diff --git "a/exp/log/log-train-2023-11-12-20-52-32-1" "b/exp/log/log-train-2023-11-12-20-52-32-1"
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+++ "b/exp/log/log-train-2023-11-12-20-52-32-1"
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+2023-11-12 20:52:32,876 INFO [train.py:699] (1/4) Training started
+2023-11-12 20:52:32,877 INFO [train.py:709] (1/4) Device: cuda:1
+2023-11-12 20:52:34,053 INFO [train.py:716] (1/4) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': -1, 'log_interval': 50, 'valid_interval': 200, 'env_info': {'k2-version': '1.24.4', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '44a9d5682af9fd3ef77074777e15278ec6d390eb', 'k2-git-date': 'Wed Sep 27 11:22:55 2023', 'lhotse-version': '0.0.0+unknown.version', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'vits', 'icefall-git-sha1': 'f55e80a7-dirty', 'icefall-git-date': 'Mon Nov 6 15:05:49 2023', 'icefall-path': '/star-zw/workspace/tts/icefall_tts', 'k2-path': '/star-zw/workspace/k2/k2/k2/python/k2/__init__.py', 'lhotse-path': '/star-zw/workspace/lhotse/lhotse_dev/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-6-0423201309-7c68fd68fb-qfn6b', 'IP address': '10.177.58.19'}, 'sampling_rate': 22050, 'frame_shift': 256, 'frame_length': 1024, 'feature_dim': 513, 'n_mels': 80, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'world_size': 4, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 1000, 'start_epoch': 1, 'exp_dir': PosixPath('vits/exp-g2p-conformer-text-encoder-new'), 'tokens': 'data/tokens.txt', 'lr': 0.0002, 'seed': 42, 'print_diagnostics': False, 'inf_check': False, 'save_every_n': 20, 'use_fp16': True, 'manifest_dir': PosixPath('data/spectrogram'), 'max_duration': 500, 'bucketing_sampler': True, 'num_buckets': 30, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': False, 'num_workers': 2, 'input_strategy': 'PrecomputedFeatures', 'blank_id': 0, 'oov_id': 2, 'vocab_size': 79}
+2023-11-12 20:52:34,053 INFO [train.py:718] (1/4) About to create model
+2023-11-12 20:52:37,019 INFO [train.py:724] (1/4) Number of parameters in generator: 35621746
+2023-11-12 20:52:37,020 INFO [train.py:726] (1/4) Number of parameters in discriminator: 50974956
+2023-11-12 20:52:37,020 INFO [train.py:727] (1/4) Total number of parameters: 86596702
+2023-11-12 20:52:43,885 INFO [train.py:734] (1/4) Using DDP
+2023-11-12 20:52:44,568 INFO [tts_datamodule.py:314] (1/4) About to get train cuts
+2023-11-12 20:52:44,858 INFO [tts_datamodule.py:169] (1/4) About to create train dataset
+2023-11-12 20:52:44,858 INFO [tts_datamodule.py:195] (1/4) Using DynamicBucketingSampler.
+2023-11-12 20:52:46,891 INFO [tts_datamodule.py:210] (1/4) About to create train dataloader
+2023-11-12 20:52:46,892 INFO [tts_datamodule.py:321] (1/4) About to get validation cuts
+2023-11-12 20:52:46,893 INFO [tts_datamodule.py:233] (1/4) About to create dev dataset
+2023-11-12 20:52:46,905 INFO [tts_datamodule.py:262] (1/4) About to create valid dataloader
+2023-11-12 20:52:46,905 INFO [train.py:628] (1/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
+2023-11-12 20:52:56,705 INFO [train.py:674] (1/4) Maximum memory allocated so far is 14175MB
+2023-11-12 20:53:00,862 INFO [train.py:674] (1/4) Maximum memory allocated so far is 14525MB
+2023-11-12 20:53:05,499 INFO [train.py:674] (1/4) Maximum memory allocated so far is 15802MB
+2023-11-12 20:53:10,155 INFO [train.py:674] (1/4) Maximum memory allocated so far is 15804MB
+2023-11-12 20:53:18,223 INFO [train.py:674] (1/4) Maximum memory allocated so far is 22872MB
+2023-11-12 20:53:26,724 INFO [train.py:674] (1/4) Maximum memory allocated so far is 22872MB
+2023-11-12 20:53:26,755 INFO [train.py:811] (1/4) Start epoch 1
+2023-11-12 20:53:41,228 INFO [train.py:467] (1/4) Epoch 1, batch 0, global_batch_idx: 0, batch size: 69, loss[discriminator_loss=5.934, discriminator_real_loss=5.934, discriminator_fake_loss=0.001226, generator_loss=974.1, generator_mel_loss=74.19, generator_kl_loss=892.9, generator_dur_loss=2.041, generator_adv_loss=4.762, generator_feat_match_loss=0.245, over 69.00 samples.], tot_loss[discriminator_loss=5.934, discriminator_real_loss=5.934, discriminator_fake_loss=0.001226, generator_loss=974.1, generator_mel_loss=74.19, generator_kl_loss=892.9, generator_dur_loss=2.041, generator_adv_loss=4.762, generator_feat_match_loss=0.245, over 69.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 2.0
+2023-11-12 20:53:41,228 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 20:53:52,378 INFO [train.py:517] (1/4) Epoch 1, validation: discriminator_loss=4.839, discriminator_real_loss=4.761, discriminator_fake_loss=0.0778, generator_loss=489.8, generator_mel_loss=72.73, generator_kl_loss=409.9, generator_dur_loss=2.185, generator_adv_loss=4.762, generator_feat_match_loss=0.2584, over 100.00 samples.
+2023-11-12 20:53:52,379 INFO [train.py:518] (1/4) Maximum memory allocated so far is 22872MB
+2023-11-12 20:57:06,480 INFO [train.py:811] (1/4) Start epoch 2
+2023-11-12 20:58:36,620 INFO [train.py:467] (1/4) Epoch 2, batch 13, global_batch_idx: 50, batch size: 64, loss[discriminator_loss=3.422, discriminator_real_loss=2.506, discriminator_fake_loss=0.9155, generator_loss=61.51, generator_mel_loss=42.79, generator_kl_loss=12.53, generator_dur_loss=1.817, generator_adv_loss=2.271, generator_feat_match_loss=2.105, over 64.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.615, discriminator_fake_loss=1.072, generator_loss=66.4, generator_mel_loss=43.57, generator_kl_loss=15.42, generator_dur_loss=1.808, generator_adv_loss=2.477, generator_feat_match_loss=3.128, over 1077.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 2.0
+2023-11-12 21:00:35,263 INFO [train.py:811] (1/4) Start epoch 3
+2023-11-12 21:02:59,082 INFO [train.py:467] (1/4) Epoch 3, batch 26, global_batch_idx: 100, batch size: 65, loss[discriminator_loss=3.203, discriminator_real_loss=1.632, discriminator_fake_loss=1.572, generator_loss=53.09, generator_mel_loss=39.63, generator_kl_loss=6.835, generator_dur_loss=1.877, generator_adv_loss=2.023, generator_feat_match_loss=2.727, over 65.00 samples.], tot_loss[discriminator_loss=2.624, discriminator_real_loss=1.449, discriminator_fake_loss=1.175, generator_loss=55.31, generator_mel_loss=39.94, generator_kl_loss=7.549, generator_dur_loss=1.859, generator_adv_loss=2.444, generator_feat_match_loss=3.513, over 1932.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 4.0
+2023-11-12 21:04:04,496 INFO [train.py:811] (1/4) Start epoch 4
+2023-11-12 21:07:36,880 INFO [train.py:811] (1/4) Start epoch 5
+2023-11-12 21:08:06,176 INFO [train.py:467] (1/4) Epoch 5, batch 2, global_batch_idx: 150, batch size: 153, loss[discriminator_loss=2.695, discriminator_real_loss=1.436, discriminator_fake_loss=1.26, generator_loss=48.52, generator_mel_loss=38.07, generator_kl_loss=4.502, generator_dur_loss=1.86, generator_adv_loss=1.669, generator_feat_match_loss=2.418, over 153.00 samples.], tot_loss[discriminator_loss=2.798, discriminator_real_loss=1.653, discriminator_fake_loss=1.145, generator_loss=48.48, generator_mel_loss=38.13, generator_kl_loss=4.439, generator_dur_loss=1.878, generator_adv_loss=1.841, generator_feat_match_loss=2.195, over 293.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 4.0
+2023-11-12 21:11:08,825 INFO [train.py:811] (1/4) Start epoch 6
+2023-11-12 21:12:49,862 INFO [train.py:467] (1/4) Epoch 6, batch 15, global_batch_idx: 200, batch size: 81, loss[discriminator_loss=2.656, discriminator_real_loss=1.463, discriminator_fake_loss=1.193, generator_loss=45.75, generator_mel_loss=36.67, generator_kl_loss=3.344, generator_dur_loss=1.897, generator_adv_loss=1.781, generator_feat_match_loss=2.062, over 81.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.433, discriminator_fake_loss=1.23, generator_loss=46.04, generator_mel_loss=36.74, generator_kl_loss=3.444, generator_dur_loss=1.899, generator_adv_loss=1.946, generator_feat_match_loss=2.018, over 1137.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 8.0
+2023-11-12 21:12:49,863 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 21:13:01,089 INFO [train.py:517] (1/4) Epoch 6, validation: discriminator_loss=2.551, discriminator_real_loss=1.182, discriminator_fake_loss=1.369, generator_loss=46.58, generator_mel_loss=36.93, generator_kl_loss=3.442, generator_dur_loss=1.942, generator_adv_loss=1.789, generator_feat_match_loss=2.469, over 100.00 samples.
+2023-11-12 21:13:01,090 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27007MB
+2023-11-12 21:14:53,667 INFO [train.py:811] (1/4) Start epoch 7
+2023-11-12 21:17:39,659 INFO [train.py:467] (1/4) Epoch 7, batch 28, global_batch_idx: 250, batch size: 69, loss[discriminator_loss=2.67, discriminator_real_loss=1.202, discriminator_fake_loss=1.468, generator_loss=44.31, generator_mel_loss=35.57, generator_kl_loss=2.637, generator_dur_loss=1.908, generator_adv_loss=2.061, generator_feat_match_loss=2.133, over 69.00 samples.], tot_loss[discriminator_loss=2.655, discriminator_real_loss=1.433, discriminator_fake_loss=1.222, generator_loss=45.07, generator_mel_loss=36.02, generator_kl_loss=2.869, generator_dur_loss=1.907, generator_adv_loss=2.068, generator_feat_match_loss=2.2, over 1970.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 8.0
+2023-11-12 21:18:23,397 INFO [train.py:811] (1/4) Start epoch 8
+2023-11-12 21:21:51,304 INFO [train.py:811] (1/4) Start epoch 9
+2023-11-12 21:22:30,260 INFO [train.py:467] (1/4) Epoch 9, batch 4, global_batch_idx: 300, batch size: 64, loss[discriminator_loss=2.496, discriminator_real_loss=1.644, discriminator_fake_loss=0.853, generator_loss=43.27, generator_mel_loss=34.33, generator_kl_loss=2.434, generator_dur_loss=1.914, generator_adv_loss=1.833, generator_feat_match_loss=2.754, over 64.00 samples.], tot_loss[discriminator_loss=2.507, discriminator_real_loss=1.304, discriminator_fake_loss=1.204, generator_loss=43.87, generator_mel_loss=34.93, generator_kl_loss=2.507, generator_dur_loss=1.914, generator_adv_loss=2.05, generator_feat_match_loss=2.469, over 340.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 8.0
+2023-11-12 21:25:27,631 INFO [train.py:811] (1/4) Start epoch 10
+2023-11-12 21:27:16,842 INFO [train.py:467] (1/4) Epoch 10, batch 17, global_batch_idx: 350, batch size: 50, loss[discriminator_loss=2.367, discriminator_real_loss=1.176, discriminator_fake_loss=1.19, generator_loss=43.5, generator_mel_loss=34.07, generator_kl_loss=2.204, generator_dur_loss=1.928, generator_adv_loss=2.426, generator_feat_match_loss=2.877, over 50.00 samples.], tot_loss[discriminator_loss=2.495, discriminator_real_loss=1.347, discriminator_fake_loss=1.148, generator_loss=43.67, generator_mel_loss=34.51, generator_kl_loss=2.344, generator_dur_loss=1.91, generator_adv_loss=2.179, generator_feat_match_loss=2.731, over 1280.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 8.0
+2023-11-12 21:28:59,605 INFO [train.py:811] (1/4) Start epoch 11
+2023-11-12 21:31:55,353 INFO [train.py:467] (1/4) Epoch 11, batch 30, global_batch_idx: 400, batch size: 153, loss[discriminator_loss=2.6, discriminator_real_loss=1.463, discriminator_fake_loss=1.137, generator_loss=43.4, generator_mel_loss=35.05, generator_kl_loss=2.423, generator_dur_loss=1.911, generator_adv_loss=1.914, generator_feat_match_loss=2.104, over 153.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.444, discriminator_fake_loss=1.274, generator_loss=42.63, generator_mel_loss=34.29, generator_kl_loss=2.249, generator_dur_loss=1.911, generator_adv_loss=1.975, generator_feat_match_loss=2.201, over 2420.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 21:31:55,355 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 21:32:06,360 INFO [train.py:517] (1/4) Epoch 11, validation: discriminator_loss=2.597, discriminator_real_loss=1.44, discriminator_fake_loss=1.157, generator_loss=43.15, generator_mel_loss=34.91, generator_kl_loss=2.243, generator_dur_loss=1.931, generator_adv_loss=1.948, generator_feat_match_loss=2.121, over 100.00 samples.
+2023-11-12 21:32:06,361 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27009MB
+2023-11-12 21:32:43,689 INFO [train.py:811] (1/4) Start epoch 12
+2023-11-12 21:36:16,268 INFO [train.py:811] (1/4) Start epoch 13
+2023-11-12 21:37:05,189 INFO [train.py:467] (1/4) Epoch 13, batch 6, global_batch_idx: 450, batch size: 71, loss[discriminator_loss=2.693, discriminator_real_loss=1.528, discriminator_fake_loss=1.165, generator_loss=40.79, generator_mel_loss=33.08, generator_kl_loss=2.041, generator_dur_loss=1.923, generator_adv_loss=1.908, generator_feat_match_loss=1.83, over 71.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.381, discriminator_fake_loss=1.312, generator_loss=41.6, generator_mel_loss=33.51, generator_kl_loss=2.081, generator_dur_loss=1.917, generator_adv_loss=2.029, generator_feat_match_loss=2.066, over 459.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 21:39:51,346 INFO [train.py:811] (1/4) Start epoch 14
+2023-11-12 21:41:39,954 INFO [train.py:467] (1/4) Epoch 14, batch 19, global_batch_idx: 500, batch size: 90, loss[discriminator_loss=2.699, discriminator_real_loss=1.347, discriminator_fake_loss=1.352, generator_loss=41.13, generator_mel_loss=33.62, generator_kl_loss=2.121, generator_dur_loss=1.907, generator_adv_loss=1.773, generator_feat_match_loss=1.707, over 90.00 samples.], tot_loss[discriminator_loss=2.786, discriminator_real_loss=1.442, discriminator_fake_loss=1.344, generator_loss=40.57, generator_mel_loss=33.09, generator_kl_loss=2.046, generator_dur_loss=1.915, generator_adv_loss=1.806, generator_feat_match_loss=1.713, over 1405.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 21:43:16,324 INFO [train.py:811] (1/4) Start epoch 15
+2023-11-12 21:46:18,660 INFO [train.py:467] (1/4) Epoch 15, batch 32, global_batch_idx: 550, batch size: 67, loss[discriminator_loss=2.762, discriminator_real_loss=1.627, discriminator_fake_loss=1.134, generator_loss=39.79, generator_mel_loss=32.16, generator_kl_loss=2.007, generator_dur_loss=1.92, generator_adv_loss=1.991, generator_feat_match_loss=1.713, over 67.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.431, discriminator_fake_loss=1.282, generator_loss=40.43, generator_mel_loss=32.62, generator_kl_loss=2.06, generator_dur_loss=1.915, generator_adv_loss=1.917, generator_feat_match_loss=1.918, over 2283.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 21:46:42,628 INFO [train.py:811] (1/4) Start epoch 16
+2023-11-12 21:50:15,687 INFO [train.py:811] (1/4) Start epoch 17
+2023-11-12 21:51:20,461 INFO [train.py:467] (1/4) Epoch 17, batch 8, global_batch_idx: 600, batch size: 95, loss[discriminator_loss=2.723, discriminator_real_loss=1.646, discriminator_fake_loss=1.078, generator_loss=39.68, generator_mel_loss=31.93, generator_kl_loss=2.189, generator_dur_loss=1.9, generator_adv_loss=1.59, generator_feat_match_loss=2.072, over 95.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.423, discriminator_fake_loss=1.274, generator_loss=40.02, generator_mel_loss=32.13, generator_kl_loss=2.151, generator_dur_loss=1.908, generator_adv_loss=1.871, generator_feat_match_loss=1.964, over 639.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 21:51:20,463 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 21:51:31,351 INFO [train.py:517] (1/4) Epoch 17, validation: discriminator_loss=2.587, discriminator_real_loss=1.127, discriminator_fake_loss=1.46, generator_loss=40.38, generator_mel_loss=32.44, generator_kl_loss=2.307, generator_dur_loss=1.91, generator_adv_loss=1.636, generator_feat_match_loss=2.091, over 100.00 samples.
+2023-11-12 21:51:31,352 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27009MB
+2023-11-12 21:54:04,319 INFO [train.py:811] (1/4) Start epoch 18
+2023-11-12 21:56:14,788 INFO [train.py:467] (1/4) Epoch 18, batch 21, global_batch_idx: 650, batch size: 101, loss[discriminator_loss=2.629, discriminator_real_loss=1.123, discriminator_fake_loss=1.506, generator_loss=40.76, generator_mel_loss=32.23, generator_kl_loss=2.116, generator_dur_loss=1.872, generator_adv_loss=2.031, generator_feat_match_loss=2.514, over 101.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.384, discriminator_fake_loss=1.282, generator_loss=40.2, generator_mel_loss=31.98, generator_kl_loss=2.155, generator_dur_loss=1.904, generator_adv_loss=1.944, generator_feat_match_loss=2.223, over 1817.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 21:57:41,252 INFO [train.py:811] (1/4) Start epoch 19
+2023-11-12 22:00:56,402 INFO [train.py:467] (1/4) Epoch 19, batch 34, global_batch_idx: 700, batch size: 126, loss[discriminator_loss=2.664, discriminator_real_loss=1.276, discriminator_fake_loss=1.388, generator_loss=41.14, generator_mel_loss=32.39, generator_kl_loss=2.13, generator_dur_loss=1.885, generator_adv_loss=2.164, generator_feat_match_loss=2.574, over 126.00 samples.], tot_loss[discriminator_loss=2.603, discriminator_real_loss=1.357, discriminator_fake_loss=1.246, generator_loss=39.89, generator_mel_loss=31.37, generator_kl_loss=2.074, generator_dur_loss=1.911, generator_adv_loss=2.079, generator_feat_match_loss=2.461, over 2654.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 22:01:12,335 INFO [train.py:811] (1/4) Start epoch 20
+2023-11-12 22:04:47,310 INFO [train.py:811] (1/4) Start epoch 21
+2023-11-12 22:06:00,323 INFO [train.py:467] (1/4) Epoch 21, batch 10, global_batch_idx: 750, batch size: 61, loss[discriminator_loss=2.564, discriminator_real_loss=1.295, discriminator_fake_loss=1.27, generator_loss=39.91, generator_mel_loss=31.37, generator_kl_loss=2.07, generator_dur_loss=1.906, generator_adv_loss=2.104, generator_feat_match_loss=2.461, over 61.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.427, discriminator_fake_loss=1.232, generator_loss=38.86, generator_mel_loss=30.64, generator_kl_loss=2.024, generator_dur_loss=1.916, generator_adv_loss=2.027, generator_feat_match_loss=2.253, over 709.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, grad_scale: 16.0
+2023-11-12 22:08:26,176 INFO [train.py:811] (1/4) Start epoch 22
+2023-11-12 22:10:46,142 INFO [train.py:467] (1/4) Epoch 22, batch 23, global_batch_idx: 800, batch size: 65, loss[discriminator_loss=2.562, discriminator_real_loss=1.245, discriminator_fake_loss=1.316, generator_loss=38.11, generator_mel_loss=30.42, generator_kl_loss=1.967, generator_dur_loss=1.923, generator_adv_loss=1.587, generator_feat_match_loss=2.213, over 65.00 samples.], tot_loss[discriminator_loss=2.643, discriminator_real_loss=1.419, discriminator_fake_loss=1.224, generator_loss=38.74, generator_mel_loss=30.54, generator_kl_loss=2.023, generator_dur_loss=1.917, generator_adv_loss=1.995, generator_feat_match_loss=2.263, over 1700.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:10:46,144 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 22:10:56,992 INFO [train.py:517] (1/4) Epoch 22, validation: discriminator_loss=2.534, discriminator_real_loss=0.9679, discriminator_fake_loss=1.566, generator_loss=40.08, generator_mel_loss=31.58, generator_kl_loss=2.227, generator_dur_loss=1.91, generator_adv_loss=1.604, generator_feat_match_loss=2.761, over 100.00 samples.
+2023-11-12 22:10:56,994 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27009MB
+2023-11-12 22:12:09,507 INFO [train.py:811] (1/4) Start epoch 23
+2023-11-12 22:15:46,220 INFO [train.py:467] (1/4) Epoch 23, batch 36, global_batch_idx: 850, batch size: 111, loss[discriminator_loss=2.844, discriminator_real_loss=1.41, discriminator_fake_loss=1.435, generator_loss=39.12, generator_mel_loss=31.55, generator_kl_loss=2.132, generator_dur_loss=1.88, generator_adv_loss=1.561, generator_feat_match_loss=2.002, over 111.00 samples.], tot_loss[discriminator_loss=2.618, discriminator_real_loss=1.375, discriminator_fake_loss=1.243, generator_loss=38.86, generator_mel_loss=30.48, generator_kl_loss=2.031, generator_dur_loss=1.911, generator_adv_loss=2.045, generator_feat_match_loss=2.395, over 2868.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:15:46,787 INFO [train.py:811] (1/4) Start epoch 24
+2023-11-12 22:19:14,582 INFO [train.py:811] (1/4) Start epoch 25
+2023-11-12 22:20:35,895 INFO [train.py:467] (1/4) Epoch 25, batch 12, global_batch_idx: 900, batch size: 60, loss[discriminator_loss=2.5, discriminator_real_loss=1.307, discriminator_fake_loss=1.194, generator_loss=38.61, generator_mel_loss=29.95, generator_kl_loss=2.071, generator_dur_loss=1.91, generator_adv_loss=2.227, generator_feat_match_loss=2.453, over 60.00 samples.], tot_loss[discriminator_loss=2.598, discriminator_real_loss=1.33, discriminator_fake_loss=1.268, generator_loss=38.19, generator_mel_loss=30.18, generator_kl_loss=1.98, generator_dur_loss=1.916, generator_adv_loss=1.943, generator_feat_match_loss=2.168, over 919.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:22:43,245 INFO [train.py:811] (1/4) Start epoch 26
+2023-11-12 22:25:14,081 INFO [train.py:467] (1/4) Epoch 26, batch 25, global_batch_idx: 950, batch size: 52, loss[discriminator_loss=2.689, discriminator_real_loss=1.455, discriminator_fake_loss=1.234, generator_loss=38.79, generator_mel_loss=30.48, generator_kl_loss=1.991, generator_dur_loss=1.925, generator_adv_loss=2.184, generator_feat_match_loss=2.207, over 52.00 samples.], tot_loss[discriminator_loss=2.65, discriminator_real_loss=1.388, discriminator_fake_loss=1.262, generator_loss=38.22, generator_mel_loss=30.06, generator_kl_loss=1.987, generator_dur_loss=1.914, generator_adv_loss=2, generator_feat_match_loss=2.256, over 1869.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:26:18,536 INFO [train.py:811] (1/4) Start epoch 27
+2023-11-12 22:29:49,077 INFO [train.py:811] (1/4) Start epoch 28
+2023-11-12 22:30:10,019 INFO [train.py:467] (1/4) Epoch 28, batch 1, global_batch_idx: 1000, batch size: 85, loss[discriminator_loss=2.596, discriminator_real_loss=1.227, discriminator_fake_loss=1.369, generator_loss=37.82, generator_mel_loss=29.74, generator_kl_loss=1.942, generator_dur_loss=1.92, generator_adv_loss=1.934, generator_feat_match_loss=2.287, over 85.00 samples.], tot_loss[discriminator_loss=2.63, discriminator_real_loss=1.306, discriminator_fake_loss=1.324, generator_loss=37.91, generator_mel_loss=29.96, generator_kl_loss=1.976, generator_dur_loss=1.929, generator_adv_loss=1.852, generator_feat_match_loss=2.194, over 149.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:30:10,020 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 22:30:21,706 INFO [train.py:517] (1/4) Epoch 28, validation: discriminator_loss=2.516, discriminator_real_loss=1.224, discriminator_fake_loss=1.292, generator_loss=38.54, generator_mel_loss=30.3, generator_kl_loss=2.126, generator_dur_loss=1.907, generator_adv_loss=1.876, generator_feat_match_loss=2.332, over 100.00 samples.
+2023-11-12 22:30:21,707 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27009MB
+2023-11-12 22:33:37,635 INFO [train.py:811] (1/4) Start epoch 29
+2023-11-12 22:35:09,471 INFO [train.py:467] (1/4) Epoch 29, batch 14, global_batch_idx: 1050, batch size: 73, loss[discriminator_loss=2.625, discriminator_real_loss=1.31, discriminator_fake_loss=1.315, generator_loss=37.17, generator_mel_loss=29.32, generator_kl_loss=2.014, generator_dur_loss=1.887, generator_adv_loss=1.821, generator_feat_match_loss=2.127, over 73.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.399, discriminator_fake_loss=1.266, generator_loss=38.09, generator_mel_loss=29.93, generator_kl_loss=1.994, generator_dur_loss=1.918, generator_adv_loss=1.97, generator_feat_match_loss=2.283, over 1040.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:37:10,338 INFO [train.py:811] (1/4) Start epoch 30
+2023-11-12 22:39:46,626 INFO [train.py:467] (1/4) Epoch 30, batch 27, global_batch_idx: 1100, batch size: 126, loss[discriminator_loss=2.551, discriminator_real_loss=1.211, discriminator_fake_loss=1.34, generator_loss=38.1, generator_mel_loss=29.72, generator_kl_loss=2.114, generator_dur_loss=1.881, generator_adv_loss=2.012, generator_feat_match_loss=2.371, over 126.00 samples.], tot_loss[discriminator_loss=2.648, discriminator_real_loss=1.377, discriminator_fake_loss=1.272, generator_loss=37.6, generator_mel_loss=29.44, generator_kl_loss=1.99, generator_dur_loss=1.912, generator_adv_loss=1.958, generator_feat_match_loss=2.304, over 2030.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:40:37,772 INFO [train.py:811] (1/4) Start epoch 31
+2023-11-12 22:44:07,620 INFO [train.py:811] (1/4) Start epoch 32
+2023-11-12 22:44:43,077 INFO [train.py:467] (1/4) Epoch 32, batch 3, global_batch_idx: 1150, batch size: 53, loss[discriminator_loss=2.629, discriminator_real_loss=1.357, discriminator_fake_loss=1.271, generator_loss=37.85, generator_mel_loss=29.46, generator_kl_loss=1.896, generator_dur_loss=1.91, generator_adv_loss=2.203, generator_feat_match_loss=2.385, over 53.00 samples.], tot_loss[discriminator_loss=2.65, discriminator_real_loss=1.343, discriminator_fake_loss=1.307, generator_loss=37.69, generator_mel_loss=29.55, generator_kl_loss=1.991, generator_dur_loss=1.908, generator_adv_loss=1.909, generator_feat_match_loss=2.338, over 313.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:47:35,325 INFO [train.py:811] (1/4) Start epoch 33
+2023-11-12 22:49:25,311 INFO [train.py:467] (1/4) Epoch 33, batch 16, global_batch_idx: 1200, batch size: 49, loss[discriminator_loss=2.617, discriminator_real_loss=1.335, discriminator_fake_loss=1.282, generator_loss=36.18, generator_mel_loss=28.12, generator_kl_loss=1.974, generator_dur_loss=1.95, generator_adv_loss=2.16, generator_feat_match_loss=1.973, over 49.00 samples.], tot_loss[discriminator_loss=2.79, discriminator_real_loss=1.511, discriminator_fake_loss=1.279, generator_loss=37.3, generator_mel_loss=29.26, generator_kl_loss=1.932, generator_dur_loss=1.917, generator_adv_loss=2.057, generator_feat_match_loss=2.133, over 1241.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:49:25,312 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 22:49:36,161 INFO [train.py:517] (1/4) Epoch 33, validation: discriminator_loss=2.588, discriminator_real_loss=1.459, discriminator_fake_loss=1.129, generator_loss=39.25, generator_mel_loss=30.77, generator_kl_loss=2.195, generator_dur_loss=1.925, generator_adv_loss=2.007, generator_feat_match_loss=2.35, over 100.00 samples.
+2023-11-12 22:49:36,162 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27009MB
+2023-11-12 22:51:17,737 INFO [train.py:811] (1/4) Start epoch 34
+2023-11-12 22:54:07,310 INFO [train.py:467] (1/4) Epoch 34, batch 29, global_batch_idx: 1250, batch size: 69, loss[discriminator_loss=2.783, discriminator_real_loss=1.54, discriminator_fake_loss=1.243, generator_loss=37.13, generator_mel_loss=29.23, generator_kl_loss=2.09, generator_dur_loss=1.926, generator_adv_loss=1.873, generator_feat_match_loss=2.008, over 69.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.39, discriminator_fake_loss=1.286, generator_loss=37, generator_mel_loss=28.98, generator_kl_loss=1.99, generator_dur_loss=1.918, generator_adv_loss=1.926, generator_feat_match_loss=2.181, over 2164.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 22:54:46,073 INFO [train.py:811] (1/4) Start epoch 35
+2023-11-12 22:58:21,154 INFO [train.py:811] (1/4) Start epoch 36
+2023-11-12 22:59:01,749 INFO [train.py:467] (1/4) Epoch 36, batch 5, global_batch_idx: 1300, batch size: 71, loss[discriminator_loss=2.559, discriminator_real_loss=1.424, discriminator_fake_loss=1.136, generator_loss=37.83, generator_mel_loss=29.21, generator_kl_loss=2.001, generator_dur_loss=1.928, generator_adv_loss=2.082, generator_feat_match_loss=2.605, over 71.00 samples.], tot_loss[discriminator_loss=2.637, discriminator_real_loss=1.38, discriminator_fake_loss=1.257, generator_loss=37.17, generator_mel_loss=28.9, generator_kl_loss=1.98, generator_dur_loss=1.921, generator_adv_loss=1.97, generator_feat_match_loss=2.399, over 387.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:01:54,210 INFO [train.py:811] (1/4) Start epoch 37
+2023-11-12 23:03:44,581 INFO [train.py:467] (1/4) Epoch 37, batch 18, global_batch_idx: 1350, batch size: 58, loss[discriminator_loss=2.596, discriminator_real_loss=1.113, discriminator_fake_loss=1.482, generator_loss=37.31, generator_mel_loss=28.95, generator_kl_loss=1.838, generator_dur_loss=1.948, generator_adv_loss=2.121, generator_feat_match_loss=2.447, over 58.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.437, discriminator_fake_loss=1.245, generator_loss=36.93, generator_mel_loss=28.7, generator_kl_loss=1.94, generator_dur_loss=1.926, generator_adv_loss=2.059, generator_feat_match_loss=2.306, over 1400.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:05:25,594 INFO [train.py:811] (1/4) Start epoch 38
+2023-11-12 23:08:29,530 INFO [train.py:467] (1/4) Epoch 38, batch 31, global_batch_idx: 1400, batch size: 95, loss[discriminator_loss=2.705, discriminator_real_loss=1.638, discriminator_fake_loss=1.067, generator_loss=37.01, generator_mel_loss=28.64, generator_kl_loss=1.962, generator_dur_loss=1.937, generator_adv_loss=2.154, generator_feat_match_loss=2.314, over 95.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.414, discriminator_fake_loss=1.267, generator_loss=36.73, generator_mel_loss=28.6, generator_kl_loss=1.976, generator_dur_loss=1.933, generator_adv_loss=2.006, generator_feat_match_loss=2.215, over 2405.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:08:29,531 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 23:08:40,527 INFO [train.py:517] (1/4) Epoch 38, validation: discriminator_loss=2.645, discriminator_real_loss=1.425, discriminator_fake_loss=1.22, generator_loss=39.17, generator_mel_loss=30.61, generator_kl_loss=2.023, generator_dur_loss=1.978, generator_adv_loss=1.968, generator_feat_match_loss=2.591, over 100.00 samples.
+2023-11-12 23:08:40,528 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27009MB
+2023-11-12 23:09:05,451 INFO [train.py:811] (1/4) Start epoch 39
+2023-11-12 23:12:38,032 INFO [train.py:811] (1/4) Start epoch 40
+2023-11-12 23:13:39,071 INFO [train.py:467] (1/4) Epoch 40, batch 7, global_batch_idx: 1450, batch size: 67, loss[discriminator_loss=2.582, discriminator_real_loss=1.28, discriminator_fake_loss=1.301, generator_loss=36.63, generator_mel_loss=28.37, generator_kl_loss=1.866, generator_dur_loss=1.958, generator_adv_loss=1.938, generator_feat_match_loss=2.496, over 67.00 samples.], tot_loss[discriminator_loss=2.64, discriminator_real_loss=1.391, discriminator_fake_loss=1.25, generator_loss=36.93, generator_mel_loss=28.79, generator_kl_loss=1.917, generator_dur_loss=1.918, generator_adv_loss=1.925, generator_feat_match_loss=2.38, over 691.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:16:10,926 INFO [train.py:811] (1/4) Start epoch 41
+2023-11-12 23:18:22,806 INFO [train.py:467] (1/4) Epoch 41, batch 20, global_batch_idx: 1500, batch size: 153, loss[discriminator_loss=2.865, discriminator_real_loss=1.281, discriminator_fake_loss=1.584, generator_loss=38.43, generator_mel_loss=29.93, generator_kl_loss=1.913, generator_dur_loss=1.892, generator_adv_loss=2.217, generator_feat_match_loss=2.48, over 153.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.392, discriminator_fake_loss=1.29, generator_loss=36.51, generator_mel_loss=28.36, generator_kl_loss=1.906, generator_dur_loss=1.931, generator_adv_loss=2.021, generator_feat_match_loss=2.298, over 1481.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:19:52,315 INFO [train.py:811] (1/4) Start epoch 42
+2023-11-12 23:23:08,920 INFO [train.py:467] (1/4) Epoch 42, batch 33, global_batch_idx: 1550, batch size: 64, loss[discriminator_loss=2.695, discriminator_real_loss=1.305, discriminator_fake_loss=1.391, generator_loss=35.62, generator_mel_loss=27.54, generator_kl_loss=1.911, generator_dur_loss=1.921, generator_adv_loss=2.119, generator_feat_match_loss=2.133, over 64.00 samples.], tot_loss[discriminator_loss=2.642, discriminator_real_loss=1.38, discriminator_fake_loss=1.262, generator_loss=36.28, generator_mel_loss=28.11, generator_kl_loss=1.925, generator_dur_loss=1.931, generator_adv_loss=1.982, generator_feat_match_loss=2.338, over 2563.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:23:26,014 INFO [train.py:811] (1/4) Start epoch 43
+2023-11-12 23:26:52,239 INFO [train.py:811] (1/4) Start epoch 44
+2023-11-12 23:27:57,936 INFO [train.py:467] (1/4) Epoch 44, batch 9, global_batch_idx: 1600, batch size: 63, loss[discriminator_loss=2.621, discriminator_real_loss=1.432, discriminator_fake_loss=1.189, generator_loss=35.68, generator_mel_loss=27.12, generator_kl_loss=1.955, generator_dur_loss=1.969, generator_adv_loss=2.219, generator_feat_match_loss=2.412, over 63.00 samples.], tot_loss[discriminator_loss=2.645, discriminator_real_loss=1.383, discriminator_fake_loss=1.262, generator_loss=35.87, generator_mel_loss=27.75, generator_kl_loss=1.937, generator_dur_loss=1.945, generator_adv_loss=1.983, generator_feat_match_loss=2.257, over 625.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:27:57,938 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 23:28:08,903 INFO [train.py:517] (1/4) Epoch 44, validation: discriminator_loss=2.572, discriminator_real_loss=1.465, discriminator_fake_loss=1.107, generator_loss=37.81, generator_mel_loss=29.18, generator_kl_loss=1.971, generator_dur_loss=1.932, generator_adv_loss=2.111, generator_feat_match_loss=2.616, over 100.00 samples.
