=0.2431, pruned_loss=0.04009, over 4912.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2524, pruned_loss=0.05432, over 980212.23 frames.], batch size: 46, aishell_tot_loss[loss=0.1763, simple_loss=0.2553, pruned_loss=0.04867, over 918274.16 frames.], datatang_tot_loss[loss=0.1828, simple_loss=0.249, pruned_loss=0.05829, over 910337.86 frames.], batch size: 46, lr: 8.04e-04
+2022-06-18 17:11:46,554 INFO [train.py:874] (0/4) Epoch 10, batch 1100, aishell_loss[loss=0.1719, simple_loss=0.2568, pruned_loss=0.04352, over 4895.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2521, pruned_loss=0.0538, over 981376.00 frames.], batch size: 42, aishell_tot_loss[loss=0.1763, simple_loss=0.2554, pruned_loss=0.04861, over 927282.93 frames.], datatang_tot_loss[loss=0.1824, simple_loss=0.2485, pruned_loss=0.05814, over 917929.48 frames.], batch size: 42, lr: 8.04e-04
+2022-06-18 17:12:16,097 INFO [train.py:874] (0/4) Epoch 10, batch 1150, datatang_loss[loss=0.1676, simple_loss=0.2331, pruned_loss=0.05106, over 4917.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2518, pruned_loss=0.0533, over 982416.64 frames.], batch size: 75, aishell_tot_loss[loss=0.1759, simple_loss=0.2552, pruned_loss=0.04833, over 934478.43 frames.], datatang_tot_loss[loss=0.1821, simple_loss=0.2483, pruned_loss=0.05801, over 925640.19 frames.], batch size: 75, lr: 8.03e-04
+2022-06-18 17:12:47,051 INFO [train.py:874] (0/4) Epoch 10, batch 1200, aishell_loss[loss=0.1747, simple_loss=0.2582, pruned_loss=0.0456, over 4916.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2509, pruned_loss=0.05323, over 983001.53 frames.], batch size: 46, aishell_tot_loss[loss=0.1755, simple_loss=0.2549, pruned_loss=0.04799, over 939162.84 frames.], datatang_tot_loss[loss=0.1819, simple_loss=0.2478, pruned_loss=0.05804, over 934083.27 frames.], batch size: 46, lr: 8.03e-04
+2022-06-18 17:13:18,856 INFO [train.py:874] (0/4) Epoch 10, batch 1250, aishell_loss[loss=0.207, simple_loss=0.2853, pruned_loss=0.06433, over 4948.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2511, pruned_loss=0.05301, over 983260.87 frames.], batch size: 64, aishell_tot_loss[loss=0.1753, simple_loss=0.2549, pruned_loss=0.04789, over 944571.72 frames.], datatang_tot_loss[loss=0.1819, simple_loss=0.2479, pruned_loss=0.05799, over 939838.72 frames.], batch size: 64, lr: 8.02e-04
+2022-06-18 17:13:47,295 INFO [train.py:874] (0/4) Epoch 10, batch 1300, datatang_loss[loss=0.1676, simple_loss=0.2376, pruned_loss=0.04878, over 4959.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2503, pruned_loss=0.05255, over 983573.50 frames.], batch size: 45, aishell_tot_loss[loss=0.1748, simple_loss=0.2544, pruned_loss=0.04762, over 948681.21 frames.], datatang_tot_loss[loss=0.1814, simple_loss=0.2475, pruned_loss=0.05768, over 945785.38 frames.], batch size: 45, lr: 8.02e-04
+2022-06-18 17:14:20,325 INFO [train.py:874] (0/4) Epoch 10, batch 1350, datatang_loss[loss=0.171, simple_loss=0.2387, pruned_loss=0.05163, over 4924.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2498, pruned_loss=0.05272, over 984028.01 frames.], batch size: 71, aishell_tot_loss[loss=0.1752, simple_loss=0.255, pruned_loss=0.04768, over 951155.82 frames.], datatang_tot_loss[loss=0.1806, simple_loss=0.2466, pruned_loss=0.05724, over 952385.86 frames.], batch size: 71, lr: 8.01e-04
+2022-06-18 17:14:52,514 INFO [train.py:874] (0/4) Epoch 10, batch 1400, datatang_loss[loss=0.1656, simple_loss=0.2413, pruned_loss=0.04492, over 4977.00 frames.], tot_loss[loss=0.178, simple_loss=0.2506, pruned_loss=0.05268, over 984480.24 frames.], batch size: 31, aishell_tot_loss[loss=0.1759, simple_loss=0.2555, pruned_loss=0.04817, over 956187.64 frames.], datatang_tot_loss[loss=0.1803, simple_loss=0.2465, pruned_loss=0.05703, over 955430.97 frames.], batch size: 31, lr: 8.01e-04
+2022-06-18 17:15:21,139 INFO [train.py:874] (0/4) Epoch 10, batch 1450, datatang_loss[loss=0.1613, simple_loss=0.2352, pruned_loss=0.04367, over 4897.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2503, pruned_loss=0.05263, over 984680.21 frames.], batch size: 42, aishell_tot_loss[loss=0.1762, simple_loss=0.2557, pruned_loss=0.04834, over 959042.34 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2461, pruned_loss=0.05668, over 959540.94 frames.], batch size: 42, lr: 8.00e-04
+2022-06-18 17:15:52,705 INFO [train.py:874] (0/4) Epoch 10, batch 1500, datatang_loss[loss=0.2173, simple_loss=0.2832, pruned_loss=0.07568, over 4912.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2509, pruned_loss=0.05299, over 984753.09 frames.], batch size: 98, aishell_tot_loss[loss=0.1769, simple_loss=0.2564, pruned_loss=0.04871, over 961807.66 frames.], datatang_tot_loss[loss=0.1796, simple_loss=0.2459, pruned_loss=0.05667, over 962826.18 frames.], batch size: 98, lr: 8.00e-04
+2022-06-18 17:16:23,733 INFO [train.py:874] (0/4) Epoch 10, batch 1550, datatang_loss[loss=0.1815, simple_loss=0.2587, pruned_loss=0.05214, over 4929.00 frames.], tot_loss[loss=0.1793, simple_loss=0.252, pruned_loss=0.05331, over 984356.46 frames.], batch size: 94, aishell_tot_loss[loss=0.1768, simple_loss=0.2563, pruned_loss=0.04866, over 964657.93 frames.], datatang_tot_loss[loss=0.1808, simple_loss=0.2469, pruned_loss=0.05734, over 964859.10 frames.], batch size: 94, lr: 7.99e-04
+2022-06-18 17:16:51,686 INFO [train.py:874] (0/4) Epoch 10, batch 1600, aishell_loss[loss=0.1837, simple_loss=0.2627, pruned_loss=0.05233, over 4872.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2519, pruned_loss=0.05322, over 984341.16 frames.], batch size: 42, aishell_tot_loss[loss=0.1772, simple_loss=0.2567, pruned_loss=0.04882, over 966785.64 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2464, pruned_loss=0.05715, over 967325.65 frames.], batch size: 42, lr: 7.99e-04
+2022-06-18 17:17:24,165 INFO [train.py:874] (0/4) Epoch 10, batch 1650, datatang_loss[loss=0.1805, simple_loss=0.2393, pruned_loss=0.06086, over 4978.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2519, pruned_loss=0.05341, over 984415.22 frames.], batch size: 45, aishell_tot_loss[loss=0.1773, simple_loss=0.2569, pruned_loss=0.0488, over 968429.50 frames.], datatang_tot_loss[loss=0.1806, simple_loss=0.2465, pruned_loss=0.05732, over 969835.42 frames.], batch size: 45, lr: 7.98e-04
+2022-06-18 17:17:56,374 INFO [train.py:874] (0/4) Epoch 10, batch 1700, aishell_loss[loss=0.2002, simple_loss=0.2869, pruned_loss=0.05674, over 4960.00 frames.], tot_loss[loss=0.179, simple_loss=0.2519, pruned_loss=0.05306, over 984789.49 frames.], batch size: 64, aishell_tot_loss[loss=0.1771, simple_loss=0.2565, pruned_loss=0.0488, over 970450.42 frames.], datatang_tot_loss[loss=0.1805, simple_loss=0.2468, pruned_loss=0.05712, over 971823.47 frames.], batch size: 64, lr: 7.98e-04
+2022-06-18 17:18:24,504 INFO [train.py:874] (0/4) Epoch 10, batch 1750, datatang_loss[loss=0.1874, simple_loss=0.2572, pruned_loss=0.05877, over 4919.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2512, pruned_loss=0.05298, over 984871.18 frames.], batch size: 77, aishell_tot_loss[loss=0.177, simple_loss=0.2564, pruned_loss=0.04877, over 972058.46 frames.], datatang_tot_loss[loss=0.1802, simple_loss=0.2463, pruned_loss=0.05711, over 973533.78 frames.], batch size: 77, lr: 7.97e-04
+2022-06-18 17:18:55,216 INFO [train.py:874] (0/4) Epoch 10, batch 1800, datatang_loss[loss=0.2031, simple_loss=0.2726, pruned_loss=0.06685, over 4929.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2515, pruned_loss=0.05276, over 985110.76 frames.], batch size: 62, aishell_tot_loss[loss=0.1769, simple_loss=0.2565, pruned_loss=0.04863, over 973743.87 frames.], datatang_tot_loss[loss=0.1802, simple_loss=0.2465, pruned_loss=0.057, over 974954.43 frames.], batch size: 62, lr: 7.97e-04
+2022-06-18 17:19:27,418 INFO [train.py:874] (0/4) Epoch 10, batch 1850, datatang_loss[loss=0.1731, simple_loss=0.2335, pruned_loss=0.05629, over 4984.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2526, pruned_loss=0.05358, over 985319.48 frames.], batch size: 34, aishell_tot_loss[loss=0.1777, simple_loss=0.2574, pruned_loss=0.04906, over 975213.68 frames.], datatang_tot_loss[loss=0.1809, simple_loss=0.2469, pruned_loss=0.05744, over 976242.55 frames.], batch size: 34, lr: 7.96e-04
+2022-06-18 17:19:55,245 INFO [train.py:874] (0/4) Epoch 10, batch 1900, aishell_loss[loss=0.1851, simple_loss=0.2655, pruned_loss=0.05234, over 4884.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2507, pruned_loss=0.0532, over 985173.79 frames.], batch size: 50, aishell_tot_loss[loss=0.1771, simple_loss=0.2565, pruned_loss=0.0489, over 975926.46 frames.], datatang_tot_loss[loss=0.1801, simple_loss=0.2461, pruned_loss=0.057, over 977592.71 frames.], batch size: 50, lr: 7.96e-04
+2022-06-18 17:20:27,318 INFO [train.py:874] (0/4) Epoch 10, batch 1950, datatang_loss[loss=0.1724, simple_loss=0.2518, pruned_loss=0.04648, over 4916.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2514, pruned_loss=0.05319, over 985118.21 frames.], batch size: 81, aishell_tot_loss[loss=0.1775, simple_loss=0.257, pruned_loss=0.04898, over 976931.41 frames.], datatang_tot_loss[loss=0.1801, simple_loss=0.2461, pruned_loss=0.05706, over 978524.19 frames.], batch size: 81, lr: 7.95e-04
+2022-06-18 17:20:59,402 INFO [train.py:874] (0/4) Epoch 10, batch 2000, aishell_loss[loss=0.1507, simple_loss=0.243, pruned_loss=0.02919, over 4946.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2507, pruned_loss=0.05219, over 985076.07 frames.], batch size: 56, aishell_tot_loss[loss=0.1773, simple_loss=0.2571, pruned_loss=0.04872, over 977804.26 frames.], datatang_tot_loss[loss=0.1789, simple_loss=0.2453, pruned_loss=0.05629, over 979337.86 frames.], batch size: 56, lr: 7.95e-04
+2022-06-18 17:20:59,405 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 17:21:15,567 INFO [train.py:914] (0/4) Epoch 10, validation: loss=0.1694, simple_loss=0.2523, pruned_loss=0.04323, over 1622729.00 frames.
+2022-06-18 17:21:47,947 INFO [train.py:874] (0/4) Epoch 10, batch 2050, aishell_loss[loss=0.1696, simple_loss=0.2544, pruned_loss=0.0424, over 4942.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2517, pruned_loss=0.05269, over 985135.40 frames.], batch size: 54, aishell_tot_loss[loss=0.177, simple_loss=0.2568, pruned_loss=0.04857, over 978934.86 frames.], datatang_tot_loss[loss=0.1802, simple_loss=0.2463, pruned_loss=0.05704, over 979814.73 frames.], batch size: 54, lr: 7.94e-04
+2022-06-18 17:22:16,702 INFO [train.py:874] (0/4) Epoch 10, batch 2100, aishell_loss[loss=0.204, simple_loss=0.2794, pruned_loss=0.06429, over 4948.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2509, pruned_loss=0.05209, over 985309.31 frames.], batch size: 49, aishell_tot_loss[loss=0.1767, simple_loss=0.2566, pruned_loss=0.04839, over 979797.49 frames.], datatang_tot_loss[loss=0.1794, simple_loss=0.2456, pruned_loss=0.05663, over 980502.49 frames.], batch size: 49, lr: 7.94e-04
+2022-06-18 17:22:48,241 INFO [train.py:874] (0/4) Epoch 10, batch 2150, aishell_loss[loss=0.1755, simple_loss=0.2601, pruned_loss=0.04548, over 4983.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2512, pruned_loss=0.05229, over 985878.09 frames.], batch size: 39, aishell_tot_loss[loss=0.1767, simple_loss=0.2567, pruned_loss=0.04836, over 980685.01 frames.], datatang_tot_loss[loss=0.1796, simple_loss=0.2458, pruned_loss=0.05669, over 981432.94 frames.], batch size: 39, lr: 7.93e-04
+2022-06-18 17:23:19,924 INFO [train.py:874] (0/4) Epoch 10, batch 2200, aishell_loss[loss=0.1816, simple_loss=0.2636, pruned_loss=0.04974, over 4942.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2513, pruned_loss=0.05261, over 985662.33 frames.], batch size: 45, aishell_tot_loss[loss=0.1767, simple_loss=0.2566, pruned_loss=0.04839, over 981188.04 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2461, pruned_loss=0.05697, over 981847.15 frames.], batch size: 45, lr: 7.93e-04
+2022-06-18 17:23:47,885 INFO [train.py:874] (0/4) Epoch 10, batch 2250, datatang_loss[loss=0.183, simple_loss=0.2473, pruned_loss=0.05929, over 4967.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2511, pruned_loss=0.0524, over 985496.83 frames.], batch size: 60, aishell_tot_loss[loss=0.1766, simple_loss=0.2565, pruned_loss=0.04834, over 981657.87 frames.], datatang_tot_loss[loss=0.1798, simple_loss=0.2458, pruned_loss=0.05686, over 982179.58 frames.], batch size: 60, lr: 7.92e-04
+2022-06-18 17:24:17,567 INFO [train.py:874] (0/4) Epoch 10, batch 2300, aishell_loss[loss=0.1586, simple_loss=0.2367, pruned_loss=0.04022, over 4930.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2517, pruned_loss=0.05258, over 985937.05 frames.], batch size: 49, aishell_tot_loss[loss=0.1766, simple_loss=0.2562, pruned_loss=0.04851, over 982469.59 frames.], datatang_tot_loss[loss=0.1803, simple_loss=0.2465, pruned_loss=0.05707, over 982685.17 frames.], batch size: 49, lr: 7.92e-04
+2022-06-18 17:24:49,700 INFO [train.py:874] (0/4) Epoch 10, batch 2350, datatang_loss[loss=0.2405, simple_loss=0.3019, pruned_loss=0.08957, over 4936.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2514, pruned_loss=0.05248, over 985548.90 frames.], batch size: 108, aishell_tot_loss[loss=0.1766, simple_loss=0.2563, pruned_loss=0.04843, over 982703.45 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2461, pruned_loss=0.05699, over 982829.74 frames.], batch size: 108, lr: 7.91e-04
+2022-06-18 17:25:17,485 INFO [train.py:874] (0/4) Epoch 10, batch 2400, datatang_loss[loss=0.1522, simple_loss=0.2251, pruned_loss=0.03967, over 4965.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2514, pruned_loss=0.05287, over 985257.57 frames.], batch size: 60, aishell_tot_loss[loss=0.1765, simple_loss=0.2562, pruned_loss=0.04839, over 982590.76 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2464, pruned_loss=0.0572, over 983301.73 frames.], batch size: 60, lr: 7.91e-04
+2022-06-18 17:25:49,002 INFO [train.py:874] (0/4) Epoch 10, batch 2450, datatang_loss[loss=0.1972, simple_loss=0.2569, pruned_loss=0.06878, over 4920.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2513, pruned_loss=0.05257, over 985327.13 frames.], batch size: 75, aishell_tot_loss[loss=0.1765, simple_loss=0.2563, pruned_loss=0.0484, over 982806.65 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2463, pruned_loss=0.05688, over 983709.09 frames.], batch size: 75, lr: 7.90e-04
+2022-06-18 17:26:19,963 INFO [train.py:874] (0/4) Epoch 10, batch 2500, aishell_loss[loss=0.1834, simple_loss=0.2641, pruned_loss=0.0514, over 4974.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2512, pruned_loss=0.05219, over 985636.50 frames.], batch size: 61, aishell_tot_loss[loss=0.1763, simple_loss=0.256, pruned_loss=0.0483, over 983476.81 frames.], datatang_tot_loss[loss=0.1799, simple_loss=0.2463, pruned_loss=0.05674, over 983863.52 frames.], batch size: 61, lr: 7.90e-04
+2022-06-18 17:26:50,240 INFO [train.py:874] (0/4) Epoch 10, batch 2550, datatang_loss[loss=0.1924, simple_loss=0.2566, pruned_loss=0.06414, over 4886.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2507, pruned_loss=0.05182, over 985225.72 frames.], batch size: 52, aishell_tot_loss[loss=0.1757, simple_loss=0.2556, pruned_loss=0.04796, over 983518.29 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2462, pruned_loss=0.05661, over 983849.35 frames.], batch size: 52, lr: 7.89e-04
+2022-06-18 17:27:22,110 INFO [train.py:874] (0/4) Epoch 10, batch 2600, datatang_loss[loss=0.1712, simple_loss=0.2351, pruned_loss=0.05371, over 4916.00 frames.], tot_loss[loss=0.177, simple_loss=0.2501, pruned_loss=0.05201, over 985478.75 frames.], batch size: 81, aishell_tot_loss[loss=0.1752, simple_loss=0.2549, pruned_loss=0.04777, over 983669.20 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2461, pruned_loss=0.05696, over 984339.41 frames.], batch size: 81, lr: 7.89e-04
+2022-06-18 17:27:53,258 INFO [train.py:874] (0/4) Epoch 10, batch 2650, aishell_loss[loss=0.1685, simple_loss=0.2434, pruned_loss=0.04679, over 4949.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2487, pruned_loss=0.05176, over 985570.30 frames.], batch size: 45, aishell_tot_loss[loss=0.1749, simple_loss=0.2544, pruned_loss=0.0477, over 983917.51 frames.], datatang_tot_loss[loss=0.1791, simple_loss=0.2451, pruned_loss=0.05653, over 984514.28 frames.], batch size: 45, lr: 7.88e-04
+2022-06-18 17:28:23,886 INFO [train.py:874] (0/4) Epoch 10, batch 2700, datatang_loss[loss=0.1691, simple_loss=0.2395, pruned_loss=0.04937, over 4940.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2486, pruned_loss=0.05241, over 985514.92 frames.], batch size: 69, aishell_tot_loss[loss=0.175, simple_loss=0.2543, pruned_loss=0.04784, over 983918.18 frames.], datatang_tot_loss[loss=0.1792, simple_loss=0.2453, pruned_loss=0.05655, over 984732.37 frames.], batch size: 69, lr: 7.88e-04
+2022-06-18 17:28:56,001 INFO [train.py:874] (0/4) Epoch 10, batch 2750, aishell_loss[loss=0.1833, simple_loss=0.2696, pruned_loss=0.04846, over 4977.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2487, pruned_loss=0.05193, over 985656.62 frames.], batch size: 51, aishell_tot_loss[loss=0.1751, simple_loss=0.2546, pruned_loss=0.04776, over 984239.12 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2448, pruned_loss=0.05609, over 984836.30 frames.], batch size: 51, lr: 7.87e-04
+2022-06-18 17:29:26,466 INFO [train.py:874] (0/4) Epoch 10, batch 2800, datatang_loss[loss=0.1982, simple_loss=0.2647, pruned_loss=0.06585, over 4924.00 frames.], tot_loss[loss=0.177, simple_loss=0.2498, pruned_loss=0.05208, over 985914.09 frames.], batch size: 81, aishell_tot_loss[loss=0.1754, simple_loss=0.255, pruned_loss=0.04792, over 984555.86 frames.], datatang_tot_loss[loss=0.1788, simple_loss=0.2452, pruned_loss=0.05615, over 985061.00 frames.], batch size: 81, lr: 7.87e-04
+2022-06-18 17:29:28,280 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-40000.pt
+2022-06-18 17:30:00,713 INFO [train.py:874] (0/4) Epoch 10, batch 2850, datatang_loss[loss=0.1788, simple_loss=0.2385, pruned_loss=0.05957, over 4959.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2495, pruned_loss=0.05175, over 985201.47 frames.], batch size: 55, aishell_tot_loss[loss=0.1751, simple_loss=0.2546, pruned_loss=0.04783, over 983993.05 frames.], datatang_tot_loss[loss=0.1786, simple_loss=0.2453, pruned_loss=0.05593, over 985132.66 frames.], batch size: 55, lr: 7.86e-04
+2022-06-18 17:30:32,966 INFO [train.py:874] (0/4) Epoch 10, batch 2900, datatang_loss[loss=0.1415, simple_loss=0.2195, pruned_loss=0.03171, over 4918.00 frames.], tot_loss[loss=0.1772, simple_loss=0.25, pruned_loss=0.05216, over 985462.65 frames.], batch size: 83, aishell_tot_loss[loss=0.1767, simple_loss=0.2558, pruned_loss=0.04875, over 984186.88 frames.], datatang_tot_loss[loss=0.1777, simple_loss=0.2446, pruned_loss=0.05537, over 985353.10 frames.], batch size: 83, lr: 7.86e-04
+2022-06-18 17:31:03,359 INFO [train.py:874] (0/4) Epoch 10, batch 2950, aishell_loss[loss=0.1694, simple_loss=0.2473, pruned_loss=0.04578, over 4982.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2502, pruned_loss=0.05201, over 985040.51 frames.], batch size: 38, aishell_tot_loss[loss=0.1763, simple_loss=0.2556, pruned_loss=0.04851, over 983808.95 frames.], datatang_tot_loss[loss=0.1779, simple_loss=0.2448, pruned_loss=0.05553, over 985466.06 frames.], batch size: 38, lr: 7.85e-04
+2022-06-18 17:31:32,443 INFO [train.py:874] (0/4) Epoch 10, batch 3000, aishell_loss[loss=0.1708, simple_loss=0.2472, pruned_loss=0.04726, over 4869.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2508, pruned_loss=0.05206, over 985114.67 frames.], batch size: 35, aishell_tot_loss[loss=0.1766, simple_loss=0.2563, pruned_loss=0.04849, over 984164.65 frames.], datatang_tot_loss[loss=0.178, simple_loss=0.2448, pruned_loss=0.05556, over 985299.42 frames.], batch size: 35, lr: 7.85e-04
+2022-06-18 17:31:32,446 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 17:31:49,158 INFO [train.py:914] (0/4) Epoch 10, validation: loss=0.1672, simple_loss=0.2512, pruned_loss=0.04165, over 1622729.00 frames.
+2022-06-18 17:32:18,362 INFO [train.py:874] (0/4) Epoch 10, batch 3050, aishell_loss[loss=0.1591, simple_loss=0.2477, pruned_loss=0.03524, over 4907.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2507, pruned_loss=0.05191, over 985407.35 frames.], batch size: 41, aishell_tot_loss[loss=0.176, simple_loss=0.2558, pruned_loss=0.04811, over 984150.82 frames.], datatang_tot_loss[loss=0.1784, simple_loss=0.2453, pruned_loss=0.05576, over 985706.37 frames.], batch size: 41, lr: 7.85e-04
+2022-06-18 17:32:48,940 INFO [train.py:874] (0/4) Epoch 10, batch 3100, aishell_loss[loss=0.138, simple_loss=0.1988, pruned_loss=0.03863, over 4942.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2505, pruned_loss=0.05204, over 984877.74 frames.], batch size: 21, aishell_tot_loss[loss=0.1758, simple_loss=0.2555, pruned_loss=0.04807, over 983738.79 frames.], datatang_tot_loss[loss=0.1786, simple_loss=0.2453, pruned_loss=0.056, over 985686.07 frames.], batch size: 21, lr: 7.84e-04
+2022-06-18 17:33:21,043 INFO [train.py:874] (0/4) Epoch 10, batch 3150, aishell_loss[loss=0.1391, simple_loss=0.2027, pruned_loss=0.03777, over 4922.00 frames.], tot_loss[loss=0.1766, simple_loss=0.25, pruned_loss=0.05153, over 985257.88 frames.], batch size: 25, aishell_tot_loss[loss=0.1754, simple_loss=0.2554, pruned_loss=0.04772, over 984032.84 frames.], datatang_tot_loss[loss=0.1783, simple_loss=0.245, pruned_loss=0.05582, over 985825.08 frames.], batch size: 25, lr: 7.84e-04
+2022-06-18 17:33:49,713 INFO [train.py:874] (0/4) Epoch 10, batch 3200, aishell_loss[loss=0.1825, simple_loss=0.27, pruned_loss=0.04748, over 4974.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2508, pruned_loss=0.05184, over 985317.91 frames.], batch size: 48, aishell_tot_loss[loss=0.176, simple_loss=0.256, pruned_loss=0.04794, over 984035.81 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2451, pruned_loss=0.05593, over 985980.05 frames.], batch size: 48, lr: 7.83e-04
+2022-06-18 17:34:21,001 INFO [train.py:874] (0/4) Epoch 10, batch 3250, datatang_loss[loss=0.1935, simple_loss=0.2515, pruned_loss=0.06774, over 4971.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2509, pruned_loss=0.05162, over 985240.41 frames.], batch size: 48, aishell_tot_loss[loss=0.1759, simple_loss=0.2562, pruned_loss=0.04777, over 983864.44 frames.], datatang_tot_loss[loss=0.1784, simple_loss=0.2448, pruned_loss=0.05601, over 986183.49 frames.], batch size: 48, lr: 7.83e-04
+2022-06-18 17:34:51,783 INFO [train.py:874] (0/4) Epoch 10, batch 3300, datatang_loss[loss=0.147, simple_loss=0.2265, pruned_loss=0.03374, over 4915.00 frames.], tot_loss[loss=0.176, simple_loss=0.2498, pruned_loss=0.05108, over 985605.07 frames.], batch size: 77, aishell_tot_loss[loss=0.1752, simple_loss=0.2556, pruned_loss=0.04739, over 984137.41 frames.], datatang_tot_loss[loss=0.1779, simple_loss=0.2445, pruned_loss=0.05559, over 986300.78 frames.], batch size: 77, lr: 7.82e-04
+2022-06-18 17:35:21,929 INFO [train.py:874] (0/4) Epoch 10, batch 3350, aishell_loss[loss=0.147, simple_loss=0.2347, pruned_loss=0.02965, over 4945.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2507, pruned_loss=0.05119, over 985389.70 frames.], batch size: 49, aishell_tot_loss[loss=0.176, simple_loss=0.2567, pruned_loss=0.04764, over 984038.43 frames.], datatang_tot_loss[loss=0.1776, simple_loss=0.2443, pruned_loss=0.05545, over 986280.12 frames.], batch size: 49, lr: 7.82e-04
+2022-06-18 17:35:53,647 INFO [train.py:874] (0/4) Epoch 10, batch 3400, aishell_loss[loss=0.1452, simple_loss=0.2242, pruned_loss=0.03307, over 4963.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2501, pruned_loss=0.05062, over 985473.52 frames.], batch size: 25, aishell_tot_loss[loss=0.1752, simple_loss=0.2561, pruned_loss=0.04718, over 984303.77 frames.], datatang_tot_loss[loss=0.1774, simple_loss=0.2441, pruned_loss=0.05539, over 986207.29 frames.], batch size: 25, lr: 7.81e-04
+2022-06-18 17:36:23,921 INFO [train.py:874] (0/4) Epoch 10, batch 3450, datatang_loss[loss=0.1796, simple_loss=0.2531, pruned_loss=0.05311, over 4854.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2511, pruned_loss=0.05111, over 985381.57 frames.], batch size: 33, aishell_tot_loss[loss=0.1759, simple_loss=0.2566, pruned_loss=0.04756, over 984305.56 frames.], datatang_tot_loss[loss=0.1777, simple_loss=0.2445, pruned_loss=0.05547, over 986196.47 frames.], batch size: 33, lr: 7.81e-04
+2022-06-18 17:36:53,798 INFO [train.py:874] (0/4) Epoch 10, batch 3500, aishell_loss[loss=0.1774, simple_loss=0.2503, pruned_loss=0.05229, over 4874.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2518, pruned_loss=0.05132, over 985354.30 frames.], batch size: 42, aishell_tot_loss[loss=0.1757, simple_loss=0.2565, pruned_loss=0.04744, over 984334.52 frames.], datatang_tot_loss[loss=0.1784, simple_loss=0.2453, pruned_loss=0.05578, over 986183.79 frames.], batch size: 42, lr: 7.80e-04
+2022-06-18 17:37:26,109 INFO [train.py:874] (0/4) Epoch 10, batch 3550, datatang_loss[loss=0.17, simple_loss=0.251, pruned_loss=0.04444, over 4944.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2517, pruned_loss=0.05194, over 985046.13 frames.], batch size: 88, aishell_tot_loss[loss=0.1755, simple_loss=0.256, pruned_loss=0.04744, over 984154.17 frames.], datatang_tot_loss[loss=0.1793, simple_loss=0.2461, pruned_loss=0.05624, over 986027.45 frames.], batch size: 88, lr: 7.80e-04
+2022-06-18 17:37:55,039 INFO [train.py:874] (0/4) Epoch 10, batch 3600, datatang_loss[loss=0.1542, simple_loss=0.2338, pruned_loss=0.03733, over 4940.00 frames.], tot_loss[loss=0.1768, simple_loss=0.251, pruned_loss=0.05134, over 984623.00 frames.], batch size: 62, aishell_tot_loss[loss=0.1753, simple_loss=0.2558, pruned_loss=0.04736, over 983860.03 frames.], datatang_tot_loss[loss=0.1787, simple_loss=0.2455, pruned_loss=0.05592, over 985917.14 frames.], batch size: 62, lr: 7.79e-04
+2022-06-18 17:38:26,112 INFO [train.py:874] (0/4) Epoch 10, batch 3650, datatang_loss[loss=0.1673, simple_loss=0.2425, pruned_loss=0.04605, over 4924.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2511, pruned_loss=0.05153, over 984847.63 frames.], batch size: 73, aishell_tot_loss[loss=0.1756, simple_loss=0.2562, pruned_loss=0.04753, over 984132.11 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2453, pruned_loss=0.05584, over 985815.48 frames.], batch size: 73, lr: 7.79e-04
+2022-06-18 17:38:57,466 INFO [train.py:874] (0/4) Epoch 10, batch 3700, aishell_loss[loss=0.153, simple_loss=0.2351, pruned_loss=0.03543, over 4868.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2515, pruned_loss=0.05202, over 984912.75 frames.], batch size: 28, aishell_tot_loss[loss=0.1765, simple_loss=0.257, pruned_loss=0.04803, over 983972.24 frames.], datatang_tot_loss[loss=0.1783, simple_loss=0.2452, pruned_loss=0.05571, over 986000.14 frames.], batch size: 28, lr: 7.78e-04
+2022-06-18 17:39:26,979 INFO [train.py:874] (0/4) Epoch 10, batch 3750, datatang_loss[loss=0.186, simple_loss=0.2509, pruned_loss=0.06053, over 4903.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2505, pruned_loss=0.05149, over 984929.48 frames.], batch size: 64, aishell_tot_loss[loss=0.1756, simple_loss=0.2562, pruned_loss=0.04751, over 984141.25 frames.], datatang_tot_loss[loss=0.1782, simple_loss=0.245, pruned_loss=0.05576, over 985852.41 frames.], batch size: 64, lr: 7.78e-04
+2022-06-18 17:39:57,895 INFO [train.py:874] (0/4) Epoch 10, batch 3800, datatang_loss[loss=0.1591, simple_loss=0.2347, pruned_loss=0.04173, over 4915.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2509, pruned_loss=0.05121, over 985487.59 frames.], batch size: 83, aishell_tot_loss[loss=0.1758, simple_loss=0.2567, pruned_loss=0.04742, over 984528.80 frames.], datatang_tot_loss[loss=0.1779, simple_loss=0.2448, pruned_loss=0.05552, over 986042.53 frames.], batch size: 83, lr: 7.77e-04
+2022-06-18 17:40:26,657 INFO [train.py:874] (0/4) Epoch 10, batch 3850, aishell_loss[loss=0.186, simple_loss=0.2695, pruned_loss=0.05123, over 4913.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2508, pruned_loss=0.05171, over 985243.67 frames.], batch size: 52, aishell_tot_loss[loss=0.1757, simple_loss=0.2564, pruned_loss=0.04751, over 984211.76 frames.], datatang_tot_loss[loss=0.1784, simple_loss=0.2451, pruned_loss=0.05589, over 986146.02 frames.], batch size: 52, lr: 7.77e-04
+2022-06-18 17:40:56,139 INFO [train.py:874] (0/4) Epoch 10, batch 3900, datatang_loss[loss=0.174, simple_loss=0.2445, pruned_loss=0.05177, over 4941.00 frames.], tot_loss[loss=0.1767, simple_loss=0.25, pruned_loss=0.05169, over 985513.05 frames.], batch size: 62, aishell_tot_loss[loss=0.1757, simple_loss=0.2563, pruned_loss=0.04759, over 984377.53 frames.], datatang_tot_loss[loss=0.1779, simple_loss=0.2446, pruned_loss=0.05562, over 986245.23 frames.], batch size: 62, lr: 7.76e-04
+2022-06-18 17:41:24,510 INFO [train.py:874] (0/4) Epoch 10, batch 3950, datatang_loss[loss=0.2034, simple_loss=0.2666, pruned_loss=0.07008, over 4973.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2499, pruned_loss=0.05134, over 985513.00 frames.], batch size: 34, aishell_tot_loss[loss=0.175, simple_loss=0.2557, pruned_loss=0.04712, over 984327.76 frames.], datatang_tot_loss[loss=0.1782, simple_loss=0.2448, pruned_loss=0.0558, over 986366.42 frames.], batch size: 34, lr: 7.76e-04
+2022-06-18 17:41:54,472 INFO [train.py:874] (0/4) Epoch 10, batch 4000, aishell_loss[loss=0.1613, simple_loss=0.2526, pruned_loss=0.03504, over 4911.00 frames.], tot_loss[loss=0.176, simple_loss=0.2499, pruned_loss=0.05104, over 985750.09 frames.], batch size: 52, aishell_tot_loss[loss=0.1748, simple_loss=0.2556, pruned_loss=0.04702, over 984608.10 frames.], datatang_tot_loss[loss=0.178, simple_loss=0.2447, pruned_loss=0.05561, over 986396.54 frames.], batch size: 52, lr: 7.76e-04
+2022-06-18 17:41:54,474 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 17:42:11,516 INFO [train.py:914] (0/4) Epoch 10, validation: loss=0.169, simple_loss=0.2525, pruned_loss=0.04271, over 1622729.00 frames.
+2022-06-18 17:42:40,784 INFO [train.py:874] (0/4) Epoch 10, batch 4050, aishell_loss[loss=0.207, simple_loss=0.2918, pruned_loss=0.06108, over 4870.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2493, pruned_loss=0.05045, over 986069.26 frames.], batch size: 37, aishell_tot_loss[loss=0.1745, simple_loss=0.2552, pruned_loss=0.0469, over 985047.29 frames.], datatang_tot_loss[loss=0.1773, simple_loss=0.2443, pruned_loss=0.05512, over 986368.45 frames.], batch size: 37, lr: 7.75e-04
+2022-06-18 17:43:07,842 INFO [train.py:874] (0/4) Epoch 10, batch 4100, aishell_loss[loss=0.1735, simple_loss=0.2625, pruned_loss=0.04218, over 4927.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2488, pruned_loss=0.04969, over 985475.50 frames.], batch size: 41, aishell_tot_loss[loss=0.1742, simple_loss=0.2552, pruned_loss=0.04663, over 984695.46 frames.], datatang_tot_loss[loss=0.1763, simple_loss=0.2434, pruned_loss=0.0546, over 986190.91 frames.], batch size: 41, lr: 7.75e-04
+2022-06-18 17:43:12,136 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-10.pt
+2022-06-18 17:44:17,191 INFO [train.py:874] (0/4) Epoch 11, batch 50, aishell_loss[loss=0.1769, simple_loss=0.2562, pruned_loss=0.04886, over 4945.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2447, pruned_loss=0.04637, over 218200.28 frames.], batch size: 56, aishell_tot_loss[loss=0.1689, simple_loss=0.2505, pruned_loss=0.04367, over 137210.19 frames.], datatang_tot_loss[loss=0.1682, simple_loss=0.2356, pruned_loss=0.05042, over 94104.50 frames.], batch size: 56, lr: 7.46e-04
+2022-06-18 17:44:48,046 INFO [train.py:874] (0/4) Epoch 11, batch 100, datatang_loss[loss=0.1459, simple_loss=0.211, pruned_loss=0.04041, over 4936.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2452, pruned_loss=0.04746, over 388470.76 frames.], batch size: 50, aishell_tot_loss[loss=0.1715, simple_loss=0.2529, pruned_loss=0.04507, over 233532.36 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2365, pruned_loss=0.05022, over 203031.11 frames.], batch size: 50, lr: 7.45e-04
+2022-06-18 17:45:16,811 INFO [train.py:874] (0/4) Epoch 11, batch 150, aishell_loss[loss=0.1729, simple_loss=0.2535, pruned_loss=0.04618, over 4957.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2447, pruned_loss=0.04822, over 521077.04 frames.], batch size: 32, aishell_tot_loss[loss=0.1733, simple_loss=0.2549, pruned_loss=0.04586, over 298763.94 frames.], datatang_tot_loss[loss=0.1681, simple_loss=0.2358, pruned_loss=0.05017, over 318912.80 frames.], batch size: 32, lr: 7.45e-04
+2022-06-18 17:45:48,301 INFO [train.py:874] (0/4) Epoch 11, batch 200, datatang_loss[loss=0.1718, simple_loss=0.2467, pruned_loss=0.04845, over 4942.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2463, pruned_loss=0.04819, over 623777.06 frames.], batch size: 55, aishell_tot_loss[loss=0.1735, simple_loss=0.256, pruned_loss=0.04555, over 385449.86 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2366, pruned_loss=0.05063, over 391454.05 frames.], batch size: 55, lr: 7.44e-04
+2022-06-18 17:46:17,708 INFO [train.py:874] (0/4) Epoch 11, batch 250, datatang_loss[loss=0.1706, simple_loss=0.2379, pruned_loss=0.05169, over 4890.00 frames.], tot_loss[loss=0.171, simple_loss=0.2462, pruned_loss=0.04792, over 703885.94 frames.], batch size: 39, aishell_tot_loss[loss=0.1732, simple_loss=0.255, pruned_loss=0.04575, over 466644.21 frames.], datatang_tot_loss[loss=0.1686, simple_loss=0.2368, pruned_loss=0.05025, over 450706.19 frames.], batch size: 39, lr: 7.44e-04
+2022-06-18 17:46:47,535 INFO [train.py:874] (0/4) Epoch 11, batch 300, datatang_loss[loss=0.1647, simple_loss=0.2352, pruned_loss=0.04706, over 4956.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2452, pruned_loss=0.04777, over 766423.40 frames.], batch size: 55, aishell_tot_loss[loss=0.1734, simple_loss=0.2545, pruned_loss=0.04613, over 516030.15 frames.], datatang_tot_loss[loss=0.1678, simple_loss=0.2366, pruned_loss=0.04945, over 525593.75 frames.], batch size: 55, lr: 7.43e-04
+2022-06-18 17:47:17,420 INFO [train.py:874] (0/4) Epoch 11, batch 350, aishell_loss[loss=0.1895, simple_loss=0.2592, pruned_loss=0.05993, over 4973.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2475, pruned_loss=0.04831, over 815168.25 frames.], batch size: 39, aishell_tot_loss[loss=0.1742, simple_loss=0.2556, pruned_loss=0.04647, over 585668.45 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2379, pruned_loss=0.05009, over 565395.23 frames.], batch size: 39, lr: 7.43e-04
+2022-06-18 17:47:47,201 INFO [train.py:874] (0/4) Epoch 11, batch 400, aishell_loss[loss=0.1594, simple_loss=0.2429, pruned_loss=0.03791, over 4977.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2465, pruned_loss=0.04795, over 853155.35 frames.], batch size: 61, aishell_tot_loss[loss=0.1731, simple_loss=0.2543, pruned_loss=0.0459, over 631013.99 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2381, pruned_loss=0.05009, over 616936.13 frames.], batch size: 61, lr: 7.42e-04
+2022-06-18 17:48:17,509 INFO [train.py:874] (0/4) Epoch 11, batch 450, datatang_loss[loss=0.1667, simple_loss=0.233, pruned_loss=0.05016, over 4935.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2469, pruned_loss=0.04866, over 882369.43 frames.], batch size: 79, aishell_tot_loss[loss=0.1728, simple_loss=0.2543, pruned_loss=0.0456, over 658050.80 frames.], datatang_tot_loss[loss=0.1709, simple_loss=0.2396, pruned_loss=0.05106, over 674813.75 frames.], batch size: 79, lr: 7.42e-04
+2022-06-18 17:48:48,061 INFO [train.py:874] (0/4) Epoch 11, batch 500, datatang_loss[loss=0.134, simple_loss=0.2019, pruned_loss=0.03303, over 4919.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2476, pruned_loss=0.04911, over 905063.21 frames.], batch size: 64, aishell_tot_loss[loss=0.1735, simple_loss=0.2544, pruned_loss=0.04631, over 696303.56 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.2406, pruned_loss=0.05107, over 711547.39 frames.], batch size: 64, lr: 7.42e-04
+2022-06-18 17:49:17,347 INFO [train.py:874] (0/4) Epoch 11, batch 550, datatang_loss[loss=0.1754, simple_loss=0.2406, pruned_loss=0.05507, over 4940.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2485, pruned_loss=0.04982, over 923005.20 frames.], batch size: 62, aishell_tot_loss[loss=0.1734, simple_loss=0.2543, pruned_loss=0.04624, over 730358.95 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.2418, pruned_loss=0.05223, over 743925.31 frames.], batch size: 62, lr: 7.41e-04
+2022-06-18 17:49:48,434 INFO [train.py:874] (0/4) Epoch 11, batch 600, datatang_loss[loss=0.2497, simple_loss=0.3044, pruned_loss=0.09748, over 4958.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2481, pruned_loss=0.04985, over 936632.02 frames.], batch size: 109, aishell_tot_loss[loss=0.1727, simple_loss=0.2535, pruned_loss=0.04595, over 755539.53 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2425, pruned_loss=0.05255, over 776665.75 frames.], batch size: 109, lr: 7.41e-04
+2022-06-18 17:50:18,131 INFO [train.py:874] (0/4) Epoch 11, batch 650, aishell_loss[loss=0.1526, simple_loss=0.2261, pruned_loss=0.03959, over 4953.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2473, pruned_loss=0.04961, over 947337.49 frames.], batch size: 27, aishell_tot_loss[loss=0.1726, simple_loss=0.2527, pruned_loss=0.04622, over 787703.53 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.242, pruned_loss=0.05236, over 796398.43 frames.], batch size: 27, lr: 7.40e-04
+2022-06-18 17:50:47,911 INFO [train.py:874] (0/4) Epoch 11, batch 700, aishell_loss[loss=0.1488, simple_loss=0.2366, pruned_loss=0.03055, over 4939.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2473, pruned_loss=0.04921, over 955708.66 frames.], batch size: 40, aishell_tot_loss[loss=0.1724, simple_loss=0.2528, pruned_loss=0.04604, over 812708.93 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.2417, pruned_loss=0.05222, over 816935.22 frames.], batch size: 40, lr: 7.40e-04
+2022-06-18 17:51:18,368 INFO [train.py:874] (0/4) Epoch 11, batch 750, aishell_loss[loss=0.184, simple_loss=0.2638, pruned_loss=0.05208, over 4954.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2466, pruned_loss=0.04929, over 962052.55 frames.], batch size: 64, aishell_tot_loss[loss=0.1717, simple_loss=0.2519, pruned_loss=0.04577, over 831726.41 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2419, pruned_loss=0.05258, over 837813.17 frames.], batch size: 64, lr: 7.39e-04
+2022-06-18 17:51:48,627 INFO [train.py:874] (0/4) Epoch 11, batch 800, datatang_loss[loss=0.1518, simple_loss=0.222, pruned_loss=0.04078, over 4898.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2478, pruned_loss=0.04946, over 967397.12 frames.], batch size: 42, aishell_tot_loss[loss=0.1727, simple_loss=0.2531, pruned_loss=0.04619, over 851219.69 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.242, pruned_loss=0.05245, over 854024.90 frames.], batch size: 42, lr: 7.39e-04
+2022-06-18 17:52:17,420 INFO [train.py:874] (0/4) Epoch 11, batch 850, aishell_loss[loss=0.1976, simple_loss=0.2757, pruned_loss=0.05975, over 4959.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2487, pruned_loss=0.04957, over 971471.08 frames.], batch size: 61, aishell_tot_loss[loss=0.1737, simple_loss=0.2543, pruned_loss=0.04657, over 868769.66 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.2418, pruned_loss=0.05235, over 867855.32 frames.], batch size: 61, lr: 7.39e-04
+2022-06-18 17:52:48,388 INFO [train.py:874] (0/4) Epoch 11, batch 900, datatang_loss[loss=0.1939, simple_loss=0.2555, pruned_loss=0.0661, over 4935.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2478, pruned_loss=0.04898, over 974639.42 frames.], batch size: 83, aishell_tot_loss[loss=0.1726, simple_loss=0.2534, pruned_loss=0.0459, over 881010.32 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.242, pruned_loss=0.05224, over 883271.61 frames.], batch size: 83, lr: 7.38e-04
+2022-06-18 17:53:18,576 INFO [train.py:874] (0/4) Epoch 11, batch 950, datatang_loss[loss=0.1664, simple_loss=0.2393, pruned_loss=0.04674, over 4928.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2478, pruned_loss=0.0492, over 976141.05 frames.], batch size: 71, aishell_tot_loss[loss=0.1722, simple_loss=0.2531, pruned_loss=0.04568, over 892189.47 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2424, pruned_loss=0.05266, over 895472.20 frames.], batch size: 71, lr: 7.38e-04
+2022-06-18 17:53:46,777 INFO [train.py:874] (0/4) Epoch 11, batch 1000, aishell_loss[loss=0.1923, simple_loss=0.2791, pruned_loss=0.05269, over 4925.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2491, pruned_loss=0.04967, over 978619.17 frames.], batch size: 78, aishell_tot_loss[loss=0.1728, simple_loss=0.2539, pruned_loss=0.04591, over 905889.42 frames.], datatang_tot_loss[loss=0.1745, simple_loss=0.2427, pruned_loss=0.05319, over 903810.70 frames.], batch size: 78, lr: 7.37e-04
+2022-06-18 17:53:46,780 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 17:54:02,791 INFO [train.py:914] (0/4) Epoch 11, validation: loss=0.1676, simple_loss=0.2521, pruned_loss=0.04158, over 1622729.00 frames.
+2022-06-18 17:54:32,112 INFO [train.py:874] (0/4) Epoch 11, batch 1050, aishell_loss[loss=0.1928, simple_loss=0.2726, pruned_loss=0.05643, over 4920.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2492, pruned_loss=0.04924, over 979891.84 frames.], batch size: 52, aishell_tot_loss[loss=0.1725, simple_loss=0.2536, pruned_loss=0.04568, over 917371.67 frames.], datatang_tot_loss[loss=0.1747, simple_loss=0.2429, pruned_loss=0.05323, over 910900.57 frames.], batch size: 52, lr: 7.37e-04
+2022-06-18 17:55:02,911 INFO [train.py:874] (0/4) Epoch 11, batch 1100, datatang_loss[loss=0.1776, simple_loss=0.24, pruned_loss=0.05761, over 4905.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2493, pruned_loss=0.04959, over 981250.96 frames.], batch size: 64, aishell_tot_loss[loss=0.1722, simple_loss=0.2531, pruned_loss=0.0457, over 926437.64 frames.], datatang_tot_loss[loss=0.1755, simple_loss=0.2436, pruned_loss=0.05366, over 918666.16 frames.], batch size: 64, lr: 7.36e-04
+2022-06-18 17:55:32,124 INFO [train.py:874] (0/4) Epoch 11, batch 1150, datatang_loss[loss=0.1458, simple_loss=0.2212, pruned_loss=0.03518, over 4957.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2494, pruned_loss=0.05011, over 981958.28 frames.], batch size: 67, aishell_tot_loss[loss=0.1727, simple_loss=0.2535, pruned_loss=0.04597, over 932304.97 frames.], datatang_tot_loss[loss=0.1757, simple_loss=0.2437, pruned_loss=0.05385, over 927463.58 frames.], batch size: 67, lr: 7.36e-04
+2022-06-18 17:56:02,933 INFO [train.py:874] (0/4) Epoch 11, batch 1200, aishell_loss[loss=0.1838, simple_loss=0.2722, pruned_loss=0.04764, over 4873.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2489, pruned_loss=0.05036, over 982411.93 frames.], batch size: 36, aishell_tot_loss[loss=0.1735, simple_loss=0.2539, pruned_loss=0.04653, over 937476.32 frames.], datatang_tot_loss[loss=0.1751, simple_loss=0.2432, pruned_loss=0.0535, over 935113.10 frames.], batch size: 36, lr: 7.36e-04
+2022-06-18 17:56:33,867 INFO [train.py:874] (0/4) Epoch 11, batch 1250, datatang_loss[loss=0.1666, simple_loss=0.2425, pruned_loss=0.04537, over 4924.00 frames.], tot_loss[loss=0.175, simple_loss=0.249, pruned_loss=0.05053, over 983181.33 frames.], batch size: 83, aishell_tot_loss[loss=0.1741, simple_loss=0.2544, pruned_loss=0.04692, over 942157.83 frames.], datatang_tot_loss[loss=0.1748, simple_loss=0.2431, pruned_loss=0.05325, over 942133.54 frames.], batch size: 83, lr: 7.35e-04
+2022-06-18 17:57:03,619 INFO [train.py:874] (0/4) Epoch 11, batch 1300, datatang_loss[loss=0.1389, simple_loss=0.2058, pruned_loss=0.03603, over 4938.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2495, pruned_loss=0.05048, over 983662.26 frames.], batch size: 34, aishell_tot_loss[loss=0.174, simple_loss=0.2546, pruned_loss=0.04666, over 946912.61 frames.], datatang_tot_loss[loss=0.1753, simple_loss=0.2436, pruned_loss=0.0535, over 947548.60 frames.], batch size: 34, lr: 7.35e-04
+2022-06-18 17:57:34,404 INFO [train.py:874] (0/4) Epoch 11, batch 1350, datatang_loss[loss=0.2363, simple_loss=0.2895, pruned_loss=0.09152, over 4932.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2502, pruned_loss=0.05077, over 983865.95 frames.], batch size: 108, aishell_tot_loss[loss=0.1736, simple_loss=0.2547, pruned_loss=0.04628, over 950912.10 frames.], datatang_tot_loss[loss=0.1764, simple_loss=0.2444, pruned_loss=0.05421, over 952334.05 frames.], batch size: 108, lr: 7.34e-04
+2022-06-18 17:58:05,165 INFO [train.py:874] (0/4) Epoch 11, batch 1400, datatang_loss[loss=0.2109, simple_loss=0.2693, pruned_loss=0.07622, over 4921.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2497, pruned_loss=0.04998, over 984005.95 frames.], batch size: 83, aishell_tot_loss[loss=0.1735, simple_loss=0.2547, pruned_loss=0.04616, over 955266.75 frames.], datatang_tot_loss[loss=0.1756, simple_loss=0.2438, pruned_loss=0.05373, over 955737.02 frames.], batch size: 83, lr: 7.34e-04
+2022-06-18 17:58:33,278 INFO [train.py:874] (0/4) Epoch 11, batch 1450, datatang_loss[loss=0.177, simple_loss=0.2511, pruned_loss=0.05145, over 4930.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2492, pruned_loss=0.04979, over 984035.18 frames.], batch size: 88, aishell_tot_loss[loss=0.1728, simple_loss=0.2538, pruned_loss=0.04592, over 959185.02 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.244, pruned_loss=0.05393, over 958559.48 frames.], batch size: 88, lr: 7.33e-04
+2022-06-18 17:59:04,227 INFO [train.py:874] (0/4) Epoch 11, batch 1500, datatang_loss[loss=0.2002, simple_loss=0.2718, pruned_loss=0.06429, over 4912.00 frames.], tot_loss[loss=0.1744, simple_loss=0.249, pruned_loss=0.04986, over 984210.33 frames.], batch size: 31, aishell_tot_loss[loss=0.1728, simple_loss=0.2539, pruned_loss=0.04587, over 962113.14 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2438, pruned_loss=0.05399, over 961738.52 frames.], batch size: 31, lr: 7.33e-04
+2022-06-18 17:59:35,808 INFO [train.py:874] (0/4) Epoch 11, batch 1550, aishell_loss[loss=0.1663, simple_loss=0.2565, pruned_loss=0.03798, over 4969.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2472, pruned_loss=0.04922, over 984280.95 frames.], batch size: 51, aishell_tot_loss[loss=0.1719, simple_loss=0.2529, pruned_loss=0.04548, over 964795.69 frames.], datatang_tot_loss[loss=0.1751, simple_loss=0.2428, pruned_loss=0.05367, over 964375.03 frames.], batch size: 51, lr: 7.33e-04
+2022-06-18 18:00:05,424 INFO [train.py:874] (0/4) Epoch 11, batch 1600, datatang_loss[loss=0.1482, simple_loss=0.213, pruned_loss=0.04172, over 4926.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2473, pruned_loss=0.04988, over 984603.95 frames.], batch size: 25, aishell_tot_loss[loss=0.1717, simple_loss=0.2525, pruned_loss=0.04546, over 967405.83 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2432, pruned_loss=0.05432, over 966763.17 frames.], batch size: 25, lr: 7.32e-04
+2022-06-18 18:00:35,333 INFO [train.py:874] (0/4) Epoch 11, batch 1650, aishell_loss[loss=0.1866, simple_loss=0.2703, pruned_loss=0.0514, over 4960.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2475, pruned_loss=0.04994, over 985186.82 frames.], batch size: 56, aishell_tot_loss[loss=0.1717, simple_loss=0.2526, pruned_loss=0.04543, over 969423.00 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2434, pruned_loss=0.05422, over 969497.16 frames.], batch size: 56, lr: 7.32e-04
+2022-06-18 18:01:06,990 INFO [train.py:874] (0/4) Epoch 11, batch 1700, aishell_loss[loss=0.1612, simple_loss=0.2365, pruned_loss=0.04293, over 4970.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2477, pruned_loss=0.04984, over 985217.96 frames.], batch size: 39, aishell_tot_loss[loss=0.1717, simple_loss=0.2525, pruned_loss=0.04544, over 971031.79 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2437, pruned_loss=0.05405, over 971627.61 frames.], batch size: 39, lr: 7.31e-04
+2022-06-18 18:01:36,157 INFO [train.py:874] (0/4) Epoch 11, batch 1750, aishell_loss[loss=0.167, simple_loss=0.2454, pruned_loss=0.04429, over 4865.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2487, pruned_loss=0.05079, over 985373.87 frames.], batch size: 35, aishell_tot_loss[loss=0.1728, simple_loss=0.2535, pruned_loss=0.04609, over 972881.69 frames.], datatang_tot_loss[loss=0.1763, simple_loss=0.2435, pruned_loss=0.0545, over 973241.57 frames.], batch size: 35, lr: 7.31e-04
+2022-06-18 18:02:06,154 INFO [train.py:874] (0/4) Epoch 11, batch 1800, datatang_loss[loss=0.1653, simple_loss=0.2326, pruned_loss=0.04902, over 4935.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2487, pruned_loss=0.05036, over 985589.63 frames.], batch size: 71, aishell_tot_loss[loss=0.1729, simple_loss=0.2537, pruned_loss=0.04604, over 974429.40 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2433, pruned_loss=0.05427, over 974829.21 frames.], batch size: 71, lr: 7.30e-04
+2022-06-18 18:02:37,427 INFO [train.py:874] (0/4) Epoch 11, batch 1850, aishell_loss[loss=0.1964, simple_loss=0.2823, pruned_loss=0.05532, over 4923.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2497, pruned_loss=0.05063, over 985467.34 frames.], batch size: 41, aishell_tot_loss[loss=0.1727, simple_loss=0.2536, pruned_loss=0.04589, over 975898.60 frames.], datatang_tot_loss[loss=0.177, simple_loss=0.2444, pruned_loss=0.05478, over 975806.22 frames.], batch size: 41, lr: 7.30e-04
+2022-06-18 18:03:06,838 INFO [train.py:874] (0/4) Epoch 11, batch 1900, datatang_loss[loss=0.1575, simple_loss=0.2301, pruned_loss=0.04248, over 4919.00 frames.], tot_loss[loss=0.175, simple_loss=0.2493, pruned_loss=0.05036, over 985852.61 frames.], batch size: 77, aishell_tot_loss[loss=0.1732, simple_loss=0.2542, pruned_loss=0.04608, over 977025.81 frames.], datatang_tot_loss[loss=0.1761, simple_loss=0.2438, pruned_loss=0.05419, over 977335.84 frames.], batch size: 77, lr: 7.30e-04
+2022-06-18 18:03:36,012 INFO [train.py:874] (0/4) Epoch 11, batch 1950, datatang_loss[loss=0.1982, simple_loss=0.2541, pruned_loss=0.07119, over 4914.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2489, pruned_loss=0.0498, over 985639.22 frames.], batch size: 34, aishell_tot_loss[loss=0.1734, simple_loss=0.2544, pruned_loss=0.04615, over 977989.37 frames.], datatang_tot_loss[loss=0.1752, simple_loss=0.2429, pruned_loss=0.05372, over 978190.96 frames.], batch size: 34, lr: 7.29e-04
+2022-06-18 18:04:07,291 INFO [train.py:874] (0/4) Epoch 11, batch 2000, aishell_loss[loss=0.1798, simple_loss=0.2759, pruned_loss=0.04184, over 4945.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2483, pruned_loss=0.04953, over 985832.52 frames.], batch size: 68, aishell_tot_loss[loss=0.1725, simple_loss=0.2538, pruned_loss=0.04563, over 978743.50 frames.], datatang_tot_loss[loss=0.1754, simple_loss=0.2431, pruned_loss=0.05387, over 979408.86 frames.], batch size: 68, lr: 7.29e-04
+2022-06-18 18:04:07,294 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 18:04:24,294 INFO [train.py:914] (0/4) Epoch 11, validation: loss=0.167, simple_loss=0.2509, pruned_loss=0.04156, over 1622729.00 frames.
+2022-06-18 18:04:53,783 INFO [train.py:874] (0/4) Epoch 11, batch 2050, aishell_loss[loss=0.1569, simple_loss=0.2417, pruned_loss=0.03606, over 4908.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2483, pruned_loss=0.04923, over 985796.95 frames.], batch size: 34, aishell_tot_loss[loss=0.1723, simple_loss=0.2536, pruned_loss=0.0455, over 979699.67 frames.], datatang_tot_loss[loss=0.1753, simple_loss=0.243, pruned_loss=0.05381, over 980008.88 frames.], batch size: 34, lr: 7.28e-04
+2022-06-18 18:05:23,745 INFO [train.py:874] (0/4) Epoch 11, batch 2100, aishell_loss[loss=0.1665, simple_loss=0.25, pruned_loss=0.04147, over 4939.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2482, pruned_loss=0.04873, over 985784.63 frames.], batch size: 58, aishell_tot_loss[loss=0.1722, simple_loss=0.2536, pruned_loss=0.04539, over 980666.67 frames.], datatang_tot_loss[loss=0.1748, simple_loss=0.2426, pruned_loss=0.05353, over 980427.68 frames.], batch size: 58, lr: 7.28e-04
+2022-06-18 18:05:53,423 INFO [train.py:874] (0/4) Epoch 11, batch 2150, datatang_loss[loss=0.1548, simple_loss=0.2277, pruned_loss=0.04097, over 4855.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2478, pruned_loss=0.04919, over 985533.82 frames.], batch size: 39, aishell_tot_loss[loss=0.1727, simple_loss=0.254, pruned_loss=0.0457, over 980955.17 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2423, pruned_loss=0.05316, over 981099.56 frames.], batch size: 39, lr: 7.28e-04
+2022-06-18 18:06:24,914 INFO [train.py:874] (0/4) Epoch 11, batch 2200, datatang_loss[loss=0.2061, simple_loss=0.2659, pruned_loss=0.07309, over 4897.00 frames.], tot_loss[loss=0.1724, simple_loss=0.247, pruned_loss=0.04887, over 985608.69 frames.], batch size: 47, aishell_tot_loss[loss=0.1719, simple_loss=0.2536, pruned_loss=0.04514, over 981500.58 frames.], datatang_tot_loss[loss=0.1742, simple_loss=0.2422, pruned_loss=0.05311, over 981680.38 frames.], batch size: 47, lr: 7.27e-04
+2022-06-18 18:06:54,554 INFO [train.py:874] (0/4) Epoch 11, batch 2250, aishell_loss[loss=0.1454, simple_loss=0.2355, pruned_loss=0.02761, over 4859.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2466, pruned_loss=0.04877, over 985257.96 frames.], batch size: 28, aishell_tot_loss[loss=0.1715, simple_loss=0.2532, pruned_loss=0.04491, over 981678.73 frames.], datatang_tot_loss[loss=0.1742, simple_loss=0.2422, pruned_loss=0.05309, over 982074.38 frames.], batch size: 28, lr: 7.27e-04
+2022-06-18 18:07:24,136 INFO [train.py:874] (0/4) Epoch 11, batch 2300, datatang_loss[loss=0.1533, simple_loss=0.2304, pruned_loss=0.03815, over 4954.00 frames.], tot_loss[loss=0.172, simple_loss=0.2466, pruned_loss=0.04875, over 985158.29 frames.], batch size: 86, aishell_tot_loss[loss=0.1714, simple_loss=0.2529, pruned_loss=0.04491, over 981751.80 frames.], datatang_tot_loss[loss=0.1741, simple_loss=0.2421, pruned_loss=0.05307, over 982703.80 frames.], batch size: 86, lr: 7.26e-04
+2022-06-18 18:07:55,326 INFO [train.py:874] (0/4) Epoch 11, batch 2350, datatang_loss[loss=0.1623, simple_loss=0.2328, pruned_loss=0.04592, over 4923.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2469, pruned_loss=0.04837, over 985371.14 frames.], batch size: 71, aishell_tot_loss[loss=0.1712, simple_loss=0.2529, pruned_loss=0.04478, over 982340.07 frames.], datatang_tot_loss[loss=0.1739, simple_loss=0.242, pruned_loss=0.05288, over 983053.29 frames.], batch size: 71, lr: 7.26e-04
+2022-06-18 18:08:25,219 INFO [train.py:874] (0/4) Epoch 11, batch 2400, aishell_loss[loss=0.185, simple_loss=0.2647, pruned_loss=0.05266, over 4958.00 frames.], tot_loss[loss=0.1729, simple_loss=0.248, pruned_loss=0.04887, over 985707.41 frames.], batch size: 32, aishell_tot_loss[loss=0.1717, simple_loss=0.2533, pruned_loss=0.04506, over 982823.98 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2425, pruned_loss=0.05313, over 983570.74 frames.], batch size: 32, lr: 7.25e-04
+2022-06-18 18:08:55,149 INFO [train.py:874] (0/4) Epoch 11, batch 2450, datatang_loss[loss=0.1675, simple_loss=0.2389, pruned_loss=0.048, over 4919.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2473, pruned_loss=0.04903, over 985649.11 frames.], batch size: 75, aishell_tot_loss[loss=0.1715, simple_loss=0.2529, pruned_loss=0.04507, over 982957.19 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2423, pruned_loss=0.05318, over 983948.19 frames.], batch size: 75, lr: 7.25e-04
+2022-06-18 18:09:26,583 INFO [train.py:874] (0/4) Epoch 11, batch 2500, datatang_loss[loss=0.1532, simple_loss=0.2227, pruned_loss=0.04187, over 4907.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2472, pruned_loss=0.04892, over 985721.41 frames.], batch size: 47, aishell_tot_loss[loss=0.1713, simple_loss=0.2528, pruned_loss=0.0449, over 983431.49 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2425, pruned_loss=0.05313, over 984050.31 frames.], batch size: 47, lr: 7.25e-04
+2022-06-18 18:09:56,446 INFO [train.py:874] (0/4) Epoch 11, batch 2550, datatang_loss[loss=0.1577, simple_loss=0.2202, pruned_loss=0.04765, over 4943.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2474, pruned_loss=0.04922, over 985673.10 frames.], batch size: 42, aishell_tot_loss[loss=0.1717, simple_loss=0.2529, pruned_loss=0.04519, over 983782.85 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2424, pruned_loss=0.05313, over 984124.11 frames.], batch size: 42, lr: 7.24e-04
+2022-06-18 18:10:26,144 INFO [train.py:874] (0/4) Epoch 11, batch 2600, datatang_loss[loss=0.1661, simple_loss=0.2378, pruned_loss=0.04726, over 4897.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2475, pruned_loss=0.04932, over 985587.48 frames.], batch size: 59, aishell_tot_loss[loss=0.1722, simple_loss=0.2534, pruned_loss=0.04549, over 983995.54 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.2419, pruned_loss=0.05301, over 984215.61 frames.], batch size: 59, lr: 7.24e-04
+2022-06-18 18:10:56,972 INFO [train.py:874] (0/4) Epoch 11, batch 2650, datatang_loss[loss=0.1905, simple_loss=0.2509, pruned_loss=0.06504, over 4942.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2483, pruned_loss=0.04949, over 985627.26 frames.], batch size: 62, aishell_tot_loss[loss=0.1728, simple_loss=0.254, pruned_loss=0.04577, over 984090.20 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.2418, pruned_loss=0.05306, over 984506.48 frames.], batch size: 62, lr: 7.23e-04
+2022-06-18 18:11:25,985 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-44000.pt
+2022-06-18 18:11:31,742 INFO [train.py:874] (0/4) Epoch 11, batch 2700, aishell_loss[loss=0.1644, simple_loss=0.2441, pruned_loss=0.0423, over 4953.00 frames.], tot_loss[loss=0.173, simple_loss=0.2479, pruned_loss=0.04909, over 985796.19 frames.], batch size: 40, aishell_tot_loss[loss=0.172, simple_loss=0.2534, pruned_loss=0.0453, over 984559.32 frames.], datatang_tot_loss[loss=0.1741, simple_loss=0.2421, pruned_loss=0.05306, over 984526.41 frames.], batch size: 40, lr: 7.23e-04
+2022-06-18 18:12:01,111 INFO [train.py:874] (0/4) Epoch 11, batch 2750, aishell_loss[loss=0.177, simple_loss=0.2656, pruned_loss=0.04424, over 4898.00 frames.], tot_loss[loss=0.173, simple_loss=0.2474, pruned_loss=0.04933, over 985560.04 frames.], batch size: 34, aishell_tot_loss[loss=0.1718, simple_loss=0.253, pruned_loss=0.04527, over 984343.73 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2422, pruned_loss=0.05324, over 984788.57 frames.], batch size: 34, lr: 7.23e-04
+2022-06-18 18:12:33,157 INFO [train.py:874] (0/4) Epoch 11, batch 2800, datatang_loss[loss=0.1778, simple_loss=0.2356, pruned_loss=0.06002, over 4935.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2474, pruned_loss=0.04905, over 985495.66 frames.], batch size: 71, aishell_tot_loss[loss=0.1711, simple_loss=0.2527, pruned_loss=0.04479, over 984559.47 frames.], datatang_tot_loss[loss=0.1747, simple_loss=0.2425, pruned_loss=0.05344, over 984742.66 frames.], batch size: 71, lr: 7.22e-04
+2022-06-18 18:13:03,706 INFO [train.py:874] (0/4) Epoch 11, batch 2850, datatang_loss[loss=0.1755, simple_loss=0.2524, pruned_loss=0.0493, over 4947.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2475, pruned_loss=0.04912, over 985388.48 frames.], batch size: 62, aishell_tot_loss[loss=0.1715, simple_loss=0.2531, pruned_loss=0.04494, over 984556.36 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2422, pruned_loss=0.0533, over 984819.50 frames.], batch size: 62, lr: 7.22e-04
+2022-06-18 18:13:33,064 INFO [train.py:874] (0/4) Epoch 11, batch 2900, datatang_loss[loss=0.2241, simple_loss=0.2825, pruned_loss=0.08286, over 4952.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2495, pruned_loss=0.05056, over 985631.06 frames.], batch size: 108, aishell_tot_loss[loss=0.1725, simple_loss=0.2538, pruned_loss=0.04556, over 984835.05 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2435, pruned_loss=0.05419, over 984965.91 frames.], batch size: 108, lr: 7.21e-04
+2022-06-18 18:14:04,182 INFO [train.py:874] (0/4) Epoch 11, batch 2950, datatang_loss[loss=0.1883, simple_loss=0.2499, pruned_loss=0.06336, over 4940.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2495, pruned_loss=0.05045, over 985796.88 frames.], batch size: 50, aishell_tot_loss[loss=0.1732, simple_loss=0.2547, pruned_loss=0.0459, over 984885.58 frames.], datatang_tot_loss[loss=0.1753, simple_loss=0.243, pruned_loss=0.05378, over 985267.49 frames.], batch size: 50, lr: 7.21e-04
+2022-06-18 18:14:33,597 INFO [train.py:874] (0/4) Epoch 11, batch 3000, datatang_loss[loss=0.1768, simple_loss=0.2464, pruned_loss=0.05358, over 4921.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2497, pruned_loss=0.05058, over 985916.29 frames.], batch size: 42, aishell_tot_loss[loss=0.1731, simple_loss=0.2545, pruned_loss=0.04584, over 985258.18 frames.], datatang_tot_loss[loss=0.1758, simple_loss=0.2435, pruned_loss=0.05411, over 985171.66 frames.], batch size: 42, lr: 7.21e-04
+2022-06-18 18:14:33,600 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 18:14:50,208 INFO [train.py:914] (0/4) Epoch 11, validation: loss=0.1662, simple_loss=0.2504, pruned_loss=0.04101, over 1622729.00 frames.
+2022-06-18 18:15:20,380 INFO [train.py:874] (0/4) Epoch 11, batch 3050, aishell_loss[loss=0.1651, simple_loss=0.2478, pruned_loss=0.04118, over 4976.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2499, pruned_loss=0.05037, over 985927.41 frames.], batch size: 39, aishell_tot_loss[loss=0.1732, simple_loss=0.2547, pruned_loss=0.04584, over 985361.02 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2436, pruned_loss=0.05411, over 985240.32 frames.], batch size: 39, lr: 7.20e-04
+2022-06-18 18:15:50,757 INFO [train.py:874] (0/4) Epoch 11, batch 3100, aishell_loss[loss=0.1818, simple_loss=0.262, pruned_loss=0.05081, over 4945.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2491, pruned_loss=0.04967, over 985801.12 frames.], batch size: 64, aishell_tot_loss[loss=0.173, simple_loss=0.2548, pruned_loss=0.04561, over 985265.93 frames.], datatang_tot_loss[loss=0.1751, simple_loss=0.2428, pruned_loss=0.05369, over 985353.93 frames.], batch size: 64, lr: 7.20e-04
+2022-06-18 18:16:23,154 INFO [train.py:874] (0/4) Epoch 11, batch 3150, datatang_loss[loss=0.155, simple_loss=0.2293, pruned_loss=0.04036, over 4941.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2485, pruned_loss=0.04939, over 985643.79 frames.], batch size: 88, aishell_tot_loss[loss=0.1731, simple_loss=0.2549, pruned_loss=0.04566, over 985198.93 frames.], datatang_tot_loss[loss=0.1745, simple_loss=0.2425, pruned_loss=0.05322, over 985358.09 frames.], batch size: 88, lr: 7.19e-04
+2022-06-18 18:16:53,747 INFO [train.py:874] (0/4) Epoch 11, batch 3200, aishell_loss[loss=0.1998, simple_loss=0.2767, pruned_loss=0.0615, over 4957.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2489, pruned_loss=0.04911, over 986008.88 frames.], batch size: 79, aishell_tot_loss[loss=0.1736, simple_loss=0.2556, pruned_loss=0.0458, over 985445.39 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.242, pruned_loss=0.05283, over 985563.02 frames.], batch size: 79, lr: 7.19e-04
+2022-06-18 18:17:23,941 INFO [train.py:874] (0/4) Epoch 11, batch 3250, datatang_loss[loss=0.1638, simple_loss=0.2337, pruned_loss=0.04699, over 4920.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2482, pruned_loss=0.04878, over 985545.99 frames.], batch size: 81, aishell_tot_loss[loss=0.1731, simple_loss=0.255, pruned_loss=0.04563, over 985021.77 frames.], datatang_tot_loss[loss=0.1736, simple_loss=0.2418, pruned_loss=0.05268, over 985621.09 frames.], batch size: 81, lr: 7.19e-04
+2022-06-18 18:17:56,614 INFO [train.py:874] (0/4) Epoch 11, batch 3300, aishell_loss[loss=0.1401, simple_loss=0.2181, pruned_loss=0.03105, over 4955.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2483, pruned_loss=0.04873, over 985565.11 frames.], batch size: 25, aishell_tot_loss[loss=0.1731, simple_loss=0.2549, pruned_loss=0.04568, over 985190.06 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.2418, pruned_loss=0.05253, over 985525.39 frames.], batch size: 25, lr: 7.18e-04
+2022-06-18 18:18:26,803 INFO [train.py:874] (0/4) Epoch 11, batch 3350, datatang_loss[loss=0.1613, simple_loss=0.2211, pruned_loss=0.05073, over 4838.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2489, pruned_loss=0.0493, over 985257.40 frames.], batch size: 30, aishell_tot_loss[loss=0.1736, simple_loss=0.2552, pruned_loss=0.04604, over 985040.96 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.2421, pruned_loss=0.05281, over 985415.67 frames.], batch size: 30, lr: 7.18e-04
+2022-06-18 18:18:57,190 INFO [train.py:874] (0/4) Epoch 11, batch 3400, aishell_loss[loss=0.1927, simple_loss=0.2712, pruned_loss=0.05714, over 4949.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2483, pruned_loss=0.04923, over 985220.53 frames.], batch size: 45, aishell_tot_loss[loss=0.1735, simple_loss=0.2551, pruned_loss=0.04595, over 984947.20 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2417, pruned_loss=0.05268, over 985454.02 frames.], batch size: 45, lr: 7.17e-04
+2022-06-18 18:19:29,615 INFO [train.py:874] (0/4) Epoch 11, batch 3450, datatang_loss[loss=0.1883, simple_loss=0.2553, pruned_loss=0.06064, over 4916.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2475, pruned_loss=0.04915, over 985121.79 frames.], batch size: 98, aishell_tot_loss[loss=0.1732, simple_loss=0.2547, pruned_loss=0.04579, over 984660.38 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2414, pruned_loss=0.05261, over 985626.00 frames.], batch size: 98, lr: 7.17e-04
+2022-06-18 18:20:00,332 INFO [train.py:874] (0/4) Epoch 11, batch 3500, datatang_loss[loss=0.1677, simple_loss=0.2329, pruned_loss=0.05126, over 4914.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2469, pruned_loss=0.04882, over 984957.00 frames.], batch size: 75, aishell_tot_loss[loss=0.1728, simple_loss=0.2543, pruned_loss=0.04563, over 984553.47 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2411, pruned_loss=0.0524, over 985554.19 frames.], batch size: 75, lr: 7.17e-04
+2022-06-18 18:20:30,444 INFO [train.py:874] (0/4) Epoch 11, batch 3550, aishell_loss[loss=0.1529, simple_loss=0.2327, pruned_loss=0.03655, over 4884.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2468, pruned_loss=0.04842, over 985226.46 frames.], batch size: 35, aishell_tot_loss[loss=0.1723, simple_loss=0.2538, pruned_loss=0.04533, over 984825.57 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2412, pruned_loss=0.05232, over 985562.49 frames.], batch size: 35, lr: 7.16e-04
+2022-06-18 18:21:01,554 INFO [train.py:874] (0/4) Epoch 11, batch 3600, datatang_loss[loss=0.1444, simple_loss=0.2104, pruned_loss=0.03924, over 4890.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2468, pruned_loss=0.04807, over 985276.48 frames.], batch size: 47, aishell_tot_loss[loss=0.1717, simple_loss=0.2535, pruned_loss=0.04499, over 984942.22 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2412, pruned_loss=0.05233, over 985519.27 frames.], batch size: 47, lr: 7.16e-04
+2022-06-18 18:21:30,548 INFO [train.py:874] (0/4) Epoch 11, batch 3650, aishell_loss[loss=0.1373, simple_loss=0.2194, pruned_loss=0.02758, over 4917.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2462, pruned_loss=0.04726, over 985433.57 frames.], batch size: 25, aishell_tot_loss[loss=0.1712, simple_loss=0.2531, pruned_loss=0.04458, over 984941.50 frames.], datatang_tot_loss[loss=0.1722, simple_loss=0.2405, pruned_loss=0.05191, over 985727.98 frames.], batch size: 25, lr: 7.15e-04
+2022-06-18 18:22:02,512 INFO [train.py:874] (0/4) Epoch 11, batch 3700, aishell_loss[loss=0.1723, simple_loss=0.2522, pruned_loss=0.0462, over 4926.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2465, pruned_loss=0.04802, over 985268.34 frames.], batch size: 32, aishell_tot_loss[loss=0.1712, simple_loss=0.2525, pruned_loss=0.04492, over 984625.41 frames.], datatang_tot_loss[loss=0.1728, simple_loss=0.2414, pruned_loss=0.05216, over 985894.38 frames.], batch size: 32, lr: 7.15e-04
+2022-06-18 18:22:32,546 INFO [train.py:874] (0/4) Epoch 11, batch 3750, aishell_loss[loss=0.1746, simple_loss=0.2622, pruned_loss=0.04349, over 4868.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2473, pruned_loss=0.04816, over 985039.04 frames.], batch size: 35, aishell_tot_loss[loss=0.1714, simple_loss=0.2529, pruned_loss=0.04493, over 984391.70 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.2415, pruned_loss=0.05239, over 985934.51 frames.], batch size: 35, lr: 7.15e-04
+2022-06-18 18:23:02,926 INFO [train.py:874] (0/4) Epoch 11, batch 3800, aishell_loss[loss=0.1684, simple_loss=0.2512, pruned_loss=0.04279, over 4969.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2466, pruned_loss=0.04817, over 984989.07 frames.], batch size: 51, aishell_tot_loss[loss=0.1711, simple_loss=0.2526, pruned_loss=0.04479, over 984515.93 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2411, pruned_loss=0.05235, over 985717.82 frames.], batch size: 51, lr: 7.14e-04
+2022-06-18 18:23:32,301 INFO [train.py:874] (0/4) Epoch 11, batch 3850, datatang_loss[loss=0.1894, simple_loss=0.2378, pruned_loss=0.07045, over 4904.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2465, pruned_loss=0.04798, over 984796.13 frames.], batch size: 42, aishell_tot_loss[loss=0.1707, simple_loss=0.2522, pruned_loss=0.04454, over 984538.78 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.241, pruned_loss=0.05247, over 985504.71 frames.], batch size: 42, lr: 7.14e-04
+2022-06-18 18:24:01,228 INFO [train.py:874] (0/4) Epoch 11, batch 3900, aishell_loss[loss=0.1944, simple_loss=0.2844, pruned_loss=0.05221, over 4904.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2466, pruned_loss=0.04792, over 985127.64 frames.], batch size: 33, aishell_tot_loss[loss=0.1706, simple_loss=0.2522, pruned_loss=0.04452, over 984699.99 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.241, pruned_loss=0.05238, over 985643.84 frames.], batch size: 33, lr: 7.14e-04
+2022-06-18 18:24:28,627 INFO [train.py:874] (0/4) Epoch 11, batch 3950, datatang_loss[loss=0.1791, simple_loss=0.2475, pruned_loss=0.05533, over 4962.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2473, pruned_loss=0.0481, over 984950.72 frames.], batch size: 67, aishell_tot_loss[loss=0.1708, simple_loss=0.2524, pruned_loss=0.04462, over 984729.86 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.2413, pruned_loss=0.05254, over 985451.76 frames.], batch size: 67, lr: 7.13e-04
+2022-06-18 18:24:59,122 INFO [train.py:874] (0/4) Epoch 11, batch 4000, aishell_loss[loss=0.1778, simple_loss=0.2561, pruned_loss=0.04979, over 4975.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2473, pruned_loss=0.04843, over 985350.16 frames.], batch size: 48, aishell_tot_loss[loss=0.1713, simple_loss=0.2528, pruned_loss=0.04493, over 985012.56 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2412, pruned_loss=0.05236, over 985548.79 frames.], batch size: 48, lr: 7.13e-04
+2022-06-18 18:24:59,125 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 18:25:16,907 INFO [train.py:914] (0/4) Epoch 11, validation: loss=0.1668, simple_loss=0.2498, pruned_loss=0.04186, over 1622729.00 frames.
+2022-06-18 18:25:45,364 INFO [train.py:874] (0/4) Epoch 11, batch 4050, aishell_loss[loss=0.1791, simple_loss=0.2601, pruned_loss=0.04908, over 4912.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2469, pruned_loss=0.04794, over 984918.34 frames.], batch size: 41, aishell_tot_loss[loss=0.1716, simple_loss=0.253, pruned_loss=0.04515, over 984676.66 frames.], datatang_tot_loss[loss=0.1719, simple_loss=0.2405, pruned_loss=0.05166, over 985464.13 frames.], batch size: 41, lr: 7.12e-04
+2022-06-18 18:26:14,012 INFO [train.py:874] (0/4) Epoch 11, batch 4100, datatang_loss[loss=0.1843, simple_loss=0.2474, pruned_loss=0.06061, over 4920.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2467, pruned_loss=0.04773, over 984500.31 frames.], batch size: 81, aishell_tot_loss[loss=0.1718, simple_loss=0.2531, pruned_loss=0.04524, over 984153.72 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.2402, pruned_loss=0.05123, over 985521.99 frames.], batch size: 81, lr: 7.12e-04
+2022-06-18 18:26:16,963 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-11.pt
+2022-06-18 18:27:19,850 INFO [train.py:874] (0/4) Epoch 12, batch 50, datatang_loss[loss=0.1778, simple_loss=0.2339, pruned_loss=0.0609, over 4895.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2478, pruned_loss=0.04893, over 218341.43 frames.], batch size: 52, aishell_tot_loss[loss=0.1708, simple_loss=0.2537, pruned_loss=0.04402, over 128734.95 frames.], datatang_tot_loss[loss=0.1757, simple_loss=0.2408, pruned_loss=0.05527, over 103074.61 frames.], batch size: 52, lr: 6.86e-04
+2022-06-18 18:27:51,807 INFO [train.py:874] (0/4) Epoch 12, batch 100, datatang_loss[loss=0.1428, simple_loss=0.2139, pruned_loss=0.03585, over 4959.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2446, pruned_loss=0.04657, over 388617.75 frames.], batch size: 55, aishell_tot_loss[loss=0.1709, simple_loss=0.254, pruned_loss=0.04388, over 233366.49 frames.], datatang_tot_loss[loss=0.1671, simple_loss=0.2342, pruned_loss=0.04999, over 203376.63 frames.], batch size: 55, lr: 6.86e-04
+2022-06-18 18:28:21,785 INFO [train.py:874] (0/4) Epoch 12, batch 150, datatang_loss[loss=0.1526, simple_loss=0.2276, pruned_loss=0.03879, over 4897.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2417, pruned_loss=0.0458, over 521110.91 frames.], batch size: 47, aishell_tot_loss[loss=0.1712, simple_loss=0.2543, pruned_loss=0.04399, over 301964.46 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2311, pruned_loss=0.04773, over 315868.49 frames.], batch size: 47, lr: 6.86e-04
+2022-06-18 18:28:52,333 INFO [train.py:874] (0/4) Epoch 12, batch 200, aishell_loss[loss=0.1947, simple_loss=0.2687, pruned_loss=0.06034, over 4917.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2421, pruned_loss=0.04623, over 623991.59 frames.], batch size: 46, aishell_tot_loss[loss=0.1713, simple_loss=0.2534, pruned_loss=0.04464, over 385273.91 frames.], datatang_tot_loss[loss=0.1637, simple_loss=0.2319, pruned_loss=0.04778, over 391903.43 frames.], batch size: 46, lr: 6.85e-04
+2022-06-18 18:29:23,915 INFO [train.py:874] (0/4) Epoch 12, batch 250, datatang_loss[loss=0.1564, simple_loss=0.2261, pruned_loss=0.0433, over 4921.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2425, pruned_loss=0.04561, over 704463.20 frames.], batch size: 73, aishell_tot_loss[loss=0.1699, simple_loss=0.2518, pruned_loss=0.04398, over 476985.91 frames.], datatang_tot_loss[loss=0.1638, simple_loss=0.2323, pruned_loss=0.0477, over 440518.05 frames.], batch size: 73, lr: 6.85e-04
+2022-06-18 18:29:54,015 INFO [train.py:874] (0/4) Epoch 12, batch 300, aishell_loss[loss=0.1708, simple_loss=0.2687, pruned_loss=0.03641, over 4952.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2412, pruned_loss=0.04521, over 766874.86 frames.], batch size: 54, aishell_tot_loss[loss=0.1681, simple_loss=0.2495, pruned_loss=0.04331, over 536773.89 frames.], datatang_tot_loss[loss=0.164, simple_loss=0.2327, pruned_loss=0.04761, over 504899.40 frames.], batch size: 54, lr: 6.85e-04
+2022-06-18 18:30:23,415 INFO [train.py:874] (0/4) Epoch 12, batch 350, aishell_loss[loss=0.1518, simple_loss=0.2416, pruned_loss=0.03098, over 4969.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2426, pruned_loss=0.04529, over 815542.33 frames.], batch size: 51, aishell_tot_loss[loss=0.1681, simple_loss=0.2503, pruned_loss=0.0429, over 587681.52 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.2341, pruned_loss=0.04805, over 563800.49 frames.], batch size: 51, lr: 6.84e-04
+2022-06-18 18:30:56,357 INFO [train.py:874] (0/4) Epoch 12, batch 400, datatang_loss[loss=0.1361, simple_loss=0.2019, pruned_loss=0.03514, over 4976.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2423, pruned_loss=0.04524, over 853726.36 frames.], batch size: 34, aishell_tot_loss[loss=0.1682, simple_loss=0.2505, pruned_loss=0.04291, over 624057.14 frames.], datatang_tot_loss[loss=0.1649, simple_loss=0.2345, pruned_loss=0.04766, over 624801.67 frames.], batch size: 34, lr: 6.84e-04
+2022-06-18 18:31:26,879 INFO [train.py:874] (0/4) Epoch 12, batch 450, aishell_loss[loss=0.1745, simple_loss=0.2472, pruned_loss=0.05085, over 4948.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2437, pruned_loss=0.04575, over 883418.93 frames.], batch size: 31, aishell_tot_loss[loss=0.1693, simple_loss=0.2513, pruned_loss=0.04364, over 670317.78 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2354, pruned_loss=0.04769, over 664064.96 frames.], batch size: 31, lr: 6.84e-04
+2022-06-18 18:31:57,601 INFO [train.py:874] (0/4) Epoch 12, batch 500, aishell_loss[loss=0.1863, simple_loss=0.2623, pruned_loss=0.05512, over 4947.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2451, pruned_loss=0.04658, over 905469.93 frames.], batch size: 32, aishell_tot_loss[loss=0.1689, simple_loss=0.2505, pruned_loss=0.04369, over 712359.53 frames.], datatang_tot_loss[loss=0.1679, simple_loss=0.2379, pruned_loss=0.04895, over 696170.30 frames.], batch size: 32, lr: 6.83e-04
+2022-06-18 18:32:29,719 INFO [train.py:874] (0/4) Epoch 12, batch 550, aishell_loss[loss=0.185, simple_loss=0.2677, pruned_loss=0.0512, over 4977.00 frames.], tot_loss[loss=0.1689, simple_loss=0.245, pruned_loss=0.04639, over 923339.39 frames.], batch size: 39, aishell_tot_loss[loss=0.1688, simple_loss=0.2505, pruned_loss=0.04358, over 740984.14 frames.], datatang_tot_loss[loss=0.1679, simple_loss=0.2382, pruned_loss=0.04876, over 734086.09 frames.], batch size: 39, lr: 6.83e-04
+2022-06-18 18:33:00,934 INFO [train.py:874] (0/4) Epoch 12, batch 600, aishell_loss[loss=0.1289, simple_loss=0.2021, pruned_loss=0.02787, over 4956.00 frames.], tot_loss[loss=0.1694, simple_loss=0.245, pruned_loss=0.04686, over 937136.71 frames.], batch size: 25, aishell_tot_loss[loss=0.1687, simple_loss=0.2502, pruned_loss=0.04357, over 763285.49 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2392, pruned_loss=0.04927, over 770199.91 frames.], batch size: 25, lr: 6.82e-04
+2022-06-18 18:33:32,101 INFO [train.py:874] (0/4) Epoch 12, batch 650, aishell_loss[loss=0.1849, simple_loss=0.2643, pruned_loss=0.05279, over 4961.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2441, pruned_loss=0.0465, over 948326.80 frames.], batch size: 44, aishell_tot_loss[loss=0.1679, simple_loss=0.2495, pruned_loss=0.04312, over 788729.31 frames.], datatang_tot_loss[loss=0.1688, simple_loss=0.2389, pruned_loss=0.04934, over 796734.39 frames.], batch size: 44, lr: 6.82e-04
+2022-06-18 18:34:02,834 INFO [train.py:874] (0/4) Epoch 12, batch 700, datatang_loss[loss=0.1397, simple_loss=0.2182, pruned_loss=0.03065, over 4933.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2437, pruned_loss=0.04595, over 956520.91 frames.], batch size: 25, aishell_tot_loss[loss=0.1674, simple_loss=0.2492, pruned_loss=0.04278, over 818727.43 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2382, pruned_loss=0.04936, over 812106.21 frames.], batch size: 25, lr: 6.82e-04
+2022-06-18 18:34:31,741 INFO [train.py:874] (0/4) Epoch 12, batch 750, datatang_loss[loss=0.1655, simple_loss=0.2409, pruned_loss=0.04501, over 4922.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2444, pruned_loss=0.04605, over 963050.40 frames.], batch size: 77, aishell_tot_loss[loss=0.1679, simple_loss=0.2496, pruned_loss=0.04304, over 840609.24 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2384, pruned_loss=0.04929, over 830275.33 frames.], batch size: 77, lr: 6.81e-04
+2022-06-18 18:35:04,205 INFO [train.py:874] (0/4) Epoch 12, batch 800, aishell_loss[loss=0.1567, simple_loss=0.2484, pruned_loss=0.03244, over 4958.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2455, pruned_loss=0.04639, over 967900.18 frames.], batch size: 61, aishell_tot_loss[loss=0.1683, simple_loss=0.2504, pruned_loss=0.04317, over 858949.97 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.239, pruned_loss=0.04959, over 847050.17 frames.], batch size: 61, lr: 6.81e-04
+2022-06-18 18:35:34,618 INFO [train.py:874] (0/4) Epoch 12, batch 850, datatang_loss[loss=0.2182, simple_loss=0.2853, pruned_loss=0.07552, over 4939.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2449, pruned_loss=0.04611, over 971583.93 frames.], batch size: 108, aishell_tot_loss[loss=0.1677, simple_loss=0.2499, pruned_loss=0.04274, over 872568.67 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2391, pruned_loss=0.04959, over 864491.05 frames.], batch size: 108, lr: 6.81e-04
+2022-06-18 18:36:04,252 INFO [train.py:874] (0/4) Epoch 12, batch 900, aishell_loss[loss=0.1371, simple_loss=0.2131, pruned_loss=0.0305, over 4827.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2443, pruned_loss=0.04572, over 974386.71 frames.], batch size: 24, aishell_tot_loss[loss=0.1669, simple_loss=0.2491, pruned_loss=0.04232, over 885122.71 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.2393, pruned_loss=0.0495, over 879228.16 frames.], batch size: 24, lr: 6.80e-04
+2022-06-18 18:36:36,373 INFO [train.py:874] (0/4) Epoch 12, batch 950, datatang_loss[loss=0.1762, simple_loss=0.2442, pruned_loss=0.05404, over 4921.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2449, pruned_loss=0.04604, over 976502.11 frames.], batch size: 83, aishell_tot_loss[loss=0.1668, simple_loss=0.2492, pruned_loss=0.04223, over 897697.27 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.2399, pruned_loss=0.04997, over 890573.93 frames.], batch size: 83, lr: 6.80e-04
+2022-06-18 18:37:06,849 INFO [train.py:874] (0/4) Epoch 12, batch 1000, aishell_loss[loss=0.1225, simple_loss=0.2121, pruned_loss=0.01645, over 4880.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2448, pruned_loss=0.04623, over 978089.37 frames.], batch size: 28, aishell_tot_loss[loss=0.1668, simple_loss=0.2491, pruned_loss=0.04226, over 908490.06 frames.], datatang_tot_loss[loss=0.1701, simple_loss=0.2398, pruned_loss=0.05023, over 900790.78 frames.], batch size: 28, lr: 6.79e-04
+2022-06-18 18:37:06,852 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 18:37:23,200 INFO [train.py:914] (0/4) Epoch 12, validation: loss=0.1654, simple_loss=0.2501, pruned_loss=0.04036, over 1622729.00 frames.
+2022-06-18 18:37:54,291 INFO [train.py:874] (0/4) Epoch 12, batch 1050, datatang_loss[loss=0.1723, simple_loss=0.2389, pruned_loss=0.05288, over 4887.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2436, pruned_loss=0.04608, over 979638.66 frames.], batch size: 39, aishell_tot_loss[loss=0.1666, simple_loss=0.2487, pruned_loss=0.0422, over 915881.91 frames.], datatang_tot_loss[loss=0.1695, simple_loss=0.2391, pruned_loss=0.04992, over 912483.54 frames.], batch size: 39, lr: 6.79e-04
+2022-06-18 18:38:26,525 INFO [train.py:874] (0/4) Epoch 12, batch 1100, aishell_loss[loss=0.1587, simple_loss=0.2423, pruned_loss=0.03749, over 4886.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2436, pruned_loss=0.04603, over 980946.58 frames.], batch size: 28, aishell_tot_loss[loss=0.1668, simple_loss=0.2489, pruned_loss=0.0424, over 923470.02 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2391, pruned_loss=0.0496, over 921745.54 frames.], batch size: 28, lr: 6.79e-04
+2022-06-18 18:38:56,143 INFO [train.py:874] (0/4) Epoch 12, batch 1150, aishell_loss[loss=0.1286, simple_loss=0.2185, pruned_loss=0.01933, over 4978.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2453, pruned_loss=0.04639, over 982071.91 frames.], batch size: 30, aishell_tot_loss[loss=0.1673, simple_loss=0.2498, pruned_loss=0.04243, over 931049.80 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2399, pruned_loss=0.05003, over 929099.64 frames.], batch size: 30, lr: 6.78e-04
+2022-06-18 18:39:27,822 INFO [train.py:874] (0/4) Epoch 12, batch 1200, aishell_loss[loss=0.1818, simple_loss=0.2569, pruned_loss=0.05332, over 4866.00 frames.], tot_loss[loss=0.169, simple_loss=0.2448, pruned_loss=0.0466, over 982889.74 frames.], batch size: 36, aishell_tot_loss[loss=0.1679, simple_loss=0.2502, pruned_loss=0.04284, over 937705.75 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.239, pruned_loss=0.0499, over 935554.34 frames.], batch size: 36, lr: 6.78e-04
+2022-06-18 18:39:58,227 INFO [train.py:874] (0/4) Epoch 12, batch 1250, aishell_loss[loss=0.1527, simple_loss=0.2439, pruned_loss=0.03081, over 4979.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2449, pruned_loss=0.04685, over 983957.82 frames.], batch size: 30, aishell_tot_loss[loss=0.168, simple_loss=0.2502, pruned_loss=0.04291, over 943339.96 frames.], datatang_tot_loss[loss=0.1698, simple_loss=0.2393, pruned_loss=0.05015, over 941965.48 frames.], batch size: 30, lr: 6.78e-04
+2022-06-18 18:40:30,426 INFO [train.py:874] (0/4) Epoch 12, batch 1300, aishell_loss[loss=0.1654, simple_loss=0.2565, pruned_loss=0.03709, over 4953.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2448, pruned_loss=0.04648, over 984275.15 frames.], batch size: 40, aishell_tot_loss[loss=0.1674, simple_loss=0.2499, pruned_loss=0.04251, over 947692.97 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.2396, pruned_loss=0.05015, over 947678.30 frames.], batch size: 40, lr: 6.77e-04
+2022-06-18 18:41:01,635 INFO [train.py:874] (0/4) Epoch 12, batch 1350, datatang_loss[loss=0.202, simple_loss=0.2711, pruned_loss=0.06644, over 4927.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2454, pruned_loss=0.04662, over 984897.95 frames.], batch size: 83, aishell_tot_loss[loss=0.1674, simple_loss=0.2501, pruned_loss=0.04235, over 951854.83 frames.], datatang_tot_loss[loss=0.1705, simple_loss=0.2401, pruned_loss=0.05044, over 952801.76 frames.], batch size: 83, lr: 6.77e-04
+2022-06-18 18:41:32,118 INFO [train.py:874] (0/4) Epoch 12, batch 1400, datatang_loss[loss=0.2476, simple_loss=0.3026, pruned_loss=0.09628, over 4920.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2441, pruned_loss=0.04621, over 985379.92 frames.], batch size: 108, aishell_tot_loss[loss=0.1671, simple_loss=0.2498, pruned_loss=0.04218, over 955655.96 frames.], datatang_tot_loss[loss=0.1697, simple_loss=0.2392, pruned_loss=0.05011, over 957179.62 frames.], batch size: 108, lr: 6.77e-04
+2022-06-18 18:42:02,774 INFO [train.py:874] (0/4) Epoch 12, batch 1450, datatang_loss[loss=0.1519, simple_loss=0.2161, pruned_loss=0.04382, over 4959.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2451, pruned_loss=0.04634, over 985478.81 frames.], batch size: 55, aishell_tot_loss[loss=0.168, simple_loss=0.2506, pruned_loss=0.04269, over 960139.10 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.239, pruned_loss=0.05005, over 959631.91 frames.], batch size: 55, lr: 6.76e-04
+2022-06-18 18:42:33,341 INFO [train.py:874] (0/4) Epoch 12, batch 1500, datatang_loss[loss=0.1649, simple_loss=0.231, pruned_loss=0.04942, over 4967.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2447, pruned_loss=0.04579, over 985507.43 frames.], batch size: 60, aishell_tot_loss[loss=0.1677, simple_loss=0.2505, pruned_loss=0.04244, over 963314.93 frames.], datatang_tot_loss[loss=0.169, simple_loss=0.2386, pruned_loss=0.04975, over 962499.47 frames.], batch size: 60, lr: 6.76e-04
+2022-06-18 18:43:02,292 INFO [train.py:874] (0/4) Epoch 12, batch 1550, aishell_loss[loss=0.1893, simple_loss=0.2634, pruned_loss=0.05766, over 4899.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2439, pruned_loss=0.04561, over 985346.20 frames.], batch size: 50, aishell_tot_loss[loss=0.1671, simple_loss=0.2498, pruned_loss=0.04225, over 966045.21 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2383, pruned_loss=0.04976, over 964912.92 frames.], batch size: 50, lr: 6.76e-04
+2022-06-18 18:43:34,345 INFO [train.py:874] (0/4) Epoch 12, batch 1600, aishell_loss[loss=0.1595, simple_loss=0.2423, pruned_loss=0.03835, over 4884.00 frames.], tot_loss[loss=0.1675, simple_loss=0.244, pruned_loss=0.0455, over 985503.30 frames.], batch size: 35, aishell_tot_loss[loss=0.166, simple_loss=0.2486, pruned_loss=0.04169, over 968570.12 frames.], datatang_tot_loss[loss=0.1698, simple_loss=0.2394, pruned_loss=0.05016, over 967233.44 frames.], batch size: 35, lr: 6.75e-04
+2022-06-18 18:44:05,015 INFO [train.py:874] (0/4) Epoch 12, batch 1650, aishell_loss[loss=0.1672, simple_loss=0.25, pruned_loss=0.04216, over 4933.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2449, pruned_loss=0.04631, over 985673.55 frames.], batch size: 58, aishell_tot_loss[loss=0.1667, simple_loss=0.2491, pruned_loss=0.04214, over 970662.65 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.2399, pruned_loss=0.05041, over 969480.64 frames.], batch size: 58, lr: 6.75e-04
+2022-06-18 18:44:35,419 INFO [train.py:874] (0/4) Epoch 12, batch 1700, aishell_loss[loss=0.1643, simple_loss=0.2585, pruned_loss=0.035, over 4948.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2448, pruned_loss=0.04593, over 985860.59 frames.], batch size: 64, aishell_tot_loss[loss=0.1674, simple_loss=0.2499, pruned_loss=0.04247, over 972435.42 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.239, pruned_loss=0.04962, over 971584.37 frames.], batch size: 64, lr: 6.74e-04
+2022-06-18 18:45:06,614 INFO [train.py:874] (0/4) Epoch 12, batch 1750, aishell_loss[loss=0.1768, simple_loss=0.2582, pruned_loss=0.04769, over 4926.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2451, pruned_loss=0.04581, over 985567.43 frames.], batch size: 52, aishell_tot_loss[loss=0.1674, simple_loss=0.25, pruned_loss=0.04244, over 974017.62 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.2393, pruned_loss=0.0496, over 972937.54 frames.], batch size: 52, lr: 6.74e-04
+2022-06-18 18:45:38,044 INFO [train.py:874] (0/4) Epoch 12, batch 1800, aishell_loss[loss=0.1537, simple_loss=0.2397, pruned_loss=0.03382, over 4953.00 frames.], tot_loss[loss=0.1684, simple_loss=0.245, pruned_loss=0.04592, over 985750.28 frames.], batch size: 54, aishell_tot_loss[loss=0.1675, simple_loss=0.2501, pruned_loss=0.04251, over 975496.18 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.2393, pruned_loss=0.04956, over 974504.99 frames.], batch size: 54, lr: 6.74e-04
+2022-06-18 18:46:08,162 INFO [train.py:874] (0/4) Epoch 12, batch 1850, aishell_loss[loss=0.1326, simple_loss=0.2212, pruned_loss=0.02199, over 4900.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2456, pruned_loss=0.04635, over 985852.14 frames.], batch size: 28, aishell_tot_loss[loss=0.1677, simple_loss=0.2503, pruned_loss=0.04255, over 976610.16 frames.], datatang_tot_loss[loss=0.1698, simple_loss=0.2397, pruned_loss=0.04993, over 976036.43 frames.], batch size: 28, lr: 6.73e-04
+2022-06-18 18:46:38,138 INFO [train.py:874] (0/4) Epoch 12, batch 1900, datatang_loss[loss=0.1504, simple_loss=0.2235, pruned_loss=0.03869, over 4944.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2459, pruned_loss=0.04643, over 986006.64 frames.], batch size: 69, aishell_tot_loss[loss=0.168, simple_loss=0.2507, pruned_loss=0.04261, over 977970.34 frames.], datatang_tot_loss[loss=0.1698, simple_loss=0.2394, pruned_loss=0.05012, over 977068.95 frames.], batch size: 69, lr: 6.73e-04
+2022-06-18 18:47:08,332 INFO [train.py:874] (0/4) Epoch 12, batch 1950, datatang_loss[loss=0.182, simple_loss=0.2448, pruned_loss=0.05958, over 4864.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2464, pruned_loss=0.04635, over 985873.99 frames.], batch size: 39, aishell_tot_loss[loss=0.1679, simple_loss=0.2509, pruned_loss=0.04241, over 979014.00 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.2397, pruned_loss=0.05044, over 977864.40 frames.], batch size: 39, lr: 6.73e-04
+2022-06-18 18:47:38,738 INFO [train.py:874] (0/4) Epoch 12, batch 2000, datatang_loss[loss=0.1662, simple_loss=0.2425, pruned_loss=0.04499, over 4953.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2465, pruned_loss=0.04687, over 985765.63 frames.], batch size: 67, aishell_tot_loss[loss=0.168, simple_loss=0.2509, pruned_loss=0.04255, over 979469.27 frames.], datatang_tot_loss[loss=0.1708, simple_loss=0.2403, pruned_loss=0.05068, over 979062.48 frames.], batch size: 67, lr: 6.72e-04
+2022-06-18 18:47:38,742 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 18:47:55,402 INFO [train.py:914] (0/4) Epoch 12, validation: loss=0.1674, simple_loss=0.25, pruned_loss=0.04245, over 1622729.00 frames.
+2022-06-18 18:48:25,434 INFO [train.py:874] (0/4) Epoch 12, batch 2050, datatang_loss[loss=0.1635, simple_loss=0.2379, pruned_loss=0.04458, over 4932.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2461, pruned_loss=0.04665, over 985639.89 frames.], batch size: 79, aishell_tot_loss[loss=0.168, simple_loss=0.2509, pruned_loss=0.04257, over 979954.44 frames.], datatang_tot_loss[loss=0.1705, simple_loss=0.2401, pruned_loss=0.05044, over 979958.80 frames.], batch size: 79, lr: 6.72e-04
+2022-06-18 18:48:56,079 INFO [train.py:874] (0/4) Epoch 12, batch 2100, aishell_loss[loss=0.2302, simple_loss=0.3041, pruned_loss=0.07817, over 4916.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2458, pruned_loss=0.04688, over 985119.87 frames.], batch size: 80, aishell_tot_loss[loss=0.168, simple_loss=0.2504, pruned_loss=0.04277, over 980208.12 frames.], datatang_tot_loss[loss=0.1708, simple_loss=0.2403, pruned_loss=0.05067, over 980503.01 frames.], batch size: 80, lr: 6.72e-04
+2022-06-18 18:49:26,401 INFO [train.py:874] (0/4) Epoch 12, batch 2150, datatang_loss[loss=0.2036, simple_loss=0.2721, pruned_loss=0.06762, over 4941.00 frames.], tot_loss[loss=0.1705, simple_loss=0.246, pruned_loss=0.04747, over 985125.78 frames.], batch size: 94, aishell_tot_loss[loss=0.1683, simple_loss=0.2504, pruned_loss=0.04313, over 980780.17 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.2405, pruned_loss=0.05102, over 981038.33 frames.], batch size: 94, lr: 6.71e-04
+2022-06-18 18:49:55,761 INFO [train.py:874] (0/4) Epoch 12, batch 2200, datatang_loss[loss=0.1661, simple_loss=0.2412, pruned_loss=0.04543, over 4919.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2462, pruned_loss=0.04747, over 984711.20 frames.], batch size: 75, aishell_tot_loss[loss=0.1688, simple_loss=0.2509, pruned_loss=0.04339, over 980993.76 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.2404, pruned_loss=0.05078, over 981362.50 frames.], batch size: 75, lr: 6.71e-04
+2022-06-18 18:50:25,833 INFO [train.py:874] (0/4) Epoch 12, batch 2250, datatang_loss[loss=0.151, simple_loss=0.2291, pruned_loss=0.03648, over 4935.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2456, pruned_loss=0.04715, over 984648.49 frames.], batch size: 69, aishell_tot_loss[loss=0.1696, simple_loss=0.2515, pruned_loss=0.04382, over 981166.59 frames.], datatang_tot_loss[loss=0.1698, simple_loss=0.2394, pruned_loss=0.05005, over 981940.82 frames.], batch size: 69, lr: 6.71e-04
+2022-06-18 18:50:57,131 INFO [train.py:874] (0/4) Epoch 12, batch 2300, datatang_loss[loss=0.1646, simple_loss=0.2299, pruned_loss=0.04966, over 4840.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2452, pruned_loss=0.04699, over 984592.38 frames.], batch size: 24, aishell_tot_loss[loss=0.1699, simple_loss=0.2518, pruned_loss=0.044, over 981687.68 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.239, pruned_loss=0.04968, over 982097.68 frames.], batch size: 24, lr: 6.70e-04
+2022-06-18 18:51:26,960 INFO [train.py:874] (0/4) Epoch 12, batch 2350, datatang_loss[loss=0.161, simple_loss=0.232, pruned_loss=0.04499, over 4940.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2454, pruned_loss=0.04662, over 984898.41 frames.], batch size: 25, aishell_tot_loss[loss=0.1702, simple_loss=0.2521, pruned_loss=0.04411, over 982211.13 frames.], datatang_tot_loss[loss=0.1686, simple_loss=0.2386, pruned_loss=0.04932, over 982549.91 frames.], batch size: 25, lr: 6.70e-04
+2022-06-18 18:51:58,525 INFO [train.py:874] (0/4) Epoch 12, batch 2400, datatang_loss[loss=0.181, simple_loss=0.2527, pruned_loss=0.05464, over 4965.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2451, pruned_loss=0.04616, over 985054.56 frames.], batch size: 55, aishell_tot_loss[loss=0.1698, simple_loss=0.252, pruned_loss=0.04384, over 982551.82 frames.], datatang_tot_loss[loss=0.1683, simple_loss=0.2386, pruned_loss=0.04904, over 982971.54 frames.], batch size: 55, lr: 6.70e-04
+2022-06-18 18:52:28,312 INFO [train.py:874] (0/4) Epoch 12, batch 2450, aishell_loss[loss=0.2018, simple_loss=0.2712, pruned_loss=0.06624, over 4937.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2447, pruned_loss=0.04554, over 985077.95 frames.], batch size: 33, aishell_tot_loss[loss=0.1695, simple_loss=0.2517, pruned_loss=0.04364, over 982885.81 frames.], datatang_tot_loss[loss=0.1677, simple_loss=0.2381, pruned_loss=0.04859, over 983189.26 frames.], batch size: 33, lr: 6.69e-04
+2022-06-18 18:52:59,325 INFO [train.py:874] (0/4) Epoch 12, batch 2500, datatang_loss[loss=0.1599, simple_loss=0.2375, pruned_loss=0.04118, over 4925.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2439, pruned_loss=0.04584, over 985530.10 frames.], batch size: 77, aishell_tot_loss[loss=0.1695, simple_loss=0.2517, pruned_loss=0.04365, over 983239.21 frames.], datatang_tot_loss[loss=0.1675, simple_loss=0.2378, pruned_loss=0.04859, over 983771.67 frames.], batch size: 77, lr: 6.69e-04
+2022-06-18 18:53:29,791 INFO [train.py:874] (0/4) Epoch 12, batch 2550, aishell_loss[loss=0.1797, simple_loss=0.2612, pruned_loss=0.04908, over 4890.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2447, pruned_loss=0.04589, over 985496.91 frames.], batch size: 34, aishell_tot_loss[loss=0.1687, simple_loss=0.251, pruned_loss=0.04321, over 983513.60 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2387, pruned_loss=0.04914, over 983939.48 frames.], batch size: 34, lr: 6.69e-04
+2022-06-18 18:53:58,765 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-48000.pt
+2022-06-18 18:54:06,335 INFO [train.py:874] (0/4) Epoch 12, batch 2600, datatang_loss[loss=0.1831, simple_loss=0.2532, pruned_loss=0.05651, over 4952.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2446, pruned_loss=0.04606, over 985692.02 frames.], batch size: 99, aishell_tot_loss[loss=0.1688, simple_loss=0.2512, pruned_loss=0.04318, over 983750.91 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2386, pruned_loss=0.04917, over 984291.80 frames.], batch size: 99, lr: 6.68e-04
+2022-06-18 18:54:36,796 INFO [train.py:874] (0/4) Epoch 12, batch 2650, datatang_loss[loss=0.1452, simple_loss=0.2237, pruned_loss=0.0334, over 4921.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2448, pruned_loss=0.04586, over 985979.87 frames.], batch size: 71, aishell_tot_loss[loss=0.1695, simple_loss=0.252, pruned_loss=0.04353, over 984231.04 frames.], datatang_tot_loss[loss=0.1675, simple_loss=0.238, pruned_loss=0.04854, over 984509.11 frames.], batch size: 71, lr: 6.68e-04
+2022-06-18 18:55:07,910 INFO [train.py:874] (0/4) Epoch 12, batch 2700, datatang_loss[loss=0.1673, simple_loss=0.2375, pruned_loss=0.04856, over 4955.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2456, pruned_loss=0.04581, over 985874.28 frames.], batch size: 86, aishell_tot_loss[loss=0.1703, simple_loss=0.2528, pruned_loss=0.04384, over 984344.88 frames.], datatang_tot_loss[loss=0.1671, simple_loss=0.2376, pruned_loss=0.04826, over 984671.28 frames.], batch size: 86, lr: 6.68e-04
+2022-06-18 18:55:38,327 INFO [train.py:874] (0/4) Epoch 12, batch 2750, datatang_loss[loss=0.1431, simple_loss=0.2232, pruned_loss=0.03149, over 4920.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2456, pruned_loss=0.04635, over 986033.84 frames.], batch size: 42, aishell_tot_loss[loss=0.1702, simple_loss=0.2526, pruned_loss=0.04391, over 984434.88 frames.], datatang_tot_loss[loss=0.1677, simple_loss=0.2381, pruned_loss=0.04868, over 985061.31 frames.], batch size: 42, lr: 6.67e-04
+2022-06-18 18:56:09,585 INFO [train.py:874] (0/4) Epoch 12, batch 2800, aishell_loss[loss=0.1593, simple_loss=0.246, pruned_loss=0.03628, over 4958.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2466, pruned_loss=0.0469, over 985758.39 frames.], batch size: 54, aishell_tot_loss[loss=0.1706, simple_loss=0.2531, pruned_loss=0.04402, over 984201.31 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2388, pruned_loss=0.04916, over 985291.70 frames.], batch size: 54, lr: 6.67e-04
+2022-06-18 18:56:40,325 INFO [train.py:874] (0/4) Epoch 12, batch 2850, datatang_loss[loss=0.2012, simple_loss=0.2541, pruned_loss=0.07418, over 4980.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2456, pruned_loss=0.04655, over 985907.36 frames.], batch size: 45, aishell_tot_loss[loss=0.1701, simple_loss=0.2525, pruned_loss=0.04382, over 984294.32 frames.], datatang_tot_loss[loss=0.1684, simple_loss=0.2388, pruned_loss=0.04899, over 985575.01 frames.], batch size: 45, lr: 6.66e-04
+2022-06-18 18:57:09,892 INFO [train.py:874] (0/4) Epoch 12, batch 2900, aishell_loss[loss=0.1819, simple_loss=0.2763, pruned_loss=0.04373, over 4902.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2466, pruned_loss=0.04691, over 985855.78 frames.], batch size: 68, aishell_tot_loss[loss=0.1698, simple_loss=0.2523, pruned_loss=0.04366, over 984459.70 frames.], datatang_tot_loss[loss=0.1695, simple_loss=0.2397, pruned_loss=0.04968, over 985616.55 frames.], batch size: 68, lr: 6.66e-04
+2022-06-18 18:57:41,736 INFO [train.py:874] (0/4) Epoch 12, batch 2950, aishell_loss[loss=0.1677, simple_loss=0.2549, pruned_loss=0.04024, over 4923.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2472, pruned_loss=0.04753, over 986091.69 frames.], batch size: 46, aishell_tot_loss[loss=0.1706, simple_loss=0.2534, pruned_loss=0.04387, over 984860.18 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.2397, pruned_loss=0.05002, over 985632.41 frames.], batch size: 46, lr: 6.66e-04
+2022-06-18 18:58:12,954 INFO [train.py:874] (0/4) Epoch 12, batch 3000, aishell_loss[loss=0.1462, simple_loss=0.2357, pruned_loss=0.02835, over 4965.00 frames.], tot_loss[loss=0.171, simple_loss=0.2474, pruned_loss=0.04728, over 986271.94 frames.], batch size: 31, aishell_tot_loss[loss=0.1709, simple_loss=0.2536, pruned_loss=0.04408, over 985206.06 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.2397, pruned_loss=0.04971, over 985679.85 frames.], batch size: 31, lr: 6.65e-04
+2022-06-18 18:58:12,957 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 18:58:29,561 INFO [train.py:914] (0/4) Epoch 12, validation: loss=0.1665, simple_loss=0.2506, pruned_loss=0.04121, over 1622729.00 frames.
