2022-05-26 10:46:41,629 INFO [train.py:906] (0/4) Training started
2022-05-26 10:46:41,632 INFO [train.py:916] (0/4) Device: cuda:0
2022-05-26 10:46:41,634 INFO [train.py:934] (0/4) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'encoder_dim': 512, 'nhead': 8, 'dim_feedforward': 2048, 'num_encoder_layers': 12, 'decoder_dim': 512, 'joiner_dim': 512, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.15.1', 'k2-build-type': 'Debug', 'k2-with-cuda': True, 'k2-git-sha1': 'ecfe7bd6d9189964bf3ff043038918d889a43185', 'k2-git-date': 'Tue May 10 10:57:55 2022', 'lhotse-version': '1.1.0', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'streaming-conformer', 'icefall-git-sha1': '364bccb-clean', 'icefall-git-date': 'Thu May 26 10:29:08 2022', 'icefall-path': '/ceph-kw/kangwei/code/icefall_reworked2', 'k2-path': '/ceph-kw/kangwei/code/k2/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-hw/kangwei/dev_tools/anaconda3/envs/rnnt2/lib/python3.8/site-packages/lhotse-1.1.0-py3.8.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-3-0307202051-57dc848959-8tmmp', 'IP address': '10.177.24.138'}, 'world_size': 4, 'master_port': 13498, 'tensorboard': True, 'num_epochs': 50, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('streaming_pruned_transducer_stateless4/exp'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 6, 'context_size': 2, 'prune_range': 5, 'lm_scale': 0.25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'save_every_n': 8000, 'keep_last_k': 20, 'average_period': 100, 'use_fp16': False, 'dynamic_chunk_training': True, 'causal_convolution': True, 'short_chunk_size': 32, 'num_left_chunks': 4, 'delay_penalty': 0.0, 'return_sym_delay': False, 'full_libri': True, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'blank_id': 0, 'vocab_size': 500}
2022-05-26 10:46:41,634 INFO [train.py:936] (0/4) About to create model
2022-05-26 10:46:42,071 INFO [train.py:940] (0/4) Number of model parameters: 78648040
2022-05-26 10:46:47,119 INFO [train.py:955] (0/4) Using DDP
2022-05-26 10:46:47,469 INFO [asr_datamodule.py:391] (0/4) About to get train-clean-100 cuts
2022-05-26 10:46:54,130 INFO [asr_datamodule.py:398] (0/4) About to get train-clean-360 cuts
2022-05-26 10:47:21,021 INFO [asr_datamodule.py:405] (0/4) About to get train-other-500 cuts
2022-05-26 10:48:07,489 INFO [asr_datamodule.py:209] (0/4) Enable MUSAN
2022-05-26 10:48:07,490 INFO [asr_datamodule.py:210] (0/4) About to get Musan cuts
2022-05-26 10:48:08,907 INFO [asr_datamodule.py:238] (0/4) Enable SpecAugment
2022-05-26 10:48:08,907 INFO [asr_datamodule.py:239] (0/4) Time warp factor: 80
2022-05-26 10:48:08,907 INFO [asr_datamodule.py:251] (0/4) Num frame mask: 10
2022-05-26 10:48:08,908 INFO [asr_datamodule.py:264] (0/4) About to create train dataset
2022-05-26 10:48:08,908 INFO [asr_datamodule.py:292] (0/4) Using BucketingSampler.
2022-05-26 10:48:13,977 INFO [asr_datamodule.py:308] (0/4) About to create train dataloader
2022-05-26 10:48:13,978 INFO [asr_datamodule.py:412] (0/4) About to get dev-clean cuts
2022-05-26 10:48:14,266 INFO [asr_datamodule.py:417] (0/4) About to get dev-other cuts
2022-05-26 10:48:14,399 INFO [asr_datamodule.py:339] (0/4) About to create dev dataset
2022-05-26 10:48:14,416 INFO [asr_datamodule.py:358] (0/4) About to create dev dataloader
2022-05-26 10:48:14,417 INFO [train.py:1082] (0/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
2022-05-26 10:48:23,779 INFO [distributed.py:874] (0/4) Reducer buckets have been rebuilt in this iteration.
2022-05-26 10:48:40,752 INFO [train.py:842] (0/4) Epoch 1, batch 0, loss[loss=0.784, simple_loss=1.568, pruned_loss=6.534, over 7290.00 frames.], tot_loss[loss=0.784, simple_loss=1.568, pruned_loss=6.534, over 7290.00 frames.], batch size: 17, lr: 3.00e-03
2022-05-26 10:49:19,668 INFO [train.py:842] (0/4) Epoch 1, batch 50, loss[loss=0.5058, simple_loss=1.012, pruned_loss=7.013, over 7164.00 frames.], tot_loss[loss=0.5659, simple_loss=1.132, pruned_loss=7.102, over 323787.58 frames.], batch size: 19, lr: 3.00e-03
2022-05-26 10:49:59,116 INFO [train.py:842] (0/4) Epoch 1, batch 100, loss[loss=0.3827, simple_loss=0.7654, pruned_loss=6.714, over 6988.00 frames.], tot_loss[loss=0.5029, simple_loss=1.006, pruned_loss=6.997, over 566429.21 frames.], batch size: 16, lr: 3.00e-03
2022-05-26 10:50:37,759 INFO [train.py:842] (0/4) Epoch 1, batch 150, loss[loss=0.3718, simple_loss=0.7435, pruned_loss=6.727, over 7447.00 frames.], tot_loss[loss=0.4746, simple_loss=0.9491, pruned_loss=6.936, over 758181.62 frames.], batch size: 17, lr: 3.00e-03
2022-05-26 10:51:16,801 INFO [train.py:842] (0/4) Epoch 1, batch 200, loss[loss=0.4291, simple_loss=0.8581, pruned_loss=6.756, over 7272.00 frames.], tot_loss[loss=0.4535, simple_loss=0.907, pruned_loss=6.887, over 908118.90 frames.], batch size: 25, lr: 3.00e-03
2022-05-26 10:51:55,438 INFO [train.py:842] (0/4) Epoch 1, batch 250, loss[loss=0.4154, simple_loss=0.8308, pruned_loss=6.659, over 7327.00 frames.], tot_loss[loss=0.4399, simple_loss=0.8799, pruned_loss=6.836, over 1017619.37 frames.], batch size: 21, lr: 3.00e-03
2022-05-26 10:52:34,306 INFO [train.py:842] (0/4) Epoch 1, batch 300, loss[loss=0.4177, simple_loss=0.8353, pruned_loss=6.776, over 7271.00 frames.], tot_loss[loss=0.4303, simple_loss=0.8605, pruned_loss=6.797, over 1109133.25 frames.], batch size: 25, lr: 3.00e-03
2022-05-26 10:53:13,223 INFO [train.py:842] (0/4) Epoch 1, batch 350, loss[loss=0.3705, simple_loss=0.7409, pruned_loss=6.61, over 7256.00 frames.], tot_loss[loss=0.4216, simple_loss=0.8432, pruned_loss=6.77, over 1178705.90 frames.], batch size: 19, lr: 3.00e-03
2022-05-26 10:53:52,158 INFO [train.py:842] (0/4) Epoch 1, batch 400, loss[loss=0.3993, simple_loss=0.7985, pruned_loss=6.79, over 7415.00 frames.], tot_loss[loss=0.4141, simple_loss=0.8281, pruned_loss=6.751, over 1230762.81 frames.], batch size: 21, lr: 3.00e-03
2022-05-26 10:54:30,787 INFO [train.py:842] (0/4) Epoch 1, batch 450, loss[loss=0.407, simple_loss=0.8141, pruned_loss=6.668, over 7418.00 frames.], tot_loss[loss=0.4078, simple_loss=0.8156, pruned_loss=6.731, over 1268656.02 frames.], batch size: 21, lr: 2.99e-03
2022-05-26 10:55:09,750 INFO [train.py:842] (0/4) Epoch 1, batch 500, loss[loss=0.394, simple_loss=0.788, pruned_loss=6.678, over 7192.00 frames.], tot_loss[loss=0.401, simple_loss=0.8021, pruned_loss=6.71, over 1304508.80 frames.], batch size: 22, lr: 2.99e-03
2022-05-26 10:55:48,092 INFO [train.py:842] (0/4) Epoch 1, batch 550, loss[loss=0.3586, simple_loss=0.7172, pruned_loss=6.657, over 7344.00 frames.], tot_loss[loss=0.3953, simple_loss=0.7906, pruned_loss=6.705, over 1330878.59 frames.], batch size: 22, lr: 2.99e-03
2022-05-26 10:56:27,036 INFO [train.py:842] (0/4) Epoch 1, batch 600, loss[loss=0.3497, simple_loss=0.6993, pruned_loss=6.711, over 7126.00 frames.], tot_loss[loss=0.3837, simple_loss=0.7675, pruned_loss=6.694, over 1351464.36 frames.], batch size: 21, lr: 2.99e-03
2022-05-26 10:57:05,654 INFO [train.py:842] (0/4) Epoch 1, batch 650, loss[loss=0.2903, simple_loss=0.5806, pruned_loss=6.585, over 7029.00 frames.], tot_loss[loss=0.3717, simple_loss=0.7435, pruned_loss=6.697, over 1369743.70 frames.], batch size: 16, lr: 2.99e-03
2022-05-26 10:57:44,552 INFO [train.py:842] (0/4) Epoch 1, batch 700, loss[loss=0.3273, simple_loss=0.6545, pruned_loss=6.822, over 7208.00 frames.], tot_loss[loss=0.3586, simple_loss=0.7172, pruned_loss=6.695, over 1381299.87 frames.], batch size: 23, lr: 2.99e-03
2022-05-26 10:58:23,525 INFO [train.py:842] (0/4) Epoch 1, batch 750, loss[loss=0.2744, simple_loss=0.5488, pruned_loss=6.648, over 7276.00 frames.], tot_loss[loss=0.346, simple_loss=0.6919, pruned_loss=6.695, over 1392933.57 frames.], batch size: 17, lr: 2.98e-03
2022-05-26 10:59:02,467 INFO [train.py:842] (0/4) Epoch 1, batch 800, loss[loss=0.2985, simple_loss=0.597, pruned_loss=6.75, over 7104.00 frames.], tot_loss[loss=0.3358, simple_loss=0.6716, pruned_loss=6.705, over 1398271.74 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 10:59:41,342 INFO [train.py:842] (0/4) Epoch 1, batch 850, loss[loss=0.3301, simple_loss=0.6602, pruned_loss=6.88, over 7230.00 frames.], tot_loss[loss=0.3258, simple_loss=0.6516, pruned_loss=6.713, over 1403068.37 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 11:00:20,376 INFO [train.py:842] (0/4) Epoch 1, batch 900, loss[loss=0.2737, simple_loss=0.5475, pruned_loss=6.687, over 7337.00 frames.], tot_loss[loss=0.3162, simple_loss=0.6325, pruned_loss=6.717, over 1407943.14 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 11:00:58,853 INFO [train.py:842] (0/4) Epoch 1, batch 950, loss[loss=0.2418, simple_loss=0.4836, pruned_loss=6.59, over 7013.00 frames.], tot_loss[loss=0.3081, simple_loss=0.6162, pruned_loss=6.721, over 1405257.81 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:01:37,553 INFO [train.py:842] (0/4) Epoch 1, batch 1000, loss[loss=0.2359, simple_loss=0.4719, pruned_loss=6.638, over 6975.00 frames.], tot_loss[loss=0.301, simple_loss=0.602, pruned_loss=6.725, over 1404991.06 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:02:16,197 INFO [train.py:842] (0/4) Epoch 1, batch 1050, loss[loss=0.2498, simple_loss=0.4996, pruned_loss=6.618, over 6990.00 frames.], tot_loss[loss=0.2951, simple_loss=0.5901, pruned_loss=6.729, over 1407362.68 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:02:54,893 INFO [train.py:842] (0/4) Epoch 1, batch 1100, loss[loss=0.2648, simple_loss=0.5296, pruned_loss=6.722, over 7217.00 frames.], tot_loss[loss=0.2896, simple_loss=0.5791, pruned_loss=6.73, over 1411554.61 frames.], batch size: 22, lr: 2.96e-03
2022-05-26 11:03:33,468 INFO [train.py:842] (0/4) Epoch 1, batch 1150, loss[loss=0.2694, simple_loss=0.5387, pruned_loss=6.77, over 6785.00 frames.], tot_loss[loss=0.2844, simple_loss=0.5688, pruned_loss=6.737, over 1412326.11 frames.], batch size: 31, lr: 2.96e-03
2022-05-26 11:04:12,434 INFO [train.py:842] (0/4) Epoch 1, batch 1200, loss[loss=0.2591, simple_loss=0.5182, pruned_loss=6.722, over 7209.00 frames.], tot_loss[loss=0.279, simple_loss=0.5581, pruned_loss=6.74, over 1420125.97 frames.], batch size: 26, lr: 2.96e-03
2022-05-26 11:04:50,802 INFO [train.py:842] (0/4) Epoch 1, batch 1250, loss[loss=0.309, simple_loss=0.618, pruned_loss=6.933, over 7386.00 frames.], tot_loss[loss=0.2741, simple_loss=0.5483, pruned_loss=6.742, over 1412927.74 frames.], batch size: 23, lr: 2.95e-03
2022-05-26 11:05:29,859 INFO [train.py:842] (0/4) Epoch 1, batch 1300, loss[loss=0.279, simple_loss=0.558, pruned_loss=6.955, over 7303.00 frames.], tot_loss[loss=0.2694, simple_loss=0.5387, pruned_loss=6.747, over 1421349.61 frames.], batch size: 24, lr: 2.95e-03
2022-05-26 11:06:08,501 INFO [train.py:842] (0/4) Epoch 1, batch 1350, loss[loss=0.2427, simple_loss=0.4855, pruned_loss=6.75, over 7132.00 frames.], tot_loss[loss=0.2652, simple_loss=0.5304, pruned_loss=6.752, over 1422297.40 frames.], batch size: 20, lr: 2.95e-03
2022-05-26 11:06:47,135 INFO [train.py:842] (0/4) Epoch 1, batch 1400, loss[loss=0.2573, simple_loss=0.5146, pruned_loss=6.875, over 7278.00 frames.], tot_loss[loss=0.2635, simple_loss=0.5269, pruned_loss=6.764, over 1418016.83 frames.], batch size: 24, lr: 2.94e-03
2022-05-26 11:07:25,754 INFO [train.py:842] (0/4) Epoch 1, batch 1450, loss[loss=0.2064, simple_loss=0.4128, pruned_loss=6.695, over 7143.00 frames.], tot_loss[loss=0.2599, simple_loss=0.5199, pruned_loss=6.767, over 1417934.65 frames.], batch size: 17, lr: 2.94e-03
2022-05-26 11:08:04,594 INFO [train.py:842] (0/4) Epoch 1, batch 1500, loss[loss=0.2327, simple_loss=0.4654, pruned_loss=6.824, over 7305.00 frames.], tot_loss[loss=0.2564, simple_loss=0.5128, pruned_loss=6.774, over 1421322.57 frames.], batch size: 24, lr: 2.94e-03
2022-05-26 11:08:43,069 INFO [train.py:842] (0/4) Epoch 1, batch 1550, loss[loss=0.2553, simple_loss=0.5106, pruned_loss=6.814, over 7107.00 frames.], tot_loss[loss=0.2532, simple_loss=0.5064, pruned_loss=6.779, over 1421558.94 frames.], batch size: 21, lr: 2.93e-03
