diff --git "a/exp/log/log-train-2022-06-18-10-29-38-1" "b/exp/log/log-train-2022-06-18-10-29-38-1"
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+2022-06-18 10:29:38,930 INFO [train.py:963] (1/4) Training started
+2022-06-18 10:29:38,930 INFO [train.py:973] (1/4) Device: cuda:1
+2022-06-18 10:29:39,172 INFO [lexicon.py:176] (1/4) Loading pre-compiled data/lang_char/Linv.pt
+2022-06-18 10:29:39,199 INFO [train.py:985] (1/4) {'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 10:29:39,200 INFO [train.py:987] (1/4) About to create model
+2022-06-18 10:29:39,965 INFO [train.py:991] (1/4) Number of model parameters: 96983734
+2022-06-18 10:29:44,959 INFO [train.py:1006] (1/4) Using DDP
+2022-06-18 10:29:46,051 INFO [aishell.py:39] (1/4) About to get train cuts from data/fbank/aishell_cuts_train.jsonl.gz
+2022-06-18 10:29:46,055 INFO [aidatatang_200zh.py:39] (1/4) About to get train cuts from data/fbank/aidatatang_cuts_train.jsonl.gz
+2022-06-18 10:29:47,841 INFO [asr_datamodule.py:163] (1/4) Enable MUSAN
+2022-06-18 10:29:47,841 INFO [asr_datamodule.py:175] (1/4) Enable SpecAugment
+2022-06-18 10:29:47,841 INFO [asr_datamodule.py:176] (1/4) Time warp factor: 80
+2022-06-18 10:29:47,841 INFO [asr_datamodule.py:188] (1/4) Num frame mask: 10
+2022-06-18 10:29:47,841 INFO [asr_datamodule.py:201] (1/4) About to create train dataset
+2022-06-18 10:29:47,841 INFO [asr_datamodule.py:229] (1/4) Using DynamicBucketingSampler.
+2022-06-18 10:29:50,138 INFO [asr_datamodule.py:238] (1/4) About to create train dataloader
+2022-06-18 10:29:50,139 INFO [asr_datamodule.py:163] (1/4) Enable MUSAN
+2022-06-18 10:29:50,139 INFO [asr_datamodule.py:175] (1/4) Enable SpecAugment
+2022-06-18 10:29:50,139 INFO [asr_datamodule.py:176] (1/4) Time warp factor: 80
+2022-06-18 10:29:50,139 INFO [asr_datamodule.py:188] (1/4) Num frame mask: 10
+2022-06-18 10:29:50,139 INFO [asr_datamodule.py:201] (1/4) About to create train dataset
+2022-06-18 10:29:50,139 INFO [asr_datamodule.py:229] (1/4) Using DynamicBucketingSampler.
+2022-06-18 10:29:52,847 INFO [asr_datamodule.py:238] (1/4) About to create train dataloader
+2022-06-18 10:29:52,848 INFO [aishell.py:45] (1/4) About to get valid cuts from data/fbank/aishell_cuts_dev.jsonl.gz
+2022-06-18 10:29:52,849 INFO [asr_datamodule.py:251] (1/4) About to create dev dataset
+2022-06-18 10:29:53,343 INFO [asr_datamodule.py:270] (1/4) About to create dev dataloader
+2022-06-18 10:29:53,344 INFO [train.py:1171] (1/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
+2022-06-18 10:31:15,534 INFO [train.py:1081] (1/4) start training from epoch 1
+2022-06-18 10:32:07,219 INFO [train.py:874] (1/4) Epoch 1, batch 50, aishell_loss[loss=0.5171, simple_loss=1.034, pruned_loss=9.153, over 4955.00 frames.], tot_loss[loss=1.382, simple_loss=2.764, pruned_loss=8.745, over 218295.20 frames.], batch size: 64, aishell_tot_loss[loss=0.7, simple_loss=1.4, pruned_loss=8.865, over 115936.32 frames.], datatang_tot_loss[loss=2.121, simple_loss=4.241, pruned_loss=8.636, over 116017.61 frames.], batch size: 64, lr: 3.00e-03
+2022-06-18 10:32:38,260 INFO [train.py:874] (1/4) Epoch 1, batch 100, datatang_loss[loss=0.3833, simple_loss=0.7665, pruned_loss=8.507, over 4925.00 frames.], tot_loss[loss=0.8409, simple_loss=1.682, pruned_loss=8.724, over 388063.54 frames.], batch size: 83, aishell_tot_loss[loss=0.5572, simple_loss=1.114, pruned_loss=9.008, over 217936.48 frames.], datatang_tot_loss[loss=1.211, simple_loss=2.423, pruned_loss=8.45, over 218511.29 frames.], batch size: 83, lr: 3.00e-03
+2022-06-18 10:33:05,801 INFO [train.py:874] (1/4) Epoch 1, batch 150, aishell_loss[loss=0.3721, simple_loss=0.7441, pruned_loss=9.036, over 4858.00 frames.], tot_loss[loss=0.6466, simple_loss=1.293, pruned_loss=8.752, over 520555.35 frames.], batch size: 36, aishell_tot_loss[loss=0.5001, simple_loss=1, pruned_loss=9.051, over 304787.34 frames.], datatang_tot_loss[loss=0.8813, simple_loss=1.763, pruned_loss=8.461, over 312424.88 frames.], batch size: 36, lr: 3.00e-03
+2022-06-18 10:33:37,022 INFO [train.py:874] (1/4) Epoch 1, batch 200, datatang_loss[loss=0.3358, simple_loss=0.6716, pruned_loss=8.309, over 4923.00 frames.], tot_loss[loss=0.5451, simple_loss=1.09, pruned_loss=8.764, over 623610.58 frames.], batch size: 75, aishell_tot_loss[loss=0.4571, simple_loss=0.9142, pruned_loss=9.101, over 396746.05 frames.], datatang_tot_loss[loss=0.7398, simple_loss=1.48, pruned_loss=8.398, over 379787.59 frames.], batch size: 75, lr: 3.00e-03
+2022-06-18 10:34:08,980 INFO [train.py:874] (1/4) Epoch 1, batch 250, aishell_loss[loss=0.3694, simple_loss=0.7389, pruned_loss=9.118, over 4890.00 frames.], tot_loss[loss=0.4828, simple_loss=0.9657, pruned_loss=8.729, over 703548.14 frames.], batch size: 50, aishell_tot_loss[loss=0.435, simple_loss=0.87, pruned_loss=9.086, over 460659.73 frames.], datatang_tot_loss[loss=0.6277, simple_loss=1.255, pruned_loss=8.377, over 456338.98 frames.], batch size: 50, lr: 3.00e-03
+2022-06-18 10:34:36,718 INFO [train.py:874] (1/4) Epoch 1, batch 300, datatang_loss[loss=0.3114, simple_loss=0.6227, pruned_loss=9.1, over 4935.00 frames.], tot_loss[loss=0.4397, simple_loss=0.8794, pruned_loss=8.766, over 766303.86 frames.], batch size: 88, aishell_tot_loss[loss=0.415, simple_loss=0.8299, pruned_loss=9.063, over 522796.18 frames.], datatang_tot_loss[loss=0.5585, simple_loss=1.117, pruned_loss=8.456, over 518600.94 frames.], batch size: 88, lr: 3.00e-03
+2022-06-18 10:35:06,994 INFO [train.py:874] (1/4) Epoch 1, batch 350, aishell_loss[loss=0.2936, simple_loss=0.5872, pruned_loss=8.657, over 4862.00 frames.], tot_loss[loss=0.4072, simple_loss=0.8143, pruned_loss=8.814, over 814445.85 frames.], batch size: 28, aishell_tot_loss[loss=0.4008, simple_loss=0.8016, pruned_loss=9.036, over 568580.09 frames.], datatang_tot_loss[loss=0.501, simple_loss=1.002, pruned_loss=8.576, over 581752.37 frames.], batch size: 28, lr: 3.00e-03
+2022-06-18 10:35:37,475 INFO [train.py:874] (1/4) Epoch 1, batch 400, aishell_loss[loss=0.3146, simple_loss=0.6292, pruned_loss=9.048, over 4935.00 frames.], tot_loss[loss=0.3838, simple_loss=0.7675, pruned_loss=8.827, over 852426.93 frames.], batch size: 32, aishell_tot_loss[loss=0.3878, simple_loss=0.7755, pruned_loss=9.01, over 617834.92 frames.], datatang_tot_loss[loss=0.4639, simple_loss=0.9277, pruned_loss=8.622, over 629220.35 frames.], batch size: 32, lr: 3.00e-03
+2022-06-18 10:36:05,194 INFO [train.py:874] (1/4) Epoch 1, batch 450, datatang_loss[loss=0.2989, simple_loss=0.5977, pruned_loss=8.945, over 4964.00 frames.], tot_loss[loss=0.3654, simple_loss=0.7309, pruned_loss=8.832, over 881934.41 frames.], batch size: 31, aishell_tot_loss[loss=0.3787, simple_loss=0.7574, pruned_loss=8.989, over 657666.46 frames.], datatang_tot_loss[loss=0.432, simple_loss=0.864, pruned_loss=8.659, over 674521.03 frames.], batch size: 31, lr: 2.99e-03
+2022-06-18 10:36:36,099 INFO [train.py:874] (1/4) Epoch 1, batch 500, aishell_loss[loss=0.3384, simple_loss=0.6767, pruned_loss=8.67, over 4944.00 frames.], tot_loss[loss=0.3516, simple_loss=0.7032, pruned_loss=8.854, over 904733.13 frames.], batch size: 45, aishell_tot_loss[loss=0.3683, simple_loss=0.7366, pruned_loss=8.97, over 700321.55 frames.], datatang_tot_loss[loss=0.4113, simple_loss=0.8226, pruned_loss=8.705, over 707102.59 frames.], batch size: 45, lr: 2.99e-03
+2022-06-18 10:37:05,652 INFO [train.py:874] (1/4) Epoch 1, batch 550, aishell_loss[loss=0.3247, simple_loss=0.6493, pruned_loss=8.829, over 4911.00 frames.], tot_loss[loss=0.341, simple_loss=0.682, pruned_loss=8.863, over 922685.57 frames.], batch size: 80, aishell_tot_loss[loss=0.3614, simple_loss=0.7229, pruned_loss=8.969, over 735324.81 frames.], datatang_tot_loss[loss=0.3924, simple_loss=0.7848, pruned_loss=8.721, over 738558.86 frames.], batch size: 80, lr: 2.99e-03
+2022-06-18 10:37:34,689 INFO [train.py:874] (1/4) Epoch 1, batch 600, datatang_loss[loss=0.2671, simple_loss=0.5342, pruned_loss=8.841, over 4959.00 frames.], tot_loss[loss=0.3323, simple_loss=0.6647, pruned_loss=8.894, over 936807.09 frames.], batch size: 55, aishell_tot_loss[loss=0.3557, simple_loss=0.7115, pruned_loss=8.96, over 761476.54 frames.], datatang_tot_loss[loss=0.3754, simple_loss=0.7509, pruned_loss=8.779, over 771065.73 frames.], batch size: 55, lr: 2.99e-03
+2022-06-18 10:38:06,369 INFO [train.py:874] (1/4) Epoch 1, batch 650, aishell_loss[loss=0.3159, simple_loss=0.6317, pruned_loss=8.921, over 4901.00 frames.], tot_loss[loss=0.3263, simple_loss=0.6526, pruned_loss=8.898, over 947795.16 frames.], batch size: 41, aishell_tot_loss[loss=0.3498, simple_loss=0.6997, pruned_loss=8.949, over 790861.54 frames.], datatang_tot_loss[loss=0.3643, simple_loss=0.7286, pruned_loss=8.799, over 793611.47 frames.], batch size: 41, lr: 2.99e-03
+2022-06-18 10:38:34,694 INFO [train.py:874] (1/4) Epoch 1, batch 700, datatang_loss[loss=0.2618, simple_loss=0.5237, pruned_loss=8.964, over 4969.00 frames.], tot_loss[loss=0.3208, simple_loss=0.6417, pruned_loss=8.907, over 956078.01 frames.], batch size: 45, aishell_tot_loss[loss=0.3458, simple_loss=0.6917, pruned_loss=8.947, over 812962.15 frames.], datatang_tot_loss[loss=0.3528, simple_loss=0.7056, pruned_loss=8.821, over 816934.86 frames.], batch size: 45, lr: 2.99e-03
+2022-06-18 10:39:03,377 INFO [train.py:874] (1/4) Epoch 1, batch 750, aishell_loss[loss=0.3056, simple_loss=0.6112, pruned_loss=9.056, over 4983.00 frames.], tot_loss[loss=0.3162, simple_loss=0.6325, pruned_loss=8.926, over 962846.88 frames.], batch size: 48, aishell_tot_loss[loss=0.3418, simple_loss=0.6836, pruned_loss=8.952, over 832617.50 frames.], datatang_tot_loss[loss=0.3432, simple_loss=0.6864, pruned_loss=8.846, over 837697.36 frames.], batch size: 48, lr: 2.98e-03
+2022-06-18 10:39:33,600 INFO [train.py:874] (1/4) Epoch 1, batch 800, datatang_loss[loss=0.2618, simple_loss=0.5237, pruned_loss=9.063, over 4905.00 frames.], tot_loss[loss=0.3116, simple_loss=0.6232, pruned_loss=8.932, over 967543.82 frames.], batch size: 42, aishell_tot_loss[loss=0.3397, simple_loss=0.6793, pruned_loss=8.945, over 846107.93 frames.], datatang_tot_loss[loss=0.3328, simple_loss=0.6657, pruned_loss=8.87, over 859005.09 frames.], batch size: 42, lr: 2.98e-03
+2022-06-18 10:40:05,970 INFO [train.py:874] (1/4) Epoch 1, batch 850, datatang_loss[loss=0.3037, simple_loss=0.6074, pruned_loss=9.04, over 4935.00 frames.], tot_loss[loss=0.3078, simple_loss=0.6156, pruned_loss=8.937, over 971579.18 frames.], batch size: 94, aishell_tot_loss[loss=0.3369, simple_loss=0.6737, pruned_loss=8.938, over 860039.35 frames.], datatang_tot_loss[loss=0.3248, simple_loss=0.6496, pruned_loss=8.889, over 876169.70 frames.], batch size: 94, lr: 2.98e-03
+2022-06-18 10:40:33,808 INFO [train.py:874] (1/4) Epoch 1, batch 900, datatang_loss[loss=0.2738, simple_loss=0.5475, pruned_loss=8.979, over 4944.00 frames.], tot_loss[loss=0.3049, simple_loss=0.6099, pruned_loss=8.951, over 974886.67 frames.], batch size: 69, aishell_tot_loss[loss=0.3327, simple_loss=0.6653, pruned_loss=8.936, over 875375.96 frames.], datatang_tot_loss[loss=0.3195, simple_loss=0.639, pruned_loss=8.914, over 888781.46 frames.], batch size: 69, lr: 2.98e-03
+2022-06-18 10:41:04,977 INFO [train.py:874] (1/4) Epoch 1, batch 950, datatang_loss[loss=0.2658, simple_loss=0.5317, pruned_loss=9.202, over 4923.00 frames.], tot_loss[loss=0.3006, simple_loss=0.6012, pruned_loss=8.966, over 977118.70 frames.], batch size: 81, aishell_tot_loss[loss=0.3299, simple_loss=0.6597, pruned_loss=8.937, over 883927.18 frames.], datatang_tot_loss[loss=0.3124, simple_loss=0.6248, pruned_loss=8.937, over 903829.37 frames.], batch size: 81, lr: 2.97e-03
+2022-06-18 10:41:36,980 INFO [train.py:874] (1/4) Epoch 1, batch 1000, datatang_loss[loss=0.2638, simple_loss=0.5275, pruned_loss=9.079, over 4921.00 frames.], tot_loss[loss=0.297, simple_loss=0.5939, pruned_loss=8.981, over 978869.87 frames.], batch size: 73, aishell_tot_loss[loss=0.3256, simple_loss=0.6512, pruned_loss=8.94, over 895701.36 frames.], datatang_tot_loss[loss=0.3072, simple_loss=0.6144, pruned_loss=8.959, over 913526.64 frames.], batch size: 73, lr: 2.97e-03
+2022-06-18 10:41:36,981 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 10:41:53,016 INFO [train.py:914] (1/4) 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] (1/4) Epoch 1, batch 1050, aishell_loss[loss=0.3091, simple_loss=0.6181, pruned_loss=9.271, over 4980.00 frames.], tot_loss[loss=0.2925, simple_loss=0.585, pruned_loss=9, over 980129.70 frames.], batch size: 39, aishell_tot_loss[loss=0.3199, simple_loss=0.6397, pruned_loss=8.946, over 907260.84 frames.], datatang_tot_loss[loss=0.3027, simple_loss=0.6054, pruned_loss=8.982, over 921016.93 frames.], batch size: 39, lr: 2.97e-03
+2022-06-18 10:42:52,051 INFO [train.py:874] (1/4) Epoch 1, batch 1100, datatang_loss[loss=0.2698, simple_loss=0.5395, pruned_loss=9.332, over 4982.00 frames.], tot_loss[loss=0.2872, simple_loss=0.5744, pruned_loss=9.024, over 981456.08 frames.], batch size: 48, aishell_tot_loss[loss=0.3145, simple_loss=0.629, pruned_loss=8.953, over 916403.29 frames.], datatang_tot_loss[loss=0.2973, simple_loss=0.5947, pruned_loss=9.011, over 928809.98 frames.], batch size: 48, lr: 2.96e-03
+2022-06-18 10:43:24,145 INFO [train.py:874] (1/4) Epoch 1, batch 1150, aishell_loss[loss=0.276, simple_loss=0.552, pruned_loss=8.988, over 4875.00 frames.], tot_loss[loss=0.2819, simple_loss=0.5638, pruned_loss=9.037, over 982295.48 frames.], batch size: 35, aishell_tot_loss[loss=0.31, simple_loss=0.6201, pruned_loss=8.955, over 923751.89 frames.], datatang_tot_loss[loss=0.2913, simple_loss=0.5827, pruned_loss=9.033, over 936112.12 frames.], batch size: 35, lr: 2.96e-03
+2022-06-18 10:43:56,382 INFO [train.py:874] (1/4) Epoch 1, batch 1200, aishell_loss[loss=0.2669, simple_loss=0.5338, pruned_loss=9.062, over 4860.00 frames.], tot_loss[loss=0.2781, simple_loss=0.5562, pruned_loss=9.046, over 982793.97 frames.], batch size: 28, aishell_tot_loss[loss=0.305, simple_loss=0.61, pruned_loss=8.959, over 931570.23 frames.], datatang_tot_loss[loss=0.2872, simple_loss=0.5745, pruned_loss=9.051, over 941316.22 frames.], batch size: 28, lr: 2.96e-03
+2022-06-18 10:44:24,361 INFO [train.py:874] (1/4) Epoch 1, batch 1250, datatang_loss[loss=0.2482, simple_loss=0.4963, pruned_loss=9.146, over 4818.00 frames.], tot_loss[loss=0.272, simple_loss=0.544, pruned_loss=9.061, over 983572.46 frames.], batch size: 24, aishell_tot_loss[loss=0.299, simple_loss=0.598, pruned_loss=8.964, over 937598.10 frames.], datatang_tot_loss[loss=0.2821, simple_loss=0.5643, pruned_loss=9.072, over 946985.81 frames.], batch size: 24, lr: 2.95e-03
+2022-06-18 10:44:55,543 INFO [train.py:874] (1/4) Epoch 1, batch 1300, datatang_loss[loss=0.2626, simple_loss=0.5253, pruned_loss=9.287, over 4948.00 frames.], tot_loss[loss=0.2656, simple_loss=0.5312, pruned_loss=9.072, over 983862.98 frames.], batch size: 34, aishell_tot_loss[loss=0.2922, simple_loss=0.5845, pruned_loss=8.965, over 943370.02 frames.], datatang_tot_loss[loss=0.2772, simple_loss=0.5544, pruned_loss=9.094, over 951292.09 frames.], batch size: 34, lr: 2.95e-03
+2022-06-18 10:45:26,700 INFO [train.py:874] (1/4) Epoch 1, batch 1350, aishell_loss[loss=0.2206, simple_loss=0.4412, pruned_loss=8.876, over 4977.00 frames.], tot_loss[loss=0.2603, simple_loss=0.5205, pruned_loss=9.08, over 984560.56 frames.], batch size: 31, aishell_tot_loss[loss=0.2866, simple_loss=0.5732, pruned_loss=8.968, over 948815.94 frames.], datatang_tot_loss[loss=0.2721, simple_loss=0.5442, pruned_loss=9.112, over 955245.97 frames.], batch size: 31, lr: 2.95e-03
+2022-06-18 10:45:54,353 INFO [train.py:874] (1/4) Epoch 1, batch 1400, aishell_loss[loss=0.255, simple_loss=0.5101, pruned_loss=8.943, over 4954.00 frames.], tot_loss[loss=0.2554, simple_loss=0.5107, pruned_loss=9.094, over 984489.31 frames.], batch size: 80, aishell_tot_loss[loss=0.2813, simple_loss=0.5627, pruned_loss=8.974, over 952828.68 frames.], datatang_tot_loss[loss=0.2674, simple_loss=0.5347, pruned_loss=9.131, over 958820.18 frames.], batch size: 80, lr: 2.94e-03
+2022-06-18 10:46:26,290 INFO [train.py:874] (1/4) Epoch 1, batch 1450, aishell_loss[loss=0.2297, simple_loss=0.4595, pruned_loss=8.987, over 4880.00 frames.], tot_loss[loss=0.249, simple_loss=0.4981, pruned_loss=9.096, over 984293.08 frames.], batch size: 47, aishell_tot_loss[loss=0.2752, simple_loss=0.5504, pruned_loss=8.971, over 956345.59 frames.], datatang_tot_loss[loss=0.2621, simple_loss=0.5242, pruned_loss=9.146, over 961852.78 frames.], batch size: 47, lr: 2.94e-03
+2022-06-18 10:46:58,000 INFO [train.py:874] (1/4) Epoch 1, batch 1500, datatang_loss[loss=0.2188, simple_loss=0.4376, pruned_loss=9.399, over 4922.00 frames.], tot_loss[loss=0.2436, simple_loss=0.4873, pruned_loss=9.1, over 984628.09 frames.], batch size: 83, aishell_tot_loss[loss=0.27, simple_loss=0.54, pruned_loss=8.968, over 959693.68 frames.], datatang_tot_loss[loss=0.2568, simple_loss=0.5135, pruned_loss=9.16, over 964738.97 frames.], batch size: 83, lr: 2.94e-03
+2022-06-18 10:47:26,504 INFO [train.py:874] (1/4) Epoch 1, batch 1550, datatang_loss[loss=0.2103, simple_loss=0.4206, pruned_loss=9.237, over 4920.00 frames.], tot_loss[loss=0.239, simple_loss=0.4781, pruned_loss=9.103, over 984625.12 frames.], batch size: 73, aishell_tot_loss[loss=0.2657, simple_loss=0.5315, pruned_loss=8.966, over 962202.38 frames.], datatang_tot_loss[loss=0.2514, simple_loss=0.5029, pruned_loss=9.171, over 967456.67 frames.], batch size: 73, lr: 2.93e-03
+2022-06-18 10:47:58,276 INFO [train.py:874] (1/4) Epoch 1, batch 1600, aishell_loss[loss=0.2487, simple_loss=0.4975, pruned_loss=8.907, over 4864.00 frames.], tot_loss[loss=0.2346, simple_loss=0.4691, pruned_loss=9.109, over 984809.91 frames.], batch size: 35, aishell_tot_loss[loss=0.2614, simple_loss=0.5229, pruned_loss=8.965, over 964617.51 frames.], datatang_tot_loss[loss=0.2465, simple_loss=0.4929, pruned_loss=9.185, over 969838.60 frames.], batch size: 35, lr: 2.93e-03
+2022-06-18 10:48:31,038 INFO [train.py:874] (1/4) Epoch 1, batch 1650, aishell_loss[loss=0.2163, simple_loss=0.4327, pruned_loss=8.955, over 4965.00 frames.], tot_loss[loss=0.2317, simple_loss=0.4634, pruned_loss=9.106, over 985377.15 frames.], batch size: 56, aishell_tot_loss[loss=0.257, simple_loss=0.5141, pruned_loss=8.963, over 967476.86 frames.], datatang_tot_loss[loss=0.243, simple_loss=0.486, pruned_loss=9.193, over 971746.54 frames.], batch size: 56, lr: 2.92e-03
+2022-06-18 10:48:59,346 INFO [train.py:874] (1/4) Epoch 1, batch 1700, aishell_loss[loss=0.2189, simple_loss=0.4377, pruned_loss=8.897, over 4943.00 frames.], tot_loss[loss=0.226, simple_loss=0.4521, pruned_loss=9.096, over 985588.46 frames.], batch size: 64, aishell_tot_loss[loss=0.2514, simple_loss=0.5027, pruned_loss=8.955, over 969691.11 frames.], datatang_tot_loss[loss=0.2382, simple_loss=0.4764, pruned_loss=9.198, over 973484.92 frames.], batch size: 64, lr: 2.92e-03
+2022-06-18 10:49:32,476 INFO [train.py:874] (1/4) Epoch 1, batch 1750, aishell_loss[loss=0.2108, simple_loss=0.4215, pruned_loss=8.836, over 4881.00 frames.], tot_loss[loss=0.222, simple_loss=0.444, pruned_loss=9.103, over 985306.91 frames.], batch size: 42, aishell_tot_loss[loss=0.248, simple_loss=0.496, pruned_loss=8.949, over 970948.59 frames.], datatang_tot_loss[loss=0.2332, simple_loss=0.4663, pruned_loss=9.209, over 975143.72 frames.], batch size: 42, lr: 2.91e-03
+2022-06-18 10:50:05,237 INFO [train.py:874] (1/4) Epoch 1, batch 1800, datatang_loss[loss=0.2044, simple_loss=0.4088, pruned_loss=9.095, over 4920.00 frames.], tot_loss[loss=0.2177, simple_loss=0.4354, pruned_loss=9.102, over 985266.73 frames.], batch size: 25, aishell_tot_loss[loss=0.243, simple_loss=0.486, pruned_loss=8.947, over 972389.66 frames.], datatang_tot_loss[loss=0.2291, simple_loss=0.4583, pruned_loss=9.216, over 976551.96 frames.], batch size: 25, lr: 2.91e-03
+2022-06-18 10:50:33,704 INFO [train.py:874] (1/4) Epoch 1, batch 1850, aishell_loss[loss=0.2306, simple_loss=0.4612, pruned_loss=8.976, over 4965.00 frames.], tot_loss[loss=0.2143, simple_loss=0.4287, pruned_loss=9.099, over 985969.96 frames.], batch size: 80, aishell_tot_loss[loss=0.2394, simple_loss=0.4788, pruned_loss=8.943, over 974131.38 frames.], datatang_tot_loss[loss=0.225, simple_loss=0.45, pruned_loss=9.217, over 978023.32 frames.], batch size: 80, lr: 2.91e-03
+2022-06-18 10:51:04,521 INFO [train.py:874] (1/4) Epoch 1, batch 1900, aishell_loss[loss=0.2084, simple_loss=0.4168, pruned_loss=8.853, over 4857.00 frames.], tot_loss[loss=0.2113, simple_loss=0.4226, pruned_loss=9.09, over 985793.45 frames.], batch size: 37, aishell_tot_loss[loss=0.2351, simple_loss=0.4702, pruned_loss=8.941, over 975436.69 frames.], datatang_tot_loss[loss=0.2218, simple_loss=0.4435, pruned_loss=9.215, over 978879.55 frames.], batch size: 37, lr: 2.90e-03
+2022-06-18 10:51:36,224 INFO [train.py:874] (1/4) Epoch 1, batch 1950, datatang_loss[loss=0.1846, simple_loss=0.3692, pruned_loss=9.177, over 4945.00 frames.], tot_loss[loss=0.208, simple_loss=0.4159, pruned_loss=9.086, over 985758.76 frames.], batch size: 67, aishell_tot_loss[loss=0.231, simple_loss=0.462, pruned_loss=8.937, over 976434.54 frames.], datatang_tot_loss[loss=0.2184, simple_loss=0.4368, pruned_loss=9.215, over 979844.17 frames.], batch size: 67, lr: 2.90e-03
+2022-06-18 10:52:03,741 INFO [train.py:874] (1/4) Epoch 1, batch 2000, aishell_loss[loss=0.1947, simple_loss=0.3893, pruned_loss=8.942, over 4925.00 frames.], tot_loss[loss=0.2049, simple_loss=0.4098, pruned_loss=9.082, over 985978.01 frames.], batch size: 49, aishell_tot_loss[loss=0.2267, simple_loss=0.4535, pruned_loss=8.937, over 977679.76 frames.], datatang_tot_loss[loss=0.2154, simple_loss=0.4308, pruned_loss=9.214, over 980612.89 frames.], batch size: 49, lr: 2.89e-03
+2022-06-18 10:52:03,742 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 10:52:20,041 INFO [train.py:914] (1/4) Epoch 1, validation: loss=8.987, simple_loss=0.3601, pruned_loss=8.807, over 1622729.00 frames.
+2022-06-18 10:52:48,752 INFO [train.py:874] (1/4) Epoch 1, batch 2050, datatang_loss[loss=0.2343, simple_loss=0.4686, pruned_loss=9.335, over 4946.00 frames.], tot_loss[loss=0.2039, simple_loss=0.4077, pruned_loss=9.081, over 986001.17 frames.], batch size: 109, aishell_tot_loss[loss=0.2242, simple_loss=0.4484, pruned_loss=8.933, over 978619.53 frames.], datatang_tot_loss[loss=0.2132, simple_loss=0.4264, pruned_loss=9.213, over 981249.25 frames.], batch size: 109, lr: 2.89e-03
+2022-06-18 10:53:19,255 INFO [train.py:874] (1/4) Epoch 1, batch 2100, datatang_loss[loss=0.1751, simple_loss=0.3503, pruned_loss=9.204, over 4922.00 frames.], tot_loss[loss=0.201, simple_loss=0.4021, pruned_loss=9.071, over 985734.84 frames.], batch size: 81, aishell_tot_loss[loss=0.2203, simple_loss=0.4407, pruned_loss=8.927, over 979394.83 frames.], datatang_tot_loss[loss=0.2104, simple_loss=0.4209, pruned_loss=9.21, over 981632.19 frames.], batch size: 81, lr: 2.88e-03
+2022-06-18 10:53:51,102 INFO [train.py:874] (1/4) Epoch 1, batch 2150, aishell_loss[loss=0.188, simple_loss=0.3759, pruned_loss=8.859, over 4900.00 frames.], tot_loss[loss=0.2001, simple_loss=0.4003, pruned_loss=9.063, over 985541.33 frames.], batch size: 33, aishell_tot_loss[loss=0.2183, simple_loss=0.4366, pruned_loss=8.92, over 979925.73 frames.], datatang_tot_loss[loss=0.2082, simple_loss=0.4165, pruned_loss=9.206, over 982111.98 frames.], batch size: 33, lr: 2.88e-03
+2022-06-18 10:54:18,518 INFO [train.py:874] (1/4) Epoch 1, batch 2200, aishell_loss[loss=0.1865, simple_loss=0.3729, pruned_loss=8.777, over 4870.00 frames.], tot_loss[loss=0.1993, simple_loss=0.3987, pruned_loss=9.055, over 985365.78 frames.], batch size: 28, aishell_tot_loss[loss=0.2162, simple_loss=0.4323, pruned_loss=8.913, over 980468.49 frames.], datatang_tot_loss[loss=0.2064, simple_loss=0.4128, pruned_loss=9.204, over 982459.63 frames.], batch size: 28, lr: 2.87e-03
+2022-06-18 10:54:50,318 INFO [train.py:874] (1/4) Epoch 1, batch 2250, aishell_loss[loss=0.1679, simple_loss=0.3358, pruned_loss=8.726, over 4940.00 frames.], tot_loss[loss=0.1975, simple_loss=0.3949, pruned_loss=9.037, over 985146.17 frames.], batch size: 27, aishell_tot_loss[loss=0.2125, simple_loss=0.4249, pruned_loss=8.906, over 980892.91 frames.], datatang_tot_loss[loss=0.205, simple_loss=0.4099, pruned_loss=9.202, over 982817.14 frames.], batch size: 27, lr: 2.86e-03
+2022-06-18 10:55:21,553 INFO [train.py:874] (1/4) Epoch 1, batch 2300, aishell_loss[loss=0.1892, simple_loss=0.3784, pruned_loss=8.804, over 4911.00 frames.], tot_loss[loss=0.1951, simple_loss=0.3901, pruned_loss=9.031, over 985342.89 frames.], batch size: 41, aishell_tot_loss[loss=0.2091, simple_loss=0.4181, pruned_loss=8.898, over 981451.41 frames.], datatang_tot_loss[loss=0.2031, simple_loss=0.4062, pruned_loss=9.201, over 983242.86 frames.], batch size: 41, lr: 2.86e-03
+2022-06-18 10:55:50,104 INFO [train.py:874] (1/4) Epoch 1, batch 2350, aishell_loss[loss=0.1949, simple_loss=0.3898, pruned_loss=8.883, over 4908.00 frames.], tot_loss[loss=0.1929, simple_loss=0.3858, pruned_loss=9.032, over 985400.10 frames.], batch size: 52, aishell_tot_loss[loss=0.2072, simple_loss=0.4144, pruned_loss=8.894, over 981934.38 frames.], datatang_tot_loss[loss=0.2002, simple_loss=0.4003, pruned_loss=9.197, over 983498.36 frames.], batch size: 52, lr: 2.85e-03
+2022-06-18 10:56:21,661 INFO [train.py:874] (1/4) Epoch 1, batch 2400, datatang_loss[loss=0.171, simple_loss=0.3421, pruned_loss=9.109, over 4969.00 frames.], tot_loss[loss=0.191, simple_loss=0.382, pruned_loss=9.028, over 985419.44 frames.], batch size: 34, aishell_tot_loss[loss=0.2045, simple_loss=0.409, pruned_loss=8.887, over 982389.28 frames.], datatang_tot_loss[loss=0.1983, simple_loss=0.3966, pruned_loss=9.197, over 983710.94 frames.], batch size: 34, lr: 2.85e-03
+2022-06-18 10:56:52,218 INFO [train.py:874] (1/4) Epoch 1, batch 2450, datatang_loss[loss=0.177, simple_loss=0.354, pruned_loss=9.168, over 4960.00 frames.], tot_loss[loss=0.1899, simple_loss=0.3798, pruned_loss=9.024, over 985576.63 frames.], batch size: 67, aishell_tot_loss[loss=0.202, simple_loss=0.4041, pruned_loss=8.885, over 982867.86 frames.], datatang_tot_loss[loss=0.197, simple_loss=0.3941, pruned_loss=9.199, over 983982.49 frames.], batch size: 67, lr: 2.84e-03
+2022-06-18 10:57:21,458 INFO [train.py:874] (1/4) Epoch 1, batch 2500, datatang_loss[loss=0.1888, simple_loss=0.3776, pruned_loss=9.232, over 4940.00 frames.], tot_loss[loss=0.1881, simple_loss=0.3762, pruned_loss=9.023, over 985872.92 frames.], batch size: 88, aishell_tot_loss[loss=0.1997, simple_loss=0.3995, pruned_loss=8.876, over 983347.24 frames.], datatang_tot_loss[loss=0.1952, simple_loss=0.3903, pruned_loss=9.198, over 984298.13 frames.], batch size: 88, lr: 2.84e-03
+2022-06-18 10:57:53,494 INFO [train.py:874] (1/4) Epoch 1, batch 2550, aishell_loss[loss=0.193, simple_loss=0.3859, pruned_loss=8.792, over 4924.00 frames.], tot_loss[loss=0.1856, simple_loss=0.3713, pruned_loss=9.014, over 985736.36 frames.], batch size: 33, aishell_tot_loss[loss=0.197, simple_loss=0.394, pruned_loss=8.868, over 983480.37 frames.], datatang_tot_loss[loss=0.193, simple_loss=0.386, pruned_loss=9.198, over 984522.20 frames.], batch size: 33, lr: 2.83e-03
+2022-06-18 10:58:22,416 INFO [train.py:874] (1/4) Epoch 1, batch 2600, datatang_loss[loss=0.1558, simple_loss=0.3116, pruned_loss=9.166, over 4924.00 frames.], tot_loss[loss=0.184, simple_loss=0.368, pruned_loss=9.005, over 985590.17 frames.], batch size: 42, aishell_tot_loss[loss=0.1941, simple_loss=0.3883, pruned_loss=8.857, over 983416.90 frames.], datatang_tot_loss[loss=0.1919, simple_loss=0.3838, pruned_loss=9.197, over 984852.14 frames.], batch size: 42, lr: 2.83e-03
+2022-06-18 10:58:52,579 INFO [train.py:874] (1/4) Epoch 1, batch 2650, datatang_loss[loss=0.1726, simple_loss=0.3451, pruned_loss=9.115, over 4907.00 frames.], tot_loss[loss=0.1831, simple_loss=0.3663, pruned_loss=9.011, over 985459.75 frames.], batch size: 64, aishell_tot_loss[loss=0.1935, simple_loss=0.387, pruned_loss=8.854, over 983617.63 frames.], datatang_tot_loss[loss=0.1896, simple_loss=0.3791, pruned_loss=9.193, over 984811.68 frames.], batch size: 64, lr: 2.82e-03
+2022-06-18 10:59:23,854 INFO [train.py:874] (1/4) Epoch 1, batch 2700, aishell_loss[loss=0.171, simple_loss=0.342, pruned_loss=8.82, over 4940.00 frames.], tot_loss[loss=0.1818, simple_loss=0.3636, pruned_loss=9.012, over 985477.12 frames.], batch size: 54, aishell_tot_loss[loss=0.1914, simple_loss=0.3829, pruned_loss=8.846, over 983768.44 frames.], datatang_tot_loss[loss=0.1882, simple_loss=0.3764, pruned_loss=9.198, over 984967.77 frames.], batch size: 54, lr: 2.81e-03
+2022-06-18 10:59:52,407 INFO [train.py:874] (1/4) Epoch 1, batch 2750, aishell_loss[loss=0.1847, simple_loss=0.3693, pruned_loss=8.81, over 4950.00 frames.], tot_loss[loss=0.1802, simple_loss=0.3604, pruned_loss=9.003, over 985790.23 frames.], batch size: 56, aishell_tot_loss[loss=0.1894, simple_loss=0.3787, pruned_loss=8.841, over 984273.22 frames.], datatang_tot_loss[loss=0.1867, simple_loss=0.3734, pruned_loss=9.196, over 985065.15 frames.], batch size: 56, lr: 2.81e-03
+2022-06-18 11:00:23,031 INFO [train.py:874] (1/4) Epoch 1, batch 2800, aishell_loss[loss=0.175, simple_loss=0.35, pruned_loss=8.837, over 4971.00 frames.], tot_loss[loss=0.1795, simple_loss=0.359, pruned_loss=8.995, over 985745.02 frames.], batch size: 48, aishell_tot_loss[loss=0.1882, simple_loss=0.3765, pruned_loss=8.836, over 984435.70 frames.], datatang_tot_loss[loss=0.1852, simple_loss=0.3704, pruned_loss=9.19, over 985121.42 frames.], batch size: 48, lr: 2.80e-03
+2022-06-18 11:00:54,638 INFO [train.py:874] (1/4) Epoch 1, batch 2850, aishell_loss[loss=0.1982, simple_loss=0.3965, pruned_loss=8.888, over 4969.00 frames.], tot_loss[loss=0.1788, simple_loss=0.3575, pruned_loss=8.988, over 985792.52 frames.], batch size: 48, aishell_tot_loss[loss=0.1867, simple_loss=0.3735, pruned_loss=8.83, over 984538.37 frames.], datatang_tot_loss[loss=0.1842, simple_loss=0.3683, pruned_loss=9.19, over 985311.36 frames.], batch size: 48, lr: 2.80e-03
+2022-06-18 11:01:22,289 INFO [train.py:874] (1/4) Epoch 1, batch 2900, datatang_loss[loss=0.1484, simple_loss=0.2967, pruned_loss=9.079, over 4975.00 frames.], tot_loss[loss=0.1782, simple_loss=0.3564, pruned_loss=8.989, over 985422.45 frames.], batch size: 31, aishell_tot_loss[loss=0.1859, simple_loss=0.3717, pruned_loss=8.823, over 984406.07 frames.], datatang_tot_loss[loss=0.1829, simple_loss=0.3658, pruned_loss=9.191, over 985252.32 frames.], batch size: 31, lr: 2.79e-03
+2022-06-18 11:01:53,459 INFO [train.py:874] (1/4) Epoch 1, batch 2950, datatang_loss[loss=0.1953, simple_loss=0.3905, pruned_loss=9.109, over 4953.00 frames.], tot_loss[loss=0.1781, simple_loss=0.3562, pruned_loss=8.995, over 985376.10 frames.], batch size: 86, aishell_tot_loss[loss=0.1853, simple_loss=0.3707, pruned_loss=8.818, over 984379.81 frames.], datatang_tot_loss[loss=0.1819, simple_loss=0.3639, pruned_loss=9.191, over 985356.69 frames.], batch size: 86, lr: 2.78e-03
+2022-06-18 11:02:24,452 INFO [train.py:874] (1/4) Epoch 1, batch 3000, aishell_loss[loss=9.012, simple_loss=0.3552, pruned_loss=8.835, over 4870.00 frames.], tot_loss[loss=0.22, simple_loss=0.3526, pruned_loss=8.992, over 985790.06 frames.], batch size: 35, aishell_tot_loss[loss=0.2277, simple_loss=0.3679, pruned_loss=8.814, over 984783.20 frames.], datatang_tot_loss[loss=0.1801, simple_loss=0.3602, pruned_loss=9.19, over 985510.41 frames.], batch size: 35, lr: 2.78e-03
+2022-06-18 11:02:24,453 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 11:02:40,211 INFO [train.py:914] (1/4) 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] (1/4) Epoch 1, batch 3050, aishell_loss[loss=0.2375, simple_loss=0.3131, pruned_loss=0.8098, over 4869.00 frames.], tot_loss[loss=0.2421, simple_loss=0.363, pruned_loss=7.263, over 986057.14 frames.], batch size: 35, aishell_tot_loss[loss=0.2371, simple_loss=0.3725, pruned_loss=7.713, over 985156.02 frames.], datatang_tot_loss[loss=0.1976, simple_loss=0.364, pruned_loss=8.469, over 985584.08 frames.], batch size: 35, lr: 2.77e-03
+2022-06-18 11:03:41,566 INFO [train.py:874] (1/4) Epoch 1, batch 3100, datatang_loss[loss=0.2271, simple_loss=0.3343, pruned_loss=0.5997, over 4922.00 frames.], tot_loss[loss=0.2422, simple_loss=0.358, pruned_loss=5.814, over 985975.99 frames.], batch size: 77, aishell_tot_loss[loss=0.238, simple_loss=0.3695, pruned_loss=7.018, over 985210.02 frames.], datatang_tot_loss[loss=0.2034, simple_loss=0.3607, pruned_loss=7.418, over 985599.29 frames.], batch size: 77, lr: 2.77e-03
+2022-06-18 11:04:10,468 INFO [train.py:874] (1/4) Epoch 1, batch 3150, aishell_loss[loss=0.2205, simple_loss=0.3421, pruned_loss=0.4948, over 4963.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3542, pruned_loss=4.648, over 985766.00 frames.], batch size: 39, aishell_tot_loss[loss=0.2365, simple_loss=0.3655, pruned_loss=6.175, over 984920.23 frames.], datatang_tot_loss[loss=0.2056, simple_loss=0.3589, pruned_loss=6.698, over 985796.54 frames.], batch size: 39, lr: 2.76e-03
+2022-06-18 11:04:41,705 INFO [train.py:874] (1/4) Epoch 1, batch 3200, datatang_loss[loss=0.2091, simple_loss=0.3409, pruned_loss=0.3866, over 4934.00 frames.], tot_loss[loss=0.2339, simple_loss=0.3529, pruned_loss=3.713, over 986178.56 frames.], batch size: 45, aishell_tot_loss[loss=0.2348, simple_loss=0.3638, pruned_loss=5.577, over 985103.50 frames.], datatang_tot_loss[loss=0.207, simple_loss=0.3575, pruned_loss=5.874, over 986133.67 frames.], batch size: 45, lr: 2.75e-03
+2022-06-18 11:05:11,785 INFO [train.py:874] (1/4) Epoch 1, batch 3250, datatang_loss[loss=0.2401, simple_loss=0.4064, pruned_loss=0.3685, over 4916.00 frames.], tot_loss[loss=0.2279, simple_loss=0.3506, pruned_loss=2.969, over 986131.46 frames.], batch size: 108, aishell_tot_loss[loss=0.2322, simple_loss=0.3616, pruned_loss=5.056, over 985258.47 frames.], datatang_tot_loss[loss=0.2068, simple_loss=0.3554, pruned_loss=5.126, over 986050.63 frames.], batch size: 108, lr: 2.75e-03
+2022-06-18 11:05:40,220 INFO [train.py:874] (1/4) Epoch 1, batch 3300, datatang_loss[loss=0.1971, simple_loss=0.3381, pruned_loss=0.281, over 4928.00 frames.], tot_loss[loss=0.2231, simple_loss=0.3494, pruned_loss=2.386, over 986076.29 frames.], batch size: 83, aishell_tot_loss[loss=0.2305, simple_loss=0.3611, pruned_loss=4.547, over 985386.12 frames.], datatang_tot_loss[loss=0.2056, simple_loss=0.3528, pruned_loss=4.515, over 985973.64 frames.], batch size: 83, lr: 2.74e-03
+2022-06-18 11:06:11,125 INFO [train.py:874] (1/4) Epoch 1, batch 3350, datatang_loss[loss=0.1887, simple_loss=0.3261, pruned_loss=0.2567, over 4958.00 frames.], tot_loss[loss=0.2169, simple_loss=0.3459, pruned_loss=1.919, over 986113.73 frames.], batch size: 67, aishell_tot_loss[loss=0.2265, simple_loss=0.3583, pruned_loss=3.99, over 985560.44 frames.], datatang_tot_loss[loss=0.2039, simple_loss=0.35, pruned_loss=4.071, over 985939.16 frames.], batch size: 67, lr: 2.73e-03
+2022-06-18 11:06:40,530 INFO [train.py:874] (1/4) Epoch 1, batch 3400, datatang_loss[loss=0.1982, simple_loss=0.3419, pruned_loss=0.2726, over 4938.00 frames.], tot_loss[loss=0.2124, simple_loss=0.3447, pruned_loss=1.553, over 985906.04 frames.], batch size: 69, aishell_tot_loss[loss=0.2229, simple_loss=0.3563, pruned_loss=3.519, over 985562.92 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.3486, pruned_loss=3.657, over 985809.56 frames.], batch size: 69, lr: 2.73e-03
+2022-06-18 11:07:10,280 INFO [train.py:874] (1/4) Epoch 1, batch 3450, aishell_loss[loss=0.2101, simple_loss=0.3611, pruned_loss=0.2956, over 4913.00 frames.], tot_loss[loss=0.2084, simple_loss=0.3428, pruned_loss=1.267, over 985718.52 frames.], batch size: 41, aishell_tot_loss[loss=0.2189, simple_loss=0.3539, pruned_loss=3.076, over 985361.25 frames.], datatang_tot_loss[loss=0.2021, simple_loss=0.3471, pruned_loss=3.32, over 985882.39 frames.], batch size: 41, lr: 2.72e-03
+2022-06-18 11:07:41,568 INFO [train.py:874] (1/4) Epoch 1, batch 3500, aishell_loss[loss=0.2023, simple_loss=0.3553, pruned_loss=0.2462, over 4952.00 frames.], tot_loss[loss=0.2056, simple_loss=0.3425, pruned_loss=1.042, over 985569.26 frames.], batch size: 40, aishell_tot_loss[loss=0.2167, simple_loss=0.3531, pruned_loss=2.769, over 985244.29 frames.], datatang_tot_loss[loss=0.2011, simple_loss=0.3458, pruned_loss=2.931, over 985857.89 frames.], batch size: 40, lr: 2.72e-03
+2022-06-18 11:08:10,088 INFO [train.py:874] (1/4) Epoch 1, batch 3550, aishell_loss[loss=0.1819, simple_loss=0.3184, pruned_loss=0.2272, over 4936.00 frames.], tot_loss[loss=0.2017, simple_loss=0.34, pruned_loss=0.8602, over 985611.17 frames.], batch size: 32, aishell_tot_loss[loss=0.213, simple_loss=0.3505, pruned_loss=2.435, over 985271.88 frames.], datatang_tot_loss[loss=0.1996, simple_loss=0.344, pruned_loss=2.649, over 985894.57 frames.], batch size: 32, lr: 2.71e-03
+2022-06-18 11:08:41,114 INFO [train.py:874] (1/4) Epoch 1, batch 3600, datatang_loss[loss=0.1736, simple_loss=0.3084, pruned_loss=0.1941, over 4958.00 frames.], tot_loss[loss=0.1988, simple_loss=0.3381, pruned_loss=0.7201, over 985677.49 frames.], batch size: 67, aishell_tot_loss[loss=0.2109, simple_loss=0.3491, pruned_loss=2.234, over 985370.89 frames.], datatang_tot_loss[loss=0.1979, simple_loss=0.342, pruned_loss=2.301, over 985858.31 frames.], batch size: 67, lr: 2.70e-03
+2022-06-18 11:09:13,093 INFO [train.py:874] (1/4) Epoch 1, batch 3650, datatang_loss[loss=0.248, simple_loss=0.4319, pruned_loss=0.3202, over 4943.00 frames.], tot_loss[loss=0.1965, simple_loss=0.3372, pruned_loss=0.6085, over 985761.36 frames.], batch size: 109, aishell_tot_loss[loss=0.2088, simple_loss=0.3478, pruned_loss=2.041, over 985594.79 frames.], datatang_tot_loss[loss=0.1966, simple_loss=0.3406, pruned_loss=2.011, over 985723.80 frames.], batch size: 109, lr: 2.70e-03
+2022-06-18 11:09:42,003 INFO [train.py:874] (1/4) Epoch 1, batch 3700, aishell_loss[loss=0.1827, simple_loss=0.3271, pruned_loss=0.191, over 4964.00 frames.], tot_loss[loss=0.1933, simple_loss=0.3339, pruned_loss=0.5199, over 985616.18 frames.], batch size: 61, aishell_tot_loss[loss=0.206, simple_loss=0.3454, pruned_loss=1.833, over 985525.28 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.3379, pruned_loss=1.791, over 985665.36 frames.], batch size: 61, lr: 2.69e-03
+2022-06-18 11:10:11,817 INFO [train.py:874] (1/4) Epoch 1, batch 3750, datatang_loss[loss=0.1961, simple_loss=0.3481, pruned_loss=0.2207, over 4956.00 frames.], tot_loss[loss=0.1915, simple_loss=0.3328, pruned_loss=0.4505, over 985842.90 frames.], batch size: 67, aishell_tot_loss[loss=0.2036, simple_loss=0.3434, pruned_loss=1.676, over 985648.35 frames.], datatang_tot_loss[loss=0.1937, simple_loss=0.3371, pruned_loss=1.572, over 985787.90 frames.], batch size: 67, lr: 2.68e-03
+2022-06-18 11:10:43,249 INFO [train.py:874] (1/4) Epoch 1, batch 3800, datatang_loss[loss=0.1794, simple_loss=0.3164, pruned_loss=0.2119, over 4876.00 frames.], tot_loss[loss=0.1901, simple_loss=0.3317, pruned_loss=0.3977, over 986016.58 frames.], batch size: 39, aishell_tot_loss[loss=0.2025, simple_loss=0.3431, pruned_loss=1.531, over 985822.01 frames.], datatang_tot_loss[loss=0.1918, simple_loss=0.3347, pruned_loss=1.386, over 985828.04 frames.], batch size: 39, lr: 2.68e-03
+2022-06-18 11:11:11,668 INFO [train.py:874] (1/4) Epoch 1, batch 3850, datatang_loss[loss=0.1851, simple_loss=0.3268, pruned_loss=0.2171, over 4958.00 frames.], tot_loss[loss=0.1889, simple_loss=0.3312, pruned_loss=0.3535, over 986180.23 frames.], batch size: 86, aishell_tot_loss[loss=0.2003, simple_loss=0.3417, pruned_loss=1.361, over 985968.28 frames.], datatang_tot_loss[loss=0.1909, simple_loss=0.3338, pruned_loss=1.258, over 985896.51 frames.], batch size: 86, lr: 2.67e-03
+2022-06-18 11:11:41,826 INFO [train.py:874] (1/4) Epoch 1, batch 3900, datatang_loss[loss=0.2002, simple_loss=0.3578, pruned_loss=0.2134, over 4922.00 frames.], tot_loss[loss=0.188, simple_loss=0.3309, pruned_loss=0.3196, over 986172.87 frames.], batch size: 98, aishell_tot_loss[loss=0.1991, simple_loss=0.3413, pruned_loss=1.244, over 986199.41 frames.], datatang_tot_loss[loss=0.1898, simple_loss=0.3325, pruned_loss=1.115, over 985737.61 frames.], batch size: 98, lr: 2.66e-03
+2022-06-18 11:12:10,014 INFO [train.py:874] (1/4) Epoch 1, batch 3950, aishell_loss[loss=0.2068, simple_loss=0.3697, pruned_loss=0.2197, over 4885.00 frames.], tot_loss[loss=0.1871, simple_loss=0.3305, pruned_loss=0.2912, over 985664.99 frames.], batch size: 42, aishell_tot_loss[loss=0.197, simple_loss=0.3398, pruned_loss=1.097, over 985806.87 frames.], datatang_tot_loss[loss=0.1893, simple_loss=0.332, pruned_loss=1.028, over 985631.98 frames.], batch size: 42, lr: 2.66e-03
+2022-06-18 11:12:39,485 INFO [train.py:874] (1/4) Epoch 1, batch 4000, datatang_loss[loss=0.2029, simple_loss=0.3593, pruned_loss=0.2327, over 4952.00 frames.], tot_loss[loss=0.1855, simple_loss=0.3288, pruned_loss=0.2679, over 985977.74 frames.], batch size: 50, aishell_tot_loss[loss=0.1945, simple_loss=0.3373, pruned_loss=0.9796, over 986010.50 frames.], datatang_tot_loss[loss=0.1885, simple_loss=0.3313, pruned_loss=0.9383, over 985748.94 frames.], batch size: 50, lr: 2.65e-03
+2022-06-18 11:12:39,486 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 11:13:00,454 INFO [train.py:914] (1/4) Epoch 1, validation: loss=0.2503, simple_loss=0.2882, pruned_loss=0.1062, over 1622729.00 frames.
+2022-06-18 11:13:28,275 INFO [train.py:874] (1/4) Epoch 1, batch 4050, aishell_loss[loss=0.1774, simple_loss=0.3201, pruned_loss=0.1733, over 4965.00 frames.], tot_loss[loss=0.1848, simple_loss=0.3282, pruned_loss=0.251, over 985792.25 frames.], batch size: 56, aishell_tot_loss[loss=0.1932, simple_loss=0.3366, pruned_loss=0.8858, over 985779.28 frames.], datatang_tot_loss[loss=0.1876, simple_loss=0.3301, pruned_loss=0.8514, over 985798.49 frames.], batch size: 56, lr: 2.64e-03
+2022-06-18 11:13:55,309 INFO [train.py:874] (1/4) Epoch 1, batch 4100, aishell_loss[loss=0.1863, simple_loss=0.3387, pruned_loss=0.1697, over 4980.00 frames.], tot_loss[loss=0.1837, simple_loss=0.327, pruned_loss=0.2359, over 986106.78 frames.], batch size: 39, aishell_tot_loss[loss=0.1915, simple_loss=0.3349, pruned_loss=0.7984, over 986006.87 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.3293, pruned_loss=0.7771, over 985888.53 frames.], batch size: 39, lr: 2.64e-03
+2022-06-18 11:14:25,455 INFO [train.py:874] (1/4) Epoch 1, batch 4150, aishell_loss[loss=0.1805, simple_loss=0.3227, pruned_loss=0.191, over 4944.00 frames.], tot_loss[loss=0.1837, simple_loss=0.3272, pruned_loss=0.2279, over 985963.60 frames.], batch size: 56, aishell_tot_loss[loss=0.1907, simple_loss=0.3342, pruned_loss=0.7337, over 985731.02 frames.], datatang_tot_loss[loss=0.1864, simple_loss=0.3289, pruned_loss=0.7034, over 986048.61 frames.], batch size: 56, lr: 2.63e-03
+2022-06-18 11:14:54,748 INFO [train.py:874] (1/4) Epoch 1, batch 4200, datatang_loss[loss=0.1936, simple_loss=0.3457, pruned_loss=0.2078, over 4934.00 frames.], tot_loss[loss=0.1823, simple_loss=0.3253, pruned_loss=0.2169, over 985415.39 frames.], batch size: 94, aishell_tot_loss[loss=0.1891, simple_loss=0.3325, pruned_loss=0.6747, over 985340.64 frames.], datatang_tot_loss[loss=0.1854, simple_loss=0.3278, pruned_loss=0.6359, over 985874.88 frames.], batch size: 94, lr: 2.63e-03
+2022-06-18 11:16:16,057 INFO [train.py:874] (1/4) Epoch 2, batch 50, datatang_loss[loss=0.179, simple_loss=0.321, pruned_loss=0.1847, over 4969.00 frames.], tot_loss[loss=0.1744, simple_loss=0.3146, pruned_loss=0.1712, over 218539.92 frames.], batch size: 60, aishell_tot_loss[loss=0.1757, simple_loss=0.319, pruned_loss=0.1618, over 120147.82 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.3104, pruned_loss=0.1812, over 112064.36 frames.], batch size: 60, lr: 2.60e-03
+2022-06-18 11:16:44,638 INFO [train.py:874] (1/4) Epoch 2, batch 100, datatang_loss[loss=0.1575, simple_loss=0.2857, pruned_loss=0.1467, over 4923.00 frames.], tot_loss[loss=0.1745, simple_loss=0.3148, pruned_loss=0.1708, over 388399.58 frames.], batch size: 73, aishell_tot_loss[loss=0.1766, simple_loss=0.3207, pruned_loss=0.162, over 237143.73 frames.], datatang_tot_loss[loss=0.1719, simple_loss=0.3076, pruned_loss=0.1812, over 199254.09 frames.], batch size: 73, lr: 2.59e-03
+2022-06-18 11:17:15,566 INFO [train.py:874] (1/4) Epoch 2, batch 150, datatang_loss[loss=0.1716, simple_loss=0.3091, pruned_loss=0.1706, over 4916.00 frames.], tot_loss[loss=0.1753, simple_loss=0.3164, pruned_loss=0.1713, over 520558.47 frames.], batch size: 75, aishell_tot_loss[loss=0.1777, simple_loss=0.3225, pruned_loss=0.1645, over 341292.21 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.3079, pruned_loss=0.1802, over 274358.42 frames.], batch size: 75, lr: 2.58e-03
+2022-06-18 11:17:45,631 INFO [train.py:874] (1/4) Epoch 2, batch 200, aishell_loss[loss=0.1779, simple_loss=0.3244, pruned_loss=0.1571, over 4879.00 frames.], tot_loss[loss=0.1747, simple_loss=0.3156, pruned_loss=0.1693, over 623865.24 frames.], batch size: 47, aishell_tot_loss[loss=0.1775, simple_loss=0.3223, pruned_loss=0.1636, over 422776.38 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.307, pruned_loss=0.1772, over 352109.53 frames.], batch size: 47, lr: 2.58e-03
+2022-06-18 11:18:14,343 INFO [train.py:874] (1/4) Epoch 2, batch 250, datatang_loss[loss=0.1537, simple_loss=0.2782, pruned_loss=0.146, over 4914.00 frames.], tot_loss[loss=0.1743, simple_loss=0.315, pruned_loss=0.1675, over 704340.10 frames.], batch size: 42, aishell_tot_loss[loss=0.1771, simple_loss=0.3217, pruned_loss=0.162, over 486959.43 frames.], datatang_tot_loss[loss=0.1712, simple_loss=0.3074, pruned_loss=0.1753, over 429437.69 frames.], batch size: 42, lr: 2.57e-03
+2022-06-18 11:18:45,603 INFO [train.py:874] (1/4) Epoch 2, batch 300, datatang_loss[loss=0.1566, simple_loss=0.2822, pruned_loss=0.1553, over 4910.00 frames.], tot_loss[loss=0.1732, simple_loss=0.3133, pruned_loss=0.1658, over 766733.72 frames.], batch size: 42, aishell_tot_loss[loss=0.1761, simple_loss=0.3202, pruned_loss=0.1601, over 552193.06 frames.], datatang_tot_loss[loss=0.1705, simple_loss=0.3061, pruned_loss=0.1742, over 487612.68 frames.], batch size: 42, lr: 2.57e-03
+2022-06-18 11:19:14,213 INFO [train.py:874] (1/4) Epoch 2, batch 350, aishell_loss[loss=0.173, simple_loss=0.3156, pruned_loss=0.1521, over 4876.00 frames.], tot_loss[loss=0.1736, simple_loss=0.3139, pruned_loss=0.1667, over 815010.73 frames.], batch size: 42, aishell_tot_loss[loss=0.1766, simple_loss=0.3211, pruned_loss=0.161, over 610769.02 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.3057, pruned_loss=0.1748, over 537154.94 frames.], batch size: 42, lr: 2.56e-03
+2022-06-18 11:19:44,615 INFO [train.py:874] (1/4) Epoch 2, batch 400, datatang_loss[loss=0.1895, simple_loss=0.3393, pruned_loss=0.1989, over 4960.00 frames.], tot_loss[loss=0.1737, simple_loss=0.3142, pruned_loss=0.1659, over 852823.56 frames.], batch size: 91, aishell_tot_loss[loss=0.1764, simple_loss=0.3208, pruned_loss=0.1598, over 648234.77 frames.], datatang_tot_loss[loss=0.1709, simple_loss=0.3071, pruned_loss=0.1738, over 597796.70 frames.], batch size: 91, lr: 2.55e-03
+2022-06-18 11:20:15,843 INFO [train.py:874] (1/4) Epoch 2, batch 450, aishell_loss[loss=0.173, simple_loss=0.3168, pruned_loss=0.146, over 4914.00 frames.], tot_loss[loss=0.1734, simple_loss=0.3141, pruned_loss=0.1634, over 882493.26 frames.], batch size: 41, aishell_tot_loss[loss=0.1758, simple_loss=0.32, pruned_loss=0.1576, over 692535.08 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.3075, pruned_loss=0.1726, over 638415.41 frames.], batch size: 41, lr: 2.55e-03
+2022-06-18 11:20:44,456 INFO [train.py:874] (1/4) Epoch 2, batch 500, datatang_loss[loss=0.1719, simple_loss=0.3106, pruned_loss=0.1662, over 4914.00 frames.], tot_loss[loss=0.1738, simple_loss=0.3149, pruned_loss=0.1631, over 905514.15 frames.], batch size: 75, aishell_tot_loss[loss=0.1759, simple_loss=0.3204, pruned_loss=0.1574, over 730768.70 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.3081, pruned_loss=0.1721, over 674988.83 frames.], batch size: 75, lr: 2.54e-03
+2022-06-18 11:21:14,567 INFO [train.py:874] (1/4) Epoch 2, batch 550, datatang_loss[loss=0.192, simple_loss=0.3447, pruned_loss=0.1963, over 4943.00 frames.], tot_loss[loss=0.1746, simple_loss=0.3164, pruned_loss=0.164, over 923349.60 frames.], batch size: 91, aishell_tot_loss[loss=0.176, simple_loss=0.3205, pruned_loss=0.1572, over 759643.27 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.3104, pruned_loss=0.173, over 713046.85 frames.], batch size: 91, lr: 2.53e-03
+2022-06-18 11:21:45,599 INFO [train.py:874] (1/4) Epoch 2, batch 600, datatang_loss[loss=0.1753, simple_loss=0.316, pruned_loss=0.1728, over 4926.00 frames.], tot_loss[loss=0.1757, simple_loss=0.318, pruned_loss=0.1674, over 937120.30 frames.], batch size: 71, aishell_tot_loss[loss=0.1768, simple_loss=0.3215, pruned_loss=0.1608, over 783294.07 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.3119, pruned_loss=0.1733, over 748657.64 frames.], batch size: 71, lr: 2.53e-03
+2022-06-18 11:22:14,820 INFO [train.py:874] (1/4) Epoch 2, batch 650, aishell_loss[loss=0.1684, simple_loss=0.3103, pruned_loss=0.1322, over 4965.00 frames.], tot_loss[loss=0.1748, simple_loss=0.3165, pruned_loss=0.1653, over 947516.98 frames.], batch size: 39, aishell_tot_loss[loss=0.1758, simple_loss=0.32, pruned_loss=0.1581, over 805867.54 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.312, pruned_loss=0.1734, over 777586.53 frames.], batch size: 39, lr: 2.52e-03
+2022-06-18 11:22:45,355 INFO [train.py:874] (1/4) Epoch 2, batch 700, datatang_loss[loss=0.1533, simple_loss=0.278, pruned_loss=0.1431, over 4912.00 frames.], tot_loss[loss=0.1741, simple_loss=0.3156, pruned_loss=0.1628, over 956229.62 frames.], batch size: 47, aishell_tot_loss[loss=0.1753, simple_loss=0.3194, pruned_loss=0.1562, over 827306.49 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.3117, pruned_loss=0.1719, over 802107.79 frames.], batch size: 47, lr: 2.51e-03
+2022-06-18 11:23:15,124 INFO [train.py:874] (1/4) Epoch 2, batch 750, aishell_loss[loss=0.1833, simple_loss=0.333, pruned_loss=0.1686, over 4939.00 frames.], tot_loss[loss=0.1734, simple_loss=0.3146, pruned_loss=0.1605, over 962479.42 frames.], batch size: 49, aishell_tot_loss[loss=0.1739, simple_loss=0.3171, pruned_loss=0.1534, over 847050.14 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.3127, pruned_loss=0.1719, over 822109.73 frames.], batch size: 49, lr: 2.51e-03
+2022-06-18 11:23:44,090 INFO [train.py:874] (1/4) Epoch 2, batch 800, aishell_loss[loss=0.1801, simple_loss=0.3278, pruned_loss=0.1622, over 4985.00 frames.], tot_loss[loss=0.1747, simple_loss=0.3167, pruned_loss=0.1636, over 967836.88 frames.], batch size: 30, aishell_tot_loss[loss=0.1748, simple_loss=0.3187, pruned_loss=0.1547, over 860910.11 frames.], datatang_tot_loss[loss=0.1742, simple_loss=0.3137, pruned_loss=0.1733, over 844461.05 frames.], batch size: 30, lr: 2.50e-03
+2022-06-18 11:24:15,002 INFO [train.py:874] (1/4) Epoch 2, batch 850, datatang_loss[loss=0.1782, simple_loss=0.3189, pruned_loss=0.1878, over 4881.00 frames.], tot_loss[loss=0.1733, simple_loss=0.3143, pruned_loss=0.1613, over 972022.00 frames.], batch size: 39, aishell_tot_loss[loss=0.1737, simple_loss=0.3168, pruned_loss=0.1529, over 874132.78 frames.], datatang_tot_loss[loss=0.1738, simple_loss=0.3131, pruned_loss=0.1721, over 862988.51 frames.], batch size: 39, lr: 2.50e-03
+2022-06-18 11:24:45,352 INFO [train.py:874] (1/4) Epoch 2, batch 900, aishell_loss[loss=0.1743, simple_loss=0.321, pruned_loss=0.1374, over 4907.00 frames.], tot_loss[loss=0.1724, simple_loss=0.3132, pruned_loss=0.1585, over 974865.15 frames.], batch size: 52, aishell_tot_loss[loss=0.1731, simple_loss=0.3161, pruned_loss=0.1512, over 887757.03 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.3122, pruned_loss=0.1703, over 876692.47 frames.], batch size: 52, lr: 2.49e-03
+2022-06-18 11:25:13,851 INFO [train.py:874] (1/4) Epoch 2, batch 950, aishell_loss[loss=0.1594, simple_loss=0.293, pruned_loss=0.129, over 4867.00 frames.], tot_loss[loss=0.1725, simple_loss=0.3133, pruned_loss=0.1589, over 977408.93 frames.], batch size: 37, aishell_tot_loss[loss=0.173, simple_loss=0.3157, pruned_loss=0.1513, over 899272.22 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.3125, pruned_loss=0.17, over 889718.47 frames.], batch size: 37, lr: 2.48e-03
+2022-06-18 11:25:45,479 INFO [train.py:874] (1/4) Epoch 2, batch 1000, datatang_loss[loss=0.1949, simple_loss=0.3512, pruned_loss=0.1933, over 4936.00 frames.], tot_loss[loss=0.1735, simple_loss=0.3152, pruned_loss=0.1592, over 979323.59 frames.], batch size: 94, aishell_tot_loss[loss=0.1732, simple_loss=0.3162, pruned_loss=0.1506, over 908326.41 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.314, pruned_loss=0.1703, over 902340.00 frames.], batch size: 94, lr: 2.48e-03
+2022-06-18 11:25:45,480 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 11:26:02,499 INFO [train.py:914] (1/4) Epoch 2, validation: loss=0.2333, simple_loss=0.2891, pruned_loss=0.08878, over 1622729.00 frames.
+2022-06-18 11:26:32,161 INFO [train.py:874] (1/4) Epoch 2, batch 1050, aishell_loss[loss=0.1429, simple_loss=0.2654, pruned_loss=0.102, over 4988.00 frames.], tot_loss[loss=0.1728, simple_loss=0.3142, pruned_loss=0.157, over 981046.27 frames.], batch size: 30, aishell_tot_loss[loss=0.1725, simple_loss=0.3152, pruned_loss=0.1488, over 919059.53 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.314, pruned_loss=0.1698, over 910725.10 frames.], batch size: 30, lr: 2.47e-03
+2022-06-18 11:27:03,421 INFO [train.py:874] (1/4) Epoch 2, batch 1100, datatang_loss[loss=0.2063, simple_loss=0.371, pruned_loss=0.2082, over 4923.00 frames.], tot_loss[loss=0.1731, simple_loss=0.3149, pruned_loss=0.1561, over 982015.37 frames.], batch size: 98, aishell_tot_loss[loss=0.1724, simple_loss=0.3153, pruned_loss=0.1476, over 926580.45 frames.], datatang_tot_loss[loss=0.1742, simple_loss=0.3146, pruned_loss=0.1692, over 919845.99 frames.], batch size: 98, lr: 2.46e-03
+2022-06-18 11:27:31,691 INFO [train.py:874] (1/4) Epoch 2, batch 1150, aishell_loss[loss=0.194, simple_loss=0.3553, pruned_loss=0.1639, over 4864.00 frames.], tot_loss[loss=0.173, simple_loss=0.3148, pruned_loss=0.1557, over 982323.18 frames.], batch size: 37, aishell_tot_loss[loss=0.1725, simple_loss=0.3156, pruned_loss=0.1469, over 934009.33 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.3141, pruned_loss=0.1693, over 926502.37 frames.], batch size: 37, lr: 2.46e-03
+2022-06-18 11:28:02,548 INFO [train.py:874] (1/4) Epoch 2, batch 1200, aishell_loss[loss=0.1686, simple_loss=0.3117, pruned_loss=0.1276, over 4957.00 frames.], tot_loss[loss=0.1723, simple_loss=0.3137, pruned_loss=0.1547, over 982773.64 frames.], batch size: 45, aishell_tot_loss[loss=0.1721, simple_loss=0.315, pruned_loss=0.1462, over 939293.86 frames.], datatang_tot_loss[loss=0.1736, simple_loss=0.3136, pruned_loss=0.1681, over 934024.41 frames.], batch size: 45, lr: 2.45e-03
+2022-06-18 11:28:34,003 INFO [train.py:874] (1/4) Epoch 2, batch 1250, datatang_loss[loss=0.1787, simple_loss=0.3234, pruned_loss=0.1698, over 4909.00 frames.], tot_loss[loss=0.1725, simple_loss=0.3133, pruned_loss=0.1581, over 983436.90 frames.], batch size: 98, aishell_tot_loss[loss=0.1726, simple_loss=0.3153, pruned_loss=0.1498, over 944642.58 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.3126, pruned_loss=0.1674, over 940217.11 frames.], batch size: 98, lr: 2.45e-03
+2022-06-18 11:29:03,385 INFO [train.py:874] (1/4) Epoch 2, batch 1300, aishell_loss[loss=0.1732, simple_loss=0.3171, pruned_loss=0.1463, over 4922.00 frames.], tot_loss[loss=0.1715, simple_loss=0.3119, pruned_loss=0.1558, over 983768.31 frames.], batch size: 52, aishell_tot_loss[loss=0.1722, simple_loss=0.3147, pruned_loss=0.1488, over 948570.22 frames.], datatang_tot_loss[loss=0.1723, simple_loss=0.3116, pruned_loss=0.1655, over 946368.12 frames.], batch size: 52, lr: 2.44e-03
+2022-06-18 11:29:33,627 INFO [train.py:874] (1/4) Epoch 2, batch 1350, datatang_loss[loss=0.1795, simple_loss=0.3265, pruned_loss=0.1625, over 4959.00 frames.], tot_loss[loss=0.1707, simple_loss=0.3109, pruned_loss=0.1526, over 984237.09 frames.], batch size: 91, aishell_tot_loss[loss=0.1718, simple_loss=0.3142, pruned_loss=0.147, over 952957.83 frames.], datatang_tot_loss[loss=0.1716, simple_loss=0.3105, pruned_loss=0.1636, over 951014.35 frames.], batch size: 91, lr: 2.43e-03
+2022-06-18 11:30:05,331 INFO [train.py:874] (1/4) Epoch 2, batch 1400, aishell_loss[loss=0.1633, simple_loss=0.3032, pruned_loss=0.1172, over 4938.00 frames.], tot_loss[loss=0.1697, simple_loss=0.3094, pruned_loss=0.1502, over 984601.25 frames.], batch size: 54, aishell_tot_loss[loss=0.1713, simple_loss=0.3135, pruned_loss=0.1454, over 956599.29 frames.], datatang_tot_loss[loss=0.1708, simple_loss=0.3093, pruned_loss=0.1618, over 955371.32 frames.], batch size: 54, lr: 2.43e-03
+2022-06-18 11:30:32,953 INFO [train.py:874] (1/4) Epoch 2, batch 1450, aishell_loss[loss=0.178, simple_loss=0.3254, pruned_loss=0.1525, over 4924.00 frames.], tot_loss[loss=0.1705, simple_loss=0.3108, pruned_loss=0.1512, over 985031.88 frames.], batch size: 80, aishell_tot_loss[loss=0.1713, simple_loss=0.3135, pruned_loss=0.145, over 960095.64 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.3103, pruned_loss=0.1626, over 959062.14 frames.], batch size: 80, lr: 2.42e-03
+2022-06-18 11:31:04,363 INFO [train.py:874] (1/4) Epoch 2, batch 1500, aishell_loss[loss=0.1883, simple_loss=0.3453, pruned_loss=0.1562, over 4962.00 frames.], tot_loss[loss=0.1707, simple_loss=0.3111, pruned_loss=0.1509, over 985334.06 frames.], batch size: 69, aishell_tot_loss[loss=0.1709, simple_loss=0.3131, pruned_loss=0.1441, over 962885.58 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.3108, pruned_loss=0.1623, over 962599.37 frames.], batch size: 69, lr: 2.42e-03
+2022-06-18 11:31:35,319 INFO [train.py:874] (1/4) Epoch 2, batch 1550, datatang_loss[loss=0.1729, simple_loss=0.3149, pruned_loss=0.1544, over 4917.00 frames.], tot_loss[loss=0.1702, simple_loss=0.3105, pruned_loss=0.15, over 985230.24 frames.], batch size: 42, aishell_tot_loss[loss=0.1706, simple_loss=0.3124, pruned_loss=0.1434, over 965363.49 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.3105, pruned_loss=0.1615, over 965342.35 frames.], batch size: 42, lr: 2.41e-03
+2022-06-18 11:32:03,491 INFO [train.py:874] (1/4) Epoch 2, batch 1600, aishell_loss[loss=0.176, simple_loss=0.3211, pruned_loss=0.155, over 4951.00 frames.], tot_loss[loss=0.1692, simple_loss=0.309, pruned_loss=0.1474, over 985330.88 frames.], batch size: 61, aishell_tot_loss[loss=0.1697, simple_loss=0.3111, pruned_loss=0.1414, over 967741.86 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.3101, pruned_loss=0.1602, over 967747.80 frames.], batch size: 61, lr: 2.40e-03
+2022-06-18 11:32:34,646 INFO [train.py:874] (1/4) Epoch 2, batch 1650, aishell_loss[loss=0.1643, simple_loss=0.3033, pruned_loss=0.1269, over 4957.00 frames.], tot_loss[loss=0.1686, simple_loss=0.3079, pruned_loss=0.1463, over 985588.56 frames.], batch size: 44, aishell_tot_loss[loss=0.1691, simple_loss=0.3101, pruned_loss=0.1402, over 970471.77 frames.], datatang_tot_loss[loss=0.1707, simple_loss=0.3095, pruned_loss=0.1599, over 969404.18 frames.], batch size: 44, lr: 2.40e-03
+2022-06-18 11:33:04,803 INFO [train.py:874] (1/4) Epoch 2, batch 1700, aishell_loss[loss=0.1571, simple_loss=0.29, pruned_loss=0.1208, over 4931.00 frames.], tot_loss[loss=0.1681, simple_loss=0.3071, pruned_loss=0.1454, over 985583.51 frames.], batch size: 33, aishell_tot_loss[loss=0.1686, simple_loss=0.3094, pruned_loss=0.1391, over 972280.69 frames.], datatang_tot_loss[loss=0.1704, simple_loss=0.3089, pruned_loss=0.1593, over 971262.79 frames.], batch size: 33, lr: 2.39e-03
+2022-06-18 11:33:33,378 INFO [train.py:874] (1/4) Epoch 2, batch 1750, datatang_loss[loss=0.1399, simple_loss=0.2583, pruned_loss=0.1079, over 4950.00 frames.], tot_loss[loss=0.1677, simple_loss=0.3066, pruned_loss=0.1443, over 985390.58 frames.], batch size: 34, aishell_tot_loss[loss=0.1682, simple_loss=0.3089, pruned_loss=0.1379, over 973538.39 frames.], datatang_tot_loss[loss=0.1701, simple_loss=0.3085, pruned_loss=0.1584, over 973054.49 frames.], batch size: 34, lr: 2.39e-03
+2022-06-18 11:34:05,521 INFO [train.py:874] (1/4) Epoch 2, batch 1800, datatang_loss[loss=0.3164, simple_loss=0.2927, pruned_loss=0.1701, over 4881.00 frames.], tot_loss[loss=0.1983, simple_loss=0.3092, pruned_loss=0.1566, over 985781.17 frames.], batch size: 47, aishell_tot_loss[loss=0.1839, simple_loss=0.3095, pruned_loss=0.1458, over 975160.61 frames.], datatang_tot_loss[loss=0.1859, simple_loss=0.31, pruned_loss=0.1622, over 974708.41 frames.], batch size: 47, lr: 2.38e-03
+2022-06-18 11:34:34,593 INFO [train.py:874] (1/4) Epoch 2, batch 1850, datatang_loss[loss=0.3447, simple_loss=0.3367, pruned_loss=0.1764, over 4965.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3111, pruned_loss=0.1581, over 985611.25 frames.], batch size: 40, aishell_tot_loss[loss=0.199, simple_loss=0.3105, pruned_loss=0.1466, over 976284.78 frames.], datatang_tot_loss[loss=0.2036, simple_loss=0.311, pruned_loss=0.1638, over 975943.41 frames.], batch size: 40, lr: 2.38e-03
+2022-06-18 11:35:04,173 INFO [train.py:874] (1/4) Epoch 2, batch 1900, datatang_loss[loss=0.3283, simple_loss=0.3074, pruned_loss=0.1746, over 4912.00 frames.], tot_loss[loss=0.2419, simple_loss=0.311, pruned_loss=0.1549, over 985516.22 frames.], batch size: 64, aishell_tot_loss[loss=0.2115, simple_loss=0.3107, pruned_loss=0.1449, over 977447.46 frames.], datatang_tot_loss[loss=0.2144, simple_loss=0.3108, pruned_loss=0.1631, over 976918.42 frames.], batch size: 64, lr: 2.37e-03
+2022-06-18 11:35:34,929 INFO [train.py:874] (1/4) Epoch 2, batch 1950, aishell_loss[loss=0.2907, simple_loss=0.3204, pruned_loss=0.1305, over 4872.00 frames.], tot_loss[loss=0.2529, simple_loss=0.3106, pruned_loss=0.1508, over 985689.70 frames.], batch size: 35, aishell_tot_loss[loss=0.2206, simple_loss=0.3106, pruned_loss=0.1424, over 978562.68 frames.], datatang_tot_loss[loss=0.224, simple_loss=0.3106, pruned_loss=0.1618, over 977948.77 frames.], batch size: 35, lr: 2.36e-03
+2022-06-18 11:36:03,038 INFO [train.py:874] (1/4) Epoch 2, batch 2000, datatang_loss[loss=0.325, simple_loss=0.3269, pruned_loss=0.1615, over 4947.00 frames.], tot_loss[loss=0.2627, simple_loss=0.3101, pruned_loss=0.1491, over 985754.86 frames.], batch size: 88, aishell_tot_loss[loss=0.2278, simple_loss=0.3096, pruned_loss=0.1406, over 979302.25 frames.], datatang_tot_loss[loss=0.2344, simple_loss=0.3109, pruned_loss=0.1614, over 979020.52 frames.], batch size: 88, lr: 2.36e-03
+2022-06-18 11:36:03,038 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 11:36:19,299 INFO [train.py:914] (1/4) Epoch 2, validation: loss=0.2142, simple_loss=0.275, pruned_loss=0.07672, over 1622729.00 frames.
+2022-06-18 11:36:48,996 INFO [train.py:874] (1/4) Epoch 2, batch 2050, datatang_loss[loss=0.3194, simple_loss=0.3159, pruned_loss=0.1615, over 4971.00 frames.], tot_loss[loss=0.2723, simple_loss=0.3104, pruned_loss=0.1493, over 985789.85 frames.], batch size: 53, aishell_tot_loss[loss=0.2369, simple_loss=0.3107, pruned_loss=0.1405, over 980041.41 frames.], datatang_tot_loss[loss=0.243, simple_loss=0.3102, pruned_loss=0.1614, over 979881.51 frames.], batch size: 53, lr: 2.35e-03
+2022-06-18 11:37:18,665 INFO [train.py:874] (1/4) Epoch 2, batch 2100, aishell_loss[loss=0.262, simple_loss=0.3018, pruned_loss=0.1111, over 4884.00 frames.], tot_loss[loss=0.2775, simple_loss=0.3104, pruned_loss=0.1474, over 985436.20 frames.], batch size: 42, aishell_tot_loss[loss=0.2429, simple_loss=0.3106, pruned_loss=0.1393, over 980357.63 frames.], datatang_tot_loss[loss=0.2504, simple_loss=0.3102, pruned_loss=0.1603, over 980562.65 frames.], batch size: 42, lr: 2.35e-03
+2022-06-18 11:37:49,774 INFO [train.py:874] (1/4) Epoch 2, batch 2150, aishell_loss[loss=0.3054, simple_loss=0.3276, pruned_loss=0.1416, over 4876.00 frames.], tot_loss[loss=0.2811, simple_loss=0.3096, pruned_loss=0.1459, over 985821.55 frames.], batch size: 42, aishell_tot_loss[loss=0.2463, simple_loss=0.3095, pruned_loss=0.1372, over 981116.22 frames.], datatang_tot_loss[loss=0.258, simple_loss=0.3105, pruned_loss=0.1601, over 981395.71 frames.], batch size: 42, lr: 2.34e-03
+2022-06-18 11:38:20,206 INFO [train.py:874] (1/4) Epoch 2, batch 2200, datatang_loss[loss=0.3481, simple_loss=0.3309, pruned_loss=0.1826, over 4903.00 frames.], tot_loss[loss=0.2841, simple_loss=0.3094, pruned_loss=0.1446, over 985787.71 frames.], batch size: 47, aishell_tot_loss[loss=0.2512, simple_loss=0.3101, pruned_loss=0.1368, over 981563.05 frames.], datatang_tot_loss[loss=0.263, simple_loss=0.3096, pruned_loss=0.1583, over 981968.61 frames.], batch size: 47, lr: 2.33e-03
+2022-06-18 11:38:48,993 INFO [train.py:874] (1/4) Epoch 2, batch 2250, datatang_loss[loss=0.3176, simple_loss=0.3149, pruned_loss=0.1601, over 4924.00 frames.], tot_loss[loss=0.2899, simple_loss=0.3101, pruned_loss=0.1467, over 985885.47 frames.], batch size: 83, aishell_tot_loss[loss=0.256, simple_loss=0.3101, pruned_loss=0.138, over 981886.81 frames.], datatang_tot_loss[loss=0.2704, simple_loss=0.3101, pruned_loss=0.1579, over 982690.14 frames.], batch size: 83, lr: 2.33e-03
+2022-06-18 11:39:20,383 INFO [train.py:874] (1/4) Epoch 2, batch 2300, aishell_loss[loss=0.3048, simple_loss=0.3297, pruned_loss=0.1399, over 4969.00 frames.], tot_loss[loss=0.2903, simple_loss=0.3101, pruned_loss=0.1444, over 985715.98 frames.], batch size: 39, aishell_tot_loss[loss=0.2597, simple_loss=0.3104, pruned_loss=0.1371, over 982067.34 frames.], datatang_tot_loss[loss=0.2733, simple_loss=0.3099, pruned_loss=0.1561, over 983189.26 frames.], batch size: 39, lr: 2.32e-03
+2022-06-18 11:39:51,122 INFO [train.py:874] (1/4) Epoch 2, batch 2350, aishell_loss[loss=0.2986, simple_loss=0.326, pruned_loss=0.1357, over 4882.00 frames.], tot_loss[loss=0.2891, simple_loss=0.3092, pruned_loss=0.1417, over 985244.05 frames.], batch size: 47, aishell_tot_loss[loss=0.2627, simple_loss=0.3111, pruned_loss=0.1362, over 982193.00 frames.], datatang_tot_loss[loss=0.2746, simple_loss=0.3081, pruned_loss=0.1535, over 983285.17 frames.], batch size: 47, lr: 2.32e-03
+2022-06-18 11:40:20,385 INFO [train.py:874] (1/4) Epoch 2, batch 2400, datatang_loss[loss=0.2963, simple_loss=0.3148, pruned_loss=0.139, over 4955.00 frames.], tot_loss[loss=0.2876, simple_loss=0.3074, pruned_loss=0.1394, over 985240.96 frames.], batch size: 91, aishell_tot_loss[loss=0.2636, simple_loss=0.3099, pruned_loss=0.1347, over 982360.27 frames.], datatang_tot_loss[loss=0.2767, simple_loss=0.3075, pruned_loss=0.1516, over 983688.18 frames.], batch size: 91, lr: 2.31e-03
+2022-06-18 11:40:51,460 INFO [train.py:874] (1/4) Epoch 2, batch 2450, aishell_loss[loss=0.2898, simple_loss=0.3184, pruned_loss=0.1306, over 4940.00 frames.], tot_loss[loss=0.2875, simple_loss=0.3071, pruned_loss=0.1383, over 985491.67 frames.], batch size: 56, aishell_tot_loss[loss=0.2652, simple_loss=0.3096, pruned_loss=0.1334, over 982881.08 frames.], datatang_tot_loss[loss=0.2791, simple_loss=0.3071, pruned_loss=0.1507, over 983947.36 frames.], batch size: 56, lr: 2.31e-03
+2022-06-18 11:41:22,608 INFO [train.py:874] (1/4) Epoch 2, batch 2500, datatang_loss[loss=0.322, simple_loss=0.3206, pruned_loss=0.1617, over 4925.00 frames.], tot_loss[loss=0.2914, simple_loss=0.3086, pruned_loss=0.1405, over 985622.74 frames.], batch size: 79, aishell_tot_loss[loss=0.2676, simple_loss=0.3098, pruned_loss=0.1325, over 983176.87 frames.], datatang_tot_loss[loss=0.2846, simple_loss=0.3081, pruned_loss=0.1534, over 984317.88 frames.], batch size: 79, lr: 2.30e-03
+2022-06-18 11:41:51,393 INFO [train.py:874] (1/4) Epoch 2, batch 2550, datatang_loss[loss=0.2807, simple_loss=0.3012, pruned_loss=0.1301, over 4949.00 frames.], tot_loss[loss=0.291, simple_loss=0.308, pruned_loss=0.1396, over 985298.80 frames.], batch size: 86, aishell_tot_loss[loss=0.2688, simple_loss=0.3091, pruned_loss=0.1316, over 983140.71 frames.], datatang_tot_loss[loss=0.2867, simple_loss=0.3081, pruned_loss=0.1529, over 984448.53 frames.], batch size: 86, lr: 2.30e-03
+2022-06-18 11:42:23,033 INFO [train.py:874] (1/4) Epoch 2, batch 2600, datatang_loss[loss=0.2806, simple_loss=0.2927, pruned_loss=0.1343, over 4942.00 frames.], tot_loss[loss=0.2875, simple_loss=0.3058, pruned_loss=0.1367, over 985683.05 frames.], batch size: 69, aishell_tot_loss[loss=0.2693, simple_loss=0.3085, pruned_loss=0.1305, over 983673.78 frames.], datatang_tot_loss[loss=0.2854, simple_loss=0.3062, pruned_loss=0.1499, over 984667.80 frames.], batch size: 69, lr: 2.29e-03
+2022-06-18 11:42:52,014 INFO [train.py:874] (1/4) Epoch 2, batch 2650, datatang_loss[loss=0.2566, simple_loss=0.283, pruned_loss=0.115, over 4897.00 frames.], tot_loss[loss=0.2887, simple_loss=0.3066, pruned_loss=0.137, over 985409.74 frames.], batch size: 52, aishell_tot_loss[loss=0.2711, simple_loss=0.3086, pruned_loss=0.1303, over 983947.82 frames.], datatang_tot_loss[loss=0.2875, simple_loss=0.3066, pruned_loss=0.15, over 984478.25 frames.], batch size: 52, lr: 2.28e-03
+2022-06-18 11:43:22,482 INFO [train.py:874] (1/4) Epoch 2, batch 2700, aishell_loss[loss=0.2595, simple_loss=0.2965, pruned_loss=0.1113, over 4961.00 frames.], tot_loss[loss=0.2862, simple_loss=0.3059, pruned_loss=0.1344, over 985253.23 frames.], batch size: 56, aishell_tot_loss[loss=0.2711, simple_loss=0.3084, pruned_loss=0.1287, over 984017.06 frames.], datatang_tot_loss[loss=0.2872, simple_loss=0.3058, pruned_loss=0.1484, over 984532.56 frames.], batch size: 56, lr: 2.28e-03
+2022-06-18 11:43:52,867 INFO [train.py:874] (1/4) Epoch 2, batch 2750, datatang_loss[loss=0.3718, simple_loss=0.3453, pruned_loss=0.1992, over 4943.00 frames.], tot_loss[loss=0.2874, simple_loss=0.3063, pruned_loss=0.1352, over 985762.08 frames.], batch size: 88, aishell_tot_loss[loss=0.2708, simple_loss=0.3074, pruned_loss=0.1276, over 984473.34 frames.], datatang_tot_loss[loss=0.2901, simple_loss=0.3069, pruned_loss=0.1489, over 984838.33 frames.], batch size: 88, lr: 2.27e-03
+2022-06-18 11:44:21,120 INFO [train.py:874] (1/4) Epoch 2, batch 2800, aishell_loss[loss=0.3049, simple_loss=0.326, pruned_loss=0.1419, over 4924.00 frames.], tot_loss[loss=0.2846, simple_loss=0.3045, pruned_loss=0.133, over 985264.98 frames.], batch size: 52, aishell_tot_loss[loss=0.2702, simple_loss=0.3065, pruned_loss=0.1263, over 984163.61 frames.], datatang_tot_loss[loss=0.2893, simple_loss=0.3056, pruned_loss=0.1472, over 984859.86 frames.], batch size: 52, lr: 2.27e-03
+2022-06-18 11:44:52,572 INFO [train.py:874] (1/4) Epoch 2, batch 2850, datatang_loss[loss=0.3209, simple_loss=0.3176, pruned_loss=0.1621, over 4918.00 frames.], tot_loss[loss=0.2839, simple_loss=0.3043, pruned_loss=0.1324, over 985455.97 frames.], batch size: 42, aishell_tot_loss[loss=0.2701, simple_loss=0.3062, pruned_loss=0.1253, over 984330.17 frames.], datatang_tot_loss[loss=0.2897, simple_loss=0.3054, pruned_loss=0.1464, over 985079.03 frames.], batch size: 42, lr: 2.26e-03
+2022-06-18 11:45:23,338 INFO [train.py:874] (1/4) Epoch 2, batch 2900, aishell_loss[loss=0.2913, simple_loss=0.3203, pruned_loss=0.1312, over 4953.00 frames.], tot_loss[loss=0.2831, simple_loss=0.3043, pruned_loss=0.1314, over 985599.01 frames.], batch size: 31, aishell_tot_loss[loss=0.2709, simple_loss=0.3063, pruned_loss=0.125, over 984686.15 frames.], datatang_tot_loss[loss=0.2891, simple_loss=0.3049, pruned_loss=0.145, over 985047.89 frames.], batch size: 31, lr: 2.26e-03
+2022-06-18 11:45:51,130 INFO [train.py:874] (1/4) Epoch 2, batch 2950, datatang_loss[loss=0.2737, simple_loss=0.3035, pruned_loss=0.122, over 4919.00 frames.], tot_loss[loss=0.2823, simple_loss=0.3045, pruned_loss=0.1304, over 985296.09 frames.], batch size: 75, aishell_tot_loss[loss=0.2704, simple_loss=0.3057, pruned_loss=0.1237, over 984429.37 frames.], datatang_tot_loss[loss=0.29, simple_loss=0.3053, pruned_loss=0.145, over 985157.57 frames.], batch size: 75, lr: 2.25e-03
+2022-06-18 11:46:22,400 INFO [train.py:874] (1/4) Epoch 2, batch 3000, aishell_loss[loss=0.2796, simple_loss=0.3127, pruned_loss=0.1233, over 4922.00 frames.], tot_loss[loss=0.2802, simple_loss=0.303, pruned_loss=0.129, over 985054.57 frames.], batch size: 32, aishell_tot_loss[loss=0.2693, simple_loss=0.3045, pruned_loss=0.1225, over 984308.94 frames.], datatang_tot_loss[loss=0.2893, simple_loss=0.3046, pruned_loss=0.1437, over 985133.91 frames.], batch size: 32, lr: 2.25e-03
+2022-06-18 11:46:22,401 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 11:46:39,816 INFO [train.py:914] (1/4) Epoch 2, validation: loss=0.2091, simple_loss=0.2742, pruned_loss=0.07205, over 1622729.00 frames.
+2022-06-18 11:47:08,832 INFO [train.py:874] (1/4) Epoch 2, batch 3050, datatang_loss[loss=0.2963, simple_loss=0.3175, pruned_loss=0.1376, over 4953.00 frames.], tot_loss[loss=0.2789, simple_loss=0.3022, pruned_loss=0.1281, over 985628.24 frames.], batch size: 99, aishell_tot_loss[loss=0.2692, simple_loss=0.3044, pruned_loss=0.1218, over 984837.56 frames.], datatang_tot_loss[loss=0.2883, simple_loss=0.3035, pruned_loss=0.1426, over 985294.68 frames.], batch size: 99, lr: 2.24e-03
+2022-06-18 11:47:40,867 INFO [train.py:874] (1/4) Epoch 2, batch 3100, aishell_loss[loss=0.2635, simple_loss=0.3001, pruned_loss=0.1134, over 4973.00 frames.], tot_loss[loss=0.2792, simple_loss=0.3028, pruned_loss=0.1279, over 985576.73 frames.], batch size: 51, aishell_tot_loss[loss=0.2695, simple_loss=0.3045, pruned_loss=0.1214, over 984879.32 frames.], datatang_tot_loss[loss=0.2888, simple_loss=0.3036, pruned_loss=0.1424, over 985357.85 frames.], batch size: 51, lr: 2.24e-03
+2022-06-18 11:48:08,414 INFO [train.py:874] (1/4) Epoch 2, batch 3150, aishell_loss[loss=0.2896, simple_loss=0.3229, pruned_loss=0.1281, over 4898.00 frames.], tot_loss[loss=0.283, simple_loss=0.3048, pruned_loss=0.1307, over 985409.21 frames.], batch size: 60, aishell_tot_loss[loss=0.2716, simple_loss=0.306, pruned_loss=0.1222, over 984809.19 frames.], datatang_tot_loss[loss=0.2909, simple_loss=0.304, pruned_loss=0.1437, over 985373.89 frames.], batch size: 60, lr: 2.23e-03
+2022-06-18 11:48:40,358 INFO [train.py:874] (1/4) Epoch 2, batch 3200, datatang_loss[loss=0.2962, simple_loss=0.3048, pruned_loss=0.1438, over 4964.00 frames.], tot_loss[loss=0.2783, simple_loss=0.3022, pruned_loss=0.1273, over 985211.25 frames.], batch size: 55, aishell_tot_loss[loss=0.2695, simple_loss=0.3045, pruned_loss=0.1204, over 984757.09 frames.], datatang_tot_loss[loss=0.2886, simple_loss=0.3027, pruned_loss=0.1415, over 985310.87 frames.], batch size: 55, lr: 2.23e-03
+2022-06-18 11:49:11,902 INFO [train.py:874] (1/4) Epoch 2, batch 3250, datatang_loss[loss=0.3094, simple_loss=0.3239, pruned_loss=0.1475, over 4952.00 frames.], tot_loss[loss=0.2768, simple_loss=0.3021, pruned_loss=0.1258, over 985518.80 frames.], batch size: 86, aishell_tot_loss[loss=0.2686, simple_loss=0.3044, pruned_loss=0.1192, over 984803.68 frames.], datatang_tot_loss[loss=0.2879, simple_loss=0.3024, pruned_loss=0.1404, over 985620.03 frames.], batch size: 86, lr: 2.22e-03
+2022-06-18 11:49:40,187 INFO [train.py:874] (1/4) Epoch 2, batch 3300, aishell_loss[loss=0.2784, simple_loss=0.3083, pruned_loss=0.1243, over 4931.00 frames.], tot_loss[loss=0.2782, simple_loss=0.3034, pruned_loss=0.1266, over 985408.32 frames.], batch size: 41, aishell_tot_loss[loss=0.2693, simple_loss=0.3047, pruned_loss=0.1194, over 984696.84 frames.], datatang_tot_loss[loss=0.2884, simple_loss=0.303, pruned_loss=0.1403, over 985694.17 frames.], batch size: 41, lr: 2.22e-03
+2022-06-18 11:50:11,294 INFO [train.py:874] (1/4) Epoch 2, batch 3350, datatang_loss[loss=0.3019, simple_loss=0.312, pruned_loss=0.1459, over 4939.00 frames.], tot_loss[loss=0.2779, simple_loss=0.3032, pruned_loss=0.1264, over 985524.82 frames.], batch size: 62, aishell_tot_loss[loss=0.2691, simple_loss=0.3047, pruned_loss=0.1189, over 984879.33 frames.], datatang_tot_loss[loss=0.288, simple_loss=0.3027, pruned_loss=0.1396, over 985696.02 frames.], batch size: 62, lr: 2.21e-03
+2022-06-18 11:50:42,260 INFO [train.py:874] (1/4) Epoch 2, batch 3400, aishell_loss[loss=0.2967, simple_loss=0.3306, pruned_loss=0.1314, over 4935.00 frames.], tot_loss[loss=0.2762, simple_loss=0.3026, pruned_loss=0.1249, over 985409.22 frames.], batch size: 54, aishell_tot_loss[loss=0.2689, simple_loss=0.3047, pruned_loss=0.1185, over 984687.86 frames.], datatang_tot_loss[loss=0.2863, simple_loss=0.302, pruned_loss=0.1379, over 985830.84 frames.], batch size: 54, lr: 2.21e-03
+2022-06-18 11:51:10,165 INFO [train.py:874] (1/4) Epoch 2, batch 3450, datatang_loss[loss=0.2684, simple_loss=0.293, pruned_loss=0.1219, over 4961.00 frames.], tot_loss[loss=0.2759, simple_loss=0.3018, pruned_loss=0.125, over 985286.28 frames.], batch size: 55, aishell_tot_loss[loss=0.2682, simple_loss=0.3043, pruned_loss=0.1177, over 984629.05 frames.], datatang_tot_loss[loss=0.2863, simple_loss=0.3013, pruned_loss=0.138, over 985799.92 frames.], batch size: 55, lr: 2.20e-03
+2022-06-18 11:51:42,279 INFO [train.py:874] (1/4) Epoch 2, batch 3500, aishell_loss[loss=0.255, simple_loss=0.3048, pruned_loss=0.1026, over 4965.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3028, pruned_loss=0.1252, over 985205.79 frames.], batch size: 56, aishell_tot_loss[loss=0.2687, simple_loss=0.3049, pruned_loss=0.1177, over 984528.56 frames.], datatang_tot_loss[loss=0.2862, simple_loss=0.3015, pruned_loss=0.1375, over 985830.94 frames.], batch size: 56, lr: 2.20e-03
+2022-06-18 11:52:10,608 INFO [train.py:874] (1/4) Epoch 2, batch 3550, aishell_loss[loss=0.2652, simple_loss=0.3051, pruned_loss=0.1126, over 4931.00 frames.], tot_loss[loss=0.2758, simple_loss=0.3028, pruned_loss=0.1245, over 985268.55 frames.], batch size: 58, aishell_tot_loss[loss=0.2681, simple_loss=0.3047, pruned_loss=0.117, over 984633.45 frames.], datatang_tot_loss[loss=0.2862, simple_loss=0.3015, pruned_loss=0.1373, over 985826.55 frames.], batch size: 58, lr: 2.19e-03
+2022-06-18 11:52:42,015 INFO [train.py:874] (1/4) Epoch 2, batch 3600, aishell_loss[loss=0.2718, simple_loss=0.3156, pruned_loss=0.114, over 4921.00 frames.], tot_loss[loss=0.2911, simple_loss=0.3079, pruned_loss=0.1371, over 985166.47 frames.], batch size: 46, aishell_tot_loss[loss=0.2708, simple_loss=0.3063, pruned_loss=0.1188, over 984587.28 frames.], datatang_tot_loss[loss=0.2992, simple_loss=0.3053, pruned_loss=0.1482, over 985771.52 frames.], batch size: 46, lr: 2.19e-03
+2022-06-18 11:53:13,265 INFO [train.py:874] (1/4) Epoch 2, batch 3650, aishell_loss[loss=0.1734, simple_loss=0.2072, pruned_loss=0.06981, over 4947.00 frames.], tot_loss[loss=0.2869, simple_loss=0.3068, pruned_loss=0.1335, over 985019.42 frames.], batch size: 21, aishell_tot_loss[loss=0.2696, simple_loss=0.3059, pruned_loss=0.1177, over 984453.39 frames.], datatang_tot_loss[loss=0.2978, simple_loss=0.3052, pruned_loss=0.1466, over 985775.82 frames.], batch size: 21, lr: 2.18e-03
+2022-06-18 11:53:42,105 INFO [train.py:874] (1/4) Epoch 2, batch 3700, aishell_loss[loss=0.2479, simple_loss=0.2942, pruned_loss=0.1008, over 4970.00 frames.], tot_loss[loss=0.281, simple_loss=0.3037, pruned_loss=0.1292, over 984932.23 frames.], batch size: 44, aishell_tot_loss[loss=0.2667, simple_loss=0.3036, pruned_loss=0.1158, over 984340.07 frames.], datatang_tot_loss[loss=0.2955, simple_loss=0.3044, pruned_loss=0.1445, over 985795.99 frames.], batch size: 44, lr: 2.18e-03
+2022-06-18 11:54:13,037 INFO [train.py:874] (1/4) Epoch 2, batch 3750, datatang_loss[loss=0.2705, simple_loss=0.2982, pruned_loss=0.1214, over 4940.00 frames.], tot_loss[loss=0.2793, simple_loss=0.3032, pruned_loss=0.1277, over 985214.72 frames.], batch size: 69, aishell_tot_loss[loss=0.2667, simple_loss=0.3037, pruned_loss=0.1156, over 984457.53 frames.], datatang_tot_loss[loss=0.2929, simple_loss=0.3037, pruned_loss=0.1422, over 985897.30 frames.], batch size: 69, lr: 2.17e-03
+2022-06-18 11:54:47,896 INFO [train.py:874] (1/4) Epoch 2, batch 3800, datatang_loss[loss=0.2638, simple_loss=0.2855, pruned_loss=0.1211, over 4921.00 frames.], tot_loss[loss=0.2794, simple_loss=0.3035, pruned_loss=0.1276, over 984851.14 frames.], batch size: 75, aishell_tot_loss[loss=0.2659, simple_loss=0.3029, pruned_loss=0.1152, over 984245.20 frames.], datatang_tot_loss[loss=0.2936, simple_loss=0.3047, pruned_loss=0.1422, over 985733.74 frames.], batch size: 75, lr: 2.17e-03
+2022-06-18 11:55:15,824 INFO [train.py:874] (1/4) Epoch 2, batch 3850, datatang_loss[loss=0.2835, simple_loss=0.3032, pruned_loss=0.1319, over 4903.00 frames.], tot_loss[loss=0.2796, simple_loss=0.3038, pruned_loss=0.1277, over 985404.59 frames.], batch size: 42, aishell_tot_loss[loss=0.268, simple_loss=0.3042, pruned_loss=0.1165, over 984637.07 frames.], datatang_tot_loss[loss=0.2916, simple_loss=0.3037, pruned_loss=0.1407, over 985886.92 frames.], batch size: 42, lr: 2.16e-03
+2022-06-18 11:55:45,603 INFO [train.py:874] (1/4) Epoch 2, batch 3900, datatang_loss[loss=0.3358, simple_loss=0.3553, pruned_loss=0.1581, over 4914.00 frames.], tot_loss[loss=0.2796, simple_loss=0.3044, pruned_loss=0.1274, over 985061.25 frames.], batch size: 98, aishell_tot_loss[loss=0.2689, simple_loss=0.3048, pruned_loss=0.117, over 984512.66 frames.], datatang_tot_loss[loss=0.2902, simple_loss=0.3036, pruned_loss=0.1391, over 985671.48 frames.], batch size: 98, lr: 2.16e-03
+2022-06-18 11:56:14,190 INFO [train.py:874] (1/4) Epoch 2, batch 3950, datatang_loss[loss=0.2581, simple_loss=0.2868, pruned_loss=0.1147, over 4930.00 frames.], tot_loss[loss=0.2781, simple_loss=0.3037, pruned_loss=0.1262, over 985230.63 frames.], batch size: 71, aishell_tot_loss[loss=0.2677, simple_loss=0.3039, pruned_loss=0.1162, over 984726.60 frames.], datatang_tot_loss[loss=0.2894, simple_loss=0.3038, pruned_loss=0.1381, over 985621.01 frames.], batch size: 71, lr: 2.15e-03
+2022-06-18 11:56:45,133 INFO [train.py:874] (1/4) Epoch 2, batch 4000, aishell_loss[loss=0.2045, simple_loss=0.2621, pruned_loss=0.07345, over 4972.00 frames.], tot_loss[loss=0.274, simple_loss=0.3013, pruned_loss=0.1233, over 985313.70 frames.], batch size: 30, aishell_tot_loss[loss=0.267, simple_loss=0.3037, pruned_loss=0.1157, over 985021.25 frames.], datatang_tot_loss[loss=0.2852, simple_loss=0.3015, pruned_loss=0.135, over 985428.43 frames.], batch size: 30, lr: 2.15e-03
+2022-06-18 11:56:45,134 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 11:57:01,102 INFO [train.py:914] (1/4) Epoch 2, validation: loss=0.2024, simple_loss=0.2723, pruned_loss=0.06625, over 1622729.00 frames.
+2022-06-18 11:57:30,571 INFO [train.py:874] (1/4) Epoch 2, batch 4050, datatang_loss[loss=0.2789, simple_loss=0.2909, pruned_loss=0.1335, over 4927.00 frames.], tot_loss[loss=0.2727, simple_loss=0.3004, pruned_loss=0.1225, over 985421.27 frames.], batch size: 42, aishell_tot_loss[loss=0.2675, simple_loss=0.3043, pruned_loss=0.1158, over 985007.60 frames.], datatang_tot_loss[loss=0.2825, simple_loss=0.2996, pruned_loss=0.1331, over 985574.47 frames.], batch size: 42, lr: 2.14e-03
+2022-06-18 11:58:00,709 INFO [train.py:874] (1/4) Epoch 2, batch 4100, aishell_loss[loss=0.2524, simple_loss=0.3006, pruned_loss=0.1021, over 4909.00 frames.], tot_loss[loss=0.2714, simple_loss=0.2997, pruned_loss=0.1215, over 985107.61 frames.], batch size: 41, aishell_tot_loss[loss=0.2673, simple_loss=0.3044, pruned_loss=0.1155, over 984629.75 frames.], datatang_tot_loss[loss=0.2806, simple_loss=0.2986, pruned_loss=0.1317, over 985648.60 frames.], batch size: 41, lr: 2.14e-03
+2022-06-18 11:59:09,053 INFO [train.py:874] (1/4) Epoch 3, batch 50, aishell_loss[loss=0.2613, simple_loss=0.3108, pruned_loss=0.1059, over 4889.00 frames.], tot_loss[loss=0.2508, simple_loss=0.2867, pruned_loss=0.1074, over 218474.02 frames.], batch size: 42, aishell_tot_loss[loss=0.2581, simple_loss=0.2998, pruned_loss=0.1082, over 120587.99 frames.], datatang_tot_loss[loss=0.2431, simple_loss=0.2726, pruned_loss=0.1068, over 111538.94 frames.], batch size: 42, lr: 2.09e-03
+2022-06-18 11:59:39,423 INFO [train.py:874] (1/4) Epoch 3, batch 100, aishell_loss[loss=0.3033, simple_loss=0.3273, pruned_loss=0.1397, over 4951.00 frames.], tot_loss[loss=0.2612, simple_loss=0.2927, pruned_loss=0.1148, over 388703.69 frames.], batch size: 64, aishell_tot_loss[loss=0.2563, simple_loss=0.2978, pruned_loss=0.1074, over 210860.95 frames.], datatang_tot_loss[loss=0.2647, simple_loss=0.2875, pruned_loss=0.121, over 226211.02 frames.], batch size: 64, lr: 2.09e-03
+2022-06-18 12:00:11,063 INFO [train.py:874] (1/4) Epoch 3, batch 150, datatang_loss[loss=0.2847, simple_loss=0.3061, pruned_loss=0.1316, over 4923.00 frames.], tot_loss[loss=0.266, simple_loss=0.293, pruned_loss=0.1195, over 521068.04 frames.], batch size: 83, aishell_tot_loss[loss=0.2662, simple_loss=0.3009, pruned_loss=0.1158, over 274249.08 frames.], datatang_tot_loss[loss=0.264, simple_loss=0.2866, pruned_loss=0.1207, over 341917.09 frames.], batch size: 83, lr: 2.08e-03
+2022-06-18 12:00:39,817 INFO [train.py:874] (1/4) Epoch 3, batch 200, datatang_loss[loss=0.2702, simple_loss=0.2932, pruned_loss=0.1236, over 4915.00 frames.], tot_loss[loss=0.2673, simple_loss=0.2967, pruned_loss=0.1189, over 624244.21 frames.], batch size: 57, aishell_tot_loss[loss=0.2669, simple_loss=0.304, pruned_loss=0.1149, over 388837.88 frames.], datatang_tot_loss[loss=0.2659, simple_loss=0.2877, pruned_loss=0.122, over 388618.32 frames.], batch size: 57, lr: 2.08e-03
+2022-06-18 12:01:10,606 INFO [train.py:874] (1/4) Epoch 3, batch 250, aishell_loss[loss=0.2719, simple_loss=0.3119, pruned_loss=0.116, over 4945.00 frames.], tot_loss[loss=0.2664, simple_loss=0.2974, pruned_loss=0.1177, over 704695.00 frames.], batch size: 58, aishell_tot_loss[loss=0.2659, simple_loss=0.3044, pruned_loss=0.1137, over 459334.22 frames.], datatang_tot_loss[loss=0.266, simple_loss=0.2889, pruned_loss=0.1215, over 459087.93 frames.], batch size: 58, lr: 2.07e-03
+2022-06-18 12:01:42,557 INFO [train.py:874] (1/4) Epoch 3, batch 300, datatang_loss[loss=0.2816, simple_loss=0.2905, pruned_loss=0.1363, over 4934.00 frames.], tot_loss[loss=0.2637, simple_loss=0.2956, pruned_loss=0.1159, over 766785.51 frames.], batch size: 42, aishell_tot_loss[loss=0.2638, simple_loss=0.3031, pruned_loss=0.1123, over 530541.10 frames.], datatang_tot_loss[loss=0.264, simple_loss=0.2872, pruned_loss=0.1204, over 511496.07 frames.], batch size: 42, lr: 2.07e-03
+2022-06-18 12:02:11,412 INFO [train.py:874] (1/4) Epoch 3, batch 350, datatang_loss[loss=0.265, simple_loss=0.2885, pruned_loss=0.1207, over 4912.00 frames.], tot_loss[loss=0.2656, simple_loss=0.2968, pruned_loss=0.1172, over 814917.21 frames.], batch size: 64, aishell_tot_loss[loss=0.2645, simple_loss=0.3037, pruned_loss=0.1126, over 575661.29 frames.], datatang_tot_loss[loss=0.2661, simple_loss=0.2891, pruned_loss=0.1216, over 575584.73 frames.], batch size: 64, lr: 2.06e-03
+2022-06-18 12:02:42,550 INFO [train.py:874] (1/4) Epoch 3, batch 400, aishell_loss[loss=0.2139, simple_loss=0.2706, pruned_loss=0.07859, over 4888.00 frames.], tot_loss[loss=0.2655, simple_loss=0.2969, pruned_loss=0.1171, over 853029.02 frames.], batch size: 28, aishell_tot_loss[loss=0.2627, simple_loss=0.3028, pruned_loss=0.1113, over 618573.93 frames.], datatang_tot_loss[loss=0.2678, simple_loss=0.2908, pruned_loss=0.1225, over 629462.51 frames.], batch size: 28, lr: 2.06e-03
+2022-06-18 12:03:13,290 INFO [train.py:874] (1/4) Epoch 3, batch 450, aishell_loss[loss=0.2266, simple_loss=0.2823, pruned_loss=0.08546, over 4972.00 frames.], tot_loss[loss=0.264, simple_loss=0.2968, pruned_loss=0.1156, over 882450.26 frames.], batch size: 39, aishell_tot_loss[loss=0.2612, simple_loss=0.3023, pruned_loss=0.1101, over 668181.17 frames.], datatang_tot_loss[loss=0.2675, simple_loss=0.2908, pruned_loss=0.1221, over 665125.93 frames.], batch size: 39, lr: 2.05e-03
+2022-06-18 12:03:41,570 INFO [train.py:874] (1/4) Epoch 3, batch 500, datatang_loss[loss=0.3481, simple_loss=0.3399, pruned_loss=0.1782, over 4923.00 frames.], tot_loss[loss=0.2661, simple_loss=0.2981, pruned_loss=0.117, over 905458.90 frames.], batch size: 83, aishell_tot_loss[loss=0.2605, simple_loss=0.3017, pruned_loss=0.1096, over 702765.07 frames.], datatang_tot_loss[loss=0.2707, simple_loss=0.2933, pruned_loss=0.124, over 705845.28 frames.], batch size: 83, lr: 2.05e-03
+2022-06-18 12:04:12,853 INFO [train.py:874] (1/4) Epoch 3, batch 550, aishell_loss[loss=0.226, simple_loss=0.2643, pruned_loss=0.09384, over 4953.00 frames.], tot_loss[loss=0.2627, simple_loss=0.2957, pruned_loss=0.1148, over 922734.95 frames.], batch size: 31, aishell_tot_loss[loss=0.2583, simple_loss=0.2998, pruned_loss=0.1084, over 735671.17 frames.], datatang_tot_loss[loss=0.2685, simple_loss=0.2923, pruned_loss=0.1224, over 738672.53 frames.], batch size: 31, lr: 2.05e-03
+2022-06-18 12:04:44,435 INFO [train.py:874] (1/4) Epoch 3, batch 600, aishell_loss[loss=0.247, simple_loss=0.2827, pruned_loss=0.1056, over 4843.00 frames.], tot_loss[loss=0.266, simple_loss=0.2965, pruned_loss=0.1178, over 936692.23 frames.], batch size: 28, aishell_tot_loss[loss=0.2595, simple_loss=0.3001, pruned_loss=0.1094, over 760562.20 frames.], datatang_tot_loss[loss=0.2712, simple_loss=0.2931, pruned_loss=0.1246, over 772183.71 frames.], batch size: 28, lr: 2.04e-03
+2022-06-18 12:05:11,864 INFO [train.py:874] (1/4) Epoch 3, batch 650, datatang_loss[loss=0.2554, simple_loss=0.2959, pruned_loss=0.1075, over 4946.00 frames.], tot_loss[loss=0.2668, simple_loss=0.2977, pruned_loss=0.118, over 947752.49 frames.], batch size: 88, aishell_tot_loss[loss=0.2602, simple_loss=0.3007, pruned_loss=0.1099, over 786095.15 frames.], datatang_tot_loss[loss=0.2717, simple_loss=0.2941, pruned_loss=0.1246, over 798472.55 frames.], batch size: 88, lr: 2.04e-03
+2022-06-18 12:05:43,569 INFO [train.py:874] (1/4) Epoch 3, batch 700, aishell_loss[loss=0.2315, simple_loss=0.2829, pruned_loss=0.09008, over 4957.00 frames.], tot_loss[loss=0.2647, simple_loss=0.2963, pruned_loss=0.1165, over 956121.10 frames.], batch size: 40, aishell_tot_loss[loss=0.2595, simple_loss=0.3003, pruned_loss=0.1094, over 805948.41 frames.], datatang_tot_loss[loss=0.2698, simple_loss=0.293, pruned_loss=0.1233, over 823833.88 frames.], batch size: 40, lr: 2.03e-03
+2022-06-18 12:06:14,590 INFO [train.py:874] (1/4) Epoch 3, batch 750, aishell_loss[loss=0.2607, simple_loss=0.3082, pruned_loss=0.1067, over 4866.00 frames.], tot_loss[loss=0.2619, simple_loss=0.2949, pruned_loss=0.1144, over 962246.43 frames.], batch size: 36, aishell_tot_loss[loss=0.2576, simple_loss=0.2991, pruned_loss=0.1081, over 829918.87 frames.], datatang_tot_loss[loss=0.2685, simple_loss=0.2924, pruned_loss=0.1223, over 839910.96 frames.], batch size: 36, lr: 2.03e-03
+2022-06-18 12:06:42,403 INFO [train.py:874] (1/4) Epoch 3, batch 800, datatang_loss[loss=0.2881, simple_loss=0.3192, pruned_loss=0.1285, over 4955.00 frames.], tot_loss[loss=0.2621, simple_loss=0.296, pruned_loss=0.1141, over 967484.53 frames.], batch size: 86, aishell_tot_loss[loss=0.2567, simple_loss=0.2989, pruned_loss=0.1073, over 851038.62 frames.], datatang_tot_loss[loss=0.2695, simple_loss=0.2934, pruned_loss=0.1228, over 854468.35 frames.], batch size: 86, lr: 2.02e-03
+2022-06-18 12:07:14,003 INFO [train.py:874] (1/4) Epoch 3, batch 850, datatang_loss[loss=0.2316, simple_loss=0.2706, pruned_loss=0.09636, over 4927.00 frames.], tot_loss[loss=0.2608, simple_loss=0.2954, pruned_loss=0.1131, over 971543.81 frames.], batch size: 83, aishell_tot_loss[loss=0.2561, simple_loss=0.2988, pruned_loss=0.1066, over 866406.06 frames.], datatang_tot_loss[loss=0.2684, simple_loss=0.2928, pruned_loss=0.122, over 870412.36 frames.], batch size: 83, lr: 2.02e-03
+2022-06-18 12:07:44,972 INFO [train.py:874] (1/4) Epoch 3, batch 900, aishell_loss[loss=0.2362, simple_loss=0.287, pruned_loss=0.09268, over 4870.00 frames.], tot_loss[loss=0.2599, simple_loss=0.295, pruned_loss=0.1124, over 974492.84 frames.], batch size: 37, aishell_tot_loss[loss=0.2561, simple_loss=0.2992, pruned_loss=0.1065, over 881982.56 frames.], datatang_tot_loss[loss=0.2671, simple_loss=0.2917, pruned_loss=0.1212, over 882291.70 frames.], batch size: 37, lr: 2.02e-03
+2022-06-18 12:08:12,992 INFO [train.py:874] (1/4) Epoch 3, batch 950, datatang_loss[loss=0.2562, simple_loss=0.2891, pruned_loss=0.1116, over 4922.00 frames.], tot_loss[loss=0.2617, simple_loss=0.2959, pruned_loss=0.1137, over 976913.11 frames.], batch size: 73, aishell_tot_loss[loss=0.256, simple_loss=0.2991, pruned_loss=0.1064, over 891771.40 frames.], datatang_tot_loss[loss=0.2685, simple_loss=0.2929, pruned_loss=0.122, over 896760.92 frames.], batch size: 73, lr: 2.01e-03
+2022-06-18 12:08:44,423 INFO [train.py:874] (1/4) Epoch 3, batch 1000, datatang_loss[loss=0.275, simple_loss=0.3024, pruned_loss=0.1238, over 4889.00 frames.], tot_loss[loss=0.2611, simple_loss=0.2958, pruned_loss=0.1132, over 978448.91 frames.], batch size: 47, aishell_tot_loss[loss=0.2555, simple_loss=0.299, pruned_loss=0.106, over 903432.62 frames.], datatang_tot_loss[loss=0.2683, simple_loss=0.2929, pruned_loss=0.1218, over 906239.66 frames.], batch size: 47, lr: 2.01e-03
+2022-06-18 12:08:44,424 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 12:09:01,537 INFO [train.py:914] (1/4) Epoch 3, validation: loss=0.1966, simple_loss=0.2677, pruned_loss=0.06274, over 1622729.00 frames.
+2022-06-18 12:09:30,025 INFO [train.py:874] (1/4) Epoch 3, batch 1050, datatang_loss[loss=0.2886, simple_loss=0.3072, pruned_loss=0.1349, over 4918.00 frames.], tot_loss[loss=0.2604, simple_loss=0.2953, pruned_loss=0.1127, over 980089.07 frames.], batch size: 64, aishell_tot_loss[loss=0.2542, simple_loss=0.2983, pruned_loss=0.1051, over 913052.24 frames.], datatang_tot_loss[loss=0.2685, simple_loss=0.293, pruned_loss=0.122, over 915712.52 frames.], batch size: 64, lr: 2.00e-03
+2022-06-18 12:10:01,704 INFO [train.py:874] (1/4) Epoch 3, batch 1100, aishell_loss[loss=0.2435, simple_loss=0.303, pruned_loss=0.09202, over 4892.00 frames.], tot_loss[loss=0.2591, simple_loss=0.295, pruned_loss=0.1116, over 981275.91 frames.], batch size: 50, aishell_tot_loss[loss=0.2546, simple_loss=0.2989, pruned_loss=0.1052, over 923078.11 frames.], datatang_tot_loss[loss=0.2669, simple_loss=0.2918, pruned_loss=0.121, over 922457.64 frames.], batch size: 50, lr: 2.00e-03
+2022-06-18 12:10:29,524 INFO [train.py:874] (1/4) Epoch 3, batch 1150, datatang_loss[loss=0.2433, simple_loss=0.2719, pruned_loss=0.1074, over 4960.00 frames.], tot_loss[loss=0.2599, simple_loss=0.2957, pruned_loss=0.1121, over 982280.39 frames.], batch size: 55, aishell_tot_loss[loss=0.2544, simple_loss=0.2991, pruned_loss=0.1049, over 929904.91 frames.], datatang_tot_loss[loss=0.2674, simple_loss=0.2923, pruned_loss=0.1213, over 930492.03 frames.], batch size: 55, lr: 2.00e-03
+2022-06-18 12:11:00,717 INFO [train.py:874] (1/4) Epoch 3, batch 1200, aishell_loss[loss=0.2501, simple_loss=0.2978, pruned_loss=0.1012, over 4927.00 frames.], tot_loss[loss=0.2621, simple_loss=0.2971, pruned_loss=0.1135, over 983309.10 frames.], batch size: 41, aishell_tot_loss[loss=0.2551, simple_loss=0.2991, pruned_loss=0.1056, over 937057.86 frames.], datatang_tot_loss[loss=0.2691, simple_loss=0.2939, pruned_loss=0.1222, over 936698.71 frames.], batch size: 41, lr: 1.99e-03
+2022-06-18 12:11:31,974 INFO [train.py:874] (1/4) Epoch 3, batch 1250, datatang_loss[loss=0.2366, simple_loss=0.2623, pruned_loss=0.1054, over 4838.00 frames.], tot_loss[loss=0.2623, simple_loss=0.2974, pruned_loss=0.1137, over 983776.37 frames.], batch size: 30, aishell_tot_loss[loss=0.2542, simple_loss=0.2985, pruned_loss=0.105, over 943472.76 frames.], datatang_tot_loss[loss=0.2706, simple_loss=0.2947, pruned_loss=0.1232, over 941708.67 frames.], batch size: 30, lr: 1.99e-03
+2022-06-18 12:11:59,378 INFO [train.py:874] (1/4) Epoch 3, batch 1300, datatang_loss[loss=0.2036, simple_loss=0.2525, pruned_loss=0.07738, over 4953.00 frames.], tot_loss[loss=0.2623, simple_loss=0.2978, pruned_loss=0.1134, over 983904.37 frames.], batch size: 50, aishell_tot_loss[loss=0.2543, simple_loss=0.2991, pruned_loss=0.1048, over 948817.52 frames.], datatang_tot_loss[loss=0.2707, simple_loss=0.2948, pruned_loss=0.1233, over 946174.60 frames.], batch size: 50, lr: 1.98e-03
+2022-06-18 12:12:30,426 INFO [train.py:874] (1/4) Epoch 3, batch 1350, datatang_loss[loss=0.2473, simple_loss=0.2849, pruned_loss=0.1048, over 4916.00 frames.], tot_loss[loss=0.2597, simple_loss=0.2957, pruned_loss=0.1119, over 984023.67 frames.], batch size: 77, aishell_tot_loss[loss=0.2534, simple_loss=0.2983, pruned_loss=0.1042, over 952258.33 frames.], datatang_tot_loss[loss=0.2684, simple_loss=0.2935, pruned_loss=0.1217, over 951457.17 frames.], batch size: 77, lr: 1.98e-03
+2022-06-18 12:13:01,674 INFO [train.py:874] (1/4) Epoch 3, batch 1400, datatang_loss[loss=0.2464, simple_loss=0.2737, pruned_loss=0.1096, over 4925.00 frames.], tot_loss[loss=0.2602, simple_loss=0.2963, pruned_loss=0.112, over 984616.63 frames.], batch size: 42, aishell_tot_loss[loss=0.2526, simple_loss=0.2976, pruned_loss=0.1038, over 957115.19 frames.], datatang_tot_loss[loss=0.2699, simple_loss=0.2948, pruned_loss=0.1225, over 954764.72 frames.], batch size: 42, lr: 1.97e-03
+2022-06-18 12:13:29,153 INFO [train.py:874] (1/4) Epoch 3, batch 1450, aishell_loss[loss=0.2375, simple_loss=0.2996, pruned_loss=0.08775, over 4913.00 frames.], tot_loss[loss=0.259, simple_loss=0.2958, pruned_loss=0.1111, over 984852.89 frames.], batch size: 52, aishell_tot_loss[loss=0.2515, simple_loss=0.2972, pruned_loss=0.1029, over 961097.96 frames.], datatang_tot_loss[loss=0.2698, simple_loss=0.2946, pruned_loss=0.1225, over 957728.52 frames.], batch size: 52, lr: 1.97e-03
+2022-06-18 12:14:01,023 INFO [train.py:874] (1/4) Epoch 3, batch 1500, datatang_loss[loss=0.2455, simple_loss=0.2769, pruned_loss=0.107, over 4926.00 frames.], tot_loss[loss=0.259, simple_loss=0.2949, pruned_loss=0.1116, over 985424.92 frames.], batch size: 64, aishell_tot_loss[loss=0.2512, simple_loss=0.2969, pruned_loss=0.1027, over 964205.20 frames.], datatang_tot_loss[loss=0.2698, simple_loss=0.2939, pruned_loss=0.1229, over 961209.66 frames.], batch size: 64, lr: 1.97e-03
+2022-06-18 12:14:30,081 INFO [train.py:874] (1/4) Epoch 3, batch 1550, aishell_loss[loss=0.2164, simple_loss=0.2806, pruned_loss=0.07609, over 4870.00 frames.], tot_loss[loss=0.2578, simple_loss=0.2942, pruned_loss=0.1107, over 985136.25 frames.], batch size: 36, aishell_tot_loss[loss=0.2499, simple_loss=0.2961, pruned_loss=0.1018, over 966317.80 frames.], datatang_tot_loss[loss=0.2695, simple_loss=0.2938, pruned_loss=0.1226, over 964141.65 frames.], batch size: 36, lr: 1.96e-03
+2022-06-18 12:14:59,946 INFO [train.py:874] (1/4) Epoch 3, batch 1600, datatang_loss[loss=0.2807, simple_loss=0.3024, pruned_loss=0.1295, over 4976.00 frames.], tot_loss[loss=0.2575, simple_loss=0.294, pruned_loss=0.1105, over 985204.22 frames.], batch size: 45, aishell_tot_loss[loss=0.2498, simple_loss=0.2962, pruned_loss=0.1017, over 968093.58 frames.], datatang_tot_loss[loss=0.2686, simple_loss=0.2935, pruned_loss=0.1218, over 967140.57 frames.], batch size: 45, lr: 1.96e-03
+2022-06-18 12:15:30,920 INFO [train.py:874] (1/4) Epoch 3, batch 1650, aishell_loss[loss=0.2478, simple_loss=0.3062, pruned_loss=0.09466, over 4973.00 frames.], tot_loss[loss=0.2566, simple_loss=0.2933, pruned_loss=0.11, over 985225.39 frames.], batch size: 44, aishell_tot_loss[loss=0.2502, simple_loss=0.2961, pruned_loss=0.1021, over 970307.66 frames.], datatang_tot_loss[loss=0.2672, simple_loss=0.2925, pruned_loss=0.121, over 969094.50 frames.], batch size: 44, lr: 1.96e-03
+2022-06-18 12:16:00,113 INFO [train.py:874] (1/4) Epoch 3, batch 1700, aishell_loss[loss=0.2161, simple_loss=0.2722, pruned_loss=0.08, over 4934.00 frames.], tot_loss[loss=0.2558, simple_loss=0.2923, pruned_loss=0.1096, over 985310.13 frames.], batch size: 32, aishell_tot_loss[loss=0.2493, simple_loss=0.2955, pruned_loss=0.1015, over 971513.66 frames.], datatang_tot_loss[loss=0.2662, simple_loss=0.2919, pruned_loss=0.1203, over 971656.35 frames.], batch size: 32, lr: 1.95e-03
+2022-06-18 12:16:30,620 INFO [train.py:874] (1/4) Epoch 3, batch 1750, datatang_loss[loss=0.2397, simple_loss=0.2767, pruned_loss=0.1013, over 4918.00 frames.], tot_loss[loss=0.255, simple_loss=0.2916, pruned_loss=0.1092, over 985578.50 frames.], batch size: 77, aishell_tot_loss[loss=0.2479, simple_loss=0.2944, pruned_loss=0.1008, over 973326.98 frames.], datatang_tot_loss[loss=0.2663, simple_loss=0.2919, pruned_loss=0.1204, over 973365.38 frames.], batch size: 77, lr: 1.95e-03
+2022-06-18 12:17:02,285 INFO [train.py:874] (1/4) Epoch 3, batch 1800, datatang_loss[loss=0.2582, simple_loss=0.2835, pruned_loss=0.1165, over 4903.00 frames.], tot_loss[loss=0.2561, simple_loss=0.2931, pruned_loss=0.1095, over 985618.46 frames.], batch size: 52, aishell_tot_loss[loss=0.2484, simple_loss=0.2949, pruned_loss=0.1009, over 974695.83 frames.], datatang_tot_loss[loss=0.2668, simple_loss=0.2926, pruned_loss=0.1205, over 974921.01 frames.], batch size: 52, lr: 1.94e-03
+2022-06-18 12:17:29,487 INFO [train.py:874] (1/4) Epoch 3, batch 1850, datatang_loss[loss=0.2589, simple_loss=0.281, pruned_loss=0.1185, over 4953.00 frames.], tot_loss[loss=0.2565, simple_loss=0.2936, pruned_loss=0.1097, over 985740.47 frames.], batch size: 45, aishell_tot_loss[loss=0.2492, simple_loss=0.2956, pruned_loss=0.1014, over 976165.26 frames.], datatang_tot_loss[loss=0.2662, simple_loss=0.2922, pruned_loss=0.1201, over 976131.25 frames.], batch size: 45, lr: 1.94e-03
+2022-06-18 12:18:00,799 INFO [train.py:874] (1/4) Epoch 3, batch 1900, aishell_loss[loss=0.2161, simple_loss=0.2828, pruned_loss=0.07476, over 4933.00 frames.], tot_loss[loss=0.2535, simple_loss=0.2923, pruned_loss=0.1074, over 985659.05 frames.], batch size: 41, aishell_tot_loss[loss=0.2481, simple_loss=0.2951, pruned_loss=0.1005, over 977203.17 frames.], datatang_tot_loss[loss=0.2639, simple_loss=0.2912, pruned_loss=0.1183, over 977262.24 frames.], batch size: 41, lr: 1.94e-03
+2022-06-18 12:18:32,147 INFO [train.py:874] (1/4) Epoch 3, batch 1950, aishell_loss[loss=0.1861, simple_loss=0.2376, pruned_loss=0.06732, over 4912.00 frames.], tot_loss[loss=0.2521, simple_loss=0.2916, pruned_loss=0.1063, over 985995.15 frames.], batch size: 25, aishell_tot_loss[loss=0.2472, simple_loss=0.2946, pruned_loss=0.09991, over 978393.67 frames.], datatang_tot_loss[loss=0.2627, simple_loss=0.2908, pruned_loss=0.1173, over 978398.73 frames.], batch size: 25, lr: 1.93e-03
+2022-06-18 12:18:59,667 INFO [train.py:874] (1/4) Epoch 3, batch 2000, aishell_loss[loss=0.2503, simple_loss=0.2998, pruned_loss=0.1004, over 4929.00 frames.], tot_loss[loss=0.2533, simple_loss=0.2919, pruned_loss=0.1073, over 985887.79 frames.], batch size: 52, aishell_tot_loss[loss=0.247, simple_loss=0.2944, pruned_loss=0.09976, over 979187.89 frames.], datatang_tot_loss[loss=0.2635, simple_loss=0.2912, pruned_loss=0.1179, over 979284.13 frames.], batch size: 52, lr: 1.93e-03
+2022-06-18 12:18:59,668 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 12:19:15,682 INFO [train.py:914] (1/4) Epoch 3, validation: loss=0.1934, simple_loss=0.2657, pruned_loss=0.0605, over 1622729.00 frames.
+2022-06-18 12:19:43,610 INFO [train.py:874] (1/4) Epoch 3, batch 2050, aishell_loss[loss=0.2237, simple_loss=0.2574, pruned_loss=0.09503, over 4975.00 frames.], tot_loss[loss=0.2535, simple_loss=0.2915, pruned_loss=0.1077, over 985944.84 frames.], batch size: 25, aishell_tot_loss[loss=0.2467, simple_loss=0.2941, pruned_loss=0.09965, over 979750.69 frames.], datatang_tot_loss[loss=0.2632, simple_loss=0.2908, pruned_loss=0.1178, over 980334.40 frames.], batch size: 25, lr: 1.92e-03
+2022-06-18 12:20:13,697 INFO [train.py:874] (1/4) Epoch 3, batch 2100, datatang_loss[loss=0.2402, simple_loss=0.2621, pruned_loss=0.1091, over 4944.00 frames.], tot_loss[loss=0.2544, simple_loss=0.2919, pruned_loss=0.1084, over 986243.30 frames.], batch size: 34, aishell_tot_loss[loss=0.2471, simple_loss=0.2945, pruned_loss=0.09988, over 980504.88 frames.], datatang_tot_loss[loss=0.2631, simple_loss=0.2908, pruned_loss=0.1177, over 981285.03 frames.], batch size: 34, lr: 1.92e-03
+2022-06-18 12:20:44,281 INFO [train.py:874] (1/4) Epoch 3, batch 2150, datatang_loss[loss=0.2591, simple_loss=0.2845, pruned_loss=0.1169, over 4943.00 frames.], tot_loss[loss=0.2551, simple_loss=0.2932, pruned_loss=0.1085, over 986200.01 frames.], batch size: 62, aishell_tot_loss[loss=0.2474, simple_loss=0.2947, pruned_loss=0.1, over 981249.84 frames.], datatang_tot_loss[loss=0.2639, simple_loss=0.2916, pruned_loss=0.1181, over 981757.81 frames.], batch size: 62, lr: 1.92e-03
+2022-06-18 12:21:13,464 INFO [train.py:874] (1/4) Epoch 3, batch 2200, aishell_loss[loss=0.252, simple_loss=0.3116, pruned_loss=0.09621, over 4896.00 frames.], tot_loss[loss=0.2548, simple_loss=0.2929, pruned_loss=0.1083, over 986264.86 frames.], batch size: 34, aishell_tot_loss[loss=0.2474, simple_loss=0.2949, pruned_loss=0.09995, over 982018.27 frames.], datatang_tot_loss[loss=0.2633, simple_loss=0.2911, pruned_loss=0.1177, over 982163.27 frames.], batch size: 34, lr: 1.91e-03
+2022-06-18 12:21:43,214 INFO [train.py:874] (1/4) Epoch 3, batch 2250, aishell_loss[loss=0.2008, simple_loss=0.2632, pruned_loss=0.06921, over 4978.00 frames.], tot_loss[loss=0.2527, simple_loss=0.2918, pruned_loss=0.1068, over 986256.66 frames.], batch size: 61, aishell_tot_loss[loss=0.2455, simple_loss=0.2934, pruned_loss=0.09878, over 982452.74 frames.], datatang_tot_loss[loss=0.2635, simple_loss=0.2913, pruned_loss=0.1178, over 982691.69 frames.], batch size: 61, lr: 1.91e-03
+2022-06-18 12:22:13,501 INFO [train.py:874] (1/4) Epoch 3, batch 2300, datatang_loss[loss=0.2312, simple_loss=0.2586, pruned_loss=0.1019, over 4960.00 frames.], tot_loss[loss=0.2523, simple_loss=0.2915, pruned_loss=0.1066, over 986046.20 frames.], batch size: 34, aishell_tot_loss[loss=0.2448, simple_loss=0.2928, pruned_loss=0.0984, over 982793.32 frames.], datatang_tot_loss[loss=0.2633, simple_loss=0.2913, pruned_loss=0.1176, over 982996.65 frames.], batch size: 34, lr: 1.91e-03
+2022-06-18 12:22:41,795 INFO [train.py:874] (1/4) Epoch 3, batch 2350, datatang_loss[loss=0.2526, simple_loss=0.2913, pruned_loss=0.107, over 4926.00 frames.], tot_loss[loss=0.2488, simple_loss=0.2886, pruned_loss=0.1045, over 985848.27 frames.], batch size: 83, aishell_tot_loss[loss=0.2424, simple_loss=0.2909, pruned_loss=0.09694, over 983035.62 frames.], datatang_tot_loss[loss=0.2617, simple_loss=0.2901, pruned_loss=0.1166, over 983292.73 frames.], batch size: 83, lr: 1.90e-03
+2022-06-18 12:23:13,295 INFO [train.py:874] (1/4) Epoch 3, batch 2400, datatang_loss[loss=0.3396, simple_loss=0.3463, pruned_loss=0.1665, over 4916.00 frames.], tot_loss[loss=0.2478, simple_loss=0.2883, pruned_loss=0.1036, over 986097.62 frames.], batch size: 98, aishell_tot_loss[loss=0.2409, simple_loss=0.29, pruned_loss=0.09587, over 983310.68 frames.], datatang_tot_loss[loss=0.2614, simple_loss=0.2902, pruned_loss=0.1163, over 983906.56 frames.], batch size: 98, lr: 1.90e-03
+2022-06-18 12:23:44,006 INFO [train.py:874] (1/4) Epoch 3, batch 2450, datatang_loss[loss=0.2358, simple_loss=0.2702, pruned_loss=0.1008, over 4921.00 frames.], tot_loss[loss=0.2484, simple_loss=0.2883, pruned_loss=0.1043, over 986050.48 frames.], batch size: 77, aishell_tot_loss[loss=0.2412, simple_loss=0.2905, pruned_loss=0.09597, over 983636.95 frames.], datatang_tot_loss[loss=0.2606, simple_loss=0.2893, pruned_loss=0.1159, over 984095.42 frames.], batch size: 77, lr: 1.89e-03
+2022-06-18 12:24:13,120 INFO [train.py:874] (1/4) Epoch 3, batch 2500, aishell_loss[loss=0.2351, simple_loss=0.2936, pruned_loss=0.08832, over 4855.00 frames.], tot_loss[loss=0.2484, simple_loss=0.2886, pruned_loss=0.1041, over 985727.40 frames.], batch size: 37, aishell_tot_loss[loss=0.2409, simple_loss=0.2904, pruned_loss=0.09574, over 983556.39 frames.], datatang_tot_loss[loss=0.2602, simple_loss=0.2894, pruned_loss=0.1155, over 984366.47 frames.], batch size: 37, lr: 1.89e-03
+2022-06-18 12:24:44,573 INFO [train.py:874] (1/4) Epoch 3, batch 2550, aishell_loss[loss=0.2627, simple_loss=0.3159, pruned_loss=0.1048, over 4922.00 frames.], tot_loss[loss=0.2495, simple_loss=0.2899, pruned_loss=0.1046, over 986008.73 frames.], batch size: 68, aishell_tot_loss[loss=0.2412, simple_loss=0.2909, pruned_loss=0.09579, over 983821.63 frames.], datatang_tot_loss[loss=0.2607, simple_loss=0.2899, pruned_loss=0.1157, over 984823.15 frames.], batch size: 68, lr: 1.89e-03
+2022-06-18 12:25:14,265 INFO [train.py:874] (1/4) Epoch 3, batch 2600, datatang_loss[loss=0.2428, simple_loss=0.2884, pruned_loss=0.0986, over 4932.00 frames.], tot_loss[loss=0.2482, simple_loss=0.289, pruned_loss=0.1038, over 985574.26 frames.], batch size: 88, aishell_tot_loss[loss=0.2392, simple_loss=0.2891, pruned_loss=0.09464, over 983726.33 frames.], datatang_tot_loss[loss=0.261, simple_loss=0.2905, pruned_loss=0.1158, over 984842.52 frames.], batch size: 88, lr: 1.88e-03
+2022-06-18 12:25:43,668 INFO [train.py:874] (1/4) Epoch 3, batch 2650, datatang_loss[loss=0.254, simple_loss=0.2816, pruned_loss=0.1133, over 4911.00 frames.], tot_loss[loss=0.2489, simple_loss=0.2894, pruned_loss=0.1042, over 985683.75 frames.], batch size: 75, aishell_tot_loss[loss=0.2396, simple_loss=0.2894, pruned_loss=0.09491, over 983819.51 frames.], datatang_tot_loss[loss=0.2608, simple_loss=0.2905, pruned_loss=0.1155, over 985174.13 frames.], batch size: 75, lr: 1.88e-03
+2022-06-18 12:26:14,979 INFO [train.py:874] (1/4) Epoch 3, batch 2700, aishell_loss[loss=0.2473, simple_loss=0.2885, pruned_loss=0.1031, over 4958.00 frames.], tot_loss[loss=0.2494, simple_loss=0.2898, pruned_loss=0.1045, over 985661.36 frames.], batch size: 31, aishell_tot_loss[loss=0.2393, simple_loss=0.2892, pruned_loss=0.09473, over 983881.82 frames.], datatang_tot_loss[loss=0.2617, simple_loss=0.2911, pruned_loss=0.1161, over 985378.95 frames.], batch size: 31, lr: 1.88e-03
+2022-06-18 12:26:44,658 INFO [train.py:874] (1/4) Epoch 3, batch 2750, aishell_loss[loss=0.2094, simple_loss=0.2733, pruned_loss=0.0728, over 4926.00 frames.], tot_loss[loss=0.2475, simple_loss=0.2887, pruned_loss=0.1032, over 986002.05 frames.], batch size: 68, aishell_tot_loss[loss=0.2384, simple_loss=0.2887, pruned_loss=0.094, over 984327.41 frames.], datatang_tot_loss[loss=0.2608, simple_loss=0.2903, pruned_loss=0.1156, over 985556.53 frames.], batch size: 68, lr: 1.87e-03
+2022-06-18 12:27:14,024 INFO [train.py:874] (1/4) Epoch 3, batch 2800, aishell_loss[loss=0.2509, simple_loss=0.2961, pruned_loss=0.1029, over 4946.00 frames.], tot_loss[loss=0.248, simple_loss=0.2886, pruned_loss=0.1037, over 985841.07 frames.], batch size: 32, aishell_tot_loss[loss=0.2384, simple_loss=0.2887, pruned_loss=0.09407, over 984394.99 frames.], datatang_tot_loss[loss=0.2605, simple_loss=0.29, pruned_loss=0.1156, over 985554.65 frames.], batch size: 32, lr: 1.87e-03
+2022-06-18 12:27:45,746 INFO [train.py:874] (1/4) Epoch 3, batch 2850, aishell_loss[loss=0.2382, simple_loss=0.291, pruned_loss=0.09273, over 4851.00 frames.], tot_loss[loss=0.2478, simple_loss=0.289, pruned_loss=0.1033, over 985648.87 frames.], batch size: 36, aishell_tot_loss[loss=0.2381, simple_loss=0.2888, pruned_loss=0.09369, over 984293.96 frames.], datatang_tot_loss[loss=0.2609, simple_loss=0.2902, pruned_loss=0.1157, over 985678.11 frames.], batch size: 36, lr: 1.87e-03
+2022-06-18 12:28:14,900 INFO [train.py:874] (1/4) Epoch 3, batch 2900, datatang_loss[loss=0.2607, simple_loss=0.2887, pruned_loss=0.1164, over 4930.00 frames.], tot_loss[loss=0.2466, simple_loss=0.288, pruned_loss=0.1026, over 985394.79 frames.], batch size: 69, aishell_tot_loss[loss=0.2367, simple_loss=0.2875, pruned_loss=0.09293, over 984016.08 frames.], datatang_tot_loss[loss=0.2607, simple_loss=0.2904, pruned_loss=0.1155, over 985833.39 frames.], batch size: 69, lr: 1.86e-03
+2022-06-18 12:28:45,259 INFO [train.py:874] (1/4) Epoch 3, batch 2950, datatang_loss[loss=0.242, simple_loss=0.28, pruned_loss=0.1019, over 4984.00 frames.], tot_loss[loss=0.2479, simple_loss=0.2885, pruned_loss=0.1036, over 985482.87 frames.], batch size: 25, aishell_tot_loss[loss=0.238, simple_loss=0.2882, pruned_loss=0.09395, over 984350.79 frames.], datatang_tot_loss[loss=0.2601, simple_loss=0.29, pruned_loss=0.1151, over 985682.94 frames.], batch size: 25, lr: 1.86e-03
+2022-06-18 12:29:16,451 INFO [train.py:874] (1/4) Epoch 3, batch 3000, aishell_loss[loss=0.2313, simple_loss=0.2819, pruned_loss=0.09036, over 4948.00 frames.], tot_loss[loss=0.2469, simple_loss=0.2885, pruned_loss=0.1027, over 985711.85 frames.], batch size: 40, aishell_tot_loss[loss=0.2377, simple_loss=0.2881, pruned_loss=0.09363, over 984532.01 frames.], datatang_tot_loss[loss=0.2595, simple_loss=0.29, pruned_loss=0.1146, over 985893.35 frames.], batch size: 40, lr: 1.86e-03
+2022-06-18 12:29:16,452 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 12:29:31,999 INFO [train.py:914] (1/4) Epoch 3, validation: loss=0.1943, simple_loss=0.2686, pruned_loss=0.05999, over 1622729.00 frames.
+2022-06-18 12:30:03,146 INFO [train.py:874] (1/4) Epoch 3, batch 3050, datatang_loss[loss=0.3831, simple_loss=0.3711, pruned_loss=0.1976, over 4949.00 frames.], tot_loss[loss=0.2481, simple_loss=0.2896, pruned_loss=0.1033, over 985669.22 frames.], batch size: 110, aishell_tot_loss[loss=0.2386, simple_loss=0.2891, pruned_loss=0.09406, over 984713.64 frames.], datatang_tot_loss[loss=0.2597, simple_loss=0.2901, pruned_loss=0.1146, over 985804.86 frames.], batch size: 110, lr: 1.85e-03
+2022-06-18 12:30:33,638 INFO [train.py:874] (1/4) Epoch 3, batch 3100, datatang_loss[loss=0.2393, simple_loss=0.2659, pruned_loss=0.1063, over 4968.00 frames.], tot_loss[loss=0.2471, simple_loss=0.2888, pruned_loss=0.1027, over 986181.71 frames.], batch size: 37, aishell_tot_loss[loss=0.2383, simple_loss=0.289, pruned_loss=0.09375, over 985239.59 frames.], datatang_tot_loss[loss=0.2586, simple_loss=0.2893, pruned_loss=0.1139, over 985918.52 frames.], batch size: 37, lr: 1.85e-03
+2022-06-18 12:31:02,149 INFO [train.py:874] (1/4) Epoch 3, batch 3150, datatang_loss[loss=0.202, simple_loss=0.2515, pruned_loss=0.07619, over 4946.00 frames.], tot_loss[loss=0.245, simple_loss=0.2874, pruned_loss=0.1013, over 986143.67 frames.], batch size: 69, aishell_tot_loss[loss=0.2377, simple_loss=0.2889, pruned_loss=0.09325, over 985338.25 frames.], datatang_tot_loss[loss=0.2566, simple_loss=0.2878, pruned_loss=0.1126, over 985911.55 frames.], batch size: 69, lr: 1.85e-03
+2022-06-18 12:31:33,994 INFO [train.py:874] (1/4) Epoch 3, batch 3200, datatang_loss[loss=0.2444, simple_loss=0.28, pruned_loss=0.1044, over 4937.00 frames.], tot_loss[loss=0.2448, simple_loss=0.2875, pruned_loss=0.1011, over 986014.65 frames.], batch size: 62, aishell_tot_loss[loss=0.2377, simple_loss=0.2891, pruned_loss=0.0931, over 985408.56 frames.], datatang_tot_loss[loss=0.2561, simple_loss=0.2875, pruned_loss=0.1124, over 985830.28 frames.], batch size: 62, lr: 1.84e-03
+2022-06-18 12:32:03,696 INFO [train.py:874] (1/4) Epoch 3, batch 3250, aishell_loss[loss=0.2429, simple_loss=0.2973, pruned_loss=0.09425, over 4947.00 frames.], tot_loss[loss=0.2459, simple_loss=0.2881, pruned_loss=0.1018, over 985791.67 frames.], batch size: 45, aishell_tot_loss[loss=0.2378, simple_loss=0.2891, pruned_loss=0.09329, over 985384.95 frames.], datatang_tot_loss[loss=0.2572, simple_loss=0.288, pruned_loss=0.1132, over 985722.57 frames.], batch size: 45, lr: 1.84e-03
+2022-06-18 12:32:33,311 INFO [train.py:874] (1/4) Epoch 3, batch 3300, aishell_loss[loss=0.238, simple_loss=0.2984, pruned_loss=0.08876, over 4955.00 frames.], tot_loss[loss=0.2463, simple_loss=0.2878, pruned_loss=0.1024, over 985752.61 frames.], batch size: 64, aishell_tot_loss[loss=0.2381, simple_loss=0.2891, pruned_loss=0.0936, over 985161.09 frames.], datatang_tot_loss[loss=0.2566, simple_loss=0.2875, pruned_loss=0.1128, over 985953.52 frames.], batch size: 64, lr: 1.84e-03
+2022-06-18 12:33:03,656 INFO [train.py:874] (1/4) Epoch 3, batch 3350, datatang_loss[loss=0.2189, simple_loss=0.2651, pruned_loss=0.08628, over 4925.00 frames.], tot_loss[loss=0.2457, simple_loss=0.2879, pruned_loss=0.1017, over 985890.77 frames.], batch size: 75, aishell_tot_loss[loss=0.2373, simple_loss=0.2887, pruned_loss=0.09297, over 985300.55 frames.], datatang_tot_loss[loss=0.2569, simple_loss=0.288, pruned_loss=0.1129, over 986021.02 frames.], batch size: 75, lr: 1.83e-03
+2022-06-18 12:33:33,269 INFO [train.py:874] (1/4) Epoch 3, batch 3400, aishell_loss[loss=0.1922, simple_loss=0.2431, pruned_loss=0.07064, over 4977.00 frames.], tot_loss[loss=0.2443, simple_loss=0.2869, pruned_loss=0.1009, over 985573.84 frames.], batch size: 25, aishell_tot_loss[loss=0.2365, simple_loss=0.2881, pruned_loss=0.09246, over 985002.98 frames.], datatang_tot_loss[loss=0.2561, simple_loss=0.2874, pruned_loss=0.1124, over 986025.32 frames.], batch size: 25, lr: 1.83e-03
+2022-06-18 12:34:03,642 INFO [train.py:874] (1/4) Epoch 3, batch 3450, datatang_loss[loss=0.2579, simple_loss=0.2836, pruned_loss=0.1161, over 4928.00 frames.], tot_loss[loss=0.2427, simple_loss=0.2859, pruned_loss=0.0998, over 985743.06 frames.], batch size: 71, aishell_tot_loss[loss=0.2358, simple_loss=0.2877, pruned_loss=0.09189, over 985083.68 frames.], datatang_tot_loss[loss=0.2549, simple_loss=0.2865, pruned_loss=0.1117, over 986158.49 frames.], batch size: 71, lr: 1.83e-03
+2022-06-18 12:34:34,625 INFO [train.py:874] (1/4) Epoch 3, batch 3500, aishell_loss[loss=0.267, simple_loss=0.3145, pruned_loss=0.1097, over 4972.00 frames.], tot_loss[loss=0.243, simple_loss=0.2866, pruned_loss=0.0997, over 985749.53 frames.], batch size: 51, aishell_tot_loss[loss=0.2363, simple_loss=0.2887, pruned_loss=0.09197, over 985289.49 frames.], datatang_tot_loss[loss=0.2539, simple_loss=0.286, pruned_loss=0.111, over 985986.65 frames.], batch size: 51, lr: 1.82e-03
+2022-06-18 12:35:02,637 INFO [train.py:874] (1/4) Epoch 3, batch 3550, datatang_loss[loss=0.253, simple_loss=0.2849, pruned_loss=0.1106, over 4962.00 frames.], tot_loss[loss=0.2441, simple_loss=0.2867, pruned_loss=0.1008, over 985470.51 frames.], batch size: 31, aishell_tot_loss[loss=0.2364, simple_loss=0.2887, pruned_loss=0.09208, over 984818.89 frames.], datatang_tot_loss[loss=0.2543, simple_loss=0.286, pruned_loss=0.1113, over 986165.31 frames.], batch size: 31, lr: 1.82e-03
+2022-06-18 12:35:34,110 INFO [train.py:874] (1/4) Epoch 3, batch 3600, aishell_loss[loss=0.2329, simple_loss=0.2935, pruned_loss=0.08612, over 4960.00 frames.], tot_loss[loss=0.2455, simple_loss=0.2874, pruned_loss=0.1017, over 985144.46 frames.], batch size: 68, aishell_tot_loss[loss=0.2372, simple_loss=0.2891, pruned_loss=0.09268, over 984546.64 frames.], datatang_tot_loss[loss=0.2547, simple_loss=0.2861, pruned_loss=0.1116, over 986101.53 frames.], batch size: 68, lr: 1.82e-03
+2022-06-18 12:36:04,025 INFO [train.py:874] (1/4) Epoch 3, batch 3650, datatang_loss[loss=0.2152, simple_loss=0.2585, pruned_loss=0.08591, over 4958.00 frames.], tot_loss[loss=0.2464, simple_loss=0.2879, pruned_loss=0.1024, over 985274.57 frames.], batch size: 55, aishell_tot_loss[loss=0.2377, simple_loss=0.2895, pruned_loss=0.09297, over 984454.20 frames.], datatang_tot_loss[loss=0.2547, simple_loss=0.2864, pruned_loss=0.1115, over 986234.64 frames.], batch size: 55, lr: 1.81e-03
+2022-06-18 12:36:39,222 INFO [train.py:874] (1/4) Epoch 3, batch 3700, aishell_loss[loss=0.2318, simple_loss=0.2871, pruned_loss=0.0882, over 4913.00 frames.], tot_loss[loss=0.2457, simple_loss=0.2875, pruned_loss=0.102, over 985618.67 frames.], batch size: 41, aishell_tot_loss[loss=0.238, simple_loss=0.2897, pruned_loss=0.09314, over 984743.14 frames.], datatang_tot_loss[loss=0.2534, simple_loss=0.2857, pruned_loss=0.1105, over 986246.99 frames.], batch size: 41, lr: 1.81e-03
+2022-06-18 12:37:08,714 INFO [train.py:874] (1/4) Epoch 3, batch 3750, aishell_loss[loss=0.2389, simple_loss=0.3039, pruned_loss=0.08692, over 4926.00 frames.], tot_loss[loss=0.2456, simple_loss=0.2873, pruned_loss=0.1019, over 985696.77 frames.], batch size: 45, aishell_tot_loss[loss=0.2378, simple_loss=0.2895, pruned_loss=0.09305, over 984841.25 frames.], datatang_tot_loss[loss=0.253, simple_loss=0.2858, pruned_loss=0.1102, over 986222.08 frames.], batch size: 45, lr: 1.81e-03
+2022-06-18 12:37:37,177 INFO [train.py:874] (1/4) Epoch 3, batch 3800, datatang_loss[loss=0.2769, simple_loss=0.2967, pruned_loss=0.1286, over 4886.00 frames.], tot_loss[loss=0.2473, simple_loss=0.2885, pruned_loss=0.1031, over 985363.86 frames.], batch size: 42, aishell_tot_loss[loss=0.2382, simple_loss=0.2897, pruned_loss=0.09334, over 984522.53 frames.], datatang_tot_loss[loss=0.254, simple_loss=0.2867, pruned_loss=0.1107, over 986189.80 frames.], batch size: 42, lr: 1.80e-03
+2022-06-18 12:38:06,929 INFO [train.py:874] (1/4) Epoch 3, batch 3850, datatang_loss[loss=0.2018, simple_loss=0.2482, pruned_loss=0.07776, over 4860.00 frames.], tot_loss[loss=0.2464, simple_loss=0.2877, pruned_loss=0.1026, over 985188.37 frames.], batch size: 39, aishell_tot_loss[loss=0.2392, simple_loss=0.2904, pruned_loss=0.09404, over 984506.01 frames.], datatang_tot_loss[loss=0.252, simple_loss=0.2854, pruned_loss=0.1093, over 985970.45 frames.], batch size: 39, lr: 1.80e-03
+2022-06-18 12:38:36,185 INFO [train.py:874] (1/4) Epoch 3, batch 3900, datatang_loss[loss=0.229, simple_loss=0.2724, pruned_loss=0.09282, over 4910.00 frames.], tot_loss[loss=0.2446, simple_loss=0.2872, pruned_loss=0.101, over 985248.78 frames.], batch size: 64, aishell_tot_loss[loss=0.2397, simple_loss=0.2911, pruned_loss=0.09413, over 984672.00 frames.], datatang_tot_loss[loss=0.2498, simple_loss=0.2842, pruned_loss=0.1077, over 985858.29 frames.], batch size: 64, lr: 1.80e-03
+2022-06-18 12:39:04,527 INFO [train.py:874] (1/4) Epoch 3, batch 3950, aishell_loss[loss=0.1909, simple_loss=0.2484, pruned_loss=0.06668, over 4952.00 frames.], tot_loss[loss=0.2434, simple_loss=0.2868, pruned_loss=0.09999, over 985272.65 frames.], batch size: 27, aishell_tot_loss[loss=0.2401, simple_loss=0.2917, pruned_loss=0.09425, over 984787.88 frames.], datatang_tot_loss[loss=0.2479, simple_loss=0.2831, pruned_loss=0.1064, over 985744.39 frames.], batch size: 27, lr: 1.79e-03
+2022-06-18 12:39:34,737 INFO [train.py:874] (1/4) Epoch 3, batch 4000, datatang_loss[loss=0.266, simple_loss=0.3052, pruned_loss=0.1135, over 4973.00 frames.], tot_loss[loss=0.2437, simple_loss=0.2869, pruned_loss=0.1003, over 985444.81 frames.], batch size: 45, aishell_tot_loss[loss=0.2402, simple_loss=0.2919, pruned_loss=0.0942, over 984824.76 frames.], datatang_tot_loss[loss=0.2479, simple_loss=0.2831, pruned_loss=0.1064, over 985876.80 frames.], batch size: 45, lr: 1.79e-03
+2022-06-18 12:39:34,737 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 12:39:51,130 INFO [train.py:914] (1/4) Epoch 3, validation: loss=0.1881, simple_loss=0.2644, pruned_loss=0.05586, over 1622729.00 frames.
+2022-06-18 12:40:21,159 INFO [train.py:874] (1/4) Epoch 3, batch 4050, datatang_loss[loss=0.2542, simple_loss=0.2803, pruned_loss=0.1141, over 4926.00 frames.], tot_loss[loss=0.2422, simple_loss=0.2859, pruned_loss=0.09926, over 985826.09 frames.], batch size: 71, aishell_tot_loss[loss=0.2393, simple_loss=0.2916, pruned_loss=0.09351, over 984889.64 frames.], datatang_tot_loss[loss=0.247, simple_loss=0.2823, pruned_loss=0.1058, over 986229.73 frames.], batch size: 71, lr: 1.79e-03
+2022-06-18 12:40:49,596 INFO [train.py:874] (1/4) Epoch 3, batch 4100, aishell_loss[loss=0.2516, simple_loss=0.3066, pruned_loss=0.09828, over 4889.00 frames.], tot_loss[loss=0.242, simple_loss=0.2859, pruned_loss=0.09906, over 985219.01 frames.], batch size: 35, aishell_tot_loss[loss=0.2392, simple_loss=0.2914, pruned_loss=0.09357, over 984364.05 frames.], datatang_tot_loss[loss=0.2467, simple_loss=0.2821, pruned_loss=0.1056, over 986183.37 frames.], batch size: 35, lr: 1.78e-03
+2022-06-18 12:42:03,119 INFO [train.py:874] (1/4) Epoch 4, batch 50, aishell_loss[loss=0.2566, simple_loss=0.312, pruned_loss=0.1006, over 4924.00 frames.], tot_loss[loss=0.2277, simple_loss=0.2745, pruned_loss=0.09046, over 218453.78 frames.], batch size: 52, aishell_tot_loss[loss=0.2313, simple_loss=0.2848, pruned_loss=0.08888, over 111483.27 frames.], datatang_tot_loss[loss=0.2245, simple_loss=0.265, pruned_loss=0.09201, over 120609.05 frames.], batch size: 52, lr: 1.73e-03
+2022-06-18 12:42:33,921 INFO [train.py:874] (1/4) Epoch 4, batch 100, aishell_loss[loss=0.3451, simple_loss=0.3723, pruned_loss=0.159, over 4939.00 frames.], tot_loss[loss=0.2272, simple_loss=0.2746, pruned_loss=0.08986, over 388692.97 frames.], batch size: 79, aishell_tot_loss[loss=0.229, simple_loss=0.2824, pruned_loss=0.08784, over 229822.14 frames.], datatang_tot_loss[loss=0.2248, simple_loss=0.2654, pruned_loss=0.09205, over 207133.80 frames.], batch size: 79, lr: 1.73e-03
+2022-06-18 12:43:05,272 INFO [train.py:874] (1/4) Epoch 4, batch 150, datatang_loss[loss=0.2044, simple_loss=0.2544, pruned_loss=0.07721, over 4956.00 frames.], tot_loss[loss=0.228, simple_loss=0.2754, pruned_loss=0.09031, over 520840.20 frames.], batch size: 86, aishell_tot_loss[loss=0.2293, simple_loss=0.2834, pruned_loss=0.08765, over 311993.86 frames.], datatang_tot_loss[loss=0.2266, simple_loss=0.267, pruned_loss=0.09303, over 305592.51 frames.], batch size: 86, lr: 1.72e-03
+2022-06-18 12:43:34,845 INFO [train.py:874] (1/4) Epoch 4, batch 200, datatang_loss[loss=0.2098, simple_loss=0.269, pruned_loss=0.07535, over 4928.00 frames.], tot_loss[loss=0.23, simple_loss=0.2769, pruned_loss=0.09152, over 623838.48 frames.], batch size: 71, aishell_tot_loss[loss=0.2293, simple_loss=0.2833, pruned_loss=0.08767, over 385233.41 frames.], datatang_tot_loss[loss=0.2301, simple_loss=0.2702, pruned_loss=0.09505, over 391741.52 frames.], batch size: 71, lr: 1.72e-03
+2022-06-18 12:44:05,337 INFO [train.py:874] (1/4) Epoch 4, batch 250, datatang_loss[loss=0.2405, simple_loss=0.2831, pruned_loss=0.09892, over 4932.00 frames.], tot_loss[loss=0.2329, simple_loss=0.279, pruned_loss=0.09341, over 704207.90 frames.], batch size: 57, aishell_tot_loss[loss=0.2326, simple_loss=0.2861, pruned_loss=0.08957, over 447860.54 frames.], datatang_tot_loss[loss=0.2317, simple_loss=0.2712, pruned_loss=0.09609, over 469728.23 frames.], batch size: 57, lr: 1.72e-03
+2022-06-18 12:44:35,733 INFO [train.py:874] (1/4) Epoch 4, batch 300, aishell_loss[loss=0.2426, simple_loss=0.2943, pruned_loss=0.09539, over 4939.00 frames.], tot_loss[loss=0.2329, simple_loss=0.2797, pruned_loss=0.09301, over 767066.39 frames.], batch size: 64, aishell_tot_loss[loss=0.2323, simple_loss=0.2859, pruned_loss=0.0894, over 516215.03 frames.], datatang_tot_loss[loss=0.2322, simple_loss=0.2724, pruned_loss=0.09597, over 526120.79 frames.], batch size: 64, lr: 1.71e-03
+2022-06-18 12:45:04,608 INFO [train.py:874] (1/4) Epoch 4, batch 350, aishell_loss[loss=0.2516, simple_loss=0.2999, pruned_loss=0.1016, over 4953.00 frames.], tot_loss[loss=0.2331, simple_loss=0.2803, pruned_loss=0.09294, over 815677.99 frames.], batch size: 31, aishell_tot_loss[loss=0.2332, simple_loss=0.2872, pruned_loss=0.08956, over 573681.38 frames.], datatang_tot_loss[loss=0.2319, simple_loss=0.272, pruned_loss=0.09589, over 578260.43 frames.], batch size: 31, lr: 1.71e-03
+2022-06-18 12:45:35,293 INFO [train.py:874] (1/4) Epoch 4, batch 400, aishell_loss[loss=0.2163, simple_loss=0.2766, pruned_loss=0.078, over 4887.00 frames.], tot_loss[loss=0.2337, simple_loss=0.2812, pruned_loss=0.09307, over 853078.05 frames.], batch size: 34, aishell_tot_loss[loss=0.2328, simple_loss=0.2872, pruned_loss=0.08919, over 638210.11 frames.], datatang_tot_loss[loss=0.2333, simple_loss=0.2726, pruned_loss=0.09702, over 609391.14 frames.], batch size: 34, lr: 1.71e-03
+2022-06-18 12:46:05,196 INFO [train.py:874] (1/4) Epoch 4, batch 450, datatang_loss[loss=0.2133, simple_loss=0.2571, pruned_loss=0.08476, over 4925.00 frames.], tot_loss[loss=0.235, simple_loss=0.282, pruned_loss=0.09398, over 882555.64 frames.], batch size: 73, aishell_tot_loss[loss=0.2317, simple_loss=0.2868, pruned_loss=0.08829, over 675977.74 frames.], datatang_tot_loss[loss=0.2366, simple_loss=0.2749, pruned_loss=0.09915, over 657161.65 frames.], batch size: 73, lr: 1.71e-03
+2022-06-18 12:46:35,411 INFO [train.py:874] (1/4) Epoch 4, batch 500, datatang_loss[loss=0.239, simple_loss=0.2759, pruned_loss=0.101, over 4944.00 frames.], tot_loss[loss=0.2344, simple_loss=0.2815, pruned_loss=0.09361, over 905308.01 frames.], batch size: 55, aishell_tot_loss[loss=0.232, simple_loss=0.2871, pruned_loss=0.08845, over 712311.91 frames.], datatang_tot_loss[loss=0.2356, simple_loss=0.2742, pruned_loss=0.09851, over 695873.84 frames.], batch size: 55, lr: 1.70e-03
+2022-06-18 12:47:05,931 INFO [train.py:874] (1/4) Epoch 4, batch 550, aishell_loss[loss=0.1715, simple_loss=0.2166, pruned_loss=0.06316, over 4876.00 frames.], tot_loss[loss=0.2346, simple_loss=0.2821, pruned_loss=0.09355, over 923354.51 frames.], batch size: 21, aishell_tot_loss[loss=0.2316, simple_loss=0.2869, pruned_loss=0.0881, over 745916.95 frames.], datatang_tot_loss[loss=0.2366, simple_loss=0.2755, pruned_loss=0.09889, over 728782.50 frames.], batch size: 21, lr: 1.70e-03
+2022-06-18 12:47:36,167 INFO [train.py:874] (1/4) Epoch 4, batch 600, datatang_loss[loss=0.2586, simple_loss=0.2862, pruned_loss=0.1155, over 4915.00 frames.], tot_loss[loss=0.2364, simple_loss=0.2831, pruned_loss=0.09487, over 937301.08 frames.], batch size: 42, aishell_tot_loss[loss=0.2327, simple_loss=0.2877, pruned_loss=0.08881, over 773214.82 frames.], datatang_tot_loss[loss=0.238, simple_loss=0.2763, pruned_loss=0.09984, over 760175.76 frames.], batch size: 42, lr: 1.70e-03
+2022-06-18 12:48:07,246 INFO [train.py:874] (1/4) Epoch 4, batch 650, aishell_loss[loss=0.1708, simple_loss=0.2087, pruned_loss=0.0665, over 4980.00 frames.], tot_loss[loss=0.237, simple_loss=0.2833, pruned_loss=0.09534, over 948093.54 frames.], batch size: 21, aishell_tot_loss[loss=0.2331, simple_loss=0.2877, pruned_loss=0.08926, over 799141.09 frames.], datatang_tot_loss[loss=0.2387, simple_loss=0.2769, pruned_loss=0.1002, over 785834.56 frames.], batch size: 21, lr: 1.69e-03
+2022-06-18 12:48:37,397 INFO [train.py:874] (1/4) Epoch 4, batch 700, datatang_loss[loss=0.2234, simple_loss=0.2698, pruned_loss=0.08851, over 4936.00 frames.], tot_loss[loss=0.2378, simple_loss=0.2835, pruned_loss=0.09604, over 956255.50 frames.], batch size: 62, aishell_tot_loss[loss=0.2329, simple_loss=0.2872, pruned_loss=0.08931, over 817576.26 frames.], datatang_tot_loss[loss=0.24, simple_loss=0.2782, pruned_loss=0.1009, over 812899.55 frames.], batch size: 62, lr: 1.69e-03
+2022-06-18 12:49:07,581 INFO [train.py:874] (1/4) Epoch 4, batch 750, aishell_loss[loss=0.2959, simple_loss=0.3389, pruned_loss=0.1264, over 4933.00 frames.], tot_loss[loss=0.2389, simple_loss=0.2841, pruned_loss=0.09682, over 963010.07 frames.], batch size: 49, aishell_tot_loss[loss=0.2334, simple_loss=0.2878, pruned_loss=0.08949, over 831398.90 frames.], datatang_tot_loss[loss=0.2408, simple_loss=0.2789, pruned_loss=0.1014, over 839394.37 frames.], batch size: 49, lr: 1.69e-03
+2022-06-18 12:49:37,665 INFO [train.py:874] (1/4) Epoch 4, batch 800, datatang_loss[loss=0.252, simple_loss=0.2744, pruned_loss=0.1148, over 4967.00 frames.], tot_loss[loss=0.2373, simple_loss=0.2835, pruned_loss=0.09557, over 967920.74 frames.], batch size: 34, aishell_tot_loss[loss=0.2332, simple_loss=0.2879, pruned_loss=0.08929, over 853389.48 frames.], datatang_tot_loss[loss=0.2398, simple_loss=0.278, pruned_loss=0.1008, over 852772.74 frames.], batch size: 34, lr: 1.69e-03
+2022-06-18 12:50:07,652 INFO [train.py:874] (1/4) Epoch 4, batch 850, aishell_loss[loss=0.2214, simple_loss=0.2832, pruned_loss=0.07981, over 4945.00 frames.], tot_loss[loss=0.2366, simple_loss=0.2832, pruned_loss=0.09498, over 972089.61 frames.], batch size: 54, aishell_tot_loss[loss=0.2325, simple_loss=0.2874, pruned_loss=0.08878, over 870765.80 frames.], datatang_tot_loss[loss=0.24, simple_loss=0.2781, pruned_loss=0.101, over 866846.62 frames.], batch size: 54, lr: 1.68e-03
+2022-06-18 12:50:37,411 INFO [train.py:874] (1/4) Epoch 4, batch 900, aishell_loss[loss=0.2508, simple_loss=0.2975, pruned_loss=0.102, over 4955.00 frames.], tot_loss[loss=0.2383, simple_loss=0.2845, pruned_loss=0.09611, over 975216.24 frames.], batch size: 40, aishell_tot_loss[loss=0.2331, simple_loss=0.2879, pruned_loss=0.08914, over 885045.76 frames.], datatang_tot_loss[loss=0.2416, simple_loss=0.2791, pruned_loss=0.102, over 880197.32 frames.], batch size: 40, lr: 1.68e-03
+2022-06-18 12:51:08,860 INFO [train.py:874] (1/4) Epoch 4, batch 950, datatang_loss[loss=0.2146, simple_loss=0.2543, pruned_loss=0.08747, over 4966.00 frames.], tot_loss[loss=0.2385, simple_loss=0.2846, pruned_loss=0.09619, over 977381.00 frames.], batch size: 45, aishell_tot_loss[loss=0.2332, simple_loss=0.288, pruned_loss=0.08921, over 896470.76 frames.], datatang_tot_loss[loss=0.2421, simple_loss=0.2796, pruned_loss=0.1022, over 892869.67 frames.], batch size: 45, lr: 1.68e-03
+2022-06-18 12:51:39,319 INFO [train.py:874] (1/4) Epoch 4, batch 1000, datatang_loss[loss=0.2703, simple_loss=0.2994, pruned_loss=0.1206, over 4947.00 frames.], tot_loss[loss=0.2379, simple_loss=0.2844, pruned_loss=0.09573, over 979526.86 frames.], batch size: 34, aishell_tot_loss[loss=0.2325, simple_loss=0.2875, pruned_loss=0.0888, over 906742.98 frames.], datatang_tot_loss[loss=0.2423, simple_loss=0.2801, pruned_loss=0.1022, over 904355.92 frames.], batch size: 34, lr: 1.67e-03
+2022-06-18 12:51:39,320 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 12:51:55,327 INFO [train.py:914] (1/4) Epoch 4, validation: loss=0.1866, simple_loss=0.2641, pruned_loss=0.05457, over 1622729.00 frames.