+2023-11-12 23:28:08,904 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27009MB
+2023-11-12 23:30:29,428 INFO [train.py:811] (1/4) Start epoch 45
+2023-11-12 23:32:41,434 INFO [train.py:467] (1/4) Epoch 45, batch 22, global_batch_idx: 1650, batch size: 126, loss[discriminator_loss=2.596, discriminator_real_loss=1.271, discriminator_fake_loss=1.325, generator_loss=36.87, generator_mel_loss=28.72, generator_kl_loss=1.998, generator_dur_loss=1.903, generator_adv_loss=1.971, generator_feat_match_loss=2.275, over 126.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.418, discriminator_fake_loss=1.296, generator_loss=35.41, generator_mel_loss=27.45, generator_kl_loss=1.934, generator_dur_loss=1.939, generator_adv_loss=1.934, generator_feat_match_loss=2.153, over 1653.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:33:56,763 INFO [train.py:811] (1/4) Start epoch 46
+2023-11-12 23:37:24,915 INFO [train.py:467] (1/4) Epoch 46, batch 35, global_batch_idx: 1700, batch size: 49, loss[discriminator_loss=2.664, discriminator_real_loss=1.213, discriminator_fake_loss=1.451, generator_loss=35.69, generator_mel_loss=27.3, generator_kl_loss=1.963, generator_dur_loss=1.963, generator_adv_loss=2.143, generator_feat_match_loss=2.316, over 49.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.417, discriminator_fake_loss=1.285, generator_loss=35.47, generator_mel_loss=27.44, generator_kl_loss=1.955, generator_dur_loss=1.942, generator_adv_loss=1.935, generator_feat_match_loss=2.19, over 2554.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:37:29,965 INFO [train.py:811] (1/4) Start epoch 47
+2023-11-12 23:41:00,945 INFO [train.py:811] (1/4) Start epoch 48
+2023-11-12 23:42:16,615 INFO [train.py:467] (1/4) Epoch 48, batch 11, global_batch_idx: 1750, batch size: 73, loss[discriminator_loss=2.613, discriminator_real_loss=1.246, discriminator_fake_loss=1.367, generator_loss=35.58, generator_mel_loss=27.27, generator_kl_loss=1.969, generator_dur_loss=1.967, generator_adv_loss=2.025, generator_feat_match_loss=2.352, over 73.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.374, discriminator_fake_loss=1.29, generator_loss=35.17, generator_mel_loss=27.17, generator_kl_loss=1.993, generator_dur_loss=1.951, generator_adv_loss=1.913, generator_feat_match_loss=2.141, over 837.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:44:30,496 INFO [train.py:811] (1/4) Start epoch 49
+2023-11-12 23:46:57,499 INFO [train.py:467] (1/4) Epoch 49, batch 24, global_batch_idx: 1800, batch size: 63, loss[discriminator_loss=2.746, discriminator_real_loss=1.484, discriminator_fake_loss=1.261, generator_loss=35.03, generator_mel_loss=26.92, generator_kl_loss=2.011, generator_dur_loss=1.986, generator_adv_loss=1.854, generator_feat_match_loss=2.252, over 63.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.425, discriminator_fake_loss=1.278, generator_loss=34.91, generator_mel_loss=26.9, generator_kl_loss=2.003, generator_dur_loss=1.948, generator_adv_loss=1.918, generator_feat_match_loss=2.142, over 1874.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:46:57,500 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-12 23:47:08,424 INFO [train.py:517] (1/4) Epoch 49, validation: discriminator_loss=2.643, discriminator_real_loss=1.239, discriminator_fake_loss=1.404, generator_loss=36.36, generator_mel_loss=28.35, generator_kl_loss=2.026, generator_dur_loss=1.943, generator_adv_loss=1.79, generator_feat_match_loss=2.245, over 100.00 samples.
+2023-11-12 23:47:08,425 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-12 23:48:15,405 INFO [train.py:811] (1/4) Start epoch 50
+2023-11-12 23:51:47,393 INFO [train.py:811] (1/4) Start epoch 51
+2023-11-12 23:52:02,391 INFO [train.py:467] (1/4) Epoch 51, batch 0, global_batch_idx: 1850, batch size: 51, loss[discriminator_loss=2.664, discriminator_real_loss=1.198, discriminator_fake_loss=1.466, generator_loss=35.22, generator_mel_loss=26.99, generator_kl_loss=1.953, generator_dur_loss=1.952, generator_adv_loss=2.082, generator_feat_match_loss=2.236, over 51.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.198, discriminator_fake_loss=1.466, generator_loss=35.22, generator_mel_loss=26.99, generator_kl_loss=1.953, generator_dur_loss=1.952, generator_adv_loss=2.082, generator_feat_match_loss=2.236, over 51.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:55:18,049 INFO [train.py:811] (1/4) Start epoch 52
+2023-11-12 23:56:40,094 INFO [train.py:467] (1/4) Epoch 52, batch 13, global_batch_idx: 1900, batch size: 154, loss[discriminator_loss=2.688, discriminator_real_loss=1.477, discriminator_fake_loss=1.212, generator_loss=34.77, generator_mel_loss=26.86, generator_kl_loss=2.107, generator_dur_loss=1.914, generator_adv_loss=1.719, generator_feat_match_loss=2.178, over 154.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.434, discriminator_fake_loss=1.31, generator_loss=34.27, generator_mel_loss=26.45, generator_kl_loss=2.015, generator_dur_loss=1.939, generator_adv_loss=1.878, generator_feat_match_loss=1.99, over 1049.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-12 23:58:45,755 INFO [train.py:811] (1/4) Start epoch 53
+2023-11-13 00:01:25,409 INFO [train.py:467] (1/4) Epoch 53, batch 26, global_batch_idx: 1950, batch size: 67, loss[discriminator_loss=2.773, discriminator_real_loss=1.533, discriminator_fake_loss=1.24, generator_loss=33.54, generator_mel_loss=26.04, generator_kl_loss=1.953, generator_dur_loss=1.943, generator_adv_loss=1.771, generator_feat_match_loss=1.83, over 67.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.442, discriminator_fake_loss=1.317, generator_loss=34.27, generator_mel_loss=26.34, generator_kl_loss=2.023, generator_dur_loss=1.932, generator_adv_loss=1.907, generator_feat_match_loss=2.066, over 2146.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 32.0
+2023-11-13 00:02:18,332 INFO [train.py:811] (1/4) Start epoch 54
+2023-11-13 00:05:44,505 INFO [train.py:811] (1/4) Start epoch 55
+2023-11-13 00:06:08,626 INFO [train.py:467] (1/4) Epoch 55, batch 2, global_batch_idx: 2000, batch size: 81, loss[discriminator_loss=2.703, discriminator_real_loss=1.473, discriminator_fake_loss=1.229, generator_loss=33.84, generator_mel_loss=25.88, generator_kl_loss=2.059, generator_dur_loss=1.947, generator_adv_loss=2, generator_feat_match_loss=1.952, over 81.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.347, discriminator_fake_loss=1.372, generator_loss=33.64, generator_mel_loss=25.74, generator_kl_loss=2.041, generator_dur_loss=1.93, generator_adv_loss=1.935, generator_feat_match_loss=1.993, over 240.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 64.0
+2023-11-13 00:06:08,626 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 00:06:20,171 INFO [train.py:517] (1/4) Epoch 55, validation: discriminator_loss=2.637, discriminator_real_loss=1.39, discriminator_fake_loss=1.247, generator_loss=34.51, generator_mel_loss=26.79, generator_kl_loss=1.921, generator_dur_loss=1.915, generator_adv_loss=1.903, generator_feat_match_loss=1.974, over 100.00 samples.
+2023-11-13 00:06:20,172 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 00:09:25,019 INFO [train.py:811] (1/4) Start epoch 56
+2023-11-13 00:11:05,098 INFO [train.py:467] (1/4) Epoch 56, batch 15, global_batch_idx: 2050, batch size: 67, loss[discriminator_loss=2.793, discriminator_real_loss=1.406, discriminator_fake_loss=1.387, generator_loss=33.47, generator_mel_loss=25.94, generator_kl_loss=1.953, generator_dur_loss=1.922, generator_adv_loss=1.761, generator_feat_match_loss=1.902, over 67.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.421, discriminator_fake_loss=1.337, generator_loss=33.71, generator_mel_loss=25.95, generator_kl_loss=1.962, generator_dur_loss=1.921, generator_adv_loss=1.899, generator_feat_match_loss=1.983, over 1187.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 64.0
+2023-11-13 00:12:58,071 INFO [train.py:811] (1/4) Start epoch 57
+2023-11-13 00:15:54,490 INFO [train.py:467] (1/4) Epoch 57, batch 28, global_batch_idx: 2100, batch size: 110, loss[discriminator_loss=2.729, discriminator_real_loss=1.336, discriminator_fake_loss=1.393, generator_loss=34.07, generator_mel_loss=26.16, generator_kl_loss=2.009, generator_dur_loss=1.88, generator_adv_loss=2, generator_feat_match_loss=2.018, over 110.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.386, discriminator_fake_loss=1.339, generator_loss=33.65, generator_mel_loss=25.78, generator_kl_loss=1.993, generator_dur_loss=1.907, generator_adv_loss=1.895, generator_feat_match_loss=2.068, over 2092.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 64.0
+2023-11-13 00:16:33,624 INFO [train.py:811] (1/4) Start epoch 58
+2023-11-13 00:20:09,530 INFO [train.py:811] (1/4) Start epoch 59
+2023-11-13 00:20:44,056 INFO [train.py:467] (1/4) Epoch 59, batch 4, global_batch_idx: 2150, batch size: 59, loss[discriminator_loss=2.742, discriminator_real_loss=1.098, discriminator_fake_loss=1.645, generator_loss=33.76, generator_mel_loss=25.88, generator_kl_loss=2.001, generator_dur_loss=1.92, generator_adv_loss=1.993, generator_feat_match_loss=1.975, over 59.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=33.39, generator_mel_loss=25.61, generator_kl_loss=1.977, generator_dur_loss=1.899, generator_adv_loss=1.882, generator_feat_match_loss=2.018, over 345.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 64.0
+2023-11-13 00:23:42,607 INFO [train.py:811] (1/4) Start epoch 60
+2023-11-13 00:25:37,824 INFO [train.py:467] (1/4) Epoch 60, batch 17, global_batch_idx: 2200, batch size: 64, loss[discriminator_loss=2.703, discriminator_real_loss=1.408, discriminator_fake_loss=1.295, generator_loss=33.61, generator_mel_loss=25.75, generator_kl_loss=1.979, generator_dur_loss=1.877, generator_adv_loss=1.867, generator_feat_match_loss=2.137, over 64.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=33.79, generator_mel_loss=25.83, generator_kl_loss=1.975, generator_dur_loss=1.875, generator_adv_loss=1.929, generator_feat_match_loss=2.177, over 1588.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 64.0
+2023-11-13 00:25:37,825 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 00:25:49,056 INFO [train.py:517] (1/4) Epoch 60, validation: discriminator_loss=2.641, discriminator_real_loss=1.307, discriminator_fake_loss=1.334, generator_loss=34.97, generator_mel_loss=27.14, generator_kl_loss=1.974, generator_dur_loss=1.873, generator_adv_loss=1.789, generator_feat_match_loss=2.193, over 100.00 samples.
+2023-11-13 00:25:49,057 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 00:27:28,351 INFO [train.py:811] (1/4) Start epoch 61
+2023-11-13 00:30:39,989 INFO [train.py:467] (1/4) Epoch 61, batch 30, global_batch_idx: 2250, batch size: 58, loss[discriminator_loss=2.748, discriminator_real_loss=1.389, discriminator_fake_loss=1.359, generator_loss=33.14, generator_mel_loss=25.25, generator_kl_loss=1.972, generator_dur_loss=1.854, generator_adv_loss=1.826, generator_feat_match_loss=2.238, over 58.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.406, discriminator_fake_loss=1.311, generator_loss=33.22, generator_mel_loss=25.39, generator_kl_loss=1.958, generator_dur_loss=1.875, generator_adv_loss=1.897, generator_feat_match_loss=2.099, over 2227.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, grad_scale: 64.0
+2023-11-13 00:31:07,247 INFO [train.py:811] (1/4) Start epoch 62
+2023-11-13 00:34:46,085 INFO [train.py:811] (1/4) Start epoch 63
+2023-11-13 00:35:39,859 INFO [train.py:467] (1/4) Epoch 63, batch 6, global_batch_idx: 2300, batch size: 63, loss[discriminator_loss=2.695, discriminator_real_loss=1.315, discriminator_fake_loss=1.381, generator_loss=33.75, generator_mel_loss=25.91, generator_kl_loss=1.985, generator_dur_loss=1.879, generator_adv_loss=1.683, generator_feat_match_loss=2.297, over 63.00 samples.], tot_loss[discriminator_loss=2.771, discriminator_real_loss=1.444, discriminator_fake_loss=1.327, generator_loss=32.93, generator_mel_loss=25.19, generator_kl_loss=1.964, generator_dur_loss=1.869, generator_adv_loss=1.881, generator_feat_match_loss=2.018, over 494.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 00:38:19,723 INFO [train.py:811] (1/4) Start epoch 64
+2023-11-13 00:40:24,037 INFO [train.py:467] (1/4) Epoch 64, batch 19, global_batch_idx: 2350, batch size: 64, loss[discriminator_loss=2.77, discriminator_real_loss=1.419, discriminator_fake_loss=1.352, generator_loss=32.73, generator_mel_loss=25.2, generator_kl_loss=2.048, generator_dur_loss=1.887, generator_adv_loss=1.753, generator_feat_match_loss=1.848, over 64.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.411, discriminator_fake_loss=1.332, generator_loss=32.71, generator_mel_loss=25.11, generator_kl_loss=1.985, generator_dur_loss=1.863, generator_adv_loss=1.847, generator_feat_match_loss=1.902, over 1389.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 00:41:52,816 INFO [train.py:811] (1/4) Start epoch 65
+2023-11-13 00:45:07,220 INFO [train.py:467] (1/4) Epoch 65, batch 32, global_batch_idx: 2400, batch size: 126, loss[discriminator_loss=2.691, discriminator_real_loss=1.379, discriminator_fake_loss=1.313, generator_loss=33.12, generator_mel_loss=25.24, generator_kl_loss=1.994, generator_dur_loss=1.849, generator_adv_loss=1.919, generator_feat_match_loss=2.111, over 126.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.403, discriminator_fake_loss=1.331, generator_loss=32.68, generator_mel_loss=24.99, generator_kl_loss=1.97, generator_dur_loss=1.859, generator_adv_loss=1.876, generator_feat_match_loss=1.986, over 2525.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 00:45:07,221 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 00:45:18,048 INFO [train.py:517] (1/4) Epoch 65, validation: discriminator_loss=2.64, discriminator_real_loss=1.369, discriminator_fake_loss=1.272, generator_loss=33.82, generator_mel_loss=25.91, generator_kl_loss=2.019, generator_dur_loss=1.845, generator_adv_loss=1.855, generator_feat_match_loss=2.192, over 100.00 samples.
+2023-11-13 00:45:18,049 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 00:45:36,271 INFO [train.py:811] (1/4) Start epoch 66
+2023-11-13 00:49:07,738 INFO [train.py:811] (1/4) Start epoch 67
+2023-11-13 00:50:03,789 INFO [train.py:467] (1/4) Epoch 67, batch 8, global_batch_idx: 2450, batch size: 65, loss[discriminator_loss=2.68, discriminator_real_loss=1.333, discriminator_fake_loss=1.346, generator_loss=31.47, generator_mel_loss=23.91, generator_kl_loss=1.986, generator_dur_loss=1.846, generator_adv_loss=1.758, generator_feat_match_loss=1.979, over 65.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.408, discriminator_fake_loss=1.335, generator_loss=31.98, generator_mel_loss=24.36, generator_kl_loss=1.973, generator_dur_loss=1.851, generator_adv_loss=1.851, generator_feat_match_loss=1.948, over 657.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 00:52:45,260 INFO [train.py:811] (1/4) Start epoch 68
+2023-11-13 00:55:03,164 INFO [train.py:467] (1/4) Epoch 68, batch 21, global_batch_idx: 2500, batch size: 55, loss[discriminator_loss=2.742, discriminator_real_loss=1.467, discriminator_fake_loss=1.275, generator_loss=32.59, generator_mel_loss=25.16, generator_kl_loss=2.08, generator_dur_loss=1.857, generator_adv_loss=1.562, generator_feat_match_loss=1.928, over 55.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.399, discriminator_fake_loss=1.339, generator_loss=32.54, generator_mel_loss=24.75, generator_kl_loss=2.023, generator_dur_loss=1.857, generator_adv_loss=1.885, generator_feat_match_loss=2.023, over 1584.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 00:56:22,220 INFO [train.py:811] (1/4) Start epoch 69
+2023-11-13 00:59:44,470 INFO [train.py:467] (1/4) Epoch 69, batch 34, global_batch_idx: 2550, batch size: 58, loss[discriminator_loss=2.777, discriminator_real_loss=1.281, discriminator_fake_loss=1.497, generator_loss=32.51, generator_mel_loss=24.76, generator_kl_loss=1.938, generator_dur_loss=1.837, generator_adv_loss=1.88, generator_feat_match_loss=2.096, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.403, discriminator_fake_loss=1.341, generator_loss=32.47, generator_mel_loss=24.72, generator_kl_loss=1.975, generator_dur_loss=1.852, generator_adv_loss=1.879, generator_feat_match_loss=2.039, over 2290.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 00:59:55,343 INFO [train.py:811] (1/4) Start epoch 70
+2023-11-13 01:03:19,282 INFO [train.py:811] (1/4) Start epoch 71
+2023-11-13 01:04:28,856 INFO [train.py:467] (1/4) Epoch 71, batch 10, global_batch_idx: 2600, batch size: 79, loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=32.14, generator_mel_loss=24.24, generator_kl_loss=2.025, generator_dur_loss=1.844, generator_adv_loss=1.891, generator_feat_match_loss=2.135, over 79.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.389, discriminator_fake_loss=1.335, generator_loss=32.4, generator_mel_loss=24.64, generator_kl_loss=1.994, generator_dur_loss=1.839, generator_adv_loss=1.88, generator_feat_match_loss=2.042, over 833.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:04:28,858 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 01:04:39,259 INFO [train.py:517] (1/4) Epoch 71, validation: discriminator_loss=2.675, discriminator_real_loss=1.302, discriminator_fake_loss=1.373, generator_loss=33.04, generator_mel_loss=25.21, generator_kl_loss=2.058, generator_dur_loss=1.819, generator_adv_loss=1.796, generator_feat_match_loss=2.157, over 100.00 samples.
+2023-11-13 01:04:39,260 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 01:06:57,401 INFO [train.py:811] (1/4) Start epoch 72
+2023-11-13 01:09:24,986 INFO [train.py:467] (1/4) Epoch 72, batch 23, global_batch_idx: 2650, batch size: 65, loss[discriminator_loss=2.695, discriminator_real_loss=1.58, discriminator_fake_loss=1.114, generator_loss=32.25, generator_mel_loss=24.4, generator_kl_loss=2.047, generator_dur_loss=1.862, generator_adv_loss=1.735, generator_feat_match_loss=2.209, over 65.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.409, discriminator_fake_loss=1.322, generator_loss=32.07, generator_mel_loss=24.34, generator_kl_loss=1.981, generator_dur_loss=1.843, generator_adv_loss=1.871, generator_feat_match_loss=2.038, over 1716.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:10:28,848 INFO [train.py:811] (1/4) Start epoch 73
+2023-11-13 01:14:02,901 INFO [train.py:467] (1/4) Epoch 73, batch 36, global_batch_idx: 2700, batch size: 61, loss[discriminator_loss=2.793, discriminator_real_loss=1.196, discriminator_fake_loss=1.596, generator_loss=31.8, generator_mel_loss=24.1, generator_kl_loss=1.886, generator_dur_loss=1.826, generator_adv_loss=1.905, generator_feat_match_loss=2.078, over 61.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.395, discriminator_fake_loss=1.33, generator_loss=32.19, generator_mel_loss=24.35, generator_kl_loss=1.998, generator_dur_loss=1.833, generator_adv_loss=1.916, generator_feat_match_loss=2.085, over 2807.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:14:03,553 INFO [train.py:811] (1/4) Start epoch 74
+2023-11-13 01:17:38,618 INFO [train.py:811] (1/4) Start epoch 75
+2023-11-13 01:18:57,343 INFO [train.py:467] (1/4) Epoch 75, batch 12, global_batch_idx: 2750, batch size: 56, loss[discriminator_loss=2.783, discriminator_real_loss=1.282, discriminator_fake_loss=1.501, generator_loss=32.45, generator_mel_loss=24.31, generator_kl_loss=1.866, generator_dur_loss=1.861, generator_adv_loss=2.336, generator_feat_match_loss=2.072, over 56.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.392, discriminator_fake_loss=1.316, generator_loss=32.28, generator_mel_loss=24.41, generator_kl_loss=1.979, generator_dur_loss=1.833, generator_adv_loss=1.898, generator_feat_match_loss=2.159, over 971.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:21:05,339 INFO [train.py:811] (1/4) Start epoch 76
+2023-11-13 01:23:36,704 INFO [train.py:467] (1/4) Epoch 76, batch 25, global_batch_idx: 2800, batch size: 59, loss[discriminator_loss=2.832, discriminator_real_loss=1.455, discriminator_fake_loss=1.376, generator_loss=32.1, generator_mel_loss=24.36, generator_kl_loss=1.953, generator_dur_loss=1.842, generator_adv_loss=1.953, generator_feat_match_loss=1.992, over 59.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.384, discriminator_fake_loss=1.355, generator_loss=32.07, generator_mel_loss=24.27, generator_kl_loss=1.981, generator_dur_loss=1.822, generator_adv_loss=1.905, generator_feat_match_loss=2.095, over 1966.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:23:36,706 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 01:23:47,555 INFO [train.py:517] (1/4) Epoch 76, validation: discriminator_loss=2.666, discriminator_real_loss=1.444, discriminator_fake_loss=1.222, generator_loss=33.52, generator_mel_loss=25.64, generator_kl_loss=2.048, generator_dur_loss=1.806, generator_adv_loss=1.952, generator_feat_match_loss=2.076, over 100.00 samples.
+2023-11-13 01:23:47,556 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 01:24:48,440 INFO [train.py:811] (1/4) Start epoch 77
+2023-11-13 01:28:18,958 INFO [train.py:811] (1/4) Start epoch 78
+2023-11-13 01:28:39,647 INFO [train.py:467] (1/4) Epoch 78, batch 1, global_batch_idx: 2850, batch size: 85, loss[discriminator_loss=2.832, discriminator_real_loss=1.643, discriminator_fake_loss=1.19, generator_loss=31.61, generator_mel_loss=24.12, generator_kl_loss=2.01, generator_dur_loss=1.831, generator_adv_loss=1.712, generator_feat_match_loss=1.932, over 85.00 samples.], tot_loss[discriminator_loss=2.808, discriminator_real_loss=1.401, discriminator_fake_loss=1.407, generator_loss=31.74, generator_mel_loss=23.98, generator_kl_loss=2, generator_dur_loss=1.818, generator_adv_loss=1.947, generator_feat_match_loss=1.995, over 164.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:31:48,625 INFO [train.py:811] (1/4) Start epoch 79
+2023-11-13 01:33:16,497 INFO [train.py:467] (1/4) Epoch 79, batch 14, global_batch_idx: 2900, batch size: 51, loss[discriminator_loss=2.68, discriminator_real_loss=1.259, discriminator_fake_loss=1.422, generator_loss=31.8, generator_mel_loss=23.97, generator_kl_loss=1.918, generator_dur_loss=1.837, generator_adv_loss=2.002, generator_feat_match_loss=2.064, over 51.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.396, discriminator_fake_loss=1.351, generator_loss=31.93, generator_mel_loss=24.12, generator_kl_loss=2.005, generator_dur_loss=1.819, generator_adv_loss=1.89, generator_feat_match_loss=2.1, over 987.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:35:20,959 INFO [train.py:811] (1/4) Start epoch 80
+2023-11-13 01:38:04,842 INFO [train.py:467] (1/4) Epoch 80, batch 27, global_batch_idx: 2950, batch size: 76, loss[discriminator_loss=2.816, discriminator_real_loss=1.419, discriminator_fake_loss=1.396, generator_loss=31.26, generator_mel_loss=23.78, generator_kl_loss=1.984, generator_dur_loss=1.802, generator_adv_loss=1.727, generator_feat_match_loss=1.969, over 76.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.392, discriminator_fake_loss=1.352, generator_loss=31.86, generator_mel_loss=24.06, generator_kl_loss=1.986, generator_dur_loss=1.814, generator_adv_loss=1.898, generator_feat_match_loss=2.095, over 2059.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:38:48,577 INFO [train.py:811] (1/4) Start epoch 81
+2023-11-13 01:42:32,748 INFO [train.py:811] (1/4) Start epoch 82
+2023-11-13 01:43:03,068 INFO [train.py:467] (1/4) Epoch 82, batch 3, global_batch_idx: 3000, batch size: 56, loss[discriminator_loss=2.758, discriminator_real_loss=1.2, discriminator_fake_loss=1.557, generator_loss=31.53, generator_mel_loss=23.79, generator_kl_loss=1.825, generator_dur_loss=1.837, generator_adv_loss=2.086, generator_feat_match_loss=1.992, over 56.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.333, discriminator_fake_loss=1.406, generator_loss=31.79, generator_mel_loss=24.04, generator_kl_loss=1.963, generator_dur_loss=1.813, generator_adv_loss=1.836, generator_feat_match_loss=2.137, over 322.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:43:03,070 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 01:43:14,686 INFO [train.py:517] (1/4) Epoch 82, validation: discriminator_loss=2.629, discriminator_real_loss=1.475, discriminator_fake_loss=1.154, generator_loss=32.79, generator_mel_loss=24.73, generator_kl_loss=2.072, generator_dur_loss=1.789, generator_adv_loss=2.068, generator_feat_match_loss=2.126, over 100.00 samples.
+2023-11-13 01:43:14,687 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 01:46:15,605 INFO [train.py:811] (1/4) Start epoch 83
+2023-11-13 01:48:02,219 INFO [train.py:467] (1/4) Epoch 83, batch 16, global_batch_idx: 3050, batch size: 73, loss[discriminator_loss=2.703, discriminator_real_loss=1.453, discriminator_fake_loss=1.25, generator_loss=30.78, generator_mel_loss=23.33, generator_kl_loss=1.902, generator_dur_loss=1.799, generator_adv_loss=1.73, generator_feat_match_loss=2.02, over 73.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.434, discriminator_fake_loss=1.325, generator_loss=31.62, generator_mel_loss=23.82, generator_kl_loss=1.978, generator_dur_loss=1.814, generator_adv_loss=1.903, generator_feat_match_loss=2.104, over 1151.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:49:53,466 INFO [train.py:811] (1/4) Start epoch 84
+2023-11-13 01:52:41,973 INFO [train.py:467] (1/4) Epoch 84, batch 29, global_batch_idx: 3100, batch size: 58, loss[discriminator_loss=2.658, discriminator_real_loss=1.254, discriminator_fake_loss=1.404, generator_loss=31.37, generator_mel_loss=23.17, generator_kl_loss=1.935, generator_dur_loss=1.806, generator_adv_loss=2.191, generator_feat_match_loss=2.262, over 58.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.399, discriminator_fake_loss=1.339, generator_loss=31.77, generator_mel_loss=23.94, generator_kl_loss=1.994, generator_dur_loss=1.811, generator_adv_loss=1.897, generator_feat_match_loss=2.135, over 2089.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 01:53:25,233 INFO [train.py:811] (1/4) Start epoch 85
+2023-11-13 01:56:53,082 INFO [train.py:811] (1/4) Start epoch 86
+2023-11-13 01:57:32,041 INFO [train.py:467] (1/4) Epoch 86, batch 5, global_batch_idx: 3150, batch size: 51, loss[discriminator_loss=2.859, discriminator_real_loss=1.379, discriminator_fake_loss=1.481, generator_loss=31.19, generator_mel_loss=23.72, generator_kl_loss=1.967, generator_dur_loss=1.819, generator_adv_loss=1.883, generator_feat_match_loss=1.806, over 51.00 samples.], tot_loss[discriminator_loss=2.79, discriminator_real_loss=1.381, discriminator_fake_loss=1.409, generator_loss=31.46, generator_mel_loss=23.85, generator_kl_loss=1.971, generator_dur_loss=1.82, generator_adv_loss=1.833, generator_feat_match_loss=1.984, over 349.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:00:25,606 INFO [train.py:811] (1/4) Start epoch 87
+2023-11-13 02:02:21,708 INFO [train.py:467] (1/4) Epoch 87, batch 18, global_batch_idx: 3200, batch size: 67, loss[discriminator_loss=2.76, discriminator_real_loss=1.448, discriminator_fake_loss=1.312, generator_loss=31.44, generator_mel_loss=23.72, generator_kl_loss=1.935, generator_dur_loss=1.817, generator_adv_loss=1.846, generator_feat_match_loss=2.117, over 67.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.385, discriminator_fake_loss=1.33, generator_loss=31.61, generator_mel_loss=23.78, generator_kl_loss=1.986, generator_dur_loss=1.8, generator_adv_loss=1.888, generator_feat_match_loss=2.152, over 1340.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:02:21,710 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 02:02:32,500 INFO [train.py:517] (1/4) Epoch 87, validation: discriminator_loss=2.654, discriminator_real_loss=1.294, discriminator_fake_loss=1.36, generator_loss=32.68, generator_mel_loss=24.71, generator_kl_loss=2.052, generator_dur_loss=1.781, generator_adv_loss=1.804, generator_feat_match_loss=2.333, over 100.00 samples.
+2023-11-13 02:02:32,501 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 02:04:11,489 INFO [train.py:811] (1/4) Start epoch 88
+2023-11-13 02:07:12,361 INFO [train.py:467] (1/4) Epoch 88, batch 31, global_batch_idx: 3250, batch size: 90, loss[discriminator_loss=2.725, discriminator_real_loss=1.625, discriminator_fake_loss=1.1, generator_loss=31.92, generator_mel_loss=24.27, generator_kl_loss=2.032, generator_dur_loss=1.823, generator_adv_loss=1.584, generator_feat_match_loss=2.211, over 90.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.412, discriminator_fake_loss=1.316, generator_loss=31.64, generator_mel_loss=23.79, generator_kl_loss=1.952, generator_dur_loss=1.808, generator_adv_loss=1.894, generator_feat_match_loss=2.194, over 2298.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:07:43,116 INFO [train.py:811] (1/4) Start epoch 89
+2023-11-13 02:11:19,304 INFO [train.py:811] (1/4) Start epoch 90
+2023-11-13 02:12:13,419 INFO [train.py:467] (1/4) Epoch 90, batch 7, global_batch_idx: 3300, batch size: 65, loss[discriminator_loss=2.785, discriminator_real_loss=1.345, discriminator_fake_loss=1.44, generator_loss=31.3, generator_mel_loss=23.43, generator_kl_loss=2.007, generator_dur_loss=1.793, generator_adv_loss=2.043, generator_feat_match_loss=2.029, over 65.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.393, discriminator_fake_loss=1.374, generator_loss=31.44, generator_mel_loss=23.73, generator_kl_loss=1.927, generator_dur_loss=1.796, generator_adv_loss=1.88, generator_feat_match_loss=2.099, over 619.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:14:56,109 INFO [train.py:811] (1/4) Start epoch 91
+2023-11-13 02:17:00,021 INFO [train.py:467] (1/4) Epoch 91, batch 20, global_batch_idx: 3350, batch size: 69, loss[discriminator_loss=2.738, discriminator_real_loss=1.395, discriminator_fake_loss=1.343, generator_loss=30.9, generator_mel_loss=23.13, generator_kl_loss=2.046, generator_dur_loss=1.812, generator_adv_loss=1.86, generator_feat_match_loss=2.051, over 69.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.399, discriminator_fake_loss=1.356, generator_loss=31.57, generator_mel_loss=23.68, generator_kl_loss=1.994, generator_dur_loss=1.8, generator_adv_loss=1.936, generator_feat_match_loss=2.156, over 1757.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:18:22,967 INFO [train.py:811] (1/4) Start epoch 92
+2023-11-13 02:21:30,560 INFO [train.py:467] (1/4) Epoch 92, batch 33, global_batch_idx: 3400, batch size: 101, loss[discriminator_loss=2.791, discriminator_real_loss=1.258, discriminator_fake_loss=1.533, generator_loss=31.89, generator_mel_loss=23.72, generator_kl_loss=1.924, generator_dur_loss=1.78, generator_adv_loss=2.152, generator_feat_match_loss=2.318, over 101.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=31.44, generator_mel_loss=23.57, generator_kl_loss=1.958, generator_dur_loss=1.797, generator_adv_loss=1.932, generator_feat_match_loss=2.187, over 2331.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:21:30,562 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 02:21:41,598 INFO [train.py:517] (1/4) Epoch 92, validation: discriminator_loss=2.768, discriminator_real_loss=1.521, discriminator_fake_loss=1.246, generator_loss=33.17, generator_mel_loss=24.83, generator_kl_loss=2.05, generator_dur_loss=1.77, generator_adv_loss=2.067, generator_feat_match_loss=2.454, over 100.00 samples.
+2023-11-13 02:21:41,599 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 02:21:59,767 INFO [train.py:811] (1/4) Start epoch 93
+2023-11-13 02:25:30,898 INFO [train.py:811] (1/4) Start epoch 94
+2023-11-13 02:26:32,257 INFO [train.py:467] (1/4) Epoch 94, batch 9, global_batch_idx: 3450, batch size: 81, loss[discriminator_loss=2.734, discriminator_real_loss=1.349, discriminator_fake_loss=1.387, generator_loss=31.54, generator_mel_loss=23.63, generator_kl_loss=2.041, generator_dur_loss=1.784, generator_adv_loss=1.967, generator_feat_match_loss=2.117, over 81.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.438, discriminator_fake_loss=1.337, generator_loss=31.14, generator_mel_loss=23.42, generator_kl_loss=1.966, generator_dur_loss=1.792, generator_adv_loss=1.878, generator_feat_match_loss=2.085, over 677.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:29:01,998 INFO [train.py:811] (1/4) Start epoch 95
+2023-11-13 02:31:26,339 INFO [train.py:467] (1/4) Epoch 95, batch 22, global_batch_idx: 3500, batch size: 73, loss[discriminator_loss=2.76, discriminator_real_loss=1.645, discriminator_fake_loss=1.115, generator_loss=31.45, generator_mel_loss=23.65, generator_kl_loss=1.993, generator_dur_loss=1.798, generator_adv_loss=1.956, generator_feat_match_loss=2.055, over 73.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.398, discriminator_fake_loss=1.364, generator_loss=31.3, generator_mel_loss=23.57, generator_kl_loss=1.975, generator_dur_loss=1.797, generator_adv_loss=1.859, generator_feat_match_loss=2.099, over 1654.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:32:38,813 INFO [train.py:811] (1/4) Start epoch 96
+2023-11-13 02:35:56,156 INFO [train.py:467] (1/4) Epoch 96, batch 35, global_batch_idx: 3550, batch size: 73, loss[discriminator_loss=2.832, discriminator_real_loss=1.355, discriminator_fake_loss=1.476, generator_loss=31.13, generator_mel_loss=23.24, generator_kl_loss=2.061, generator_dur_loss=1.793, generator_adv_loss=1.914, generator_feat_match_loss=2.115, over 73.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.387, discriminator_fake_loss=1.356, generator_loss=31.26, generator_mel_loss=23.45, generator_kl_loss=1.971, generator_dur_loss=1.788, generator_adv_loss=1.884, generator_feat_match_loss=2.163, over 2768.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:36:02,692 INFO [train.py:811] (1/4) Start epoch 97
+2023-11-13 02:39:34,222 INFO [train.py:811] (1/4) Start epoch 98
+2023-11-13 02:40:52,731 INFO [train.py:467] (1/4) Epoch 98, batch 11, global_batch_idx: 3600, batch size: 110, loss[discriminator_loss=2.766, discriminator_real_loss=1.375, discriminator_fake_loss=1.392, generator_loss=31.5, generator_mel_loss=23.72, generator_kl_loss=2.005, generator_dur_loss=1.784, generator_adv_loss=1.898, generator_feat_match_loss=2.094, over 110.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.401, discriminator_fake_loss=1.351, generator_loss=30.98, generator_mel_loss=23.34, generator_kl_loss=1.96, generator_dur_loss=1.793, generator_adv_loss=1.845, generator_feat_match_loss=2.042, over 959.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:40:52,732 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 02:41:03,532 INFO [train.py:517] (1/4) Epoch 98, validation: discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=32.06, generator_mel_loss=24.26, generator_kl_loss=2.058, generator_dur_loss=1.766, generator_adv_loss=1.862, generator_feat_match_loss=2.108, over 100.00 samples.