2022-05-26 11:09:22,177 INFO [train.py:842] (0/4) Epoch 1, batch 1600, loss[loss=0.2564, simple_loss=0.5127, pruned_loss=6.83, over 7326.00 frames.], tot_loss[loss=0.25, simple_loss=0.5, pruned_loss=6.781, over 1420706.31 frames.], batch size: 20, lr: 2.93e-03
2022-05-26 11:10:01,420 INFO [train.py:842] (0/4) Epoch 1, batch 1650, loss[loss=0.2364, simple_loss=0.4728, pruned_loss=6.815, over 7153.00 frames.], tot_loss[loss=0.2467, simple_loss=0.4935, pruned_loss=6.786, over 1422854.74 frames.], batch size: 18, lr: 2.92e-03
2022-05-26 11:10:40,799 INFO [train.py:842] (0/4) Epoch 1, batch 1700, loss[loss=0.2381, simple_loss=0.4763, pruned_loss=6.822, over 6293.00 frames.], tot_loss[loss=0.2449, simple_loss=0.4898, pruned_loss=6.791, over 1417681.49 frames.], batch size: 37, lr: 2.92e-03
2022-05-26 11:11:19,945 INFO [train.py:842] (0/4) Epoch 1, batch 1750, loss[loss=0.2491, simple_loss=0.4982, pruned_loss=6.739, over 6380.00 frames.], tot_loss[loss=0.2422, simple_loss=0.4843, pruned_loss=6.792, over 1417955.63 frames.], batch size: 37, lr: 2.91e-03
2022-05-26 11:11:59,998 INFO [train.py:842] (0/4) Epoch 1, batch 1800, loss[loss=0.2499, simple_loss=0.4997, pruned_loss=6.874, over 7066.00 frames.], tot_loss[loss=0.2404, simple_loss=0.4807, pruned_loss=6.797, over 1418143.78 frames.], batch size: 28, lr: 2.91e-03
2022-05-26 11:12:39,025 INFO [train.py:842] (0/4) Epoch 1, batch 1850, loss[loss=0.2581, simple_loss=0.5162, pruned_loss=6.869, over 5240.00 frames.], tot_loss[loss=0.2384, simple_loss=0.4768, pruned_loss=6.801, over 1418995.55 frames.], batch size: 55, lr: 2.91e-03
2022-05-26 11:13:18,082 INFO [train.py:842] (0/4) Epoch 1, batch 1900, loss[loss=0.2252, simple_loss=0.4504, pruned_loss=6.893, over 7260.00 frames.], tot_loss[loss=0.2371, simple_loss=0.4742, pruned_loss=6.804, over 1419026.54 frames.], batch size: 19, lr: 2.90e-03
2022-05-26 11:13:56,904 INFO [train.py:842] (0/4) Epoch 1, batch 1950, loss[loss=0.2174, simple_loss=0.4349, pruned_loss=6.805, over 7320.00 frames.], tot_loss[loss=0.235, simple_loss=0.47, pruned_loss=6.797, over 1421939.18 frames.], batch size: 21, lr: 2.90e-03
2022-05-26 11:14:35,965 INFO [train.py:842] (0/4) Epoch 1, batch 2000, loss[loss=0.1976, simple_loss=0.3952, pruned_loss=6.704, over 6831.00 frames.], tot_loss[loss=0.2326, simple_loss=0.4653, pruned_loss=6.792, over 1422015.14 frames.], batch size: 15, lr: 2.89e-03
2022-05-26 11:15:15,095 INFO [train.py:842] (0/4) Epoch 1, batch 2050, loss[loss=0.2332, simple_loss=0.4663, pruned_loss=6.854, over 7170.00 frames.], tot_loss[loss=0.2313, simple_loss=0.4626, pruned_loss=6.792, over 1420442.88 frames.], batch size: 26, lr: 2.89e-03
2022-05-26 11:15:53,879 INFO [train.py:842] (0/4) Epoch 1, batch 2100, loss[loss=0.2075, simple_loss=0.415, pruned_loss=6.655, over 7180.00 frames.], tot_loss[loss=0.2304, simple_loss=0.4609, pruned_loss=6.795, over 1418082.21 frames.], batch size: 18, lr: 2.88e-03
2022-05-26 11:16:32,684 INFO [train.py:842] (0/4) Epoch 1, batch 2150, loss[loss=0.2243, simple_loss=0.4485, pruned_loss=6.843, over 7341.00 frames.], tot_loss[loss=0.2289, simple_loss=0.4579, pruned_loss=6.796, over 1422372.14 frames.], batch size: 22, lr: 2.88e-03
2022-05-26 11:17:11,542 INFO [train.py:842] (0/4) Epoch 1, batch 2200, loss[loss=0.2257, simple_loss=0.4515, pruned_loss=6.891, over 7324.00 frames.], tot_loss[loss=0.2281, simple_loss=0.4563, pruned_loss=6.792, over 1421991.23 frames.], batch size: 25, lr: 2.87e-03
2022-05-26 11:17:50,098 INFO [train.py:842] (0/4) Epoch 1, batch 2250, loss[loss=0.2342, simple_loss=0.4685, pruned_loss=6.834, over 7216.00 frames.], tot_loss[loss=0.227, simple_loss=0.454, pruned_loss=6.796, over 1420357.49 frames.], batch size: 21, lr: 2.86e-03
2022-05-26 11:18:28,792 INFO [train.py:842] (0/4) Epoch 1, batch 2300, loss[loss=0.2194, simple_loss=0.4389, pruned_loss=6.713, over 7270.00 frames.], tot_loss[loss=0.2267, simple_loss=0.4534, pruned_loss=6.801, over 1415292.70 frames.], batch size: 19, lr: 2.86e-03
2022-05-26 11:19:07,806 INFO [train.py:842] (0/4) Epoch 1, batch 2350, loss[loss=0.2265, simple_loss=0.453, pruned_loss=6.892, over 5124.00 frames.], tot_loss[loss=0.2267, simple_loss=0.4534, pruned_loss=6.804, over 1415393.47 frames.], batch size: 52, lr: 2.85e-03
2022-05-26 11:19:47,045 INFO [train.py:842] (0/4) Epoch 1, batch 2400, loss[loss=0.2109, simple_loss=0.4218, pruned_loss=6.796, over 7419.00 frames.], tot_loss[loss=0.2254, simple_loss=0.4508, pruned_loss=6.807, over 1410888.37 frames.], batch size: 20, lr: 2.85e-03
2022-05-26 11:20:25,501 INFO [train.py:842] (0/4) Epoch 1, batch 2450, loss[loss=0.2448, simple_loss=0.4895, pruned_loss=6.841, over 5193.00 frames.], tot_loss[loss=0.2248, simple_loss=0.4496, pruned_loss=6.805, over 1410887.59 frames.], batch size: 53, lr: 2.84e-03
2022-05-26 11:21:04,495 INFO [train.py:842] (0/4) Epoch 1, batch 2500, loss[loss=0.2316, simple_loss=0.4632, pruned_loss=6.867, over 7319.00 frames.], tot_loss[loss=0.222, simple_loss=0.444, pruned_loss=6.803, over 1417263.32 frames.], batch size: 20, lr: 2.84e-03
2022-05-26 11:21:42,892 INFO [train.py:842] (0/4) Epoch 1, batch 2550, loss[loss=0.196, simple_loss=0.392, pruned_loss=6.793, over 7406.00 frames.], tot_loss[loss=0.2227, simple_loss=0.4455, pruned_loss=6.808, over 1418544.68 frames.], batch size: 18, lr: 2.83e-03
2022-05-26 11:22:21,926 INFO [train.py:842] (0/4) Epoch 1, batch 2600, loss[loss=0.2393, simple_loss=0.4785, pruned_loss=6.938, over 7240.00 frames.], tot_loss[loss=0.2206, simple_loss=0.4411, pruned_loss=6.802, over 1421652.87 frames.], batch size: 20, lr: 2.83e-03
2022-05-26 11:23:00,462 INFO [train.py:842] (0/4) Epoch 1, batch 2650, loss[loss=0.2014, simple_loss=0.4029, pruned_loss=6.771, over 7234.00 frames.], tot_loss[loss=0.2186, simple_loss=0.4373, pruned_loss=6.793, over 1422753.88 frames.], batch size: 20, lr: 2.82e-03
2022-05-26 11:23:39,467 INFO [train.py:842] (0/4) Epoch 1, batch 2700, loss[loss=0.2136, simple_loss=0.4273, pruned_loss=6.851, over 7147.00 frames.], tot_loss[loss=0.218, simple_loss=0.436, pruned_loss=6.788, over 1422424.12 frames.], batch size: 20, lr: 2.81e-03
2022-05-26 11:24:17,966 INFO [train.py:842] (0/4) Epoch 1, batch 2750, loss[loss=0.2052, simple_loss=0.4104, pruned_loss=6.834, over 7324.00 frames.], tot_loss[loss=0.2169, simple_loss=0.4339, pruned_loss=6.787, over 1423446.62 frames.], batch size: 20, lr: 2.81e-03
2022-05-26 11:24:56,712 INFO [train.py:842] (0/4) Epoch 1, batch 2800, loss[loss=0.2107, simple_loss=0.4215, pruned_loss=6.795, over 7147.00 frames.], tot_loss[loss=0.2172, simple_loss=0.4344, pruned_loss=6.791, over 1422233.31 frames.], batch size: 20, lr: 2.80e-03
2022-05-26 11:25:35,295 INFO [train.py:842] (0/4) Epoch 1, batch 2850, loss[loss=0.2245, simple_loss=0.4491, pruned_loss=6.74, over 7360.00 frames.], tot_loss[loss=0.2168, simple_loss=0.4337, pruned_loss=6.793, over 1424762.33 frames.], batch size: 19, lr: 2.80e-03
2022-05-26 11:26:13,803 INFO [train.py:842] (0/4) Epoch 1, batch 2900, loss[loss=0.2036, simple_loss=0.4072, pruned_loss=6.778, over 7342.00 frames.], tot_loss[loss=0.2167, simple_loss=0.4334, pruned_loss=6.797, over 1420410.66 frames.], batch size: 20, lr: 2.79e-03
2022-05-26 11:26:52,582 INFO [train.py:842] (0/4) Epoch 1, batch 2950, loss[loss=0.2019, simple_loss=0.4038, pruned_loss=6.794, over 7150.00 frames.], tot_loss[loss=0.2147, simple_loss=0.4294, pruned_loss=6.795, over 1417160.93 frames.], batch size: 26, lr: 2.78e-03
2022-05-26 11:27:31,318 INFO [train.py:842] (0/4) Epoch 1, batch 3000, loss[loss=0.356, simple_loss=0.3797, pruned_loss=1.662, over 7281.00 frames.], tot_loss[loss=0.248, simple_loss=0.4285, pruned_loss=6.765, over 1421118.41 frames.], batch size: 17, lr: 2.78e-03
2022-05-26 11:27:31,319 INFO [train.py:862] (0/4) Computing validation loss
2022-05-26 11:27:40,551 INFO [train.py:871] (0/4) Epoch 1, validation: loss=2.017, simple_loss=0.4861, pruned_loss=1.774, over 868885.00 frames.
2022-05-26 11:28:19,136 INFO [train.py:842] (0/4) Epoch 1, batch 3050, loss[loss=0.3566, simple_loss=0.495, pruned_loss=1.091, over 6463.00 frames.], tot_loss[loss=0.2752, simple_loss=0.4393, pruned_loss=5.562, over 1419999.13 frames.], batch size: 38, lr: 2.77e-03
2022-05-26 11:28:58,769 INFO [train.py:842] (0/4) Epoch 1, batch 3100, loss[loss=0.3004, simple_loss=0.4499, pruned_loss=0.7541, over 7420.00 frames.], tot_loss[loss=0.2798, simple_loss=0.4349, pruned_loss=4.503, over 1426509.22 frames.], batch size: 21, lr: 2.77e-03
2022-05-26 11:29:37,514 INFO [train.py:842] (0/4) Epoch 1, batch 3150, loss[loss=0.2875, simple_loss=0.4663, pruned_loss=0.5437, over 7406.00 frames.], tot_loss[loss=0.2788, simple_loss=0.4336, pruned_loss=3.637, over 1427277.74 frames.], batch size: 21, lr: 2.76e-03
2022-05-26 11:30:16,512 INFO [train.py:842] (0/4) Epoch 1, batch 3200, loss[loss=0.2713, simple_loss=0.4543, pruned_loss=0.4412, over 7291.00 frames.], tot_loss[loss=0.2747, simple_loss=0.4331, pruned_loss=2.936, over 1423862.67 frames.], batch size: 24, lr: 2.75e-03
2022-05-26 11:30:54,979 INFO [train.py:842] (0/4) Epoch 1, batch 3250, loss[loss=0.2604, simple_loss=0.4486, pruned_loss=0.3616, over 7144.00 frames.], tot_loss[loss=0.2696, simple_loss=0.4324, pruned_loss=2.367, over 1423544.71 frames.], batch size: 20, lr: 2.75e-03
2022-05-26 11:31:34,088 INFO [train.py:842] (0/4) Epoch 1, batch 3300, loss[loss=0.2377, simple_loss=0.4174, pruned_loss=0.2897, over 7382.00 frames.], tot_loss[loss=0.265, simple_loss=0.4319, pruned_loss=1.921, over 1419222.53 frames.], batch size: 23, lr: 2.74e-03
2022-05-26 11:32:12,602 INFO [train.py:842] (0/4) Epoch 1, batch 3350, loss[loss=0.241, simple_loss=0.4242, pruned_loss=0.2892, over 7280.00 frames.], tot_loss[loss=0.2586, simple_loss=0.4281, pruned_loss=1.555, over 1423609.77 frames.], batch size: 24, lr: 2.73e-03
2022-05-26 11:32:51,525 INFO [train.py:842] (0/4) Epoch 1, batch 3400, loss[loss=0.2217, simple_loss=0.3933, pruned_loss=0.2504, over 7262.00 frames.], tot_loss[loss=0.2537, simple_loss=0.4258, pruned_loss=1.272, over 1424137.60 frames.], batch size: 19, lr: 2.73e-03
2022-05-26 11:33:30,151 INFO [train.py:842] (0/4) Epoch 1, batch 3450, loss[loss=0.2499, simple_loss=0.4421, pruned_loss=0.2892, over 7343.00 frames.], tot_loss[loss=0.249, simple_loss=0.4227, pruned_loss=1.049, over 1423925.42 frames.], batch size: 25, lr: 2.72e-03
2022-05-26 11:34:09,057 INFO [train.py:842] (0/4) Epoch 1, batch 3500, loss[loss=0.3113, simple_loss=0.5322, pruned_loss=0.4521, over 7183.00 frames.], tot_loss[loss=0.247, simple_loss=0.4232, pruned_loss=0.8774, over 1422489.85 frames.], batch size: 26, lr: 2.72e-03
2022-05-26 11:34:47,674 INFO [train.py:842] (0/4) Epoch 1, batch 3550, loss[loss=0.2196, simple_loss=0.3913, pruned_loss=0.2393, over 7210.00 frames.], tot_loss[loss=0.2431, simple_loss=0.42, pruned_loss=0.7383, over 1423518.80 frames.], batch size: 21, lr: 2.71e-03
2022-05-26 11:35:26,427 INFO [train.py:842] (0/4) Epoch 1, batch 3600, loss[loss=0.1914, simple_loss=0.3459, pruned_loss=0.1845, over 6998.00 frames.], tot_loss[loss=0.2402, simple_loss=0.4181, pruned_loss=0.6294, over 1422338.60 frames.], batch size: 16, lr: 2.70e-03
2022-05-26 11:36:05,092 INFO [train.py:842] (0/4) Epoch 1, batch 3650, loss[loss=0.2059, simple_loss=0.3756, pruned_loss=0.1809, over 7215.00 frames.], tot_loss[loss=0.2375, simple_loss=0.416, pruned_loss=0.5424, over 1422898.08 frames.], batch size: 21, lr: 2.70e-03
2022-05-26 11:36:43,959 INFO [train.py:842] (0/4) Epoch 1, batch 3700, loss[loss=0.2244, simple_loss=0.404, pruned_loss=0.2242, over 6711.00 frames.], tot_loss[loss=0.2355, simple_loss=0.4145, pruned_loss=0.4738, over 1427080.33 frames.], batch size: 31, lr: 2.69e-03
2022-05-26 11:37:22,481 INFO [train.py:842] (0/4) Epoch 1, batch 3750, loss[loss=0.1806, simple_loss=0.3274, pruned_loss=0.1687, over 7293.00 frames.], tot_loss[loss=0.2337, simple_loss=0.413, pruned_loss=0.4223, over 1418626.42 frames.], batch size: 18, lr: 2.68e-03
2022-05-26 11:38:01,292 INFO [train.py:842] (0/4) Epoch 1, batch 3800, loss[loss=0.205, simple_loss=0.3693, pruned_loss=0.2035, over 7133.00 frames.], tot_loss[loss=0.2338, simple_loss=0.4144, pruned_loss=0.3832, over 1417108.20 frames.], batch size: 17, lr: 2.68e-03