+2022-06-18 12:52:26,586 INFO [train.py:874] (1/4) Epoch 4, batch 1050, aishell_loss[loss=0.2553, simple_loss=0.3137, pruned_loss=0.0984, over 4923.00 frames.], tot_loss[loss=0.2366, simple_loss=0.2832, pruned_loss=0.09498, over 980988.15 frames.], batch size: 68, aishell_tot_loss[loss=0.232, simple_loss=0.2871, pruned_loss=0.08847, over 914970.99 frames.], datatang_tot_loss[loss=0.2413, simple_loss=0.2794, pruned_loss=0.1016, over 915120.35 frames.], batch size: 68, lr: 1.67e-03
+2022-06-18 12:52:56,827 INFO [train.py:874] (1/4) Epoch 4, batch 1100, datatang_loss[loss=0.2165, simple_loss=0.2664, pruned_loss=0.08325, over 4918.00 frames.], tot_loss[loss=0.2351, simple_loss=0.2822, pruned_loss=0.09397, over 981587.03 frames.], batch size: 75, aishell_tot_loss[loss=0.231, simple_loss=0.2865, pruned_loss=0.08779, over 923039.78 frames.], datatang_tot_loss[loss=0.2406, simple_loss=0.2788, pruned_loss=0.1012, over 923222.44 frames.], batch size: 75, lr: 1.67e-03
+2022-06-18 12:53:26,761 INFO [train.py:874] (1/4) Epoch 4, batch 1150, aishell_loss[loss=0.2537, simple_loss=0.3103, pruned_loss=0.09857, over 4971.00 frames.], tot_loss[loss=0.2365, simple_loss=0.2836, pruned_loss=0.09467, over 982556.49 frames.], batch size: 51, aishell_tot_loss[loss=0.2307, simple_loss=0.2864, pruned_loss=0.08746, over 932080.13 frames.], datatang_tot_loss[loss=0.2427, simple_loss=0.2802, pruned_loss=0.1026, over 928923.32 frames.], batch size: 51, lr: 1.67e-03
+2022-06-18 12:53:57,027 INFO [train.py:874] (1/4) Epoch 4, batch 1200, datatang_loss[loss=0.2608, simple_loss=0.2887, pruned_loss=0.1164, over 4915.00 frames.], tot_loss[loss=0.2363, simple_loss=0.2834, pruned_loss=0.09461, over 983197.79 frames.], batch size: 77, aishell_tot_loss[loss=0.2297, simple_loss=0.286, pruned_loss=0.08672, over 938300.11 frames.], datatang_tot_loss[loss=0.2434, simple_loss=0.2805, pruned_loss=0.1031, over 935633.91 frames.], batch size: 77, lr: 1.66e-03
+2022-06-18 12:54:27,045 INFO [train.py:874] (1/4) Epoch 4, batch 1250, datatang_loss[loss=0.2544, simple_loss=0.2823, pruned_loss=0.1132, over 4914.00 frames.], tot_loss[loss=0.2373, simple_loss=0.2845, pruned_loss=0.09503, over 983591.13 frames.], batch size: 64, aishell_tot_loss[loss=0.2297, simple_loss=0.2863, pruned_loss=0.08656, over 944086.92 frames.], datatang_tot_loss[loss=0.2445, simple_loss=0.2814, pruned_loss=0.1038, over 941138.86 frames.], batch size: 64, lr: 1.66e-03
+2022-06-18 12:54:57,600 INFO [train.py:874] (1/4) Epoch 4, batch 1300, datatang_loss[loss=0.2381, simple_loss=0.2812, pruned_loss=0.09745, over 4954.00 frames.], tot_loss[loss=0.2362, simple_loss=0.2841, pruned_loss=0.09413, over 984258.19 frames.], batch size: 91, aishell_tot_loss[loss=0.229, simple_loss=0.2859, pruned_loss=0.08605, over 950177.83 frames.], datatang_tot_loss[loss=0.2445, simple_loss=0.2813, pruned_loss=0.1038, over 945302.11 frames.], batch size: 91, lr: 1.66e-03
+2022-06-18 12:55:28,215 INFO [train.py:874] (1/4) Epoch 4, batch 1350, aishell_loss[loss=0.1936, simple_loss=0.2684, pruned_loss=0.05936, over 4969.00 frames.], tot_loss[loss=0.2357, simple_loss=0.284, pruned_loss=0.09367, over 984379.90 frames.], batch size: 44, aishell_tot_loss[loss=0.2282, simple_loss=0.2855, pruned_loss=0.08546, over 955039.18 frames.], datatang_tot_loss[loss=0.2451, simple_loss=0.2818, pruned_loss=0.1042, over 949041.25 frames.], batch size: 44, lr: 1.66e-03
+2022-06-18 12:55:57,857 INFO [train.py:874] (1/4) Epoch 4, batch 1400, datatang_loss[loss=0.2444, simple_loss=0.2867, pruned_loss=0.1011, over 4926.00 frames.], tot_loss[loss=0.2361, simple_loss=0.2837, pruned_loss=0.09422, over 984557.98 frames.], batch size: 83, aishell_tot_loss[loss=0.2285, simple_loss=0.2856, pruned_loss=0.08564, over 957640.21 frames.], datatang_tot_loss[loss=0.2446, simple_loss=0.2816, pruned_loss=0.1038, over 954364.56 frames.], batch size: 83, lr: 1.65e-03
+2022-06-18 12:56:27,848 INFO [train.py:874] (1/4) Epoch 4, batch 1450, datatang_loss[loss=0.2761, simple_loss=0.3065, pruned_loss=0.1229, over 4915.00 frames.], tot_loss[loss=0.2357, simple_loss=0.2839, pruned_loss=0.0938, over 984386.70 frames.], batch size: 57, aishell_tot_loss[loss=0.2281, simple_loss=0.2854, pruned_loss=0.08538, over 961358.51 frames.], datatang_tot_loss[loss=0.245, simple_loss=0.2818, pruned_loss=0.1041, over 957065.97 frames.], batch size: 57, lr: 1.65e-03
+2022-06-18 12:57:00,071 INFO [train.py:874] (1/4) Epoch 4, batch 1500, aishell_loss[loss=0.2372, simple_loss=0.2941, pruned_loss=0.09013, over 4956.00 frames.], tot_loss[loss=0.2358, simple_loss=0.2838, pruned_loss=0.09391, over 984920.58 frames.], batch size: 54, aishell_tot_loss[loss=0.2278, simple_loss=0.2853, pruned_loss=0.08518, over 964440.20 frames.], datatang_tot_loss[loss=0.245, simple_loss=0.2819, pruned_loss=0.104, over 960492.26 frames.], batch size: 54, lr: 1.65e-03
+2022-06-18 12:57:28,971 INFO [train.py:874] (1/4) Epoch 4, batch 1550, datatang_loss[loss=0.2289, simple_loss=0.2699, pruned_loss=0.09394, over 4913.00 frames.], tot_loss[loss=0.2354, simple_loss=0.283, pruned_loss=0.09389, over 984769.88 frames.], batch size: 77, aishell_tot_loss[loss=0.2268, simple_loss=0.2839, pruned_loss=0.08484, over 966919.16 frames.], datatang_tot_loss[loss=0.2458, simple_loss=0.2824, pruned_loss=0.1045, over 963096.56 frames.], batch size: 77, lr: 1.65e-03
+2022-06-18 12:57:59,846 INFO [train.py:874] (1/4) Epoch 4, batch 1600, datatang_loss[loss=0.2675, simple_loss=0.3039, pruned_loss=0.1156, over 4924.00 frames.], tot_loss[loss=0.2348, simple_loss=0.2824, pruned_loss=0.0936, over 985112.01 frames.], batch size: 77, aishell_tot_loss[loss=0.2263, simple_loss=0.2837, pruned_loss=0.08444, over 968762.58 frames.], datatang_tot_loss[loss=0.2448, simple_loss=0.282, pruned_loss=0.1038, over 966364.47 frames.], batch size: 77, lr: 1.64e-03
+2022-06-18 12:58:30,952 INFO [train.py:874] (1/4) Epoch 4, batch 1650, datatang_loss[loss=0.2003, simple_loss=0.2499, pruned_loss=0.07539, over 4887.00 frames.], tot_loss[loss=0.2329, simple_loss=0.2812, pruned_loss=0.09228, over 985241.90 frames.], batch size: 47, aishell_tot_loss[loss=0.2258, simple_loss=0.2835, pruned_loss=0.08408, over 970612.33 frames.], datatang_tot_loss[loss=0.2431, simple_loss=0.2808, pruned_loss=0.1026, over 968793.27 frames.], batch size: 47, lr: 1.64e-03
+2022-06-18 12:59:01,538 INFO [train.py:874] (1/4) Epoch 4, batch 1700, datatang_loss[loss=0.2039, simple_loss=0.2556, pruned_loss=0.07613, over 4861.00 frames.], tot_loss[loss=0.2338, simple_loss=0.2817, pruned_loss=0.09293, over 985207.15 frames.], batch size: 25, aishell_tot_loss[loss=0.2268, simple_loss=0.2844, pruned_loss=0.08457, over 972074.83 frames.], datatang_tot_loss[loss=0.2425, simple_loss=0.2803, pruned_loss=0.1023, over 970986.17 frames.], batch size: 25, lr: 1.64e-03
+2022-06-18 12:59:32,076 INFO [train.py:874] (1/4) Epoch 4, batch 1750, aishell_loss[loss=0.2219, simple_loss=0.277, pruned_loss=0.08342, over 4886.00 frames.], tot_loss[loss=0.2336, simple_loss=0.282, pruned_loss=0.09257, over 985149.06 frames.], batch size: 47, aishell_tot_loss[loss=0.2268, simple_loss=0.2843, pruned_loss=0.08462, over 973597.12 frames.], datatang_tot_loss[loss=0.2423, simple_loss=0.2805, pruned_loss=0.1021, over 972616.06 frames.], batch size: 47, lr: 1.63e-03
+2022-06-18 13:00:02,766 INFO [train.py:874] (1/4) Epoch 4, batch 1800, aishell_loss[loss=0.2066, simple_loss=0.2818, pruned_loss=0.0657, over 4918.00 frames.], tot_loss[loss=0.2354, simple_loss=0.2834, pruned_loss=0.09372, over 984987.69 frames.], batch size: 52, aishell_tot_loss[loss=0.2267, simple_loss=0.2842, pruned_loss=0.08457, over 974575.09 frames.], datatang_tot_loss[loss=0.2442, simple_loss=0.282, pruned_loss=0.1032, over 974292.07 frames.], batch size: 52, lr: 1.63e-03
+2022-06-18 13:00:32,616 INFO [train.py:874] (1/4) Epoch 4, batch 1850, aishell_loss[loss=0.2246, simple_loss=0.282, pruned_loss=0.08359, over 4909.00 frames.], tot_loss[loss=0.234, simple_loss=0.2825, pruned_loss=0.09269, over 985398.19 frames.], batch size: 41, aishell_tot_loss[loss=0.2263, simple_loss=0.2837, pruned_loss=0.08441, over 975891.99 frames.], datatang_tot_loss[loss=0.2431, simple_loss=0.2817, pruned_loss=0.1022, over 975898.46 frames.], batch size: 41, lr: 1.63e-03
+2022-06-18 13:01:02,819 INFO [train.py:874] (1/4) Epoch 4, batch 1900, aishell_loss[loss=0.2512, simple_loss=0.3008, pruned_loss=0.1008, over 4976.00 frames.], tot_loss[loss=0.2357, simple_loss=0.2833, pruned_loss=0.09402, over 985680.84 frames.], batch size: 48, aishell_tot_loss[loss=0.2263, simple_loss=0.2837, pruned_loss=0.08449, over 977165.42 frames.], datatang_tot_loss[loss=0.2447, simple_loss=0.2825, pruned_loss=0.1034, over 977150.52 frames.], batch size: 48, lr: 1.63e-03
+2022-06-18 13:01:34,322 INFO [train.py:874] (1/4) Epoch 4, batch 1950, aishell_loss[loss=0.2162, simple_loss=0.2773, pruned_loss=0.07756, over 4918.00 frames.], tot_loss[loss=0.2373, simple_loss=0.2834, pruned_loss=0.09557, over 985256.19 frames.], batch size: 52, aishell_tot_loss[loss=0.2263, simple_loss=0.2835, pruned_loss=0.08454, over 977795.22 frames.], datatang_tot_loss[loss=0.2466, simple_loss=0.2829, pruned_loss=0.1052, over 978056.53 frames.], batch size: 52, lr: 1.62e-03
+2022-06-18 13:02:04,139 INFO [train.py:874] (1/4) Epoch 4, batch 2000, datatang_loss[loss=0.237, simple_loss=0.2916, pruned_loss=0.09117, over 4947.00 frames.], tot_loss[loss=0.2363, simple_loss=0.283, pruned_loss=0.09483, over 985194.71 frames.], batch size: 91, aishell_tot_loss[loss=0.2257, simple_loss=0.2829, pruned_loss=0.08421, over 978571.84 frames.], datatang_tot_loss[loss=0.246, simple_loss=0.283, pruned_loss=0.1045, over 978922.80 frames.], batch size: 91, lr: 1.62e-03
+2022-06-18 13:02:04,140 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 13:02:20,119 INFO [train.py:914] (1/4) Epoch 4, validation: loss=0.1875, simple_loss=0.2609, pruned_loss=0.05705, over 1622729.00 frames.
+2022-06-18 13:02:49,815 INFO [train.py:874] (1/4) Epoch 4, batch 2050, aishell_loss[loss=0.2444, simple_loss=0.3044, pruned_loss=0.09216, over 4981.00 frames.], tot_loss[loss=0.2348, simple_loss=0.282, pruned_loss=0.09382, over 985186.93 frames.], batch size: 51, aishell_tot_loss[loss=0.2255, simple_loss=0.2828, pruned_loss=0.08412, over 979195.44 frames.], datatang_tot_loss[loss=0.2446, simple_loss=0.2822, pruned_loss=0.1035, over 979819.84 frames.], batch size: 51, lr: 1.62e-03
+2022-06-18 13:03:20,619 INFO [train.py:874] (1/4) Epoch 4, batch 2100, datatang_loss[loss=0.2179, simple_loss=0.2759, pruned_loss=0.07994, over 4892.00 frames.], tot_loss[loss=0.234, simple_loss=0.2818, pruned_loss=0.0931, over 985481.62 frames.], batch size: 47, aishell_tot_loss[loss=0.2254, simple_loss=0.2828, pruned_loss=0.08399, over 979880.80 frames.], datatang_tot_loss[loss=0.2436, simple_loss=0.2819, pruned_loss=0.1026, over 980777.91 frames.], batch size: 47, lr: 1.62e-03
+2022-06-18 13:03:50,532 INFO [train.py:874] (1/4) Epoch 4, batch 2150, datatang_loss[loss=0.2399, simple_loss=0.2717, pruned_loss=0.1041, over 4907.00 frames.], tot_loss[loss=0.2319, simple_loss=0.2805, pruned_loss=0.09163, over 985427.55 frames.], batch size: 52, aishell_tot_loss[loss=0.2245, simple_loss=0.282, pruned_loss=0.08348, over 980335.18 frames.], datatang_tot_loss[loss=0.2423, simple_loss=0.2812, pruned_loss=0.1017, over 981483.54 frames.], batch size: 52, lr: 1.61e-03
+2022-06-18 13:04:20,270 INFO [train.py:874] (1/4) Epoch 4, batch 2200, datatang_loss[loss=0.2313, simple_loss=0.2739, pruned_loss=0.09432, over 4946.00 frames.], tot_loss[loss=0.2314, simple_loss=0.2804, pruned_loss=0.09116, over 985145.68 frames.], batch size: 62, aishell_tot_loss[loss=0.2247, simple_loss=0.2822, pruned_loss=0.08354, over 980676.94 frames.], datatang_tot_loss[loss=0.2414, simple_loss=0.2806, pruned_loss=0.1011, over 981922.96 frames.], batch size: 62, lr: 1.61e-03
+2022-06-18 13:04:51,371 INFO [train.py:874] (1/4) Epoch 4, batch 2250, aishell_loss[loss=0.1897, simple_loss=0.2511, pruned_loss=0.06411, over 4905.00 frames.], tot_loss[loss=0.2299, simple_loss=0.2797, pruned_loss=0.09007, over 985491.87 frames.], batch size: 27, aishell_tot_loss[loss=0.2239, simple_loss=0.2818, pruned_loss=0.08301, over 981318.75 frames.], datatang_tot_loss[loss=0.2403, simple_loss=0.2801, pruned_loss=0.1003, over 982547.17 frames.], batch size: 27, lr: 1.61e-03
+2022-06-18 13:05:21,496 INFO [train.py:874] (1/4) Epoch 4, batch 2300, datatang_loss[loss=0.2961, simple_loss=0.333, pruned_loss=0.1297, over 4911.00 frames.], tot_loss[loss=0.231, simple_loss=0.2807, pruned_loss=0.0906, over 985453.23 frames.], batch size: 42, aishell_tot_loss[loss=0.2234, simple_loss=0.2817, pruned_loss=0.0826, over 981633.04 frames.], datatang_tot_loss[loss=0.2413, simple_loss=0.2809, pruned_loss=0.1008, over 983033.34 frames.], batch size: 42, lr: 1.61e-03
+2022-06-18 13:05:52,002 INFO [train.py:874] (1/4) Epoch 4, batch 2350, datatang_loss[loss=0.2309, simple_loss=0.2687, pruned_loss=0.09652, over 4925.00 frames.], tot_loss[loss=0.2312, simple_loss=0.2808, pruned_loss=0.09077, over 985115.33 frames.], batch size: 77, aishell_tot_loss[loss=0.2227, simple_loss=0.2812, pruned_loss=0.08212, over 981758.60 frames.], datatang_tot_loss[loss=0.2416, simple_loss=0.2813, pruned_loss=0.101, over 983278.36 frames.], batch size: 77, lr: 1.60e-03
+2022-06-18 13:06:21,651 INFO [train.py:874] (1/4) Epoch 4, batch 2400, aishell_loss[loss=0.2253, simple_loss=0.2815, pruned_loss=0.08454, over 4942.00 frames.], tot_loss[loss=0.2315, simple_loss=0.2812, pruned_loss=0.09087, over 984917.15 frames.], batch size: 49, aishell_tot_loss[loss=0.2236, simple_loss=0.2818, pruned_loss=0.0827, over 981906.92 frames.], datatang_tot_loss[loss=0.2413, simple_loss=0.281, pruned_loss=0.1007, over 983563.09 frames.], batch size: 49, lr: 1.60e-03
+2022-06-18 13:06:51,596 INFO [train.py:874] (1/4) Epoch 4, batch 2450, datatang_loss[loss=0.2342, simple_loss=0.2807, pruned_loss=0.09386, over 4913.00 frames.], tot_loss[loss=0.2302, simple_loss=0.2804, pruned_loss=0.09001, over 984670.21 frames.], batch size: 42, aishell_tot_loss[loss=0.2225, simple_loss=0.281, pruned_loss=0.08196, over 981996.23 frames.], datatang_tot_loss[loss=0.241, simple_loss=0.2809, pruned_loss=0.1006, over 983752.90 frames.], batch size: 42, lr: 1.60e-03
+2022-06-18 13:07:23,704 INFO [train.py:874] (1/4) Epoch 4, batch 2500, datatang_loss[loss=0.3055, simple_loss=0.3344, pruned_loss=0.1383, over 4930.00 frames.], tot_loss[loss=0.2305, simple_loss=0.2802, pruned_loss=0.09044, over 984724.25 frames.], batch size: 108, aishell_tot_loss[loss=0.2223, simple_loss=0.2808, pruned_loss=0.08192, over 982109.23 frames.], datatang_tot_loss[loss=0.2404, simple_loss=0.2807, pruned_loss=0.1001, over 984060.35 frames.], batch size: 108, lr: 1.60e-03
+2022-06-18 13:07:53,518 INFO [train.py:874] (1/4) Epoch 4, batch 2550, datatang_loss[loss=0.2229, simple_loss=0.2693, pruned_loss=0.08826, over 4971.00 frames.], tot_loss[loss=0.2298, simple_loss=0.279, pruned_loss=0.09029, over 985156.27 frames.], batch size: 37, aishell_tot_loss[loss=0.2231, simple_loss=0.2811, pruned_loss=0.08252, over 982528.22 frames.], datatang_tot_loss[loss=0.2381, simple_loss=0.2791, pruned_loss=0.0985, over 984403.73 frames.], batch size: 37, lr: 1.60e-03
+2022-06-18 13:08:25,264 INFO [train.py:874] (1/4) Epoch 4, batch 2600, aishell_loss[loss=0.2819, simple_loss=0.3318, pruned_loss=0.1161, over 4907.00 frames.], tot_loss[loss=0.2295, simple_loss=0.2788, pruned_loss=0.09006, over 984921.72 frames.], batch size: 46, aishell_tot_loss[loss=0.2234, simple_loss=0.2815, pruned_loss=0.08262, over 982482.57 frames.], datatang_tot_loss[loss=0.2368, simple_loss=0.2783, pruned_loss=0.09765, over 984550.72 frames.], batch size: 46, lr: 1.59e-03
+2022-06-18 13:08:55,094 INFO [train.py:874] (1/4) Epoch 4, batch 2650, aishell_loss[loss=0.2613, simple_loss=0.3226, pruned_loss=0.1001, over 4976.00 frames.], tot_loss[loss=0.2302, simple_loss=0.2795, pruned_loss=0.09047, over 985179.21 frames.], batch size: 48, aishell_tot_loss[loss=0.2245, simple_loss=0.2822, pruned_loss=0.08346, over 983190.80 frames.], datatang_tot_loss[loss=0.2366, simple_loss=0.2781, pruned_loss=0.09759, over 984464.64 frames.], batch size: 48, lr: 1.59e-03
+2022-06-18 13:09:25,299 INFO [train.py:874] (1/4) Epoch 4, batch 2700, datatang_loss[loss=0.2303, simple_loss=0.2712, pruned_loss=0.09467, over 4912.00 frames.], tot_loss[loss=0.2313, simple_loss=0.2801, pruned_loss=0.09128, over 985442.31 frames.], batch size: 47, aishell_tot_loss[loss=0.2254, simple_loss=0.2827, pruned_loss=0.08409, over 983563.03 frames.], datatang_tot_loss[loss=0.2367, simple_loss=0.278, pruned_loss=0.0977, over 984672.63 frames.], batch size: 47, lr: 1.59e-03
+2022-06-18 13:09:56,900 INFO [train.py:874] (1/4) Epoch 4, batch 2750, datatang_loss[loss=0.2308, simple_loss=0.2835, pruned_loss=0.08904, over 4952.00 frames.], tot_loss[loss=0.2317, simple_loss=0.281, pruned_loss=0.09121, over 985614.41 frames.], batch size: 86, aishell_tot_loss[loss=0.2258, simple_loss=0.2834, pruned_loss=0.08406, over 983789.44 frames.], datatang_tot_loss[loss=0.2366, simple_loss=0.2783, pruned_loss=0.09745, over 984925.22 frames.], batch size: 86, lr: 1.59e-03
+2022-06-18 13:10:26,846 INFO [train.py:874] (1/4) Epoch 4, batch 2800, datatang_loss[loss=0.2611, simple_loss=0.3059, pruned_loss=0.1081, over 4953.00 frames.], tot_loss[loss=0.2304, simple_loss=0.28, pruned_loss=0.09043, over 985447.25 frames.], batch size: 86, aishell_tot_loss[loss=0.2252, simple_loss=0.2832, pruned_loss=0.08364, over 983928.51 frames.], datatang_tot_loss[loss=0.2358, simple_loss=0.2776, pruned_loss=0.09703, over 984900.40 frames.], batch size: 86, lr: 1.58e-03
+2022-06-18 13:10:56,180 INFO [train.py:874] (1/4) Epoch 4, batch 2850, datatang_loss[loss=0.2231, simple_loss=0.2704, pruned_loss=0.08792, over 4930.00 frames.], tot_loss[loss=0.2302, simple_loss=0.2801, pruned_loss=0.09019, over 985872.02 frames.], batch size: 79, aishell_tot_loss[loss=0.2239, simple_loss=0.2823, pruned_loss=0.08276, over 984153.15 frames.], datatang_tot_loss[loss=0.2368, simple_loss=0.2784, pruned_loss=0.09759, over 985350.30 frames.], batch size: 79, lr: 1.58e-03
+2022-06-18 13:11:27,467 INFO [train.py:874] (1/4) Epoch 4, batch 2900, aishell_loss[loss=0.2214, simple_loss=0.2783, pruned_loss=0.08226, over 4860.00 frames.], tot_loss[loss=0.2303, simple_loss=0.2802, pruned_loss=0.09022, over 985882.47 frames.], batch size: 37, aishell_tot_loss[loss=0.2241, simple_loss=0.2827, pruned_loss=0.08274, over 984381.98 frames.], datatang_tot_loss[loss=0.2364, simple_loss=0.2782, pruned_loss=0.09729, over 985391.15 frames.], batch size: 37, lr: 1.58e-03
+2022-06-18 13:11:56,667 INFO [train.py:874] (1/4) Epoch 4, batch 2950, aishell_loss[loss=0.2166, simple_loss=0.2841, pruned_loss=0.07454, over 4919.00 frames.], tot_loss[loss=0.2297, simple_loss=0.2795, pruned_loss=0.08991, over 985821.91 frames.], batch size: 46, aishell_tot_loss[loss=0.2239, simple_loss=0.2826, pruned_loss=0.08262, over 984537.11 frames.], datatang_tot_loss[loss=0.2355, simple_loss=0.2777, pruned_loss=0.09671, over 985393.94 frames.], batch size: 46, lr: 1.58e-03
+2022-06-18 13:12:25,370 INFO [train.py:874] (1/4) Epoch 4, batch 3000, aishell_loss[loss=0.2267, simple_loss=0.2933, pruned_loss=0.08, over 4934.00 frames.], tot_loss[loss=0.2277, simple_loss=0.2782, pruned_loss=0.08856, over 985833.00 frames.], batch size: 45, aishell_tot_loss[loss=0.2232, simple_loss=0.282, pruned_loss=0.08221, over 984536.89 frames.], datatang_tot_loss[loss=0.2342, simple_loss=0.2767, pruned_loss=0.09588, over 985619.33 frames.], batch size: 45, lr: 1.57e-03
+2022-06-18 13:12:25,371 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 13:12:41,438 INFO [train.py:914] (1/4) Epoch 4, validation: loss=0.1874, simple_loss=0.266, pruned_loss=0.05438, over 1622729.00 frames.
+2022-06-18 13:13:12,184 INFO [train.py:874] (1/4) Epoch 4, batch 3050, datatang_loss[loss=0.2029, simple_loss=0.2499, pruned_loss=0.07793, over 4913.00 frames.], tot_loss[loss=0.2268, simple_loss=0.2772, pruned_loss=0.08822, over 985943.49 frames.], batch size: 64, aishell_tot_loss[loss=0.2227, simple_loss=0.2816, pruned_loss=0.08195, over 984883.58 frames.], datatang_tot_loss[loss=0.2335, simple_loss=0.2757, pruned_loss=0.09568, over 985582.13 frames.], batch size: 64, lr: 1.57e-03
+2022-06-18 13:13:42,312 INFO [train.py:874] (1/4) Epoch 4, batch 3100, aishell_loss[loss=0.1605, simple_loss=0.2036, pruned_loss=0.05876, over 4860.00 frames.], tot_loss[loss=0.2262, simple_loss=0.2771, pruned_loss=0.08768, over 985743.87 frames.], batch size: 21, aishell_tot_loss[loss=0.2227, simple_loss=0.2817, pruned_loss=0.08187, over 984794.81 frames.], datatang_tot_loss[loss=0.2328, simple_loss=0.2752, pruned_loss=0.0952, over 985634.15 frames.], batch size: 21, lr: 1.57e-03
+2022-06-18 13:14:11,458 INFO [train.py:874] (1/4) Epoch 4, batch 3150, datatang_loss[loss=0.2139, simple_loss=0.2656, pruned_loss=0.08115, over 4934.00 frames.], tot_loss[loss=0.2249, simple_loss=0.2765, pruned_loss=0.08661, over 985999.25 frames.], batch size: 62, aishell_tot_loss[loss=0.2222, simple_loss=0.2815, pruned_loss=0.08146, over 985125.64 frames.], datatang_tot_loss[loss=0.2316, simple_loss=0.2743, pruned_loss=0.09442, over 985704.23 frames.], batch size: 62, lr: 1.57e-03
+2022-06-18 13:14:41,509 INFO [train.py:874] (1/4) Epoch 4, batch 3200, datatang_loss[loss=0.2252, simple_loss=0.2742, pruned_loss=0.08814, over 4950.00 frames.], tot_loss[loss=0.2267, simple_loss=0.2772, pruned_loss=0.08808, over 986001.70 frames.], batch size: 50, aishell_tot_loss[loss=0.2224, simple_loss=0.2814, pruned_loss=0.08171, over 985148.29 frames.], datatang_tot_loss[loss=0.2325, simple_loss=0.275, pruned_loss=0.09502, over 985803.15 frames.], batch size: 50, lr: 1.57e-03
+2022-06-18 13:15:11,999 INFO [train.py:874] (1/4) Epoch 4, batch 3250, aishell_loss[loss=0.2141, simple_loss=0.2861, pruned_loss=0.07106, over 4968.00 frames.], tot_loss[loss=0.2264, simple_loss=0.2776, pruned_loss=0.08763, over 985967.21 frames.], batch size: 64, aishell_tot_loss[loss=0.2225, simple_loss=0.2818, pruned_loss=0.0816, over 985131.31 frames.], datatang_tot_loss[loss=0.2318, simple_loss=0.2748, pruned_loss=0.09444, over 985898.64 frames.], batch size: 64, lr: 1.56e-03
+2022-06-18 13:15:41,370 INFO [train.py:874] (1/4) Epoch 4, batch 3300, datatang_loss[loss=0.2324, simple_loss=0.2658, pruned_loss=0.09948, over 4958.00 frames.], tot_loss[loss=0.2265, simple_loss=0.2779, pruned_loss=0.08753, over 985967.18 frames.], batch size: 34, aishell_tot_loss[loss=0.2227, simple_loss=0.2823, pruned_loss=0.08154, over 985148.39 frames.], datatang_tot_loss[loss=0.2316, simple_loss=0.2746, pruned_loss=0.09427, over 985986.76 frames.], batch size: 34, lr: 1.56e-03
+2022-06-18 13:16:12,234 INFO [train.py:874] (1/4) Epoch 4, batch 3350, aishell_loss[loss=0.2318, simple_loss=0.2981, pruned_loss=0.08273, over 4969.00 frames.], tot_loss[loss=0.2271, simple_loss=0.279, pruned_loss=0.08757, over 986294.24 frames.], batch size: 44, aishell_tot_loss[loss=0.2223, simple_loss=0.2824, pruned_loss=0.08106, over 985380.89 frames.], datatang_tot_loss[loss=0.2325, simple_loss=0.2753, pruned_loss=0.0948, over 986196.78 frames.], batch size: 44, lr: 1.56e-03
+2022-06-18 13:16:42,777 INFO [train.py:874] (1/4) Epoch 4, batch 3400, aishell_loss[loss=0.2197, simple_loss=0.2828, pruned_loss=0.07828, over 4869.00 frames.], tot_loss[loss=0.2258, simple_loss=0.2783, pruned_loss=0.08665, over 985698.65 frames.], batch size: 37, aishell_tot_loss[loss=0.2219, simple_loss=0.2824, pruned_loss=0.0807, over 985192.17 frames.], datatang_tot_loss[loss=0.2317, simple_loss=0.2746, pruned_loss=0.09435, over 985904.69 frames.], batch size: 37, lr: 1.56e-03
+2022-06-18 13:17:12,119 INFO [train.py:874] (1/4) Epoch 4, batch 3450, aishell_loss[loss=0.2061, simple_loss=0.2742, pruned_loss=0.069, over 4968.00 frames.], tot_loss[loss=0.2264, simple_loss=0.279, pruned_loss=0.08691, over 985830.33 frames.], batch size: 44, aishell_tot_loss[loss=0.2221, simple_loss=0.2827, pruned_loss=0.08078, over 985198.41 frames.], datatang_tot_loss[loss=0.2319, simple_loss=0.2748, pruned_loss=0.09449, over 986071.81 frames.], batch size: 44, lr: 1.55e-03
+2022-06-18 13:17:41,244 INFO [train.py:874] (1/4) Epoch 4, batch 3500, datatang_loss[loss=0.3416, simple_loss=0.328, pruned_loss=0.1776, over 4917.00 frames.], tot_loss[loss=0.2264, simple_loss=0.2789, pruned_loss=0.08694, over 985574.44 frames.], batch size: 98, aishell_tot_loss[loss=0.2213, simple_loss=0.2818, pruned_loss=0.08036, over 985028.89 frames.], datatang_tot_loss[loss=0.2329, simple_loss=0.2755, pruned_loss=0.09512, over 986040.29 frames.], batch size: 98, lr: 1.55e-03
+2022-06-18 13:18:17,393 INFO [train.py:874] (1/4) Epoch 4, batch 3550, datatang_loss[loss=0.1997, simple_loss=0.251, pruned_loss=0.07424, over 4913.00 frames.], tot_loss[loss=0.2251, simple_loss=0.2774, pruned_loss=0.08635, over 985326.77 frames.], batch size: 75, aishell_tot_loss[loss=0.2215, simple_loss=0.2821, pruned_loss=0.08044, over 984690.29 frames.], datatang_tot_loss[loss=0.231, simple_loss=0.2738, pruned_loss=0.09411, over 986094.78 frames.], batch size: 75, lr: 1.55e-03
+2022-06-18 13:18:45,983 INFO [train.py:874] (1/4) Epoch 4, batch 3600, aishell_loss[loss=0.226, simple_loss=0.2874, pruned_loss=0.08225, over 4948.00 frames.], tot_loss[loss=0.2253, simple_loss=0.2778, pruned_loss=0.08639, over 985419.98 frames.], batch size: 54, aishell_tot_loss[loss=0.2219, simple_loss=0.2826, pruned_loss=0.0806, over 984806.76 frames.], datatang_tot_loss[loss=0.2305, simple_loss=0.2735, pruned_loss=0.09374, over 986044.65 frames.], batch size: 54, lr: 1.55e-03
+2022-06-18 13:19:16,708 INFO [train.py:874] (1/4) Epoch 4, batch 3650, aishell_loss[loss=0.223, simple_loss=0.2888, pruned_loss=0.07861, over 4964.00 frames.], tot_loss[loss=0.2252, simple_loss=0.2773, pruned_loss=0.08655, over 985627.43 frames.], batch size: 61, aishell_tot_loss[loss=0.2211, simple_loss=0.282, pruned_loss=0.08014, over 984804.69 frames.], datatang_tot_loss[loss=0.2307, simple_loss=0.2739, pruned_loss=0.09371, over 986207.76 frames.], batch size: 61, lr: 1.54e-03
+2022-06-18 13:19:48,114 INFO [train.py:874] (1/4) Epoch 4, batch 3700, datatang_loss[loss=0.2241, simple_loss=0.2772, pruned_loss=0.08548, over 4930.00 frames.], tot_loss[loss=0.2254, simple_loss=0.2778, pruned_loss=0.08656, over 985617.00 frames.], batch size: 73, aishell_tot_loss[loss=0.2206, simple_loss=0.2815, pruned_loss=0.07987, over 984797.61 frames.], datatang_tot_loss[loss=0.2311, simple_loss=0.2746, pruned_loss=0.09382, over 986227.63 frames.], batch size: 73, lr: 1.54e-03
+2022-06-18 13:20:16,795 INFO [train.py:874] (1/4) Epoch 4, batch 3750, datatang_loss[loss=0.2283, simple_loss=0.2804, pruned_loss=0.08812, over 4949.00 frames.], tot_loss[loss=0.2249, simple_loss=0.2774, pruned_loss=0.08622, over 985563.79 frames.], batch size: 34, aishell_tot_loss[loss=0.2208, simple_loss=0.2814, pruned_loss=0.08009, over 984856.20 frames.], datatang_tot_loss[loss=0.2304, simple_loss=0.2745, pruned_loss=0.09313, over 986138.16 frames.], batch size: 34, lr: 1.54e-03
+2022-06-18 13:20:47,155 INFO [train.py:874] (1/4) Epoch 4, batch 3800, datatang_loss[loss=0.2545, simple_loss=0.2872, pruned_loss=0.1109, over 4916.00 frames.], tot_loss[loss=0.2244, simple_loss=0.2769, pruned_loss=0.08591, over 985592.83 frames.], batch size: 42, aishell_tot_loss[loss=0.2198, simple_loss=0.2806, pruned_loss=0.07952, over 984870.43 frames.], datatang_tot_loss[loss=0.2307, simple_loss=0.2746, pruned_loss=0.09335, over 986163.08 frames.], batch size: 42, lr: 1.54e-03
+2022-06-18 13:21:17,335 INFO [train.py:874] (1/4) Epoch 4, batch 3850, datatang_loss[loss=0.1919, simple_loss=0.245, pruned_loss=0.06941, over 4963.00 frames.], tot_loss[loss=0.2227, simple_loss=0.2752, pruned_loss=0.08503, over 985051.93 frames.], batch size: 34, aishell_tot_loss[loss=0.2198, simple_loss=0.2804, pruned_loss=0.07954, over 984687.58 frames.], datatang_tot_loss[loss=0.2286, simple_loss=0.273, pruned_loss=0.09215, over 985771.00 frames.], batch size: 34, lr: 1.54e-03
+2022-06-18 13:21:46,277 INFO [train.py:874] (1/4) Epoch 4, batch 3900, aishell_loss[loss=0.2443, simple_loss=0.3033, pruned_loss=0.09269, over 4963.00 frames.], tot_loss[loss=0.2244, simple_loss=0.2767, pruned_loss=0.08611, over 985018.27 frames.], batch size: 40, aishell_tot_loss[loss=0.2204, simple_loss=0.2808, pruned_loss=0.08, over 984738.05 frames.], datatang_tot_loss[loss=0.2296, simple_loss=0.2736, pruned_loss=0.09285, over 985674.82 frames.], batch size: 40, lr: 1.53e-03
+2022-06-18 13:22:14,509 INFO [train.py:874] (1/4) Epoch 4, batch 3950, datatang_loss[loss=0.2362, simple_loss=0.2809, pruned_loss=0.09572, over 4917.00 frames.], tot_loss[loss=0.2235, simple_loss=0.2765, pruned_loss=0.08532, over 984938.30 frames.], batch size: 75, aishell_tot_loss[loss=0.2202, simple_loss=0.2808, pruned_loss=0.07984, over 984434.36 frames.], datatang_tot_loss[loss=0.2287, simple_loss=0.2733, pruned_loss=0.0921, over 985841.61 frames.], batch size: 75, lr: 1.53e-03
+2022-06-18 13:22:44,205 INFO [train.py:874] (1/4) Epoch 4, batch 4000, aishell_loss[loss=0.2517, simple_loss=0.3074, pruned_loss=0.09795, over 4979.00 frames.], tot_loss[loss=0.2238, simple_loss=0.2763, pruned_loss=0.08565, over 984824.75 frames.], batch size: 48, aishell_tot_loss[loss=0.2196, simple_loss=0.2803, pruned_loss=0.07947, over 984446.17 frames.], datatang_tot_loss[loss=0.2294, simple_loss=0.2735, pruned_loss=0.09269, over 985676.93 frames.], batch size: 48, lr: 1.53e-03
+2022-06-18 13:22:44,206 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 13:23:00,878 INFO [train.py:914] (1/4) Epoch 4, validation: loss=0.1818, simple_loss=0.2607, pruned_loss=0.0515, over 1622729.00 frames.
+2022-06-18 13:23:30,354 INFO [train.py:874] (1/4) Epoch 4, batch 4050, aishell_loss[loss=0.2183, simple_loss=0.2887, pruned_loss=0.07389, over 4935.00 frames.], tot_loss[loss=0.2248, simple_loss=0.2768, pruned_loss=0.08643, over 985353.60 frames.], batch size: 54, aishell_tot_loss[loss=0.22, simple_loss=0.2806, pruned_loss=0.0797, over 984827.34 frames.], datatang_tot_loss[loss=0.2299, simple_loss=0.2735, pruned_loss=0.09315, over 985803.03 frames.], batch size: 54, lr: 1.53e-03
+2022-06-18 13:24:00,233 INFO [train.py:874] (1/4) Epoch 4, batch 4100, datatang_loss[loss=0.2156, simple_loss=0.2738, pruned_loss=0.07867, over 4953.00 frames.], tot_loss[loss=0.2254, simple_loss=0.2772, pruned_loss=0.08681, over 985421.88 frames.], batch size: 86, aishell_tot_loss[loss=0.2205, simple_loss=0.2812, pruned_loss=0.07994, over 985058.66 frames.], datatang_tot_loss[loss=0.2297, simple_loss=0.2736, pruned_loss=0.09294, over 985644.09 frames.], batch size: 86, lr: 1.53e-03
+2022-06-18 13:24:29,184 INFO [train.py:874] (1/4) Epoch 4, batch 4150, aishell_loss[loss=0.2236, simple_loss=0.2821, pruned_loss=0.08252, over 4922.00 frames.], tot_loss[loss=0.2246, simple_loss=0.2764, pruned_loss=0.08642, over 984990.55 frames.], batch size: 41, aishell_tot_loss[loss=0.2202, simple_loss=0.2806, pruned_loss=0.0799, over 984559.95 frames.], datatang_tot_loss[loss=0.2293, simple_loss=0.2733, pruned_loss=0.09261, over 985688.91 frames.], batch size: 41, lr: 1.52e-03
+2022-06-18 13:25:55,760 INFO [train.py:874] (1/4) Epoch 5, batch 50, datatang_loss[loss=0.1972, simple_loss=0.2492, pruned_loss=0.07259, over 4958.00 frames.], tot_loss[loss=0.217, simple_loss=0.2724, pruned_loss=0.08076, over 218858.92 frames.], batch size: 67, aishell_tot_loss[loss=0.2175, simple_loss=0.2819, pruned_loss=0.07654, over 98688.01 frames.], datatang_tot_loss[loss=0.2167, simple_loss=0.2659, pruned_loss=0.08379, over 133513.21 frames.], batch size: 67, lr: 1.47e-03
+2022-06-18 13:26:26,148 INFO [train.py:874] (1/4) Epoch 5, batch 100, aishell_loss[loss=0.2076, simple_loss=0.2799, pruned_loss=0.0676, over 4866.00 frames.], tot_loss[loss=0.214, simple_loss=0.2706, pruned_loss=0.07868, over 388602.79 frames.], batch size: 36, aishell_tot_loss[loss=0.2164, simple_loss=0.2807, pruned_loss=0.07605, over 195102.85 frames.], datatang_tot_loss[loss=0.2123, simple_loss=0.2629, pruned_loss=0.08091, over 241279.52 frames.], batch size: 36, lr: 1.46e-03
+2022-06-18 13:26:56,294 INFO [train.py:874] (1/4) Epoch 5, batch 150, aishell_loss[loss=0.172, simple_loss=0.2325, pruned_loss=0.05577, over 4814.00 frames.], tot_loss[loss=0.2155, simple_loss=0.272, pruned_loss=0.07957, over 521114.04 frames.], batch size: 26, aishell_tot_loss[loss=0.2198, simple_loss=0.2832, pruned_loss=0.07816, over 291603.31 frames.], datatang_tot_loss[loss=0.2117, simple_loss=0.2618, pruned_loss=0.08084, over 325915.44 frames.], batch size: 26, lr: 1.46e-03
+2022-06-18 13:27:26,069 INFO [train.py:874] (1/4) Epoch 5, batch 200, aishell_loss[loss=0.2292, simple_loss=0.2842, pruned_loss=0.08709, over 4974.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2703, pruned_loss=0.0799, over 623931.84 frames.], batch size: 39, aishell_tot_loss[loss=0.2177, simple_loss=0.2801, pruned_loss=0.07764, over 373190.37 frames.], datatang_tot_loss[loss=0.2128, simple_loss=0.2617, pruned_loss=0.082, over 403595.10 frames.], batch size: 39, lr: 1.46e-03
+2022-06-18 13:27:56,610 INFO [train.py:874] (1/4) Epoch 5, batch 250, datatang_loss[loss=0.2655, simple_loss=0.3046, pruned_loss=0.1131, over 4913.00 frames.], tot_loss[loss=0.216, simple_loss=0.2708, pruned_loss=0.08056, over 704103.14 frames.], batch size: 98, aishell_tot_loss[loss=0.2175, simple_loss=0.2804, pruned_loss=0.07734, over 437210.55 frames.], datatang_tot_loss[loss=0.2143, simple_loss=0.2624, pruned_loss=0.08315, over 479730.13 frames.], batch size: 98, lr: 1.46e-03
+2022-06-18 13:28:27,269 INFO [train.py:874] (1/4) Epoch 5, batch 300, datatang_loss[loss=0.2345, simple_loss=0.2722, pruned_loss=0.09844, over 4945.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2714, pruned_loss=0.08205, over 766193.18 frames.], batch size: 69, aishell_tot_loss[loss=0.2183, simple_loss=0.2799, pruned_loss=0.07841, over 503886.44 frames.], datatang_tot_loss[loss=0.2162, simple_loss=0.2633, pruned_loss=0.08453, over 537044.55 frames.], batch size: 69, lr: 1.46e-03
+2022-06-18 13:28:56,551 INFO [train.py:874] (1/4) Epoch 5, batch 350, datatang_loss[loss=0.2461, simple_loss=0.2633, pruned_loss=0.1144, over 4973.00 frames.], tot_loss[loss=0.2185, simple_loss=0.2721, pruned_loss=0.08242, over 814641.49 frames.], batch size: 40, aishell_tot_loss[loss=0.2183, simple_loss=0.2797, pruned_loss=0.07848, over 564639.73 frames.], datatang_tot_loss[loss=0.2174, simple_loss=0.2642, pruned_loss=0.08532, over 585904.10 frames.], batch size: 40, lr: 1.45e-03
+2022-06-18 13:29:27,463 INFO [train.py:874] (1/4) Epoch 5, batch 400, aishell_loss[loss=0.1581, simple_loss=0.236, pruned_loss=0.04005, over 4859.00 frames.], tot_loss[loss=0.217, simple_loss=0.2713, pruned_loss=0.08136, over 852632.59 frames.], batch size: 28, aishell_tot_loss[loss=0.2182, simple_loss=0.2797, pruned_loss=0.07831, over 604688.23 frames.], datatang_tot_loss[loss=0.2158, simple_loss=0.2637, pruned_loss=0.08397, over 641911.37 frames.], batch size: 28, lr: 1.45e-03
+2022-06-18 13:29:57,471 INFO [train.py:874] (1/4) Epoch 5, batch 450, aishell_loss[loss=0.2145, simple_loss=0.2831, pruned_loss=0.07296, over 4906.00 frames.], tot_loss[loss=0.2168, simple_loss=0.2723, pruned_loss=0.0806, over 881948.00 frames.], batch size: 34, aishell_tot_loss[loss=0.2174, simple_loss=0.2796, pruned_loss=0.07754, over 655940.54 frames.], datatang_tot_loss[loss=0.2162, simple_loss=0.2646, pruned_loss=0.08391, over 676375.28 frames.], batch size: 34, lr: 1.45e-03
+2022-06-18 13:30:27,315 INFO [train.py:874] (1/4) Epoch 5, batch 500, datatang_loss[loss=0.2019, simple_loss=0.2577, pruned_loss=0.07302, over 4922.00 frames.], tot_loss[loss=0.2178, simple_loss=0.2732, pruned_loss=0.08122, over 905011.03 frames.], batch size: 83, aishell_tot_loss[loss=0.2166, simple_loss=0.2793, pruned_loss=0.077, over 690434.96 frames.], datatang_tot_loss[loss=0.2183, simple_loss=0.2666, pruned_loss=0.08504, over 716885.99 frames.], batch size: 83, lr: 1.45e-03
+2022-06-18 13:30:57,315 INFO [train.py:874] (1/4) Epoch 5, batch 550, aishell_loss[loss=0.1936, simple_loss=0.2662, pruned_loss=0.06049, over 4880.00 frames.], tot_loss[loss=0.2175, simple_loss=0.2733, pruned_loss=0.08089, over 922490.14 frames.], batch size: 47, aishell_tot_loss[loss=0.2172, simple_loss=0.2799, pruned_loss=0.07723, over 726233.00 frames.], datatang_tot_loss[loss=0.2175, simple_loss=0.2661, pruned_loss=0.0845, over 747218.92 frames.], batch size: 47, lr: 1.45e-03
+2022-06-18 13:31:27,952 INFO [train.py:874] (1/4) Epoch 5, batch 600, aishell_loss[loss=0.219, simple_loss=0.2939, pruned_loss=0.07211, over 4962.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2754, pruned_loss=0.08217, over 936535.63 frames.], batch size: 64, aishell_tot_loss[loss=0.2181, simple_loss=0.2807, pruned_loss=0.07777, over 759011.50 frames.], datatang_tot_loss[loss=0.2197, simple_loss=0.2679, pruned_loss=0.08576, over 773300.80 frames.], batch size: 64, lr: 1.44e-03
+2022-06-18 13:31:56,148 INFO [train.py:874] (1/4) Epoch 5, batch 650, aishell_loss[loss=0.1996, simple_loss=0.263, pruned_loss=0.06811, over 4933.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2751, pruned_loss=0.08207, over 947148.05 frames.], batch size: 32, aishell_tot_loss[loss=0.2173, simple_loss=0.2799, pruned_loss=0.07731, over 788440.11 frames.], datatang_tot_loss[loss=0.2205, simple_loss=0.2684, pruned_loss=0.08631, over 795424.64 frames.], batch size: 32, lr: 1.44e-03
+2022-06-18 13:32:27,641 INFO [train.py:874] (1/4) Epoch 5, batch 700, aishell_loss[loss=0.1969, simple_loss=0.2699, pruned_loss=0.06198, over 4921.00 frames.], tot_loss[loss=0.2211, simple_loss=0.2764, pruned_loss=0.08287, over 955550.88 frames.], batch size: 52, aishell_tot_loss[loss=0.2177, simple_loss=0.2805, pruned_loss=0.07745, over 814360.20 frames.], datatang_tot_loss[loss=0.2221, simple_loss=0.2694, pruned_loss=0.08744, over 815060.93 frames.], batch size: 52, lr: 1.44e-03
+2022-06-18 13:32:56,847 INFO [train.py:874] (1/4) Epoch 5, batch 750, datatang_loss[loss=0.1983, simple_loss=0.2439, pruned_loss=0.07631, over 4960.00 frames.], tot_loss[loss=0.2226, simple_loss=0.277, pruned_loss=0.08405, over 961777.36 frames.], batch size: 34, aishell_tot_loss[loss=0.2174, simple_loss=0.2801, pruned_loss=0.0774, over 834792.56 frames.], datatang_tot_loss[loss=0.2246, simple_loss=0.271, pruned_loss=0.08908, over 834421.90 frames.], batch size: 34, lr: 1.44e-03
+2022-06-18 13:33:26,668 INFO [train.py:874] (1/4) Epoch 5, batch 800, datatang_loss[loss=0.2098, simple_loss=0.2656, pruned_loss=0.07701, over 4971.00 frames.], tot_loss[loss=0.2218, simple_loss=0.2763, pruned_loss=0.08365, over 966964.38 frames.], batch size: 37, aishell_tot_loss[loss=0.2163, simple_loss=0.2792, pruned_loss=0.07668, over 851171.89 frames.], datatang_tot_loss[loss=0.2251, simple_loss=0.2714, pruned_loss=0.08941, over 853491.68 frames.], batch size: 37, lr: 1.44e-03
+2022-06-18 13:33:57,570 INFO [train.py:874] (1/4) Epoch 5, batch 850, aishell_loss[loss=0.2042, simple_loss=0.2734, pruned_loss=0.06745, over 4858.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2753, pruned_loss=0.08228, over 970996.59 frames.], batch size: 35, aishell_tot_loss[loss=0.2156, simple_loss=0.2789, pruned_loss=0.07618, over 866239.38 frames.], datatang_tot_loss[loss=0.2238, simple_loss=0.2709, pruned_loss=0.08838, over 869705.45 frames.], batch size: 35, lr: 1.43e-03
+2022-06-18 13:34:26,194 INFO [train.py:874] (1/4) Epoch 5, batch 900, aishell_loss[loss=0.2087, simple_loss=0.2792, pruned_loss=0.06911, over 4942.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2746, pruned_loss=0.08161, over 974060.55 frames.], batch size: 45, aishell_tot_loss[loss=0.215, simple_loss=0.2785, pruned_loss=0.07578, over 879448.09 frames.], datatang_tot_loss[loss=0.2232, simple_loss=0.2706, pruned_loss=0.08791, over 884016.19 frames.], batch size: 45, lr: 1.43e-03
+2022-06-18 13:34:56,206 INFO [train.py:874] (1/4) Epoch 5, batch 950, datatang_loss[loss=0.1872, simple_loss=0.2452, pruned_loss=0.06455, over 4912.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2744, pruned_loss=0.08161, over 976604.56 frames.], batch size: 52, aishell_tot_loss[loss=0.2154, simple_loss=0.279, pruned_loss=0.07591, over 890724.54 frames.], datatang_tot_loss[loss=0.2226, simple_loss=0.27, pruned_loss=0.08759, over 897140.86 frames.], batch size: 52, lr: 1.43e-03
+2022-06-18 13:35:26,997 INFO [train.py:874] (1/4) Epoch 5, batch 1000, datatang_loss[loss=0.2229, simple_loss=0.2746, pruned_loss=0.08562, over 4961.00 frames.], tot_loss[loss=0.2188, simple_loss=0.2738, pruned_loss=0.08195, over 978300.14 frames.], batch size: 86, aishell_tot_loss[loss=0.2149, simple_loss=0.2782, pruned_loss=0.07578, over 901479.05 frames.], datatang_tot_loss[loss=0.2232, simple_loss=0.2701, pruned_loss=0.0881, over 907666.83 frames.], batch size: 86, lr: 1.43e-03
+2022-06-18 13:35:26,998 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 13:35:43,412 INFO [train.py:914] (1/4) Epoch 5, validation: loss=0.1786, simple_loss=0.258, pruned_loss=0.04955, over 1622729.00 frames.
+2022-06-18 13:36:13,976 INFO [train.py:874] (1/4) Epoch 5, batch 1050, aishell_loss[loss=0.205, simple_loss=0.2695, pruned_loss=0.0702, over 4962.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2738, pruned_loss=0.08284, over 979814.38 frames.], batch size: 40, aishell_tot_loss[loss=0.2146, simple_loss=0.2778, pruned_loss=0.07575, over 909502.39 frames.], datatang_tot_loss[loss=0.2242, simple_loss=0.2706, pruned_loss=0.08885, over 918440.77 frames.], batch size: 40, lr: 1.43e-03
+2022-06-18 13:36:44,161 INFO [train.py:874] (1/4) Epoch 5, batch 1100, datatang_loss[loss=0.1996, simple_loss=0.2602, pruned_loss=0.06955, over 4925.00 frames.], tot_loss[loss=0.2196, simple_loss=0.2743, pruned_loss=0.08251, over 981020.98 frames.], batch size: 83, aishell_tot_loss[loss=0.215, simple_loss=0.2783, pruned_loss=0.07587, over 919679.27 frames.], datatang_tot_loss[loss=0.2239, simple_loss=0.2703, pruned_loss=0.08875, over 925201.17 frames.], batch size: 83, lr: 1.43e-03
+2022-06-18 13:37:13,243 INFO [train.py:874] (1/4) Epoch 5, batch 1150, datatang_loss[loss=0.2132, simple_loss=0.2571, pruned_loss=0.08462, over 4935.00 frames.], tot_loss[loss=0.2212, simple_loss=0.2756, pruned_loss=0.08341, over 982496.94 frames.], batch size: 79, aishell_tot_loss[loss=0.2147, simple_loss=0.2782, pruned_loss=0.07553, over 926745.51 frames.], datatang_tot_loss[loss=0.226, simple_loss=0.2721, pruned_loss=0.08998, over 933407.24 frames.], batch size: 79, lr: 1.42e-03
+2022-06-18 13:37:45,217 INFO [train.py:874] (1/4) Epoch 5, batch 1200, aishell_loss[loss=0.2178, simple_loss=0.2808, pruned_loss=0.07743, over 4882.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2753, pruned_loss=0.08264, over 982862.11 frames.], batch size: 47, aishell_tot_loss[loss=0.2148, simple_loss=0.2784, pruned_loss=0.07564, over 933453.75 frames.], datatang_tot_loss[loss=0.225, simple_loss=0.2717, pruned_loss=0.08917, over 939444.00 frames.], batch size: 47, lr: 1.42e-03
+2022-06-18 13:38:15,657 INFO [train.py:874] (1/4) Epoch 5, batch 1250, aishell_loss[loss=0.1872, simple_loss=0.2552, pruned_loss=0.0596, over 4884.00 frames.], tot_loss[loss=0.2192, simple_loss=0.2743, pruned_loss=0.08203, over 983400.06 frames.], batch size: 42, aishell_tot_loss[loss=0.2142, simple_loss=0.278, pruned_loss=0.07521, over 939731.06 frames.], datatang_tot_loss[loss=0.2245, simple_loss=0.2711, pruned_loss=0.089, over 944686.18 frames.], batch size: 42, lr: 1.42e-03
+2022-06-18 13:38:44,449 INFO [train.py:874] (1/4) Epoch 5, batch 1300, datatang_loss[loss=0.2066, simple_loss=0.2574, pruned_loss=0.07788, over 4918.00 frames.], tot_loss[loss=0.2201, simple_loss=0.2749, pruned_loss=0.08263, over 983958.37 frames.], batch size: 75, aishell_tot_loss[loss=0.2148, simple_loss=0.2782, pruned_loss=0.07565, over 945009.22 frames.], datatang_tot_loss[loss=0.2248, simple_loss=0.2713, pruned_loss=0.08918, over 949712.39 frames.], batch size: 75, lr: 1.42e-03
+2022-06-18 13:39:15,005 INFO [train.py:874] (1/4) Epoch 5, batch 1350, datatang_loss[loss=0.2253, simple_loss=0.2714, pruned_loss=0.08958, over 4904.00 frames.], tot_loss[loss=0.22, simple_loss=0.2748, pruned_loss=0.08258, over 984590.61 frames.], batch size: 64, aishell_tot_loss[loss=0.2142, simple_loss=0.2778, pruned_loss=0.07533, over 949726.26 frames.], datatang_tot_loss[loss=0.2253, simple_loss=0.2718, pruned_loss=0.08945, over 954256.62 frames.], batch size: 64, lr: 1.42e-03
+2022-06-18 13:39:44,959 INFO [train.py:874] (1/4) Epoch 5, batch 1400, datatang_loss[loss=0.2454, simple_loss=0.2768, pruned_loss=0.1069, over 4961.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2746, pruned_loss=0.08251, over 984658.48 frames.], batch size: 67, aishell_tot_loss[loss=0.2142, simple_loss=0.2778, pruned_loss=0.07533, over 953570.90 frames.], datatang_tot_loss[loss=0.2252, simple_loss=0.2715, pruned_loss=0.08942, over 958170.39 frames.], batch size: 67, lr: 1.41e-03
+2022-06-18 13:40:14,814 INFO [train.py:874] (1/4) Epoch 5, batch 1450, aishell_loss[loss=0.2035, simple_loss=0.2755, pruned_loss=0.06575, over 4871.00 frames.], tot_loss[loss=0.2209, simple_loss=0.2757, pruned_loss=0.08303, over 984565.76 frames.], batch size: 35, aishell_tot_loss[loss=0.214, simple_loss=0.2776, pruned_loss=0.07514, over 957195.03 frames.], datatang_tot_loss[loss=0.2267, simple_loss=0.2728, pruned_loss=0.09023, over 961254.80 frames.], batch size: 35, lr: 1.41e-03
+2022-06-18 13:40:45,876 INFO [train.py:874] (1/4) Epoch 5, batch 1500, datatang_loss[loss=0.2279, simple_loss=0.2772, pruned_loss=0.08934, over 4954.00 frames.], tot_loss[loss=0.22, simple_loss=0.2748, pruned_loss=0.08264, over 985046.59 frames.], batch size: 86, aishell_tot_loss[loss=0.2134, simple_loss=0.2771, pruned_loss=0.07488, over 960180.61 frames.], datatang_tot_loss[loss=0.226, simple_loss=0.2725, pruned_loss=0.08976, over 964648.77 frames.], batch size: 86, lr: 1.41e-03
+2022-06-18 13:41:16,036 INFO [train.py:874] (1/4) Epoch 5, batch 1550, datatang_loss[loss=0.2272, simple_loss=0.2877, pruned_loss=0.08334, over 4977.00 frames.], tot_loss[loss=0.2198, simple_loss=0.2749, pruned_loss=0.08237, over 985130.80 frames.], batch size: 60, aishell_tot_loss[loss=0.2138, simple_loss=0.2776, pruned_loss=0.07501, over 962871.81 frames.], datatang_tot_loss[loss=0.2253, simple_loss=0.2722, pruned_loss=0.08921, over 967300.80 frames.], batch size: 60, lr: 1.41e-03
+2022-06-18 13:41:45,624 INFO [train.py:874] (1/4) Epoch 5, batch 1600, aishell_loss[loss=0.2497, simple_loss=0.3111, pruned_loss=0.09419, over 4942.00 frames.], tot_loss[loss=0.2203, simple_loss=0.2748, pruned_loss=0.0829, over 984857.94 frames.], batch size: 54, aishell_tot_loss[loss=0.2146, simple_loss=0.2774, pruned_loss=0.07593, over 965153.57 frames.], datatang_tot_loss[loss=0.2252, simple_loss=0.2723, pruned_loss=0.08909, over 969493.55 frames.], batch size: 54, lr: 1.41e-03
+2022-06-18 13:42:15,551 INFO [train.py:874] (1/4) Epoch 5, batch 1650, datatang_loss[loss=0.2185, simple_loss=0.2749, pruned_loss=0.08103, over 4954.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2746, pruned_loss=0.0826, over 985208.25 frames.], batch size: 67, aishell_tot_loss[loss=0.2141, simple_loss=0.2772, pruned_loss=0.07545, over 967317.74 frames.], datatang_tot_loss[loss=0.2253, simple_loss=0.2723, pruned_loss=0.08909, over 971760.96 frames.], batch size: 67, lr: 1.40e-03
+2022-06-18 13:42:46,146 INFO [train.py:874] (1/4) Epoch 5, batch 1700, aishell_loss[loss=0.2723, simple_loss=0.3268, pruned_loss=0.1089, over 4909.00 frames.], tot_loss[loss=0.2199, simple_loss=0.2744, pruned_loss=0.08265, over 985480.69 frames.], batch size: 52, aishell_tot_loss[loss=0.2144, simple_loss=0.2773, pruned_loss=0.07571, over 969638.60 frames.], datatang_tot_loss[loss=0.2249, simple_loss=0.2719, pruned_loss=0.0889, over 973419.18 frames.], batch size: 52, lr: 1.40e-03
+2022-06-18 13:43:15,675 INFO [train.py:874] (1/4) Epoch 5, batch 1750, datatang_loss[loss=0.207, simple_loss=0.2509, pruned_loss=0.08156, over 4952.00 frames.], tot_loss[loss=0.219, simple_loss=0.2744, pruned_loss=0.08185, over 985655.84 frames.], batch size: 86, aishell_tot_loss[loss=0.2144, simple_loss=0.2778, pruned_loss=0.07547, over 971513.69 frames.], datatang_tot_loss[loss=0.224, simple_loss=0.2714, pruned_loss=0.08832, over 975011.13 frames.], batch size: 86, lr: 1.40e-03
+2022-06-18 13:43:46,686 INFO [train.py:874] (1/4) Epoch 5, batch 1800, aishell_loss[loss=0.2229, simple_loss=0.2883, pruned_loss=0.07874, over 4969.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2741, pruned_loss=0.08183, over 985888.74 frames.], batch size: 40, aishell_tot_loss[loss=0.2142, simple_loss=0.2779, pruned_loss=0.07528, over 972794.13 frames.], datatang_tot_loss[loss=0.2236, simple_loss=0.271, pruned_loss=0.08806, over 976762.09 frames.], batch size: 40, lr: 1.40e-03
+2022-06-18 13:44:17,129 INFO [train.py:874] (1/4) Epoch 5, batch 1850, datatang_loss[loss=0.1844, simple_loss=0.2369, pruned_loss=0.06598, over 4899.00 frames.], tot_loss[loss=0.2189, simple_loss=0.2744, pruned_loss=0.08167, over 985892.35 frames.], batch size: 42, aishell_tot_loss[loss=0.2147, simple_loss=0.2786, pruned_loss=0.07538, over 974223.52 frames.], datatang_tot_loss[loss=0.2231, simple_loss=0.2707, pruned_loss=0.08777, over 977928.11 frames.], batch size: 42, lr: 1.40e-03
+2022-06-18 13:44:47,046 INFO [train.py:874] (1/4) Epoch 5, batch 1900, datatang_loss[loss=0.2107, simple_loss=0.2592, pruned_loss=0.08112, over 4937.00 frames.], tot_loss[loss=0.2172, simple_loss=0.2733, pruned_loss=0.08059, over 985944.99 frames.], batch size: 50, aishell_tot_loss[loss=0.2135, simple_loss=0.2777, pruned_loss=0.07465, over 975665.68 frames.], datatang_tot_loss[loss=0.2226, simple_loss=0.2702, pruned_loss=0.08748, over 978867.55 frames.], batch size: 50, lr: 1.40e-03
+2022-06-18 13:45:17,707 INFO [train.py:874] (1/4) Epoch 5, batch 1950, datatang_loss[loss=0.1974, simple_loss=0.2458, pruned_loss=0.07451, over 4850.00 frames.], tot_loss[loss=0.218, simple_loss=0.2738, pruned_loss=0.08115, over 985721.98 frames.], batch size: 24, aishell_tot_loss[loss=0.2146, simple_loss=0.2789, pruned_loss=0.07508, over 976689.33 frames.], datatang_tot_loss[loss=0.2221, simple_loss=0.2695, pruned_loss=0.0874, over 979628.66 frames.], batch size: 24, lr: 1.39e-03
+2022-06-18 13:45:46,803 INFO [train.py:874] (1/4) Epoch 5, batch 2000, aishell_loss[loss=0.1622, simple_loss=0.2241, pruned_loss=0.05018, over 4960.00 frames.], tot_loss[loss=0.2185, simple_loss=0.2743, pruned_loss=0.08139, over 985745.05 frames.], batch size: 25, aishell_tot_loss[loss=0.2142, simple_loss=0.2786, pruned_loss=0.0749, over 977596.10 frames.], datatang_tot_loss[loss=0.223, simple_loss=0.2701, pruned_loss=0.08795, over 980565.82 frames.], batch size: 25, lr: 1.39e-03
+2022-06-18 13:45:46,804 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 13:46:03,087 INFO [train.py:914] (1/4) 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] (1/4) Epoch 5, batch 2050, datatang_loss[loss=0.2224, simple_loss=0.2723, pruned_loss=0.08621, over 4861.00 frames.], tot_loss[loss=0.2174, simple_loss=0.2733, pruned_loss=0.08074, over 985236.49 frames.], batch size: 39, aishell_tot_loss[loss=0.2132, simple_loss=0.2775, pruned_loss=0.07441, over 978195.26 frames.], datatang_tot_loss[loss=0.2228, simple_loss=0.2701, pruned_loss=0.08776, over 980999.89 frames.], batch size: 39, lr: 1.39e-03
+2022-06-18 13:47:01,879 INFO [train.py:874] (1/4) Epoch 5, batch 2100, datatang_loss[loss=0.2087, simple_loss=0.261, pruned_loss=0.07816, over 4923.00 frames.], tot_loss[loss=0.218, simple_loss=0.2745, pruned_loss=0.08074, over 985746.05 frames.], batch size: 77, aishell_tot_loss[loss=0.2134, simple_loss=0.278, pruned_loss=0.07445, over 979597.06 frames.], datatang_tot_loss[loss=0.2234, simple_loss=0.2705, pruned_loss=0.08808, over 981533.36 frames.], batch size: 77, lr: 1.39e-03
+2022-06-18 13:47:31,754 INFO [train.py:874] (1/4) Epoch 5, batch 2150, aishell_loss[loss=0.2216, simple_loss=0.2864, pruned_loss=0.07835, over 4919.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2736, pruned_loss=0.07938, over 985232.37 frames.], batch size: 41, aishell_tot_loss[loss=0.2122, simple_loss=0.2771, pruned_loss=0.07359, over 980026.26 frames.], datatang_tot_loss[loss=0.2229, simple_loss=0.2703, pruned_loss=0.08777, over 981824.69 frames.], batch size: 41, lr: 1.39e-03
+2022-06-18 13:48:01,815 INFO [train.py:874] (1/4) Epoch 5, batch 2200, datatang_loss[loss=0.2006, simple_loss=0.2502, pruned_loss=0.07555, over 4920.00 frames.], tot_loss[loss=0.2158, simple_loss=0.2726, pruned_loss=0.07952, over 985061.72 frames.], batch size: 73, aishell_tot_loss[loss=0.2119, simple_loss=0.277, pruned_loss=0.07341, over 980365.17 frames.], datatang_tot_loss[loss=0.2221, simple_loss=0.2697, pruned_loss=0.08724, over 982252.22 frames.], batch size: 73, lr: 1.39e-03
+2022-06-18 13:48:32,484 INFO [train.py:874] (1/4) Epoch 5, batch 2250, datatang_loss[loss=0.1673, simple_loss=0.2186, pruned_loss=0.058, over 4929.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2728, pruned_loss=0.08117, over 985156.01 frames.], batch size: 37, aishell_tot_loss[loss=0.2128, simple_loss=0.2772, pruned_loss=0.07418, over 980542.95 frames.], datatang_tot_loss[loss=0.2225, simple_loss=0.2697, pruned_loss=0.08763, over 982991.95 frames.], batch size: 37, lr: 1.38e-03
+2022-06-18 13:49:03,296 INFO [train.py:874] (1/4) Epoch 5, batch 2300, datatang_loss[loss=0.3095, simple_loss=0.3343, pruned_loss=0.1423, over 4955.00 frames.], tot_loss[loss=0.219, simple_loss=0.2737, pruned_loss=0.0822, over 985253.66 frames.], batch size: 99, aishell_tot_loss[loss=0.2131, simple_loss=0.2773, pruned_loss=0.07451, over 980771.59 frames.], datatang_tot_loss[loss=0.2233, simple_loss=0.2706, pruned_loss=0.08803, over 983571.13 frames.], batch size: 99, lr: 1.38e-03
+2022-06-18 13:49:34,053 INFO [train.py:874] (1/4) Epoch 5, batch 2350, aishell_loss[loss=0.1672, simple_loss=0.2386, pruned_loss=0.0479, over 4975.00 frames.], tot_loss[loss=0.2177, simple_loss=0.273, pruned_loss=0.08116, over 985316.17 frames.], batch size: 27, aishell_tot_loss[loss=0.2126, simple_loss=0.2769, pruned_loss=0.07413, over 981317.81 frames.], datatang_tot_loss[loss=0.2227, simple_loss=0.2701, pruned_loss=0.08759, over 983812.03 frames.], batch size: 27, lr: 1.38e-03
+2022-06-18 13:50:03,371 INFO [train.py:874] (1/4) Epoch 5, batch 2400, datatang_loss[loss=0.1688, simple_loss=0.2211, pruned_loss=0.05828, over 4906.00 frames.], tot_loss[loss=0.2179, simple_loss=0.2735, pruned_loss=0.08115, over 985620.98 frames.], batch size: 47, aishell_tot_loss[loss=0.2129, simple_loss=0.2774, pruned_loss=0.07424, over 981785.55 frames.], datatang_tot_loss[loss=0.2226, simple_loss=0.2702, pruned_loss=0.08748, over 984290.03 frames.], batch size: 47, lr: 1.38e-03
+2022-06-18 13:50:34,569 INFO [train.py:874] (1/4) Epoch 5, batch 2450, aishell_loss[loss=0.2488, simple_loss=0.311, pruned_loss=0.09333, over 4926.00 frames.], tot_loss[loss=0.217, simple_loss=0.2727, pruned_loss=0.0807, over 985669.41 frames.], batch size: 79, aishell_tot_loss[loss=0.2131, simple_loss=0.2774, pruned_loss=0.07438, over 981976.71 frames.], datatang_tot_loss[loss=0.2213, simple_loss=0.2694, pruned_loss=0.08663, over 984694.99 frames.], batch size: 79, lr: 1.38e-03
+2022-06-18 13:51:05,481 INFO [train.py:874] (1/4) Epoch 5, batch 2500, datatang_loss[loss=0.179, simple_loss=0.2417, pruned_loss=0.05808, over 4954.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2712, pruned_loss=0.07929, over 985836.61 frames.], batch size: 67, aishell_tot_loss[loss=0.2125, simple_loss=0.2772, pruned_loss=0.07391, over 982539.32 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2679, pruned_loss=0.08565, over 984861.08 frames.], batch size: 67, lr: 1.38e-03
+2022-06-18 13:51:35,049 INFO [train.py:874] (1/4) Epoch 5, batch 2550, datatang_loss[loss=0.1807, simple_loss=0.2482, pruned_loss=0.05658, over 4936.00 frames.], tot_loss[loss=0.2155, simple_loss=0.2722, pruned_loss=0.07937, over 985457.58 frames.], batch size: 69, aishell_tot_loss[loss=0.213, simple_loss=0.2777, pruned_loss=0.07413, over 982529.00 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2679, pruned_loss=0.08567, over 985026.84 frames.], batch size: 69, lr: 1.37e-03
+2022-06-18 13:52:06,133 INFO [train.py:874] (1/4) Epoch 5, batch 2600, datatang_loss[loss=0.241, simple_loss=0.286, pruned_loss=0.09796, over 4845.00 frames.], tot_loss[loss=0.2143, simple_loss=0.2718, pruned_loss=0.07838, over 985324.86 frames.], batch size: 33, aishell_tot_loss[loss=0.212, simple_loss=0.2772, pruned_loss=0.0734, over 982722.00 frames.], datatang_tot_loss[loss=0.2191, simple_loss=0.2677, pruned_loss=0.0853, over 985119.14 frames.], batch size: 33, lr: 1.37e-03
+2022-06-18 13:52:35,434 INFO [train.py:874] (1/4) Epoch 5, batch 2650, datatang_loss[loss=0.201, simple_loss=0.2614, pruned_loss=0.07024, over 4864.00 frames.], tot_loss[loss=0.2151, simple_loss=0.2723, pruned_loss=0.07893, over 985270.39 frames.], batch size: 39, aishell_tot_loss[loss=0.2122, simple_loss=0.2773, pruned_loss=0.07354, over 982846.55 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2681, pruned_loss=0.08555, over 985266.76 frames.], batch size: 39, lr: 1.37e-03
+2022-06-18 13:53:06,283 INFO [train.py:874] (1/4) Epoch 5, batch 2700, datatang_loss[loss=0.2186, simple_loss=0.2571, pruned_loss=0.09007, over 4939.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2714, pruned_loss=0.0785, over 985236.56 frames.], batch size: 50, aishell_tot_loss[loss=0.2119, simple_loss=0.2767, pruned_loss=0.07358, over 983195.24 frames.], datatang_tot_loss[loss=0.2188, simple_loss=0.2676, pruned_loss=0.08496, over 985173.26 frames.], batch size: 50, lr: 1.37e-03
+2022-06-18 13:53:36,281 INFO [train.py:874] (1/4) Epoch 5, batch 2750, datatang_loss[loss=0.2799, simple_loss=0.3169, pruned_loss=0.1214, over 4915.00 frames.], tot_loss[loss=0.2147, simple_loss=0.2718, pruned_loss=0.07881, over 985426.88 frames.], batch size: 108, aishell_tot_loss[loss=0.2123, simple_loss=0.2771, pruned_loss=0.07373, over 983472.56 frames.], datatang_tot_loss[loss=0.2187, simple_loss=0.2674, pruned_loss=0.08496, over 985351.05 frames.], batch size: 108, lr: 1.37e-03
+2022-06-18 13:54:05,690 INFO [train.py:874] (1/4) Epoch 5, batch 2800, datatang_loss[loss=0.1999, simple_loss=0.2571, pruned_loss=0.0713, over 4926.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2724, pruned_loss=0.07917, over 985617.51 frames.], batch size: 79, aishell_tot_loss[loss=0.2125, simple_loss=0.2773, pruned_loss=0.07389, over 983747.92 frames.], datatang_tot_loss[loss=0.2189, simple_loss=0.2677, pruned_loss=0.08507, over 985507.06 frames.], batch size: 79, lr: 1.37e-03
+2022-06-18 13:54:37,285 INFO [train.py:874] (1/4) Epoch 5, batch 2850, datatang_loss[loss=0.2147, simple_loss=0.2664, pruned_loss=0.08149, over 4936.00 frames.], tot_loss[loss=0.2136, simple_loss=0.2711, pruned_loss=0.07805, over 985535.16 frames.], batch size: 34, aishell_tot_loss[loss=0.2116, simple_loss=0.2766, pruned_loss=0.07329, over 983876.48 frames.], datatang_tot_loss[loss=0.2179, simple_loss=0.2673, pruned_loss=0.08423, over 985492.52 frames.], batch size: 34, lr: 1.36e-03
+2022-06-18 13:55:07,711 INFO [train.py:874] (1/4) Epoch 5, batch 2900, datatang_loss[loss=0.223, simple_loss=0.2747, pruned_loss=0.08566, over 4921.00 frames.], tot_loss[loss=0.213, simple_loss=0.271, pruned_loss=0.07751, over 986234.59 frames.], batch size: 83, aishell_tot_loss[loss=0.2115, simple_loss=0.2768, pruned_loss=0.07315, over 984521.28 frames.], datatang_tot_loss[loss=0.2171, simple_loss=0.2667, pruned_loss=0.08379, over 985815.19 frames.], batch size: 83, lr: 1.36e-03
+2022-06-18 13:55:36,551 INFO [train.py:874] (1/4) Epoch 5, batch 2950, aishell_loss[loss=0.2048, simple_loss=0.2793, pruned_loss=0.0651, over 4931.00 frames.], tot_loss[loss=0.2139, simple_loss=0.272, pruned_loss=0.07789, over 985929.79 frames.], batch size: 49, aishell_tot_loss[loss=0.2112, simple_loss=0.2768, pruned_loss=0.07281, over 984339.27 frames.], datatang_tot_loss[loss=0.218, simple_loss=0.2675, pruned_loss=0.08425, over 985907.98 frames.], batch size: 49, lr: 1.36e-03
+2022-06-18 13:56:08,923 INFO [train.py:874] (1/4) Epoch 5, batch 3000, datatang_loss[loss=0.2769, simple_loss=0.303, pruned_loss=0.1254, over 4942.00 frames.], tot_loss[loss=0.2131, simple_loss=0.2714, pruned_loss=0.07741, over 985911.43 frames.], batch size: 34, aishell_tot_loss[loss=0.2101, simple_loss=0.2759, pruned_loss=0.07212, over 984317.64 frames.], datatang_tot_loss[loss=0.2182, simple_loss=0.2676, pruned_loss=0.08438, over 986098.00 frames.], batch size: 34, lr: 1.36e-03
+2022-06-18 13:56:08,924 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 13:56:26,006 INFO [train.py:914] (1/4) 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] (1/4) Epoch 5, batch 3050, aishell_loss[loss=0.2262, simple_loss=0.2922, pruned_loss=0.0801, over 4911.00 frames.], tot_loss[loss=0.2134, simple_loss=0.2715, pruned_loss=0.0777, over 985969.90 frames.], batch size: 41, aishell_tot_loss[loss=0.2099, simple_loss=0.2758, pruned_loss=0.07205, over 984413.03 frames.], datatang_tot_loss[loss=0.2185, simple_loss=0.2677, pruned_loss=0.08464, over 986239.54 frames.], batch size: 41, lr: 1.36e-03
+2022-06-18 13:57:26,862 INFO [train.py:874] (1/4) Epoch 5, batch 3100, datatang_loss[loss=0.2172, simple_loss=0.2615, pruned_loss=0.08641, over 4923.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2717, pruned_loss=0.07836, over 985786.13 frames.], batch size: 47, aishell_tot_loss[loss=0.2106, simple_loss=0.2763, pruned_loss=0.07245, over 984514.50 frames.], datatang_tot_loss[loss=0.2183, simple_loss=0.2675, pruned_loss=0.08457, over 986078.02 frames.], batch size: 47, lr: 1.36e-03
+2022-06-18 13:57:57,630 INFO [train.py:874] (1/4) Epoch 5, batch 3150, aishell_loss[loss=0.2339, simple_loss=0.2845, pruned_loss=0.09163, over 4878.00 frames.], tot_loss[loss=0.2154, simple_loss=0.2722, pruned_loss=0.07931, over 985906.78 frames.], batch size: 36, aishell_tot_loss[loss=0.2113, simple_loss=0.2766, pruned_loss=0.07296, over 984693.84 frames.], datatang_tot_loss[loss=0.2188, simple_loss=0.2678, pruned_loss=0.08486, over 986120.19 frames.], batch size: 36, lr: 1.35e-03
+2022-06-18 13:58:27,915 INFO [train.py:874] (1/4) Epoch 5, batch 3200, aishell_loss[loss=0.1958, simple_loss=0.273, pruned_loss=0.05927, over 4921.00 frames.], tot_loss[loss=0.2153, simple_loss=0.272, pruned_loss=0.07925, over 985695.12 frames.], batch size: 46, aishell_tot_loss[loss=0.2111, simple_loss=0.2766, pruned_loss=0.07278, over 984713.71 frames.], datatang_tot_loss[loss=0.2188, simple_loss=0.2679, pruned_loss=0.08483, over 985966.98 frames.], batch size: 46, lr: 1.35e-03
+2022-06-18 13:58:58,336 INFO [train.py:874] (1/4) Epoch 5, batch 3250, datatang_loss[loss=0.2143, simple_loss=0.2567, pruned_loss=0.08595, over 4864.00 frames.], tot_loss[loss=0.2149, simple_loss=0.2716, pruned_loss=0.07914, over 985834.96 frames.], batch size: 30, aishell_tot_loss[loss=0.2097, simple_loss=0.2752, pruned_loss=0.07214, over 985005.37 frames.], datatang_tot_loss[loss=0.2199, simple_loss=0.2687, pruned_loss=0.08559, over 985906.24 frames.], batch size: 30, lr: 1.35e-03
+2022-06-18 13:59:26,746 INFO [train.py:874] (1/4) Epoch 5, batch 3300, aishell_loss[loss=0.168, simple_loss=0.239, pruned_loss=0.04852, over 4967.00 frames.], tot_loss[loss=0.2142, simple_loss=0.2713, pruned_loss=0.07852, over 985820.63 frames.], batch size: 25, aishell_tot_loss[loss=0.2094, simple_loss=0.2749, pruned_loss=0.07193, over 985153.44 frames.], datatang_tot_loss[loss=0.2199, simple_loss=0.2684, pruned_loss=0.08569, over 985861.23 frames.], batch size: 25, lr: 1.35e-03
+2022-06-18 13:59:58,744 INFO [train.py:874] (1/4) Epoch 5, batch 3350, datatang_loss[loss=0.199, simple_loss=0.2572, pruned_loss=0.07036, over 4911.00 frames.], tot_loss[loss=0.2139, simple_loss=0.2714, pruned_loss=0.07823, over 985664.08 frames.], batch size: 75, aishell_tot_loss[loss=0.2099, simple_loss=0.2756, pruned_loss=0.07207, over 985072.10 frames.], datatang_tot_loss[loss=0.219, simple_loss=0.2678, pruned_loss=0.0851, over 985851.07 frames.], batch size: 75, lr: 1.35e-03
+2022-06-18 14:00:33,672 INFO [train.py:874] (1/4) Epoch 5, batch 3400, datatang_loss[loss=0.1917, simple_loss=0.2437, pruned_loss=0.06986, over 4964.00 frames.], tot_loss[loss=0.2137, simple_loss=0.2719, pruned_loss=0.07771, over 985926.76 frames.], batch size: 67, aishell_tot_loss[loss=0.2095, simple_loss=0.2757, pruned_loss=0.07169, over 985324.45 frames.], datatang_tot_loss[loss=0.2192, simple_loss=0.2679, pruned_loss=0.08529, over 985942.48 frames.], batch size: 67, lr: 1.35e-03
+2022-06-18 14:01:04,098 INFO [train.py:874] (1/4) Epoch 5, batch 3450, aishell_loss[loss=0.2078, simple_loss=0.2776, pruned_loss=0.06895, over 4945.00 frames.], tot_loss[loss=0.2126, simple_loss=0.2703, pruned_loss=0.07749, over 985888.21 frames.], batch size: 56, aishell_tot_loss[loss=0.2089, simple_loss=0.2748, pruned_loss=0.07152, over 985168.68 frames.], datatang_tot_loss[loss=0.2185, simple_loss=0.2672, pruned_loss=0.08486, over 986101.15 frames.], batch size: 56, lr: 1.34e-03
+2022-06-18 14:01:33,996 INFO [train.py:874] (1/4) Epoch 5, batch 3500, aishell_loss[loss=0.1623, simple_loss=0.2332, pruned_loss=0.04565, over 4900.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2718, pruned_loss=0.07822, over 985963.70 frames.], batch size: 28, aishell_tot_loss[loss=0.2091, simple_loss=0.2753, pruned_loss=0.0715, over 985189.00 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2683, pruned_loss=0.08549, over 986218.45 frames.], batch size: 28, lr: 1.34e-03
+2022-06-18 14:02:04,011 INFO [train.py:874] (1/4) Epoch 5, batch 3550, datatang_loss[loss=0.2078, simple_loss=0.261, pruned_loss=0.07731, over 4892.00 frames.], tot_loss[loss=0.215, simple_loss=0.2726, pruned_loss=0.07866, over 985770.37 frames.], batch size: 30, aishell_tot_loss[loss=0.2097, simple_loss=0.2757, pruned_loss=0.07186, over 985110.45 frames.], datatang_tot_loss[loss=0.22, simple_loss=0.2686, pruned_loss=0.08576, over 986176.38 frames.], batch size: 30, lr: 1.34e-03
+2022-06-18 14:02:33,814 INFO [train.py:874] (1/4) Epoch 5, batch 3600, aishell_loss[loss=0.1939, simple_loss=0.2701, pruned_loss=0.05885, over 4913.00 frames.], tot_loss[loss=0.2176, simple_loss=0.2748, pruned_loss=0.08016, over 985819.84 frames.], batch size: 52, aishell_tot_loss[loss=0.211, simple_loss=0.2769, pruned_loss=0.07259, over 985085.86 frames.], datatang_tot_loss[loss=0.2215, simple_loss=0.2698, pruned_loss=0.08664, over 986298.98 frames.], batch size: 52, lr: 1.34e-03
+2022-06-18 14:03:03,179 INFO [train.py:874] (1/4) Epoch 5, batch 3650, datatang_loss[loss=0.221, simple_loss=0.2787, pruned_loss=0.08164, over 4942.00 frames.], tot_loss[loss=0.2162, simple_loss=0.2737, pruned_loss=0.0793, over 986188.05 frames.], batch size: 88, aishell_tot_loss[loss=0.2102, simple_loss=0.2762, pruned_loss=0.07215, over 985249.33 frames.], datatang_tot_loss[loss=0.221, simple_loss=0.2698, pruned_loss=0.08611, over 986551.42 frames.], batch size: 88, lr: 1.34e-03
+2022-06-18 14:03:33,727 INFO [train.py:874] (1/4) Epoch 5, batch 3700, aishell_loss[loss=0.1576, simple_loss=0.2289, pruned_loss=0.04315, over 4949.00 frames.], tot_loss[loss=0.2148, simple_loss=0.2725, pruned_loss=0.0786, over 986187.09 frames.], batch size: 25, aishell_tot_loss[loss=0.2102, simple_loss=0.2759, pruned_loss=0.07226, over 985235.07 frames.], datatang_tot_loss[loss=0.2197, simple_loss=0.2689, pruned_loss=0.08527, over 986635.77 frames.], batch size: 25, lr: 1.34e-03
+2022-06-18 14:04:03,481 INFO [train.py:874] (1/4) Epoch 5, batch 3750, aishell_loss[loss=0.204, simple_loss=0.2711, pruned_loss=0.06844, over 4912.00 frames.], tot_loss[loss=0.2157, simple_loss=0.2733, pruned_loss=0.07906, over 986181.03 frames.], batch size: 52, aishell_tot_loss[loss=0.2114, simple_loss=0.2768, pruned_loss=0.07295, over 985364.03 frames.], datatang_tot_loss[loss=0.2195, simple_loss=0.2688, pruned_loss=0.0851, over 986574.42 frames.], batch size: 52, lr: 1.34e-03
+2022-06-18 14:04:33,268 INFO [train.py:874] (1/4) Epoch 5, batch 3800, aishell_loss[loss=0.1813, simple_loss=0.2627, pruned_loss=0.04995, over 4831.00 frames.], tot_loss[loss=0.2148, simple_loss=0.273, pruned_loss=0.07823, over 985748.62 frames.], batch size: 29, aishell_tot_loss[loss=0.2106, simple_loss=0.2764, pruned_loss=0.07236, over 985127.09 frames.], datatang_tot_loss[loss=0.2196, simple_loss=0.2688, pruned_loss=0.08519, over 986451.63 frames.], batch size: 29, lr: 1.33e-03
+2022-06-18 14:05:02,183 INFO [train.py:874] (1/4) Epoch 5, batch 3850, datatang_loss[loss=0.2107, simple_loss=0.2716, pruned_loss=0.07488, over 4932.00 frames.], tot_loss[loss=0.2129, simple_loss=0.2719, pruned_loss=0.07693, over 985607.89 frames.], batch size: 77, aishell_tot_loss[loss=0.2095, simple_loss=0.2756, pruned_loss=0.07167, over 985033.68 frames.], datatang_tot_loss[loss=0.2188, simple_loss=0.2682, pruned_loss=0.08472, over 986426.87 frames.], batch size: 77, lr: 1.33e-03
+2022-06-18 14:05:32,251 INFO [train.py:874] (1/4) Epoch 5, batch 3900, datatang_loss[loss=0.1963, simple_loss=0.2566, pruned_loss=0.06796, over 4944.00 frames.], tot_loss[loss=0.2125, simple_loss=0.2718, pruned_loss=0.07655, over 986054.99 frames.], batch size: 62, aishell_tot_loss[loss=0.2095, simple_loss=0.2759, pruned_loss=0.07161, over 985462.91 frames.], datatang_tot_loss[loss=0.2181, simple_loss=0.2679, pruned_loss=0.08414, over 986444.43 frames.], batch size: 62, lr: 1.33e-03
+2022-06-18 14:06:00,054 INFO [train.py:874] (1/4) Epoch 5, batch 3950, datatang_loss[loss=0.202, simple_loss=0.2465, pruned_loss=0.07878, over 4921.00 frames.], tot_loss[loss=0.2127, simple_loss=0.2719, pruned_loss=0.07675, over 986071.77 frames.], batch size: 57, aishell_tot_loss[loss=0.2091, simple_loss=0.2756, pruned_loss=0.0713, over 985571.90 frames.], datatang_tot_loss[loss=0.2185, simple_loss=0.268, pruned_loss=0.08449, over 986376.43 frames.], batch size: 57, lr: 1.33e-03
+2022-06-18 14:06:30,658 INFO [train.py:874] (1/4) Epoch 5, batch 4000, aishell_loss[loss=0.2194, simple_loss=0.2912, pruned_loss=0.07384, over 4906.00 frames.], tot_loss[loss=0.2123, simple_loss=0.2714, pruned_loss=0.07665, over 986097.88 frames.], batch size: 34, aishell_tot_loss[loss=0.2086, simple_loss=0.2751, pruned_loss=0.07102, over 985694.95 frames.], datatang_tot_loss[loss=0.2184, simple_loss=0.268, pruned_loss=0.08436, over 986296.38 frames.], batch size: 34, lr: 1.33e-03
+2022-06-18 14:06:30,658 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 14:06:46,839 INFO [train.py:914] (1/4) 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] (1/4) Epoch 5, batch 4050, aishell_loss[loss=0.2067, simple_loss=0.2761, pruned_loss=0.06864, over 4969.00 frames.], tot_loss[loss=0.2143, simple_loss=0.272, pruned_loss=0.07829, over 986016.12 frames.], batch size: 44, aishell_tot_loss[loss=0.2093, simple_loss=0.2752, pruned_loss=0.07174, over 985633.97 frames.], datatang_tot_loss[loss=0.2191, simple_loss=0.2688, pruned_loss=0.08476, over 986277.16 frames.], batch size: 44, lr: 1.33e-03
+2022-06-18 14:07:45,346 INFO [train.py:874] (1/4) Epoch 5, batch 4100, aishell_loss[loss=0.2349, simple_loss=0.2917, pruned_loss=0.08902, over 4876.00 frames.], tot_loss[loss=0.2141, simple_loss=0.2715, pruned_loss=0.07838, over 985756.80 frames.], batch size: 42, aishell_tot_loss[loss=0.2091, simple_loss=0.2749, pruned_loss=0.07165, over 985413.92 frames.], datatang_tot_loss[loss=0.219, simple_loss=0.2686, pruned_loss=0.08469, over 986219.77 frames.], batch size: 42, lr: 1.32e-03
+2022-06-18 14:08:14,089 INFO [train.py:874] (1/4) Epoch 5, batch 4150, aishell_loss[loss=0.2036, simple_loss=0.2716, pruned_loss=0.06779, over 4894.00 frames.], tot_loss[loss=0.2146, simple_loss=0.2727, pruned_loss=0.07825, over 985578.03 frames.], batch size: 34, aishell_tot_loss[loss=0.2098, simple_loss=0.2757, pruned_loss=0.0719, over 985208.45 frames.], datatang_tot_loss[loss=0.2188, simple_loss=0.269, pruned_loss=0.08431, over 986227.02 frames.], batch size: 34, lr: 1.32e-03
+2022-06-18 14:09:32,220 INFO [train.py:874] (1/4) Epoch 6, batch 50, datatang_loss[loss=0.1866, simple_loss=0.2464, pruned_loss=0.06343, over 4948.00 frames.], tot_loss[loss=0.1991, simple_loss=0.261, pruned_loss=0.0686, over 218389.08 frames.], batch size: 62, aishell_tot_loss[loss=0.2053, simple_loss=0.2734, pruned_loss=0.06862, over 120215.51 frames.], datatang_tot_loss[loss=0.1925, simple_loss=0.2476, pruned_loss=0.06875, over 111818.81 frames.], batch size: 62, lr: 1.27e-03
+2022-06-18 14:10:03,278 INFO [train.py:874] (1/4) Epoch 6, batch 100, datatang_loss[loss=0.1985, simple_loss=0.2506, pruned_loss=0.07324, over 4919.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2604, pruned_loss=0.06958, over 388345.22 frames.], batch size: 47, aishell_tot_loss[loss=0.2051, simple_loss=0.2728, pruned_loss=0.06872, over 214270.72 frames.], datatang_tot_loss[loss=0.1947, simple_loss=0.2487, pruned_loss=0.07031, over 222475.81 frames.], batch size: 47, lr: 1.27e-03
+2022-06-18 14:10:32,559 INFO [train.py:874] (1/4) Epoch 6, batch 150, datatang_loss[loss=0.1748, simple_loss=0.2364, pruned_loss=0.05656, over 4925.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2597, pruned_loss=0.06973, over 520759.76 frames.], batch size: 73, aishell_tot_loss[loss=0.2046, simple_loss=0.272, pruned_loss=0.06863, over 287720.10 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.2497, pruned_loss=0.07056, over 329129.72 frames.], batch size: 73, lr: 1.27e-03
+2022-06-18 14:11:03,733 INFO [train.py:874] (1/4) Epoch 6, batch 200, aishell_loss[loss=0.2334, simple_loss=0.2981, pruned_loss=0.08435, over 4908.00 frames.], tot_loss[loss=0.2005, simple_loss=0.261, pruned_loss=0.07001, over 623911.60 frames.], batch size: 68, aishell_tot_loss[loss=0.204, simple_loss=0.2716, pruned_loss=0.06822, over 363720.26 frames.], datatang_tot_loss[loss=0.1973, simple_loss=0.2518, pruned_loss=0.07137, over 412339.39 frames.], batch size: 68, lr: 1.26e-03
+2022-06-18 14:11:33,000 INFO [train.py:874] (1/4) Epoch 6, batch 250, datatang_loss[loss=0.2409, simple_loss=0.2841, pruned_loss=0.09885, over 4899.00 frames.], tot_loss[loss=0.203, simple_loss=0.2638, pruned_loss=0.07107, over 703840.81 frames.], batch size: 52, aishell_tot_loss[loss=0.206, simple_loss=0.2728, pruned_loss=0.06964, over 455476.20 frames.], datatang_tot_loss[loss=0.1987, simple_loss=0.2535, pruned_loss=0.0719, over 461945.15 frames.], batch size: 52, lr: 1.26e-03
+2022-06-18 14:12:03,454 INFO [train.py:874] (1/4) Epoch 6, batch 300, datatang_loss[loss=0.2055, simple_loss=0.2756, pruned_loss=0.06769, over 4962.00 frames.], tot_loss[loss=0.2054, simple_loss=0.266, pruned_loss=0.07238, over 766534.18 frames.], batch size: 31, aishell_tot_loss[loss=0.2066, simple_loss=0.2735, pruned_loss=0.06985, over 513222.81 frames.], datatang_tot_loss[loss=0.2023, simple_loss=0.2568, pruned_loss=0.07387, over 528465.16 frames.], batch size: 31, lr: 1.26e-03
+2022-06-18 14:12:34,550 INFO [train.py:874] (1/4) Epoch 6, batch 350, datatang_loss[loss=0.1666, simple_loss=0.2345, pruned_loss=0.04932, over 4925.00 frames.], tot_loss[loss=0.205, simple_loss=0.2656, pruned_loss=0.07222, over 814976.11 frames.], batch size: 71, aishell_tot_loss[loss=0.2075, simple_loss=0.2744, pruned_loss=0.07028, over 566518.54 frames.], datatang_tot_loss[loss=0.2014, simple_loss=0.2559, pruned_loss=0.07342, over 584430.94 frames.], batch size: 71, lr: 1.26e-03
+2022-06-18 14:13:03,868 INFO [train.py:874] (1/4) Epoch 6, batch 400, datatang_loss[loss=0.1784, simple_loss=0.2432, pruned_loss=0.05681, over 4917.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2658, pruned_loss=0.07177, over 852613.41 frames.], batch size: 83, aishell_tot_loss[loss=0.206, simple_loss=0.2734, pruned_loss=0.06933, over 621221.81 frames.], datatang_tot_loss[loss=0.2023, simple_loss=0.2569, pruned_loss=0.07386, over 626285.86 frames.], batch size: 83, lr: 1.26e-03
+2022-06-18 14:13:33,620 INFO [train.py:874] (1/4) Epoch 6, batch 450, aishell_loss[loss=0.2171, simple_loss=0.2809, pruned_loss=0.07661, over 4959.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2655, pruned_loss=0.07133, over 882099.52 frames.], batch size: 56, aishell_tot_loss[loss=0.2057, simple_loss=0.273, pruned_loss=0.0692, over 667238.09 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.2569, pruned_loss=0.07349, over 665511.42 frames.], batch size: 56, lr: 1.26e-03
+2022-06-18 14:14:05,036 INFO [train.py:874] (1/4) Epoch 6, batch 500, datatang_loss[loss=0.2131, simple_loss=0.2745, pruned_loss=0.07584, over 4921.00 frames.], tot_loss[loss=0.2055, simple_loss=0.267, pruned_loss=0.07203, over 904937.91 frames.], batch size: 81, aishell_tot_loss[loss=0.2057, simple_loss=0.2733, pruned_loss=0.06904, over 708982.18 frames.], datatang_tot_loss[loss=0.2039, simple_loss=0.2584, pruned_loss=0.07466, over 698765.87 frames.], batch size: 81, lr: 1.26e-03
+2022-06-18 14:14:34,031 INFO [train.py:874] (1/4) Epoch 6, batch 550, aishell_loss[loss=0.173, simple_loss=0.2383, pruned_loss=0.05387, over 4978.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2673, pruned_loss=0.07174, over 922519.58 frames.], batch size: 30, aishell_tot_loss[loss=0.2048, simple_loss=0.2727, pruned_loss=0.06844, over 755462.77 frames.], datatang_tot_loss[loss=0.2049, simple_loss=0.2588, pruned_loss=0.07546, over 716895.60 frames.], batch size: 30, lr: 1.25e-03
+2022-06-18 14:15:04,016 INFO [train.py:874] (1/4) Epoch 6, batch 600, datatang_loss[loss=0.2182, simple_loss=0.2702, pruned_loss=0.08311, over 4925.00 frames.], tot_loss[loss=0.2067, simple_loss=0.268, pruned_loss=0.07266, over 936364.36 frames.], batch size: 81, aishell_tot_loss[loss=0.2048, simple_loss=0.2726, pruned_loss=0.06845, over 781358.47 frames.], datatang_tot_loss[loss=0.2067, simple_loss=0.2603, pruned_loss=0.07656, over 749762.42 frames.], batch size: 81, lr: 1.25e-03
+2022-06-18 14:15:34,709 INFO [train.py:874] (1/4) Epoch 6, batch 650, datatang_loss[loss=0.1977, simple_loss=0.2549, pruned_loss=0.07023, over 4922.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2682, pruned_loss=0.07334, over 947358.71 frames.], batch size: 83, aishell_tot_loss[loss=0.2052, simple_loss=0.2727, pruned_loss=0.06881, over 804407.69 frames.], datatang_tot_loss[loss=0.2076, simple_loss=0.261, pruned_loss=0.07709, over 778763.16 frames.], batch size: 83, lr: 1.25e-03
+2022-06-18 14:16:03,559 INFO [train.py:874] (1/4) Epoch 6, batch 700, aishell_loss[loss=0.1938, simple_loss=0.2639, pruned_loss=0.06186, over 4860.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2675, pruned_loss=0.07329, over 955455.67 frames.], batch size: 36, aishell_tot_loss[loss=0.2032, simple_loss=0.2708, pruned_loss=0.06776, over 825330.61 frames.], datatang_tot_loss[loss=0.2095, simple_loss=0.2625, pruned_loss=0.07824, over 803134.45 frames.], batch size: 36, lr: 1.25e-03
+2022-06-18 14:16:34,007 INFO [train.py:874] (1/4) Epoch 6, batch 750, aishell_loss[loss=0.1671, simple_loss=0.2334, pruned_loss=0.05038, over 4962.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2672, pruned_loss=0.07349, over 962199.23 frames.], batch size: 27, aishell_tot_loss[loss=0.2026, simple_loss=0.2704, pruned_loss=0.0674, over 841313.35 frames.], datatang_tot_loss[loss=0.2102, simple_loss=0.263, pruned_loss=0.07867, over 827917.93 frames.], batch size: 27, lr: 1.25e-03
+2022-06-18 14:17:06,172 INFO [train.py:874] (1/4) Epoch 6, batch 800, datatang_loss[loss=0.192, simple_loss=0.2397, pruned_loss=0.07218, over 4904.00 frames.], tot_loss[loss=0.2081, simple_loss=0.2672, pruned_loss=0.07451, over 967013.08 frames.], batch size: 47, aishell_tot_loss[loss=0.202, simple_loss=0.2696, pruned_loss=0.06722, over 855576.50 frames.], datatang_tot_loss[loss=0.2119, simple_loss=0.264, pruned_loss=0.07991, over 849013.88 frames.], batch size: 47, lr: 1.25e-03
+2022-06-18 14:17:35,691 INFO [train.py:874] (1/4) Epoch 6, batch 850, datatang_loss[loss=0.215, simple_loss=0.2619, pruned_loss=0.08406, over 4947.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2682, pruned_loss=0.07512, over 971264.23 frames.], batch size: 67, aishell_tot_loss[loss=0.203, simple_loss=0.2704, pruned_loss=0.0678, over 869867.10 frames.], datatang_tot_loss[loss=0.2125, simple_loss=0.2646, pruned_loss=0.08019, over 866301.54 frames.], batch size: 67, lr: 1.25e-03
+2022-06-18 14:18:05,631 INFO [train.py:874] (1/4) Epoch 6, batch 900, aishell_loss[loss=0.2053, simple_loss=0.2671, pruned_loss=0.07174, over 4914.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2686, pruned_loss=0.07479, over 974533.43 frames.], batch size: 33, aishell_tot_loss[loss=0.203, simple_loss=0.2704, pruned_loss=0.06776, over 884919.17 frames.], datatang_tot_loss[loss=0.2129, simple_loss=0.2652, pruned_loss=0.08032, over 878957.56 frames.], batch size: 33, lr: 1.25e-03
+2022-06-18 14:18:36,667 INFO [train.py:874] (1/4) Epoch 6, batch 950, aishell_loss[loss=0.1817, simple_loss=0.2566, pruned_loss=0.0534, over 4918.00 frames.], tot_loss[loss=0.2082, simple_loss=0.2682, pruned_loss=0.07405, over 977040.11 frames.], batch size: 33, aishell_tot_loss[loss=0.2023, simple_loss=0.27, pruned_loss=0.06727, over 897341.84 frames.], datatang_tot_loss[loss=0.2129, simple_loss=0.2652, pruned_loss=0.08023, over 890969.42 frames.], batch size: 33, lr: 1.24e-03
+2022-06-18 14:19:06,996 INFO [train.py:874] (1/4) Epoch 6, batch 1000, datatang_loss[loss=0.1987, simple_loss=0.2484, pruned_loss=0.0745, over 4875.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2677, pruned_loss=0.07387, over 978872.02 frames.], batch size: 39, aishell_tot_loss[loss=0.2027, simple_loss=0.2704, pruned_loss=0.06752, over 907433.90 frames.], datatang_tot_loss[loss=0.2121, simple_loss=0.2645, pruned_loss=0.07985, over 902353.81 frames.], batch size: 39, lr: 1.24e-03
+2022-06-18 14:19:06,996 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 14:19:23,421 INFO [train.py:914] (1/4) Epoch 6, validation: loss=0.175, simple_loss=0.2564, pruned_loss=0.04677, over 1622729.00 frames.
+2022-06-18 14:19:52,891 INFO [train.py:874] (1/4) Epoch 6, batch 1050, datatang_loss[loss=0.1876, simple_loss=0.2461, pruned_loss=0.06459, over 4795.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2679, pruned_loss=0.07345, over 980254.42 frames.], batch size: 24, aishell_tot_loss[loss=0.2025, simple_loss=0.2704, pruned_loss=0.06731, over 917286.25 frames.], datatang_tot_loss[loss=0.2121, simple_loss=0.2648, pruned_loss=0.07977, over 911361.70 frames.], batch size: 24, lr: 1.24e-03
+2022-06-18 14:20:23,048 INFO [train.py:874] (1/4) Epoch 6, batch 1100, aishell_loss[loss=0.2275, simple_loss=0.2833, pruned_loss=0.08586, over 4984.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2687, pruned_loss=0.0755, over 981368.70 frames.], batch size: 39, aishell_tot_loss[loss=0.2039, simple_loss=0.2708, pruned_loss=0.06852, over 923550.49 frames.], datatang_tot_loss[loss=0.2132, simple_loss=0.2654, pruned_loss=0.08053, over 921911.98 frames.], batch size: 39, lr: 1.24e-03
+2022-06-18 14:20:54,772 INFO [train.py:874] (1/4) Epoch 6, batch 1150, aishell_loss[loss=0.208, simple_loss=0.2831, pruned_loss=0.06643, over 4963.00 frames.], tot_loss[loss=0.211, simple_loss=0.2696, pruned_loss=0.07616, over 982347.22 frames.], batch size: 61, aishell_tot_loss[loss=0.2046, simple_loss=0.2712, pruned_loss=0.06904, over 929364.89 frames.], datatang_tot_loss[loss=0.2139, simple_loss=0.2664, pruned_loss=0.08073, over 930951.14 frames.], batch size: 61, lr: 1.24e-03
+2022-06-18 14:21:24,833 INFO [train.py:874] (1/4) Epoch 6, batch 1200, datatang_loss[loss=0.1897, simple_loss=0.2562, pruned_loss=0.06158, over 4946.00 frames.], tot_loss[loss=0.2109, simple_loss=0.2702, pruned_loss=0.07575, over 983483.15 frames.], batch size: 79, aishell_tot_loss[loss=0.2047, simple_loss=0.2717, pruned_loss=0.06889, over 935576.08 frames.], datatang_tot_loss[loss=0.214, simple_loss=0.2668, pruned_loss=0.08059, over 938215.00 frames.], batch size: 79, lr: 1.24e-03
+2022-06-18 14:21:54,088 INFO [train.py:874] (1/4) Epoch 6, batch 1250, datatang_loss[loss=0.2245, simple_loss=0.2808, pruned_loss=0.08412, over 4958.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2698, pruned_loss=0.07553, over 984262.72 frames.], batch size: 45, aishell_tot_loss[loss=0.2047, simple_loss=0.2716, pruned_loss=0.06886, over 941365.79 frames.], datatang_tot_loss[loss=0.2139, simple_loss=0.2666, pruned_loss=0.08058, over 944216.71 frames.], batch size: 45, lr: 1.24e-03
+2022-06-18 14:22:25,456 INFO [train.py:874] (1/4) Epoch 6, batch 1300, aishell_loss[loss=0.1501, simple_loss=0.2235, pruned_loss=0.03833, over 4959.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2697, pruned_loss=0.07528, over 984362.85 frames.], batch size: 27, aishell_tot_loss[loss=0.2042, simple_loss=0.2714, pruned_loss=0.06849, over 946196.60 frames.], datatang_tot_loss[loss=0.2143, simple_loss=0.2669, pruned_loss=0.08083, over 949234.57 frames.], batch size: 27, lr: 1.23e-03
+2022-06-18 14:22:55,206 INFO [train.py:874] (1/4) Epoch 6, batch 1350, aishell_loss[loss=0.208, simple_loss=0.2676, pruned_loss=0.07423, over 4933.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2698, pruned_loss=0.07564, over 984920.84 frames.], batch size: 49, aishell_tot_loss[loss=0.2042, simple_loss=0.2715, pruned_loss=0.06845, over 950211.83 frames.], datatang_tot_loss[loss=0.2148, simple_loss=0.2672, pruned_loss=0.08117, over 954368.42 frames.], batch size: 49, lr: 1.23e-03
+2022-06-18 14:23:24,947 INFO [train.py:874] (1/4) Epoch 6, batch 1400, aishell_loss[loss=0.2057, simple_loss=0.2779, pruned_loss=0.06678, over 4926.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2699, pruned_loss=0.07461, over 984795.15 frames.], batch size: 58, aishell_tot_loss[loss=0.2035, simple_loss=0.2712, pruned_loss=0.06788, over 955124.91 frames.], datatang_tot_loss[loss=0.215, simple_loss=0.2675, pruned_loss=0.0812, over 957102.78 frames.], batch size: 58, lr: 1.23e-03
+2022-06-18 14:23:56,878 INFO [train.py:874] (1/4) Epoch 6, batch 1450, aishell_loss[loss=0.1985, simple_loss=0.2703, pruned_loss=0.06337, over 4958.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2699, pruned_loss=0.07395, over 984983.70 frames.], batch size: 44, aishell_tot_loss[loss=0.2029, simple_loss=0.2711, pruned_loss=0.06732, over 959076.36 frames.], datatang_tot_loss[loss=0.2152, simple_loss=0.2677, pruned_loss=0.08129, over 960130.91 frames.], batch size: 44, lr: 1.23e-03
+2022-06-18 14:24:26,585 INFO [train.py:874] (1/4) Epoch 6, batch 1500, aishell_loss[loss=0.1996, simple_loss=0.2691, pruned_loss=0.06506, over 4947.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2699, pruned_loss=0.07351, over 985019.84 frames.], batch size: 49, aishell_tot_loss[loss=0.2029, simple_loss=0.2715, pruned_loss=0.06718, over 962657.18 frames.], datatang_tot_loss[loss=0.2149, simple_loss=0.2674, pruned_loss=0.08121, over 962569.37 frames.], batch size: 49, lr: 1.23e-03
+2022-06-18 14:24:56,258 INFO [train.py:874] (1/4) Epoch 6, batch 1550, aishell_loss[loss=0.2106, simple_loss=0.2848, pruned_loss=0.0682, over 4919.00 frames.], tot_loss[loss=0.209, simple_loss=0.2708, pruned_loss=0.07364, over 984996.84 frames.], batch size: 52, aishell_tot_loss[loss=0.2037, simple_loss=0.2725, pruned_loss=0.06742, over 965709.54 frames.], datatang_tot_loss[loss=0.2149, simple_loss=0.2673, pruned_loss=0.08127, over 964745.88 frames.], batch size: 52, lr: 1.23e-03
+2022-06-18 14:25:27,122 INFO [train.py:874] (1/4) Epoch 6, batch 1600, aishell_loss[loss=0.1878, simple_loss=0.2598, pruned_loss=0.05791, over 4851.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2698, pruned_loss=0.07296, over 985143.10 frames.], batch size: 28, aishell_tot_loss[loss=0.2021, simple_loss=0.2712, pruned_loss=0.06653, over 968416.77 frames.], datatang_tot_loss[loss=0.2155, simple_loss=0.2676, pruned_loss=0.08167, over 966814.23 frames.], batch size: 28, lr: 1.23e-03
+2022-06-18 14:25:56,108 INFO [train.py:874] (1/4) Epoch 6, batch 1650, datatang_loss[loss=0.2222, simple_loss=0.2717, pruned_loss=0.08639, over 4902.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2696, pruned_loss=0.07388, over 985230.66 frames.], batch size: 52, aishell_tot_loss[loss=0.2016, simple_loss=0.2705, pruned_loss=0.06635, over 970191.48 frames.], datatang_tot_loss[loss=0.2164, simple_loss=0.2682, pruned_loss=0.0823, over 969301.23 frames.], batch size: 52, lr: 1.23e-03
+2022-06-18 14:26:28,016 INFO [train.py:874] (1/4) Epoch 6, batch 1700, datatang_loss[loss=0.2782, simple_loss=0.3259, pruned_loss=0.1152, over 4922.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2696, pruned_loss=0.07369, over 985371.57 frames.], batch size: 108, aishell_tot_loss[loss=0.201, simple_loss=0.2702, pruned_loss=0.06592, over 972063.44 frames.], datatang_tot_loss[loss=0.2167, simple_loss=0.2686, pruned_loss=0.08235, over 971231.09 frames.], batch size: 108, lr: 1.22e-03
+2022-06-18 14:26:57,265 INFO [train.py:874] (1/4) Epoch 6, batch 1750, aishell_loss[loss=0.2253, simple_loss=0.2972, pruned_loss=0.07667, over 4917.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2688, pruned_loss=0.0734, over 985240.53 frames.], batch size: 46, aishell_tot_loss[loss=0.2009, simple_loss=0.2701, pruned_loss=0.06588, over 973557.44 frames.], datatang_tot_loss[loss=0.2158, simple_loss=0.2679, pruned_loss=0.08189, over 972839.62 frames.], batch size: 46, lr: 1.22e-03
+2022-06-18 14:27:27,456 INFO [train.py:874] (1/4) Epoch 6, batch 1800, aishell_loss[loss=0.1978, simple_loss=0.2626, pruned_loss=0.06645, over 4878.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2681, pruned_loss=0.07328, over 985199.70 frames.], batch size: 28, aishell_tot_loss[loss=0.2011, simple_loss=0.27, pruned_loss=0.06609, over 974951.12 frames.], datatang_tot_loss[loss=0.2149, simple_loss=0.2672, pruned_loss=0.08136, over 974249.14 frames.], batch size: 28, lr: 1.22e-03
+2022-06-18 14:27:59,153 INFO [train.py:874] (1/4) Epoch 6, batch 1850, aishell_loss[loss=0.1883, simple_loss=0.2632, pruned_loss=0.05676, over 4954.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2681, pruned_loss=0.07242, over 985927.17 frames.], batch size: 64, aishell_tot_loss[loss=0.201, simple_loss=0.27, pruned_loss=0.06596, over 976776.98 frames.], datatang_tot_loss[loss=0.2141, simple_loss=0.267, pruned_loss=0.08067, over 975688.18 frames.], batch size: 64, lr: 1.22e-03
+2022-06-18 14:28:29,046 INFO [train.py:874] (1/4) Epoch 6, batch 1900, aishell_loss[loss=0.2553, simple_loss=0.312, pruned_loss=0.09929, over 4948.00 frames.], tot_loss[loss=0.206, simple_loss=0.268, pruned_loss=0.07198, over 986056.21 frames.], batch size: 54, aishell_tot_loss[loss=0.2013, simple_loss=0.2701, pruned_loss=0.06621, over 978153.21 frames.], datatang_tot_loss[loss=0.2135, simple_loss=0.2666, pruned_loss=0.08024, over 976700.58 frames.], batch size: 54, lr: 1.22e-03
+2022-06-18 14:28:59,014 INFO [train.py:874] (1/4) Epoch 6, batch 1950, aishell_loss[loss=0.2279, simple_loss=0.2873, pruned_loss=0.08418, over 4928.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2677, pruned_loss=0.07241, over 985850.36 frames.], batch size: 33, aishell_tot_loss[loss=0.2016, simple_loss=0.2704, pruned_loss=0.06642, over 978570.33 frames.], datatang_tot_loss[loss=0.2129, simple_loss=0.2658, pruned_loss=0.07999, over 978114.41 frames.], batch size: 33, lr: 1.22e-03
+2022-06-18 14:29:30,644 INFO [train.py:874] (1/4) Epoch 6, batch 2000, datatang_loss[loss=0.2222, simple_loss=0.2744, pruned_loss=0.08501, over 4925.00 frames.], tot_loss[loss=0.2062, simple_loss=0.2675, pruned_loss=0.07248, over 985591.25 frames.], batch size: 71, aishell_tot_loss[loss=0.2016, simple_loss=0.2705, pruned_loss=0.06637, over 979102.29 frames.], datatang_tot_loss[loss=0.2127, simple_loss=0.2655, pruned_loss=0.07999, over 979068.62 frames.], batch size: 71, lr: 1.22e-03
+2022-06-18 14:29:30,645 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 14:29:46,437 INFO [train.py:914] (1/4) Epoch 6, validation: loss=0.1751, simple_loss=0.2557, pruned_loss=0.04723, over 1622729.00 frames.
+2022-06-18 14:30:18,808 INFO [train.py:874] (1/4) Epoch 6, batch 2050, aishell_loss[loss=0.2137, simple_loss=0.2835, pruned_loss=0.07194, over 4870.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2684, pruned_loss=0.07314, over 985457.77 frames.], batch size: 42, aishell_tot_loss[loss=0.2016, simple_loss=0.2706, pruned_loss=0.06629, over 979587.40 frames.], datatang_tot_loss[loss=0.2136, simple_loss=0.2663, pruned_loss=0.08042, over 979976.36 frames.], batch size: 42, lr: 1.22e-03
+2022-06-18 14:30:50,050 INFO [train.py:874] (1/4) Epoch 6, batch 2100, datatang_loss[loss=0.2271, simple_loss=0.278, pruned_loss=0.08812, over 4927.00 frames.], tot_loss[loss=0.2059, simple_loss=0.2671, pruned_loss=0.07237, over 985674.45 frames.], batch size: 71, aishell_tot_loss[loss=0.2006, simple_loss=0.2698, pruned_loss=0.06567, over 980236.45 frames.], datatang_tot_loss[loss=0.2128, simple_loss=0.2657, pruned_loss=0.07994, over 980881.56 frames.], batch size: 71, lr: 1.21e-03
+2022-06-18 14:31:18,746 INFO [train.py:874] (1/4) Epoch 6, batch 2150, datatang_loss[loss=0.2277, simple_loss=0.2799, pruned_loss=0.08777, over 4932.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2674, pruned_loss=0.0719, over 985514.93 frames.], batch size: 71, aishell_tot_loss[loss=0.2003, simple_loss=0.2697, pruned_loss=0.06543, over 980801.29 frames.], datatang_tot_loss[loss=0.2128, simple_loss=0.2658, pruned_loss=0.07993, over 981374.32 frames.], batch size: 71, lr: 1.21e-03
+2022-06-18 14:31:50,703 INFO [train.py:874] (1/4) Epoch 6, batch 2200, aishell_loss[loss=0.1923, simple_loss=0.2637, pruned_loss=0.06047, over 4933.00 frames.], tot_loss[loss=0.2054, simple_loss=0.2669, pruned_loss=0.07192, over 986144.10 frames.], batch size: 54, aishell_tot_loss[loss=0.1997, simple_loss=0.2687, pruned_loss=0.06533, over 982155.12 frames.], datatang_tot_loss[loss=0.2136, simple_loss=0.266, pruned_loss=0.08055, over 981724.01 frames.], batch size: 54, lr: 1.21e-03
+2022-06-18 14:32:20,686 INFO [train.py:874] (1/4) Epoch 6, batch 2250, datatang_loss[loss=0.1733, simple_loss=0.2329, pruned_loss=0.05687, over 4919.00 frames.], tot_loss[loss=0.2047, simple_loss=0.2662, pruned_loss=0.07158, over 986077.59 frames.], batch size: 57, aishell_tot_loss[loss=0.1996, simple_loss=0.2687, pruned_loss=0.06521, over 982530.83 frames.], datatang_tot_loss[loss=0.2126, simple_loss=0.2653, pruned_loss=0.07997, over 982268.68 frames.], batch size: 57, lr: 1.21e-03
+2022-06-18 14:32:50,365 INFO [train.py:874] (1/4) Epoch 6, batch 2300, datatang_loss[loss=0.2101, simple_loss=0.2562, pruned_loss=0.08202, over 4914.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2666, pruned_loss=0.07086, over 985870.61 frames.], batch size: 64, aishell_tot_loss[loss=0.1993, simple_loss=0.2689, pruned_loss=0.06488, over 982869.07 frames.], datatang_tot_loss[loss=0.2124, simple_loss=0.2652, pruned_loss=0.07981, over 982567.95 frames.], batch size: 64, lr: 1.21e-03
+2022-06-18 14:33:22,411 INFO [train.py:874] (1/4) Epoch 6, batch 2350, datatang_loss[loss=0.2147, simple_loss=0.2737, pruned_loss=0.07784, over 4922.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2663, pruned_loss=0.07075, over 985976.30 frames.], batch size: 94, aishell_tot_loss[loss=0.199, simple_loss=0.2685, pruned_loss=0.06476, over 983350.54 frames.], datatang_tot_loss[loss=0.2122, simple_loss=0.2651, pruned_loss=0.07969, over 982935.70 frames.], batch size: 94, lr: 1.21e-03
+2022-06-18 14:33:50,371 INFO [train.py:874] (1/4) Epoch 6, batch 2400, aishell_loss[loss=0.1894, simple_loss=0.2655, pruned_loss=0.05658, over 4970.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2652, pruned_loss=0.07, over 985760.88 frames.], batch size: 40, aishell_tot_loss[loss=0.1979, simple_loss=0.2676, pruned_loss=0.06408, over 983614.81 frames.], datatang_tot_loss[loss=0.2119, simple_loss=0.2647, pruned_loss=0.07953, over 983105.46 frames.], batch size: 40, lr: 1.21e-03
+2022-06-18 14:34:22,272 INFO [train.py:874] (1/4) Epoch 6, batch 2450, aishell_loss[loss=0.207, simple_loss=0.2758, pruned_loss=0.06912, over 4910.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2645, pruned_loss=0.06967, over 985442.49 frames.], batch size: 33, aishell_tot_loss[loss=0.1975, simple_loss=0.2672, pruned_loss=0.06387, over 983819.95 frames.], datatang_tot_loss[loss=0.211, simple_loss=0.2642, pruned_loss=0.07893, over 983145.20 frames.], batch size: 33, lr: 1.21e-03
+2022-06-18 14:34:52,672 INFO [train.py:874] (1/4) Epoch 6, batch 2500, datatang_loss[loss=0.1947, simple_loss=0.2592, pruned_loss=0.06513, over 4953.00 frames.], tot_loss[loss=0.203, simple_loss=0.2654, pruned_loss=0.07026, over 985402.74 frames.], batch size: 86, aishell_tot_loss[loss=0.1981, simple_loss=0.2681, pruned_loss=0.06402, over 984133.54 frames.], datatang_tot_loss[loss=0.2107, simple_loss=0.264, pruned_loss=0.07874, over 983264.94 frames.], batch size: 86, lr: 1.20e-03
+2022-06-18 14:35:21,329 INFO [train.py:874] (1/4) Epoch 6, batch 2550, datatang_loss[loss=0.1769, simple_loss=0.2441, pruned_loss=0.05487, over 4931.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2667, pruned_loss=0.07084, over 985261.50 frames.], batch size: 79, aishell_tot_loss[loss=0.198, simple_loss=0.2681, pruned_loss=0.0639, over 984044.40 frames.], datatang_tot_loss[loss=0.2117, simple_loss=0.2652, pruned_loss=0.07913, over 983586.52 frames.], batch size: 79, lr: 1.20e-03
+2022-06-18 14:35:53,628 INFO [train.py:874] (1/4) Epoch 6, batch 2600, aishell_loss[loss=0.1919, simple_loss=0.2596, pruned_loss=0.06206, over 4864.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2656, pruned_loss=0.07077, over 984987.24 frames.], batch size: 37, aishell_tot_loss[loss=0.1983, simple_loss=0.268, pruned_loss=0.06432, over 983921.31 frames.], datatang_tot_loss[loss=0.2106, simple_loss=0.2643, pruned_loss=0.07847, over 983757.30 frames.], batch size: 37, lr: 1.20e-03
+2022-06-18 14:36:23,543 INFO [train.py:874] (1/4) Epoch 6, batch 2650, aishell_loss[loss=0.1746, simple_loss=0.2386, pruned_loss=0.05528, over 4808.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2644, pruned_loss=0.07029, over 984529.16 frames.], batch size: 26, aishell_tot_loss[loss=0.1973, simple_loss=0.267, pruned_loss=0.06387, over 983627.45 frames.], datatang_tot_loss[loss=0.2105, simple_loss=0.2638, pruned_loss=0.07856, over 983851.09 frames.], batch size: 26, lr: 1.20e-03
+2022-06-18 14:36:53,123 INFO [train.py:874] (1/4) Epoch 6, batch 2700, datatang_loss[loss=0.2268, simple_loss=0.2894, pruned_loss=0.08215, over 4915.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2656, pruned_loss=0.07114, over 984905.04 frames.], batch size: 98, aishell_tot_loss[loss=0.1982, simple_loss=0.2679, pruned_loss=0.06425, over 983920.64 frames.], datatang_tot_loss[loss=0.2106, simple_loss=0.264, pruned_loss=0.07859, over 984137.44 frames.], batch size: 98, lr: 1.20e-03
+2022-06-18 14:37:24,163 INFO [train.py:874] (1/4) Epoch 6, batch 2750, datatang_loss[loss=0.2215, simple_loss=0.2771, pruned_loss=0.08291, over 4935.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2658, pruned_loss=0.07147, over 984931.84 frames.], batch size: 94, aishell_tot_loss[loss=0.1988, simple_loss=0.2685, pruned_loss=0.06459, over 983987.78 frames.], datatang_tot_loss[loss=0.2103, simple_loss=0.2636, pruned_loss=0.0785, over 984291.57 frames.], batch size: 94, lr: 1.20e-03
+2022-06-18 14:37:54,765 INFO [train.py:874] (1/4) Epoch 6, batch 2800, datatang_loss[loss=0.2312, simple_loss=0.285, pruned_loss=0.08867, over 4948.00 frames.], tot_loss[loss=0.204, simple_loss=0.2655, pruned_loss=0.07128, over 985171.98 frames.], batch size: 50, aishell_tot_loss[loss=0.1987, simple_loss=0.2682, pruned_loss=0.06457, over 984099.45 frames.], datatang_tot_loss[loss=0.21, simple_loss=0.2635, pruned_loss=0.07824, over 984604.99 frames.], batch size: 50, lr: 1.20e-03
+2022-06-18 14:38:23,488 INFO [train.py:874] (1/4) Epoch 6, batch 2850, datatang_loss[loss=0.2128, simple_loss=0.2715, pruned_loss=0.07702, over 4939.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2664, pruned_loss=0.07191, over 985216.72 frames.], batch size: 88, aishell_tot_loss[loss=0.1995, simple_loss=0.269, pruned_loss=0.06502, over 984129.66 frames.], datatang_tot_loss[loss=0.2102, simple_loss=0.2635, pruned_loss=0.07844, over 984811.17 frames.], batch size: 88, lr: 1.20e-03
+2022-06-18 14:38:55,056 INFO [train.py:874] (1/4) Epoch 6, batch 2900, aishell_loss[loss=0.2058, simple_loss=0.2741, pruned_loss=0.06879, over 4910.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2656, pruned_loss=0.07079, over 985353.67 frames.], batch size: 33, aishell_tot_loss[loss=0.1996, simple_loss=0.2695, pruned_loss=0.0649, over 984242.17 frames.], datatang_tot_loss[loss=0.2084, simple_loss=0.2622, pruned_loss=0.0773, over 985013.36 frames.], batch size: 33, lr: 1.19e-03
+2022-06-18 14:39:25,936 INFO [train.py:874] (1/4) Epoch 6, batch 2950, datatang_loss[loss=0.2051, simple_loss=0.2601, pruned_loss=0.07502, over 4928.00 frames.], tot_loss[loss=0.204, simple_loss=0.2657, pruned_loss=0.07118, over 985666.01 frames.], batch size: 47, aishell_tot_loss[loss=0.1997, simple_loss=0.2695, pruned_loss=0.06493, over 984355.98 frames.], datatang_tot_loss[loss=0.2087, simple_loss=0.2623, pruned_loss=0.07752, over 985391.15 frames.], batch size: 47, lr: 1.19e-03
+2022-06-18 14:39:54,954 INFO [train.py:874] (1/4) Epoch 6, batch 3000, aishell_loss[loss=0.1705, simple_loss=0.231, pruned_loss=0.05504, over 4956.00 frames.], tot_loss[loss=0.203, simple_loss=0.2647, pruned_loss=0.07063, over 985815.53 frames.], batch size: 25, aishell_tot_loss[loss=0.1992, simple_loss=0.2691, pruned_loss=0.06467, over 984619.12 frames.], datatang_tot_loss[loss=0.208, simple_loss=0.2618, pruned_loss=0.07709, over 985464.14 frames.], batch size: 25, lr: 1.19e-03
+2022-06-18 14:39:54,955 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 14:40:11,300 INFO [train.py:914] (1/4) 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] (1/4) Epoch 6, batch 3050, aishell_loss[loss=0.2266, simple_loss=0.2894, pruned_loss=0.08196, over 4975.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2637, pruned_loss=0.06979, over 985740.08 frames.], batch size: 44, aishell_tot_loss[loss=0.1985, simple_loss=0.2682, pruned_loss=0.06439, over 984758.76 frames.], datatang_tot_loss[loss=0.2072, simple_loss=0.2612, pruned_loss=0.07665, over 985432.24 frames.], batch size: 44, lr: 1.19e-03
+2022-06-18 14:41:11,397 INFO [train.py:874] (1/4) Epoch 6, batch 3100, aishell_loss[loss=0.1739, simple_loss=0.2232, pruned_loss=0.06232, over 4813.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2635, pruned_loss=0.0699, over 985641.23 frames.], batch size: 20, aishell_tot_loss[loss=0.1977, simple_loss=0.2675, pruned_loss=0.06397, over 984783.69 frames.], datatang_tot_loss[loss=0.2079, simple_loss=0.2614, pruned_loss=0.07713, over 985465.99 frames.], batch size: 20, lr: 1.19e-03
+2022-06-18 14:41:41,825 INFO [train.py:874] (1/4) Epoch 6, batch 3150, datatang_loss[loss=0.1685, simple_loss=0.2305, pruned_loss=0.05322, over 4931.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2637, pruned_loss=0.06945, over 985742.73 frames.], batch size: 71, aishell_tot_loss[loss=0.1974, simple_loss=0.2673, pruned_loss=0.06375, over 985057.47 frames.], datatang_tot_loss[loss=0.2076, simple_loss=0.2615, pruned_loss=0.07681, over 985431.46 frames.], batch size: 71, lr: 1.19e-03
+2022-06-18 14:42:15,279 INFO [train.py:874] (1/4) Epoch 6, batch 3200, aishell_loss[loss=0.1858, simple_loss=0.2623, pruned_loss=0.05465, over 4979.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2638, pruned_loss=0.06924, over 985883.84 frames.], batch size: 48, aishell_tot_loss[loss=0.1979, simple_loss=0.2678, pruned_loss=0.06404, over 985351.41 frames.], datatang_tot_loss[loss=0.2068, simple_loss=0.2609, pruned_loss=0.07635, over 985401.95 frames.], batch size: 48, lr: 1.19e-03
+2022-06-18 14:42:46,474 INFO [train.py:874] (1/4) Epoch 6, batch 3250, datatang_loss[loss=0.2046, simple_loss=0.2635, pruned_loss=0.07284, over 4841.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2638, pruned_loss=0.06929, over 985481.21 frames.], batch size: 30, aishell_tot_loss[loss=0.197, simple_loss=0.2669, pruned_loss=0.06357, over 985063.38 frames.], datatang_tot_loss[loss=0.2076, simple_loss=0.2615, pruned_loss=0.07682, over 985390.35 frames.], batch size: 30, lr: 1.19e-03
+2022-06-18 14:43:16,354 INFO [train.py:874] (1/4) Epoch 6, batch 3300, aishell_loss[loss=0.1809, simple_loss=0.2487, pruned_loss=0.05661, over 4862.00 frames.], tot_loss[loss=0.2011, simple_loss=0.2638, pruned_loss=0.0692, over 985486.53 frames.], batch size: 28, aishell_tot_loss[loss=0.1969, simple_loss=0.267, pruned_loss=0.06339, over 985079.52 frames.], datatang_tot_loss[loss=0.2073, simple_loss=0.2614, pruned_loss=0.07657, over 985440.28 frames.], batch size: 28, lr: 1.18e-03
+2022-06-18 14:43:45,795 INFO [train.py:874] (1/4) Epoch 6, batch 3350, aishell_loss[loss=0.1782, simple_loss=0.2435, pruned_loss=0.0565, over 4983.00 frames.], tot_loss[loss=0.201, simple_loss=0.264, pruned_loss=0.06896, over 985324.88 frames.], batch size: 30, aishell_tot_loss[loss=0.1967, simple_loss=0.267, pruned_loss=0.06321, over 984895.32 frames.], datatang_tot_loss[loss=0.2073, simple_loss=0.2614, pruned_loss=0.07656, over 985515.76 frames.], batch size: 30, lr: 1.18e-03
+2022-06-18 14:44:17,241 INFO [train.py:874] (1/4) Epoch 6, batch 3400, aishell_loss[loss=0.1661, simple_loss=0.2323, pruned_loss=0.04998, over 4955.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2648, pruned_loss=0.06933, over 985236.59 frames.], batch size: 25, aishell_tot_loss[loss=0.1969, simple_loss=0.2671, pruned_loss=0.06339, over 984908.53 frames.], datatang_tot_loss[loss=0.2079, simple_loss=0.262, pruned_loss=0.07695, over 985458.82 frames.], batch size: 25, lr: 1.18e-03
+2022-06-18 14:44:46,246 INFO [train.py:874] (1/4) Epoch 6, batch 3450, aishell_loss[loss=0.1337, simple_loss=0.2089, pruned_loss=0.02927, over 4974.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2661, pruned_loss=0.07017, over 985350.48 frames.], batch size: 27, aishell_tot_loss[loss=0.1977, simple_loss=0.2681, pruned_loss=0.06368, over 984918.91 frames.], datatang_tot_loss[loss=0.2086, simple_loss=0.2624, pruned_loss=0.07746, over 985584.09 frames.], batch size: 27, lr: 1.18e-03
+2022-06-18 14:45:16,420 INFO [train.py:874] (1/4) Epoch 6, batch 3500, datatang_loss[loss=0.182, simple_loss=0.2487, pruned_loss=0.05761, over 4961.00 frames.], tot_loss[loss=0.2033, simple_loss=0.267, pruned_loss=0.06976, over 985683.97 frames.], batch size: 67, aishell_tot_loss[loss=0.198, simple_loss=0.2689, pruned_loss=0.0635, over 985182.59 frames.], datatang_tot_loss[loss=0.2087, simple_loss=0.2625, pruned_loss=0.07744, over 985717.24 frames.], batch size: 67, lr: 1.18e-03
+2022-06-18 14:45:47,005 INFO [train.py:874] (1/4) Epoch 6, batch 3550, aishell_loss[loss=0.2068, simple_loss=0.2725, pruned_loss=0.07059, over 4974.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2679, pruned_loss=0.06992, over 985687.34 frames.], batch size: 44, aishell_tot_loss[loss=0.1988, simple_loss=0.2696, pruned_loss=0.06396, over 985106.48 frames.], datatang_tot_loss[loss=0.2089, simple_loss=0.2629, pruned_loss=0.07747, over 985879.90 frames.], batch size: 44, lr: 1.18e-03
+2022-06-18 14:46:15,902 INFO [train.py:874] (1/4) Epoch 6, batch 3600, aishell_loss[loss=0.2057, simple_loss=0.2813, pruned_loss=0.06503, over 4882.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2662, pruned_loss=0.0692, over 985761.73 frames.], batch size: 47, aishell_tot_loss[loss=0.1981, simple_loss=0.2691, pruned_loss=0.06357, over 985011.24 frames.], datatang_tot_loss[loss=0.2079, simple_loss=0.2619, pruned_loss=0.07691, over 986103.80 frames.], batch size: 47, lr: 1.18e-03
+2022-06-18 14:46:46,859 INFO [train.py:874] (1/4) Epoch 6, batch 3650, datatang_loss[loss=0.2179, simple_loss=0.2706, pruned_loss=0.08262, over 4930.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2654, pruned_loss=0.06901, over 985899.38 frames.], batch size: 24, aishell_tot_loss[loss=0.198, simple_loss=0.2692, pruned_loss=0.0634, over 985026.97 frames.], datatang_tot_loss[loss=0.207, simple_loss=0.2613, pruned_loss=0.07636, over 986258.50 frames.], batch size: 24, lr: 1.18e-03
+2022-06-18 14:47:16,915 INFO [train.py:874] (1/4) Epoch 6, batch 3700, datatang_loss[loss=0.2035, simple_loss=0.2652, pruned_loss=0.07085, over 4942.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2653, pruned_loss=0.06885, over 985814.90 frames.], batch size: 94, aishell_tot_loss[loss=0.1974, simple_loss=0.2686, pruned_loss=0.06311, over 985072.20 frames.], datatang_tot_loss[loss=0.2072, simple_loss=0.2617, pruned_loss=0.07634, over 986179.05 frames.], batch size: 94, lr: 1.18e-03
+2022-06-18 14:47:44,895 INFO [train.py:874] (1/4) Epoch 6, batch 3750, datatang_loss[loss=0.2628, simple_loss=0.3089, pruned_loss=0.1083, over 4947.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2662, pruned_loss=0.07007, over 985806.49 frames.], batch size: 109, aishell_tot_loss[loss=0.1976, simple_loss=0.2687, pruned_loss=0.06327, over 985002.43 frames.], datatang_tot_loss[loss=0.2084, simple_loss=0.2627, pruned_loss=0.07707, over 986272.20 frames.], batch size: 109, lr: 1.17e-03
+2022-06-18 14:48:15,175 INFO [train.py:874] (1/4) Epoch 6, batch 3800, aishell_loss[loss=0.2, simple_loss=0.2698, pruned_loss=0.0651, over 4911.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2663, pruned_loss=0.07024, over 985864.23 frames.], batch size: 68, aishell_tot_loss[loss=0.1989, simple_loss=0.2697, pruned_loss=0.06407, over 985000.81 frames.], datatang_tot_loss[loss=0.2074, simple_loss=0.2621, pruned_loss=0.07632, over 986361.87 frames.], batch size: 68, lr: 1.17e-03
+2022-06-18 14:48:43,815 INFO [train.py:874] (1/4) Epoch 6, batch 3850, datatang_loss[loss=0.224, simple_loss=0.2745, pruned_loss=0.08677, over 4957.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2664, pruned_loss=0.07063, over 985930.85 frames.], batch size: 67, aishell_tot_loss[loss=0.1987, simple_loss=0.2692, pruned_loss=0.06412, over 984968.39 frames.], datatang_tot_loss[loss=0.2079, simple_loss=0.2628, pruned_loss=0.07654, over 986477.01 frames.], batch size: 67, lr: 1.17e-03
+2022-06-18 14:49:12,062 INFO [train.py:874] (1/4) Epoch 6, batch 3900, aishell_loss[loss=0.1753, simple_loss=0.2467, pruned_loss=0.05196, over 4979.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2663, pruned_loss=0.07088, over 985727.32 frames.], batch size: 30, aishell_tot_loss[loss=0.1994, simple_loss=0.2696, pruned_loss=0.06459, over 985138.92 frames.], datatang_tot_loss[loss=0.2078, simple_loss=0.2624, pruned_loss=0.07662, over 986169.28 frames.], batch size: 30, lr: 1.17e-03
+2022-06-18 14:49:41,278 INFO [train.py:874] (1/4) Epoch 6, batch 3950, datatang_loss[loss=0.1989, simple_loss=0.2632, pruned_loss=0.06729, over 4926.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2659, pruned_loss=0.07023, over 985756.96 frames.], batch size: 64, aishell_tot_loss[loss=0.1992, simple_loss=0.2694, pruned_loss=0.06453, over 985236.35 frames.], datatang_tot_loss[loss=0.2072, simple_loss=0.2622, pruned_loss=0.07608, over 986103.84 frames.], batch size: 64, lr: 1.17e-03
+2022-06-18 14:50:10,158 INFO [train.py:874] (1/4) Epoch 6, batch 4000, aishell_loss[loss=0.2237, simple_loss=0.2972, pruned_loss=0.0751, over 4924.00 frames.], tot_loss[loss=0.203, simple_loss=0.266, pruned_loss=0.07006, over 985680.69 frames.], batch size: 41, aishell_tot_loss[loss=0.199, simple_loss=0.2693, pruned_loss=0.06433, over 985120.83 frames.], datatang_tot_loss[loss=0.2074, simple_loss=0.2624, pruned_loss=0.07614, over 986175.83 frames.], batch size: 41, lr: 1.17e-03
+2022-06-18 14:50:10,158 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 14:50:26,075 INFO [train.py:914] (1/4) Epoch 6, validation: loss=0.1741, simple_loss=0.2559, pruned_loss=0.04619, over 1622729.00 frames.
+2022-06-18 14:51:43,917 INFO [train.py:874] (1/4) Epoch 7, batch 50, aishell_loss[loss=0.2017, simple_loss=0.2701, pruned_loss=0.06668, over 4955.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2571, pruned_loss=0.06353, over 219025.66 frames.], batch size: 40, aishell_tot_loss[loss=0.1988, simple_loss=0.2678, pruned_loss=0.0649, over 116437.43 frames.], datatang_tot_loss[loss=0.1856, simple_loss=0.2466, pruned_loss=0.06233, over 116300.89 frames.], batch size: 40, lr: 1.12e-03
+2022-06-18 14:52:15,042 INFO [train.py:874] (1/4) Epoch 7, batch 100, datatang_loss[loss=0.2249, simple_loss=0.2626, pruned_loss=0.09354, over 4943.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2589, pruned_loss=0.06424, over 388915.20 frames.], batch size: 50, aishell_tot_loss[loss=0.1952, simple_loss=0.2668, pruned_loss=0.06181, over 226297.26 frames.], datatang_tot_loss[loss=0.192, simple_loss=0.2502, pruned_loss=0.0669, over 211066.87 frames.], batch size: 50, lr: 1.12e-03
+2022-06-18 14:52:44,506 INFO [train.py:874] (1/4) Epoch 7, batch 150, aishell_loss[loss=0.2124, simple_loss=0.2876, pruned_loss=0.06866, over 4919.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2616, pruned_loss=0.06502, over 521250.56 frames.], batch size: 32, aishell_tot_loss[loss=0.1986, simple_loss=0.2693, pruned_loss=0.06393, over 345127.03 frames.], datatang_tot_loss[loss=0.1915, simple_loss=0.2502, pruned_loss=0.06635, over 270970.01 frames.], batch size: 32, lr: 1.12e-03
+2022-06-18 14:53:13,180 INFO [train.py:874] (1/4) Epoch 7, batch 200, datatang_loss[loss=0.2214, simple_loss=0.2701, pruned_loss=0.08635, over 4924.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2597, pruned_loss=0.06548, over 624466.53 frames.], batch size: 83, aishell_tot_loss[loss=0.1977, simple_loss=0.2679, pruned_loss=0.06372, over 406528.15 frames.], datatang_tot_loss[loss=0.1926, simple_loss=0.2507, pruned_loss=0.06721, over 370722.20 frames.], batch size: 83, lr: 1.12e-03
+2022-06-18 14:53:45,407 INFO [train.py:874] (1/4) Epoch 7, batch 250, aishell_loss[loss=0.215, simple_loss=0.2838, pruned_loss=0.07308, over 4912.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2606, pruned_loss=0.06588, over 704631.82 frames.], batch size: 33, aishell_tot_loss[loss=0.1968, simple_loss=0.2677, pruned_loss=0.06293, over 474735.02 frames.], datatang_tot_loss[loss=0.1952, simple_loss=0.2527, pruned_loss=0.06883, over 443253.99 frames.], batch size: 33, lr: 1.11e-03
+2022-06-18 14:54:14,193 INFO [train.py:874] (1/4) Epoch 7, batch 300, aishell_loss[loss=0.2213, simple_loss=0.2852, pruned_loss=0.07874, over 4971.00 frames.], tot_loss[loss=0.1975, simple_loss=0.262, pruned_loss=0.06646, over 766850.55 frames.], batch size: 44, aishell_tot_loss[loss=0.197, simple_loss=0.2676, pruned_loss=0.06314, over 546063.63 frames.], datatang_tot_loss[loss=0.1971, simple_loss=0.2546, pruned_loss=0.06982, over 494908.91 frames.], batch size: 44, lr: 1.11e-03
+2022-06-18 14:54:43,153 INFO [train.py:874] (1/4) Epoch 7, batch 350, aishell_loss[loss=0.2315, simple_loss=0.2968, pruned_loss=0.0831, over 4865.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2631, pruned_loss=0.0674, over 815530.48 frames.], batch size: 35, aishell_tot_loss[loss=0.1978, simple_loss=0.2681, pruned_loss=0.0637, over 594033.04 frames.], datatang_tot_loss[loss=0.1987, simple_loss=0.2562, pruned_loss=0.07056, over 557119.31 frames.], batch size: 35, lr: 1.11e-03
+2022-06-18 14:55:14,503 INFO [train.py:874] (1/4) Epoch 7, batch 400, aishell_loss[loss=0.1588, simple_loss=0.2413, pruned_loss=0.0382, over 4962.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2649, pruned_loss=0.06836, over 853369.78 frames.], batch size: 56, aishell_tot_loss[loss=0.1969, simple_loss=0.2676, pruned_loss=0.06308, over 647069.82 frames.], datatang_tot_loss[loss=0.2028, simple_loss=0.2596, pruned_loss=0.07302, over 600005.13 frames.], batch size: 56, lr: 1.11e-03
+2022-06-18 14:55:44,359 INFO [train.py:874] (1/4) Epoch 7, batch 450, datatang_loss[loss=0.1897, simple_loss=0.2574, pruned_loss=0.061, over 4918.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2645, pruned_loss=0.06838, over 882486.21 frames.], batch size: 81, aishell_tot_loss[loss=0.1958, simple_loss=0.267, pruned_loss=0.06236, over 679329.66 frames.], datatang_tot_loss[loss=0.2037, simple_loss=0.2603, pruned_loss=0.07351, over 653664.72 frames.], batch size: 81, lr: 1.11e-03
+2022-06-18 14:56:12,601 INFO [train.py:874] (1/4) Epoch 7, batch 500, aishell_loss[loss=0.1743, simple_loss=0.257, pruned_loss=0.04578, over 4922.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2645, pruned_loss=0.06816, over 905435.81 frames.], batch size: 52, aishell_tot_loss[loss=0.1956, simple_loss=0.267, pruned_loss=0.06208, over 716706.55 frames.], datatang_tot_loss[loss=0.204, simple_loss=0.2606, pruned_loss=0.07371, over 691437.97 frames.], batch size: 52, lr: 1.11e-03
+2022-06-18 14:56:43,875 INFO [train.py:874] (1/4) Epoch 7, batch 550, aishell_loss[loss=0.1952, simple_loss=0.2695, pruned_loss=0.06042, over 4881.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2639, pruned_loss=0.06754, over 923452.07 frames.], batch size: 34, aishell_tot_loss[loss=0.196, simple_loss=0.2678, pruned_loss=0.06211, over 742551.68 frames.], datatang_tot_loss[loss=0.2023, simple_loss=0.2594, pruned_loss=0.07258, over 732584.30 frames.], batch size: 34, lr: 1.11e-03
+2022-06-18 14:57:13,434 INFO [train.py:874] (1/4) Epoch 7, batch 600, datatang_loss[loss=0.2305, simple_loss=0.2786, pruned_loss=0.09118, over 4892.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2639, pruned_loss=0.06817, over 937211.06 frames.], batch size: 47, aishell_tot_loss[loss=0.1958, simple_loss=0.2676, pruned_loss=0.06205, over 764496.09 frames.], datatang_tot_loss[loss=0.2031, simple_loss=0.26, pruned_loss=0.07315, over 769113.74 frames.], batch size: 47, lr: 1.11e-03
+2022-06-18 14:57:42,493 INFO [train.py:874] (1/4) Epoch 7, batch 650, aishell_loss[loss=0.207, simple_loss=0.2817, pruned_loss=0.06615, over 4851.00 frames.], tot_loss[loss=0.2, simple_loss=0.264, pruned_loss=0.06802, over 947768.28 frames.], batch size: 36, aishell_tot_loss[loss=0.196, simple_loss=0.2676, pruned_loss=0.0622, over 794168.24 frames.], datatang_tot_loss[loss=0.2032, simple_loss=0.2599, pruned_loss=0.07323, over 790818.80 frames.], batch size: 36, lr: 1.11e-03
+2022-06-18 14:58:14,072 INFO [train.py:874] (1/4) Epoch 7, batch 700, datatang_loss[loss=0.185, simple_loss=0.2421, pruned_loss=0.06393, over 4893.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2625, pruned_loss=0.06754, over 955690.12 frames.], batch size: 47, aishell_tot_loss[loss=0.1953, simple_loss=0.2668, pruned_loss=0.0619, over 816308.07 frames.], datatang_tot_loss[loss=0.2025, simple_loss=0.2589, pruned_loss=0.07299, over 813686.45 frames.], batch size: 47, lr: 1.11e-03
+2022-06-18 14:58:45,052 INFO [train.py:874] (1/4) Epoch 7, batch 750, datatang_loss[loss=0.175, simple_loss=0.2492, pruned_loss=0.05042, over 4930.00 frames.], tot_loss[loss=0.1996, simple_loss=0.2634, pruned_loss=0.0679, over 962025.54 frames.], batch size: 73, aishell_tot_loss[loss=0.1959, simple_loss=0.2675, pruned_loss=0.06218, over 834491.20 frames.], datatang_tot_loss[loss=0.2026, simple_loss=0.2593, pruned_loss=0.07299, over 835379.72 frames.], batch size: 73, lr: 1.10e-03
+2022-06-18 14:59:13,867 INFO [train.py:874] (1/4) Epoch 7, batch 800, datatang_loss[loss=0.2087, simple_loss=0.2655, pruned_loss=0.07588, over 4944.00 frames.], tot_loss[loss=0.2019, simple_loss=0.265, pruned_loss=0.06936, over 967160.42 frames.], batch size: 69, aishell_tot_loss[loss=0.1971, simple_loss=0.2683, pruned_loss=0.063, over 852203.33 frames.], datatang_tot_loss[loss=0.2043, simple_loss=0.2605, pruned_loss=0.07402, over 853058.50 frames.], batch size: 69, lr: 1.10e-03
+2022-06-18 14:59:44,735 INFO [train.py:874] (1/4) Epoch 7, batch 850, aishell_loss[loss=0.1755, simple_loss=0.2434, pruned_loss=0.05385, over 4869.00 frames.], tot_loss[loss=0.1998, simple_loss=0.2632, pruned_loss=0.06818, over 970826.60 frames.], batch size: 28, aishell_tot_loss[loss=0.1961, simple_loss=0.2674, pruned_loss=0.06244, over 866435.02 frames.], datatang_tot_loss[loss=0.203, simple_loss=0.2595, pruned_loss=0.07327, over 869699.40 frames.], batch size: 28, lr: 1.10e-03
+2022-06-18 15:00:16,350 INFO [train.py:874] (1/4) Epoch 7, batch 900, datatang_loss[loss=0.1818, simple_loss=0.2394, pruned_loss=0.06205, over 4894.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2626, pruned_loss=0.06763, over 973708.91 frames.], batch size: 52, aishell_tot_loss[loss=0.1958, simple_loss=0.2671, pruned_loss=0.0622, over 878871.11 frames.], datatang_tot_loss[loss=0.2023, simple_loss=0.2591, pruned_loss=0.07278, over 884468.18 frames.], batch size: 52, lr: 1.10e-03
+2022-06-18 15:00:45,816 INFO [train.py:874] (1/4) Epoch 7, batch 950, aishell_loss[loss=0.2111, simple_loss=0.2681, pruned_loss=0.07707, over 4865.00 frames.], tot_loss[loss=0.1999, simple_loss=0.2633, pruned_loss=0.06825, over 976309.72 frames.], batch size: 36, aishell_tot_loss[loss=0.1957, simple_loss=0.2669, pruned_loss=0.06225, over 891328.81 frames.], datatang_tot_loss[loss=0.2035, simple_loss=0.26, pruned_loss=0.07346, over 896456.72 frames.], batch size: 36, lr: 1.10e-03
+2022-06-18 15:01:17,331 INFO [train.py:874] (1/4) Epoch 7, batch 1000, datatang_loss[loss=0.235, simple_loss=0.2906, pruned_loss=0.08972, over 4936.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2634, pruned_loss=0.06841, over 978787.20 frames.], batch size: 34, aishell_tot_loss[loss=0.1951, simple_loss=0.2665, pruned_loss=0.06183, over 901378.41 frames.], datatang_tot_loss[loss=0.2042, simple_loss=0.2605, pruned_loss=0.07393, over 908364.14 frames.], batch size: 34, lr: 1.10e-03
+2022-06-18 15:01:17,332 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 15:01:33,319 INFO [train.py:914] (1/4) Epoch 7, validation: loss=0.1728, simple_loss=0.2548, pruned_loss=0.04541, over 1622729.00 frames.
+2022-06-18 15:02:05,423 INFO [train.py:874] (1/4) Epoch 7, batch 1050, datatang_loss[loss=0.203, simple_loss=0.2618, pruned_loss=0.0721, over 4934.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2635, pruned_loss=0.06839, over 980293.61 frames.], batch size: 94, aishell_tot_loss[loss=0.1957, simple_loss=0.2672, pruned_loss=0.06212, over 909915.14 frames.], datatang_tot_loss[loss=0.2036, simple_loss=0.26, pruned_loss=0.07354, over 918672.26 frames.], batch size: 94, lr: 1.10e-03
+2022-06-18 15:02:35,723 INFO [train.py:874] (1/4) Epoch 7, batch 1100, datatang_loss[loss=0.211, simple_loss=0.2741, pruned_loss=0.07394, over 4944.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2634, pruned_loss=0.06752, over 981460.33 frames.], batch size: 88, aishell_tot_loss[loss=0.1954, simple_loss=0.267, pruned_loss=0.06189, over 921319.13 frames.], datatang_tot_loss[loss=0.2032, simple_loss=0.2598, pruned_loss=0.07334, over 924228.15 frames.], batch size: 88, lr: 1.10e-03
+2022-06-18 15:03:04,437 INFO [train.py:874] (1/4) Epoch 7, batch 1150, aishell_loss[loss=0.2112, simple_loss=0.28, pruned_loss=0.07121, over 4920.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2633, pruned_loss=0.06747, over 982134.26 frames.], batch size: 33, aishell_tot_loss[loss=0.1955, simple_loss=0.2672, pruned_loss=0.06188, over 928307.20 frames.], datatang_tot_loss[loss=0.203, simple_loss=0.2595, pruned_loss=0.0732, over 931768.79 frames.], batch size: 33, lr: 1.10e-03
+2022-06-18 15:03:35,929 INFO [train.py:874] (1/4) Epoch 7, batch 1200, datatang_loss[loss=0.1758, simple_loss=0.2469, pruned_loss=0.05229, over 4960.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2631, pruned_loss=0.06712, over 982936.99 frames.], batch size: 86, aishell_tot_loss[loss=0.1951, simple_loss=0.267, pruned_loss=0.06162, over 935173.89 frames.], datatang_tot_loss[loss=0.2029, simple_loss=0.2595, pruned_loss=0.07314, over 938056.46 frames.], batch size: 86, lr: 1.10e-03
+2022-06-18 15:04:06,613 INFO [train.py:874] (1/4) Epoch 7, batch 1250, aishell_loss[loss=0.2165, simple_loss=0.2818, pruned_loss=0.07563, over 4934.00 frames.], tot_loss[loss=0.198, simple_loss=0.2627, pruned_loss=0.06668, over 983922.01 frames.], batch size: 45, aishell_tot_loss[loss=0.1952, simple_loss=0.2671, pruned_loss=0.0616, over 942429.18 frames.], datatang_tot_loss[loss=0.2022, simple_loss=0.2587, pruned_loss=0.07289, over 942789.35 frames.], batch size: 45, lr: 1.09e-03
+2022-06-18 15:04:34,742 INFO [train.py:874] (1/4) Epoch 7, batch 1300, aishell_loss[loss=0.2064, simple_loss=0.2789, pruned_loss=0.06698, over 4855.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2629, pruned_loss=0.06665, over 984575.78 frames.], batch size: 36, aishell_tot_loss[loss=0.1954, simple_loss=0.2676, pruned_loss=0.06164, over 947462.72 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.2585, pruned_loss=0.07263, over 948185.53 frames.], batch size: 36, lr: 1.09e-03
+2022-06-18 15:05:04,914 INFO [train.py:874] (1/4) Epoch 7, batch 1350, aishell_loss[loss=0.1628, simple_loss=0.2289, pruned_loss=0.04831, over 4949.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2627, pruned_loss=0.06694, over 984346.68 frames.], batch size: 27, aishell_tot_loss[loss=0.1949, simple_loss=0.2669, pruned_loss=0.06148, over 951354.47 frames.], datatang_tot_loss[loss=0.2024, simple_loss=0.2589, pruned_loss=0.07301, over 952699.25 frames.], batch size: 27, lr: 1.09e-03
+2022-06-18 15:05:36,473 INFO [train.py:874] (1/4) Epoch 7, batch 1400, aishell_loss[loss=0.2192, simple_loss=0.2829, pruned_loss=0.07775, over 4946.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2635, pruned_loss=0.06732, over 985024.66 frames.], batch size: 54, aishell_tot_loss[loss=0.1957, simple_loss=0.2676, pruned_loss=0.06187, over 955537.79 frames.], datatang_tot_loss[loss=0.2025, simple_loss=0.2591, pruned_loss=0.07296, over 956841.72 frames.], batch size: 54, lr: 1.09e-03
+2022-06-18 15:06:05,398 INFO [train.py:874] (1/4) Epoch 7, batch 1450, aishell_loss[loss=0.2235, simple_loss=0.2915, pruned_loss=0.07769, over 4933.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2631, pruned_loss=0.06706, over 984901.45 frames.], batch size: 54, aishell_tot_loss[loss=0.1949, simple_loss=0.267, pruned_loss=0.06146, over 958710.74 frames.], datatang_tot_loss[loss=0.2026, simple_loss=0.2593, pruned_loss=0.07299, over 960322.34 frames.], batch size: 54, lr: 1.09e-03
+2022-06-18 15:06:36,105 INFO [train.py:874] (1/4) Epoch 7, batch 1500, datatang_loss[loss=0.2074, simple_loss=0.2563, pruned_loss=0.07928, over 4959.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2621, pruned_loss=0.06669, over 984844.51 frames.], batch size: 55, aishell_tot_loss[loss=0.1946, simple_loss=0.2664, pruned_loss=0.06137, over 961348.06 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.2589, pruned_loss=0.07249, over 963582.52 frames.], batch size: 55, lr: 1.09e-03
+2022-06-18 15:07:07,639 INFO [train.py:874] (1/4) Epoch 7, batch 1550, aishell_loss[loss=0.1796, simple_loss=0.246, pruned_loss=0.05659, over 4924.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2624, pruned_loss=0.06637, over 984441.62 frames.], batch size: 33, aishell_tot_loss[loss=0.1946, simple_loss=0.2666, pruned_loss=0.06136, over 963990.51 frames.], datatang_tot_loss[loss=0.2015, simple_loss=0.2589, pruned_loss=0.07208, over 965780.61 frames.], batch size: 33, lr: 1.09e-03
+2022-06-18 15:07:35,789 INFO [train.py:874] (1/4) Epoch 7, batch 1600, datatang_loss[loss=0.2085, simple_loss=0.2747, pruned_loss=0.07113, over 4952.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2629, pruned_loss=0.06541, over 984519.75 frames.], batch size: 91, aishell_tot_loss[loss=0.1944, simple_loss=0.2669, pruned_loss=0.06102, over 967239.44 frames.], datatang_tot_loss[loss=0.2012, simple_loss=0.2589, pruned_loss=0.07174, over 967257.62 frames.], batch size: 91, lr: 1.09e-03
+2022-06-18 15:08:07,455 INFO [train.py:874] (1/4) Epoch 7, batch 1650, datatang_loss[loss=0.1811, simple_loss=0.2428, pruned_loss=0.0597, over 4970.00 frames.], tot_loss[loss=0.1963, simple_loss=0.262, pruned_loss=0.06536, over 984955.53 frames.], batch size: 37, aishell_tot_loss[loss=0.1946, simple_loss=0.2668, pruned_loss=0.06122, over 968973.02 frames.], datatang_tot_loss[loss=0.2, simple_loss=0.258, pruned_loss=0.071, over 970040.66 frames.], batch size: 37, lr: 1.09e-03
+2022-06-18 15:08:38,338 INFO [train.py:874] (1/4) Epoch 7, batch 1700, datatang_loss[loss=0.2065, simple_loss=0.2635, pruned_loss=0.0748, over 4972.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2619, pruned_loss=0.06536, over 985410.34 frames.], batch size: 60, aishell_tot_loss[loss=0.1943, simple_loss=0.2665, pruned_loss=0.06106, over 970973.74 frames.], datatang_tot_loss[loss=0.2, simple_loss=0.2583, pruned_loss=0.07091, over 972138.75 frames.], batch size: 60, lr: 1.09e-03
+2022-06-18 15:09:08,394 INFO [train.py:874] (1/4) Epoch 7, batch 1750, aishell_loss[loss=0.1946, simple_loss=0.2725, pruned_loss=0.05834, over 4969.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2629, pruned_loss=0.06633, over 985452.62 frames.], batch size: 48, aishell_tot_loss[loss=0.1948, simple_loss=0.2669, pruned_loss=0.06136, over 972327.01 frames.], datatang_tot_loss[loss=0.2008, simple_loss=0.259, pruned_loss=0.07132, over 974054.94 frames.], batch size: 48, lr: 1.08e-03
+2022-06-18 15:09:38,842 INFO [train.py:874] (1/4) Epoch 7, batch 1800, aishell_loss[loss=0.1577, simple_loss=0.213, pruned_loss=0.05125, over 4709.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2625, pruned_loss=0.0661, over 984751.29 frames.], batch size: 20, aishell_tot_loss[loss=0.195, simple_loss=0.2669, pruned_loss=0.06153, over 973239.07 frames.], datatang_tot_loss[loss=0.2002, simple_loss=0.2584, pruned_loss=0.071, over 975322.11 frames.], batch size: 20, lr: 1.08e-03
+2022-06-18 15:10:08,338 INFO [train.py:874] (1/4) Epoch 7, batch 1850, datatang_loss[loss=0.2186, simple_loss=0.2822, pruned_loss=0.07751, over 4952.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2633, pruned_loss=0.06698, over 984962.63 frames.], batch size: 99, aishell_tot_loss[loss=0.1949, simple_loss=0.2668, pruned_loss=0.06152, over 974570.59 frames.], datatang_tot_loss[loss=0.2015, simple_loss=0.2592, pruned_loss=0.07193, over 976679.83 frames.], batch size: 99, lr: 1.08e-03
+2022-06-18 15:10:38,753 INFO [train.py:874] (1/4) Epoch 7, batch 1900, aishell_loss[loss=0.2042, simple_loss=0.2839, pruned_loss=0.06221, over 4966.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2632, pruned_loss=0.06708, over 985288.43 frames.], batch size: 44, aishell_tot_loss[loss=0.1946, simple_loss=0.2665, pruned_loss=0.06141, over 975993.24 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.2595, pruned_loss=0.07218, over 977809.61 frames.], batch size: 44, lr: 1.08e-03
+2022-06-18 15:11:09,996 INFO [train.py:874] (1/4) Epoch 7, batch 1950, aishell_loss[loss=0.1721, simple_loss=0.2432, pruned_loss=0.05049, over 4982.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2638, pruned_loss=0.06817, over 985450.91 frames.], batch size: 25, aishell_tot_loss[loss=0.1962, simple_loss=0.2672, pruned_loss=0.06261, over 976742.48 frames.], datatang_tot_loss[loss=0.2017, simple_loss=0.2597, pruned_loss=0.07189, over 979136.17 frames.], batch size: 25, lr: 1.08e-03
+2022-06-18 15:11:38,537 INFO [train.py:874] (1/4) Epoch 7, batch 2000, aishell_loss[loss=0.2021, simple_loss=0.2816, pruned_loss=0.06128, over 4905.00 frames.], tot_loss[loss=0.1992, simple_loss=0.2638, pruned_loss=0.06731, over 985452.93 frames.], batch size: 68, aishell_tot_loss[loss=0.1954, simple_loss=0.2668, pruned_loss=0.06201, over 977879.33 frames.], datatang_tot_loss[loss=0.202, simple_loss=0.26, pruned_loss=0.07195, over 979788.73 frames.], batch size: 68, lr: 1.08e-03
+2022-06-18 15:11:38,538 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 15:11:55,040 INFO [train.py:914] (1/4) Epoch 7, validation: loss=0.1748, simple_loss=0.2567, pruned_loss=0.04647, over 1622729.00 frames.