+2023-11-13 02:41:03,533 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 02:43:14,241 INFO [train.py:811] (1/4) Start epoch 99
+2023-11-13 02:45:42,525 INFO [train.py:467] (1/4) Epoch 99, batch 24, global_batch_idx: 3650, batch size: 76, loss[discriminator_loss=2.832, discriminator_real_loss=1.656, discriminator_fake_loss=1.176, generator_loss=31.33, generator_mel_loss=23.47, generator_kl_loss=2.056, generator_dur_loss=1.776, generator_adv_loss=1.708, generator_feat_match_loss=2.32, over 76.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.405, discriminator_fake_loss=1.35, generator_loss=31.26, generator_mel_loss=23.45, generator_kl_loss=1.984, generator_dur_loss=1.789, generator_adv_loss=1.871, generator_feat_match_loss=2.167, over 1765.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:46:48,836 INFO [train.py:811] (1/4) Start epoch 100
+2023-11-13 02:50:27,381 INFO [train.py:811] (1/4) Start epoch 101
+2023-11-13 02:50:46,111 INFO [train.py:467] (1/4) Epoch 101, batch 0, global_batch_idx: 3700, batch size: 65, loss[discriminator_loss=2.809, discriminator_real_loss=1.301, discriminator_fake_loss=1.508, generator_loss=30.51, generator_mel_loss=23.02, generator_kl_loss=1.948, generator_dur_loss=1.805, generator_adv_loss=1.752, generator_feat_match_loss=1.98, over 65.00 samples.], tot_loss[discriminator_loss=2.809, discriminator_real_loss=1.301, discriminator_fake_loss=1.508, generator_loss=30.51, generator_mel_loss=23.02, generator_kl_loss=1.948, generator_dur_loss=1.805, generator_adv_loss=1.752, generator_feat_match_loss=1.98, over 65.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, grad_scale: 64.0
+2023-11-13 02:54:03,207 INFO [train.py:811] (1/4) Start epoch 102
+2023-11-13 02:55:22,972 INFO [train.py:467] (1/4) Epoch 102, batch 13, global_batch_idx: 3750, batch size: 49, loss[discriminator_loss=2.727, discriminator_real_loss=1.552, discriminator_fake_loss=1.175, generator_loss=29.61, generator_mel_loss=22.13, generator_kl_loss=1.901, generator_dur_loss=1.803, generator_adv_loss=1.668, generator_feat_match_loss=2.107, over 49.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.385, discriminator_fake_loss=1.334, generator_loss=30.79, generator_mel_loss=23.1, generator_kl_loss=1.935, generator_dur_loss=1.782, generator_adv_loss=1.834, generator_feat_match_loss=2.137, over 908.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 64.0
+2023-11-13 02:57:40,820 INFO [train.py:811] (1/4) Start epoch 103
+2023-11-13 03:00:04,361 INFO [train.py:467] (1/4) Epoch 103, batch 26, global_batch_idx: 3800, batch size: 51, loss[discriminator_loss=2.777, discriminator_real_loss=1.283, discriminator_fake_loss=1.493, generator_loss=31.67, generator_mel_loss=23.75, generator_kl_loss=1.97, generator_dur_loss=1.792, generator_adv_loss=1.915, generator_feat_match_loss=2.25, over 51.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.385, discriminator_fake_loss=1.339, generator_loss=31.02, generator_mel_loss=23.26, generator_kl_loss=1.967, generator_dur_loss=1.791, generator_adv_loss=1.849, generator_feat_match_loss=2.148, over 1805.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 64.0
+2023-11-13 03:00:04,362 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 03:00:15,277 INFO [train.py:517] (1/4) Epoch 103, validation: discriminator_loss=2.699, discriminator_real_loss=1.437, discriminator_fake_loss=1.263, generator_loss=31.77, generator_mel_loss=23.74, generator_kl_loss=2.119, generator_dur_loss=1.75, generator_adv_loss=1.923, generator_feat_match_loss=2.244, over 100.00 samples.
+2023-11-13 03:00:15,278 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 03:01:13,800 INFO [train.py:811] (1/4) Start epoch 104
+2023-11-13 03:04:45,269 INFO [train.py:811] (1/4) Start epoch 105
+2023-11-13 03:05:16,136 INFO [train.py:467] (1/4) Epoch 105, batch 2, global_batch_idx: 3850, batch size: 55, loss[discriminator_loss=2.77, discriminator_real_loss=1.462, discriminator_fake_loss=1.307, generator_loss=31.38, generator_mel_loss=23.46, generator_kl_loss=1.915, generator_dur_loss=1.82, generator_adv_loss=2.049, generator_feat_match_loss=2.141, over 55.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.397, discriminator_fake_loss=1.351, generator_loss=31.13, generator_mel_loss=23.2, generator_kl_loss=2, generator_dur_loss=1.792, generator_adv_loss=1.961, generator_feat_match_loss=2.18, over 187.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 64.0
+2023-11-13 03:08:13,751 INFO [train.py:811] (1/4) Start epoch 106
+2023-11-13 03:09:43,375 INFO [train.py:467] (1/4) Epoch 106, batch 15, global_batch_idx: 3900, batch size: 51, loss[discriminator_loss=2.797, discriminator_real_loss=1.562, discriminator_fake_loss=1.235, generator_loss=31.32, generator_mel_loss=23.26, generator_kl_loss=2.042, generator_dur_loss=1.763, generator_adv_loss=2.002, generator_feat_match_loss=2.25, over 51.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.408, discriminator_fake_loss=1.358, generator_loss=30.83, generator_mel_loss=23.12, generator_kl_loss=1.969, generator_dur_loss=1.777, generator_adv_loss=1.865, generator_feat_match_loss=2.104, over 1079.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 64.0
+2023-11-13 03:11:40,244 INFO [train.py:811] (1/4) Start epoch 107
+2023-11-13 03:14:19,914 INFO [train.py:467] (1/4) Epoch 107, batch 28, global_batch_idx: 3950, batch size: 50, loss[discriminator_loss=2.777, discriminator_real_loss=1.503, discriminator_fake_loss=1.274, generator_loss=29.76, generator_mel_loss=22.4, generator_kl_loss=1.867, generator_dur_loss=1.802, generator_adv_loss=1.812, generator_feat_match_loss=1.881, over 50.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.415, discriminator_fake_loss=1.346, generator_loss=30.9, generator_mel_loss=23.16, generator_kl_loss=1.937, generator_dur_loss=1.781, generator_adv_loss=1.883, generator_feat_match_loss=2.139, over 1970.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 64.0
+2023-11-13 03:15:13,749 INFO [train.py:811] (1/4) Start epoch 108
+2023-11-13 03:18:42,980 INFO [train.py:811] (1/4) Start epoch 109
+2023-11-13 03:19:19,576 INFO [train.py:467] (1/4) Epoch 109, batch 4, global_batch_idx: 4000, batch size: 51, loss[discriminator_loss=2.711, discriminator_real_loss=1.296, discriminator_fake_loss=1.414, generator_loss=31.02, generator_mel_loss=23.21, generator_kl_loss=1.917, generator_dur_loss=1.75, generator_adv_loss=2.016, generator_feat_match_loss=2.131, over 51.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.412, discriminator_fake_loss=1.334, generator_loss=30.08, generator_mel_loss=22.56, generator_kl_loss=1.92, generator_dur_loss=1.784, generator_adv_loss=1.843, generator_feat_match_loss=1.974, over 316.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:19:19,583 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 03:19:31,524 INFO [train.py:517] (1/4) Epoch 109, validation: discriminator_loss=2.658, discriminator_real_loss=1.349, discriminator_fake_loss=1.309, generator_loss=31.43, generator_mel_loss=23.69, generator_kl_loss=1.953, generator_dur_loss=1.745, generator_adv_loss=1.882, generator_feat_match_loss=2.155, over 100.00 samples.
+2023-11-13 03:19:31,525 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 03:22:31,748 INFO [train.py:811] (1/4) Start epoch 110
+2023-11-13 03:24:17,792 INFO [train.py:467] (1/4) Epoch 110, batch 17, global_batch_idx: 4050, batch size: 85, loss[discriminator_loss=2.762, discriminator_real_loss=1.321, discriminator_fake_loss=1.44, generator_loss=30.81, generator_mel_loss=22.84, generator_kl_loss=1.907, generator_dur_loss=1.779, generator_adv_loss=2.199, generator_feat_match_loss=2.086, over 85.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.371, discriminator_fake_loss=1.355, generator_loss=30.9, generator_mel_loss=23.02, generator_kl_loss=1.972, generator_dur_loss=1.776, generator_adv_loss=1.889, generator_feat_match_loss=2.24, over 1330.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:26:04,214 INFO [train.py:811] (1/4) Start epoch 111
+2023-11-13 03:29:07,991 INFO [train.py:467] (1/4) Epoch 111, batch 30, global_batch_idx: 4100, batch size: 52, loss[discriminator_loss=2.963, discriminator_real_loss=1.788, discriminator_fake_loss=1.175, generator_loss=29.67, generator_mel_loss=22.44, generator_kl_loss=1.812, generator_dur_loss=1.791, generator_adv_loss=1.577, generator_feat_match_loss=2.053, over 52.00 samples.], tot_loss[discriminator_loss=2.789, discriminator_real_loss=1.423, discriminator_fake_loss=1.366, generator_loss=30.83, generator_mel_loss=23.02, generator_kl_loss=1.984, generator_dur_loss=1.78, generator_adv_loss=1.899, generator_feat_match_loss=2.145, over 2145.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:29:39,225 INFO [train.py:811] (1/4) Start epoch 112
+2023-11-13 03:33:13,564 INFO [train.py:811] (1/4) Start epoch 113
+2023-11-13 03:33:57,803 INFO [train.py:467] (1/4) Epoch 113, batch 6, global_batch_idx: 4150, batch size: 71, loss[discriminator_loss=2.711, discriminator_real_loss=1.219, discriminator_fake_loss=1.492, generator_loss=31.73, generator_mel_loss=23.49, generator_kl_loss=2.007, generator_dur_loss=1.773, generator_adv_loss=2.16, generator_feat_match_loss=2.303, over 71.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.359, discriminator_fake_loss=1.364, generator_loss=31.18, generator_mel_loss=23.25, generator_kl_loss=2.013, generator_dur_loss=1.774, generator_adv_loss=1.91, generator_feat_match_loss=2.234, over 544.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:36:40,033 INFO [train.py:811] (1/4) Start epoch 114
+2023-11-13 03:38:42,383 INFO [train.py:467] (1/4) Epoch 114, batch 19, global_batch_idx: 4200, batch size: 61, loss[discriminator_loss=2.715, discriminator_real_loss=1.334, discriminator_fake_loss=1.381, generator_loss=30.84, generator_mel_loss=22.85, generator_kl_loss=1.97, generator_dur_loss=1.799, generator_adv_loss=1.984, generator_feat_match_loss=2.229, over 61.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=30.81, generator_mel_loss=23, generator_kl_loss=1.968, generator_dur_loss=1.773, generator_adv_loss=1.854, generator_feat_match_loss=2.209, over 1467.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:38:42,384 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 03:38:54,190 INFO [train.py:517] (1/4) Epoch 114, validation: discriminator_loss=2.653, discriminator_real_loss=1.35, discriminator_fake_loss=1.303, generator_loss=31.92, generator_mel_loss=23.96, generator_kl_loss=2.078, generator_dur_loss=1.744, generator_adv_loss=1.877, generator_feat_match_loss=2.255, over 100.00 samples.
+2023-11-13 03:38:54,191 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 03:40:19,543 INFO [train.py:811] (1/4) Start epoch 115
+2023-11-13 03:43:25,686 INFO [train.py:467] (1/4) Epoch 115, batch 32, global_batch_idx: 4250, batch size: 126, loss[discriminator_loss=2.732, discriminator_real_loss=1.402, discriminator_fake_loss=1.33, generator_loss=31.04, generator_mel_loss=23.21, generator_kl_loss=1.984, generator_dur_loss=1.755, generator_adv_loss=1.851, generator_feat_match_loss=2.244, over 126.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.402, discriminator_fake_loss=1.353, generator_loss=30.61, generator_mel_loss=22.85, generator_kl_loss=1.964, generator_dur_loss=1.772, generator_adv_loss=1.856, generator_feat_match_loss=2.171, over 2389.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:43:48,581 INFO [train.py:811] (1/4) Start epoch 116
+2023-11-13 03:47:13,996 INFO [train.py:811] (1/4) Start epoch 117
+2023-11-13 03:48:13,608 INFO [train.py:467] (1/4) Epoch 117, batch 8, global_batch_idx: 4300, batch size: 52, loss[discriminator_loss=2.793, discriminator_real_loss=1.574, discriminator_fake_loss=1.218, generator_loss=30.78, generator_mel_loss=23.09, generator_kl_loss=1.957, generator_dur_loss=1.782, generator_adv_loss=1.793, generator_feat_match_loss=2.166, over 52.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.405, discriminator_fake_loss=1.352, generator_loss=30.45, generator_mel_loss=22.75, generator_kl_loss=1.922, generator_dur_loss=1.768, generator_adv_loss=1.839, generator_feat_match_loss=2.171, over 629.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:50:52,071 INFO [train.py:811] (1/4) Start epoch 118
+2023-11-13 03:53:08,253 INFO [train.py:467] (1/4) Epoch 118, batch 21, global_batch_idx: 4350, batch size: 85, loss[discriminator_loss=2.76, discriminator_real_loss=1.361, discriminator_fake_loss=1.398, generator_loss=31.19, generator_mel_loss=23.3, generator_kl_loss=2.022, generator_dur_loss=1.757, generator_adv_loss=1.963, generator_feat_match_loss=2.146, over 85.00 samples.], tot_loss[discriminator_loss=2.782, discriminator_real_loss=1.424, discriminator_fake_loss=1.358, generator_loss=30.48, generator_mel_loss=22.77, generator_kl_loss=1.97, generator_dur_loss=1.771, generator_adv_loss=1.849, generator_feat_match_loss=2.125, over 1582.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:54:26,548 INFO [train.py:811] (1/4) Start epoch 119
+2023-11-13 03:57:50,232 INFO [train.py:467] (1/4) Epoch 119, batch 34, global_batch_idx: 4400, batch size: 49, loss[discriminator_loss=2.721, discriminator_real_loss=1.355, discriminator_fake_loss=1.365, generator_loss=29.87, generator_mel_loss=22.34, generator_kl_loss=1.931, generator_dur_loss=1.778, generator_adv_loss=1.86, generator_feat_match_loss=1.959, over 49.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.401, discriminator_fake_loss=1.348, generator_loss=30.6, generator_mel_loss=22.78, generator_kl_loss=1.977, generator_dur_loss=1.765, generator_adv_loss=1.862, generator_feat_match_loss=2.216, over 2795.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 03:57:50,233 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 03:58:00,794 INFO [train.py:517] (1/4) Epoch 119, validation: discriminator_loss=2.664, discriminator_real_loss=1.22, discriminator_fake_loss=1.443, generator_loss=31.92, generator_mel_loss=23.95, generator_kl_loss=1.953, generator_dur_loss=1.737, generator_adv_loss=1.761, generator_feat_match_loss=2.518, over 100.00 samples.
+2023-11-13 03:58:00,795 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 03:58:10,848 INFO [train.py:811] (1/4) Start epoch 120
+2023-11-13 04:01:42,992 INFO [train.py:811] (1/4) Start epoch 121
+2023-11-13 04:02:50,453 INFO [train.py:467] (1/4) Epoch 121, batch 10, global_batch_idx: 4450, batch size: 54, loss[discriminator_loss=2.73, discriminator_real_loss=1.351, discriminator_fake_loss=1.379, generator_loss=30.18, generator_mel_loss=22.35, generator_kl_loss=1.999, generator_dur_loss=1.795, generator_adv_loss=1.784, generator_feat_match_loss=2.248, over 54.00 samples.], tot_loss[discriminator_loss=2.772, discriminator_real_loss=1.42, discriminator_fake_loss=1.352, generator_loss=30.36, generator_mel_loss=22.65, generator_kl_loss=1.975, generator_dur_loss=1.767, generator_adv_loss=1.831, generator_feat_match_loss=2.138, over 822.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:05:14,795 INFO [train.py:811] (1/4) Start epoch 122
+2023-11-13 04:07:37,436 INFO [train.py:467] (1/4) Epoch 122, batch 23, global_batch_idx: 4500, batch size: 55, loss[discriminator_loss=2.68, discriminator_real_loss=1.405, discriminator_fake_loss=1.275, generator_loss=30.46, generator_mel_loss=22.51, generator_kl_loss=1.963, generator_dur_loss=1.789, generator_adv_loss=1.885, generator_feat_match_loss=2.314, over 55.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.346, generator_loss=30.51, generator_mel_loss=22.68, generator_kl_loss=1.967, generator_dur_loss=1.773, generator_adv_loss=1.867, generator_feat_match_loss=2.228, over 1651.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:08:46,302 INFO [train.py:811] (1/4) Start epoch 123
+2023-11-13 04:12:13,055 INFO [train.py:467] (1/4) Epoch 123, batch 36, global_batch_idx: 4550, batch size: 67, loss[discriminator_loss=2.719, discriminator_real_loss=1.217, discriminator_fake_loss=1.501, generator_loss=30.79, generator_mel_loss=22.65, generator_kl_loss=1.931, generator_dur_loss=1.78, generator_adv_loss=2.186, generator_feat_match_loss=2.25, over 67.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.404, discriminator_fake_loss=1.37, generator_loss=30.53, generator_mel_loss=22.81, generator_kl_loss=1.967, generator_dur_loss=1.762, generator_adv_loss=1.856, generator_feat_match_loss=2.134, over 2804.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:12:13,646 INFO [train.py:811] (1/4) Start epoch 124
+2023-11-13 04:15:43,122 INFO [train.py:811] (1/4) Start epoch 125
+2023-11-13 04:17:08,827 INFO [train.py:467] (1/4) Epoch 125, batch 12, global_batch_idx: 4600, batch size: 101, loss[discriminator_loss=2.717, discriminator_real_loss=1.406, discriminator_fake_loss=1.311, generator_loss=30.65, generator_mel_loss=22.85, generator_kl_loss=1.895, generator_dur_loss=1.765, generator_adv_loss=1.851, generator_feat_match_loss=2.293, over 101.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=30.65, generator_mel_loss=22.8, generator_kl_loss=1.953, generator_dur_loss=1.759, generator_adv_loss=1.862, generator_feat_match_loss=2.277, over 1019.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:17:08,829 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 04:17:19,764 INFO [train.py:517] (1/4) Epoch 125, validation: discriminator_loss=2.659, discriminator_real_loss=1.309, discriminator_fake_loss=1.35, generator_loss=31.88, generator_mel_loss=23.86, generator_kl_loss=1.967, generator_dur_loss=1.736, generator_adv_loss=1.793, generator_feat_match_loss=2.523, over 100.00 samples.
+2023-11-13 04:17:19,765 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 04:19:29,549 INFO [train.py:811] (1/4) Start epoch 126
+2023-11-13 04:21:56,589 INFO [train.py:467] (1/4) Epoch 126, batch 25, global_batch_idx: 4650, batch size: 53, loss[discriminator_loss=2.727, discriminator_real_loss=1.441, discriminator_fake_loss=1.285, generator_loss=30.32, generator_mel_loss=22.5, generator_kl_loss=1.969, generator_dur_loss=1.762, generator_adv_loss=1.851, generator_feat_match_loss=2.234, over 53.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.377, discriminator_fake_loss=1.355, generator_loss=30.61, generator_mel_loss=22.7, generator_kl_loss=1.976, generator_dur_loss=1.759, generator_adv_loss=1.893, generator_feat_match_loss=2.277, over 1925.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:22:55,995 INFO [train.py:811] (1/4) Start epoch 127
+2023-11-13 04:26:33,846 INFO [train.py:811] (1/4) Start epoch 128
+2023-11-13 04:26:58,851 INFO [train.py:467] (1/4) Epoch 128, batch 1, global_batch_idx: 4700, batch size: 52, loss[discriminator_loss=2.725, discriminator_real_loss=1.385, discriminator_fake_loss=1.34, generator_loss=30.75, generator_mel_loss=22.68, generator_kl_loss=1.939, generator_dur_loss=1.782, generator_adv_loss=2.102, generator_feat_match_loss=2.25, over 52.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.302, discriminator_fake_loss=1.427, generator_loss=30.71, generator_mel_loss=22.77, generator_kl_loss=1.929, generator_dur_loss=1.777, generator_adv_loss=1.96, generator_feat_match_loss=2.274, over 101.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:30:11,198 INFO [train.py:811] (1/4) Start epoch 129
+2023-11-13 04:31:40,387 INFO [train.py:467] (1/4) Epoch 129, batch 14, global_batch_idx: 4750, batch size: 60, loss[discriminator_loss=2.811, discriminator_real_loss=1.213, discriminator_fake_loss=1.598, generator_loss=31.18, generator_mel_loss=23.09, generator_kl_loss=1.909, generator_dur_loss=1.77, generator_adv_loss=2.195, generator_feat_match_loss=2.213, over 60.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.395, discriminator_fake_loss=1.364, generator_loss=30.66, generator_mel_loss=22.8, generator_kl_loss=1.949, generator_dur_loss=1.76, generator_adv_loss=1.886, generator_feat_match_loss=2.272, over 929.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:33:45,707 INFO [train.py:811] (1/4) Start epoch 130
+2023-11-13 04:36:24,715 INFO [train.py:467] (1/4) Epoch 130, batch 27, global_batch_idx: 4800, batch size: 126, loss[discriminator_loss=2.746, discriminator_real_loss=1.442, discriminator_fake_loss=1.303, generator_loss=30.3, generator_mel_loss=22.54, generator_kl_loss=1.982, generator_dur_loss=1.746, generator_adv_loss=1.791, generator_feat_match_loss=2.242, over 126.00 samples.], tot_loss[discriminator_loss=2.797, discriminator_real_loss=1.436, discriminator_fake_loss=1.361, generator_loss=30.21, generator_mel_loss=22.53, generator_kl_loss=1.971, generator_dur_loss=1.757, generator_adv_loss=1.853, generator_feat_match_loss=2.096, over 2055.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:36:24,716 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 04:36:35,812 INFO [train.py:517] (1/4) Epoch 130, validation: discriminator_loss=2.648, discriminator_real_loss=1.241, discriminator_fake_loss=1.407, generator_loss=30.79, generator_mel_loss=23.05, generator_kl_loss=2.004, generator_dur_loss=1.733, generator_adv_loss=1.731, generator_feat_match_loss=2.275, over 100.00 samples.
+2023-11-13 04:36:35,813 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 04:37:25,964 INFO [train.py:811] (1/4) Start epoch 131
+2023-11-13 04:40:55,926 INFO [train.py:811] (1/4) Start epoch 132
+2023-11-13 04:41:29,789 INFO [train.py:467] (1/4) Epoch 132, batch 3, global_batch_idx: 4850, batch size: 60, loss[discriminator_loss=2.707, discriminator_real_loss=1.396, discriminator_fake_loss=1.311, generator_loss=31.53, generator_mel_loss=23.48, generator_kl_loss=2.05, generator_dur_loss=1.769, generator_adv_loss=1.891, generator_feat_match_loss=2.342, over 60.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.383, discriminator_fake_loss=1.362, generator_loss=30.91, generator_mel_loss=22.92, generator_kl_loss=1.954, generator_dur_loss=1.764, generator_adv_loss=1.916, generator_feat_match_loss=2.353, over 253.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:44:25,833 INFO [train.py:811] (1/4) Start epoch 133
+2023-11-13 04:46:02,993 INFO [train.py:467] (1/4) Epoch 133, batch 16, global_batch_idx: 4900, batch size: 95, loss[discriminator_loss=2.793, discriminator_real_loss=1.438, discriminator_fake_loss=1.354, generator_loss=30.38, generator_mel_loss=22.62, generator_kl_loss=1.897, generator_dur_loss=1.76, generator_adv_loss=1.857, generator_feat_match_loss=2.238, over 95.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.418, discriminator_fake_loss=1.345, generator_loss=30.48, generator_mel_loss=22.62, generator_kl_loss=1.968, generator_dur_loss=1.758, generator_adv_loss=1.874, generator_feat_match_loss=2.259, over 1207.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:47:56,956 INFO [train.py:811] (1/4) Start epoch 134
+2023-11-13 04:50:46,793 INFO [train.py:467] (1/4) Epoch 134, batch 29, global_batch_idx: 4950, batch size: 61, loss[discriminator_loss=2.771, discriminator_real_loss=1.338, discriminator_fake_loss=1.434, generator_loss=30.02, generator_mel_loss=22.17, generator_kl_loss=1.908, generator_dur_loss=1.757, generator_adv_loss=1.895, generator_feat_match_loss=2.295, over 61.00 samples.], tot_loss[discriminator_loss=2.788, discriminator_real_loss=1.406, discriminator_fake_loss=1.381, generator_loss=30.28, generator_mel_loss=22.52, generator_kl_loss=1.971, generator_dur_loss=1.754, generator_adv_loss=1.841, generator_feat_match_loss=2.19, over 2039.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:51:26,201 INFO [train.py:811] (1/4) Start epoch 135
+2023-11-13 04:54:55,873 INFO [train.py:811] (1/4) Start epoch 136
+2023-11-13 04:55:38,407 INFO [train.py:467] (1/4) Epoch 136, batch 5, global_batch_idx: 5000, batch size: 71, loss[discriminator_loss=2.686, discriminator_real_loss=1.289, discriminator_fake_loss=1.396, generator_loss=30.76, generator_mel_loss=22.94, generator_kl_loss=1.864, generator_dur_loss=1.748, generator_adv_loss=1.84, generator_feat_match_loss=2.375, over 71.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.36, discriminator_fake_loss=1.36, generator_loss=30.7, generator_mel_loss=22.76, generator_kl_loss=1.932, generator_dur_loss=1.75, generator_adv_loss=1.877, generator_feat_match_loss=2.376, over 500.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 04:55:38,408 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 04:55:50,229 INFO [train.py:517] (1/4) Epoch 136, validation: discriminator_loss=2.601, discriminator_real_loss=1.183, discriminator_fake_loss=1.418, generator_loss=31.35, generator_mel_loss=23.4, generator_kl_loss=1.903, generator_dur_loss=1.72, generator_adv_loss=1.731, generator_feat_match_loss=2.595, over 100.00 samples.
+2023-11-13 04:55:50,230 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 04:58:35,193 INFO [train.py:811] (1/4) Start epoch 137
+2023-11-13 05:00:30,158 INFO [train.py:467] (1/4) Epoch 137, batch 18, global_batch_idx: 5050, batch size: 61, loss[discriminator_loss=2.766, discriminator_real_loss=1.18, discriminator_fake_loss=1.587, generator_loss=30.17, generator_mel_loss=22.11, generator_kl_loss=2.1, generator_dur_loss=1.741, generator_adv_loss=2.156, generator_feat_match_loss=2.068, over 61.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.398, discriminator_fake_loss=1.372, generator_loss=30.38, generator_mel_loss=22.55, generator_kl_loss=1.983, generator_dur_loss=1.752, generator_adv_loss=1.879, generator_feat_match_loss=2.216, over 1337.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 05:02:10,316 INFO [train.py:811] (1/4) Start epoch 138
+2023-11-13 05:05:17,234 INFO [train.py:467] (1/4) Epoch 138, batch 31, global_batch_idx: 5100, batch size: 126, loss[discriminator_loss=2.734, discriminator_real_loss=1.393, discriminator_fake_loss=1.341, generator_loss=31.19, generator_mel_loss=23.2, generator_kl_loss=1.962, generator_dur_loss=1.738, generator_adv_loss=1.926, generator_feat_match_loss=2.361, over 126.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.414, discriminator_fake_loss=1.347, generator_loss=30.48, generator_mel_loss=22.64, generator_kl_loss=1.976, generator_dur_loss=1.745, generator_adv_loss=1.852, generator_feat_match_loss=2.268, over 2457.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 05:05:45,551 INFO [train.py:811] (1/4) Start epoch 139
+2023-11-13 05:09:14,753 INFO [train.py:811] (1/4) Start epoch 140
+2023-11-13 05:10:06,741 INFO [train.py:467] (1/4) Epoch 140, batch 7, global_batch_idx: 5150, batch size: 49, loss[discriminator_loss=2.818, discriminator_real_loss=1.594, discriminator_fake_loss=1.225, generator_loss=29.67, generator_mel_loss=22.17, generator_kl_loss=1.976, generator_dur_loss=1.751, generator_adv_loss=1.8, generator_feat_match_loss=1.97, over 49.00 samples.], tot_loss[discriminator_loss=2.785, discriminator_real_loss=1.43, discriminator_fake_loss=1.356, generator_loss=30.35, generator_mel_loss=22.54, generator_kl_loss=2.01, generator_dur_loss=1.752, generator_adv_loss=1.876, generator_feat_match_loss=2.173, over 575.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 05:12:47,475 INFO [train.py:811] (1/4) Start epoch 141
+2023-11-13 05:14:54,171 INFO [train.py:467] (1/4) Epoch 141, batch 20, global_batch_idx: 5200, batch size: 60, loss[discriminator_loss=2.789, discriminator_real_loss=1.48, discriminator_fake_loss=1.308, generator_loss=30.23, generator_mel_loss=22.42, generator_kl_loss=1.946, generator_dur_loss=1.79, generator_adv_loss=1.805, generator_feat_match_loss=2.277, over 60.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.418, discriminator_fake_loss=1.341, generator_loss=30.55, generator_mel_loss=22.65, generator_kl_loss=1.956, generator_dur_loss=1.746, generator_adv_loss=1.884, generator_feat_match_loss=2.315, over 1540.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 05:14:54,172 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 05:15:05,086 INFO [train.py:517] (1/4) Epoch 141, validation: discriminator_loss=2.734, discriminator_real_loss=1.313, discriminator_fake_loss=1.421, generator_loss=31.4, generator_mel_loss=23.61, generator_kl_loss=2.005, generator_dur_loss=1.718, generator_adv_loss=1.729, generator_feat_match_loss=2.339, over 100.00 samples.
+2023-11-13 05:15:05,087 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 05:16:27,007 INFO [train.py:811] (1/4) Start epoch 142
+2023-11-13 05:19:39,456 INFO [train.py:467] (1/4) Epoch 142, batch 33, global_batch_idx: 5250, batch size: 76, loss[discriminator_loss=2.744, discriminator_real_loss=1.383, discriminator_fake_loss=1.361, generator_loss=29.36, generator_mel_loss=21.69, generator_kl_loss=1.916, generator_dur_loss=1.745, generator_adv_loss=1.815, generator_feat_match_loss=2.193, over 76.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.384, discriminator_fake_loss=1.364, generator_loss=30.42, generator_mel_loss=22.53, generator_kl_loss=1.951, generator_dur_loss=1.75, generator_adv_loss=1.866, generator_feat_match_loss=2.324, over 2367.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, grad_scale: 128.0
+2023-11-13 05:19:57,138 INFO [train.py:811] (1/4) Start epoch 143
+2023-11-13 05:23:26,078 INFO [train.py:811] (1/4) Start epoch 144
+2023-11-13 05:24:33,424 INFO [train.py:467] (1/4) Epoch 144, batch 9, global_batch_idx: 5300, batch size: 67, loss[discriminator_loss=2.695, discriminator_real_loss=1.483, discriminator_fake_loss=1.213, generator_loss=29.99, generator_mel_loss=22.18, generator_kl_loss=2.015, generator_dur_loss=1.761, generator_adv_loss=1.769, generator_feat_match_loss=2.27, over 67.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.357, discriminator_fake_loss=1.336, generator_loss=30.41, generator_mel_loss=22.43, generator_kl_loss=1.977, generator_dur_loss=1.746, generator_adv_loss=1.874, generator_feat_match_loss=2.377, over 716.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:27:00,336 INFO [train.py:811] (1/4) Start epoch 145
+2023-11-13 05:29:15,252 INFO [train.py:467] (1/4) Epoch 145, batch 22, global_batch_idx: 5350, batch size: 59, loss[discriminator_loss=2.688, discriminator_real_loss=1.248, discriminator_fake_loss=1.44, generator_loss=29.53, generator_mel_loss=21.61, generator_kl_loss=2.033, generator_dur_loss=1.759, generator_adv_loss=1.862, generator_feat_match_loss=2.264, over 59.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.387, discriminator_fake_loss=1.374, generator_loss=30.37, generator_mel_loss=22.48, generator_kl_loss=2.009, generator_dur_loss=1.752, generator_adv_loss=1.864, generator_feat_match_loss=2.267, over 1618.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:30:35,956 INFO [train.py:811] (1/4) Start epoch 146
+2023-11-13 05:34:02,603 INFO [train.py:467] (1/4) Epoch 146, batch 35, global_batch_idx: 5400, batch size: 95, loss[discriminator_loss=2.793, discriminator_real_loss=1.223, discriminator_fake_loss=1.57, generator_loss=30.7, generator_mel_loss=22.53, generator_kl_loss=1.932, generator_dur_loss=1.716, generator_adv_loss=2.078, generator_feat_match_loss=2.438, over 95.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.4, discriminator_fake_loss=1.355, generator_loss=30.23, generator_mel_loss=22.36, generator_kl_loss=1.962, generator_dur_loss=1.744, generator_adv_loss=1.892, generator_feat_match_loss=2.27, over 2397.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:34:02,605 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 05:34:13,538 INFO [train.py:517] (1/4) Epoch 146, validation: discriminator_loss=2.781, discriminator_real_loss=1.589, discriminator_fake_loss=1.192, generator_loss=31.94, generator_mel_loss=23.76, generator_kl_loss=2.033, generator_dur_loss=1.711, generator_adv_loss=2.037, generator_feat_match_loss=2.401, over 100.00 samples.
+2023-11-13 05:34:13,539 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 05:34:18,853 INFO [train.py:811] (1/4) Start epoch 147
+2023-11-13 05:37:48,417 INFO [train.py:811] (1/4) Start epoch 148
+2023-11-13 05:38:57,710 INFO [train.py:467] (1/4) Epoch 148, batch 11, global_batch_idx: 5450, batch size: 53, loss[discriminator_loss=2.746, discriminator_real_loss=1.32, discriminator_fake_loss=1.426, generator_loss=30.56, generator_mel_loss=22.71, generator_kl_loss=1.912, generator_dur_loss=1.765, generator_adv_loss=1.838, generator_feat_match_loss=2.332, over 53.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.405, discriminator_fake_loss=1.36, generator_loss=30.18, generator_mel_loss=22.39, generator_kl_loss=1.984, generator_dur_loss=1.748, generator_adv_loss=1.839, generator_feat_match_loss=2.217, over 779.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:41:17,247 INFO [train.py:811] (1/4) Start epoch 149
+2023-11-13 05:43:45,086 INFO [train.py:467] (1/4) Epoch 149, batch 24, global_batch_idx: 5500, batch size: 49, loss[discriminator_loss=2.715, discriminator_real_loss=1.475, discriminator_fake_loss=1.241, generator_loss=30.16, generator_mel_loss=22.29, generator_kl_loss=1.867, generator_dur_loss=1.749, generator_adv_loss=2.027, generator_feat_match_loss=2.232, over 49.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.395, discriminator_fake_loss=1.36, generator_loss=30.17, generator_mel_loss=22.31, generator_kl_loss=1.949, generator_dur_loss=1.747, generator_adv_loss=1.87, generator_feat_match_loss=2.3, over 1922.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:44:52,136 INFO [train.py:811] (1/4) Start epoch 150
+2023-11-13 05:48:19,076 INFO [train.py:811] (1/4) Start epoch 151
+2023-11-13 05:48:33,688 INFO [train.py:467] (1/4) Epoch 151, batch 0, global_batch_idx: 5550, batch size: 73, loss[discriminator_loss=2.674, discriminator_real_loss=1.31, discriminator_fake_loss=1.364, generator_loss=30.4, generator_mel_loss=22.33, generator_kl_loss=1.898, generator_dur_loss=1.756, generator_adv_loss=1.967, generator_feat_match_loss=2.457, over 73.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.31, discriminator_fake_loss=1.364, generator_loss=30.4, generator_mel_loss=22.33, generator_kl_loss=1.898, generator_dur_loss=1.756, generator_adv_loss=1.967, generator_feat_match_loss=2.457, over 73.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:51:49,031 INFO [train.py:811] (1/4) Start epoch 152
+2023-11-13 05:53:09,441 INFO [train.py:467] (1/4) Epoch 152, batch 13, global_batch_idx: 5600, batch size: 60, loss[discriminator_loss=2.732, discriminator_real_loss=1.375, discriminator_fake_loss=1.357, generator_loss=29.98, generator_mel_loss=22.08, generator_kl_loss=2.001, generator_dur_loss=1.734, generator_adv_loss=1.909, generator_feat_match_loss=2.258, over 60.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.39, discriminator_fake_loss=1.352, generator_loss=30.39, generator_mel_loss=22.5, generator_kl_loss=1.984, generator_dur_loss=1.743, generator_adv_loss=1.843, generator_feat_match_loss=2.326, over 1176.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:53:09,442 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 05:53:20,499 INFO [train.py:517] (1/4) Epoch 152, validation: discriminator_loss=2.72, discriminator_real_loss=1.382, discriminator_fake_loss=1.337, generator_loss=31.62, generator_mel_loss=23.69, generator_kl_loss=2.044, generator_dur_loss=1.705, generator_adv_loss=1.797, generator_feat_match_loss=2.381, over 100.00 samples.