2022-05-26 11:38:40,179 INFO [train.py:842] (0/4) Epoch 1, batch 3850, loss[loss=0.2209, simple_loss=0.3919, pruned_loss=0.249, over 7135.00 frames.], tot_loss[loss=0.2324, simple_loss=0.4132, pruned_loss=0.3484, over 1423130.09 frames.], batch size: 17, lr: 2.67e-03
2022-05-26 11:39:18,907 INFO [train.py:842] (0/4) Epoch 1, batch 3900, loss[loss=0.1706, simple_loss=0.3109, pruned_loss=0.1509, over 6825.00 frames.], tot_loss[loss=0.2316, simple_loss=0.4129, pruned_loss=0.3227, over 1419840.09 frames.], batch size: 15, lr: 2.66e-03
2022-05-26 11:39:57,452 INFO [train.py:842] (0/4) Epoch 1, batch 3950, loss[loss=0.2746, simple_loss=0.4878, pruned_loss=0.3067, over 6802.00 frames.], tot_loss[loss=0.2303, simple_loss=0.4114, pruned_loss=0.301, over 1418700.25 frames.], batch size: 31, lr: 2.66e-03
2022-05-26 11:40:36,019 INFO [train.py:842] (0/4) Epoch 1, batch 4000, loss[loss=0.2274, simple_loss=0.4119, pruned_loss=0.2144, over 7188.00 frames.], tot_loss[loss=0.23, simple_loss=0.4118, pruned_loss=0.2841, over 1419833.67 frames.], batch size: 26, lr: 2.65e-03
2022-05-26 11:41:14,456 INFO [train.py:842] (0/4) Epoch 1, batch 4050, loss[loss=0.2715, simple_loss=0.4815, pruned_loss=0.3074, over 4814.00 frames.], tot_loss[loss=0.2285, simple_loss=0.4099, pruned_loss=0.2687, over 1421414.20 frames.], batch size: 52, lr: 2.64e-03
2022-05-26 11:41:53,376 INFO [train.py:842] (0/4) Epoch 1, batch 4100, loss[loss=0.2151, simple_loss=0.3889, pruned_loss=0.2067, over 6294.00 frames.], tot_loss[loss=0.2271, simple_loss=0.4082, pruned_loss=0.2564, over 1419160.90 frames.], batch size: 37, lr: 2.64e-03
2022-05-26 11:42:32,053 INFO [train.py:842] (0/4) Epoch 1, batch 4150, loss[loss=0.1967, simple_loss=0.3607, pruned_loss=0.1633, over 7419.00 frames.], tot_loss[loss=0.2267, simple_loss=0.4078, pruned_loss=0.2477, over 1424525.71 frames.], batch size: 20, lr: 2.63e-03
2022-05-26 11:43:10,837 INFO [train.py:842] (0/4) Epoch 1, batch 4200, loss[loss=0.2246, simple_loss=0.4075, pruned_loss=0.2082, over 7316.00 frames.], tot_loss[loss=0.2253, simple_loss=0.4059, pruned_loss=0.2389, over 1428506.06 frames.], batch size: 21, lr: 2.63e-03
2022-05-26 11:43:49,376 INFO [train.py:842] (0/4) Epoch 1, batch 4250, loss[loss=0.1998, simple_loss=0.365, pruned_loss=0.1727, over 7148.00 frames.], tot_loss[loss=0.2238, simple_loss=0.4038, pruned_loss=0.2317, over 1427654.39 frames.], batch size: 20, lr: 2.62e-03
2022-05-26 11:44:28,192 INFO [train.py:842] (0/4) Epoch 1, batch 4300, loss[loss=0.2763, simple_loss=0.4959, pruned_loss=0.283, over 7190.00 frames.], tot_loss[loss=0.2226, simple_loss=0.4019, pruned_loss=0.2259, over 1424429.29 frames.], batch size: 22, lr: 2.61e-03
2022-05-26 11:45:06,706 INFO [train.py:842] (0/4) Epoch 1, batch 4350, loss[loss=0.2067, simple_loss=0.3765, pruned_loss=0.185, over 7157.00 frames.], tot_loss[loss=0.222, simple_loss=0.4011, pruned_loss=0.2223, over 1426218.39 frames.], batch size: 19, lr: 2.61e-03
2022-05-26 11:45:45,323 INFO [train.py:842] (0/4) Epoch 1, batch 4400, loss[loss=0.2235, simple_loss=0.4071, pruned_loss=0.199, over 7223.00 frames.], tot_loss[loss=0.2234, simple_loss=0.4038, pruned_loss=0.2211, over 1426557.55 frames.], batch size: 21, lr: 2.60e-03
2022-05-26 11:46:23,886 INFO [train.py:842] (0/4) Epoch 1, batch 4450, loss[loss=0.2092, simple_loss=0.3806, pruned_loss=0.1888, over 7165.00 frames.], tot_loss[loss=0.223, simple_loss=0.4031, pruned_loss=0.2183, over 1428932.28 frames.], batch size: 19, lr: 2.59e-03
2022-05-26 11:47:02,775 INFO [train.py:842] (0/4) Epoch 1, batch 4500, loss[loss=0.218, simple_loss=0.3956, pruned_loss=0.2022, over 7255.00 frames.], tot_loss[loss=0.2226, simple_loss=0.4028, pruned_loss=0.2149, over 1431556.74 frames.], batch size: 19, lr: 2.59e-03
2022-05-26 11:47:41,422 INFO [train.py:842] (0/4) Epoch 1, batch 4550, loss[loss=0.1993, simple_loss=0.3643, pruned_loss=0.1713, over 7061.00 frames.], tot_loss[loss=0.2218, simple_loss=0.4016, pruned_loss=0.2134, over 1429692.39 frames.], batch size: 18, lr: 2.58e-03
2022-05-26 11:48:20,132 INFO [train.py:842] (0/4) Epoch 1, batch 4600, loss[loss=0.2043, simple_loss=0.3734, pruned_loss=0.1756, over 7249.00 frames.], tot_loss[loss=0.2204, simple_loss=0.3995, pruned_loss=0.2089, over 1428670.69 frames.], batch size: 19, lr: 2.57e-03
2022-05-26 11:48:58,607 INFO [train.py:842] (0/4) Epoch 1, batch 4650, loss[loss=0.2431, simple_loss=0.4383, pruned_loss=0.2396, over 7090.00 frames.], tot_loss[loss=0.2204, simple_loss=0.3997, pruned_loss=0.2071, over 1429568.51 frames.], batch size: 28, lr: 2.57e-03
2022-05-26 11:49:37,488 INFO [train.py:842] (0/4) Epoch 1, batch 4700, loss[loss=0.2024, simple_loss=0.3686, pruned_loss=0.1814, over 7288.00 frames.], tot_loss[loss=0.2201, simple_loss=0.3991, pruned_loss=0.2064, over 1428974.32 frames.], batch size: 17, lr: 2.56e-03
2022-05-26 11:50:15,957 INFO [train.py:842] (0/4) Epoch 1, batch 4750, loss[loss=0.2578, simple_loss=0.459, pruned_loss=0.283, over 5373.00 frames.], tot_loss[loss=0.2217, simple_loss=0.4019, pruned_loss=0.2081, over 1427664.88 frames.], batch size: 52, lr: 2.55e-03
2022-05-26 11:50:54,731 INFO [train.py:842] (0/4) Epoch 1, batch 4800, loss[loss=0.1903, simple_loss=0.3504, pruned_loss=0.1513, over 7434.00 frames.], tot_loss[loss=0.2203, simple_loss=0.3999, pruned_loss=0.2044, over 1429578.02 frames.], batch size: 20, lr: 2.55e-03
2022-05-26 11:51:33,198 INFO [train.py:842] (0/4) Epoch 1, batch 4850, loss[loss=0.2285, simple_loss=0.4107, pruned_loss=0.2318, over 7256.00 frames.], tot_loss[loss=0.2208, simple_loss=0.4009, pruned_loss=0.2047, over 1426592.52 frames.], batch size: 19, lr: 2.54e-03
2022-05-26 11:52:11,922 INFO [train.py:842] (0/4) Epoch 1, batch 4900, loss[loss=0.21, simple_loss=0.382, pruned_loss=0.1901, over 7321.00 frames.], tot_loss[loss=0.2201, simple_loss=0.3997, pruned_loss=0.2026, over 1428116.24 frames.], batch size: 20, lr: 2.54e-03
2022-05-26 11:52:50,297 INFO [train.py:842] (0/4) Epoch 1, batch 4950, loss[loss=0.2103, simple_loss=0.382, pruned_loss=0.1927, over 7358.00 frames.], tot_loss[loss=0.2207, simple_loss=0.4009, pruned_loss=0.2029, over 1423683.74 frames.], batch size: 19, lr: 2.53e-03
2022-05-26 11:53:29,132 INFO [train.py:842] (0/4) Epoch 1, batch 5000, loss[loss=0.2392, simple_loss=0.4331, pruned_loss=0.2268, over 7341.00 frames.], tot_loss[loss=0.2208, simple_loss=0.4012, pruned_loss=0.2023, over 1423145.91 frames.], batch size: 22, lr: 2.52e-03
2022-05-26 11:54:07,521 INFO [train.py:842] (0/4) Epoch 1, batch 5050, loss[loss=0.2574, simple_loss=0.4624, pruned_loss=0.2623, over 7324.00 frames.], tot_loss[loss=0.2196, simple_loss=0.3993, pruned_loss=0.1997, over 1422617.45 frames.], batch size: 21, lr: 2.52e-03
2022-05-26 11:54:46,104 INFO [train.py:842] (0/4) Epoch 1, batch 5100, loss[loss=0.2179, simple_loss=0.3996, pruned_loss=0.1809, over 7200.00 frames.], tot_loss[loss=0.2181, simple_loss=0.3967, pruned_loss=0.1974, over 1420917.37 frames.], batch size: 22, lr: 2.51e-03
2022-05-26 11:55:24,621 INFO [train.py:842] (0/4) Epoch 1, batch 5150, loss[loss=0.2463, simple_loss=0.4439, pruned_loss=0.2432, over 7429.00 frames.], tot_loss[loss=0.2168, simple_loss=0.3946, pruned_loss=0.1948, over 1423251.49 frames.], batch size: 20, lr: 2.50e-03
2022-05-26 11:56:03,336 INFO [train.py:842] (0/4) Epoch 1, batch 5200, loss[loss=0.2365, simple_loss=0.4296, pruned_loss=0.2166, over 7336.00 frames.], tot_loss[loss=0.2177, simple_loss=0.3962, pruned_loss=0.196, over 1421537.25 frames.], batch size: 25, lr: 2.50e-03
2022-05-26 11:56:41,767 INFO [train.py:842] (0/4) Epoch 1, batch 5250, loss[loss=0.2448, simple_loss=0.4394, pruned_loss=0.2505, over 5025.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3944, pruned_loss=0.1938, over 1419366.50 frames.], batch size: 52, lr: 2.49e-03
2022-05-26 11:57:20,422 INFO [train.py:842] (0/4) Epoch 1, batch 5300, loss[loss=0.2245, simple_loss=0.4025, pruned_loss=0.2319, over 7288.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3941, pruned_loss=0.1933, over 1416829.71 frames.], batch size: 17, lr: 2.49e-03
2022-05-26 11:57:58,747 INFO [train.py:842] (0/4) Epoch 1, batch 5350, loss[loss=0.2178, simple_loss=0.3972, pruned_loss=0.192, over 7385.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3939, pruned_loss=0.1926, over 1414483.02 frames.], batch size: 23, lr: 2.48e-03
2022-05-26 11:58:37,511 INFO [train.py:842] (0/4) Epoch 1, batch 5400, loss[loss=0.2141, simple_loss=0.394, pruned_loss=0.1712, over 7026.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3946, pruned_loss=0.193, over 1419758.10 frames.], batch size: 28, lr: 2.47e-03
2022-05-26 11:59:16,032 INFO [train.py:842] (0/4) Epoch 1, batch 5450, loss[loss=0.2257, simple_loss=0.41, pruned_loss=0.2067, over 7151.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3933, pruned_loss=0.1915, over 1420793.75 frames.], batch size: 20, lr: 2.47e-03
2022-05-26 11:59:54,869 INFO [train.py:842] (0/4) Epoch 1, batch 5500, loss[loss=0.251, simple_loss=0.4516, pruned_loss=0.2516, over 5129.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3927, pruned_loss=0.191, over 1420316.83 frames.], batch size: 52, lr: 2.46e-03
2022-05-26 12:00:33,672 INFO [train.py:842] (0/4) Epoch 1, batch 5550, loss[loss=0.2462, simple_loss=0.4376, pruned_loss=0.2738, over 6824.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3924, pruned_loss=0.1915, over 1422188.07 frames.], batch size: 15, lr: 2.45e-03
2022-05-26 12:01:12,572 INFO [train.py:842] (0/4) Epoch 1, batch 5600, loss[loss=0.2431, simple_loss=0.4391, pruned_loss=0.235, over 6419.00 frames.], tot_loss[loss=0.2153, simple_loss=0.3925, pruned_loss=0.1906, over 1422593.45 frames.], batch size: 38, lr: 2.45e-03
2022-05-26 12:01:51,145 INFO [train.py:842] (0/4) Epoch 1, batch 5650, loss[loss=0.1951, simple_loss=0.356, pruned_loss=0.1713, over 7272.00 frames.], tot_loss[loss=0.215, simple_loss=0.3921, pruned_loss=0.1901, over 1419619.82 frames.], batch size: 17, lr: 2.44e-03
2022-05-26 12:02:29,840 INFO [train.py:842] (0/4) Epoch 1, batch 5700, loss[loss=0.1968, simple_loss=0.3624, pruned_loss=0.156, over 7422.00 frames.], tot_loss[loss=0.215, simple_loss=0.3921, pruned_loss=0.1899, over 1420183.04 frames.], batch size: 20, lr: 2.44e-03
2022-05-26 12:03:08,168 INFO [train.py:842] (0/4) Epoch 1, batch 5750, loss[loss=0.1862, simple_loss=0.3439, pruned_loss=0.143, over 7281.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3928, pruned_loss=0.1902, over 1421928.55 frames.], batch size: 18, lr: 2.43e-03
2022-05-26 12:03:47,051 INFO [train.py:842] (0/4) Epoch 1, batch 5800, loss[loss=0.2041, simple_loss=0.3764, pruned_loss=0.1591, over 7209.00 frames.], tot_loss[loss=0.2149, simple_loss=0.3921, pruned_loss=0.1883, over 1427954.34 frames.], batch size: 22, lr: 2.42e-03
2022-05-26 12:04:25,394 INFO [train.py:842] (0/4) Epoch 1, batch 5850, loss[loss=0.2393, simple_loss=0.431, pruned_loss=0.2385, over 7425.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3929, pruned_loss=0.1893, over 1426989.41 frames.], batch size: 20, lr: 2.42e-03
2022-05-26 12:05:04,399 INFO [train.py:842] (0/4) Epoch 1, batch 5900, loss[loss=0.253, simple_loss=0.4583, pruned_loss=0.2383, over 7322.00 frames.], tot_loss[loss=0.214, simple_loss=0.3906, pruned_loss=0.1873, over 1430197.25 frames.], batch size: 21, lr: 2.41e-03
2022-05-26 12:05:43,139 INFO [train.py:842] (0/4) Epoch 1, batch 5950, loss[loss=0.2184, simple_loss=0.397, pruned_loss=0.1993, over 7168.00 frames.], tot_loss[loss=0.2134, simple_loss=0.3895, pruned_loss=0.1861, over 1430975.40 frames.], batch size: 19, lr: 2.41e-03
2022-05-26 12:06:21,960 INFO [train.py:842] (0/4) Epoch 1, batch 6000, loss[loss=0.4581, simple_loss=0.4171, pruned_loss=0.2495, over 7155.00 frames.], tot_loss[loss=0.2149, simple_loss=0.3888, pruned_loss=0.185, over 1427705.57 frames.], batch size: 26, lr: 2.40e-03
2022-05-26 12:06:21,961 INFO [train.py:862] (0/4) Computing validation loss
2022-05-26 12:06:31,824 INFO [train.py:871] (0/4) Epoch 1, validation: loss=0.2892, simple_loss=0.3436, pruned_loss=0.1174, over 868885.00 frames.