+2022-06-18 15:12:25,170 INFO [train.py:874] (1/4) Epoch 7, batch 2050, aishell_loss[loss=0.2055, simple_loss=0.2851, pruned_loss=0.06297, over 4870.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2636, pruned_loss=0.06682, over 985433.33 frames.], batch size: 37, aishell_tot_loss[loss=0.195, simple_loss=0.2669, pruned_loss=0.06159, over 978864.94 frames.], datatang_tot_loss[loss=0.2019, simple_loss=0.2597, pruned_loss=0.07201, over 980362.05 frames.], batch size: 37, lr: 1.08e-03
+2022-06-18 15:12:54,798 INFO [train.py:874] (1/4) Epoch 7, batch 2100, aishell_loss[loss=0.1568, simple_loss=0.2422, pruned_loss=0.03566, over 4925.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2634, pruned_loss=0.06663, over 985397.76 frames.], batch size: 46, aishell_tot_loss[loss=0.1941, simple_loss=0.266, pruned_loss=0.06108, over 979743.95 frames.], datatang_tot_loss[loss=0.2028, simple_loss=0.2603, pruned_loss=0.07268, over 980870.28 frames.], batch size: 46, lr: 1.08e-03
+2022-06-18 15:13:25,726 INFO [train.py:874] (1/4) Epoch 7, batch 2150, datatang_loss[loss=0.2185, simple_loss=0.2747, pruned_loss=0.08118, over 4967.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2625, pruned_loss=0.06539, over 985138.22 frames.], batch size: 34, aishell_tot_loss[loss=0.1937, simple_loss=0.2658, pruned_loss=0.06077, over 980417.47 frames.], datatang_tot_loss[loss=0.2016, simple_loss=0.2595, pruned_loss=0.07188, over 981145.24 frames.], batch size: 34, lr: 1.08e-03
+2022-06-18 15:13:55,546 INFO [train.py:874] (1/4) Epoch 7, batch 2200, datatang_loss[loss=0.204, simple_loss=0.266, pruned_loss=0.07095, over 4953.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2621, pruned_loss=0.06581, over 985544.29 frames.], batch size: 67, aishell_tot_loss[loss=0.1939, simple_loss=0.2662, pruned_loss=0.06082, over 981004.10 frames.], datatang_tot_loss[loss=0.201, simple_loss=0.2588, pruned_loss=0.07159, over 981972.66 frames.], batch size: 67, lr: 1.08e-03
+2022-06-18 15:14:26,121 INFO [train.py:874] (1/4) Epoch 7, batch 2250, aishell_loss[loss=0.2072, simple_loss=0.2795, pruned_loss=0.06744, over 4919.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2616, pruned_loss=0.06556, over 985520.87 frames.], batch size: 68, aishell_tot_loss[loss=0.194, simple_loss=0.2662, pruned_loss=0.06092, over 981610.67 frames.], datatang_tot_loss[loss=0.2004, simple_loss=0.2582, pruned_loss=0.07126, over 982310.47 frames.], batch size: 68, lr: 1.07e-03
+2022-06-18 15:14:56,307 INFO [train.py:874] (1/4) Epoch 7, batch 2300, aishell_loss[loss=0.213, simple_loss=0.2802, pruned_loss=0.07288, over 4940.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2618, pruned_loss=0.06513, over 985350.17 frames.], batch size: 79, aishell_tot_loss[loss=0.1939, simple_loss=0.266, pruned_loss=0.0609, over 982139.34 frames.], datatang_tot_loss[loss=0.2002, simple_loss=0.2582, pruned_loss=0.07106, over 982461.17 frames.], batch size: 79, lr: 1.07e-03
+2022-06-18 15:15:26,202 INFO [train.py:874] (1/4) Epoch 7, batch 2350, datatang_loss[loss=0.2242, simple_loss=0.2798, pruned_loss=0.08424, over 4981.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2627, pruned_loss=0.06634, over 985276.11 frames.], batch size: 37, aishell_tot_loss[loss=0.1943, simple_loss=0.2665, pruned_loss=0.06104, over 982401.05 frames.], datatang_tot_loss[loss=0.2013, simple_loss=0.2587, pruned_loss=0.07189, over 982835.55 frames.], batch size: 37, lr: 1.07e-03
+2022-06-18 15:15:56,419 INFO [train.py:874] (1/4) Epoch 7, batch 2400, datatang_loss[loss=0.1993, simple_loss=0.2616, pruned_loss=0.06848, over 4978.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2613, pruned_loss=0.0656, over 985210.69 frames.], batch size: 34, aishell_tot_loss[loss=0.1933, simple_loss=0.2656, pruned_loss=0.06047, over 982737.47 frames.], datatang_tot_loss[loss=0.2007, simple_loss=0.2583, pruned_loss=0.0716, over 983064.76 frames.], batch size: 34, lr: 1.07e-03
+2022-06-18 15:16:27,326 INFO [train.py:874] (1/4) Epoch 7, batch 2450, aishell_loss[loss=0.2371, simple_loss=0.3024, pruned_loss=0.08587, over 4942.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2618, pruned_loss=0.06552, over 985139.82 frames.], batch size: 78, aishell_tot_loss[loss=0.1938, simple_loss=0.2663, pruned_loss=0.06066, over 982880.31 frames.], datatang_tot_loss[loss=0.2003, simple_loss=0.2579, pruned_loss=0.07133, over 983394.30 frames.], batch size: 78, lr: 1.07e-03
+2022-06-18 15:16:56,372 INFO [train.py:874] (1/4) Epoch 7, batch 2500, datatang_loss[loss=0.2014, simple_loss=0.2614, pruned_loss=0.07072, over 4923.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2628, pruned_loss=0.06633, over 985076.24 frames.], batch size: 77, aishell_tot_loss[loss=0.194, simple_loss=0.2664, pruned_loss=0.06078, over 982896.03 frames.], datatang_tot_loss[loss=0.2013, simple_loss=0.2586, pruned_loss=0.07199, over 983791.41 frames.], batch size: 77, lr: 1.07e-03
+2022-06-18 15:17:26,866 INFO [train.py:874] (1/4) Epoch 7, batch 2550, aishell_loss[loss=0.1646, simple_loss=0.2201, pruned_loss=0.05454, over 4891.00 frames.], tot_loss[loss=0.197, simple_loss=0.2622, pruned_loss=0.06593, over 985191.59 frames.], batch size: 21, aishell_tot_loss[loss=0.1937, simple_loss=0.266, pruned_loss=0.06072, over 983140.22 frames.], datatang_tot_loss[loss=0.2011, simple_loss=0.2583, pruned_loss=0.07194, over 984102.85 frames.], batch size: 21, lr: 1.07e-03
+2022-06-18 15:17:58,013 INFO [train.py:874] (1/4) Epoch 7, batch 2600, aishell_loss[loss=0.2079, simple_loss=0.2749, pruned_loss=0.07044, over 4975.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2617, pruned_loss=0.06546, over 985325.26 frames.], batch size: 51, aishell_tot_loss[loss=0.193, simple_loss=0.2655, pruned_loss=0.06024, over 983386.12 frames.], datatang_tot_loss[loss=0.201, simple_loss=0.2582, pruned_loss=0.07185, over 984369.09 frames.], batch size: 51, lr: 1.07e-03
+2022-06-18 15:18:28,167 INFO [train.py:874] (1/4) Epoch 7, batch 2650, aishell_loss[loss=0.2038, simple_loss=0.2715, pruned_loss=0.06806, over 4932.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2613, pruned_loss=0.06491, over 985397.25 frames.], batch size: 49, aishell_tot_loss[loss=0.1932, simple_loss=0.2659, pruned_loss=0.06021, over 983727.90 frames.], datatang_tot_loss[loss=0.1999, simple_loss=0.2574, pruned_loss=0.0712, over 984443.70 frames.], batch size: 49, lr: 1.07e-03
+2022-06-18 15:18:58,541 INFO [train.py:874] (1/4) Epoch 7, batch 2700, aishell_loss[loss=0.2038, simple_loss=0.2629, pruned_loss=0.07229, over 4954.00 frames.], tot_loss[loss=0.196, simple_loss=0.2618, pruned_loss=0.06513, over 985569.29 frames.], batch size: 40, aishell_tot_loss[loss=0.1929, simple_loss=0.266, pruned_loss=0.05996, over 984170.09 frames.], datatang_tot_loss[loss=0.2004, simple_loss=0.2577, pruned_loss=0.07158, over 984500.76 frames.], batch size: 40, lr: 1.07e-03
+2022-06-18 15:19:28,845 INFO [train.py:874] (1/4) Epoch 7, batch 2750, aishell_loss[loss=0.1952, simple_loss=0.2773, pruned_loss=0.05655, over 4931.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2617, pruned_loss=0.0651, over 985655.59 frames.], batch size: 78, aishell_tot_loss[loss=0.1927, simple_loss=0.2658, pruned_loss=0.05981, over 984229.39 frames.], datatang_tot_loss[loss=0.2005, simple_loss=0.2576, pruned_loss=0.07165, over 984826.36 frames.], batch size: 78, lr: 1.07e-03
+2022-06-18 15:19:58,435 INFO [train.py:874] (1/4) Epoch 7, batch 2800, aishell_loss[loss=0.1762, simple_loss=0.2577, pruned_loss=0.04735, over 4936.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2617, pruned_loss=0.06493, over 985487.77 frames.], batch size: 49, aishell_tot_loss[loss=0.1931, simple_loss=0.2664, pruned_loss=0.05986, over 984275.59 frames.], datatang_tot_loss[loss=0.1997, simple_loss=0.2573, pruned_loss=0.07109, over 984858.85 frames.], batch size: 49, lr: 1.06e-03
+2022-06-18 15:20:29,268 INFO [train.py:874] (1/4) Epoch 7, batch 2850, aishell_loss[loss=0.2191, simple_loss=0.291, pruned_loss=0.0736, over 4961.00 frames.], tot_loss[loss=0.1954, simple_loss=0.2614, pruned_loss=0.06475, over 985766.46 frames.], batch size: 44, aishell_tot_loss[loss=0.1928, simple_loss=0.2664, pruned_loss=0.05965, over 984619.98 frames.], datatang_tot_loss[loss=0.1994, simple_loss=0.257, pruned_loss=0.07093, over 985021.96 frames.], batch size: 44, lr: 1.06e-03
+2022-06-18 15:21:00,380 INFO [train.py:874] (1/4) Epoch 7, batch 2900, aishell_loss[loss=0.1745, simple_loss=0.2539, pruned_loss=0.04752, over 4966.00 frames.], tot_loss[loss=0.1946, simple_loss=0.2603, pruned_loss=0.06441, over 985777.49 frames.], batch size: 40, aishell_tot_loss[loss=0.1929, simple_loss=0.2665, pruned_loss=0.05963, over 984590.33 frames.], datatang_tot_loss[loss=0.1981, simple_loss=0.2559, pruned_loss=0.07011, over 985263.43 frames.], batch size: 40, lr: 1.06e-03
+2022-06-18 15:21:30,578 INFO [train.py:874] (1/4) Epoch 7, batch 2950, aishell_loss[loss=0.1861, simple_loss=0.2605, pruned_loss=0.05586, over 4915.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2597, pruned_loss=0.06468, over 985502.39 frames.], batch size: 33, aishell_tot_loss[loss=0.1926, simple_loss=0.266, pruned_loss=0.05965, over 984323.41 frames.], datatang_tot_loss[loss=0.198, simple_loss=0.2559, pruned_loss=0.07, over 985407.69 frames.], batch size: 33, lr: 1.06e-03
+2022-06-18 15:22:01,255 INFO [train.py:874] (1/4) Epoch 7, batch 3000, datatang_loss[loss=0.2197, simple_loss=0.2805, pruned_loss=0.07948, over 4904.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2607, pruned_loss=0.06535, over 985976.83 frames.], batch size: 47, aishell_tot_loss[loss=0.1927, simple_loss=0.2658, pruned_loss=0.05977, over 984839.23 frames.], datatang_tot_loss[loss=0.1989, simple_loss=0.2571, pruned_loss=0.07033, over 985531.73 frames.], batch size: 47, lr: 1.06e-03
+2022-06-18 15:22:01,256 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 15:22:18,149 INFO [train.py:914] (1/4) 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] (1/4) Epoch 7, batch 3050, aishell_loss[loss=0.1396, simple_loss=0.2019, pruned_loss=0.0386, over 4883.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2602, pruned_loss=0.06471, over 986150.60 frames.], batch size: 21, aishell_tot_loss[loss=0.1927, simple_loss=0.2658, pruned_loss=0.05984, over 985119.48 frames.], datatang_tot_loss[loss=0.1978, simple_loss=0.2564, pruned_loss=0.06964, over 985626.87 frames.], batch size: 21, lr: 1.06e-03
+2022-06-18 15:23:18,129 INFO [train.py:874] (1/4) Epoch 7, batch 3100, datatang_loss[loss=0.2394, simple_loss=0.2927, pruned_loss=0.09299, over 4857.00 frames.], tot_loss[loss=0.194, simple_loss=0.2595, pruned_loss=0.06421, over 985904.67 frames.], batch size: 33, aishell_tot_loss[loss=0.192, simple_loss=0.2652, pruned_loss=0.05945, over 985079.31 frames.], datatang_tot_loss[loss=0.1976, simple_loss=0.2559, pruned_loss=0.06964, over 985601.85 frames.], batch size: 33, lr: 1.06e-03
+2022-06-18 15:23:50,868 INFO [train.py:874] (1/4) Epoch 7, batch 3150, aishell_loss[loss=0.1589, simple_loss=0.2505, pruned_loss=0.03364, over 4904.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2595, pruned_loss=0.06341, over 985802.67 frames.], batch size: 34, aishell_tot_loss[loss=0.1916, simple_loss=0.265, pruned_loss=0.05907, over 985017.22 frames.], datatang_tot_loss[loss=0.197, simple_loss=0.2556, pruned_loss=0.06922, over 985685.95 frames.], batch size: 34, lr: 1.06e-03
+2022-06-18 15:24:21,585 INFO [train.py:874] (1/4) Epoch 7, batch 3200, datatang_loss[loss=0.1785, simple_loss=0.2398, pruned_loss=0.05858, over 4907.00 frames.], tot_loss[loss=0.193, simple_loss=0.2593, pruned_loss=0.06339, over 985519.61 frames.], batch size: 64, aishell_tot_loss[loss=0.1914, simple_loss=0.2647, pruned_loss=0.05908, over 984898.87 frames.], datatang_tot_loss[loss=0.1968, simple_loss=0.2556, pruned_loss=0.06895, over 985596.19 frames.], batch size: 64, lr: 1.06e-03
+2022-06-18 15:24:53,202 INFO [train.py:874] (1/4) Epoch 7, batch 3250, aishell_loss[loss=0.2508, simple_loss=0.3293, pruned_loss=0.08616, over 4927.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2601, pruned_loss=0.06335, over 985809.75 frames.], batch size: 78, aishell_tot_loss[loss=0.1918, simple_loss=0.2652, pruned_loss=0.05919, over 985080.09 frames.], datatang_tot_loss[loss=0.1967, simple_loss=0.2554, pruned_loss=0.06899, over 985803.90 frames.], batch size: 78, lr: 1.06e-03
+2022-06-18 15:25:21,869 INFO [train.py:874] (1/4) Epoch 7, batch 3300, datatang_loss[loss=0.1628, simple_loss=0.235, pruned_loss=0.04532, over 4929.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2607, pruned_loss=0.06376, over 985734.49 frames.], batch size: 26, aishell_tot_loss[loss=0.1917, simple_loss=0.2653, pruned_loss=0.05905, over 985123.52 frames.], datatang_tot_loss[loss=0.1974, simple_loss=0.256, pruned_loss=0.06937, over 985772.12 frames.], batch size: 26, lr: 1.05e-03
+2022-06-18 15:25:52,844 INFO [train.py:874] (1/4) Epoch 7, batch 3350, datatang_loss[loss=0.2183, simple_loss=0.2689, pruned_loss=0.0839, over 4931.00 frames.], tot_loss[loss=0.1941, simple_loss=0.261, pruned_loss=0.06362, over 985577.02 frames.], batch size: 79, aishell_tot_loss[loss=0.1912, simple_loss=0.265, pruned_loss=0.05871, over 985010.77 frames.], datatang_tot_loss[loss=0.1979, simple_loss=0.2563, pruned_loss=0.06973, over 985800.50 frames.], batch size: 79, lr: 1.05e-03
+2022-06-18 15:26:24,280 INFO [train.py:874] (1/4) Epoch 7, batch 3400, aishell_loss[loss=0.2122, simple_loss=0.2808, pruned_loss=0.07181, over 4949.00 frames.], tot_loss[loss=0.1945, simple_loss=0.2612, pruned_loss=0.06394, over 985552.01 frames.], batch size: 56, aishell_tot_loss[loss=0.1913, simple_loss=0.2651, pruned_loss=0.0588, over 985263.35 frames.], datatang_tot_loss[loss=0.1981, simple_loss=0.2567, pruned_loss=0.06981, over 985548.24 frames.], batch size: 56, lr: 1.05e-03
+2022-06-18 15:26:53,560 INFO [train.py:874] (1/4) Epoch 7, batch 3450, datatang_loss[loss=0.173, simple_loss=0.2423, pruned_loss=0.05184, over 4923.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2612, pruned_loss=0.06373, over 985411.19 frames.], batch size: 73, aishell_tot_loss[loss=0.1914, simple_loss=0.2651, pruned_loss=0.05883, over 985042.32 frames.], datatang_tot_loss[loss=0.1979, simple_loss=0.2566, pruned_loss=0.06961, over 985666.34 frames.], batch size: 73, lr: 1.05e-03
+2022-06-18 15:27:24,024 INFO [train.py:874] (1/4) Epoch 7, batch 3500, aishell_loss[loss=0.2249, simple_loss=0.2891, pruned_loss=0.0804, over 4945.00 frames.], tot_loss[loss=0.1955, simple_loss=0.2623, pruned_loss=0.06433, over 985520.06 frames.], batch size: 58, aishell_tot_loss[loss=0.1924, simple_loss=0.2659, pruned_loss=0.05942, over 985009.18 frames.], datatang_tot_loss[loss=0.198, simple_loss=0.2571, pruned_loss=0.06942, over 985817.24 frames.], batch size: 58, lr: 1.05e-03
+2022-06-18 15:27:53,566 INFO [train.py:874] (1/4) Epoch 7, batch 3550, aishell_loss[loss=0.1705, simple_loss=0.2495, pruned_loss=0.04573, over 4952.00 frames.], tot_loss[loss=0.1961, simple_loss=0.2629, pruned_loss=0.06461, over 985279.20 frames.], batch size: 31, aishell_tot_loss[loss=0.1926, simple_loss=0.2661, pruned_loss=0.05953, over 984845.29 frames.], datatang_tot_loss[loss=0.1985, simple_loss=0.2577, pruned_loss=0.06968, over 985741.44 frames.], batch size: 31, lr: 1.05e-03
+2022-06-18 15:28:23,444 INFO [train.py:874] (1/4) Epoch 7, batch 3600, aishell_loss[loss=0.1998, simple_loss=0.2773, pruned_loss=0.06115, over 4874.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2636, pruned_loss=0.06468, over 985315.43 frames.], batch size: 36, aishell_tot_loss[loss=0.1925, simple_loss=0.2663, pruned_loss=0.05936, over 984849.69 frames.], datatang_tot_loss[loss=0.1993, simple_loss=0.2584, pruned_loss=0.07012, over 985778.75 frames.], batch size: 36, lr: 1.05e-03
+2022-06-18 15:28:54,592 INFO [train.py:874] (1/4) Epoch 7, batch 3650, aishell_loss[loss=0.1865, simple_loss=0.2691, pruned_loss=0.05189, over 4886.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2636, pruned_loss=0.06505, over 985233.12 frames.], batch size: 50, aishell_tot_loss[loss=0.193, simple_loss=0.2665, pruned_loss=0.05979, over 984779.66 frames.], datatang_tot_loss[loss=0.1993, simple_loss=0.2586, pruned_loss=0.06997, over 985748.09 frames.], batch size: 50, lr: 1.05e-03
+2022-06-18 15:29:24,186 INFO [train.py:874] (1/4) Epoch 7, batch 3700, aishell_loss[loss=0.2051, simple_loss=0.2753, pruned_loss=0.0675, over 4954.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2628, pruned_loss=0.06437, over 985433.47 frames.], batch size: 40, aishell_tot_loss[loss=0.1921, simple_loss=0.2658, pruned_loss=0.05923, over 984942.47 frames.], datatang_tot_loss[loss=0.1992, simple_loss=0.2586, pruned_loss=0.06991, over 985799.61 frames.], batch size: 40, lr: 1.05e-03
+2022-06-18 15:29:54,972 INFO [train.py:874] (1/4) Epoch 7, batch 3750, aishell_loss[loss=0.2272, simple_loss=0.2946, pruned_loss=0.07991, over 4967.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2621, pruned_loss=0.06428, over 985399.78 frames.], batch size: 44, aishell_tot_loss[loss=0.1912, simple_loss=0.265, pruned_loss=0.05874, over 984756.10 frames.], datatang_tot_loss[loss=0.1997, simple_loss=0.2588, pruned_loss=0.07026, over 985964.69 frames.], batch size: 44, lr: 1.05e-03
+2022-06-18 15:30:23,978 INFO [train.py:874] (1/4) Epoch 7, batch 3800, aishell_loss[loss=0.1822, simple_loss=0.2642, pruned_loss=0.05016, over 4984.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2607, pruned_loss=0.06337, over 985809.59 frames.], batch size: 38, aishell_tot_loss[loss=0.1907, simple_loss=0.2646, pruned_loss=0.05841, over 984987.88 frames.], datatang_tot_loss[loss=0.1985, simple_loss=0.2578, pruned_loss=0.06965, over 986182.95 frames.], batch size: 38, lr: 1.05e-03
+2022-06-18 15:30:52,416 INFO [train.py:874] (1/4) Epoch 7, batch 3850, datatang_loss[loss=0.2222, simple_loss=0.2728, pruned_loss=0.0858, over 4931.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2595, pruned_loss=0.0625, over 985487.37 frames.], batch size: 79, aishell_tot_loss[loss=0.1902, simple_loss=0.264, pruned_loss=0.05822, over 984784.31 frames.], datatang_tot_loss[loss=0.1973, simple_loss=0.2569, pruned_loss=0.06886, over 986108.22 frames.], batch size: 79, lr: 1.05e-03
+2022-06-18 15:31:22,654 INFO [train.py:874] (1/4) Epoch 7, batch 3900, aishell_loss[loss=0.2146, simple_loss=0.2757, pruned_loss=0.07673, over 4938.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2597, pruned_loss=0.06253, over 985465.43 frames.], batch size: 54, aishell_tot_loss[loss=0.1905, simple_loss=0.2644, pruned_loss=0.05826, over 984717.12 frames.], datatang_tot_loss[loss=0.1967, simple_loss=0.2566, pruned_loss=0.06842, over 986140.56 frames.], batch size: 54, lr: 1.04e-03
+2022-06-18 15:31:51,714 INFO [train.py:874] (1/4) Epoch 7, batch 3950, aishell_loss[loss=0.2191, simple_loss=0.2884, pruned_loss=0.07488, over 4922.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2605, pruned_loss=0.06279, over 985706.32 frames.], batch size: 79, aishell_tot_loss[loss=0.1904, simple_loss=0.2647, pruned_loss=0.05809, over 984807.47 frames.], datatang_tot_loss[loss=0.1972, simple_loss=0.257, pruned_loss=0.06867, over 986324.53 frames.], batch size: 79, lr: 1.04e-03
+2022-06-18 15:32:19,177 INFO [train.py:874] (1/4) Epoch 7, batch 4000, aishell_loss[loss=0.2014, simple_loss=0.2713, pruned_loss=0.06579, over 4878.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2603, pruned_loss=0.06218, over 985737.33 frames.], batch size: 28, aishell_tot_loss[loss=0.1904, simple_loss=0.2648, pruned_loss=0.05794, over 984689.10 frames.], datatang_tot_loss[loss=0.1966, simple_loss=0.2564, pruned_loss=0.06834, over 986562.00 frames.], batch size: 28, lr: 1.04e-03
+2022-06-18 15:32:19,178 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 15:32:35,474 INFO [train.py:914] (1/4) 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] (1/4) Epoch 7, batch 4050, datatang_loss[loss=0.1977, simple_loss=0.2564, pruned_loss=0.06954, over 4920.00 frames.], tot_loss[loss=0.1918, simple_loss=0.2595, pruned_loss=0.06209, over 985314.34 frames.], batch size: 75, aishell_tot_loss[loss=0.1901, simple_loss=0.2644, pruned_loss=0.05791, over 984336.37 frames.], datatang_tot_loss[loss=0.196, simple_loss=0.256, pruned_loss=0.06804, over 986483.81 frames.], batch size: 75, lr: 1.04e-03
+2022-06-18 15:33:33,294 INFO [train.py:874] (1/4) Epoch 7, batch 4100, aishell_loss[loss=0.1851, simple_loss=0.2612, pruned_loss=0.05448, over 4902.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2599, pruned_loss=0.06272, over 984695.44 frames.], batch size: 41, aishell_tot_loss[loss=0.1901, simple_loss=0.2642, pruned_loss=0.05796, over 983923.39 frames.], datatang_tot_loss[loss=0.1967, simple_loss=0.2564, pruned_loss=0.0685, over 986243.57 frames.], batch size: 41, lr: 1.04e-03
+2022-06-18 15:34:37,403 INFO [train.py:874] (1/4) Epoch 8, batch 50, datatang_loss[loss=0.1701, simple_loss=0.2367, pruned_loss=0.05173, over 4944.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2538, pruned_loss=0.05837, over 218421.88 frames.], batch size: 50, aishell_tot_loss[loss=0.2005, simple_loss=0.2751, pruned_loss=0.06301, over 102882.81 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.2368, pruned_loss=0.0547, over 129003.04 frames.], batch size: 50, lr: 9.97e-04
+2022-06-18 15:35:06,763 INFO [train.py:874] (1/4) Epoch 8, batch 100, aishell_loss[loss=0.1687, simple_loss=0.2325, pruned_loss=0.05249, over 4838.00 frames.], tot_loss[loss=0.1877, simple_loss=0.257, pruned_loss=0.05918, over 388441.48 frames.], batch size: 24, aishell_tot_loss[loss=0.1946, simple_loss=0.2695, pruned_loss=0.05983, over 229843.98 frames.], datatang_tot_loss[loss=0.1794, simple_loss=0.2422, pruned_loss=0.05835, over 206851.10 frames.], batch size: 24, lr: 9.97e-04
+2022-06-18 15:35:37,008 INFO [train.py:874] (1/4) Epoch 8, batch 150, aishell_loss[loss=0.1707, simple_loss=0.244, pruned_loss=0.04871, over 4977.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2554, pruned_loss=0.05855, over 521213.86 frames.], batch size: 39, aishell_tot_loss[loss=0.1919, simple_loss=0.2672, pruned_loss=0.05833, over 319103.28 frames.], datatang_tot_loss[loss=0.1801, simple_loss=0.2424, pruned_loss=0.0589, over 298724.88 frames.], batch size: 39, lr: 9.96e-04
+2022-06-18 15:36:06,932 INFO [train.py:874] (1/4) Epoch 8, batch 200, datatang_loss[loss=0.2368, simple_loss=0.2759, pruned_loss=0.09889, over 4955.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2543, pruned_loss=0.05855, over 624028.86 frames.], batch size: 67, aishell_tot_loss[loss=0.1905, simple_loss=0.2655, pruned_loss=0.05779, over 400434.53 frames.], datatang_tot_loss[loss=0.1805, simple_loss=0.2423, pruned_loss=0.0594, over 376551.36 frames.], batch size: 67, lr: 9.95e-04
+2022-06-18 15:36:36,913 INFO [train.py:874] (1/4) Epoch 8, batch 250, aishell_loss[loss=0.209, simple_loss=0.2873, pruned_loss=0.06537, over 4974.00 frames.], tot_loss[loss=0.188, simple_loss=0.2565, pruned_loss=0.05971, over 704152.36 frames.], batch size: 39, aishell_tot_loss[loss=0.1916, simple_loss=0.2672, pruned_loss=0.05804, over 464215.48 frames.], datatang_tot_loss[loss=0.1834, simple_loss=0.2449, pruned_loss=0.06097, over 453549.22 frames.], batch size: 39, lr: 9.94e-04
+2022-06-18 15:37:06,797 INFO [train.py:874] (1/4) Epoch 8, batch 300, datatang_loss[loss=0.2015, simple_loss=0.2598, pruned_loss=0.07159, over 4945.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2568, pruned_loss=0.0608, over 766064.24 frames.], batch size: 50, aishell_tot_loss[loss=0.1905, simple_loss=0.2652, pruned_loss=0.05793, over 524892.18 frames.], datatang_tot_loss[loss=0.1866, simple_loss=0.2476, pruned_loss=0.06287, over 516426.94 frames.], batch size: 50, lr: 9.93e-04
+2022-06-18 15:37:37,433 INFO [train.py:874] (1/4) Epoch 8, batch 350, datatang_loss[loss=0.2082, simple_loss=0.2686, pruned_loss=0.07391, over 4955.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2566, pruned_loss=0.0612, over 814491.29 frames.], batch size: 99, aishell_tot_loss[loss=0.1901, simple_loss=0.2646, pruned_loss=0.05781, over 564491.42 frames.], datatang_tot_loss[loss=0.1879, simple_loss=0.249, pruned_loss=0.06341, over 585849.22 frames.], batch size: 99, lr: 9.93e-04
+2022-06-18 15:38:07,249 INFO [train.py:874] (1/4) Epoch 8, batch 400, aishell_loss[loss=0.1926, simple_loss=0.2643, pruned_loss=0.0604, over 4953.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2566, pruned_loss=0.06108, over 852631.47 frames.], batch size: 56, aishell_tot_loss[loss=0.1895, simple_loss=0.264, pruned_loss=0.05754, over 610619.13 frames.], datatang_tot_loss[loss=0.1885, simple_loss=0.2497, pruned_loss=0.06361, over 636389.20 frames.], batch size: 56, lr: 9.92e-04
+2022-06-18 15:38:37,479 INFO [train.py:874] (1/4) Epoch 8, batch 450, aishell_loss[loss=0.1527, simple_loss=0.2317, pruned_loss=0.03687, over 4980.00 frames.], tot_loss[loss=0.19, simple_loss=0.2577, pruned_loss=0.06109, over 882123.95 frames.], batch size: 30, aishell_tot_loss[loss=0.1878, simple_loss=0.2625, pruned_loss=0.05658, over 662706.10 frames.], datatang_tot_loss[loss=0.1911, simple_loss=0.2522, pruned_loss=0.06499, over 669980.64 frames.], batch size: 30, lr: 9.91e-04
+2022-06-18 15:39:07,772 INFO [train.py:874] (1/4) Epoch 8, batch 500, aishell_loss[loss=0.2426, simple_loss=0.3009, pruned_loss=0.09221, over 4930.00 frames.], tot_loss[loss=0.1907, simple_loss=0.258, pruned_loss=0.06166, over 904860.01 frames.], batch size: 78, aishell_tot_loss[loss=0.1885, simple_loss=0.2625, pruned_loss=0.05719, over 697644.38 frames.], datatang_tot_loss[loss=0.1915, simple_loss=0.2528, pruned_loss=0.06513, over 709944.87 frames.], batch size: 78, lr: 9.90e-04
+2022-06-18 15:39:37,949 INFO [train.py:874] (1/4) Epoch 8, batch 550, datatang_loss[loss=0.2058, simple_loss=0.2592, pruned_loss=0.0762, over 4862.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2582, pruned_loss=0.06226, over 923030.64 frames.], batch size: 39, aishell_tot_loss[loss=0.188, simple_loss=0.2617, pruned_loss=0.05719, over 730409.03 frames.], datatang_tot_loss[loss=0.1929, simple_loss=0.2539, pruned_loss=0.06598, over 743787.31 frames.], batch size: 39, lr: 9.89e-04
+2022-06-18 15:40:08,003 INFO [train.py:874] (1/4) Epoch 8, batch 600, aishell_loss[loss=0.1735, simple_loss=0.25, pruned_loss=0.04851, over 4882.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2586, pruned_loss=0.06337, over 936336.18 frames.], batch size: 42, aishell_tot_loss[loss=0.1881, simple_loss=0.2616, pruned_loss=0.05726, over 753260.00 frames.], datatang_tot_loss[loss=0.1947, simple_loss=0.2549, pruned_loss=0.06721, over 778323.29 frames.], batch size: 42, lr: 9.88e-04
+2022-06-18 15:40:37,299 INFO [train.py:874] (1/4) Epoch 8, batch 650, aishell_loss[loss=0.1697, simple_loss=0.2458, pruned_loss=0.04682, over 4974.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2586, pruned_loss=0.06257, over 947435.55 frames.], batch size: 31, aishell_tot_loss[loss=0.1876, simple_loss=0.2617, pruned_loss=0.05676, over 784875.07 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.2548, pruned_loss=0.06725, over 799017.83 frames.], batch size: 31, lr: 9.88e-04
+2022-06-18 15:41:07,620 INFO [train.py:874] (1/4) Epoch 8, batch 700, datatang_loss[loss=0.2261, simple_loss=0.2866, pruned_loss=0.08275, over 4930.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2588, pruned_loss=0.06214, over 955933.92 frames.], batch size: 94, aishell_tot_loss[loss=0.1872, simple_loss=0.2617, pruned_loss=0.05636, over 807773.39 frames.], datatang_tot_loss[loss=0.1948, simple_loss=0.2552, pruned_loss=0.0672, over 821726.38 frames.], batch size: 94, lr: 9.87e-04
+2022-06-18 15:41:37,807 INFO [train.py:874] (1/4) Epoch 8, batch 750, aishell_loss[loss=0.1963, simple_loss=0.2735, pruned_loss=0.05956, over 4956.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2592, pruned_loss=0.06213, over 962244.02 frames.], batch size: 40, aishell_tot_loss[loss=0.1872, simple_loss=0.2619, pruned_loss=0.05628, over 827903.67 frames.], datatang_tot_loss[loss=0.1951, simple_loss=0.2556, pruned_loss=0.06732, over 841494.56 frames.], batch size: 40, lr: 9.86e-04
+2022-06-18 15:42:08,049 INFO [train.py:874] (1/4) Epoch 8, batch 800, datatang_loss[loss=0.2043, simple_loss=0.2638, pruned_loss=0.07238, over 4909.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2577, pruned_loss=0.06152, over 967689.97 frames.], batch size: 42, aishell_tot_loss[loss=0.1862, simple_loss=0.2611, pruned_loss=0.0557, over 846095.76 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.2548, pruned_loss=0.06718, over 859077.82 frames.], batch size: 42, lr: 9.85e-04
+2022-06-18 15:42:37,768 INFO [train.py:874] (1/4) Epoch 8, batch 850, aishell_loss[loss=0.1544, simple_loss=0.2297, pruned_loss=0.03959, over 4926.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2589, pruned_loss=0.06129, over 971954.36 frames.], batch size: 32, aishell_tot_loss[loss=0.1864, simple_loss=0.2618, pruned_loss=0.05554, over 863178.96 frames.], datatang_tot_loss[loss=0.1948, simple_loss=0.2554, pruned_loss=0.0671, over 873691.00 frames.], batch size: 32, lr: 9.84e-04
+2022-06-18 15:43:07,862 INFO [train.py:874] (1/4) Epoch 8, batch 900, datatang_loss[loss=0.1894, simple_loss=0.2588, pruned_loss=0.06002, over 4953.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2591, pruned_loss=0.06182, over 974948.64 frames.], batch size: 91, aishell_tot_loss[loss=0.1868, simple_loss=0.2617, pruned_loss=0.05591, over 878378.16 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.2557, pruned_loss=0.06749, over 886133.91 frames.], batch size: 91, lr: 9.84e-04
+2022-06-18 15:43:39,064 INFO [train.py:874] (1/4) Epoch 8, batch 950, datatang_loss[loss=0.1815, simple_loss=0.2462, pruned_loss=0.05836, over 4898.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2593, pruned_loss=0.06207, over 977471.77 frames.], batch size: 47, aishell_tot_loss[loss=0.1869, simple_loss=0.2621, pruned_loss=0.05582, over 886787.55 frames.], datatang_tot_loss[loss=0.1954, simple_loss=0.256, pruned_loss=0.06737, over 901745.75 frames.], batch size: 47, lr: 9.83e-04
+2022-06-18 15:44:08,431 INFO [train.py:874] (1/4) Epoch 8, batch 1000, datatang_loss[loss=0.177, simple_loss=0.2405, pruned_loss=0.05669, over 4932.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2593, pruned_loss=0.06193, over 978907.41 frames.], batch size: 57, aishell_tot_loss[loss=0.1867, simple_loss=0.262, pruned_loss=0.05573, over 898725.28 frames.], datatang_tot_loss[loss=0.1955, simple_loss=0.2561, pruned_loss=0.06749, over 911041.81 frames.], batch size: 57, lr: 9.82e-04
+2022-06-18 15:44:08,432 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 15:44:24,591 INFO [train.py:914] (1/4) 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] (1/4) Epoch 8, batch 1050, datatang_loss[loss=0.1969, simple_loss=0.2616, pruned_loss=0.06607, over 4965.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2596, pruned_loss=0.06172, over 980312.13 frames.], batch size: 67, aishell_tot_loss[loss=0.1875, simple_loss=0.2628, pruned_loss=0.05612, over 908852.42 frames.], datatang_tot_loss[loss=0.1948, simple_loss=0.2557, pruned_loss=0.06697, over 919849.33 frames.], batch size: 67, lr: 9.81e-04
+2022-06-18 15:45:25,069 INFO [train.py:874] (1/4) Epoch 8, batch 1100, aishell_loss[loss=0.1658, simple_loss=0.2034, pruned_loss=0.06408, over 4981.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2589, pruned_loss=0.06149, over 981527.73 frames.], batch size: 21, aishell_tot_loss[loss=0.1868, simple_loss=0.2619, pruned_loss=0.05584, over 918964.80 frames.], datatang_tot_loss[loss=0.1951, simple_loss=0.2558, pruned_loss=0.06721, over 926698.64 frames.], batch size: 21, lr: 9.80e-04
+2022-06-18 15:45:54,910 INFO [train.py:874] (1/4) Epoch 8, batch 1150, datatang_loss[loss=0.2141, simple_loss=0.2692, pruned_loss=0.07948, over 4938.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2586, pruned_loss=0.06154, over 982084.91 frames.], batch size: 94, aishell_tot_loss[loss=0.1876, simple_loss=0.2626, pruned_loss=0.05629, over 926026.01 frames.], datatang_tot_loss[loss=0.1942, simple_loss=0.2549, pruned_loss=0.06674, over 934007.16 frames.], batch size: 94, lr: 9.80e-04
+2022-06-18 15:46:24,545 INFO [train.py:874] (1/4) Epoch 8, batch 1200, aishell_loss[loss=0.2039, simple_loss=0.2738, pruned_loss=0.06701, over 4978.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2592, pruned_loss=0.06213, over 982780.01 frames.], batch size: 44, aishell_tot_loss[loss=0.1889, simple_loss=0.2634, pruned_loss=0.05718, over 934013.52 frames.], datatang_tot_loss[loss=0.1939, simple_loss=0.2545, pruned_loss=0.06666, over 939101.87 frames.], batch size: 44, lr: 9.79e-04
+2022-06-18 15:46:55,065 INFO [train.py:874] (1/4) Epoch 8, batch 1250, datatang_loss[loss=0.171, simple_loss=0.2334, pruned_loss=0.05425, over 4951.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2589, pruned_loss=0.06213, over 983305.26 frames.], batch size: 50, aishell_tot_loss[loss=0.1882, simple_loss=0.2625, pruned_loss=0.05691, over 940489.14 frames.], datatang_tot_loss[loss=0.1945, simple_loss=0.2549, pruned_loss=0.06708, over 944120.00 frames.], batch size: 50, lr: 9.78e-04
+2022-06-18 15:47:24,820 INFO [train.py:874] (1/4) Epoch 8, batch 1300, aishell_loss[loss=0.1962, simple_loss=0.2751, pruned_loss=0.05861, over 4925.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2582, pruned_loss=0.0612, over 983808.98 frames.], batch size: 52, aishell_tot_loss[loss=0.1876, simple_loss=0.2622, pruned_loss=0.05649, over 945654.41 frames.], datatang_tot_loss[loss=0.1938, simple_loss=0.2545, pruned_loss=0.06657, over 949153.87 frames.], batch size: 52, lr: 9.77e-04
+2022-06-18 15:47:55,252 INFO [train.py:874] (1/4) Epoch 8, batch 1350, datatang_loss[loss=0.2033, simple_loss=0.2721, pruned_loss=0.06725, over 4920.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2582, pruned_loss=0.06123, over 984468.32 frames.], batch size: 94, aishell_tot_loss[loss=0.1866, simple_loss=0.2611, pruned_loss=0.05605, over 950443.89 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.2555, pruned_loss=0.06689, over 953642.12 frames.], batch size: 94, lr: 9.76e-04
+2022-06-18 15:48:24,916 INFO [train.py:874] (1/4) Epoch 8, batch 1400, aishell_loss[loss=0.2033, simple_loss=0.2725, pruned_loss=0.06706, over 4874.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2595, pruned_loss=0.06157, over 984519.93 frames.], batch size: 37, aishell_tot_loss[loss=0.1871, simple_loss=0.2618, pruned_loss=0.05622, over 955029.80 frames.], datatang_tot_loss[loss=0.1953, simple_loss=0.2561, pruned_loss=0.06729, over 956808.40 frames.], batch size: 37, lr: 9.76e-04
+2022-06-18 15:48:55,376 INFO [train.py:874] (1/4) Epoch 8, batch 1450, aishell_loss[loss=0.1977, simple_loss=0.2711, pruned_loss=0.06218, over 4854.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2587, pruned_loss=0.06107, over 984900.67 frames.], batch size: 36, aishell_tot_loss[loss=0.1868, simple_loss=0.2616, pruned_loss=0.05597, over 958674.84 frames.], datatang_tot_loss[loss=0.1947, simple_loss=0.2555, pruned_loss=0.06692, over 960321.17 frames.], batch size: 36, lr: 9.75e-04
+2022-06-18 15:49:25,886 INFO [train.py:874] (1/4) Epoch 8, batch 1500, datatang_loss[loss=0.2733, simple_loss=0.309, pruned_loss=0.1188, over 4924.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2583, pruned_loss=0.06065, over 984512.57 frames.], batch size: 109, aishell_tot_loss[loss=0.1859, simple_loss=0.2612, pruned_loss=0.05533, over 961396.73 frames.], datatang_tot_loss[loss=0.1948, simple_loss=0.2556, pruned_loss=0.06706, over 963190.73 frames.], batch size: 109, lr: 9.74e-04
+2022-06-18 15:49:55,599 INFO [train.py:874] (1/4) Epoch 8, batch 1550, datatang_loss[loss=0.1645, simple_loss=0.2335, pruned_loss=0.04774, over 4907.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2577, pruned_loss=0.06101, over 984749.88 frames.], batch size: 47, aishell_tot_loss[loss=0.1863, simple_loss=0.2618, pruned_loss=0.05541, over 963420.06 frames.], datatang_tot_loss[loss=0.1941, simple_loss=0.2545, pruned_loss=0.06681, over 966576.81 frames.], batch size: 47, lr: 9.73e-04
+2022-06-18 15:50:25,932 INFO [train.py:874] (1/4) Epoch 8, batch 1600, datatang_loss[loss=0.1692, simple_loss=0.2333, pruned_loss=0.0525, over 4886.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2566, pruned_loss=0.06006, over 984676.66 frames.], batch size: 25, aishell_tot_loss[loss=0.1852, simple_loss=0.2606, pruned_loss=0.05487, over 966449.53 frames.], datatang_tot_loss[loss=0.1937, simple_loss=0.2542, pruned_loss=0.06663, over 968205.25 frames.], batch size: 25, lr: 9.73e-04
+2022-06-18 15:50:55,412 INFO [train.py:874] (1/4) Epoch 8, batch 1650, aishell_loss[loss=0.1971, simple_loss=0.2702, pruned_loss=0.06202, over 4916.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2582, pruned_loss=0.06069, over 985122.74 frames.], batch size: 46, aishell_tot_loss[loss=0.1858, simple_loss=0.2614, pruned_loss=0.05507, over 968838.73 frames.], datatang_tot_loss[loss=0.1945, simple_loss=0.2549, pruned_loss=0.06704, over 970384.58 frames.], batch size: 46, lr: 9.72e-04
+2022-06-18 15:51:24,651 INFO [train.py:874] (1/4) Epoch 8, batch 1700, datatang_loss[loss=0.1803, simple_loss=0.2394, pruned_loss=0.06059, over 4899.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2578, pruned_loss=0.0599, over 985174.21 frames.], batch size: 42, aishell_tot_loss[loss=0.1848, simple_loss=0.2607, pruned_loss=0.0545, over 971029.25 frames.], datatang_tot_loss[loss=0.1944, simple_loss=0.2551, pruned_loss=0.06688, over 971925.28 frames.], batch size: 42, lr: 9.71e-04
+2022-06-18 15:51:53,643 INFO [train.py:874] (1/4) Epoch 8, batch 1750, aishell_loss[loss=0.1804, simple_loss=0.2645, pruned_loss=0.04812, over 4863.00 frames.], tot_loss[loss=0.1895, simple_loss=0.259, pruned_loss=0.06001, over 985127.62 frames.], batch size: 37, aishell_tot_loss[loss=0.1852, simple_loss=0.2612, pruned_loss=0.05462, over 972852.33 frames.], datatang_tot_loss[loss=0.1949, simple_loss=0.2557, pruned_loss=0.06704, over 973300.67 frames.], batch size: 37, lr: 9.70e-04
+2022-06-18 15:52:24,178 INFO [train.py:874] (1/4) Epoch 8, batch 1800, datatang_loss[loss=0.1951, simple_loss=0.2629, pruned_loss=0.06363, over 4937.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2581, pruned_loss=0.06024, over 985460.75 frames.], batch size: 88, aishell_tot_loss[loss=0.1851, simple_loss=0.261, pruned_loss=0.05455, over 973968.22 frames.], datatang_tot_loss[loss=0.1944, simple_loss=0.2553, pruned_loss=0.06672, over 975354.36 frames.], batch size: 88, lr: 9.69e-04
+2022-06-18 15:52:53,918 INFO [train.py:874] (1/4) Epoch 8, batch 1850, datatang_loss[loss=0.1788, simple_loss=0.2251, pruned_loss=0.06629, over 4919.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2581, pruned_loss=0.05962, over 985699.48 frames.], batch size: 50, aishell_tot_loss[loss=0.1845, simple_loss=0.2609, pruned_loss=0.05406, over 975706.30 frames.], datatang_tot_loss[loss=0.1943, simple_loss=0.2553, pruned_loss=0.06664, over 976425.41 frames.], batch size: 50, lr: 9.69e-04
+2022-06-18 15:53:23,134 INFO [train.py:874] (1/4) Epoch 8, batch 1900, aishell_loss[loss=0.1824, simple_loss=0.2544, pruned_loss=0.05522, over 4963.00 frames.], tot_loss[loss=0.1905, simple_loss=0.259, pruned_loss=0.06105, over 985760.83 frames.], batch size: 31, aishell_tot_loss[loss=0.1856, simple_loss=0.2617, pruned_loss=0.05476, over 976683.50 frames.], datatang_tot_loss[loss=0.1948, simple_loss=0.2556, pruned_loss=0.06696, over 977756.63 frames.], batch size: 31, lr: 9.68e-04
+2022-06-18 15:53:54,911 INFO [train.py:874] (1/4) Epoch 8, batch 1950, aishell_loss[loss=0.2164, simple_loss=0.2999, pruned_loss=0.06641, over 4925.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2584, pruned_loss=0.06091, over 985897.08 frames.], batch size: 78, aishell_tot_loss[loss=0.1851, simple_loss=0.2612, pruned_loss=0.05447, over 977688.54 frames.], datatang_tot_loss[loss=0.1946, simple_loss=0.2555, pruned_loss=0.06689, over 978890.03 frames.], batch size: 78, lr: 9.67e-04
+2022-06-18 15:54:24,708 INFO [train.py:874] (1/4) Epoch 8, batch 2000, datatang_loss[loss=0.2011, simple_loss=0.2612, pruned_loss=0.07046, over 4949.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2586, pruned_loss=0.06124, over 985750.82 frames.], batch size: 25, aishell_tot_loss[loss=0.1854, simple_loss=0.2615, pruned_loss=0.05467, over 978248.68 frames.], datatang_tot_loss[loss=0.1949, simple_loss=0.2555, pruned_loss=0.06711, over 979963.53 frames.], batch size: 25, lr: 9.66e-04
+2022-06-18 15:54:24,709 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 15:54:40,501 INFO [train.py:914] (1/4) Epoch 8, validation: loss=0.1711, simple_loss=0.254, pruned_loss=0.04413, over 1622729.00 frames.
+2022-06-18 15:55:10,326 INFO [train.py:874] (1/4) Epoch 8, batch 2050, aishell_loss[loss=0.1664, simple_loss=0.2518, pruned_loss=0.04047, over 4934.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2579, pruned_loss=0.06032, over 985524.62 frames.], batch size: 49, aishell_tot_loss[loss=0.1858, simple_loss=0.2618, pruned_loss=0.05488, over 978989.92 frames.], datatang_tot_loss[loss=0.1933, simple_loss=0.2544, pruned_loss=0.06614, over 980575.08 frames.], batch size: 49, lr: 9.66e-04
+2022-06-18 15:55:40,580 INFO [train.py:874] (1/4) Epoch 8, batch 2100, datatang_loss[loss=0.1964, simple_loss=0.2596, pruned_loss=0.06655, over 4983.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2573, pruned_loss=0.06067, over 985571.64 frames.], batch size: 45, aishell_tot_loss[loss=0.1857, simple_loss=0.2615, pruned_loss=0.05494, over 979594.88 frames.], datatang_tot_loss[loss=0.1933, simple_loss=0.2542, pruned_loss=0.06619, over 981349.38 frames.], batch size: 45, lr: 9.65e-04
+2022-06-18 15:56:10,906 INFO [train.py:874] (1/4) Epoch 8, batch 2150, datatang_loss[loss=0.189, simple_loss=0.2627, pruned_loss=0.05767, over 4951.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2576, pruned_loss=0.06102, over 985865.06 frames.], batch size: 86, aishell_tot_loss[loss=0.1858, simple_loss=0.2617, pruned_loss=0.05501, over 980412.39 frames.], datatang_tot_loss[loss=0.1935, simple_loss=0.2542, pruned_loss=0.06643, over 982044.05 frames.], batch size: 86, lr: 9.64e-04
+2022-06-18 15:56:40,021 INFO [train.py:874] (1/4) Epoch 8, batch 2200, datatang_loss[loss=0.1857, simple_loss=0.2445, pruned_loss=0.06344, over 4944.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2576, pruned_loss=0.06054, over 986159.49 frames.], batch size: 67, aishell_tot_loss[loss=0.1863, simple_loss=0.2623, pruned_loss=0.05513, over 981087.42 frames.], datatang_tot_loss[loss=0.1926, simple_loss=0.2536, pruned_loss=0.06582, over 982763.42 frames.], batch size: 67, lr: 9.63e-04
+2022-06-18 15:57:10,683 INFO [train.py:874] (1/4) Epoch 8, batch 2250, aishell_loss[loss=0.1595, simple_loss=0.2386, pruned_loss=0.04016, over 4937.00 frames.], tot_loss[loss=0.188, simple_loss=0.2564, pruned_loss=0.05978, over 985942.22 frames.], batch size: 54, aishell_tot_loss[loss=0.1855, simple_loss=0.2613, pruned_loss=0.05484, over 981675.79 frames.], datatang_tot_loss[loss=0.192, simple_loss=0.2531, pruned_loss=0.06543, over 982954.26 frames.], batch size: 54, lr: 9.63e-04
+2022-06-18 15:57:40,727 INFO [train.py:874] (1/4) Epoch 8, batch 2300, datatang_loss[loss=0.1931, simple_loss=0.2527, pruned_loss=0.06675, over 4944.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2558, pruned_loss=0.05885, over 985573.98 frames.], batch size: 69, aishell_tot_loss[loss=0.1848, simple_loss=0.2609, pruned_loss=0.05435, over 981911.51 frames.], datatang_tot_loss[loss=0.1914, simple_loss=0.2526, pruned_loss=0.06504, over 983202.44 frames.], batch size: 69, lr: 9.62e-04
+2022-06-18 15:58:10,147 INFO [train.py:874] (1/4) Epoch 8, batch 2350, aishell_loss[loss=0.1589, simple_loss=0.2379, pruned_loss=0.03993, over 4873.00 frames.], tot_loss[loss=0.1874, simple_loss=0.257, pruned_loss=0.0589, over 985410.66 frames.], batch size: 28, aishell_tot_loss[loss=0.1852, simple_loss=0.2613, pruned_loss=0.05452, over 982283.53 frames.], datatang_tot_loss[loss=0.1916, simple_loss=0.2529, pruned_loss=0.06516, over 983416.87 frames.], batch size: 28, lr: 9.61e-04
+2022-06-18 15:58:39,608 INFO [train.py:874] (1/4) Epoch 8, batch 2400, datatang_loss[loss=0.1709, simple_loss=0.2387, pruned_loss=0.05149, over 4900.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2571, pruned_loss=0.0591, over 985106.89 frames.], batch size: 59, aishell_tot_loss[loss=0.1848, simple_loss=0.261, pruned_loss=0.05434, over 982281.82 frames.], datatang_tot_loss[loss=0.1921, simple_loss=0.2531, pruned_loss=0.06553, over 983708.08 frames.], batch size: 59, lr: 9.60e-04
+2022-06-18 15:59:09,433 INFO [train.py:874] (1/4) Epoch 8, batch 2450, aishell_loss[loss=0.1672, simple_loss=0.2528, pruned_loss=0.04076, over 4955.00 frames.], tot_loss[loss=0.188, simple_loss=0.2578, pruned_loss=0.0591, over 985223.46 frames.], batch size: 54, aishell_tot_loss[loss=0.1846, simple_loss=0.2609, pruned_loss=0.05413, over 982712.58 frames.], datatang_tot_loss[loss=0.1929, simple_loss=0.2539, pruned_loss=0.06594, over 983948.32 frames.], batch size: 54, lr: 9.60e-04
+2022-06-18 15:59:39,453 INFO [train.py:874] (1/4) Epoch 8, batch 2500, aishell_loss[loss=0.2145, simple_loss=0.2828, pruned_loss=0.07309, over 4980.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2575, pruned_loss=0.05942, over 985361.86 frames.], batch size: 44, aishell_tot_loss[loss=0.1849, simple_loss=0.2608, pruned_loss=0.05448, over 982806.88 frames.], datatang_tot_loss[loss=0.1925, simple_loss=0.2537, pruned_loss=0.06566, over 984441.28 frames.], batch size: 44, lr: 9.59e-04
+2022-06-18 16:00:09,591 INFO [train.py:874] (1/4) Epoch 8, batch 2550, aishell_loss[loss=0.1501, simple_loss=0.2312, pruned_loss=0.03448, over 4903.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2563, pruned_loss=0.05947, over 985686.42 frames.], batch size: 34, aishell_tot_loss[loss=0.1843, simple_loss=0.2604, pruned_loss=0.05411, over 983050.18 frames.], datatang_tot_loss[loss=0.192, simple_loss=0.2532, pruned_loss=0.06546, over 984872.98 frames.], batch size: 34, lr: 9.58e-04
+2022-06-18 16:00:41,124 INFO [train.py:874] (1/4) Epoch 8, batch 2600, datatang_loss[loss=0.1736, simple_loss=0.2415, pruned_loss=0.05287, over 4933.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2562, pruned_loss=0.0592, over 986053.19 frames.], batch size: 69, aishell_tot_loss[loss=0.184, simple_loss=0.2604, pruned_loss=0.05382, over 983201.23 frames.], datatang_tot_loss[loss=0.1918, simple_loss=0.2532, pruned_loss=0.06523, over 985488.38 frames.], batch size: 69, lr: 9.57e-04
+2022-06-18 16:01:09,774 INFO [train.py:874] (1/4) Epoch 8, batch 2650, aishell_loss[loss=0.1686, simple_loss=0.2428, pruned_loss=0.04721, over 4882.00 frames.], tot_loss[loss=0.188, simple_loss=0.257, pruned_loss=0.05953, over 985531.92 frames.], batch size: 42, aishell_tot_loss[loss=0.1847, simple_loss=0.261, pruned_loss=0.05418, over 983229.06 frames.], datatang_tot_loss[loss=0.1917, simple_loss=0.2533, pruned_loss=0.0651, over 985315.71 frames.], batch size: 42, lr: 9.57e-04
+2022-06-18 16:01:39,858 INFO [train.py:874] (1/4) Epoch 8, batch 2700, datatang_loss[loss=0.1999, simple_loss=0.2583, pruned_loss=0.07072, over 4889.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2576, pruned_loss=0.05942, over 985752.20 frames.], batch size: 42, aishell_tot_loss[loss=0.1846, simple_loss=0.2613, pruned_loss=0.05398, over 983658.87 frames.], datatang_tot_loss[loss=0.1921, simple_loss=0.2535, pruned_loss=0.06534, over 985452.41 frames.], batch size: 42, lr: 9.56e-04
+2022-06-18 16:02:09,343 INFO [train.py:874] (1/4) Epoch 8, batch 2750, datatang_loss[loss=0.189, simple_loss=0.2503, pruned_loss=0.06383, over 4927.00 frames.], tot_loss[loss=0.1898, simple_loss=0.259, pruned_loss=0.06024, over 985942.23 frames.], batch size: 71, aishell_tot_loss[loss=0.186, simple_loss=0.2624, pruned_loss=0.05475, over 984056.26 frames.], datatang_tot_loss[loss=0.1924, simple_loss=0.2539, pruned_loss=0.06549, over 985557.76 frames.], batch size: 71, lr: 9.55e-04
+2022-06-18 16:02:39,820 INFO [train.py:874] (1/4) Epoch 8, batch 2800, datatang_loss[loss=0.1878, simple_loss=0.2537, pruned_loss=0.06101, over 4924.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2579, pruned_loss=0.05974, over 985901.20 frames.], batch size: 94, aishell_tot_loss[loss=0.186, simple_loss=0.2625, pruned_loss=0.05472, over 984255.37 frames.], datatang_tot_loss[loss=0.1914, simple_loss=0.2528, pruned_loss=0.06502, over 985584.11 frames.], batch size: 94, lr: 9.54e-04
+2022-06-18 16:03:10,498 INFO [train.py:874] (1/4) Epoch 8, batch 2850, aishell_loss[loss=0.2009, simple_loss=0.2847, pruned_loss=0.05854, over 4943.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2582, pruned_loss=0.05964, over 985857.58 frames.], batch size: 45, aishell_tot_loss[loss=0.1859, simple_loss=0.2626, pruned_loss=0.0546, over 984474.74 frames.], datatang_tot_loss[loss=0.1916, simple_loss=0.2531, pruned_loss=0.06509, over 985559.88 frames.], batch size: 45, lr: 9.54e-04
+2022-06-18 16:03:39,760 INFO [train.py:874] (1/4) Epoch 8, batch 2900, aishell_loss[loss=0.1874, simple_loss=0.2591, pruned_loss=0.05785, over 4968.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2576, pruned_loss=0.05979, over 985883.74 frames.], batch size: 61, aishell_tot_loss[loss=0.1859, simple_loss=0.2625, pruned_loss=0.05465, over 984622.59 frames.], datatang_tot_loss[loss=0.1913, simple_loss=0.2527, pruned_loss=0.06497, over 985632.03 frames.], batch size: 61, lr: 9.53e-04
+2022-06-18 16:04:10,428 INFO [train.py:874] (1/4) Epoch 8, batch 2950, aishell_loss[loss=0.179, simple_loss=0.2556, pruned_loss=0.0512, over 4977.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2576, pruned_loss=0.05947, over 985781.23 frames.], batch size: 48, aishell_tot_loss[loss=0.1861, simple_loss=0.2626, pruned_loss=0.05482, over 984583.56 frames.], datatang_tot_loss[loss=0.1908, simple_loss=0.2525, pruned_loss=0.06455, over 985737.77 frames.], batch size: 48, lr: 9.52e-04
+2022-06-18 16:04:40,904 INFO [train.py:874] (1/4) Epoch 8, batch 3000, aishell_loss[loss=0.2592, simple_loss=0.3131, pruned_loss=0.1027, over 4939.00 frames.], tot_loss[loss=0.1879, simple_loss=0.257, pruned_loss=0.0594, over 985797.54 frames.], batch size: 68, aishell_tot_loss[loss=0.186, simple_loss=0.2623, pruned_loss=0.05486, over 984782.90 frames.], datatang_tot_loss[loss=0.1905, simple_loss=0.2523, pruned_loss=0.06434, over 985714.10 frames.], batch size: 68, lr: 9.52e-04
+2022-06-18 16:04:40,905 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 16:04:57,747 INFO [train.py:914] (1/4) Epoch 8, validation: loss=0.1712, simple_loss=0.2536, pruned_loss=0.04441, over 1622729.00 frames.
+2022-06-18 16:05:32,038 INFO [train.py:874] (1/4) Epoch 8, batch 3050, datatang_loss[loss=0.1847, simple_loss=0.258, pruned_loss=0.05572, over 4934.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2576, pruned_loss=0.0596, over 985698.18 frames.], batch size: 94, aishell_tot_loss[loss=0.1855, simple_loss=0.2621, pruned_loss=0.05441, over 984673.43 frames.], datatang_tot_loss[loss=0.1914, simple_loss=0.253, pruned_loss=0.06484, over 985824.80 frames.], batch size: 94, lr: 9.51e-04
+2022-06-18 16:06:02,993 INFO [train.py:874] (1/4) Epoch 8, batch 3100, aishell_loss[loss=0.1798, simple_loss=0.2636, pruned_loss=0.04799, over 4916.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2576, pruned_loss=0.05949, over 985735.59 frames.], batch size: 46, aishell_tot_loss[loss=0.1858, simple_loss=0.2625, pruned_loss=0.0546, over 984812.69 frames.], datatang_tot_loss[loss=0.1908, simple_loss=0.2529, pruned_loss=0.06436, over 985824.97 frames.], batch size: 46, lr: 9.50e-04
+2022-06-18 16:06:31,629 INFO [train.py:874] (1/4) Epoch 8, batch 3150, datatang_loss[loss=0.2741, simple_loss=0.3168, pruned_loss=0.1157, over 4949.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2571, pruned_loss=0.05935, over 985836.59 frames.], batch size: 108, aishell_tot_loss[loss=0.1856, simple_loss=0.2619, pruned_loss=0.05462, over 984895.20 frames.], datatang_tot_loss[loss=0.1906, simple_loss=0.2528, pruned_loss=0.06422, over 985936.16 frames.], batch size: 108, lr: 9.49e-04
+2022-06-18 16:07:02,061 INFO [train.py:874] (1/4) Epoch 8, batch 3200, datatang_loss[loss=0.2654, simple_loss=0.3037, pruned_loss=0.1135, over 4954.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2577, pruned_loss=0.05982, over 985640.96 frames.], batch size: 109, aishell_tot_loss[loss=0.1851, simple_loss=0.2616, pruned_loss=0.05425, over 984699.88 frames.], datatang_tot_loss[loss=0.1918, simple_loss=0.2538, pruned_loss=0.06488, over 986011.76 frames.], batch size: 109, lr: 9.49e-04
+2022-06-18 16:07:32,561 INFO [train.py:874] (1/4) Epoch 8, batch 3250, datatang_loss[loss=0.1856, simple_loss=0.2525, pruned_loss=0.05931, over 4960.00 frames.], tot_loss[loss=0.1883, simple_loss=0.2574, pruned_loss=0.05964, over 985422.33 frames.], batch size: 91, aishell_tot_loss[loss=0.1849, simple_loss=0.2614, pruned_loss=0.05422, over 984691.23 frames.], datatang_tot_loss[loss=0.1917, simple_loss=0.2537, pruned_loss=0.06488, over 985848.88 frames.], batch size: 91, lr: 9.48e-04
+2022-06-18 16:08:02,004 INFO [train.py:874] (1/4) Epoch 8, batch 3300, datatang_loss[loss=0.1755, simple_loss=0.2382, pruned_loss=0.05639, over 4970.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2566, pruned_loss=0.05908, over 985469.08 frames.], batch size: 24, aishell_tot_loss[loss=0.1847, simple_loss=0.261, pruned_loss=0.05422, over 984676.19 frames.], datatang_tot_loss[loss=0.1908, simple_loss=0.2531, pruned_loss=0.06425, over 985945.22 frames.], batch size: 24, lr: 9.47e-04
+2022-06-18 16:08:32,930 INFO [train.py:874] (1/4) Epoch 8, batch 3350, aishell_loss[loss=0.1903, simple_loss=0.2742, pruned_loss=0.05314, over 4938.00 frames.], tot_loss[loss=0.1891, simple_loss=0.2579, pruned_loss=0.06014, over 985737.54 frames.], batch size: 45, aishell_tot_loss[loss=0.1852, simple_loss=0.2616, pruned_loss=0.05437, over 984555.40 frames.], datatang_tot_loss[loss=0.1919, simple_loss=0.2539, pruned_loss=0.06497, over 986347.41 frames.], batch size: 45, lr: 9.46e-04
+2022-06-18 16:09:02,961 INFO [train.py:874] (1/4) Epoch 8, batch 3400, aishell_loss[loss=0.1918, simple_loss=0.2712, pruned_loss=0.05622, over 4924.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2581, pruned_loss=0.06034, over 985748.86 frames.], batch size: 33, aishell_tot_loss[loss=0.1856, simple_loss=0.2618, pruned_loss=0.0547, over 984641.67 frames.], datatang_tot_loss[loss=0.1919, simple_loss=0.2541, pruned_loss=0.06487, over 986335.70 frames.], batch size: 33, lr: 9.46e-04
+2022-06-18 16:09:31,652 INFO [train.py:874] (1/4) Epoch 8, batch 3450, datatang_loss[loss=0.1833, simple_loss=0.2451, pruned_loss=0.06072, over 4871.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2568, pruned_loss=0.05976, over 985600.95 frames.], batch size: 25, aishell_tot_loss[loss=0.1852, simple_loss=0.2612, pruned_loss=0.05458, over 984666.97 frames.], datatang_tot_loss[loss=0.1912, simple_loss=0.2532, pruned_loss=0.06462, over 986234.79 frames.], batch size: 25, lr: 9.45e-04
+2022-06-18 16:10:01,616 INFO [train.py:874] (1/4) Epoch 8, batch 3500, datatang_loss[loss=0.2126, simple_loss=0.2736, pruned_loss=0.0758, over 4903.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2576, pruned_loss=0.05956, over 985842.44 frames.], batch size: 52, aishell_tot_loss[loss=0.1852, simple_loss=0.2615, pruned_loss=0.05451, over 984966.76 frames.], datatang_tot_loss[loss=0.1915, simple_loss=0.2537, pruned_loss=0.06466, over 986248.26 frames.], batch size: 52, lr: 9.44e-04
+2022-06-18 16:10:31,543 INFO [train.py:874] (1/4) Epoch 8, batch 3550, datatang_loss[loss=0.1614, simple_loss=0.231, pruned_loss=0.04593, over 4968.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2571, pruned_loss=0.05918, over 986117.93 frames.], batch size: 31, aishell_tot_loss[loss=0.1854, simple_loss=0.2616, pruned_loss=0.05465, over 985194.63 frames.], datatang_tot_loss[loss=0.1906, simple_loss=0.2531, pruned_loss=0.06406, over 986364.68 frames.], batch size: 31, lr: 9.44e-04
+2022-06-18 16:11:02,121 INFO [train.py:874] (1/4) Epoch 8, batch 3600, datatang_loss[loss=0.241, simple_loss=0.2733, pruned_loss=0.1043, over 4932.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2561, pruned_loss=0.05868, over 985896.43 frames.], batch size: 34, aishell_tot_loss[loss=0.1852, simple_loss=0.2615, pruned_loss=0.05443, over 985063.97 frames.], datatang_tot_loss[loss=0.1896, simple_loss=0.2522, pruned_loss=0.06348, over 986303.64 frames.], batch size: 34, lr: 9.43e-04
+2022-06-18 16:11:32,844 INFO [train.py:874] (1/4) Epoch 8, batch 3650, datatang_loss[loss=0.1906, simple_loss=0.2577, pruned_loss=0.06177, over 4930.00 frames.], tot_loss[loss=0.1865, simple_loss=0.256, pruned_loss=0.05853, over 985639.80 frames.], batch size: 71, aishell_tot_loss[loss=0.185, simple_loss=0.2613, pruned_loss=0.0544, over 985061.73 frames.], datatang_tot_loss[loss=0.1894, simple_loss=0.2521, pruned_loss=0.0633, over 986067.76 frames.], batch size: 71, lr: 9.42e-04
+2022-06-18 16:12:03,747 INFO [train.py:874] (1/4) Epoch 8, batch 3700, aishell_loss[loss=0.1668, simple_loss=0.2199, pruned_loss=0.05685, over 4893.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2553, pruned_loss=0.05856, over 985760.96 frames.], batch size: 20, aishell_tot_loss[loss=0.1852, simple_loss=0.2613, pruned_loss=0.05456, over 985020.67 frames.], datatang_tot_loss[loss=0.1886, simple_loss=0.2514, pruned_loss=0.06287, over 986221.16 frames.], batch size: 20, lr: 9.42e-04
+2022-06-18 16:12:32,578 INFO [train.py:874] (1/4) Epoch 8, batch 3750, datatang_loss[loss=0.1889, simple_loss=0.2489, pruned_loss=0.06445, over 4962.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2557, pruned_loss=0.05837, over 985607.44 frames.], batch size: 45, aishell_tot_loss[loss=0.1849, simple_loss=0.2613, pruned_loss=0.0543, over 984971.11 frames.], datatang_tot_loss[loss=0.1888, simple_loss=0.2515, pruned_loss=0.063, over 986162.69 frames.], batch size: 45, lr: 9.41e-04
+2022-06-18 16:13:02,544 INFO [train.py:874] (1/4) Epoch 8, batch 3800, datatang_loss[loss=0.1672, simple_loss=0.2301, pruned_loss=0.05219, over 4939.00 frames.], tot_loss[loss=0.187, simple_loss=0.2563, pruned_loss=0.05888, over 985407.95 frames.], batch size: 34, aishell_tot_loss[loss=0.1853, simple_loss=0.2616, pruned_loss=0.05445, over 984760.57 frames.], datatang_tot_loss[loss=0.1892, simple_loss=0.2518, pruned_loss=0.06332, over 986161.94 frames.], batch size: 34, lr: 9.40e-04
+2022-06-18 16:13:31,933 INFO [train.py:874] (1/4) Epoch 8, batch 3850, aishell_loss[loss=0.1788, simple_loss=0.257, pruned_loss=0.05024, over 4886.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2571, pruned_loss=0.05874, over 985454.30 frames.], batch size: 47, aishell_tot_loss[loss=0.1859, simple_loss=0.2625, pruned_loss=0.05462, over 984809.72 frames.], datatang_tot_loss[loss=0.1888, simple_loss=0.2515, pruned_loss=0.06298, over 986152.15 frames.], batch size: 47, lr: 9.39e-04
+2022-06-18 16:14:00,683 INFO [train.py:874] (1/4) Epoch 8, batch 3900, aishell_loss[loss=0.2161, simple_loss=0.295, pruned_loss=0.06861, over 4970.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2562, pruned_loss=0.05781, over 985661.31 frames.], batch size: 79, aishell_tot_loss[loss=0.1854, simple_loss=0.2621, pruned_loss=0.05434, over 984991.99 frames.], datatang_tot_loss[loss=0.1879, simple_loss=0.2509, pruned_loss=0.06243, over 986201.38 frames.], batch size: 79, lr: 9.39e-04
+2022-06-18 16:14:30,205 INFO [train.py:874] (1/4) Epoch 8, batch 3950, datatang_loss[loss=0.1618, simple_loss=0.2335, pruned_loss=0.04504, over 4932.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2554, pruned_loss=0.05777, over 985651.78 frames.], batch size: 71, aishell_tot_loss[loss=0.1857, simple_loss=0.2625, pruned_loss=0.05449, over 985042.70 frames.], datatang_tot_loss[loss=0.1868, simple_loss=0.2499, pruned_loss=0.0619, over 986118.52 frames.], batch size: 71, lr: 9.38e-04
+2022-06-18 16:14:59,697 INFO [train.py:874] (1/4) Epoch 8, batch 4000, datatang_loss[loss=0.2115, simple_loss=0.2706, pruned_loss=0.07621, over 4940.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2561, pruned_loss=0.05828, over 985155.07 frames.], batch size: 94, aishell_tot_loss[loss=0.1857, simple_loss=0.2624, pruned_loss=0.05454, over 984601.20 frames.], datatang_tot_loss[loss=0.1876, simple_loss=0.2507, pruned_loss=0.06222, over 986034.26 frames.], batch size: 94, lr: 9.37e-04
+2022-06-18 16:14:59,698 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 16:15:16,494 INFO [train.py:914] (1/4) Epoch 8, validation: loss=0.1678, simple_loss=0.2518, pruned_loss=0.0419, over 1622729.00 frames.
+2022-06-18 16:15:46,525 INFO [train.py:874] (1/4) Epoch 8, batch 4050, datatang_loss[loss=0.1562, simple_loss=0.2263, pruned_loss=0.04307, over 4912.00 frames.], tot_loss[loss=0.1868, simple_loss=0.2563, pruned_loss=0.05871, over 985206.30 frames.], batch size: 64, aishell_tot_loss[loss=0.1864, simple_loss=0.2629, pruned_loss=0.05494, over 984551.76 frames.], datatang_tot_loss[loss=0.1874, simple_loss=0.2504, pruned_loss=0.06218, over 986094.81 frames.], batch size: 64, lr: 9.37e-04
+2022-06-18 16:16:15,500 INFO [train.py:874] (1/4) Epoch 8, batch 4100, datatang_loss[loss=0.1673, simple_loss=0.2366, pruned_loss=0.04903, over 4972.00 frames.], tot_loss[loss=0.188, simple_loss=0.2573, pruned_loss=0.05933, over 985244.76 frames.], batch size: 67, aishell_tot_loss[loss=0.1868, simple_loss=0.2633, pruned_loss=0.05515, over 984416.35 frames.], datatang_tot_loss[loss=0.1881, simple_loss=0.2511, pruned_loss=0.06252, over 986220.47 frames.], batch size: 67, lr: 9.36e-04
+2022-06-18 16:16:43,399 INFO [train.py:874] (1/4) Epoch 8, batch 4150, aishell_loss[loss=0.1867, simple_loss=0.2661, pruned_loss=0.05369, over 4911.00 frames.], tot_loss[loss=0.1873, simple_loss=0.257, pruned_loss=0.05877, over 985095.53 frames.], batch size: 41, aishell_tot_loss[loss=0.1868, simple_loss=0.2636, pruned_loss=0.055, over 984515.37 frames.], datatang_tot_loss[loss=0.1874, simple_loss=0.2505, pruned_loss=0.06218, over 985953.18 frames.], batch size: 41, lr: 9.35e-04
+2022-06-18 16:18:01,444 INFO [train.py:874] (1/4) Epoch 9, batch 50, datatang_loss[loss=0.1716, simple_loss=0.238, pruned_loss=0.05263, over 4825.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2517, pruned_loss=0.05337, over 218123.49 frames.], batch size: 24, aishell_tot_loss[loss=0.1826, simple_loss=0.2587, pruned_loss=0.0533, over 133088.05 frames.], datatang_tot_loss[loss=0.1744, simple_loss=0.2422, pruned_loss=0.05334, over 98355.12 frames.], batch size: 24, lr: 8.97e-04
+2022-06-18 16:18:32,027 INFO [train.py:874] (1/4) Epoch 9, batch 100, datatang_loss[loss=0.1763, simple_loss=0.2352, pruned_loss=0.05874, over 4915.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2509, pruned_loss=0.0554, over 388347.05 frames.], batch size: 81, aishell_tot_loss[loss=0.1854, simple_loss=0.2602, pruned_loss=0.05527, over 218362.30 frames.], datatang_tot_loss[loss=0.1763, simple_loss=0.2421, pruned_loss=0.05529, over 218377.09 frames.], batch size: 81, lr: 8.96e-04
+2022-06-18 16:19:01,239 INFO [train.py:874] (1/4) Epoch 9, batch 150, datatang_loss[loss=0.192, simple_loss=0.251, pruned_loss=0.06655, over 4893.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2517, pruned_loss=0.05553, over 520939.36 frames.], batch size: 52, aishell_tot_loss[loss=0.1845, simple_loss=0.2605, pruned_loss=0.05423, over 294996.14 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2439, pruned_loss=0.0565, over 322396.30 frames.], batch size: 52, lr: 8.96e-04
+2022-06-18 16:19:31,588 INFO [train.py:874] (1/4) Epoch 9, batch 200, datatang_loss[loss=0.1684, simple_loss=0.2247, pruned_loss=0.05602, over 4986.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2511, pruned_loss=0.05495, over 624104.31 frames.], batch size: 34, aishell_tot_loss[loss=0.1836, simple_loss=0.259, pruned_loss=0.05409, over 397271.00 frames.], datatang_tot_loss[loss=0.1772, simple_loss=0.2427, pruned_loss=0.05589, over 379854.18 frames.], batch size: 34, lr: 8.95e-04
+2022-06-18 16:20:02,108 INFO [train.py:874] (1/4) Epoch 9, batch 250, datatang_loss[loss=0.1668, simple_loss=0.2374, pruned_loss=0.04808, over 4934.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2499, pruned_loss=0.05423, over 703969.74 frames.], batch size: 69, aishell_tot_loss[loss=0.1829, simple_loss=0.2586, pruned_loss=0.05364, over 466340.58 frames.], datatang_tot_loss[loss=0.1758, simple_loss=0.2413, pruned_loss=0.05515, over 451123.58 frames.], batch size: 69, lr: 8.94e-04
+2022-06-18 16:20:30,902 INFO [train.py:874] (1/4) Epoch 9, batch 300, datatang_loss[loss=0.1864, simple_loss=0.2525, pruned_loss=0.06021, over 4923.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2502, pruned_loss=0.05402, over 766308.48 frames.], batch size: 83, aishell_tot_loss[loss=0.1825, simple_loss=0.2587, pruned_loss=0.05319, over 531975.36 frames.], datatang_tot_loss[loss=0.1759, simple_loss=0.2413, pruned_loss=0.05521, over 509310.00 frames.], batch size: 83, lr: 8.94e-04
+2022-06-18 16:21:02,268 INFO [train.py:874] (1/4) Epoch 9, batch 350, aishell_loss[loss=0.1981, simple_loss=0.2827, pruned_loss=0.05676, over 4886.00 frames.], tot_loss[loss=0.1791, simple_loss=0.25, pruned_loss=0.05415, over 814790.47 frames.], batch size: 50, aishell_tot_loss[loss=0.1827, simple_loss=0.2587, pruned_loss=0.05332, over 589066.21 frames.], datatang_tot_loss[loss=0.1757, simple_loss=0.2409, pruned_loss=0.05523, over 561360.67 frames.], batch size: 50, lr: 8.93e-04
+2022-06-18 16:21:31,228 INFO [train.py:874] (1/4) Epoch 9, batch 400, datatang_loss[loss=0.2331, simple_loss=0.2897, pruned_loss=0.08826, over 4934.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2513, pruned_loss=0.05472, over 852601.43 frames.], batch size: 108, aishell_tot_loss[loss=0.1833, simple_loss=0.2593, pruned_loss=0.05368, over 649599.26 frames.], datatang_tot_loss[loss=0.1762, simple_loss=0.241, pruned_loss=0.05572, over 595880.33 frames.], batch size: 108, lr: 8.92e-04
+2022-06-18 16:22:01,508 INFO [train.py:874] (1/4) Epoch 9, batch 450, datatang_loss[loss=0.1651, simple_loss=0.2318, pruned_loss=0.04921, over 4952.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2515, pruned_loss=0.05496, over 882232.31 frames.], batch size: 55, aishell_tot_loss[loss=0.1831, simple_loss=0.2596, pruned_loss=0.05326, over 686274.32 frames.], datatang_tot_loss[loss=0.1773, simple_loss=0.2417, pruned_loss=0.05648, over 645275.61 frames.], batch size: 55, lr: 8.92e-04
+2022-06-18 16:22:32,027 INFO [train.py:874] (1/4) Epoch 9, batch 500, datatang_loss[loss=0.1603, simple_loss=0.2337, pruned_loss=0.04345, over 4927.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2519, pruned_loss=0.05552, over 905544.65 frames.], batch size: 73, aishell_tot_loss[loss=0.1829, simple_loss=0.2597, pruned_loss=0.05299, over 713515.40 frames.], datatang_tot_loss[loss=0.179, simple_loss=0.2432, pruned_loss=0.05738, over 694620.10 frames.], batch size: 73, lr: 8.91e-04
+2022-06-18 16:23:02,676 INFO [train.py:874] (1/4) Epoch 9, batch 550, datatang_loss[loss=0.1723, simple_loss=0.2303, pruned_loss=0.05721, over 4941.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2526, pruned_loss=0.05615, over 923648.99 frames.], batch size: 37, aishell_tot_loss[loss=0.183, simple_loss=0.2599, pruned_loss=0.053, over 739715.67 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2445, pruned_loss=0.05816, over 735374.73 frames.], batch size: 37, lr: 8.90e-04
+2022-06-18 16:23:32,309 INFO [train.py:874] (1/4) Epoch 9, batch 600, aishell_loss[loss=0.1848, simple_loss=0.2525, pruned_loss=0.05856, over 4887.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2538, pruned_loss=0.05687, over 937493.39 frames.], batch size: 28, aishell_tot_loss[loss=0.1833, simple_loss=0.26, pruned_loss=0.05331, over 770888.07 frames.], datatang_tot_loss[loss=0.1821, simple_loss=0.246, pruned_loss=0.05907, over 762691.20 frames.], batch size: 28, lr: 8.90e-04
+2022-06-18 16:24:02,268 INFO [train.py:874] (1/4) Epoch 9, batch 650, datatang_loss[loss=0.1498, simple_loss=0.2217, pruned_loss=0.03891, over 4894.00 frames.], tot_loss[loss=0.184, simple_loss=0.2543, pruned_loss=0.05682, over 948229.34 frames.], batch size: 47, aishell_tot_loss[loss=0.1832, simple_loss=0.26, pruned_loss=0.05315, over 797047.26 frames.], datatang_tot_loss[loss=0.1828, simple_loss=0.2468, pruned_loss=0.05944, over 788114.59 frames.], batch size: 47, lr: 8.89e-04
+2022-06-18 16:24:31,768 INFO [train.py:874] (1/4) Epoch 9, batch 700, aishell_loss[loss=0.179, simple_loss=0.2718, pruned_loss=0.04306, over 4862.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2549, pruned_loss=0.05676, over 956148.55 frames.], batch size: 36, aishell_tot_loss[loss=0.1826, simple_loss=0.2596, pruned_loss=0.05278, over 821383.77 frames.], datatang_tot_loss[loss=0.184, simple_loss=0.248, pruned_loss=0.06001, over 808709.12 frames.], batch size: 36, lr: 8.88e-04
+2022-06-18 16:25:02,544 INFO [train.py:874] (1/4) Epoch 9, batch 750, aishell_loss[loss=0.1464, simple_loss=0.2068, pruned_loss=0.04303, over 4862.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2548, pruned_loss=0.05634, over 962926.53 frames.], batch size: 21, aishell_tot_loss[loss=0.1824, simple_loss=0.2595, pruned_loss=0.05271, over 844374.26 frames.], datatang_tot_loss[loss=0.1839, simple_loss=0.248, pruned_loss=0.0599, over 825783.83 frames.], batch size: 21, lr: 8.88e-04
+2022-06-18 16:25:33,513 INFO [train.py:874] (1/4) Epoch 9, batch 800, aishell_loss[loss=0.1709, simple_loss=0.2555, pruned_loss=0.04317, over 4936.00 frames.], tot_loss[loss=0.1832, simple_loss=0.254, pruned_loss=0.05624, over 967658.30 frames.], batch size: 54, aishell_tot_loss[loss=0.1808, simple_loss=0.2581, pruned_loss=0.05179, over 861018.72 frames.], datatang_tot_loss[loss=0.1851, simple_loss=0.2487, pruned_loss=0.06075, over 844274.68 frames.], batch size: 54, lr: 8.87e-04
+2022-06-18 16:26:02,653 INFO [train.py:874] (1/4) Epoch 9, batch 850, aishell_loss[loss=0.2013, simple_loss=0.2741, pruned_loss=0.06424, over 4982.00 frames.], tot_loss[loss=0.183, simple_loss=0.2536, pruned_loss=0.05619, over 971569.31 frames.], batch size: 48, aishell_tot_loss[loss=0.1809, simple_loss=0.2578, pruned_loss=0.05197, over 874510.43 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.2487, pruned_loss=0.0604, over 862152.40 frames.], batch size: 48, lr: 8.87e-04
+2022-06-18 16:26:33,076 INFO [train.py:874] (1/4) Epoch 9, batch 900, datatang_loss[loss=0.1818, simple_loss=0.2562, pruned_loss=0.05371, over 4840.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2546, pruned_loss=0.05596, over 974658.76 frames.], batch size: 30, aishell_tot_loss[loss=0.1818, simple_loss=0.2586, pruned_loss=0.05251, over 892828.98 frames.], datatang_tot_loss[loss=0.1844, simple_loss=0.2487, pruned_loss=0.06007, over 870514.72 frames.], batch size: 30, lr: 8.86e-04
+2022-06-18 16:27:02,540 INFO [train.py:874] (1/4) Epoch 9, batch 950, aishell_loss[loss=0.192, simple_loss=0.2636, pruned_loss=0.0602, over 4953.00 frames.], tot_loss[loss=0.186, simple_loss=0.257, pruned_loss=0.05747, over 977107.25 frames.], batch size: 64, aishell_tot_loss[loss=0.1832, simple_loss=0.2602, pruned_loss=0.05314, over 906120.34 frames.], datatang_tot_loss[loss=0.1863, simple_loss=0.2499, pruned_loss=0.06138, over 881052.44 frames.], batch size: 64, lr: 8.85e-04
+2022-06-18 16:27:32,412 INFO [train.py:874] (1/4) Epoch 9, batch 1000, datatang_loss[loss=0.1658, simple_loss=0.2354, pruned_loss=0.04809, over 4922.00 frames.], tot_loss[loss=0.1847, simple_loss=0.256, pruned_loss=0.05674, over 978850.45 frames.], batch size: 73, aishell_tot_loss[loss=0.1823, simple_loss=0.2594, pruned_loss=0.05262, over 916299.76 frames.], datatang_tot_loss[loss=0.1862, simple_loss=0.2499, pruned_loss=0.06124, over 892057.09 frames.], batch size: 73, lr: 8.85e-04
+2022-06-18 16:27:32,413 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 16:27:48,781 INFO [train.py:914] (1/4) Epoch 9, validation: loss=0.1688, simple_loss=0.2514, pruned_loss=0.04309, over 1622729.00 frames.
+2022-06-18 16:28:18,766 INFO [train.py:874] (1/4) Epoch 9, batch 1050, aishell_loss[loss=0.1678, simple_loss=0.2489, pruned_loss=0.04338, over 4827.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2551, pruned_loss=0.05607, over 980105.98 frames.], batch size: 29, aishell_tot_loss[loss=0.1813, simple_loss=0.2586, pruned_loss=0.052, over 924485.82 frames.], datatang_tot_loss[loss=0.1862, simple_loss=0.2501, pruned_loss=0.06111, over 902701.02 frames.], batch size: 29, lr: 8.84e-04
+2022-06-18 16:28:49,631 INFO [train.py:874] (1/4) Epoch 9, batch 1100, datatang_loss[loss=0.1678, simple_loss=0.239, pruned_loss=0.04825, over 4925.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2562, pruned_loss=0.05636, over 981205.26 frames.], batch size: 57, aishell_tot_loss[loss=0.1813, simple_loss=0.2589, pruned_loss=0.0519, over 931790.69 frames.], datatang_tot_loss[loss=0.187, simple_loss=0.2511, pruned_loss=0.06146, over 912125.57 frames.], batch size: 57, lr: 8.83e-04
+2022-06-18 16:29:19,119 INFO [train.py:874] (1/4) Epoch 9, batch 1150, datatang_loss[loss=0.221, simple_loss=0.2748, pruned_loss=0.08362, over 4950.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2552, pruned_loss=0.05599, over 982036.90 frames.], batch size: 34, aishell_tot_loss[loss=0.18, simple_loss=0.2577, pruned_loss=0.05115, over 938068.51 frames.], datatang_tot_loss[loss=0.1875, simple_loss=0.2515, pruned_loss=0.06174, over 920678.98 frames.], batch size: 34, lr: 8.83e-04
+2022-06-18 16:29:49,953 INFO [train.py:874] (1/4) Epoch 9, batch 1200, aishell_loss[loss=0.1818, simple_loss=0.2686, pruned_loss=0.0475, over 4955.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2555, pruned_loss=0.05607, over 982810.66 frames.], batch size: 40, aishell_tot_loss[loss=0.1809, simple_loss=0.2583, pruned_loss=0.05172, over 944665.40 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.2512, pruned_loss=0.06133, over 926972.19 frames.], batch size: 40, lr: 8.82e-04
+2022-06-18 16:30:20,545 INFO [train.py:874] (1/4) Epoch 9, batch 1250, datatang_loss[loss=0.2179, simple_loss=0.2639, pruned_loss=0.08591, over 4920.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2545, pruned_loss=0.05565, over 983436.74 frames.], batch size: 64, aishell_tot_loss[loss=0.1796, simple_loss=0.2569, pruned_loss=0.05116, over 950901.63 frames.], datatang_tot_loss[loss=0.1875, simple_loss=0.2515, pruned_loss=0.06171, over 931772.28 frames.], batch size: 64, lr: 8.82e-04
+2022-06-18 16:30:49,219 INFO [train.py:874] (1/4) Epoch 9, batch 1300, aishell_loss[loss=0.1765, simple_loss=0.2645, pruned_loss=0.04424, over 4893.00 frames.], tot_loss[loss=0.184, simple_loss=0.256, pruned_loss=0.05603, over 983895.49 frames.], batch size: 34, aishell_tot_loss[loss=0.1801, simple_loss=0.2577, pruned_loss=0.05123, over 955169.49 frames.], datatang_tot_loss[loss=0.1882, simple_loss=0.2523, pruned_loss=0.06205, over 937665.26 frames.], batch size: 34, lr: 8.81e-04
+2022-06-18 16:31:19,936 INFO [train.py:874] (1/4) Epoch 9, batch 1350, aishell_loss[loss=0.1711, simple_loss=0.2497, pruned_loss=0.04623, over 4880.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2568, pruned_loss=0.05639, over 984323.10 frames.], batch size: 42, aishell_tot_loss[loss=0.1797, simple_loss=0.2578, pruned_loss=0.05083, over 959243.63 frames.], datatang_tot_loss[loss=0.1896, simple_loss=0.2533, pruned_loss=0.0629, over 942627.81 frames.], batch size: 42, lr: 8.80e-04
+2022-06-18 16:31:50,506 INFO [train.py:874] (1/4) Epoch 9, batch 1400, aishell_loss[loss=0.1819, simple_loss=0.2668, pruned_loss=0.04848, over 4947.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2562, pruned_loss=0.05636, over 984384.92 frames.], batch size: 45, aishell_tot_loss[loss=0.1797, simple_loss=0.2575, pruned_loss=0.05093, over 962331.19 frames.], datatang_tot_loss[loss=0.1894, simple_loss=0.2532, pruned_loss=0.06284, over 947242.06 frames.], batch size: 45, lr: 8.80e-04
+2022-06-18 16:32:21,316 INFO [train.py:874] (1/4) Epoch 9, batch 1450, datatang_loss[loss=0.1577, simple_loss=0.2243, pruned_loss=0.04558, over 4972.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2552, pruned_loss=0.0562, over 983780.30 frames.], batch size: 45, aishell_tot_loss[loss=0.1794, simple_loss=0.2572, pruned_loss=0.05083, over 964666.46 frames.], datatang_tot_loss[loss=0.189, simple_loss=0.2525, pruned_loss=0.0627, over 951178.67 frames.], batch size: 45, lr: 8.79e-04
+2022-06-18 16:32:52,181 INFO [train.py:874] (1/4) Epoch 9, batch 1500, datatang_loss[loss=0.175, simple_loss=0.2345, pruned_loss=0.05772, over 4920.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2555, pruned_loss=0.05575, over 984194.55 frames.], batch size: 57, aishell_tot_loss[loss=0.1797, simple_loss=0.2579, pruned_loss=0.05078, over 966997.30 frames.], datatang_tot_loss[loss=0.1882, simple_loss=0.2523, pruned_loss=0.06207, over 955433.63 frames.], batch size: 57, lr: 8.78e-04
+2022-06-18 16:33:21,719 INFO [train.py:874] (1/4) Epoch 9, batch 1550, aishell_loss[loss=0.1533, simple_loss=0.2159, pruned_loss=0.04533, over 4885.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2549, pruned_loss=0.05603, over 984231.00 frames.], batch size: 21, aishell_tot_loss[loss=0.1795, simple_loss=0.2574, pruned_loss=0.05082, over 968804.05 frames.], datatang_tot_loss[loss=0.1883, simple_loss=0.2523, pruned_loss=0.06208, over 959179.90 frames.], batch size: 21, lr: 8.78e-04
+2022-06-18 16:33:52,470 INFO [train.py:874] (1/4) Epoch 9, batch 1600, datatang_loss[loss=0.2011, simple_loss=0.2558, pruned_loss=0.07323, over 4908.00 frames.], tot_loss[loss=0.1828, simple_loss=0.254, pruned_loss=0.05576, over 984323.87 frames.], batch size: 64, aishell_tot_loss[loss=0.1794, simple_loss=0.2571, pruned_loss=0.05081, over 970795.65 frames.], datatang_tot_loss[loss=0.1876, simple_loss=0.2516, pruned_loss=0.06184, over 961921.47 frames.], batch size: 64, lr: 8.77e-04
+2022-06-18 16:34:23,096 INFO [train.py:874] (1/4) Epoch 9, batch 1650, datatang_loss[loss=0.1708, simple_loss=0.2424, pruned_loss=0.04961, over 4940.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2542, pruned_loss=0.05556, over 984683.42 frames.], batch size: 88, aishell_tot_loss[loss=0.1798, simple_loss=0.2578, pruned_loss=0.0509, over 972620.59 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.251, pruned_loss=0.06146, over 964663.93 frames.], batch size: 88, lr: 8.77e-04
+2022-06-18 16:34:54,131 INFO [train.py:874] (1/4) Epoch 9, batch 1700, datatang_loss[loss=0.1996, simple_loss=0.2693, pruned_loss=0.0649, over 4906.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2528, pruned_loss=0.05487, over 984493.85 frames.], batch size: 52, aishell_tot_loss[loss=0.1788, simple_loss=0.2569, pruned_loss=0.05032, over 973592.23 frames.], datatang_tot_loss[loss=0.1862, simple_loss=0.2504, pruned_loss=0.06104, over 967333.05 frames.], batch size: 52, lr: 8.76e-04
+2022-06-18 16:35:25,096 INFO [train.py:874] (1/4) Epoch 9, batch 1750, aishell_loss[loss=0.2162, simple_loss=0.2885, pruned_loss=0.072, over 4984.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2533, pruned_loss=0.05575, over 984946.04 frames.], batch size: 38, aishell_tot_loss[loss=0.1787, simple_loss=0.2569, pruned_loss=0.05026, over 974762.61 frames.], datatang_tot_loss[loss=0.187, simple_loss=0.2509, pruned_loss=0.06157, over 970078.50 frames.], batch size: 38, lr: 8.75e-04
+2022-06-18 16:35:56,451 INFO [train.py:874] (1/4) Epoch 9, batch 1800, aishell_loss[loss=0.1831, simple_loss=0.2669, pruned_loss=0.04959, over 4932.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2544, pruned_loss=0.05677, over 984964.77 frames.], batch size: 45, aishell_tot_loss[loss=0.1794, simple_loss=0.2571, pruned_loss=0.05082, over 975749.36 frames.], datatang_tot_loss[loss=0.1878, simple_loss=0.2517, pruned_loss=0.06197, over 972120.48 frames.], batch size: 45, lr: 8.75e-04
+2022-06-18 16:36:25,540 INFO [train.py:874] (1/4) Epoch 9, batch 1850, aishell_loss[loss=0.2033, simple_loss=0.2719, pruned_loss=0.06734, over 4872.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2533, pruned_loss=0.056, over 984693.29 frames.], batch size: 37, aishell_tot_loss[loss=0.1792, simple_loss=0.257, pruned_loss=0.05067, over 976641.28 frames.], datatang_tot_loss[loss=0.1868, simple_loss=0.2506, pruned_loss=0.06147, over 973493.82 frames.], batch size: 37, lr: 8.74e-04
+2022-06-18 16:36:56,414 INFO [train.py:874] (1/4) Epoch 9, batch 1900, datatang_loss[loss=0.1521, simple_loss=0.2219, pruned_loss=0.04118, over 4912.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2551, pruned_loss=0.05661, over 985187.78 frames.], batch size: 64, aishell_tot_loss[loss=0.1806, simple_loss=0.2583, pruned_loss=0.05149, over 977948.68 frames.], datatang_tot_loss[loss=0.1868, simple_loss=0.251, pruned_loss=0.06129, over 974966.76 frames.], batch size: 64, lr: 8.73e-04
+2022-06-18 16:37:28,047 INFO [train.py:874] (1/4) Epoch 9, batch 1950, aishell_loss[loss=0.1907, simple_loss=0.2718, pruned_loss=0.05473, over 4858.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2552, pruned_loss=0.05616, over 985073.47 frames.], batch size: 37, aishell_tot_loss[loss=0.181, simple_loss=0.2589, pruned_loss=0.05156, over 978519.83 frames.], datatang_tot_loss[loss=0.1861, simple_loss=0.2507, pruned_loss=0.06075, over 976353.99 frames.], batch size: 37, lr: 8.73e-04
+2022-06-18 16:37:57,023 INFO [train.py:874] (1/4) Epoch 9, batch 2000, datatang_loss[loss=0.1691, simple_loss=0.2291, pruned_loss=0.05456, over 4873.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2551, pruned_loss=0.05614, over 984869.40 frames.], batch size: 39, aishell_tot_loss[loss=0.1812, simple_loss=0.259, pruned_loss=0.05172, over 978918.46 frames.], datatang_tot_loss[loss=0.1859, simple_loss=0.2505, pruned_loss=0.06059, over 977559.55 frames.], batch size: 39, lr: 8.72e-04
+2022-06-18 16:37:57,024 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 16:38:13,637 INFO [train.py:914] (1/4) Epoch 9, validation: loss=0.1694, simple_loss=0.2525, pruned_loss=0.04315, over 1622729.00 frames.
+2022-06-18 16:38:43,888 INFO [train.py:874] (1/4) Epoch 9, batch 2050, datatang_loss[loss=0.2009, simple_loss=0.2517, pruned_loss=0.07509, over 4918.00 frames.], tot_loss[loss=0.1835, simple_loss=0.255, pruned_loss=0.05605, over 985187.05 frames.], batch size: 47, aishell_tot_loss[loss=0.1807, simple_loss=0.2589, pruned_loss=0.05128, over 979571.65 frames.], datatang_tot_loss[loss=0.1862, simple_loss=0.2507, pruned_loss=0.06085, over 978807.67 frames.], batch size: 47, lr: 8.72e-04
+2022-06-18 16:39:14,762 INFO [train.py:874] (1/4) Epoch 9, batch 2100, aishell_loss[loss=0.1766, simple_loss=0.2574, pruned_loss=0.04793, over 4940.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2563, pruned_loss=0.0569, over 985443.61 frames.], batch size: 58, aishell_tot_loss[loss=0.1815, simple_loss=0.2597, pruned_loss=0.05167, over 980423.74 frames.], datatang_tot_loss[loss=0.187, simple_loss=0.2515, pruned_loss=0.06124, over 979649.10 frames.], batch size: 58, lr: 8.71e-04
+2022-06-18 16:39:46,139 INFO [train.py:874] (1/4) Epoch 9, batch 2150, aishell_loss[loss=0.1771, simple_loss=0.2495, pruned_loss=0.05232, over 4906.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2547, pruned_loss=0.05589, over 985683.39 frames.], batch size: 34, aishell_tot_loss[loss=0.1804, simple_loss=0.2586, pruned_loss=0.05112, over 981154.89 frames.], datatang_tot_loss[loss=0.1864, simple_loss=0.2511, pruned_loss=0.0608, over 980443.63 frames.], batch size: 34, lr: 8.70e-04
+2022-06-18 16:40:16,372 INFO [train.py:874] (1/4) Epoch 9, batch 2200, aishell_loss[loss=0.2063, simple_loss=0.2771, pruned_loss=0.06779, over 4870.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2551, pruned_loss=0.05629, over 985733.01 frames.], batch size: 37, aishell_tot_loss[loss=0.1807, simple_loss=0.2589, pruned_loss=0.05124, over 981397.93 frames.], datatang_tot_loss[loss=0.1866, simple_loss=0.2513, pruned_loss=0.06101, over 981416.15 frames.], batch size: 37, lr: 8.70e-04
+2022-06-18 16:40:46,509 INFO [train.py:874] (1/4) Epoch 9, batch 2250, datatang_loss[loss=0.1899, simple_loss=0.2563, pruned_loss=0.06172, over 4952.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2543, pruned_loss=0.05595, over 985745.02 frames.], batch size: 86, aishell_tot_loss[loss=0.1797, simple_loss=0.2581, pruned_loss=0.05067, over 981754.65 frames.], datatang_tot_loss[loss=0.1869, simple_loss=0.2513, pruned_loss=0.06124, over 982091.46 frames.], batch size: 86, lr: 8.69e-04
+2022-06-18 16:41:17,730 INFO [train.py:874] (1/4) Epoch 9, batch 2300, datatang_loss[loss=0.1742, simple_loss=0.2337, pruned_loss=0.05731, over 4982.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2539, pruned_loss=0.05544, over 985678.08 frames.], batch size: 34, aishell_tot_loss[loss=0.1792, simple_loss=0.2577, pruned_loss=0.0504, over 982027.52 frames.], datatang_tot_loss[loss=0.1867, simple_loss=0.2511, pruned_loss=0.0611, over 982647.46 frames.], batch size: 34, lr: 8.69e-04
+2022-06-18 16:41:48,091 INFO [train.py:874] (1/4) Epoch 9, batch 2350, aishell_loss[loss=0.1952, simple_loss=0.2719, pruned_loss=0.0593, over 4934.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2531, pruned_loss=0.05488, over 985798.43 frames.], batch size: 49, aishell_tot_loss[loss=0.1798, simple_loss=0.258, pruned_loss=0.05075, over 982428.52 frames.], datatang_tot_loss[loss=0.185, simple_loss=0.2498, pruned_loss=0.0601, over 983155.75 frames.], batch size: 49, lr: 8.68e-04
+2022-06-18 16:42:20,332 INFO [train.py:874] (1/4) Epoch 9, batch 2400, aishell_loss[loss=0.1696, simple_loss=0.2549, pruned_loss=0.04208, over 4874.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2538, pruned_loss=0.05538, over 985952.23 frames.], batch size: 34, aishell_tot_loss[loss=0.1796, simple_loss=0.2578, pruned_loss=0.05072, over 982767.13 frames.], datatang_tot_loss[loss=0.1859, simple_loss=0.2507, pruned_loss=0.06054, over 983699.82 frames.], batch size: 34, lr: 8.67e-04
+2022-06-18 16:42:51,835 INFO [train.py:874] (1/4) Epoch 9, batch 2450, datatang_loss[loss=0.1741, simple_loss=0.2341, pruned_loss=0.05708, over 4949.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2537, pruned_loss=0.05537, over 985937.29 frames.], batch size: 34, aishell_tot_loss[loss=0.1795, simple_loss=0.2577, pruned_loss=0.05067, over 983260.83 frames.], datatang_tot_loss[loss=0.186, simple_loss=0.2504, pruned_loss=0.06079, over 983853.17 frames.], batch size: 34, lr: 8.67e-04
+2022-06-18 16:43:22,137 INFO [train.py:874] (1/4) Epoch 9, batch 2500, datatang_loss[loss=0.1583, simple_loss=0.2276, pruned_loss=0.04452, over 4845.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2536, pruned_loss=0.05535, over 985836.14 frames.], batch size: 30, aishell_tot_loss[loss=0.1792, simple_loss=0.2576, pruned_loss=0.05042, over 983442.35 frames.], datatang_tot_loss[loss=0.186, simple_loss=0.2503, pruned_loss=0.06087, over 984112.00 frames.], batch size: 30, lr: 8.66e-04
+2022-06-18 16:43:51,853 INFO [train.py:874] (1/4) Epoch 9, batch 2550, aishell_loss[loss=0.1712, simple_loss=0.2528, pruned_loss=0.04479, over 4942.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2529, pruned_loss=0.05535, over 986030.87 frames.], batch size: 56, aishell_tot_loss[loss=0.1789, simple_loss=0.2574, pruned_loss=0.05025, over 983874.10 frames.], datatang_tot_loss[loss=0.1857, simple_loss=0.2497, pruned_loss=0.06085, over 984364.05 frames.], batch size: 56, lr: 8.66e-04
+2022-06-18 16:44:23,863 INFO [train.py:874] (1/4) Epoch 9, batch 2600, datatang_loss[loss=0.2666, simple_loss=0.3128, pruned_loss=0.1102, over 4929.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2533, pruned_loss=0.0555, over 985736.70 frames.], batch size: 108, aishell_tot_loss[loss=0.1792, simple_loss=0.2577, pruned_loss=0.05038, over 983966.19 frames.], datatang_tot_loss[loss=0.1856, simple_loss=0.2497, pruned_loss=0.06077, over 984422.85 frames.], batch size: 108, lr: 8.65e-04
+2022-06-18 16:44:54,187 INFO [train.py:874] (1/4) Epoch 9, batch 2650, aishell_loss[loss=0.2241, simple_loss=0.2903, pruned_loss=0.07896, over 4936.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2525, pruned_loss=0.05482, over 985674.56 frames.], batch size: 79, aishell_tot_loss[loss=0.1786, simple_loss=0.257, pruned_loss=0.05006, over 984038.08 frames.], datatang_tot_loss[loss=0.185, simple_loss=0.2494, pruned_loss=0.06035, over 984646.38 frames.], batch size: 79, lr: 8.64e-04
+2022-06-18 16:45:24,692 INFO [train.py:874] (1/4) Epoch 9, batch 2700, aishell_loss[loss=0.1514, simple_loss=0.2185, pruned_loss=0.04209, over 4902.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2521, pruned_loss=0.05515, over 985481.91 frames.], batch size: 25, aishell_tot_loss[loss=0.1786, simple_loss=0.257, pruned_loss=0.05004, over 984114.58 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.249, pruned_loss=0.06027, over 984657.84 frames.], batch size: 25, lr: 8.64e-04
+2022-06-18 16:45:55,554 INFO [train.py:874] (1/4) Epoch 9, batch 2750, datatang_loss[loss=0.1651, simple_loss=0.2232, pruned_loss=0.05353, over 4890.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2521, pruned_loss=0.05469, over 985508.68 frames.], batch size: 52, aishell_tot_loss[loss=0.1788, simple_loss=0.2573, pruned_loss=0.05015, over 984352.43 frames.], datatang_tot_loss[loss=0.1841, simple_loss=0.2485, pruned_loss=0.05983, over 984723.92 frames.], batch size: 52, lr: 8.63e-04
+2022-06-18 16:46:25,602 INFO [train.py:874] (1/4) Epoch 9, batch 2800, datatang_loss[loss=0.2112, simple_loss=0.2722, pruned_loss=0.07509, over 4959.00 frames.], tot_loss[loss=0.182, simple_loss=0.2537, pruned_loss=0.05514, over 985718.56 frames.], batch size: 67, aishell_tot_loss[loss=0.1792, simple_loss=0.2581, pruned_loss=0.05018, over 984469.52 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.2492, pruned_loss=0.06025, over 985066.80 frames.], batch size: 67, lr: 8.63e-04
+2022-06-18 16:46:55,361 INFO [train.py:874] (1/4) Epoch 9, batch 2850, datatang_loss[loss=0.1906, simple_loss=0.2608, pruned_loss=0.06021, over 4864.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2539, pruned_loss=0.05514, over 986174.64 frames.], batch size: 39, aishell_tot_loss[loss=0.1788, simple_loss=0.2578, pruned_loss=0.04988, over 985052.31 frames.], datatang_tot_loss[loss=0.1854, simple_loss=0.2496, pruned_loss=0.06062, over 985198.97 frames.], batch size: 39, lr: 8.62e-04
+2022-06-18 16:47:30,692 INFO [train.py:874] (1/4) Epoch 9, batch 2900, datatang_loss[loss=0.1591, simple_loss=0.229, pruned_loss=0.04464, over 4921.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2534, pruned_loss=0.05454, over 986110.25 frames.], batch size: 83, aishell_tot_loss[loss=0.1786, simple_loss=0.2578, pruned_loss=0.04969, over 985340.58 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.2491, pruned_loss=0.06025, over 985094.35 frames.], batch size: 83, lr: 8.61e-04
+2022-06-18 16:48:01,975 INFO [train.py:874] (1/4) Epoch 9, batch 2950, datatang_loss[loss=0.1834, simple_loss=0.2513, pruned_loss=0.05777, over 4944.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2532, pruned_loss=0.05479, over 986117.88 frames.], batch size: 50, aishell_tot_loss[loss=0.1786, simple_loss=0.2579, pruned_loss=0.04963, over 985437.52 frames.], datatang_tot_loss[loss=0.1846, simple_loss=0.249, pruned_loss=0.06016, over 985222.25 frames.], batch size: 50, lr: 8.61e-04
+2022-06-18 16:48:32,284 INFO [train.py:874] (1/4) Epoch 9, batch 3000, datatang_loss[loss=0.1816, simple_loss=0.239, pruned_loss=0.06213, over 4944.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2539, pruned_loss=0.05489, over 986042.64 frames.], batch size: 37, aishell_tot_loss[loss=0.179, simple_loss=0.2583, pruned_loss=0.04983, over 985496.53 frames.], datatang_tot_loss[loss=0.1847, simple_loss=0.2492, pruned_loss=0.06011, over 985267.89 frames.], batch size: 37, lr: 8.60e-04
+2022-06-18 16:48:32,285 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 16:48:48,781 INFO [train.py:914] (1/4) Epoch 9, validation: loss=0.169, simple_loss=0.2511, pruned_loss=0.04348, over 1622729.00 frames.
+2022-06-18 16:49:18,618 INFO [train.py:874] (1/4) Epoch 9, batch 3050, datatang_loss[loss=0.1614, simple_loss=0.2298, pruned_loss=0.04647, over 4919.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2542, pruned_loss=0.05455, over 986283.90 frames.], batch size: 79, aishell_tot_loss[loss=0.1797, simple_loss=0.2591, pruned_loss=0.0501, over 985612.82 frames.], datatang_tot_loss[loss=0.1839, simple_loss=0.2486, pruned_loss=0.05959, over 985572.63 frames.], batch size: 79, lr: 8.60e-04
+2022-06-18 16:49:51,930 INFO [train.py:874] (1/4) Epoch 9, batch 3100, datatang_loss[loss=0.1509, simple_loss=0.2276, pruned_loss=0.03709, over 4948.00 frames.], tot_loss[loss=0.181, simple_loss=0.2534, pruned_loss=0.05435, over 986333.71 frames.], batch size: 24, aishell_tot_loss[loss=0.1795, simple_loss=0.2589, pruned_loss=0.05003, over 985732.58 frames.], datatang_tot_loss[loss=0.1833, simple_loss=0.2481, pruned_loss=0.05927, over 985664.19 frames.], batch size: 24, lr: 8.59e-04
+2022-06-18 16:50:22,217 INFO [train.py:874] (1/4) Epoch 9, batch 3150, datatang_loss[loss=0.1504, simple_loss=0.2213, pruned_loss=0.03974, over 4907.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2528, pruned_loss=0.05423, over 985950.17 frames.], batch size: 25, aishell_tot_loss[loss=0.1791, simple_loss=0.2585, pruned_loss=0.04987, over 985434.86 frames.], datatang_tot_loss[loss=0.1832, simple_loss=0.248, pruned_loss=0.05916, over 985702.83 frames.], batch size: 25, lr: 8.59e-04
+2022-06-18 16:50:52,583 INFO [train.py:874] (1/4) Epoch 9, batch 3200, datatang_loss[loss=0.2023, simple_loss=0.2672, pruned_loss=0.06868, over 4956.00 frames.], tot_loss[loss=0.1807, simple_loss=0.253, pruned_loss=0.05423, over 986158.56 frames.], batch size: 60, aishell_tot_loss[loss=0.1791, simple_loss=0.2586, pruned_loss=0.04984, over 985578.44 frames.], datatang_tot_loss[loss=0.1831, simple_loss=0.2481, pruned_loss=0.0591, over 985872.67 frames.], batch size: 60, lr: 8.58e-04
+2022-06-18 16:51:25,242 INFO [train.py:874] (1/4) Epoch 9, batch 3250, datatang_loss[loss=0.1487, simple_loss=0.2191, pruned_loss=0.03915, over 4937.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2534, pruned_loss=0.05491, over 986012.94 frames.], batch size: 69, aishell_tot_loss[loss=0.1797, simple_loss=0.2589, pruned_loss=0.05023, over 985492.80 frames.], datatang_tot_loss[loss=0.1833, simple_loss=0.2483, pruned_loss=0.05914, over 985906.32 frames.], batch size: 69, lr: 8.57e-04
+2022-06-18 16:51:55,892 INFO [train.py:874] (1/4) Epoch 9, batch 3300, aishell_loss[loss=0.2025, simple_loss=0.2804, pruned_loss=0.06229, over 4986.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2526, pruned_loss=0.05531, over 985886.50 frames.], batch size: 38, aishell_tot_loss[loss=0.1789, simple_loss=0.2579, pruned_loss=0.04998, over 985479.38 frames.], datatang_tot_loss[loss=0.184, simple_loss=0.2487, pruned_loss=0.05966, over 985852.08 frames.], batch size: 38, lr: 8.57e-04
+2022-06-18 16:52:26,648 INFO [train.py:874] (1/4) Epoch 9, batch 3350, datatang_loss[loss=0.2571, simple_loss=0.3095, pruned_loss=0.1024, over 4947.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2539, pruned_loss=0.0557, over 985283.68 frames.], batch size: 109, aishell_tot_loss[loss=0.1794, simple_loss=0.2584, pruned_loss=0.05018, over 985278.78 frames.], datatang_tot_loss[loss=0.1847, simple_loss=0.2494, pruned_loss=0.05993, over 985448.70 frames.], batch size: 109, lr: 8.56e-04
+2022-06-18 16:52:57,986 INFO [train.py:874] (1/4) Epoch 9, batch 3400, datatang_loss[loss=0.1533, simple_loss=0.2257, pruned_loss=0.04039, over 4933.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2527, pruned_loss=0.05503, over 985256.79 frames.], batch size: 71, aishell_tot_loss[loss=0.1786, simple_loss=0.2574, pruned_loss=0.04988, over 985050.69 frames.], datatang_tot_loss[loss=0.1843, simple_loss=0.2492, pruned_loss=0.05965, over 985631.66 frames.], batch size: 71, lr: 8.56e-04
+2022-06-18 16:53:28,217 INFO [train.py:874] (1/4) Epoch 9, batch 3450, datatang_loss[loss=0.2079, simple_loss=0.2634, pruned_loss=0.07615, over 4931.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2535, pruned_loss=0.05567, over 985322.11 frames.], batch size: 42, aishell_tot_loss[loss=0.1787, simple_loss=0.2575, pruned_loss=0.04996, over 984949.29 frames.], datatang_tot_loss[loss=0.1852, simple_loss=0.2497, pruned_loss=0.06034, over 985801.27 frames.], batch size: 42, lr: 8.55e-04
+2022-06-18 16:53:59,570 INFO [train.py:874] (1/4) Epoch 9, batch 3500, aishell_loss[loss=0.1926, simple_loss=0.2729, pruned_loss=0.0562, over 4880.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2543, pruned_loss=0.05598, over 985211.70 frames.], batch size: 34, aishell_tot_loss[loss=0.1791, simple_loss=0.2581, pruned_loss=0.05009, over 984529.51 frames.], datatang_tot_loss[loss=0.1855, simple_loss=0.2502, pruned_loss=0.06047, over 986080.55 frames.], batch size: 34, lr: 8.55e-04
+2022-06-18 16:54:30,571 INFO [train.py:874] (1/4) Epoch 9, batch 3550, aishell_loss[loss=0.1326, simple_loss=0.2082, pruned_loss=0.02853, over 4949.00 frames.], tot_loss[loss=0.1845, simple_loss=0.2556, pruned_loss=0.05667, over 985170.22 frames.], batch size: 25, aishell_tot_loss[loss=0.1795, simple_loss=0.2584, pruned_loss=0.05032, over 984262.68 frames.], datatang_tot_loss[loss=0.1868, simple_loss=0.2512, pruned_loss=0.06124, over 986286.70 frames.], batch size: 25, lr: 8.54e-04
+2022-06-18 16:55:00,706 INFO [train.py:874] (1/4) Epoch 9, batch 3600, datatang_loss[loss=0.1663, simple_loss=0.2292, pruned_loss=0.05174, over 4927.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2548, pruned_loss=0.05579, over 984671.14 frames.], batch size: 79, aishell_tot_loss[loss=0.1798, simple_loss=0.2587, pruned_loss=0.05041, over 984044.74 frames.], datatang_tot_loss[loss=0.1855, simple_loss=0.2502, pruned_loss=0.06036, over 985954.16 frames.], batch size: 79, lr: 8.53e-04
+2022-06-18 16:55:30,667 INFO [train.py:874] (1/4) Epoch 9, batch 3650, aishell_loss[loss=0.1485, simple_loss=0.2185, pruned_loss=0.03923, over 4839.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2544, pruned_loss=0.05521, over 984717.72 frames.], batch size: 24, aishell_tot_loss[loss=0.1791, simple_loss=0.258, pruned_loss=0.05007, over 984102.16 frames.], datatang_tot_loss[loss=0.1857, simple_loss=0.2504, pruned_loss=0.06047, over 985921.43 frames.], batch size: 24, lr: 8.53e-04
+2022-06-18 16:56:03,445 INFO [train.py:874] (1/4) Epoch 9, batch 3700, datatang_loss[loss=0.2299, simple_loss=0.2945, pruned_loss=0.08263, over 4959.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2548, pruned_loss=0.05513, over 985130.38 frames.], batch size: 99, aishell_tot_loss[loss=0.1788, simple_loss=0.2578, pruned_loss=0.04989, over 984536.57 frames.], datatang_tot_loss[loss=0.1862, simple_loss=0.2508, pruned_loss=0.06078, over 985889.60 frames.], batch size: 99, lr: 8.52e-04
+2022-06-18 16:56:32,387 INFO [train.py:874] (1/4) Epoch 9, batch 3750, datatang_loss[loss=0.1837, simple_loss=0.2645, pruned_loss=0.05145, over 4876.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2542, pruned_loss=0.05447, over 985267.16 frames.], batch size: 30, aishell_tot_loss[loss=0.1785, simple_loss=0.2576, pruned_loss=0.04972, over 984602.01 frames.], datatang_tot_loss[loss=0.1853, simple_loss=0.2505, pruned_loss=0.06008, over 985929.34 frames.], batch size: 30, lr: 8.52e-04
+2022-06-18 16:57:03,082 INFO [train.py:874] (1/4) Epoch 9, batch 3800, aishell_loss[loss=0.1596, simple_loss=0.2332, pruned_loss=0.04304, over 4785.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2532, pruned_loss=0.05435, over 985400.85 frames.], batch size: 24, aishell_tot_loss[loss=0.1782, simple_loss=0.2572, pruned_loss=0.0496, over 984483.87 frames.], datatang_tot_loss[loss=0.1848, simple_loss=0.2501, pruned_loss=0.05974, over 986162.56 frames.], batch size: 24, lr: 8.51e-04
+2022-06-18 16:57:32,528 INFO [train.py:874] (1/4) Epoch 9, batch 3850, aishell_loss[loss=0.1836, simple_loss=0.2632, pruned_loss=0.05193, over 4957.00 frames.], tot_loss[loss=0.181, simple_loss=0.2532, pruned_loss=0.05444, over 985911.47 frames.], batch size: 56, aishell_tot_loss[loss=0.1789, simple_loss=0.2578, pruned_loss=0.04995, over 984865.44 frames.], datatang_tot_loss[loss=0.184, simple_loss=0.2494, pruned_loss=0.05927, over 986315.33 frames.], batch size: 56, lr: 8.51e-04
+2022-06-18 16:58:01,282 INFO [train.py:874] (1/4) Epoch 9, batch 3900, aishell_loss[loss=0.167, simple_loss=0.2494, pruned_loss=0.04236, over 4929.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2522, pruned_loss=0.05418, over 985055.68 frames.], batch size: 33, aishell_tot_loss[loss=0.1779, simple_loss=0.2566, pruned_loss=0.04962, over 984178.56 frames.], datatang_tot_loss[loss=0.1841, simple_loss=0.2494, pruned_loss=0.05941, over 986190.71 frames.], batch size: 33, lr: 8.50e-04
+2022-06-18 16:58:29,655 INFO [train.py:874] (1/4) Epoch 9, batch 3950, datatang_loss[loss=0.1907, simple_loss=0.2512, pruned_loss=0.06512, over 4936.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2508, pruned_loss=0.05288, over 984969.68 frames.], batch size: 34, aishell_tot_loss[loss=0.1772, simple_loss=0.2561, pruned_loss=0.04916, over 984079.60 frames.], datatang_tot_loss[loss=0.1826, simple_loss=0.2481, pruned_loss=0.05854, over 986194.59 frames.], batch size: 34, lr: 8.49e-04
+2022-06-18 16:58:57,894 INFO [train.py:874] (1/4) Epoch 9, batch 4000, aishell_loss[loss=0.1643, simple_loss=0.2494, pruned_loss=0.03963, over 4895.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2508, pruned_loss=0.05236, over 984845.23 frames.], batch size: 34, aishell_tot_loss[loss=0.1773, simple_loss=0.2563, pruned_loss=0.04912, over 984072.26 frames.], datatang_tot_loss[loss=0.1817, simple_loss=0.2474, pruned_loss=0.05802, over 986083.43 frames.], batch size: 34, lr: 8.49e-04
+2022-06-18 16:58:57,895 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 16:59:14,870 INFO [train.py:914] (1/4) Epoch 9, validation: loss=0.1682, simple_loss=0.2517, pruned_loss=0.04232, over 1622729.00 frames.
+2022-06-18 16:59:43,934 INFO [train.py:874] (1/4) Epoch 9, batch 4050, datatang_loss[loss=0.2085, simple_loss=0.2788, pruned_loss=0.06915, over 4957.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2508, pruned_loss=0.05238, over 984678.13 frames.], batch size: 99, aishell_tot_loss[loss=0.1769, simple_loss=0.2562, pruned_loss=0.04882, over 983870.24 frames.], datatang_tot_loss[loss=0.1815, simple_loss=0.2474, pruned_loss=0.05784, over 986012.79 frames.], batch size: 99, lr: 8.48e-04
+2022-06-18 17:00:57,160 INFO [train.py:874] (1/4) Epoch 10, batch 50, datatang_loss[loss=0.1731, simple_loss=0.2423, pruned_loss=0.05189, over 4947.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2494, pruned_loss=0.05386, over 218629.65 frames.], batch size: 91, aishell_tot_loss[loss=0.1848, simple_loss=0.2612, pruned_loss=0.05427, over 111821.89 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.2391, pruned_loss=0.05362, over 120465.39 frames.], batch size: 91, lr: 8.15e-04
+2022-06-18 17:01:24,363 INFO [train.py:874] (1/4) Epoch 10, batch 100, aishell_loss[loss=0.1598, simple_loss=0.2488, pruned_loss=0.03537, over 4959.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2508, pruned_loss=0.05151, over 388853.15 frames.], batch size: 56, aishell_tot_loss[loss=0.1811, simple_loss=0.2609, pruned_loss=0.05066, over 245213.57 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.2372, pruned_loss=0.0531, over 191158.86 frames.], batch size: 56, lr: 8.14e-04
+2022-06-18 17:01:55,762 INFO [train.py:874] (1/4) Epoch 10, batch 150, aishell_loss[loss=0.203, simple_loss=0.2839, pruned_loss=0.06105, over 4914.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2509, pruned_loss=0.05218, over 520831.35 frames.], batch size: 68, aishell_tot_loss[loss=0.1817, simple_loss=0.2602, pruned_loss=0.05159, over 348160.62 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.2379, pruned_loss=0.05312, over 267021.78 frames.], batch size: 68, lr: 8.14e-04
+2022-06-18 17:02:27,249 INFO [train.py:874] (1/4) Epoch 10, batch 200, datatang_loss[loss=0.1544, simple_loss=0.2212, pruned_loss=0.04383, over 4971.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2501, pruned_loss=0.05121, over 624177.67 frames.], batch size: 37, aishell_tot_loss[loss=0.1805, simple_loss=0.2595, pruned_loss=0.05071, over 431862.10 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.2376, pruned_loss=0.05235, over 342142.58 frames.], batch size: 37, lr: 8.13e-04
+2022-06-18 17:02:55,480 INFO [train.py:874] (1/4) Epoch 10, batch 250, datatang_loss[loss=0.1983, simple_loss=0.2682, pruned_loss=0.06415, over 4934.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2494, pruned_loss=0.05123, over 704351.83 frames.], batch size: 88, aishell_tot_loss[loss=0.18, simple_loss=0.2591, pruned_loss=0.05041, over 499577.24 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.2376, pruned_loss=0.05262, over 415081.83 frames.], batch size: 88, lr: 8.13e-04
+2022-06-18 17:03:27,369 INFO [train.py:874] (1/4) Epoch 10, batch 300, aishell_loss[loss=0.1662, simple_loss=0.2506, pruned_loss=0.04089, over 4918.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2485, pruned_loss=0.05054, over 766546.70 frames.], batch size: 46, aishell_tot_loss[loss=0.1783, simple_loss=0.2578, pruned_loss=0.04945, over 556735.98 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.238, pruned_loss=0.05242, over 482203.86 frames.], batch size: 46, lr: 8.12e-04
+2022-06-18 17:03:58,468 INFO [train.py:874] (1/4) Epoch 10, batch 350, datatang_loss[loss=0.1703, simple_loss=0.2399, pruned_loss=0.05032, over 4933.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2481, pruned_loss=0.0509, over 815099.13 frames.], batch size: 83, aishell_tot_loss[loss=0.1777, simple_loss=0.257, pruned_loss=0.0492, over 605211.85 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2386, pruned_loss=0.05306, over 543846.70 frames.], batch size: 83, lr: 8.12e-04
+2022-06-18 17:04:26,545 INFO [train.py:874] (1/4) Epoch 10, batch 400, datatang_loss[loss=0.1505, simple_loss=0.2201, pruned_loss=0.04044, over 4919.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2499, pruned_loss=0.05216, over 852791.88 frames.], batch size: 75, aishell_tot_loss[loss=0.177, simple_loss=0.2564, pruned_loss=0.04882, over 651469.09 frames.], datatang_tot_loss[loss=0.1763, simple_loss=0.2419, pruned_loss=0.05536, over 594034.67 frames.], batch size: 75, lr: 8.11e-04
+2022-06-18 17:04:57,206 INFO [train.py:874] (1/4) Epoch 10, batch 450, aishell_loss[loss=0.1335, simple_loss=0.1944, pruned_loss=0.03628, over 4776.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2505, pruned_loss=0.05252, over 881872.28 frames.], batch size: 19, aishell_tot_loss[loss=0.1778, simple_loss=0.257, pruned_loss=0.0493, over 699233.32 frames.], datatang_tot_loss[loss=0.1767, simple_loss=0.242, pruned_loss=0.05569, over 629564.96 frames.], batch size: 19, lr: 8.11e-04
+2022-06-18 17:05:26,439 INFO [train.py:874] (1/4) Epoch 10, batch 500, aishell_loss[loss=0.1803, simple_loss=0.2656, pruned_loss=0.04751, over 4872.00 frames.], tot_loss[loss=0.177, simple_loss=0.2508, pruned_loss=0.05167, over 904781.22 frames.], batch size: 35, aishell_tot_loss[loss=0.177, simple_loss=0.2567, pruned_loss=0.0486, over 739082.59 frames.], datatang_tot_loss[loss=0.1768, simple_loss=0.2425, pruned_loss=0.05549, over 663568.87 frames.], batch size: 35, lr: 8.10e-04
+2022-06-18 17:05:55,039 INFO [train.py:874] (1/4) Epoch 10, batch 550, datatang_loss[loss=0.1397, simple_loss=0.2036, pruned_loss=0.03792, over 4938.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2524, pruned_loss=0.05263, over 922723.93 frames.], batch size: 45, aishell_tot_loss[loss=0.1774, simple_loss=0.2572, pruned_loss=0.04882, over 773376.74 frames.], datatang_tot_loss[loss=0.1788, simple_loss=0.2442, pruned_loss=0.05671, over 694434.47 frames.], batch size: 45, lr: 8.10e-04
+2022-06-18 17:06:26,459 INFO [train.py:874] (1/4) Epoch 10, batch 600, aishell_loss[loss=0.1667, simple_loss=0.2461, pruned_loss=0.0436, over 4954.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2519, pruned_loss=0.05258, over 936521.15 frames.], batch size: 40, aishell_tot_loss[loss=0.1767, simple_loss=0.2564, pruned_loss=0.04847, over 800150.11 frames.], datatang_tot_loss[loss=0.1795, simple_loss=0.2449, pruned_loss=0.05712, over 726073.98 frames.], batch size: 40, lr: 8.09e-04
+2022-06-18 17:06:55,505 INFO [train.py:874] (1/4) Epoch 10, batch 650, datatang_loss[loss=0.1944, simple_loss=0.2598, pruned_loss=0.06451, over 4977.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2514, pruned_loss=0.05273, over 947680.43 frames.], batch size: 45, aishell_tot_loss[loss=0.1762, simple_loss=0.2561, pruned_loss=0.04819, over 818746.04 frames.], datatang_tot_loss[loss=0.1799, simple_loss=0.2452, pruned_loss=0.05729, over 761412.17 frames.], batch size: 45, lr: 8.09e-04
+2022-06-18 17:07:24,977 INFO [train.py:874] (1/4) Epoch 10, batch 700, aishell_loss[loss=0.185, simple_loss=0.2605, pruned_loss=0.05479, over 4964.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2514, pruned_loss=0.05282, over 956317.81 frames.], batch size: 31, aishell_tot_loss[loss=0.1762, simple_loss=0.2561, pruned_loss=0.04815, over 839972.06 frames.], datatang_tot_loss[loss=0.1802, simple_loss=0.2453, pruned_loss=0.05756, over 785973.79 frames.], batch size: 31, lr: 8.08e-04
+2022-06-18 17:07:56,720 INFO [train.py:874] (1/4) Epoch 10, batch 750, aishell_loss[loss=0.1797, simple_loss=0.2674, pruned_loss=0.046, over 4983.00 frames.], tot_loss[loss=0.178, simple_loss=0.2508, pruned_loss=0.05259, over 962297.28 frames.], batch size: 38, aishell_tot_loss[loss=0.1757, simple_loss=0.2555, pruned_loss=0.04798, over 854785.16 frames.], datatang_tot_loss[loss=0.18, simple_loss=0.2454, pruned_loss=0.05731, over 811968.76 frames.], batch size: 38, lr: 8.08e-04
+2022-06-18 17:08:27,665 INFO [train.py:874] (1/4) Epoch 10, batch 800, aishell_loss[loss=0.1817, simple_loss=0.2602, pruned_loss=0.05154, over 4954.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2516, pruned_loss=0.05299, over 967508.79 frames.], batch size: 64, aishell_tot_loss[loss=0.176, simple_loss=0.2559, pruned_loss=0.04807, over 868392.98 frames.], datatang_tot_loss[loss=0.1807, simple_loss=0.2463, pruned_loss=0.05753, over 834794.95 frames.], batch size: 64, lr: 8.07e-04
+2022-06-18 17:08:57,236 INFO [train.py:874] (1/4) Epoch 10, batch 850, datatang_loss[loss=0.1702, simple_loss=0.2502, pruned_loss=0.04512, over 4919.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2518, pruned_loss=0.05304, over 971513.76 frames.], batch size: 57, aishell_tot_loss[loss=0.1762, simple_loss=0.2561, pruned_loss=0.04815, over 880742.22 frames.], datatang_tot_loss[loss=0.1807, simple_loss=0.2467, pruned_loss=0.05738, over 854442.03 frames.], batch size: 57, lr: 8.07e-04
+2022-06-18 17:09:29,312 INFO [train.py:874] (1/4) Epoch 10, batch 900, aishell_loss[loss=0.1815, simple_loss=0.2617, pruned_loss=0.05064, over 4875.00 frames.], tot_loss[loss=0.1788, simple_loss=0.252, pruned_loss=0.0528, over 974607.34 frames.], batch size: 42, aishell_tot_loss[loss=0.1764, simple_loss=0.2564, pruned_loss=0.04826, over 893496.85 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2466, pruned_loss=0.05709, over 869336.22 frames.], batch size: 42, lr: 8.06e-04
+2022-06-18 17:09:58,849 INFO [train.py:874] (1/4) Epoch 10, batch 950, datatang_loss[loss=0.1364, simple_loss=0.2089, pruned_loss=0.03194, over 4978.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2518, pruned_loss=0.05273, over 977237.21 frames.], batch size: 55, aishell_tot_loss[loss=0.1762, simple_loss=0.2561, pruned_loss=0.04813, over 904757.05 frames.], datatang_tot_loss[loss=0.1806, simple_loss=0.2468, pruned_loss=0.05723, over 882720.34 frames.], batch size: 55, lr: 8.05e-04
+2022-06-18 17:10:29,024 INFO [train.py:874] (1/4) Epoch 10, batch 1000, aishell_loss[loss=0.1515, simple_loss=0.2388, pruned_loss=0.03212, over 4970.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2512, pruned_loss=0.05264, over 979338.36 frames.], batch size: 44, aishell_tot_loss[loss=0.1756, simple_loss=0.2555, pruned_loss=0.04783, over 913545.69 frames.], datatang_tot_loss[loss=0.1808, simple_loss=0.2469, pruned_loss=0.05735, over 896013.25 frames.], batch size: 44, lr: 8.05e-04
+2022-06-18 17:10:29,025 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 17:10:46,216 INFO [train.py:914] (1/4) 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] (1/4) Epoch 10, batch 1050, aishell_loss[loss=0.1945, simple_loss=0.2627, pruned_loss=0.06315, over 4901.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2507, pruned_loss=0.05294, over 981138.51 frames.], batch size: 41, aishell_tot_loss[loss=0.1757, simple_loss=0.2555, pruned_loss=0.04793, over 918936.81 frames.], datatang_tot_loss[loss=0.1805, simple_loss=0.2467, pruned_loss=0.05712, over 910690.40 frames.], batch size: 41, lr: 8.04e-04
+2022-06-18 17:11:46,553 INFO [train.py:874] (1/4) Epoch 10, batch 1100, aishell_loss[loss=0.1952, simple_loss=0.2706, pruned_loss=0.0599, over 4963.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2516, pruned_loss=0.05301, over 982164.72 frames.], batch size: 61, aishell_tot_loss[loss=0.1758, simple_loss=0.2559, pruned_loss=0.04789, over 927986.89 frames.], datatang_tot_loss[loss=0.1811, simple_loss=0.2471, pruned_loss=0.05753, over 918167.91 frames.], batch size: 61, lr: 8.04e-04
+2022-06-18 17:12:16,091 INFO [train.py:874] (1/4) Epoch 10, batch 1150, datatang_loss[loss=0.1826, simple_loss=0.2543, pruned_loss=0.05545, over 4835.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2501, pruned_loss=0.05215, over 982624.15 frames.], batch size: 30, aishell_tot_loss[loss=0.1747, simple_loss=0.2548, pruned_loss=0.04736, over 934880.00 frames.], datatang_tot_loss[loss=0.1805, simple_loss=0.2465, pruned_loss=0.05721, over 925640.01 frames.], batch size: 30, lr: 8.03e-04
+2022-06-18 17:12:47,090 INFO [train.py:874] (1/4) Epoch 10, batch 1200, aishell_loss[loss=0.1679, simple_loss=0.2508, pruned_loss=0.04254, over 4976.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2505, pruned_loss=0.05286, over 983305.80 frames.], batch size: 39, aishell_tot_loss[loss=0.1752, simple_loss=0.2551, pruned_loss=0.04766, over 939699.62 frames.], datatang_tot_loss[loss=0.1808, simple_loss=0.2467, pruned_loss=0.05745, over 934053.52 frames.], batch size: 39, lr: 8.03e-04
+2022-06-18 17:13:18,851 INFO [train.py:874] (1/4) Epoch 10, batch 1250, aishell_loss[loss=0.1882, simple_loss=0.2804, pruned_loss=0.04801, over 4977.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2507, pruned_loss=0.0526, over 983967.82 frames.], batch size: 48, aishell_tot_loss[loss=0.175, simple_loss=0.2551, pruned_loss=0.04744, over 945338.44 frames.], datatang_tot_loss[loss=0.1809, simple_loss=0.2468, pruned_loss=0.05753, over 940032.75 frames.], batch size: 48, lr: 8.02e-04
+2022-06-18 17:13:47,296 INFO [train.py:874] (1/4) Epoch 10, batch 1300, datatang_loss[loss=0.1891, simple_loss=0.2594, pruned_loss=0.05941, over 4952.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2504, pruned_loss=0.05259, over 984259.47 frames.], batch size: 50, aishell_tot_loss[loss=0.1747, simple_loss=0.2549, pruned_loss=0.04728, over 949597.43 frames.], datatang_tot_loss[loss=0.181, simple_loss=0.2467, pruned_loss=0.05761, over 945839.14 frames.], batch size: 50, lr: 8.02e-04
+2022-06-18 17:14:20,326 INFO [train.py:874] (1/4) Epoch 10, batch 1350, datatang_loss[loss=0.1856, simple_loss=0.2564, pruned_loss=0.05738, over 4926.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2502, pruned_loss=0.05341, over 984068.48 frames.], batch size: 57, aishell_tot_loss[loss=0.1747, simple_loss=0.2548, pruned_loss=0.04726, over 951659.31 frames.], datatang_tot_loss[loss=0.1815, simple_loss=0.2469, pruned_loss=0.05799, over 952265.89 frames.], batch size: 57, lr: 8.01e-04
+2022-06-18 17:14:52,511 INFO [train.py:874] (1/4) Epoch 10, batch 1400, datatang_loss[loss=0.1588, simple_loss=0.2339, pruned_loss=0.04184, over 4924.00 frames.], tot_loss[loss=0.178, simple_loss=0.2498, pruned_loss=0.05313, over 984075.38 frames.], batch size: 75, aishell_tot_loss[loss=0.1748, simple_loss=0.2542, pruned_loss=0.04768, over 956187.21 frames.], datatang_tot_loss[loss=0.1811, simple_loss=0.2466, pruned_loss=0.05776, over 955307.66 frames.], batch size: 75, lr: 8.01e-04
+2022-06-18 17:15:21,140 INFO [train.py:874] (1/4) Epoch 10, batch 1450, datatang_loss[loss=0.1747, simple_loss=0.2444, pruned_loss=0.0525, over 4963.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2491, pruned_loss=0.05258, over 984415.37 frames.], batch size: 45, aishell_tot_loss[loss=0.1747, simple_loss=0.254, pruned_loss=0.04773, over 959139.96 frames.], datatang_tot_loss[loss=0.1801, simple_loss=0.2461, pruned_loss=0.05706, over 959397.30 frames.], batch size: 45, lr: 8.00e-04
+2022-06-18 17:15:52,706 INFO [train.py:874] (1/4) Epoch 10, batch 1500, datatang_loss[loss=0.2071, simple_loss=0.279, pruned_loss=0.06763, over 4925.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2501, pruned_loss=0.05281, over 984704.58 frames.], batch size: 94, aishell_tot_loss[loss=0.1754, simple_loss=0.2549, pruned_loss=0.0479, over 961962.94 frames.], datatang_tot_loss[loss=0.1801, simple_loss=0.2461, pruned_loss=0.05708, over 962789.22 frames.], batch size: 94, lr: 8.00e-04
+2022-06-18 17:16:23,734 INFO [train.py:874] (1/4) Epoch 10, batch 1550, datatang_loss[loss=0.2638, simple_loss=0.3039, pruned_loss=0.1119, over 4946.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2519, pruned_loss=0.05342, over 984951.26 frames.], batch size: 108, aishell_tot_loss[loss=0.176, simple_loss=0.2556, pruned_loss=0.04821, over 965352.62 frames.], datatang_tot_loss[loss=0.1813, simple_loss=0.2471, pruned_loss=0.05775, over 964927.56 frames.], batch size: 108, lr: 7.99e-04
+2022-06-18 17:16:51,684 INFO [train.py:874] (1/4) Epoch 10, batch 1600, aishell_loss[loss=0.1699, simple_loss=0.2537, pruned_loss=0.043, over 4865.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2527, pruned_loss=0.0537, over 985156.90 frames.], batch size: 36, aishell_tot_loss[loss=0.1763, simple_loss=0.2561, pruned_loss=0.04821, over 967692.17 frames.], datatang_tot_loss[loss=0.1819, simple_loss=0.2476, pruned_loss=0.05813, over 967481.84 frames.], batch size: 36, lr: 7.99e-04
+2022-06-18 17:17:24,165 INFO [train.py:874] (1/4) Epoch 10, batch 1650, datatang_loss[loss=0.201, simple_loss=0.2564, pruned_loss=0.07281, over 4949.00 frames.], tot_loss[loss=0.1806, simple_loss=0.253, pruned_loss=0.05415, over 985291.81 frames.], batch size: 34, aishell_tot_loss[loss=0.1756, simple_loss=0.2555, pruned_loss=0.04787, over 969400.10 frames.], datatang_tot_loss[loss=0.1833, simple_loss=0.2487, pruned_loss=0.05891, over 970074.67 frames.], batch size: 34, lr: 7.98e-04
+2022-06-18 17:17:56,375 INFO [train.py:874] (1/4) Epoch 10, batch 1700, aishell_loss[loss=0.1606, simple_loss=0.2166, pruned_loss=0.05228, over 4940.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2534, pruned_loss=0.05396, over 985199.92 frames.], batch size: 21, aishell_tot_loss[loss=0.1766, simple_loss=0.2564, pruned_loss=0.04839, over 971276.19 frames.], datatang_tot_loss[loss=0.1827, simple_loss=0.2484, pruned_loss=0.05848, over 971763.88 frames.], batch size: 21, lr: 7.98e-04
+2022-06-18 17:18:24,504 INFO [train.py:874] (1/4) Epoch 10, batch 1750, datatang_loss[loss=0.1824, simple_loss=0.252, pruned_loss=0.0564, over 4912.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2523, pruned_loss=0.05339, over 985311.02 frames.], batch size: 75, aishell_tot_loss[loss=0.1762, simple_loss=0.2558, pruned_loss=0.04828, over 972926.95 frames.], datatang_tot_loss[loss=0.1821, simple_loss=0.248, pruned_loss=0.05811, over 973460.22 frames.], batch size: 75, lr: 7.97e-04
+2022-06-18 17:18:55,217 INFO [train.py:874] (1/4) Epoch 10, batch 1800, datatang_loss[loss=0.163, simple_loss=0.229, pruned_loss=0.04847, over 4921.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2519, pruned_loss=0.05326, over 985697.85 frames.], batch size: 71, aishell_tot_loss[loss=0.1761, simple_loss=0.2556, pruned_loss=0.04833, over 974729.83 frames.], datatang_tot_loss[loss=0.1819, simple_loss=0.2479, pruned_loss=0.05795, over 974926.30 frames.], batch size: 71, lr: 7.97e-04
+2022-06-18 17:19:27,438 INFO [train.py:874] (1/4) Epoch 10, batch 1850, datatang_loss[loss=0.1816, simple_loss=0.2526, pruned_loss=0.05526, over 4917.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2529, pruned_loss=0.0544, over 985572.72 frames.], batch size: 62, aishell_tot_loss[loss=0.1764, simple_loss=0.2557, pruned_loss=0.04851, over 975864.27 frames.], datatang_tot_loss[loss=0.1835, simple_loss=0.249, pruned_loss=0.05903, over 976220.27 frames.], batch size: 62, lr: 7.96e-04
+2022-06-18 17:19:55,259 INFO [train.py:874] (1/4) Epoch 10, batch 1900, aishell_loss[loss=0.1807, simple_loss=0.2583, pruned_loss=0.0515, over 4858.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2531, pruned_loss=0.05475, over 985796.07 frames.], batch size: 38, aishell_tot_loss[loss=0.1763, simple_loss=0.2558, pruned_loss=0.04845, over 976561.93 frames.], datatang_tot_loss[loss=0.1839, simple_loss=0.2494, pruned_loss=0.05922, over 977988.71 frames.], batch size: 38, lr: 7.96e-04
+2022-06-18 17:20:27,319 INFO [train.py:874] (1/4) Epoch 10, batch 1950, datatang_loss[loss=0.1881, simple_loss=0.2583, pruned_loss=0.05896, over 4933.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2536, pruned_loss=0.05474, over 986069.56 frames.], batch size: 50, aishell_tot_loss[loss=0.177, simple_loss=0.2564, pruned_loss=0.04881, over 977915.07 frames.], datatang_tot_loss[loss=0.1839, simple_loss=0.2494, pruned_loss=0.05915, over 978943.75 frames.], batch size: 50, lr: 7.95e-04
+2022-06-18 17:20:59,403 INFO [train.py:874] (1/4) Epoch 10, batch 2000, aishell_loss[loss=0.1928, simple_loss=0.2639, pruned_loss=0.06088, over 4973.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2534, pruned_loss=0.05414, over 986180.14 frames.], batch size: 31, aishell_tot_loss[loss=0.1772, simple_loss=0.2567, pruned_loss=0.04883, over 979088.11 frames.], datatang_tot_loss[loss=0.1831, simple_loss=0.249, pruned_loss=0.05864, over 979683.76 frames.], batch size: 31, lr: 7.95e-04
+2022-06-18 17:20:59,404 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 17:21:15,566 INFO [train.py:914] (1/4) Epoch 10, validation: loss=0.1694, simple_loss=0.2523, pruned_loss=0.04323, over 1622729.00 frames.
+2022-06-18 17:21:47,960 INFO [train.py:874] (1/4) Epoch 10, batch 2050, aishell_loss[loss=0.1798, simple_loss=0.2598, pruned_loss=0.0499, over 4971.00 frames.], tot_loss[loss=0.1811, simple_loss=0.254, pruned_loss=0.05409, over 986261.41 frames.], batch size: 44, aishell_tot_loss[loss=0.1774, simple_loss=0.2572, pruned_loss=0.0488, over 980024.41 frames.], datatang_tot_loss[loss=0.1835, simple_loss=0.2492, pruned_loss=0.05889, over 980454.93 frames.], batch size: 44, lr: 7.94e-04
+2022-06-18 17:22:16,703 INFO [train.py:874] (1/4) Epoch 10, batch 2100, aishell_loss[loss=0.1489, simple_loss=0.2173, pruned_loss=0.04022, over 4973.00 frames.], tot_loss[loss=0.18, simple_loss=0.253, pruned_loss=0.05347, over 986518.10 frames.], batch size: 25, aishell_tot_loss[loss=0.1771, simple_loss=0.2568, pruned_loss=0.04867, over 980956.14 frames.], datatang_tot_loss[loss=0.1828, simple_loss=0.2486, pruned_loss=0.05852, over 981216.31 frames.], batch size: 25, lr: 7.94e-04
+2022-06-18 17:22:48,239 INFO [train.py:874] (1/4) Epoch 10, batch 2150, aishell_loss[loss=0.1583, simple_loss=0.2461, pruned_loss=0.0353, over 4948.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2518, pruned_loss=0.05295, over 986405.06 frames.], batch size: 54, aishell_tot_loss[loss=0.1763, simple_loss=0.2561, pruned_loss=0.04827, over 981431.43 frames.], datatang_tot_loss[loss=0.1823, simple_loss=0.2481, pruned_loss=0.05827, over 981892.12 frames.], batch size: 54, lr: 7.93e-04
+2022-06-18 17:23:19,887 INFO [train.py:874] (1/4) Epoch 10, batch 2200, aishell_loss[loss=0.1846, simple_loss=0.2652, pruned_loss=0.05199, over 4945.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2521, pruned_loss=0.05301, over 986559.38 frames.], batch size: 40, aishell_tot_loss[loss=0.1764, simple_loss=0.2563, pruned_loss=0.04822, over 982093.03 frames.], datatang_tot_loss[loss=0.1824, simple_loss=0.2482, pruned_loss=0.05831, over 982513.30 frames.], batch size: 40, lr: 7.93e-04
+2022-06-18 17:23:47,886 INFO [train.py:874] (1/4) Epoch 10, batch 2250, datatang_loss[loss=0.1511, simple_loss=0.2251, pruned_loss=0.03853, over 4927.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2509, pruned_loss=0.05215, over 986016.08 frames.], batch size: 77, aishell_tot_loss[loss=0.176, simple_loss=0.2558, pruned_loss=0.04811, over 982425.10 frames.], datatang_tot_loss[loss=0.1811, simple_loss=0.2472, pruned_loss=0.05754, over 982590.06 frames.], batch size: 77, lr: 7.92e-04
+2022-06-18 17:24:17,606 INFO [train.py:874] (1/4) Epoch 10, batch 2300, aishell_loss[loss=0.1791, simple_loss=0.259, pruned_loss=0.04965, over 4936.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2514, pruned_loss=0.05238, over 985953.01 frames.], batch size: 45, aishell_tot_loss[loss=0.1764, simple_loss=0.256, pruned_loss=0.04843, over 983022.97 frames.], datatang_tot_loss[loss=0.1812, simple_loss=0.2472, pruned_loss=0.05758, over 982738.84 frames.], batch size: 45, lr: 7.92e-04
+2022-06-18 17:24:49,700 INFO [train.py:874] (1/4) Epoch 10, batch 2350, datatang_loss[loss=0.1976, simple_loss=0.2584, pruned_loss=0.06836, over 4951.00 frames.], tot_loss[loss=0.1772, simple_loss=0.251, pruned_loss=0.05172, over 985863.47 frames.], batch size: 67, aishell_tot_loss[loss=0.1761, simple_loss=0.256, pruned_loss=0.04815, over 983234.06 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2466, pruned_loss=0.05704, over 983153.75 frames.], batch size: 67, lr: 7.91e-04
+2022-06-18 17:25:17,486 INFO [train.py:874] (1/4) Epoch 10, batch 2400, datatang_loss[loss=0.1676, simple_loss=0.2354, pruned_loss=0.04995, over 4924.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2514, pruned_loss=0.05224, over 986038.32 frames.], batch size: 64, aishell_tot_loss[loss=0.176, simple_loss=0.256, pruned_loss=0.04799, over 983644.69 frames.], datatang_tot_loss[loss=0.1809, simple_loss=0.2472, pruned_loss=0.05735, over 983554.50 frames.], batch size: 64, lr: 7.91e-04
+2022-06-18 17:25:49,003 INFO [train.py:874] (1/4) Epoch 10, batch 2450, datatang_loss[loss=0.1585, simple_loss=0.2318, pruned_loss=0.0426, over 4913.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2502, pruned_loss=0.05165, over 985790.69 frames.], batch size: 75, aishell_tot_loss[loss=0.1755, simple_loss=0.2555, pruned_loss=0.04776, over 983848.86 frames.], datatang_tot_loss[loss=0.1801, simple_loss=0.2464, pruned_loss=0.05688, over 983667.79 frames.], batch size: 75, lr: 7.90e-04
+2022-06-18 17:26:19,964 INFO [train.py:874] (1/4) Epoch 10, batch 2500, aishell_loss[loss=0.1714, simple_loss=0.262, pruned_loss=0.04036, over 4977.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2504, pruned_loss=0.05148, over 986132.26 frames.], batch size: 39, aishell_tot_loss[loss=0.1749, simple_loss=0.255, pruned_loss=0.04736, over 984277.73 frames.], datatang_tot_loss[loss=0.1805, simple_loss=0.2468, pruned_loss=0.05712, over 984059.08 frames.], batch size: 39, lr: 7.90e-04
+2022-06-18 17:26:50,241 INFO [train.py:874] (1/4) Epoch 10, batch 2550, datatang_loss[loss=0.1902, simple_loss=0.2598, pruned_loss=0.06034, over 4946.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2509, pruned_loss=0.05167, over 986159.43 frames.], batch size: 69, aishell_tot_loss[loss=0.1749, simple_loss=0.2552, pruned_loss=0.04725, over 984425.38 frames.], datatang_tot_loss[loss=0.1808, simple_loss=0.247, pruned_loss=0.05728, over 984403.28 frames.], batch size: 69, lr: 7.89e-04
+2022-06-18 17:27:22,111 INFO [train.py:874] (1/4) Epoch 10, batch 2600, datatang_loss[loss=0.1705, simple_loss=0.2326, pruned_loss=0.05424, over 4928.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2507, pruned_loss=0.05146, over 985698.92 frames.], batch size: 79, aishell_tot_loss[loss=0.1754, simple_loss=0.2558, pruned_loss=0.04756, over 984035.76 frames.], datatang_tot_loss[loss=0.1798, simple_loss=0.2462, pruned_loss=0.05671, over 984718.97 frames.], batch size: 79, lr: 7.89e-04
+2022-06-18 17:27:53,226 INFO [train.py:874] (1/4) Epoch 10, batch 2650, aishell_loss[loss=0.1659, simple_loss=0.24, pruned_loss=0.0459, over 4957.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2511, pruned_loss=0.05188, over 985630.48 frames.], batch size: 27, aishell_tot_loss[loss=0.1759, simple_loss=0.2563, pruned_loss=0.04773, over 984206.80 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2461, pruned_loss=0.05667, over 984773.15 frames.], batch size: 27, lr: 7.88e-04
+2022-06-18 17:28:23,887 INFO [train.py:874] (1/4) Epoch 10, batch 2700, datatang_loss[loss=0.1674, simple_loss=0.2367, pruned_loss=0.04908, over 4924.00 frames.], tot_loss[loss=0.1787, simple_loss=0.251, pruned_loss=0.0532, over 985684.89 frames.], batch size: 71, aishell_tot_loss[loss=0.1763, simple_loss=0.2567, pruned_loss=0.04795, over 984132.40 frames.], datatang_tot_loss[loss=0.1804, simple_loss=0.2462, pruned_loss=0.05733, over 985132.18 frames.], batch size: 71, lr: 7.88e-04
+2022-06-18 17:28:56,002 INFO [train.py:874] (1/4) Epoch 10, batch 2750, aishell_loss[loss=0.1938, simple_loss=0.2757, pruned_loss=0.05593, over 4932.00 frames.], tot_loss[loss=0.1786, simple_loss=0.251, pruned_loss=0.05309, over 985595.52 frames.], batch size: 80, aishell_tot_loss[loss=0.1769, simple_loss=0.2572, pruned_loss=0.04832, over 984258.59 frames.], datatang_tot_loss[loss=0.1797, simple_loss=0.2456, pruned_loss=0.0569, over 985144.81 frames.], batch size: 80, lr: 7.87e-04
+2022-06-18 17:29:26,471 INFO [train.py:874] (1/4) Epoch 10, batch 2800, datatang_loss[loss=0.2059, simple_loss=0.2713, pruned_loss=0.07022, over 4902.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2507, pruned_loss=0.05257, over 985492.70 frames.], batch size: 64, aishell_tot_loss[loss=0.1769, simple_loss=0.2569, pruned_loss=0.0484, over 984314.37 frames.], datatang_tot_loss[loss=0.1791, simple_loss=0.2454, pruned_loss=0.05641, over 985200.13 frames.], batch size: 64, lr: 7.87e-04
+2022-06-18 17:30:00,714 INFO [train.py:874] (1/4) Epoch 10, batch 2850, datatang_loss[loss=0.1475, simple_loss=0.2085, pruned_loss=0.04322, over 4806.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2504, pruned_loss=0.05212, over 985564.80 frames.], batch size: 24, aishell_tot_loss[loss=0.1767, simple_loss=0.2568, pruned_loss=0.04828, over 984472.97 frames.], datatang_tot_loss[loss=0.1786, simple_loss=0.245, pruned_loss=0.05609, over 985306.21 frames.], batch size: 24, lr: 7.86e-04
+2022-06-18 17:30:32,967 INFO [train.py:874] (1/4) Epoch 10, batch 2900, datatang_loss[loss=0.17, simple_loss=0.2453, pruned_loss=0.04733, over 4964.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2515, pruned_loss=0.05265, over 985697.95 frames.], batch size: 40, aishell_tot_loss[loss=0.1772, simple_loss=0.2572, pruned_loss=0.04855, over 984539.34 frames.], datatang_tot_loss[loss=0.1792, simple_loss=0.2457, pruned_loss=0.05631, over 985536.19 frames.], batch size: 40, lr: 7.86e-04
+2022-06-18 17:31:03,360 INFO [train.py:874] (1/4) Epoch 10, batch 2950, aishell_loss[loss=0.1491, simple_loss=0.2193, pruned_loss=0.03948, over 4930.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2509, pruned_loss=0.05187, over 985494.63 frames.], batch size: 25, aishell_tot_loss[loss=0.1764, simple_loss=0.2566, pruned_loss=0.04813, over 984563.98 frames.], datatang_tot_loss[loss=0.1788, simple_loss=0.2456, pruned_loss=0.05601, over 985470.72 frames.], batch size: 25, lr: 7.85e-04
+2022-06-18 17:31:32,444 INFO [train.py:874] (1/4) Epoch 10, batch 3000, aishell_loss[loss=0.1864, simple_loss=0.2613, pruned_loss=0.05575, over 4981.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2502, pruned_loss=0.05171, over 985527.37 frames.], batch size: 38, aishell_tot_loss[loss=0.176, simple_loss=0.256, pruned_loss=0.04803, over 984610.20 frames.], datatang_tot_loss[loss=0.1786, simple_loss=0.2454, pruned_loss=0.05586, over 985562.37 frames.], batch size: 38, lr: 7.85e-04
+2022-06-18 17:31:32,445 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 17:31:49,158 INFO [train.py:914] (1/4) Epoch 10, validation: loss=0.1672, simple_loss=0.2512, pruned_loss=0.04165, over 1622729.00 frames.
+2022-06-18 17:32:18,363 INFO [train.py:874] (1/4) Epoch 10, batch 3050, aishell_loss[loss=0.1567, simple_loss=0.2399, pruned_loss=0.03674, over 4932.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2502, pruned_loss=0.0513, over 985464.80 frames.], batch size: 49, aishell_tot_loss[loss=0.1755, simple_loss=0.2557, pruned_loss=0.04759, over 984646.44 frames.], datatang_tot_loss[loss=0.1786, simple_loss=0.2456, pruned_loss=0.05578, over 985556.96 frames.], batch size: 49, lr: 7.85e-04
+2022-06-18 17:32:48,941 INFO [train.py:874] (1/4) Epoch 10, batch 3100, aishell_loss[loss=0.1565, simple_loss=0.2303, pruned_loss=0.04134, over 4955.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2508, pruned_loss=0.05178, over 985836.62 frames.], batch size: 27, aishell_tot_loss[loss=0.1751, simple_loss=0.2555, pruned_loss=0.04734, over 984921.90 frames.], datatang_tot_loss[loss=0.1796, simple_loss=0.2463, pruned_loss=0.05652, over 985776.49 frames.], batch size: 27, lr: 7.84e-04
+2022-06-18 17:33:21,044 INFO [train.py:874] (1/4) Epoch 10, batch 3150, aishell_loss[loss=0.1728, simple_loss=0.262, pruned_loss=0.04173, over 4913.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2509, pruned_loss=0.05168, over 985809.63 frames.], batch size: 52, aishell_tot_loss[loss=0.175, simple_loss=0.2555, pruned_loss=0.0472, over 984965.95 frames.], datatang_tot_loss[loss=0.1798, simple_loss=0.2464, pruned_loss=0.05655, over 985837.08 frames.], batch size: 52, lr: 7.84e-04
+2022-06-18 17:33:49,752 INFO [train.py:874] (1/4) Epoch 10, batch 3200, aishell_loss[loss=0.1834, simple_loss=0.2643, pruned_loss=0.05125, over 4940.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2512, pruned_loss=0.05169, over 985528.89 frames.], batch size: 45, aishell_tot_loss[loss=0.1756, simple_loss=0.2561, pruned_loss=0.04753, over 984745.93 frames.], datatang_tot_loss[loss=0.1792, simple_loss=0.246, pruned_loss=0.05622, over 985882.36 frames.], batch size: 45, lr: 7.83e-04
+2022-06-18 17:34:21,002 INFO [train.py:874] (1/4) Epoch 10, batch 3250, datatang_loss[loss=0.1695, simple_loss=0.2273, pruned_loss=0.05584, over 4955.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2503, pruned_loss=0.05067, over 985496.39 frames.], batch size: 45, aishell_tot_loss[loss=0.1748, simple_loss=0.2557, pruned_loss=0.04698, over 984669.95 frames.], datatang_tot_loss[loss=0.1785, simple_loss=0.2453, pruned_loss=0.05582, over 985987.11 frames.], batch size: 45, lr: 7.83e-04
+2022-06-18 17:34:51,784 INFO [train.py:874] (1/4) Epoch 10, batch 3300, datatang_loss[loss=0.1421, simple_loss=0.2179, pruned_loss=0.03318, over 4929.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2498, pruned_loss=0.05118, over 985667.83 frames.], batch size: 79, aishell_tot_loss[loss=0.175, simple_loss=0.2556, pruned_loss=0.04721, over 984811.61 frames.], datatang_tot_loss[loss=0.1784, simple_loss=0.2451, pruned_loss=0.05582, over 986041.50 frames.], batch size: 79, lr: 7.82e-04
+2022-06-18 17:35:21,930 INFO [train.py:874] (1/4) Epoch 10, batch 3350, aishell_loss[loss=0.1793, simple_loss=0.262, pruned_loss=0.04829, over 4907.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2494, pruned_loss=0.05072, over 985872.60 frames.], batch size: 41, aishell_tot_loss[loss=0.1746, simple_loss=0.2552, pruned_loss=0.04702, over 985020.40 frames.], datatang_tot_loss[loss=0.178, simple_loss=0.2448, pruned_loss=0.05557, over 986124.84 frames.], batch size: 41, lr: 7.82e-04
+2022-06-18 17:35:53,648 INFO [train.py:874] (1/4) Epoch 10, batch 3400, aishell_loss[loss=0.136, simple_loss=0.2162, pruned_loss=0.02794, over 4892.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2489, pruned_loss=0.05026, over 986172.54 frames.], batch size: 28, aishell_tot_loss[loss=0.174, simple_loss=0.2545, pruned_loss=0.04671, over 985347.85 frames.], datatang_tot_loss[loss=0.1777, simple_loss=0.2446, pruned_loss=0.05543, over 986203.29 frames.], batch size: 28, lr: 7.81e-04
+2022-06-18 17:36:23,922 INFO [train.py:874] (1/4) Epoch 10, batch 3450, datatang_loss[loss=0.2055, simple_loss=0.2662, pruned_loss=0.07236, over 4910.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2487, pruned_loss=0.05022, over 985818.72 frames.], batch size: 64, aishell_tot_loss[loss=0.1743, simple_loss=0.2545, pruned_loss=0.04701, over 985224.83 frames.], datatang_tot_loss[loss=0.1771, simple_loss=0.2442, pruned_loss=0.05503, over 986058.49 frames.], batch size: 64, lr: 7.81e-04
+2022-06-18 17:36:53,799 INFO [train.py:874] (1/4) Epoch 10, batch 3500, aishell_loss[loss=0.1964, simple_loss=0.2736, pruned_loss=0.05965, over 4924.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2486, pruned_loss=0.04986, over 986222.46 frames.], batch size: 33, aishell_tot_loss[loss=0.1733, simple_loss=0.2538, pruned_loss=0.04644, over 985571.53 frames.], datatang_tot_loss[loss=0.1774, simple_loss=0.2445, pruned_loss=0.05509, over 986188.85 frames.], batch size: 33, lr: 7.80e-04
+2022-06-18 17:37:26,113 INFO [train.py:874] (1/4) Epoch 10, batch 3550, datatang_loss[loss=0.1664, simple_loss=0.2377, pruned_loss=0.04749, over 4926.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2492, pruned_loss=0.05047, over 986025.21 frames.], batch size: 73, aishell_tot_loss[loss=0.1738, simple_loss=0.2545, pruned_loss=0.04657, over 985463.16 frames.], datatang_tot_loss[loss=0.1775, simple_loss=0.2446, pruned_loss=0.05523, over 986164.40 frames.], batch size: 73, lr: 7.80e-04
+2022-06-18 17:37:55,040 INFO [train.py:874] (1/4) Epoch 10, batch 3600, datatang_loss[loss=0.1715, simple_loss=0.2394, pruned_loss=0.05175, over 4897.00 frames.], tot_loss[loss=0.176, simple_loss=0.2506, pruned_loss=0.05073, over 986072.02 frames.], batch size: 52, aishell_tot_loss[loss=0.1747, simple_loss=0.2555, pruned_loss=0.0469, over 985554.99 frames.], datatang_tot_loss[loss=0.1776, simple_loss=0.2447, pruned_loss=0.05529, over 986192.83 frames.], batch size: 52, lr: 7.79e-04
+2022-06-18 17:38:26,098 INFO [train.py:874] (1/4) Epoch 10, batch 3650, datatang_loss[loss=0.1674, simple_loss=0.24, pruned_loss=0.04739, over 4953.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2489, pruned_loss=0.05011, over 985409.05 frames.], batch size: 86, aishell_tot_loss[loss=0.1737, simple_loss=0.2543, pruned_loss=0.0465, over 985017.25 frames.], datatang_tot_loss[loss=0.177, simple_loss=0.2443, pruned_loss=0.05491, over 986070.79 frames.], batch size: 86, lr: 7.79e-04
+2022-06-18 17:38:57,471 INFO [train.py:874] (1/4) Epoch 10, batch 3700, aishell_loss[loss=0.1639, simple_loss=0.2472, pruned_loss=0.04029, over 4894.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2485, pruned_loss=0.05014, over 985233.64 frames.], batch size: 42, aishell_tot_loss[loss=0.1737, simple_loss=0.2544, pruned_loss=0.0465, over 985222.76 frames.], datatang_tot_loss[loss=0.1766, simple_loss=0.2438, pruned_loss=0.05469, over 985656.19 frames.], batch size: 42, lr: 7.78e-04
+2022-06-18 17:39:26,932 INFO [train.py:874] (1/4) Epoch 10, batch 3750, datatang_loss[loss=0.187, simple_loss=0.2623, pruned_loss=0.05589, over 4950.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2503, pruned_loss=0.05113, over 985214.35 frames.], batch size: 50, aishell_tot_loss[loss=0.1745, simple_loss=0.255, pruned_loss=0.04702, over 985368.31 frames.], datatang_tot_loss[loss=0.1776, simple_loss=0.2449, pruned_loss=0.05518, over 985437.39 frames.], batch size: 50, lr: 7.78e-04
+2022-06-18 17:39:57,896 INFO [train.py:874] (1/4) Epoch 10, batch 3800, datatang_loss[loss=0.2074, simple_loss=0.2793, pruned_loss=0.06774, over 4955.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2502, pruned_loss=0.05114, over 985472.14 frames.], batch size: 99, aishell_tot_loss[loss=0.1748, simple_loss=0.2553, pruned_loss=0.04718, over 985463.19 frames.], datatang_tot_loss[loss=0.1773, simple_loss=0.2444, pruned_loss=0.05505, over 985560.13 frames.], batch size: 99, lr: 7.77e-04
+2022-06-18 17:40:26,658 INFO [train.py:874] (1/4) Epoch 10, batch 3850, aishell_loss[loss=0.2053, simple_loss=0.2801, pruned_loss=0.06526, over 4979.00 frames.], tot_loss[loss=0.177, simple_loss=0.2504, pruned_loss=0.05185, over 985549.97 frames.], batch size: 51, aishell_tot_loss[loss=0.1756, simple_loss=0.2558, pruned_loss=0.04769, over 985733.13 frames.], datatang_tot_loss[loss=0.1774, simple_loss=0.2443, pruned_loss=0.05531, over 985357.73 frames.], batch size: 51, lr: 7.77e-04
+2022-06-18 17:40:56,136 INFO [train.py:874] (1/4) Epoch 10, batch 3900, datatang_loss[loss=0.1861, simple_loss=0.2503, pruned_loss=0.0609, over 4922.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2498, pruned_loss=0.05172, over 985374.11 frames.], batch size: 81, aishell_tot_loss[loss=0.1752, simple_loss=0.2557, pruned_loss=0.04736, over 985504.74 frames.], datatang_tot_loss[loss=0.1775, simple_loss=0.2441, pruned_loss=0.0554, over 985411.03 frames.], batch size: 81, lr: 7.76e-04
+2022-06-18 17:41:24,505 INFO [train.py:874] (1/4) Epoch 10, batch 3950, datatang_loss[loss=0.1497, simple_loss=0.2129, pruned_loss=0.04325, over 4915.00 frames.], tot_loss[loss=0.1777, simple_loss=0.251, pruned_loss=0.05215, over 985532.25 frames.], batch size: 64, aishell_tot_loss[loss=0.1757, simple_loss=0.2561, pruned_loss=0.04763, over 985549.61 frames.], datatang_tot_loss[loss=0.1782, simple_loss=0.2449, pruned_loss=0.05577, over 985523.06 frames.], batch size: 64, lr: 7.76e-04
+2022-06-18 17:41:54,473 INFO [train.py:874] (1/4) Epoch 10, batch 4000, aishell_loss[loss=0.1389, simple_loss=0.2137, pruned_loss=0.03201, over 4967.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2507, pruned_loss=0.05178, over 985456.80 frames.], batch size: 25, aishell_tot_loss[loss=0.1756, simple_loss=0.2559, pruned_loss=0.04762, over 985597.64 frames.], datatang_tot_loss[loss=0.1779, simple_loss=0.2447, pruned_loss=0.05556, over 985388.73 frames.], batch size: 25, lr: 7.76e-04
+2022-06-18 17:41:54,474 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 17:42:11,515 INFO [train.py:914] (1/4) 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] (1/4) Epoch 10, batch 4050, aishell_loss[loss=0.1616, simple_loss=0.2408, pruned_loss=0.04121, over 4881.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2509, pruned_loss=0.05179, over 985511.09 frames.], batch size: 34, aishell_tot_loss[loss=0.1756, simple_loss=0.2558, pruned_loss=0.04771, over 985553.88 frames.], datatang_tot_loss[loss=0.1782, simple_loss=0.2451, pruned_loss=0.05563, over 985486.51 frames.], batch size: 34, lr: 7.75e-04
+2022-06-18 17:43:07,843 INFO [train.py:874] (1/4) Epoch 10, batch 4100, aishell_loss[loss=0.184, simple_loss=0.2662, pruned_loss=0.05092, over 4910.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2509, pruned_loss=0.05106, over 984908.40 frames.], batch size: 41, aishell_tot_loss[loss=0.1757, simple_loss=0.2562, pruned_loss=0.04761, over 984918.31 frames.], datatang_tot_loss[loss=0.1774, simple_loss=0.2446, pruned_loss=0.05511, over 985482.84 frames.], batch size: 41, lr: 7.75e-04
+2022-06-18 17:44:17,191 INFO [train.py:874] (1/4) Epoch 11, batch 50, aishell_loss[loss=0.1477, simple_loss=0.228, pruned_loss=0.03369, over 4803.00 frames.], tot_loss[loss=0.1667, simple_loss=0.244, pruned_loss=0.0447, over 218334.69 frames.], batch size: 26, aishell_tot_loss[loss=0.17, simple_loss=0.2537, pruned_loss=0.04317, over 137313.50 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2294, pruned_loss=0.04714, over 94156.37 frames.], batch size: 26, lr: 7.46e-04
+2022-06-18 17:44:48,047 INFO [train.py:874] (1/4) Epoch 11, batch 100, datatang_loss[loss=0.16, simple_loss=0.2282, pruned_loss=0.04594, over 4946.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2447, pruned_loss=0.04631, over 388570.02 frames.], batch size: 69, aishell_tot_loss[loss=0.1716, simple_loss=0.2545, pruned_loss=0.04437, over 233365.88 frames.], datatang_tot_loss[loss=0.165, simple_loss=0.2333, pruned_loss=0.04832, over 203319.34 frames.], batch size: 69, lr: 7.45e-04
+2022-06-18 17:45:16,813 INFO [train.py:874] (1/4) Epoch 11, batch 150, aishell_loss[loss=0.1698, simple_loss=0.2587, pruned_loss=0.04047, over 4966.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2449, pruned_loss=0.04778, over 520715.13 frames.], batch size: 44, aishell_tot_loss[loss=0.172, simple_loss=0.2547, pruned_loss=0.04472, over 298149.17 frames.], datatang_tot_loss[loss=0.1682, simple_loss=0.2361, pruned_loss=0.05011, over 319137.04 frames.], batch size: 44, lr: 7.45e-04
+2022-06-18 17:45:48,302 INFO [train.py:874] (1/4) Epoch 11, batch 200, datatang_loss[loss=0.1564, simple_loss=0.2228, pruned_loss=0.04501, over 4915.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2439, pruned_loss=0.04753, over 623884.53 frames.], batch size: 75, aishell_tot_loss[loss=0.1731, simple_loss=0.2546, pruned_loss=0.0458, over 385128.84 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2337, pruned_loss=0.04895, over 391863.29 frames.], batch size: 75, lr: 7.44e-04
+2022-06-18 17:46:17,709 INFO [train.py:874] (1/4) Epoch 11, batch 250, datatang_loss[loss=0.1821, simple_loss=0.2499, pruned_loss=0.0571, over 4923.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2452, pruned_loss=0.04747, over 704039.01 frames.], batch size: 64, aishell_tot_loss[loss=0.1725, simple_loss=0.2544, pruned_loss=0.04533, over 466139.30 frames.], datatang_tot_loss[loss=0.1672, simple_loss=0.2352, pruned_loss=0.04957, over 451390.85 frames.], batch size: 64, lr: 7.44e-04
+2022-06-18 17:46:47,565 INFO [train.py:874] (1/4) Epoch 11, batch 300, datatang_loss[loss=0.1697, simple_loss=0.2411, pruned_loss=0.04917, over 4936.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2449, pruned_loss=0.04744, over 766597.52 frames.], batch size: 79, aishell_tot_loss[loss=0.1725, simple_loss=0.2547, pruned_loss=0.04516, over 515865.61 frames.], datatang_tot_loss[loss=0.1672, simple_loss=0.2354, pruned_loss=0.04953, over 525941.56 frames.], batch size: 79, lr: 7.43e-04
+2022-06-18 17:47:17,419 INFO [train.py:874] (1/4) Epoch 11, batch 350, aishell_loss[loss=0.1801, simple_loss=0.2698, pruned_loss=0.04522, over 4951.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2469, pruned_loss=0.04832, over 815259.98 frames.], batch size: 79, aishell_tot_loss[loss=0.1738, simple_loss=0.2557, pruned_loss=0.046, over 585625.44 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2364, pruned_loss=0.0503, over 565570.37 frames.], batch size: 79, lr: 7.43e-04
+2022-06-18 17:47:47,202 INFO [train.py:874] (1/4) Epoch 11, batch 400, aishell_loss[loss=0.1747, simple_loss=0.2471, pruned_loss=0.05116, over 4947.00 frames.], tot_loss[loss=0.1721, simple_loss=0.247, pruned_loss=0.04857, over 853165.43 frames.], batch size: 45, aishell_tot_loss[loss=0.173, simple_loss=0.2543, pruned_loss=0.04588, over 631054.52 frames.], datatang_tot_loss[loss=0.1701, simple_loss=0.2385, pruned_loss=0.05084, over 616951.55 frames.], batch size: 45, lr: 7.42e-04
+2022-06-18 17:48:17,509 INFO [train.py:874] (1/4) Epoch 11, batch 450, datatang_loss[loss=0.1778, simple_loss=0.2377, pruned_loss=0.05891, over 4904.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2461, pruned_loss=0.0488, over 882574.29 frames.], batch size: 47, aishell_tot_loss[loss=0.172, simple_loss=0.2529, pruned_loss=0.04558, over 658169.27 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.2396, pruned_loss=0.05118, over 674965.23 frames.], batch size: 47, lr: 7.42e-04
+2022-06-18 17:48:48,062 INFO [train.py:874] (1/4) Epoch 11, batch 500, datatang_loss[loss=0.2063, simple_loss=0.2672, pruned_loss=0.07269, over 4945.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2465, pruned_loss=0.04906, over 905205.70 frames.], batch size: 62, aishell_tot_loss[loss=0.1718, simple_loss=0.2527, pruned_loss=0.04543, over 696504.56 frames.], datatang_tot_loss[loss=0.1719, simple_loss=0.2402, pruned_loss=0.05178, over 711560.87 frames.], batch size: 62, lr: 7.42e-04
+2022-06-18 17:49:17,348 INFO [train.py:874] (1/4) Epoch 11, batch 550, datatang_loss[loss=0.1737, simple_loss=0.2408, pruned_loss=0.05336, over 4947.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2458, pruned_loss=0.04889, over 922585.53 frames.], batch size: 34, aishell_tot_loss[loss=0.171, simple_loss=0.2519, pruned_loss=0.04509, over 730208.57 frames.], datatang_tot_loss[loss=0.1722, simple_loss=0.2402, pruned_loss=0.05206, over 743725.02 frames.], batch size: 34, lr: 7.41e-04
+2022-06-18 17:49:48,435 INFO [train.py:874] (1/4) Epoch 11, batch 600, datatang_loss[loss=0.1592, simple_loss=0.2249, pruned_loss=0.04669, over 4920.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2454, pruned_loss=0.04875, over 936633.06 frames.], batch size: 42, aishell_tot_loss[loss=0.1711, simple_loss=0.2516, pruned_loss=0.04536, over 755853.88 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.2402, pruned_loss=0.05156, over 776399.31 frames.], batch size: 42, lr: 7.41e-04
+2022-06-18 17:50:18,135 INFO [train.py:874] (1/4) Epoch 11, batch 650, aishell_loss[loss=0.1408, simple_loss=0.2152, pruned_loss=0.03316, over 4852.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2479, pruned_loss=0.04978, over 947495.01 frames.], batch size: 28, aishell_tot_loss[loss=0.1722, simple_loss=0.2527, pruned_loss=0.04588, over 787771.73 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.2417, pruned_loss=0.05258, over 796531.21 frames.], batch size: 28, lr: 7.40e-04
+2022-06-18 17:50:47,912 INFO [train.py:874] (1/4) Epoch 11, batch 700, aishell_loss[loss=0.171, simple_loss=0.2601, pruned_loss=0.04096, over 4979.00 frames.], tot_loss[loss=0.1733, simple_loss=0.248, pruned_loss=0.04928, over 955721.13 frames.], batch size: 39, aishell_tot_loss[loss=0.1721, simple_loss=0.2527, pruned_loss=0.04574, over 812674.40 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2419, pruned_loss=0.05234, over 817024.61 frames.], batch size: 39, lr: 7.40e-04
+2022-06-18 17:51:18,376 INFO [train.py:874] (1/4) Epoch 11, batch 750, aishell_loss[loss=0.1673, simple_loss=0.2474, pruned_loss=0.04365, over 4934.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2491, pruned_loss=0.05059, over 962344.79 frames.], batch size: 41, aishell_tot_loss[loss=0.1719, simple_loss=0.2524, pruned_loss=0.04568, over 832171.11 frames.], datatang_tot_loss[loss=0.1758, simple_loss=0.2437, pruned_loss=0.05397, over 837726.72 frames.], batch size: 41, lr: 7.39e-04
+2022-06-18 17:51:48,628 INFO [train.py:874] (1/4) Epoch 11, batch 800, datatang_loss[loss=0.1725, simple_loss=0.2455, pruned_loss=0.04974, over 4893.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2498, pruned_loss=0.05058, over 967678.33 frames.], batch size: 52, aishell_tot_loss[loss=0.1735, simple_loss=0.2539, pruned_loss=0.04648, over 851749.04 frames.], datatang_tot_loss[loss=0.1751, simple_loss=0.2433, pruned_loss=0.05348, over 853857.03 frames.], batch size: 52, lr: 7.39e-04
+2022-06-18 17:52:17,432 INFO [train.py:874] (1/4) Epoch 11, batch 850, aishell_loss[loss=0.1821, simple_loss=0.265, pruned_loss=0.04957, over 4956.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2505, pruned_loss=0.05059, over 971864.02 frames.], batch size: 80, aishell_tot_loss[loss=0.1746, simple_loss=0.2551, pruned_loss=0.04701, over 869533.58 frames.], datatang_tot_loss[loss=0.1748, simple_loss=0.2431, pruned_loss=0.05324, over 867581.62 frames.], batch size: 80, lr: 7.39e-04
+2022-06-18 17:52:48,389 INFO [train.py:874] (1/4) Epoch 11, batch 900, datatang_loss[loss=0.1954, simple_loss=0.263, pruned_loss=0.06394, over 4926.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2497, pruned_loss=0.05032, over 975037.75 frames.], batch size: 79, aishell_tot_loss[loss=0.174, simple_loss=0.2548, pruned_loss=0.04665, over 881490.78 frames.], datatang_tot_loss[loss=0.1749, simple_loss=0.2431, pruned_loss=0.0533, over 883332.97 frames.], batch size: 79, lr: 7.38e-04
+2022-06-18 17:53:18,577 INFO [train.py:874] (1/4) Epoch 11, batch 950, datatang_loss[loss=0.1653, simple_loss=0.2343, pruned_loss=0.0482, over 4844.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2498, pruned_loss=0.05026, over 977053.92 frames.], batch size: 24, aishell_tot_loss[loss=0.1739, simple_loss=0.2548, pruned_loss=0.04655, over 893029.53 frames.], datatang_tot_loss[loss=0.1751, simple_loss=0.2434, pruned_loss=0.05339, over 895767.06 frames.], batch size: 24, lr: 7.38e-04
+2022-06-18 17:53:46,778 INFO [train.py:874] (1/4) Epoch 11, batch 1000, aishell_loss[loss=0.134, simple_loss=0.2064, pruned_loss=0.03086, over 4823.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2493, pruned_loss=0.04982, over 978667.83 frames.], batch size: 24, aishell_tot_loss[loss=0.173, simple_loss=0.2539, pruned_loss=0.04601, over 906138.09 frames.], datatang_tot_loss[loss=0.1756, simple_loss=0.2437, pruned_loss=0.05372, over 903857.39 frames.], batch size: 24, lr: 7.37e-04
+2022-06-18 17:53:46,778 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 17:54:02,790 INFO [train.py:914] (1/4) 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] (1/4) Epoch 11, batch 1050, aishell_loss[loss=0.2083, simple_loss=0.287, pruned_loss=0.06483, over 4977.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2495, pruned_loss=0.04929, over 979895.48 frames.], batch size: 39, aishell_tot_loss[loss=0.1725, simple_loss=0.2537, pruned_loss=0.04568, over 917468.38 frames.], datatang_tot_loss[loss=0.1757, simple_loss=0.2439, pruned_loss=0.0537, over 911033.90 frames.], batch size: 39, lr: 7.37e-04
+2022-06-18 17:55:02,908 INFO [train.py:874] (1/4) Epoch 11, batch 1100, datatang_loss[loss=0.1682, simple_loss=0.2393, pruned_loss=0.0486, over 4927.00 frames.], tot_loss[loss=0.174, simple_loss=0.2496, pruned_loss=0.04917, over 981193.78 frames.], batch size: 64, aishell_tot_loss[loss=0.1726, simple_loss=0.2539, pruned_loss=0.04567, over 926252.91 frames.], datatang_tot_loss[loss=0.1755, simple_loss=0.2438, pruned_loss=0.05358, over 918998.50 frames.], batch size: 64, lr: 7.36e-04
+2022-06-18 17:55:32,125 INFO [train.py:874] (1/4) Epoch 11, batch 1150, datatang_loss[loss=0.1477, simple_loss=0.2302, pruned_loss=0.03255, over 4922.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2488, pruned_loss=0.04948, over 982101.74 frames.], batch size: 83, aishell_tot_loss[loss=0.1728, simple_loss=0.2539, pruned_loss=0.04581, over 932181.10 frames.], datatang_tot_loss[loss=0.1752, simple_loss=0.2432, pruned_loss=0.05359, over 927908.62 frames.], batch size: 83, lr: 7.36e-04
+2022-06-18 17:56:02,934 INFO [train.py:874] (1/4) Epoch 11, batch 1200, aishell_loss[loss=0.1796, simple_loss=0.2623, pruned_loss=0.04847, over 4947.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2491, pruned_loss=0.04984, over 983211.94 frames.], batch size: 54, aishell_tot_loss[loss=0.1738, simple_loss=0.2549, pruned_loss=0.04641, over 937780.38 frames.], datatang_tot_loss[loss=0.1746, simple_loss=0.2429, pruned_loss=0.05319, over 935807.58 frames.], batch size: 54, lr: 7.36e-04
+2022-06-18 17:56:33,868 INFO [train.py:874] (1/4) Epoch 11, batch 1250, datatang_loss[loss=0.1437, simple_loss=0.2157, pruned_loss=0.03581, over 4949.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2481, pruned_loss=0.04954, over 983398.12 frames.], batch size: 50, aishell_tot_loss[loss=0.174, simple_loss=0.2549, pruned_loss=0.04652, over 942283.99 frames.], datatang_tot_loss[loss=0.1737, simple_loss=0.2421, pruned_loss=0.05264, over 942457.55 frames.], batch size: 50, lr: 7.35e-04
+2022-06-18 17:57:03,620 INFO [train.py:874] (1/4) Epoch 11, batch 1300, datatang_loss[loss=0.1712, simple_loss=0.2461, pruned_loss=0.04821, over 4932.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2481, pruned_loss=0.04956, over 983720.21 frames.], batch size: 88, aishell_tot_loss[loss=0.174, simple_loss=0.2549, pruned_loss=0.04657, over 947095.45 frames.], datatang_tot_loss[loss=0.1736, simple_loss=0.2421, pruned_loss=0.05255, over 947647.89 frames.], batch size: 88, lr: 7.35e-04
+2022-06-18 17:57:34,405 INFO [train.py:874] (1/4) Epoch 11, batch 1350, datatang_loss[loss=0.1569, simple_loss=0.2265, pruned_loss=0.04366, over 4857.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2488, pruned_loss=0.04944, over 984375.00 frames.], batch size: 36, aishell_tot_loss[loss=0.1736, simple_loss=0.2548, pruned_loss=0.04616, over 951693.03 frames.], datatang_tot_loss[loss=0.1742, simple_loss=0.2428, pruned_loss=0.05277, over 952311.20 frames.], batch size: 36, lr: 7.34e-04
+2022-06-18 17:58:05,156 INFO [train.py:874] (1/4) Epoch 11, batch 1400, datatang_loss[loss=0.2512, simple_loss=0.3065, pruned_loss=0.09796, over 4958.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2493, pruned_loss=0.04952, over 984687.71 frames.], batch size: 108, aishell_tot_loss[loss=0.1735, simple_loss=0.2546, pruned_loss=0.04622, over 956256.08 frames.], datatang_tot_loss[loss=0.1746, simple_loss=0.2433, pruned_loss=0.0529, over 955704.65 frames.], batch size: 108, lr: 7.34e-04
+2022-06-18 17:58:33,279 INFO [train.py:874] (1/4) Epoch 11, batch 1450, datatang_loss[loss=0.1594, simple_loss=0.227, pruned_loss=0.04595, over 4966.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2496, pruned_loss=0.04982, over 984990.27 frames.], batch size: 60, aishell_tot_loss[loss=0.1733, simple_loss=0.2543, pruned_loss=0.04619, over 960420.51 frames.], datatang_tot_loss[loss=0.1753, simple_loss=0.2438, pruned_loss=0.05338, over 958599.00 frames.], batch size: 60, lr: 7.33e-04
+2022-06-18 17:59:04,227 INFO [train.py:874] (1/4) Epoch 11, batch 1500, datatang_loss[loss=0.1563, simple_loss=0.2277, pruned_loss=0.04246, over 4924.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2496, pruned_loss=0.05001, over 984914.04 frames.], batch size: 73, aishell_tot_loss[loss=0.1732, simple_loss=0.2541, pruned_loss=0.04621, over 963214.64 frames.], datatang_tot_loss[loss=0.1757, simple_loss=0.2442, pruned_loss=0.05358, over 961723.85 frames.], batch size: 73, lr: 7.33e-04
+2022-06-18 17:59:35,780 INFO [train.py:874] (1/4) Epoch 11, batch 1550, aishell_loss[loss=0.1684, simple_loss=0.2522, pruned_loss=0.04226, over 4910.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2503, pruned_loss=0.04994, over 985161.85 frames.], batch size: 41, aishell_tot_loss[loss=0.1737, simple_loss=0.2548, pruned_loss=0.04623, over 965876.35 frames.], datatang_tot_loss[loss=0.1756, simple_loss=0.2442, pruned_loss=0.05348, over 964606.08 frames.], batch size: 41, lr: 7.33e-04
+2022-06-18 18:00:05,424 INFO [train.py:874] (1/4) Epoch 11, batch 1600, datatang_loss[loss=0.1727, simple_loss=0.2358, pruned_loss=0.05477, over 4919.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2497, pruned_loss=0.0497, over 985139.69 frames.], batch size: 73, aishell_tot_loss[loss=0.1731, simple_loss=0.2544, pruned_loss=0.04586, over 968075.35 frames.], datatang_tot_loss[loss=0.1757, simple_loss=0.2443, pruned_loss=0.05359, over 967087.84 frames.], batch size: 73, lr: 7.32e-04
+2022-06-18 18:00:35,333 INFO [train.py:874] (1/4) Epoch 11, batch 1650, aishell_loss[loss=0.1785, simple_loss=0.2704, pruned_loss=0.04329, over 4960.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2492, pruned_loss=0.04977, over 985250.77 frames.], batch size: 51, aishell_tot_loss[loss=0.1731, simple_loss=0.2546, pruned_loss=0.0458, over 969740.14 frames.], datatang_tot_loss[loss=0.1755, simple_loss=0.2438, pruned_loss=0.05358, over 969677.49 frames.], batch size: 51, lr: 7.32e-04
+2022-06-18 18:01:06,991 INFO [train.py:874] (1/4) Epoch 11, batch 1700, aishell_loss[loss=0.1977, simple_loss=0.2763, pruned_loss=0.05954, over 4929.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2491, pruned_loss=0.05, over 985286.57 frames.], batch size: 33, aishell_tot_loss[loss=0.1732, simple_loss=0.2545, pruned_loss=0.04595, over 971362.49 frames.], datatang_tot_loss[loss=0.1756, simple_loss=0.244, pruned_loss=0.05362, over 971768.46 frames.], batch size: 33, lr: 7.31e-04
+2022-06-18 18:01:36,119 INFO [train.py:874] (1/4) Epoch 11, batch 1750, aishell_loss[loss=0.173, simple_loss=0.2553, pruned_loss=0.04535, over 4981.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2507, pruned_loss=0.05081, over 985486.68 frames.], batch size: 39, aishell_tot_loss[loss=0.1744, simple_loss=0.2556, pruned_loss=0.04661, over 973211.90 frames.], datatang_tot_loss[loss=0.1761, simple_loss=0.2443, pruned_loss=0.05396, over 973362.95 frames.], batch size: 39, lr: 7.31e-04
+2022-06-18 18:02:06,154 INFO [train.py:874] (1/4) Epoch 11, batch 1800, datatang_loss[loss=0.1427, simple_loss=0.2222, pruned_loss=0.03163, over 4923.00 frames.], tot_loss[loss=0.175, simple_loss=0.2496, pruned_loss=0.05015, over 985650.15 frames.], batch size: 73, aishell_tot_loss[loss=0.1743, simple_loss=0.2554, pruned_loss=0.04662, over 974921.08 frames.], datatang_tot_loss[loss=0.1752, simple_loss=0.2436, pruned_loss=0.05341, over 974686.61 frames.], batch size: 73, lr: 7.30e-04
+2022-06-18 18:02:37,427 INFO [train.py:874] (1/4) Epoch 11, batch 1850, aishell_loss[loss=0.1593, simple_loss=0.2518, pruned_loss=0.03337, over 4920.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2492, pruned_loss=0.04963, over 985488.17 frames.], batch size: 52, aishell_tot_loss[loss=0.1742, simple_loss=0.2553, pruned_loss=0.04653, over 976045.86 frames.], datatang_tot_loss[loss=0.1746, simple_loss=0.2432, pruned_loss=0.05301, over 975943.59 frames.], batch size: 52, lr: 7.30e-04
+2022-06-18 18:03:06,839 INFO [train.py:874] (1/4) Epoch 11, batch 1900, datatang_loss[loss=0.155, simple_loss=0.2283, pruned_loss=0.04088, over 4964.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2491, pruned_loss=0.04973, over 985848.51 frames.], batch size: 60, aishell_tot_loss[loss=0.1741, simple_loss=0.2554, pruned_loss=0.04642, over 977111.05 frames.], datatang_tot_loss[loss=0.1747, simple_loss=0.2433, pruned_loss=0.05309, over 977497.55 frames.], batch size: 60, lr: 7.30e-04
+2022-06-18 18:03:36,013 INFO [train.py:874] (1/4) Epoch 11, batch 1950, datatang_loss[loss=0.1907, simple_loss=0.2514, pruned_loss=0.06501, over 4961.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2493, pruned_loss=0.04956, over 985888.91 frames.], batch size: 55, aishell_tot_loss[loss=0.1745, simple_loss=0.2557, pruned_loss=0.0466, over 978256.85 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2428, pruned_loss=0.05286, over 978412.03 frames.], batch size: 55, lr: 7.29e-04
+2022-06-18 18:04:07,291 INFO [train.py:874] (1/4) Epoch 11, batch 2000, aishell_loss[loss=0.1521, simple_loss=0.2356, pruned_loss=0.03426, over 4963.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2486, pruned_loss=0.04956, over 986041.29 frames.], batch size: 40, aishell_tot_loss[loss=0.1736, simple_loss=0.2549, pruned_loss=0.04618, over 979291.12 frames.], datatang_tot_loss[loss=0.1747, simple_loss=0.2429, pruned_loss=0.05322, over 979300.37 frames.], batch size: 40, lr: 7.29e-04
+2022-06-18 18:04:07,292 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 18:04:24,294 INFO [train.py:914] (1/4) Epoch 11, validation: loss=0.167, simple_loss=0.2509, pruned_loss=0.04156, over 1622729.00 frames.