+2023-11-13 05:53:20,500 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 05:55:27,400 INFO [train.py:811] (1/4) Start epoch 153
+2023-11-13 05:58:05,087 INFO [train.py:467] (1/4) Epoch 153, batch 26, global_batch_idx: 5650, batch size: 52, loss[discriminator_loss=2.711, discriminator_real_loss=1.381, discriminator_fake_loss=1.331, generator_loss=30.41, generator_mel_loss=22.36, generator_kl_loss=1.988, generator_dur_loss=1.769, generator_adv_loss=1.906, generator_feat_match_loss=2.395, over 52.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.404, discriminator_fake_loss=1.354, generator_loss=30.2, generator_mel_loss=22.34, generator_kl_loss=1.996, generator_dur_loss=1.734, generator_adv_loss=1.862, generator_feat_match_loss=2.272, over 2102.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 05:59:03,245 INFO [train.py:811] (1/4) Start epoch 154
+2023-11-13 06:02:31,561 INFO [train.py:811] (1/4) Start epoch 155
+2023-11-13 06:02:58,820 INFO [train.py:467] (1/4) Epoch 155, batch 2, global_batch_idx: 5700, batch size: 63, loss[discriminator_loss=2.826, discriminator_real_loss=1.367, discriminator_fake_loss=1.459, generator_loss=29.44, generator_mel_loss=21.61, generator_kl_loss=1.986, generator_dur_loss=1.754, generator_adv_loss=1.957, generator_feat_match_loss=2.135, over 63.00 samples.], tot_loss[discriminator_loss=2.794, discriminator_real_loss=1.415, discriminator_fake_loss=1.379, generator_loss=29.63, generator_mel_loss=21.87, generator_kl_loss=1.914, generator_dur_loss=1.753, generator_adv_loss=1.894, generator_feat_match_loss=2.203, over 179.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 06:06:06,747 INFO [train.py:811] (1/4) Start epoch 156
+2023-11-13 06:07:45,324 INFO [train.py:467] (1/4) Epoch 156, batch 15, global_batch_idx: 5750, batch size: 65, loss[discriminator_loss=2.707, discriminator_real_loss=1.255, discriminator_fake_loss=1.453, generator_loss=30.76, generator_mel_loss=22.59, generator_kl_loss=1.954, generator_dur_loss=1.736, generator_adv_loss=2.002, generator_feat_match_loss=2.475, over 65.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.386, discriminator_fake_loss=1.35, generator_loss=30.25, generator_mel_loss=22.23, generator_kl_loss=1.996, generator_dur_loss=1.739, generator_adv_loss=1.876, generator_feat_match_loss=2.404, over 1180.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 128.0
+2023-11-13 06:09:43,615 INFO [train.py:811] (1/4) Start epoch 157
+2023-11-13 06:12:34,789 INFO [train.py:467] (1/4) Epoch 157, batch 28, global_batch_idx: 5800, batch size: 63, loss[discriminator_loss=2.818, discriminator_real_loss=1.259, discriminator_fake_loss=1.56, generator_loss=30.07, generator_mel_loss=22.25, generator_kl_loss=1.895, generator_dur_loss=1.751, generator_adv_loss=2.016, generator_feat_match_loss=2.156, over 63.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.403, discriminator_fake_loss=1.362, generator_loss=30.28, generator_mel_loss=22.29, generator_kl_loss=1.965, generator_dur_loss=1.735, generator_adv_loss=1.923, generator_feat_match_loss=2.373, over 2128.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 64.0
+2023-11-13 06:12:34,790 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 06:12:46,007 INFO [train.py:517] (1/4) Epoch 157, validation: discriminator_loss=2.75, discriminator_real_loss=1.503, discriminator_fake_loss=1.247, generator_loss=31.28, generator_mel_loss=23.14, generator_kl_loss=2.07, generator_dur_loss=1.715, generator_adv_loss=1.982, generator_feat_match_loss=2.37, over 100.00 samples.
+2023-11-13 06:12:46,008 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 06:13:25,076 INFO [train.py:811] (1/4) Start epoch 158
+2023-11-13 06:16:56,409 INFO [train.py:811] (1/4) Start epoch 159
+2023-11-13 06:17:37,074 INFO [train.py:467] (1/4) Epoch 159, batch 4, global_batch_idx: 5850, batch size: 101, loss[discriminator_loss=2.699, discriminator_real_loss=1.324, discriminator_fake_loss=1.375, generator_loss=30.95, generator_mel_loss=22.85, generator_kl_loss=1.999, generator_dur_loss=1.697, generator_adv_loss=1.849, generator_feat_match_loss=2.551, over 101.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.344, discriminator_fake_loss=1.363, generator_loss=30.45, generator_mel_loss=22.34, generator_kl_loss=2.002, generator_dur_loss=1.731, generator_adv_loss=1.903, generator_feat_match_loss=2.471, over 439.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 64.0
+2023-11-13 06:20:24,501 INFO [train.py:811] (1/4) Start epoch 160
+2023-11-13 06:22:15,050 INFO [train.py:467] (1/4) Epoch 160, batch 17, global_batch_idx: 5900, batch size: 51, loss[discriminator_loss=2.521, discriminator_real_loss=1.338, discriminator_fake_loss=1.184, generator_loss=31.55, generator_mel_loss=22.62, generator_kl_loss=1.988, generator_dur_loss=1.776, generator_adv_loss=2.025, generator_feat_match_loss=3.133, over 51.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.354, discriminator_fake_loss=1.351, generator_loss=31.01, generator_mel_loss=22.43, generator_kl_loss=1.969, generator_dur_loss=1.738, generator_adv_loss=2.127, generator_feat_match_loss=2.751, over 1316.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 32.0
+2023-11-13 06:23:58,053 INFO [train.py:811] (1/4) Start epoch 161
+2023-11-13 06:27:03,360 INFO [train.py:467] (1/4) Epoch 161, batch 30, global_batch_idx: 5950, batch size: 81, loss[discriminator_loss=2.828, discriminator_real_loss=1.268, discriminator_fake_loss=1.561, generator_loss=29.06, generator_mel_loss=21.06, generator_kl_loss=1.928, generator_dur_loss=1.753, generator_adv_loss=2.014, generator_feat_match_loss=2.307, over 81.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.321, discriminator_fake_loss=1.338, generator_loss=30.95, generator_mel_loss=22.35, generator_kl_loss=1.977, generator_dur_loss=1.733, generator_adv_loss=2.103, generator_feat_match_loss=2.783, over 2183.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 32.0
+2023-11-13 06:27:31,234 INFO [train.py:811] (1/4) Start epoch 162
+2023-11-13 06:31:06,566 INFO [train.py:811] (1/4) Start epoch 163
+2023-11-13 06:31:59,804 INFO [train.py:467] (1/4) Epoch 163, batch 6, global_batch_idx: 6000, batch size: 60, loss[discriminator_loss=2.67, discriminator_real_loss=1.333, discriminator_fake_loss=1.337, generator_loss=30.77, generator_mel_loss=22.85, generator_kl_loss=1.948, generator_dur_loss=1.742, generator_adv_loss=1.83, generator_feat_match_loss=2.395, over 60.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.395, discriminator_fake_loss=1.319, generator_loss=30.07, generator_mel_loss=22.14, generator_kl_loss=1.942, generator_dur_loss=1.746, generator_adv_loss=1.904, generator_feat_match_loss=2.335, over 412.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 32.0
+2023-11-13 06:31:59,805 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 06:32:13,074 INFO [train.py:517] (1/4) Epoch 163, validation: discriminator_loss=2.666, discriminator_real_loss=1.218, discriminator_fake_loss=1.447, generator_loss=31.02, generator_mel_loss=23.15, generator_kl_loss=2.002, generator_dur_loss=1.701, generator_adv_loss=1.687, generator_feat_match_loss=2.479, over 100.00 samples.
+2023-11-13 06:32:13,075 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 06:34:52,673 INFO [train.py:811] (1/4) Start epoch 164
+2023-11-13 06:36:51,831 INFO [train.py:467] (1/4) Epoch 164, batch 19, global_batch_idx: 6050, batch size: 73, loss[discriminator_loss=2.57, discriminator_real_loss=1.337, discriminator_fake_loss=1.232, generator_loss=29.97, generator_mel_loss=21.68, generator_kl_loss=2.009, generator_dur_loss=1.74, generator_adv_loss=1.911, generator_feat_match_loss=2.627, over 73.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.406, discriminator_fake_loss=1.31, generator_loss=31.07, generator_mel_loss=22.3, generator_kl_loss=1.94, generator_dur_loss=1.731, generator_adv_loss=2.165, generator_feat_match_loss=2.936, over 1412.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 32.0
+2023-11-13 06:38:26,648 INFO [train.py:811] (1/4) Start epoch 165
+2023-11-13 06:41:30,872 INFO [train.py:467] (1/4) Epoch 165, batch 32, global_batch_idx: 6100, batch size: 52, loss[discriminator_loss=2.529, discriminator_real_loss=1.301, discriminator_fake_loss=1.229, generator_loss=30.87, generator_mel_loss=22.06, generator_kl_loss=1.871, generator_dur_loss=1.741, generator_adv_loss=2.148, generator_feat_match_loss=3.051, over 52.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.325, discriminator_fake_loss=1.347, generator_loss=30.68, generator_mel_loss=22.13, generator_kl_loss=1.933, generator_dur_loss=1.733, generator_adv_loss=2.103, generator_feat_match_loss=2.783, over 2332.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 06:41:51,959 INFO [train.py:811] (1/4) Start epoch 166
+2023-11-13 06:45:29,307 INFO [train.py:811] (1/4) Start epoch 167
+2023-11-13 06:46:33,490 INFO [train.py:467] (1/4) Epoch 167, batch 8, global_batch_idx: 6150, batch size: 56, loss[discriminator_loss=2.523, discriminator_real_loss=1.242, discriminator_fake_loss=1.281, generator_loss=31.42, generator_mel_loss=22.72, generator_kl_loss=1.939, generator_dur_loss=1.715, generator_adv_loss=2.146, generator_feat_match_loss=2.902, over 56.00 samples.], tot_loss[discriminator_loss=2.646, discriminator_real_loss=1.3, discriminator_fake_loss=1.345, generator_loss=30.83, generator_mel_loss=22.3, generator_kl_loss=1.97, generator_dur_loss=1.737, generator_adv_loss=2.011, generator_feat_match_loss=2.812, over 725.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 06:49:02,987 INFO [train.py:811] (1/4) Start epoch 168
+2023-11-13 06:51:24,158 INFO [train.py:467] (1/4) Epoch 168, batch 21, global_batch_idx: 6200, batch size: 79, loss[discriminator_loss=3.002, discriminator_real_loss=1.479, discriminator_fake_loss=1.522, generator_loss=30.23, generator_mel_loss=22.39, generator_kl_loss=1.969, generator_dur_loss=1.718, generator_adv_loss=1.736, generator_feat_match_loss=2.414, over 79.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=31.13, generator_mel_loss=22.24, generator_kl_loss=1.983, generator_dur_loss=1.734, generator_adv_loss=2.183, generator_feat_match_loss=2.989, over 1577.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 06:51:24,160 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 06:51:35,085 INFO [train.py:517] (1/4) Epoch 168, validation: discriminator_loss=2.937, discriminator_real_loss=1.581, discriminator_fake_loss=1.356, generator_loss=30.96, generator_mel_loss=22.99, generator_kl_loss=1.986, generator_dur_loss=1.7, generator_adv_loss=1.922, generator_feat_match_loss=2.362, over 100.00 samples.
+2023-11-13 06:51:35,086 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 06:52:50,412 INFO [train.py:811] (1/4) Start epoch 169
+2023-11-13 06:56:14,850 INFO [train.py:467] (1/4) Epoch 169, batch 34, global_batch_idx: 6250, batch size: 67, loss[discriminator_loss=2.746, discriminator_real_loss=1.251, discriminator_fake_loss=1.496, generator_loss=32.08, generator_mel_loss=23, generator_kl_loss=2.035, generator_dur_loss=1.722, generator_adv_loss=2.141, generator_feat_match_loss=3.18, over 67.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.354, discriminator_fake_loss=1.318, generator_loss=30.54, generator_mel_loss=22.2, generator_kl_loss=1.95, generator_dur_loss=1.728, generator_adv_loss=2.008, generator_feat_match_loss=2.652, over 2523.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 06:56:24,820 INFO [train.py:811] (1/4) Start epoch 170
+2023-11-13 06:59:57,624 INFO [train.py:811] (1/4) Start epoch 171
+2023-11-13 07:01:11,296 INFO [train.py:467] (1/4) Epoch 171, batch 10, global_batch_idx: 6300, batch size: 153, loss[discriminator_loss=2.639, discriminator_real_loss=1.31, discriminator_fake_loss=1.329, generator_loss=32.03, generator_mel_loss=22.48, generator_kl_loss=1.934, generator_dur_loss=1.743, generator_adv_loss=2.256, generator_feat_match_loss=3.621, over 153.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.347, discriminator_fake_loss=1.354, generator_loss=31.32, generator_mel_loss=22.22, generator_kl_loss=1.954, generator_dur_loss=1.731, generator_adv_loss=2.245, generator_feat_match_loss=3.176, over 908.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:03:36,265 INFO [train.py:811] (1/4) Start epoch 172
+2023-11-13 07:05:49,562 INFO [train.py:467] (1/4) Epoch 172, batch 23, global_batch_idx: 6350, batch size: 81, loss[discriminator_loss=2.586, discriminator_real_loss=1.346, discriminator_fake_loss=1.24, generator_loss=30.42, generator_mel_loss=21.86, generator_kl_loss=1.96, generator_dur_loss=1.731, generator_adv_loss=2.082, generator_feat_match_loss=2.789, over 81.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.332, discriminator_fake_loss=1.327, generator_loss=30.53, generator_mel_loss=22.03, generator_kl_loss=1.967, generator_dur_loss=1.727, generator_adv_loss=2.035, generator_feat_match_loss=2.779, over 1810.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:07:05,998 INFO [train.py:811] (1/4) Start epoch 173
+2023-11-13 07:10:38,025 INFO [train.py:467] (1/4) Epoch 173, batch 36, global_batch_idx: 6400, batch size: 110, loss[discriminator_loss=2.656, discriminator_real_loss=1.446, discriminator_fake_loss=1.21, generator_loss=30.63, generator_mel_loss=22.51, generator_kl_loss=1.987, generator_dur_loss=1.722, generator_adv_loss=1.841, generator_feat_match_loss=2.57, over 110.00 samples.], tot_loss[discriminator_loss=2.641, discriminator_real_loss=1.333, discriminator_fake_loss=1.308, generator_loss=30.97, generator_mel_loss=22.03, generator_kl_loss=1.97, generator_dur_loss=1.73, generator_adv_loss=2.207, generator_feat_match_loss=3.032, over 2591.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 32.0
+2023-11-13 07:10:38,026 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 07:10:50,207 INFO [train.py:517] (1/4) Epoch 173, validation: discriminator_loss=2.858, discriminator_real_loss=1.201, discriminator_fake_loss=1.657, generator_loss=30.66, generator_mel_loss=22.74, generator_kl_loss=1.988, generator_dur_loss=1.701, generator_adv_loss=1.712, generator_feat_match_loss=2.527, over 100.00 samples.
+2023-11-13 07:10:50,208 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 07:10:50,798 INFO [train.py:811] (1/4) Start epoch 174
+2023-11-13 07:14:25,807 INFO [train.py:811] (1/4) Start epoch 175
+2023-11-13 07:15:51,292 INFO [train.py:467] (1/4) Epoch 175, batch 12, global_batch_idx: 6450, batch size: 73, loss[discriminator_loss=2.422, discriminator_real_loss=1.164, discriminator_fake_loss=1.259, generator_loss=31.34, generator_mel_loss=21.74, generator_kl_loss=2.042, generator_dur_loss=1.735, generator_adv_loss=2.119, generator_feat_match_loss=3.699, over 73.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.391, discriminator_fake_loss=1.309, generator_loss=30.97, generator_mel_loss=22.09, generator_kl_loss=1.98, generator_dur_loss=1.73, generator_adv_loss=2.186, generator_feat_match_loss=2.988, over 940.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:17:58,998 INFO [train.py:811] (1/4) Start epoch 176
+2023-11-13 07:20:24,768 INFO [train.py:467] (1/4) Epoch 176, batch 25, global_batch_idx: 6500, batch size: 110, loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=30.9, generator_mel_loss=22.48, generator_kl_loss=1.996, generator_dur_loss=1.707, generator_adv_loss=2.012, generator_feat_match_loss=2.715, over 110.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.36, discriminator_fake_loss=1.336, generator_loss=30.22, generator_mel_loss=22.09, generator_kl_loss=1.946, generator_dur_loss=1.729, generator_adv_loss=1.951, generator_feat_match_loss=2.509, over 1929.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:21:29,803 INFO [train.py:811] (1/4) Start epoch 177
+2023-11-13 07:25:01,270 INFO [train.py:811] (1/4) Start epoch 178
+2023-11-13 07:25:21,839 INFO [train.py:467] (1/4) Epoch 178, batch 1, global_batch_idx: 6550, batch size: 55, loss[discriminator_loss=2.564, discriminator_real_loss=1.267, discriminator_fake_loss=1.298, generator_loss=30.81, generator_mel_loss=21.84, generator_kl_loss=1.971, generator_dur_loss=1.752, generator_adv_loss=2.496, generator_feat_match_loss=2.758, over 55.00 samples.], tot_loss[discriminator_loss=2.568, discriminator_real_loss=1.23, discriminator_fake_loss=1.337, generator_loss=30.97, generator_mel_loss=21.75, generator_kl_loss=1.952, generator_dur_loss=1.741, generator_adv_loss=2.391, generator_feat_match_loss=3.138, over 119.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:28:34,265 INFO [train.py:811] (1/4) Start epoch 179
+2023-11-13 07:30:03,365 INFO [train.py:467] (1/4) Epoch 179, batch 14, global_batch_idx: 6600, batch size: 153, loss[discriminator_loss=3.273, discriminator_real_loss=1.998, discriminator_fake_loss=1.274, generator_loss=30.84, generator_mel_loss=22.4, generator_kl_loss=2.021, generator_dur_loss=1.685, generator_adv_loss=2.35, generator_feat_match_loss=2.383, over 153.00 samples.], tot_loss[discriminator_loss=2.777, discriminator_real_loss=1.448, discriminator_fake_loss=1.329, generator_loss=31.03, generator_mel_loss=22.19, generator_kl_loss=1.951, generator_dur_loss=1.725, generator_adv_loss=2.254, generator_feat_match_loss=2.91, over 1148.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:30:03,366 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 07:30:14,357 INFO [train.py:517] (1/4) Epoch 179, validation: discriminator_loss=2.851, discriminator_real_loss=1.733, discriminator_fake_loss=1.118, generator_loss=31.68, generator_mel_loss=23.22, generator_kl_loss=1.976, generator_dur_loss=1.695, generator_adv_loss=2.28, generator_feat_match_loss=2.509, over 100.00 samples.
+2023-11-13 07:30:14,358 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 07:32:14,041 INFO [train.py:811] (1/4) Start epoch 180
+2023-11-13 07:35:02,499 INFO [train.py:467] (1/4) Epoch 180, batch 27, global_batch_idx: 6650, batch size: 69, loss[discriminator_loss=2.621, discriminator_real_loss=1.326, discriminator_fake_loss=1.296, generator_loss=30.7, generator_mel_loss=21.96, generator_kl_loss=1.942, generator_dur_loss=1.721, generator_adv_loss=2.105, generator_feat_match_loss=2.973, over 69.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.349, discriminator_fake_loss=1.342, generator_loss=30.08, generator_mel_loss=21.96, generator_kl_loss=1.948, generator_dur_loss=1.722, generator_adv_loss=1.989, generator_feat_match_loss=2.464, over 2140.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:35:48,329 INFO [train.py:811] (1/4) Start epoch 181
+2023-11-13 07:39:21,120 INFO [train.py:811] (1/4) Start epoch 182
+2023-11-13 07:39:52,312 INFO [train.py:467] (1/4) Epoch 182, batch 3, global_batch_idx: 6700, batch size: 81, loss[discriminator_loss=2.898, discriminator_real_loss=1.367, discriminator_fake_loss=1.531, generator_loss=30.22, generator_mel_loss=22.11, generator_kl_loss=2.031, generator_dur_loss=1.706, generator_adv_loss=1.884, generator_feat_match_loss=2.486, over 81.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.359, discriminator_fake_loss=1.348, generator_loss=30.69, generator_mel_loss=21.7, generator_kl_loss=1.98, generator_dur_loss=1.739, generator_adv_loss=2.257, generator_feat_match_loss=3.015, over 238.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:42:47,307 INFO [train.py:811] (1/4) Start epoch 183
+2023-11-13 07:44:30,912 INFO [train.py:467] (1/4) Epoch 183, batch 16, global_batch_idx: 6750, batch size: 64, loss[discriminator_loss=2.594, discriminator_real_loss=1.229, discriminator_fake_loss=1.366, generator_loss=30.57, generator_mel_loss=21.92, generator_kl_loss=2.015, generator_dur_loss=1.722, generator_adv_loss=2.047, generator_feat_match_loss=2.861, over 64.00 samples.], tot_loss[discriminator_loss=2.627, discriminator_real_loss=1.322, discriminator_fake_loss=1.305, generator_loss=30.42, generator_mel_loss=22.08, generator_kl_loss=1.98, generator_dur_loss=1.72, generator_adv_loss=1.972, generator_feat_match_loss=2.661, over 1427.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, grad_scale: 16.0
+2023-11-13 07:46:19,807 INFO [train.py:811] (1/4) Start epoch 184
+2023-11-13 07:49:18,127 INFO [train.py:467] (1/4) Epoch 184, batch 29, global_batch_idx: 6800, batch size: 81, loss[discriminator_loss=2.596, discriminator_real_loss=1.223, discriminator_fake_loss=1.373, generator_loss=30.04, generator_mel_loss=21.6, generator_kl_loss=1.998, generator_dur_loss=1.719, generator_adv_loss=2.066, generator_feat_match_loss=2.65, over 81.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.354, discriminator_fake_loss=1.316, generator_loss=31.04, generator_mel_loss=22.16, generator_kl_loss=1.972, generator_dur_loss=1.724, generator_adv_loss=2.187, generator_feat_match_loss=3.002, over 2208.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 32.0
+2023-11-13 07:49:18,128 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 07:49:29,713 INFO [train.py:517] (1/4) Epoch 184, validation: discriminator_loss=2.926, discriminator_real_loss=1.293, discriminator_fake_loss=1.633, generator_loss=30.87, generator_mel_loss=23.04, generator_kl_loss=2.013, generator_dur_loss=1.695, generator_adv_loss=1.598, generator_feat_match_loss=2.517, over 100.00 samples.
+2023-11-13 07:49:29,714 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 07:50:04,025 INFO [train.py:811] (1/4) Start epoch 185
+2023-11-13 07:53:35,839 INFO [train.py:811] (1/4) Start epoch 186
+2023-11-13 07:54:17,625 INFO [train.py:467] (1/4) Epoch 186, batch 5, global_batch_idx: 6850, batch size: 79, loss[discriminator_loss=2.979, discriminator_real_loss=1.299, discriminator_fake_loss=1.68, generator_loss=29.89, generator_mel_loss=21.99, generator_kl_loss=1.885, generator_dur_loss=1.715, generator_adv_loss=1.934, generator_feat_match_loss=2.371, over 79.00 samples.], tot_loss[discriminator_loss=2.608, discriminator_real_loss=1.294, discriminator_fake_loss=1.315, generator_loss=30.88, generator_mel_loss=21.97, generator_kl_loss=1.897, generator_dur_loss=1.714, generator_adv_loss=2.123, generator_feat_match_loss=3.17, over 428.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 07:57:04,005 INFO [train.py:811] (1/4) Start epoch 187
+2023-11-13 07:58:58,546 INFO [train.py:467] (1/4) Epoch 187, batch 18, global_batch_idx: 6900, batch size: 50, loss[discriminator_loss=2.805, discriminator_real_loss=1.603, discriminator_fake_loss=1.201, generator_loss=30.6, generator_mel_loss=21.78, generator_kl_loss=1.882, generator_dur_loss=1.713, generator_adv_loss=2.357, generator_feat_match_loss=2.865, over 50.00 samples.], tot_loss[discriminator_loss=2.614, discriminator_real_loss=1.299, discriminator_fake_loss=1.315, generator_loss=30.87, generator_mel_loss=21.95, generator_kl_loss=1.942, generator_dur_loss=1.718, generator_adv_loss=2.185, generator_feat_match_loss=3.08, over 1603.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:00:37,736 INFO [train.py:811] (1/4) Start epoch 188
+2023-11-13 08:03:33,559 INFO [train.py:467] (1/4) Epoch 188, batch 31, global_batch_idx: 6950, batch size: 55, loss[discriminator_loss=2.406, discriminator_real_loss=1.288, discriminator_fake_loss=1.117, generator_loss=32.86, generator_mel_loss=22.41, generator_kl_loss=1.896, generator_dur_loss=1.753, generator_adv_loss=2.598, generator_feat_match_loss=4.203, over 55.00 samples.], tot_loss[discriminator_loss=2.59, discriminator_real_loss=1.316, discriminator_fake_loss=1.274, generator_loss=31.11, generator_mel_loss=21.98, generator_kl_loss=1.963, generator_dur_loss=1.723, generator_adv_loss=2.251, generator_feat_match_loss=3.187, over 2257.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:04:05,131 INFO [train.py:811] (1/4) Start epoch 189
+2023-11-13 08:07:35,006 INFO [train.py:811] (1/4) Start epoch 190
+2023-11-13 08:08:35,972 INFO [train.py:467] (1/4) Epoch 190, batch 7, global_batch_idx: 7000, batch size: 85, loss[discriminator_loss=2.633, discriminator_real_loss=1.252, discriminator_fake_loss=1.382, generator_loss=29.74, generator_mel_loss=21.63, generator_kl_loss=1.928, generator_dur_loss=1.728, generator_adv_loss=1.963, generator_feat_match_loss=2.492, over 85.00 samples.], tot_loss[discriminator_loss=2.645, discriminator_real_loss=1.338, discriminator_fake_loss=1.308, generator_loss=29.89, generator_mel_loss=21.62, generator_kl_loss=1.932, generator_dur_loss=1.737, generator_adv_loss=2, generator_feat_match_loss=2.601, over 523.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:08:35,973 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 08:08:47,371 INFO [train.py:517] (1/4) Epoch 190, validation: discriminator_loss=2.559, discriminator_real_loss=1.268, discriminator_fake_loss=1.291, generator_loss=31.28, generator_mel_loss=22.84, generator_kl_loss=1.986, generator_dur_loss=1.694, generator_adv_loss=1.96, generator_feat_match_loss=2.801, over 100.00 samples.
+2023-11-13 08:08:47,372 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 08:11:22,300 INFO [train.py:811] (1/4) Start epoch 191
+2023-11-13 08:13:23,848 INFO [train.py:467] (1/4) Epoch 191, batch 20, global_batch_idx: 7050, batch size: 64, loss[discriminator_loss=2.477, discriminator_real_loss=1.229, discriminator_fake_loss=1.248, generator_loss=30.67, generator_mel_loss=21.69, generator_kl_loss=1.876, generator_dur_loss=1.714, generator_adv_loss=2.236, generator_feat_match_loss=3.148, over 64.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.355, discriminator_fake_loss=1.304, generator_loss=30.75, generator_mel_loss=21.79, generator_kl_loss=1.965, generator_dur_loss=1.722, generator_adv_loss=2.172, generator_feat_match_loss=3.098, over 1408.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:14:52,731 INFO [train.py:811] (1/4) Start epoch 192
+2023-11-13 08:18:07,493 INFO [train.py:467] (1/4) Epoch 192, batch 33, global_batch_idx: 7100, batch size: 101, loss[discriminator_loss=2.551, discriminator_real_loss=1.21, discriminator_fake_loss=1.341, generator_loss=31.15, generator_mel_loss=22.25, generator_kl_loss=1.95, generator_dur_loss=1.728, generator_adv_loss=2.189, generator_feat_match_loss=3.027, over 101.00 samples.], tot_loss[discriminator_loss=2.648, discriminator_real_loss=1.335, discriminator_fake_loss=1.313, generator_loss=30.67, generator_mel_loss=22.04, generator_kl_loss=1.964, generator_dur_loss=1.722, generator_adv_loss=2.093, generator_feat_match_loss=2.853, over 2569.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:18:24,226 INFO [train.py:811] (1/4) Start epoch 193
+2023-11-13 08:21:46,747 INFO [train.py:811] (1/4) Start epoch 194
+2023-11-13 08:22:44,474 INFO [train.py:467] (1/4) Epoch 194, batch 9, global_batch_idx: 7150, batch size: 60, loss[discriminator_loss=2.574, discriminator_real_loss=1.231, discriminator_fake_loss=1.342, generator_loss=31.3, generator_mel_loss=21.46, generator_kl_loss=1.869, generator_dur_loss=1.715, generator_adv_loss=2.713, generator_feat_match_loss=3.547, over 60.00 samples.], tot_loss[discriminator_loss=2.612, discriminator_real_loss=1.347, discriminator_fake_loss=1.265, generator_loss=31.16, generator_mel_loss=21.76, generator_kl_loss=1.935, generator_dur_loss=1.723, generator_adv_loss=2.403, generator_feat_match_loss=3.341, over 648.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:25:12,669 INFO [train.py:811] (1/4) Start epoch 195
+2023-11-13 08:27:14,766 INFO [train.py:467] (1/4) Epoch 195, batch 22, global_batch_idx: 7200, batch size: 73, loss[discriminator_loss=2.666, discriminator_real_loss=1.322, discriminator_fake_loss=1.344, generator_loss=31.14, generator_mel_loss=21.93, generator_kl_loss=1.924, generator_dur_loss=1.703, generator_adv_loss=2.244, generator_feat_match_loss=3.338, over 73.00 samples.], tot_loss[discriminator_loss=2.605, discriminator_real_loss=1.311, discriminator_fake_loss=1.294, generator_loss=30.65, generator_mel_loss=21.94, generator_kl_loss=1.952, generator_dur_loss=1.717, generator_adv_loss=2.09, generator_feat_match_loss=2.951, over 1548.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 32.0
+2023-11-13 08:27:14,767 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 08:27:25,457 INFO [train.py:517] (1/4) Epoch 195, validation: discriminator_loss=2.731, discriminator_real_loss=1.228, discriminator_fake_loss=1.504, generator_loss=31.59, generator_mel_loss=23.37, generator_kl_loss=2.062, generator_dur_loss=1.684, generator_adv_loss=1.696, generator_feat_match_loss=2.776, over 100.00 samples.
+2023-11-13 08:27:25,458 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 08:28:48,425 INFO [train.py:811] (1/4) Start epoch 196
+2023-11-13 08:32:12,712 INFO [train.py:467] (1/4) Epoch 196, batch 35, global_batch_idx: 7250, batch size: 50, loss[discriminator_loss=2.434, discriminator_real_loss=1.211, discriminator_fake_loss=1.224, generator_loss=31.42, generator_mel_loss=21.8, generator_kl_loss=1.951, generator_dur_loss=1.706, generator_adv_loss=2.312, generator_feat_match_loss=3.652, over 50.00 samples.], tot_loss[discriminator_loss=2.645, discriminator_real_loss=1.336, discriminator_fake_loss=1.309, generator_loss=30.73, generator_mel_loss=21.74, generator_kl_loss=1.936, generator_dur_loss=1.716, generator_adv_loss=2.233, generator_feat_match_loss=3.106, over 2518.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:32:18,269 INFO [train.py:811] (1/4) Start epoch 197
+2023-11-13 08:35:48,362 INFO [train.py:811] (1/4) Start epoch 198
+2023-11-13 08:37:03,840 INFO [train.py:467] (1/4) Epoch 198, batch 11, global_batch_idx: 7300, batch size: 71, loss[discriminator_loss=2.617, discriminator_real_loss=1.303, discriminator_fake_loss=1.315, generator_loss=31.14, generator_mel_loss=22.04, generator_kl_loss=1.83, generator_dur_loss=1.715, generator_adv_loss=2.207, generator_feat_match_loss=3.346, over 71.00 samples.], tot_loss[discriminator_loss=2.633, discriminator_real_loss=1.295, discriminator_fake_loss=1.338, generator_loss=30.68, generator_mel_loss=21.74, generator_kl_loss=1.963, generator_dur_loss=1.717, generator_adv_loss=2.162, generator_feat_match_loss=3.103, over 970.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:39:16,747 INFO [train.py:811] (1/4) Start epoch 199
+2023-11-13 08:41:45,053 INFO [train.py:467] (1/4) Epoch 199, batch 24, global_batch_idx: 7350, batch size: 153, loss[discriminator_loss=2.471, discriminator_real_loss=1.281, discriminator_fake_loss=1.189, generator_loss=31.72, generator_mel_loss=22.23, generator_kl_loss=1.895, generator_dur_loss=1.728, generator_adv_loss=2.301, generator_feat_match_loss=3.566, over 153.00 samples.], tot_loss[discriminator_loss=2.641, discriminator_real_loss=1.352, discriminator_fake_loss=1.289, generator_loss=30.97, generator_mel_loss=22.07, generator_kl_loss=1.936, generator_dur_loss=1.72, generator_adv_loss=2.181, generator_feat_match_loss=3.066, over 1848.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:42:50,815 INFO [train.py:811] (1/4) Start epoch 200
+2023-11-13 08:46:22,619 INFO [train.py:811] (1/4) Start epoch 201
+2023-11-13 08:46:42,998 INFO [train.py:467] (1/4) Epoch 201, batch 0, global_batch_idx: 7400, batch size: 50, loss[discriminator_loss=2.5, discriminator_real_loss=1.314, discriminator_fake_loss=1.187, generator_loss=31.71, generator_mel_loss=22.45, generator_kl_loss=1.941, generator_dur_loss=1.765, generator_adv_loss=2.24, generator_feat_match_loss=3.307, over 50.00 samples.], tot_loss[discriminator_loss=2.5, discriminator_real_loss=1.314, discriminator_fake_loss=1.187, generator_loss=31.71, generator_mel_loss=22.45, generator_kl_loss=1.941, generator_dur_loss=1.765, generator_adv_loss=2.24, generator_feat_match_loss=3.307, over 50.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:46:42,999 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 08:46:55,223 INFO [train.py:517] (1/4) Epoch 201, validation: discriminator_loss=2.565, discriminator_real_loss=1.226, discriminator_fake_loss=1.339, generator_loss=31.02, generator_mel_loss=22.61, generator_kl_loss=2.034, generator_dur_loss=1.688, generator_adv_loss=1.874, generator_feat_match_loss=2.82, over 100.00 samples.