+2022-06-18 18:59:00,900 INFO [train.py:874] (0/4) Epoch 12, batch 3050, aishell_loss[loss=0.1499, simple_loss=0.2345, pruned_loss=0.03266, over 4980.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2469, pruned_loss=0.04715, over 986208.60 frames.], batch size: 30, aishell_tot_loss[loss=0.1702, simple_loss=0.253, pruned_loss=0.04373, over 985263.96 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2401, pruned_loss=0.04994, over 985737.62 frames.], batch size: 30, lr: 6.65e-04
+2022-06-18 18:59:32,407 INFO [train.py:874] (0/4) Epoch 12, batch 3100, aishell_loss[loss=0.1408, simple_loss=0.2193, pruned_loss=0.03113, over 4875.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2464, pruned_loss=0.04647, over 986349.86 frames.], batch size: 28, aishell_tot_loss[loss=0.1695, simple_loss=0.2525, pruned_loss=0.04328, over 985399.35 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.2402, pruned_loss=0.04979, over 985920.96 frames.], batch size: 28, lr: 6.65e-04
+2022-06-18 19:00:02,795 INFO [train.py:874] (0/4) Epoch 12, batch 3150, aishell_loss[loss=0.1694, simple_loss=0.2418, pruned_loss=0.0485, over 4864.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2459, pruned_loss=0.04647, over 986306.69 frames.], batch size: 35, aishell_tot_loss[loss=0.1693, simple_loss=0.2522, pruned_loss=0.0432, over 985394.38 frames.], datatang_tot_loss[loss=0.1698, simple_loss=0.2402, pruned_loss=0.04972, over 986020.06 frames.], batch size: 35, lr: 6.64e-04
+2022-06-18 19:00:33,839 INFO [train.py:874] (0/4) Epoch 12, batch 3200, aishell_loss[loss=0.161, simple_loss=0.2528, pruned_loss=0.03461, over 4978.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2441, pruned_loss=0.04541, over 986558.63 frames.], batch size: 51, aishell_tot_loss[loss=0.1684, simple_loss=0.2513, pruned_loss=0.04274, over 985917.82 frames.], datatang_tot_loss[loss=0.1687, simple_loss=0.239, pruned_loss=0.04918, over 985902.02 frames.], batch size: 51, lr: 6.64e-04
+2022-06-18 19:01:04,180 INFO [train.py:874] (0/4) Epoch 12, batch 3250, aishell_loss[loss=0.1582, simple_loss=0.2458, pruned_loss=0.03533, over 4970.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2446, pruned_loss=0.04558, over 986243.04 frames.], batch size: 44, aishell_tot_loss[loss=0.1684, simple_loss=0.2514, pruned_loss=0.04268, over 985931.07 frames.], datatang_tot_loss[loss=0.1688, simple_loss=0.2392, pruned_loss=0.04925, over 985700.50 frames.], batch size: 44, lr: 6.64e-04
+2022-06-18 19:01:35,674 INFO [train.py:874] (0/4) Epoch 12, batch 3300, datatang_loss[loss=0.1519, simple_loss=0.2265, pruned_loss=0.03868, over 4915.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2453, pruned_loss=0.04558, over 985910.99 frames.], batch size: 64, aishell_tot_loss[loss=0.1683, simple_loss=0.2515, pruned_loss=0.04255, over 985670.91 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.2398, pruned_loss=0.04931, over 985713.47 frames.], batch size: 64, lr: 6.63e-04
+2022-06-18 19:02:06,901 INFO [train.py:874] (0/4) Epoch 12, batch 3350, aishell_loss[loss=0.1585, simple_loss=0.2528, pruned_loss=0.03203, over 4974.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2455, pruned_loss=0.04532, over 986004.36 frames.], batch size: 39, aishell_tot_loss[loss=0.1681, simple_loss=0.2514, pruned_loss=0.04241, over 985637.07 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2396, pruned_loss=0.04927, over 985896.23 frames.], batch size: 39, lr: 6.63e-04
+2022-06-18 19:02:37,683 INFO [train.py:874] (0/4) Epoch 12, batch 3400, datatang_loss[loss=0.1415, simple_loss=0.2165, pruned_loss=0.0332, over 4958.00 frames.], tot_loss[loss=0.168, simple_loss=0.2452, pruned_loss=0.04539, over 985577.01 frames.], batch size: 55, aishell_tot_loss[loss=0.1679, simple_loss=0.2512, pruned_loss=0.04234, over 985188.00 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2394, pruned_loss=0.04939, over 985948.35 frames.], batch size: 55, lr: 6.63e-04
+2022-06-18 19:03:08,070 INFO [train.py:874] (0/4) Epoch 12, batch 3450, aishell_loss[loss=0.2685, simple_loss=0.3282, pruned_loss=0.1044, over 4967.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2455, pruned_loss=0.04537, over 985640.39 frames.], batch size: 79, aishell_tot_loss[loss=0.1678, simple_loss=0.251, pruned_loss=0.04226, over 985146.49 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.2397, pruned_loss=0.04938, over 986065.91 frames.], batch size: 79, lr: 6.62e-04
+2022-06-18 19:03:38,669 INFO [train.py:874] (0/4) Epoch 12, batch 3500, aishell_loss[loss=0.1619, simple_loss=0.2473, pruned_loss=0.03827, over 4941.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2458, pruned_loss=0.04555, over 985530.63 frames.], batch size: 58, aishell_tot_loss[loss=0.1675, simple_loss=0.2508, pruned_loss=0.04205, over 985048.00 frames.], datatang_tot_loss[loss=0.1698, simple_loss=0.2401, pruned_loss=0.04973, over 986050.57 frames.], batch size: 58, lr: 6.62e-04
+2022-06-18 19:04:07,462 INFO [train.py:874] (0/4) Epoch 12, batch 3550, datatang_loss[loss=0.164, simple_loss=0.2372, pruned_loss=0.04539, over 4958.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2455, pruned_loss=0.0458, over 985550.03 frames.], batch size: 86, aishell_tot_loss[loss=0.1674, simple_loss=0.2508, pruned_loss=0.04203, over 985096.52 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.2399, pruned_loss=0.0499, over 986002.78 frames.], batch size: 86, lr: 6.62e-04
+2022-06-18 19:04:39,572 INFO [train.py:874] (0/4) Epoch 12, batch 3600, datatang_loss[loss=0.1403, simple_loss=0.2184, pruned_loss=0.03107, over 4963.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2449, pruned_loss=0.04523, over 985541.42 frames.], batch size: 55, aishell_tot_loss[loss=0.1669, simple_loss=0.2501, pruned_loss=0.04179, over 985045.99 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.2399, pruned_loss=0.04961, over 986054.78 frames.], batch size: 55, lr: 6.61e-04
+2022-06-18 19:05:11,050 INFO [train.py:874] (0/4) Epoch 12, batch 3650, aishell_loss[loss=0.1586, simple_loss=0.2398, pruned_loss=0.03866, over 4935.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2447, pruned_loss=0.04535, over 985819.81 frames.], batch size: 32, aishell_tot_loss[loss=0.1669, simple_loss=0.25, pruned_loss=0.04193, over 985131.87 frames.], datatang_tot_loss[loss=0.1694, simple_loss=0.2398, pruned_loss=0.04948, over 986272.71 frames.], batch size: 32, lr: 6.61e-04
+2022-06-18 19:05:40,303 INFO [train.py:874] (0/4) Epoch 12, batch 3700, datatang_loss[loss=0.1697, simple_loss=0.2436, pruned_loss=0.04786, over 4964.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2436, pruned_loss=0.04527, over 986108.71 frames.], batch size: 91, aishell_tot_loss[loss=0.1665, simple_loss=0.2494, pruned_loss=0.04175, over 985425.06 frames.], datatang_tot_loss[loss=0.169, simple_loss=0.2392, pruned_loss=0.04935, over 986291.39 frames.], batch size: 91, lr: 6.61e-04
+2022-06-18 19:06:10,953 INFO [train.py:874] (0/4) Epoch 12, batch 3750, aishell_loss[loss=0.1609, simple_loss=0.256, pruned_loss=0.03287, over 4910.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2438, pruned_loss=0.04472, over 986048.75 frames.], batch size: 52, aishell_tot_loss[loss=0.1661, simple_loss=0.2491, pruned_loss=0.04152, over 985586.71 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2391, pruned_loss=0.0493, over 986156.04 frames.], batch size: 52, lr: 6.60e-04
+2022-06-18 19:06:39,856 INFO [train.py:874] (0/4) Epoch 12, batch 3800, datatang_loss[loss=0.1912, simple_loss=0.2593, pruned_loss=0.06158, over 4822.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2444, pruned_loss=0.04499, over 985936.09 frames.], batch size: 25, aishell_tot_loss[loss=0.1657, simple_loss=0.2489, pruned_loss=0.04127, over 985963.43 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.2398, pruned_loss=0.04968, over 985717.50 frames.], batch size: 25, lr: 6.60e-04
+2022-06-18 19:07:09,914 INFO [train.py:874] (0/4) Epoch 12, batch 3850, aishell_loss[loss=0.1742, simple_loss=0.2559, pruned_loss=0.04622, over 4936.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2441, pruned_loss=0.04507, over 986074.98 frames.], batch size: 56, aishell_tot_loss[loss=0.1661, simple_loss=0.2492, pruned_loss=0.04144, over 985955.49 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2392, pruned_loss=0.04947, over 985894.19 frames.], batch size: 56, lr: 6.60e-04
+2022-06-18 19:07:38,522 INFO [train.py:874] (0/4) Epoch 12, batch 3900, aishell_loss[loss=0.1666, simple_loss=0.2562, pruned_loss=0.03849, over 4934.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2436, pruned_loss=0.04477, over 985797.02 frames.], batch size: 49, aishell_tot_loss[loss=0.166, simple_loss=0.2492, pruned_loss=0.04138, over 985944.09 frames.], datatang_tot_loss[loss=0.1683, simple_loss=0.2389, pruned_loss=0.04888, over 985663.98 frames.], batch size: 49, lr: 6.59e-04
+2022-06-18 19:08:09,096 INFO [train.py:874] (0/4) Epoch 12, batch 3950, aishell_loss[loss=0.1717, simple_loss=0.2628, pruned_loss=0.04026, over 4923.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2427, pruned_loss=0.04529, over 985869.44 frames.], batch size: 52, aishell_tot_loss[loss=0.166, simple_loss=0.2488, pruned_loss=0.04163, over 985998.17 frames.], datatang_tot_loss[loss=0.1682, simple_loss=0.2386, pruned_loss=0.04889, over 985696.45 frames.], batch size: 52, lr: 6.59e-04
+2022-06-18 19:08:37,851 INFO [train.py:874] (0/4) Epoch 12, batch 4000, datatang_loss[loss=0.1635, simple_loss=0.2384, pruned_loss=0.04429, over 4926.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2432, pruned_loss=0.04501, over 985539.27 frames.], batch size: 73, aishell_tot_loss[loss=0.1667, simple_loss=0.2495, pruned_loss=0.04198, over 985395.10 frames.], datatang_tot_loss[loss=0.1674, simple_loss=0.2381, pruned_loss=0.04831, over 985966.11 frames.], batch size: 73, lr: 6.59e-04
+2022-06-18 19:08:37,854 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 19:08:54,416 INFO [train.py:914] (0/4) Epoch 12, validation: loss=0.1659, simple_loss=0.2501, pruned_loss=0.0409, over 1622729.00 frames.
+2022-06-18 19:09:24,431 INFO [train.py:874] (0/4) Epoch 12, batch 4050, datatang_loss[loss=0.1475, simple_loss=0.2177, pruned_loss=0.03867, over 4946.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2432, pruned_loss=0.04534, over 985550.75 frames.], batch size: 62, aishell_tot_loss[loss=0.1667, simple_loss=0.2494, pruned_loss=0.04198, over 985190.91 frames.], datatang_tot_loss[loss=0.1676, simple_loss=0.2383, pruned_loss=0.04848, over 986130.87 frames.], batch size: 62, lr: 6.58e-04
+2022-06-18 19:09:52,462 INFO [train.py:874] (0/4) Epoch 12, batch 4100, datatang_loss[loss=0.1661, simple_loss=0.2449, pruned_loss=0.04366, over 4944.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2438, pruned_loss=0.04533, over 985331.04 frames.], batch size: 62, aishell_tot_loss[loss=0.1668, simple_loss=0.2494, pruned_loss=0.04208, over 984806.07 frames.], datatang_tot_loss[loss=0.1678, simple_loss=0.2385, pruned_loss=0.04851, over 986283.72 frames.], batch size: 62, lr: 6.58e-04
+2022-06-18 19:10:05,187 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-12.pt
+2022-06-18 19:11:11,422 INFO [train.py:874] (0/4) Epoch 13, batch 50, datatang_loss[loss=0.1391, simple_loss=0.2174, pruned_loss=0.03041, over 4918.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2327, pruned_loss=0.04154, over 218469.73 frames.], batch size: 81, aishell_tot_loss[loss=0.1663, simple_loss=0.2478, pruned_loss=0.0424, over 107080.60 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2202, pruned_loss=0.04098, over 124973.44 frames.], batch size: 81, lr: 6.36e-04
+2022-06-18 19:11:41,572 INFO [train.py:874] (0/4) Epoch 13, batch 100, aishell_loss[loss=0.1574, simple_loss=0.2386, pruned_loss=0.03814, over 4933.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2388, pruned_loss=0.04208, over 388141.68 frames.], batch size: 32, aishell_tot_loss[loss=0.1681, simple_loss=0.2512, pruned_loss=0.04254, over 229323.60 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.224, pruned_loss=0.04169, over 207072.72 frames.], batch size: 32, lr: 6.35e-04
+2022-06-18 19:12:12,255 INFO [train.py:874] (0/4) Epoch 13, batch 150, datatang_loss[loss=0.1646, simple_loss=0.2424, pruned_loss=0.04335, over 4919.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2392, pruned_loss=0.04271, over 520608.02 frames.], batch size: 98, aishell_tot_loss[loss=0.1682, simple_loss=0.2514, pruned_loss=0.04251, over 308142.34 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2266, pruned_loss=0.04288, over 309181.86 frames.], batch size: 98, lr: 6.35e-04
+2022-06-18 19:12:44,585 INFO [train.py:874] (0/4) Epoch 13, batch 200, datatang_loss[loss=0.1642, simple_loss=0.2334, pruned_loss=0.0475, over 4926.00 frames.], tot_loss[loss=0.163, simple_loss=0.2398, pruned_loss=0.04311, over 623962.54 frames.], batch size: 42, aishell_tot_loss[loss=0.1688, simple_loss=0.2511, pruned_loss=0.04322, over 379044.87 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2289, pruned_loss=0.04287, over 397878.15 frames.], batch size: 42, lr: 6.35e-04
+2022-06-18 19:13:14,987 INFO [train.py:874] (0/4) Epoch 13, batch 250, datatang_loss[loss=0.1369, simple_loss=0.2163, pruned_loss=0.0288, over 4912.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2386, pruned_loss=0.04274, over 703896.05 frames.], batch size: 42, aishell_tot_loss[loss=0.1673, simple_loss=0.2499, pruned_loss=0.04236, over 445008.48 frames.], datatang_tot_loss[loss=0.1575, simple_loss=0.2286, pruned_loss=0.04316, over 472098.89 frames.], batch size: 42, lr: 6.34e-04
+2022-06-18 19:13:46,324 INFO [train.py:874] (0/4) Epoch 13, batch 300, datatang_loss[loss=0.1826, simple_loss=0.2507, pruned_loss=0.05724, over 4929.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2393, pruned_loss=0.04312, over 766611.12 frames.], batch size: 83, aishell_tot_loss[loss=0.1655, simple_loss=0.2486, pruned_loss=0.04123, over 499234.20 frames.], datatang_tot_loss[loss=0.1603, simple_loss=0.2312, pruned_loss=0.04469, over 541632.50 frames.], batch size: 83, lr: 6.34e-04
+2022-06-18 19:14:18,045 INFO [train.py:874] (0/4) Epoch 13, batch 350, aishell_loss[loss=0.1534, simple_loss=0.2448, pruned_loss=0.03099, over 4977.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2399, pruned_loss=0.04388, over 815119.17 frames.], batch size: 61, aishell_tot_loss[loss=0.1661, simple_loss=0.2491, pruned_loss=0.04155, over 549822.87 frames.], datatang_tot_loss[loss=0.1614, simple_loss=0.2319, pruned_loss=0.04547, over 599909.99 frames.], batch size: 61, lr: 6.34e-04
+2022-06-18 19:14:49,253 INFO [train.py:874] (0/4) Epoch 13, batch 400, aishell_loss[loss=0.1526, simple_loss=0.2349, pruned_loss=0.03519, over 4935.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2404, pruned_loss=0.04435, over 852655.90 frames.], batch size: 49, aishell_tot_loss[loss=0.1659, simple_loss=0.2484, pruned_loss=0.04168, over 599090.90 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2331, pruned_loss=0.04606, over 646897.77 frames.], batch size: 49, lr: 6.33e-04
+2022-06-18 19:15:19,249 INFO [train.py:874] (0/4) Epoch 13, batch 450, aishell_loss[loss=0.1463, simple_loss=0.2342, pruned_loss=0.02916, over 4907.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2408, pruned_loss=0.04481, over 882154.40 frames.], batch size: 52, aishell_tot_loss[loss=0.1656, simple_loss=0.2481, pruned_loss=0.04158, over 635616.65 frames.], datatang_tot_loss[loss=0.164, simple_loss=0.2343, pruned_loss=0.04683, over 694557.85 frames.], batch size: 52, lr: 6.33e-04
+2022-06-18 19:15:51,457 INFO [train.py:874] (0/4) Epoch 13, batch 500, aishell_loss[loss=0.1587, simple_loss=0.2502, pruned_loss=0.03358, over 4950.00 frames.], tot_loss[loss=0.165, simple_loss=0.2414, pruned_loss=0.04434, over 905543.76 frames.], batch size: 56, aishell_tot_loss[loss=0.1654, simple_loss=0.2482, pruned_loss=0.0413, over 680267.29 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2347, pruned_loss=0.04673, over 726381.53 frames.], batch size: 56, lr: 6.33e-04
+2022-06-18 19:16:22,035 INFO [train.py:874] (0/4) Epoch 13, batch 550, datatang_loss[loss=0.1669, simple_loss=0.2416, pruned_loss=0.04604, over 4939.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2411, pruned_loss=0.04436, over 923305.89 frames.], batch size: 88, aishell_tot_loss[loss=0.1652, simple_loss=0.2478, pruned_loss=0.04123, over 717672.90 frames.], datatang_tot_loss[loss=0.1642, simple_loss=0.2345, pruned_loss=0.04695, over 755673.48 frames.], batch size: 88, lr: 6.32e-04
+2022-06-18 19:16:51,637 INFO [train.py:874] (0/4) Epoch 13, batch 600, datatang_loss[loss=0.1661, simple_loss=0.2397, pruned_loss=0.04629, over 4849.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2411, pruned_loss=0.04424, over 937126.21 frames.], batch size: 24, aishell_tot_loss[loss=0.1649, simple_loss=0.2474, pruned_loss=0.04114, over 750508.97 frames.], datatang_tot_loss[loss=0.1644, simple_loss=0.2348, pruned_loss=0.04696, over 781647.38 frames.], batch size: 24, lr: 6.32e-04
+2022-06-18 19:17:23,489 INFO [train.py:874] (0/4) Epoch 13, batch 650, aishell_loss[loss=0.1411, simple_loss=0.2276, pruned_loss=0.02724, over 4966.00 frames.], tot_loss[loss=0.1664, simple_loss=0.243, pruned_loss=0.04488, over 948234.00 frames.], batch size: 30, aishell_tot_loss[loss=0.1664, simple_loss=0.2491, pruned_loss=0.04183, over 782730.14 frames.], datatang_tot_loss[loss=0.1649, simple_loss=0.2353, pruned_loss=0.04728, over 801984.40 frames.], batch size: 30, lr: 6.32e-04
+2022-06-18 19:17:54,564 INFO [train.py:874] (0/4) Epoch 13, batch 700, aishell_loss[loss=0.1861, simple_loss=0.2666, pruned_loss=0.05282, over 4948.00 frames.], tot_loss[loss=0.167, simple_loss=0.2441, pruned_loss=0.04492, over 956788.56 frames.], batch size: 54, aishell_tot_loss[loss=0.1665, simple_loss=0.2495, pruned_loss=0.04175, over 808679.74 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2364, pruned_loss=0.04758, over 821990.47 frames.], batch size: 54, lr: 6.31e-04
+2022-06-18 19:18:24,369 INFO [train.py:874] (0/4) Epoch 13, batch 750, aishell_loss[loss=0.1628, simple_loss=0.2472, pruned_loss=0.03922, over 4895.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2435, pruned_loss=0.04468, over 963152.91 frames.], batch size: 34, aishell_tot_loss[loss=0.1659, simple_loss=0.249, pruned_loss=0.04144, over 830159.12 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2364, pruned_loss=0.04771, over 840653.61 frames.], batch size: 34, lr: 6.31e-04
+2022-06-18 19:18:56,616 INFO [train.py:874] (0/4) Epoch 13, batch 800, aishell_loss[loss=0.17, simple_loss=0.2578, pruned_loss=0.04106, over 4942.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2441, pruned_loss=0.04459, over 968565.03 frames.], batch size: 45, aishell_tot_loss[loss=0.1664, simple_loss=0.2497, pruned_loss=0.04155, over 849370.33 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2367, pruned_loss=0.04754, over 857318.01 frames.], batch size: 45, lr: 6.31e-04
+2022-06-18 19:19:27,045 INFO [train.py:874] (0/4) Epoch 13, batch 850, aishell_loss[loss=0.1311, simple_loss=0.2078, pruned_loss=0.02722, over 4949.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2437, pruned_loss=0.04451, over 972599.42 frames.], batch size: 25, aishell_tot_loss[loss=0.1666, simple_loss=0.2499, pruned_loss=0.04171, over 863704.15 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2364, pruned_loss=0.04721, over 874267.48 frames.], batch size: 25, lr: 6.31e-04
+2022-06-18 19:19:57,536 INFO [train.py:874] (0/4) Epoch 13, batch 900, aishell_loss[loss=0.156, simple_loss=0.2359, pruned_loss=0.03803, over 4895.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2433, pruned_loss=0.04418, over 975450.13 frames.], batch size: 28, aishell_tot_loss[loss=0.166, simple_loss=0.2493, pruned_loss=0.04137, over 878371.61 frames.], datatang_tot_loss[loss=0.1655, simple_loss=0.2366, pruned_loss=0.04719, over 887075.78 frames.], batch size: 28, lr: 6.30e-04
+2022-06-18 19:20:28,919 INFO [train.py:874] (0/4) Epoch 13, batch 950, aishell_loss[loss=0.132, simple_loss=0.2073, pruned_loss=0.02841, over 4989.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2438, pruned_loss=0.0439, over 977698.17 frames.], batch size: 25, aishell_tot_loss[loss=0.1656, simple_loss=0.249, pruned_loss=0.04107, over 894148.11 frames.], datatang_tot_loss[loss=0.166, simple_loss=0.2372, pruned_loss=0.04736, over 895678.41 frames.], batch size: 25, lr: 6.30e-04
+2022-06-18 19:20:58,023 INFO [train.py:874] (0/4) Epoch 13, batch 1000, aishell_loss[loss=0.1245, simple_loss=0.2009, pruned_loss=0.0241, over 4964.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2434, pruned_loss=0.04415, over 979495.21 frames.], batch size: 25, aishell_tot_loss[loss=0.1653, simple_loss=0.2482, pruned_loss=0.04119, over 905194.25 frames.], datatang_tot_loss[loss=0.1663, simple_loss=0.2376, pruned_loss=0.04748, over 906045.96 frames.], batch size: 25, lr: 6.30e-04
+2022-06-18 19:20:58,026 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 19:21:15,125 INFO [train.py:914] (0/4) Epoch 13, validation: loss=0.1646, simple_loss=0.2486, pruned_loss=0.04032, over 1622729.00 frames.
+2022-06-18 19:21:45,793 INFO [train.py:874] (0/4) Epoch 13, batch 1050, datatang_loss[loss=0.1623, simple_loss=0.2318, pruned_loss=0.04642, over 4912.00 frames.], tot_loss[loss=0.166, simple_loss=0.2435, pruned_loss=0.04429, over 980901.17 frames.], batch size: 75, aishell_tot_loss[loss=0.1654, simple_loss=0.2484, pruned_loss=0.04119, over 915379.71 frames.], datatang_tot_loss[loss=0.1664, simple_loss=0.2375, pruned_loss=0.04764, over 914753.35 frames.], batch size: 75, lr: 6.29e-04
+2022-06-18 19:22:16,622 INFO [train.py:874] (0/4) Epoch 13, batch 1100, datatang_loss[loss=0.1736, simple_loss=0.2429, pruned_loss=0.0522, over 4915.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2429, pruned_loss=0.04377, over 982309.04 frames.], batch size: 42, aishell_tot_loss[loss=0.1659, simple_loss=0.2489, pruned_loss=0.04139, over 923102.23 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.2367, pruned_loss=0.04677, over 924031.76 frames.], batch size: 42, lr: 6.29e-04
+2022-06-18 19:22:47,009 INFO [train.py:874] (0/4) Epoch 13, batch 1150, aishell_loss[loss=0.1513, simple_loss=0.2076, pruned_loss=0.04746, over 4949.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2429, pruned_loss=0.04393, over 982626.24 frames.], batch size: 21, aishell_tot_loss[loss=0.1656, simple_loss=0.2485, pruned_loss=0.04131, over 930837.40 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2369, pruned_loss=0.047, over 930462.05 frames.], batch size: 21, lr: 6.29e-04
+2022-06-18 19:23:17,720 INFO [train.py:874] (0/4) Epoch 13, batch 1200, datatang_loss[loss=0.1634, simple_loss=0.2358, pruned_loss=0.04547, over 4925.00 frames.], tot_loss[loss=0.1658, simple_loss=0.243, pruned_loss=0.04427, over 983329.29 frames.], batch size: 71, aishell_tot_loss[loss=0.1658, simple_loss=0.2487, pruned_loss=0.04148, over 936951.96 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.237, pruned_loss=0.04708, over 937324.76 frames.], batch size: 71, lr: 6.28e-04
+2022-06-18 19:23:48,003 INFO [train.py:874] (0/4) Epoch 13, batch 1250, datatang_loss[loss=0.1964, simple_loss=0.2625, pruned_loss=0.06515, over 4939.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2429, pruned_loss=0.04449, over 983031.82 frames.], batch size: 62, aishell_tot_loss[loss=0.1657, simple_loss=0.2484, pruned_loss=0.04151, over 942172.45 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2371, pruned_loss=0.0473, over 942710.14 frames.], batch size: 62, lr: 6.28e-04
+2022-06-18 19:24:19,115 INFO [train.py:874] (0/4) Epoch 13, batch 1300, datatang_loss[loss=0.2167, simple_loss=0.2726, pruned_loss=0.08038, over 4925.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2428, pruned_loss=0.04468, over 983122.51 frames.], batch size: 98, aishell_tot_loss[loss=0.1653, simple_loss=0.248, pruned_loss=0.04127, over 945982.43 frames.], datatang_tot_loss[loss=0.1665, simple_loss=0.2377, pruned_loss=0.04758, over 948525.37 frames.], batch size: 98, lr: 6.28e-04
+2022-06-18 19:24:49,538 INFO [train.py:874] (0/4) Epoch 13, batch 1350, aishell_loss[loss=0.1462, simple_loss=0.2245, pruned_loss=0.03398, over 4914.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2424, pruned_loss=0.04474, over 983606.47 frames.], batch size: 33, aishell_tot_loss[loss=0.1654, simple_loss=0.2479, pruned_loss=0.04141, over 949854.90 frames.], datatang_tot_loss[loss=0.1662, simple_loss=0.2375, pruned_loss=0.04745, over 953563.12 frames.], batch size: 33, lr: 6.27e-04
+2022-06-18 19:25:19,786 INFO [train.py:874] (0/4) Epoch 13, batch 1400, datatang_loss[loss=0.1786, simple_loss=0.2477, pruned_loss=0.05477, over 4922.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2429, pruned_loss=0.04521, over 984194.48 frames.], batch size: 71, aishell_tot_loss[loss=0.1656, simple_loss=0.2483, pruned_loss=0.04144, over 953552.12 frames.], datatang_tot_loss[loss=0.1668, simple_loss=0.2378, pruned_loss=0.04787, over 957938.72 frames.], batch size: 71, lr: 6.27e-04
+2022-06-18 19:25:50,291 INFO [train.py:874] (0/4) Epoch 13, batch 1450, aishell_loss[loss=0.1694, simple_loss=0.2511, pruned_loss=0.04386, over 4958.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2437, pruned_loss=0.04547, over 984741.16 frames.], batch size: 31, aishell_tot_loss[loss=0.1659, simple_loss=0.2487, pruned_loss=0.04156, over 957301.37 frames.], datatang_tot_loss[loss=0.1673, simple_loss=0.2383, pruned_loss=0.04812, over 961459.23 frames.], batch size: 31, lr: 6.27e-04
+2022-06-18 19:26:20,090 INFO [train.py:874] (0/4) Epoch 13, batch 1500, datatang_loss[loss=0.1592, simple_loss=0.2295, pruned_loss=0.04447, over 4963.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2434, pruned_loss=0.04515, over 985161.63 frames.], batch size: 45, aishell_tot_loss[loss=0.1659, simple_loss=0.249, pruned_loss=0.04144, over 960730.51 frames.], datatang_tot_loss[loss=0.167, simple_loss=0.2378, pruned_loss=0.04807, over 964455.01 frames.], batch size: 45, lr: 6.27e-04
+2022-06-18 19:26:50,902 INFO [train.py:874] (0/4) Epoch 13, batch 1550, aishell_loss[loss=0.1387, simple_loss=0.1983, pruned_loss=0.03961, over 4868.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2439, pruned_loss=0.04525, over 985183.20 frames.], batch size: 21, aishell_tot_loss[loss=0.166, simple_loss=0.2491, pruned_loss=0.04151, over 963905.07 frames.], datatang_tot_loss[loss=0.1673, simple_loss=0.2381, pruned_loss=0.04828, over 966678.74 frames.], batch size: 21, lr: 6.26e-04
+2022-06-18 19:27:20,339 INFO [train.py:874] (0/4) Epoch 13, batch 1600, datatang_loss[loss=0.1382, simple_loss=0.2207, pruned_loss=0.0278, over 4930.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2444, pruned_loss=0.04506, over 985127.42 frames.], batch size: 79, aishell_tot_loss[loss=0.1659, simple_loss=0.2492, pruned_loss=0.04131, over 966370.83 frames.], datatang_tot_loss[loss=0.1676, simple_loss=0.2385, pruned_loss=0.04836, over 968830.96 frames.], batch size: 79, lr: 6.26e-04
+2022-06-18 19:27:49,904 INFO [train.py:874] (0/4) Epoch 13, batch 1650, datatang_loss[loss=0.1588, simple_loss=0.2319, pruned_loss=0.04283, over 4924.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2444, pruned_loss=0.0452, over 985767.21 frames.], batch size: 81, aishell_tot_loss[loss=0.1654, simple_loss=0.2486, pruned_loss=0.04112, over 968968.91 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2394, pruned_loss=0.04879, over 971032.56 frames.], batch size: 81, lr: 6.26e-04
+2022-06-18 19:28:22,046 INFO [train.py:874] (0/4) Epoch 13, batch 1700, aishell_loss[loss=0.1698, simple_loss=0.2579, pruned_loss=0.04082, over 4970.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2446, pruned_loss=0.04499, over 985325.38 frames.], batch size: 64, aishell_tot_loss[loss=0.1658, simple_loss=0.2491, pruned_loss=0.0413, over 970309.13 frames.], datatang_tot_loss[loss=0.168, simple_loss=0.2393, pruned_loss=0.04829, over 972890.26 frames.], batch size: 64, lr: 6.25e-04
+2022-06-18 19:28:51,115 INFO [train.py:874] (0/4) Epoch 13, batch 1750, datatang_loss[loss=0.1626, simple_loss=0.2374, pruned_loss=0.04384, over 4931.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2446, pruned_loss=0.04477, over 985565.72 frames.], batch size: 77, aishell_tot_loss[loss=0.1658, simple_loss=0.2489, pruned_loss=0.0414, over 972382.21 frames.], datatang_tot_loss[loss=0.1678, simple_loss=0.2395, pruned_loss=0.04811, over 974334.99 frames.], batch size: 77, lr: 6.25e-04
+2022-06-18 19:29:20,515 INFO [train.py:874] (0/4) Epoch 13, batch 1800, aishell_loss[loss=0.1385, simple_loss=0.2295, pruned_loss=0.02373, over 4903.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2449, pruned_loss=0.04468, over 985646.55 frames.], batch size: 28, aishell_tot_loss[loss=0.1658, simple_loss=0.2489, pruned_loss=0.04133, over 974175.98 frames.], datatang_tot_loss[loss=0.1681, simple_loss=0.2397, pruned_loss=0.04822, over 975541.09 frames.], batch size: 28, lr: 6.25e-04
+2022-06-18 19:29:52,014 INFO [train.py:874] (0/4) Epoch 13, batch 1850, datatang_loss[loss=0.1678, simple_loss=0.2425, pruned_loss=0.04653, over 4959.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2441, pruned_loss=0.04443, over 985727.61 frames.], batch size: 86, aishell_tot_loss[loss=0.1662, simple_loss=0.2493, pruned_loss=0.04152, over 975458.96 frames.], datatang_tot_loss[loss=0.167, simple_loss=0.2386, pruned_loss=0.04774, over 976877.41 frames.], batch size: 86, lr: 6.24e-04
+2022-06-18 19:30:21,951 INFO [train.py:874] (0/4) Epoch 13, batch 1900, datatang_loss[loss=0.1626, simple_loss=0.2403, pruned_loss=0.04246, over 4952.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2439, pruned_loss=0.04458, over 985888.52 frames.], batch size: 37, aishell_tot_loss[loss=0.1666, simple_loss=0.2498, pruned_loss=0.04172, over 976541.77 frames.], datatang_tot_loss[loss=0.1666, simple_loss=0.2381, pruned_loss=0.04755, over 978185.03 frames.], batch size: 37, lr: 6.24e-04
+2022-06-18 19:30:51,589 INFO [train.py:874] (0/4) Epoch 13, batch 1950, datatang_loss[loss=0.1191, simple_loss=0.1857, pruned_loss=0.02629, over 4930.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2429, pruned_loss=0.04469, over 985858.47 frames.], batch size: 37, aishell_tot_loss[loss=0.1669, simple_loss=0.2497, pruned_loss=0.04207, over 977301.00 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2375, pruned_loss=0.04719, over 979367.78 frames.], batch size: 37, lr: 6.24e-04
+2022-06-18 19:31:23,221 INFO [train.py:874] (0/4) Epoch 13, batch 2000, aishell_loss[loss=0.1746, simple_loss=0.2599, pruned_loss=0.04469, over 4970.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2438, pruned_loss=0.04491, over 985793.95 frames.], batch size: 44, aishell_tot_loss[loss=0.1673, simple_loss=0.2502, pruned_loss=0.04223, over 978494.00 frames.], datatang_tot_loss[loss=0.1661, simple_loss=0.2374, pruned_loss=0.04742, over 979926.60 frames.], batch size: 44, lr: 6.24e-04
+2022-06-18 19:31:23,224 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 19:31:40,588 INFO [train.py:914] (0/4) Epoch 13, validation: loss=0.1662, simple_loss=0.2512, pruned_loss=0.04057, over 1622729.00 frames.
+2022-06-18 19:32:10,592 INFO [train.py:874] (0/4) Epoch 13, batch 2050, aishell_loss[loss=0.161, simple_loss=0.2341, pruned_loss=0.04391, over 4960.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2442, pruned_loss=0.04502, over 985475.02 frames.], batch size: 27, aishell_tot_loss[loss=0.168, simple_loss=0.2507, pruned_loss=0.04261, over 979064.11 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2372, pruned_loss=0.04723, over 980577.99 frames.], batch size: 27, lr: 6.23e-04
+2022-06-18 19:32:41,259 INFO [train.py:874] (0/4) Epoch 13, batch 2100, aishell_loss[loss=0.1768, simple_loss=0.2598, pruned_loss=0.0469, over 4891.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2448, pruned_loss=0.04527, over 985376.51 frames.], batch size: 34, aishell_tot_loss[loss=0.1685, simple_loss=0.2512, pruned_loss=0.04291, over 979714.09 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2375, pruned_loss=0.04718, over 981137.41 frames.], batch size: 34, lr: 6.23e-04
+2022-06-18 19:33:09,877 INFO [train.py:874] (0/4) Epoch 13, batch 2150, datatang_loss[loss=0.1405, simple_loss=0.2058, pruned_loss=0.03762, over 4944.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2445, pruned_loss=0.04511, over 985718.22 frames.], batch size: 67, aishell_tot_loss[loss=0.169, simple_loss=0.2518, pruned_loss=0.04315, over 980613.86 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2368, pruned_loss=0.04682, over 981763.53 frames.], batch size: 67, lr: 6.23e-04
+2022-06-18 19:33:41,241 INFO [train.py:874] (0/4) Epoch 13, batch 2200, aishell_loss[loss=0.1897, simple_loss=0.281, pruned_loss=0.04917, over 4936.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2433, pruned_loss=0.04463, over 985821.52 frames.], batch size: 80, aishell_tot_loss[loss=0.1685, simple_loss=0.2512, pruned_loss=0.04288, over 981099.84 frames.], datatang_tot_loss[loss=0.1647, simple_loss=0.2362, pruned_loss=0.04661, over 982461.74 frames.], batch size: 80, lr: 6.22e-04
+2022-06-18 19:34:10,780 INFO [train.py:874] (0/4) Epoch 13, batch 2250, aishell_loss[loss=0.145, simple_loss=0.2237, pruned_loss=0.0331, over 4928.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2435, pruned_loss=0.04491, over 986089.30 frames.], batch size: 32, aishell_tot_loss[loss=0.1683, simple_loss=0.2509, pruned_loss=0.04286, over 981967.60 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2365, pruned_loss=0.04693, over 982828.96 frames.], batch size: 32, lr: 6.22e-04
+2022-06-18 19:34:40,254 INFO [train.py:874] (0/4) Epoch 13, batch 2300, datatang_loss[loss=0.1776, simple_loss=0.251, pruned_loss=0.05208, over 4930.00 frames.], tot_loss[loss=0.166, simple_loss=0.2428, pruned_loss=0.04463, over 986402.01 frames.], batch size: 42, aishell_tot_loss[loss=0.1678, simple_loss=0.2505, pruned_loss=0.04255, over 982657.88 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2363, pruned_loss=0.04689, over 983328.41 frames.], batch size: 42, lr: 6.22e-04
+2022-06-18 19:35:11,408 INFO [train.py:874] (0/4) Epoch 13, batch 2350, aishell_loss[loss=0.144, simple_loss=0.2354, pruned_loss=0.02631, over 4945.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2432, pruned_loss=0.04417, over 986189.43 frames.], batch size: 58, aishell_tot_loss[loss=0.167, simple_loss=0.25, pruned_loss=0.04198, over 982992.40 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2367, pruned_loss=0.04706, over 983589.17 frames.], batch size: 58, lr: 6.21e-04
+2022-06-18 19:35:41,093 INFO [train.py:874] (0/4) Epoch 13, batch 2400, datatang_loss[loss=0.1719, simple_loss=0.2477, pruned_loss=0.04808, over 4973.00 frames.], tot_loss[loss=0.166, simple_loss=0.2431, pruned_loss=0.04445, over 985973.43 frames.], batch size: 37, aishell_tot_loss[loss=0.1663, simple_loss=0.2491, pruned_loss=0.04173, over 983120.69 frames.], datatang_tot_loss[loss=0.1663, simple_loss=0.2376, pruned_loss=0.04748, over 983904.46 frames.], batch size: 37, lr: 6.21e-04
+2022-06-18 19:36:10,582 INFO [train.py:874] (0/4) Epoch 13, batch 2450, datatang_loss[loss=0.1454, simple_loss=0.2159, pruned_loss=0.03749, over 4944.00 frames.], tot_loss[loss=0.1655, simple_loss=0.243, pruned_loss=0.044, over 985947.45 frames.], batch size: 57, aishell_tot_loss[loss=0.1668, simple_loss=0.25, pruned_loss=0.04184, over 983378.07 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.2365, pruned_loss=0.04687, over 984193.41 frames.], batch size: 57, lr: 6.21e-04
+2022-06-18 19:36:29,222 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-52000.pt
+2022-06-18 19:36:46,668 INFO [train.py:874] (0/4) Epoch 13, batch 2500, datatang_loss[loss=0.1361, simple_loss=0.2115, pruned_loss=0.03035, over 4918.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2426, pruned_loss=0.04377, over 985840.81 frames.], batch size: 77, aishell_tot_loss[loss=0.1663, simple_loss=0.2495, pruned_loss=0.04156, over 983553.47 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.2364, pruned_loss=0.04691, over 984438.64 frames.], batch size: 77, lr: 6.21e-04
+2022-06-18 19:37:16,483 INFO [train.py:874] (0/4) Epoch 13, batch 2550, datatang_loss[loss=0.1839, simple_loss=0.2607, pruned_loss=0.05356, over 4955.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2418, pruned_loss=0.04321, over 985969.84 frames.], batch size: 99, aishell_tot_loss[loss=0.1653, simple_loss=0.2485, pruned_loss=0.0411, over 983921.85 frames.], datatang_tot_loss[loss=0.1649, simple_loss=0.2364, pruned_loss=0.04666, over 984643.86 frames.], batch size: 99, lr: 6.20e-04
+2022-06-18 19:37:46,578 INFO [train.py:874] (0/4) Epoch 13, batch 2600, datatang_loss[loss=0.1449, simple_loss=0.2241, pruned_loss=0.03288, over 4941.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2417, pruned_loss=0.04322, over 985483.42 frames.], batch size: 50, aishell_tot_loss[loss=0.1648, simple_loss=0.248, pruned_loss=0.04078, over 983611.05 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2366, pruned_loss=0.04684, over 984838.05 frames.], batch size: 50, lr: 6.20e-04
+2022-06-18 19:38:17,672 INFO [train.py:874] (0/4) Epoch 13, batch 2650, aishell_loss[loss=0.1677, simple_loss=0.2549, pruned_loss=0.04019, over 4897.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2426, pruned_loss=0.04324, over 985310.87 frames.], batch size: 41, aishell_tot_loss[loss=0.1651, simple_loss=0.2485, pruned_loss=0.04087, over 983608.13 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.2369, pruned_loss=0.04668, over 984975.77 frames.], batch size: 41, lr: 6.20e-04
+2022-06-18 19:38:46,884 INFO [train.py:874] (0/4) Epoch 13, batch 2700, datatang_loss[loss=0.1668, simple_loss=0.2354, pruned_loss=0.04911, over 4947.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2419, pruned_loss=0.04354, over 985014.87 frames.], batch size: 69, aishell_tot_loss[loss=0.1651, simple_loss=0.2482, pruned_loss=0.04101, over 983708.22 frames.], datatang_tot_loss[loss=0.1649, simple_loss=0.2366, pruned_loss=0.0466, over 984782.92 frames.], batch size: 69, lr: 6.19e-04
+2022-06-18 19:39:16,955 INFO [train.py:874] (0/4) Epoch 13, batch 2750, datatang_loss[loss=0.1691, simple_loss=0.235, pruned_loss=0.05157, over 4915.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2419, pruned_loss=0.04369, over 984757.15 frames.], batch size: 81, aishell_tot_loss[loss=0.1655, simple_loss=0.2488, pruned_loss=0.04115, over 983957.56 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.2361, pruned_loss=0.04651, over 984451.04 frames.], batch size: 81, lr: 6.19e-04
+2022-06-18 19:39:48,811 INFO [train.py:874] (0/4) Epoch 13, batch 2800, aishell_loss[loss=0.1378, simple_loss=0.2075, pruned_loss=0.03402, over 4914.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2409, pruned_loss=0.04315, over 984739.30 frames.], batch size: 25, aishell_tot_loss[loss=0.1648, simple_loss=0.2483, pruned_loss=0.04068, over 983929.67 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2356, pruned_loss=0.04631, over 984587.33 frames.], batch size: 25, lr: 6.19e-04
+2022-06-18 19:40:18,683 INFO [train.py:874] (0/4) Epoch 13, batch 2850, aishell_loss[loss=0.1579, simple_loss=0.2416, pruned_loss=0.03705, over 4939.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2417, pruned_loss=0.04301, over 985172.78 frames.], batch size: 54, aishell_tot_loss[loss=0.1648, simple_loss=0.2484, pruned_loss=0.04064, over 984267.21 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2356, pruned_loss=0.04626, over 984836.46 frames.], batch size: 54, lr: 6.18e-04
+2022-06-18 19:40:48,092 INFO [train.py:874] (0/4) Epoch 13, batch 2900, datatang_loss[loss=0.1405, simple_loss=0.2163, pruned_loss=0.03239, over 4983.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2422, pruned_loss=0.04334, over 985046.91 frames.], batch size: 31, aishell_tot_loss[loss=0.1649, simple_loss=0.2484, pruned_loss=0.04069, over 984184.98 frames.], datatang_tot_loss[loss=0.1645, simple_loss=0.2359, pruned_loss=0.04654, over 984936.96 frames.], batch size: 31, lr: 6.18e-04
+2022-06-18 19:41:19,749 INFO [train.py:874] (0/4) Epoch 13, batch 2950, aishell_loss[loss=0.1637, simple_loss=0.2546, pruned_loss=0.03638, over 4858.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2422, pruned_loss=0.04334, over 985209.41 frames.], batch size: 36, aishell_tot_loss[loss=0.1651, simple_loss=0.2486, pruned_loss=0.0408, over 984241.67 frames.], datatang_tot_loss[loss=0.1643, simple_loss=0.2359, pruned_loss=0.0463, over 985172.56 frames.], batch size: 36, lr: 6.18e-04
+2022-06-18 19:41:48,995 INFO [train.py:874] (0/4) Epoch 13, batch 3000, aishell_loss[loss=0.1783, simple_loss=0.2596, pruned_loss=0.04844, over 4945.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2418, pruned_loss=0.04315, over 984847.30 frames.], batch size: 54, aishell_tot_loss[loss=0.1647, simple_loss=0.2479, pruned_loss=0.04069, over 984231.75 frames.], datatang_tot_loss[loss=0.1642, simple_loss=0.2363, pruned_loss=0.04609, over 984900.57 frames.], batch size: 54, lr: 6.18e-04
+2022-06-18 19:41:48,999 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 19:42:05,183 INFO [train.py:914] (0/4) Epoch 13, validation: loss=0.1653, simple_loss=0.2497, pruned_loss=0.04043, over 1622729.00 frames.
+2022-06-18 19:42:35,294 INFO [train.py:874] (0/4) Epoch 13, batch 3050, datatang_loss[loss=0.141, simple_loss=0.2183, pruned_loss=0.03188, over 4898.00 frames.], tot_loss[loss=0.164, simple_loss=0.2416, pruned_loss=0.04324, over 985068.35 frames.], batch size: 47, aishell_tot_loss[loss=0.1644, simple_loss=0.2476, pruned_loss=0.04059, over 984392.31 frames.], datatang_tot_loss[loss=0.1644, simple_loss=0.2364, pruned_loss=0.0462, over 985036.12 frames.], batch size: 47, lr: 6.17e-04
+2022-06-18 19:43:05,342 INFO [train.py:874] (0/4) Epoch 13, batch 3100, datatang_loss[loss=0.1659, simple_loss=0.2414, pruned_loss=0.0452, over 4952.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2418, pruned_loss=0.04386, over 985214.81 frames.], batch size: 86, aishell_tot_loss[loss=0.1639, simple_loss=0.247, pruned_loss=0.04045, over 984341.79 frames.], datatang_tot_loss[loss=0.1655, simple_loss=0.2374, pruned_loss=0.04683, over 985292.53 frames.], batch size: 86, lr: 6.17e-04
+2022-06-18 19:43:36,269 INFO [train.py:874] (0/4) Epoch 13, batch 3150, aishell_loss[loss=0.1504, simple_loss=0.2398, pruned_loss=0.03054, over 4963.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2416, pruned_loss=0.0436, over 985606.85 frames.], batch size: 44, aishell_tot_loss[loss=0.1641, simple_loss=0.2471, pruned_loss=0.0405, over 984657.82 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2369, pruned_loss=0.04657, over 985480.09 frames.], batch size: 44, lr: 6.17e-04
+2022-06-18 19:44:06,010 INFO [train.py:874] (0/4) Epoch 13, batch 3200, aishell_loss[loss=0.199, simple_loss=0.2759, pruned_loss=0.06106, over 4915.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2407, pruned_loss=0.04291, over 985129.66 frames.], batch size: 68, aishell_tot_loss[loss=0.1639, simple_loss=0.2472, pruned_loss=0.04035, over 984742.65 frames.], datatang_tot_loss[loss=0.1638, simple_loss=0.2359, pruned_loss=0.04587, over 984985.04 frames.], batch size: 68, lr: 6.16e-04
+2022-06-18 19:44:35,533 INFO [train.py:874] (0/4) Epoch 13, batch 3250, datatang_loss[loss=0.1542, simple_loss=0.2224, pruned_loss=0.04305, over 4914.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2401, pruned_loss=0.04248, over 985230.34 frames.], batch size: 81, aishell_tot_loss[loss=0.1639, simple_loss=0.2471, pruned_loss=0.04031, over 984727.71 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2352, pruned_loss=0.04538, over 985156.59 frames.], batch size: 81, lr: 6.16e-04
+2022-06-18 19:45:06,293 INFO [train.py:874] (0/4) Epoch 13, batch 3300, datatang_loss[loss=0.1691, simple_loss=0.2463, pruned_loss=0.04597, over 4957.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2413, pruned_loss=0.04247, over 985212.93 frames.], batch size: 91, aishell_tot_loss[loss=0.164, simple_loss=0.2474, pruned_loss=0.04032, over 984659.20 frames.], datatang_tot_loss[loss=0.1632, simple_loss=0.2358, pruned_loss=0.04532, over 985279.87 frames.], batch size: 91, lr: 6.16e-04
+2022-06-18 19:45:35,768 INFO [train.py:874] (0/4) Epoch 13, batch 3350, datatang_loss[loss=0.1595, simple_loss=0.2298, pruned_loss=0.04464, over 4957.00 frames.], tot_loss[loss=0.164, simple_loss=0.2421, pruned_loss=0.04298, over 985105.74 frames.], batch size: 55, aishell_tot_loss[loss=0.1639, simple_loss=0.2474, pruned_loss=0.04017, over 984566.67 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2366, pruned_loss=0.04582, over 985299.07 frames.], batch size: 55, lr: 6.16e-04
+2022-06-18 19:46:06,204 INFO [train.py:874] (0/4) Epoch 13, batch 3400, datatang_loss[loss=0.1702, simple_loss=0.2434, pruned_loss=0.04848, over 4941.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2426, pruned_loss=0.04395, over 985250.04 frames.], batch size: 88, aishell_tot_loss[loss=0.1641, simple_loss=0.2473, pruned_loss=0.04044, over 984501.45 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2374, pruned_loss=0.04647, over 985538.51 frames.], batch size: 88, lr: 6.15e-04
+2022-06-18 19:46:38,158 INFO [train.py:874] (0/4) Epoch 13, batch 3450, datatang_loss[loss=0.2165, simple_loss=0.2807, pruned_loss=0.07618, over 4949.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2429, pruned_loss=0.04401, over 985485.46 frames.], batch size: 109, aishell_tot_loss[loss=0.1643, simple_loss=0.2477, pruned_loss=0.04042, over 984744.59 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2376, pruned_loss=0.04663, over 985590.09 frames.], batch size: 109, lr: 6.15e-04
+2022-06-18 19:47:07,975 INFO [train.py:874] (0/4) Epoch 13, batch 3500, datatang_loss[loss=0.2044, simple_loss=0.2708, pruned_loss=0.06898, over 4950.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2436, pruned_loss=0.04413, over 985853.85 frames.], batch size: 55, aishell_tot_loss[loss=0.1645, simple_loss=0.2481, pruned_loss=0.04048, over 984983.38 frames.], datatang_tot_loss[loss=0.1657, simple_loss=0.2378, pruned_loss=0.04682, over 985813.91 frames.], batch size: 55, lr: 6.15e-04
+2022-06-18 19:47:37,790 INFO [train.py:874] (0/4) Epoch 13, batch 3550, aishell_loss[loss=0.1618, simple_loss=0.2416, pruned_loss=0.04094, over 4978.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2432, pruned_loss=0.04375, over 985800.83 frames.], batch size: 48, aishell_tot_loss[loss=0.1649, simple_loss=0.2485, pruned_loss=0.04067, over 985136.43 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2371, pruned_loss=0.0464, over 985715.34 frames.], batch size: 48, lr: 6.14e-04
+2022-06-18 19:48:09,655 INFO [train.py:874] (0/4) Epoch 13, batch 3600, datatang_loss[loss=0.1685, simple_loss=0.2351, pruned_loss=0.05093, over 4913.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2436, pruned_loss=0.04406, over 985745.58 frames.], batch size: 64, aishell_tot_loss[loss=0.1649, simple_loss=0.2484, pruned_loss=0.04072, over 985148.39 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2376, pruned_loss=0.04681, over 985733.07 frames.], batch size: 64, lr: 6.14e-04
+2022-06-18 19:48:39,129 INFO [train.py:874] (0/4) Epoch 13, batch 3650, datatang_loss[loss=0.165, simple_loss=0.2459, pruned_loss=0.04204, over 4961.00 frames.], tot_loss[loss=0.165, simple_loss=0.243, pruned_loss=0.04353, over 985930.95 frames.], batch size: 91, aishell_tot_loss[loss=0.1642, simple_loss=0.2478, pruned_loss=0.04028, over 985242.61 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2376, pruned_loss=0.04677, over 985907.83 frames.], batch size: 91, lr: 6.14e-04
+2022-06-18 19:49:09,236 INFO [train.py:874] (0/4) Epoch 13, batch 3700, datatang_loss[loss=0.1542, simple_loss=0.2259, pruned_loss=0.0412, over 4921.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2419, pruned_loss=0.04324, over 985616.41 frames.], batch size: 77, aishell_tot_loss[loss=0.1638, simple_loss=0.2472, pruned_loss=0.0402, over 985146.41 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2373, pruned_loss=0.04655, over 985739.80 frames.], batch size: 77, lr: 6.14e-04
+2022-06-18 19:49:40,858 INFO [train.py:874] (0/4) Epoch 13, batch 3750, aishell_loss[loss=0.1818, simple_loss=0.2661, pruned_loss=0.04874, over 4948.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2421, pruned_loss=0.04326, over 985513.68 frames.], batch size: 56, aishell_tot_loss[loss=0.1635, simple_loss=0.2471, pruned_loss=0.04002, over 984960.54 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2374, pruned_loss=0.04671, over 985854.19 frames.], batch size: 56, lr: 6.13e-04
+2022-06-18 19:50:08,797 INFO [train.py:874] (0/4) Epoch 13, batch 3800, aishell_loss[loss=0.1467, simple_loss=0.2294, pruned_loss=0.03198, over 4918.00 frames.], tot_loss[loss=0.1641, simple_loss=0.242, pruned_loss=0.04313, over 985675.44 frames.], batch size: 33, aishell_tot_loss[loss=0.1635, simple_loss=0.2471, pruned_loss=0.03996, over 985137.46 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2372, pruned_loss=0.04661, over 985874.96 frames.], batch size: 33, lr: 6.13e-04
+2022-06-18 19:50:39,556 INFO [train.py:874] (0/4) Epoch 13, batch 3850, datatang_loss[loss=0.1569, simple_loss=0.2133, pruned_loss=0.05027, over 4965.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2411, pruned_loss=0.04317, over 985103.69 frames.], batch size: 40, aishell_tot_loss[loss=0.1635, simple_loss=0.2467, pruned_loss=0.04013, over 984582.20 frames.], datatang_tot_loss[loss=0.1648, simple_loss=0.2365, pruned_loss=0.04651, over 985853.66 frames.], batch size: 40, lr: 6.13e-04
+2022-06-18 19:51:08,420 INFO [train.py:874] (0/4) Epoch 13, batch 3900, aishell_loss[loss=0.1394, simple_loss=0.2183, pruned_loss=0.03024, over 4975.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2414, pruned_loss=0.0435, over 985168.07 frames.], batch size: 27, aishell_tot_loss[loss=0.1635, simple_loss=0.2466, pruned_loss=0.04018, over 984719.18 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2369, pruned_loss=0.04679, over 985761.28 frames.], batch size: 27, lr: 6.12e-04
+2022-06-18 19:51:38,455 INFO [train.py:874] (0/4) Epoch 13, batch 3950, aishell_loss[loss=0.1745, simple_loss=0.2657, pruned_loss=0.04165, over 4862.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2422, pruned_loss=0.04346, over 985103.41 frames.], batch size: 36, aishell_tot_loss[loss=0.1645, simple_loss=0.2477, pruned_loss=0.04064, over 984516.36 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.2364, pruned_loss=0.04636, over 985894.71 frames.], batch size: 36, lr: 6.12e-04
+2022-06-18 19:52:05,670 INFO [train.py:874] (0/4) Epoch 13, batch 4000, datatang_loss[loss=0.1538, simple_loss=0.228, pruned_loss=0.03979, over 4914.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2419, pruned_loss=0.04335, over 985278.52 frames.], batch size: 57, aishell_tot_loss[loss=0.1644, simple_loss=0.2475, pruned_loss=0.04061, over 984737.86 frames.], datatang_tot_loss[loss=0.1644, simple_loss=0.2362, pruned_loss=0.04628, over 985833.26 frames.], batch size: 57, lr: 6.12e-04
+2022-06-18 19:52:05,673 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 19:52:22,450 INFO [train.py:914] (0/4) Epoch 13, validation: loss=0.1652, simple_loss=0.2502, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-18 19:52:50,035 INFO [train.py:874] (0/4) Epoch 13, batch 4050, datatang_loss[loss=0.1609, simple_loss=0.2279, pruned_loss=0.04695, over 4953.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2433, pruned_loss=0.04395, over 985701.27 frames.], batch size: 67, aishell_tot_loss[loss=0.1648, simple_loss=0.2481, pruned_loss=0.04073, over 985024.56 frames.], datatang_tot_loss[loss=0.1653, simple_loss=0.2371, pruned_loss=0.04677, over 985999.99 frames.], batch size: 67, lr: 6.12e-04
+2022-06-18 19:53:20,286 INFO [train.py:874] (0/4) Epoch 13, batch 4100, datatang_loss[loss=0.1692, simple_loss=0.2385, pruned_loss=0.04993, over 4937.00 frames.], tot_loss[loss=0.167, simple_loss=0.2447, pruned_loss=0.04465, over 985699.79 frames.], batch size: 42, aishell_tot_loss[loss=0.1664, simple_loss=0.2497, pruned_loss=0.04152, over 985066.71 frames.], datatang_tot_loss[loss=0.1653, simple_loss=0.2369, pruned_loss=0.04689, over 986032.78 frames.], batch size: 42, lr: 6.11e-04
+2022-06-18 19:53:48,790 INFO [train.py:874] (0/4) Epoch 13, batch 4150, aishell_loss[loss=0.1735, simple_loss=0.2715, pruned_loss=0.03777, over 4921.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2445, pruned_loss=0.04467, over 985274.63 frames.], batch size: 68, aishell_tot_loss[loss=0.167, simple_loss=0.2504, pruned_loss=0.04178, over 984789.78 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2366, pruned_loss=0.04667, over 985878.89 frames.], batch size: 68, lr: 6.11e-04
+2022-06-18 19:54:07,944 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-13.pt
+2022-06-18 19:55:12,945 INFO [train.py:874] (0/4) Epoch 14, batch 50, aishell_loss[loss=0.1593, simple_loss=0.24, pruned_loss=0.03933, over 4864.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2286, pruned_loss=0.03879, over 218402.89 frames.], batch size: 36, aishell_tot_loss[loss=0.1661, simple_loss=0.248, pruned_loss=0.04211, over 93903.64 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2155, pruned_loss=0.03672, over 137622.25 frames.], batch size: 36, lr: 5.91e-04
+2022-06-18 19:55:42,407 INFO [train.py:874] (0/4) Epoch 14, batch 100, datatang_loss[loss=0.1583, simple_loss=0.2316, pruned_loss=0.04249, over 4967.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2342, pruned_loss=0.03997, over 388201.13 frames.], batch size: 55, aishell_tot_loss[loss=0.1657, simple_loss=0.2494, pruned_loss=0.041, over 194596.75 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2215, pruned_loss=0.03912, over 241327.86 frames.], batch size: 55, lr: 5.91e-04
+2022-06-18 19:56:14,028 INFO [train.py:874] (0/4) Epoch 14, batch 150, datatang_loss[loss=0.1711, simple_loss=0.2457, pruned_loss=0.04828, over 4956.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2348, pruned_loss=0.04047, over 520803.98 frames.], batch size: 91, aishell_tot_loss[loss=0.1644, simple_loss=0.2484, pruned_loss=0.04018, over 266579.71 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2246, pruned_loss=0.04062, over 348493.84 frames.], batch size: 91, lr: 5.91e-04
+2022-06-18 19:56:43,538 INFO [train.py:874] (0/4) Epoch 14, batch 200, datatang_loss[loss=0.1414, simple_loss=0.2101, pruned_loss=0.03633, over 4959.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2358, pruned_loss=0.03983, over 623645.97 frames.], batch size: 55, aishell_tot_loss[loss=0.1636, simple_loss=0.2483, pruned_loss=0.03948, over 354328.99 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2251, pruned_loss=0.04028, over 420611.22 frames.], batch size: 55, lr: 5.90e-04
+2022-06-18 19:57:13,589 INFO [train.py:874] (0/4) Epoch 14, batch 250, aishell_loss[loss=0.1783, simple_loss=0.2551, pruned_loss=0.05079, over 4938.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2378, pruned_loss=0.04057, over 703792.25 frames.], batch size: 33, aishell_tot_loss[loss=0.1656, simple_loss=0.2498, pruned_loss=0.0407, over 439747.60 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2254, pruned_loss=0.04026, over 476949.70 frames.], batch size: 33, lr: 5.90e-04
+2022-06-18 19:57:44,738 INFO [train.py:874] (0/4) Epoch 14, batch 300, aishell_loss[loss=0.1409, simple_loss=0.2242, pruned_loss=0.02878, over 4968.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2386, pruned_loss=0.04081, over 766398.89 frames.], batch size: 30, aishell_tot_loss[loss=0.1652, simple_loss=0.2493, pruned_loss=0.04057, over 513898.81 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2265, pruned_loss=0.04081, over 527621.05 frames.], batch size: 30, lr: 5.90e-04
+2022-06-18 19:58:15,354 INFO [train.py:874] (0/4) Epoch 14, batch 350, datatang_loss[loss=0.1613, simple_loss=0.2294, pruned_loss=0.04656, over 4889.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2379, pruned_loss=0.04078, over 814946.66 frames.], batch size: 39, aishell_tot_loss[loss=0.1647, simple_loss=0.248, pruned_loss=0.04065, over 569378.15 frames.], datatang_tot_loss[loss=0.1542, simple_loss=0.227, pruned_loss=0.04074, over 581592.03 frames.], batch size: 39, lr: 5.89e-04
+2022-06-18 19:58:44,788 INFO [train.py:874] (0/4) Epoch 14, batch 400, datatang_loss[loss=0.1692, simple_loss=0.2378, pruned_loss=0.05028, over 4935.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2373, pruned_loss=0.04107, over 852889.00 frames.], batch size: 88, aishell_tot_loss[loss=0.165, simple_loss=0.2482, pruned_loss=0.04084, over 605388.22 frames.], datatang_tot_loss[loss=0.1547, simple_loss=0.2273, pruned_loss=0.04101, over 641475.91 frames.], batch size: 88, lr: 5.89e-04
+2022-06-18 19:59:15,071 INFO [train.py:874] (0/4) Epoch 14, batch 450, datatang_loss[loss=0.2082, simple_loss=0.2621, pruned_loss=0.07713, over 4957.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2379, pruned_loss=0.04137, over 881951.40 frames.], batch size: 55, aishell_tot_loss[loss=0.1645, simple_loss=0.2478, pruned_loss=0.04058, over 649872.87 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2283, pruned_loss=0.0417, over 681912.11 frames.], batch size: 55, lr: 5.89e-04
+2022-06-18 19:59:45,587 INFO [train.py:874] (0/4) Epoch 14, batch 500, datatang_loss[loss=0.2048, simple_loss=0.2714, pruned_loss=0.06909, over 4947.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2389, pruned_loss=0.04172, over 904994.71 frames.], batch size: 109, aishell_tot_loss[loss=0.1642, simple_loss=0.2476, pruned_loss=0.04046, over 692332.76 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2297, pruned_loss=0.04238, over 715080.49 frames.], batch size: 109, lr: 5.89e-04
+2022-06-18 20:00:15,461 INFO [train.py:874] (0/4) Epoch 14, batch 550, datatang_loss[loss=0.1645, simple_loss=0.2378, pruned_loss=0.04562, over 4924.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2385, pruned_loss=0.04162, over 922788.05 frames.], batch size: 81, aishell_tot_loss[loss=0.164, simple_loss=0.2473, pruned_loss=0.04035, over 725408.48 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2296, pruned_loss=0.04243, over 748214.85 frames.], batch size: 81, lr: 5.88e-04
+2022-06-18 20:00:45,856 INFO [train.py:874] (0/4) Epoch 14, batch 600, datatang_loss[loss=0.1971, simple_loss=0.273, pruned_loss=0.0606, over 4959.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2398, pruned_loss=0.04177, over 937008.56 frames.], batch size: 99, aishell_tot_loss[loss=0.1643, simple_loss=0.2479, pruned_loss=0.04034, over 758535.42 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2305, pruned_loss=0.0427, over 774184.97 frames.], batch size: 99, lr: 5.88e-04
+2022-06-18 20:01:15,112 INFO [train.py:874] (0/4) Epoch 14, batch 650, aishell_loss[loss=0.1748, simple_loss=0.2584, pruned_loss=0.04559, over 4933.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2408, pruned_loss=0.04193, over 947626.21 frames.], batch size: 56, aishell_tot_loss[loss=0.1646, simple_loss=0.2485, pruned_loss=0.04032, over 783070.59 frames.], datatang_tot_loss[loss=0.1587, simple_loss=0.2315, pruned_loss=0.04296, over 800969.63 frames.], batch size: 56, lr: 5.88e-04
+2022-06-18 20:01:44,220 INFO [train.py:874] (0/4) Epoch 14, batch 700, datatang_loss[loss=0.1771, simple_loss=0.2533, pruned_loss=0.05045, over 4937.00 frames.], tot_loss[loss=0.1616, simple_loss=0.24, pruned_loss=0.04156, over 956396.75 frames.], batch size: 94, aishell_tot_loss[loss=0.164, simple_loss=0.2478, pruned_loss=0.04013, over 807182.37 frames.], datatang_tot_loss[loss=0.1585, simple_loss=0.2314, pruned_loss=0.04277, over 822831.86 frames.], batch size: 94, lr: 5.88e-04
+2022-06-18 20:02:13,951 INFO [train.py:874] (0/4) Epoch 14, batch 750, aishell_loss[loss=0.1603, simple_loss=0.2501, pruned_loss=0.03524, over 4977.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2409, pruned_loss=0.04272, over 963107.23 frames.], batch size: 51, aishell_tot_loss[loss=0.1642, simple_loss=0.248, pruned_loss=0.04021, over 826612.27 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2326, pruned_loss=0.04409, over 843676.86 frames.], batch size: 51, lr: 5.87e-04
+2022-06-18 20:02:45,442 INFO [train.py:874] (0/4) Epoch 14, batch 800, datatang_loss[loss=0.1632, simple_loss=0.2288, pruned_loss=0.04877, over 4842.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2418, pruned_loss=0.04325, over 967805.04 frames.], batch size: 30, aishell_tot_loss[loss=0.165, simple_loss=0.2487, pruned_loss=0.04063, over 842688.71 frames.], datatang_tot_loss[loss=0.1611, simple_loss=0.2333, pruned_loss=0.04439, over 862465.67 frames.], batch size: 30, lr: 5.87e-04
+2022-06-18 20:03:15,189 INFO [train.py:874] (0/4) Epoch 14, batch 850, aishell_loss[loss=0.1797, simple_loss=0.2605, pruned_loss=0.04948, over 4861.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2423, pruned_loss=0.04262, over 971588.10 frames.], batch size: 36, aishell_tot_loss[loss=0.1648, simple_loss=0.2487, pruned_loss=0.04043, over 862412.96 frames.], datatang_tot_loss[loss=0.161, simple_loss=0.2337, pruned_loss=0.04413, over 874287.28 frames.], batch size: 36, lr: 5.87e-04
+2022-06-18 20:03:44,471 INFO [train.py:874] (0/4) Epoch 14, batch 900, aishell_loss[loss=0.1257, simple_loss=0.2059, pruned_loss=0.02278, over 4986.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2418, pruned_loss=0.04274, over 974506.38 frames.], batch size: 27, aishell_tot_loss[loss=0.1645, simple_loss=0.2482, pruned_loss=0.04039, over 877401.66 frames.], datatang_tot_loss[loss=0.1614, simple_loss=0.2339, pruned_loss=0.04444, over 886776.55 frames.], batch size: 27, lr: 5.86e-04
+2022-06-18 20:04:15,152 INFO [train.py:874] (0/4) Epoch 14, batch 950, datatang_loss[loss=0.2382, simple_loss=0.2964, pruned_loss=0.09002, over 4919.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2423, pruned_loss=0.04305, over 977003.10 frames.], batch size: 109, aishell_tot_loss[loss=0.1648, simple_loss=0.2487, pruned_loss=0.0405, over 887635.64 frames.], datatang_tot_loss[loss=0.162, simple_loss=0.2346, pruned_loss=0.04468, over 900706.95 frames.], batch size: 109, lr: 5.86e-04
+2022-06-18 20:04:45,283 INFO [train.py:874] (0/4) Epoch 14, batch 1000, datatang_loss[loss=0.1433, simple_loss=0.2075, pruned_loss=0.03958, over 4970.00 frames.], tot_loss[loss=0.164, simple_loss=0.2421, pruned_loss=0.04297, over 978991.48 frames.], batch size: 60, aishell_tot_loss[loss=0.1644, simple_loss=0.2483, pruned_loss=0.04022, over 898395.53 frames.], datatang_tot_loss[loss=0.1624, simple_loss=0.2351, pruned_loss=0.04491, over 911442.59 frames.], batch size: 60, lr: 5.86e-04
+2022-06-18 20:04:45,286 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 20:05:01,200 INFO [train.py:914] (0/4) Epoch 14, validation: loss=0.1656, simple_loss=0.2496, pruned_loss=0.04079, over 1622729.00 frames.
+2022-06-18 20:05:31,520 INFO [train.py:874] (0/4) Epoch 14, batch 1050, aishell_loss[loss=0.1598, simple_loss=0.2486, pruned_loss=0.03545, over 4912.00 frames.], tot_loss[loss=0.163, simple_loss=0.241, pruned_loss=0.04248, over 980521.58 frames.], batch size: 46, aishell_tot_loss[loss=0.1643, simple_loss=0.248, pruned_loss=0.04031, over 908991.73 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.2342, pruned_loss=0.04441, over 919988.87 frames.], batch size: 46, lr: 5.86e-04
+2022-06-18 20:06:01,965 INFO [train.py:874] (0/4) Epoch 14, batch 1100, aishell_loss[loss=0.1743, simple_loss=0.2556, pruned_loss=0.04649, over 4961.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2416, pruned_loss=0.04298, over 982122.04 frames.], batch size: 61, aishell_tot_loss[loss=0.1644, simple_loss=0.2481, pruned_loss=0.04033, over 916401.32 frames.], datatang_tot_loss[loss=0.1624, simple_loss=0.235, pruned_loss=0.04488, over 929541.09 frames.], batch size: 61, lr: 5.85e-04
+2022-06-18 20:06:31,906 INFO [train.py:874] (0/4) Epoch 14, batch 1150, datatang_loss[loss=0.2076, simple_loss=0.2812, pruned_loss=0.06696, over 4920.00 frames.], tot_loss[loss=0.164, simple_loss=0.242, pruned_loss=0.043, over 983035.56 frames.], batch size: 98, aishell_tot_loss[loss=0.1642, simple_loss=0.2481, pruned_loss=0.04015, over 925126.00 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2353, pruned_loss=0.04518, over 935812.72 frames.], batch size: 98, lr: 5.85e-04
+2022-06-18 20:07:00,188 INFO [train.py:874] (0/4) Epoch 14, batch 1200, datatang_loss[loss=0.1509, simple_loss=0.2242, pruned_loss=0.03884, over 4934.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2421, pruned_loss=0.0429, over 983736.29 frames.], batch size: 71, aishell_tot_loss[loss=0.1643, simple_loss=0.2482, pruned_loss=0.04015, over 933587.32 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2351, pruned_loss=0.04525, over 940683.35 frames.], batch size: 71, lr: 5.85e-04
+2022-06-18 20:07:32,155 INFO [train.py:874] (0/4) Epoch 14, batch 1250, aishell_loss[loss=0.1558, simple_loss=0.2396, pruned_loss=0.03606, over 4914.00 frames.], tot_loss[loss=0.1642, simple_loss=0.242, pruned_loss=0.0432, over 984276.42 frames.], batch size: 41, aishell_tot_loss[loss=0.1648, simple_loss=0.2486, pruned_loss=0.04053, over 939403.12 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2349, pruned_loss=0.04524, over 946384.06 frames.], batch size: 41, lr: 5.85e-04
+2022-06-18 20:08:00,562 INFO [train.py:874] (0/4) Epoch 14, batch 1300, aishell_loss[loss=0.1488, simple_loss=0.2446, pruned_loss=0.02649, over 4921.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2424, pruned_loss=0.04259, over 984454.03 frames.], batch size: 60, aishell_tot_loss[loss=0.1643, simple_loss=0.2484, pruned_loss=0.04011, over 946211.17 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2351, pruned_loss=0.04522, over 949680.42 frames.], batch size: 60, lr: 5.84e-04
+2022-06-18 20:08:31,642 INFO [train.py:874] (0/4) Epoch 14, batch 1350, aishell_loss[loss=0.1728, simple_loss=0.2705, pruned_loss=0.03755, over 4886.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2411, pruned_loss=0.04286, over 985035.54 frames.], batch size: 42, aishell_tot_loss[loss=0.1637, simple_loss=0.2475, pruned_loss=0.03995, over 950242.36 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2349, pruned_loss=0.04554, over 954792.78 frames.], batch size: 42, lr: 5.84e-04
+2022-06-18 20:09:02,305 INFO [train.py:874] (0/4) Epoch 14, batch 1400, aishell_loss[loss=0.1651, simple_loss=0.2514, pruned_loss=0.03934, over 4865.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2406, pruned_loss=0.04292, over 985070.93 frames.], batch size: 37, aishell_tot_loss[loss=0.163, simple_loss=0.2465, pruned_loss=0.03975, over 955025.47 frames.], datatang_tot_loss[loss=0.1635, simple_loss=0.2351, pruned_loss=0.04596, over 957775.79 frames.], batch size: 37, lr: 5.84e-04
+2022-06-18 20:09:32,255 INFO [train.py:874] (0/4) Epoch 14, batch 1450, aishell_loss[loss=0.1691, simple_loss=0.2607, pruned_loss=0.03874, over 4972.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2409, pruned_loss=0.04289, over 985138.82 frames.], batch size: 44, aishell_tot_loss[loss=0.1632, simple_loss=0.2466, pruned_loss=0.03994, over 958377.87 frames.], datatang_tot_loss[loss=0.1634, simple_loss=0.2353, pruned_loss=0.04575, over 961263.35 frames.], batch size: 44, lr: 5.84e-04
+2022-06-18 20:10:03,009 INFO [train.py:874] (0/4) Epoch 14, batch 1500, datatang_loss[loss=0.1461, simple_loss=0.2268, pruned_loss=0.03267, over 4913.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2417, pruned_loss=0.04311, over 984944.41 frames.], batch size: 42, aishell_tot_loss[loss=0.1635, simple_loss=0.247, pruned_loss=0.04, over 960860.65 frames.], datatang_tot_loss[loss=0.1638, simple_loss=0.2359, pruned_loss=0.04588, over 964499.53 frames.], batch size: 42, lr: 5.83e-04
+2022-06-18 20:10:33,046 INFO [train.py:874] (0/4) Epoch 14, batch 1550, datatang_loss[loss=0.1658, simple_loss=0.2337, pruned_loss=0.04895, over 4925.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2406, pruned_loss=0.04258, over 985196.61 frames.], batch size: 83, aishell_tot_loss[loss=0.1632, simple_loss=0.2466, pruned_loss=0.03986, over 963754.09 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2352, pruned_loss=0.0455, over 967114.39 frames.], batch size: 83, lr: 5.83e-04
+2022-06-18 20:11:02,408 INFO [train.py:874] (0/4) Epoch 14, batch 1600, aishell_loss[loss=0.178, simple_loss=0.2649, pruned_loss=0.04554, over 4918.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2398, pruned_loss=0.04154, over 985162.97 frames.], batch size: 68, aishell_tot_loss[loss=0.1621, simple_loss=0.2454, pruned_loss=0.03934, over 967329.22 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2347, pruned_loss=0.04521, over 968286.26 frames.], batch size: 68, lr: 5.83e-04
+2022-06-18 20:11:33,205 INFO [train.py:874] (0/4) Epoch 14, batch 1650, datatang_loss[loss=0.1556, simple_loss=0.22, pruned_loss=0.0456, over 4922.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2397, pruned_loss=0.04155, over 985008.18 frames.], batch size: 42, aishell_tot_loss[loss=0.1617, simple_loss=0.2453, pruned_loss=0.03908, over 969168.55 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2347, pruned_loss=0.0453, over 970377.32 frames.], batch size: 42, lr: 5.83e-04
+2022-06-18 20:12:02,943 INFO [train.py:874] (0/4) Epoch 14, batch 1700, aishell_loss[loss=0.1574, simple_loss=0.2503, pruned_loss=0.03223, over 4883.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2397, pruned_loss=0.04183, over 984565.19 frames.], batch size: 42, aishell_tot_loss[loss=0.1611, simple_loss=0.2448, pruned_loss=0.03873, over 970663.85 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.235, pruned_loss=0.04582, over 971988.96 frames.], batch size: 42, lr: 5.82e-04
+2022-06-18 20:12:32,527 INFO [train.py:874] (0/4) Epoch 14, batch 1750, datatang_loss[loss=0.1561, simple_loss=0.2233, pruned_loss=0.04447, over 4977.00 frames.], tot_loss[loss=0.162, simple_loss=0.2397, pruned_loss=0.04211, over 984438.25 frames.], batch size: 37, aishell_tot_loss[loss=0.1611, simple_loss=0.2448, pruned_loss=0.03876, over 972379.38 frames.], datatang_tot_loss[loss=0.1635, simple_loss=0.2351, pruned_loss=0.04599, over 973248.49 frames.], batch size: 37, lr: 5.82e-04
+2022-06-18 20:13:04,405 INFO [train.py:874] (0/4) Epoch 14, batch 1800, datatang_loss[loss=0.153, simple_loss=0.2338, pruned_loss=0.03611, over 4937.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2396, pruned_loss=0.04204, over 984944.45 frames.], batch size: 34, aishell_tot_loss[loss=0.1615, simple_loss=0.2455, pruned_loss=0.03872, over 973721.12 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2343, pruned_loss=0.04581, over 975147.53 frames.], batch size: 34, lr: 5.82e-04
+2022-06-18 20:13:34,476 INFO [train.py:874] (0/4) Epoch 14, batch 1850, datatang_loss[loss=0.1738, simple_loss=0.2449, pruned_loss=0.05138, over 4947.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2399, pruned_loss=0.04254, over 985008.66 frames.], batch size: 86, aishell_tot_loss[loss=0.1617, simple_loss=0.2456, pruned_loss=0.03893, over 974761.86 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2348, pruned_loss=0.04589, over 976600.72 frames.], batch size: 86, lr: 5.81e-04
+2022-06-18 20:14:03,928 INFO [train.py:874] (0/4) Epoch 14, batch 1900, aishell_loss[loss=0.1625, simple_loss=0.2516, pruned_loss=0.03667, over 4879.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2399, pruned_loss=0.04249, over 985340.21 frames.], batch size: 35, aishell_tot_loss[loss=0.1618, simple_loss=0.2456, pruned_loss=0.03902, over 976024.44 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2349, pruned_loss=0.04569, over 977860.15 frames.], batch size: 35, lr: 5.81e-04
+2022-06-18 20:14:35,014 INFO [train.py:874] (0/4) Epoch 14, batch 1950, datatang_loss[loss=0.1451, simple_loss=0.219, pruned_loss=0.03564, over 4842.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2401, pruned_loss=0.04244, over 985305.74 frames.], batch size: 25, aishell_tot_loss[loss=0.1625, simple_loss=0.2462, pruned_loss=0.03936, over 977267.35 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2345, pruned_loss=0.04537, over 978568.03 frames.], batch size: 25, lr: 5.81e-04
+2022-06-18 20:15:05,413 INFO [train.py:874] (0/4) Epoch 14, batch 2000, aishell_loss[loss=0.1684, simple_loss=0.241, pruned_loss=0.04787, over 4914.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2409, pruned_loss=0.04245, over 985331.17 frames.], batch size: 33, aishell_tot_loss[loss=0.1626, simple_loss=0.2462, pruned_loss=0.03946, over 978303.42 frames.], datatang_tot_loss[loss=0.1629, simple_loss=0.2348, pruned_loss=0.04553, over 979314.01 frames.], batch size: 33, lr: 5.81e-04
+2022-06-18 20:15:05,416 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 20:15:22,471 INFO [train.py:914] (0/4) Epoch 14, validation: loss=0.1654, simple_loss=0.2497, pruned_loss=0.04057, over 1622729.00 frames.
+2022-06-18 20:15:52,261 INFO [train.py:874] (0/4) Epoch 14, batch 2050, datatang_loss[loss=0.1822, simple_loss=0.2635, pruned_loss=0.05048, over 4945.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2405, pruned_loss=0.04207, over 985771.15 frames.], batch size: 69, aishell_tot_loss[loss=0.1621, simple_loss=0.2458, pruned_loss=0.03917, over 979536.88 frames.], datatang_tot_loss[loss=0.1629, simple_loss=0.2349, pruned_loss=0.04542, over 980090.14 frames.], batch size: 69, lr: 5.80e-04
+2022-06-18 20:16:21,744 INFO [train.py:874] (0/4) Epoch 14, batch 2100, datatang_loss[loss=0.1399, simple_loss=0.2214, pruned_loss=0.02926, over 4952.00 frames.], tot_loss[loss=0.1622, simple_loss=0.24, pruned_loss=0.0422, over 986040.00 frames.], batch size: 67, aishell_tot_loss[loss=0.1623, simple_loss=0.2456, pruned_loss=0.03945, over 980335.46 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2347, pruned_loss=0.04518, over 980967.11 frames.], batch size: 67, lr: 5.80e-04
+2022-06-18 20:16:52,390 INFO [train.py:874] (0/4) Epoch 14, batch 2150, datatang_loss[loss=0.168, simple_loss=0.2366, pruned_loss=0.04975, over 4903.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2405, pruned_loss=0.0419, over 986261.27 frames.], batch size: 52, aishell_tot_loss[loss=0.1626, simple_loss=0.246, pruned_loss=0.03958, over 981330.94 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2344, pruned_loss=0.04491, over 981489.73 frames.], batch size: 52, lr: 5.80e-04
+2022-06-18 20:17:23,084 INFO [train.py:874] (0/4) Epoch 14, batch 2200, datatang_loss[loss=0.1557, simple_loss=0.2432, pruned_loss=0.03406, over 4898.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2404, pruned_loss=0.04191, over 986339.26 frames.], batch size: 52, aishell_tot_loss[loss=0.1631, simple_loss=0.2465, pruned_loss=0.03978, over 981799.96 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.234, pruned_loss=0.0446, over 982249.12 frames.], batch size: 52, lr: 5.80e-04
+2022-06-18 20:17:52,999 INFO [train.py:874] (0/4) Epoch 14, batch 2250, aishell_loss[loss=0.1559, simple_loss=0.2473, pruned_loss=0.03225, over 4935.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2412, pruned_loss=0.04201, over 985964.30 frames.], batch size: 27, aishell_tot_loss[loss=0.1628, simple_loss=0.2465, pruned_loss=0.03957, over 982232.13 frames.], datatang_tot_loss[loss=0.1623, simple_loss=0.2346, pruned_loss=0.04497, over 982443.66 frames.], batch size: 27, lr: 5.79e-04
+2022-06-18 20:18:24,026 INFO [train.py:874] (0/4) Epoch 14, batch 2300, datatang_loss[loss=0.1496, simple_loss=0.2169, pruned_loss=0.04112, over 4927.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2397, pruned_loss=0.04203, over 986126.21 frames.], batch size: 73, aishell_tot_loss[loss=0.1626, simple_loss=0.2459, pruned_loss=0.0396, over 982674.04 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2342, pruned_loss=0.04471, over 983031.57 frames.], batch size: 73, lr: 5.79e-04
+2022-06-18 20:18:26,002 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-56000.pt
+2022-06-18 20:18:58,988 INFO [train.py:874] (0/4) Epoch 14, batch 2350, datatang_loss[loss=0.1725, simple_loss=0.2459, pruned_loss=0.04954, over 4915.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2403, pruned_loss=0.04196, over 985694.16 frames.], batch size: 64, aishell_tot_loss[loss=0.163, simple_loss=0.2465, pruned_loss=0.03977, over 982765.08 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.2339, pruned_loss=0.04455, over 983280.14 frames.], batch size: 64, lr: 5.79e-04
+2022-06-18 20:19:28,607 INFO [train.py:874] (0/4) Epoch 14, batch 2400, aishell_loss[loss=0.1491, simple_loss=0.2334, pruned_loss=0.03237, over 4853.00 frames.], tot_loss[loss=0.162, simple_loss=0.2401, pruned_loss=0.04194, over 985421.64 frames.], batch size: 35, aishell_tot_loss[loss=0.1632, simple_loss=0.2467, pruned_loss=0.03983, over 982896.19 frames.], datatang_tot_loss[loss=0.1612, simple_loss=0.2337, pruned_loss=0.04433, over 983490.21 frames.], batch size: 35, lr: 5.79e-04
+2022-06-18 20:19:59,656 INFO [train.py:874] (0/4) Epoch 14, batch 2450, datatang_loss[loss=0.2089, simple_loss=0.273, pruned_loss=0.07243, over 4933.00 frames.], tot_loss[loss=0.1621, simple_loss=0.24, pruned_loss=0.04211, over 985749.28 frames.], batch size: 94, aishell_tot_loss[loss=0.1631, simple_loss=0.2467, pruned_loss=0.03973, over 983299.73 frames.], datatang_tot_loss[loss=0.1614, simple_loss=0.2339, pruned_loss=0.04448, over 983947.19 frames.], batch size: 94, lr: 5.78e-04
+2022-06-18 20:20:30,395 INFO [train.py:874] (0/4) Epoch 14, batch 2500, datatang_loss[loss=0.1737, simple_loss=0.251, pruned_loss=0.0482, over 4928.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2408, pruned_loss=0.04215, over 985370.62 frames.], batch size: 57, aishell_tot_loss[loss=0.1632, simple_loss=0.247, pruned_loss=0.03967, over 983235.29 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.234, pruned_loss=0.04467, over 984112.61 frames.], batch size: 57, lr: 5.78e-04
+2022-06-18 20:20:59,946 INFO [train.py:874] (0/4) Epoch 14, batch 2550, datatang_loss[loss=0.1495, simple_loss=0.2305, pruned_loss=0.0343, over 4935.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2399, pruned_loss=0.04187, over 985419.76 frames.], batch size: 71, aishell_tot_loss[loss=0.1623, simple_loss=0.2463, pruned_loss=0.0392, over 983326.46 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2339, pruned_loss=0.04487, over 984467.84 frames.], batch size: 71, lr: 5.78e-04
+2022-06-18 20:21:29,264 INFO [train.py:874] (0/4) Epoch 14, batch 2600, datatang_loss[loss=0.1646, simple_loss=0.238, pruned_loss=0.04562, over 4954.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2405, pruned_loss=0.04244, over 985065.16 frames.], batch size: 62, aishell_tot_loss[loss=0.1625, simple_loss=0.2464, pruned_loss=0.03933, over 983041.72 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2345, pruned_loss=0.04525, over 984725.82 frames.], batch size: 62, lr: 5.78e-04
+2022-06-18 20:22:01,015 INFO [train.py:874] (0/4) Epoch 14, batch 2650, datatang_loss[loss=0.148, simple_loss=0.2197, pruned_loss=0.03811, over 4960.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2402, pruned_loss=0.04206, over 985374.11 frames.], batch size: 60, aishell_tot_loss[loss=0.163, simple_loss=0.2468, pruned_loss=0.03953, over 983334.10 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.2339, pruned_loss=0.04464, over 985032.50 frames.], batch size: 60, lr: 5.77e-04
+2022-06-18 20:22:30,526 INFO [train.py:874] (0/4) Epoch 14, batch 2700, datatang_loss[loss=0.2075, simple_loss=0.2723, pruned_loss=0.07139, over 4901.00 frames.], tot_loss[loss=0.1631, simple_loss=0.241, pruned_loss=0.04266, over 985160.97 frames.], batch size: 42, aishell_tot_loss[loss=0.1637, simple_loss=0.2476, pruned_loss=0.03989, over 983491.45 frames.], datatang_tot_loss[loss=0.1619, simple_loss=0.2341, pruned_loss=0.04483, over 984903.46 frames.], batch size: 42, lr: 5.77e-04
+2022-06-18 20:22:59,814 INFO [train.py:874] (0/4) Epoch 14, batch 2750, datatang_loss[loss=0.1914, simple_loss=0.2542, pruned_loss=0.06434, over 4931.00 frames.], tot_loss[loss=0.1628, simple_loss=0.241, pruned_loss=0.04226, over 985539.05 frames.], batch size: 94, aishell_tot_loss[loss=0.1636, simple_loss=0.2476, pruned_loss=0.03974, over 984129.26 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2339, pruned_loss=0.04471, over 984897.14 frames.], batch size: 94, lr: 5.77e-04
+2022-06-18 20:23:30,612 INFO [train.py:874] (0/4) Epoch 14, batch 2800, aishell_loss[loss=0.152, simple_loss=0.2453, pruned_loss=0.02939, over 4956.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2407, pruned_loss=0.04213, over 986070.59 frames.], batch size: 40, aishell_tot_loss[loss=0.1632, simple_loss=0.2471, pruned_loss=0.03972, over 984766.94 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.234, pruned_loss=0.04465, over 985070.27 frames.], batch size: 40, lr: 5.77e-04
+2022-06-18 20:24:01,525 INFO [train.py:874] (0/4) Epoch 14, batch 2850, aishell_loss[loss=0.1895, simple_loss=0.2667, pruned_loss=0.05619, over 4854.00 frames.], tot_loss[loss=0.162, simple_loss=0.24, pruned_loss=0.04194, over 986086.65 frames.], batch size: 37, aishell_tot_loss[loss=0.1627, simple_loss=0.2464, pruned_loss=0.03946, over 984976.85 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2339, pruned_loss=0.04469, over 985152.48 frames.], batch size: 37, lr: 5.76e-04
+2022-06-18 20:24:30,565 INFO [train.py:874] (0/4) Epoch 14, batch 2900, aishell_loss[loss=0.1624, simple_loss=0.2532, pruned_loss=0.0358, over 4931.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2396, pruned_loss=0.04164, over 985880.76 frames.], batch size: 58, aishell_tot_loss[loss=0.1626, simple_loss=0.2465, pruned_loss=0.03934, over 984852.92 frames.], datatang_tot_loss[loss=0.1612, simple_loss=0.2336, pruned_loss=0.04438, over 985294.89 frames.], batch size: 58, lr: 5.76e-04
+2022-06-18 20:25:01,150 INFO [train.py:874] (0/4) Epoch 14, batch 2950, aishell_loss[loss=0.1574, simple_loss=0.2436, pruned_loss=0.03562, over 4965.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2404, pruned_loss=0.04205, over 985893.55 frames.], batch size: 61, aishell_tot_loss[loss=0.1629, simple_loss=0.247, pruned_loss=0.03939, over 984933.00 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.2339, pruned_loss=0.0447, over 985421.92 frames.], batch size: 61, lr: 5.76e-04
+2022-06-18 20:25:31,691 INFO [train.py:874] (0/4) Epoch 14, batch 3000, datatang_loss[loss=0.1334, simple_loss=0.2073, pruned_loss=0.02975, over 4904.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2407, pruned_loss=0.04232, over 985753.55 frames.], batch size: 42, aishell_tot_loss[loss=0.1631, simple_loss=0.2473, pruned_loss=0.03947, over 984850.09 frames.], datatang_tot_loss[loss=0.1619, simple_loss=0.2341, pruned_loss=0.04482, over 985497.28 frames.], batch size: 42, lr: 5.76e-04
+2022-06-18 20:25:31,695 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 20:25:47,766 INFO [train.py:914] (0/4) Epoch 14, validation: loss=0.1656, simple_loss=0.2493, pruned_loss=0.0409, over 1622729.00 frames.
+2022-06-18 20:26:17,836 INFO [train.py:874] (0/4) Epoch 14, batch 3050, aishell_loss[loss=0.1612, simple_loss=0.2579, pruned_loss=0.03224, over 4948.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2408, pruned_loss=0.04206, over 986075.55 frames.], batch size: 64, aishell_tot_loss[loss=0.1631, simple_loss=0.2474, pruned_loss=0.03941, over 985154.61 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2341, pruned_loss=0.04468, over 985682.10 frames.], batch size: 64, lr: 5.75e-04
+2022-06-18 20:26:48,726 INFO [train.py:874] (0/4) Epoch 14, batch 3100, datatang_loss[loss=0.1498, simple_loss=0.2327, pruned_loss=0.03343, over 4937.00 frames.], tot_loss[loss=0.1623, simple_loss=0.24, pruned_loss=0.04229, over 985502.74 frames.], batch size: 94, aishell_tot_loss[loss=0.1632, simple_loss=0.2474, pruned_loss=0.03952, over 984873.38 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.2338, pruned_loss=0.04465, over 985497.19 frames.], batch size: 94, lr: 5.75e-04
+2022-06-18 20:27:19,561 INFO [train.py:874] (0/4) Epoch 14, batch 3150, aishell_loss[loss=0.2007, simple_loss=0.268, pruned_loss=0.06672, over 4976.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2396, pruned_loss=0.04231, over 985652.26 frames.], batch size: 44, aishell_tot_loss[loss=0.1632, simple_loss=0.2473, pruned_loss=0.03959, over 985067.85 frames.], datatang_tot_loss[loss=0.1613, simple_loss=0.2334, pruned_loss=0.04458, over 985519.31 frames.], batch size: 44, lr: 5.75e-04
+2022-06-18 20:27:49,660 INFO [train.py:874] (0/4) Epoch 14, batch 3200, aishell_loss[loss=0.1363, simple_loss=0.2227, pruned_loss=0.025, over 4815.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2395, pruned_loss=0.04177, over 985550.35 frames.], batch size: 26, aishell_tot_loss[loss=0.1633, simple_loss=0.2473, pruned_loss=0.03961, over 985005.62 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.233, pruned_loss=0.04406, over 985560.64 frames.], batch size: 26, lr: 5.75e-04
+2022-06-18 20:28:20,123 INFO [train.py:874] (0/4) Epoch 14, batch 3250, datatang_loss[loss=0.1344, simple_loss=0.2083, pruned_loss=0.03026, over 4930.00 frames.], tot_loss[loss=0.161, simple_loss=0.2392, pruned_loss=0.04145, over 985470.94 frames.], batch size: 73, aishell_tot_loss[loss=0.1632, simple_loss=0.2471, pruned_loss=0.03963, over 984928.55 frames.], datatang_tot_loss[loss=0.16, simple_loss=0.2324, pruned_loss=0.04379, over 985624.06 frames.], batch size: 73, lr: 5.74e-04
+2022-06-18 20:28:49,465 INFO [train.py:874] (0/4) Epoch 14, batch 3300, aishell_loss[loss=0.1591, simple_loss=0.2372, pruned_loss=0.04054, over 4898.00 frames.], tot_loss[loss=0.161, simple_loss=0.2392, pruned_loss=0.04137, over 985463.15 frames.], batch size: 34, aishell_tot_loss[loss=0.1631, simple_loss=0.247, pruned_loss=0.03959, over 985211.14 frames.], datatang_tot_loss[loss=0.1598, simple_loss=0.2321, pruned_loss=0.04377, over 985396.27 frames.], batch size: 34, lr: 5.74e-04
+2022-06-18 20:29:19,700 INFO [train.py:874] (0/4) Epoch 14, batch 3350, datatang_loss[loss=0.1979, simple_loss=0.2661, pruned_loss=0.06484, over 4923.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2392, pruned_loss=0.04121, over 985789.73 frames.], batch size: 42, aishell_tot_loss[loss=0.1627, simple_loss=0.2465, pruned_loss=0.03943, over 985637.66 frames.], datatang_tot_loss[loss=0.1599, simple_loss=0.2324, pruned_loss=0.04373, over 985337.35 frames.], batch size: 42, lr: 5.74e-04
+2022-06-18 20:29:49,997 INFO [train.py:874] (0/4) Epoch 14, batch 3400, aishell_loss[loss=0.1737, simple_loss=0.2565, pruned_loss=0.04547, over 4919.00 frames.], tot_loss[loss=0.161, simple_loss=0.2399, pruned_loss=0.04108, over 985937.32 frames.], batch size: 41, aishell_tot_loss[loss=0.1629, simple_loss=0.2469, pruned_loss=0.03942, over 985840.52 frames.], datatang_tot_loss[loss=0.1598, simple_loss=0.2325, pruned_loss=0.0436, over 985357.25 frames.], batch size: 41, lr: 5.74e-04
+2022-06-18 20:30:18,782 INFO [train.py:874] (0/4) Epoch 14, batch 3450, aishell_loss[loss=0.1794, simple_loss=0.2575, pruned_loss=0.05062, over 4956.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2396, pruned_loss=0.04112, over 986021.93 frames.], batch size: 31, aishell_tot_loss[loss=0.162, simple_loss=0.2459, pruned_loss=0.03903, over 985937.52 frames.], datatang_tot_loss[loss=0.1605, simple_loss=0.233, pruned_loss=0.04397, over 985439.06 frames.], batch size: 31, lr: 5.73e-04
+2022-06-18 20:30:50,304 INFO [train.py:874] (0/4) Epoch 14, batch 3500, aishell_loss[loss=0.1618, simple_loss=0.2496, pruned_loss=0.03695, over 4881.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2401, pruned_loss=0.04177, over 985782.64 frames.], batch size: 47, aishell_tot_loss[loss=0.1621, simple_loss=0.2459, pruned_loss=0.03913, over 985787.55 frames.], datatang_tot_loss[loss=0.1613, simple_loss=0.2338, pruned_loss=0.04444, over 985431.19 frames.], batch size: 47, lr: 5.73e-04
+2022-06-18 20:31:19,803 INFO [train.py:874] (0/4) Epoch 14, batch 3550, datatang_loss[loss=0.1745, simple_loss=0.2423, pruned_loss=0.05338, over 4904.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2397, pruned_loss=0.04166, over 985712.93 frames.], batch size: 52, aishell_tot_loss[loss=0.1613, simple_loss=0.2452, pruned_loss=0.03865, over 985770.74 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2341, pruned_loss=0.04477, over 985420.97 frames.], batch size: 52, lr: 5.73e-04
+2022-06-18 20:31:49,278 INFO [train.py:874] (0/4) Epoch 14, batch 3600, aishell_loss[loss=0.1478, simple_loss=0.2408, pruned_loss=0.02744, over 4945.00 frames.], tot_loss[loss=0.161, simple_loss=0.2399, pruned_loss=0.04107, over 985509.66 frames.], batch size: 54, aishell_tot_loss[loss=0.1606, simple_loss=0.245, pruned_loss=0.03814, over 985614.76 frames.], datatang_tot_loss[loss=0.1619, simple_loss=0.2344, pruned_loss=0.04475, over 985405.83 frames.], batch size: 54, lr: 5.73e-04
+2022-06-18 20:32:20,192 INFO [train.py:874] (0/4) Epoch 14, batch 3650, datatang_loss[loss=0.1504, simple_loss=0.2271, pruned_loss=0.03687, over 4969.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2401, pruned_loss=0.04156, over 985625.63 frames.], batch size: 67, aishell_tot_loss[loss=0.161, simple_loss=0.2453, pruned_loss=0.03835, over 985424.12 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2344, pruned_loss=0.0449, over 985714.42 frames.], batch size: 67, lr: 5.72e-04
+2022-06-18 20:32:50,537 INFO [train.py:874] (0/4) Epoch 14, batch 3700, aishell_loss[loss=0.1676, simple_loss=0.2485, pruned_loss=0.04338, over 4979.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2393, pruned_loss=0.04165, over 985356.16 frames.], batch size: 39, aishell_tot_loss[loss=0.1614, simple_loss=0.2453, pruned_loss=0.03874, over 985138.71 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.2338, pruned_loss=0.04457, over 985725.38 frames.], batch size: 39, lr: 5.72e-04
+2022-06-18 20:33:20,167 INFO [train.py:874] (0/4) Epoch 14, batch 3750, datatang_loss[loss=0.2378, simple_loss=0.2888, pruned_loss=0.09339, over 4946.00 frames.], tot_loss[loss=0.162, simple_loss=0.2399, pruned_loss=0.04207, over 985336.71 frames.], batch size: 110, aishell_tot_loss[loss=0.1613, simple_loss=0.245, pruned_loss=0.03881, over 985154.03 frames.], datatang_tot_loss[loss=0.1623, simple_loss=0.2345, pruned_loss=0.045, over 985677.52 frames.], batch size: 110, lr: 5.72e-04
+2022-06-18 20:33:50,717 INFO [train.py:874] (0/4) Epoch 14, batch 3800, datatang_loss[loss=0.1423, simple_loss=0.22, pruned_loss=0.03234, over 4923.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2395, pruned_loss=0.04205, over 985161.68 frames.], batch size: 42, aishell_tot_loss[loss=0.1617, simple_loss=0.2453, pruned_loss=0.03906, over 985035.83 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.2339, pruned_loss=0.04464, over 985558.99 frames.], batch size: 42, lr: 5.72e-04
+2022-06-18 20:34:19,275 INFO [train.py:874] (0/4) Epoch 14, batch 3850, aishell_loss[loss=0.174, simple_loss=0.2572, pruned_loss=0.04537, over 4975.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2393, pruned_loss=0.04163, over 985285.40 frames.], batch size: 39, aishell_tot_loss[loss=0.161, simple_loss=0.2446, pruned_loss=0.03873, over 985068.11 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2345, pruned_loss=0.04455, over 985616.78 frames.], batch size: 39, lr: 5.71e-04
+2022-06-18 20:34:49,125 INFO [train.py:874] (0/4) Epoch 14, batch 3900, aishell_loss[loss=0.1772, simple_loss=0.2633, pruned_loss=0.04561, over 4862.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2394, pruned_loss=0.04163, over 985074.76 frames.], batch size: 35, aishell_tot_loss[loss=0.1609, simple_loss=0.2445, pruned_loss=0.03867, over 984765.83 frames.], datatang_tot_loss[loss=0.1619, simple_loss=0.2345, pruned_loss=0.04465, over 985699.74 frames.], batch size: 35, lr: 5.71e-04
+2022-06-18 20:35:17,101 INFO [train.py:874] (0/4) Epoch 14, batch 3950, datatang_loss[loss=0.1532, simple_loss=0.2196, pruned_loss=0.04337, over 4954.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2397, pruned_loss=0.04159, over 984934.99 frames.], batch size: 67, aishell_tot_loss[loss=0.1615, simple_loss=0.2449, pruned_loss=0.03899, over 984621.24 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.2342, pruned_loss=0.04438, over 985675.40 frames.], batch size: 67, lr: 5.71e-04
+2022-06-18 20:35:46,848 INFO [train.py:874] (0/4) Epoch 14, batch 4000, datatang_loss[loss=0.1501, simple_loss=0.2255, pruned_loss=0.03736, over 4923.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2386, pruned_loss=0.04128, over 984928.72 frames.], batch size: 75, aishell_tot_loss[loss=0.1609, simple_loss=0.2444, pruned_loss=0.03872, over 984368.96 frames.], datatang_tot_loss[loss=0.1611, simple_loss=0.2339, pruned_loss=0.04421, over 985858.40 frames.], batch size: 75, lr: 5.71e-04
+2022-06-18 20:35:46,851 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 20:36:02,938 INFO [train.py:914] (0/4) Epoch 14, validation: loss=0.1656, simple_loss=0.2513, pruned_loss=0.03999, over 1622729.00 frames.
+2022-06-18 20:36:31,813 INFO [train.py:874] (0/4) Epoch 14, batch 4050, datatang_loss[loss=0.1593, simple_loss=0.2312, pruned_loss=0.04367, over 4940.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2386, pruned_loss=0.04109, over 984890.36 frames.], batch size: 67, aishell_tot_loss[loss=0.1609, simple_loss=0.2445, pruned_loss=0.03871, over 984138.10 frames.], datatang_tot_loss[loss=0.1608, simple_loss=0.2334, pruned_loss=0.04408, over 986041.62 frames.], batch size: 67, lr: 5.70e-04
+2022-06-18 20:36:59,613 INFO [train.py:874] (0/4) Epoch 14, batch 4100, datatang_loss[loss=0.1793, simple_loss=0.2476, pruned_loss=0.05549, over 4949.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2382, pruned_loss=0.04079, over 984505.46 frames.], batch size: 99, aishell_tot_loss[loss=0.1605, simple_loss=0.244, pruned_loss=0.03852, over 983644.48 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.2336, pruned_loss=0.04381, over 986064.39 frames.], batch size: 99, lr: 5.70e-04
+2022-06-18 20:37:15,275 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-14.pt
+2022-06-18 20:38:15,679 INFO [train.py:874] (0/4) Epoch 15, batch 50, datatang_loss[loss=0.1432, simple_loss=0.2152, pruned_loss=0.03558, over 4964.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2351, pruned_loss=0.04038, over 218345.86 frames.], batch size: 25, aishell_tot_loss[loss=0.1643, simple_loss=0.2475, pruned_loss=0.04048, over 115966.49 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2231, pruned_loss=0.04035, over 116029.59 frames.], batch size: 25, lr: 5.52e-04
+2022-06-18 20:38:46,654 INFO [train.py:874] (0/4) Epoch 15, batch 100, aishell_loss[loss=0.1564, simple_loss=0.2467, pruned_loss=0.033, over 4956.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2357, pruned_loss=0.04029, over 388261.07 frames.], batch size: 56, aishell_tot_loss[loss=0.1632, simple_loss=0.2462, pruned_loss=0.04014, over 225719.06 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.2244, pruned_loss=0.04051, over 210866.43 frames.], batch size: 56, lr: 5.52e-04
+2022-06-18 20:39:16,995 INFO [train.py:874] (0/4) Epoch 15, batch 150, aishell_loss[loss=0.1484, simple_loss=0.2456, pruned_loss=0.02558, over 4931.00 frames.], tot_loss[loss=0.1555, simple_loss=0.233, pruned_loss=0.03898, over 520422.28 frames.], batch size: 58, aishell_tot_loss[loss=0.16, simple_loss=0.2427, pruned_loss=0.03867, over 321564.59 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2226, pruned_loss=0.03965, over 295286.39 frames.], batch size: 58, lr: 5.52e-04
+2022-06-18 20:39:45,837 INFO [train.py:874] (0/4) Epoch 15, batch 200, aishell_loss[loss=0.1217, simple_loss=0.2046, pruned_loss=0.01936, over 4945.00 frames.], tot_loss[loss=0.157, simple_loss=0.2348, pruned_loss=0.03966, over 623449.97 frames.], batch size: 25, aishell_tot_loss[loss=0.1616, simple_loss=0.2442, pruned_loss=0.03955, over 393605.94 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2251, pruned_loss=0.03992, over 382832.15 frames.], batch size: 25, lr: 5.52e-04
+2022-06-18 20:40:16,146 INFO [train.py:874] (0/4) Epoch 15, batch 250, aishell_loss[loss=0.1406, simple_loss=0.2167, pruned_loss=0.03227, over 4957.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2353, pruned_loss=0.03854, over 703650.32 frames.], batch size: 31, aishell_tot_loss[loss=0.1599, simple_loss=0.2433, pruned_loss=0.03828, over 488830.65 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.225, pruned_loss=0.03945, over 426422.78 frames.], batch size: 31, lr: 5.51e-04
+2022-06-18 20:40:47,473 INFO [train.py:874] (0/4) Epoch 15, batch 300, datatang_loss[loss=0.1545, simple_loss=0.2368, pruned_loss=0.03608, over 4946.00 frames.], tot_loss[loss=0.156, simple_loss=0.2352, pruned_loss=0.0384, over 766116.24 frames.], batch size: 50, aishell_tot_loss[loss=0.16, simple_loss=0.2435, pruned_loss=0.03821, over 553821.35 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2246, pruned_loss=0.03917, over 484826.90 frames.], batch size: 50, lr: 5.51e-04
+2022-06-18 20:41:16,893 INFO [train.py:874] (0/4) Epoch 15, batch 350, datatang_loss[loss=0.1652, simple_loss=0.2391, pruned_loss=0.04565, over 4934.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2356, pruned_loss=0.03885, over 814609.69 frames.], batch size: 79, aishell_tot_loss[loss=0.1601, simple_loss=0.2437, pruned_loss=0.0383, over 602520.51 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2257, pruned_loss=0.03968, over 546090.93 frames.], batch size: 79, lr: 5.51e-04
+2022-06-18 20:41:47,886 INFO [train.py:874] (0/4) Epoch 15, batch 400, aishell_loss[loss=0.1637, simple_loss=0.2506, pruned_loss=0.03845, over 4873.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2361, pruned_loss=0.03939, over 852944.81 frames.], batch size: 36, aishell_tot_loss[loss=0.16, simple_loss=0.2434, pruned_loss=0.03833, over 647890.39 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.227, pruned_loss=0.04043, over 598073.25 frames.], batch size: 36, lr: 5.51e-04
+2022-06-18 20:42:19,938 INFO [train.py:874] (0/4) Epoch 15, batch 450, aishell_loss[loss=0.1474, simple_loss=0.2414, pruned_loss=0.02677, over 4981.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2368, pruned_loss=0.04005, over 882246.65 frames.], batch size: 39, aishell_tot_loss[loss=0.16, simple_loss=0.2434, pruned_loss=0.03829, over 678625.44 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.229, pruned_loss=0.04133, over 653680.65 frames.], batch size: 39, lr: 5.51e-04
+2022-06-18 20:42:49,001 INFO [train.py:874] (0/4) Epoch 15, batch 500, aishell_loss[loss=0.1663, simple_loss=0.2502, pruned_loss=0.04127, over 4942.00 frames.], tot_loss[loss=0.1598, simple_loss=0.238, pruned_loss=0.04081, over 905084.76 frames.], batch size: 49, aishell_tot_loss[loss=0.1604, simple_loss=0.2438, pruned_loss=0.03849, over 706227.10 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2311, pruned_loss=0.04214, over 701646.02 frames.], batch size: 49, lr: 5.50e-04
+2022-06-18 20:43:19,964 INFO [train.py:874] (0/4) Epoch 15, batch 550, aishell_loss[loss=0.1512, simple_loss=0.2256, pruned_loss=0.03842, over 4789.00 frames.], tot_loss[loss=0.1613, simple_loss=0.239, pruned_loss=0.04182, over 922936.98 frames.], batch size: 24, aishell_tot_loss[loss=0.1605, simple_loss=0.2442, pruned_loss=0.03844, over 733922.47 frames.], datatang_tot_loss[loss=0.1598, simple_loss=0.2325, pruned_loss=0.04352, over 740286.56 frames.], batch size: 24, lr: 5.50e-04
+2022-06-18 20:43:50,630 INFO [train.py:874] (0/4) Epoch 15, batch 600, aishell_loss[loss=0.1646, simple_loss=0.2563, pruned_loss=0.03646, over 4976.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2394, pruned_loss=0.0417, over 936895.69 frames.], batch size: 51, aishell_tot_loss[loss=0.1602, simple_loss=0.2439, pruned_loss=0.03823, over 765653.85 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.2333, pruned_loss=0.04388, over 767188.28 frames.], batch size: 51, lr: 5.50e-04
+2022-06-18 20:44:19,782 INFO [train.py:874] (0/4) Epoch 15, batch 650, aishell_loss[loss=0.1306, simple_loss=0.2132, pruned_loss=0.02399, over 4942.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2401, pruned_loss=0.04204, over 947414.07 frames.], batch size: 31, aishell_tot_loss[loss=0.1603, simple_loss=0.244, pruned_loss=0.03823, over 792330.73 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2342, pruned_loss=0.04457, over 791853.17 frames.], batch size: 31, lr: 5.50e-04
+2022-06-18 20:44:51,125 INFO [train.py:874] (0/4) Epoch 15, batch 700, aishell_loss[loss=0.1782, simple_loss=0.2651, pruned_loss=0.04559, over 4923.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2394, pruned_loss=0.04212, over 955339.46 frames.], batch size: 78, aishell_tot_loss[loss=0.1602, simple_loss=0.2435, pruned_loss=0.03841, over 810235.09 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2344, pruned_loss=0.04448, over 818854.78 frames.], batch size: 78, lr: 5.49e-04
+2022-06-18 20:45:21,686 INFO [train.py:874] (0/4) Epoch 15, batch 750, datatang_loss[loss=0.1624, simple_loss=0.2383, pruned_loss=0.04324, over 4901.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2394, pruned_loss=0.0418, over 961799.69 frames.], batch size: 52, aishell_tot_loss[loss=0.1604, simple_loss=0.2438, pruned_loss=0.03855, over 832286.38 frames.], datatang_tot_loss[loss=0.1612, simple_loss=0.2341, pruned_loss=0.0442, over 836896.78 frames.], batch size: 52, lr: 5.49e-04
+2022-06-18 20:45:51,523 INFO [train.py:874] (0/4) Epoch 15, batch 800, datatang_loss[loss=0.1529, simple_loss=0.2316, pruned_loss=0.03709, over 4927.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2384, pruned_loss=0.04157, over 966829.50 frames.], batch size: 34, aishell_tot_loss[loss=0.1606, simple_loss=0.2437, pruned_loss=0.03868, over 846619.04 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2333, pruned_loss=0.04374, over 857671.40 frames.], batch size: 34, lr: 5.49e-04
+2022-06-18 20:46:21,636 INFO [train.py:874] (0/4) Epoch 15, batch 850, datatang_loss[loss=0.1545, simple_loss=0.2351, pruned_loss=0.03693, over 4935.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2383, pruned_loss=0.04138, over 970496.73 frames.], batch size: 57, aishell_tot_loss[loss=0.161, simple_loss=0.2445, pruned_loss=0.0388, over 861936.76 frames.], datatang_tot_loss[loss=0.1597, simple_loss=0.2326, pruned_loss=0.04346, over 873214.82 frames.], batch size: 57, lr: 5.49e-04
+2022-06-18 20:46:52,652 INFO [train.py:874] (0/4) Epoch 15, batch 900, aishell_loss[loss=0.1288, simple_loss=0.2101, pruned_loss=0.02379, over 4875.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2379, pruned_loss=0.04114, over 974125.97 frames.], batch size: 28, aishell_tot_loss[loss=0.1605, simple_loss=0.2439, pruned_loss=0.03857, over 874964.42 frames.], datatang_tot_loss[loss=0.1598, simple_loss=0.2328, pruned_loss=0.0434, over 888122.61 frames.], batch size: 28, lr: 5.48e-04
+2022-06-18 20:47:21,755 INFO [train.py:874] (0/4) Epoch 15, batch 950, datatang_loss[loss=0.1528, simple_loss=0.2294, pruned_loss=0.0381, over 4937.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2381, pruned_loss=0.04129, over 976512.73 frames.], batch size: 88, aishell_tot_loss[loss=0.1606, simple_loss=0.2441, pruned_loss=0.03853, over 886331.90 frames.], datatang_tot_loss[loss=0.16, simple_loss=0.2329, pruned_loss=0.04357, over 900890.00 frames.], batch size: 88, lr: 5.48e-04
+2022-06-18 20:47:53,005 INFO [train.py:874] (0/4) Epoch 15, batch 1000, aishell_loss[loss=0.1702, simple_loss=0.2522, pruned_loss=0.04407, over 4884.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2379, pruned_loss=0.04081, over 978507.68 frames.], batch size: 35, aishell_tot_loss[loss=0.1609, simple_loss=0.2445, pruned_loss=0.03872, over 898569.68 frames.], datatang_tot_loss[loss=0.1589, simple_loss=0.2321, pruned_loss=0.04291, over 910353.94 frames.], batch size: 35, lr: 5.48e-04
+2022-06-18 20:47:53,007 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 20:48:10,192 INFO [train.py:914] (0/4) Epoch 15, validation: loss=0.1651, simple_loss=0.2495, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-18 20:48:40,014 INFO [train.py:874] (0/4) Epoch 15, batch 1050, datatang_loss[loss=0.1316, simple_loss=0.2077, pruned_loss=0.02771, over 4808.00 frames.], tot_loss[loss=0.159, simple_loss=0.2372, pruned_loss=0.04036, over 979961.70 frames.], batch size: 25, aishell_tot_loss[loss=0.1611, simple_loss=0.2448, pruned_loss=0.03871, over 907531.98 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2311, pruned_loss=0.04235, over 920222.43 frames.], batch size: 25, lr: 5.48e-04
+2022-06-18 20:49:10,955 INFO [train.py:874] (0/4) Epoch 15, batch 1100, aishell_loss[loss=0.1716, simple_loss=0.2498, pruned_loss=0.04669, over 4923.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2374, pruned_loss=0.03998, over 981341.48 frames.], batch size: 33, aishell_tot_loss[loss=0.161, simple_loss=0.2449, pruned_loss=0.03852, over 918074.47 frames.], datatang_tot_loss[loss=0.1575, simple_loss=0.2308, pruned_loss=0.04216, over 926929.08 frames.], batch size: 33, lr: 5.48e-04
+2022-06-18 20:49:39,093 INFO [train.py:874] (0/4) Epoch 15, batch 1150, datatang_loss[loss=0.1625, simple_loss=0.2295, pruned_loss=0.04769, over 4973.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2379, pruned_loss=0.04047, over 982411.50 frames.], batch size: 55, aishell_tot_loss[loss=0.1604, simple_loss=0.2442, pruned_loss=0.03829, over 927043.70 frames.], datatang_tot_loss[loss=0.1587, simple_loss=0.2316, pruned_loss=0.04291, over 933065.89 frames.], batch size: 55, lr: 5.47e-04
+2022-06-18 20:50:10,388 INFO [train.py:874] (0/4) Epoch 15, batch 1200, aishell_loss[loss=0.1512, simple_loss=0.225, pruned_loss=0.03871, over 4971.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2379, pruned_loss=0.04054, over 983036.04 frames.], batch size: 25, aishell_tot_loss[loss=0.1606, simple_loss=0.2443, pruned_loss=0.03844, over 933517.46 frames.], datatang_tot_loss[loss=0.1586, simple_loss=0.2316, pruned_loss=0.04277, over 939546.99 frames.], batch size: 25, lr: 5.47e-04
+2022-06-18 20:50:40,892 INFO [train.py:874] (0/4) Epoch 15, batch 1250, aishell_loss[loss=0.1729, simple_loss=0.2591, pruned_loss=0.04335, over 4963.00 frames.], tot_loss[loss=0.1594, simple_loss=0.238, pruned_loss=0.04042, over 983813.63 frames.], batch size: 61, aishell_tot_loss[loss=0.1608, simple_loss=0.2447, pruned_loss=0.03847, over 939465.14 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2314, pruned_loss=0.04257, over 945357.87 frames.], batch size: 61, lr: 5.47e-04
+2022-06-18 20:51:09,555 INFO [train.py:874] (0/4) Epoch 15, batch 1300, aishell_loss[loss=0.1657, simple_loss=0.2555, pruned_loss=0.03797, over 4912.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2373, pruned_loss=0.04004, over 984272.66 frames.], batch size: 41, aishell_tot_loss[loss=0.1606, simple_loss=0.2444, pruned_loss=0.03846, over 945733.46 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.2308, pruned_loss=0.04223, over 949423.17 frames.], batch size: 41, lr: 5.47e-04
+2022-06-18 20:51:39,856 INFO [train.py:874] (0/4) Epoch 15, batch 1350, datatang_loss[loss=0.1517, simple_loss=0.2312, pruned_loss=0.03608, over 4877.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2379, pruned_loss=0.04036, over 984464.56 frames.], batch size: 39, aishell_tot_loss[loss=0.1606, simple_loss=0.2446, pruned_loss=0.03835, over 949307.33 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2315, pruned_loss=0.04251, over 954583.06 frames.], batch size: 39, lr: 5.46e-04
+2022-06-18 20:52:11,236 INFO [train.py:874] (0/4) Epoch 15, batch 1400, datatang_loss[loss=0.2041, simple_loss=0.2582, pruned_loss=0.075, over 4897.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2392, pruned_loss=0.0415, over 984876.49 frames.], batch size: 52, aishell_tot_loss[loss=0.161, simple_loss=0.2448, pruned_loss=0.03856, over 953181.74 frames.], datatang_tot_loss[loss=0.1598, simple_loss=0.2327, pruned_loss=0.04344, over 958721.97 frames.], batch size: 52, lr: 5.46e-04
+2022-06-18 20:52:39,830 INFO [train.py:874] (0/4) Epoch 15, batch 1450, aishell_loss[loss=0.1872, simple_loss=0.2684, pruned_loss=0.05299, over 4881.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2388, pruned_loss=0.04134, over 984921.54 frames.], batch size: 47, aishell_tot_loss[loss=0.1615, simple_loss=0.2453, pruned_loss=0.03881, over 956934.32 frames.], datatang_tot_loss[loss=0.1591, simple_loss=0.2319, pruned_loss=0.04313, over 961833.93 frames.], batch size: 47, lr: 5.46e-04
+2022-06-18 20:53:10,615 INFO [train.py:874] (0/4) Epoch 15, batch 1500, aishell_loss[loss=0.1978, simple_loss=0.2808, pruned_loss=0.05746, over 4937.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2393, pruned_loss=0.04145, over 985161.44 frames.], batch size: 79, aishell_tot_loss[loss=0.1616, simple_loss=0.2456, pruned_loss=0.03878, over 959810.81 frames.], datatang_tot_loss[loss=0.1594, simple_loss=0.2323, pruned_loss=0.04326, over 965127.93 frames.], batch size: 79, lr: 5.46e-04
+2022-06-18 20:53:41,104 INFO [train.py:874] (0/4) Epoch 15, batch 1550, aishell_loss[loss=0.1369, simple_loss=0.225, pruned_loss=0.02444, over 4958.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2392, pruned_loss=0.0413, over 985376.24 frames.], batch size: 44, aishell_tot_loss[loss=0.1616, simple_loss=0.2455, pruned_loss=0.03882, over 963050.43 frames.], datatang_tot_loss[loss=0.1594, simple_loss=0.2325, pruned_loss=0.04318, over 967471.12 frames.], batch size: 44, lr: 5.45e-04
+2022-06-18 20:54:09,708 INFO [train.py:874] (0/4) Epoch 15, batch 1600, datatang_loss[loss=0.1599, simple_loss=0.2387, pruned_loss=0.04062, over 4941.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2387, pruned_loss=0.0411, over 985243.22 frames.], batch size: 45, aishell_tot_loss[loss=0.1609, simple_loss=0.2449, pruned_loss=0.03846, over 965240.12 frames.], datatang_tot_loss[loss=0.1597, simple_loss=0.2327, pruned_loss=0.04333, over 969833.88 frames.], batch size: 45, lr: 5.45e-04
+2022-06-18 20:54:39,883 INFO [train.py:874] (0/4) Epoch 15, batch 1650, aishell_loss[loss=0.1583, simple_loss=0.2409, pruned_loss=0.03779, over 4864.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2392, pruned_loss=0.04108, over 985224.12 frames.], batch size: 37, aishell_tot_loss[loss=0.1611, simple_loss=0.2452, pruned_loss=0.03854, over 967350.19 frames.], datatang_tot_loss[loss=0.1597, simple_loss=0.2328, pruned_loss=0.04331, over 971886.20 frames.], batch size: 37, lr: 5.45e-04
+2022-06-18 20:55:11,393 INFO [train.py:874] (0/4) Epoch 15, batch 1700, aishell_loss[loss=0.186, simple_loss=0.2658, pruned_loss=0.05312, over 4942.00 frames.], tot_loss[loss=0.16, simple_loss=0.238, pruned_loss=0.04098, over 985632.92 frames.], batch size: 58, aishell_tot_loss[loss=0.1612, simple_loss=0.245, pruned_loss=0.03867, over 968699.87 frames.], datatang_tot_loss[loss=0.159, simple_loss=0.2322, pruned_loss=0.04291, over 974469.68 frames.], batch size: 58, lr: 5.45e-04
+2022-06-18 20:55:40,695 INFO [train.py:874] (0/4) Epoch 15, batch 1750, aishell_loss[loss=0.1588, simple_loss=0.2456, pruned_loss=0.03598, over 4889.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2376, pruned_loss=0.04106, over 985171.71 frames.], batch size: 42, aishell_tot_loss[loss=0.1607, simple_loss=0.2446, pruned_loss=0.03838, over 970245.24 frames.], datatang_tot_loss[loss=0.1593, simple_loss=0.2323, pruned_loss=0.04321, over 975634.36 frames.], batch size: 42, lr: 5.45e-04
+2022-06-18 20:56:11,380 INFO [train.py:874] (0/4) Epoch 15, batch 1800, datatang_loss[loss=0.1847, simple_loss=0.2553, pruned_loss=0.05707, over 4933.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2379, pruned_loss=0.04085, over 985358.70 frames.], batch size: 109, aishell_tot_loss[loss=0.1606, simple_loss=0.2449, pruned_loss=0.03817, over 971820.18 frames.], datatang_tot_loss[loss=0.1593, simple_loss=0.2322, pruned_loss=0.04318, over 977066.21 frames.], batch size: 109, lr: 5.44e-04
+2022-06-18 20:56:41,295 INFO [train.py:874] (0/4) Epoch 15, batch 1850, datatang_loss[loss=0.1669, simple_loss=0.2275, pruned_loss=0.05312, over 4891.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2377, pruned_loss=0.04037, over 985579.33 frames.], batch size: 47, aishell_tot_loss[loss=0.1602, simple_loss=0.2446, pruned_loss=0.03788, over 973603.14 frames.], datatang_tot_loss[loss=0.1591, simple_loss=0.2321, pruned_loss=0.04303, over 978124.08 frames.], batch size: 47, lr: 5.44e-04
+2022-06-18 20:57:10,549 INFO [train.py:874] (0/4) Epoch 15, batch 1900, aishell_loss[loss=0.149, simple_loss=0.2168, pruned_loss=0.04056, over 4984.00 frames.], tot_loss[loss=0.16, simple_loss=0.2383, pruned_loss=0.04088, over 985684.22 frames.], batch size: 25, aishell_tot_loss[loss=0.1607, simple_loss=0.2454, pruned_loss=0.03804, over 975041.36 frames.], datatang_tot_loss[loss=0.1593, simple_loss=0.2318, pruned_loss=0.04341, over 979104.32 frames.], batch size: 25, lr: 5.44e-04
+2022-06-18 20:57:39,898 INFO [train.py:874] (0/4) Epoch 15, batch 1950, aishell_loss[loss=0.1899, simple_loss=0.2809, pruned_loss=0.04941, over 4956.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2387, pruned_loss=0.04055, over 985396.47 frames.], batch size: 64, aishell_tot_loss[loss=0.1611, simple_loss=0.2459, pruned_loss=0.03816, over 976044.52 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2316, pruned_loss=0.04298, over 979805.95 frames.], batch size: 64, lr: 5.44e-04
+2022-06-18 20:58:10,943 INFO [train.py:874] (0/4) Epoch 15, batch 2000, aishell_loss[loss=0.1425, simple_loss=0.2371, pruned_loss=0.02392, over 4945.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2386, pruned_loss=0.04113, over 985690.85 frames.], batch size: 58, aishell_tot_loss[loss=0.1608, simple_loss=0.2454, pruned_loss=0.0381, over 977290.05 frames.], datatang_tot_loss[loss=0.1596, simple_loss=0.2321, pruned_loss=0.04355, over 980582.02 frames.], batch size: 58, lr: 5.43e-04
+2022-06-18 20:58:10,946 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 20:58:27,551 INFO [train.py:914] (0/4) Epoch 15, validation: loss=0.166, simple_loss=0.2499, pruned_loss=0.04104, over 1622729.00 frames.