+2022-06-18 18:04:53,781 INFO [train.py:874] (1/4) Epoch 11, batch 2050, aishell_loss[loss=0.188, simple_loss=0.271, pruned_loss=0.05253, over 4946.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2497, pruned_loss=0.04966, over 986004.03 frames.], batch size: 64, aishell_tot_loss[loss=0.1741, simple_loss=0.2556, pruned_loss=0.04631, over 980109.01 frames.], datatang_tot_loss[loss=0.1749, simple_loss=0.2431, pruned_loss=0.05331, over 980043.37 frames.], batch size: 64, lr: 7.28e-04
+2022-06-18 18:05:23,745 INFO [train.py:874] (1/4) Epoch 11, batch 2100, aishell_loss[loss=0.182, simple_loss=0.2751, pruned_loss=0.04441, over 4954.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2491, pruned_loss=0.04955, over 985903.14 frames.], batch size: 81, aishell_tot_loss[loss=0.1733, simple_loss=0.2544, pruned_loss=0.04606, over 980812.41 frames.], datatang_tot_loss[loss=0.1753, simple_loss=0.2434, pruned_loss=0.05361, over 980621.92 frames.], batch size: 81, lr: 7.28e-04
+2022-06-18 18:05:53,427 INFO [train.py:874] (1/4) Epoch 11, batch 2150, datatang_loss[loss=0.1624, simple_loss=0.2285, pruned_loss=0.0481, over 4926.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2485, pruned_loss=0.04992, over 985734.16 frames.], batch size: 50, aishell_tot_loss[loss=0.1733, simple_loss=0.2542, pruned_loss=0.0462, over 980908.03 frames.], datatang_tot_loss[loss=0.1752, simple_loss=0.2436, pruned_loss=0.05346, over 981554.09 frames.], batch size: 50, lr: 7.28e-04
+2022-06-18 18:06:24,915 INFO [train.py:874] (1/4) Epoch 11, batch 2200, datatang_loss[loss=0.1946, simple_loss=0.2604, pruned_loss=0.06438, over 4930.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2479, pruned_loss=0.04998, over 985173.28 frames.], batch size: 57, aishell_tot_loss[loss=0.1733, simple_loss=0.2541, pruned_loss=0.04626, over 981082.98 frames.], datatang_tot_loss[loss=0.1749, simple_loss=0.2433, pruned_loss=0.05327, over 981827.97 frames.], batch size: 57, lr: 7.27e-04
+2022-06-18 18:06:54,554 INFO [train.py:874] (1/4) Epoch 11, batch 2250, aishell_loss[loss=0.1606, simple_loss=0.2429, pruned_loss=0.03913, over 4928.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2476, pruned_loss=0.04985, over 985258.10 frames.], batch size: 33, aishell_tot_loss[loss=0.1733, simple_loss=0.2542, pruned_loss=0.04618, over 981361.02 frames.], datatang_tot_loss[loss=0.1746, simple_loss=0.2428, pruned_loss=0.05319, over 982502.76 frames.], batch size: 33, lr: 7.27e-04
+2022-06-18 18:07:24,137 INFO [train.py:874] (1/4) Epoch 11, batch 2300, datatang_loss[loss=0.1834, simple_loss=0.2528, pruned_loss=0.05697, over 4952.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2474, pruned_loss=0.04914, over 985590.22 frames.], batch size: 91, aishell_tot_loss[loss=0.1726, simple_loss=0.2538, pruned_loss=0.04572, over 981926.26 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2428, pruned_loss=0.05297, over 983085.25 frames.], batch size: 91, lr: 7.26e-04
+2022-06-18 18:07:55,327 INFO [train.py:874] (1/4) Epoch 11, batch 2350, datatang_loss[loss=0.1908, simple_loss=0.2523, pruned_loss=0.06462, over 4957.00 frames.], tot_loss[loss=0.173, simple_loss=0.2477, pruned_loss=0.04913, over 985515.41 frames.], batch size: 91, aishell_tot_loss[loss=0.1727, simple_loss=0.2538, pruned_loss=0.04584, over 982360.77 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.2427, pruned_loss=0.05293, over 983336.41 frames.], batch size: 91, lr: 7.26e-04
+2022-06-18 18:08:25,220 INFO [train.py:874] (1/4) Epoch 11, batch 2400, aishell_loss[loss=0.1924, simple_loss=0.271, pruned_loss=0.05691, over 4899.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2483, pruned_loss=0.0499, over 985445.21 frames.], batch size: 34, aishell_tot_loss[loss=0.1726, simple_loss=0.2535, pruned_loss=0.04589, over 982757.39 frames.], datatang_tot_loss[loss=0.1754, simple_loss=0.2434, pruned_loss=0.05372, over 983507.24 frames.], batch size: 34, lr: 7.25e-04
+2022-06-18 18:08:55,149 INFO [train.py:874] (1/4) Epoch 11, batch 2450, datatang_loss[loss=0.1679, simple_loss=0.2455, pruned_loss=0.04518, over 4917.00 frames.], tot_loss[loss=0.174, simple_loss=0.2483, pruned_loss=0.0498, over 985654.17 frames.], batch size: 83, aishell_tot_loss[loss=0.173, simple_loss=0.2538, pruned_loss=0.04604, over 983239.59 frames.], datatang_tot_loss[loss=0.175, simple_loss=0.2431, pruned_loss=0.05345, over 983803.06 frames.], batch size: 83, lr: 7.25e-04
+2022-06-18 18:09:26,585 INFO [train.py:874] (1/4) Epoch 11, batch 2500, datatang_loss[loss=0.177, simple_loss=0.2532, pruned_loss=0.05037, over 4922.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2477, pruned_loss=0.049, over 985653.51 frames.], batch size: 75, aishell_tot_loss[loss=0.1726, simple_loss=0.2538, pruned_loss=0.04572, over 983354.29 frames.], datatang_tot_loss[loss=0.1741, simple_loss=0.2426, pruned_loss=0.05284, over 984184.22 frames.], batch size: 75, lr: 7.25e-04
+2022-06-18 18:09:56,448 INFO [train.py:874] (1/4) Epoch 11, batch 2550, datatang_loss[loss=0.1632, simple_loss=0.2366, pruned_loss=0.04492, over 4940.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2474, pruned_loss=0.04876, over 985912.65 frames.], batch size: 69, aishell_tot_loss[loss=0.1725, simple_loss=0.2538, pruned_loss=0.04559, over 983983.95 frames.], datatang_tot_loss[loss=0.1737, simple_loss=0.2422, pruned_loss=0.05262, over 984276.18 frames.], batch size: 69, lr: 7.24e-04
+2022-06-18 18:10:26,149 INFO [train.py:874] (1/4) Epoch 11, batch 2600, datatang_loss[loss=0.1564, simple_loss=0.2248, pruned_loss=0.04398, over 4960.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2465, pruned_loss=0.04833, over 985882.14 frames.], batch size: 67, aishell_tot_loss[loss=0.1721, simple_loss=0.2533, pruned_loss=0.04544, over 984153.04 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.2415, pruned_loss=0.05231, over 984498.90 frames.], batch size: 67, lr: 7.24e-04
+2022-06-18 18:10:56,971 INFO [train.py:874] (1/4) Epoch 11, batch 2650, datatang_loss[loss=0.2079, simple_loss=0.2641, pruned_loss=0.07586, over 4931.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2472, pruned_loss=0.04851, over 985977.46 frames.], batch size: 50, aishell_tot_loss[loss=0.1721, simple_loss=0.2535, pruned_loss=0.04537, over 984418.75 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2415, pruned_loss=0.05253, over 984690.96 frames.], batch size: 50, lr: 7.23e-04
+2022-06-18 18:11:31,743 INFO [train.py:874] (1/4) Epoch 11, batch 2700, aishell_loss[loss=0.1708, simple_loss=0.2569, pruned_loss=0.0424, over 4919.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2472, pruned_loss=0.04856, over 985663.94 frames.], batch size: 41, aishell_tot_loss[loss=0.1716, simple_loss=0.2531, pruned_loss=0.04503, over 984376.78 frames.], datatang_tot_loss[loss=0.1737, simple_loss=0.2419, pruned_loss=0.05278, over 984735.19 frames.], batch size: 41, lr: 7.23e-04
+2022-06-18 18:12:01,112 INFO [train.py:874] (1/4) Epoch 11, batch 2750, aishell_loss[loss=0.1744, simple_loss=0.258, pruned_loss=0.04538, over 4934.00 frames.], tot_loss[loss=0.1718, simple_loss=0.247, pruned_loss=0.04831, over 985354.97 frames.], batch size: 49, aishell_tot_loss[loss=0.1715, simple_loss=0.2531, pruned_loss=0.04496, over 984127.61 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2418, pruned_loss=0.05243, over 984905.89 frames.], batch size: 49, lr: 7.23e-04
+2022-06-18 18:12:33,119 INFO [train.py:874] (1/4) Epoch 11, batch 2800, datatang_loss[loss=0.1508, simple_loss=0.2225, pruned_loss=0.03957, over 4946.00 frames.], tot_loss[loss=0.172, simple_loss=0.247, pruned_loss=0.04847, over 985352.49 frames.], batch size: 34, aishell_tot_loss[loss=0.1715, simple_loss=0.253, pruned_loss=0.04497, over 984054.52 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2418, pruned_loss=0.05246, over 985162.41 frames.], batch size: 34, lr: 7.22e-04
+2022-06-18 18:13:03,709 INFO [train.py:874] (1/4) Epoch 11, batch 2850, datatang_loss[loss=0.1627, simple_loss=0.2352, pruned_loss=0.04508, over 4941.00 frames.], tot_loss[loss=0.1711, simple_loss=0.246, pruned_loss=0.04805, over 985414.41 frames.], batch size: 88, aishell_tot_loss[loss=0.1706, simple_loss=0.2521, pruned_loss=0.0446, over 984115.29 frames.], datatang_tot_loss[loss=0.1731, simple_loss=0.2416, pruned_loss=0.0523, over 985345.38 frames.], batch size: 88, lr: 7.22e-04
+2022-06-18 18:13:33,065 INFO [train.py:874] (1/4) Epoch 11, batch 2900, datatang_loss[loss=0.1882, simple_loss=0.2541, pruned_loss=0.06116, over 4979.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2467, pruned_loss=0.04836, over 985995.80 frames.], batch size: 48, aishell_tot_loss[loss=0.1707, simple_loss=0.2522, pruned_loss=0.04462, over 984620.97 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2419, pruned_loss=0.05252, over 985604.04 frames.], batch size: 48, lr: 7.21e-04
+2022-06-18 18:14:04,188 INFO [train.py:874] (1/4) Epoch 11, batch 2950, datatang_loss[loss=0.1705, simple_loss=0.2409, pruned_loss=0.05003, over 4949.00 frames.], tot_loss[loss=0.1714, simple_loss=0.246, pruned_loss=0.04835, over 986152.32 frames.], batch size: 45, aishell_tot_loss[loss=0.1705, simple_loss=0.252, pruned_loss=0.04449, over 984772.27 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.2415, pruned_loss=0.05247, over 985806.22 frames.], batch size: 45, lr: 7.21e-04
+2022-06-18 18:14:33,559 INFO [train.py:874] (1/4) Epoch 11, batch 3000, datatang_loss[loss=0.2012, simple_loss=0.2629, pruned_loss=0.0698, over 4947.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2469, pruned_loss=0.04876, over 986061.94 frames.], batch size: 55, aishell_tot_loss[loss=0.1712, simple_loss=0.2527, pruned_loss=0.04483, over 984869.20 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2416, pruned_loss=0.05254, over 985819.29 frames.], batch size: 55, lr: 7.21e-04
+2022-06-18 18:14:33,560 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 18:14:50,208 INFO [train.py:914] (1/4) Epoch 11, validation: loss=0.1662, simple_loss=0.2504, pruned_loss=0.04101, over 1622729.00 frames.
+2022-06-18 18:15:20,342 INFO [train.py:874] (1/4) Epoch 11, batch 3050, aishell_loss[loss=0.1775, simple_loss=0.2617, pruned_loss=0.04663, over 4948.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2474, pruned_loss=0.04899, over 985899.76 frames.], batch size: 64, aishell_tot_loss[loss=0.1713, simple_loss=0.2529, pruned_loss=0.04489, over 984889.95 frames.], datatang_tot_loss[loss=0.1736, simple_loss=0.2417, pruned_loss=0.05277, over 985811.22 frames.], batch size: 64, lr: 7.20e-04
+2022-06-18 18:15:50,758 INFO [train.py:874] (1/4) Epoch 11, batch 3100, aishell_loss[loss=0.159, simple_loss=0.2405, pruned_loss=0.03873, over 4869.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2474, pruned_loss=0.04908, over 985596.03 frames.], batch size: 28, aishell_tot_loss[loss=0.1711, simple_loss=0.2528, pruned_loss=0.04474, over 984786.31 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.2419, pruned_loss=0.05308, over 985716.95 frames.], batch size: 28, lr: 7.20e-04
+2022-06-18 18:16:23,156 INFO [train.py:874] (1/4) Epoch 11, batch 3150, datatang_loss[loss=0.1568, simple_loss=0.23, pruned_loss=0.04176, over 4931.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2479, pruned_loss=0.04939, over 985649.40 frames.], batch size: 79, aishell_tot_loss[loss=0.1714, simple_loss=0.2534, pruned_loss=0.04473, over 984791.23 frames.], datatang_tot_loss[loss=0.1743, simple_loss=0.242, pruned_loss=0.05332, over 985832.50 frames.], batch size: 79, lr: 7.19e-04
+2022-06-18 18:16:53,748 INFO [train.py:874] (1/4) Epoch 11, batch 3200, aishell_loss[loss=0.1629, simple_loss=0.2454, pruned_loss=0.04024, over 4956.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2471, pruned_loss=0.04826, over 985536.27 frames.], batch size: 31, aishell_tot_loss[loss=0.1707, simple_loss=0.2529, pruned_loss=0.0443, over 984700.69 frames.], datatang_tot_loss[loss=0.1735, simple_loss=0.2416, pruned_loss=0.05272, over 985898.61 frames.], batch size: 31, lr: 7.19e-04
+2022-06-18 18:17:23,942 INFO [train.py:874] (1/4) Epoch 11, batch 3250, datatang_loss[loss=0.2036, simple_loss=0.2668, pruned_loss=0.07017, over 4921.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2467, pruned_loss=0.04773, over 985604.59 frames.], batch size: 98, aishell_tot_loss[loss=0.1703, simple_loss=0.2525, pruned_loss=0.04405, over 984777.63 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.2415, pruned_loss=0.05242, over 985951.73 frames.], batch size: 98, lr: 7.19e-04
+2022-06-18 18:17:56,618 INFO [train.py:874] (1/4) Epoch 11, batch 3300, aishell_loss[loss=0.1407, simple_loss=0.2197, pruned_loss=0.03083, over 4910.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2466, pruned_loss=0.04737, over 985874.94 frames.], batch size: 28, aishell_tot_loss[loss=0.1701, simple_loss=0.2525, pruned_loss=0.04387, over 984852.02 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.2412, pruned_loss=0.05215, over 986241.09 frames.], batch size: 28, lr: 7.18e-04
+2022-06-18 18:18:26,827 INFO [train.py:874] (1/4) Epoch 11, batch 3350, datatang_loss[loss=0.182, simple_loss=0.2383, pruned_loss=0.06284, over 4915.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2469, pruned_loss=0.04785, over 985939.74 frames.], batch size: 47, aishell_tot_loss[loss=0.1701, simple_loss=0.2522, pruned_loss=0.04405, over 984943.68 frames.], datatang_tot_loss[loss=0.1733, simple_loss=0.2417, pruned_loss=0.05246, over 986324.81 frames.], batch size: 47, lr: 7.18e-04
+2022-06-18 18:18:57,191 INFO [train.py:874] (1/4) Epoch 11, batch 3400, aishell_loss[loss=0.1856, simple_loss=0.2696, pruned_loss=0.05077, over 4919.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2471, pruned_loss=0.04829, over 985619.34 frames.], batch size: 33, aishell_tot_loss[loss=0.1699, simple_loss=0.252, pruned_loss=0.0439, over 984992.53 frames.], datatang_tot_loss[loss=0.174, simple_loss=0.2423, pruned_loss=0.05287, over 985992.71 frames.], batch size: 33, lr: 7.17e-04
+2022-06-18 18:19:29,655 INFO [train.py:874] (1/4) Epoch 11, batch 3450, datatang_loss[loss=0.156, simple_loss=0.2378, pruned_loss=0.03705, over 4955.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2456, pruned_loss=0.048, over 985616.92 frames.], batch size: 86, aishell_tot_loss[loss=0.1693, simple_loss=0.251, pruned_loss=0.04382, over 984831.36 frames.], datatang_tot_loss[loss=0.1734, simple_loss=0.2417, pruned_loss=0.05248, over 986152.82 frames.], batch size: 86, lr: 7.17e-04
+2022-06-18 18:20:00,336 INFO [train.py:874] (1/4) Epoch 11, batch 3500, datatang_loss[loss=0.1458, simple_loss=0.2169, pruned_loss=0.03733, over 4935.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2461, pruned_loss=0.04785, over 985920.97 frames.], batch size: 69, aishell_tot_loss[loss=0.1697, simple_loss=0.2516, pruned_loss=0.04389, over 985073.45 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2415, pruned_loss=0.05217, over 986253.29 frames.], batch size: 69, lr: 7.17e-04
+2022-06-18 18:20:30,448 INFO [train.py:874] (1/4) Epoch 11, batch 3550, aishell_loss[loss=0.1742, simple_loss=0.2537, pruned_loss=0.04738, over 4970.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2458, pruned_loss=0.04789, over 986206.27 frames.], batch size: 51, aishell_tot_loss[loss=0.1695, simple_loss=0.2512, pruned_loss=0.04389, over 985493.28 frames.], datatang_tot_loss[loss=0.1729, simple_loss=0.2413, pruned_loss=0.05225, over 986192.05 frames.], batch size: 51, lr: 7.16e-04
+2022-06-18 18:21:01,554 INFO [train.py:874] (1/4) Epoch 11, batch 3600, datatang_loss[loss=0.1677, simple_loss=0.2449, pruned_loss=0.04526, over 4955.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2462, pruned_loss=0.04769, over 986103.54 frames.], batch size: 86, aishell_tot_loss[loss=0.17, simple_loss=0.2518, pruned_loss=0.04412, over 985472.24 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2409, pruned_loss=0.0519, over 986206.05 frames.], batch size: 86, lr: 7.16e-04
+2022-06-18 18:21:30,549 INFO [train.py:874] (1/4) Epoch 11, batch 3650, aishell_loss[loss=0.186, simple_loss=0.2585, pruned_loss=0.05679, over 4956.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2464, pruned_loss=0.04747, over 985878.58 frames.], batch size: 31, aishell_tot_loss[loss=0.1698, simple_loss=0.2516, pruned_loss=0.04405, over 985373.82 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2412, pruned_loss=0.05183, over 986146.19 frames.], batch size: 31, lr: 7.15e-04
+2022-06-18 18:22:02,551 INFO [train.py:874] (1/4) Epoch 11, batch 3700, aishell_loss[loss=0.1521, simple_loss=0.2472, pruned_loss=0.02853, over 4952.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2461, pruned_loss=0.04772, over 985784.27 frames.], batch size: 44, aishell_tot_loss[loss=0.1701, simple_loss=0.2516, pruned_loss=0.04434, over 985361.46 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.2407, pruned_loss=0.05169, over 986082.85 frames.], batch size: 44, lr: 7.15e-04
+2022-06-18 18:22:32,548 INFO [train.py:874] (1/4) Epoch 11, batch 3750, aishell_loss[loss=0.1521, simple_loss=0.2348, pruned_loss=0.03475, over 4871.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2471, pruned_loss=0.04752, over 985407.16 frames.], batch size: 42, aishell_tot_loss[loss=0.1699, simple_loss=0.2518, pruned_loss=0.04403, over 985033.06 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.2413, pruned_loss=0.05194, over 986055.90 frames.], batch size: 42, lr: 7.15e-04
+2022-06-18 18:23:02,888 INFO [train.py:874] (1/4) Epoch 11, batch 3800, aishell_loss[loss=0.1751, simple_loss=0.2642, pruned_loss=0.04295, over 4913.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2465, pruned_loss=0.04756, over 985374.51 frames.], batch size: 46, aishell_tot_loss[loss=0.1694, simple_loss=0.2511, pruned_loss=0.04383, over 984923.39 frames.], datatang_tot_loss[loss=0.1727, simple_loss=0.2415, pruned_loss=0.05197, over 986078.05 frames.], batch size: 46, lr: 7.14e-04
+2022-06-18 18:23:32,302 INFO [train.py:874] (1/4) Epoch 11, batch 3850, datatang_loss[loss=0.1836, simple_loss=0.2525, pruned_loss=0.05741, over 4839.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2474, pruned_loss=0.04824, over 985293.93 frames.], batch size: 30, aishell_tot_loss[loss=0.1703, simple_loss=0.2519, pruned_loss=0.04441, over 985209.77 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2415, pruned_loss=0.05222, over 985710.20 frames.], batch size: 30, lr: 7.14e-04
+2022-06-18 18:24:01,226 INFO [train.py:874] (1/4) Epoch 11, batch 3900, aishell_loss[loss=0.1612, simple_loss=0.245, pruned_loss=0.03869, over 4884.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2464, pruned_loss=0.04787, over 985102.70 frames.], batch size: 42, aishell_tot_loss[loss=0.1707, simple_loss=0.2519, pruned_loss=0.0447, over 985058.09 frames.], datatang_tot_loss[loss=0.1718, simple_loss=0.2405, pruned_loss=0.05157, over 985619.87 frames.], batch size: 42, lr: 7.14e-04
+2022-06-18 18:24:28,629 INFO [train.py:874] (1/4) Epoch 11, batch 3950, datatang_loss[loss=0.1458, simple_loss=0.214, pruned_loss=0.03883, over 4960.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2478, pruned_loss=0.0485, over 985222.74 frames.], batch size: 55, aishell_tot_loss[loss=0.1709, simple_loss=0.2524, pruned_loss=0.04468, over 984927.49 frames.], datatang_tot_loss[loss=0.173, simple_loss=0.2413, pruned_loss=0.05238, over 985835.62 frames.], batch size: 55, lr: 7.13e-04
+2022-06-18 18:24:59,120 INFO [train.py:874] (1/4) Epoch 11, batch 4000, aishell_loss[loss=0.1848, simple_loss=0.2643, pruned_loss=0.0526, over 4893.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2466, pruned_loss=0.04795, over 985289.88 frames.], batch size: 34, aishell_tot_loss[loss=0.1703, simple_loss=0.2519, pruned_loss=0.04433, over 985012.13 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.2409, pruned_loss=0.05203, over 985759.51 frames.], batch size: 34, lr: 7.13e-04
+2022-06-18 18:24:59,121 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 18:25:16,906 INFO [train.py:914] (1/4) Epoch 11, validation: loss=0.1668, simple_loss=0.2498, pruned_loss=0.04186, over 1622729.00 frames.
+2022-06-18 18:25:45,365 INFO [train.py:874] (1/4) Epoch 11, batch 4050, aishell_loss[loss=0.1633, simple_loss=0.248, pruned_loss=0.03931, over 4881.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2467, pruned_loss=0.04783, over 984897.74 frames.], batch size: 47, aishell_tot_loss[loss=0.1701, simple_loss=0.2516, pruned_loss=0.04431, over 984635.13 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.2412, pruned_loss=0.05196, over 985710.22 frames.], batch size: 47, lr: 7.12e-04
+2022-06-18 18:26:14,013 INFO [train.py:874] (1/4) Epoch 11, batch 4100, datatang_loss[loss=0.1508, simple_loss=0.2304, pruned_loss=0.0356, over 4927.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2456, pruned_loss=0.0469, over 984573.11 frames.], batch size: 71, aishell_tot_loss[loss=0.1698, simple_loss=0.2512, pruned_loss=0.04416, over 984242.10 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.2406, pruned_loss=0.05105, over 985708.52 frames.], batch size: 71, lr: 7.12e-04
+2022-06-18 18:27:19,854 INFO [train.py:874] (1/4) Epoch 12, batch 50, datatang_loss[loss=0.1717, simple_loss=0.2291, pruned_loss=0.05721, over 4917.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2439, pruned_loss=0.04716, over 218360.12 frames.], batch size: 42, aishell_tot_loss[loss=0.1747, simple_loss=0.2559, pruned_loss=0.04676, over 128947.88 frames.], datatang_tot_loss[loss=0.1623, simple_loss=0.229, pruned_loss=0.04776, over 102856.37 frames.], batch size: 42, lr: 6.86e-04
+2022-06-18 18:27:51,807 INFO [train.py:874] (1/4) Epoch 12, batch 100, datatang_loss[loss=0.1493, simple_loss=0.2313, pruned_loss=0.03368, over 4959.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2409, pruned_loss=0.04538, over 388525.16 frames.], batch size: 91, aishell_tot_loss[loss=0.1705, simple_loss=0.2522, pruned_loss=0.04436, over 233748.60 frames.], datatang_tot_loss[loss=0.161, simple_loss=0.2283, pruned_loss=0.04683, over 202882.87 frames.], batch size: 91, lr: 6.86e-04
+2022-06-18 18:28:21,825 INFO [train.py:874] (1/4) Epoch 12, batch 150, datatang_loss[loss=0.1534, simple_loss=0.2202, pruned_loss=0.04328, over 4923.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2417, pruned_loss=0.04624, over 520680.12 frames.], batch size: 71, aishell_tot_loss[loss=0.1701, simple_loss=0.2519, pruned_loss=0.04408, over 302063.55 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.2327, pruned_loss=0.04821, over 315301.90 frames.], batch size: 71, lr: 6.86e-04
+2022-06-18 18:28:52,334 INFO [train.py:874] (1/4) Epoch 12, batch 200, aishell_loss[loss=0.1414, simple_loss=0.2237, pruned_loss=0.02949, over 4970.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2411, pruned_loss=0.0459, over 623917.07 frames.], batch size: 44, aishell_tot_loss[loss=0.168, simple_loss=0.2494, pruned_loss=0.04333, over 385636.38 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2335, pruned_loss=0.04843, over 391398.49 frames.], batch size: 44, lr: 6.85e-04
+2022-06-18 18:29:23,911 INFO [train.py:874] (1/4) Epoch 12, batch 250, datatang_loss[loss=0.1562, simple_loss=0.2105, pruned_loss=0.05091, over 4969.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2422, pruned_loss=0.0454, over 703832.14 frames.], batch size: 45, aishell_tot_loss[loss=0.1674, simple_loss=0.2491, pruned_loss=0.04285, over 476712.69 frames.], datatang_tot_loss[loss=0.1657, simple_loss=0.2342, pruned_loss=0.04854, over 440046.92 frames.], batch size: 45, lr: 6.85e-04
+2022-06-18 18:29:54,015 INFO [train.py:874] (1/4) Epoch 12, batch 300, aishell_loss[loss=0.1741, simple_loss=0.2625, pruned_loss=0.04287, over 4886.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2424, pruned_loss=0.04545, over 766719.72 frames.], batch size: 47, aishell_tot_loss[loss=0.1673, simple_loss=0.2493, pruned_loss=0.04271, over 537255.26 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2346, pruned_loss=0.04861, over 504158.54 frames.], batch size: 47, lr: 6.85e-04
+2022-06-18 18:30:23,415 INFO [train.py:874] (1/4) Epoch 12, batch 350, aishell_loss[loss=0.1768, simple_loss=0.2584, pruned_loss=0.04763, over 4864.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2435, pruned_loss=0.046, over 815298.94 frames.], batch size: 36, aishell_tot_loss[loss=0.1684, simple_loss=0.2503, pruned_loss=0.04325, over 588031.95 frames.], datatang_tot_loss[loss=0.1666, simple_loss=0.2356, pruned_loss=0.04878, over 563082.19 frames.], batch size: 36, lr: 6.84e-04
+2022-06-18 18:30:56,357 INFO [train.py:874] (1/4) Epoch 12, batch 400, datatang_loss[loss=0.1991, simple_loss=0.2631, pruned_loss=0.06762, over 4913.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2426, pruned_loss=0.04582, over 852878.06 frames.], batch size: 57, aishell_tot_loss[loss=0.1687, simple_loss=0.2505, pruned_loss=0.04345, over 623981.10 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.235, pruned_loss=0.04807, over 623861.35 frames.], batch size: 57, lr: 6.84e-04
+2022-06-18 18:31:26,879 INFO [train.py:874] (1/4) Epoch 12, batch 450, aishell_loss[loss=0.2009, simple_loss=0.2776, pruned_loss=0.06208, over 4976.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2421, pruned_loss=0.04539, over 882403.49 frames.], batch size: 61, aishell_tot_loss[loss=0.1679, simple_loss=0.2495, pruned_loss=0.0431, over 669745.55 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.235, pruned_loss=0.04791, over 663361.99 frames.], batch size: 61, lr: 6.84e-04
+2022-06-18 18:31:57,606 INFO [train.py:874] (1/4) Epoch 12, batch 500, aishell_loss[loss=0.166, simple_loss=0.2521, pruned_loss=0.03998, over 4956.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2436, pruned_loss=0.04545, over 906006.94 frames.], batch size: 61, aishell_tot_loss[loss=0.168, simple_loss=0.2501, pruned_loss=0.04294, over 712962.52 frames.], datatang_tot_loss[loss=0.1663, simple_loss=0.2361, pruned_loss=0.04825, over 695840.49 frames.], batch size: 61, lr: 6.83e-04
+2022-06-18 18:32:29,682 INFO [train.py:874] (1/4) Epoch 12, batch 550, aishell_loss[loss=0.1879, simple_loss=0.2686, pruned_loss=0.05364, over 4971.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2435, pruned_loss=0.04569, over 923402.92 frames.], batch size: 44, aishell_tot_loss[loss=0.1685, simple_loss=0.2505, pruned_loss=0.0432, over 741232.27 frames.], datatang_tot_loss[loss=0.1662, simple_loss=0.236, pruned_loss=0.04819, over 733717.88 frames.], batch size: 44, lr: 6.83e-04
+2022-06-18 18:33:00,939 INFO [train.py:874] (1/4) Epoch 12, batch 600, aishell_loss[loss=0.1727, simple_loss=0.2529, pruned_loss=0.04619, over 4938.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2439, pruned_loss=0.04693, over 937131.14 frames.], batch size: 58, aishell_tot_loss[loss=0.168, simple_loss=0.2498, pruned_loss=0.04306, over 763466.24 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2377, pruned_loss=0.04967, over 769871.70 frames.], batch size: 58, lr: 6.82e-04
+2022-06-18 18:33:32,102 INFO [train.py:874] (1/4) Epoch 12, batch 650, aishell_loss[loss=0.165, simple_loss=0.2582, pruned_loss=0.03589, over 4921.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2444, pruned_loss=0.04688, over 947927.93 frames.], batch size: 52, aishell_tot_loss[loss=0.1678, simple_loss=0.2498, pruned_loss=0.04292, over 788566.50 frames.], datatang_tot_loss[loss=0.169, simple_loss=0.2384, pruned_loss=0.04985, over 796355.59 frames.], batch size: 52, lr: 6.82e-04
+2022-06-18 18:34:02,835 INFO [train.py:874] (1/4) Epoch 12, batch 700, datatang_loss[loss=0.1641, simple_loss=0.233, pruned_loss=0.04762, over 4941.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2455, pruned_loss=0.04691, over 956392.16 frames.], batch size: 50, aishell_tot_loss[loss=0.1678, simple_loss=0.2498, pruned_loss=0.04285, over 818710.26 frames.], datatang_tot_loss[loss=0.1701, simple_loss=0.2394, pruned_loss=0.0504, over 811816.68 frames.], batch size: 50, lr: 6.82e-04
+2022-06-18 18:34:31,742 INFO [train.py:874] (1/4) Epoch 12, batch 750, datatang_loss[loss=0.1862, simple_loss=0.2445, pruned_loss=0.06395, over 4886.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2462, pruned_loss=0.04736, over 962796.44 frames.], batch size: 39, aishell_tot_loss[loss=0.1687, simple_loss=0.2507, pruned_loss=0.04338, over 840418.89 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.2394, pruned_loss=0.05061, over 830030.70 frames.], batch size: 39, lr: 6.81e-04
+2022-06-18 18:35:04,178 INFO [train.py:874] (1/4) Epoch 12, batch 800, aishell_loss[loss=0.1615, simple_loss=0.2451, pruned_loss=0.03894, over 4858.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2457, pruned_loss=0.0468, over 967848.01 frames.], batch size: 37, aishell_tot_loss[loss=0.1682, simple_loss=0.2501, pruned_loss=0.0431, over 858840.87 frames.], datatang_tot_loss[loss=0.1702, simple_loss=0.2395, pruned_loss=0.05039, over 846916.95 frames.], batch size: 37, lr: 6.81e-04
+2022-06-18 18:35:34,613 INFO [train.py:874] (1/4) Epoch 12, batch 850, datatang_loss[loss=0.1592, simple_loss=0.2193, pruned_loss=0.04952, over 4965.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2447, pruned_loss=0.04675, over 971968.12 frames.], batch size: 55, aishell_tot_loss[loss=0.1686, simple_loss=0.25, pruned_loss=0.04354, over 872609.70 frames.], datatang_tot_loss[loss=0.1692, simple_loss=0.2388, pruned_loss=0.04982, over 864699.13 frames.], batch size: 55, lr: 6.81e-04
+2022-06-18 18:36:04,254 INFO [train.py:874] (1/4) Epoch 12, batch 900, aishell_loss[loss=0.1704, simple_loss=0.2606, pruned_loss=0.04005, over 4882.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2448, pruned_loss=0.04697, over 975004.97 frames.], batch size: 42, aishell_tot_loss[loss=0.1691, simple_loss=0.2506, pruned_loss=0.04385, over 885393.13 frames.], datatang_tot_loss[loss=0.169, simple_loss=0.2386, pruned_loss=0.04971, over 879509.45 frames.], batch size: 42, lr: 6.80e-04
+2022-06-18 18:36:36,374 INFO [train.py:874] (1/4) Epoch 12, batch 950, datatang_loss[loss=0.149, simple_loss=0.2233, pruned_loss=0.03737, over 4971.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2436, pruned_loss=0.0459, over 977468.66 frames.], batch size: 60, aishell_tot_loss[loss=0.1677, simple_loss=0.2492, pruned_loss=0.0431, over 898021.98 frames.], datatang_tot_loss[loss=0.1686, simple_loss=0.2385, pruned_loss=0.04932, over 891223.69 frames.], batch size: 60, lr: 6.80e-04
+2022-06-18 18:37:06,850 INFO [train.py:874] (1/4) Epoch 12, batch 1000, aishell_loss[loss=0.1384, simple_loss=0.2257, pruned_loss=0.0256, over 4835.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2436, pruned_loss=0.0459, over 978962.61 frames.], batch size: 28, aishell_tot_loss[loss=0.1677, simple_loss=0.2491, pruned_loss=0.04311, over 908644.09 frames.], datatang_tot_loss[loss=0.1685, simple_loss=0.2384, pruned_loss=0.04929, over 901652.42 frames.], batch size: 28, lr: 6.79e-04
+2022-06-18 18:37:06,851 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 18:37:23,199 INFO [train.py:914] (1/4) Epoch 12, validation: loss=0.1654, simple_loss=0.2501, pruned_loss=0.04036, over 1622729.00 frames.
+2022-06-18 18:37:54,285 INFO [train.py:874] (1/4) Epoch 12, batch 1050, datatang_loss[loss=0.1643, simple_loss=0.2424, pruned_loss=0.04303, over 4955.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2441, pruned_loss=0.04646, over 980364.12 frames.], batch size: 91, aishell_tot_loss[loss=0.168, simple_loss=0.2495, pruned_loss=0.04327, over 915887.75 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2388, pruned_loss=0.04952, over 913390.29 frames.], batch size: 91, lr: 6.79e-04
+2022-06-18 18:38:26,526 INFO [train.py:874] (1/4) Epoch 12, batch 1100, aishell_loss[loss=0.1736, simple_loss=0.2657, pruned_loss=0.04075, over 4974.00 frames.], tot_loss[loss=0.17, simple_loss=0.2454, pruned_loss=0.04723, over 981794.65 frames.], batch size: 69, aishell_tot_loss[loss=0.1685, simple_loss=0.25, pruned_loss=0.04346, over 923486.91 frames.], datatang_tot_loss[loss=0.1701, simple_loss=0.2399, pruned_loss=0.05015, over 922811.34 frames.], batch size: 69, lr: 6.79e-04
+2022-06-18 18:38:56,147 INFO [train.py:874] (1/4) Epoch 12, batch 1150, aishell_loss[loss=0.1781, simple_loss=0.2709, pruned_loss=0.04262, over 4917.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2456, pruned_loss=0.04706, over 982272.71 frames.], batch size: 41, aishell_tot_loss[loss=0.1683, simple_loss=0.2499, pruned_loss=0.04338, over 930818.48 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.2403, pruned_loss=0.05013, over 929795.37 frames.], batch size: 41, lr: 6.78e-04
+2022-06-18 18:39:27,823 INFO [train.py:874] (1/4) Epoch 12, batch 1200, aishell_loss[loss=0.1582, simple_loss=0.2479, pruned_loss=0.03425, over 4926.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2465, pruned_loss=0.048, over 983239.39 frames.], batch size: 49, aishell_tot_loss[loss=0.1695, simple_loss=0.2507, pruned_loss=0.04418, over 937474.48 frames.], datatang_tot_loss[loss=0.1707, simple_loss=0.2405, pruned_loss=0.05047, over 936410.79 frames.], batch size: 49, lr: 6.78e-04
+2022-06-18 18:39:58,228 INFO [train.py:874] (1/4) Epoch 12, batch 1250, aishell_loss[loss=0.1759, simple_loss=0.2575, pruned_loss=0.04709, over 4917.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2468, pruned_loss=0.04809, over 983397.97 frames.], batch size: 52, aishell_tot_loss[loss=0.1698, simple_loss=0.251, pruned_loss=0.04428, over 942669.24 frames.], datatang_tot_loss[loss=0.171, simple_loss=0.2408, pruned_loss=0.0506, over 942289.24 frames.], batch size: 52, lr: 6.78e-04
+2022-06-18 18:40:30,427 INFO [train.py:874] (1/4) Epoch 12, batch 1300, aishell_loss[loss=0.1852, simple_loss=0.2604, pruned_loss=0.05495, over 4856.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2457, pruned_loss=0.04736, over 983871.24 frames.], batch size: 36, aishell_tot_loss[loss=0.1696, simple_loss=0.251, pruned_loss=0.04405, over 947195.90 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2398, pruned_loss=0.05012, over 947926.16 frames.], batch size: 36, lr: 6.77e-04
+2022-06-18 18:41:01,636 INFO [train.py:874] (1/4) Epoch 12, batch 1350, datatang_loss[loss=0.212, simple_loss=0.2733, pruned_loss=0.07542, over 4948.00 frames.], tot_loss[loss=0.1695, simple_loss=0.245, pruned_loss=0.04695, over 984425.85 frames.], batch size: 69, aishell_tot_loss[loss=0.169, simple_loss=0.2506, pruned_loss=0.04364, over 951423.12 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.2397, pruned_loss=0.05011, over 952824.22 frames.], batch size: 69, lr: 6.77e-04
+2022-06-18 18:41:32,119 INFO [train.py:874] (1/4) Epoch 12, batch 1400, datatang_loss[loss=0.1448, simple_loss=0.213, pruned_loss=0.03825, over 4908.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2442, pruned_loss=0.0464, over 984972.07 frames.], batch size: 25, aishell_tot_loss[loss=0.1682, simple_loss=0.2501, pruned_loss=0.0432, over 955240.67 frames.], datatang_tot_loss[loss=0.1696, simple_loss=0.2395, pruned_loss=0.04987, over 957159.87 frames.], batch size: 25, lr: 6.77e-04
+2022-06-18 18:42:02,774 INFO [train.py:874] (1/4) Epoch 12, batch 1450, datatang_loss[loss=0.1768, simple_loss=0.2518, pruned_loss=0.0509, over 4961.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2462, pruned_loss=0.04677, over 984918.67 frames.], batch size: 67, aishell_tot_loss[loss=0.1692, simple_loss=0.2513, pruned_loss=0.04355, over 959692.26 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2398, pruned_loss=0.05012, over 959465.28 frames.], batch size: 67, lr: 6.76e-04
+2022-06-18 18:42:33,341 INFO [train.py:874] (1/4) Epoch 12, batch 1500, datatang_loss[loss=0.175, simple_loss=0.24, pruned_loss=0.055, over 4927.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2454, pruned_loss=0.04654, over 984947.52 frames.], batch size: 83, aishell_tot_loss[loss=0.168, simple_loss=0.25, pruned_loss=0.04304, over 962753.99 frames.], datatang_tot_loss[loss=0.1706, simple_loss=0.2404, pruned_loss=0.05042, over 962409.88 frames.], batch size: 83, lr: 6.76e-04
+2022-06-18 18:43:02,293 INFO [train.py:874] (1/4) Epoch 12, batch 1550, aishell_loss[loss=0.1891, simple_loss=0.2712, pruned_loss=0.05355, over 4969.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2462, pruned_loss=0.04676, over 985320.23 frames.], batch size: 79, aishell_tot_loss[loss=0.168, simple_loss=0.2503, pruned_loss=0.04287, over 965824.69 frames.], datatang_tot_loss[loss=0.1713, simple_loss=0.2409, pruned_loss=0.05091, over 964994.40 frames.], batch size: 79, lr: 6.76e-04
+2022-06-18 18:43:34,359 INFO [train.py:874] (1/4) Epoch 12, batch 1600, aishell_loss[loss=0.1748, simple_loss=0.2487, pruned_loss=0.05049, over 4866.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2472, pruned_loss=0.04758, over 985285.45 frames.], batch size: 35, aishell_tot_loss[loss=0.1683, simple_loss=0.2505, pruned_loss=0.04304, over 968054.82 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.2417, pruned_loss=0.05164, over 967413.00 frames.], batch size: 35, lr: 6.75e-04
+2022-06-18 18:44:05,017 INFO [train.py:874] (1/4) Epoch 12, batch 1650, aishell_loss[loss=0.1878, simple_loss=0.2669, pruned_loss=0.05439, over 4865.00 frames.], tot_loss[loss=0.171, simple_loss=0.2468, pruned_loss=0.04757, over 984915.58 frames.], batch size: 36, aishell_tot_loss[loss=0.1684, simple_loss=0.2507, pruned_loss=0.04306, over 969712.99 frames.], datatang_tot_loss[loss=0.1724, simple_loss=0.2415, pruned_loss=0.05164, over 969495.11 frames.], batch size: 36, lr: 6.75e-04
+2022-06-18 18:44:35,460 INFO [train.py:874] (1/4) Epoch 12, batch 1700, aishell_loss[loss=0.1794, simple_loss=0.2613, pruned_loss=0.04877, over 4938.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2467, pruned_loss=0.04732, over 984804.43 frames.], batch size: 41, aishell_tot_loss[loss=0.1685, simple_loss=0.251, pruned_loss=0.04302, over 971386.05 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.2413, pruned_loss=0.05142, over 971312.01 frames.], batch size: 41, lr: 6.74e-04
+2022-06-18 18:45:06,615 INFO [train.py:874] (1/4) Epoch 12, batch 1750, aishell_loss[loss=0.1757, simple_loss=0.2703, pruned_loss=0.0405, over 4955.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2458, pruned_loss=0.04704, over 985148.06 frames.], batch size: 78, aishell_tot_loss[loss=0.1679, simple_loss=0.2503, pruned_loss=0.04277, over 973462.05 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.241, pruned_loss=0.05152, over 972744.74 frames.], batch size: 78, lr: 6.74e-04
+2022-06-18 18:45:38,044 INFO [train.py:874] (1/4) Epoch 12, batch 1800, aishell_loss[loss=0.1328, simple_loss=0.2033, pruned_loss=0.03113, over 4859.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2453, pruned_loss=0.04675, over 985191.93 frames.], batch size: 21, aishell_tot_loss[loss=0.1679, simple_loss=0.2503, pruned_loss=0.04275, over 974690.93 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.2406, pruned_loss=0.0511, over 974402.13 frames.], batch size: 21, lr: 6.74e-04
+2022-06-18 18:46:08,163 INFO [train.py:874] (1/4) Epoch 12, batch 1850, aishell_loss[loss=0.1761, simple_loss=0.2568, pruned_loss=0.04765, over 4967.00 frames.], tot_loss[loss=0.169, simple_loss=0.2453, pruned_loss=0.04639, over 985466.84 frames.], batch size: 61, aishell_tot_loss[loss=0.1678, simple_loss=0.2502, pruned_loss=0.04268, over 975983.12 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.2406, pruned_loss=0.05075, over 975903.97 frames.], batch size: 61, lr: 6.73e-04
+2022-06-18 18:46:38,138 INFO [train.py:874] (1/4) Epoch 12, batch 1900, datatang_loss[loss=0.1557, simple_loss=0.2146, pruned_loss=0.04843, over 4983.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2449, pruned_loss=0.04631, over 985516.97 frames.], batch size: 34, aishell_tot_loss[loss=0.1675, simple_loss=0.2498, pruned_loss=0.04257, over 977064.47 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.2404, pruned_loss=0.05086, over 977111.07 frames.], batch size: 34, lr: 6.73e-04
+2022-06-18 18:47:08,333 INFO [train.py:874] (1/4) Epoch 12, batch 1950, datatang_loss[loss=0.1802, simple_loss=0.2554, pruned_loss=0.05249, over 4972.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2445, pruned_loss=0.04543, over 985486.97 frames.], batch size: 40, aishell_tot_loss[loss=0.167, simple_loss=0.2495, pruned_loss=0.04231, over 978247.05 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.24, pruned_loss=0.05026, over 977873.09 frames.], batch size: 40, lr: 6.73e-04
+2022-06-18 18:47:38,738 INFO [train.py:874] (1/4) Epoch 12, batch 2000, datatang_loss[loss=0.1821, simple_loss=0.2439, pruned_loss=0.06015, over 4874.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2446, pruned_loss=0.04614, over 985441.76 frames.], batch size: 44, aishell_tot_loss[loss=0.1676, simple_loss=0.2498, pruned_loss=0.04272, over 978719.70 frames.], datatang_tot_loss[loss=0.1702, simple_loss=0.2399, pruned_loss=0.05027, over 979112.71 frames.], batch size: 44, lr: 6.72e-04
+2022-06-18 18:47:38,739 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 18:47:55,402 INFO [train.py:914] (1/4) Epoch 12, validation: loss=0.1674, simple_loss=0.25, pruned_loss=0.04245, over 1622729.00 frames.
+2022-06-18 18:48:25,433 INFO [train.py:874] (1/4) Epoch 12, batch 2050, datatang_loss[loss=0.1872, simple_loss=0.2577, pruned_loss=0.05831, over 4936.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2442, pruned_loss=0.04622, over 985661.22 frames.], batch size: 94, aishell_tot_loss[loss=0.1674, simple_loss=0.2495, pruned_loss=0.04272, over 979492.59 frames.], datatang_tot_loss[loss=0.1702, simple_loss=0.2398, pruned_loss=0.05023, over 980102.36 frames.], batch size: 94, lr: 6.72e-04
+2022-06-18 18:48:56,080 INFO [train.py:874] (1/4) Epoch 12, batch 2100, aishell_loss[loss=0.2026, simple_loss=0.2872, pruned_loss=0.05896, over 4921.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2442, pruned_loss=0.04612, over 985541.05 frames.], batch size: 33, aishell_tot_loss[loss=0.1674, simple_loss=0.2494, pruned_loss=0.04272, over 980187.67 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2396, pruned_loss=0.05021, over 980677.09 frames.], batch size: 33, lr: 6.72e-04
+2022-06-18 18:49:26,410 INFO [train.py:874] (1/4) Epoch 12, batch 2150, datatang_loss[loss=0.1759, simple_loss=0.2426, pruned_loss=0.05454, over 4973.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2467, pruned_loss=0.04743, over 985957.00 frames.], batch size: 40, aishell_tot_loss[loss=0.1684, simple_loss=0.2506, pruned_loss=0.04316, over 981124.42 frames.], datatang_tot_loss[loss=0.1716, simple_loss=0.241, pruned_loss=0.05114, over 981375.20 frames.], batch size: 40, lr: 6.71e-04
+2022-06-18 18:49:55,762 INFO [train.py:874] (1/4) Epoch 12, batch 2200, datatang_loss[loss=0.2083, simple_loss=0.2761, pruned_loss=0.07019, over 4932.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2466, pruned_loss=0.04794, over 985752.60 frames.], batch size: 109, aishell_tot_loss[loss=0.1681, simple_loss=0.25, pruned_loss=0.04308, over 981468.30 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.2417, pruned_loss=0.05169, over 981923.92 frames.], batch size: 109, lr: 6.71e-04
+2022-06-18 18:50:25,834 INFO [train.py:874] (1/4) Epoch 12, batch 2250, datatang_loss[loss=0.1529, simple_loss=0.2242, pruned_loss=0.04078, over 4984.00 frames.], tot_loss[loss=0.172, simple_loss=0.2468, pruned_loss=0.0486, over 985429.20 frames.], batch size: 40, aishell_tot_loss[loss=0.1691, simple_loss=0.2505, pruned_loss=0.0438, over 981508.72 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.2416, pruned_loss=0.05174, over 982511.17 frames.], batch size: 40, lr: 6.71e-04
+2022-06-18 18:50:57,133 INFO [train.py:874] (1/4) Epoch 12, batch 2300, datatang_loss[loss=0.1491, simple_loss=0.2116, pruned_loss=0.04331, over 4932.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2471, pruned_loss=0.04866, over 985590.72 frames.], batch size: 50, aishell_tot_loss[loss=0.1696, simple_loss=0.2511, pruned_loss=0.04402, over 982233.90 frames.], datatang_tot_loss[loss=0.1725, simple_loss=0.2417, pruned_loss=0.05164, over 982741.18 frames.], batch size: 50, lr: 6.70e-04
+2022-06-18 18:51:26,967 INFO [train.py:874] (1/4) Epoch 12, batch 2350, datatang_loss[loss=0.2208, simple_loss=0.2874, pruned_loss=0.07713, over 4945.00 frames.], tot_loss[loss=0.172, simple_loss=0.2477, pruned_loss=0.04814, over 985569.08 frames.], batch size: 109, aishell_tot_loss[loss=0.169, simple_loss=0.2509, pruned_loss=0.04352, over 982443.28 frames.], datatang_tot_loss[loss=0.1732, simple_loss=0.2426, pruned_loss=0.05193, over 983249.30 frames.], batch size: 109, lr: 6.70e-04
+2022-06-18 18:51:58,526 INFO [train.py:874] (1/4) Epoch 12, batch 2400, datatang_loss[loss=0.1835, simple_loss=0.2565, pruned_loss=0.05525, over 4975.00 frames.], tot_loss[loss=0.171, simple_loss=0.2468, pruned_loss=0.04756, over 985666.38 frames.], batch size: 37, aishell_tot_loss[loss=0.1686, simple_loss=0.2508, pruned_loss=0.04322, over 982633.03 frames.], datatang_tot_loss[loss=0.1726, simple_loss=0.242, pruned_loss=0.05162, over 983802.30 frames.], batch size: 37, lr: 6.70e-04
+2022-06-18 18:52:28,275 INFO [train.py:874] (1/4) Epoch 12, batch 2450, aishell_loss[loss=0.1559, simple_loss=0.2429, pruned_loss=0.03449, over 4877.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2465, pruned_loss=0.04753, over 985407.57 frames.], batch size: 34, aishell_tot_loss[loss=0.1689, simple_loss=0.2509, pruned_loss=0.04339, over 982896.88 frames.], datatang_tot_loss[loss=0.1723, simple_loss=0.2414, pruned_loss=0.05158, over 983864.07 frames.], batch size: 34, lr: 6.69e-04
+2022-06-18 18:52:59,294 INFO [train.py:874] (1/4) Epoch 12, batch 2500, datatang_loss[loss=0.2037, simple_loss=0.2752, pruned_loss=0.06609, over 4929.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2465, pruned_loss=0.04766, over 985891.40 frames.], batch size: 94, aishell_tot_loss[loss=0.1692, simple_loss=0.2514, pruned_loss=0.04354, over 983385.19 frames.], datatang_tot_loss[loss=0.172, simple_loss=0.2414, pruned_loss=0.05129, over 984327.33 frames.], batch size: 94, lr: 6.69e-04
+2022-06-18 18:53:29,792 INFO [train.py:874] (1/4) Epoch 12, batch 2550, aishell_loss[loss=0.1654, simple_loss=0.2453, pruned_loss=0.04271, over 4864.00 frames.], tot_loss[loss=0.171, simple_loss=0.2467, pruned_loss=0.04763, over 985504.80 frames.], batch size: 36, aishell_tot_loss[loss=0.1694, simple_loss=0.2514, pruned_loss=0.04372, over 983527.74 frames.], datatang_tot_loss[loss=0.1721, simple_loss=0.2415, pruned_loss=0.05136, over 984279.74 frames.], batch size: 36, lr: 6.69e-04
+2022-06-18 18:54:06,336 INFO [train.py:874] (1/4) Epoch 12, batch 2600, datatang_loss[loss=0.185, simple_loss=0.2453, pruned_loss=0.06233, over 4961.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2457, pruned_loss=0.04719, over 985713.82 frames.], batch size: 91, aishell_tot_loss[loss=0.1692, simple_loss=0.2511, pruned_loss=0.04359, over 983783.25 frames.], datatang_tot_loss[loss=0.1714, simple_loss=0.241, pruned_loss=0.05091, over 984620.47 frames.], batch size: 91, lr: 6.68e-04
+2022-06-18 18:54:36,796 INFO [train.py:874] (1/4) Epoch 12, batch 2650, datatang_loss[loss=0.1545, simple_loss=0.2229, pruned_loss=0.04308, over 4962.00 frames.], tot_loss[loss=0.1692, simple_loss=0.245, pruned_loss=0.04672, over 985820.72 frames.], batch size: 34, aishell_tot_loss[loss=0.1693, simple_loss=0.2513, pruned_loss=0.04359, over 983982.92 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.24, pruned_loss=0.05035, over 984874.40 frames.], batch size: 34, lr: 6.68e-04
+2022-06-18 18:55:07,915 INFO [train.py:874] (1/4) Epoch 12, batch 2700, datatang_loss[loss=0.1775, simple_loss=0.2294, pruned_loss=0.06282, over 4894.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2453, pruned_loss=0.04667, over 985462.75 frames.], batch size: 42, aishell_tot_loss[loss=0.1694, simple_loss=0.2514, pruned_loss=0.04369, over 983816.60 frames.], datatang_tot_loss[loss=0.1703, simple_loss=0.2399, pruned_loss=0.05033, over 985014.65 frames.], batch size: 42, lr: 6.68e-04
+2022-06-18 18:55:38,367 INFO [train.py:874] (1/4) Epoch 12, batch 2750, datatang_loss[loss=0.206, simple_loss=0.2762, pruned_loss=0.06788, over 4947.00 frames.], tot_loss[loss=0.1694, simple_loss=0.245, pruned_loss=0.04686, over 985277.53 frames.], batch size: 45, aishell_tot_loss[loss=0.1694, simple_loss=0.2513, pruned_loss=0.04374, over 983996.67 frames.], datatang_tot_loss[loss=0.1701, simple_loss=0.2396, pruned_loss=0.05032, over 984869.72 frames.], batch size: 45, lr: 6.67e-04
+2022-06-18 18:56:09,576 INFO [train.py:874] (1/4) Epoch 12, batch 2800, aishell_loss[loss=0.1198, simple_loss=0.2071, pruned_loss=0.01627, over 4957.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2448, pruned_loss=0.04667, over 985517.11 frames.], batch size: 25, aishell_tot_loss[loss=0.1692, simple_loss=0.2512, pruned_loss=0.0436, over 984259.83 frames.], datatang_tot_loss[loss=0.1699, simple_loss=0.2395, pruned_loss=0.0502, over 985053.12 frames.], batch size: 25, lr: 6.67e-04
+2022-06-18 18:56:40,326 INFO [train.py:874] (1/4) Epoch 12, batch 2850, datatang_loss[loss=0.2047, simple_loss=0.2626, pruned_loss=0.07338, over 4950.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2456, pruned_loss=0.04666, over 985556.64 frames.], batch size: 67, aishell_tot_loss[loss=0.1694, simple_loss=0.2516, pruned_loss=0.04358, over 984086.86 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2399, pruned_loss=0.05007, over 985449.03 frames.], batch size: 67, lr: 6.66e-04
+2022-06-18 18:57:09,893 INFO [train.py:874] (1/4) Epoch 12, batch 2900, aishell_loss[loss=0.1901, simple_loss=0.2881, pruned_loss=0.04602, over 4961.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2456, pruned_loss=0.04664, over 985905.38 frames.], batch size: 68, aishell_tot_loss[loss=0.1687, simple_loss=0.251, pruned_loss=0.04322, over 984692.58 frames.], datatang_tot_loss[loss=0.1706, simple_loss=0.2402, pruned_loss=0.05048, over 985413.74 frames.], batch size: 68, lr: 6.66e-04
+2022-06-18 18:57:41,737 INFO [train.py:874] (1/4) Epoch 12, batch 2950, aishell_loss[loss=0.1632, simple_loss=0.2473, pruned_loss=0.03951, over 4970.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2466, pruned_loss=0.04742, over 985564.68 frames.], batch size: 44, aishell_tot_loss[loss=0.1689, simple_loss=0.2514, pruned_loss=0.04319, over 984714.62 frames.], datatang_tot_loss[loss=0.1717, simple_loss=0.2411, pruned_loss=0.0511, over 985221.27 frames.], batch size: 44, lr: 6.66e-04
+2022-06-18 18:58:12,916 INFO [train.py:874] (1/4) Epoch 12, batch 3000, aishell_loss[loss=0.1384, simple_loss=0.1965, pruned_loss=0.04014, over 4808.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2451, pruned_loss=0.04702, over 985698.34 frames.], batch size: 21, aishell_tot_loss[loss=0.1688, simple_loss=0.2505, pruned_loss=0.04351, over 984753.56 frames.], datatang_tot_loss[loss=0.1707, simple_loss=0.2404, pruned_loss=0.05045, over 985483.27 frames.], batch size: 21, lr: 6.65e-04
+2022-06-18 18:58:12,917 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 18:58:29,560 INFO [train.py:914] (1/4) Epoch 12, validation: loss=0.1665, simple_loss=0.2506, pruned_loss=0.04121, over 1622729.00 frames.
+2022-06-18 18:59:00,855 INFO [train.py:874] (1/4) Epoch 12, batch 3050, aishell_loss[loss=0.1736, simple_loss=0.2559, pruned_loss=0.04563, over 4972.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2449, pruned_loss=0.04698, over 985337.01 frames.], batch size: 40, aishell_tot_loss[loss=0.1683, simple_loss=0.25, pruned_loss=0.0433, over 984462.60 frames.], datatang_tot_loss[loss=0.1709, simple_loss=0.2408, pruned_loss=0.05054, over 985514.11 frames.], batch size: 40, lr: 6.65e-04
+2022-06-18 18:59:32,408 INFO [train.py:874] (1/4) Epoch 12, batch 3100, aishell_loss[loss=0.1755, simple_loss=0.2633, pruned_loss=0.04383, over 4960.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2451, pruned_loss=0.04689, over 985654.31 frames.], batch size: 78, aishell_tot_loss[loss=0.1683, simple_loss=0.2502, pruned_loss=0.04319, over 984786.14 frames.], datatang_tot_loss[loss=0.1709, simple_loss=0.2406, pruned_loss=0.05062, over 985612.18 frames.], batch size: 78, lr: 6.65e-04
+2022-06-18 19:00:02,836 INFO [train.py:874] (1/4) Epoch 12, batch 3150, aishell_loss[loss=0.1712, simple_loss=0.2486, pruned_loss=0.04688, over 4919.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2447, pruned_loss=0.04685, over 985426.49 frames.], batch size: 41, aishell_tot_loss[loss=0.1678, simple_loss=0.2497, pruned_loss=0.04297, over 984793.48 frames.], datatang_tot_loss[loss=0.1711, simple_loss=0.2409, pruned_loss=0.05061, over 985450.67 frames.], batch size: 41, lr: 6.64e-04
+2022-06-18 19:00:33,840 INFO [train.py:874] (1/4) Epoch 12, batch 3200, aishell_loss[loss=0.1784, simple_loss=0.2615, pruned_loss=0.04766, over 4898.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2442, pruned_loss=0.04618, over 985419.49 frames.], batch size: 60, aishell_tot_loss[loss=0.1672, simple_loss=0.2492, pruned_loss=0.04266, over 984895.18 frames.], datatang_tot_loss[loss=0.1707, simple_loss=0.2406, pruned_loss=0.05039, over 985406.57 frames.], batch size: 60, lr: 6.64e-04
+2022-06-18 19:01:04,142 INFO [train.py:874] (1/4) Epoch 12, batch 3250, aishell_loss[loss=0.1561, simple_loss=0.2406, pruned_loss=0.03582, over 4863.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2432, pruned_loss=0.04624, over 985155.34 frames.], batch size: 37, aishell_tot_loss[loss=0.1668, simple_loss=0.2483, pruned_loss=0.04261, over 984766.78 frames.], datatang_tot_loss[loss=0.1706, simple_loss=0.2403, pruned_loss=0.0504, over 985318.68 frames.], batch size: 37, lr: 6.64e-04
+2022-06-18 19:01:35,675 INFO [train.py:874] (1/4) Epoch 12, batch 3300, datatang_loss[loss=0.1518, simple_loss=0.2323, pruned_loss=0.0357, over 4961.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2431, pruned_loss=0.04582, over 985005.82 frames.], batch size: 86, aishell_tot_loss[loss=0.166, simple_loss=0.2478, pruned_loss=0.04209, over 984474.01 frames.], datatang_tot_loss[loss=0.1706, simple_loss=0.2405, pruned_loss=0.0504, over 985472.80 frames.], batch size: 86, lr: 6.63e-04
+2022-06-18 19:02:06,902 INFO [train.py:874] (1/4) Epoch 12, batch 3350, aishell_loss[loss=0.1657, simple_loss=0.2464, pruned_loss=0.0425, over 4926.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2435, pruned_loss=0.04562, over 984883.32 frames.], batch size: 49, aishell_tot_loss[loss=0.1659, simple_loss=0.2479, pruned_loss=0.04196, over 984362.31 frames.], datatang_tot_loss[loss=0.1706, simple_loss=0.2403, pruned_loss=0.05043, over 985499.00 frames.], batch size: 49, lr: 6.63e-04
+2022-06-18 19:02:37,646 INFO [train.py:874] (1/4) Epoch 12, batch 3400, datatang_loss[loss=0.1634, simple_loss=0.2281, pruned_loss=0.04936, over 4954.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2433, pruned_loss=0.04574, over 985101.13 frames.], batch size: 67, aishell_tot_loss[loss=0.1664, simple_loss=0.2483, pruned_loss=0.04222, over 984532.15 frames.], datatang_tot_loss[loss=0.17, simple_loss=0.2395, pruned_loss=0.05029, over 985559.26 frames.], batch size: 67, lr: 6.63e-04
+2022-06-18 19:03:08,071 INFO [train.py:874] (1/4) Epoch 12, batch 3450, aishell_loss[loss=0.193, simple_loss=0.2369, pruned_loss=0.07459, over 4952.00 frames.], tot_loss[loss=0.1669, simple_loss=0.243, pruned_loss=0.04544, over 985294.99 frames.], batch size: 22, aishell_tot_loss[loss=0.1668, simple_loss=0.2486, pruned_loss=0.04248, over 984566.52 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2385, pruned_loss=0.04965, over 985751.37 frames.], batch size: 22, lr: 6.62e-04
+2022-06-18 19:03:38,708 INFO [train.py:874] (1/4) Epoch 12, batch 3500, aishell_loss[loss=0.1529, simple_loss=0.2354, pruned_loss=0.0352, over 4952.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2433, pruned_loss=0.04587, over 985056.66 frames.], batch size: 31, aishell_tot_loss[loss=0.1669, simple_loss=0.2485, pruned_loss=0.04262, over 984279.80 frames.], datatang_tot_loss[loss=0.1693, simple_loss=0.2388, pruned_loss=0.04991, over 985829.19 frames.], batch size: 31, lr: 6.62e-04
+2022-06-18 19:04:07,462 INFO [train.py:874] (1/4) Epoch 12, batch 3550, datatang_loss[loss=0.1715, simple_loss=0.245, pruned_loss=0.04897, over 4941.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2437, pruned_loss=0.04589, over 985007.84 frames.], batch size: 88, aishell_tot_loss[loss=0.1672, simple_loss=0.2491, pruned_loss=0.04269, over 984219.13 frames.], datatang_tot_loss[loss=0.169, simple_loss=0.2386, pruned_loss=0.04972, over 985822.54 frames.], batch size: 88, lr: 6.62e-04
+2022-06-18 19:04:39,573 INFO [train.py:874] (1/4) Epoch 12, batch 3600, datatang_loss[loss=0.1466, simple_loss=0.2253, pruned_loss=0.03393, over 4922.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2445, pruned_loss=0.04611, over 985366.48 frames.], batch size: 83, aishell_tot_loss[loss=0.1679, simple_loss=0.2498, pruned_loss=0.043, over 984441.93 frames.], datatang_tot_loss[loss=0.1689, simple_loss=0.2384, pruned_loss=0.04968, over 986001.97 frames.], batch size: 83, lr: 6.61e-04
+2022-06-18 19:05:11,051 INFO [train.py:874] (1/4) Epoch 12, batch 3650, aishell_loss[loss=0.186, simple_loss=0.2671, pruned_loss=0.05243, over 4935.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2448, pruned_loss=0.04604, over 985451.07 frames.], batch size: 79, aishell_tot_loss[loss=0.1681, simple_loss=0.25, pruned_loss=0.04304, over 984360.66 frames.], datatang_tot_loss[loss=0.1688, simple_loss=0.2387, pruned_loss=0.04948, over 986214.43 frames.], batch size: 79, lr: 6.61e-04
+2022-06-18 19:05:40,304 INFO [train.py:874] (1/4) Epoch 12, batch 3700, datatang_loss[loss=0.1513, simple_loss=0.228, pruned_loss=0.03731, over 4958.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2451, pruned_loss=0.04627, over 985581.57 frames.], batch size: 91, aishell_tot_loss[loss=0.168, simple_loss=0.2504, pruned_loss=0.04284, over 984471.44 frames.], datatang_tot_loss[loss=0.1693, simple_loss=0.239, pruned_loss=0.04979, over 986237.99 frames.], batch size: 91, lr: 6.61e-04
+2022-06-18 19:06:10,954 INFO [train.py:874] (1/4) Epoch 12, batch 3750, aishell_loss[loss=0.1582, simple_loss=0.2316, pruned_loss=0.04235, over 4930.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2447, pruned_loss=0.04557, over 985460.02 frames.], batch size: 49, aishell_tot_loss[loss=0.1674, simple_loss=0.2498, pruned_loss=0.0425, over 984770.93 frames.], datatang_tot_loss[loss=0.1691, simple_loss=0.2387, pruned_loss=0.04974, over 985929.40 frames.], batch size: 49, lr: 6.60e-04
+2022-06-18 19:06:39,857 INFO [train.py:874] (1/4) Epoch 12, batch 3800, datatang_loss[loss=0.159, simple_loss=0.2396, pruned_loss=0.0392, over 4922.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2443, pruned_loss=0.04535, over 986007.66 frames.], batch size: 77, aishell_tot_loss[loss=0.1673, simple_loss=0.2496, pruned_loss=0.04246, over 985097.53 frames.], datatang_tot_loss[loss=0.1687, simple_loss=0.2386, pruned_loss=0.04944, over 986206.66 frames.], batch size: 77, lr: 6.60e-04
+2022-06-18 19:07:09,913 INFO [train.py:874] (1/4) Epoch 12, batch 3850, aishell_loss[loss=0.1793, simple_loss=0.2579, pruned_loss=0.05042, over 4950.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2438, pruned_loss=0.04499, over 985690.10 frames.], batch size: 40, aishell_tot_loss[loss=0.1669, simple_loss=0.2494, pruned_loss=0.04217, over 985099.58 frames.], datatang_tot_loss[loss=0.1684, simple_loss=0.2383, pruned_loss=0.04919, over 985962.52 frames.], batch size: 40, lr: 6.60e-04
+2022-06-18 19:07:38,511 INFO [train.py:874] (1/4) Epoch 12, batch 3900, aishell_loss[loss=0.1634, simple_loss=0.2462, pruned_loss=0.04027, over 4974.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2432, pruned_loss=0.04476, over 985998.15 frames.], batch size: 64, aishell_tot_loss[loss=0.1666, simple_loss=0.2492, pruned_loss=0.042, over 985104.79 frames.], datatang_tot_loss[loss=0.1678, simple_loss=0.2382, pruned_loss=0.04872, over 986296.15 frames.], batch size: 64, lr: 6.59e-04
+2022-06-18 19:08:09,097 INFO [train.py:874] (1/4) Epoch 12, batch 3950, aishell_loss[loss=0.165, simple_loss=0.2496, pruned_loss=0.04022, over 4934.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2431, pruned_loss=0.04487, over 985771.65 frames.], batch size: 49, aishell_tot_loss[loss=0.167, simple_loss=0.2496, pruned_loss=0.04223, over 985127.43 frames.], datatang_tot_loss[loss=0.1672, simple_loss=0.2379, pruned_loss=0.0483, over 986069.31 frames.], batch size: 49, lr: 6.59e-04
+2022-06-18 19:08:37,852 INFO [train.py:874] (1/4) Epoch 12, batch 4000, datatang_loss[loss=0.1457, simple_loss=0.2255, pruned_loss=0.03296, over 4958.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2436, pruned_loss=0.04502, over 985863.24 frames.], batch size: 86, aishell_tot_loss[loss=0.1674, simple_loss=0.25, pruned_loss=0.04237, over 985153.41 frames.], datatang_tot_loss[loss=0.1672, simple_loss=0.2378, pruned_loss=0.04827, over 986184.25 frames.], batch size: 86, lr: 6.59e-04
+2022-06-18 19:08:37,853 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 19:08:54,416 INFO [train.py:914] (1/4) Epoch 12, validation: loss=0.1659, simple_loss=0.2501, pruned_loss=0.0409, over 1622729.00 frames.
+2022-06-18 19:09:24,449 INFO [train.py:874] (1/4) Epoch 12, batch 4050, datatang_loss[loss=0.1441, simple_loss=0.2208, pruned_loss=0.03374, over 4891.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2444, pruned_loss=0.04572, over 985778.46 frames.], batch size: 52, aishell_tot_loss[loss=0.1673, simple_loss=0.2498, pruned_loss=0.04243, over 984994.86 frames.], datatang_tot_loss[loss=0.1682, simple_loss=0.2389, pruned_loss=0.04877, over 986275.05 frames.], batch size: 52, lr: 6.58e-04
+2022-06-18 19:09:52,462 INFO [train.py:874] (1/4) Epoch 12, batch 4100, datatang_loss[loss=0.1512, simple_loss=0.1947, pruned_loss=0.05385, over 4919.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2438, pruned_loss=0.04551, over 985002.05 frames.], batch size: 42, aishell_tot_loss[loss=0.1676, simple_loss=0.2498, pruned_loss=0.04276, over 984353.64 frames.], datatang_tot_loss[loss=0.1675, simple_loss=0.2382, pruned_loss=0.04834, over 986163.40 frames.], batch size: 42, lr: 6.58e-04
+2022-06-18 19:11:11,422 INFO [train.py:874] (1/4) Epoch 13, batch 50, datatang_loss[loss=0.1426, simple_loss=0.2184, pruned_loss=0.03339, over 4981.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2354, pruned_loss=0.0434, over 218819.87 frames.], batch size: 31, aishell_tot_loss[loss=0.1664, simple_loss=0.2471, pruned_loss=0.0428, over 107447.21 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2259, pruned_loss=0.04413, over 124980.92 frames.], batch size: 31, lr: 6.36e-04
+2022-06-18 19:11:41,573 INFO [train.py:874] (1/4) Epoch 13, batch 100, aishell_loss[loss=0.1573, simple_loss=0.2455, pruned_loss=0.03458, over 4948.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2371, pruned_loss=0.04225, over 388719.68 frames.], batch size: 56, aishell_tot_loss[loss=0.1666, simple_loss=0.2482, pruned_loss=0.04246, over 229870.87 frames.], datatang_tot_loss[loss=0.1543, simple_loss=0.2242, pruned_loss=0.04221, over 207157.83 frames.], batch size: 56, lr: 6.35e-04
+2022-06-18 19:12:12,256 INFO [train.py:874] (1/4) Epoch 13, batch 150, datatang_loss[loss=0.1465, simple_loss=0.2159, pruned_loss=0.03856, over 4925.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2368, pruned_loss=0.04192, over 521392.09 frames.], batch size: 73, aishell_tot_loss[loss=0.1665, simple_loss=0.2482, pruned_loss=0.04244, over 309109.34 frames.], datatang_tot_loss[loss=0.1545, simple_loss=0.2256, pruned_loss=0.04169, over 309143.98 frames.], batch size: 73, lr: 6.35e-04
+2022-06-18 19:12:44,585 INFO [train.py:874] (1/4) Epoch 13, batch 200, datatang_loss[loss=0.1858, simple_loss=0.2464, pruned_loss=0.06256, over 4982.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2388, pruned_loss=0.04264, over 624430.91 frames.], batch size: 40, aishell_tot_loss[loss=0.1679, simple_loss=0.25, pruned_loss=0.04293, over 379899.71 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2281, pruned_loss=0.04236, over 397708.82 frames.], batch size: 40, lr: 6.35e-04
+2022-06-18 19:13:14,988 INFO [train.py:874] (1/4) Epoch 13, batch 250, datatang_loss[loss=0.1397, simple_loss=0.2134, pruned_loss=0.03301, over 4917.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2398, pruned_loss=0.04343, over 704517.34 frames.], batch size: 83, aishell_tot_loss[loss=0.1688, simple_loss=0.2507, pruned_loss=0.04344, over 445906.72 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2294, pruned_loss=0.04321, over 472098.42 frames.], batch size: 83, lr: 6.34e-04
+2022-06-18 19:13:46,325 INFO [train.py:874] (1/4) Epoch 13, batch 300, datatang_loss[loss=0.1921, simple_loss=0.2668, pruned_loss=0.05873, over 4927.00 frames.], tot_loss[loss=0.1649, simple_loss=0.241, pruned_loss=0.04443, over 766489.57 frames.], batch size: 94, aishell_tot_loss[loss=0.1675, simple_loss=0.2491, pruned_loss=0.04298, over 499528.49 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2332, pruned_loss=0.04512, over 541508.71 frames.], batch size: 94, lr: 6.34e-04
+2022-06-18 19:14:18,045 INFO [train.py:874] (1/4) Epoch 13, batch 350, aishell_loss[loss=0.1525, simple_loss=0.2417, pruned_loss=0.03164, over 4970.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2405, pruned_loss=0.04423, over 815062.30 frames.], batch size: 51, aishell_tot_loss[loss=0.1669, simple_loss=0.2487, pruned_loss=0.04257, over 550157.54 frames.], datatang_tot_loss[loss=0.1619, simple_loss=0.2332, pruned_loss=0.04529, over 599783.69 frames.], batch size: 51, lr: 6.34e-04
+2022-06-18 19:14:49,254 INFO [train.py:874] (1/4) Epoch 13, batch 400, aishell_loss[loss=0.1671, simple_loss=0.2443, pruned_loss=0.04497, over 4881.00 frames.], tot_loss[loss=0.1648, simple_loss=0.241, pruned_loss=0.04435, over 852762.07 frames.], batch size: 42, aishell_tot_loss[loss=0.1675, simple_loss=0.2495, pruned_loss=0.04271, over 599427.96 frames.], datatang_tot_loss[loss=0.1619, simple_loss=0.233, pruned_loss=0.04536, over 646887.30 frames.], batch size: 42, lr: 6.33e-04
+2022-06-18 19:15:19,250 INFO [train.py:874] (1/4) Epoch 13, batch 450, aishell_loss[loss=0.1483, simple_loss=0.2308, pruned_loss=0.0329, over 4941.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2409, pruned_loss=0.04409, over 882291.05 frames.], batch size: 45, aishell_tot_loss[loss=0.1677, simple_loss=0.2499, pruned_loss=0.04273, over 636254.36 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.233, pruned_loss=0.04508, over 694270.11 frames.], batch size: 45, lr: 6.33e-04
+2022-06-18 19:15:51,458 INFO [train.py:874] (1/4) Epoch 13, batch 500, aishell_loss[loss=0.1748, simple_loss=0.2531, pruned_loss=0.04828, over 4862.00 frames.], tot_loss[loss=0.165, simple_loss=0.2413, pruned_loss=0.04434, over 904893.54 frames.], batch size: 37, aishell_tot_loss[loss=0.1665, simple_loss=0.2486, pruned_loss=0.04216, over 680130.82 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2343, pruned_loss=0.04599, over 726015.47 frames.], batch size: 37, lr: 6.33e-04
+2022-06-18 19:16:22,054 INFO [train.py:874] (1/4) Epoch 13, batch 550, datatang_loss[loss=0.1527, simple_loss=0.2325, pruned_loss=0.03647, over 4929.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2427, pruned_loss=0.04474, over 922735.73 frames.], batch size: 94, aishell_tot_loss[loss=0.1679, simple_loss=0.2499, pruned_loss=0.04291, over 717495.66 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2348, pruned_loss=0.0459, over 755341.42 frames.], batch size: 94, lr: 6.32e-04
+2022-06-18 19:16:51,638 INFO [train.py:874] (1/4) Epoch 13, batch 600, datatang_loss[loss=0.158, simple_loss=0.2301, pruned_loss=0.04301, over 4918.00 frames.], tot_loss[loss=0.166, simple_loss=0.2427, pruned_loss=0.04465, over 937090.72 frames.], batch size: 75, aishell_tot_loss[loss=0.1681, simple_loss=0.2501, pruned_loss=0.0431, over 750564.29 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2347, pruned_loss=0.04573, over 781583.34 frames.], batch size: 75, lr: 6.32e-04
+2022-06-18 19:17:23,529 INFO [train.py:874] (1/4) Epoch 13, batch 650, aishell_loss[loss=0.1343, simple_loss=0.223, pruned_loss=0.0228, over 4906.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2425, pruned_loss=0.04388, over 947999.57 frames.], batch size: 34, aishell_tot_loss[loss=0.1674, simple_loss=0.2496, pruned_loss=0.04259, over 782590.24 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2346, pruned_loss=0.0454, over 801908.90 frames.], batch size: 34, lr: 6.32e-04
+2022-06-18 19:17:54,565 INFO [train.py:874] (1/4) Epoch 13, batch 700, aishell_loss[loss=0.1631, simple_loss=0.248, pruned_loss=0.03909, over 4947.00 frames.], tot_loss[loss=0.1659, simple_loss=0.243, pruned_loss=0.04441, over 956238.30 frames.], batch size: 31, aishell_tot_loss[loss=0.167, simple_loss=0.2493, pruned_loss=0.04232, over 808178.80 frames.], datatang_tot_loss[loss=0.1641, simple_loss=0.2355, pruned_loss=0.04635, over 821911.07 frames.], batch size: 31, lr: 6.31e-04
+2022-06-18 19:18:24,369 INFO [train.py:874] (1/4) Epoch 13, batch 750, aishell_loss[loss=0.161, simple_loss=0.2513, pruned_loss=0.0353, over 4951.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2425, pruned_loss=0.04419, over 962657.67 frames.], batch size: 56, aishell_tot_loss[loss=0.1667, simple_loss=0.2491, pruned_loss=0.04221, over 829608.16 frames.], datatang_tot_loss[loss=0.1639, simple_loss=0.2353, pruned_loss=0.04623, over 840621.75 frames.], batch size: 56, lr: 6.31e-04
+2022-06-18 19:18:56,615 INFO [train.py:874] (1/4) Epoch 13, batch 800, aishell_loss[loss=0.1469, simple_loss=0.2336, pruned_loss=0.03012, over 4917.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2426, pruned_loss=0.04426, over 967773.87 frames.], batch size: 46, aishell_tot_loss[loss=0.1669, simple_loss=0.2492, pruned_loss=0.04228, over 848631.08 frames.], datatang_tot_loss[loss=0.1639, simple_loss=0.2352, pruned_loss=0.04624, over 857114.57 frames.], batch size: 46, lr: 6.31e-04
+2022-06-18 19:19:27,014 INFO [train.py:874] (1/4) Epoch 13, batch 850, aishell_loss[loss=0.1483, simple_loss=0.238, pruned_loss=0.02931, over 4966.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2432, pruned_loss=0.04494, over 971978.56 frames.], batch size: 44, aishell_tot_loss[loss=0.1674, simple_loss=0.2498, pruned_loss=0.04253, over 862897.16 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.2358, pruned_loss=0.04675, over 874231.07 frames.], batch size: 44, lr: 6.31e-04
+2022-06-18 19:19:57,541 INFO [train.py:874] (1/4) Epoch 13, batch 900, aishell_loss[loss=0.1629, simple_loss=0.2492, pruned_loss=0.03835, over 4940.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2438, pruned_loss=0.04498, over 974914.11 frames.], batch size: 54, aishell_tot_loss[loss=0.1671, simple_loss=0.2495, pruned_loss=0.04239, over 877788.85 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2368, pruned_loss=0.04703, over 886861.76 frames.], batch size: 54, lr: 6.30e-04
+2022-06-18 19:20:28,920 INFO [train.py:874] (1/4) Epoch 13, batch 950, aishell_loss[loss=0.1885, simple_loss=0.2701, pruned_loss=0.05347, over 4923.00 frames.], tot_loss[loss=0.1666, simple_loss=0.244, pruned_loss=0.04462, over 976915.73 frames.], batch size: 52, aishell_tot_loss[loss=0.1667, simple_loss=0.2493, pruned_loss=0.04212, over 893339.39 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2369, pruned_loss=0.04711, over 895417.62 frames.], batch size: 52, lr: 6.30e-04
+2022-06-18 19:20:58,039 INFO [train.py:874] (1/4) Epoch 13, batch 1000, aishell_loss[loss=0.1039, simple_loss=0.1773, pruned_loss=0.01531, over 4949.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2434, pruned_loss=0.04435, over 978525.41 frames.], batch size: 21, aishell_tot_loss[loss=0.166, simple_loss=0.2486, pruned_loss=0.04167, over 904227.25 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2371, pruned_loss=0.04728, over 905681.44 frames.], batch size: 21, lr: 6.30e-04
+2022-06-18 19:20:58,040 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 19:21:15,124 INFO [train.py:914] (1/4) Epoch 13, validation: loss=0.1646, simple_loss=0.2486, pruned_loss=0.04032, over 1622729.00 frames.
+2022-06-18 19:21:45,794 INFO [train.py:874] (1/4) Epoch 13, batch 1050, datatang_loss[loss=0.2342, simple_loss=0.2949, pruned_loss=0.08673, over 4914.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2436, pruned_loss=0.04465, over 979948.16 frames.], batch size: 108, aishell_tot_loss[loss=0.1654, simple_loss=0.248, pruned_loss=0.04139, over 914553.40 frames.], datatang_tot_loss[loss=0.1669, simple_loss=0.2379, pruned_loss=0.04793, over 914191.10 frames.], batch size: 108, lr: 6.29e-04
+2022-06-18 19:22:16,623 INFO [train.py:874] (1/4) Epoch 13, batch 1100, datatang_loss[loss=0.1295, simple_loss=0.2126, pruned_loss=0.0232, over 4971.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2433, pruned_loss=0.0446, over 981331.51 frames.], batch size: 25, aishell_tot_loss[loss=0.1656, simple_loss=0.2481, pruned_loss=0.04152, over 922303.92 frames.], datatang_tot_loss[loss=0.1666, simple_loss=0.2378, pruned_loss=0.0477, over 923337.72 frames.], batch size: 25, lr: 6.29e-04
+2022-06-18 19:22:46,972 INFO [train.py:874] (1/4) Epoch 13, batch 1150, aishell_loss[loss=0.1695, simple_loss=0.2552, pruned_loss=0.0419, over 4975.00 frames.], tot_loss[loss=0.1659, simple_loss=0.243, pruned_loss=0.04436, over 982645.45 frames.], batch size: 48, aishell_tot_loss[loss=0.1655, simple_loss=0.2481, pruned_loss=0.04141, over 930696.43 frames.], datatang_tot_loss[loss=0.1664, simple_loss=0.2375, pruned_loss=0.04763, over 930115.90 frames.], batch size: 48, lr: 6.29e-04
+2022-06-18 19:23:17,707 INFO [train.py:874] (1/4) Epoch 13, batch 1200, datatang_loss[loss=0.1951, simple_loss=0.2643, pruned_loss=0.06293, over 4957.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2435, pruned_loss=0.04468, over 983451.00 frames.], batch size: 99, aishell_tot_loss[loss=0.1661, simple_loss=0.2486, pruned_loss=0.04174, over 936703.85 frames.], datatang_tot_loss[loss=0.1663, simple_loss=0.2376, pruned_loss=0.04752, over 937262.78 frames.], batch size: 99, lr: 6.28e-04
+2022-06-18 19:23:48,004 INFO [train.py:874] (1/4) Epoch 13, batch 1250, datatang_loss[loss=0.22, simple_loss=0.284, pruned_loss=0.07796, over 4932.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2445, pruned_loss=0.04526, over 983957.60 frames.], batch size: 94, aishell_tot_loss[loss=0.1664, simple_loss=0.2491, pruned_loss=0.04185, over 942668.38 frames.], datatang_tot_loss[loss=0.1673, simple_loss=0.2383, pruned_loss=0.04813, over 942782.88 frames.], batch size: 94, lr: 6.28e-04
+2022-06-18 19:24:19,116 INFO [train.py:874] (1/4) Epoch 13, batch 1300, datatang_loss[loss=0.1578, simple_loss=0.2171, pruned_loss=0.04928, over 4904.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2445, pruned_loss=0.04549, over 984466.63 frames.], batch size: 64, aishell_tot_loss[loss=0.1663, simple_loss=0.2491, pruned_loss=0.04181, over 946765.42 frames.], datatang_tot_loss[loss=0.1677, simple_loss=0.2388, pruned_loss=0.04831, over 948915.91 frames.], batch size: 64, lr: 6.28e-04
+2022-06-18 19:24:49,546 INFO [train.py:874] (1/4) Epoch 13, batch 1350, aishell_loss[loss=0.1559, simple_loss=0.2446, pruned_loss=0.03359, over 4918.00 frames.], tot_loss[loss=0.166, simple_loss=0.2429, pruned_loss=0.04454, over 984525.81 frames.], batch size: 41, aishell_tot_loss[loss=0.1662, simple_loss=0.2492, pruned_loss=0.04161, over 950528.95 frames.], datatang_tot_loss[loss=0.1661, simple_loss=0.2373, pruned_loss=0.04748, over 953789.11 frames.], batch size: 41, lr: 6.27e-04
+2022-06-18 19:25:19,791 INFO [train.py:874] (1/4) Epoch 13, batch 1400, datatang_loss[loss=0.1793, simple_loss=0.2382, pruned_loss=0.06017, over 4964.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2419, pruned_loss=0.0443, over 984381.78 frames.], batch size: 37, aishell_tot_loss[loss=0.166, simple_loss=0.249, pruned_loss=0.04149, over 953595.75 frames.], datatang_tot_loss[loss=0.1655, simple_loss=0.2366, pruned_loss=0.04719, over 958103.22 frames.], batch size: 37, lr: 6.27e-04
+2022-06-18 19:25:50,253 INFO [train.py:874] (1/4) Epoch 13, batch 1450, aishell_loss[loss=0.1483, simple_loss=0.2294, pruned_loss=0.03357, over 4978.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2412, pruned_loss=0.04393, over 984832.88 frames.], batch size: 30, aishell_tot_loss[loss=0.1658, simple_loss=0.2488, pruned_loss=0.04144, over 957530.38 frames.], datatang_tot_loss[loss=0.1648, simple_loss=0.2359, pruned_loss=0.04682, over 961344.73 frames.], batch size: 30, lr: 6.27e-04
+2022-06-18 19:26:20,098 INFO [train.py:874] (1/4) Epoch 13, batch 1500, datatang_loss[loss=0.1584, simple_loss=0.235, pruned_loss=0.04092, over 4923.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2416, pruned_loss=0.04453, over 984856.50 frames.], batch size: 83, aishell_tot_loss[loss=0.1657, simple_loss=0.2484, pruned_loss=0.04156, over 960504.89 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2365, pruned_loss=0.04733, over 964379.15 frames.], batch size: 83, lr: 6.27e-04
+2022-06-18 19:26:50,907 INFO [train.py:874] (1/4) Epoch 13, batch 1550, aishell_loss[loss=0.1768, simple_loss=0.259, pruned_loss=0.0473, over 4919.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2418, pruned_loss=0.04433, over 985137.96 frames.], batch size: 46, aishell_tot_loss[loss=0.1653, simple_loss=0.248, pruned_loss=0.04126, over 963841.40 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2367, pruned_loss=0.0475, over 966661.01 frames.], batch size: 46, lr: 6.26e-04
+2022-06-18 19:27:20,340 INFO [train.py:874] (1/4) Epoch 13, batch 1600, datatang_loss[loss=0.1515, simple_loss=0.2202, pruned_loss=0.04138, over 4920.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2417, pruned_loss=0.04404, over 985241.93 frames.], batch size: 75, aishell_tot_loss[loss=0.1651, simple_loss=0.2481, pruned_loss=0.04101, over 966522.65 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2364, pruned_loss=0.04742, over 968776.19 frames.], batch size: 75, lr: 6.26e-04
+2022-06-18 19:27:49,905 INFO [train.py:874] (1/4) Epoch 13, batch 1650, datatang_loss[loss=0.1465, simple_loss=0.2252, pruned_loss=0.03393, over 4926.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2421, pruned_loss=0.04422, over 985503.10 frames.], batch size: 79, aishell_tot_loss[loss=0.1657, simple_loss=0.2486, pruned_loss=0.04138, over 968987.86 frames.], datatang_tot_loss[loss=0.1652, simple_loss=0.2361, pruned_loss=0.0472, over 970727.99 frames.], batch size: 79, lr: 6.26e-04
+2022-06-18 19:28:22,047 INFO [train.py:874] (1/4) Epoch 13, batch 1700, aishell_loss[loss=0.1522, simple_loss=0.2485, pruned_loss=0.028, over 4953.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2432, pruned_loss=0.04464, over 985651.04 frames.], batch size: 58, aishell_tot_loss[loss=0.1663, simple_loss=0.2495, pruned_loss=0.0416, over 970616.36 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2366, pruned_loss=0.04729, over 972894.51 frames.], batch size: 58, lr: 6.25e-04
+2022-06-18 19:28:51,113 INFO [train.py:874] (1/4) Epoch 13, batch 1750, datatang_loss[loss=0.14, simple_loss=0.2094, pruned_loss=0.03537, over 4969.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2425, pruned_loss=0.04439, over 985184.48 frames.], batch size: 34, aishell_tot_loss[loss=0.1655, simple_loss=0.2485, pruned_loss=0.04129, over 972069.57 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2366, pruned_loss=0.04747, over 974263.55 frames.], batch size: 34, lr: 6.25e-04
+2022-06-18 19:29:20,516 INFO [train.py:874] (1/4) Epoch 13, batch 1800, aishell_loss[loss=0.1469, simple_loss=0.2318, pruned_loss=0.03102, over 4943.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2434, pruned_loss=0.04463, over 985124.88 frames.], batch size: 54, aishell_tot_loss[loss=0.1662, simple_loss=0.2491, pruned_loss=0.0416, over 973597.39 frames.], datatang_tot_loss[loss=0.1659, simple_loss=0.2368, pruned_loss=0.04751, over 975535.09 frames.], batch size: 54, lr: 6.25e-04
+2022-06-18 19:29:52,015 INFO [train.py:874] (1/4) Epoch 13, batch 1850, datatang_loss[loss=0.1639, simple_loss=0.2296, pruned_loss=0.04911, over 4949.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2431, pruned_loss=0.04489, over 985339.73 frames.], batch size: 50, aishell_tot_loss[loss=0.167, simple_loss=0.2498, pruned_loss=0.04208, over 974803.26 frames.], datatang_tot_loss[loss=0.1653, simple_loss=0.236, pruned_loss=0.0473, over 977027.27 frames.], batch size: 50, lr: 6.24e-04
+2022-06-18 19:30:21,957 INFO [train.py:874] (1/4) Epoch 13, batch 1900, datatang_loss[loss=0.1702, simple_loss=0.2392, pruned_loss=0.05064, over 4922.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2414, pruned_loss=0.04397, over 985073.03 frames.], batch size: 83, aishell_tot_loss[loss=0.1659, simple_loss=0.2487, pruned_loss=0.0415, over 975450.93 frames.], datatang_tot_loss[loss=0.1645, simple_loss=0.2354, pruned_loss=0.04682, over 978268.96 frames.], batch size: 83, lr: 6.24e-04
+2022-06-18 19:30:51,629 INFO [train.py:874] (1/4) Epoch 13, batch 1950, datatang_loss[loss=0.1681, simple_loss=0.2403, pruned_loss=0.04794, over 4904.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2412, pruned_loss=0.04382, over 985170.04 frames.], batch size: 85, aishell_tot_loss[loss=0.1654, simple_loss=0.2484, pruned_loss=0.04121, over 976194.79 frames.], datatang_tot_loss[loss=0.1647, simple_loss=0.2357, pruned_loss=0.04679, over 979497.59 frames.], batch size: 85, lr: 6.24e-04
+2022-06-18 19:31:23,220 INFO [train.py:874] (1/4) Epoch 13, batch 2000, aishell_loss[loss=0.1615, simple_loss=0.2433, pruned_loss=0.03986, over 4941.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2421, pruned_loss=0.04406, over 985371.13 frames.], batch size: 58, aishell_tot_loss[loss=0.1658, simple_loss=0.2486, pruned_loss=0.04154, over 977434.23 frames.], datatang_tot_loss[loss=0.1648, simple_loss=0.2359, pruned_loss=0.0468, over 980271.18 frames.], batch size: 58, lr: 6.24e-04
+2022-06-18 19:31:23,221 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 19:31:40,587 INFO [train.py:914] (1/4) Epoch 13, validation: loss=0.1662, simple_loss=0.2512, pruned_loss=0.04057, over 1622729.00 frames.
+2022-06-18 19:32:10,590 INFO [train.py:874] (1/4) Epoch 13, batch 2050, aishell_loss[loss=0.1818, simple_loss=0.2746, pruned_loss=0.04455, over 4947.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2437, pruned_loss=0.04483, over 985432.29 frames.], batch size: 64, aishell_tot_loss[loss=0.1667, simple_loss=0.2493, pruned_loss=0.04201, over 978463.20 frames.], datatang_tot_loss[loss=0.1655, simple_loss=0.2367, pruned_loss=0.04718, over 980861.47 frames.], batch size: 64, lr: 6.23e-04
+2022-06-18 19:32:41,260 INFO [train.py:874] (1/4) Epoch 13, batch 2100, aishell_loss[loss=0.1814, simple_loss=0.2519, pruned_loss=0.05549, over 4872.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2425, pruned_loss=0.04486, over 984752.92 frames.], batch size: 35, aishell_tot_loss[loss=0.1664, simple_loss=0.2488, pruned_loss=0.04201, over 978738.64 frames.], datatang_tot_loss[loss=0.1653, simple_loss=0.2363, pruned_loss=0.04719, over 981211.78 frames.], batch size: 35, lr: 6.23e-04
+2022-06-18 19:33:09,893 INFO [train.py:874] (1/4) Epoch 13, batch 2150, datatang_loss[loss=0.1473, simple_loss=0.2211, pruned_loss=0.03676, over 4925.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2419, pruned_loss=0.04434, over 984987.73 frames.], batch size: 71, aishell_tot_loss[loss=0.166, simple_loss=0.2486, pruned_loss=0.04174, over 979391.67 frames.], datatang_tot_loss[loss=0.1649, simple_loss=0.2358, pruned_loss=0.04695, over 981928.81 frames.], batch size: 71, lr: 6.23e-04
+2022-06-18 19:33:41,241 INFO [train.py:874] (1/4) Epoch 13, batch 2200, aishell_loss[loss=0.1937, simple_loss=0.2859, pruned_loss=0.05072, over 4938.00 frames.], tot_loss[loss=0.1653, simple_loss=0.242, pruned_loss=0.04431, over 985386.74 frames.], batch size: 68, aishell_tot_loss[loss=0.1654, simple_loss=0.248, pruned_loss=0.04141, over 980120.97 frames.], datatang_tot_loss[loss=0.1655, simple_loss=0.2365, pruned_loss=0.04726, over 982643.07 frames.], batch size: 68, lr: 6.22e-04
+2022-06-18 19:34:10,781 INFO [train.py:874] (1/4) Epoch 13, batch 2250, aishell_loss[loss=0.1652, simple_loss=0.2518, pruned_loss=0.03931, over 4985.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2424, pruned_loss=0.04447, over 985939.63 frames.], batch size: 39, aishell_tot_loss[loss=0.1652, simple_loss=0.2479, pruned_loss=0.04122, over 981156.67 frames.], datatang_tot_loss[loss=0.166, simple_loss=0.2368, pruned_loss=0.04763, over 983163.44 frames.], batch size: 39, lr: 6.22e-04
+2022-06-18 19:34:40,238 INFO [train.py:874] (1/4) Epoch 13, batch 2300, datatang_loss[loss=0.159, simple_loss=0.2258, pruned_loss=0.04603, over 4934.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2428, pruned_loss=0.0443, over 985751.30 frames.], batch size: 79, aishell_tot_loss[loss=0.1655, simple_loss=0.2483, pruned_loss=0.04133, over 981440.22 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2371, pruned_loss=0.0473, over 983557.18 frames.], batch size: 79, lr: 6.22e-04
+2022-06-18 19:35:11,408 INFO [train.py:874] (1/4) Epoch 13, batch 2350, aishell_loss[loss=0.1678, simple_loss=0.2546, pruned_loss=0.04049, over 4955.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2437, pruned_loss=0.04428, over 985832.24 frames.], batch size: 58, aishell_tot_loss[loss=0.1658, simple_loss=0.2488, pruned_loss=0.04141, over 981965.21 frames.], datatang_tot_loss[loss=0.166, simple_loss=0.2373, pruned_loss=0.04732, over 983924.74 frames.], batch size: 58, lr: 6.21e-04
+2022-06-18 19:35:41,094 INFO [train.py:874] (1/4) Epoch 13, batch 2400, datatang_loss[loss=0.1727, simple_loss=0.2425, pruned_loss=0.05148, over 4865.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2442, pruned_loss=0.04495, over 985369.78 frames.], batch size: 39, aishell_tot_loss[loss=0.1656, simple_loss=0.2485, pruned_loss=0.04135, over 981998.51 frames.], datatang_tot_loss[loss=0.1671, simple_loss=0.2383, pruned_loss=0.04797, over 984047.56 frames.], batch size: 39, lr: 6.21e-04
+2022-06-18 19:36:10,583 INFO [train.py:874] (1/4) Epoch 13, batch 2450, datatang_loss[loss=0.1555, simple_loss=0.2264, pruned_loss=0.0423, over 4906.00 frames.], tot_loss[loss=0.167, simple_loss=0.2447, pruned_loss=0.04466, over 985428.14 frames.], batch size: 52, aishell_tot_loss[loss=0.1657, simple_loss=0.2491, pruned_loss=0.04121, over 982461.41 frames.], datatang_tot_loss[loss=0.1671, simple_loss=0.2384, pruned_loss=0.04792, over 984205.46 frames.], batch size: 52, lr: 6.21e-04
+2022-06-18 19:36:46,669 INFO [train.py:874] (1/4) Epoch 13, batch 2500, datatang_loss[loss=0.1571, simple_loss=0.2263, pruned_loss=0.04395, over 4919.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2445, pruned_loss=0.04428, over 985015.76 frames.], batch size: 75, aishell_tot_loss[loss=0.1654, simple_loss=0.2488, pruned_loss=0.04099, over 982636.57 frames.], datatang_tot_loss[loss=0.1671, simple_loss=0.2386, pruned_loss=0.04784, over 984118.14 frames.], batch size: 75, lr: 6.21e-04
+2022-06-18 19:37:16,500 INFO [train.py:874] (1/4) Epoch 13, batch 2550, datatang_loss[loss=0.175, simple_loss=0.2442, pruned_loss=0.05292, over 4946.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2428, pruned_loss=0.04334, over 985003.93 frames.], batch size: 69, aishell_tot_loss[loss=0.1649, simple_loss=0.2485, pruned_loss=0.04065, over 982858.67 frames.], datatang_tot_loss[loss=0.1658, simple_loss=0.2373, pruned_loss=0.04716, over 984269.70 frames.], batch size: 69, lr: 6.20e-04
+2022-06-18 19:37:46,579 INFO [train.py:874] (1/4) Epoch 13, batch 2600, datatang_loss[loss=0.1442, simple_loss=0.2145, pruned_loss=0.03693, over 4974.00 frames.], tot_loss[loss=0.166, simple_loss=0.2441, pruned_loss=0.04393, over 985280.73 frames.], batch size: 34, aishell_tot_loss[loss=0.1653, simple_loss=0.2491, pruned_loss=0.04074, over 983193.18 frames.], datatang_tot_loss[loss=0.1666, simple_loss=0.238, pruned_loss=0.04756, over 984562.95 frames.], batch size: 34, lr: 6.20e-04
+2022-06-18 19:38:17,672 INFO [train.py:874] (1/4) Epoch 13, batch 2650, aishell_loss[loss=0.1351, simple_loss=0.2086, pruned_loss=0.03078, over 4959.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2433, pruned_loss=0.04411, over 985675.40 frames.], batch size: 25, aishell_tot_loss[loss=0.1654, simple_loss=0.2487, pruned_loss=0.04104, over 983606.79 frames.], datatang_tot_loss[loss=0.1662, simple_loss=0.2376, pruned_loss=0.04743, over 984906.38 frames.], batch size: 25, lr: 6.20e-04
+2022-06-18 19:38:46,885 INFO [train.py:874] (1/4) Epoch 13, batch 2700, datatang_loss[loss=0.1756, simple_loss=0.2297, pruned_loss=0.06076, over 4893.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2435, pruned_loss=0.04502, over 985621.47 frames.], batch size: 59, aishell_tot_loss[loss=0.1655, simple_loss=0.2486, pruned_loss=0.04119, over 983678.36 frames.], datatang_tot_loss[loss=0.1672, simple_loss=0.2382, pruned_loss=0.04805, over 985081.47 frames.], batch size: 59, lr: 6.19e-04
+2022-06-18 19:39:16,956 INFO [train.py:874] (1/4) Epoch 13, batch 2750, datatang_loss[loss=0.1841, simple_loss=0.2552, pruned_loss=0.05654, over 4952.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2428, pruned_loss=0.04446, over 985812.33 frames.], batch size: 91, aishell_tot_loss[loss=0.1649, simple_loss=0.2481, pruned_loss=0.04086, over 984043.52 frames.], datatang_tot_loss[loss=0.1668, simple_loss=0.238, pruned_loss=0.04779, over 985204.36 frames.], batch size: 91, lr: 6.19e-04
+2022-06-18 19:39:48,811 INFO [train.py:874] (1/4) Epoch 13, batch 2800, aishell_loss[loss=0.1575, simple_loss=0.2479, pruned_loss=0.03356, over 4954.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2424, pruned_loss=0.044, over 985589.31 frames.], batch size: 64, aishell_tot_loss[loss=0.1647, simple_loss=0.2482, pruned_loss=0.04064, over 984112.28 frames.], datatang_tot_loss[loss=0.1663, simple_loss=0.2375, pruned_loss=0.0475, over 985162.97 frames.], batch size: 64, lr: 6.19e-04
+2022-06-18 19:40:18,683 INFO [train.py:874] (1/4) Epoch 13, batch 2850, aishell_loss[loss=0.1484, simple_loss=0.2341, pruned_loss=0.0314, over 4914.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2423, pruned_loss=0.0438, over 985277.83 frames.], batch size: 33, aishell_tot_loss[loss=0.165, simple_loss=0.2483, pruned_loss=0.04083, over 983915.23 frames.], datatang_tot_loss[loss=0.1657, simple_loss=0.237, pruned_loss=0.04723, over 985287.18 frames.], batch size: 33, lr: 6.18e-04
+2022-06-18 19:40:48,090 INFO [train.py:874] (1/4) Epoch 13, batch 2900, datatang_loss[loss=0.1766, simple_loss=0.2472, pruned_loss=0.05303, over 4950.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2424, pruned_loss=0.04369, over 985522.79 frames.], batch size: 62, aishell_tot_loss[loss=0.165, simple_loss=0.2484, pruned_loss=0.04076, over 984294.38 frames.], datatang_tot_loss[loss=0.1656, simple_loss=0.2368, pruned_loss=0.0472, over 985335.62 frames.], batch size: 62, lr: 6.18e-04
+2022-06-18 19:41:19,759 INFO [train.py:874] (1/4) Epoch 13, batch 2950, aishell_loss[loss=0.1711, simple_loss=0.2564, pruned_loss=0.04292, over 4959.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2426, pruned_loss=0.0438, over 985374.52 frames.], batch size: 64, aishell_tot_loss[loss=0.1652, simple_loss=0.2486, pruned_loss=0.04088, over 984345.53 frames.], datatang_tot_loss[loss=0.1654, simple_loss=0.2368, pruned_loss=0.04705, over 985274.57 frames.], batch size: 64, lr: 6.18e-04
+2022-06-18 19:41:48,996 INFO [train.py:874] (1/4) Epoch 13, batch 3000, aishell_loss[loss=0.2025, simple_loss=0.2736, pruned_loss=0.06565, over 4879.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2417, pruned_loss=0.04379, over 985282.87 frames.], batch size: 47, aishell_tot_loss[loss=0.1644, simple_loss=0.2476, pruned_loss=0.04059, over 984386.73 frames.], datatang_tot_loss[loss=0.1657, simple_loss=0.237, pruned_loss=0.04721, over 985255.06 frames.], batch size: 47, lr: 6.18e-04
+2022-06-18 19:41:48,997 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 19:42:05,184 INFO [train.py:914] (1/4) Epoch 13, validation: loss=0.1653, simple_loss=0.2497, pruned_loss=0.04043, over 1622729.00 frames.
+2022-06-18 19:42:35,291 INFO [train.py:874] (1/4) Epoch 13, batch 3050, datatang_loss[loss=0.1444, simple_loss=0.2245, pruned_loss=0.03211, over 4974.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2406, pruned_loss=0.04309, over 985750.88 frames.], batch size: 60, aishell_tot_loss[loss=0.1636, simple_loss=0.2468, pruned_loss=0.04019, over 984782.77 frames.], datatang_tot_loss[loss=0.1651, simple_loss=0.2365, pruned_loss=0.0468, over 985453.52 frames.], batch size: 60, lr: 6.17e-04
+2022-06-18 19:43:05,341 INFO [train.py:874] (1/4) Epoch 13, batch 3100, datatang_loss[loss=0.1503, simple_loss=0.2192, pruned_loss=0.04071, over 4922.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2407, pruned_loss=0.04343, over 985988.55 frames.], batch size: 71, aishell_tot_loss[loss=0.1642, simple_loss=0.2475, pruned_loss=0.0405, over 985063.47 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.236, pruned_loss=0.04661, over 985571.01 frames.], batch size: 71, lr: 6.17e-04
+2022-06-18 19:43:36,264 INFO [train.py:874] (1/4) Epoch 13, batch 3150, aishell_loss[loss=0.151, simple_loss=0.2352, pruned_loss=0.03333, over 4924.00 frames.], tot_loss[loss=0.164, simple_loss=0.2409, pruned_loss=0.04358, over 986057.44 frames.], batch size: 46, aishell_tot_loss[loss=0.1648, simple_loss=0.2479, pruned_loss=0.04082, over 985299.17 frames.], datatang_tot_loss[loss=0.1642, simple_loss=0.2355, pruned_loss=0.04644, over 985564.51 frames.], batch size: 46, lr: 6.17e-04
+2022-06-18 19:44:06,010 INFO [train.py:874] (1/4) Epoch 13, batch 3200, aishell_loss[loss=0.1652, simple_loss=0.248, pruned_loss=0.0412, over 4945.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2413, pruned_loss=0.04406, over 986141.28 frames.], batch size: 58, aishell_tot_loss[loss=0.1648, simple_loss=0.2481, pruned_loss=0.04074, over 985168.70 frames.], datatang_tot_loss[loss=0.1648, simple_loss=0.2358, pruned_loss=0.04689, over 985925.59 frames.], batch size: 58, lr: 6.16e-04
+2022-06-18 19:44:35,534 INFO [train.py:874] (1/4) Epoch 13, batch 3250, datatang_loss[loss=0.1805, simple_loss=0.2391, pruned_loss=0.06096, over 4956.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2404, pruned_loss=0.04352, over 986191.62 frames.], batch size: 37, aishell_tot_loss[loss=0.1649, simple_loss=0.2481, pruned_loss=0.04083, over 985233.01 frames.], datatang_tot_loss[loss=0.1636, simple_loss=0.2347, pruned_loss=0.04625, over 986043.88 frames.], batch size: 37, lr: 6.16e-04
+2022-06-18 19:45:06,293 INFO [train.py:874] (1/4) Epoch 13, batch 3300, datatang_loss[loss=0.1704, simple_loss=0.2371, pruned_loss=0.05186, over 4960.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2408, pruned_loss=0.04369, over 985711.55 frames.], batch size: 55, aishell_tot_loss[loss=0.1649, simple_loss=0.2479, pruned_loss=0.04095, over 984752.10 frames.], datatang_tot_loss[loss=0.1638, simple_loss=0.2349, pruned_loss=0.04635, over 986172.60 frames.], batch size: 55, lr: 6.16e-04
+2022-06-18 19:45:35,740 INFO [train.py:874] (1/4) Epoch 13, batch 3350, datatang_loss[loss=0.1308, simple_loss=0.2045, pruned_loss=0.0285, over 4820.00 frames.], tot_loss[loss=0.1631, simple_loss=0.24, pruned_loss=0.04313, over 985875.55 frames.], batch size: 24, aishell_tot_loss[loss=0.1645, simple_loss=0.2478, pruned_loss=0.04056, over 984992.12 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2341, pruned_loss=0.04607, over 986139.28 frames.], batch size: 24, lr: 6.16e-04
+2022-06-18 19:46:06,224 INFO [train.py:874] (1/4) Epoch 13, batch 3400, datatang_loss[loss=0.1467, simple_loss=0.2296, pruned_loss=0.03191, over 4945.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2398, pruned_loss=0.04288, over 985169.55 frames.], batch size: 50, aishell_tot_loss[loss=0.1644, simple_loss=0.2477, pruned_loss=0.04049, over 984541.57 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.234, pruned_loss=0.04574, over 985910.23 frames.], batch size: 50, lr: 6.15e-04
+2022-06-18 19:46:38,162 INFO [train.py:874] (1/4) Epoch 13, batch 3450, datatang_loss[loss=0.1702, simple_loss=0.2516, pruned_loss=0.04437, over 4931.00 frames.], tot_loss[loss=0.163, simple_loss=0.2398, pruned_loss=0.04313, over 985139.62 frames.], batch size: 94, aishell_tot_loss[loss=0.1641, simple_loss=0.2474, pruned_loss=0.04041, over 984598.93 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2342, pruned_loss=0.04597, over 985788.63 frames.], batch size: 94, lr: 6.15e-04
+2022-06-18 19:47:08,006 INFO [train.py:874] (1/4) Epoch 13, batch 3500, datatang_loss[loss=0.1475, simple_loss=0.205, pruned_loss=0.04498, over 4917.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2404, pruned_loss=0.04338, over 985107.07 frames.], batch size: 34, aishell_tot_loss[loss=0.1645, simple_loss=0.2476, pruned_loss=0.04069, over 984592.85 frames.], datatang_tot_loss[loss=0.1632, simple_loss=0.2344, pruned_loss=0.04597, over 985776.68 frames.], batch size: 34, lr: 6.15e-04
+2022-06-18 19:47:37,791 INFO [train.py:874] (1/4) Epoch 13, batch 3550, aishell_loss[loss=0.1641, simple_loss=0.2422, pruned_loss=0.04298, over 4953.00 frames.], tot_loss[loss=0.1641, simple_loss=0.241, pruned_loss=0.04358, over 985252.25 frames.], batch size: 31, aishell_tot_loss[loss=0.1648, simple_loss=0.2478, pruned_loss=0.04087, over 984731.62 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2345, pruned_loss=0.04607, over 985787.15 frames.], batch size: 31, lr: 6.14e-04
+2022-06-18 19:48:09,656 INFO [train.py:874] (1/4) Epoch 13, batch 3600, datatang_loss[loss=0.196, simple_loss=0.257, pruned_loss=0.06744, over 4961.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2421, pruned_loss=0.04404, over 985193.51 frames.], batch size: 50, aishell_tot_loss[loss=0.1647, simple_loss=0.2479, pruned_loss=0.04072, over 984750.45 frames.], datatang_tot_loss[loss=0.1645, simple_loss=0.2354, pruned_loss=0.04678, over 985717.94 frames.], batch size: 50, lr: 6.14e-04
+2022-06-18 19:48:39,130 INFO [train.py:874] (1/4) Epoch 13, batch 3650, datatang_loss[loss=0.1529, simple_loss=0.2367, pruned_loss=0.03451, over 4937.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2429, pruned_loss=0.04418, over 985567.60 frames.], batch size: 88, aishell_tot_loss[loss=0.1653, simple_loss=0.2486, pruned_loss=0.04104, over 985049.39 frames.], datatang_tot_loss[loss=0.1645, simple_loss=0.2356, pruned_loss=0.0467, over 985801.32 frames.], batch size: 88, lr: 6.14e-04
+2022-06-18 19:49:09,237 INFO [train.py:874] (1/4) Epoch 13, batch 3700, datatang_loss[loss=0.2121, simple_loss=0.2774, pruned_loss=0.07343, over 4924.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2418, pruned_loss=0.04345, over 985397.47 frames.], batch size: 98, aishell_tot_loss[loss=0.165, simple_loss=0.2482, pruned_loss=0.04091, over 984931.11 frames.], datatang_tot_loss[loss=0.1636, simple_loss=0.2351, pruned_loss=0.0461, over 985768.95 frames.], batch size: 98, lr: 6.14e-04
+2022-06-18 19:49:40,821 INFO [train.py:874] (1/4) Epoch 13, batch 3750, aishell_loss[loss=0.1769, simple_loss=0.2667, pruned_loss=0.04357, over 4949.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2406, pruned_loss=0.04294, over 985044.31 frames.], batch size: 64, aishell_tot_loss[loss=0.1644, simple_loss=0.2475, pruned_loss=0.0406, over 984487.89 frames.], datatang_tot_loss[loss=0.1632, simple_loss=0.2346, pruned_loss=0.04588, over 985872.92 frames.], batch size: 64, lr: 6.13e-04
+2022-06-18 19:50:08,760 INFO [train.py:874] (1/4) Epoch 13, batch 3800, aishell_loss[loss=0.1679, simple_loss=0.2589, pruned_loss=0.03847, over 4969.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2408, pruned_loss=0.04305, over 984985.91 frames.], batch size: 51, aishell_tot_loss[loss=0.1643, simple_loss=0.2474, pruned_loss=0.04065, over 984136.91 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2348, pruned_loss=0.04588, over 986122.57 frames.], batch size: 51, lr: 6.13e-04
+2022-06-18 19:50:39,538 INFO [train.py:874] (1/4) Epoch 13, batch 3850, datatang_loss[loss=0.1312, simple_loss=0.1912, pruned_loss=0.03564, over 4867.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2408, pruned_loss=0.04281, over 984871.00 frames.], batch size: 36, aishell_tot_loss[loss=0.1643, simple_loss=0.2473, pruned_loss=0.04069, over 984001.45 frames.], datatang_tot_loss[loss=0.1629, simple_loss=0.2346, pruned_loss=0.04562, over 986141.04 frames.], batch size: 36, lr: 6.13e-04
+2022-06-18 19:51:08,421 INFO [train.py:874] (1/4) Epoch 13, batch 3900, aishell_loss[loss=0.2149, simple_loss=0.2923, pruned_loss=0.06878, over 4913.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2416, pruned_loss=0.04297, over 985344.59 frames.], batch size: 79, aishell_tot_loss[loss=0.1649, simple_loss=0.248, pruned_loss=0.04097, over 984199.61 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2348, pruned_loss=0.04539, over 986398.76 frames.], batch size: 79, lr: 6.12e-04
+2022-06-18 19:51:38,456 INFO [train.py:874] (1/4) Epoch 13, batch 3950, aishell_loss[loss=0.1734, simple_loss=0.264, pruned_loss=0.04139, over 4859.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2419, pruned_loss=0.04291, over 985415.13 frames.], batch size: 36, aishell_tot_loss[loss=0.1651, simple_loss=0.2481, pruned_loss=0.04107, over 984213.26 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2348, pruned_loss=0.04521, over 986498.55 frames.], batch size: 36, lr: 6.12e-04
+2022-06-18 19:52:05,670 INFO [train.py:874] (1/4) Epoch 13, batch 4000, datatang_loss[loss=0.177, simple_loss=0.2519, pruned_loss=0.05107, over 4958.00 frames.], tot_loss[loss=0.164, simple_loss=0.2416, pruned_loss=0.04323, over 985285.16 frames.], batch size: 108, aishell_tot_loss[loss=0.1644, simple_loss=0.2473, pruned_loss=0.04074, over 984072.41 frames.], datatang_tot_loss[loss=0.1636, simple_loss=0.2355, pruned_loss=0.04588, over 986522.00 frames.], batch size: 108, lr: 6.12e-04
+2022-06-18 19:52:05,671 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 19:52:22,449 INFO [train.py:914] (1/4) Epoch 13, validation: loss=0.1652, simple_loss=0.2502, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-18 19:52:50,039 INFO [train.py:874] (1/4) Epoch 13, batch 4050, datatang_loss[loss=0.1367, simple_loss=0.2119, pruned_loss=0.03075, over 4928.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2419, pruned_loss=0.04365, over 985154.54 frames.], batch size: 34, aishell_tot_loss[loss=0.1648, simple_loss=0.2476, pruned_loss=0.04104, over 983995.45 frames.], datatang_tot_loss[loss=0.1638, simple_loss=0.2355, pruned_loss=0.04602, over 986460.45 frames.], batch size: 34, lr: 6.12e-04
+2022-06-18 19:53:20,287 INFO [train.py:874] (1/4) Epoch 13, batch 4100, datatang_loss[loss=0.1651, simple_loss=0.2348, pruned_loss=0.04772, over 4927.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2417, pruned_loss=0.04351, over 985371.34 frames.], batch size: 73, aishell_tot_loss[loss=0.1648, simple_loss=0.2474, pruned_loss=0.04104, over 984297.19 frames.], datatang_tot_loss[loss=0.1637, simple_loss=0.2354, pruned_loss=0.046, over 986425.20 frames.], batch size: 73, lr: 6.11e-04
+2022-06-18 19:53:48,794 INFO [train.py:874] (1/4) Epoch 13, batch 4150, aishell_loss[loss=0.1634, simple_loss=0.2405, pruned_loss=0.04318, over 4858.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2412, pruned_loss=0.04359, over 985301.46 frames.], batch size: 35, aishell_tot_loss[loss=0.1654, simple_loss=0.248, pruned_loss=0.04137, over 984170.20 frames.], datatang_tot_loss[loss=0.1629, simple_loss=0.2346, pruned_loss=0.04566, over 986450.23 frames.], batch size: 35, lr: 6.11e-04
+2022-06-18 19:55:12,922 INFO [train.py:874] (1/4) Epoch 14, batch 50, aishell_loss[loss=0.1564, simple_loss=0.2348, pruned_loss=0.03903, over 4938.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2324, pruned_loss=0.03955, over 218422.91 frames.], batch size: 32, aishell_tot_loss[loss=0.1699, simple_loss=0.2504, pruned_loss=0.04467, over 93997.01 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2199, pruned_loss=0.03603, over 137551.95 frames.], batch size: 32, lr: 5.91e-04
+2022-06-18 19:55:42,408 INFO [train.py:874] (1/4) Epoch 14, batch 100, datatang_loss[loss=0.1816, simple_loss=0.2539, pruned_loss=0.05459, over 4924.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2331, pruned_loss=0.03934, over 388786.24 frames.], batch size: 98, aishell_tot_loss[loss=0.1663, simple_loss=0.2474, pruned_loss=0.04257, over 195286.94 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2213, pruned_loss=0.03668, over 241261.65 frames.], batch size: 98, lr: 5.91e-04
+2022-06-18 19:56:14,048 INFO [train.py:874] (1/4) Epoch 14, batch 150, datatang_loss[loss=0.1345, simple_loss=0.2119, pruned_loss=0.02851, over 4990.00 frames.], tot_loss[loss=0.1582, simple_loss=0.235, pruned_loss=0.04071, over 520990.15 frames.], batch size: 31, aishell_tot_loss[loss=0.1656, simple_loss=0.2473, pruned_loss=0.04197, over 267198.61 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2255, pruned_loss=0.03956, over 348176.48 frames.], batch size: 31, lr: 5.91e-04
+2022-06-18 19:56:43,539 INFO [train.py:874] (1/4) Epoch 14, batch 200, datatang_loss[loss=0.1512, simple_loss=0.2153, pruned_loss=0.04349, over 4921.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2344, pruned_loss=0.04031, over 624171.39 frames.], batch size: 73, aishell_tot_loss[loss=0.1631, simple_loss=0.2453, pruned_loss=0.04044, over 355276.13 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2252, pruned_loss=0.04019, over 420318.99 frames.], batch size: 73, lr: 5.90e-04
+2022-06-18 19:57:13,590 INFO [train.py:874] (1/4) Epoch 14, batch 250, aishell_loss[loss=0.1706, simple_loss=0.2532, pruned_loss=0.04401, over 4967.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2345, pruned_loss=0.03943, over 704372.95 frames.], batch size: 44, aishell_tot_loss[loss=0.1615, simple_loss=0.2444, pruned_loss=0.03932, over 440635.45 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2249, pruned_loss=0.03985, over 476785.04 frames.], batch size: 44, lr: 5.90e-04
+2022-06-18 19:57:44,767 INFO [train.py:874] (1/4) Epoch 14, batch 300, aishell_loss[loss=0.2124, simple_loss=0.2961, pruned_loss=0.06434, over 4933.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2357, pruned_loss=0.0398, over 766732.11 frames.], batch size: 79, aishell_tot_loss[loss=0.1626, simple_loss=0.2454, pruned_loss=0.03987, over 514453.51 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.225, pruned_loss=0.0398, over 527559.67 frames.], batch size: 79, lr: 5.90e-04
+2022-06-18 19:58:15,355 INFO [train.py:874] (1/4) Epoch 14, batch 350, datatang_loss[loss=0.183, simple_loss=0.252, pruned_loss=0.05702, over 4901.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2372, pruned_loss=0.04013, over 815121.50 frames.], batch size: 47, aishell_tot_loss[loss=0.1634, simple_loss=0.2468, pruned_loss=0.04, over 569813.32 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2261, pruned_loss=0.04014, over 581516.91 frames.], batch size: 47, lr: 5.89e-04
+2022-06-18 19:58:44,789 INFO [train.py:874] (1/4) Epoch 14, batch 400, datatang_loss[loss=0.1793, simple_loss=0.2611, pruned_loss=0.04881, over 4931.00 frames.], tot_loss[loss=0.1608, simple_loss=0.238, pruned_loss=0.04179, over 853155.51 frames.], batch size: 98, aishell_tot_loss[loss=0.1634, simple_loss=0.2466, pruned_loss=0.04006, over 605769.85 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2288, pruned_loss=0.0424, over 641571.10 frames.], batch size: 98, lr: 5.89e-04
+2022-06-18 19:59:15,071 INFO [train.py:874] (1/4) Epoch 14, batch 450, datatang_loss[loss=0.2172, simple_loss=0.2786, pruned_loss=0.07788, over 4957.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2372, pruned_loss=0.04146, over 882564.09 frames.], batch size: 37, aishell_tot_loss[loss=0.1619, simple_loss=0.2451, pruned_loss=0.03942, over 650480.20 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2293, pruned_loss=0.04269, over 682174.89 frames.], batch size: 37, lr: 5.89e-04
+2022-06-18 19:59:45,588 INFO [train.py:874] (1/4) Epoch 14, batch 500, datatang_loss[loss=0.1548, simple_loss=0.2231, pruned_loss=0.04327, over 4929.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2378, pruned_loss=0.0417, over 905397.42 frames.], batch size: 73, aishell_tot_loss[loss=0.1632, simple_loss=0.2462, pruned_loss=0.04014, over 692998.29 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2288, pruned_loss=0.04249, over 715094.51 frames.], batch size: 73, lr: 5.89e-04
+2022-06-18 20:00:15,501 INFO [train.py:874] (1/4) Epoch 14, batch 550, datatang_loss[loss=0.1554, simple_loss=0.2307, pruned_loss=0.04004, over 4945.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2385, pruned_loss=0.04188, over 923065.41 frames.], batch size: 50, aishell_tot_loss[loss=0.1642, simple_loss=0.247, pruned_loss=0.04064, over 725947.09 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.229, pruned_loss=0.04234, over 748228.31 frames.], batch size: 50, lr: 5.88e-04
+2022-06-18 20:00:45,857 INFO [train.py:874] (1/4) Epoch 14, batch 600, datatang_loss[loss=0.1992, simple_loss=0.2715, pruned_loss=0.06344, over 4910.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2405, pruned_loss=0.04287, over 936730.07 frames.], batch size: 98, aishell_tot_loss[loss=0.1642, simple_loss=0.2471, pruned_loss=0.04068, over 758672.89 frames.], datatang_tot_loss[loss=0.1594, simple_loss=0.2315, pruned_loss=0.04368, over 774012.78 frames.], batch size: 98, lr: 5.88e-04
+2022-06-18 20:01:15,112 INFO [train.py:874] (1/4) Epoch 14, batch 650, aishell_loss[loss=0.1436, simple_loss=0.2361, pruned_loss=0.02554, over 4971.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2414, pruned_loss=0.04296, over 947834.51 frames.], batch size: 61, aishell_tot_loss[loss=0.1642, simple_loss=0.2473, pruned_loss=0.04049, over 783571.48 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.2329, pruned_loss=0.04417, over 800888.38 frames.], batch size: 61, lr: 5.88e-04
+2022-06-18 20:01:44,221 INFO [train.py:874] (1/4) Epoch 14, batch 700, datatang_loss[loss=0.1892, simple_loss=0.2548, pruned_loss=0.06178, over 4911.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2413, pruned_loss=0.04287, over 956241.40 frames.], batch size: 47, aishell_tot_loss[loss=0.1639, simple_loss=0.2469, pruned_loss=0.0404, over 807280.30 frames.], datatang_tot_loss[loss=0.161, simple_loss=0.2335, pruned_loss=0.04432, over 822759.52 frames.], batch size: 47, lr: 5.88e-04
+2022-06-18 20:02:13,952 INFO [train.py:874] (1/4) Epoch 14, batch 750, aishell_loss[loss=0.1674, simple_loss=0.2538, pruned_loss=0.04052, over 4968.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2404, pruned_loss=0.04265, over 962653.17 frames.], batch size: 48, aishell_tot_loss[loss=0.1629, simple_loss=0.2461, pruned_loss=0.03988, over 826733.40 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.2336, pruned_loss=0.04465, over 843236.32 frames.], batch size: 48, lr: 5.87e-04
+2022-06-18 20:02:45,443 INFO [train.py:874] (1/4) Epoch 14, batch 800, datatang_loss[loss=0.1649, simple_loss=0.2395, pruned_loss=0.04512, over 4922.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2407, pruned_loss=0.04298, over 967840.28 frames.], batch size: 81, aishell_tot_loss[loss=0.1636, simple_loss=0.2467, pruned_loss=0.04026, over 843325.16 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.2338, pruned_loss=0.04468, over 861986.47 frames.], batch size: 81, lr: 5.87e-04
+2022-06-18 20:03:15,190 INFO [train.py:874] (1/4) Epoch 14, batch 850, aishell_loss[loss=0.15, simple_loss=0.2306, pruned_loss=0.03476, over 4960.00 frames.], tot_loss[loss=0.163, simple_loss=0.2406, pruned_loss=0.04272, over 971708.06 frames.], batch size: 27, aishell_tot_loss[loss=0.1629, simple_loss=0.246, pruned_loss=0.03991, over 862977.04 frames.], datatang_tot_loss[loss=0.162, simple_loss=0.2341, pruned_loss=0.04496, over 873910.07 frames.], batch size: 27, lr: 5.87e-04
+2022-06-18 20:03:44,511 INFO [train.py:874] (1/4) Epoch 14, batch 900, aishell_loss[loss=0.175, simple_loss=0.2609, pruned_loss=0.04452, over 4899.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2401, pruned_loss=0.04236, over 974394.37 frames.], batch size: 34, aishell_tot_loss[loss=0.1622, simple_loss=0.2455, pruned_loss=0.03945, over 877601.99 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2341, pruned_loss=0.04508, over 886509.38 frames.], batch size: 34, lr: 5.86e-04
+2022-06-18 20:04:15,153 INFO [train.py:874] (1/4) Epoch 14, batch 950, datatang_loss[loss=0.1624, simple_loss=0.234, pruned_loss=0.04538, over 4881.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2392, pruned_loss=0.04264, over 976746.41 frames.], batch size: 39, aishell_tot_loss[loss=0.1616, simple_loss=0.2445, pruned_loss=0.03932, over 887801.50 frames.], datatang_tot_loss[loss=0.1626, simple_loss=0.2344, pruned_loss=0.0454, over 900314.31 frames.], batch size: 39, lr: 5.86e-04
+2022-06-18 20:04:45,285 INFO [train.py:874] (1/4) Epoch 14, batch 1000, datatang_loss[loss=0.1468, simple_loss=0.2276, pruned_loss=0.03299, over 4919.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2401, pruned_loss=0.04308, over 979066.44 frames.], batch size: 81, aishell_tot_loss[loss=0.1622, simple_loss=0.2452, pruned_loss=0.03958, over 898461.89 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2347, pruned_loss=0.04567, over 911472.48 frames.], batch size: 81, lr: 5.86e-04
+2022-06-18 20:04:45,286 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 20:05:01,198 INFO [train.py:914] (1/4) Epoch 14, validation: loss=0.1656, simple_loss=0.2496, pruned_loss=0.04079, over 1622729.00 frames.
+2022-06-18 20:05:31,521 INFO [train.py:874] (1/4) Epoch 14, batch 1050, aishell_loss[loss=0.1306, simple_loss=0.2119, pruned_loss=0.02463, over 4958.00 frames.], tot_loss[loss=0.163, simple_loss=0.2403, pruned_loss=0.04281, over 980842.97 frames.], batch size: 27, aishell_tot_loss[loss=0.162, simple_loss=0.2452, pruned_loss=0.03942, over 909379.73 frames.], datatang_tot_loss[loss=0.1632, simple_loss=0.235, pruned_loss=0.04568, over 919947.57 frames.], batch size: 27, lr: 5.86e-04
+2022-06-18 20:06:01,967 INFO [train.py:874] (1/4) Epoch 14, batch 1100, aishell_loss[loss=0.1552, simple_loss=0.2461, pruned_loss=0.03217, over 4935.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2404, pruned_loss=0.04302, over 981781.56 frames.], batch size: 33, aishell_tot_loss[loss=0.1622, simple_loss=0.2456, pruned_loss=0.03941, over 916604.89 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.235, pruned_loss=0.04583, over 929032.47 frames.], batch size: 33, lr: 5.85e-04
+2022-06-18 20:06:31,910 INFO [train.py:874] (1/4) Epoch 14, batch 1150, datatang_loss[loss=0.1575, simple_loss=0.2374, pruned_loss=0.03881, over 4914.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2409, pruned_loss=0.04261, over 982674.92 frames.], batch size: 75, aishell_tot_loss[loss=0.1625, simple_loss=0.2461, pruned_loss=0.03943, over 925330.83 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2349, pruned_loss=0.0455, over 935240.80 frames.], batch size: 75, lr: 5.85e-04
+2022-06-18 20:07:00,189 INFO [train.py:874] (1/4) Epoch 14, batch 1200, datatang_loss[loss=0.1784, simple_loss=0.2433, pruned_loss=0.05675, over 4946.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2415, pruned_loss=0.04268, over 983363.28 frames.], batch size: 69, aishell_tot_loss[loss=0.1624, simple_loss=0.2461, pruned_loss=0.03931, over 933552.62 frames.], datatang_tot_loss[loss=0.1636, simple_loss=0.2354, pruned_loss=0.04588, over 940266.47 frames.], batch size: 69, lr: 5.85e-04
+2022-06-18 20:07:32,156 INFO [train.py:874] (1/4) Epoch 14, batch 1250, aishell_loss[loss=0.1935, simple_loss=0.2714, pruned_loss=0.05782, over 4925.00 frames.], tot_loss[loss=0.1631, simple_loss=0.241, pruned_loss=0.04259, over 984260.88 frames.], batch size: 46, aishell_tot_loss[loss=0.1625, simple_loss=0.2461, pruned_loss=0.03941, over 939696.58 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2352, pruned_loss=0.04569, over 945980.11 frames.], batch size: 46, lr: 5.85e-04
+2022-06-18 20:08:00,525 INFO [train.py:874] (1/4) Epoch 14, batch 1300, aishell_loss[loss=0.1755, simple_loss=0.2607, pruned_loss=0.04513, over 4955.00 frames.], tot_loss[loss=0.1627, simple_loss=0.241, pruned_loss=0.04218, over 984323.47 frames.], batch size: 54, aishell_tot_loss[loss=0.1626, simple_loss=0.2462, pruned_loss=0.03947, over 946256.30 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2347, pruned_loss=0.04539, over 949390.80 frames.], batch size: 54, lr: 5.84e-04
+2022-06-18 20:08:31,643 INFO [train.py:874] (1/4) Epoch 14, batch 1350, aishell_loss[loss=0.1425, simple_loss=0.237, pruned_loss=0.02398, over 4977.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2405, pruned_loss=0.04222, over 984703.42 frames.], batch size: 51, aishell_tot_loss[loss=0.162, simple_loss=0.2458, pruned_loss=0.0391, over 950167.53 frames.], datatang_tot_loss[loss=0.1631, simple_loss=0.2349, pruned_loss=0.04566, over 954418.65 frames.], batch size: 51, lr: 5.84e-04
+2022-06-18 20:09:02,306 INFO [train.py:874] (1/4) Epoch 14, batch 1400, aishell_loss[loss=0.1809, simple_loss=0.2603, pruned_loss=0.05079, over 4952.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2401, pruned_loss=0.04183, over 984980.79 frames.], batch size: 64, aishell_tot_loss[loss=0.1613, simple_loss=0.2451, pruned_loss=0.03873, over 954974.80 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2352, pruned_loss=0.04569, over 957601.43 frames.], batch size: 64, lr: 5.84e-04
+2022-06-18 20:09:32,254 INFO [train.py:874] (1/4) Epoch 14, batch 1450, aishell_loss[loss=0.1343, simple_loss=0.2221, pruned_loss=0.0233, over 4870.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2401, pruned_loss=0.04178, over 985001.63 frames.], batch size: 28, aishell_tot_loss[loss=0.1609, simple_loss=0.2446, pruned_loss=0.03861, over 958398.48 frames.], datatang_tot_loss[loss=0.1635, simple_loss=0.2357, pruned_loss=0.04567, over 960961.56 frames.], batch size: 28, lr: 5.84e-04
+2022-06-18 20:10:03,010 INFO [train.py:874] (1/4) Epoch 14, batch 1500, datatang_loss[loss=0.17, simple_loss=0.2457, pruned_loss=0.04716, over 4946.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2401, pruned_loss=0.04203, over 985104.56 frames.], batch size: 88, aishell_tot_loss[loss=0.1615, simple_loss=0.2452, pruned_loss=0.03893, over 961158.25 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.2351, pruned_loss=0.04543, over 964247.75 frames.], batch size: 88, lr: 5.83e-04
+2022-06-18 20:10:33,050 INFO [train.py:874] (1/4) Epoch 14, batch 1550, datatang_loss[loss=0.2318, simple_loss=0.2927, pruned_loss=0.08551, over 4930.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2397, pruned_loss=0.04202, over 985218.55 frames.], batch size: 108, aishell_tot_loss[loss=0.1617, simple_loss=0.245, pruned_loss=0.03916, over 963939.41 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2348, pruned_loss=0.04516, over 966867.11 frames.], batch size: 108, lr: 5.83e-04
+2022-06-18 20:11:02,418 INFO [train.py:874] (1/4) Epoch 14, batch 1600, aishell_loss[loss=0.1842, simple_loss=0.2603, pruned_loss=0.05411, over 4907.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2405, pruned_loss=0.04209, over 985114.07 frames.], batch size: 33, aishell_tot_loss[loss=0.162, simple_loss=0.2453, pruned_loss=0.03929, over 967444.54 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2347, pruned_loss=0.0454, over 968005.14 frames.], batch size: 33, lr: 5.83e-04
+2022-06-18 20:11:33,204 INFO [train.py:874] (1/4) Epoch 14, batch 1650, datatang_loss[loss=0.1357, simple_loss=0.2141, pruned_loss=0.0287, over 4924.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2405, pruned_loss=0.04226, over 985212.27 frames.], batch size: 77, aishell_tot_loss[loss=0.1622, simple_loss=0.2456, pruned_loss=0.03945, over 969682.81 frames.], datatang_tot_loss[loss=0.1627, simple_loss=0.2346, pruned_loss=0.04538, over 969970.94 frames.], batch size: 77, lr: 5.83e-04
+2022-06-18 20:12:02,976 INFO [train.py:874] (1/4) Epoch 14, batch 1700, aishell_loss[loss=0.1647, simple_loss=0.2528, pruned_loss=0.03836, over 4854.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2403, pruned_loss=0.04256, over 985333.35 frames.], batch size: 36, aishell_tot_loss[loss=0.1618, simple_loss=0.245, pruned_loss=0.0393, over 971471.89 frames.], datatang_tot_loss[loss=0.1634, simple_loss=0.235, pruned_loss=0.04585, over 971934.13 frames.], batch size: 36, lr: 5.82e-04
+2022-06-18 20:12:32,528 INFO [train.py:874] (1/4) Epoch 14, batch 1750, datatang_loss[loss=0.1501, simple_loss=0.2337, pruned_loss=0.03326, over 4949.00 frames.], tot_loss[loss=0.1636, simple_loss=0.241, pruned_loss=0.0431, over 985315.32 frames.], batch size: 62, aishell_tot_loss[loss=0.1623, simple_loss=0.2454, pruned_loss=0.03965, over 972883.80 frames.], datatang_tot_loss[loss=0.1638, simple_loss=0.2354, pruned_loss=0.04609, over 973725.24 frames.], batch size: 62, lr: 5.82e-04
+2022-06-18 20:13:04,406 INFO [train.py:874] (1/4) Epoch 14, batch 1800, datatang_loss[loss=0.1902, simple_loss=0.2649, pruned_loss=0.05776, over 4960.00 frames.], tot_loss[loss=0.164, simple_loss=0.2413, pruned_loss=0.04331, over 984713.28 frames.], batch size: 91, aishell_tot_loss[loss=0.1629, simple_loss=0.2461, pruned_loss=0.0399, over 973892.97 frames.], datatang_tot_loss[loss=0.1638, simple_loss=0.2354, pruned_loss=0.04607, over 974893.12 frames.], batch size: 91, lr: 5.82e-04
+2022-06-18 20:13:34,477 INFO [train.py:874] (1/4) Epoch 14, batch 1850, datatang_loss[loss=0.1768, simple_loss=0.2432, pruned_loss=0.05526, over 4943.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2408, pruned_loss=0.04317, over 985061.71 frames.], batch size: 69, aishell_tot_loss[loss=0.1624, simple_loss=0.2456, pruned_loss=0.03964, over 975013.93 frames.], datatang_tot_loss[loss=0.164, simple_loss=0.2358, pruned_loss=0.04607, over 976531.48 frames.], batch size: 69, lr: 5.81e-04
+2022-06-18 20:14:03,929 INFO [train.py:874] (1/4) Epoch 14, batch 1900, aishell_loss[loss=0.1836, simple_loss=0.2581, pruned_loss=0.05457, over 4929.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2415, pruned_loss=0.0438, over 985350.61 frames.], batch size: 33, aishell_tot_loss[loss=0.1627, simple_loss=0.246, pruned_loss=0.03974, over 976128.23 frames.], datatang_tot_loss[loss=0.1648, simple_loss=0.2363, pruned_loss=0.04659, over 977878.19 frames.], batch size: 33, lr: 5.81e-04
+2022-06-18 20:14:34,999 INFO [train.py:874] (1/4) Epoch 14, batch 1950, datatang_loss[loss=0.1619, simple_loss=0.2278, pruned_loss=0.04803, over 4956.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2414, pruned_loss=0.04369, over 985583.25 frames.], batch size: 67, aishell_tot_loss[loss=0.1631, simple_loss=0.2465, pruned_loss=0.03987, over 977317.59 frames.], datatang_tot_loss[loss=0.1643, simple_loss=0.2357, pruned_loss=0.0465, over 978918.24 frames.], batch size: 67, lr: 5.81e-04
+2022-06-18 20:15:05,414 INFO [train.py:874] (1/4) Epoch 14, batch 2000, aishell_loss[loss=0.1828, simple_loss=0.2704, pruned_loss=0.04758, over 4947.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2415, pruned_loss=0.0433, over 985607.86 frames.], batch size: 45, aishell_tot_loss[loss=0.1627, simple_loss=0.2462, pruned_loss=0.03962, over 978452.83 frames.], datatang_tot_loss[loss=0.1646, simple_loss=0.2358, pruned_loss=0.04666, over 979607.63 frames.], batch size: 45, lr: 5.81e-04
+2022-06-18 20:15:05,415 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 20:15:22,470 INFO [train.py:914] (1/4) Epoch 14, validation: loss=0.1654, simple_loss=0.2497, pruned_loss=0.04057, over 1622729.00 frames.
+2022-06-18 20:15:52,262 INFO [train.py:874] (1/4) Epoch 14, batch 2050, datatang_loss[loss=0.187, simple_loss=0.2672, pruned_loss=0.05338, over 4930.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2412, pruned_loss=0.04304, over 985506.24 frames.], batch size: 34, aishell_tot_loss[loss=0.1625, simple_loss=0.2461, pruned_loss=0.03947, over 979042.93 frames.], datatang_tot_loss[loss=0.1644, simple_loss=0.2357, pruned_loss=0.04656, over 980455.41 frames.], batch size: 34, lr: 5.80e-04
+2022-06-18 20:16:21,742 INFO [train.py:874] (1/4) Epoch 14, batch 2100, datatang_loss[loss=0.1493, simple_loss=0.2154, pruned_loss=0.04157, over 4912.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2417, pruned_loss=0.04371, over 985336.95 frames.], batch size: 42, aishell_tot_loss[loss=0.1635, simple_loss=0.2469, pruned_loss=0.04006, over 979570.73 frames.], datatang_tot_loss[loss=0.1644, simple_loss=0.2355, pruned_loss=0.04663, over 981089.55 frames.], batch size: 42, lr: 5.80e-04
+2022-06-18 20:16:52,391 INFO [train.py:874] (1/4) Epoch 14, batch 2150, datatang_loss[loss=0.1406, simple_loss=0.2161, pruned_loss=0.03255, over 4915.00 frames.], tot_loss[loss=0.1634, simple_loss=0.241, pruned_loss=0.04286, over 985687.90 frames.], batch size: 75, aishell_tot_loss[loss=0.1627, simple_loss=0.246, pruned_loss=0.03968, over 980830.30 frames.], datatang_tot_loss[loss=0.1642, simple_loss=0.2355, pruned_loss=0.04645, over 981433.71 frames.], batch size: 75, lr: 5.80e-04
+2022-06-18 20:17:23,084 INFO [train.py:874] (1/4) Epoch 14, batch 2200, datatang_loss[loss=0.1826, simple_loss=0.2532, pruned_loss=0.05599, over 4910.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2414, pruned_loss=0.04312, over 985532.16 frames.], batch size: 75, aishell_tot_loss[loss=0.1632, simple_loss=0.2466, pruned_loss=0.03984, over 981184.42 frames.], datatang_tot_loss[loss=0.1642, simple_loss=0.2354, pruned_loss=0.04645, over 981989.82 frames.], batch size: 75, lr: 5.80e-04
+2022-06-18 20:17:52,961 INFO [train.py:874] (1/4) Epoch 14, batch 2250, aishell_loss[loss=0.1806, simple_loss=0.2527, pruned_loss=0.05423, over 4912.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2416, pruned_loss=0.04327, over 985408.72 frames.], batch size: 33, aishell_tot_loss[loss=0.1635, simple_loss=0.2467, pruned_loss=0.04009, over 981622.30 frames.], datatang_tot_loss[loss=0.1642, simple_loss=0.2354, pruned_loss=0.0465, over 982374.51 frames.], batch size: 33, lr: 5.79e-04
+2022-06-18 20:18:23,986 INFO [train.py:874] (1/4) Epoch 14, batch 2300, datatang_loss[loss=0.146, simple_loss=0.227, pruned_loss=0.0325, over 4805.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2404, pruned_loss=0.04274, over 985453.80 frames.], batch size: 25, aishell_tot_loss[loss=0.1632, simple_loss=0.2465, pruned_loss=0.0399, over 982116.14 frames.], datatang_tot_loss[loss=0.1633, simple_loss=0.2348, pruned_loss=0.04591, over 982708.77 frames.], batch size: 25, lr: 5.79e-04
+2022-06-18 20:18:58,992 INFO [train.py:874] (1/4) Epoch 14, batch 2350, datatang_loss[loss=0.1356, simple_loss=0.2162, pruned_loss=0.02749, over 4935.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2405, pruned_loss=0.04244, over 985521.96 frames.], batch size: 88, aishell_tot_loss[loss=0.1633, simple_loss=0.2464, pruned_loss=0.04012, over 982649.20 frames.], datatang_tot_loss[loss=0.1628, simple_loss=0.2346, pruned_loss=0.04546, over 982977.07 frames.], batch size: 88, lr: 5.79e-04
+2022-06-18 20:19:28,607 INFO [train.py:874] (1/4) Epoch 14, batch 2400, aishell_loss[loss=0.1288, simple_loss=0.2219, pruned_loss=0.01783, over 4977.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2406, pruned_loss=0.04295, over 985681.54 frames.], batch size: 30, aishell_tot_loss[loss=0.1632, simple_loss=0.2463, pruned_loss=0.04011, over 982921.33 frames.], datatang_tot_loss[loss=0.1634, simple_loss=0.2351, pruned_loss=0.04585, over 983497.93 frames.], batch size: 30, lr: 5.79e-04
+2022-06-18 20:19:59,657 INFO [train.py:874] (1/4) Epoch 14, batch 2450, datatang_loss[loss=0.1631, simple_loss=0.2294, pruned_loss=0.04836, over 4940.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2405, pruned_loss=0.04303, over 985284.38 frames.], batch size: 50, aishell_tot_loss[loss=0.1629, simple_loss=0.246, pruned_loss=0.03993, over 982835.82 frames.], datatang_tot_loss[loss=0.1637, simple_loss=0.2355, pruned_loss=0.04597, over 983710.81 frames.], batch size: 50, lr: 5.78e-04
+2022-06-18 20:20:30,393 INFO [train.py:874] (1/4) Epoch 14, batch 2500, datatang_loss[loss=0.146, simple_loss=0.2306, pruned_loss=0.03075, over 4837.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2402, pruned_loss=0.04253, over 985372.27 frames.], batch size: 30, aishell_tot_loss[loss=0.163, simple_loss=0.246, pruned_loss=0.04003, over 983235.91 frames.], datatang_tot_loss[loss=0.163, simple_loss=0.235, pruned_loss=0.04548, over 983895.69 frames.], batch size: 30, lr: 5.78e-04
+2022-06-18 20:20:59,933 INFO [train.py:874] (1/4) Epoch 14, batch 2550, datatang_loss[loss=0.1473, simple_loss=0.2263, pruned_loss=0.03418, over 4914.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2392, pruned_loss=0.04175, over 985090.57 frames.], batch size: 64, aishell_tot_loss[loss=0.1625, simple_loss=0.2456, pruned_loss=0.03967, over 983401.16 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2342, pruned_loss=0.04499, over 983849.56 frames.], batch size: 64, lr: 5.78e-04
+2022-06-18 20:21:29,265 INFO [train.py:874] (1/4) Epoch 14, batch 2600, datatang_loss[loss=0.1621, simple_loss=0.2371, pruned_loss=0.04358, over 4909.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2398, pruned_loss=0.04224, over 985016.90 frames.], batch size: 59, aishell_tot_loss[loss=0.1627, simple_loss=0.2458, pruned_loss=0.03973, over 983565.42 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2345, pruned_loss=0.04526, over 983938.80 frames.], batch size: 59, lr: 5.78e-04
+2022-06-18 20:22:01,016 INFO [train.py:874] (1/4) Epoch 14, batch 2650, datatang_loss[loss=0.1301, simple_loss=0.2012, pruned_loss=0.0295, over 4859.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2379, pruned_loss=0.0415, over 985500.45 frames.], batch size: 30, aishell_tot_loss[loss=0.1619, simple_loss=0.2449, pruned_loss=0.03944, over 983927.76 frames.], datatang_tot_loss[loss=0.1614, simple_loss=0.2335, pruned_loss=0.04464, over 984373.66 frames.], batch size: 30, lr: 5.77e-04
+2022-06-18 20:22:30,527 INFO [train.py:874] (1/4) Epoch 14, batch 2700, datatang_loss[loss=0.1578, simple_loss=0.2347, pruned_loss=0.04044, over 4970.00 frames.], tot_loss[loss=0.1614, simple_loss=0.239, pruned_loss=0.04189, over 985120.69 frames.], batch size: 48, aishell_tot_loss[loss=0.1624, simple_loss=0.2455, pruned_loss=0.03965, over 983820.50 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.2339, pruned_loss=0.04465, over 984392.52 frames.], batch size: 48, lr: 5.77e-04
+2022-06-18 20:22:59,815 INFO [train.py:874] (1/4) Epoch 14, batch 2750, datatang_loss[loss=0.1872, simple_loss=0.2358, pruned_loss=0.06934, over 4925.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2398, pruned_loss=0.04214, over 985161.66 frames.], batch size: 42, aishell_tot_loss[loss=0.1629, simple_loss=0.2461, pruned_loss=0.03983, over 983897.45 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2338, pruned_loss=0.04476, over 984607.13 frames.], batch size: 42, lr: 5.77e-04
+2022-06-18 20:23:30,613 INFO [train.py:874] (1/4) Epoch 14, batch 2800, aishell_loss[loss=0.1604, simple_loss=0.2382, pruned_loss=0.04126, over 4983.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2392, pruned_loss=0.0417, over 984842.70 frames.], batch size: 51, aishell_tot_loss[loss=0.1623, simple_loss=0.2456, pruned_loss=0.03949, over 983723.84 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.2336, pruned_loss=0.04469, over 984639.91 frames.], batch size: 51, lr: 5.77e-04
+2022-06-18 20:24:01,488 INFO [train.py:874] (1/4) Epoch 14, batch 2850, aishell_loss[loss=0.1445, simple_loss=0.2314, pruned_loss=0.02874, over 4894.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2398, pruned_loss=0.0422, over 984996.24 frames.], batch size: 42, aishell_tot_loss[loss=0.1624, simple_loss=0.2458, pruned_loss=0.03946, over 983976.47 frames.], datatang_tot_loss[loss=0.1621, simple_loss=0.2338, pruned_loss=0.04518, over 984718.10 frames.], batch size: 42, lr: 5.76e-04
+2022-06-18 20:24:30,569 INFO [train.py:874] (1/4) Epoch 14, batch 2900, aishell_loss[loss=0.1408, simple_loss=0.2269, pruned_loss=0.02738, over 4945.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2392, pruned_loss=0.04193, over 984823.22 frames.], batch size: 45, aishell_tot_loss[loss=0.162, simple_loss=0.2457, pruned_loss=0.03918, over 983835.85 frames.], datatang_tot_loss[loss=0.1618, simple_loss=0.2335, pruned_loss=0.04505, over 984815.92 frames.], batch size: 45, lr: 5.76e-04
+2022-06-18 20:25:01,155 INFO [train.py:874] (1/4) Epoch 14, batch 2950, aishell_loss[loss=0.1471, simple_loss=0.2349, pruned_loss=0.02969, over 4949.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2393, pruned_loss=0.04197, over 985026.86 frames.], batch size: 45, aishell_tot_loss[loss=0.162, simple_loss=0.2457, pruned_loss=0.03909, over 983767.81 frames.], datatang_tot_loss[loss=0.1619, simple_loss=0.2336, pruned_loss=0.0451, over 985205.07 frames.], batch size: 45, lr: 5.76e-04
+2022-06-18 20:25:31,692 INFO [train.py:874] (1/4) Epoch 14, batch 3000, datatang_loss[loss=0.1861, simple_loss=0.2585, pruned_loss=0.05683, over 4927.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2393, pruned_loss=0.04177, over 985018.89 frames.], batch size: 94, aishell_tot_loss[loss=0.1623, simple_loss=0.2464, pruned_loss=0.03917, over 984035.09 frames.], datatang_tot_loss[loss=0.1612, simple_loss=0.233, pruned_loss=0.04468, over 985037.93 frames.], batch size: 94, lr: 5.76e-04
+2022-06-18 20:25:31,693 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 20:25:47,765 INFO [train.py:914] (1/4) 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] (1/4) Epoch 14, batch 3050, aishell_loss[loss=0.1486, simple_loss=0.2379, pruned_loss=0.02962, over 4934.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2395, pruned_loss=0.04202, over 985335.50 frames.], batch size: 58, aishell_tot_loss[loss=0.1625, simple_loss=0.2464, pruned_loss=0.03933, over 984260.31 frames.], datatang_tot_loss[loss=0.1613, simple_loss=0.2331, pruned_loss=0.04477, over 985250.84 frames.], batch size: 58, lr: 5.75e-04
+2022-06-18 20:26:48,727 INFO [train.py:874] (1/4) Epoch 14, batch 3100, datatang_loss[loss=0.1319, simple_loss=0.2085, pruned_loss=0.02763, over 4917.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2381, pruned_loss=0.04168, over 985254.20 frames.], batch size: 77, aishell_tot_loss[loss=0.162, simple_loss=0.2458, pruned_loss=0.03906, over 984454.31 frames.], datatang_tot_loss[loss=0.1608, simple_loss=0.2326, pruned_loss=0.0445, over 985093.60 frames.], batch size: 77, lr: 5.75e-04
+2022-06-18 20:27:19,561 INFO [train.py:874] (1/4) Epoch 14, batch 3150, aishell_loss[loss=0.1689, simple_loss=0.2249, pruned_loss=0.05645, over 4871.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2388, pruned_loss=0.04195, over 985506.14 frames.], batch size: 21, aishell_tot_loss[loss=0.1624, simple_loss=0.2463, pruned_loss=0.03918, over 984692.08 frames.], datatang_tot_loss[loss=0.1609, simple_loss=0.2327, pruned_loss=0.04456, over 985231.05 frames.], batch size: 21, lr: 5.75e-04
+2022-06-18 20:27:49,660 INFO [train.py:874] (1/4) Epoch 14, batch 3200, aishell_loss[loss=0.1676, simple_loss=0.2538, pruned_loss=0.04069, over 4913.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2393, pruned_loss=0.0419, over 985417.20 frames.], batch size: 52, aishell_tot_loss[loss=0.1629, simple_loss=0.2469, pruned_loss=0.0395, over 984767.78 frames.], datatang_tot_loss[loss=0.1605, simple_loss=0.2325, pruned_loss=0.04425, over 985184.23 frames.], batch size: 52, lr: 5.75e-04
+2022-06-18 20:28:20,115 INFO [train.py:874] (1/4) Epoch 14, batch 3250, datatang_loss[loss=0.1672, simple_loss=0.2459, pruned_loss=0.04425, over 4951.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2399, pruned_loss=0.04187, over 985410.65 frames.], batch size: 91, aishell_tot_loss[loss=0.1634, simple_loss=0.2473, pruned_loss=0.03978, over 984853.32 frames.], datatang_tot_loss[loss=0.1602, simple_loss=0.2323, pruned_loss=0.04405, over 985208.91 frames.], batch size: 91, lr: 5.74e-04
+2022-06-18 20:28:49,466 INFO [train.py:874] (1/4) Epoch 14, batch 3300, aishell_loss[loss=0.1562, simple_loss=0.2341, pruned_loss=0.03915, over 4914.00 frames.], tot_loss[loss=0.1617, simple_loss=0.24, pruned_loss=0.04168, over 985613.05 frames.], batch size: 33, aishell_tot_loss[loss=0.1629, simple_loss=0.2468, pruned_loss=0.03954, over 984904.26 frames.], datatang_tot_loss[loss=0.1605, simple_loss=0.2327, pruned_loss=0.0442, over 985470.95 frames.], batch size: 33, lr: 5.74e-04
+2022-06-18 20:29:19,701 INFO [train.py:874] (1/4) Epoch 14, batch 3350, datatang_loss[loss=0.1812, simple_loss=0.255, pruned_loss=0.0537, over 4928.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2401, pruned_loss=0.04134, over 985541.43 frames.], batch size: 94, aishell_tot_loss[loss=0.1625, simple_loss=0.2467, pruned_loss=0.03919, over 984883.20 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.2329, pruned_loss=0.04416, over 985524.40 frames.], batch size: 94, lr: 5.74e-04
+2022-06-18 20:29:49,997 INFO [train.py:874] (1/4) Epoch 14, batch 3400, aishell_loss[loss=0.1618, simple_loss=0.2487, pruned_loss=0.03743, over 4856.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2392, pruned_loss=0.04099, over 985599.94 frames.], batch size: 35, aishell_tot_loss[loss=0.162, simple_loss=0.246, pruned_loss=0.03897, over 985044.96 frames.], datatang_tot_loss[loss=0.1602, simple_loss=0.2324, pruned_loss=0.04402, over 985538.02 frames.], batch size: 35, lr: 5.74e-04
+2022-06-18 20:30:18,780 INFO [train.py:874] (1/4) Epoch 14, batch 3450, aishell_loss[loss=0.1668, simple_loss=0.2516, pruned_loss=0.04098, over 4888.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2396, pruned_loss=0.04133, over 985347.83 frames.], batch size: 50, aishell_tot_loss[loss=0.1622, simple_loss=0.2461, pruned_loss=0.03914, over 984910.71 frames.], datatang_tot_loss[loss=0.1605, simple_loss=0.2326, pruned_loss=0.04417, over 985504.11 frames.], batch size: 50, lr: 5.73e-04
+2022-06-18 20:30:50,305 INFO [train.py:874] (1/4) Epoch 14, batch 3500, aishell_loss[loss=0.1721, simple_loss=0.2613, pruned_loss=0.04145, over 4977.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2405, pruned_loss=0.04189, over 985252.18 frames.], batch size: 39, aishell_tot_loss[loss=0.1626, simple_loss=0.2465, pruned_loss=0.03931, over 984835.03 frames.], datatang_tot_loss[loss=0.1611, simple_loss=0.2333, pruned_loss=0.04448, over 985514.66 frames.], batch size: 39, lr: 5.73e-04
+2022-06-18 20:31:19,804 INFO [train.py:874] (1/4) Epoch 14, batch 3550, datatang_loss[loss=0.1652, simple_loss=0.2367, pruned_loss=0.04683, over 4899.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2407, pruned_loss=0.04251, over 985343.91 frames.], batch size: 52, aishell_tot_loss[loss=0.1633, simple_loss=0.2471, pruned_loss=0.03973, over 984844.07 frames.], datatang_tot_loss[loss=0.1613, simple_loss=0.2331, pruned_loss=0.0447, over 985627.51 frames.], batch size: 52, lr: 5.73e-04
+2022-06-18 20:31:49,279 INFO [train.py:874] (1/4) Epoch 14, batch 3600, aishell_loss[loss=0.1787, simple_loss=0.2596, pruned_loss=0.04889, over 4922.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2409, pruned_loss=0.04225, over 985350.02 frames.], batch size: 49, aishell_tot_loss[loss=0.1632, simple_loss=0.2471, pruned_loss=0.03968, over 984839.83 frames.], datatang_tot_loss[loss=0.1613, simple_loss=0.2333, pruned_loss=0.04466, over 985678.95 frames.], batch size: 49, lr: 5.73e-04
+2022-06-18 20:32:20,185 INFO [train.py:874] (1/4) Epoch 14, batch 3650, datatang_loss[loss=0.1531, simple_loss=0.2209, pruned_loss=0.04267, over 4913.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2402, pruned_loss=0.04218, over 985401.15 frames.], batch size: 57, aishell_tot_loss[loss=0.1628, simple_loss=0.2467, pruned_loss=0.03949, over 984696.30 frames.], datatang_tot_loss[loss=0.1614, simple_loss=0.2333, pruned_loss=0.04474, over 985896.17 frames.], batch size: 57, lr: 5.72e-04
+2022-06-18 20:32:50,538 INFO [train.py:874] (1/4) Epoch 14, batch 3700, aishell_loss[loss=0.1512, simple_loss=0.2364, pruned_loss=0.03298, over 4899.00 frames.], tot_loss[loss=0.162, simple_loss=0.2398, pruned_loss=0.04207, over 984877.17 frames.], batch size: 34, aishell_tot_loss[loss=0.1622, simple_loss=0.2461, pruned_loss=0.03913, over 984318.52 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2336, pruned_loss=0.04495, over 985752.37 frames.], batch size: 34, lr: 5.72e-04
+2022-06-18 20:33:20,169 INFO [train.py:874] (1/4) Epoch 14, batch 3750, datatang_loss[loss=0.1464, simple_loss=0.2209, pruned_loss=0.03601, over 4961.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2402, pruned_loss=0.04198, over 984591.12 frames.], batch size: 67, aishell_tot_loss[loss=0.1625, simple_loss=0.2466, pruned_loss=0.03921, over 983958.46 frames.], datatang_tot_loss[loss=0.1616, simple_loss=0.2334, pruned_loss=0.04487, over 985795.37 frames.], batch size: 67, lr: 5.72e-04
+2022-06-18 20:33:50,760 INFO [train.py:874] (1/4) Epoch 14, batch 3800, datatang_loss[loss=0.1365, simple_loss=0.2212, pruned_loss=0.02584, over 4954.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2398, pruned_loss=0.04181, over 985006.06 frames.], batch size: 86, aishell_tot_loss[loss=0.1629, simple_loss=0.2469, pruned_loss=0.03941, over 984054.28 frames.], datatang_tot_loss[loss=0.1608, simple_loss=0.2329, pruned_loss=0.04436, over 986033.12 frames.], batch size: 86, lr: 5.72e-04
+2022-06-18 20:34:19,272 INFO [train.py:874] (1/4) Epoch 14, batch 3850, aishell_loss[loss=0.131, simple_loss=0.2235, pruned_loss=0.01921, over 4907.00 frames.], tot_loss[loss=0.1624, simple_loss=0.24, pruned_loss=0.04242, over 985056.32 frames.], batch size: 46, aishell_tot_loss[loss=0.1621, simple_loss=0.2459, pruned_loss=0.03914, over 984276.52 frames.], datatang_tot_loss[loss=0.1623, simple_loss=0.2341, pruned_loss=0.04525, over 985849.65 frames.], batch size: 46, lr: 5.71e-04
+2022-06-18 20:34:49,128 INFO [train.py:874] (1/4) Epoch 14, batch 3900, aishell_loss[loss=0.1663, simple_loss=0.2552, pruned_loss=0.03868, over 4933.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2402, pruned_loss=0.04235, over 985156.29 frames.], batch size: 49, aishell_tot_loss[loss=0.1621, simple_loss=0.2459, pruned_loss=0.0391, over 984240.81 frames.], datatang_tot_loss[loss=0.1625, simple_loss=0.2343, pruned_loss=0.04532, over 985998.37 frames.], batch size: 49, lr: 5.71e-04
+2022-06-18 20:35:17,084 INFO [train.py:874] (1/4) Epoch 14, batch 3950, datatang_loss[loss=0.162, simple_loss=0.2358, pruned_loss=0.04407, over 4952.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2409, pruned_loss=0.04238, over 985612.94 frames.], batch size: 91, aishell_tot_loss[loss=0.1626, simple_loss=0.2463, pruned_loss=0.03945, over 984649.96 frames.], datatang_tot_loss[loss=0.1623, simple_loss=0.2344, pruned_loss=0.04512, over 986115.71 frames.], batch size: 91, lr: 5.71e-04
+2022-06-18 20:35:46,849 INFO [train.py:874] (1/4) Epoch 14, batch 4000, datatang_loss[loss=0.1812, simple_loss=0.2532, pruned_loss=0.05455, over 4957.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2406, pruned_loss=0.04209, over 985620.18 frames.], batch size: 99, aishell_tot_loss[loss=0.1628, simple_loss=0.2465, pruned_loss=0.03954, over 984780.85 frames.], datatang_tot_loss[loss=0.1617, simple_loss=0.2341, pruned_loss=0.04465, over 986033.93 frames.], batch size: 99, lr: 5.71e-04
+2022-06-18 20:35:46,850 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 20:36:02,938 INFO [train.py:914] (1/4) 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] (1/4) Epoch 14, batch 4050, datatang_loss[loss=0.2415, simple_loss=0.2913, pruned_loss=0.09585, over 4946.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2402, pruned_loss=0.04179, over 985011.56 frames.], batch size: 109, aishell_tot_loss[loss=0.1624, simple_loss=0.246, pruned_loss=0.03941, over 984418.18 frames.], datatang_tot_loss[loss=0.1615, simple_loss=0.234, pruned_loss=0.04454, over 985834.83 frames.], batch size: 109, lr: 5.70e-04
+2022-06-18 20:36:59,614 INFO [train.py:874] (1/4) Epoch 14, batch 4100, datatang_loss[loss=0.1433, simple_loss=0.2094, pruned_loss=0.03856, over 4972.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2391, pruned_loss=0.04123, over 984447.17 frames.], batch size: 37, aishell_tot_loss[loss=0.1624, simple_loss=0.246, pruned_loss=0.03939, over 983794.95 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2332, pruned_loss=0.04386, over 985812.72 frames.], batch size: 37, lr: 5.70e-04
+2022-06-18 20:38:15,681 INFO [train.py:874] (1/4) Epoch 15, batch 50, datatang_loss[loss=0.1485, simple_loss=0.2203, pruned_loss=0.03833, over 4940.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2343, pruned_loss=0.03921, over 218258.37 frames.], batch size: 62, aishell_tot_loss[loss=0.1647, simple_loss=0.2482, pruned_loss=0.04057, over 115830.23 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2209, pruned_loss=0.03796, over 116076.44 frames.], batch size: 62, lr: 5.52e-04
+2022-06-18 20:38:46,642 INFO [train.py:874] (1/4) Epoch 15, batch 100, aishell_loss[loss=0.1782, simple_loss=0.2682, pruned_loss=0.04409, over 4967.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2369, pruned_loss=0.03937, over 388542.64 frames.], batch size: 39, aishell_tot_loss[loss=0.1666, simple_loss=0.2518, pruned_loss=0.04076, over 226090.42 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2206, pruned_loss=0.03783, over 210773.37 frames.], batch size: 39, lr: 5.52e-04
+2022-06-18 20:39:16,996 INFO [train.py:874] (1/4) Epoch 15, batch 150, aishell_loss[loss=0.1714, simple_loss=0.2636, pruned_loss=0.03965, over 4950.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2386, pruned_loss=0.03997, over 520892.43 frames.], batch size: 45, aishell_tot_loss[loss=0.1659, simple_loss=0.2512, pruned_loss=0.0403, over 322236.32 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2241, pruned_loss=0.03948, over 295130.11 frames.], batch size: 45, lr: 5.52e-04
+2022-06-18 20:39:45,838 INFO [train.py:874] (1/4) Epoch 15, batch 200, aishell_loss[loss=0.1576, simple_loss=0.2519, pruned_loss=0.03163, over 4954.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2367, pruned_loss=0.03971, over 624256.03 frames.], batch size: 64, aishell_tot_loss[loss=0.1656, simple_loss=0.2502, pruned_loss=0.04049, over 394614.68 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2232, pruned_loss=0.03898, over 382730.47 frames.], batch size: 64, lr: 5.52e-04
+2022-06-18 20:40:16,146 INFO [train.py:874] (1/4) Epoch 15, batch 250, aishell_loss[loss=0.1569, simple_loss=0.2398, pruned_loss=0.03701, over 4876.00 frames.], tot_loss[loss=0.158, simple_loss=0.2376, pruned_loss=0.03922, over 703984.20 frames.], batch size: 36, aishell_tot_loss[loss=0.1636, simple_loss=0.2484, pruned_loss=0.03943, over 489396.83 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2239, pruned_loss=0.03921, over 426304.65 frames.], batch size: 36, lr: 5.51e-04
+2022-06-18 20:40:47,473 INFO [train.py:874] (1/4) Epoch 15, batch 300, datatang_loss[loss=0.1704, simple_loss=0.2457, pruned_loss=0.04752, over 4916.00 frames.], tot_loss[loss=0.1577, simple_loss=0.237, pruned_loss=0.03922, over 766148.79 frames.], batch size: 57, aishell_tot_loss[loss=0.1631, simple_loss=0.2474, pruned_loss=0.03944, over 554064.93 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2239, pruned_loss=0.03911, over 484734.63 frames.], batch size: 57, lr: 5.51e-04
+2022-06-18 20:41:16,894 INFO [train.py:874] (1/4) Epoch 15, batch 350, datatang_loss[loss=0.1505, simple_loss=0.2217, pruned_loss=0.0397, over 4909.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2374, pruned_loss=0.0396, over 815102.73 frames.], batch size: 42, aishell_tot_loss[loss=0.1627, simple_loss=0.247, pruned_loss=0.03922, over 603161.87 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2259, pruned_loss=0.03997, over 546085.77 frames.], batch size: 42, lr: 5.51e-04
+2022-06-18 20:41:47,847 INFO [train.py:874] (1/4) Epoch 15, batch 400, aishell_loss[loss=0.154, simple_loss=0.2398, pruned_loss=0.03409, over 4972.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2377, pruned_loss=0.03958, over 852840.83 frames.], batch size: 44, aishell_tot_loss[loss=0.1629, simple_loss=0.2469, pruned_loss=0.03945, over 648261.15 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2267, pruned_loss=0.0397, over 597732.73 frames.], batch size: 44, lr: 5.51e-04
+2022-06-18 20:42:19,900 INFO [train.py:874] (1/4) Epoch 15, batch 450, aishell_loss[loss=0.182, simple_loss=0.2698, pruned_loss=0.04706, over 4881.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2374, pruned_loss=0.03981, over 882222.81 frames.], batch size: 47, aishell_tot_loss[loss=0.1621, simple_loss=0.2464, pruned_loss=0.0389, over 678726.54 frames.], datatang_tot_loss[loss=0.1546, simple_loss=0.228, pruned_loss=0.04059, over 653677.65 frames.], batch size: 47, lr: 5.51e-04
+2022-06-18 20:42:49,002 INFO [train.py:874] (1/4) Epoch 15, batch 500, aishell_loss[loss=0.1528, simple_loss=0.2324, pruned_loss=0.03655, over 4982.00 frames.], tot_loss[loss=0.1582, simple_loss=0.237, pruned_loss=0.03971, over 905144.49 frames.], batch size: 27, aishell_tot_loss[loss=0.1621, simple_loss=0.2462, pruned_loss=0.03902, over 706537.23 frames.], datatang_tot_loss[loss=0.1545, simple_loss=0.2285, pruned_loss=0.04029, over 701516.57 frames.], batch size: 27, lr: 5.50e-04
+2022-06-18 20:43:19,965 INFO [train.py:874] (1/4) Epoch 15, batch 550, aishell_loss[loss=0.1675, simple_loss=0.2561, pruned_loss=0.03941, over 4926.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2377, pruned_loss=0.04066, over 922956.98 frames.], batch size: 33, aishell_tot_loss[loss=0.1625, simple_loss=0.2464, pruned_loss=0.03934, over 734384.24 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2295, pruned_loss=0.0412, over 739960.11 frames.], batch size: 33, lr: 5.50e-04
+2022-06-18 20:43:50,628 INFO [train.py:874] (1/4) Epoch 15, batch 600, aishell_loss[loss=0.1765, simple_loss=0.2547, pruned_loss=0.04913, over 4923.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2379, pruned_loss=0.04074, over 936985.85 frames.], batch size: 46, aishell_tot_loss[loss=0.1618, simple_loss=0.2457, pruned_loss=0.03898, over 766309.93 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2303, pruned_loss=0.04179, over 766721.10 frames.], batch size: 46, lr: 5.50e-04
+2022-06-18 20:44:19,783 INFO [train.py:874] (1/4) Epoch 15, batch 650, aishell_loss[loss=0.1503, simple_loss=0.2371, pruned_loss=0.03178, over 4877.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2376, pruned_loss=0.04033, over 947532.39 frames.], batch size: 35, aishell_tot_loss[loss=0.161, simple_loss=0.245, pruned_loss=0.03849, over 792923.37 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2304, pruned_loss=0.0419, over 791456.27 frames.], batch size: 35, lr: 5.50e-04
+2022-06-18 20:44:51,126 INFO [train.py:874] (1/4) Epoch 15, batch 700, aishell_loss[loss=0.1838, simple_loss=0.2655, pruned_loss=0.05101, over 4920.00 frames.], tot_loss[loss=0.159, simple_loss=0.2371, pruned_loss=0.0405, over 956241.66 frames.], batch size: 41, aishell_tot_loss[loss=0.161, simple_loss=0.2448, pruned_loss=0.03859, over 811575.97 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2302, pruned_loss=0.04195, over 818583.08 frames.], batch size: 41, lr: 5.49e-04
+2022-06-18 20:45:21,687 INFO [train.py:874] (1/4) Epoch 15, batch 750, datatang_loss[loss=0.1626, simple_loss=0.2268, pruned_loss=0.04926, over 4899.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2376, pruned_loss=0.04046, over 962782.26 frames.], batch size: 47, aishell_tot_loss[loss=0.1612, simple_loss=0.2453, pruned_loss=0.03852, over 833579.33 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.23, pruned_loss=0.04208, over 836833.77 frames.], batch size: 47, lr: 5.49e-04
+2022-06-18 20:45:51,546 INFO [train.py:874] (1/4) Epoch 15, batch 800, datatang_loss[loss=0.1392, simple_loss=0.2195, pruned_loss=0.02948, over 4959.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2362, pruned_loss=0.03998, over 967902.86 frames.], batch size: 34, aishell_tot_loss[loss=0.1606, simple_loss=0.2448, pruned_loss=0.03822, over 847973.87 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2293, pruned_loss=0.04175, over 857747.39 frames.], batch size: 34, lr: 5.49e-04
+2022-06-18 20:46:21,641 INFO [train.py:874] (1/4) Epoch 15, batch 850, datatang_loss[loss=0.1689, simple_loss=0.2413, pruned_loss=0.04823, over 4903.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2361, pruned_loss=0.04009, over 971665.69 frames.], batch size: 64, aishell_tot_loss[loss=0.1606, simple_loss=0.2446, pruned_loss=0.03836, over 863408.95 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2293, pruned_loss=0.04173, over 873358.37 frames.], batch size: 64, lr: 5.49e-04
+2022-06-18 20:46:52,653 INFO [train.py:874] (1/4) Epoch 15, batch 900, aishell_loss[loss=0.1786, simple_loss=0.2762, pruned_loss=0.04054, over 4902.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2362, pruned_loss=0.04027, over 974619.84 frames.], batch size: 34, aishell_tot_loss[loss=0.1606, simple_loss=0.2444, pruned_loss=0.03837, over 876012.02 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2296, pruned_loss=0.04189, over 888068.37 frames.], batch size: 34, lr: 5.48e-04
+2022-06-18 20:47:21,756 INFO [train.py:874] (1/4) Epoch 15, batch 950, datatang_loss[loss=0.1549, simple_loss=0.2321, pruned_loss=0.03887, over 4933.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2369, pruned_loss=0.04065, over 977135.96 frames.], batch size: 88, aishell_tot_loss[loss=0.1608, simple_loss=0.2444, pruned_loss=0.03859, over 887424.22 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2303, pruned_loss=0.04206, over 900960.34 frames.], batch size: 88, lr: 5.48e-04
+2022-06-18 20:47:53,006 INFO [train.py:874] (1/4) Epoch 15, batch 1000, aishell_loss[loss=0.1754, simple_loss=0.2642, pruned_loss=0.04326, over 4944.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2378, pruned_loss=0.04098, over 978667.93 frames.], batch size: 58, aishell_tot_loss[loss=0.161, simple_loss=0.2446, pruned_loss=0.03868, over 899116.45 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2309, pruned_loss=0.04244, over 910491.90 frames.], batch size: 58, lr: 5.48e-04
+2022-06-18 20:47:53,007 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 20:48:10,191 INFO [train.py:914] (1/4) Epoch 15, validation: loss=0.1651, simple_loss=0.2495, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-18 20:48:40,053 INFO [train.py:874] (1/4) Epoch 15, batch 1050, datatang_loss[loss=0.1445, simple_loss=0.2244, pruned_loss=0.03235, over 4970.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2377, pruned_loss=0.0406, over 980298.99 frames.], batch size: 67, aishell_tot_loss[loss=0.1613, simple_loss=0.2453, pruned_loss=0.03868, over 908119.49 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2304, pruned_loss=0.04209, over 920454.35 frames.], batch size: 67, lr: 5.48e-04
+2022-06-18 20:49:10,956 INFO [train.py:874] (1/4) Epoch 15, batch 1100, aishell_loss[loss=0.1748, simple_loss=0.2583, pruned_loss=0.0456, over 4935.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2378, pruned_loss=0.0406, over 981436.22 frames.], batch size: 45, aishell_tot_loss[loss=0.1612, simple_loss=0.2449, pruned_loss=0.03879, over 918480.59 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2306, pruned_loss=0.0421, over 927065.94 frames.], batch size: 45, lr: 5.48e-04
+2022-06-18 20:49:39,094 INFO [train.py:874] (1/4) Epoch 15, batch 1150, datatang_loss[loss=0.155, simple_loss=0.2373, pruned_loss=0.0363, over 4917.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2387, pruned_loss=0.04051, over 982657.48 frames.], batch size: 83, aishell_tot_loss[loss=0.1613, simple_loss=0.2452, pruned_loss=0.03871, over 927503.01 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2311, pruned_loss=0.04215, over 933247.51 frames.], batch size: 83, lr: 5.47e-04
+2022-06-18 20:50:10,389 INFO [train.py:874] (1/4) Epoch 15, batch 1200, aishell_loss[loss=0.1647, simple_loss=0.2478, pruned_loss=0.04079, over 4958.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2384, pruned_loss=0.0405, over 983086.54 frames.], batch size: 56, aishell_tot_loss[loss=0.1611, simple_loss=0.245, pruned_loss=0.0386, over 933909.61 frames.], datatang_tot_loss[loss=0.1578, simple_loss=0.2311, pruned_loss=0.04224, over 939580.32 frames.], batch size: 56, lr: 5.47e-04
+2022-06-18 20:50:40,894 INFO [train.py:874] (1/4) Epoch 15, batch 1250, aishell_loss[loss=0.1608, simple_loss=0.2399, pruned_loss=0.04087, over 4925.00 frames.], tot_loss[loss=0.1596, simple_loss=0.238, pruned_loss=0.04063, over 983070.33 frames.], batch size: 52, aishell_tot_loss[loss=0.161, simple_loss=0.2447, pruned_loss=0.03869, over 939470.38 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2312, pruned_loss=0.04227, over 944887.22 frames.], batch size: 52, lr: 5.47e-04
+2022-06-18 20:51:09,555 INFO [train.py:874] (1/4) Epoch 15, batch 1300, aishell_loss[loss=0.1518, simple_loss=0.2355, pruned_loss=0.03401, over 4822.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2382, pruned_loss=0.04035, over 983492.17 frames.], batch size: 29, aishell_tot_loss[loss=0.1608, simple_loss=0.2444, pruned_loss=0.03859, over 945565.26 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2315, pruned_loss=0.0422, over 948958.75 frames.], batch size: 29, lr: 5.47e-04
+2022-06-18 20:51:39,855 INFO [train.py:874] (1/4) Epoch 15, batch 1350, datatang_loss[loss=0.1624, simple_loss=0.2313, pruned_loss=0.04675, over 4945.00 frames.], tot_loss[loss=0.1592, simple_loss=0.238, pruned_loss=0.04024, over 983924.94 frames.], batch size: 62, aishell_tot_loss[loss=0.1609, simple_loss=0.2448, pruned_loss=0.03849, over 949395.45 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2312, pruned_loss=0.04207, over 954021.51 frames.], batch size: 62, lr: 5.46e-04
+2022-06-18 20:52:11,225 INFO [train.py:874] (1/4) Epoch 15, batch 1400, datatang_loss[loss=0.1412, simple_loss=0.2158, pruned_loss=0.03327, over 4947.00 frames.], tot_loss[loss=0.159, simple_loss=0.2376, pruned_loss=0.04021, over 984725.84 frames.], batch size: 67, aishell_tot_loss[loss=0.1608, simple_loss=0.245, pruned_loss=0.03827, over 953248.82 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.2308, pruned_loss=0.04221, over 958531.47 frames.], batch size: 67, lr: 5.46e-04
+2022-06-18 20:52:39,831 INFO [train.py:874] (1/4) Epoch 15, batch 1450, aishell_loss[loss=0.1333, simple_loss=0.1987, pruned_loss=0.03396, over 4814.00 frames.], tot_loss[loss=0.1593, simple_loss=0.238, pruned_loss=0.04028, over 984809.07 frames.], batch size: 21, aishell_tot_loss[loss=0.161, simple_loss=0.2452, pruned_loss=0.03841, over 956803.91 frames.], datatang_tot_loss[loss=0.1576, simple_loss=0.2309, pruned_loss=0.04216, over 961869.52 frames.], batch size: 21, lr: 5.46e-04
+2022-06-18 20:53:10,615 INFO [train.py:874] (1/4) Epoch 15, batch 1500, aishell_loss[loss=0.118, simple_loss=0.1797, pruned_loss=0.02815, over 4908.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2366, pruned_loss=0.04017, over 984886.33 frames.], batch size: 21, aishell_tot_loss[loss=0.1607, simple_loss=0.2445, pruned_loss=0.03848, over 959505.95 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2305, pruned_loss=0.0419, over 965151.68 frames.], batch size: 21, lr: 5.46e-04
+2022-06-18 20:53:41,104 INFO [train.py:874] (1/4) Epoch 15, batch 1550, aishell_loss[loss=0.163, simple_loss=0.2447, pruned_loss=0.04066, over 4969.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2368, pruned_loss=0.04025, over 984681.65 frames.], batch size: 44, aishell_tot_loss[loss=0.1602, simple_loss=0.2435, pruned_loss=0.03839, over 962372.61 frames.], datatang_tot_loss[loss=0.1578, simple_loss=0.2313, pruned_loss=0.0421, over 967403.16 frames.], batch size: 44, lr: 5.45e-04
+2022-06-18 20:54:09,709 INFO [train.py:874] (1/4) Epoch 15, batch 1600, datatang_loss[loss=0.1506, simple_loss=0.2254, pruned_loss=0.0379, over 4965.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2365, pruned_loss=0.03962, over 985108.56 frames.], batch size: 31, aishell_tot_loss[loss=0.16, simple_loss=0.2436, pruned_loss=0.03817, over 965091.38 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2309, pruned_loss=0.04161, over 969751.24 frames.], batch size: 31, lr: 5.45e-04
+2022-06-18 20:54:39,884 INFO [train.py:874] (1/4) Epoch 15, batch 1650, aishell_loss[loss=0.1834, simple_loss=0.2596, pruned_loss=0.05355, over 4920.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2378, pruned_loss=0.04019, over 985260.55 frames.], batch size: 46, aishell_tot_loss[loss=0.1602, simple_loss=0.244, pruned_loss=0.03816, over 967525.46 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.2317, pruned_loss=0.04219, over 971642.36 frames.], batch size: 46, lr: 5.45e-04
+2022-06-18 20:55:11,395 INFO [train.py:874] (1/4) Epoch 15, batch 1700, aishell_loss[loss=0.1576, simple_loss=0.24, pruned_loss=0.03756, over 4940.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2375, pruned_loss=0.04057, over 985309.24 frames.], batch size: 32, aishell_tot_loss[loss=0.1603, simple_loss=0.2443, pruned_loss=0.0382, over 969196.26 frames.], datatang_tot_loss[loss=0.1582, simple_loss=0.2316, pruned_loss=0.04242, over 973559.92 frames.], batch size: 32, lr: 5.45e-04
+2022-06-18 20:55:40,716 INFO [train.py:874] (1/4) Epoch 15, batch 1750, aishell_loss[loss=0.1593, simple_loss=0.2506, pruned_loss=0.03398, over 4963.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2377, pruned_loss=0.04067, over 985548.72 frames.], batch size: 40, aishell_tot_loss[loss=0.1607, simple_loss=0.2447, pruned_loss=0.03838, over 970890.73 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2316, pruned_loss=0.04231, over 975303.68 frames.], batch size: 40, lr: 5.45e-04
+2022-06-18 20:56:11,380 INFO [train.py:874] (1/4) Epoch 15, batch 1800, datatang_loss[loss=0.1732, simple_loss=0.2329, pruned_loss=0.05676, over 4940.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2372, pruned_loss=0.04063, over 985963.77 frames.], batch size: 42, aishell_tot_loss[loss=0.1605, simple_loss=0.2445, pruned_loss=0.03828, over 972646.61 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.2312, pruned_loss=0.0424, over 976862.11 frames.], batch size: 42, lr: 5.44e-04
+2022-06-18 20:56:41,294 INFO [train.py:874] (1/4) Epoch 15, batch 1850, datatang_loss[loss=0.1505, simple_loss=0.2336, pruned_loss=0.03369, over 4951.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2374, pruned_loss=0.04023, over 985487.59 frames.], batch size: 50, aishell_tot_loss[loss=0.1609, simple_loss=0.2451, pruned_loss=0.0384, over 973833.77 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2307, pruned_loss=0.04192, over 977846.63 frames.], batch size: 50, lr: 5.44e-04
+2022-06-18 20:57:10,550 INFO [train.py:874] (1/4) Epoch 15, batch 1900, aishell_loss[loss=0.174, simple_loss=0.2594, pruned_loss=0.04435, over 4924.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2379, pruned_loss=0.04064, over 985443.42 frames.], batch size: 41, aishell_tot_loss[loss=0.1606, simple_loss=0.2447, pruned_loss=0.03825, over 974979.25 frames.], datatang_tot_loss[loss=0.1583, simple_loss=0.2314, pruned_loss=0.04255, over 978933.52 frames.], batch size: 41, lr: 5.44e-04
+2022-06-18 20:57:39,884 INFO [train.py:874] (1/4) Epoch 15, batch 1950, aishell_loss[loss=0.1618, simple_loss=0.2528, pruned_loss=0.03539, over 4892.00 frames.], tot_loss[loss=0.16, simple_loss=0.2386, pruned_loss=0.04071, over 985943.47 frames.], batch size: 42, aishell_tot_loss[loss=0.1614, simple_loss=0.2457, pruned_loss=0.03859, over 976405.91 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2312, pruned_loss=0.04236, over 980043.91 frames.], batch size: 42, lr: 5.44e-04
+2022-06-18 20:58:10,943 INFO [train.py:874] (1/4) Epoch 15, batch 2000, aishell_loss[loss=0.1612, simple_loss=0.254, pruned_loss=0.03415, over 4856.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2391, pruned_loss=0.04089, over 985830.84 frames.], batch size: 36, aishell_tot_loss[loss=0.162, simple_loss=0.2464, pruned_loss=0.03883, over 977119.77 frames.], datatang_tot_loss[loss=0.1579, simple_loss=0.2311, pruned_loss=0.04231, over 980976.13 frames.], batch size: 36, lr: 5.43e-04
+2022-06-18 20:58:10,944 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 20:58:27,551 INFO [train.py:914] (1/4) Epoch 15, validation: loss=0.166, simple_loss=0.2499, pruned_loss=0.04104, over 1622729.00 frames.