+2023-11-13 08:46:55,224 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 08:50:15,810 INFO [train.py:811] (1/4) Start epoch 202
+2023-11-13 08:51:44,155 INFO [train.py:467] (1/4) Epoch 202, batch 13, global_batch_idx: 7450, batch size: 61, loss[discriminator_loss=2.32, discriminator_real_loss=1.218, discriminator_fake_loss=1.102, generator_loss=32.54, generator_mel_loss=21.81, generator_kl_loss=2.001, generator_dur_loss=1.73, generator_adv_loss=2.557, generator_feat_match_loss=4.438, over 61.00 samples.], tot_loss[discriminator_loss=2.492, discriminator_real_loss=1.273, discriminator_fake_loss=1.219, generator_loss=31.06, generator_mel_loss=21.57, generator_kl_loss=1.924, generator_dur_loss=1.725, generator_adv_loss=2.337, generator_feat_match_loss=3.503, over 888.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:53:50,139 INFO [train.py:811] (1/4) Start epoch 203
+2023-11-13 08:56:20,595 INFO [train.py:467] (1/4) Epoch 203, batch 26, global_batch_idx: 7500, batch size: 50, loss[discriminator_loss=2.457, discriminator_real_loss=1.235, discriminator_fake_loss=1.223, generator_loss=30, generator_mel_loss=20.99, generator_kl_loss=1.96, generator_dur_loss=1.709, generator_adv_loss=2.457, generator_feat_match_loss=2.891, over 50.00 samples.], tot_loss[discriminator_loss=2.47, discriminator_real_loss=1.253, discriminator_fake_loss=1.218, generator_loss=31.28, generator_mel_loss=21.58, generator_kl_loss=1.932, generator_dur_loss=1.716, generator_adv_loss=2.385, generator_feat_match_loss=3.668, over 1892.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 08:57:21,384 INFO [train.py:811] (1/4) Start epoch 204
+2023-11-13 09:00:58,169 INFO [train.py:811] (1/4) Start epoch 205
+2023-11-13 09:01:25,516 INFO [train.py:467] (1/4) Epoch 205, batch 2, global_batch_idx: 7550, batch size: 51, loss[discriminator_loss=2.266, discriminator_real_loss=1.118, discriminator_fake_loss=1.146, generator_loss=32.53, generator_mel_loss=21.52, generator_kl_loss=2.057, generator_dur_loss=1.708, generator_adv_loss=2.527, generator_feat_match_loss=4.723, over 51.00 samples.], tot_loss[discriminator_loss=2.314, discriminator_real_loss=1.176, discriminator_fake_loss=1.137, generator_loss=32.57, generator_mel_loss=21.97, generator_kl_loss=1.982, generator_dur_loss=1.708, generator_adv_loss=2.445, generator_feat_match_loss=4.473, over 187.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:04:25,210 INFO [train.py:811] (1/4) Start epoch 206
+2023-11-13 09:06:00,671 INFO [train.py:467] (1/4) Epoch 206, batch 15, global_batch_idx: 7600, batch size: 50, loss[discriminator_loss=2.402, discriminator_real_loss=1.127, discriminator_fake_loss=1.275, generator_loss=30.73, generator_mel_loss=21.01, generator_kl_loss=1.85, generator_dur_loss=1.702, generator_adv_loss=2.502, generator_feat_match_loss=3.662, over 50.00 samples.], tot_loss[discriminator_loss=2.522, discriminator_real_loss=1.246, discriminator_fake_loss=1.276, generator_loss=30.66, generator_mel_loss=21.25, generator_kl_loss=1.926, generator_dur_loss=1.722, generator_adv_loss=2.324, generator_feat_match_loss=3.431, over 1115.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 32.0
+2023-11-13 09:06:00,672 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 09:06:11,531 INFO [train.py:517] (1/4) Epoch 206, validation: discriminator_loss=2.259, discriminator_real_loss=1.189, discriminator_fake_loss=1.07, generator_loss=32.32, generator_mel_loss=22.16, generator_kl_loss=2.046, generator_dur_loss=1.682, generator_adv_loss=2.501, generator_feat_match_loss=3.928, over 100.00 samples.
+2023-11-13 09:06:11,532 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 09:08:09,374 INFO [train.py:811] (1/4) Start epoch 207
+2023-11-13 09:10:59,134 INFO [train.py:467] (1/4) Epoch 207, batch 28, global_batch_idx: 7650, batch size: 49, loss[discriminator_loss=2.703, discriminator_real_loss=1.389, discriminator_fake_loss=1.314, generator_loss=29.47, generator_mel_loss=21.35, generator_kl_loss=2.005, generator_dur_loss=1.714, generator_adv_loss=1.966, generator_feat_match_loss=2.43, over 49.00 samples.], tot_loss[discriminator_loss=2.644, discriminator_real_loss=1.325, discriminator_fake_loss=1.319, generator_loss=30.49, generator_mel_loss=21.62, generator_kl_loss=1.974, generator_dur_loss=1.715, generator_adv_loss=2.167, generator_feat_match_loss=3.012, over 2053.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:11:41,280 INFO [train.py:811] (1/4) Start epoch 208
+2023-11-13 09:15:09,761 INFO [train.py:811] (1/4) Start epoch 209
+2023-11-13 09:15:44,321 INFO [train.py:467] (1/4) Epoch 209, batch 4, global_batch_idx: 7700, batch size: 54, loss[discriminator_loss=2.516, discriminator_real_loss=1.221, discriminator_fake_loss=1.296, generator_loss=31.54, generator_mel_loss=22.24, generator_kl_loss=1.937, generator_dur_loss=1.719, generator_adv_loss=1.968, generator_feat_match_loss=3.68, over 54.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.354, discriminator_fake_loss=1.308, generator_loss=30.61, generator_mel_loss=21.64, generator_kl_loss=1.941, generator_dur_loss=1.724, generator_adv_loss=2.172, generator_feat_match_loss=3.131, over 293.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:18:42,593 INFO [train.py:811] (1/4) Start epoch 210
+2023-11-13 09:20:35,027 INFO [train.py:467] (1/4) Epoch 210, batch 17, global_batch_idx: 7750, batch size: 79, loss[discriminator_loss=2.986, discriminator_real_loss=1.746, discriminator_fake_loss=1.24, generator_loss=30.62, generator_mel_loss=21.89, generator_kl_loss=1.88, generator_dur_loss=1.697, generator_adv_loss=2.299, generator_feat_match_loss=2.857, over 79.00 samples.], tot_loss[discriminator_loss=2.643, discriminator_real_loss=1.295, discriminator_fake_loss=1.348, generator_loss=30.91, generator_mel_loss=21.66, generator_kl_loss=1.977, generator_dur_loss=1.711, generator_adv_loss=2.263, generator_feat_match_loss=3.3, over 1434.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:22:21,315 INFO [train.py:811] (1/4) Start epoch 211
+2023-11-13 09:25:29,382 INFO [train.py:467] (1/4) Epoch 211, batch 30, global_batch_idx: 7800, batch size: 81, loss[discriminator_loss=2.709, discriminator_real_loss=1.471, discriminator_fake_loss=1.238, generator_loss=31.28, generator_mel_loss=22.14, generator_kl_loss=1.971, generator_dur_loss=1.717, generator_adv_loss=2.154, generator_feat_match_loss=3.295, over 81.00 samples.], tot_loss[discriminator_loss=2.636, discriminator_real_loss=1.313, discriminator_fake_loss=1.322, generator_loss=30.65, generator_mel_loss=21.81, generator_kl_loss=1.956, generator_dur_loss=1.709, generator_adv_loss=2.146, generator_feat_match_loss=3.029, over 2510.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:25:29,383 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 09:25:40,565 INFO [train.py:517] (1/4) Epoch 211, validation: discriminator_loss=2.683, discriminator_real_loss=1.262, discriminator_fake_loss=1.421, generator_loss=30.94, generator_mel_loss=22.57, generator_kl_loss=1.999, generator_dur_loss=1.684, generator_adv_loss=1.803, generator_feat_match_loss=2.877, over 100.00 samples.
+2023-11-13 09:25:40,566 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 09:26:08,291 INFO [train.py:811] (1/4) Start epoch 212
+2023-11-13 09:29:28,961 INFO [train.py:811] (1/4) Start epoch 213
+2023-11-13 09:30:20,044 INFO [train.py:467] (1/4) Epoch 213, batch 6, global_batch_idx: 7850, batch size: 69, loss[discriminator_loss=2.449, discriminator_real_loss=1.283, discriminator_fake_loss=1.167, generator_loss=30.82, generator_mel_loss=21.26, generator_kl_loss=1.954, generator_dur_loss=1.727, generator_adv_loss=2.438, generator_feat_match_loss=3.445, over 69.00 samples.], tot_loss[discriminator_loss=2.873, discriminator_real_loss=1.408, discriminator_fake_loss=1.465, generator_loss=30.27, generator_mel_loss=21.46, generator_kl_loss=1.95, generator_dur_loss=1.714, generator_adv_loss=2.148, generator_feat_match_loss=3.002, over 527.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:32:56,712 INFO [train.py:811] (1/4) Start epoch 214
+2023-11-13 09:34:53,751 INFO [train.py:467] (1/4) Epoch 214, batch 19, global_batch_idx: 7900, batch size: 50, loss[discriminator_loss=2.385, discriminator_real_loss=1.248, discriminator_fake_loss=1.137, generator_loss=31.07, generator_mel_loss=21.47, generator_kl_loss=1.896, generator_dur_loss=1.719, generator_adv_loss=2.473, generator_feat_match_loss=3.518, over 50.00 samples.], tot_loss[discriminator_loss=2.562, discriminator_real_loss=1.303, discriminator_fake_loss=1.259, generator_loss=30.91, generator_mel_loss=21.56, generator_kl_loss=1.933, generator_dur_loss=1.707, generator_adv_loss=2.304, generator_feat_match_loss=3.413, over 1325.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:36:31,127 INFO [train.py:811] (1/4) Start epoch 215
+2023-11-13 09:39:38,446 INFO [train.py:467] (1/4) Epoch 215, batch 32, global_batch_idx: 7950, batch size: 79, loss[discriminator_loss=2.926, discriminator_real_loss=1.347, discriminator_fake_loss=1.58, generator_loss=30.73, generator_mel_loss=21.72, generator_kl_loss=1.973, generator_dur_loss=1.735, generator_adv_loss=2.297, generator_feat_match_loss=3.008, over 79.00 samples.], tot_loss[discriminator_loss=2.619, discriminator_real_loss=1.295, discriminator_fake_loss=1.324, generator_loss=30.8, generator_mel_loss=21.85, generator_kl_loss=1.98, generator_dur_loss=1.708, generator_adv_loss=2.165, generator_feat_match_loss=3.1, over 2720.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 8.0
+2023-11-13 09:40:01,965 INFO [train.py:811] (1/4) Start epoch 216
+2023-11-13 09:43:37,327 INFO [train.py:811] (1/4) Start epoch 217
+2023-11-13 09:44:42,978 INFO [train.py:467] (1/4) Epoch 217, batch 8, global_batch_idx: 8000, batch size: 55, loss[discriminator_loss=2.688, discriminator_real_loss=1.352, discriminator_fake_loss=1.337, generator_loss=31.75, generator_mel_loss=22.28, generator_kl_loss=1.994, generator_dur_loss=1.765, generator_adv_loss=2.404, generator_feat_match_loss=3.312, over 55.00 samples.], tot_loss[discriminator_loss=2.652, discriminator_real_loss=1.324, discriminator_fake_loss=1.328, generator_loss=30.7, generator_mel_loss=21.82, generator_kl_loss=1.947, generator_dur_loss=1.713, generator_adv_loss=2.136, generator_feat_match_loss=3.078, over 666.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:44:42,979 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 09:44:53,741 INFO [train.py:517] (1/4) Epoch 217, validation: discriminator_loss=2.619, discriminator_real_loss=1.454, discriminator_fake_loss=1.165, generator_loss=31.28, generator_mel_loss=22.22, generator_kl_loss=1.973, generator_dur_loss=1.683, generator_adv_loss=2.305, generator_feat_match_loss=3.1, over 100.00 samples.
+2023-11-13 09:44:53,742 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 09:47:21,163 INFO [train.py:811] (1/4) Start epoch 218
+2023-11-13 09:49:24,654 INFO [train.py:467] (1/4) Epoch 218, batch 21, global_batch_idx: 8050, batch size: 73, loss[discriminator_loss=2.828, discriminator_real_loss=1.725, discriminator_fake_loss=1.104, generator_loss=30.45, generator_mel_loss=21.42, generator_kl_loss=1.932, generator_dur_loss=1.699, generator_adv_loss=2.293, generator_feat_match_loss=3.109, over 73.00 samples.], tot_loss[discriminator_loss=2.547, discriminator_real_loss=1.271, discriminator_fake_loss=1.277, generator_loss=31.15, generator_mel_loss=21.64, generator_kl_loss=1.947, generator_dur_loss=1.712, generator_adv_loss=2.305, generator_feat_match_loss=3.539, over 1594.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:50:44,366 INFO [train.py:811] (1/4) Start epoch 219
+2023-11-13 09:54:06,234 INFO [train.py:467] (1/4) Epoch 219, batch 34, global_batch_idx: 8100, batch size: 79, loss[discriminator_loss=2.309, discriminator_real_loss=1.105, discriminator_fake_loss=1.203, generator_loss=31.48, generator_mel_loss=21.23, generator_kl_loss=1.876, generator_dur_loss=1.724, generator_adv_loss=2.611, generator_feat_match_loss=4.043, over 79.00 samples.], tot_loss[discriminator_loss=2.584, discriminator_real_loss=1.31, discriminator_fake_loss=1.274, generator_loss=30.79, generator_mel_loss=21.68, generator_kl_loss=1.969, generator_dur_loss=1.71, generator_adv_loss=2.22, generator_feat_match_loss=3.215, over 2561.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 09:54:15,039 INFO [train.py:811] (1/4) Start epoch 220
+2023-11-13 09:57:43,864 INFO [train.py:811] (1/4) Start epoch 221
+2023-11-13 09:58:59,660 INFO [train.py:467] (1/4) Epoch 221, batch 10, global_batch_idx: 8150, batch size: 90, loss[discriminator_loss=2.641, discriminator_real_loss=1.426, discriminator_fake_loss=1.214, generator_loss=29.72, generator_mel_loss=21.04, generator_kl_loss=1.943, generator_dur_loss=1.692, generator_adv_loss=2.225, generator_feat_match_loss=2.818, over 90.00 samples.], tot_loss[discriminator_loss=2.597, discriminator_real_loss=1.285, discriminator_fake_loss=1.312, generator_loss=30.44, generator_mel_loss=21.29, generator_kl_loss=1.954, generator_dur_loss=1.703, generator_adv_loss=2.257, generator_feat_match_loss=3.235, over 861.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 10:01:15,610 INFO [train.py:811] (1/4) Start epoch 222
+2023-11-13 10:03:37,524 INFO [train.py:467] (1/4) Epoch 222, batch 23, global_batch_idx: 8200, batch size: 101, loss[discriminator_loss=2.926, discriminator_real_loss=1.52, discriminator_fake_loss=1.405, generator_loss=29.92, generator_mel_loss=21.62, generator_kl_loss=2.013, generator_dur_loss=1.683, generator_adv_loss=2.053, generator_feat_match_loss=2.559, over 101.00 samples.], tot_loss[discriminator_loss=2.618, discriminator_real_loss=1.31, discriminator_fake_loss=1.308, generator_loss=31.2, generator_mel_loss=21.91, generator_kl_loss=1.973, generator_dur_loss=1.707, generator_adv_loss=2.293, generator_feat_match_loss=3.31, over 1693.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 10:03:37,526 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 10:03:48,041 INFO [train.py:517] (1/4) Epoch 222, validation: discriminator_loss=2.788, discriminator_real_loss=1.489, discriminator_fake_loss=1.298, generator_loss=31.44, generator_mel_loss=22.81, generator_kl_loss=1.963, generator_dur_loss=1.681, generator_adv_loss=2.115, generator_feat_match_loss=2.867, over 100.00 samples.
+2023-11-13 10:03:48,042 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 10:05:01,286 INFO [train.py:811] (1/4) Start epoch 223
+2023-11-13 10:08:33,687 INFO [train.py:467] (1/4) Epoch 223, batch 36, global_batch_idx: 8250, batch size: 60, loss[discriminator_loss=2.355, discriminator_real_loss=1.149, discriminator_fake_loss=1.205, generator_loss=31.09, generator_mel_loss=21.12, generator_kl_loss=1.909, generator_dur_loss=1.701, generator_adv_loss=2.461, generator_feat_match_loss=3.902, over 60.00 samples.], tot_loss[discriminator_loss=2.635, discriminator_real_loss=1.343, discriminator_fake_loss=1.292, generator_loss=30.74, generator_mel_loss=21.72, generator_kl_loss=1.928, generator_dur_loss=1.71, generator_adv_loss=2.19, generator_feat_match_loss=3.197, over 2759.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, grad_scale: 16.0
+2023-11-13 10:08:34,317 INFO [train.py:811] (1/4) Start epoch 224
+2023-11-13 10:12:10,984 INFO [train.py:811] (1/4) Start epoch 225
+2023-11-13 10:13:30,073 INFO [train.py:467] (1/4) Epoch 225, batch 12, global_batch_idx: 8300, batch size: 110, loss[discriminator_loss=2.668, discriminator_real_loss=1.238, discriminator_fake_loss=1.43, generator_loss=29.96, generator_mel_loss=21.67, generator_kl_loss=2.007, generator_dur_loss=1.661, generator_adv_loss=1.928, generator_feat_match_loss=2.699, over 110.00 samples.], tot_loss[discriminator_loss=2.614, discriminator_real_loss=1.308, discriminator_fake_loss=1.306, generator_loss=30.12, generator_mel_loss=21.48, generator_kl_loss=1.948, generator_dur_loss=1.699, generator_adv_loss=2.1, generator_feat_match_loss=2.898, over 859.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:15:42,810 INFO [train.py:811] (1/4) Start epoch 226
+2023-11-13 10:18:17,269 INFO [train.py:467] (1/4) Epoch 226, batch 25, global_batch_idx: 8350, batch size: 153, loss[discriminator_loss=2.713, discriminator_real_loss=1.217, discriminator_fake_loss=1.496, generator_loss=30.66, generator_mel_loss=21.87, generator_kl_loss=2.044, generator_dur_loss=1.682, generator_adv_loss=2.137, generator_feat_match_loss=2.924, over 153.00 samples.], tot_loss[discriminator_loss=2.599, discriminator_real_loss=1.314, discriminator_fake_loss=1.285, generator_loss=30.87, generator_mel_loss=21.82, generator_kl_loss=1.946, generator_dur_loss=1.708, generator_adv_loss=2.22, generator_feat_match_loss=3.178, over 1972.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:19:17,375 INFO [train.py:811] (1/4) Start epoch 227
+2023-11-13 10:22:52,766 INFO [train.py:811] (1/4) Start epoch 228
+2023-11-13 10:23:13,953 INFO [train.py:467] (1/4) Epoch 228, batch 1, global_batch_idx: 8400, batch size: 110, loss[discriminator_loss=2.576, discriminator_real_loss=1.271, discriminator_fake_loss=1.305, generator_loss=30.85, generator_mel_loss=22.1, generator_kl_loss=2.047, generator_dur_loss=1.701, generator_adv_loss=2.078, generator_feat_match_loss=2.93, over 110.00 samples.], tot_loss[discriminator_loss=2.585, discriminator_real_loss=1.301, discriminator_fake_loss=1.283, generator_loss=30.87, generator_mel_loss=22.01, generator_kl_loss=2.016, generator_dur_loss=1.699, generator_adv_loss=2.108, generator_feat_match_loss=3.033, over 159.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 32.0
+2023-11-13 10:23:13,954 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 10:23:25,526 INFO [train.py:517] (1/4) Epoch 228, validation: discriminator_loss=2.709, discriminator_real_loss=1.193, discriminator_fake_loss=1.516, generator_loss=29.8, generator_mel_loss=22.04, generator_kl_loss=1.973, generator_dur_loss=1.681, generator_adv_loss=1.645, generator_feat_match_loss=2.456, over 100.00 samples.
+2023-11-13 10:23:25,527 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 10:26:38,194 INFO [train.py:811] (1/4) Start epoch 229
+2023-11-13 10:27:57,123 INFO [train.py:467] (1/4) Epoch 229, batch 14, global_batch_idx: 8450, batch size: 79, loss[discriminator_loss=2.355, discriminator_real_loss=1.207, discriminator_fake_loss=1.147, generator_loss=31.81, generator_mel_loss=21.4, generator_kl_loss=1.898, generator_dur_loss=1.681, generator_adv_loss=2.545, generator_feat_match_loss=4.285, over 79.00 samples.], tot_loss[discriminator_loss=2.508, discriminator_real_loss=1.282, discriminator_fake_loss=1.226, generator_loss=31.07, generator_mel_loss=21.68, generator_kl_loss=1.939, generator_dur_loss=1.712, generator_adv_loss=2.298, generator_feat_match_loss=3.441, over 917.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:30:04,477 INFO [train.py:811] (1/4) Start epoch 230
+2023-11-13 10:32:46,505 INFO [train.py:467] (1/4) Epoch 230, batch 27, global_batch_idx: 8500, batch size: 51, loss[discriminator_loss=2.557, discriminator_real_loss=1.334, discriminator_fake_loss=1.223, generator_loss=30.76, generator_mel_loss=20.85, generator_kl_loss=1.996, generator_dur_loss=1.701, generator_adv_loss=2.557, generator_feat_match_loss=3.654, over 51.00 samples.], tot_loss[discriminator_loss=2.605, discriminator_real_loss=1.318, discriminator_fake_loss=1.287, generator_loss=30.77, generator_mel_loss=21.69, generator_kl_loss=1.965, generator_dur_loss=1.708, generator_adv_loss=2.204, generator_feat_match_loss=3.196, over 2055.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:33:39,018 INFO [train.py:811] (1/4) Start epoch 231
+2023-11-13 10:37:09,111 INFO [train.py:811] (1/4) Start epoch 232
+2023-11-13 10:37:40,410 INFO [train.py:467] (1/4) Epoch 232, batch 3, global_batch_idx: 8550, batch size: 85, loss[discriminator_loss=2.676, discriminator_real_loss=1.361, discriminator_fake_loss=1.315, generator_loss=30.34, generator_mel_loss=21.92, generator_kl_loss=1.9, generator_dur_loss=1.696, generator_adv_loss=1.929, generator_feat_match_loss=2.895, over 85.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.332, discriminator_fake_loss=1.332, generator_loss=30.6, generator_mel_loss=22.05, generator_kl_loss=1.938, generator_dur_loss=1.703, generator_adv_loss=1.972, generator_feat_match_loss=2.94, over 342.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:40:43,440 INFO [train.py:811] (1/4) Start epoch 233
+2023-11-13 10:42:29,907 INFO [train.py:467] (1/4) Epoch 233, batch 16, global_batch_idx: 8600, batch size: 81, loss[discriminator_loss=2.408, discriminator_real_loss=1.271, discriminator_fake_loss=1.137, generator_loss=31.72, generator_mel_loss=21.34, generator_kl_loss=2.022, generator_dur_loss=1.699, generator_adv_loss=2.809, generator_feat_match_loss=3.855, over 81.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.443, discriminator_fake_loss=1.288, generator_loss=31.5, generator_mel_loss=21.52, generator_kl_loss=1.968, generator_dur_loss=1.702, generator_adv_loss=2.575, generator_feat_match_loss=3.736, over 1325.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:42:29,908 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 10:42:41,018 INFO [train.py:517] (1/4) Epoch 233, validation: discriminator_loss=2.891, discriminator_real_loss=1.424, discriminator_fake_loss=1.467, generator_loss=33.18, generator_mel_loss=22.47, generator_kl_loss=2.009, generator_dur_loss=1.672, generator_adv_loss=2.831, generator_feat_match_loss=4.196, over 100.00 samples.
+2023-11-13 10:42:41,019 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 10:44:22,933 INFO [train.py:811] (1/4) Start epoch 234
+2023-11-13 10:47:18,947 INFO [train.py:467] (1/4) Epoch 234, batch 29, global_batch_idx: 8650, batch size: 126, loss[discriminator_loss=2.781, discriminator_real_loss=1.412, discriminator_fake_loss=1.369, generator_loss=30.69, generator_mel_loss=21.54, generator_kl_loss=1.97, generator_dur_loss=1.693, generator_adv_loss=2.318, generator_feat_match_loss=3.172, over 126.00 samples.], tot_loss[discriminator_loss=2.792, discriminator_real_loss=1.424, discriminator_fake_loss=1.368, generator_loss=29.45, generator_mel_loss=21.31, generator_kl_loss=1.939, generator_dur_loss=1.705, generator_adv_loss=1.954, generator_feat_match_loss=2.543, over 2088.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:47:57,087 INFO [train.py:811] (1/4) Start epoch 235
+2023-11-13 10:51:26,027 INFO [train.py:811] (1/4) Start epoch 236
+2023-11-13 10:52:11,299 INFO [train.py:467] (1/4) Epoch 236, batch 5, global_batch_idx: 8700, batch size: 73, loss[discriminator_loss=2.672, discriminator_real_loss=1.26, discriminator_fake_loss=1.411, generator_loss=30.1, generator_mel_loss=21.46, generator_kl_loss=2.009, generator_dur_loss=1.71, generator_adv_loss=1.987, generator_feat_match_loss=2.936, over 73.00 samples.], tot_loss[discriminator_loss=2.776, discriminator_real_loss=1.394, discriminator_fake_loss=1.381, generator_loss=29.7, generator_mel_loss=21.43, generator_kl_loss=1.955, generator_dur_loss=1.705, generator_adv_loss=1.936, generator_feat_match_loss=2.68, over 453.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:54:56,133 INFO [train.py:811] (1/4) Start epoch 237
+2023-11-13 10:56:45,543 INFO [train.py:467] (1/4) Epoch 237, batch 18, global_batch_idx: 8750, batch size: 126, loss[discriminator_loss=2.703, discriminator_real_loss=1.149, discriminator_fake_loss=1.555, generator_loss=30.78, generator_mel_loss=22.07, generator_kl_loss=1.933, generator_dur_loss=1.706, generator_adv_loss=2.195, generator_feat_match_loss=2.877, over 126.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.335, discriminator_fake_loss=1.35, generator_loss=30.25, generator_mel_loss=21.59, generator_kl_loss=1.923, generator_dur_loss=1.707, generator_adv_loss=2.077, generator_feat_match_loss=2.959, over 1485.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 10:58:28,067 INFO [train.py:811] (1/4) Start epoch 238
+2023-11-13 11:01:28,579 INFO [train.py:467] (1/4) Epoch 238, batch 31, global_batch_idx: 8800, batch size: 50, loss[discriminator_loss=2.73, discriminator_real_loss=1.368, discriminator_fake_loss=1.363, generator_loss=29.81, generator_mel_loss=21.27, generator_kl_loss=1.849, generator_dur_loss=1.7, generator_adv_loss=2.051, generator_feat_match_loss=2.934, over 50.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=30.17, generator_mel_loss=21.62, generator_kl_loss=1.961, generator_dur_loss=1.7, generator_adv_loss=2.025, generator_feat_match_loss=2.871, over 2302.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 32.0
+2023-11-13 11:01:28,581 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 11:01:39,346 INFO [train.py:517] (1/4) Epoch 238, validation: discriminator_loss=2.529, discriminator_real_loss=1.238, discriminator_fake_loss=1.29, generator_loss=31.39, generator_mel_loss=22.59, generator_kl_loss=1.976, generator_dur_loss=1.667, generator_adv_loss=1.969, generator_feat_match_loss=3.19, over 100.00 samples.
+2023-11-13 11:01:39,347 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 11:02:11,895 INFO [train.py:811] (1/4) Start epoch 239
+2023-11-13 11:05:43,992 INFO [train.py:811] (1/4) Start epoch 240
+2023-11-13 11:06:41,775 INFO [train.py:467] (1/4) Epoch 240, batch 7, global_batch_idx: 8850, batch size: 79, loss[discriminator_loss=2.686, discriminator_real_loss=1.162, discriminator_fake_loss=1.523, generator_loss=30.82, generator_mel_loss=21.44, generator_kl_loss=2.006, generator_dur_loss=1.692, generator_adv_loss=2.354, generator_feat_match_loss=3.322, over 79.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.339, discriminator_fake_loss=1.37, generator_loss=30.31, generator_mel_loss=21.66, generator_kl_loss=1.946, generator_dur_loss=1.71, generator_adv_loss=2.103, generator_feat_match_loss=2.885, over 579.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:09:14,870 INFO [train.py:811] (1/4) Start epoch 241
+2023-11-13 11:11:24,360 INFO [train.py:467] (1/4) Epoch 241, batch 20, global_batch_idx: 8900, batch size: 65, loss[discriminator_loss=2.926, discriminator_real_loss=1.688, discriminator_fake_loss=1.238, generator_loss=29.36, generator_mel_loss=21.46, generator_kl_loss=1.854, generator_dur_loss=1.697, generator_adv_loss=1.807, generator_feat_match_loss=2.547, over 65.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.403, discriminator_fake_loss=1.34, generator_loss=30.23, generator_mel_loss=21.44, generator_kl_loss=1.925, generator_dur_loss=1.702, generator_adv_loss=2.136, generator_feat_match_loss=3.028, over 1561.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:12:43,672 INFO [train.py:811] (1/4) Start epoch 242
+2023-11-13 11:15:59,142 INFO [train.py:467] (1/4) Epoch 242, batch 33, global_batch_idx: 8950, batch size: 52, loss[discriminator_loss=2.762, discriminator_real_loss=1.267, discriminator_fake_loss=1.494, generator_loss=30.32, generator_mel_loss=21.68, generator_kl_loss=1.906, generator_dur_loss=1.713, generator_adv_loss=2.08, generator_feat_match_loss=2.939, over 52.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.36, discriminator_fake_loss=1.318, generator_loss=30.22, generator_mel_loss=21.7, generator_kl_loss=1.959, generator_dur_loss=1.698, generator_adv_loss=2.009, generator_feat_match_loss=2.849, over 2540.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:16:14,406 INFO [train.py:811] (1/4) Start epoch 243
+2023-11-13 11:19:44,082 INFO [train.py:811] (1/4) Start epoch 244
+2023-11-13 11:20:50,557 INFO [train.py:467] (1/4) Epoch 244, batch 9, global_batch_idx: 9000, batch size: 55, loss[discriminator_loss=2.648, discriminator_real_loss=1.455, discriminator_fake_loss=1.192, generator_loss=30.08, generator_mel_loss=21.52, generator_kl_loss=1.961, generator_dur_loss=1.727, generator_adv_loss=2.109, generator_feat_match_loss=2.77, over 55.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.379, discriminator_fake_loss=1.328, generator_loss=30.34, generator_mel_loss=21.58, generator_kl_loss=1.937, generator_dur_loss=1.711, generator_adv_loss=2.174, generator_feat_match_loss=2.943, over 736.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:20:50,558 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 11:21:01,413 INFO [train.py:517] (1/4) Epoch 244, validation: discriminator_loss=2.725, discriminator_real_loss=1.361, discriminator_fake_loss=1.365, generator_loss=30.89, generator_mel_loss=22.52, generator_kl_loss=2.073, generator_dur_loss=1.669, generator_adv_loss=1.862, generator_feat_match_loss=2.76, over 100.00 samples.
+2023-11-13 11:21:01,414 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 11:23:20,596 INFO [train.py:811] (1/4) Start epoch 245
+2023-11-13 11:25:37,384 INFO [train.py:467] (1/4) Epoch 245, batch 22, global_batch_idx: 9050, batch size: 69, loss[discriminator_loss=2.668, discriminator_real_loss=1.271, discriminator_fake_loss=1.397, generator_loss=29.72, generator_mel_loss=21.41, generator_kl_loss=1.894, generator_dur_loss=1.683, generator_adv_loss=1.977, generator_feat_match_loss=2.752, over 69.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.322, generator_loss=30.4, generator_mel_loss=21.77, generator_kl_loss=1.934, generator_dur_loss=1.701, generator_adv_loss=2.044, generator_feat_match_loss=2.942, over 1638.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:26:54,808 INFO [train.py:811] (1/4) Start epoch 246
+2023-11-13 11:30:22,509 INFO [train.py:467] (1/4) Epoch 246, batch 35, global_batch_idx: 9100, batch size: 101, loss[discriminator_loss=2.684, discriminator_real_loss=1.521, discriminator_fake_loss=1.164, generator_loss=31.44, generator_mel_loss=21.85, generator_kl_loss=1.894, generator_dur_loss=1.695, generator_adv_loss=2.344, generator_feat_match_loss=3.656, over 101.00 samples.], tot_loss[discriminator_loss=2.652, discriminator_real_loss=1.346, discriminator_fake_loss=1.307, generator_loss=30.7, generator_mel_loss=21.63, generator_kl_loss=1.941, generator_dur_loss=1.703, generator_adv_loss=2.21, generator_feat_match_loss=3.217, over 2613.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:30:31,238 INFO [train.py:811] (1/4) Start epoch 247
+2023-11-13 11:34:00,720 INFO [train.py:811] (1/4) Start epoch 248
+2023-11-13 11:35:20,492 INFO [train.py:467] (1/4) Epoch 248, batch 11, global_batch_idx: 9150, batch size: 126, loss[discriminator_loss=2.904, discriminator_real_loss=1.176, discriminator_fake_loss=1.729, generator_loss=30, generator_mel_loss=21.85, generator_kl_loss=1.903, generator_dur_loss=1.69, generator_adv_loss=1.803, generator_feat_match_loss=2.76, over 126.00 samples.], tot_loss[discriminator_loss=2.607, discriminator_real_loss=1.251, discriminator_fake_loss=1.356, generator_loss=30.73, generator_mel_loss=21.57, generator_kl_loss=1.976, generator_dur_loss=1.693, generator_adv_loss=2.174, generator_feat_match_loss=3.312, over 979.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:37:36,988 INFO [train.py:811] (1/4) Start epoch 249
+2023-11-13 11:40:01,569 INFO [train.py:467] (1/4) Epoch 249, batch 24, global_batch_idx: 9200, batch size: 51, loss[discriminator_loss=2.68, discriminator_real_loss=1.379, discriminator_fake_loss=1.301, generator_loss=30.6, generator_mel_loss=21.6, generator_kl_loss=1.934, generator_dur_loss=1.714, generator_adv_loss=2.201, generator_feat_match_loss=3.148, over 51.00 samples.], tot_loss[discriminator_loss=2.624, discriminator_real_loss=1.325, discriminator_fake_loss=1.299, generator_loss=30.57, generator_mel_loss=21.5, generator_kl_loss=1.94, generator_dur_loss=1.697, generator_adv_loss=2.171, generator_feat_match_loss=3.26, over 2009.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 32.0
+2023-11-13 11:40:01,570 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 11:40:12,655 INFO [train.py:517] (1/4) Epoch 249, validation: discriminator_loss=2.564, discriminator_real_loss=1.379, discriminator_fake_loss=1.185, generator_loss=31.21, generator_mel_loss=22.41, generator_kl_loss=1.995, generator_dur_loss=1.668, generator_adv_loss=2.13, generator_feat_match_loss=3.014, over 100.00 samples.