2022-05-26 12:07:10,558 INFO [train.py:842] (0/4) Epoch 1, batch 6050, loss[loss=0.3636, simple_loss=0.3634, pruned_loss=0.1819, over 7266.00 frames.], tot_loss[loss=0.259, simple_loss=0.3925, pruned_loss=0.191, over 1424958.80 frames.], batch size: 16, lr: 2.39e-03
2022-05-26 12:07:49,761 INFO [train.py:842] (0/4) Epoch 1, batch 6100, loss[loss=0.3233, simple_loss=0.3497, pruned_loss=0.1484, over 6806.00 frames.], tot_loss[loss=0.2839, simple_loss=0.3904, pruned_loss=0.1884, over 1427153.91 frames.], batch size: 15, lr: 2.39e-03
2022-05-26 12:08:28,487 INFO [train.py:842] (0/4) Epoch 1, batch 6150, loss[loss=0.3645, simple_loss=0.3782, pruned_loss=0.1754, over 7116.00 frames.], tot_loss[loss=0.3059, simple_loss=0.3913, pruned_loss=0.1878, over 1427397.82 frames.], batch size: 21, lr: 2.38e-03
2022-05-26 12:09:07,159 INFO [train.py:842] (0/4) Epoch 1, batch 6200, loss[loss=0.4063, simple_loss=0.406, pruned_loss=0.2033, over 7342.00 frames.], tot_loss[loss=0.3204, simple_loss=0.391, pruned_loss=0.1852, over 1427919.20 frames.], batch size: 22, lr: 2.38e-03
2022-05-26 12:09:45,799 INFO [train.py:842] (0/4) Epoch 1, batch 6250, loss[loss=0.4259, simple_loss=0.4276, pruned_loss=0.2121, over 7362.00 frames.], tot_loss[loss=0.333, simple_loss=0.3915, pruned_loss=0.1842, over 1429875.68 frames.], batch size: 23, lr: 2.37e-03
2022-05-26 12:10:25,213 INFO [train.py:842] (0/4) Epoch 1, batch 6300, loss[loss=0.4287, simple_loss=0.4311, pruned_loss=0.2132, over 7281.00 frames.], tot_loss[loss=0.3422, simple_loss=0.3912, pruned_loss=0.1831, over 1428116.34 frames.], batch size: 18, lr: 2.37e-03
2022-05-26 12:11:03,817 INFO [train.py:842] (0/4) Epoch 1, batch 6350, loss[loss=0.3284, simple_loss=0.3611, pruned_loss=0.1478, over 7146.00 frames.], tot_loss[loss=0.3468, simple_loss=0.3896, pruned_loss=0.1804, over 1427807.12 frames.], batch size: 20, lr: 2.36e-03
2022-05-26 12:11:42,701 INFO [train.py:842] (0/4) Epoch 1, batch 6400, loss[loss=0.3665, simple_loss=0.3901, pruned_loss=0.1714, over 7363.00 frames.], tot_loss[loss=0.3538, simple_loss=0.3902, pruned_loss=0.1808, over 1427647.66 frames.], batch size: 19, lr: 2.35e-03
2022-05-26 12:12:21,091 INFO [train.py:842] (0/4) Epoch 1, batch 6450, loss[loss=0.4298, simple_loss=0.4262, pruned_loss=0.2167, over 7125.00 frames.], tot_loss[loss=0.3597, simple_loss=0.3918, pruned_loss=0.181, over 1427702.78 frames.], batch size: 21, lr: 2.35e-03
2022-05-26 12:12:59,893 INFO [train.py:842] (0/4) Epoch 1, batch 6500, loss[loss=0.3362, simple_loss=0.3592, pruned_loss=0.1566, over 7138.00 frames.], tot_loss[loss=0.3601, simple_loss=0.3902, pruned_loss=0.1785, over 1423019.08 frames.], batch size: 17, lr: 2.34e-03
2022-05-26 12:13:38,142 INFO [train.py:842] (0/4) Epoch 1, batch 6550, loss[loss=0.4067, simple_loss=0.4215, pruned_loss=0.1959, over 7316.00 frames.], tot_loss[loss=0.3612, simple_loss=0.3896, pruned_loss=0.1769, over 1418883.33 frames.], batch size: 21, lr: 2.34e-03
2022-05-26 12:14:17,053 INFO [train.py:842] (0/4) Epoch 1, batch 6600, loss[loss=0.3335, simple_loss=0.3766, pruned_loss=0.1452, over 7190.00 frames.], tot_loss[loss=0.3639, simple_loss=0.3905, pruned_loss=0.1768, over 1423693.85 frames.], batch size: 26, lr: 2.33e-03
2022-05-26 12:14:55,785 INFO [train.py:842] (0/4) Epoch 1, batch 6650, loss[loss=0.3711, simple_loss=0.3856, pruned_loss=0.1783, over 7063.00 frames.], tot_loss[loss=0.3669, simple_loss=0.3916, pruned_loss=0.1774, over 1421950.43 frames.], batch size: 18, lr: 2.33e-03
2022-05-26 12:15:34,734 INFO [train.py:842] (0/4) Epoch 1, batch 6700, loss[loss=0.584, simple_loss=0.5194, pruned_loss=0.3242, over 4581.00 frames.], tot_loss[loss=0.3671, simple_loss=0.3917, pruned_loss=0.1762, over 1422581.71 frames.], batch size: 52, lr: 2.32e-03
2022-05-26 12:16:13,264 INFO [train.py:842] (0/4) Epoch 1, batch 6750, loss[loss=0.3301, simple_loss=0.3679, pruned_loss=0.1462, over 7283.00 frames.], tot_loss[loss=0.3655, simple_loss=0.3902, pruned_loss=0.1742, over 1426583.31 frames.], batch size: 24, lr: 2.31e-03
2022-05-26 12:16:52,110 INFO [train.py:842] (0/4) Epoch 1, batch 6800, loss[loss=0.3536, simple_loss=0.3706, pruned_loss=0.1683, over 7431.00 frames.], tot_loss[loss=0.3635, simple_loss=0.3889, pruned_loss=0.172, over 1428508.65 frames.], batch size: 20, lr: 2.31e-03
2022-05-26 12:17:30,583 INFO [train.py:842] (0/4) Epoch 1, batch 6850, loss[loss=0.3112, simple_loss=0.3677, pruned_loss=0.1273, over 7196.00 frames.], tot_loss[loss=0.3627, simple_loss=0.3886, pruned_loss=0.1707, over 1426717.85 frames.], batch size: 23, lr: 2.30e-03
2022-05-26 12:18:19,073 INFO [train.py:842] (0/4) Epoch 1, batch 6900, loss[loss=0.3578, simple_loss=0.387, pruned_loss=0.1643, over 7414.00 frames.], tot_loss[loss=0.3603, simple_loss=0.3865, pruned_loss=0.1688, over 1427895.41 frames.], batch size: 21, lr: 2.30e-03
2022-05-26 12:18:57,536 INFO [train.py:842] (0/4) Epoch 1, batch 6950, loss[loss=0.3328, simple_loss=0.3532, pruned_loss=0.1562, over 7271.00 frames.], tot_loss[loss=0.3618, simple_loss=0.3874, pruned_loss=0.1695, over 1422748.48 frames.], batch size: 18, lr: 2.29e-03
2022-05-26 12:19:36,268 INFO [train.py:842] (0/4) Epoch 1, batch 7000, loss[loss=0.3356, simple_loss=0.3666, pruned_loss=0.1523, over 7153.00 frames.], tot_loss[loss=0.3642, simple_loss=0.3892, pruned_loss=0.1707, over 1420545.91 frames.], batch size: 18, lr: 2.29e-03
2022-05-26 12:20:14,735 INFO [train.py:842] (0/4) Epoch 1, batch 7050, loss[loss=0.3505, simple_loss=0.3774, pruned_loss=0.1618, over 7166.00 frames.], tot_loss[loss=0.3618, simple_loss=0.3877, pruned_loss=0.1688, over 1421803.53 frames.], batch size: 19, lr: 2.28e-03
2022-05-26 12:20:53,601 INFO [train.py:842] (0/4) Epoch 1, batch 7100, loss[loss=0.3123, simple_loss=0.3704, pruned_loss=0.1271, over 7342.00 frames.], tot_loss[loss=0.3619, simple_loss=0.3878, pruned_loss=0.1687, over 1423981.86 frames.], batch size: 22, lr: 2.28e-03
2022-05-26 12:21:32,657 INFO [train.py:842] (0/4) Epoch 1, batch 7150, loss[loss=0.3883, simple_loss=0.4022, pruned_loss=0.1872, over 7204.00 frames.], tot_loss[loss=0.3611, simple_loss=0.3867, pruned_loss=0.1683, over 1418171.13 frames.], batch size: 22, lr: 2.27e-03
2022-05-26 12:22:11,492 INFO [train.py:842] (0/4) Epoch 1, batch 7200, loss[loss=0.355, simple_loss=0.3961, pruned_loss=0.1569, over 7346.00 frames.], tot_loss[loss=0.36, simple_loss=0.3869, pruned_loss=0.1669, over 1420770.40 frames.], batch size: 22, lr: 2.27e-03
2022-05-26 12:22:50,077 INFO [train.py:842] (0/4) Epoch 1, batch 7250, loss[loss=0.3941, simple_loss=0.4047, pruned_loss=0.1918, over 7061.00 frames.], tot_loss[loss=0.3622, simple_loss=0.3884, pruned_loss=0.1683, over 1416062.40 frames.], batch size: 18, lr: 2.26e-03
2022-05-26 12:23:28,695 INFO [train.py:842] (0/4) Epoch 1, batch 7300, loss[loss=0.3737, simple_loss=0.4098, pruned_loss=0.1688, over 7067.00 frames.], tot_loss[loss=0.3627, simple_loss=0.3892, pruned_loss=0.1684, over 1416594.02 frames.], batch size: 28, lr: 2.26e-03
2022-05-26 12:24:07,152 INFO [train.py:842] (0/4) Epoch 1, batch 7350, loss[loss=0.31, simple_loss=0.3418, pruned_loss=0.1391, over 7218.00 frames.], tot_loss[loss=0.3606, simple_loss=0.3879, pruned_loss=0.1668, over 1416611.07 frames.], batch size: 16, lr: 2.25e-03
2022-05-26 12:24:45,812 INFO [train.py:842] (0/4) Epoch 1, batch 7400, loss[loss=0.2582, simple_loss=0.3105, pruned_loss=0.1029, over 7402.00 frames.], tot_loss[loss=0.3612, simple_loss=0.3885, pruned_loss=0.1671, over 1417748.21 frames.], batch size: 18, lr: 2.24e-03
2022-05-26 12:25:24,571 INFO [train.py:842] (0/4) Epoch 1, batch 7450, loss[loss=0.3592, simple_loss=0.3845, pruned_loss=0.167, over 7397.00 frames.], tot_loss[loss=0.3629, simple_loss=0.3904, pruned_loss=0.1679, over 1425460.15 frames.], batch size: 18, lr: 2.24e-03
2022-05-26 12:26:03,336 INFO [train.py:842] (0/4) Epoch 1, batch 7500, loss[loss=0.4152, simple_loss=0.4213, pruned_loss=0.2046, over 7432.00 frames.], tot_loss[loss=0.3665, simple_loss=0.3924, pruned_loss=0.1704, over 1422886.18 frames.], batch size: 20, lr: 2.23e-03
2022-05-26 12:26:41,950 INFO [train.py:842] (0/4) Epoch 1, batch 7550, loss[loss=0.3082, simple_loss=0.3492, pruned_loss=0.1337, over 7329.00 frames.], tot_loss[loss=0.3624, simple_loss=0.3894, pruned_loss=0.1677, over 1420722.91 frames.], batch size: 20, lr: 2.23e-03
2022-05-26 12:27:21,041 INFO [train.py:842] (0/4) Epoch 1, batch 7600, loss[loss=0.3289, simple_loss=0.3675, pruned_loss=0.1452, over 7421.00 frames.], tot_loss[loss=0.3578, simple_loss=0.3865, pruned_loss=0.1646, over 1424353.22 frames.], batch size: 21, lr: 2.22e-03
2022-05-26 12:28:28,318 INFO [train.py:842] (0/4) Epoch 1, batch 7650, loss[loss=0.4288, simple_loss=0.4249, pruned_loss=0.2164, over 7333.00 frames.], tot_loss[loss=0.3594, simple_loss=0.3879, pruned_loss=0.1655, over 1427571.25 frames.], batch size: 20, lr: 2.22e-03
2022-05-26 12:29:07,133 INFO [train.py:842] (0/4) Epoch 1, batch 7700, loss[loss=0.365, simple_loss=0.3945, pruned_loss=0.1677, over 7239.00 frames.], tot_loss[loss=0.3602, simple_loss=0.3882, pruned_loss=0.1662, over 1424765.81 frames.], batch size: 20, lr: 2.21e-03
2022-05-26 12:29:46,009 INFO [train.py:842] (0/4) Epoch 1, batch 7750, loss[loss=0.3356, simple_loss=0.3649, pruned_loss=0.1532, over 7360.00 frames.], tot_loss[loss=0.3577, simple_loss=0.3867, pruned_loss=0.1644, over 1425837.59 frames.], batch size: 19, lr: 2.21e-03
2022-05-26 12:30:24,812 INFO [train.py:842] (0/4) Epoch 1, batch 7800, loss[loss=0.4515, simple_loss=0.4411, pruned_loss=0.2309, over 7071.00 frames.], tot_loss[loss=0.3597, simple_loss=0.3878, pruned_loss=0.1658, over 1427722.09 frames.], batch size: 28, lr: 2.20e-03
2022-05-26 12:31:03,146 INFO [train.py:842] (0/4) Epoch 1, batch 7850, loss[loss=0.3404, simple_loss=0.3911, pruned_loss=0.1448, over 7289.00 frames.], tot_loss[loss=0.3585, simple_loss=0.3874, pruned_loss=0.1648, over 1429709.76 frames.], batch size: 24, lr: 2.20e-03
2022-05-26 12:31:41,887 INFO [train.py:842] (0/4) Epoch 1, batch 7900, loss[loss=0.4451, simple_loss=0.4374, pruned_loss=0.2264, over 7433.00 frames.], tot_loss[loss=0.3576, simple_loss=0.3868, pruned_loss=0.1642, over 1427764.73 frames.], batch size: 20, lr: 2.19e-03
2022-05-26 12:32:20,417 INFO [train.py:842] (0/4) Epoch 1, batch 7950, loss[loss=0.3926, simple_loss=0.4128, pruned_loss=0.1863, over 6569.00 frames.], tot_loss[loss=0.3547, simple_loss=0.3848, pruned_loss=0.1623, over 1422853.58 frames.], batch size: 38, lr: 2.19e-03
2022-05-26 12:32:58,422 INFO [checkpoint.py:75] (0/4) Saving checkpoint to streaming_pruned_transducer_stateless4/exp/checkpoint-8000.pt
2022-05-26 12:33:01,920 INFO [train.py:842] (0/4) Epoch 1, batch 8000, loss[loss=0.3569, simple_loss=0.3671, pruned_loss=0.1733, over 7144.00 frames.], tot_loss[loss=0.3537, simple_loss=0.3843, pruned_loss=0.1616, over 1425270.20 frames.], batch size: 17, lr: 2.18e-03
2022-05-26 12:33:40,591 INFO [train.py:842] (0/4) Epoch 1, batch 8050, loss[loss=0.2441, simple_loss=0.2852, pruned_loss=0.1015, over 7146.00 frames.], tot_loss[loss=0.3512, simple_loss=0.3828, pruned_loss=0.1597, over 1428906.56 frames.], batch size: 17, lr: 2.18e-03
2022-05-26 12:34:19,298 INFO [train.py:842] (0/4) Epoch 1, batch 8100, loss[loss=0.4043, simple_loss=0.4158, pruned_loss=0.1964, over 7257.00 frames.], tot_loss[loss=0.3519, simple_loss=0.3835, pruned_loss=0.1601, over 1428116.30 frames.], batch size: 19, lr: 2.17e-03
2022-05-26 12:34:57,730 INFO [train.py:842] (0/4) Epoch 1, batch 8150, loss[loss=0.3794, simple_loss=0.401, pruned_loss=0.1789, over 7212.00 frames.], tot_loss[loss=0.3553, simple_loss=0.3858, pruned_loss=0.1624, over 1423228.70 frames.], batch size: 22, lr: 2.17e-03
2022-05-26 12:35:36,455 INFO [train.py:842] (0/4) Epoch 1, batch 8200, loss[loss=0.3918, simple_loss=0.4046, pruned_loss=0.1895, over 7172.00 frames.], tot_loss[loss=0.3524, simple_loss=0.384, pruned_loss=0.1604, over 1421450.34 frames.], batch size: 18, lr: 2.16e-03
2022-05-26 12:36:15,285 INFO [train.py:842] (0/4) Epoch 1, batch 8250, loss[loss=0.2808, simple_loss=0.3205, pruned_loss=0.1205, over 7247.00 frames.], tot_loss[loss=0.3516, simple_loss=0.3828, pruned_loss=0.1602, over 1421870.53 frames.], batch size: 19, lr: 2.16e-03
2022-05-26 12:36:53,993 INFO [train.py:842] (0/4) Epoch 1, batch 8300, loss[loss=0.3731, simple_loss=0.4047, pruned_loss=0.1707, over 6711.00 frames.], tot_loss[loss=0.3511, simple_loss=0.3829, pruned_loss=0.1596, over 1421967.79 frames.], batch size: 31, lr: 2.15e-03
2022-05-26 12:37:32,673 INFO [train.py:842] (0/4) Epoch 1, batch 8350, loss[loss=0.3089, simple_loss=0.347, pruned_loss=0.1354, over 7276.00 frames.], tot_loss[loss=0.3493, simple_loss=0.3818, pruned_loss=0.1584, over 1424472.66 frames.], batch size: 18, lr: 2.15e-03
2022-05-26 12:38:11,573 INFO [train.py:842] (0/4) Epoch 1, batch 8400, loss[loss=0.4013, simple_loss=0.4311, pruned_loss=0.1858, over 7301.00 frames.], tot_loss[loss=0.3484, simple_loss=0.3819, pruned_loss=0.1574, over 1424227.82 frames.], batch size: 25, lr: 2.15e-03
2022-05-26 12:38:49,920 INFO [train.py:842] (0/4) Epoch 1, batch 8450, loss[loss=0.3543, simple_loss=0.386, pruned_loss=0.1613, over 7116.00 frames.], tot_loss[loss=0.348, simple_loss=0.3819, pruned_loss=0.1571, over 1423616.38 frames.], batch size: 21, lr: 2.14e-03
2022-05-26 12:39:28,659 INFO [train.py:842] (0/4) Epoch 1, batch 8500, loss[loss=0.3258, simple_loss=0.3701, pruned_loss=0.1408, over 7148.00 frames.], tot_loss[loss=0.3477, simple_loss=0.3813, pruned_loss=0.157, over 1423192.48 frames.], batch size: 20, lr: 2.14e-03
2022-05-26 12:40:07,535 INFO [train.py:842] (0/4) Epoch 1, batch 8550, loss[loss=0.3518, simple_loss=0.3732, pruned_loss=0.1652, over 7163.00 frames.], tot_loss[loss=0.3461, simple_loss=0.3801, pruned_loss=0.1561, over 1424251.33 frames.], batch size: 18, lr: 2.13e-03
2022-05-26 12:40:46,238 INFO [train.py:842] (0/4) Epoch 1, batch 8600, loss[loss=0.3705, simple_loss=0.4002, pruned_loss=0.1704, over 7057.00 frames.], tot_loss[loss=0.3484, simple_loss=0.3815, pruned_loss=0.1576, over 1420786.40 frames.], batch size: 18, lr: 2.13e-03
2022-05-26 12:41:24,634 INFO [train.py:842] (0/4) Epoch 1, batch 8650, loss[loss=0.3265, simple_loss=0.3715, pruned_loss=0.1407, over 7312.00 frames.], tot_loss[loss=0.3497, simple_loss=0.383, pruned_loss=0.1582, over 1414157.61 frames.], batch size: 21, lr: 2.12e-03
2022-05-26 12:42:03,383 INFO [train.py:842] (0/4) Epoch 1, batch 8700, loss[loss=0.3132, simple_loss=0.3447, pruned_loss=0.1408, over 7147.00 frames.], tot_loss[loss=0.3506, simple_loss=0.384, pruned_loss=0.1587, over 1411332.89 frames.], batch size: 17, lr: 2.12e-03
2022-05-26 12:42:41,796 INFO [train.py:842] (0/4) Epoch 1, batch 8750, loss[loss=0.3504, simple_loss=0.3974, pruned_loss=0.1517, over 6789.00 frames.], tot_loss[loss=0.3529, simple_loss=0.3855, pruned_loss=0.1601, over 1412986.52 frames.], batch size: 31, lr: 2.11e-03
2022-05-26 12:43:20,372 INFO [train.py:842] (0/4) Epoch 1, batch 8800, loss[loss=0.2974, simple_loss=0.3495, pruned_loss=0.1227, over 6741.00 frames.], tot_loss[loss=0.3494, simple_loss=0.3836, pruned_loss=0.1576, over 1416529.47 frames.], batch size: 31, lr: 2.11e-03
2022-05-26 12:43:58,596 INFO [train.py:842] (0/4) Epoch 1, batch 8850, loss[loss=0.457, simple_loss=0.4651, pruned_loss=0.2244, over 4962.00 frames.], tot_loss[loss=0.3495, simple_loss=0.3841, pruned_loss=0.1575, over 1412717.32 frames.], batch size: 52, lr: 2.10e-03
2022-05-26 12:44:37,288 INFO [train.py:842] (0/4) Epoch 1, batch 8900, loss[loss=0.3112, simple_loss=0.3505, pruned_loss=0.136, over 6969.00 frames.], tot_loss[loss=0.3501, simple_loss=0.384, pruned_loss=0.158, over 1403945.36 frames.], batch size: 16, lr: 2.10e-03
2022-05-26 12:45:15,601 INFO [train.py:842] (0/4) Epoch 1, batch 8950, loss[loss=0.3336, simple_loss=0.3927, pruned_loss=0.1373, over 7313.00 frames.], tot_loss[loss=0.3492, simple_loss=0.3842, pruned_loss=0.1572, over 1406208.79 frames.], batch size: 21, lr: 2.10e-03
2022-05-26 12:45:54,292 INFO [train.py:842] (0/4) Epoch 1, batch 9000, loss[loss=0.4914, simple_loss=0.4665, pruned_loss=0.2581, over 5027.00 frames.], tot_loss[loss=0.3517, simple_loss=0.3867, pruned_loss=0.1584, over 1399371.88 frames.], batch size: 53, lr: 2.09e-03
2022-05-26 12:45:54,294 INFO [train.py:862] (0/4) Computing validation loss
2022-05-26 12:46:03,568 INFO [train.py:871] (0/4) Epoch 1, validation: loss=0.2508, simple_loss=0.3369, pruned_loss=0.08236, over 868885.00 frames.