+2022-06-18 20:58:58,077 INFO [train.py:874] (1/4) Epoch 15, batch 2050, datatang_loss[loss=0.1602, simple_loss=0.2324, pruned_loss=0.04399, over 4926.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2387, pruned_loss=0.04041, over 985997.03 frames.], batch size: 57, aishell_tot_loss[loss=0.1616, simple_loss=0.2465, pruned_loss=0.03838, over 978287.44 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2307, pruned_loss=0.04235, over 981598.78 frames.], batch size: 57, lr: 5.43e-04
+2022-06-18 20:59:28,245 INFO [train.py:874] (1/4) Epoch 15, batch 2100, datatang_loss[loss=0.1985, simple_loss=0.2717, pruned_loss=0.06271, over 4956.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2381, pruned_loss=0.03981, over 985643.06 frames.], batch size: 99, aishell_tot_loss[loss=0.1612, simple_loss=0.2462, pruned_loss=0.03807, over 978853.86 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2304, pruned_loss=0.04203, over 982076.72 frames.], batch size: 99, lr: 5.43e-04
+2022-06-18 20:59:58,266 INFO [train.py:874] (1/4) Epoch 15, batch 2150, aishell_loss[loss=0.1225, simple_loss=0.1989, pruned_loss=0.0231, over 4957.00 frames.], tot_loss[loss=0.159, simple_loss=0.2376, pruned_loss=0.04018, over 985606.68 frames.], batch size: 25, aishell_tot_loss[loss=0.1613, simple_loss=0.246, pruned_loss=0.03831, over 979253.44 frames.], datatang_tot_loss[loss=0.1572, simple_loss=0.2303, pruned_loss=0.04206, over 982798.58 frames.], batch size: 25, lr: 5.43e-04
+2022-06-18 21:00:33,785 INFO [train.py:874] (1/4) Epoch 15, batch 2200, aishell_loss[loss=0.2069, simple_loss=0.2869, pruned_loss=0.06345, over 4936.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2386, pruned_loss=0.04094, over 985320.88 frames.], batch size: 54, aishell_tot_loss[loss=0.161, simple_loss=0.2457, pruned_loss=0.03815, over 979730.60 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2315, pruned_loss=0.04299, over 983085.93 frames.], batch size: 54, lr: 5.43e-04
+2022-06-18 21:01:02,747 INFO [train.py:874] (1/4) Epoch 15, batch 2250, aishell_loss[loss=0.1682, simple_loss=0.2501, pruned_loss=0.04316, over 4888.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2391, pruned_loss=0.04096, over 985287.86 frames.], batch size: 34, aishell_tot_loss[loss=0.1612, simple_loss=0.246, pruned_loss=0.03826, over 980362.58 frames.], datatang_tot_loss[loss=0.1589, simple_loss=0.2316, pruned_loss=0.04309, over 983423.45 frames.], batch size: 34, lr: 5.42e-04
+2022-06-18 21:01:33,950 INFO [train.py:874] (1/4) Epoch 15, batch 2300, datatang_loss[loss=0.1706, simple_loss=0.2347, pruned_loss=0.05325, over 4936.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2392, pruned_loss=0.04111, over 985715.68 frames.], batch size: 73, aishell_tot_loss[loss=0.1614, simple_loss=0.246, pruned_loss=0.03841, over 981168.02 frames.], datatang_tot_loss[loss=0.1591, simple_loss=0.2319, pruned_loss=0.04314, over 983852.96 frames.], batch size: 73, lr: 5.42e-04
+2022-06-18 21:02:05,451 INFO [train.py:874] (1/4) Epoch 15, batch 2350, aishell_loss[loss=0.1445, simple_loss=0.2241, pruned_loss=0.03248, over 4982.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2378, pruned_loss=0.04074, over 985909.02 frames.], batch size: 27, aishell_tot_loss[loss=0.1609, simple_loss=0.2452, pruned_loss=0.0383, over 981665.43 frames.], datatang_tot_loss[loss=0.1586, simple_loss=0.2312, pruned_loss=0.04295, over 984326.03 frames.], batch size: 27, lr: 5.42e-04
+2022-06-18 21:02:33,672 INFO [train.py:874] (1/4) Epoch 15, batch 2400, datatang_loss[loss=0.1644, simple_loss=0.2383, pruned_loss=0.04528, over 4939.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2369, pruned_loss=0.04062, over 985683.12 frames.], batch size: 69, aishell_tot_loss[loss=0.1602, simple_loss=0.2442, pruned_loss=0.0381, over 981893.45 frames.], datatang_tot_loss[loss=0.1587, simple_loss=0.2311, pruned_loss=0.0431, over 984559.54 frames.], batch size: 69, lr: 5.42e-04
+2022-06-18 21:03:04,146 INFO [train.py:874] (1/4) Epoch 15, batch 2450, aishell_loss[loss=0.1679, simple_loss=0.2518, pruned_loss=0.04194, over 4919.00 frames.], tot_loss[loss=0.1593, simple_loss=0.237, pruned_loss=0.0408, over 985426.32 frames.], batch size: 52, aishell_tot_loss[loss=0.1602, simple_loss=0.2443, pruned_loss=0.03808, over 982123.56 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2313, pruned_loss=0.04319, over 984572.61 frames.], batch size: 52, lr: 5.41e-04
+2022-06-18 21:03:35,630 INFO [train.py:874] (1/4) Epoch 15, batch 2500, aishell_loss[loss=0.1443, simple_loss=0.2355, pruned_loss=0.02658, over 4967.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2373, pruned_loss=0.04025, over 985278.10 frames.], batch size: 61, aishell_tot_loss[loss=0.16, simple_loss=0.2442, pruned_loss=0.03787, over 982652.55 frames.], datatang_tot_loss[loss=0.1585, simple_loss=0.2312, pruned_loss=0.04295, over 984418.48 frames.], batch size: 61, lr: 5.41e-04
+2022-06-18 21:04:05,192 INFO [train.py:874] (1/4) Epoch 15, batch 2550, datatang_loss[loss=0.1889, simple_loss=0.2555, pruned_loss=0.06112, over 4927.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2383, pruned_loss=0.04045, over 985393.58 frames.], batch size: 94, aishell_tot_loss[loss=0.1602, simple_loss=0.2446, pruned_loss=0.03791, over 983098.40 frames.], datatang_tot_loss[loss=0.1589, simple_loss=0.2316, pruned_loss=0.04313, over 984512.47 frames.], batch size: 94, lr: 5.41e-04
+2022-06-18 21:04:36,452 INFO [train.py:874] (1/4) Epoch 15, batch 2600, datatang_loss[loss=0.1611, simple_loss=0.2307, pruned_loss=0.04572, over 4914.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2388, pruned_loss=0.04098, over 985580.30 frames.], batch size: 75, aishell_tot_loss[loss=0.1604, simple_loss=0.2446, pruned_loss=0.03805, over 983533.49 frames.], datatang_tot_loss[loss=0.1596, simple_loss=0.2321, pruned_loss=0.04356, over 984644.34 frames.], batch size: 75, lr: 5.41e-04
+2022-06-18 21:05:08,094 INFO [train.py:874] (1/4) Epoch 15, batch 2650, datatang_loss[loss=0.184, simple_loss=0.2537, pruned_loss=0.05716, over 4919.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2392, pruned_loss=0.04185, over 985714.31 frames.], batch size: 98, aishell_tot_loss[loss=0.1608, simple_loss=0.245, pruned_loss=0.03829, over 983773.52 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2323, pruned_loss=0.04426, over 984895.40 frames.], batch size: 98, lr: 5.41e-04
+2022-06-18 21:05:37,261 INFO [train.py:874] (1/4) Epoch 15, batch 2700, datatang_loss[loss=0.1557, simple_loss=0.2216, pruned_loss=0.0449, over 4949.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2392, pruned_loss=0.04176, over 985846.88 frames.], batch size: 62, aishell_tot_loss[loss=0.1605, simple_loss=0.2448, pruned_loss=0.03805, over 983776.52 frames.], datatang_tot_loss[loss=0.1609, simple_loss=0.233, pruned_loss=0.04438, over 985305.79 frames.], batch size: 62, lr: 5.40e-04
+2022-06-18 21:06:06,768 INFO [train.py:874] (1/4) Epoch 15, batch 2750, datatang_loss[loss=0.1276, simple_loss=0.2044, pruned_loss=0.02534, over 4889.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2391, pruned_loss=0.04138, over 985810.59 frames.], batch size: 52, aishell_tot_loss[loss=0.1605, simple_loss=0.245, pruned_loss=0.03806, over 984305.13 frames.], datatang_tot_loss[loss=0.1605, simple_loss=0.2327, pruned_loss=0.04422, over 985062.49 frames.], batch size: 52, lr: 5.40e-04
+2022-06-18 21:06:38,006 INFO [train.py:874] (1/4) Epoch 15, batch 2800, datatang_loss[loss=0.1556, simple_loss=0.2256, pruned_loss=0.04283, over 4912.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2392, pruned_loss=0.04154, over 985952.42 frames.], batch size: 77, aishell_tot_loss[loss=0.1609, simple_loss=0.245, pruned_loss=0.03841, over 984621.08 frames.], datatang_tot_loss[loss=0.1605, simple_loss=0.2329, pruned_loss=0.04404, over 985157.80 frames.], batch size: 77, lr: 5.40e-04
+2022-06-18 21:07:05,639 INFO [train.py:874] (1/4) Epoch 15, batch 2850, aishell_loss[loss=0.179, simple_loss=0.2623, pruned_loss=0.0478, over 4921.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2386, pruned_loss=0.0413, over 985815.53 frames.], batch size: 41, aishell_tot_loss[loss=0.1605, simple_loss=0.2446, pruned_loss=0.03821, over 984526.39 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2326, pruned_loss=0.04409, over 985360.59 frames.], batch size: 41, lr: 5.40e-04
+2022-06-18 21:07:36,983 INFO [train.py:874] (1/4) Epoch 15, batch 2900, datatang_loss[loss=0.1283, simple_loss=0.2036, pruned_loss=0.02652, over 4972.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2386, pruned_loss=0.04131, over 985809.45 frames.], batch size: 60, aishell_tot_loss[loss=0.1603, simple_loss=0.2445, pruned_loss=0.03809, over 984483.26 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.233, pruned_loss=0.04411, over 985579.43 frames.], batch size: 60, lr: 5.39e-04
+2022-06-18 21:08:07,683 INFO [train.py:874] (1/4) Epoch 15, batch 2950, aishell_loss[loss=0.1513, simple_loss=0.2273, pruned_loss=0.03763, over 4954.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2382, pruned_loss=0.04061, over 985922.80 frames.], batch size: 40, aishell_tot_loss[loss=0.16, simple_loss=0.2444, pruned_loss=0.03784, over 984891.85 frames.], datatang_tot_loss[loss=0.16, simple_loss=0.2325, pruned_loss=0.04378, over 985483.47 frames.], batch size: 40, lr: 5.39e-04
+2022-06-18 21:08:37,248 INFO [train.py:874] (1/4) Epoch 15, batch 3000, datatang_loss[loss=0.1732, simple_loss=0.2379, pruned_loss=0.05423, over 4939.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2384, pruned_loss=0.04118, over 985941.83 frames.], batch size: 69, aishell_tot_loss[loss=0.1604, simple_loss=0.2446, pruned_loss=0.03813, over 984747.30 frames.], datatang_tot_loss[loss=0.1602, simple_loss=0.2325, pruned_loss=0.04397, over 985812.97 frames.], batch size: 69, lr: 5.39e-04
+2022-06-18 21:08:37,249 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 21:08:54,379 INFO [train.py:914] (1/4) Epoch 15, validation: loss=0.1663, simple_loss=0.2504, pruned_loss=0.04115, over 1622729.00 frames.
+2022-06-18 21:09:23,248 INFO [train.py:874] (1/4) Epoch 15, batch 3050, aishell_loss[loss=0.1599, simple_loss=0.2569, pruned_loss=0.0315, over 4979.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2385, pruned_loss=0.04089, over 985926.58 frames.], batch size: 39, aishell_tot_loss[loss=0.1603, simple_loss=0.2445, pruned_loss=0.03802, over 984824.65 frames.], datatang_tot_loss[loss=0.1601, simple_loss=0.2324, pruned_loss=0.04397, over 985903.20 frames.], batch size: 39, lr: 5.39e-04
+2022-06-18 21:09:54,998 INFO [train.py:874] (1/4) Epoch 15, batch 3100, aishell_loss[loss=0.1454, simple_loss=0.2181, pruned_loss=0.03636, over 4944.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2395, pruned_loss=0.04105, over 986003.79 frames.], batch size: 25, aishell_tot_loss[loss=0.1612, simple_loss=0.2456, pruned_loss=0.03841, over 984962.84 frames.], datatang_tot_loss[loss=0.1599, simple_loss=0.2325, pruned_loss=0.04368, over 985962.24 frames.], batch size: 25, lr: 5.39e-04
+2022-06-18 21:10:26,280 INFO [train.py:874] (1/4) Epoch 15, batch 3150, aishell_loss[loss=0.1732, simple_loss=0.2471, pruned_loss=0.04964, over 4921.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2398, pruned_loss=0.04119, over 986094.22 frames.], batch size: 33, aishell_tot_loss[loss=0.1608, simple_loss=0.2452, pruned_loss=0.0382, over 985164.69 frames.], datatang_tot_loss[loss=0.1606, simple_loss=0.2332, pruned_loss=0.04404, over 985973.19 frames.], batch size: 33, lr: 5.38e-04
+2022-06-18 21:10:56,706 INFO [train.py:874] (1/4) Epoch 15, batch 3200, datatang_loss[loss=0.1347, simple_loss=0.2012, pruned_loss=0.03413, over 4870.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2398, pruned_loss=0.04097, over 986000.50 frames.], batch size: 39, aishell_tot_loss[loss=0.1609, simple_loss=0.2454, pruned_loss=0.0382, over 985193.40 frames.], datatang_tot_loss[loss=0.1604, simple_loss=0.2331, pruned_loss=0.04386, over 985981.55 frames.], batch size: 39, lr: 5.38e-04
+2022-06-18 21:11:27,556 INFO [train.py:874] (1/4) Epoch 15, batch 3250, aishell_loss[loss=0.1309, simple_loss=0.21, pruned_loss=0.02589, over 4979.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2388, pruned_loss=0.04075, over 986096.31 frames.], batch size: 30, aishell_tot_loss[loss=0.1607, simple_loss=0.2453, pruned_loss=0.03808, over 985385.01 frames.], datatang_tot_loss[loss=0.1599, simple_loss=0.2325, pruned_loss=0.04364, over 985976.37 frames.], batch size: 30, lr: 5.38e-04
+2022-06-18 21:11:59,179 INFO [train.py:874] (1/4) Epoch 15, batch 3300, datatang_loss[loss=0.1635, simple_loss=0.2481, pruned_loss=0.03948, over 4949.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2391, pruned_loss=0.0407, over 986297.35 frames.], batch size: 91, aishell_tot_loss[loss=0.1608, simple_loss=0.2456, pruned_loss=0.03801, over 985525.60 frames.], datatang_tot_loss[loss=0.1599, simple_loss=0.2327, pruned_loss=0.04355, over 986128.32 frames.], batch size: 91, lr: 5.38e-04
+2022-06-18 21:12:28,842 INFO [train.py:874] (1/4) Epoch 15, batch 3350, aishell_loss[loss=0.1575, simple_loss=0.2477, pruned_loss=0.03368, over 4937.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2395, pruned_loss=0.04092, over 986401.42 frames.], batch size: 49, aishell_tot_loss[loss=0.161, simple_loss=0.2457, pruned_loss=0.03816, over 985584.47 frames.], datatang_tot_loss[loss=0.1601, simple_loss=0.233, pruned_loss=0.0436, over 986293.34 frames.], batch size: 49, lr: 5.37e-04
+2022-06-18 21:12:59,952 INFO [train.py:874] (1/4) Epoch 15, batch 3400, datatang_loss[loss=0.1578, simple_loss=0.2326, pruned_loss=0.04151, over 4923.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2389, pruned_loss=0.04027, over 985931.71 frames.], batch size: 83, aishell_tot_loss[loss=0.1604, simple_loss=0.2452, pruned_loss=0.03777, over 985302.25 frames.], datatang_tot_loss[loss=0.1597, simple_loss=0.2327, pruned_loss=0.04338, over 986202.10 frames.], batch size: 83, lr: 5.37e-04
+2022-06-18 21:13:28,565 INFO [train.py:874] (1/4) Epoch 15, batch 3450, aishell_loss[loss=0.1653, simple_loss=0.2512, pruned_loss=0.03966, over 4964.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2388, pruned_loss=0.03995, over 985806.01 frames.], batch size: 51, aishell_tot_loss[loss=0.1602, simple_loss=0.245, pruned_loss=0.03766, over 985232.45 frames.], datatang_tot_loss[loss=0.1594, simple_loss=0.2326, pruned_loss=0.04313, over 986191.85 frames.], batch size: 51, lr: 5.37e-04
+2022-06-18 21:13:59,930 INFO [train.py:874] (1/4) Epoch 15, batch 3500, aishell_loss[loss=0.1579, simple_loss=0.2406, pruned_loss=0.03758, over 4977.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2377, pruned_loss=0.03971, over 985963.95 frames.], batch size: 48, aishell_tot_loss[loss=0.1606, simple_loss=0.2454, pruned_loss=0.03792, over 985441.78 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2314, pruned_loss=0.04239, over 986144.07 frames.], batch size: 48, lr: 5.37e-04
+2022-06-18 21:14:32,189 INFO [train.py:874] (1/4) Epoch 15, batch 3550, aishell_loss[loss=0.1798, simple_loss=0.2564, pruned_loss=0.05166, over 4961.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2383, pruned_loss=0.04, over 985669.86 frames.], batch size: 61, aishell_tot_loss[loss=0.161, simple_loss=0.2455, pruned_loss=0.0382, over 985146.77 frames.], datatang_tot_loss[loss=0.1582, simple_loss=0.2315, pruned_loss=0.04241, over 986176.92 frames.], batch size: 61, lr: 5.37e-04
+2022-06-18 21:15:01,902 INFO [train.py:874] (1/4) Epoch 15, batch 3600, aishell_loss[loss=0.1861, simple_loss=0.2473, pruned_loss=0.06246, over 4956.00 frames.], tot_loss[loss=0.1591, simple_loss=0.238, pruned_loss=0.04016, over 985809.35 frames.], batch size: 31, aishell_tot_loss[loss=0.161, simple_loss=0.2454, pruned_loss=0.03828, over 985185.48 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.2314, pruned_loss=0.04233, over 986283.91 frames.], batch size: 31, lr: 5.36e-04
+2022-06-18 21:15:34,175 INFO [train.py:874] (1/4) Epoch 15, batch 3650, datatang_loss[loss=0.1371, simple_loss=0.2125, pruned_loss=0.03087, over 4973.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2379, pruned_loss=0.04039, over 985987.76 frames.], batch size: 45, aishell_tot_loss[loss=0.1612, simple_loss=0.2455, pruned_loss=0.03842, over 985240.58 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2315, pruned_loss=0.04232, over 986395.90 frames.], batch size: 45, lr: 5.36e-04
+2022-06-18 21:16:05,734 INFO [train.py:874] (1/4) Epoch 15, batch 3700, aishell_loss[loss=0.1555, simple_loss=0.2427, pruned_loss=0.03411, over 4973.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2387, pruned_loss=0.04048, over 985792.38 frames.], batch size: 44, aishell_tot_loss[loss=0.1612, simple_loss=0.2455, pruned_loss=0.03841, over 985276.31 frames.], datatang_tot_loss[loss=0.1584, simple_loss=0.2319, pruned_loss=0.0425, over 986204.84 frames.], batch size: 44, lr: 5.36e-04
+2022-06-18 21:16:34,880 INFO [train.py:874] (1/4) Epoch 15, batch 3750, datatang_loss[loss=0.1436, simple_loss=0.221, pruned_loss=0.03309, over 4948.00 frames.], tot_loss[loss=0.1584, simple_loss=0.237, pruned_loss=0.03985, over 985484.16 frames.], batch size: 69, aishell_tot_loss[loss=0.1607, simple_loss=0.245, pruned_loss=0.03821, over 985303.15 frames.], datatang_tot_loss[loss=0.1574, simple_loss=0.2309, pruned_loss=0.04198, over 985851.63 frames.], batch size: 69, lr: 5.36e-04
+2022-06-18 21:17:05,023 INFO [train.py:874] (1/4) Epoch 15, batch 3800, aishell_loss[loss=0.187, simple_loss=0.2768, pruned_loss=0.04863, over 4938.00 frames.], tot_loss[loss=0.1591, simple_loss=0.238, pruned_loss=0.04006, over 985650.33 frames.], batch size: 79, aishell_tot_loss[loss=0.1609, simple_loss=0.2453, pruned_loss=0.03828, over 985526.27 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2312, pruned_loss=0.04215, over 985781.37 frames.], batch size: 79, lr: 5.35e-04
+2022-06-18 21:17:36,747 INFO [train.py:874] (1/4) Epoch 15, batch 3850, aishell_loss[loss=0.1643, simple_loss=0.2438, pruned_loss=0.04241, over 4914.00 frames.], tot_loss[loss=0.159, simple_loss=0.238, pruned_loss=0.04002, over 985229.99 frames.], batch size: 41, aishell_tot_loss[loss=0.1605, simple_loss=0.2448, pruned_loss=0.03813, over 985151.92 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.2314, pruned_loss=0.04227, over 985707.37 frames.], batch size: 41, lr: 5.35e-04
+2022-06-18 21:18:05,695 INFO [train.py:874] (1/4) Epoch 15, batch 3900, datatang_loss[loss=0.1462, simple_loss=0.2342, pruned_loss=0.02911, over 4931.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2375, pruned_loss=0.03995, over 985221.32 frames.], batch size: 94, aishell_tot_loss[loss=0.1601, simple_loss=0.2445, pruned_loss=0.03783, over 985021.30 frames.], datatang_tot_loss[loss=0.158, simple_loss=0.2313, pruned_loss=0.04234, over 985765.39 frames.], batch size: 94, lr: 5.35e-04
+2022-06-18 21:18:34,804 INFO [train.py:874] (1/4) Epoch 15, batch 3950, datatang_loss[loss=0.161, simple_loss=0.2368, pruned_loss=0.04262, over 4925.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2385, pruned_loss=0.04029, over 985329.30 frames.], batch size: 42, aishell_tot_loss[loss=0.1597, simple_loss=0.2442, pruned_loss=0.03765, over 984931.41 frames.], datatang_tot_loss[loss=0.1591, simple_loss=0.2323, pruned_loss=0.043, over 985947.45 frames.], batch size: 42, lr: 5.35e-04
+2022-06-18 21:19:03,472 INFO [train.py:874] (1/4) Epoch 15, batch 4000, datatang_loss[loss=0.183, simple_loss=0.236, pruned_loss=0.06499, over 4967.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2384, pruned_loss=0.04021, over 985383.43 frames.], batch size: 34, aishell_tot_loss[loss=0.1596, simple_loss=0.2439, pruned_loss=0.0376, over 984921.04 frames.], datatang_tot_loss[loss=0.1592, simple_loss=0.2324, pruned_loss=0.04306, over 985998.54 frames.], batch size: 34, lr: 5.35e-04
+2022-06-18 21:19:03,473 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 21:19:20,713 INFO [train.py:914] (1/4) Epoch 15, validation: loss=0.1641, simple_loss=0.2483, pruned_loss=0.0399, over 1622729.00 frames.
+2022-06-18 21:19:49,326 INFO [train.py:874] (1/4) Epoch 15, batch 4050, datatang_loss[loss=0.1385, simple_loss=0.2223, pruned_loss=0.02733, over 4852.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2378, pruned_loss=0.03969, over 985355.34 frames.], batch size: 30, aishell_tot_loss[loss=0.1591, simple_loss=0.2435, pruned_loss=0.03737, over 984982.33 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2321, pruned_loss=0.04276, over 985896.20 frames.], batch size: 30, lr: 5.34e-04
+2022-06-18 21:20:18,330 INFO [train.py:874] (1/4) Epoch 15, batch 4100, aishell_loss[loss=0.1437, simple_loss=0.2296, pruned_loss=0.02892, over 4869.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2378, pruned_loss=0.03967, over 984748.09 frames.], batch size: 36, aishell_tot_loss[loss=0.159, simple_loss=0.2434, pruned_loss=0.03734, over 984383.09 frames.], datatang_tot_loss[loss=0.1588, simple_loss=0.2321, pruned_loss=0.04278, over 985864.74 frames.], batch size: 36, lr: 5.34e-04
+2022-06-18 21:20:47,955 INFO [train.py:874] (1/4) Epoch 15, batch 4150, datatang_loss[loss=0.1529, simple_loss=0.2278, pruned_loss=0.03898, over 4897.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2383, pruned_loss=0.04026, over 984841.29 frames.], batch size: 47, aishell_tot_loss[loss=0.1595, simple_loss=0.2438, pruned_loss=0.03758, over 984492.94 frames.], datatang_tot_loss[loss=0.1592, simple_loss=0.2324, pruned_loss=0.04299, over 985737.39 frames.], batch size: 47, lr: 5.34e-04
+2022-06-18 21:22:22,379 INFO [train.py:874] (1/4) Epoch 16, batch 50, aishell_loss[loss=0.1748, simple_loss=0.2553, pruned_loss=0.0472, over 4971.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2282, pruned_loss=0.03501, over 218803.20 frames.], batch size: 39, aishell_tot_loss[loss=0.154, simple_loss=0.2389, pruned_loss=0.03459, over 111863.81 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2185, pruned_loss=0.03529, over 120603.80 frames.], batch size: 39, lr: 5.18e-04
+2022-06-18 21:22:53,187 INFO [train.py:874] (1/4) Epoch 16, batch 100, aishell_loss[loss=0.1221, simple_loss=0.1839, pruned_loss=0.03013, over 4872.00 frames.], tot_loss[loss=0.148, simple_loss=0.2265, pruned_loss=0.03472, over 388791.91 frames.], batch size: 20, aishell_tot_loss[loss=0.1518, simple_loss=0.2366, pruned_loss=0.03355, over 202821.29 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2182, pruned_loss=0.03563, over 234139.88 frames.], batch size: 20, lr: 5.18e-04
+2022-06-18 21:23:22,589 INFO [train.py:874] (1/4) Epoch 16, batch 150, aishell_loss[loss=0.1567, simple_loss=0.2455, pruned_loss=0.0339, over 4903.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2253, pruned_loss=0.03492, over 521048.33 frames.], batch size: 34, aishell_tot_loss[loss=0.1515, simple_loss=0.2357, pruned_loss=0.03371, over 280836.64 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2172, pruned_loss=0.0358, over 335951.78 frames.], batch size: 34, lr: 5.18e-04
+2022-06-18 21:23:53,696 INFO [train.py:874] (1/4) Epoch 16, batch 200, aishell_loss[loss=0.1402, simple_loss=0.2313, pruned_loss=0.02453, over 4950.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2283, pruned_loss=0.03642, over 623857.26 frames.], batch size: 40, aishell_tot_loss[loss=0.1526, simple_loss=0.2372, pruned_loss=0.03397, over 350978.81 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2209, pruned_loss=0.03793, over 423921.88 frames.], batch size: 40, lr: 5.17e-04
+2022-06-18 21:24:24,698 INFO [train.py:874] (1/4) Epoch 16, batch 250, aishell_loss[loss=0.1452, simple_loss=0.2367, pruned_loss=0.02686, over 4879.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2291, pruned_loss=0.03624, over 704123.34 frames.], batch size: 35, aishell_tot_loss[loss=0.1537, simple_loss=0.2383, pruned_loss=0.0346, over 439662.53 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.22, pruned_loss=0.0375, over 477492.19 frames.], batch size: 35, lr: 5.17e-04
+2022-06-18 21:24:55,265 INFO [train.py:874] (1/4) Epoch 16, batch 300, datatang_loss[loss=0.1742, simple_loss=0.2321, pruned_loss=0.05817, over 4933.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2307, pruned_loss=0.03673, over 766232.35 frames.], batch size: 37, aishell_tot_loss[loss=0.1549, simple_loss=0.2395, pruned_loss=0.03512, over 508550.73 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2208, pruned_loss=0.03783, over 532725.47 frames.], batch size: 37, lr: 5.17e-04
+2022-06-18 21:25:25,776 INFO [train.py:874] (1/4) Epoch 16, batch 350, datatang_loss[loss=0.1679, simple_loss=0.2328, pruned_loss=0.05145, over 4973.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2315, pruned_loss=0.03736, over 814959.42 frames.], batch size: 48, aishell_tot_loss[loss=0.1551, simple_loss=0.2396, pruned_loss=0.03525, over 554166.98 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.223, pruned_loss=0.03867, over 595935.97 frames.], batch size: 48, lr: 5.17e-04
+2022-06-18 21:25:56,578 INFO [train.py:874] (1/4) Epoch 16, batch 400, datatang_loss[loss=0.1492, simple_loss=0.2234, pruned_loss=0.03747, over 4914.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2328, pruned_loss=0.03794, over 852615.64 frames.], batch size: 81, aishell_tot_loss[loss=0.1565, simple_loss=0.2409, pruned_loss=0.03607, over 604882.98 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2237, pruned_loss=0.03885, over 641742.23 frames.], batch size: 81, lr: 5.17e-04
+2022-06-18 21:26:25,942 INFO [train.py:874] (1/4) Epoch 16, batch 450, datatang_loss[loss=0.155, simple_loss=0.2282, pruned_loss=0.04093, over 4941.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2336, pruned_loss=0.03827, over 882084.06 frames.], batch size: 69, aishell_tot_loss[loss=0.1571, simple_loss=0.2412, pruned_loss=0.03654, over 658027.25 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2242, pruned_loss=0.03912, over 674545.38 frames.], batch size: 69, lr: 5.16e-04
+2022-06-18 21:26:54,482 INFO [train.py:874] (1/4) Epoch 16, batch 500, datatang_loss[loss=0.1424, simple_loss=0.2192, pruned_loss=0.03282, over 4959.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2348, pruned_loss=0.03851, over 905054.12 frames.], batch size: 37, aishell_tot_loss[loss=0.1579, simple_loss=0.2418, pruned_loss=0.03701, over 710683.36 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2244, pruned_loss=0.03929, over 697143.90 frames.], batch size: 37, lr: 5.16e-04
+2022-06-18 21:27:26,328 INFO [train.py:874] (1/4) Epoch 16, batch 550, aishell_loss[loss=0.1679, simple_loss=0.2507, pruned_loss=0.04259, over 4932.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2337, pruned_loss=0.0384, over 922808.86 frames.], batch size: 33, aishell_tot_loss[loss=0.157, simple_loss=0.2404, pruned_loss=0.03677, over 742883.86 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.225, pruned_loss=0.03952, over 731195.67 frames.], batch size: 33, lr: 5.16e-04
+2022-06-18 21:27:56,232 INFO [train.py:874] (1/4) Epoch 16, batch 600, aishell_loss[loss=0.1706, simple_loss=0.2515, pruned_loss=0.04488, over 4951.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2349, pruned_loss=0.03885, over 936931.32 frames.], batch size: 64, aishell_tot_loss[loss=0.1572, simple_loss=0.2407, pruned_loss=0.03689, over 771653.23 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2265, pruned_loss=0.04007, over 761190.30 frames.], batch size: 64, lr: 5.16e-04
+2022-06-18 21:28:26,888 INFO [train.py:874] (1/4) Epoch 16, batch 650, datatang_loss[loss=0.1693, simple_loss=0.2552, pruned_loss=0.04166, over 4851.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2356, pruned_loss=0.03938, over 947965.82 frames.], batch size: 36, aishell_tot_loss[loss=0.1575, simple_loss=0.241, pruned_loss=0.03698, over 793944.24 frames.], datatang_tot_loss[loss=0.1546, simple_loss=0.2277, pruned_loss=0.04072, over 790897.23 frames.], batch size: 36, lr: 5.16e-04
+2022-06-18 21:28:57,668 INFO [train.py:874] (1/4) Epoch 16, batch 700, aishell_loss[loss=0.1714, simple_loss=0.2606, pruned_loss=0.0411, over 4959.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2368, pruned_loss=0.03978, over 956332.56 frames.], batch size: 79, aishell_tot_loss[loss=0.1584, simple_loss=0.2423, pruned_loss=0.03724, over 813218.79 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2286, pruned_loss=0.04103, over 817127.49 frames.], batch size: 79, lr: 5.15e-04
+2022-06-18 21:29:27,945 INFO [train.py:874] (1/4) Epoch 16, batch 750, aishell_loss[loss=0.175, simple_loss=0.2581, pruned_loss=0.04599, over 4925.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2367, pruned_loss=0.03991, over 963454.94 frames.], batch size: 52, aishell_tot_loss[loss=0.1588, simple_loss=0.2428, pruned_loss=0.03746, over 830204.67 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2288, pruned_loss=0.04105, over 840786.36 frames.], batch size: 52, lr: 5.15e-04
+2022-06-18 21:29:58,390 INFO [train.py:874] (1/4) Epoch 16, batch 800, aishell_loss[loss=0.1625, simple_loss=0.2493, pruned_loss=0.03783, over 4867.00 frames.], tot_loss[loss=0.1582, simple_loss=0.237, pruned_loss=0.03969, over 968405.53 frames.], batch size: 36, aishell_tot_loss[loss=0.1583, simple_loss=0.2424, pruned_loss=0.03709, over 848540.19 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2298, pruned_loss=0.04132, over 857868.75 frames.], batch size: 36, lr: 5.15e-04
+2022-06-18 21:30:28,973 INFO [train.py:874] (1/4) Epoch 16, batch 850, aishell_loss[loss=0.1582, simple_loss=0.2393, pruned_loss=0.03858, over 4949.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2364, pruned_loss=0.03931, over 972383.04 frames.], batch size: 56, aishell_tot_loss[loss=0.1578, simple_loss=0.2421, pruned_loss=0.03672, over 863542.42 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2297, pruned_loss=0.04132, over 874147.69 frames.], batch size: 56, lr: 5.15e-04
+2022-06-18 21:30:58,973 INFO [train.py:874] (1/4) Epoch 16, batch 900, aishell_loss[loss=0.121, simple_loss=0.2101, pruned_loss=0.01595, over 4967.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2363, pruned_loss=0.03912, over 975324.34 frames.], batch size: 30, aishell_tot_loss[loss=0.1573, simple_loss=0.2418, pruned_loss=0.03642, over 875189.53 frames.], datatang_tot_loss[loss=0.1566, simple_loss=0.2304, pruned_loss=0.04135, over 889694.05 frames.], batch size: 30, lr: 5.15e-04
+2022-06-18 21:31:29,481 INFO [train.py:874] (1/4) Epoch 16, batch 950, datatang_loss[loss=0.1763, simple_loss=0.2388, pruned_loss=0.05686, over 4955.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2354, pruned_loss=0.03878, over 977807.88 frames.], batch size: 67, aishell_tot_loss[loss=0.1569, simple_loss=0.2414, pruned_loss=0.03621, over 886492.73 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2301, pruned_loss=0.0411, over 902618.67 frames.], batch size: 67, lr: 5.14e-04
+2022-06-18 21:31:59,327 INFO [train.py:874] (1/4) Epoch 16, batch 1000, aishell_loss[loss=0.1596, simple_loss=0.2426, pruned_loss=0.03827, over 4944.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2366, pruned_loss=0.03883, over 979387.01 frames.], batch size: 54, aishell_tot_loss[loss=0.1576, simple_loss=0.2422, pruned_loss=0.03652, over 901239.50 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.23, pruned_loss=0.04105, over 909593.48 frames.], batch size: 54, lr: 5.14e-04
+2022-06-18 21:31:59,328 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 21:32:15,095 INFO [train.py:914] (1/4) Epoch 16, validation: loss=0.1652, simple_loss=0.2489, pruned_loss=0.04072, over 1622729.00 frames.
+2022-06-18 21:32:45,792 INFO [train.py:874] (1/4) Epoch 16, batch 1050, aishell_loss[loss=0.1641, simple_loss=0.2496, pruned_loss=0.03933, over 4936.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2363, pruned_loss=0.03857, over 980762.05 frames.], batch size: 49, aishell_tot_loss[loss=0.1576, simple_loss=0.2423, pruned_loss=0.03646, over 909968.72 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2298, pruned_loss=0.04075, over 919627.10 frames.], batch size: 49, lr: 5.14e-04
+2022-06-18 21:33:17,605 INFO [train.py:874] (1/4) Epoch 16, batch 1100, aishell_loss[loss=0.1547, simple_loss=0.2367, pruned_loss=0.03635, over 4930.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2364, pruned_loss=0.03901, over 981429.55 frames.], batch size: 54, aishell_tot_loss[loss=0.1577, simple_loss=0.2421, pruned_loss=0.03665, over 917417.78 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2304, pruned_loss=0.04098, over 928283.05 frames.], batch size: 54, lr: 5.14e-04
+2022-06-18 21:33:46,463 INFO [train.py:874] (1/4) Epoch 16, batch 1150, aishell_loss[loss=0.1611, simple_loss=0.2443, pruned_loss=0.03893, over 4913.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2369, pruned_loss=0.03925, over 982284.09 frames.], batch size: 41, aishell_tot_loss[loss=0.1575, simple_loss=0.2421, pruned_loss=0.03647, over 925440.33 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2309, pruned_loss=0.04145, over 935009.60 frames.], batch size: 41, lr: 5.14e-04
+2022-06-18 21:34:16,210 INFO [train.py:874] (1/4) Epoch 16, batch 1200, aishell_loss[loss=0.1916, simple_loss=0.2794, pruned_loss=0.05185, over 4919.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2378, pruned_loss=0.03925, over 983165.81 frames.], batch size: 46, aishell_tot_loss[loss=0.1585, simple_loss=0.2432, pruned_loss=0.03687, over 933368.61 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2307, pruned_loss=0.04119, over 940411.99 frames.], batch size: 46, lr: 5.13e-04
+2022-06-18 21:34:46,014 INFO [train.py:874] (1/4) Epoch 16, batch 1250, aishell_loss[loss=0.1635, simple_loss=0.2536, pruned_loss=0.0367, over 4946.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2387, pruned_loss=0.0393, over 983749.40 frames.], batch size: 56, aishell_tot_loss[loss=0.1591, simple_loss=0.2441, pruned_loss=0.03708, over 941083.73 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.2306, pruned_loss=0.04123, over 944354.97 frames.], batch size: 56, lr: 5.13e-04
+2022-06-18 21:35:16,592 INFO [train.py:874] (1/4) Epoch 16, batch 1300, datatang_loss[loss=0.1569, simple_loss=0.2399, pruned_loss=0.03697, over 4938.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2381, pruned_loss=0.03939, over 984229.06 frames.], batch size: 62, aishell_tot_loss[loss=0.1593, simple_loss=0.2442, pruned_loss=0.03721, over 945735.93 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.2303, pruned_loss=0.0412, over 949813.59 frames.], batch size: 62, lr: 5.13e-04
+2022-06-18 21:35:48,249 INFO [train.py:874] (1/4) Epoch 16, batch 1350, datatang_loss[loss=0.1373, simple_loss=0.2153, pruned_loss=0.02962, over 4857.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2392, pruned_loss=0.04008, over 984600.28 frames.], batch size: 30, aishell_tot_loss[loss=0.1591, simple_loss=0.2442, pruned_loss=0.03701, over 950746.20 frames.], datatang_tot_loss[loss=0.1581, simple_loss=0.2317, pruned_loss=0.04225, over 953828.41 frames.], batch size: 30, lr: 5.13e-04
+2022-06-18 21:36:18,258 INFO [train.py:874] (1/4) Epoch 16, batch 1400, aishell_loss[loss=0.2038, simple_loss=0.2855, pruned_loss=0.06103, over 4935.00 frames.], tot_loss[loss=0.159, simple_loss=0.238, pruned_loss=0.03997, over 984481.86 frames.], batch size: 68, aishell_tot_loss[loss=0.159, simple_loss=0.2439, pruned_loss=0.03703, over 953978.15 frames.], datatang_tot_loss[loss=0.1577, simple_loss=0.2312, pruned_loss=0.04213, over 958015.62 frames.], batch size: 68, lr: 5.13e-04
+2022-06-18 21:36:47,790 INFO [train.py:874] (1/4) Epoch 16, batch 1450, datatang_loss[loss=0.161, simple_loss=0.2379, pruned_loss=0.04208, over 4891.00 frames.], tot_loss[loss=0.158, simple_loss=0.2371, pruned_loss=0.03947, over 984505.00 frames.], batch size: 47, aishell_tot_loss[loss=0.1589, simple_loss=0.2438, pruned_loss=0.03703, over 957428.37 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2304, pruned_loss=0.04168, over 961324.85 frames.], batch size: 47, lr: 5.12e-04
+2022-06-18 21:37:19,306 INFO [train.py:874] (1/4) Epoch 16, batch 1500, datatang_loss[loss=0.1375, simple_loss=0.2179, pruned_loss=0.02859, over 4961.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2364, pruned_loss=0.03909, over 984932.70 frames.], batch size: 31, aishell_tot_loss[loss=0.1587, simple_loss=0.2433, pruned_loss=0.03704, over 961019.51 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.23, pruned_loss=0.04132, over 964131.32 frames.], batch size: 31, lr: 5.12e-04
+2022-06-18 21:37:48,656 INFO [train.py:874] (1/4) Epoch 16, batch 1550, datatang_loss[loss=0.1275, simple_loss=0.2047, pruned_loss=0.02512, over 4958.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2359, pruned_loss=0.03871, over 985173.08 frames.], batch size: 37, aishell_tot_loss[loss=0.1584, simple_loss=0.2431, pruned_loss=0.03687, over 963974.07 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2295, pruned_loss=0.04108, over 966699.63 frames.], batch size: 37, lr: 5.12e-04
+2022-06-18 21:38:18,590 INFO [train.py:874] (1/4) Epoch 16, batch 1600, datatang_loss[loss=0.1547, simple_loss=0.2317, pruned_loss=0.03881, over 4864.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2367, pruned_loss=0.03935, over 985150.76 frames.], batch size: 39, aishell_tot_loss[loss=0.159, simple_loss=0.2436, pruned_loss=0.03719, over 966436.81 frames.], datatang_tot_loss[loss=0.1564, simple_loss=0.23, pruned_loss=0.04137, over 968883.32 frames.], batch size: 39, lr: 5.12e-04
+2022-06-18 21:38:49,750 INFO [train.py:874] (1/4) Epoch 16, batch 1650, aishell_loss[loss=0.1427, simple_loss=0.2379, pruned_loss=0.02378, over 4909.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2364, pruned_loss=0.03922, over 984880.38 frames.], batch size: 41, aishell_tot_loss[loss=0.1588, simple_loss=0.2433, pruned_loss=0.03713, over 968001.97 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2302, pruned_loss=0.04124, over 971100.43 frames.], batch size: 41, lr: 5.12e-04
+2022-06-18 21:39:20,229 INFO [train.py:874] (1/4) Epoch 16, batch 1700, aishell_loss[loss=0.1478, simple_loss=0.2363, pruned_loss=0.02963, over 4969.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2364, pruned_loss=0.03912, over 984946.97 frames.], batch size: 61, aishell_tot_loss[loss=0.1583, simple_loss=0.243, pruned_loss=0.03682, over 970011.07 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2305, pruned_loss=0.04141, over 972736.68 frames.], batch size: 61, lr: 5.11e-04
+2022-06-18 21:39:48,454 INFO [train.py:874] (1/4) Epoch 16, batch 1750, datatang_loss[loss=0.1717, simple_loss=0.241, pruned_loss=0.05124, over 4925.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2372, pruned_loss=0.03966, over 985225.91 frames.], batch size: 88, aishell_tot_loss[loss=0.1586, simple_loss=0.2433, pruned_loss=0.03694, over 971818.64 frames.], datatang_tot_loss[loss=0.1573, simple_loss=0.2309, pruned_loss=0.04185, over 974419.02 frames.], batch size: 88, lr: 5.11e-04
+2022-06-18 21:40:19,018 INFO [train.py:874] (1/4) Epoch 16, batch 1800, aishell_loss[loss=0.1583, simple_loss=0.2495, pruned_loss=0.03354, over 4980.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2362, pruned_loss=0.03898, over 985603.22 frames.], batch size: 30, aishell_tot_loss[loss=0.158, simple_loss=0.2427, pruned_loss=0.03659, over 974011.22 frames.], datatang_tot_loss[loss=0.1567, simple_loss=0.2302, pruned_loss=0.04164, over 975539.08 frames.], batch size: 30, lr: 5.11e-04
+2022-06-18 21:40:47,577 INFO [train.py:874] (1/4) Epoch 16, batch 1850, aishell_loss[loss=0.1804, simple_loss=0.2598, pruned_loss=0.05046, over 4909.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2361, pruned_loss=0.03935, over 985917.38 frames.], batch size: 41, aishell_tot_loss[loss=0.1582, simple_loss=0.2428, pruned_loss=0.03674, over 975308.87 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.23, pruned_loss=0.04179, over 977121.02 frames.], batch size: 41, lr: 5.11e-04
+2022-06-18 21:41:17,888 INFO [train.py:874] (1/4) Epoch 16, batch 1900, datatang_loss[loss=0.1617, simple_loss=0.2363, pruned_loss=0.04353, over 4923.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2368, pruned_loss=0.03907, over 986030.15 frames.], batch size: 83, aishell_tot_loss[loss=0.158, simple_loss=0.243, pruned_loss=0.03655, over 976876.43 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2303, pruned_loss=0.04182, over 977992.84 frames.], batch size: 83, lr: 5.11e-04
+2022-06-18 21:41:47,574 INFO [train.py:874] (1/4) Epoch 16, batch 1950, aishell_loss[loss=0.1496, simple_loss=0.241, pruned_loss=0.02907, over 4979.00 frames.], tot_loss[loss=0.1579, simple_loss=0.237, pruned_loss=0.03935, over 985670.16 frames.], batch size: 30, aishell_tot_loss[loss=0.1583, simple_loss=0.2433, pruned_loss=0.03665, over 977395.57 frames.], datatang_tot_loss[loss=0.1571, simple_loss=0.2303, pruned_loss=0.04199, over 979117.82 frames.], batch size: 30, lr: 5.10e-04
+2022-06-18 21:42:22,162 INFO [train.py:874] (1/4) Epoch 16, batch 2000, datatang_loss[loss=0.1183, simple_loss=0.1937, pruned_loss=0.02149, over 4929.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2357, pruned_loss=0.03841, over 985518.76 frames.], batch size: 37, aishell_tot_loss[loss=0.1576, simple_loss=0.2427, pruned_loss=0.03626, over 978227.03 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2295, pruned_loss=0.04141, over 979899.39 frames.], batch size: 37, lr: 5.10e-04
+2022-06-18 21:42:22,162 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 21:42:38,235 INFO [train.py:914] (1/4) Epoch 16, validation: loss=0.1651, simple_loss=0.2493, pruned_loss=0.04046, over 1622729.00 frames.
+2022-06-18 21:43:07,841 INFO [train.py:874] (1/4) Epoch 16, batch 2050, datatang_loss[loss=0.1634, simple_loss=0.238, pruned_loss=0.04443, over 4976.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2355, pruned_loss=0.03867, over 985559.68 frames.], batch size: 37, aishell_tot_loss[loss=0.158, simple_loss=0.2428, pruned_loss=0.03658, over 978776.42 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.2293, pruned_loss=0.04115, over 980880.40 frames.], batch size: 37, lr: 5.10e-04
+2022-06-18 21:43:37,760 INFO [train.py:874] (1/4) Epoch 16, batch 2100, aishell_loss[loss=0.1593, simple_loss=0.2411, pruned_loss=0.03875, over 4975.00 frames.], tot_loss[loss=0.155, simple_loss=0.2346, pruned_loss=0.03771, over 985852.60 frames.], batch size: 30, aishell_tot_loss[loss=0.1568, simple_loss=0.2418, pruned_loss=0.03591, over 979802.13 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2291, pruned_loss=0.04082, over 981529.10 frames.], batch size: 30, lr: 5.10e-04
+2022-06-18 21:44:09,097 INFO [train.py:874] (1/4) Epoch 16, batch 2150, aishell_loss[loss=0.1692, simple_loss=0.2543, pruned_loss=0.04201, over 4952.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2344, pruned_loss=0.03762, over 986155.83 frames.], batch size: 56, aishell_tot_loss[loss=0.1561, simple_loss=0.2409, pruned_loss=0.03563, over 980823.51 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2296, pruned_loss=0.04086, over 982068.20 frames.], batch size: 56, lr: 5.10e-04
+2022-06-18 21:44:39,264 INFO [train.py:874] (1/4) Epoch 16, batch 2200, datatang_loss[loss=0.1664, simple_loss=0.2359, pruned_loss=0.04843, over 4889.00 frames.], tot_loss[loss=0.1551, simple_loss=0.235, pruned_loss=0.03757, over 986348.99 frames.], batch size: 52, aishell_tot_loss[loss=0.1565, simple_loss=0.2416, pruned_loss=0.03573, over 981664.52 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.2293, pruned_loss=0.04056, over 982552.39 frames.], batch size: 52, lr: 5.09e-04
+2022-06-18 21:45:09,177 INFO [train.py:874] (1/4) Epoch 16, batch 2250, datatang_loss[loss=0.1453, simple_loss=0.2271, pruned_loss=0.03177, over 4886.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2351, pruned_loss=0.03821, over 986478.96 frames.], batch size: 39, aishell_tot_loss[loss=0.1562, simple_loss=0.2411, pruned_loss=0.03566, over 982100.82 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.23, pruned_loss=0.04112, over 983245.51 frames.], batch size: 39, lr: 5.09e-04
+2022-06-18 21:45:40,318 INFO [train.py:874] (1/4) Epoch 16, batch 2300, aishell_loss[loss=0.1372, simple_loss=0.2179, pruned_loss=0.02821, over 4959.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2354, pruned_loss=0.0382, over 986256.69 frames.], batch size: 25, aishell_tot_loss[loss=0.1562, simple_loss=0.2412, pruned_loss=0.03562, over 982577.99 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2301, pruned_loss=0.04111, over 983414.46 frames.], batch size: 25, lr: 5.09e-04
+2022-06-18 21:46:10,853 INFO [train.py:874] (1/4) Epoch 16, batch 2350, aishell_loss[loss=0.1442, simple_loss=0.2352, pruned_loss=0.02656, over 4934.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2359, pruned_loss=0.03844, over 986243.84 frames.], batch size: 58, aishell_tot_loss[loss=0.1565, simple_loss=0.2417, pruned_loss=0.03568, over 983061.84 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.23, pruned_loss=0.04129, over 983689.29 frames.], batch size: 58, lr: 5.09e-04
+2022-06-18 21:46:40,971 INFO [train.py:874] (1/4) Epoch 16, batch 2400, datatang_loss[loss=0.1483, simple_loss=0.2262, pruned_loss=0.03522, over 4955.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2355, pruned_loss=0.03867, over 986076.23 frames.], batch size: 69, aishell_tot_loss[loss=0.1563, simple_loss=0.2415, pruned_loss=0.03557, over 983188.05 frames.], datatang_tot_loss[loss=0.1565, simple_loss=0.23, pruned_loss=0.04151, over 984053.77 frames.], batch size: 69, lr: 5.09e-04
+2022-06-18 21:47:10,430 INFO [train.py:874] (1/4) Epoch 16, batch 2450, datatang_loss[loss=0.1785, simple_loss=0.235, pruned_loss=0.061, over 4886.00 frames.], tot_loss[loss=0.157, simple_loss=0.2369, pruned_loss=0.0385, over 985722.25 frames.], batch size: 52, aishell_tot_loss[loss=0.1565, simple_loss=0.242, pruned_loss=0.03554, over 983420.99 frames.], datatang_tot_loss[loss=0.1569, simple_loss=0.2304, pruned_loss=0.04168, over 984053.03 frames.], batch size: 52, lr: 5.08e-04
+2022-06-18 21:47:41,018 INFO [train.py:874] (1/4) Epoch 16, batch 2500, datatang_loss[loss=0.1269, simple_loss=0.2063, pruned_loss=0.02373, over 4933.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2368, pruned_loss=0.03885, over 985736.85 frames.], batch size: 79, aishell_tot_loss[loss=0.157, simple_loss=0.2422, pruned_loss=0.03591, over 983648.18 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2302, pruned_loss=0.04167, over 984301.81 frames.], batch size: 79, lr: 5.08e-04
+2022-06-18 21:48:11,423 INFO [train.py:874] (1/4) Epoch 16, batch 2550, datatang_loss[loss=0.1253, simple_loss=0.2008, pruned_loss=0.02495, over 4904.00 frames.], tot_loss[loss=0.1563, simple_loss=0.236, pruned_loss=0.03831, over 985297.14 frames.], batch size: 34, aishell_tot_loss[loss=0.1572, simple_loss=0.2423, pruned_loss=0.03602, over 983562.39 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2291, pruned_loss=0.04112, over 984353.45 frames.], batch size: 34, lr: 5.08e-04
+2022-06-18 21:48:41,691 INFO [train.py:874] (1/4) Epoch 16, batch 2600, datatang_loss[loss=0.1427, simple_loss=0.2155, pruned_loss=0.03493, over 4915.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2362, pruned_loss=0.03853, over 985641.36 frames.], batch size: 75, aishell_tot_loss[loss=0.1578, simple_loss=0.243, pruned_loss=0.03631, over 983927.71 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2289, pruned_loss=0.04097, over 984658.12 frames.], batch size: 75, lr: 5.08e-04
+2022-06-18 21:49:12,448 INFO [train.py:874] (1/4) Epoch 16, batch 2650, datatang_loss[loss=0.1796, simple_loss=0.2451, pruned_loss=0.05701, over 4909.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2369, pruned_loss=0.03889, over 985683.73 frames.], batch size: 47, aishell_tot_loss[loss=0.1578, simple_loss=0.2433, pruned_loss=0.03613, over 984223.33 frames.], datatang_tot_loss[loss=0.1562, simple_loss=0.2296, pruned_loss=0.04144, over 984727.11 frames.], batch size: 47, lr: 5.08e-04
+2022-06-18 21:49:42,476 INFO [train.py:874] (1/4) Epoch 16, batch 2700, aishell_loss[loss=0.1509, simple_loss=0.2349, pruned_loss=0.03347, over 4858.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2369, pruned_loss=0.03843, over 985433.67 frames.], batch size: 36, aishell_tot_loss[loss=0.1572, simple_loss=0.2429, pruned_loss=0.03575, over 983906.34 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2296, pruned_loss=0.04149, over 985086.98 frames.], batch size: 36, lr: 5.07e-04
+2022-06-18 21:50:12,449 INFO [train.py:874] (1/4) Epoch 16, batch 2750, datatang_loss[loss=0.1385, simple_loss=0.2171, pruned_loss=0.02996, over 4926.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2375, pruned_loss=0.03907, over 985517.12 frames.], batch size: 73, aishell_tot_loss[loss=0.1578, simple_loss=0.2434, pruned_loss=0.03607, over 983933.82 frames.], datatang_tot_loss[loss=0.1568, simple_loss=0.2301, pruned_loss=0.04172, over 985345.68 frames.], batch size: 73, lr: 5.07e-04
+2022-06-18 21:50:43,002 INFO [train.py:874] (1/4) Epoch 16, batch 2800, datatang_loss[loss=0.1406, simple_loss=0.2187, pruned_loss=0.03123, over 4954.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2361, pruned_loss=0.03861, over 985764.85 frames.], batch size: 67, aishell_tot_loss[loss=0.1575, simple_loss=0.243, pruned_loss=0.036, over 984198.43 frames.], datatang_tot_loss[loss=0.156, simple_loss=0.2294, pruned_loss=0.04128, over 985528.21 frames.], batch size: 67, lr: 5.07e-04
+2022-06-18 21:51:13,687 INFO [train.py:874] (1/4) Epoch 16, batch 2850, aishell_loss[loss=0.1495, simple_loss=0.2404, pruned_loss=0.02932, over 4934.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2357, pruned_loss=0.03844, over 985568.39 frames.], batch size: 58, aishell_tot_loss[loss=0.1576, simple_loss=0.2429, pruned_loss=0.03613, over 984240.84 frames.], datatang_tot_loss[loss=0.1555, simple_loss=0.2292, pruned_loss=0.04094, over 985480.50 frames.], batch size: 58, lr: 5.07e-04
+2022-06-18 21:51:43,107 INFO [train.py:874] (1/4) Epoch 16, batch 2900, datatang_loss[loss=0.1556, simple_loss=0.2256, pruned_loss=0.04282, over 4931.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2358, pruned_loss=0.03838, over 985646.84 frames.], batch size: 79, aishell_tot_loss[loss=0.1581, simple_loss=0.2434, pruned_loss=0.03642, over 984435.39 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2286, pruned_loss=0.04061, over 985549.20 frames.], batch size: 79, lr: 5.07e-04
+2022-06-18 21:52:12,510 INFO [train.py:874] (1/4) Epoch 16, batch 2950, datatang_loss[loss=0.1757, simple_loss=0.2369, pruned_loss=0.05725, over 4900.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2352, pruned_loss=0.03862, over 985801.60 frames.], batch size: 47, aishell_tot_loss[loss=0.1583, simple_loss=0.2432, pruned_loss=0.03668, over 984504.57 frames.], datatang_tot_loss[loss=0.1547, simple_loss=0.2286, pruned_loss=0.04043, over 985749.81 frames.], batch size: 47, lr: 5.06e-04
+2022-06-18 21:52:43,980 INFO [train.py:874] (1/4) Epoch 16, batch 3000, aishell_loss[loss=0.1661, simple_loss=0.2541, pruned_loss=0.03898, over 4935.00 frames.], tot_loss[loss=0.1558, simple_loss=0.2354, pruned_loss=0.0381, over 986028.70 frames.], batch size: 68, aishell_tot_loss[loss=0.1574, simple_loss=0.2425, pruned_loss=0.03614, over 984635.32 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2292, pruned_loss=0.04048, over 986034.77 frames.], batch size: 68, lr: 5.06e-04
+2022-06-18 21:52:43,981 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 21:53:00,709 INFO [train.py:914] (1/4) Epoch 16, validation: loss=0.1647, simple_loss=0.249, pruned_loss=0.04025, over 1622729.00 frames.
+2022-06-18 21:53:29,644 INFO [train.py:874] (1/4) Epoch 16, batch 3050, datatang_loss[loss=0.1499, simple_loss=0.2246, pruned_loss=0.03759, over 4938.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2368, pruned_loss=0.03866, over 986069.76 frames.], batch size: 57, aishell_tot_loss[loss=0.1579, simple_loss=0.2431, pruned_loss=0.03639, over 984850.16 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2296, pruned_loss=0.0409, over 986075.03 frames.], batch size: 57, lr: 5.06e-04
+2022-06-18 21:53:59,441 INFO [train.py:874] (1/4) Epoch 16, batch 3100, datatang_loss[loss=0.1369, simple_loss=0.2103, pruned_loss=0.03177, over 4932.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2373, pruned_loss=0.03852, over 986226.79 frames.], batch size: 77, aishell_tot_loss[loss=0.1578, simple_loss=0.2431, pruned_loss=0.03626, over 985195.00 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2299, pruned_loss=0.04098, over 986040.77 frames.], batch size: 77, lr: 5.06e-04
+2022-06-18 21:54:29,100 INFO [train.py:874] (1/4) Epoch 16, batch 3150, aishell_loss[loss=0.1567, simple_loss=0.2378, pruned_loss=0.03779, over 4970.00 frames.], tot_loss[loss=0.157, simple_loss=0.2373, pruned_loss=0.03832, over 985611.56 frames.], batch size: 31, aishell_tot_loss[loss=0.1581, simple_loss=0.2435, pruned_loss=0.03635, over 984728.88 frames.], datatang_tot_loss[loss=0.1555, simple_loss=0.2296, pruned_loss=0.04067, over 986019.16 frames.], batch size: 31, lr: 5.06e-04
+2022-06-18 21:55:00,820 INFO [train.py:874] (1/4) Epoch 16, batch 3200, aishell_loss[loss=0.1779, simple_loss=0.2663, pruned_loss=0.04477, over 4940.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2366, pruned_loss=0.0381, over 985524.14 frames.], batch size: 54, aishell_tot_loss[loss=0.158, simple_loss=0.2432, pruned_loss=0.03639, over 984804.68 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2291, pruned_loss=0.0404, over 985921.45 frames.], batch size: 54, lr: 5.05e-04
+2022-06-18 21:55:30,450 INFO [train.py:874] (1/4) Epoch 16, batch 3250, datatang_loss[loss=0.1307, simple_loss=0.2136, pruned_loss=0.02386, over 4945.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2348, pruned_loss=0.03772, over 985868.64 frames.], batch size: 69, aishell_tot_loss[loss=0.1575, simple_loss=0.2425, pruned_loss=0.03627, over 985002.77 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2283, pruned_loss=0.03996, over 986103.56 frames.], batch size: 69, lr: 5.05e-04
+2022-06-18 21:55:59,806 INFO [train.py:874] (1/4) Epoch 16, batch 3300, datatang_loss[loss=0.161, simple_loss=0.2314, pruned_loss=0.0453, over 4944.00 frames.], tot_loss[loss=0.1548, simple_loss=0.234, pruned_loss=0.03775, over 985904.53 frames.], batch size: 55, aishell_tot_loss[loss=0.1574, simple_loss=0.2422, pruned_loss=0.03628, over 985209.57 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2279, pruned_loss=0.03981, over 985990.82 frames.], batch size: 55, lr: 5.05e-04
+2022-06-18 21:56:30,788 INFO [train.py:874] (1/4) Epoch 16, batch 3350, datatang_loss[loss=0.1624, simple_loss=0.2384, pruned_loss=0.04323, over 4939.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2353, pruned_loss=0.03804, over 985803.13 frames.], batch size: 88, aishell_tot_loss[loss=0.1577, simple_loss=0.2423, pruned_loss=0.03648, over 985257.13 frames.], datatang_tot_loss[loss=0.1542, simple_loss=0.2285, pruned_loss=0.03997, over 985942.24 frames.], batch size: 88, lr: 5.05e-04
+2022-06-18 21:57:00,629 INFO [train.py:874] (1/4) Epoch 16, batch 3400, aishell_loss[loss=0.1367, simple_loss=0.2145, pruned_loss=0.02948, over 4958.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2349, pruned_loss=0.0378, over 985694.19 frames.], batch size: 25, aishell_tot_loss[loss=0.1575, simple_loss=0.2423, pruned_loss=0.03642, over 985420.37 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2282, pruned_loss=0.03968, over 985709.05 frames.], batch size: 25, lr: 5.05e-04
+2022-06-18 21:57:30,327 INFO [train.py:874] (1/4) Epoch 16, batch 3450, aishell_loss[loss=0.1579, simple_loss=0.2455, pruned_loss=0.03513, over 4941.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2353, pruned_loss=0.03801, over 985813.82 frames.], batch size: 45, aishell_tot_loss[loss=0.1579, simple_loss=0.2425, pruned_loss=0.03662, over 985383.84 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.228, pruned_loss=0.03975, over 985912.70 frames.], batch size: 45, lr: 5.05e-04
+2022-06-18 21:58:01,269 INFO [train.py:874] (1/4) Epoch 16, batch 3500, aishell_loss[loss=0.1523, simple_loss=0.2441, pruned_loss=0.03021, over 4924.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2354, pruned_loss=0.03862, over 985446.63 frames.], batch size: 52, aishell_tot_loss[loss=0.158, simple_loss=0.2423, pruned_loss=0.03685, over 984866.04 frames.], datatang_tot_loss[loss=0.1543, simple_loss=0.2284, pruned_loss=0.04012, over 986079.64 frames.], batch size: 52, lr: 5.04e-04
+2022-06-18 21:58:30,717 INFO [train.py:874] (1/4) Epoch 16, batch 3550, aishell_loss[loss=0.1691, simple_loss=0.2556, pruned_loss=0.04136, over 4870.00 frames.], tot_loss[loss=0.1557, simple_loss=0.235, pruned_loss=0.03823, over 985254.69 frames.], batch size: 36, aishell_tot_loss[loss=0.1582, simple_loss=0.2426, pruned_loss=0.03687, over 984767.52 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2275, pruned_loss=0.0397, over 985978.30 frames.], batch size: 36, lr: 5.04e-04
+2022-06-18 21:59:01,871 INFO [train.py:874] (1/4) Epoch 16, batch 3600, datatang_loss[loss=0.1295, simple_loss=0.2088, pruned_loss=0.02505, over 4827.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2355, pruned_loss=0.03841, over 984974.57 frames.], batch size: 25, aishell_tot_loss[loss=0.1586, simple_loss=0.243, pruned_loss=0.03715, over 984591.08 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2276, pruned_loss=0.03963, over 985841.42 frames.], batch size: 25, lr: 5.04e-04
+2022-06-18 21:59:31,583 INFO [train.py:874] (1/4) Epoch 16, batch 3650, aishell_loss[loss=0.1462, simple_loss=0.2445, pruned_loss=0.02393, over 4916.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2359, pruned_loss=0.03854, over 985532.18 frames.], batch size: 68, aishell_tot_loss[loss=0.1581, simple_loss=0.2426, pruned_loss=0.03676, over 984912.68 frames.], datatang_tot_loss[loss=0.1544, simple_loss=0.2283, pruned_loss=0.0402, over 986076.49 frames.], batch size: 68, lr: 5.04e-04
+2022-06-18 22:00:02,457 INFO [train.py:874] (1/4) Epoch 16, batch 3700, datatang_loss[loss=0.2084, simple_loss=0.2737, pruned_loss=0.07157, over 4961.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2362, pruned_loss=0.03907, over 985208.78 frames.], batch size: 99, aishell_tot_loss[loss=0.1579, simple_loss=0.2424, pruned_loss=0.03673, over 984898.84 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2292, pruned_loss=0.04071, over 985754.16 frames.], batch size: 99, lr: 5.04e-04
+2022-06-18 22:00:32,975 INFO [train.py:874] (1/4) Epoch 16, batch 3750, aishell_loss[loss=0.1385, simple_loss=0.2245, pruned_loss=0.02628, over 4975.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2351, pruned_loss=0.03863, over 985284.74 frames.], batch size: 30, aishell_tot_loss[loss=0.1572, simple_loss=0.2415, pruned_loss=0.03646, over 985200.92 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2289, pruned_loss=0.04062, over 985507.17 frames.], batch size: 30, lr: 5.03e-04
+2022-06-18 22:01:02,946 INFO [train.py:874] (1/4) Epoch 16, batch 3800, datatang_loss[loss=0.1409, simple_loss=0.2142, pruned_loss=0.03379, over 4923.00 frames.], tot_loss[loss=0.1561, simple_loss=0.235, pruned_loss=0.03864, over 984927.63 frames.], batch size: 71, aishell_tot_loss[loss=0.1568, simple_loss=0.2412, pruned_loss=0.03624, over 984689.63 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2292, pruned_loss=0.04082, over 985598.09 frames.], batch size: 71, lr: 5.03e-04
+2022-06-18 22:01:31,722 INFO [train.py:874] (1/4) Epoch 16, batch 3850, datatang_loss[loss=0.1375, simple_loss=0.2157, pruned_loss=0.02966, over 4926.00 frames.], tot_loss[loss=0.1555, simple_loss=0.2348, pruned_loss=0.03808, over 984746.54 frames.], batch size: 81, aishell_tot_loss[loss=0.1564, simple_loss=0.2411, pruned_loss=0.03582, over 984689.63 frames.], datatang_tot_loss[loss=0.1552, simple_loss=0.229, pruned_loss=0.0407, over 985354.62 frames.], batch size: 81, lr: 5.03e-04
+2022-06-18 22:01:59,926 INFO [train.py:874] (1/4) Epoch 16, batch 3900, datatang_loss[loss=0.1369, simple_loss=0.2204, pruned_loss=0.02667, over 4951.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2343, pruned_loss=0.03794, over 985130.49 frames.], batch size: 26, aishell_tot_loss[loss=0.1563, simple_loss=0.241, pruned_loss=0.03582, over 984953.31 frames.], datatang_tot_loss[loss=0.1548, simple_loss=0.2285, pruned_loss=0.04051, over 985438.60 frames.], batch size: 26, lr: 5.03e-04
+2022-06-18 22:02:29,027 INFO [train.py:874] (1/4) Epoch 16, batch 3950, aishell_loss[loss=0.1797, simple_loss=0.2613, pruned_loss=0.049, over 4969.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2349, pruned_loss=0.03843, over 985467.77 frames.], batch size: 39, aishell_tot_loss[loss=0.1569, simple_loss=0.2413, pruned_loss=0.03623, over 985036.15 frames.], datatang_tot_loss[loss=0.155, simple_loss=0.2288, pruned_loss=0.04057, over 985690.13 frames.], batch size: 39, lr: 5.03e-04
+2022-06-18 22:02:58,088 INFO [train.py:874] (1/4) Epoch 16, batch 4000, aishell_loss[loss=0.1357, simple_loss=0.2216, pruned_loss=0.02494, over 4976.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2352, pruned_loss=0.03865, over 985279.57 frames.], batch size: 30, aishell_tot_loss[loss=0.1572, simple_loss=0.2416, pruned_loss=0.03641, over 984802.37 frames.], datatang_tot_loss[loss=0.155, simple_loss=0.2287, pruned_loss=0.04065, over 985762.49 frames.], batch size: 30, lr: 5.02e-04
+2022-06-18 22:02:58,089 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 22:03:14,114 INFO [train.py:914] (1/4) Epoch 16, validation: loss=0.164, simple_loss=0.2482, pruned_loss=0.03992, over 1622729.00 frames.
+2022-06-18 22:03:42,691 INFO [train.py:874] (1/4) Epoch 16, batch 4050, datatang_loss[loss=0.1819, simple_loss=0.2575, pruned_loss=0.0531, over 4915.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2351, pruned_loss=0.03887, over 985348.75 frames.], batch size: 98, aishell_tot_loss[loss=0.1579, simple_loss=0.2421, pruned_loss=0.03683, over 984878.08 frames.], datatang_tot_loss[loss=0.1546, simple_loss=0.2284, pruned_loss=0.0404, over 985739.64 frames.], batch size: 98, lr: 5.02e-04
+2022-06-18 22:04:12,005 INFO [train.py:874] (1/4) Epoch 16, batch 4100, aishell_loss[loss=0.1672, simple_loss=0.2581, pruned_loss=0.03816, over 4949.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2351, pruned_loss=0.03867, over 985432.28 frames.], batch size: 61, aishell_tot_loss[loss=0.157, simple_loss=0.2414, pruned_loss=0.0363, over 984673.73 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.229, pruned_loss=0.04079, over 986023.00 frames.], batch size: 61, lr: 5.02e-04
+2022-06-18 22:05:35,076 INFO [train.py:874] (1/4) Epoch 17, batch 50, datatang_loss[loss=0.1658, simple_loss=0.2321, pruned_loss=0.04974, over 4947.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2338, pruned_loss=0.03539, over 218199.60 frames.], batch size: 34, aishell_tot_loss[loss=0.1552, simple_loss=0.2415, pruned_loss=0.03449, over 128631.51 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2243, pruned_loss=0.03648, over 103015.91 frames.], batch size: 34, lr: 4.88e-04
+2022-06-18 22:06:05,303 INFO [train.py:874] (1/4) Epoch 17, batch 100, aishell_loss[loss=0.1564, simple_loss=0.2455, pruned_loss=0.03366, over 4917.00 frames.], tot_loss[loss=0.152, simple_loss=0.2327, pruned_loss=0.03563, over 388574.68 frames.], batch size: 52, aishell_tot_loss[loss=0.1553, simple_loss=0.2424, pruned_loss=0.0341, over 222029.53 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2228, pruned_loss=0.03713, over 214909.73 frames.], batch size: 52, lr: 4.88e-04
+2022-06-18 22:06:34,638 INFO [train.py:874] (1/4) Epoch 17, batch 150, datatang_loss[loss=0.1631, simple_loss=0.2316, pruned_loss=0.04727, over 4917.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2323, pruned_loss=0.03635, over 521158.46 frames.], batch size: 81, aishell_tot_loss[loss=0.1563, simple_loss=0.2426, pruned_loss=0.03501, over 301825.92 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2228, pruned_loss=0.03731, over 316009.30 frames.], batch size: 81, lr: 4.87e-04
+2022-06-18 22:07:06,318 INFO [train.py:874] (1/4) Epoch 17, batch 200, datatang_loss[loss=0.1593, simple_loss=0.2386, pruned_loss=0.04, over 4914.00 frames.], tot_loss[loss=0.153, simple_loss=0.2327, pruned_loss=0.03668, over 623899.44 frames.], batch size: 57, aishell_tot_loss[loss=0.1572, simple_loss=0.2433, pruned_loss=0.03558, over 379265.67 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2227, pruned_loss=0.03739, over 397665.25 frames.], batch size: 57, lr: 4.87e-04
+2022-06-18 22:07:35,625 INFO [train.py:874] (1/4) Epoch 17, batch 250, datatang_loss[loss=0.1442, simple_loss=0.2157, pruned_loss=0.03638, over 4921.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2322, pruned_loss=0.03612, over 704234.00 frames.], batch size: 34, aishell_tot_loss[loss=0.1574, simple_loss=0.244, pruned_loss=0.03544, over 448043.81 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2214, pruned_loss=0.03671, over 469614.43 frames.], batch size: 34, lr: 4.87e-04
+2022-06-18 22:08:05,785 INFO [train.py:874] (1/4) Epoch 17, batch 300, aishell_loss[loss=0.1306, simple_loss=0.2058, pruned_loss=0.02764, over 4932.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2319, pruned_loss=0.0363, over 766597.10 frames.], batch size: 25, aishell_tot_loss[loss=0.1565, simple_loss=0.2425, pruned_loss=0.03528, over 508780.92 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2225, pruned_loss=0.03713, over 532833.44 frames.], batch size: 25, lr: 4.87e-04
+2022-06-18 22:08:36,966 INFO [train.py:874] (1/4) Epoch 17, batch 350, aishell_loss[loss=0.1332, simple_loss=0.2051, pruned_loss=0.03071, over 4954.00 frames.], tot_loss[loss=0.153, simple_loss=0.2323, pruned_loss=0.0368, over 815352.98 frames.], batch size: 25, aishell_tot_loss[loss=0.1567, simple_loss=0.2424, pruned_loss=0.03548, over 562873.98 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2231, pruned_loss=0.03772, over 588324.69 frames.], batch size: 25, lr: 4.87e-04
+2022-06-18 22:09:07,068 INFO [train.py:874] (1/4) Epoch 17, batch 400, datatang_loss[loss=0.1384, simple_loss=0.214, pruned_loss=0.0314, over 4960.00 frames.], tot_loss[loss=0.154, simple_loss=0.2328, pruned_loss=0.03762, over 853403.30 frames.], batch size: 67, aishell_tot_loss[loss=0.1563, simple_loss=0.2413, pruned_loss=0.03563, over 618394.25 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2244, pruned_loss=0.03886, over 629983.15 frames.], batch size: 67, lr: 4.87e-04
+2022-06-18 22:09:36,432 INFO [train.py:874] (1/4) Epoch 17, batch 450, datatang_loss[loss=0.1312, simple_loss=0.2021, pruned_loss=0.03016, over 4804.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2336, pruned_loss=0.03786, over 882492.52 frames.], batch size: 25, aishell_tot_loss[loss=0.1565, simple_loss=0.2417, pruned_loss=0.03572, over 659882.61 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2252, pruned_loss=0.03921, over 673345.85 frames.], batch size: 25, lr: 4.86e-04
+2022-06-18 22:10:07,603 INFO [train.py:874] (1/4) Epoch 17, batch 500, datatang_loss[loss=0.1905, simple_loss=0.2588, pruned_loss=0.06116, over 4972.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2338, pruned_loss=0.03753, over 905525.62 frames.], batch size: 60, aishell_tot_loss[loss=0.1565, simple_loss=0.2419, pruned_loss=0.03558, over 702793.07 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2249, pruned_loss=0.03907, over 705874.32 frames.], batch size: 60, lr: 4.86e-04
+2022-06-18 22:10:36,766 INFO [train.py:874] (1/4) Epoch 17, batch 550, aishell_loss[loss=0.1902, simple_loss=0.2715, pruned_loss=0.05447, over 4975.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2342, pruned_loss=0.03787, over 923432.12 frames.], batch size: 48, aishell_tot_loss[loss=0.157, simple_loss=0.2421, pruned_loss=0.03593, over 729791.56 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2258, pruned_loss=0.03915, over 745076.54 frames.], batch size: 48, lr: 4.86e-04
+2022-06-18 22:11:06,754 INFO [train.py:874] (1/4) Epoch 17, batch 600, datatang_loss[loss=0.1391, simple_loss=0.2143, pruned_loss=0.03188, over 4929.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2346, pruned_loss=0.03858, over 936761.07 frames.], batch size: 71, aishell_tot_loss[loss=0.1571, simple_loss=0.242, pruned_loss=0.03609, over 751431.98 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2272, pruned_loss=0.03984, over 780662.64 frames.], batch size: 71, lr: 4.86e-04
+2022-06-18 22:11:38,225 INFO [train.py:874] (1/4) Epoch 17, batch 650, aishell_loss[loss=0.1506, simple_loss=0.2385, pruned_loss=0.03132, over 4861.00 frames.], tot_loss[loss=0.156, simple_loss=0.2352, pruned_loss=0.03842, over 947386.59 frames.], batch size: 37, aishell_tot_loss[loss=0.1571, simple_loss=0.242, pruned_loss=0.03605, over 780938.18 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2278, pruned_loss=0.03989, over 802913.26 frames.], batch size: 37, lr: 4.86e-04
+2022-06-18 22:12:08,252 INFO [train.py:874] (1/4) Epoch 17, batch 700, datatang_loss[loss=0.1641, simple_loss=0.237, pruned_loss=0.04557, over 4966.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2358, pruned_loss=0.03847, over 956294.20 frames.], batch size: 60, aishell_tot_loss[loss=0.157, simple_loss=0.242, pruned_loss=0.03597, over 806039.22 frames.], datatang_tot_loss[loss=0.1544, simple_loss=0.2285, pruned_loss=0.0402, over 823992.52 frames.], batch size: 60, lr: 4.86e-04
+2022-06-18 22:12:37,686 INFO [train.py:874] (1/4) Epoch 17, batch 750, aishell_loss[loss=0.1138, simple_loss=0.179, pruned_loss=0.02428, over 4958.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2367, pruned_loss=0.03888, over 962758.01 frames.], batch size: 22, aishell_tot_loss[loss=0.1573, simple_loss=0.242, pruned_loss=0.03627, over 827866.37 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2296, pruned_loss=0.04051, over 842380.48 frames.], batch size: 22, lr: 4.85e-04
+2022-06-18 22:13:08,737 INFO [train.py:874] (1/4) Epoch 17, batch 800, aishell_loss[loss=0.1559, simple_loss=0.24, pruned_loss=0.03587, over 4876.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2366, pruned_loss=0.03854, over 967439.15 frames.], batch size: 36, aishell_tot_loss[loss=0.1568, simple_loss=0.2418, pruned_loss=0.03597, over 846732.97 frames.], datatang_tot_loss[loss=0.1555, simple_loss=0.2299, pruned_loss=0.04058, over 858614.39 frames.], batch size: 36, lr: 4.85e-04
+2022-06-18 22:13:38,515 INFO [train.py:874] (1/4) Epoch 17, batch 850, aishell_loss[loss=0.1691, simple_loss=0.2453, pruned_loss=0.04645, over 4980.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2359, pruned_loss=0.03797, over 971379.46 frames.], batch size: 48, aishell_tot_loss[loss=0.1566, simple_loss=0.2414, pruned_loss=0.03586, over 863550.36 frames.], datatang_tot_loss[loss=0.1548, simple_loss=0.2295, pruned_loss=0.04008, over 873068.31 frames.], batch size: 48, lr: 4.85e-04
+2022-06-18 22:14:08,881 INFO [train.py:874] (1/4) Epoch 17, batch 900, datatang_loss[loss=0.1583, simple_loss=0.2355, pruned_loss=0.04057, over 4934.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2365, pruned_loss=0.03844, over 974359.06 frames.], batch size: 79, aishell_tot_loss[loss=0.157, simple_loss=0.2419, pruned_loss=0.03606, over 878468.87 frames.], datatang_tot_loss[loss=0.1554, simple_loss=0.2298, pruned_loss=0.04046, over 885635.08 frames.], batch size: 79, lr: 4.85e-04
+2022-06-18 22:14:39,884 INFO [train.py:874] (1/4) Epoch 17, batch 950, datatang_loss[loss=0.1636, simple_loss=0.2337, pruned_loss=0.04677, over 4919.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2368, pruned_loss=0.03909, over 976621.38 frames.], batch size: 77, aishell_tot_loss[loss=0.1572, simple_loss=0.242, pruned_loss=0.03615, over 890474.21 frames.], datatang_tot_loss[loss=0.1563, simple_loss=0.2303, pruned_loss=0.04114, over 897743.75 frames.], batch size: 77, lr: 4.85e-04
+2022-06-18 22:15:10,094 INFO [train.py:874] (1/4) Epoch 17, batch 1000, datatang_loss[loss=0.1447, simple_loss=0.2285, pruned_loss=0.03044, over 4959.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2366, pruned_loss=0.03882, over 978589.97 frames.], batch size: 86, aishell_tot_loss[loss=0.1575, simple_loss=0.2424, pruned_loss=0.03628, over 901165.66 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.2299, pruned_loss=0.04078, over 908550.31 frames.], batch size: 86, lr: 4.84e-04
+2022-06-18 22:15:10,095 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 22:15:27,676 INFO [train.py:914] (1/4) Epoch 17, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-18 22:15:57,377 INFO [train.py:874] (1/4) Epoch 17, batch 1050, aishell_loss[loss=0.1537, simple_loss=0.2391, pruned_loss=0.0342, over 4965.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2369, pruned_loss=0.03823, over 979990.45 frames.], batch size: 31, aishell_tot_loss[loss=0.1578, simple_loss=0.2429, pruned_loss=0.03634, over 914183.70 frames.], datatang_tot_loss[loss=0.1549, simple_loss=0.2292, pruned_loss=0.0403, over 914515.74 frames.], batch size: 31, lr: 4.84e-04
+2022-06-18 22:16:27,408 INFO [train.py:874] (1/4) Epoch 17, batch 1100, aishell_loss[loss=0.162, simple_loss=0.2356, pruned_loss=0.04421, over 4908.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2366, pruned_loss=0.03848, over 981285.48 frames.], batch size: 34, aishell_tot_loss[loss=0.157, simple_loss=0.2417, pruned_loss=0.03614, over 923755.35 frames.], datatang_tot_loss[loss=0.1558, simple_loss=0.23, pruned_loss=0.0408, over 921766.68 frames.], batch size: 34, lr: 4.84e-04
+2022-06-18 22:16:58,182 INFO [train.py:874] (1/4) Epoch 17, batch 1150, datatang_loss[loss=0.1786, simple_loss=0.2423, pruned_loss=0.05743, over 4925.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2352, pruned_loss=0.03861, over 981810.01 frames.], batch size: 81, aishell_tot_loss[loss=0.1564, simple_loss=0.2407, pruned_loss=0.03605, over 928409.62 frames.], datatang_tot_loss[loss=0.1559, simple_loss=0.2301, pruned_loss=0.04086, over 931432.39 frames.], batch size: 81, lr: 4.84e-04
+2022-06-18 22:17:27,465 INFO [train.py:874] (1/4) Epoch 17, batch 1200, aishell_loss[loss=0.1463, simple_loss=0.2328, pruned_loss=0.02986, over 4923.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2359, pruned_loss=0.03851, over 982254.80 frames.], batch size: 46, aishell_tot_loss[loss=0.1566, simple_loss=0.241, pruned_loss=0.03607, over 936043.63 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2303, pruned_loss=0.0409, over 936520.27 frames.], batch size: 46, lr: 4.84e-04
+2022-06-18 22:17:58,142 INFO [train.py:874] (1/4) Epoch 17, batch 1250, datatang_loss[loss=0.1724, simple_loss=0.2393, pruned_loss=0.05273, over 4906.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2361, pruned_loss=0.03873, over 983128.82 frames.], batch size: 39, aishell_tot_loss[loss=0.1569, simple_loss=0.2415, pruned_loss=0.03618, over 941264.27 frames.], datatang_tot_loss[loss=0.1561, simple_loss=0.2302, pruned_loss=0.04096, over 943032.45 frames.], batch size: 39, lr: 4.84e-04
+2022-06-18 22:18:27,925 INFO [train.py:874] (1/4) Epoch 17, batch 1300, aishell_loss[loss=0.1501, simple_loss=0.2348, pruned_loss=0.0327, over 4911.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2357, pruned_loss=0.03835, over 983513.96 frames.], batch size: 46, aishell_tot_loss[loss=0.1567, simple_loss=0.2414, pruned_loss=0.03599, over 946507.77 frames.], datatang_tot_loss[loss=0.1557, simple_loss=0.2299, pruned_loss=0.04078, over 947853.45 frames.], batch size: 46, lr: 4.83e-04
+2022-06-18 22:18:58,665 INFO [train.py:874] (1/4) Epoch 17, batch 1350, aishell_loss[loss=0.1663, simple_loss=0.2569, pruned_loss=0.03782, over 4938.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2356, pruned_loss=0.03832, over 983339.97 frames.], batch size: 54, aishell_tot_loss[loss=0.1572, simple_loss=0.2418, pruned_loss=0.03628, over 951188.73 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2292, pruned_loss=0.04049, over 951536.00 frames.], batch size: 54, lr: 4.83e-04
+2022-06-18 22:19:29,796 INFO [train.py:874] (1/4) Epoch 17, batch 1400, aishell_loss[loss=0.1552, simple_loss=0.2369, pruned_loss=0.03675, over 4929.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2359, pruned_loss=0.03841, over 983943.79 frames.], batch size: 32, aishell_tot_loss[loss=0.1572, simple_loss=0.2421, pruned_loss=0.0362, over 955341.42 frames.], datatang_tot_loss[loss=0.1553, simple_loss=0.2294, pruned_loss=0.04063, over 955567.22 frames.], batch size: 32, lr: 4.83e-04
+2022-06-18 22:19:58,837 INFO [train.py:874] (1/4) Epoch 17, batch 1450, aishell_loss[loss=0.1786, simple_loss=0.2589, pruned_loss=0.04912, over 4969.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2355, pruned_loss=0.03816, over 984593.30 frames.], batch size: 44, aishell_tot_loss[loss=0.157, simple_loss=0.2417, pruned_loss=0.03612, over 958970.92 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2294, pruned_loss=0.04043, over 959333.43 frames.], batch size: 44, lr: 4.83e-04
+2022-06-18 22:20:29,675 INFO [train.py:874] (1/4) Epoch 17, batch 1500, datatang_loss[loss=0.155, simple_loss=0.2301, pruned_loss=0.0399, over 4924.00 frames.], tot_loss[loss=0.1561, simple_loss=0.2357, pruned_loss=0.03827, over 984954.69 frames.], batch size: 71, aishell_tot_loss[loss=0.1572, simple_loss=0.2421, pruned_loss=0.03611, over 961758.63 frames.], datatang_tot_loss[loss=0.1551, simple_loss=0.2294, pruned_loss=0.04046, over 962908.81 frames.], batch size: 71, lr: 4.83e-04
+2022-06-18 22:20:59,674 INFO [train.py:874] (1/4) Epoch 17, batch 1550, datatang_loss[loss=0.1617, simple_loss=0.2316, pruned_loss=0.04588, over 4952.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2351, pruned_loss=0.03779, over 984938.98 frames.], batch size: 55, aishell_tot_loss[loss=0.1571, simple_loss=0.2421, pruned_loss=0.0361, over 964559.11 frames.], datatang_tot_loss[loss=0.1543, simple_loss=0.2287, pruned_loss=0.04001, over 965414.28 frames.], batch size: 55, lr: 4.82e-04
+2022-06-18 22:21:29,215 INFO [train.py:874] (1/4) Epoch 17, batch 1600, aishell_loss[loss=0.1555, simple_loss=0.2527, pruned_loss=0.02917, over 4929.00 frames.], tot_loss[loss=0.1549, simple_loss=0.235, pruned_loss=0.03743, over 985520.47 frames.], batch size: 58, aishell_tot_loss[loss=0.1568, simple_loss=0.2419, pruned_loss=0.03583, over 966738.22 frames.], datatang_tot_loss[loss=0.1542, simple_loss=0.2289, pruned_loss=0.03971, over 968524.95 frames.], batch size: 58, lr: 4.82e-04
+2022-06-18 22:21:59,599 INFO [train.py:874] (1/4) Epoch 17, batch 1650, aishell_loss[loss=0.1679, simple_loss=0.2454, pruned_loss=0.0452, over 4965.00 frames.], tot_loss[loss=0.1545, simple_loss=0.2348, pruned_loss=0.03711, over 985655.13 frames.], batch size: 44, aishell_tot_loss[loss=0.1563, simple_loss=0.2416, pruned_loss=0.03552, over 969174.95 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2289, pruned_loss=0.03961, over 970456.72 frames.], batch size: 44, lr: 4.82e-04
+2022-06-18 22:22:30,209 INFO [train.py:874] (1/4) Epoch 17, batch 1700, datatang_loss[loss=0.1424, simple_loss=0.2217, pruned_loss=0.03161, over 4945.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2351, pruned_loss=0.03733, over 985556.44 frames.], batch size: 69, aishell_tot_loss[loss=0.1564, simple_loss=0.2415, pruned_loss=0.03564, over 971005.53 frames.], datatang_tot_loss[loss=0.1542, simple_loss=0.2291, pruned_loss=0.03966, over 972266.37 frames.], batch size: 69, lr: 4.82e-04
+2022-06-18 22:22:59,536 INFO [train.py:874] (1/4) Epoch 17, batch 1750, datatang_loss[loss=0.1469, simple_loss=0.2263, pruned_loss=0.03374, over 4959.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2336, pruned_loss=0.03674, over 985323.02 frames.], batch size: 67, aishell_tot_loss[loss=0.1559, simple_loss=0.2411, pruned_loss=0.03539, over 971932.78 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2284, pruned_loss=0.03906, over 974297.44 frames.], batch size: 67, lr: 4.82e-04
+2022-06-18 22:23:31,053 INFO [train.py:874] (1/4) Epoch 17, batch 1800, datatang_loss[loss=0.1568, simple_loss=0.2324, pruned_loss=0.04062, over 4930.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2332, pruned_loss=0.03686, over 985338.19 frames.], batch size: 77, aishell_tot_loss[loss=0.1556, simple_loss=0.2408, pruned_loss=0.03522, over 973150.29 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2282, pruned_loss=0.03915, over 975928.86 frames.], batch size: 77, lr: 4.82e-04
+2022-06-18 22:24:01,631 INFO [train.py:874] (1/4) Epoch 17, batch 1850, aishell_loss[loss=0.173, simple_loss=0.2527, pruned_loss=0.04664, over 4921.00 frames.], tot_loss[loss=0.1536, simple_loss=0.233, pruned_loss=0.03713, over 985238.71 frames.], batch size: 33, aishell_tot_loss[loss=0.1555, simple_loss=0.2404, pruned_loss=0.03529, over 974322.28 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.228, pruned_loss=0.0393, over 977208.03 frames.], batch size: 33, lr: 4.81e-04
+2022-06-18 22:24:35,561 INFO [train.py:874] (1/4) Epoch 17, batch 1900, aishell_loss[loss=0.1839, simple_loss=0.2733, pruned_loss=0.04723, over 4936.00 frames.], tot_loss[loss=0.1545, simple_loss=0.234, pruned_loss=0.03755, over 985564.72 frames.], batch size: 77, aishell_tot_loss[loss=0.1559, simple_loss=0.241, pruned_loss=0.03546, over 975549.76 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.2284, pruned_loss=0.03944, over 978510.17 frames.], batch size: 77, lr: 4.81e-04
+2022-06-18 22:25:06,014 INFO [train.py:874] (1/4) Epoch 17, batch 1950, datatang_loss[loss=0.1998, simple_loss=0.2522, pruned_loss=0.07364, over 4967.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2343, pruned_loss=0.03802, over 985358.89 frames.], batch size: 55, aishell_tot_loss[loss=0.1565, simple_loss=0.2414, pruned_loss=0.03579, over 976409.21 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.2283, pruned_loss=0.03958, over 979405.73 frames.], batch size: 55, lr: 4.81e-04
+2022-06-18 22:25:36,668 INFO [train.py:874] (1/4) Epoch 17, batch 2000, datatang_loss[loss=0.1228, simple_loss=0.1958, pruned_loss=0.02492, over 4953.00 frames.], tot_loss[loss=0.155, simple_loss=0.2345, pruned_loss=0.0378, over 984940.57 frames.], batch size: 67, aishell_tot_loss[loss=0.1563, simple_loss=0.2417, pruned_loss=0.03551, over 977081.19 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2282, pruned_loss=0.03965, over 980034.33 frames.], batch size: 67, lr: 4.81e-04
+2022-06-18 22:25:36,668 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 22:25:53,690 INFO [train.py:914] (1/4) Epoch 17, validation: loss=0.1656, simple_loss=0.2494, pruned_loss=0.04087, over 1622729.00 frames.
+2022-06-18 22:26:23,378 INFO [train.py:874] (1/4) Epoch 17, batch 2050, aishell_loss[loss=0.1452, simple_loss=0.229, pruned_loss=0.03072, over 4878.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2344, pruned_loss=0.03753, over 985056.68 frames.], batch size: 42, aishell_tot_loss[loss=0.156, simple_loss=0.2413, pruned_loss=0.03533, over 978312.85 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2282, pruned_loss=0.03968, over 980467.05 frames.], batch size: 42, lr: 4.81e-04
+2022-06-18 22:26:54,516 INFO [train.py:874] (1/4) Epoch 17, batch 2100, aishell_loss[loss=0.1334, simple_loss=0.2137, pruned_loss=0.02654, over 4981.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2337, pruned_loss=0.03752, over 985024.58 frames.], batch size: 30, aishell_tot_loss[loss=0.1556, simple_loss=0.2408, pruned_loss=0.03523, over 978843.45 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.2278, pruned_loss=0.03977, over 981231.71 frames.], batch size: 30, lr: 4.81e-04
+2022-06-18 22:27:23,938 INFO [train.py:874] (1/4) Epoch 17, batch 2150, datatang_loss[loss=0.1587, simple_loss=0.242, pruned_loss=0.03766, over 4935.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2343, pruned_loss=0.03748, over 985486.32 frames.], batch size: 88, aishell_tot_loss[loss=0.1557, simple_loss=0.2408, pruned_loss=0.03532, over 979884.87 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2283, pruned_loss=0.03965, over 981867.36 frames.], batch size: 88, lr: 4.80e-04
+2022-06-18 22:27:55,173 INFO [train.py:874] (1/4) Epoch 17, batch 2200, datatang_loss[loss=0.1369, simple_loss=0.2183, pruned_loss=0.02777, over 4934.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2352, pruned_loss=0.03762, over 985666.71 frames.], batch size: 62, aishell_tot_loss[loss=0.1565, simple_loss=0.2419, pruned_loss=0.03562, over 980785.70 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.228, pruned_loss=0.03954, over 982260.18 frames.], batch size: 62, lr: 4.80e-04
+2022-06-18 22:28:25,158 INFO [train.py:874] (1/4) Epoch 17, batch 2250, datatang_loss[loss=0.1292, simple_loss=0.1978, pruned_loss=0.03027, over 4982.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2355, pruned_loss=0.03765, over 985864.32 frames.], batch size: 40, aishell_tot_loss[loss=0.157, simple_loss=0.2423, pruned_loss=0.0358, over 981447.49 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2277, pruned_loss=0.03947, over 982806.61 frames.], batch size: 40, lr: 4.80e-04
+2022-06-18 22:28:55,864 INFO [train.py:874] (1/4) Epoch 17, batch 2300, aishell_loss[loss=0.1538, simple_loss=0.2407, pruned_loss=0.03338, over 4907.00 frames.], tot_loss[loss=0.1548, simple_loss=0.235, pruned_loss=0.03728, over 985864.10 frames.], batch size: 52, aishell_tot_loss[loss=0.1567, simple_loss=0.2421, pruned_loss=0.03565, over 981914.68 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2272, pruned_loss=0.0393, over 983233.61 frames.], batch size: 52, lr: 4.80e-04
+2022-06-18 22:29:26,630 INFO [train.py:874] (1/4) Epoch 17, batch 2350, aishell_loss[loss=0.144, simple_loss=0.2367, pruned_loss=0.02568, over 4961.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2349, pruned_loss=0.03689, over 985729.78 frames.], batch size: 40, aishell_tot_loss[loss=0.1567, simple_loss=0.2421, pruned_loss=0.03567, over 982432.05 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.227, pruned_loss=0.03891, over 983380.73 frames.], batch size: 40, lr: 4.80e-04
+2022-06-18 22:29:55,936 INFO [train.py:874] (1/4) Epoch 17, batch 2400, datatang_loss[loss=0.1702, simple_loss=0.2482, pruned_loss=0.04612, over 4934.00 frames.], tot_loss[loss=0.1541, simple_loss=0.235, pruned_loss=0.0366, over 985732.34 frames.], batch size: 94, aishell_tot_loss[loss=0.157, simple_loss=0.2426, pruned_loss=0.03565, over 982728.14 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2267, pruned_loss=0.03855, over 983752.31 frames.], batch size: 94, lr: 4.79e-04
+2022-06-18 22:30:25,171 INFO [train.py:874] (1/4) Epoch 17, batch 2450, aishell_loss[loss=0.1813, simple_loss=0.2688, pruned_loss=0.04684, over 4958.00 frames.], tot_loss[loss=0.1544, simple_loss=0.235, pruned_loss=0.03695, over 985680.22 frames.], batch size: 40, aishell_tot_loss[loss=0.1567, simple_loss=0.2421, pruned_loss=0.03568, over 983180.78 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2271, pruned_loss=0.03881, over 983839.88 frames.], batch size: 40, lr: 4.79e-04
+2022-06-18 22:30:56,070 INFO [train.py:874] (1/4) Epoch 17, batch 2500, aishell_loss[loss=0.1481, simple_loss=0.238, pruned_loss=0.02909, over 4850.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2343, pruned_loss=0.03672, over 985414.26 frames.], batch size: 36, aishell_tot_loss[loss=0.1559, simple_loss=0.2412, pruned_loss=0.03535, over 983434.53 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2273, pruned_loss=0.03884, over 983830.80 frames.], batch size: 36, lr: 4.79e-04
+2022-06-18 22:31:26,711 INFO [train.py:874] (1/4) Epoch 17, batch 2550, aishell_loss[loss=0.1402, simple_loss=0.2219, pruned_loss=0.02928, over 4870.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2346, pruned_loss=0.03691, over 985495.50 frames.], batch size: 28, aishell_tot_loss[loss=0.1562, simple_loss=0.2415, pruned_loss=0.03547, over 983467.35 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2272, pruned_loss=0.03886, over 984310.90 frames.], batch size: 28, lr: 4.79e-04
+2022-06-18 22:31:56,349 INFO [train.py:874] (1/4) Epoch 17, batch 2600, aishell_loss[loss=0.1816, simple_loss=0.2639, pruned_loss=0.0496, over 4983.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2346, pruned_loss=0.03704, over 985324.38 frames.], batch size: 38, aishell_tot_loss[loss=0.1561, simple_loss=0.2411, pruned_loss=0.03557, over 983464.14 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2276, pruned_loss=0.03885, over 984509.98 frames.], batch size: 38, lr: 4.79e-04
+2022-06-18 22:32:27,296 INFO [train.py:874] (1/4) Epoch 17, batch 2650, aishell_loss[loss=0.1485, simple_loss=0.2392, pruned_loss=0.02885, over 4939.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2351, pruned_loss=0.03732, over 985419.53 frames.], batch size: 58, aishell_tot_loss[loss=0.1569, simple_loss=0.242, pruned_loss=0.03595, over 983590.61 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2275, pruned_loss=0.03869, over 984789.38 frames.], batch size: 58, lr: 4.79e-04
+2022-06-18 22:32:58,303 INFO [train.py:874] (1/4) Epoch 17, batch 2700, datatang_loss[loss=0.1847, simple_loss=0.2556, pruned_loss=0.05694, over 4934.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2355, pruned_loss=0.0376, over 985245.64 frames.], batch size: 88, aishell_tot_loss[loss=0.1567, simple_loss=0.2417, pruned_loss=0.03583, over 983684.10 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2286, pruned_loss=0.03903, over 984767.69 frames.], batch size: 88, lr: 4.78e-04
+2022-06-18 22:33:27,882 INFO [train.py:874] (1/4) Epoch 17, batch 2750, datatang_loss[loss=0.1443, simple_loss=0.2206, pruned_loss=0.03399, over 4953.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2347, pruned_loss=0.03744, over 985469.16 frames.], batch size: 69, aishell_tot_loss[loss=0.1564, simple_loss=0.2414, pruned_loss=0.03569, over 984052.52 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2281, pruned_loss=0.03905, over 984867.12 frames.], batch size: 69, lr: 4.78e-04
+2022-06-18 22:33:57,731 INFO [train.py:874] (1/4) Epoch 17, batch 2800, aishell_loss[loss=0.1534, simple_loss=0.2345, pruned_loss=0.03615, over 4968.00 frames.], tot_loss[loss=0.1551, simple_loss=0.2352, pruned_loss=0.03751, over 985427.19 frames.], batch size: 44, aishell_tot_loss[loss=0.1567, simple_loss=0.2418, pruned_loss=0.0358, over 984311.91 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2279, pruned_loss=0.03907, over 984813.12 frames.], batch size: 44, lr: 4.78e-04
+2022-06-18 22:34:28,501 INFO [train.py:874] (1/4) Epoch 17, batch 2850, aishell_loss[loss=0.1452, simple_loss=0.2398, pruned_loss=0.02524, over 4949.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2363, pruned_loss=0.03822, over 985809.81 frames.], batch size: 64, aishell_tot_loss[loss=0.157, simple_loss=0.2422, pruned_loss=0.03587, over 984668.81 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2286, pruned_loss=0.03982, over 985079.00 frames.], batch size: 64, lr: 4.78e-04
+2022-06-18 22:34:58,999 INFO [train.py:874] (1/4) Epoch 17, batch 2900, datatang_loss[loss=0.1407, simple_loss=0.2156, pruned_loss=0.03287, over 4934.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2355, pruned_loss=0.03739, over 985334.56 frames.], batch size: 69, aishell_tot_loss[loss=0.1564, simple_loss=0.2419, pruned_loss=0.03543, over 984414.29 frames.], datatang_tot_loss[loss=0.1536, simple_loss=0.228, pruned_loss=0.03956, over 985055.86 frames.], batch size: 69, lr: 4.78e-04
+2022-06-18 22:35:28,952 INFO [train.py:874] (1/4) Epoch 17, batch 2950, aishell_loss[loss=0.1426, simple_loss=0.2366, pruned_loss=0.02425, over 4968.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2356, pruned_loss=0.03762, over 985116.33 frames.], batch size: 51, aishell_tot_loss[loss=0.1566, simple_loss=0.2421, pruned_loss=0.03548, over 984346.80 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.2281, pruned_loss=0.03971, over 985021.07 frames.], batch size: 51, lr: 4.78e-04
+2022-06-18 22:35:58,592 INFO [train.py:874] (1/4) Epoch 17, batch 3000, datatang_loss[loss=0.1638, simple_loss=0.2403, pruned_loss=0.04363, over 4961.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2355, pruned_loss=0.0375, over 985200.70 frames.], batch size: 55, aishell_tot_loss[loss=0.1566, simple_loss=0.2424, pruned_loss=0.03541, over 984291.69 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.2281, pruned_loss=0.0396, over 985244.13 frames.], batch size: 55, lr: 4.77e-04
+2022-06-18 22:35:58,593 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 22:36:15,620 INFO [train.py:914] (1/4) Epoch 17, validation: loss=0.1675, simple_loss=0.2537, pruned_loss=0.04061, over 1622729.00 frames.
+2022-06-18 22:36:45,564 INFO [train.py:874] (1/4) Epoch 17, batch 3050, aishell_loss[loss=0.1571, simple_loss=0.239, pruned_loss=0.03755, over 4974.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2354, pruned_loss=0.03772, over 985619.79 frames.], batch size: 27, aishell_tot_loss[loss=0.1566, simple_loss=0.2424, pruned_loss=0.03545, over 984482.89 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.2284, pruned_loss=0.03973, over 985582.58 frames.], batch size: 27, lr: 4.77e-04
+2022-06-18 22:37:16,151 INFO [train.py:874] (1/4) Epoch 17, batch 3100, datatang_loss[loss=0.1428, simple_loss=0.2227, pruned_loss=0.0314, over 4924.00 frames.], tot_loss[loss=0.1553, simple_loss=0.2351, pruned_loss=0.03775, over 985589.42 frames.], batch size: 83, aishell_tot_loss[loss=0.1565, simple_loss=0.242, pruned_loss=0.03547, over 984487.71 frames.], datatang_tot_loss[loss=0.154, simple_loss=0.2288, pruned_loss=0.03967, over 985638.53 frames.], batch size: 83, lr: 4.77e-04
+2022-06-18 22:37:46,647 INFO [train.py:874] (1/4) Epoch 17, batch 3150, aishell_loss[loss=0.1222, simple_loss=0.2023, pruned_loss=0.02099, over 4955.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2354, pruned_loss=0.03768, over 985482.00 frames.], batch size: 25, aishell_tot_loss[loss=0.1567, simple_loss=0.2423, pruned_loss=0.03553, over 984624.12 frames.], datatang_tot_loss[loss=0.1539, simple_loss=0.2287, pruned_loss=0.03956, over 985506.73 frames.], batch size: 25, lr: 4.77e-04
+2022-06-18 22:38:17,499 INFO [train.py:874] (1/4) Epoch 17, batch 3200, aishell_loss[loss=0.1665, simple_loss=0.2568, pruned_loss=0.03813, over 4919.00 frames.], tot_loss[loss=0.1558, simple_loss=0.236, pruned_loss=0.03776, over 985522.41 frames.], batch size: 46, aishell_tot_loss[loss=0.1569, simple_loss=0.2425, pruned_loss=0.0356, over 984488.58 frames.], datatang_tot_loss[loss=0.1541, simple_loss=0.2289, pruned_loss=0.03967, over 985807.43 frames.], batch size: 46, lr: 4.77e-04
+2022-06-18 22:38:47,533 INFO [train.py:874] (1/4) Epoch 17, batch 3250, aishell_loss[loss=0.1263, simple_loss=0.2223, pruned_loss=0.01514, over 4977.00 frames.], tot_loss[loss=0.155, simple_loss=0.235, pruned_loss=0.03753, over 985679.28 frames.], batch size: 30, aishell_tot_loss[loss=0.1565, simple_loss=0.2423, pruned_loss=0.03538, over 984879.73 frames.], datatang_tot_loss[loss=0.1538, simple_loss=0.2285, pruned_loss=0.03954, over 985637.11 frames.], batch size: 30, lr: 4.77e-04
+2022-06-18 22:39:17,039 INFO [train.py:874] (1/4) Epoch 17, batch 3300, aishell_loss[loss=0.1514, simple_loss=0.2422, pruned_loss=0.03025, over 4908.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2341, pruned_loss=0.03715, over 985747.46 frames.], batch size: 52, aishell_tot_loss[loss=0.1564, simple_loss=0.2421, pruned_loss=0.03537, over 984767.82 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2277, pruned_loss=0.03917, over 985914.60 frames.], batch size: 52, lr: 4.76e-04
+2022-06-18 22:39:47,774 INFO [train.py:874] (1/4) Epoch 17, batch 3350, aishell_loss[loss=0.1568, simple_loss=0.2427, pruned_loss=0.03546, over 4975.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2344, pruned_loss=0.03715, over 985883.89 frames.], batch size: 31, aishell_tot_loss[loss=0.1564, simple_loss=0.2422, pruned_loss=0.03533, over 985093.00 frames.], datatang_tot_loss[loss=0.1531, simple_loss=0.2278, pruned_loss=0.0392, over 985828.14 frames.], batch size: 31, lr: 4.76e-04
+2022-06-18 22:40:18,452 INFO [train.py:874] (1/4) Epoch 17, batch 3400, datatang_loss[loss=0.1505, simple_loss=0.2162, pruned_loss=0.04238, over 4909.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2342, pruned_loss=0.03678, over 985671.26 frames.], batch size: 52, aishell_tot_loss[loss=0.1554, simple_loss=0.2413, pruned_loss=0.0347, over 984952.59 frames.], datatang_tot_loss[loss=0.1535, simple_loss=0.2281, pruned_loss=0.03942, over 985864.38 frames.], batch size: 52, lr: 4.76e-04
+2022-06-18 22:40:47,879 INFO [train.py:874] (1/4) Epoch 17, batch 3450, aishell_loss[loss=0.1599, simple_loss=0.2427, pruned_loss=0.03859, over 4854.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2345, pruned_loss=0.03709, over 985886.50 frames.], batch size: 38, aishell_tot_loss[loss=0.1556, simple_loss=0.2415, pruned_loss=0.03484, over 985068.76 frames.], datatang_tot_loss[loss=0.1536, simple_loss=0.2282, pruned_loss=0.0395, over 986042.74 frames.], batch size: 38, lr: 4.76e-04
+2022-06-18 22:41:18,972 INFO [train.py:874] (1/4) Epoch 17, batch 3500, aishell_loss[loss=0.1677, simple_loss=0.2507, pruned_loss=0.04236, over 4962.00 frames.], tot_loss[loss=0.1552, simple_loss=0.2354, pruned_loss=0.03755, over 986048.86 frames.], batch size: 61, aishell_tot_loss[loss=0.1565, simple_loss=0.2423, pruned_loss=0.03533, over 985207.76 frames.], datatang_tot_loss[loss=0.1536, simple_loss=0.2282, pruned_loss=0.03949, over 986143.93 frames.], batch size: 61, lr: 4.76e-04
+2022-06-18 22:41:49,998 INFO [train.py:874] (1/4) Epoch 17, batch 3550, datatang_loss[loss=0.1492, simple_loss=0.2241, pruned_loss=0.0371, over 4839.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2349, pruned_loss=0.03737, over 985763.14 frames.], batch size: 30, aishell_tot_loss[loss=0.1564, simple_loss=0.2422, pruned_loss=0.03532, over 985263.99 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2282, pruned_loss=0.03922, over 985844.09 frames.], batch size: 30, lr: 4.76e-04
+2022-06-18 22:42:20,085 INFO [train.py:874] (1/4) Epoch 17, batch 3600, datatang_loss[loss=0.1319, simple_loss=0.2047, pruned_loss=0.02958, over 4924.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2346, pruned_loss=0.03731, over 985051.60 frames.], batch size: 71, aishell_tot_loss[loss=0.1571, simple_loss=0.2425, pruned_loss=0.03586, over 984615.54 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2271, pruned_loss=0.03874, over 985798.94 frames.], batch size: 71, lr: 4.75e-04
+2022-06-18 22:42:49,390 INFO [train.py:874] (1/4) Epoch 17, batch 3650, aishell_loss[loss=0.1823, simple_loss=0.2619, pruned_loss=0.0514, over 4921.00 frames.], tot_loss[loss=0.1552, simple_loss=0.235, pruned_loss=0.03768, over 985369.21 frames.], batch size: 41, aishell_tot_loss[loss=0.1562, simple_loss=0.2414, pruned_loss=0.03548, over 984709.95 frames.], datatang_tot_loss[loss=0.1537, simple_loss=0.2284, pruned_loss=0.03953, over 985993.28 frames.], batch size: 41, lr: 4.75e-04
+2022-06-18 22:43:21,330 INFO [train.py:874] (1/4) Epoch 17, batch 3700, aishell_loss[loss=0.1625, simple_loss=0.2469, pruned_loss=0.03905, over 4921.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2344, pruned_loss=0.03763, over 985565.16 frames.], batch size: 52, aishell_tot_loss[loss=0.156, simple_loss=0.2413, pruned_loss=0.0354, over 984893.48 frames.], datatang_tot_loss[loss=0.1536, simple_loss=0.228, pruned_loss=0.0396, over 986018.50 frames.], batch size: 52, lr: 4.75e-04
+2022-06-18 22:43:51,463 INFO [train.py:874] (1/4) Epoch 17, batch 3750, aishell_loss[loss=0.1618, simple_loss=0.2445, pruned_loss=0.0395, over 4897.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2338, pruned_loss=0.03739, over 985681.59 frames.], batch size: 34, aishell_tot_loss[loss=0.1558, simple_loss=0.2411, pruned_loss=0.03529, over 985070.44 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2277, pruned_loss=0.03944, over 985992.25 frames.], batch size: 34, lr: 4.75e-04
+2022-06-18 22:44:21,504 INFO [train.py:874] (1/4) Epoch 17, batch 3800, aishell_loss[loss=0.1527, simple_loss=0.2362, pruned_loss=0.0346, over 4923.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2332, pruned_loss=0.03705, over 985344.42 frames.], batch size: 46, aishell_tot_loss[loss=0.1557, simple_loss=0.241, pruned_loss=0.03526, over 985000.74 frames.], datatang_tot_loss[loss=0.1527, simple_loss=0.2271, pruned_loss=0.03914, over 985738.79 frames.], batch size: 46, lr: 4.75e-04
+2022-06-18 22:44:50,827 INFO [train.py:874] (1/4) Epoch 17, batch 3850, aishell_loss[loss=0.1643, simple_loss=0.2477, pruned_loss=0.04049, over 4969.00 frames.], tot_loss[loss=0.1546, simple_loss=0.2344, pruned_loss=0.03735, over 985374.71 frames.], batch size: 64, aishell_tot_loss[loss=0.1562, simple_loss=0.2415, pruned_loss=0.03544, over 985032.35 frames.], datatang_tot_loss[loss=0.153, simple_loss=0.2274, pruned_loss=0.03933, over 985740.21 frames.], batch size: 64, lr: 4.75e-04
+2022-06-18 22:45:20,645 INFO [train.py:874] (1/4) Epoch 17, batch 3900, aishell_loss[loss=0.1501, simple_loss=0.246, pruned_loss=0.02714, over 4945.00 frames.], tot_loss[loss=0.1556, simple_loss=0.2358, pruned_loss=0.03776, over 985191.70 frames.], batch size: 56, aishell_tot_loss[loss=0.1572, simple_loss=0.2424, pruned_loss=0.03599, over 984879.31 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.228, pruned_loss=0.03919, over 985707.65 frames.], batch size: 56, lr: 4.74e-04
+2022-06-18 22:45:49,952 INFO [train.py:874] (1/4) Epoch 17, batch 3950, aishell_loss[loss=0.1546, simple_loss=0.2502, pruned_loss=0.02954, over 4882.00 frames.], tot_loss[loss=0.1558, simple_loss=0.236, pruned_loss=0.03781, over 985246.65 frames.], batch size: 34, aishell_tot_loss[loss=0.1574, simple_loss=0.2427, pruned_loss=0.03604, over 985018.32 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2281, pruned_loss=0.03923, over 985608.70 frames.], batch size: 34, lr: 4.74e-04
+2022-06-18 22:46:19,548 INFO [train.py:874] (1/4) Epoch 17, batch 4000, datatang_loss[loss=0.1606, simple_loss=0.2337, pruned_loss=0.04371, over 4904.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2354, pruned_loss=0.03769, over 985282.47 frames.], batch size: 47, aishell_tot_loss[loss=0.1575, simple_loss=0.2428, pruned_loss=0.03612, over 985178.51 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2274, pruned_loss=0.03905, over 985479.25 frames.], batch size: 47, lr: 4.74e-04
+2022-06-18 22:46:19,549 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 22:46:35,709 INFO [train.py:914] (1/4) 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] (1/4) Epoch 17, batch 4050, datatang_loss[loss=0.1486, simple_loss=0.2258, pruned_loss=0.03571, over 4922.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2344, pruned_loss=0.03698, over 985486.56 frames.], batch size: 73, aishell_tot_loss[loss=0.1571, simple_loss=0.2424, pruned_loss=0.03591, over 985195.79 frames.], datatang_tot_loss[loss=0.152, simple_loss=0.2271, pruned_loss=0.03849, over 985656.28 frames.], batch size: 73, lr: 4.74e-04
+2022-06-18 22:47:35,448 INFO [train.py:874] (1/4) Epoch 17, batch 4100, aishell_loss[loss=0.1607, simple_loss=0.2363, pruned_loss=0.04254, over 4866.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2334, pruned_loss=0.03682, over 985414.29 frames.], batch size: 37, aishell_tot_loss[loss=0.1568, simple_loss=0.2422, pruned_loss=0.03567, over 984910.57 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2266, pruned_loss=0.03842, over 985869.39 frames.], batch size: 37, lr: 4.74e-04
+2022-06-18 22:48:03,148 INFO [train.py:874] (1/4) Epoch 17, batch 4150, aishell_loss[loss=0.1595, simple_loss=0.2508, pruned_loss=0.0341, over 4925.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2347, pruned_loss=0.03699, over 985353.97 frames.], batch size: 58, aishell_tot_loss[loss=0.1575, simple_loss=0.2431, pruned_loss=0.03595, over 984931.53 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2268, pruned_loss=0.03826, over 985779.20 frames.], batch size: 58, lr: 4.73e-04
+2022-06-18 22:49:24,002 INFO [train.py:874] (1/4) Epoch 18, batch 50, aishell_loss[loss=0.1654, simple_loss=0.2527, pruned_loss=0.03901, over 4905.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2292, pruned_loss=0.03432, over 218708.61 frames.], batch size: 34, aishell_tot_loss[loss=0.1553, simple_loss=0.2397, pruned_loss=0.03542, over 129097.74 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2159, pruned_loss=0.03298, over 103101.58 frames.], batch size: 34, lr: 4.61e-04
+2022-06-18 22:49:54,853 INFO [train.py:874] (1/4) Epoch 18, batch 100, datatang_loss[loss=0.1261, simple_loss=0.1982, pruned_loss=0.02698, over 4957.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2234, pruned_loss=0.03357, over 388835.10 frames.], batch size: 55, aishell_tot_loss[loss=0.1515, simple_loss=0.2345, pruned_loss=0.03425, over 222188.49 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.213, pruned_loss=0.03305, over 215102.67 frames.], batch size: 55, lr: 4.61e-04
+2022-06-18 22:50:25,804 INFO [train.py:874] (1/4) Epoch 18, batch 150, datatang_loss[loss=0.1241, simple_loss=0.2009, pruned_loss=0.02363, over 4896.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2267, pruned_loss=0.03379, over 521420.48 frames.], batch size: 47, aishell_tot_loss[loss=0.1527, simple_loss=0.237, pruned_loss=0.03425, over 325701.29 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2151, pruned_loss=0.03339, over 292168.42 frames.], batch size: 47, lr: 4.60e-04
+2022-06-18 22:50:54,381 INFO [train.py:874] (1/4) Epoch 18, batch 200, aishell_loss[loss=0.1503, simple_loss=0.2412, pruned_loss=0.02968, over 4971.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2289, pruned_loss=0.03482, over 623985.80 frames.], batch size: 61, aishell_tot_loss[loss=0.1537, simple_loss=0.2384, pruned_loss=0.03446, over 414691.00 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2167, pruned_loss=0.03497, over 361383.88 frames.], batch size: 61, lr: 4.60e-04
+2022-06-18 22:51:24,567 INFO [train.py:874] (1/4) Epoch 18, batch 250, aishell_loss[loss=0.1695, simple_loss=0.257, pruned_loss=0.04097, over 4949.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2306, pruned_loss=0.03523, over 704007.86 frames.], batch size: 64, aishell_tot_loss[loss=0.1543, simple_loss=0.2395, pruned_loss=0.03455, over 479236.12 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2193, pruned_loss=0.03562, over 437665.83 frames.], batch size: 64, lr: 4.60e-04
+2022-06-18 22:51:56,059 INFO [train.py:874] (1/4) Epoch 18, batch 300, datatang_loss[loss=0.1498, simple_loss=0.202, pruned_loss=0.04879, over 4956.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2295, pruned_loss=0.03535, over 766354.09 frames.], batch size: 34, aishell_tot_loss[loss=0.1543, simple_loss=0.2393, pruned_loss=0.03467, over 527435.04 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2192, pruned_loss=0.03568, over 514204.29 frames.], batch size: 34, lr: 4.60e-04
+2022-06-18 22:52:24,076 INFO [train.py:874] (1/4) Epoch 18, batch 350, datatang_loss[loss=0.1453, simple_loss=0.2237, pruned_loss=0.03342, over 4919.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2307, pruned_loss=0.03528, over 815208.42 frames.], batch size: 83, aishell_tot_loss[loss=0.1546, simple_loss=0.2399, pruned_loss=0.0347, over 595570.04 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2194, pruned_loss=0.03568, over 554901.98 frames.], batch size: 83, lr: 4.60e-04
+2022-06-18 22:52:55,582 INFO [train.py:874] (1/4) Epoch 18, batch 400, datatang_loss[loss=0.1569, simple_loss=0.2332, pruned_loss=0.04032, over 4944.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2315, pruned_loss=0.03597, over 853254.79 frames.], batch size: 88, aishell_tot_loss[loss=0.1549, simple_loss=0.2399, pruned_loss=0.0349, over 632680.50 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2217, pruned_loss=0.03649, over 615416.94 frames.], batch size: 88, lr: 4.60e-04
+2022-06-18 22:53:26,694 INFO [train.py:874] (1/4) Epoch 18, batch 450, datatang_loss[loss=0.1667, simple_loss=0.2317, pruned_loss=0.05086, over 4893.00 frames.], tot_loss[loss=0.152, simple_loss=0.2316, pruned_loss=0.03618, over 882673.45 frames.], batch size: 52, aishell_tot_loss[loss=0.1548, simple_loss=0.2398, pruned_loss=0.0349, over 675829.55 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2221, pruned_loss=0.0369, over 657432.24 frames.], batch size: 52, lr: 4.59e-04
+2022-06-18 22:53:54,538 INFO [train.py:874] (1/4) Epoch 18, batch 500, datatang_loss[loss=0.1378, simple_loss=0.2061, pruned_loss=0.03476, over 4927.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2317, pruned_loss=0.03619, over 905346.82 frames.], batch size: 73, aishell_tot_loss[loss=0.155, simple_loss=0.2401, pruned_loss=0.03492, over 712123.75 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2222, pruned_loss=0.03699, over 696109.35 frames.], batch size: 73, lr: 4.59e-04
+2022-06-18 22:54:26,287 INFO [train.py:874] (1/4) Epoch 18, batch 550, datatang_loss[loss=0.13, simple_loss=0.2069, pruned_loss=0.02656, over 4941.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2316, pruned_loss=0.03607, over 923395.42 frames.], batch size: 69, aishell_tot_loss[loss=0.1537, simple_loss=0.2388, pruned_loss=0.0343, over 746822.19 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2233, pruned_loss=0.03761, over 727821.66 frames.], batch size: 69, lr: 4.59e-04
+2022-06-18 22:54:57,084 INFO [train.py:874] (1/4) Epoch 18, batch 600, datatang_loss[loss=0.161, simple_loss=0.2303, pruned_loss=0.04582, over 4939.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2317, pruned_loss=0.03646, over 937216.29 frames.], batch size: 69, aishell_tot_loss[loss=0.1541, simple_loss=0.2393, pruned_loss=0.03447, over 769604.54 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2237, pruned_loss=0.0379, over 763826.71 frames.], batch size: 69, lr: 4.59e-04
+2022-06-18 22:55:24,764 INFO [train.py:874] (1/4) Epoch 18, batch 650, datatang_loss[loss=0.1335, simple_loss=0.2145, pruned_loss=0.02628, over 4835.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2323, pruned_loss=0.03635, over 947839.22 frames.], batch size: 30, aishell_tot_loss[loss=0.1548, simple_loss=0.2399, pruned_loss=0.03485, over 796005.03 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2237, pruned_loss=0.03748, over 788851.96 frames.], batch size: 30, lr: 4.59e-04
+2022-06-18 22:55:56,306 INFO [train.py:874] (1/4) Epoch 18, batch 700, datatang_loss[loss=0.1166, simple_loss=0.1952, pruned_loss=0.01903, over 4904.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2328, pruned_loss=0.0365, over 956253.21 frames.], batch size: 34, aishell_tot_loss[loss=0.155, simple_loss=0.2402, pruned_loss=0.03494, over 821824.01 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.224, pruned_loss=0.0377, over 808394.44 frames.], batch size: 34, lr: 4.59e-04
+2022-06-18 22:56:25,863 INFO [train.py:874] (1/4) Epoch 18, batch 750, aishell_loss[loss=0.1478, simple_loss=0.2327, pruned_loss=0.03146, over 4924.00 frames.], tot_loss[loss=0.1529, simple_loss=0.233, pruned_loss=0.03637, over 962944.65 frames.], batch size: 68, aishell_tot_loss[loss=0.1548, simple_loss=0.24, pruned_loss=0.03482, over 841816.13 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.2244, pruned_loss=0.03772, over 828687.10 frames.], batch size: 68, lr: 4.58e-04
+2022-06-18 22:56:55,810 INFO [train.py:874] (1/4) Epoch 18, batch 800, aishell_loss[loss=0.1532, simple_loss=0.2244, pruned_loss=0.04103, over 4953.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2337, pruned_loss=0.0368, over 967693.56 frames.], batch size: 31, aishell_tot_loss[loss=0.1553, simple_loss=0.2405, pruned_loss=0.03507, over 856796.71 frames.], datatang_tot_loss[loss=0.1506, simple_loss=0.2253, pruned_loss=0.03795, over 848976.52 frames.], batch size: 31, lr: 4.58e-04
+2022-06-18 22:57:27,087 INFO [train.py:874] (1/4) Epoch 18, batch 850, aishell_loss[loss=0.1291, simple_loss=0.1974, pruned_loss=0.03042, over 4895.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2343, pruned_loss=0.03706, over 971395.54 frames.], batch size: 21, aishell_tot_loss[loss=0.1554, simple_loss=0.2405, pruned_loss=0.03514, over 872340.58 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2261, pruned_loss=0.03829, over 864383.09 frames.], batch size: 21, lr: 4.58e-04
+2022-06-18 22:57:56,125 INFO [train.py:874] (1/4) Epoch 18, batch 900, datatang_loss[loss=0.1622, simple_loss=0.2448, pruned_loss=0.03976, over 4915.00 frames.], tot_loss[loss=0.1548, simple_loss=0.2344, pruned_loss=0.03757, over 974886.03 frames.], batch size: 98, aishell_tot_loss[loss=0.1555, simple_loss=0.2407, pruned_loss=0.03519, over 884858.72 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2264, pruned_loss=0.03885, over 879898.91 frames.], batch size: 98, lr: 4.58e-04
+2022-06-18 22:58:25,697 INFO [train.py:874] (1/4) Epoch 18, batch 950, datatang_loss[loss=0.1569, simple_loss=0.2283, pruned_loss=0.04278, over 4953.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2346, pruned_loss=0.03812, over 977263.58 frames.], batch size: 62, aishell_tot_loss[loss=0.1561, simple_loss=0.2413, pruned_loss=0.03544, over 893407.62 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2267, pruned_loss=0.03922, over 895708.83 frames.], batch size: 62, lr: 4.58e-04
+2022-06-18 22:58:57,123 INFO [train.py:874] (1/4) Epoch 18, batch 1000, aishell_loss[loss=0.1134, simple_loss=0.1814, pruned_loss=0.02275, over 4852.00 frames.], tot_loss[loss=0.1554, simple_loss=0.2347, pruned_loss=0.03801, over 979020.20 frames.], batch size: 21, aishell_tot_loss[loss=0.1554, simple_loss=0.2404, pruned_loss=0.03526, over 906309.90 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2276, pruned_loss=0.03964, over 904161.66 frames.], batch size: 21, lr: 4.58e-04
+2022-06-18 22:58:57,124 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 22:59:14,039 INFO [train.py:914] (1/4) Epoch 18, validation: loss=0.1636, simple_loss=0.2479, pruned_loss=0.03969, over 1622729.00 frames.
+2022-06-18 22:59:44,458 INFO [train.py:874] (1/4) Epoch 18, batch 1050, datatang_loss[loss=0.1519, simple_loss=0.2228, pruned_loss=0.04054, over 4929.00 frames.], tot_loss[loss=0.1546, simple_loss=0.234, pruned_loss=0.03761, over 980773.14 frames.], batch size: 50, aishell_tot_loss[loss=0.1557, simple_loss=0.2407, pruned_loss=0.03538, over 915204.45 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2268, pruned_loss=0.03915, over 914525.65 frames.], batch size: 50, lr: 4.58e-04
+2022-06-18 23:00:16,722 INFO [train.py:874] (1/4) Epoch 18, batch 1100, datatang_loss[loss=0.1724, simple_loss=0.2473, pruned_loss=0.04878, over 4981.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2341, pruned_loss=0.03767, over 981739.74 frames.], batch size: 34, aishell_tot_loss[loss=0.1552, simple_loss=0.24, pruned_loss=0.03513, over 922699.46 frames.], datatang_tot_loss[loss=0.1534, simple_loss=0.2277, pruned_loss=0.0395, over 923557.73 frames.], batch size: 34, lr: 4.57e-04
+2022-06-18 23:00:44,266 INFO [train.py:874] (1/4) Epoch 18, batch 1150, datatang_loss[loss=0.154, simple_loss=0.2095, pruned_loss=0.0493, over 4884.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2338, pruned_loss=0.03731, over 982641.49 frames.], batch size: 39, aishell_tot_loss[loss=0.1549, simple_loss=0.2401, pruned_loss=0.03484, over 929133.07 frames.], datatang_tot_loss[loss=0.1532, simple_loss=0.2275, pruned_loss=0.03941, over 931875.43 frames.], batch size: 39, lr: 4.57e-04
+2022-06-18 23:01:15,667 INFO [train.py:874] (1/4) Epoch 18, batch 1200, aishell_loss[loss=0.1686, simple_loss=0.2537, pruned_loss=0.04178, over 4937.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2336, pruned_loss=0.03692, over 983574.79 frames.], batch size: 58, aishell_tot_loss[loss=0.1547, simple_loss=0.2398, pruned_loss=0.03483, over 936463.83 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2275, pruned_loss=0.03907, over 937842.53 frames.], batch size: 58, lr: 4.57e-04
+2022-06-18 23:01:47,500 INFO [train.py:874] (1/4) Epoch 18, batch 1250, datatang_loss[loss=0.1235, simple_loss=0.2076, pruned_loss=0.01969, over 4929.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2338, pruned_loss=0.03657, over 984076.58 frames.], batch size: 71, aishell_tot_loss[loss=0.1554, simple_loss=0.2408, pruned_loss=0.03504, over 942306.80 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2267, pruned_loss=0.03848, over 943472.05 frames.], batch size: 71, lr: 4.57e-04
+2022-06-18 23:02:15,955 INFO [train.py:874] (1/4) Epoch 18, batch 1300, datatang_loss[loss=0.1314, simple_loss=0.2004, pruned_loss=0.03117, over 4890.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2336, pruned_loss=0.0369, over 984701.94 frames.], batch size: 39, aishell_tot_loss[loss=0.1553, simple_loss=0.2406, pruned_loss=0.03506, over 946505.12 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.227, pruned_loss=0.0387, over 949593.88 frames.], batch size: 39, lr: 4.57e-04
+2022-06-18 23:02:45,771 INFO [train.py:874] (1/4) Epoch 18, batch 1350, aishell_loss[loss=0.1742, simple_loss=0.2483, pruned_loss=0.05008, over 4874.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2342, pruned_loss=0.03726, over 984888.80 frames.], batch size: 42, aishell_tot_loss[loss=0.1559, simple_loss=0.2411, pruned_loss=0.03535, over 951346.79 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.227, pruned_loss=0.03885, over 953604.99 frames.], batch size: 42, lr: 4.57e-04
+2022-06-18 23:03:17,855 INFO [train.py:874] (1/4) Epoch 18, batch 1400, datatang_loss[loss=0.1587, simple_loss=0.233, pruned_loss=0.04223, over 4949.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2339, pruned_loss=0.03723, over 985132.88 frames.], batch size: 55, aishell_tot_loss[loss=0.156, simple_loss=0.2411, pruned_loss=0.03548, over 955061.24 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2267, pruned_loss=0.03871, over 957772.79 frames.], batch size: 55, lr: 4.56e-04
+2022-06-18 23:03:45,940 INFO [train.py:874] (1/4) Epoch 18, batch 1450, aishell_loss[loss=0.1257, simple_loss=0.2129, pruned_loss=0.01927, over 4985.00 frames.], tot_loss[loss=0.1538, simple_loss=0.2333, pruned_loss=0.03715, over 985151.60 frames.], batch size: 30, aishell_tot_loss[loss=0.1552, simple_loss=0.2402, pruned_loss=0.03514, over 958512.82 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2271, pruned_loss=0.039, over 961095.86 frames.], batch size: 30, lr: 4.56e-04
+2022-06-18 23:04:16,697 INFO [train.py:874] (1/4) Epoch 18, batch 1500, datatang_loss[loss=0.1526, simple_loss=0.2261, pruned_loss=0.03956, over 4898.00 frames.], tot_loss[loss=0.1539, simple_loss=0.2337, pruned_loss=0.03703, over 985511.23 frames.], batch size: 59, aishell_tot_loss[loss=0.1553, simple_loss=0.2405, pruned_loss=0.03511, over 961892.85 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2271, pruned_loss=0.03892, over 964062.38 frames.], batch size: 59, lr: 4.56e-04
+2022-06-18 23:04:45,926 INFO [train.py:874] (1/4) Epoch 18, batch 1550, aishell_loss[loss=0.1494, simple_loss=0.2376, pruned_loss=0.03065, over 4872.00 frames.], tot_loss[loss=0.154, simple_loss=0.2341, pruned_loss=0.03694, over 985752.24 frames.], batch size: 28, aishell_tot_loss[loss=0.1553, simple_loss=0.2405, pruned_loss=0.03501, over 965120.69 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2272, pruned_loss=0.03902, over 966440.80 frames.], batch size: 28, lr: 4.56e-04
+2022-06-18 23:05:15,884 INFO [train.py:874] (1/4) Epoch 18, batch 1600, datatang_loss[loss=0.1375, simple_loss=0.2158, pruned_loss=0.02962, over 4897.00 frames.], tot_loss[loss=0.1544, simple_loss=0.2344, pruned_loss=0.03719, over 985889.68 frames.], batch size: 52, aishell_tot_loss[loss=0.1557, simple_loss=0.2409, pruned_loss=0.03524, over 967600.59 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.227, pruned_loss=0.03906, over 968823.13 frames.], batch size: 52, lr: 4.56e-04
+2022-06-18 23:05:47,559 INFO [train.py:874] (1/4) Epoch 18, batch 1650, datatang_loss[loss=0.1585, simple_loss=0.2381, pruned_loss=0.03945, over 4922.00 frames.], tot_loss[loss=0.1549, simple_loss=0.2349, pruned_loss=0.03748, over 986008.79 frames.], batch size: 98, aishell_tot_loss[loss=0.1556, simple_loss=0.2409, pruned_loss=0.03516, over 969656.42 frames.], datatang_tot_loss[loss=0.1533, simple_loss=0.2278, pruned_loss=0.03939, over 971024.25 frames.], batch size: 98, lr: 4.56e-04
+2022-06-18 23:06:22,687 INFO [train.py:874] (1/4) Epoch 18, batch 1700, datatang_loss[loss=0.1417, simple_loss=0.2199, pruned_loss=0.03173, over 4931.00 frames.], tot_loss[loss=0.1547, simple_loss=0.2346, pruned_loss=0.03742, over 985953.27 frames.], batch size: 69, aishell_tot_loss[loss=0.156, simple_loss=0.241, pruned_loss=0.03549, over 971416.48 frames.], datatang_tot_loss[loss=0.1528, simple_loss=0.2275, pruned_loss=0.03904, over 972898.60 frames.], batch size: 69, lr: 4.55e-04
+2022-06-18 23:06:51,254 INFO [train.py:874] (1/4) Epoch 18, batch 1750, aishell_loss[loss=0.1429, simple_loss=0.2184, pruned_loss=0.03369, over 4966.00 frames.], tot_loss[loss=0.1549, simple_loss=0.235, pruned_loss=0.03741, over 986067.21 frames.], batch size: 25, aishell_tot_loss[loss=0.1564, simple_loss=0.2415, pruned_loss=0.03571, over 973253.87 frames.], datatang_tot_loss[loss=0.1526, simple_loss=0.2275, pruned_loss=0.03887, over 974426.74 frames.], batch size: 25, lr: 4.55e-04
+2022-06-18 23:07:22,828 INFO [train.py:874] (1/4) Epoch 18, batch 1800, datatang_loss[loss=0.1653, simple_loss=0.2386, pruned_loss=0.04597, over 4929.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2343, pruned_loss=0.03714, over 986029.47 frames.], batch size: 79, aishell_tot_loss[loss=0.1562, simple_loss=0.2413, pruned_loss=0.03556, over 974513.36 frames.], datatang_tot_loss[loss=0.1524, simple_loss=0.2274, pruned_loss=0.03869, over 975979.83 frames.], batch size: 79, lr: 4.55e-04
+2022-06-18 23:07:53,201 INFO [train.py:874] (1/4) Epoch 18, batch 1850, aishell_loss[loss=0.1578, simple_loss=0.2437, pruned_loss=0.03596, over 4926.00 frames.], tot_loss[loss=0.1534, simple_loss=0.2334, pruned_loss=0.03668, over 985949.32 frames.], batch size: 33, aishell_tot_loss[loss=0.156, simple_loss=0.2412, pruned_loss=0.03544, over 975568.33 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2265, pruned_loss=0.03833, over 977382.79 frames.], batch size: 33, lr: 4.55e-04
+2022-06-18 23:08:22,141 INFO [train.py:874] (1/4) Epoch 18, batch 1900, aishell_loss[loss=0.1455, simple_loss=0.2396, pruned_loss=0.02575, over 4950.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2326, pruned_loss=0.03611, over 985585.35 frames.], batch size: 45, aishell_tot_loss[loss=0.1552, simple_loss=0.2403, pruned_loss=0.03502, over 976668.47 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.2262, pruned_loss=0.03814, over 978141.58 frames.], batch size: 45, lr: 4.55e-04
+2022-06-18 23:08:53,184 INFO [train.py:874] (1/4) Epoch 18, batch 1950, datatang_loss[loss=0.1406, simple_loss=0.207, pruned_loss=0.03709, over 4955.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2324, pruned_loss=0.03638, over 985806.67 frames.], batch size: 55, aishell_tot_loss[loss=0.1548, simple_loss=0.24, pruned_loss=0.0348, over 977477.50 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2266, pruned_loss=0.03843, over 979438.32 frames.], batch size: 55, lr: 4.55e-04
+2022-06-18 23:09:24,292 INFO [train.py:874] (1/4) Epoch 18, batch 2000, datatang_loss[loss=0.1569, simple_loss=0.2326, pruned_loss=0.04062, over 4937.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2328, pruned_loss=0.03644, over 985776.98 frames.], batch size: 62, aishell_tot_loss[loss=0.1552, simple_loss=0.2404, pruned_loss=0.03497, over 978311.40 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2266, pruned_loss=0.03825, over 980275.75 frames.], batch size: 62, lr: 4.55e-04
+2022-06-18 23:09:24,293 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 23:09:40,067 INFO [train.py:914] (1/4) 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] (1/4) Epoch 18, batch 2050, aishell_loss[loss=0.1547, simple_loss=0.2421, pruned_loss=0.03365, over 4933.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2325, pruned_loss=0.0363, over 985636.21 frames.], batch size: 31, aishell_tot_loss[loss=0.1547, simple_loss=0.24, pruned_loss=0.03465, over 978976.25 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.2265, pruned_loss=0.0384, over 980999.97 frames.], batch size: 31, lr: 4.54e-04
+2022-06-18 23:10:39,546 INFO [train.py:874] (1/4) Epoch 18, batch 2100, aishell_loss[loss=0.1438, simple_loss=0.2404, pruned_loss=0.02356, over 4983.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2327, pruned_loss=0.03602, over 985547.60 frames.], batch size: 38, aishell_tot_loss[loss=0.1545, simple_loss=0.2401, pruned_loss=0.03443, over 979970.18 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2263, pruned_loss=0.0383, over 981250.04 frames.], batch size: 38, lr: 4.54e-04
+2022-06-18 23:11:11,041 INFO [train.py:874] (1/4) Epoch 18, batch 2150, datatang_loss[loss=0.1349, simple_loss=0.2105, pruned_loss=0.02961, over 4965.00 frames.], tot_loss[loss=0.152, simple_loss=0.2325, pruned_loss=0.03575, over 985247.67 frames.], batch size: 55, aishell_tot_loss[loss=0.1538, simple_loss=0.2395, pruned_loss=0.03406, over 980489.93 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2265, pruned_loss=0.03834, over 981587.00 frames.], batch size: 55, lr: 4.54e-04
+2022-06-18 23:11:42,805 INFO [train.py:874] (1/4) Epoch 18, batch 2200, aishell_loss[loss=0.1282, simple_loss=0.2105, pruned_loss=0.02294, over 4819.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2327, pruned_loss=0.03561, over 984955.55 frames.], batch size: 24, aishell_tot_loss[loss=0.1539, simple_loss=0.2397, pruned_loss=0.034, over 980564.29 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2264, pruned_loss=0.03812, over 982184.09 frames.], batch size: 24, lr: 4.54e-04
+2022-06-18 23:12:10,479 INFO [train.py:874] (1/4) Epoch 18, batch 2250, datatang_loss[loss=0.15, simple_loss=0.2387, pruned_loss=0.03061, over 4930.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2316, pruned_loss=0.03512, over 984881.59 frames.], batch size: 42, aishell_tot_loss[loss=0.1538, simple_loss=0.2395, pruned_loss=0.03402, over 980853.58 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2256, pruned_loss=0.03742, over 982625.74 frames.], batch size: 42, lr: 4.54e-04
+2022-06-18 23:12:42,744 INFO [train.py:874] (1/4) Epoch 18, batch 2300, datatang_loss[loss=0.1379, simple_loss=0.216, pruned_loss=0.02991, over 4919.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2325, pruned_loss=0.03557, over 984893.88 frames.], batch size: 81, aishell_tot_loss[loss=0.1538, simple_loss=0.2396, pruned_loss=0.03398, over 981391.48 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2264, pruned_loss=0.03776, over 982832.14 frames.], batch size: 81, lr: 4.54e-04
+2022-06-18 23:13:13,531 INFO [train.py:874] (1/4) Epoch 18, batch 2350, aishell_loss[loss=0.1218, simple_loss=0.2004, pruned_loss=0.02159, over 4917.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2332, pruned_loss=0.03604, over 985201.11 frames.], batch size: 25, aishell_tot_loss[loss=0.1539, simple_loss=0.2398, pruned_loss=0.03399, over 982159.20 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2266, pruned_loss=0.03824, over 983052.50 frames.], batch size: 25, lr: 4.53e-04
+2022-06-18 23:13:42,678 INFO [train.py:874] (1/4) Epoch 18, batch 2400, datatang_loss[loss=0.1637, simple_loss=0.2283, pruned_loss=0.04952, over 4944.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2337, pruned_loss=0.0368, over 985274.36 frames.], batch size: 62, aishell_tot_loss[loss=0.1543, simple_loss=0.24, pruned_loss=0.0343, over 982694.93 frames.], datatang_tot_loss[loss=0.1522, simple_loss=0.227, pruned_loss=0.03867, over 983202.88 frames.], batch size: 62, lr: 4.53e-04
+2022-06-18 23:14:13,857 INFO [train.py:874] (1/4) Epoch 18, batch 2450, aishell_loss[loss=0.1135, simple_loss=0.1963, pruned_loss=0.0154, over 4970.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2344, pruned_loss=0.03699, over 985513.39 frames.], batch size: 27, aishell_tot_loss[loss=0.1542, simple_loss=0.2397, pruned_loss=0.03436, over 983140.34 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2277, pruned_loss=0.03903, over 983572.23 frames.], batch size: 27, lr: 4.53e-04
+2022-06-18 23:14:43,647 INFO [train.py:874] (1/4) Epoch 18, batch 2500, aishell_loss[loss=0.1441, simple_loss=0.2445, pruned_loss=0.02192, over 4951.00 frames.], tot_loss[loss=0.153, simple_loss=0.2336, pruned_loss=0.03617, over 985553.33 frames.], batch size: 54, aishell_tot_loss[loss=0.1534, simple_loss=0.2391, pruned_loss=0.0339, over 983453.13 frames.], datatang_tot_loss[loss=0.1525, simple_loss=0.2273, pruned_loss=0.03887, over 983811.44 frames.], batch size: 54, lr: 4.53e-04
+2022-06-18 23:15:12,787 INFO [train.py:874] (1/4) Epoch 18, batch 2550, datatang_loss[loss=0.1317, simple_loss=0.2171, pruned_loss=0.02316, over 4912.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2334, pruned_loss=0.03578, over 985674.27 frames.], batch size: 64, aishell_tot_loss[loss=0.1536, simple_loss=0.2392, pruned_loss=0.03398, over 983748.39 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2268, pruned_loss=0.03842, over 984104.79 frames.], batch size: 64, lr: 4.53e-04
+2022-06-18 23:15:44,503 INFO [train.py:874] (1/4) Epoch 18, batch 2600, aishell_loss[loss=0.1419, simple_loss=0.2373, pruned_loss=0.02323, over 4917.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2333, pruned_loss=0.03627, over 985573.28 frames.], batch size: 46, aishell_tot_loss[loss=0.1537, simple_loss=0.2392, pruned_loss=0.03413, over 983877.69 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2267, pruned_loss=0.03871, over 984287.03 frames.], batch size: 46, lr: 4.53e-04
+2022-06-18 23:16:12,057 INFO [train.py:874] (1/4) Epoch 18, batch 2650, aishell_loss[loss=0.2023, simple_loss=0.2806, pruned_loss=0.06202, over 4868.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2334, pruned_loss=0.03661, over 985439.49 frames.], batch size: 37, aishell_tot_loss[loss=0.154, simple_loss=0.2395, pruned_loss=0.03424, over 983832.59 frames.], datatang_tot_loss[loss=0.1523, simple_loss=0.2267, pruned_loss=0.03891, over 984529.42 frames.], batch size: 37, lr: 4.52e-04
+2022-06-18 23:16:42,927 INFO [train.py:874] (1/4) Epoch 18, batch 2700, aishell_loss[loss=0.1616, simple_loss=0.2527, pruned_loss=0.03527, over 4905.00 frames.], tot_loss[loss=0.1542, simple_loss=0.2341, pruned_loss=0.03715, over 985578.94 frames.], batch size: 52, aishell_tot_loss[loss=0.1544, simple_loss=0.2397, pruned_loss=0.03453, over 984012.74 frames.], datatang_tot_loss[loss=0.1529, simple_loss=0.2273, pruned_loss=0.0392, over 984783.97 frames.], batch size: 52, lr: 4.52e-04
+2022-06-18 23:17:14,055 INFO [train.py:874] (1/4) Epoch 18, batch 2750, aishell_loss[loss=0.1391, simple_loss=0.2198, pruned_loss=0.02921, over 4979.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2324, pruned_loss=0.03663, over 985077.85 frames.], batch size: 30, aishell_tot_loss[loss=0.1538, simple_loss=0.2391, pruned_loss=0.03426, over 983944.21 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2265, pruned_loss=0.03888, over 984585.24 frames.], batch size: 30, lr: 4.52e-04
+2022-06-18 23:17:42,078 INFO [train.py:874] (1/4) Epoch 18, batch 2800, aishell_loss[loss=0.1435, simple_loss=0.2378, pruned_loss=0.02459, over 4957.00 frames.], tot_loss[loss=0.1524, simple_loss=0.2324, pruned_loss=0.03617, over 985107.36 frames.], batch size: 44, aishell_tot_loss[loss=0.1535, simple_loss=0.239, pruned_loss=0.03401, over 983913.19 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2265, pruned_loss=0.03866, over 984836.45 frames.], batch size: 44, lr: 4.52e-04
+2022-06-18 23:18:13,840 INFO [train.py:874] (1/4) Epoch 18, batch 2850, aishell_loss[loss=0.1427, simple_loss=0.2277, pruned_loss=0.02884, over 4942.00 frames.], tot_loss[loss=0.152, simple_loss=0.2319, pruned_loss=0.036, over 985041.59 frames.], batch size: 58, aishell_tot_loss[loss=0.1533, simple_loss=0.2388, pruned_loss=0.03388, over 983870.73 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2261, pruned_loss=0.03856, over 984977.77 frames.], batch size: 58, lr: 4.52e-04
+2022-06-18 23:18:44,555 INFO [train.py:874] (1/4) Epoch 18, batch 2900, aishell_loss[loss=0.1426, simple_loss=0.2265, pruned_loss=0.02937, over 4955.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2315, pruned_loss=0.03559, over 985215.38 frames.], batch size: 44, aishell_tot_loss[loss=0.1531, simple_loss=0.2388, pruned_loss=0.03373, over 984179.01 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2257, pruned_loss=0.0382, over 984987.40 frames.], batch size: 44, lr: 4.52e-04
+2022-06-18 23:19:14,112 INFO [train.py:874] (1/4) Epoch 18, batch 2950, datatang_loss[loss=0.1546, simple_loss=0.2319, pruned_loss=0.03868, over 4953.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2319, pruned_loss=0.03608, over 985599.28 frames.], batch size: 91, aishell_tot_loss[loss=0.1536, simple_loss=0.2392, pruned_loss=0.03397, over 984590.49 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.226, pruned_loss=0.03823, over 985111.28 frames.], batch size: 91, lr: 4.52e-04
+2022-06-18 23:19:45,409 INFO [train.py:874] (1/4) Epoch 18, batch 3000, aishell_loss[loss=0.1701, simple_loss=0.2498, pruned_loss=0.04524, over 4886.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2327, pruned_loss=0.03678, over 985831.72 frames.], batch size: 42, aishell_tot_loss[loss=0.1541, simple_loss=0.2396, pruned_loss=0.03429, over 984803.62 frames.], datatang_tot_loss[loss=0.1518, simple_loss=0.2266, pruned_loss=0.03855, over 985307.45 frames.], batch size: 42, lr: 4.51e-04
+2022-06-18 23:19:45,410 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 23:20:02,474 INFO [train.py:914] (1/4) 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] (1/4) Epoch 18, batch 3050, datatang_loss[loss=0.1598, simple_loss=0.2374, pruned_loss=0.04105, over 4887.00 frames.], tot_loss[loss=0.153, simple_loss=0.2329, pruned_loss=0.03659, over 985760.30 frames.], batch size: 42, aishell_tot_loss[loss=0.1545, simple_loss=0.2399, pruned_loss=0.03454, over 984886.99 frames.], datatang_tot_loss[loss=0.1514, simple_loss=0.2264, pruned_loss=0.03818, over 985319.07 frames.], batch size: 42, lr: 4.51e-04
+2022-06-18 23:21:04,153 INFO [train.py:874] (1/4) Epoch 18, batch 3100, datatang_loss[loss=0.1474, simple_loss=0.2263, pruned_loss=0.03428, over 4916.00 frames.], tot_loss[loss=0.1533, simple_loss=0.233, pruned_loss=0.03681, over 985586.65 frames.], batch size: 81, aishell_tot_loss[loss=0.155, simple_loss=0.2404, pruned_loss=0.03479, over 984785.95 frames.], datatang_tot_loss[loss=0.1512, simple_loss=0.226, pruned_loss=0.03821, over 985409.46 frames.], batch size: 81, lr: 4.51e-04
+2022-06-18 23:21:34,390 INFO [train.py:874] (1/4) Epoch 18, batch 3150, datatang_loss[loss=0.1448, simple_loss=0.2278, pruned_loss=0.03088, over 4950.00 frames.], tot_loss[loss=0.1536, simple_loss=0.2337, pruned_loss=0.03673, over 985596.47 frames.], batch size: 91, aishell_tot_loss[loss=0.1553, simple_loss=0.2409, pruned_loss=0.0349, over 984740.73 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.226, pruned_loss=0.03812, over 985573.03 frames.], batch size: 91, lr: 4.51e-04
+2022-06-18 23:22:05,223 INFO [train.py:874] (1/4) Epoch 18, batch 3200, datatang_loss[loss=0.1396, simple_loss=0.2068, pruned_loss=0.03624, over 4910.00 frames.], tot_loss[loss=0.1537, simple_loss=0.2334, pruned_loss=0.03702, over 985637.63 frames.], batch size: 47, aishell_tot_loss[loss=0.1552, simple_loss=0.2406, pruned_loss=0.03486, over 984895.87 frames.], datatang_tot_loss[loss=0.1516, simple_loss=0.2264, pruned_loss=0.03842, over 985542.53 frames.], batch size: 47, lr: 4.51e-04
+2022-06-18 23:22:37,293 INFO [train.py:874] (1/4) Epoch 18, batch 3250, datatang_loss[loss=0.1516, simple_loss=0.2365, pruned_loss=0.0334, over 4955.00 frames.], tot_loss[loss=0.1535, simple_loss=0.2334, pruned_loss=0.03677, over 985518.81 frames.], batch size: 86, aishell_tot_loss[loss=0.1548, simple_loss=0.2403, pruned_loss=0.03462, over 984724.04 frames.], datatang_tot_loss[loss=0.1519, simple_loss=0.2268, pruned_loss=0.03848, over 985668.98 frames.], batch size: 86, lr: 4.51e-04
+2022-06-18 23:23:06,595 INFO [train.py:874] (1/4) Epoch 18, batch 3300, aishell_loss[loss=0.1407, simple_loss=0.2227, pruned_loss=0.0293, over 4904.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2331, pruned_loss=0.03634, over 985305.03 frames.], batch size: 34, aishell_tot_loss[loss=0.154, simple_loss=0.2395, pruned_loss=0.03424, over 984483.30 frames.], datatang_tot_loss[loss=0.1521, simple_loss=0.2273, pruned_loss=0.03849, over 985766.90 frames.], batch size: 34, lr: 4.50e-04
+2022-06-18 23:23:37,937 INFO [train.py:874] (1/4) Epoch 18, batch 3350, datatang_loss[loss=0.1598, simple_loss=0.2373, pruned_loss=0.04117, over 4951.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2323, pruned_loss=0.03545, over 985189.93 frames.], batch size: 86, aishell_tot_loss[loss=0.1538, simple_loss=0.2397, pruned_loss=0.03398, over 984536.42 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2264, pruned_loss=0.03775, over 985597.16 frames.], batch size: 86, lr: 4.50e-04
+2022-06-18 23:24:09,441 INFO [train.py:874] (1/4) Epoch 18, batch 3400, datatang_loss[loss=0.135, simple_loss=0.218, pruned_loss=0.02604, over 4943.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2318, pruned_loss=0.03544, over 985376.03 frames.], batch size: 25, aishell_tot_loss[loss=0.1538, simple_loss=0.2395, pruned_loss=0.03406, over 984598.93 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2261, pruned_loss=0.03748, over 985737.21 frames.], batch size: 25, lr: 4.50e-04
+2022-06-18 23:24:39,079 INFO [train.py:874] (1/4) Epoch 18, batch 3450, datatang_loss[loss=0.1778, simple_loss=0.2425, pruned_loss=0.05653, over 4953.00 frames.], tot_loss[loss=0.1518, simple_loss=0.2316, pruned_loss=0.03596, over 985506.90 frames.], batch size: 37, aishell_tot_loss[loss=0.1545, simple_loss=0.24, pruned_loss=0.03449, over 984600.13 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2255, pruned_loss=0.03746, over 985890.34 frames.], batch size: 37, lr: 4.50e-04
+2022-06-18 23:25:10,229 INFO [train.py:874] (1/4) Epoch 18, batch 3500, aishell_loss[loss=0.1677, simple_loss=0.2436, pruned_loss=0.04589, over 4882.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2315, pruned_loss=0.03618, over 985518.41 frames.], batch size: 28, aishell_tot_loss[loss=0.1543, simple_loss=0.2396, pruned_loss=0.03451, over 984735.47 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.2258, pruned_loss=0.03764, over 985801.64 frames.], batch size: 28, lr: 4.50e-04
+2022-06-18 23:25:41,314 INFO [train.py:874] (1/4) Epoch 18, batch 3550, aishell_loss[loss=0.16, simple_loss=0.2449, pruned_loss=0.03751, over 4967.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2322, pruned_loss=0.03601, over 985832.89 frames.], batch size: 61, aishell_tot_loss[loss=0.1544, simple_loss=0.2399, pruned_loss=0.03446, over 984943.42 frames.], datatang_tot_loss[loss=0.1505, simple_loss=0.226, pruned_loss=0.03751, over 985973.76 frames.], batch size: 61, lr: 4.50e-04
+2022-06-18 23:26:10,891 INFO [train.py:874] (1/4) Epoch 18, batch 3600, aishell_loss[loss=0.1365, simple_loss=0.2239, pruned_loss=0.02457, over 4859.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2324, pruned_loss=0.03593, over 985721.70 frames.], batch size: 38, aishell_tot_loss[loss=0.1541, simple_loss=0.2397, pruned_loss=0.03426, over 984914.54 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2262, pruned_loss=0.03764, over 985974.61 frames.], batch size: 38, lr: 4.50e-04
+2022-06-18 23:26:42,442 INFO [train.py:874] (1/4) Epoch 18, batch 3650, aishell_loss[loss=0.1692, simple_loss=0.2445, pruned_loss=0.04693, over 4906.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2322, pruned_loss=0.03557, over 986097.20 frames.], batch size: 33, aishell_tot_loss[loss=0.1534, simple_loss=0.239, pruned_loss=0.03392, over 985184.60 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2264, pruned_loss=0.03759, over 986175.35 frames.], batch size: 33, lr: 4.49e-04
+2022-06-18 23:27:14,775 INFO [train.py:874] (1/4) Epoch 18, batch 3700, aishell_loss[loss=0.1665, simple_loss=0.2507, pruned_loss=0.0412, over 4880.00 frames.], tot_loss[loss=0.1522, simple_loss=0.2324, pruned_loss=0.03603, over 985979.28 frames.], batch size: 34, aishell_tot_loss[loss=0.1539, simple_loss=0.2392, pruned_loss=0.03427, over 985122.67 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2265, pruned_loss=0.03769, over 986217.73 frames.], batch size: 34, lr: 4.49e-04
+2022-06-18 23:27:43,280 INFO [train.py:874] (1/4) Epoch 18, batch 3750, aishell_loss[loss=0.1551, simple_loss=0.2437, pruned_loss=0.0333, over 4889.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2322, pruned_loss=0.03584, over 986045.28 frames.], batch size: 34, aishell_tot_loss[loss=0.154, simple_loss=0.2393, pruned_loss=0.03434, over 985005.73 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2259, pruned_loss=0.03747, over 986496.74 frames.], batch size: 34, lr: 4.49e-04
+2022-06-18 23:28:15,705 INFO [train.py:874] (1/4) Epoch 18, batch 3800, datatang_loss[loss=0.1473, simple_loss=0.2283, pruned_loss=0.03317, over 4927.00 frames.], tot_loss[loss=0.1531, simple_loss=0.2337, pruned_loss=0.03627, over 985811.34 frames.], batch size: 71, aishell_tot_loss[loss=0.1546, simple_loss=0.2399, pruned_loss=0.03462, over 984904.72 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2263, pruned_loss=0.03773, over 986474.74 frames.], batch size: 71, lr: 4.49e-04
+2022-06-18 23:28:45,423 INFO [train.py:874] (1/4) Epoch 18, batch 3850, datatang_loss[loss=0.1672, simple_loss=0.2465, pruned_loss=0.044, over 4932.00 frames.], tot_loss[loss=0.1533, simple_loss=0.2339, pruned_loss=0.03641, over 985544.12 frames.], batch size: 94, aishell_tot_loss[loss=0.1539, simple_loss=0.2391, pruned_loss=0.03435, over 984704.82 frames.], datatang_tot_loss[loss=0.1517, simple_loss=0.227, pruned_loss=0.03822, over 986458.69 frames.], batch size: 94, lr: 4.49e-04
+2022-06-18 23:29:15,481 INFO [train.py:874] (1/4) Epoch 18, batch 3900, datatang_loss[loss=0.1648, simple_loss=0.245, pruned_loss=0.04225, over 4959.00 frames.], tot_loss[loss=0.1529, simple_loss=0.2336, pruned_loss=0.03616, over 985495.93 frames.], batch size: 99, aishell_tot_loss[loss=0.1538, simple_loss=0.2392, pruned_loss=0.03422, over 984905.37 frames.], datatang_tot_loss[loss=0.1515, simple_loss=0.2268, pruned_loss=0.03813, over 986203.46 frames.], batch size: 99, lr: 4.49e-04
+2022-06-18 23:29:45,385 INFO [train.py:874] (1/4) Epoch 18, batch 3950, datatang_loss[loss=0.1778, simple_loss=0.2562, pruned_loss=0.04967, over 4931.00 frames.], tot_loss[loss=0.1525, simple_loss=0.2334, pruned_loss=0.03582, over 985330.27 frames.], batch size: 108, aishell_tot_loss[loss=0.1539, simple_loss=0.2393, pruned_loss=0.03421, over 984816.95 frames.], datatang_tot_loss[loss=0.1511, simple_loss=0.2267, pruned_loss=0.03772, over 986074.51 frames.], batch size: 108, lr: 4.49e-04
+2022-06-18 23:30:15,533 INFO [train.py:874] (1/4) Epoch 18, batch 4000, aishell_loss[loss=0.1665, simple_loss=0.2642, pruned_loss=0.03442, over 4871.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2328, pruned_loss=0.03523, over 985277.74 frames.], batch size: 36, aishell_tot_loss[loss=0.1534, simple_loss=0.2391, pruned_loss=0.03383, over 984856.22 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2266, pruned_loss=0.03738, over 985933.93 frames.], batch size: 36, lr: 4.48e-04
+2022-06-18 23:30:15,534 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 23:30:33,177 INFO [train.py:914] (1/4) Epoch 18, validation: loss=0.1643, simple_loss=0.249, pruned_loss=0.03975, over 1622729.00 frames.
+2022-06-18 23:31:01,711 INFO [train.py:874] (1/4) Epoch 18, batch 4050, aishell_loss[loss=0.1792, simple_loss=0.2661, pruned_loss=0.04617, over 4974.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2328, pruned_loss=0.03504, over 985697.82 frames.], batch size: 39, aishell_tot_loss[loss=0.1535, simple_loss=0.2393, pruned_loss=0.03382, over 985058.07 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2263, pruned_loss=0.03709, over 986162.76 frames.], batch size: 39, lr: 4.48e-04
+2022-06-18 23:31:31,895 INFO [train.py:874] (1/4) Epoch 18, batch 4100, datatang_loss[loss=0.154, simple_loss=0.236, pruned_loss=0.03603, over 4966.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2326, pruned_loss=0.0352, over 985243.14 frames.], batch size: 99, aishell_tot_loss[loss=0.1534, simple_loss=0.2391, pruned_loss=0.03381, over 984652.07 frames.], datatang_tot_loss[loss=0.1503, simple_loss=0.2264, pruned_loss=0.03714, over 986097.42 frames.], batch size: 99, lr: 4.48e-04
+2022-06-18 23:32:01,291 INFO [train.py:874] (1/4) Epoch 18, batch 4150, aishell_loss[loss=0.1757, simple_loss=0.2646, pruned_loss=0.04343, over 4863.00 frames.], tot_loss[loss=0.152, simple_loss=0.2333, pruned_loss=0.03539, over 985583.35 frames.], batch size: 35, aishell_tot_loss[loss=0.1541, simple_loss=0.24, pruned_loss=0.03405, over 984761.54 frames.], datatang_tot_loss[loss=0.1502, simple_loss=0.2264, pruned_loss=0.03699, over 986297.68 frames.], batch size: 35, lr: 4.48e-04
+2022-06-18 23:32:31,803 INFO [train.py:874] (1/4) Epoch 18, batch 4200, datatang_loss[loss=0.1374, simple_loss=0.2209, pruned_loss=0.02691, over 4959.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2339, pruned_loss=0.03569, over 985475.89 frames.], batch size: 45, aishell_tot_loss[loss=0.1549, simple_loss=0.241, pruned_loss=0.03444, over 984582.77 frames.], datatang_tot_loss[loss=0.1499, simple_loss=0.226, pruned_loss=0.03688, over 986382.80 frames.], batch size: 45, lr: 4.48e-04
+2022-06-18 23:33:39,357 INFO [train.py:874] (1/4) Epoch 19, batch 50, aishell_loss[loss=0.1198, simple_loss=0.1897, pruned_loss=0.02498, over 4921.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2259, pruned_loss=0.03217, over 218592.57 frames.], batch size: 21, aishell_tot_loss[loss=0.1451, simple_loss=0.2324, pruned_loss=0.02893, over 111685.93 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2201, pruned_loss=0.03512, over 120546.75 frames.], batch size: 21, lr: 4.36e-04
+2022-06-18 23:34:10,843 INFO [train.py:874] (1/4) Epoch 19, batch 100, aishell_loss[loss=0.1619, simple_loss=0.2476, pruned_loss=0.03805, over 4963.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2277, pruned_loss=0.03377, over 388703.79 frames.], batch size: 44, aishell_tot_loss[loss=0.1521, simple_loss=0.2369, pruned_loss=0.03365, over 214655.32 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2184, pruned_loss=0.03358, over 222480.41 frames.], batch size: 44, lr: 4.36e-04
+2022-06-18 23:34:43,824 INFO [train.py:874] (1/4) Epoch 19, batch 150, datatang_loss[loss=0.1309, simple_loss=0.2142, pruned_loss=0.02386, over 4930.00 frames.], tot_loss[loss=0.147, simple_loss=0.228, pruned_loss=0.03298, over 521372.42 frames.], batch size: 94, aishell_tot_loss[loss=0.1534, simple_loss=0.239, pruned_loss=0.03386, over 298684.37 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2175, pruned_loss=0.03215, over 319366.93 frames.], batch size: 94, lr: 4.36e-04
+2022-06-18 23:35:13,703 INFO [train.py:874] (1/4) Epoch 19, batch 200, aishell_loss[loss=0.1583, simple_loss=0.2458, pruned_loss=0.03538, over 4964.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2285, pruned_loss=0.03389, over 624761.02 frames.], batch size: 40, aishell_tot_loss[loss=0.1529, simple_loss=0.2386, pruned_loss=0.03361, over 370682.96 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2192, pruned_loss=0.03391, over 406831.74 frames.], batch size: 40, lr: 4.36e-04
+2022-06-18 23:35:44,911 INFO [train.py:874] (1/4) Epoch 19, batch 250, datatang_loss[loss=0.1561, simple_loss=0.2265, pruned_loss=0.04286, over 4883.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2293, pruned_loss=0.03395, over 704875.18 frames.], batch size: 52, aishell_tot_loss[loss=0.1541, simple_loss=0.2403, pruned_loss=0.03396, over 445887.39 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2184, pruned_loss=0.03374, over 472497.43 frames.], batch size: 52, lr: 4.36e-04
+2022-06-18 23:36:17,239 INFO [train.py:874] (1/4) Epoch 19, batch 300, aishell_loss[loss=0.1419, simple_loss=0.2292, pruned_loss=0.0273, over 4968.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2286, pruned_loss=0.03376, over 766855.66 frames.], batch size: 30, aishell_tot_loss[loss=0.1532, simple_loss=0.2398, pruned_loss=0.03326, over 506999.82 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.218, pruned_loss=0.03414, over 534984.70 frames.], batch size: 30, lr: 4.36e-04
+2022-06-18 23:36:46,751 INFO [train.py:874] (1/4) Epoch 19, batch 350, aishell_loss[loss=0.1405, simple_loss=0.225, pruned_loss=0.02804, over 4947.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2285, pruned_loss=0.03369, over 815031.64 frames.], batch size: 45, aishell_tot_loss[loss=0.1531, simple_loss=0.2397, pruned_loss=0.03327, over 565365.99 frames.], datatang_tot_loss[loss=0.1429, simple_loss=0.2177, pruned_loss=0.03406, over 585830.26 frames.], batch size: 45, lr: 4.35e-04
+2022-06-18 23:37:18,103 INFO [train.py:874] (1/4) Epoch 19, batch 400, aishell_loss[loss=0.1699, simple_loss=0.2382, pruned_loss=0.05077, over 4961.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2274, pruned_loss=0.0336, over 852489.62 frames.], batch size: 31, aishell_tot_loss[loss=0.1527, simple_loss=0.2389, pruned_loss=0.03322, over 611029.58 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2172, pruned_loss=0.03395, over 636161.81 frames.], batch size: 31, lr: 4.35e-04
+2022-06-18 23:37:49,208 INFO [train.py:874] (1/4) Epoch 19, batch 450, datatang_loss[loss=0.1763, simple_loss=0.2462, pruned_loss=0.05327, over 4942.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2298, pruned_loss=0.0343, over 881897.42 frames.], batch size: 91, aishell_tot_loss[loss=0.1542, simple_loss=0.2408, pruned_loss=0.03385, over 656633.51 frames.], datatang_tot_loss[loss=0.1434, simple_loss=0.2182, pruned_loss=0.03428, over 675835.28 frames.], batch size: 91, lr: 4.35e-04
+2022-06-18 23:38:17,524 INFO [train.py:874] (1/4) Epoch 19, batch 500, aishell_loss[loss=0.1351, simple_loss=0.2257, pruned_loss=0.02227, over 4948.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2294, pruned_loss=0.0342, over 904959.44 frames.], batch size: 56, aishell_tot_loss[loss=0.1539, simple_loss=0.24, pruned_loss=0.03397, over 695260.42 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2184, pruned_loss=0.0341, over 712516.06 frames.], batch size: 56, lr: 4.35e-04
+2022-06-18 23:38:49,411 INFO [train.py:874] (1/4) Epoch 19, batch 550, aishell_loss[loss=0.1528, simple_loss=0.2439, pruned_loss=0.03083, over 4962.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2304, pruned_loss=0.03454, over 923239.04 frames.], batch size: 61, aishell_tot_loss[loss=0.1542, simple_loss=0.2404, pruned_loss=0.03405, over 729792.20 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2194, pruned_loss=0.03452, over 744770.75 frames.], batch size: 61, lr: 4.35e-04
+2022-06-18 23:39:21,331 INFO [train.py:874] (1/4) Epoch 19, batch 600, aishell_loss[loss=0.1543, simple_loss=0.2283, pruned_loss=0.0402, over 4973.00 frames.], tot_loss[loss=0.15, simple_loss=0.2307, pruned_loss=0.03466, over 937147.95 frames.], batch size: 27, aishell_tot_loss[loss=0.154, simple_loss=0.2401, pruned_loss=0.03396, over 761142.83 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2201, pruned_loss=0.03484, over 772065.11 frames.], batch size: 27, lr: 4.35e-04
+2022-06-18 23:39:49,967 INFO [train.py:874] (1/4) Epoch 19, batch 650, aishell_loss[loss=0.1489, simple_loss=0.243, pruned_loss=0.02741, over 4914.00 frames.], tot_loss[loss=0.1507, simple_loss=0.231, pruned_loss=0.03516, over 947893.06 frames.], batch size: 68, aishell_tot_loss[loss=0.1537, simple_loss=0.2394, pruned_loss=0.03399, over 786375.27 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2214, pruned_loss=0.03553, over 798383.47 frames.], batch size: 68, lr: 4.35e-04
+2022-06-18 23:40:22,383 INFO [train.py:874] (1/4) Epoch 19, batch 700, datatang_loss[loss=0.1389, simple_loss=0.2134, pruned_loss=0.03224, over 4911.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2321, pruned_loss=0.03545, over 955985.49 frames.], batch size: 64, aishell_tot_loss[loss=0.1542, simple_loss=0.2402, pruned_loss=0.03405, over 808947.10 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2223, pruned_loss=0.03591, over 821004.43 frames.], batch size: 64, lr: 4.34e-04
+2022-06-18 23:40:54,863 INFO [train.py:874] (1/4) Epoch 19, batch 750, aishell_loss[loss=0.1136, simple_loss=0.1818, pruned_loss=0.02267, over 4935.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2315, pruned_loss=0.03547, over 962163.83 frames.], batch size: 21, aishell_tot_loss[loss=0.1537, simple_loss=0.2394, pruned_loss=0.03405, over 828822.99 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2227, pruned_loss=0.03604, over 840876.24 frames.], batch size: 21, lr: 4.34e-04
+2022-06-18 23:41:23,179 INFO [train.py:874] (1/4) Epoch 19, batch 800, aishell_loss[loss=0.1791, simple_loss=0.2726, pruned_loss=0.04275, over 4925.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2321, pruned_loss=0.03546, over 967388.24 frames.], batch size: 78, aishell_tot_loss[loss=0.1542, simple_loss=0.2398, pruned_loss=0.03425, over 850070.84 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2227, pruned_loss=0.03598, over 855328.34 frames.], batch size: 78, lr: 4.34e-04
+2022-06-18 23:41:55,286 INFO [train.py:874] (1/4) Epoch 19, batch 850, aishell_loss[loss=0.147, simple_loss=0.221, pruned_loss=0.03654, over 4981.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2325, pruned_loss=0.03603, over 971921.18 frames.], batch size: 44, aishell_tot_loss[loss=0.1546, simple_loss=0.2401, pruned_loss=0.03454, over 866434.02 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2231, pruned_loss=0.03646, over 870795.57 frames.], batch size: 44, lr: 4.34e-04
+2022-06-18 23:42:25,136 INFO [train.py:874] (1/4) Epoch 19, batch 900, aishell_loss[loss=0.1366, simple_loss=0.2299, pruned_loss=0.0217, over 4919.00 frames.], tot_loss[loss=0.1525, simple_loss=0.233, pruned_loss=0.03598, over 974590.90 frames.], batch size: 52, aishell_tot_loss[loss=0.1554, simple_loss=0.2411, pruned_loss=0.03487, over 880667.79 frames.], datatang_tot_loss[loss=0.1477, simple_loss=0.223, pruned_loss=0.03624, over 883747.82 frames.], batch size: 52, lr: 4.34e-04
+2022-06-18 23:42:56,492 INFO [train.py:874] (1/4) Epoch 19, batch 950, aishell_loss[loss=0.1641, simple_loss=0.2422, pruned_loss=0.04301, over 4923.00 frames.], tot_loss[loss=0.1523, simple_loss=0.2327, pruned_loss=0.03593, over 977109.37 frames.], batch size: 32, aishell_tot_loss[loss=0.155, simple_loss=0.2408, pruned_loss=0.03458, over 892245.57 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2234, pruned_loss=0.03655, over 896566.13 frames.], batch size: 32, lr: 4.34e-04
+2022-06-18 23:43:29,206 INFO [train.py:874] (1/4) Epoch 19, batch 1000, aishell_loss[loss=0.1268, simple_loss=0.2092, pruned_loss=0.0222, over 4952.00 frames.], tot_loss[loss=0.1527, simple_loss=0.2328, pruned_loss=0.03631, over 978762.25 frames.], batch size: 25, aishell_tot_loss[loss=0.1548, simple_loss=0.2406, pruned_loss=0.03447, over 900499.40 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2242, pruned_loss=0.03713, over 909357.06 frames.], batch size: 25, lr: 4.34e-04
+2022-06-18 23:43:29,207 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 23:43:46,289 INFO [train.py:914] (1/4) Epoch 19, validation: loss=0.1644, simple_loss=0.2486, pruned_loss=0.04009, over 1622729.00 frames.