+2022-06-18 20:58:58,075 INFO [train.py:874] (0/4) Epoch 15, batch 2050, datatang_loss[loss=0.1436, simple_loss=0.222, pruned_loss=0.0326, over 4976.00 frames.], tot_loss[loss=0.1598, simple_loss=0.238, pruned_loss=0.04081, over 985423.06 frames.], batch size: 45, aishell_tot_loss[loss=0.1604, simple_loss=0.2448, pruned_loss=0.03797, over 978072.05 frames.], datatang_tot_loss[loss=0.1594, simple_loss=0.2319, pruned_loss=0.04343, over 981093.18 frames.], batch size: 45, lr: 5.43e-04
+2022-06-18 20:59:28,187 INFO [train.py:874] (0/4) Epoch 15, batch 2100, datatang_loss[loss=0.1677, simple_loss=0.2421, pruned_loss=0.04663, over 4977.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2377, pruned_loss=0.04065, over 985646.92 frames.], batch size: 37, aishell_tot_loss[loss=0.1601, simple_loss=0.2443, pruned_loss=0.03792, over 978884.85 frames.], datatang_tot_loss[loss=0.1593, simple_loss=0.232, pruned_loss=0.04332, over 981892.23 frames.], batch size: 37, lr: 5.43e-04
+2022-06-18 20:59:58,264 INFO [train.py:874] (0/4) Epoch 15, batch 2150, aishell_loss[loss=0.1632, simple_loss=0.2549, pruned_loss=0.03579, over 4902.00 frames.], tot_loss[loss=0.1598, simple_loss=0.238, pruned_loss=0.04074, over 985600.90 frames.], batch size: 68, aishell_tot_loss[loss=0.1608, simple_loss=0.245, pruned_loss=0.03828, over 979439.67 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2318, pruned_loss=0.04295, over 982485.15 frames.], batch size: 68, lr: 5.43e-04
+2022-06-18 21:00:18,426 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-60000.pt
+2022-06-18 21:00:33,784 INFO [train.py:874] (0/4) Epoch 15, batch 2200, aishell_loss[loss=0.1544, simple_loss=0.2335, pruned_loss=0.03763, over 4936.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2386, pruned_loss=0.04087, over 985745.78 frames.], batch size: 31, aishell_tot_loss[loss=0.161, simple_loss=0.2452, pruned_loss=0.0384, over 980230.73 frames.], datatang_tot_loss[loss=0.159, simple_loss=0.2321, pruned_loss=0.04297, over 982918.20 frames.], batch size: 31, lr: 5.43e-04
+2022-06-18 21:01:02,746 INFO [train.py:874] (0/4) Epoch 15, batch 2250, aishell_loss[loss=0.1628, simple_loss=0.2557, pruned_loss=0.03492, over 4929.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2385, pruned_loss=0.04067, over 986021.60 frames.], batch size: 58, aishell_tot_loss[loss=0.1608, simple_loss=0.245, pruned_loss=0.03828, over 981263.30 frames.], datatang_tot_loss[loss=0.159, simple_loss=0.2319, pruned_loss=0.04304, over 983207.69 frames.], batch size: 58, lr: 5.42e-04
+2022-06-18 21:01:33,951 INFO [train.py:874] (0/4) Epoch 15, batch 2300, datatang_loss[loss=0.1695, simple_loss=0.2443, pruned_loss=0.04735, over 4925.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2384, pruned_loss=0.04058, over 986155.05 frames.], batch size: 42, aishell_tot_loss[loss=0.1605, simple_loss=0.2448, pruned_loss=0.03813, over 981912.26 frames.], datatang_tot_loss[loss=0.1591, simple_loss=0.2321, pruned_loss=0.04305, over 983582.11 frames.], batch size: 42, lr: 5.42e-04
+2022-06-18 21:02:05,450 INFO [train.py:874] (0/4) Epoch 15, batch 2350, aishell_loss[loss=0.1772, simple_loss=0.2655, pruned_loss=0.04443, over 4967.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2381, pruned_loss=0.04054, over 985728.82 frames.], batch size: 44, aishell_tot_loss[loss=0.1607, simple_loss=0.2448, pruned_loss=0.03835, over 982061.45 frames.], datatang_tot_loss[loss=0.1587, simple_loss=0.2318, pruned_loss=0.04279, over 983785.89 frames.], batch size: 44, lr: 5.42e-04
+2022-06-18 21:02:33,671 INFO [train.py:874] (0/4) Epoch 15, batch 2400, datatang_loss[loss=0.1841, simple_loss=0.2624, pruned_loss=0.05294, over 4928.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2391, pruned_loss=0.0409, over 985152.17 frames.], batch size: 73, aishell_tot_loss[loss=0.1612, simple_loss=0.2454, pruned_loss=0.03848, over 981915.10 frames.], datatang_tot_loss[loss=0.1592, simple_loss=0.2321, pruned_loss=0.04309, over 983992.99 frames.], batch size: 73, lr: 5.42e-04
+2022-06-18 21:03:04,145 INFO [train.py:874] (0/4) Epoch 15, batch 2450, aishell_loss[loss=0.1518, simple_loss=0.2359, pruned_loss=0.0338, over 4943.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2387, pruned_loss=0.04114, over 985322.53 frames.], batch size: 56, aishell_tot_loss[loss=0.1607, simple_loss=0.2447, pruned_loss=0.03835, over 982015.05 frames.], datatang_tot_loss[loss=0.1598, simple_loss=0.2329, pruned_loss=0.0434, over 984523.39 frames.], batch size: 56, lr: 5.41e-04
+2022-06-18 21:03:35,629 INFO [train.py:874] (0/4) Epoch 15, batch 2500, aishell_loss[loss=0.1574, simple_loss=0.2359, pruned_loss=0.03946, over 4955.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2377, pruned_loss=0.04036, over 985600.65 frames.], batch size: 31, aishell_tot_loss[loss=0.16, simple_loss=0.2438, pruned_loss=0.03805, over 982485.96 frames.], datatang_tot_loss[loss=0.1592, simple_loss=0.2323, pruned_loss=0.04304, over 984898.10 frames.], batch size: 31, lr: 5.41e-04
+2022-06-18 21:04:05,191 INFO [train.py:874] (0/4) Epoch 15, batch 2550, datatang_loss[loss=0.1671, simple_loss=0.2357, pruned_loss=0.04924, over 4918.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2369, pruned_loss=0.03978, over 985172.61 frames.], batch size: 57, aishell_tot_loss[loss=0.1592, simple_loss=0.2432, pruned_loss=0.03763, over 982520.80 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.232, pruned_loss=0.04284, over 984879.77 frames.], batch size: 57, lr: 5.41e-04
+2022-06-18 21:04:36,452 INFO [train.py:874] (0/4) Epoch 15, batch 2600, datatang_loss[loss=0.1352, simple_loss=0.2083, pruned_loss=0.03103, over 4936.00 frames.], tot_loss[loss=0.159, simple_loss=0.2378, pruned_loss=0.04011, over 985306.80 frames.], batch size: 55, aishell_tot_loss[loss=0.1599, simple_loss=0.244, pruned_loss=0.03791, over 982894.63 frames.], datatang_tot_loss[loss=0.1589, simple_loss=0.232, pruned_loss=0.04286, over 985001.67 frames.], batch size: 55, lr: 5.41e-04
+2022-06-18 21:05:08,094 INFO [train.py:874] (0/4) Epoch 15, batch 2650, datatang_loss[loss=0.1536, simple_loss=0.2256, pruned_loss=0.04078, over 4969.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2376, pruned_loss=0.03996, over 984982.56 frames.], batch size: 60, aishell_tot_loss[loss=0.16, simple_loss=0.2441, pruned_loss=0.03792, over 982951.25 frames.], datatang_tot_loss[loss=0.1584, simple_loss=0.2316, pruned_loss=0.04261, over 984914.01 frames.], batch size: 60, lr: 5.41e-04
+2022-06-18 21:05:37,257 INFO [train.py:874] (0/4) Epoch 15, batch 2700, datatang_loss[loss=0.1852, simple_loss=0.2533, pruned_loss=0.05851, over 4963.00 frames.], tot_loss[loss=0.158, simple_loss=0.2363, pruned_loss=0.03985, over 985096.54 frames.], batch size: 55, aishell_tot_loss[loss=0.1602, simple_loss=0.2444, pruned_loss=0.03801, over 983335.09 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2303, pruned_loss=0.04222, over 984842.34 frames.], batch size: 55, lr: 5.40e-04
+2022-06-18 21:06:06,727 INFO [train.py:874] (0/4) Epoch 15, batch 2750, datatang_loss[loss=0.173, simple_loss=0.2497, pruned_loss=0.04821, over 4942.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2362, pruned_loss=0.03937, over 984651.10 frames.], batch size: 88, aishell_tot_loss[loss=0.1597, simple_loss=0.2439, pruned_loss=0.03777, over 983128.10 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.2301, pruned_loss=0.04194, over 984840.70 frames.], batch size: 88, lr: 5.40e-04
+2022-06-18 21:06:38,044 INFO [train.py:874] (0/4) Epoch 15, batch 2800, datatang_loss[loss=0.1376, simple_loss=0.2173, pruned_loss=0.02897, over 4859.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2362, pruned_loss=0.03904, over 984976.66 frames.], batch size: 30, aishell_tot_loss[loss=0.1601, simple_loss=0.2444, pruned_loss=0.03785, over 983546.00 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2296, pruned_loss=0.04136, over 984925.48 frames.], batch size: 30, lr: 5.40e-04
+2022-06-18 21:07:05,638 INFO [train.py:874] (0/4) Epoch 15, batch 2850, aishell_loss[loss=0.1681, simple_loss=0.2302, pruned_loss=0.05295, over 4946.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2376, pruned_loss=0.03986, over 984977.43 frames.], batch size: 25, aishell_tot_loss[loss=0.1608, simple_loss=0.245, pruned_loss=0.03825, over 983463.18 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2302, pruned_loss=0.04172, over 985173.71 frames.], batch size: 25, lr: 5.40e-04
+2022-06-18 21:07:36,987 INFO [train.py:874] (0/4) Epoch 15, batch 2900, datatang_loss[loss=0.1386, simple_loss=0.218, pruned_loss=0.0296, over 4961.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2373, pruned_loss=0.03984, over 985237.23 frames.], batch size: 67, aishell_tot_loss[loss=0.161, simple_loss=0.2454, pruned_loss=0.03827, over 983705.60 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2298, pruned_loss=0.04157, over 985327.51 frames.], batch size: 67, lr: 5.39e-04
+2022-06-18 21:08:07,678 INFO [train.py:874] (0/4) Epoch 15, batch 2950, aishell_loss[loss=0.1463, simple_loss=0.2253, pruned_loss=0.03363, over 4876.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2378, pruned_loss=0.03972, over 985552.15 frames.], batch size: 28, aishell_tot_loss[loss=0.161, simple_loss=0.2456, pruned_loss=0.03814, over 984017.51 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2297, pruned_loss=0.04163, over 985553.79 frames.], batch size: 28, lr: 5.39e-04
+2022-06-18 21:08:37,247 INFO [train.py:874] (0/4) Epoch 15, batch 3000, datatang_loss[loss=0.1475, simple_loss=0.2115, pruned_loss=0.04177, over 4956.00 frames.], tot_loss[loss=0.159, simple_loss=0.2377, pruned_loss=0.04018, over 985476.04 frames.], batch size: 50, aishell_tot_loss[loss=0.161, simple_loss=0.2456, pruned_loss=0.03815, over 984155.45 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.2299, pruned_loss=0.04203, over 985499.58 frames.], batch size: 50, lr: 5.39e-04
+2022-06-18 21:08:37,250 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 21:08:54,379 INFO [train.py:914] (0/4) Epoch 15, validation: loss=0.1663, simple_loss=0.2504, pruned_loss=0.04115, over 1622729.00 frames.
+2022-06-18 21:09:23,247 INFO [train.py:874] (0/4) Epoch 15, batch 3050, aishell_loss[loss=0.1539, simple_loss=0.239, pruned_loss=0.03443, over 4925.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2386, pruned_loss=0.04015, over 985562.62 frames.], batch size: 33, aishell_tot_loss[loss=0.1613, simple_loss=0.2462, pruned_loss=0.03816, over 984537.73 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2299, pruned_loss=0.04212, over 985394.90 frames.], batch size: 33, lr: 5.39e-04
+2022-06-18 21:09:54,958 INFO [train.py:874] (0/4) Epoch 15, batch 3100, aishell_loss[loss=0.1317, simple_loss=0.2134, pruned_loss=0.02503, over 4882.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2381, pruned_loss=0.0403, over 985436.42 frames.], batch size: 28, aishell_tot_loss[loss=0.1607, simple_loss=0.2455, pruned_loss=0.03799, over 984622.85 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.2303, pruned_loss=0.04241, over 985290.84 frames.], batch size: 28, lr: 5.39e-04
+2022-06-18 21:10:26,285 INFO [train.py:874] (0/4) Epoch 15, batch 3150, aishell_loss[loss=0.1368, simple_loss=0.2247, pruned_loss=0.02443, over 4975.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2374, pruned_loss=0.03976, over 985526.54 frames.], batch size: 27, aishell_tot_loss[loss=0.1601, simple_loss=0.2451, pruned_loss=0.03758, over 984605.12 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2299, pruned_loss=0.04229, over 985521.80 frames.], batch size: 27, lr: 5.38e-04
+2022-06-18 21:10:56,708 INFO [train.py:874] (0/4) Epoch 15, batch 3200, datatang_loss[loss=0.178, simple_loss=0.2554, pruned_loss=0.05032, over 4908.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2379, pruned_loss=0.03978, over 985532.21 frames.], batch size: 52, aishell_tot_loss[loss=0.1601, simple_loss=0.245, pruned_loss=0.03755, over 984618.00 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.2305, pruned_loss=0.04233, over 985617.47 frames.], batch size: 52, lr: 5.38e-04
+2022-06-18 21:11:27,555 INFO [train.py:874] (0/4) Epoch 15, batch 3250, aishell_loss[loss=0.1103, simple_loss=0.1827, pruned_loss=0.01901, over 4797.00 frames.], tot_loss[loss=0.1594, simple_loss=0.238, pruned_loss=0.04042, over 985457.88 frames.], batch size: 20, aishell_tot_loss[loss=0.1601, simple_loss=0.2451, pruned_loss=0.03752, over 984510.72 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2308, pruned_loss=0.0429, over 985720.10 frames.], batch size: 20, lr: 5.38e-04
+2022-06-18 21:11:59,149 INFO [train.py:874] (0/4) Epoch 15, batch 3300, datatang_loss[loss=0.1712, simple_loss=0.2386, pruned_loss=0.05193, over 4930.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2381, pruned_loss=0.04027, over 985393.21 frames.], batch size: 42, aishell_tot_loss[loss=0.1602, simple_loss=0.2453, pruned_loss=0.03751, over 984392.82 frames.], datatang_tot_loss[loss=0.1582, simple_loss=0.2309, pruned_loss=0.04272, over 985826.05 frames.], batch size: 42, lr: 5.38e-04
+2022-06-18 21:12:28,842 INFO [train.py:874] (0/4) Epoch 15, batch 3350, aishell_loss[loss=0.1956, simple_loss=0.2679, pruned_loss=0.06167, over 4872.00 frames.], tot_loss[loss=0.1591, simple_loss=0.238, pruned_loss=0.0401, over 985255.30 frames.], batch size: 36, aishell_tot_loss[loss=0.1599, simple_loss=0.245, pruned_loss=0.03741, over 984228.38 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2312, pruned_loss=0.04267, over 985918.61 frames.], batch size: 36, lr: 5.37e-04
+2022-06-18 21:12:59,961 INFO [train.py:874] (0/4) Epoch 15, batch 3400, datatang_loss[loss=0.189, simple_loss=0.254, pruned_loss=0.06201, over 4966.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2386, pruned_loss=0.03994, over 985255.32 frames.], batch size: 34, aishell_tot_loss[loss=0.1602, simple_loss=0.2456, pruned_loss=0.03743, over 984266.25 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.231, pruned_loss=0.04257, over 985945.84 frames.], batch size: 34, lr: 5.37e-04
+2022-06-18 21:13:28,564 INFO [train.py:874] (0/4) Epoch 15, batch 3450, aishell_loss[loss=0.1535, simple_loss=0.2432, pruned_loss=0.03186, over 4916.00 frames.], tot_loss[loss=0.159, simple_loss=0.2381, pruned_loss=0.0399, over 985246.80 frames.], batch size: 52, aishell_tot_loss[loss=0.1601, simple_loss=0.2454, pruned_loss=0.0374, over 984470.95 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.2307, pruned_loss=0.04262, over 985783.49 frames.], batch size: 52, lr: 5.37e-04
+2022-06-18 21:13:59,926 INFO [train.py:874] (0/4) Epoch 15, batch 3500, aishell_loss[loss=0.1825, simple_loss=0.2623, pruned_loss=0.05129, over 4943.00 frames.], tot_loss[loss=0.1592, simple_loss=0.238, pruned_loss=0.04021, over 984993.00 frames.], batch size: 58, aishell_tot_loss[loss=0.16, simple_loss=0.2451, pruned_loss=0.03745, over 984211.19 frames.], datatang_tot_loss[loss=0.1584, simple_loss=0.2313, pruned_loss=0.04276, over 985783.16 frames.], batch size: 58, lr: 5.37e-04
+2022-06-18 21:14:32,149 INFO [train.py:874] (0/4) Epoch 15, batch 3550, aishell_loss[loss=0.147, simple_loss=0.2339, pruned_loss=0.03007, over 4909.00 frames.], tot_loss[loss=0.1595, simple_loss=0.239, pruned_loss=0.03995, over 985000.26 frames.], batch size: 34, aishell_tot_loss[loss=0.1599, simple_loss=0.2452, pruned_loss=0.03733, over 984186.83 frames.], datatang_tot_loss[loss=0.1587, simple_loss=0.2319, pruned_loss=0.04273, over 985840.41 frames.], batch size: 34, lr: 5.37e-04
+2022-06-18 21:15:01,900 INFO [train.py:874] (0/4) Epoch 15, batch 3600, aishell_loss[loss=0.178, simple_loss=0.2511, pruned_loss=0.05251, over 4940.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2386, pruned_loss=0.03983, over 985226.84 frames.], batch size: 58, aishell_tot_loss[loss=0.1604, simple_loss=0.2455, pruned_loss=0.03759, over 984493.36 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2314, pruned_loss=0.04224, over 985754.68 frames.], batch size: 58, lr: 5.36e-04
+2022-06-18 21:15:34,135 INFO [train.py:874] (0/4) Epoch 15, batch 3650, datatang_loss[loss=0.1622, simple_loss=0.2365, pruned_loss=0.04396, over 4918.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2375, pruned_loss=0.03989, over 985318.71 frames.], batch size: 73, aishell_tot_loss[loss=0.1608, simple_loss=0.2457, pruned_loss=0.03797, over 984721.79 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2305, pruned_loss=0.04183, over 985617.09 frames.], batch size: 73, lr: 5.36e-04
+2022-06-18 21:16:05,722 INFO [train.py:874] (0/4) Epoch 15, batch 3700, aishell_loss[loss=0.163, simple_loss=0.2501, pruned_loss=0.03791, over 4912.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2379, pruned_loss=0.03945, over 984866.74 frames.], batch size: 52, aishell_tot_loss[loss=0.1604, simple_loss=0.2455, pruned_loss=0.03761, over 984445.10 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2307, pruned_loss=0.0418, over 985465.68 frames.], batch size: 52, lr: 5.36e-04
+2022-06-18 21:16:34,909 INFO [train.py:874] (0/4) Epoch 15, batch 3750, datatang_loss[loss=0.1511, simple_loss=0.2223, pruned_loss=0.04, over 4942.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2376, pruned_loss=0.03938, over 985438.53 frames.], batch size: 88, aishell_tot_loss[loss=0.1606, simple_loss=0.2458, pruned_loss=0.03777, over 984736.76 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.2303, pruned_loss=0.04145, over 985748.27 frames.], batch size: 88, lr: 5.36e-04
+2022-06-18 21:17:05,022 INFO [train.py:874] (0/4) Epoch 15, batch 3800, aishell_loss[loss=0.1708, simple_loss=0.2537, pruned_loss=0.04394, over 4985.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2359, pruned_loss=0.0389, over 985293.05 frames.], batch size: 37, aishell_tot_loss[loss=0.1592, simple_loss=0.2437, pruned_loss=0.03739, over 984728.15 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2302, pruned_loss=0.04132, over 985650.31 frames.], batch size: 37, lr: 5.35e-04
+2022-06-18 21:17:36,745 INFO [train.py:874] (0/4) Epoch 15, batch 3850, aishell_loss[loss=0.1179, simple_loss=0.1884, pruned_loss=0.02375, over 4977.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2368, pruned_loss=0.03905, over 985368.85 frames.], batch size: 22, aishell_tot_loss[loss=0.1592, simple_loss=0.2437, pruned_loss=0.03733, over 984813.08 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2308, pruned_loss=0.04148, over 985690.58 frames.], batch size: 22, lr: 5.35e-04
+2022-06-18 21:18:05,688 INFO [train.py:874] (0/4) Epoch 15, batch 3900, datatang_loss[loss=0.157, simple_loss=0.2296, pruned_loss=0.04217, over 4947.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2364, pruned_loss=0.03911, over 985615.12 frames.], batch size: 69, aishell_tot_loss[loss=0.1589, simple_loss=0.2435, pruned_loss=0.03715, over 984956.11 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2307, pruned_loss=0.04155, over 985818.84 frames.], batch size: 69, lr: 5.35e-04
+2022-06-18 21:18:34,803 INFO [train.py:874] (0/4) Epoch 15, batch 3950, datatang_loss[loss=0.15, simple_loss=0.2243, pruned_loss=0.03781, over 4936.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2361, pruned_loss=0.03884, over 985621.35 frames.], batch size: 57, aishell_tot_loss[loss=0.1586, simple_loss=0.2431, pruned_loss=0.03705, over 985059.93 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2302, pruned_loss=0.04142, over 985810.84 frames.], batch size: 57, lr: 5.35e-04
+2022-06-18 21:19:03,470 INFO [train.py:874] (0/4) Epoch 15, batch 4000, datatang_loss[loss=0.1903, simple_loss=0.2568, pruned_loss=0.06187, over 4941.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2376, pruned_loss=0.03942, over 985801.34 frames.], batch size: 94, aishell_tot_loss[loss=0.1594, simple_loss=0.2439, pruned_loss=0.03741, over 985339.03 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2306, pruned_loss=0.04165, over 985770.06 frames.], batch size: 94, lr: 5.35e-04
+2022-06-18 21:19:03,473 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 21:19:20,713 INFO [train.py:914] (0/4) Epoch 15, validation: loss=0.1641, simple_loss=0.2483, pruned_loss=0.0399, over 1622729.00 frames.
+2022-06-18 21:19:49,323 INFO [train.py:874] (0/4) Epoch 15, batch 4050, datatang_loss[loss=0.1942, simple_loss=0.2595, pruned_loss=0.06439, over 4913.00 frames.], tot_loss[loss=0.159, simple_loss=0.2379, pruned_loss=0.04001, over 985540.00 frames.], batch size: 64, aishell_tot_loss[loss=0.1601, simple_loss=0.2443, pruned_loss=0.03794, over 985209.14 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.2306, pruned_loss=0.04174, over 985684.97 frames.], batch size: 64, lr: 5.34e-04
+2022-06-18 21:20:18,338 INFO [train.py:874] (0/4) Epoch 15, batch 4100, aishell_loss[loss=0.1478, simple_loss=0.2352, pruned_loss=0.03025, over 4960.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2387, pruned_loss=0.04082, over 985511.45 frames.], batch size: 31, aishell_tot_loss[loss=0.1602, simple_loss=0.2441, pruned_loss=0.03818, over 985029.13 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2314, pruned_loss=0.04246, over 985875.32 frames.], batch size: 31, lr: 5.34e-04
+2022-06-18 21:20:47,955 INFO [train.py:874] (0/4) Epoch 15, batch 4150, datatang_loss[loss=0.1431, simple_loss=0.2245, pruned_loss=0.03086, over 4897.00 frames.], tot_loss[loss=0.1585, simple_loss=0.237, pruned_loss=0.04002, over 985325.30 frames.], batch size: 59, aishell_tot_loss[loss=0.1595, simple_loss=0.2432, pruned_loss=0.03786, over 984751.94 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2309, pruned_loss=0.04197, over 985960.56 frames.], batch size: 59, lr: 5.34e-04
+2022-06-18 21:21:18,426 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-15.pt
+2022-06-18 21:22:22,377 INFO [train.py:874] (0/4) Epoch 16, batch 50, aishell_loss[loss=0.1365, simple_loss=0.1986, pruned_loss=0.03722, over 4887.00 frames.], tot_loss[loss=0.153, simple_loss=0.2321, pruned_loss=0.03696, over 218566.56 frames.], batch size: 21, aishell_tot_loss[loss=0.1592, simple_loss=0.2429, pruned_loss=0.03772, over 111535.67 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2224, pruned_loss=0.03621, over 120668.11 frames.], batch size: 21, lr: 5.18e-04
+2022-06-18 21:22:53,186 INFO [train.py:874] (0/4) Epoch 16, batch 100, aishell_loss[loss=0.1503, simple_loss=0.2366, pruned_loss=0.032, over 4871.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2294, pruned_loss=0.03574, over 388055.75 frames.], batch size: 36, aishell_tot_loss[loss=0.1566, simple_loss=0.2406, pruned_loss=0.03637, over 202266.99 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2201, pruned_loss=0.03524, over 233862.62 frames.], batch size: 36, lr: 5.18e-04
+2022-06-18 21:23:22,588 INFO [train.py:874] (0/4) Epoch 16, batch 150, aishell_loss[loss=0.1438, simple_loss=0.227, pruned_loss=0.03026, over 4933.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2289, pruned_loss=0.03621, over 520536.02 frames.], batch size: 32, aishell_tot_loss[loss=0.1572, simple_loss=0.2411, pruned_loss=0.0367, over 280435.28 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2193, pruned_loss=0.03572, over 335672.91 frames.], batch size: 32, lr: 5.18e-04
+2022-06-18 21:23:53,696 INFO [train.py:874] (0/4) Epoch 16, batch 200, aishell_loss[loss=0.1678, simple_loss=0.2421, pruned_loss=0.04676, over 4929.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2292, pruned_loss=0.03728, over 623962.37 frames.], batch size: 58, aishell_tot_loss[loss=0.1576, simple_loss=0.241, pruned_loss=0.03716, over 351030.10 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2202, pruned_loss=0.03709, over 423811.88 frames.], batch size: 58, lr: 5.17e-04
+2022-06-18 21:24:24,689 INFO [train.py:874] (0/4) Epoch 16, batch 250, aishell_loss[loss=0.1679, simple_loss=0.253, pruned_loss=0.04135, over 4913.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2321, pruned_loss=0.0382, over 703764.91 frames.], batch size: 41, aishell_tot_loss[loss=0.1589, simple_loss=0.2425, pruned_loss=0.03764, over 439439.29 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2217, pruned_loss=0.03818, over 477183.54 frames.], batch size: 41, lr: 5.17e-04
+2022-06-18 21:24:55,265 INFO [train.py:874] (0/4) Epoch 16, batch 300, datatang_loss[loss=0.1719, simple_loss=0.243, pruned_loss=0.05036, over 4925.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2326, pruned_loss=0.03755, over 765689.66 frames.], batch size: 73, aishell_tot_loss[loss=0.1578, simple_loss=0.2417, pruned_loss=0.03689, over 508061.59 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2227, pruned_loss=0.03802, over 532474.33 frames.], batch size: 73, lr: 5.17e-04
+2022-06-18 21:25:25,761 INFO [train.py:874] (0/4) Epoch 16, batch 350, datatang_loss[loss=0.1798, simple_loss=0.2579, pruned_loss=0.0508, over 4931.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2319, pruned_loss=0.03767, over 814791.10 frames.], batch size: 94, aishell_tot_loss[loss=0.1579, simple_loss=0.2417, pruned_loss=0.03711, over 553668.29 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2228, pruned_loss=0.03799, over 596043.47 frames.], batch size: 94, lr: 5.17e-04
+2022-06-18 21:25:56,578 INFO [train.py:874] (0/4) Epoch 16, batch 400, datatang_loss[loss=0.1274, simple_loss=0.2052, pruned_loss=0.02478, over 4950.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2318, pruned_loss=0.0377, over 852895.14 frames.], batch size: 31, aishell_tot_loss[loss=0.1575, simple_loss=0.2413, pruned_loss=0.03687, over 604655.77 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2228, pruned_loss=0.03828, over 642060.14 frames.], batch size: 31, lr: 5.17e-04
+2022-06-18 21:26:25,941 INFO [train.py:874] (0/4) Epoch 16, batch 450, datatang_loss[loss=0.1637, simple_loss=0.2287, pruned_loss=0.04932, over 4933.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2333, pruned_loss=0.038, over 882756.89 frames.], batch size: 57, aishell_tot_loss[loss=0.1585, simple_loss=0.2426, pruned_loss=0.03722, over 658218.04 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.223, pruned_loss=0.03843, over 674935.06 frames.], batch size: 57, lr: 5.16e-04
+2022-06-18 21:26:54,478 INFO [train.py:874] (0/4) Epoch 16, batch 500, datatang_loss[loss=0.1473, simple_loss=0.2154, pruned_loss=0.03954, over 4922.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2347, pruned_loss=0.03834, over 905213.10 frames.], batch size: 57, aishell_tot_loss[loss=0.1585, simple_loss=0.2425, pruned_loss=0.03724, over 710410.94 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.224, pruned_loss=0.03904, over 697565.34 frames.], batch size: 57, lr: 5.16e-04
+2022-06-18 21:27:26,306 INFO [train.py:874] (0/4) Epoch 16, batch 550, aishell_loss[loss=0.1586, simple_loss=0.2455, pruned_loss=0.0359, over 4935.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2362, pruned_loss=0.03899, over 923144.24 frames.], batch size: 49, aishell_tot_loss[loss=0.1583, simple_loss=0.2427, pruned_loss=0.037, over 742965.52 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2262, pruned_loss=0.04025, over 731464.05 frames.], batch size: 49, lr: 5.16e-04
+2022-06-18 21:27:56,231 INFO [train.py:874] (0/4) Epoch 16, batch 600, aishell_loss[loss=0.1627, simple_loss=0.2469, pruned_loss=0.03922, over 4929.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2371, pruned_loss=0.03958, over 936922.11 frames.], batch size: 49, aishell_tot_loss[loss=0.1589, simple_loss=0.2431, pruned_loss=0.03734, over 771451.72 frames.], datatang_tot_loss[loss=0.1545, simple_loss=0.2275, pruned_loss=0.04077, over 761412.40 frames.], batch size: 49, lr: 5.16e-04
+2022-06-18 21:28:26,892 INFO [train.py:874] (0/4) Epoch 16, batch 650, datatang_loss[loss=0.214, simple_loss=0.2848, pruned_loss=0.07165, over 4917.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2377, pruned_loss=0.04044, over 947936.47 frames.], batch size: 98, aishell_tot_loss[loss=0.1596, simple_loss=0.2436, pruned_loss=0.03775, over 793825.71 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.2285, pruned_loss=0.04151, over 790983.72 frames.], batch size: 98, lr: 5.16e-04
+2022-06-18 21:28:57,668 INFO [train.py:874] (0/4) Epoch 16, batch 700, aishell_loss[loss=0.1747, simple_loss=0.259, pruned_loss=0.0452, over 4884.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2371, pruned_loss=0.04009, over 956153.71 frames.], batch size: 47, aishell_tot_loss[loss=0.1592, simple_loss=0.2436, pruned_loss=0.03739, over 812616.96 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2286, pruned_loss=0.04157, over 817550.63 frames.], batch size: 47, lr: 5.15e-04
+2022-06-18 21:29:27,944 INFO [train.py:874] (0/4) Epoch 16, batch 750, aishell_loss[loss=0.1539, simple_loss=0.2451, pruned_loss=0.03131, over 4917.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2378, pruned_loss=0.04074, over 962820.84 frames.], batch size: 33, aishell_tot_loss[loss=0.1585, simple_loss=0.2428, pruned_loss=0.03713, over 829175.24 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2309, pruned_loss=0.04265, over 841109.42 frames.], batch size: 33, lr: 5.15e-04
+2022-06-18 21:29:58,389 INFO [train.py:874] (0/4) Epoch 16, batch 800, aishell_loss[loss=0.1658, simple_loss=0.2567, pruned_loss=0.03745, over 4857.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2379, pruned_loss=0.04043, over 967886.29 frames.], batch size: 37, aishell_tot_loss[loss=0.1585, simple_loss=0.2427, pruned_loss=0.03709, over 847471.21 frames.], datatang_tot_loss[loss=0.1582, simple_loss=0.2313, pruned_loss=0.04254, over 858277.79 frames.], batch size: 37, lr: 5.15e-04
+2022-06-18 21:30:28,972 INFO [train.py:874] (0/4) Epoch 16, batch 850, aishell_loss[loss=0.1749, simple_loss=0.2617, pruned_loss=0.04404, over 4956.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2384, pruned_loss=0.04059, over 971691.50 frames.], batch size: 64, aishell_tot_loss[loss=0.1588, simple_loss=0.2432, pruned_loss=0.03718, over 862439.77 frames.], datatang_tot_loss[loss=0.1587, simple_loss=0.2318, pruned_loss=0.04276, over 874335.74 frames.], batch size: 64, lr: 5.15e-04
+2022-06-18 21:30:58,973 INFO [train.py:874] (0/4) Epoch 16, batch 900, aishell_loss[loss=0.1478, simple_loss=0.2279, pruned_loss=0.03387, over 4807.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2375, pruned_loss=0.0401, over 974874.63 frames.], batch size: 26, aishell_tot_loss[loss=0.1581, simple_loss=0.2427, pruned_loss=0.03681, over 874005.86 frames.], datatang_tot_loss[loss=0.1584, simple_loss=0.2318, pruned_loss=0.04256, over 890163.48 frames.], batch size: 26, lr: 5.15e-04
+2022-06-18 21:31:29,480 INFO [train.py:874] (0/4) Epoch 16, batch 950, datatang_loss[loss=0.1922, simple_loss=0.2744, pruned_loss=0.05504, over 4941.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2374, pruned_loss=0.04014, over 976891.05 frames.], batch size: 99, aishell_tot_loss[loss=0.1582, simple_loss=0.2428, pruned_loss=0.0368, over 884882.37 frames.], datatang_tot_loss[loss=0.1585, simple_loss=0.2318, pruned_loss=0.0426, over 902948.85 frames.], batch size: 99, lr: 5.14e-04
+2022-06-18 21:31:59,327 INFO [train.py:874] (0/4) Epoch 16, batch 1000, aishell_loss[loss=0.1581, simple_loss=0.2508, pruned_loss=0.03272, over 4921.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2384, pruned_loss=0.04011, over 979064.27 frames.], batch size: 68, aishell_tot_loss[loss=0.1593, simple_loss=0.2441, pruned_loss=0.03728, over 900418.89 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2313, pruned_loss=0.04241, over 909775.48 frames.], batch size: 68, lr: 5.14e-04
+2022-06-18 21:31:59,330 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 21:32:15,096 INFO [train.py:914] (0/4) Epoch 16, validation: loss=0.1652, simple_loss=0.2489, pruned_loss=0.04072, over 1622729.00 frames.
+2022-06-18 21:32:45,791 INFO [train.py:874] (0/4) Epoch 16, batch 1050, aishell_loss[loss=0.1373, simple_loss=0.2036, pruned_loss=0.03553, over 4973.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2372, pruned_loss=0.03969, over 980433.48 frames.], batch size: 22, aishell_tot_loss[loss=0.1586, simple_loss=0.2431, pruned_loss=0.03709, over 909136.44 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2313, pruned_loss=0.04208, over 919797.88 frames.], batch size: 22, lr: 5.14e-04
+2022-06-18 21:33:17,604 INFO [train.py:874] (0/4) Epoch 16, batch 1100, aishell_loss[loss=0.1598, simple_loss=0.2508, pruned_loss=0.03439, over 4899.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2376, pruned_loss=0.03983, over 981848.21 frames.], batch size: 60, aishell_tot_loss[loss=0.1589, simple_loss=0.2434, pruned_loss=0.03721, over 917284.44 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2315, pruned_loss=0.0421, over 928532.11 frames.], batch size: 60, lr: 5.14e-04
+2022-06-18 21:33:46,463 INFO [train.py:874] (0/4) Epoch 16, batch 1150, aishell_loss[loss=0.1755, simple_loss=0.2582, pruned_loss=0.04639, over 4950.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2379, pruned_loss=0.0397, over 982516.56 frames.], batch size: 64, aishell_tot_loss[loss=0.1586, simple_loss=0.2433, pruned_loss=0.03694, over 925599.07 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.232, pruned_loss=0.04229, over 934865.82 frames.], batch size: 64, lr: 5.14e-04
+2022-06-18 21:34:16,209 INFO [train.py:874] (0/4) Epoch 16, batch 1200, aishell_loss[loss=0.1911, simple_loss=0.2786, pruned_loss=0.05174, over 4943.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2378, pruned_loss=0.03955, over 982923.14 frames.], batch size: 58, aishell_tot_loss[loss=0.1582, simple_loss=0.2429, pruned_loss=0.03679, over 933062.35 frames.], datatang_tot_loss[loss=0.1585, simple_loss=0.2322, pruned_loss=0.04239, over 940264.47 frames.], batch size: 58, lr: 5.13e-04
+2022-06-18 21:34:46,013 INFO [train.py:874] (0/4) Epoch 16, batch 1250, aishell_loss[loss=0.1822, simple_loss=0.2625, pruned_loss=0.05098, over 4980.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2386, pruned_loss=0.03964, over 983289.72 frames.], batch size: 39, aishell_tot_loss[loss=0.1592, simple_loss=0.2437, pruned_loss=0.03733, over 940438.02 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.232, pruned_loss=0.04207, over 944339.93 frames.], batch size: 39, lr: 5.13e-04
+2022-06-18 21:35:16,551 INFO [train.py:874] (0/4) Epoch 16, batch 1300, datatang_loss[loss=0.1615, simple_loss=0.2374, pruned_loss=0.04286, over 4945.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2377, pruned_loss=0.03931, over 983977.62 frames.], batch size: 88, aishell_tot_loss[loss=0.1589, simple_loss=0.2436, pruned_loss=0.03713, over 945151.61 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.2315, pruned_loss=0.04185, over 949930.53 frames.], batch size: 88, lr: 5.13e-04
+2022-06-18 21:35:48,248 INFO [train.py:874] (0/4) Epoch 16, batch 1350, datatang_loss[loss=0.1554, simple_loss=0.2244, pruned_loss=0.04315, over 4926.00 frames.], tot_loss[loss=0.159, simple_loss=0.2383, pruned_loss=0.03985, over 984186.52 frames.], batch size: 79, aishell_tot_loss[loss=0.1591, simple_loss=0.2436, pruned_loss=0.03731, over 950110.72 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.232, pruned_loss=0.04228, over 953797.74 frames.], batch size: 79, lr: 5.13e-04
+2022-06-18 21:36:18,261 INFO [train.py:874] (0/4) Epoch 16, batch 1400, aishell_loss[loss=0.1642, simple_loss=0.2553, pruned_loss=0.03661, over 4919.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2381, pruned_loss=0.04026, over 984566.77 frames.], batch size: 68, aishell_tot_loss[loss=0.1596, simple_loss=0.2439, pruned_loss=0.03767, over 953599.22 frames.], datatang_tot_loss[loss=0.1582, simple_loss=0.2319, pruned_loss=0.0423, over 958231.67 frames.], batch size: 68, lr: 5.13e-04
+2022-06-18 21:36:47,790 INFO [train.py:874] (0/4) Epoch 16, batch 1450, datatang_loss[loss=0.162, simple_loss=0.2319, pruned_loss=0.0461, over 4922.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2379, pruned_loss=0.03999, over 984867.15 frames.], batch size: 57, aishell_tot_loss[loss=0.1597, simple_loss=0.2441, pruned_loss=0.03766, over 957635.55 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2316, pruned_loss=0.0421, over 961284.59 frames.], batch size: 57, lr: 5.12e-04
+2022-06-18 21:37:19,308 INFO [train.py:874] (0/4) Epoch 16, batch 1500, datatang_loss[loss=0.1504, simple_loss=0.2206, pruned_loss=0.04012, over 4929.00 frames.], tot_loss[loss=0.158, simple_loss=0.2368, pruned_loss=0.03957, over 985165.50 frames.], batch size: 50, aishell_tot_loss[loss=0.1592, simple_loss=0.2433, pruned_loss=0.03756, over 961064.59 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2311, pruned_loss=0.0418, over 964178.58 frames.], batch size: 50, lr: 5.12e-04
+2022-06-18 21:37:48,659 INFO [train.py:874] (0/4) Epoch 16, batch 1550, datatang_loss[loss=0.1444, simple_loss=0.2273, pruned_loss=0.03076, over 4925.00 frames.], tot_loss[loss=0.157, simple_loss=0.236, pruned_loss=0.03895, over 984859.19 frames.], batch size: 71, aishell_tot_loss[loss=0.1582, simple_loss=0.2423, pruned_loss=0.03707, over 963643.25 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2311, pruned_loss=0.04162, over 966596.12 frames.], batch size: 71, lr: 5.12e-04
+2022-06-18 21:38:18,582 INFO [train.py:874] (0/4) Epoch 16, batch 1600, datatang_loss[loss=0.1445, simple_loss=0.206, pruned_loss=0.04152, over 4941.00 frames.], tot_loss[loss=0.1581, simple_loss=0.237, pruned_loss=0.03962, over 984936.28 frames.], batch size: 34, aishell_tot_loss[loss=0.1587, simple_loss=0.2428, pruned_loss=0.03726, over 965932.33 frames.], datatang_tot_loss[loss=0.1578, simple_loss=0.2315, pruned_loss=0.04205, over 969028.30 frames.], batch size: 34, lr: 5.12e-04
+2022-06-18 21:38:49,711 INFO [train.py:874] (0/4) Epoch 16, batch 1650, aishell_loss[loss=0.1335, simple_loss=0.2172, pruned_loss=0.02494, over 4964.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2366, pruned_loss=0.03927, over 985396.30 frames.], batch size: 30, aishell_tot_loss[loss=0.1588, simple_loss=0.243, pruned_loss=0.0373, over 968050.09 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2311, pruned_loss=0.04152, over 971434.36 frames.], batch size: 30, lr: 5.12e-04
+2022-06-18 21:39:20,228 INFO [train.py:874] (0/4) Epoch 16, batch 1700, aishell_loss[loss=0.1371, simple_loss=0.2198, pruned_loss=0.02719, over 4972.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2358, pruned_loss=0.03878, over 985720.16 frames.], batch size: 39, aishell_tot_loss[loss=0.1583, simple_loss=0.2426, pruned_loss=0.03702, over 970159.27 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.2307, pruned_loss=0.04121, over 973316.42 frames.], batch size: 39, lr: 5.11e-04
+2022-06-18 21:39:48,492 INFO [train.py:874] (0/4) Epoch 16, batch 1750, datatang_loss[loss=0.1996, simple_loss=0.2686, pruned_loss=0.06537, over 4949.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2361, pruned_loss=0.03889, over 985682.39 frames.], batch size: 108, aishell_tot_loss[loss=0.1579, simple_loss=0.2424, pruned_loss=0.03675, over 971916.72 frames.], datatang_tot_loss[loss=0.157, simple_loss=0.231, pruned_loss=0.0415, over 974802.81 frames.], batch size: 108, lr: 5.11e-04
+2022-06-18 21:40:19,017 INFO [train.py:874] (0/4) Epoch 16, batch 1800, aishell_loss[loss=0.1512, simple_loss=0.236, pruned_loss=0.03323, over 4914.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2365, pruned_loss=0.03883, over 985579.00 frames.], batch size: 41, aishell_tot_loss[loss=0.1582, simple_loss=0.2425, pruned_loss=0.03693, over 973687.73 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2308, pruned_loss=0.04134, over 975876.44 frames.], batch size: 41, lr: 5.11e-04
+2022-06-18 21:40:47,575 INFO [train.py:874] (0/4) Epoch 16, batch 1850, aishell_loss[loss=0.1767, simple_loss=0.2628, pruned_loss=0.04531, over 4963.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2372, pruned_loss=0.03933, over 985548.36 frames.], batch size: 44, aishell_tot_loss[loss=0.1584, simple_loss=0.2427, pruned_loss=0.03706, over 974921.28 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2315, pruned_loss=0.04163, over 977125.31 frames.], batch size: 44, lr: 5.11e-04
+2022-06-18 21:41:17,887 INFO [train.py:874] (0/4) Epoch 16, batch 1900, datatang_loss[loss=0.1789, simple_loss=0.2541, pruned_loss=0.05187, over 4955.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2387, pruned_loss=0.03983, over 985255.26 frames.], batch size: 99, aishell_tot_loss[loss=0.1591, simple_loss=0.2436, pruned_loss=0.0373, over 976068.60 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.232, pruned_loss=0.04201, over 977954.70 frames.], batch size: 99, lr: 5.11e-04
+2022-06-18 21:41:47,574 INFO [train.py:874] (0/4) Epoch 16, batch 1950, aishell_loss[loss=0.1559, simple_loss=0.2403, pruned_loss=0.03579, over 4944.00 frames.], tot_loss[loss=0.158, simple_loss=0.2376, pruned_loss=0.0392, over 985497.35 frames.], batch size: 56, aishell_tot_loss[loss=0.1588, simple_loss=0.2435, pruned_loss=0.03709, over 977280.52 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2313, pruned_loss=0.04161, over 978917.09 frames.], batch size: 56, lr: 5.10e-04
+2022-06-18 21:42:07,605 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-64000.pt
+2022-06-18 21:42:22,199 INFO [train.py:874] (0/4) Epoch 16, batch 2000, datatang_loss[loss=0.1424, simple_loss=0.2039, pruned_loss=0.04045, over 4872.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2369, pruned_loss=0.03892, over 985692.89 frames.], batch size: 25, aishell_tot_loss[loss=0.1585, simple_loss=0.243, pruned_loss=0.03702, over 978401.02 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.231, pruned_loss=0.04135, over 979753.74 frames.], batch size: 25, lr: 5.10e-04
+2022-06-18 21:42:22,202 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 21:42:38,236 INFO [train.py:914] (0/4) Epoch 16, validation: loss=0.1651, simple_loss=0.2493, pruned_loss=0.04046, over 1622729.00 frames.
+2022-06-18 21:43:07,840 INFO [train.py:874] (0/4) Epoch 16, batch 2050, datatang_loss[loss=0.1683, simple_loss=0.2334, pruned_loss=0.05162, over 4930.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2367, pruned_loss=0.03916, over 985927.39 frames.], batch size: 79, aishell_tot_loss[loss=0.1592, simple_loss=0.2436, pruned_loss=0.03735, over 979148.52 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2304, pruned_loss=0.04113, over 980781.03 frames.], batch size: 79, lr: 5.10e-04
+2022-06-18 21:43:37,759 INFO [train.py:874] (0/4) Epoch 16, batch 2100, aishell_loss[loss=0.1364, simple_loss=0.2233, pruned_loss=0.02475, over 4937.00 frames.], tot_loss[loss=0.157, simple_loss=0.2363, pruned_loss=0.03886, over 985842.44 frames.], batch size: 54, aishell_tot_loss[loss=0.1589, simple_loss=0.2433, pruned_loss=0.03723, over 979979.79 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2301, pruned_loss=0.04097, over 981269.67 frames.], batch size: 54, lr: 5.10e-04
+2022-06-18 21:44:09,095 INFO [train.py:874] (0/4) Epoch 16, batch 2150, aishell_loss[loss=0.2001, simple_loss=0.2665, pruned_loss=0.06687, over 4854.00 frames.], tot_loss[loss=0.1578, simple_loss=0.237, pruned_loss=0.03932, over 985990.29 frames.], batch size: 35, aishell_tot_loss[loss=0.1596, simple_loss=0.2439, pruned_loss=0.03762, over 980747.73 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.23, pruned_loss=0.04101, over 981890.28 frames.], batch size: 35, lr: 5.10e-04
+2022-06-18 21:44:39,263 INFO [train.py:874] (0/4) Epoch 16, batch 2200, datatang_loss[loss=0.1777, simple_loss=0.2492, pruned_loss=0.05306, over 4931.00 frames.], tot_loss[loss=0.1577, simple_loss=0.237, pruned_loss=0.03923, over 985655.89 frames.], batch size: 69, aishell_tot_loss[loss=0.1599, simple_loss=0.2443, pruned_loss=0.03771, over 980938.31 frames.], datatang_tot_loss[loss=0.1556, simple_loss=0.2297, pruned_loss=0.0408, over 982448.72 frames.], batch size: 69, lr: 5.09e-04
+2022-06-18 21:45:09,176 INFO [train.py:874] (0/4) Epoch 16, batch 2250, datatang_loss[loss=0.1356, simple_loss=0.2107, pruned_loss=0.03028, over 4951.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2366, pruned_loss=0.03905, over 985673.72 frames.], batch size: 55, aishell_tot_loss[loss=0.1595, simple_loss=0.244, pruned_loss=0.03754, over 981464.62 frames.], datatang_tot_loss[loss=0.1556, simple_loss=0.2297, pruned_loss=0.04074, over 982846.28 frames.], batch size: 55, lr: 5.09e-04
+2022-06-18 21:45:40,317 INFO [train.py:874] (0/4) Epoch 16, batch 2300, aishell_loss[loss=0.1614, simple_loss=0.2472, pruned_loss=0.03783, over 4916.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2375, pruned_loss=0.03919, over 985719.22 frames.], batch size: 52, aishell_tot_loss[loss=0.1598, simple_loss=0.2444, pruned_loss=0.03755, over 982162.37 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2302, pruned_loss=0.04088, over 983015.39 frames.], batch size: 52, lr: 5.09e-04
+2022-06-18 21:46:10,857 INFO [train.py:874] (0/4) Epoch 16, batch 2350, aishell_loss[loss=0.1704, simple_loss=0.2626, pruned_loss=0.03906, over 4950.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2372, pruned_loss=0.0389, over 985201.16 frames.], batch size: 54, aishell_tot_loss[loss=0.1592, simple_loss=0.2439, pruned_loss=0.03725, over 982135.10 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2304, pruned_loss=0.04088, over 983229.94 frames.], batch size: 54, lr: 5.09e-04
+2022-06-18 21:46:40,970 INFO [train.py:874] (0/4) Epoch 16, batch 2400, datatang_loss[loss=0.1447, simple_loss=0.2248, pruned_loss=0.03231, over 4925.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2366, pruned_loss=0.03842, over 985103.74 frames.], batch size: 75, aishell_tot_loss[loss=0.1592, simple_loss=0.244, pruned_loss=0.0372, over 982247.89 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.23, pruned_loss=0.04034, over 983581.00 frames.], batch size: 75, lr: 5.09e-04
+2022-06-18 21:47:10,429 INFO [train.py:874] (0/4) Epoch 16, batch 2450, datatang_loss[loss=0.1884, simple_loss=0.2408, pruned_loss=0.06804, over 4964.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2374, pruned_loss=0.03869, over 985396.82 frames.], batch size: 55, aishell_tot_loss[loss=0.1589, simple_loss=0.2435, pruned_loss=0.03719, over 982946.44 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2306, pruned_loss=0.04071, over 983756.67 frames.], batch size: 55, lr: 5.08e-04
+2022-06-18 21:47:41,017 INFO [train.py:874] (0/4) Epoch 16, batch 2500, datatang_loss[loss=0.1202, simple_loss=0.2003, pruned_loss=0.02011, over 4927.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2367, pruned_loss=0.03814, over 985397.81 frames.], batch size: 73, aishell_tot_loss[loss=0.1588, simple_loss=0.2434, pruned_loss=0.03706, over 983231.04 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.23, pruned_loss=0.0402, over 983944.40 frames.], batch size: 73, lr: 5.08e-04
+2022-06-18 21:48:11,422 INFO [train.py:874] (0/4) Epoch 16, batch 2550, datatang_loss[loss=0.1567, simple_loss=0.2418, pruned_loss=0.03577, over 4902.00 frames.], tot_loss[loss=0.1558, simple_loss=0.236, pruned_loss=0.03781, over 985464.98 frames.], batch size: 59, aishell_tot_loss[loss=0.1582, simple_loss=0.2426, pruned_loss=0.03689, over 983585.47 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2298, pruned_loss=0.03999, over 984099.15 frames.], batch size: 59, lr: 5.08e-04
+2022-06-18 21:48:41,675 INFO [train.py:874] (0/4) Epoch 16, batch 2600, datatang_loss[loss=0.1355, simple_loss=0.2138, pruned_loss=0.02859, over 4942.00 frames.], tot_loss[loss=0.156, simple_loss=0.2363, pruned_loss=0.03782, over 985722.05 frames.], batch size: 62, aishell_tot_loss[loss=0.1579, simple_loss=0.2427, pruned_loss=0.03653, over 983684.63 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2301, pruned_loss=0.04015, over 984634.71 frames.], batch size: 62, lr: 5.08e-04
+2022-06-18 21:49:12,447 INFO [train.py:874] (0/4) Epoch 16, batch 2650, datatang_loss[loss=0.1538, simple_loss=0.2307, pruned_loss=0.03847, over 4964.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2373, pruned_loss=0.03871, over 985887.74 frames.], batch size: 67, aishell_tot_loss[loss=0.1583, simple_loss=0.2432, pruned_loss=0.03672, over 983861.48 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2307, pruned_loss=0.04075, over 984969.25 frames.], batch size: 67, lr: 5.08e-04
+2022-06-18 21:49:42,478 INFO [train.py:874] (0/4) Epoch 16, batch 2700, aishell_loss[loss=0.1575, simple_loss=0.2441, pruned_loss=0.0355, over 4942.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2365, pruned_loss=0.03832, over 985913.92 frames.], batch size: 45, aishell_tot_loss[loss=0.1578, simple_loss=0.2426, pruned_loss=0.03646, over 984172.24 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.2301, pruned_loss=0.04069, over 985059.08 frames.], batch size: 45, lr: 5.07e-04
+2022-06-18 21:50:12,448 INFO [train.py:874] (0/4) Epoch 16, batch 2750, datatang_loss[loss=0.1411, simple_loss=0.2136, pruned_loss=0.0343, over 4925.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2369, pruned_loss=0.03907, over 985752.57 frames.], batch size: 73, aishell_tot_loss[loss=0.1576, simple_loss=0.2425, pruned_loss=0.03638, over 984443.20 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2309, pruned_loss=0.04143, over 984897.80 frames.], batch size: 73, lr: 5.07e-04
+2022-06-18 21:50:43,002 INFO [train.py:874] (0/4) Epoch 16, batch 2800, datatang_loss[loss=0.1323, simple_loss=0.2095, pruned_loss=0.02759, over 4932.00 frames.], tot_loss[loss=0.1579, simple_loss=0.237, pruned_loss=0.03942, over 985702.85 frames.], batch size: 62, aishell_tot_loss[loss=0.1583, simple_loss=0.2431, pruned_loss=0.03668, over 984559.36 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2306, pruned_loss=0.04147, over 984984.69 frames.], batch size: 62, lr: 5.07e-04
+2022-06-18 21:51:13,691 INFO [train.py:874] (0/4) Epoch 16, batch 2850, aishell_loss[loss=0.1577, simple_loss=0.2416, pruned_loss=0.03691, over 4916.00 frames.], tot_loss[loss=0.157, simple_loss=0.2366, pruned_loss=0.03866, over 985441.75 frames.], batch size: 41, aishell_tot_loss[loss=0.1578, simple_loss=0.243, pruned_loss=0.03633, over 984426.82 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2304, pruned_loss=0.04107, over 985068.22 frames.], batch size: 41, lr: 5.07e-04
+2022-06-18 21:51:43,106 INFO [train.py:874] (0/4) Epoch 16, batch 2900, datatang_loss[loss=0.1745, simple_loss=0.2453, pruned_loss=0.05191, over 4951.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2361, pruned_loss=0.03841, over 985841.96 frames.], batch size: 86, aishell_tot_loss[loss=0.1568, simple_loss=0.242, pruned_loss=0.03577, over 984800.26 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2308, pruned_loss=0.04137, over 985282.06 frames.], batch size: 86, lr: 5.07e-04
+2022-06-18 21:52:12,469 INFO [train.py:874] (0/4) Epoch 16, batch 2950, datatang_loss[loss=0.1606, simple_loss=0.2302, pruned_loss=0.04556, over 4958.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2357, pruned_loss=0.03865, over 985634.97 frames.], batch size: 37, aishell_tot_loss[loss=0.1575, simple_loss=0.2428, pruned_loss=0.03612, over 984852.06 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2298, pruned_loss=0.04105, over 985189.41 frames.], batch size: 37, lr: 5.06e-04
+2022-06-18 21:52:43,981 INFO [train.py:874] (0/4) Epoch 16, batch 3000, aishell_loss[loss=0.1412, simple_loss=0.2295, pruned_loss=0.02649, over 4968.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2353, pruned_loss=0.03812, over 985733.16 frames.], batch size: 48, aishell_tot_loss[loss=0.1573, simple_loss=0.2427, pruned_loss=0.03595, over 985176.59 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2292, pruned_loss=0.04074, over 985099.26 frames.], batch size: 48, lr: 5.06e-04
+2022-06-18 21:52:43,984 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 21:53:00,710 INFO [train.py:914] (0/4) Epoch 16, validation: loss=0.1647, simple_loss=0.249, pruned_loss=0.04025, over 1622729.00 frames.
+2022-06-18 21:53:29,682 INFO [train.py:874] (0/4) Epoch 16, batch 3050, datatang_loss[loss=0.169, simple_loss=0.2467, pruned_loss=0.04562, over 4906.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2364, pruned_loss=0.03845, over 986170.77 frames.], batch size: 47, aishell_tot_loss[loss=0.1582, simple_loss=0.2435, pruned_loss=0.03648, over 985604.56 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2289, pruned_loss=0.04064, over 985278.60 frames.], batch size: 47, lr: 5.06e-04
+2022-06-18 21:53:59,437 INFO [train.py:874] (0/4) Epoch 16, batch 3100, datatang_loss[loss=0.165, simple_loss=0.2464, pruned_loss=0.04182, over 4921.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2365, pruned_loss=0.0385, over 985778.97 frames.], batch size: 47, aishell_tot_loss[loss=0.1577, simple_loss=0.2429, pruned_loss=0.03624, over 985286.86 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2294, pruned_loss=0.04101, over 985368.01 frames.], batch size: 47, lr: 5.06e-04
+2022-06-18 21:54:29,100 INFO [train.py:874] (0/4) Epoch 16, batch 3150, aishell_loss[loss=0.1544, simple_loss=0.2334, pruned_loss=0.03771, over 4962.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2362, pruned_loss=0.03853, over 985355.41 frames.], batch size: 31, aishell_tot_loss[loss=0.1573, simple_loss=0.2423, pruned_loss=0.03616, over 985096.05 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2297, pruned_loss=0.04114, over 985249.65 frames.], batch size: 31, lr: 5.06e-04
+2022-06-18 21:55:00,820 INFO [train.py:874] (0/4) Epoch 16, batch 3200, aishell_loss[loss=0.1697, simple_loss=0.2551, pruned_loss=0.04211, over 4918.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2358, pruned_loss=0.038, over 985593.30 frames.], batch size: 46, aishell_tot_loss[loss=0.1574, simple_loss=0.2422, pruned_loss=0.03631, over 985192.40 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2293, pruned_loss=0.04045, over 985457.06 frames.], batch size: 46, lr: 5.05e-04
+2022-06-18 21:55:30,449 INFO [train.py:874] (0/4) Epoch 16, batch 3250, datatang_loss[loss=0.1678, simple_loss=0.2415, pruned_loss=0.04701, over 4967.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2352, pruned_loss=0.03804, over 986144.31 frames.], batch size: 55, aishell_tot_loss[loss=0.1569, simple_loss=0.2417, pruned_loss=0.0361, over 985487.97 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2295, pruned_loss=0.04052, over 985796.12 frames.], batch size: 55, lr: 5.05e-04
+2022-06-18 21:55:59,809 INFO [train.py:874] (0/4) Epoch 16, batch 3300, datatang_loss[loss=0.1492, simple_loss=0.2299, pruned_loss=0.0343, over 4843.00 frames.], tot_loss[loss=0.1554, simple_loss=0.235, pruned_loss=0.03792, over 985939.01 frames.], batch size: 25, aishell_tot_loss[loss=0.1572, simple_loss=0.242, pruned_loss=0.03615, over 985418.87 frames.], datatang_tot_loss[loss=0.1547, simple_loss=0.2291, pruned_loss=0.04016, over 985759.11 frames.], batch size: 25, lr: 5.05e-04
+2022-06-18 21:56:30,787 INFO [train.py:874] (0/4) Epoch 16, batch 3350, datatang_loss[loss=0.1586, simple_loss=0.2364, pruned_loss=0.04036, over 4957.00 frames.], tot_loss[loss=0.157, simple_loss=0.2369, pruned_loss=0.03852, over 985999.64 frames.], batch size: 91, aishell_tot_loss[loss=0.1583, simple_loss=0.2431, pruned_loss=0.03682, over 985448.12 frames.], datatang_tot_loss[loss=0.155, simple_loss=0.2296, pruned_loss=0.04024, over 985890.60 frames.], batch size: 91, lr: 5.05e-04
+2022-06-18 21:57:00,633 INFO [train.py:874] (0/4) Epoch 16, batch 3400, aishell_loss[loss=0.1642, simple_loss=0.2553, pruned_loss=0.0365, over 4937.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2369, pruned_loss=0.03881, over 986438.73 frames.], batch size: 49, aishell_tot_loss[loss=0.1583, simple_loss=0.2431, pruned_loss=0.03676, over 985738.80 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2298, pruned_loss=0.04056, over 986145.98 frames.], batch size: 49, lr: 5.05e-04
+2022-06-18 21:57:30,327 INFO [train.py:874] (0/4) Epoch 16, batch 3450, aishell_loss[loss=0.1489, simple_loss=0.2437, pruned_loss=0.02708, over 4964.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2371, pruned_loss=0.03837, over 986402.41 frames.], batch size: 44, aishell_tot_loss[loss=0.1583, simple_loss=0.2433, pruned_loss=0.03664, over 985839.66 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2296, pruned_loss=0.04037, over 986129.99 frames.], batch size: 44, lr: 5.05e-04
+2022-06-18 21:58:01,218 INFO [train.py:874] (0/4) Epoch 16, batch 3500, aishell_loss[loss=0.1539, simple_loss=0.2407, pruned_loss=0.03353, over 4949.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2377, pruned_loss=0.03893, over 986191.75 frames.], batch size: 45, aishell_tot_loss[loss=0.1583, simple_loss=0.2433, pruned_loss=0.03662, over 985452.57 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2304, pruned_loss=0.04093, over 986397.66 frames.], batch size: 45, lr: 5.04e-04
+2022-06-18 21:58:30,716 INFO [train.py:874] (0/4) Epoch 16, batch 3550, aishell_loss[loss=0.1186, simple_loss=0.2013, pruned_loss=0.0179, over 4830.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2373, pruned_loss=0.03857, over 985839.65 frames.], batch size: 24, aishell_tot_loss[loss=0.1581, simple_loss=0.2432, pruned_loss=0.03645, over 985305.69 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2302, pruned_loss=0.04079, over 986245.55 frames.], batch size: 24, lr: 5.04e-04
+2022-06-18 21:59:01,909 INFO [train.py:874] (0/4) Epoch 16, batch 3600, datatang_loss[loss=0.1309, simple_loss=0.212, pruned_loss=0.0249, over 4943.00 frames.], tot_loss[loss=0.1571, simple_loss=0.237, pruned_loss=0.03858, over 985674.73 frames.], batch size: 50, aishell_tot_loss[loss=0.1579, simple_loss=0.243, pruned_loss=0.03639, over 985123.83 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2301, pruned_loss=0.04087, over 986271.01 frames.], batch size: 50, lr: 5.04e-04
+2022-06-18 21:59:31,582 INFO [train.py:874] (0/4) Epoch 16, batch 3650, aishell_loss[loss=0.139, simple_loss=0.2221, pruned_loss=0.02788, over 4943.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2361, pruned_loss=0.0383, over 985528.27 frames.], batch size: 49, aishell_tot_loss[loss=0.1576, simple_loss=0.2425, pruned_loss=0.03633, over 985082.24 frames.], datatang_tot_loss[loss=0.1555, simple_loss=0.2297, pruned_loss=0.04068, over 986163.13 frames.], batch size: 49, lr: 5.04e-04
+2022-06-18 22:00:02,455 INFO [train.py:874] (0/4) Epoch 16, batch 3700, datatang_loss[loss=0.1386, simple_loss=0.2172, pruned_loss=0.03003, over 4865.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2368, pruned_loss=0.03882, over 985657.26 frames.], batch size: 39, aishell_tot_loss[loss=0.1583, simple_loss=0.2434, pruned_loss=0.03658, over 985148.08 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2297, pruned_loss=0.04087, over 986188.47 frames.], batch size: 39, lr: 5.04e-04
+2022-06-18 22:00:32,935 INFO [train.py:874] (0/4) Epoch 16, batch 3750, aishell_loss[loss=0.1157, simple_loss=0.1994, pruned_loss=0.01599, over 4935.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2375, pruned_loss=0.03879, over 985364.07 frames.], batch size: 27, aishell_tot_loss[loss=0.1581, simple_loss=0.2434, pruned_loss=0.03644, over 985149.38 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2305, pruned_loss=0.04103, over 985881.07 frames.], batch size: 27, lr: 5.03e-04
+2022-06-18 22:01:02,945 INFO [train.py:874] (0/4) Epoch 16, batch 3800, datatang_loss[loss=0.1607, simple_loss=0.235, pruned_loss=0.04323, over 4937.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2364, pruned_loss=0.03822, over 985392.60 frames.], batch size: 88, aishell_tot_loss[loss=0.1576, simple_loss=0.2432, pruned_loss=0.03603, over 985237.58 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2297, pruned_loss=0.04079, over 985780.91 frames.], batch size: 88, lr: 5.03e-04
+2022-06-18 22:01:31,724 INFO [train.py:874] (0/4) Epoch 16, batch 3850, datatang_loss[loss=0.1714, simple_loss=0.252, pruned_loss=0.04536, over 4951.00 frames.], tot_loss[loss=0.156, simple_loss=0.2358, pruned_loss=0.03805, over 985349.80 frames.], batch size: 99, aishell_tot_loss[loss=0.1571, simple_loss=0.2425, pruned_loss=0.03579, over 985087.87 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2298, pruned_loss=0.04081, over 985855.01 frames.], batch size: 99, lr: 5.03e-04
+2022-06-18 22:01:59,926 INFO [train.py:874] (0/4) Epoch 16, batch 3900, datatang_loss[loss=0.1624, simple_loss=0.2216, pruned_loss=0.05158, over 4977.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2355, pruned_loss=0.03797, over 985138.24 frames.], batch size: 45, aishell_tot_loss[loss=0.1571, simple_loss=0.2425, pruned_loss=0.03586, over 984880.88 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2294, pruned_loss=0.04057, over 985811.13 frames.], batch size: 45, lr: 5.03e-04
+2022-06-18 22:02:29,023 INFO [train.py:874] (0/4) Epoch 16, batch 3950, aishell_loss[loss=0.1219, simple_loss=0.1847, pruned_loss=0.02955, over 4967.00 frames.], tot_loss[loss=0.155, simple_loss=0.2349, pruned_loss=0.03752, over 985086.04 frames.], batch size: 21, aishell_tot_loss[loss=0.1565, simple_loss=0.2419, pruned_loss=0.03559, over 984889.26 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2293, pruned_loss=0.0403, over 985704.50 frames.], batch size: 21, lr: 5.03e-04
+2022-06-18 22:02:58,082 INFO [train.py:874] (0/4) Epoch 16, batch 4000, aishell_loss[loss=0.1759, simple_loss=0.2649, pruned_loss=0.04349, over 4961.00 frames.], tot_loss[loss=0.154, simple_loss=0.2341, pruned_loss=0.03697, over 984954.39 frames.], batch size: 79, aishell_tot_loss[loss=0.1564, simple_loss=0.2415, pruned_loss=0.0356, over 984693.64 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.2286, pruned_loss=0.03961, over 985712.21 frames.], batch size: 79, lr: 5.02e-04
+2022-06-18 22:02:58,085 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 22:03:14,115 INFO [train.py:914] (0/4) Epoch 16, validation: loss=0.164, simple_loss=0.2482, pruned_loss=0.03992, over 1622729.00 frames.
+2022-06-18 22:03:42,689 INFO [train.py:874] (0/4) Epoch 16, batch 4050, datatang_loss[loss=0.1519, simple_loss=0.2265, pruned_loss=0.03867, over 4960.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2335, pruned_loss=0.03675, over 984845.65 frames.], batch size: 67, aishell_tot_loss[loss=0.1566, simple_loss=0.2419, pruned_loss=0.03564, over 984540.81 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2276, pruned_loss=0.03908, over 985651.50 frames.], batch size: 67, lr: 5.02e-04
+2022-06-18 22:04:12,004 INFO [train.py:874] (0/4) Epoch 16, batch 4100, aishell_loss[loss=0.1353, simple_loss=0.213, pruned_loss=0.02875, over 4941.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2331, pruned_loss=0.03659, over 984679.38 frames.], batch size: 25, aishell_tot_loss[loss=0.1555, simple_loss=0.2405, pruned_loss=0.0352, over 984416.79 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2282, pruned_loss=0.03922, over 985560.58 frames.], batch size: 25, lr: 5.02e-04
+2022-06-18 22:04:31,441 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-16.pt
+2022-06-18 22:05:35,079 INFO [train.py:874] (0/4) Epoch 17, batch 50, datatang_loss[loss=0.1887, simple_loss=0.2647, pruned_loss=0.05634, over 4945.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2343, pruned_loss=0.03698, over 218289.29 frames.], batch size: 110, aishell_tot_loss[loss=0.161, simple_loss=0.2458, pruned_loss=0.03816, over 128995.08 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2201, pruned_loss=0.03552, over 102766.05 frames.], batch size: 110, lr: 4.88e-04
+2022-06-18 22:06:05,302 INFO [train.py:874] (0/4) Epoch 17, batch 100, aishell_loss[loss=0.1581, simple_loss=0.2456, pruned_loss=0.03529, over 4946.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2313, pruned_loss=0.03577, over 388351.89 frames.], batch size: 45, aishell_tot_loss[loss=0.1592, simple_loss=0.244, pruned_loss=0.03723, over 222066.50 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2187, pruned_loss=0.03447, over 214682.40 frames.], batch size: 45, lr: 4.88e-04
+2022-06-18 22:06:34,676 INFO [train.py:874] (0/4) Epoch 17, batch 150, datatang_loss[loss=0.1449, simple_loss=0.2114, pruned_loss=0.03926, over 4875.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2314, pruned_loss=0.03618, over 520597.07 frames.], batch size: 39, aishell_tot_loss[loss=0.1605, simple_loss=0.2454, pruned_loss=0.03781, over 301621.96 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2186, pruned_loss=0.03464, over 315633.24 frames.], batch size: 39, lr: 4.87e-04
+2022-06-18 22:07:06,317 INFO [train.py:874] (0/4) Epoch 17, batch 200, datatang_loss[loss=0.1407, simple_loss=0.2176, pruned_loss=0.03186, over 4915.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2309, pruned_loss=0.03661, over 623668.27 frames.], batch size: 75, aishell_tot_loss[loss=0.1604, simple_loss=0.2441, pruned_loss=0.03832, over 379102.67 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.219, pruned_loss=0.0349, over 397519.45 frames.], batch size: 75, lr: 4.87e-04
+2022-06-18 22:07:35,621 INFO [train.py:874] (0/4) Epoch 17, batch 250, datatang_loss[loss=0.1265, simple_loss=0.2089, pruned_loss=0.02205, over 4928.00 frames.], tot_loss[loss=0.152, simple_loss=0.231, pruned_loss=0.03652, over 703932.06 frames.], batch size: 79, aishell_tot_loss[loss=0.1601, simple_loss=0.2441, pruned_loss=0.03807, over 447717.77 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2192, pruned_loss=0.03505, over 469544.02 frames.], batch size: 79, lr: 4.87e-04
+2022-06-18 22:08:05,780 INFO [train.py:874] (0/4) Epoch 17, batch 300, aishell_loss[loss=0.1463, simple_loss=0.2361, pruned_loss=0.0283, over 4940.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2318, pruned_loss=0.03644, over 766299.88 frames.], batch size: 58, aishell_tot_loss[loss=0.1585, simple_loss=0.2427, pruned_loss=0.03719, over 508460.64 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2216, pruned_loss=0.03573, over 532742.44 frames.], batch size: 58, lr: 4.87e-04
+2022-06-18 22:08:37,004 INFO [train.py:874] (0/4) Epoch 17, batch 350, aishell_loss[loss=0.1577, simple_loss=0.2464, pruned_loss=0.03448, over 4952.00 frames.], tot_loss[loss=0.1524, simple_loss=0.232, pruned_loss=0.03641, over 814878.97 frames.], batch size: 56, aishell_tot_loss[loss=0.1578, simple_loss=0.2425, pruned_loss=0.03658, over 562632.72 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2221, pruned_loss=0.03627, over 587937.60 frames.], batch size: 56, lr: 4.87e-04
+2022-06-18 22:09:07,067 INFO [train.py:874] (0/4) Epoch 17, batch 400, datatang_loss[loss=0.1313, simple_loss=0.2105, pruned_loss=0.02609, over 4969.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2335, pruned_loss=0.03715, over 852746.30 frames.], batch size: 67, aishell_tot_loss[loss=0.1583, simple_loss=0.243, pruned_loss=0.03676, over 617944.14 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2231, pruned_loss=0.0371, over 629553.60 frames.], batch size: 67, lr: 4.87e-04
+2022-06-18 22:09:36,432 INFO [train.py:874] (0/4) Epoch 17, batch 450, datatang_loss[loss=0.1432, simple_loss=0.2224, pruned_loss=0.03196, over 4964.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2335, pruned_loss=0.03701, over 882250.47 frames.], batch size: 55, aishell_tot_loss[loss=0.1582, simple_loss=0.243, pruned_loss=0.03672, over 659551.16 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2236, pruned_loss=0.03706, over 673164.19 frames.], batch size: 55, lr: 4.86e-04
+2022-06-18 22:10:07,603 INFO [train.py:874] (0/4) Epoch 17, batch 500, datatang_loss[loss=0.1404, simple_loss=0.2121, pruned_loss=0.03435, over 4798.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2335, pruned_loss=0.03673, over 905185.00 frames.], batch size: 24, aishell_tot_loss[loss=0.1577, simple_loss=0.2425, pruned_loss=0.03644, over 702361.25 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2238, pruned_loss=0.03702, over 705696.42 frames.], batch size: 24, lr: 4.86e-04
+2022-06-18 22:10:36,770 INFO [train.py:874] (0/4) Epoch 17, batch 550, aishell_loss[loss=0.1529, simple_loss=0.2379, pruned_loss=0.03397, over 4930.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2327, pruned_loss=0.03677, over 922503.25 frames.], batch size: 32, aishell_tot_loss[loss=0.1568, simple_loss=0.2415, pruned_loss=0.03603, over 729311.20 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2243, pruned_loss=0.03746, over 744300.89 frames.], batch size: 32, lr: 4.86e-04
+2022-06-18 22:11:06,715 INFO [train.py:874] (0/4) Epoch 17, batch 600, datatang_loss[loss=0.143, simple_loss=0.2153, pruned_loss=0.03536, over 4910.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2333, pruned_loss=0.03726, over 936626.11 frames.], batch size: 64, aishell_tot_loss[loss=0.1578, simple_loss=0.2428, pruned_loss=0.03644, over 751599.49 frames.], datatang_tot_loss[loss=0.15, simple_loss=0.2247, pruned_loss=0.03764, over 780043.40 frames.], batch size: 64, lr: 4.86e-04
+2022-06-18 22:11:38,225 INFO [train.py:874] (0/4) Epoch 17, batch 650, aishell_loss[loss=0.1517, simple_loss=0.2462, pruned_loss=0.02859, over 4879.00 frames.], tot_loss[loss=0.1532, simple_loss=0.233, pruned_loss=0.0367, over 947506.54 frames.], batch size: 35, aishell_tot_loss[loss=0.156, simple_loss=0.2413, pruned_loss=0.03541, over 781139.98 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2254, pruned_loss=0.03806, over 802526.43 frames.], batch size: 35, lr: 4.86e-04
+2022-06-18 22:12:08,228 INFO [train.py:874] (0/4) Epoch 17, batch 700, datatang_loss[loss=0.1443, simple_loss=0.2215, pruned_loss=0.03352, over 4928.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2336, pruned_loss=0.03749, over 955871.36 frames.], batch size: 62, aishell_tot_loss[loss=0.1557, simple_loss=0.2405, pruned_loss=0.03549, over 805896.02 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2267, pruned_loss=0.03898, over 823422.32 frames.], batch size: 62, lr: 4.86e-04
+2022-06-18 22:12:37,685 INFO [train.py:874] (0/4) Epoch 17, batch 750, aishell_loss[loss=0.1271, simple_loss=0.2048, pruned_loss=0.0247, over 4979.00 frames.], tot_loss[loss=0.1538, simple_loss=0.233, pruned_loss=0.03733, over 962406.39 frames.], batch size: 25, aishell_tot_loss[loss=0.1547, simple_loss=0.2393, pruned_loss=0.03507, over 827587.66 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2272, pruned_loss=0.03929, over 842029.29 frames.], batch size: 25, lr: 4.85e-04
+2022-06-18 22:13:08,737 INFO [train.py:874] (0/4) Epoch 17, batch 800, aishell_loss[loss=0.1379, simple_loss=0.2248, pruned_loss=0.02555, over 4986.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2329, pruned_loss=0.0373, over 967206.40 frames.], batch size: 30, aishell_tot_loss[loss=0.1544, simple_loss=0.2389, pruned_loss=0.03497, over 846455.18 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2273, pruned_loss=0.03943, over 858380.12 frames.], batch size: 30, lr: 4.85e-04
+2022-06-18 22:13:38,522 INFO [train.py:874] (0/4) Epoch 17, batch 850, aishell_loss[loss=0.1458, simple_loss=0.231, pruned_loss=0.03029, over 4979.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2332, pruned_loss=0.03709, over 971417.22 frames.], batch size: 48, aishell_tot_loss[loss=0.1548, simple_loss=0.2393, pruned_loss=0.03511, over 863512.57 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.227, pruned_loss=0.03909, over 872889.33 frames.], batch size: 48, lr: 4.85e-04
+2022-06-18 22:14:08,879 INFO [train.py:874] (0/4) Epoch 17, batch 900, datatang_loss[loss=0.1271, simple_loss=0.2115, pruned_loss=0.02134, over 4930.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2338, pruned_loss=0.03781, over 974666.39 frames.], batch size: 71, aishell_tot_loss[loss=0.1554, simple_loss=0.2393, pruned_loss=0.03576, over 878510.44 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2277, pruned_loss=0.03933, over 885701.31 frames.], batch size: 71, lr: 4.85e-04
+2022-06-18 22:14:39,883 INFO [train.py:874] (0/4) Epoch 17, batch 950, datatang_loss[loss=0.1536, simple_loss=0.2156, pruned_loss=0.04584, over 4882.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2348, pruned_loss=0.03837, over 977042.21 frames.], batch size: 30, aishell_tot_loss[loss=0.1563, simple_loss=0.2403, pruned_loss=0.03619, over 890445.30 frames.], datatang_tot_loss[loss=0.1536, simple_loss=0.228, pruned_loss=0.03959, over 898064.61 frames.], batch size: 30, lr: 4.85e-04
+2022-06-18 22:15:10,093 INFO [train.py:874] (0/4) Epoch 17, batch 1000, datatang_loss[loss=0.1601, simple_loss=0.2399, pruned_loss=0.04013, over 4960.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2351, pruned_loss=0.03838, over 979410.26 frames.], batch size: 91, aishell_tot_loss[loss=0.1563, simple_loss=0.2404, pruned_loss=0.03613, over 901581.43 frames.], datatang_tot_loss[loss=0.154, simple_loss=0.2285, pruned_loss=0.03975, over 908903.43 frames.], batch size: 91, lr: 4.84e-04
+2022-06-18 22:15:10,096 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 22:15:27,677 INFO [train.py:914] (0/4) Epoch 17, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-18 22:15:57,376 INFO [train.py:874] (0/4) Epoch 17, batch 1050, aishell_loss[loss=0.1463, simple_loss=0.2254, pruned_loss=0.03356, over 4893.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2348, pruned_loss=0.03812, over 980446.06 frames.], batch size: 28, aishell_tot_loss[loss=0.156, simple_loss=0.2399, pruned_loss=0.03605, over 914080.25 frames.], datatang_tot_loss[loss=0.154, simple_loss=0.2284, pruned_loss=0.03981, over 915130.43 frames.], batch size: 28, lr: 4.84e-04
+2022-06-18 22:16:27,408 INFO [train.py:874] (0/4) Epoch 17, batch 1100, aishell_loss[loss=0.158, simple_loss=0.2509, pruned_loss=0.0325, over 4979.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2364, pruned_loss=0.03829, over 982034.49 frames.], batch size: 39, aishell_tot_loss[loss=0.1564, simple_loss=0.2408, pruned_loss=0.03604, over 924031.19 frames.], datatang_tot_loss[loss=0.1547, simple_loss=0.2292, pruned_loss=0.04013, over 922364.13 frames.], batch size: 39, lr: 4.84e-04
+2022-06-18 22:16:58,197 INFO [train.py:874] (0/4) Epoch 17, batch 1150, datatang_loss[loss=0.1277, simple_loss=0.1982, pruned_loss=0.02862, over 4899.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2367, pruned_loss=0.03874, over 982991.36 frames.], batch size: 42, aishell_tot_loss[loss=0.1571, simple_loss=0.2415, pruned_loss=0.03633, over 929181.33 frames.], datatang_tot_loss[loss=0.155, simple_loss=0.2296, pruned_loss=0.04023, over 932055.43 frames.], batch size: 42, lr: 4.84e-04
+2022-06-18 22:17:27,461 INFO [train.py:874] (0/4) Epoch 17, batch 1200, aishell_loss[loss=0.1662, simple_loss=0.2543, pruned_loss=0.03902, over 4957.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2362, pruned_loss=0.03811, over 983576.29 frames.], batch size: 40, aishell_tot_loss[loss=0.156, simple_loss=0.2407, pruned_loss=0.03567, over 937170.77 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2299, pruned_loss=0.0404, over 937053.28 frames.], batch size: 40, lr: 4.84e-04
+2022-06-18 22:17:58,099 INFO [train.py:874] (0/4) Epoch 17, batch 1250, datatang_loss[loss=0.1593, simple_loss=0.2328, pruned_loss=0.04293, over 4965.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2366, pruned_loss=0.0383, over 984493.27 frames.], batch size: 86, aishell_tot_loss[loss=0.1567, simple_loss=0.2413, pruned_loss=0.03606, over 942458.41 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2301, pruned_loss=0.04016, over 943678.69 frames.], batch size: 86, lr: 4.84e-04
+2022-06-18 22:18:27,927 INFO [train.py:874] (0/4) Epoch 17, batch 1300, aishell_loss[loss=0.1679, simple_loss=0.2483, pruned_loss=0.04375, over 4965.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2364, pruned_loss=0.03817, over 985216.98 frames.], batch size: 51, aishell_tot_loss[loss=0.1569, simple_loss=0.2416, pruned_loss=0.03616, over 948196.07 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2299, pruned_loss=0.03999, over 948473.73 frames.], batch size: 51, lr: 4.83e-04
+2022-06-18 22:18:58,665 INFO [train.py:874] (0/4) Epoch 17, batch 1350, aishell_loss[loss=0.1512, simple_loss=0.2478, pruned_loss=0.02725, over 4968.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2373, pruned_loss=0.03864, over 985252.93 frames.], batch size: 39, aishell_tot_loss[loss=0.1575, simple_loss=0.2423, pruned_loss=0.03635, over 952929.43 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.23, pruned_loss=0.04038, over 952458.30 frames.], batch size: 39, lr: 4.83e-04
+2022-06-18 22:19:29,795 INFO [train.py:874] (0/4) Epoch 17, batch 1400, aishell_loss[loss=0.138, simple_loss=0.2282, pruned_loss=0.02384, over 4941.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2375, pruned_loss=0.03882, over 985620.01 frames.], batch size: 58, aishell_tot_loss[loss=0.1578, simple_loss=0.2427, pruned_loss=0.0365, over 956951.67 frames.], datatang_tot_loss[loss=0.1556, simple_loss=0.2303, pruned_loss=0.0405, over 956500.97 frames.], batch size: 58, lr: 4.83e-04
+2022-06-18 22:19:58,837 INFO [train.py:874] (0/4) Epoch 17, batch 1450, aishell_loss[loss=0.1612, simple_loss=0.2526, pruned_loss=0.03484, over 4866.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2367, pruned_loss=0.03826, over 985801.95 frames.], batch size: 37, aishell_tot_loss[loss=0.1574, simple_loss=0.2425, pruned_loss=0.03612, over 960396.81 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2298, pruned_loss=0.04034, over 960054.09 frames.], batch size: 37, lr: 4.83e-04
+2022-06-18 22:20:29,675 INFO [train.py:874] (0/4) Epoch 17, batch 1500, datatang_loss[loss=0.1532, simple_loss=0.2255, pruned_loss=0.04042, over 4870.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2361, pruned_loss=0.03818, over 985543.05 frames.], batch size: 39, aishell_tot_loss[loss=0.1572, simple_loss=0.2424, pruned_loss=0.03596, over 962717.23 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2296, pruned_loss=0.04035, over 963488.94 frames.], batch size: 39, lr: 4.83e-04
+2022-06-18 22:20:59,675 INFO [train.py:874] (0/4) Epoch 17, batch 1550, datatang_loss[loss=0.1493, simple_loss=0.2286, pruned_loss=0.03503, over 4924.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2358, pruned_loss=0.03783, over 985263.68 frames.], batch size: 81, aishell_tot_loss[loss=0.1566, simple_loss=0.2418, pruned_loss=0.03572, over 965516.78 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2298, pruned_loss=0.04029, over 965687.84 frames.], batch size: 81, lr: 4.82e-04
+2022-06-18 22:21:29,213 INFO [train.py:874] (0/4) Epoch 17, batch 1600, aishell_loss[loss=0.1768, simple_loss=0.2558, pruned_loss=0.04893, over 4955.00 frames.], tot_loss[loss=0.1549, simple_loss=0.235, pruned_loss=0.03741, over 985094.90 frames.], batch size: 56, aishell_tot_loss[loss=0.1562, simple_loss=0.2415, pruned_loss=0.03549, over 967124.17 frames.], datatang_tot_loss[loss=0.1546, simple_loss=0.2295, pruned_loss=0.0399, over 968514.08 frames.], batch size: 56, lr: 4.82e-04
+2022-06-18 22:21:59,640 INFO [train.py:874] (0/4) Epoch 17, batch 1650, aishell_loss[loss=0.15, simple_loss=0.2244, pruned_loss=0.03776, over 4926.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2353, pruned_loss=0.0376, over 984805.93 frames.], batch size: 33, aishell_tot_loss[loss=0.1563, simple_loss=0.2412, pruned_loss=0.03568, over 969195.89 frames.], datatang_tot_loss[loss=0.1548, simple_loss=0.2299, pruned_loss=0.03986, over 970218.70 frames.], batch size: 33, lr: 4.82e-04
+2022-06-18 22:22:30,208 INFO [train.py:874] (0/4) Epoch 17, batch 1700, datatang_loss[loss=0.1709, simple_loss=0.2512, pruned_loss=0.04529, over 4951.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2357, pruned_loss=0.03764, over 984923.55 frames.], batch size: 45, aishell_tot_loss[loss=0.1561, simple_loss=0.2409, pruned_loss=0.03568, over 971261.98 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2305, pruned_loss=0.03991, over 971846.87 frames.], batch size: 45, lr: 4.82e-04
+2022-06-18 22:22:59,502 INFO [train.py:874] (0/4) Epoch 17, batch 1750, datatang_loss[loss=0.15, simple_loss=0.2179, pruned_loss=0.04103, over 4945.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2343, pruned_loss=0.0375, over 985098.58 frames.], batch size: 45, aishell_tot_loss[loss=0.1561, simple_loss=0.2408, pruned_loss=0.03566, over 972556.17 frames.], datatang_tot_loss[loss=0.1543, simple_loss=0.2294, pruned_loss=0.03957, over 973859.55 frames.], batch size: 45, lr: 4.82e-04
+2022-06-18 22:23:31,053 INFO [train.py:874] (0/4) Epoch 17, batch 1800, datatang_loss[loss=0.1324, simple_loss=0.2131, pruned_loss=0.02583, over 4967.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2345, pruned_loss=0.03769, over 985230.46 frames.], batch size: 60, aishell_tot_loss[loss=0.1561, simple_loss=0.2411, pruned_loss=0.03559, over 973936.96 frames.], datatang_tot_loss[loss=0.1545, simple_loss=0.2295, pruned_loss=0.03973, over 975387.83 frames.], batch size: 60, lr: 4.82e-04
+2022-06-18 22:24:01,629 INFO [train.py:874] (0/4) Epoch 17, batch 1850, aishell_loss[loss=0.1865, simple_loss=0.2595, pruned_loss=0.05674, over 4885.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2352, pruned_loss=0.0379, over 985620.98 frames.], batch size: 34, aishell_tot_loss[loss=0.1566, simple_loss=0.2416, pruned_loss=0.03577, over 975519.70 frames.], datatang_tot_loss[loss=0.1544, simple_loss=0.2294, pruned_loss=0.03975, over 976717.27 frames.], batch size: 34, lr: 4.81e-04
+2022-06-18 22:24:03,736 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-68000.pt
+2022-06-18 22:24:35,560 INFO [train.py:874] (0/4) Epoch 17, batch 1900, aishell_loss[loss=0.1296, simple_loss=0.2142, pruned_loss=0.02253, over 4973.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2349, pruned_loss=0.03779, over 985892.02 frames.], batch size: 27, aishell_tot_loss[loss=0.1562, simple_loss=0.2415, pruned_loss=0.0355, over 976552.41 frames.], datatang_tot_loss[loss=0.1546, simple_loss=0.2294, pruned_loss=0.03986, over 978172.55 frames.], batch size: 27, lr: 4.81e-04
+2022-06-18 22:25:06,014 INFO [train.py:874] (0/4) Epoch 17, batch 1950, datatang_loss[loss=0.1545, simple_loss=0.2246, pruned_loss=0.04215, over 4955.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2352, pruned_loss=0.03828, over 985620.93 frames.], batch size: 67, aishell_tot_loss[loss=0.1565, simple_loss=0.2415, pruned_loss=0.03576, over 977152.04 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2297, pruned_loss=0.04008, over 979264.74 frames.], batch size: 67, lr: 4.81e-04
+2022-06-18 22:25:36,666 INFO [train.py:874] (0/4) Epoch 17, batch 2000, datatang_loss[loss=0.134, simple_loss=0.2037, pruned_loss=0.03213, over 4930.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2339, pruned_loss=0.03757, over 985848.75 frames.], batch size: 37, aishell_tot_loss[loss=0.1562, simple_loss=0.241, pruned_loss=0.03568, over 978070.89 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.2288, pruned_loss=0.03945, over 980311.83 frames.], batch size: 37, lr: 4.81e-04
+2022-06-18 22:25:36,670 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 22:25:53,691 INFO [train.py:914] (0/4) Epoch 17, validation: loss=0.1656, simple_loss=0.2494, pruned_loss=0.04087, over 1622729.00 frames.
+2022-06-18 22:26:23,335 INFO [train.py:874] (0/4) Epoch 17, batch 2050, aishell_loss[loss=0.1521, simple_loss=0.2456, pruned_loss=0.02933, over 4953.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2355, pruned_loss=0.03803, over 985827.51 frames.], batch size: 56, aishell_tot_loss[loss=0.1562, simple_loss=0.2412, pruned_loss=0.0356, over 979115.01 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2299, pruned_loss=0.04009, over 980841.32 frames.], batch size: 56, lr: 4.81e-04
+2022-06-18 22:26:54,515 INFO [train.py:874] (0/4) Epoch 17, batch 2100, aishell_loss[loss=0.1238, simple_loss=0.1982, pruned_loss=0.02468, over 4989.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2351, pruned_loss=0.03805, over 985995.05 frames.], batch size: 25, aishell_tot_loss[loss=0.1557, simple_loss=0.2406, pruned_loss=0.03541, over 979957.75 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2301, pruned_loss=0.04035, over 981548.15 frames.], batch size: 25, lr: 4.81e-04
+2022-06-18 22:27:23,934 INFO [train.py:874] (0/4) Epoch 17, batch 2150, datatang_loss[loss=0.1751, simple_loss=0.2489, pruned_loss=0.05071, over 4808.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2347, pruned_loss=0.03769, over 985713.70 frames.], batch size: 24, aishell_tot_loss[loss=0.1554, simple_loss=0.2403, pruned_loss=0.03523, over 980637.09 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.23, pruned_loss=0.04023, over 981823.57 frames.], batch size: 24, lr: 4.80e-04
+2022-06-18 22:27:55,171 INFO [train.py:874] (0/4) Epoch 17, batch 2200, datatang_loss[loss=0.1429, simple_loss=0.2162, pruned_loss=0.03483, over 4974.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2342, pruned_loss=0.03735, over 985961.08 frames.], batch size: 37, aishell_tot_loss[loss=0.155, simple_loss=0.2398, pruned_loss=0.03512, over 981367.85 frames.], datatang_tot_loss[loss=0.1548, simple_loss=0.2296, pruned_loss=0.04002, over 982420.52 frames.], batch size: 37, lr: 4.80e-04
+2022-06-18 22:28:25,157 INFO [train.py:874] (0/4) Epoch 17, batch 2250, datatang_loss[loss=0.2491, simple_loss=0.3057, pruned_loss=0.09628, over 4940.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2353, pruned_loss=0.03775, over 985740.66 frames.], batch size: 108, aishell_tot_loss[loss=0.1558, simple_loss=0.2407, pruned_loss=0.03545, over 981762.40 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2296, pruned_loss=0.04013, over 982759.69 frames.], batch size: 108, lr: 4.80e-04
+2022-06-18 22:28:55,862 INFO [train.py:874] (0/4) Epoch 17, batch 2300, aishell_loss[loss=0.1652, simple_loss=0.2445, pruned_loss=0.04289, over 4926.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2346, pruned_loss=0.03729, over 985307.95 frames.], batch size: 33, aishell_tot_loss[loss=0.155, simple_loss=0.2398, pruned_loss=0.03515, over 981802.05 frames.], datatang_tot_loss[loss=0.1548, simple_loss=0.2296, pruned_loss=0.04, over 983093.01 frames.], batch size: 33, lr: 4.80e-04
+2022-06-18 22:29:26,647 INFO [train.py:874] (0/4) Epoch 17, batch 2350, aishell_loss[loss=0.1608, simple_loss=0.2482, pruned_loss=0.03669, over 4938.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2345, pruned_loss=0.03737, over 985417.48 frames.], batch size: 58, aishell_tot_loss[loss=0.1554, simple_loss=0.24, pruned_loss=0.03538, over 982229.12 frames.], datatang_tot_loss[loss=0.1544, simple_loss=0.2291, pruned_loss=0.03988, over 983469.43 frames.], batch size: 58, lr: 4.80e-04
+2022-06-18 22:29:55,935 INFO [train.py:874] (0/4) Epoch 17, batch 2400, datatang_loss[loss=0.1239, simple_loss=0.1896, pruned_loss=0.0291, over 4867.00 frames.], tot_loss[loss=0.154, simple_loss=0.2336, pruned_loss=0.03716, over 984980.61 frames.], batch size: 25, aishell_tot_loss[loss=0.1553, simple_loss=0.2398, pruned_loss=0.03538, over 982224.81 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2284, pruned_loss=0.0396, over 983617.35 frames.], batch size: 25, lr: 4.79e-04
+2022-06-18 22:30:25,171 INFO [train.py:874] (0/4) Epoch 17, batch 2450, aishell_loss[loss=0.1372, simple_loss=0.2189, pruned_loss=0.02776, over 4973.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2334, pruned_loss=0.03692, over 984857.95 frames.], batch size: 25, aishell_tot_loss[loss=0.1555, simple_loss=0.2401, pruned_loss=0.03551, over 982384.73 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2277, pruned_loss=0.03917, over 983821.94 frames.], batch size: 25, lr: 4.79e-04
+2022-06-18 22:30:56,066 INFO [train.py:874] (0/4) Epoch 17, batch 2500, aishell_loss[loss=0.1597, simple_loss=0.2532, pruned_loss=0.03312, over 4976.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2341, pruned_loss=0.03736, over 985061.11 frames.], batch size: 39, aishell_tot_loss[loss=0.1558, simple_loss=0.2405, pruned_loss=0.03554, over 982843.69 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2278, pruned_loss=0.03953, over 983985.03 frames.], batch size: 39, lr: 4.79e-04
+2022-06-18 22:31:26,749 INFO [train.py:874] (0/4) Epoch 17, batch 2550, aishell_loss[loss=0.2062, simple_loss=0.2877, pruned_loss=0.06238, over 4945.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2338, pruned_loss=0.03729, over 984999.91 frames.], batch size: 54, aishell_tot_loss[loss=0.1557, simple_loss=0.2403, pruned_loss=0.03557, over 983077.36 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2275, pruned_loss=0.03942, over 984076.59 frames.], batch size: 54, lr: 4.79e-04
+2022-06-18 22:31:56,348 INFO [train.py:874] (0/4) Epoch 17, batch 2600, aishell_loss[loss=0.1711, simple_loss=0.2511, pruned_loss=0.04552, over 4913.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2337, pruned_loss=0.03705, over 984966.74 frames.], batch size: 41, aishell_tot_loss[loss=0.156, simple_loss=0.2405, pruned_loss=0.03569, over 983332.97 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2271, pruned_loss=0.03902, over 984136.75 frames.], batch size: 41, lr: 4.79e-04
+2022-06-18 22:32:27,308 INFO [train.py:874] (0/4) Epoch 17, batch 2650, aishell_loss[loss=0.1589, simple_loss=0.2492, pruned_loss=0.03429, over 4905.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2341, pruned_loss=0.03719, over 985232.23 frames.], batch size: 41, aishell_tot_loss[loss=0.1564, simple_loss=0.2413, pruned_loss=0.03571, over 983632.65 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2269, pruned_loss=0.03904, over 984394.42 frames.], batch size: 41, lr: 4.79e-04
+2022-06-18 22:32:58,303 INFO [train.py:874] (0/4) Epoch 17, batch 2700, datatang_loss[loss=0.142, simple_loss=0.2159, pruned_loss=0.03403, over 4916.00 frames.], tot_loss[loss=0.154, simple_loss=0.2336, pruned_loss=0.03719, over 985043.65 frames.], batch size: 75, aishell_tot_loss[loss=0.1563, simple_loss=0.2412, pruned_loss=0.03569, over 983612.03 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2268, pruned_loss=0.03893, over 984468.90 frames.], batch size: 75, lr: 4.78e-04
+2022-06-18 22:33:27,879 INFO [train.py:874] (0/4) Epoch 17, batch 2750, datatang_loss[loss=0.1643, simple_loss=0.2488, pruned_loss=0.03989, over 4934.00 frames.], tot_loss[loss=0.1542, simple_loss=0.234, pruned_loss=0.0372, over 985443.78 frames.], batch size: 94, aishell_tot_loss[loss=0.1561, simple_loss=0.2413, pruned_loss=0.0354, over 984163.88 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.227, pruned_loss=0.03921, over 984568.18 frames.], batch size: 94, lr: 4.78e-04
+2022-06-18 22:33:57,747 INFO [train.py:874] (0/4) Epoch 17, batch 2800, aishell_loss[loss=0.1169, simple_loss=0.1846, pruned_loss=0.02455, over 4826.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2338, pruned_loss=0.03703, over 985167.43 frames.], batch size: 21, aishell_tot_loss[loss=0.1561, simple_loss=0.2412, pruned_loss=0.03553, over 984175.81 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2267, pruned_loss=0.03892, over 984534.86 frames.], batch size: 21, lr: 4.78e-04
+2022-06-18 22:34:28,503 INFO [train.py:874] (0/4) Epoch 17, batch 2850, aishell_loss[loss=0.1529, simple_loss=0.2366, pruned_loss=0.03455, over 4863.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2343, pruned_loss=0.03725, over 984926.87 frames.], batch size: 35, aishell_tot_loss[loss=0.156, simple_loss=0.241, pruned_loss=0.03552, over 984094.60 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.227, pruned_loss=0.03915, over 984563.11 frames.], batch size: 35, lr: 4.78e-04
+2022-06-18 22:34:58,999 INFO [train.py:874] (0/4) Epoch 17, batch 2900, datatang_loss[loss=0.1497, simple_loss=0.2182, pruned_loss=0.04059, over 4936.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2337, pruned_loss=0.03687, over 985133.80 frames.], batch size: 57, aishell_tot_loss[loss=0.1552, simple_loss=0.2401, pruned_loss=0.03518, over 984332.26 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.2271, pruned_loss=0.03915, over 984688.14 frames.], batch size: 57, lr: 4.78e-04
+2022-06-18 22:35:28,951 INFO [train.py:874] (0/4) Epoch 17, batch 2950, aishell_loss[loss=0.1316, simple_loss=0.2128, pruned_loss=0.02521, over 4946.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2332, pruned_loss=0.03685, over 985352.48 frames.], batch size: 25, aishell_tot_loss[loss=0.1552, simple_loss=0.2399, pruned_loss=0.03526, over 984599.41 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2269, pruned_loss=0.03896, over 984787.94 frames.], batch size: 25, lr: 4.78e-04
+2022-06-18 22:35:58,642 INFO [train.py:874] (0/4) Epoch 17, batch 3000, datatang_loss[loss=0.141, simple_loss=0.2132, pruned_loss=0.03439, over 4946.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2336, pruned_loss=0.03775, over 985385.31 frames.], batch size: 50, aishell_tot_loss[loss=0.1559, simple_loss=0.2405, pruned_loss=0.03565, over 984617.69 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2269, pruned_loss=0.03941, over 984935.76 frames.], batch size: 50, lr: 4.77e-04
+2022-06-18 22:35:58,645 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 22:36:15,620 INFO [train.py:914] (0/4) Epoch 17, validation: loss=0.1675, simple_loss=0.2537, pruned_loss=0.04061, over 1622729.00 frames.