2022-05-26 12:46:41,376 INFO [train.py:842] (0/4) Epoch 1, batch 9050, loss[loss=0.5309, simple_loss=0.4847, pruned_loss=0.2886, over 5113.00 frames.], tot_loss[loss=0.3535, simple_loss=0.3881, pruned_loss=0.1595, over 1387518.43 frames.], batch size: 55, lr: 2.09e-03
2022-05-26 12:47:18,742 INFO [train.py:842] (0/4) Epoch 1, batch 9100, loss[loss=0.3806, simple_loss=0.4064, pruned_loss=0.1774, over 5177.00 frames.], tot_loss[loss=0.3589, simple_loss=0.3919, pruned_loss=0.163, over 1343375.13 frames.], batch size: 54, lr: 2.08e-03
2022-05-26 12:47:56,211 INFO [train.py:842] (0/4) Epoch 1, batch 9150, loss[loss=0.4318, simple_loss=0.4349, pruned_loss=0.2143, over 5318.00 frames.], tot_loss[loss=0.3643, simple_loss=0.395, pruned_loss=0.1668, over 1286627.94 frames.], batch size: 52, lr: 2.08e-03
2022-05-26 12:48:28,770 INFO [checkpoint.py:75] (0/4) Saving checkpoint to streaming_pruned_transducer_stateless4/exp/epoch-1.pt
2022-05-26 12:48:47,801 INFO [train.py:842] (0/4) Epoch 2, batch 0, loss[loss=0.4658, simple_loss=0.4807, pruned_loss=0.2255, over 7160.00 frames.], tot_loss[loss=0.4658, simple_loss=0.4807, pruned_loss=0.2255, over 7160.00 frames.], batch size: 26, lr: 2.06e-03
2022-05-26 12:49:27,354 INFO [train.py:842] (0/4) Epoch 2, batch 50, loss[loss=0.3564, simple_loss=0.3933, pruned_loss=0.1598, over 7242.00 frames.], tot_loss[loss=0.3464, simple_loss=0.3825, pruned_loss=0.1551, over 310714.08 frames.], batch size: 20, lr: 2.06e-03
2022-05-26 12:50:06,215 INFO [train.py:842] (0/4) Epoch 2, batch 100, loss[loss=0.383, simple_loss=0.4053, pruned_loss=0.1803, over 7425.00 frames.], tot_loss[loss=0.3453, simple_loss=0.3805, pruned_loss=0.1551, over 559080.38 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 12:50:45,170 INFO [train.py:842] (0/4) Epoch 2, batch 150, loss[loss=0.3128, simple_loss=0.3582, pruned_loss=0.1337, over 7320.00 frames.], tot_loss[loss=0.3414, simple_loss=0.3784, pruned_loss=0.1522, over 750064.73 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 12:51:23,759 INFO [train.py:842] (0/4) Epoch 2, batch 200, loss[loss=0.3928, simple_loss=0.405, pruned_loss=0.1903, over 7163.00 frames.], tot_loss[loss=0.3397, simple_loss=0.3769, pruned_loss=0.1513, over 899600.05 frames.], batch size: 19, lr: 2.04e-03
2022-05-26 12:52:03,051 INFO [train.py:842] (0/4) Epoch 2, batch 250, loss[loss=0.4019, simple_loss=0.4243, pruned_loss=0.1898, over 7390.00 frames.], tot_loss[loss=0.3427, simple_loss=0.3787, pruned_loss=0.1534, over 1014665.10 frames.], batch size: 23, lr: 2.04e-03
2022-05-26 12:52:42,055 INFO [train.py:842] (0/4) Epoch 2, batch 300, loss[loss=0.2916, simple_loss=0.3453, pruned_loss=0.1189, over 7259.00 frames.], tot_loss[loss=0.3432, simple_loss=0.3802, pruned_loss=0.1531, over 1103630.59 frames.], batch size: 19, lr: 2.03e-03
2022-05-26 12:53:21,155 INFO [train.py:842] (0/4) Epoch 2, batch 350, loss[loss=0.3706, simple_loss=0.3959, pruned_loss=0.1726, over 7225.00 frames.], tot_loss[loss=0.337, simple_loss=0.3757, pruned_loss=0.1492, over 1172735.56 frames.], batch size: 21, lr: 2.03e-03
2022-05-26 12:53:59,748 INFO [train.py:842] (0/4) Epoch 2, batch 400, loss[loss=0.3729, simple_loss=0.3936, pruned_loss=0.176, over 7135.00 frames.], tot_loss[loss=0.3362, simple_loss=0.3751, pruned_loss=0.1487, over 1229478.66 frames.], batch size: 20, lr: 2.03e-03
2022-05-26 12:54:38,394 INFO [train.py:842] (0/4) Epoch 2, batch 450, loss[loss=0.3196, simple_loss=0.367, pruned_loss=0.1361, over 7158.00 frames.], tot_loss[loss=0.3372, simple_loss=0.376, pruned_loss=0.1492, over 1274709.63 frames.], batch size: 19, lr: 2.02e-03
2022-05-26 12:55:16,799 INFO [train.py:842] (0/4) Epoch 2, batch 500, loss[loss=0.2699, simple_loss=0.325, pruned_loss=0.1073, over 7170.00 frames.], tot_loss[loss=0.3354, simple_loss=0.3746, pruned_loss=0.1481, over 1306170.00 frames.], batch size: 18, lr: 2.02e-03
2022-05-26 12:55:56,046 INFO [train.py:842] (0/4) Epoch 2, batch 550, loss[loss=0.328, simple_loss=0.3634, pruned_loss=0.1463, over 7362.00 frames.], tot_loss[loss=0.3369, simple_loss=0.3756, pruned_loss=0.1491, over 1332006.51 frames.], batch size: 19, lr: 2.01e-03
2022-05-26 12:56:34,277 INFO [train.py:842] (0/4) Epoch 2, batch 600, loss[loss=0.3411, simple_loss=0.3727, pruned_loss=0.1547, over 7370.00 frames.], tot_loss[loss=0.339, simple_loss=0.3775, pruned_loss=0.1503, over 1353927.69 frames.], batch size: 23, lr: 2.01e-03
2022-05-26 12:57:13,120 INFO [train.py:842] (0/4) Epoch 2, batch 650, loss[loss=0.2615, simple_loss=0.3213, pruned_loss=0.1009, over 7272.00 frames.], tot_loss[loss=0.3353, simple_loss=0.375, pruned_loss=0.1477, over 1368032.79 frames.], batch size: 18, lr: 2.01e-03
2022-05-26 12:57:51,836 INFO [train.py:842] (0/4) Epoch 2, batch 700, loss[loss=0.4018, simple_loss=0.4141, pruned_loss=0.1948, over 5187.00 frames.], tot_loss[loss=0.3312, simple_loss=0.3724, pruned_loss=0.145, over 1379724.32 frames.], batch size: 52, lr: 2.00e-03
2022-05-26 12:58:30,909 INFO [train.py:842] (0/4) Epoch 2, batch 750, loss[loss=0.3319, simple_loss=0.3616, pruned_loss=0.1511, over 7256.00 frames.], tot_loss[loss=0.3337, simple_loss=0.3742, pruned_loss=0.1466, over 1390727.13 frames.], batch size: 19, lr: 2.00e-03
2022-05-26 12:59:09,560 INFO [train.py:842] (0/4) Epoch 2, batch 800, loss[loss=0.2663, simple_loss=0.3276, pruned_loss=0.1025, over 7065.00 frames.], tot_loss[loss=0.3325, simple_loss=0.3734, pruned_loss=0.1458, over 1400198.93 frames.], batch size: 18, lr: 1.99e-03
2022-05-26 12:59:48,503 INFO [train.py:842] (0/4) Epoch 2, batch 850, loss[loss=0.3308, simple_loss=0.3606, pruned_loss=0.1505, over 7331.00 frames.], tot_loss[loss=0.3321, simple_loss=0.3728, pruned_loss=0.1457, over 1408694.74 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 13:00:27,172 INFO [train.py:842] (0/4) Epoch 2, batch 900, loss[loss=0.3587, simple_loss=0.3874, pruned_loss=0.165, over 7441.00 frames.], tot_loss[loss=0.3336, simple_loss=0.374, pruned_loss=0.1466, over 1413752.33 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 13:01:06,368 INFO [train.py:842] (0/4) Epoch 2, batch 950, loss[loss=0.3038, simple_loss=0.3387, pruned_loss=0.1345, over 7253.00 frames.], tot_loss[loss=0.3334, simple_loss=0.3739, pruned_loss=0.1464, over 1415166.48 frames.], batch size: 19, lr: 1.98e-03
2022-05-26 13:01:45,059 INFO [train.py:842] (0/4) Epoch 2, batch 1000, loss[loss=0.3836, simple_loss=0.4059, pruned_loss=0.1806, over 6805.00 frames.], tot_loss[loss=0.3306, simple_loss=0.372, pruned_loss=0.1446, over 1416430.60 frames.], batch size: 31, lr: 1.98e-03
2022-05-26 13:02:24,223 INFO [train.py:842] (0/4) Epoch 2, batch 1050, loss[loss=0.302, simple_loss=0.3415, pruned_loss=0.1312, over 7429.00 frames.], tot_loss[loss=0.3325, simple_loss=0.373, pruned_loss=0.146, over 1418576.63 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 13:03:02,483 INFO [train.py:842] (0/4) Epoch 2, batch 1100, loss[loss=0.3411, simple_loss=0.3672, pruned_loss=0.1575, over 7153.00 frames.], tot_loss[loss=0.336, simple_loss=0.3756, pruned_loss=0.1482, over 1419396.12 frames.], batch size: 18, lr: 1.97e-03
2022-05-26 13:03:41,564 INFO [train.py:842] (0/4) Epoch 2, batch 1150, loss[loss=0.3138, simple_loss=0.3706, pruned_loss=0.1285, over 7233.00 frames.], tot_loss[loss=0.3333, simple_loss=0.3737, pruned_loss=0.1464, over 1423282.03 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 13:04:19,996 INFO [train.py:842] (0/4) Epoch 2, batch 1200, loss[loss=0.3399, simple_loss=0.394, pruned_loss=0.1429, over 7054.00 frames.], tot_loss[loss=0.3325, simple_loss=0.3734, pruned_loss=0.1458, over 1422340.50 frames.], batch size: 28, lr: 1.96e-03
2022-05-26 13:04:58,724 INFO [train.py:842] (0/4) Epoch 2, batch 1250, loss[loss=0.2558, simple_loss=0.314, pruned_loss=0.09882, over 7282.00 frames.], tot_loss[loss=0.3326, simple_loss=0.3743, pruned_loss=0.1454, over 1422723.20 frames.], batch size: 18, lr: 1.96e-03
2022-05-26 13:05:37,175 INFO [train.py:842] (0/4) Epoch 2, batch 1300, loss[loss=0.3226, simple_loss=0.3763, pruned_loss=0.1345, over 7218.00 frames.], tot_loss[loss=0.3316, simple_loss=0.3736, pruned_loss=0.1448, over 1416720.90 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 13:06:15,947 INFO [train.py:842] (0/4) Epoch 2, batch 1350, loss[loss=0.2966, simple_loss=0.3213, pruned_loss=0.136, over 7264.00 frames.], tot_loss[loss=0.3329, simple_loss=0.3743, pruned_loss=0.1457, over 1420217.79 frames.], batch size: 17, lr: 1.95e-03
2022-05-26 13:06:54,264 INFO [train.py:842] (0/4) Epoch 2, batch 1400, loss[loss=0.3484, simple_loss=0.3791, pruned_loss=0.1588, over 7224.00 frames.], tot_loss[loss=0.3325, simple_loss=0.3738, pruned_loss=0.1456, over 1419234.68 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 13:07:33,450 INFO [train.py:842] (0/4) Epoch 2, batch 1450, loss[loss=0.3716, simple_loss=0.4086, pruned_loss=0.1672, over 7167.00 frames.], tot_loss[loss=0.3328, simple_loss=0.374, pruned_loss=0.1458, over 1422230.83 frames.], batch size: 26, lr: 1.94e-03
2022-05-26 13:08:12,021 INFO [train.py:842] (0/4) Epoch 2, batch 1500, loss[loss=0.4094, simple_loss=0.426, pruned_loss=0.1964, over 6488.00 frames.], tot_loss[loss=0.3323, simple_loss=0.3736, pruned_loss=0.1455, over 1421195.28 frames.], batch size: 38, lr: 1.94e-03
2022-05-26 13:08:50,744 INFO [train.py:842] (0/4) Epoch 2, batch 1550, loss[loss=0.3139, simple_loss=0.3547, pruned_loss=0.1366, over 7433.00 frames.], tot_loss[loss=0.3304, simple_loss=0.3727, pruned_loss=0.1441, over 1425137.46 frames.], batch size: 20, lr: 1.94e-03
2022-05-26 13:09:29,421 INFO [train.py:842] (0/4) Epoch 2, batch 1600, loss[loss=0.3242, simple_loss=0.3617, pruned_loss=0.1434, over 7156.00 frames.], tot_loss[loss=0.3276, simple_loss=0.37, pruned_loss=0.1426, over 1424882.25 frames.], batch size: 18, lr: 1.93e-03
2022-05-26 13:10:08,283 INFO [train.py:842] (0/4) Epoch 2, batch 1650, loss[loss=0.3435, simple_loss=0.3777, pruned_loss=0.1547, over 7423.00 frames.], tot_loss[loss=0.3243, simple_loss=0.3675, pruned_loss=0.1405, over 1424865.63 frames.], batch size: 20, lr: 1.93e-03
2022-05-26 13:10:46,848 INFO [train.py:842] (0/4) Epoch 2, batch 1700, loss[loss=0.4109, simple_loss=0.4321, pruned_loss=0.1948, over 7406.00 frames.], tot_loss[loss=0.3231, simple_loss=0.3668, pruned_loss=0.1397, over 1423789.21 frames.], batch size: 21, lr: 1.92e-03
2022-05-26 13:11:25,780 INFO [train.py:842] (0/4) Epoch 2, batch 1750, loss[loss=0.2635, simple_loss=0.3118, pruned_loss=0.1076, over 7278.00 frames.], tot_loss[loss=0.3246, simple_loss=0.368, pruned_loss=0.1406, over 1423108.95 frames.], batch size: 18, lr: 1.92e-03
2022-05-26 13:12:04,211 INFO [train.py:842] (0/4) Epoch 2, batch 1800, loss[loss=0.3183, simple_loss=0.3645, pruned_loss=0.1361, over 7362.00 frames.], tot_loss[loss=0.3258, simple_loss=0.3687, pruned_loss=0.1414, over 1424568.56 frames.], batch size: 19, lr: 1.92e-03
2022-05-26 13:12:43,080 INFO [train.py:842] (0/4) Epoch 2, batch 1850, loss[loss=0.2818, simple_loss=0.3339, pruned_loss=0.1148, over 7328.00 frames.], tot_loss[loss=0.322, simple_loss=0.366, pruned_loss=0.139, over 1424261.38 frames.], batch size: 20, lr: 1.91e-03
2022-05-26 13:13:21,382 INFO [train.py:842] (0/4) Epoch 2, batch 1900, loss[loss=0.3056, simple_loss=0.3434, pruned_loss=0.1339, over 6999.00 frames.], tot_loss[loss=0.3226, simple_loss=0.3673, pruned_loss=0.1389, over 1427997.60 frames.], batch size: 16, lr: 1.91e-03
2022-05-26 13:14:00,097 INFO [train.py:842] (0/4) Epoch 2, batch 1950, loss[loss=0.2707, simple_loss=0.3131, pruned_loss=0.1142, over 7258.00 frames.], tot_loss[loss=0.326, simple_loss=0.37, pruned_loss=0.141, over 1428479.21 frames.], batch size: 18, lr: 1.91e-03
2022-05-26 13:14:38,224 INFO [train.py:842] (0/4) Epoch 2, batch 2000, loss[loss=0.3171, simple_loss=0.3763, pruned_loss=0.129, over 7118.00 frames.], tot_loss[loss=0.3269, simple_loss=0.3711, pruned_loss=0.1413, over 1421923.15 frames.], batch size: 21, lr: 1.90e-03
2022-05-26 13:15:17,084 INFO [train.py:842] (0/4) Epoch 2, batch 2050, loss[loss=0.3243, simple_loss=0.3898, pruned_loss=0.1294, over 7130.00 frames.], tot_loss[loss=0.3269, simple_loss=0.3714, pruned_loss=0.1412, over 1423475.31 frames.], batch size: 28, lr: 1.90e-03
2022-05-26 13:15:55,559 INFO [train.py:842] (0/4) Epoch 2, batch 2100, loss[loss=0.3336, simple_loss=0.3584, pruned_loss=0.1544, over 7424.00 frames.], tot_loss[loss=0.3271, simple_loss=0.3712, pruned_loss=0.1415, over 1424556.90 frames.], batch size: 18, lr: 1.90e-03