+2022-06-18 23:44:16,847 INFO [train.py:874] (1/4) Epoch 19, batch 1050, datatang_loss[loss=0.1482, simple_loss=0.2281, pruned_loss=0.03415, over 4987.00 frames.], tot_loss[loss=0.153, simple_loss=0.233, pruned_loss=0.03648, over 980545.60 frames.], batch size: 37, aishell_tot_loss[loss=0.1545, simple_loss=0.2402, pruned_loss=0.03441, over 909975.44 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2252, pruned_loss=0.03748, over 919071.57 frames.], batch size: 37, lr: 4.33e-04
+2022-06-18 23:44:49,111 INFO [train.py:874] (1/4) Epoch 19, batch 1100, aishell_loss[loss=0.1656, simple_loss=0.2554, pruned_loss=0.0379, over 4972.00 frames.], tot_loss[loss=0.153, simple_loss=0.2328, pruned_loss=0.03662, over 981399.80 frames.], batch size: 44, aishell_tot_loss[loss=0.1547, simple_loss=0.2402, pruned_loss=0.03459, over 917794.18 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2251, pruned_loss=0.03753, over 927561.02 frames.], batch size: 44, lr: 4.33e-04
+2022-06-18 23:45:17,769 INFO [train.py:874] (1/4) Epoch 19, batch 1150, datatang_loss[loss=0.1512, simple_loss=0.2355, pruned_loss=0.03345, over 4906.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2324, pruned_loss=0.0366, over 982164.03 frames.], batch size: 42, aishell_tot_loss[loss=0.1543, simple_loss=0.2398, pruned_loss=0.03442, over 924591.81 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2253, pruned_loss=0.03778, over 935263.37 frames.], batch size: 42, lr: 4.33e-04
+2022-06-18 23:45:50,865 INFO [train.py:874] (1/4) Epoch 19, batch 1200, aishell_loss[loss=0.161, simple_loss=0.2349, pruned_loss=0.04349, over 4981.00 frames.], tot_loss[loss=0.1528, simple_loss=0.2324, pruned_loss=0.0366, over 982629.85 frames.], batch size: 39, aishell_tot_loss[loss=0.1539, simple_loss=0.2392, pruned_loss=0.03434, over 931795.03 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2259, pruned_loss=0.03799, over 940892.89 frames.], batch size: 39, lr: 4.33e-04
+2022-06-18 23:46:23,209 INFO [train.py:874] (1/4) Epoch 19, batch 1250, aishell_loss[loss=0.1689, simple_loss=0.2554, pruned_loss=0.04118, over 4936.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2311, pruned_loss=0.03581, over 982818.86 frames.], batch size: 54, aishell_tot_loss[loss=0.1529, simple_loss=0.2381, pruned_loss=0.03387, over 938172.91 frames.], datatang_tot_loss[loss=0.1504, simple_loss=0.2254, pruned_loss=0.03772, over 945712.35 frames.], batch size: 54, lr: 4.33e-04
+2022-06-18 23:46:52,173 INFO [train.py:874] (1/4) Epoch 19, batch 1300, aishell_loss[loss=0.1458, simple_loss=0.2222, pruned_loss=0.03475, over 4872.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2315, pruned_loss=0.03587, over 983339.37 frames.], batch size: 28, aishell_tot_loss[loss=0.153, simple_loss=0.2384, pruned_loss=0.03378, over 942366.17 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2258, pruned_loss=0.03778, over 951462.78 frames.], batch size: 28, lr: 4.33e-04
+2022-06-18 23:47:23,374 INFO [train.py:874] (1/4) Epoch 19, batch 1350, aishell_loss[loss=0.1581, simple_loss=0.2527, pruned_loss=0.03173, over 4974.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2314, pruned_loss=0.03542, over 984097.91 frames.], batch size: 51, aishell_tot_loss[loss=0.1532, simple_loss=0.2385, pruned_loss=0.03396, over 948254.80 frames.], datatang_tot_loss[loss=0.1498, simple_loss=0.2253, pruned_loss=0.0372, over 955075.24 frames.], batch size: 51, lr: 4.33e-04
+2022-06-18 23:47:56,175 INFO [train.py:874] (1/4) Epoch 19, batch 1400, aishell_loss[loss=0.1395, simple_loss=0.2247, pruned_loss=0.02718, over 4975.00 frames.], tot_loss[loss=0.1514, simple_loss=0.2318, pruned_loss=0.0355, over 984304.47 frames.], batch size: 48, aishell_tot_loss[loss=0.1536, simple_loss=0.239, pruned_loss=0.03411, over 951295.51 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2254, pruned_loss=0.03701, over 959648.04 frames.], batch size: 48, lr: 4.32e-04
+2022-06-18 23:48:24,807 INFO [train.py:874] (1/4) Epoch 19, batch 1450, datatang_loss[loss=0.1446, simple_loss=0.2232, pruned_loss=0.03299, over 4918.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2315, pruned_loss=0.03521, over 984942.82 frames.], batch size: 81, aishell_tot_loss[loss=0.1535, simple_loss=0.239, pruned_loss=0.03396, over 955803.84 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2249, pruned_loss=0.03686, over 962685.16 frames.], batch size: 81, lr: 4.32e-04
+2022-06-18 23:49:02,514 INFO [train.py:874] (1/4) Epoch 19, batch 1500, datatang_loss[loss=0.1417, simple_loss=0.2163, pruned_loss=0.0335, over 4924.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2311, pruned_loss=0.03488, over 984916.22 frames.], batch size: 79, aishell_tot_loss[loss=0.153, simple_loss=0.2386, pruned_loss=0.03369, over 959282.98 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.2247, pruned_loss=0.03677, over 965304.09 frames.], batch size: 79, lr: 4.32e-04
+2022-06-18 23:49:33,542 INFO [train.py:874] (1/4) Epoch 19, batch 1550, aishell_loss[loss=0.1584, simple_loss=0.2414, pruned_loss=0.0377, over 4896.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2319, pruned_loss=0.03517, over 985269.54 frames.], batch size: 42, aishell_tot_loss[loss=0.1537, simple_loss=0.2392, pruned_loss=0.03406, over 963063.95 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2245, pruned_loss=0.0367, over 967335.04 frames.], batch size: 42, lr: 4.32e-04
+2022-06-18 23:50:04,162 INFO [train.py:874] (1/4) Epoch 19, batch 1600, aishell_loss[loss=0.1219, simple_loss=0.1865, pruned_loss=0.02866, over 4844.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2309, pruned_loss=0.0348, over 985101.86 frames.], batch size: 21, aishell_tot_loss[loss=0.1527, simple_loss=0.2383, pruned_loss=0.03356, over 965395.68 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2244, pruned_loss=0.03674, over 969542.78 frames.], batch size: 21, lr: 4.32e-04
+2022-06-18 23:50:37,462 INFO [train.py:874] (1/4) Epoch 19, batch 1650, aishell_loss[loss=0.1454, simple_loss=0.2261, pruned_loss=0.03237, over 4959.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2307, pruned_loss=0.0346, over 985018.24 frames.], batch size: 40, aishell_tot_loss[loss=0.1521, simple_loss=0.2376, pruned_loss=0.03334, over 967986.05 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2245, pruned_loss=0.03674, over 971111.36 frames.], batch size: 40, lr: 4.32e-04
+2022-06-18 23:51:09,472 INFO [train.py:874] (1/4) Epoch 19, batch 1700, datatang_loss[loss=0.1655, simple_loss=0.2487, pruned_loss=0.04113, over 4913.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2302, pruned_loss=0.03449, over 985260.90 frames.], batch size: 64, aishell_tot_loss[loss=0.1521, simple_loss=0.2375, pruned_loss=0.03331, over 969812.14 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2242, pruned_loss=0.03645, over 973115.32 frames.], batch size: 64, lr: 4.32e-04
+2022-06-18 23:51:39,498 INFO [train.py:874] (1/4) Epoch 19, batch 1750, datatang_loss[loss=0.1573, simple_loss=0.2376, pruned_loss=0.03849, over 4968.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2303, pruned_loss=0.03477, over 985563.80 frames.], batch size: 40, aishell_tot_loss[loss=0.1524, simple_loss=0.2377, pruned_loss=0.03352, over 971444.81 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2242, pruned_loss=0.03641, over 974979.76 frames.], batch size: 40, lr: 4.31e-04
+2022-06-18 23:52:12,624 INFO [train.py:874] (1/4) Epoch 19, batch 1800, aishell_loss[loss=0.1824, simple_loss=0.2652, pruned_loss=0.04982, over 4935.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2306, pruned_loss=0.03493, over 985752.64 frames.], batch size: 49, aishell_tot_loss[loss=0.1524, simple_loss=0.2379, pruned_loss=0.03346, over 973363.28 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2241, pruned_loss=0.03661, over 976179.20 frames.], batch size: 49, lr: 4.31e-04
+2022-06-18 23:52:41,170 INFO [train.py:874] (1/4) Epoch 19, batch 1850, datatang_loss[loss=0.1432, simple_loss=0.2185, pruned_loss=0.03392, over 4846.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2312, pruned_loss=0.03457, over 985724.03 frames.], batch size: 30, aishell_tot_loss[loss=0.1522, simple_loss=0.2379, pruned_loss=0.03323, over 975203.66 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2242, pruned_loss=0.03656, over 976973.55 frames.], batch size: 30, lr: 4.31e-04
+2022-06-18 23:53:13,001 INFO [train.py:874] (1/4) Epoch 19, batch 1900, aishell_loss[loss=0.1569, simple_loss=0.2425, pruned_loss=0.03566, over 4964.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2327, pruned_loss=0.03497, over 985627.57 frames.], batch size: 40, aishell_tot_loss[loss=0.1526, simple_loss=0.2388, pruned_loss=0.03327, over 976344.48 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2249, pruned_loss=0.03688, over 978001.50 frames.], batch size: 40, lr: 4.31e-04
+2022-06-18 23:53:45,785 INFO [train.py:874] (1/4) Epoch 19, batch 1950, datatang_loss[loss=0.1655, simple_loss=0.2439, pruned_loss=0.04359, over 4921.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2329, pruned_loss=0.03518, over 985891.17 frames.], batch size: 98, aishell_tot_loss[loss=0.1527, simple_loss=0.2388, pruned_loss=0.03326, over 977530.64 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2253, pruned_loss=0.03709, over 979085.54 frames.], batch size: 98, lr: 4.31e-04
+2022-06-18 23:54:14,557 INFO [train.py:874] (1/4) Epoch 19, batch 2000, datatang_loss[loss=0.1258, simple_loss=0.204, pruned_loss=0.02385, over 4908.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2318, pruned_loss=0.03494, over 986008.44 frames.], batch size: 47, aishell_tot_loss[loss=0.1523, simple_loss=0.2383, pruned_loss=0.0331, over 978750.29 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2249, pruned_loss=0.03701, over 979770.21 frames.], batch size: 47, lr: 4.31e-04
+2022-06-18 23:54:14,558 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-18 23:54:31,135 INFO [train.py:914] (1/4) Epoch 19, validation: loss=0.1648, simple_loss=0.2482, pruned_loss=0.04066, over 1622729.00 frames.
+2022-06-18 23:55:03,607 INFO [train.py:874] (1/4) Epoch 19, batch 2050, datatang_loss[loss=0.133, simple_loss=0.2097, pruned_loss=0.02817, over 4932.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2337, pruned_loss=0.03574, over 985836.96 frames.], batch size: 73, aishell_tot_loss[loss=0.1527, simple_loss=0.239, pruned_loss=0.03321, over 979645.43 frames.], datatang_tot_loss[loss=0.1508, simple_loss=0.2259, pruned_loss=0.03781, over 980300.75 frames.], batch size: 73, lr: 4.31e-04
+2022-06-18 23:55:34,117 INFO [train.py:874] (1/4) Epoch 19, batch 2100, datatang_loss[loss=0.1719, simple_loss=0.2469, pruned_loss=0.04851, over 4910.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2335, pruned_loss=0.03586, over 985903.25 frames.], batch size: 98, aishell_tot_loss[loss=0.1527, simple_loss=0.2391, pruned_loss=0.0332, over 980351.83 frames.], datatang_tot_loss[loss=0.151, simple_loss=0.2262, pruned_loss=0.03791, over 981046.30 frames.], batch size: 98, lr: 4.30e-04
+2022-06-18 23:56:07,332 INFO [train.py:874] (1/4) Epoch 19, batch 2150, aishell_loss[loss=0.1873, simple_loss=0.2714, pruned_loss=0.05162, over 4933.00 frames.], tot_loss[loss=0.1526, simple_loss=0.2332, pruned_loss=0.03599, over 986004.36 frames.], batch size: 45, aishell_tot_loss[loss=0.1527, simple_loss=0.2389, pruned_loss=0.03321, over 981013.95 frames.], datatang_tot_loss[loss=0.1513, simple_loss=0.2265, pruned_loss=0.03801, over 981697.82 frames.], batch size: 45, lr: 4.30e-04
+2022-06-18 23:56:40,096 INFO [train.py:874] (1/4) Epoch 19, batch 2200, datatang_loss[loss=0.1238, simple_loss=0.2094, pruned_loss=0.01914, over 4925.00 frames.], tot_loss[loss=0.1521, simple_loss=0.2327, pruned_loss=0.03576, over 986120.75 frames.], batch size: 83, aishell_tot_loss[loss=0.1526, simple_loss=0.2387, pruned_loss=0.03325, over 981853.89 frames.], datatang_tot_loss[loss=0.1509, simple_loss=0.2263, pruned_loss=0.03777, over 982081.58 frames.], batch size: 83, lr: 4.30e-04
+2022-06-18 23:57:09,676 INFO [train.py:874] (1/4) Epoch 19, batch 2250, aishell_loss[loss=0.1131, simple_loss=0.1916, pruned_loss=0.01732, over 4808.00 frames.], tot_loss[loss=0.151, simple_loss=0.2312, pruned_loss=0.03544, over 986055.82 frames.], batch size: 26, aishell_tot_loss[loss=0.1522, simple_loss=0.238, pruned_loss=0.03318, over 981994.42 frames.], datatang_tot_loss[loss=0.1503, simple_loss=0.2257, pruned_loss=0.0375, over 982839.10 frames.], batch size: 26, lr: 4.30e-04
+2022-06-18 23:57:42,509 INFO [train.py:874] (1/4) Epoch 19, batch 2300, datatang_loss[loss=0.1387, simple_loss=0.2217, pruned_loss=0.02781, over 4925.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2313, pruned_loss=0.03507, over 985712.37 frames.], batch size: 83, aishell_tot_loss[loss=0.1527, simple_loss=0.2386, pruned_loss=0.03342, over 982210.90 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2249, pruned_loss=0.03691, over 983130.85 frames.], batch size: 83, lr: 4.30e-04
+2022-06-18 23:58:14,309 INFO [train.py:874] (1/4) Epoch 19, batch 2350, aishell_loss[loss=0.1636, simple_loss=0.25, pruned_loss=0.03863, over 4957.00 frames.], tot_loss[loss=0.151, simple_loss=0.2317, pruned_loss=0.03509, over 985833.48 frames.], batch size: 40, aishell_tot_loss[loss=0.153, simple_loss=0.2388, pruned_loss=0.03356, over 982658.93 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2246, pruned_loss=0.03685, over 983545.25 frames.], batch size: 40, lr: 4.30e-04
+2022-06-18 23:58:44,224 INFO [train.py:874] (1/4) Epoch 19, batch 2400, datatang_loss[loss=0.1388, simple_loss=0.2196, pruned_loss=0.02907, over 4957.00 frames.], tot_loss[loss=0.1509, simple_loss=0.2314, pruned_loss=0.03519, over 985740.82 frames.], batch size: 55, aishell_tot_loss[loss=0.1525, simple_loss=0.238, pruned_loss=0.03348, over 982989.53 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2252, pruned_loss=0.03698, over 983760.94 frames.], batch size: 55, lr: 4.30e-04
+2022-06-18 23:59:16,927 INFO [train.py:874] (1/4) Epoch 19, batch 2450, datatang_loss[loss=0.1269, simple_loss=0.2143, pruned_loss=0.01978, over 4924.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2308, pruned_loss=0.03495, over 985591.00 frames.], batch size: 73, aishell_tot_loss[loss=0.1525, simple_loss=0.2382, pruned_loss=0.03347, over 983051.99 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2247, pruned_loss=0.03672, over 984071.43 frames.], batch size: 73, lr: 4.30e-04
+2022-06-18 23:59:48,702 INFO [train.py:874] (1/4) Epoch 19, batch 2500, datatang_loss[loss=0.1345, simple_loss=0.2249, pruned_loss=0.02206, over 4946.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2312, pruned_loss=0.03457, over 985431.67 frames.], batch size: 25, aishell_tot_loss[loss=0.1524, simple_loss=0.2383, pruned_loss=0.03325, over 983339.74 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2245, pruned_loss=0.03657, over 984108.66 frames.], batch size: 25, lr: 4.29e-04
+2022-06-19 00:00:18,832 INFO [train.py:874] (1/4) Epoch 19, batch 2550, aishell_loss[loss=0.1514, simple_loss=0.238, pruned_loss=0.03235, over 4912.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2317, pruned_loss=0.0349, over 985555.22 frames.], batch size: 41, aishell_tot_loss[loss=0.153, simple_loss=0.239, pruned_loss=0.0335, over 983643.11 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2242, pruned_loss=0.03658, over 984343.73 frames.], batch size: 41, lr: 4.29e-04
+2022-06-19 00:00:52,790 INFO [train.py:874] (1/4) Epoch 19, batch 2600, datatang_loss[loss=0.1464, simple_loss=0.2232, pruned_loss=0.0348, over 4924.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2315, pruned_loss=0.03499, over 985731.71 frames.], batch size: 57, aishell_tot_loss[loss=0.1531, simple_loss=0.2393, pruned_loss=0.03347, over 983962.21 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.224, pruned_loss=0.03665, over 984573.68 frames.], batch size: 57, lr: 4.29e-04
+2022-06-19 00:01:22,178 INFO [train.py:874] (1/4) Epoch 19, batch 2650, aishell_loss[loss=0.1699, simple_loss=0.2483, pruned_loss=0.04576, over 4945.00 frames.], tot_loss[loss=0.151, simple_loss=0.2317, pruned_loss=0.03515, over 985927.92 frames.], batch size: 32, aishell_tot_loss[loss=0.1531, simple_loss=0.2393, pruned_loss=0.03347, over 984219.43 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2243, pruned_loss=0.03679, over 984868.65 frames.], batch size: 32, lr: 4.29e-04
+2022-06-19 00:01:54,097 INFO [train.py:874] (1/4) Epoch 19, batch 2700, datatang_loss[loss=0.1349, simple_loss=0.2134, pruned_loss=0.02822, over 4928.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2309, pruned_loss=0.03499, over 985756.08 frames.], batch size: 79, aishell_tot_loss[loss=0.1526, simple_loss=0.2387, pruned_loss=0.03321, over 984129.65 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2241, pruned_loss=0.03686, over 985090.59 frames.], batch size: 79, lr: 4.29e-04
+2022-06-19 00:02:27,629 INFO [train.py:874] (1/4) Epoch 19, batch 2750, aishell_loss[loss=0.1609, simple_loss=0.2451, pruned_loss=0.03835, over 4931.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2295, pruned_loss=0.03481, over 985677.74 frames.], batch size: 58, aishell_tot_loss[loss=0.1521, simple_loss=0.238, pruned_loss=0.03314, over 984163.74 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2237, pruned_loss=0.0366, over 985194.86 frames.], batch size: 58, lr: 4.29e-04
+2022-06-19 00:03:00,486 INFO [train.py:874] (1/4) Epoch 19, batch 2800, aishell_loss[loss=0.1492, simple_loss=0.2311, pruned_loss=0.03364, over 4962.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2297, pruned_loss=0.03492, over 985622.75 frames.], batch size: 44, aishell_tot_loss[loss=0.1519, simple_loss=0.2376, pruned_loss=0.03315, over 984159.99 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2244, pruned_loss=0.0366, over 985331.36 frames.], batch size: 44, lr: 4.29e-04
+2022-06-19 00:03:29,850 INFO [train.py:874] (1/4) Epoch 19, batch 2850, aishell_loss[loss=0.1568, simple_loss=0.2456, pruned_loss=0.03403, over 4970.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2305, pruned_loss=0.0349, over 985939.46 frames.], batch size: 61, aishell_tot_loss[loss=0.1522, simple_loss=0.2378, pruned_loss=0.03334, over 984542.81 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2243, pruned_loss=0.0365, over 985518.61 frames.], batch size: 61, lr: 4.28e-04
+2022-06-19 00:04:03,076 INFO [train.py:874] (1/4) Epoch 19, batch 2900, datatang_loss[loss=0.1243, simple_loss=0.2023, pruned_loss=0.02312, over 4955.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2309, pruned_loss=0.03495, over 985623.81 frames.], batch size: 67, aishell_tot_loss[loss=0.1524, simple_loss=0.238, pruned_loss=0.03341, over 984390.86 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2242, pruned_loss=0.03654, over 985582.18 frames.], batch size: 67, lr: 4.28e-04
+2022-06-19 00:04:31,755 INFO [train.py:874] (1/4) Epoch 19, batch 2950, datatang_loss[loss=0.1425, simple_loss=0.2173, pruned_loss=0.03389, over 4919.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2319, pruned_loss=0.03578, over 985535.24 frames.], batch size: 75, aishell_tot_loss[loss=0.1528, simple_loss=0.2385, pruned_loss=0.03359, over 984476.74 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2248, pruned_loss=0.0372, over 985559.54 frames.], batch size: 75, lr: 4.28e-04
+2022-06-19 00:05:05,081 INFO [train.py:874] (1/4) Epoch 19, batch 3000, aishell_loss[loss=0.1557, simple_loss=0.24, pruned_loss=0.03568, over 4927.00 frames.], tot_loss[loss=0.1515, simple_loss=0.2318, pruned_loss=0.03556, over 985634.12 frames.], batch size: 49, aishell_tot_loss[loss=0.1526, simple_loss=0.2383, pruned_loss=0.03348, over 984634.10 frames.], datatang_tot_loss[loss=0.1496, simple_loss=0.2248, pruned_loss=0.03721, over 985631.22 frames.], batch size: 49, lr: 4.28e-04
+2022-06-19 00:05:05,082 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 00:05:22,959 INFO [train.py:914] (1/4) Epoch 19, validation: loss=0.1643, simple_loss=0.2485, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 00:05:51,966 INFO [train.py:874] (1/4) Epoch 19, batch 3050, aishell_loss[loss=0.1448, simple_loss=0.2424, pruned_loss=0.02366, over 4943.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2326, pruned_loss=0.03535, over 985746.92 frames.], batch size: 54, aishell_tot_loss[loss=0.1532, simple_loss=0.239, pruned_loss=0.03366, over 984683.90 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2245, pruned_loss=0.037, over 985857.76 frames.], batch size: 54, lr: 4.28e-04
+2022-06-19 00:06:24,278 INFO [train.py:874] (1/4) Epoch 19, batch 3100, datatang_loss[loss=0.1331, simple_loss=0.2052, pruned_loss=0.03056, over 4940.00 frames.], tot_loss[loss=0.1511, simple_loss=0.2319, pruned_loss=0.03516, over 985732.40 frames.], batch size: 25, aishell_tot_loss[loss=0.153, simple_loss=0.2389, pruned_loss=0.0336, over 984762.41 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.224, pruned_loss=0.03687, over 985878.96 frames.], batch size: 25, lr: 4.28e-04
+2022-06-19 00:06:52,808 INFO [train.py:874] (1/4) Epoch 19, batch 3150, aishell_loss[loss=0.145, simple_loss=0.2386, pruned_loss=0.02566, over 4930.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2312, pruned_loss=0.03457, over 985497.83 frames.], batch size: 49, aishell_tot_loss[loss=0.1528, simple_loss=0.2391, pruned_loss=0.03325, over 984844.90 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2232, pruned_loss=0.03656, over 985629.96 frames.], batch size: 49, lr: 4.28e-04
+2022-06-19 00:07:26,191 INFO [train.py:874] (1/4) Epoch 19, batch 3200, aishell_loss[loss=0.1505, simple_loss=0.2431, pruned_loss=0.029, over 4910.00 frames.], tot_loss[loss=0.1508, simple_loss=0.2309, pruned_loss=0.03534, over 985672.84 frames.], batch size: 46, aishell_tot_loss[loss=0.1527, simple_loss=0.2387, pruned_loss=0.03332, over 985129.46 frames.], datatang_tot_loss[loss=0.149, simple_loss=0.2235, pruned_loss=0.03718, over 985581.19 frames.], batch size: 46, lr: 4.27e-04
+2022-06-19 00:07:58,159 INFO [train.py:874] (1/4) Epoch 19, batch 3250, aishell_loss[loss=0.1564, simple_loss=0.2399, pruned_loss=0.03646, over 4893.00 frames.], tot_loss[loss=0.152, simple_loss=0.2319, pruned_loss=0.03601, over 985549.88 frames.], batch size: 34, aishell_tot_loss[loss=0.1534, simple_loss=0.2391, pruned_loss=0.03384, over 985010.00 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2239, pruned_loss=0.03744, over 985642.44 frames.], batch size: 34, lr: 4.27e-04
+2022-06-19 00:08:26,503 INFO [train.py:874] (1/4) Epoch 19, batch 3300, aishell_loss[loss=0.153, simple_loss=0.2412, pruned_loss=0.03237, over 4856.00 frames.], tot_loss[loss=0.152, simple_loss=0.232, pruned_loss=0.03596, over 985342.80 frames.], batch size: 37, aishell_tot_loss[loss=0.1538, simple_loss=0.2396, pruned_loss=0.034, over 984909.67 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2237, pruned_loss=0.03732, over 985577.86 frames.], batch size: 37, lr: 4.27e-04
+2022-06-19 00:08:58,830 INFO [train.py:874] (1/4) Epoch 19, batch 3350, aishell_loss[loss=0.1269, simple_loss=0.2116, pruned_loss=0.02108, over 4873.00 frames.], tot_loss[loss=0.1517, simple_loss=0.2319, pruned_loss=0.03576, over 985634.61 frames.], batch size: 28, aishell_tot_loss[loss=0.1537, simple_loss=0.2396, pruned_loss=0.03389, over 985143.08 frames.], datatang_tot_loss[loss=0.1491, simple_loss=0.2236, pruned_loss=0.03731, over 985686.33 frames.], batch size: 28, lr: 4.27e-04
+2022-06-19 00:09:31,437 INFO [train.py:874] (1/4) Epoch 19, batch 3400, aishell_loss[loss=0.1673, simple_loss=0.247, pruned_loss=0.04376, over 4971.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2317, pruned_loss=0.03549, over 985565.09 frames.], batch size: 48, aishell_tot_loss[loss=0.1536, simple_loss=0.2396, pruned_loss=0.0338, over 984981.85 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2236, pruned_loss=0.03711, over 985817.63 frames.], batch size: 48, lr: 4.27e-04
+2022-06-19 00:09:59,983 INFO [train.py:874] (1/4) Epoch 19, batch 3450, datatang_loss[loss=0.1525, simple_loss=0.2294, pruned_loss=0.03783, over 4924.00 frames.], tot_loss[loss=0.1514, simple_loss=0.232, pruned_loss=0.03537, over 985588.91 frames.], batch size: 83, aishell_tot_loss[loss=0.1536, simple_loss=0.2399, pruned_loss=0.03367, over 984990.11 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2236, pruned_loss=0.03709, over 985879.16 frames.], batch size: 83, lr: 4.27e-04
+2022-06-19 00:10:33,081 INFO [train.py:874] (1/4) Epoch 19, batch 3500, aishell_loss[loss=0.1321, simple_loss=0.2167, pruned_loss=0.02377, over 4974.00 frames.], tot_loss[loss=0.1513, simple_loss=0.232, pruned_loss=0.03534, over 985650.68 frames.], batch size: 27, aishell_tot_loss[loss=0.1538, simple_loss=0.2399, pruned_loss=0.03383, over 985292.12 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2235, pruned_loss=0.03694, over 985673.36 frames.], batch size: 27, lr: 4.27e-04
+2022-06-19 00:11:03,927 INFO [train.py:874] (1/4) Epoch 19, batch 3550, aishell_loss[loss=0.1472, simple_loss=0.2397, pruned_loss=0.02733, over 4938.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2318, pruned_loss=0.0353, over 985981.00 frames.], batch size: 56, aishell_tot_loss[loss=0.1535, simple_loss=0.2395, pruned_loss=0.03376, over 985628.30 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2239, pruned_loss=0.03696, over 985730.28 frames.], batch size: 56, lr: 4.26e-04
+2022-06-19 00:11:33,772 INFO [train.py:874] (1/4) Epoch 19, batch 3600, aishell_loss[loss=0.1526, simple_loss=0.2352, pruned_loss=0.03505, over 4931.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2321, pruned_loss=0.03558, over 985715.05 frames.], batch size: 32, aishell_tot_loss[loss=0.1538, simple_loss=0.2398, pruned_loss=0.03388, over 985302.06 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2243, pruned_loss=0.03703, over 985829.55 frames.], batch size: 32, lr: 4.26e-04
+2022-06-19 00:12:05,691 INFO [train.py:874] (1/4) Epoch 19, batch 3650, aishell_loss[loss=0.1749, simple_loss=0.2622, pruned_loss=0.04375, over 4874.00 frames.], tot_loss[loss=0.1516, simple_loss=0.2325, pruned_loss=0.03539, over 985703.52 frames.], batch size: 36, aishell_tot_loss[loss=0.1542, simple_loss=0.2402, pruned_loss=0.03407, over 985439.75 frames.], datatang_tot_loss[loss=0.1488, simple_loss=0.2242, pruned_loss=0.03669, over 985707.40 frames.], batch size: 36, lr: 4.26e-04
+2022-06-19 00:12:38,524 INFO [train.py:874] (1/4) Epoch 19, batch 3700, datatang_loss[loss=0.1201, simple_loss=0.1936, pruned_loss=0.02327, over 4899.00 frames.], tot_loss[loss=0.1512, simple_loss=0.2317, pruned_loss=0.03534, over 985838.47 frames.], batch size: 52, aishell_tot_loss[loss=0.1539, simple_loss=0.2396, pruned_loss=0.03409, over 985387.39 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2243, pruned_loss=0.03657, over 985924.27 frames.], batch size: 52, lr: 4.26e-04
+2022-06-19 00:13:06,426 INFO [train.py:874] (1/4) Epoch 19, batch 3750, datatang_loss[loss=0.1259, simple_loss=0.201, pruned_loss=0.02539, over 4946.00 frames.], tot_loss[loss=0.151, simple_loss=0.2315, pruned_loss=0.03525, over 985638.25 frames.], batch size: 55, aishell_tot_loss[loss=0.1536, simple_loss=0.2392, pruned_loss=0.03401, over 985165.48 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2243, pruned_loss=0.03656, over 985982.39 frames.], batch size: 55, lr: 4.26e-04
+2022-06-19 00:13:39,401 INFO [train.py:874] (1/4) Epoch 19, batch 3800, aishell_loss[loss=0.1256, simple_loss=0.2197, pruned_loss=0.01571, over 4955.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2307, pruned_loss=0.03444, over 985576.82 frames.], batch size: 56, aishell_tot_loss[loss=0.1529, simple_loss=0.2387, pruned_loss=0.03358, over 985222.67 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2238, pruned_loss=0.03616, over 985883.79 frames.], batch size: 56, lr: 4.26e-04
+2022-06-19 00:14:10,380 INFO [train.py:874] (1/4) Epoch 19, batch 3850, aishell_loss[loss=0.1426, simple_loss=0.224, pruned_loss=0.03058, over 4892.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2308, pruned_loss=0.03474, over 985520.14 frames.], batch size: 34, aishell_tot_loss[loss=0.1528, simple_loss=0.2383, pruned_loss=0.03364, over 985295.11 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2244, pruned_loss=0.03626, over 985750.48 frames.], batch size: 34, lr: 4.26e-04
+2022-06-19 00:14:40,619 INFO [train.py:874] (1/4) Epoch 19, batch 3900, aishell_loss[loss=0.1352, simple_loss=0.2205, pruned_loss=0.02496, over 4914.00 frames.], tot_loss[loss=0.151, simple_loss=0.2313, pruned_loss=0.03536, over 985481.20 frames.], batch size: 46, aishell_tot_loss[loss=0.1532, simple_loss=0.2387, pruned_loss=0.03386, over 985352.51 frames.], datatang_tot_loss[loss=0.1489, simple_loss=0.2245, pruned_loss=0.03663, over 985639.27 frames.], batch size: 46, lr: 4.26e-04
+2022-06-19 00:15:09,676 INFO [train.py:874] (1/4) Epoch 19, batch 3950, aishell_loss[loss=0.1187, simple_loss=0.2006, pruned_loss=0.01835, over 4985.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2301, pruned_loss=0.03452, over 985337.09 frames.], batch size: 30, aishell_tot_loss[loss=0.1523, simple_loss=0.2378, pruned_loss=0.03343, over 985263.12 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2239, pruned_loss=0.03623, over 985575.20 frames.], batch size: 30, lr: 4.25e-04
+2022-06-19 00:15:40,633 INFO [train.py:874] (1/4) Epoch 19, batch 4000, aishell_loss[loss=0.1457, simple_loss=0.2355, pruned_loss=0.02799, over 4925.00 frames.], tot_loss[loss=0.15, simple_loss=0.2307, pruned_loss=0.03466, over 985848.88 frames.], batch size: 54, aishell_tot_loss[loss=0.1529, simple_loss=0.2382, pruned_loss=0.03373, over 985526.37 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2239, pruned_loss=0.03601, over 985843.81 frames.], batch size: 54, lr: 4.25e-04
+2022-06-19 00:15:40,634 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 00:15:58,331 INFO [train.py:914] (1/4) Epoch 19, validation: loss=0.165, simple_loss=0.2492, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 00:16:27,774 INFO [train.py:874] (1/4) Epoch 19, batch 4050, aishell_loss[loss=0.1682, simple_loss=0.2515, pruned_loss=0.04249, over 4942.00 frames.], tot_loss[loss=0.1498, simple_loss=0.231, pruned_loss=0.03437, over 985640.62 frames.], batch size: 54, aishell_tot_loss[loss=0.153, simple_loss=0.2387, pruned_loss=0.03359, over 985509.24 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2235, pruned_loss=0.03578, over 985676.34 frames.], batch size: 54, lr: 4.25e-04
+2022-06-19 00:16:58,279 INFO [train.py:874] (1/4) Epoch 19, batch 4100, datatang_loss[loss=0.1515, simple_loss=0.2296, pruned_loss=0.03669, over 4961.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2307, pruned_loss=0.03469, over 985770.93 frames.], batch size: 86, aishell_tot_loss[loss=0.153, simple_loss=0.2386, pruned_loss=0.03368, over 985521.18 frames.], datatang_tot_loss[loss=0.1477, simple_loss=0.2236, pruned_loss=0.03594, over 985804.21 frames.], batch size: 86, lr: 4.25e-04
+2022-06-19 00:17:27,288 INFO [train.py:874] (1/4) Epoch 19, batch 4150, aishell_loss[loss=0.137, simple_loss=0.2223, pruned_loss=0.02586, over 4881.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2307, pruned_loss=0.03448, over 985578.61 frames.], batch size: 47, aishell_tot_loss[loss=0.1527, simple_loss=0.2383, pruned_loss=0.03351, over 985284.02 frames.], datatang_tot_loss[loss=0.1476, simple_loss=0.2235, pruned_loss=0.03588, over 985871.78 frames.], batch size: 47, lr: 4.25e-04
+2022-06-19 00:19:01,263 INFO [train.py:874] (1/4) Epoch 20, batch 50, datatang_loss[loss=0.1372, simple_loss=0.2201, pruned_loss=0.02717, over 4932.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2193, pruned_loss=0.03042, over 218480.19 frames.], batch size: 94, aishell_tot_loss[loss=0.148, simple_loss=0.2316, pruned_loss=0.03225, over 89300.95 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2117, pruned_loss=0.02929, over 142056.45 frames.], batch size: 94, lr: 4.14e-04
+2022-06-19 00:19:31,413 INFO [train.py:874] (1/4) Epoch 20, batch 100, aishell_loss[loss=0.2262, simple_loss=0.2903, pruned_loss=0.08108, over 4945.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2285, pruned_loss=0.03355, over 388136.48 frames.], batch size: 54, aishell_tot_loss[loss=0.1568, simple_loss=0.2422, pruned_loss=0.03577, over 214106.86 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2137, pruned_loss=0.03092, over 222426.95 frames.], batch size: 54, lr: 4.14e-04
+2022-06-19 00:20:03,664 INFO [train.py:874] (1/4) Epoch 20, batch 150, datatang_loss[loss=0.1408, simple_loss=0.225, pruned_loss=0.0283, over 4946.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2291, pruned_loss=0.03353, over 520641.75 frames.], batch size: 91, aishell_tot_loss[loss=0.1558, simple_loss=0.2413, pruned_loss=0.03513, over 311669.15 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2155, pruned_loss=0.03152, over 305683.49 frames.], batch size: 91, lr: 4.14e-04
+2022-06-19 00:20:32,881 INFO [train.py:874] (1/4) Epoch 20, batch 200, datatang_loss[loss=0.1511, simple_loss=0.2217, pruned_loss=0.04025, over 4916.00 frames.], tot_loss[loss=0.148, simple_loss=0.2286, pruned_loss=0.03373, over 623638.49 frames.], batch size: 83, aishell_tot_loss[loss=0.1561, simple_loss=0.2417, pruned_loss=0.03521, over 390969.65 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2145, pruned_loss=0.03189, over 385768.61 frames.], batch size: 83, lr: 4.14e-04
+2022-06-19 00:21:05,314 INFO [train.py:874] (1/4) Epoch 20, batch 250, aishell_loss[loss=0.1278, simple_loss=0.2246, pruned_loss=0.0155, over 4933.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2278, pruned_loss=0.03349, over 704180.29 frames.], batch size: 58, aishell_tot_loss[loss=0.155, simple_loss=0.2407, pruned_loss=0.03459, over 463786.70 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.214, pruned_loss=0.03217, over 453911.93 frames.], batch size: 58, lr: 4.14e-04
+2022-06-19 00:21:37,014 INFO [train.py:874] (1/4) Epoch 20, batch 300, datatang_loss[loss=0.136, simple_loss=0.2201, pruned_loss=0.02597, over 4942.00 frames.], tot_loss[loss=0.147, simple_loss=0.2278, pruned_loss=0.03305, over 766563.27 frames.], batch size: 50, aishell_tot_loss[loss=0.1545, simple_loss=0.2405, pruned_loss=0.0342, over 520361.59 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2149, pruned_loss=0.03202, over 521453.36 frames.], batch size: 50, lr: 4.14e-04
+2022-06-19 00:22:05,468 INFO [train.py:874] (1/4) Epoch 20, batch 350, aishell_loss[loss=0.1534, simple_loss=0.2503, pruned_loss=0.02829, over 4878.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2293, pruned_loss=0.03326, over 814797.85 frames.], batch size: 34, aishell_tot_loss[loss=0.154, simple_loss=0.2404, pruned_loss=0.03381, over 580922.33 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2168, pruned_loss=0.03266, over 569934.91 frames.], batch size: 34, lr: 4.14e-04
+2022-06-19 00:22:38,814 INFO [train.py:874] (1/4) Epoch 20, batch 400, datatang_loss[loss=0.1396, simple_loss=0.2139, pruned_loss=0.03268, over 4930.00 frames.], tot_loss[loss=0.1478, simple_loss=0.229, pruned_loss=0.03333, over 852660.76 frames.], batch size: 57, aishell_tot_loss[loss=0.1536, simple_loss=0.2398, pruned_loss=0.03364, over 616041.32 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2184, pruned_loss=0.03298, over 631361.82 frames.], batch size: 57, lr: 4.13e-04
+2022-06-19 00:23:11,541 INFO [train.py:874] (1/4) Epoch 20, batch 450, aishell_loss[loss=0.1515, simple_loss=0.2384, pruned_loss=0.03227, over 4970.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2288, pruned_loss=0.03353, over 882171.71 frames.], batch size: 44, aishell_tot_loss[loss=0.1532, simple_loss=0.2388, pruned_loss=0.03384, over 661170.77 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2189, pruned_loss=0.03306, over 671573.96 frames.], batch size: 44, lr: 4.13e-04
+2022-06-19 00:23:40,912 INFO [train.py:874] (1/4) Epoch 20, batch 500, datatang_loss[loss=0.1479, simple_loss=0.2265, pruned_loss=0.0347, over 4952.00 frames.], tot_loss[loss=0.148, simple_loss=0.2292, pruned_loss=0.03337, over 905149.85 frames.], batch size: 34, aishell_tot_loss[loss=0.1533, simple_loss=0.2391, pruned_loss=0.03374, over 696335.28 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2195, pruned_loss=0.03295, over 711527.28 frames.], batch size: 34, lr: 4.13e-04
+2022-06-19 00:24:13,538 INFO [train.py:874] (1/4) Epoch 20, batch 550, aishell_loss[loss=0.1358, simple_loss=0.2223, pruned_loss=0.02464, over 4970.00 frames.], tot_loss[loss=0.149, simple_loss=0.2297, pruned_loss=0.03412, over 923160.17 frames.], batch size: 44, aishell_tot_loss[loss=0.1537, simple_loss=0.2392, pruned_loss=0.03411, over 729140.36 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2201, pruned_loss=0.0336, over 745180.91 frames.], batch size: 44, lr: 4.13e-04
+2022-06-19 00:24:45,796 INFO [train.py:874] (1/4) Epoch 20, batch 600, aishell_loss[loss=0.1801, simple_loss=0.2589, pruned_loss=0.05068, over 4886.00 frames.], tot_loss[loss=0.15, simple_loss=0.2311, pruned_loss=0.03448, over 936830.52 frames.], batch size: 47, aishell_tot_loss[loss=0.1541, simple_loss=0.2398, pruned_loss=0.03421, over 761306.33 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2211, pruned_loss=0.03403, over 771469.77 frames.], batch size: 47, lr: 4.13e-04
+2022-06-19 00:25:14,717 INFO [train.py:874] (1/4) Epoch 20, batch 650, datatang_loss[loss=0.1663, simple_loss=0.2395, pruned_loss=0.04656, over 4932.00 frames.], tot_loss[loss=0.1498, simple_loss=0.231, pruned_loss=0.03434, over 947767.93 frames.], batch size: 83, aishell_tot_loss[loss=0.1537, simple_loss=0.2393, pruned_loss=0.03403, over 789671.93 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2214, pruned_loss=0.03413, over 794956.34 frames.], batch size: 83, lr: 4.13e-04
+2022-06-19 00:25:47,543 INFO [train.py:874] (1/4) Epoch 20, batch 700, datatang_loss[loss=0.1349, simple_loss=0.2138, pruned_loss=0.02797, over 4926.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2313, pruned_loss=0.03457, over 956323.62 frames.], batch size: 71, aishell_tot_loss[loss=0.1538, simple_loss=0.2395, pruned_loss=0.03407, over 812710.15 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2219, pruned_loss=0.03447, over 817609.88 frames.], batch size: 71, lr: 4.13e-04
+2022-06-19 00:26:18,442 INFO [train.py:874] (1/4) Epoch 20, batch 750, datatang_loss[loss=0.1178, simple_loss=0.1894, pruned_loss=0.02306, over 4868.00 frames.], tot_loss[loss=0.1512, simple_loss=0.232, pruned_loss=0.03514, over 962407.84 frames.], batch size: 39, aishell_tot_loss[loss=0.1545, simple_loss=0.2399, pruned_loss=0.03454, over 838886.63 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2218, pruned_loss=0.0348, over 831092.87 frames.], batch size: 39, lr: 4.13e-04
+2022-06-19 00:26:48,881 INFO [train.py:874] (1/4) Epoch 20, batch 800, aishell_loss[loss=0.1847, simple_loss=0.2659, pruned_loss=0.05178, over 4943.00 frames.], tot_loss[loss=0.1507, simple_loss=0.2317, pruned_loss=0.03487, over 967820.33 frames.], batch size: 58, aishell_tot_loss[loss=0.154, simple_loss=0.2393, pruned_loss=0.03438, over 855695.40 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2224, pruned_loss=0.03474, over 850090.27 frames.], batch size: 58, lr: 4.12e-04
+2022-06-19 00:27:20,462 INFO [train.py:874] (1/4) Epoch 20, batch 850, aishell_loss[loss=0.1541, simple_loss=0.2401, pruned_loss=0.03403, over 4980.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2316, pruned_loss=0.03476, over 972162.46 frames.], batch size: 48, aishell_tot_loss[loss=0.1533, simple_loss=0.2386, pruned_loss=0.03404, over 873498.22 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2228, pruned_loss=0.03505, over 863834.33 frames.], batch size: 48, lr: 4.12e-04
+2022-06-19 00:27:50,515 INFO [train.py:874] (1/4) Epoch 20, batch 900, aishell_loss[loss=0.1755, simple_loss=0.2531, pruned_loss=0.04894, over 4959.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2314, pruned_loss=0.03491, over 974997.27 frames.], batch size: 31, aishell_tot_loss[loss=0.1536, simple_loss=0.239, pruned_loss=0.03409, over 884048.33 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2229, pruned_loss=0.03519, over 880815.23 frames.], batch size: 31, lr: 4.12e-04
+2022-06-19 00:28:20,649 INFO [train.py:874] (1/4) Epoch 20, batch 950, aishell_loss[loss=0.1465, simple_loss=0.2378, pruned_loss=0.02758, over 4889.00 frames.], tot_loss[loss=0.1492, simple_loss=0.23, pruned_loss=0.03414, over 977096.48 frames.], batch size: 50, aishell_tot_loss[loss=0.1522, simple_loss=0.2375, pruned_loss=0.03342, over 896134.04 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2228, pruned_loss=0.03505, over 892759.55 frames.], batch size: 50, lr: 4.12e-04
+2022-06-19 00:28:53,579 INFO [train.py:874] (1/4) Epoch 20, batch 1000, aishell_loss[loss=0.1489, simple_loss=0.229, pruned_loss=0.03441, over 4950.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2293, pruned_loss=0.03392, over 979001.34 frames.], batch size: 31, aishell_tot_loss[loss=0.1519, simple_loss=0.2373, pruned_loss=0.03328, over 905214.20 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2224, pruned_loss=0.03488, over 905186.76 frames.], batch size: 31, lr: 4.12e-04
+2022-06-19 00:28:53,580 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 00:29:10,060 INFO [train.py:914] (1/4) Epoch 20, validation: loss=0.1639, simple_loss=0.248, pruned_loss=0.0399, over 1622729.00 frames.
+2022-06-19 00:29:43,680 INFO [train.py:874] (1/4) Epoch 20, batch 1050, aishell_loss[loss=0.1767, simple_loss=0.2739, pruned_loss=0.03973, over 4915.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2304, pruned_loss=0.03447, over 980524.09 frames.], batch size: 78, aishell_tot_loss[loss=0.1522, simple_loss=0.2376, pruned_loss=0.03342, over 914341.97 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2232, pruned_loss=0.03532, over 915060.91 frames.], batch size: 78, lr: 4.12e-04
+2022-06-19 00:30:16,592 INFO [train.py:874] (1/4) Epoch 20, batch 1100, aishell_loss[loss=0.1533, simple_loss=0.2445, pruned_loss=0.03103, over 4888.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2313, pruned_loss=0.03467, over 981901.75 frames.], batch size: 34, aishell_tot_loss[loss=0.1523, simple_loss=0.2378, pruned_loss=0.0334, over 922968.15 frames.], datatang_tot_loss[loss=0.1476, simple_loss=0.2241, pruned_loss=0.0356, over 923409.30 frames.], batch size: 34, lr: 4.12e-04
+2022-06-19 00:30:45,814 INFO [train.py:874] (1/4) Epoch 20, batch 1150, aishell_loss[loss=0.1485, simple_loss=0.2294, pruned_loss=0.03379, over 4885.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2302, pruned_loss=0.03418, over 982551.56 frames.], batch size: 28, aishell_tot_loss[loss=0.1517, simple_loss=0.2374, pruned_loss=0.03299, over 929762.17 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2235, pruned_loss=0.03547, over 931121.77 frames.], batch size: 28, lr: 4.11e-04
+2022-06-19 00:31:19,124 INFO [train.py:874] (1/4) Epoch 20, batch 1200, aishell_loss[loss=0.1471, simple_loss=0.2316, pruned_loss=0.03127, over 4955.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2288, pruned_loss=0.03396, over 983027.91 frames.], batch size: 56, aishell_tot_loss[loss=0.1505, simple_loss=0.236, pruned_loss=0.03255, over 935335.51 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2235, pruned_loss=0.03561, over 938283.76 frames.], batch size: 56, lr: 4.11e-04
+2022-06-19 00:31:51,400 INFO [train.py:874] (1/4) Epoch 20, batch 1250, datatang_loss[loss=0.1595, simple_loss=0.2333, pruned_loss=0.04282, over 4944.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2283, pruned_loss=0.03374, over 983335.05 frames.], batch size: 69, aishell_tot_loss[loss=0.1504, simple_loss=0.2359, pruned_loss=0.03248, over 940744.08 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2228, pruned_loss=0.03538, over 944066.28 frames.], batch size: 69, lr: 4.11e-04
+2022-06-19 00:32:25,048 INFO [train.py:874] (1/4) Epoch 20, batch 1300, aishell_loss[loss=0.1419, simple_loss=0.2318, pruned_loss=0.02599, over 4925.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2287, pruned_loss=0.03413, over 983621.65 frames.], batch size: 52, aishell_tot_loss[loss=0.1506, simple_loss=0.2361, pruned_loss=0.03258, over 945522.72 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2229, pruned_loss=0.03565, over 949233.86 frames.], batch size: 52, lr: 4.11e-04
+2022-06-19 00:32:57,717 INFO [train.py:874] (1/4) Epoch 20, batch 1350, aishell_loss[loss=0.2028, simple_loss=0.2784, pruned_loss=0.06354, over 4922.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2292, pruned_loss=0.03409, over 984197.30 frames.], batch size: 46, aishell_tot_loss[loss=0.1509, simple_loss=0.2363, pruned_loss=0.0327, over 950745.87 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2229, pruned_loss=0.03553, over 953188.17 frames.], batch size: 46, lr: 4.11e-04
+2022-06-19 00:33:29,215 INFO [train.py:874] (1/4) Epoch 20, batch 1400, datatang_loss[loss=0.1526, simple_loss=0.2231, pruned_loss=0.04104, over 4895.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2302, pruned_loss=0.03434, over 984423.50 frames.], batch size: 47, aishell_tot_loss[loss=0.1515, simple_loss=0.2371, pruned_loss=0.03292, over 955149.75 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.223, pruned_loss=0.03563, over 956642.73 frames.], batch size: 47, lr: 4.11e-04
+2022-06-19 00:33:59,640 INFO [train.py:874] (1/4) Epoch 20, batch 1450, datatang_loss[loss=0.1387, simple_loss=0.2116, pruned_loss=0.03292, over 4940.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2302, pruned_loss=0.03426, over 984103.48 frames.], batch size: 69, aishell_tot_loss[loss=0.1517, simple_loss=0.2371, pruned_loss=0.03311, over 959230.29 frames.], datatang_tot_loss[loss=0.1467, simple_loss=0.2225, pruned_loss=0.03547, over 958953.74 frames.], batch size: 69, lr: 4.11e-04
+2022-06-19 00:34:33,119 INFO [train.py:874] (1/4) Epoch 20, batch 1500, aishell_loss[loss=0.1532, simple_loss=0.2355, pruned_loss=0.03541, over 4860.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2304, pruned_loss=0.03431, over 984115.04 frames.], batch size: 37, aishell_tot_loss[loss=0.1514, simple_loss=0.237, pruned_loss=0.0329, over 961880.84 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.223, pruned_loss=0.03574, over 962203.27 frames.], batch size: 37, lr: 4.11e-04
+2022-06-19 00:35:03,383 INFO [train.py:874] (1/4) Epoch 20, batch 1550, datatang_loss[loss=0.1538, simple_loss=0.2281, pruned_loss=0.03972, over 4929.00 frames.], tot_loss[loss=0.149, simple_loss=0.2294, pruned_loss=0.03431, over 984436.04 frames.], batch size: 83, aishell_tot_loss[loss=0.1512, simple_loss=0.2368, pruned_loss=0.03278, over 963658.53 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2228, pruned_loss=0.03573, over 965904.87 frames.], batch size: 83, lr: 4.10e-04
+2022-06-19 00:35:34,670 INFO [train.py:874] (1/4) Epoch 20, batch 1600, datatang_loss[loss=0.1316, simple_loss=0.1974, pruned_loss=0.03289, over 4931.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2303, pruned_loss=0.0345, over 985038.20 frames.], batch size: 42, aishell_tot_loss[loss=0.151, simple_loss=0.2366, pruned_loss=0.0327, over 966962.86 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2237, pruned_loss=0.03608, over 967902.01 frames.], batch size: 42, lr: 4.10e-04
+2022-06-19 00:36:07,048 INFO [train.py:874] (1/4) Epoch 20, batch 1650, datatang_loss[loss=0.1356, simple_loss=0.2119, pruned_loss=0.02968, over 4958.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2312, pruned_loss=0.0348, over 985114.25 frames.], batch size: 67, aishell_tot_loss[loss=0.1513, simple_loss=0.2371, pruned_loss=0.03272, over 969035.96 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2243, pruned_loss=0.03639, over 970053.73 frames.], batch size: 67, lr: 4.10e-04
+2022-06-19 00:36:36,976 INFO [train.py:874] (1/4) Epoch 20, batch 1700, aishell_loss[loss=0.1776, simple_loss=0.2573, pruned_loss=0.04897, over 4939.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2297, pruned_loss=0.03383, over 984943.13 frames.], batch size: 58, aishell_tot_loss[loss=0.1503, simple_loss=0.2361, pruned_loss=0.03227, over 970583.87 frames.], datatang_tot_loss[loss=0.1477, simple_loss=0.2238, pruned_loss=0.03583, over 971993.40 frames.], batch size: 58, lr: 4.10e-04
+2022-06-19 00:37:08,900 INFO [train.py:874] (1/4) Epoch 20, batch 1750, aishell_loss[loss=0.1307, simple_loss=0.2246, pruned_loss=0.01838, over 4946.00 frames.], tot_loss[loss=0.149, simple_loss=0.2298, pruned_loss=0.03406, over 985211.95 frames.], batch size: 56, aishell_tot_loss[loss=0.1509, simple_loss=0.2368, pruned_loss=0.0325, over 972283.38 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.2233, pruned_loss=0.03576, over 973767.15 frames.], batch size: 56, lr: 4.10e-04
+2022-06-19 00:37:42,117 INFO [train.py:874] (1/4) Epoch 20, batch 1800, aishell_loss[loss=0.1377, simple_loss=0.2203, pruned_loss=0.02753, over 4968.00 frames.], tot_loss[loss=0.149, simple_loss=0.2304, pruned_loss=0.03384, over 986022.57 frames.], batch size: 51, aishell_tot_loss[loss=0.1507, simple_loss=0.2367, pruned_loss=0.03239, over 974859.66 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2236, pruned_loss=0.03569, over 974936.14 frames.], batch size: 51, lr: 4.10e-04
+2022-06-19 00:38:12,139 INFO [train.py:874] (1/4) Epoch 20, batch 1850, aishell_loss[loss=0.1328, simple_loss=0.2176, pruned_loss=0.02396, over 4978.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2308, pruned_loss=0.03404, over 985940.83 frames.], batch size: 27, aishell_tot_loss[loss=0.1506, simple_loss=0.2369, pruned_loss=0.03218, over 976257.03 frames.], datatang_tot_loss[loss=0.148, simple_loss=0.2238, pruned_loss=0.03615, over 976061.87 frames.], batch size: 27, lr: 4.10e-04
+2022-06-19 00:38:43,857 INFO [train.py:874] (1/4) Epoch 20, batch 1900, aishell_loss[loss=0.1481, simple_loss=0.2374, pruned_loss=0.02938, over 4946.00 frames.], tot_loss[loss=0.1498, simple_loss=0.2311, pruned_loss=0.0342, over 986018.88 frames.], batch size: 32, aishell_tot_loss[loss=0.151, simple_loss=0.2372, pruned_loss=0.03239, over 977091.96 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2242, pruned_loss=0.036, over 977619.63 frames.], batch size: 32, lr: 4.10e-04
+2022-06-19 00:39:16,002 INFO [train.py:874] (1/4) Epoch 20, batch 1950, datatang_loss[loss=0.1352, simple_loss=0.2214, pruned_loss=0.02452, over 4970.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2306, pruned_loss=0.03397, over 985935.13 frames.], batch size: 45, aishell_tot_loss[loss=0.1505, simple_loss=0.2365, pruned_loss=0.03228, over 978023.65 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2243, pruned_loss=0.03593, over 978644.63 frames.], batch size: 45, lr: 4.09e-04
+2022-06-19 00:39:45,782 INFO [train.py:874] (1/4) Epoch 20, batch 2000, aishell_loss[loss=0.1516, simple_loss=0.2324, pruned_loss=0.03541, over 4962.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2311, pruned_loss=0.03398, over 986183.45 frames.], batch size: 44, aishell_tot_loss[loss=0.151, simple_loss=0.2372, pruned_loss=0.03239, over 979073.09 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2242, pruned_loss=0.03581, over 979655.94 frames.], batch size: 44, lr: 4.09e-04
+2022-06-19 00:39:45,783 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 00:40:02,387 INFO [train.py:914] (1/4) Epoch 20, validation: loss=0.1645, simple_loss=0.2482, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 00:40:32,679 INFO [train.py:874] (1/4) Epoch 20, batch 2050, datatang_loss[loss=0.1714, simple_loss=0.2399, pruned_loss=0.05147, over 4923.00 frames.], tot_loss[loss=0.1505, simple_loss=0.2319, pruned_loss=0.03458, over 985831.24 frames.], batch size: 73, aishell_tot_loss[loss=0.1516, simple_loss=0.2376, pruned_loss=0.03281, over 979815.15 frames.], datatang_tot_loss[loss=0.1483, simple_loss=0.2246, pruned_loss=0.03604, over 980147.02 frames.], batch size: 73, lr: 4.09e-04
+2022-06-19 00:41:04,537 INFO [train.py:874] (1/4) Epoch 20, batch 2100, datatang_loss[loss=0.1927, simple_loss=0.2689, pruned_loss=0.05822, over 4941.00 frames.], tot_loss[loss=0.1506, simple_loss=0.2317, pruned_loss=0.03473, over 985591.95 frames.], batch size: 109, aishell_tot_loss[loss=0.1516, simple_loss=0.2377, pruned_loss=0.03274, over 980239.82 frames.], datatang_tot_loss[loss=0.1487, simple_loss=0.2248, pruned_loss=0.03623, over 980819.51 frames.], batch size: 109, lr: 4.09e-04
+2022-06-19 00:41:37,686 INFO [train.py:874] (1/4) Epoch 20, batch 2150, aishell_loss[loss=0.1497, simple_loss=0.2246, pruned_loss=0.03736, over 4943.00 frames.], tot_loss[loss=0.1513, simple_loss=0.2319, pruned_loss=0.03532, over 985791.67 frames.], batch size: 25, aishell_tot_loss[loss=0.1516, simple_loss=0.2376, pruned_loss=0.03279, over 980716.26 frames.], datatang_tot_loss[loss=0.1495, simple_loss=0.2255, pruned_loss=0.03678, over 981714.29 frames.], batch size: 25, lr: 4.09e-04
+2022-06-19 00:42:09,209 INFO [train.py:874] (1/4) Epoch 20, batch 2200, aishell_loss[loss=0.1474, simple_loss=0.2454, pruned_loss=0.02473, over 4967.00 frames.], tot_loss[loss=0.1519, simple_loss=0.2323, pruned_loss=0.03571, over 985822.51 frames.], batch size: 64, aishell_tot_loss[loss=0.1512, simple_loss=0.2373, pruned_loss=0.0326, over 981253.04 frames.], datatang_tot_loss[loss=0.1507, simple_loss=0.2266, pruned_loss=0.03745, over 982270.11 frames.], batch size: 64, lr: 4.09e-04
+2022-06-19 00:42:40,347 INFO [train.py:874] (1/4) Epoch 20, batch 2250, datatang_loss[loss=0.1326, simple_loss=0.2064, pruned_loss=0.02943, over 4921.00 frames.], tot_loss[loss=0.1504, simple_loss=0.2304, pruned_loss=0.03515, over 985773.79 frames.], batch size: 64, aishell_tot_loss[loss=0.1505, simple_loss=0.2363, pruned_loss=0.03233, over 981587.66 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2259, pruned_loss=0.03716, over 982817.11 frames.], batch size: 64, lr: 4.09e-04
+2022-06-19 00:43:13,256 INFO [train.py:874] (1/4) Epoch 20, batch 2300, aishell_loss[loss=0.1825, simple_loss=0.2619, pruned_loss=0.05151, over 4922.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2302, pruned_loss=0.03481, over 985499.13 frames.], batch size: 78, aishell_tot_loss[loss=0.15, simple_loss=0.2357, pruned_loss=0.03212, over 982025.94 frames.], datatang_tot_loss[loss=0.1501, simple_loss=0.2259, pruned_loss=0.0372, over 982957.84 frames.], batch size: 78, lr: 4.09e-04
+2022-06-19 00:43:44,358 INFO [train.py:874] (1/4) Epoch 20, batch 2350, datatang_loss[loss=0.1536, simple_loss=0.2313, pruned_loss=0.03793, over 4898.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2297, pruned_loss=0.03426, over 985333.76 frames.], batch size: 52, aishell_tot_loss[loss=0.1498, simple_loss=0.2356, pruned_loss=0.03204, over 982264.65 frames.], datatang_tot_loss[loss=0.1494, simple_loss=0.2254, pruned_loss=0.03669, over 983245.86 frames.], batch size: 52, lr: 4.08e-04
+2022-06-19 00:44:15,711 INFO [train.py:874] (1/4) Epoch 20, batch 2400, datatang_loss[loss=0.1336, simple_loss=0.2081, pruned_loss=0.02961, over 4921.00 frames.], tot_loss[loss=0.15, simple_loss=0.2307, pruned_loss=0.03461, over 985322.50 frames.], batch size: 77, aishell_tot_loss[loss=0.1503, simple_loss=0.2364, pruned_loss=0.03212, over 982539.54 frames.], datatang_tot_loss[loss=0.1497, simple_loss=0.2256, pruned_loss=0.03689, over 983549.70 frames.], batch size: 77, lr: 4.08e-04
+2022-06-19 00:44:49,143 INFO [train.py:874] (1/4) Epoch 20, batch 2450, aishell_loss[loss=0.1629, simple_loss=0.2462, pruned_loss=0.03979, over 4917.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2305, pruned_loss=0.03447, over 985676.43 frames.], batch size: 46, aishell_tot_loss[loss=0.1505, simple_loss=0.2367, pruned_loss=0.03218, over 982884.60 frames.], datatang_tot_loss[loss=0.1492, simple_loss=0.2252, pruned_loss=0.03664, over 984104.87 frames.], batch size: 46, lr: 4.08e-04
+2022-06-19 00:45:21,531 INFO [train.py:874] (1/4) Epoch 20, batch 2500, datatang_loss[loss=0.159, simple_loss=0.2432, pruned_loss=0.03742, over 4956.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2306, pruned_loss=0.03476, over 985413.86 frames.], batch size: 99, aishell_tot_loss[loss=0.1508, simple_loss=0.2369, pruned_loss=0.03235, over 982869.00 frames.], datatang_tot_loss[loss=0.1493, simple_loss=0.2251, pruned_loss=0.03671, over 984336.92 frames.], batch size: 99, lr: 4.08e-04
+2022-06-19 00:45:51,628 INFO [train.py:874] (1/4) Epoch 20, batch 2550, datatang_loss[loss=0.136, simple_loss=0.2124, pruned_loss=0.02977, over 4924.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2296, pruned_loss=0.03454, over 985644.16 frames.], batch size: 42, aishell_tot_loss[loss=0.151, simple_loss=0.2371, pruned_loss=0.03243, over 983128.23 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2241, pruned_loss=0.03636, over 984721.65 frames.], batch size: 42, lr: 4.08e-04
+2022-06-19 00:46:25,326 INFO [train.py:874] (1/4) Epoch 20, batch 2600, datatang_loss[loss=0.1918, simple_loss=0.2636, pruned_loss=0.06005, over 4916.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2301, pruned_loss=0.03488, over 985700.17 frames.], batch size: 109, aishell_tot_loss[loss=0.1514, simple_loss=0.2374, pruned_loss=0.0327, over 983563.87 frames.], datatang_tot_loss[loss=0.1486, simple_loss=0.2243, pruned_loss=0.03643, over 984738.54 frames.], batch size: 109, lr: 4.08e-04
+2022-06-19 00:46:57,788 INFO [train.py:874] (1/4) Epoch 20, batch 2650, aishell_loss[loss=0.1586, simple_loss=0.2396, pruned_loss=0.03878, over 4896.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2303, pruned_loss=0.03456, over 985795.06 frames.], batch size: 34, aishell_tot_loss[loss=0.1513, simple_loss=0.2374, pruned_loss=0.03257, over 983983.96 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2242, pruned_loss=0.03632, over 984795.79 frames.], batch size: 34, lr: 4.08e-04
+2022-06-19 00:47:28,184 INFO [train.py:874] (1/4) Epoch 20, batch 2700, aishell_loss[loss=0.1639, simple_loss=0.2445, pruned_loss=0.04162, over 4981.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2299, pruned_loss=0.03442, over 985848.21 frames.], batch size: 44, aishell_tot_loss[loss=0.1513, simple_loss=0.2374, pruned_loss=0.03258, over 984146.55 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2238, pruned_loss=0.03616, over 985012.00 frames.], batch size: 44, lr: 4.08e-04
+2022-06-19 00:48:02,055 INFO [train.py:874] (1/4) Epoch 20, batch 2750, datatang_loss[loss=0.1489, simple_loss=0.2293, pruned_loss=0.03424, over 4931.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2289, pruned_loss=0.03451, over 985501.09 frames.], batch size: 94, aishell_tot_loss[loss=0.1506, simple_loss=0.2363, pruned_loss=0.0325, over 984035.18 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2238, pruned_loss=0.03633, over 985052.36 frames.], batch size: 94, lr: 4.07e-04
+2022-06-19 00:48:34,584 INFO [train.py:874] (1/4) Epoch 20, batch 2800, aishell_loss[loss=0.1411, simple_loss=0.2364, pruned_loss=0.02294, over 4912.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2294, pruned_loss=0.03448, over 985455.26 frames.], batch size: 33, aishell_tot_loss[loss=0.1506, simple_loss=0.2364, pruned_loss=0.03238, over 983973.16 frames.], datatang_tot_loss[loss=0.1485, simple_loss=0.2241, pruned_loss=0.03644, over 985283.15 frames.], batch size: 33, lr: 4.07e-04
+2022-06-19 00:49:04,722 INFO [train.py:874] (1/4) Epoch 20, batch 2850, aishell_loss[loss=0.1292, simple_loss=0.2176, pruned_loss=0.02038, over 4927.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2292, pruned_loss=0.03396, over 985379.93 frames.], batch size: 52, aishell_tot_loss[loss=0.1501, simple_loss=0.2361, pruned_loss=0.03209, over 984151.72 frames.], datatang_tot_loss[loss=0.1482, simple_loss=0.2241, pruned_loss=0.03615, over 985221.31 frames.], batch size: 52, lr: 4.07e-04
+2022-06-19 00:49:38,231 INFO [train.py:874] (1/4) Epoch 20, batch 2900, aishell_loss[loss=0.1479, simple_loss=0.2421, pruned_loss=0.02685, over 4942.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2293, pruned_loss=0.03394, over 985689.59 frames.], batch size: 58, aishell_tot_loss[loss=0.1498, simple_loss=0.2357, pruned_loss=0.03197, over 984474.24 frames.], datatang_tot_loss[loss=0.1484, simple_loss=0.2243, pruned_loss=0.03625, over 985391.40 frames.], batch size: 58, lr: 4.07e-04
+2022-06-19 00:50:09,754 INFO [train.py:874] (1/4) Epoch 20, batch 2950, datatang_loss[loss=0.1356, simple_loss=0.2163, pruned_loss=0.0274, over 4911.00 frames.], tot_loss[loss=0.148, simple_loss=0.2288, pruned_loss=0.03357, over 985581.86 frames.], batch size: 75, aishell_tot_loss[loss=0.1495, simple_loss=0.2353, pruned_loss=0.0318, over 984524.33 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.224, pruned_loss=0.03592, over 985396.97 frames.], batch size: 75, lr: 4.07e-04
+2022-06-19 00:50:39,773 INFO [train.py:874] (1/4) Epoch 20, batch 3000, datatang_loss[loss=0.1393, simple_loss=0.2203, pruned_loss=0.02913, over 4937.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2297, pruned_loss=0.03371, over 985677.26 frames.], batch size: 57, aishell_tot_loss[loss=0.1501, simple_loss=0.2363, pruned_loss=0.03191, over 984945.97 frames.], datatang_tot_loss[loss=0.1479, simple_loss=0.2239, pruned_loss=0.03589, over 985235.28 frames.], batch size: 57, lr: 4.07e-04
+2022-06-19 00:50:39,775 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 00:50:56,984 INFO [train.py:914] (1/4) Epoch 20, validation: loss=0.164, simple_loss=0.2487, pruned_loss=0.03966, over 1622729.00 frames.
+2022-06-19 00:51:26,461 INFO [train.py:874] (1/4) Epoch 20, batch 3050, datatang_loss[loss=0.1397, simple_loss=0.2175, pruned_loss=0.03093, over 4922.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2297, pruned_loss=0.03381, over 985887.97 frames.], batch size: 75, aishell_tot_loss[loss=0.1502, simple_loss=0.2366, pruned_loss=0.03187, over 985103.88 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2237, pruned_loss=0.03592, over 985436.75 frames.], batch size: 75, lr: 4.07e-04
+2022-06-19 00:51:58,723 INFO [train.py:874] (1/4) Epoch 20, batch 3100, datatang_loss[loss=0.1322, simple_loss=0.2008, pruned_loss=0.03174, over 4980.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2303, pruned_loss=0.034, over 985465.28 frames.], batch size: 34, aishell_tot_loss[loss=0.1507, simple_loss=0.2372, pruned_loss=0.03208, over 984891.36 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2238, pruned_loss=0.03586, over 985322.60 frames.], batch size: 34, lr: 4.07e-04
+2022-06-19 00:52:31,196 INFO [train.py:874] (1/4) Epoch 20, batch 3150, datatang_loss[loss=0.1366, simple_loss=0.2167, pruned_loss=0.02828, over 4958.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2306, pruned_loss=0.03428, over 985616.96 frames.], batch size: 86, aishell_tot_loss[loss=0.1512, simple_loss=0.2377, pruned_loss=0.03235, over 984747.61 frames.], datatang_tot_loss[loss=0.1478, simple_loss=0.2239, pruned_loss=0.03581, over 985706.00 frames.], batch size: 86, lr: 4.06e-04
+2022-06-19 00:53:02,564 INFO [train.py:874] (1/4) Epoch 20, batch 3200, datatang_loss[loss=0.1437, simple_loss=0.229, pruned_loss=0.02921, over 4830.00 frames.], tot_loss[loss=0.1499, simple_loss=0.2314, pruned_loss=0.03422, over 985910.37 frames.], batch size: 30, aishell_tot_loss[loss=0.1517, simple_loss=0.2383, pruned_loss=0.03257, over 985163.12 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2236, pruned_loss=0.03568, over 985722.85 frames.], batch size: 30, lr: 4.06e-04
+2022-06-19 00:53:34,157 INFO [train.py:874] (1/4) Epoch 20, batch 3250, datatang_loss[loss=0.2018, simple_loss=0.2736, pruned_loss=0.065, over 4923.00 frames.], tot_loss[loss=0.1502, simple_loss=0.2315, pruned_loss=0.03441, over 986384.54 frames.], batch size: 34, aishell_tot_loss[loss=0.1521, simple_loss=0.2386, pruned_loss=0.0328, over 985628.68 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2236, pruned_loss=0.03569, over 985880.34 frames.], batch size: 34, lr: 4.06e-04
+2022-06-19 00:54:06,320 INFO [train.py:874] (1/4) Epoch 20, batch 3300, datatang_loss[loss=0.1492, simple_loss=0.2289, pruned_loss=0.03471, over 4921.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2308, pruned_loss=0.03423, over 986274.62 frames.], batch size: 75, aishell_tot_loss[loss=0.1523, simple_loss=0.2387, pruned_loss=0.03294, over 985597.20 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.223, pruned_loss=0.03536, over 985952.72 frames.], batch size: 75, lr: 4.06e-04
+2022-06-19 00:54:36,926 INFO [train.py:874] (1/4) Epoch 20, batch 3350, datatang_loss[loss=0.1387, simple_loss=0.2101, pruned_loss=0.03364, over 4968.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2306, pruned_loss=0.03414, over 986071.58 frames.], batch size: 65, aishell_tot_loss[loss=0.1519, simple_loss=0.2382, pruned_loss=0.03284, over 985472.19 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2233, pruned_loss=0.03541, over 985983.12 frames.], batch size: 65, lr: 4.06e-04
+2022-06-19 00:55:10,647 INFO [train.py:874] (1/4) Epoch 20, batch 3400, datatang_loss[loss=0.1314, simple_loss=0.2079, pruned_loss=0.02744, over 4915.00 frames.], tot_loss[loss=0.1497, simple_loss=0.231, pruned_loss=0.03424, over 986012.77 frames.], batch size: 75, aishell_tot_loss[loss=0.1518, simple_loss=0.2383, pruned_loss=0.03263, over 985371.12 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2234, pruned_loss=0.03573, over 986102.42 frames.], batch size: 75, lr: 4.06e-04
+2022-06-19 00:55:42,705 INFO [train.py:874] (1/4) Epoch 20, batch 3450, datatang_loss[loss=0.153, simple_loss=0.2391, pruned_loss=0.03344, over 4929.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2307, pruned_loss=0.03422, over 985946.53 frames.], batch size: 94, aishell_tot_loss[loss=0.1517, simple_loss=0.2385, pruned_loss=0.03247, over 985513.24 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.2234, pruned_loss=0.03579, over 985940.75 frames.], batch size: 94, lr: 4.06e-04
+2022-06-19 00:56:12,510 INFO [train.py:874] (1/4) Epoch 20, batch 3500, datatang_loss[loss=0.1446, simple_loss=0.2091, pruned_loss=0.04001, over 4862.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2306, pruned_loss=0.03422, over 985726.12 frames.], batch size: 39, aishell_tot_loss[loss=0.1518, simple_loss=0.2386, pruned_loss=0.03253, over 985351.88 frames.], datatang_tot_loss[loss=0.1473, simple_loss=0.2231, pruned_loss=0.03578, over 985915.67 frames.], batch size: 39, lr: 4.06e-04
+2022-06-19 00:56:46,200 INFO [train.py:874] (1/4) Epoch 20, batch 3550, aishell_loss[loss=0.1696, simple_loss=0.2632, pruned_loss=0.03796, over 4940.00 frames.], tot_loss[loss=0.1486, simple_loss=0.2297, pruned_loss=0.03371, over 985208.14 frames.], batch size: 78, aishell_tot_loss[loss=0.1514, simple_loss=0.2384, pruned_loss=0.03222, over 985056.62 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2227, pruned_loss=0.03548, over 985661.43 frames.], batch size: 78, lr: 4.05e-04
+2022-06-19 00:57:18,977 INFO [train.py:874] (1/4) Epoch 20, batch 3600, aishell_loss[loss=0.1604, simple_loss=0.246, pruned_loss=0.03743, over 4877.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2294, pruned_loss=0.03405, over 985349.19 frames.], batch size: 42, aishell_tot_loss[loss=0.1523, simple_loss=0.2389, pruned_loss=0.03279, over 985110.19 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.222, pruned_loss=0.03518, over 985710.73 frames.], batch size: 42, lr: 4.05e-04
+2022-06-19 00:57:49,532 INFO [train.py:874] (1/4) Epoch 20, batch 3650, aishell_loss[loss=0.1503, simple_loss=0.2329, pruned_loss=0.03389, over 4987.00 frames.], tot_loss[loss=0.1483, simple_loss=0.229, pruned_loss=0.03383, over 985789.48 frames.], batch size: 30, aishell_tot_loss[loss=0.1522, simple_loss=0.2387, pruned_loss=0.03285, over 985395.29 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2217, pruned_loss=0.03488, over 985867.51 frames.], batch size: 30, lr: 4.05e-04
+2022-06-19 00:58:21,861 INFO [train.py:874] (1/4) Epoch 20, batch 3700, aishell_loss[loss=0.1445, simple_loss=0.2254, pruned_loss=0.03178, over 4956.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2285, pruned_loss=0.03362, over 985924.20 frames.], batch size: 40, aishell_tot_loss[loss=0.1519, simple_loss=0.2381, pruned_loss=0.0328, over 985519.31 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2216, pruned_loss=0.03468, over 985918.37 frames.], batch size: 40, lr: 4.05e-04
+2022-06-19 00:58:52,956 INFO [train.py:874] (1/4) Epoch 20, batch 3750, aishell_loss[loss=0.1243, simple_loss=0.1982, pruned_loss=0.02525, over 4935.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2283, pruned_loss=0.03328, over 986025.56 frames.], batch size: 25, aishell_tot_loss[loss=0.1514, simple_loss=0.2377, pruned_loss=0.03251, over 985479.34 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2217, pruned_loss=0.03457, over 986117.02 frames.], batch size: 25, lr: 4.05e-04
+2022-06-19 00:59:23,338 INFO [train.py:874] (1/4) Epoch 20, batch 3800, aishell_loss[loss=0.1468, simple_loss=0.2314, pruned_loss=0.03106, over 4856.00 frames.], tot_loss[loss=0.147, simple_loss=0.2281, pruned_loss=0.03296, over 985907.40 frames.], batch size: 37, aishell_tot_loss[loss=0.1513, simple_loss=0.2376, pruned_loss=0.03256, over 985422.73 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2211, pruned_loss=0.03415, over 986119.08 frames.], batch size: 37, lr: 4.05e-04
+2022-06-19 00:59:56,336 INFO [train.py:874] (1/4) Epoch 20, batch 3850, aishell_loss[loss=0.1379, simple_loss=0.2292, pruned_loss=0.0233, over 4897.00 frames.], tot_loss[loss=0.1473, simple_loss=0.228, pruned_loss=0.03334, over 985690.04 frames.], batch size: 47, aishell_tot_loss[loss=0.1514, simple_loss=0.2378, pruned_loss=0.03255, over 985357.57 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.2211, pruned_loss=0.0344, over 985950.81 frames.], batch size: 47, lr: 4.05e-04
+2022-06-19 01:00:26,603 INFO [train.py:874] (1/4) Epoch 20, batch 3900, aishell_loss[loss=0.1404, simple_loss=0.2278, pruned_loss=0.02651, over 4896.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2283, pruned_loss=0.03343, over 985920.78 frames.], batch size: 52, aishell_tot_loss[loss=0.1517, simple_loss=0.2378, pruned_loss=0.03277, over 985392.76 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2212, pruned_loss=0.03425, over 986164.70 frames.], batch size: 52, lr: 4.05e-04
+2022-06-19 01:00:57,349 INFO [train.py:874] (1/4) Epoch 20, batch 3950, aishell_loss[loss=0.1495, simple_loss=0.2365, pruned_loss=0.03128, over 4890.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2285, pruned_loss=0.03321, over 985860.81 frames.], batch size: 28, aishell_tot_loss[loss=0.1518, simple_loss=0.2382, pruned_loss=0.03269, over 985357.65 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2207, pruned_loss=0.03407, over 986168.58 frames.], batch size: 28, lr: 4.04e-04
+2022-06-19 01:01:28,307 INFO [train.py:874] (1/4) Epoch 20, batch 4000, datatang_loss[loss=0.1436, simple_loss=0.2295, pruned_loss=0.02883, over 4943.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2286, pruned_loss=0.03337, over 985833.20 frames.], batch size: 91, aishell_tot_loss[loss=0.1514, simple_loss=0.2379, pruned_loss=0.0325, over 985343.45 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.2212, pruned_loss=0.03436, over 986162.32 frames.], batch size: 91, lr: 4.04e-04
+2022-06-19 01:01:28,308 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 01:01:44,967 INFO [train.py:914] (1/4) Epoch 20, validation: loss=0.1658, simple_loss=0.2493, pruned_loss=0.04118, over 1622729.00 frames.
+2022-06-19 01:02:16,039 INFO [train.py:874] (1/4) Epoch 20, batch 4050, aishell_loss[loss=0.1458, simple_loss=0.229, pruned_loss=0.03132, over 4882.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2293, pruned_loss=0.03376, over 985635.85 frames.], batch size: 28, aishell_tot_loss[loss=0.1517, simple_loss=0.2382, pruned_loss=0.03257, over 985201.81 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2216, pruned_loss=0.03464, over 986093.06 frames.], batch size: 28, lr: 4.04e-04
+2022-06-19 01:02:45,509 INFO [train.py:874] (1/4) Epoch 20, batch 4100, aishell_loss[loss=0.1446, simple_loss=0.2374, pruned_loss=0.02593, over 4857.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2296, pruned_loss=0.03363, over 985829.98 frames.], batch size: 28, aishell_tot_loss[loss=0.152, simple_loss=0.2385, pruned_loss=0.03276, over 985317.13 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.2212, pruned_loss=0.03437, over 986203.53 frames.], batch size: 28, lr: 4.04e-04
+2022-06-19 01:03:16,322 INFO [train.py:874] (1/4) Epoch 20, batch 4150, datatang_loss[loss=0.1262, simple_loss=0.2047, pruned_loss=0.02386, over 4923.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2293, pruned_loss=0.03341, over 985854.36 frames.], batch size: 73, aishell_tot_loss[loss=0.1517, simple_loss=0.2382, pruned_loss=0.03259, over 985281.24 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2212, pruned_loss=0.03434, over 986294.53 frames.], batch size: 73, lr: 4.04e-04
+2022-06-19 01:03:46,130 INFO [train.py:874] (1/4) Epoch 20, batch 4200, aishell_loss[loss=0.1487, simple_loss=0.2331, pruned_loss=0.03218, over 4936.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2294, pruned_loss=0.0337, over 985676.87 frames.], batch size: 58, aishell_tot_loss[loss=0.1516, simple_loss=0.2381, pruned_loss=0.03249, over 985299.66 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2215, pruned_loss=0.03471, over 986105.57 frames.], batch size: 58, lr: 4.04e-04
+2022-06-19 01:04:16,242 INFO [train.py:874] (1/4) Epoch 20, batch 4250, aishell_loss[loss=0.1475, simple_loss=0.2367, pruned_loss=0.02916, over 4901.00 frames.], tot_loss[loss=0.148, simple_loss=0.2289, pruned_loss=0.03352, over 985444.77 frames.], batch size: 28, aishell_tot_loss[loss=0.1517, simple_loss=0.2383, pruned_loss=0.03258, over 984934.45 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2208, pruned_loss=0.03444, over 986217.05 frames.], batch size: 28, lr: 4.04e-04
+2022-06-19 01:05:47,337 INFO [train.py:874] (1/4) Epoch 21, batch 50, datatang_loss[loss=0.1579, simple_loss=0.2378, pruned_loss=0.03897, over 4926.00 frames.], tot_loss[loss=0.142, simple_loss=0.2229, pruned_loss=0.03056, over 218638.56 frames.], batch size: 94, aishell_tot_loss[loss=0.1517, simple_loss=0.2371, pruned_loss=0.03319, over 94035.57 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2134, pruned_loss=0.02883, over 137727.17 frames.], batch size: 94, lr: 3.94e-04
+2022-06-19 01:06:17,243 INFO [train.py:874] (1/4) Epoch 21, batch 100, aishell_loss[loss=0.1465, simple_loss=0.2348, pruned_loss=0.02911, over 4922.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2243, pruned_loss=0.03103, over 388308.88 frames.], batch size: 58, aishell_tot_loss[loss=0.1525, simple_loss=0.2383, pruned_loss=0.03331, over 198870.94 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2124, pruned_loss=0.02907, over 237400.93 frames.], batch size: 58, lr: 3.94e-04
+2022-06-19 01:06:49,569 INFO [train.py:874] (1/4) Epoch 21, batch 150, aishell_loss[loss=0.1552, simple_loss=0.2393, pruned_loss=0.03557, over 4973.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2265, pruned_loss=0.03247, over 520813.54 frames.], batch size: 39, aishell_tot_loss[loss=0.1538, simple_loss=0.2389, pruned_loss=0.03433, over 298299.65 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2141, pruned_loss=0.03036, over 319138.58 frames.], batch size: 39, lr: 3.94e-04
+2022-06-19 01:07:21,662 INFO [train.py:874] (1/4) Epoch 21, batch 200, datatang_loss[loss=0.1048, simple_loss=0.1911, pruned_loss=0.009215, over 4930.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2274, pruned_loss=0.03199, over 623925.14 frames.], batch size: 71, aishell_tot_loss[loss=0.1546, simple_loss=0.2404, pruned_loss=0.03444, over 391278.33 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2128, pruned_loss=0.02929, over 385807.51 frames.], batch size: 71, lr: 3.94e-04
+2022-06-19 01:07:50,959 INFO [train.py:874] (1/4) Epoch 21, batch 250, datatang_loss[loss=0.1519, simple_loss=0.2169, pruned_loss=0.04349, over 4926.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2283, pruned_loss=0.03249, over 704557.32 frames.], batch size: 57, aishell_tot_loss[loss=0.1544, simple_loss=0.2402, pruned_loss=0.0343, over 477116.22 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2134, pruned_loss=0.03005, over 440476.47 frames.], batch size: 57, lr: 3.94e-04
+2022-06-19 01:08:22,406 INFO [train.py:874] (1/4) Epoch 21, batch 300, datatang_loss[loss=0.133, simple_loss=0.216, pruned_loss=0.02504, over 4918.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2276, pruned_loss=0.03197, over 767060.55 frames.], batch size: 81, aishell_tot_loss[loss=0.1535, simple_loss=0.2392, pruned_loss=0.03395, over 548125.76 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2129, pruned_loss=0.02955, over 492666.03 frames.], batch size: 81, lr: 3.94e-04
+2022-06-19 01:08:53,928 INFO [train.py:874] (1/4) Epoch 21, batch 350, aishell_loss[loss=0.1615, simple_loss=0.2604, pruned_loss=0.0313, over 4969.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2282, pruned_loss=0.03217, over 815578.03 frames.], batch size: 39, aishell_tot_loss[loss=0.1537, simple_loss=0.2399, pruned_loss=0.03377, over 601565.49 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2134, pruned_loss=0.03011, over 548626.89 frames.], batch size: 39, lr: 3.93e-04
+2022-06-19 01:09:24,473 INFO [train.py:874] (1/4) Epoch 21, batch 400, datatang_loss[loss=0.1783, simple_loss=0.2502, pruned_loss=0.05317, over 4899.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2283, pruned_loss=0.03253, over 853002.32 frames.], batch size: 59, aishell_tot_loss[loss=0.1538, simple_loss=0.2398, pruned_loss=0.03389, over 649803.87 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2137, pruned_loss=0.03056, over 596296.39 frames.], batch size: 59, lr: 3.93e-04
+2022-06-19 01:09:56,124 INFO [train.py:874] (1/4) Epoch 21, batch 450, datatang_loss[loss=0.1582, simple_loss=0.2261, pruned_loss=0.04517, over 4914.00 frames.], tot_loss[loss=0.1465, simple_loss=0.228, pruned_loss=0.03253, over 882326.35 frames.], batch size: 81, aishell_tot_loss[loss=0.1541, simple_loss=0.2405, pruned_loss=0.03387, over 686234.97 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2137, pruned_loss=0.03074, over 645629.93 frames.], batch size: 81, lr: 3.93e-04
+2022-06-19 01:10:28,216 INFO [train.py:874] (1/4) Epoch 21, batch 500, aishell_loss[loss=0.1792, simple_loss=0.2653, pruned_loss=0.04656, over 4917.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2274, pruned_loss=0.03256, over 905595.32 frames.], batch size: 41, aishell_tot_loss[loss=0.1533, simple_loss=0.2398, pruned_loss=0.03336, over 717662.87 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2145, pruned_loss=0.03144, over 690383.64 frames.], batch size: 41, lr: 3.93e-04
+2022-06-19 01:10:58,436 INFO [train.py:874] (1/4) Epoch 21, batch 550, aishell_loss[loss=0.1259, simple_loss=0.2018, pruned_loss=0.02496, over 4810.00 frames.], tot_loss[loss=0.1457, simple_loss=0.227, pruned_loss=0.03216, over 923339.05 frames.], batch size: 24, aishell_tot_loss[loss=0.1525, simple_loss=0.2391, pruned_loss=0.03297, over 748146.82 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2148, pruned_loss=0.03137, over 726347.32 frames.], batch size: 24, lr: 3.93e-04
+2022-06-19 01:11:30,247 INFO [train.py:874] (1/4) Epoch 21, batch 600, datatang_loss[loss=0.1538, simple_loss=0.2176, pruned_loss=0.04505, over 4947.00 frames.], tot_loss[loss=0.146, simple_loss=0.2272, pruned_loss=0.03246, over 937312.46 frames.], batch size: 37, aishell_tot_loss[loss=0.1527, simple_loss=0.2392, pruned_loss=0.03313, over 776319.38 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.215, pruned_loss=0.03158, over 756865.57 frames.], batch size: 37, lr: 3.93e-04
+2022-06-19 01:12:03,150 INFO [train.py:874] (1/4) Epoch 21, batch 650, datatang_loss[loss=0.1219, simple_loss=0.1983, pruned_loss=0.02278, over 4906.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2267, pruned_loss=0.03248, over 947550.25 frames.], batch size: 57, aishell_tot_loss[loss=0.1521, simple_loss=0.2386, pruned_loss=0.03285, over 793738.33 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2161, pruned_loss=0.03197, over 790892.26 frames.], batch size: 57, lr: 3.93e-04
+2022-06-19 01:12:34,558 INFO [train.py:874] (1/4) Epoch 21, batch 700, datatang_loss[loss=0.1531, simple_loss=0.2331, pruned_loss=0.03651, over 4915.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2275, pruned_loss=0.03254, over 956038.98 frames.], batch size: 77, aishell_tot_loss[loss=0.1518, simple_loss=0.2384, pruned_loss=0.03259, over 814522.81 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2173, pruned_loss=0.03233, over 815699.28 frames.], batch size: 77, lr: 3.93e-04
+2022-06-19 01:13:05,867 INFO [train.py:874] (1/4) Epoch 21, batch 750, aishell_loss[loss=0.1399, simple_loss=0.2385, pruned_loss=0.02068, over 4882.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2269, pruned_loss=0.03202, over 962549.62 frames.], batch size: 47, aishell_tot_loss[loss=0.1509, simple_loss=0.2377, pruned_loss=0.03206, over 834699.40 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2172, pruned_loss=0.03224, over 835645.47 frames.], batch size: 47, lr: 3.93e-04
+2022-06-19 01:13:36,443 INFO [train.py:874] (1/4) Epoch 21, batch 800, datatang_loss[loss=0.1518, simple_loss=0.2133, pruned_loss=0.04516, over 4958.00 frames.], tot_loss[loss=0.146, simple_loss=0.2273, pruned_loss=0.03234, over 967453.46 frames.], batch size: 40, aishell_tot_loss[loss=0.151, simple_loss=0.2376, pruned_loss=0.03218, over 852921.47 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2176, pruned_loss=0.03248, over 852635.65 frames.], batch size: 40, lr: 3.92e-04
+2022-06-19 01:14:08,623 INFO [train.py:874] (1/4) Epoch 21, batch 850, aishell_loss[loss=0.1454, simple_loss=0.2405, pruned_loss=0.02518, over 4950.00 frames.], tot_loss[loss=0.146, simple_loss=0.2278, pruned_loss=0.03211, over 971353.60 frames.], batch size: 40, aishell_tot_loss[loss=0.1508, simple_loss=0.2376, pruned_loss=0.03196, over 868570.91 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.218, pruned_loss=0.03243, over 868154.00 frames.], batch size: 40, lr: 3.92e-04
+2022-06-19 01:14:40,492 INFO [train.py:874] (1/4) Epoch 21, batch 900, aishell_loss[loss=0.1678, simple_loss=0.2444, pruned_loss=0.04563, over 4890.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2281, pruned_loss=0.03258, over 974540.08 frames.], batch size: 34, aishell_tot_loss[loss=0.1508, simple_loss=0.2373, pruned_loss=0.03218, over 882875.37 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2186, pruned_loss=0.03272, over 881494.20 frames.], batch size: 34, lr: 3.92e-04
+2022-06-19 01:15:10,909 INFO [train.py:874] (1/4) Epoch 21, batch 950, datatang_loss[loss=0.143, simple_loss=0.2186, pruned_loss=0.03375, over 4955.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2278, pruned_loss=0.03238, over 976813.45 frames.], batch size: 45, aishell_tot_loss[loss=0.1501, simple_loss=0.2363, pruned_loss=0.03198, over 898363.85 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2184, pruned_loss=0.03274, over 889981.02 frames.], batch size: 45, lr: 3.92e-04
+2022-06-19 01:15:49,479 INFO [train.py:874] (1/4) Epoch 21, batch 1000, aishell_loss[loss=0.1463, simple_loss=0.2298, pruned_loss=0.03143, over 4936.00 frames.], tot_loss[loss=0.1467, simple_loss=0.228, pruned_loss=0.03275, over 978879.07 frames.], batch size: 52, aishell_tot_loss[loss=0.1499, simple_loss=0.2359, pruned_loss=0.0319, over 907161.84 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2195, pruned_loss=0.03321, over 902946.40 frames.], batch size: 52, lr: 3.92e-04
+2022-06-19 01:15:49,480 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 01:16:06,685 INFO [train.py:914] (1/4) Epoch 21, validation: loss=0.1651, simple_loss=0.2487, pruned_loss=0.04071, over 1622729.00 frames.
+2022-06-19 01:16:39,086 INFO [train.py:874] (1/4) Epoch 21, batch 1050, datatang_loss[loss=0.1379, simple_loss=0.2094, pruned_loss=0.03317, over 4938.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2285, pruned_loss=0.03317, over 980420.63 frames.], batch size: 62, aishell_tot_loss[loss=0.15, simple_loss=0.2359, pruned_loss=0.03201, over 917816.57 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2199, pruned_loss=0.03366, over 911224.31 frames.], batch size: 62, lr: 3.92e-04
+2022-06-19 01:17:11,995 INFO [train.py:874] (1/4) Epoch 21, batch 1100, datatang_loss[loss=0.146, simple_loss=0.2205, pruned_loss=0.03573, over 4914.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2292, pruned_loss=0.03325, over 981462.65 frames.], batch size: 42, aishell_tot_loss[loss=0.1497, simple_loss=0.2358, pruned_loss=0.03183, over 927477.64 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2205, pruned_loss=0.03409, over 917978.55 frames.], batch size: 42, lr: 3.92e-04
+2022-06-19 01:17:43,375 INFO [train.py:874] (1/4) Epoch 21, batch 1150, datatang_loss[loss=0.1406, simple_loss=0.2151, pruned_loss=0.03302, over 4933.00 frames.], tot_loss[loss=0.1483, simple_loss=0.2301, pruned_loss=0.03329, over 982164.32 frames.], batch size: 50, aishell_tot_loss[loss=0.1496, simple_loss=0.2358, pruned_loss=0.03171, over 934330.79 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2216, pruned_loss=0.03435, over 925682.73 frames.], batch size: 50, lr: 3.92e-04
+2022-06-19 01:18:16,586 INFO [train.py:874] (1/4) Epoch 21, batch 1200, datatang_loss[loss=0.1573, simple_loss=0.2242, pruned_loss=0.04518, over 4911.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2312, pruned_loss=0.03354, over 983104.06 frames.], batch size: 47, aishell_tot_loss[loss=0.15, simple_loss=0.2364, pruned_loss=0.03182, over 941091.58 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2225, pruned_loss=0.03465, over 932088.85 frames.], batch size: 47, lr: 3.91e-04
+2022-06-19 01:18:47,830 INFO [train.py:874] (1/4) Epoch 21, batch 1250, aishell_loss[loss=0.1469, simple_loss=0.2365, pruned_loss=0.02864, over 4869.00 frames.], tot_loss[loss=0.1487, simple_loss=0.2307, pruned_loss=0.03332, over 983579.34 frames.], batch size: 37, aishell_tot_loss[loss=0.1498, simple_loss=0.2362, pruned_loss=0.03172, over 947288.34 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2222, pruned_loss=0.03467, over 937091.14 frames.], batch size: 37, lr: 3.91e-04
+2022-06-19 01:19:18,421 INFO [train.py:874] (1/4) Epoch 21, batch 1300, aishell_loss[loss=0.1806, simple_loss=0.271, pruned_loss=0.04513, over 4907.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2308, pruned_loss=0.03367, over 983782.22 frames.], batch size: 78, aishell_tot_loss[loss=0.1504, simple_loss=0.2367, pruned_loss=0.032, over 951785.43 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.222, pruned_loss=0.03484, over 942464.25 frames.], batch size: 78, lr: 3.91e-04
+2022-06-19 01:19:51,537 INFO [train.py:874] (1/4) Epoch 21, batch 1350, aishell_loss[loss=0.1644, simple_loss=0.2547, pruned_loss=0.03708, over 4881.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2308, pruned_loss=0.03413, over 984015.35 frames.], batch size: 42, aishell_tot_loss[loss=0.1507, simple_loss=0.2369, pruned_loss=0.03224, over 955315.31 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2221, pruned_loss=0.03512, over 947852.29 frames.], batch size: 42, lr: 3.91e-04
+2022-06-19 01:20:23,985 INFO [train.py:874] (1/4) Epoch 21, batch 1400, datatang_loss[loss=0.1721, simple_loss=0.2456, pruned_loss=0.04934, over 4953.00 frames.], tot_loss[loss=0.1503, simple_loss=0.2311, pruned_loss=0.03477, over 984602.07 frames.], batch size: 91, aishell_tot_loss[loss=0.1507, simple_loss=0.2368, pruned_loss=0.03231, over 959019.29 frames.], datatang_tot_loss[loss=0.1474, simple_loss=0.223, pruned_loss=0.03586, over 952446.18 frames.], batch size: 91, lr: 3.91e-04
+2022-06-19 01:20:54,656 INFO [train.py:874] (1/4) Epoch 21, batch 1450, datatang_loss[loss=0.1423, simple_loss=0.2145, pruned_loss=0.03507, over 4948.00 frames.], tot_loss[loss=0.1495, simple_loss=0.2304, pruned_loss=0.03431, over 984893.15 frames.], batch size: 62, aishell_tot_loss[loss=0.1507, simple_loss=0.2368, pruned_loss=0.03227, over 962017.51 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2225, pruned_loss=0.03555, over 956568.70 frames.], batch size: 62, lr: 3.91e-04
+2022-06-19 01:21:28,253 INFO [train.py:874] (1/4) Epoch 21, batch 1500, datatang_loss[loss=0.1498, simple_loss=0.2246, pruned_loss=0.03754, over 4914.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2298, pruned_loss=0.03394, over 985109.30 frames.], batch size: 75, aishell_tot_loss[loss=0.1505, simple_loss=0.2367, pruned_loss=0.03211, over 964399.72 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2223, pruned_loss=0.03533, over 960505.17 frames.], batch size: 75, lr: 3.91e-04
+2022-06-19 01:21:57,783 INFO [train.py:874] (1/4) Epoch 21, batch 1550, aishell_loss[loss=0.1452, simple_loss=0.2338, pruned_loss=0.02834, over 4971.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2299, pruned_loss=0.03398, over 984740.70 frames.], batch size: 39, aishell_tot_loss[loss=0.1511, simple_loss=0.2371, pruned_loss=0.03251, over 966542.94 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2219, pruned_loss=0.03505, over 963279.06 frames.], batch size: 39, lr: 3.91e-04
+2022-06-19 01:22:31,039 INFO [train.py:874] (1/4) Epoch 21, batch 1600, datatang_loss[loss=0.1254, simple_loss=0.2082, pruned_loss=0.02129, over 4932.00 frames.], tot_loss[loss=0.1494, simple_loss=0.2306, pruned_loss=0.03414, over 984669.29 frames.], batch size: 79, aishell_tot_loss[loss=0.1511, simple_loss=0.237, pruned_loss=0.03267, over 968731.11 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.2226, pruned_loss=0.03515, over 965611.96 frames.], batch size: 79, lr: 3.91e-04
+2022-06-19 01:23:02,999 INFO [train.py:874] (1/4) Epoch 21, batch 1650, datatang_loss[loss=0.1431, simple_loss=0.2224, pruned_loss=0.0319, over 4960.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2298, pruned_loss=0.03361, over 984946.05 frames.], batch size: 86, aishell_tot_loss[loss=0.1511, simple_loss=0.2371, pruned_loss=0.03253, over 970412.49 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2219, pruned_loss=0.03477, over 968353.79 frames.], batch size: 86, lr: 3.90e-04
+2022-06-19 01:23:32,745 INFO [train.py:874] (1/4) Epoch 21, batch 1700, aishell_loss[loss=0.1622, simple_loss=0.2487, pruned_loss=0.03785, over 4865.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2298, pruned_loss=0.03357, over 985102.53 frames.], batch size: 37, aishell_tot_loss[loss=0.1506, simple_loss=0.2366, pruned_loss=0.03232, over 971962.16 frames.], datatang_tot_loss[loss=0.1462, simple_loss=0.2226, pruned_loss=0.03489, over 970676.48 frames.], batch size: 37, lr: 3.90e-04
+2022-06-19 01:24:06,675 INFO [train.py:874] (1/4) Epoch 21, batch 1750, aishell_loss[loss=0.1361, simple_loss=0.2198, pruned_loss=0.02624, over 4885.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2291, pruned_loss=0.03329, over 985472.10 frames.], batch size: 28, aishell_tot_loss[loss=0.1506, simple_loss=0.2367, pruned_loss=0.03228, over 973304.84 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2222, pruned_loss=0.03457, over 973002.71 frames.], batch size: 28, lr: 3.90e-04
+2022-06-19 01:24:40,280 INFO [train.py:874] (1/4) Epoch 21, batch 1800, datatang_loss[loss=0.1464, simple_loss=0.2192, pruned_loss=0.03685, over 4921.00 frames.], tot_loss[loss=0.1489, simple_loss=0.2303, pruned_loss=0.03374, over 985581.42 frames.], batch size: 57, aishell_tot_loss[loss=0.1512, simple_loss=0.2373, pruned_loss=0.0326, over 974877.90 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2227, pruned_loss=0.03474, over 974433.79 frames.], batch size: 57, lr: 3.90e-04
+2022-06-19 01:25:09,428 INFO [train.py:874] (1/4) Epoch 21, batch 1850, datatang_loss[loss=0.1483, simple_loss=0.231, pruned_loss=0.03281, over 4980.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2307, pruned_loss=0.03383, over 985811.01 frames.], batch size: 45, aishell_tot_loss[loss=0.1519, simple_loss=0.238, pruned_loss=0.03297, over 976333.18 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2223, pruned_loss=0.03452, over 975791.44 frames.], batch size: 45, lr: 3.90e-04
+2022-06-19 01:25:41,904 INFO [train.py:874] (1/4) Epoch 21, batch 1900, aishell_loss[loss=0.1634, simple_loss=0.2478, pruned_loss=0.03953, over 4873.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2314, pruned_loss=0.03436, over 985867.50 frames.], batch size: 42, aishell_tot_loss[loss=0.1529, simple_loss=0.2386, pruned_loss=0.03357, over 977492.99 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2222, pruned_loss=0.03452, over 976976.23 frames.], batch size: 42, lr: 3.90e-04
+2022-06-19 01:26:13,749 INFO [train.py:874] (1/4) Epoch 21, batch 1950, datatang_loss[loss=0.1179, simple_loss=0.2045, pruned_loss=0.01565, over 4934.00 frames.], tot_loss[loss=0.1501, simple_loss=0.2314, pruned_loss=0.03444, over 985838.72 frames.], batch size: 79, aishell_tot_loss[loss=0.1525, simple_loss=0.2381, pruned_loss=0.03339, over 978318.03 frames.], datatang_tot_loss[loss=0.1464, simple_loss=0.223, pruned_loss=0.03486, over 978149.05 frames.], batch size: 79, lr: 3.90e-04
+2022-06-19 01:26:45,043 INFO [train.py:874] (1/4) Epoch 21, batch 2000, aishell_loss[loss=0.1533, simple_loss=0.2432, pruned_loss=0.03175, over 4959.00 frames.], tot_loss[loss=0.1497, simple_loss=0.2314, pruned_loss=0.03402, over 986102.25 frames.], batch size: 40, aishell_tot_loss[loss=0.1515, simple_loss=0.2375, pruned_loss=0.03277, over 979363.13 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2236, pruned_loss=0.03516, over 979169.65 frames.], batch size: 40, lr: 3.90e-04
+2022-06-19 01:26:45,044 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 01:27:02,716 INFO [train.py:914] (1/4) Epoch 21, validation: loss=0.164, simple_loss=0.2481, pruned_loss=0.03994, over 1622729.00 frames.
+2022-06-19 01:27:32,172 INFO [train.py:874] (1/4) Epoch 21, batch 2050, aishell_loss[loss=0.1362, simple_loss=0.2283, pruned_loss=0.02208, over 4962.00 frames.], tot_loss[loss=0.1496, simple_loss=0.2311, pruned_loss=0.03404, over 985787.88 frames.], batch size: 64, aishell_tot_loss[loss=0.1513, simple_loss=0.2375, pruned_loss=0.03257, over 980035.11 frames.], datatang_tot_loss[loss=0.1472, simple_loss=0.2237, pruned_loss=0.03534, over 979767.63 frames.], batch size: 64, lr: 3.90e-04
+2022-06-19 01:28:06,088 INFO [train.py:874] (1/4) Epoch 21, batch 2100, aishell_loss[loss=0.1133, simple_loss=0.1947, pruned_loss=0.0159, over 4973.00 frames.], tot_loss[loss=0.1493, simple_loss=0.2305, pruned_loss=0.03407, over 985577.23 frames.], batch size: 27, aishell_tot_loss[loss=0.1507, simple_loss=0.2369, pruned_loss=0.03226, over 980591.85 frames.], datatang_tot_loss[loss=0.1477, simple_loss=0.224, pruned_loss=0.0357, over 980364.82 frames.], batch size: 27, lr: 3.89e-04
+2022-06-19 01:28:39,722 INFO [train.py:874] (1/4) Epoch 21, batch 2150, aishell_loss[loss=0.1405, simple_loss=0.2267, pruned_loss=0.02718, over 4946.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2303, pruned_loss=0.03408, over 985646.54 frames.], batch size: 45, aishell_tot_loss[loss=0.1503, simple_loss=0.2364, pruned_loss=0.03205, over 981191.69 frames.], datatang_tot_loss[loss=0.1481, simple_loss=0.2243, pruned_loss=0.03591, over 981051.99 frames.], batch size: 45, lr: 3.89e-04
+2022-06-19 01:29:09,141 INFO [train.py:874] (1/4) Epoch 21, batch 2200, aishell_loss[loss=0.1739, simple_loss=0.2615, pruned_loss=0.04317, over 4919.00 frames.], tot_loss[loss=0.1492, simple_loss=0.2306, pruned_loss=0.03391, over 985733.39 frames.], batch size: 68, aishell_tot_loss[loss=0.1508, simple_loss=0.2372, pruned_loss=0.03219, over 981577.33 frames.], datatang_tot_loss[loss=0.1475, simple_loss=0.224, pruned_loss=0.03557, over 981818.92 frames.], batch size: 68, lr: 3.89e-04
+2022-06-19 01:29:41,095 INFO [train.py:874] (1/4) Epoch 21, batch 2250, datatang_loss[loss=0.1213, simple_loss=0.1972, pruned_loss=0.0227, over 4911.00 frames.], tot_loss[loss=0.149, simple_loss=0.2304, pruned_loss=0.03378, over 985765.47 frames.], batch size: 57, aishell_tot_loss[loss=0.151, simple_loss=0.2374, pruned_loss=0.03227, over 982012.26 frames.], datatang_tot_loss[loss=0.1471, simple_loss=0.2234, pruned_loss=0.0354, over 982363.46 frames.], batch size: 57, lr: 3.89e-04
+2022-06-19 01:30:15,115 INFO [train.py:874] (1/4) Epoch 21, batch 2300, aishell_loss[loss=0.1538, simple_loss=0.2493, pruned_loss=0.02916, over 4951.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2299, pruned_loss=0.03349, over 985914.90 frames.], batch size: 64, aishell_tot_loss[loss=0.151, simple_loss=0.2375, pruned_loss=0.03225, over 982605.01 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.223, pruned_loss=0.03508, over 982751.74 frames.], batch size: 64, lr: 3.89e-04
+2022-06-19 01:30:45,557 INFO [train.py:874] (1/4) Epoch 21, batch 2350, aishell_loss[loss=0.1484, simple_loss=0.2288, pruned_loss=0.03404, over 4984.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2291, pruned_loss=0.03353, over 985657.77 frames.], batch size: 37, aishell_tot_loss[loss=0.1506, simple_loss=0.2369, pruned_loss=0.03218, over 982719.78 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2227, pruned_loss=0.03516, over 983123.53 frames.], batch size: 37, lr: 3.89e-04
+2022-06-19 01:31:19,320 INFO [train.py:874] (1/4) Epoch 21, batch 2400, datatang_loss[loss=0.1804, simple_loss=0.2567, pruned_loss=0.05208, over 4931.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2287, pruned_loss=0.03316, over 985436.00 frames.], batch size: 94, aishell_tot_loss[loss=0.1499, simple_loss=0.2361, pruned_loss=0.03187, over 983082.12 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2228, pruned_loss=0.03507, over 983159.46 frames.], batch size: 94, lr: 3.89e-04
+2022-06-19 01:31:50,754 INFO [train.py:874] (1/4) Epoch 21, batch 2450, datatang_loss[loss=0.1815, simple_loss=0.2586, pruned_loss=0.05221, over 4921.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2292, pruned_loss=0.0332, over 985513.64 frames.], batch size: 108, aishell_tot_loss[loss=0.1496, simple_loss=0.2359, pruned_loss=0.03164, over 983258.07 frames.], datatang_tot_loss[loss=0.147, simple_loss=0.2235, pruned_loss=0.03525, over 983622.11 frames.], batch size: 108, lr: 3.89e-04
+2022-06-19 01:32:21,753 INFO [train.py:874] (1/4) Epoch 21, batch 2500, aishell_loss[loss=0.1752, simple_loss=0.2507, pruned_loss=0.04987, over 4939.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2297, pruned_loss=0.03336, over 985485.34 frames.], batch size: 32, aishell_tot_loss[loss=0.15, simple_loss=0.2361, pruned_loss=0.03198, over 983357.22 frames.], datatang_tot_loss[loss=0.1468, simple_loss=0.2233, pruned_loss=0.0351, over 983995.66 frames.], batch size: 32, lr: 3.89e-04
+2022-06-19 01:32:56,166 INFO [train.py:874] (1/4) Epoch 21, batch 2550, aishell_loss[loss=0.1065, simple_loss=0.1696, pruned_loss=0.02171, over 4830.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2292, pruned_loss=0.03334, over 985731.93 frames.], batch size: 21, aishell_tot_loss[loss=0.1499, simple_loss=0.236, pruned_loss=0.03195, over 983706.63 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2231, pruned_loss=0.035, over 984327.75 frames.], batch size: 21, lr: 3.88e-04
+2022-06-19 01:33:29,090 INFO [train.py:874] (1/4) Epoch 21, batch 2600, datatang_loss[loss=0.1601, simple_loss=0.2336, pruned_loss=0.04327, over 4858.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2283, pruned_loss=0.03276, over 985505.06 frames.], batch size: 30, aishell_tot_loss[loss=0.1497, simple_loss=0.2356, pruned_loss=0.03189, over 983710.74 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2224, pruned_loss=0.03441, over 984506.53 frames.], batch size: 30, lr: 3.88e-04
+2022-06-19 01:33:59,965 INFO [train.py:874] (1/4) Epoch 21, batch 2650, aishell_loss[loss=0.1306, simple_loss=0.2116, pruned_loss=0.02478, over 4869.00 frames.], tot_loss[loss=0.1464, simple_loss=0.228, pruned_loss=0.03241, over 985219.29 frames.], batch size: 28, aishell_tot_loss[loss=0.1493, simple_loss=0.2353, pruned_loss=0.03167, over 983592.49 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2223, pruned_loss=0.03415, over 984669.43 frames.], batch size: 28, lr: 3.88e-04
+2022-06-19 01:34:33,245 INFO [train.py:874] (1/4) Epoch 21, batch 2700, aishell_loss[loss=0.1293, simple_loss=0.2149, pruned_loss=0.02186, over 4890.00 frames.], tot_loss[loss=0.147, simple_loss=0.228, pruned_loss=0.03298, over 985501.37 frames.], batch size: 28, aishell_tot_loss[loss=0.1493, simple_loss=0.235, pruned_loss=0.03174, over 983861.12 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2226, pruned_loss=0.03451, over 984936.77 frames.], batch size: 28, lr: 3.88e-04
+2022-06-19 01:35:05,888 INFO [train.py:874] (1/4) Epoch 21, batch 2750, aishell_loss[loss=0.1564, simple_loss=0.2354, pruned_loss=0.03867, over 4968.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2276, pruned_loss=0.03254, over 985582.37 frames.], batch size: 31, aishell_tot_loss[loss=0.1491, simple_loss=0.235, pruned_loss=0.03159, over 983754.56 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2223, pruned_loss=0.03412, over 985374.76 frames.], batch size: 31, lr: 3.88e-04
+2022-06-19 01:35:36,798 INFO [train.py:874] (1/4) Epoch 21, batch 2800, aishell_loss[loss=0.1879, simple_loss=0.2672, pruned_loss=0.05428, over 4900.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2279, pruned_loss=0.03243, over 985691.50 frames.], batch size: 34, aishell_tot_loss[loss=0.1492, simple_loss=0.2352, pruned_loss=0.03163, over 984062.71 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2221, pruned_loss=0.0339, over 985439.25 frames.], batch size: 34, lr: 3.88e-04
+2022-06-19 01:36:09,375 INFO [train.py:874] (1/4) Epoch 21, batch 2850, datatang_loss[loss=0.1457, simple_loss=0.2231, pruned_loss=0.03416, over 4975.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2283, pruned_loss=0.03277, over 985651.23 frames.], batch size: 53, aishell_tot_loss[loss=0.1494, simple_loss=0.2352, pruned_loss=0.03181, over 984019.23 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.222, pruned_loss=0.03404, over 985705.40 frames.], batch size: 53, lr: 3.88e-04
+2022-06-19 01:36:39,715 INFO [train.py:874] (1/4) Epoch 21, batch 2900, datatang_loss[loss=0.1286, simple_loss=0.2122, pruned_loss=0.0225, over 4913.00 frames.], tot_loss[loss=0.1469, simple_loss=0.228, pruned_loss=0.03294, over 985769.86 frames.], batch size: 64, aishell_tot_loss[loss=0.1496, simple_loss=0.2353, pruned_loss=0.03194, over 984060.12 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2217, pruned_loss=0.03405, over 985968.23 frames.], batch size: 64, lr: 3.88e-04
+2022-06-19 01:37:10,802 INFO [train.py:874] (1/4) Epoch 21, batch 2950, datatang_loss[loss=0.1984, simple_loss=0.269, pruned_loss=0.06392, over 4943.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2288, pruned_loss=0.03277, over 985815.14 frames.], batch size: 109, aishell_tot_loss[loss=0.1492, simple_loss=0.2353, pruned_loss=0.03157, over 984432.69 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2221, pruned_loss=0.03426, over 985865.24 frames.], batch size: 109, lr: 3.87e-04
+2022-06-19 01:37:43,719 INFO [train.py:874] (1/4) Epoch 21, batch 3000, aishell_loss[loss=0.1401, simple_loss=0.2349, pruned_loss=0.0226, over 4939.00 frames.], tot_loss[loss=0.1474, simple_loss=0.229, pruned_loss=0.03293, over 985280.42 frames.], batch size: 49, aishell_tot_loss[loss=0.1488, simple_loss=0.2348, pruned_loss=0.03141, over 984238.69 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2228, pruned_loss=0.03456, over 985658.77 frames.], batch size: 49, lr: 3.87e-04
+2022-06-19 01:37:43,720 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 01:38:00,374 INFO [train.py:914] (1/4) Epoch 21, validation: loss=0.1648, simple_loss=0.2487, pruned_loss=0.04045, over 1622729.00 frames.
+2022-06-19 01:38:34,161 INFO [train.py:874] (1/4) Epoch 21, batch 3050, aishell_loss[loss=0.1459, simple_loss=0.2299, pruned_loss=0.03094, over 4962.00 frames.], tot_loss[loss=0.1475, simple_loss=0.229, pruned_loss=0.03304, over 985400.67 frames.], batch size: 56, aishell_tot_loss[loss=0.1488, simple_loss=0.2346, pruned_loss=0.03146, over 984260.52 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2228, pruned_loss=0.03469, over 985870.31 frames.], batch size: 56, lr: 3.87e-04
+2022-06-19 01:39:07,205 INFO [train.py:874] (1/4) Epoch 21, batch 3100, aishell_loss[loss=0.1422, simple_loss=0.2367, pruned_loss=0.02385, over 4937.00 frames.], tot_loss[loss=0.147, simple_loss=0.2283, pruned_loss=0.03281, over 985309.86 frames.], batch size: 54, aishell_tot_loss[loss=0.1485, simple_loss=0.2344, pruned_loss=0.03129, over 984335.25 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2224, pruned_loss=0.0346, over 985767.75 frames.], batch size: 54, lr: 3.87e-04
+2022-06-19 01:39:37,320 INFO [train.py:874] (1/4) Epoch 21, batch 3150, aishell_loss[loss=0.1682, simple_loss=0.2508, pruned_loss=0.04283, over 4987.00 frames.], tot_loss[loss=0.1479, simple_loss=0.229, pruned_loss=0.03343, over 985321.01 frames.], batch size: 39, aishell_tot_loss[loss=0.1493, simple_loss=0.235, pruned_loss=0.0318, over 984416.77 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2227, pruned_loss=0.03467, over 985722.49 frames.], batch size: 39, lr: 3.87e-04
+2022-06-19 01:40:10,718 INFO [train.py:874] (1/4) Epoch 21, batch 3200, aishell_loss[loss=0.1443, simple_loss=0.2299, pruned_loss=0.02938, over 4922.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2295, pruned_loss=0.03314, over 985696.02 frames.], batch size: 41, aishell_tot_loss[loss=0.1494, simple_loss=0.2354, pruned_loss=0.03169, over 984723.51 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2224, pruned_loss=0.03466, over 985920.54 frames.], batch size: 41, lr: 3.87e-04
+2022-06-19 01:40:43,359 INFO [train.py:874] (1/4) Epoch 21, batch 3250, datatang_loss[loss=0.1625, simple_loss=0.241, pruned_loss=0.04197, over 4961.00 frames.], tot_loss[loss=0.1485, simple_loss=0.2298, pruned_loss=0.03363, over 985468.73 frames.], batch size: 91, aishell_tot_loss[loss=0.1492, simple_loss=0.2354, pruned_loss=0.03154, over 984750.11 frames.], datatang_tot_loss[loss=0.1469, simple_loss=0.2233, pruned_loss=0.03521, over 985700.26 frames.], batch size: 91, lr: 3.87e-04
+2022-06-19 01:41:14,431 INFO [train.py:874] (1/4) Epoch 21, batch 3300, datatang_loss[loss=0.1331, simple_loss=0.2145, pruned_loss=0.02585, over 4933.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2294, pruned_loss=0.03352, over 985697.88 frames.], batch size: 79, aishell_tot_loss[loss=0.1493, simple_loss=0.2355, pruned_loss=0.03153, over 984941.41 frames.], datatang_tot_loss[loss=0.1466, simple_loss=0.2228, pruned_loss=0.03522, over 985813.72 frames.], batch size: 79, lr: 3.87e-04
+2022-06-19 01:41:47,797 INFO [train.py:874] (1/4) Epoch 21, batch 3350, datatang_loss[loss=0.1401, simple_loss=0.2154, pruned_loss=0.03242, over 4927.00 frames.], tot_loss[loss=0.1484, simple_loss=0.2298, pruned_loss=0.03349, over 985326.85 frames.], batch size: 42, aishell_tot_loss[loss=0.1498, simple_loss=0.236, pruned_loss=0.03181, over 984828.69 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2225, pruned_loss=0.03502, over 985636.12 frames.], batch size: 42, lr: 3.87e-04
+2022-06-19 01:42:19,508 INFO [train.py:874] (1/4) Epoch 21, batch 3400, datatang_loss[loss=0.1315, simple_loss=0.2143, pruned_loss=0.02438, over 4936.00 frames.], tot_loss[loss=0.1482, simple_loss=0.2298, pruned_loss=0.03325, over 985474.46 frames.], batch size: 71, aishell_tot_loss[loss=0.1494, simple_loss=0.2358, pruned_loss=0.03146, over 985088.39 frames.], datatang_tot_loss[loss=0.1465, simple_loss=0.2226, pruned_loss=0.03525, over 985566.95 frames.], batch size: 71, lr: 3.86e-04
+2022-06-19 01:42:51,684 INFO [train.py:874] (1/4) Epoch 21, batch 3450, datatang_loss[loss=0.1075, simple_loss=0.1845, pruned_loss=0.01522, over 4915.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2289, pruned_loss=0.03295, over 985680.76 frames.], batch size: 75, aishell_tot_loss[loss=0.1496, simple_loss=0.2359, pruned_loss=0.03164, over 985159.44 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2218, pruned_loss=0.03467, over 985744.04 frames.], batch size: 75, lr: 3.86e-04
+2022-06-19 01:43:25,914 INFO [train.py:874] (1/4) Epoch 21, batch 3500, datatang_loss[loss=0.15, simple_loss=0.2246, pruned_loss=0.03776, over 4897.00 frames.], tot_loss[loss=0.1475, simple_loss=0.2287, pruned_loss=0.03311, over 985643.48 frames.], batch size: 39, aishell_tot_loss[loss=0.1495, simple_loss=0.2355, pruned_loss=0.03176, over 985081.85 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2221, pruned_loss=0.03463, over 985837.77 frames.], batch size: 39, lr: 3.86e-04
+2022-06-19 01:43:56,502 INFO [train.py:874] (1/4) Epoch 21, batch 3550, datatang_loss[loss=0.1618, simple_loss=0.2344, pruned_loss=0.04464, over 4981.00 frames.], tot_loss[loss=0.147, simple_loss=0.228, pruned_loss=0.03294, over 985670.70 frames.], batch size: 31, aishell_tot_loss[loss=0.1489, simple_loss=0.2351, pruned_loss=0.03137, over 985023.76 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2219, pruned_loss=0.0348, over 985963.91 frames.], batch size: 31, lr: 3.86e-04
+2022-06-19 01:44:29,763 INFO [train.py:874] (1/4) Epoch 21, batch 3600, aishell_loss[loss=0.1672, simple_loss=0.251, pruned_loss=0.04172, over 4915.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2286, pruned_loss=0.03336, over 985888.24 frames.], batch size: 46, aishell_tot_loss[loss=0.149, simple_loss=0.2352, pruned_loss=0.03146, over 985273.43 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2224, pruned_loss=0.03508, over 985972.23 frames.], batch size: 46, lr: 3.86e-04
+2022-06-19 01:45:03,483 INFO [train.py:874] (1/4) Epoch 21, batch 3650, datatang_loss[loss=0.1407, simple_loss=0.2218, pruned_loss=0.02985, over 4913.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2279, pruned_loss=0.03282, over 985863.97 frames.], batch size: 57, aishell_tot_loss[loss=0.1486, simple_loss=0.2349, pruned_loss=0.03116, over 985301.98 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.222, pruned_loss=0.03481, over 985998.56 frames.], batch size: 57, lr: 3.86e-04
+2022-06-19 01:45:34,390 INFO [train.py:874] (1/4) Epoch 21, batch 3700, datatang_loss[loss=0.1364, simple_loss=0.2184, pruned_loss=0.02714, over 4938.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2278, pruned_loss=0.03287, over 985664.63 frames.], batch size: 69, aishell_tot_loss[loss=0.1486, simple_loss=0.2348, pruned_loss=0.03122, over 984956.22 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.222, pruned_loss=0.03474, over 986180.19 frames.], batch size: 69, lr: 3.86e-04
+2022-06-19 01:46:07,231 INFO [train.py:874] (1/4) Epoch 21, batch 3750, aishell_loss[loss=0.1663, simple_loss=0.2569, pruned_loss=0.03786, over 4909.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2276, pruned_loss=0.03294, over 985390.74 frames.], batch size: 68, aishell_tot_loss[loss=0.1482, simple_loss=0.2343, pruned_loss=0.03102, over 984491.13 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2221, pruned_loss=0.03498, over 986376.08 frames.], batch size: 68, lr: 3.86e-04
+2022-06-19 01:46:36,116 INFO [train.py:874] (1/4) Epoch 21, batch 3800, aishell_loss[loss=0.1678, simple_loss=0.2554, pruned_loss=0.04009, over 4919.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2286, pruned_loss=0.03261, over 985291.03 frames.], batch size: 46, aishell_tot_loss[loss=0.1485, simple_loss=0.2348, pruned_loss=0.0311, over 984487.75 frames.], datatang_tot_loss[loss=0.1457, simple_loss=0.2221, pruned_loss=0.03471, over 986348.95 frames.], batch size: 46, lr: 3.86e-04
+2022-06-19 01:47:09,280 INFO [train.py:874] (1/4) Epoch 21, batch 3850, datatang_loss[loss=0.1884, simple_loss=0.257, pruned_loss=0.05987, over 4881.00 frames.], tot_loss[loss=0.148, simple_loss=0.2293, pruned_loss=0.03333, over 985303.03 frames.], batch size: 39, aishell_tot_loss[loss=0.1496, simple_loss=0.2355, pruned_loss=0.03181, over 984539.94 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2223, pruned_loss=0.03464, over 986245.10 frames.], batch size: 39, lr: 3.85e-04
+2022-06-19 01:47:38,019 INFO [train.py:874] (1/4) Epoch 21, batch 3900, datatang_loss[loss=0.1253, simple_loss=0.1999, pruned_loss=0.02537, over 4921.00 frames.], tot_loss[loss=0.147, simple_loss=0.2287, pruned_loss=0.03261, over 984974.58 frames.], batch size: 71, aishell_tot_loss[loss=0.1488, simple_loss=0.2348, pruned_loss=0.03135, over 984334.24 frames.], datatang_tot_loss[loss=0.1455, simple_loss=0.2221, pruned_loss=0.03447, over 986132.90 frames.], batch size: 71, lr: 3.85e-04
+2022-06-19 01:48:11,143 INFO [train.py:874] (1/4) Epoch 21, batch 3950, datatang_loss[loss=0.1391, simple_loss=0.2198, pruned_loss=0.02922, over 4923.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2284, pruned_loss=0.03288, over 984558.87 frames.], batch size: 81, aishell_tot_loss[loss=0.1491, simple_loss=0.2351, pruned_loss=0.0316, over 984254.31 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2218, pruned_loss=0.03442, over 985712.22 frames.], batch size: 81, lr: 3.85e-04
+2022-06-19 01:48:40,027 INFO [train.py:874] (1/4) Epoch 21, batch 4000, aishell_loss[loss=0.1306, simple_loss=0.2126, pruned_loss=0.02431, over 4861.00 frames.], tot_loss[loss=0.1471, simple_loss=0.2285, pruned_loss=0.03282, over 984355.49 frames.], batch size: 28, aishell_tot_loss[loss=0.149, simple_loss=0.235, pruned_loss=0.03148, over 983773.50 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2219, pruned_loss=0.03448, over 985898.00 frames.], batch size: 28, lr: 3.85e-04
+2022-06-19 01:48:40,028 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 01:48:57,402 INFO [train.py:914] (1/4) Epoch 21, validation: loss=0.1643, simple_loss=0.2479, pruned_loss=0.04039, over 1622729.00 frames.
+2022-06-19 01:49:25,766 INFO [train.py:874] (1/4) Epoch 21, batch 4050, datatang_loss[loss=0.1273, simple_loss=0.2143, pruned_loss=0.02017, over 4939.00 frames.], tot_loss[loss=0.1473, simple_loss=0.229, pruned_loss=0.03281, over 984539.79 frames.], batch size: 62, aishell_tot_loss[loss=0.1493, simple_loss=0.2355, pruned_loss=0.03154, over 983679.00 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2217, pruned_loss=0.03439, over 986108.71 frames.], batch size: 62, lr: 3.85e-04
+2022-06-19 01:50:39,844 INFO [train.py:874] (1/4) Epoch 22, batch 50, datatang_loss[loss=0.1388, simple_loss=0.2066, pruned_loss=0.03544, over 4895.00 frames.], tot_loss[loss=0.143, simple_loss=0.2253, pruned_loss=0.03032, over 218728.70 frames.], batch size: 52, aishell_tot_loss[loss=0.1472, simple_loss=0.2349, pruned_loss=0.02976, over 120671.22 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2147, pruned_loss=0.03079, over 111725.80 frames.], batch size: 52, lr: 3.76e-04
+2022-06-19 01:51:13,533 INFO [train.py:874] (1/4) Epoch 22, batch 100, datatang_loss[loss=0.1387, simple_loss=0.2162, pruned_loss=0.03062, over 4958.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2265, pruned_loss=0.03114, over 389170.27 frames.], batch size: 86, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.03083, over 226549.74 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.218, pruned_loss=0.03126, over 211036.48 frames.], batch size: 86, lr: 3.76e-04
+2022-06-19 01:51:45,195 INFO [train.py:874] (1/4) Epoch 22, batch 150, aishell_loss[loss=0.1348, simple_loss=0.2088, pruned_loss=0.03037, over 4983.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2278, pruned_loss=0.03201, over 521586.57 frames.], batch size: 27, aishell_tot_loss[loss=0.1485, simple_loss=0.2349, pruned_loss=0.03105, over 312611.34 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2202, pruned_loss=0.03267, over 305865.41 frames.], batch size: 27, lr: 3.76e-04
+2022-06-19 01:52:17,278 INFO [train.py:874] (1/4) Epoch 22, batch 200, aishell_loss[loss=0.1438, simple_loss=0.2246, pruned_loss=0.03149, over 4926.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2283, pruned_loss=0.03198, over 624713.20 frames.], batch size: 32, aishell_tot_loss[loss=0.1504, simple_loss=0.2367, pruned_loss=0.03199, over 391970.97 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2191, pruned_loss=0.03172, over 386110.99 frames.], batch size: 32, lr: 3.76e-04
+2022-06-19 01:52:50,993 INFO [train.py:874] (1/4) Epoch 22, batch 250, aishell_loss[loss=0.152, simple_loss=0.2544, pruned_loss=0.02484, over 4921.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2281, pruned_loss=0.03146, over 704477.30 frames.], batch size: 78, aishell_tot_loss[loss=0.1498, simple_loss=0.2364, pruned_loss=0.03155, over 477081.01 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2182, pruned_loss=0.03134, over 440642.51 frames.], batch size: 78, lr: 3.76e-04
+2022-06-19 01:53:21,202 INFO [train.py:874] (1/4) Epoch 22, batch 300, datatang_loss[loss=0.134, simple_loss=0.2153, pruned_loss=0.02636, over 4897.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2281, pruned_loss=0.03138, over 767035.28 frames.], batch size: 52, aishell_tot_loss[loss=0.1504, simple_loss=0.2368, pruned_loss=0.03196, over 541538.85 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2177, pruned_loss=0.03079, over 500150.96 frames.], batch size: 52, lr: 3.76e-04
+2022-06-19 01:53:54,111 INFO [train.py:874] (1/4) Epoch 22, batch 350, datatang_loss[loss=0.1563, simple_loss=0.2307, pruned_loss=0.04093, over 4906.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2277, pruned_loss=0.03144, over 815319.05 frames.], batch size: 64, aishell_tot_loss[loss=0.1503, simple_loss=0.2366, pruned_loss=0.03198, over 593736.04 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2177, pruned_loss=0.03085, over 557224.08 frames.], batch size: 64, lr: 3.76e-04
+2022-06-19 01:54:25,641 INFO [train.py:874] (1/4) Epoch 22, batch 400, aishell_loss[loss=0.1762, simple_loss=0.2578, pruned_loss=0.04731, over 4912.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2292, pruned_loss=0.032, over 853324.57 frames.], batch size: 33, aishell_tot_loss[loss=0.1506, simple_loss=0.2372, pruned_loss=0.03195, over 636512.82 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2198, pruned_loss=0.03176, over 611607.62 frames.], batch size: 33, lr: 3.76e-04
+2022-06-19 01:54:58,587 INFO [train.py:874] (1/4) Epoch 22, batch 450, datatang_loss[loss=0.206, simple_loss=0.2613, pruned_loss=0.0754, over 4925.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2288, pruned_loss=0.03204, over 882537.35 frames.], batch size: 71, aishell_tot_loss[loss=0.1504, simple_loss=0.237, pruned_loss=0.03189, over 669657.57 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2202, pruned_loss=0.03196, over 663845.02 frames.], batch size: 71, lr: 3.76e-04
+2022-06-19 01:55:29,419 INFO [train.py:874] (1/4) Epoch 22, batch 500, datatang_loss[loss=0.1461, simple_loss=0.2216, pruned_loss=0.03536, over 4920.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2269, pruned_loss=0.03159, over 904957.19 frames.], batch size: 42, aishell_tot_loss[loss=0.1491, simple_loss=0.2357, pruned_loss=0.03118, over 706554.61 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.219, pruned_loss=0.03207, over 701604.51 frames.], batch size: 42, lr: 3.75e-04
+2022-06-19 01:56:02,060 INFO [train.py:874] (1/4) Epoch 22, batch 550, datatang_loss[loss=0.1681, simple_loss=0.2426, pruned_loss=0.04678, over 4947.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2274, pruned_loss=0.03203, over 922966.59 frames.], batch size: 62, aishell_tot_loss[loss=0.1493, simple_loss=0.2355, pruned_loss=0.03155, over 736925.72 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.22, pruned_loss=0.03227, over 737706.60 frames.], batch size: 62, lr: 3.75e-04
+2022-06-19 01:56:34,618 INFO [train.py:874] (1/4) Epoch 22, batch 600, aishell_loss[loss=0.1408, simple_loss=0.2249, pruned_loss=0.02834, over 4936.00 frames.], tot_loss[loss=0.145, simple_loss=0.2266, pruned_loss=0.03168, over 936898.88 frames.], batch size: 58, aishell_tot_loss[loss=0.1487, simple_loss=0.2349, pruned_loss=0.03124, over 763996.01 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2197, pruned_loss=0.03216, over 769148.49 frames.], batch size: 58, lr: 3.75e-04
+2022-06-19 01:57:06,351 INFO [train.py:874] (1/4) Epoch 22, batch 650, aishell_loss[loss=0.1368, simple_loss=0.2285, pruned_loss=0.02253, over 4914.00 frames.], tot_loss[loss=0.1456, simple_loss=0.227, pruned_loss=0.0321, over 947551.93 frames.], batch size: 52, aishell_tot_loss[loss=0.1487, simple_loss=0.235, pruned_loss=0.0312, over 787811.77 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.22, pruned_loss=0.03269, over 796690.98 frames.], batch size: 52, lr: 3.75e-04
+2022-06-19 01:57:38,737 INFO [train.py:874] (1/4) Epoch 22, batch 700, aishell_loss[loss=0.155, simple_loss=0.2439, pruned_loss=0.03302, over 4959.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2269, pruned_loss=0.03205, over 956168.26 frames.], batch size: 61, aishell_tot_loss[loss=0.1488, simple_loss=0.2349, pruned_loss=0.03137, over 811052.05 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2198, pruned_loss=0.03251, over 819174.34 frames.], batch size: 61, lr: 3.75e-04
+2022-06-19 01:58:11,464 INFO [train.py:874] (1/4) Epoch 22, batch 750, datatang_loss[loss=0.1354, simple_loss=0.2274, pruned_loss=0.02169, over 4877.00 frames.], tot_loss[loss=0.145, simple_loss=0.2264, pruned_loss=0.03185, over 962773.76 frames.], batch size: 30, aishell_tot_loss[loss=0.1487, simple_loss=0.2344, pruned_loss=0.03151, over 833056.96 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2195, pruned_loss=0.03218, over 837473.95 frames.], batch size: 30, lr: 3.75e-04
+2022-06-19 01:58:43,769 INFO [train.py:874] (1/4) Epoch 22, batch 800, datatang_loss[loss=0.1605, simple_loss=0.2347, pruned_loss=0.04316, over 4887.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2271, pruned_loss=0.03253, over 967704.95 frames.], batch size: 52, aishell_tot_loss[loss=0.1486, simple_loss=0.234, pruned_loss=0.03157, over 848935.32 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2207, pruned_loss=0.03289, over 856775.66 frames.], batch size: 52, lr: 3.75e-04
+2022-06-19 01:59:14,946 INFO [train.py:874] (1/4) Epoch 22, batch 850, aishell_loss[loss=0.1447, simple_loss=0.2358, pruned_loss=0.02678, over 4936.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2272, pruned_loss=0.03182, over 971813.52 frames.], batch size: 45, aishell_tot_loss[loss=0.1485, simple_loss=0.2345, pruned_loss=0.03129, over 865869.59 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2202, pruned_loss=0.03241, over 871278.59 frames.], batch size: 45, lr: 3.75e-04
+2022-06-19 01:59:46,507 INFO [train.py:874] (1/4) Epoch 22, batch 900, aishell_loss[loss=0.1213, simple_loss=0.2077, pruned_loss=0.01739, over 4886.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2266, pruned_loss=0.03194, over 975071.52 frames.], batch size: 28, aishell_tot_loss[loss=0.148, simple_loss=0.2336, pruned_loss=0.03126, over 878888.85 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2205, pruned_loss=0.03254, over 885984.89 frames.], batch size: 28, lr: 3.75e-04
+2022-06-19 02:00:23,993 INFO [train.py:874] (1/4) Epoch 22, batch 950, aishell_loss[loss=0.1403, simple_loss=0.2314, pruned_loss=0.02461, over 4894.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2269, pruned_loss=0.03201, over 977460.03 frames.], batch size: 34, aishell_tot_loss[loss=0.1479, simple_loss=0.2336, pruned_loss=0.03114, over 891469.05 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2206, pruned_loss=0.03274, over 897763.34 frames.], batch size: 34, lr: 3.74e-04
+2022-06-19 02:00:55,122 INFO [train.py:874] (1/4) Epoch 22, batch 1000, aishell_loss[loss=0.1603, simple_loss=0.2516, pruned_loss=0.03447, over 4942.00 frames.], tot_loss[loss=0.146, simple_loss=0.2275, pruned_loss=0.03221, over 979438.36 frames.], batch size: 45, aishell_tot_loss[loss=0.1483, simple_loss=0.2341, pruned_loss=0.03127, over 903753.28 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2206, pruned_loss=0.03289, over 907150.53 frames.], batch size: 45, lr: 3.74e-04
+2022-06-19 02:00:55,122 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 02:01:12,465 INFO [train.py:914] (1/4) Epoch 22, validation: loss=0.1645, simple_loss=0.2481, pruned_loss=0.04044, over 1622729.00 frames.
+2022-06-19 02:01:46,825 INFO [train.py:874] (1/4) Epoch 22, batch 1050, datatang_loss[loss=0.1213, simple_loss=0.2031, pruned_loss=0.01972, over 4930.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2269, pruned_loss=0.03213, over 980859.76 frames.], batch size: 64, aishell_tot_loss[loss=0.148, simple_loss=0.234, pruned_loss=0.03097, over 908202.80 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.221, pruned_loss=0.03298, over 921088.46 frames.], batch size: 64, lr: 3.74e-04
+2022-06-19 02:02:17,962 INFO [train.py:874] (1/4) Epoch 22, batch 1100, datatang_loss[loss=0.1213, simple_loss=0.198, pruned_loss=0.02225, over 4911.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2277, pruned_loss=0.03226, over 981957.02 frames.], batch size: 75, aishell_tot_loss[loss=0.1489, simple_loss=0.2349, pruned_loss=0.03141, over 917861.61 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2208, pruned_loss=0.03278, over 928298.38 frames.], batch size: 75, lr: 3.74e-04
+2022-06-19 02:02:49,866 INFO [train.py:874] (1/4) Epoch 22, batch 1150, datatang_loss[loss=0.1367, simple_loss=0.2215, pruned_loss=0.02596, over 4947.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2279, pruned_loss=0.03222, over 982810.77 frames.], batch size: 86, aishell_tot_loss[loss=0.1485, simple_loss=0.2346, pruned_loss=0.03123, over 925425.23 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2214, pruned_loss=0.03294, over 935454.73 frames.], batch size: 86, lr: 3.74e-04
+2022-06-19 02:03:20,661 INFO [train.py:874] (1/4) Epoch 22, batch 1200, aishell_loss[loss=0.1064, simple_loss=0.1808, pruned_loss=0.01599, over 4668.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2276, pruned_loss=0.03191, over 982757.76 frames.], batch size: 20, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.03085, over 933139.87 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2213, pruned_loss=0.03305, over 940184.46 frames.], batch size: 20, lr: 3.74e-04
+2022-06-19 02:03:54,479 INFO [train.py:874] (1/4) Epoch 22, batch 1250, datatang_loss[loss=0.1469, simple_loss=0.2303, pruned_loss=0.03172, over 4961.00 frames.], tot_loss[loss=0.1468, simple_loss=0.2289, pruned_loss=0.03232, over 983366.93 frames.], batch size: 91, aishell_tot_loss[loss=0.1485, simple_loss=0.2346, pruned_loss=0.03116, over 941252.29 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2216, pruned_loss=0.03332, over 943768.31 frames.], batch size: 91, lr: 3.74e-04
+2022-06-19 02:04:26,502 INFO [train.py:874] (1/4) Epoch 22, batch 1300, datatang_loss[loss=0.1359, simple_loss=0.2142, pruned_loss=0.02883, over 4926.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2284, pruned_loss=0.03213, over 983571.51 frames.], batch size: 77, aishell_tot_loss[loss=0.1478, simple_loss=0.2341, pruned_loss=0.03079, over 946706.25 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2216, pruned_loss=0.03355, over 948177.63 frames.], batch size: 77, lr: 3.74e-04
+2022-06-19 02:04:58,066 INFO [train.py:874] (1/4) Epoch 22, batch 1350, datatang_loss[loss=0.1393, simple_loss=0.2173, pruned_loss=0.03065, over 4926.00 frames.], tot_loss[loss=0.1461, simple_loss=0.228, pruned_loss=0.03203, over 983744.41 frames.], batch size: 57, aishell_tot_loss[loss=0.1479, simple_loss=0.2342, pruned_loss=0.03079, over 951391.94 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2211, pruned_loss=0.03344, over 952208.27 frames.], batch size: 57, lr: 3.74e-04
+2022-06-19 02:05:30,699 INFO [train.py:874] (1/4) Epoch 22, batch 1400, datatang_loss[loss=0.15, simple_loss=0.2307, pruned_loss=0.03469, over 4871.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2278, pruned_loss=0.03202, over 983967.73 frames.], batch size: 39, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.03083, over 955453.22 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.221, pruned_loss=0.03338, over 955907.60 frames.], batch size: 39, lr: 3.73e-04
+2022-06-19 02:06:03,236 INFO [train.py:874] (1/4) Epoch 22, batch 1450, aishell_loss[loss=0.1615, simple_loss=0.2493, pruned_loss=0.03685, over 4934.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2274, pruned_loss=0.03164, over 984184.41 frames.], batch size: 54, aishell_tot_loss[loss=0.1474, simple_loss=0.2336, pruned_loss=0.03057, over 959636.32 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2207, pruned_loss=0.03326, over 958604.79 frames.], batch size: 54, lr: 3.73e-04
+2022-06-19 02:06:34,664 INFO [train.py:874] (1/4) Epoch 22, batch 1500, aishell_loss[loss=0.1736, simple_loss=0.26, pruned_loss=0.04362, over 4960.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2282, pruned_loss=0.03246, over 984704.16 frames.], batch size: 64, aishell_tot_loss[loss=0.148, simple_loss=0.2342, pruned_loss=0.03085, over 962800.23 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2211, pruned_loss=0.03377, over 961898.18 frames.], batch size: 64, lr: 3.73e-04
+2022-06-19 02:07:06,454 INFO [train.py:874] (1/4) Epoch 22, batch 1550, datatang_loss[loss=0.1375, simple_loss=0.2182, pruned_loss=0.02837, over 4953.00 frames.], tot_loss[loss=0.1477, simple_loss=0.2291, pruned_loss=0.03315, over 984768.10 frames.], batch size: 86, aishell_tot_loss[loss=0.1484, simple_loss=0.2345, pruned_loss=0.03112, over 965280.40 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2218, pruned_loss=0.03429, over 964738.62 frames.], batch size: 86, lr: 3.73e-04
+2022-06-19 02:07:39,208 INFO [train.py:874] (1/4) Epoch 22, batch 1600, datatang_loss[loss=0.1449, simple_loss=0.2205, pruned_loss=0.03465, over 4956.00 frames.], tot_loss[loss=0.147, simple_loss=0.2284, pruned_loss=0.03278, over 984770.90 frames.], batch size: 45, aishell_tot_loss[loss=0.1484, simple_loss=0.2345, pruned_loss=0.03112, over 967914.19 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.221, pruned_loss=0.03407, over 966739.92 frames.], batch size: 45, lr: 3.73e-04
+2022-06-19 02:08:12,781 INFO [train.py:874] (1/4) Epoch 22, batch 1650, aishell_loss[loss=0.1172, simple_loss=0.1859, pruned_loss=0.02426, over 4908.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2282, pruned_loss=0.0328, over 984786.23 frames.], batch size: 21, aishell_tot_loss[loss=0.1483, simple_loss=0.2344, pruned_loss=0.03112, over 969612.96 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2213, pruned_loss=0.0341, over 969193.09 frames.], batch size: 21, lr: 3.73e-04
+2022-06-19 02:08:44,573 INFO [train.py:874] (1/4) Epoch 22, batch 1700, datatang_loss[loss=0.1585, simple_loss=0.2333, pruned_loss=0.04189, over 4945.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2282, pruned_loss=0.03254, over 984880.11 frames.], batch size: 50, aishell_tot_loss[loss=0.1483, simple_loss=0.2344, pruned_loss=0.03109, over 971652.80 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2211, pruned_loss=0.03395, over 970862.31 frames.], batch size: 50, lr: 3.73e-04
+2022-06-19 02:09:16,516 INFO [train.py:874] (1/4) Epoch 22, batch 1750, datatang_loss[loss=0.1712, simple_loss=0.2469, pruned_loss=0.04777, over 4957.00 frames.], tot_loss[loss=0.146, simple_loss=0.2276, pruned_loss=0.03223, over 984840.07 frames.], batch size: 91, aishell_tot_loss[loss=0.1478, simple_loss=0.2339, pruned_loss=0.03082, over 972917.45 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2211, pruned_loss=0.03388, over 972747.73 frames.], batch size: 91, lr: 3.73e-04
+2022-06-19 02:09:50,784 INFO [train.py:874] (1/4) Epoch 22, batch 1800, aishell_loss[loss=0.1164, simple_loss=0.1979, pruned_loss=0.01747, over 4822.00 frames.], tot_loss[loss=0.145, simple_loss=0.2265, pruned_loss=0.03171, over 984939.10 frames.], batch size: 24, aishell_tot_loss[loss=0.1473, simple_loss=0.2334, pruned_loss=0.03055, over 974165.34 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2207, pruned_loss=0.03349, over 974438.45 frames.], batch size: 24, lr: 3.73e-04
+2022-06-19 02:10:22,907 INFO [train.py:874] (1/4) Epoch 22, batch 1850, datatang_loss[loss=0.1576, simple_loss=0.237, pruned_loss=0.03908, over 4923.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2274, pruned_loss=0.03239, over 984991.27 frames.], batch size: 73, aishell_tot_loss[loss=0.1475, simple_loss=0.2336, pruned_loss=0.03069, over 975302.54 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2212, pruned_loss=0.03401, over 975857.12 frames.], batch size: 73, lr: 3.73e-04
+2022-06-19 02:10:56,163 INFO [train.py:874] (1/4) Epoch 22, batch 1900, datatang_loss[loss=0.1405, simple_loss=0.2369, pruned_loss=0.02206, over 4932.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2277, pruned_loss=0.03267, over 985296.53 frames.], batch size: 57, aishell_tot_loss[loss=0.1479, simple_loss=0.2339, pruned_loss=0.03099, over 976616.98 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2213, pruned_loss=0.034, over 977084.02 frames.], batch size: 57, lr: 3.72e-04
+2022-06-19 02:11:27,906 INFO [train.py:874] (1/4) Epoch 22, batch 1950, aishell_loss[loss=0.1524, simple_loss=0.2343, pruned_loss=0.03529, over 4960.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2275, pruned_loss=0.03256, over 985278.56 frames.], batch size: 31, aishell_tot_loss[loss=0.1478, simple_loss=0.2337, pruned_loss=0.03097, over 977593.72 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2213, pruned_loss=0.03396, over 978078.98 frames.], batch size: 31, lr: 3.72e-04
+2022-06-19 02:12:00,355 INFO [train.py:874] (1/4) Epoch 22, batch 2000, datatang_loss[loss=0.1488, simple_loss=0.2374, pruned_loss=0.0301, over 4870.00 frames.], tot_loss[loss=0.147, simple_loss=0.2284, pruned_loss=0.03282, over 985769.47 frames.], batch size: 30, aishell_tot_loss[loss=0.1485, simple_loss=0.2345, pruned_loss=0.0312, over 978892.40 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2214, pruned_loss=0.03403, over 979047.37 frames.], batch size: 30, lr: 3.72e-04
+2022-06-19 02:12:00,356 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 02:12:18,452 INFO [train.py:914] (1/4) Epoch 22, validation: loss=0.165, simple_loss=0.2489, pruned_loss=0.04058, over 1622729.00 frames.
+2022-06-19 02:12:50,740 INFO [train.py:874] (1/4) Epoch 22, batch 2050, aishell_loss[loss=0.1423, simple_loss=0.2175, pruned_loss=0.03351, over 4905.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2276, pruned_loss=0.0328, over 985471.78 frames.], batch size: 34, aishell_tot_loss[loss=0.1485, simple_loss=0.2344, pruned_loss=0.03131, over 979243.50 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2209, pruned_loss=0.03394, over 979966.70 frames.], batch size: 34, lr: 3.72e-04
+2022-06-19 02:13:23,369 INFO [train.py:874] (1/4) Epoch 22, batch 2100, aishell_loss[loss=0.1511, simple_loss=0.2305, pruned_loss=0.03586, over 4954.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2279, pruned_loss=0.03276, over 985575.88 frames.], batch size: 31, aishell_tot_loss[loss=0.1486, simple_loss=0.2346, pruned_loss=0.03134, over 980048.99 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2209, pruned_loss=0.03391, over 980662.38 frames.], batch size: 31, lr: 3.72e-04
+2022-06-19 02:13:54,808 INFO [train.py:874] (1/4) Epoch 22, batch 2150, datatang_loss[loss=0.21, simple_loss=0.2702, pruned_loss=0.07494, over 4937.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2278, pruned_loss=0.03265, over 985515.08 frames.], batch size: 109, aishell_tot_loss[loss=0.1483, simple_loss=0.2343, pruned_loss=0.03111, over 980547.70 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2212, pruned_loss=0.03402, over 981336.03 frames.], batch size: 109, lr: 3.72e-04
+2022-06-19 02:14:25,705 INFO [train.py:874] (1/4) Epoch 22, batch 2200, aishell_loss[loss=0.1273, simple_loss=0.1862, pruned_loss=0.03421, over 4779.00 frames.], tot_loss[loss=0.1473, simple_loss=0.2287, pruned_loss=0.03301, over 985204.23 frames.], batch size: 21, aishell_tot_loss[loss=0.1484, simple_loss=0.2343, pruned_loss=0.03126, over 980781.95 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2221, pruned_loss=0.03425, over 981852.74 frames.], batch size: 21, lr: 3.72e-04
+2022-06-19 02:14:59,112 INFO [train.py:874] (1/4) Epoch 22, batch 2250, datatang_loss[loss=0.1309, simple_loss=0.2065, pruned_loss=0.02759, over 4905.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2288, pruned_loss=0.03297, over 985195.20 frames.], batch size: 47, aishell_tot_loss[loss=0.1484, simple_loss=0.2343, pruned_loss=0.03125, over 981237.69 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2223, pruned_loss=0.03429, over 982305.90 frames.], batch size: 47, lr: 3.72e-04
+2022-06-19 02:15:30,350 INFO [train.py:874] (1/4) Epoch 22, batch 2300, datatang_loss[loss=0.1452, simple_loss=0.212, pruned_loss=0.03926, over 4916.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2284, pruned_loss=0.03296, over 985338.65 frames.], batch size: 64, aishell_tot_loss[loss=0.1483, simple_loss=0.2342, pruned_loss=0.0312, over 981711.05 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2221, pruned_loss=0.03437, over 982793.15 frames.], batch size: 64, lr: 3.72e-04
+2022-06-19 02:16:02,660 INFO [train.py:874] (1/4) Epoch 22, batch 2350, aishell_loss[loss=0.1429, simple_loss=0.2264, pruned_loss=0.02968, over 4986.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2281, pruned_loss=0.03247, over 985470.08 frames.], batch size: 38, aishell_tot_loss[loss=0.1482, simple_loss=0.2342, pruned_loss=0.03105, over 982275.11 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2215, pruned_loss=0.0341, over 983126.55 frames.], batch size: 38, lr: 3.72e-04
+2022-06-19 02:16:33,519 INFO [train.py:874] (1/4) Epoch 22, batch 2400, datatang_loss[loss=0.1242, simple_loss=0.2031, pruned_loss=0.02264, over 4957.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2281, pruned_loss=0.03236, over 985407.07 frames.], batch size: 67, aishell_tot_loss[loss=0.148, simple_loss=0.2342, pruned_loss=0.03095, over 982509.62 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2214, pruned_loss=0.0341, over 983486.00 frames.], batch size: 67, lr: 3.71e-04
+2022-06-19 02:17:04,621 INFO [train.py:874] (1/4) Epoch 22, batch 2450, datatang_loss[loss=0.1455, simple_loss=0.2258, pruned_loss=0.03262, over 4930.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2281, pruned_loss=0.03249, over 985586.94 frames.], batch size: 83, aishell_tot_loss[loss=0.1478, simple_loss=0.234, pruned_loss=0.03081, over 982929.27 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2219, pruned_loss=0.03431, over 983803.43 frames.], batch size: 83, lr: 3.71e-04
+2022-06-19 02:17:37,741 INFO [train.py:874] (1/4) Epoch 22, batch 2500, aishell_loss[loss=0.1097, simple_loss=0.1767, pruned_loss=0.02137, over 4881.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2284, pruned_loss=0.03228, over 985510.25 frames.], batch size: 21, aishell_tot_loss[loss=0.1475, simple_loss=0.2339, pruned_loss=0.03059, over 983188.24 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2223, pruned_loss=0.0343, over 983992.59 frames.], batch size: 21, lr: 3.71e-04
+2022-06-19 02:18:09,172 INFO [train.py:874] (1/4) Epoch 22, batch 2550, aishell_loss[loss=0.111, simple_loss=0.1837, pruned_loss=0.01909, over 4782.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2283, pruned_loss=0.03225, over 985336.47 frames.], batch size: 20, aishell_tot_loss[loss=0.147, simple_loss=0.2333, pruned_loss=0.03039, over 983397.36 frames.], datatang_tot_loss[loss=0.1458, simple_loss=0.2227, pruned_loss=0.03447, over 984067.53 frames.], batch size: 20, lr: 3.71e-04
+2022-06-19 02:18:41,340 INFO [train.py:874] (1/4) Epoch 22, batch 2600, aishell_loss[loss=0.1602, simple_loss=0.245, pruned_loss=0.03777, over 4949.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2278, pruned_loss=0.03225, over 985429.18 frames.], batch size: 54, aishell_tot_loss[loss=0.147, simple_loss=0.2333, pruned_loss=0.03034, over 983568.72 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2223, pruned_loss=0.03448, over 984372.11 frames.], batch size: 54, lr: 3.71e-04
+2022-06-19 02:19:14,419 INFO [train.py:874] (1/4) Epoch 22, batch 2650, datatang_loss[loss=0.1417, simple_loss=0.2073, pruned_loss=0.0381, over 4951.00 frames.], tot_loss[loss=0.1462, simple_loss=0.228, pruned_loss=0.03222, over 985426.30 frames.], batch size: 45, aishell_tot_loss[loss=0.1472, simple_loss=0.2333, pruned_loss=0.03059, over 983761.76 frames.], datatang_tot_loss[loss=0.1454, simple_loss=0.2222, pruned_loss=0.03428, over 984548.28 frames.], batch size: 45, lr: 3.71e-04
+2022-06-19 02:19:44,852 INFO [train.py:874] (1/4) Epoch 22, batch 2700, aishell_loss[loss=0.1591, simple_loss=0.2482, pruned_loss=0.03497, over 4861.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2272, pruned_loss=0.03182, over 984906.87 frames.], batch size: 37, aishell_tot_loss[loss=0.1469, simple_loss=0.2328, pruned_loss=0.0305, over 983562.53 frames.], datatang_tot_loss[loss=0.1448, simple_loss=0.2216, pruned_loss=0.03399, over 984521.38 frames.], batch size: 37, lr: 3.71e-04
+2022-06-19 02:20:16,437 INFO [train.py:874] (1/4) Epoch 22, batch 2750, datatang_loss[loss=0.1463, simple_loss=0.2124, pruned_loss=0.04012, over 4894.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2284, pruned_loss=0.03232, over 984944.79 frames.], batch size: 47, aishell_tot_loss[loss=0.148, simple_loss=0.2339, pruned_loss=0.03108, over 983641.14 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2215, pruned_loss=0.0339, over 984711.48 frames.], batch size: 47, lr: 3.71e-04
+2022-06-19 02:20:48,480 INFO [train.py:874] (1/4) Epoch 22, batch 2800, aishell_loss[loss=0.1601, simple_loss=0.2433, pruned_loss=0.03842, over 4904.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2287, pruned_loss=0.03217, over 985147.19 frames.], batch size: 52, aishell_tot_loss[loss=0.1483, simple_loss=0.2343, pruned_loss=0.03118, over 983932.83 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2213, pruned_loss=0.03367, over 984833.56 frames.], batch size: 52, lr: 3.71e-04
+2022-06-19 02:21:19,580 INFO [train.py:874] (1/4) Epoch 22, batch 2850, aishell_loss[loss=0.1755, simple_loss=0.2598, pruned_loss=0.04557, over 4957.00 frames.], tot_loss[loss=0.1466, simple_loss=0.229, pruned_loss=0.03209, over 985744.95 frames.], batch size: 40, aishell_tot_loss[loss=0.1485, simple_loss=0.2347, pruned_loss=0.03114, over 984474.03 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2212, pruned_loss=0.03356, over 985116.82 frames.], batch size: 40, lr: 3.70e-04
+2022-06-19 02:21:52,038 INFO [train.py:874] (1/4) Epoch 22, batch 2900, aishell_loss[loss=0.1416, simple_loss=0.2255, pruned_loss=0.02882, over 4944.00 frames.], tot_loss[loss=0.1462, simple_loss=0.228, pruned_loss=0.03217, over 986001.13 frames.], batch size: 49, aishell_tot_loss[loss=0.1485, simple_loss=0.2346, pruned_loss=0.03116, over 984656.81 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2207, pruned_loss=0.0335, over 985409.15 frames.], batch size: 49, lr: 3.70e-04
+2022-06-19 02:22:24,846 INFO [train.py:874] (1/4) Epoch 22, batch 2950, aishell_loss[loss=0.1388, simple_loss=0.22, pruned_loss=0.02886, over 4870.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2279, pruned_loss=0.03235, over 986074.11 frames.], batch size: 28, aishell_tot_loss[loss=0.1482, simple_loss=0.2344, pruned_loss=0.03102, over 984599.19 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.221, pruned_loss=0.03374, over 985767.20 frames.], batch size: 28, lr: 3.70e-04
+2022-06-19 02:22:56,251 INFO [train.py:874] (1/4) Epoch 22, batch 3000, datatang_loss[loss=0.1288, simple_loss=0.2097, pruned_loss=0.02394, over 4941.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2286, pruned_loss=0.03231, over 985960.95 frames.], batch size: 69, aishell_tot_loss[loss=0.1485, simple_loss=0.2349, pruned_loss=0.03106, over 984554.97 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.221, pruned_loss=0.03372, over 985935.97 frames.], batch size: 69, lr: 3.70e-04
+2022-06-19 02:22:56,252 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 02:23:14,259 INFO [train.py:914] (1/4) 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] (1/4) Epoch 22, batch 3050, datatang_loss[loss=0.147, simple_loss=0.2271, pruned_loss=0.03343, over 4948.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2281, pruned_loss=0.03187, over 985374.87 frames.], batch size: 86, aishell_tot_loss[loss=0.1481, simple_loss=0.2346, pruned_loss=0.03086, over 984490.21 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2211, pruned_loss=0.03341, over 985530.14 frames.], batch size: 86, lr: 3.70e-04
+2022-06-19 02:24:17,450 INFO [train.py:874] (1/4) Epoch 22, batch 3100, aishell_loss[loss=0.1476, simple_loss=0.2221, pruned_loss=0.03659, over 4940.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2282, pruned_loss=0.03199, over 985467.25 frames.], batch size: 31, aishell_tot_loss[loss=0.1482, simple_loss=0.2345, pruned_loss=0.03092, over 984631.86 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2216, pruned_loss=0.03339, over 985558.12 frames.], batch size: 31, lr: 3.70e-04
+2022-06-19 02:24:49,586 INFO [train.py:874] (1/4) Epoch 22, batch 3150, aishell_loss[loss=0.1479, simple_loss=0.2295, pruned_loss=0.03311, over 4927.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2285, pruned_loss=0.03204, over 985124.23 frames.], batch size: 33, aishell_tot_loss[loss=0.1484, simple_loss=0.2349, pruned_loss=0.03101, over 984671.77 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2211, pruned_loss=0.0334, over 985260.03 frames.], batch size: 33, lr: 3.70e-04
+2022-06-19 02:25:22,893 INFO [train.py:874] (1/4) Epoch 22, batch 3200, datatang_loss[loss=0.1338, simple_loss=0.2216, pruned_loss=0.02301, over 4925.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2277, pruned_loss=0.03205, over 985169.12 frames.], batch size: 57, aishell_tot_loss[loss=0.1488, simple_loss=0.2351, pruned_loss=0.03121, over 984961.77 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2203, pruned_loss=0.03313, over 985056.12 frames.], batch size: 57, lr: 3.70e-04
+2022-06-19 02:25:55,782 INFO [train.py:874] (1/4) Epoch 22, batch 3250, datatang_loss[loss=0.1378, simple_loss=0.2167, pruned_loss=0.02948, over 4930.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2276, pruned_loss=0.03255, over 985384.22 frames.], batch size: 77, aishell_tot_loss[loss=0.1486, simple_loss=0.2348, pruned_loss=0.03121, over 985058.93 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2206, pruned_loss=0.03361, over 985220.28 frames.], batch size: 77, lr: 3.70e-04
+2022-06-19 02:26:28,522 INFO [train.py:874] (1/4) Epoch 22, batch 3300, datatang_loss[loss=0.1453, simple_loss=0.2211, pruned_loss=0.03469, over 4911.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2294, pruned_loss=0.03305, over 985370.29 frames.], batch size: 81, aishell_tot_loss[loss=0.1497, simple_loss=0.2361, pruned_loss=0.03163, over 984995.90 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.221, pruned_loss=0.0338, over 985325.69 frames.], batch size: 81, lr: 3.70e-04
+2022-06-19 02:27:02,233 INFO [train.py:874] (1/4) Epoch 22, batch 3350, aishell_loss[loss=0.1448, simple_loss=0.2287, pruned_loss=0.03042, over 4919.00 frames.], tot_loss[loss=0.1479, simple_loss=0.2296, pruned_loss=0.03305, over 985201.24 frames.], batch size: 33, aishell_tot_loss[loss=0.1495, simple_loss=0.236, pruned_loss=0.03148, over 984828.50 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2213, pruned_loss=0.03409, over 985369.36 frames.], batch size: 33, lr: 3.69e-04
+2022-06-19 02:27:34,677 INFO [train.py:874] (1/4) Epoch 22, batch 3400, aishell_loss[loss=0.1334, simple_loss=0.2016, pruned_loss=0.03264, over 4880.00 frames.], tot_loss[loss=0.1474, simple_loss=0.2292, pruned_loss=0.03279, over 985597.91 frames.], batch size: 21, aishell_tot_loss[loss=0.1492, simple_loss=0.2355, pruned_loss=0.03141, over 984896.26 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2216, pruned_loss=0.03391, over 985749.12 frames.], batch size: 21, lr: 3.69e-04
+2022-06-19 02:28:08,668 INFO [train.py:874] (1/4) Epoch 22, batch 3450, aishell_loss[loss=0.1532, simple_loss=0.2439, pruned_loss=0.03128, over 4914.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2285, pruned_loss=0.03267, over 985656.26 frames.], batch size: 52, aishell_tot_loss[loss=0.1485, simple_loss=0.2351, pruned_loss=0.03093, over 984807.30 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2219, pruned_loss=0.03415, over 985923.45 frames.], batch size: 52, lr: 3.69e-04
+2022-06-19 02:28:40,665 INFO [train.py:874] (1/4) Epoch 22, batch 3500, aishell_loss[loss=0.1493, simple_loss=0.2437, pruned_loss=0.02745, over 4947.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2285, pruned_loss=0.03289, over 985623.89 frames.], batch size: 54, aishell_tot_loss[loss=0.1481, simple_loss=0.2347, pruned_loss=0.03077, over 984935.19 frames.], datatang_tot_loss[loss=0.1459, simple_loss=0.2227, pruned_loss=0.03452, over 985806.73 frames.], batch size: 54, lr: 3.69e-04
+2022-06-19 02:29:12,238 INFO [train.py:874] (1/4) Epoch 22, batch 3550, aishell_loss[loss=0.1449, simple_loss=0.24, pruned_loss=0.02492, over 4928.00 frames.], tot_loss[loss=0.1478, simple_loss=0.2294, pruned_loss=0.03304, over 985441.69 frames.], batch size: 68, aishell_tot_loss[loss=0.1485, simple_loss=0.2352, pruned_loss=0.03086, over 984893.84 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2228, pruned_loss=0.03471, over 985730.78 frames.], batch size: 68, lr: 3.69e-04
+2022-06-19 02:29:44,438 INFO [train.py:874] (1/4) Epoch 22, batch 3600, datatang_loss[loss=0.1999, simple_loss=0.2706, pruned_loss=0.06454, over 4934.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2293, pruned_loss=0.03298, over 985738.54 frames.], batch size: 108, aishell_tot_loss[loss=0.1485, simple_loss=0.2355, pruned_loss=0.03077, over 984991.24 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2226, pruned_loss=0.03476, over 985971.31 frames.], batch size: 108, lr: 3.69e-04
+2022-06-19 02:30:17,396 INFO [train.py:874] (1/4) Epoch 22, batch 3650, datatang_loss[loss=0.1421, simple_loss=0.2191, pruned_loss=0.03252, over 4974.00 frames.], tot_loss[loss=0.147, simple_loss=0.2288, pruned_loss=0.03258, over 985293.90 frames.], batch size: 31, aishell_tot_loss[loss=0.1487, simple_loss=0.2357, pruned_loss=0.03085, over 984671.51 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2219, pruned_loss=0.03428, over 985855.43 frames.], batch size: 31, lr: 3.69e-04
+2022-06-19 02:30:48,796 INFO [train.py:874] (1/4) Epoch 22, batch 3700, datatang_loss[loss=0.1359, simple_loss=0.2116, pruned_loss=0.03004, over 4918.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2291, pruned_loss=0.03269, over 985588.16 frames.], batch size: 73, aishell_tot_loss[loss=0.1491, simple_loss=0.2362, pruned_loss=0.03104, over 984881.66 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2218, pruned_loss=0.03421, over 985962.20 frames.], batch size: 73, lr: 3.69e-04
+2022-06-19 02:31:19,479 INFO [train.py:874] (1/4) Epoch 22, batch 3750, datatang_loss[loss=0.1431, simple_loss=0.2104, pruned_loss=0.03786, over 4939.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2289, pruned_loss=0.03232, over 985354.06 frames.], batch size: 45, aishell_tot_loss[loss=0.1487, simple_loss=0.2357, pruned_loss=0.0308, over 984669.17 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.2217, pruned_loss=0.03414, over 986001.73 frames.], batch size: 45, lr: 3.69e-04
+2022-06-19 02:31:49,499 INFO [train.py:874] (1/4) Epoch 22, batch 3800, aishell_loss[loss=0.1453, simple_loss=0.2357, pruned_loss=0.02745, over 4920.00 frames.], tot_loss[loss=0.146, simple_loss=0.2276, pruned_loss=0.03214, over 985422.59 frames.], batch size: 52, aishell_tot_loss[loss=0.148, simple_loss=0.2349, pruned_loss=0.03061, over 984736.98 frames.], datatang_tot_loss[loss=0.1447, simple_loss=0.2213, pruned_loss=0.03411, over 986002.15 frames.], batch size: 52, lr: 3.69e-04
+2022-06-19 02:32:20,316 INFO [train.py:874] (1/4) Epoch 22, batch 3850, datatang_loss[loss=0.1589, simple_loss=0.2258, pruned_loss=0.04606, over 4974.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2276, pruned_loss=0.03205, over 985480.20 frames.], batch size: 40, aishell_tot_loss[loss=0.1481, simple_loss=0.235, pruned_loss=0.03063, over 984913.99 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2213, pruned_loss=0.03387, over 985883.61 frames.], batch size: 40, lr: 3.68e-04
+2022-06-19 02:32:51,364 INFO [train.py:874] (1/4) Epoch 22, batch 3900, aishell_loss[loss=0.1607, simple_loss=0.2366, pruned_loss=0.04237, over 4926.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2267, pruned_loss=0.03183, over 985642.86 frames.], batch size: 33, aishell_tot_loss[loss=0.1476, simple_loss=0.2344, pruned_loss=0.03045, over 984959.84 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2213, pruned_loss=0.03364, over 985994.87 frames.], batch size: 33, lr: 3.68e-04
+2022-06-19 02:33:21,711 INFO [train.py:874] (1/4) Epoch 22, batch 3950, aishell_loss[loss=0.1418, simple_loss=0.2357, pruned_loss=0.02396, over 4948.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2275, pruned_loss=0.03174, over 985684.68 frames.], batch size: 56, aishell_tot_loss[loss=0.1477, simple_loss=0.2346, pruned_loss=0.03042, over 985285.09 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2213, pruned_loss=0.03362, over 985759.95 frames.], batch size: 56, lr: 3.68e-04
+2022-06-19 02:33:51,301 INFO [train.py:874] (1/4) Epoch 22, batch 4000, aishell_loss[loss=0.175, simple_loss=0.2566, pruned_loss=0.04666, over 4883.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2281, pruned_loss=0.0321, over 985328.06 frames.], batch size: 50, aishell_tot_loss[loss=0.1481, simple_loss=0.235, pruned_loss=0.03058, over 984972.10 frames.], datatang_tot_loss[loss=0.1445, simple_loss=0.2215, pruned_loss=0.03374, over 985729.47 frames.], batch size: 50, lr: 3.68e-04
+2022-06-19 02:33:51,302 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 02:34:08,826 INFO [train.py:914] (1/4) Epoch 22, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04074, over 1622729.00 frames.