+2022-06-18 22:36:45,524 INFO [train.py:874] (0/4) Epoch 17, batch 3050, aishell_loss[loss=0.1386, simple_loss=0.2311, pruned_loss=0.02305, over 4870.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2338, pruned_loss=0.03816, over 985537.35 frames.], batch size: 42, aishell_tot_loss[loss=0.1563, simple_loss=0.241, pruned_loss=0.03583, over 984739.57 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2269, pruned_loss=0.03964, over 985113.96 frames.], batch size: 42, lr: 4.77e-04
+2022-06-18 22:37:16,149 INFO [train.py:874] (0/4) Epoch 17, batch 3100, datatang_loss[loss=0.1642, simple_loss=0.237, pruned_loss=0.04566, over 4924.00 frames.], tot_loss[loss=0.1554, simple_loss=0.234, pruned_loss=0.03839, over 985574.92 frames.], batch size: 71, aishell_tot_loss[loss=0.157, simple_loss=0.2417, pruned_loss=0.03618, over 984994.78 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2266, pruned_loss=0.03957, over 985033.19 frames.], batch size: 71, lr: 4.77e-04
+2022-06-18 22:37:46,646 INFO [train.py:874] (0/4) Epoch 17, batch 3150, aishell_loss[loss=0.1636, simple_loss=0.2544, pruned_loss=0.03642, over 4888.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2334, pruned_loss=0.03754, over 985701.90 frames.], batch size: 42, aishell_tot_loss[loss=0.1568, simple_loss=0.2417, pruned_loss=0.03597, over 984952.91 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2261, pruned_loss=0.03898, over 985362.47 frames.], batch size: 42, lr: 4.77e-04
+2022-06-18 22:38:17,536 INFO [train.py:874] (0/4) Epoch 17, batch 3200, aishell_loss[loss=0.1317, simple_loss=0.215, pruned_loss=0.0242, over 4965.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2332, pruned_loss=0.03692, over 985492.86 frames.], batch size: 27, aishell_tot_loss[loss=0.1557, simple_loss=0.2405, pruned_loss=0.03545, over 984804.21 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2267, pruned_loss=0.03893, over 985437.95 frames.], batch size: 27, lr: 4.77e-04
+2022-06-18 22:38:47,532 INFO [train.py:874] (0/4) Epoch 17, batch 3250, aishell_loss[loss=0.1871, simple_loss=0.2629, pruned_loss=0.05561, over 4976.00 frames.], tot_loss[loss=0.154, simple_loss=0.2332, pruned_loss=0.03742, over 985796.19 frames.], batch size: 48, aishell_tot_loss[loss=0.1559, simple_loss=0.2407, pruned_loss=0.03555, over 984932.84 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2269, pruned_loss=0.03919, over 985687.84 frames.], batch size: 48, lr: 4.77e-04
+2022-06-18 22:39:17,038 INFO [train.py:874] (0/4) Epoch 17, batch 3300, aishell_loss[loss=0.1487, simple_loss=0.2398, pruned_loss=0.02884, over 4918.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2336, pruned_loss=0.03766, over 985891.49 frames.], batch size: 52, aishell_tot_loss[loss=0.1559, simple_loss=0.2408, pruned_loss=0.03551, over 985209.33 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.227, pruned_loss=0.03951, over 985624.28 frames.], batch size: 52, lr: 4.76e-04
+2022-06-18 22:39:47,759 INFO [train.py:874] (0/4) Epoch 17, batch 3350, aishell_loss[loss=0.1536, simple_loss=0.2395, pruned_loss=0.03386, over 4928.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2329, pruned_loss=0.03714, over 986031.68 frames.], batch size: 58, aishell_tot_loss[loss=0.1561, simple_loss=0.241, pruned_loss=0.03563, over 985373.44 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2261, pruned_loss=0.03889, over 985722.53 frames.], batch size: 58, lr: 4.76e-04
+2022-06-18 22:40:18,435 INFO [train.py:874] (0/4) Epoch 17, batch 3400, datatang_loss[loss=0.1306, simple_loss=0.2094, pruned_loss=0.02588, over 4917.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2329, pruned_loss=0.03733, over 985891.79 frames.], batch size: 75, aishell_tot_loss[loss=0.1562, simple_loss=0.2408, pruned_loss=0.03581, over 985108.55 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.226, pruned_loss=0.03891, over 985954.26 frames.], batch size: 75, lr: 4.76e-04
+2022-06-18 22:40:47,877 INFO [train.py:874] (0/4) Epoch 17, batch 3450, aishell_loss[loss=0.1557, simple_loss=0.2462, pruned_loss=0.03261, over 4914.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2338, pruned_loss=0.03762, over 985913.20 frames.], batch size: 52, aishell_tot_loss[loss=0.1567, simple_loss=0.2415, pruned_loss=0.03595, over 985343.40 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2261, pruned_loss=0.03904, over 985824.79 frames.], batch size: 52, lr: 4.76e-04
+2022-06-18 22:41:18,970 INFO [train.py:874] (0/4) Epoch 17, batch 3500, aishell_loss[loss=0.15, simple_loss=0.2414, pruned_loss=0.02928, over 4956.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2332, pruned_loss=0.03687, over 986279.76 frames.], batch size: 68, aishell_tot_loss[loss=0.1562, simple_loss=0.2413, pruned_loss=0.03555, over 985648.63 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2258, pruned_loss=0.03871, over 985979.32 frames.], batch size: 68, lr: 4.76e-04
+2022-06-18 22:41:49,997 INFO [train.py:874] (0/4) Epoch 17, batch 3550, datatang_loss[loss=0.1462, simple_loss=0.2197, pruned_loss=0.03632, over 4945.00 frames.], tot_loss[loss=0.1535, simple_loss=0.233, pruned_loss=0.03699, over 985974.52 frames.], batch size: 69, aishell_tot_loss[loss=0.156, simple_loss=0.2408, pruned_loss=0.03562, over 985533.05 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2263, pruned_loss=0.03863, over 985868.57 frames.], batch size: 69, lr: 4.76e-04
+2022-06-18 22:42:20,084 INFO [train.py:874] (0/4) Epoch 17, batch 3600, datatang_loss[loss=0.1259, simple_loss=0.2106, pruned_loss=0.02062, over 4929.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2337, pruned_loss=0.03698, over 985902.93 frames.], batch size: 57, aishell_tot_loss[loss=0.156, simple_loss=0.2408, pruned_loss=0.03566, over 985565.57 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2266, pruned_loss=0.03863, over 985819.25 frames.], batch size: 57, lr: 4.75e-04
+2022-06-18 22:42:49,411 INFO [train.py:874] (0/4) Epoch 17, batch 3650, aishell_loss[loss=0.2, simple_loss=0.2717, pruned_loss=0.06412, over 4870.00 frames.], tot_loss[loss=0.155, simple_loss=0.2348, pruned_loss=0.03761, over 985816.08 frames.], batch size: 36, aishell_tot_loss[loss=0.1561, simple_loss=0.2409, pruned_loss=0.03565, over 985431.43 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2275, pruned_loss=0.03925, over 985910.40 frames.], batch size: 36, lr: 4.75e-04
+2022-06-18 22:43:21,326 INFO [train.py:874] (0/4) Epoch 17, batch 3700, aishell_loss[loss=0.1479, simple_loss=0.2403, pruned_loss=0.02776, over 4970.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2344, pruned_loss=0.03757, over 985397.37 frames.], batch size: 44, aishell_tot_loss[loss=0.1558, simple_loss=0.2406, pruned_loss=0.03549, over 985064.46 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2276, pruned_loss=0.0394, over 985857.63 frames.], batch size: 44, lr: 4.75e-04
+2022-06-18 22:43:51,462 INFO [train.py:874] (0/4) Epoch 17, batch 3750, aishell_loss[loss=0.1653, simple_loss=0.2557, pruned_loss=0.03745, over 4901.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2354, pruned_loss=0.03781, over 985442.68 frames.], batch size: 46, aishell_tot_loss[loss=0.1567, simple_loss=0.2417, pruned_loss=0.03582, over 985048.32 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2278, pruned_loss=0.03932, over 985892.19 frames.], batch size: 46, lr: 4.75e-04
+2022-06-18 22:44:21,504 INFO [train.py:874] (0/4) Epoch 17, batch 3800, aishell_loss[loss=0.1819, simple_loss=0.2537, pruned_loss=0.05508, over 4980.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2354, pruned_loss=0.03758, over 986092.79 frames.], batch size: 44, aishell_tot_loss[loss=0.1568, simple_loss=0.2419, pruned_loss=0.03588, over 985559.06 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2277, pruned_loss=0.03908, over 986065.39 frames.], batch size: 44, lr: 4.75e-04
+2022-06-18 22:44:50,865 INFO [train.py:874] (0/4) Epoch 17, batch 3850, aishell_loss[loss=0.1753, simple_loss=0.2639, pruned_loss=0.04335, over 4949.00 frames.], tot_loss[loss=0.155, simple_loss=0.2355, pruned_loss=0.03725, over 986020.85 frames.], batch size: 40, aishell_tot_loss[loss=0.1572, simple_loss=0.2425, pruned_loss=0.03599, over 985598.29 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2271, pruned_loss=0.0387, over 986038.85 frames.], batch size: 40, lr: 4.75e-04
+2022-06-18 22:45:20,644 INFO [train.py:874] (0/4) Epoch 17, batch 3900, aishell_loss[loss=0.1571, simple_loss=0.2416, pruned_loss=0.03633, over 4882.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2348, pruned_loss=0.0369, over 986126.63 frames.], batch size: 42, aishell_tot_loss[loss=0.1573, simple_loss=0.2425, pruned_loss=0.03606, over 985813.92 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2265, pruned_loss=0.03823, over 985975.11 frames.], batch size: 42, lr: 4.74e-04
+2022-06-18 22:45:49,951 INFO [train.py:874] (0/4) Epoch 17, batch 3950, aishell_loss[loss=0.1429, simple_loss=0.2217, pruned_loss=0.03208, over 4945.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2342, pruned_loss=0.03685, over 986066.80 frames.], batch size: 45, aishell_tot_loss[loss=0.1568, simple_loss=0.2419, pruned_loss=0.03586, over 985717.19 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2265, pruned_loss=0.03836, over 986053.35 frames.], batch size: 45, lr: 4.74e-04
+2022-06-18 22:46:19,533 INFO [train.py:874] (0/4) Epoch 17, batch 4000, datatang_loss[loss=0.149, simple_loss=0.2313, pruned_loss=0.03337, over 4933.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2343, pruned_loss=0.03699, over 985741.31 frames.], batch size: 79, aishell_tot_loss[loss=0.1571, simple_loss=0.2423, pruned_loss=0.03593, over 985463.36 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2262, pruned_loss=0.03835, over 986004.16 frames.], batch size: 79, lr: 4.74e-04
+2022-06-18 22:46:19,535 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 22:46:35,710 INFO [train.py:914] (0/4) Epoch 17, validation: loss=0.1673, simple_loss=0.2516, pruned_loss=0.04154, over 1622729.00 frames.
+2022-06-18 22:47:04,907 INFO [train.py:874] (0/4) Epoch 17, batch 4050, datatang_loss[loss=0.1405, simple_loss=0.2143, pruned_loss=0.03328, over 4945.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2339, pruned_loss=0.03687, over 985432.46 frames.], batch size: 50, aishell_tot_loss[loss=0.1571, simple_loss=0.2426, pruned_loss=0.03582, over 985271.73 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2259, pruned_loss=0.03825, over 985856.53 frames.], batch size: 50, lr: 4.74e-04
+2022-06-18 22:47:35,448 INFO [train.py:874] (0/4) Epoch 17, batch 4100, aishell_loss[loss=0.1736, simple_loss=0.2574, pruned_loss=0.04487, over 4922.00 frames.], tot_loss[loss=0.154, simple_loss=0.2341, pruned_loss=0.03695, over 985424.10 frames.], batch size: 46, aishell_tot_loss[loss=0.1571, simple_loss=0.2426, pruned_loss=0.03584, over 985351.74 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2264, pruned_loss=0.03821, over 985726.65 frames.], batch size: 46, lr: 4.74e-04
+2022-06-18 22:48:03,147 INFO [train.py:874] (0/4) Epoch 17, batch 4150, aishell_loss[loss=0.1525, simple_loss=0.2483, pruned_loss=0.0283, over 4861.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2338, pruned_loss=0.03685, over 985479.04 frames.], batch size: 37, aishell_tot_loss[loss=0.1573, simple_loss=0.2427, pruned_loss=0.03592, over 985272.79 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2259, pruned_loss=0.03802, over 985842.03 frames.], batch size: 37, lr: 4.73e-04
+2022-06-18 22:48:24,511 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-17.pt
+2022-06-18 22:49:24,002 INFO [train.py:874] (0/4) Epoch 18, batch 50, aishell_loss[loss=0.1699, simple_loss=0.2538, pruned_loss=0.043, over 4981.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2274, pruned_loss=0.03289, over 218490.40 frames.], batch size: 51, aishell_tot_loss[loss=0.1532, simple_loss=0.2375, pruned_loss=0.0344, over 129162.66 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2142, pruned_loss=0.03082, over 102806.56 frames.], batch size: 51, lr: 4.61e-04
+2022-06-18 22:49:54,849 INFO [train.py:874] (0/4) Epoch 18, batch 100, datatang_loss[loss=0.1303, simple_loss=0.2077, pruned_loss=0.02644, over 4917.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2296, pruned_loss=0.03405, over 388624.05 frames.], batch size: 64, aishell_tot_loss[loss=0.1546, simple_loss=0.2399, pruned_loss=0.03469, over 222214.84 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.219, pruned_loss=0.03319, over 214816.90 frames.], batch size: 64, lr: 4.61e-04
+2022-06-18 22:50:25,805 INFO [train.py:874] (0/4) Epoch 18, batch 150, datatang_loss[loss=0.1256, simple_loss=0.1993, pruned_loss=0.02597, over 4945.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2298, pruned_loss=0.03392, over 520939.30 frames.], batch size: 45, aishell_tot_loss[loss=0.1536, simple_loss=0.2389, pruned_loss=0.03412, over 325228.40 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2193, pruned_loss=0.03359, over 292061.17 frames.], batch size: 45, lr: 4.60e-04
+2022-06-18 22:50:54,381 INFO [train.py:874] (0/4) Epoch 18, batch 200, aishell_loss[loss=0.1469, simple_loss=0.2267, pruned_loss=0.03354, over 4863.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2304, pruned_loss=0.0345, over 624097.51 frames.], batch size: 35, aishell_tot_loss[loss=0.1537, simple_loss=0.2394, pruned_loss=0.03405, over 414682.45 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2193, pruned_loss=0.03477, over 361408.50 frames.], batch size: 35, lr: 4.60e-04
+2022-06-18 22:51:24,566 INFO [train.py:874] (0/4) Epoch 18, batch 250, aishell_loss[loss=0.1556, simple_loss=0.2382, pruned_loss=0.03648, over 4872.00 frames.], tot_loss[loss=0.151, simple_loss=0.23, pruned_loss=0.03598, over 704253.12 frames.], batch size: 43, aishell_tot_loss[loss=0.1549, simple_loss=0.2392, pruned_loss=0.03533, over 479351.79 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2197, pruned_loss=0.03593, over 437732.14 frames.], batch size: 43, lr: 4.60e-04
+2022-06-18 22:51:56,057 INFO [train.py:874] (0/4) Epoch 18, batch 300, datatang_loss[loss=0.1404, simple_loss=0.2252, pruned_loss=0.02782, over 4949.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2296, pruned_loss=0.03553, over 766813.42 frames.], batch size: 69, aishell_tot_loss[loss=0.1545, simple_loss=0.2393, pruned_loss=0.03487, over 527809.58 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2199, pruned_loss=0.0358, over 514270.96 frames.], batch size: 69, lr: 4.60e-04
+2022-06-18 22:52:24,075 INFO [train.py:874] (0/4) Epoch 18, batch 350, datatang_loss[loss=0.1633, simple_loss=0.2415, pruned_loss=0.04251, over 4905.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2303, pruned_loss=0.03535, over 815049.56 frames.], batch size: 52, aishell_tot_loss[loss=0.1549, simple_loss=0.2397, pruned_loss=0.03507, over 595556.25 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2196, pruned_loss=0.03543, over 554747.81 frames.], batch size: 52, lr: 4.60e-04
+2022-06-18 22:52:55,582 INFO [train.py:874] (0/4) Epoch 18, batch 400, datatang_loss[loss=0.1718, simple_loss=0.2396, pruned_loss=0.05198, over 4894.00 frames.], tot_loss[loss=0.1516, simple_loss=0.231, pruned_loss=0.03608, over 852788.14 frames.], batch size: 47, aishell_tot_loss[loss=0.155, simple_loss=0.2398, pruned_loss=0.03514, over 632682.31 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2214, pruned_loss=0.03643, over 614918.93 frames.], batch size: 47, lr: 4.60e-04
+2022-06-18 22:53:26,693 INFO [train.py:874] (0/4) Epoch 18, batch 450, datatang_loss[loss=0.1325, simple_loss=0.2102, pruned_loss=0.02736, over 4913.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2311, pruned_loss=0.03562, over 882041.97 frames.], batch size: 81, aishell_tot_loss[loss=0.1546, simple_loss=0.2395, pruned_loss=0.03485, over 675556.55 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2219, pruned_loss=0.0362, over 656968.40 frames.], batch size: 81, lr: 4.59e-04
+2022-06-18 22:53:54,537 INFO [train.py:874] (0/4) Epoch 18, batch 500, datatang_loss[loss=0.2223, simple_loss=0.2837, pruned_loss=0.08045, over 4950.00 frames.], tot_loss[loss=0.153, simple_loss=0.2333, pruned_loss=0.03637, over 904687.87 frames.], batch size: 108, aishell_tot_loss[loss=0.1553, simple_loss=0.2406, pruned_loss=0.03496, over 711917.61 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2238, pruned_loss=0.03712, over 695490.76 frames.], batch size: 108, lr: 4.59e-04
+2022-06-18 22:54:26,284 INFO [train.py:874] (0/4) Epoch 18, batch 550, datatang_loss[loss=0.1338, simple_loss=0.2162, pruned_loss=0.02568, over 4923.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2336, pruned_loss=0.03655, over 923036.74 frames.], batch size: 73, aishell_tot_loss[loss=0.1555, simple_loss=0.2407, pruned_loss=0.03517, over 746849.05 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2243, pruned_loss=0.03729, over 727232.90 frames.], batch size: 73, lr: 4.59e-04
+2022-06-18 22:54:57,086 INFO [train.py:874] (0/4) Epoch 18, batch 600, datatang_loss[loss=0.1507, simple_loss=0.2251, pruned_loss=0.03811, over 4932.00 frames.], tot_loss[loss=0.153, simple_loss=0.2329, pruned_loss=0.0366, over 936900.51 frames.], batch size: 79, aishell_tot_loss[loss=0.1553, simple_loss=0.2405, pruned_loss=0.03507, over 769607.08 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2244, pruned_loss=0.0375, over 763305.83 frames.], batch size: 79, lr: 4.59e-04
+2022-06-18 22:55:24,768 INFO [train.py:874] (0/4) Epoch 18, batch 650, datatang_loss[loss=0.1443, simple_loss=0.2208, pruned_loss=0.03392, over 4942.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2329, pruned_loss=0.03695, over 947571.73 frames.], batch size: 62, aishell_tot_loss[loss=0.1549, simple_loss=0.2398, pruned_loss=0.035, over 795942.45 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2252, pruned_loss=0.0381, over 788425.51 frames.], batch size: 62, lr: 4.59e-04
+2022-06-18 22:55:56,306 INFO [train.py:874] (0/4) Epoch 18, batch 700, datatang_loss[loss=0.131, simple_loss=0.2116, pruned_loss=0.02515, over 4944.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2331, pruned_loss=0.03666, over 955894.48 frames.], batch size: 55, aishell_tot_loss[loss=0.155, simple_loss=0.24, pruned_loss=0.03498, over 821297.75 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2251, pruned_loss=0.03795, over 808317.95 frames.], batch size: 55, lr: 4.59e-04
+2022-06-18 22:56:25,862 INFO [train.py:874] (0/4) Epoch 18, batch 750, aishell_loss[loss=0.136, simple_loss=0.2242, pruned_loss=0.02394, over 4965.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2326, pruned_loss=0.03659, over 962434.76 frames.], batch size: 31, aishell_tot_loss[loss=0.1547, simple_loss=0.2396, pruned_loss=0.03487, over 841330.61 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.225, pruned_loss=0.03805, over 828404.61 frames.], batch size: 31, lr: 4.58e-04
+2022-06-18 22:56:55,812 INFO [train.py:874] (0/4) Epoch 18, batch 800, aishell_loss[loss=0.1461, simple_loss=0.2377, pruned_loss=0.02728, over 4978.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2333, pruned_loss=0.03714, over 967730.86 frames.], batch size: 51, aishell_tot_loss[loss=0.1553, simple_loss=0.2401, pruned_loss=0.03522, over 856550.59 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2257, pruned_loss=0.03837, over 848988.04 frames.], batch size: 51, lr: 4.58e-04
+2022-06-18 22:57:27,086 INFO [train.py:874] (0/4) Epoch 18, batch 850, aishell_loss[loss=0.1682, simple_loss=0.2497, pruned_loss=0.04338, over 4883.00 frames.], tot_loss[loss=0.1533, simple_loss=0.233, pruned_loss=0.03681, over 971795.24 frames.], batch size: 34, aishell_tot_loss[loss=0.1551, simple_loss=0.2399, pruned_loss=0.0351, over 872276.29 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2256, pruned_loss=0.03822, over 864643.40 frames.], batch size: 34, lr: 4.58e-04
+2022-06-18 22:57:56,124 INFO [train.py:874] (0/4) Epoch 18, batch 900, datatang_loss[loss=0.1651, simple_loss=0.2414, pruned_loss=0.04434, over 4918.00 frames.], tot_loss[loss=0.1541, simple_loss=0.2338, pruned_loss=0.03713, over 974837.23 frames.], batch size: 98, aishell_tot_loss[loss=0.155, simple_loss=0.2401, pruned_loss=0.03497, over 884449.29 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2266, pruned_loss=0.0387, over 880098.54 frames.], batch size: 98, lr: 4.58e-04
+2022-06-18 22:58:25,697 INFO [train.py:874] (0/4) Epoch 18, batch 950, datatang_loss[loss=0.1273, simple_loss=0.2056, pruned_loss=0.02448, over 4928.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2327, pruned_loss=0.03691, over 976943.41 frames.], batch size: 77, aishell_tot_loss[loss=0.1552, simple_loss=0.2403, pruned_loss=0.03502, over 892968.38 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2257, pruned_loss=0.03835, over 895639.78 frames.], batch size: 77, lr: 4.58e-04
+2022-06-18 22:58:57,122 INFO [train.py:874] (0/4) Epoch 18, batch 1000, aishell_loss[loss=0.1609, simple_loss=0.2442, pruned_loss=0.03876, over 4979.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2326, pruned_loss=0.03637, over 978827.75 frames.], batch size: 39, aishell_tot_loss[loss=0.1546, simple_loss=0.2398, pruned_loss=0.03475, over 906257.47 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2255, pruned_loss=0.03817, over 903817.34 frames.], batch size: 39, lr: 4.58e-04
+2022-06-18 22:58:57,125 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 22:59:14,040 INFO [train.py:914] (0/4) Epoch 18, validation: loss=0.1636, simple_loss=0.2479, pruned_loss=0.03969, over 1622729.00 frames.
+2022-06-18 22:59:44,461 INFO [train.py:874] (0/4) Epoch 18, batch 1050, datatang_loss[loss=0.1434, simple_loss=0.219, pruned_loss=0.0339, over 4941.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2324, pruned_loss=0.03623, over 980265.20 frames.], batch size: 69, aishell_tot_loss[loss=0.1545, simple_loss=0.2397, pruned_loss=0.03464, over 914634.46 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2256, pruned_loss=0.03808, over 914351.79 frames.], batch size: 69, lr: 4.58e-04
+2022-06-18 23:00:16,717 INFO [train.py:874] (0/4) Epoch 18, batch 1100, datatang_loss[loss=0.1618, simple_loss=0.2369, pruned_loss=0.04332, over 4931.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2327, pruned_loss=0.03638, over 981137.63 frames.], batch size: 83, aishell_tot_loss[loss=0.1543, simple_loss=0.2393, pruned_loss=0.03464, over 921984.90 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2265, pruned_loss=0.03816, over 923414.19 frames.], batch size: 83, lr: 4.57e-04
+2022-06-18 23:00:44,289 INFO [train.py:874] (0/4) Epoch 18, batch 1150, datatang_loss[loss=0.1548, simple_loss=0.2359, pruned_loss=0.03689, over 4962.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2334, pruned_loss=0.0365, over 982003.26 frames.], batch size: 55, aishell_tot_loss[loss=0.1544, simple_loss=0.2396, pruned_loss=0.03463, over 928471.64 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2271, pruned_loss=0.03827, over 931584.12 frames.], batch size: 55, lr: 4.57e-04
+2022-06-18 23:01:15,667 INFO [train.py:874] (0/4) Epoch 18, batch 1200, aishell_loss[loss=0.1639, simple_loss=0.2463, pruned_loss=0.04075, over 4873.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2331, pruned_loss=0.03621, over 982784.37 frames.], batch size: 28, aishell_tot_loss[loss=0.1539, simple_loss=0.239, pruned_loss=0.03439, over 935488.65 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2271, pruned_loss=0.03824, over 937669.27 frames.], batch size: 28, lr: 4.57e-04
+2022-06-18 23:01:47,506 INFO [train.py:874] (0/4) Epoch 18, batch 1250, datatang_loss[loss=0.1533, simple_loss=0.2206, pruned_loss=0.04294, over 4955.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2339, pruned_loss=0.03653, over 983454.47 frames.], batch size: 67, aishell_tot_loss[loss=0.1547, simple_loss=0.2401, pruned_loss=0.03465, over 941680.50 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2269, pruned_loss=0.03832, over 943081.63 frames.], batch size: 67, lr: 4.57e-04
+2022-06-18 23:02:15,951 INFO [train.py:874] (0/4) Epoch 18, batch 1300, datatang_loss[loss=0.1547, simple_loss=0.2267, pruned_loss=0.04134, over 4921.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2337, pruned_loss=0.03698, over 983904.72 frames.], batch size: 73, aishell_tot_loss[loss=0.1549, simple_loss=0.2403, pruned_loss=0.03472, over 945610.14 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2269, pruned_loss=0.03867, over 949239.97 frames.], batch size: 73, lr: 4.57e-04
+2022-06-18 23:02:45,770 INFO [train.py:874] (0/4) Epoch 18, batch 1350, aishell_loss[loss=0.1709, simple_loss=0.254, pruned_loss=0.04386, over 4916.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2337, pruned_loss=0.03703, over 984182.54 frames.], batch size: 41, aishell_tot_loss[loss=0.1551, simple_loss=0.2403, pruned_loss=0.03496, over 950587.34 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2268, pruned_loss=0.03859, over 953185.60 frames.], batch size: 41, lr: 4.57e-04
+2022-06-18 23:03:17,885 INFO [train.py:874] (0/4) Epoch 18, batch 1400, datatang_loss[loss=0.1365, simple_loss=0.2065, pruned_loss=0.03326, over 4985.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2334, pruned_loss=0.03689, over 984996.52 frames.], batch size: 37, aishell_tot_loss[loss=0.1548, simple_loss=0.24, pruned_loss=0.03482, over 954822.02 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2269, pruned_loss=0.03862, over 957397.35 frames.], batch size: 37, lr: 4.56e-04
+2022-06-18 23:03:45,939 INFO [train.py:874] (0/4) Epoch 18, batch 1450, aishell_loss[loss=0.1455, simple_loss=0.2304, pruned_loss=0.03033, over 4950.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2333, pruned_loss=0.03658, over 985188.26 frames.], batch size: 54, aishell_tot_loss[loss=0.1542, simple_loss=0.2396, pruned_loss=0.03446, over 958322.00 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2272, pruned_loss=0.03868, over 960905.52 frames.], batch size: 54, lr: 4.56e-04
+2022-06-18 23:04:16,693 INFO [train.py:874] (0/4) Epoch 18, batch 1500, datatang_loss[loss=0.1561, simple_loss=0.231, pruned_loss=0.04063, over 4956.00 frames.], tot_loss[loss=0.1526, simple_loss=0.233, pruned_loss=0.03615, over 985226.17 frames.], batch size: 86, aishell_tot_loss[loss=0.1537, simple_loss=0.2392, pruned_loss=0.0341, over 961135.55 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2273, pruned_loss=0.03859, over 964170.08 frames.], batch size: 86, lr: 4.56e-04
+2022-06-18 23:04:45,963 INFO [train.py:874] (0/4) Epoch 18, batch 1550, aishell_loss[loss=0.1645, simple_loss=0.2495, pruned_loss=0.03974, over 4979.00 frames.], tot_loss[loss=0.153, simple_loss=0.2332, pruned_loss=0.03633, over 985181.04 frames.], batch size: 51, aishell_tot_loss[loss=0.1541, simple_loss=0.2394, pruned_loss=0.03443, over 964414.77 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.227, pruned_loss=0.0385, over 966216.28 frames.], batch size: 51, lr: 4.56e-04
+2022-06-18 23:05:15,892 INFO [train.py:874] (0/4) Epoch 18, batch 1600, datatang_loss[loss=0.1453, simple_loss=0.2223, pruned_loss=0.03414, over 4926.00 frames.], tot_loss[loss=0.1531, simple_loss=0.233, pruned_loss=0.03657, over 985078.13 frames.], batch size: 77, aishell_tot_loss[loss=0.1537, simple_loss=0.2387, pruned_loss=0.03439, over 966828.10 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2273, pruned_loss=0.03881, over 968379.86 frames.], batch size: 77, lr: 4.56e-04
+2022-06-18 23:05:47,558 INFO [train.py:874] (0/4) Epoch 18, batch 1650, datatang_loss[loss=0.1512, simple_loss=0.223, pruned_loss=0.03972, over 4988.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2335, pruned_loss=0.03688, over 985412.47 frames.], batch size: 34, aishell_tot_loss[loss=0.1546, simple_loss=0.2395, pruned_loss=0.03485, over 969070.67 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2272, pruned_loss=0.0386, over 970597.54 frames.], batch size: 34, lr: 4.56e-04
+2022-06-18 23:05:59,928 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-72000.pt
+2022-06-18 23:06:22,682 INFO [train.py:874] (0/4) Epoch 18, batch 1700, datatang_loss[loss=0.1416, simple_loss=0.2096, pruned_loss=0.03687, over 4973.00 frames.], tot_loss[loss=0.153, simple_loss=0.2332, pruned_loss=0.03642, over 985669.06 frames.], batch size: 60, aishell_tot_loss[loss=0.1544, simple_loss=0.2395, pruned_loss=0.03466, over 971156.63 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.227, pruned_loss=0.03837, over 972459.17 frames.], batch size: 60, lr: 4.55e-04
+2022-06-18 23:06:51,253 INFO [train.py:874] (0/4) Epoch 18, batch 1750, aishell_loss[loss=0.1571, simple_loss=0.2369, pruned_loss=0.03868, over 4948.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2331, pruned_loss=0.03608, over 985739.72 frames.], batch size: 40, aishell_tot_loss[loss=0.1537, simple_loss=0.2389, pruned_loss=0.03427, over 972952.56 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2274, pruned_loss=0.03837, over 974003.48 frames.], batch size: 40, lr: 4.55e-04
+2022-06-18 23:07:22,827 INFO [train.py:874] (0/4) Epoch 18, batch 1800, datatang_loss[loss=0.1622, simple_loss=0.2344, pruned_loss=0.04493, over 4881.00 frames.], tot_loss[loss=0.153, simple_loss=0.2333, pruned_loss=0.03633, over 986083.91 frames.], batch size: 39, aishell_tot_loss[loss=0.1538, simple_loss=0.2391, pruned_loss=0.03429, over 974305.97 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2277, pruned_loss=0.03847, over 975875.26 frames.], batch size: 39, lr: 4.55e-04
+2022-06-18 23:07:53,159 INFO [train.py:874] (0/4) Epoch 18, batch 1850, aishell_loss[loss=0.1399, simple_loss=0.2256, pruned_loss=0.02707, over 4983.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2328, pruned_loss=0.03631, over 986243.58 frames.], batch size: 30, aishell_tot_loss[loss=0.1536, simple_loss=0.2388, pruned_loss=0.03423, over 975880.36 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2273, pruned_loss=0.03855, over 977065.89 frames.], batch size: 30, lr: 4.55e-04
+2022-06-18 23:08:22,176 INFO [train.py:874] (0/4) Epoch 18, batch 1900, aishell_loss[loss=0.1466, simple_loss=0.2291, pruned_loss=0.03205, over 4967.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2332, pruned_loss=0.03669, over 986277.31 frames.], batch size: 27, aishell_tot_loss[loss=0.1543, simple_loss=0.2392, pruned_loss=0.03472, over 977110.03 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2271, pruned_loss=0.03851, over 978197.44 frames.], batch size: 27, lr: 4.55e-04
+2022-06-18 23:08:53,188 INFO [train.py:874] (0/4) Epoch 18, batch 1950, datatang_loss[loss=0.15, simple_loss=0.2255, pruned_loss=0.03721, over 4974.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2334, pruned_loss=0.03685, over 986189.19 frames.], batch size: 60, aishell_tot_loss[loss=0.1548, simple_loss=0.2397, pruned_loss=0.0349, over 977786.36 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2273, pruned_loss=0.0384, over 979411.84 frames.], batch size: 60, lr: 4.55e-04
+2022-06-18 23:09:24,291 INFO [train.py:874] (0/4) Epoch 18, batch 2000, datatang_loss[loss=0.1454, simple_loss=0.2267, pruned_loss=0.03204, over 4929.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2326, pruned_loss=0.03624, over 986422.77 frames.], batch size: 79, aishell_tot_loss[loss=0.1543, simple_loss=0.2394, pruned_loss=0.03457, over 978831.73 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2269, pruned_loss=0.0381, over 980388.06 frames.], batch size: 79, lr: 4.55e-04
+2022-06-18 23:09:24,293 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 23:09:40,068 INFO [train.py:914] (0/4) Epoch 18, validation: loss=0.1648, simple_loss=0.2489, pruned_loss=0.0403, over 1622729.00 frames.
+2022-06-18 23:10:11,559 INFO [train.py:874] (0/4) Epoch 18, batch 2050, aishell_loss[loss=0.1451, simple_loss=0.2302, pruned_loss=0.02995, over 4978.00 frames.], tot_loss[loss=0.1517, simple_loss=0.232, pruned_loss=0.03576, over 986296.14 frames.], batch size: 39, aishell_tot_loss[loss=0.1532, simple_loss=0.2384, pruned_loss=0.03404, over 979686.93 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.227, pruned_loss=0.0381, over 980990.82 frames.], batch size: 39, lr: 4.54e-04
+2022-06-18 23:10:39,545 INFO [train.py:874] (0/4) Epoch 18, batch 2100, aishell_loss[loss=0.1252, simple_loss=0.2146, pruned_loss=0.01788, over 4838.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2316, pruned_loss=0.03552, over 986231.91 frames.], batch size: 28, aishell_tot_loss[loss=0.1528, simple_loss=0.238, pruned_loss=0.03385, over 980509.59 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2268, pruned_loss=0.03798, over 981519.37 frames.], batch size: 28, lr: 4.54e-04
+2022-06-18 23:11:11,040 INFO [train.py:874] (0/4) Epoch 18, batch 2150, datatang_loss[loss=0.1487, simple_loss=0.2194, pruned_loss=0.03897, over 4965.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2321, pruned_loss=0.03576, over 985995.28 frames.], batch size: 34, aishell_tot_loss[loss=0.1528, simple_loss=0.2381, pruned_loss=0.03381, over 980932.43 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.227, pruned_loss=0.03818, over 982082.09 frames.], batch size: 34, lr: 4.54e-04
+2022-06-18 23:11:42,800 INFO [train.py:874] (0/4) Epoch 18, batch 2200, aishell_loss[loss=0.1586, simple_loss=0.2368, pruned_loss=0.04025, over 4934.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2324, pruned_loss=0.03564, over 985946.88 frames.], batch size: 32, aishell_tot_loss[loss=0.1525, simple_loss=0.2379, pruned_loss=0.03358, over 981301.67 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2276, pruned_loss=0.03822, over 982704.73 frames.], batch size: 32, lr: 4.54e-04
+2022-06-18 23:12:10,479 INFO [train.py:874] (0/4) Epoch 18, batch 2250, datatang_loss[loss=0.1453, simple_loss=0.2266, pruned_loss=0.03202, over 4930.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2321, pruned_loss=0.03538, over 986307.96 frames.], batch size: 71, aishell_tot_loss[loss=0.1523, simple_loss=0.2379, pruned_loss=0.03342, over 982137.75 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2272, pruned_loss=0.038, over 983156.33 frames.], batch size: 71, lr: 4.54e-04
+2022-06-18 23:12:42,743 INFO [train.py:874] (0/4) Epoch 18, batch 2300, datatang_loss[loss=0.1476, simple_loss=0.2269, pruned_loss=0.03419, over 4896.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2324, pruned_loss=0.03574, over 985999.13 frames.], batch size: 52, aishell_tot_loss[loss=0.1526, simple_loss=0.2381, pruned_loss=0.0336, over 982382.09 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2273, pruned_loss=0.03805, over 983453.07 frames.], batch size: 52, lr: 4.54e-04
+2022-06-18 23:13:13,530 INFO [train.py:874] (0/4) Epoch 18, batch 2350, aishell_loss[loss=0.1643, simple_loss=0.2466, pruned_loss=0.04098, over 4972.00 frames.], tot_loss[loss=0.1524, simple_loss=0.233, pruned_loss=0.03591, over 985859.21 frames.], batch size: 39, aishell_tot_loss[loss=0.1528, simple_loss=0.2384, pruned_loss=0.03361, over 982723.28 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2274, pruned_loss=0.03824, over 983698.87 frames.], batch size: 39, lr: 4.53e-04
+2022-06-18 23:13:42,677 INFO [train.py:874] (0/4) Epoch 18, batch 2400, datatang_loss[loss=0.1642, simple_loss=0.2392, pruned_loss=0.04458, over 4957.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2331, pruned_loss=0.03614, over 985842.23 frames.], batch size: 86, aishell_tot_loss[loss=0.1529, simple_loss=0.2385, pruned_loss=0.03363, over 983138.54 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.2275, pruned_loss=0.03843, over 983867.85 frames.], batch size: 86, lr: 4.53e-04
+2022-06-18 23:14:13,856 INFO [train.py:874] (0/4) Epoch 18, batch 2450, aishell_loss[loss=0.1482, simple_loss=0.2385, pruned_loss=0.02892, over 4885.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2344, pruned_loss=0.0365, over 985669.57 frames.], batch size: 42, aishell_tot_loss[loss=0.1537, simple_loss=0.2392, pruned_loss=0.03405, over 983415.59 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2278, pruned_loss=0.03854, over 983969.03 frames.], batch size: 42, lr: 4.53e-04
+2022-06-18 23:14:43,649 INFO [train.py:874] (0/4) Epoch 18, batch 2500, aishell_loss[loss=0.1447, simple_loss=0.2341, pruned_loss=0.02761, over 4933.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2339, pruned_loss=0.03617, over 985692.92 frames.], batch size: 56, aishell_tot_loss[loss=0.1532, simple_loss=0.2385, pruned_loss=0.03394, over 983620.88 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2278, pruned_loss=0.03849, over 984264.48 frames.], batch size: 56, lr: 4.53e-04
+2022-06-18 23:15:12,825 INFO [train.py:874] (0/4) Epoch 18, batch 2550, datatang_loss[loss=0.1493, simple_loss=0.2337, pruned_loss=0.03248, over 4951.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2333, pruned_loss=0.03585, over 985976.06 frames.], batch size: 37, aishell_tot_loss[loss=0.1527, simple_loss=0.2382, pruned_loss=0.03366, over 984153.54 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2276, pruned_loss=0.03852, over 984456.49 frames.], batch size: 37, lr: 4.53e-04
+2022-06-18 23:15:44,502 INFO [train.py:874] (0/4) Epoch 18, batch 2600, aishell_loss[loss=0.1639, simple_loss=0.252, pruned_loss=0.03793, over 4975.00 frames.], tot_loss[loss=0.1534, simple_loss=0.234, pruned_loss=0.03645, over 985917.16 frames.], batch size: 61, aishell_tot_loss[loss=0.1533, simple_loss=0.2386, pruned_loss=0.03396, over 984275.91 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2279, pruned_loss=0.03883, over 984659.42 frames.], batch size: 61, lr: 4.53e-04
+2022-06-18 23:16:12,045 INFO [train.py:874] (0/4) Epoch 18, batch 2650, aishell_loss[loss=0.1685, simple_loss=0.2522, pruned_loss=0.04242, over 4946.00 frames.], tot_loss[loss=0.1525, simple_loss=0.233, pruned_loss=0.03598, over 985924.10 frames.], batch size: 79, aishell_tot_loss[loss=0.1531, simple_loss=0.2387, pruned_loss=0.0338, over 984490.09 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.227, pruned_loss=0.03849, over 984793.02 frames.], batch size: 79, lr: 4.52e-04
+2022-06-18 23:16:42,926 INFO [train.py:874] (0/4) Epoch 18, batch 2700, aishell_loss[loss=0.1535, simple_loss=0.2417, pruned_loss=0.03263, over 4937.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2332, pruned_loss=0.03625, over 985628.19 frames.], batch size: 54, aishell_tot_loss[loss=0.1536, simple_loss=0.239, pruned_loss=0.03411, over 984506.33 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2268, pruned_loss=0.03842, over 984759.04 frames.], batch size: 54, lr: 4.52e-04
+2022-06-18 23:17:14,055 INFO [train.py:874] (0/4) Epoch 18, batch 2750, aishell_loss[loss=0.149, simple_loss=0.2359, pruned_loss=0.03111, over 4886.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2322, pruned_loss=0.03583, over 985679.03 frames.], batch size: 50, aishell_tot_loss[loss=0.1531, simple_loss=0.2385, pruned_loss=0.03384, over 984795.83 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2267, pruned_loss=0.03815, over 984756.13 frames.], batch size: 50, lr: 4.52e-04
+2022-06-18 23:17:42,078 INFO [train.py:874] (0/4) Epoch 18, batch 2800, aishell_loss[loss=0.1361, simple_loss=0.2213, pruned_loss=0.0254, over 4873.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2319, pruned_loss=0.03567, over 985712.77 frames.], batch size: 28, aishell_tot_loss[loss=0.1527, simple_loss=0.2381, pruned_loss=0.03364, over 984862.82 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2267, pruned_loss=0.03815, over 984929.13 frames.], batch size: 28, lr: 4.52e-04
+2022-06-18 23:18:13,865 INFO [train.py:874] (0/4) Epoch 18, batch 2850, aishell_loss[loss=0.1605, simple_loss=0.2494, pruned_loss=0.03578, over 4933.00 frames.], tot_loss[loss=0.1517, simple_loss=0.232, pruned_loss=0.03571, over 985461.35 frames.], batch size: 58, aishell_tot_loss[loss=0.1531, simple_loss=0.2386, pruned_loss=0.03387, over 984796.57 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2262, pruned_loss=0.03789, over 984920.82 frames.], batch size: 58, lr: 4.52e-04
+2022-06-18 23:18:44,553 INFO [train.py:874] (0/4) Epoch 18, batch 2900, aishell_loss[loss=0.1304, simple_loss=0.2082, pruned_loss=0.02629, over 4946.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2311, pruned_loss=0.03555, over 985425.74 frames.], batch size: 25, aishell_tot_loss[loss=0.1524, simple_loss=0.2376, pruned_loss=0.03359, over 984840.82 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2262, pruned_loss=0.03795, over 984972.05 frames.], batch size: 25, lr: 4.52e-04
+2022-06-18 23:19:14,111 INFO [train.py:874] (0/4) Epoch 18, batch 2950, datatang_loss[loss=0.15, simple_loss=0.2264, pruned_loss=0.03675, over 4915.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2311, pruned_loss=0.03576, over 985700.75 frames.], batch size: 83, aishell_tot_loss[loss=0.1526, simple_loss=0.2377, pruned_loss=0.03376, over 984922.24 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2263, pruned_loss=0.03782, over 985300.44 frames.], batch size: 83, lr: 4.52e-04
+2022-06-18 23:19:45,377 INFO [train.py:874] (0/4) Epoch 18, batch 3000, aishell_loss[loss=0.1594, simple_loss=0.2453, pruned_loss=0.03676, over 4938.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2304, pruned_loss=0.03535, over 985560.08 frames.], batch size: 64, aishell_tot_loss[loss=0.1527, simple_loss=0.2378, pruned_loss=0.03379, over 985076.31 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2256, pruned_loss=0.03727, over 985129.31 frames.], batch size: 64, lr: 4.51e-04
+2022-06-18 23:19:45,379 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 23:20:02,475 INFO [train.py:914] (0/4) Epoch 18, validation: loss=0.165, simple_loss=0.2484, pruned_loss=0.04079, over 1622729.00 frames.
+2022-06-18 23:20:31,595 INFO [train.py:874] (0/4) Epoch 18, batch 3050, datatang_loss[loss=0.146, simple_loss=0.2281, pruned_loss=0.03191, over 4937.00 frames.], tot_loss[loss=0.1513, simple_loss=0.231, pruned_loss=0.03574, over 985644.30 frames.], batch size: 69, aishell_tot_loss[loss=0.1536, simple_loss=0.2386, pruned_loss=0.03429, over 985088.06 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2251, pruned_loss=0.03714, over 985305.77 frames.], batch size: 69, lr: 4.51e-04
+2022-06-18 23:21:04,152 INFO [train.py:874] (0/4) Epoch 18, batch 3100, datatang_loss[loss=0.1317, simple_loss=0.2142, pruned_loss=0.02461, over 4987.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2315, pruned_loss=0.03558, over 985671.33 frames.], batch size: 31, aishell_tot_loss[loss=0.1535, simple_loss=0.2385, pruned_loss=0.0342, over 985056.63 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2254, pruned_loss=0.03707, over 985465.98 frames.], batch size: 31, lr: 4.51e-04
+2022-06-18 23:21:34,389 INFO [train.py:874] (0/4) Epoch 18, batch 3150, datatang_loss[loss=0.1875, simple_loss=0.2614, pruned_loss=0.05682, over 4963.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2332, pruned_loss=0.0366, over 986100.75 frames.], batch size: 99, aishell_tot_loss[loss=0.1539, simple_loss=0.239, pruned_loss=0.03438, over 985387.78 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2266, pruned_loss=0.03796, over 985684.01 frames.], batch size: 99, lr: 4.51e-04
+2022-06-18 23:22:05,205 INFO [train.py:874] (0/4) Epoch 18, batch 3200, datatang_loss[loss=0.1364, simple_loss=0.2179, pruned_loss=0.02739, over 4826.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2326, pruned_loss=0.03649, over 986198.91 frames.], batch size: 25, aishell_tot_loss[loss=0.1541, simple_loss=0.2392, pruned_loss=0.03449, over 985399.60 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2262, pruned_loss=0.03776, over 985909.65 frames.], batch size: 25, lr: 4.51e-04
+2022-06-18 23:22:37,281 INFO [train.py:874] (0/4) Epoch 18, batch 3250, datatang_loss[loss=0.1851, simple_loss=0.2671, pruned_loss=0.05149, over 4894.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2333, pruned_loss=0.03663, over 986114.28 frames.], batch size: 52, aishell_tot_loss[loss=0.1538, simple_loss=0.2393, pruned_loss=0.03421, over 985546.90 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.227, pruned_loss=0.03825, over 985789.87 frames.], batch size: 52, lr: 4.51e-04
+2022-06-18 23:23:06,594 INFO [train.py:874] (0/4) Epoch 18, batch 3300, aishell_loss[loss=0.131, simple_loss=0.1913, pruned_loss=0.03535, over 4874.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2331, pruned_loss=0.03679, over 986242.60 frames.], batch size: 21, aishell_tot_loss[loss=0.1532, simple_loss=0.2384, pruned_loss=0.03395, over 985605.33 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2276, pruned_loss=0.03876, over 985963.60 frames.], batch size: 21, lr: 4.50e-04
+2022-06-18 23:23:37,936 INFO [train.py:874] (0/4) Epoch 18, batch 3350, datatang_loss[loss=0.1331, simple_loss=0.2135, pruned_loss=0.0263, over 4941.00 frames.], tot_loss[loss=0.1522, simple_loss=0.232, pruned_loss=0.03622, over 985739.15 frames.], batch size: 69, aishell_tot_loss[loss=0.1528, simple_loss=0.2383, pruned_loss=0.03368, over 985548.39 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2267, pruned_loss=0.03849, over 985598.86 frames.], batch size: 69, lr: 4.50e-04
+2022-06-18 23:24:09,440 INFO [train.py:874] (0/4) Epoch 18, batch 3400, datatang_loss[loss=0.1473, simple_loss=0.2298, pruned_loss=0.03236, over 4935.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2325, pruned_loss=0.03662, over 985628.11 frames.], batch size: 88, aishell_tot_loss[loss=0.1535, simple_loss=0.2388, pruned_loss=0.03406, over 985317.13 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2268, pruned_loss=0.03845, over 985761.13 frames.], batch size: 88, lr: 4.50e-04
+2022-06-18 23:24:39,082 INFO [train.py:874] (0/4) Epoch 18, batch 3450, datatang_loss[loss=0.1743, simple_loss=0.2377, pruned_loss=0.05545, over 4891.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2323, pruned_loss=0.03698, over 985773.68 frames.], batch size: 52, aishell_tot_loss[loss=0.1539, simple_loss=0.2391, pruned_loss=0.03434, over 985340.81 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2265, pruned_loss=0.03853, over 985899.77 frames.], batch size: 52, lr: 4.50e-04
+2022-06-18 23:25:10,228 INFO [train.py:874] (0/4) Epoch 18, batch 3500, aishell_loss[loss=0.147, simple_loss=0.2475, pruned_loss=0.02325, over 4922.00 frames.], tot_loss[loss=0.152, simple_loss=0.2312, pruned_loss=0.03636, over 985527.40 frames.], batch size: 68, aishell_tot_loss[loss=0.154, simple_loss=0.2392, pruned_loss=0.03438, over 985316.90 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2253, pruned_loss=0.0379, over 985674.79 frames.], batch size: 68, lr: 4.50e-04
+2022-06-18 23:25:41,314 INFO [train.py:874] (0/4) Epoch 18, batch 3550, aishell_loss[loss=0.1483, simple_loss=0.2361, pruned_loss=0.03025, over 4920.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2323, pruned_loss=0.03643, over 985863.59 frames.], batch size: 46, aishell_tot_loss[loss=0.1546, simple_loss=0.2399, pruned_loss=0.03467, over 985525.81 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2256, pruned_loss=0.03773, over 985831.20 frames.], batch size: 46, lr: 4.50e-04
+2022-06-18 23:26:10,888 INFO [train.py:874] (0/4) Epoch 18, batch 3600, aishell_loss[loss=0.1541, simple_loss=0.2345, pruned_loss=0.03685, over 4872.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2316, pruned_loss=0.03609, over 985723.63 frames.], batch size: 37, aishell_tot_loss[loss=0.1539, simple_loss=0.2391, pruned_loss=0.03438, over 985532.79 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2255, pruned_loss=0.03773, over 985739.66 frames.], batch size: 37, lr: 4.50e-04
+2022-06-18 23:26:42,441 INFO [train.py:874] (0/4) Epoch 18, batch 3650, aishell_loss[loss=0.122, simple_loss=0.1949, pruned_loss=0.02449, over 4935.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2313, pruned_loss=0.03588, over 985502.68 frames.], batch size: 25, aishell_tot_loss[loss=0.154, simple_loss=0.2391, pruned_loss=0.03445, over 985251.58 frames.], datatang_tot_loss[loss=0.15, simple_loss=0.2251, pruned_loss=0.03745, over 985806.69 frames.], batch size: 25, lr: 4.49e-04
+2022-06-18 23:27:14,774 INFO [train.py:874] (0/4) Epoch 18, batch 3700, aishell_loss[loss=0.1383, simple_loss=0.2309, pruned_loss=0.02286, over 4974.00 frames.], tot_loss[loss=0.1516, simple_loss=0.231, pruned_loss=0.03609, over 985275.52 frames.], batch size: 51, aishell_tot_loss[loss=0.154, simple_loss=0.2388, pruned_loss=0.03459, over 985125.26 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2248, pruned_loss=0.03751, over 985661.07 frames.], batch size: 51, lr: 4.49e-04
+2022-06-18 23:27:43,238 INFO [train.py:874] (0/4) Epoch 18, batch 3750, aishell_loss[loss=0.1551, simple_loss=0.2427, pruned_loss=0.03376, over 4911.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2311, pruned_loss=0.03593, over 984900.71 frames.], batch size: 41, aishell_tot_loss[loss=0.1538, simple_loss=0.2388, pruned_loss=0.0344, over 984897.85 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2246, pruned_loss=0.03761, over 985482.64 frames.], batch size: 41, lr: 4.49e-04
+2022-06-18 23:28:15,700 INFO [train.py:874] (0/4) Epoch 18, batch 3800, datatang_loss[loss=0.173, simple_loss=0.2491, pruned_loss=0.04847, over 4943.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2319, pruned_loss=0.03581, over 984998.26 frames.], batch size: 50, aishell_tot_loss[loss=0.1535, simple_loss=0.2386, pruned_loss=0.03415, over 984881.92 frames.], datatang_tot_loss[loss=0.1503, simple_loss=0.2249, pruned_loss=0.03779, over 985550.37 frames.], batch size: 50, lr: 4.49e-04
+2022-06-18 23:28:45,423 INFO [train.py:874] (0/4) Epoch 18, batch 3850, datatang_loss[loss=0.157, simple_loss=0.2138, pruned_loss=0.05013, over 4917.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2325, pruned_loss=0.0364, over 985021.38 frames.], batch size: 34, aishell_tot_loss[loss=0.1533, simple_loss=0.2385, pruned_loss=0.03403, over 984824.09 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2254, pruned_loss=0.03858, over 985590.32 frames.], batch size: 34, lr: 4.49e-04
+2022-06-18 23:29:15,514 INFO [train.py:874] (0/4) Epoch 18, batch 3900, datatang_loss[loss=0.1573, simple_loss=0.2317, pruned_loss=0.04149, over 4957.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2328, pruned_loss=0.03633, over 985422.06 frames.], batch size: 37, aishell_tot_loss[loss=0.1536, simple_loss=0.2388, pruned_loss=0.03416, over 984895.49 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2256, pruned_loss=0.03842, over 985890.95 frames.], batch size: 37, lr: 4.49e-04
+2022-06-18 23:29:45,384 INFO [train.py:874] (0/4) Epoch 18, batch 3950, datatang_loss[loss=0.1401, simple_loss=0.2146, pruned_loss=0.0328, over 4927.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2329, pruned_loss=0.03632, over 985103.12 frames.], batch size: 42, aishell_tot_loss[loss=0.1536, simple_loss=0.239, pruned_loss=0.03408, over 984790.41 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2258, pruned_loss=0.03843, over 985664.96 frames.], batch size: 42, lr: 4.49e-04
+2022-06-18 23:30:15,571 INFO [train.py:874] (0/4) Epoch 18, batch 4000, aishell_loss[loss=0.1759, simple_loss=0.2665, pruned_loss=0.04269, over 4870.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2327, pruned_loss=0.03604, over 985026.61 frames.], batch size: 42, aishell_tot_loss[loss=0.1542, simple_loss=0.2398, pruned_loss=0.0343, over 984739.09 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2251, pruned_loss=0.03786, over 985599.70 frames.], batch size: 42, lr: 4.48e-04
+2022-06-18 23:30:15,574 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 23:30:33,178 INFO [train.py:914] (0/4) Epoch 18, validation: loss=0.1643, simple_loss=0.249, pruned_loss=0.03975, over 1622729.00 frames.
+2022-06-18 23:31:01,714 INFO [train.py:874] (0/4) Epoch 18, batch 4050, aishell_loss[loss=0.1813, simple_loss=0.268, pruned_loss=0.04732, over 4879.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2319, pruned_loss=0.03556, over 985016.37 frames.], batch size: 50, aishell_tot_loss[loss=0.1535, simple_loss=0.2391, pruned_loss=0.03402, over 984520.91 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2251, pruned_loss=0.03763, over 985789.03 frames.], batch size: 50, lr: 4.48e-04
+2022-06-18 23:31:31,894 INFO [train.py:874] (0/4) Epoch 18, batch 4100, datatang_loss[loss=0.1521, simple_loss=0.2302, pruned_loss=0.03702, over 4936.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2309, pruned_loss=0.0352, over 984932.21 frames.], batch size: 79, aishell_tot_loss[loss=0.1532, simple_loss=0.2386, pruned_loss=0.03385, over 984554.29 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2245, pruned_loss=0.03731, over 985618.95 frames.], batch size: 79, lr: 4.48e-04
+2022-06-18 23:32:01,290 INFO [train.py:874] (0/4) Epoch 18, batch 4150, aishell_loss[loss=0.1578, simple_loss=0.2405, pruned_loss=0.03755, over 4927.00 frames.], tot_loss[loss=0.152, simple_loss=0.232, pruned_loss=0.03601, over 985365.41 frames.], batch size: 41, aishell_tot_loss[loss=0.1547, simple_loss=0.24, pruned_loss=0.03464, over 984799.64 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2243, pruned_loss=0.03723, over 985802.63 frames.], batch size: 41, lr: 4.48e-04
+2022-06-18 23:32:31,803 INFO [train.py:874] (0/4) Epoch 18, batch 4200, datatang_loss[loss=0.1366, simple_loss=0.2177, pruned_loss=0.02771, over 4924.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2337, pruned_loss=0.03677, over 985214.55 frames.], batch size: 81, aishell_tot_loss[loss=0.1548, simple_loss=0.2404, pruned_loss=0.03458, over 984681.61 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2257, pruned_loss=0.03813, over 985781.18 frames.], batch size: 81, lr: 4.48e-04
+2022-06-18 23:32:34,360 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-18.pt
+2022-06-18 23:33:39,356 INFO [train.py:874] (0/4) Epoch 19, batch 50, aishell_loss[loss=0.156, simple_loss=0.2381, pruned_loss=0.03692, over 4961.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2252, pruned_loss=0.03273, over 218692.08 frames.], batch size: 61, aishell_tot_loss[loss=0.1505, simple_loss=0.2368, pruned_loss=0.03209, over 111838.15 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2147, pruned_loss=0.03328, over 120524.11 frames.], batch size: 61, lr: 4.36e-04
+2022-06-18 23:34:10,842 INFO [train.py:874] (0/4) Epoch 19, batch 100, aishell_loss[loss=0.1497, simple_loss=0.235, pruned_loss=0.03218, over 4970.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2279, pruned_loss=0.03349, over 388748.89 frames.], batch size: 51, aishell_tot_loss[loss=0.1539, simple_loss=0.2399, pruned_loss=0.03393, over 214722.37 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2156, pruned_loss=0.03286, over 222475.15 frames.], batch size: 51, lr: 4.36e-04
+2022-06-18 23:34:43,807 INFO [train.py:874] (0/4) Epoch 19, batch 150, datatang_loss[loss=0.1349, simple_loss=0.219, pruned_loss=0.02537, over 4942.00 frames.], tot_loss[loss=0.1465, simple_loss=0.227, pruned_loss=0.03304, over 521226.05 frames.], batch size: 86, aishell_tot_loss[loss=0.1533, simple_loss=0.239, pruned_loss=0.03376, over 298724.79 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2157, pruned_loss=0.03229, over 319183.06 frames.], batch size: 86, lr: 4.36e-04
+2022-06-18 23:35:13,706 INFO [train.py:874] (0/4) Epoch 19, batch 200, aishell_loss[loss=0.1637, simple_loss=0.2423, pruned_loss=0.04252, over 4939.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2272, pruned_loss=0.03325, over 624099.72 frames.], batch size: 32, aishell_tot_loss[loss=0.1533, simple_loss=0.2393, pruned_loss=0.03371, over 370377.90 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2161, pruned_loss=0.03269, over 406432.55 frames.], batch size: 32, lr: 4.36e-04
+2022-06-18 23:35:44,911 INFO [train.py:874] (0/4) Epoch 19, batch 250, datatang_loss[loss=0.1479, simple_loss=0.2191, pruned_loss=0.03841, over 4979.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2284, pruned_loss=0.03355, over 704239.94 frames.], batch size: 45, aishell_tot_loss[loss=0.1538, simple_loss=0.2397, pruned_loss=0.03399, over 445366.47 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2171, pruned_loss=0.0329, over 472260.69 frames.], batch size: 45, lr: 4.36e-04
+2022-06-18 23:36:17,238 INFO [train.py:874] (0/4) Epoch 19, batch 300, aishell_loss[loss=0.1569, simple_loss=0.2401, pruned_loss=0.03685, over 4974.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2279, pruned_loss=0.03346, over 766532.94 frames.], batch size: 51, aishell_tot_loss[loss=0.1536, simple_loss=0.2395, pruned_loss=0.03387, over 506489.60 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2166, pruned_loss=0.03289, over 535005.31 frames.], batch size: 51, lr: 4.36e-04
+2022-06-18 23:36:46,752 INFO [train.py:874] (0/4) Epoch 19, batch 350, aishell_loss[loss=0.1507, simple_loss=0.2326, pruned_loss=0.03439, over 4981.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2279, pruned_loss=0.03376, over 815164.12 frames.], batch size: 44, aishell_tot_loss[loss=0.1532, simple_loss=0.2384, pruned_loss=0.03399, over 564859.45 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2174, pruned_loss=0.03324, over 586306.10 frames.], batch size: 44, lr: 4.35e-04
+2022-06-18 23:37:18,103 INFO [train.py:874] (0/4) Epoch 19, batch 400, aishell_loss[loss=0.1667, simple_loss=0.2514, pruned_loss=0.04098, over 4942.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2295, pruned_loss=0.03476, over 853181.24 frames.], batch size: 49, aishell_tot_loss[loss=0.1551, simple_loss=0.2404, pruned_loss=0.03494, over 610747.44 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2182, pruned_loss=0.03386, over 637015.71 frames.], batch size: 49, lr: 4.35e-04
+2022-06-18 23:37:49,207 INFO [train.py:874] (0/4) Epoch 19, batch 450, datatang_loss[loss=0.1415, simple_loss=0.222, pruned_loss=0.03052, over 4921.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2295, pruned_loss=0.03451, over 882704.17 frames.], batch size: 75, aishell_tot_loss[loss=0.1551, simple_loss=0.2405, pruned_loss=0.03486, over 656798.76 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.218, pruned_loss=0.03368, over 676442.43 frames.], batch size: 75, lr: 4.35e-04
+2022-06-18 23:38:17,523 INFO [train.py:874] (0/4) Epoch 19, batch 500, aishell_loss[loss=0.1653, simple_loss=0.2524, pruned_loss=0.0391, over 4914.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2295, pruned_loss=0.03453, over 905109.78 frames.], batch size: 68, aishell_tot_loss[loss=0.154, simple_loss=0.2391, pruned_loss=0.03442, over 695016.55 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2194, pruned_loss=0.03422, over 712957.92 frames.], batch size: 68, lr: 4.35e-04
+2022-06-18 23:38:49,411 INFO [train.py:874] (0/4) Epoch 19, batch 550, aishell_loss[loss=0.1511, simple_loss=0.2462, pruned_loss=0.02794, over 4957.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2294, pruned_loss=0.0345, over 922605.64 frames.], batch size: 61, aishell_tot_loss[loss=0.1541, simple_loss=0.2391, pruned_loss=0.03452, over 728790.57 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2194, pruned_loss=0.03416, over 745173.83 frames.], batch size: 61, lr: 4.35e-04
+2022-06-18 23:39:21,384 INFO [train.py:874] (0/4) Epoch 19, batch 600, aishell_loss[loss=0.1851, simple_loss=0.2698, pruned_loss=0.05017, over 4939.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2302, pruned_loss=0.0351, over 936361.46 frames.], batch size: 79, aishell_tot_loss[loss=0.1537, simple_loss=0.2387, pruned_loss=0.03433, over 759963.94 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2206, pruned_loss=0.03516, over 772401.29 frames.], batch size: 79, lr: 4.35e-04
+2022-06-18 23:39:49,966 INFO [train.py:874] (0/4) Epoch 19, batch 650, aishell_loss[loss=0.1554, simple_loss=0.2457, pruned_loss=0.03249, over 4947.00 frames.], tot_loss[loss=0.151, simple_loss=0.231, pruned_loss=0.0355, over 947444.13 frames.], batch size: 45, aishell_tot_loss[loss=0.1538, simple_loss=0.239, pruned_loss=0.03431, over 785453.68 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2217, pruned_loss=0.03582, over 798710.60 frames.], batch size: 45, lr: 4.35e-04
+2022-06-18 23:40:22,382 INFO [train.py:874] (0/4) Epoch 19, batch 700, datatang_loss[loss=0.1431, simple_loss=0.221, pruned_loss=0.03258, over 4919.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2315, pruned_loss=0.03538, over 956130.60 frames.], batch size: 75, aishell_tot_loss[loss=0.1535, simple_loss=0.2387, pruned_loss=0.0341, over 808219.29 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2228, pruned_loss=0.03598, over 821701.91 frames.], batch size: 75, lr: 4.34e-04
+2022-06-18 23:40:54,863 INFO [train.py:874] (0/4) Epoch 19, batch 750, aishell_loss[loss=0.1572, simple_loss=0.2426, pruned_loss=0.03591, over 4957.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2316, pruned_loss=0.03575, over 962363.69 frames.], batch size: 56, aishell_tot_loss[loss=0.1528, simple_loss=0.2378, pruned_loss=0.03389, over 827999.45 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2242, pruned_loss=0.03671, over 841732.74 frames.], batch size: 56, lr: 4.34e-04
+2022-06-18 23:41:23,178 INFO [train.py:874] (0/4) Epoch 19, batch 800, aishell_loss[loss=0.1618, simple_loss=0.2464, pruned_loss=0.03861, over 4886.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2322, pruned_loss=0.03582, over 967405.75 frames.], batch size: 42, aishell_tot_loss[loss=0.1534, simple_loss=0.2383, pruned_loss=0.0342, over 849167.24 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2242, pruned_loss=0.03666, over 856145.52 frames.], batch size: 42, lr: 4.34e-04
+2022-06-18 23:41:55,285 INFO [train.py:874] (0/4) Epoch 19, batch 850, aishell_loss[loss=0.1589, simple_loss=0.2445, pruned_loss=0.03658, over 4982.00 frames.], tot_loss[loss=0.152, simple_loss=0.2324, pruned_loss=0.03578, over 971159.27 frames.], batch size: 39, aishell_tot_loss[loss=0.153, simple_loss=0.2381, pruned_loss=0.03389, over 865055.28 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2249, pruned_loss=0.03705, over 871260.16 frames.], batch size: 39, lr: 4.34e-04
+2022-06-18 23:42:25,136 INFO [train.py:874] (0/4) Epoch 19, batch 900, aishell_loss[loss=0.1769, simple_loss=0.26, pruned_loss=0.04687, over 4982.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2334, pruned_loss=0.03641, over 974579.68 frames.], batch size: 38, aishell_tot_loss[loss=0.1536, simple_loss=0.2386, pruned_loss=0.03426, over 879805.27 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2257, pruned_loss=0.03751, over 884420.38 frames.], batch size: 38, lr: 4.34e-04
+2022-06-18 23:42:56,451 INFO [train.py:874] (0/4) Epoch 19, batch 950, aishell_loss[loss=0.1867, simple_loss=0.2741, pruned_loss=0.04963, over 4915.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2324, pruned_loss=0.03593, over 977145.05 frames.], batch size: 68, aishell_tot_loss[loss=0.1534, simple_loss=0.2385, pruned_loss=0.03409, over 891322.59 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2251, pruned_loss=0.03719, over 897358.77 frames.], batch size: 68, lr: 4.34e-04
+2022-06-18 23:43:29,205 INFO [train.py:874] (0/4) Epoch 19, batch 1000, aishell_loss[loss=0.1659, simple_loss=0.2587, pruned_loss=0.03658, over 4914.00 frames.], tot_loss[loss=0.1531, simple_loss=0.233, pruned_loss=0.0366, over 978859.31 frames.], batch size: 46, aishell_tot_loss[loss=0.1534, simple_loss=0.2385, pruned_loss=0.03412, over 899683.08 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2263, pruned_loss=0.0379, over 910090.37 frames.], batch size: 46, lr: 4.34e-04
+2022-06-18 23:43:29,208 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 23:43:46,290 INFO [train.py:914] (0/4) Epoch 19, validation: loss=0.1644, simple_loss=0.2486, pruned_loss=0.04009, over 1622729.00 frames.
+2022-06-18 23:44:16,850 INFO [train.py:874] (0/4) Epoch 19, batch 1050, datatang_loss[loss=0.1443, simple_loss=0.2334, pruned_loss=0.02763, over 4927.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2322, pruned_loss=0.03578, over 980572.73 frames.], batch size: 94, aishell_tot_loss[loss=0.1528, simple_loss=0.2383, pruned_loss=0.03369, over 909178.65 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.226, pruned_loss=0.0375, over 919733.58 frames.], batch size: 94, lr: 4.33e-04
+2022-06-18 23:44:49,110 INFO [train.py:874] (0/4) Epoch 19, batch 1100, aishell_loss[loss=0.1644, simple_loss=0.2486, pruned_loss=0.04013, over 4953.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2325, pruned_loss=0.0363, over 981253.16 frames.], batch size: 40, aishell_tot_loss[loss=0.1528, simple_loss=0.2381, pruned_loss=0.03378, over 916855.05 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2267, pruned_loss=0.03798, over 928188.18 frames.], batch size: 40, lr: 4.33e-04
+2022-06-18 23:45:17,768 INFO [train.py:874] (0/4) Epoch 19, batch 1150, datatang_loss[loss=0.1368, simple_loss=0.2144, pruned_loss=0.02957, over 4954.00 frames.], tot_loss[loss=0.1519, simple_loss=0.232, pruned_loss=0.03595, over 982143.26 frames.], batch size: 67, aishell_tot_loss[loss=0.1522, simple_loss=0.2375, pruned_loss=0.03348, over 923802.22 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2268, pruned_loss=0.03792, over 935863.13 frames.], batch size: 67, lr: 4.33e-04
+2022-06-18 23:45:50,864 INFO [train.py:874] (0/4) Epoch 19, batch 1200, aishell_loss[loss=0.1606, simple_loss=0.252, pruned_loss=0.03459, over 4869.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2325, pruned_loss=0.03608, over 982890.68 frames.], batch size: 37, aishell_tot_loss[loss=0.1529, simple_loss=0.2382, pruned_loss=0.03381, over 931297.55 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2266, pruned_loss=0.03782, over 941545.01 frames.], batch size: 37, lr: 4.33e-04
+2022-06-18 23:46:23,204 INFO [train.py:874] (0/4) Epoch 19, batch 1250, aishell_loss[loss=0.1209, simple_loss=0.2088, pruned_loss=0.01651, over 4977.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2313, pruned_loss=0.03517, over 983420.63 frames.], batch size: 30, aishell_tot_loss[loss=0.1523, simple_loss=0.2379, pruned_loss=0.03334, over 938086.05 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2257, pruned_loss=0.03738, over 946342.33 frames.], batch size: 30, lr: 4.33e-04
+2022-06-18 23:46:52,171 INFO [train.py:874] (0/4) Epoch 19, batch 1300, aishell_loss[loss=0.1641, simple_loss=0.2451, pruned_loss=0.04156, over 4925.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2316, pruned_loss=0.03547, over 983717.22 frames.], batch size: 58, aishell_tot_loss[loss=0.1529, simple_loss=0.2386, pruned_loss=0.03364, over 942158.63 frames.], datatang_tot_loss[loss=0.15, simple_loss=0.2255, pruned_loss=0.03724, over 952041.53 frames.], batch size: 58, lr: 4.33e-04
+2022-06-18 23:47:23,374 INFO [train.py:874] (0/4) Epoch 19, batch 1350, aishell_loss[loss=0.156, simple_loss=0.248, pruned_loss=0.03202, over 4966.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2321, pruned_loss=0.03535, over 984139.99 frames.], batch size: 51, aishell_tot_loss[loss=0.1531, simple_loss=0.2389, pruned_loss=0.03368, over 947780.03 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2254, pruned_loss=0.03714, over 955607.63 frames.], batch size: 51, lr: 4.33e-04
+2022-06-18 23:47:56,179 INFO [train.py:874] (0/4) Epoch 19, batch 1400, aishell_loss[loss=0.1661, simple_loss=0.2477, pruned_loss=0.0423, over 4986.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2321, pruned_loss=0.03565, over 984730.63 frames.], batch size: 38, aishell_tot_loss[loss=0.1536, simple_loss=0.2392, pruned_loss=0.034, over 951149.56 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2255, pruned_loss=0.03703, over 960221.55 frames.], batch size: 38, lr: 4.32e-04
+2022-06-18 23:48:24,799 INFO [train.py:874] (0/4) Epoch 19, batch 1450, datatang_loss[loss=0.1528, simple_loss=0.223, pruned_loss=0.04124, over 4892.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2331, pruned_loss=0.03567, over 984634.56 frames.], batch size: 47, aishell_tot_loss[loss=0.154, simple_loss=0.2398, pruned_loss=0.0341, over 955106.07 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2258, pruned_loss=0.03698, over 963076.82 frames.], batch size: 47, lr: 4.32e-04
+2022-06-18 23:48:41,784 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-76000.pt
+2022-06-18 23:49:02,513 INFO [train.py:874] (0/4) Epoch 19, batch 1500, datatang_loss[loss=0.1429, simple_loss=0.2131, pruned_loss=0.0363, over 4872.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2333, pruned_loss=0.03595, over 984903.98 frames.], batch size: 39, aishell_tot_loss[loss=0.1542, simple_loss=0.2397, pruned_loss=0.03436, over 958937.01 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2261, pruned_loss=0.03709, over 965617.82 frames.], batch size: 39, lr: 4.32e-04
+2022-06-18 23:49:33,578 INFO [train.py:874] (0/4) Epoch 19, batch 1550, aishell_loss[loss=0.1391, simple_loss=0.2215, pruned_loss=0.0283, over 4888.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2339, pruned_loss=0.03617, over 984723.58 frames.], batch size: 28, aishell_tot_loss[loss=0.1543, simple_loss=0.24, pruned_loss=0.0343, over 962258.16 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2263, pruned_loss=0.0375, over 967554.88 frames.], batch size: 28, lr: 4.32e-04
+2022-06-18 23:50:04,156 INFO [train.py:874] (0/4) Epoch 19, batch 1600, aishell_loss[loss=0.1428, simple_loss=0.2299, pruned_loss=0.02781, over 4940.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2337, pruned_loss=0.03599, over 984778.74 frames.], batch size: 54, aishell_tot_loss[loss=0.1543, simple_loss=0.24, pruned_loss=0.03423, over 964852.63 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2262, pruned_loss=0.03742, over 969689.58 frames.], batch size: 54, lr: 4.32e-04
+2022-06-18 23:50:37,457 INFO [train.py:874] (0/4) Epoch 19, batch 1650, aishell_loss[loss=0.1879, simple_loss=0.2771, pruned_loss=0.04933, over 4914.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2332, pruned_loss=0.03586, over 984686.29 frames.], batch size: 76, aishell_tot_loss[loss=0.1534, simple_loss=0.2391, pruned_loss=0.03385, over 967461.79 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2264, pruned_loss=0.03777, over 971211.92 frames.], batch size: 76, lr: 4.32e-04
+2022-06-18 23:51:09,496 INFO [train.py:874] (0/4) Epoch 19, batch 1700, datatang_loss[loss=0.1657, simple_loss=0.2384, pruned_loss=0.04652, over 4964.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2326, pruned_loss=0.03541, over 985173.02 frames.], batch size: 60, aishell_tot_loss[loss=0.1531, simple_loss=0.239, pruned_loss=0.03359, over 969190.07 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2262, pruned_loss=0.03745, over 973533.39 frames.], batch size: 60, lr: 4.32e-04
+2022-06-18 23:51:39,498 INFO [train.py:874] (0/4) Epoch 19, batch 1750, datatang_loss[loss=0.1696, simple_loss=0.254, pruned_loss=0.04262, over 4982.00 frames.], tot_loss[loss=0.152, simple_loss=0.2326, pruned_loss=0.03573, over 985020.29 frames.], batch size: 37, aishell_tot_loss[loss=0.1534, simple_loss=0.2391, pruned_loss=0.03383, over 970608.85 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2264, pruned_loss=0.03746, over 975124.07 frames.], batch size: 37, lr: 4.31e-04
+2022-06-18 23:52:12,624 INFO [train.py:874] (0/4) Epoch 19, batch 1800, aishell_loss[loss=0.1533, simple_loss=0.2369, pruned_loss=0.03483, over 4957.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2327, pruned_loss=0.03571, over 984842.43 frames.], batch size: 40, aishell_tot_loss[loss=0.1534, simple_loss=0.2392, pruned_loss=0.03377, over 972081.92 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2263, pruned_loss=0.03754, over 976345.11 frames.], batch size: 40, lr: 4.31e-04
+2022-06-18 23:52:41,181 INFO [train.py:874] (0/4) Epoch 19, batch 1850, datatang_loss[loss=0.2215, simple_loss=0.2841, pruned_loss=0.0794, over 4948.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2336, pruned_loss=0.03563, over 985235.15 frames.], batch size: 109, aishell_tot_loss[loss=0.153, simple_loss=0.239, pruned_loss=0.03351, over 974161.03 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.227, pruned_loss=0.03784, over 977299.33 frames.], batch size: 109, lr: 4.31e-04
+2022-06-18 23:53:12,992 INFO [train.py:874] (0/4) Epoch 19, batch 1900, aishell_loss[loss=0.1309, simple_loss=0.2065, pruned_loss=0.02758, over 4972.00 frames.], tot_loss[loss=0.1522, simple_loss=0.233, pruned_loss=0.03574, over 985402.54 frames.], batch size: 25, aishell_tot_loss[loss=0.1529, simple_loss=0.2388, pruned_loss=0.03354, over 975570.84 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2267, pruned_loss=0.03795, over 978326.29 frames.], batch size: 25, lr: 4.31e-04
+2022-06-18 23:53:45,784 INFO [train.py:874] (0/4) Epoch 19, batch 1950, datatang_loss[loss=0.1693, simple_loss=0.2514, pruned_loss=0.04363, over 4921.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2328, pruned_loss=0.03567, over 985801.35 frames.], batch size: 42, aishell_tot_loss[loss=0.1529, simple_loss=0.2389, pruned_loss=0.03345, over 976875.47 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2265, pruned_loss=0.03798, over 979430.65 frames.], batch size: 42, lr: 4.31e-04
+2022-06-18 23:54:14,557 INFO [train.py:874] (0/4) Epoch 19, batch 2000, datatang_loss[loss=0.1355, simple_loss=0.2054, pruned_loss=0.0328, over 4930.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2324, pruned_loss=0.03547, over 985536.08 frames.], batch size: 50, aishell_tot_loss[loss=0.1524, simple_loss=0.2384, pruned_loss=0.03321, over 977685.53 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2265, pruned_loss=0.03797, over 980117.21 frames.], batch size: 50, lr: 4.31e-04
+2022-06-18 23:54:14,560 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-18 23:54:31,136 INFO [train.py:914] (0/4) Epoch 19, validation: loss=0.1648, simple_loss=0.2482, pruned_loss=0.04066, over 1622729.00 frames.
+2022-06-18 23:55:03,606 INFO [train.py:874] (0/4) Epoch 19, batch 2050, datatang_loss[loss=0.1622, simple_loss=0.2401, pruned_loss=0.04214, over 4947.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2324, pruned_loss=0.0354, over 985511.66 frames.], batch size: 67, aishell_tot_loss[loss=0.1524, simple_loss=0.2382, pruned_loss=0.03333, over 978617.24 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2266, pruned_loss=0.03783, over 980754.40 frames.], batch size: 67, lr: 4.31e-04
+2022-06-18 23:55:34,117 INFO [train.py:874] (0/4) Epoch 19, batch 2100, datatang_loss[loss=0.1459, simple_loss=0.2253, pruned_loss=0.03326, over 4939.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2328, pruned_loss=0.03601, over 985590.33 frames.], batch size: 88, aishell_tot_loss[loss=0.1523, simple_loss=0.2382, pruned_loss=0.03318, over 979230.45 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2272, pruned_loss=0.03847, over 981543.90 frames.], batch size: 88, lr: 4.30e-04
+2022-06-18 23:56:07,330 INFO [train.py:874] (0/4) Epoch 19, batch 2150, aishell_loss[loss=0.1862, simple_loss=0.2746, pruned_loss=0.04884, over 4940.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2319, pruned_loss=0.03562, over 985665.43 frames.], batch size: 64, aishell_tot_loss[loss=0.1518, simple_loss=0.2377, pruned_loss=0.03293, over 980052.54 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2269, pruned_loss=0.03824, over 982014.52 frames.], batch size: 64, lr: 4.30e-04
+2022-06-18 23:56:40,093 INFO [train.py:874] (0/4) Epoch 19, batch 2200, datatang_loss[loss=0.1467, simple_loss=0.2207, pruned_loss=0.03632, over 4932.00 frames.], tot_loss[loss=0.151, simple_loss=0.2311, pruned_loss=0.0355, over 985597.64 frames.], batch size: 81, aishell_tot_loss[loss=0.152, simple_loss=0.2379, pruned_loss=0.03311, over 980642.95 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2258, pruned_loss=0.03794, over 982430.09 frames.], batch size: 81, lr: 4.30e-04
+2022-06-18 23:57:09,675 INFO [train.py:874] (0/4) Epoch 19, batch 2250, aishell_loss[loss=0.1346, simple_loss=0.2292, pruned_loss=0.01998, over 4916.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2318, pruned_loss=0.03562, over 985417.08 frames.], batch size: 52, aishell_tot_loss[loss=0.1525, simple_loss=0.2383, pruned_loss=0.03334, over 980924.15 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2262, pruned_loss=0.03779, over 982888.69 frames.], batch size: 52, lr: 4.30e-04
+2022-06-18 23:57:42,508 INFO [train.py:874] (0/4) Epoch 19, batch 2300, datatang_loss[loss=0.1596, simple_loss=0.2255, pruned_loss=0.04688, over 4901.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2318, pruned_loss=0.03543, over 985251.18 frames.], batch size: 25, aishell_tot_loss[loss=0.1525, simple_loss=0.2385, pruned_loss=0.03321, over 981322.07 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2258, pruned_loss=0.03778, over 983163.44 frames.], batch size: 25, lr: 4.30e-04
+2022-06-18 23:58:14,308 INFO [train.py:874] (0/4) Epoch 19, batch 2350, aishell_loss[loss=0.1185, simple_loss=0.211, pruned_loss=0.01302, over 4982.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2324, pruned_loss=0.03529, over 985427.36 frames.], batch size: 30, aishell_tot_loss[loss=0.1529, simple_loss=0.2391, pruned_loss=0.0334, over 982047.66 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2254, pruned_loss=0.03753, over 983362.68 frames.], batch size: 30, lr: 4.30e-04
+2022-06-18 23:58:44,223 INFO [train.py:874] (0/4) Epoch 19, batch 2400, datatang_loss[loss=0.1402, simple_loss=0.2205, pruned_loss=0.02991, over 4933.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2334, pruned_loss=0.03606, over 985061.33 frames.], batch size: 62, aishell_tot_loss[loss=0.1534, simple_loss=0.2394, pruned_loss=0.03369, over 982063.49 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2264, pruned_loss=0.03797, over 983580.93 frames.], batch size: 62, lr: 4.30e-04
+2022-06-18 23:59:16,965 INFO [train.py:874] (0/4) Epoch 19, batch 2450, datatang_loss[loss=0.1494, simple_loss=0.225, pruned_loss=0.03693, over 4906.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2338, pruned_loss=0.03624, over 985222.17 frames.], batch size: 64, aishell_tot_loss[loss=0.1533, simple_loss=0.2394, pruned_loss=0.03364, over 982255.52 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.227, pruned_loss=0.0382, over 984064.44 frames.], batch size: 64, lr: 4.30e-04
+2022-06-18 23:59:48,702 INFO [train.py:874] (0/4) Epoch 19, batch 2500, datatang_loss[loss=0.1516, simple_loss=0.2343, pruned_loss=0.03444, over 4934.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2338, pruned_loss=0.03601, over 985184.91 frames.], batch size: 88, aishell_tot_loss[loss=0.1536, simple_loss=0.2394, pruned_loss=0.03391, over 982464.39 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.227, pruned_loss=0.03787, over 984347.05 frames.], batch size: 88, lr: 4.29e-04
+2022-06-19 00:00:18,831 INFO [train.py:874] (0/4) Epoch 19, batch 2550, aishell_loss[loss=0.1639, simple_loss=0.2548, pruned_loss=0.03653, over 4978.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2337, pruned_loss=0.03576, over 985409.65 frames.], batch size: 48, aishell_tot_loss[loss=0.1536, simple_loss=0.2393, pruned_loss=0.03395, over 982806.24 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.227, pruned_loss=0.03761, over 984682.88 frames.], batch size: 48, lr: 4.29e-04
+2022-06-19 00:00:52,789 INFO [train.py:874] (0/4) Epoch 19, batch 2600, datatang_loss[loss=0.15, simple_loss=0.2291, pruned_loss=0.03544, over 4955.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2334, pruned_loss=0.03559, over 985264.03 frames.], batch size: 86, aishell_tot_loss[loss=0.1535, simple_loss=0.2392, pruned_loss=0.03387, over 982982.91 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2272, pruned_loss=0.03747, over 984711.15 frames.], batch size: 86, lr: 4.29e-04
+2022-06-19 00:01:22,177 INFO [train.py:874] (0/4) Epoch 19, batch 2650, aishell_loss[loss=0.1282, simple_loss=0.2145, pruned_loss=0.02095, over 4978.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2325, pruned_loss=0.03558, over 985661.55 frames.], batch size: 30, aishell_tot_loss[loss=0.1529, simple_loss=0.2385, pruned_loss=0.03361, over 983646.15 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.227, pruned_loss=0.03771, over 984790.00 frames.], batch size: 30, lr: 4.29e-04
+2022-06-19 00:01:54,086 INFO [train.py:874] (0/4) Epoch 19, batch 2700, datatang_loss[loss=0.1389, simple_loss=0.2182, pruned_loss=0.02979, over 4781.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2317, pruned_loss=0.03521, over 985803.02 frames.], batch size: 24, aishell_tot_loss[loss=0.1522, simple_loss=0.2379, pruned_loss=0.03331, over 984040.97 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2268, pruned_loss=0.03756, over 984898.17 frames.], batch size: 24, lr: 4.29e-04
+2022-06-19 00:02:27,629 INFO [train.py:874] (0/4) Epoch 19, batch 2750, aishell_loss[loss=0.1806, simple_loss=0.2613, pruned_loss=0.05001, over 4914.00 frames.], tot_loss[loss=0.151, simple_loss=0.231, pruned_loss=0.03548, over 985854.04 frames.], batch size: 46, aishell_tot_loss[loss=0.1524, simple_loss=0.2378, pruned_loss=0.03344, over 984209.06 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2262, pruned_loss=0.03752, over 985059.56 frames.], batch size: 46, lr: 4.29e-04
+2022-06-19 00:03:00,485 INFO [train.py:874] (0/4) Epoch 19, batch 2800, aishell_loss[loss=0.1898, simple_loss=0.2707, pruned_loss=0.05438, over 4916.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2311, pruned_loss=0.03566, over 985976.03 frames.], batch size: 41, aishell_tot_loss[loss=0.1528, simple_loss=0.2382, pruned_loss=0.03373, over 984335.00 frames.], datatang_tot_loss[loss=0.1503, simple_loss=0.2261, pruned_loss=0.03728, over 985319.27 frames.], batch size: 41, lr: 4.29e-04
+2022-06-19 00:03:29,848 INFO [train.py:874] (0/4) Epoch 19, batch 2850, aishell_loss[loss=0.1673, simple_loss=0.2623, pruned_loss=0.03613, over 4955.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2321, pruned_loss=0.03548, over 985923.37 frames.], batch size: 61, aishell_tot_loss[loss=0.1528, simple_loss=0.2383, pruned_loss=0.03365, over 984456.95 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2264, pruned_loss=0.03732, over 985434.83 frames.], batch size: 61, lr: 4.28e-04
+2022-06-19 00:04:03,076 INFO [train.py:874] (0/4) Epoch 19, batch 2900, datatang_loss[loss=0.1552, simple_loss=0.2317, pruned_loss=0.03931, over 4906.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2321, pruned_loss=0.03523, over 985753.95 frames.], batch size: 57, aishell_tot_loss[loss=0.1528, simple_loss=0.2385, pruned_loss=0.0335, over 984606.35 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2259, pruned_loss=0.03727, over 985359.38 frames.], batch size: 57, lr: 4.28e-04
+2022-06-19 00:04:31,793 INFO [train.py:874] (0/4) Epoch 19, batch 2950, datatang_loss[loss=0.1427, simple_loss=0.215, pruned_loss=0.03516, over 4913.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2323, pruned_loss=0.0359, over 985393.59 frames.], batch size: 42, aishell_tot_loss[loss=0.1528, simple_loss=0.2383, pruned_loss=0.03364, over 984305.66 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2264, pruned_loss=0.03777, over 985447.78 frames.], batch size: 42, lr: 4.28e-04
+2022-06-19 00:05:05,080 INFO [train.py:874] (0/4) Epoch 19, batch 3000, aishell_loss[loss=0.1534, simple_loss=0.2349, pruned_loss=0.03601, over 4894.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2336, pruned_loss=0.03615, over 985351.63 frames.], batch size: 34, aishell_tot_loss[loss=0.1533, simple_loss=0.2391, pruned_loss=0.03376, over 984491.67 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2268, pruned_loss=0.03799, over 985357.50 frames.], batch size: 34, lr: 4.28e-04
+2022-06-19 00:05:05,084 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 00:05:22,959 INFO [train.py:914] (0/4) Epoch 19, validation: loss=0.1643, simple_loss=0.2485, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 00:05:51,965 INFO [train.py:874] (0/4) Epoch 19, batch 3050, aishell_loss[loss=0.1415, simple_loss=0.2189, pruned_loss=0.03203, over 4881.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2336, pruned_loss=0.03587, over 985622.70 frames.], batch size: 28, aishell_tot_loss[loss=0.1529, simple_loss=0.2387, pruned_loss=0.03355, over 984893.47 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.227, pruned_loss=0.03813, over 985366.58 frames.], batch size: 28, lr: 4.28e-04
+2022-06-19 00:06:24,277 INFO [train.py:874] (0/4) Epoch 19, batch 3100, datatang_loss[loss=0.145, simple_loss=0.2211, pruned_loss=0.03449, over 4948.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2332, pruned_loss=0.03586, over 985445.45 frames.], batch size: 88, aishell_tot_loss[loss=0.153, simple_loss=0.2384, pruned_loss=0.03376, over 984775.60 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.227, pruned_loss=0.03794, over 985427.47 frames.], batch size: 88, lr: 4.28e-04
+2022-06-19 00:06:52,802 INFO [train.py:874] (0/4) Epoch 19, batch 3150, aishell_loss[loss=0.1481, simple_loss=0.2429, pruned_loss=0.0267, over 4985.00 frames.], tot_loss[loss=0.1514, simple_loss=0.232, pruned_loss=0.03535, over 985482.89 frames.], batch size: 39, aishell_tot_loss[loss=0.1528, simple_loss=0.2383, pruned_loss=0.03363, over 984846.17 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.226, pruned_loss=0.0375, over 985475.64 frames.], batch size: 39, lr: 4.28e-04
+2022-06-19 00:07:26,191 INFO [train.py:874] (0/4) Epoch 19, batch 3200, aishell_loss[loss=0.1475, simple_loss=0.2243, pruned_loss=0.03537, over 4898.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2321, pruned_loss=0.03572, over 985390.74 frames.], batch size: 28, aishell_tot_loss[loss=0.1529, simple_loss=0.2383, pruned_loss=0.03376, over 984711.21 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2262, pruned_loss=0.03767, over 985590.33 frames.], batch size: 28, lr: 4.27e-04
+2022-06-19 00:07:58,162 INFO [train.py:874] (0/4) Epoch 19, batch 3250, aishell_loss[loss=0.1573, simple_loss=0.2489, pruned_loss=0.03282, over 4960.00 frames.], tot_loss[loss=0.1525, simple_loss=0.233, pruned_loss=0.03598, over 985284.67 frames.], batch size: 40, aishell_tot_loss[loss=0.1536, simple_loss=0.2389, pruned_loss=0.03417, over 984654.01 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2265, pruned_loss=0.0376, over 985607.00 frames.], batch size: 40, lr: 4.27e-04
+2022-06-19 00:08:26,506 INFO [train.py:874] (0/4) Epoch 19, batch 3300, aishell_loss[loss=0.1506, simple_loss=0.2361, pruned_loss=0.03259, over 4947.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2331, pruned_loss=0.03629, over 985533.46 frames.], batch size: 45, aishell_tot_loss[loss=0.1537, simple_loss=0.2388, pruned_loss=0.03427, over 985063.93 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2267, pruned_loss=0.03786, over 985489.91 frames.], batch size: 45, lr: 4.27e-04
+2022-06-19 00:08:58,828 INFO [train.py:874] (0/4) Epoch 19, batch 3350, aishell_loss[loss=0.1258, simple_loss=0.1986, pruned_loss=0.02652, over 4952.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2328, pruned_loss=0.03593, over 986018.78 frames.], batch size: 25, aishell_tot_loss[loss=0.1536, simple_loss=0.2388, pruned_loss=0.03417, over 985316.30 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2264, pruned_loss=0.03762, over 985819.94 frames.], batch size: 25, lr: 4.27e-04
+2022-06-19 00:09:31,443 INFO [train.py:874] (0/4) Epoch 19, batch 3400, aishell_loss[loss=0.1613, simple_loss=0.2449, pruned_loss=0.03883, over 4949.00 frames.], tot_loss[loss=0.151, simple_loss=0.2315, pruned_loss=0.03528, over 985879.01 frames.], batch size: 56, aishell_tot_loss[loss=0.1531, simple_loss=0.2381, pruned_loss=0.03405, over 985334.16 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2259, pruned_loss=0.03702, over 985758.74 frames.], batch size: 56, lr: 4.27e-04
+2022-06-19 00:09:59,982 INFO [train.py:874] (0/4) Epoch 19, batch 3450, datatang_loss[loss=0.1299, simple_loss=0.213, pruned_loss=0.02335, over 4917.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2317, pruned_loss=0.03537, over 985946.83 frames.], batch size: 81, aishell_tot_loss[loss=0.1534, simple_loss=0.2386, pruned_loss=0.03405, over 985322.76 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2255, pruned_loss=0.03704, over 985921.67 frames.], batch size: 81, lr: 4.27e-04
+2022-06-19 00:10:33,079 INFO [train.py:874] (0/4) Epoch 19, batch 3500, aishell_loss[loss=0.1348, simple_loss=0.2313, pruned_loss=0.01918, over 4957.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2317, pruned_loss=0.03477, over 985648.96 frames.], batch size: 40, aishell_tot_loss[loss=0.1531, simple_loss=0.2386, pruned_loss=0.0338, over 985062.13 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2254, pruned_loss=0.03666, over 985938.44 frames.], batch size: 40, lr: 4.27e-04
+2022-06-19 00:11:03,925 INFO [train.py:874] (0/4) Epoch 19, batch 3550, aishell_loss[loss=0.1332, simple_loss=0.2247, pruned_loss=0.02085, over 4920.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2315, pruned_loss=0.03486, over 985667.49 frames.], batch size: 41, aishell_tot_loss[loss=0.1527, simple_loss=0.2381, pruned_loss=0.03361, over 985101.36 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2256, pruned_loss=0.03682, over 985948.40 frames.], batch size: 41, lr: 4.26e-04
+2022-06-19 00:11:33,776 INFO [train.py:874] (0/4) Epoch 19, batch 3600, aishell_loss[loss=0.1501, simple_loss=0.2265, pruned_loss=0.03683, over 4977.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2324, pruned_loss=0.03537, over 985328.05 frames.], batch size: 39, aishell_tot_loss[loss=0.1531, simple_loss=0.2388, pruned_loss=0.0337, over 984940.01 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2262, pruned_loss=0.03712, over 985757.83 frames.], batch size: 39, lr: 4.26e-04
+2022-06-19 00:12:05,687 INFO [train.py:874] (0/4) Epoch 19, batch 3650, aishell_loss[loss=0.1459, simple_loss=0.2411, pruned_loss=0.02539, over 4904.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2323, pruned_loss=0.03528, over 985747.36 frames.], batch size: 60, aishell_tot_loss[loss=0.1531, simple_loss=0.2388, pruned_loss=0.03367, over 985230.91 frames.], datatang_tot_loss[loss=0.15, simple_loss=0.2259, pruned_loss=0.03706, over 985917.32 frames.], batch size: 60, lr: 4.26e-04
+2022-06-19 00:12:38,526 INFO [train.py:874] (0/4) Epoch 19, batch 3700, datatang_loss[loss=0.1198, simple_loss=0.1972, pruned_loss=0.0212, over 4910.00 frames.], tot_loss[loss=0.151, simple_loss=0.2318, pruned_loss=0.03509, over 985583.56 frames.], batch size: 52, aishell_tot_loss[loss=0.1528, simple_loss=0.2385, pruned_loss=0.03354, over 985087.09 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2259, pruned_loss=0.03691, over 985909.46 frames.], batch size: 52, lr: 4.26e-04
+2022-06-19 00:13:06,425 INFO [train.py:874] (0/4) Epoch 19, batch 3750, datatang_loss[loss=0.1222, simple_loss=0.2061, pruned_loss=0.01917, over 4966.00 frames.], tot_loss[loss=0.151, simple_loss=0.2318, pruned_loss=0.0351, over 985954.23 frames.], batch size: 67, aishell_tot_loss[loss=0.1531, simple_loss=0.2386, pruned_loss=0.03378, over 985546.59 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2255, pruned_loss=0.03668, over 985866.36 frames.], batch size: 67, lr: 4.26e-04
+2022-06-19 00:13:39,400 INFO [train.py:874] (0/4) Epoch 19, batch 3800, aishell_loss[loss=0.1541, simple_loss=0.2421, pruned_loss=0.03308, over 4903.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2312, pruned_loss=0.03469, over 985504.64 frames.], batch size: 52, aishell_tot_loss[loss=0.1527, simple_loss=0.2381, pruned_loss=0.03361, over 985172.97 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2252, pruned_loss=0.03642, over 985836.81 frames.], batch size: 52, lr: 4.26e-04
+2022-06-19 00:14:10,392 INFO [train.py:874] (0/4) Epoch 19, batch 3850, aishell_loss[loss=0.1511, simple_loss=0.2379, pruned_loss=0.03215, over 4930.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2312, pruned_loss=0.03478, over 985483.06 frames.], batch size: 58, aishell_tot_loss[loss=0.1526, simple_loss=0.2381, pruned_loss=0.0335, over 985246.19 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.2253, pruned_loss=0.03647, over 985736.23 frames.], batch size: 58, lr: 4.26e-04
+2022-06-19 00:14:40,619 INFO [train.py:874] (0/4) Epoch 19, batch 3900, aishell_loss[loss=0.146, simple_loss=0.2402, pruned_loss=0.02594, over 4855.00 frames.], tot_loss[loss=0.1512, simple_loss=0.232, pruned_loss=0.03523, over 985593.01 frames.], batch size: 35, aishell_tot_loss[loss=0.1535, simple_loss=0.239, pruned_loss=0.03403, over 985236.39 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2252, pruned_loss=0.03635, over 985842.24 frames.], batch size: 35, lr: 4.26e-04
+2022-06-19 00:15:09,675 INFO [train.py:874] (0/4) Epoch 19, batch 3950, aishell_loss[loss=0.1683, simple_loss=0.2522, pruned_loss=0.04215, over 4947.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2314, pruned_loss=0.03491, over 985553.37 frames.], batch size: 54, aishell_tot_loss[loss=0.1534, simple_loss=0.2388, pruned_loss=0.03399, over 985131.78 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2246, pruned_loss=0.03608, over 985933.68 frames.], batch size: 54, lr: 4.25e-04
+2022-06-19 00:15:40,629 INFO [train.py:874] (0/4) Epoch 19, batch 4000, aishell_loss[loss=0.1459, simple_loss=0.233, pruned_loss=0.02941, over 4972.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2313, pruned_loss=0.03492, over 985478.52 frames.], batch size: 44, aishell_tot_loss[loss=0.1536, simple_loss=0.239, pruned_loss=0.03416, over 984994.38 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2242, pruned_loss=0.0359, over 985989.03 frames.], batch size: 44, lr: 4.25e-04
+2022-06-19 00:15:40,632 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 00:15:58,332 INFO [train.py:914] (0/4) Epoch 19, validation: loss=0.165, simple_loss=0.2492, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 00:16:27,765 INFO [train.py:874] (0/4) Epoch 19, batch 4050, aishell_loss[loss=0.1568, simple_loss=0.2471, pruned_loss=0.03329, over 4885.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2304, pruned_loss=0.03455, over 985203.73 frames.], batch size: 50, aishell_tot_loss[loss=0.1534, simple_loss=0.2387, pruned_loss=0.03401, over 984789.24 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2233, pruned_loss=0.03562, over 985910.92 frames.], batch size: 50, lr: 4.25e-04
+2022-06-19 00:16:58,278 INFO [train.py:874] (0/4) Epoch 19, batch 4100, datatang_loss[loss=0.2453, simple_loss=0.2948, pruned_loss=0.09787, over 4918.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2312, pruned_loss=0.03523, over 985535.89 frames.], batch size: 107, aishell_tot_loss[loss=0.153, simple_loss=0.2385, pruned_loss=0.03376, over 984964.08 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2244, pruned_loss=0.03648, over 986042.15 frames.], batch size: 107, lr: 4.25e-04
+2022-06-19 00:17:27,287 INFO [train.py:874] (0/4) Epoch 19, batch 4150, aishell_loss[loss=0.1452, simple_loss=0.2321, pruned_loss=0.02911, over 4965.00 frames.], tot_loss[loss=0.1505, simple_loss=0.231, pruned_loss=0.03498, over 985577.13 frames.], batch size: 44, aishell_tot_loss[loss=0.1526, simple_loss=0.2382, pruned_loss=0.03352, over 984978.44 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2243, pruned_loss=0.03652, over 986100.77 frames.], batch size: 44, lr: 4.25e-04
+2022-06-19 00:18:00,676 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-19.pt
+2022-06-19 00:19:01,264 INFO [train.py:874] (0/4) Epoch 20, batch 50, datatang_loss[loss=0.1254, simple_loss=0.2029, pruned_loss=0.02394, over 4972.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2214, pruned_loss=0.03095, over 218455.28 frames.], batch size: 37, aishell_tot_loss[loss=0.1535, simple_loss=0.2397, pruned_loss=0.03362, over 89482.02 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2102, pruned_loss=0.02932, over 141851.00 frames.], batch size: 37, lr: 4.14e-04
+2022-06-19 00:19:31,471 INFO [train.py:874] (0/4) Epoch 20, batch 100, aishell_loss[loss=0.2411, simple_loss=0.2999, pruned_loss=0.09113, over 4956.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2255, pruned_loss=0.03268, over 388321.20 frames.], batch size: 64, aishell_tot_loss[loss=0.1543, simple_loss=0.239, pruned_loss=0.03486, over 214395.62 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2114, pruned_loss=0.03022, over 222327.87 frames.], batch size: 64, lr: 4.14e-04
+2022-06-19 00:20:03,664 INFO [train.py:874] (0/4) Epoch 20, batch 150, datatang_loss[loss=0.1197, simple_loss=0.203, pruned_loss=0.01819, over 4918.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2269, pruned_loss=0.03275, over 520424.34 frames.], batch size: 64, aishell_tot_loss[loss=0.1547, simple_loss=0.2399, pruned_loss=0.03475, over 311829.76 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2125, pruned_loss=0.03037, over 305295.81 frames.], batch size: 64, lr: 4.14e-04
+2022-06-19 00:20:32,881 INFO [train.py:874] (0/4) Epoch 20, batch 200, datatang_loss[loss=0.1304, simple_loss=0.2085, pruned_loss=0.02609, over 4926.00 frames.], tot_loss[loss=0.146, simple_loss=0.2266, pruned_loss=0.03269, over 623587.03 frames.], batch size: 71, aishell_tot_loss[loss=0.1541, simple_loss=0.2394, pruned_loss=0.03444, over 391078.06 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.213, pruned_loss=0.03074, over 385554.20 frames.], batch size: 71, lr: 4.14e-04
+2022-06-19 00:21:05,352 INFO [train.py:874] (0/4) Epoch 20, batch 250, aishell_loss[loss=0.1412, simple_loss=0.2284, pruned_loss=0.02702, over 4924.00 frames.], tot_loss[loss=0.1463, simple_loss=0.227, pruned_loss=0.03277, over 703390.86 frames.], batch size: 49, aishell_tot_loss[loss=0.1538, simple_loss=0.2394, pruned_loss=0.03409, over 463445.40 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2134, pruned_loss=0.03125, over 453380.37 frames.], batch size: 49, lr: 4.14e-04
+2022-06-19 00:21:36,973 INFO [train.py:874] (0/4) Epoch 20, batch 300, datatang_loss[loss=0.1458, simple_loss=0.2262, pruned_loss=0.03265, over 4934.00 frames.], tot_loss[loss=0.147, simple_loss=0.2275, pruned_loss=0.03326, over 765891.61 frames.], batch size: 94, aishell_tot_loss[loss=0.1539, simple_loss=0.2393, pruned_loss=0.03427, over 520182.29 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2152, pruned_loss=0.03191, over 520792.57 frames.], batch size: 94, lr: 4.14e-04
+2022-06-19 00:22:05,467 INFO [train.py:874] (0/4) Epoch 20, batch 350, aishell_loss[loss=0.144, simple_loss=0.2422, pruned_loss=0.02286, over 4956.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2288, pruned_loss=0.03316, over 814710.78 frames.], batch size: 56, aishell_tot_loss[loss=0.1539, simple_loss=0.2396, pruned_loss=0.03408, over 581138.29 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2164, pruned_loss=0.03199, over 569424.50 frames.], batch size: 56, lr: 4.14e-04
+2022-06-19 00:22:38,814 INFO [train.py:874] (0/4) Epoch 20, batch 400, datatang_loss[loss=0.128, simple_loss=0.2105, pruned_loss=0.02269, over 4922.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2294, pruned_loss=0.03339, over 852361.03 frames.], batch size: 81, aishell_tot_loss[loss=0.1536, simple_loss=0.2399, pruned_loss=0.03368, over 615743.12 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2185, pruned_loss=0.03282, over 631122.61 frames.], batch size: 81, lr: 4.13e-04
+2022-06-19 00:23:11,536 INFO [train.py:874] (0/4) Epoch 20, batch 450, aishell_loss[loss=0.1233, simple_loss=0.1893, pruned_loss=0.02868, over 4890.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2286, pruned_loss=0.03333, over 881861.35 frames.], batch size: 21, aishell_tot_loss[loss=0.1525, simple_loss=0.2386, pruned_loss=0.03319, over 660648.77 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2186, pruned_loss=0.03328, over 671554.41 frames.], batch size: 21, lr: 4.13e-04
+2022-06-19 00:23:40,924 INFO [train.py:874] (0/4) Epoch 20, batch 500, datatang_loss[loss=0.1417, simple_loss=0.2004, pruned_loss=0.04153, over 4959.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2283, pruned_loss=0.03315, over 904786.23 frames.], batch size: 45, aishell_tot_loss[loss=0.1521, simple_loss=0.2385, pruned_loss=0.03288, over 695848.69 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2186, pruned_loss=0.03335, over 711397.01 frames.], batch size: 45, lr: 4.13e-04
+2022-06-19 00:24:13,537 INFO [train.py:874] (0/4) Epoch 20, batch 550, aishell_loss[loss=0.1616, simple_loss=0.2602, pruned_loss=0.03149, over 4961.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2294, pruned_loss=0.03384, over 922869.69 frames.], batch size: 64, aishell_tot_loss[loss=0.1517, simple_loss=0.2382, pruned_loss=0.03263, over 728691.03 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2205, pruned_loss=0.03455, over 745091.28 frames.], batch size: 64, lr: 4.13e-04
+2022-06-19 00:24:45,799 INFO [train.py:874] (0/4) Epoch 20, batch 600, aishell_loss[loss=0.1542, simple_loss=0.2474, pruned_loss=0.03048, over 4917.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2299, pruned_loss=0.03399, over 936514.01 frames.], batch size: 33, aishell_tot_loss[loss=0.1516, simple_loss=0.238, pruned_loss=0.03258, over 760980.20 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2212, pruned_loss=0.03489, over 771224.58 frames.], batch size: 33, lr: 4.13e-04
+2022-06-19 00:25:14,716 INFO [train.py:874] (0/4) Epoch 20, batch 650, datatang_loss[loss=0.1713, simple_loss=0.2363, pruned_loss=0.05315, over 4916.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2304, pruned_loss=0.03435, over 947043.51 frames.], batch size: 57, aishell_tot_loss[loss=0.152, simple_loss=0.2381, pruned_loss=0.03295, over 789096.42 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2215, pruned_loss=0.03508, over 794502.65 frames.], batch size: 57, lr: 4.13e-04
+2022-06-19 00:25:47,542 INFO [train.py:874] (0/4) Epoch 20, batch 700, datatang_loss[loss=0.1231, simple_loss=0.1966, pruned_loss=0.02478, over 4948.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2306, pruned_loss=0.03455, over 955566.58 frames.], batch size: 45, aishell_tot_loss[loss=0.1523, simple_loss=0.238, pruned_loss=0.0333, over 812111.71 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2221, pruned_loss=0.03508, over 817106.89 frames.], batch size: 45, lr: 4.13e-04
+2022-06-19 00:26:18,440 INFO [train.py:874] (0/4) Epoch 20, batch 750, datatang_loss[loss=0.1285, simple_loss=0.212, pruned_loss=0.0225, over 4929.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2322, pruned_loss=0.03522, over 961977.98 frames.], batch size: 71, aishell_tot_loss[loss=0.1531, simple_loss=0.2389, pruned_loss=0.03367, over 838317.11 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2225, pruned_loss=0.03568, over 830855.75 frames.], batch size: 71, lr: 4.13e-04
+2022-06-19 00:26:48,886 INFO [train.py:874] (0/4) Epoch 20, batch 800, aishell_loss[loss=0.1749, simple_loss=0.2627, pruned_loss=0.04359, over 4923.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2317, pruned_loss=0.03469, over 967646.07 frames.], batch size: 52, aishell_tot_loss[loss=0.1527, simple_loss=0.2387, pruned_loss=0.03334, over 855204.47 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2226, pruned_loss=0.03551, over 850017.50 frames.], batch size: 52, lr: 4.12e-04
+2022-06-19 00:27:20,460 INFO [train.py:874] (0/4) Epoch 20, batch 850, aishell_loss[loss=0.1452, simple_loss=0.2332, pruned_loss=0.02859, over 4975.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2312, pruned_loss=0.03427, over 971594.81 frames.], batch size: 39, aishell_tot_loss[loss=0.1521, simple_loss=0.2382, pruned_loss=0.033, over 872711.77 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2225, pruned_loss=0.03549, over 863660.81 frames.], batch size: 39, lr: 4.12e-04
+2022-06-19 00:27:50,514 INFO [train.py:874] (0/4) Epoch 20, batch 900, aishell_loss[loss=0.1706, simple_loss=0.2617, pruned_loss=0.03973, over 4947.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2306, pruned_loss=0.03405, over 974805.98 frames.], batch size: 64, aishell_tot_loss[loss=0.1519, simple_loss=0.238, pruned_loss=0.03289, over 883462.17 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2226, pruned_loss=0.03528, over 880813.71 frames.], batch size: 64, lr: 4.12e-04
+2022-06-19 00:28:20,649 INFO [train.py:874] (0/4) Epoch 20, batch 950, aishell_loss[loss=0.1651, simple_loss=0.2514, pruned_loss=0.03946, over 4935.00 frames.], tot_loss[loss=0.1486, simple_loss=0.23, pruned_loss=0.03362, over 976937.48 frames.], batch size: 32, aishell_tot_loss[loss=0.1512, simple_loss=0.2373, pruned_loss=0.03249, over 895659.01 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2225, pruned_loss=0.03517, over 892685.25 frames.], batch size: 32, lr: 4.12e-04
+2022-06-19 00:28:53,578 INFO [train.py:874] (0/4) Epoch 20, batch 1000, aishell_loss[loss=0.1332, simple_loss=0.2205, pruned_loss=0.02299, over 4856.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2304, pruned_loss=0.03408, over 978523.53 frames.], batch size: 28, aishell_tot_loss[loss=0.1506, simple_loss=0.2366, pruned_loss=0.03225, over 904873.21 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2239, pruned_loss=0.03584, over 904691.02 frames.], batch size: 28, lr: 4.12e-04
+2022-06-19 00:28:53,581 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 00:29:10,060 INFO [train.py:914] (0/4) Epoch 20, validation: loss=0.1639, simple_loss=0.248, pruned_loss=0.0399, over 1622729.00 frames.