2022-05-26 13:16:34,411 INFO [train.py:842] (0/4) Epoch 2, batch 2150, loss[loss=0.3572, simple_loss=0.4056, pruned_loss=0.1544, over 7418.00 frames.], tot_loss[loss=0.3269, simple_loss=0.3706, pruned_loss=0.1416, over 1423310.72 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:17:12,972 INFO [train.py:842] (0/4) Epoch 2, batch 2200, loss[loss=0.4682, simple_loss=0.456, pruned_loss=0.2402, over 7115.00 frames.], tot_loss[loss=0.3246, simple_loss=0.3691, pruned_loss=0.14, over 1423088.65 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:17:52,126 INFO [train.py:842] (0/4) Epoch 2, batch 2250, loss[loss=0.2977, simple_loss=0.3657, pruned_loss=0.1149, over 7224.00 frames.], tot_loss[loss=0.3238, simple_loss=0.3683, pruned_loss=0.1396, over 1424413.58 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:18:30,733 INFO [train.py:842] (0/4) Epoch 2, batch 2300, loss[loss=0.3716, simple_loss=0.4032, pruned_loss=0.17, over 7209.00 frames.], tot_loss[loss=0.3237, simple_loss=0.3686, pruned_loss=0.1394, over 1424778.58 frames.], batch size: 22, lr: 1.88e-03
2022-05-26 13:19:09,872 INFO [train.py:842] (0/4) Epoch 2, batch 2350, loss[loss=0.3203, simple_loss=0.363, pruned_loss=0.1388, over 7224.00 frames.], tot_loss[loss=0.3224, simple_loss=0.3675, pruned_loss=0.1386, over 1422216.39 frames.], batch size: 20, lr: 1.88e-03
2022-05-26 13:19:48,354 INFO [train.py:842] (0/4) Epoch 2, batch 2400, loss[loss=0.4103, simple_loss=0.4295, pruned_loss=0.1956, over 7311.00 frames.], tot_loss[loss=0.3238, simple_loss=0.3685, pruned_loss=0.1395, over 1422520.67 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 13:20:27,512 INFO [train.py:842] (0/4) Epoch 2, batch 2450, loss[loss=0.2949, simple_loss=0.3557, pruned_loss=0.1171, over 7308.00 frames.], tot_loss[loss=0.3243, simple_loss=0.3693, pruned_loss=0.1396, over 1426702.09 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 13:21:06,101 INFO [train.py:842] (0/4) Epoch 2, batch 2500, loss[loss=0.3532, simple_loss=0.3894, pruned_loss=0.1585, over 7133.00 frames.], tot_loss[loss=0.3225, simple_loss=0.3682, pruned_loss=0.1385, over 1427509.35 frames.], batch size: 26, lr: 1.87e-03
2022-05-26 13:21:44,924 INFO [train.py:842] (0/4) Epoch 2, batch 2550, loss[loss=0.3168, simple_loss=0.3426, pruned_loss=0.1455, over 7013.00 frames.], tot_loss[loss=0.3225, simple_loss=0.3684, pruned_loss=0.1383, over 1427511.94 frames.], batch size: 16, lr: 1.86e-03
2022-05-26 13:22:23,546 INFO [train.py:842] (0/4) Epoch 2, batch 2600, loss[loss=0.3287, simple_loss=0.3821, pruned_loss=0.1376, over 7180.00 frames.], tot_loss[loss=0.3215, simple_loss=0.3674, pruned_loss=0.1378, over 1429259.73 frames.], batch size: 26, lr: 1.86e-03
2022-05-26 13:23:02,363 INFO [train.py:842] (0/4) Epoch 2, batch 2650, loss[loss=0.3139, simple_loss=0.3634, pruned_loss=0.1322, over 6317.00 frames.], tot_loss[loss=0.3208, simple_loss=0.3669, pruned_loss=0.1374, over 1427139.37 frames.], batch size: 37, lr: 1.86e-03
2022-05-26 13:23:41,053 INFO [train.py:842] (0/4) Epoch 2, batch 2700, loss[loss=0.3396, simple_loss=0.3782, pruned_loss=0.1505, over 6739.00 frames.], tot_loss[loss=0.3187, simple_loss=0.3653, pruned_loss=0.136, over 1426621.94 frames.], batch size: 31, lr: 1.85e-03
2022-05-26 13:24:20,333 INFO [train.py:842] (0/4) Epoch 2, batch 2750, loss[loss=0.3678, simple_loss=0.3976, pruned_loss=0.169, over 7300.00 frames.], tot_loss[loss=0.3189, simple_loss=0.3654, pruned_loss=0.1362, over 1423536.71 frames.], batch size: 24, lr: 1.85e-03
2022-05-26 13:24:58,803 INFO [train.py:842] (0/4) Epoch 2, batch 2800, loss[loss=0.3317, simple_loss=0.3846, pruned_loss=0.1394, over 7206.00 frames.], tot_loss[loss=0.3192, simple_loss=0.3657, pruned_loss=0.1364, over 1426325.48 frames.], batch size: 23, lr: 1.85e-03
2022-05-26 13:25:37,893 INFO [train.py:842] (0/4) Epoch 2, batch 2850, loss[loss=0.3149, simple_loss=0.3628, pruned_loss=0.1335, over 7282.00 frames.], tot_loss[loss=0.3212, simple_loss=0.3667, pruned_loss=0.1378, over 1426213.49 frames.], batch size: 24, lr: 1.84e-03
2022-05-26 13:26:16,459 INFO [train.py:842] (0/4) Epoch 2, batch 2900, loss[loss=0.3949, simple_loss=0.4168, pruned_loss=0.1866, over 7241.00 frames.], tot_loss[loss=0.3234, simple_loss=0.3683, pruned_loss=0.1392, over 1420931.73 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 13:26:55,514 INFO [train.py:842] (0/4) Epoch 2, batch 2950, loss[loss=0.2671, simple_loss=0.3312, pruned_loss=0.1015, over 7228.00 frames.], tot_loss[loss=0.3212, simple_loss=0.3669, pruned_loss=0.1378, over 1422018.71 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 13:27:34,165 INFO [train.py:842] (0/4) Epoch 2, batch 3000, loss[loss=0.2783, simple_loss=0.3305, pruned_loss=0.1131, over 7281.00 frames.], tot_loss[loss=0.3184, simple_loss=0.365, pruned_loss=0.1359, over 1425366.07 frames.], batch size: 17, lr: 1.84e-03
2022-05-26 13:27:34,166 INFO [train.py:862] (0/4) Computing validation loss
2022-05-26 13:27:43,282 INFO [train.py:871] (0/4) Epoch 2, validation: loss=0.238, simple_loss=0.3286, pruned_loss=0.07369, over 868885.00 frames.
2022-05-26 13:28:22,611 INFO [train.py:842] (0/4) Epoch 2, batch 3050, loss[loss=0.2693, simple_loss=0.3169, pruned_loss=0.1108, over 7277.00 frames.], tot_loss[loss=0.3208, simple_loss=0.3665, pruned_loss=0.1375, over 1421208.21 frames.], batch size: 18, lr: 1.83e-03
2022-05-26 13:29:00,967 INFO [train.py:842] (0/4) Epoch 2, batch 3100, loss[loss=0.4686, simple_loss=0.4725, pruned_loss=0.2323, over 5364.00 frames.], tot_loss[loss=0.3206, simple_loss=0.3665, pruned_loss=0.1374, over 1421456.21 frames.], batch size: 52, lr: 1.83e-03
2022-05-26 13:29:39,782 INFO [train.py:842] (0/4) Epoch 2, batch 3150, loss[loss=0.2661, simple_loss=0.3155, pruned_loss=0.1083, over 6788.00 frames.], tot_loss[loss=0.32, simple_loss=0.3659, pruned_loss=0.137, over 1423299.55 frames.], batch size: 15, lr: 1.83e-03
2022-05-26 13:30:18,106 INFO [train.py:842] (0/4) Epoch 2, batch 3200, loss[loss=0.298, simple_loss=0.3361, pruned_loss=0.1299, over 4839.00 frames.], tot_loss[loss=0.3236, simple_loss=0.3687, pruned_loss=0.1393, over 1412342.71 frames.], batch size: 53, lr: 1.82e-03
2022-05-26 13:30:56,890 INFO [train.py:842] (0/4) Epoch 2, batch 3250, loss[loss=0.2998, simple_loss=0.3605, pruned_loss=0.1195, over 7204.00 frames.], tot_loss[loss=0.3251, simple_loss=0.3697, pruned_loss=0.1402, over 1415061.01 frames.], batch size: 23, lr: 1.82e-03
2022-05-26 13:31:35,491 INFO [train.py:842] (0/4) Epoch 2, batch 3300, loss[loss=0.3127, simple_loss=0.3638, pruned_loss=0.1308, over 7209.00 frames.], tot_loss[loss=0.322, simple_loss=0.3675, pruned_loss=0.1383, over 1419727.52 frames.], batch size: 22, lr: 1.82e-03
2022-05-26 13:32:14,257 INFO [train.py:842] (0/4) Epoch 2, batch 3350, loss[loss=0.2973, simple_loss=0.3546, pruned_loss=0.12, over 7191.00 frames.], tot_loss[loss=0.3216, simple_loss=0.3678, pruned_loss=0.1377, over 1422736.40 frames.], batch size: 26, lr: 1.81e-03
2022-05-26 13:32:52,898 INFO [train.py:842] (0/4) Epoch 2, batch 3400, loss[loss=0.2842, simple_loss=0.326, pruned_loss=0.1212, over 7156.00 frames.], tot_loss[loss=0.3206, simple_loss=0.3668, pruned_loss=0.1373, over 1424812.81 frames.], batch size: 17, lr: 1.81e-03
2022-05-26 13:33:31,547 INFO [train.py:842] (0/4) Epoch 2, batch 3450, loss[loss=0.294, simple_loss=0.3609, pruned_loss=0.1135, over 7295.00 frames.], tot_loss[loss=0.3184, simple_loss=0.3656, pruned_loss=0.1356, over 1426655.71 frames.], batch size: 24, lr: 1.81e-03
2022-05-26 13:34:10,026 INFO [train.py:842] (0/4) Epoch 2, batch 3500, loss[loss=0.3436, simple_loss=0.3828, pruned_loss=0.1522, over 6273.00 frames.], tot_loss[loss=0.3177, simple_loss=0.365, pruned_loss=0.1352, over 1423986.62 frames.], batch size: 37, lr: 1.80e-03
2022-05-26 13:34:48,895 INFO [train.py:842] (0/4) Epoch 2, batch 3550, loss[loss=0.3517, simple_loss=0.395, pruned_loss=0.1542, over 7312.00 frames.], tot_loss[loss=0.3194, simple_loss=0.3664, pruned_loss=0.1362, over 1424415.13 frames.], batch size: 25, lr: 1.80e-03
2022-05-26 13:35:27,117 INFO [train.py:842] (0/4) Epoch 2, batch 3600, loss[loss=0.4865, simple_loss=0.4573, pruned_loss=0.2579, over 7230.00 frames.], tot_loss[loss=0.3189, simple_loss=0.3663, pruned_loss=0.1358, over 1425592.74 frames.], batch size: 20, lr: 1.80e-03
2022-05-26 13:36:06,067 INFO [train.py:842] (0/4) Epoch 2, batch 3650, loss[loss=0.2698, simple_loss=0.3231, pruned_loss=0.1082, over 7196.00 frames.], tot_loss[loss=0.317, simple_loss=0.3647, pruned_loss=0.1346, over 1427878.45 frames.], batch size: 16, lr: 1.79e-03
2022-05-26 13:36:44,543 INFO [train.py:842] (0/4) Epoch 2, batch 3700, loss[loss=0.2694, simple_loss=0.3356, pruned_loss=0.1017, over 7156.00 frames.], tot_loss[loss=0.3138, simple_loss=0.3629, pruned_loss=0.1324, over 1429803.89 frames.], batch size: 19, lr: 1.79e-03
2022-05-26 13:37:23,446 INFO [train.py:842] (0/4) Epoch 2, batch 3750, loss[loss=0.2851, simple_loss=0.3527, pruned_loss=0.1087, over 7275.00 frames.], tot_loss[loss=0.3144, simple_loss=0.3634, pruned_loss=0.1327, over 1430441.22 frames.], batch size: 24, lr: 1.79e-03
2022-05-26 13:38:02,063 INFO [train.py:842] (0/4) Epoch 2, batch 3800, loss[loss=0.2788, simple_loss=0.3242, pruned_loss=0.1167, over 6983.00 frames.], tot_loss[loss=0.3152, simple_loss=0.3637, pruned_loss=0.1333, over 1431455.57 frames.], batch size: 16, lr: 1.79e-03
2022-05-26 13:38:40,878 INFO [train.py:842] (0/4) Epoch 2, batch 3850, loss[loss=0.296, simple_loss=0.3562, pruned_loss=0.1179, over 7194.00 frames.], tot_loss[loss=0.313, simple_loss=0.3625, pruned_loss=0.1318, over 1431685.81 frames.], batch size: 22, lr: 1.78e-03
2022-05-26 13:39:19,556 INFO [train.py:842] (0/4) Epoch 2, batch 3900, loss[loss=0.3312, simple_loss=0.3837, pruned_loss=0.1393, over 6668.00 frames.], tot_loss[loss=0.3116, simple_loss=0.3617, pruned_loss=0.1308, over 1434161.73 frames.], batch size: 38, lr: 1.78e-03
2022-05-26 13:39:58,481 INFO [train.py:842] (0/4) Epoch 2, batch 3950, loss[loss=0.3629, simple_loss=0.4066, pruned_loss=0.1596, over 7311.00 frames.], tot_loss[loss=0.311, simple_loss=0.3609, pruned_loss=0.1306, over 1432239.67 frames.], batch size: 21, lr: 1.78e-03
2022-05-26 13:40:36,944 INFO [train.py:842] (0/4) Epoch 2, batch 4000, loss[loss=0.4032, simple_loss=0.4114, pruned_loss=0.1975, over 4998.00 frames.], tot_loss[loss=0.3119, simple_loss=0.362, pruned_loss=0.1309, over 1432240.47 frames.], batch size: 52, lr: 1.77e-03
2022-05-26 13:41:15,590 INFO [train.py:842] (0/4) Epoch 2, batch 4050, loss[loss=0.2992, simple_loss=0.3603, pruned_loss=0.1191, over 6865.00 frames.], tot_loss[loss=0.3137, simple_loss=0.3633, pruned_loss=0.132, over 1427768.06 frames.], batch size: 31, lr: 1.77e-03
2022-05-26 13:41:54,111 INFO [train.py:842] (0/4) Epoch 2, batch 4100, loss[loss=0.3345, simple_loss=0.3903, pruned_loss=0.1393, over 7122.00 frames.], tot_loss[loss=0.3159, simple_loss=0.365, pruned_loss=0.1334, over 1429518.69 frames.], batch size: 28, lr: 1.77e-03
2022-05-26 13:42:32,939 INFO [train.py:842] (0/4) Epoch 2, batch 4150, loss[loss=0.3797, simple_loss=0.4091, pruned_loss=0.1751, over 7134.00 frames.], tot_loss[loss=0.3152, simple_loss=0.3637, pruned_loss=0.1334, over 1427396.59 frames.], batch size: 26, lr: 1.76e-03
2022-05-26 13:43:11,631 INFO [train.py:842] (0/4) Epoch 2, batch 4200, loss[loss=0.2299, simple_loss=0.2872, pruned_loss=0.08627, over 7005.00 frames.], tot_loss[loss=0.3128, simple_loss=0.3622, pruned_loss=0.1317, over 1425642.80 frames.], batch size: 16, lr: 1.76e-03
2022-05-26 13:43:50,287 INFO [train.py:842] (0/4) Epoch 2, batch 4250, loss[loss=0.2626, simple_loss=0.3339, pruned_loss=0.09568, over 7215.00 frames.], tot_loss[loss=0.3122, simple_loss=0.362, pruned_loss=0.1311, over 1424008.27 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 13:44:28,859 INFO [train.py:842] (0/4) Epoch 2, batch 4300, loss[loss=0.3121, simple_loss=0.3646, pruned_loss=0.1298, over 7329.00 frames.], tot_loss[loss=0.309, simple_loss=0.3596, pruned_loss=0.1292, over 1426063.30 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 13:45:07,462 INFO [train.py:842] (0/4) Epoch 2, batch 4350, loss[loss=0.2406, simple_loss=0.3062, pruned_loss=0.08755, over 7163.00 frames.], tot_loss[loss=0.3094, simple_loss=0.3598, pruned_loss=0.1295, over 1422688.45 frames.], batch size: 19, lr: 1.75e-03
2022-05-26 13:45:45,797 INFO [train.py:842] (0/4) Epoch 2, batch 4400, loss[loss=0.2755, simple_loss=0.3373, pruned_loss=0.1069, over 7270.00 frames.], tot_loss[loss=0.3108, simple_loss=0.3611, pruned_loss=0.1302, over 1423356.02 frames.], batch size: 24, lr: 1.75e-03
2022-05-26 13:46:25,232 INFO [train.py:842] (0/4) Epoch 2, batch 4450, loss[loss=0.3168, simple_loss=0.3505, pruned_loss=0.1415, over 7409.00 frames.], tot_loss[loss=0.3112, simple_loss=0.3613, pruned_loss=0.1306, over 1423661.71 frames.], batch size: 18, lr: 1.75e-03
2022-05-26 13:47:03,778 INFO [train.py:842] (0/4) Epoch 2, batch 4500, loss[loss=0.2651, simple_loss=0.3223, pruned_loss=0.104, over 7333.00 frames.], tot_loss[loss=0.3108, simple_loss=0.3608, pruned_loss=0.1304, over 1425320.65 frames.], batch size: 20, lr: 1.74e-03