+2022-06-19 02:34:38,060 INFO [train.py:874] (1/4) Epoch 22, batch 4050, aishell_loss[loss=0.1567, simple_loss=0.2395, pruned_loss=0.03695, over 4910.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2285, pruned_loss=0.03199, over 985039.26 frames.], batch size: 41, aishell_tot_loss[loss=0.1483, simple_loss=0.2353, pruned_loss=0.03067, over 984583.88 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2214, pruned_loss=0.03355, over 985832.58 frames.], batch size: 41, lr: 3.68e-04
+2022-06-19 02:36:04,873 INFO [train.py:874] (1/4) Epoch 23, batch 50, datatang_loss[loss=0.1304, simple_loss=0.2001, pruned_loss=0.03041, over 4899.00 frames.], tot_loss[loss=0.145, simple_loss=0.2256, pruned_loss=0.03217, over 218698.49 frames.], batch size: 52, aishell_tot_loss[loss=0.1518, simple_loss=0.2373, pruned_loss=0.03315, over 107617.14 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2159, pruned_loss=0.03145, over 124694.21 frames.], batch size: 52, lr: 3.60e-04
+2022-06-19 02:36:35,954 INFO [train.py:874] (1/4) Epoch 23, batch 100, datatang_loss[loss=0.155, simple_loss=0.2324, pruned_loss=0.03882, over 4928.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2256, pruned_loss=0.03073, over 388968.83 frames.], batch size: 57, aishell_tot_loss[loss=0.1511, simple_loss=0.2382, pruned_loss=0.03201, over 211101.60 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2139, pruned_loss=0.02973, over 226297.07 frames.], batch size: 57, lr: 3.60e-04
+2022-06-19 02:37:08,833 INFO [train.py:874] (1/4) Epoch 23, batch 150, aishell_loss[loss=0.1555, simple_loss=0.2557, pruned_loss=0.02769, over 4910.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2236, pruned_loss=0.03037, over 520848.25 frames.], batch size: 68, aishell_tot_loss[loss=0.1492, simple_loss=0.2358, pruned_loss=0.03125, over 281004.92 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2144, pruned_loss=0.02989, over 335615.20 frames.], batch size: 68, lr: 3.60e-04
+2022-06-19 02:37:40,903 INFO [train.py:874] (1/4) Epoch 23, batch 200, aishell_loss[loss=0.1731, simple_loss=0.2657, pruned_loss=0.04029, over 4941.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2259, pruned_loss=0.03118, over 624058.57 frames.], batch size: 68, aishell_tot_loss[loss=0.1502, simple_loss=0.2365, pruned_loss=0.03198, over 388619.99 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2147, pruned_loss=0.03029, over 388689.48 frames.], batch size: 68, lr: 3.60e-04
+2022-06-19 02:38:12,801 INFO [train.py:874] (1/4) Epoch 23, batch 250, aishell_loss[loss=0.1588, simple_loss=0.2439, pruned_loss=0.03684, over 4956.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2255, pruned_loss=0.03119, over 704452.75 frames.], batch size: 56, aishell_tot_loss[loss=0.1494, simple_loss=0.2357, pruned_loss=0.03155, over 459035.32 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2148, pruned_loss=0.03071, over 459161.95 frames.], batch size: 56, lr: 3.60e-04
+2022-06-19 02:38:44,863 INFO [train.py:874] (1/4) Epoch 23, batch 300, datatang_loss[loss=0.1453, simple_loss=0.2233, pruned_loss=0.03368, over 4932.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2259, pruned_loss=0.03128, over 766816.87 frames.], batch size: 34, aishell_tot_loss[loss=0.1496, simple_loss=0.2358, pruned_loss=0.03168, over 518656.18 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2156, pruned_loss=0.03075, over 523596.95 frames.], batch size: 34, lr: 3.60e-04
+2022-06-19 02:39:17,351 INFO [train.py:874] (1/4) Epoch 23, batch 350, aishell_loss[loss=0.1537, simple_loss=0.2388, pruned_loss=0.03428, over 4906.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2253, pruned_loss=0.03124, over 815260.23 frames.], batch size: 34, aishell_tot_loss[loss=0.1488, simple_loss=0.2349, pruned_loss=0.03138, over 577769.42 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2154, pruned_loss=0.03102, over 573837.91 frames.], batch size: 34, lr: 3.59e-04
+2022-06-19 02:39:48,706 INFO [train.py:874] (1/4) Epoch 23, batch 400, datatang_loss[loss=0.1337, simple_loss=0.2211, pruned_loss=0.02319, over 4930.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2247, pruned_loss=0.03051, over 853034.36 frames.], batch size: 77, aishell_tot_loss[loss=0.1479, simple_loss=0.2343, pruned_loss=0.03075, over 618279.74 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2158, pruned_loss=0.03061, over 629767.13 frames.], batch size: 77, lr: 3.59e-04
+2022-06-19 02:40:20,257 INFO [train.py:874] (1/4) Epoch 23, batch 450, datatang_loss[loss=0.1323, simple_loss=0.205, pruned_loss=0.02987, over 4898.00 frames.], tot_loss[loss=0.1438, simple_loss=0.226, pruned_loss=0.0308, over 882342.19 frames.], batch size: 52, aishell_tot_loss[loss=0.1476, simple_loss=0.2342, pruned_loss=0.03049, over 669463.06 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2171, pruned_loss=0.03124, over 663722.51 frames.], batch size: 52, lr: 3.59e-04
+2022-06-19 02:40:53,036 INFO [train.py:874] (1/4) Epoch 23, batch 500, datatang_loss[loss=0.1361, simple_loss=0.2189, pruned_loss=0.02665, over 4932.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2269, pruned_loss=0.03119, over 904901.04 frames.], batch size: 94, aishell_tot_loss[loss=0.1472, simple_loss=0.2337, pruned_loss=0.03029, over 710356.96 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2187, pruned_loss=0.03195, over 697492.17 frames.], batch size: 94, lr: 3.59e-04
+2022-06-19 02:41:25,229 INFO [train.py:874] (1/4) Epoch 23, batch 550, datatang_loss[loss=0.1433, simple_loss=0.2178, pruned_loss=0.03444, over 4917.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2276, pruned_loss=0.03163, over 923085.83 frames.], batch size: 81, aishell_tot_loss[loss=0.1479, simple_loss=0.2344, pruned_loss=0.03067, over 744260.38 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2191, pruned_loss=0.0322, over 730194.72 frames.], batch size: 81, lr: 3.59e-04
+2022-06-19 02:41:56,287 INFO [train.py:874] (1/4) Epoch 23, batch 600, aishell_loss[loss=0.1165, simple_loss=0.1896, pruned_loss=0.02167, over 4786.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2277, pruned_loss=0.03171, over 937038.39 frames.], batch size: 21, aishell_tot_loss[loss=0.1477, simple_loss=0.234, pruned_loss=0.03071, over 772770.55 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2198, pruned_loss=0.03231, over 760298.38 frames.], batch size: 21, lr: 3.59e-04
+2022-06-19 02:42:28,258 INFO [train.py:874] (1/4) Epoch 23, batch 650, datatang_loss[loss=0.168, simple_loss=0.2365, pruned_loss=0.04971, over 4883.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2271, pruned_loss=0.03161, over 947749.73 frames.], batch size: 39, aishell_tot_loss[loss=0.1478, simple_loss=0.2338, pruned_loss=0.03087, over 799746.33 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2193, pruned_loss=0.03209, over 784741.55 frames.], batch size: 39, lr: 3.59e-04
+2022-06-19 02:42:59,740 INFO [train.py:874] (1/4) Epoch 23, batch 700, aishell_loss[loss=0.1457, simple_loss=0.2328, pruned_loss=0.02929, over 4927.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2278, pruned_loss=0.03206, over 955985.26 frames.], batch size: 33, aishell_tot_loss[loss=0.1481, simple_loss=0.2341, pruned_loss=0.03101, over 820711.43 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2201, pruned_loss=0.03256, over 809206.48 frames.], batch size: 33, lr: 3.59e-04
+2022-06-19 02:43:32,100 INFO [train.py:874] (1/4) Epoch 23, batch 750, datatang_loss[loss=0.1628, simple_loss=0.2371, pruned_loss=0.04428, over 4944.00 frames.], tot_loss[loss=0.1469, simple_loss=0.2286, pruned_loss=0.03254, over 962358.78 frames.], batch size: 50, aishell_tot_loss[loss=0.1489, simple_loss=0.2352, pruned_loss=0.0313, over 838379.53 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2204, pruned_loss=0.03293, over 831628.56 frames.], batch size: 50, lr: 3.59e-04
+2022-06-19 02:44:03,150 INFO [train.py:874] (1/4) Epoch 23, batch 800, aishell_loss[loss=0.1574, simple_loss=0.2425, pruned_loss=0.03613, over 4940.00 frames.], tot_loss[loss=0.1455, simple_loss=0.2275, pruned_loss=0.03176, over 967552.84 frames.], batch size: 32, aishell_tot_loss[loss=0.1477, simple_loss=0.234, pruned_loss=0.03072, over 855646.28 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2205, pruned_loss=0.0327, over 849875.23 frames.], batch size: 32, lr: 3.59e-04
+2022-06-19 02:44:41,819 INFO [train.py:874] (1/4) Epoch 23, batch 850, aishell_loss[loss=0.1644, simple_loss=0.2499, pruned_loss=0.03947, over 4979.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2274, pruned_loss=0.03168, over 971578.82 frames.], batch size: 39, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03073, over 873750.29 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2202, pruned_loss=0.03267, over 862899.73 frames.], batch size: 39, lr: 3.58e-04
+2022-06-19 02:45:13,061 INFO [train.py:874] (1/4) Epoch 23, batch 900, datatang_loss[loss=0.1445, simple_loss=0.2155, pruned_loss=0.03672, over 4903.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2268, pruned_loss=0.03148, over 974507.47 frames.], batch size: 42, aishell_tot_loss[loss=0.1477, simple_loss=0.2338, pruned_loss=0.0308, over 886769.63 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2198, pruned_loss=0.03235, over 877319.35 frames.], batch size: 42, lr: 3.58e-04
+2022-06-19 02:45:44,643 INFO [train.py:874] (1/4) Epoch 23, batch 950, datatang_loss[loss=0.1209, simple_loss=0.1983, pruned_loss=0.02177, over 4956.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2274, pruned_loss=0.03148, over 977203.83 frames.], batch size: 45, aishell_tot_loss[loss=0.1479, simple_loss=0.2342, pruned_loss=0.03077, over 900691.63 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2196, pruned_loss=0.03243, over 887770.99 frames.], batch size: 45, lr: 3.58e-04
+2022-06-19 02:46:17,103 INFO [train.py:874] (1/4) Epoch 23, batch 1000, datatang_loss[loss=0.162, simple_loss=0.2376, pruned_loss=0.04318, over 4959.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2281, pruned_loss=0.03175, over 979045.70 frames.], batch size: 91, aishell_tot_loss[loss=0.1483, simple_loss=0.2345, pruned_loss=0.03105, over 911414.12 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2202, pruned_loss=0.03242, over 898428.33 frames.], batch size: 91, lr: 3.58e-04
+2022-06-19 02:46:17,104 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 02:46:34,399 INFO [train.py:914] (1/4) Epoch 23, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04077, over 1622729.00 frames.
+2022-06-19 02:47:05,339 INFO [train.py:874] (1/4) Epoch 23, batch 1050, datatang_loss[loss=0.1351, simple_loss=0.2158, pruned_loss=0.02723, over 4924.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2274, pruned_loss=0.03162, over 980637.05 frames.], batch size: 81, aishell_tot_loss[loss=0.1476, simple_loss=0.2337, pruned_loss=0.03071, over 919229.65 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2205, pruned_loss=0.03258, over 909924.99 frames.], batch size: 81, lr: 3.58e-04
+2022-06-19 02:47:36,019 INFO [train.py:874] (1/4) Epoch 23, batch 1100, datatang_loss[loss=0.1316, simple_loss=0.2102, pruned_loss=0.02643, over 4987.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2263, pruned_loss=0.03118, over 982303.06 frames.], batch size: 26, aishell_tot_loss[loss=0.1472, simple_loss=0.2333, pruned_loss=0.03052, over 926463.45 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2199, pruned_loss=0.03224, over 920128.71 frames.], batch size: 26, lr: 3.58e-04
+2022-06-19 02:48:07,577 INFO [train.py:874] (1/4) Epoch 23, batch 1150, aishell_loss[loss=0.1513, simple_loss=0.2425, pruned_loss=0.03011, over 4938.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2266, pruned_loss=0.03135, over 983111.92 frames.], batch size: 64, aishell_tot_loss[loss=0.1471, simple_loss=0.233, pruned_loss=0.03056, over 933748.28 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2205, pruned_loss=0.03234, over 927577.04 frames.], batch size: 64, lr: 3.58e-04
+2022-06-19 02:48:40,913 INFO [train.py:874] (1/4) Epoch 23, batch 1200, datatang_loss[loss=0.1098, simple_loss=0.1879, pruned_loss=0.01587, over 4929.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2268, pruned_loss=0.03143, over 983743.72 frames.], batch size: 71, aishell_tot_loss[loss=0.1469, simple_loss=0.233, pruned_loss=0.03037, over 939299.10 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.2208, pruned_loss=0.03257, over 935092.88 frames.], batch size: 71, lr: 3.58e-04
+2022-06-19 02:49:12,892 INFO [train.py:874] (1/4) Epoch 23, batch 1250, datatang_loss[loss=0.1488, simple_loss=0.221, pruned_loss=0.03831, over 4941.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2263, pruned_loss=0.03149, over 983851.21 frames.], batch size: 55, aishell_tot_loss[loss=0.1468, simple_loss=0.2328, pruned_loss=0.03038, over 944950.52 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2203, pruned_loss=0.03265, over 940495.46 frames.], batch size: 55, lr: 3.58e-04
+2022-06-19 02:49:43,401 INFO [train.py:874] (1/4) Epoch 23, batch 1300, aishell_loss[loss=0.142, simple_loss=0.2275, pruned_loss=0.02823, over 4930.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2278, pruned_loss=0.03224, over 984185.70 frames.], batch size: 49, aishell_tot_loss[loss=0.1474, simple_loss=0.2334, pruned_loss=0.03071, over 950561.26 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2211, pruned_loss=0.03321, over 944795.30 frames.], batch size: 49, lr: 3.58e-04
+2022-06-19 02:50:15,879 INFO [train.py:874] (1/4) Epoch 23, batch 1350, aishell_loss[loss=0.1244, simple_loss=0.211, pruned_loss=0.01885, over 4866.00 frames.], tot_loss[loss=0.1472, simple_loss=0.2283, pruned_loss=0.03304, over 984584.00 frames.], batch size: 28, aishell_tot_loss[loss=0.1472, simple_loss=0.2332, pruned_loss=0.03059, over 953723.76 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2221, pruned_loss=0.03421, over 950761.02 frames.], batch size: 28, lr: 3.57e-04
+2022-06-19 02:50:47,391 INFO [train.py:874] (1/4) Epoch 23, batch 1400, datatang_loss[loss=0.1549, simple_loss=0.2297, pruned_loss=0.04006, over 4936.00 frames.], tot_loss[loss=0.1481, simple_loss=0.2292, pruned_loss=0.03345, over 985123.55 frames.], batch size: 62, aishell_tot_loss[loss=0.1473, simple_loss=0.2336, pruned_loss=0.03051, over 957939.87 frames.], datatang_tot_loss[loss=0.1463, simple_loss=0.2229, pruned_loss=0.03486, over 954735.88 frames.], batch size: 62, lr: 3.57e-04
+2022-06-19 02:51:19,371 INFO [train.py:874] (1/4) Epoch 23, batch 1450, aishell_loss[loss=0.167, simple_loss=0.2517, pruned_loss=0.04116, over 4936.00 frames.], tot_loss[loss=0.1476, simple_loss=0.2287, pruned_loss=0.03326, over 985384.91 frames.], batch size: 68, aishell_tot_loss[loss=0.1474, simple_loss=0.2337, pruned_loss=0.03059, over 961045.68 frames.], datatang_tot_loss[loss=0.146, simple_loss=0.2225, pruned_loss=0.03473, over 958699.78 frames.], batch size: 68, lr: 3.57e-04
+2022-06-19 02:51:53,290 INFO [train.py:874] (1/4) Epoch 23, batch 1500, aishell_loss[loss=0.1574, simple_loss=0.239, pruned_loss=0.03793, over 4861.00 frames.], tot_loss[loss=0.147, simple_loss=0.2278, pruned_loss=0.03311, over 985223.78 frames.], batch size: 35, aishell_tot_loss[loss=0.1469, simple_loss=0.2329, pruned_loss=0.03044, over 963352.95 frames.], datatang_tot_loss[loss=0.1461, simple_loss=0.2225, pruned_loss=0.03484, over 962252.45 frames.], batch size: 35, lr: 3.57e-04
+2022-06-19 02:52:24,982 INFO [train.py:874] (1/4) Epoch 23, batch 1550, aishell_loss[loss=0.1446, simple_loss=0.2326, pruned_loss=0.02832, over 4874.00 frames.], tot_loss[loss=0.147, simple_loss=0.2287, pruned_loss=0.03263, over 984682.65 frames.], batch size: 42, aishell_tot_loss[loss=0.1474, simple_loss=0.2338, pruned_loss=0.03047, over 966091.19 frames.], datatang_tot_loss[loss=0.1456, simple_loss=0.2222, pruned_loss=0.03454, over 964165.06 frames.], batch size: 42, lr: 3.57e-04
+2022-06-19 02:52:56,563 INFO [train.py:874] (1/4) Epoch 23, batch 1600, datatang_loss[loss=0.1266, simple_loss=0.2065, pruned_loss=0.02335, over 4922.00 frames.], tot_loss[loss=0.1466, simple_loss=0.228, pruned_loss=0.03254, over 984494.32 frames.], batch size: 73, aishell_tot_loss[loss=0.1476, simple_loss=0.2339, pruned_loss=0.03062, over 967580.00 frames.], datatang_tot_loss[loss=0.1452, simple_loss=0.2218, pruned_loss=0.03426, over 967098.10 frames.], batch size: 73, lr: 3.57e-04
+2022-06-19 02:53:27,753 INFO [train.py:874] (1/4) Epoch 23, batch 1650, datatang_loss[loss=0.1429, simple_loss=0.2037, pruned_loss=0.04109, over 4953.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2276, pruned_loss=0.03246, over 984682.04 frames.], batch size: 24, aishell_tot_loss[loss=0.1471, simple_loss=0.2333, pruned_loss=0.03049, over 969993.43 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.2219, pruned_loss=0.03438, over 968927.85 frames.], batch size: 24, lr: 3.57e-04
+2022-06-19 02:54:00,681 INFO [train.py:874] (1/4) Epoch 23, batch 1700, aishell_loss[loss=0.1443, simple_loss=0.2305, pruned_loss=0.02908, over 4848.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2276, pruned_loss=0.03225, over 984654.23 frames.], batch size: 35, aishell_tot_loss[loss=0.1472, simple_loss=0.2332, pruned_loss=0.03056, over 971841.14 frames.], datatang_tot_loss[loss=0.1451, simple_loss=0.2218, pruned_loss=0.03416, over 970614.82 frames.], batch size: 35, lr: 3.57e-04
+2022-06-19 02:54:33,028 INFO [train.py:874] (1/4) Epoch 23, batch 1750, aishell_loss[loss=0.1584, simple_loss=0.25, pruned_loss=0.03344, over 4910.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2282, pruned_loss=0.03251, over 984906.09 frames.], batch size: 41, aishell_tot_loss[loss=0.1475, simple_loss=0.2336, pruned_loss=0.03072, over 973311.05 frames.], datatang_tot_loss[loss=0.1453, simple_loss=0.222, pruned_loss=0.03425, over 972569.07 frames.], batch size: 41, lr: 3.57e-04
+2022-06-19 02:55:05,101 INFO [train.py:874] (1/4) Epoch 23, batch 1800, aishell_loss[loss=0.1337, simple_loss=0.2154, pruned_loss=0.02602, over 4966.00 frames.], tot_loss[loss=0.146, simple_loss=0.2274, pruned_loss=0.03231, over 984886.24 frames.], batch size: 31, aishell_tot_loss[loss=0.1474, simple_loss=0.2335, pruned_loss=0.03061, over 974565.45 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2216, pruned_loss=0.03412, over 974124.80 frames.], batch size: 31, lr: 3.57e-04
+2022-06-19 02:55:38,718 INFO [train.py:874] (1/4) Epoch 23, batch 1850, datatang_loss[loss=0.1393, simple_loss=0.2252, pruned_loss=0.02671, over 4930.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2284, pruned_loss=0.03206, over 985464.84 frames.], batch size: 77, aishell_tot_loss[loss=0.1476, simple_loss=0.2341, pruned_loss=0.03056, over 976204.76 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.222, pruned_loss=0.0339, over 975579.68 frames.], batch size: 77, lr: 3.57e-04
+2022-06-19 02:56:09,935 INFO [train.py:874] (1/4) Epoch 23, batch 1900, aishell_loss[loss=0.1661, simple_loss=0.2568, pruned_loss=0.03767, over 4941.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2278, pruned_loss=0.0318, over 985701.82 frames.], batch size: 79, aishell_tot_loss[loss=0.1479, simple_loss=0.2345, pruned_loss=0.03067, over 977471.44 frames.], datatang_tot_loss[loss=0.1439, simple_loss=0.2209, pruned_loss=0.03348, over 976822.81 frames.], batch size: 79, lr: 3.56e-04
+2022-06-19 02:56:41,478 INFO [train.py:874] (1/4) Epoch 23, batch 1950, datatang_loss[loss=0.1522, simple_loss=0.2272, pruned_loss=0.03857, over 4920.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2278, pruned_loss=0.03251, over 985530.27 frames.], batch size: 73, aishell_tot_loss[loss=0.1484, simple_loss=0.235, pruned_loss=0.03095, over 977915.02 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.2209, pruned_loss=0.0338, over 978214.76 frames.], batch size: 73, lr: 3.56e-04
+2022-06-19 02:57:14,386 INFO [train.py:874] (1/4) Epoch 23, batch 2000, datatang_loss[loss=0.1354, simple_loss=0.2139, pruned_loss=0.02847, over 4935.00 frames.], tot_loss[loss=0.1466, simple_loss=0.2276, pruned_loss=0.03275, over 985478.62 frames.], batch size: 25, aishell_tot_loss[loss=0.1486, simple_loss=0.2351, pruned_loss=0.03105, over 978487.49 frames.], datatang_tot_loss[loss=0.1444, simple_loss=0.2209, pruned_loss=0.03392, over 979327.33 frames.], batch size: 25, lr: 3.56e-04
+2022-06-19 02:57:14,387 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 02:57:32,300 INFO [train.py:914] (1/4) Epoch 23, validation: loss=0.1648, simple_loss=0.2485, pruned_loss=0.04055, over 1622729.00 frames.
+2022-06-19 02:58:03,737 INFO [train.py:874] (1/4) Epoch 23, batch 2050, datatang_loss[loss=0.151, simple_loss=0.2361, pruned_loss=0.033, over 4921.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2277, pruned_loss=0.03282, over 985191.40 frames.], batch size: 77, aishell_tot_loss[loss=0.1485, simple_loss=0.2349, pruned_loss=0.03111, over 978933.52 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2211, pruned_loss=0.034, over 980138.17 frames.], batch size: 77, lr: 3.56e-04
+2022-06-19 02:58:35,258 INFO [train.py:874] (1/4) Epoch 23, batch 2100, aishell_loss[loss=0.1536, simple_loss=0.2379, pruned_loss=0.03465, over 4873.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2273, pruned_loss=0.03259, over 985329.33 frames.], batch size: 35, aishell_tot_loss[loss=0.1484, simple_loss=0.2345, pruned_loss=0.03109, over 979686.18 frames.], datatang_tot_loss[loss=0.1443, simple_loss=0.221, pruned_loss=0.03381, over 980861.76 frames.], batch size: 35, lr: 3.56e-04
+2022-06-19 02:59:07,977 INFO [train.py:874] (1/4) Epoch 23, batch 2150, datatang_loss[loss=0.1519, simple_loss=0.228, pruned_loss=0.03787, over 4927.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2267, pruned_loss=0.03244, over 985082.58 frames.], batch size: 71, aishell_tot_loss[loss=0.1481, simple_loss=0.2343, pruned_loss=0.03092, over 979985.71 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2208, pruned_loss=0.03376, over 981459.35 frames.], batch size: 71, lr: 3.56e-04
+2022-06-19 02:59:40,132 INFO [train.py:874] (1/4) Epoch 23, batch 2200, datatang_loss[loss=0.1307, simple_loss=0.1984, pruned_loss=0.03152, over 4959.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2268, pruned_loss=0.03194, over 984902.82 frames.], batch size: 34, aishell_tot_loss[loss=0.1479, simple_loss=0.2344, pruned_loss=0.03072, over 980295.23 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2206, pruned_loss=0.03347, over 981970.14 frames.], batch size: 34, lr: 3.56e-04
+2022-06-19 03:00:11,258 INFO [train.py:874] (1/4) Epoch 23, batch 2250, datatang_loss[loss=0.1413, simple_loss=0.2376, pruned_loss=0.02246, over 4837.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2262, pruned_loss=0.03202, over 984966.68 frames.], batch size: 30, aishell_tot_loss[loss=0.1474, simple_loss=0.2337, pruned_loss=0.03054, over 980868.29 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2208, pruned_loss=0.03365, over 982320.04 frames.], batch size: 30, lr: 3.56e-04
+2022-06-19 03:00:43,964 INFO [train.py:874] (1/4) Epoch 23, batch 2300, aishell_loss[loss=0.16, simple_loss=0.2422, pruned_loss=0.03893, over 4870.00 frames.], tot_loss[loss=0.1464, simple_loss=0.2273, pruned_loss=0.03278, over 985160.69 frames.], batch size: 36, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03068, over 981095.82 frames.], datatang_tot_loss[loss=0.145, simple_loss=0.2216, pruned_loss=0.03425, over 983071.40 frames.], batch size: 36, lr: 3.56e-04
+2022-06-19 03:01:16,186 INFO [train.py:874] (1/4) Epoch 23, batch 2350, aishell_loss[loss=0.1446, simple_loss=0.2363, pruned_loss=0.02646, over 4914.00 frames.], tot_loss[loss=0.1462, simple_loss=0.2273, pruned_loss=0.03259, over 984933.68 frames.], batch size: 52, aishell_tot_loss[loss=0.1476, simple_loss=0.234, pruned_loss=0.03061, over 981533.06 frames.], datatang_tot_loss[loss=0.1449, simple_loss=0.2214, pruned_loss=0.03415, over 983107.41 frames.], batch size: 52, lr: 3.56e-04
+2022-06-19 03:01:47,565 INFO [train.py:874] (1/4) Epoch 23, batch 2400, aishell_loss[loss=0.1626, simple_loss=0.2465, pruned_loss=0.03931, over 4926.00 frames.], tot_loss[loss=0.146, simple_loss=0.2266, pruned_loss=0.03265, over 984880.99 frames.], batch size: 54, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03075, over 981587.57 frames.], datatang_tot_loss[loss=0.1446, simple_loss=0.2211, pruned_loss=0.03404, over 983579.45 frames.], batch size: 54, lr: 3.55e-04
+2022-06-19 03:02:18,517 INFO [train.py:874] (1/4) Epoch 23, batch 2450, aishell_loss[loss=0.1387, simple_loss=0.2172, pruned_loss=0.03013, over 4874.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2267, pruned_loss=0.03198, over 985062.60 frames.], batch size: 42, aishell_tot_loss[loss=0.1474, simple_loss=0.2338, pruned_loss=0.03052, over 982111.49 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2207, pruned_loss=0.0337, over 983827.69 frames.], batch size: 42, lr: 3.55e-04
+2022-06-19 03:02:51,513 INFO [train.py:874] (1/4) Epoch 23, batch 2500, datatang_loss[loss=0.1321, simple_loss=0.21, pruned_loss=0.0271, over 4946.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2258, pruned_loss=0.03174, over 985146.86 frames.], batch size: 86, aishell_tot_loss[loss=0.1472, simple_loss=0.2334, pruned_loss=0.03045, over 982437.79 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2203, pruned_loss=0.03342, over 984033.39 frames.], batch size: 86, lr: 3.55e-04
+2022-06-19 03:03:23,713 INFO [train.py:874] (1/4) Epoch 23, batch 2550, datatang_loss[loss=0.1228, simple_loss=0.2071, pruned_loss=0.01922, over 4919.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2262, pruned_loss=0.03215, over 985614.01 frames.], batch size: 77, aishell_tot_loss[loss=0.1476, simple_loss=0.2336, pruned_loss=0.03078, over 982856.93 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2205, pruned_loss=0.03344, over 984531.92 frames.], batch size: 77, lr: 3.55e-04
+2022-06-19 03:03:55,767 INFO [train.py:874] (1/4) Epoch 23, batch 2600, aishell_loss[loss=0.1269, simple_loss=0.2161, pruned_loss=0.01886, over 4957.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2259, pruned_loss=0.03228, over 985377.92 frames.], batch size: 31, aishell_tot_loss[loss=0.1471, simple_loss=0.233, pruned_loss=0.0306, over 982839.31 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2206, pruned_loss=0.03375, over 984716.61 frames.], batch size: 31, lr: 3.55e-04
+2022-06-19 03:04:28,496 INFO [train.py:874] (1/4) Epoch 23, batch 2650, datatang_loss[loss=0.1507, simple_loss=0.23, pruned_loss=0.03573, over 4925.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2266, pruned_loss=0.03214, over 985641.64 frames.], batch size: 71, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03063, over 983374.45 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.2199, pruned_loss=0.03365, over 984873.40 frames.], batch size: 71, lr: 3.55e-04
+2022-06-19 03:05:00,051 INFO [train.py:874] (1/4) Epoch 23, batch 2700, aishell_loss[loss=0.1427, simple_loss=0.2313, pruned_loss=0.02702, over 4874.00 frames.], tot_loss[loss=0.1459, simple_loss=0.227, pruned_loss=0.03243, over 985785.05 frames.], batch size: 28, aishell_tot_loss[loss=0.1476, simple_loss=0.2338, pruned_loss=0.0307, over 983754.36 frames.], datatang_tot_loss[loss=0.1441, simple_loss=0.2205, pruned_loss=0.03387, over 985008.99 frames.], batch size: 28, lr: 3.55e-04
+2022-06-19 03:05:31,312 INFO [train.py:874] (1/4) Epoch 23, batch 2750, aishell_loss[loss=0.1559, simple_loss=0.2327, pruned_loss=0.03956, over 4951.00 frames.], tot_loss[loss=0.1463, simple_loss=0.2274, pruned_loss=0.03257, over 985557.19 frames.], batch size: 31, aishell_tot_loss[loss=0.1479, simple_loss=0.2341, pruned_loss=0.03083, over 983755.10 frames.], datatang_tot_loss[loss=0.1442, simple_loss=0.2205, pruned_loss=0.03392, over 985103.17 frames.], batch size: 31, lr: 3.55e-04
+2022-06-19 03:06:03,718 INFO [train.py:874] (1/4) Epoch 23, batch 2800, aishell_loss[loss=0.1365, simple_loss=0.2316, pruned_loss=0.02068, over 4912.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2267, pruned_loss=0.0324, over 985346.13 frames.], batch size: 41, aishell_tot_loss[loss=0.148, simple_loss=0.2341, pruned_loss=0.03093, over 983887.00 frames.], datatang_tot_loss[loss=0.1436, simple_loss=0.22, pruned_loss=0.03365, over 984990.21 frames.], batch size: 41, lr: 3.55e-04
+2022-06-19 03:06:35,759 INFO [train.py:874] (1/4) Epoch 23, batch 2850, aishell_loss[loss=0.123, simple_loss=0.1879, pruned_loss=0.02902, over 4941.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2267, pruned_loss=0.03233, over 985166.15 frames.], batch size: 21, aishell_tot_loss[loss=0.148, simple_loss=0.234, pruned_loss=0.03099, over 983999.50 frames.], datatang_tot_loss[loss=0.1435, simple_loss=0.2199, pruned_loss=0.03357, over 984916.86 frames.], batch size: 21, lr: 3.55e-04
+2022-06-19 03:07:06,070 INFO [train.py:874] (1/4) Epoch 23, batch 2900, aishell_loss[loss=0.1489, simple_loss=0.2378, pruned_loss=0.02999, over 4972.00 frames.], tot_loss[loss=0.1457, simple_loss=0.2272, pruned_loss=0.03208, over 985420.15 frames.], batch size: 31, aishell_tot_loss[loss=0.1477, simple_loss=0.234, pruned_loss=0.03073, over 984173.66 frames.], datatang_tot_loss[loss=0.1438, simple_loss=0.2205, pruned_loss=0.03355, over 985170.00 frames.], batch size: 31, lr: 3.55e-04
+2022-06-19 03:07:38,865 INFO [train.py:874] (1/4) Epoch 23, batch 2950, datatang_loss[loss=0.115, simple_loss=0.1976, pruned_loss=0.01621, over 4957.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2275, pruned_loss=0.03234, over 985777.51 frames.], batch size: 67, aishell_tot_loss[loss=0.1479, simple_loss=0.2342, pruned_loss=0.03085, over 984645.72 frames.], datatang_tot_loss[loss=0.144, simple_loss=0.2206, pruned_loss=0.03375, over 985259.84 frames.], batch size: 67, lr: 3.54e-04
+2022-06-19 03:08:11,546 INFO [train.py:874] (1/4) Epoch 23, batch 3000, aishell_loss[loss=0.1459, simple_loss=0.2349, pruned_loss=0.02845, over 4863.00 frames.], tot_loss[loss=0.1458, simple_loss=0.2273, pruned_loss=0.03216, over 985879.29 frames.], batch size: 35, aishell_tot_loss[loss=0.1479, simple_loss=0.2342, pruned_loss=0.03086, over 984757.32 frames.], datatang_tot_loss[loss=0.1437, simple_loss=0.2203, pruned_loss=0.03359, over 985458.43 frames.], batch size: 35, lr: 3.54e-04
+2022-06-19 03:08:11,547 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 03:08:29,177 INFO [train.py:914] (1/4) Epoch 23, validation: loss=0.1651, simple_loss=0.2488, pruned_loss=0.04074, over 1622729.00 frames.
+2022-06-19 03:09:00,953 INFO [train.py:874] (1/4) Epoch 23, batch 3050, datatang_loss[loss=0.1371, simple_loss=0.2095, pruned_loss=0.03238, over 4947.00 frames.], tot_loss[loss=0.145, simple_loss=0.2265, pruned_loss=0.03177, over 985490.10 frames.], batch size: 50, aishell_tot_loss[loss=0.1475, simple_loss=0.2335, pruned_loss=0.0307, over 984559.67 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2196, pruned_loss=0.03338, over 985447.37 frames.], batch size: 50, lr: 3.54e-04
+2022-06-19 03:09:32,345 INFO [train.py:874] (1/4) Epoch 23, batch 3100, datatang_loss[loss=0.1349, simple_loss=0.2144, pruned_loss=0.02774, over 4954.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2272, pruned_loss=0.03177, over 985277.13 frames.], batch size: 67, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03078, over 984538.24 frames.], datatang_tot_loss[loss=0.1432, simple_loss=0.2197, pruned_loss=0.03329, over 985385.54 frames.], batch size: 67, lr: 3.54e-04
+2022-06-19 03:10:04,599 INFO [train.py:874] (1/4) Epoch 23, batch 3150, datatang_loss[loss=0.1381, simple_loss=0.2149, pruned_loss=0.03063, over 4943.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2271, pruned_loss=0.03179, over 985393.15 frames.], batch size: 69, aishell_tot_loss[loss=0.1483, simple_loss=0.2344, pruned_loss=0.03104, over 984686.16 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2193, pruned_loss=0.03297, over 985416.35 frames.], batch size: 69, lr: 3.54e-04
+2022-06-19 03:10:38,020 INFO [train.py:874] (1/4) Epoch 23, batch 3200, datatang_loss[loss=0.1475, simple_loss=0.2306, pruned_loss=0.03225, over 4936.00 frames.], tot_loss[loss=0.1453, simple_loss=0.2266, pruned_loss=0.03197, over 985076.34 frames.], batch size: 94, aishell_tot_loss[loss=0.1479, simple_loss=0.2339, pruned_loss=0.03098, over 984495.18 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2192, pruned_loss=0.03317, over 985355.58 frames.], batch size: 94, lr: 3.54e-04
+2022-06-19 03:11:09,275 INFO [train.py:874] (1/4) Epoch 23, batch 3250, datatang_loss[loss=0.1447, simple_loss=0.2312, pruned_loss=0.02909, over 4964.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2266, pruned_loss=0.03138, over 984978.90 frames.], batch size: 67, aishell_tot_loss[loss=0.1474, simple_loss=0.2337, pruned_loss=0.03051, over 984579.54 frames.], datatang_tot_loss[loss=0.1427, simple_loss=0.2193, pruned_loss=0.03299, over 985210.09 frames.], batch size: 67, lr: 3.54e-04
+2022-06-19 03:11:41,514 INFO [train.py:874] (1/4) Epoch 23, batch 3300, aishell_loss[loss=0.1486, simple_loss=0.2326, pruned_loss=0.03229, over 4930.00 frames.], tot_loss[loss=0.1452, simple_loss=0.2272, pruned_loss=0.03164, over 984725.04 frames.], batch size: 32, aishell_tot_loss[loss=0.1483, simple_loss=0.2347, pruned_loss=0.03093, over 984404.27 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2188, pruned_loss=0.03279, over 985140.45 frames.], batch size: 32, lr: 3.54e-04
+2022-06-19 03:12:14,963 INFO [train.py:874] (1/4) Epoch 23, batch 3350, aishell_loss[loss=0.1573, simple_loss=0.2524, pruned_loss=0.03108, over 4949.00 frames.], tot_loss[loss=0.1454, simple_loss=0.2273, pruned_loss=0.03175, over 984914.91 frames.], batch size: 56, aishell_tot_loss[loss=0.1482, simple_loss=0.2346, pruned_loss=0.03094, over 984335.85 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2191, pruned_loss=0.03283, over 985391.71 frames.], batch size: 56, lr: 3.54e-04
+2022-06-19 03:12:46,725 INFO [train.py:874] (1/4) Epoch 23, batch 3400, datatang_loss[loss=0.1332, simple_loss=0.2204, pruned_loss=0.02302, over 4948.00 frames.], tot_loss[loss=0.1451, simple_loss=0.2272, pruned_loss=0.03145, over 985241.57 frames.], batch size: 88, aishell_tot_loss[loss=0.1477, simple_loss=0.2343, pruned_loss=0.03061, over 984518.03 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2197, pruned_loss=0.03279, over 985551.89 frames.], batch size: 88, lr: 3.54e-04
+2022-06-19 03:13:18,809 INFO [train.py:874] (1/4) Epoch 23, batch 3450, aishell_loss[loss=0.1256, simple_loss=0.194, pruned_loss=0.02862, over 4901.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2261, pruned_loss=0.03102, over 984841.93 frames.], batch size: 21, aishell_tot_loss[loss=0.148, simple_loss=0.2343, pruned_loss=0.03079, over 984259.49 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2187, pruned_loss=0.03206, over 985419.72 frames.], batch size: 21, lr: 3.54e-04
+2022-06-19 03:13:51,964 INFO [train.py:874] (1/4) Epoch 23, batch 3500, datatang_loss[loss=0.1237, simple_loss=0.2016, pruned_loss=0.02285, over 4922.00 frames.], tot_loss[loss=0.144, simple_loss=0.2259, pruned_loss=0.03104, over 984761.02 frames.], batch size: 75, aishell_tot_loss[loss=0.1475, simple_loss=0.2341, pruned_loss=0.03049, over 984157.78 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.219, pruned_loss=0.03225, over 985407.87 frames.], batch size: 75, lr: 3.53e-04
+2022-06-19 03:14:23,651 INFO [train.py:874] (1/4) Epoch 23, batch 3550, datatang_loss[loss=0.1258, simple_loss=0.2109, pruned_loss=0.02039, over 4928.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2255, pruned_loss=0.03105, over 984657.54 frames.], batch size: 79, aishell_tot_loss[loss=0.1474, simple_loss=0.2339, pruned_loss=0.03043, over 984052.30 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2188, pruned_loss=0.03222, over 985366.59 frames.], batch size: 79, lr: 3.53e-04
+2022-06-19 03:14:55,475 INFO [train.py:874] (1/4) Epoch 23, batch 3600, aishell_loss[loss=0.1591, simple_loss=0.2415, pruned_loss=0.0384, over 4943.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2255, pruned_loss=0.0311, over 984796.13 frames.], batch size: 45, aishell_tot_loss[loss=0.1474, simple_loss=0.2339, pruned_loss=0.03042, over 984289.79 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2189, pruned_loss=0.03216, over 985219.43 frames.], batch size: 45, lr: 3.53e-04
+2022-06-19 03:15:29,867 INFO [train.py:874] (1/4) Epoch 23, batch 3650, aishell_loss[loss=0.1343, simple_loss=0.2203, pruned_loss=0.02415, over 4928.00 frames.], tot_loss[loss=0.1443, simple_loss=0.226, pruned_loss=0.03128, over 984804.95 frames.], batch size: 49, aishell_tot_loss[loss=0.1475, simple_loss=0.2342, pruned_loss=0.03041, over 984247.45 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2192, pruned_loss=0.0323, over 985268.98 frames.], batch size: 49, lr: 3.53e-04
+2022-06-19 03:16:01,416 INFO [train.py:874] (1/4) Epoch 23, batch 3700, datatang_loss[loss=0.1694, simple_loss=0.2501, pruned_loss=0.04441, over 4943.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2252, pruned_loss=0.03096, over 984790.64 frames.], batch size: 109, aishell_tot_loss[loss=0.1471, simple_loss=0.2337, pruned_loss=0.03025, over 984113.60 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2191, pruned_loss=0.03204, over 985362.51 frames.], batch size: 109, lr: 3.53e-04
+2022-06-19 03:16:34,254 INFO [train.py:874] (1/4) Epoch 23, batch 3750, datatang_loss[loss=0.118, simple_loss=0.1969, pruned_loss=0.0195, over 4928.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2255, pruned_loss=0.03066, over 985272.06 frames.], batch size: 71, aishell_tot_loss[loss=0.147, simple_loss=0.2339, pruned_loss=0.03005, over 984456.55 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2189, pruned_loss=0.03188, over 985549.89 frames.], batch size: 71, lr: 3.53e-04
+2022-06-19 03:17:06,328 INFO [train.py:874] (1/4) Epoch 23, batch 3800, datatang_loss[loss=0.1353, simple_loss=0.2025, pruned_loss=0.03402, over 4933.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2263, pruned_loss=0.03065, over 985650.66 frames.], batch size: 34, aishell_tot_loss[loss=0.1468, simple_loss=0.2339, pruned_loss=0.02981, over 984712.95 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2194, pruned_loss=0.03207, over 985768.82 frames.], batch size: 34, lr: 3.53e-04
+2022-06-19 03:17:37,348 INFO [train.py:874] (1/4) Epoch 23, batch 3850, aishell_loss[loss=0.1635, simple_loss=0.252, pruned_loss=0.03748, over 4941.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2268, pruned_loss=0.0308, over 985379.28 frames.], batch size: 56, aishell_tot_loss[loss=0.1468, simple_loss=0.234, pruned_loss=0.02981, over 984703.52 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2194, pruned_loss=0.03222, over 985625.86 frames.], batch size: 56, lr: 3.53e-04
+2022-06-19 03:18:07,537 INFO [train.py:874] (1/4) Epoch 23, batch 3900, aishell_loss[loss=0.1518, simple_loss=0.2433, pruned_loss=0.03022, over 4935.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2267, pruned_loss=0.03099, over 985379.57 frames.], batch size: 45, aishell_tot_loss[loss=0.1466, simple_loss=0.2337, pruned_loss=0.02978, over 984369.84 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2197, pruned_loss=0.03239, over 985991.79 frames.], batch size: 45, lr: 3.53e-04
+2022-06-19 03:18:38,071 INFO [train.py:874] (1/4) Epoch 23, batch 3950, datatang_loss[loss=0.1555, simple_loss=0.2378, pruned_loss=0.0366, over 4953.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2268, pruned_loss=0.03093, over 984878.91 frames.], batch size: 91, aishell_tot_loss[loss=0.147, simple_loss=0.2339, pruned_loss=0.03006, over 984160.40 frames.], datatang_tot_loss[loss=0.1416, simple_loss=0.2191, pruned_loss=0.03209, over 985743.82 frames.], batch size: 91, lr: 3.53e-04
+2022-06-19 03:19:10,354 INFO [train.py:874] (1/4) Epoch 23, batch 4000, aishell_loss[loss=0.1497, simple_loss=0.2447, pruned_loss=0.02736, over 4939.00 frames.], tot_loss[loss=0.1446, simple_loss=0.227, pruned_loss=0.03116, over 984835.07 frames.], batch size: 81, aishell_tot_loss[loss=0.1473, simple_loss=0.2343, pruned_loss=0.03019, over 984082.07 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.219, pruned_loss=0.03218, over 985761.25 frames.], batch size: 81, lr: 3.52e-04
+2022-06-19 03:19:10,355 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 03:19:27,334 INFO [train.py:914] (1/4) Epoch 23, validation: loss=0.166, simple_loss=0.2494, pruned_loss=0.04127, over 1622729.00 frames.
+2022-06-19 03:19:59,954 INFO [train.py:874] (1/4) Epoch 23, batch 4050, datatang_loss[loss=0.1492, simple_loss=0.2196, pruned_loss=0.03939, over 4920.00 frames.], tot_loss[loss=0.145, simple_loss=0.2266, pruned_loss=0.0317, over 985209.96 frames.], batch size: 75, aishell_tot_loss[loss=0.1477, simple_loss=0.2346, pruned_loss=0.03041, over 984378.34 frames.], datatang_tot_loss[loss=0.142, simple_loss=0.2191, pruned_loss=0.03243, over 985768.01 frames.], batch size: 75, lr: 3.52e-04
+2022-06-19 03:20:30,739 INFO [train.py:874] (1/4) Epoch 23, batch 4100, datatang_loss[loss=0.1145, simple_loss=0.1947, pruned_loss=0.01712, over 4930.00 frames.], tot_loss[loss=0.1461, simple_loss=0.2274, pruned_loss=0.0324, over 985198.60 frames.], batch size: 62, aishell_tot_loss[loss=0.1482, simple_loss=0.235, pruned_loss=0.03069, over 984282.84 frames.], datatang_tot_loss[loss=0.1428, simple_loss=0.2196, pruned_loss=0.03296, over 985876.31 frames.], batch size: 62, lr: 3.52e-04
+2022-06-19 03:21:01,187 INFO [train.py:874] (1/4) Epoch 23, batch 4150, datatang_loss[loss=0.161, simple_loss=0.2403, pruned_loss=0.04089, over 4959.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2272, pruned_loss=0.03194, over 985057.89 frames.], batch size: 99, aishell_tot_loss[loss=0.1483, simple_loss=0.2351, pruned_loss=0.03073, over 984234.45 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2192, pruned_loss=0.03258, over 985825.82 frames.], batch size: 99, lr: 3.52e-04
+2022-06-19 03:22:22,967 INFO [train.py:874] (1/4) Epoch 24, batch 50, aishell_loss[loss=0.1442, simple_loss=0.2343, pruned_loss=0.02706, over 4871.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2221, pruned_loss=0.02927, over 218507.18 frames.], batch size: 35, aishell_tot_loss[loss=0.1468, simple_loss=0.2336, pruned_loss=0.02999, over 124636.85 frames.], datatang_tot_loss[loss=0.1331, simple_loss=0.2091, pruned_loss=0.02857, over 107475.29 frames.], batch size: 35, lr: 3.45e-04
+2022-06-19 03:22:55,071 INFO [train.py:874] (1/4) Epoch 24, batch 100, datatang_loss[loss=0.1324, simple_loss=0.2035, pruned_loss=0.03064, over 4856.00 frames.], tot_loss[loss=0.144, simple_loss=0.2258, pruned_loss=0.03106, over 388498.75 frames.], batch size: 25, aishell_tot_loss[loss=0.1512, simple_loss=0.2382, pruned_loss=0.03214, over 237497.67 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2101, pruned_loss=0.02943, over 198966.66 frames.], batch size: 25, lr: 3.45e-04
+2022-06-19 03:23:26,190 INFO [train.py:874] (1/4) Epoch 24, batch 150, aishell_loss[loss=0.1334, simple_loss=0.2161, pruned_loss=0.02529, over 4870.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2251, pruned_loss=0.03079, over 521399.40 frames.], batch size: 28, aishell_tot_loss[loss=0.1495, simple_loss=0.2368, pruned_loss=0.03115, over 335738.85 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2107, pruned_loss=0.03019, over 281389.87 frames.], batch size: 28, lr: 3.45e-04
+2022-06-19 03:23:59,935 INFO [train.py:874] (1/4) Epoch 24, batch 200, datatang_loss[loss=0.1389, simple_loss=0.2081, pruned_loss=0.03487, over 4922.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2251, pruned_loss=0.03083, over 624173.31 frames.], batch size: 77, aishell_tot_loss[loss=0.1483, simple_loss=0.2357, pruned_loss=0.03049, over 409324.28 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2131, pruned_loss=0.03106, over 367380.05 frames.], batch size: 77, lr: 3.45e-04
+2022-06-19 03:24:28,441 INFO [train.py:874] (1/4) Epoch 24, batch 250, aishell_loss[loss=0.1243, simple_loss=0.2152, pruned_loss=0.01672, over 4971.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2245, pruned_loss=0.03017, over 704159.67 frames.], batch size: 30, aishell_tot_loss[loss=0.147, simple_loss=0.2345, pruned_loss=0.02971, over 484761.70 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.213, pruned_loss=0.03086, over 431805.79 frames.], batch size: 30, lr: 3.44e-04
+2022-06-19 03:25:02,111 INFO [train.py:874] (1/4) Epoch 24, batch 300, datatang_loss[loss=0.1275, simple_loss=0.2122, pruned_loss=0.02144, over 4927.00 frames.], tot_loss[loss=0.1426, simple_loss=0.224, pruned_loss=0.03057, over 766327.45 frames.], batch size: 57, aishell_tot_loss[loss=0.1469, simple_loss=0.2341, pruned_loss=0.02982, over 527837.86 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2142, pruned_loss=0.03125, over 513757.45 frames.], batch size: 57, lr: 3.44e-04
+2022-06-19 03:25:34,575 INFO [train.py:874] (1/4) Epoch 24, batch 350, aishell_loss[loss=0.137, simple_loss=0.2244, pruned_loss=0.0248, over 4959.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2228, pruned_loss=0.03008, over 815110.72 frames.], batch size: 54, aishell_tot_loss[loss=0.1459, simple_loss=0.2328, pruned_loss=0.02952, over 583950.49 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2134, pruned_loss=0.03087, over 567262.85 frames.], batch size: 54, lr: 3.44e-04
+2022-06-19 03:26:05,119 INFO [train.py:874] (1/4) Epoch 24, batch 400, datatang_loss[loss=0.1564, simple_loss=0.2304, pruned_loss=0.04122, over 4912.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2238, pruned_loss=0.03028, over 853136.68 frames.], batch size: 64, aishell_tot_loss[loss=0.1465, simple_loss=0.2335, pruned_loss=0.0298, over 624275.33 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2146, pruned_loss=0.03076, over 623914.14 frames.], batch size: 64, lr: 3.44e-04
+2022-06-19 03:26:39,129 INFO [train.py:874] (1/4) Epoch 24, batch 450, datatang_loss[loss=0.1337, simple_loss=0.2091, pruned_loss=0.02918, over 4960.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2251, pruned_loss=0.03086, over 882581.70 frames.], batch size: 67, aishell_tot_loss[loss=0.1476, simple_loss=0.2349, pruned_loss=0.03015, over 655561.31 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2159, pruned_loss=0.03116, over 677495.50 frames.], batch size: 67, lr: 3.44e-04
+2022-06-19 03:27:12,088 INFO [train.py:874] (1/4) Epoch 24, batch 500, aishell_loss[loss=0.1255, simple_loss=0.2171, pruned_loss=0.01702, over 4964.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2251, pruned_loss=0.03064, over 905684.59 frames.], batch size: 44, aishell_tot_loss[loss=0.1471, simple_loss=0.2343, pruned_loss=0.03002, over 700342.55 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2159, pruned_loss=0.03111, over 708435.19 frames.], batch size: 44, lr: 3.44e-04
+2022-06-19 03:27:40,940 INFO [train.py:874] (1/4) Epoch 24, batch 550, datatang_loss[loss=0.141, simple_loss=0.231, pruned_loss=0.02551, over 4877.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2248, pruned_loss=0.03063, over 923089.36 frames.], batch size: 25, aishell_tot_loss[loss=0.1469, simple_loss=0.2338, pruned_loss=0.02997, over 734891.47 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2159, pruned_loss=0.03116, over 739831.17 frames.], batch size: 25, lr: 3.44e-04
+2022-06-19 03:28:15,528 INFO [train.py:874] (1/4) Epoch 24, batch 600, datatang_loss[loss=0.1321, simple_loss=0.1971, pruned_loss=0.03357, over 4987.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2255, pruned_loss=0.03112, over 937382.21 frames.], batch size: 40, aishell_tot_loss[loss=0.1469, simple_loss=0.2338, pruned_loss=0.03005, over 764635.45 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2168, pruned_loss=0.03174, over 769013.55 frames.], batch size: 40, lr: 3.44e-04
+2022-06-19 03:28:47,970 INFO [train.py:874] (1/4) Epoch 24, batch 650, aishell_loss[loss=0.161, simple_loss=0.2488, pruned_loss=0.03663, over 4940.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2256, pruned_loss=0.0313, over 948194.30 frames.], batch size: 79, aishell_tot_loss[loss=0.1472, simple_loss=0.2336, pruned_loss=0.03043, over 797532.96 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2166, pruned_loss=0.03175, over 787673.12 frames.], batch size: 79, lr: 3.44e-04
+2022-06-19 03:29:24,092 INFO [train.py:874] (1/4) Epoch 24, batch 700, aishell_loss[loss=0.1255, simple_loss=0.2132, pruned_loss=0.01894, over 4938.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2263, pruned_loss=0.03155, over 956445.15 frames.], batch size: 32, aishell_tot_loss[loss=0.1472, simple_loss=0.2334, pruned_loss=0.03049, over 820501.76 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2176, pruned_loss=0.03207, over 810080.46 frames.], batch size: 32, lr: 3.44e-04
+2022-06-19 03:29:56,690 INFO [train.py:874] (1/4) Epoch 24, batch 750, aishell_loss[loss=0.1558, simple_loss=0.2347, pruned_loss=0.03843, over 4881.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2256, pruned_loss=0.03114, over 962483.61 frames.], batch size: 34, aishell_tot_loss[loss=0.1469, simple_loss=0.2333, pruned_loss=0.0302, over 835871.34 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2175, pruned_loss=0.03189, over 834518.31 frames.], batch size: 34, lr: 3.44e-04
+2022-06-19 03:30:29,821 INFO [train.py:874] (1/4) Epoch 24, batch 800, datatang_loss[loss=0.1316, simple_loss=0.2221, pruned_loss=0.0206, over 4912.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2265, pruned_loss=0.03109, over 967739.03 frames.], batch size: 81, aishell_tot_loss[loss=0.147, simple_loss=0.2336, pruned_loss=0.03018, over 856828.07 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2181, pruned_loss=0.03193, over 849019.89 frames.], batch size: 81, lr: 3.44e-04
+2022-06-19 03:30:59,443 INFO [train.py:874] (1/4) Epoch 24, batch 850, aishell_loss[loss=0.1498, simple_loss=0.2351, pruned_loss=0.03224, over 4967.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2255, pruned_loss=0.0309, over 971420.39 frames.], batch size: 61, aishell_tot_loss[loss=0.1463, simple_loss=0.2327, pruned_loss=0.02999, over 871328.64 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2179, pruned_loss=0.03189, over 865497.90 frames.], batch size: 61, lr: 3.43e-04
+2022-06-19 03:31:31,308 INFO [train.py:874] (1/4) Epoch 24, batch 900, aishell_loss[loss=0.1514, simple_loss=0.2408, pruned_loss=0.03095, over 4951.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2267, pruned_loss=0.03125, over 974608.52 frames.], batch size: 64, aishell_tot_loss[loss=0.1468, simple_loss=0.2333, pruned_loss=0.03012, over 885778.47 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2186, pruned_loss=0.03217, over 878637.79 frames.], batch size: 64, lr: 3.43e-04
+2022-06-19 03:31:59,209 INFO [train.py:874] (1/4) Epoch 24, batch 950, aishell_loss[loss=0.1491, simple_loss=0.2433, pruned_loss=0.02748, over 4937.00 frames.], tot_loss[loss=0.145, simple_loss=0.2267, pruned_loss=0.03168, over 977136.44 frames.], batch size: 54, aishell_tot_loss[loss=0.1475, simple_loss=0.2337, pruned_loss=0.03068, over 897892.33 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2183, pruned_loss=0.03215, over 890944.48 frames.], batch size: 54, lr: 3.43e-04
+2022-06-19 03:32:28,606 INFO [train.py:874] (1/4) Epoch 24, batch 1000, datatang_loss[loss=0.1419, simple_loss=0.2209, pruned_loss=0.0314, over 4931.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2264, pruned_loss=0.03143, over 978634.77 frames.], batch size: 57, aishell_tot_loss[loss=0.1472, simple_loss=0.2336, pruned_loss=0.03041, over 907745.65 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2183, pruned_loss=0.03218, over 902164.77 frames.], batch size: 57, lr: 3.43e-04
+2022-06-19 03:32:28,607 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 03:32:45,105 INFO [train.py:914] (1/4) 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] (1/4) Epoch 24, batch 1050, aishell_loss[loss=0.1541, simple_loss=0.2412, pruned_loss=0.03354, over 4867.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2269, pruned_loss=0.03138, over 980140.29 frames.], batch size: 36, aishell_tot_loss[loss=0.1468, simple_loss=0.2334, pruned_loss=0.03016, over 918579.29 frames.], datatang_tot_loss[loss=0.1418, simple_loss=0.2188, pruned_loss=0.03246, over 910108.57 frames.], batch size: 36, lr: 3.43e-04
+2022-06-19 03:33:42,074 INFO [train.py:874] (1/4) Epoch 24, batch 1100, aishell_loss[loss=0.1777, simple_loss=0.2482, pruned_loss=0.05365, over 4912.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2266, pruned_loss=0.03135, over 980919.72 frames.], batch size: 41, aishell_tot_loss[loss=0.1472, simple_loss=0.2335, pruned_loss=0.03042, over 926196.09 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2185, pruned_loss=0.03216, over 918821.77 frames.], batch size: 41, lr: 3.43e-04
+2022-06-19 03:34:11,132 INFO [train.py:874] (1/4) Epoch 24, batch 1150, aishell_loss[loss=0.1543, simple_loss=0.239, pruned_loss=0.0348, over 4917.00 frames.], tot_loss[loss=0.145, simple_loss=0.227, pruned_loss=0.03151, over 981883.70 frames.], batch size: 68, aishell_tot_loss[loss=0.1477, simple_loss=0.234, pruned_loss=0.03072, over 934154.92 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2183, pruned_loss=0.03207, over 925486.49 frames.], batch size: 68, lr: 3.43e-04
+2022-06-19 03:34:38,367 INFO [train.py:874] (1/4) Epoch 24, batch 1200, datatang_loss[loss=0.1419, simple_loss=0.2189, pruned_loss=0.03246, over 4914.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2268, pruned_loss=0.03142, over 982605.81 frames.], batch size: 64, aishell_tot_loss[loss=0.148, simple_loss=0.2344, pruned_loss=0.03084, over 939144.07 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2181, pruned_loss=0.03186, over 933663.76 frames.], batch size: 64, lr: 3.43e-04
+2022-06-19 03:35:08,062 INFO [train.py:874] (1/4) Epoch 24, batch 1250, datatang_loss[loss=0.1657, simple_loss=0.2235, pruned_loss=0.05392, over 4950.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2267, pruned_loss=0.0315, over 983234.43 frames.], batch size: 34, aishell_tot_loss[loss=0.148, simple_loss=0.2344, pruned_loss=0.03081, over 943125.72 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2186, pruned_loss=0.03194, over 941333.94 frames.], batch size: 34, lr: 3.43e-04
+2022-06-19 03:35:37,361 INFO [train.py:874] (1/4) Epoch 24, batch 1300, datatang_loss[loss=0.149, simple_loss=0.2282, pruned_loss=0.0349, over 4939.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2262, pruned_loss=0.03161, over 983783.51 frames.], batch size: 71, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03067, over 947964.91 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2185, pruned_loss=0.03222, over 946747.57 frames.], batch size: 71, lr: 3.43e-04
+2022-06-19 03:36:04,144 INFO [train.py:874] (1/4) Epoch 24, batch 1350, aishell_loss[loss=0.1393, simple_loss=0.2256, pruned_loss=0.02651, over 4950.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2262, pruned_loss=0.0315, over 984564.82 frames.], batch size: 32, aishell_tot_loss[loss=0.1475, simple_loss=0.2338, pruned_loss=0.03057, over 953114.78 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2184, pruned_loss=0.03226, over 950978.17 frames.], batch size: 32, lr: 3.43e-04
+2022-06-19 03:36:34,126 INFO [train.py:874] (1/4) Epoch 24, batch 1400, aishell_loss[loss=0.1623, simple_loss=0.2531, pruned_loss=0.03577, over 4865.00 frames.], tot_loss[loss=0.1449, simple_loss=0.2263, pruned_loss=0.03172, over 984937.37 frames.], batch size: 35, aishell_tot_loss[loss=0.1474, simple_loss=0.2337, pruned_loss=0.03061, over 956498.13 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2189, pruned_loss=0.03246, over 955665.45 frames.], batch size: 35, lr: 3.42e-04
+2022-06-19 03:37:01,579 INFO [train.py:874] (1/4) Epoch 24, batch 1450, datatang_loss[loss=0.1422, simple_loss=0.2198, pruned_loss=0.03233, over 4952.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2263, pruned_loss=0.03159, over 985195.51 frames.], batch size: 67, aishell_tot_loss[loss=0.1473, simple_loss=0.2335, pruned_loss=0.03055, over 959945.41 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.219, pruned_loss=0.03241, over 959302.15 frames.], batch size: 67, lr: 3.42e-04
+2022-06-19 03:37:30,959 INFO [train.py:874] (1/4) Epoch 24, batch 1500, aishell_loss[loss=0.152, simple_loss=0.2476, pruned_loss=0.02819, over 4950.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2265, pruned_loss=0.03145, over 985279.76 frames.], batch size: 54, aishell_tot_loss[loss=0.1473, simple_loss=0.2337, pruned_loss=0.03047, over 962795.99 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2192, pruned_loss=0.03235, over 962556.03 frames.], batch size: 54, lr: 3.42e-04
+2022-06-19 03:38:00,677 INFO [train.py:874] (1/4) Epoch 24, batch 1550, datatang_loss[loss=0.1132, simple_loss=0.2012, pruned_loss=0.01259, over 4939.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2262, pruned_loss=0.03136, over 985104.82 frames.], batch size: 79, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03062, over 965131.89 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2187, pruned_loss=0.0321, over 965354.21 frames.], batch size: 79, lr: 3.42e-04
+2022-06-19 03:38:29,170 INFO [train.py:874] (1/4) Epoch 24, batch 1600, datatang_loss[loss=0.1463, simple_loss=0.2276, pruned_loss=0.03245, over 4940.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2257, pruned_loss=0.03085, over 985088.56 frames.], batch size: 62, aishell_tot_loss[loss=0.1472, simple_loss=0.2336, pruned_loss=0.03039, over 967317.50 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2187, pruned_loss=0.03176, over 967833.33 frames.], batch size: 62, lr: 3.42e-04
+2022-06-19 03:38:56,951 INFO [train.py:874] (1/4) Epoch 24, batch 1650, aishell_loss[loss=0.1377, simple_loss=0.2269, pruned_loss=0.02427, over 4979.00 frames.], tot_loss[loss=0.144, simple_loss=0.2259, pruned_loss=0.03108, over 985273.95 frames.], batch size: 37, aishell_tot_loss[loss=0.147, simple_loss=0.2334, pruned_loss=0.0303, over 969280.54 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2188, pruned_loss=0.03205, over 970189.65 frames.], batch size: 37, lr: 3.42e-04
+2022-06-19 03:39:27,573 INFO [train.py:874] (1/4) Epoch 24, batch 1700, datatang_loss[loss=0.1357, simple_loss=0.2118, pruned_loss=0.02983, over 4930.00 frames.], tot_loss[loss=0.144, simple_loss=0.2252, pruned_loss=0.03144, over 985620.16 frames.], batch size: 62, aishell_tot_loss[loss=0.1467, simple_loss=0.233, pruned_loss=0.03021, over 970853.07 frames.], datatang_tot_loss[loss=0.1419, simple_loss=0.2189, pruned_loss=0.03243, over 972596.34 frames.], batch size: 62, lr: 3.42e-04
+2022-06-19 03:39:57,076 INFO [train.py:874] (1/4) Epoch 24, batch 1750, aishell_loss[loss=0.1187, simple_loss=0.1929, pruned_loss=0.02226, over 4889.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2252, pruned_loss=0.03147, over 985332.69 frames.], batch size: 21, aishell_tot_loss[loss=0.1467, simple_loss=0.233, pruned_loss=0.03019, over 971687.45 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2194, pruned_loss=0.03246, over 974655.00 frames.], batch size: 21, lr: 3.42e-04
+2022-06-19 03:40:24,391 INFO [train.py:874] (1/4) Epoch 24, batch 1800, datatang_loss[loss=0.1436, simple_loss=0.2203, pruned_loss=0.03346, over 4896.00 frames.], tot_loss[loss=0.1438, simple_loss=0.225, pruned_loss=0.03124, over 985307.11 frames.], batch size: 59, aishell_tot_loss[loss=0.1462, simple_loss=0.2325, pruned_loss=0.0299, over 973033.38 frames.], datatang_tot_loss[loss=0.1423, simple_loss=0.2197, pruned_loss=0.03248, over 976090.79 frames.], batch size: 59, lr: 3.42e-04
+2022-06-19 03:40:54,967 INFO [train.py:874] (1/4) Epoch 24, batch 1850, datatang_loss[loss=0.133, simple_loss=0.2128, pruned_loss=0.02661, over 4836.00 frames.], tot_loss[loss=0.1438, simple_loss=0.225, pruned_loss=0.03133, over 985081.61 frames.], batch size: 23, aishell_tot_loss[loss=0.1463, simple_loss=0.2324, pruned_loss=0.03011, over 974234.76 frames.], datatang_tot_loss[loss=0.1421, simple_loss=0.2195, pruned_loss=0.03239, over 977171.87 frames.], batch size: 23, lr: 3.42e-04
+2022-06-19 03:41:25,560 INFO [train.py:874] (1/4) Epoch 24, batch 1900, datatang_loss[loss=0.1573, simple_loss=0.2287, pruned_loss=0.04296, over 4930.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2249, pruned_loss=0.03103, over 985223.46 frames.], batch size: 79, aishell_tot_loss[loss=0.1458, simple_loss=0.2321, pruned_loss=0.02977, over 975530.89 frames.], datatang_tot_loss[loss=0.1422, simple_loss=0.2194, pruned_loss=0.03246, over 978281.01 frames.], batch size: 79, lr: 3.42e-04
+2022-06-19 03:41:52,662 INFO [train.py:874] (1/4) Epoch 24, batch 1950, datatang_loss[loss=0.1302, simple_loss=0.2169, pruned_loss=0.02172, over 4952.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2237, pruned_loss=0.03061, over 985441.96 frames.], batch size: 86, aishell_tot_loss[loss=0.1455, simple_loss=0.2316, pruned_loss=0.02966, over 976551.69 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2188, pruned_loss=0.03204, over 979356.69 frames.], batch size: 86, lr: 3.41e-04
+2022-06-19 03:42:22,513 INFO [train.py:874] (1/4) Epoch 24, batch 2000, datatang_loss[loss=0.1234, simple_loss=0.2083, pruned_loss=0.01924, over 4952.00 frames.], tot_loss[loss=0.142, simple_loss=0.2235, pruned_loss=0.03027, over 985463.35 frames.], batch size: 86, aishell_tot_loss[loss=0.1453, simple_loss=0.2314, pruned_loss=0.02957, over 977344.11 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2186, pruned_loss=0.03167, over 980293.78 frames.], batch size: 86, lr: 3.41e-04
+2022-06-19 03:42:22,514 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 03:42:39,281 INFO [train.py:914] (1/4) Epoch 24, validation: loss=0.1651, simple_loss=0.2488, pruned_loss=0.04067, over 1622729.00 frames.
+2022-06-19 03:43:08,909 INFO [train.py:874] (1/4) Epoch 24, batch 2050, aishell_loss[loss=0.1628, simple_loss=0.2557, pruned_loss=0.03498, over 4979.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2252, pruned_loss=0.03102, over 985705.68 frames.], batch size: 30, aishell_tot_loss[loss=0.1461, simple_loss=0.2321, pruned_loss=0.0301, over 978598.57 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2193, pruned_loss=0.03189, over 980888.19 frames.], batch size: 30, lr: 3.41e-04
+2022-06-19 03:43:38,479 INFO [train.py:874] (1/4) Epoch 24, batch 2100, datatang_loss[loss=0.1275, simple_loss=0.2121, pruned_loss=0.02147, over 4917.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2256, pruned_loss=0.03098, over 985427.92 frames.], batch size: 77, aishell_tot_loss[loss=0.1465, simple_loss=0.2326, pruned_loss=0.0302, over 978892.64 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2193, pruned_loss=0.03173, over 981654.35 frames.], batch size: 77, lr: 3.41e-04
+2022-06-19 03:44:05,030 INFO [train.py:874] (1/4) Epoch 24, batch 2150, aishell_loss[loss=0.1528, simple_loss=0.2345, pruned_loss=0.03555, over 4947.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2256, pruned_loss=0.03099, over 985736.85 frames.], batch size: 58, aishell_tot_loss[loss=0.1463, simple_loss=0.2324, pruned_loss=0.03011, over 979700.24 frames.], datatang_tot_loss[loss=0.1415, simple_loss=0.2194, pruned_loss=0.0318, over 982373.38 frames.], batch size: 58, lr: 3.41e-04
+2022-06-19 03:44:35,446 INFO [train.py:874] (1/4) Epoch 24, batch 2200, aishell_loss[loss=0.1408, simple_loss=0.2301, pruned_loss=0.02573, over 4878.00 frames.], tot_loss[loss=0.144, simple_loss=0.226, pruned_loss=0.03101, over 985626.53 frames.], batch size: 42, aishell_tot_loss[loss=0.147, simple_loss=0.2332, pruned_loss=0.03038, over 980291.70 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2189, pruned_loss=0.0316, over 982814.63 frames.], batch size: 42, lr: 3.41e-04
+2022-06-19 03:45:05,802 INFO [train.py:874] (1/4) Epoch 24, batch 2250, datatang_loss[loss=0.1578, simple_loss=0.2259, pruned_loss=0.04484, over 4919.00 frames.], tot_loss[loss=0.144, simple_loss=0.2261, pruned_loss=0.031, over 985653.94 frames.], batch size: 81, aishell_tot_loss[loss=0.1474, simple_loss=0.2338, pruned_loss=0.03051, over 981072.71 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2185, pruned_loss=0.03143, over 982984.32 frames.], batch size: 81, lr: 3.41e-04
+2022-06-19 03:45:33,373 INFO [train.py:874] (1/4) Epoch 24, batch 2300, aishell_loss[loss=0.1592, simple_loss=0.2653, pruned_loss=0.02655, over 4918.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2252, pruned_loss=0.03103, over 985636.43 frames.], batch size: 41, aishell_tot_loss[loss=0.147, simple_loss=0.2335, pruned_loss=0.03026, over 981365.38 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2182, pruned_loss=0.03169, over 983479.58 frames.], batch size: 41, lr: 3.41e-04
+2022-06-19 03:46:03,316 INFO [train.py:874] (1/4) Epoch 24, batch 2350, datatang_loss[loss=0.1697, simple_loss=0.2375, pruned_loss=0.05096, over 4964.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2246, pruned_loss=0.03104, over 985746.55 frames.], batch size: 60, aishell_tot_loss[loss=0.1468, simple_loss=0.233, pruned_loss=0.03031, over 981646.63 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2179, pruned_loss=0.03164, over 984061.26 frames.], batch size: 60, lr: 3.41e-04
+2022-06-19 03:46:33,436 INFO [train.py:874] (1/4) Epoch 24, batch 2400, datatang_loss[loss=0.1342, simple_loss=0.2199, pruned_loss=0.0242, over 4929.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2248, pruned_loss=0.03122, over 985420.08 frames.], batch size: 94, aishell_tot_loss[loss=0.147, simple_loss=0.233, pruned_loss=0.03054, over 981679.51 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2181, pruned_loss=0.03161, over 984338.86 frames.], batch size: 94, lr: 3.41e-04
+2022-06-19 03:46:59,465 INFO [train.py:874] (1/4) Epoch 24, batch 2450, datatang_loss[loss=0.1487, simple_loss=0.2277, pruned_loss=0.03487, over 4961.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2254, pruned_loss=0.03142, over 985315.71 frames.], batch size: 60, aishell_tot_loss[loss=0.1477, simple_loss=0.2335, pruned_loss=0.03089, over 981846.23 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.218, pruned_loss=0.03152, over 984619.76 frames.], batch size: 60, lr: 3.41e-04
+2022-06-19 03:47:29,043 INFO [train.py:874] (1/4) Epoch 24, batch 2500, aishell_loss[loss=0.1632, simple_loss=0.2539, pruned_loss=0.03622, over 4943.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2258, pruned_loss=0.03135, over 985589.28 frames.], batch size: 64, aishell_tot_loss[loss=0.1478, simple_loss=0.2338, pruned_loss=0.0309, over 982384.84 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2181, pruned_loss=0.03148, over 984843.94 frames.], batch size: 64, lr: 3.41e-04
+2022-06-19 03:47:57,737 INFO [train.py:874] (1/4) Epoch 24, batch 2550, aishell_loss[loss=0.1524, simple_loss=0.231, pruned_loss=0.03687, over 4965.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2263, pruned_loss=0.03104, over 985759.34 frames.], batch size: 31, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03071, over 983051.74 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2181, pruned_loss=0.03139, over 984895.46 frames.], batch size: 31, lr: 3.40e-04
+2022-06-19 03:48:24,739 INFO [train.py:874] (1/4) Epoch 24, batch 2600, aishell_loss[loss=0.1475, simple_loss=0.2292, pruned_loss=0.03289, over 4931.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2266, pruned_loss=0.03115, over 985814.21 frames.], batch size: 58, aishell_tot_loss[loss=0.1473, simple_loss=0.2337, pruned_loss=0.03047, over 983578.33 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2185, pruned_loss=0.0318, over 984871.14 frames.], batch size: 58, lr: 3.40e-04
+2022-06-19 03:48:54,376 INFO [train.py:874] (1/4) Epoch 24, batch 2650, aishell_loss[loss=0.1551, simple_loss=0.2505, pruned_loss=0.02988, over 4958.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2264, pruned_loss=0.03129, over 985850.43 frames.], batch size: 56, aishell_tot_loss[loss=0.1475, simple_loss=0.2338, pruned_loss=0.03062, over 983909.88 frames.], datatang_tot_loss[loss=0.141, simple_loss=0.2183, pruned_loss=0.03179, over 984961.10 frames.], batch size: 56, lr: 3.40e-04
+2022-06-19 03:49:23,860 INFO [train.py:874] (1/4) Epoch 24, batch 2700, datatang_loss[loss=0.1335, simple_loss=0.199, pruned_loss=0.03398, over 4898.00 frames.], tot_loss[loss=0.145, simple_loss=0.2266, pruned_loss=0.03168, over 985807.41 frames.], batch size: 42, aishell_tot_loss[loss=0.1477, simple_loss=0.2341, pruned_loss=0.03067, over 983966.97 frames.], datatang_tot_loss[loss=0.1414, simple_loss=0.2186, pruned_loss=0.03213, over 985167.74 frames.], batch size: 42, lr: 3.40e-04
+2022-06-19 03:49:50,612 INFO [train.py:874] (1/4) Epoch 24, batch 2750, aishell_loss[loss=0.1444, simple_loss=0.2255, pruned_loss=0.03168, over 4944.00 frames.], tot_loss[loss=0.1467, simple_loss=0.2287, pruned_loss=0.03236, over 985729.92 frames.], batch size: 31, aishell_tot_loss[loss=0.1486, simple_loss=0.2351, pruned_loss=0.03103, over 984086.66 frames.], datatang_tot_loss[loss=0.1424, simple_loss=0.2195, pruned_loss=0.03264, over 985302.85 frames.], batch size: 31, lr: 3.40e-04
+2022-06-19 03:50:20,311 INFO [train.py:874] (1/4) Epoch 24, batch 2800, aishell_loss[loss=0.1514, simple_loss=0.2452, pruned_loss=0.02881, over 4930.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2282, pruned_loss=0.03241, over 985961.46 frames.], batch size: 68, aishell_tot_loss[loss=0.148, simple_loss=0.2345, pruned_loss=0.03078, over 984377.03 frames.], datatang_tot_loss[loss=0.143, simple_loss=0.22, pruned_loss=0.03305, over 985495.10 frames.], batch size: 68, lr: 3.40e-04
+2022-06-19 03:50:47,474 INFO [train.py:874] (1/4) Epoch 24, batch 2850, aishell_loss[loss=0.149, simple_loss=0.244, pruned_loss=0.027, over 4878.00 frames.], tot_loss[loss=0.1459, simple_loss=0.2274, pruned_loss=0.03225, over 985289.07 frames.], batch size: 35, aishell_tot_loss[loss=0.1475, simple_loss=0.234, pruned_loss=0.03053, over 984105.66 frames.], datatang_tot_loss[loss=0.1433, simple_loss=0.2201, pruned_loss=0.03321, over 985259.26 frames.], batch size: 35, lr: 3.40e-04
+2022-06-19 03:51:16,576 INFO [train.py:874] (1/4) Epoch 24, batch 2900, aishell_loss[loss=0.1595, simple_loss=0.2443, pruned_loss=0.03734, over 4868.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2271, pruned_loss=0.03207, over 985326.17 frames.], batch size: 35, aishell_tot_loss[loss=0.1474, simple_loss=0.2338, pruned_loss=0.03051, over 984148.11 frames.], datatang_tot_loss[loss=0.1431, simple_loss=0.2198, pruned_loss=0.03317, over 985429.46 frames.], batch size: 35, lr: 3.40e-04
+2022-06-19 03:51:46,003 INFO [train.py:874] (1/4) Epoch 24, batch 2950, datatang_loss[loss=0.1444, simple_loss=0.221, pruned_loss=0.03389, over 4935.00 frames.], tot_loss[loss=0.1447, simple_loss=0.2262, pruned_loss=0.03164, over 985482.43 frames.], batch size: 79, aishell_tot_loss[loss=0.147, simple_loss=0.2336, pruned_loss=0.03022, over 984202.25 frames.], datatang_tot_loss[loss=0.1426, simple_loss=0.2191, pruned_loss=0.03308, over 985668.08 frames.], batch size: 79, lr: 3.40e-04
+2022-06-19 03:52:13,914 INFO [train.py:874] (1/4) Epoch 24, batch 3000, aishell_loss[loss=0.1451, simple_loss=0.2348, pruned_loss=0.02766, over 4944.00 frames.], tot_loss[loss=0.1446, simple_loss=0.226, pruned_loss=0.03158, over 985329.95 frames.], batch size: 54, aishell_tot_loss[loss=0.147, simple_loss=0.2334, pruned_loss=0.03027, over 984197.92 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2191, pruned_loss=0.03294, over 985638.47 frames.], batch size: 54, lr: 3.40e-04
+2022-06-19 03:52:13,915 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 03:52:29,517 INFO [train.py:914] (1/4) Epoch 24, validation: loss=0.1647, simple_loss=0.2484, pruned_loss=0.04048, over 1622729.00 frames.
+2022-06-19 03:52:58,017 INFO [train.py:874] (1/4) Epoch 24, batch 3050, aishell_loss[loss=0.1431, simple_loss=0.2217, pruned_loss=0.0323, over 4912.00 frames.], tot_loss[loss=0.1456, simple_loss=0.2268, pruned_loss=0.03215, over 985379.52 frames.], batch size: 41, aishell_tot_loss[loss=0.148, simple_loss=0.2342, pruned_loss=0.0309, over 984150.73 frames.], datatang_tot_loss[loss=0.1425, simple_loss=0.2191, pruned_loss=0.03294, over 985840.57 frames.], batch size: 41, lr: 3.40e-04
+2022-06-19 03:53:24,994 INFO [train.py:874] (1/4) Epoch 24, batch 3100, aishell_loss[loss=0.146, simple_loss=0.2424, pruned_loss=0.02479, over 4861.00 frames.], tot_loss[loss=0.1444, simple_loss=0.226, pruned_loss=0.03139, over 985392.90 frames.], batch size: 36, aishell_tot_loss[loss=0.1476, simple_loss=0.2339, pruned_loss=0.03066, over 984206.32 frames.], datatang_tot_loss[loss=0.1417, simple_loss=0.2186, pruned_loss=0.03241, over 985878.95 frames.], batch size: 36, lr: 3.39e-04
+2022-06-19 03:53:54,430 INFO [train.py:874] (1/4) Epoch 24, batch 3150, aishell_loss[loss=0.1561, simple_loss=0.2402, pruned_loss=0.03604, over 4919.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2248, pruned_loss=0.03089, over 985731.85 frames.], batch size: 33, aishell_tot_loss[loss=0.1468, simple_loss=0.2331, pruned_loss=0.03027, over 984392.86 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2179, pruned_loss=0.03228, over 986166.72 frames.], batch size: 33, lr: 3.39e-04
+2022-06-19 03:54:21,502 INFO [train.py:874] (1/4) Epoch 24, batch 3200, datatang_loss[loss=0.1425, simple_loss=0.2234, pruned_loss=0.03077, over 4959.00 frames.], tot_loss[loss=0.1434, simple_loss=0.225, pruned_loss=0.03087, over 985380.90 frames.], batch size: 86, aishell_tot_loss[loss=0.1467, simple_loss=0.2329, pruned_loss=0.03028, over 984108.13 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2179, pruned_loss=0.03219, over 986219.26 frames.], batch size: 86, lr: 3.39e-04
+2022-06-19 03:54:50,558 INFO [train.py:874] (1/4) Epoch 24, batch 3250, datatang_loss[loss=0.1441, simple_loss=0.2304, pruned_loss=0.02891, over 4942.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2253, pruned_loss=0.03095, over 984992.45 frames.], batch size: 62, aishell_tot_loss[loss=0.1467, simple_loss=0.2329, pruned_loss=0.03025, over 983818.82 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2179, pruned_loss=0.03225, over 986166.58 frames.], batch size: 62, lr: 3.39e-04
+2022-06-19 03:55:21,754 INFO [train.py:874] (1/4) Epoch 24, batch 3300, aishell_loss[loss=0.16, simple_loss=0.2501, pruned_loss=0.03495, over 4922.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2256, pruned_loss=0.03089, over 985316.65 frames.], batch size: 32, aishell_tot_loss[loss=0.1468, simple_loss=0.233, pruned_loss=0.03035, over 984044.37 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2182, pruned_loss=0.032, over 986269.10 frames.], batch size: 32, lr: 3.39e-04
+2022-06-19 03:55:47,623 INFO [train.py:874] (1/4) Epoch 24, batch 3350, aishell_loss[loss=0.169, simple_loss=0.2622, pruned_loss=0.03786, over 4955.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2265, pruned_loss=0.031, over 985122.85 frames.], batch size: 79, aishell_tot_loss[loss=0.1474, simple_loss=0.2337, pruned_loss=0.03058, over 983987.18 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.218, pruned_loss=0.03189, over 986227.58 frames.], batch size: 79, lr: 3.39e-04
+2022-06-19 03:56:18,374 INFO [train.py:874] (1/4) Epoch 24, batch 3400, aishell_loss[loss=0.1407, simple_loss=0.2259, pruned_loss=0.02769, over 4976.00 frames.], tot_loss[loss=0.1438, simple_loss=0.226, pruned_loss=0.03078, over 985034.64 frames.], batch size: 39, aishell_tot_loss[loss=0.1471, simple_loss=0.2333, pruned_loss=0.03047, over 983934.78 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2179, pruned_loss=0.03171, over 986193.53 frames.], batch size: 39, lr: 3.39e-04
+2022-06-19 03:56:47,583 INFO [train.py:874] (1/4) Epoch 24, batch 3450, aishell_loss[loss=0.1547, simple_loss=0.2312, pruned_loss=0.0391, over 4965.00 frames.], tot_loss[loss=0.1442, simple_loss=0.226, pruned_loss=0.03127, over 985152.63 frames.], batch size: 40, aishell_tot_loss[loss=0.1472, simple_loss=0.2333, pruned_loss=0.0306, over 984045.50 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2184, pruned_loss=0.03201, over 986146.87 frames.], batch size: 40, lr: 3.39e-04
+2022-06-19 03:57:14,726 INFO [train.py:874] (1/4) Epoch 24, batch 3500, aishell_loss[loss=0.1418, simple_loss=0.2261, pruned_loss=0.02876, over 4879.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2258, pruned_loss=0.03133, over 985143.11 frames.], batch size: 28, aishell_tot_loss[loss=0.1472, simple_loss=0.2334, pruned_loss=0.03054, over 984106.49 frames.], datatang_tot_loss[loss=0.1413, simple_loss=0.2184, pruned_loss=0.03212, over 986055.24 frames.], batch size: 28, lr: 3.39e-04
+2022-06-19 03:57:44,568 INFO [train.py:874] (1/4) Epoch 24, batch 3550, datatang_loss[loss=0.1293, simple_loss=0.2154, pruned_loss=0.02154, over 4961.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2256, pruned_loss=0.03148, over 985144.41 frames.], batch size: 60, aishell_tot_loss[loss=0.1476, simple_loss=0.2338, pruned_loss=0.03072, over 983855.57 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2182, pruned_loss=0.03205, over 986254.57 frames.], batch size: 60, lr: 3.39e-04
+2022-06-19 03:58:14,378 INFO [train.py:874] (1/4) Epoch 24, batch 3600, datatang_loss[loss=0.1764, simple_loss=0.2428, pruned_loss=0.05501, over 4975.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2255, pruned_loss=0.03131, over 985313.92 frames.], batch size: 55, aishell_tot_loss[loss=0.1474, simple_loss=0.2337, pruned_loss=0.03057, over 983889.34 frames.], datatang_tot_loss[loss=0.1412, simple_loss=0.2182, pruned_loss=0.03203, over 986402.10 frames.], batch size: 55, lr: 3.39e-04
+2022-06-19 03:58:41,413 INFO [train.py:874] (1/4) Epoch 24, batch 3650, aishell_loss[loss=0.1512, simple_loss=0.2458, pruned_loss=0.02827, over 4945.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2259, pruned_loss=0.03124, over 985437.69 frames.], batch size: 56, aishell_tot_loss[loss=0.1478, simple_loss=0.2344, pruned_loss=0.03059, over 983955.10 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2178, pruned_loss=0.03194, over 986524.19 frames.], batch size: 56, lr: 3.39e-04
+2022-06-19 03:59:11,569 INFO [train.py:874] (1/4) Epoch 24, batch 3700, aishell_loss[loss=0.1561, simple_loss=0.2402, pruned_loss=0.03601, over 4935.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2265, pruned_loss=0.03118, over 985183.70 frames.], batch size: 45, aishell_tot_loss[loss=0.1479, simple_loss=0.2348, pruned_loss=0.03051, over 984006.18 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2182, pruned_loss=0.03194, over 986221.61 frames.], batch size: 45, lr: 3.38e-04
+2022-06-19 03:59:40,796 INFO [train.py:874] (1/4) Epoch 24, batch 3750, aishell_loss[loss=0.1491, simple_loss=0.2385, pruned_loss=0.0298, over 4950.00 frames.], tot_loss[loss=0.144, simple_loss=0.2262, pruned_loss=0.03091, over 985438.24 frames.], batch size: 31, aishell_tot_loss[loss=0.1477, simple_loss=0.2346, pruned_loss=0.03034, over 984273.70 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.218, pruned_loss=0.03181, over 986225.04 frames.], batch size: 31, lr: 3.38e-04
+2022-06-19 04:00:08,470 INFO [train.py:874] (1/4) Epoch 24, batch 3800, datatang_loss[loss=0.1367, simple_loss=0.2121, pruned_loss=0.03061, over 4943.00 frames.], tot_loss[loss=0.143, simple_loss=0.2251, pruned_loss=0.0304, over 985538.74 frames.], batch size: 69, aishell_tot_loss[loss=0.1469, simple_loss=0.2339, pruned_loss=0.02991, over 984412.25 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2177, pruned_loss=0.03166, over 986216.81 frames.], batch size: 69, lr: 3.38e-04
+2022-06-19 04:00:37,996 INFO [train.py:874] (1/4) Epoch 24, batch 3850, datatang_loss[loss=0.1319, simple_loss=0.2147, pruned_loss=0.02455, over 4924.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2254, pruned_loss=0.03054, over 985459.17 frames.], batch size: 73, aishell_tot_loss[loss=0.147, simple_loss=0.234, pruned_loss=0.03002, over 984499.56 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2174, pruned_loss=0.03166, over 986138.89 frames.], batch size: 73, lr: 3.38e-04
+2022-06-19 04:01:05,127 INFO [train.py:874] (1/4) Epoch 24, batch 3900, aishell_loss[loss=0.1565, simple_loss=0.2446, pruned_loss=0.03415, over 4978.00 frames.], tot_loss[loss=0.143, simple_loss=0.2251, pruned_loss=0.03048, over 985655.13 frames.], batch size: 48, aishell_tot_loss[loss=0.1471, simple_loss=0.234, pruned_loss=0.03013, over 984825.11 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2173, pruned_loss=0.03139, over 986029.62 frames.], batch size: 48, lr: 3.38e-04
+2022-06-19 04:01:33,263 INFO [train.py:874] (1/4) Epoch 24, batch 3950, aishell_loss[loss=0.1407, simple_loss=0.2164, pruned_loss=0.03249, over 4954.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2235, pruned_loss=0.03014, over 985702.32 frames.], batch size: 32, aishell_tot_loss[loss=0.1464, simple_loss=0.233, pruned_loss=0.02988, over 984856.58 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2166, pruned_loss=0.03118, over 986077.28 frames.], batch size: 32, lr: 3.38e-04
+2022-06-19 04:01:59,974 INFO [train.py:874] (1/4) Epoch 24, batch 4000, aishell_loss[loss=0.1537, simple_loss=0.2408, pruned_loss=0.03332, over 4934.00 frames.], tot_loss[loss=0.1419, simple_loss=0.224, pruned_loss=0.02997, over 985388.46 frames.], batch size: 49, aishell_tot_loss[loss=0.1463, simple_loss=0.2329, pruned_loss=0.02988, over 984557.55 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2166, pruned_loss=0.03093, over 986119.35 frames.], batch size: 49, lr: 3.38e-04
+2022-06-19 04:01:59,975 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 04:02:15,553 INFO [train.py:914] (1/4) Epoch 24, validation: loss=0.1649, simple_loss=0.2488, pruned_loss=0.04056, over 1622729.00 frames.
+2022-06-19 04:02:42,074 INFO [train.py:874] (1/4) Epoch 24, batch 4050, aishell_loss[loss=0.1169, simple_loss=0.1895, pruned_loss=0.02221, over 4880.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2239, pruned_loss=0.02993, over 985282.18 frames.], batch size: 21, aishell_tot_loss[loss=0.1461, simple_loss=0.2325, pruned_loss=0.02982, over 984441.10 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2167, pruned_loss=0.03087, over 986148.22 frames.], batch size: 21, lr: 3.38e-04
+2022-06-19 04:03:10,324 INFO [train.py:874] (1/4) Epoch 24, batch 4100, datatang_loss[loss=0.1354, simple_loss=0.2112, pruned_loss=0.02986, over 4894.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2249, pruned_loss=0.03006, over 985116.46 frames.], batch size: 24, aishell_tot_loss[loss=0.1466, simple_loss=0.2334, pruned_loss=0.02995, over 984433.65 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2167, pruned_loss=0.03079, over 985980.09 frames.], batch size: 24, lr: 3.38e-04
+2022-06-19 04:03:36,843 INFO [train.py:874] (1/4) Epoch 24, batch 4150, aishell_loss[loss=0.1613, simple_loss=0.242, pruned_loss=0.0403, over 4892.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2246, pruned_loss=0.03034, over 985106.47 frames.], batch size: 34, aishell_tot_loss[loss=0.1466, simple_loss=0.2333, pruned_loss=0.03, over 984238.24 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2165, pruned_loss=0.03095, over 986127.97 frames.], batch size: 34, lr: 3.38e-04
+2022-06-19 04:04:55,976 INFO [train.py:874] (1/4) Epoch 25, batch 50, datatang_loss[loss=0.1334, simple_loss=0.2077, pruned_loss=0.02958, over 4924.00 frames.], tot_loss[loss=0.1465, simple_loss=0.2294, pruned_loss=0.03184, over 218780.31 frames.], batch size: 81, aishell_tot_loss[loss=0.1535, simple_loss=0.2397, pruned_loss=0.0337, over 142041.00 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2131, pruned_loss=0.02906, over 89632.74 frames.], batch size: 81, lr: 3.31e-04
+2022-06-19 04:05:25,169 INFO [train.py:874] (1/4) Epoch 25, batch 100, datatang_loss[loss=0.1242, simple_loss=0.1867, pruned_loss=0.03083, over 4902.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2229, pruned_loss=0.02986, over 388977.71 frames.], batch size: 25, aishell_tot_loss[loss=0.1488, simple_loss=0.2352, pruned_loss=0.03118, over 233956.84 frames.], datatang_tot_loss[loss=0.1337, simple_loss=0.2099, pruned_loss=0.0287, over 203199.90 frames.], batch size: 25, lr: 3.31e-04
+2022-06-19 04:05:54,547 INFO [train.py:874] (1/4) Epoch 25, batch 150, datatang_loss[loss=0.1224, simple_loss=0.2007, pruned_loss=0.02206, over 4958.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2211, pruned_loss=0.02927, over 521489.32 frames.], batch size: 86, aishell_tot_loss[loss=0.1473, simple_loss=0.2338, pruned_loss=0.03041, over 305656.74 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.2101, pruned_loss=0.02851, over 312700.15 frames.], batch size: 86, lr: 3.31e-04
+2022-06-19 04:06:21,630 INFO [train.py:874] (1/4) Epoch 25, batch 200, aishell_loss[loss=0.1418, simple_loss=0.229, pruned_loss=0.02731, over 4953.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2204, pruned_loss=0.02834, over 624127.12 frames.], batch size: 56, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.02919, over 394340.56 frames.], datatang_tot_loss[loss=0.1326, simple_loss=0.2091, pruned_loss=0.02799, over 383054.95 frames.], batch size: 56, lr: 3.31e-04
+2022-06-19 04:06:50,345 INFO [train.py:874] (1/4) Epoch 25, batch 250, datatang_loss[loss=0.1847, simple_loss=0.2567, pruned_loss=0.05633, over 4920.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2206, pruned_loss=0.02828, over 704232.70 frames.], batch size: 108, aishell_tot_loss[loss=0.1442, simple_loss=0.2312, pruned_loss=0.02862, over 458652.45 frames.], datatang_tot_loss[loss=0.1338, simple_loss=0.2109, pruned_loss=0.02834, over 459363.22 frames.], batch size: 108, lr: 3.31e-04
+2022-06-19 04:07:19,535 INFO [train.py:874] (1/4) Epoch 25, batch 300, datatang_loss[loss=0.1543, simple_loss=0.2222, pruned_loss=0.04319, over 4923.00 frames.], tot_loss[loss=0.139, simple_loss=0.2207, pruned_loss=0.02868, over 766781.73 frames.], batch size: 57, aishell_tot_loss[loss=0.1449, simple_loss=0.2316, pruned_loss=0.02911, over 520850.72 frames.], datatang_tot_loss[loss=0.1335, simple_loss=0.2103, pruned_loss=0.02833, over 521379.55 frames.], batch size: 57, lr: 3.30e-04
+2022-06-19 04:07:46,663 INFO [train.py:874] (1/4) Epoch 25, batch 350, datatang_loss[loss=0.1254, simple_loss=0.2031, pruned_loss=0.02379, over 4867.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2213, pruned_loss=0.02876, over 815449.73 frames.], batch size: 39, aishell_tot_loss[loss=0.1453, simple_loss=0.2319, pruned_loss=0.02933, over 583824.36 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2102, pruned_loss=0.0282, over 567835.86 frames.], batch size: 39, lr: 3.30e-04
+2022-06-19 04:08:15,444 INFO [train.py:874] (1/4) Epoch 25, batch 400, datatang_loss[loss=0.1244, simple_loss=0.2052, pruned_loss=0.02184, over 4961.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2208, pruned_loss=0.02872, over 853116.58 frames.], batch size: 60, aishell_tot_loss[loss=0.1454, simple_loss=0.2317, pruned_loss=0.02954, over 625691.92 frames.], datatang_tot_loss[loss=0.1331, simple_loss=0.2103, pruned_loss=0.02798, over 622563.86 frames.], batch size: 60, lr: 3.30e-04
+2022-06-19 04:08:43,831 INFO [train.py:874] (1/4) Epoch 25, batch 450, datatang_loss[loss=0.2148, simple_loss=0.2742, pruned_loss=0.0777, over 4940.00 frames.], tot_loss[loss=0.1407, simple_loss=0.222, pruned_loss=0.02971, over 882532.77 frames.], batch size: 110, aishell_tot_loss[loss=0.1455, simple_loss=0.2316, pruned_loss=0.02966, over 658353.75 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2128, pruned_loss=0.02926, over 674893.70 frames.], batch size: 110, lr: 3.30e-04
+2022-06-19 04:09:15,237 INFO [train.py:874] (1/4) Epoch 25, batch 500, aishell_loss[loss=0.14, simple_loss=0.2277, pruned_loss=0.02616, over 4892.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2236, pruned_loss=0.02993, over 905730.28 frames.], batch size: 42, aishell_tot_loss[loss=0.1462, simple_loss=0.233, pruned_loss=0.02967, over 698548.36 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2133, pruned_loss=0.02963, over 710278.23 frames.], batch size: 42, lr: 3.30e-04
+2022-06-19 04:09:43,981 INFO [train.py:874] (1/4) Epoch 25, batch 550, aishell_loss[loss=0.2039, simple_loss=0.2879, pruned_loss=0.05996, over 4949.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2248, pruned_loss=0.03068, over 923822.71 frames.], batch size: 40, aishell_tot_loss[loss=0.1468, simple_loss=0.2336, pruned_loss=0.03001, over 733813.39 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2143, pruned_loss=0.03036, over 741705.43 frames.], batch size: 40, lr: 3.30e-04
+2022-06-19 04:10:12,935 INFO [train.py:874] (1/4) Epoch 25, batch 600, aishell_loss[loss=0.152, simple_loss=0.2419, pruned_loss=0.03101, over 4886.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2248, pruned_loss=0.03038, over 937328.69 frames.], batch size: 42, aishell_tot_loss[loss=0.1461, simple_loss=0.2326, pruned_loss=0.02976, over 767805.05 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2151, pruned_loss=0.03039, over 765942.25 frames.], batch size: 42, lr: 3.30e-04
+2022-06-19 04:10:41,777 INFO [train.py:874] (1/4) Epoch 25, batch 650, aishell_loss[loss=0.1377, simple_loss=0.2285, pruned_loss=0.02344, over 4852.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2262, pruned_loss=0.03029, over 947877.35 frames.], batch size: 36, aishell_tot_loss[loss=0.1466, simple_loss=0.2335, pruned_loss=0.02984, over 799858.12 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2156, pruned_loss=0.03029, over 784968.17 frames.], batch size: 36, lr: 3.30e-04
+2022-06-19 04:11:11,130 INFO [train.py:874] (1/4) Epoch 25, batch 700, datatang_loss[loss=0.1453, simple_loss=0.2338, pruned_loss=0.02836, over 4957.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2262, pruned_loss=0.03022, over 956606.38 frames.], batch size: 55, aishell_tot_loss[loss=0.1461, simple_loss=0.2331, pruned_loss=0.02958, over 822807.73 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2163, pruned_loss=0.03055, over 807849.75 frames.], batch size: 55, lr: 3.30e-04
+2022-06-19 04:11:37,450 INFO [train.py:874] (1/4) Epoch 25, batch 750, datatang_loss[loss=0.1283, simple_loss=0.2076, pruned_loss=0.02456, over 4924.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2263, pruned_loss=0.03011, over 962735.65 frames.], batch size: 73, aishell_tot_loss[loss=0.1461, simple_loss=0.2335, pruned_loss=0.02939, over 843339.05 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2162, pruned_loss=0.03065, over 826966.32 frames.], batch size: 73, lr: 3.30e-04
+2022-06-19 04:12:06,292 INFO [train.py:874] (1/4) Epoch 25, batch 800, datatang_loss[loss=0.1178, simple_loss=0.1888, pruned_loss=0.02336, over 4979.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2258, pruned_loss=0.03031, over 967976.82 frames.], batch size: 40, aishell_tot_loss[loss=0.1462, simple_loss=0.2333, pruned_loss=0.02956, over 858044.30 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2166, pruned_loss=0.03075, over 848047.61 frames.], batch size: 40, lr: 3.30e-04
+2022-06-19 04:12:35,216 INFO [train.py:874] (1/4) Epoch 25, batch 850, datatang_loss[loss=0.1421, simple_loss=0.2239, pruned_loss=0.03014, over 4923.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2262, pruned_loss=0.03054, over 971834.14 frames.], batch size: 57, aishell_tot_loss[loss=0.146, simple_loss=0.2332, pruned_loss=0.02944, over 871871.47 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2175, pruned_loss=0.03118, over 865475.53 frames.], batch size: 57, lr: 3.30e-04
+2022-06-19 04:13:03,058 INFO [train.py:874] (1/4) Epoch 25, batch 900, datatang_loss[loss=0.1337, simple_loss=0.2087, pruned_loss=0.02934, over 4947.00 frames.], tot_loss[loss=0.1436, simple_loss=0.226, pruned_loss=0.03061, over 975171.04 frames.], batch size: 69, aishell_tot_loss[loss=0.146, simple_loss=0.2331, pruned_loss=0.02948, over 884453.37 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2177, pruned_loss=0.03125, over 880779.60 frames.], batch size: 69, lr: 3.29e-04
+2022-06-19 04:13:30,843 INFO [train.py:874] (1/4) Epoch 25, batch 950, aishell_loss[loss=0.1364, simple_loss=0.2324, pruned_loss=0.02015, over 4918.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2259, pruned_loss=0.0306, over 977635.36 frames.], batch size: 46, aishell_tot_loss[loss=0.146, simple_loss=0.233, pruned_loss=0.02946, over 895984.38 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.218, pruned_loss=0.0313, over 893696.85 frames.], batch size: 46, lr: 3.29e-04
+2022-06-19 04:14:00,738 INFO [train.py:874] (1/4) Epoch 25, batch 1000, datatang_loss[loss=0.1212, simple_loss=0.2034, pruned_loss=0.01954, over 4915.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2259, pruned_loss=0.0301, over 979552.26 frames.], batch size: 75, aishell_tot_loss[loss=0.1455, simple_loss=0.2326, pruned_loss=0.02924, over 909043.28 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2181, pruned_loss=0.03105, over 902042.84 frames.], batch size: 75, lr: 3.29e-04
+2022-06-19 04:14:00,738 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 04:14:17,422 INFO [train.py:914] (1/4) Epoch 25, validation: loss=0.1644, simple_loss=0.248, pruned_loss=0.0404, over 1622729.00 frames.
+2022-06-19 04:14:45,775 INFO [train.py:874] (1/4) Epoch 25, batch 1050, datatang_loss[loss=0.1516, simple_loss=0.2347, pruned_loss=0.0343, over 4962.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2265, pruned_loss=0.03007, over 980509.58 frames.], batch size: 99, aishell_tot_loss[loss=0.1453, simple_loss=0.2325, pruned_loss=0.02911, over 918680.43 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2187, pruned_loss=0.03116, over 910743.88 frames.], batch size: 99, lr: 3.29e-04
+2022-06-19 04:15:14,184 INFO [train.py:874] (1/4) Epoch 25, batch 1100, aishell_loss[loss=0.1521, simple_loss=0.2305, pruned_loss=0.03684, over 4940.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2262, pruned_loss=0.03031, over 981416.25 frames.], batch size: 32, aishell_tot_loss[loss=0.1458, simple_loss=0.2325, pruned_loss=0.02952, over 928279.85 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2182, pruned_loss=0.03106, over 917278.37 frames.], batch size: 32, lr: 3.29e-04
+2022-06-19 04:15:41,605 INFO [train.py:874] (1/4) Epoch 25, batch 1150, aishell_loss[loss=0.142, simple_loss=0.2357, pruned_loss=0.02409, over 4976.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2267, pruned_loss=0.03022, over 982264.75 frames.], batch size: 44, aishell_tot_loss[loss=0.146, simple_loss=0.233, pruned_loss=0.02952, over 935332.81 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2182, pruned_loss=0.03098, over 924888.96 frames.], batch size: 44, lr: 3.29e-04
+2022-06-19 04:16:10,678 INFO [train.py:874] (1/4) Epoch 25, batch 1200, aishell_loss[loss=0.1616, simple_loss=0.2555, pruned_loss=0.03387, over 4960.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2265, pruned_loss=0.03027, over 983033.70 frames.], batch size: 40, aishell_tot_loss[loss=0.1461, simple_loss=0.233, pruned_loss=0.02961, over 941872.97 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2181, pruned_loss=0.03096, over 931304.15 frames.], batch size: 40, lr: 3.29e-04
+2022-06-19 04:16:37,443 INFO [train.py:874] (1/4) Epoch 25, batch 1250, datatang_loss[loss=0.148, simple_loss=0.2187, pruned_loss=0.03869, over 4933.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2266, pruned_loss=0.03061, over 983144.05 frames.], batch size: 79, aishell_tot_loss[loss=0.1466, simple_loss=0.2332, pruned_loss=0.02995, over 946921.97 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2181, pruned_loss=0.03101, over 937220.39 frames.], batch size: 79, lr: 3.29e-04
+2022-06-19 04:17:06,239 INFO [train.py:874] (1/4) Epoch 25, batch 1300, aishell_loss[loss=0.1601, simple_loss=0.2456, pruned_loss=0.03733, over 4944.00 frames.], tot_loss[loss=0.1443, simple_loss=0.2269, pruned_loss=0.03085, over 983603.53 frames.], batch size: 79, aishell_tot_loss[loss=0.1472, simple_loss=0.2339, pruned_loss=0.03027, over 950846.09 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2182, pruned_loss=0.03097, over 943576.10 frames.], batch size: 79, lr: 3.29e-04
+2022-06-19 04:17:37,371 INFO [train.py:874] (1/4) Epoch 25, batch 1350, datatang_loss[loss=0.1633, simple_loss=0.2384, pruned_loss=0.0441, over 4902.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2271, pruned_loss=0.03128, over 983665.66 frames.], batch size: 34, aishell_tot_loss[loss=0.1476, simple_loss=0.2342, pruned_loss=0.03048, over 954421.93 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2185, pruned_loss=0.03128, over 948708.35 frames.], batch size: 34, lr: 3.29e-04
+2022-06-19 04:18:04,166 INFO [train.py:874] (1/4) Epoch 25, batch 1400, datatang_loss[loss=0.1439, simple_loss=0.2136, pruned_loss=0.03707, over 4897.00 frames.], tot_loss[loss=0.1446, simple_loss=0.2266, pruned_loss=0.03133, over 983998.09 frames.], batch size: 52, aishell_tot_loss[loss=0.1474, simple_loss=0.2339, pruned_loss=0.03049, over 957814.03 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2186, pruned_loss=0.03141, over 953280.14 frames.], batch size: 52, lr: 3.29e-04
+2022-06-19 04:18:32,337 INFO [train.py:874] (1/4) Epoch 25, batch 1450, datatang_loss[loss=0.1326, simple_loss=0.2186, pruned_loss=0.02332, over 4922.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2257, pruned_loss=0.03099, over 984481.65 frames.], batch size: 88, aishell_tot_loss[loss=0.1468, simple_loss=0.2333, pruned_loss=0.03019, over 960929.23 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2185, pruned_loss=0.03142, over 957419.71 frames.], batch size: 88, lr: 3.29e-04
+2022-06-19 04:19:02,039 INFO [train.py:874] (1/4) Epoch 25, batch 1500, aishell_loss[loss=0.145, simple_loss=0.237, pruned_loss=0.02653, over 4941.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2259, pruned_loss=0.03097, over 984421.46 frames.], batch size: 56, aishell_tot_loss[loss=0.147, simple_loss=0.2335, pruned_loss=0.03027, over 963377.30 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2186, pruned_loss=0.03135, over 960890.72 frames.], batch size: 56, lr: 3.28e-04
+2022-06-19 04:19:29,739 INFO [train.py:874] (1/4) Epoch 25, batch 1550, datatang_loss[loss=0.1314, simple_loss=0.2039, pruned_loss=0.02942, over 4831.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2257, pruned_loss=0.03107, over 984466.10 frames.], batch size: 25, aishell_tot_loss[loss=0.1471, simple_loss=0.2336, pruned_loss=0.03026, over 965476.89 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2184, pruned_loss=0.0315, over 964117.86 frames.], batch size: 25, lr: 3.28e-04
+2022-06-19 04:19:59,148 INFO [train.py:874] (1/4) Epoch 25, batch 1600, aishell_loss[loss=0.1634, simple_loss=0.2543, pruned_loss=0.03628, over 4931.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2262, pruned_loss=0.03103, over 984449.49 frames.], batch size: 79, aishell_tot_loss[loss=0.1474, simple_loss=0.2339, pruned_loss=0.03048, over 967851.65 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2184, pruned_loss=0.03127, over 966335.83 frames.], batch size: 79, lr: 3.28e-04
+2022-06-19 04:20:27,788 INFO [train.py:874] (1/4) Epoch 25, batch 1650, aishell_loss[loss=0.1357, simple_loss=0.2203, pruned_loss=0.02555, over 4889.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2262, pruned_loss=0.03098, over 984486.87 frames.], batch size: 34, aishell_tot_loss[loss=0.1476, simple_loss=0.234, pruned_loss=0.03059, over 969586.86 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2183, pruned_loss=0.03114, over 968746.13 frames.], batch size: 34, lr: 3.28e-04
+2022-06-19 04:20:55,475 INFO [train.py:874] (1/4) Epoch 25, batch 1700, datatang_loss[loss=0.1495, simple_loss=0.2244, pruned_loss=0.03735, over 4926.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2254, pruned_loss=0.03068, over 984555.67 frames.], batch size: 73, aishell_tot_loss[loss=0.1475, simple_loss=0.2338, pruned_loss=0.03056, over 971162.31 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2178, pruned_loss=0.03087, over 970840.23 frames.], batch size: 73, lr: 3.28e-04
+2022-06-19 04:21:24,461 INFO [train.py:874] (1/4) Epoch 25, batch 1750, aishell_loss[loss=0.137, simple_loss=0.2315, pruned_loss=0.02131, over 4909.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2252, pruned_loss=0.03074, over 984646.97 frames.], batch size: 46, aishell_tot_loss[loss=0.1473, simple_loss=0.2337, pruned_loss=0.03045, over 972530.67 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2176, pruned_loss=0.03103, over 972772.03 frames.], batch size: 46, lr: 3.28e-04
+2022-06-19 04:21:53,971 INFO [train.py:874] (1/4) Epoch 25, batch 1800, datatang_loss[loss=0.1406, simple_loss=0.2107, pruned_loss=0.03524, over 4876.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2252, pruned_loss=0.03106, over 984666.74 frames.], batch size: 39, aishell_tot_loss[loss=0.1476, simple_loss=0.234, pruned_loss=0.03056, over 973598.44 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2178, pruned_loss=0.03124, over 974527.28 frames.], batch size: 39, lr: 3.28e-04
+2022-06-19 04:22:21,499 INFO [train.py:874] (1/4) Epoch 25, batch 1850, datatang_loss[loss=0.1262, simple_loss=0.2104, pruned_loss=0.02098, over 4923.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2251, pruned_loss=0.03091, over 984730.88 frames.], batch size: 83, aishell_tot_loss[loss=0.147, simple_loss=0.2333, pruned_loss=0.03032, over 974943.43 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2179, pruned_loss=0.03137, over 975756.95 frames.], batch size: 83, lr: 3.28e-04
+2022-06-19 04:22:52,303 INFO [train.py:874] (1/4) Epoch 25, batch 1900, datatang_loss[loss=0.1383, simple_loss=0.214, pruned_loss=0.03126, over 4904.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2265, pruned_loss=0.03128, over 984647.74 frames.], batch size: 52, aishell_tot_loss[loss=0.1475, simple_loss=0.234, pruned_loss=0.03053, over 976280.38 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2183, pruned_loss=0.0316, over 976557.49 frames.], batch size: 52, lr: 3.28e-04
+2022-06-19 04:23:20,040 INFO [train.py:874] (1/4) Epoch 25, batch 1950, aishell_loss[loss=0.1622, simple_loss=0.2542, pruned_loss=0.03507, over 4915.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2265, pruned_loss=0.03128, over 985044.57 frames.], batch size: 68, aishell_tot_loss[loss=0.1476, simple_loss=0.234, pruned_loss=0.03061, over 977390.30 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2183, pruned_loss=0.03158, over 977820.41 frames.], batch size: 68, lr: 3.28e-04
+2022-06-19 04:23:47,733 INFO [train.py:874] (1/4) Epoch 25, batch 2000, datatang_loss[loss=0.1243, simple_loss=0.2024, pruned_loss=0.0231, over 4950.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2265, pruned_loss=0.03111, over 985049.83 frames.], batch size: 69, aishell_tot_loss[loss=0.1478, simple_loss=0.2341, pruned_loss=0.03072, over 978423.43 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2182, pruned_loss=0.03132, over 978553.18 frames.], batch size: 69, lr: 3.28e-04
+2022-06-19 04:23:47,733 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 04:24:04,381 INFO [train.py:914] (1/4) 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] (1/4) Epoch 25, batch 2050, aishell_loss[loss=0.1258, simple_loss=0.1853, pruned_loss=0.03311, over 4937.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2268, pruned_loss=0.03081, over 985185.23 frames.], batch size: 21, aishell_tot_loss[loss=0.1475, simple_loss=0.2339, pruned_loss=0.03051, over 979447.57 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2181, pruned_loss=0.03124, over 979223.36 frames.], batch size: 21, lr: 3.28e-04
+2022-06-19 04:24:59,613 INFO [train.py:874] (1/4) Epoch 25, batch 2100, aishell_loss[loss=0.1248, simple_loss=0.2084, pruned_loss=0.02059, over 4870.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2259, pruned_loss=0.0306, over 985639.05 frames.], batch size: 28, aishell_tot_loss[loss=0.1468, simple_loss=0.233, pruned_loss=0.03024, over 980421.51 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2179, pruned_loss=0.03131, over 980096.71 frames.], batch size: 28, lr: 3.28e-04
+2022-06-19 04:25:26,936 INFO [train.py:874] (1/4) Epoch 25, batch 2150, datatang_loss[loss=0.1322, simple_loss=0.2137, pruned_loss=0.02533, over 4940.00 frames.], tot_loss[loss=0.1445, simple_loss=0.2267, pruned_loss=0.03113, over 985743.19 frames.], batch size: 88, aishell_tot_loss[loss=0.147, simple_loss=0.2336, pruned_loss=0.03026, over 981006.41 frames.], datatang_tot_loss[loss=0.1411, simple_loss=0.2186, pruned_loss=0.03177, over 980892.46 frames.], batch size: 88, lr: 3.27e-04
+2022-06-19 04:25:55,451 INFO [train.py:874] (1/4) Epoch 25, batch 2200, datatang_loss[loss=0.1295, simple_loss=0.2137, pruned_loss=0.02265, over 4917.00 frames.], tot_loss[loss=0.1441, simple_loss=0.2264, pruned_loss=0.03089, over 985876.54 frames.], batch size: 81, aishell_tot_loss[loss=0.1472, simple_loss=0.2335, pruned_loss=0.0304, over 981401.47 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2185, pruned_loss=0.0314, over 981770.83 frames.], batch size: 81, lr: 3.27e-04
+2022-06-19 04:26:24,824 INFO [train.py:874] (1/4) Epoch 25, batch 2250, aishell_loss[loss=0.1657, simple_loss=0.254, pruned_loss=0.03865, over 4920.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2259, pruned_loss=0.03066, over 986011.54 frames.], batch size: 46, aishell_tot_loss[loss=0.1473, simple_loss=0.2338, pruned_loss=0.03039, over 981940.78 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2179, pruned_loss=0.03116, over 982373.40 frames.], batch size: 46, lr: 3.27e-04
+2022-06-19 04:26:54,171 INFO [train.py:874] (1/4) Epoch 25, batch 2300, datatang_loss[loss=0.1256, simple_loss=0.2062, pruned_loss=0.02254, over 4922.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2262, pruned_loss=0.03071, over 985848.07 frames.], batch size: 73, aishell_tot_loss[loss=0.1477, simple_loss=0.2342, pruned_loss=0.03053, over 982300.02 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2178, pruned_loss=0.03105, over 982742.18 frames.], batch size: 73, lr: 3.27e-04
+2022-06-19 04:27:21,752 INFO [train.py:874] (1/4) Epoch 25, batch 2350, aishell_loss[loss=0.1501, simple_loss=0.2364, pruned_loss=0.03189, over 4975.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2263, pruned_loss=0.03072, over 986075.39 frames.], batch size: 39, aishell_tot_loss[loss=0.1476, simple_loss=0.2344, pruned_loss=0.03039, over 982752.95 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2179, pruned_loss=0.03118, over 983311.03 frames.], batch size: 39, lr: 3.27e-04
+2022-06-19 04:27:52,155 INFO [train.py:874] (1/4) Epoch 25, batch 2400, aishell_loss[loss=0.145, simple_loss=0.2359, pruned_loss=0.02706, over 4965.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2262, pruned_loss=0.03114, over 986422.84 frames.], batch size: 61, aishell_tot_loss[loss=0.1478, simple_loss=0.2345, pruned_loss=0.03055, over 983273.06 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2182, pruned_loss=0.03142, over 983857.43 frames.], batch size: 61, lr: 3.27e-04
+2022-06-19 04:28:20,098 INFO [train.py:874] (1/4) Epoch 25, batch 2450, aishell_loss[loss=0.1334, simple_loss=0.2304, pruned_loss=0.01819, over 4945.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2258, pruned_loss=0.03066, over 986248.46 frames.], batch size: 58, aishell_tot_loss[loss=0.1477, simple_loss=0.2345, pruned_loss=0.03046, over 983610.69 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2179, pruned_loss=0.03105, over 984020.94 frames.], batch size: 58, lr: 3.27e-04
+2022-06-19 04:28:48,389 INFO [train.py:874] (1/4) Epoch 25, batch 2500, aishell_loss[loss=0.1435, simple_loss=0.2348, pruned_loss=0.0261, over 4970.00 frames.], tot_loss[loss=0.1438, simple_loss=0.226, pruned_loss=0.03084, over 986044.98 frames.], batch size: 44, aishell_tot_loss[loss=0.1479, simple_loss=0.2344, pruned_loss=0.0307, over 983833.03 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2179, pruned_loss=0.03099, over 984155.17 frames.], batch size: 44, lr: 3.27e-04
+2022-06-19 04:29:17,548 INFO [train.py:874] (1/4) Epoch 25, batch 2550, aishell_loss[loss=0.1518, simple_loss=0.2298, pruned_loss=0.03692, over 4964.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2263, pruned_loss=0.03125, over 985446.84 frames.], batch size: 31, aishell_tot_loss[loss=0.1479, simple_loss=0.2342, pruned_loss=0.03077, over 983663.05 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.218, pruned_loss=0.03138, over 984166.26 frames.], batch size: 31, lr: 3.27e-04
+2022-06-19 04:29:45,265 INFO [train.py:874] (1/4) Epoch 25, batch 2600, aishell_loss[loss=0.1327, simple_loss=0.2264, pruned_loss=0.01956, over 4945.00 frames.], tot_loss[loss=0.144, simple_loss=0.2261, pruned_loss=0.03096, over 985659.82 frames.], batch size: 54, aishell_tot_loss[loss=0.1477, simple_loss=0.2339, pruned_loss=0.03073, over 984065.86 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.218, pruned_loss=0.03116, over 984338.36 frames.], batch size: 54, lr: 3.27e-04
+2022-06-19 04:30:13,912 INFO [train.py:874] (1/4) Epoch 25, batch 2650, aishell_loss[loss=0.1638, simple_loss=0.2476, pruned_loss=0.04002, over 4916.00 frames.], tot_loss[loss=0.1436, simple_loss=0.2256, pruned_loss=0.03083, over 985210.67 frames.], batch size: 68, aishell_tot_loss[loss=0.1476, simple_loss=0.2338, pruned_loss=0.03069, over 983660.44 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2178, pruned_loss=0.03106, over 984607.06 frames.], batch size: 68, lr: 3.27e-04
+2022-06-19 04:30:43,850 INFO [train.py:874] (1/4) Epoch 25, batch 2700, aishell_loss[loss=0.1548, simple_loss=0.2358, pruned_loss=0.03685, over 4894.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2254, pruned_loss=0.03044, over 985209.83 frames.], batch size: 42, aishell_tot_loss[loss=0.1474, simple_loss=0.2338, pruned_loss=0.03048, over 983872.26 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2175, pruned_loss=0.03086, over 984664.06 frames.], batch size: 42, lr: 3.27e-04
+2022-06-19 04:31:12,110 INFO [train.py:874] (1/4) Epoch 25, batch 2750, datatang_loss[loss=0.1778, simple_loss=0.2568, pruned_loss=0.04939, over 4926.00 frames.], tot_loss[loss=0.1437, simple_loss=0.2262, pruned_loss=0.03066, over 985408.27 frames.], batch size: 108, aishell_tot_loss[loss=0.1473, simple_loss=0.2338, pruned_loss=0.03041, over 984150.16 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2183, pruned_loss=0.0311, over 984818.25 frames.], batch size: 108, lr: 3.26e-04
+2022-06-19 04:31:39,174 INFO [train.py:874] (1/4) Epoch 25, batch 2800, aishell_loss[loss=0.1494, simple_loss=0.2473, pruned_loss=0.02569, over 4981.00 frames.], tot_loss[loss=0.1448, simple_loss=0.2272, pruned_loss=0.03114, over 985199.10 frames.], batch size: 51, aishell_tot_loss[loss=0.1476, simple_loss=0.2341, pruned_loss=0.0305, over 984201.04 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2187, pruned_loss=0.03151, over 984791.69 frames.], batch size: 51, lr: 3.26e-04
+2022-06-19 04:32:09,649 INFO [train.py:874] (1/4) Epoch 25, batch 2850, aishell_loss[loss=0.1478, simple_loss=0.2347, pruned_loss=0.03044, over 4948.00 frames.], tot_loss[loss=0.1444, simple_loss=0.2263, pruned_loss=0.03119, over 985190.65 frames.], batch size: 61, aishell_tot_loss[loss=0.1476, simple_loss=0.234, pruned_loss=0.0306, over 984113.16 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2183, pruned_loss=0.03149, over 985019.57 frames.], batch size: 61, lr: 3.26e-04
+2022-06-19 04:32:36,507 INFO [train.py:874] (1/4) Epoch 25, batch 2900, datatang_loss[loss=0.1315, simple_loss=0.2112, pruned_loss=0.02592, over 4927.00 frames.], tot_loss[loss=0.1439, simple_loss=0.2261, pruned_loss=0.03088, over 985188.30 frames.], batch size: 79, aishell_tot_loss[loss=0.147, simple_loss=0.2334, pruned_loss=0.03027, over 984222.39 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2184, pruned_loss=0.03155, over 985061.78 frames.], batch size: 79, lr: 3.26e-04
+2022-06-19 04:33:06,599 INFO [train.py:874] (1/4) Epoch 25, batch 2950, datatang_loss[loss=0.1405, simple_loss=0.2257, pruned_loss=0.02765, over 4935.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2255, pruned_loss=0.03057, over 985427.75 frames.], batch size: 88, aishell_tot_loss[loss=0.1466, simple_loss=0.2333, pruned_loss=0.02998, over 984441.99 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.218, pruned_loss=0.03149, over 985219.74 frames.], batch size: 88, lr: 3.26e-04
+2022-06-19 04:33:34,208 INFO [train.py:874] (1/4) Epoch 25, batch 3000, aishell_loss[loss=0.1078, simple_loss=0.1749, pruned_loss=0.02031, over 4782.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2252, pruned_loss=0.03023, over 985202.00 frames.], batch size: 20, aishell_tot_loss[loss=0.1462, simple_loss=0.2329, pruned_loss=0.02971, over 984261.01 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2178, pruned_loss=0.0314, over 985336.17 frames.], batch size: 20, lr: 3.26e-04
+2022-06-19 04:33:34,208 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 04:33:49,970 INFO [train.py:914] (1/4) Epoch 25, validation: loss=0.1645, simple_loss=0.2487, pruned_loss=0.04017, over 1622729.00 frames.
+2022-06-19 04:34:20,839 INFO [train.py:874] (1/4) Epoch 25, batch 3050, aishell_loss[loss=0.1699, simple_loss=0.2522, pruned_loss=0.04386, over 4862.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2251, pruned_loss=0.03009, over 985296.31 frames.], batch size: 35, aishell_tot_loss[loss=0.1463, simple_loss=0.2329, pruned_loss=0.02987, over 984417.57 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2173, pruned_loss=0.03103, over 985399.67 frames.], batch size: 35, lr: 3.26e-04
+2022-06-19 04:34:49,380 INFO [train.py:874] (1/4) Epoch 25, batch 3100, aishell_loss[loss=0.1475, simple_loss=0.2294, pruned_loss=0.03274, over 4918.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2254, pruned_loss=0.03008, over 985428.33 frames.], batch size: 58, aishell_tot_loss[loss=0.1462, simple_loss=0.2328, pruned_loss=0.02979, over 984672.59 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2173, pruned_loss=0.03106, over 985395.64 frames.], batch size: 58, lr: 3.26e-04
+2022-06-19 04:35:19,309 INFO [train.py:874] (1/4) Epoch 25, batch 3150, datatang_loss[loss=0.111, simple_loss=0.1958, pruned_loss=0.0131, over 4930.00 frames.], tot_loss[loss=0.142, simple_loss=0.2248, pruned_loss=0.02955, over 985552.48 frames.], batch size: 79, aishell_tot_loss[loss=0.1456, simple_loss=0.2323, pruned_loss=0.0294, over 984904.53 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2173, pruned_loss=0.0308, over 985394.67 frames.], batch size: 79, lr: 3.26e-04
+2022-06-19 04:35:49,025 INFO [train.py:874] (1/4) Epoch 25, batch 3200, datatang_loss[loss=0.1489, simple_loss=0.2239, pruned_loss=0.03696, over 4980.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2248, pruned_loss=0.02946, over 985718.95 frames.], batch size: 26, aishell_tot_loss[loss=0.1454, simple_loss=0.2322, pruned_loss=0.02928, over 985074.92 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2172, pruned_loss=0.03072, over 985490.84 frames.], batch size: 26, lr: 3.26e-04
+2022-06-19 04:36:17,747 INFO [train.py:874] (1/4) Epoch 25, batch 3250, aishell_loss[loss=0.1649, simple_loss=0.2434, pruned_loss=0.04321, over 4964.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2251, pruned_loss=0.02952, over 985735.41 frames.], batch size: 44, aishell_tot_loss[loss=0.1446, simple_loss=0.2315, pruned_loss=0.02885, over 985092.67 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.218, pruned_loss=0.03113, over 985582.59 frames.], batch size: 44, lr: 3.26e-04
+2022-06-19 04:36:47,045 INFO [train.py:874] (1/4) Epoch 25, batch 3300, datatang_loss[loss=0.1398, simple_loss=0.2208, pruned_loss=0.02941, over 4859.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2249, pruned_loss=0.02995, over 985345.34 frames.], batch size: 39, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02905, over 985106.71 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.218, pruned_loss=0.03123, over 985253.91 frames.], batch size: 39, lr: 3.26e-04
+2022-06-19 04:37:14,506 INFO [train.py:874] (1/4) Epoch 25, batch 3350, datatang_loss[loss=0.1386, simple_loss=0.2246, pruned_loss=0.02623, over 4917.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2254, pruned_loss=0.03025, over 985363.08 frames.], batch size: 81, aishell_tot_loss[loss=0.1451, simple_loss=0.2318, pruned_loss=0.02918, over 985032.66 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2184, pruned_loss=0.03139, over 985384.53 frames.], batch size: 81, lr: 3.26e-04
+2022-06-19 04:37:42,869 INFO [train.py:874] (1/4) Epoch 25, batch 3400, aishell_loss[loss=0.1691, simple_loss=0.2437, pruned_loss=0.04724, over 4893.00 frames.], tot_loss[loss=0.143, simple_loss=0.2254, pruned_loss=0.0303, over 985086.36 frames.], batch size: 34, aishell_tot_loss[loss=0.1454, simple_loss=0.2319, pruned_loss=0.02944, over 984561.52 frames.], datatang_tot_loss[loss=0.1402, simple_loss=0.2181, pruned_loss=0.03121, over 985603.74 frames.], batch size: 34, lr: 3.25e-04
+2022-06-19 04:38:11,614 INFO [train.py:874] (1/4) Epoch 25, batch 3450, datatang_loss[loss=0.1229, simple_loss=0.1976, pruned_loss=0.02408, over 4947.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2261, pruned_loss=0.03035, over 985140.01 frames.], batch size: 67, aishell_tot_loss[loss=0.1456, simple_loss=0.2321, pruned_loss=0.02952, over 984545.03 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2185, pruned_loss=0.03122, over 985697.71 frames.], batch size: 67, lr: 3.25e-04
+2022-06-19 04:38:39,599 INFO [train.py:874] (1/4) Epoch 25, batch 3500, aishell_loss[loss=0.1565, simple_loss=0.2451, pruned_loss=0.03402, over 4979.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2257, pruned_loss=0.02991, over 984971.02 frames.], batch size: 51, aishell_tot_loss[loss=0.1455, simple_loss=0.2321, pruned_loss=0.02948, over 984459.49 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2177, pruned_loss=0.03084, over 985642.94 frames.], batch size: 51, lr: 3.25e-04
+2022-06-19 04:39:08,257 INFO [train.py:874] (1/4) Epoch 25, batch 3550, datatang_loss[loss=0.1552, simple_loss=0.2349, pruned_loss=0.03778, over 4935.00 frames.], tot_loss[loss=0.1431, simple_loss=0.226, pruned_loss=0.03017, over 985523.08 frames.], batch size: 94, aishell_tot_loss[loss=0.1457, simple_loss=0.2325, pruned_loss=0.02947, over 984975.23 frames.], datatang_tot_loss[loss=0.14, simple_loss=0.2178, pruned_loss=0.03106, over 985697.36 frames.], batch size: 94, lr: 3.25e-04
+2022-06-19 04:39:38,076 INFO [train.py:874] (1/4) Epoch 25, batch 3600, aishell_loss[loss=0.1506, simple_loss=0.2392, pruned_loss=0.03105, over 4936.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2265, pruned_loss=0.0302, over 985445.44 frames.], batch size: 49, aishell_tot_loss[loss=0.1463, simple_loss=0.2331, pruned_loss=0.02981, over 984966.08 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2175, pruned_loss=0.03079, over 985687.60 frames.], batch size: 49, lr: 3.25e-04
+2022-06-19 04:40:04,735 INFO [train.py:874] (1/4) Epoch 25, batch 3650, aishell_loss[loss=0.1243, simple_loss=0.213, pruned_loss=0.01779, over 4978.00 frames.], tot_loss[loss=0.1435, simple_loss=0.2264, pruned_loss=0.03027, over 984964.45 frames.], batch size: 44, aishell_tot_loss[loss=0.1461, simple_loss=0.2326, pruned_loss=0.02975, over 984491.12 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2179, pruned_loss=0.03091, over 985692.52 frames.], batch size: 44, lr: 3.25e-04
+2022-06-19 04:40:33,806 INFO [train.py:874] (1/4) Epoch 25, batch 3700, datatang_loss[loss=0.1217, simple_loss=0.2061, pruned_loss=0.01861, over 4958.00 frames.], tot_loss[loss=0.1432, simple_loss=0.226, pruned_loss=0.03018, over 984662.46 frames.], batch size: 34, aishell_tot_loss[loss=0.1462, simple_loss=0.2328, pruned_loss=0.02978, over 983979.40 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2177, pruned_loss=0.03074, over 985824.57 frames.], batch size: 34, lr: 3.25e-04
+2022-06-19 04:41:00,275 INFO [train.py:874] (1/4) Epoch 25, batch 3750, datatang_loss[loss=0.141, simple_loss=0.2203, pruned_loss=0.03078, over 4966.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2267, pruned_loss=0.03081, over 984863.40 frames.], batch size: 50, aishell_tot_loss[loss=0.1465, simple_loss=0.2331, pruned_loss=0.02996, over 984009.65 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2182, pruned_loss=0.03121, over 985956.74 frames.], batch size: 50, lr: 3.25e-04
+2022-06-19 04:41:29,147 INFO [train.py:874] (1/4) Epoch 25, batch 3800, datatang_loss[loss=0.1189, simple_loss=0.2024, pruned_loss=0.01769, over 4929.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2254, pruned_loss=0.03047, over 984624.38 frames.], batch size: 71, aishell_tot_loss[loss=0.1462, simple_loss=0.2327, pruned_loss=0.02988, over 984023.63 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2177, pruned_loss=0.03098, over 985655.96 frames.], batch size: 71, lr: 3.25e-04
+2022-06-19 04:41:57,149 INFO [train.py:874] (1/4) Epoch 25, batch 3850, datatang_loss[loss=0.1521, simple_loss=0.2162, pruned_loss=0.04401, over 4887.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2249, pruned_loss=0.03038, over 984862.30 frames.], batch size: 24, aishell_tot_loss[loss=0.1461, simple_loss=0.2325, pruned_loss=0.02985, over 984071.41 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2174, pruned_loss=0.03092, over 985820.55 frames.], batch size: 24, lr: 3.25e-04
+2022-06-19 04:42:25,188 INFO [train.py:874] (1/4) Epoch 25, batch 3900, aishell_loss[loss=0.1453, simple_loss=0.2331, pruned_loss=0.02875, over 4950.00 frames.], tot_loss[loss=0.1434, simple_loss=0.2256, pruned_loss=0.03065, over 985347.92 frames.], batch size: 45, aishell_tot_loss[loss=0.1458, simple_loss=0.2325, pruned_loss=0.0296, over 984365.42 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2179, pruned_loss=0.03145, over 986038.96 frames.], batch size: 45, lr: 3.25e-04
+2022-06-19 04:42:52,852 INFO [train.py:874] (1/4) Epoch 25, batch 3950, aishell_loss[loss=0.1413, simple_loss=0.2324, pruned_loss=0.02516, over 4931.00 frames.], tot_loss[loss=0.1438, simple_loss=0.2261, pruned_loss=0.03074, over 985630.04 frames.], batch size: 68, aishell_tot_loss[loss=0.1458, simple_loss=0.2326, pruned_loss=0.0295, over 984687.30 frames.], datatang_tot_loss[loss=0.1409, simple_loss=0.2184, pruned_loss=0.03168, over 986046.84 frames.], batch size: 68, lr: 3.25e-04
+2022-06-19 04:43:21,322 INFO [train.py:874] (1/4) Epoch 25, batch 4000, aishell_loss[loss=0.1136, simple_loss=0.1944, pruned_loss=0.01644, over 4871.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2261, pruned_loss=0.03026, over 985162.11 frames.], batch size: 28, aishell_tot_loss[loss=0.1459, simple_loss=0.233, pruned_loss=0.02944, over 984652.18 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.2181, pruned_loss=0.03129, over 985646.71 frames.], batch size: 28, lr: 3.25e-04
+2022-06-19 04:43:21,323 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 04:43:37,169 INFO [train.py:914] (1/4) Epoch 25, validation: loss=0.1639, simple_loss=0.2478, pruned_loss=0.03995, over 1622729.00 frames.
+2022-06-19 04:44:42,287 INFO [train.py:874] (1/4) Epoch 26, batch 50, aishell_loss[loss=0.1682, simple_loss=0.2446, pruned_loss=0.04596, over 4919.00 frames.], tot_loss[loss=0.1403, simple_loss=0.222, pruned_loss=0.02931, over 218502.74 frames.], batch size: 41, aishell_tot_loss[loss=0.1471, simple_loss=0.2348, pruned_loss=0.02965, over 98381.30 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2124, pruned_loss=0.02906, over 133431.79 frames.], batch size: 41, lr: 3.18e-04
+2022-06-19 04:45:12,043 INFO [train.py:874] (1/4) Epoch 26, batch 100, datatang_loss[loss=0.1377, simple_loss=0.216, pruned_loss=0.02968, over 4906.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2213, pruned_loss=0.02928, over 388373.33 frames.], batch size: 52, aishell_tot_loss[loss=0.1448, simple_loss=0.2314, pruned_loss=0.0291, over 206685.42 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2122, pruned_loss=0.02947, over 229935.11 frames.], batch size: 52, lr: 3.18e-04
+2022-06-19 04:45:40,326 INFO [train.py:874] (1/4) Epoch 26, batch 150, datatang_loss[loss=0.1296, simple_loss=0.2107, pruned_loss=0.0243, over 4928.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2228, pruned_loss=0.02982, over 520997.58 frames.], batch size: 77, aishell_tot_loss[loss=0.1458, simple_loss=0.2323, pruned_loss=0.02967, over 298589.99 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2135, pruned_loss=0.02984, over 319025.26 frames.], batch size: 77, lr: 3.18e-04
+2022-06-19 04:46:09,977 INFO [train.py:874] (1/4) Epoch 26, batch 200, datatang_loss[loss=0.1516, simple_loss=0.2188, pruned_loss=0.04222, over 4949.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2223, pruned_loss=0.02945, over 623674.46 frames.], batch size: 34, aishell_tot_loss[loss=0.1448, simple_loss=0.2315, pruned_loss=0.02903, over 370163.84 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2139, pruned_loss=0.02986, over 406112.18 frames.], batch size: 34, lr: 3.18e-04
+2022-06-19 04:46:40,012 INFO [train.py:874] (1/4) Epoch 26, batch 250, datatang_loss[loss=0.1269, simple_loss=0.2076, pruned_loss=0.02308, over 4976.00 frames.], tot_loss[loss=0.14, simple_loss=0.2218, pruned_loss=0.02907, over 704337.39 frames.], batch size: 65, aishell_tot_loss[loss=0.1439, simple_loss=0.231, pruned_loss=0.02836, over 423555.31 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2145, pruned_loss=0.02985, over 492151.24 frames.], batch size: 65, lr: 3.18e-04
+2022-06-19 04:47:07,487 INFO [train.py:874] (1/4) Epoch 26, batch 300, aishell_loss[loss=0.1321, simple_loss=0.2089, pruned_loss=0.02767, over 4972.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2216, pruned_loss=0.02878, over 766320.05 frames.], batch size: 27, aishell_tot_loss[loss=0.1427, simple_loss=0.2299, pruned_loss=0.02781, over 481735.76 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2152, pruned_loss=0.02984, over 556882.62 frames.], batch size: 27, lr: 3.18e-04
+2022-06-19 04:47:36,378 INFO [train.py:874] (1/4) Epoch 26, batch 350, aishell_loss[loss=0.162, simple_loss=0.2522, pruned_loss=0.03596, over 4962.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2224, pruned_loss=0.02936, over 814891.07 frames.], batch size: 79, aishell_tot_loss[loss=0.144, simple_loss=0.2309, pruned_loss=0.0286, over 536570.79 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2154, pruned_loss=0.02995, over 611138.30 frames.], batch size: 79, lr: 3.18e-04
+2022-06-19 04:48:05,363 INFO [train.py:874] (1/4) Epoch 26, batch 400, aishell_loss[loss=0.1405, simple_loss=0.2284, pruned_loss=0.0263, over 4912.00 frames.], tot_loss[loss=0.1407, simple_loss=0.223, pruned_loss=0.02917, over 852495.37 frames.], batch size: 33, aishell_tot_loss[loss=0.1433, simple_loss=0.2302, pruned_loss=0.02824, over 595026.52 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2163, pruned_loss=0.03009, over 650321.03 frames.], batch size: 33, lr: 3.18e-04
+2022-06-19 04:48:32,767 INFO [train.py:874] (1/4) Epoch 26, batch 450, aishell_loss[loss=0.1308, simple_loss=0.2126, pruned_loss=0.02445, over 4939.00 frames.], tot_loss[loss=0.142, simple_loss=0.2245, pruned_loss=0.0297, over 882121.48 frames.], batch size: 33, aishell_tot_loss[loss=0.1441, simple_loss=0.2309, pruned_loss=0.02861, over 644689.46 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2174, pruned_loss=0.03049, over 686734.84 frames.], batch size: 33, lr: 3.18e-04
+2022-06-19 04:49:06,609 INFO [train.py:874] (1/4) Epoch 26, batch 500, datatang_loss[loss=0.1237, simple_loss=0.2002, pruned_loss=0.02363, over 4929.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2232, pruned_loss=0.02953, over 904912.99 frames.], batch size: 71, aishell_tot_loss[loss=0.1444, simple_loss=0.231, pruned_loss=0.02887, over 675462.74 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2163, pruned_loss=0.03003, over 729780.46 frames.], batch size: 71, lr: 3.18e-04
+2022-06-19 04:49:36,230 INFO [train.py:874] (1/4) Epoch 26, batch 550, aishell_loss[loss=0.162, simple_loss=0.2543, pruned_loss=0.03484, over 4946.00 frames.], tot_loss[loss=0.1414, simple_loss=0.224, pruned_loss=0.02937, over 922941.71 frames.], batch size: 45, aishell_tot_loss[loss=0.1448, simple_loss=0.2318, pruned_loss=0.02888, over 718880.54 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2159, pruned_loss=0.02987, over 754207.18 frames.], batch size: 45, lr: 3.17e-04
+2022-06-19 04:50:04,780 INFO [train.py:874] (1/4) Epoch 26, batch 600, datatang_loss[loss=0.1266, simple_loss=0.2023, pruned_loss=0.02543, over 4828.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2237, pruned_loss=0.02959, over 936970.01 frames.], batch size: 30, aishell_tot_loss[loss=0.1446, simple_loss=0.2314, pruned_loss=0.0289, over 749181.44 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2161, pruned_loss=0.03013, over 782564.03 frames.], batch size: 30, lr: 3.17e-04
+2022-06-19 04:50:33,421 INFO [train.py:874] (1/4) Epoch 26, batch 650, datatang_loss[loss=0.14, simple_loss=0.2211, pruned_loss=0.02942, over 4944.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2236, pruned_loss=0.02954, over 947947.86 frames.], batch size: 88, aishell_tot_loss[loss=0.1445, simple_loss=0.2313, pruned_loss=0.02892, over 778125.84 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.216, pruned_loss=0.0301, over 805729.75 frames.], batch size: 88, lr: 3.17e-04
+2022-06-19 04:51:04,493 INFO [train.py:874] (1/4) Epoch 26, batch 700, datatang_loss[loss=0.1348, simple_loss=0.2181, pruned_loss=0.02573, over 4960.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2248, pruned_loss=0.03004, over 956131.44 frames.], batch size: 60, aishell_tot_loss[loss=0.1453, simple_loss=0.2319, pruned_loss=0.02937, over 804438.83 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2167, pruned_loss=0.0303, over 825082.87 frames.], batch size: 60, lr: 3.17e-04
+2022-06-19 04:51:32,315 INFO [train.py:874] (1/4) Epoch 26, batch 750, aishell_loss[loss=0.1518, simple_loss=0.2363, pruned_loss=0.03369, over 4871.00 frames.], tot_loss[loss=0.1424, simple_loss=0.225, pruned_loss=0.02993, over 962376.03 frames.], batch size: 35, aishell_tot_loss[loss=0.1454, simple_loss=0.2324, pruned_loss=0.02922, over 825700.00 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2166, pruned_loss=0.03035, over 843768.38 frames.], batch size: 35, lr: 3.17e-04
+2022-06-19 04:52:00,215 INFO [train.py:874] (1/4) Epoch 26, batch 800, aishell_loss[loss=0.168, simple_loss=0.2593, pruned_loss=0.03832, over 4967.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2251, pruned_loss=0.02999, over 967546.73 frames.], batch size: 61, aishell_tot_loss[loss=0.1454, simple_loss=0.2325, pruned_loss=0.02916, over 840951.94 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2171, pruned_loss=0.03049, over 863577.82 frames.], batch size: 61, lr: 3.17e-04
+2022-06-19 04:52:30,613 INFO [train.py:874] (1/4) Epoch 26, batch 850, aishell_loss[loss=0.1697, simple_loss=0.246, pruned_loss=0.04669, over 4974.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2239, pruned_loss=0.02967, over 971516.96 frames.], batch size: 44, aishell_tot_loss[loss=0.1449, simple_loss=0.2314, pruned_loss=0.02917, over 860985.79 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2165, pruned_loss=0.03015, over 875352.07 frames.], batch size: 44, lr: 3.17e-04
+2022-06-19 04:52:59,209 INFO [train.py:874] (1/4) Epoch 26, batch 900, aishell_loss[loss=0.1395, simple_loss=0.2193, pruned_loss=0.02986, over 4862.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2236, pruned_loss=0.02968, over 974242.62 frames.], batch size: 35, aishell_tot_loss[loss=0.145, simple_loss=0.2317, pruned_loss=0.02921, over 871975.35 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2164, pruned_loss=0.0301, over 891114.24 frames.], batch size: 35, lr: 3.17e-04
+2022-06-19 04:53:26,724 INFO [train.py:874] (1/4) Epoch 26, batch 950, aishell_loss[loss=0.1466, simple_loss=0.2212, pruned_loss=0.03602, over 4936.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2232, pruned_loss=0.02953, over 976912.85 frames.], batch size: 32, aishell_tot_loss[loss=0.1444, simple_loss=0.2309, pruned_loss=0.029, over 884518.68 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2167, pruned_loss=0.03015, over 903069.01 frames.], batch size: 32, lr: 3.17e-04
+2022-06-19 04:53:57,637 INFO [train.py:874] (1/4) Epoch 26, batch 1000, datatang_loss[loss=0.1442, simple_loss=0.2256, pruned_loss=0.03143, over 4933.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2238, pruned_loss=0.03014, over 978838.43 frames.], batch size: 79, aishell_tot_loss[loss=0.1448, simple_loss=0.2314, pruned_loss=0.0291, over 894359.49 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2171, pruned_loss=0.03071, over 914491.36 frames.], batch size: 79, lr: 3.17e-04
+2022-06-19 04:53:57,638 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 04:54:14,575 INFO [train.py:914] (1/4) Epoch 26, validation: loss=0.165, simple_loss=0.2487, pruned_loss=0.04059, over 1622729.00 frames.
+2022-06-19 04:54:43,208 INFO [train.py:874] (1/4) Epoch 26, batch 1050, aishell_loss[loss=0.1492, simple_loss=0.241, pruned_loss=0.02869, over 4956.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2246, pruned_loss=0.03018, over 980442.65 frames.], batch size: 64, aishell_tot_loss[loss=0.1455, simple_loss=0.2325, pruned_loss=0.02928, over 904887.33 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2169, pruned_loss=0.03064, over 923112.74 frames.], batch size: 64, lr: 3.17e-04
+2022-06-19 04:55:11,952 INFO [train.py:874] (1/4) Epoch 26, batch 1100, datatang_loss[loss=0.1335, simple_loss=0.2196, pruned_loss=0.02372, over 4935.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2245, pruned_loss=0.03012, over 981608.09 frames.], batch size: 94, aishell_tot_loss[loss=0.1459, simple_loss=0.233, pruned_loss=0.02938, over 911808.95 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2168, pruned_loss=0.03047, over 932442.48 frames.], batch size: 94, lr: 3.17e-04
+2022-06-19 04:55:39,895 INFO [train.py:874] (1/4) Epoch 26, batch 1150, aishell_loss[loss=0.1556, simple_loss=0.2308, pruned_loss=0.04019, over 4945.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2243, pruned_loss=0.03019, over 982470.89 frames.], batch size: 32, aishell_tot_loss[loss=0.1458, simple_loss=0.2328, pruned_loss=0.02936, over 920050.60 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2168, pruned_loss=0.03058, over 939028.39 frames.], batch size: 32, lr: 3.17e-04
+2022-06-19 04:56:09,243 INFO [train.py:874] (1/4) Epoch 26, batch 1200, datatang_loss[loss=0.1425, simple_loss=0.2159, pruned_loss=0.03453, over 4960.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2251, pruned_loss=0.03024, over 983323.27 frames.], batch size: 45, aishell_tot_loss[loss=0.146, simple_loss=0.233, pruned_loss=0.0295, over 929003.22 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.217, pruned_loss=0.03058, over 943761.02 frames.], batch size: 45, lr: 3.16e-04
+2022-06-19 04:56:37,388 INFO [train.py:874] (1/4) Epoch 26, batch 1250, datatang_loss[loss=0.1267, simple_loss=0.2066, pruned_loss=0.02338, over 4921.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2244, pruned_loss=0.0295, over 983653.06 frames.], batch size: 81, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.0292, over 937360.22 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2163, pruned_loss=0.03017, over 947262.29 frames.], batch size: 81, lr: 3.16e-04
+2022-06-19 04:57:05,522 INFO [train.py:874] (1/4) Epoch 26, batch 1300, aishell_loss[loss=0.1436, simple_loss=0.2313, pruned_loss=0.02797, over 4861.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2246, pruned_loss=0.02986, over 984213.39 frames.], batch size: 38, aishell_tot_loss[loss=0.1458, simple_loss=0.2329, pruned_loss=0.02934, over 942224.33 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2163, pruned_loss=0.03038, over 952548.77 frames.], batch size: 38, lr: 3.16e-04
+2022-06-19 04:57:34,761 INFO [train.py:874] (1/4) Epoch 26, batch 1350, datatang_loss[loss=0.1298, simple_loss=0.2043, pruned_loss=0.0276, over 4915.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2247, pruned_loss=0.03005, over 984297.94 frames.], batch size: 77, aishell_tot_loss[loss=0.1456, simple_loss=0.2328, pruned_loss=0.02923, over 947265.17 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2165, pruned_loss=0.03069, over 956281.43 frames.], batch size: 77, lr: 3.16e-04
+2022-06-19 04:58:03,280 INFO [train.py:874] (1/4) Epoch 26, batch 1400, datatang_loss[loss=0.1454, simple_loss=0.2286, pruned_loss=0.03114, over 4957.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2246, pruned_loss=0.03047, over 984483.74 frames.], batch size: 91, aishell_tot_loss[loss=0.1457, simple_loss=0.2326, pruned_loss=0.02939, over 951270.00 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2168, pruned_loss=0.03098, over 960053.89 frames.], batch size: 91, lr: 3.16e-04
+2022-06-19 04:58:31,204 INFO [train.py:874] (1/4) Epoch 26, batch 1450, aishell_loss[loss=0.1246, simple_loss=0.2167, pruned_loss=0.01631, over 4913.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2248, pruned_loss=0.03027, over 984353.30 frames.], batch size: 46, aishell_tot_loss[loss=0.146, simple_loss=0.233, pruned_loss=0.02953, over 954661.35 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.03067, over 963216.08 frames.], batch size: 46, lr: 3.16e-04
+2022-06-19 04:59:01,374 INFO [train.py:874] (1/4) Epoch 26, batch 1500, aishell_loss[loss=0.1518, simple_loss=0.2399, pruned_loss=0.03185, over 4938.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2248, pruned_loss=0.03005, over 984405.65 frames.], batch size: 54, aishell_tot_loss[loss=0.1461, simple_loss=0.2332, pruned_loss=0.0295, over 958324.59 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2162, pruned_loss=0.03051, over 965700.75 frames.], batch size: 54, lr: 3.16e-04
+2022-06-19 04:59:30,329 INFO [train.py:874] (1/4) Epoch 26, batch 1550, aishell_loss[loss=0.1358, simple_loss=0.2258, pruned_loss=0.02295, over 4859.00 frames.], tot_loss[loss=0.1417, simple_loss=0.224, pruned_loss=0.02967, over 983890.00 frames.], batch size: 37, aishell_tot_loss[loss=0.1453, simple_loss=0.2321, pruned_loss=0.02923, over 961103.98 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2162, pruned_loss=0.03038, over 967740.48 frames.], batch size: 37, lr: 3.16e-04
+2022-06-19 04:59:58,546 INFO [train.py:874] (1/4) Epoch 26, batch 1600, datatang_loss[loss=0.1284, simple_loss=0.2036, pruned_loss=0.02657, over 4915.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2239, pruned_loss=0.02981, over 983974.10 frames.], batch size: 57, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02907, over 963496.79 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2166, pruned_loss=0.03068, over 969996.05 frames.], batch size: 57, lr: 3.16e-04
+2022-06-19 05:00:28,447 INFO [train.py:874] (1/4) Epoch 26, batch 1650, aishell_loss[loss=0.1601, simple_loss=0.2481, pruned_loss=0.03611, over 4876.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2246, pruned_loss=0.03028, over 984005.51 frames.], batch size: 42, aishell_tot_loss[loss=0.1458, simple_loss=0.2326, pruned_loss=0.02944, over 965182.78 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2167, pruned_loss=0.03075, over 972245.06 frames.], batch size: 42, lr: 3.16e-04
+2022-06-19 05:00:57,262 INFO [train.py:874] (1/4) Epoch 26, batch 1700, datatang_loss[loss=0.1208, simple_loss=0.2011, pruned_loss=0.02022, over 4916.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2246, pruned_loss=0.03013, over 984083.89 frames.], batch size: 83, aishell_tot_loss[loss=0.1456, simple_loss=0.2325, pruned_loss=0.0293, over 967307.76 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2168, pruned_loss=0.03079, over 973818.79 frames.], batch size: 83, lr: 3.16e-04
+2022-06-19 05:01:26,595 INFO [train.py:874] (1/4) Epoch 26, batch 1750, datatang_loss[loss=0.09532, simple_loss=0.1706, pruned_loss=0.01002, over 4957.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2241, pruned_loss=0.02967, over 984533.93 frames.], batch size: 45, aishell_tot_loss[loss=0.1456, simple_loss=0.2327, pruned_loss=0.02928, over 969404.40 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2163, pruned_loss=0.03036, over 975329.69 frames.], batch size: 45, lr: 3.16e-04
+2022-06-19 05:01:57,143 INFO [train.py:874] (1/4) Epoch 26, batch 1800, aishell_loss[loss=0.1439, simple_loss=0.2358, pruned_loss=0.02601, over 4954.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2247, pruned_loss=0.0303, over 984604.99 frames.], batch size: 56, aishell_tot_loss[loss=0.1462, simple_loss=0.2331, pruned_loss=0.02966, over 970633.47 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2167, pruned_loss=0.03058, over 976931.90 frames.], batch size: 56, lr: 3.16e-04
+2022-06-19 05:02:25,517 INFO [train.py:874] (1/4) Epoch 26, batch 1850, datatang_loss[loss=0.1379, simple_loss=0.2154, pruned_loss=0.03021, over 4894.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2247, pruned_loss=0.03045, over 984832.80 frames.], batch size: 59, aishell_tot_loss[loss=0.1465, simple_loss=0.2334, pruned_loss=0.02983, over 972010.41 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2169, pruned_loss=0.03058, over 978153.28 frames.], batch size: 59, lr: 3.16e-04
+2022-06-19 05:02:55,375 INFO [train.py:874] (1/4) Epoch 26, batch 1900, datatang_loss[loss=0.137, simple_loss=0.2157, pruned_loss=0.02919, over 4926.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2245, pruned_loss=0.03041, over 984934.00 frames.], batch size: 57, aishell_tot_loss[loss=0.1462, simple_loss=0.2329, pruned_loss=0.0297, over 973646.24 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.2169, pruned_loss=0.03073, over 978967.34 frames.], batch size: 57, lr: 3.15e-04
+2022-06-19 05:03:25,573 INFO [train.py:874] (1/4) Epoch 26, batch 1950, datatang_loss[loss=0.1278, simple_loss=0.2054, pruned_loss=0.02504, over 4927.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2248, pruned_loss=0.03044, over 985158.52 frames.], batch size: 73, aishell_tot_loss[loss=0.1466, simple_loss=0.2334, pruned_loss=0.02997, over 975220.78 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2165, pruned_loss=0.03056, over 979722.28 frames.], batch size: 73, lr: 3.15e-04
+2022-06-19 05:03:53,622 INFO [train.py:874] (1/4) Epoch 26, batch 2000, datatang_loss[loss=0.1438, simple_loss=0.2204, pruned_loss=0.0336, over 4949.00 frames.], tot_loss[loss=0.1425, simple_loss=0.2247, pruned_loss=0.03022, over 985302.88 frames.], batch size: 86, aishell_tot_loss[loss=0.1464, simple_loss=0.2332, pruned_loss=0.02976, over 976479.27 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2164, pruned_loss=0.03057, over 980473.28 frames.], batch size: 86, lr: 3.15e-04
+2022-06-19 05:03:53,622 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 05:04:09,288 INFO [train.py:914] (1/4) Epoch 26, validation: loss=0.1659, simple_loss=0.2484, pruned_loss=0.04175, over 1622729.00 frames.
+2022-06-19 05:04:38,988 INFO [train.py:874] (1/4) Epoch 26, batch 2050, aishell_loss[loss=0.1566, simple_loss=0.234, pruned_loss=0.03964, over 4899.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2251, pruned_loss=0.03029, over 985341.72 frames.], batch size: 34, aishell_tot_loss[loss=0.1465, simple_loss=0.2333, pruned_loss=0.02987, over 977511.01 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2168, pruned_loss=0.03055, over 981075.06 frames.], batch size: 34, lr: 3.15e-04
+2022-06-19 05:05:07,641 INFO [train.py:874] (1/4) Epoch 26, batch 2100, datatang_loss[loss=0.1391, simple_loss=0.2193, pruned_loss=0.0295, over 4926.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2253, pruned_loss=0.03024, over 985303.04 frames.], batch size: 83, aishell_tot_loss[loss=0.1462, simple_loss=0.2333, pruned_loss=0.02958, over 978306.27 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2171, pruned_loss=0.03079, over 981652.62 frames.], batch size: 83, lr: 3.15e-04
+2022-06-19 05:05:37,219 INFO [train.py:874] (1/4) Epoch 26, batch 2150, aishell_loss[loss=0.1553, simple_loss=0.2522, pruned_loss=0.02916, over 4978.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2256, pruned_loss=0.03037, over 985289.36 frames.], batch size: 51, aishell_tot_loss[loss=0.1462, simple_loss=0.2333, pruned_loss=0.02955, over 979079.13 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2172, pruned_loss=0.03101, over 982176.89 frames.], batch size: 51, lr: 3.15e-04
+2022-06-19 05:06:06,681 INFO [train.py:874] (1/4) Epoch 26, batch 2200, datatang_loss[loss=0.1132, simple_loss=0.1946, pruned_loss=0.01589, over 4928.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2252, pruned_loss=0.03052, over 985754.41 frames.], batch size: 75, aishell_tot_loss[loss=0.1461, simple_loss=0.2331, pruned_loss=0.02958, over 979928.01 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2172, pruned_loss=0.03112, over 982858.90 frames.], batch size: 75, lr: 3.15e-04
+2022-06-19 05:06:34,931 INFO [train.py:874] (1/4) Epoch 26, batch 2250, datatang_loss[loss=0.1609, simple_loss=0.2367, pruned_loss=0.0426, over 4924.00 frames.], tot_loss[loss=0.1434, simple_loss=0.226, pruned_loss=0.03044, over 985904.63 frames.], batch size: 57, aishell_tot_loss[loss=0.1457, simple_loss=0.2328, pruned_loss=0.02934, over 980791.79 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2181, pruned_loss=0.03136, over 983248.58 frames.], batch size: 57, lr: 3.15e-04
+2022-06-19 05:07:05,043 INFO [train.py:874] (1/4) Epoch 26, batch 2300, datatang_loss[loss=0.1501, simple_loss=0.2264, pruned_loss=0.03688, over 4941.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2251, pruned_loss=0.03023, over 985746.67 frames.], batch size: 67, aishell_tot_loss[loss=0.1455, simple_loss=0.2325, pruned_loss=0.02927, over 981272.72 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2177, pruned_loss=0.03121, over 983486.61 frames.], batch size: 67, lr: 3.15e-04
+2022-06-19 05:07:33,215 INFO [train.py:874] (1/4) Epoch 26, batch 2350, aishell_loss[loss=0.1422, simple_loss=0.2383, pruned_loss=0.02302, over 4942.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2242, pruned_loss=0.02948, over 985745.97 frames.], batch size: 45, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.02911, over 981673.58 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2171, pruned_loss=0.03057, over 983862.05 frames.], batch size: 45, lr: 3.15e-04
+2022-06-19 05:08:00,963 INFO [train.py:874] (1/4) Epoch 26, batch 2400, datatang_loss[loss=0.1352, simple_loss=0.2166, pruned_loss=0.02696, over 4924.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2246, pruned_loss=0.03003, over 985784.37 frames.], batch size: 73, aishell_tot_loss[loss=0.1456, simple_loss=0.2324, pruned_loss=0.02938, over 982028.22 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2173, pruned_loss=0.0308, over 984244.98 frames.], batch size: 73, lr: 3.15e-04
+2022-06-19 05:08:30,376 INFO [train.py:874] (1/4) Epoch 26, batch 2450, aishell_loss[loss=0.1608, simple_loss=0.2465, pruned_loss=0.03759, over 4937.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2231, pruned_loss=0.02971, over 985703.62 frames.], batch size: 64, aishell_tot_loss[loss=0.1452, simple_loss=0.2319, pruned_loss=0.0293, over 982206.25 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2163, pruned_loss=0.03051, over 984567.10 frames.], batch size: 64, lr: 3.15e-04
+2022-06-19 05:08:59,237 INFO [train.py:874] (1/4) Epoch 26, batch 2500, aishell_loss[loss=0.1117, simple_loss=0.1963, pruned_loss=0.01359, over 4878.00 frames.], tot_loss[loss=0.1417, simple_loss=0.224, pruned_loss=0.02969, over 985615.05 frames.], batch size: 28, aishell_tot_loss[loss=0.1452, simple_loss=0.2322, pruned_loss=0.02907, over 982648.31 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2167, pruned_loss=0.03071, over 984596.72 frames.], batch size: 28, lr: 3.15e-04
+2022-06-19 05:09:26,862 INFO [train.py:874] (1/4) Epoch 26, batch 2550, aishell_loss[loss=0.1471, simple_loss=0.2368, pruned_loss=0.02871, over 4975.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2247, pruned_loss=0.02937, over 985532.66 frames.], batch size: 44, aishell_tot_loss[loss=0.1454, simple_loss=0.2328, pruned_loss=0.02906, over 982968.07 frames.], datatang_tot_loss[loss=0.1385, simple_loss=0.2163, pruned_loss=0.03037, over 984714.35 frames.], batch size: 44, lr: 3.14e-04
+2022-06-19 05:09:56,427 INFO [train.py:874] (1/4) Epoch 26, batch 2600, datatang_loss[loss=0.1239, simple_loss=0.2106, pruned_loss=0.01861, over 4972.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2248, pruned_loss=0.02935, over 985323.51 frames.], batch size: 31, aishell_tot_loss[loss=0.1452, simple_loss=0.2325, pruned_loss=0.02899, over 982906.55 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2166, pruned_loss=0.03036, over 984937.40 frames.], batch size: 31, lr: 3.14e-04
+2022-06-19 05:10:26,626 INFO [train.py:874] (1/4) Epoch 26, batch 2650, datatang_loss[loss=0.144, simple_loss=0.2132, pruned_loss=0.03738, over 4944.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2247, pruned_loss=0.0298, over 985630.26 frames.], batch size: 67, aishell_tot_loss[loss=0.1452, simple_loss=0.2323, pruned_loss=0.02902, over 983330.23 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.217, pruned_loss=0.03071, over 985136.40 frames.], batch size: 67, lr: 3.14e-04
+2022-06-19 05:10:56,030 INFO [train.py:874] (1/4) Epoch 26, batch 2700, datatang_loss[loss=0.1369, simple_loss=0.206, pruned_loss=0.03393, over 4910.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2248, pruned_loss=0.03003, over 986158.41 frames.], batch size: 52, aishell_tot_loss[loss=0.1452, simple_loss=0.2322, pruned_loss=0.02904, over 983944.03 frames.], datatang_tot_loss[loss=0.1396, simple_loss=0.2173, pruned_loss=0.03092, over 985417.57 frames.], batch size: 52, lr: 3.14e-04
+2022-06-19 05:11:24,557 INFO [train.py:874] (1/4) Epoch 26, batch 2750, aishell_loss[loss=0.1386, simple_loss=0.2286, pruned_loss=0.0243, over 4910.00 frames.], tot_loss[loss=0.142, simple_loss=0.2246, pruned_loss=0.02966, over 985806.57 frames.], batch size: 52, aishell_tot_loss[loss=0.145, simple_loss=0.2323, pruned_loss=0.02888, over 983970.16 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2168, pruned_loss=0.03076, over 985397.59 frames.], batch size: 52, lr: 3.14e-04
+2022-06-19 05:11:53,311 INFO [train.py:874] (1/4) Epoch 26, batch 2800, aishell_loss[loss=0.1465, simple_loss=0.2236, pruned_loss=0.03468, over 4926.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2253, pruned_loss=0.02975, over 985801.21 frames.], batch size: 33, aishell_tot_loss[loss=0.1453, simple_loss=0.2325, pruned_loss=0.02907, over 984175.25 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2165, pruned_loss=0.03073, over 985531.25 frames.], batch size: 33, lr: 3.14e-04
+2022-06-19 05:12:22,080 INFO [train.py:874] (1/4) Epoch 26, batch 2850, aishell_loss[loss=0.1046, simple_loss=0.1761, pruned_loss=0.0165, over 4950.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2248, pruned_loss=0.02924, over 985863.72 frames.], batch size: 21, aishell_tot_loss[loss=0.1451, simple_loss=0.2323, pruned_loss=0.02894, over 984693.47 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2161, pruned_loss=0.03031, over 985317.95 frames.], batch size: 21, lr: 3.14e-04
+2022-06-19 05:12:50,266 INFO [train.py:874] (1/4) Epoch 26, batch 2900, datatang_loss[loss=0.1681, simple_loss=0.2535, pruned_loss=0.04139, over 4842.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2245, pruned_loss=0.02922, over 985554.64 frames.], batch size: 33, aishell_tot_loss[loss=0.1447, simple_loss=0.2315, pruned_loss=0.02892, over 984588.95 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2163, pruned_loss=0.03026, over 985326.26 frames.], batch size: 33, lr: 3.14e-04
+2022-06-19 05:13:18,681 INFO [train.py:874] (1/4) Epoch 26, batch 2950, aishell_loss[loss=0.1385, simple_loss=0.2309, pruned_loss=0.02307, over 4858.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2239, pruned_loss=0.02908, over 985584.77 frames.], batch size: 38, aishell_tot_loss[loss=0.1442, simple_loss=0.231, pruned_loss=0.0287, over 984703.66 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2161, pruned_loss=0.03025, over 985385.15 frames.], batch size: 38, lr: 3.14e-04
+2022-06-19 05:13:48,853 INFO [train.py:874] (1/4) Epoch 26, batch 3000, aishell_loss[loss=0.1563, simple_loss=0.2415, pruned_loss=0.03554, over 4938.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2252, pruned_loss=0.02975, over 985361.40 frames.], batch size: 49, aishell_tot_loss[loss=0.1449, simple_loss=0.2315, pruned_loss=0.02911, over 984526.89 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2166, pruned_loss=0.03051, over 985477.35 frames.], batch size: 49, lr: 3.14e-04
+2022-06-19 05:13:48,854 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 05:14:05,913 INFO [train.py:914] (1/4) Epoch 26, validation: loss=0.1644, simple_loss=0.2479, pruned_loss=0.04043, over 1622729.00 frames.
+2022-06-19 05:14:34,725 INFO [train.py:874] (1/4) Epoch 26, batch 3050, aishell_loss[loss=0.1308, simple_loss=0.2198, pruned_loss=0.02088, over 4921.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2251, pruned_loss=0.03005, over 985686.53 frames.], batch size: 46, aishell_tot_loss[loss=0.1452, simple_loss=0.2319, pruned_loss=0.02926, over 984432.38 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.217, pruned_loss=0.0306, over 985935.39 frames.], batch size: 46, lr: 3.14e-04
+2022-06-19 05:15:02,363 INFO [train.py:874] (1/4) Epoch 26, batch 3100, datatang_loss[loss=0.1621, simple_loss=0.2462, pruned_loss=0.03903, over 4932.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2239, pruned_loss=0.02948, over 985415.55 frames.], batch size: 42, aishell_tot_loss[loss=0.1446, simple_loss=0.2313, pruned_loss=0.02894, over 984260.13 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2169, pruned_loss=0.03031, over 985893.42 frames.], batch size: 42, lr: 3.14e-04
+2022-06-19 05:15:30,148 INFO [train.py:874] (1/4) Epoch 26, batch 3150, datatang_loss[loss=0.1546, simple_loss=0.234, pruned_loss=0.03756, over 4952.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2232, pruned_loss=0.02965, over 985940.59 frames.], batch size: 86, aishell_tot_loss[loss=0.1443, simple_loss=0.2308, pruned_loss=0.02892, over 984610.11 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2166, pruned_loss=0.03048, over 986184.43 frames.], batch size: 86, lr: 3.14e-04
+2022-06-19 05:16:00,420 INFO [train.py:874] (1/4) Epoch 26, batch 3200, datatang_loss[loss=0.1389, simple_loss=0.214, pruned_loss=0.0319, over 4863.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2238, pruned_loss=0.03018, over 985676.67 frames.], batch size: 39, aishell_tot_loss[loss=0.145, simple_loss=0.2312, pruned_loss=0.0294, over 984631.81 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2166, pruned_loss=0.03056, over 986018.95 frames.], batch size: 39, lr: 3.14e-04
+2022-06-19 05:16:28,119 INFO [train.py:874] (1/4) Epoch 26, batch 3250, aishell_loss[loss=0.1179, simple_loss=0.1911, pruned_loss=0.02235, over 4830.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2242, pruned_loss=0.02994, over 985767.29 frames.], batch size: 21, aishell_tot_loss[loss=0.1451, simple_loss=0.2314, pruned_loss=0.02938, over 984809.32 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2165, pruned_loss=0.03041, over 986044.06 frames.], batch size: 21, lr: 3.13e-04
+2022-06-19 05:16:56,615 INFO [train.py:874] (1/4) Epoch 26, batch 3300, datatang_loss[loss=0.1361, simple_loss=0.2143, pruned_loss=0.02897, over 4982.00 frames.], tot_loss[loss=0.1432, simple_loss=0.2254, pruned_loss=0.03047, over 985654.70 frames.], batch size: 40, aishell_tot_loss[loss=0.1454, simple_loss=0.232, pruned_loss=0.02934, over 984735.14 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2176, pruned_loss=0.03098, over 986035.21 frames.], batch size: 40, lr: 3.13e-04
+2022-06-19 05:17:26,234 INFO [train.py:874] (1/4) Epoch 26, batch 3350, datatang_loss[loss=0.146, simple_loss=0.2259, pruned_loss=0.03307, over 4919.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2251, pruned_loss=0.03015, over 985805.81 frames.], batch size: 71, aishell_tot_loss[loss=0.1447, simple_loss=0.2314, pruned_loss=0.02899, over 984900.67 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.218, pruned_loss=0.03108, over 986100.22 frames.], batch size: 71, lr: 3.13e-04
+2022-06-19 05:17:54,022 INFO [train.py:874] (1/4) Epoch 26, batch 3400, datatang_loss[loss=0.1368, simple_loss=0.2134, pruned_loss=0.03004, over 4938.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2255, pruned_loss=0.03005, over 985675.52 frames.], batch size: 69, aishell_tot_loss[loss=0.1449, simple_loss=0.2318, pruned_loss=0.02907, over 984938.52 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2179, pruned_loss=0.03094, over 986011.01 frames.], batch size: 69, lr: 3.13e-04
+2022-06-19 05:18:21,625 INFO [train.py:874] (1/4) Epoch 26, batch 3450, aishell_loss[loss=0.1632, simple_loss=0.2464, pruned_loss=0.04001, over 4941.00 frames.], tot_loss[loss=0.1442, simple_loss=0.2265, pruned_loss=0.0309, over 985863.35 frames.], batch size: 32, aishell_tot_loss[loss=0.1456, simple_loss=0.2323, pruned_loss=0.02942, over 984980.80 frames.], datatang_tot_loss[loss=0.1408, simple_loss=0.2187, pruned_loss=0.03148, over 986207.05 frames.], batch size: 32, lr: 3.13e-04
+2022-06-19 05:18:52,451 INFO [train.py:874] (1/4) Epoch 26, batch 3500, datatang_loss[loss=0.1821, simple_loss=0.255, pruned_loss=0.05456, over 4935.00 frames.], tot_loss[loss=0.1437, simple_loss=0.226, pruned_loss=0.03074, over 985888.37 frames.], batch size: 108, aishell_tot_loss[loss=0.1456, simple_loss=0.2326, pruned_loss=0.02934, over 984993.31 frames.], datatang_tot_loss[loss=0.1407, simple_loss=0.2184, pruned_loss=0.03145, over 986266.24 frames.], batch size: 108, lr: 3.13e-04
+2022-06-19 05:19:22,078 INFO [train.py:874] (1/4) Epoch 26, batch 3550, datatang_loss[loss=0.1706, simple_loss=0.2414, pruned_loss=0.04991, over 4907.00 frames.], tot_loss[loss=0.1431, simple_loss=0.2254, pruned_loss=0.03046, over 985857.77 frames.], batch size: 42, aishell_tot_loss[loss=0.1453, simple_loss=0.2323, pruned_loss=0.02917, over 985107.61 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2181, pruned_loss=0.03137, over 986178.27 frames.], batch size: 42, lr: 3.13e-04
+2022-06-19 05:19:50,838 INFO [train.py:874] (1/4) Epoch 26, batch 3600, aishell_loss[loss=0.1339, simple_loss=0.2208, pruned_loss=0.02353, over 4901.00 frames.], tot_loss[loss=0.1427, simple_loss=0.225, pruned_loss=0.03014, over 985697.33 frames.], batch size: 34, aishell_tot_loss[loss=0.1446, simple_loss=0.2315, pruned_loss=0.02888, over 984868.63 frames.], datatang_tot_loss[loss=0.1406, simple_loss=0.2184, pruned_loss=0.03137, over 986313.35 frames.], batch size: 34, lr: 3.13e-04
+2022-06-19 05:20:19,581 INFO [train.py:874] (1/4) Epoch 26, batch 3650, aishell_loss[loss=0.1473, simple_loss=0.2284, pruned_loss=0.03312, over 4915.00 frames.], tot_loss[loss=0.1433, simple_loss=0.2257, pruned_loss=0.03044, over 985521.95 frames.], batch size: 33, aishell_tot_loss[loss=0.1453, simple_loss=0.232, pruned_loss=0.0293, over 984817.87 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2183, pruned_loss=0.03131, over 986237.30 frames.], batch size: 33, lr: 3.13e-04
+2022-06-19 05:20:49,059 INFO [train.py:874] (1/4) Epoch 26, batch 3700, datatang_loss[loss=0.1546, simple_loss=0.239, pruned_loss=0.03512, over 4943.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2247, pruned_loss=0.03032, over 985646.80 frames.], batch size: 99, aishell_tot_loss[loss=0.145, simple_loss=0.2316, pruned_loss=0.02919, over 984840.72 frames.], datatang_tot_loss[loss=0.1403, simple_loss=0.218, pruned_loss=0.03127, over 986310.03 frames.], batch size: 99, lr: 3.13e-04
+2022-06-19 05:21:16,721 INFO [train.py:874] (1/4) Epoch 26, batch 3750, aishell_loss[loss=0.1562, simple_loss=0.2516, pruned_loss=0.03036, over 4931.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2243, pruned_loss=0.02996, over 985413.72 frames.], batch size: 68, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02909, over 984592.64 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2175, pruned_loss=0.03103, over 986343.38 frames.], batch size: 68, lr: 3.13e-04
+2022-06-19 05:21:44,668 INFO [train.py:874] (1/4) Epoch 26, batch 3800, aishell_loss[loss=0.1524, simple_loss=0.2494, pruned_loss=0.02776, over 4931.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2244, pruned_loss=0.03003, over 985473.33 frames.], batch size: 68, aishell_tot_loss[loss=0.1455, simple_loss=0.2323, pruned_loss=0.02936, over 984728.12 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.217, pruned_loss=0.03083, over 986279.79 frames.], batch size: 68, lr: 3.13e-04
+2022-06-19 05:22:11,980 INFO [train.py:874] (1/4) Epoch 26, batch 3850, aishell_loss[loss=0.1567, simple_loss=0.247, pruned_loss=0.03321, over 4921.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2242, pruned_loss=0.02964, over 985544.40 frames.], batch size: 46, aishell_tot_loss[loss=0.1451, simple_loss=0.232, pruned_loss=0.02908, over 984727.41 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.03071, over 986379.67 frames.], batch size: 46, lr: 3.13e-04
+2022-06-19 05:22:40,499 INFO [train.py:874] (1/4) Epoch 26, batch 3900, datatang_loss[loss=0.11, simple_loss=0.1973, pruned_loss=0.01136, over 4879.00 frames.], tot_loss[loss=0.1417, simple_loss=0.224, pruned_loss=0.02968, over 985276.53 frames.], batch size: 39, aishell_tot_loss[loss=0.1453, simple_loss=0.2324, pruned_loss=0.0291, over 984563.60 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2165, pruned_loss=0.03064, over 986206.41 frames.], batch size: 39, lr: 3.12e-04
+2022-06-19 05:23:06,640 INFO [train.py:874] (1/4) Epoch 26, batch 3950, aishell_loss[loss=0.1362, simple_loss=0.2253, pruned_loss=0.02351, over 4943.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2238, pruned_loss=0.02924, over 985340.72 frames.], batch size: 40, aishell_tot_loss[loss=0.1449, simple_loss=0.232, pruned_loss=0.02887, over 984786.28 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2164, pruned_loss=0.03039, over 986046.68 frames.], batch size: 40, lr: 3.12e-04
+2022-06-19 05:23:34,141 INFO [train.py:874] (1/4) Epoch 26, batch 4000, aishell_loss[loss=0.1755, simple_loss=0.2573, pruned_loss=0.04683, over 4941.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2239, pruned_loss=0.02952, over 985793.84 frames.], batch size: 79, aishell_tot_loss[loss=0.1455, simple_loss=0.2324, pruned_loss=0.02931, over 985093.35 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.216, pruned_loss=0.03014, over 986191.74 frames.], batch size: 79, lr: 3.12e-04
+2022-06-19 05:23:34,141 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 05:23:50,808 INFO [train.py:914] (1/4) Epoch 26, validation: loss=0.1644, simple_loss=0.2484, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-19 05:24:19,240 INFO [train.py:874] (1/4) Epoch 26, batch 4050, aishell_loss[loss=0.1357, simple_loss=0.2381, pruned_loss=0.01661, over 4918.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2234, pruned_loss=0.02918, over 985569.07 frames.], batch size: 52, aishell_tot_loss[loss=0.145, simple_loss=0.2319, pruned_loss=0.02907, over 984831.04 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2158, pruned_loss=0.02997, over 986263.50 frames.], batch size: 52, lr: 3.12e-04
+2022-06-19 05:24:46,158 INFO [train.py:874] (1/4) Epoch 26, batch 4100, datatang_loss[loss=0.1676, simple_loss=0.2461, pruned_loss=0.04458, over 4927.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2235, pruned_loss=0.02884, over 985405.40 frames.], batch size: 94, aishell_tot_loss[loss=0.1451, simple_loss=0.2322, pruned_loss=0.02903, over 984879.23 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2155, pruned_loss=0.02957, over 986073.80 frames.], batch size: 94, lr: 3.12e-04
+2022-06-19 05:26:04,715 INFO [train.py:874] (1/4) Epoch 27, batch 50, datatang_loss[loss=0.1481, simple_loss=0.2185, pruned_loss=0.03886, over 4950.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2173, pruned_loss=0.02687, over 218599.47 frames.], batch size: 62, aishell_tot_loss[loss=0.1393, simple_loss=0.2268, pruned_loss=0.02585, over 111781.20 frames.], datatang_tot_loss[loss=0.132, simple_loss=0.2085, pruned_loss=0.02779, over 120458.28 frames.], batch size: 62, lr: 3.06e-04
+2022-06-19 05:26:34,279 INFO [train.py:874] (1/4) Epoch 27, batch 100, datatang_loss[loss=0.1237, simple_loss=0.2014, pruned_loss=0.02298, over 4959.00 frames.], tot_loss[loss=0.1336, simple_loss=0.2145, pruned_loss=0.02633, over 388743.40 frames.], batch size: 67, aishell_tot_loss[loss=0.1365, simple_loss=0.2224, pruned_loss=0.02526, over 206673.89 frames.], datatang_tot_loss[loss=0.1313, simple_loss=0.2079, pruned_loss=0.02731, over 230334.88 frames.], batch size: 67, lr: 3.06e-04
+2022-06-19 05:27:02,756 INFO [train.py:874] (1/4) Epoch 27, batch 150, aishell_loss[loss=0.1403, simple_loss=0.2206, pruned_loss=0.02998, over 4964.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2199, pruned_loss=0.02725, over 521167.85 frames.], batch size: 61, aishell_tot_loss[loss=0.1409, simple_loss=0.2279, pruned_loss=0.02694, over 335309.03 frames.], datatang_tot_loss[loss=0.1318, simple_loss=0.2086, pruned_loss=0.0275, over 281612.16 frames.], batch size: 61, lr: 3.06e-04
+2022-06-19 05:27:29,180 INFO [train.py:874] (1/4) Epoch 27, batch 200, aishell_loss[loss=0.1293, simple_loss=0.2205, pruned_loss=0.0191, over 4926.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2216, pruned_loss=0.02712, over 624323.01 frames.], batch size: 52, aishell_tot_loss[loss=0.1407, simple_loss=0.2285, pruned_loss=0.02648, over 426159.06 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2108, pruned_loss=0.02787, over 348906.05 frames.], batch size: 52, lr: 3.06e-04
+2022-06-19 05:27:59,034 INFO [train.py:874] (1/4) Epoch 27, batch 250, datatang_loss[loss=0.1261, simple_loss=0.2139, pruned_loss=0.01912, over 4952.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2212, pruned_loss=0.02779, over 704499.26 frames.], batch size: 86, aishell_tot_loss[loss=0.1404, simple_loss=0.2277, pruned_loss=0.02653, over 489330.01 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2123, pruned_loss=0.02891, over 427024.78 frames.], batch size: 86, lr: 3.06e-04
+2022-06-19 05:28:28,379 INFO [train.py:874] (1/4) Epoch 27, batch 300, datatang_loss[loss=0.1186, simple_loss=0.1905, pruned_loss=0.02336, over 4961.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2205, pruned_loss=0.02753, over 766722.20 frames.], batch size: 37, aishell_tot_loss[loss=0.1393, simple_loss=0.2265, pruned_loss=0.02607, over 549874.73 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2129, pruned_loss=0.02903, over 490335.29 frames.], batch size: 37, lr: 3.06e-04
+2022-06-19 05:29:00,123 INFO [train.py:874] (1/4) Epoch 27, batch 350, datatang_loss[loss=0.154, simple_loss=0.2297, pruned_loss=0.03913, over 4899.00 frames.], tot_loss[loss=0.139, simple_loss=0.2211, pruned_loss=0.02846, over 814727.27 frames.], batch size: 47, aishell_tot_loss[loss=0.1412, simple_loss=0.2278, pruned_loss=0.02726, over 594977.76 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2132, pruned_loss=0.02914, over 555051.91 frames.], batch size: 47, lr: 3.06e-04
+2022-06-19 05:29:29,887 INFO [train.py:874] (1/4) Epoch 27, batch 400, datatang_loss[loss=0.1287, simple_loss=0.2134, pruned_loss=0.02198, over 4928.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2219, pruned_loss=0.02852, over 852265.03 frames.], batch size: 94, aishell_tot_loss[loss=0.1426, simple_loss=0.2297, pruned_loss=0.02781, over 635518.29 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2129, pruned_loss=0.02871, over 611309.40 frames.], batch size: 94, lr: 3.06e-04
+2022-06-19 05:29:59,162 INFO [train.py:874] (1/4) Epoch 27, batch 450, datatang_loss[loss=0.145, simple_loss=0.2337, pruned_loss=0.02814, over 4959.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2212, pruned_loss=0.02878, over 881977.74 frames.], batch size: 99, aishell_tot_loss[loss=0.1431, simple_loss=0.2299, pruned_loss=0.02818, over 664165.66 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2129, pruned_loss=0.02876, over 668482.19 frames.], batch size: 99, lr: 3.06e-04
+2022-06-19 05:30:26,711 INFO [train.py:874] (1/4) Epoch 27, batch 500, datatang_loss[loss=0.1476, simple_loss=0.2185, pruned_loss=0.03833, over 4959.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2218, pruned_loss=0.029, over 905279.67 frames.], batch size: 67, aishell_tot_loss[loss=0.1437, simple_loss=0.2306, pruned_loss=0.02843, over 700821.02 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2132, pruned_loss=0.02889, over 707354.80 frames.], batch size: 67, lr: 3.06e-04
+2022-06-19 05:30:56,947 INFO [train.py:874] (1/4) Epoch 27, batch 550, datatang_loss[loss=0.157, simple_loss=0.2376, pruned_loss=0.03827, over 4976.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2225, pruned_loss=0.0293, over 923192.27 frames.], batch size: 40, aishell_tot_loss[loss=0.1438, simple_loss=0.2306, pruned_loss=0.0285, over 730545.88 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2143, pruned_loss=0.02936, over 743905.58 frames.], batch size: 40, lr: 3.06e-04
+2022-06-19 05:31:26,629 INFO [train.py:874] (1/4) Epoch 27, batch 600, datatang_loss[loss=0.1635, simple_loss=0.2369, pruned_loss=0.04503, over 4957.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2234, pruned_loss=0.02938, over 936686.19 frames.], batch size: 67, aishell_tot_loss[loss=0.1437, simple_loss=0.2305, pruned_loss=0.02841, over 764711.57 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2152, pruned_loss=0.02969, over 768023.36 frames.], batch size: 67, lr: 3.06e-04
+2022-06-19 05:31:54,494 INFO [train.py:874] (1/4) Epoch 27, batch 650, aishell_loss[loss=0.1572, simple_loss=0.2465, pruned_loss=0.03395, over 4922.00 frames.], tot_loss[loss=0.1407, simple_loss=0.223, pruned_loss=0.02924, over 947121.76 frames.], batch size: 46, aishell_tot_loss[loss=0.1438, simple_loss=0.2307, pruned_loss=0.02845, over 790306.62 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2147, pruned_loss=0.02957, over 793627.51 frames.], batch size: 46, lr: 3.05e-04
+2022-06-19 05:32:23,280 INFO [train.py:874] (1/4) Epoch 27, batch 700, datatang_loss[loss=0.1801, simple_loss=0.2482, pruned_loss=0.05602, over 4962.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2235, pruned_loss=0.02905, over 955312.92 frames.], batch size: 67, aishell_tot_loss[loss=0.1432, simple_loss=0.2303, pruned_loss=0.02801, over 819215.00 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2151, pruned_loss=0.02989, over 809860.70 frames.], batch size: 67, lr: 3.05e-04
+2022-06-19 05:32:53,906 INFO [train.py:874] (1/4) Epoch 27, batch 750, aishell_loss[loss=0.1611, simple_loss=0.2518, pruned_loss=0.03524, over 4882.00 frames.], tot_loss[loss=0.141, simple_loss=0.2237, pruned_loss=0.02919, over 962111.03 frames.], batch size: 42, aishell_tot_loss[loss=0.1437, simple_loss=0.231, pruned_loss=0.02815, over 838093.42 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.215, pruned_loss=0.02995, over 831404.21 frames.], batch size: 42, lr: 3.05e-04
+2022-06-19 05:33:22,658 INFO [train.py:874] (1/4) Epoch 27, batch 800, aishell_loss[loss=0.1395, simple_loss=0.2236, pruned_loss=0.02771, over 4863.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2228, pruned_loss=0.02874, over 967365.01 frames.], batch size: 37, aishell_tot_loss[loss=0.1426, simple_loss=0.2296, pruned_loss=0.02777, over 854677.20 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2157, pruned_loss=0.02983, over 850425.99 frames.], batch size: 37, lr: 3.05e-04
+2022-06-19 05:33:51,881 INFO [train.py:874] (1/4) Epoch 27, batch 850, datatang_loss[loss=0.1312, simple_loss=0.2121, pruned_loss=0.02519, over 4933.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2229, pruned_loss=0.0289, over 971661.51 frames.], batch size: 69, aishell_tot_loss[loss=0.1426, simple_loss=0.2296, pruned_loss=0.02787, over 870735.68 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2158, pruned_loss=0.0299, over 865949.69 frames.], batch size: 69, lr: 3.05e-04
+2022-06-19 05:34:20,054 INFO [train.py:874] (1/4) Epoch 27, batch 900, datatang_loss[loss=0.117, simple_loss=0.1964, pruned_loss=0.01885, over 4934.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2229, pruned_loss=0.02935, over 974455.78 frames.], batch size: 69, aishell_tot_loss[loss=0.1424, simple_loss=0.229, pruned_loss=0.02789, over 882810.02 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2166, pruned_loss=0.03039, over 881206.37 frames.], batch size: 69, lr: 3.05e-04
+2022-06-19 05:34:50,669 INFO [train.py:874] (1/4) Epoch 27, batch 950, datatang_loss[loss=0.1494, simple_loss=0.2324, pruned_loss=0.03316, over 4934.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2227, pruned_loss=0.0291, over 976895.11 frames.], batch size: 50, aishell_tot_loss[loss=0.1423, simple_loss=0.229, pruned_loss=0.02783, over 893572.33 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2165, pruned_loss=0.03018, over 894796.10 frames.], batch size: 50, lr: 3.05e-04
+2022-06-19 05:35:20,657 INFO [train.py:874] (1/4) Epoch 27, batch 1000, datatang_loss[loss=0.14, simple_loss=0.2267, pruned_loss=0.0267, over 4924.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2232, pruned_loss=0.02916, over 978846.46 frames.], batch size: 81, aishell_tot_loss[loss=0.1425, simple_loss=0.229, pruned_loss=0.02798, over 907634.14 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2166, pruned_loss=0.03025, over 902209.71 frames.], batch size: 81, lr: 3.05e-04
+2022-06-19 05:35:20,657 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 05:35:36,379 INFO [train.py:914] (1/4) Epoch 27, validation: loss=0.1644, simple_loss=0.2483, pruned_loss=0.04028, over 1622729.00 frames.