+2022-06-19 00:29:43,679 INFO [train.py:874] (0/4) Epoch 20, batch 1050, aishell_loss[loss=0.1383, simple_loss=0.2304, pruned_loss=0.02316, over 4944.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2308, pruned_loss=0.03431, over 980378.75 frames.], batch size: 49, aishell_tot_loss[loss=0.1507, simple_loss=0.2369, pruned_loss=0.03222, over 914138.15 frames.], datatang_tot_loss[loss=0.1483, simple_loss=0.2243, pruned_loss=0.03614, over 914763.07 frames.], batch size: 49, lr: 4.12e-04
+2022-06-19 00:30:16,591 INFO [train.py:874] (0/4) Epoch 20, batch 1100, aishell_loss[loss=0.1533, simple_loss=0.2431, pruned_loss=0.03174, over 4946.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2312, pruned_loss=0.03454, over 981450.59 frames.], batch size: 58, aishell_tot_loss[loss=0.1504, simple_loss=0.2366, pruned_loss=0.03213, over 922504.57 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.2251, pruned_loss=0.03653, over 923076.36 frames.], batch size: 58, lr: 4.12e-04
+2022-06-19 00:30:45,814 INFO [train.py:874] (0/4) Epoch 20, batch 1150, aishell_loss[loss=0.1494, simple_loss=0.2438, pruned_loss=0.02756, over 4963.00 frames.], tot_loss[loss=0.1493, simple_loss=0.23, pruned_loss=0.03428, over 982159.36 frames.], batch size: 61, aishell_tot_loss[loss=0.1504, simple_loss=0.2362, pruned_loss=0.03229, over 929325.28 frames.], datatang_tot_loss[loss=0.1483, simple_loss=0.2244, pruned_loss=0.0361, over 930804.32 frames.], batch size: 61, lr: 4.11e-04
+2022-06-19 00:31:19,120 INFO [train.py:874] (0/4) Epoch 20, batch 1200, aishell_loss[loss=0.1658, simple_loss=0.2516, pruned_loss=0.03997, over 4944.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2308, pruned_loss=0.035, over 983067.23 frames.], batch size: 32, aishell_tot_loss[loss=0.1509, simple_loss=0.2365, pruned_loss=0.03259, over 935238.18 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.225, pruned_loss=0.03655, over 938100.39 frames.], batch size: 32, lr: 4.11e-04
+2022-06-19 00:31:51,402 INFO [train.py:874] (0/4) Epoch 20, batch 1250, datatang_loss[loss=0.1354, simple_loss=0.2095, pruned_loss=0.03065, over 4910.00 frames.], tot_loss[loss=0.15, simple_loss=0.2301, pruned_loss=0.03497, over 983369.41 frames.], batch size: 57, aishell_tot_loss[loss=0.151, simple_loss=0.2363, pruned_loss=0.03278, over 940669.59 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2246, pruned_loss=0.03641, over 943895.55 frames.], batch size: 57, lr: 4.11e-04
+2022-06-19 00:32:06,337 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-80000.pt
+2022-06-19 00:32:25,047 INFO [train.py:874] (0/4) Epoch 20, batch 1300, aishell_loss[loss=0.1612, simple_loss=0.2444, pruned_loss=0.03903, over 4919.00 frames.], tot_loss[loss=0.15, simple_loss=0.2302, pruned_loss=0.0349, over 983697.87 frames.], batch size: 46, aishell_tot_loss[loss=0.1508, simple_loss=0.2363, pruned_loss=0.03269, over 945613.88 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2246, pruned_loss=0.0365, over 948983.84 frames.], batch size: 46, lr: 4.11e-04
+2022-06-19 00:32:57,723 INFO [train.py:874] (0/4) Epoch 20, batch 1350, aishell_loss[loss=0.1942, simple_loss=0.2813, pruned_loss=0.05358, over 4888.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2307, pruned_loss=0.03493, over 984108.53 frames.], batch size: 50, aishell_tot_loss[loss=0.1514, simple_loss=0.2366, pruned_loss=0.03305, over 950490.61 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2247, pruned_loss=0.03628, over 953131.30 frames.], batch size: 50, lr: 4.11e-04
+2022-06-19 00:33:29,214 INFO [train.py:874] (0/4) Epoch 20, batch 1400, datatang_loss[loss=0.1511, simple_loss=0.2285, pruned_loss=0.03688, over 4961.00 frames.], tot_loss[loss=0.15, simple_loss=0.2305, pruned_loss=0.03481, over 984333.92 frames.], batch size: 91, aishell_tot_loss[loss=0.1516, simple_loss=0.2366, pruned_loss=0.03327, over 954760.60 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2244, pruned_loss=0.03603, over 956735.67 frames.], batch size: 91, lr: 4.11e-04
+2022-06-19 00:33:59,641 INFO [train.py:874] (0/4) Epoch 20, batch 1450, datatang_loss[loss=0.1694, simple_loss=0.237, pruned_loss=0.05088, over 4918.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2315, pruned_loss=0.03498, over 985077.93 frames.], batch size: 75, aishell_tot_loss[loss=0.1521, simple_loss=0.2372, pruned_loss=0.03349, over 959823.63 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2244, pruned_loss=0.03615, over 959221.01 frames.], batch size: 75, lr: 4.11e-04
+2022-06-19 00:34:33,119 INFO [train.py:874] (0/4) Epoch 20, batch 1500, aishell_loss[loss=0.1218, simple_loss=0.2133, pruned_loss=0.01516, over 4886.00 frames.], tot_loss[loss=0.151, simple_loss=0.2317, pruned_loss=0.03518, over 985003.85 frames.], batch size: 50, aishell_tot_loss[loss=0.1519, simple_loss=0.2371, pruned_loss=0.03333, over 962548.15 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2249, pruned_loss=0.03655, over 962439.12 frames.], batch size: 50, lr: 4.11e-04
+2022-06-19 00:35:03,370 INFO [train.py:874] (0/4) Epoch 20, batch 1550, datatang_loss[loss=0.1357, simple_loss=0.2173, pruned_loss=0.0271, over 4918.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2307, pruned_loss=0.03509, over 985022.47 frames.], batch size: 81, aishell_tot_loss[loss=0.1516, simple_loss=0.237, pruned_loss=0.03312, over 964178.87 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2247, pruned_loss=0.03655, over 966081.75 frames.], batch size: 81, lr: 4.10e-04
+2022-06-19 00:35:34,669 INFO [train.py:874] (0/4) Epoch 20, batch 1600, datatang_loss[loss=0.165, simple_loss=0.2414, pruned_loss=0.04427, over 4916.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2304, pruned_loss=0.03489, over 984953.53 frames.], batch size: 98, aishell_tot_loss[loss=0.151, simple_loss=0.2361, pruned_loss=0.03294, over 966832.71 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.225, pruned_loss=0.03664, over 968059.49 frames.], batch size: 98, lr: 4.10e-04
+2022-06-19 00:36:07,047 INFO [train.py:874] (0/4) Epoch 20, batch 1650, datatang_loss[loss=0.1565, simple_loss=0.2353, pruned_loss=0.03884, over 4984.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2308, pruned_loss=0.03503, over 985124.28 frames.], batch size: 31, aishell_tot_loss[loss=0.1515, simple_loss=0.2367, pruned_loss=0.03312, over 968856.15 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2248, pruned_loss=0.03662, over 970335.72 frames.], batch size: 31, lr: 4.10e-04
+2022-06-19 00:36:36,981 INFO [train.py:874] (0/4) Epoch 20, batch 1700, aishell_loss[loss=0.1532, simple_loss=0.2394, pruned_loss=0.03349, over 4953.00 frames.], tot_loss[loss=0.1495, simple_loss=0.23, pruned_loss=0.0345, over 985117.41 frames.], batch size: 54, aishell_tot_loss[loss=0.1514, simple_loss=0.2368, pruned_loss=0.03299, over 970601.21 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.224, pruned_loss=0.0362, over 972230.70 frames.], batch size: 54, lr: 4.10e-04
+2022-06-19 00:37:08,872 INFO [train.py:874] (0/4) Epoch 20, batch 1750, aishell_loss[loss=0.162, simple_loss=0.2554, pruned_loss=0.03432, over 4913.00 frames.], tot_loss[loss=0.1496, simple_loss=0.23, pruned_loss=0.03458, over 985283.45 frames.], batch size: 79, aishell_tot_loss[loss=0.1512, simple_loss=0.2367, pruned_loss=0.03284, over 972165.20 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2242, pruned_loss=0.03639, over 974041.31 frames.], batch size: 79, lr: 4.10e-04
+2022-06-19 00:37:42,116 INFO [train.py:874] (0/4) Epoch 20, batch 1800, aishell_loss[loss=0.1496, simple_loss=0.2341, pruned_loss=0.03254, over 4918.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2313, pruned_loss=0.03483, over 985398.35 frames.], batch size: 33, aishell_tot_loss[loss=0.1516, simple_loss=0.2371, pruned_loss=0.03304, over 974202.01 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2247, pruned_loss=0.03654, over 975038.99 frames.], batch size: 33, lr: 4.10e-04
+2022-06-19 00:38:12,139 INFO [train.py:874] (0/4) Epoch 20, batch 1850, aishell_loss[loss=0.1774, simple_loss=0.2681, pruned_loss=0.04341, over 4978.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2322, pruned_loss=0.03458, over 985620.78 frames.], batch size: 48, aishell_tot_loss[loss=0.1519, simple_loss=0.2378, pruned_loss=0.03296, over 975669.44 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2248, pruned_loss=0.03641, over 976363.04 frames.], batch size: 48, lr: 4.10e-04
+2022-06-19 00:38:43,849 INFO [train.py:874] (0/4) Epoch 20, batch 1900, aishell_loss[loss=0.1503, simple_loss=0.2277, pruned_loss=0.0365, over 4971.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2317, pruned_loss=0.03435, over 985845.94 frames.], batch size: 51, aishell_tot_loss[loss=0.1523, simple_loss=0.2384, pruned_loss=0.03312, over 976720.66 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2241, pruned_loss=0.03589, over 977784.15 frames.], batch size: 51, lr: 4.10e-04
+2022-06-19 00:39:16,002 INFO [train.py:874] (0/4) Epoch 20, batch 1950, datatang_loss[loss=0.154, simple_loss=0.2305, pruned_loss=0.03878, over 4915.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2323, pruned_loss=0.03457, over 985936.66 frames.], batch size: 81, aishell_tot_loss[loss=0.1523, simple_loss=0.2383, pruned_loss=0.03311, over 977959.99 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2248, pruned_loss=0.03612, over 978671.50 frames.], batch size: 81, lr: 4.09e-04
+2022-06-19 00:39:45,776 INFO [train.py:874] (0/4) Epoch 20, batch 2000, aishell_loss[loss=0.1038, simple_loss=0.176, pruned_loss=0.01581, over 4782.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2306, pruned_loss=0.03396, over 986144.56 frames.], batch size: 21, aishell_tot_loss[loss=0.1515, simple_loss=0.2374, pruned_loss=0.03286, over 978969.65 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2242, pruned_loss=0.03573, over 979688.46 frames.], batch size: 21, lr: 4.09e-04
+2022-06-19 00:39:45,781 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 00:40:02,387 INFO [train.py:914] (0/4) Epoch 20, validation: loss=0.1645, simple_loss=0.2482, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 00:40:32,678 INFO [train.py:874] (0/4) Epoch 20, batch 2050, datatang_loss[loss=0.1617, simple_loss=0.2361, pruned_loss=0.0436, over 4958.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2314, pruned_loss=0.03451, over 986017.56 frames.], batch size: 62, aishell_tot_loss[loss=0.1517, simple_loss=0.2376, pruned_loss=0.03285, over 979844.55 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2246, pruned_loss=0.03627, over 980292.97 frames.], batch size: 62, lr: 4.09e-04
+2022-06-19 00:41:04,529 INFO [train.py:874] (0/4) Epoch 20, batch 2100, datatang_loss[loss=0.1395, simple_loss=0.2098, pruned_loss=0.03459, over 4930.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2308, pruned_loss=0.03447, over 985376.40 frames.], batch size: 79, aishell_tot_loss[loss=0.1515, simple_loss=0.2377, pruned_loss=0.03269, over 979915.51 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2243, pruned_loss=0.03629, over 980926.88 frames.], batch size: 79, lr: 4.09e-04
+2022-06-19 00:41:37,685 INFO [train.py:874] (0/4) Epoch 20, batch 2150, aishell_loss[loss=0.1357, simple_loss=0.2184, pruned_loss=0.02647, over 4883.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2309, pruned_loss=0.03472, over 985582.25 frames.], batch size: 42, aishell_tot_loss[loss=0.1517, simple_loss=0.2378, pruned_loss=0.03282, over 980452.82 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2245, pruned_loss=0.03635, over 981743.67 frames.], batch size: 42, lr: 4.09e-04
+2022-06-19 00:42:09,208 INFO [train.py:874] (0/4) Epoch 20, batch 2200, aishell_loss[loss=0.1693, simple_loss=0.2536, pruned_loss=0.04248, over 4900.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2307, pruned_loss=0.03479, over 985567.40 frames.], batch size: 60, aishell_tot_loss[loss=0.1515, simple_loss=0.2377, pruned_loss=0.03264, over 981160.03 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2246, pruned_loss=0.03655, over 982079.42 frames.], batch size: 60, lr: 4.09e-04
+2022-06-19 00:42:40,347 INFO [train.py:874] (0/4) Epoch 20, batch 2250, datatang_loss[loss=0.1756, simple_loss=0.2489, pruned_loss=0.05113, over 4928.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2309, pruned_loss=0.03493, over 985893.72 frames.], batch size: 88, aishell_tot_loss[loss=0.1521, simple_loss=0.2383, pruned_loss=0.03291, over 981641.71 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2244, pruned_loss=0.03637, over 982842.18 frames.], batch size: 88, lr: 4.09e-04
+2022-06-19 00:43:13,273 INFO [train.py:874] (0/4) Epoch 20, batch 2300, aishell_loss[loss=0.165, simple_loss=0.2532, pruned_loss=0.03836, over 4887.00 frames.], tot_loss[loss=0.151, simple_loss=0.2318, pruned_loss=0.03507, over 985597.45 frames.], batch size: 42, aishell_tot_loss[loss=0.1523, simple_loss=0.2385, pruned_loss=0.03303, over 982109.13 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2247, pruned_loss=0.03654, over 982960.90 frames.], batch size: 42, lr: 4.09e-04
+2022-06-19 00:43:44,363 INFO [train.py:874] (0/4) Epoch 20, batch 2350, datatang_loss[loss=0.136, simple_loss=0.2223, pruned_loss=0.02489, over 4938.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2313, pruned_loss=0.03476, over 985334.75 frames.], batch size: 69, aishell_tot_loss[loss=0.1526, simple_loss=0.2387, pruned_loss=0.03322, over 982262.23 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2242, pruned_loss=0.03607, over 983239.75 frames.], batch size: 69, lr: 4.08e-04
+2022-06-19 00:44:15,712 INFO [train.py:874] (0/4) Epoch 20, batch 2400, datatang_loss[loss=0.155, simple_loss=0.2286, pruned_loss=0.04074, over 4960.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2309, pruned_loss=0.03481, over 986041.14 frames.], batch size: 67, aishell_tot_loss[loss=0.1525, simple_loss=0.2386, pruned_loss=0.03316, over 983081.00 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2239, pruned_loss=0.03617, over 983776.53 frames.], batch size: 67, lr: 4.08e-04
+2022-06-19 00:44:49,142 INFO [train.py:874] (0/4) Epoch 20, batch 2450, aishell_loss[loss=0.1435, simple_loss=0.2344, pruned_loss=0.02634, over 4912.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2304, pruned_loss=0.03467, over 985858.29 frames.], batch size: 52, aishell_tot_loss[loss=0.1521, simple_loss=0.2384, pruned_loss=0.03291, over 983342.54 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2237, pruned_loss=0.03626, over 983947.68 frames.], batch size: 52, lr: 4.08e-04
+2022-06-19 00:45:21,531 INFO [train.py:874] (0/4) Epoch 20, batch 2500, datatang_loss[loss=0.1232, simple_loss=0.1896, pruned_loss=0.02833, over 4935.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2304, pruned_loss=0.03467, over 985766.13 frames.], batch size: 50, aishell_tot_loss[loss=0.1521, simple_loss=0.2385, pruned_loss=0.03289, over 983456.56 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2237, pruned_loss=0.03623, over 984228.42 frames.], batch size: 50, lr: 4.08e-04
+2022-06-19 00:45:51,626 INFO [train.py:874] (0/4) Epoch 20, batch 2550, datatang_loss[loss=0.1176, simple_loss=0.1892, pruned_loss=0.02303, over 4939.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2303, pruned_loss=0.0347, over 985788.09 frames.], batch size: 55, aishell_tot_loss[loss=0.153, simple_loss=0.2392, pruned_loss=0.0334, over 983731.18 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2231, pruned_loss=0.03573, over 984419.63 frames.], batch size: 55, lr: 4.08e-04
+2022-06-19 00:46:25,314 INFO [train.py:874] (0/4) Epoch 20, batch 2600, datatang_loss[loss=0.1802, simple_loss=0.2531, pruned_loss=0.0537, over 4935.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2296, pruned_loss=0.03442, over 985630.35 frames.], batch size: 34, aishell_tot_loss[loss=0.1528, simple_loss=0.2389, pruned_loss=0.03334, over 983835.09 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2227, pruned_loss=0.03548, over 984533.45 frames.], batch size: 34, lr: 4.08e-04
+2022-06-19 00:46:57,788 INFO [train.py:874] (0/4) Epoch 20, batch 2650, aishell_loss[loss=0.1448, simple_loss=0.2356, pruned_loss=0.02697, over 4961.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2302, pruned_loss=0.03458, over 985675.82 frames.], batch size: 56, aishell_tot_loss[loss=0.153, simple_loss=0.2391, pruned_loss=0.03344, over 984193.21 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2228, pruned_loss=0.03558, over 984563.23 frames.], batch size: 56, lr: 4.08e-04
+2022-06-19 00:47:28,183 INFO [train.py:874] (0/4) Epoch 20, batch 2700, aishell_loss[loss=0.1581, simple_loss=0.2503, pruned_loss=0.03298, over 4966.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2306, pruned_loss=0.03457, over 985565.88 frames.], batch size: 69, aishell_tot_loss[loss=0.1527, simple_loss=0.2389, pruned_loss=0.03321, over 984186.91 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2234, pruned_loss=0.03579, over 984750.88 frames.], batch size: 69, lr: 4.08e-04
+2022-06-19 00:48:02,014 INFO [train.py:874] (0/4) Epoch 20, batch 2750, datatang_loss[loss=0.137, simple_loss=0.2161, pruned_loss=0.02894, over 4929.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2302, pruned_loss=0.03437, over 986003.93 frames.], batch size: 62, aishell_tot_loss[loss=0.1528, simple_loss=0.2391, pruned_loss=0.03326, over 984596.32 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2228, pruned_loss=0.03551, over 985059.35 frames.], batch size: 62, lr: 4.07e-04
+2022-06-19 00:48:34,584 INFO [train.py:874] (0/4) Epoch 20, batch 2800, aishell_loss[loss=0.1403, simple_loss=0.2362, pruned_loss=0.02221, over 4929.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2299, pruned_loss=0.03454, over 986011.43 frames.], batch size: 68, aishell_tot_loss[loss=0.1523, simple_loss=0.2385, pruned_loss=0.03301, over 984700.29 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2229, pruned_loss=0.03594, over 985224.19 frames.], batch size: 68, lr: 4.07e-04
+2022-06-19 00:49:04,725 INFO [train.py:874] (0/4) Epoch 20, batch 2850, aishell_loss[loss=0.1421, simple_loss=0.2357, pruned_loss=0.02425, over 4960.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2296, pruned_loss=0.03411, over 986160.02 frames.], batch size: 64, aishell_tot_loss[loss=0.1524, simple_loss=0.2386, pruned_loss=0.03307, over 985118.70 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2224, pruned_loss=0.03542, over 985218.11 frames.], batch size: 64, lr: 4.07e-04
+2022-06-19 00:49:38,231 INFO [train.py:874] (0/4) Epoch 20, batch 2900, aishell_loss[loss=0.1435, simple_loss=0.2341, pruned_loss=0.02641, over 4975.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2292, pruned_loss=0.03408, over 986264.02 frames.], batch size: 30, aishell_tot_loss[loss=0.1521, simple_loss=0.2381, pruned_loss=0.03299, over 985343.11 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2222, pruned_loss=0.03547, over 985335.96 frames.], batch size: 30, lr: 4.07e-04
+2022-06-19 00:50:09,754 INFO [train.py:874] (0/4) Epoch 20, batch 2950, datatang_loss[loss=0.1313, simple_loss=0.2192, pruned_loss=0.02169, over 4960.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2291, pruned_loss=0.03386, over 986154.31 frames.], batch size: 86, aishell_tot_loss[loss=0.1518, simple_loss=0.2379, pruned_loss=0.03281, over 985254.80 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2223, pruned_loss=0.03536, over 985505.02 frames.], batch size: 86, lr: 4.07e-04
+2022-06-19 00:50:39,733 INFO [train.py:874] (0/4) Epoch 20, batch 3000, datatang_loss[loss=0.1614, simple_loss=0.229, pruned_loss=0.04693, over 4925.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2292, pruned_loss=0.03377, over 986263.17 frames.], batch size: 83, aishell_tot_loss[loss=0.1515, simple_loss=0.2378, pruned_loss=0.03256, over 985492.68 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2223, pruned_loss=0.03545, over 985564.30 frames.], batch size: 83, lr: 4.07e-04
+2022-06-19 00:50:39,736 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 00:50:56,985 INFO [train.py:914] (0/4) Epoch 20, validation: loss=0.164, simple_loss=0.2487, pruned_loss=0.03966, over 1622729.00 frames.
+2022-06-19 00:51:26,499 INFO [train.py:874] (0/4) Epoch 20, batch 3050, datatang_loss[loss=0.1448, simple_loss=0.2226, pruned_loss=0.03347, over 4921.00 frames.], tot_loss[loss=0.1486, simple_loss=0.229, pruned_loss=0.0341, over 986039.69 frames.], batch size: 77, aishell_tot_loss[loss=0.1516, simple_loss=0.2377, pruned_loss=0.03273, over 985451.71 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2222, pruned_loss=0.03549, over 985508.28 frames.], batch size: 77, lr: 4.07e-04
+2022-06-19 00:51:58,722 INFO [train.py:874] (0/4) Epoch 20, batch 3100, datatang_loss[loss=0.1586, simple_loss=0.2426, pruned_loss=0.03727, over 4930.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2298, pruned_loss=0.03439, over 985872.28 frames.], batch size: 25, aishell_tot_loss[loss=0.152, simple_loss=0.2381, pruned_loss=0.03301, over 985289.01 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2227, pruned_loss=0.03549, over 985625.81 frames.], batch size: 25, lr: 4.07e-04
+2022-06-19 00:52:31,195 INFO [train.py:874] (0/4) Epoch 20, batch 3150, datatang_loss[loss=0.1285, simple_loss=0.2126, pruned_loss=0.02221, over 4919.00 frames.], tot_loss[loss=0.148, simple_loss=0.2284, pruned_loss=0.03378, over 985812.49 frames.], batch size: 77, aishell_tot_loss[loss=0.1515, simple_loss=0.2375, pruned_loss=0.03276, over 985324.62 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2219, pruned_loss=0.03507, over 985613.26 frames.], batch size: 77, lr: 4.06e-04
+2022-06-19 00:53:02,564 INFO [train.py:874] (0/4) Epoch 20, batch 3200, datatang_loss[loss=0.153, simple_loss=0.2377, pruned_loss=0.03412, over 4901.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2294, pruned_loss=0.03383, over 985712.49 frames.], batch size: 47, aishell_tot_loss[loss=0.1513, simple_loss=0.2372, pruned_loss=0.03264, over 985115.77 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2223, pruned_loss=0.03528, over 985807.12 frames.], batch size: 47, lr: 4.06e-04
+2022-06-19 00:53:34,153 INFO [train.py:874] (0/4) Epoch 20, batch 3250, datatang_loss[loss=0.1876, simple_loss=0.2452, pruned_loss=0.06499, over 4880.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2302, pruned_loss=0.03439, over 985142.28 frames.], batch size: 47, aishell_tot_loss[loss=0.1515, simple_loss=0.2374, pruned_loss=0.03283, over 985121.05 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.223, pruned_loss=0.03565, over 985260.98 frames.], batch size: 47, lr: 4.06e-04
+2022-06-19 00:54:06,319 INFO [train.py:874] (0/4) Epoch 20, batch 3300, datatang_loss[loss=0.1446, simple_loss=0.2154, pruned_loss=0.03688, over 4923.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2295, pruned_loss=0.03413, over 985041.98 frames.], batch size: 77, aishell_tot_loss[loss=0.1513, simple_loss=0.237, pruned_loss=0.03279, over 984943.65 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2227, pruned_loss=0.03539, over 985328.52 frames.], batch size: 77, lr: 4.06e-04
+2022-06-19 00:54:36,925 INFO [train.py:874] (0/4) Epoch 20, batch 3350, datatang_loss[loss=0.1305, simple_loss=0.2162, pruned_loss=0.02235, over 4954.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2298, pruned_loss=0.03403, over 985078.14 frames.], batch size: 86, aishell_tot_loss[loss=0.1518, simple_loss=0.2374, pruned_loss=0.03307, over 984771.95 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2226, pruned_loss=0.03503, over 985525.37 frames.], batch size: 86, lr: 4.06e-04
+2022-06-19 00:55:10,646 INFO [train.py:874] (0/4) Epoch 20, batch 3400, datatang_loss[loss=0.1116, simple_loss=0.1899, pruned_loss=0.0166, over 4853.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2296, pruned_loss=0.03384, over 985102.57 frames.], batch size: 30, aishell_tot_loss[loss=0.1515, simple_loss=0.2373, pruned_loss=0.03286, over 984815.45 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2224, pruned_loss=0.03506, over 985491.50 frames.], batch size: 30, lr: 4.06e-04
+2022-06-19 00:55:42,665 INFO [train.py:874] (0/4) Epoch 20, batch 3450, datatang_loss[loss=0.1338, simple_loss=0.2068, pruned_loss=0.03039, over 4973.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2297, pruned_loss=0.03392, over 985423.96 frames.], batch size: 60, aishell_tot_loss[loss=0.1515, simple_loss=0.2376, pruned_loss=0.03269, over 984869.14 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2226, pruned_loss=0.03524, over 985747.44 frames.], batch size: 60, lr: 4.06e-04
+2022-06-19 00:56:12,508 INFO [train.py:874] (0/4) Epoch 20, batch 3500, datatang_loss[loss=0.1333, simple_loss=0.2033, pruned_loss=0.03167, over 4954.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2291, pruned_loss=0.03374, over 985363.36 frames.], batch size: 37, aishell_tot_loss[loss=0.151, simple_loss=0.237, pruned_loss=0.03248, over 985219.59 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2224, pruned_loss=0.03526, over 985363.26 frames.], batch size: 37, lr: 4.06e-04
+2022-06-19 00:56:46,160 INFO [train.py:874] (0/4) Epoch 20, batch 3550, aishell_loss[loss=0.1701, simple_loss=0.2691, pruned_loss=0.03553, over 4913.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2294, pruned_loss=0.03413, over 985590.80 frames.], batch size: 78, aishell_tot_loss[loss=0.1512, simple_loss=0.2373, pruned_loss=0.03257, over 985510.90 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2226, pruned_loss=0.03546, over 985326.76 frames.], batch size: 78, lr: 4.05e-04
+2022-06-19 00:57:18,976 INFO [train.py:874] (0/4) Epoch 20, batch 3600, aishell_loss[loss=0.1542, simple_loss=0.2428, pruned_loss=0.03277, over 4927.00 frames.], tot_loss[loss=0.1491, simple_loss=0.23, pruned_loss=0.03404, over 985747.89 frames.], batch size: 49, aishell_tot_loss[loss=0.152, simple_loss=0.2382, pruned_loss=0.0329, over 985549.28 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2226, pruned_loss=0.03502, over 985491.28 frames.], batch size: 49, lr: 4.05e-04
+2022-06-19 00:57:49,530 INFO [train.py:874] (0/4) Epoch 20, batch 3650, aishell_loss[loss=0.1496, simple_loss=0.2325, pruned_loss=0.03336, over 4944.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2294, pruned_loss=0.03393, over 985544.72 frames.], batch size: 45, aishell_tot_loss[loss=0.1519, simple_loss=0.2378, pruned_loss=0.03298, over 985266.44 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2224, pruned_loss=0.03479, over 985608.63 frames.], batch size: 45, lr: 4.05e-04
+2022-06-19 00:58:21,860 INFO [train.py:874] (0/4) Epoch 20, batch 3700, aishell_loss[loss=0.1768, simple_loss=0.2545, pruned_loss=0.04952, over 4933.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2295, pruned_loss=0.03355, over 985893.03 frames.], batch size: 80, aishell_tot_loss[loss=0.1521, simple_loss=0.2382, pruned_loss=0.03299, over 985515.76 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2221, pruned_loss=0.03438, over 985759.90 frames.], batch size: 80, lr: 4.05e-04
+2022-06-19 00:58:52,958 INFO [train.py:874] (0/4) Epoch 20, batch 3750, aishell_loss[loss=0.1595, simple_loss=0.2471, pruned_loss=0.0359, over 4976.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2302, pruned_loss=0.03369, over 986170.52 frames.], batch size: 39, aishell_tot_loss[loss=0.1523, simple_loss=0.2385, pruned_loss=0.03309, over 985730.05 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2226, pruned_loss=0.03439, over 985890.98 frames.], batch size: 39, lr: 4.05e-04
+2022-06-19 00:59:23,341 INFO [train.py:874] (0/4) Epoch 20, batch 3800, aishell_loss[loss=0.1467, simple_loss=0.2383, pruned_loss=0.02759, over 4969.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2302, pruned_loss=0.03348, over 985962.26 frames.], batch size: 39, aishell_tot_loss[loss=0.1521, simple_loss=0.2384, pruned_loss=0.03292, over 985592.60 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2223, pruned_loss=0.03436, over 985901.62 frames.], batch size: 39, lr: 4.05e-04
+2022-06-19 00:59:56,335 INFO [train.py:874] (0/4) Epoch 20, batch 3850, aishell_loss[loss=0.1508, simple_loss=0.2333, pruned_loss=0.0341, over 4925.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2298, pruned_loss=0.0339, over 985547.49 frames.], batch size: 58, aishell_tot_loss[loss=0.1521, simple_loss=0.2385, pruned_loss=0.03283, over 985308.90 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2224, pruned_loss=0.03478, over 985764.58 frames.], batch size: 58, lr: 4.05e-04
+2022-06-19 01:00:26,602 INFO [train.py:874] (0/4) Epoch 20, batch 3900, aishell_loss[loss=0.1389, simple_loss=0.232, pruned_loss=0.02286, over 4962.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2298, pruned_loss=0.03378, over 985597.84 frames.], batch size: 64, aishell_tot_loss[loss=0.1522, simple_loss=0.2384, pruned_loss=0.03296, over 985100.69 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2225, pruned_loss=0.03457, over 986017.76 frames.], batch size: 64, lr: 4.05e-04
+2022-06-19 01:00:57,346 INFO [train.py:874] (0/4) Epoch 20, batch 3950, aishell_loss[loss=0.152, simple_loss=0.2443, pruned_loss=0.02983, over 4914.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2284, pruned_loss=0.03325, over 985488.01 frames.], batch size: 41, aishell_tot_loss[loss=0.1517, simple_loss=0.2379, pruned_loss=0.0328, over 985044.30 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2214, pruned_loss=0.03416, over 985955.67 frames.], batch size: 41, lr: 4.04e-04
+2022-06-19 01:01:28,305 INFO [train.py:874] (0/4) Epoch 20, batch 4000, datatang_loss[loss=0.15, simple_loss=0.228, pruned_loss=0.03595, over 4955.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2291, pruned_loss=0.03358, over 985674.41 frames.], batch size: 45, aishell_tot_loss[loss=0.1518, simple_loss=0.238, pruned_loss=0.03275, over 985199.64 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2219, pruned_loss=0.03447, over 985979.21 frames.], batch size: 45, lr: 4.04e-04
+2022-06-19 01:01:28,308 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 01:01:44,967 INFO [train.py:914] (0/4) Epoch 20, validation: loss=0.1658, simple_loss=0.2493, pruned_loss=0.04118, over 1622729.00 frames.
+2022-06-19 01:02:16,037 INFO [train.py:874] (0/4) Epoch 20, batch 4050, aishell_loss[loss=0.15, simple_loss=0.233, pruned_loss=0.0335, over 4960.00 frames.], tot_loss[loss=0.1489, simple_loss=0.23, pruned_loss=0.03394, over 985767.36 frames.], batch size: 61, aishell_tot_loss[loss=0.152, simple_loss=0.2383, pruned_loss=0.03279, over 985194.13 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2227, pruned_loss=0.03479, over 986072.08 frames.], batch size: 61, lr: 4.04e-04
+2022-06-19 01:02:45,507 INFO [train.py:874] (0/4) Epoch 20, batch 4100, aishell_loss[loss=0.1453, simple_loss=0.2415, pruned_loss=0.02449, over 4969.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2306, pruned_loss=0.03411, over 985899.57 frames.], batch size: 61, aishell_tot_loss[loss=0.1519, simple_loss=0.2383, pruned_loss=0.03275, over 985561.60 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.223, pruned_loss=0.0351, over 985887.62 frames.], batch size: 61, lr: 4.04e-04
+2022-06-19 01:03:16,360 INFO [train.py:874] (0/4) Epoch 20, batch 4150, datatang_loss[loss=0.1375, simple_loss=0.2139, pruned_loss=0.03054, over 4938.00 frames.], tot_loss[loss=0.149, simple_loss=0.2301, pruned_loss=0.03394, over 986295.19 frames.], batch size: 57, aishell_tot_loss[loss=0.1517, simple_loss=0.2378, pruned_loss=0.03275, over 985824.32 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2229, pruned_loss=0.03493, over 986078.76 frames.], batch size: 57, lr: 4.04e-04
+2022-06-19 01:03:46,146 INFO [train.py:874] (0/4) Epoch 20, batch 4200, aishell_loss[loss=0.1634, simple_loss=0.2467, pruned_loss=0.03998, over 4903.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2306, pruned_loss=0.03432, over 985902.41 frames.], batch size: 52, aishell_tot_loss[loss=0.1524, simple_loss=0.2385, pruned_loss=0.03313, over 985530.87 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2229, pruned_loss=0.03497, over 986032.44 frames.], batch size: 52, lr: 4.04e-04
+2022-06-19 01:04:16,242 INFO [train.py:874] (0/4) Epoch 20, batch 4250, aishell_loss[loss=0.1649, simple_loss=0.2546, pruned_loss=0.03763, over 4931.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2314, pruned_loss=0.03483, over 985666.31 frames.], batch size: 68, aishell_tot_loss[loss=0.153, simple_loss=0.2391, pruned_loss=0.03346, over 985150.69 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2232, pruned_loss=0.03525, over 986185.55 frames.], batch size: 68, lr: 4.04e-04
+2022-06-19 01:04:45,764 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-20.pt
+2022-06-19 01:05:47,336 INFO [train.py:874] (0/4) Epoch 21, batch 50, datatang_loss[loss=0.12, simple_loss=0.1999, pruned_loss=0.02007, over 4905.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2256, pruned_loss=0.03294, over 218245.17 frames.], batch size: 64, aishell_tot_loss[loss=0.153, simple_loss=0.2387, pruned_loss=0.03365, over 93969.16 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2169, pruned_loss=0.03245, over 137396.52 frames.], batch size: 64, lr: 3.94e-04
+2022-06-19 01:06:17,243 INFO [train.py:874] (0/4) Epoch 21, batch 100, aishell_loss[loss=0.1285, simple_loss=0.2058, pruned_loss=0.02564, over 4796.00 frames.], tot_loss[loss=0.1438, simple_loss=0.223, pruned_loss=0.03227, over 387789.69 frames.], batch size: 24, aishell_tot_loss[loss=0.1494, simple_loss=0.2338, pruned_loss=0.03254, over 198299.87 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2143, pruned_loss=0.03207, over 237383.18 frames.], batch size: 24, lr: 3.94e-04
+2022-06-19 01:06:49,578 INFO [train.py:874] (0/4) Epoch 21, batch 150, aishell_loss[loss=0.1213, simple_loss=0.2041, pruned_loss=0.01926, over 4960.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2247, pruned_loss=0.0325, over 520450.87 frames.], batch size: 27, aishell_tot_loss[loss=0.1508, simple_loss=0.2354, pruned_loss=0.03306, over 297965.94 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2144, pruned_loss=0.03191, over 318984.74 frames.], batch size: 27, lr: 3.94e-04
+2022-06-19 01:07:21,609 INFO [train.py:874] (0/4) Epoch 21, batch 200, datatang_loss[loss=0.1225, simple_loss=0.1935, pruned_loss=0.02576, over 4932.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2248, pruned_loss=0.03218, over 623543.63 frames.], batch size: 34, aishell_tot_loss[loss=0.1507, simple_loss=0.2354, pruned_loss=0.03302, over 390879.97 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2133, pruned_loss=0.03134, over 385684.21 frames.], batch size: 34, lr: 3.94e-04
+2022-06-19 01:07:50,961 INFO [train.py:874] (0/4) Epoch 21, batch 250, datatang_loss[loss=0.1158, simple_loss=0.1916, pruned_loss=0.01994, over 4962.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2258, pruned_loss=0.03162, over 704027.64 frames.], batch size: 50, aishell_tot_loss[loss=0.1504, simple_loss=0.2356, pruned_loss=0.03259, over 476787.56 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2136, pruned_loss=0.03074, over 440088.16 frames.], batch size: 50, lr: 3.94e-04
+2022-06-19 01:08:22,372 INFO [train.py:874] (0/4) Epoch 21, batch 300, datatang_loss[loss=0.1475, simple_loss=0.2174, pruned_loss=0.0388, over 4921.00 frames.], tot_loss[loss=0.146, simple_loss=0.2277, pruned_loss=0.03211, over 766507.56 frames.], batch size: 57, aishell_tot_loss[loss=0.1517, simple_loss=0.2372, pruned_loss=0.03309, over 547758.12 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2145, pruned_loss=0.03088, over 492250.63 frames.], batch size: 57, lr: 3.94e-04
+2022-06-19 01:08:53,928 INFO [train.py:874] (0/4) Epoch 21, batch 350, aishell_loss[loss=0.1736, simple_loss=0.2654, pruned_loss=0.04085, over 4952.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2274, pruned_loss=0.03205, over 815271.24 frames.], batch size: 80, aishell_tot_loss[loss=0.1511, simple_loss=0.2369, pruned_loss=0.03269, over 601549.79 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2149, pruned_loss=0.03126, over 548082.67 frames.], batch size: 80, lr: 3.93e-04
+2022-06-19 01:09:24,473 INFO [train.py:874] (0/4) Epoch 21, batch 400, datatang_loss[loss=0.1511, simple_loss=0.2183, pruned_loss=0.04192, over 4871.00 frames.], tot_loss[loss=0.147, simple_loss=0.2282, pruned_loss=0.03289, over 852580.94 frames.], batch size: 30, aishell_tot_loss[loss=0.1506, simple_loss=0.2365, pruned_loss=0.03237, over 649899.29 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2169, pruned_loss=0.03283, over 595508.78 frames.], batch size: 30, lr: 3.93e-04
+2022-06-19 01:09:56,120 INFO [train.py:874] (0/4) Epoch 21, batch 450, datatang_loss[loss=0.1649, simple_loss=0.2353, pruned_loss=0.04727, over 4976.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2278, pruned_loss=0.03359, over 882162.77 frames.], batch size: 60, aishell_tot_loss[loss=0.1507, simple_loss=0.2361, pruned_loss=0.03262, over 686520.68 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2175, pruned_loss=0.0337, over 644925.46 frames.], batch size: 60, lr: 3.93e-04
+2022-06-19 01:10:28,238 INFO [train.py:874] (0/4) Epoch 21, batch 500, aishell_loss[loss=0.1591, simple_loss=0.2502, pruned_loss=0.03405, over 4977.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2275, pruned_loss=0.03343, over 905220.74 frames.], batch size: 61, aishell_tot_loss[loss=0.1501, simple_loss=0.2358, pruned_loss=0.03223, over 717778.09 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.218, pruned_loss=0.03398, over 689629.12 frames.], batch size: 61, lr: 3.93e-04
+2022-06-19 01:10:58,426 INFO [train.py:874] (0/4) Epoch 21, batch 550, aishell_loss[loss=0.1536, simple_loss=0.2455, pruned_loss=0.03083, over 4951.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2268, pruned_loss=0.03278, over 922927.44 frames.], batch size: 64, aishell_tot_loss[loss=0.1493, simple_loss=0.2353, pruned_loss=0.03165, over 748030.00 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.218, pruned_loss=0.0338, over 725778.19 frames.], batch size: 64, lr: 3.93e-04
+2022-06-19 01:11:30,246 INFO [train.py:874] (0/4) Epoch 21, batch 600, datatang_loss[loss=0.149, simple_loss=0.2284, pruned_loss=0.03482, over 4956.00 frames.], tot_loss[loss=0.146, simple_loss=0.227, pruned_loss=0.03252, over 936776.52 frames.], batch size: 91, aishell_tot_loss[loss=0.1499, simple_loss=0.2361, pruned_loss=0.03178, over 775832.57 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2174, pruned_loss=0.03334, over 756501.73 frames.], batch size: 91, lr: 3.93e-04
+2022-06-19 01:12:03,188 INFO [train.py:874] (0/4) Epoch 21, batch 650, datatang_loss[loss=0.1307, simple_loss=0.1985, pruned_loss=0.03144, over 4955.00 frames.], tot_loss[loss=0.146, simple_loss=0.2265, pruned_loss=0.03277, over 947781.15 frames.], batch size: 45, aishell_tot_loss[loss=0.1504, simple_loss=0.2367, pruned_loss=0.03206, over 793991.46 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2171, pruned_loss=0.03329, over 790570.85 frames.], batch size: 45, lr: 3.93e-04
+2022-06-19 01:12:34,559 INFO [train.py:874] (0/4) Epoch 21, batch 700, datatang_loss[loss=0.1372, simple_loss=0.2154, pruned_loss=0.02956, over 4956.00 frames.], tot_loss[loss=0.147, simple_loss=0.2275, pruned_loss=0.03325, over 956313.25 frames.], batch size: 55, aishell_tot_loss[loss=0.1503, simple_loss=0.2366, pruned_loss=0.032, over 815106.23 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2186, pruned_loss=0.03398, over 815169.81 frames.], batch size: 55, lr: 3.93e-04
+2022-06-19 01:13:05,866 INFO [train.py:874] (0/4) Epoch 21, batch 750, aishell_loss[loss=0.1607, simple_loss=0.2406, pruned_loss=0.04036, over 4916.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2274, pruned_loss=0.03314, over 962627.53 frames.], batch size: 33, aishell_tot_loss[loss=0.1504, simple_loss=0.2367, pruned_loss=0.03202, over 835188.16 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2183, pruned_loss=0.03389, over 835053.75 frames.], batch size: 33, lr: 3.93e-04
+2022-06-19 01:13:36,443 INFO [train.py:874] (0/4) Epoch 21, batch 800, datatang_loss[loss=0.15, simple_loss=0.2273, pruned_loss=0.03637, over 4913.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2272, pruned_loss=0.03291, over 967323.33 frames.], batch size: 75, aishell_tot_loss[loss=0.15, simple_loss=0.2363, pruned_loss=0.03187, over 853223.00 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2185, pruned_loss=0.03386, over 852038.63 frames.], batch size: 75, lr: 3.92e-04
+2022-06-19 01:14:08,622 INFO [train.py:874] (0/4) Epoch 21, batch 850, aishell_loss[loss=0.1377, simple_loss=0.2193, pruned_loss=0.028, over 4955.00 frames.], tot_loss[loss=0.1469, simple_loss=0.228, pruned_loss=0.03288, over 971292.18 frames.], batch size: 32, aishell_tot_loss[loss=0.1503, simple_loss=0.2366, pruned_loss=0.03196, over 868636.57 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.219, pruned_loss=0.03371, over 867865.03 frames.], batch size: 32, lr: 3.92e-04
+2022-06-19 01:14:40,453 INFO [train.py:874] (0/4) Epoch 21, batch 900, aishell_loss[loss=0.1468, simple_loss=0.2373, pruned_loss=0.02813, over 4974.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2279, pruned_loss=0.03235, over 974664.89 frames.], batch size: 51, aishell_tot_loss[loss=0.1498, simple_loss=0.2364, pruned_loss=0.03165, over 883059.41 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2191, pruned_loss=0.03341, over 881274.56 frames.], batch size: 51, lr: 3.92e-04
+2022-06-19 01:15:10,905 INFO [train.py:874] (0/4) Epoch 21, batch 950, datatang_loss[loss=0.1319, simple_loss=0.22, pruned_loss=0.0219, over 4958.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2291, pruned_loss=0.03238, over 977119.60 frames.], batch size: 67, aishell_tot_loss[loss=0.1504, simple_loss=0.2371, pruned_loss=0.03188, over 898573.72 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.219, pruned_loss=0.03324, over 889950.44 frames.], batch size: 67, lr: 3.92e-04
+2022-06-19 01:15:30,701 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-84000.pt
+2022-06-19 01:15:49,478 INFO [train.py:874] (0/4) Epoch 21, batch 1000, aishell_loss[loss=0.1743, simple_loss=0.2572, pruned_loss=0.04569, over 4918.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2291, pruned_loss=0.03284, over 979132.44 frames.], batch size: 52, aishell_tot_loss[loss=0.1507, simple_loss=0.2373, pruned_loss=0.03208, over 907340.73 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2195, pruned_loss=0.03346, over 902963.91 frames.], batch size: 52, lr: 3.92e-04
+2022-06-19 01:15:49,481 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 01:16:06,686 INFO [train.py:914] (0/4) Epoch 21, validation: loss=0.1651, simple_loss=0.2487, pruned_loss=0.04071, over 1622729.00 frames.
+2022-06-19 01:16:39,085 INFO [train.py:874] (0/4) Epoch 21, batch 1050, datatang_loss[loss=0.1629, simple_loss=0.2321, pruned_loss=0.04685, over 4933.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2302, pruned_loss=0.03383, over 980546.08 frames.], batch size: 79, aishell_tot_loss[loss=0.151, simple_loss=0.2374, pruned_loss=0.03233, over 917726.55 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2204, pruned_loss=0.03437, over 911405.65 frames.], batch size: 79, lr: 3.92e-04
+2022-06-19 01:17:11,986 INFO [train.py:874] (0/4) Epoch 21, batch 1100, datatang_loss[loss=0.1362, simple_loss=0.215, pruned_loss=0.0287, over 4922.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2305, pruned_loss=0.03349, over 981602.84 frames.], batch size: 57, aishell_tot_loss[loss=0.1504, simple_loss=0.2368, pruned_loss=0.03201, over 927393.66 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2211, pruned_loss=0.03449, over 918174.80 frames.], batch size: 57, lr: 3.92e-04
+2022-06-19 01:17:43,412 INFO [train.py:874] (0/4) Epoch 21, batch 1150, datatang_loss[loss=0.1312, simple_loss=0.2053, pruned_loss=0.02854, over 4923.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2311, pruned_loss=0.03417, over 982395.96 frames.], batch size: 64, aishell_tot_loss[loss=0.1505, simple_loss=0.2368, pruned_loss=0.03205, over 934442.48 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2221, pruned_loss=0.0353, over 925773.29 frames.], batch size: 64, lr: 3.92e-04
+2022-06-19 01:18:16,587 INFO [train.py:874] (0/4) Epoch 21, batch 1200, datatang_loss[loss=0.1309, simple_loss=0.2104, pruned_loss=0.02566, over 4911.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2313, pruned_loss=0.03414, over 982931.75 frames.], batch size: 64, aishell_tot_loss[loss=0.1505, simple_loss=0.2367, pruned_loss=0.03219, over 940941.59 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2227, pruned_loss=0.0353, over 932042.50 frames.], batch size: 64, lr: 3.91e-04
+2022-06-19 01:18:47,829 INFO [train.py:874] (0/4) Epoch 21, batch 1250, aishell_loss[loss=0.1637, simple_loss=0.2504, pruned_loss=0.03851, over 4936.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2314, pruned_loss=0.03398, over 983421.57 frames.], batch size: 58, aishell_tot_loss[loss=0.1503, simple_loss=0.2365, pruned_loss=0.03208, over 947175.34 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.223, pruned_loss=0.03543, over 936997.88 frames.], batch size: 58, lr: 3.91e-04
+2022-06-19 01:19:18,410 INFO [train.py:874] (0/4) Epoch 21, batch 1300, aishell_loss[loss=0.1707, simple_loss=0.266, pruned_loss=0.03772, over 4933.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2306, pruned_loss=0.03355, over 983912.06 frames.], batch size: 78, aishell_tot_loss[loss=0.1497, simple_loss=0.236, pruned_loss=0.03175, over 951997.76 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2229, pruned_loss=0.03538, over 942322.83 frames.], batch size: 78, lr: 3.91e-04
+2022-06-19 01:19:51,537 INFO [train.py:874] (0/4) Epoch 21, batch 1350, aishell_loss[loss=0.1458, simple_loss=0.2292, pruned_loss=0.03122, over 4977.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2307, pruned_loss=0.03393, over 984374.54 frames.], batch size: 48, aishell_tot_loss[loss=0.1498, simple_loss=0.236, pruned_loss=0.03179, over 955892.65 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2234, pruned_loss=0.03573, over 947612.41 frames.], batch size: 48, lr: 3.91e-04
+2022-06-19 01:20:23,986 INFO [train.py:874] (0/4) Epoch 21, batch 1400, datatang_loss[loss=0.1586, simple_loss=0.2209, pruned_loss=0.04818, over 4953.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2308, pruned_loss=0.03409, over 984884.19 frames.], batch size: 45, aishell_tot_loss[loss=0.1501, simple_loss=0.2362, pruned_loss=0.032, over 959373.03 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2236, pruned_loss=0.03573, over 952394.44 frames.], batch size: 45, lr: 3.91e-04
+2022-06-19 01:20:54,658 INFO [train.py:874] (0/4) Epoch 21, batch 1450, datatang_loss[loss=0.1139, simple_loss=0.1967, pruned_loss=0.01552, over 4896.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2301, pruned_loss=0.03344, over 984546.50 frames.], batch size: 52, aishell_tot_loss[loss=0.1501, simple_loss=0.2364, pruned_loss=0.03193, over 962096.58 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2229, pruned_loss=0.03516, over 956137.88 frames.], batch size: 52, lr: 3.91e-04
+2022-06-19 01:21:28,252 INFO [train.py:874] (0/4) Epoch 21, batch 1500, datatang_loss[loss=0.1475, simple_loss=0.2173, pruned_loss=0.03882, over 4925.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2297, pruned_loss=0.03371, over 984852.79 frames.], batch size: 42, aishell_tot_loss[loss=0.1499, simple_loss=0.2358, pruned_loss=0.03199, over 964614.16 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2233, pruned_loss=0.03529, over 960011.83 frames.], batch size: 42, lr: 3.91e-04
+2022-06-19 01:21:57,780 INFO [train.py:874] (0/4) Epoch 21, batch 1550, aishell_loss[loss=0.1397, simple_loss=0.2304, pruned_loss=0.02448, over 4981.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2292, pruned_loss=0.03301, over 985127.07 frames.], batch size: 39, aishell_tot_loss[loss=0.1493, simple_loss=0.2353, pruned_loss=0.03167, over 967381.63 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2232, pruned_loss=0.03489, over 962800.29 frames.], batch size: 39, lr: 3.91e-04
+2022-06-19 01:22:31,062 INFO [train.py:874] (0/4) Epoch 21, batch 1600, datatang_loss[loss=0.1439, simple_loss=0.2301, pruned_loss=0.02884, over 4956.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2295, pruned_loss=0.03299, over 985167.00 frames.], batch size: 91, aishell_tot_loss[loss=0.1495, simple_loss=0.2355, pruned_loss=0.03179, over 969617.79 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2231, pruned_loss=0.03476, over 965239.63 frames.], batch size: 91, lr: 3.91e-04
+2022-06-19 01:23:02,969 INFO [train.py:874] (0/4) Epoch 21, batch 1650, datatang_loss[loss=0.1517, simple_loss=0.2292, pruned_loss=0.03713, over 4935.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2303, pruned_loss=0.03351, over 985393.42 frames.], batch size: 79, aishell_tot_loss[loss=0.15, simple_loss=0.2357, pruned_loss=0.03211, over 971511.10 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2238, pruned_loss=0.03489, over 967782.66 frames.], batch size: 79, lr: 3.90e-04
+2022-06-19 01:23:32,744 INFO [train.py:874] (0/4) Epoch 21, batch 1700, aishell_loss[loss=0.1496, simple_loss=0.2409, pruned_loss=0.02919, over 4951.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2302, pruned_loss=0.03374, over 985844.73 frames.], batch size: 54, aishell_tot_loss[loss=0.1499, simple_loss=0.2359, pruned_loss=0.03194, over 973233.22 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2238, pruned_loss=0.03528, over 970289.03 frames.], batch size: 54, lr: 3.90e-04
+2022-06-19 01:24:06,678 INFO [train.py:874] (0/4) Epoch 21, batch 1750, aishell_loss[loss=0.1802, simple_loss=0.2671, pruned_loss=0.04662, over 4904.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2288, pruned_loss=0.0334, over 985595.34 frames.], batch size: 68, aishell_tot_loss[loss=0.149, simple_loss=0.2349, pruned_loss=0.0316, over 974044.51 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2237, pruned_loss=0.03519, over 972578.73 frames.], batch size: 68, lr: 3.90e-04
+2022-06-19 01:24:40,279 INFO [train.py:874] (0/4) Epoch 21, batch 1800, datatang_loss[loss=0.1695, simple_loss=0.2406, pruned_loss=0.04919, over 4939.00 frames.], tot_loss[loss=0.1477, simple_loss=0.229, pruned_loss=0.03323, over 985705.85 frames.], batch size: 69, aishell_tot_loss[loss=0.1491, simple_loss=0.2352, pruned_loss=0.03148, over 975564.55 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2232, pruned_loss=0.03521, over 974035.55 frames.], batch size: 69, lr: 3.90e-04
+2022-06-19 01:25:09,427 INFO [train.py:874] (0/4) Epoch 21, batch 1850, datatang_loss[loss=0.1324, simple_loss=0.213, pruned_loss=0.02594, over 4918.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2289, pruned_loss=0.03295, over 985362.08 frames.], batch size: 77, aishell_tot_loss[loss=0.1493, simple_loss=0.2357, pruned_loss=0.03151, over 976688.70 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2226, pruned_loss=0.03489, over 975091.78 frames.], batch size: 77, lr: 3.90e-04
+2022-06-19 01:25:41,903 INFO [train.py:874] (0/4) Epoch 21, batch 1900, aishell_loss[loss=0.1488, simple_loss=0.2352, pruned_loss=0.0312, over 4877.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2296, pruned_loss=0.0328, over 985342.61 frames.], batch size: 35, aishell_tot_loss[loss=0.1496, simple_loss=0.2361, pruned_loss=0.03152, over 977761.36 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2225, pruned_loss=0.03474, over 976184.44 frames.], batch size: 35, lr: 3.90e-04
+2022-06-19 01:26:13,757 INFO [train.py:874] (0/4) Epoch 21, batch 1950, datatang_loss[loss=0.1171, simple_loss=0.1968, pruned_loss=0.01871, over 4918.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2292, pruned_loss=0.03275, over 985375.97 frames.], batch size: 75, aishell_tot_loss[loss=0.1495, simple_loss=0.236, pruned_loss=0.03152, over 978428.87 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2224, pruned_loss=0.03461, over 977535.69 frames.], batch size: 75, lr: 3.90e-04
+2022-06-19 01:26:45,043 INFO [train.py:874] (0/4) Epoch 21, batch 2000, aishell_loss[loss=0.1502, simple_loss=0.2325, pruned_loss=0.0339, over 4934.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2293, pruned_loss=0.03288, over 985260.73 frames.], batch size: 33, aishell_tot_loss[loss=0.1496, simple_loss=0.2361, pruned_loss=0.03159, over 978997.93 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2222, pruned_loss=0.03466, over 978592.70 frames.], batch size: 33, lr: 3.90e-04
+2022-06-19 01:26:45,046 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 01:27:02,717 INFO [train.py:914] (0/4) Epoch 21, validation: loss=0.164, simple_loss=0.2481, pruned_loss=0.03994, over 1622729.00 frames.
+2022-06-19 01:27:32,171 INFO [train.py:874] (0/4) Epoch 21, batch 2050, aishell_loss[loss=0.1602, simple_loss=0.2511, pruned_loss=0.03464, over 4918.00 frames.], tot_loss[loss=0.148, simple_loss=0.2296, pruned_loss=0.03322, over 985524.00 frames.], batch size: 46, aishell_tot_loss[loss=0.1497, simple_loss=0.2362, pruned_loss=0.03162, over 979747.97 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2226, pruned_loss=0.03489, over 979651.14 frames.], batch size: 46, lr: 3.90e-04
+2022-06-19 01:28:06,084 INFO [train.py:874] (0/4) Epoch 21, batch 2100, aishell_loss[loss=0.1682, simple_loss=0.2537, pruned_loss=0.04131, over 4967.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2296, pruned_loss=0.03347, over 985782.82 frames.], batch size: 44, aishell_tot_loss[loss=0.1496, simple_loss=0.2359, pruned_loss=0.03161, over 980469.83 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2229, pruned_loss=0.03513, over 980578.01 frames.], batch size: 44, lr: 3.89e-04
+2022-06-19 01:28:39,721 INFO [train.py:874] (0/4) Epoch 21, batch 2150, aishell_loss[loss=0.1612, simple_loss=0.2464, pruned_loss=0.03801, over 4880.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2297, pruned_loss=0.03399, over 985659.21 frames.], batch size: 47, aishell_tot_loss[loss=0.15, simple_loss=0.236, pruned_loss=0.03199, over 980732.39 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2231, pruned_loss=0.0353, over 981426.88 frames.], batch size: 47, lr: 3.89e-04
+2022-06-19 01:29:09,144 INFO [train.py:874] (0/4) Epoch 21, batch 2200, aishell_loss[loss=0.1582, simple_loss=0.2473, pruned_loss=0.03457, over 4922.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2306, pruned_loss=0.03437, over 985667.45 frames.], batch size: 52, aishell_tot_loss[loss=0.1502, simple_loss=0.2363, pruned_loss=0.03204, over 981162.23 frames.], datatang_tot_loss[loss=0.1477, simple_loss=0.2239, pruned_loss=0.03571, over 982069.96 frames.], batch size: 52, lr: 3.89e-04
+2022-06-19 01:29:41,095 INFO [train.py:874] (0/4) Epoch 21, batch 2250, datatang_loss[loss=0.1452, simple_loss=0.2205, pruned_loss=0.03493, over 4869.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2301, pruned_loss=0.03433, over 985928.83 frames.], batch size: 25, aishell_tot_loss[loss=0.1501, simple_loss=0.236, pruned_loss=0.03214, over 981790.57 frames.], datatang_tot_loss[loss=0.1476, simple_loss=0.2238, pruned_loss=0.03568, over 982675.19 frames.], batch size: 25, lr: 3.89e-04
+2022-06-19 01:30:15,117 INFO [train.py:874] (0/4) Epoch 21, batch 2300, aishell_loss[loss=0.1385, simple_loss=0.2214, pruned_loss=0.02783, over 4879.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2299, pruned_loss=0.03461, over 985877.91 frames.], batch size: 28, aishell_tot_loss[loss=0.1507, simple_loss=0.2364, pruned_loss=0.03252, over 982158.33 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2233, pruned_loss=0.03568, over 983124.89 frames.], batch size: 28, lr: 3.89e-04
+2022-06-19 01:30:45,555 INFO [train.py:874] (0/4) Epoch 21, batch 2350, aishell_loss[loss=0.1589, simple_loss=0.2414, pruned_loss=0.03817, over 4969.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2301, pruned_loss=0.03435, over 985759.08 frames.], batch size: 61, aishell_tot_loss[loss=0.1512, simple_loss=0.2369, pruned_loss=0.03271, over 982584.91 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.223, pruned_loss=0.03534, over 983326.63 frames.], batch size: 61, lr: 3.89e-04
+2022-06-19 01:31:19,319 INFO [train.py:874] (0/4) Epoch 21, batch 2400, datatang_loss[loss=0.1436, simple_loss=0.2287, pruned_loss=0.02929, over 4950.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2301, pruned_loss=0.03418, over 986060.22 frames.], batch size: 91, aishell_tot_loss[loss=0.1511, simple_loss=0.237, pruned_loss=0.03259, over 983155.58 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2229, pruned_loss=0.03539, over 983746.41 frames.], batch size: 91, lr: 3.89e-04
+2022-06-19 01:31:50,753 INFO [train.py:874] (0/4) Epoch 21, batch 2450, datatang_loss[loss=0.1443, simple_loss=0.2145, pruned_loss=0.03704, over 4936.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2296, pruned_loss=0.03408, over 985505.99 frames.], batch size: 50, aishell_tot_loss[loss=0.151, simple_loss=0.2368, pruned_loss=0.03259, over 983092.57 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2228, pruned_loss=0.03529, over 983836.28 frames.], batch size: 50, lr: 3.89e-04
+2022-06-19 01:32:21,752 INFO [train.py:874] (0/4) Epoch 21, batch 2500, aishell_loss[loss=0.1641, simple_loss=0.2529, pruned_loss=0.03768, over 4942.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2296, pruned_loss=0.03398, over 985168.66 frames.], batch size: 58, aishell_tot_loss[loss=0.1511, simple_loss=0.2366, pruned_loss=0.03277, over 983120.21 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2226, pruned_loss=0.0351, over 983954.98 frames.], batch size: 58, lr: 3.89e-04
+2022-06-19 01:32:56,165 INFO [train.py:874] (0/4) Epoch 21, batch 2550, aishell_loss[loss=0.1576, simple_loss=0.2371, pruned_loss=0.0391, over 4856.00 frames.], tot_loss[loss=0.1484, simple_loss=0.229, pruned_loss=0.03388, over 985291.38 frames.], batch size: 37, aishell_tot_loss[loss=0.1509, simple_loss=0.2364, pruned_loss=0.03273, over 983210.99 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2222, pruned_loss=0.035, over 984365.81 frames.], batch size: 37, lr: 3.88e-04
+2022-06-19 01:33:29,089 INFO [train.py:874] (0/4) Epoch 21, batch 2600, datatang_loss[loss=0.1571, simple_loss=0.2281, pruned_loss=0.04304, over 4936.00 frames.], tot_loss[loss=0.1482, simple_loss=0.229, pruned_loss=0.03371, over 985553.95 frames.], batch size: 34, aishell_tot_loss[loss=0.1505, simple_loss=0.236, pruned_loss=0.0325, over 983684.93 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2226, pruned_loss=0.03506, over 984514.87 frames.], batch size: 34, lr: 3.88e-04
+2022-06-19 01:33:59,963 INFO [train.py:874] (0/4) Epoch 21, batch 2650, aishell_loss[loss=0.2, simple_loss=0.2826, pruned_loss=0.05871, over 4917.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2293, pruned_loss=0.03363, over 985569.04 frames.], batch size: 79, aishell_tot_loss[loss=0.1508, simple_loss=0.2365, pruned_loss=0.03251, over 983754.63 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2222, pruned_loss=0.03492, over 984796.75 frames.], batch size: 79, lr: 3.88e-04
+2022-06-19 01:34:33,251 INFO [train.py:874] (0/4) Epoch 21, batch 2700, aishell_loss[loss=0.1477, simple_loss=0.233, pruned_loss=0.03118, over 4902.00 frames.], tot_loss[loss=0.149, simple_loss=0.2296, pruned_loss=0.03417, over 985486.35 frames.], batch size: 34, aishell_tot_loss[loss=0.1507, simple_loss=0.2368, pruned_loss=0.03229, over 983841.99 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2227, pruned_loss=0.03562, over 984893.25 frames.], batch size: 34, lr: 3.88e-04
+2022-06-19 01:35:05,888 INFO [train.py:874] (0/4) Epoch 21, batch 2750, aishell_loss[loss=0.1461, simple_loss=0.2348, pruned_loss=0.02868, over 4919.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2294, pruned_loss=0.03403, over 985086.33 frames.], batch size: 46, aishell_tot_loss[loss=0.1505, simple_loss=0.2366, pruned_loss=0.03219, over 983719.63 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2228, pruned_loss=0.03559, over 984841.93 frames.], batch size: 46, lr: 3.88e-04
+2022-06-19 01:35:36,798 INFO [train.py:874] (0/4) Epoch 21, batch 2800, aishell_loss[loss=0.1714, simple_loss=0.2484, pruned_loss=0.0472, over 4963.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2294, pruned_loss=0.03426, over 985382.89 frames.], batch size: 69, aishell_tot_loss[loss=0.151, simple_loss=0.2368, pruned_loss=0.03258, over 984050.49 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2224, pruned_loss=0.03548, over 985020.66 frames.], batch size: 69, lr: 3.88e-04
+2022-06-19 01:36:09,390 INFO [train.py:874] (0/4) Epoch 21, batch 2850, datatang_loss[loss=0.1427, simple_loss=0.219, pruned_loss=0.03316, over 4929.00 frames.], tot_loss[loss=0.1488, simple_loss=0.2295, pruned_loss=0.03407, over 985333.48 frames.], batch size: 79, aishell_tot_loss[loss=0.1508, simple_loss=0.2364, pruned_loss=0.03264, over 984119.35 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2226, pruned_loss=0.03534, over 985129.47 frames.], batch size: 79, lr: 3.88e-04
+2022-06-19 01:36:39,720 INFO [train.py:874] (0/4) Epoch 21, batch 2900, datatang_loss[loss=0.1402, simple_loss=0.2208, pruned_loss=0.02987, over 4933.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2287, pruned_loss=0.03329, over 985314.94 frames.], batch size: 69, aishell_tot_loss[loss=0.1507, simple_loss=0.2363, pruned_loss=0.03254, over 984142.73 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2219, pruned_loss=0.03461, over 985241.78 frames.], batch size: 69, lr: 3.88e-04
+2022-06-19 01:37:10,801 INFO [train.py:874] (0/4) Epoch 21, batch 2950, datatang_loss[loss=0.1477, simple_loss=0.2253, pruned_loss=0.03501, over 4923.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2287, pruned_loss=0.03283, over 985255.52 frames.], batch size: 83, aishell_tot_loss[loss=0.1502, simple_loss=0.236, pruned_loss=0.03219, over 984094.19 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2218, pruned_loss=0.03446, over 985391.01 frames.], batch size: 83, lr: 3.87e-04
+2022-06-19 01:37:43,719 INFO [train.py:874] (0/4) Epoch 21, batch 3000, aishell_loss[loss=0.1125, simple_loss=0.199, pruned_loss=0.01303, over 4974.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2287, pruned_loss=0.03297, over 985288.59 frames.], batch size: 30, aishell_tot_loss[loss=0.15, simple_loss=0.2359, pruned_loss=0.03205, over 984144.80 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.222, pruned_loss=0.03464, over 985497.29 frames.], batch size: 30, lr: 3.87e-04
+2022-06-19 01:37:43,722 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 01:38:00,375 INFO [train.py:914] (0/4) Epoch 21, validation: loss=0.1648, simple_loss=0.2487, pruned_loss=0.04045, over 1622729.00 frames.
+2022-06-19 01:38:34,160 INFO [train.py:874] (0/4) Epoch 21, batch 3050, aishell_loss[loss=0.1527, simple_loss=0.2406, pruned_loss=0.03245, over 4966.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2298, pruned_loss=0.03322, over 985538.97 frames.], batch size: 61, aishell_tot_loss[loss=0.1505, simple_loss=0.2367, pruned_loss=0.03215, over 984382.07 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.222, pruned_loss=0.03478, over 985663.76 frames.], batch size: 61, lr: 3.87e-04
+2022-06-19 01:39:07,204 INFO [train.py:874] (0/4) Epoch 21, batch 3100, aishell_loss[loss=0.1863, simple_loss=0.2734, pruned_loss=0.04957, over 4876.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2302, pruned_loss=0.03337, over 985420.29 frames.], batch size: 35, aishell_tot_loss[loss=0.1505, simple_loss=0.2369, pruned_loss=0.03208, over 984287.98 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2223, pruned_loss=0.03495, over 985749.56 frames.], batch size: 35, lr: 3.87e-04
+2022-06-19 01:39:37,319 INFO [train.py:874] (0/4) Epoch 21, batch 3150, aishell_loss[loss=0.1258, simple_loss=0.2177, pruned_loss=0.01695, over 4983.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2297, pruned_loss=0.03294, over 985591.05 frames.], batch size: 27, aishell_tot_loss[loss=0.1503, simple_loss=0.2368, pruned_loss=0.03193, over 984753.05 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2222, pruned_loss=0.03456, over 985539.39 frames.], batch size: 27, lr: 3.87e-04
+2022-06-19 01:40:10,717 INFO [train.py:874] (0/4) Epoch 21, batch 3200, aishell_loss[loss=0.1501, simple_loss=0.2415, pruned_loss=0.02932, over 4931.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2309, pruned_loss=0.03299, over 985606.66 frames.], batch size: 56, aishell_tot_loss[loss=0.1504, simple_loss=0.2373, pruned_loss=0.03182, over 984837.55 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2227, pruned_loss=0.03478, over 985590.94 frames.], batch size: 56, lr: 3.87e-04
+2022-06-19 01:40:43,325 INFO [train.py:874] (0/4) Epoch 21, batch 3250, datatang_loss[loss=0.1394, simple_loss=0.2156, pruned_loss=0.03158, over 4950.00 frames.], tot_loss[loss=0.149, simple_loss=0.231, pruned_loss=0.03351, over 985164.99 frames.], batch size: 50, aishell_tot_loss[loss=0.1505, simple_loss=0.2374, pruned_loss=0.03183, over 984416.35 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2233, pruned_loss=0.03514, over 985595.63 frames.], batch size: 50, lr: 3.87e-04
+2022-06-19 01:41:14,430 INFO [train.py:874] (0/4) Epoch 21, batch 3300, datatang_loss[loss=0.1412, simple_loss=0.2207, pruned_loss=0.0309, over 4932.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2309, pruned_loss=0.03347, over 985167.11 frames.], batch size: 88, aishell_tot_loss[loss=0.1502, simple_loss=0.2372, pruned_loss=0.03157, over 984454.42 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2234, pruned_loss=0.03538, over 985616.81 frames.], batch size: 88, lr: 3.87e-04
+2022-06-19 01:41:47,796 INFO [train.py:874] (0/4) Epoch 21, batch 3350, datatang_loss[loss=0.127, simple_loss=0.2012, pruned_loss=0.02635, over 4928.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2301, pruned_loss=0.03352, over 985784.56 frames.], batch size: 57, aishell_tot_loss[loss=0.1499, simple_loss=0.2368, pruned_loss=0.03151, over 984852.44 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2229, pruned_loss=0.03556, over 985917.41 frames.], batch size: 57, lr: 3.87e-04
+2022-06-19 01:42:19,553 INFO [train.py:874] (0/4) Epoch 21, batch 3400, datatang_loss[loss=0.1238, simple_loss=0.2006, pruned_loss=0.02351, over 4856.00 frames.], tot_loss[loss=0.1482, simple_loss=0.23, pruned_loss=0.03317, over 985352.69 frames.], batch size: 30, aishell_tot_loss[loss=0.1496, simple_loss=0.2365, pruned_loss=0.03134, over 984821.76 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2228, pruned_loss=0.03548, over 985611.79 frames.], batch size: 30, lr: 3.86e-04
+2022-06-19 01:42:51,701 INFO [train.py:874] (0/4) Epoch 21, batch 3450, datatang_loss[loss=0.1235, simple_loss=0.2035, pruned_loss=0.02171, over 4921.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2304, pruned_loss=0.03355, over 985318.08 frames.], batch size: 71, aishell_tot_loss[loss=0.1503, simple_loss=0.2373, pruned_loss=0.03166, over 984893.36 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2226, pruned_loss=0.03545, over 985516.95 frames.], batch size: 71, lr: 3.86e-04
+2022-06-19 01:43:25,913 INFO [train.py:874] (0/4) Epoch 21, batch 3500, datatang_loss[loss=0.1644, simple_loss=0.242, pruned_loss=0.04339, over 4931.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2305, pruned_loss=0.03368, over 985234.62 frames.], batch size: 94, aishell_tot_loss[loss=0.1505, simple_loss=0.2373, pruned_loss=0.03187, over 984711.21 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.223, pruned_loss=0.03534, over 985628.15 frames.], batch size: 94, lr: 3.86e-04
+2022-06-19 01:43:56,502 INFO [train.py:874] (0/4) Epoch 21, batch 3550, datatang_loss[loss=0.1268, simple_loss=0.2128, pruned_loss=0.02041, over 4921.00 frames.], tot_loss[loss=0.149, simple_loss=0.2304, pruned_loss=0.03378, over 985380.43 frames.], batch size: 73, aishell_tot_loss[loss=0.1509, simple_loss=0.2374, pruned_loss=0.03219, over 984587.26 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.223, pruned_loss=0.03512, over 985920.50 frames.], batch size: 73, lr: 3.86e-04
+2022-06-19 01:44:29,723 INFO [train.py:874] (0/4) Epoch 21, batch 3600, aishell_loss[loss=0.139, simple_loss=0.2355, pruned_loss=0.02122, over 4962.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2309, pruned_loss=0.03381, over 985670.27 frames.], batch size: 61, aishell_tot_loss[loss=0.1509, simple_loss=0.2376, pruned_loss=0.0321, over 984866.48 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2234, pruned_loss=0.03525, over 985968.79 frames.], batch size: 61, lr: 3.86e-04
+2022-06-19 01:45:03,482 INFO [train.py:874] (0/4) Epoch 21, batch 3650, datatang_loss[loss=0.111, simple_loss=0.1923, pruned_loss=0.01483, over 4929.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2307, pruned_loss=0.03401, over 985324.62 frames.], batch size: 79, aishell_tot_loss[loss=0.1514, simple_loss=0.2379, pruned_loss=0.03242, over 984695.70 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2232, pruned_loss=0.03519, over 985828.57 frames.], batch size: 79, lr: 3.86e-04
+2022-06-19 01:45:34,389 INFO [train.py:874] (0/4) Epoch 21, batch 3700, datatang_loss[loss=0.1291, simple_loss=0.2079, pruned_loss=0.02513, over 4925.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2294, pruned_loss=0.03352, over 985571.68 frames.], batch size: 83, aishell_tot_loss[loss=0.1509, simple_loss=0.2375, pruned_loss=0.03218, over 984923.63 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2224, pruned_loss=0.03493, over 985874.73 frames.], batch size: 83, lr: 3.86e-04
+2022-06-19 01:46:07,230 INFO [train.py:874] (0/4) Epoch 21, batch 3750, aishell_loss[loss=0.1498, simple_loss=0.2306, pruned_loss=0.03449, over 4884.00 frames.], tot_loss[loss=0.147, simple_loss=0.2283, pruned_loss=0.03282, over 985347.20 frames.], batch size: 47, aishell_tot_loss[loss=0.1504, simple_loss=0.2369, pruned_loss=0.03191, over 984773.00 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2217, pruned_loss=0.03444, over 985817.57 frames.], batch size: 47, lr: 3.86e-04
+2022-06-19 01:46:36,115 INFO [train.py:874] (0/4) Epoch 21, batch 3800, aishell_loss[loss=0.1318, simple_loss=0.2274, pruned_loss=0.01806, over 4914.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2292, pruned_loss=0.03265, over 985201.33 frames.], batch size: 41, aishell_tot_loss[loss=0.1504, simple_loss=0.2371, pruned_loss=0.0318, over 984823.76 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2216, pruned_loss=0.0344, over 985671.97 frames.], batch size: 41, lr: 3.86e-04
+2022-06-19 01:47:09,279 INFO [train.py:874] (0/4) Epoch 21, batch 3850, datatang_loss[loss=0.1375, simple_loss=0.2182, pruned_loss=0.02842, over 4926.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2296, pruned_loss=0.03311, over 985473.88 frames.], batch size: 83, aishell_tot_loss[loss=0.1506, simple_loss=0.2373, pruned_loss=0.03193, over 984998.98 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2222, pruned_loss=0.0346, over 985755.86 frames.], batch size: 83, lr: 3.85e-04
+2022-06-19 01:47:38,018 INFO [train.py:874] (0/4) Epoch 21, batch 3900, datatang_loss[loss=0.1291, simple_loss=0.2113, pruned_loss=0.02343, over 4927.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2298, pruned_loss=0.03273, over 985194.67 frames.], batch size: 79, aishell_tot_loss[loss=0.1505, simple_loss=0.2372, pruned_loss=0.03191, over 984699.29 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2218, pruned_loss=0.03427, over 985805.34 frames.], batch size: 79, lr: 3.85e-04
+2022-06-19 01:48:11,143 INFO [train.py:874] (0/4) Epoch 21, batch 3950, datatang_loss[loss=0.1416, simple_loss=0.2138, pruned_loss=0.03466, over 4903.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2291, pruned_loss=0.03259, over 985289.05 frames.], batch size: 52, aishell_tot_loss[loss=0.1502, simple_loss=0.2367, pruned_loss=0.0319, over 984641.89 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.222, pruned_loss=0.03402, over 985932.24 frames.], batch size: 52, lr: 3.85e-04
+2022-06-19 01:48:40,065 INFO [train.py:874] (0/4) Epoch 21, batch 4000, aishell_loss[loss=0.1461, simple_loss=0.2287, pruned_loss=0.03173, over 4939.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2292, pruned_loss=0.03254, over 985324.38 frames.], batch size: 49, aishell_tot_loss[loss=0.1508, simple_loss=0.2372, pruned_loss=0.03217, over 984873.20 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2214, pruned_loss=0.03362, over 985755.58 frames.], batch size: 49, lr: 3.85e-04
+2022-06-19 01:48:40,070 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 01:48:57,402 INFO [train.py:914] (0/4) Epoch 21, validation: loss=0.1643, simple_loss=0.2479, pruned_loss=0.04039, over 1622729.00 frames.
+2022-06-19 01:49:25,765 INFO [train.py:874] (0/4) Epoch 21, batch 4050, datatang_loss[loss=0.1245, simple_loss=0.2071, pruned_loss=0.02093, over 4912.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2289, pruned_loss=0.03221, over 985325.53 frames.], batch size: 75, aishell_tot_loss[loss=0.1502, simple_loss=0.2366, pruned_loss=0.03188, over 984809.28 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2213, pruned_loss=0.03348, over 985814.95 frames.], batch size: 75, lr: 3.85e-04
+2022-06-19 01:49:33,983 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-21.pt
+2022-06-19 01:50:39,843 INFO [train.py:874] (0/4) Epoch 22, batch 50, datatang_loss[loss=0.1428, simple_loss=0.2171, pruned_loss=0.03423, over 4911.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2274, pruned_loss=0.03085, over 218640.95 frames.], batch size: 57, aishell_tot_loss[loss=0.1547, simple_loss=0.2421, pruned_loss=0.03365, over 120597.82 frames.], datatang_tot_loss[loss=0.1338, simple_loss=0.2117, pruned_loss=0.02801, over 111702.27 frames.], batch size: 57, lr: 3.76e-04
+2022-06-19 01:51:13,461 INFO [train.py:874] (0/4) Epoch 22, batch 100, datatang_loss[loss=0.159, simple_loss=0.2385, pruned_loss=0.03973, over 4953.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2266, pruned_loss=0.03041, over 388542.94 frames.], batch size: 91, aishell_tot_loss[loss=0.1528, simple_loss=0.2401, pruned_loss=0.03277, over 226312.60 frames.], datatang_tot_loss[loss=0.1339, simple_loss=0.212, pruned_loss=0.02792, over 210586.94 frames.], batch size: 91, lr: 3.76e-04
+2022-06-19 01:51:45,194 INFO [train.py:874] (0/4) Epoch 22, batch 150, aishell_loss[loss=0.158, simple_loss=0.2431, pruned_loss=0.03645, over 4953.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2269, pruned_loss=0.03147, over 520716.71 frames.], batch size: 40, aishell_tot_loss[loss=0.154, simple_loss=0.2405, pruned_loss=0.03377, over 312154.39 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.213, pruned_loss=0.02897, over 305316.23 frames.], batch size: 40, lr: 3.76e-04
+2022-06-19 01:52:17,267 INFO [train.py:874] (0/4) Epoch 22, batch 200, aishell_loss[loss=0.1526, simple_loss=0.2412, pruned_loss=0.032, over 4943.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2271, pruned_loss=0.03129, over 623594.97 frames.], batch size: 45, aishell_tot_loss[loss=0.1527, simple_loss=0.2397, pruned_loss=0.03285, over 391243.50 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2141, pruned_loss=0.02966, over 385485.99 frames.], batch size: 45, lr: 3.76e-04
+2022-06-19 01:52:50,984 INFO [train.py:874] (0/4) Epoch 22, batch 250, aishell_loss[loss=0.1504, simple_loss=0.2429, pruned_loss=0.02897, over 4915.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2265, pruned_loss=0.03088, over 703973.66 frames.], batch size: 46, aishell_tot_loss[loss=0.151, simple_loss=0.2375, pruned_loss=0.03228, over 476723.79 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2138, pruned_loss=0.02937, over 440197.98 frames.], batch size: 46, lr: 3.76e-04
+2022-06-19 01:53:21,202 INFO [train.py:874] (0/4) Epoch 22, batch 300, datatang_loss[loss=0.1442, simple_loss=0.2204, pruned_loss=0.03398, over 4923.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2263, pruned_loss=0.03123, over 766178.89 frames.], batch size: 77, aishell_tot_loss[loss=0.1506, simple_loss=0.2369, pruned_loss=0.03211, over 540862.96 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2141, pruned_loss=0.03009, over 499616.86 frames.], batch size: 77, lr: 3.76e-04
+2022-06-19 01:53:54,111 INFO [train.py:874] (0/4) Epoch 22, batch 350, datatang_loss[loss=0.1944, simple_loss=0.2685, pruned_loss=0.06015, over 4939.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2275, pruned_loss=0.03183, over 814686.51 frames.], batch size: 109, aishell_tot_loss[loss=0.1501, simple_loss=0.2361, pruned_loss=0.03202, over 593092.27 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2171, pruned_loss=0.03114, over 556863.21 frames.], batch size: 109, lr: 3.76e-04
+2022-06-19 01:54:25,640 INFO [train.py:874] (0/4) Epoch 22, batch 400, aishell_loss[loss=0.1519, simple_loss=0.2383, pruned_loss=0.03278, over 4886.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2273, pruned_loss=0.03185, over 852060.80 frames.], batch size: 34, aishell_tot_loss[loss=0.1496, simple_loss=0.2359, pruned_loss=0.03171, over 635410.61 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2177, pruned_loss=0.03159, over 610996.63 frames.], batch size: 34, lr: 3.76e-04
+2022-06-19 01:54:58,586 INFO [train.py:874] (0/4) Epoch 22, batch 450, datatang_loss[loss=0.2093, simple_loss=0.2789, pruned_loss=0.06986, over 4955.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2274, pruned_loss=0.03199, over 881696.09 frames.], batch size: 91, aishell_tot_loss[loss=0.1497, simple_loss=0.2361, pruned_loss=0.03168, over 668606.65 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2183, pruned_loss=0.03187, over 663528.36 frames.], batch size: 91, lr: 3.76e-04
+2022-06-19 01:55:29,418 INFO [train.py:874] (0/4) Epoch 22, batch 500, datatang_loss[loss=0.1461, simple_loss=0.2309, pruned_loss=0.03067, over 4932.00 frames.], tot_loss[loss=0.146, simple_loss=0.2278, pruned_loss=0.03207, over 904899.88 frames.], batch size: 62, aishell_tot_loss[loss=0.1499, simple_loss=0.2365, pruned_loss=0.03167, over 705923.51 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2186, pruned_loss=0.03204, over 701650.59 frames.], batch size: 62, lr: 3.75e-04
+2022-06-19 01:56:02,059 INFO [train.py:874] (0/4) Epoch 22, batch 550, datatang_loss[loss=0.2022, simple_loss=0.2569, pruned_loss=0.07376, over 4945.00 frames.], tot_loss[loss=0.146, simple_loss=0.2276, pruned_loss=0.03226, over 922814.85 frames.], batch size: 62, aishell_tot_loss[loss=0.1496, simple_loss=0.2361, pruned_loss=0.03157, over 736346.19 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.219, pruned_loss=0.03245, over 737652.78 frames.], batch size: 62, lr: 3.75e-04
+2022-06-19 01:56:34,617 INFO [train.py:874] (0/4) Epoch 22, batch 600, aishell_loss[loss=0.1557, simple_loss=0.2464, pruned_loss=0.03253, over 4932.00 frames.], tot_loss[loss=0.146, simple_loss=0.2273, pruned_loss=0.03236, over 936602.13 frames.], batch size: 68, aishell_tot_loss[loss=0.1493, simple_loss=0.2354, pruned_loss=0.03161, over 763522.37 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2196, pruned_loss=0.0326, over 768863.05 frames.], batch size: 68, lr: 3.75e-04
+2022-06-19 01:57:06,341 INFO [train.py:874] (0/4) Epoch 22, batch 650, aishell_loss[loss=0.1306, simple_loss=0.2188, pruned_loss=0.02121, over 4857.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2269, pruned_loss=0.03198, over 947441.16 frames.], batch size: 28, aishell_tot_loss[loss=0.149, simple_loss=0.2351, pruned_loss=0.03145, over 787650.53 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2194, pruned_loss=0.03234, over 796344.03 frames.], batch size: 28, lr: 3.75e-04
+2022-06-19 01:57:38,736 INFO [train.py:874] (0/4) Epoch 22, batch 700, aishell_loss[loss=0.1272, simple_loss=0.2101, pruned_loss=0.02211, over 4887.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2279, pruned_loss=0.03227, over 955815.75 frames.], batch size: 28, aishell_tot_loss[loss=0.1486, simple_loss=0.2348, pruned_loss=0.03115, over 810752.19 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2209, pruned_loss=0.03304, over 818758.64 frames.], batch size: 28, lr: 3.75e-04
+2022-06-19 01:58:11,421 INFO [train.py:874] (0/4) Epoch 22, batch 750, datatang_loss[loss=0.1321, simple_loss=0.215, pruned_loss=0.02464, over 4934.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2273, pruned_loss=0.03253, over 962564.44 frames.], batch size: 88, aishell_tot_loss[loss=0.1487, simple_loss=0.2345, pruned_loss=0.03141, over 832939.66 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2204, pruned_loss=0.03316, over 837020.57 frames.], batch size: 88, lr: 3.75e-04
+2022-06-19 01:58:43,762 INFO [train.py:874] (0/4) Epoch 22, batch 800, datatang_loss[loss=0.147, simple_loss=0.2186, pruned_loss=0.03767, over 4924.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2273, pruned_loss=0.03225, over 967411.01 frames.], batch size: 77, aishell_tot_loss[loss=0.1487, simple_loss=0.2348, pruned_loss=0.03131, over 848784.24 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2204, pruned_loss=0.03298, over 856289.48 frames.], batch size: 77, lr: 3.75e-04
+2022-06-19 01:59:14,946 INFO [train.py:874] (0/4) Epoch 22, batch 850, aishell_loss[loss=0.1633, simple_loss=0.2524, pruned_loss=0.03706, over 4970.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2273, pruned_loss=0.03201, over 971200.23 frames.], batch size: 40, aishell_tot_loss[loss=0.1488, simple_loss=0.2348, pruned_loss=0.03144, over 865188.39 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2201, pruned_loss=0.03258, over 871014.05 frames.], batch size: 40, lr: 3.75e-04
+2022-06-19 01:59:46,506 INFO [train.py:874] (0/4) Epoch 22, batch 900, aishell_loss[loss=0.1645, simple_loss=0.2532, pruned_loss=0.03783, over 4932.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2278, pruned_loss=0.03235, over 974381.64 frames.], batch size: 68, aishell_tot_loss[loss=0.1488, simple_loss=0.235, pruned_loss=0.03133, over 878202.63 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2205, pruned_loss=0.03308, over 885596.48 frames.], batch size: 68, lr: 3.75e-04
+2022-06-19 02:00:00,819 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-88000.pt
+2022-06-19 02:00:24,001 INFO [train.py:874] (0/4) Epoch 22, batch 950, aishell_loss[loss=0.1612, simple_loss=0.2459, pruned_loss=0.03829, over 4940.00 frames.], tot_loss[loss=0.146, simple_loss=0.2277, pruned_loss=0.0322, over 977060.63 frames.], batch size: 49, aishell_tot_loss[loss=0.149, simple_loss=0.2353, pruned_loss=0.0314, over 891041.56 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2201, pruned_loss=0.03287, over 897386.90 frames.], batch size: 49, lr: 3.74e-04
+2022-06-19 02:00:55,122 INFO [train.py:874] (0/4) Epoch 22, batch 1000, aishell_loss[loss=0.1466, simple_loss=0.2443, pruned_loss=0.0244, over 4907.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2285, pruned_loss=0.03235, over 979162.33 frames.], batch size: 46, aishell_tot_loss[loss=0.1491, simple_loss=0.2355, pruned_loss=0.03133, over 903468.57 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2206, pruned_loss=0.03317, over 906781.95 frames.], batch size: 46, lr: 3.74e-04
+2022-06-19 02:00:55,126 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 02:01:12,462 INFO [train.py:914] (0/4) Epoch 22, validation: loss=0.1645, simple_loss=0.2481, pruned_loss=0.04044, over 1622729.00 frames.
+2022-06-19 02:01:46,818 INFO [train.py:874] (0/4) Epoch 22, batch 1050, datatang_loss[loss=0.1897, simple_loss=0.2669, pruned_loss=0.05622, over 4937.00 frames.], tot_loss[loss=0.146, simple_loss=0.2273, pruned_loss=0.03237, over 980713.83 frames.], batch size: 108, aishell_tot_loss[loss=0.1485, simple_loss=0.2351, pruned_loss=0.03101, over 908026.27 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2209, pruned_loss=0.03338, over 920760.76 frames.], batch size: 108, lr: 3.74e-04
+2022-06-19 02:02:17,962 INFO [train.py:874] (0/4) Epoch 22, batch 1100, datatang_loss[loss=0.1538, simple_loss=0.2319, pruned_loss=0.03782, over 4923.00 frames.], tot_loss[loss=0.146, simple_loss=0.2272, pruned_loss=0.03237, over 981579.73 frames.], batch size: 83, aishell_tot_loss[loss=0.1487, simple_loss=0.235, pruned_loss=0.03118, over 917623.81 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2205, pruned_loss=0.03329, over 927823.77 frames.], batch size: 83, lr: 3.74e-04
+2022-06-19 02:02:49,826 INFO [train.py:874] (0/4) Epoch 22, batch 1150, datatang_loss[loss=0.1308, simple_loss=0.2104, pruned_loss=0.02564, over 4931.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2276, pruned_loss=0.03257, over 982288.69 frames.], batch size: 71, aishell_tot_loss[loss=0.1494, simple_loss=0.2357, pruned_loss=0.03153, over 925116.43 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2202, pruned_loss=0.03316, over 934906.05 frames.], batch size: 71, lr: 3.74e-04
+2022-06-19 02:03:20,661 INFO [train.py:874] (0/4) Epoch 22, batch 1200, aishell_loss[loss=0.1155, simple_loss=0.2031, pruned_loss=0.01391, over 4902.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2283, pruned_loss=0.03266, over 982362.64 frames.], batch size: 28, aishell_tot_loss[loss=0.1487, simple_loss=0.2351, pruned_loss=0.03115, over 932674.15 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2212, pruned_loss=0.03375, over 939902.38 frames.], batch size: 28, lr: 3.74e-04
+2022-06-19 02:03:54,479 INFO [train.py:874] (0/4) Epoch 22, batch 1250, datatang_loss[loss=0.1329, simple_loss=0.2143, pruned_loss=0.02572, over 4925.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2292, pruned_loss=0.03287, over 982915.74 frames.], batch size: 77, aishell_tot_loss[loss=0.1492, simple_loss=0.2356, pruned_loss=0.03144, over 940725.65 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2211, pruned_loss=0.03388, over 943509.09 frames.], batch size: 77, lr: 3.74e-04
+2022-06-19 02:04:26,501 INFO [train.py:874] (0/4) Epoch 22, batch 1300, datatang_loss[loss=0.1539, simple_loss=0.24, pruned_loss=0.03395, over 4938.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2288, pruned_loss=0.03305, over 983434.70 frames.], batch size: 94, aishell_tot_loss[loss=0.1486, simple_loss=0.2348, pruned_loss=0.03115, over 946067.95 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2215, pruned_loss=0.03445, over 948354.85 frames.], batch size: 94, lr: 3.74e-04
+2022-06-19 02:04:58,062 INFO [train.py:874] (0/4) Epoch 22, batch 1350, datatang_loss[loss=0.1449, simple_loss=0.2206, pruned_loss=0.03456, over 4886.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2291, pruned_loss=0.03307, over 983973.02 frames.], batch size: 47, aishell_tot_loss[loss=0.1482, simple_loss=0.2346, pruned_loss=0.03092, over 950888.87 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2221, pruned_loss=0.03479, over 952647.28 frames.], batch size: 47, lr: 3.74e-04
+2022-06-19 02:05:30,698 INFO [train.py:874] (0/4) Epoch 22, batch 1400, datatang_loss[loss=0.1856, simple_loss=0.26, pruned_loss=0.0556, over 4936.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2286, pruned_loss=0.03285, over 984304.21 frames.], batch size: 50, aishell_tot_loss[loss=0.1482, simple_loss=0.2344, pruned_loss=0.03104, over 954857.41 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2219, pruned_loss=0.03448, over 956624.51 frames.], batch size: 50, lr: 3.73e-04
+2022-06-19 02:06:03,236 INFO [train.py:874] (0/4) Epoch 22, batch 1450, aishell_loss[loss=0.1624, simple_loss=0.25, pruned_loss=0.03736, over 4977.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2292, pruned_loss=0.03315, over 984466.98 frames.], batch size: 48, aishell_tot_loss[loss=0.1486, simple_loss=0.2345, pruned_loss=0.03131, over 959095.03 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2222, pruned_loss=0.03466, over 959311.39 frames.], batch size: 48, lr: 3.73e-04
+2022-06-19 02:06:34,663 INFO [train.py:874] (0/4) Epoch 22, batch 1500, aishell_loss[loss=0.1261, simple_loss=0.2143, pruned_loss=0.0189, over 4945.00 frames.], tot_loss[loss=0.147, simple_loss=0.2287, pruned_loss=0.03269, over 984420.24 frames.], batch size: 25, aishell_tot_loss[loss=0.1486, simple_loss=0.2345, pruned_loss=0.03131, over 961971.44 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.222, pruned_loss=0.03418, over 962348.17 frames.], batch size: 25, lr: 3.73e-04
+2022-06-19 02:07:06,453 INFO [train.py:874] (0/4) Epoch 22, batch 1550, datatang_loss[loss=0.1404, simple_loss=0.2114, pruned_loss=0.03472, over 4991.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2287, pruned_loss=0.03272, over 984717.99 frames.], batch size: 25, aishell_tot_loss[loss=0.1486, simple_loss=0.2347, pruned_loss=0.03129, over 964875.88 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2218, pruned_loss=0.03421, over 964997.96 frames.], batch size: 25, lr: 3.73e-04
+2022-06-19 02:07:39,205 INFO [train.py:874] (0/4) Epoch 22, batch 1600, datatang_loss[loss=0.1473, simple_loss=0.2333, pruned_loss=0.03069, over 4951.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2289, pruned_loss=0.03265, over 985015.35 frames.], batch size: 91, aishell_tot_loss[loss=0.1483, simple_loss=0.2347, pruned_loss=0.03099, over 967865.75 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2219, pruned_loss=0.03452, over 966967.16 frames.], batch size: 91, lr: 3.73e-04
+2022-06-19 02:08:12,765 INFO [train.py:874] (0/4) Epoch 22, batch 1650, aishell_loss[loss=0.1794, simple_loss=0.2618, pruned_loss=0.0485, over 4943.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2292, pruned_loss=0.03309, over 985355.95 frames.], batch size: 40, aishell_tot_loss[loss=0.149, simple_loss=0.2354, pruned_loss=0.03125, over 970010.91 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2218, pruned_loss=0.03465, over 969339.09 frames.], batch size: 40, lr: 3.73e-04
+2022-06-19 02:08:44,567 INFO [train.py:874] (0/4) Epoch 22, batch 1700, datatang_loss[loss=0.144, simple_loss=0.2221, pruned_loss=0.03299, over 4956.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2289, pruned_loss=0.03278, over 985852.43 frames.], batch size: 86, aishell_tot_loss[loss=0.149, simple_loss=0.2354, pruned_loss=0.03125, over 972388.99 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2213, pruned_loss=0.03442, over 971140.42 frames.], batch size: 86, lr: 3.73e-04
+2022-06-19 02:09:16,523 INFO [train.py:874] (0/4) Epoch 22, batch 1750, datatang_loss[loss=0.1674, simple_loss=0.2493, pruned_loss=0.04276, over 4913.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2292, pruned_loss=0.0328, over 985689.67 frames.], batch size: 98, aishell_tot_loss[loss=0.1487, simple_loss=0.2353, pruned_loss=0.03105, over 973798.68 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2219, pruned_loss=0.03464, over 972886.09 frames.], batch size: 98, lr: 3.73e-04
+2022-06-19 02:09:50,783 INFO [train.py:874] (0/4) Epoch 22, batch 1800, aishell_loss[loss=0.1167, simple_loss=0.1939, pruned_loss=0.01979, over 4806.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2281, pruned_loss=0.03264, over 985665.42 frames.], batch size: 26, aishell_tot_loss[loss=0.1486, simple_loss=0.2349, pruned_loss=0.03114, over 974785.97 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2215, pruned_loss=0.0343, over 974788.45 frames.], batch size: 26, lr: 3.73e-04
+2022-06-19 02:10:22,907 INFO [train.py:874] (0/4) Epoch 22, batch 1850, datatang_loss[loss=0.1332, simple_loss=0.2101, pruned_loss=0.02818, over 4930.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2284, pruned_loss=0.03257, over 985883.33 frames.], batch size: 71, aishell_tot_loss[loss=0.1482, simple_loss=0.2346, pruned_loss=0.03092, over 976206.54 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2221, pruned_loss=0.03444, over 976160.28 frames.], batch size: 71, lr: 3.73e-04
+2022-06-19 02:10:56,162 INFO [train.py:874] (0/4) Epoch 22, batch 1900, datatang_loss[loss=0.1562, simple_loss=0.2323, pruned_loss=0.04, over 4927.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2284, pruned_loss=0.03253, over 985911.40 frames.], batch size: 71, aishell_tot_loss[loss=0.148, simple_loss=0.2342, pruned_loss=0.03087, over 977046.94 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2226, pruned_loss=0.0344, over 977613.97 frames.], batch size: 71, lr: 3.72e-04
+2022-06-19 02:11:27,939 INFO [train.py:874] (0/4) Epoch 22, batch 1950, aishell_loss[loss=0.1383, simple_loss=0.2278, pruned_loss=0.02443, over 4945.00 frames.], tot_loss[loss=0.1454, simple_loss=0.227, pruned_loss=0.03189, over 985251.48 frames.], batch size: 58, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.03088, over 977729.31 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2212, pruned_loss=0.0337, over 978265.46 frames.], batch size: 58, lr: 3.72e-04
+2022-06-19 02:12:00,316 INFO [train.py:874] (0/4) Epoch 22, batch 2000, datatang_loss[loss=0.1527, simple_loss=0.233, pruned_loss=0.03615, over 4970.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2274, pruned_loss=0.03202, over 985414.75 frames.], batch size: 40, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.03088, over 978825.84 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2214, pruned_loss=0.03375, over 979050.84 frames.], batch size: 40, lr: 3.72e-04
+2022-06-19 02:12:00,320 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 02:12:18,452 INFO [train.py:914] (0/4) Epoch 22, validation: loss=0.165, simple_loss=0.2489, pruned_loss=0.04058, over 1622729.00 frames.
+2022-06-19 02:12:50,761 INFO [train.py:874] (0/4) Epoch 22, batch 2050, aishell_loss[loss=0.1455, simple_loss=0.2219, pruned_loss=0.03451, over 4851.00 frames.], tot_loss[loss=0.145, simple_loss=0.2267, pruned_loss=0.03163, over 985565.25 frames.], batch size: 28, aishell_tot_loss[loss=0.1476, simple_loss=0.2337, pruned_loss=0.03075, over 979463.05 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2212, pruned_loss=0.03337, over 980104.88 frames.], batch size: 28, lr: 3.72e-04
+2022-06-19 02:13:23,362 INFO [train.py:874] (0/4) Epoch 22, batch 2100, aishell_loss[loss=0.155, simple_loss=0.2443, pruned_loss=0.03291, over 4968.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2267, pruned_loss=0.03119, over 985069.74 frames.], batch size: 31, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03065, over 980066.23 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2205, pruned_loss=0.03292, over 980345.41 frames.], batch size: 31, lr: 3.72e-04
+2022-06-19 02:13:54,807 INFO [train.py:874] (0/4) Epoch 22, batch 2150, datatang_loss[loss=0.1449, simple_loss=0.2201, pruned_loss=0.03485, over 4947.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2272, pruned_loss=0.03153, over 985225.48 frames.], batch size: 45, aishell_tot_loss[loss=0.148, simple_loss=0.2345, pruned_loss=0.03074, over 980510.41 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2206, pruned_loss=0.03304, over 981204.73 frames.], batch size: 45, lr: 3.72e-04
+2022-06-19 02:14:25,665 INFO [train.py:874] (0/4) Epoch 22, batch 2200, aishell_loss[loss=0.1454, simple_loss=0.2356, pruned_loss=0.02759, over 4956.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2272, pruned_loss=0.03155, over 985104.94 frames.], batch size: 40, aishell_tot_loss[loss=0.1479, simple_loss=0.2343, pruned_loss=0.03079, over 980848.29 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2207, pruned_loss=0.03293, over 981755.60 frames.], batch size: 40, lr: 3.72e-04
+2022-06-19 02:14:59,110 INFO [train.py:874] (0/4) Epoch 22, batch 2250, datatang_loss[loss=0.1556, simple_loss=0.2153, pruned_loss=0.048, over 4960.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2275, pruned_loss=0.03196, over 985389.52 frames.], batch size: 37, aishell_tot_loss[loss=0.1484, simple_loss=0.2348, pruned_loss=0.03097, over 981662.50 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2204, pruned_loss=0.03312, over 982145.17 frames.], batch size: 37, lr: 3.72e-04
+2022-06-19 02:15:30,309 INFO [train.py:874] (0/4) Epoch 22, batch 2300, datatang_loss[loss=0.1607, simple_loss=0.2298, pruned_loss=0.04578, over 4950.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2281, pruned_loss=0.03278, over 985820.69 frames.], batch size: 45, aishell_tot_loss[loss=0.149, simple_loss=0.2354, pruned_loss=0.03136, over 982360.50 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2205, pruned_loss=0.03358, over 982720.79 frames.], batch size: 45, lr: 3.72e-04
+2022-06-19 02:16:02,659 INFO [train.py:874] (0/4) Epoch 22, batch 2350, aishell_loss[loss=0.1395, simple_loss=0.2296, pruned_loss=0.02468, over 4869.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2277, pruned_loss=0.03262, over 985073.99 frames.], batch size: 36, aishell_tot_loss[loss=0.1487, simple_loss=0.2349, pruned_loss=0.03123, over 982274.12 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2203, pruned_loss=0.03368, over 982811.62 frames.], batch size: 36, lr: 3.72e-04
+2022-06-19 02:16:33,519 INFO [train.py:874] (0/4) Epoch 22, batch 2400, datatang_loss[loss=0.1474, simple_loss=0.2282, pruned_loss=0.03325, over 4918.00 frames.], tot_loss[loss=0.1465, simple_loss=0.228, pruned_loss=0.03254, over 985305.25 frames.], batch size: 75, aishell_tot_loss[loss=0.149, simple_loss=0.2352, pruned_loss=0.03136, over 982679.17 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2201, pruned_loss=0.03354, over 983248.48 frames.], batch size: 75, lr: 3.71e-04
+2022-06-19 02:17:04,621 INFO [train.py:874] (0/4) Epoch 22, batch 2450, datatang_loss[loss=0.1436, simple_loss=0.2127, pruned_loss=0.03725, over 4933.00 frames.], tot_loss[loss=0.1457, simple_loss=0.227, pruned_loss=0.03219, over 985436.88 frames.], batch size: 79, aishell_tot_loss[loss=0.1486, simple_loss=0.235, pruned_loss=0.0311, over 982956.99 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2195, pruned_loss=0.03343, over 983641.05 frames.], batch size: 79, lr: 3.71e-04
+2022-06-19 02:17:37,741 INFO [train.py:874] (0/4) Epoch 22, batch 2500, aishell_loss[loss=0.1404, simple_loss=0.2255, pruned_loss=0.0276, over 4979.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2274, pruned_loss=0.03181, over 985700.20 frames.], batch size: 51, aishell_tot_loss[loss=0.1486, simple_loss=0.2354, pruned_loss=0.03092, over 983353.41 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2194, pruned_loss=0.0332, over 984039.54 frames.], batch size: 51, lr: 3.71e-04
+2022-06-19 02:18:09,171 INFO [train.py:874] (0/4) Epoch 22, batch 2550, aishell_loss[loss=0.1493, simple_loss=0.235, pruned_loss=0.03183, over 4937.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2284, pruned_loss=0.03172, over 985720.70 frames.], batch size: 68, aishell_tot_loss[loss=0.1488, simple_loss=0.2359, pruned_loss=0.03091, over 983650.17 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2199, pruned_loss=0.03307, over 984240.09 frames.], batch size: 68, lr: 3.71e-04
+2022-06-19 02:18:41,339 INFO [train.py:874] (0/4) Epoch 22, batch 2600, aishell_loss[loss=0.1722, simple_loss=0.257, pruned_loss=0.04366, over 4888.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2279, pruned_loss=0.03161, over 985819.09 frames.], batch size: 42, aishell_tot_loss[loss=0.1493, simple_loss=0.2363, pruned_loss=0.0311, over 983965.41 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2189, pruned_loss=0.03269, over 984445.52 frames.], batch size: 42, lr: 3.71e-04
+2022-06-19 02:19:14,419 INFO [train.py:874] (0/4) Epoch 22, batch 2650, datatang_loss[loss=0.1246, simple_loss=0.1944, pruned_loss=0.02739, over 4952.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2279, pruned_loss=0.03133, over 985691.22 frames.], batch size: 31, aishell_tot_loss[loss=0.1491, simple_loss=0.2364, pruned_loss=0.03093, over 984143.91 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2185, pruned_loss=0.03253, over 984526.19 frames.], batch size: 31, lr: 3.71e-04
+2022-06-19 02:19:44,848 INFO [train.py:874] (0/4) Epoch 22, batch 2700, aishell_loss[loss=0.1393, simple_loss=0.2318, pruned_loss=0.02341, over 4890.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2278, pruned_loss=0.03127, over 985728.75 frames.], batch size: 34, aishell_tot_loss[loss=0.1485, simple_loss=0.2358, pruned_loss=0.03063, over 984368.03 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2185, pruned_loss=0.0327, over 984659.43 frames.], batch size: 34, lr: 3.71e-04
+2022-06-19 02:20:16,436 INFO [train.py:874] (0/4) Epoch 22, batch 2750, datatang_loss[loss=0.1557, simple_loss=0.238, pruned_loss=0.03663, over 4950.00 frames.], tot_loss[loss=0.146, simple_loss=0.2286, pruned_loss=0.03168, over 986092.13 frames.], batch size: 69, aishell_tot_loss[loss=0.1491, simple_loss=0.2361, pruned_loss=0.03106, over 984958.05 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2189, pruned_loss=0.03266, over 984727.85 frames.], batch size: 69, lr: 3.71e-04
+2022-06-19 02:20:48,482 INFO [train.py:874] (0/4) Epoch 22, batch 2800, aishell_loss[loss=0.187, simple_loss=0.2626, pruned_loss=0.05574, over 4898.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2299, pruned_loss=0.03235, over 985846.40 frames.], batch size: 34, aishell_tot_loss[loss=0.15, simple_loss=0.237, pruned_loss=0.03146, over 984835.95 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2192, pruned_loss=0.03294, over 984898.62 frames.], batch size: 34, lr: 3.71e-04
+2022-06-19 02:21:19,582 INFO [train.py:874] (0/4) Epoch 22, batch 2850, aishell_loss[loss=0.1383, simple_loss=0.226, pruned_loss=0.0253, over 4872.00 frames.], tot_loss[loss=0.147, simple_loss=0.2298, pruned_loss=0.03216, over 985618.60 frames.], batch size: 36, aishell_tot_loss[loss=0.1499, simple_loss=0.2369, pruned_loss=0.03141, over 984811.05 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2194, pruned_loss=0.03284, over 984923.64 frames.], batch size: 36, lr: 3.70e-04
+2022-06-19 02:21:52,034 INFO [train.py:874] (0/4) Epoch 22, batch 2900, aishell_loss[loss=0.1317, simple_loss=0.2193, pruned_loss=0.02205, over 4920.00 frames.], tot_loss[loss=0.146, simple_loss=0.2284, pruned_loss=0.0318, over 985629.31 frames.], batch size: 33, aishell_tot_loss[loss=0.1496, simple_loss=0.2368, pruned_loss=0.0312, over 984733.09 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2186, pruned_loss=0.03264, over 985186.32 frames.], batch size: 33, lr: 3.70e-04
+2022-06-19 02:22:24,871 INFO [train.py:874] (0/4) Epoch 22, batch 2950, aishell_loss[loss=0.1571, simple_loss=0.2447, pruned_loss=0.03476, over 4969.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2287, pruned_loss=0.03196, over 985586.17 frames.], batch size: 61, aishell_tot_loss[loss=0.1493, simple_loss=0.2366, pruned_loss=0.03102, over 984819.05 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2195, pruned_loss=0.03293, over 985196.40 frames.], batch size: 61, lr: 3.70e-04
+2022-06-19 02:22:56,251 INFO [train.py:874] (0/4) Epoch 22, batch 3000, datatang_loss[loss=0.1235, simple_loss=0.1936, pruned_loss=0.02673, over 4970.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2289, pruned_loss=0.03228, over 985460.36 frames.], batch size: 45, aishell_tot_loss[loss=0.1493, simple_loss=0.2363, pruned_loss=0.0311, over 984756.31 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2199, pruned_loss=0.03325, over 985269.10 frames.], batch size: 45, lr: 3.70e-04
+2022-06-19 02:22:56,254 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 02:23:14,260 INFO [train.py:914] (0/4) Epoch 22, validation: loss=0.1638, simple_loss=0.2477, pruned_loss=0.03991, over 1622729.00 frames.
+2022-06-19 02:23:45,734 INFO [train.py:874] (0/4) Epoch 22, batch 3050, datatang_loss[loss=0.1551, simple_loss=0.2293, pruned_loss=0.04052, over 4912.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2287, pruned_loss=0.03235, over 985564.89 frames.], batch size: 75, aishell_tot_loss[loss=0.1493, simple_loss=0.2362, pruned_loss=0.03115, over 984867.80 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.22, pruned_loss=0.03327, over 985367.31 frames.], batch size: 75, lr: 3.70e-04
+2022-06-19 02:24:17,446 INFO [train.py:874] (0/4) Epoch 22, batch 3100, aishell_loss[loss=0.1398, simple_loss=0.2247, pruned_loss=0.02745, over 4892.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2285, pruned_loss=0.0323, over 985404.29 frames.], batch size: 34, aishell_tot_loss[loss=0.1491, simple_loss=0.2361, pruned_loss=0.03108, over 984899.59 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2203, pruned_loss=0.03331, over 985255.38 frames.], batch size: 34, lr: 3.70e-04
+2022-06-19 02:24:49,625 INFO [train.py:874] (0/4) Epoch 22, batch 3150, aishell_loss[loss=0.1569, simple_loss=0.2415, pruned_loss=0.03615, over 4968.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2283, pruned_loss=0.03212, over 985159.97 frames.], batch size: 51, aishell_tot_loss[loss=0.1487, simple_loss=0.2355, pruned_loss=0.03099, over 984695.88 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2202, pruned_loss=0.03331, over 985278.46 frames.], batch size: 51, lr: 3.70e-04
+2022-06-19 02:25:22,893 INFO [train.py:874] (0/4) Epoch 22, batch 3200, datatang_loss[loss=0.1324, simple_loss=0.2061, pruned_loss=0.0293, over 4877.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2279, pruned_loss=0.03217, over 985049.55 frames.], batch size: 24, aishell_tot_loss[loss=0.1487, simple_loss=0.2355, pruned_loss=0.03093, over 984620.61 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2202, pruned_loss=0.03338, over 985257.60 frames.], batch size: 24, lr: 3.70e-04
+2022-06-19 02:25:55,781 INFO [train.py:874] (0/4) Epoch 22, batch 3250, datatang_loss[loss=0.1394, simple_loss=0.2193, pruned_loss=0.02976, over 4927.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2278, pruned_loss=0.03199, over 985369.84 frames.], batch size: 75, aishell_tot_loss[loss=0.1485, simple_loss=0.2353, pruned_loss=0.03079, over 985012.70 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2203, pruned_loss=0.0333, over 985229.26 frames.], batch size: 75, lr: 3.70e-04
+2022-06-19 02:26:28,525 INFO [train.py:874] (0/4) Epoch 22, batch 3300, datatang_loss[loss=0.1415, simple_loss=0.2124, pruned_loss=0.03529, over 4945.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2282, pruned_loss=0.03212, over 985443.21 frames.], batch size: 34, aishell_tot_loss[loss=0.1483, simple_loss=0.2351, pruned_loss=0.03074, over 985155.88 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2208, pruned_loss=0.03351, over 985226.97 frames.], batch size: 34, lr: 3.70e-04
+2022-06-19 02:27:02,233 INFO [train.py:874] (0/4) Epoch 22, batch 3350, aishell_loss[loss=0.1276, simple_loss=0.2001, pruned_loss=0.02752, over 4981.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2276, pruned_loss=0.03156, over 985422.97 frames.], batch size: 25, aishell_tot_loss[loss=0.1476, simple_loss=0.2347, pruned_loss=0.03031, over 985240.39 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2204, pruned_loss=0.0334, over 985172.20 frames.], batch size: 25, lr: 3.69e-04
+2022-06-19 02:27:34,676 INFO [train.py:874] (0/4) Epoch 22, batch 3400, aishell_loss[loss=0.1561, simple_loss=0.2384, pruned_loss=0.0369, over 4951.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2267, pruned_loss=0.03133, over 985343.29 frames.], batch size: 64, aishell_tot_loss[loss=0.1473, simple_loss=0.2341, pruned_loss=0.03025, over 985251.22 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2201, pruned_loss=0.03316, over 985136.76 frames.], batch size: 64, lr: 3.69e-04
+2022-06-19 02:28:08,666 INFO [train.py:874] (0/4) Epoch 22, batch 3450, aishell_loss[loss=0.1216, simple_loss=0.1903, pruned_loss=0.02643, over 4740.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2257, pruned_loss=0.03155, over 985124.43 frames.], batch size: 20, aishell_tot_loss[loss=0.1473, simple_loss=0.234, pruned_loss=0.03032, over 985043.21 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2196, pruned_loss=0.03311, over 985169.24 frames.], batch size: 20, lr: 3.69e-04
+2022-06-19 02:28:40,664 INFO [train.py:874] (0/4) Epoch 22, batch 3500, aishell_loss[loss=0.1636, simple_loss=0.2549, pruned_loss=0.0361, over 4891.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2269, pruned_loss=0.03183, over 985620.38 frames.], batch size: 42, aishell_tot_loss[loss=0.1475, simple_loss=0.2343, pruned_loss=0.03035, over 985156.84 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2207, pruned_loss=0.0333, over 985581.29 frames.], batch size: 42, lr: 3.69e-04
+2022-06-19 02:29:12,237 INFO [train.py:874] (0/4) Epoch 22, batch 3550, aishell_loss[loss=0.1592, simple_loss=0.2396, pruned_loss=0.03945, over 4936.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2265, pruned_loss=0.03193, over 985317.45 frames.], batch size: 45, aishell_tot_loss[loss=0.1475, simple_loss=0.2338, pruned_loss=0.03056, over 984987.36 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2204, pruned_loss=0.03322, over 985485.76 frames.], batch size: 45, lr: 3.69e-04
+2022-06-19 02:29:44,437 INFO [train.py:874] (0/4) Epoch 22, batch 3600, datatang_loss[loss=0.157, simple_loss=0.2389, pruned_loss=0.03757, over 4918.00 frames.], tot_loss[loss=0.1458, simple_loss=0.227, pruned_loss=0.03232, over 985346.55 frames.], batch size: 75, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.0308, over 985010.40 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2206, pruned_loss=0.03339, over 985511.72 frames.], batch size: 75, lr: 3.69e-04
+2022-06-19 02:30:17,395 INFO [train.py:874] (0/4) Epoch 22, batch 3650, datatang_loss[loss=0.1541, simple_loss=0.2375, pruned_loss=0.0354, over 4929.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2273, pruned_loss=0.0326, over 985742.67 frames.], batch size: 42, aishell_tot_loss[loss=0.1482, simple_loss=0.2344, pruned_loss=0.03099, over 985294.63 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2207, pruned_loss=0.03356, over 985662.77 frames.], batch size: 42, lr: 3.69e-04
+2022-06-19 02:30:48,793 INFO [train.py:874] (0/4) Epoch 22, batch 3700, datatang_loss[loss=0.1789, simple_loss=0.2559, pruned_loss=0.05095, over 4914.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2274, pruned_loss=0.03275, over 985674.25 frames.], batch size: 98, aishell_tot_loss[loss=0.1482, simple_loss=0.2341, pruned_loss=0.03109, over 985138.24 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2209, pruned_loss=0.03368, over 985814.65 frames.], batch size: 98, lr: 3.69e-04
+2022-06-19 02:31:19,478 INFO [train.py:874] (0/4) Epoch 22, batch 3750, datatang_loss[loss=0.1544, simple_loss=0.2225, pruned_loss=0.04312, over 4945.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2276, pruned_loss=0.03276, over 985874.73 frames.], batch size: 62, aishell_tot_loss[loss=0.1479, simple_loss=0.2339, pruned_loss=0.03095, over 985247.53 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2209, pruned_loss=0.03402, over 985991.02 frames.], batch size: 62, lr: 3.69e-04
+2022-06-19 02:31:49,498 INFO [train.py:874] (0/4) Epoch 22, batch 3800, aishell_loss[loss=0.1318, simple_loss=0.2284, pruned_loss=0.01759, over 4826.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2281, pruned_loss=0.03258, over 985639.10 frames.], batch size: 29, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03077, over 985071.34 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2216, pruned_loss=0.03408, over 985987.75 frames.], batch size: 29, lr: 3.69e-04
+2022-06-19 02:32:20,311 INFO [train.py:874] (0/4) Epoch 22, batch 3850, datatang_loss[loss=0.1543, simple_loss=0.2361, pruned_loss=0.03621, over 4927.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2277, pruned_loss=0.03238, over 985234.16 frames.], batch size: 98, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03077, over 984883.57 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2215, pruned_loss=0.03384, over 985749.17 frames.], batch size: 98, lr: 3.68e-04
+2022-06-19 02:32:51,363 INFO [train.py:874] (0/4) Epoch 22, batch 3900, aishell_loss[loss=0.1128, simple_loss=0.1803, pruned_loss=0.02263, over 4810.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2271, pruned_loss=0.03225, over 985234.67 frames.], batch size: 20, aishell_tot_loss[loss=0.1479, simple_loss=0.234, pruned_loss=0.03092, over 985014.50 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2212, pruned_loss=0.03347, over 985598.08 frames.], batch size: 20, lr: 3.68e-04
+2022-06-19 02:33:21,710 INFO [train.py:874] (0/4) Epoch 22, batch 3950, aishell_loss[loss=0.1449, simple_loss=0.2398, pruned_loss=0.02501, over 4922.00 frames.], tot_loss[loss=0.146, simple_loss=0.2276, pruned_loss=0.0322, over 985391.06 frames.], batch size: 68, aishell_tot_loss[loss=0.1482, simple_loss=0.2342, pruned_loss=0.03113, over 984919.98 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2211, pruned_loss=0.03329, over 985876.68 frames.], batch size: 68, lr: 3.68e-04
+2022-06-19 02:33:51,301 INFO [train.py:874] (0/4) Epoch 22, batch 4000, aishell_loss[loss=0.1663, simple_loss=0.2433, pruned_loss=0.04464, over 4954.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2277, pruned_loss=0.03205, over 985721.34 frames.], batch size: 31, aishell_tot_loss[loss=0.1485, simple_loss=0.2346, pruned_loss=0.03119, over 985101.99 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2208, pruned_loss=0.03302, over 986048.96 frames.], batch size: 31, lr: 3.68e-04
+2022-06-19 02:33:51,304 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 02:34:08,827 INFO [train.py:914] (0/4) Epoch 22, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04074, over 1622729.00 frames.