2022-05-26 13:47:42,573 INFO [train.py:842] (0/4) Epoch 2, batch 4550, loss[loss=0.4127, simple_loss=0.4214, pruned_loss=0.202, over 7275.00 frames.], tot_loss[loss=0.3122, simple_loss=0.3617, pruned_loss=0.1314, over 1426037.82 frames.], batch size: 18, lr: 1.74e-03
2022-05-26 13:48:20,814 INFO [train.py:842] (0/4) Epoch 2, batch 4600, loss[loss=0.2938, simple_loss=0.3535, pruned_loss=0.117, over 7201.00 frames.], tot_loss[loss=0.3121, simple_loss=0.3616, pruned_loss=0.1313, over 1421071.45 frames.], batch size: 22, lr: 1.74e-03
2022-05-26 13:48:59,680 INFO [train.py:842] (0/4) Epoch 2, batch 4650, loss[loss=0.327, simple_loss=0.3816, pruned_loss=0.1362, over 7265.00 frames.], tot_loss[loss=0.3099, simple_loss=0.3602, pruned_loss=0.1298, over 1424222.51 frames.], batch size: 25, lr: 1.74e-03
2022-05-26 13:49:38,508 INFO [train.py:842] (0/4) Epoch 2, batch 4700, loss[loss=0.3141, simple_loss=0.3671, pruned_loss=0.1306, over 7318.00 frames.], tot_loss[loss=0.3102, simple_loss=0.3599, pruned_loss=0.1303, over 1424918.93 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 13:50:16,914 INFO [train.py:842] (0/4) Epoch 2, batch 4750, loss[loss=0.3369, simple_loss=0.3851, pruned_loss=0.1444, over 7410.00 frames.], tot_loss[loss=0.3127, simple_loss=0.3614, pruned_loss=0.132, over 1417559.00 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 13:50:55,337 INFO [train.py:842] (0/4) Epoch 2, batch 4800, loss[loss=0.3368, simple_loss=0.3831, pruned_loss=0.1453, over 7301.00 frames.], tot_loss[loss=0.3143, simple_loss=0.3631, pruned_loss=0.1328, over 1415849.02 frames.], batch size: 24, lr: 1.73e-03
2022-05-26 13:51:34,134 INFO [train.py:842] (0/4) Epoch 2, batch 4850, loss[loss=0.2651, simple_loss=0.3228, pruned_loss=0.1037, over 7150.00 frames.], tot_loss[loss=0.3151, simple_loss=0.3634, pruned_loss=0.1333, over 1415946.88 frames.], batch size: 18, lr: 1.73e-03
2022-05-26 13:52:12,637 INFO [train.py:842] (0/4) Epoch 2, batch 4900, loss[loss=0.2168, simple_loss=0.2759, pruned_loss=0.07888, over 7276.00 frames.], tot_loss[loss=0.3118, simple_loss=0.3611, pruned_loss=0.1313, over 1418653.37 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 13:52:51,828 INFO [train.py:842] (0/4) Epoch 2, batch 4950, loss[loss=0.2968, simple_loss=0.362, pruned_loss=0.1158, over 7240.00 frames.], tot_loss[loss=0.312, simple_loss=0.3609, pruned_loss=0.1316, over 1421182.04 frames.], batch size: 20, lr: 1.72e-03
2022-05-26 13:53:30,406 INFO [train.py:842] (0/4) Epoch 2, batch 5000, loss[loss=0.321, simple_loss=0.3502, pruned_loss=0.1459, over 7285.00 frames.], tot_loss[loss=0.3112, simple_loss=0.3608, pruned_loss=0.1308, over 1423476.22 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 13:54:08,956 INFO [train.py:842] (0/4) Epoch 2, batch 5050, loss[loss=0.3242, simple_loss=0.3755, pruned_loss=0.1364, over 7414.00 frames.], tot_loss[loss=0.3131, simple_loss=0.3625, pruned_loss=0.1319, over 1417393.53 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 13:54:47,653 INFO [train.py:842] (0/4) Epoch 2, batch 5100, loss[loss=0.2672, simple_loss=0.3376, pruned_loss=0.09841, over 7158.00 frames.], tot_loss[loss=0.3108, simple_loss=0.3607, pruned_loss=0.1305, over 1419448.24 frames.], batch size: 19, lr: 1.71e-03
2022-05-26 13:55:26,640 INFO [train.py:842] (0/4) Epoch 2, batch 5150, loss[loss=0.3949, simple_loss=0.4238, pruned_loss=0.183, over 7222.00 frames.], tot_loss[loss=0.3129, simple_loss=0.3621, pruned_loss=0.1318, over 1421226.94 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 13:56:05,013 INFO [train.py:842] (0/4) Epoch 2, batch 5200, loss[loss=0.4194, simple_loss=0.4392, pruned_loss=0.1998, over 7283.00 frames.], tot_loss[loss=0.3116, simple_loss=0.3613, pruned_loss=0.1309, over 1422589.00 frames.], batch size: 25, lr: 1.71e-03
2022-05-26 13:56:43,797 INFO [train.py:842] (0/4) Epoch 2, batch 5250, loss[loss=0.3567, simple_loss=0.3971, pruned_loss=0.1581, over 6952.00 frames.], tot_loss[loss=0.3107, simple_loss=0.3609, pruned_loss=0.1302, over 1424991.62 frames.], batch size: 32, lr: 1.70e-03
2022-05-26 13:57:22,561 INFO [train.py:842] (0/4) Epoch 2, batch 5300, loss[loss=0.2915, simple_loss=0.3475, pruned_loss=0.1177, over 7373.00 frames.], tot_loss[loss=0.3094, simple_loss=0.3598, pruned_loss=0.1294, over 1421943.71 frames.], batch size: 23, lr: 1.70e-03
2022-05-26 13:58:01,638 INFO [train.py:842] (0/4) Epoch 2, batch 5350, loss[loss=0.2939, simple_loss=0.3554, pruned_loss=0.1162, over 7353.00 frames.], tot_loss[loss=0.3076, simple_loss=0.3584, pruned_loss=0.1283, over 1419169.17 frames.], batch size: 19, lr: 1.70e-03
2022-05-26 13:58:40,204 INFO [train.py:842] (0/4) Epoch 2, batch 5400, loss[loss=0.3269, simple_loss=0.3803, pruned_loss=0.1367, over 6296.00 frames.], tot_loss[loss=0.3065, simple_loss=0.3576, pruned_loss=0.1277, over 1419700.28 frames.], batch size: 37, lr: 1.70e-03
2022-05-26 13:59:19,543 INFO [train.py:842] (0/4) Epoch 2, batch 5450, loss[loss=0.3064, simple_loss=0.3457, pruned_loss=0.1336, over 6799.00 frames.], tot_loss[loss=0.3048, simple_loss=0.3561, pruned_loss=0.1267, over 1421266.45 frames.], batch size: 15, lr: 1.69e-03
2022-05-26 13:59:58,074 INFO [train.py:842] (0/4) Epoch 2, batch 5500, loss[loss=0.2624, simple_loss=0.3162, pruned_loss=0.1043, over 7131.00 frames.], tot_loss[loss=0.3058, simple_loss=0.3564, pruned_loss=0.1276, over 1422566.08 frames.], batch size: 17, lr: 1.69e-03
2022-05-26 14:00:37,017 INFO [train.py:842] (0/4) Epoch 2, batch 5550, loss[loss=0.2938, simple_loss=0.3514, pruned_loss=0.1181, over 6992.00 frames.], tot_loss[loss=0.3063, simple_loss=0.3565, pruned_loss=0.128, over 1422978.52 frames.], batch size: 16, lr: 1.69e-03
2022-05-26 14:01:15,380 INFO [train.py:842] (0/4) Epoch 2, batch 5600, loss[loss=0.3209, simple_loss=0.3699, pruned_loss=0.1359, over 7286.00 frames.], tot_loss[loss=0.3091, simple_loss=0.359, pruned_loss=0.1296, over 1423625.70 frames.], batch size: 24, lr: 1.69e-03
2022-05-26 14:01:54,145 INFO [train.py:842] (0/4) Epoch 2, batch 5650, loss[loss=0.3133, simple_loss=0.3796, pruned_loss=0.1235, over 7227.00 frames.], tot_loss[loss=0.3082, simple_loss=0.3587, pruned_loss=0.1289, over 1424675.85 frames.], batch size: 23, lr: 1.68e-03
2022-05-26 14:02:32,792 INFO [train.py:842] (0/4) Epoch 2, batch 5700, loss[loss=0.2217, simple_loss=0.2951, pruned_loss=0.07416, over 7277.00 frames.], tot_loss[loss=0.3059, simple_loss=0.3566, pruned_loss=0.1276, over 1424371.95 frames.], batch size: 18, lr: 1.68e-03
2022-05-26 14:03:11,573 INFO [train.py:842] (0/4) Epoch 2, batch 5750, loss[loss=0.319, simple_loss=0.3799, pruned_loss=0.1291, over 7306.00 frames.], tot_loss[loss=0.3052, simple_loss=0.3564, pruned_loss=0.127, over 1422667.85 frames.], batch size: 21, lr: 1.68e-03
2022-05-26 14:03:50,309 INFO [train.py:842] (0/4) Epoch 2, batch 5800, loss[loss=0.2916, simple_loss=0.3547, pruned_loss=0.1143, over 7145.00 frames.], tot_loss[loss=0.3045, simple_loss=0.356, pruned_loss=0.1265, over 1426674.19 frames.], batch size: 26, lr: 1.68e-03
2022-05-26 14:04:29,348 INFO [train.py:842] (0/4) Epoch 2, batch 5850, loss[loss=0.3512, simple_loss=0.3884, pruned_loss=0.157, over 7405.00 frames.], tot_loss[loss=0.3063, simple_loss=0.3575, pruned_loss=0.1276, over 1422870.48 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 14:05:07,952 INFO [train.py:842] (0/4) Epoch 2, batch 5900, loss[loss=0.3605, simple_loss=0.3763, pruned_loss=0.1724, over 7282.00 frames.], tot_loss[loss=0.306, simple_loss=0.3573, pruned_loss=0.1273, over 1424640.98 frames.], batch size: 17, lr: 1.67e-03
2022-05-26 14:05:46,678 INFO [train.py:842] (0/4) Epoch 2, batch 5950, loss[loss=0.3563, simple_loss=0.3956, pruned_loss=0.1585, over 7200.00 frames.], tot_loss[loss=0.3082, simple_loss=0.3593, pruned_loss=0.1285, over 1423781.91 frames.], batch size: 22, lr: 1.67e-03
2022-05-26 14:06:25,158 INFO [train.py:842] (0/4) Epoch 2, batch 6000, loss[loss=0.2648, simple_loss=0.3325, pruned_loss=0.09852, over 7405.00 frames.], tot_loss[loss=0.3077, simple_loss=0.359, pruned_loss=0.1282, over 1420434.44 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 14:06:25,159 INFO [train.py:862] (0/4) Computing validation loss
2022-05-26 14:06:34,411 INFO [train.py:871] (0/4) Epoch 2, validation: loss=0.2262, simple_loss=0.3196, pruned_loss=0.0664, over 868885.00 frames.
2022-05-26 14:07:13,292 INFO [train.py:842] (0/4) Epoch 2, batch 6050, loss[loss=0.298, simple_loss=0.36, pruned_loss=0.118, over 7200.00 frames.], tot_loss[loss=0.3058, simple_loss=0.3576, pruned_loss=0.1269, over 1424145.28 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 14:07:51,787 INFO [train.py:842] (0/4) Epoch 2, batch 6100, loss[loss=0.2984, simple_loss=0.3614, pruned_loss=0.1177, over 7360.00 frames.], tot_loss[loss=0.3049, simple_loss=0.357, pruned_loss=0.1264, over 1426710.01 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 14:08:30,698 INFO [train.py:842] (0/4) Epoch 2, batch 6150, loss[loss=0.3289, simple_loss=0.3653, pruned_loss=0.1462, over 6988.00 frames.], tot_loss[loss=0.3046, simple_loss=0.3567, pruned_loss=0.1263, over 1426576.95 frames.], batch size: 28, lr: 1.66e-03
2022-05-26 14:09:09,176 INFO [train.py:842] (0/4) Epoch 2, batch 6200, loss[loss=0.4046, simple_loss=0.4329, pruned_loss=0.1882, over 6726.00 frames.], tot_loss[loss=0.3056, simple_loss=0.3576, pruned_loss=0.1268, over 1424669.59 frames.], batch size: 31, lr: 1.66e-03
2022-05-26 14:09:47,987 INFO [train.py:842] (0/4) Epoch 2, batch 6250, loss[loss=0.3359, simple_loss=0.3899, pruned_loss=0.1409, over 7109.00 frames.], tot_loss[loss=0.3051, simple_loss=0.3573, pruned_loss=0.1264, over 1427991.60 frames.], batch size: 21, lr: 1.65e-03
2022-05-26 14:10:27,074 INFO [train.py:842] (0/4) Epoch 2, batch 6300, loss[loss=0.3176, simple_loss=0.3629, pruned_loss=0.1362, over 7149.00 frames.], tot_loss[loss=0.306, simple_loss=0.3581, pruned_loss=0.127, over 1432149.10 frames.], batch size: 26, lr: 1.65e-03
2022-05-26 14:11:05,661 INFO [train.py:842] (0/4) Epoch 2, batch 6350, loss[loss=0.3631, simple_loss=0.4033, pruned_loss=0.1614, over 6481.00 frames.], tot_loss[loss=0.3083, simple_loss=0.3601, pruned_loss=0.1283, over 1430266.23 frames.], batch size: 38, lr: 1.65e-03
2022-05-26 14:11:44,200 INFO [train.py:842] (0/4) Epoch 2, batch 6400, loss[loss=0.2781, simple_loss=0.3499, pruned_loss=0.1031, over 6740.00 frames.], tot_loss[loss=0.3058, simple_loss=0.3577, pruned_loss=0.1269, over 1425780.11 frames.], batch size: 31, lr: 1.65e-03
2022-05-26 14:12:23,047 INFO [train.py:842] (0/4) Epoch 2, batch 6450, loss[loss=0.2486, simple_loss=0.3032, pruned_loss=0.09703, over 7417.00 frames.], tot_loss[loss=0.3049, simple_loss=0.3567, pruned_loss=0.1266, over 1425575.17 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:13:01,662 INFO [train.py:842] (0/4) Epoch 2, batch 6500, loss[loss=0.3099, simple_loss=0.3624, pruned_loss=0.1286, over 7201.00 frames.], tot_loss[loss=0.3036, simple_loss=0.3559, pruned_loss=0.1256, over 1424325.44 frames.], batch size: 22, lr: 1.64e-03
2022-05-26 14:13:40,427 INFO [train.py:842] (0/4) Epoch 2, batch 6550, loss[loss=0.2571, simple_loss=0.3183, pruned_loss=0.098, over 7453.00 frames.], tot_loss[loss=0.3044, simple_loss=0.3567, pruned_loss=0.126, over 1422313.58 frames.], batch size: 19, lr: 1.64e-03
2022-05-26 14:14:18,959 INFO [train.py:842] (0/4) Epoch 2, batch 6600, loss[loss=0.2804, simple_loss=0.3318, pruned_loss=0.1145, over 7268.00 frames.], tot_loss[loss=0.302, simple_loss=0.355, pruned_loss=0.1245, over 1421964.36 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:14:57,584 INFO [train.py:842] (0/4) Epoch 2, batch 6650, loss[loss=0.3094, simple_loss=0.3675, pruned_loss=0.1256, over 7211.00 frames.], tot_loss[loss=0.3056, simple_loss=0.3575, pruned_loss=0.1268, over 1414573.48 frames.], batch size: 23, lr: 1.63e-03
2022-05-26 14:15:36,108 INFO [train.py:842] (0/4) Epoch 2, batch 6700, loss[loss=0.2928, simple_loss=0.3397, pruned_loss=0.123, over 7276.00 frames.], tot_loss[loss=0.304, simple_loss=0.3565, pruned_loss=0.1257, over 1419701.40 frames.], batch size: 17, lr: 1.63e-03
2022-05-26 14:16:14,706 INFO [train.py:842] (0/4) Epoch 2, batch 6750, loss[loss=0.2973, simple_loss=0.3536, pruned_loss=0.1205, over 7232.00 frames.], tot_loss[loss=0.3016, simple_loss=0.3554, pruned_loss=0.1239, over 1422341.11 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 14:16:53,059 INFO [train.py:842] (0/4) Epoch 2, batch 6800, loss[loss=0.3136, simple_loss=0.371, pruned_loss=0.1281, over 7107.00 frames.], tot_loss[loss=0.3002, simple_loss=0.3545, pruned_loss=0.1229, over 1424525.78 frames.], batch size: 21, lr: 1.63e-03
2022-05-26 14:16:57,935 INFO [checkpoint.py:75] (0/4) Saving checkpoint to streaming_pruned_transducer_stateless4/exp/checkpoint-16000.pt