+2022-06-19 05:36:07,025 INFO [train.py:874] (1/4) Epoch 27, batch 1050, aishell_loss[loss=0.1403, simple_loss=0.2261, pruned_loss=0.02721, over 4915.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2234, pruned_loss=0.02938, over 980304.55 frames.], batch size: 41, aishell_tot_loss[loss=0.1424, simple_loss=0.2288, pruned_loss=0.02797, over 916410.78 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2172, pruned_loss=0.03053, over 912425.66 frames.], batch size: 41, lr: 3.05e-04
+2022-06-19 05:36:36,806 INFO [train.py:874] (1/4) Epoch 27, batch 1100, aishell_loss[loss=0.142, simple_loss=0.2339, pruned_loss=0.02502, over 4981.00 frames.], tot_loss[loss=0.14, simple_loss=0.2224, pruned_loss=0.02885, over 981312.84 frames.], batch size: 39, aishell_tot_loss[loss=0.1417, simple_loss=0.2279, pruned_loss=0.02776, over 925334.84 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2169, pruned_loss=0.03022, over 920030.06 frames.], batch size: 39, lr: 3.05e-04
+2022-06-19 05:37:06,345 INFO [train.py:874] (1/4) Epoch 27, batch 1150, datatang_loss[loss=0.1285, simple_loss=0.207, pruned_loss=0.02501, over 4920.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2227, pruned_loss=0.02893, over 982285.22 frames.], batch size: 77, aishell_tot_loss[loss=0.1426, simple_loss=0.2289, pruned_loss=0.02809, over 932837.60 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2162, pruned_loss=0.02997, over 927359.06 frames.], batch size: 77, lr: 3.05e-04
+2022-06-19 05:37:36,216 INFO [train.py:874] (1/4) Epoch 27, batch 1200, aishell_loss[loss=0.1169, simple_loss=0.1918, pruned_loss=0.02096, over 4983.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2223, pruned_loss=0.02892, over 983081.98 frames.], batch size: 25, aishell_tot_loss[loss=0.1422, simple_loss=0.2285, pruned_loss=0.02798, over 938383.50 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2164, pruned_loss=0.03003, over 934989.15 frames.], batch size: 25, lr: 3.05e-04
+2022-06-19 05:38:04,664 INFO [train.py:874] (1/4) Epoch 27, batch 1250, aishell_loss[loss=0.1497, simple_loss=0.2438, pruned_loss=0.02784, over 4935.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2224, pruned_loss=0.02901, over 983421.33 frames.], batch size: 79, aishell_tot_loss[loss=0.1423, simple_loss=0.2287, pruned_loss=0.02795, over 943275.33 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2164, pruned_loss=0.03012, over 941409.84 frames.], batch size: 79, lr: 3.05e-04
+2022-06-19 05:38:34,999 INFO [train.py:874] (1/4) Epoch 27, batch 1300, aishell_loss[loss=0.1371, simple_loss=0.2256, pruned_loss=0.02426, over 4978.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2228, pruned_loss=0.02868, over 983889.06 frames.], batch size: 51, aishell_tot_loss[loss=0.1424, simple_loss=0.2292, pruned_loss=0.02782, over 947938.34 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2164, pruned_loss=0.02986, over 946918.49 frames.], batch size: 51, lr: 3.05e-04
+2022-06-19 05:39:05,111 INFO [train.py:874] (1/4) Epoch 27, batch 1350, datatang_loss[loss=0.1279, simple_loss=0.2052, pruned_loss=0.02533, over 4951.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2217, pruned_loss=0.02874, over 983835.40 frames.], batch size: 55, aishell_tot_loss[loss=0.1422, simple_loss=0.2288, pruned_loss=0.02778, over 950686.06 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2161, pruned_loss=0.02985, over 952678.49 frames.], batch size: 55, lr: 3.04e-04
+2022-06-19 05:39:34,201 INFO [train.py:874] (1/4) Epoch 27, batch 1400, datatang_loss[loss=0.1295, simple_loss=0.2121, pruned_loss=0.02347, over 4912.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2224, pruned_loss=0.02861, over 984101.50 frames.], batch size: 75, aishell_tot_loss[loss=0.1421, simple_loss=0.2288, pruned_loss=0.02769, over 954460.91 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2167, pruned_loss=0.02978, over 956768.05 frames.], batch size: 75, lr: 3.04e-04
+2022-06-19 05:40:02,660 INFO [train.py:874] (1/4) Epoch 27, batch 1450, datatang_loss[loss=0.1349, simple_loss=0.2139, pruned_loss=0.02796, over 4916.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2227, pruned_loss=0.02869, over 984318.89 frames.], batch size: 77, aishell_tot_loss[loss=0.1423, simple_loss=0.2292, pruned_loss=0.02767, over 957912.18 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2167, pruned_loss=0.02984, over 960237.91 frames.], batch size: 77, lr: 3.04e-04
+2022-06-19 05:40:32,275 INFO [train.py:874] (1/4) Epoch 27, batch 1500, aishell_loss[loss=0.1707, simple_loss=0.2375, pruned_loss=0.05194, over 4954.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2231, pruned_loss=0.02906, over 984330.25 frames.], batch size: 32, aishell_tot_loss[loss=0.1426, simple_loss=0.2295, pruned_loss=0.02788, over 960974.82 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2167, pruned_loss=0.03002, over 963113.41 frames.], batch size: 32, lr: 3.04e-04
+2022-06-19 05:41:00,510 INFO [train.py:874] (1/4) Epoch 27, batch 1550, aishell_loss[loss=0.1475, simple_loss=0.2333, pruned_loss=0.03092, over 4931.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2231, pruned_loss=0.02904, over 984390.70 frames.], batch size: 32, aishell_tot_loss[loss=0.1426, simple_loss=0.2295, pruned_loss=0.02786, over 963471.16 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2168, pruned_loss=0.03, over 965905.53 frames.], batch size: 32, lr: 3.04e-04
+2022-06-19 05:41:29,481 INFO [train.py:874] (1/4) Epoch 27, batch 1600, aishell_loss[loss=0.1462, simple_loss=0.2304, pruned_loss=0.03094, over 4954.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2237, pruned_loss=0.02891, over 984296.08 frames.], batch size: 56, aishell_tot_loss[loss=0.1429, simple_loss=0.2299, pruned_loss=0.02798, over 966495.06 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2166, pruned_loss=0.02982, over 967476.75 frames.], batch size: 56, lr: 3.04e-04
+2022-06-19 05:41:58,667 INFO [train.py:874] (1/4) Epoch 27, batch 1650, aishell_loss[loss=0.1431, simple_loss=0.233, pruned_loss=0.02664, over 4906.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2247, pruned_loss=0.0292, over 984895.42 frames.], batch size: 34, aishell_tot_loss[loss=0.1433, simple_loss=0.2305, pruned_loss=0.02804, over 968847.85 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2172, pruned_loss=0.03005, over 969836.70 frames.], batch size: 34, lr: 3.04e-04
+2022-06-19 05:42:26,853 INFO [train.py:874] (1/4) Epoch 27, batch 1700, aishell_loss[loss=0.1723, simple_loss=0.2655, pruned_loss=0.03953, over 4941.00 frames.], tot_loss[loss=0.1429, simple_loss=0.2258, pruned_loss=0.02997, over 985366.09 frames.], batch size: 40, aishell_tot_loss[loss=0.1442, simple_loss=0.2314, pruned_loss=0.0285, over 970896.60 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2177, pruned_loss=0.03046, over 971947.01 frames.], batch size: 40, lr: 3.04e-04
+2022-06-19 05:42:56,307 INFO [train.py:874] (1/4) Epoch 27, batch 1750, datatang_loss[loss=0.1622, simple_loss=0.2334, pruned_loss=0.04553, over 4952.00 frames.], tot_loss[loss=0.1424, simple_loss=0.2255, pruned_loss=0.02963, over 985182.15 frames.], batch size: 91, aishell_tot_loss[loss=0.1441, simple_loss=0.2312, pruned_loss=0.02848, over 972685.92 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2176, pruned_loss=0.03027, over 973266.69 frames.], batch size: 91, lr: 3.04e-04
+2022-06-19 05:43:25,975 INFO [train.py:874] (1/4) Epoch 27, batch 1800, aishell_loss[loss=0.1157, simple_loss=0.1933, pruned_loss=0.019, over 4805.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2246, pruned_loss=0.02979, over 984715.28 frames.], batch size: 24, aishell_tot_loss[loss=0.1442, simple_loss=0.231, pruned_loss=0.0287, over 973545.33 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2172, pruned_loss=0.03029, over 974778.93 frames.], batch size: 24, lr: 3.04e-04
+2022-06-19 05:43:54,026 INFO [train.py:874] (1/4) Epoch 27, batch 1850, aishell_loss[loss=0.1394, simple_loss=0.2283, pruned_loss=0.02523, over 4852.00 frames.], tot_loss[loss=0.142, simple_loss=0.2248, pruned_loss=0.02963, over 984786.12 frames.], batch size: 36, aishell_tot_loss[loss=0.1445, simple_loss=0.2315, pruned_loss=0.02877, over 975168.22 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2166, pruned_loss=0.03016, over 975754.51 frames.], batch size: 36, lr: 3.04e-04
+2022-06-19 05:44:24,531 INFO [train.py:874] (1/4) Epoch 27, batch 1900, aishell_loss[loss=0.1427, simple_loss=0.2292, pruned_loss=0.02808, over 4937.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2249, pruned_loss=0.02985, over 985226.77 frames.], batch size: 33, aishell_tot_loss[loss=0.1446, simple_loss=0.2317, pruned_loss=0.02877, over 976515.18 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2166, pruned_loss=0.03045, over 977078.71 frames.], batch size: 33, lr: 3.04e-04
+2022-06-19 05:44:54,844 INFO [train.py:874] (1/4) Epoch 27, batch 1950, aishell_loss[loss=0.1439, simple_loss=0.228, pruned_loss=0.02984, over 4965.00 frames.], tot_loss[loss=0.1422, simple_loss=0.225, pruned_loss=0.02971, over 985421.53 frames.], batch size: 61, aishell_tot_loss[loss=0.1443, simple_loss=0.2315, pruned_loss=0.02858, over 977808.38 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2169, pruned_loss=0.03056, over 977975.98 frames.], batch size: 61, lr: 3.04e-04
+2022-06-19 05:45:24,369 INFO [train.py:874] (1/4) Epoch 27, batch 2000, datatang_loss[loss=0.1157, simple_loss=0.1904, pruned_loss=0.02054, over 4943.00 frames.], tot_loss[loss=0.1421, simple_loss=0.225, pruned_loss=0.02955, over 985390.04 frames.], batch size: 50, aishell_tot_loss[loss=0.1439, simple_loss=0.2313, pruned_loss=0.02825, over 978648.53 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2172, pruned_loss=0.03075, over 978863.63 frames.], batch size: 50, lr: 3.04e-04
+2022-06-19 05:45:24,370 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 05:45:41,440 INFO [train.py:914] (1/4) Epoch 27, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04011, over 1622729.00 frames.
+2022-06-19 05:46:10,066 INFO [train.py:874] (1/4) Epoch 27, batch 2050, aishell_loss[loss=0.1416, simple_loss=0.2231, pruned_loss=0.03001, over 4957.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2237, pruned_loss=0.02942, over 985799.38 frames.], batch size: 56, aishell_tot_loss[loss=0.1432, simple_loss=0.2304, pruned_loss=0.02797, over 979570.36 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2172, pruned_loss=0.03085, over 979922.12 frames.], batch size: 56, lr: 3.04e-04
+2022-06-19 05:46:39,369 INFO [train.py:874] (1/4) Epoch 27, batch 2100, datatang_loss[loss=0.123, simple_loss=0.1942, pruned_loss=0.02595, over 4837.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2245, pruned_loss=0.0293, over 985637.15 frames.], batch size: 30, aishell_tot_loss[loss=0.1435, simple_loss=0.2309, pruned_loss=0.02806, over 980343.17 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2172, pruned_loss=0.03072, over 980421.00 frames.], batch size: 30, lr: 3.03e-04
+2022-06-19 05:47:08,910 INFO [train.py:874] (1/4) Epoch 27, batch 2150, aishell_loss[loss=0.1249, simple_loss=0.2072, pruned_loss=0.02129, over 4958.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2243, pruned_loss=0.02924, over 985682.23 frames.], batch size: 40, aishell_tot_loss[loss=0.1437, simple_loss=0.2311, pruned_loss=0.02817, over 981027.47 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2169, pruned_loss=0.03054, over 981007.94 frames.], batch size: 40, lr: 3.03e-04
+2022-06-19 05:47:37,123 INFO [train.py:874] (1/4) Epoch 27, batch 2200, aishell_loss[loss=0.1617, simple_loss=0.2511, pruned_loss=0.03619, over 4944.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2242, pruned_loss=0.02951, over 985823.43 frames.], batch size: 56, aishell_tot_loss[loss=0.1434, simple_loss=0.2306, pruned_loss=0.02812, over 981515.57 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.2171, pruned_loss=0.03085, over 981789.44 frames.], batch size: 56, lr: 3.03e-04
+2022-06-19 05:48:07,807 INFO [train.py:874] (1/4) Epoch 27, batch 2250, aishell_loss[loss=0.1366, simple_loss=0.2197, pruned_loss=0.02673, over 4923.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2241, pruned_loss=0.02953, over 986022.10 frames.], batch size: 33, aishell_tot_loss[loss=0.1435, simple_loss=0.2309, pruned_loss=0.02805, over 982187.48 frames.], datatang_tot_loss[loss=0.1394, simple_loss=0.217, pruned_loss=0.03091, over 982300.72 frames.], batch size: 33, lr: 3.03e-04
+2022-06-19 05:48:35,820 INFO [train.py:874] (1/4) Epoch 27, batch 2300, aishell_loss[loss=0.1704, simple_loss=0.2597, pruned_loss=0.04058, over 4868.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2242, pruned_loss=0.02962, over 985934.20 frames.], batch size: 36, aishell_tot_loss[loss=0.1431, simple_loss=0.2304, pruned_loss=0.02791, over 982421.99 frames.], datatang_tot_loss[loss=0.1399, simple_loss=0.2173, pruned_loss=0.03123, over 982878.77 frames.], batch size: 36, lr: 3.03e-04
+2022-06-19 05:49:05,341 INFO [train.py:874] (1/4) Epoch 27, batch 2350, aishell_loss[loss=0.1427, simple_loss=0.2355, pruned_loss=0.02497, over 4972.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2251, pruned_loss=0.03025, over 985843.99 frames.], batch size: 69, aishell_tot_loss[loss=0.1438, simple_loss=0.2311, pruned_loss=0.02826, over 982664.27 frames.], datatang_tot_loss[loss=0.1405, simple_loss=0.2179, pruned_loss=0.03154, over 983322.77 frames.], batch size: 69, lr: 3.03e-04
+2022-06-19 05:49:34,605 INFO [train.py:874] (1/4) Epoch 27, batch 2400, aishell_loss[loss=0.1532, simple_loss=0.2456, pruned_loss=0.03038, over 4923.00 frames.], tot_loss[loss=0.1425, simple_loss=0.225, pruned_loss=0.02995, over 985860.25 frames.], batch size: 52, aishell_tot_loss[loss=0.1436, simple_loss=0.231, pruned_loss=0.0281, over 982987.71 frames.], datatang_tot_loss[loss=0.1404, simple_loss=0.2178, pruned_loss=0.0315, over 983680.21 frames.], batch size: 52, lr: 3.03e-04
+2022-06-19 05:50:01,633 INFO [train.py:874] (1/4) Epoch 27, batch 2450, datatang_loss[loss=0.1259, simple_loss=0.1938, pruned_loss=0.02894, over 4966.00 frames.], tot_loss[loss=0.1427, simple_loss=0.2252, pruned_loss=0.03008, over 985848.59 frames.], batch size: 37, aishell_tot_loss[loss=0.1442, simple_loss=0.2316, pruned_loss=0.02842, over 983236.29 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2175, pruned_loss=0.03136, over 984020.90 frames.], batch size: 37, lr: 3.03e-04
+2022-06-19 05:50:30,999 INFO [train.py:874] (1/4) Epoch 27, batch 2500, aishell_loss[loss=0.1575, simple_loss=0.2454, pruned_loss=0.03482, over 4955.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2257, pruned_loss=0.03, over 985568.70 frames.], batch size: 61, aishell_tot_loss[loss=0.1445, simple_loss=0.2317, pruned_loss=0.02861, over 983259.91 frames.], datatang_tot_loss[loss=0.1401, simple_loss=0.2179, pruned_loss=0.03113, over 984230.42 frames.], batch size: 61, lr: 3.03e-04
+2022-06-19 05:51:00,252 INFO [train.py:874] (1/4) Epoch 27, batch 2550, datatang_loss[loss=0.1581, simple_loss=0.2329, pruned_loss=0.04164, over 4951.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2243, pruned_loss=0.02941, over 985666.16 frames.], batch size: 69, aishell_tot_loss[loss=0.1441, simple_loss=0.2312, pruned_loss=0.02845, over 983695.21 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2172, pruned_loss=0.0307, over 984322.75 frames.], batch size: 69, lr: 3.03e-04
+2022-06-19 05:51:28,206 INFO [train.py:874] (1/4) Epoch 27, batch 2600, aishell_loss[loss=0.1131, simple_loss=0.2036, pruned_loss=0.01128, over 4893.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2244, pruned_loss=0.02937, over 985441.83 frames.], batch size: 28, aishell_tot_loss[loss=0.144, simple_loss=0.2311, pruned_loss=0.02842, over 983893.25 frames.], datatang_tot_loss[loss=0.1392, simple_loss=0.217, pruned_loss=0.03073, over 984288.23 frames.], batch size: 28, lr: 3.03e-04
+2022-06-19 05:51:58,807 INFO [train.py:874] (1/4) Epoch 27, batch 2650, aishell_loss[loss=0.1579, simple_loss=0.2416, pruned_loss=0.03715, over 4942.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2247, pruned_loss=0.03022, over 985638.85 frames.], batch size: 45, aishell_tot_loss[loss=0.1448, simple_loss=0.2317, pruned_loss=0.02899, over 984130.58 frames.], datatang_tot_loss[loss=0.1395, simple_loss=0.2171, pruned_loss=0.03095, over 984565.38 frames.], batch size: 45, lr: 3.03e-04
+2022-06-19 05:52:27,276 INFO [train.py:874] (1/4) Epoch 27, batch 2700, datatang_loss[loss=0.1492, simple_loss=0.2228, pruned_loss=0.03775, over 4976.00 frames.], tot_loss[loss=0.1431, simple_loss=0.225, pruned_loss=0.03059, over 985314.91 frames.], batch size: 40, aishell_tot_loss[loss=0.1452, simple_loss=0.2318, pruned_loss=0.0293, over 983958.17 frames.], datatang_tot_loss[loss=0.1397, simple_loss=0.2171, pruned_loss=0.03114, over 984700.42 frames.], batch size: 40, lr: 3.03e-04
+2022-06-19 05:52:55,941 INFO [train.py:874] (1/4) Epoch 27, batch 2750, aishell_loss[loss=0.1082, simple_loss=0.1923, pruned_loss=0.01201, over 4966.00 frames.], tot_loss[loss=0.1428, simple_loss=0.2246, pruned_loss=0.0305, over 985430.58 frames.], batch size: 25, aishell_tot_loss[loss=0.1451, simple_loss=0.2315, pruned_loss=0.02928, over 984070.15 frames.], datatang_tot_loss[loss=0.1398, simple_loss=0.2173, pruned_loss=0.03112, over 984912.18 frames.], batch size: 25, lr: 3.03e-04
+2022-06-19 05:53:25,469 INFO [train.py:874] (1/4) Epoch 27, batch 2800, aishell_loss[loss=0.0972, simple_loss=0.1634, pruned_loss=0.01548, over 4819.00 frames.], tot_loss[loss=0.1421, simple_loss=0.2241, pruned_loss=0.03006, over 984985.36 frames.], batch size: 20, aishell_tot_loss[loss=0.1451, simple_loss=0.2318, pruned_loss=0.02924, over 983759.43 frames.], datatang_tot_loss[loss=0.1391, simple_loss=0.2166, pruned_loss=0.03076, over 984970.21 frames.], batch size: 20, lr: 3.02e-04
+2022-06-19 05:53:55,467 INFO [train.py:874] (1/4) Epoch 27, batch 2850, datatang_loss[loss=0.1279, simple_loss=0.2086, pruned_loss=0.02357, over 4950.00 frames.], tot_loss[loss=0.1419, simple_loss=0.2241, pruned_loss=0.02985, over 985310.09 frames.], batch size: 67, aishell_tot_loss[loss=0.1447, simple_loss=0.2315, pruned_loss=0.02897, over 984191.35 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2169, pruned_loss=0.03081, over 985027.29 frames.], batch size: 67, lr: 3.02e-04
+2022-06-19 05:54:23,635 INFO [train.py:874] (1/4) Epoch 27, batch 2900, datatang_loss[loss=0.1261, simple_loss=0.2016, pruned_loss=0.02528, over 4928.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2235, pruned_loss=0.02944, over 985355.04 frames.], batch size: 71, aishell_tot_loss[loss=0.1449, simple_loss=0.2317, pruned_loss=0.02903, over 984380.56 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.216, pruned_loss=0.03032, over 985046.94 frames.], batch size: 71, lr: 3.02e-04
+2022-06-19 05:54:53,400 INFO [train.py:874] (1/4) Epoch 27, batch 2950, datatang_loss[loss=0.1524, simple_loss=0.2279, pruned_loss=0.0384, over 4884.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2228, pruned_loss=0.02936, over 985544.62 frames.], batch size: 47, aishell_tot_loss[loss=0.1441, simple_loss=0.2307, pruned_loss=0.02872, over 984621.44 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2164, pruned_loss=0.03046, over 985148.06 frames.], batch size: 47, lr: 3.02e-04
+2022-06-19 05:55:22,533 INFO [train.py:874] (1/4) Epoch 27, batch 3000, aishell_loss[loss=0.1439, simple_loss=0.2411, pruned_loss=0.02335, over 4927.00 frames.], tot_loss[loss=0.1408, simple_loss=0.223, pruned_loss=0.02932, over 985565.50 frames.], batch size: 33, aishell_tot_loss[loss=0.1438, simple_loss=0.2307, pruned_loss=0.02843, over 984656.76 frames.], datatang_tot_loss[loss=0.139, simple_loss=0.2167, pruned_loss=0.03063, over 985276.21 frames.], batch size: 33, lr: 3.02e-04
+2022-06-19 05:55:22,534 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 05:55:39,145 INFO [train.py:914] (1/4) Epoch 27, validation: loss=0.1642, simple_loss=0.2486, pruned_loss=0.03985, over 1622729.00 frames.
+2022-06-19 05:56:10,086 INFO [train.py:874] (1/4) Epoch 27, batch 3050, datatang_loss[loss=0.1257, simple_loss=0.2037, pruned_loss=0.02386, over 4958.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2236, pruned_loss=0.02944, over 986012.56 frames.], batch size: 45, aishell_tot_loss[loss=0.1437, simple_loss=0.2307, pruned_loss=0.02841, over 985074.16 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2173, pruned_loss=0.03071, over 985478.51 frames.], batch size: 45, lr: 3.02e-04
+2022-06-19 05:56:38,536 INFO [train.py:874] (1/4) Epoch 27, batch 3100, datatang_loss[loss=0.1664, simple_loss=0.2355, pruned_loss=0.04869, over 4914.00 frames.], tot_loss[loss=0.141, simple_loss=0.2236, pruned_loss=0.02918, over 985937.11 frames.], batch size: 52, aishell_tot_loss[loss=0.1438, simple_loss=0.2309, pruned_loss=0.02837, over 985117.95 frames.], datatang_tot_loss[loss=0.1389, simple_loss=0.2169, pruned_loss=0.03043, over 985536.74 frames.], batch size: 52, lr: 3.02e-04
+2022-06-19 05:57:09,319 INFO [train.py:874] (1/4) Epoch 27, batch 3150, datatang_loss[loss=0.1198, simple_loss=0.2028, pruned_loss=0.01835, over 4981.00 frames.], tot_loss[loss=0.1412, simple_loss=0.224, pruned_loss=0.02919, over 986154.57 frames.], batch size: 60, aishell_tot_loss[loss=0.1442, simple_loss=0.2314, pruned_loss=0.02851, over 985478.95 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.217, pruned_loss=0.03024, over 985549.20 frames.], batch size: 60, lr: 3.02e-04
+2022-06-19 05:57:40,483 INFO [train.py:874] (1/4) Epoch 27, batch 3200, datatang_loss[loss=0.1495, simple_loss=0.2362, pruned_loss=0.03143, over 4896.00 frames.], tot_loss[loss=0.1423, simple_loss=0.2251, pruned_loss=0.0298, over 986272.88 frames.], batch size: 42, aishell_tot_loss[loss=0.1448, simple_loss=0.232, pruned_loss=0.02876, over 985458.07 frames.], datatang_tot_loss[loss=0.1393, simple_loss=0.2175, pruned_loss=0.03062, over 985856.24 frames.], batch size: 42, lr: 3.02e-04
+2022-06-19 05:58:09,157 INFO [train.py:874] (1/4) Epoch 27, batch 3250, aishell_loss[loss=0.1344, simple_loss=0.229, pruned_loss=0.01991, over 4915.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2235, pruned_loss=0.02918, over 986118.64 frames.], batch size: 52, aishell_tot_loss[loss=0.1441, simple_loss=0.2313, pruned_loss=0.02846, over 985521.97 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2165, pruned_loss=0.03029, over 985777.77 frames.], batch size: 52, lr: 3.02e-04
+2022-06-19 05:58:39,175 INFO [train.py:874] (1/4) Epoch 27, batch 3300, aishell_loss[loss=0.1441, simple_loss=0.2395, pruned_loss=0.02434, over 4946.00 frames.], tot_loss[loss=0.1415, simple_loss=0.224, pruned_loss=0.02947, over 985802.87 frames.], batch size: 45, aishell_tot_loss[loss=0.1445, simple_loss=0.2315, pruned_loss=0.02874, over 985411.59 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2167, pruned_loss=0.03027, over 985662.92 frames.], batch size: 45, lr: 3.02e-04
+2022-06-19 05:59:09,429 INFO [train.py:874] (1/4) Epoch 27, batch 3350, aishell_loss[loss=0.1409, simple_loss=0.2359, pruned_loss=0.023, over 4934.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2232, pruned_loss=0.02956, over 986215.31 frames.], batch size: 68, aishell_tot_loss[loss=0.1441, simple_loss=0.2309, pruned_loss=0.02859, over 985634.72 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2168, pruned_loss=0.03047, over 985933.54 frames.], batch size: 68, lr: 3.02e-04
+2022-06-19 05:59:36,782 INFO [train.py:874] (1/4) Epoch 27, batch 3400, aishell_loss[loss=0.1407, simple_loss=0.2326, pruned_loss=0.02437, over 4916.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2233, pruned_loss=0.02959, over 986419.02 frames.], batch size: 68, aishell_tot_loss[loss=0.1444, simple_loss=0.2314, pruned_loss=0.02864, over 985758.45 frames.], datatang_tot_loss[loss=0.1387, simple_loss=0.2166, pruned_loss=0.03042, over 986117.27 frames.], batch size: 68, lr: 3.02e-04
+2022-06-19 06:00:05,981 INFO [train.py:874] (1/4) Epoch 27, batch 3450, aishell_loss[loss=0.1271, simple_loss=0.2126, pruned_loss=0.02078, over 4889.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2224, pruned_loss=0.02888, over 985920.25 frames.], batch size: 28, aishell_tot_loss[loss=0.1431, simple_loss=0.2303, pruned_loss=0.02798, over 985266.13 frames.], datatang_tot_loss[loss=0.1386, simple_loss=0.2166, pruned_loss=0.03034, over 986186.16 frames.], batch size: 28, lr: 3.02e-04
+2022-06-19 06:00:35,351 INFO [train.py:874] (1/4) Epoch 27, batch 3500, aishell_loss[loss=0.133, simple_loss=0.2195, pruned_loss=0.02329, over 4900.00 frames.], tot_loss[loss=0.1399, simple_loss=0.222, pruned_loss=0.02895, over 985691.63 frames.], batch size: 34, aishell_tot_loss[loss=0.1435, simple_loss=0.2303, pruned_loss=0.02833, over 985127.94 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.216, pruned_loss=0.02999, over 986125.14 frames.], batch size: 34, lr: 3.02e-04
+2022-06-19 06:01:04,234 INFO [train.py:874] (1/4) Epoch 27, batch 3550, datatang_loss[loss=0.1185, simple_loss=0.2008, pruned_loss=0.01806, over 4918.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2213, pruned_loss=0.02868, over 985471.11 frames.], batch size: 75, aishell_tot_loss[loss=0.143, simple_loss=0.2298, pruned_loss=0.0281, over 984920.80 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2159, pruned_loss=0.02984, over 986074.17 frames.], batch size: 75, lr: 3.01e-04
+2022-06-19 06:01:33,726 INFO [train.py:874] (1/4) Epoch 27, batch 3600, aishell_loss[loss=0.1638, simple_loss=0.2535, pruned_loss=0.03709, over 4956.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2223, pruned_loss=0.02892, over 985605.11 frames.], batch size: 40, aishell_tot_loss[loss=0.1436, simple_loss=0.2305, pruned_loss=0.02834, over 984969.51 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.216, pruned_loss=0.02979, over 986149.98 frames.], batch size: 40, lr: 3.01e-04
+2022-06-19 06:02:05,263 INFO [train.py:874] (1/4) Epoch 27, batch 3650, aishell_loss[loss=0.1426, simple_loss=0.2283, pruned_loss=0.02847, over 4925.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2224, pruned_loss=0.02895, over 985806.96 frames.], batch size: 32, aishell_tot_loss[loss=0.1441, simple_loss=0.2313, pruned_loss=0.02843, over 985006.46 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2156, pruned_loss=0.02963, over 986281.37 frames.], batch size: 32, lr: 3.01e-04
+2022-06-19 06:02:32,479 INFO [train.py:874] (1/4) Epoch 27, batch 3700, datatang_loss[loss=0.1336, simple_loss=0.2139, pruned_loss=0.0266, over 4941.00 frames.], tot_loss[loss=0.1409, simple_loss=0.2231, pruned_loss=0.02929, over 985308.41 frames.], batch size: 25, aishell_tot_loss[loss=0.1443, simple_loss=0.2315, pruned_loss=0.02854, over 984762.99 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2156, pruned_loss=0.02985, over 986050.37 frames.], batch size: 25, lr: 3.01e-04
+2022-06-19 06:03:03,250 INFO [train.py:874] (1/4) Epoch 27, batch 3750, datatang_loss[loss=0.1338, simple_loss=0.2166, pruned_loss=0.02551, over 4947.00 frames.], tot_loss[loss=0.1399, simple_loss=0.222, pruned_loss=0.0289, over 985652.91 frames.], batch size: 88, aishell_tot_loss[loss=0.144, simple_loss=0.2312, pruned_loss=0.02841, over 984973.13 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2149, pruned_loss=0.02957, over 986163.66 frames.], batch size: 88, lr: 3.01e-04
+2022-06-19 06:03:32,613 INFO [train.py:874] (1/4) Epoch 27, batch 3800, aishell_loss[loss=0.1341, simple_loss=0.2279, pruned_loss=0.02017, over 4871.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2216, pruned_loss=0.02857, over 985459.41 frames.], batch size: 42, aishell_tot_loss[loss=0.1438, simple_loss=0.2308, pruned_loss=0.02834, over 984771.13 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2145, pruned_loss=0.02928, over 986199.47 frames.], batch size: 42, lr: 3.01e-04
+2022-06-19 06:04:01,606 INFO [train.py:874] (1/4) Epoch 27, batch 3850, datatang_loss[loss=0.1382, simple_loss=0.2202, pruned_loss=0.02808, over 4954.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2219, pruned_loss=0.0288, over 985584.24 frames.], batch size: 86, aishell_tot_loss[loss=0.1434, simple_loss=0.2305, pruned_loss=0.02809, over 985063.84 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2151, pruned_loss=0.0297, over 986028.04 frames.], batch size: 86, lr: 3.01e-04
+2022-06-19 06:04:28,991 INFO [train.py:874] (1/4) Epoch 27, batch 3900, aishell_loss[loss=0.1412, simple_loss=0.2309, pruned_loss=0.02577, over 4868.00 frames.], tot_loss[loss=0.1405, simple_loss=0.223, pruned_loss=0.02894, over 985236.42 frames.], batch size: 42, aishell_tot_loss[loss=0.1441, simple_loss=0.2313, pruned_loss=0.02846, over 984661.13 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2147, pruned_loss=0.0295, over 986115.90 frames.], batch size: 42, lr: 3.01e-04
+2022-06-19 06:04:58,551 INFO [train.py:874] (1/4) Epoch 27, batch 3950, aishell_loss[loss=0.1516, simple_loss=0.2476, pruned_loss=0.02775, over 4948.00 frames.], tot_loss[loss=0.1395, simple_loss=0.222, pruned_loss=0.02849, over 985279.00 frames.], batch size: 64, aishell_tot_loss[loss=0.1442, simple_loss=0.2314, pruned_loss=0.02852, over 984642.85 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2139, pruned_loss=0.02894, over 986113.10 frames.], batch size: 64, lr: 3.01e-04
+2022-06-19 06:05:27,301 INFO [train.py:874] (1/4) Epoch 27, batch 4000, datatang_loss[loss=0.1162, simple_loss=0.1993, pruned_loss=0.01659, over 4953.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2232, pruned_loss=0.02876, over 985504.69 frames.], batch size: 91, aishell_tot_loss[loss=0.1448, simple_loss=0.2321, pruned_loss=0.02873, over 984891.00 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2142, pruned_loss=0.02896, over 986089.69 frames.], batch size: 91, lr: 3.01e-04
+2022-06-19 06:05:27,302 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 06:05:43,550 INFO [train.py:914] (1/4) Epoch 27, validation: loss=0.1646, simple_loss=0.249, pruned_loss=0.0401, over 1622729.00 frames.
+2022-06-19 06:06:11,904 INFO [train.py:874] (1/4) Epoch 27, batch 4050, aishell_loss[loss=0.1542, simple_loss=0.2415, pruned_loss=0.03339, over 4862.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2232, pruned_loss=0.02874, over 985339.38 frames.], batch size: 37, aishell_tot_loss[loss=0.1452, simple_loss=0.2325, pruned_loss=0.02896, over 984859.28 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.214, pruned_loss=0.02869, over 985945.04 frames.], batch size: 37, lr: 3.01e-04
+2022-06-19 06:06:38,088 INFO [train.py:874] (1/4) Epoch 27, batch 4100, datatang_loss[loss=0.1413, simple_loss=0.2251, pruned_loss=0.02871, over 4925.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2237, pruned_loss=0.02862, over 985665.11 frames.], batch size: 57, aishell_tot_loss[loss=0.1446, simple_loss=0.2319, pruned_loss=0.02866, over 985040.15 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2148, pruned_loss=0.02883, over 986114.20 frames.], batch size: 57, lr: 3.01e-04
+2022-06-19 06:07:06,626 INFO [train.py:874] (1/4) Epoch 27, batch 4150, aishell_loss[loss=0.1512, simple_loss=0.2347, pruned_loss=0.03383, over 4916.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2234, pruned_loss=0.02845, over 985221.09 frames.], batch size: 41, aishell_tot_loss[loss=0.1445, simple_loss=0.2317, pruned_loss=0.0286, over 984446.22 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.215, pruned_loss=0.02867, over 986235.90 frames.], batch size: 41, lr: 3.01e-04
+2022-06-19 06:08:12,730 INFO [train.py:874] (1/4) Epoch 28, batch 50, datatang_loss[loss=0.1516, simple_loss=0.2229, pruned_loss=0.04015, over 4983.00 frames.], tot_loss[loss=0.1345, simple_loss=0.2172, pruned_loss=0.02592, over 218237.31 frames.], batch size: 40, aishell_tot_loss[loss=0.1423, simple_loss=0.2296, pruned_loss=0.02749, over 120149.68 frames.], datatang_tot_loss[loss=0.1261, simple_loss=0.2039, pruned_loss=0.02414, over 111715.24 frames.], batch size: 40, lr: 2.95e-04
+2022-06-19 06:08:39,692 INFO [train.py:874] (1/4) Epoch 28, batch 100, aishell_loss[loss=0.1492, simple_loss=0.2345, pruned_loss=0.03196, over 4933.00 frames.], tot_loss[loss=0.136, simple_loss=0.2185, pruned_loss=0.02675, over 388324.68 frames.], batch size: 49, aishell_tot_loss[loss=0.1419, simple_loss=0.2287, pruned_loss=0.02756, over 229568.99 frames.], datatang_tot_loss[loss=0.129, simple_loss=0.2067, pruned_loss=0.02569, over 206985.78 frames.], batch size: 49, lr: 2.95e-04
+2022-06-19 06:09:09,739 INFO [train.py:874] (1/4) Epoch 28, batch 150, aishell_loss[loss=0.1119, simple_loss=0.1866, pruned_loss=0.01862, over 4993.00 frames.], tot_loss[loss=0.1365, simple_loss=0.2197, pruned_loss=0.02669, over 521098.21 frames.], batch size: 21, aishell_tot_loss[loss=0.1418, simple_loss=0.2291, pruned_loss=0.02727, over 335438.98 frames.], datatang_tot_loss[loss=0.1295, simple_loss=0.2074, pruned_loss=0.02582, over 281324.68 frames.], batch size: 21, lr: 2.95e-04
+2022-06-19 06:09:42,617 INFO [train.py:874] (1/4) Epoch 28, batch 200, datatang_loss[loss=0.1114, simple_loss=0.1932, pruned_loss=0.01478, over 4938.00 frames.], tot_loss[loss=0.1356, simple_loss=0.219, pruned_loss=0.02616, over 624306.70 frames.], batch size: 62, aishell_tot_loss[loss=0.1416, simple_loss=0.2289, pruned_loss=0.02714, over 423755.93 frames.], datatang_tot_loss[loss=0.128, simple_loss=0.2061, pruned_loss=0.02498, over 351579.09 frames.], batch size: 62, lr: 2.95e-04
+2022-06-19 06:10:12,369 INFO [train.py:874] (1/4) Epoch 28, batch 250, datatang_loss[loss=0.1308, simple_loss=0.2119, pruned_loss=0.02486, over 4919.00 frames.], tot_loss[loss=0.1359, simple_loss=0.2183, pruned_loss=0.02674, over 704309.40 frames.], batch size: 57, aishell_tot_loss[loss=0.1415, simple_loss=0.2283, pruned_loss=0.02732, over 484779.33 frames.], datatang_tot_loss[loss=0.1293, simple_loss=0.2069, pruned_loss=0.02591, over 431902.86 frames.], batch size: 57, lr: 2.95e-04
+2022-06-19 06:10:42,402 INFO [train.py:874] (1/4) Epoch 28, batch 300, aishell_loss[loss=0.1402, simple_loss=0.2344, pruned_loss=0.02306, over 4957.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2198, pruned_loss=0.02743, over 766706.02 frames.], batch size: 61, aishell_tot_loss[loss=0.1427, simple_loss=0.2295, pruned_loss=0.02792, over 545970.13 frames.], datatang_tot_loss[loss=0.1304, simple_loss=0.2081, pruned_loss=0.02641, over 494705.70 frames.], batch size: 61, lr: 2.95e-04
+2022-06-19 06:11:09,571 INFO [train.py:874] (1/4) Epoch 28, batch 350, datatang_loss[loss=0.1124, simple_loss=0.199, pruned_loss=0.01291, over 4923.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2198, pruned_loss=0.02791, over 815066.48 frames.], batch size: 73, aishell_tot_loss[loss=0.1431, simple_loss=0.2298, pruned_loss=0.02824, over 587803.63 frames.], datatang_tot_loss[loss=0.1316, simple_loss=0.2093, pruned_loss=0.02699, over 563168.49 frames.], batch size: 73, lr: 2.95e-04
+2022-06-19 06:11:39,738 INFO [train.py:874] (1/4) Epoch 28, batch 400, aishell_loss[loss=0.1428, simple_loss=0.2315, pruned_loss=0.02703, over 4865.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2212, pruned_loss=0.02814, over 852633.55 frames.], batch size: 35, aishell_tot_loss[loss=0.1432, simple_loss=0.23, pruned_loss=0.02822, over 629128.77 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2116, pruned_loss=0.02749, over 618439.43 frames.], batch size: 35, lr: 2.95e-04
+2022-06-19 06:12:10,378 INFO [train.py:874] (1/4) Epoch 28, batch 450, datatang_loss[loss=0.1543, simple_loss=0.2312, pruned_loss=0.03872, over 4919.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2209, pruned_loss=0.02831, over 881870.77 frames.], batch size: 98, aishell_tot_loss[loss=0.1435, simple_loss=0.2303, pruned_loss=0.02837, over 666271.82 frames.], datatang_tot_loss[loss=0.1334, simple_loss=0.2115, pruned_loss=0.02767, over 666337.89 frames.], batch size: 98, lr: 2.95e-04
+2022-06-19 06:12:37,879 INFO [train.py:874] (1/4) Epoch 28, batch 500, aishell_loss[loss=0.1591, simple_loss=0.242, pruned_loss=0.03805, over 4963.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2217, pruned_loss=0.02843, over 904852.67 frames.], batch size: 44, aishell_tot_loss[loss=0.143, simple_loss=0.23, pruned_loss=0.02804, over 707994.46 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2124, pruned_loss=0.02824, over 699758.25 frames.], batch size: 44, lr: 2.95e-04
+2022-06-19 06:13:06,537 INFO [train.py:874] (1/4) Epoch 28, batch 550, datatang_loss[loss=0.1242, simple_loss=0.202, pruned_loss=0.02327, over 4930.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2215, pruned_loss=0.02837, over 922601.38 frames.], batch size: 69, aishell_tot_loss[loss=0.1425, simple_loss=0.2292, pruned_loss=0.02788, over 742718.00 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2129, pruned_loss=0.02841, over 731143.92 frames.], batch size: 69, lr: 2.95e-04
+2022-06-19 06:13:35,832 INFO [train.py:874] (1/4) Epoch 28, batch 600, aishell_loss[loss=0.1766, simple_loss=0.2545, pruned_loss=0.04933, over 4884.00 frames.], tot_loss[loss=0.1394, simple_loss=0.222, pruned_loss=0.02839, over 936745.09 frames.], batch size: 42, aishell_tot_loss[loss=0.1426, simple_loss=0.2293, pruned_loss=0.02796, over 777872.40 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2129, pruned_loss=0.02843, over 754251.48 frames.], batch size: 42, lr: 2.95e-04
+2022-06-19 06:14:02,745 INFO [train.py:874] (1/4) Epoch 28, batch 650, aishell_loss[loss=0.1469, simple_loss=0.2356, pruned_loss=0.02907, over 4932.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2215, pruned_loss=0.02797, over 947438.00 frames.], batch size: 58, aishell_tot_loss[loss=0.1424, simple_loss=0.2291, pruned_loss=0.02782, over 802123.55 frames.], datatang_tot_loss[loss=0.1345, simple_loss=0.2128, pruned_loss=0.02808, over 781577.85 frames.], batch size: 58, lr: 2.94e-04
+2022-06-19 06:14:32,404 INFO [train.py:874] (1/4) Epoch 28, batch 700, aishell_loss[loss=0.1336, simple_loss=0.231, pruned_loss=0.01812, over 4880.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2219, pruned_loss=0.02789, over 956008.18 frames.], batch size: 42, aishell_tot_loss[loss=0.1425, simple_loss=0.2294, pruned_loss=0.02778, over 822258.50 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2133, pruned_loss=0.02803, over 807352.03 frames.], batch size: 42, lr: 2.94e-04
+2022-06-19 06:15:01,765 INFO [train.py:874] (1/4) Epoch 28, batch 750, aishell_loss[loss=0.144, simple_loss=0.2362, pruned_loss=0.02593, over 4897.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2222, pruned_loss=0.02785, over 962558.43 frames.], batch size: 41, aishell_tot_loss[loss=0.1422, simple_loss=0.2292, pruned_loss=0.0276, over 841514.52 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.214, pruned_loss=0.02816, over 828309.38 frames.], batch size: 41, lr: 2.94e-04
+2022-06-19 06:15:30,193 INFO [train.py:874] (1/4) Epoch 28, batch 800, datatang_loss[loss=0.1434, simple_loss=0.2259, pruned_loss=0.03049, over 4935.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2224, pruned_loss=0.0281, over 967919.52 frames.], batch size: 71, aishell_tot_loss[loss=0.1424, simple_loss=0.2296, pruned_loss=0.02765, over 854074.18 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2147, pruned_loss=0.02837, over 851759.56 frames.], batch size: 71, lr: 2.94e-04
+2022-06-19 06:16:00,394 INFO [train.py:874] (1/4) Epoch 28, batch 850, aishell_loss[loss=0.14, simple_loss=0.2294, pruned_loss=0.02534, over 4961.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2224, pruned_loss=0.02829, over 971767.63 frames.], batch size: 61, aishell_tot_loss[loss=0.1426, simple_loss=0.2296, pruned_loss=0.02777, over 870069.47 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2146, pruned_loss=0.0285, over 866919.48 frames.], batch size: 61, lr: 2.94e-04
+2022-06-19 06:16:30,558 INFO [train.py:874] (1/4) Epoch 28, batch 900, datatang_loss[loss=0.149, simple_loss=0.2303, pruned_loss=0.03389, over 4893.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2222, pruned_loss=0.02844, over 975439.21 frames.], batch size: 39, aishell_tot_loss[loss=0.1424, simple_loss=0.2295, pruned_loss=0.02769, over 884124.09 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2145, pruned_loss=0.02881, over 881091.59 frames.], batch size: 39, lr: 2.94e-04
+2022-06-19 06:16:57,985 INFO [train.py:874] (1/4) Epoch 28, batch 950, datatang_loss[loss=0.1252, simple_loss=0.202, pruned_loss=0.02418, over 4918.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2222, pruned_loss=0.02833, over 977527.07 frames.], batch size: 77, aishell_tot_loss[loss=0.1426, simple_loss=0.2296, pruned_loss=0.02777, over 895434.81 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2146, pruned_loss=0.02866, over 893877.88 frames.], batch size: 77, lr: 2.94e-04
+2022-06-19 06:17:29,792 INFO [train.py:874] (1/4) Epoch 28, batch 1000, datatang_loss[loss=0.136, simple_loss=0.2181, pruned_loss=0.02695, over 4967.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2221, pruned_loss=0.02848, over 979163.61 frames.], batch size: 60, aishell_tot_loss[loss=0.1426, simple_loss=0.2297, pruned_loss=0.0278, over 906066.32 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2143, pruned_loss=0.02886, over 904495.54 frames.], batch size: 60, lr: 2.94e-04
+2022-06-19 06:17:29,793 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 06:17:46,266 INFO [train.py:914] (1/4) Epoch 28, validation: loss=0.1641, simple_loss=0.2478, pruned_loss=0.0402, over 1622729.00 frames.
+2022-06-19 06:18:14,332 INFO [train.py:874] (1/4) Epoch 28, batch 1050, aishell_loss[loss=0.1274, simple_loss=0.2155, pruned_loss=0.01966, over 4970.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2233, pruned_loss=0.02896, over 980845.16 frames.], batch size: 27, aishell_tot_loss[loss=0.1436, simple_loss=0.2306, pruned_loss=0.02825, over 915592.36 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.029, over 914163.47 frames.], batch size: 27, lr: 2.94e-04
+2022-06-19 06:18:45,862 INFO [train.py:874] (1/4) Epoch 28, batch 1100, aishell_loss[loss=0.151, simple_loss=0.2524, pruned_loss=0.02483, over 4922.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2225, pruned_loss=0.02895, over 981855.90 frames.], batch size: 76, aishell_tot_loss[loss=0.1439, simple_loss=0.2309, pruned_loss=0.0285, over 923087.79 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2139, pruned_loss=0.02883, over 923296.29 frames.], batch size: 76, lr: 2.94e-04
+2022-06-19 06:19:12,535 INFO [train.py:874] (1/4) Epoch 28, batch 1150, aishell_loss[loss=0.1479, simple_loss=0.2455, pruned_loss=0.0252, over 4860.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2238, pruned_loss=0.0293, over 982975.93 frames.], batch size: 38, aishell_tot_loss[loss=0.1442, simple_loss=0.2313, pruned_loss=0.02851, over 930273.37 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2151, pruned_loss=0.02927, over 931131.35 frames.], batch size: 38, lr: 2.94e-04
+2022-06-19 06:19:43,164 INFO [train.py:874] (1/4) Epoch 28, batch 1200, datatang_loss[loss=0.1456, simple_loss=0.219, pruned_loss=0.03609, over 4916.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2237, pruned_loss=0.02894, over 983515.64 frames.], batch size: 77, aishell_tot_loss[loss=0.1439, simple_loss=0.2312, pruned_loss=0.02823, over 937073.78 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.215, pruned_loss=0.02927, over 937232.98 frames.], batch size: 77, lr: 2.94e-04
+2022-06-19 06:20:13,510 INFO [train.py:874] (1/4) Epoch 28, batch 1250, aishell_loss[loss=0.1525, simple_loss=0.2408, pruned_loss=0.03208, over 4892.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2249, pruned_loss=0.02936, over 984201.98 frames.], batch size: 34, aishell_tot_loss[loss=0.1446, simple_loss=0.2319, pruned_loss=0.02867, over 943314.04 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2156, pruned_loss=0.02934, over 942643.49 frames.], batch size: 34, lr: 2.94e-04
+2022-06-19 06:20:39,910 INFO [train.py:874] (1/4) Epoch 28, batch 1300, datatang_loss[loss=0.1514, simple_loss=0.2236, pruned_loss=0.03962, over 4957.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2249, pruned_loss=0.02891, over 984194.08 frames.], batch size: 45, aishell_tot_loss[loss=0.1444, simple_loss=0.2319, pruned_loss=0.02848, over 948570.88 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2157, pruned_loss=0.02912, over 947050.76 frames.], batch size: 45, lr: 2.94e-04
+2022-06-19 06:21:10,131 INFO [train.py:874] (1/4) Epoch 28, batch 1350, datatang_loss[loss=0.1433, simple_loss=0.2149, pruned_loss=0.0358, over 4937.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2251, pruned_loss=0.02898, over 984634.97 frames.], batch size: 79, aishell_tot_loss[loss=0.1439, simple_loss=0.2316, pruned_loss=0.0281, over 952758.62 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2165, pruned_loss=0.02956, over 951905.69 frames.], batch size: 79, lr: 2.94e-04
+2022-06-19 06:21:39,808 INFO [train.py:874] (1/4) Epoch 28, batch 1400, datatang_loss[loss=0.1654, simple_loss=0.2406, pruned_loss=0.04509, over 4957.00 frames.], tot_loss[loss=0.1426, simple_loss=0.2259, pruned_loss=0.02967, over 985047.56 frames.], batch size: 86, aishell_tot_loss[loss=0.1443, simple_loss=0.2319, pruned_loss=0.02836, over 956265.16 frames.], datatang_tot_loss[loss=0.1388, simple_loss=0.2174, pruned_loss=0.03007, over 956441.61 frames.], batch size: 86, lr: 2.93e-04
+2022-06-19 06:22:07,138 INFO [train.py:874] (1/4) Epoch 28, batch 1450, aishell_loss[loss=0.1362, simple_loss=0.2288, pruned_loss=0.02175, over 4880.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2248, pruned_loss=0.02902, over 985523.27 frames.], batch size: 47, aishell_tot_loss[loss=0.144, simple_loss=0.2317, pruned_loss=0.0281, over 959758.31 frames.], datatang_tot_loss[loss=0.1381, simple_loss=0.2168, pruned_loss=0.0297, over 960198.86 frames.], batch size: 47, lr: 2.93e-04
+2022-06-19 06:22:39,118 INFO [train.py:874] (1/4) Epoch 28, batch 1500, datatang_loss[loss=0.1514, simple_loss=0.2193, pruned_loss=0.04173, over 4969.00 frames.], tot_loss[loss=0.1415, simple_loss=0.2242, pruned_loss=0.02943, over 985439.85 frames.], batch size: 45, aishell_tot_loss[loss=0.1444, simple_loss=0.2318, pruned_loss=0.02851, over 962145.76 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2163, pruned_loss=0.02973, over 963753.87 frames.], batch size: 45, lr: 2.93e-04
+2022-06-19 06:23:09,510 INFO [train.py:874] (1/4) Epoch 28, batch 1550, aishell_loss[loss=0.1463, simple_loss=0.2323, pruned_loss=0.0301, over 4983.00 frames.], tot_loss[loss=0.1422, simple_loss=0.2244, pruned_loss=0.02994, over 985245.29 frames.], batch size: 38, aishell_tot_loss[loss=0.1453, simple_loss=0.2324, pruned_loss=0.02908, over 964255.51 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2161, pruned_loss=0.02979, over 966712.08 frames.], batch size: 38, lr: 2.93e-04
+2022-06-19 06:23:37,901 INFO [train.py:874] (1/4) Epoch 28, batch 1600, datatang_loss[loss=0.1159, simple_loss=0.1963, pruned_loss=0.01778, over 4900.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2242, pruned_loss=0.02975, over 985264.56 frames.], batch size: 47, aishell_tot_loss[loss=0.1455, simple_loss=0.2327, pruned_loss=0.02918, over 966499.84 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2158, pruned_loss=0.02961, over 969133.68 frames.], batch size: 47, lr: 2.93e-04
+2022-06-19 06:24:07,778 INFO [train.py:874] (1/4) Epoch 28, batch 1650, datatang_loss[loss=0.1577, simple_loss=0.2245, pruned_loss=0.04548, over 4954.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2242, pruned_loss=0.02955, over 985378.82 frames.], batch size: 34, aishell_tot_loss[loss=0.1455, simple_loss=0.2327, pruned_loss=0.02913, over 968710.59 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2158, pruned_loss=0.02952, over 971140.20 frames.], batch size: 34, lr: 2.93e-04
+2022-06-19 06:24:36,421 INFO [train.py:874] (1/4) Epoch 28, batch 1700, aishell_loss[loss=0.1394, simple_loss=0.2293, pruned_loss=0.02475, over 4892.00 frames.], tot_loss[loss=0.1411, simple_loss=0.2242, pruned_loss=0.02896, over 985065.36 frames.], batch size: 50, aishell_tot_loss[loss=0.1453, simple_loss=0.2325, pruned_loss=0.029, over 970961.95 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2154, pruned_loss=0.02909, over 972267.67 frames.], batch size: 50, lr: 2.93e-04
+2022-06-19 06:25:04,619 INFO [train.py:874] (1/4) Epoch 28, batch 1750, datatang_loss[loss=0.1805, simple_loss=0.2539, pruned_loss=0.05355, over 4933.00 frames.], tot_loss[loss=0.1406, simple_loss=0.2236, pruned_loss=0.02879, over 985350.60 frames.], batch size: 108, aishell_tot_loss[loss=0.1448, simple_loss=0.2321, pruned_loss=0.02871, over 972483.00 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2153, pruned_loss=0.02916, over 974202.36 frames.], batch size: 108, lr: 2.93e-04
+2022-06-19 06:25:35,063 INFO [train.py:874] (1/4) Epoch 28, batch 1800, datatang_loss[loss=0.1278, simple_loss=0.2068, pruned_loss=0.0244, over 4933.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2239, pruned_loss=0.02874, over 985220.72 frames.], batch size: 79, aishell_tot_loss[loss=0.1449, simple_loss=0.2323, pruned_loss=0.02873, over 973876.92 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.215, pruned_loss=0.02906, over 975546.06 frames.], batch size: 79, lr: 2.93e-04
+2022-06-19 06:26:02,301 INFO [train.py:874] (1/4) Epoch 28, batch 1850, datatang_loss[loss=0.1436, simple_loss=0.2314, pruned_loss=0.02787, over 4911.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2231, pruned_loss=0.02873, over 985407.62 frames.], batch size: 26, aishell_tot_loss[loss=0.1447, simple_loss=0.2322, pruned_loss=0.02864, over 974965.64 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2145, pruned_loss=0.02909, over 977091.65 frames.], batch size: 26, lr: 2.93e-04
+2022-06-19 06:26:30,987 INFO [train.py:874] (1/4) Epoch 28, batch 1900, aishell_loss[loss=0.1459, simple_loss=0.2334, pruned_loss=0.02926, over 4964.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2233, pruned_loss=0.0292, over 985739.19 frames.], batch size: 44, aishell_tot_loss[loss=0.1448, simple_loss=0.2322, pruned_loss=0.02873, over 976268.08 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2147, pruned_loss=0.02948, over 978344.34 frames.], batch size: 44, lr: 2.93e-04
+2022-06-19 06:27:02,461 INFO [train.py:874] (1/4) Epoch 28, batch 1950, aishell_loss[loss=0.1431, simple_loss=0.2296, pruned_loss=0.02826, over 4984.00 frames.], tot_loss[loss=0.1408, simple_loss=0.2233, pruned_loss=0.02917, over 985940.04 frames.], batch size: 30, aishell_tot_loss[loss=0.1443, simple_loss=0.2318, pruned_loss=0.02842, over 977437.25 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2153, pruned_loss=0.02976, over 979344.03 frames.], batch size: 30, lr: 2.93e-04
+2022-06-19 06:27:30,173 INFO [train.py:874] (1/4) Epoch 28, batch 2000, datatang_loss[loss=0.1914, simple_loss=0.2573, pruned_loss=0.06274, over 4916.00 frames.], tot_loss[loss=0.141, simple_loss=0.2234, pruned_loss=0.02932, over 985979.38 frames.], batch size: 98, aishell_tot_loss[loss=0.1444, simple_loss=0.2316, pruned_loss=0.02862, over 978575.75 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2152, pruned_loss=0.02977, over 980061.06 frames.], batch size: 98, lr: 2.93e-04
+2022-06-19 06:27:30,173 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 06:27:46,238 INFO [train.py:914] (1/4) Epoch 28, validation: loss=0.1643, simple_loss=0.2484, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 06:28:15,605 INFO [train.py:874] (1/4) Epoch 28, batch 2050, datatang_loss[loss=0.1285, simple_loss=0.2147, pruned_loss=0.0212, over 4931.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2231, pruned_loss=0.02883, over 985898.75 frames.], batch size: 83, aishell_tot_loss[loss=0.1442, simple_loss=0.2317, pruned_loss=0.02841, over 979394.61 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2149, pruned_loss=0.02947, over 980732.25 frames.], batch size: 83, lr: 2.93e-04
+2022-06-19 06:28:43,207 INFO [train.py:874] (1/4) Epoch 28, batch 2100, datatang_loss[loss=0.1284, simple_loss=0.207, pruned_loss=0.02483, over 4956.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2223, pruned_loss=0.02851, over 985785.01 frames.], batch size: 86, aishell_tot_loss[loss=0.1434, simple_loss=0.2306, pruned_loss=0.02807, over 980022.24 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2148, pruned_loss=0.02946, over 981374.72 frames.], batch size: 86, lr: 2.93e-04
+2022-06-19 06:29:13,535 INFO [train.py:874] (1/4) Epoch 28, batch 2150, datatang_loss[loss=0.1309, simple_loss=0.2128, pruned_loss=0.02445, over 4960.00 frames.], tot_loss[loss=0.14, simple_loss=0.2226, pruned_loss=0.02872, over 986151.10 frames.], batch size: 45, aishell_tot_loss[loss=0.1436, simple_loss=0.2308, pruned_loss=0.02823, over 980843.75 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2152, pruned_loss=0.02941, over 982099.83 frames.], batch size: 45, lr: 2.93e-04
+2022-06-19 06:29:42,213 INFO [train.py:874] (1/4) Epoch 28, batch 2200, aishell_loss[loss=0.1269, simple_loss=0.2132, pruned_loss=0.02037, over 4975.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2225, pruned_loss=0.02889, over 985995.96 frames.], batch size: 30, aishell_tot_loss[loss=0.1436, simple_loss=0.2307, pruned_loss=0.02825, over 981267.53 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2154, pruned_loss=0.02954, over 982606.41 frames.], batch size: 30, lr: 2.92e-04
+2022-06-19 06:30:10,920 INFO [train.py:874] (1/4) Epoch 28, batch 2250, aishell_loss[loss=0.1536, simple_loss=0.2425, pruned_loss=0.03239, over 4946.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2237, pruned_loss=0.02937, over 985961.21 frames.], batch size: 32, aishell_tot_loss[loss=0.1434, simple_loss=0.2305, pruned_loss=0.0281, over 981910.74 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2164, pruned_loss=0.03022, over 982891.42 frames.], batch size: 32, lr: 2.92e-04
+2022-06-19 06:30:41,936 INFO [train.py:874] (1/4) Epoch 28, batch 2300, datatang_loss[loss=0.1297, simple_loss=0.2089, pruned_loss=0.02528, over 4913.00 frames.], tot_loss[loss=0.1413, simple_loss=0.2238, pruned_loss=0.02943, over 985784.59 frames.], batch size: 64, aishell_tot_loss[loss=0.1438, simple_loss=0.2311, pruned_loss=0.0283, over 982274.13 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2161, pruned_loss=0.03015, over 983180.03 frames.], batch size: 64, lr: 2.92e-04
+2022-06-19 06:31:09,122 INFO [train.py:874] (1/4) Epoch 28, batch 2350, datatang_loss[loss=0.1154, simple_loss=0.1929, pruned_loss=0.01894, over 4966.00 frames.], tot_loss[loss=0.1418, simple_loss=0.2246, pruned_loss=0.02947, over 985779.74 frames.], batch size: 45, aishell_tot_loss[loss=0.1446, simple_loss=0.2318, pruned_loss=0.0287, over 982806.56 frames.], datatang_tot_loss[loss=0.1379, simple_loss=0.2161, pruned_loss=0.02987, over 983373.53 frames.], batch size: 45, lr: 2.92e-04
+2022-06-19 06:31:38,574 INFO [train.py:874] (1/4) Epoch 28, batch 2400, aishell_loss[loss=0.1717, simple_loss=0.2536, pruned_loss=0.04487, over 4942.00 frames.], tot_loss[loss=0.1412, simple_loss=0.2246, pruned_loss=0.02895, over 985915.85 frames.], batch size: 79, aishell_tot_loss[loss=0.1448, simple_loss=0.2321, pruned_loss=0.02875, over 983184.17 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2155, pruned_loss=0.02933, over 983772.43 frames.], batch size: 79, lr: 2.92e-04
+2022-06-19 06:32:08,610 INFO [train.py:874] (1/4) Epoch 28, batch 2450, aishell_loss[loss=0.1339, simple_loss=0.2229, pruned_loss=0.02252, over 4953.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2238, pruned_loss=0.02854, over 986021.15 frames.], batch size: 40, aishell_tot_loss[loss=0.1442, simple_loss=0.2317, pruned_loss=0.0284, over 983633.01 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.215, pruned_loss=0.02924, over 984020.16 frames.], batch size: 40, lr: 2.92e-04
+2022-06-19 06:32:35,291 INFO [train.py:874] (1/4) Epoch 28, batch 2500, datatang_loss[loss=0.1594, simple_loss=0.233, pruned_loss=0.04296, over 4950.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2237, pruned_loss=0.02866, over 985936.38 frames.], batch size: 67, aishell_tot_loss[loss=0.1443, simple_loss=0.2317, pruned_loss=0.02844, over 983794.73 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2151, pruned_loss=0.02925, over 984267.67 frames.], batch size: 67, lr: 2.92e-04
+2022-06-19 06:33:05,158 INFO [train.py:874] (1/4) Epoch 28, batch 2550, aishell_loss[loss=0.1385, simple_loss=0.2267, pruned_loss=0.02517, over 4962.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2236, pruned_loss=0.02872, over 986184.57 frames.], batch size: 40, aishell_tot_loss[loss=0.1443, simple_loss=0.2318, pruned_loss=0.02845, over 984092.22 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2148, pruned_loss=0.02927, over 984686.84 frames.], batch size: 40, lr: 2.92e-04
+2022-06-19 06:33:35,652 INFO [train.py:874] (1/4) Epoch 28, batch 2600, datatang_loss[loss=0.129, simple_loss=0.2082, pruned_loss=0.02494, over 4932.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2238, pruned_loss=0.02884, over 985989.24 frames.], batch size: 50, aishell_tot_loss[loss=0.1442, simple_loss=0.2316, pruned_loss=0.02841, over 984289.80 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2149, pruned_loss=0.02941, over 984712.80 frames.], batch size: 50, lr: 2.92e-04
+2022-06-19 06:34:01,434 INFO [train.py:874] (1/4) Epoch 28, batch 2650, datatang_loss[loss=0.125, simple_loss=0.2107, pruned_loss=0.01969, over 4912.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2234, pruned_loss=0.02897, over 985910.92 frames.], batch size: 75, aishell_tot_loss[loss=0.144, simple_loss=0.231, pruned_loss=0.02851, over 984413.48 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2151, pruned_loss=0.02944, over 984850.40 frames.], batch size: 75, lr: 2.92e-04
+2022-06-19 06:34:31,378 INFO [train.py:874] (1/4) Epoch 28, batch 2700, datatang_loss[loss=0.1294, simple_loss=0.2137, pruned_loss=0.02253, over 4937.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2244, pruned_loss=0.02941, over 985772.12 frames.], batch size: 88, aishell_tot_loss[loss=0.1446, simple_loss=0.2316, pruned_loss=0.0288, over 984306.86 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2154, pruned_loss=0.02961, over 985131.26 frames.], batch size: 88, lr: 2.92e-04
+2022-06-19 06:34:59,557 INFO [train.py:874] (1/4) Epoch 28, batch 2750, aishell_loss[loss=0.1237, simple_loss=0.1851, pruned_loss=0.03112, over 4863.00 frames.], tot_loss[loss=0.1417, simple_loss=0.2246, pruned_loss=0.02942, over 985837.55 frames.], batch size: 21, aishell_tot_loss[loss=0.1445, simple_loss=0.2312, pruned_loss=0.02884, over 984587.68 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2159, pruned_loss=0.02966, over 985187.56 frames.], batch size: 21, lr: 2.92e-04
+2022-06-19 06:35:27,839 INFO [train.py:874] (1/4) Epoch 28, batch 2800, aishell_loss[loss=0.1296, simple_loss=0.2125, pruned_loss=0.02333, over 4879.00 frames.], tot_loss[loss=0.1416, simple_loss=0.2242, pruned_loss=0.0295, over 985839.94 frames.], batch size: 28, aishell_tot_loss[loss=0.144, simple_loss=0.2308, pruned_loss=0.0286, over 984666.83 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.216, pruned_loss=0.03001, over 985341.83 frames.], batch size: 28, lr: 2.92e-04
+2022-06-19 06:35:57,602 INFO [train.py:874] (1/4) Epoch 28, batch 2850, datatang_loss[loss=0.16, simple_loss=0.2502, pruned_loss=0.03485, over 4957.00 frames.], tot_loss[loss=0.1412, simple_loss=0.224, pruned_loss=0.02924, over 985898.68 frames.], batch size: 109, aishell_tot_loss[loss=0.1435, simple_loss=0.2305, pruned_loss=0.02829, over 984758.42 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2164, pruned_loss=0.03009, over 985498.98 frames.], batch size: 109, lr: 2.92e-04
+2022-06-19 06:36:25,972 INFO [train.py:874] (1/4) Epoch 28, batch 2900, aishell_loss[loss=0.1462, simple_loss=0.2331, pruned_loss=0.02966, over 4950.00 frames.], tot_loss[loss=0.141, simple_loss=0.2231, pruned_loss=0.02946, over 985935.73 frames.], batch size: 31, aishell_tot_loss[loss=0.1435, simple_loss=0.2302, pruned_loss=0.02837, over 984842.60 frames.], datatang_tot_loss[loss=0.1383, simple_loss=0.2162, pruned_loss=0.03022, over 985611.01 frames.], batch size: 31, lr: 2.92e-04
+2022-06-19 06:36:54,061 INFO [train.py:874] (1/4) Epoch 28, batch 2950, aishell_loss[loss=0.1094, simple_loss=0.1709, pruned_loss=0.02393, over 4861.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2221, pruned_loss=0.02902, over 985856.70 frames.], batch size: 20, aishell_tot_loss[loss=0.143, simple_loss=0.2296, pruned_loss=0.02818, over 985044.89 frames.], datatang_tot_loss[loss=0.1378, simple_loss=0.2156, pruned_loss=0.02998, over 985498.56 frames.], batch size: 20, lr: 2.91e-04
+2022-06-19 06:37:24,545 INFO [train.py:874] (1/4) Epoch 28, batch 3000, aishell_loss[loss=0.1336, simple_loss=0.2201, pruned_loss=0.02352, over 4976.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2222, pruned_loss=0.02916, over 985668.44 frames.], batch size: 44, aishell_tot_loss[loss=0.1431, simple_loss=0.2297, pruned_loss=0.02828, over 985014.86 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.216, pruned_loss=0.02997, over 985460.26 frames.], batch size: 44, lr: 2.91e-04
+2022-06-19 06:37:24,545 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 06:37:41,302 INFO [train.py:914] (1/4) Epoch 28, validation: loss=0.1644, simple_loss=0.2482, pruned_loss=0.04027, over 1622729.00 frames.
+2022-06-19 06:38:10,639 INFO [train.py:874] (1/4) Epoch 28, batch 3050, datatang_loss[loss=0.1215, simple_loss=0.2039, pruned_loss=0.01959, over 4916.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2226, pruned_loss=0.02915, over 985743.21 frames.], batch size: 81, aishell_tot_loss[loss=0.1431, simple_loss=0.2297, pruned_loss=0.02824, over 985096.02 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2164, pruned_loss=0.03002, over 985552.92 frames.], batch size: 81, lr: 2.91e-04
+2022-06-19 06:38:41,011 INFO [train.py:874] (1/4) Epoch 28, batch 3100, datatang_loss[loss=0.1343, simple_loss=0.2037, pruned_loss=0.0324, over 4868.00 frames.], tot_loss[loss=0.1407, simple_loss=0.2228, pruned_loss=0.02929, over 985607.07 frames.], batch size: 24, aishell_tot_loss[loss=0.1432, simple_loss=0.23, pruned_loss=0.02824, over 985180.95 frames.], datatang_tot_loss[loss=0.1384, simple_loss=0.2164, pruned_loss=0.03013, over 985422.65 frames.], batch size: 24, lr: 2.91e-04
+2022-06-19 06:39:07,312 INFO [train.py:874] (1/4) Epoch 28, batch 3150, aishell_loss[loss=0.1528, simple_loss=0.2389, pruned_loss=0.03334, over 4925.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2227, pruned_loss=0.02896, over 985360.23 frames.], batch size: 41, aishell_tot_loss[loss=0.1428, simple_loss=0.2296, pruned_loss=0.02802, over 985012.12 frames.], datatang_tot_loss[loss=0.1382, simple_loss=0.2164, pruned_loss=0.03007, over 985394.39 frames.], batch size: 41, lr: 2.91e-04
+2022-06-19 06:39:36,944 INFO [train.py:874] (1/4) Epoch 28, batch 3200, datatang_loss[loss=0.1348, simple_loss=0.2169, pruned_loss=0.02642, over 4956.00 frames.], tot_loss[loss=0.14, simple_loss=0.2224, pruned_loss=0.02877, over 985804.26 frames.], batch size: 31, aishell_tot_loss[loss=0.1427, simple_loss=0.2295, pruned_loss=0.02793, over 985208.80 frames.], datatang_tot_loss[loss=0.138, simple_loss=0.2161, pruned_loss=0.02995, over 985706.30 frames.], batch size: 31, lr: 2.91e-04
+2022-06-19 06:40:07,282 INFO [train.py:874] (1/4) Epoch 28, batch 3250, aishell_loss[loss=0.133, simple_loss=0.219, pruned_loss=0.0235, over 4957.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2222, pruned_loss=0.02864, over 985858.27 frames.], batch size: 31, aishell_tot_loss[loss=0.143, simple_loss=0.2299, pruned_loss=0.02804, over 985252.80 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2154, pruned_loss=0.02965, over 985793.25 frames.], batch size: 31, lr: 2.91e-04
+2022-06-19 06:40:34,281 INFO [train.py:874] (1/4) Epoch 28, batch 3300, aishell_loss[loss=0.117, simple_loss=0.2064, pruned_loss=0.01374, over 4952.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2216, pruned_loss=0.02833, over 986014.98 frames.], batch size: 27, aishell_tot_loss[loss=0.1427, simple_loss=0.2298, pruned_loss=0.02784, over 985598.67 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2149, pruned_loss=0.02948, over 985692.22 frames.], batch size: 27, lr: 2.91e-04
+2022-06-19 06:41:04,427 INFO [train.py:874] (1/4) Epoch 28, batch 3350, aishell_loss[loss=0.1137, simple_loss=0.2041, pruned_loss=0.01162, over 4987.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2221, pruned_loss=0.02868, over 985915.59 frames.], batch size: 30, aishell_tot_loss[loss=0.1432, simple_loss=0.2297, pruned_loss=0.02834, over 985602.61 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2148, pruned_loss=0.0293, over 985666.76 frames.], batch size: 30, lr: 2.91e-04
+2022-06-19 06:41:34,600 INFO [train.py:874] (1/4) Epoch 28, batch 3400, datatang_loss[loss=0.1321, simple_loss=0.2089, pruned_loss=0.02767, over 4962.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2215, pruned_loss=0.0285, over 985750.17 frames.], batch size: 40, aishell_tot_loss[loss=0.1429, simple_loss=0.2295, pruned_loss=0.0282, over 985476.31 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2143, pruned_loss=0.02922, over 985687.36 frames.], batch size: 40, lr: 2.91e-04
+2022-06-19 06:42:02,107 INFO [train.py:874] (1/4) Epoch 28, batch 3450, datatang_loss[loss=0.1917, simple_loss=0.262, pruned_loss=0.0607, over 4918.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2217, pruned_loss=0.02878, over 985290.97 frames.], batch size: 83, aishell_tot_loss[loss=0.1424, simple_loss=0.2289, pruned_loss=0.02792, over 985058.70 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2152, pruned_loss=0.02973, over 985633.35 frames.], batch size: 83, lr: 2.91e-04
+2022-06-19 06:42:33,136 INFO [train.py:874] (1/4) Epoch 28, batch 3500, datatang_loss[loss=0.1405, simple_loss=0.2224, pruned_loss=0.02933, over 4955.00 frames.], tot_loss[loss=0.1399, simple_loss=0.222, pruned_loss=0.02888, over 985250.09 frames.], batch size: 34, aishell_tot_loss[loss=0.1424, simple_loss=0.2291, pruned_loss=0.02783, over 984935.55 frames.], datatang_tot_loss[loss=0.1375, simple_loss=0.2152, pruned_loss=0.02992, over 985708.19 frames.], batch size: 34, lr: 2.91e-04
+2022-06-19 06:43:02,759 INFO [train.py:874] (1/4) Epoch 28, batch 3550, aishell_loss[loss=0.1239, simple_loss=0.2082, pruned_loss=0.01979, over 4983.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2214, pruned_loss=0.02845, over 985285.96 frames.], batch size: 27, aishell_tot_loss[loss=0.1417, simple_loss=0.2286, pruned_loss=0.02747, over 984974.57 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.215, pruned_loss=0.02979, over 985681.10 frames.], batch size: 27, lr: 2.91e-04
+2022-06-19 06:43:30,716 INFO [train.py:874] (1/4) Epoch 28, batch 3600, aishell_loss[loss=0.1617, simple_loss=0.2518, pruned_loss=0.03579, over 4931.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2225, pruned_loss=0.02914, over 985555.09 frames.], batch size: 54, aishell_tot_loss[loss=0.1431, simple_loss=0.2298, pruned_loss=0.02821, over 985071.99 frames.], datatang_tot_loss[loss=0.1373, simple_loss=0.2152, pruned_loss=0.02971, over 985851.83 frames.], batch size: 54, lr: 2.91e-04
+2022-06-19 06:44:01,930 INFO [train.py:874] (1/4) Epoch 28, batch 3650, datatang_loss[loss=0.1284, simple_loss=0.2041, pruned_loss=0.02633, over 4938.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2226, pruned_loss=0.02877, over 985488.95 frames.], batch size: 69, aishell_tot_loss[loss=0.1432, simple_loss=0.2301, pruned_loss=0.02812, over 985066.13 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2148, pruned_loss=0.02946, over 985823.81 frames.], batch size: 69, lr: 2.91e-04
+2022-06-19 06:44:30,421 INFO [train.py:874] (1/4) Epoch 28, batch 3700, datatang_loss[loss=0.1245, simple_loss=0.2114, pruned_loss=0.01882, over 4881.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2222, pruned_loss=0.02873, over 985485.42 frames.], batch size: 47, aishell_tot_loss[loss=0.1432, simple_loss=0.2301, pruned_loss=0.02817, over 985131.53 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2142, pruned_loss=0.02937, over 985757.85 frames.], batch size: 47, lr: 2.91e-04
+2022-06-19 06:44:59,336 INFO [train.py:874] (1/4) Epoch 28, batch 3750, datatang_loss[loss=0.1286, simple_loss=0.2129, pruned_loss=0.02217, over 4927.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2228, pruned_loss=0.02887, over 985543.35 frames.], batch size: 57, aishell_tot_loss[loss=0.1431, simple_loss=0.23, pruned_loss=0.02805, over 985144.49 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2147, pruned_loss=0.02966, over 985816.11 frames.], batch size: 57, lr: 2.90e-04
+2022-06-19 06:45:28,857 INFO [train.py:874] (1/4) Epoch 28, batch 3800, aishell_loss[loss=0.1336, simple_loss=0.2193, pruned_loss=0.02397, over 4904.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2226, pruned_loss=0.0289, over 985724.80 frames.], batch size: 52, aishell_tot_loss[loss=0.1431, simple_loss=0.23, pruned_loss=0.02814, over 985340.18 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2148, pruned_loss=0.02961, over 985820.02 frames.], batch size: 52, lr: 2.90e-04
+2022-06-19 06:45:56,691 INFO [train.py:874] (1/4) Epoch 28, batch 3850, datatang_loss[loss=0.1307, simple_loss=0.2119, pruned_loss=0.0248, over 4940.00 frames.], tot_loss[loss=0.139, simple_loss=0.2211, pruned_loss=0.02847, over 984983.73 frames.], batch size: 88, aishell_tot_loss[loss=0.1426, simple_loss=0.2292, pruned_loss=0.02803, over 984662.73 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.214, pruned_loss=0.02927, over 985738.54 frames.], batch size: 88, lr: 2.90e-04
+2022-06-19 06:46:25,682 INFO [train.py:874] (1/4) Epoch 28, batch 3900, aishell_loss[loss=0.1504, simple_loss=0.2289, pruned_loss=0.03596, over 4946.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2212, pruned_loss=0.02854, over 984800.13 frames.], batch size: 54, aishell_tot_loss[loss=0.1429, simple_loss=0.2293, pruned_loss=0.02822, over 984472.69 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2137, pruned_loss=0.02915, over 985711.79 frames.], batch size: 54, lr: 2.90e-04
+2022-06-19 06:46:53,822 INFO [train.py:874] (1/4) Epoch 28, batch 3950, aishell_loss[loss=0.1288, simple_loss=0.2162, pruned_loss=0.02073, over 4936.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2208, pruned_loss=0.02785, over 984906.82 frames.], batch size: 54, aishell_tot_loss[loss=0.1428, simple_loss=0.2293, pruned_loss=0.02815, over 984599.77 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2131, pruned_loss=0.02846, over 985632.08 frames.], batch size: 54, lr: 2.90e-04
+2022-06-19 06:47:20,488 INFO [train.py:874] (1/4) Epoch 28, batch 4000, aishell_loss[loss=0.1348, simple_loss=0.229, pruned_loss=0.02023, over 4957.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2217, pruned_loss=0.02808, over 985338.70 frames.], batch size: 40, aishell_tot_loss[loss=0.1431, simple_loss=0.2298, pruned_loss=0.02813, over 985006.61 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2131, pruned_loss=0.02863, over 985640.39 frames.], batch size: 40, lr: 2.90e-04
+2022-06-19 06:47:20,489 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 06:47:36,386 INFO [train.py:914] (1/4) Epoch 28, validation: loss=0.1646, simple_loss=0.2484, pruned_loss=0.04036, over 1622729.00 frames.
+2022-06-19 06:48:02,201 INFO [train.py:874] (1/4) Epoch 28, batch 4050, aishell_loss[loss=0.1654, simple_loss=0.2505, pruned_loss=0.04013, over 4886.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2222, pruned_loss=0.028, over 985291.95 frames.], batch size: 34, aishell_tot_loss[loss=0.143, simple_loss=0.2299, pruned_loss=0.02799, over 984998.09 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.213, pruned_loss=0.02863, over 985622.32 frames.], batch size: 34, lr: 2.90e-04
+2022-06-19 06:49:07,971 INFO [train.py:874] (1/4) Epoch 29, batch 50, aishell_loss[loss=0.1415, simple_loss=0.2389, pruned_loss=0.02199, over 4953.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2189, pruned_loss=0.02774, over 218512.48 frames.], batch size: 80, aishell_tot_loss[loss=0.1412, simple_loss=0.2278, pruned_loss=0.0273, over 120424.83 frames.], datatang_tot_loss[loss=0.1331, simple_loss=0.2094, pruned_loss=0.02835, over 111733.94 frames.], batch size: 80, lr: 2.85e-04
+2022-06-19 06:49:37,783 INFO [train.py:874] (1/4) Epoch 29, batch 100, datatang_loss[loss=0.134, simple_loss=0.2129, pruned_loss=0.02762, over 4990.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2199, pruned_loss=0.02758, over 388439.17 frames.], batch size: 31, aishell_tot_loss[loss=0.141, simple_loss=0.2284, pruned_loss=0.02684, over 222150.50 frames.], datatang_tot_loss[loss=0.1339, simple_loss=0.2109, pruned_loss=0.02842, over 214677.21 frames.], batch size: 31, lr: 2.85e-04
+2022-06-19 06:50:11,751 INFO [train.py:874] (1/4) Epoch 29, batch 150, aishell_loss[loss=0.1776, simple_loss=0.2658, pruned_loss=0.04475, over 4947.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2203, pruned_loss=0.02768, over 520805.96 frames.], batch size: 54, aishell_tot_loss[loss=0.1423, simple_loss=0.2294, pruned_loss=0.02763, over 318694.20 frames.], datatang_tot_loss[loss=0.1329, simple_loss=0.2103, pruned_loss=0.02778, over 298687.78 frames.], batch size: 54, lr: 2.85e-04
+2022-06-19 06:50:40,059 INFO [train.py:874] (1/4) Epoch 29, batch 200, datatang_loss[loss=0.1138, simple_loss=0.2012, pruned_loss=0.01318, over 4933.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2198, pruned_loss=0.02735, over 623114.78 frames.], batch size: 79, aishell_tot_loss[loss=0.1417, simple_loss=0.2289, pruned_loss=0.02726, over 405613.58 frames.], datatang_tot_loss[loss=0.1324, simple_loss=0.2095, pruned_loss=0.0276, over 370044.18 frames.], batch size: 79, lr: 2.85e-04
+2022-06-19 06:51:09,814 INFO [train.py:874] (1/4) Epoch 29, batch 250, datatang_loss[loss=0.1382, simple_loss=0.2096, pruned_loss=0.0334, over 4944.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2212, pruned_loss=0.02786, over 703547.74 frames.], batch size: 42, aishell_tot_loss[loss=0.1421, simple_loss=0.2296, pruned_loss=0.0273, over 473902.73 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2115, pruned_loss=0.02838, over 442647.55 frames.], batch size: 42, lr: 2.85e-04
+2022-06-19 06:51:37,966 INFO [train.py:874] (1/4) Epoch 29, batch 300, datatang_loss[loss=0.1213, simple_loss=0.198, pruned_loss=0.02228, over 4925.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2206, pruned_loss=0.02746, over 766195.19 frames.], batch size: 73, aishell_tot_loss[loss=0.1417, simple_loss=0.2291, pruned_loss=0.02715, over 549700.13 frames.], datatang_tot_loss[loss=0.1331, simple_loss=0.2102, pruned_loss=0.02797, over 489662.17 frames.], batch size: 73, lr: 2.85e-04
+2022-06-19 06:52:07,632 INFO [train.py:874] (1/4) Epoch 29, batch 350, aishell_loss[loss=0.1891, simple_loss=0.2822, pruned_loss=0.04805, over 4950.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2217, pruned_loss=0.02748, over 815051.19 frames.], batch size: 79, aishell_tot_loss[loss=0.1421, simple_loss=0.2298, pruned_loss=0.02722, over 610613.06 frames.], datatang_tot_loss[loss=0.1333, simple_loss=0.2107, pruned_loss=0.02791, over 537151.21 frames.], batch size: 79, lr: 2.85e-04
+2022-06-19 06:52:37,145 INFO [train.py:874] (1/4) Epoch 29, batch 400, aishell_loss[loss=0.1506, simple_loss=0.2353, pruned_loss=0.03294, over 4916.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2214, pruned_loss=0.02753, over 852872.06 frames.], batch size: 41, aishell_tot_loss[loss=0.1414, simple_loss=0.2286, pruned_loss=0.0271, over 656331.80 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.212, pruned_loss=0.02809, over 588103.16 frames.], batch size: 41, lr: 2.85e-04
+2022-06-19 06:53:05,424 INFO [train.py:874] (1/4) Epoch 29, batch 450, datatang_loss[loss=0.1344, simple_loss=0.2068, pruned_loss=0.03095, over 4961.00 frames.], tot_loss[loss=0.1389, simple_loss=0.222, pruned_loss=0.02796, over 882562.39 frames.], batch size: 40, aishell_tot_loss[loss=0.1419, simple_loss=0.2292, pruned_loss=0.02728, over 699594.25 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2122, pruned_loss=0.02852, over 629737.98 frames.], batch size: 40, lr: 2.85e-04
+2022-06-19 06:53:35,502 INFO [train.py:874] (1/4) Epoch 29, batch 500, datatang_loss[loss=0.1475, simple_loss=0.2235, pruned_loss=0.03576, over 4947.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2208, pruned_loss=0.02801, over 905361.73 frames.], batch size: 69, aishell_tot_loss[loss=0.1418, simple_loss=0.2288, pruned_loss=0.02741, over 733175.18 frames.], datatang_tot_loss[loss=0.1341, simple_loss=0.2114, pruned_loss=0.02845, over 671717.03 frames.], batch size: 69, lr: 2.84e-04
+2022-06-19 06:54:05,086 INFO [train.py:874] (1/4) Epoch 29, batch 550, aishell_loss[loss=0.1284, simple_loss=0.2108, pruned_loss=0.02298, over 4890.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2207, pruned_loss=0.02803, over 923329.73 frames.], batch size: 34, aishell_tot_loss[loss=0.1419, simple_loss=0.2289, pruned_loss=0.02747, over 757496.42 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2119, pruned_loss=0.02842, over 715493.92 frames.], batch size: 34, lr: 2.84e-04
+2022-06-19 06:54:33,118 INFO [train.py:874] (1/4) Epoch 29, batch 600, datatang_loss[loss=0.1307, simple_loss=0.2089, pruned_loss=0.0263, over 4860.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2212, pruned_loss=0.02823, over 937082.37 frames.], batch size: 39, aishell_tot_loss[loss=0.142, simple_loss=0.2286, pruned_loss=0.02767, over 786268.20 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2128, pruned_loss=0.02853, over 744956.06 frames.], batch size: 39, lr: 2.84e-04
+2022-06-19 06:55:02,477 INFO [train.py:874] (1/4) Epoch 29, batch 650, aishell_loss[loss=0.116, simple_loss=0.2093, pruned_loss=0.01131, over 4881.00 frames.], tot_loss[loss=0.139, simple_loss=0.2216, pruned_loss=0.02826, over 947752.38 frames.], batch size: 28, aishell_tot_loss[loss=0.1425, simple_loss=0.2291, pruned_loss=0.02789, over 809569.55 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2128, pruned_loss=0.02837, over 773398.87 frames.], batch size: 28, lr: 2.84e-04
+2022-06-19 06:55:31,656 INFO [train.py:874] (1/4) Epoch 29, batch 700, datatang_loss[loss=0.1177, simple_loss=0.1947, pruned_loss=0.02034, over 4917.00 frames.], tot_loss[loss=0.139, simple_loss=0.2211, pruned_loss=0.02852, over 956294.70 frames.], batch size: 25, aishell_tot_loss[loss=0.1426, simple_loss=0.2291, pruned_loss=0.02803, over 828049.60 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2127, pruned_loss=0.02859, over 801255.77 frames.], batch size: 25, lr: 2.84e-04
+2022-06-19 06:56:00,596 INFO [train.py:874] (1/4) Epoch 29, batch 750, aishell_loss[loss=0.1471, simple_loss=0.2283, pruned_loss=0.03297, over 4981.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2213, pruned_loss=0.02845, over 962659.72 frames.], batch size: 39, aishell_tot_loss[loss=0.1423, simple_loss=0.2287, pruned_loss=0.02794, over 847257.78 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2134, pruned_loss=0.02865, over 822049.15 frames.], batch size: 39, lr: 2.84e-04
+2022-06-19 06:56:31,385 INFO [train.py:874] (1/4) Epoch 29, batch 800, aishell_loss[loss=0.1376, simple_loss=0.2201, pruned_loss=0.02755, over 4954.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2213, pruned_loss=0.02861, over 967730.47 frames.], batch size: 27, aishell_tot_loss[loss=0.142, simple_loss=0.2281, pruned_loss=0.02794, over 865244.45 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2139, pruned_loss=0.02889, over 839248.58 frames.], batch size: 27, lr: 2.84e-04
+2022-06-19 06:57:01,120 INFO [train.py:874] (1/4) Epoch 29, batch 850, aishell_loss[loss=0.1486, simple_loss=0.2406, pruned_loss=0.02827, over 4969.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2212, pruned_loss=0.02849, over 971986.89 frames.], batch size: 61, aishell_tot_loss[loss=0.1417, simple_loss=0.2278, pruned_loss=0.0278, over 880019.12 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.214, pruned_loss=0.02895, over 856101.31 frames.], batch size: 61, lr: 2.84e-04
+2022-06-19 06:57:29,414 INFO [train.py:874] (1/4) Epoch 29, batch 900, aishell_loss[loss=0.1398, simple_loss=0.2263, pruned_loss=0.0267, over 4940.00 frames.], tot_loss[loss=0.139, simple_loss=0.2211, pruned_loss=0.02845, over 974854.65 frames.], batch size: 49, aishell_tot_loss[loss=0.1418, simple_loss=0.2279, pruned_loss=0.02787, over 891291.27 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.214, pruned_loss=0.02884, over 872573.06 frames.], batch size: 49, lr: 2.84e-04
+2022-06-19 06:57:59,986 INFO [train.py:874] (1/4) Epoch 29, batch 950, aishell_loss[loss=0.1896, simple_loss=0.2802, pruned_loss=0.04953, over 4856.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2214, pruned_loss=0.02844, over 977367.86 frames.], batch size: 37, aishell_tot_loss[loss=0.1418, simple_loss=0.2279, pruned_loss=0.02787, over 902375.19 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2144, pruned_loss=0.02887, over 886059.01 frames.], batch size: 37, lr: 2.84e-04
+2022-06-19 06:58:28,345 INFO [train.py:874] (1/4) Epoch 29, batch 1000, aishell_loss[loss=0.1332, simple_loss=0.2214, pruned_loss=0.02245, over 4941.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2225, pruned_loss=0.02901, over 978891.22 frames.], batch size: 45, aishell_tot_loss[loss=0.1425, simple_loss=0.2286, pruned_loss=0.02822, over 910907.54 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2151, pruned_loss=0.02917, over 898927.48 frames.], batch size: 45, lr: 2.84e-04
+2022-06-19 06:58:28,345 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 06:58:44,050 INFO [train.py:914] (1/4) Epoch 29, validation: loss=0.165, simple_loss=0.2483, pruned_loss=0.04086, over 1622729.00 frames.
+2022-06-19 06:59:14,570 INFO [train.py:874] (1/4) Epoch 29, batch 1050, aishell_loss[loss=0.1529, simple_loss=0.2381, pruned_loss=0.03383, over 4980.00 frames.], tot_loss[loss=0.1405, simple_loss=0.2228, pruned_loss=0.02909, over 980469.59 frames.], batch size: 48, aishell_tot_loss[loss=0.1429, simple_loss=0.2291, pruned_loss=0.02832, over 919369.57 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2152, pruned_loss=0.02923, over 909616.38 frames.], batch size: 48, lr: 2.84e-04
+2022-06-19 06:59:43,106 INFO [train.py:874] (1/4) Epoch 29, batch 1100, aishell_loss[loss=0.1545, simple_loss=0.2426, pruned_loss=0.03319, over 4981.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2227, pruned_loss=0.02886, over 981600.30 frames.], batch size: 39, aishell_tot_loss[loss=0.1428, simple_loss=0.2292, pruned_loss=0.02821, over 926725.78 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2152, pruned_loss=0.02915, over 919036.83 frames.], batch size: 39, lr: 2.84e-04
+2022-06-19 07:00:12,939 INFO [train.py:874] (1/4) Epoch 29, batch 1150, aishell_loss[loss=0.1357, simple_loss=0.2309, pruned_loss=0.02026, over 4914.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2227, pruned_loss=0.02881, over 982455.53 frames.], batch size: 52, aishell_tot_loss[loss=0.143, simple_loss=0.2293, pruned_loss=0.02832, over 932957.95 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2154, pruned_loss=0.02902, over 927618.85 frames.], batch size: 52, lr: 2.84e-04
+2022-06-19 07:00:42,821 INFO [train.py:874] (1/4) Epoch 29, batch 1200, datatang_loss[loss=0.1246, simple_loss=0.1988, pruned_loss=0.02519, over 4931.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2214, pruned_loss=0.02874, over 983570.00 frames.], batch size: 77, aishell_tot_loss[loss=0.1424, simple_loss=0.2284, pruned_loss=0.02823, over 937393.07 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2155, pruned_loss=0.02904, over 936763.95 frames.], batch size: 77, lr: 2.84e-04
+2022-06-19 07:01:11,932 INFO [train.py:874] (1/4) Epoch 29, batch 1250, aishell_loss[loss=0.1354, simple_loss=0.2267, pruned_loss=0.02207, over 4965.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2205, pruned_loss=0.02831, over 984195.48 frames.], batch size: 61, aishell_tot_loss[loss=0.1419, simple_loss=0.2279, pruned_loss=0.02796, over 942666.46 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.215, pruned_loss=0.02885, over 943099.95 frames.], batch size: 61, lr: 2.84e-04
+2022-06-19 07:01:42,640 INFO [train.py:874] (1/4) Epoch 29, batch 1300, datatang_loss[loss=0.1213, simple_loss=0.1911, pruned_loss=0.02576, over 4939.00 frames.], tot_loss[loss=0.1389, simple_loss=0.221, pruned_loss=0.02846, over 984464.47 frames.], batch size: 50, aishell_tot_loss[loss=0.1418, simple_loss=0.228, pruned_loss=0.02778, over 947377.43 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2153, pruned_loss=0.02915, over 948399.38 frames.], batch size: 50, lr: 2.84e-04
+2022-06-19 07:02:12,781 INFO [train.py:874] (1/4) Epoch 29, batch 1350, aishell_loss[loss=0.1313, simple_loss=0.2203, pruned_loss=0.02121, over 4877.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2211, pruned_loss=0.02865, over 984384.70 frames.], batch size: 47, aishell_tot_loss[loss=0.1417, simple_loss=0.228, pruned_loss=0.02775, over 951342.12 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2154, pruned_loss=0.02936, over 952953.00 frames.], batch size: 47, lr: 2.83e-04
+2022-06-19 07:02:41,611 INFO [train.py:874] (1/4) Epoch 29, batch 1400, datatang_loss[loss=0.1269, simple_loss=0.2138, pruned_loss=0.02003, over 4920.00 frames.], tot_loss[loss=0.1387, simple_loss=0.221, pruned_loss=0.02824, over 985077.85 frames.], batch size: 75, aishell_tot_loss[loss=0.1416, simple_loss=0.2277, pruned_loss=0.02771, over 956085.48 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2152, pruned_loss=0.02902, over 956551.75 frames.], batch size: 75, lr: 2.83e-04
+2022-06-19 07:03:11,148 INFO [train.py:874] (1/4) Epoch 29, batch 1450, aishell_loss[loss=0.151, simple_loss=0.2465, pruned_loss=0.02774, over 4940.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2219, pruned_loss=0.02849, over 985449.31 frames.], batch size: 45, aishell_tot_loss[loss=0.1418, simple_loss=0.2281, pruned_loss=0.02773, over 960075.60 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2155, pruned_loss=0.02923, over 959724.48 frames.], batch size: 45, lr: 2.83e-04
+2022-06-19 07:03:40,959 INFO [train.py:874] (1/4) Epoch 29, batch 1500, datatang_loss[loss=0.1444, simple_loss=0.2217, pruned_loss=0.03358, over 4932.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2217, pruned_loss=0.02892, over 985623.86 frames.], batch size: 71, aishell_tot_loss[loss=0.1417, simple_loss=0.2279, pruned_loss=0.02774, over 962664.96 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.2158, pruned_loss=0.02967, over 963322.14 frames.], batch size: 71, lr: 2.83e-04
+2022-06-19 07:04:10,512 INFO [train.py:874] (1/4) Epoch 29, batch 1550, datatang_loss[loss=0.14, simple_loss=0.2189, pruned_loss=0.03059, over 4981.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2217, pruned_loss=0.02844, over 985770.36 frames.], batch size: 25, aishell_tot_loss[loss=0.1413, simple_loss=0.2278, pruned_loss=0.0274, over 965345.73 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2158, pruned_loss=0.02953, over 966110.93 frames.], batch size: 25, lr: 2.83e-04
+2022-06-19 07:04:40,810 INFO [train.py:874] (1/4) Epoch 29, batch 1600, aishell_loss[loss=0.1315, simple_loss=0.2146, pruned_loss=0.02415, over 4967.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2205, pruned_loss=0.02794, over 985730.62 frames.], batch size: 31, aishell_tot_loss[loss=0.1413, simple_loss=0.2277, pruned_loss=0.02745, over 967255.59 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2149, pruned_loss=0.02889, over 968860.72 frames.], batch size: 31, lr: 2.83e-04
+2022-06-19 07:05:09,435 INFO [train.py:874] (1/4) Epoch 29, batch 1650, datatang_loss[loss=0.1494, simple_loss=0.237, pruned_loss=0.0309, over 4964.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2211, pruned_loss=0.0283, over 985574.65 frames.], batch size: 34, aishell_tot_loss[loss=0.1417, simple_loss=0.228, pruned_loss=0.02766, over 969075.47 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.215, pruned_loss=0.02901, over 971042.14 frames.], batch size: 34, lr: 2.83e-04
+2022-06-19 07:05:37,791 INFO [train.py:874] (1/4) Epoch 29, batch 1700, aishell_loss[loss=0.111, simple_loss=0.1929, pruned_loss=0.01455, over 4950.00 frames.], tot_loss[loss=0.1377, simple_loss=0.22, pruned_loss=0.02776, over 985446.97 frames.], batch size: 27, aishell_tot_loss[loss=0.1411, simple_loss=0.2273, pruned_loss=0.02747, over 970848.81 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2144, pruned_loss=0.02862, over 972799.58 frames.], batch size: 27, lr: 2.83e-04
+2022-06-19 07:06:08,733 INFO [train.py:874] (1/4) Epoch 29, batch 1750, datatang_loss[loss=0.1546, simple_loss=0.2319, pruned_loss=0.03866, over 4900.00 frames.], tot_loss[loss=0.138, simple_loss=0.2203, pruned_loss=0.02781, over 985183.29 frames.], batch size: 47, aishell_tot_loss[loss=0.1413, simple_loss=0.2276, pruned_loss=0.02748, over 972527.07 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2143, pruned_loss=0.0286, over 974065.55 frames.], batch size: 47, lr: 2.83e-04
+2022-06-19 07:06:37,234 INFO [train.py:874] (1/4) Epoch 29, batch 1800, datatang_loss[loss=0.1259, simple_loss=0.2043, pruned_loss=0.02369, over 4933.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2215, pruned_loss=0.02809, over 985313.61 frames.], batch size: 71, aishell_tot_loss[loss=0.1416, simple_loss=0.2284, pruned_loss=0.02745, over 974081.97 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2147, pruned_loss=0.02888, over 975443.76 frames.], batch size: 71, lr: 2.83e-04
+2022-06-19 07:07:05,212 INFO [train.py:874] (1/4) Epoch 29, batch 1850, datatang_loss[loss=0.1259, simple_loss=0.2062, pruned_loss=0.02281, over 4961.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2225, pruned_loss=0.02816, over 985467.12 frames.], batch size: 31, aishell_tot_loss[loss=0.1421, simple_loss=0.2289, pruned_loss=0.02765, over 975808.20 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2147, pruned_loss=0.02877, over 976403.45 frames.], batch size: 31, lr: 2.83e-04
+2022-06-19 07:07:34,102 INFO [train.py:874] (1/4) Epoch 29, batch 1900, aishell_loss[loss=0.1036, simple_loss=0.1668, pruned_loss=0.02022, over 4940.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2227, pruned_loss=0.02848, over 985490.26 frames.], batch size: 21, aishell_tot_loss[loss=0.142, simple_loss=0.2286, pruned_loss=0.02772, over 977249.13 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.215, pruned_loss=0.02908, over 977190.70 frames.], batch size: 21, lr: 2.83e-04
+2022-06-19 07:08:03,015 INFO [train.py:874] (1/4) Epoch 29, batch 1950, aishell_loss[loss=0.1344, simple_loss=0.2172, pruned_loss=0.02579, over 4936.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2222, pruned_loss=0.02818, over 985015.44 frames.], batch size: 49, aishell_tot_loss[loss=0.1418, simple_loss=0.2282, pruned_loss=0.02765, over 977935.19 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2148, pruned_loss=0.02887, over 977948.31 frames.], batch size: 49, lr: 2.83e-04
+2022-06-19 07:08:32,532 INFO [train.py:874] (1/4) Epoch 29, batch 2000, aishell_loss[loss=0.1111, simple_loss=0.2003, pruned_loss=0.01091, over 4896.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2236, pruned_loss=0.02856, over 985215.40 frames.], batch size: 28, aishell_tot_loss[loss=0.1423, simple_loss=0.2287, pruned_loss=0.0279, over 979048.96 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2154, pruned_loss=0.02906, over 978723.62 frames.], batch size: 28, lr: 2.83e-04
+2022-06-19 07:08:32,532 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 07:08:49,526 INFO [train.py:914] (1/4) Epoch 29, validation: loss=0.1647, simple_loss=0.2482, pruned_loss=0.04062, over 1622729.00 frames.
+2022-06-19 07:09:18,420 INFO [train.py:874] (1/4) Epoch 29, batch 2050, datatang_loss[loss=0.1322, simple_loss=0.2156, pruned_loss=0.02438, over 4912.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2234, pruned_loss=0.02855, over 985111.29 frames.], batch size: 64, aishell_tot_loss[loss=0.1425, simple_loss=0.2291, pruned_loss=0.02799, over 979578.49 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2153, pruned_loss=0.02899, over 979578.67 frames.], batch size: 64, lr: 2.83e-04
+2022-06-19 07:09:48,714 INFO [train.py:874] (1/4) Epoch 29, batch 2100, datatang_loss[loss=0.1376, simple_loss=0.2213, pruned_loss=0.02696, over 4868.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2222, pruned_loss=0.02814, over 984877.81 frames.], batch size: 39, aishell_tot_loss[loss=0.1419, simple_loss=0.2285, pruned_loss=0.02764, over 980020.40 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2152, pruned_loss=0.02891, over 980210.68 frames.], batch size: 39, lr: 2.83e-04
+2022-06-19 07:10:18,286 INFO [train.py:874] (1/4) Epoch 29, batch 2150, datatang_loss[loss=0.1161, simple_loss=0.194, pruned_loss=0.01908, over 4882.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2226, pruned_loss=0.02829, over 984871.11 frames.], batch size: 25, aishell_tot_loss[loss=0.1423, simple_loss=0.229, pruned_loss=0.0278, over 980605.00 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2151, pruned_loss=0.02888, over 980754.15 frames.], batch size: 25, lr: 2.82e-04
+2022-06-19 07:10:47,512 INFO [train.py:874] (1/4) Epoch 29, batch 2200, datatang_loss[loss=0.1243, simple_loss=0.2069, pruned_loss=0.02081, over 4922.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2226, pruned_loss=0.02846, over 984755.98 frames.], batch size: 83, aishell_tot_loss[loss=0.1429, simple_loss=0.2294, pruned_loss=0.0282, over 980937.18 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2148, pruned_loss=0.02866, over 981281.55 frames.], batch size: 83, lr: 2.82e-04
+2022-06-19 07:11:17,485 INFO [train.py:874] (1/4) Epoch 29, batch 2250, datatang_loss[loss=0.1377, simple_loss=0.2173, pruned_loss=0.02902, over 4947.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2222, pruned_loss=0.02852, over 984764.06 frames.], batch size: 62, aishell_tot_loss[loss=0.1428, simple_loss=0.2291, pruned_loss=0.02821, over 981273.42 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2149, pruned_loss=0.02873, over 981802.41 frames.], batch size: 62, lr: 2.82e-04
+2022-06-19 07:11:46,211 INFO [train.py:874] (1/4) Epoch 29, batch 2300, datatang_loss[loss=0.1283, simple_loss=0.2093, pruned_loss=0.02362, over 4951.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2222, pruned_loss=0.02928, over 984756.65 frames.], batch size: 86, aishell_tot_loss[loss=0.1429, simple_loss=0.2291, pruned_loss=0.02835, over 981192.15 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2154, pruned_loss=0.02939, over 982605.79 frames.], batch size: 86, lr: 2.82e-04
+2022-06-19 07:12:16,323 INFO [train.py:874] (1/4) Epoch 29, batch 2350, datatang_loss[loss=0.1396, simple_loss=0.2093, pruned_loss=0.03499, over 4888.00 frames.], tot_loss[loss=0.1414, simple_loss=0.2234, pruned_loss=0.02975, over 985114.19 frames.], batch size: 42, aishell_tot_loss[loss=0.1436, simple_loss=0.2298, pruned_loss=0.02871, over 981817.97 frames.], datatang_tot_loss[loss=0.1377, simple_loss=0.2162, pruned_loss=0.02962, over 983019.38 frames.], batch size: 42, lr: 2.82e-04
+2022-06-19 07:12:46,290 INFO [train.py:874] (1/4) Epoch 29, batch 2400, aishell_loss[loss=0.1478, simple_loss=0.2471, pruned_loss=0.02427, over 4964.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2219, pruned_loss=0.02895, over 984990.86 frames.], batch size: 44, aishell_tot_loss[loss=0.1434, simple_loss=0.2296, pruned_loss=0.02858, over 981983.10 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2153, pruned_loss=0.02902, over 983307.14 frames.], batch size: 44, lr: 2.82e-04
+2022-06-19 07:13:14,706 INFO [train.py:874] (1/4) Epoch 29, batch 2450, aishell_loss[loss=0.122, simple_loss=0.2125, pruned_loss=0.01571, over 4990.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2213, pruned_loss=0.02853, over 985280.86 frames.], batch size: 27, aishell_tot_loss[loss=0.1431, simple_loss=0.2295, pruned_loss=0.02839, over 982558.65 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2146, pruned_loss=0.0288, over 983588.63 frames.], batch size: 27, lr: 2.82e-04
+2022-06-19 07:13:44,302 INFO [train.py:874] (1/4) Epoch 29, batch 2500, aishell_loss[loss=0.154, simple_loss=0.2347, pruned_loss=0.03661, over 4967.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2218, pruned_loss=0.02825, over 985320.25 frames.], batch size: 40, aishell_tot_loss[loss=0.1429, simple_loss=0.2295, pruned_loss=0.02813, over 982834.72 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2148, pruned_loss=0.02876, over 983874.42 frames.], batch size: 40, lr: 2.82e-04
+2022-06-19 07:14:14,335 INFO [train.py:874] (1/4) Epoch 29, batch 2550, aishell_loss[loss=0.1559, simple_loss=0.2493, pruned_loss=0.03127, over 4918.00 frames.], tot_loss[loss=0.1394, simple_loss=0.2219, pruned_loss=0.02846, over 985239.78 frames.], batch size: 78, aishell_tot_loss[loss=0.1431, simple_loss=0.2295, pruned_loss=0.02839, over 982863.27 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2144, pruned_loss=0.02868, over 984255.14 frames.], batch size: 78, lr: 2.82e-04
+2022-06-19 07:14:43,061 INFO [train.py:874] (1/4) Epoch 29, batch 2600, aishell_loss[loss=0.139, simple_loss=0.2247, pruned_loss=0.02668, over 4877.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2206, pruned_loss=0.02815, over 985273.02 frames.], batch size: 35, aishell_tot_loss[loss=0.1424, simple_loss=0.2287, pruned_loss=0.0281, over 983146.43 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.214, pruned_loss=0.02861, over 984380.62 frames.], batch size: 35, lr: 2.82e-04
+2022-06-19 07:15:12,751 INFO [train.py:874] (1/4) Epoch 29, batch 2650, datatang_loss[loss=0.128, simple_loss=0.2108, pruned_loss=0.02261, over 4940.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2207, pruned_loss=0.02793, over 985331.75 frames.], batch size: 88, aishell_tot_loss[loss=0.1422, simple_loss=0.2286, pruned_loss=0.02793, over 983533.77 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2138, pruned_loss=0.02851, over 984441.09 frames.], batch size: 88, lr: 2.82e-04
+2022-06-19 07:15:41,970 INFO [train.py:874] (1/4) Epoch 29, batch 2700, datatang_loss[loss=0.1183, simple_loss=0.2085, pruned_loss=0.01405, over 4926.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2212, pruned_loss=0.02761, over 984777.36 frames.], batch size: 71, aishell_tot_loss[loss=0.142, simple_loss=0.2287, pruned_loss=0.02766, over 983246.12 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2137, pruned_loss=0.02839, over 984494.40 frames.], batch size: 71, lr: 2.82e-04
+2022-06-19 07:16:09,396 INFO [train.py:874] (1/4) Epoch 29, batch 2750, aishell_loss[loss=0.1378, simple_loss=0.2263, pruned_loss=0.02463, over 4867.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2204, pruned_loss=0.02729, over 984355.20 frames.], batch size: 36, aishell_tot_loss[loss=0.1417, simple_loss=0.2281, pruned_loss=0.02767, over 982852.08 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2133, pruned_loss=0.02795, over 984671.39 frames.], batch size: 36, lr: 2.82e-04
+2022-06-19 07:16:40,526 INFO [train.py:874] (1/4) Epoch 29, batch 2800, datatang_loss[loss=0.1392, simple_loss=0.2206, pruned_loss=0.02883, over 4930.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2215, pruned_loss=0.02848, over 984402.78 frames.], batch size: 73, aishell_tot_loss[loss=0.1423, simple_loss=0.2288, pruned_loss=0.02794, over 982973.66 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.214, pruned_loss=0.02879, over 984696.09 frames.], batch size: 73, lr: 2.82e-04
+2022-06-19 07:17:10,569 INFO [train.py:874] (1/4) Epoch 29, batch 2850, aishell_loss[loss=0.1321, simple_loss=0.2159, pruned_loss=0.02413, over 4941.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2214, pruned_loss=0.02801, over 984677.97 frames.], batch size: 49, aishell_tot_loss[loss=0.142, simple_loss=0.2287, pruned_loss=0.02767, over 983293.72 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2139, pruned_loss=0.0286, over 984812.12 frames.], batch size: 49, lr: 2.82e-04
+2022-06-19 07:17:38,888 INFO [train.py:874] (1/4) Epoch 29, batch 2900, aishell_loss[loss=0.1559, simple_loss=0.2353, pruned_loss=0.03828, over 4866.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2217, pruned_loss=0.02848, over 984753.71 frames.], batch size: 35, aishell_tot_loss[loss=0.1423, simple_loss=0.2288, pruned_loss=0.0279, over 983540.37 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2141, pruned_loss=0.02885, over 984800.65 frames.], batch size: 35, lr: 2.82e-04
+2022-06-19 07:18:09,830 INFO [train.py:874] (1/4) Epoch 29, batch 2950, datatang_loss[loss=0.1415, simple_loss=0.2163, pruned_loss=0.0333, over 4978.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2216, pruned_loss=0.02835, over 984987.61 frames.], batch size: 40, aishell_tot_loss[loss=0.1423, simple_loss=0.2289, pruned_loss=0.02781, over 983613.36 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2139, pruned_loss=0.0288, over 985098.50 frames.], batch size: 40, lr: 2.82e-04
+2022-06-19 07:18:38,430 INFO [train.py:874] (1/4) Epoch 29, batch 3000, datatang_loss[loss=0.138, simple_loss=0.2146, pruned_loss=0.0307, over 4904.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2214, pruned_loss=0.02784, over 985163.47 frames.], batch size: 64, aishell_tot_loss[loss=0.1419, simple_loss=0.2288, pruned_loss=0.02753, over 983797.89 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2136, pruned_loss=0.02858, over 985270.66 frames.], batch size: 64, lr: 2.81e-04
+2022-06-19 07:18:38,431 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 07:18:54,223 INFO [train.py:914] (1/4) Epoch 29, validation: loss=0.165, simple_loss=0.2488, pruned_loss=0.0406, over 1622729.00 frames.
+2022-06-19 07:19:24,185 INFO [train.py:874] (1/4) Epoch 29, batch 3050, datatang_loss[loss=0.1524, simple_loss=0.2383, pruned_loss=0.03329, over 4948.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2219, pruned_loss=0.02833, over 985444.82 frames.], batch size: 99, aishell_tot_loss[loss=0.1418, simple_loss=0.2287, pruned_loss=0.02744, over 984158.18 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2147, pruned_loss=0.02908, over 985329.56 frames.], batch size: 99, lr: 2.81e-04
+2022-06-19 07:19:51,652 INFO [train.py:874] (1/4) Epoch 29, batch 3100, aishell_loss[loss=0.1667, simple_loss=0.2484, pruned_loss=0.04247, over 4943.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2225, pruned_loss=0.02884, over 985506.59 frames.], batch size: 32, aishell_tot_loss[loss=0.1424, simple_loss=0.2294, pruned_loss=0.02767, over 984517.01 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2147, pruned_loss=0.02938, over 985209.58 frames.], batch size: 32, lr: 2.81e-04
+2022-06-19 07:20:22,494 INFO [train.py:874] (1/4) Epoch 29, batch 3150, datatang_loss[loss=0.1316, simple_loss=0.2071, pruned_loss=0.02801, over 4953.00 frames.], tot_loss[loss=0.14, simple_loss=0.2225, pruned_loss=0.02875, over 985621.75 frames.], batch size: 55, aishell_tot_loss[loss=0.1424, simple_loss=0.2292, pruned_loss=0.02781, over 984547.13 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2148, pruned_loss=0.02922, over 985458.93 frames.], batch size: 55, lr: 2.81e-04
+2022-06-19 07:20:52,234 INFO [train.py:874] (1/4) Epoch 29, batch 3200, aishell_loss[loss=0.1596, simple_loss=0.2559, pruned_loss=0.03162, over 4926.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2224, pruned_loss=0.02856, over 985444.85 frames.], batch size: 68, aishell_tot_loss[loss=0.1426, simple_loss=0.2295, pruned_loss=0.02788, over 984550.23 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2145, pruned_loss=0.02901, over 985403.46 frames.], batch size: 68, lr: 2.81e-04
+2022-06-19 07:21:20,845 INFO [train.py:874] (1/4) Epoch 29, batch 3250, aishell_loss[loss=0.1514, simple_loss=0.2483, pruned_loss=0.02721, over 4969.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2217, pruned_loss=0.02847, over 985269.68 frames.], batch size: 79, aishell_tot_loss[loss=0.142, simple_loss=0.2285, pruned_loss=0.02769, over 984413.02 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2148, pruned_loss=0.02911, over 985452.32 frames.], batch size: 79, lr: 2.81e-04
+2022-06-19 07:21:51,294 INFO [train.py:874] (1/4) Epoch 29, batch 3300, aishell_loss[loss=0.1279, simple_loss=0.2174, pruned_loss=0.01923, over 4899.00 frames.], tot_loss[loss=0.1395, simple_loss=0.2224, pruned_loss=0.02831, over 985586.10 frames.], batch size: 34, aishell_tot_loss[loss=0.1419, simple_loss=0.2287, pruned_loss=0.0275, over 984679.65 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2154, pruned_loss=0.02913, over 985613.73 frames.], batch size: 34, lr: 2.81e-04
+2022-06-19 07:22:20,993 INFO [train.py:874] (1/4) Epoch 29, batch 3350, datatang_loss[loss=0.1442, simple_loss=0.2243, pruned_loss=0.03207, over 4955.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2218, pruned_loss=0.02821, over 985434.91 frames.], batch size: 50, aishell_tot_loss[loss=0.1415, simple_loss=0.228, pruned_loss=0.02746, over 984482.86 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2155, pruned_loss=0.02908, over 985751.32 frames.], batch size: 50, lr: 2.81e-04
+2022-06-19 07:22:48,907 INFO [train.py:874] (1/4) Epoch 29, batch 3400, datatang_loss[loss=0.123, simple_loss=0.2108, pruned_loss=0.01761, over 4949.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2207, pruned_loss=0.0277, over 985554.56 frames.], batch size: 67, aishell_tot_loss[loss=0.1413, simple_loss=0.2277, pruned_loss=0.02748, over 984470.29 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2149, pruned_loss=0.02847, over 985920.50 frames.], batch size: 67, lr: 2.81e-04
+2022-06-19 07:23:20,400 INFO [train.py:874] (1/4) Epoch 29, batch 3450, datatang_loss[loss=0.1414, simple_loss=0.2192, pruned_loss=0.03182, over 4917.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2211, pruned_loss=0.02775, over 985522.36 frames.], batch size: 75, aishell_tot_loss[loss=0.1412, simple_loss=0.2277, pruned_loss=0.02733, over 984571.63 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.215, pruned_loss=0.02863, over 985900.73 frames.], batch size: 75, lr: 2.81e-04
+2022-06-19 07:23:50,335 INFO [train.py:874] (1/4) Epoch 29, batch 3500, aishell_loss[loss=0.1406, simple_loss=0.2267, pruned_loss=0.02723, over 4932.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2214, pruned_loss=0.02805, over 985293.54 frames.], batch size: 54, aishell_tot_loss[loss=0.1413, simple_loss=0.2277, pruned_loss=0.02741, over 984560.91 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2152, pruned_loss=0.02881, over 985741.95 frames.], batch size: 54, lr: 2.81e-04
+2022-06-19 07:24:19,342 INFO [train.py:874] (1/4) Epoch 29, batch 3550, aishell_loss[loss=0.1424, simple_loss=0.2269, pruned_loss=0.02898, over 4933.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2222, pruned_loss=0.02821, over 985921.92 frames.], batch size: 32, aishell_tot_loss[loss=0.1413, simple_loss=0.2278, pruned_loss=0.02741, over 984924.20 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2158, pruned_loss=0.02897, over 986086.92 frames.], batch size: 32, lr: 2.81e-04
+2022-06-19 07:24:50,026 INFO [train.py:874] (1/4) Epoch 29, batch 3600, datatang_loss[loss=0.1368, simple_loss=0.2279, pruned_loss=0.02284, over 4974.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2222, pruned_loss=0.02805, over 986133.07 frames.], batch size: 31, aishell_tot_loss[loss=0.1414, simple_loss=0.2281, pruned_loss=0.02739, over 985233.84 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2156, pruned_loss=0.02882, over 986098.57 frames.], batch size: 31, lr: 2.81e-04
+2022-06-19 07:25:18,915 INFO [train.py:874] (1/4) Epoch 29, batch 3650, datatang_loss[loss=0.1109, simple_loss=0.1934, pruned_loss=0.01421, over 4930.00 frames.], tot_loss[loss=0.1383, simple_loss=0.221, pruned_loss=0.02786, over 985924.52 frames.], batch size: 79, aishell_tot_loss[loss=0.1414, simple_loss=0.2281, pruned_loss=0.02732, over 985173.94 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2147, pruned_loss=0.02866, over 986031.68 frames.], batch size: 79, lr: 2.81e-04
+2022-06-19 07:25:46,584 INFO [train.py:874] (1/4) Epoch 29, batch 3700, aishell_loss[loss=0.1417, simple_loss=0.2339, pruned_loss=0.02478, over 4919.00 frames.], tot_loss[loss=0.1375, simple_loss=0.22, pruned_loss=0.02752, over 985706.10 frames.], batch size: 41, aishell_tot_loss[loss=0.1413, simple_loss=0.2279, pruned_loss=0.02734, over 985100.96 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2139, pruned_loss=0.02825, over 985933.48 frames.], batch size: 41, lr: 2.81e-04
+2022-06-19 07:26:16,109 INFO [train.py:874] (1/4) Epoch 29, batch 3750, aishell_loss[loss=0.1191, simple_loss=0.2055, pruned_loss=0.01635, over 4885.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2204, pruned_loss=0.02755, over 985291.07 frames.], batch size: 28, aishell_tot_loss[loss=0.141, simple_loss=0.2274, pruned_loss=0.02725, over 984627.07 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.214, pruned_loss=0.02835, over 986057.34 frames.], batch size: 28, lr: 2.81e-04
+2022-06-19 07:26:43,588 INFO [train.py:874] (1/4) Epoch 29, batch 3800, datatang_loss[loss=0.1275, simple_loss=0.2088, pruned_loss=0.02306, over 4958.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2213, pruned_loss=0.02813, over 985463.87 frames.], batch size: 62, aishell_tot_loss[loss=0.1416, simple_loss=0.228, pruned_loss=0.02766, over 984783.58 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2144, pruned_loss=0.0285, over 986056.41 frames.], batch size: 62, lr: 2.81e-04
+2022-06-19 07:27:13,069 INFO [train.py:874] (1/4) Epoch 29, batch 3850, aishell_loss[loss=0.146, simple_loss=0.234, pruned_loss=0.02904, over 4954.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2207, pruned_loss=0.02773, over 985527.51 frames.], batch size: 64, aishell_tot_loss[loss=0.1411, simple_loss=0.2274, pruned_loss=0.02738, over 984848.90 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2142, pruned_loss=0.02837, over 986062.22 frames.], batch size: 64, lr: 2.80e-04
+2022-06-19 07:27:40,474 INFO [train.py:874] (1/4) Epoch 29, batch 3900, datatang_loss[loss=0.1258, simple_loss=0.2088, pruned_loss=0.02145, over 4922.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2194, pruned_loss=0.02749, over 985670.62 frames.], batch size: 71, aishell_tot_loss[loss=0.1409, simple_loss=0.2272, pruned_loss=0.02727, over 984893.56 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.2136, pruned_loss=0.02815, over 986123.35 frames.], batch size: 71, lr: 2.80e-04
+2022-06-19 07:28:09,836 INFO [train.py:874] (1/4) Epoch 29, batch 3950, aishell_loss[loss=0.135, simple_loss=0.2247, pruned_loss=0.02266, over 4918.00 frames.], tot_loss[loss=0.1373, simple_loss=0.2197, pruned_loss=0.02743, over 985522.39 frames.], batch size: 46, aishell_tot_loss[loss=0.1408, simple_loss=0.2274, pruned_loss=0.02708, over 985064.83 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2137, pruned_loss=0.02822, over 985825.03 frames.], batch size: 46, lr: 2.80e-04
+2022-06-19 07:28:37,200 INFO [train.py:874] (1/4) Epoch 29, batch 4000, aishell_loss[loss=0.1724, simple_loss=0.2595, pruned_loss=0.04261, over 4949.00 frames.], tot_loss[loss=0.138, simple_loss=0.22, pruned_loss=0.02798, over 985388.12 frames.], batch size: 56, aishell_tot_loss[loss=0.1409, simple_loss=0.2277, pruned_loss=0.02706, over 985004.29 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2137, pruned_loss=0.02874, over 985735.22 frames.], batch size: 56, lr: 2.80e-04
+2022-06-19 07:28:37,201 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 07:28:54,128 INFO [train.py:914] (1/4) Epoch 29, validation: loss=0.165, simple_loss=0.2485, pruned_loss=0.04075, over 1622729.00 frames.
+2022-06-19 07:29:22,710 INFO [train.py:874] (1/4) Epoch 29, batch 4050, aishell_loss[loss=0.1198, simple_loss=0.2038, pruned_loss=0.01789, over 4948.00 frames.], tot_loss[loss=0.1376, simple_loss=0.2198, pruned_loss=0.02771, over 985084.58 frames.], batch size: 27, aishell_tot_loss[loss=0.1412, simple_loss=0.2278, pruned_loss=0.0273, over 984602.81 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2129, pruned_loss=0.02827, over 985847.20 frames.], batch size: 27, lr: 2.80e-04
+2022-06-19 07:29:48,243 INFO [train.py:874] (1/4) Epoch 29, batch 4100, aishell_loss[loss=0.1523, simple_loss=0.245, pruned_loss=0.02986, over 4912.00 frames.], tot_loss[loss=0.139, simple_loss=0.2211, pruned_loss=0.02848, over 984712.45 frames.], batch size: 41, aishell_tot_loss[loss=0.1424, simple_loss=0.2288, pruned_loss=0.02794, over 984365.60 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.213, pruned_loss=0.02843, over 985667.08 frames.], batch size: 41, lr: 2.80e-04
+2022-06-19 07:30:56,065 INFO [train.py:874] (1/4) Epoch 30, batch 50, datatang_loss[loss=0.1618, simple_loss=0.2437, pruned_loss=0.0399, over 4944.00 frames.], tot_loss[loss=0.1331, simple_loss=0.2158, pruned_loss=0.02524, over 218286.56 frames.], batch size: 110, aishell_tot_loss[loss=0.1382, simple_loss=0.2249, pruned_loss=0.02579, over 120357.45 frames.], datatang_tot_loss[loss=0.1276, simple_loss=0.2059, pruned_loss=0.02462, over 111554.59 frames.], batch size: 110, lr: 2.75e-04
+2022-06-19 07:31:24,114 INFO [train.py:874] (1/4) Epoch 30, batch 100, datatang_loss[loss=0.1243, simple_loss=0.2035, pruned_loss=0.02251, over 4922.00 frames.], tot_loss[loss=0.1329, simple_loss=0.2155, pruned_loss=0.02519, over 388350.28 frames.], batch size: 57, aishell_tot_loss[loss=0.1382, simple_loss=0.2246, pruned_loss=0.02585, over 218249.42 frames.], datatang_tot_loss[loss=0.1276, simple_loss=0.2063, pruned_loss=0.02448, over 218488.37 frames.], batch size: 57, lr: 2.75e-04
+2022-06-19 07:31:54,681 INFO [train.py:874] (1/4) Epoch 30, batch 150, aishell_loss[loss=0.1513, simple_loss=0.2423, pruned_loss=0.03016, over 4908.00 frames.], tot_loss[loss=0.1343, simple_loss=0.2171, pruned_loss=0.02577, over 520530.15 frames.], batch size: 46, aishell_tot_loss[loss=0.1398, simple_loss=0.2263, pruned_loss=0.02667, over 328447.89 frames.], datatang_tot_loss[loss=0.1274, simple_loss=0.2058, pruned_loss=0.02455, over 288186.44 frames.], batch size: 46, lr: 2.75e-04
+2022-06-19 07:32:24,880 INFO [train.py:874] (1/4) Epoch 30, batch 200, aishell_loss[loss=0.1429, simple_loss=0.2462, pruned_loss=0.01979, over 4924.00 frames.], tot_loss[loss=0.1355, simple_loss=0.2176, pruned_loss=0.02667, over 623728.53 frames.], batch size: 68, aishell_tot_loss[loss=0.1393, simple_loss=0.2252, pruned_loss=0.02671, over 397089.68 frames.], datatang_tot_loss[loss=0.1308, simple_loss=0.2091, pruned_loss=0.02623, over 379590.06 frames.], batch size: 68, lr: 2.75e-04
+2022-06-19 07:32:53,105 INFO [train.py:874] (1/4) Epoch 30, batch 250, datatang_loss[loss=0.1422, simple_loss=0.2238, pruned_loss=0.03026, over 4954.00 frames.], tot_loss[loss=0.1372, simple_loss=0.219, pruned_loss=0.02769, over 703945.33 frames.], batch size: 50, aishell_tot_loss[loss=0.1411, simple_loss=0.2273, pruned_loss=0.02743, over 461160.84 frames.], datatang_tot_loss[loss=0.1321, simple_loss=0.2097, pruned_loss=0.02724, over 456287.62 frames.], batch size: 50, lr: 2.75e-04
+2022-06-19 07:33:24,730 INFO [train.py:874] (1/4) Epoch 30, batch 300, aishell_loss[loss=0.1201, simple_loss=0.1985, pruned_loss=0.02089, over 4819.00 frames.], tot_loss[loss=0.1372, simple_loss=0.2189, pruned_loss=0.02772, over 766638.93 frames.], batch size: 26, aishell_tot_loss[loss=0.1417, simple_loss=0.2279, pruned_loss=0.02779, over 522947.19 frames.], datatang_tot_loss[loss=0.1317, simple_loss=0.2093, pruned_loss=0.02708, over 518892.56 frames.], batch size: 26, lr: 2.75e-04
+2022-06-19 07:33:54,718 INFO [train.py:874] (1/4) Epoch 30, batch 350, aishell_loss[loss=0.1591, simple_loss=0.2412, pruned_loss=0.03847, over 4860.00 frames.], tot_loss[loss=0.1379, simple_loss=0.22, pruned_loss=0.02795, over 814911.46 frames.], batch size: 36, aishell_tot_loss[loss=0.1414, simple_loss=0.2275, pruned_loss=0.02762, over 593213.37 frames.], datatang_tot_loss[loss=0.1328, simple_loss=0.2102, pruned_loss=0.02768, over 556996.75 frames.], batch size: 36, lr: 2.75e-04
+2022-06-19 07:34:23,751 INFO [train.py:874] (1/4) Epoch 30, batch 400, datatang_loss[loss=0.1218, simple_loss=0.203, pruned_loss=0.02029, over 4919.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2198, pruned_loss=0.02756, over 852970.64 frames.], batch size: 75, aishell_tot_loss[loss=0.1407, simple_loss=0.227, pruned_loss=0.02721, over 642996.89 frames.], datatang_tot_loss[loss=0.1329, simple_loss=0.2106, pruned_loss=0.02765, over 603786.11 frames.], batch size: 75, lr: 2.75e-04
+2022-06-19 07:34:53,350 INFO [train.py:874] (1/4) Epoch 30, batch 450, datatang_loss[loss=0.1184, simple_loss=0.1992, pruned_loss=0.01878, over 4982.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2201, pruned_loss=0.02773, over 882292.41 frames.], batch size: 40, aishell_tot_loss[loss=0.1408, simple_loss=0.2271, pruned_loss=0.0272, over 681680.83 frames.], datatang_tot_loss[loss=0.1337, simple_loss=0.2114, pruned_loss=0.02798, over 650491.01 frames.], batch size: 40, lr: 2.75e-04
+2022-06-19 07:35:22,856 INFO [train.py:874] (1/4) Epoch 30, batch 500, datatang_loss[loss=0.1275, simple_loss=0.21, pruned_loss=0.02252, over 4956.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2206, pruned_loss=0.02762, over 905247.43 frames.], batch size: 67, aishell_tot_loss[loss=0.1408, simple_loss=0.2275, pruned_loss=0.02707, over 722673.87 frames.], datatang_tot_loss[loss=0.1338, simple_loss=0.2116, pruned_loss=0.02804, over 684150.76 frames.], batch size: 67, lr: 2.75e-04
+2022-06-19 07:35:50,042 INFO [train.py:874] (1/4) Epoch 30, batch 550, aishell_loss[loss=0.1491, simple_loss=0.2423, pruned_loss=0.02799, over 4924.00 frames.], tot_loss[loss=0.138, simple_loss=0.2211, pruned_loss=0.02749, over 922613.97 frames.], batch size: 52, aishell_tot_loss[loss=0.141, simple_loss=0.2278, pruned_loss=0.02716, over 762419.74 frames.], datatang_tot_loss[loss=0.1336, simple_loss=0.2115, pruned_loss=0.02785, over 708640.56 frames.], batch size: 52, lr: 2.75e-04
+2022-06-19 07:36:21,125 INFO [train.py:874] (1/4) Epoch 30, batch 600, datatang_loss[loss=0.1197, simple_loss=0.2062, pruned_loss=0.01662, over 4945.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2215, pruned_loss=0.02749, over 936254.59 frames.], batch size: 57, aishell_tot_loss[loss=0.1413, simple_loss=0.2282, pruned_loss=0.0272, over 789680.02 frames.], datatang_tot_loss[loss=0.1337, simple_loss=0.2118, pruned_loss=0.02782, over 739694.66 frames.], batch size: 57, lr: 2.75e-04
+2022-06-19 07:36:51,312 INFO [train.py:874] (1/4) Epoch 30, batch 650, datatang_loss[loss=0.156, simple_loss=0.2282, pruned_loss=0.04186, over 4953.00 frames.], tot_loss[loss=0.138, simple_loss=0.2209, pruned_loss=0.02752, over 947339.56 frames.], batch size: 91, aishell_tot_loss[loss=0.1408, simple_loss=0.2276, pruned_loss=0.02699, over 812903.80 frames.], datatang_tot_loss[loss=0.1342, simple_loss=0.2122, pruned_loss=0.02808, over 768664.89 frames.], batch size: 91, lr: 2.75e-04
+2022-06-19 07:37:20,542 INFO [train.py:874] (1/4) Epoch 30, batch 700, datatang_loss[loss=0.1336, simple_loss=0.2075, pruned_loss=0.02988, over 4947.00 frames.], tot_loss[loss=0.1374, simple_loss=0.2203, pruned_loss=0.02719, over 956016.18 frames.], batch size: 67, aishell_tot_loss[loss=0.1407, simple_loss=0.2277, pruned_loss=0.02684, over 830849.97 frames.], datatang_tot_loss[loss=0.1338, simple_loss=0.212, pruned_loss=0.0278, over 797367.07 frames.], batch size: 67, lr: 2.75e-04
+2022-06-19 07:37:50,962 INFO [train.py:874] (1/4) Epoch 30, batch 750, aishell_loss[loss=0.1128, simple_loss=0.1904, pruned_loss=0.01759, over 4867.00 frames.], tot_loss[loss=0.1375, simple_loss=0.2205, pruned_loss=0.02727, over 962338.25 frames.], batch size: 21, aishell_tot_loss[loss=0.1402, simple_loss=0.2269, pruned_loss=0.02673, over 850156.41 frames.], datatang_tot_loss[loss=0.1344, simple_loss=0.2129, pruned_loss=0.02797, over 817890.56 frames.], batch size: 21, lr: 2.75e-04
+2022-06-19 07:38:21,524 INFO [train.py:874] (1/4) Epoch 30, batch 800, datatang_loss[loss=0.1648, simple_loss=0.2395, pruned_loss=0.04503, over 4941.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2212, pruned_loss=0.02716, over 967641.62 frames.], batch size: 62, aishell_tot_loss[loss=0.1402, simple_loss=0.2271, pruned_loss=0.02667, over 869049.06 frames.], datatang_tot_loss[loss=0.1346, simple_loss=0.2133, pruned_loss=0.02789, over 834062.52 frames.], batch size: 62, lr: 2.75e-04
+2022-06-19 07:38:51,850 INFO [train.py:874] (1/4) Epoch 30, batch 850, datatang_loss[loss=0.111, simple_loss=0.1987, pruned_loss=0.01163, over 4924.00 frames.], tot_loss[loss=0.138, simple_loss=0.2213, pruned_loss=0.02735, over 971776.32 frames.], batch size: 81, aishell_tot_loss[loss=0.1404, simple_loss=0.2273, pruned_loss=0.02675, over 879677.46 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.214, pruned_loss=0.02797, over 855995.54 frames.], batch size: 81, lr: 2.75e-04
+2022-06-19 07:39:22,130 INFO [train.py:874] (1/4) Epoch 30, batch 900, datatang_loss[loss=0.1382, simple_loss=0.2312, pruned_loss=0.02256, over 4958.00 frames.], tot_loss[loss=0.138, simple_loss=0.2212, pruned_loss=0.02746, over 974893.15 frames.], batch size: 60, aishell_tot_loss[loss=0.1408, simple_loss=0.2278, pruned_loss=0.02689, over 889747.01 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2139, pruned_loss=0.02791, over 874186.33 frames.], batch size: 60, lr: 2.74e-04
+2022-06-19 07:39:50,710 INFO [train.py:874] (1/4) Epoch 30, batch 950, aishell_loss[loss=0.132, simple_loss=0.221, pruned_loss=0.02149, over 4978.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2214, pruned_loss=0.02778, over 977389.17 frames.], batch size: 39, aishell_tot_loss[loss=0.1413, simple_loss=0.2282, pruned_loss=0.02726, over 901817.17 frames.], datatang_tot_loss[loss=0.1348, simple_loss=0.2137, pruned_loss=0.02796, over 886523.77 frames.], batch size: 39, lr: 2.74e-04
+2022-06-19 07:40:20,533 INFO [train.py:874] (1/4) Epoch 30, batch 1000, datatang_loss[loss=0.1353, simple_loss=0.2079, pruned_loss=0.03135, over 4924.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2219, pruned_loss=0.02753, over 979561.68 frames.], batch size: 71, aishell_tot_loss[loss=0.1415, simple_loss=0.2287, pruned_loss=0.02719, over 912692.73 frames.], datatang_tot_loss[loss=0.1347, simple_loss=0.2138, pruned_loss=0.02779, over 897411.87 frames.], batch size: 71, lr: 2.74e-04
+2022-06-19 07:40:20,534 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 07:40:38,847 INFO [train.py:914] (1/4) Epoch 30, validation: loss=0.1647, simple_loss=0.2491, pruned_loss=0.04015, over 1622729.00 frames.
+2022-06-19 07:41:06,101 INFO [train.py:874] (1/4) Epoch 30, batch 1050, datatang_loss[loss=0.1313, simple_loss=0.2121, pruned_loss=0.02531, over 4927.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2226, pruned_loss=0.0278, over 980883.79 frames.], batch size: 83, aishell_tot_loss[loss=0.1418, simple_loss=0.2289, pruned_loss=0.02734, over 921659.77 frames.], datatang_tot_loss[loss=0.1351, simple_loss=0.2143, pruned_loss=0.02797, over 907256.47 frames.], batch size: 83, lr: 2.74e-04
+2022-06-19 07:41:34,466 INFO [train.py:874] (1/4) Epoch 30, batch 1100, datatang_loss[loss=0.1403, simple_loss=0.2125, pruned_loss=0.03401, over 4884.00 frames.], tot_loss[loss=0.1395, simple_loss=0.223, pruned_loss=0.02803, over 982090.44 frames.], batch size: 47, aishell_tot_loss[loss=0.1419, simple_loss=0.229, pruned_loss=0.02739, over 930497.58 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2145, pruned_loss=0.02822, over 915056.87 frames.], batch size: 47, lr: 2.74e-04
+2022-06-19 07:42:04,643 INFO [train.py:874] (1/4) Epoch 30, batch 1150, datatang_loss[loss=0.1203, simple_loss=0.2047, pruned_loss=0.018, over 4929.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2223, pruned_loss=0.02807, over 982831.58 frames.], batch size: 83, aishell_tot_loss[loss=0.1422, simple_loss=0.2294, pruned_loss=0.02747, over 935050.82 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2143, pruned_loss=0.0282, over 925701.72 frames.], batch size: 83, lr: 2.74e-04
+2022-06-19 07:42:33,971 INFO [train.py:874] (1/4) Epoch 30, batch 1200, datatang_loss[loss=0.122, simple_loss=0.196, pruned_loss=0.02404, over 4740.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2222, pruned_loss=0.02821, over 983259.68 frames.], batch size: 25, aishell_tot_loss[loss=0.1423, simple_loss=0.2295, pruned_loss=0.02752, over 939795.12 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2146, pruned_loss=0.02834, over 933968.42 frames.], batch size: 25, lr: 2.74e-04
+2022-06-19 07:43:03,425 INFO [train.py:874] (1/4) Epoch 30, batch 1250, aishell_loss[loss=0.1705, simple_loss=0.2571, pruned_loss=0.04196, over 4973.00 frames.], tot_loss[loss=0.1404, simple_loss=0.2237, pruned_loss=0.02855, over 983963.40 frames.], batch size: 44, aishell_tot_loss[loss=0.1429, simple_loss=0.2303, pruned_loss=0.02778, over 946654.79 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.215, pruned_loss=0.02857, over 938551.83 frames.], batch size: 44, lr: 2.74e-04
+2022-06-19 07:43:31,117 INFO [train.py:874] (1/4) Epoch 30, batch 1300, aishell_loss[loss=0.1587, simple_loss=0.2368, pruned_loss=0.0403, over 4963.00 frames.], tot_loss[loss=0.1403, simple_loss=0.2238, pruned_loss=0.02836, over 983862.69 frames.], batch size: 40, aishell_tot_loss[loss=0.1423, simple_loss=0.2296, pruned_loss=0.02752, over 952287.11 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2153, pruned_loss=0.02875, over 942116.18 frames.], batch size: 40, lr: 2.74e-04
+2022-06-19 07:44:01,894 INFO [train.py:874] (1/4) Epoch 30, batch 1350, datatang_loss[loss=0.1294, simple_loss=0.2031, pruned_loss=0.02783, over 4977.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2224, pruned_loss=0.02859, over 984321.28 frames.], batch size: 25, aishell_tot_loss[loss=0.1417, simple_loss=0.2286, pruned_loss=0.02744, over 955883.05 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2153, pruned_loss=0.02915, over 947729.73 frames.], batch size: 25, lr: 2.74e-04
+2022-06-19 07:44:33,761 INFO [train.py:874] (1/4) Epoch 30, batch 1400, aishell_loss[loss=0.1243, simple_loss=0.2071, pruned_loss=0.02074, over 4930.00 frames.], tot_loss[loss=0.1412, simple_loss=0.224, pruned_loss=0.02917, over 984872.02 frames.], batch size: 32, aishell_tot_loss[loss=0.1424, simple_loss=0.2292, pruned_loss=0.02778, over 960579.56 frames.], datatang_tot_loss[loss=0.1376, simple_loss=0.216, pruned_loss=0.02958, over 950934.03 frames.], batch size: 32, lr: 2.74e-04
+2022-06-19 07:45:01,995 INFO [train.py:874] (1/4) Epoch 30, batch 1450, aishell_loss[loss=0.1416, simple_loss=0.2217, pruned_loss=0.03078, over 4867.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2224, pruned_loss=0.02874, over 984801.12 frames.], batch size: 35, aishell_tot_loss[loss=0.142, simple_loss=0.2285, pruned_loss=0.02774, over 963347.55 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2153, pruned_loss=0.02925, over 954908.59 frames.], batch size: 35, lr: 2.74e-04
+2022-06-19 07:45:31,975 INFO [train.py:874] (1/4) Epoch 30, batch 1500, datatang_loss[loss=0.1241, simple_loss=0.2081, pruned_loss=0.02007, over 4928.00 frames.], tot_loss[loss=0.1399, simple_loss=0.2227, pruned_loss=0.02857, over 984564.46 frames.], batch size: 42, aishell_tot_loss[loss=0.1426, simple_loss=0.2293, pruned_loss=0.0279, over 965644.38 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.02898, over 958362.00 frames.], batch size: 42, lr: 2.74e-04
+2022-06-19 07:46:02,918 INFO [train.py:874] (1/4) Epoch 30, batch 1550, datatang_loss[loss=0.1249, simple_loss=0.2114, pruned_loss=0.01919, over 4954.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2229, pruned_loss=0.02865, over 984973.98 frames.], batch size: 91, aishell_tot_loss[loss=0.1424, simple_loss=0.2292, pruned_loss=0.02778, over 968091.32 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.2154, pruned_loss=0.02922, over 961699.73 frames.], batch size: 91, lr: 2.74e-04
+2022-06-19 07:46:30,636 INFO [train.py:874] (1/4) Epoch 30, batch 1600, datatang_loss[loss=0.135, simple_loss=0.2109, pruned_loss=0.0296, over 4891.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2225, pruned_loss=0.02835, over 985343.87 frames.], batch size: 47, aishell_tot_loss[loss=0.1422, simple_loss=0.2294, pruned_loss=0.02756, over 970351.20 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.2149, pruned_loss=0.02917, over 964585.72 frames.], batch size: 47, lr: 2.74e-04
+2022-06-19 07:47:01,044 INFO [train.py:874] (1/4) Epoch 30, batch 1650, aishell_loss[loss=0.1507, simple_loss=0.2399, pruned_loss=0.03074, over 4975.00 frames.], tot_loss[loss=0.1392, simple_loss=0.222, pruned_loss=0.02825, over 985287.59 frames.], batch size: 39, aishell_tot_loss[loss=0.1425, simple_loss=0.2297, pruned_loss=0.02765, over 971836.26 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2145, pruned_loss=0.02891, over 967361.64 frames.], batch size: 39, lr: 2.74e-04
+2022-06-19 07:47:31,288 INFO [train.py:874] (1/4) Epoch 30, batch 1700, datatang_loss[loss=0.1086, simple_loss=0.1854, pruned_loss=0.01586, over 4872.00 frames.], tot_loss[loss=0.139, simple_loss=0.2223, pruned_loss=0.02784, over 984935.28 frames.], batch size: 39, aishell_tot_loss[loss=0.1428, simple_loss=0.2303, pruned_loss=0.02767, over 973036.40 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2142, pruned_loss=0.02846, over 969482.40 frames.], batch size: 39, lr: 2.74e-04
+2022-06-19 07:47:59,751 INFO [train.py:874] (1/4) Epoch 30, batch 1750, datatang_loss[loss=0.1176, simple_loss=0.1889, pruned_loss=0.02312, over 4977.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2222, pruned_loss=0.02771, over 985425.41 frames.], batch size: 40, aishell_tot_loss[loss=0.1426, simple_loss=0.23, pruned_loss=0.02762, over 974808.92 frames.], datatang_tot_loss[loss=0.1355, simple_loss=0.2144, pruned_loss=0.0283, over 971446.82 frames.], batch size: 40, lr: 2.74e-04
+2022-06-19 07:48:29,645 INFO [train.py:874] (1/4) Epoch 30, batch 1800, datatang_loss[loss=0.1204, simple_loss=0.1911, pruned_loss=0.02483, over 4969.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2221, pruned_loss=0.02791, over 985652.57 frames.], batch size: 34, aishell_tot_loss[loss=0.1425, simple_loss=0.2298, pruned_loss=0.02762, over 976257.62 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2144, pruned_loss=0.02846, over 973137.49 frames.], batch size: 34, lr: 2.73e-04
+2022-06-19 07:49:00,480 INFO [train.py:874] (1/4) Epoch 30, batch 1850, datatang_loss[loss=0.1354, simple_loss=0.2206, pruned_loss=0.02506, over 4923.00 frames.], tot_loss[loss=0.139, simple_loss=0.2218, pruned_loss=0.02805, over 986089.79 frames.], batch size: 81, aishell_tot_loss[loss=0.1423, simple_loss=0.2297, pruned_loss=0.02752, over 977583.43 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2145, pruned_loss=0.02867, over 974896.02 frames.], batch size: 81, lr: 2.73e-04
+2022-06-19 07:49:28,727 INFO [train.py:874] (1/4) Epoch 30, batch 1900, datatang_loss[loss=0.1613, simple_loss=0.232, pruned_loss=0.0453, over 4963.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2218, pruned_loss=0.02786, over 985874.78 frames.], batch size: 40, aishell_tot_loss[loss=0.1422, simple_loss=0.2298, pruned_loss=0.02733, over 978611.96 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2141, pruned_loss=0.02866, over 975927.66 frames.], batch size: 40, lr: 2.73e-04
+2022-06-19 07:49:58,518 INFO [train.py:874] (1/4) Epoch 30, batch 1950, aishell_loss[loss=0.1367, simple_loss=0.2292, pruned_loss=0.02212, over 4914.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2215, pruned_loss=0.02778, over 985694.27 frames.], batch size: 41, aishell_tot_loss[loss=0.1415, simple_loss=0.2288, pruned_loss=0.02714, over 979088.91 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2145, pruned_loss=0.02874, over 977256.76 frames.], batch size: 41, lr: 2.73e-04
+2022-06-19 07:50:27,892 INFO [train.py:874] (1/4) Epoch 30, batch 2000, datatang_loss[loss=0.1441, simple_loss=0.2284, pruned_loss=0.02986, over 4940.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2232, pruned_loss=0.02812, over 986032.03 frames.], batch size: 88, aishell_tot_loss[loss=0.1421, simple_loss=0.2297, pruned_loss=0.02724, over 980300.16 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.215, pruned_loss=0.029, over 978105.96 frames.], batch size: 88, lr: 2.73e-04
+2022-06-19 07:50:27,892 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 07:50:43,695 INFO [train.py:914] (1/4) Epoch 30, validation: loss=0.1643, simple_loss=0.2485, pruned_loss=0.04007, over 1622729.00 frames.
+2022-06-19 07:51:13,782 INFO [train.py:874] (1/4) Epoch 30, batch 2050, datatang_loss[loss=0.1267, simple_loss=0.2034, pruned_loss=0.02503, over 4885.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2225, pruned_loss=0.02801, over 985885.85 frames.], batch size: 47, aishell_tot_loss[loss=0.1418, simple_loss=0.2295, pruned_loss=0.02707, over 980783.83 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2145, pruned_loss=0.02907, over 979066.56 frames.], batch size: 47, lr: 2.73e-04
+2022-06-19 07:51:43,741 INFO [train.py:874] (1/4) Epoch 30, batch 2100, datatang_loss[loss=0.1427, simple_loss=0.2165, pruned_loss=0.03448, over 4931.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2211, pruned_loss=0.02795, over 985925.40 frames.], batch size: 71, aishell_tot_loss[loss=0.1412, simple_loss=0.2286, pruned_loss=0.02691, over 981142.02 frames.], datatang_tot_loss[loss=0.1362, simple_loss=0.2142, pruned_loss=0.02911, over 980160.15 frames.], batch size: 71, lr: 2.73e-04
+2022-06-19 07:52:12,619 INFO [train.py:874] (1/4) Epoch 30, batch 2150, datatang_loss[loss=0.1411, simple_loss=0.2134, pruned_loss=0.03445, over 4946.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2206, pruned_loss=0.02759, over 985982.26 frames.], batch size: 67, aishell_tot_loss[loss=0.1406, simple_loss=0.2281, pruned_loss=0.0266, over 981607.31 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2142, pruned_loss=0.02901, over 980987.18 frames.], batch size: 67, lr: 2.73e-04
+2022-06-19 07:52:42,921 INFO [train.py:874] (1/4) Epoch 30, batch 2200, aishell_loss[loss=0.156, simple_loss=0.2465, pruned_loss=0.03274, over 4961.00 frames.], tot_loss[loss=0.1378, simple_loss=0.2207, pruned_loss=0.02752, over 986006.53 frames.], batch size: 61, aishell_tot_loss[loss=0.1414, simple_loss=0.2289, pruned_loss=0.02693, over 982162.04 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2134, pruned_loss=0.02853, over 981561.54 frames.], batch size: 61, lr: 2.73e-04
+2022-06-19 07:53:12,734 INFO [train.py:874] (1/4) Epoch 30, batch 2250, aishell_loss[loss=0.1508, simple_loss=0.234, pruned_loss=0.03377, over 4915.00 frames.], tot_loss[loss=0.1379, simple_loss=0.2208, pruned_loss=0.02747, over 986031.70 frames.], batch size: 46, aishell_tot_loss[loss=0.1416, simple_loss=0.2292, pruned_loss=0.02703, over 982622.77 frames.], datatang_tot_loss[loss=0.1349, simple_loss=0.213, pruned_loss=0.02837, over 982097.13 frames.], batch size: 46, lr: 2.73e-04
+2022-06-19 07:53:44,000 INFO [train.py:874] (1/4) Epoch 30, batch 2300, aishell_loss[loss=0.1453, simple_loss=0.242, pruned_loss=0.02429, over 4976.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2214, pruned_loss=0.02762, over 985979.09 frames.], batch size: 51, aishell_tot_loss[loss=0.1417, simple_loss=0.2294, pruned_loss=0.02701, over 982895.82 frames.], datatang_tot_loss[loss=0.1352, simple_loss=0.2135, pruned_loss=0.02845, over 982631.93 frames.], batch size: 51, lr: 2.73e-04
+2022-06-19 07:54:14,770 INFO [train.py:874] (1/4) Epoch 30, batch 2350, aishell_loss[loss=0.1542, simple_loss=0.2444, pruned_loss=0.03195, over 4903.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2208, pruned_loss=0.02795, over 986039.44 frames.], batch size: 79, aishell_tot_loss[loss=0.1417, simple_loss=0.2292, pruned_loss=0.02714, over 983217.96 frames.], datatang_tot_loss[loss=0.1354, simple_loss=0.2135, pruned_loss=0.02863, over 983134.53 frames.], batch size: 79, lr: 2.73e-04
+2022-06-19 07:54:42,740 INFO [train.py:874] (1/4) Epoch 30, batch 2400, datatang_loss[loss=0.1375, simple_loss=0.2156, pruned_loss=0.02971, over 4975.00 frames.], tot_loss[loss=0.138, simple_loss=0.2206, pruned_loss=0.02772, over 985650.74 frames.], batch size: 31, aishell_tot_loss[loss=0.1418, simple_loss=0.2291, pruned_loss=0.02721, over 983059.60 frames.], datatang_tot_loss[loss=0.135, simple_loss=0.2133, pruned_loss=0.02832, over 983543.29 frames.], batch size: 31, lr: 2.73e-04
+2022-06-19 07:55:12,750 INFO [train.py:874] (1/4) Epoch 30, batch 2450, datatang_loss[loss=0.158, simple_loss=0.2291, pruned_loss=0.04349, over 4928.00 frames.], tot_loss[loss=0.1389, simple_loss=0.2211, pruned_loss=0.0283, over 986042.34 frames.], batch size: 88, aishell_tot_loss[loss=0.1419, simple_loss=0.2292, pruned_loss=0.02727, over 983458.55 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2141, pruned_loss=0.02882, over 984100.06 frames.], batch size: 88, lr: 2.73e-04
+2022-06-19 07:55:43,669 INFO [train.py:874] (1/4) Epoch 30, batch 2500, aishell_loss[loss=0.1282, simple_loss=0.2129, pruned_loss=0.02176, over 4985.00 frames.], tot_loss[loss=0.1387, simple_loss=0.2213, pruned_loss=0.02803, over 985856.34 frames.], batch size: 27, aishell_tot_loss[loss=0.1416, simple_loss=0.2291, pruned_loss=0.02706, over 983780.46 frames.], datatang_tot_loss[loss=0.1359, simple_loss=0.2141, pruned_loss=0.02881, over 984108.45 frames.], batch size: 27, lr: 2.73e-04
+2022-06-19 07:56:12,507 INFO [train.py:874] (1/4) Epoch 30, batch 2550, datatang_loss[loss=0.132, simple_loss=0.212, pruned_loss=0.02598, over 4945.00 frames.], tot_loss[loss=0.1396, simple_loss=0.2223, pruned_loss=0.02842, over 985900.62 frames.], batch size: 88, aishell_tot_loss[loss=0.142, simple_loss=0.2294, pruned_loss=0.0273, over 984074.94 frames.], datatang_tot_loss[loss=0.1363, simple_loss=0.2146, pruned_loss=0.02902, over 984310.17 frames.], batch size: 88, lr: 2.73e-04
+2022-06-19 07:56:42,335 INFO [train.py:874] (1/4) Epoch 30, batch 2600, aishell_loss[loss=0.1368, simple_loss=0.222, pruned_loss=0.02581, over 4865.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2222, pruned_loss=0.02862, over 986257.92 frames.], batch size: 36, aishell_tot_loss[loss=0.1422, simple_loss=0.2296, pruned_loss=0.02737, over 984350.09 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2146, pruned_loss=0.02922, over 984810.12 frames.], batch size: 36, lr: 2.73e-04
+2022-06-19 07:57:13,794 INFO [train.py:874] (1/4) Epoch 30, batch 2650, aishell_loss[loss=0.1401, simple_loss=0.2321, pruned_loss=0.02406, over 4932.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2215, pruned_loss=0.02849, over 986252.19 frames.], batch size: 32, aishell_tot_loss[loss=0.1415, simple_loss=0.229, pruned_loss=0.02701, over 984585.84 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2147, pruned_loss=0.02949, over 984978.39 frames.], batch size: 32, lr: 2.73e-04
+2022-06-19 07:57:41,435 INFO [train.py:874] (1/4) Epoch 30, batch 2700, datatang_loss[loss=0.1395, simple_loss=0.2174, pruned_loss=0.03075, over 4975.00 frames.], tot_loss[loss=0.1392, simple_loss=0.2216, pruned_loss=0.02835, over 986181.15 frames.], batch size: 53, aishell_tot_loss[loss=0.1416, simple_loss=0.2291, pruned_loss=0.02707, over 984648.38 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2143, pruned_loss=0.02937, over 985188.44 frames.], batch size: 53, lr: 2.72e-04
+2022-06-19 07:58:11,545 INFO [train.py:874] (1/4) Epoch 30, batch 2750, aishell_loss[loss=0.1501, simple_loss=0.2365, pruned_loss=0.03192, over 4922.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2226, pruned_loss=0.02847, over 986386.23 frames.], batch size: 41, aishell_tot_loss[loss=0.142, simple_loss=0.2295, pruned_loss=0.02723, over 985007.30 frames.], datatang_tot_loss[loss=0.1367, simple_loss=0.2146, pruned_loss=0.02942, over 985360.14 frames.], batch size: 41, lr: 2.72e-04
+2022-06-19 07:58:40,962 INFO [train.py:874] (1/4) Epoch 30, batch 2800, aishell_loss[loss=0.1766, simple_loss=0.2659, pruned_loss=0.04365, over 4892.00 frames.], tot_loss[loss=0.1399, simple_loss=0.223, pruned_loss=0.02844, over 986371.86 frames.], batch size: 34, aishell_tot_loss[loss=0.1419, simple_loss=0.2294, pruned_loss=0.02714, over 984989.97 frames.], datatang_tot_loss[loss=0.1371, simple_loss=0.2152, pruned_loss=0.02952, over 985654.27 frames.], batch size: 34, lr: 2.72e-04
+2022-06-19 07:59:11,188 INFO [train.py:874] (1/4) Epoch 30, batch 2850, datatang_loss[loss=0.1309, simple_loss=0.2144, pruned_loss=0.02366, over 4956.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2225, pruned_loss=0.02843, over 986238.17 frames.], batch size: 45, aishell_tot_loss[loss=0.142, simple_loss=0.2296, pruned_loss=0.02725, over 984881.51 frames.], datatang_tot_loss[loss=0.1369, simple_loss=0.215, pruned_loss=0.02936, over 985855.32 frames.], batch size: 45, lr: 2.72e-04
+2022-06-19 07:59:41,810 INFO [train.py:874] (1/4) Epoch 30, batch 2900, aishell_loss[loss=0.1411, simple_loss=0.2253, pruned_loss=0.02841, over 4977.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2224, pruned_loss=0.02816, over 986299.70 frames.], batch size: 48, aishell_tot_loss[loss=0.142, simple_loss=0.2296, pruned_loss=0.02721, over 984971.02 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2147, pruned_loss=0.02915, over 986021.65 frames.], batch size: 48, lr: 2.72e-04
+2022-06-19 08:00:11,902 INFO [train.py:874] (1/4) Epoch 30, batch 2950, datatang_loss[loss=0.1462, simple_loss=0.2247, pruned_loss=0.03385, over 4969.00 frames.], tot_loss[loss=0.1401, simple_loss=0.2231, pruned_loss=0.0285, over 985987.57 frames.], batch size: 60, aishell_tot_loss[loss=0.1419, simple_loss=0.2297, pruned_loss=0.02709, over 984986.91 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2157, pruned_loss=0.02958, over 985846.85 frames.], batch size: 60, lr: 2.72e-04
+2022-06-19 08:00:40,794 INFO [train.py:874] (1/4) Epoch 30, batch 3000, datatang_loss[loss=0.1307, simple_loss=0.2153, pruned_loss=0.02305, over 4907.00 frames.], tot_loss[loss=0.1391, simple_loss=0.2221, pruned_loss=0.02805, over 986081.83 frames.], batch size: 64, aishell_tot_loss[loss=0.1413, simple_loss=0.2289, pruned_loss=0.02685, over 985196.53 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2152, pruned_loss=0.02943, over 985886.53 frames.], batch size: 64, lr: 2.72e-04
+2022-06-19 08:00:40,795 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 08:00:57,061 INFO [train.py:914] (1/4) Epoch 30, validation: loss=0.165, simple_loss=0.2488, pruned_loss=0.04058, over 1622729.00 frames.
+2022-06-19 08:01:25,991 INFO [train.py:874] (1/4) Epoch 30, batch 3050, datatang_loss[loss=0.1126, simple_loss=0.1986, pruned_loss=0.01334, over 4913.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2225, pruned_loss=0.02759, over 985487.40 frames.], batch size: 75, aishell_tot_loss[loss=0.1415, simple_loss=0.2294, pruned_loss=0.02682, over 985000.43 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.02903, over 985585.66 frames.], batch size: 75, lr: 2.72e-04
+2022-06-19 08:01:55,868 INFO [train.py:874] (1/4) Epoch 30, batch 3100, datatang_loss[loss=0.1433, simple_loss=0.2273, pruned_loss=0.02966, over 4890.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2216, pruned_loss=0.02769, over 985025.61 frames.], batch size: 52, aishell_tot_loss[loss=0.1411, simple_loss=0.2288, pruned_loss=0.02669, over 984890.94 frames.], datatang_tot_loss[loss=0.1366, simple_loss=0.215, pruned_loss=0.0291, over 985229.42 frames.], batch size: 52, lr: 2.72e-04
+2022-06-19 08:02:25,659 INFO [train.py:874] (1/4) Epoch 30, batch 3150, datatang_loss[loss=0.1192, simple_loss=0.1955, pruned_loss=0.02151, over 4844.00 frames.], tot_loss[loss=0.1383, simple_loss=0.2214, pruned_loss=0.02759, over 985348.25 frames.], batch size: 30, aishell_tot_loss[loss=0.141, simple_loss=0.2287, pruned_loss=0.02667, over 985047.22 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.2151, pruned_loss=0.02895, over 985406.84 frames.], batch size: 30, lr: 2.72e-04
+2022-06-19 08:02:54,298 INFO [train.py:874] (1/4) Epoch 30, batch 3200, datatang_loss[loss=0.1905, simple_loss=0.2661, pruned_loss=0.0574, over 4928.00 frames.], tot_loss[loss=0.1381, simple_loss=0.221, pruned_loss=0.02757, over 985454.13 frames.], batch size: 108, aishell_tot_loss[loss=0.1407, simple_loss=0.2282, pruned_loss=0.02659, over 985017.11 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2148, pruned_loss=0.02897, over 985567.55 frames.], batch size: 108, lr: 2.72e-04
+2022-06-19 08:03:24,481 INFO [train.py:874] (1/4) Epoch 30, batch 3250, datatang_loss[loss=0.1905, simple_loss=0.2675, pruned_loss=0.05673, over 4934.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2212, pruned_loss=0.02758, over 985389.25 frames.], batch size: 109, aishell_tot_loss[loss=0.1406, simple_loss=0.2281, pruned_loss=0.02657, over 984992.03 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2149, pruned_loss=0.02897, over 985583.45 frames.], batch size: 109, lr: 2.72e-04
+2022-06-19 08:03:54,541 INFO [train.py:874] (1/4) Epoch 30, batch 3300, datatang_loss[loss=0.1278, simple_loss=0.2121, pruned_loss=0.02176, over 4903.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2213, pruned_loss=0.02788, over 985658.60 frames.], batch size: 59, aishell_tot_loss[loss=0.1404, simple_loss=0.2277, pruned_loss=0.02651, over 985064.52 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2156, pruned_loss=0.02924, over 985814.54 frames.], batch size: 59, lr: 2.72e-04
+2022-06-19 08:04:23,887 INFO [train.py:874] (1/4) Epoch 30, batch 3350, aishell_loss[loss=0.1159, simple_loss=0.2012, pruned_loss=0.01529, over 4988.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2213, pruned_loss=0.02782, over 985847.25 frames.], batch size: 30, aishell_tot_loss[loss=0.1404, simple_loss=0.2276, pruned_loss=0.02659, over 985254.02 frames.], datatang_tot_loss[loss=0.1368, simple_loss=0.2153, pruned_loss=0.02919, over 985895.96 frames.], batch size: 30, lr: 2.72e-04
+2022-06-19 08:04:55,170 INFO [train.py:874] (1/4) Epoch 30, batch 3400, aishell_loss[loss=0.1512, simple_loss=0.2441, pruned_loss=0.02916, over 4883.00 frames.], tot_loss[loss=0.1397, simple_loss=0.2225, pruned_loss=0.02844, over 985647.49 frames.], batch size: 42, aishell_tot_loss[loss=0.1415, simple_loss=0.2287, pruned_loss=0.02714, over 985146.75 frames.], datatang_tot_loss[loss=0.137, simple_loss=0.2154, pruned_loss=0.02927, over 985846.92 frames.], batch size: 42, lr: 2.72e-04
+2022-06-19 08:05:24,151 INFO [train.py:874] (1/4) Epoch 30, batch 3450, aishell_loss[loss=0.1178, simple_loss=0.1832, pruned_loss=0.02614, over 4799.00 frames.], tot_loss[loss=0.1402, simple_loss=0.2231, pruned_loss=0.02864, over 985486.81 frames.], batch size: 21, aishell_tot_loss[loss=0.1417, simple_loss=0.2288, pruned_loss=0.02724, over 985117.88 frames.], datatang_tot_loss[loss=0.1374, simple_loss=0.2159, pruned_loss=0.02946, over 985765.21 frames.], batch size: 21, lr: 2.72e-04
+2022-06-19 08:05:54,701 INFO [train.py:874] (1/4) Epoch 30, batch 3500, datatang_loss[loss=0.198, simple_loss=0.266, pruned_loss=0.06503, over 4944.00 frames.], tot_loss[loss=0.1398, simple_loss=0.2227, pruned_loss=0.02845, over 985530.25 frames.], batch size: 109, aishell_tot_loss[loss=0.1417, simple_loss=0.2287, pruned_loss=0.0273, over 985085.12 frames.], datatang_tot_loss[loss=0.1372, simple_loss=0.2159, pruned_loss=0.02923, over 985850.69 frames.], batch size: 109, lr: 2.72e-04
+2022-06-19 08:06:24,511 INFO [train.py:874] (1/4) Epoch 30, batch 3550, datatang_loss[loss=0.1562, simple_loss=0.23, pruned_loss=0.04117, over 4948.00 frames.], tot_loss[loss=0.1385, simple_loss=0.2209, pruned_loss=0.02801, over 985756.99 frames.], batch size: 34, aishell_tot_loss[loss=0.1413, simple_loss=0.2281, pruned_loss=0.02723, over 985346.64 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2151, pruned_loss=0.02885, over 985837.75 frames.], batch size: 34, lr: 2.72e-04
+2022-06-19 08:06:53,794 INFO [train.py:874] (1/4) Epoch 30, batch 3600, datatang_loss[loss=0.1179, simple_loss=0.1961, pruned_loss=0.01982, over 4933.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2208, pruned_loss=0.02818, over 985772.91 frames.], batch size: 79, aishell_tot_loss[loss=0.1412, simple_loss=0.2278, pruned_loss=0.02727, over 985302.02 frames.], datatang_tot_loss[loss=0.1365, simple_loss=0.215, pruned_loss=0.029, over 985953.53 frames.], batch size: 79, lr: 2.71e-04
+2022-06-19 08:07:23,926 INFO [train.py:874] (1/4) Epoch 30, batch 3650, datatang_loss[loss=0.1116, simple_loss=0.1925, pruned_loss=0.01534, over 4905.00 frames.], tot_loss[loss=0.1384, simple_loss=0.2204, pruned_loss=0.02815, over 985739.04 frames.], batch size: 64, aishell_tot_loss[loss=0.1413, simple_loss=0.2282, pruned_loss=0.02726, over 985268.84 frames.], datatang_tot_loss[loss=0.1361, simple_loss=0.2145, pruned_loss=0.02892, over 985970.07 frames.], batch size: 64, lr: 2.71e-04
+2022-06-19 08:07:54,536 INFO [train.py:874] (1/4) Epoch 30, batch 3700, aishell_loss[loss=0.1652, simple_loss=0.2571, pruned_loss=0.03664, over 4964.00 frames.], tot_loss[loss=0.1382, simple_loss=0.2201, pruned_loss=0.02817, over 986068.93 frames.], batch size: 61, aishell_tot_loss[loss=0.1414, simple_loss=0.2282, pruned_loss=0.02733, over 985444.66 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2144, pruned_loss=0.02884, over 986138.82 frames.], batch size: 61, lr: 2.71e-04
+2022-06-19 08:08:23,939 INFO [train.py:874] (1/4) Epoch 30, batch 3750, aishell_loss[loss=0.1569, simple_loss=0.2404, pruned_loss=0.03666, over 4874.00 frames.], tot_loss[loss=0.1377, simple_loss=0.2194, pruned_loss=0.02797, over 985580.13 frames.], batch size: 35, aishell_tot_loss[loss=0.1408, simple_loss=0.2275, pruned_loss=0.02703, over 984849.87 frames.], datatang_tot_loss[loss=0.136, simple_loss=0.2141, pruned_loss=0.02893, over 986275.24 frames.], batch size: 35, lr: 2.71e-04
+2022-06-19 08:08:52,770 INFO [train.py:874] (1/4) Epoch 30, batch 3800, datatang_loss[loss=0.1357, simple_loss=0.2063, pruned_loss=0.03249, over 4983.00 frames.], tot_loss[loss=0.1381, simple_loss=0.2199, pruned_loss=0.02819, over 985556.70 frames.], batch size: 37, aishell_tot_loss[loss=0.1407, simple_loss=0.2274, pruned_loss=0.02704, over 985017.08 frames.], datatang_tot_loss[loss=0.1364, simple_loss=0.2145, pruned_loss=0.02919, over 986089.16 frames.], batch size: 37, lr: 2.71e-04
+2022-06-19 08:09:20,697 INFO [train.py:874] (1/4) Epoch 30, batch 3850, datatang_loss[loss=0.1167, simple_loss=0.1978, pruned_loss=0.01775, over 4947.00 frames.], tot_loss[loss=0.138, simple_loss=0.2203, pruned_loss=0.02788, over 985742.12 frames.], batch size: 37, aishell_tot_loss[loss=0.1412, simple_loss=0.2279, pruned_loss=0.02723, over 985340.03 frames.], datatang_tot_loss[loss=0.1356, simple_loss=0.2138, pruned_loss=0.02872, over 985985.24 frames.], batch size: 37, lr: 2.71e-04
+2022-06-19 08:09:50,453 INFO [train.py:874] (1/4) Epoch 30, batch 3900, aishell_loss[loss=0.1309, simple_loss=0.2235, pruned_loss=0.01918, over 4980.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2215, pruned_loss=0.02809, over 985769.32 frames.], batch size: 30, aishell_tot_loss[loss=0.1416, simple_loss=0.2284, pruned_loss=0.02741, over 985416.97 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2138, pruned_loss=0.02878, over 985986.88 frames.], batch size: 30, lr: 2.71e-04
+2022-06-19 08:10:17,945 INFO [train.py:874] (1/4) Epoch 30, batch 3950, datatang_loss[loss=0.1408, simple_loss=0.2208, pruned_loss=0.03044, over 4930.00 frames.], tot_loss[loss=0.1388, simple_loss=0.2217, pruned_loss=0.02795, over 985713.87 frames.], batch size: 88, aishell_tot_loss[loss=0.1417, simple_loss=0.2287, pruned_loss=0.0273, over 985480.01 frames.], datatang_tot_loss[loss=0.1357, simple_loss=0.2138, pruned_loss=0.02876, over 985893.46 frames.], batch size: 88, lr: 2.71e-04
+2022-06-19 08:10:51,421 INFO [train.py:874] (1/4) Epoch 30, batch 4000, aishell_loss[loss=0.1401, simple_loss=0.2363, pruned_loss=0.022, over 4876.00 frames.], tot_loss[loss=0.1393, simple_loss=0.2221, pruned_loss=0.02827, over 985466.25 frames.], batch size: 35, aishell_tot_loss[loss=0.142, simple_loss=0.229, pruned_loss=0.02753, over 985437.08 frames.], datatang_tot_loss[loss=0.1358, simple_loss=0.2139, pruned_loss=0.02885, over 985677.53 frames.], batch size: 35, lr: 2.71e-04
+2022-06-19 08:10:51,422 INFO [train.py:905] (1/4) Computing validation loss
+2022-06-19 08:11:08,528 INFO [train.py:914] (1/4) Epoch 30, validation: loss=0.1651, simple_loss=0.249, pruned_loss=0.04054, over 1622729.00 frames.
+2022-06-19 08:11:36,711 INFO [train.py:874] (1/4) Epoch 30, batch 4050, aishell_loss[loss=0.1516, simple_loss=0.243, pruned_loss=0.03006, over 4918.00 frames.], tot_loss[loss=0.1386, simple_loss=0.2213, pruned_loss=0.02799, over 984996.20 frames.], batch size: 41, aishell_tot_loss[loss=0.142, simple_loss=0.229, pruned_loss=0.02746, over 984956.57 frames.], datatang_tot_loss[loss=0.1353, simple_loss=0.2133, pruned_loss=0.02863, over 985653.72 frames.], batch size: 41, lr: 2.71e-04
+2022-06-19 08:11:58,140 INFO [train.py:1125] (1/4) Done!