+2022-06-19 02:34:38,020 INFO [train.py:874] (0/4) Epoch 22, batch 4050, aishell_loss[loss=0.157, simple_loss=0.2362, pruned_loss=0.03886, over 4905.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2282, pruned_loss=0.03221, over 985321.59 frames.], batch size: 34, aishell_tot_loss[loss=0.1491, simple_loss=0.2352, pruned_loss=0.03153, over 984813.21 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2205, pruned_loss=0.03285, over 985988.81 frames.], batch size: 34, lr: 3.68e-04
+2022-06-19 02:35:02,638 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-22.pt
+2022-06-19 02:36:04,873 INFO [train.py:874] (0/4) Epoch 23, batch 50, datatang_loss[loss=0.1377, simple_loss=0.2251, pruned_loss=0.02513, over 4961.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2205, pruned_loss=0.03062, over 218158.03 frames.], batch size: 91, aishell_tot_loss[loss=0.1445, simple_loss=0.2291, pruned_loss=0.02996, over 106611.95 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2135, pruned_loss=0.03127, over 125100.84 frames.], batch size: 91, lr: 3.60e-04
+2022-06-19 02:36:35,971 INFO [train.py:874] (0/4) Epoch 23, batch 100, datatang_loss[loss=0.1305, simple_loss=0.2002, pruned_loss=0.03042, over 4890.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2224, pruned_loss=0.03015, over 388499.66 frames.], batch size: 52, aishell_tot_loss[loss=0.1469, simple_loss=0.2337, pruned_loss=0.03006, over 210386.59 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2118, pruned_loss=0.03029, over 226434.30 frames.], batch size: 52, lr: 3.60e-04
+2022-06-19 02:37:08,833 INFO [train.py:874] (0/4) Epoch 23, batch 150, aishell_loss[loss=0.147, simple_loss=0.2334, pruned_loss=0.03029, over 4879.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2231, pruned_loss=0.03075, over 520979.99 frames.], batch size: 47, aishell_tot_loss[loss=0.1494, simple_loss=0.2363, pruned_loss=0.03126, over 280821.60 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2124, pruned_loss=0.03027, over 335809.26 frames.], batch size: 47, lr: 3.60e-04
+2022-06-19 02:37:40,905 INFO [train.py:874] (0/4) Epoch 23, batch 200, aishell_loss[loss=0.1397, simple_loss=0.2183, pruned_loss=0.03051, over 4926.00 frames.], tot_loss[loss=0.1438, simple_loss=0.225, pruned_loss=0.03131, over 624264.50 frames.], batch size: 32, aishell_tot_loss[loss=0.1491, simple_loss=0.2355, pruned_loss=0.03138, over 388514.89 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2134, pruned_loss=0.031, over 388939.40 frames.], batch size: 32, lr: 3.60e-04
+2022-06-19 02:38:12,802 INFO [train.py:874] (0/4) Epoch 23, batch 250, aishell_loss[loss=0.1478, simple_loss=0.2372, pruned_loss=0.02919, over 4931.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2261, pruned_loss=0.03157, over 704060.07 frames.], batch size: 58, aishell_tot_loss[loss=0.1496, simple_loss=0.236, pruned_loss=0.03155, over 458435.84 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.215, pruned_loss=0.03129, over 459276.95 frames.], batch size: 58, lr: 3.60e-04
+2022-06-19 02:38:44,863 INFO [train.py:874] (0/4) Epoch 23, batch 300, datatang_loss[loss=0.1344, simple_loss=0.2093, pruned_loss=0.02975, over 4945.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2261, pruned_loss=0.03144, over 766740.26 frames.], batch size: 55, aishell_tot_loss[loss=0.1493, simple_loss=0.2358, pruned_loss=0.03141, over 518185.86 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2155, pruned_loss=0.03129, over 523876.96 frames.], batch size: 55, lr: 3.60e-04
+2022-06-19 02:39:17,390 INFO [train.py:874] (0/4) Epoch 23, batch 350, aishell_loss[loss=0.1446, simple_loss=0.2392, pruned_loss=0.02495, over 4884.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2266, pruned_loss=0.03114, over 815044.04 frames.], batch size: 47, aishell_tot_loss[loss=0.15, simple_loss=0.2371, pruned_loss=0.03151, over 577496.32 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2149, pruned_loss=0.03078, over 573792.90 frames.], batch size: 47, lr: 3.59e-04
+2022-06-19 02:39:48,747 INFO [train.py:874] (0/4) Epoch 23, batch 400, datatang_loss[loss=0.1302, simple_loss=0.2196, pruned_loss=0.0204, over 4960.00 frames.], tot_loss[loss=0.1439, simple_loss=0.226, pruned_loss=0.03087, over 852705.05 frames.], batch size: 91, aishell_tot_loss[loss=0.1492, simple_loss=0.2362, pruned_loss=0.03107, over 618121.59 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2158, pruned_loss=0.03082, over 629491.71 frames.], batch size: 91, lr: 3.59e-04
+2022-06-19 02:40:20,257 INFO [train.py:874] (0/4) Epoch 23, batch 450, datatang_loss[loss=0.1411, simple_loss=0.2206, pruned_loss=0.03075, over 4936.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2268, pruned_loss=0.03104, over 882303.03 frames.], batch size: 69, aishell_tot_loss[loss=0.1493, simple_loss=0.2365, pruned_loss=0.031, over 669390.83 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.216, pruned_loss=0.03111, over 663654.59 frames.], batch size: 69, lr: 3.59e-04
+2022-06-19 02:40:53,001 INFO [train.py:874] (0/4) Epoch 23, batch 500, datatang_loss[loss=0.1552, simple_loss=0.2366, pruned_loss=0.03692, over 4918.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2271, pruned_loss=0.03115, over 905485.25 frames.], batch size: 81, aishell_tot_loss[loss=0.1483, simple_loss=0.2353, pruned_loss=0.03064, over 710962.81 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2174, pruned_loss=0.0316, over 697408.22 frames.], batch size: 81, lr: 3.59e-04
+2022-06-19 02:41:25,228 INFO [train.py:874] (0/4) Epoch 23, batch 550, datatang_loss[loss=0.1379, simple_loss=0.2153, pruned_loss=0.03023, over 4942.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2268, pruned_loss=0.03128, over 923589.32 frames.], batch size: 69, aishell_tot_loss[loss=0.1481, simple_loss=0.2352, pruned_loss=0.03053, over 744692.33 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2173, pruned_loss=0.03189, over 730258.50 frames.], batch size: 69, lr: 3.59e-04
+2022-06-19 02:41:56,286 INFO [train.py:874] (0/4) Epoch 23, batch 600, aishell_loss[loss=0.1547, simple_loss=0.2477, pruned_loss=0.03089, over 4874.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2271, pruned_loss=0.03168, over 937221.40 frames.], batch size: 35, aishell_tot_loss[loss=0.148, simple_loss=0.235, pruned_loss=0.03055, over 772897.75 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.218, pruned_loss=0.03243, over 760372.25 frames.], batch size: 35, lr: 3.59e-04
+2022-06-19 02:42:28,258 INFO [train.py:874] (0/4) Epoch 23, batch 650, datatang_loss[loss=0.1444, simple_loss=0.2355, pruned_loss=0.02662, over 4890.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2259, pruned_loss=0.03113, over 948136.74 frames.], batch size: 52, aishell_tot_loss[loss=0.1473, simple_loss=0.234, pruned_loss=0.03033, over 799921.33 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2174, pruned_loss=0.03201, over 784994.97 frames.], batch size: 52, lr: 3.59e-04
+2022-06-19 02:42:59,739 INFO [train.py:874] (0/4) Epoch 23, batch 700, aishell_loss[loss=0.1776, simple_loss=0.2639, pruned_loss=0.04568, over 4954.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2271, pruned_loss=0.03201, over 956576.47 frames.], batch size: 79, aishell_tot_loss[loss=0.1481, simple_loss=0.2346, pruned_loss=0.03082, over 821110.95 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2185, pruned_loss=0.0326, over 809524.80 frames.], batch size: 79, lr: 3.59e-04
+2022-06-19 02:43:32,099 INFO [train.py:874] (0/4) Epoch 23, batch 750, datatang_loss[loss=0.2139, simple_loss=0.2838, pruned_loss=0.07199, over 4949.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2269, pruned_loss=0.03184, over 963182.06 frames.], batch size: 109, aishell_tot_loss[loss=0.1478, simple_loss=0.2342, pruned_loss=0.03068, over 839026.52 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.219, pruned_loss=0.03261, over 831988.35 frames.], batch size: 109, lr: 3.59e-04
+2022-06-19 02:44:03,150 INFO [train.py:874] (0/4) Epoch 23, batch 800, aishell_loss[loss=0.1243, simple_loss=0.2116, pruned_loss=0.01849, over 4938.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2271, pruned_loss=0.03183, over 968489.93 frames.], batch size: 32, aishell_tot_loss[loss=0.1483, simple_loss=0.2347, pruned_loss=0.03094, over 856541.67 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2188, pruned_loss=0.03239, over 850182.68 frames.], batch size: 32, lr: 3.59e-04
+2022-06-19 02:44:28,325 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-92000.pt
+2022-06-19 02:44:41,859 INFO [train.py:874] (0/4) Epoch 23, batch 850, aishell_loss[loss=0.1563, simple_loss=0.2506, pruned_loss=0.03106, over 4922.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2282, pruned_loss=0.03214, over 972459.45 frames.], batch size: 68, aishell_tot_loss[loss=0.1492, simple_loss=0.2357, pruned_loss=0.03131, over 874740.82 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2187, pruned_loss=0.03249, over 863113.67 frames.], batch size: 68, lr: 3.58e-04
+2022-06-19 02:45:13,064 INFO [train.py:874] (0/4) Epoch 23, batch 900, datatang_loss[loss=0.1363, simple_loss=0.2251, pruned_loss=0.02378, over 4943.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2283, pruned_loss=0.03212, over 975327.99 frames.], batch size: 86, aishell_tot_loss[loss=0.1497, simple_loss=0.2361, pruned_loss=0.03161, over 887702.74 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2186, pruned_loss=0.03224, over 877577.07 frames.], batch size: 86, lr: 3.58e-04
+2022-06-19 02:45:44,643 INFO [train.py:874] (0/4) Epoch 23, batch 950, datatang_loss[loss=0.1213, simple_loss=0.2001, pruned_loss=0.02126, over 4939.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2289, pruned_loss=0.03218, over 977683.89 frames.], batch size: 34, aishell_tot_loss[loss=0.1501, simple_loss=0.2368, pruned_loss=0.03171, over 901518.17 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2185, pruned_loss=0.0323, over 887810.28 frames.], batch size: 34, lr: 3.58e-04
+2022-06-19 02:46:17,102 INFO [train.py:874] (0/4) Epoch 23, batch 1000, datatang_loss[loss=0.1485, simple_loss=0.2207, pruned_loss=0.03817, over 4924.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2289, pruned_loss=0.03205, over 979164.19 frames.], batch size: 81, aishell_tot_loss[loss=0.1503, simple_loss=0.237, pruned_loss=0.03181, over 911973.13 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2184, pruned_loss=0.03209, over 898359.57 frames.], batch size: 81, lr: 3.58e-04
+2022-06-19 02:46:17,105 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 02:46:34,399 INFO [train.py:914] (0/4) Epoch 23, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04077, over 1622729.00 frames.
+2022-06-19 02:47:05,337 INFO [train.py:874] (0/4) Epoch 23, batch 1050, datatang_loss[loss=0.1395, simple_loss=0.218, pruned_loss=0.03048, over 4898.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2282, pruned_loss=0.03182, over 980754.80 frames.], batch size: 52, aishell_tot_loss[loss=0.1494, simple_loss=0.2363, pruned_loss=0.03128, over 919860.20 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2189, pruned_loss=0.03238, over 909745.63 frames.], batch size: 52, lr: 3.58e-04
+2022-06-19 02:47:36,018 INFO [train.py:874] (0/4) Epoch 23, batch 1100, datatang_loss[loss=0.1424, simple_loss=0.2373, pruned_loss=0.02372, over 4940.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2275, pruned_loss=0.03177, over 981447.61 frames.], batch size: 69, aishell_tot_loss[loss=0.1485, simple_loss=0.2352, pruned_loss=0.03091, over 926168.38 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2196, pruned_loss=0.03268, over 919805.32 frames.], batch size: 69, lr: 3.58e-04
+2022-06-19 02:48:07,577 INFO [train.py:874] (0/4) Epoch 23, batch 1150, aishell_loss[loss=0.1417, simple_loss=0.2372, pruned_loss=0.02307, over 4954.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2274, pruned_loss=0.03171, over 982601.05 frames.], batch size: 61, aishell_tot_loss[loss=0.1486, simple_loss=0.2352, pruned_loss=0.031, over 933530.72 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2194, pruned_loss=0.03252, over 927401.73 frames.], batch size: 61, lr: 3.58e-04
+2022-06-19 02:48:40,912 INFO [train.py:874] (0/4) Epoch 23, batch 1200, datatang_loss[loss=0.1422, simple_loss=0.2189, pruned_loss=0.0328, over 4940.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2278, pruned_loss=0.03168, over 983201.63 frames.], batch size: 62, aishell_tot_loss[loss=0.1486, simple_loss=0.2355, pruned_loss=0.03079, over 939090.57 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2197, pruned_loss=0.03268, over 934803.29 frames.], batch size: 62, lr: 3.58e-04
+2022-06-19 02:49:12,891 INFO [train.py:874] (0/4) Epoch 23, batch 1250, datatang_loss[loss=0.1356, simple_loss=0.2155, pruned_loss=0.02789, over 4944.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2279, pruned_loss=0.03185, over 983674.22 frames.], batch size: 34, aishell_tot_loss[loss=0.1486, simple_loss=0.2356, pruned_loss=0.0308, over 944815.14 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2196, pruned_loss=0.03286, over 940419.25 frames.], batch size: 34, lr: 3.58e-04
+2022-06-19 02:49:43,400 INFO [train.py:874] (0/4) Epoch 23, batch 1300, aishell_loss[loss=0.1526, simple_loss=0.2454, pruned_loss=0.02993, over 4914.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2282, pruned_loss=0.03214, over 984186.84 frames.], batch size: 41, aishell_tot_loss[loss=0.1478, simple_loss=0.2346, pruned_loss=0.03053, over 950513.87 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2207, pruned_loss=0.03349, over 944814.56 frames.], batch size: 41, lr: 3.58e-04
+2022-06-19 02:50:15,888 INFO [train.py:874] (0/4) Epoch 23, batch 1350, aishell_loss[loss=0.1482, simple_loss=0.2389, pruned_loss=0.02873, over 4937.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2278, pruned_loss=0.03202, over 984763.73 frames.], batch size: 58, aishell_tot_loss[loss=0.1479, simple_loss=0.2347, pruned_loss=0.03053, over 953919.17 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2206, pruned_loss=0.03332, over 950731.36 frames.], batch size: 58, lr: 3.57e-04
+2022-06-19 02:50:47,390 INFO [train.py:874] (0/4) Epoch 23, batch 1400, datatang_loss[loss=0.1298, simple_loss=0.2148, pruned_loss=0.02237, over 4927.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2282, pruned_loss=0.03219, over 984953.34 frames.], batch size: 57, aishell_tot_loss[loss=0.1481, simple_loss=0.2347, pruned_loss=0.03077, over 957644.44 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.221, pruned_loss=0.03329, over 954825.74 frames.], batch size: 57, lr: 3.57e-04
+2022-06-19 02:51:19,370 INFO [train.py:874] (0/4) Epoch 23, batch 1450, aishell_loss[loss=0.1422, simple_loss=0.2197, pruned_loss=0.03239, over 4930.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2277, pruned_loss=0.03231, over 985393.90 frames.], batch size: 32, aishell_tot_loss[loss=0.1483, simple_loss=0.2347, pruned_loss=0.03094, over 960944.15 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2206, pruned_loss=0.03328, over 958768.85 frames.], batch size: 32, lr: 3.57e-04
+2022-06-19 02:51:53,282 INFO [train.py:874] (0/4) Epoch 23, batch 1500, aishell_loss[loss=0.1668, simple_loss=0.2491, pruned_loss=0.04224, over 4911.00 frames.], tot_loss[loss=0.146, simple_loss=0.2276, pruned_loss=0.03214, over 985481.17 frames.], batch size: 46, aishell_tot_loss[loss=0.1484, simple_loss=0.2348, pruned_loss=0.03099, over 963660.61 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2205, pruned_loss=0.03309, over 962179.86 frames.], batch size: 46, lr: 3.57e-04
+2022-06-19 02:52:24,984 INFO [train.py:874] (0/4) Epoch 23, batch 1550, aishell_loss[loss=0.1376, simple_loss=0.2214, pruned_loss=0.02694, over 4819.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2271, pruned_loss=0.03161, over 985379.03 frames.], batch size: 29, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03058, over 966714.69 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2201, pruned_loss=0.03303, over 964259.01 frames.], batch size: 29, lr: 3.57e-04
+2022-06-19 02:52:56,563 INFO [train.py:874] (0/4) Epoch 23, batch 1600, datatang_loss[loss=0.158, simple_loss=0.2378, pruned_loss=0.03905, over 4901.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2261, pruned_loss=0.03124, over 985112.64 frames.], batch size: 59, aishell_tot_loss[loss=0.147, simple_loss=0.2333, pruned_loss=0.0303, over 968206.75 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.22, pruned_loss=0.0328, over 967189.13 frames.], batch size: 59, lr: 3.57e-04
+2022-06-19 02:53:27,754 INFO [train.py:874] (0/4) Epoch 23, batch 1650, datatang_loss[loss=0.13, simple_loss=0.212, pruned_loss=0.02398, over 4931.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2263, pruned_loss=0.03127, over 984933.11 frames.], batch size: 79, aishell_tot_loss[loss=0.1473, simple_loss=0.2335, pruned_loss=0.03049, over 970266.40 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2198, pruned_loss=0.0326, over 969021.41 frames.], batch size: 79, lr: 3.57e-04
+2022-06-19 02:54:00,679 INFO [train.py:874] (0/4) Epoch 23, batch 1700, aishell_loss[loss=0.1506, simple_loss=0.2334, pruned_loss=0.03391, over 4950.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2272, pruned_loss=0.03187, over 985039.60 frames.], batch size: 56, aishell_tot_loss[loss=0.1475, simple_loss=0.2337, pruned_loss=0.03061, over 972219.04 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2202, pruned_loss=0.0331, over 970766.89 frames.], batch size: 56, lr: 3.57e-04
+2022-06-19 02:54:33,024 INFO [train.py:874] (0/4) Epoch 23, batch 1750, aishell_loss[loss=0.1446, simple_loss=0.2349, pruned_loss=0.02713, over 4981.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2273, pruned_loss=0.03161, over 985229.88 frames.], batch size: 39, aishell_tot_loss[loss=0.1472, simple_loss=0.2339, pruned_loss=0.03028, over 973943.84 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2203, pruned_loss=0.03312, over 972454.94 frames.], batch size: 39, lr: 3.57e-04
+2022-06-19 02:55:05,061 INFO [train.py:874] (0/4) Epoch 23, batch 1800, aishell_loss[loss=0.1409, simple_loss=0.2286, pruned_loss=0.02657, over 4917.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2276, pruned_loss=0.03163, over 985292.66 frames.], batch size: 52, aishell_tot_loss[loss=0.1473, simple_loss=0.2342, pruned_loss=0.03019, over 975052.64 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2205, pruned_loss=0.03319, over 974259.94 frames.], batch size: 52, lr: 3.57e-04
+2022-06-19 02:55:38,721 INFO [train.py:874] (0/4) Epoch 23, batch 1850, datatang_loss[loss=0.1488, simple_loss=0.2175, pruned_loss=0.04008, over 4934.00 frames.], tot_loss[loss=0.145, simple_loss=0.2271, pruned_loss=0.0315, over 985539.44 frames.], batch size: 34, aishell_tot_loss[loss=0.1474, simple_loss=0.2342, pruned_loss=0.03034, over 976419.20 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2199, pruned_loss=0.03288, over 975672.90 frames.], batch size: 34, lr: 3.57e-04
+2022-06-19 02:56:09,933 INFO [train.py:874] (0/4) Epoch 23, batch 1900, aishell_loss[loss=0.1521, simple_loss=0.2504, pruned_loss=0.02687, over 4975.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2273, pruned_loss=0.03152, over 985805.38 frames.], batch size: 51, aishell_tot_loss[loss=0.1475, simple_loss=0.2345, pruned_loss=0.03029, over 977563.75 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2198, pruned_loss=0.03293, over 977035.77 frames.], batch size: 51, lr: 3.56e-04
+2022-06-19 02:56:41,438 INFO [train.py:874] (0/4) Epoch 23, batch 1950, datatang_loss[loss=0.1224, simple_loss=0.1946, pruned_loss=0.02513, over 4920.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2264, pruned_loss=0.03153, over 985830.67 frames.], batch size: 24, aishell_tot_loss[loss=0.1474, simple_loss=0.2343, pruned_loss=0.03028, over 978287.72 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2195, pruned_loss=0.03284, over 978342.99 frames.], batch size: 24, lr: 3.56e-04
+2022-06-19 02:57:14,386 INFO [train.py:874] (0/4) Epoch 23, batch 2000, datatang_loss[loss=0.1337, simple_loss=0.1977, pruned_loss=0.03482, over 4852.00 frames.], tot_loss[loss=0.1445, simple_loss=0.226, pruned_loss=0.03145, over 985798.86 frames.], batch size: 30, aishell_tot_loss[loss=0.1479, simple_loss=0.2348, pruned_loss=0.03044, over 978793.11 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2189, pruned_loss=0.03253, over 979567.32 frames.], batch size: 30, lr: 3.56e-04
+2022-06-19 02:57:14,388 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 02:57:32,301 INFO [train.py:914] (0/4) Epoch 23, validation: loss=0.1648, simple_loss=0.2485, pruned_loss=0.04055, over 1622729.00 frames.
+2022-06-19 02:58:03,725 INFO [train.py:874] (0/4) Epoch 23, batch 2050, datatang_loss[loss=0.1262, simple_loss=0.2066, pruned_loss=0.02292, over 4937.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2267, pruned_loss=0.03144, over 985874.12 frames.], batch size: 79, aishell_tot_loss[loss=0.1479, simple_loss=0.2348, pruned_loss=0.03047, over 979433.58 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2193, pruned_loss=0.03249, over 980570.06 frames.], batch size: 79, lr: 3.56e-04
+2022-06-19 02:58:35,258 INFO [train.py:874] (0/4) Epoch 23, batch 2100, aishell_loss[loss=0.1442, simple_loss=0.2306, pruned_loss=0.02894, over 4863.00 frames.], tot_loss[loss=0.145, simple_loss=0.2269, pruned_loss=0.0316, over 985544.90 frames.], batch size: 36, aishell_tot_loss[loss=0.1481, simple_loss=0.2349, pruned_loss=0.03071, over 979902.28 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2194, pruned_loss=0.03238, over 981137.09 frames.], batch size: 36, lr: 3.56e-04
+2022-06-19 02:59:07,976 INFO [train.py:874] (0/4) Epoch 23, batch 2150, datatang_loss[loss=0.1252, simple_loss=0.2085, pruned_loss=0.0209, over 4927.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2265, pruned_loss=0.03118, over 985290.68 frames.], batch size: 83, aishell_tot_loss[loss=0.1482, simple_loss=0.2351, pruned_loss=0.03059, over 980185.97 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2192, pruned_loss=0.03202, over 981728.50 frames.], batch size: 83, lr: 3.56e-04
+2022-06-19 02:59:40,169 INFO [train.py:874] (0/4) Epoch 23, batch 2200, datatang_loss[loss=0.1404, simple_loss=0.2164, pruned_loss=0.03219, over 4927.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2271, pruned_loss=0.03164, over 985426.63 frames.], batch size: 79, aishell_tot_loss[loss=0.1486, simple_loss=0.2354, pruned_loss=0.03092, over 980793.02 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2193, pruned_loss=0.03217, over 982293.49 frames.], batch size: 79, lr: 3.56e-04
+2022-06-19 03:00:11,257 INFO [train.py:874] (0/4) Epoch 23, batch 2250, datatang_loss[loss=0.1724, simple_loss=0.2537, pruned_loss=0.04557, over 4956.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2272, pruned_loss=0.03179, over 985416.36 frames.], batch size: 67, aishell_tot_loss[loss=0.1485, simple_loss=0.2354, pruned_loss=0.03076, over 981176.03 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2197, pruned_loss=0.03247, over 982779.20 frames.], batch size: 67, lr: 3.56e-04
+2022-06-19 03:00:43,963 INFO [train.py:874] (0/4) Epoch 23, batch 2300, aishell_loss[loss=0.1587, simple_loss=0.247, pruned_loss=0.03516, over 4970.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2272, pruned_loss=0.03162, over 985331.72 frames.], batch size: 51, aishell_tot_loss[loss=0.149, simple_loss=0.236, pruned_loss=0.03099, over 981552.52 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2192, pruned_loss=0.03208, over 983111.58 frames.], batch size: 51, lr: 3.56e-04
+2022-06-19 03:01:16,153 INFO [train.py:874] (0/4) Epoch 23, batch 2350, aishell_loss[loss=0.1548, simple_loss=0.2323, pruned_loss=0.03864, over 4942.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2264, pruned_loss=0.03171, over 985268.72 frames.], batch size: 32, aishell_tot_loss[loss=0.1484, simple_loss=0.235, pruned_loss=0.03092, over 981839.83 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2193, pruned_loss=0.03227, over 983447.83 frames.], batch size: 32, lr: 3.56e-04
+2022-06-19 03:01:47,564 INFO [train.py:874] (0/4) Epoch 23, batch 2400, aishell_loss[loss=0.1465, simple_loss=0.2398, pruned_loss=0.02657, over 4881.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2265, pruned_loss=0.03192, over 985164.38 frames.], batch size: 47, aishell_tot_loss[loss=0.1487, simple_loss=0.2354, pruned_loss=0.03101, over 981983.24 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2192, pruned_loss=0.0324, over 983781.07 frames.], batch size: 47, lr: 3.55e-04
+2022-06-19 03:02:18,516 INFO [train.py:874] (0/4) Epoch 23, batch 2450, aishell_loss[loss=0.1382, simple_loss=0.2353, pruned_loss=0.02059, over 4946.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2259, pruned_loss=0.03142, over 985205.31 frames.], batch size: 56, aishell_tot_loss[loss=0.1475, simple_loss=0.2339, pruned_loss=0.03059, over 982286.57 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2196, pruned_loss=0.03237, over 984114.65 frames.], batch size: 56, lr: 3.55e-04
+2022-06-19 03:02:51,512 INFO [train.py:874] (0/4) Epoch 23, batch 2500, datatang_loss[loss=0.1478, simple_loss=0.2186, pruned_loss=0.03848, over 4911.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2256, pruned_loss=0.03126, over 985035.06 frames.], batch size: 52, aishell_tot_loss[loss=0.1474, simple_loss=0.2338, pruned_loss=0.03055, over 982157.85 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2195, pruned_loss=0.0322, over 984484.01 frames.], batch size: 52, lr: 3.55e-04
+2022-06-19 03:03:23,716 INFO [train.py:874] (0/4) Epoch 23, batch 2550, datatang_loss[loss=0.1511, simple_loss=0.2316, pruned_loss=0.03527, over 4934.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2264, pruned_loss=0.03176, over 985155.96 frames.], batch size: 57, aishell_tot_loss[loss=0.1476, simple_loss=0.2342, pruned_loss=0.03051, over 982561.90 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2199, pruned_loss=0.03272, over 984580.87 frames.], batch size: 57, lr: 3.55e-04
+2022-06-19 03:03:55,766 INFO [train.py:874] (0/4) Epoch 23, batch 2600, aishell_loss[loss=0.1736, simple_loss=0.2501, pruned_loss=0.04858, over 4925.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2274, pruned_loss=0.03223, over 985329.13 frames.], batch size: 41, aishell_tot_loss[loss=0.1481, simple_loss=0.2346, pruned_loss=0.03084, over 982928.27 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2205, pruned_loss=0.03286, over 984762.22 frames.], batch size: 41, lr: 3.55e-04
+2022-06-19 03:04:28,495 INFO [train.py:874] (0/4) Epoch 23, batch 2650, datatang_loss[loss=0.1404, simple_loss=0.2287, pruned_loss=0.02603, over 4945.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2273, pruned_loss=0.03197, over 985507.51 frames.], batch size: 69, aishell_tot_loss[loss=0.148, simple_loss=0.2345, pruned_loss=0.03079, over 983567.88 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2202, pruned_loss=0.03276, over 984685.52 frames.], batch size: 69, lr: 3.55e-04
+2022-06-19 03:05:00,011 INFO [train.py:874] (0/4) Epoch 23, batch 2700, aishell_loss[loss=0.1519, simple_loss=0.2383, pruned_loss=0.0328, over 4900.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2273, pruned_loss=0.03217, over 985161.89 frames.], batch size: 41, aishell_tot_loss[loss=0.148, simple_loss=0.2344, pruned_loss=0.0308, over 983626.38 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2203, pruned_loss=0.033, over 984593.93 frames.], batch size: 41, lr: 3.55e-04
+2022-06-19 03:05:31,318 INFO [train.py:874] (0/4) Epoch 23, batch 2750, aishell_loss[loss=0.1713, simple_loss=0.2475, pruned_loss=0.04751, over 4920.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2269, pruned_loss=0.03211, over 985568.74 frames.], batch size: 46, aishell_tot_loss[loss=0.1487, simple_loss=0.2349, pruned_loss=0.03129, over 983961.71 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2194, pruned_loss=0.0325, over 984925.05 frames.], batch size: 46, lr: 3.55e-04
+2022-06-19 03:06:03,721 INFO [train.py:874] (0/4) Epoch 23, batch 2800, aishell_loss[loss=0.1367, simple_loss=0.2296, pruned_loss=0.0219, over 4978.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2261, pruned_loss=0.03169, over 985557.28 frames.], batch size: 61, aishell_tot_loss[loss=0.1486, simple_loss=0.2349, pruned_loss=0.03119, over 983923.79 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2186, pruned_loss=0.03219, over 985179.48 frames.], batch size: 61, lr: 3.55e-04
+2022-06-19 03:06:35,740 INFO [train.py:874] (0/4) Epoch 23, batch 2850, aishell_loss[loss=0.143, simple_loss=0.231, pruned_loss=0.0275, over 4861.00 frames.], tot_loss[loss=0.1457, simple_loss=0.227, pruned_loss=0.03221, over 985339.98 frames.], batch size: 38, aishell_tot_loss[loss=0.149, simple_loss=0.2351, pruned_loss=0.03139, over 983889.69 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.219, pruned_loss=0.03256, over 985250.93 frames.], batch size: 38, lr: 3.55e-04
+2022-06-19 03:07:06,069 INFO [train.py:874] (0/4) Epoch 23, batch 2900, aishell_loss[loss=0.1463, simple_loss=0.2411, pruned_loss=0.02576, over 4926.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2263, pruned_loss=0.03191, over 985836.59 frames.], batch size: 68, aishell_tot_loss[loss=0.1484, simple_loss=0.2344, pruned_loss=0.03121, over 984319.68 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2192, pruned_loss=0.03246, over 985516.38 frames.], batch size: 68, lr: 3.55e-04
+2022-06-19 03:07:38,864 INFO [train.py:874] (0/4) Epoch 23, batch 2950, datatang_loss[loss=0.1271, simple_loss=0.1977, pruned_loss=0.02828, over 4949.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2267, pruned_loss=0.03196, over 985981.04 frames.], batch size: 25, aishell_tot_loss[loss=0.1486, simple_loss=0.2346, pruned_loss=0.03131, over 984543.92 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2191, pruned_loss=0.03246, over 985677.26 frames.], batch size: 25, lr: 3.54e-04
+2022-06-19 03:08:11,512 INFO [train.py:874] (0/4) Epoch 23, batch 3000, aishell_loss[loss=0.1503, simple_loss=0.2362, pruned_loss=0.03217, over 4960.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2264, pruned_loss=0.0316, over 985888.49 frames.], batch size: 51, aishell_tot_loss[loss=0.1483, simple_loss=0.2346, pruned_loss=0.03102, over 984510.43 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2186, pruned_loss=0.03238, over 985831.79 frames.], batch size: 51, lr: 3.54e-04
+2022-06-19 03:08:11,515 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 03:08:29,177 INFO [train.py:914] (0/4) Epoch 23, validation: loss=0.1651, simple_loss=0.2488, pruned_loss=0.04074, over 1622729.00 frames.
+2022-06-19 03:09:00,948 INFO [train.py:874] (0/4) Epoch 23, batch 3050, datatang_loss[loss=0.1464, simple_loss=0.2261, pruned_loss=0.03337, over 4945.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2273, pruned_loss=0.03166, over 985251.11 frames.], batch size: 86, aishell_tot_loss[loss=0.1482, simple_loss=0.2347, pruned_loss=0.0309, over 984214.52 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.219, pruned_loss=0.0326, over 985648.92 frames.], batch size: 86, lr: 3.54e-04
+2022-06-19 03:09:32,345 INFO [train.py:874] (0/4) Epoch 23, batch 3100, datatang_loss[loss=0.1337, simple_loss=0.2118, pruned_loss=0.02784, over 4927.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2283, pruned_loss=0.03159, over 985116.11 frames.], batch size: 77, aishell_tot_loss[loss=0.1484, simple_loss=0.235, pruned_loss=0.03087, over 984332.55 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2195, pruned_loss=0.03258, over 985483.40 frames.], batch size: 77, lr: 3.54e-04
+2022-06-19 03:10:04,559 INFO [train.py:874] (0/4) Epoch 23, batch 3150, datatang_loss[loss=0.1696, simple_loss=0.2403, pruned_loss=0.04941, over 4855.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2278, pruned_loss=0.0318, over 985579.08 frames.], batch size: 36, aishell_tot_loss[loss=0.1482, simple_loss=0.2347, pruned_loss=0.0308, over 984632.15 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2196, pruned_loss=0.03284, over 985715.80 frames.], batch size: 36, lr: 3.54e-04
+2022-06-19 03:10:38,019 INFO [train.py:874] (0/4) Epoch 23, batch 3200, datatang_loss[loss=0.1286, simple_loss=0.2031, pruned_loss=0.02706, over 4940.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2274, pruned_loss=0.03172, over 985346.75 frames.], batch size: 79, aishell_tot_loss[loss=0.1483, simple_loss=0.235, pruned_loss=0.03086, over 984494.03 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2191, pruned_loss=0.0327, over 985710.68 frames.], batch size: 79, lr: 3.54e-04
+2022-06-19 03:11:09,275 INFO [train.py:874] (0/4) Epoch 23, batch 3250, datatang_loss[loss=0.125, simple_loss=0.1998, pruned_loss=0.02511, over 4969.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2266, pruned_loss=0.03115, over 985390.59 frames.], batch size: 45, aishell_tot_loss[loss=0.1477, simple_loss=0.2344, pruned_loss=0.03046, over 984597.32 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2188, pruned_loss=0.03252, over 985721.53 frames.], batch size: 45, lr: 3.54e-04
+2022-06-19 03:11:41,513 INFO [train.py:874] (0/4) Epoch 23, batch 3300, aishell_loss[loss=0.1287, simple_loss=0.2053, pruned_loss=0.02602, over 4858.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2269, pruned_loss=0.03139, over 985489.07 frames.], batch size: 28, aishell_tot_loss[loss=0.1481, simple_loss=0.2348, pruned_loss=0.03069, over 984668.78 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2186, pruned_loss=0.0325, over 985821.78 frames.], batch size: 28, lr: 3.54e-04
+2022-06-19 03:12:14,929 INFO [train.py:874] (0/4) Epoch 23, batch 3350, aishell_loss[loss=0.171, simple_loss=0.2385, pruned_loss=0.05174, over 4903.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2268, pruned_loss=0.03171, over 986041.94 frames.], batch size: 34, aishell_tot_loss[loss=0.1481, simple_loss=0.2347, pruned_loss=0.03081, over 984870.42 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2187, pruned_loss=0.03265, over 986258.53 frames.], batch size: 34, lr: 3.54e-04
+2022-06-19 03:12:46,725 INFO [train.py:874] (0/4) Epoch 23, batch 3400, datatang_loss[loss=0.2079, simple_loss=0.2789, pruned_loss=0.06849, over 4922.00 frames.], tot_loss[loss=0.145, simple_loss=0.2268, pruned_loss=0.03155, over 986042.83 frames.], batch size: 108, aishell_tot_loss[loss=0.1481, simple_loss=0.2348, pruned_loss=0.0307, over 985082.23 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2188, pruned_loss=0.03255, over 986152.37 frames.], batch size: 108, lr: 3.54e-04
+2022-06-19 03:13:18,806 INFO [train.py:874] (0/4) Epoch 23, batch 3450, aishell_loss[loss=0.1442, simple_loss=0.2226, pruned_loss=0.03285, over 4945.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2266, pruned_loss=0.03159, over 985753.80 frames.], batch size: 56, aishell_tot_loss[loss=0.148, simple_loss=0.2348, pruned_loss=0.03061, over 985122.89 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2187, pruned_loss=0.03264, over 985897.71 frames.], batch size: 56, lr: 3.54e-04
+2022-06-19 03:13:51,951 INFO [train.py:874] (0/4) Epoch 23, batch 3500, datatang_loss[loss=0.144, simple_loss=0.217, pruned_loss=0.03544, over 4920.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2263, pruned_loss=0.03118, over 985641.05 frames.], batch size: 81, aishell_tot_loss[loss=0.1482, simple_loss=0.2351, pruned_loss=0.03064, over 985043.54 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2184, pruned_loss=0.03211, over 985910.02 frames.], batch size: 81, lr: 3.53e-04
+2022-06-19 03:14:23,651 INFO [train.py:874] (0/4) Epoch 23, batch 3550, datatang_loss[loss=0.1213, simple_loss=0.1918, pruned_loss=0.02541, over 4973.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2263, pruned_loss=0.03137, over 985868.32 frames.], batch size: 55, aishell_tot_loss[loss=0.1481, simple_loss=0.235, pruned_loss=0.03065, over 985159.31 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2187, pruned_loss=0.03223, over 986054.11 frames.], batch size: 55, lr: 3.53e-04
+2022-06-19 03:14:55,474 INFO [train.py:874] (0/4) Epoch 23, batch 3600, aishell_loss[loss=0.1669, simple_loss=0.2487, pruned_loss=0.04253, over 4903.00 frames.], tot_loss[loss=0.1445, simple_loss=0.226, pruned_loss=0.03154, over 985728.87 frames.], batch size: 34, aishell_tot_loss[loss=0.1477, simple_loss=0.2345, pruned_loss=0.0304, over 984955.29 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2191, pruned_loss=0.03255, over 986148.96 frames.], batch size: 34, lr: 3.53e-04
+2022-06-19 03:15:29,863 INFO [train.py:874] (0/4) Epoch 23, batch 3650, aishell_loss[loss=0.1529, simple_loss=0.2462, pruned_loss=0.02986, over 4862.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2265, pruned_loss=0.03171, over 985770.64 frames.], batch size: 37, aishell_tot_loss[loss=0.1475, simple_loss=0.2346, pruned_loss=0.03018, over 984948.23 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2195, pruned_loss=0.03296, over 986245.25 frames.], batch size: 37, lr: 3.53e-04
+2022-06-19 03:16:01,414 INFO [train.py:874] (0/4) Epoch 23, batch 3700, datatang_loss[loss=0.1602, simple_loss=0.2301, pruned_loss=0.04516, over 4924.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2266, pruned_loss=0.03181, over 985614.22 frames.], batch size: 94, aishell_tot_loss[loss=0.1477, simple_loss=0.2349, pruned_loss=0.03027, over 984760.85 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2198, pruned_loss=0.03294, over 986260.14 frames.], batch size: 94, lr: 3.53e-04
+2022-06-19 03:16:34,254 INFO [train.py:874] (0/4) Epoch 23, batch 3750, datatang_loss[loss=0.143, simple_loss=0.2289, pruned_loss=0.02858, over 4956.00 frames.], tot_loss[loss=0.146, simple_loss=0.228, pruned_loss=0.03199, over 985563.71 frames.], batch size: 91, aishell_tot_loss[loss=0.1485, simple_loss=0.2357, pruned_loss=0.0307, over 985064.47 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2201, pruned_loss=0.03272, over 985929.37 frames.], batch size: 91, lr: 3.53e-04
+2022-06-19 03:17:06,327 INFO [train.py:874] (0/4) Epoch 23, batch 3800, datatang_loss[loss=0.124, simple_loss=0.2077, pruned_loss=0.02015, over 4939.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2276, pruned_loss=0.03148, over 985480.15 frames.], batch size: 79, aishell_tot_loss[loss=0.1483, simple_loss=0.2356, pruned_loss=0.03052, over 985016.36 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2198, pruned_loss=0.03245, over 985916.06 frames.], batch size: 79, lr: 3.53e-04
+2022-06-19 03:17:37,350 INFO [train.py:874] (0/4) Epoch 23, batch 3850, aishell_loss[loss=0.1329, simple_loss=0.2236, pruned_loss=0.02107, over 4980.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2273, pruned_loss=0.03143, over 985331.35 frames.], batch size: 30, aishell_tot_loss[loss=0.1481, simple_loss=0.2353, pruned_loss=0.03047, over 984948.96 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2193, pruned_loss=0.0325, over 985845.07 frames.], batch size: 30, lr: 3.53e-04
+2022-06-19 03:18:07,537 INFO [train.py:874] (0/4) Epoch 23, batch 3900, aishell_loss[loss=0.1544, simple_loss=0.2462, pruned_loss=0.03133, over 4906.00 frames.], tot_loss[loss=0.145, simple_loss=0.227, pruned_loss=0.03148, over 985392.42 frames.], batch size: 41, aishell_tot_loss[loss=0.1478, simple_loss=0.235, pruned_loss=0.03028, over 984715.36 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2193, pruned_loss=0.03272, over 986128.53 frames.], batch size: 41, lr: 3.53e-04
+2022-06-19 03:18:38,101 INFO [train.py:874] (0/4) Epoch 23, batch 3950, datatang_loss[loss=0.1407, simple_loss=0.2262, pruned_loss=0.02762, over 4931.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2273, pruned_loss=0.03165, over 985279.30 frames.], batch size: 50, aishell_tot_loss[loss=0.1481, simple_loss=0.2348, pruned_loss=0.03071, over 984426.61 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2193, pruned_loss=0.03251, over 986325.74 frames.], batch size: 50, lr: 3.53e-04
+2022-06-19 03:19:10,313 INFO [train.py:874] (0/4) Epoch 23, batch 4000, aishell_loss[loss=0.1543, simple_loss=0.2425, pruned_loss=0.03306, over 4966.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2274, pruned_loss=0.03178, over 985491.52 frames.], batch size: 39, aishell_tot_loss[loss=0.1486, simple_loss=0.2352, pruned_loss=0.03093, over 984715.91 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2191, pruned_loss=0.03245, over 986242.40 frames.], batch size: 39, lr: 3.52e-04
+2022-06-19 03:19:10,316 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 03:19:27,334 INFO [train.py:914] (0/4) Epoch 23, validation: loss=0.166, simple_loss=0.2494, pruned_loss=0.04127, over 1622729.00 frames.
+2022-06-19 03:19:59,953 INFO [train.py:874] (0/4) Epoch 23, batch 4050, datatang_loss[loss=0.1457, simple_loss=0.2196, pruned_loss=0.03589, over 4964.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2264, pruned_loss=0.03153, over 985855.75 frames.], batch size: 50, aishell_tot_loss[loss=0.1484, simple_loss=0.2351, pruned_loss=0.03085, over 984988.47 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.219, pruned_loss=0.0322, over 986291.22 frames.], batch size: 50, lr: 3.52e-04
+2022-06-19 03:20:30,739 INFO [train.py:874] (0/4) Epoch 23, batch 4100, datatang_loss[loss=0.1461, simple_loss=0.2173, pruned_loss=0.03747, over 4930.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2257, pruned_loss=0.03186, over 985947.93 frames.], batch size: 57, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03061, over 984974.83 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2192, pruned_loss=0.03279, over 986441.98 frames.], batch size: 57, lr: 3.52e-04
+2022-06-19 03:21:01,185 INFO [train.py:874] (0/4) Epoch 23, batch 4150, datatang_loss[loss=0.1404, simple_loss=0.2201, pruned_loss=0.03041, over 4942.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2266, pruned_loss=0.03181, over 985361.27 frames.], batch size: 69, aishell_tot_loss[loss=0.1479, simple_loss=0.2345, pruned_loss=0.03066, over 984576.15 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2194, pruned_loss=0.03278, over 986305.81 frames.], batch size: 69, lr: 3.52e-04
+2022-06-19 03:21:18,180 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-23.pt
+2022-06-19 03:22:22,969 INFO [train.py:874] (0/4) Epoch 24, batch 50, aishell_loss[loss=0.143, simple_loss=0.2278, pruned_loss=0.02909, over 4973.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2239, pruned_loss=0.02926, over 218406.70 frames.], batch size: 61, aishell_tot_loss[loss=0.1484, simple_loss=0.2356, pruned_loss=0.03065, over 124631.32 frames.], datatang_tot_loss[loss=0.1328, simple_loss=0.2102, pruned_loss=0.0277, over 107348.24 frames.], batch size: 61, lr: 3.45e-04
+2022-06-19 03:22:55,071 INFO [train.py:874] (0/4) Epoch 24, batch 100, datatang_loss[loss=0.1384, simple_loss=0.2203, pruned_loss=0.02828, over 4927.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2246, pruned_loss=0.03023, over 388655.42 frames.], batch size: 79, aishell_tot_loss[loss=0.1484, simple_loss=0.2351, pruned_loss=0.03084, over 237534.04 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2116, pruned_loss=0.02934, over 199062.77 frames.], batch size: 79, lr: 3.45e-04
+2022-06-19 03:23:26,189 INFO [train.py:874] (0/4) Epoch 24, batch 150, aishell_loss[loss=0.1524, simple_loss=0.2331, pruned_loss=0.03585, over 4974.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2249, pruned_loss=0.0304, over 520993.73 frames.], batch size: 51, aishell_tot_loss[loss=0.1488, simple_loss=0.2356, pruned_loss=0.031, over 335509.64 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2114, pruned_loss=0.02952, over 281163.48 frames.], batch size: 51, lr: 3.45e-04
+2022-06-19 03:23:59,934 INFO [train.py:874] (0/4) Epoch 24, batch 200, datatang_loss[loss=0.1503, simple_loss=0.2223, pruned_loss=0.03911, over 4922.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2239, pruned_loss=0.03056, over 623710.07 frames.], batch size: 42, aishell_tot_loss[loss=0.1493, simple_loss=0.2354, pruned_loss=0.0316, over 409067.42 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2112, pruned_loss=0.02925, over 367060.78 frames.], batch size: 42, lr: 3.45e-04
+2022-06-19 03:24:28,441 INFO [train.py:874] (0/4) Epoch 24, batch 250, aishell_loss[loss=0.1633, simple_loss=0.256, pruned_loss=0.03537, over 4917.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2238, pruned_loss=0.03031, over 704179.20 frames.], batch size: 46, aishell_tot_loss[loss=0.1483, simple_loss=0.2347, pruned_loss=0.03088, over 484758.27 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2112, pruned_loss=0.02963, over 431701.65 frames.], batch size: 46, lr: 3.44e-04
+2022-06-19 03:25:02,149 INFO [train.py:874] (0/4) Epoch 24, batch 300, datatang_loss[loss=0.1488, simple_loss=0.2268, pruned_loss=0.0354, over 4948.00 frames.], tot_loss[loss=0.1422, simple_loss=0.223, pruned_loss=0.03066, over 766588.29 frames.], batch size: 55, aishell_tot_loss[loss=0.1483, simple_loss=0.2346, pruned_loss=0.03098, over 527971.37 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2118, pruned_loss=0.03015, over 513815.76 frames.], batch size: 55, lr: 3.44e-04
+2022-06-19 03:25:34,574 INFO [train.py:874] (0/4) Epoch 24, batch 350, aishell_loss[loss=0.1505, simple_loss=0.2394, pruned_loss=0.0308, over 4907.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2239, pruned_loss=0.03028, over 815582.94 frames.], batch size: 41, aishell_tot_loss[loss=0.1482, simple_loss=0.2351, pruned_loss=0.03063, over 584076.86 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2124, pruned_loss=0.02999, over 567581.69 frames.], batch size: 41, lr: 3.44e-04
+2022-06-19 03:26:05,101 INFO [train.py:874] (0/4) Epoch 24, batch 400, datatang_loss[loss=0.1243, simple_loss=0.2046, pruned_loss=0.02197, over 4923.00 frames.], tot_loss[loss=0.142, simple_loss=0.2236, pruned_loss=0.03015, over 852887.67 frames.], batch size: 73, aishell_tot_loss[loss=0.1487, simple_loss=0.2357, pruned_loss=0.03082, over 623856.28 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2122, pruned_loss=0.02964, over 624058.15 frames.], batch size: 73, lr: 3.44e-04
+2022-06-19 03:26:39,127 INFO [train.py:874] (0/4) Epoch 24, batch 450, datatang_loss[loss=0.1431, simple_loss=0.2233, pruned_loss=0.03146, over 4924.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2235, pruned_loss=0.0306, over 882410.13 frames.], batch size: 57, aishell_tot_loss[loss=0.1484, simple_loss=0.2352, pruned_loss=0.03081, over 654852.67 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2135, pruned_loss=0.03028, over 677955.17 frames.], batch size: 57, lr: 3.44e-04
+2022-06-19 03:27:12,087 INFO [train.py:874] (0/4) Epoch 24, batch 500, aishell_loss[loss=0.1545, simple_loss=0.2486, pruned_loss=0.03015, over 4919.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2244, pruned_loss=0.03057, over 904903.05 frames.], batch size: 52, aishell_tot_loss[loss=0.1484, simple_loss=0.2352, pruned_loss=0.03081, over 699302.76 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2138, pruned_loss=0.03024, over 708608.34 frames.], batch size: 52, lr: 3.44e-04
+2022-06-19 03:27:40,939 INFO [train.py:874] (0/4) Epoch 24, batch 550, datatang_loss[loss=0.1404, simple_loss=0.2074, pruned_loss=0.03671, over 4913.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2243, pruned_loss=0.0306, over 922790.60 frames.], batch size: 42, aishell_tot_loss[loss=0.1477, simple_loss=0.2345, pruned_loss=0.0304, over 734201.21 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2143, pruned_loss=0.03066, over 740089.46 frames.], batch size: 42, lr: 3.44e-04
+2022-06-19 03:28:15,527 INFO [train.py:874] (0/4) Epoch 24, batch 600, datatang_loss[loss=0.1388, simple_loss=0.2176, pruned_loss=0.03005, over 4923.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2244, pruned_loss=0.03048, over 936909.11 frames.], batch size: 34, aishell_tot_loss[loss=0.1475, simple_loss=0.2342, pruned_loss=0.0304, over 764018.21 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2146, pruned_loss=0.03051, over 769000.70 frames.], batch size: 34, lr: 3.44e-04
+2022-06-19 03:28:47,968 INFO [train.py:874] (0/4) Epoch 24, batch 650, aishell_loss[loss=0.134, simple_loss=0.2298, pruned_loss=0.01907, over 4971.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2248, pruned_loss=0.03043, over 947781.33 frames.], batch size: 44, aishell_tot_loss[loss=0.1468, simple_loss=0.2334, pruned_loss=0.03012, over 796869.49 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2152, pruned_loss=0.03075, over 787758.08 frames.], batch size: 44, lr: 3.44e-04
+2022-06-19 03:28:58,003 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-96000.pt
+2022-06-19 03:29:24,091 INFO [train.py:874] (0/4) Epoch 24, batch 700, aishell_loss[loss=0.1279, simple_loss=0.2236, pruned_loss=0.01613, over 4890.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2264, pruned_loss=0.03159, over 956409.40 frames.], batch size: 42, aishell_tot_loss[loss=0.1473, simple_loss=0.2336, pruned_loss=0.03052, over 820207.88 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2171, pruned_loss=0.03181, over 810168.86 frames.], batch size: 42, lr: 3.44e-04
+2022-06-19 03:29:56,694 INFO [train.py:874] (0/4) Epoch 24, batch 750, aishell_loss[loss=0.1511, simple_loss=0.2476, pruned_loss=0.02733, over 4958.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2262, pruned_loss=0.03163, over 962944.85 frames.], batch size: 69, aishell_tot_loss[loss=0.1476, simple_loss=0.2338, pruned_loss=0.03064, over 836049.26 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2173, pruned_loss=0.03183, over 834668.44 frames.], batch size: 69, lr: 3.44e-04
+2022-06-19 03:30:29,820 INFO [train.py:874] (0/4) Epoch 24, batch 800, datatang_loss[loss=0.1324, simple_loss=0.2039, pruned_loss=0.03046, over 4917.00 frames.], tot_loss[loss=0.145, simple_loss=0.2269, pruned_loss=0.0316, over 968004.82 frames.], batch size: 77, aishell_tot_loss[loss=0.1471, simple_loss=0.2334, pruned_loss=0.03038, over 856875.49 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2183, pruned_loss=0.03221, over 849170.62 frames.], batch size: 77, lr: 3.44e-04
+2022-06-19 03:30:59,480 INFO [train.py:874] (0/4) Epoch 24, batch 850, aishell_loss[loss=0.1165, simple_loss=0.1937, pruned_loss=0.01962, over 4964.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2261, pruned_loss=0.0313, over 971847.13 frames.], batch size: 25, aishell_tot_loss[loss=0.147, simple_loss=0.2332, pruned_loss=0.03043, over 871507.25 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.218, pruned_loss=0.03186, over 865741.29 frames.], batch size: 25, lr: 3.43e-04
+2022-06-19 03:31:31,346 INFO [train.py:874] (0/4) Epoch 24, batch 900, aishell_loss[loss=0.1527, simple_loss=0.2377, pruned_loss=0.03387, over 4861.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2264, pruned_loss=0.03136, over 975046.58 frames.], batch size: 37, aishell_tot_loss[loss=0.147, simple_loss=0.2331, pruned_loss=0.03047, over 885931.76 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2184, pruned_loss=0.03196, over 878971.62 frames.], batch size: 37, lr: 3.43e-04
+2022-06-19 03:31:59,247 INFO [train.py:874] (0/4) Epoch 24, batch 950, aishell_loss[loss=0.1481, simple_loss=0.2348, pruned_loss=0.03074, over 4863.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2268, pruned_loss=0.03165, over 977322.73 frames.], batch size: 37, aishell_tot_loss[loss=0.1476, simple_loss=0.2338, pruned_loss=0.03071, over 898041.15 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2183, pruned_loss=0.03208, over 891066.45 frames.], batch size: 37, lr: 3.43e-04
+2022-06-19 03:32:28,606 INFO [train.py:874] (0/4) Epoch 24, batch 1000, datatang_loss[loss=0.1464, simple_loss=0.2204, pruned_loss=0.03616, over 4900.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2273, pruned_loss=0.03176, over 979547.54 frames.], batch size: 52, aishell_tot_loss[loss=0.1476, simple_loss=0.2338, pruned_loss=0.03067, over 908574.16 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.219, pruned_loss=0.03235, over 902403.47 frames.], batch size: 52, lr: 3.43e-04
+2022-06-19 03:32:28,609 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 03:32:45,106 INFO [train.py:914] (0/4) Epoch 24, validation: loss=0.1641, simple_loss=0.2485, pruned_loss=0.03987, over 1622729.00 frames.
+2022-06-19 03:33:11,281 INFO [train.py:874] (0/4) Epoch 24, batch 1050, aishell_loss[loss=0.1424, simple_loss=0.2348, pruned_loss=0.02496, over 4930.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2281, pruned_loss=0.03169, over 980936.22 frames.], batch size: 68, aishell_tot_loss[loss=0.1473, simple_loss=0.2339, pruned_loss=0.03039, over 919157.76 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2197, pruned_loss=0.03266, over 910554.55 frames.], batch size: 68, lr: 3.43e-04
+2022-06-19 03:33:42,074 INFO [train.py:874] (0/4) Epoch 24, batch 1100, aishell_loss[loss=0.1454, simple_loss=0.2373, pruned_loss=0.02676, over 4905.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2279, pruned_loss=0.03127, over 981824.93 frames.], batch size: 46, aishell_tot_loss[loss=0.147, simple_loss=0.2337, pruned_loss=0.03011, over 926782.65 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.22, pruned_loss=0.03251, over 919448.79 frames.], batch size: 46, lr: 3.43e-04
+2022-06-19 03:34:11,135 INFO [train.py:874] (0/4) Epoch 24, batch 1150, aishell_loss[loss=0.137, simple_loss=0.2266, pruned_loss=0.02363, over 4893.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2281, pruned_loss=0.03145, over 982533.42 frames.], batch size: 50, aishell_tot_loss[loss=0.1472, simple_loss=0.2339, pruned_loss=0.03023, over 934672.66 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.22, pruned_loss=0.03265, over 925988.22 frames.], batch size: 50, lr: 3.43e-04
+2022-06-19 03:34:38,366 INFO [train.py:874] (0/4) Epoch 24, batch 1200, datatang_loss[loss=0.125, simple_loss=0.202, pruned_loss=0.02399, over 4912.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2268, pruned_loss=0.03101, over 983086.80 frames.], batch size: 47, aishell_tot_loss[loss=0.1471, simple_loss=0.2342, pruned_loss=0.03006, over 939832.08 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2188, pruned_loss=0.03227, over 933851.34 frames.], batch size: 47, lr: 3.43e-04
+2022-06-19 03:35:08,064 INFO [train.py:874] (0/4) Epoch 24, batch 1250, datatang_loss[loss=0.1442, simple_loss=0.2257, pruned_loss=0.03131, over 4872.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2259, pruned_loss=0.03086, over 983395.63 frames.], batch size: 30, aishell_tot_loss[loss=0.1472, simple_loss=0.2342, pruned_loss=0.03012, over 943898.81 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2183, pruned_loss=0.03197, over 941133.49 frames.], batch size: 30, lr: 3.43e-04
+2022-06-19 03:35:37,363 INFO [train.py:874] (0/4) Epoch 24, batch 1300, datatang_loss[loss=0.13, simple_loss=0.2082, pruned_loss=0.02593, over 4910.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2266, pruned_loss=0.03132, over 984233.40 frames.], batch size: 47, aishell_tot_loss[loss=0.1471, simple_loss=0.2341, pruned_loss=0.03005, over 948776.72 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2191, pruned_loss=0.03248, over 946784.33 frames.], batch size: 47, lr: 3.43e-04
+2022-06-19 03:36:04,146 INFO [train.py:874] (0/4) Epoch 24, batch 1350, aishell_loss[loss=0.1363, simple_loss=0.2235, pruned_loss=0.02456, over 4878.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2268, pruned_loss=0.03124, over 984632.48 frames.], batch size: 35, aishell_tot_loss[loss=0.1474, simple_loss=0.2343, pruned_loss=0.03027, over 953688.16 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.219, pruned_loss=0.03224, over 950836.68 frames.], batch size: 35, lr: 3.43e-04
+2022-06-19 03:36:34,128 INFO [train.py:874] (0/4) Epoch 24, batch 1400, aishell_loss[loss=0.1742, simple_loss=0.2504, pruned_loss=0.04902, over 4957.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2265, pruned_loss=0.03124, over 984881.82 frames.], batch size: 40, aishell_tot_loss[loss=0.1478, simple_loss=0.2345, pruned_loss=0.03053, over 957002.87 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2186, pruned_loss=0.03195, over 955435.34 frames.], batch size: 40, lr: 3.42e-04
+2022-06-19 03:37:01,578 INFO [train.py:874] (0/4) Epoch 24, batch 1450, datatang_loss[loss=0.1413, simple_loss=0.2205, pruned_loss=0.03108, over 4862.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2262, pruned_loss=0.03095, over 984759.30 frames.], batch size: 30, aishell_tot_loss[loss=0.1478, simple_loss=0.2345, pruned_loss=0.03052, over 960291.06 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2183, pruned_loss=0.03168, over 958785.50 frames.], batch size: 30, lr: 3.42e-04
+2022-06-19 03:37:30,958 INFO [train.py:874] (0/4) Epoch 24, batch 1500, aishell_loss[loss=0.1361, simple_loss=0.2196, pruned_loss=0.02627, over 4958.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2257, pruned_loss=0.03129, over 984675.97 frames.], batch size: 31, aishell_tot_loss[loss=0.1472, simple_loss=0.2337, pruned_loss=0.03034, over 962549.02 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2186, pruned_loss=0.03215, over 962353.36 frames.], batch size: 31, lr: 3.42e-04
+2022-06-19 03:38:00,677 INFO [train.py:874] (0/4) Epoch 24, batch 1550, datatang_loss[loss=0.1355, simple_loss=0.2151, pruned_loss=0.02797, over 4962.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2255, pruned_loss=0.03159, over 984825.81 frames.], batch size: 86, aishell_tot_loss[loss=0.1473, simple_loss=0.2335, pruned_loss=0.03055, over 964948.79 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2186, pruned_loss=0.03227, over 965332.89 frames.], batch size: 86, lr: 3.42e-04
+2022-06-19 03:38:29,166 INFO [train.py:874] (0/4) Epoch 24, batch 1600, datatang_loss[loss=0.1576, simple_loss=0.238, pruned_loss=0.03861, over 4928.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2254, pruned_loss=0.03127, over 985033.06 frames.], batch size: 98, aishell_tot_loss[loss=0.1467, simple_loss=0.2333, pruned_loss=0.03006, over 967212.62 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2187, pruned_loss=0.03244, over 967943.70 frames.], batch size: 98, lr: 3.42e-04
+2022-06-19 03:38:56,950 INFO [train.py:874] (0/4) Epoch 24, batch 1650, aishell_loss[loss=0.1484, simple_loss=0.2375, pruned_loss=0.02962, over 4964.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2266, pruned_loss=0.03176, over 985492.98 frames.], batch size: 44, aishell_tot_loss[loss=0.1475, simple_loss=0.2341, pruned_loss=0.03043, over 969842.52 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.219, pruned_loss=0.03262, over 969919.02 frames.], batch size: 44, lr: 3.42e-04
+2022-06-19 03:39:27,572 INFO [train.py:874] (0/4) Epoch 24, batch 1700, datatang_loss[loss=0.16, simple_loss=0.227, pruned_loss=0.04651, over 4908.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2253, pruned_loss=0.03151, over 985184.31 frames.], batch size: 42, aishell_tot_loss[loss=0.147, simple_loss=0.2333, pruned_loss=0.03029, over 970836.70 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2189, pruned_loss=0.03245, over 972242.14 frames.], batch size: 42, lr: 3.42e-04
+2022-06-19 03:39:57,075 INFO [train.py:874] (0/4) Epoch 24, batch 1750, aishell_loss[loss=0.1515, simple_loss=0.2395, pruned_loss=0.0318, over 4879.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2248, pruned_loss=0.03152, over 985207.31 frames.], batch size: 47, aishell_tot_loss[loss=0.1472, simple_loss=0.2334, pruned_loss=0.03051, over 971640.23 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2187, pruned_loss=0.03223, over 974576.71 frames.], batch size: 47, lr: 3.42e-04
+2022-06-19 03:40:24,390 INFO [train.py:874] (0/4) Epoch 24, batch 1800, datatang_loss[loss=0.1282, simple_loss=0.2072, pruned_loss=0.02459, over 4955.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2249, pruned_loss=0.03137, over 985102.14 frames.], batch size: 37, aishell_tot_loss[loss=0.1476, simple_loss=0.234, pruned_loss=0.03063, over 972839.66 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2181, pruned_loss=0.03196, over 976055.36 frames.], batch size: 37, lr: 3.42e-04
+2022-06-19 03:40:54,927 INFO [train.py:874] (0/4) Epoch 24, batch 1850, datatang_loss[loss=0.122, simple_loss=0.1923, pruned_loss=0.02583, over 4966.00 frames.], tot_loss[loss=0.143, simple_loss=0.2246, pruned_loss=0.03071, over 985435.36 frames.], batch size: 55, aishell_tot_loss[loss=0.1477, simple_loss=0.2344, pruned_loss=0.03049, over 974427.44 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2172, pruned_loss=0.03139, over 977326.25 frames.], batch size: 55, lr: 3.42e-04
+2022-06-19 03:41:25,560 INFO [train.py:874] (0/4) Epoch 24, batch 1900, datatang_loss[loss=0.1643, simple_loss=0.2455, pruned_loss=0.04151, over 4914.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2258, pruned_loss=0.03088, over 985657.95 frames.], batch size: 108, aishell_tot_loss[loss=0.1477, simple_loss=0.2345, pruned_loss=0.03039, over 975847.37 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2178, pruned_loss=0.03163, over 978430.85 frames.], batch size: 108, lr: 3.42e-04
+2022-06-19 03:41:52,661 INFO [train.py:874] (0/4) Epoch 24, batch 1950, datatang_loss[loss=0.1409, simple_loss=0.224, pruned_loss=0.02893, over 4933.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2258, pruned_loss=0.03118, over 985460.40 frames.], batch size: 94, aishell_tot_loss[loss=0.1479, simple_loss=0.2347, pruned_loss=0.03051, over 976309.87 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2182, pruned_loss=0.03178, over 979690.42 frames.], batch size: 94, lr: 3.41e-04
+2022-06-19 03:42:22,513 INFO [train.py:874] (0/4) Epoch 24, batch 2000, datatang_loss[loss=0.1464, simple_loss=0.2269, pruned_loss=0.0329, over 4956.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2252, pruned_loss=0.03103, over 985602.13 frames.], batch size: 67, aishell_tot_loss[loss=0.1481, simple_loss=0.2349, pruned_loss=0.03068, over 977449.80 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2174, pruned_loss=0.03144, over 980420.64 frames.], batch size: 67, lr: 3.41e-04
+2022-06-19 03:42:22,515 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 03:42:39,283 INFO [train.py:914] (0/4) Epoch 24, validation: loss=0.1651, simple_loss=0.2488, pruned_loss=0.04067, over 1622729.00 frames.
+2022-06-19 03:43:08,908 INFO [train.py:874] (0/4) Epoch 24, batch 2050, aishell_loss[loss=0.1616, simple_loss=0.2459, pruned_loss=0.03868, over 4911.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2263, pruned_loss=0.03211, over 985527.95 frames.], batch size: 52, aishell_tot_loss[loss=0.1488, simple_loss=0.2352, pruned_loss=0.03125, over 978453.96 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.218, pruned_loss=0.03202, over 980928.31 frames.], batch size: 52, lr: 3.41e-04
+2022-06-19 03:43:38,478 INFO [train.py:874] (0/4) Epoch 24, batch 2100, datatang_loss[loss=0.1402, simple_loss=0.2183, pruned_loss=0.03102, over 4925.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2255, pruned_loss=0.03154, over 985299.18 frames.], batch size: 57, aishell_tot_loss[loss=0.1483, simple_loss=0.2346, pruned_loss=0.03103, over 978914.98 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2178, pruned_loss=0.03174, over 981570.60 frames.], batch size: 57, lr: 3.41e-04
+2022-06-19 03:44:05,029 INFO [train.py:874] (0/4) Epoch 24, batch 2150, aishell_loss[loss=0.159, simple_loss=0.2414, pruned_loss=0.0383, over 4871.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2264, pruned_loss=0.03191, over 985292.82 frames.], batch size: 37, aishell_tot_loss[loss=0.1485, simple_loss=0.2349, pruned_loss=0.03106, over 979617.04 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2184, pruned_loss=0.03213, over 982033.00 frames.], batch size: 37, lr: 3.41e-04
+2022-06-19 03:44:35,445 INFO [train.py:874] (0/4) Epoch 24, batch 2200, aishell_loss[loss=0.1405, simple_loss=0.2333, pruned_loss=0.02387, over 4918.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2264, pruned_loss=0.03188, over 985362.95 frames.], batch size: 41, aishell_tot_loss[loss=0.1481, simple_loss=0.2341, pruned_loss=0.03102, over 980217.07 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2189, pruned_loss=0.03221, over 982597.99 frames.], batch size: 41, lr: 3.41e-04
+2022-06-19 03:45:05,762 INFO [train.py:874] (0/4) Epoch 24, batch 2250, datatang_loss[loss=0.136, simple_loss=0.2083, pruned_loss=0.03186, over 4833.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2258, pruned_loss=0.03154, over 984989.71 frames.], batch size: 30, aishell_tot_loss[loss=0.148, simple_loss=0.2342, pruned_loss=0.03091, over 980621.19 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2184, pruned_loss=0.03202, over 982707.21 frames.], batch size: 30, lr: 3.41e-04
+2022-06-19 03:45:33,372 INFO [train.py:874] (0/4) Epoch 24, batch 2300, aishell_loss[loss=0.1519, simple_loss=0.2342, pruned_loss=0.03482, over 4945.00 frames.], tot_loss[loss=0.145, simple_loss=0.2263, pruned_loss=0.03191, over 984855.71 frames.], batch size: 54, aishell_tot_loss[loss=0.1482, simple_loss=0.2344, pruned_loss=0.03098, over 980722.16 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2189, pruned_loss=0.03232, over 983210.58 frames.], batch size: 54, lr: 3.41e-04
+2022-06-19 03:46:03,317 INFO [train.py:874] (0/4) Epoch 24, batch 2350, datatang_loss[loss=0.1609, simple_loss=0.2334, pruned_loss=0.0442, over 4930.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2271, pruned_loss=0.03234, over 984974.46 frames.], batch size: 42, aishell_tot_loss[loss=0.1481, simple_loss=0.2346, pruned_loss=0.03083, over 981198.27 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2195, pruned_loss=0.03299, over 983553.14 frames.], batch size: 42, lr: 3.41e-04
+2022-06-19 03:46:33,435 INFO [train.py:874] (0/4) Epoch 24, batch 2400, datatang_loss[loss=0.1361, simple_loss=0.2239, pruned_loss=0.02419, over 4915.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2264, pruned_loss=0.03202, over 984814.00 frames.], batch size: 75, aishell_tot_loss[loss=0.148, simple_loss=0.2344, pruned_loss=0.03077, over 981326.45 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2191, pruned_loss=0.03279, over 983836.96 frames.], batch size: 75, lr: 3.41e-04
+2022-06-19 03:46:59,464 INFO [train.py:874] (0/4) Epoch 24, batch 2450, datatang_loss[loss=0.1458, simple_loss=0.2239, pruned_loss=0.03379, over 4926.00 frames.], tot_loss[loss=0.145, simple_loss=0.2264, pruned_loss=0.03182, over 984347.15 frames.], batch size: 83, aishell_tot_loss[loss=0.1477, simple_loss=0.2344, pruned_loss=0.0305, over 981237.22 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.219, pruned_loss=0.0329, over 983960.16 frames.], batch size: 83, lr: 3.41e-04
+2022-06-19 03:47:29,042 INFO [train.py:874] (0/4) Epoch 24, batch 2500, aishell_loss[loss=0.1541, simple_loss=0.2406, pruned_loss=0.0338, over 4918.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2258, pruned_loss=0.03134, over 984255.30 frames.], batch size: 46, aishell_tot_loss[loss=0.1472, simple_loss=0.234, pruned_loss=0.03021, over 981358.32 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2188, pruned_loss=0.03268, over 984152.53 frames.], batch size: 46, lr: 3.41e-04
+2022-06-19 03:47:57,736 INFO [train.py:874] (0/4) Epoch 24, batch 2550, aishell_loss[loss=0.1933, simple_loss=0.2841, pruned_loss=0.05123, over 4934.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2267, pruned_loss=0.03142, over 984522.56 frames.], batch size: 80, aishell_tot_loss[loss=0.1474, simple_loss=0.2341, pruned_loss=0.03037, over 981882.96 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.219, pruned_loss=0.03264, over 984343.59 frames.], batch size: 80, lr: 3.40e-04
+2022-06-19 03:48:24,738 INFO [train.py:874] (0/4) Epoch 24, batch 2600, aishell_loss[loss=0.15, simple_loss=0.2367, pruned_loss=0.03163, over 4979.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2267, pruned_loss=0.03123, over 984875.27 frames.], batch size: 51, aishell_tot_loss[loss=0.1476, simple_loss=0.2344, pruned_loss=0.03036, over 982439.76 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2186, pruned_loss=0.03247, over 984524.05 frames.], batch size: 51, lr: 3.40e-04
+2022-06-19 03:48:54,375 INFO [train.py:874] (0/4) Epoch 24, batch 2650, aishell_loss[loss=0.1203, simple_loss=0.2066, pruned_loss=0.01705, over 4986.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2268, pruned_loss=0.03126, over 985091.75 frames.], batch size: 27, aishell_tot_loss[loss=0.1475, simple_loss=0.2344, pruned_loss=0.03034, over 982749.40 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2187, pruned_loss=0.03246, over 984764.10 frames.], batch size: 27, lr: 3.40e-04
+2022-06-19 03:49:23,859 INFO [train.py:874] (0/4) Epoch 24, batch 2700, datatang_loss[loss=0.1654, simple_loss=0.2366, pruned_loss=0.04709, over 4962.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2267, pruned_loss=0.0315, over 985137.61 frames.], batch size: 37, aishell_tot_loss[loss=0.147, simple_loss=0.2338, pruned_loss=0.03005, over 982823.79 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2196, pruned_loss=0.03291, over 984998.87 frames.], batch size: 37, lr: 3.40e-04
+2022-06-19 03:49:50,611 INFO [train.py:874] (0/4) Epoch 24, batch 2750, aishell_loss[loss=0.1107, simple_loss=0.1752, pruned_loss=0.02307, over 4874.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2278, pruned_loss=0.03215, over 985213.60 frames.], batch size: 21, aishell_tot_loss[loss=0.1478, simple_loss=0.2346, pruned_loss=0.03055, over 983120.20 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2196, pruned_loss=0.03317, over 985114.14 frames.], batch size: 21, lr: 3.40e-04
+2022-06-19 03:50:20,308 INFO [train.py:874] (0/4) Epoch 24, batch 2800, aishell_loss[loss=0.1346, simple_loss=0.2194, pruned_loss=0.0249, over 4879.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2272, pruned_loss=0.03163, over 985182.59 frames.], batch size: 34, aishell_tot_loss[loss=0.1478, simple_loss=0.2347, pruned_loss=0.0304, over 983186.48 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.219, pruned_loss=0.03282, over 985256.98 frames.], batch size: 34, lr: 3.40e-04
+2022-06-19 03:50:47,474 INFO [train.py:874] (0/4) Epoch 24, batch 2850, aishell_loss[loss=0.1645, simple_loss=0.2529, pruned_loss=0.03805, over 4876.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2274, pruned_loss=0.03163, over 985605.95 frames.], batch size: 42, aishell_tot_loss[loss=0.1477, simple_loss=0.2349, pruned_loss=0.03029, over 983755.61 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2195, pruned_loss=0.03288, over 985339.49 frames.], batch size: 42, lr: 3.40e-04
+2022-06-19 03:51:16,575 INFO [train.py:874] (0/4) Epoch 24, batch 2900, aishell_loss[loss=0.1697, simple_loss=0.2595, pruned_loss=0.03992, over 4937.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2275, pruned_loss=0.0314, over 985604.77 frames.], batch size: 32, aishell_tot_loss[loss=0.1474, simple_loss=0.2346, pruned_loss=0.03014, over 983805.69 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2196, pruned_loss=0.03285, over 985560.14 frames.], batch size: 32, lr: 3.40e-04
+2022-06-19 03:51:46,002 INFO [train.py:874] (0/4) Epoch 24, batch 2950, datatang_loss[loss=0.1304, simple_loss=0.2117, pruned_loss=0.02458, over 4944.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2271, pruned_loss=0.03116, over 985868.76 frames.], batch size: 50, aishell_tot_loss[loss=0.147, simple_loss=0.2342, pruned_loss=0.02995, over 984060.23 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2197, pruned_loss=0.03276, over 985804.28 frames.], batch size: 50, lr: 3.40e-04
+2022-06-19 03:52:13,913 INFO [train.py:874] (0/4) Epoch 24, batch 3000, aishell_loss[loss=0.1576, simple_loss=0.2412, pruned_loss=0.03705, over 4865.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2272, pruned_loss=0.03151, over 985858.94 frames.], batch size: 35, aishell_tot_loss[loss=0.1474, simple_loss=0.2343, pruned_loss=0.03021, over 984171.73 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2197, pruned_loss=0.03282, over 985907.34 frames.], batch size: 35, lr: 3.40e-04
+2022-06-19 03:52:13,915 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 03:52:29,518 INFO [train.py:914] (0/4) Epoch 24, validation: loss=0.1647, simple_loss=0.2484, pruned_loss=0.04048, over 1622729.00 frames.
+2022-06-19 03:52:58,013 INFO [train.py:874] (0/4) Epoch 24, batch 3050, aishell_loss[loss=0.1388, simple_loss=0.2241, pruned_loss=0.02676, over 4913.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2281, pruned_loss=0.03189, over 985641.07 frames.], batch size: 46, aishell_tot_loss[loss=0.1481, simple_loss=0.2352, pruned_loss=0.03049, over 984221.49 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2199, pruned_loss=0.03296, over 985835.18 frames.], batch size: 46, lr: 3.40e-04
+2022-06-19 03:53:24,993 INFO [train.py:874] (0/4) Epoch 24, batch 3100, aishell_loss[loss=0.1337, simple_loss=0.2225, pruned_loss=0.02247, over 4937.00 frames.], tot_loss[loss=0.145, simple_loss=0.2271, pruned_loss=0.03146, over 985890.27 frames.], batch size: 32, aishell_tot_loss[loss=0.1478, simple_loss=0.2349, pruned_loss=0.03028, over 984382.83 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2192, pruned_loss=0.03273, over 986085.53 frames.], batch size: 32, lr: 3.39e-04
+2022-06-19 03:53:54,429 INFO [train.py:874] (0/4) Epoch 24, batch 3150, aishell_loss[loss=0.1255, simple_loss=0.2043, pruned_loss=0.02334, over 4870.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2271, pruned_loss=0.0315, over 985894.32 frames.], batch size: 28, aishell_tot_loss[loss=0.1477, simple_loss=0.2348, pruned_loss=0.03027, over 984637.17 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2191, pruned_loss=0.03283, over 986032.50 frames.], batch size: 28, lr: 3.39e-04
+2022-06-19 03:54:21,501 INFO [train.py:874] (0/4) Epoch 24, batch 3200, datatang_loss[loss=0.1292, simple_loss=0.2089, pruned_loss=0.0248, over 4962.00 frames.], tot_loss[loss=0.1446, simple_loss=0.227, pruned_loss=0.03113, over 985583.35 frames.], batch size: 60, aishell_tot_loss[loss=0.1472, simple_loss=0.2344, pruned_loss=0.02999, over 984281.19 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2192, pruned_loss=0.03277, over 986212.17 frames.], batch size: 60, lr: 3.39e-04
+2022-06-19 03:54:50,558 INFO [train.py:874] (0/4) Epoch 24, batch 3250, datatang_loss[loss=0.1327, simple_loss=0.2041, pruned_loss=0.03063, over 4928.00 frames.], tot_loss[loss=0.145, simple_loss=0.2275, pruned_loss=0.03127, over 985758.73 frames.], batch size: 79, aishell_tot_loss[loss=0.1472, simple_loss=0.2344, pruned_loss=0.02998, over 984594.53 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2197, pruned_loss=0.0329, over 986190.94 frames.], batch size: 79, lr: 3.39e-04
+2022-06-19 03:55:21,753 INFO [train.py:874] (0/4) Epoch 24, batch 3300, aishell_loss[loss=0.1618, simple_loss=0.2483, pruned_loss=0.03769, over 4888.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2269, pruned_loss=0.03095, over 985548.33 frames.], batch size: 34, aishell_tot_loss[loss=0.1471, simple_loss=0.2344, pruned_loss=0.0299, over 984661.81 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2192, pruned_loss=0.03258, over 985976.96 frames.], batch size: 34, lr: 3.39e-04
+2022-06-19 03:55:47,623 INFO [train.py:874] (0/4) Epoch 24, batch 3350, aishell_loss[loss=0.1636, simple_loss=0.2461, pruned_loss=0.04058, over 4915.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2273, pruned_loss=0.03105, over 986297.88 frames.], batch size: 34, aishell_tot_loss[loss=0.147, simple_loss=0.2342, pruned_loss=0.02991, over 985200.69 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2194, pruned_loss=0.0327, over 986308.55 frames.], batch size: 34, lr: 3.39e-04
+2022-06-19 03:56:18,373 INFO [train.py:874] (0/4) Epoch 24, batch 3400, aishell_loss[loss=0.1635, simple_loss=0.2382, pruned_loss=0.04438, over 4878.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2275, pruned_loss=0.03116, over 986140.18 frames.], batch size: 35, aishell_tot_loss[loss=0.1473, simple_loss=0.2346, pruned_loss=0.02998, over 985186.14 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2194, pruned_loss=0.03269, over 986290.53 frames.], batch size: 35, lr: 3.39e-04
+2022-06-19 03:56:47,582 INFO [train.py:874] (0/4) Epoch 24, batch 3450, aishell_loss[loss=0.1561, simple_loss=0.2507, pruned_loss=0.03079, over 4976.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2271, pruned_loss=0.03124, over 986141.89 frames.], batch size: 39, aishell_tot_loss[loss=0.1473, simple_loss=0.2348, pruned_loss=0.02994, over 985143.59 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2192, pruned_loss=0.03271, over 986393.28 frames.], batch size: 39, lr: 3.39e-04
+2022-06-19 03:57:14,725 INFO [train.py:874] (0/4) Epoch 24, batch 3500, aishell_loss[loss=0.1559, simple_loss=0.244, pruned_loss=0.03389, over 4929.00 frames.], tot_loss[loss=0.1448, simple_loss=0.227, pruned_loss=0.03131, over 986013.23 frames.], batch size: 68, aishell_tot_loss[loss=0.1476, simple_loss=0.2351, pruned_loss=0.03008, over 985213.81 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.219, pruned_loss=0.03259, over 986245.72 frames.], batch size: 68, lr: 3.39e-04
+2022-06-19 03:57:44,567 INFO [train.py:874] (0/4) Epoch 24, batch 3550, datatang_loss[loss=0.1409, simple_loss=0.2178, pruned_loss=0.03193, over 4939.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2264, pruned_loss=0.03158, over 985727.70 frames.], batch size: 62, aishell_tot_loss[loss=0.1475, simple_loss=0.2347, pruned_loss=0.03013, over 984792.39 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2192, pruned_loss=0.03272, over 986396.43 frames.], batch size: 62, lr: 3.39e-04
+2022-06-19 03:58:14,378 INFO [train.py:874] (0/4) Epoch 24, batch 3600, datatang_loss[loss=0.1792, simple_loss=0.2429, pruned_loss=0.05774, over 4908.00 frames.], tot_loss[loss=0.1456, simple_loss=0.227, pruned_loss=0.03207, over 985745.62 frames.], batch size: 52, aishell_tot_loss[loss=0.1477, simple_loss=0.2348, pruned_loss=0.03027, over 984864.95 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2198, pruned_loss=0.0331, over 986354.09 frames.], batch size: 52, lr: 3.39e-04
+2022-06-19 03:58:41,413 INFO [train.py:874] (0/4) Epoch 24, batch 3650, aishell_loss[loss=0.1603, simple_loss=0.2395, pruned_loss=0.04056, over 4902.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2271, pruned_loss=0.0317, over 985846.58 frames.], batch size: 34, aishell_tot_loss[loss=0.1478, simple_loss=0.2348, pruned_loss=0.0304, over 985142.71 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2198, pruned_loss=0.03269, over 986228.04 frames.], batch size: 34, lr: 3.39e-04
+2022-06-19 03:59:11,606 INFO [train.py:874] (0/4) Epoch 24, batch 3700, aishell_loss[loss=0.1479, simple_loss=0.2349, pruned_loss=0.03045, over 4938.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2271, pruned_loss=0.032, over 985912.45 frames.], batch size: 45, aishell_tot_loss[loss=0.1477, simple_loss=0.2348, pruned_loss=0.0303, over 985268.29 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2199, pruned_loss=0.03312, over 986195.08 frames.], batch size: 45, lr: 3.38e-04
+2022-06-19 03:59:40,795 INFO [train.py:874] (0/4) Epoch 24, batch 3750, aishell_loss[loss=0.1574, simple_loss=0.2446, pruned_loss=0.03511, over 4961.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2273, pruned_loss=0.03202, over 985866.80 frames.], batch size: 40, aishell_tot_loss[loss=0.1477, simple_loss=0.2348, pruned_loss=0.03026, over 985267.05 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2201, pruned_loss=0.03324, over 986174.47 frames.], batch size: 40, lr: 3.38e-04
+2022-06-19 04:00:08,472 INFO [train.py:874] (0/4) Epoch 24, batch 3800, datatang_loss[loss=0.1295, simple_loss=0.2078, pruned_loss=0.02565, over 4913.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2253, pruned_loss=0.03103, over 985598.28 frames.], batch size: 52, aishell_tot_loss[loss=0.1468, simple_loss=0.2338, pruned_loss=0.02991, over 985204.94 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2189, pruned_loss=0.03258, over 985988.96 frames.], batch size: 52, lr: 3.38e-04
+2022-06-19 04:00:37,995 INFO [train.py:874] (0/4) Epoch 24, batch 3850, datatang_loss[loss=0.1577, simple_loss=0.2244, pruned_loss=0.04548, over 4937.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2248, pruned_loss=0.03086, over 985269.08 frames.], batch size: 79, aishell_tot_loss[loss=0.1464, simple_loss=0.2331, pruned_loss=0.02981, over 984952.16 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2186, pruned_loss=0.03251, over 985914.42 frames.], batch size: 79, lr: 3.38e-04
+2022-06-19 04:01:05,126 INFO [train.py:874] (0/4) Epoch 24, batch 3900, aishell_loss[loss=0.1184, simple_loss=0.1989, pruned_loss=0.01894, over 4971.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2244, pruned_loss=0.03061, over 985601.13 frames.], batch size: 25, aishell_tot_loss[loss=0.1463, simple_loss=0.2329, pruned_loss=0.02979, over 985166.07 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2183, pruned_loss=0.03215, over 985989.80 frames.], batch size: 25, lr: 3.38e-04
+2022-06-19 04:01:33,262 INFO [train.py:874] (0/4) Epoch 24, batch 3950, aishell_loss[loss=0.1239, simple_loss=0.2052, pruned_loss=0.02123, over 4885.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2239, pruned_loss=0.03029, over 985192.91 frames.], batch size: 28, aishell_tot_loss[loss=0.1458, simple_loss=0.2326, pruned_loss=0.02953, over 984801.07 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.218, pruned_loss=0.03195, over 985920.90 frames.], batch size: 28, lr: 3.38e-04
+2022-06-19 04:01:59,976 INFO [train.py:874] (0/4) Epoch 24, batch 4000, aishell_loss[loss=0.1609, simple_loss=0.243, pruned_loss=0.03944, over 4905.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2239, pruned_loss=0.03001, over 985456.73 frames.], batch size: 34, aishell_tot_loss[loss=0.1455, simple_loss=0.2322, pruned_loss=0.02936, over 985146.32 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2178, pruned_loss=0.03176, over 985822.80 frames.], batch size: 34, lr: 3.38e-04
+2022-06-19 04:01:59,979 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 04:02:15,554 INFO [train.py:914] (0/4) Epoch 24, validation: loss=0.1649, simple_loss=0.2488, pruned_loss=0.04056, over 1622729.00 frames.
+2022-06-19 04:02:42,070 INFO [train.py:874] (0/4) Epoch 24, batch 4050, aishell_loss[loss=0.1329, simple_loss=0.2318, pruned_loss=0.01694, over 4967.00 frames.], tot_loss[loss=0.142, simple_loss=0.224, pruned_loss=0.03003, over 985472.04 frames.], batch size: 61, aishell_tot_loss[loss=0.1456, simple_loss=0.2324, pruned_loss=0.02939, over 985252.49 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2174, pruned_loss=0.03164, over 985722.94 frames.], batch size: 61, lr: 3.38e-04
+2022-06-19 04:03:10,320 INFO [train.py:874] (0/4) Epoch 24, batch 4100, datatang_loss[loss=0.1245, simple_loss=0.2016, pruned_loss=0.02367, over 4909.00 frames.], tot_loss[loss=0.1431, simple_loss=0.225, pruned_loss=0.03055, over 985782.19 frames.], batch size: 52, aishell_tot_loss[loss=0.1464, simple_loss=0.2332, pruned_loss=0.02981, over 985526.27 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2174, pruned_loss=0.03168, over 985783.27 frames.], batch size: 52, lr: 3.38e-04
+2022-06-19 04:03:36,843 INFO [train.py:874] (0/4) Epoch 24, batch 4150, aishell_loss[loss=0.1466, simple_loss=0.2377, pruned_loss=0.02777, over 4904.00 frames.], tot_loss[loss=0.143, simple_loss=0.2253, pruned_loss=0.03037, over 985656.01 frames.], batch size: 34, aishell_tot_loss[loss=0.1464, simple_loss=0.2333, pruned_loss=0.02975, over 985258.46 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2176, pruned_loss=0.03149, over 985935.62 frames.], batch size: 34, lr: 3.38e-04
+2022-06-19 04:03:59,689 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-24.pt
+2022-06-19 04:04:55,978 INFO [train.py:874] (0/4) Epoch 25, batch 50, datatang_loss[loss=0.1576, simple_loss=0.2244, pruned_loss=0.04539, over 4915.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2232, pruned_loss=0.02883, over 218854.88 frames.], batch size: 57, aishell_tot_loss[loss=0.1443, simple_loss=0.2301, pruned_loss=0.02923, over 142028.79 frames.], datatang_tot_loss[loss=0.1343, simple_loss=0.2124, pruned_loss=0.02816, over 89743.56 frames.], batch size: 57, lr: 3.31e-04
+2022-06-19 04:05:25,165 INFO [train.py:874] (0/4) Epoch 25, batch 100, datatang_loss[loss=0.1281, simple_loss=0.2112, pruned_loss=0.02244, over 4951.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2223, pruned_loss=0.02923, over 389032.14 frames.], batch size: 45, aishell_tot_loss[loss=0.1458, simple_loss=0.2313, pruned_loss=0.03008, over 233828.71 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2122, pruned_loss=0.02833, over 203412.89 frames.], batch size: 45, lr: 3.31e-04
+2022-06-19 04:05:54,546 INFO [train.py:874] (0/4) Epoch 25, batch 150, datatang_loss[loss=0.1365, simple_loss=0.219, pruned_loss=0.02696, over 4932.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2223, pruned_loss=0.02975, over 521260.87 frames.], batch size: 69, aishell_tot_loss[loss=0.1471, simple_loss=0.233, pruned_loss=0.03061, over 305463.59 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2124, pruned_loss=0.02885, over 312668.87 frames.], batch size: 69, lr: 3.31e-04
+2022-06-19 04:06:21,630 INFO [train.py:874] (0/4) Epoch 25, batch 200, aishell_loss[loss=0.1295, simple_loss=0.2209, pruned_loss=0.01907, over 4918.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2225, pruned_loss=0.0294, over 624219.43 frames.], batch size: 46, aishell_tot_loss[loss=0.1468, simple_loss=0.2333, pruned_loss=0.03015, over 394458.71 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2115, pruned_loss=0.02868, over 383015.88 frames.], batch size: 46, lr: 3.31e-04
+2022-06-19 04:06:50,344 INFO [train.py:874] (0/4) Epoch 25, batch 250, datatang_loss[loss=0.1315, simple_loss=0.2093, pruned_loss=0.0269, over 4913.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2225, pruned_loss=0.02916, over 704882.66 frames.], batch size: 75, aishell_tot_loss[loss=0.1463, simple_loss=0.2332, pruned_loss=0.02969, over 459179.01 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.212, pruned_loss=0.02874, over 459535.80 frames.], batch size: 75, lr: 3.31e-04
+2022-06-19 04:07:19,531 INFO [train.py:874] (0/4) Epoch 25, batch 300, datatang_loss[loss=0.1245, simple_loss=0.2052, pruned_loss=0.02191, over 4941.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2213, pruned_loss=0.02898, over 767089.62 frames.], batch size: 31, aishell_tot_loss[loss=0.1452, simple_loss=0.2313, pruned_loss=0.02949, over 521040.51 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2119, pruned_loss=0.02865, over 521607.61 frames.], batch size: 31, lr: 3.30e-04
+2022-06-19 04:07:46,663 INFO [train.py:874] (0/4) Epoch 25, batch 350, datatang_loss[loss=0.1297, simple_loss=0.2061, pruned_loss=0.02667, over 4920.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2215, pruned_loss=0.029, over 815296.94 frames.], batch size: 81, aishell_tot_loss[loss=0.1456, simple_loss=0.2318, pruned_loss=0.02975, over 583895.35 frames.], datatang_tot_loss[loss=0.1339, simple_loss=0.211, pruned_loss=0.02838, over 567711.15 frames.], batch size: 81, lr: 3.30e-04
+2022-06-19 04:08:15,442 INFO [train.py:874] (0/4) Epoch 25, batch 400, datatang_loss[loss=0.1326, simple_loss=0.2096, pruned_loss=0.02779, over 4930.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2215, pruned_loss=0.02931, over 852484.63 frames.], batch size: 77, aishell_tot_loss[loss=0.1457, simple_loss=0.2318, pruned_loss=0.02977, over 625319.72 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2116, pruned_loss=0.02883, over 622365.68 frames.], batch size: 77, lr: 3.30e-04
+2022-06-19 04:08:43,830 INFO [train.py:874] (0/4) Epoch 25, batch 450, datatang_loss[loss=0.1317, simple_loss=0.2147, pruned_loss=0.02436, over 4905.00 frames.], tot_loss[loss=0.141, simple_loss=0.2222, pruned_loss=0.02987, over 881948.78 frames.], batch size: 75, aishell_tot_loss[loss=0.1467, simple_loss=0.2328, pruned_loss=0.03028, over 657791.79 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2122, pruned_loss=0.02913, over 674851.87 frames.], batch size: 75, lr: 3.30e-04
+2022-06-19 04:08:59,876 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-100000.pt
+2022-06-19 04:09:15,236 INFO [train.py:874] (0/4) Epoch 25, batch 500, aishell_loss[loss=0.1336, simple_loss=0.2183, pruned_loss=0.02451, over 4935.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2231, pruned_loss=0.0301, over 904738.31 frames.], batch size: 54, aishell_tot_loss[loss=0.1463, simple_loss=0.2326, pruned_loss=0.03007, over 697426.45 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2135, pruned_loss=0.02971, over 710295.70 frames.], batch size: 54, lr: 3.30e-04
+2022-06-19 04:09:43,980 INFO [train.py:874] (0/4) Epoch 25, batch 550, aishell_loss[loss=0.209, simple_loss=0.294, pruned_loss=0.06203, over 4955.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2243, pruned_loss=0.03044, over 922687.91 frames.], batch size: 54, aishell_tot_loss[loss=0.147, simple_loss=0.2337, pruned_loss=0.0302, over 732595.31 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2139, pruned_loss=0.03007, over 741574.55 frames.], batch size: 54, lr: 3.30e-04
+2022-06-19 04:10:12,935 INFO [train.py:874] (0/4) Epoch 25, batch 600, aishell_loss[loss=0.1447, simple_loss=0.2337, pruned_loss=0.02787, over 4929.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2238, pruned_loss=0.03003, over 936769.87 frames.], batch size: 49, aishell_tot_loss[loss=0.1469, simple_loss=0.2332, pruned_loss=0.03032, over 766718.46 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2133, pruned_loss=0.02951, over 766207.81 frames.], batch size: 49, lr: 3.30e-04
+2022-06-19 04:10:41,819 INFO [train.py:874] (0/4) Epoch 25, batch 650, aishell_loss[loss=0.1606, simple_loss=0.2522, pruned_loss=0.03447, over 4956.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2248, pruned_loss=0.03025, over 947804.21 frames.], batch size: 54, aishell_tot_loss[loss=0.1469, simple_loss=0.2332, pruned_loss=0.03033, over 799109.81 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.214, pruned_loss=0.02979, over 785402.73 frames.], batch size: 54, lr: 3.30e-04
+2022-06-19 04:11:11,129 INFO [train.py:874] (0/4) Epoch 25, batch 700, datatang_loss[loss=0.1211, simple_loss=0.2023, pruned_loss=0.01999, over 4908.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2248, pruned_loss=0.02984, over 956120.58 frames.], batch size: 64, aishell_tot_loss[loss=0.1472, simple_loss=0.2336, pruned_loss=0.03039, over 821729.75 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2137, pruned_loss=0.02925, over 808200.83 frames.], batch size: 64, lr: 3.30e-04
+2022-06-19 04:11:37,449 INFO [train.py:874] (0/4) Epoch 25, batch 750, datatang_loss[loss=0.141, simple_loss=0.2215, pruned_loss=0.03022, over 4944.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2252, pruned_loss=0.03014, over 962753.15 frames.], batch size: 62, aishell_tot_loss[loss=0.1467, simple_loss=0.2332, pruned_loss=0.03009, over 842559.31 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2148, pruned_loss=0.02992, over 827530.40 frames.], batch size: 62, lr: 3.30e-04
+2022-06-19 04:12:06,294 INFO [train.py:874] (0/4) Epoch 25, batch 800, datatang_loss[loss=0.1282, simple_loss=0.2031, pruned_loss=0.0267, over 4948.00 frames.], tot_loss[loss=0.1425, simple_loss=0.225, pruned_loss=0.02995, over 967918.56 frames.], batch size: 55, aishell_tot_loss[loss=0.146, simple_loss=0.2328, pruned_loss=0.02962, over 857320.80 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2156, pruned_loss=0.03019, over 848494.80 frames.], batch size: 55, lr: 3.30e-04
+2022-06-19 04:12:35,215 INFO [train.py:874] (0/4) Epoch 25, batch 850, datatang_loss[loss=0.1378, simple_loss=0.2226, pruned_loss=0.02654, over 4873.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2256, pruned_loss=0.02995, over 971999.29 frames.], batch size: 39, aishell_tot_loss[loss=0.1462, simple_loss=0.2333, pruned_loss=0.02955, over 871399.99 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2162, pruned_loss=0.03026, over 865899.68 frames.], batch size: 39, lr: 3.30e-04
+2022-06-19 04:13:03,058 INFO [train.py:874] (0/4) Epoch 25, batch 900, datatang_loss[loss=0.1267, simple_loss=0.2112, pruned_loss=0.02108, over 4924.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2249, pruned_loss=0.02962, over 975313.90 frames.], batch size: 77, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.02918, over 884029.45 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2168, pruned_loss=0.03024, over 881178.48 frames.], batch size: 77, lr: 3.29e-04
+2022-06-19 04:13:30,843 INFO [train.py:874] (0/4) Epoch 25, batch 950, aishell_loss[loss=0.1244, simple_loss=0.2167, pruned_loss=0.01607, over 4965.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2257, pruned_loss=0.03002, over 977837.58 frames.], batch size: 40, aishell_tot_loss[loss=0.145, simple_loss=0.2319, pruned_loss=0.02902, over 895699.39 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2182, pruned_loss=0.0308, over 894042.64 frames.], batch size: 40, lr: 3.29e-04
+2022-06-19 04:14:00,698 INFO [train.py:874] (0/4) Epoch 25, batch 1000, datatang_loss[loss=0.1533, simple_loss=0.2219, pruned_loss=0.04236, over 4908.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2256, pruned_loss=0.02986, over 979585.81 frames.], batch size: 75, aishell_tot_loss[loss=0.1446, simple_loss=0.2315, pruned_loss=0.02886, over 908660.19 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2181, pruned_loss=0.03086, over 902362.72 frames.], batch size: 75, lr: 3.29e-04
+2022-06-19 04:14:00,701 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 04:14:17,422 INFO [train.py:914] (0/4) Epoch 25, validation: loss=0.1644, simple_loss=0.248, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 04:14:45,771 INFO [train.py:874] (0/4) Epoch 25, batch 1050, datatang_loss[loss=0.1366, simple_loss=0.2086, pruned_loss=0.03226, over 4903.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2255, pruned_loss=0.02951, over 981019.64 frames.], batch size: 47, aishell_tot_loss[loss=0.1446, simple_loss=0.2315, pruned_loss=0.02881, over 918767.68 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2181, pruned_loss=0.03054, over 911131.76 frames.], batch size: 47, lr: 3.29e-04
+2022-06-19 04:15:14,183 INFO [train.py:874] (0/4) Epoch 25, batch 1100, aishell_loss[loss=0.1533, simple_loss=0.2457, pruned_loss=0.03046, over 4926.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2263, pruned_loss=0.02992, over 982151.40 frames.], batch size: 49, aishell_tot_loss[loss=0.1449, simple_loss=0.2319, pruned_loss=0.02892, over 928850.38 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2183, pruned_loss=0.03091, over 917493.45 frames.], batch size: 49, lr: 3.29e-04
+2022-06-19 04:15:41,601 INFO [train.py:874] (0/4) Epoch 25, batch 1150, aishell_loss[loss=0.1326, simple_loss=0.2179, pruned_loss=0.0237, over 4983.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2261, pruned_loss=0.02958, over 982947.47 frames.], batch size: 27, aishell_tot_loss[loss=0.1446, simple_loss=0.2322, pruned_loss=0.02853, over 935773.28 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2181, pruned_loss=0.03095, over 925256.59 frames.], batch size: 27, lr: 3.29e-04
+2022-06-19 04:16:10,682 INFO [train.py:874] (0/4) Epoch 25, batch 1200, aishell_loss[loss=0.1494, simple_loss=0.2362, pruned_loss=0.0313, over 4918.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2258, pruned_loss=0.03, over 983491.88 frames.], batch size: 52, aishell_tot_loss[loss=0.1444, simple_loss=0.2315, pruned_loss=0.02863, over 942102.31 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2184, pruned_loss=0.0313, over 931699.63 frames.], batch size: 52, lr: 3.29e-04
+2022-06-19 04:16:37,443 INFO [train.py:874] (0/4) Epoch 25, batch 1250, datatang_loss[loss=0.1246, simple_loss=0.2062, pruned_loss=0.02151, over 4936.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2253, pruned_loss=0.02986, over 983596.75 frames.], batch size: 79, aishell_tot_loss[loss=0.1443, simple_loss=0.2311, pruned_loss=0.02873, over 947202.18 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2184, pruned_loss=0.03107, over 937593.46 frames.], batch size: 79, lr: 3.29e-04
+2022-06-19 04:17:06,239 INFO [train.py:874] (0/4) Epoch 25, batch 1300, aishell_loss[loss=0.1577, simple_loss=0.249, pruned_loss=0.03317, over 4967.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2246, pruned_loss=0.0303, over 983955.82 frames.], batch size: 40, aishell_tot_loss[loss=0.1445, simple_loss=0.2309, pruned_loss=0.02903, over 950960.38 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2181, pruned_loss=0.03119, over 944026.70 frames.], batch size: 40, lr: 3.29e-04
+2022-06-19 04:17:37,328 INFO [train.py:874] (0/4) Epoch 25, batch 1350, datatang_loss[loss=0.1323, simple_loss=0.2088, pruned_loss=0.02791, over 4929.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2248, pruned_loss=0.03054, over 984440.90 frames.], batch size: 79, aishell_tot_loss[loss=0.1449, simple_loss=0.2313, pruned_loss=0.02928, over 954842.09 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2181, pruned_loss=0.03125, over 949314.88 frames.], batch size: 79, lr: 3.29e-04
+2022-06-19 04:18:04,166 INFO [train.py:874] (0/4) Epoch 25, batch 1400, datatang_loss[loss=0.1397, simple_loss=0.2224, pruned_loss=0.02851, over 4930.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2242, pruned_loss=0.03076, over 984123.13 frames.], batch size: 79, aishell_tot_loss[loss=0.1446, simple_loss=0.2307, pruned_loss=0.02925, over 957584.45 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2182, pruned_loss=0.03157, over 953907.03 frames.], batch size: 79, lr: 3.29e-04
+2022-06-19 04:18:32,336 INFO [train.py:874] (0/4) Epoch 25, batch 1450, datatang_loss[loss=0.168, simple_loss=0.2596, pruned_loss=0.03816, over 4881.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2252, pruned_loss=0.03126, over 984514.17 frames.], batch size: 47, aishell_tot_loss[loss=0.1451, simple_loss=0.2313, pruned_loss=0.02949, over 960599.78 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2187, pruned_loss=0.03192, over 958017.93 frames.], batch size: 47, lr: 3.29e-04
+2022-06-19 04:19:02,038 INFO [train.py:874] (0/4) Epoch 25, batch 1500, aishell_loss[loss=0.1485, simple_loss=0.2437, pruned_loss=0.0267, over 4907.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2255, pruned_loss=0.031, over 984887.24 frames.], batch size: 41, aishell_tot_loss[loss=0.1453, simple_loss=0.2318, pruned_loss=0.02934, over 963309.78 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2186, pruned_loss=0.03189, over 961657.25 frames.], batch size: 41, lr: 3.28e-04
+2022-06-19 04:19:29,739 INFO [train.py:874] (0/4) Epoch 25, batch 1550, datatang_loss[loss=0.1543, simple_loss=0.2234, pruned_loss=0.04255, over 4927.00 frames.], tot_loss[loss=0.1442, simple_loss=0.226, pruned_loss=0.03121, over 985222.03 frames.], batch size: 79, aishell_tot_loss[loss=0.1455, simple_loss=0.2319, pruned_loss=0.0295, over 965721.32 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2193, pruned_loss=0.03202, over 964895.35 frames.], batch size: 79, lr: 3.28e-04
+2022-06-19 04:19:59,147 INFO [train.py:874] (0/4) Epoch 25, batch 1600, aishell_loss[loss=0.1577, simple_loss=0.2429, pruned_loss=0.03624, over 4965.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2252, pruned_loss=0.03097, over 985638.67 frames.], batch size: 44, aishell_tot_loss[loss=0.1451, simple_loss=0.2313, pruned_loss=0.02945, over 968524.98 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2191, pruned_loss=0.03195, over 967196.29 frames.], batch size: 44, lr: 3.28e-04
+2022-06-19 04:20:27,786 INFO [train.py:874] (0/4) Epoch 25, batch 1650, aishell_loss[loss=0.1569, simple_loss=0.2454, pruned_loss=0.0342, over 4945.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2255, pruned_loss=0.03098, over 985821.20 frames.], batch size: 56, aishell_tot_loss[loss=0.1455, simple_loss=0.2317, pruned_loss=0.02963, over 970520.05 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2191, pruned_loss=0.03184, over 969573.31 frames.], batch size: 56, lr: 3.28e-04
+2022-06-19 04:20:55,474 INFO [train.py:874] (0/4) Epoch 25, batch 1700, datatang_loss[loss=0.1422, simple_loss=0.2297, pruned_loss=0.02731, over 4942.00 frames.], tot_loss[loss=0.144, simple_loss=0.2257, pruned_loss=0.03121, over 985990.19 frames.], batch size: 94, aishell_tot_loss[loss=0.1462, simple_loss=0.2325, pruned_loss=0.02995, over 972304.05 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2186, pruned_loss=0.03181, over 971676.13 frames.], batch size: 94, lr: 3.28e-04
+2022-06-19 04:21:24,461 INFO [train.py:874] (0/4) Epoch 25, batch 1750, aishell_loss[loss=0.1427, simple_loss=0.2324, pruned_loss=0.02651, over 4920.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2259, pruned_loss=0.03141, over 986112.75 frames.], batch size: 41, aishell_tot_loss[loss=0.1468, simple_loss=0.2331, pruned_loss=0.03026, over 973889.31 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2183, pruned_loss=0.03179, over 973536.46 frames.], batch size: 41, lr: 3.28e-04
+2022-06-19 04:21:53,970 INFO [train.py:874] (0/4) Epoch 25, batch 1800, datatang_loss[loss=0.1346, simple_loss=0.2133, pruned_loss=0.02798, over 4921.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2251, pruned_loss=0.03069, over 986097.81 frames.], batch size: 57, aishell_tot_loss[loss=0.1466, simple_loss=0.2332, pruned_loss=0.03002, over 975049.12 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.218, pruned_loss=0.03128, over 975285.57 frames.], batch size: 57, lr: 3.28e-04
+2022-06-19 04:22:21,498 INFO [train.py:874] (0/4) Epoch 25, batch 1850, datatang_loss[loss=0.1315, simple_loss=0.2077, pruned_loss=0.02765, over 4913.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2253, pruned_loss=0.03065, over 986253.28 frames.], batch size: 75, aishell_tot_loss[loss=0.1464, simple_loss=0.2329, pruned_loss=0.02995, over 976632.61 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.218, pruned_loss=0.03135, over 976443.80 frames.], batch size: 75, lr: 3.28e-04
+2022-06-19 04:22:52,303 INFO [train.py:874] (0/4) Epoch 25, batch 1900, datatang_loss[loss=0.135, simple_loss=0.2077, pruned_loss=0.03114, over 4962.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2259, pruned_loss=0.03095, over 986413.85 frames.], batch size: 34, aishell_tot_loss[loss=0.1465, simple_loss=0.2331, pruned_loss=0.02995, over 978048.45 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2183, pruned_loss=0.0317, over 977488.65 frames.], batch size: 34, lr: 3.28e-04
+2022-06-19 04:23:20,040 INFO [train.py:874] (0/4) Epoch 25, batch 1950, aishell_loss[loss=0.1464, simple_loss=0.2436, pruned_loss=0.02457, over 4916.00 frames.], tot_loss[loss=0.1447, simple_loss=0.227, pruned_loss=0.03121, over 986257.99 frames.], batch size: 52, aishell_tot_loss[loss=0.1476, simple_loss=0.2343, pruned_loss=0.03043, over 978794.98 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2181, pruned_loss=0.03155, over 978612.05 frames.], batch size: 52, lr: 3.28e-04
+2022-06-19 04:23:47,732 INFO [train.py:874] (0/4) Epoch 25, batch 2000, datatang_loss[loss=0.131, simple_loss=0.2012, pruned_loss=0.03042, over 4979.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2269, pruned_loss=0.03078, over 986214.62 frames.], batch size: 25, aishell_tot_loss[loss=0.1475, simple_loss=0.2344, pruned_loss=0.03029, over 979727.50 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.218, pruned_loss=0.03128, over 979419.22 frames.], batch size: 25, lr: 3.28e-04
+2022-06-19 04:23:47,734 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 04:24:04,382 INFO [train.py:914] (0/4) Epoch 25, validation: loss=0.1644, simple_loss=0.2485, pruned_loss=0.04013, over 1622729.00 frames.
+2022-06-19 04:24:31,265 INFO [train.py:874] (0/4) Epoch 25, batch 2050, aishell_loss[loss=0.1311, simple_loss=0.2149, pruned_loss=0.0236, over 4857.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2271, pruned_loss=0.0305, over 986011.23 frames.], batch size: 37, aishell_tot_loss[loss=0.1472, simple_loss=0.2343, pruned_loss=0.03009, over 980499.33 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2179, pruned_loss=0.03121, over 979995.69 frames.], batch size: 37, lr: 3.28e-04
+2022-06-19 04:24:59,612 INFO [train.py:874] (0/4) Epoch 25, batch 2100, aishell_loss[loss=0.1404, simple_loss=0.2313, pruned_loss=0.0248, over 4883.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2269, pruned_loss=0.03023, over 986052.31 frames.], batch size: 50, aishell_tot_loss[loss=0.1467, simple_loss=0.2337, pruned_loss=0.02987, over 981267.97 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2181, pruned_loss=0.03113, over 980619.62 frames.], batch size: 50, lr: 3.28e-04
+2022-06-19 04:25:26,934 INFO [train.py:874] (0/4) Epoch 25, batch 2150, datatang_loss[loss=0.1452, simple_loss=0.2081, pruned_loss=0.04116, over 4888.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2268, pruned_loss=0.03076, over 985970.97 frames.], batch size: 47, aishell_tot_loss[loss=0.1469, simple_loss=0.2339, pruned_loss=0.02993, over 981792.76 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2183, pruned_loss=0.03153, over 981221.39 frames.], batch size: 47, lr: 3.27e-04
+2022-06-19 04:25:55,450 INFO [train.py:874] (0/4) Epoch 25, batch 2200, datatang_loss[loss=0.1342, simple_loss=0.2148, pruned_loss=0.02683, over 4912.00 frames.], tot_loss[loss=0.144, simple_loss=0.227, pruned_loss=0.03049, over 985880.03 frames.], batch size: 64, aishell_tot_loss[loss=0.147, simple_loss=0.2342, pruned_loss=0.02995, over 982129.67 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2184, pruned_loss=0.03122, over 981846.42 frames.], batch size: 64, lr: 3.27e-04
+2022-06-19 04:26:24,823 INFO [train.py:874] (0/4) Epoch 25, batch 2250, aishell_loss[loss=0.1388, simple_loss=0.2201, pruned_loss=0.0288, over 4947.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2259, pruned_loss=0.03049, over 985741.05 frames.], batch size: 45, aishell_tot_loss[loss=0.1467, simple_loss=0.2337, pruned_loss=0.02988, over 982324.02 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.218, pruned_loss=0.03127, over 982436.88 frames.], batch size: 45, lr: 3.27e-04
+2022-06-19 04:26:54,170 INFO [train.py:874] (0/4) Epoch 25, batch 2300, datatang_loss[loss=0.1216, simple_loss=0.2047, pruned_loss=0.01929, over 4911.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2253, pruned_loss=0.03013, over 985385.20 frames.], batch size: 75, aishell_tot_loss[loss=0.1462, simple_loss=0.2329, pruned_loss=0.0297, over 982470.62 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2182, pruned_loss=0.03103, over 982701.90 frames.], batch size: 75, lr: 3.27e-04
+2022-06-19 04:27:21,752 INFO [train.py:874] (0/4) Epoch 25, batch 2350, aishell_loss[loss=0.1215, simple_loss=0.1952, pruned_loss=0.02391, over 4796.00 frames.], tot_loss[loss=0.142, simple_loss=0.2247, pruned_loss=0.02968, over 985070.98 frames.], batch size: 24, aishell_tot_loss[loss=0.146, simple_loss=0.2328, pruned_loss=0.02955, over 982532.22 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2177, pruned_loss=0.03064, over 982972.13 frames.], batch size: 24, lr: 3.27e-04
+2022-06-19 04:27:52,154 INFO [train.py:874] (0/4) Epoch 25, batch 2400, aishell_loss[loss=0.1711, simple_loss=0.2534, pruned_loss=0.04445, over 4964.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2247, pruned_loss=0.03003, over 985267.48 frames.], batch size: 61, aishell_tot_loss[loss=0.1461, simple_loss=0.233, pruned_loss=0.02962, over 982577.30 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2179, pruned_loss=0.03079, over 983657.38 frames.], batch size: 61, lr: 3.27e-04
+2022-06-19 04:28:20,097 INFO [train.py:874] (0/4) Epoch 25, batch 2450, aishell_loss[loss=0.1431, simple_loss=0.2372, pruned_loss=0.02449, over 4927.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2243, pruned_loss=0.02993, over 985599.16 frames.], batch size: 54, aishell_tot_loss[loss=0.1457, simple_loss=0.2326, pruned_loss=0.02944, over 983028.51 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2177, pruned_loss=0.03084, over 984066.54 frames.], batch size: 54, lr: 3.27e-04
+2022-06-19 04:28:48,388 INFO [train.py:874] (0/4) Epoch 25, batch 2500, aishell_loss[loss=0.1583, simple_loss=0.2476, pruned_loss=0.0345, over 4946.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2248, pruned_loss=0.03028, over 985302.27 frames.], batch size: 79, aishell_tot_loss[loss=0.1463, simple_loss=0.233, pruned_loss=0.02977, over 983066.38 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2175, pruned_loss=0.03084, over 984207.37 frames.], batch size: 79, lr: 3.27e-04
+2022-06-19 04:29:17,543 INFO [train.py:874] (0/4) Epoch 25, batch 2550, aishell_loss[loss=0.1658, simple_loss=0.2401, pruned_loss=0.04571, over 4970.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2253, pruned_loss=0.03028, over 985630.58 frames.], batch size: 39, aishell_tot_loss[loss=0.1463, simple_loss=0.2329, pruned_loss=0.02984, over 983692.06 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2175, pruned_loss=0.03079, over 984356.83 frames.], batch size: 39, lr: 3.27e-04
+2022-06-19 04:29:45,264 INFO [train.py:874] (0/4) Epoch 25, batch 2600, aishell_loss[loss=0.1355, simple_loss=0.2197, pruned_loss=0.02565, over 4979.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2251, pruned_loss=0.03009, over 985407.26 frames.], batch size: 27, aishell_tot_loss[loss=0.1457, simple_loss=0.2325, pruned_loss=0.02939, over 983932.22 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2176, pruned_loss=0.031, over 984253.06 frames.], batch size: 27, lr: 3.27e-04
+2022-06-19 04:30:13,947 INFO [train.py:874] (0/4) Epoch 25, batch 2650, aishell_loss[loss=0.1488, simple_loss=0.2412, pruned_loss=0.02821, over 4981.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2252, pruned_loss=0.03008, over 985678.54 frames.], batch size: 51, aishell_tot_loss[loss=0.1458, simple_loss=0.233, pruned_loss=0.0293, over 984116.84 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2175, pruned_loss=0.03104, over 984657.83 frames.], batch size: 51, lr: 3.27e-04
+2022-06-19 04:30:43,849 INFO [train.py:874] (0/4) Epoch 25, batch 2700, aishell_loss[loss=0.1398, simple_loss=0.2296, pruned_loss=0.02506, over 4957.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2259, pruned_loss=0.02984, over 985899.24 frames.], batch size: 61, aishell_tot_loss[loss=0.1458, simple_loss=0.2333, pruned_loss=0.02919, over 984265.91 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2177, pruned_loss=0.03087, over 985055.10 frames.], batch size: 61, lr: 3.27e-04
+2022-06-19 04:31:12,109 INFO [train.py:874] (0/4) Epoch 25, batch 2750, datatang_loss[loss=0.1424, simple_loss=0.2107, pruned_loss=0.03703, over 4973.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2248, pruned_loss=0.02989, over 986283.24 frames.], batch size: 45, aishell_tot_loss[loss=0.1458, simple_loss=0.2328, pruned_loss=0.02934, over 984660.93 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2172, pruned_loss=0.03073, over 985354.16 frames.], batch size: 45, lr: 3.26e-04
+2022-06-19 04:31:39,173 INFO [train.py:874] (0/4) Epoch 25, batch 2800, aishell_loss[loss=0.1322, simple_loss=0.2243, pruned_loss=0.02007, over 4872.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2252, pruned_loss=0.02977, over 986117.24 frames.], batch size: 28, aishell_tot_loss[loss=0.146, simple_loss=0.2332, pruned_loss=0.02935, over 984793.43 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.0306, over 985349.06 frames.], batch size: 28, lr: 3.26e-04
+2022-06-19 04:32:09,650 INFO [train.py:874] (0/4) Epoch 25, batch 2850, aishell_loss[loss=0.15, simple_loss=0.2352, pruned_loss=0.03244, over 4930.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2254, pruned_loss=0.02993, over 986018.31 frames.], batch size: 41, aishell_tot_loss[loss=0.1464, simple_loss=0.2338, pruned_loss=0.02951, over 984587.24 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2166, pruned_loss=0.03052, over 985679.89 frames.], batch size: 41, lr: 3.26e-04
+2022-06-19 04:32:36,511 INFO [train.py:874] (0/4) Epoch 25, batch 2900, datatang_loss[loss=0.1335, simple_loss=0.2156, pruned_loss=0.02567, over 4918.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2254, pruned_loss=0.02998, over 986018.67 frames.], batch size: 81, aishell_tot_loss[loss=0.1465, simple_loss=0.234, pruned_loss=0.02951, over 984923.15 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2162, pruned_loss=0.03057, over 985582.04 frames.], batch size: 81, lr: 3.26e-04
+2022-06-19 04:33:06,598 INFO [train.py:874] (0/4) Epoch 25, batch 2950, datatang_loss[loss=0.1308, simple_loss=0.2057, pruned_loss=0.02791, over 4924.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2241, pruned_loss=0.02964, over 985681.38 frames.], batch size: 71, aishell_tot_loss[loss=0.146, simple_loss=0.2333, pruned_loss=0.02939, over 984583.69 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2158, pruned_loss=0.03032, over 985730.73 frames.], batch size: 71, lr: 3.26e-04
+2022-06-19 04:33:34,207 INFO [train.py:874] (0/4) Epoch 25, batch 3000, aishell_loss[loss=0.1417, simple_loss=0.2254, pruned_loss=0.02906, over 4920.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2246, pruned_loss=0.02976, over 985607.54 frames.], batch size: 46, aishell_tot_loss[loss=0.1458, simple_loss=0.233, pruned_loss=0.02923, over 984610.42 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2161, pruned_loss=0.03059, over 985793.33 frames.], batch size: 46, lr: 3.26e-04
+2022-06-19 04:33:34,209 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 04:33:49,970 INFO [train.py:914] (0/4) Epoch 25, validation: loss=0.1645, simple_loss=0.2487, pruned_loss=0.04017, over 1622729.00 frames.