+2023-11-13 11:40:12,656 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 11:41:14,879 INFO [train.py:811] (1/4) Start epoch 250
+2023-11-13 11:44:43,283 INFO [train.py:811] (1/4) Start epoch 251
+2023-11-13 11:44:59,108 INFO [train.py:467] (1/4) Epoch 251, batch 0, global_batch_idx: 9250, batch size: 69, loss[discriminator_loss=2.52, discriminator_real_loss=1.457, discriminator_fake_loss=1.062, generator_loss=31.22, generator_mel_loss=21.52, generator_kl_loss=1.857, generator_dur_loss=1.69, generator_adv_loss=2.375, generator_feat_match_loss=3.775, over 69.00 samples.], tot_loss[discriminator_loss=2.52, discriminator_real_loss=1.457, discriminator_fake_loss=1.062, generator_loss=31.22, generator_mel_loss=21.52, generator_kl_loss=1.857, generator_dur_loss=1.69, generator_adv_loss=2.375, generator_feat_match_loss=3.775, over 69.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:48:15,421 INFO [train.py:811] (1/4) Start epoch 252
+2023-11-13 11:49:38,892 INFO [train.py:467] (1/4) Epoch 252, batch 13, global_batch_idx: 9300, batch size: 56, loss[discriminator_loss=2.568, discriminator_real_loss=1.353, discriminator_fake_loss=1.216, generator_loss=30.94, generator_mel_loss=21.59, generator_kl_loss=1.969, generator_dur_loss=1.7, generator_adv_loss=2.207, generator_feat_match_loss=3.477, over 56.00 samples.], tot_loss[discriminator_loss=2.633, discriminator_real_loss=1.331, discriminator_fake_loss=1.301, generator_loss=30.43, generator_mel_loss=21.7, generator_kl_loss=1.959, generator_dur_loss=1.706, generator_adv_loss=2.064, generator_feat_match_loss=3.008, over 923.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:51:46,640 INFO [train.py:811] (1/4) Start epoch 253
+2023-11-13 11:54:37,187 INFO [train.py:467] (1/4) Epoch 253, batch 26, global_batch_idx: 9350, batch size: 69, loss[discriminator_loss=2.812, discriminator_real_loss=1.181, discriminator_fake_loss=1.632, generator_loss=30.11, generator_mel_loss=21.43, generator_kl_loss=1.886, generator_dur_loss=1.69, generator_adv_loss=2.191, generator_feat_match_loss=2.92, over 69.00 samples.], tot_loss[discriminator_loss=2.589, discriminator_real_loss=1.318, discriminator_fake_loss=1.271, generator_loss=30.99, generator_mel_loss=21.53, generator_kl_loss=1.929, generator_dur_loss=1.697, generator_adv_loss=2.328, generator_feat_match_loss=3.508, over 2230.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:55:24,011 INFO [train.py:811] (1/4) Start epoch 254
+2023-11-13 11:58:55,845 INFO [train.py:811] (1/4) Start epoch 255
+2023-11-13 11:59:23,492 INFO [train.py:467] (1/4) Epoch 255, batch 2, global_batch_idx: 9400, batch size: 63, loss[discriminator_loss=2.727, discriminator_real_loss=1.357, discriminator_fake_loss=1.37, generator_loss=29.74, generator_mel_loss=21.4, generator_kl_loss=1.977, generator_dur_loss=1.715, generator_adv_loss=1.929, generator_feat_match_loss=2.719, over 63.00 samples.], tot_loss[discriminator_loss=2.607, discriminator_real_loss=1.285, discriminator_fake_loss=1.322, generator_loss=30.32, generator_mel_loss=21.46, generator_kl_loss=1.968, generator_dur_loss=1.726, generator_adv_loss=2.109, generator_feat_match_loss=3.057, over 199.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 11:59:23,493 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 11:59:35,614 INFO [train.py:517] (1/4) Epoch 255, validation: discriminator_loss=2.662, discriminator_real_loss=1.208, discriminator_fake_loss=1.454, generator_loss=31.34, generator_mel_loss=22.31, generator_kl_loss=2.044, generator_dur_loss=1.678, generator_adv_loss=2.136, generator_feat_match_loss=3.177, over 100.00 samples.
+2023-11-13 11:59:35,616 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 12:02:43,992 INFO [train.py:811] (1/4) Start epoch 256
+2023-11-13 12:04:14,539 INFO [train.py:467] (1/4) Epoch 256, batch 15, global_batch_idx: 9450, batch size: 61, loss[discriminator_loss=2.592, discriminator_real_loss=1.242, discriminator_fake_loss=1.35, generator_loss=31.03, generator_mel_loss=21.93, generator_kl_loss=1.907, generator_dur_loss=1.704, generator_adv_loss=2.266, generator_feat_match_loss=3.219, over 61.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.371, discriminator_fake_loss=1.317, generator_loss=30.17, generator_mel_loss=21.52, generator_kl_loss=1.944, generator_dur_loss=1.7, generator_adv_loss=2.074, generator_feat_match_loss=2.927, over 1097.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 12:06:17,401 INFO [train.py:811] (1/4) Start epoch 257
+2023-11-13 12:09:11,871 INFO [train.py:467] (1/4) Epoch 257, batch 28, global_batch_idx: 9500, batch size: 55, loss[discriminator_loss=2.605, discriminator_real_loss=1.236, discriminator_fake_loss=1.37, generator_loss=30.52, generator_mel_loss=21.28, generator_kl_loss=1.958, generator_dur_loss=1.722, generator_adv_loss=2.047, generator_feat_match_loss=3.512, over 55.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.368, discriminator_fake_loss=1.315, generator_loss=30.44, generator_mel_loss=21.51, generator_kl_loss=1.943, generator_dur_loss=1.704, generator_adv_loss=2.148, generator_feat_match_loss=3.137, over 1885.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 12:09:52,353 INFO [train.py:811] (1/4) Start epoch 258
+2023-11-13 12:13:22,963 INFO [train.py:811] (1/4) Start epoch 259
+2023-11-13 12:14:01,643 INFO [train.py:467] (1/4) Epoch 259, batch 4, global_batch_idx: 9550, batch size: 60, loss[discriminator_loss=2.549, discriminator_real_loss=1.291, discriminator_fake_loss=1.258, generator_loss=30.89, generator_mel_loss=21.24, generator_kl_loss=1.916, generator_dur_loss=1.716, generator_adv_loss=2.439, generator_feat_match_loss=3.576, over 60.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.298, discriminator_fake_loss=1.437, generator_loss=30.31, generator_mel_loss=21.41, generator_kl_loss=1.89, generator_dur_loss=1.696, generator_adv_loss=2.07, generator_feat_match_loss=3.239, over 381.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 12:16:53,785 INFO [train.py:811] (1/4) Start epoch 260
+2023-11-13 12:18:39,473 INFO [train.py:467] (1/4) Epoch 260, batch 17, global_batch_idx: 9600, batch size: 55, loss[discriminator_loss=2.805, discriminator_real_loss=1.538, discriminator_fake_loss=1.268, generator_loss=29.62, generator_mel_loss=21.59, generator_kl_loss=1.912, generator_dur_loss=1.728, generator_adv_loss=1.838, generator_feat_match_loss=2.551, over 55.00 samples.], tot_loss[discriminator_loss=2.642, discriminator_real_loss=1.332, discriminator_fake_loss=1.31, generator_loss=30.33, generator_mel_loss=21.65, generator_kl_loss=1.932, generator_dur_loss=1.697, generator_adv_loss=2.069, generator_feat_match_loss=2.988, over 1193.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 32.0
+2023-11-13 12:18:39,474 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 12:18:51,923 INFO [train.py:517] (1/4) Epoch 260, validation: discriminator_loss=2.6, discriminator_real_loss=1.279, discriminator_fake_loss=1.322, generator_loss=31.63, generator_mel_loss=22.92, generator_kl_loss=1.995, generator_dur_loss=1.666, generator_adv_loss=1.953, generator_feat_match_loss=3.099, over 100.00 samples.
+2023-11-13 12:18:51,924 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 12:20:37,658 INFO [train.py:811] (1/4) Start epoch 261
+2023-11-13 12:23:39,484 INFO [train.py:467] (1/4) Epoch 261, batch 30, global_batch_idx: 9650, batch size: 95, loss[discriminator_loss=3.07, discriminator_real_loss=1.464, discriminator_fake_loss=1.605, generator_loss=29.78, generator_mel_loss=21.54, generator_kl_loss=1.885, generator_dur_loss=1.687, generator_adv_loss=1.84, generator_feat_match_loss=2.834, over 95.00 samples.], tot_loss[discriminator_loss=2.608, discriminator_real_loss=1.32, discriminator_fake_loss=1.288, generator_loss=30.94, generator_mel_loss=21.6, generator_kl_loss=1.933, generator_dur_loss=1.701, generator_adv_loss=2.272, generator_feat_match_loss=3.434, over 2114.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 12:24:11,396 INFO [train.py:811] (1/4) Start epoch 262
+2023-11-13 12:27:37,981 INFO [train.py:811] (1/4) Start epoch 263
+2023-11-13 12:28:24,527 INFO [train.py:467] (1/4) Epoch 263, batch 6, global_batch_idx: 9700, batch size: 60, loss[discriminator_loss=2.711, discriminator_real_loss=1.32, discriminator_fake_loss=1.39, generator_loss=30.08, generator_mel_loss=21.26, generator_kl_loss=2.005, generator_dur_loss=1.724, generator_adv_loss=2.178, generator_feat_match_loss=2.91, over 60.00 samples.], tot_loss[discriminator_loss=2.644, discriminator_real_loss=1.336, discriminator_fake_loss=1.308, generator_loss=30.03, generator_mel_loss=21.37, generator_kl_loss=1.982, generator_dur_loss=1.698, generator_adv_loss=2.054, generator_feat_match_loss=2.934, over 418.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 12:31:11,081 INFO [train.py:811] (1/4) Start epoch 264
+2023-11-13 12:33:01,563 INFO [train.py:467] (1/4) Epoch 264, batch 19, global_batch_idx: 9750, batch size: 126, loss[discriminator_loss=2.637, discriminator_real_loss=1.363, discriminator_fake_loss=1.273, generator_loss=30.79, generator_mel_loss=21.82, generator_kl_loss=1.908, generator_dur_loss=1.681, generator_adv_loss=2.25, generator_feat_match_loss=3.135, over 126.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.362, discriminator_fake_loss=1.288, generator_loss=30.39, generator_mel_loss=21.52, generator_kl_loss=1.93, generator_dur_loss=1.694, generator_adv_loss=2.133, generator_feat_match_loss=3.109, over 1468.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 12:34:31,212 INFO [train.py:811] (1/4) Start epoch 265
+2023-11-13 12:37:46,657 INFO [train.py:467] (1/4) Epoch 265, batch 32, global_batch_idx: 9800, batch size: 65, loss[discriminator_loss=2.314, discriminator_real_loss=1.127, discriminator_fake_loss=1.188, generator_loss=32.09, generator_mel_loss=21.54, generator_kl_loss=1.936, generator_dur_loss=1.695, generator_adv_loss=2.582, generator_feat_match_loss=4.336, over 65.00 samples.], tot_loss[discriminator_loss=2.629, discriminator_real_loss=1.325, discriminator_fake_loss=1.304, generator_loss=31, generator_mel_loss=21.68, generator_kl_loss=1.944, generator_dur_loss=1.695, generator_adv_loss=2.269, generator_feat_match_loss=3.409, over 2434.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, grad_scale: 16.0
+2023-11-13 12:37:46,658 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 12:37:57,627 INFO [train.py:517] (1/4) Epoch 265, validation: discriminator_loss=2.392, discriminator_real_loss=1.221, discriminator_fake_loss=1.171, generator_loss=32.52, generator_mel_loss=22.43, generator_kl_loss=2.05, generator_dur_loss=1.678, generator_adv_loss=2.219, generator_feat_match_loss=4.139, over 100.00 samples.
+2023-11-13 12:37:57,629 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 12:38:15,270 INFO [train.py:811] (1/4) Start epoch 266
+2023-11-13 12:41:48,785 INFO [train.py:811] (1/4) Start epoch 267
+2023-11-13 12:42:51,429 INFO [train.py:467] (1/4) Epoch 267, batch 8, global_batch_idx: 9850, batch size: 59, loss[discriminator_loss=2.621, discriminator_real_loss=1.324, discriminator_fake_loss=1.297, generator_loss=30.41, generator_mel_loss=21.81, generator_kl_loss=1.91, generator_dur_loss=1.693, generator_adv_loss=1.954, generator_feat_match_loss=3.043, over 59.00 samples.], tot_loss[discriminator_loss=2.643, discriminator_real_loss=1.326, discriminator_fake_loss=1.317, generator_loss=30.12, generator_mel_loss=21.55, generator_kl_loss=1.937, generator_dur_loss=1.703, generator_adv_loss=1.995, generator_feat_match_loss=2.935, over 636.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 12:45:23,263 INFO [train.py:811] (1/4) Start epoch 268
+2023-11-13 12:47:39,266 INFO [train.py:467] (1/4) Epoch 268, batch 21, global_batch_idx: 9900, batch size: 53, loss[discriminator_loss=2.797, discriminator_real_loss=1.601, discriminator_fake_loss=1.197, generator_loss=30.88, generator_mel_loss=21.67, generator_kl_loss=1.921, generator_dur_loss=1.701, generator_adv_loss=2.355, generator_feat_match_loss=3.232, over 53.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.338, discriminator_fake_loss=1.338, generator_loss=30.59, generator_mel_loss=21.55, generator_kl_loss=1.948, generator_dur_loss=1.69, generator_adv_loss=2.167, generator_feat_match_loss=3.238, over 1897.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 12:48:59,481 INFO [train.py:811] (1/4) Start epoch 269
+2023-11-13 12:52:21,649 INFO [train.py:467] (1/4) Epoch 269, batch 34, global_batch_idx: 9950, batch size: 153, loss[discriminator_loss=2.416, discriminator_real_loss=1.229, discriminator_fake_loss=1.187, generator_loss=32.14, generator_mel_loss=21.76, generator_kl_loss=1.941, generator_dur_loss=1.69, generator_adv_loss=2.494, generator_feat_match_loss=4.258, over 153.00 samples.], tot_loss[discriminator_loss=2.64, discriminator_real_loss=1.345, discriminator_fake_loss=1.295, generator_loss=30.5, generator_mel_loss=21.46, generator_kl_loss=1.936, generator_dur_loss=1.697, generator_adv_loss=2.176, generator_feat_match_loss=3.225, over 2614.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 12:52:32,469 INFO [train.py:811] (1/4) Start epoch 270
+2023-11-13 12:55:58,922 INFO [train.py:811] (1/4) Start epoch 271
+2023-11-13 12:57:08,125 INFO [train.py:467] (1/4) Epoch 271, batch 10, global_batch_idx: 10000, batch size: 126, loss[discriminator_loss=2.68, discriminator_real_loss=1.395, discriminator_fake_loss=1.285, generator_loss=30.92, generator_mel_loss=22.09, generator_kl_loss=1.986, generator_dur_loss=1.671, generator_adv_loss=2.16, generator_feat_match_loss=3.014, over 126.00 samples.], tot_loss[discriminator_loss=2.638, discriminator_real_loss=1.332, discriminator_fake_loss=1.306, generator_loss=30.15, generator_mel_loss=21.5, generator_kl_loss=1.967, generator_dur_loss=1.69, generator_adv_loss=2.025, generator_feat_match_loss=2.968, over 828.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 32.0
+2023-11-13 12:57:08,127 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 12:57:18,945 INFO [train.py:517] (1/4) Epoch 271, validation: discriminator_loss=2.664, discriminator_real_loss=1.391, discriminator_fake_loss=1.273, generator_loss=31.42, generator_mel_loss=22.54, generator_kl_loss=1.989, generator_dur_loss=1.673, generator_adv_loss=2.122, generator_feat_match_loss=3.091, over 100.00 samples.
+2023-11-13 12:57:18,946 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 12:59:41,358 INFO [train.py:811] (1/4) Start epoch 272
+2023-11-13 13:01:58,829 INFO [train.py:467] (1/4) Epoch 272, batch 23, global_batch_idx: 10050, batch size: 81, loss[discriminator_loss=2.494, discriminator_real_loss=1.286, discriminator_fake_loss=1.208, generator_loss=31.14, generator_mel_loss=21.63, generator_kl_loss=1.937, generator_dur_loss=1.689, generator_adv_loss=2.277, generator_feat_match_loss=3.604, over 81.00 samples.], tot_loss[discriminator_loss=2.624, discriminator_real_loss=1.328, discriminator_fake_loss=1.296, generator_loss=30.47, generator_mel_loss=21.47, generator_kl_loss=1.93, generator_dur_loss=1.699, generator_adv_loss=2.168, generator_feat_match_loss=3.199, over 1756.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:03:12,752 INFO [train.py:811] (1/4) Start epoch 273
+2023-11-13 13:06:49,423 INFO [train.py:467] (1/4) Epoch 273, batch 36, global_batch_idx: 10100, batch size: 101, loss[discriminator_loss=2.695, discriminator_real_loss=1.32, discriminator_fake_loss=1.375, generator_loss=30.17, generator_mel_loss=21.55, generator_kl_loss=1.925, generator_dur_loss=1.686, generator_adv_loss=1.932, generator_feat_match_loss=3.076, over 101.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.353, discriminator_fake_loss=1.312, generator_loss=30.49, generator_mel_loss=21.38, generator_kl_loss=1.932, generator_dur_loss=1.693, generator_adv_loss=2.173, generator_feat_match_loss=3.31, over 2517.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:06:50,099 INFO [train.py:811] (1/4) Start epoch 274
+2023-11-13 13:10:22,992 INFO [train.py:811] (1/4) Start epoch 275
+2023-11-13 13:11:43,907 INFO [train.py:467] (1/4) Epoch 275, batch 12, global_batch_idx: 10150, batch size: 59, loss[discriminator_loss=2.789, discriminator_real_loss=1.405, discriminator_fake_loss=1.385, generator_loss=29.66, generator_mel_loss=21.33, generator_kl_loss=1.942, generator_dur_loss=1.73, generator_adv_loss=1.979, generator_feat_match_loss=2.68, over 59.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.35, discriminator_fake_loss=1.33, generator_loss=30.22, generator_mel_loss=21.34, generator_kl_loss=1.931, generator_dur_loss=1.704, generator_adv_loss=2.102, generator_feat_match_loss=3.147, over 770.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:13:56,049 INFO [train.py:811] (1/4) Start epoch 276
+2023-11-13 13:16:19,365 INFO [train.py:467] (1/4) Epoch 276, batch 25, global_batch_idx: 10200, batch size: 110, loss[discriminator_loss=2.602, discriminator_real_loss=1.388, discriminator_fake_loss=1.213, generator_loss=31.36, generator_mel_loss=21.64, generator_kl_loss=1.988, generator_dur_loss=1.69, generator_adv_loss=2.355, generator_feat_match_loss=3.691, over 110.00 samples.], tot_loss[discriminator_loss=2.635, discriminator_real_loss=1.343, discriminator_fake_loss=1.291, generator_loss=30.54, generator_mel_loss=21.6, generator_kl_loss=1.967, generator_dur_loss=1.695, generator_adv_loss=2.139, generator_feat_match_loss=3.141, over 1845.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:16:19,367 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 13:16:30,529 INFO [train.py:517] (1/4) Epoch 276, validation: discriminator_loss=2.581, discriminator_real_loss=1.203, discriminator_fake_loss=1.377, generator_loss=31.56, generator_mel_loss=22.84, generator_kl_loss=2.009, generator_dur_loss=1.67, generator_adv_loss=1.857, generator_feat_match_loss=3.182, over 100.00 samples.
+2023-11-13 13:16:30,530 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 13:17:37,656 INFO [train.py:811] (1/4) Start epoch 277
+2023-11-13 13:21:10,477 INFO [train.py:811] (1/4) Start epoch 278
+2023-11-13 13:21:33,438 INFO [train.py:467] (1/4) Epoch 278, batch 1, global_batch_idx: 10250, batch size: 56, loss[discriminator_loss=2.688, discriminator_real_loss=1.3, discriminator_fake_loss=1.387, generator_loss=30.3, generator_mel_loss=21.71, generator_kl_loss=1.86, generator_dur_loss=1.71, generator_adv_loss=2.012, generator_feat_match_loss=3.008, over 56.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.357, discriminator_fake_loss=1.312, generator_loss=30.4, generator_mel_loss=21.81, generator_kl_loss=1.898, generator_dur_loss=1.698, generator_adv_loss=2.001, generator_feat_match_loss=2.987, over 166.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:24:40,218 INFO [train.py:811] (1/4) Start epoch 279
+2023-11-13 13:26:13,108 INFO [train.py:467] (1/4) Epoch 279, batch 14, global_batch_idx: 10300, batch size: 71, loss[discriminator_loss=2.703, discriminator_real_loss=1.257, discriminator_fake_loss=1.445, generator_loss=30.71, generator_mel_loss=21.66, generator_kl_loss=1.773, generator_dur_loss=1.68, generator_adv_loss=2.398, generator_feat_match_loss=3.201, over 71.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.375, discriminator_fake_loss=1.316, generator_loss=30.56, generator_mel_loss=21.6, generator_kl_loss=1.962, generator_dur_loss=1.697, generator_adv_loss=2.154, generator_feat_match_loss=3.144, over 1081.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:28:15,371 INFO [train.py:811] (1/4) Start epoch 280
+2023-11-13 13:30:56,377 INFO [train.py:467] (1/4) Epoch 280, batch 27, global_batch_idx: 10350, batch size: 61, loss[discriminator_loss=2.766, discriminator_real_loss=1.53, discriminator_fake_loss=1.235, generator_loss=29.89, generator_mel_loss=21.23, generator_kl_loss=1.982, generator_dur_loss=1.71, generator_adv_loss=2.279, generator_feat_match_loss=2.693, over 61.00 samples.], tot_loss[discriminator_loss=2.646, discriminator_real_loss=1.35, discriminator_fake_loss=1.297, generator_loss=30.63, generator_mel_loss=21.34, generator_kl_loss=1.958, generator_dur_loss=1.695, generator_adv_loss=2.25, generator_feat_match_loss=3.384, over 1876.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:31:43,832 INFO [train.py:811] (1/4) Start epoch 281
+2023-11-13 13:35:19,349 INFO [train.py:811] (1/4) Start epoch 282
+2023-11-13 13:35:52,403 INFO [train.py:467] (1/4) Epoch 282, batch 3, global_batch_idx: 10400, batch size: 53, loss[discriminator_loss=2.691, discriminator_real_loss=1.421, discriminator_fake_loss=1.27, generator_loss=29.97, generator_mel_loss=20.99, generator_kl_loss=1.936, generator_dur_loss=1.679, generator_adv_loss=2.352, generator_feat_match_loss=3.012, over 53.00 samples.], tot_loss[discriminator_loss=2.6, discriminator_real_loss=1.274, discriminator_fake_loss=1.327, generator_loss=30.83, generator_mel_loss=21.46, generator_kl_loss=1.97, generator_dur_loss=1.7, generator_adv_loss=2.237, generator_feat_match_loss=3.47, over 247.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 32.0
+2023-11-13 13:35:52,405 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 13:36:04,175 INFO [train.py:517] (1/4) Epoch 282, validation: discriminator_loss=2.715, discriminator_real_loss=1.545, discriminator_fake_loss=1.17, generator_loss=31.62, generator_mel_loss=22.3, generator_kl_loss=2.052, generator_dur_loss=1.661, generator_adv_loss=2.386, generator_feat_match_loss=3.221, over 100.00 samples.
+2023-11-13 13:36:04,176 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 13:39:00,319 INFO [train.py:811] (1/4) Start epoch 283
+2023-11-13 13:40:33,501 INFO [train.py:467] (1/4) Epoch 283, batch 16, global_batch_idx: 10450, batch size: 71, loss[discriminator_loss=2.453, discriminator_real_loss=1.18, discriminator_fake_loss=1.273, generator_loss=30.84, generator_mel_loss=21.46, generator_kl_loss=1.861, generator_dur_loss=1.667, generator_adv_loss=2.18, generator_feat_match_loss=3.672, over 71.00 samples.], tot_loss[discriminator_loss=2.618, discriminator_real_loss=1.319, discriminator_fake_loss=1.299, generator_loss=30.28, generator_mel_loss=21.35, generator_kl_loss=1.949, generator_dur_loss=1.694, generator_adv_loss=2.116, generator_feat_match_loss=3.165, over 1220.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:42:27,494 INFO [train.py:811] (1/4) Start epoch 284
+2023-11-13 13:45:23,199 INFO [train.py:467] (1/4) Epoch 284, batch 29, global_batch_idx: 10500, batch size: 153, loss[discriminator_loss=2.66, discriminator_real_loss=1.405, discriminator_fake_loss=1.255, generator_loss=30.79, generator_mel_loss=21.89, generator_kl_loss=1.922, generator_dur_loss=1.679, generator_adv_loss=2.061, generator_feat_match_loss=3.242, over 153.00 samples.], tot_loss[discriminator_loss=2.639, discriminator_real_loss=1.356, discriminator_fake_loss=1.284, generator_loss=30.35, generator_mel_loss=21.32, generator_kl_loss=1.923, generator_dur_loss=1.689, generator_adv_loss=2.147, generator_feat_match_loss=3.269, over 2290.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:46:06,886 INFO [train.py:811] (1/4) Start epoch 285
+2023-11-13 13:49:43,223 INFO [train.py:811] (1/4) Start epoch 286
+2023-11-13 13:50:24,583 INFO [train.py:467] (1/4) Epoch 286, batch 5, global_batch_idx: 10550, batch size: 90, loss[discriminator_loss=2.805, discriminator_real_loss=1.349, discriminator_fake_loss=1.456, generator_loss=29.42, generator_mel_loss=21.12, generator_kl_loss=1.931, generator_dur_loss=1.686, generator_adv_loss=1.963, generator_feat_match_loss=2.723, over 90.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.379, discriminator_fake_loss=1.358, generator_loss=29.97, generator_mel_loss=21.2, generator_kl_loss=1.93, generator_dur_loss=1.69, generator_adv_loss=2.037, generator_feat_match_loss=3.112, over 420.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:53:17,509 INFO [train.py:811] (1/4) Start epoch 287
+2023-11-13 13:55:11,094 INFO [train.py:467] (1/4) Epoch 287, batch 18, global_batch_idx: 10600, batch size: 54, loss[discriminator_loss=2.559, discriminator_real_loss=1.382, discriminator_fake_loss=1.178, generator_loss=31.53, generator_mel_loss=21.95, generator_kl_loss=2.043, generator_dur_loss=1.686, generator_adv_loss=2.301, generator_feat_match_loss=3.543, over 54.00 samples.], tot_loss[discriminator_loss=2.594, discriminator_real_loss=1.29, discriminator_fake_loss=1.304, generator_loss=30.71, generator_mel_loss=21.48, generator_kl_loss=1.956, generator_dur_loss=1.685, generator_adv_loss=2.203, generator_feat_match_loss=3.391, over 1395.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 13:55:11,095 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 13:55:22,043 INFO [train.py:517] (1/4) Epoch 287, validation: discriminator_loss=2.661, discriminator_real_loss=1.14, discriminator_fake_loss=1.521, generator_loss=30.62, generator_mel_loss=21.94, generator_kl_loss=2.034, generator_dur_loss=1.667, generator_adv_loss=1.799, generator_feat_match_loss=3.179, over 100.00 samples.
+2023-11-13 13:55:22,044 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 13:56:58,627 INFO [train.py:811] (1/4) Start epoch 288
+2023-11-13 13:59:59,267 INFO [train.py:467] (1/4) Epoch 288, batch 31, global_batch_idx: 10650, batch size: 65, loss[discriminator_loss=2.648, discriminator_real_loss=1.515, discriminator_fake_loss=1.135, generator_loss=30.17, generator_mel_loss=21.3, generator_kl_loss=2.014, generator_dur_loss=1.673, generator_adv_loss=2.061, generator_feat_match_loss=3.125, over 65.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.327, discriminator_fake_loss=1.329, generator_loss=30.46, generator_mel_loss=21.34, generator_kl_loss=1.929, generator_dur_loss=1.693, generator_adv_loss=2.169, generator_feat_match_loss=3.331, over 2529.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:00:30,365 INFO [train.py:811] (1/4) Start epoch 289
+2023-11-13 14:04:01,539 INFO [train.py:811] (1/4) Start epoch 290
+2023-11-13 14:05:00,432 INFO [train.py:467] (1/4) Epoch 290, batch 7, global_batch_idx: 10700, batch size: 56, loss[discriminator_loss=2.645, discriminator_real_loss=1.459, discriminator_fake_loss=1.187, generator_loss=29.31, generator_mel_loss=20.58, generator_kl_loss=1.847, generator_dur_loss=1.695, generator_adv_loss=2.172, generator_feat_match_loss=3.016, over 56.00 samples.], tot_loss[discriminator_loss=2.586, discriminator_real_loss=1.292, discriminator_fake_loss=1.293, generator_loss=30.18, generator_mel_loss=20.94, generator_kl_loss=1.929, generator_dur_loss=1.692, generator_adv_loss=2.202, generator_feat_match_loss=3.414, over 562.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:07:32,198 INFO [train.py:811] (1/4) Start epoch 291
+2023-11-13 14:09:36,401 INFO [train.py:467] (1/4) Epoch 291, batch 20, global_batch_idx: 10750, batch size: 51, loss[discriminator_loss=2.742, discriminator_real_loss=1.902, discriminator_fake_loss=0.8398, generator_loss=31.39, generator_mel_loss=21.45, generator_kl_loss=2.015, generator_dur_loss=1.682, generator_adv_loss=2.363, generator_feat_match_loss=3.881, over 51.00 samples.], tot_loss[discriminator_loss=2.61, discriminator_real_loss=1.329, discriminator_fake_loss=1.281, generator_loss=30.92, generator_mel_loss=21.54, generator_kl_loss=1.96, generator_dur_loss=1.688, generator_adv_loss=2.273, generator_feat_match_loss=3.459, over 1654.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:11:01,226 INFO [train.py:811] (1/4) Start epoch 292
+2023-11-13 14:14:14,498 INFO [train.py:467] (1/4) Epoch 292, batch 33, global_batch_idx: 10800, batch size: 95, loss[discriminator_loss=2.629, discriminator_real_loss=1.291, discriminator_fake_loss=1.339, generator_loss=30.28, generator_mel_loss=21.39, generator_kl_loss=2.01, generator_dur_loss=1.658, generator_adv_loss=2.049, generator_feat_match_loss=3.17, over 95.00 samples.], tot_loss[discriminator_loss=2.643, discriminator_real_loss=1.354, discriminator_fake_loss=1.289, generator_loss=30.33, generator_mel_loss=21.31, generator_kl_loss=1.944, generator_dur_loss=1.691, generator_adv_loss=2.133, generator_feat_match_loss=3.246, over 2326.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 32.0
+2023-11-13 14:14:14,499 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 14:14:25,391 INFO [train.py:517] (1/4) Epoch 292, validation: discriminator_loss=2.609, discriminator_real_loss=1.31, discriminator_fake_loss=1.299, generator_loss=30.94, generator_mel_loss=22.17, generator_kl_loss=1.969, generator_dur_loss=1.665, generator_adv_loss=2.03, generator_feat_match_loss=3.101, over 100.00 samples.
+2023-11-13 14:14:25,392 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 14:14:46,202 INFO [train.py:811] (1/4) Start epoch 293
+2023-11-13 14:18:20,601 INFO [train.py:811] (1/4) Start epoch 294
+2023-11-13 14:19:23,584 INFO [train.py:467] (1/4) Epoch 294, batch 9, global_batch_idx: 10850, batch size: 59, loss[discriminator_loss=2.578, discriminator_real_loss=1.241, discriminator_fake_loss=1.338, generator_loss=30.25, generator_mel_loss=21.42, generator_kl_loss=1.999, generator_dur_loss=1.696, generator_adv_loss=1.929, generator_feat_match_loss=3.201, over 59.00 samples.], tot_loss[discriminator_loss=2.639, discriminator_real_loss=1.344, discriminator_fake_loss=1.295, generator_loss=30.35, generator_mel_loss=21.46, generator_kl_loss=1.93, generator_dur_loss=1.694, generator_adv_loss=2.095, generator_feat_match_loss=3.174, over 675.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:21:52,309 INFO [train.py:811] (1/4) Start epoch 295
+2023-11-13 14:23:59,400 INFO [train.py:467] (1/4) Epoch 295, batch 22, global_batch_idx: 10900, batch size: 110, loss[discriminator_loss=2.418, discriminator_real_loss=1.314, discriminator_fake_loss=1.104, generator_loss=31.26, generator_mel_loss=21.34, generator_kl_loss=1.97, generator_dur_loss=1.681, generator_adv_loss=2.383, generator_feat_match_loss=3.889, over 110.00 samples.], tot_loss[discriminator_loss=2.558, discriminator_real_loss=1.299, discriminator_fake_loss=1.26, generator_loss=30.89, generator_mel_loss=21.24, generator_kl_loss=1.935, generator_dur_loss=1.691, generator_adv_loss=2.315, generator_feat_match_loss=3.705, over 1862.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:25:18,360 INFO [train.py:811] (1/4) Start epoch 296
+2023-11-13 14:28:45,974 INFO [train.py:467] (1/4) Epoch 296, batch 35, global_batch_idx: 10950, batch size: 90, loss[discriminator_loss=2.93, discriminator_real_loss=1.537, discriminator_fake_loss=1.392, generator_loss=30.62, generator_mel_loss=21.83, generator_kl_loss=1.999, generator_dur_loss=1.699, generator_adv_loss=2.143, generator_feat_match_loss=2.945, over 90.00 samples.], tot_loss[discriminator_loss=2.641, discriminator_real_loss=1.337, discriminator_fake_loss=1.303, generator_loss=30.65, generator_mel_loss=21.44, generator_kl_loss=1.946, generator_dur_loss=1.69, generator_adv_loss=2.204, generator_feat_match_loss=3.379, over 2681.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:28:52,608 INFO [train.py:811] (1/4) Start epoch 297
+2023-11-13 14:32:25,269 INFO [train.py:811] (1/4) Start epoch 298
+2023-11-13 14:33:34,833 INFO [train.py:467] (1/4) Epoch 298, batch 11, global_batch_idx: 11000, batch size: 64, loss[discriminator_loss=2.555, discriminator_real_loss=1.379, discriminator_fake_loss=1.176, generator_loss=31.15, generator_mel_loss=21.02, generator_kl_loss=1.991, generator_dur_loss=1.721, generator_adv_loss=2.486, generator_feat_match_loss=3.934, over 64.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.296, discriminator_fake_loss=1.274, generator_loss=30.46, generator_mel_loss=21.22, generator_kl_loss=1.961, generator_dur_loss=1.687, generator_adv_loss=2.215, generator_feat_match_loss=3.379, over 806.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:33:34,834 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 14:33:46,168 INFO [train.py:517] (1/4) Epoch 298, validation: discriminator_loss=2.492, discriminator_real_loss=1.252, discriminator_fake_loss=1.24, generator_loss=30.79, generator_mel_loss=21.56, generator_kl_loss=1.967, generator_dur_loss=1.664, generator_adv_loss=2.107, generator_feat_match_loss=3.486, over 100.00 samples.
+2023-11-13 14:33:46,170 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 14:36:01,102 INFO [train.py:811] (1/4) Start epoch 299
+2023-11-13 14:38:28,902 INFO [train.py:467] (1/4) Epoch 299, batch 24, global_batch_idx: 11050, batch size: 56, loss[discriminator_loss=2.695, discriminator_real_loss=1.391, discriminator_fake_loss=1.304, generator_loss=29.3, generator_mel_loss=21.04, generator_kl_loss=1.904, generator_dur_loss=1.677, generator_adv_loss=1.963, generator_feat_match_loss=2.723, over 56.00 samples.], tot_loss[discriminator_loss=2.613, discriminator_real_loss=1.327, discriminator_fake_loss=1.286, generator_loss=30.55, generator_mel_loss=21.11, generator_kl_loss=1.92, generator_dur_loss=1.688, generator_adv_loss=2.284, generator_feat_match_loss=3.55, over 1791.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:39:35,501 INFO [train.py:811] (1/4) Start epoch 300
+2023-11-13 14:43:07,599 INFO [train.py:811] (1/4) Start epoch 301
+2023-11-13 14:43:25,749 INFO [train.py:467] (1/4) Epoch 301, batch 0, global_batch_idx: 11100, batch size: 71, loss[discriminator_loss=2.691, discriminator_real_loss=1.387, discriminator_fake_loss=1.305, generator_loss=29.94, generator_mel_loss=21.23, generator_kl_loss=1.976, generator_dur_loss=1.684, generator_adv_loss=2.061, generator_feat_match_loss=2.99, over 71.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.387, discriminator_fake_loss=1.305, generator_loss=29.94, generator_mel_loss=21.23, generator_kl_loss=1.976, generator_dur_loss=1.684, generator_adv_loss=2.061, generator_feat_match_loss=2.99, over 71.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:46:38,671 INFO [train.py:811] (1/4) Start epoch 302
+2023-11-13 14:47:59,377 INFO [train.py:467] (1/4) Epoch 302, batch 13, global_batch_idx: 11150, batch size: 73, loss[discriminator_loss=2.553, discriminator_real_loss=1.278, discriminator_fake_loss=1.274, generator_loss=30.31, generator_mel_loss=21.1, generator_kl_loss=1.939, generator_dur_loss=1.687, generator_adv_loss=2.125, generator_feat_match_loss=3.459, over 73.00 samples.], tot_loss[discriminator_loss=2.616, discriminator_real_loss=1.33, discriminator_fake_loss=1.286, generator_loss=30.76, generator_mel_loss=21.4, generator_kl_loss=1.924, generator_dur_loss=1.694, generator_adv_loss=2.225, generator_feat_match_loss=3.514, over 854.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:50:08,607 INFO [train.py:811] (1/4) Start epoch 303
+2023-11-13 14:52:45,890 INFO [train.py:467] (1/4) Epoch 303, batch 26, global_batch_idx: 11200, batch size: 73, loss[discriminator_loss=2.881, discriminator_real_loss=1.59, discriminator_fake_loss=1.291, generator_loss=30.35, generator_mel_loss=21.44, generator_kl_loss=1.925, generator_dur_loss=1.672, generator_adv_loss=2.051, generator_feat_match_loss=3.258, over 73.00 samples.], tot_loss[discriminator_loss=2.633, discriminator_real_loss=1.337, discriminator_fake_loss=1.296, generator_loss=30.71, generator_mel_loss=21.23, generator_kl_loss=1.933, generator_dur_loss=1.686, generator_adv_loss=2.288, generator_feat_match_loss=3.57, over 1841.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 14:52:45,891 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 14:52:57,017 INFO [train.py:517] (1/4) Epoch 303, validation: discriminator_loss=2.558, discriminator_real_loss=1.27, discriminator_fake_loss=1.287, generator_loss=31.35, generator_mel_loss=22.14, generator_kl_loss=2.025, generator_dur_loss=1.664, generator_adv_loss=2.113, generator_feat_match_loss=3.409, over 100.00 samples.