2022-05-26 14:17:34,587 INFO [train.py:842] (0/4) Epoch 2, batch 6850, loss[loss=0.2625, simple_loss=0.3186, pruned_loss=0.1032, over 7328.00 frames.], tot_loss[loss=0.3007, simple_loss=0.355, pruned_loss=0.1232, over 1421156.74 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 14:18:13,245 INFO [train.py:842] (0/4) Epoch 2, batch 6900, loss[loss=0.2647, simple_loss=0.3242, pruned_loss=0.1026, over 7426.00 frames.], tot_loss[loss=0.3007, simple_loss=0.355, pruned_loss=0.1231, over 1420458.42 frames.], batch size: 20, lr: 1.62e-03
2022-05-26 14:18:52,139 INFO [train.py:842] (0/4) Epoch 2, batch 6950, loss[loss=0.2548, simple_loss=0.3276, pruned_loss=0.09099, over 7288.00 frames.], tot_loss[loss=0.2992, simple_loss=0.3534, pruned_loss=0.1225, over 1420741.03 frames.], batch size: 18, lr: 1.62e-03
2022-05-26 14:19:30,566 INFO [train.py:842] (0/4) Epoch 2, batch 7000, loss[loss=0.2643, simple_loss=0.3223, pruned_loss=0.1032, over 7325.00 frames.], tot_loss[loss=0.2982, simple_loss=0.3533, pruned_loss=0.1216, over 1422796.89 frames.], batch size: 21, lr: 1.62e-03
2022-05-26 14:20:09,754 INFO [train.py:842] (0/4) Epoch 2, batch 7050, loss[loss=0.3176, simple_loss=0.3545, pruned_loss=0.1404, over 5363.00 frames.], tot_loss[loss=0.2966, simple_loss=0.3517, pruned_loss=0.1208, over 1425503.17 frames.], batch size: 52, lr: 1.62e-03
2022-05-26 14:20:48,374 INFO [train.py:842] (0/4) Epoch 2, batch 7100, loss[loss=0.2868, simple_loss=0.347, pruned_loss=0.1133, over 7112.00 frames.], tot_loss[loss=0.3, simple_loss=0.3541, pruned_loss=0.123, over 1425121.55 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 14:21:26,952 INFO [train.py:842] (0/4) Epoch 2, batch 7150, loss[loss=0.2855, simple_loss=0.3479, pruned_loss=0.1115, over 7416.00 frames.], tot_loss[loss=0.3029, simple_loss=0.356, pruned_loss=0.1249, over 1422132.13 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 14:22:05,293 INFO [train.py:842] (0/4) Epoch 2, batch 7200, loss[loss=0.2315, simple_loss=0.2893, pruned_loss=0.08682, over 6991.00 frames.], tot_loss[loss=0.3026, simple_loss=0.3555, pruned_loss=0.1248, over 1419725.14 frames.], batch size: 16, lr: 1.61e-03
2022-05-26 14:22:44,438 INFO [train.py:842] (0/4) Epoch 2, batch 7250, loss[loss=0.2656, simple_loss=0.3353, pruned_loss=0.09794, over 7236.00 frames.], tot_loss[loss=0.3015, simple_loss=0.355, pruned_loss=0.124, over 1425049.89 frames.], batch size: 20, lr: 1.61e-03
2022-05-26 14:23:22,967 INFO [train.py:842] (0/4) Epoch 2, batch 7300, loss[loss=0.3138, simple_loss=0.3659, pruned_loss=0.1309, over 7210.00 frames.], tot_loss[loss=0.3032, simple_loss=0.3558, pruned_loss=0.1254, over 1427524.08 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 14:24:01,873 INFO [train.py:842] (0/4) Epoch 2, batch 7350, loss[loss=0.4031, simple_loss=0.4264, pruned_loss=0.1899, over 4822.00 frames.], tot_loss[loss=0.3004, simple_loss=0.3535, pruned_loss=0.1236, over 1423703.50 frames.], batch size: 52, lr: 1.60e-03
2022-05-26 14:24:40,512 INFO [train.py:842] (0/4) Epoch 2, batch 7400, loss[loss=0.2508, simple_loss=0.313, pruned_loss=0.09431, over 6985.00 frames.], tot_loss[loss=0.2999, simple_loss=0.3532, pruned_loss=0.1233, over 1423095.26 frames.], batch size: 16, lr: 1.60e-03
2022-05-26 14:25:19,330 INFO [train.py:842] (0/4) Epoch 2, batch 7450, loss[loss=0.2896, simple_loss=0.3417, pruned_loss=0.1187, over 7343.00 frames.], tot_loss[loss=0.3006, simple_loss=0.3537, pruned_loss=0.1238, over 1418852.70 frames.], batch size: 19, lr: 1.60e-03
2022-05-26 14:25:57,996 INFO [train.py:842] (0/4) Epoch 2, batch 7500, loss[loss=0.2714, simple_loss=0.3458, pruned_loss=0.0985, over 7213.00 frames.], tot_loss[loss=0.302, simple_loss=0.355, pruned_loss=0.1244, over 1419843.71 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 14:26:36,915 INFO [train.py:842] (0/4) Epoch 2, batch 7550, loss[loss=0.3039, simple_loss=0.3596, pruned_loss=0.1241, over 7404.00 frames.], tot_loss[loss=0.3003, simple_loss=0.354, pruned_loss=0.1233, over 1420963.12 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 14:27:15,663 INFO [train.py:842] (0/4) Epoch 2, batch 7600, loss[loss=0.3808, simple_loss=0.4069, pruned_loss=0.1773, over 5391.00 frames.], tot_loss[loss=0.3001, simple_loss=0.3535, pruned_loss=0.1233, over 1421102.83 frames.], batch size: 52, lr: 1.59e-03
2022-05-26 14:27:54,288 INFO [train.py:842] (0/4) Epoch 2, batch 7650, loss[loss=0.3183, simple_loss=0.3721, pruned_loss=0.1322, over 7420.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3531, pruned_loss=0.1227, over 1421699.63 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 14:28:32,786 INFO [train.py:842] (0/4) Epoch 2, batch 7700, loss[loss=0.3323, simple_loss=0.3852, pruned_loss=0.1397, over 7341.00 frames.], tot_loss[loss=0.2979, simple_loss=0.3521, pruned_loss=0.1218, over 1423316.38 frames.], batch size: 22, lr: 1.59e-03
2022-05-26 14:29:11,466 INFO [train.py:842] (0/4) Epoch 2, batch 7750, loss[loss=0.3157, simple_loss=0.381, pruned_loss=0.1252, over 7078.00 frames.], tot_loss[loss=0.298, simple_loss=0.3526, pruned_loss=0.1217, over 1424497.74 frames.], batch size: 28, lr: 1.59e-03
2022-05-26 14:29:50,000 INFO [train.py:842] (0/4) Epoch 2, batch 7800, loss[loss=0.3816, simple_loss=0.3983, pruned_loss=0.1824, over 7141.00 frames.], tot_loss[loss=0.2995, simple_loss=0.3539, pruned_loss=0.1226, over 1423731.93 frames.], batch size: 20, lr: 1.58e-03
2022-05-26 14:30:28,804 INFO [train.py:842] (0/4) Epoch 2, batch 7850, loss[loss=0.369, simple_loss=0.4027, pruned_loss=0.1677, over 7320.00 frames.], tot_loss[loss=0.2983, simple_loss=0.3529, pruned_loss=0.1218, over 1424272.71 frames.], batch size: 21, lr: 1.58e-03
2022-05-26 14:31:07,199 INFO [train.py:842] (0/4) Epoch 2, batch 7900, loss[loss=0.4059, simple_loss=0.415, pruned_loss=0.1984, over 5202.00 frames.], tot_loss[loss=0.3006, simple_loss=0.3545, pruned_loss=0.1234, over 1426656.01 frames.], batch size: 53, lr: 1.58e-03
2022-05-26 14:31:46,043 INFO [train.py:842] (0/4) Epoch 2, batch 7950, loss[loss=0.288, simple_loss=0.344, pruned_loss=0.1161, over 7159.00 frames.], tot_loss[loss=0.2984, simple_loss=0.3532, pruned_loss=0.1219, over 1428315.63 frames.], batch size: 18, lr: 1.58e-03
2022-05-26 14:32:24,256 INFO [train.py:842] (0/4) Epoch 2, batch 8000, loss[loss=0.2746, simple_loss=0.3423, pruned_loss=0.1034, over 7211.00 frames.], tot_loss[loss=0.2971, simple_loss=0.3524, pruned_loss=0.1209, over 1426745.97 frames.], batch size: 21, lr: 1.57e-03
2022-05-26 14:33:02,919 INFO [train.py:842] (0/4) Epoch 2, batch 8050, loss[loss=0.3373, simple_loss=0.3807, pruned_loss=0.1469, over 6473.00 frames.], tot_loss[loss=0.299, simple_loss=0.3538, pruned_loss=0.122, over 1424708.70 frames.], batch size: 37, lr: 1.57e-03
2022-05-26 14:33:41,415 INFO [train.py:842] (0/4) Epoch 2, batch 8100, loss[loss=0.3343, simple_loss=0.3768, pruned_loss=0.1459, over 7196.00 frames.], tot_loss[loss=0.2982, simple_loss=0.3532, pruned_loss=0.1216, over 1427660.09 frames.], batch size: 26, lr: 1.57e-03
2022-05-26 14:34:20,565 INFO [train.py:842] (0/4) Epoch 2, batch 8150, loss[loss=0.3023, simple_loss=0.3543, pruned_loss=0.1251, over 7076.00 frames.], tot_loss[loss=0.2965, simple_loss=0.3516, pruned_loss=0.1207, over 1429183.01 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 14:34:58,967 INFO [train.py:842] (0/4) Epoch 2, batch 8200, loss[loss=0.2643, simple_loss=0.324, pruned_loss=0.1023, over 7271.00 frames.], tot_loss[loss=0.2971, simple_loss=0.3521, pruned_loss=0.1211, over 1424677.95 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 14:35:38,135 INFO [train.py:842] (0/4) Epoch 2, batch 8250, loss[loss=0.3314, simple_loss=0.3752, pruned_loss=0.1438, over 7070.00 frames.], tot_loss[loss=0.296, simple_loss=0.3505, pruned_loss=0.1208, over 1422250.89 frames.], batch size: 28, lr: 1.56e-03
2022-05-26 14:36:16,475 INFO [train.py:842] (0/4) Epoch 2, batch 8300, loss[loss=0.3009, simple_loss=0.3566, pruned_loss=0.1226, over 7145.00 frames.], tot_loss[loss=0.2958, simple_loss=0.3505, pruned_loss=0.1206, over 1420461.16 frames.], batch size: 20, lr: 1.56e-03
2022-05-26 14:36:55,279 INFO [train.py:842] (0/4) Epoch 2, batch 8350, loss[loss=0.4366, simple_loss=0.4468, pruned_loss=0.2132, over 4922.00 frames.], tot_loss[loss=0.2936, simple_loss=0.3494, pruned_loss=0.1188, over 1420153.56 frames.], batch size: 53, lr: 1.56e-03
2022-05-26 14:37:33,565 INFO [train.py:842] (0/4) Epoch 2, batch 8400, loss[loss=0.2579, simple_loss=0.3177, pruned_loss=0.09903, over 7148.00 frames.], tot_loss[loss=0.2941, simple_loss=0.3499, pruned_loss=0.1191, over 1419376.02 frames.], batch size: 17, lr: 1.56e-03
2022-05-26 14:38:12,064 INFO [train.py:842] (0/4) Epoch 2, batch 8450, loss[loss=0.3288, simple_loss=0.4024, pruned_loss=0.1275, over 7211.00 frames.], tot_loss[loss=0.2953, simple_loss=0.3512, pruned_loss=0.1197, over 1415027.74 frames.], batch size: 22, lr: 1.56e-03
2022-05-26 14:38:50,512 INFO [train.py:842] (0/4) Epoch 2, batch 8500, loss[loss=0.3838, simple_loss=0.3916, pruned_loss=0.188, over 7137.00 frames.], tot_loss[loss=0.2961, simple_loss=0.3512, pruned_loss=0.1205, over 1418660.75 frames.], batch size: 17, lr: 1.55e-03
2022-05-26 14:39:29,175 INFO [train.py:842] (0/4) Epoch 2, batch 8550, loss[loss=0.2275, simple_loss=0.2929, pruned_loss=0.08105, over 7342.00 frames.], tot_loss[loss=0.2967, simple_loss=0.3521, pruned_loss=0.1207, over 1423267.61 frames.], batch size: 19, lr: 1.55e-03
2022-05-26 14:40:07,849 INFO [train.py:842] (0/4) Epoch 2, batch 8600, loss[loss=0.374, simple_loss=0.4082, pruned_loss=0.1699, over 6446.00 frames.], tot_loss[loss=0.2945, simple_loss=0.3498, pruned_loss=0.1196, over 1420207.10 frames.], batch size: 37, lr: 1.55e-03
2022-05-26 14:40:46,956 INFO [train.py:842] (0/4) Epoch 2, batch 8650, loss[loss=0.2873, simple_loss=0.355, pruned_loss=0.1098, over 7149.00 frames.], tot_loss[loss=0.2959, simple_loss=0.3509, pruned_loss=0.1204, over 1422166.54 frames.], batch size: 20, lr: 1.55e-03
2022-05-26 14:41:25,748 INFO [train.py:842] (0/4) Epoch 2, batch 8700, loss[loss=0.269, simple_loss=0.327, pruned_loss=0.1055, over 7075.00 frames.], tot_loss[loss=0.2932, simple_loss=0.3493, pruned_loss=0.1186, over 1421121.19 frames.], batch size: 18, lr: 1.55e-03
2022-05-26 14:42:04,176 INFO [train.py:842] (0/4) Epoch 2, batch 8750, loss[loss=0.2349, simple_loss=0.301, pruned_loss=0.08436, over 7160.00 frames.], tot_loss[loss=0.2922, simple_loss=0.3487, pruned_loss=0.1179, over 1420532.75 frames.], batch size: 18, lr: 1.54e-03
2022-05-26 14:42:42,567 INFO [train.py:842] (0/4) Epoch 2, batch 8800, loss[loss=0.3222, simple_loss=0.3763, pruned_loss=0.134, over 7333.00 frames.], tot_loss[loss=0.2976, simple_loss=0.3518, pruned_loss=0.1217, over 1412882.90 frames.], batch size: 22, lr: 1.54e-03
2022-05-26 14:43:21,145 INFO [train.py:842] (0/4) Epoch 2, batch 8850, loss[loss=0.3963, simple_loss=0.4275, pruned_loss=0.1826, over 7267.00 frames.], tot_loss[loss=0.2979, simple_loss=0.3521, pruned_loss=0.1219, over 1411056.68 frames.], batch size: 24, lr: 1.54e-03
2022-05-26 14:43:59,278 INFO [train.py:842] (0/4) Epoch 2, batch 8900, loss[loss=0.2722, simple_loss=0.3359, pruned_loss=0.1042, over 6667.00 frames.], tot_loss[loss=0.2988, simple_loss=0.3526, pruned_loss=0.1225, over 1401951.45 frames.], batch size: 31, lr: 1.54e-03
2022-05-26 14:44:37,748 INFO [train.py:842] (0/4) Epoch 2, batch 8950, loss[loss=0.3059, simple_loss=0.3656, pruned_loss=0.123, over 7111.00 frames.], tot_loss[loss=0.2978, simple_loss=0.352, pruned_loss=0.1219, over 1402203.66 frames.], batch size: 21, lr: 1.54e-03
2022-05-26 14:45:16,040 INFO [train.py:842] (0/4) Epoch 2, batch 9000, loss[loss=0.3234, simple_loss=0.362, pruned_loss=0.1424, over 7271.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3532, pruned_loss=0.1227, over 1397438.09 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 14:45:16,041 INFO [train.py:862] (0/4) Computing validation loss
2022-05-26 14:45:25,234 INFO [train.py:871] (0/4) Epoch 2, validation: loss=0.2179, simple_loss=0.3144, pruned_loss=0.06069, over 868885.00 frames.
2022-05-26 14:46:03,593 INFO [train.py:842] (0/4) Epoch 2, batch 9050, loss[loss=0.2595, simple_loss=0.3231, pruned_loss=0.09797, over 7274.00 frames.], tot_loss[loss=0.3014, simple_loss=0.3545, pruned_loss=0.1241, over 1382698.79 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 14:46:40,966 INFO [train.py:842] (0/4) Epoch 2, batch 9100, loss[loss=0.3601, simple_loss=0.4026, pruned_loss=0.1588, over 5105.00 frames.], tot_loss[loss=0.3054, simple_loss=0.3572, pruned_loss=0.1268, over 1329781.40 frames.], batch size: 52, lr: 1.53e-03
2022-05-26 14:47:18,817 INFO [train.py:842] (0/4) Epoch 2, batch 9150, loss[loss=0.337, simple_loss=0.3778, pruned_loss=0.1481, over 5021.00 frames.], tot_loss[loss=0.3137, simple_loss=0.3626, pruned_loss=0.1324, over 1258555.56 frames.], batch size: 52, lr: 1.53e-03