+2022-06-19 04:34:20,877 INFO [train.py:874] (0/4) Epoch 25, batch 3050, aishell_loss[loss=0.1322, simple_loss=0.221, pruned_loss=0.02173, over 4879.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2252, pruned_loss=0.02994, over 985267.14 frames.], batch size: 28, aishell_tot_loss[loss=0.1457, simple_loss=0.2328, pruned_loss=0.02924, over 984293.81 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2166, pruned_loss=0.03078, over 985869.62 frames.], batch size: 28, lr: 3.26e-04
+2022-06-19 04:34:49,418 INFO [train.py:874] (0/4) Epoch 25, batch 3100, aishell_loss[loss=0.1375, simple_loss=0.2206, pruned_loss=0.02722, over 4885.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2246, pruned_loss=0.02981, over 985279.79 frames.], batch size: 34, aishell_tot_loss[loss=0.1454, simple_loss=0.2323, pruned_loss=0.02926, over 984527.84 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2162, pruned_loss=0.03066, over 985732.86 frames.], batch size: 34, lr: 3.26e-04
+2022-06-19 04:35:19,346 INFO [train.py:874] (0/4) Epoch 25, batch 3150, datatang_loss[loss=0.1205, simple_loss=0.1991, pruned_loss=0.02097, over 4900.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2241, pruned_loss=0.02981, over 985183.91 frames.], batch size: 25, aishell_tot_loss[loss=0.1455, simple_loss=0.2321, pruned_loss=0.02947, over 984403.77 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2159, pruned_loss=0.03042, over 985783.73 frames.], batch size: 25, lr: 3.26e-04
+2022-06-19 04:35:49,063 INFO [train.py:874] (0/4) Epoch 25, batch 3200, datatang_loss[loss=0.1424, simple_loss=0.2197, pruned_loss=0.03255, over 4950.00 frames.], tot_loss[loss=0.141, simple_loss=0.2234, pruned_loss=0.02929, over 985010.89 frames.], batch size: 91, aishell_tot_loss[loss=0.1445, simple_loss=0.2312, pruned_loss=0.02892, over 984406.52 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2159, pruned_loss=0.03041, over 985630.03 frames.], batch size: 91, lr: 3.26e-04
+2022-06-19 04:36:17,745 INFO [train.py:874] (0/4) Epoch 25, batch 3250, aishell_loss[loss=0.1355, simple_loss=0.2309, pruned_loss=0.02009, over 4963.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2236, pruned_loss=0.02943, over 985129.61 frames.], batch size: 40, aishell_tot_loss[loss=0.1442, simple_loss=0.2309, pruned_loss=0.02876, over 984589.67 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2162, pruned_loss=0.03067, over 985580.76 frames.], batch size: 40, lr: 3.26e-04
+2022-06-19 04:36:47,044 INFO [train.py:874] (0/4) Epoch 25, batch 3300, datatang_loss[loss=0.1532, simple_loss=0.2203, pruned_loss=0.04299, over 4974.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2229, pruned_loss=0.02984, over 985330.12 frames.], batch size: 37, aishell_tot_loss[loss=0.1437, simple_loss=0.2302, pruned_loss=0.02859, over 984668.68 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2165, pruned_loss=0.03115, over 985706.80 frames.], batch size: 37, lr: 3.26e-04
+2022-06-19 04:37:14,505 INFO [train.py:874] (0/4) Epoch 25, batch 3350, datatang_loss[loss=0.1418, simple_loss=0.2218, pruned_loss=0.03095, over 4943.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2235, pruned_loss=0.02972, over 985159.97 frames.], batch size: 88, aishell_tot_loss[loss=0.1439, simple_loss=0.2303, pruned_loss=0.02871, over 984610.02 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2169, pruned_loss=0.03093, over 985624.93 frames.], batch size: 88, lr: 3.26e-04
+2022-06-19 04:37:42,868 INFO [train.py:874] (0/4) Epoch 25, batch 3400, aishell_loss[loss=0.1468, simple_loss=0.2259, pruned_loss=0.03386, over 4927.00 frames.], tot_loss[loss=0.1417, simple_loss=0.224, pruned_loss=0.02972, over 985343.25 frames.], batch size: 32, aishell_tot_loss[loss=0.1441, simple_loss=0.2305, pruned_loss=0.02883, over 984569.60 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2168, pruned_loss=0.03084, over 985902.44 frames.], batch size: 32, lr: 3.25e-04
+2022-06-19 04:38:11,574 INFO [train.py:874] (0/4) Epoch 25, batch 3450, datatang_loss[loss=0.137, simple_loss=0.2151, pruned_loss=0.02942, over 4928.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2239, pruned_loss=0.02954, over 985341.53 frames.], batch size: 71, aishell_tot_loss[loss=0.1442, simple_loss=0.2307, pruned_loss=0.02889, over 984609.36 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2164, pruned_loss=0.03058, over 985901.01 frames.], batch size: 71, lr: 3.25e-04
+2022-06-19 04:38:39,598 INFO [train.py:874] (0/4) Epoch 25, batch 3500, aishell_loss[loss=0.1387, simple_loss=0.2261, pruned_loss=0.02566, over 4921.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2238, pruned_loss=0.02902, over 985208.21 frames.], batch size: 41, aishell_tot_loss[loss=0.1439, simple_loss=0.2306, pruned_loss=0.02861, over 984579.34 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2159, pruned_loss=0.03032, over 985851.86 frames.], batch size: 41, lr: 3.25e-04
+2022-06-19 04:39:08,256 INFO [train.py:874] (0/4) Epoch 25, batch 3550, datatang_loss[loss=0.1278, simple_loss=0.21, pruned_loss=0.02276, over 4923.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2241, pruned_loss=0.02937, over 985333.81 frames.], batch size: 79, aishell_tot_loss[loss=0.1443, simple_loss=0.2311, pruned_loss=0.02873, over 984663.77 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2159, pruned_loss=0.03046, over 985899.67 frames.], batch size: 79, lr: 3.25e-04
+2022-06-19 04:39:38,078 INFO [train.py:874] (0/4) Epoch 25, batch 3600, aishell_loss[loss=0.1422, simple_loss=0.2327, pruned_loss=0.02586, over 4880.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2258, pruned_loss=0.02986, over 985484.39 frames.], batch size: 28, aishell_tot_loss[loss=0.145, simple_loss=0.2319, pruned_loss=0.02906, over 984881.63 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2165, pruned_loss=0.03066, over 985892.17 frames.], batch size: 28, lr: 3.25e-04
+2022-06-19 04:40:04,734 INFO [train.py:874] (0/4) Epoch 25, batch 3650, aishell_loss[loss=0.1333, simple_loss=0.221, pruned_loss=0.02278, over 4932.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2253, pruned_loss=0.02996, over 985682.23 frames.], batch size: 45, aishell_tot_loss[loss=0.1446, simple_loss=0.2313, pruned_loss=0.0289, over 984974.30 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2167, pruned_loss=0.03097, over 986043.59 frames.], batch size: 45, lr: 3.25e-04
+2022-06-19 04:40:33,805 INFO [train.py:874] (0/4) Epoch 25, batch 3700, datatang_loss[loss=0.1393, simple_loss=0.2207, pruned_loss=0.02897, over 4945.00 frames.], tot_loss[loss=0.143, simple_loss=0.2251, pruned_loss=0.03045, over 985731.64 frames.], batch size: 88, aishell_tot_loss[loss=0.1449, simple_loss=0.2314, pruned_loss=0.02918, over 984907.04 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.217, pruned_loss=0.03121, over 986193.43 frames.], batch size: 88, lr: 3.25e-04
+2022-06-19 04:41:00,274 INFO [train.py:874] (0/4) Epoch 25, batch 3750, datatang_loss[loss=0.143, simple_loss=0.2229, pruned_loss=0.03157, over 4908.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2255, pruned_loss=0.03074, over 985737.37 frames.], batch size: 57, aishell_tot_loss[loss=0.1453, simple_loss=0.2319, pruned_loss=0.02931, over 984964.53 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2171, pruned_loss=0.03146, over 986190.14 frames.], batch size: 57, lr: 3.25e-04
+2022-06-19 04:41:29,108 INFO [train.py:874] (0/4) Epoch 25, batch 3800, datatang_loss[loss=0.1326, simple_loss=0.2118, pruned_loss=0.02666, over 4943.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2247, pruned_loss=0.03033, over 985528.80 frames.], batch size: 62, aishell_tot_loss[loss=0.145, simple_loss=0.2317, pruned_loss=0.02916, over 984742.66 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2169, pruned_loss=0.03124, over 986225.50 frames.], batch size: 62, lr: 3.25e-04
+2022-06-19 04:41:57,149 INFO [train.py:874] (0/4) Epoch 25, batch 3850, datatang_loss[loss=0.172, simple_loss=0.2473, pruned_loss=0.04831, over 4918.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2254, pruned_loss=0.03062, over 985667.68 frames.], batch size: 98, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02901, over 984956.01 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2177, pruned_loss=0.03172, over 986177.36 frames.], batch size: 98, lr: 3.25e-04
+2022-06-19 04:42:25,187 INFO [train.py:874] (0/4) Epoch 25, batch 3900, aishell_loss[loss=0.148, simple_loss=0.2402, pruned_loss=0.02788, over 4932.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2257, pruned_loss=0.03053, over 985434.32 frames.], batch size: 58, aishell_tot_loss[loss=0.1446, simple_loss=0.2314, pruned_loss=0.02886, over 985122.22 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2182, pruned_loss=0.03186, over 985801.99 frames.], batch size: 58, lr: 3.25e-04
+2022-06-19 04:42:52,851 INFO [train.py:874] (0/4) Epoch 25, batch 3950, aishell_loss[loss=0.1558, simple_loss=0.2428, pruned_loss=0.03441, over 4893.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2262, pruned_loss=0.03076, over 985416.14 frames.], batch size: 34, aishell_tot_loss[loss=0.1448, simple_loss=0.2318, pruned_loss=0.02887, over 984939.16 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2186, pruned_loss=0.03212, over 985958.08 frames.], batch size: 34, lr: 3.25e-04
+2022-06-19 04:43:21,320 INFO [train.py:874] (0/4) Epoch 25, batch 4000, aishell_loss[loss=0.1633, simple_loss=0.2468, pruned_loss=0.03991, over 4945.00 frames.], tot_loss[loss=0.143, simple_loss=0.2258, pruned_loss=0.03013, over 985579.58 frames.], batch size: 54, aishell_tot_loss[loss=0.1445, simple_loss=0.2318, pruned_loss=0.02863, over 984956.17 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2182, pruned_loss=0.03179, over 986140.88 frames.], batch size: 54, lr: 3.25e-04
+2022-06-19 04:43:21,324 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 04:43:37,170 INFO [train.py:914] (0/4) Epoch 25, validation: loss=0.1639, simple_loss=0.2478, pruned_loss=0.03995, over 1622729.00 frames.
+2022-06-19 04:43:45,204 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-25.pt
+2022-06-19 04:44:42,286 INFO [train.py:874] (0/4) Epoch 26, batch 50, aishell_loss[loss=0.1379, simple_loss=0.2264, pruned_loss=0.0247, over 4826.00 frames.], tot_loss[loss=0.1379, simple_loss=0.22, pruned_loss=0.02786, over 218584.54 frames.], batch size: 29, aishell_tot_loss[loss=0.1439, simple_loss=0.2309, pruned_loss=0.02844, over 98338.33 frames.], datatang_tot_loss[loss=0.1334, simple_loss=0.212, pruned_loss=0.02739, over 133571.97 frames.], batch size: 29, lr: 3.18e-04
+2022-06-19 04:45:12,041 INFO [train.py:874] (0/4) Epoch 26, batch 100, datatang_loss[loss=0.1246, simple_loss=0.1938, pruned_loss=0.02773, over 4958.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2221, pruned_loss=0.02874, over 388216.44 frames.], batch size: 26, aishell_tot_loss[loss=0.1447, simple_loss=0.2318, pruned_loss=0.02879, over 206318.60 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2131, pruned_loss=0.02853, over 230133.73 frames.], batch size: 26, lr: 3.18e-04
+2022-06-19 04:45:40,324 INFO [train.py:874] (0/4) Epoch 26, batch 150, datatang_loss[loss=0.1259, simple_loss=0.2115, pruned_loss=0.02014, over 4928.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2234, pruned_loss=0.02936, over 520399.35 frames.], batch size: 77, aishell_tot_loss[loss=0.146, simple_loss=0.2329, pruned_loss=0.02954, over 297882.78 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2141, pruned_loss=0.02895, over 319070.34 frames.], batch size: 77, lr: 3.18e-04
+2022-06-19 04:46:09,976 INFO [train.py:874] (0/4) Epoch 26, batch 200, datatang_loss[loss=0.1418, simple_loss=0.218, pruned_loss=0.0328, over 4927.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2237, pruned_loss=0.02951, over 623656.60 frames.], batch size: 83, aishell_tot_loss[loss=0.1457, simple_loss=0.2326, pruned_loss=0.02936, over 369863.53 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2154, pruned_loss=0.02944, over 406310.50 frames.], batch size: 83, lr: 3.18e-04
+2022-06-19 04:46:40,011 INFO [train.py:874] (0/4) Epoch 26, batch 250, datatang_loss[loss=0.1207, simple_loss=0.2006, pruned_loss=0.02037, over 4917.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2221, pruned_loss=0.02916, over 703785.72 frames.], batch size: 57, aishell_tot_loss[loss=0.1451, simple_loss=0.2319, pruned_loss=0.02912, over 422795.85 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2145, pruned_loss=0.02916, over 492233.49 frames.], batch size: 57, lr: 3.18e-04
+2022-06-19 04:47:07,486 INFO [train.py:874] (0/4) Epoch 26, batch 300, aishell_loss[loss=0.1514, simple_loss=0.2403, pruned_loss=0.03122, over 4937.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2214, pruned_loss=0.02865, over 766172.29 frames.], batch size: 45, aishell_tot_loss[loss=0.144, simple_loss=0.2308, pruned_loss=0.02865, over 481345.24 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2144, pruned_loss=0.02882, over 556958.22 frames.], batch size: 45, lr: 3.18e-04
+2022-06-19 04:47:36,380 INFO [train.py:874] (0/4) Epoch 26, batch 350, aishell_loss[loss=0.1549, simple_loss=0.2494, pruned_loss=0.03023, over 4909.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2224, pruned_loss=0.02953, over 815049.65 frames.], batch size: 52, aishell_tot_loss[loss=0.1447, simple_loss=0.2308, pruned_loss=0.02929, over 536221.67 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2156, pruned_loss=0.02947, over 611482.17 frames.], batch size: 52, lr: 3.18e-04
+2022-06-19 04:48:05,362 INFO [train.py:874] (0/4) Epoch 26, batch 400, aishell_loss[loss=0.1356, simple_loss=0.2249, pruned_loss=0.02317, over 4891.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2219, pruned_loss=0.02886, over 852497.70 frames.], batch size: 60, aishell_tot_loss[loss=0.1434, simple_loss=0.2298, pruned_loss=0.02846, over 594862.66 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2152, pruned_loss=0.02938, over 650366.05 frames.], batch size: 60, lr: 3.18e-04
+2022-06-19 04:48:32,766 INFO [train.py:874] (0/4) Epoch 26, batch 450, aishell_loss[loss=0.1393, simple_loss=0.2313, pruned_loss=0.02365, over 4979.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2214, pruned_loss=0.02853, over 881977.47 frames.], batch size: 48, aishell_tot_loss[loss=0.1427, simple_loss=0.2289, pruned_loss=0.02821, over 644309.88 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2149, pruned_loss=0.02914, over 686850.18 frames.], batch size: 48, lr: 3.18e-04
+2022-06-19 04:48:44,865 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-104000.pt
+2022-06-19 04:49:06,565 INFO [train.py:874] (0/4) Epoch 26, batch 500, datatang_loss[loss=0.1344, simple_loss=0.2173, pruned_loss=0.02572, over 4933.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2214, pruned_loss=0.02878, over 904855.06 frames.], batch size: 88, aishell_tot_loss[loss=0.1425, simple_loss=0.2287, pruned_loss=0.02819, over 675268.44 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2155, pruned_loss=0.02939, over 729777.31 frames.], batch size: 88, lr: 3.18e-04
+2022-06-19 04:49:36,264 INFO [train.py:874] (0/4) Epoch 26, batch 550, aishell_loss[loss=0.1463, simple_loss=0.2281, pruned_loss=0.03228, over 4972.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2229, pruned_loss=0.02912, over 923026.61 frames.], batch size: 39, aishell_tot_loss[loss=0.1435, simple_loss=0.2299, pruned_loss=0.02853, over 718737.67 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2156, pruned_loss=0.02954, over 754330.62 frames.], batch size: 39, lr: 3.17e-04
+2022-06-19 04:50:04,780 INFO [train.py:874] (0/4) Epoch 26, batch 600, datatang_loss[loss=0.1149, simple_loss=0.194, pruned_loss=0.01788, over 4926.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2226, pruned_loss=0.02917, over 936722.25 frames.], batch size: 73, aishell_tot_loss[loss=0.144, simple_loss=0.2307, pruned_loss=0.02865, over 749005.96 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2146, pruned_loss=0.02952, over 782375.06 frames.], batch size: 73, lr: 3.17e-04
+2022-06-19 04:50:33,458 INFO [train.py:874] (0/4) Epoch 26, batch 650, datatang_loss[loss=0.128, simple_loss=0.2111, pruned_loss=0.02246, over 4930.00 frames.], tot_loss[loss=0.1409, simple_loss=0.223, pruned_loss=0.02937, over 947690.02 frames.], batch size: 79, aishell_tot_loss[loss=0.1441, simple_loss=0.2307, pruned_loss=0.02873, over 778045.40 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2151, pruned_loss=0.02975, over 805422.63 frames.], batch size: 79, lr: 3.17e-04
+2022-06-19 04:51:04,491 INFO [train.py:874] (0/4) Epoch 26, batch 700, datatang_loss[loss=0.142, simple_loss=0.2197, pruned_loss=0.03213, over 4781.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2236, pruned_loss=0.02955, over 955594.27 frames.], batch size: 25, aishell_tot_loss[loss=0.1445, simple_loss=0.2312, pruned_loss=0.02887, over 803939.31 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.215, pruned_loss=0.02989, over 824906.44 frames.], batch size: 25, lr: 3.17e-04
+2022-06-19 04:51:32,314 INFO [train.py:874] (0/4) Epoch 26, batch 750, aishell_loss[loss=0.1516, simple_loss=0.2441, pruned_loss=0.0296, over 4887.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2232, pruned_loss=0.02944, over 961791.41 frames.], batch size: 42, aishell_tot_loss[loss=0.1441, simple_loss=0.2307, pruned_loss=0.0287, over 824838.06 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2152, pruned_loss=0.02995, over 843856.49 frames.], batch size: 42, lr: 3.17e-04
+2022-06-19 04:52:00,214 INFO [train.py:874] (0/4) Epoch 26, batch 800, aishell_loss[loss=0.1109, simple_loss=0.1993, pruned_loss=0.01128, over 4958.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2226, pruned_loss=0.02935, over 966876.08 frames.], batch size: 27, aishell_tot_loss[loss=0.1439, simple_loss=0.2306, pruned_loss=0.02853, over 840174.03 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.215, pruned_loss=0.03, over 863442.82 frames.], batch size: 27, lr: 3.17e-04
+2022-06-19 04:52:30,654 INFO [train.py:874] (0/4) Epoch 26, batch 850, aishell_loss[loss=0.1586, simple_loss=0.2404, pruned_loss=0.0384, over 4940.00 frames.], tot_loss[loss=0.141, simple_loss=0.2234, pruned_loss=0.02935, over 971346.61 frames.], batch size: 32, aishell_tot_loss[loss=0.1442, simple_loss=0.2313, pruned_loss=0.02858, over 860717.53 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2148, pruned_loss=0.02999, over 875197.37 frames.], batch size: 32, lr: 3.17e-04
+2022-06-19 04:52:59,208 INFO [train.py:874] (0/4) Epoch 26, batch 900, aishell_loss[loss=0.1642, simple_loss=0.2489, pruned_loss=0.03971, over 4887.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2237, pruned_loss=0.0295, over 974413.85 frames.], batch size: 42, aishell_tot_loss[loss=0.1442, simple_loss=0.2313, pruned_loss=0.02854, over 871808.92 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2157, pruned_loss=0.03017, over 891234.67 frames.], batch size: 42, lr: 3.17e-04
+2022-06-19 04:53:26,722 INFO [train.py:874] (0/4) Epoch 26, batch 950, aishell_loss[loss=0.1357, simple_loss=0.2279, pruned_loss=0.02179, over 4961.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2236, pruned_loss=0.02948, over 977100.15 frames.], batch size: 31, aishell_tot_loss[loss=0.1443, simple_loss=0.2312, pruned_loss=0.02865, over 884367.25 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2159, pruned_loss=0.03005, over 903236.43 frames.], batch size: 31, lr: 3.17e-04
+2022-06-19 04:53:57,635 INFO [train.py:874] (0/4) Epoch 26, batch 1000, datatang_loss[loss=0.1504, simple_loss=0.238, pruned_loss=0.0314, over 4920.00 frames.], tot_loss[loss=0.1419, simple_loss=0.224, pruned_loss=0.02993, over 979079.63 frames.], batch size: 98, aishell_tot_loss[loss=0.1447, simple_loss=0.2316, pruned_loss=0.02891, over 894384.24 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2163, pruned_loss=0.03033, over 914564.85 frames.], batch size: 98, lr: 3.17e-04
+2022-06-19 04:53:57,637 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 04:54:14,575 INFO [train.py:914] (0/4) Epoch 26, validation: loss=0.165, simple_loss=0.2487, pruned_loss=0.04059, over 1622729.00 frames.
+2022-06-19 04:54:43,207 INFO [train.py:874] (0/4) Epoch 26, batch 1050, aishell_loss[loss=0.1166, simple_loss=0.195, pruned_loss=0.01905, over 4801.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2242, pruned_loss=0.0298, over 980574.58 frames.], batch size: 24, aishell_tot_loss[loss=0.1448, simple_loss=0.2316, pruned_loss=0.02902, over 904741.60 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2166, pruned_loss=0.03016, over 923301.65 frames.], batch size: 24, lr: 3.17e-04
+2022-06-19 04:55:11,949 INFO [train.py:874] (0/4) Epoch 26, batch 1100, datatang_loss[loss=0.1297, simple_loss=0.2053, pruned_loss=0.02708, over 4960.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2236, pruned_loss=0.02973, over 981874.11 frames.], batch size: 45, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02902, over 911903.02 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2164, pruned_loss=0.03009, over 932548.58 frames.], batch size: 45, lr: 3.17e-04
+2022-06-19 04:55:39,894 INFO [train.py:874] (0/4) Epoch 26, batch 1150, aishell_loss[loss=0.1468, simple_loss=0.2314, pruned_loss=0.03108, over 4864.00 frames.], tot_loss[loss=0.1416, simple_loss=0.224, pruned_loss=0.02963, over 982443.06 frames.], batch size: 35, aishell_tot_loss[loss=0.1449, simple_loss=0.2318, pruned_loss=0.02896, over 919919.81 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2168, pruned_loss=0.03008, over 939094.25 frames.], batch size: 35, lr: 3.17e-04
+2022-06-19 04:56:09,242 INFO [train.py:874] (0/4) Epoch 26, batch 1200, datatang_loss[loss=0.1449, simple_loss=0.2167, pruned_loss=0.03653, over 4899.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2239, pruned_loss=0.02948, over 982819.16 frames.], batch size: 42, aishell_tot_loss[loss=0.1448, simple_loss=0.2316, pruned_loss=0.029, over 928372.99 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2166, pruned_loss=0.02994, over 943827.39 frames.], batch size: 42, lr: 3.16e-04
+2022-06-19 04:56:37,387 INFO [train.py:874] (0/4) Epoch 26, batch 1250, datatang_loss[loss=0.1508, simple_loss=0.2274, pruned_loss=0.03714, over 4956.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2238, pruned_loss=0.02929, over 982925.56 frames.], batch size: 99, aishell_tot_loss[loss=0.1442, simple_loss=0.2308, pruned_loss=0.02878, over 936590.32 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2166, pruned_loss=0.02998, over 947190.53 frames.], batch size: 99, lr: 3.16e-04
+2022-06-19 04:57:05,521 INFO [train.py:874] (0/4) Epoch 26, batch 1300, aishell_loss[loss=0.1366, simple_loss=0.2289, pruned_loss=0.02213, over 4949.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2236, pruned_loss=0.02947, over 983217.81 frames.], batch size: 54, aishell_tot_loss[loss=0.1446, simple_loss=0.2312, pruned_loss=0.02902, over 941304.62 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2163, pruned_loss=0.0299, over 952235.36 frames.], batch size: 54, lr: 3.16e-04
+2022-06-19 04:57:34,760 INFO [train.py:874] (0/4) Epoch 26, batch 1350, datatang_loss[loss=0.1355, simple_loss=0.213, pruned_loss=0.02902, over 4942.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2238, pruned_loss=0.02943, over 983447.88 frames.], batch size: 62, aishell_tot_loss[loss=0.1449, simple_loss=0.2316, pruned_loss=0.02908, over 946318.93 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2161, pruned_loss=0.02981, over 956061.36 frames.], batch size: 62, lr: 3.16e-04
+2022-06-19 04:58:03,278 INFO [train.py:874] (0/4) Epoch 26, batch 1400, datatang_loss[loss=0.1454, simple_loss=0.2293, pruned_loss=0.03078, over 4918.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2239, pruned_loss=0.02955, over 983859.56 frames.], batch size: 94, aishell_tot_loss[loss=0.1448, simple_loss=0.2315, pruned_loss=0.02911, over 950455.55 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2165, pruned_loss=0.02989, over 959869.75 frames.], batch size: 94, lr: 3.16e-04
+2022-06-19 04:58:31,203 INFO [train.py:874] (0/4) Epoch 26, batch 1450, aishell_loss[loss=0.1306, simple_loss=0.2164, pruned_loss=0.02245, over 4876.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2244, pruned_loss=0.02999, over 984144.70 frames.], batch size: 47, aishell_tot_loss[loss=0.1453, simple_loss=0.2319, pruned_loss=0.02934, over 954153.74 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2168, pruned_loss=0.03014, over 963141.22 frames.], batch size: 47, lr: 3.16e-04
+2022-06-19 04:59:01,372 INFO [train.py:874] (0/4) Epoch 26, batch 1500, aishell_loss[loss=0.151, simple_loss=0.2502, pruned_loss=0.02588, over 4880.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2254, pruned_loss=0.0305, over 984700.43 frames.], batch size: 42, aishell_tot_loss[loss=0.1453, simple_loss=0.2321, pruned_loss=0.02931, over 958513.89 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2174, pruned_loss=0.03081, over 965496.22 frames.], batch size: 42, lr: 3.16e-04
+2022-06-19 04:59:30,328 INFO [train.py:874] (0/4) Epoch 26, batch 1550, aishell_loss[loss=0.1239, simple_loss=0.1995, pruned_loss=0.02413, over 4815.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2255, pruned_loss=0.03039, over 984514.63 frames.], batch size: 26, aishell_tot_loss[loss=0.1454, simple_loss=0.2321, pruned_loss=0.02932, over 961594.21 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2174, pruned_loss=0.03081, over 967654.99 frames.], batch size: 26, lr: 3.16e-04
+2022-06-19 04:59:58,545 INFO [train.py:874] (0/4) Epoch 26, batch 1600, datatang_loss[loss=0.1365, simple_loss=0.2131, pruned_loss=0.0299, over 4960.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2255, pruned_loss=0.03029, over 984470.37 frames.], batch size: 86, aishell_tot_loss[loss=0.1452, simple_loss=0.2319, pruned_loss=0.02919, over 963968.20 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2178, pruned_loss=0.03092, over 969895.16 frames.], batch size: 86, lr: 3.16e-04
+2022-06-19 05:00:28,446 INFO [train.py:874] (0/4) Epoch 26, batch 1650, aishell_loss[loss=0.1359, simple_loss=0.2191, pruned_loss=0.02636, over 4921.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2241, pruned_loss=0.03002, over 984613.10 frames.], batch size: 33, aishell_tot_loss[loss=0.145, simple_loss=0.2316, pruned_loss=0.02922, over 965744.41 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2172, pruned_loss=0.03061, over 972254.85 frames.], batch size: 33, lr: 3.16e-04
+2022-06-19 05:00:57,261 INFO [train.py:874] (0/4) Epoch 26, batch 1700, datatang_loss[loss=0.1279, simple_loss=0.1965, pruned_loss=0.02969, over 4848.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2236, pruned_loss=0.02996, over 984623.68 frames.], batch size: 30, aishell_tot_loss[loss=0.1446, simple_loss=0.2311, pruned_loss=0.02912, over 967802.90 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.217, pruned_loss=0.03068, over 973891.03 frames.], batch size: 30, lr: 3.16e-04
+2022-06-19 05:01:26,594 INFO [train.py:874] (0/4) Epoch 26, batch 1750, datatang_loss[loss=0.1403, simple_loss=0.2267, pruned_loss=0.02692, over 4887.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2234, pruned_loss=0.02985, over 984909.89 frames.], batch size: 30, aishell_tot_loss[loss=0.1444, simple_loss=0.231, pruned_loss=0.02892, over 969743.72 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2169, pruned_loss=0.03076, over 975449.02 frames.], batch size: 30, lr: 3.16e-04
+2022-06-19 05:01:57,180 INFO [train.py:874] (0/4) Epoch 26, batch 1800, aishell_loss[loss=0.1215, simple_loss=0.1896, pruned_loss=0.02671, over 4790.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2229, pruned_loss=0.0297, over 984813.48 frames.], batch size: 20, aishell_tot_loss[loss=0.1444, simple_loss=0.2309, pruned_loss=0.02896, over 970994.07 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2167, pruned_loss=0.03054, over 976892.74 frames.], batch size: 20, lr: 3.16e-04
+2022-06-19 05:02:25,555 INFO [train.py:874] (0/4) Epoch 26, batch 1850, datatang_loss[loss=0.1358, simple_loss=0.2157, pruned_loss=0.02792, over 4938.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2222, pruned_loss=0.02975, over 985243.59 frames.], batch size: 69, aishell_tot_loss[loss=0.1441, simple_loss=0.2304, pruned_loss=0.0289, over 972174.85 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2166, pruned_loss=0.03058, over 978525.56 frames.], batch size: 69, lr: 3.16e-04
+2022-06-19 05:02:55,417 INFO [train.py:874] (0/4) Epoch 26, batch 1900, datatang_loss[loss=0.1664, simple_loss=0.2419, pruned_loss=0.04542, over 4951.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2232, pruned_loss=0.03009, over 985409.46 frames.], batch size: 60, aishell_tot_loss[loss=0.1444, simple_loss=0.2307, pruned_loss=0.02908, over 973921.83 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2169, pruned_loss=0.03077, over 979336.90 frames.], batch size: 60, lr: 3.15e-04
+2022-06-19 05:03:25,568 INFO [train.py:874] (0/4) Epoch 26, batch 1950, datatang_loss[loss=0.1172, simple_loss=0.1986, pruned_loss=0.01788, over 4914.00 frames.], tot_loss[loss=0.1418, simple_loss=0.224, pruned_loss=0.02985, over 985450.92 frames.], batch size: 64, aishell_tot_loss[loss=0.1451, simple_loss=0.2317, pruned_loss=0.02922, over 975564.01 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2165, pruned_loss=0.03044, over 979844.86 frames.], batch size: 64, lr: 3.15e-04
+2022-06-19 05:03:53,621 INFO [train.py:874] (0/4) Epoch 26, batch 2000, datatang_loss[loss=0.1182, simple_loss=0.2071, pruned_loss=0.01466, over 4929.00 frames.], tot_loss[loss=0.142, simple_loss=0.2246, pruned_loss=0.02971, over 984949.24 frames.], batch size: 73, aishell_tot_loss[loss=0.1452, simple_loss=0.2319, pruned_loss=0.02926, over 976537.90 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2168, pruned_loss=0.03026, over 980194.13 frames.], batch size: 73, lr: 3.15e-04
+2022-06-19 05:03:53,625 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 05:04:09,288 INFO [train.py:914] (0/4) Epoch 26, validation: loss=0.1659, simple_loss=0.2484, pruned_loss=0.04175, over 1622729.00 frames.
+2022-06-19 05:04:38,949 INFO [train.py:874] (0/4) Epoch 26, batch 2050, aishell_loss[loss=0.1452, simple_loss=0.2304, pruned_loss=0.03002, over 4934.00 frames.], tot_loss[loss=0.141, simple_loss=0.2235, pruned_loss=0.02924, over 984993.15 frames.], batch size: 33, aishell_tot_loss[loss=0.1447, simple_loss=0.2314, pruned_loss=0.02896, over 977546.90 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2161, pruned_loss=0.03005, over 980763.80 frames.], batch size: 33, lr: 3.15e-04
+2022-06-19 05:05:07,639 INFO [train.py:874] (0/4) Epoch 26, batch 2100, datatang_loss[loss=0.1901, simple_loss=0.2655, pruned_loss=0.05734, over 4921.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2243, pruned_loss=0.02969, over 985514.06 frames.], batch size: 98, aishell_tot_loss[loss=0.1449, simple_loss=0.2316, pruned_loss=0.02914, over 978617.74 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2168, pruned_loss=0.03025, over 981604.41 frames.], batch size: 98, lr: 3.15e-04
+2022-06-19 05:05:37,216 INFO [train.py:874] (0/4) Epoch 26, batch 2150, aishell_loss[loss=0.1617, simple_loss=0.2541, pruned_loss=0.03465, over 4929.00 frames.], tot_loss[loss=0.142, simple_loss=0.2248, pruned_loss=0.02964, over 985463.73 frames.], batch size: 79, aishell_tot_loss[loss=0.1447, simple_loss=0.2316, pruned_loss=0.02886, over 979450.08 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.217, pruned_loss=0.03052, over 982047.98 frames.], batch size: 79, lr: 3.15e-04
+2022-06-19 05:06:06,719 INFO [train.py:874] (0/4) Epoch 26, batch 2200, datatang_loss[loss=0.1669, simple_loss=0.2482, pruned_loss=0.04287, over 4929.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2247, pruned_loss=0.03015, over 985515.66 frames.], batch size: 94, aishell_tot_loss[loss=0.1444, simple_loss=0.2313, pruned_loss=0.02875, over 979951.89 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2176, pruned_loss=0.03111, over 982671.22 frames.], batch size: 94, lr: 3.15e-04
+2022-06-19 05:06:34,930 INFO [train.py:874] (0/4) Epoch 26, batch 2250, datatang_loss[loss=0.1398, simple_loss=0.2207, pruned_loss=0.02942, over 4952.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2248, pruned_loss=0.03007, over 984925.39 frames.], batch size: 67, aishell_tot_loss[loss=0.1443, simple_loss=0.2309, pruned_loss=0.02882, over 980168.84 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2178, pruned_loss=0.03105, over 982898.31 frames.], batch size: 67, lr: 3.15e-04
+2022-06-19 05:07:05,042 INFO [train.py:874] (0/4) Epoch 26, batch 2300, datatang_loss[loss=0.1202, simple_loss=0.1951, pruned_loss=0.02271, over 4898.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2247, pruned_loss=0.0304, over 984922.48 frames.], batch size: 30, aishell_tot_loss[loss=0.1443, simple_loss=0.2309, pruned_loss=0.02887, over 980761.08 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.218, pruned_loss=0.03136, over 983090.12 frames.], batch size: 30, lr: 3.15e-04
+2022-06-19 05:07:33,214 INFO [train.py:874] (0/4) Epoch 26, batch 2350, aishell_loss[loss=0.114, simple_loss=0.1786, pruned_loss=0.02468, over 4980.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2245, pruned_loss=0.03036, over 985118.48 frames.], batch size: 21, aishell_tot_loss[loss=0.1445, simple_loss=0.2312, pruned_loss=0.02887, over 981349.84 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2176, pruned_loss=0.03138, over 983414.02 frames.], batch size: 21, lr: 3.15e-04
+2022-06-19 05:08:00,962 INFO [train.py:874] (0/4) Epoch 26, batch 2400, datatang_loss[loss=0.1381, simple_loss=0.2209, pruned_loss=0.0277, over 4861.00 frames.], tot_loss[loss=0.1428, simple_loss=0.225, pruned_loss=0.03032, over 985391.58 frames.], batch size: 30, aishell_tot_loss[loss=0.1452, simple_loss=0.2321, pruned_loss=0.02913, over 981891.03 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2172, pruned_loss=0.03113, over 983814.53 frames.], batch size: 30, lr: 3.15e-04
+2022-06-19 05:08:30,374 INFO [train.py:874] (0/4) Epoch 26, batch 2450, aishell_loss[loss=0.1428, simple_loss=0.2253, pruned_loss=0.03021, over 4981.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2241, pruned_loss=0.02999, over 985710.58 frames.], batch size: 30, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02907, over 982357.21 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.217, pruned_loss=0.03086, over 984246.19 frames.], batch size: 30, lr: 3.15e-04
+2022-06-19 05:08:59,236 INFO [train.py:874] (0/4) Epoch 26, batch 2500, aishell_loss[loss=0.1439, simple_loss=0.2345, pruned_loss=0.02669, over 4918.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2237, pruned_loss=0.02999, over 985923.04 frames.], batch size: 68, aishell_tot_loss[loss=0.1447, simple_loss=0.2313, pruned_loss=0.02909, over 982868.85 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2169, pruned_loss=0.03084, over 984543.26 frames.], batch size: 68, lr: 3.15e-04
+2022-06-19 05:09:26,861 INFO [train.py:874] (0/4) Epoch 26, batch 2550, aishell_loss[loss=0.1504, simple_loss=0.2399, pruned_loss=0.03047, over 4906.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2239, pruned_loss=0.0295, over 985994.05 frames.], batch size: 41, aishell_tot_loss[loss=0.1444, simple_loss=0.2312, pruned_loss=0.02885, over 983437.03 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2168, pruned_loss=0.03064, over 984612.48 frames.], batch size: 41, lr: 3.14e-04
+2022-06-19 05:09:56,386 INFO [train.py:874] (0/4) Epoch 26, batch 2600, datatang_loss[loss=0.1389, simple_loss=0.2178, pruned_loss=0.03003, over 4924.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2245, pruned_loss=0.02981, over 985732.33 frames.], batch size: 50, aishell_tot_loss[loss=0.145, simple_loss=0.2317, pruned_loss=0.02909, over 983481.10 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.03069, over 984770.60 frames.], batch size: 50, lr: 3.14e-04
+2022-06-19 05:10:26,646 INFO [train.py:874] (0/4) Epoch 26, batch 2650, datatang_loss[loss=0.1904, simple_loss=0.2581, pruned_loss=0.06139, over 4927.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2239, pruned_loss=0.02969, over 985575.87 frames.], batch size: 108, aishell_tot_loss[loss=0.1446, simple_loss=0.2314, pruned_loss=0.02893, over 983433.22 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2166, pruned_loss=0.03064, over 985016.71 frames.], batch size: 108, lr: 3.14e-04
+2022-06-19 05:10:56,028 INFO [train.py:874] (0/4) Epoch 26, batch 2700, datatang_loss[loss=0.143, simple_loss=0.2147, pruned_loss=0.03562, over 4977.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2239, pruned_loss=0.02975, over 985844.46 frames.], batch size: 25, aishell_tot_loss[loss=0.1448, simple_loss=0.2315, pruned_loss=0.02904, over 983803.50 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2166, pruned_loss=0.03058, over 985244.60 frames.], batch size: 25, lr: 3.14e-04
+2022-06-19 05:11:24,556 INFO [train.py:874] (0/4) Epoch 26, batch 2750, aishell_loss[loss=0.1414, simple_loss=0.2313, pruned_loss=0.02571, over 4971.00 frames.], tot_loss[loss=0.1415, simple_loss=0.224, pruned_loss=0.02951, over 986050.55 frames.], batch size: 39, aishell_tot_loss[loss=0.1444, simple_loss=0.2312, pruned_loss=0.02875, over 984352.70 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2165, pruned_loss=0.03062, over 985246.98 frames.], batch size: 39, lr: 3.14e-04
+2022-06-19 05:11:53,309 INFO [train.py:874] (0/4) Epoch 26, batch 2800, aishell_loss[loss=0.1462, simple_loss=0.2398, pruned_loss=0.02632, over 4927.00 frames.], tot_loss[loss=0.1421, simple_loss=0.225, pruned_loss=0.02956, over 985651.09 frames.], batch size: 49, aishell_tot_loss[loss=0.1441, simple_loss=0.2312, pruned_loss=0.02853, over 984229.70 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.217, pruned_loss=0.03096, over 985293.21 frames.], batch size: 49, lr: 3.14e-04
+2022-06-19 05:12:22,079 INFO [train.py:874] (0/4) Epoch 26, batch 2850, aishell_loss[loss=0.1258, simple_loss=0.2219, pruned_loss=0.01486, over 4855.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2254, pruned_loss=0.02968, over 985349.16 frames.], batch size: 36, aishell_tot_loss[loss=0.1447, simple_loss=0.2318, pruned_loss=0.02884, over 983926.00 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2166, pruned_loss=0.03078, over 985526.17 frames.], batch size: 36, lr: 3.14e-04
+2022-06-19 05:12:50,264 INFO [train.py:874] (0/4) Epoch 26, batch 2900, datatang_loss[loss=0.1388, simple_loss=0.2208, pruned_loss=0.02841, over 4950.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2253, pruned_loss=0.02973, over 985589.94 frames.], batch size: 86, aishell_tot_loss[loss=0.1446, simple_loss=0.2317, pruned_loss=0.02876, over 984019.66 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2167, pruned_loss=0.03092, over 985866.64 frames.], batch size: 86, lr: 3.14e-04
+2022-06-19 05:13:18,642 INFO [train.py:874] (0/4) Epoch 26, batch 2950, aishell_loss[loss=0.1513, simple_loss=0.2384, pruned_loss=0.0321, over 4880.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2248, pruned_loss=0.02942, over 985529.64 frames.], batch size: 42, aishell_tot_loss[loss=0.1444, simple_loss=0.2314, pruned_loss=0.02869, over 984136.81 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2165, pruned_loss=0.03067, over 985866.84 frames.], batch size: 42, lr: 3.14e-04
+2022-06-19 05:13:48,852 INFO [train.py:874] (0/4) Epoch 26, batch 3000, aishell_loss[loss=0.1279, simple_loss=0.2145, pruned_loss=0.02059, over 4950.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2259, pruned_loss=0.0293, over 986140.42 frames.], batch size: 27, aishell_tot_loss[loss=0.1448, simple_loss=0.2323, pruned_loss=0.02864, over 984805.94 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2164, pruned_loss=0.03058, over 986018.44 frames.], batch size: 27, lr: 3.14e-04
+2022-06-19 05:13:48,859 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 05:14:05,914 INFO [train.py:914] (0/4) Epoch 26, validation: loss=0.1644, simple_loss=0.2479, pruned_loss=0.04043, over 1622729.00 frames.
+2022-06-19 05:14:34,724 INFO [train.py:874] (0/4) Epoch 26, batch 3050, aishell_loss[loss=0.1472, simple_loss=0.2398, pruned_loss=0.02727, over 4879.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2245, pruned_loss=0.02897, over 985895.96 frames.], batch size: 47, aishell_tot_loss[loss=0.1452, simple_loss=0.2327, pruned_loss=0.02883, over 985037.04 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2157, pruned_loss=0.02991, over 985676.77 frames.], batch size: 47, lr: 3.14e-04
+2022-06-19 05:15:02,362 INFO [train.py:874] (0/4) Epoch 26, batch 3100, datatang_loss[loss=0.1665, simple_loss=0.2393, pruned_loss=0.04681, over 4922.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2241, pruned_loss=0.02928, over 985777.06 frames.], batch size: 94, aishell_tot_loss[loss=0.1451, simple_loss=0.2328, pruned_loss=0.02871, over 985292.86 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2156, pruned_loss=0.03023, over 985404.04 frames.], batch size: 94, lr: 3.14e-04
+2022-06-19 05:15:30,147 INFO [train.py:874] (0/4) Epoch 26, batch 3150, datatang_loss[loss=0.1342, simple_loss=0.2036, pruned_loss=0.03237, over 4930.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2233, pruned_loss=0.02921, over 985594.92 frames.], batch size: 81, aishell_tot_loss[loss=0.1443, simple_loss=0.2317, pruned_loss=0.02845, over 985113.26 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2157, pruned_loss=0.03039, over 985488.48 frames.], batch size: 81, lr: 3.14e-04
+2022-06-19 05:16:00,419 INFO [train.py:874] (0/4) Epoch 26, batch 3200, datatang_loss[loss=0.1348, simple_loss=0.2057, pruned_loss=0.03191, over 4892.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2244, pruned_loss=0.02959, over 985434.44 frames.], batch size: 59, aishell_tot_loss[loss=0.1449, simple_loss=0.2324, pruned_loss=0.02874, over 984909.97 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2161, pruned_loss=0.03044, over 985578.26 frames.], batch size: 59, lr: 3.14e-04
+2022-06-19 05:16:28,118 INFO [train.py:874] (0/4) Epoch 26, batch 3250, aishell_loss[loss=0.1307, simple_loss=0.2099, pruned_loss=0.02578, over 4954.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2253, pruned_loss=0.02946, over 985670.61 frames.], batch size: 27, aishell_tot_loss[loss=0.1446, simple_loss=0.2324, pruned_loss=0.02844, over 985029.96 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2166, pruned_loss=0.03063, over 985777.95 frames.], batch size: 27, lr: 3.13e-04
+2022-06-19 05:16:56,614 INFO [train.py:874] (0/4) Epoch 26, batch 3300, datatang_loss[loss=0.1094, simple_loss=0.1879, pruned_loss=0.01545, over 4898.00 frames.], tot_loss[loss=0.142, simple_loss=0.2246, pruned_loss=0.02967, over 985352.39 frames.], batch size: 52, aishell_tot_loss[loss=0.1448, simple_loss=0.2324, pruned_loss=0.02855, over 984732.53 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2166, pruned_loss=0.03067, over 985780.54 frames.], batch size: 52, lr: 3.13e-04
+2022-06-19 05:17:26,233 INFO [train.py:874] (0/4) Epoch 26, batch 3350, datatang_loss[loss=0.155, simple_loss=0.219, pruned_loss=0.04544, over 4884.00 frames.], tot_loss[loss=0.142, simple_loss=0.2245, pruned_loss=0.0298, over 985337.64 frames.], batch size: 39, aishell_tot_loss[loss=0.1448, simple_loss=0.2323, pruned_loss=0.02864, over 984760.69 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2165, pruned_loss=0.03073, over 985763.22 frames.], batch size: 39, lr: 3.13e-04
+2022-06-19 05:17:54,020 INFO [train.py:874] (0/4) Epoch 26, batch 3400, datatang_loss[loss=0.1351, simple_loss=0.2199, pruned_loss=0.02516, over 4920.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2239, pruned_loss=0.0292, over 985251.99 frames.], batch size: 47, aishell_tot_loss[loss=0.1443, simple_loss=0.2318, pruned_loss=0.02843, over 984815.65 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2161, pruned_loss=0.03035, over 985635.46 frames.], batch size: 47, lr: 3.13e-04
+2022-06-19 05:18:21,624 INFO [train.py:874] (0/4) Epoch 26, batch 3450, aishell_loss[loss=0.1657, simple_loss=0.2568, pruned_loss=0.03728, over 4861.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2245, pruned_loss=0.0294, over 985139.20 frames.], batch size: 36, aishell_tot_loss[loss=0.1447, simple_loss=0.2323, pruned_loss=0.02856, over 984847.65 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2164, pruned_loss=0.03036, over 985494.06 frames.], batch size: 36, lr: 3.13e-04
+2022-06-19 05:18:52,412 INFO [train.py:874] (0/4) Epoch 26, batch 3500, datatang_loss[loss=0.1319, simple_loss=0.2076, pruned_loss=0.02808, over 4924.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2233, pruned_loss=0.02957, over 985260.76 frames.], batch size: 42, aishell_tot_loss[loss=0.1446, simple_loss=0.2319, pruned_loss=0.02866, over 984754.78 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2159, pruned_loss=0.03038, over 985686.47 frames.], batch size: 42, lr: 3.13e-04
+2022-06-19 05:19:22,038 INFO [train.py:874] (0/4) Epoch 26, batch 3550, datatang_loss[loss=0.1515, simple_loss=0.2294, pruned_loss=0.0368, over 4899.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2238, pruned_loss=0.02994, over 985051.69 frames.], batch size: 25, aishell_tot_loss[loss=0.1447, simple_loss=0.2319, pruned_loss=0.02874, over 984529.66 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2163, pruned_loss=0.0307, over 985701.70 frames.], batch size: 25, lr: 3.13e-04
+2022-06-19 05:19:50,838 INFO [train.py:874] (0/4) Epoch 26, batch 3600, aishell_loss[loss=0.135, simple_loss=0.2268, pruned_loss=0.02159, over 4958.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2241, pruned_loss=0.02988, over 985210.84 frames.], batch size: 40, aishell_tot_loss[loss=0.1442, simple_loss=0.2314, pruned_loss=0.02847, over 984552.45 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2169, pruned_loss=0.03099, over 985847.74 frames.], batch size: 40, lr: 3.13e-04
+2022-06-19 05:20:19,579 INFO [train.py:874] (0/4) Epoch 26, batch 3650, aishell_loss[loss=0.1237, simple_loss=0.2116, pruned_loss=0.01796, over 4978.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2248, pruned_loss=0.02999, over 985021.10 frames.], batch size: 30, aishell_tot_loss[loss=0.1446, simple_loss=0.2317, pruned_loss=0.02879, over 984370.67 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2171, pruned_loss=0.03087, over 985863.43 frames.], batch size: 30, lr: 3.13e-04
+2022-06-19 05:20:49,058 INFO [train.py:874] (0/4) Epoch 26, batch 3700, datatang_loss[loss=0.1243, simple_loss=0.2058, pruned_loss=0.02139, over 4927.00 frames.], tot_loss[loss=0.1419, simple_loss=0.224, pruned_loss=0.02993, over 985191.90 frames.], batch size: 57, aishell_tot_loss[loss=0.1448, simple_loss=0.2318, pruned_loss=0.02888, over 984580.41 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2165, pruned_loss=0.0307, over 985780.82 frames.], batch size: 57, lr: 3.13e-04
+2022-06-19 05:21:16,720 INFO [train.py:874] (0/4) Epoch 26, batch 3750, aishell_loss[loss=0.1409, simple_loss=0.2233, pruned_loss=0.02924, over 4866.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2242, pruned_loss=0.02959, over 985086.89 frames.], batch size: 35, aishell_tot_loss[loss=0.1445, simple_loss=0.2317, pruned_loss=0.0287, over 984545.23 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2167, pruned_loss=0.03054, over 985731.25 frames.], batch size: 35, lr: 3.13e-04
+2022-06-19 05:21:44,666 INFO [train.py:874] (0/4) Epoch 26, batch 3800, aishell_loss[loss=0.1126, simple_loss=0.2, pruned_loss=0.01261, over 4872.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2242, pruned_loss=0.02955, over 984933.51 frames.], batch size: 28, aishell_tot_loss[loss=0.144, simple_loss=0.231, pruned_loss=0.02851, over 984383.17 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2173, pruned_loss=0.03069, over 985717.91 frames.], batch size: 28, lr: 3.13e-04
+2022-06-19 05:22:11,979 INFO [train.py:874] (0/4) Epoch 26, batch 3850, aishell_loss[loss=0.1608, simple_loss=0.2527, pruned_loss=0.03448, over 4940.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2247, pruned_loss=0.02949, over 985048.07 frames.], batch size: 68, aishell_tot_loss[loss=0.144, simple_loss=0.2311, pruned_loss=0.0285, over 984623.03 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2175, pruned_loss=0.03066, over 985593.12 frames.], batch size: 68, lr: 3.13e-04
+2022-06-19 05:22:40,498 INFO [train.py:874] (0/4) Epoch 26, batch 3900, datatang_loss[loss=0.1165, simple_loss=0.1933, pruned_loss=0.01987, over 4920.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2239, pruned_loss=0.02941, over 985250.14 frames.], batch size: 73, aishell_tot_loss[loss=0.1442, simple_loss=0.2311, pruned_loss=0.02864, over 984835.08 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2171, pruned_loss=0.03036, over 985553.43 frames.], batch size: 73, lr: 3.12e-04
+2022-06-19 05:23:06,639 INFO [train.py:874] (0/4) Epoch 26, batch 3950, aishell_loss[loss=0.1461, simple_loss=0.2256, pruned_loss=0.03329, over 4941.00 frames.], tot_loss[loss=0.1414, simple_loss=0.224, pruned_loss=0.02936, over 985350.20 frames.], batch size: 54, aishell_tot_loss[loss=0.1438, simple_loss=0.2308, pruned_loss=0.02842, over 984775.09 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2173, pruned_loss=0.03052, over 985743.80 frames.], batch size: 54, lr: 3.12e-04
+2022-06-19 05:23:34,140 INFO [train.py:874] (0/4) Epoch 26, batch 4000, aishell_loss[loss=0.1533, simple_loss=0.237, pruned_loss=0.03478, over 4979.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2238, pruned_loss=0.02948, over 985408.19 frames.], batch size: 37, aishell_tot_loss[loss=0.1441, simple_loss=0.2309, pruned_loss=0.02865, over 985020.17 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2171, pruned_loss=0.03037, over 985579.06 frames.], batch size: 37, lr: 3.12e-04
+2022-06-19 05:23:34,143 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 05:23:50,809 INFO [train.py:914] (0/4) Epoch 26, validation: loss=0.1644, simple_loss=0.2484, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-19 05:24:19,239 INFO [train.py:874] (0/4) Epoch 26, batch 4050, aishell_loss[loss=0.1543, simple_loss=0.2442, pruned_loss=0.03217, over 4930.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2246, pruned_loss=0.02963, over 985165.49 frames.], batch size: 58, aishell_tot_loss[loss=0.1448, simple_loss=0.2315, pruned_loss=0.02902, over 984592.47 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2172, pruned_loss=0.03017, over 985779.95 frames.], batch size: 58, lr: 3.12e-04
+2022-06-19 05:24:46,158 INFO [train.py:874] (0/4) Epoch 26, batch 4100, datatang_loss[loss=0.1312, simple_loss=0.2068, pruned_loss=0.02777, over 4915.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2241, pruned_loss=0.02952, over 985184.76 frames.], batch size: 81, aishell_tot_loss[loss=0.1447, simple_loss=0.2315, pruned_loss=0.02892, over 984708.51 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2166, pruned_loss=0.03016, over 985693.90 frames.], batch size: 81, lr: 3.12e-04
+2022-06-19 05:25:11,543 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-26.pt
+2022-06-19 05:26:04,675 INFO [train.py:874] (0/4) Epoch 27, batch 50, datatang_loss[loss=0.127, simple_loss=0.2092, pruned_loss=0.02246, over 4844.00 frames.], tot_loss[loss=0.142, simple_loss=0.2249, pruned_loss=0.0295, over 218468.59 frames.], batch size: 30, aishell_tot_loss[loss=0.1467, simple_loss=0.2334, pruned_loss=0.03007, over 111724.53 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.217, pruned_loss=0.02907, over 120400.25 frames.], batch size: 30, lr: 3.06e-04
+2022-06-19 05:26:34,279 INFO [train.py:874] (0/4) Epoch 27, batch 100, datatang_loss[loss=0.1273, simple_loss=0.2034, pruned_loss=0.02555, over 4890.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2219, pruned_loss=0.02884, over 388607.89 frames.], batch size: 59, aishell_tot_loss[loss=0.1455, simple_loss=0.2322, pruned_loss=0.02944, over 206862.06 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2131, pruned_loss=0.02835, over 230018.61 frames.], batch size: 59, lr: 3.06e-04
+2022-06-19 05:27:02,756 INFO [train.py:874] (0/4) Epoch 27, batch 150, aishell_loss[loss=0.1458, simple_loss=0.2387, pruned_loss=0.0265, over 4891.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2238, pruned_loss=0.02805, over 520881.66 frames.], batch size: 47, aishell_tot_loss[loss=0.1455, simple_loss=0.2332, pruned_loss=0.02889, over 335437.78 frames.], datatang_tot_loss[loss=0.1329, simple_loss=0.2114, pruned_loss=0.02723, over 281141.28 frames.], batch size: 47, lr: 3.06e-04
+2022-06-19 05:27:29,179 INFO [train.py:874] (0/4) Epoch 27, batch 200, aishell_loss[loss=0.147, simple_loss=0.2308, pruned_loss=0.03156, over 4946.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2229, pruned_loss=0.02817, over 623355.50 frames.], batch size: 49, aishell_tot_loss[loss=0.1441, simple_loss=0.2316, pruned_loss=0.02833, over 425702.82 frames.], datatang_tot_loss[loss=0.1337, simple_loss=0.2112, pruned_loss=0.02805, over 348284.25 frames.], batch size: 49, lr: 3.06e-04
+2022-06-19 05:27:59,034 INFO [train.py:874] (0/4) Epoch 27, batch 250, datatang_loss[loss=0.1401, simple_loss=0.2214, pruned_loss=0.02935, over 4935.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2224, pruned_loss=0.02852, over 703605.95 frames.], batch size: 88, aishell_tot_loss[loss=0.145, simple_loss=0.232, pruned_loss=0.02901, over 489111.87 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2109, pruned_loss=0.02784, over 426149.91 frames.], batch size: 88, lr: 3.06e-04
+2022-06-19 05:28:28,377 INFO [train.py:874] (0/4) Epoch 27, batch 300, datatang_loss[loss=0.1778, simple_loss=0.2408, pruned_loss=0.0574, over 4980.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2222, pruned_loss=0.02884, over 765920.30 frames.], batch size: 40, aishell_tot_loss[loss=0.145, simple_loss=0.2314, pruned_loss=0.02928, over 549573.40 frames.], datatang_tot_loss[loss=0.1337, simple_loss=0.2113, pruned_loss=0.02804, over 489542.66 frames.], batch size: 40, lr: 3.06e-04
+2022-06-19 05:28:41,605 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-108000.pt
+2022-06-19 05:29:00,124 INFO [train.py:874] (0/4) Epoch 27, batch 350, datatang_loss[loss=0.143, simple_loss=0.2159, pruned_loss=0.03502, over 4939.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2229, pruned_loss=0.02983, over 814610.78 frames.], batch size: 88, aishell_tot_loss[loss=0.1462, simple_loss=0.2326, pruned_loss=0.02986, over 595165.44 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.212, pruned_loss=0.02905, over 554428.45 frames.], batch size: 88, lr: 3.06e-04
+2022-06-19 05:29:29,886 INFO [train.py:874] (0/4) Epoch 27, batch 400, datatang_loss[loss=0.1427, simple_loss=0.2251, pruned_loss=0.03016, over 4956.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2231, pruned_loss=0.03023, over 852708.81 frames.], batch size: 67, aishell_tot_loss[loss=0.146, simple_loss=0.2325, pruned_loss=0.02979, over 635866.46 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.213, pruned_loss=0.02987, over 611152.77 frames.], batch size: 67, lr: 3.06e-04
+2022-06-19 05:29:59,161 INFO [train.py:874] (0/4) Epoch 27, batch 450, datatang_loss[loss=0.1174, simple_loss=0.1823, pruned_loss=0.02626, over 4956.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2226, pruned_loss=0.02979, over 882201.60 frames.], batch size: 45, aishell_tot_loss[loss=0.1463, simple_loss=0.2329, pruned_loss=0.02982, over 664435.53 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2131, pruned_loss=0.02938, over 668272.12 frames.], batch size: 45, lr: 3.06e-04
+2022-06-19 05:30:26,671 INFO [train.py:874] (0/4) Epoch 27, batch 500, datatang_loss[loss=0.1387, simple_loss=0.2164, pruned_loss=0.03048, over 4942.00 frames.], tot_loss[loss=0.1403, simple_loss=0.222, pruned_loss=0.02925, over 905259.00 frames.], batch size: 50, aishell_tot_loss[loss=0.1457, simple_loss=0.2323, pruned_loss=0.02952, over 700985.56 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2129, pruned_loss=0.02901, over 707038.26 frames.], batch size: 50, lr: 3.06e-04
+2022-06-19 05:30:56,945 INFO [train.py:874] (0/4) Epoch 27, batch 550, datatang_loss[loss=0.1188, simple_loss=0.2016, pruned_loss=0.01798, over 4946.00 frames.], tot_loss[loss=0.1395, simple_loss=0.221, pruned_loss=0.02903, over 922799.07 frames.], batch size: 69, aishell_tot_loss[loss=0.1449, simple_loss=0.2314, pruned_loss=0.02922, over 730428.28 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2126, pruned_loss=0.02904, over 743515.29 frames.], batch size: 69, lr: 3.06e-04
+2022-06-19 05:31:26,628 INFO [train.py:874] (0/4) Epoch 27, batch 600, datatang_loss[loss=0.151, simple_loss=0.227, pruned_loss=0.03752, over 4925.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2227, pruned_loss=0.02918, over 936901.81 frames.], batch size: 79, aishell_tot_loss[loss=0.1451, simple_loss=0.2319, pruned_loss=0.02918, over 765106.95 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2136, pruned_loss=0.02924, over 767731.06 frames.], batch size: 79, lr: 3.06e-04
+2022-06-19 05:31:54,493 INFO [train.py:874] (0/4) Epoch 27, batch 650, aishell_loss[loss=0.1444, simple_loss=0.2264, pruned_loss=0.03116, over 4944.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2229, pruned_loss=0.02919, over 947824.80 frames.], batch size: 32, aishell_tot_loss[loss=0.1453, simple_loss=0.2322, pruned_loss=0.02919, over 791241.46 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2136, pruned_loss=0.02924, over 793356.06 frames.], batch size: 32, lr: 3.05e-04
+2022-06-19 05:32:23,240 INFO [train.py:874] (0/4) Epoch 27, batch 700, datatang_loss[loss=0.1557, simple_loss=0.2291, pruned_loss=0.04116, over 4918.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2243, pruned_loss=0.02932, over 956077.05 frames.], batch size: 81, aishell_tot_loss[loss=0.1453, simple_loss=0.2326, pruned_loss=0.02897, over 819770.97 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2143, pruned_loss=0.02963, over 810099.20 frames.], batch size: 81, lr: 3.05e-04
+2022-06-19 05:32:53,867 INFO [train.py:874] (0/4) Epoch 27, batch 750, aishell_loss[loss=0.1598, simple_loss=0.2495, pruned_loss=0.03503, over 4969.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2242, pruned_loss=0.02954, over 962646.33 frames.], batch size: 39, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.02915, over 838635.10 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2148, pruned_loss=0.02974, over 831491.71 frames.], batch size: 39, lr: 3.05e-04
+2022-06-19 05:33:22,658 INFO [train.py:874] (0/4) Epoch 27, batch 800, aishell_loss[loss=0.1299, simple_loss=0.2129, pruned_loss=0.02347, over 4861.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2237, pruned_loss=0.02926, over 967224.02 frames.], batch size: 28, aishell_tot_loss[loss=0.1454, simple_loss=0.2324, pruned_loss=0.0292, over 854770.98 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2143, pruned_loss=0.02936, over 850294.70 frames.], batch size: 28, lr: 3.05e-04
+2022-06-19 05:33:51,889 INFO [train.py:874] (0/4) Epoch 27, batch 850, datatang_loss[loss=0.1362, simple_loss=0.2158, pruned_loss=0.02827, over 4960.00 frames.], tot_loss[loss=0.1411, simple_loss=0.224, pruned_loss=0.02909, over 971572.22 frames.], batch size: 37, aishell_tot_loss[loss=0.1451, simple_loss=0.2324, pruned_loss=0.02896, over 870839.87 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2146, pruned_loss=0.02938, over 865836.41 frames.], batch size: 37, lr: 3.05e-04
+2022-06-19 05:34:20,054 INFO [train.py:874] (0/4) Epoch 27, batch 900, datatang_loss[loss=0.1391, simple_loss=0.209, pruned_loss=0.03457, over 4922.00 frames.], tot_loss[loss=0.141, simple_loss=0.2237, pruned_loss=0.02917, over 974077.65 frames.], batch size: 42, aishell_tot_loss[loss=0.1444, simple_loss=0.2316, pruned_loss=0.02854, over 883101.87 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2152, pruned_loss=0.02985, over 880591.88 frames.], batch size: 42, lr: 3.05e-04
+2022-06-19 05:34:50,707 INFO [train.py:874] (0/4) Epoch 27, batch 950, datatang_loss[loss=0.1552, simple_loss=0.222, pruned_loss=0.04426, over 4878.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2225, pruned_loss=0.02911, over 975850.97 frames.], batch size: 39, aishell_tot_loss[loss=0.1437, simple_loss=0.2306, pruned_loss=0.02837, over 893182.03 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2152, pruned_loss=0.02989, over 894120.36 frames.], batch size: 39, lr: 3.05e-04
+2022-06-19 05:35:20,656 INFO [train.py:874] (0/4) Epoch 27, batch 1000, datatang_loss[loss=0.1385, simple_loss=0.2174, pruned_loss=0.02979, over 4869.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2229, pruned_loss=0.02868, over 977493.79 frames.], batch size: 39, aishell_tot_loss[loss=0.1432, simple_loss=0.2302, pruned_loss=0.02808, over 906834.37 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2153, pruned_loss=0.02974, over 901490.04 frames.], batch size: 39, lr: 3.05e-04
+2022-06-19 05:35:20,659 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 05:35:36,380 INFO [train.py:914] (0/4) Epoch 27, validation: loss=0.1644, simple_loss=0.2483, pruned_loss=0.04028, over 1622729.00 frames.
+2022-06-19 05:36:07,023 INFO [train.py:874] (0/4) Epoch 27, batch 1050, aishell_loss[loss=0.157, simple_loss=0.2446, pruned_loss=0.03472, over 4870.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2236, pruned_loss=0.029, over 979169.12 frames.], batch size: 35, aishell_tot_loss[loss=0.1434, simple_loss=0.2305, pruned_loss=0.02809, over 915837.65 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2158, pruned_loss=0.03002, over 911566.70 frames.], batch size: 35, lr: 3.05e-04
+2022-06-19 05:36:36,805 INFO [train.py:874] (0/4) Epoch 27, batch 1100, aishell_loss[loss=0.1517, simple_loss=0.2382, pruned_loss=0.03258, over 4876.00 frames.], tot_loss[loss=0.1408, simple_loss=0.224, pruned_loss=0.02883, over 980741.48 frames.], batch size: 36, aishell_tot_loss[loss=0.1432, simple_loss=0.2304, pruned_loss=0.02796, over 925303.89 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2164, pruned_loss=0.02998, over 919118.11 frames.], batch size: 36, lr: 3.05e-04
+2022-06-19 05:37:06,344 INFO [train.py:874] (0/4) Epoch 27, batch 1150, datatang_loss[loss=0.128, simple_loss=0.2093, pruned_loss=0.02338, over 4919.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2248, pruned_loss=0.02912, over 981793.11 frames.], batch size: 71, aishell_tot_loss[loss=0.144, simple_loss=0.2312, pruned_loss=0.02844, over 932577.70 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2165, pruned_loss=0.0298, over 926713.40 frames.], batch size: 71, lr: 3.05e-04
+2022-06-19 05:37:36,215 INFO [train.py:874] (0/4) Epoch 27, batch 1200, aishell_loss[loss=0.1557, simple_loss=0.2405, pruned_loss=0.03544, over 4914.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2239, pruned_loss=0.02919, over 982696.21 frames.], batch size: 52, aishell_tot_loss[loss=0.1437, simple_loss=0.2308, pruned_loss=0.02831, over 938517.30 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2163, pruned_loss=0.03, over 934055.10 frames.], batch size: 52, lr: 3.05e-04
+2022-06-19 05:38:04,625 INFO [train.py:874] (0/4) Epoch 27, batch 1250, aishell_loss[loss=0.1452, simple_loss=0.226, pruned_loss=0.03218, over 4975.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2232, pruned_loss=0.0289, over 983422.60 frames.], batch size: 39, aishell_tot_loss[loss=0.1435, simple_loss=0.2306, pruned_loss=0.02822, over 943748.57 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2159, pruned_loss=0.02977, over 940556.95 frames.], batch size: 39, lr: 3.05e-04
+2022-06-19 05:38:35,037 INFO [train.py:874] (0/4) Epoch 27, batch 1300, aishell_loss[loss=0.1362, simple_loss=0.2231, pruned_loss=0.02466, over 4950.00 frames.], tot_loss[loss=0.14, simple_loss=0.2226, pruned_loss=0.02872, over 983696.62 frames.], batch size: 45, aishell_tot_loss[loss=0.143, simple_loss=0.2299, pruned_loss=0.02804, over 948366.98 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2161, pruned_loss=0.02971, over 945966.00 frames.], batch size: 45, lr: 3.05e-04
+2022-06-19 05:39:05,110 INFO [train.py:874] (0/4) Epoch 27, batch 1350, datatang_loss[loss=0.1241, simple_loss=0.2102, pruned_loss=0.01897, over 4949.00 frames.], tot_loss[loss=0.14, simple_loss=0.2225, pruned_loss=0.02876, over 984196.51 frames.], batch size: 69, aishell_tot_loss[loss=0.1429, simple_loss=0.2299, pruned_loss=0.02792, over 951181.72 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2164, pruned_loss=0.02976, over 952312.21 frames.], batch size: 69, lr: 3.04e-04
+2022-06-19 05:39:34,201 INFO [train.py:874] (0/4) Epoch 27, batch 1400, datatang_loss[loss=0.1267, simple_loss=0.201, pruned_loss=0.02621, over 4919.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2228, pruned_loss=0.02869, over 984511.67 frames.], batch size: 57, aishell_tot_loss[loss=0.1431, simple_loss=0.2303, pruned_loss=0.02793, over 955309.17 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2162, pruned_loss=0.02965, over 956157.75 frames.], batch size: 57, lr: 3.04e-04
+2022-06-19 05:40:02,655 INFO [train.py:874] (0/4) Epoch 27, batch 1450, datatang_loss[loss=0.1288, simple_loss=0.21, pruned_loss=0.02375, over 4948.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2231, pruned_loss=0.02905, over 984396.90 frames.], batch size: 67, aishell_tot_loss[loss=0.1431, simple_loss=0.2302, pruned_loss=0.02796, over 958115.77 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2167, pruned_loss=0.02997, over 959992.17 frames.], batch size: 67, lr: 3.04e-04
+2022-06-19 05:40:32,271 INFO [train.py:874] (0/4) Epoch 27, batch 1500, aishell_loss[loss=0.1566, simple_loss=0.2414, pruned_loss=0.0359, over 4875.00 frames.], tot_loss[loss=0.1418, simple_loss=0.224, pruned_loss=0.02976, over 984556.23 frames.], batch size: 42, aishell_tot_loss[loss=0.1435, simple_loss=0.2307, pruned_loss=0.02812, over 961228.89 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2171, pruned_loss=0.03062, over 963010.88 frames.], batch size: 42, lr: 3.04e-04
+2022-06-19 05:41:00,509 INFO [train.py:874] (0/4) Epoch 27, batch 1550, aishell_loss[loss=0.1176, simple_loss=0.1971, pruned_loss=0.01902, over 4980.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2234, pruned_loss=0.02962, over 985073.16 frames.], batch size: 27, aishell_tot_loss[loss=0.1437, simple_loss=0.2307, pruned_loss=0.02831, over 964014.80 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2166, pruned_loss=0.03034, over 966044.78 frames.], batch size: 27, lr: 3.04e-04
+2022-06-19 05:41:29,480 INFO [train.py:874] (0/4) Epoch 27, batch 1600, aishell_loss[loss=0.1574, simple_loss=0.2383, pruned_loss=0.03827, over 4856.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2233, pruned_loss=0.02868, over 984979.24 frames.], batch size: 36, aishell_tot_loss[loss=0.1432, simple_loss=0.2307, pruned_loss=0.02784, over 966932.21 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.216, pruned_loss=0.02991, over 967780.92 frames.], batch size: 36, lr: 3.04e-04
+2022-06-19 05:41:58,667 INFO [train.py:874] (0/4) Epoch 27, batch 1650, aishell_loss[loss=0.163, simple_loss=0.2496, pruned_loss=0.03821, over 4929.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2229, pruned_loss=0.02864, over 985138.66 frames.], batch size: 68, aishell_tot_loss[loss=0.1433, simple_loss=0.2308, pruned_loss=0.02792, over 969024.73 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2155, pruned_loss=0.02972, over 970017.04 frames.], batch size: 68, lr: 3.04e-04
+2022-06-19 05:42:26,891 INFO [train.py:874] (0/4) Epoch 27, batch 1700, aishell_loss[loss=0.148, simple_loss=0.2361, pruned_loss=0.02997, over 4952.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2237, pruned_loss=0.02907, over 984747.64 frames.], batch size: 31, aishell_tot_loss[loss=0.1437, simple_loss=0.2311, pruned_loss=0.02811, over 970553.81 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.216, pruned_loss=0.02993, over 971752.81 frames.], batch size: 31, lr: 3.04e-04
+2022-06-19 05:42:56,262 INFO [train.py:874] (0/4) Epoch 27, batch 1750, datatang_loss[loss=0.17, simple_loss=0.2375, pruned_loss=0.0512, over 4916.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2239, pruned_loss=0.02871, over 984838.05 frames.], batch size: 64, aishell_tot_loss[loss=0.1437, simple_loss=0.2315, pruned_loss=0.02799, over 972543.27 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2157, pruned_loss=0.0297, over 973069.30 frames.], batch size: 64, lr: 3.04e-04
+2022-06-19 05:43:25,973 INFO [train.py:874] (0/4) Epoch 27, batch 1800, aishell_loss[loss=0.135, simple_loss=0.2198, pruned_loss=0.02511, over 4868.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2246, pruned_loss=0.02927, over 984938.12 frames.], batch size: 35, aishell_tot_loss[loss=0.1441, simple_loss=0.2316, pruned_loss=0.02828, over 974104.38 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2164, pruned_loss=0.02998, over 974463.61 frames.], batch size: 35, lr: 3.04e-04
+2022-06-19 05:43:54,064 INFO [train.py:874] (0/4) Epoch 27, batch 1850, aishell_loss[loss=0.1102, simple_loss=0.1977, pruned_loss=0.01136, over 4956.00 frames.], tot_loss[loss=0.1409, simple_loss=0.224, pruned_loss=0.02896, over 984745.04 frames.], batch size: 27, aishell_tot_loss[loss=0.1432, simple_loss=0.2304, pruned_loss=0.02801, over 975382.32 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2166, pruned_loss=0.03, over 975492.99 frames.], batch size: 27, lr: 3.04e-04
+2022-06-19 05:44:24,568 INFO [train.py:874] (0/4) Epoch 27, batch 1900, aishell_loss[loss=0.1136, simple_loss=0.1862, pruned_loss=0.02048, over 4886.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2242, pruned_loss=0.02943, over 985099.59 frames.], batch size: 21, aishell_tot_loss[loss=0.1433, simple_loss=0.2306, pruned_loss=0.02801, over 976558.61 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2167, pruned_loss=0.03054, over 976882.74 frames.], batch size: 21, lr: 3.04e-04
+2022-06-19 05:44:54,881 INFO [train.py:874] (0/4) Epoch 27, batch 1950, aishell_loss[loss=0.1212, simple_loss=0.2053, pruned_loss=0.01855, over 4948.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2241, pruned_loss=0.02961, over 985371.13 frames.], batch size: 25, aishell_tot_loss[loss=0.1433, simple_loss=0.2304, pruned_loss=0.02807, over 977912.51 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2168, pruned_loss=0.03076, over 977787.78 frames.], batch size: 25, lr: 3.04e-04
+2022-06-19 05:45:24,370 INFO [train.py:874] (0/4) Epoch 27, batch 2000, datatang_loss[loss=0.1103, simple_loss=0.1862, pruned_loss=0.01722, over 4981.00 frames.], tot_loss[loss=0.1405, simple_loss=0.223, pruned_loss=0.02901, over 985658.87 frames.], batch size: 31, aishell_tot_loss[loss=0.1427, simple_loss=0.2296, pruned_loss=0.02785, over 978993.01 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2164, pruned_loss=0.0304, over 978787.68 frames.], batch size: 31, lr: 3.04e-04
+2022-06-19 05:45:24,373 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 05:45:41,441 INFO [train.py:914] (0/4) Epoch 27, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04011, over 1622729.00 frames.
+2022-06-19 05:46:10,064 INFO [train.py:874] (0/4) Epoch 27, batch 2050, aishell_loss[loss=0.1448, simple_loss=0.2325, pruned_loss=0.0286, over 4957.00 frames.], tot_loss[loss=0.141, simple_loss=0.2232, pruned_loss=0.02941, over 985801.16 frames.], batch size: 61, aishell_tot_loss[loss=0.1425, simple_loss=0.2295, pruned_loss=0.02772, over 979507.03 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2171, pruned_loss=0.03089, over 980014.48 frames.], batch size: 61, lr: 3.04e-04
+2022-06-19 05:46:39,363 INFO [train.py:874] (0/4) Epoch 27, batch 2100, datatang_loss[loss=0.1288, simple_loss=0.2059, pruned_loss=0.02581, over 4946.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2237, pruned_loss=0.0289, over 986010.16 frames.], batch size: 69, aishell_tot_loss[loss=0.1426, simple_loss=0.23, pruned_loss=0.02759, over 980490.32 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2168, pruned_loss=0.03057, over 980684.34 frames.], batch size: 69, lr: 3.03e-04
+2022-06-19 05:47:08,911 INFO [train.py:874] (0/4) Epoch 27, batch 2150, aishell_loss[loss=0.1188, simple_loss=0.2129, pruned_loss=0.01229, over 4878.00 frames.], tot_loss[loss=0.141, simple_loss=0.2235, pruned_loss=0.02923, over 986041.54 frames.], batch size: 28, aishell_tot_loss[loss=0.1428, simple_loss=0.2299, pruned_loss=0.02783, over 981221.34 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.03063, over 981264.81 frames.], batch size: 28, lr: 3.03e-04
+2022-06-19 05:47:37,122 INFO [train.py:874] (0/4) Epoch 27, batch 2200, aishell_loss[loss=0.1483, simple_loss=0.2416, pruned_loss=0.02748, over 4915.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2232, pruned_loss=0.02902, over 986255.43 frames.], batch size: 60, aishell_tot_loss[loss=0.1425, simple_loss=0.2295, pruned_loss=0.02774, over 981946.55 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2167, pruned_loss=0.03053, over 981900.77 frames.], batch size: 60, lr: 3.03e-04
+2022-06-19 05:48:07,805 INFO [train.py:874] (0/4) Epoch 27, batch 2250, aishell_loss[loss=0.1247, simple_loss=0.2155, pruned_loss=0.01696, over 4859.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2228, pruned_loss=0.02913, over 986114.12 frames.], batch size: 37, aishell_tot_loss[loss=0.1423, simple_loss=0.2293, pruned_loss=0.02765, over 982090.03 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2166, pruned_loss=0.03069, over 982628.82 frames.], batch size: 37, lr: 3.03e-04
+2022-06-19 05:48:35,819 INFO [train.py:874] (0/4) Epoch 27, batch 2300, aishell_loss[loss=0.1465, simple_loss=0.2365, pruned_loss=0.02827, over 4942.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2231, pruned_loss=0.02864, over 985607.42 frames.], batch size: 49, aishell_tot_loss[loss=0.1422, simple_loss=0.2295, pruned_loss=0.02742, over 982051.98 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2164, pruned_loss=0.03045, over 983025.07 frames.], batch size: 49, lr: 3.03e-04
+2022-06-19 05:49:05,339 INFO [train.py:874] (0/4) Epoch 27, batch 2350, aishell_loss[loss=0.1463, simple_loss=0.2408, pruned_loss=0.02586, over 4958.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2226, pruned_loss=0.02849, over 985461.53 frames.], batch size: 61, aishell_tot_loss[loss=0.1419, simple_loss=0.2291, pruned_loss=0.02735, over 982072.03 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2163, pruned_loss=0.03028, over 983573.19 frames.], batch size: 61, lr: 3.03e-04
+2022-06-19 05:49:34,600 INFO [train.py:874] (0/4) Epoch 27, batch 2400, aishell_loss[loss=0.1562, simple_loss=0.2513, pruned_loss=0.03058, over 4883.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2228, pruned_loss=0.02913, over 985349.68 frames.], batch size: 47, aishell_tot_loss[loss=0.1423, simple_loss=0.2292, pruned_loss=0.0277, over 982301.39 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2164, pruned_loss=0.03054, over 983855.45 frames.], batch size: 47, lr: 3.03e-04
+2022-06-19 05:50:01,635 INFO [train.py:874] (0/4) Epoch 27, batch 2450, datatang_loss[loss=0.1291, simple_loss=0.206, pruned_loss=0.02609, over 4926.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2227, pruned_loss=0.02946, over 985518.97 frames.], batch size: 79, aishell_tot_loss[loss=0.1423, simple_loss=0.2288, pruned_loss=0.0279, over 982660.62 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2165, pruned_loss=0.03071, over 984226.33 frames.], batch size: 79, lr: 3.03e-04
+2022-06-19 05:50:30,999 INFO [train.py:874] (0/4) Epoch 27, batch 2500, aishell_loss[loss=0.1426, simple_loss=0.2273, pruned_loss=0.02895, over 4911.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2223, pruned_loss=0.02901, over 985695.84 frames.], batch size: 52, aishell_tot_loss[loss=0.1424, simple_loss=0.2289, pruned_loss=0.02792, over 983030.70 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2158, pruned_loss=0.03024, over 984545.86 frames.], batch size: 52, lr: 3.03e-04
+2022-06-19 05:51:00,251 INFO [train.py:874] (0/4) Epoch 27, batch 2550, datatang_loss[loss=0.1312, simple_loss=0.2121, pruned_loss=0.02518, over 4936.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2226, pruned_loss=0.02951, over 985580.25 frames.], batch size: 69, aishell_tot_loss[loss=0.1426, simple_loss=0.2291, pruned_loss=0.02804, over 983073.00 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.216, pruned_loss=0.03061, over 984813.98 frames.], batch size: 69, lr: 3.03e-04
+2022-06-19 05:51:28,167 INFO [train.py:874] (0/4) Epoch 27, batch 2600, aishell_loss[loss=0.1396, simple_loss=0.238, pruned_loss=0.02056, over 4909.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2237, pruned_loss=0.02928, over 985754.51 frames.], batch size: 41, aishell_tot_loss[loss=0.1427, simple_loss=0.2297, pruned_loss=0.02786, over 983456.82 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2162, pruned_loss=0.03068, over 985062.10 frames.], batch size: 41, lr: 3.03e-04
+2022-06-19 05:51:58,806 INFO [train.py:874] (0/4) Epoch 27, batch 2650, aishell_loss[loss=0.1242, simple_loss=0.2165, pruned_loss=0.01599, over 4979.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2242, pruned_loss=0.02966, over 986050.94 frames.], batch size: 48, aishell_tot_loss[loss=0.1435, simple_loss=0.2305, pruned_loss=0.02824, over 983680.77 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2164, pruned_loss=0.03064, over 985447.20 frames.], batch size: 48, lr: 3.03e-04
+2022-06-19 05:52:27,282 INFO [train.py:874] (0/4) Epoch 27, batch 2700, datatang_loss[loss=0.1174, simple_loss=0.1938, pruned_loss=0.02046, over 4886.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2246, pruned_loss=0.0299, over 986230.66 frames.], batch size: 47, aishell_tot_loss[loss=0.1441, simple_loss=0.2311, pruned_loss=0.02854, over 984343.28 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2161, pruned_loss=0.0307, over 985348.83 frames.], batch size: 47, lr: 3.03e-04
+2022-06-19 05:52:55,898 INFO [train.py:874] (0/4) Epoch 27, batch 2750, aishell_loss[loss=0.1481, simple_loss=0.2306, pruned_loss=0.03275, over 4877.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2236, pruned_loss=0.02941, over 985619.71 frames.], batch size: 42, aishell_tot_loss[loss=0.1439, simple_loss=0.2308, pruned_loss=0.02849, over 984020.24 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2157, pruned_loss=0.03027, over 985333.91 frames.], batch size: 42, lr: 3.03e-04
+2022-06-19 05:53:25,468 INFO [train.py:874] (0/4) Epoch 27, batch 2800, aishell_loss[loss=0.1467, simple_loss=0.2381, pruned_loss=0.02766, over 4869.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2239, pruned_loss=0.02955, over 985461.70 frames.], batch size: 35, aishell_tot_loss[loss=0.1437, simple_loss=0.2308, pruned_loss=0.0283, over 983858.98 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.216, pruned_loss=0.0306, over 985545.49 frames.], batch size: 35, lr: 3.02e-04
+2022-06-19 05:53:55,466 INFO [train.py:874] (0/4) Epoch 27, batch 2850, datatang_loss[loss=0.1277, simple_loss=0.2017, pruned_loss=0.02681, over 4975.00 frames.], tot_loss[loss=0.1403, simple_loss=0.223, pruned_loss=0.02879, over 985729.06 frames.], batch size: 53, aishell_tot_loss[loss=0.1432, simple_loss=0.2303, pruned_loss=0.02805, over 984075.37 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2158, pruned_loss=0.03006, over 985790.33 frames.], batch size: 53, lr: 3.02e-04
+2022-06-19 05:54:23,634 INFO [train.py:874] (0/4) Epoch 27, batch 2900, datatang_loss[loss=0.1464, simple_loss=0.2208, pruned_loss=0.03601, over 4959.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2238, pruned_loss=0.02934, over 985824.68 frames.], batch size: 37, aishell_tot_loss[loss=0.1435, simple_loss=0.2306, pruned_loss=0.02819, over 984302.04 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2163, pruned_loss=0.03044, over 985853.31 frames.], batch size: 37, lr: 3.02e-04
+2022-06-19 05:54:53,402 INFO [train.py:874] (0/4) Epoch 27, batch 2950, datatang_loss[loss=0.1443, simple_loss=0.2294, pruned_loss=0.02956, over 4855.00 frames.], tot_loss[loss=0.141, simple_loss=0.2234, pruned_loss=0.02929, over 985675.18 frames.], batch size: 30, aishell_tot_loss[loss=0.1435, simple_loss=0.2306, pruned_loss=0.02814, over 984403.01 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2162, pruned_loss=0.03042, over 985763.81 frames.], batch size: 30, lr: 3.02e-04
+2022-06-19 05:55:22,532 INFO [train.py:874] (0/4) Epoch 27, batch 3000, aishell_loss[loss=0.1143, simple_loss=0.2032, pruned_loss=0.01273, over 4878.00 frames.], tot_loss[loss=0.1405, simple_loss=0.223, pruned_loss=0.029, over 985520.62 frames.], batch size: 28, aishell_tot_loss[loss=0.1428, simple_loss=0.2299, pruned_loss=0.02783, over 984333.53 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2168, pruned_loss=0.0304, over 985776.64 frames.], batch size: 28, lr: 3.02e-04
+2022-06-19 05:55:22,535 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 05:55:39,145 INFO [train.py:914] (0/4) Epoch 27, validation: loss=0.1642, simple_loss=0.2486, pruned_loss=0.03985, over 1622729.00 frames.
+2022-06-19 05:56:10,024 INFO [train.py:874] (0/4) Epoch 27, batch 3050, datatang_loss[loss=0.1557, simple_loss=0.2283, pruned_loss=0.04158, over 4892.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2244, pruned_loss=0.02944, over 985662.00 frames.], batch size: 47, aishell_tot_loss[loss=0.1435, simple_loss=0.2307, pruned_loss=0.02819, over 984527.72 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2173, pruned_loss=0.03047, over 985826.76 frames.], batch size: 47, lr: 3.02e-04
+2022-06-19 05:56:38,535 INFO [train.py:874] (0/4) Epoch 27, batch 3100, datatang_loss[loss=0.128, simple_loss=0.2022, pruned_loss=0.02694, over 4944.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2238, pruned_loss=0.0294, over 985851.84 frames.], batch size: 69, aishell_tot_loss[loss=0.1439, simple_loss=0.231, pruned_loss=0.02835, over 984488.29 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2166, pruned_loss=0.03027, over 986173.20 frames.], batch size: 69, lr: 3.02e-04
+2022-06-19 05:57:09,319 INFO [train.py:874] (0/4) Epoch 27, batch 3150, datatang_loss[loss=0.1406, simple_loss=0.2234, pruned_loss=0.0289, over 4944.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2228, pruned_loss=0.02891, over 985667.88 frames.], batch size: 62, aishell_tot_loss[loss=0.1434, simple_loss=0.2304, pruned_loss=0.02814, over 984526.63 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2163, pruned_loss=0.02997, over 986033.14 frames.], batch size: 62, lr: 3.02e-04
+2022-06-19 05:57:40,483 INFO [train.py:874] (0/4) Epoch 27, batch 3200, datatang_loss[loss=0.134, simple_loss=0.2112, pruned_loss=0.02841, over 4911.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2234, pruned_loss=0.02905, over 985671.58 frames.], batch size: 64, aishell_tot_loss[loss=0.1438, simple_loss=0.231, pruned_loss=0.02833, over 984548.42 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2162, pruned_loss=0.02992, over 986136.70 frames.], batch size: 64, lr: 3.02e-04
+2022-06-19 05:58:09,156 INFO [train.py:874] (0/4) Epoch 27, batch 3250, aishell_loss[loss=0.1467, simple_loss=0.2378, pruned_loss=0.02785, over 4940.00 frames.], tot_loss[loss=0.14, simple_loss=0.2223, pruned_loss=0.02883, over 985302.36 frames.], batch size: 45, aishell_tot_loss[loss=0.1429, simple_loss=0.2298, pruned_loss=0.02805, over 984469.14 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2162, pruned_loss=0.02997, over 985907.10 frames.], batch size: 45, lr: 3.02e-04
+2022-06-19 05:58:39,174 INFO [train.py:874] (0/4) Epoch 27, batch 3300, aishell_loss[loss=0.1476, simple_loss=0.2385, pruned_loss=0.02839, over 4980.00 frames.], tot_loss[loss=0.1406, simple_loss=0.223, pruned_loss=0.0291, over 985626.62 frames.], batch size: 44, aishell_tot_loss[loss=0.143, simple_loss=0.2299, pruned_loss=0.02808, over 984678.58 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2165, pruned_loss=0.03019, over 986072.30 frames.], batch size: 44, lr: 3.02e-04
+2022-06-19 05:59:09,429 INFO [train.py:874] (0/4) Epoch 27, batch 3350, aishell_loss[loss=0.1174, simple_loss=0.2008, pruned_loss=0.01694, over 4955.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2228, pruned_loss=0.02936, over 985699.27 frames.], batch size: 31, aishell_tot_loss[loss=0.1435, simple_loss=0.2304, pruned_loss=0.0283, over 984954.18 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2161, pruned_loss=0.03017, over 985899.38 frames.], batch size: 31, lr: 3.02e-04
+2022-06-19 05:59:36,781 INFO [train.py:874] (0/4) Epoch 27, batch 3400, aishell_loss[loss=0.1453, simple_loss=0.2341, pruned_loss=0.02827, over 4920.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2218, pruned_loss=0.02893, over 984928.80 frames.], batch size: 52, aishell_tot_loss[loss=0.1434, simple_loss=0.2304, pruned_loss=0.02824, over 984436.76 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2153, pruned_loss=0.02974, over 985617.70 frames.], batch size: 52, lr: 3.02e-04
+2022-06-19 06:00:05,980 INFO [train.py:874] (0/4) Epoch 27, batch 3450, aishell_loss[loss=0.1442, simple_loss=0.2275, pruned_loss=0.03043, over 4928.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2221, pruned_loss=0.02905, over 985173.84 frames.], batch size: 45, aishell_tot_loss[loss=0.1439, simple_loss=0.2307, pruned_loss=0.02857, over 984604.68 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.215, pruned_loss=0.02954, over 985696.50 frames.], batch size: 45, lr: 3.02e-04
+2022-06-19 06:00:35,350 INFO [train.py:874] (0/4) Epoch 27, batch 3500, aishell_loss[loss=0.1481, simple_loss=0.2324, pruned_loss=0.03191, over 4946.00 frames.], tot_loss[loss=0.14, simple_loss=0.2218, pruned_loss=0.02907, over 985335.22 frames.], batch size: 32, aishell_tot_loss[loss=0.1436, simple_loss=0.2303, pruned_loss=0.02848, over 984750.39 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2151, pruned_loss=0.02962, over 985709.44 frames.], batch size: 32, lr: 3.02e-04
+2022-06-19 06:01:04,233 INFO [train.py:874] (0/4) Epoch 27, batch 3550, datatang_loss[loss=0.1288, simple_loss=0.1971, pruned_loss=0.03026, over 4871.00 frames.], tot_loss[loss=0.14, simple_loss=0.2216, pruned_loss=0.02924, over 985307.37 frames.], batch size: 39, aishell_tot_loss[loss=0.1436, simple_loss=0.2303, pruned_loss=0.02844, over 984722.89 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2152, pruned_loss=0.02981, over 985717.21 frames.], batch size: 39, lr: 3.01e-04
+2022-06-19 06:01:33,764 INFO [train.py:874] (0/4) Epoch 27, batch 3600, aishell_loss[loss=0.1081, simple_loss=0.1772, pruned_loss=0.01952, over 4820.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2212, pruned_loss=0.02866, over 984974.92 frames.], batch size: 21, aishell_tot_loss[loss=0.1431, simple_loss=0.2298, pruned_loss=0.02818, over 984375.53 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.215, pruned_loss=0.0295, over 985734.38 frames.], batch size: 21, lr: 3.01e-04
+2022-06-19 06:02:05,263 INFO [train.py:874] (0/4) Epoch 27, batch 3650, aishell_loss[loss=0.1591, simple_loss=0.2363, pruned_loss=0.04098, over 4940.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2204, pruned_loss=0.02839, over 984923.75 frames.], batch size: 32, aishell_tot_loss[loss=0.1429, simple_loss=0.2295, pruned_loss=0.02817, over 984243.46 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2147, pruned_loss=0.02913, over 985751.95 frames.], batch size: 32, lr: 3.01e-04
+2022-06-19 06:02:32,479 INFO [train.py:874] (0/4) Epoch 27, batch 3700, datatang_loss[loss=0.1405, simple_loss=0.2123, pruned_loss=0.03439, over 4915.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2216, pruned_loss=0.02854, over 984964.89 frames.], batch size: 81, aishell_tot_loss[loss=0.1432, simple_loss=0.2301, pruned_loss=0.0282, over 984246.50 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2148, pruned_loss=0.02921, over 985803.30 frames.], batch size: 81, lr: 3.01e-04
+2022-06-19 06:03:03,251 INFO [train.py:874] (0/4) Epoch 27, batch 3750, datatang_loss[loss=0.128, simple_loss=0.2104, pruned_loss=0.02273, over 4952.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2213, pruned_loss=0.02871, over 985339.34 frames.], batch size: 55, aishell_tot_loss[loss=0.1432, simple_loss=0.2299, pruned_loss=0.0283, over 984768.54 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2147, pruned_loss=0.02923, over 985652.50 frames.], batch size: 55, lr: 3.01e-04
+2022-06-19 06:03:32,613 INFO [train.py:874] (0/4) Epoch 27, batch 3800, aishell_loss[loss=0.1298, simple_loss=0.219, pruned_loss=0.02032, over 4960.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2214, pruned_loss=0.02848, over 985251.40 frames.], batch size: 56, aishell_tot_loss[loss=0.1433, simple_loss=0.2301, pruned_loss=0.0283, over 984698.06 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2142, pruned_loss=0.02898, over 985664.87 frames.], batch size: 56, lr: 3.01e-04
+2022-06-19 06:04:01,605 INFO [train.py:874] (0/4) Epoch 27, batch 3850, datatang_loss[loss=0.1294, simple_loss=0.2126, pruned_loss=0.02307, over 4923.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2211, pruned_loss=0.02817, over 985407.11 frames.], batch size: 83, aishell_tot_loss[loss=0.1436, simple_loss=0.2303, pruned_loss=0.02846, over 984725.75 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2137, pruned_loss=0.02845, over 985809.95 frames.], batch size: 83, lr: 3.01e-04
+2022-06-19 06:04:28,990 INFO [train.py:874] (0/4) Epoch 27, batch 3900, aishell_loss[loss=0.116, simple_loss=0.194, pruned_loss=0.01895, over 4977.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2221, pruned_loss=0.02817, over 985854.05 frames.], batch size: 25, aishell_tot_loss[loss=0.1432, simple_loss=0.2302, pruned_loss=0.02809, over 985253.99 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.214, pruned_loss=0.02876, over 985804.37 frames.], batch size: 25, lr: 3.01e-04
+2022-06-19 06:04:58,550 INFO [train.py:874] (0/4) Epoch 27, batch 3950, aishell_loss[loss=0.1455, simple_loss=0.2294, pruned_loss=0.03084, over 4922.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2217, pruned_loss=0.02805, over 985752.17 frames.], batch size: 33, aishell_tot_loss[loss=0.1431, simple_loss=0.2302, pruned_loss=0.028, over 985363.00 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.214, pruned_loss=0.02864, over 985661.69 frames.], batch size: 33, lr: 3.01e-04
+2022-06-19 06:05:27,300 INFO [train.py:874] (0/4) Epoch 27, batch 4000, datatang_loss[loss=0.1242, simple_loss=0.2018, pruned_loss=0.02331, over 4931.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2222, pruned_loss=0.02783, over 985935.68 frames.], batch size: 71, aishell_tot_loss[loss=0.1435, simple_loss=0.231, pruned_loss=0.02799, over 985627.25 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2135, pruned_loss=0.02835, over 985656.05 frames.], batch size: 71, lr: 3.01e-04
+2022-06-19 06:05:27,303 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 06:05:43,551 INFO [train.py:914] (0/4) Epoch 27, validation: loss=0.1646, simple_loss=0.249, pruned_loss=0.0401, over 1622729.00 frames.
+2022-06-19 06:06:11,903 INFO [train.py:874] (0/4) Epoch 27, batch 4050, aishell_loss[loss=0.1735, simple_loss=0.259, pruned_loss=0.04396, over 4865.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2219, pruned_loss=0.02815, over 985729.63 frames.], batch size: 36, aishell_tot_loss[loss=0.1441, simple_loss=0.2313, pruned_loss=0.02843, over 985371.42 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2129, pruned_loss=0.02817, over 985753.41 frames.], batch size: 36, lr: 3.01e-04
+2022-06-19 06:06:38,048 INFO [train.py:874] (0/4) Epoch 27, batch 4100, datatang_loss[loss=0.1418, simple_loss=0.2318, pruned_loss=0.02592, over 4919.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2216, pruned_loss=0.02793, over 985343.20 frames.], batch size: 98, aishell_tot_loss[loss=0.1438, simple_loss=0.2309, pruned_loss=0.02831, over 984952.91 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2128, pruned_loss=0.028, over 985783.52 frames.], batch size: 98, lr: 3.01e-04
+2022-06-19 06:07:06,625 INFO [train.py:874] (0/4) Epoch 27, batch 4150, aishell_loss[loss=0.1331, simple_loss=0.2269, pruned_loss=0.01963, over 4917.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2207, pruned_loss=0.02755, over 984860.29 frames.], batch size: 41, aishell_tot_loss[loss=0.1433, simple_loss=0.2305, pruned_loss=0.02803, over 984668.78 frames.], datatang_tot_loss[loss=0.134, simple_loss=0.2124, pruned_loss=0.02784, over 985544.65 frames.], batch size: 41, lr: 3.01e-04
+2022-06-19 06:07:13,597 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-27.pt
+2022-06-19 06:08:12,729 INFO [train.py:874] (0/4) Epoch 28, batch 50, datatang_loss[loss=0.1254, simple_loss=0.2049, pruned_loss=0.02298, over 4912.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2216, pruned_loss=0.02793, over 218430.87 frames.], batch size: 75, aishell_tot_loss[loss=0.1494, simple_loss=0.2372, pruned_loss=0.03081, over 120280.92 frames.], datatang_tot_loss[loss=0.1275, simple_loss=0.2052, pruned_loss=0.02485, over 111807.75 frames.], batch size: 75, lr: 2.95e-04
+2022-06-19 06:08:39,691 INFO [train.py:874] (0/4) Epoch 28, batch 100, aishell_loss[loss=0.1344, simple_loss=0.2299, pruned_loss=0.01946, over 4954.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2214, pruned_loss=0.02702, over 388723.59 frames.], batch size: 56, aishell_tot_loss[loss=0.1462, simple_loss=0.2345, pruned_loss=0.02891, over 229872.11 frames.], datatang_tot_loss[loss=0.1283, simple_loss=0.2067, pruned_loss=0.02501, over 207141.65 frames.], batch size: 56, lr: 2.95e-04
+2022-06-19 06:09:09,739 INFO [train.py:874] (0/4) Epoch 28, batch 150, aishell_loss[loss=0.137, simple_loss=0.2269, pruned_loss=0.02354, over 4911.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2227, pruned_loss=0.02759, over 520562.35 frames.], batch size: 41, aishell_tot_loss[loss=0.1463, simple_loss=0.2345, pruned_loss=0.02903, over 335093.30 frames.], datatang_tot_loss[loss=0.1295, simple_loss=0.2077, pruned_loss=0.0257, over 281167.93 frames.], batch size: 41, lr: 2.95e-04
+2022-06-19 06:09:18,396 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-112000.pt
+2022-06-19 06:09:42,579 INFO [train.py:874] (0/4) Epoch 28, batch 200, datatang_loss[loss=0.1361, simple_loss=0.2212, pruned_loss=0.02547, over 4953.00 frames.], tot_loss[loss=0.1391, simple_loss=0.223, pruned_loss=0.02756, over 623752.48 frames.], batch size: 91, aishell_tot_loss[loss=0.1451, simple_loss=0.2336, pruned_loss=0.02832, over 423083.21 frames.], datatang_tot_loss[loss=0.131, simple_loss=0.209, pruned_loss=0.02651, over 351672.06 frames.], batch size: 91, lr: 2.95e-04
+2022-06-19 06:10:12,330 INFO [train.py:874] (0/4) Epoch 28, batch 250, datatang_loss[loss=0.1278, simple_loss=0.2069, pruned_loss=0.02434, over 4916.00 frames.], tot_loss[loss=0.137, simple_loss=0.2206, pruned_loss=0.02671, over 704003.13 frames.], batch size: 75, aishell_tot_loss[loss=0.1438, simple_loss=0.2324, pruned_loss=0.02759, over 484264.82 frames.], datatang_tot_loss[loss=0.1297, simple_loss=0.2076, pruned_loss=0.02595, over 432014.07 frames.], batch size: 75, lr: 2.95e-04
+2022-06-19 06:10:42,401 INFO [train.py:874] (0/4) Epoch 28, batch 300, aishell_loss[loss=0.1425, simple_loss=0.2264, pruned_loss=0.02934, over 4943.00 frames.], tot_loss[loss=0.1366, simple_loss=0.22, pruned_loss=0.02656, over 766945.95 frames.], batch size: 54, aishell_tot_loss[loss=0.1435, simple_loss=0.2318, pruned_loss=0.0276, over 545673.78 frames.], datatang_tot_loss[loss=0.1294, simple_loss=0.2075, pruned_loss=0.02566, over 495159.36 frames.], batch size: 54, lr: 2.95e-04
+2022-06-19 06:11:09,564 INFO [train.py:874] (0/4) Epoch 28, batch 350, datatang_loss[loss=0.127, simple_loss=0.2075, pruned_loss=0.02327, over 4927.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2201, pruned_loss=0.02723, over 815511.73 frames.], batch size: 71, aishell_tot_loss[loss=0.1432, simple_loss=0.2315, pruned_loss=0.02744, over 587619.60 frames.], datatang_tot_loss[loss=0.1314, simple_loss=0.2091, pruned_loss=0.0269, over 563756.19 frames.], batch size: 71, lr: 2.95e-04
+2022-06-19 06:11:39,737 INFO [train.py:874] (0/4) Epoch 28, batch 400, aishell_loss[loss=0.1357, simple_loss=0.2198, pruned_loss=0.02575, over 4937.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2206, pruned_loss=0.02759, over 853492.91 frames.], batch size: 32, aishell_tot_loss[loss=0.143, simple_loss=0.2311, pruned_loss=0.02747, over 629202.26 frames.], datatang_tot_loss[loss=0.1327, simple_loss=0.2105, pruned_loss=0.0274, over 619248.74 frames.], batch size: 32, lr: 2.95e-04
+2022-06-19 06:12:10,377 INFO [train.py:874] (0/4) Epoch 28, batch 450, datatang_loss[loss=0.1318, simple_loss=0.2112, pruned_loss=0.02623, over 4927.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2211, pruned_loss=0.028, over 882792.66 frames.], batch size: 81, aishell_tot_loss[loss=0.1437, simple_loss=0.2317, pruned_loss=0.02785, over 666367.51 frames.], datatang_tot_loss[loss=0.1332, simple_loss=0.2111, pruned_loss=0.02769, over 667280.53 frames.], batch size: 81, lr: 2.95e-04
+2022-06-19 06:12:37,878 INFO [train.py:874] (0/4) Epoch 28, batch 500, aishell_loss[loss=0.1537, simple_loss=0.2426, pruned_loss=0.03238, over 4956.00 frames.], tot_loss[loss=0.1382, simple_loss=0.221, pruned_loss=0.0277, over 905303.10 frames.], batch size: 40, aishell_tot_loss[loss=0.1432, simple_loss=0.2311, pruned_loss=0.02766, over 707994.49 frames.], datatang_tot_loss[loss=0.133, simple_loss=0.211, pruned_loss=0.02754, over 700378.20 frames.], batch size: 40, lr: 2.95e-04
+2022-06-19 06:13:06,536 INFO [train.py:874] (0/4) Epoch 28, batch 550, datatang_loss[loss=0.1536, simple_loss=0.2299, pruned_loss=0.0386, over 4938.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2207, pruned_loss=0.02786, over 922729.73 frames.], batch size: 34, aishell_tot_loss[loss=0.1426, simple_loss=0.2301, pruned_loss=0.02757, over 742713.32 frames.], datatang_tot_loss[loss=0.1335, simple_loss=0.2114, pruned_loss=0.02785, over 731463.07 frames.], batch size: 34, lr: 2.95e-04
+2022-06-19 06:13:35,830 INFO [train.py:874] (0/4) Epoch 28, batch 600, aishell_loss[loss=0.1514, simple_loss=0.2365, pruned_loss=0.0332, over 4964.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2209, pruned_loss=0.0277, over 936792.55 frames.], batch size: 39, aishell_tot_loss[loss=0.1416, simple_loss=0.2289, pruned_loss=0.0272, over 777592.98 frames.], datatang_tot_loss[loss=0.1342, simple_loss=0.2122, pruned_loss=0.02808, over 754754.80 frames.], batch size: 39, lr: 2.95e-04
+2022-06-19 06:14:02,778 INFO [train.py:874] (0/4) Epoch 28, batch 650, aishell_loss[loss=0.1642, simple_loss=0.254, pruned_loss=0.03714, over 4897.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2207, pruned_loss=0.02788, over 947386.68 frames.], batch size: 34, aishell_tot_loss[loss=0.1412, simple_loss=0.2283, pruned_loss=0.02709, over 801812.84 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2126, pruned_loss=0.02846, over 781978.60 frames.], batch size: 34, lr: 2.94e-04
+2022-06-19 06:14:32,408 INFO [train.py:874] (0/4) Epoch 28, batch 700, aishell_loss[loss=0.1522, simple_loss=0.2401, pruned_loss=0.03214, over 4939.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2215, pruned_loss=0.02836, over 955482.39 frames.], batch size: 54, aishell_tot_loss[loss=0.1416, simple_loss=0.2287, pruned_loss=0.02726, over 821303.20 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2134, pruned_loss=0.0289, over 807896.11 frames.], batch size: 54, lr: 2.94e-04
+2022-06-19 06:15:01,764 INFO [train.py:874] (0/4) Epoch 28, batch 750, aishell_loss[loss=0.1228, simple_loss=0.2152, pruned_loss=0.01519, over 4820.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2218, pruned_loss=0.02843, over 961974.85 frames.], batch size: 29, aishell_tot_loss[loss=0.1418, simple_loss=0.2288, pruned_loss=0.02739, over 840339.55 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2138, pruned_loss=0.02894, over 828945.86 frames.], batch size: 29, lr: 2.94e-04
+2022-06-19 06:15:30,190 INFO [train.py:874] (0/4) Epoch 28, batch 800, datatang_loss[loss=0.1551, simple_loss=0.2248, pruned_loss=0.04265, over 4905.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2218, pruned_loss=0.02865, over 967095.68 frames.], batch size: 25, aishell_tot_loss[loss=0.1422, simple_loss=0.2293, pruned_loss=0.02755, over 852863.42 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2141, pruned_loss=0.02906, over 852053.14 frames.], batch size: 25, lr: 2.94e-04
+2022-06-19 06:16:00,394 INFO [train.py:874] (0/4) Epoch 28, batch 850, aishell_loss[loss=0.1398, simple_loss=0.2279, pruned_loss=0.02585, over 4938.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2222, pruned_loss=0.02848, over 971182.48 frames.], batch size: 49, aishell_tot_loss[loss=0.1422, simple_loss=0.2295, pruned_loss=0.0275, over 868950.54 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2144, pruned_loss=0.02903, over 867314.25 frames.], batch size: 49, lr: 2.94e-04
+2022-06-19 06:16:30,557 INFO [train.py:874] (0/4) Epoch 28, batch 900, datatang_loss[loss=0.1225, simple_loss=0.2036, pruned_loss=0.02073, over 4930.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2219, pruned_loss=0.02819, over 974372.89 frames.], batch size: 79, aishell_tot_loss[loss=0.1424, simple_loss=0.2299, pruned_loss=0.02744, over 882696.24 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2136, pruned_loss=0.02881, over 881232.18 frames.], batch size: 79, lr: 2.94e-04
+2022-06-19 06:16:57,984 INFO [train.py:874] (0/4) Epoch 28, batch 950, datatang_loss[loss=0.119, simple_loss=0.1985, pruned_loss=0.01974, over 4941.00 frames.], tot_loss[loss=0.1392, simple_loss=0.222, pruned_loss=0.02821, over 976802.69 frames.], batch size: 79, aishell_tot_loss[loss=0.1423, simple_loss=0.2298, pruned_loss=0.02739, over 894215.52 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2139, pruned_loss=0.02888, over 894068.79 frames.], batch size: 79, lr: 2.94e-04
+2022-06-19 06:17:29,795 INFO [train.py:874] (0/4) Epoch 28, batch 1000, datatang_loss[loss=0.1041, simple_loss=0.1858, pruned_loss=0.01118, over 4917.00 frames.], tot_loss[loss=0.1401, simple_loss=0.223, pruned_loss=0.02865, over 978516.60 frames.], batch size: 75, aishell_tot_loss[loss=0.1429, simple_loss=0.2301, pruned_loss=0.02781, over 905269.79 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2147, pruned_loss=0.029, over 904312.61 frames.], batch size: 75, lr: 2.94e-04
+2022-06-19 06:17:29,798 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 06:17:46,267 INFO [train.py:914] (0/4) Epoch 28, validation: loss=0.1641, simple_loss=0.2478, pruned_loss=0.0402, over 1622729.00 frames.