+2023-11-13 14:52:57,018 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 14:53:49,238 INFO [train.py:811] (1/4) Start epoch 304
+2023-11-13 14:57:24,571 INFO [train.py:811] (1/4) Start epoch 305
+2023-11-13 14:57:50,890 INFO [train.py:467] (1/4) Epoch 305, batch 2, global_batch_idx: 11250, batch size: 56, loss[discriminator_loss=2.531, discriminator_real_loss=1.258, discriminator_fake_loss=1.272, generator_loss=30.65, generator_mel_loss=21.07, generator_kl_loss=2.03, generator_dur_loss=1.706, generator_adv_loss=2.273, generator_feat_match_loss=3.572, over 56.00 samples.], tot_loss[discriminator_loss=2.562, discriminator_real_loss=1.282, discriminator_fake_loss=1.28, generator_loss=30.61, generator_mel_loss=21.41, generator_kl_loss=2, generator_dur_loss=1.704, generator_adv_loss=2.163, generator_feat_match_loss=3.34, over 206.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 15:00:51,783 INFO [train.py:811] (1/4) Start epoch 306
+2023-11-13 15:02:25,454 INFO [train.py:467] (1/4) Epoch 306, batch 15, global_batch_idx: 11300, batch size: 71, loss[discriminator_loss=2.633, discriminator_real_loss=1.39, discriminator_fake_loss=1.243, generator_loss=30.34, generator_mel_loss=21.62, generator_kl_loss=1.906, generator_dur_loss=1.675, generator_adv_loss=2.191, generator_feat_match_loss=2.951, over 71.00 samples.], tot_loss[discriminator_loss=2.585, discriminator_real_loss=1.291, discriminator_fake_loss=1.294, generator_loss=30.34, generator_mel_loss=21.1, generator_kl_loss=1.927, generator_dur_loss=1.689, generator_adv_loss=2.205, generator_feat_match_loss=3.418, over 1111.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, grad_scale: 16.0
+2023-11-13 15:04:23,313 INFO [train.py:811] (1/4) Start epoch 307
+2023-11-13 15:07:05,925 INFO [train.py:467] (1/4) Epoch 307, batch 28, global_batch_idx: 11350, batch size: 76, loss[discriminator_loss=2.441, discriminator_real_loss=1.251, discriminator_fake_loss=1.191, generator_loss=30.79, generator_mel_loss=21.36, generator_kl_loss=1.916, generator_dur_loss=1.708, generator_adv_loss=2.287, generator_feat_match_loss=3.518, over 76.00 samples.], tot_loss[discriminator_loss=2.579, discriminator_real_loss=1.305, discriminator_fake_loss=1.275, generator_loss=30.85, generator_mel_loss=21.38, generator_kl_loss=1.94, generator_dur_loss=1.682, generator_adv_loss=2.25, generator_feat_match_loss=3.594, over 2141.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:07:52,373 INFO [train.py:811] (1/4) Start epoch 308
+2023-11-13 15:11:22,301 INFO [train.py:811] (1/4) Start epoch 309
+2023-11-13 15:11:56,210 INFO [train.py:467] (1/4) Epoch 309, batch 4, global_batch_idx: 11400, batch size: 67, loss[discriminator_loss=2.566, discriminator_real_loss=1.417, discriminator_fake_loss=1.15, generator_loss=30.19, generator_mel_loss=20.94, generator_kl_loss=1.9, generator_dur_loss=1.714, generator_adv_loss=2.094, generator_feat_match_loss=3.545, over 67.00 samples.], tot_loss[discriminator_loss=2.622, discriminator_real_loss=1.347, discriminator_fake_loss=1.275, generator_loss=30.21, generator_mel_loss=21.17, generator_kl_loss=1.926, generator_dur_loss=1.694, generator_adv_loss=2.125, generator_feat_match_loss=3.303, over 330.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:11:56,211 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 15:12:08,157 INFO [train.py:517] (1/4) Epoch 309, validation: discriminator_loss=2.516, discriminator_real_loss=1.074, discriminator_fake_loss=1.442, generator_loss=31.1, generator_mel_loss=22.33, generator_kl_loss=1.964, generator_dur_loss=1.66, generator_adv_loss=1.733, generator_feat_match_loss=3.416, over 100.00 samples.
+2023-11-13 15:12:08,158 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 15:15:09,998 INFO [train.py:811] (1/4) Start epoch 310
+2023-11-13 15:17:04,698 INFO [train.py:467] (1/4) Epoch 310, batch 17, global_batch_idx: 11450, batch size: 79, loss[discriminator_loss=2.684, discriminator_real_loss=1.321, discriminator_fake_loss=1.363, generator_loss=29.88, generator_mel_loss=21.27, generator_kl_loss=1.908, generator_dur_loss=1.675, generator_adv_loss=1.963, generator_feat_match_loss=3.062, over 79.00 samples.], tot_loss[discriminator_loss=2.643, discriminator_real_loss=1.342, discriminator_fake_loss=1.301, generator_loss=30.06, generator_mel_loss=21.17, generator_kl_loss=1.922, generator_dur_loss=1.689, generator_adv_loss=2.045, generator_feat_match_loss=3.234, over 1305.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:18:42,206 INFO [train.py:811] (1/4) Start epoch 311
+2023-11-13 15:21:34,825 INFO [train.py:467] (1/4) Epoch 311, batch 30, global_batch_idx: 11500, batch size: 67, loss[discriminator_loss=2.699, discriminator_real_loss=1.297, discriminator_fake_loss=1.402, generator_loss=30.69, generator_mel_loss=21.82, generator_kl_loss=1.84, generator_dur_loss=1.685, generator_adv_loss=2.074, generator_feat_match_loss=3.27, over 67.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.345, discriminator_fake_loss=1.318, generator_loss=30.19, generator_mel_loss=21.34, generator_kl_loss=1.932, generator_dur_loss=1.692, generator_adv_loss=2.062, generator_feat_match_loss=3.166, over 2110.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:22:10,014 INFO [train.py:811] (1/4) Start epoch 312
+2023-11-13 15:25:38,579 INFO [train.py:811] (1/4) Start epoch 313
+2023-11-13 15:26:27,484 INFO [train.py:467] (1/4) Epoch 313, batch 6, global_batch_idx: 11550, batch size: 52, loss[discriminator_loss=2.709, discriminator_real_loss=1.311, discriminator_fake_loss=1.398, generator_loss=30.73, generator_mel_loss=21.45, generator_kl_loss=1.925, generator_dur_loss=1.703, generator_adv_loss=2.125, generator_feat_match_loss=3.527, over 52.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.376, discriminator_fake_loss=1.282, generator_loss=30.45, generator_mel_loss=21.36, generator_kl_loss=1.944, generator_dur_loss=1.69, generator_adv_loss=2.127, generator_feat_match_loss=3.336, over 475.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:29:11,603 INFO [train.py:811] (1/4) Start epoch 314
+2023-11-13 15:31:19,108 INFO [train.py:467] (1/4) Epoch 314, batch 19, global_batch_idx: 11600, batch size: 59, loss[discriminator_loss=2.674, discriminator_real_loss=1.313, discriminator_fake_loss=1.36, generator_loss=30.13, generator_mel_loss=21.59, generator_kl_loss=1.877, generator_dur_loss=1.695, generator_adv_loss=1.971, generator_feat_match_loss=2.992, over 59.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.339, discriminator_fake_loss=1.324, generator_loss=30.62, generator_mel_loss=21.55, generator_kl_loss=1.948, generator_dur_loss=1.685, generator_adv_loss=2.139, generator_feat_match_loss=3.296, over 1581.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 32.0
+2023-11-13 15:31:19,110 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 15:31:30,952 INFO [train.py:517] (1/4) Epoch 314, validation: discriminator_loss=2.669, discriminator_real_loss=1.306, discriminator_fake_loss=1.363, generator_loss=30.58, generator_mel_loss=22.17, generator_kl_loss=1.924, generator_dur_loss=1.659, generator_adv_loss=1.836, generator_feat_match_loss=2.989, over 100.00 samples.
+2023-11-13 15:31:30,953 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 15:32:58,469 INFO [train.py:811] (1/4) Start epoch 315
+2023-11-13 15:36:17,441 INFO [train.py:467] (1/4) Epoch 315, batch 32, global_batch_idx: 11650, batch size: 49, loss[discriminator_loss=2.352, discriminator_real_loss=1.187, discriminator_fake_loss=1.165, generator_loss=32.28, generator_mel_loss=21.21, generator_kl_loss=1.859, generator_dur_loss=1.721, generator_adv_loss=2.871, generator_feat_match_loss=4.613, over 49.00 samples.], tot_loss[discriminator_loss=2.608, discriminator_real_loss=1.319, discriminator_fake_loss=1.289, generator_loss=30.74, generator_mel_loss=21.34, generator_kl_loss=1.925, generator_dur_loss=1.688, generator_adv_loss=2.247, generator_feat_match_loss=3.533, over 2453.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:36:37,135 INFO [train.py:811] (1/4) Start epoch 316
+2023-11-13 15:40:13,655 INFO [train.py:811] (1/4) Start epoch 317
+2023-11-13 15:41:18,025 INFO [train.py:467] (1/4) Epoch 317, batch 8, global_batch_idx: 11700, batch size: 64, loss[discriminator_loss=2.797, discriminator_real_loss=1.395, discriminator_fake_loss=1.401, generator_loss=28.29, generator_mel_loss=20.48, generator_kl_loss=1.875, generator_dur_loss=1.684, generator_adv_loss=1.818, generator_feat_match_loss=2.43, over 64.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.324, discriminator_fake_loss=1.35, generator_loss=29.77, generator_mel_loss=20.89, generator_kl_loss=1.932, generator_dur_loss=1.691, generator_adv_loss=2.125, generator_feat_match_loss=3.124, over 529.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:43:45,583 INFO [train.py:811] (1/4) Start epoch 318
+2023-11-13 15:46:01,191 INFO [train.py:467] (1/4) Epoch 318, batch 21, global_batch_idx: 11750, batch size: 61, loss[discriminator_loss=2.578, discriminator_real_loss=1.301, discriminator_fake_loss=1.277, generator_loss=30.24, generator_mel_loss=21.53, generator_kl_loss=1.931, generator_dur_loss=1.689, generator_adv_loss=2.02, generator_feat_match_loss=3.072, over 61.00 samples.], tot_loss[discriminator_loss=2.613, discriminator_real_loss=1.32, discriminator_fake_loss=1.293, generator_loss=30.27, generator_mel_loss=21.21, generator_kl_loss=1.941, generator_dur_loss=1.681, generator_adv_loss=2.148, generator_feat_match_loss=3.292, over 1707.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:47:19,116 INFO [train.py:811] (1/4) Start epoch 319
+2023-11-13 15:50:47,696 INFO [train.py:467] (1/4) Epoch 319, batch 34, global_batch_idx: 11800, batch size: 60, loss[discriminator_loss=2.508, discriminator_real_loss=1.222, discriminator_fake_loss=1.287, generator_loss=31.14, generator_mel_loss=21.28, generator_kl_loss=1.931, generator_dur_loss=1.706, generator_adv_loss=2.414, generator_feat_match_loss=3.811, over 60.00 samples.], tot_loss[discriminator_loss=2.578, discriminator_real_loss=1.292, discriminator_fake_loss=1.286, generator_loss=30.82, generator_mel_loss=21.25, generator_kl_loss=1.945, generator_dur_loss=1.685, generator_adv_loss=2.281, generator_feat_match_loss=3.651, over 2779.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:50:47,697 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 15:51:00,597 INFO [train.py:517] (1/4) Epoch 319, validation: discriminator_loss=2.383, discriminator_real_loss=1.151, discriminator_fake_loss=1.232, generator_loss=31.38, generator_mel_loss=22.12, generator_kl_loss=1.926, generator_dur_loss=1.659, generator_adv_loss=2.087, generator_feat_match_loss=3.581, over 100.00 samples.
+2023-11-13 15:51:00,598 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 15:51:10,353 INFO [train.py:811] (1/4) Start epoch 320
+2023-11-13 15:54:48,067 INFO [train.py:811] (1/4) Start epoch 321
+2023-11-13 15:55:58,415 INFO [train.py:467] (1/4) Epoch 321, batch 10, global_batch_idx: 11850, batch size: 51, loss[discriminator_loss=2.68, discriminator_real_loss=1.209, discriminator_fake_loss=1.471, generator_loss=30.55, generator_mel_loss=21.33, generator_kl_loss=1.982, generator_dur_loss=1.669, generator_adv_loss=2.111, generator_feat_match_loss=3.461, over 51.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.352, discriminator_fake_loss=1.299, generator_loss=30.58, generator_mel_loss=21.45, generator_kl_loss=1.954, generator_dur_loss=1.694, generator_adv_loss=2.169, generator_feat_match_loss=3.31, over 708.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 15:58:24,712 INFO [train.py:811] (1/4) Start epoch 322
+2023-11-13 16:00:41,138 INFO [train.py:467] (1/4) Epoch 322, batch 23, global_batch_idx: 11900, batch size: 51, loss[discriminator_loss=2.566, discriminator_real_loss=1.269, discriminator_fake_loss=1.298, generator_loss=30.58, generator_mel_loss=21.5, generator_kl_loss=1.952, generator_dur_loss=1.717, generator_adv_loss=2.133, generator_feat_match_loss=3.281, over 51.00 samples.], tot_loss[discriminator_loss=2.625, discriminator_real_loss=1.323, discriminator_fake_loss=1.302, generator_loss=30.13, generator_mel_loss=21.15, generator_kl_loss=1.934, generator_dur_loss=1.687, generator_adv_loss=2.113, generator_feat_match_loss=3.239, over 1586.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:01:56,368 INFO [train.py:811] (1/4) Start epoch 323
+2023-11-13 16:05:28,930 INFO [train.py:467] (1/4) Epoch 323, batch 36, global_batch_idx: 11950, batch size: 64, loss[discriminator_loss=2.9, discriminator_real_loss=1.741, discriminator_fake_loss=1.159, generator_loss=29.9, generator_mel_loss=21.23, generator_kl_loss=1.842, generator_dur_loss=1.662, generator_adv_loss=2.135, generator_feat_match_loss=3.031, over 64.00 samples.], tot_loss[discriminator_loss=2.649, discriminator_real_loss=1.353, discriminator_fake_loss=1.295, generator_loss=30.68, generator_mel_loss=21.39, generator_kl_loss=1.943, generator_dur_loss=1.682, generator_adv_loss=2.201, generator_feat_match_loss=3.463, over 2712.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:05:29,678 INFO [train.py:811] (1/4) Start epoch 324
+2023-11-13 16:09:06,141 INFO [train.py:811] (1/4) Start epoch 325
+2023-11-13 16:10:33,185 INFO [train.py:467] (1/4) Epoch 325, batch 12, global_batch_idx: 12000, batch size: 71, loss[discriminator_loss=2.648, discriminator_real_loss=1.34, discriminator_fake_loss=1.31, generator_loss=30.11, generator_mel_loss=21.34, generator_kl_loss=1.975, generator_dur_loss=1.674, generator_adv_loss=2.031, generator_feat_match_loss=3.094, over 71.00 samples.], tot_loss[discriminator_loss=2.602, discriminator_real_loss=1.332, discriminator_fake_loss=1.27, generator_loss=30.48, generator_mel_loss=21.29, generator_kl_loss=1.959, generator_dur_loss=1.682, generator_adv_loss=2.156, generator_feat_match_loss=3.395, over 915.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 32.0
+2023-11-13 16:10:33,187 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 16:10:45,041 INFO [train.py:517] (1/4) Epoch 325, validation: discriminator_loss=2.661, discriminator_real_loss=1.313, discriminator_fake_loss=1.347, generator_loss=31.06, generator_mel_loss=22.35, generator_kl_loss=2.023, generator_dur_loss=1.656, generator_adv_loss=1.906, generator_feat_match_loss=3.13, over 100.00 samples.
+2023-11-13 16:10:45,042 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 16:12:57,432 INFO [train.py:811] (1/4) Start epoch 326
+2023-11-13 16:15:34,195 INFO [train.py:467] (1/4) Epoch 326, batch 25, global_batch_idx: 12050, batch size: 153, loss[discriminator_loss=2.672, discriminator_real_loss=1.326, discriminator_fake_loss=1.345, generator_loss=31.38, generator_mel_loss=21.66, generator_kl_loss=1.827, generator_dur_loss=1.651, generator_adv_loss=2.432, generator_feat_match_loss=3.809, over 153.00 samples.], tot_loss[discriminator_loss=2.617, discriminator_real_loss=1.316, discriminator_fake_loss=1.301, generator_loss=30.68, generator_mel_loss=21.4, generator_kl_loss=1.948, generator_dur_loss=1.681, generator_adv_loss=2.184, generator_feat_match_loss=3.467, over 2140.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:16:33,815 INFO [train.py:811] (1/4) Start epoch 327
+2023-11-13 16:20:13,967 INFO [train.py:811] (1/4) Start epoch 328
+2023-11-13 16:20:36,027 INFO [train.py:467] (1/4) Epoch 328, batch 1, global_batch_idx: 12100, batch size: 73, loss[discriminator_loss=2.566, discriminator_real_loss=1.252, discriminator_fake_loss=1.314, generator_loss=30.94, generator_mel_loss=21.62, generator_kl_loss=1.885, generator_dur_loss=1.687, generator_adv_loss=2.154, generator_feat_match_loss=3.594, over 73.00 samples.], tot_loss[discriminator_loss=2.608, discriminator_real_loss=1.388, discriminator_fake_loss=1.22, generator_loss=30.85, generator_mel_loss=21.51, generator_kl_loss=1.911, generator_dur_loss=1.669, generator_adv_loss=2.138, generator_feat_match_loss=3.615, over 199.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:23:46,657 INFO [train.py:811] (1/4) Start epoch 329
+2023-11-13 16:25:14,803 INFO [train.py:467] (1/4) Epoch 329, batch 14, global_batch_idx: 12150, batch size: 56, loss[discriminator_loss=2.623, discriminator_real_loss=1.297, discriminator_fake_loss=1.326, generator_loss=29.23, generator_mel_loss=20.61, generator_kl_loss=1.926, generator_dur_loss=1.661, generator_adv_loss=2.084, generator_feat_match_loss=2.955, over 56.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.345, discriminator_fake_loss=1.307, generator_loss=30.43, generator_mel_loss=21.38, generator_kl_loss=1.926, generator_dur_loss=1.683, generator_adv_loss=2.138, generator_feat_match_loss=3.305, over 1048.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:27:13,404 INFO [train.py:811] (1/4) Start epoch 330
+2023-11-13 16:29:59,120 INFO [train.py:467] (1/4) Epoch 330, batch 27, global_batch_idx: 12200, batch size: 71, loss[discriminator_loss=2.746, discriminator_real_loss=1.41, discriminator_fake_loss=1.335, generator_loss=29.61, generator_mel_loss=21.07, generator_kl_loss=1.978, generator_dur_loss=1.659, generator_adv_loss=1.995, generator_feat_match_loss=2.906, over 71.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.371, discriminator_fake_loss=1.31, generator_loss=30.23, generator_mel_loss=21.16, generator_kl_loss=1.93, generator_dur_loss=1.681, generator_adv_loss=2.116, generator_feat_match_loss=3.341, over 2120.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:29:59,122 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 16:30:10,578 INFO [train.py:517] (1/4) Epoch 330, validation: discriminator_loss=2.728, discriminator_real_loss=1.36, discriminator_fake_loss=1.368, generator_loss=30.56, generator_mel_loss=22.03, generator_kl_loss=2.067, generator_dur_loss=1.651, generator_adv_loss=1.844, generator_feat_match_loss=2.969, over 100.00 samples.
+2023-11-13 16:30:10,579 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 16:30:57,031 INFO [train.py:811] (1/4) Start epoch 331
+2023-11-13 16:34:26,095 INFO [train.py:811] (1/4) Start epoch 332
+2023-11-13 16:34:55,283 INFO [train.py:467] (1/4) Epoch 332, batch 3, global_batch_idx: 12250, batch size: 59, loss[discriminator_loss=2.703, discriminator_real_loss=1.379, discriminator_fake_loss=1.323, generator_loss=29.88, generator_mel_loss=21.17, generator_kl_loss=2.04, generator_dur_loss=1.736, generator_adv_loss=1.99, generator_feat_match_loss=2.941, over 59.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.35, discriminator_fake_loss=1.36, generator_loss=29.97, generator_mel_loss=21.22, generator_kl_loss=1.968, generator_dur_loss=1.687, generator_adv_loss=2.03, generator_feat_match_loss=3.064, over 295.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:37:57,088 INFO [train.py:811] (1/4) Start epoch 333
+2023-11-13 16:39:39,145 INFO [train.py:467] (1/4) Epoch 333, batch 16, global_batch_idx: 12300, batch size: 56, loss[discriminator_loss=2.602, discriminator_real_loss=1.306, discriminator_fake_loss=1.297, generator_loss=30.15, generator_mel_loss=21.01, generator_kl_loss=1.868, generator_dur_loss=1.686, generator_adv_loss=2.297, generator_feat_match_loss=3.295, over 56.00 samples.], tot_loss[discriminator_loss=2.634, discriminator_real_loss=1.333, discriminator_fake_loss=1.302, generator_loss=30.63, generator_mel_loss=21.49, generator_kl_loss=1.951, generator_dur_loss=1.682, generator_adv_loss=2.124, generator_feat_match_loss=3.374, over 1226.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:41:36,126 INFO [train.py:811] (1/4) Start epoch 334
+2023-11-13 16:44:27,060 INFO [train.py:467] (1/4) Epoch 334, batch 29, global_batch_idx: 12350, batch size: 64, loss[discriminator_loss=3.092, discriminator_real_loss=1.599, discriminator_fake_loss=1.493, generator_loss=28.72, generator_mel_loss=20.18, generator_kl_loss=1.966, generator_dur_loss=1.69, generator_adv_loss=2.072, generator_feat_match_loss=2.809, over 64.00 samples.], tot_loss[discriminator_loss=2.575, discriminator_real_loss=1.288, discriminator_fake_loss=1.288, generator_loss=30.64, generator_mel_loss=21.04, generator_kl_loss=1.944, generator_dur_loss=1.681, generator_adv_loss=2.297, generator_feat_match_loss=3.678, over 2193.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:45:06,666 INFO [train.py:811] (1/4) Start epoch 335
+2023-11-13 16:48:44,361 INFO [train.py:811] (1/4) Start epoch 336
+2023-11-13 16:49:29,997 INFO [train.py:467] (1/4) Epoch 336, batch 5, global_batch_idx: 12400, batch size: 71, loss[discriminator_loss=2.627, discriminator_real_loss=1.225, discriminator_fake_loss=1.402, generator_loss=30.65, generator_mel_loss=21.45, generator_kl_loss=1.921, generator_dur_loss=1.671, generator_adv_loss=2.211, generator_feat_match_loss=3.4, over 71.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=30.22, generator_mel_loss=21.28, generator_kl_loss=1.915, generator_dur_loss=1.697, generator_adv_loss=2.097, generator_feat_match_loss=3.233, over 444.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 32.0
+2023-11-13 16:49:29,999 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 16:49:42,792 INFO [train.py:517] (1/4) Epoch 336, validation: discriminator_loss=2.72, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=30.43, generator_mel_loss=21.91, generator_kl_loss=2.01, generator_dur_loss=1.664, generator_adv_loss=1.865, generator_feat_match_loss=2.978, over 100.00 samples.
+2023-11-13 16:49:42,793 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 16:52:32,936 INFO [train.py:811] (1/4) Start epoch 337
+2023-11-13 16:54:26,101 INFO [train.py:467] (1/4) Epoch 337, batch 18, global_batch_idx: 12450, batch size: 81, loss[discriminator_loss=2.508, discriminator_real_loss=1.122, discriminator_fake_loss=1.387, generator_loss=31.26, generator_mel_loss=21.38, generator_kl_loss=1.914, generator_dur_loss=1.682, generator_adv_loss=2.205, generator_feat_match_loss=4.078, over 81.00 samples.], tot_loss[discriminator_loss=2.608, discriminator_real_loss=1.325, discriminator_fake_loss=1.283, generator_loss=30.47, generator_mel_loss=21.16, generator_kl_loss=1.922, generator_dur_loss=1.677, generator_adv_loss=2.214, generator_feat_match_loss=3.498, over 1289.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:56:04,587 INFO [train.py:811] (1/4) Start epoch 338
+2023-11-13 16:59:07,657 INFO [train.py:467] (1/4) Epoch 338, batch 31, global_batch_idx: 12500, batch size: 67, loss[discriminator_loss=2.705, discriminator_real_loss=1.434, discriminator_fake_loss=1.271, generator_loss=30, generator_mel_loss=21.42, generator_kl_loss=1.977, generator_dur_loss=1.666, generator_adv_loss=1.964, generator_feat_match_loss=2.979, over 67.00 samples.], tot_loss[discriminator_loss=2.621, discriminator_real_loss=1.331, discriminator_fake_loss=1.291, generator_loss=30.37, generator_mel_loss=21.22, generator_kl_loss=1.949, generator_dur_loss=1.679, generator_adv_loss=2.138, generator_feat_match_loss=3.38, over 2448.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 16:59:39,622 INFO [train.py:811] (1/4) Start epoch 339
+2023-11-13 17:03:14,479 INFO [train.py:811] (1/4) Start epoch 340
+2023-11-13 17:04:11,840 INFO [train.py:467] (1/4) Epoch 340, batch 7, global_batch_idx: 12550, batch size: 95, loss[discriminator_loss=2.637, discriminator_real_loss=1.394, discriminator_fake_loss=1.242, generator_loss=30.68, generator_mel_loss=21.54, generator_kl_loss=2.019, generator_dur_loss=1.683, generator_adv_loss=2.01, generator_feat_match_loss=3.43, over 95.00 samples.], tot_loss[discriminator_loss=2.621, discriminator_real_loss=1.339, discriminator_fake_loss=1.283, generator_loss=30.72, generator_mel_loss=21.33, generator_kl_loss=1.959, generator_dur_loss=1.682, generator_adv_loss=2.188, generator_feat_match_loss=3.562, over 553.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 17:06:47,923 INFO [train.py:811] (1/4) Start epoch 341
+2023-11-13 17:09:05,283 INFO [train.py:467] (1/4) Epoch 341, batch 20, global_batch_idx: 12600, batch size: 126, loss[discriminator_loss=2.617, discriminator_real_loss=1.41, discriminator_fake_loss=1.206, generator_loss=31.13, generator_mel_loss=21.36, generator_kl_loss=1.977, generator_dur_loss=1.664, generator_adv_loss=2.422, generator_feat_match_loss=3.705, over 126.00 samples.], tot_loss[discriminator_loss=2.631, discriminator_real_loss=1.341, discriminator_fake_loss=1.29, generator_loss=30.66, generator_mel_loss=21.31, generator_kl_loss=1.929, generator_dur_loss=1.682, generator_adv_loss=2.217, generator_feat_match_loss=3.529, over 1623.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 17:09:05,284 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 17:09:16,645 INFO [train.py:517] (1/4) Epoch 341, validation: discriminator_loss=2.477, discriminator_real_loss=1.291, discriminator_fake_loss=1.186, generator_loss=31.98, generator_mel_loss=22.19, generator_kl_loss=2.086, generator_dur_loss=1.654, generator_adv_loss=2.258, generator_feat_match_loss=3.797, over 100.00 samples.
+2023-11-13 17:09:16,646 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 17:10:39,501 INFO [train.py:811] (1/4) Start epoch 342
+2023-11-13 17:13:57,603 INFO [train.py:467] (1/4) Epoch 342, batch 33, global_batch_idx: 12650, batch size: 85, loss[discriminator_loss=2.383, discriminator_real_loss=1.288, discriminator_fake_loss=1.095, generator_loss=30.61, generator_mel_loss=20.58, generator_kl_loss=1.944, generator_dur_loss=1.672, generator_adv_loss=2.443, generator_feat_match_loss=3.973, over 85.00 samples.], tot_loss[discriminator_loss=2.618, discriminator_real_loss=1.325, discriminator_fake_loss=1.293, generator_loss=30.78, generator_mel_loss=21.25, generator_kl_loss=1.953, generator_dur_loss=1.684, generator_adv_loss=2.25, generator_feat_match_loss=3.641, over 2507.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 17:14:14,240 INFO [train.py:811] (1/4) Start epoch 343
+2023-11-13 17:17:50,724 INFO [train.py:811] (1/4) Start epoch 344
+2023-11-13 17:18:55,280 INFO [train.py:467] (1/4) Epoch 344, batch 9, global_batch_idx: 12700, batch size: 55, loss[discriminator_loss=2.635, discriminator_real_loss=1.256, discriminator_fake_loss=1.379, generator_loss=30.94, generator_mel_loss=21.54, generator_kl_loss=1.948, generator_dur_loss=1.714, generator_adv_loss=2.359, generator_feat_match_loss=3.383, over 55.00 samples.], tot_loss[discriminator_loss=2.615, discriminator_real_loss=1.36, discriminator_fake_loss=1.256, generator_loss=30.49, generator_mel_loss=21.28, generator_kl_loss=1.938, generator_dur_loss=1.689, generator_adv_loss=2.165, generator_feat_match_loss=3.425, over 670.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 17:21:26,510 INFO [train.py:811] (1/4) Start epoch 345
+2023-11-13 17:23:36,717 INFO [train.py:467] (1/4) Epoch 345, batch 22, global_batch_idx: 12750, batch size: 60, loss[discriminator_loss=2.703, discriminator_real_loss=1.329, discriminator_fake_loss=1.375, generator_loss=30.71, generator_mel_loss=21.6, generator_kl_loss=1.924, generator_dur_loss=1.688, generator_adv_loss=2.025, generator_feat_match_loss=3.473, over 60.00 samples.], tot_loss[discriminator_loss=2.646, discriminator_real_loss=1.339, discriminator_fake_loss=1.307, generator_loss=30.34, generator_mel_loss=21.14, generator_kl_loss=1.935, generator_dur_loss=1.685, generator_adv_loss=2.148, generator_feat_match_loss=3.44, over 1558.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 17:25:02,164 INFO [train.py:811] (1/4) Start epoch 346
+2023-11-13 17:28:34,568 INFO [train.py:467] (1/4) Epoch 346, batch 35, global_batch_idx: 12800, batch size: 63, loss[discriminator_loss=2.574, discriminator_real_loss=1.182, discriminator_fake_loss=1.394, generator_loss=30.61, generator_mel_loss=21.04, generator_kl_loss=1.922, generator_dur_loss=1.7, generator_adv_loss=2.301, generator_feat_match_loss=3.646, over 63.00 samples.], tot_loss[discriminator_loss=2.647, discriminator_real_loss=1.329, discriminator_fake_loss=1.318, generator_loss=30.4, generator_mel_loss=21.21, generator_kl_loss=1.927, generator_dur_loss=1.683, generator_adv_loss=2.144, generator_feat_match_loss=3.431, over 2390.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 32.0
+2023-11-13 17:28:34,570 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 17:28:46,965 INFO [train.py:517] (1/4) Epoch 346, validation: discriminator_loss=2.767, discriminator_real_loss=1.383, discriminator_fake_loss=1.384, generator_loss=30.62, generator_mel_loss=22.02, generator_kl_loss=2.037, generator_dur_loss=1.65, generator_adv_loss=1.855, generator_feat_match_loss=3.06, over 100.00 samples.
+2023-11-13 17:28:46,966 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 17:28:52,290 INFO [train.py:811] (1/4) Start epoch 347
+2023-11-13 17:32:24,641 INFO [train.py:811] (1/4) Start epoch 348
+2023-11-13 17:33:38,056 INFO [train.py:467] (1/4) Epoch 348, batch 11, global_batch_idx: 12850, batch size: 58, loss[discriminator_loss=2.422, discriminator_real_loss=1.228, discriminator_fake_loss=1.195, generator_loss=31.15, generator_mel_loss=21.18, generator_kl_loss=1.928, generator_dur_loss=1.671, generator_adv_loss=2.371, generator_feat_match_loss=4.004, over 58.00 samples.], tot_loss[discriminator_loss=2.565, discriminator_real_loss=1.297, discriminator_fake_loss=1.268, generator_loss=30.95, generator_mel_loss=21.27, generator_kl_loss=1.934, generator_dur_loss=1.677, generator_adv_loss=2.31, generator_feat_match_loss=3.755, over 893.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, grad_scale: 16.0
+2023-11-13 17:35:54,364 INFO [train.py:811] (1/4) Start epoch 349
+2023-11-13 17:38:21,620 INFO [train.py:467] (1/4) Epoch 349, batch 24, global_batch_idx: 12900, batch size: 52, loss[discriminator_loss=2.703, discriminator_real_loss=1.272, discriminator_fake_loss=1.43, generator_loss=30.32, generator_mel_loss=21.46, generator_kl_loss=1.914, generator_dur_loss=1.679, generator_adv_loss=1.999, generator_feat_match_loss=3.26, over 52.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.373, discriminator_fake_loss=1.315, generator_loss=30.15, generator_mel_loss=21.25, generator_kl_loss=1.933, generator_dur_loss=1.683, generator_adv_loss=2.074, generator_feat_match_loss=3.203, over 1816.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 17:39:22,790 INFO [train.py:811] (1/4) Start epoch 350
+2023-11-13 17:42:58,445 INFO [train.py:811] (1/4) Start epoch 351
+2023-11-13 17:43:14,237 INFO [train.py:467] (1/4) Epoch 351, batch 0, global_batch_idx: 12950, batch size: 71, loss[discriminator_loss=2.764, discriminator_real_loss=1.329, discriminator_fake_loss=1.435, generator_loss=30.07, generator_mel_loss=21.44, generator_kl_loss=1.932, generator_dur_loss=1.669, generator_adv_loss=1.908, generator_feat_match_loss=3.121, over 71.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.329, discriminator_fake_loss=1.435, generator_loss=30.07, generator_mel_loss=21.44, generator_kl_loss=1.932, generator_dur_loss=1.669, generator_adv_loss=1.908, generator_feat_match_loss=3.121, over 71.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 17:46:33,221 INFO [train.py:811] (1/4) Start epoch 352
+2023-11-13 17:47:51,785 INFO [train.py:467] (1/4) Epoch 352, batch 13, global_batch_idx: 13000, batch size: 85, loss[discriminator_loss=2.693, discriminator_real_loss=1.197, discriminator_fake_loss=1.496, generator_loss=30.29, generator_mel_loss=21.33, generator_kl_loss=1.888, generator_dur_loss=1.699, generator_adv_loss=1.87, generator_feat_match_loss=3.498, over 85.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.33, discriminator_fake_loss=1.326, generator_loss=30.27, generator_mel_loss=21.05, generator_kl_loss=1.92, generator_dur_loss=1.683, generator_adv_loss=2.17, generator_feat_match_loss=3.446, over 906.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 17:47:51,787 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 17:48:03,377 INFO [train.py:517] (1/4) Epoch 352, validation: discriminator_loss=2.671, discriminator_real_loss=1.116, discriminator_fake_loss=1.555, generator_loss=30.09, generator_mel_loss=21.66, generator_kl_loss=2.014, generator_dur_loss=1.65, generator_adv_loss=1.618, generator_feat_match_loss=3.148, over 100.00 samples.