+2022-06-19 06:18:14,331 INFO [train.py:874] (0/4) Epoch 28, batch 1050, aishell_loss[loss=0.1407, simple_loss=0.2387, pruned_loss=0.02131, over 4954.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2229, pruned_loss=0.02881, over 980001.36 frames.], batch size: 40, aishell_tot_loss[loss=0.1424, simple_loss=0.2293, pruned_loss=0.02768, over 914659.67 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2156, pruned_loss=0.02939, over 913858.48 frames.], batch size: 40, lr: 2.94e-04
+2022-06-19 06:18:45,862 INFO [train.py:874] (0/4) Epoch 28, batch 1100, aishell_loss[loss=0.1719, simple_loss=0.2626, pruned_loss=0.04061, over 4966.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2232, pruned_loss=0.02896, over 981448.16 frames.], batch size: 61, aishell_tot_loss[loss=0.1432, simple_loss=0.2305, pruned_loss=0.02801, over 922486.74 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.215, pruned_loss=0.02927, over 923050.84 frames.], batch size: 61, lr: 2.94e-04
+2022-06-19 06:19:12,534 INFO [train.py:874] (0/4) Epoch 28, batch 1150, aishell_loss[loss=0.1563, simple_loss=0.2587, pruned_loss=0.02696, over 4957.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2231, pruned_loss=0.02885, over 982190.12 frames.], batch size: 61, aishell_tot_loss[loss=0.1431, simple_loss=0.2302, pruned_loss=0.02802, over 929440.49 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2154, pruned_loss=0.0292, over 930708.60 frames.], batch size: 61, lr: 2.94e-04
+2022-06-19 06:19:43,163 INFO [train.py:874] (0/4) Epoch 28, batch 1200, datatang_loss[loss=0.1383, simple_loss=0.2129, pruned_loss=0.03187, over 4979.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2228, pruned_loss=0.02895, over 982504.85 frames.], batch size: 48, aishell_tot_loss[loss=0.1427, simple_loss=0.2294, pruned_loss=0.02802, over 936237.41 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2157, pruned_loss=0.02939, over 936536.00 frames.], batch size: 48, lr: 2.94e-04
+2022-06-19 06:20:13,506 INFO [train.py:874] (0/4) Epoch 28, batch 1250, aishell_loss[loss=0.1554, simple_loss=0.2438, pruned_loss=0.0335, over 4921.00 frames.], tot_loss[loss=0.141, simple_loss=0.2235, pruned_loss=0.02923, over 983017.89 frames.], batch size: 52, aishell_tot_loss[loss=0.1435, simple_loss=0.2301, pruned_loss=0.02842, over 942454.33 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2158, pruned_loss=0.02937, over 941729.15 frames.], batch size: 52, lr: 2.94e-04
+2022-06-19 06:20:39,905 INFO [train.py:874] (0/4) Epoch 28, batch 1300, datatang_loss[loss=0.1285, simple_loss=0.2084, pruned_loss=0.02433, over 4973.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2237, pruned_loss=0.02895, over 984024.04 frames.], batch size: 37, aishell_tot_loss[loss=0.1434, simple_loss=0.2303, pruned_loss=0.02829, over 948488.07 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2157, pruned_loss=0.02929, over 946382.41 frames.], batch size: 37, lr: 2.94e-04
+2022-06-19 06:21:10,130 INFO [train.py:874] (0/4) Epoch 28, batch 1350, datatang_loss[loss=0.143, simple_loss=0.223, pruned_loss=0.03151, over 4917.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2232, pruned_loss=0.0292, over 984539.10 frames.], batch size: 75, aishell_tot_loss[loss=0.1433, simple_loss=0.2298, pruned_loss=0.02839, over 952827.62 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2159, pruned_loss=0.0295, over 951225.69 frames.], batch size: 75, lr: 2.94e-04
+2022-06-19 06:21:39,808 INFO [train.py:874] (0/4) Epoch 28, batch 1400, datatang_loss[loss=0.142, simple_loss=0.2249, pruned_loss=0.02951, over 4950.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2231, pruned_loss=0.02893, over 985038.28 frames.], batch size: 55, aishell_tot_loss[loss=0.1436, simple_loss=0.2303, pruned_loss=0.02845, over 956384.25 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2154, pruned_loss=0.0292, over 955853.25 frames.], batch size: 55, lr: 2.93e-04
+2022-06-19 06:22:07,138 INFO [train.py:874] (0/4) Epoch 28, batch 1450, aishell_loss[loss=0.1507, simple_loss=0.2426, pruned_loss=0.02944, over 4979.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2233, pruned_loss=0.0292, over 985111.34 frames.], batch size: 48, aishell_tot_loss[loss=0.1436, simple_loss=0.2305, pruned_loss=0.02841, over 959413.73 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2157, pruned_loss=0.02958, over 959721.95 frames.], batch size: 48, lr: 2.93e-04
+2022-06-19 06:22:39,156 INFO [train.py:874] (0/4) Epoch 28, batch 1500, datatang_loss[loss=0.124, simple_loss=0.2064, pruned_loss=0.0208, over 4946.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2229, pruned_loss=0.02915, over 984996.39 frames.], batch size: 88, aishell_tot_loss[loss=0.1436, simple_loss=0.2302, pruned_loss=0.0285, over 962090.31 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2157, pruned_loss=0.02948, over 962932.20 frames.], batch size: 88, lr: 2.93e-04
+2022-06-19 06:23:09,548 INFO [train.py:874] (0/4) Epoch 28, batch 1550, aishell_loss[loss=0.1451, simple_loss=0.2371, pruned_loss=0.0266, over 4911.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2226, pruned_loss=0.02926, over 985124.74 frames.], batch size: 52, aishell_tot_loss[loss=0.1437, simple_loss=0.2302, pruned_loss=0.02863, over 964567.23 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2155, pruned_loss=0.02948, over 965875.58 frames.], batch size: 52, lr: 2.93e-04
+2022-06-19 06:23:37,900 INFO [train.py:874] (0/4) Epoch 28, batch 1600, datatang_loss[loss=0.1469, simple_loss=0.2237, pruned_loss=0.03501, over 4943.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2222, pruned_loss=0.02914, over 985181.95 frames.], batch size: 50, aishell_tot_loss[loss=0.1434, simple_loss=0.2301, pruned_loss=0.02839, over 966728.26 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2152, pruned_loss=0.02962, over 968447.62 frames.], batch size: 50, lr: 2.93e-04
+2022-06-19 06:24:07,778 INFO [train.py:874] (0/4) Epoch 28, batch 1650, datatang_loss[loss=0.1169, simple_loss=0.1881, pruned_loss=0.02291, over 4941.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2226, pruned_loss=0.02902, over 985457.29 frames.], batch size: 50, aishell_tot_loss[loss=0.1438, simple_loss=0.2308, pruned_loss=0.0284, over 968923.18 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2148, pruned_loss=0.02954, over 970694.67 frames.], batch size: 50, lr: 2.93e-04
+2022-06-19 06:24:36,381 INFO [train.py:874] (0/4) Epoch 28, batch 1700, aishell_loss[loss=0.1545, simple_loss=0.2363, pruned_loss=0.03636, over 4884.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2235, pruned_loss=0.02909, over 985625.04 frames.], batch size: 34, aishell_tot_loss[loss=0.1438, simple_loss=0.2307, pruned_loss=0.02846, over 971362.34 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2154, pruned_loss=0.02962, over 972152.22 frames.], batch size: 34, lr: 2.93e-04
+2022-06-19 06:25:04,619 INFO [train.py:874] (0/4) Epoch 28, batch 1750, datatang_loss[loss=0.1246, simple_loss=0.207, pruned_loss=0.02105, over 4933.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2225, pruned_loss=0.02886, over 985516.57 frames.], batch size: 88, aishell_tot_loss[loss=0.1429, simple_loss=0.2297, pruned_loss=0.02802, over 972748.43 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2155, pruned_loss=0.0298, over 973903.64 frames.], batch size: 88, lr: 2.93e-04
+2022-06-19 06:25:35,069 INFO [train.py:874] (0/4) Epoch 28, batch 1800, datatang_loss[loss=0.1335, simple_loss=0.2156, pruned_loss=0.02572, over 4951.00 frames.], tot_loss[loss=0.141, simple_loss=0.2236, pruned_loss=0.02924, over 985093.90 frames.], batch size: 67, aishell_tot_loss[loss=0.1437, simple_loss=0.2304, pruned_loss=0.02847, over 974221.79 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2155, pruned_loss=0.02978, over 974869.44 frames.], batch size: 67, lr: 2.93e-04
+2022-06-19 06:26:02,301 INFO [train.py:874] (0/4) Epoch 28, batch 1850, datatang_loss[loss=0.1594, simple_loss=0.2171, pruned_loss=0.05083, over 4963.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2226, pruned_loss=0.02913, over 985435.06 frames.], batch size: 50, aishell_tot_loss[loss=0.143, simple_loss=0.2297, pruned_loss=0.0281, over 975593.76 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2156, pruned_loss=0.03003, over 976322.60 frames.], batch size: 50, lr: 2.93e-04
+2022-06-19 06:26:30,986 INFO [train.py:874] (0/4) Epoch 28, batch 1900, aishell_loss[loss=0.1416, simple_loss=0.2238, pruned_loss=0.02964, over 4975.00 frames.], tot_loss[loss=0.1406, simple_loss=0.223, pruned_loss=0.0291, over 985549.61 frames.], batch size: 31, aishell_tot_loss[loss=0.1434, simple_loss=0.2303, pruned_loss=0.02827, over 976696.33 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2154, pruned_loss=0.02981, over 977586.08 frames.], batch size: 31, lr: 2.93e-04
+2022-06-19 06:27:02,499 INFO [train.py:874] (0/4) Epoch 28, batch 1950, aishell_loss[loss=0.1472, simple_loss=0.2315, pruned_loss=0.0315, over 4944.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2226, pruned_loss=0.02908, over 985589.13 frames.], batch size: 56, aishell_tot_loss[loss=0.1434, simple_loss=0.2301, pruned_loss=0.02837, over 977584.26 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2154, pruned_loss=0.02968, over 978711.71 frames.], batch size: 56, lr: 2.93e-04
+2022-06-19 06:27:30,172 INFO [train.py:874] (0/4) Epoch 28, batch 2000, datatang_loss[loss=0.1479, simple_loss=0.2334, pruned_loss=0.03121, over 4861.00 frames.], tot_loss[loss=0.141, simple_loss=0.2233, pruned_loss=0.02937, over 985337.54 frames.], batch size: 39, aishell_tot_loss[loss=0.1433, simple_loss=0.2296, pruned_loss=0.02849, over 978316.60 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2163, pruned_loss=0.02991, over 979502.32 frames.], batch size: 39, lr: 2.93e-04
+2022-06-19 06:27:30,175 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 06:27:46,238 INFO [train.py:914] (0/4) Epoch 28, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 06:28:15,604 INFO [train.py:874] (0/4) Epoch 28, batch 2050, datatang_loss[loss=0.1415, simple_loss=0.2182, pruned_loss=0.03246, over 4911.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2233, pruned_loss=0.02945, over 985551.68 frames.], batch size: 52, aishell_tot_loss[loss=0.1437, simple_loss=0.2299, pruned_loss=0.02873, over 979075.96 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2162, pruned_loss=0.02979, over 980484.71 frames.], batch size: 52, lr: 2.93e-04
+2022-06-19 06:28:43,206 INFO [train.py:874] (0/4) Epoch 28, batch 2100, datatang_loss[loss=0.1225, simple_loss=0.2038, pruned_loss=0.02061, over 4923.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2233, pruned_loss=0.02928, over 985872.84 frames.], batch size: 73, aishell_tot_loss[loss=0.1436, simple_loss=0.2298, pruned_loss=0.02873, over 980079.90 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2162, pruned_loss=0.02968, over 981200.52 frames.], batch size: 73, lr: 2.93e-04
+2022-06-19 06:29:13,534 INFO [train.py:874] (0/4) Epoch 28, batch 2150, datatang_loss[loss=0.1292, simple_loss=0.2045, pruned_loss=0.02698, over 4937.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2227, pruned_loss=0.02927, over 985583.37 frames.], batch size: 79, aishell_tot_loss[loss=0.1438, simple_loss=0.2301, pruned_loss=0.02875, over 980483.71 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2157, pruned_loss=0.02965, over 981683.42 frames.], batch size: 79, lr: 2.93e-04
+2022-06-19 06:29:42,212 INFO [train.py:874] (0/4) Epoch 28, batch 2200, aishell_loss[loss=0.1259, simple_loss=0.2061, pruned_loss=0.02285, over 4970.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2226, pruned_loss=0.02916, over 985769.46 frames.], batch size: 27, aishell_tot_loss[loss=0.1439, simple_loss=0.2302, pruned_loss=0.02881, over 981337.64 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2156, pruned_loss=0.02948, over 982066.33 frames.], batch size: 27, lr: 2.92e-04
+2022-06-19 06:30:10,880 INFO [train.py:874] (0/4) Epoch 28, batch 2250, aishell_loss[loss=0.1578, simple_loss=0.2394, pruned_loss=0.03807, over 4955.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2222, pruned_loss=0.02895, over 985850.70 frames.], batch size: 31, aishell_tot_loss[loss=0.1433, simple_loss=0.2294, pruned_loss=0.0286, over 981956.51 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2157, pruned_loss=0.02949, over 982502.19 frames.], batch size: 31, lr: 2.92e-04
+2022-06-19 06:30:41,935 INFO [train.py:874] (0/4) Epoch 28, batch 2300, datatang_loss[loss=0.1288, simple_loss=0.2177, pruned_loss=0.01993, over 4882.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2217, pruned_loss=0.02866, over 985911.60 frames.], batch size: 47, aishell_tot_loss[loss=0.1432, simple_loss=0.2292, pruned_loss=0.02861, over 982429.91 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2154, pruned_loss=0.02915, over 982947.61 frames.], batch size: 47, lr: 2.92e-04
+2022-06-19 06:31:09,121 INFO [train.py:874] (0/4) Epoch 28, batch 2350, datatang_loss[loss=0.1171, simple_loss=0.1962, pruned_loss=0.01904, over 4925.00 frames.], tot_loss[loss=0.14, simple_loss=0.2227, pruned_loss=0.02871, over 986127.20 frames.], batch size: 73, aishell_tot_loss[loss=0.1437, simple_loss=0.2299, pruned_loss=0.02874, over 982972.03 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2154, pruned_loss=0.02904, over 983399.41 frames.], batch size: 73, lr: 2.92e-04
+2022-06-19 06:31:38,570 INFO [train.py:874] (0/4) Epoch 28, batch 2400, aishell_loss[loss=0.1379, simple_loss=0.2252, pruned_loss=0.0253, over 4912.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2233, pruned_loss=0.02854, over 985966.73 frames.], batch size: 46, aishell_tot_loss[loss=0.144, simple_loss=0.2306, pruned_loss=0.02871, over 983352.81 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2151, pruned_loss=0.02884, over 983545.27 frames.], batch size: 46, lr: 2.92e-04
+2022-06-19 06:32:08,609 INFO [train.py:874] (0/4) Epoch 28, batch 2450, aishell_loss[loss=0.1163, simple_loss=0.2099, pruned_loss=0.01133, over 4986.00 frames.], tot_loss[loss=0.14, simple_loss=0.2227, pruned_loss=0.02864, over 985628.78 frames.], batch size: 30, aishell_tot_loss[loss=0.1434, simple_loss=0.2296, pruned_loss=0.02853, over 983246.64 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2152, pruned_loss=0.02909, over 983894.78 frames.], batch size: 30, lr: 2.92e-04
+2022-06-19 06:32:35,290 INFO [train.py:874] (0/4) Epoch 28, batch 2500, datatang_loss[loss=0.1249, simple_loss=0.2044, pruned_loss=0.02272, over 4927.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2233, pruned_loss=0.02866, over 985629.03 frames.], batch size: 71, aishell_tot_loss[loss=0.1435, simple_loss=0.2301, pruned_loss=0.02847, over 983352.40 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2156, pruned_loss=0.02916, over 984274.91 frames.], batch size: 71, lr: 2.92e-04
+2022-06-19 06:33:05,159 INFO [train.py:874] (0/4) Epoch 28, batch 2550, aishell_loss[loss=0.1607, simple_loss=0.2504, pruned_loss=0.03549, over 4968.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2236, pruned_loss=0.0285, over 985641.37 frames.], batch size: 80, aishell_tot_loss[loss=0.1436, simple_loss=0.2306, pruned_loss=0.02836, over 983896.19 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2155, pruned_loss=0.02905, over 984179.08 frames.], batch size: 80, lr: 2.92e-04
+2022-06-19 06:33:35,651 INFO [train.py:874] (0/4) Epoch 28, batch 2600, datatang_loss[loss=0.1532, simple_loss=0.2338, pruned_loss=0.03633, over 4938.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2242, pruned_loss=0.02844, over 985215.33 frames.], batch size: 94, aishell_tot_loss[loss=0.1436, simple_loss=0.2306, pruned_loss=0.02831, over 983614.84 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2158, pruned_loss=0.02899, over 984407.02 frames.], batch size: 94, lr: 2.92e-04
+2022-06-19 06:34:01,433 INFO [train.py:874] (0/4) Epoch 28, batch 2650, datatang_loss[loss=0.1334, simple_loss=0.2124, pruned_loss=0.02717, over 4928.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2247, pruned_loss=0.02896, over 985511.17 frames.], batch size: 73, aishell_tot_loss[loss=0.1439, simple_loss=0.2307, pruned_loss=0.02853, over 984017.85 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2163, pruned_loss=0.0293, over 984603.00 frames.], batch size: 73, lr: 2.92e-04
+2022-06-19 06:34:31,376 INFO [train.py:874] (0/4) Epoch 28, batch 2700, datatang_loss[loss=0.1323, simple_loss=0.2118, pruned_loss=0.02641, over 4924.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2234, pruned_loss=0.02872, over 985259.43 frames.], batch size: 83, aishell_tot_loss[loss=0.1435, simple_loss=0.2299, pruned_loss=0.0285, over 983812.09 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2158, pruned_loss=0.02909, over 984826.82 frames.], batch size: 83, lr: 2.92e-04
+2022-06-19 06:34:59,553 INFO [train.py:874] (0/4) Epoch 28, batch 2750, aishell_loss[loss=0.1378, simple_loss=0.2268, pruned_loss=0.0244, over 4913.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2241, pruned_loss=0.02918, over 985215.43 frames.], batch size: 41, aishell_tot_loss[loss=0.1439, simple_loss=0.2303, pruned_loss=0.02872, over 983958.51 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.216, pruned_loss=0.02934, over 984873.83 frames.], batch size: 41, lr: 2.92e-04
+2022-06-19 06:35:27,838 INFO [train.py:874] (0/4) Epoch 28, batch 2800, aishell_loss[loss=0.1486, simple_loss=0.2321, pruned_loss=0.03258, over 4943.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2237, pruned_loss=0.02904, over 985022.96 frames.], batch size: 32, aishell_tot_loss[loss=0.1436, simple_loss=0.23, pruned_loss=0.02856, over 983893.36 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.216, pruned_loss=0.0294, over 984911.98 frames.], batch size: 32, lr: 2.92e-04
+2022-06-19 06:35:57,601 INFO [train.py:874] (0/4) Epoch 28, batch 2850, datatang_loss[loss=0.1369, simple_loss=0.2155, pruned_loss=0.02916, over 4943.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2226, pruned_loss=0.02893, over 984861.09 frames.], batch size: 50, aishell_tot_loss[loss=0.1435, simple_loss=0.2299, pruned_loss=0.02853, over 983978.71 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2152, pruned_loss=0.02932, over 984796.52 frames.], batch size: 50, lr: 2.92e-04
+2022-06-19 06:36:25,975 INFO [train.py:874] (0/4) Epoch 28, batch 2900, aishell_loss[loss=0.1384, simple_loss=0.2265, pruned_loss=0.02515, over 4924.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2223, pruned_loss=0.02879, over 985472.59 frames.], batch size: 46, aishell_tot_loss[loss=0.1433, simple_loss=0.2298, pruned_loss=0.02842, over 984349.15 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2153, pruned_loss=0.02926, over 985173.28 frames.], batch size: 46, lr: 2.92e-04
+2022-06-19 06:36:54,064 INFO [train.py:874] (0/4) Epoch 28, batch 2950, aishell_loss[loss=0.1379, simple_loss=0.2284, pruned_loss=0.02368, over 4925.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2229, pruned_loss=0.02878, over 985661.07 frames.], batch size: 46, aishell_tot_loss[loss=0.1433, simple_loss=0.23, pruned_loss=0.0283, over 984379.50 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2156, pruned_loss=0.02934, over 985515.91 frames.], batch size: 46, lr: 2.91e-04
+2022-06-19 06:37:24,543 INFO [train.py:874] (0/4) Epoch 28, batch 3000, aishell_loss[loss=0.1161, simple_loss=0.2051, pruned_loss=0.01362, over 4973.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2227, pruned_loss=0.02841, over 985701.91 frames.], batch size: 30, aishell_tot_loss[loss=0.1433, simple_loss=0.2302, pruned_loss=0.02819, over 984606.05 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2156, pruned_loss=0.02903, over 985463.57 frames.], batch size: 30, lr: 2.91e-04
+2022-06-19 06:37:24,545 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 06:37:41,303 INFO [train.py:914] (0/4) Epoch 28, validation: loss=0.1644, simple_loss=0.2482, pruned_loss=0.04027, over 1622729.00 frames.
+2022-06-19 06:38:10,638 INFO [train.py:874] (0/4) Epoch 28, batch 3050, datatang_loss[loss=0.1314, simple_loss=0.2119, pruned_loss=0.02547, over 4938.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2222, pruned_loss=0.02826, over 985855.27 frames.], batch size: 62, aishell_tot_loss[loss=0.1431, simple_loss=0.2298, pruned_loss=0.02817, over 984754.87 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2154, pruned_loss=0.02886, over 985636.85 frames.], batch size: 62, lr: 2.91e-04
+2022-06-19 06:38:41,007 INFO [train.py:874] (0/4) Epoch 28, batch 3100, datatang_loss[loss=0.124, simple_loss=0.213, pruned_loss=0.0175, over 4927.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2221, pruned_loss=0.02842, over 985714.18 frames.], batch size: 94, aishell_tot_loss[loss=0.1433, simple_loss=0.23, pruned_loss=0.02831, over 984846.91 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2154, pruned_loss=0.02879, over 985540.98 frames.], batch size: 94, lr: 2.91e-04
+2022-06-19 06:39:07,312 INFO [train.py:874] (0/4) Epoch 28, batch 3150, aishell_loss[loss=0.1504, simple_loss=0.2445, pruned_loss=0.02816, over 4911.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2227, pruned_loss=0.02841, over 985442.96 frames.], batch size: 41, aishell_tot_loss[loss=0.1431, simple_loss=0.2302, pruned_loss=0.02807, over 984897.09 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2154, pruned_loss=0.02899, over 985336.02 frames.], batch size: 41, lr: 2.91e-04
+2022-06-19 06:39:36,943 INFO [train.py:874] (0/4) Epoch 28, batch 3200, datatang_loss[loss=0.1364, simple_loss=0.2099, pruned_loss=0.03149, over 4929.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2223, pruned_loss=0.02821, over 985627.75 frames.], batch size: 79, aishell_tot_loss[loss=0.1426, simple_loss=0.2298, pruned_loss=0.02769, over 985061.27 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2152, pruned_loss=0.02914, over 985438.26 frames.], batch size: 79, lr: 2.91e-04
+2022-06-19 06:40:07,281 INFO [train.py:874] (0/4) Epoch 28, batch 3250, aishell_loss[loss=0.1493, simple_loss=0.2403, pruned_loss=0.02915, over 4954.00 frames.], tot_loss[loss=0.139, simple_loss=0.2215, pruned_loss=0.02826, over 985965.78 frames.], batch size: 56, aishell_tot_loss[loss=0.1426, simple_loss=0.2296, pruned_loss=0.02778, over 985319.22 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2147, pruned_loss=0.02905, over 985616.93 frames.], batch size: 56, lr: 2.91e-04
+2022-06-19 06:40:34,280 INFO [train.py:874] (0/4) Epoch 28, batch 3300, aishell_loss[loss=0.1333, simple_loss=0.2195, pruned_loss=0.02356, over 4955.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2219, pruned_loss=0.02833, over 985566.04 frames.], batch size: 31, aishell_tot_loss[loss=0.1427, simple_loss=0.2297, pruned_loss=0.02787, over 985200.24 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2149, pruned_loss=0.02897, over 985426.95 frames.], batch size: 31, lr: 2.91e-04
+2022-06-19 06:41:04,427 INFO [train.py:874] (0/4) Epoch 28, batch 3350, aishell_loss[loss=0.155, simple_loss=0.2353, pruned_loss=0.03736, over 4964.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2224, pruned_loss=0.02834, over 985570.08 frames.], batch size: 44, aishell_tot_loss[loss=0.1429, simple_loss=0.2297, pruned_loss=0.02807, over 985144.14 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2149, pruned_loss=0.02878, over 985556.41 frames.], batch size: 44, lr: 2.91e-04
+2022-06-19 06:41:34,599 INFO [train.py:874] (0/4) Epoch 28, batch 3400, datatang_loss[loss=0.1456, simple_loss=0.2233, pruned_loss=0.03391, over 4927.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2227, pruned_loss=0.02823, over 985554.56 frames.], batch size: 25, aishell_tot_loss[loss=0.1427, simple_loss=0.2298, pruned_loss=0.02779, over 985340.58 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.215, pruned_loss=0.02894, over 985384.19 frames.], batch size: 25, lr: 2.91e-04
+2022-06-19 06:42:02,106 INFO [train.py:874] (0/4) Epoch 28, batch 3450, datatang_loss[loss=0.1878, simple_loss=0.2447, pruned_loss=0.06548, over 4910.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2233, pruned_loss=0.02907, over 985716.74 frames.], batch size: 64, aishell_tot_loss[loss=0.1432, simple_loss=0.2302, pruned_loss=0.0281, over 985435.00 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2157, pruned_loss=0.02947, over 985501.05 frames.], batch size: 64, lr: 2.91e-04
+2022-06-19 06:42:33,135 INFO [train.py:874] (0/4) Epoch 28, batch 3500, datatang_loss[loss=0.1563, simple_loss=0.2344, pruned_loss=0.0391, over 4971.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2236, pruned_loss=0.02894, over 985786.82 frames.], batch size: 60, aishell_tot_loss[loss=0.1429, simple_loss=0.23, pruned_loss=0.02788, over 985304.64 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2161, pruned_loss=0.02963, over 985769.40 frames.], batch size: 60, lr: 2.91e-04
+2022-06-19 06:43:02,758 INFO [train.py:874] (0/4) Epoch 28, batch 3550, aishell_loss[loss=0.1499, simple_loss=0.242, pruned_loss=0.02892, over 4925.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2232, pruned_loss=0.0288, over 985510.04 frames.], batch size: 45, aishell_tot_loss[loss=0.1425, simple_loss=0.2296, pruned_loss=0.02774, over 985074.17 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2163, pruned_loss=0.02965, over 985755.45 frames.], batch size: 45, lr: 2.91e-04
+2022-06-19 06:43:30,715 INFO [train.py:874] (0/4) Epoch 28, batch 3600, aishell_loss[loss=0.148, simple_loss=0.2399, pruned_loss=0.02808, over 4919.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2233, pruned_loss=0.02913, over 985614.51 frames.], batch size: 33, aishell_tot_loss[loss=0.1431, simple_loss=0.2302, pruned_loss=0.02802, over 985176.16 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2162, pruned_loss=0.0297, over 985785.34 frames.], batch size: 33, lr: 2.91e-04
+2022-06-19 06:44:01,890 INFO [train.py:874] (0/4) Epoch 28, batch 3650, datatang_loss[loss=0.1326, simple_loss=0.2217, pruned_loss=0.02176, over 4931.00 frames.], tot_loss[loss=0.1404, simple_loss=0.223, pruned_loss=0.02887, over 985033.42 frames.], batch size: 94, aishell_tot_loss[loss=0.1429, simple_loss=0.2297, pruned_loss=0.02804, over 984762.15 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2161, pruned_loss=0.02948, over 985644.62 frames.], batch size: 94, lr: 2.91e-04
+2022-06-19 06:44:30,420 INFO [train.py:874] (0/4) Epoch 28, batch 3700, datatang_loss[loss=0.1286, simple_loss=0.2117, pruned_loss=0.02278, over 4893.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2225, pruned_loss=0.0287, over 984891.79 frames.], batch size: 59, aishell_tot_loss[loss=0.1426, simple_loss=0.2293, pruned_loss=0.02793, over 984626.67 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2159, pruned_loss=0.02945, over 985590.45 frames.], batch size: 59, lr: 2.91e-04
+2022-06-19 06:44:59,335 INFO [train.py:874] (0/4) Epoch 28, batch 3750, datatang_loss[loss=0.1113, simple_loss=0.1867, pruned_loss=0.018, over 4849.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2229, pruned_loss=0.02901, over 984867.77 frames.], batch size: 30, aishell_tot_loss[loss=0.1428, simple_loss=0.2294, pruned_loss=0.02806, over 984610.62 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.216, pruned_loss=0.0297, over 985542.88 frames.], batch size: 30, lr: 2.90e-04
+2022-06-19 06:45:28,857 INFO [train.py:874] (0/4) Epoch 28, batch 3800, aishell_loss[loss=0.1226, simple_loss=0.2083, pruned_loss=0.01846, over 4906.00 frames.], tot_loss[loss=0.14, simple_loss=0.2224, pruned_loss=0.02875, over 985179.09 frames.], batch size: 28, aishell_tot_loss[loss=0.1429, simple_loss=0.2296, pruned_loss=0.02813, over 984816.79 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2155, pruned_loss=0.02939, over 985616.64 frames.], batch size: 28, lr: 2.90e-04
+2022-06-19 06:45:56,690 INFO [train.py:874] (0/4) Epoch 28, batch 3850, datatang_loss[loss=0.1246, simple_loss=0.2069, pruned_loss=0.02116, over 4928.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2221, pruned_loss=0.02876, over 985313.97 frames.], batch size: 71, aishell_tot_loss[loss=0.143, simple_loss=0.2296, pruned_loss=0.0282, over 984882.64 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2151, pruned_loss=0.02933, over 985696.14 frames.], batch size: 71, lr: 2.90e-04
+2022-06-19 06:46:25,681 INFO [train.py:874] (0/4) Epoch 28, batch 3900, aishell_loss[loss=0.1605, simple_loss=0.2381, pruned_loss=0.04146, over 4977.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2221, pruned_loss=0.0286, over 985730.64 frames.], batch size: 39, aishell_tot_loss[loss=0.143, simple_loss=0.2296, pruned_loss=0.02817, over 985207.64 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2149, pruned_loss=0.02919, over 985827.70 frames.], batch size: 39, lr: 2.90e-04
+2022-06-19 06:46:53,821 INFO [train.py:874] (0/4) Epoch 28, batch 3950, aishell_loss[loss=0.147, simple_loss=0.2288, pruned_loss=0.03261, over 4971.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2224, pruned_loss=0.02889, over 985443.21 frames.], batch size: 51, aishell_tot_loss[loss=0.1433, simple_loss=0.2296, pruned_loss=0.0285, over 984888.35 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.215, pruned_loss=0.02913, over 985928.34 frames.], batch size: 51, lr: 2.90e-04
+2022-06-19 06:47:20,487 INFO [train.py:874] (0/4) Epoch 28, batch 4000, aishell_loss[loss=0.1417, simple_loss=0.2296, pruned_loss=0.02689, over 4978.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2232, pruned_loss=0.02855, over 985161.34 frames.], batch size: 51, aishell_tot_loss[loss=0.1434, simple_loss=0.2301, pruned_loss=0.02841, over 984725.43 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2151, pruned_loss=0.02891, over 985826.25 frames.], batch size: 51, lr: 2.90e-04
+2022-06-19 06:47:20,491 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 06:47:36,386 INFO [train.py:914] (0/4) Epoch 28, validation: loss=0.1646, simple_loss=0.2484, pruned_loss=0.04036, over 1622729.00 frames.
+2022-06-19 06:48:02,200 INFO [train.py:874] (0/4) Epoch 28, batch 4050, aishell_loss[loss=0.1543, simple_loss=0.2438, pruned_loss=0.03241, over 4960.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2237, pruned_loss=0.02856, over 985058.76 frames.], batch size: 69, aishell_tot_loss[loss=0.1434, simple_loss=0.2303, pruned_loss=0.02828, over 984683.32 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.215, pruned_loss=0.02902, over 985757.51 frames.], batch size: 69, lr: 2.90e-04
+2022-06-19 06:48:12,565 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-28.pt
+2022-06-19 06:49:07,971 INFO [train.py:874] (0/4) Epoch 29, batch 50, aishell_loss[loss=0.1374, simple_loss=0.2307, pruned_loss=0.02207, over 4972.00 frames.], tot_loss[loss=0.131, simple_loss=0.2119, pruned_loss=0.02502, over 218400.93 frames.], batch size: 44, aishell_tot_loss[loss=0.1355, simple_loss=0.2206, pruned_loss=0.02522, over 120464.52 frames.], datatang_tot_loss[loss=0.1259, simple_loss=0.2024, pruned_loss=0.0247, over 111579.32 frames.], batch size: 44, lr: 2.85e-04
+2022-06-19 06:49:37,782 INFO [train.py:874] (0/4) Epoch 29, batch 100, datatang_loss[loss=0.1079, simple_loss=0.1824, pruned_loss=0.01671, over 4931.00 frames.], tot_loss[loss=0.1344, simple_loss=0.216, pruned_loss=0.02635, over 388878.07 frames.], batch size: 57, aishell_tot_loss[loss=0.1372, simple_loss=0.223, pruned_loss=0.02572, over 222558.18 frames.], datatang_tot_loss[loss=0.131, simple_loss=0.2083, pruned_loss=0.02684, over 214747.08 frames.], batch size: 57, lr: 2.85e-04
+2022-06-19 06:49:41,429 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-116000.pt
+2022-06-19 06:50:11,747 INFO [train.py:874] (0/4) Epoch 29, batch 150, aishell_loss[loss=0.1594, simple_loss=0.238, pruned_loss=0.0404, over 4968.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2188, pruned_loss=0.02802, over 521278.23 frames.], batch size: 51, aishell_tot_loss[loss=0.1413, simple_loss=0.2264, pruned_loss=0.02809, over 319125.35 frames.], datatang_tot_loss[loss=0.1323, simple_loss=0.2096, pruned_loss=0.02747, over 298825.30 frames.], batch size: 51, lr: 2.85e-04
+2022-06-19 06:50:40,058 INFO [train.py:874] (0/4) Epoch 29, batch 200, datatang_loss[loss=0.139, simple_loss=0.2023, pruned_loss=0.03778, over 4976.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2198, pruned_loss=0.02764, over 624019.35 frames.], batch size: 34, aishell_tot_loss[loss=0.1413, simple_loss=0.2273, pruned_loss=0.02765, over 406084.17 frames.], datatang_tot_loss[loss=0.1326, simple_loss=0.2104, pruned_loss=0.02742, over 370655.16 frames.], batch size: 34, lr: 2.85e-04
+2022-06-19 06:51:09,813 INFO [train.py:874] (0/4) Epoch 29, batch 250, datatang_loss[loss=0.1198, simple_loss=0.2074, pruned_loss=0.01611, over 4918.00 frames.], tot_loss[loss=0.1371, simple_loss=0.22, pruned_loss=0.02706, over 704376.10 frames.], batch size: 77, aishell_tot_loss[loss=0.1412, simple_loss=0.2277, pruned_loss=0.02736, over 474417.72 frames.], datatang_tot_loss[loss=0.1322, simple_loss=0.2108, pruned_loss=0.02678, over 443208.19 frames.], batch size: 77, lr: 2.85e-04
+2022-06-19 06:51:37,965 INFO [train.py:874] (0/4) Epoch 29, batch 300, datatang_loss[loss=0.1438, simple_loss=0.2163, pruned_loss=0.03566, over 4927.00 frames.], tot_loss[loss=0.1389, simple_loss=0.222, pruned_loss=0.02789, over 766662.93 frames.], batch size: 50, aishell_tot_loss[loss=0.1418, simple_loss=0.2287, pruned_loss=0.02748, over 550084.10 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2122, pruned_loss=0.028, over 490036.06 frames.], batch size: 50, lr: 2.85e-04
+2022-06-19 06:52:07,632 INFO [train.py:874] (0/4) Epoch 29, batch 350, aishell_loss[loss=0.1365, simple_loss=0.2264, pruned_loss=0.02333, over 4937.00 frames.], tot_loss[loss=0.138, simple_loss=0.221, pruned_loss=0.02751, over 815241.68 frames.], batch size: 54, aishell_tot_loss[loss=0.1412, simple_loss=0.2278, pruned_loss=0.02728, over 610915.31 frames.], datatang_tot_loss[loss=0.1335, simple_loss=0.2115, pruned_loss=0.02773, over 537329.43 frames.], batch size: 54, lr: 2.85e-04
+2022-06-19 06:52:37,144 INFO [train.py:874] (0/4) Epoch 29, batch 400, aishell_loss[loss=0.151, simple_loss=0.2345, pruned_loss=0.03381, over 4925.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2214, pruned_loss=0.02742, over 853225.17 frames.], batch size: 33, aishell_tot_loss[loss=0.1412, simple_loss=0.2284, pruned_loss=0.02703, over 656926.04 frames.], datatang_tot_loss[loss=0.1338, simple_loss=0.212, pruned_loss=0.02786, over 588137.34 frames.], batch size: 33, lr: 2.85e-04
+2022-06-19 06:53:05,423 INFO [train.py:874] (0/4) Epoch 29, batch 450, datatang_loss[loss=0.1529, simple_loss=0.2257, pruned_loss=0.04004, over 4912.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2215, pruned_loss=0.02805, over 882461.98 frames.], batch size: 47, aishell_tot_loss[loss=0.1412, simple_loss=0.2276, pruned_loss=0.02739, over 699770.30 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2131, pruned_loss=0.02839, over 629697.00 frames.], batch size: 47, lr: 2.85e-04
+2022-06-19 06:53:35,539 INFO [train.py:874] (0/4) Epoch 29, batch 500, datatang_loss[loss=0.1233, simple_loss=0.2032, pruned_loss=0.02176, over 4925.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2209, pruned_loss=0.02766, over 905347.92 frames.], batch size: 77, aishell_tot_loss[loss=0.1406, simple_loss=0.2272, pruned_loss=0.02699, over 733401.22 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.213, pruned_loss=0.02834, over 671669.09 frames.], batch size: 77, lr: 2.84e-04
+2022-06-19 06:54:05,085 INFO [train.py:874] (0/4) Epoch 29, batch 550, aishell_loss[loss=0.1476, simple_loss=0.2291, pruned_loss=0.03304, over 4938.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2211, pruned_loss=0.02775, over 923050.47 frames.], batch size: 45, aishell_tot_loss[loss=0.1411, simple_loss=0.2279, pruned_loss=0.02716, over 757292.24 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2133, pruned_loss=0.02824, over 715575.46 frames.], batch size: 45, lr: 2.84e-04
+2022-06-19 06:54:33,117 INFO [train.py:874] (0/4) Epoch 29, batch 600, datatang_loss[loss=0.1104, simple_loss=0.1846, pruned_loss=0.01812, over 4964.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2211, pruned_loss=0.02789, over 936974.13 frames.], batch size: 67, aishell_tot_loss[loss=0.1418, simple_loss=0.2285, pruned_loss=0.02757, over 786362.77 frames.], datatang_tot_loss[loss=0.1343, simple_loss=0.2125, pruned_loss=0.02804, over 744876.67 frames.], batch size: 67, lr: 2.84e-04
+2022-06-19 06:55:02,475 INFO [train.py:874] (0/4) Epoch 29, batch 650, aishell_loss[loss=0.1401, simple_loss=0.2349, pruned_loss=0.02269, over 4944.00 frames.], tot_loss[loss=0.1393, simple_loss=0.222, pruned_loss=0.02826, over 947967.34 frames.], batch size: 45, aishell_tot_loss[loss=0.1428, simple_loss=0.2295, pruned_loss=0.02808, over 809856.71 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2129, pruned_loss=0.028, over 773420.26 frames.], batch size: 45, lr: 2.84e-04
+2022-06-19 06:55:31,655 INFO [train.py:874] (0/4) Epoch 29, batch 700, datatang_loss[loss=0.172, simple_loss=0.2504, pruned_loss=0.04675, over 4947.00 frames.], tot_loss[loss=0.14, simple_loss=0.2227, pruned_loss=0.02863, over 956555.16 frames.], batch size: 99, aishell_tot_loss[loss=0.1431, simple_loss=0.23, pruned_loss=0.02813, over 828388.87 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2138, pruned_loss=0.02848, over 801287.97 frames.], batch size: 99, lr: 2.84e-04
+2022-06-19 06:56:00,597 INFO [train.py:874] (0/4) Epoch 29, batch 750, aishell_loss[loss=0.137, simple_loss=0.2246, pruned_loss=0.02469, over 4982.00 frames.], tot_loss[loss=0.1403, simple_loss=0.223, pruned_loss=0.02877, over 962792.41 frames.], batch size: 39, aishell_tot_loss[loss=0.1431, simple_loss=0.2301, pruned_loss=0.02808, over 847384.09 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2143, pruned_loss=0.02882, over 822191.51 frames.], batch size: 39, lr: 2.84e-04
+2022-06-19 06:56:31,384 INFO [train.py:874] (0/4) Epoch 29, batch 800, aishell_loss[loss=0.1587, simple_loss=0.2437, pruned_loss=0.03688, over 4928.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2231, pruned_loss=0.0289, over 967484.06 frames.], batch size: 58, aishell_tot_loss[loss=0.1429, simple_loss=0.2297, pruned_loss=0.028, over 865084.04 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2147, pruned_loss=0.02913, over 839276.19 frames.], batch size: 58, lr: 2.84e-04
+2022-06-19 06:57:01,120 INFO [train.py:874] (0/4) Epoch 29, batch 850, aishell_loss[loss=0.1304, simple_loss=0.2104, pruned_loss=0.02517, over 4911.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2226, pruned_loss=0.0289, over 971129.21 frames.], batch size: 52, aishell_tot_loss[loss=0.1432, simple_loss=0.2299, pruned_loss=0.0282, over 879253.52 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.214, pruned_loss=0.02901, over 856021.10 frames.], batch size: 52, lr: 2.84e-04
+2022-06-19 06:57:29,417 INFO [train.py:874] (0/4) Epoch 29, batch 900, aishell_loss[loss=0.1263, simple_loss=0.2183, pruned_loss=0.01716, over 4945.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2219, pruned_loss=0.02852, over 974636.51 frames.], batch size: 54, aishell_tot_loss[loss=0.1424, simple_loss=0.2294, pruned_loss=0.02773, over 890951.08 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2139, pruned_loss=0.02912, over 872625.13 frames.], batch size: 54, lr: 2.84e-04
+2022-06-19 06:57:59,985 INFO [train.py:874] (0/4) Epoch 29, batch 950, aishell_loss[loss=0.1613, simple_loss=0.252, pruned_loss=0.03531, over 4968.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2219, pruned_loss=0.02833, over 976883.85 frames.], batch size: 61, aishell_tot_loss[loss=0.1429, simple_loss=0.2297, pruned_loss=0.02799, over 901974.18 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2137, pruned_loss=0.02867, over 885847.01 frames.], batch size: 61, lr: 2.84e-04
+2022-06-19 06:58:28,346 INFO [train.py:874] (0/4) Epoch 29, batch 1000, aishell_loss[loss=0.1608, simple_loss=0.2369, pruned_loss=0.04237, over 4924.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2217, pruned_loss=0.02866, over 978615.25 frames.], batch size: 33, aishell_tot_loss[loss=0.1429, simple_loss=0.2294, pruned_loss=0.02822, over 910537.70 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2141, pruned_loss=0.0288, over 898876.44 frames.], batch size: 33, lr: 2.84e-04
+2022-06-19 06:58:28,349 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 06:58:44,050 INFO [train.py:914] (0/4) Epoch 29, validation: loss=0.165, simple_loss=0.2483, pruned_loss=0.04086, over 1622729.00 frames.
+2022-06-19 06:59:14,569 INFO [train.py:874] (0/4) Epoch 29, batch 1050, aishell_loss[loss=0.1502, simple_loss=0.2373, pruned_loss=0.0316, over 4942.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2218, pruned_loss=0.02866, over 979862.95 frames.], batch size: 58, aishell_tot_loss[loss=0.1425, simple_loss=0.229, pruned_loss=0.02804, over 918700.88 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2147, pruned_loss=0.02902, over 909476.12 frames.], batch size: 58, lr: 2.84e-04
+2022-06-19 06:59:43,105 INFO [train.py:874] (0/4) Epoch 29, batch 1100, aishell_loss[loss=0.1312, simple_loss=0.2163, pruned_loss=0.02304, over 4931.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2219, pruned_loss=0.02861, over 981357.49 frames.], batch size: 27, aishell_tot_loss[loss=0.1428, simple_loss=0.2294, pruned_loss=0.02811, over 926325.44 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2145, pruned_loss=0.02893, over 918969.30 frames.], batch size: 27, lr: 2.84e-04
+2022-06-19 07:00:12,939 INFO [train.py:874] (0/4) Epoch 29, batch 1150, aishell_loss[loss=0.1295, simple_loss=0.2067, pruned_loss=0.02613, over 4859.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2222, pruned_loss=0.02883, over 982455.10 frames.], batch size: 28, aishell_tot_loss[loss=0.143, simple_loss=0.2295, pruned_loss=0.02824, over 932768.33 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2148, pruned_loss=0.02907, over 927586.61 frames.], batch size: 28, lr: 2.84e-04
+2022-06-19 07:00:42,821 INFO [train.py:874] (0/4) Epoch 29, batch 1200, datatang_loss[loss=0.1245, simple_loss=0.2013, pruned_loss=0.02389, over 4896.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2217, pruned_loss=0.02886, over 983170.10 frames.], batch size: 47, aishell_tot_loss[loss=0.143, simple_loss=0.2294, pruned_loss=0.02824, over 937153.39 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2149, pruned_loss=0.02911, over 936376.78 frames.], batch size: 47, lr: 2.84e-04
+2022-06-19 07:01:11,893 INFO [train.py:874] (0/4) Epoch 29, batch 1250, aishell_loss[loss=0.1435, simple_loss=0.2318, pruned_loss=0.02763, over 4948.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2224, pruned_loss=0.02908, over 984051.57 frames.], batch size: 32, aishell_tot_loss[loss=0.1436, simple_loss=0.2301, pruned_loss=0.02854, over 942346.40 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2152, pruned_loss=0.02908, over 943050.30 frames.], batch size: 32, lr: 2.84e-04
+2022-06-19 07:01:42,640 INFO [train.py:874] (0/4) Epoch 29, batch 1300, datatang_loss[loss=0.1455, simple_loss=0.2266, pruned_loss=0.03223, over 4923.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2227, pruned_loss=0.02896, over 984103.57 frames.], batch size: 64, aishell_tot_loss[loss=0.1431, simple_loss=0.2296, pruned_loss=0.02829, over 947027.71 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.216, pruned_loss=0.02926, over 948158.45 frames.], batch size: 64, lr: 2.84e-04
+2022-06-19 07:02:12,782 INFO [train.py:874] (0/4) Epoch 29, batch 1350, aishell_loss[loss=0.1259, simple_loss=0.2167, pruned_loss=0.0176, over 4922.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2219, pruned_loss=0.0284, over 984739.29 frames.], batch size: 58, aishell_tot_loss[loss=0.1429, simple_loss=0.2296, pruned_loss=0.02808, over 951402.18 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2152, pruned_loss=0.02889, over 953050.25 frames.], batch size: 58, lr: 2.83e-04
+2022-06-19 07:02:41,614 INFO [train.py:874] (0/4) Epoch 29, batch 1400, datatang_loss[loss=0.1497, simple_loss=0.2324, pruned_loss=0.03349, over 4936.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2223, pruned_loss=0.02854, over 984810.81 frames.], batch size: 94, aishell_tot_loss[loss=0.1429, simple_loss=0.2296, pruned_loss=0.02811, over 955645.35 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2152, pruned_loss=0.02898, over 956551.72 frames.], batch size: 94, lr: 2.83e-04
+2022-06-19 07:03:11,147 INFO [train.py:874] (0/4) Epoch 29, batch 1450, aishell_loss[loss=0.1304, simple_loss=0.2219, pruned_loss=0.01944, over 4965.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2219, pruned_loss=0.0282, over 985138.79 frames.], batch size: 40, aishell_tot_loss[loss=0.1424, simple_loss=0.2292, pruned_loss=0.02781, over 959450.62 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2151, pruned_loss=0.02891, over 959860.61 frames.], batch size: 40, lr: 2.83e-04
+2022-06-19 07:03:40,958 INFO [train.py:874] (0/4) Epoch 29, batch 1500, datatang_loss[loss=0.1137, simple_loss=0.1913, pruned_loss=0.0181, over 4929.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2206, pruned_loss=0.02755, over 985374.89 frames.], batch size: 42, aishell_tot_loss[loss=0.1418, simple_loss=0.2286, pruned_loss=0.02746, over 961959.60 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2144, pruned_loss=0.02849, over 963580.36 frames.], batch size: 42, lr: 2.83e-04
+2022-06-19 07:04:10,550 INFO [train.py:874] (0/4) Epoch 29, batch 1550, datatang_loss[loss=0.1308, simple_loss=0.2147, pruned_loss=0.02343, over 4946.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2208, pruned_loss=0.02773, over 985550.77 frames.], batch size: 91, aishell_tot_loss[loss=0.142, simple_loss=0.229, pruned_loss=0.02752, over 964765.61 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.214, pruned_loss=0.02854, over 966285.26 frames.], batch size: 91, lr: 2.83e-04
+2022-06-19 07:04:40,809 INFO [train.py:874] (0/4) Epoch 29, batch 1600, aishell_loss[loss=0.1161, simple_loss=0.2016, pruned_loss=0.01533, over 4881.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2204, pruned_loss=0.02787, over 985565.91 frames.], batch size: 28, aishell_tot_loss[loss=0.1419, simple_loss=0.2288, pruned_loss=0.02753, over 966785.78 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.214, pruned_loss=0.0286, over 968983.05 frames.], batch size: 28, lr: 2.83e-04
+2022-06-19 07:05:09,434 INFO [train.py:874] (0/4) Epoch 29, batch 1650, datatang_loss[loss=0.1547, simple_loss=0.2333, pruned_loss=0.03802, over 4955.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2203, pruned_loss=0.02764, over 985619.61 frames.], batch size: 99, aishell_tot_loss[loss=0.1419, simple_loss=0.229, pruned_loss=0.02739, over 968840.00 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2135, pruned_loss=0.02843, over 971147.10 frames.], batch size: 99, lr: 2.83e-04
+2022-06-19 07:05:37,787 INFO [train.py:874] (0/4) Epoch 29, batch 1700, aishell_loss[loss=0.1567, simple_loss=0.2454, pruned_loss=0.03396, over 4862.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2201, pruned_loss=0.02758, over 984939.52 frames.], batch size: 35, aishell_tot_loss[loss=0.1419, simple_loss=0.2288, pruned_loss=0.02752, over 970447.39 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2132, pruned_loss=0.02819, over 972528.24 frames.], batch size: 35, lr: 2.83e-04
+2022-06-19 07:06:08,732 INFO [train.py:874] (0/4) Epoch 29, batch 1750, datatang_loss[loss=0.1225, simple_loss=0.2019, pruned_loss=0.02156, over 4927.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2196, pruned_loss=0.02759, over 984969.49 frames.], batch size: 81, aishell_tot_loss[loss=0.1413, simple_loss=0.2279, pruned_loss=0.02733, over 972069.48 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2134, pruned_loss=0.02833, over 974089.71 frames.], batch size: 81, lr: 2.83e-04
+2022-06-19 07:06:37,233 INFO [train.py:874] (0/4) Epoch 29, batch 1800, datatang_loss[loss=0.1308, simple_loss=0.2089, pruned_loss=0.02635, over 4914.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2202, pruned_loss=0.02748, over 984901.31 frames.], batch size: 83, aishell_tot_loss[loss=0.1412, simple_loss=0.2281, pruned_loss=0.02713, over 973289.28 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2136, pruned_loss=0.02833, over 975590.62 frames.], batch size: 83, lr: 2.83e-04
+2022-06-19 07:07:05,211 INFO [train.py:874] (0/4) Epoch 29, batch 1850, datatang_loss[loss=0.1219, simple_loss=0.2078, pruned_loss=0.01796, over 4940.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2209, pruned_loss=0.02732, over 984952.49 frames.], batch size: 42, aishell_tot_loss[loss=0.1412, simple_loss=0.2285, pruned_loss=0.02693, over 974914.45 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2133, pruned_loss=0.02835, over 976551.97 frames.], batch size: 42, lr: 2.83e-04
+2022-06-19 07:07:34,100 INFO [train.py:874] (0/4) Epoch 29, batch 1900, aishell_loss[loss=0.1471, simple_loss=0.2392, pruned_loss=0.02752, over 4973.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2219, pruned_loss=0.02768, over 985069.68 frames.], batch size: 61, aishell_tot_loss[loss=0.142, simple_loss=0.2294, pruned_loss=0.0273, over 976319.16 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.213, pruned_loss=0.02833, over 977487.08 frames.], batch size: 61, lr: 2.83e-04
+2022-06-19 07:08:03,015 INFO [train.py:874] (0/4) Epoch 29, batch 1950, aishell_loss[loss=0.1299, simple_loss=0.2189, pruned_loss=0.02043, over 4922.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2223, pruned_loss=0.02803, over 985184.58 frames.], batch size: 41, aishell_tot_loss[loss=0.1421, simple_loss=0.2296, pruned_loss=0.02731, over 977466.65 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2132, pruned_loss=0.02868, over 978406.56 frames.], batch size: 41, lr: 2.83e-04
+2022-06-19 07:08:32,531 INFO [train.py:874] (0/4) Epoch 29, batch 2000, aishell_loss[loss=0.1218, simple_loss=0.2073, pruned_loss=0.01816, over 4952.00 frames.], tot_loss[loss=0.139, simple_loss=0.2228, pruned_loss=0.02762, over 985159.22 frames.], batch size: 25, aishell_tot_loss[loss=0.1416, simple_loss=0.2293, pruned_loss=0.02696, over 978454.54 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2134, pruned_loss=0.02867, over 979139.71 frames.], batch size: 25, lr: 2.83e-04
+2022-06-19 07:08:32,533 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 07:08:49,527 INFO [train.py:914] (0/4) Epoch 29, validation: loss=0.1647, simple_loss=0.2482, pruned_loss=0.04062, over 1622729.00 frames.
+2022-06-19 07:09:18,419 INFO [train.py:874] (0/4) Epoch 29, batch 2050, datatang_loss[loss=0.1605, simple_loss=0.2482, pruned_loss=0.03642, over 4963.00 frames.], tot_loss[loss=0.1384, simple_loss=0.222, pruned_loss=0.02743, over 985650.52 frames.], batch size: 99, aishell_tot_loss[loss=0.1415, simple_loss=0.2292, pruned_loss=0.02687, over 979503.20 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2132, pruned_loss=0.0285, over 980115.96 frames.], batch size: 99, lr: 2.83e-04
+2022-06-19 07:09:48,751 INFO [train.py:874] (0/4) Epoch 29, batch 2100, datatang_loss[loss=0.1565, simple_loss=0.232, pruned_loss=0.04056, over 4904.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2219, pruned_loss=0.02789, over 985516.66 frames.], batch size: 47, aishell_tot_loss[loss=0.1413, simple_loss=0.2289, pruned_loss=0.02682, over 979863.84 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2139, pruned_loss=0.02894, over 980986.54 frames.], batch size: 47, lr: 2.83e-04
+2022-06-19 07:10:18,286 INFO [train.py:874] (0/4) Epoch 29, batch 2150, datatang_loss[loss=0.1466, simple_loss=0.2351, pruned_loss=0.02903, over 4960.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2227, pruned_loss=0.02808, over 985332.17 frames.], batch size: 99, aishell_tot_loss[loss=0.1417, simple_loss=0.2293, pruned_loss=0.02708, over 980579.76 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2144, pruned_loss=0.0289, over 981277.76 frames.], batch size: 99, lr: 2.82e-04
+2022-06-19 07:10:47,508 INFO [train.py:874] (0/4) Epoch 29, batch 2200, datatang_loss[loss=0.1299, simple_loss=0.217, pruned_loss=0.02134, over 4955.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2233, pruned_loss=0.02816, over 985366.35 frames.], batch size: 86, aishell_tot_loss[loss=0.1423, simple_loss=0.23, pruned_loss=0.02728, over 980980.67 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2145, pruned_loss=0.02881, over 981931.42 frames.], batch size: 86, lr: 2.82e-04
+2022-06-19 07:11:17,485 INFO [train.py:874] (0/4) Epoch 29, batch 2250, datatang_loss[loss=0.1597, simple_loss=0.2311, pruned_loss=0.0441, over 4963.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2234, pruned_loss=0.02842, over 985277.61 frames.], batch size: 45, aishell_tot_loss[loss=0.1423, simple_loss=0.2299, pruned_loss=0.02735, over 981256.76 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.215, pruned_loss=0.02903, over 982481.42 frames.], batch size: 45, lr: 2.82e-04
+2022-06-19 07:11:46,210 INFO [train.py:874] (0/4) Epoch 29, batch 2300, datatang_loss[loss=0.1193, simple_loss=0.2103, pruned_loss=0.01419, over 4933.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2221, pruned_loss=0.02835, over 985259.89 frames.], batch size: 83, aishell_tot_loss[loss=0.142, simple_loss=0.2294, pruned_loss=0.02725, over 981316.05 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2148, pruned_loss=0.02905, over 983165.32 frames.], batch size: 83, lr: 2.82e-04
+2022-06-19 07:12:16,287 INFO [train.py:874] (0/4) Epoch 29, batch 2350, datatang_loss[loss=0.1488, simple_loss=0.2255, pruned_loss=0.03603, over 4952.00 frames.], tot_loss[loss=0.1396, simple_loss=0.222, pruned_loss=0.02861, over 984697.01 frames.], batch size: 60, aishell_tot_loss[loss=0.1422, simple_loss=0.2292, pruned_loss=0.02755, over 981344.92 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.215, pruned_loss=0.02907, over 983235.99 frames.], batch size: 60, lr: 2.82e-04
+2022-06-19 07:12:46,285 INFO [train.py:874] (0/4) Epoch 29, batch 2400, aishell_loss[loss=0.1409, simple_loss=0.2386, pruned_loss=0.02158, over 4921.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2218, pruned_loss=0.02831, over 984491.23 frames.], batch size: 41, aishell_tot_loss[loss=0.1426, simple_loss=0.2298, pruned_loss=0.02772, over 981624.29 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2146, pruned_loss=0.02863, over 983259.59 frames.], batch size: 41, lr: 2.82e-04
+2022-06-19 07:13:14,701 INFO [train.py:874] (0/4) Epoch 29, batch 2450, aishell_loss[loss=0.1774, simple_loss=0.2586, pruned_loss=0.04812, over 4888.00 frames.], tot_loss[loss=0.1385, simple_loss=0.221, pruned_loss=0.02805, over 984840.00 frames.], batch size: 42, aishell_tot_loss[loss=0.1427, simple_loss=0.2297, pruned_loss=0.02783, over 982165.88 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2137, pruned_loss=0.02826, over 983572.24 frames.], batch size: 42, lr: 2.82e-04
+2022-06-19 07:13:44,298 INFO [train.py:874] (0/4) Epoch 29, batch 2500, aishell_loss[loss=0.1553, simple_loss=0.2465, pruned_loss=0.03205, over 4966.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2201, pruned_loss=0.02774, over 984690.07 frames.], batch size: 79, aishell_tot_loss[loss=0.1418, simple_loss=0.2287, pruned_loss=0.02744, over 982337.60 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2135, pruned_loss=0.02834, over 983731.85 frames.], batch size: 79, lr: 2.82e-04
+2022-06-19 07:14:14,335 INFO [train.py:874] (0/4) Epoch 29, batch 2550, aishell_loss[loss=0.1486, simple_loss=0.2355, pruned_loss=0.03085, over 4978.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2216, pruned_loss=0.02789, over 984755.92 frames.], batch size: 39, aishell_tot_loss[loss=0.1419, simple_loss=0.2293, pruned_loss=0.02726, over 982776.53 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2138, pruned_loss=0.02867, over 983806.00 frames.], batch size: 39, lr: 2.82e-04
+2022-06-19 07:14:43,060 INFO [train.py:874] (0/4) Epoch 29, batch 2600, aishell_loss[loss=0.1476, simple_loss=0.2366, pruned_loss=0.02931, over 4858.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2213, pruned_loss=0.02789, over 985468.54 frames.], batch size: 35, aishell_tot_loss[loss=0.1422, simple_loss=0.2296, pruned_loss=0.02745, over 983109.12 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2135, pruned_loss=0.02843, over 984559.15 frames.], batch size: 35, lr: 2.82e-04
+2022-06-19 07:15:12,750 INFO [train.py:874] (0/4) Epoch 29, batch 2650, datatang_loss[loss=0.1429, simple_loss=0.232, pruned_loss=0.02688, over 4919.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2218, pruned_loss=0.02781, over 985762.63 frames.], batch size: 94, aishell_tot_loss[loss=0.1419, simple_loss=0.2292, pruned_loss=0.02729, over 983651.87 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.214, pruned_loss=0.02853, over 984739.61 frames.], batch size: 94, lr: 2.82e-04
+2022-06-19 07:15:41,969 INFO [train.py:874] (0/4) Epoch 29, batch 2700, datatang_loss[loss=0.126, simple_loss=0.1987, pruned_loss=0.02668, over 4923.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2217, pruned_loss=0.02757, over 985937.75 frames.], batch size: 73, aishell_tot_loss[loss=0.1417, simple_loss=0.229, pruned_loss=0.02724, over 984015.59 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2137, pruned_loss=0.02833, over 984964.74 frames.], batch size: 73, lr: 2.82e-04
+2022-06-19 07:16:09,396 INFO [train.py:874] (0/4) Epoch 29, batch 2750, aishell_loss[loss=0.1264, simple_loss=0.2141, pruned_loss=0.01933, over 4935.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2226, pruned_loss=0.02835, over 985925.87 frames.], batch size: 54, aishell_tot_loss[loss=0.1424, simple_loss=0.2297, pruned_loss=0.02751, over 984232.77 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2138, pruned_loss=0.02884, over 985075.81 frames.], batch size: 54, lr: 2.82e-04
+2022-06-19 07:16:40,486 INFO [train.py:874] (0/4) Epoch 29, batch 2800, datatang_loss[loss=0.1345, simple_loss=0.2058, pruned_loss=0.03165, over 4836.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2229, pruned_loss=0.02866, over 985836.50 frames.], batch size: 25, aishell_tot_loss[loss=0.1426, simple_loss=0.23, pruned_loss=0.0276, over 984458.64 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2145, pruned_loss=0.02908, over 985048.00 frames.], batch size: 25, lr: 2.82e-04
+2022-06-19 07:17:10,568 INFO [train.py:874] (0/4) Epoch 29, batch 2850, aishell_loss[loss=0.1509, simple_loss=0.2334, pruned_loss=0.03419, over 4914.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2231, pruned_loss=0.02863, over 985662.00 frames.], batch size: 41, aishell_tot_loss[loss=0.1426, simple_loss=0.23, pruned_loss=0.02759, over 984444.04 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2147, pruned_loss=0.02914, over 985136.85 frames.], batch size: 41, lr: 2.82e-04
+2022-06-19 07:17:38,887 INFO [train.py:874] (0/4) Epoch 29, batch 2900, aishell_loss[loss=0.1584, simple_loss=0.2422, pruned_loss=0.03728, over 4856.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2224, pruned_loss=0.02806, over 986061.68 frames.], batch size: 37, aishell_tot_loss[loss=0.1424, simple_loss=0.2297, pruned_loss=0.02754, over 984954.43 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2142, pruned_loss=0.02862, over 985268.63 frames.], batch size: 37, lr: 2.82e-04
+2022-06-19 07:18:09,829 INFO [train.py:874] (0/4) Epoch 29, batch 2950, datatang_loss[loss=0.117, simple_loss=0.2, pruned_loss=0.01703, over 4923.00 frames.], tot_loss[loss=0.1388, simple_loss=0.222, pruned_loss=0.0278, over 985810.19 frames.], batch size: 79, aishell_tot_loss[loss=0.1424, simple_loss=0.2298, pruned_loss=0.02749, over 985012.91 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2139, pruned_loss=0.0284, over 985178.55 frames.], batch size: 79, lr: 2.82e-04
+2022-06-19 07:18:38,430 INFO [train.py:874] (0/4) Epoch 29, batch 3000, datatang_loss[loss=0.1237, simple_loss=0.2126, pruned_loss=0.01736, over 4920.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2218, pruned_loss=0.02759, over 985678.94 frames.], batch size: 83, aishell_tot_loss[loss=0.142, simple_loss=0.2295, pruned_loss=0.02732, over 984955.14 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2138, pruned_loss=0.02832, over 985273.92 frames.], batch size: 83, lr: 2.81e-04
+2022-06-19 07:18:38,433 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 07:18:54,223 INFO [train.py:914] (0/4) Epoch 29, validation: loss=0.165, simple_loss=0.2488, pruned_loss=0.0406, over 1622729.00 frames.
+2022-06-19 07:19:24,184 INFO [train.py:874] (0/4) Epoch 29, batch 3050, datatang_loss[loss=0.1383, simple_loss=0.2163, pruned_loss=0.03018, over 4942.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2215, pruned_loss=0.02786, over 985957.64 frames.], batch size: 69, aishell_tot_loss[loss=0.1424, simple_loss=0.2298, pruned_loss=0.02751, over 985088.13 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2137, pruned_loss=0.02835, over 985563.60 frames.], batch size: 69, lr: 2.81e-04
+2022-06-19 07:19:51,652 INFO [train.py:874] (0/4) Epoch 29, batch 3100, aishell_loss[loss=0.152, simple_loss=0.2478, pruned_loss=0.02812, over 4930.00 frames.], tot_loss[loss=0.1394, simple_loss=0.222, pruned_loss=0.02842, over 985504.35 frames.], batch size: 68, aishell_tot_loss[loss=0.1429, simple_loss=0.2302, pruned_loss=0.02784, over 985220.09 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2138, pruned_loss=0.0286, over 985098.34 frames.], batch size: 68, lr: 2.81e-04
+2022-06-19 07:20:22,493 INFO [train.py:874] (0/4) Epoch 29, batch 3150, datatang_loss[loss=0.117, simple_loss=0.1987, pruned_loss=0.01768, over 4908.00 frames.], tot_loss[loss=0.1388, simple_loss=0.222, pruned_loss=0.02782, over 985358.75 frames.], batch size: 64, aishell_tot_loss[loss=0.1426, simple_loss=0.23, pruned_loss=0.02757, over 984842.11 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2138, pruned_loss=0.02829, over 985401.69 frames.], batch size: 64, lr: 2.81e-04
+2022-06-19 07:20:52,233 INFO [train.py:874] (0/4) Epoch 29, batch 3200, aishell_loss[loss=0.1421, simple_loss=0.2226, pruned_loss=0.03085, over 4910.00 frames.], tot_loss[loss=0.1389, simple_loss=0.222, pruned_loss=0.02792, over 985012.10 frames.], batch size: 52, aishell_tot_loss[loss=0.1426, simple_loss=0.2302, pruned_loss=0.02754, over 984873.21 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2137, pruned_loss=0.02839, over 985075.11 frames.], batch size: 52, lr: 2.81e-04
+2022-06-19 07:21:20,844 INFO [train.py:874] (0/4) Epoch 29, batch 3250, aishell_loss[loss=0.1349, simple_loss=0.226, pruned_loss=0.0219, over 4967.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2223, pruned_loss=0.02827, over 985185.93 frames.], batch size: 69, aishell_tot_loss[loss=0.1428, simple_loss=0.2303, pruned_loss=0.02762, over 984949.07 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2139, pruned_loss=0.02866, over 985184.11 frames.], batch size: 69, lr: 2.81e-04
+2022-06-19 07:21:51,294 INFO [train.py:874] (0/4) Epoch 29, batch 3300, aishell_loss[loss=0.1461, simple_loss=0.2285, pruned_loss=0.03188, over 4867.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2222, pruned_loss=0.02835, over 985300.72 frames.], batch size: 35, aishell_tot_loss[loss=0.1427, simple_loss=0.2301, pruned_loss=0.02762, over 984885.80 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.214, pruned_loss=0.02878, over 985401.97 frames.], batch size: 35, lr: 2.81e-04
+2022-06-19 07:22:20,992 INFO [train.py:874] (0/4) Epoch 29, batch 3350, datatang_loss[loss=0.1237, simple_loss=0.2081, pruned_loss=0.01969, over 4961.00 frames.], tot_loss[loss=0.139, simple_loss=0.222, pruned_loss=0.02801, over 985397.66 frames.], batch size: 86, aishell_tot_loss[loss=0.143, simple_loss=0.2304, pruned_loss=0.02779, over 984798.67 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2134, pruned_loss=0.02828, over 985631.28 frames.], batch size: 86, lr: 2.81e-04
+2022-06-19 07:22:48,945 INFO [train.py:874] (0/4) Epoch 29, batch 3400, datatang_loss[loss=0.1157, simple_loss=0.1962, pruned_loss=0.01759, over 4927.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2217, pruned_loss=0.02794, over 985656.39 frames.], batch size: 73, aishell_tot_loss[loss=0.1427, simple_loss=0.2301, pruned_loss=0.02765, over 984951.02 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2138, pruned_loss=0.02832, over 985784.20 frames.], batch size: 73, lr: 2.81e-04
+2022-06-19 07:23:20,399 INFO [train.py:874] (0/4) Epoch 29, batch 3450, datatang_loss[loss=0.1405, simple_loss=0.2179, pruned_loss=0.03151, over 4899.00 frames.], tot_loss[loss=0.139, simple_loss=0.222, pruned_loss=0.02802, over 985563.64 frames.], batch size: 47, aishell_tot_loss[loss=0.1424, simple_loss=0.2298, pruned_loss=0.0275, over 984750.87 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2142, pruned_loss=0.02858, over 985979.50 frames.], batch size: 47, lr: 2.81e-04
+2022-06-19 07:23:50,334 INFO [train.py:874] (0/4) Epoch 29, batch 3500, aishell_loss[loss=0.1368, simple_loss=0.231, pruned_loss=0.02131, over 4935.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2216, pruned_loss=0.02809, over 985520.63 frames.], batch size: 49, aishell_tot_loss[loss=0.1423, simple_loss=0.2295, pruned_loss=0.02752, over 984810.05 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2141, pruned_loss=0.02865, over 985944.62 frames.], batch size: 49, lr: 2.81e-04
+2022-06-19 07:24:19,341 INFO [train.py:874] (0/4) Epoch 29, batch 3550, aishell_loss[loss=0.1367, simple_loss=0.2235, pruned_loss=0.02493, over 4826.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2215, pruned_loss=0.02808, over 985299.99 frames.], batch size: 29, aishell_tot_loss[loss=0.1424, simple_loss=0.2296, pruned_loss=0.0276, over 984840.22 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2136, pruned_loss=0.02857, over 985711.78 frames.], batch size: 29, lr: 2.81e-04
+2022-06-19 07:24:50,025 INFO [train.py:874] (0/4) Epoch 29, batch 3600, datatang_loss[loss=0.1085, simple_loss=0.183, pruned_loss=0.01702, over 4955.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2212, pruned_loss=0.02784, over 985347.87 frames.], batch size: 31, aishell_tot_loss[loss=0.1425, simple_loss=0.2298, pruned_loss=0.02754, over 984723.64 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2131, pruned_loss=0.02839, over 985880.16 frames.], batch size: 31, lr: 2.81e-04
+2022-06-19 07:25:18,915 INFO [train.py:874] (0/4) Epoch 29, batch 3650, datatang_loss[loss=0.1347, simple_loss=0.2129, pruned_loss=0.02826, over 4975.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2212, pruned_loss=0.02757, over 985533.64 frames.], batch size: 60, aishell_tot_loss[loss=0.1424, simple_loss=0.2299, pruned_loss=0.02741, over 984779.04 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2133, pruned_loss=0.02818, over 986021.57 frames.], batch size: 60, lr: 2.81e-04
+2022-06-19 07:25:46,583 INFO [train.py:874] (0/4) Epoch 29, batch 3700, aishell_loss[loss=0.1351, simple_loss=0.2303, pruned_loss=0.01998, over 4919.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2207, pruned_loss=0.02779, over 985563.95 frames.], batch size: 46, aishell_tot_loss[loss=0.1424, simple_loss=0.2296, pruned_loss=0.02756, over 984861.04 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2131, pruned_loss=0.02823, over 985990.19 frames.], batch size: 46, lr: 2.81e-04
+2022-06-19 07:26:16,110 INFO [train.py:874] (0/4) Epoch 29, batch 3750, aishell_loss[loss=0.1387, simple_loss=0.2252, pruned_loss=0.02605, over 4867.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2211, pruned_loss=0.02776, over 985181.90 frames.], batch size: 35, aishell_tot_loss[loss=0.1419, simple_loss=0.229, pruned_loss=0.02742, over 984667.41 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2133, pruned_loss=0.02833, over 985870.88 frames.], batch size: 35, lr: 2.81e-04
+2022-06-19 07:26:43,585 INFO [train.py:874] (0/4) Epoch 29, batch 3800, datatang_loss[loss=0.1335, simple_loss=0.2113, pruned_loss=0.0279, over 4943.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2215, pruned_loss=0.02797, over 985136.81 frames.], batch size: 69, aishell_tot_loss[loss=0.1423, simple_loss=0.2293, pruned_loss=0.02762, over 984663.67 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2135, pruned_loss=0.02834, over 985802.09 frames.], batch size: 69, lr: 2.81e-04
+2022-06-19 07:27:13,068 INFO [train.py:874] (0/4) Epoch 29, batch 3850, aishell_loss[loss=0.1307, simple_loss=0.2224, pruned_loss=0.01951, over 4880.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2213, pruned_loss=0.0277, over 985012.05 frames.], batch size: 34, aishell_tot_loss[loss=0.142, simple_loss=0.2289, pruned_loss=0.02752, over 984551.77 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2135, pruned_loss=0.02816, over 985765.98 frames.], batch size: 34, lr: 2.80e-04
+2022-06-19 07:27:40,474 INFO [train.py:874] (0/4) Epoch 29, batch 3900, datatang_loss[loss=0.117, simple_loss=0.1954, pruned_loss=0.01935, over 4917.00 frames.], tot_loss[loss=0.1374, simple_loss=0.22, pruned_loss=0.02735, over 985135.20 frames.], batch size: 57, aishell_tot_loss[loss=0.1416, simple_loss=0.2286, pruned_loss=0.02726, over 984546.90 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2132, pruned_loss=0.02799, over 985809.77 frames.], batch size: 57, lr: 2.80e-04
+2022-06-19 07:28:09,835 INFO [train.py:874] (0/4) Epoch 29, batch 3950, aishell_loss[loss=0.1503, simple_loss=0.248, pruned_loss=0.02628, over 4962.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2207, pruned_loss=0.02727, over 985781.06 frames.], batch size: 40, aishell_tot_loss[loss=0.1418, simple_loss=0.2291, pruned_loss=0.02726, over 985009.02 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2134, pruned_loss=0.02782, over 986006.49 frames.], batch size: 40, lr: 2.80e-04
+2022-06-19 07:28:37,199 INFO [train.py:874] (0/4) Epoch 29, batch 4000, aishell_loss[loss=0.1479, simple_loss=0.2376, pruned_loss=0.02905, over 4955.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2208, pruned_loss=0.02738, over 985716.48 frames.], batch size: 58, aishell_tot_loss[loss=0.1416, simple_loss=0.229, pruned_loss=0.02712, over 985040.54 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2136, pruned_loss=0.02801, over 985953.04 frames.], batch size: 58, lr: 2.80e-04
+2022-06-19 07:28:37,201 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 07:28:54,128 INFO [train.py:914] (0/4) Epoch 29, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04075, over 1622729.00 frames.
+2022-06-19 07:29:22,709 INFO [train.py:874] (0/4) Epoch 29, batch 4050, aishell_loss[loss=0.115, simple_loss=0.2003, pruned_loss=0.01487, over 4965.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2215, pruned_loss=0.02732, over 986030.07 frames.], batch size: 27, aishell_tot_loss[loss=0.1415, simple_loss=0.2291, pruned_loss=0.02698, over 985441.84 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2139, pruned_loss=0.02804, over 985938.90 frames.], batch size: 27, lr: 2.80e-04
+2022-06-19 07:29:48,240 INFO [train.py:874] (0/4) Epoch 29, batch 4100, aishell_loss[loss=0.1394, simple_loss=0.2276, pruned_loss=0.0256, over 4914.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2217, pruned_loss=0.02796, over 985646.81 frames.], batch size: 41, aishell_tot_loss[loss=0.1427, simple_loss=0.2299, pruned_loss=0.02774, over 985191.67 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2132, pruned_loss=0.02793, over 985880.43 frames.], batch size: 41, lr: 2.80e-04
+2022-06-19 07:29:50,598 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-120000.pt
+2022-06-19 07:30:00,473 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-29.pt
+2022-06-19 07:30:56,064 INFO [train.py:874] (0/4) Epoch 30, batch 50, datatang_loss[loss=0.1309, simple_loss=0.2145, pruned_loss=0.02366, over 4925.00 frames.], tot_loss[loss=0.135, simple_loss=0.2159, pruned_loss=0.02708, over 218215.72 frames.], batch size: 94, aishell_tot_loss[loss=0.1421, simple_loss=0.2277, pruned_loss=0.0282, over 120365.75 frames.], datatang_tot_loss[loss=0.1274, simple_loss=0.2031, pruned_loss=0.02586, over 111498.53 frames.], batch size: 94, lr: 2.75e-04
+2022-06-19 07:31:24,113 INFO [train.py:874] (0/4) Epoch 30, batch 100, datatang_loss[loss=0.1469, simple_loss=0.2195, pruned_loss=0.03718, over 4937.00 frames.], tot_loss[loss=0.1346, simple_loss=0.2168, pruned_loss=0.02618, over 388246.94 frames.], batch size: 69, aishell_tot_loss[loss=0.1416, simple_loss=0.2288, pruned_loss=0.02717, over 218483.93 frames.], datatang_tot_loss[loss=0.1276, simple_loss=0.2047, pruned_loss=0.02527, over 218153.46 frames.], batch size: 69, lr: 2.75e-04
+2022-06-19 07:31:54,679 INFO [train.py:874] (0/4) Epoch 30, batch 150, aishell_loss[loss=0.1524, simple_loss=0.2308, pruned_loss=0.03698, over 4978.00 frames.], tot_loss[loss=0.1362, simple_loss=0.2188, pruned_loss=0.02679, over 520403.57 frames.], batch size: 31, aishell_tot_loss[loss=0.1435, simple_loss=0.2305, pruned_loss=0.02826, over 328576.98 frames.], datatang_tot_loss[loss=0.1271, simple_loss=0.2042, pruned_loss=0.02496, over 287891.56 frames.], batch size: 31, lr: 2.75e-04
+2022-06-19 07:32:24,880 INFO [train.py:874] (0/4) Epoch 30, batch 200, aishell_loss[loss=0.1269, simple_loss=0.2182, pruned_loss=0.01781, over 4874.00 frames.], tot_loss[loss=0.1362, simple_loss=0.219, pruned_loss=0.02665, over 623299.43 frames.], batch size: 28, aishell_tot_loss[loss=0.1436, simple_loss=0.231, pruned_loss=0.02809, over 396893.12 frames.], datatang_tot_loss[loss=0.1281, simple_loss=0.206, pruned_loss=0.02513, over 379292.23 frames.], batch size: 28, lr: 2.75e-04
+2022-06-19 07:32:53,142 INFO [train.py:874] (0/4) Epoch 30, batch 250, datatang_loss[loss=0.1216, simple_loss=0.1914, pruned_loss=0.02593, over 4941.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2192, pruned_loss=0.02687, over 703811.05 frames.], batch size: 50, aishell_tot_loss[loss=0.1434, simple_loss=0.231, pruned_loss=0.02794, over 461136.61 frames.], datatang_tot_loss[loss=0.1293, simple_loss=0.2071, pruned_loss=0.02572, over 456100.07 frames.], batch size: 50, lr: 2.75e-04
+2022-06-19 07:33:24,729 INFO [train.py:874] (0/4) Epoch 30, batch 300, aishell_loss[loss=0.1544, simple_loss=0.2346, pruned_loss=0.03714, over 4867.00 frames.], tot_loss[loss=0.1369, simple_loss=0.2202, pruned_loss=0.02687, over 765992.16 frames.], batch size: 37, aishell_tot_loss[loss=0.1438, simple_loss=0.2313, pruned_loss=0.02817, over 522742.59 frames.], datatang_tot_loss[loss=0.1296, simple_loss=0.2082, pruned_loss=0.02551, over 518315.79 frames.], batch size: 37, lr: 2.75e-04
+2022-06-19 07:33:54,678 INFO [train.py:874] (0/4) Epoch 30, batch 350, aishell_loss[loss=0.1393, simple_loss=0.2349, pruned_loss=0.02189, over 4953.00 frames.], tot_loss[loss=0.1375, simple_loss=0.221, pruned_loss=0.02695, over 815211.48 frames.], batch size: 56, aishell_tot_loss[loss=0.143, simple_loss=0.2306, pruned_loss=0.02776, over 593758.63 frames.], datatang_tot_loss[loss=0.1304, simple_loss=0.209, pruned_loss=0.02591, over 556602.84 frames.], batch size: 56, lr: 2.75e-04
+2022-06-19 07:34:23,750 INFO [train.py:874] (0/4) Epoch 30, batch 400, datatang_loss[loss=0.1536, simple_loss=0.2229, pruned_loss=0.04212, over 4939.00 frames.], tot_loss[loss=0.138, simple_loss=0.2214, pruned_loss=0.02726, over 853173.98 frames.], batch size: 34, aishell_tot_loss[loss=0.143, simple_loss=0.2307, pruned_loss=0.02762, over 643485.12 frames.], datatang_tot_loss[loss=0.1313, simple_loss=0.2095, pruned_loss=0.02654, over 603386.50 frames.], batch size: 34, lr: 2.75e-04
+2022-06-19 07:34:53,344 INFO [train.py:874] (0/4) Epoch 30, batch 450, datatang_loss[loss=0.1325, simple_loss=0.2158, pruned_loss=0.02464, over 4892.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2215, pruned_loss=0.02781, over 882423.71 frames.], batch size: 30, aishell_tot_loss[loss=0.1428, simple_loss=0.2303, pruned_loss=0.0277, over 682343.47 frames.], datatang_tot_loss[loss=0.1327, simple_loss=0.2108, pruned_loss=0.02733, over 649895.81 frames.], batch size: 30, lr: 2.75e-04
+2022-06-19 07:35:22,817 INFO [train.py:874] (0/4) Epoch 30, batch 500, datatang_loss[loss=0.1455, simple_loss=0.2238, pruned_loss=0.03359, over 4931.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2215, pruned_loss=0.02782, over 905361.35 frames.], batch size: 26, aishell_tot_loss[loss=0.1422, simple_loss=0.2295, pruned_loss=0.02746, over 723410.34 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2113, pruned_loss=0.02766, over 683456.50 frames.], batch size: 26, lr: 2.75e-04
+2022-06-19 07:35:50,041 INFO [train.py:874] (0/4) Epoch 30, batch 550, aishell_loss[loss=0.1032, simple_loss=0.1857, pruned_loss=0.01036, over 4828.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2213, pruned_loss=0.02779, over 922629.09 frames.], batch size: 24, aishell_tot_loss[loss=0.142, simple_loss=0.2288, pruned_loss=0.02764, over 762838.08 frames.], datatang_tot_loss[loss=0.1332, simple_loss=0.2113, pruned_loss=0.02753, over 708145.13 frames.], batch size: 24, lr: 2.75e-04
+2022-06-19 07:36:21,161 INFO [train.py:874] (0/4) Epoch 30, batch 600, datatang_loss[loss=0.1259, simple_loss=0.2053, pruned_loss=0.02321, over 4926.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2214, pruned_loss=0.02798, over 936827.63 frames.], batch size: 75, aishell_tot_loss[loss=0.1421, simple_loss=0.2289, pruned_loss=0.02768, over 790304.04 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.2116, pruned_loss=0.02783, over 739603.82 frames.], batch size: 75, lr: 2.75e-04
+2022-06-19 07:36:51,307 INFO [train.py:874] (0/4) Epoch 30, batch 650, datatang_loss[loss=0.1352, simple_loss=0.2261, pruned_loss=0.02217, over 4909.00 frames.], tot_loss[loss=0.138, simple_loss=0.2209, pruned_loss=0.02754, over 947464.85 frames.], batch size: 64, aishell_tot_loss[loss=0.1415, simple_loss=0.2281, pruned_loss=0.02744, over 813260.91 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.2121, pruned_loss=0.02759, over 768443.93 frames.], batch size: 64, lr: 2.75e-04
+2022-06-19 07:37:20,502 INFO [train.py:874] (0/4) Epoch 30, batch 700, datatang_loss[loss=0.1373, simple_loss=0.2246, pruned_loss=0.02496, over 4951.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2204, pruned_loss=0.02733, over 955746.33 frames.], batch size: 86, aishell_tot_loss[loss=0.1413, simple_loss=0.228, pruned_loss=0.02728, over 831020.75 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.2122, pruned_loss=0.0275, over 796938.14 frames.], batch size: 86, lr: 2.75e-04
+2022-06-19 07:37:51,000 INFO [train.py:874] (0/4) Epoch 30, batch 750, aishell_loss[loss=0.1208, simple_loss=0.196, pruned_loss=0.02284, over 4942.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2208, pruned_loss=0.0273, over 962144.49 frames.], batch size: 25, aishell_tot_loss[loss=0.141, simple_loss=0.2279, pruned_loss=0.02704, over 850274.30 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2127, pruned_loss=0.0277, over 817571.20 frames.], batch size: 25, lr: 2.75e-04
+2022-06-19 07:38:21,523 INFO [train.py:874] (0/4) Epoch 30, batch 800, datatang_loss[loss=0.1434, simple_loss=0.223, pruned_loss=0.03186, over 4907.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2215, pruned_loss=0.02757, over 967039.12 frames.], batch size: 47, aishell_tot_loss[loss=0.1411, simple_loss=0.2279, pruned_loss=0.02717, over 868886.36 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2133, pruned_loss=0.02788, over 833558.95 frames.], batch size: 47, lr: 2.75e-04
+2022-06-19 07:38:51,807 INFO [train.py:874] (0/4) Epoch 30, batch 850, datatang_loss[loss=0.1365, simple_loss=0.2214, pruned_loss=0.02574, over 4942.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2213, pruned_loss=0.02772, over 971162.63 frames.], batch size: 94, aishell_tot_loss[loss=0.1411, simple_loss=0.228, pruned_loss=0.0271, over 879333.75 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2136, pruned_loss=0.02814, over 855602.76 frames.], batch size: 94, lr: 2.75e-04
+2022-06-19 07:39:22,160 INFO [train.py:874] (0/4) Epoch 30, batch 900, datatang_loss[loss=0.1617, simple_loss=0.2515, pruned_loss=0.03593, over 4938.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2207, pruned_loss=0.02795, over 974621.04 frames.], batch size: 109, aishell_tot_loss[loss=0.1412, simple_loss=0.2278, pruned_loss=0.02729, over 889570.82 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2137, pruned_loss=0.02821, over 873906.14 frames.], batch size: 109, lr: 2.74e-04
+2022-06-19 07:39:50,709 INFO [train.py:874] (0/4) Epoch 30, batch 950, aishell_loss[loss=0.1558, simple_loss=0.242, pruned_loss=0.03478, over 4885.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2213, pruned_loss=0.02782, over 976762.78 frames.], batch size: 47, aishell_tot_loss[loss=0.1415, simple_loss=0.2283, pruned_loss=0.02733, over 901316.51 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2138, pruned_loss=0.02811, over 886166.68 frames.], batch size: 47, lr: 2.74e-04
+2022-06-19 07:40:20,532 INFO [train.py:874] (0/4) Epoch 30, batch 1000, datatang_loss[loss=0.1196, simple_loss=0.2088, pruned_loss=0.0152, over 4931.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2212, pruned_loss=0.02804, over 977872.34 frames.], batch size: 71, aishell_tot_loss[loss=0.1412, simple_loss=0.2277, pruned_loss=0.02734, over 911272.94 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.214, pruned_loss=0.02838, over 896810.03 frames.], batch size: 71, lr: 2.74e-04
+2022-06-19 07:40:20,535 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 07:40:38,847 INFO [train.py:914] (0/4) Epoch 30, validation: loss=0.1647, simple_loss=0.2491, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-19 07:41:06,100 INFO [train.py:874] (0/4) Epoch 30, batch 1050, datatang_loss[loss=0.1301, simple_loss=0.2149, pruned_loss=0.02262, over 4881.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2209, pruned_loss=0.02775, over 979197.63 frames.], batch size: 30, aishell_tot_loss[loss=0.1411, simple_loss=0.2278, pruned_loss=0.02725, over 920585.61 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2136, pruned_loss=0.02819, over 906154.00 frames.], batch size: 30, lr: 2.74e-04
+2022-06-19 07:41:34,465 INFO [train.py:874] (0/4) Epoch 30, batch 1100, datatang_loss[loss=0.1039, simple_loss=0.1892, pruned_loss=0.009306, over 4922.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2222, pruned_loss=0.02784, over 980437.93 frames.], batch size: 57, aishell_tot_loss[loss=0.1414, simple_loss=0.2284, pruned_loss=0.02719, over 929158.51 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2142, pruned_loss=0.02839, over 914113.89 frames.], batch size: 57, lr: 2.74e-04
+2022-06-19 07:42:04,642 INFO [train.py:874] (0/4) Epoch 30, batch 1150, datatang_loss[loss=0.1479, simple_loss=0.2269, pruned_loss=0.03441, over 4928.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2217, pruned_loss=0.02773, over 981313.31 frames.], batch size: 42, aishell_tot_loss[loss=0.1415, simple_loss=0.2286, pruned_loss=0.02721, over 933573.85 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2143, pruned_loss=0.02824, over 924918.00 frames.], batch size: 42, lr: 2.74e-04
+2022-06-19 07:42:33,971 INFO [train.py:874] (0/4) Epoch 30, batch 1200, datatang_loss[loss=0.1348, simple_loss=0.2053, pruned_loss=0.03211, over 4918.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2213, pruned_loss=0.02773, over 982067.76 frames.], batch size: 81, aishell_tot_loss[loss=0.1411, simple_loss=0.2281, pruned_loss=0.02708, over 938299.86 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2148, pruned_loss=0.02836, over 933433.04 frames.], batch size: 81, lr: 2.74e-04
+2022-06-19 07:43:03,424 INFO [train.py:874] (0/4) Epoch 30, batch 1250, aishell_loss[loss=0.1335, simple_loss=0.2051, pruned_loss=0.03096, over 4862.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2208, pruned_loss=0.02726, over 982513.33 frames.], batch size: 28, aishell_tot_loss[loss=0.1407, simple_loss=0.2276, pruned_loss=0.0269, over 944908.66 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2142, pruned_loss=0.02806, over 937997.12 frames.], batch size: 28, lr: 2.74e-04
+2022-06-19 07:43:31,116 INFO [train.py:874] (0/4) Epoch 30, batch 1300, aishell_loss[loss=0.133, simple_loss=0.2175, pruned_loss=0.02418, over 4864.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2207, pruned_loss=0.02695, over 983043.04 frames.], batch size: 28, aishell_tot_loss[loss=0.1403, simple_loss=0.2272, pruned_loss=0.02676, over 951368.12 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.214, pruned_loss=0.02787, over 941377.84 frames.], batch size: 28, lr: 2.74e-04
+2022-06-19 07:44:01,899 INFO [train.py:874] (0/4) Epoch 30, batch 1350, datatang_loss[loss=0.1187, simple_loss=0.1947, pruned_loss=0.02131, over 4916.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2208, pruned_loss=0.02734, over 983538.02 frames.], batch size: 75, aishell_tot_loss[loss=0.1404, simple_loss=0.227, pruned_loss=0.02688, over 954956.39 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2143, pruned_loss=0.02808, over 947041.52 frames.], batch size: 75, lr: 2.74e-04
+2022-06-19 07:44:33,760 INFO [train.py:874] (0/4) Epoch 30, batch 1400, aishell_loss[loss=0.132, simple_loss=0.223, pruned_loss=0.02045, over 4937.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2214, pruned_loss=0.02743, over 983938.03 frames.], batch size: 49, aishell_tot_loss[loss=0.1408, simple_loss=0.2274, pruned_loss=0.02709, over 959407.18 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2142, pruned_loss=0.02799, over 950365.94 frames.], batch size: 49, lr: 2.74e-04
+2022-06-19 07:45:01,994 INFO [train.py:874] (0/4) Epoch 30, batch 1450, aishell_loss[loss=0.1383, simple_loss=0.2284, pruned_loss=0.02405, over 4930.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2215, pruned_loss=0.02788, over 984065.93 frames.], batch size: 56, aishell_tot_loss[loss=0.1406, simple_loss=0.227, pruned_loss=0.02712, over 962378.72 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2148, pruned_loss=0.02844, over 954328.87 frames.], batch size: 56, lr: 2.74e-04
+2022-06-19 07:45:31,975 INFO [train.py:874] (0/4) Epoch 30, batch 1500, datatang_loss[loss=0.1237, simple_loss=0.1969, pruned_loss=0.02521, over 4867.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2209, pruned_loss=0.0279, over 984481.83 frames.], batch size: 39, aishell_tot_loss[loss=0.1403, simple_loss=0.2266, pruned_loss=0.02699, over 965339.04 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2146, pruned_loss=0.02864, over 957854.82 frames.], batch size: 39, lr: 2.74e-04
+2022-06-19 07:46:02,917 INFO [train.py:874] (0/4) Epoch 30, batch 1550, datatang_loss[loss=0.1247, simple_loss=0.2085, pruned_loss=0.02043, over 4898.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2209, pruned_loss=0.02767, over 984673.54 frames.], batch size: 52, aishell_tot_loss[loss=0.1406, simple_loss=0.2269, pruned_loss=0.02713, over 967650.69 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2145, pruned_loss=0.02827, over 961171.92 frames.], batch size: 52, lr: 2.74e-04
+2022-06-19 07:46:30,635 INFO [train.py:874] (0/4) Epoch 30, batch 1600, datatang_loss[loss=0.1199, simple_loss=0.1968, pruned_loss=0.02153, over 4967.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2211, pruned_loss=0.02792, over 985384.48 frames.], batch size: 60, aishell_tot_loss[loss=0.1406, simple_loss=0.2269, pruned_loss=0.02712, over 969996.22 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2148, pruned_loss=0.02853, over 964374.91 frames.], batch size: 60, lr: 2.74e-04
+2022-06-19 07:47:01,036 INFO [train.py:874] (0/4) Epoch 30, batch 1650, aishell_loss[loss=0.1551, simple_loss=0.2491, pruned_loss=0.03058, over 4945.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2215, pruned_loss=0.02808, over 985415.86 frames.], batch size: 56, aishell_tot_loss[loss=0.141, simple_loss=0.2275, pruned_loss=0.02727, over 971379.93 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2149, pruned_loss=0.02854, over 967428.99 frames.], batch size: 56, lr: 2.74e-04
+2022-06-19 07:47:31,286 INFO [train.py:874] (0/4) Epoch 30, batch 1700, datatang_loss[loss=0.125, simple_loss=0.2008, pruned_loss=0.02457, over 4960.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2222, pruned_loss=0.02844, over 985446.44 frames.], batch size: 34, aishell_tot_loss[loss=0.1418, simple_loss=0.2284, pruned_loss=0.02757, over 973146.00 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2148, pruned_loss=0.02866, over 969444.90 frames.], batch size: 34, lr: 2.74e-04
+2022-06-19 07:47:59,750 INFO [train.py:874] (0/4) Epoch 30, batch 1750, datatang_loss[loss=0.1469, simple_loss=0.2288, pruned_loss=0.03257, over 4949.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2229, pruned_loss=0.02835, over 985372.41 frames.], batch size: 62, aishell_tot_loss[loss=0.1419, simple_loss=0.2287, pruned_loss=0.02753, over 974436.45 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2154, pruned_loss=0.02867, over 971419.31 frames.], batch size: 62, lr: 2.74e-04
+2022-06-19 07:48:29,648 INFO [train.py:874] (0/4) Epoch 30, batch 1800, datatang_loss[loss=0.1253, simple_loss=0.2078, pruned_loss=0.02136, over 4911.00 frames.], tot_loss[loss=0.14, simple_loss=0.2231, pruned_loss=0.0285, over 985169.12 frames.], batch size: 64, aishell_tot_loss[loss=0.1422, simple_loss=0.2291, pruned_loss=0.02765, over 975191.71 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2155, pruned_loss=0.02878, over 973416.23 frames.], batch size: 64, lr: 2.73e-04
+2022-06-19 07:49:00,479 INFO [train.py:874] (0/4) Epoch 30, batch 1850, datatang_loss[loss=0.1672, simple_loss=0.2205, pruned_loss=0.05701, over 4957.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2218, pruned_loss=0.02831, over 985172.94 frames.], batch size: 37, aishell_tot_loss[loss=0.1417, simple_loss=0.2285, pruned_loss=0.02746, over 976121.69 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2151, pruned_loss=0.02881, over 975049.31 frames.], batch size: 37, lr: 2.73e-04
+2022-06-19 07:49:28,727 INFO [train.py:874] (0/4) Epoch 30, batch 1900, datatang_loss[loss=0.131, simple_loss=0.2127, pruned_loss=0.02469, over 4918.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2217, pruned_loss=0.02801, over 985067.24 frames.], batch size: 77, aishell_tot_loss[loss=0.1414, simple_loss=0.2282, pruned_loss=0.02726, over 977169.89 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2151, pruned_loss=0.02878, over 976128.82 frames.], batch size: 77, lr: 2.73e-04
+2022-06-19 07:49:58,556 INFO [train.py:874] (0/4) Epoch 30, batch 1950, aishell_loss[loss=0.1499, simple_loss=0.2321, pruned_loss=0.03383, over 4951.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2208, pruned_loss=0.02746, over 984876.32 frames.], batch size: 56, aishell_tot_loss[loss=0.1409, simple_loss=0.2275, pruned_loss=0.02709, over 978000.77 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2147, pruned_loss=0.02837, over 977071.69 frames.], batch size: 56, lr: 2.73e-04
+2022-06-19 07:50:27,892 INFO [train.py:874] (0/4) Epoch 30, batch 2000, datatang_loss[loss=0.132, simple_loss=0.2112, pruned_loss=0.02643, over 4948.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2214, pruned_loss=0.02773, over 984955.89 frames.], batch size: 86, aishell_tot_loss[loss=0.1409, simple_loss=0.2275, pruned_loss=0.02715, over 978833.23 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2149, pruned_loss=0.02857, over 978010.62 frames.], batch size: 86, lr: 2.73e-04
+2022-06-19 07:50:27,895 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 07:50:43,695 INFO [train.py:914] (0/4) Epoch 30, validation: loss=0.1643, simple_loss=0.2485, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 07:51:13,781 INFO [train.py:874] (0/4) Epoch 30, batch 2050, datatang_loss[loss=0.1258, simple_loss=0.21, pruned_loss=0.02085, over 4947.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2211, pruned_loss=0.02727, over 985155.90 frames.], batch size: 69, aishell_tot_loss[loss=0.1407, simple_loss=0.2276, pruned_loss=0.02688, over 979715.07 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2144, pruned_loss=0.0283, over 978867.23 frames.], batch size: 69, lr: 2.73e-04
+2022-06-19 07:51:43,741 INFO [train.py:874] (0/4) Epoch 30, batch 2100, datatang_loss[loss=0.1425, simple_loss=0.2251, pruned_loss=0.02999, over 4887.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2209, pruned_loss=0.02763, over 984872.74 frames.], batch size: 52, aishell_tot_loss[loss=0.1411, simple_loss=0.2278, pruned_loss=0.0272, over 980005.94 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2142, pruned_loss=0.02827, over 979678.25 frames.], batch size: 52, lr: 2.73e-04
+2022-06-19 07:52:12,654 INFO [train.py:874] (0/4) Epoch 30, batch 2150, datatang_loss[loss=0.1289, simple_loss=0.2064, pruned_loss=0.02571, over 4952.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2209, pruned_loss=0.02745, over 985187.82 frames.], batch size: 42, aishell_tot_loss[loss=0.1408, simple_loss=0.2279, pruned_loss=0.02686, over 980524.50 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2143, pruned_loss=0.02839, over 980665.33 frames.], batch size: 42, lr: 2.73e-04
+2022-06-19 07:52:42,920 INFO [train.py:874] (0/4) Epoch 30, batch 2200, aishell_loss[loss=0.147, simple_loss=0.2316, pruned_loss=0.03121, over 4934.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2219, pruned_loss=0.02781, over 985671.21 frames.], batch size: 58, aishell_tot_loss[loss=0.1408, simple_loss=0.2279, pruned_loss=0.02682, over 981427.06 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2154, pruned_loss=0.02876, over 981349.51 frames.], batch size: 58, lr: 2.73e-04
+2022-06-19 07:53:12,771 INFO [train.py:874] (0/4) Epoch 30, batch 2250, aishell_loss[loss=0.1449, simple_loss=0.2311, pruned_loss=0.02933, over 4857.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2218, pruned_loss=0.02764, over 985701.48 frames.], batch size: 35, aishell_tot_loss[loss=0.1408, simple_loss=0.2278, pruned_loss=0.02689, over 981906.06 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2152, pruned_loss=0.02854, over 981904.22 frames.], batch size: 35, lr: 2.73e-04
+2022-06-19 07:53:43,999 INFO [train.py:874] (0/4) Epoch 30, batch 2300, aishell_loss[loss=0.156, simple_loss=0.2399, pruned_loss=0.03606, over 4867.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2229, pruned_loss=0.02818, over 985921.81 frames.], batch size: 36, aishell_tot_loss[loss=0.1416, simple_loss=0.2287, pruned_loss=0.02718, over 982345.23 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2157, pruned_loss=0.02876, over 982596.11 frames.], batch size: 36, lr: 2.73e-04
+2022-06-19 07:54:14,770 INFO [train.py:874] (0/4) Epoch 30, batch 2350, aishell_loss[loss=0.1557, simple_loss=0.2371, pruned_loss=0.03716, over 4884.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2227, pruned_loss=0.02808, over 985976.80 frames.], batch size: 47, aishell_tot_loss[loss=0.1413, simple_loss=0.2286, pruned_loss=0.02696, over 982713.41 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2159, pruned_loss=0.02886, over 983089.94 frames.], batch size: 47, lr: 2.73e-04
+2022-06-19 07:54:42,739 INFO [train.py:874] (0/4) Epoch 30, batch 2400, datatang_loss[loss=0.122, simple_loss=0.2082, pruned_loss=0.01785, over 4953.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2218, pruned_loss=0.02804, over 985941.09 frames.], batch size: 91, aishell_tot_loss[loss=0.1415, simple_loss=0.2287, pruned_loss=0.02718, over 983124.60 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2152, pruned_loss=0.02863, over 983365.87 frames.], batch size: 91, lr: 2.73e-04
+2022-06-19 07:55:12,749 INFO [train.py:874] (0/4) Epoch 30, batch 2450, datatang_loss[loss=0.114, simple_loss=0.1956, pruned_loss=0.01613, over 4916.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2211, pruned_loss=0.02779, over 986102.15 frames.], batch size: 77, aishell_tot_loss[loss=0.1411, simple_loss=0.2282, pruned_loss=0.02697, over 983581.03 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2153, pruned_loss=0.02857, over 983702.02 frames.], batch size: 77, lr: 2.73e-04
+2022-06-19 07:55:43,710 INFO [train.py:874] (0/4) Epoch 30, batch 2500, aishell_loss[loss=0.1345, simple_loss=0.2275, pruned_loss=0.02076, over 4918.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2212, pruned_loss=0.02767, over 986106.31 frames.], batch size: 78, aishell_tot_loss[loss=0.1414, simple_loss=0.2287, pruned_loss=0.02707, over 983888.05 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2146, pruned_loss=0.02838, over 983988.53 frames.], batch size: 78, lr: 2.73e-04
+2022-06-19 07:56:12,506 INFO [train.py:874] (0/4) Epoch 30, batch 2550, datatang_loss[loss=0.1206, simple_loss=0.2033, pruned_loss=0.01902, over 4925.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2216, pruned_loss=0.02746, over 986054.08 frames.], batch size: 73, aishell_tot_loss[loss=0.1418, simple_loss=0.2294, pruned_loss=0.02714, over 984074.61 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2141, pruned_loss=0.02809, over 984258.77 frames.], batch size: 73, lr: 2.73e-04
+2022-06-19 07:56:42,334 INFO [train.py:874] (0/4) Epoch 30, batch 2600, aishell_loss[loss=0.143, simple_loss=0.2365, pruned_loss=0.02477, over 4974.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2212, pruned_loss=0.02752, over 985977.30 frames.], batch size: 64, aishell_tot_loss[loss=0.1417, simple_loss=0.2294, pruned_loss=0.02703, over 984152.72 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2139, pruned_loss=0.0282, over 984539.45 frames.], batch size: 64, lr: 2.73e-04
+2022-06-19 07:57:13,793 INFO [train.py:874] (0/4) Epoch 30, batch 2650, aishell_loss[loss=0.1492, simple_loss=0.238, pruned_loss=0.03021, over 4957.00 frames.], tot_loss[loss=0.138, simple_loss=0.2209, pruned_loss=0.02759, over 985824.50 frames.], batch size: 32, aishell_tot_loss[loss=0.1418, simple_loss=0.2294, pruned_loss=0.02711, over 984370.42 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2136, pruned_loss=0.02817, over 984533.81 frames.], batch size: 32, lr: 2.73e-04
+2022-06-19 07:57:41,434 INFO [train.py:874] (0/4) Epoch 30, batch 2700, datatang_loss[loss=0.1425, simple_loss=0.2215, pruned_loss=0.0317, over 4939.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2217, pruned_loss=0.02774, over 985729.06 frames.], batch size: 88, aishell_tot_loss[loss=0.1425, simple_loss=0.2304, pruned_loss=0.02729, over 984370.75 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2131, pruned_loss=0.02816, over 984756.80 frames.], batch size: 88, lr: 2.72e-04
+2022-06-19 07:58:11,583 INFO [train.py:874] (0/4) Epoch 30, batch 2750, aishell_loss[loss=0.125, simple_loss=0.2166, pruned_loss=0.01676, over 4950.00 frames.], tot_loss[loss=0.1388, simple_loss=0.222, pruned_loss=0.02777, over 985682.46 frames.], batch size: 56, aishell_tot_loss[loss=0.1425, simple_loss=0.2304, pruned_loss=0.02736, over 984481.39 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2131, pruned_loss=0.02814, over 984879.68 frames.], batch size: 56, lr: 2.72e-04
+2022-06-19 07:58:40,960 INFO [train.py:874] (0/4) Epoch 30, batch 2800, aishell_loss[loss=0.1561, simple_loss=0.249, pruned_loss=0.03157, over 4928.00 frames.], tot_loss[loss=0.138, simple_loss=0.221, pruned_loss=0.02748, over 985384.28 frames.], batch size: 64, aishell_tot_loss[loss=0.1415, simple_loss=0.2292, pruned_loss=0.02693, over 984361.00 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2132, pruned_loss=0.02828, over 984919.33 frames.], batch size: 64, lr: 2.72e-04
+2022-06-19 07:59:11,149 INFO [train.py:874] (0/4) Epoch 30, batch 2850, datatang_loss[loss=0.129, simple_loss=0.2172, pruned_loss=0.02041, over 4957.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2211, pruned_loss=0.02779, over 985434.00 frames.], batch size: 91, aishell_tot_loss[loss=0.1417, simple_loss=0.2292, pruned_loss=0.02713, over 984325.44 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2137, pruned_loss=0.02835, over 985174.25 frames.], batch size: 91, lr: 2.72e-04
+2022-06-19 07:59:41,818 INFO [train.py:874] (0/4) Epoch 30, batch 2900, aishell_loss[loss=0.1627, simple_loss=0.2411, pruned_loss=0.04211, over 4960.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2211, pruned_loss=0.02785, over 985462.30 frames.], batch size: 40, aishell_tot_loss[loss=0.1416, simple_loss=0.2288, pruned_loss=0.02719, over 984372.06 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2139, pruned_loss=0.02837, over 985320.02 frames.], batch size: 40, lr: 2.72e-04
+2022-06-19 08:00:11,901 INFO [train.py:874] (0/4) Epoch 30, batch 2950, datatang_loss[loss=0.1153, simple_loss=0.1951, pruned_loss=0.01771, over 4943.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2212, pruned_loss=0.02794, over 985671.07 frames.], batch size: 69, aishell_tot_loss[loss=0.1418, simple_loss=0.2292, pruned_loss=0.02724, over 984562.26 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2138, pruned_loss=0.0284, over 985477.27 frames.], batch size: 69, lr: 2.72e-04
+2022-06-19 08:00:40,793 INFO [train.py:874] (0/4) Epoch 30, batch 3000, datatang_loss[loss=0.1337, simple_loss=0.2076, pruned_loss=0.02991, over 4975.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2215, pruned_loss=0.0281, over 985737.58 frames.], batch size: 60, aishell_tot_loss[loss=0.1419, simple_loss=0.2292, pruned_loss=0.02729, over 984892.96 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2137, pruned_loss=0.02856, over 985372.86 frames.], batch size: 60, lr: 2.72e-04
+2022-06-19 08:00:40,796 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 08:00:57,063 INFO [train.py:914] (0/4) Epoch 30, validation: loss=0.165, simple_loss=0.2488, pruned_loss=0.04058, over 1622729.00 frames.
+2022-06-19 08:01:25,990 INFO [train.py:874] (0/4) Epoch 30, batch 3050, datatang_loss[loss=0.1217, simple_loss=0.2062, pruned_loss=0.01857, over 4920.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2223, pruned_loss=0.02812, over 986180.54 frames.], batch size: 71, aishell_tot_loss[loss=0.142, simple_loss=0.2293, pruned_loss=0.02732, over 985318.43 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2141, pruned_loss=0.02862, over 985560.86 frames.], batch size: 71, lr: 2.72e-04
+2022-06-19 08:01:55,873 INFO [train.py:874] (0/4) Epoch 30, batch 3100, datatang_loss[loss=0.1246, simple_loss=0.2009, pruned_loss=0.02419, over 4938.00 frames.], tot_loss[loss=0.139, simple_loss=0.2218, pruned_loss=0.02805, over 986161.51 frames.], batch size: 69, aishell_tot_loss[loss=0.142, simple_loss=0.2294, pruned_loss=0.02726, over 985341.00 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.214, pruned_loss=0.0286, over 985688.25 frames.], batch size: 69, lr: 2.72e-04
+2022-06-19 08:02:25,658 INFO [train.py:874] (0/4) Epoch 30, batch 3150, datatang_loss[loss=0.1328, simple_loss=0.2077, pruned_loss=0.02897, over 4886.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2213, pruned_loss=0.028, over 985916.50 frames.], batch size: 52, aishell_tot_loss[loss=0.1421, simple_loss=0.2295, pruned_loss=0.02732, over 985463.04 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2137, pruned_loss=0.02849, over 985459.11 frames.], batch size: 52, lr: 2.72e-04
+2022-06-19 08:02:54,297 INFO [train.py:874] (0/4) Epoch 30, batch 3200, datatang_loss[loss=0.1242, simple_loss=0.2055, pruned_loss=0.02147, over 4964.00 frames.], tot_loss[loss=0.138, simple_loss=0.221, pruned_loss=0.02754, over 985949.15 frames.], batch size: 67, aishell_tot_loss[loss=0.1416, simple_loss=0.2289, pruned_loss=0.02714, over 985498.41 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2138, pruned_loss=0.02823, over 985557.03 frames.], batch size: 67, lr: 2.72e-04
+2022-06-19 08:03:24,480 INFO [train.py:874] (0/4) Epoch 30, batch 3250, datatang_loss[loss=0.1388, simple_loss=0.2176, pruned_loss=0.03, over 4975.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2221, pruned_loss=0.02814, over 985564.10 frames.], batch size: 34, aishell_tot_loss[loss=0.1418, simple_loss=0.229, pruned_loss=0.02727, over 985165.18 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2146, pruned_loss=0.0287, over 985577.32 frames.], batch size: 34, lr: 2.72e-04
+2022-06-19 08:03:54,502 INFO [train.py:874] (0/4) Epoch 30, batch 3300, datatang_loss[loss=0.1275, simple_loss=0.1977, pruned_loss=0.02861, over 4979.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2227, pruned_loss=0.02874, over 985881.22 frames.], batch size: 45, aishell_tot_loss[loss=0.1423, simple_loss=0.2294, pruned_loss=0.02758, over 985414.30 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2152, pruned_loss=0.02904, over 985691.82 frames.], batch size: 45, lr: 2.72e-04
+2022-06-19 08:04:23,848 INFO [train.py:874] (0/4) Epoch 30, batch 3350, aishell_loss[loss=0.1419, simple_loss=0.2328, pruned_loss=0.02552, over 4977.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2235, pruned_loss=0.02887, over 985916.53 frames.], batch size: 51, aishell_tot_loss[loss=0.1426, simple_loss=0.2298, pruned_loss=0.02771, over 985549.38 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2153, pruned_loss=0.02919, over 985667.81 frames.], batch size: 51, lr: 2.72e-04
+2022-06-19 08:04:55,169 INFO [train.py:874] (0/4) Epoch 30, batch 3400, aishell_loss[loss=0.1163, simple_loss=0.2022, pruned_loss=0.01517, over 4982.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2232, pruned_loss=0.0288, over 985684.71 frames.], batch size: 27, aishell_tot_loss[loss=0.1425, simple_loss=0.2297, pruned_loss=0.02762, over 985385.91 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2153, pruned_loss=0.02931, over 985659.46 frames.], batch size: 27, lr: 2.72e-04
+2022-06-19 08:05:24,109 INFO [train.py:874] (0/4) Epoch 30, batch 3450, aishell_loss[loss=0.1283, simple_loss=0.2227, pruned_loss=0.01698, over 4915.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2226, pruned_loss=0.02845, over 985615.63 frames.], batch size: 41, aishell_tot_loss[loss=0.1424, simple_loss=0.2296, pruned_loss=0.02757, over 985361.07 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2149, pruned_loss=0.02908, over 985656.11 frames.], batch size: 41, lr: 2.72e-04
+2022-06-19 08:05:54,700 INFO [train.py:874] (0/4) Epoch 30, batch 3500, datatang_loss[loss=0.1328, simple_loss=0.2066, pruned_loss=0.02956, over 4912.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2214, pruned_loss=0.02823, over 985656.40 frames.], batch size: 52, aishell_tot_loss[loss=0.1425, simple_loss=0.2296, pruned_loss=0.02775, over 985224.60 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2139, pruned_loss=0.02868, over 985850.19 frames.], batch size: 52, lr: 2.72e-04
+2022-06-19 08:06:24,514 INFO [train.py:874] (0/4) Epoch 30, batch 3550, datatang_loss[loss=0.1371, simple_loss=0.221, pruned_loss=0.02658, over 4956.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2213, pruned_loss=0.02814, over 985580.99 frames.], batch size: 86, aishell_tot_loss[loss=0.1424, simple_loss=0.2294, pruned_loss=0.0277, over 985198.44 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2143, pruned_loss=0.02861, over 985812.38 frames.], batch size: 86, lr: 2.72e-04
+2022-06-19 08:06:53,799 INFO [train.py:874] (0/4) Epoch 30, batch 3600, datatang_loss[loss=0.1144, simple_loss=0.1955, pruned_loss=0.01668, over 4925.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2206, pruned_loss=0.02803, over 985397.59 frames.], batch size: 77, aishell_tot_loss[loss=0.142, simple_loss=0.2288, pruned_loss=0.02758, over 984871.72 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2139, pruned_loss=0.02861, over 985978.22 frames.], batch size: 77, lr: 2.71e-04
+2022-06-19 08:07:23,925 INFO [train.py:874] (0/4) Epoch 30, batch 3650, datatang_loss[loss=0.1243, simple_loss=0.2048, pruned_loss=0.02191, over 4912.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2207, pruned_loss=0.02828, over 985490.11 frames.], batch size: 75, aishell_tot_loss[loss=0.1423, simple_loss=0.2291, pruned_loss=0.02775, over 985005.15 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2139, pruned_loss=0.02866, over 985918.46 frames.], batch size: 75, lr: 2.71e-04
+2022-06-19 08:07:54,536 INFO [train.py:874] (0/4) Epoch 30, batch 3700, aishell_loss[loss=0.1592, simple_loss=0.2447, pruned_loss=0.03682, over 4941.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2205, pruned_loss=0.02812, over 985628.50 frames.], batch size: 58, aishell_tot_loss[loss=0.1425, simple_loss=0.2294, pruned_loss=0.0278, over 985184.51 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2139, pruned_loss=0.02842, over 985874.49 frames.], batch size: 58, lr: 2.71e-04
+2022-06-19 08:08:23,938 INFO [train.py:874] (0/4) Epoch 30, batch 3750, aishell_loss[loss=0.15, simple_loss=0.2377, pruned_loss=0.0311, over 4938.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2205, pruned_loss=0.02797, over 985518.22 frames.], batch size: 45, aishell_tot_loss[loss=0.1426, simple_loss=0.2295, pruned_loss=0.02791, over 985229.88 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2135, pruned_loss=0.02816, over 985747.92 frames.], batch size: 45, lr: 2.71e-04
+2022-06-19 08:08:52,768 INFO [train.py:874] (0/4) Epoch 30, batch 3800, datatang_loss[loss=0.1382, simple_loss=0.2195, pruned_loss=0.02848, over 4897.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2206, pruned_loss=0.02797, over 985327.83 frames.], batch size: 47, aishell_tot_loss[loss=0.1427, simple_loss=0.2295, pruned_loss=0.02798, over 985002.82 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2133, pruned_loss=0.02808, over 985786.21 frames.], batch size: 47, lr: 2.71e-04
+2022-06-19 08:09:20,697 INFO [train.py:874] (0/4) Epoch 30, batch 3850, datatang_loss[loss=0.1204, simple_loss=0.1995, pruned_loss=0.02064, over 4904.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2218, pruned_loss=0.02819, over 985129.29 frames.], batch size: 64, aishell_tot_loss[loss=0.1428, simple_loss=0.2295, pruned_loss=0.02807, over 984665.80 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2141, pruned_loss=0.02822, over 985913.51 frames.], batch size: 64, lr: 2.71e-04
+2022-06-19 08:09:50,452 INFO [train.py:874] (0/4) Epoch 30, batch 3900, aishell_loss[loss=0.1192, simple_loss=0.2055, pruned_loss=0.01651, over 4975.00 frames.], tot_loss[loss=0.139, simple_loss=0.2219, pruned_loss=0.02807, over 985439.33 frames.], batch size: 30, aishell_tot_loss[loss=0.143, simple_loss=0.2296, pruned_loss=0.02823, over 984850.36 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2135, pruned_loss=0.02795, over 986070.86 frames.], batch size: 30, lr: 2.71e-04
+2022-06-19 08:10:17,947 INFO [train.py:874] (0/4) Epoch 30, batch 3950, datatang_loss[loss=0.1214, simple_loss=0.2113, pruned_loss=0.01571, over 4916.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2209, pruned_loss=0.02749, over 985348.64 frames.], batch size: 83, aishell_tot_loss[loss=0.1425, simple_loss=0.2292, pruned_loss=0.02794, over 984824.63 frames.], datatang_tot_loss[loss=0.134, simple_loss=0.2128, pruned_loss=0.02764, over 986009.49 frames.], batch size: 83, lr: 2.71e-04
+2022-06-19 08:10:45,787 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/checkpoint-124000.pt
+2022-06-19 08:10:51,420 INFO [train.py:874] (0/4) Epoch 30, batch 4000, aishell_loss[loss=0.1366, simple_loss=0.2253, pruned_loss=0.02389, over 4921.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2213, pruned_loss=0.02744, over 985243.44 frames.], batch size: 41, aishell_tot_loss[loss=0.1426, simple_loss=0.2294, pruned_loss=0.02786, over 984836.26 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2129, pruned_loss=0.0276, over 985874.26 frames.], batch size: 41, lr: 2.71e-04
+2022-06-19 08:10:51,424 INFO [train.py:905] (0/4) Computing validation loss
+2022-06-19 08:11:08,528 INFO [train.py:914] (0/4) Epoch 30, validation: loss=0.1651, simple_loss=0.249, pruned_loss=0.04054, over 1622729.00 frames.
+2022-06-19 08:11:36,710 INFO [train.py:874] (0/4) Epoch 30, batch 4050, aishell_loss[loss=0.1756, simple_loss=0.2728, pruned_loss=0.03922, over 4922.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2215, pruned_loss=0.02787, over 984818.61 frames.], batch size: 41, aishell_tot_loss[loss=0.1431, simple_loss=0.2299, pruned_loss=0.02813, over 984227.09 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2127, pruned_loss=0.02769, over 985991.53 frames.], batch size: 41, lr: 2.71e-04
+2022-06-19 08:11:57,958 INFO [checkpoint.py:75] (0/4) Saving checkpoint to pruned_transducer_stateless3/exp-context-size-1/epoch-30.pt
+2022-06-19 08:12:06,408 INFO [train.py:1125] (0/4) Done!