+2023-11-13 17:48:03,378 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 17:50:17,696 INFO [train.py:811] (1/4) Start epoch 353
+2023-11-13 17:52:59,115 INFO [train.py:467] (1/4) Epoch 353, batch 26, global_batch_idx: 13050, batch size: 85, loss[discriminator_loss=2.865, discriminator_real_loss=1.213, discriminator_fake_loss=1.652, generator_loss=29.19, generator_mel_loss=20.82, generator_kl_loss=1.941, generator_dur_loss=1.656, generator_adv_loss=2.078, generator_feat_match_loss=2.695, over 85.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.341, discriminator_fake_loss=1.322, generator_loss=30.6, generator_mel_loss=21.21, generator_kl_loss=1.942, generator_dur_loss=1.68, generator_adv_loss=2.237, generator_feat_match_loss=3.535, over 2050.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 17:53:53,783 INFO [train.py:811] (1/4) Start epoch 354
+2023-11-13 17:57:27,493 INFO [train.py:811] (1/4) Start epoch 355
+2023-11-13 17:57:57,733 INFO [train.py:467] (1/4) Epoch 355, batch 2, global_batch_idx: 13100, batch size: 95, loss[discriminator_loss=2.604, discriminator_real_loss=1.323, discriminator_fake_loss=1.28, generator_loss=30.09, generator_mel_loss=21.01, generator_kl_loss=1.947, generator_dur_loss=1.659, generator_adv_loss=2.043, generator_feat_match_loss=3.438, over 95.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.376, discriminator_fake_loss=1.292, generator_loss=29.84, generator_mel_loss=21.02, generator_kl_loss=1.962, generator_dur_loss=1.689, generator_adv_loss=1.992, generator_feat_match_loss=3.172, over 233.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:00:58,656 INFO [train.py:811] (1/4) Start epoch 356
+2023-11-13 18:02:38,843 INFO [train.py:467] (1/4) Epoch 356, batch 15, global_batch_idx: 13150, batch size: 85, loss[discriminator_loss=2.617, discriminator_real_loss=1.378, discriminator_fake_loss=1.24, generator_loss=30.47, generator_mel_loss=21.26, generator_kl_loss=1.933, generator_dur_loss=1.66, generator_adv_loss=2.18, generator_feat_match_loss=3.438, over 85.00 samples.], tot_loss[discriminator_loss=2.65, discriminator_real_loss=1.342, discriminator_fake_loss=1.308, generator_loss=30.36, generator_mel_loss=21.3, generator_kl_loss=1.963, generator_dur_loss=1.678, generator_adv_loss=2.095, generator_feat_match_loss=3.325, over 1293.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:04:30,477 INFO [train.py:811] (1/4) Start epoch 357
+2023-11-13 18:07:13,696 INFO [train.py:467] (1/4) Epoch 357, batch 28, global_batch_idx: 13200, batch size: 95, loss[discriminator_loss=2.578, discriminator_real_loss=1.219, discriminator_fake_loss=1.359, generator_loss=30.57, generator_mel_loss=21.11, generator_kl_loss=1.948, generator_dur_loss=1.643, generator_adv_loss=2.271, generator_feat_match_loss=3.598, over 95.00 samples.], tot_loss[discriminator_loss=2.649, discriminator_real_loss=1.341, discriminator_fake_loss=1.309, generator_loss=30.43, generator_mel_loss=21.25, generator_kl_loss=1.948, generator_dur_loss=1.676, generator_adv_loss=2.128, generator_feat_match_loss=3.428, over 2093.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 32.0
+2023-11-13 18:07:13,698 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 18:07:25,632 INFO [train.py:517] (1/4) Epoch 357, validation: discriminator_loss=2.6, discriminator_real_loss=1.316, discriminator_fake_loss=1.283, generator_loss=31.01, generator_mel_loss=21.87, generator_kl_loss=2.01, generator_dur_loss=1.656, generator_adv_loss=1.989, generator_feat_match_loss=3.487, over 100.00 samples.
+2023-11-13 18:07:25,633 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 18:08:08,162 INFO [train.py:811] (1/4) Start epoch 358
+2023-11-13 18:11:43,379 INFO [train.py:811] (1/4) Start epoch 359
+2023-11-13 18:12:22,401 INFO [train.py:467] (1/4) Epoch 359, batch 4, global_batch_idx: 13250, batch size: 65, loss[discriminator_loss=3.135, discriminator_real_loss=1.732, discriminator_fake_loss=1.402, generator_loss=30.47, generator_mel_loss=21.19, generator_kl_loss=1.903, generator_dur_loss=1.693, generator_adv_loss=2.461, generator_feat_match_loss=3.225, over 65.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.374, discriminator_fake_loss=1.376, generator_loss=30.96, generator_mel_loss=21.36, generator_kl_loss=1.965, generator_dur_loss=1.679, generator_adv_loss=2.382, generator_feat_match_loss=3.578, over 378.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:15:08,073 INFO [train.py:811] (1/4) Start epoch 360
+2023-11-13 18:17:09,776 INFO [train.py:467] (1/4) Epoch 360, batch 17, global_batch_idx: 13300, batch size: 65, loss[discriminator_loss=2.523, discriminator_real_loss=1.312, discriminator_fake_loss=1.213, generator_loss=29.43, generator_mel_loss=20.3, generator_kl_loss=1.873, generator_dur_loss=1.67, generator_adv_loss=2.285, generator_feat_match_loss=3.301, over 65.00 samples.], tot_loss[discriminator_loss=2.623, discriminator_real_loss=1.339, discriminator_fake_loss=1.284, generator_loss=30.21, generator_mel_loss=20.85, generator_kl_loss=1.917, generator_dur_loss=1.688, generator_adv_loss=2.208, generator_feat_match_loss=3.543, over 1226.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:18:47,590 INFO [train.py:811] (1/4) Start epoch 361
+2023-11-13 18:21:58,714 INFO [train.py:467] (1/4) Epoch 361, batch 30, global_batch_idx: 13350, batch size: 63, loss[discriminator_loss=2.641, discriminator_real_loss=1.149, discriminator_fake_loss=1.492, generator_loss=30.79, generator_mel_loss=21.11, generator_kl_loss=1.988, generator_dur_loss=1.699, generator_adv_loss=2.203, generator_feat_match_loss=3.793, over 63.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.369, discriminator_fake_loss=1.287, generator_loss=30.55, generator_mel_loss=21.19, generator_kl_loss=1.93, generator_dur_loss=1.675, generator_adv_loss=2.196, generator_feat_match_loss=3.563, over 2468.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:22:31,717 INFO [train.py:811] (1/4) Start epoch 362
+2023-11-13 18:26:08,305 INFO [train.py:811] (1/4) Start epoch 363
+2023-11-13 18:26:49,659 INFO [train.py:467] (1/4) Epoch 363, batch 6, global_batch_idx: 13400, batch size: 81, loss[discriminator_loss=2.684, discriminator_real_loss=1.265, discriminator_fake_loss=1.418, generator_loss=30.13, generator_mel_loss=21.07, generator_kl_loss=1.996, generator_dur_loss=1.697, generator_adv_loss=2.057, generator_feat_match_loss=3.309, over 81.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.335, discriminator_fake_loss=1.328, generator_loss=30.23, generator_mel_loss=21.14, generator_kl_loss=1.966, generator_dur_loss=1.691, generator_adv_loss=2.073, generator_feat_match_loss=3.362, over 470.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:26:49,661 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 18:27:02,174 INFO [train.py:517] (1/4) Epoch 363, validation: discriminator_loss=2.582, discriminator_real_loss=1.272, discriminator_fake_loss=1.31, generator_loss=30.95, generator_mel_loss=21.97, generator_kl_loss=2.024, generator_dur_loss=1.651, generator_adv_loss=1.956, generator_feat_match_loss=3.342, over 100.00 samples.
+2023-11-13 18:27:02,176 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 18:29:58,010 INFO [train.py:811] (1/4) Start epoch 364
+2023-11-13 18:32:03,099 INFO [train.py:467] (1/4) Epoch 364, batch 19, global_batch_idx: 13450, batch size: 71, loss[discriminator_loss=2.566, discriminator_real_loss=1.225, discriminator_fake_loss=1.341, generator_loss=30.88, generator_mel_loss=21.46, generator_kl_loss=1.968, generator_dur_loss=1.673, generator_adv_loss=2.037, generator_feat_match_loss=3.746, over 71.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.39, discriminator_fake_loss=1.279, generator_loss=30.65, generator_mel_loss=21.24, generator_kl_loss=1.928, generator_dur_loss=1.675, generator_adv_loss=2.229, generator_feat_match_loss=3.573, over 1381.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:33:36,393 INFO [train.py:811] (1/4) Start epoch 365
+2023-11-13 18:36:45,184 INFO [train.py:467] (1/4) Epoch 365, batch 32, global_batch_idx: 13500, batch size: 60, loss[discriminator_loss=2.539, discriminator_real_loss=1.29, discriminator_fake_loss=1.249, generator_loss=30.83, generator_mel_loss=21.06, generator_kl_loss=1.889, generator_dur_loss=1.702, generator_adv_loss=2.312, generator_feat_match_loss=3.861, over 60.00 samples.], tot_loss[discriminator_loss=2.589, discriminator_real_loss=1.292, discriminator_fake_loss=1.297, generator_loss=30.34, generator_mel_loss=21.07, generator_kl_loss=1.94, generator_dur_loss=1.679, generator_adv_loss=2.155, generator_feat_match_loss=3.499, over 2474.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:37:07,795 INFO [train.py:811] (1/4) Start epoch 366
+2023-11-13 18:40:43,273 INFO [train.py:811] (1/4) Start epoch 367
+2023-11-13 18:41:44,767 INFO [train.py:467] (1/4) Epoch 367, batch 8, global_batch_idx: 13550, batch size: 101, loss[discriminator_loss=2.562, discriminator_real_loss=1.399, discriminator_fake_loss=1.162, generator_loss=30.32, generator_mel_loss=21.13, generator_kl_loss=1.925, generator_dur_loss=1.663, generator_adv_loss=2.105, generator_feat_match_loss=3.498, over 101.00 samples.], tot_loss[discriminator_loss=2.632, discriminator_real_loss=1.364, discriminator_fake_loss=1.268, generator_loss=30.36, generator_mel_loss=21.26, generator_kl_loss=1.938, generator_dur_loss=1.675, generator_adv_loss=2.092, generator_feat_match_loss=3.39, over 752.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:44:16,099 INFO [train.py:811] (1/4) Start epoch 368
+2023-11-13 18:46:36,912 INFO [train.py:467] (1/4) Epoch 368, batch 21, global_batch_idx: 13600, batch size: 61, loss[discriminator_loss=2.574, discriminator_real_loss=1.311, discriminator_fake_loss=1.264, generator_loss=30.38, generator_mel_loss=20.8, generator_kl_loss=1.849, generator_dur_loss=1.646, generator_adv_loss=2.133, generator_feat_match_loss=3.953, over 61.00 samples.], tot_loss[discriminator_loss=2.655, discriminator_real_loss=1.346, discriminator_fake_loss=1.309, generator_loss=30.37, generator_mel_loss=21.21, generator_kl_loss=1.925, generator_dur_loss=1.673, generator_adv_loss=2.145, generator_feat_match_loss=3.419, over 1785.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 32.0
+2023-11-13 18:46:36,914 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 18:46:48,970 INFO [train.py:517] (1/4) Epoch 368, validation: discriminator_loss=2.436, discriminator_real_loss=1.103, discriminator_fake_loss=1.333, generator_loss=31.34, generator_mel_loss=21.77, generator_kl_loss=2.029, generator_dur_loss=1.646, generator_adv_loss=1.987, generator_feat_match_loss=3.909, over 100.00 samples.
+2023-11-13 18:46:48,971 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 18:48:07,712 INFO [train.py:811] (1/4) Start epoch 369
+2023-11-13 18:51:27,680 INFO [train.py:467] (1/4) Epoch 369, batch 34, global_batch_idx: 13650, batch size: 76, loss[discriminator_loss=2.582, discriminator_real_loss=1.314, discriminator_fake_loss=1.267, generator_loss=29.89, generator_mel_loss=20.62, generator_kl_loss=1.888, generator_dur_loss=1.689, generator_adv_loss=2.178, generator_feat_match_loss=3.516, over 76.00 samples.], tot_loss[discriminator_loss=2.644, discriminator_real_loss=1.335, discriminator_fake_loss=1.31, generator_loss=30.27, generator_mel_loss=21.16, generator_kl_loss=1.929, generator_dur_loss=1.677, generator_adv_loss=2.108, generator_feat_match_loss=3.39, over 2519.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:51:41,879 INFO [train.py:811] (1/4) Start epoch 370
+2023-11-13 18:55:16,271 INFO [train.py:811] (1/4) Start epoch 371
+2023-11-13 18:56:25,868 INFO [train.py:467] (1/4) Epoch 371, batch 10, global_batch_idx: 13700, batch size: 52, loss[discriminator_loss=2.605, discriminator_real_loss=1.241, discriminator_fake_loss=1.365, generator_loss=30.49, generator_mel_loss=20.67, generator_kl_loss=2, generator_dur_loss=1.66, generator_adv_loss=2.16, generator_feat_match_loss=4.004, over 52.00 samples.], tot_loss[discriminator_loss=2.569, discriminator_real_loss=1.291, discriminator_fake_loss=1.278, generator_loss=30.45, generator_mel_loss=20.86, generator_kl_loss=1.953, generator_dur_loss=1.677, generator_adv_loss=2.229, generator_feat_match_loss=3.724, over 750.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 18:58:47,516 INFO [train.py:811] (1/4) Start epoch 372
+2023-11-13 19:01:09,289 INFO [train.py:467] (1/4) Epoch 372, batch 23, global_batch_idx: 13750, batch size: 69, loss[discriminator_loss=2.584, discriminator_real_loss=1.264, discriminator_fake_loss=1.32, generator_loss=29.97, generator_mel_loss=20.74, generator_kl_loss=1.975, generator_dur_loss=1.676, generator_adv_loss=2.113, generator_feat_match_loss=3.461, over 69.00 samples.], tot_loss[discriminator_loss=2.572, discriminator_real_loss=1.298, discriminator_fake_loss=1.274, generator_loss=30.43, generator_mel_loss=21.02, generator_kl_loss=1.957, generator_dur_loss=1.682, generator_adv_loss=2.186, generator_feat_match_loss=3.588, over 1593.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 19:02:20,990 INFO [train.py:811] (1/4) Start epoch 373
+2023-11-13 19:05:55,170 INFO [train.py:467] (1/4) Epoch 373, batch 36, global_batch_idx: 13800, batch size: 52, loss[discriminator_loss=2.498, discriminator_real_loss=1.328, discriminator_fake_loss=1.17, generator_loss=30.66, generator_mel_loss=20.78, generator_kl_loss=1.962, generator_dur_loss=1.661, generator_adv_loss=2.344, generator_feat_match_loss=3.918, over 52.00 samples.], tot_loss[discriminator_loss=2.62, discriminator_real_loss=1.337, discriminator_fake_loss=1.282, generator_loss=30.71, generator_mel_loss=21.09, generator_kl_loss=1.946, generator_dur_loss=1.682, generator_adv_loss=2.25, generator_feat_match_loss=3.746, over 2708.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:05:55,172 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 19:06:07,434 INFO [train.py:517] (1/4) Epoch 373, validation: discriminator_loss=2.515, discriminator_real_loss=1.122, discriminator_fake_loss=1.393, generator_loss=31.29, generator_mel_loss=21.7, generator_kl_loss=2.09, generator_dur_loss=1.652, generator_adv_loss=2.007, generator_feat_match_loss=3.836, over 100.00 samples.
+2023-11-13 19:06:07,435 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 19:06:08,145 INFO [train.py:811] (1/4) Start epoch 374
+2023-11-13 19:09:38,958 INFO [train.py:811] (1/4) Start epoch 375
+2023-11-13 19:10:53,230 INFO [train.py:467] (1/4) Epoch 375, batch 12, global_batch_idx: 13850, batch size: 76, loss[discriminator_loss=2.617, discriminator_real_loss=1.29, discriminator_fake_loss=1.328, generator_loss=30.39, generator_mel_loss=20.99, generator_kl_loss=1.947, generator_dur_loss=1.65, generator_adv_loss=2.289, generator_feat_match_loss=3.514, over 76.00 samples.], tot_loss[discriminator_loss=2.625, discriminator_real_loss=1.324, discriminator_fake_loss=1.302, generator_loss=30.41, generator_mel_loss=21.23, generator_kl_loss=1.936, generator_dur_loss=1.678, generator_adv_loss=2.145, generator_feat_match_loss=3.428, over 878.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:13:09,717 INFO [train.py:811] (1/4) Start epoch 376
+2023-11-13 19:15:39,006 INFO [train.py:467] (1/4) Epoch 376, batch 25, global_batch_idx: 13900, batch size: 64, loss[discriminator_loss=2.863, discriminator_real_loss=1.632, discriminator_fake_loss=1.231, generator_loss=30.13, generator_mel_loss=20.96, generator_kl_loss=1.866, generator_dur_loss=1.668, generator_adv_loss=2.316, generator_feat_match_loss=3.32, over 64.00 samples.], tot_loss[discriminator_loss=2.634, discriminator_real_loss=1.328, discriminator_fake_loss=1.306, generator_loss=30.71, generator_mel_loss=21.15, generator_kl_loss=1.958, generator_dur_loss=1.676, generator_adv_loss=2.245, generator_feat_match_loss=3.678, over 2127.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:16:40,662 INFO [train.py:811] (1/4) Start epoch 377
+2023-11-13 19:20:13,293 INFO [train.py:811] (1/4) Start epoch 378
+2023-11-13 19:20:36,050 INFO [train.py:467] (1/4) Epoch 378, batch 1, global_batch_idx: 13950, batch size: 110, loss[discriminator_loss=2.619, discriminator_real_loss=1.312, discriminator_fake_loss=1.308, generator_loss=30.64, generator_mel_loss=21.38, generator_kl_loss=1.93, generator_dur_loss=1.686, generator_adv_loss=2.031, generator_feat_match_loss=3.609, over 110.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.397, discriminator_fake_loss=1.269, generator_loss=30.06, generator_mel_loss=21.03, generator_kl_loss=1.911, generator_dur_loss=1.691, generator_adv_loss=2.035, generator_feat_match_loss=3.394, over 173.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:23:53,759 INFO [train.py:811] (1/4) Start epoch 379
+2023-11-13 19:25:22,639 INFO [train.py:467] (1/4) Epoch 379, batch 14, global_batch_idx: 14000, batch size: 49, loss[discriminator_loss=2.777, discriminator_real_loss=1.266, discriminator_fake_loss=1.511, generator_loss=30.19, generator_mel_loss=21.12, generator_kl_loss=2.011, generator_dur_loss=1.684, generator_adv_loss=2.141, generator_feat_match_loss=3.234, over 49.00 samples.], tot_loss[discriminator_loss=2.613, discriminator_real_loss=1.324, discriminator_fake_loss=1.289, generator_loss=30.44, generator_mel_loss=21.2, generator_kl_loss=1.973, generator_dur_loss=1.682, generator_adv_loss=2.122, generator_feat_match_loss=3.468, over 971.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 19:25:22,641 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 19:25:34,172 INFO [train.py:517] (1/4) Epoch 379, validation: discriminator_loss=2.685, discriminator_real_loss=1.545, discriminator_fake_loss=1.14, generator_loss=31.28, generator_mel_loss=21.94, generator_kl_loss=2.153, generator_dur_loss=1.648, generator_adv_loss=2.175, generator_feat_match_loss=3.365, over 100.00 samples.
+2023-11-13 19:25:34,173 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 19:27:37,106 INFO [train.py:811] (1/4) Start epoch 380
+2023-11-13 19:30:11,316 INFO [train.py:467] (1/4) Epoch 380, batch 27, global_batch_idx: 14050, batch size: 64, loss[discriminator_loss=2.664, discriminator_real_loss=1.483, discriminator_fake_loss=1.18, generator_loss=29.9, generator_mel_loss=20.79, generator_kl_loss=1.88, generator_dur_loss=1.642, generator_adv_loss=2.342, generator_feat_match_loss=3.248, over 64.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.377, discriminator_fake_loss=1.316, generator_loss=30.47, generator_mel_loss=21.25, generator_kl_loss=1.934, generator_dur_loss=1.677, generator_adv_loss=2.167, generator_feat_match_loss=3.445, over 2036.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 19:31:03,459 INFO [train.py:811] (1/4) Start epoch 381
+2023-11-13 19:34:38,485 INFO [train.py:811] (1/4) Start epoch 382
+2023-11-13 19:35:11,623 INFO [train.py:467] (1/4) Epoch 382, batch 3, global_batch_idx: 14100, batch size: 60, loss[discriminator_loss=2.512, discriminator_real_loss=1.325, discriminator_fake_loss=1.187, generator_loss=30.79, generator_mel_loss=21.11, generator_kl_loss=2.044, generator_dur_loss=1.677, generator_adv_loss=2.191, generator_feat_match_loss=3.764, over 60.00 samples.], tot_loss[discriminator_loss=2.596, discriminator_real_loss=1.33, discriminator_fake_loss=1.266, generator_loss=30.46, generator_mel_loss=21.07, generator_kl_loss=1.912, generator_dur_loss=1.683, generator_adv_loss=2.18, generator_feat_match_loss=3.616, over 259.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 19:38:13,259 INFO [train.py:811] (1/4) Start epoch 383
+2023-11-13 19:40:00,831 INFO [train.py:467] (1/4) Epoch 383, batch 16, global_batch_idx: 14150, batch size: 153, loss[discriminator_loss=2.562, discriminator_real_loss=1.213, discriminator_fake_loss=1.351, generator_loss=31.05, generator_mel_loss=20.96, generator_kl_loss=1.948, generator_dur_loss=1.65, generator_adv_loss=2.174, generator_feat_match_loss=4.316, over 153.00 samples.], tot_loss[discriminator_loss=2.637, discriminator_real_loss=1.321, discriminator_fake_loss=1.316, generator_loss=30.62, generator_mel_loss=21.12, generator_kl_loss=1.949, generator_dur_loss=1.678, generator_adv_loss=2.227, generator_feat_match_loss=3.642, over 1249.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:41:50,253 INFO [train.py:811] (1/4) Start epoch 384
+2023-11-13 19:44:47,557 INFO [train.py:467] (1/4) Epoch 384, batch 29, global_batch_idx: 14200, batch size: 79, loss[discriminator_loss=2.664, discriminator_real_loss=1.196, discriminator_fake_loss=1.467, generator_loss=29.8, generator_mel_loss=20.6, generator_kl_loss=1.904, generator_dur_loss=1.675, generator_adv_loss=1.988, generator_feat_match_loss=3.639, over 79.00 samples.], tot_loss[discriminator_loss=2.596, discriminator_real_loss=1.316, discriminator_fake_loss=1.28, generator_loss=30.45, generator_mel_loss=20.87, generator_kl_loss=1.931, generator_dur_loss=1.67, generator_adv_loss=2.223, generator_feat_match_loss=3.753, over 2363.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:44:47,559 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 19:44:59,378 INFO [train.py:517] (1/4) Epoch 384, validation: discriminator_loss=2.806, discriminator_real_loss=1.156, discriminator_fake_loss=1.65, generator_loss=30.87, generator_mel_loss=22.28, generator_kl_loss=1.928, generator_dur_loss=1.651, generator_adv_loss=1.578, generator_feat_match_loss=3.435, over 100.00 samples.
+2023-11-13 19:44:59,380 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 19:45:34,923 INFO [train.py:811] (1/4) Start epoch 385
+2023-11-13 19:49:10,616 INFO [train.py:811] (1/4) Start epoch 386
+2023-11-13 19:49:54,855 INFO [train.py:467] (1/4) Epoch 386, batch 5, global_batch_idx: 14250, batch size: 53, loss[discriminator_loss=2.883, discriminator_real_loss=1.59, discriminator_fake_loss=1.292, generator_loss=31.73, generator_mel_loss=21.7, generator_kl_loss=1.901, generator_dur_loss=1.713, generator_adv_loss=2.555, generator_feat_match_loss=3.861, over 53.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.41, discriminator_fake_loss=1.27, generator_loss=31.15, generator_mel_loss=21.28, generator_kl_loss=1.944, generator_dur_loss=1.683, generator_adv_loss=2.373, generator_feat_match_loss=3.874, over 394.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:52:48,277 INFO [train.py:811] (1/4) Start epoch 387
+2023-11-13 19:54:42,898 INFO [train.py:467] (1/4) Epoch 387, batch 18, global_batch_idx: 14300, batch size: 52, loss[discriminator_loss=2.631, discriminator_real_loss=1.252, discriminator_fake_loss=1.379, generator_loss=30.08, generator_mel_loss=20.95, generator_kl_loss=2.005, generator_dur_loss=1.704, generator_adv_loss=2.211, generator_feat_match_loss=3.203, over 52.00 samples.], tot_loss[discriminator_loss=2.644, discriminator_real_loss=1.34, discriminator_fake_loss=1.304, generator_loss=30.16, generator_mel_loss=21.08, generator_kl_loss=1.979, generator_dur_loss=1.683, generator_adv_loss=2.075, generator_feat_match_loss=3.347, over 1424.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:56:22,335 INFO [train.py:811] (1/4) Start epoch 388
+2023-11-13 19:59:33,677 INFO [train.py:467] (1/4) Epoch 388, batch 31, global_batch_idx: 14350, batch size: 65, loss[discriminator_loss=2.512, discriminator_real_loss=1.382, discriminator_fake_loss=1.131, generator_loss=31.16, generator_mel_loss=21.06, generator_kl_loss=2.005, generator_dur_loss=1.672, generator_adv_loss=2.373, generator_feat_match_loss=4.055, over 65.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.39, discriminator_fake_loss=1.292, generator_loss=30.39, generator_mel_loss=21, generator_kl_loss=1.959, generator_dur_loss=1.676, generator_adv_loss=2.188, generator_feat_match_loss=3.568, over 2426.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 8.0
+2023-11-13 19:59:56,225 INFO [train.py:811] (1/4) Start epoch 389
+2023-11-13 20:03:27,372 INFO [train.py:811] (1/4) Start epoch 390
+2023-11-13 20:04:17,243 INFO [train.py:467] (1/4) Epoch 390, batch 7, global_batch_idx: 14400, batch size: 56, loss[discriminator_loss=2.734, discriminator_real_loss=1.486, discriminator_fake_loss=1.248, generator_loss=30.02, generator_mel_loss=21.18, generator_kl_loss=1.91, generator_dur_loss=1.674, generator_adv_loss=2.031, generator_feat_match_loss=3.223, over 56.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=30.23, generator_mel_loss=21.14, generator_kl_loss=1.924, generator_dur_loss=1.677, generator_adv_loss=2.115, generator_feat_match_loss=3.377, over 618.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, grad_scale: 16.0
+2023-11-13 20:04:17,245 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 20:04:28,951 INFO [train.py:517] (1/4) Epoch 390, validation: discriminator_loss=2.847, discriminator_real_loss=1.415, discriminator_fake_loss=1.432, generator_loss=30.63, generator_mel_loss=21.9, generator_kl_loss=2.055, generator_dur_loss=1.644, generator_adv_loss=1.844, generator_feat_match_loss=3.191, over 100.00 samples.
+2023-11-13 20:04:28,952 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 20:07:11,169 INFO [train.py:811] (1/4) Start epoch 391
+2023-11-13 20:09:14,881 INFO [train.py:467] (1/4) Epoch 391, batch 20, global_batch_idx: 14450, batch size: 65, loss[discriminator_loss=2.633, discriminator_real_loss=1.383, discriminator_fake_loss=1.251, generator_loss=30.67, generator_mel_loss=21.27, generator_kl_loss=1.949, generator_dur_loss=1.684, generator_adv_loss=2.137, generator_feat_match_loss=3.631, over 65.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.365, discriminator_fake_loss=1.312, generator_loss=30.24, generator_mel_loss=21.11, generator_kl_loss=1.938, generator_dur_loss=1.67, generator_adv_loss=2.105, generator_feat_match_loss=3.42, over 1608.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:10:43,978 INFO [train.py:811] (1/4) Start epoch 392
+2023-11-13 20:13:52,949 INFO [train.py:467] (1/4) Epoch 392, batch 33, global_batch_idx: 14500, batch size: 95, loss[discriminator_loss=2.676, discriminator_real_loss=1.534, discriminator_fake_loss=1.143, generator_loss=30.5, generator_mel_loss=21.24, generator_kl_loss=1.925, generator_dur_loss=1.676, generator_adv_loss=1.883, generator_feat_match_loss=3.781, over 95.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.38, discriminator_fake_loss=1.309, generator_loss=30.21, generator_mel_loss=21.14, generator_kl_loss=1.944, generator_dur_loss=1.677, generator_adv_loss=2.095, generator_feat_match_loss=3.354, over 2194.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:14:14,820 INFO [train.py:811] (1/4) Start epoch 393
+2023-11-13 20:17:40,813 INFO [train.py:811] (1/4) Start epoch 394
+2023-11-13 20:18:50,329 INFO [train.py:467] (1/4) Epoch 394, batch 9, global_batch_idx: 14550, batch size: 73, loss[discriminator_loss=2.588, discriminator_real_loss=1.25, discriminator_fake_loss=1.338, generator_loss=30.09, generator_mel_loss=20.84, generator_kl_loss=1.981, generator_dur_loss=1.654, generator_adv_loss=2.156, generator_feat_match_loss=3.459, over 73.00 samples.], tot_loss[discriminator_loss=2.615, discriminator_real_loss=1.328, discriminator_fake_loss=1.287, generator_loss=30.05, generator_mel_loss=20.86, generator_kl_loss=1.919, generator_dur_loss=1.67, generator_adv_loss=2.139, generator_feat_match_loss=3.46, over 699.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:21:12,405 INFO [train.py:811] (1/4) Start epoch 395
+2023-11-13 20:23:27,801 INFO [train.py:467] (1/4) Epoch 395, batch 22, global_batch_idx: 14600, batch size: 63, loss[discriminator_loss=2.533, discriminator_real_loss=1.273, discriminator_fake_loss=1.26, generator_loss=30.66, generator_mel_loss=21, generator_kl_loss=1.943, generator_dur_loss=1.69, generator_adv_loss=2.295, generator_feat_match_loss=3.73, over 63.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.306, discriminator_fake_loss=1.264, generator_loss=30.48, generator_mel_loss=20.98, generator_kl_loss=1.975, generator_dur_loss=1.671, generator_adv_loss=2.204, generator_feat_match_loss=3.652, over 1706.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:23:27,803 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 20:23:39,072 INFO [train.py:517] (1/4) Epoch 395, validation: discriminator_loss=2.607, discriminator_real_loss=1.199, discriminator_fake_loss=1.408, generator_loss=31.02, generator_mel_loss=21.93, generator_kl_loss=2.106, generator_dur_loss=1.653, generator_adv_loss=1.804, generator_feat_match_loss=3.531, over 100.00 samples.
+2023-11-13 20:23:39,073 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 20:24:57,089 INFO [train.py:811] (1/4) Start epoch 396
+2023-11-13 20:28:20,215 INFO [train.py:467] (1/4) Epoch 396, batch 35, global_batch_idx: 14650, batch size: 79, loss[discriminator_loss=2.629, discriminator_real_loss=1.459, discriminator_fake_loss=1.171, generator_loss=30.17, generator_mel_loss=21.11, generator_kl_loss=1.913, generator_dur_loss=1.691, generator_adv_loss=2, generator_feat_match_loss=3.453, over 79.00 samples.], tot_loss[discriminator_loss=2.634, discriminator_real_loss=1.339, discriminator_fake_loss=1.294, generator_loss=30.45, generator_mel_loss=20.92, generator_kl_loss=1.963, generator_dur_loss=1.674, generator_adv_loss=2.214, generator_feat_match_loss=3.677, over 2715.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:28:27,859 INFO [train.py:811] (1/4) Start epoch 397
+2023-11-13 20:32:03,345 INFO [train.py:811] (1/4) Start epoch 398
+2023-11-13 20:33:20,256 INFO [train.py:467] (1/4) Epoch 398, batch 11, global_batch_idx: 14700, batch size: 49, loss[discriminator_loss=2.707, discriminator_real_loss=1.363, discriminator_fake_loss=1.343, generator_loss=30.14, generator_mel_loss=21.35, generator_kl_loss=2.006, generator_dur_loss=1.664, generator_adv_loss=2.006, generator_feat_match_loss=3.105, over 49.00 samples.], tot_loss[discriminator_loss=2.63, discriminator_real_loss=1.321, discriminator_fake_loss=1.309, generator_loss=30.25, generator_mel_loss=21.04, generator_kl_loss=1.989, generator_dur_loss=1.675, generator_adv_loss=2.109, generator_feat_match_loss=3.442, over 762.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:35:35,848 INFO [train.py:811] (1/4) Start epoch 399
+2023-11-13 20:38:01,212 INFO [train.py:467] (1/4) Epoch 399, batch 24, global_batch_idx: 14750, batch size: 95, loss[discriminator_loss=2.645, discriminator_real_loss=1.308, discriminator_fake_loss=1.336, generator_loss=30.85, generator_mel_loss=21.4, generator_kl_loss=1.965, generator_dur_loss=1.662, generator_adv_loss=2.201, generator_feat_match_loss=3.629, over 95.00 samples.], tot_loss[discriminator_loss=2.637, discriminator_real_loss=1.336, discriminator_fake_loss=1.301, generator_loss=30.35, generator_mel_loss=21.11, generator_kl_loss=1.968, generator_dur_loss=1.672, generator_adv_loss=2.114, generator_feat_match_loss=3.492, over 1868.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:39:10,536 INFO [train.py:811] (1/4) Start epoch 400
+2023-11-13 20:42:42,979 INFO [train.py:811] (1/4) Start epoch 401
+2023-11-13 20:42:59,070 INFO [train.py:467] (1/4) Epoch 401, batch 0, global_batch_idx: 14800, batch size: 61, loss[discriminator_loss=2.676, discriminator_real_loss=1.439, discriminator_fake_loss=1.235, generator_loss=30.18, generator_mel_loss=21.06, generator_kl_loss=2.018, generator_dur_loss=1.715, generator_adv_loss=1.861, generator_feat_match_loss=3.525, over 61.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.439, discriminator_fake_loss=1.235, generator_loss=30.18, generator_mel_loss=21.06, generator_kl_loss=2.018, generator_dur_loss=1.715, generator_adv_loss=1.861, generator_feat_match_loss=3.525, over 61.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:42:59,071 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 20:43:10,796 INFO [train.py:517] (1/4) Epoch 401, validation: discriminator_loss=2.685, discriminator_real_loss=1.134, discriminator_fake_loss=1.55, generator_loss=30.75, generator_mel_loss=21.67, generator_kl_loss=2.09, generator_dur_loss=1.65, generator_adv_loss=1.705, generator_feat_match_loss=3.639, over 100.00 samples.
+2023-11-13 20:43:10,797 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 20:46:21,827 INFO [train.py:811] (1/4) Start epoch 402
+2023-11-13 20:47:50,205 INFO [train.py:467] (1/4) Epoch 402, batch 13, global_batch_idx: 14850, batch size: 153, loss[discriminator_loss=2.412, discriminator_real_loss=1.132, discriminator_fake_loss=1.28, generator_loss=31.99, generator_mel_loss=21.25, generator_kl_loss=1.963, generator_dur_loss=1.65, generator_adv_loss=2.406, generator_feat_match_loss=4.719, over 153.00 samples.], tot_loss[discriminator_loss=2.609, discriminator_real_loss=1.339, discriminator_fake_loss=1.27, generator_loss=30.99, generator_mel_loss=21.09, generator_kl_loss=1.971, generator_dur_loss=1.673, generator_adv_loss=2.278, generator_feat_match_loss=3.979, over 1098.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:49:53,049 INFO [train.py:811] (1/4) Start epoch 403
+2023-11-13 20:52:22,041 INFO [train.py:467] (1/4) Epoch 403, batch 26, global_batch_idx: 14900, batch size: 126, loss[discriminator_loss=2.625, discriminator_real_loss=1.371, discriminator_fake_loss=1.253, generator_loss=30.6, generator_mel_loss=21.42, generator_kl_loss=1.947, generator_dur_loss=1.659, generator_adv_loss=1.965, generator_feat_match_loss=3.609, over 126.00 samples.], tot_loss[discriminator_loss=2.64, discriminator_real_loss=1.337, discriminator_fake_loss=1.303, generator_loss=30.34, generator_mel_loss=21.08, generator_kl_loss=1.982, generator_dur_loss=1.67, generator_adv_loss=2.107, generator_feat_match_loss=3.498, over 1967.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 20:53:20,890 INFO [train.py:811] (1/4) Start epoch 404
+2023-11-13 20:56:47,676 INFO [train.py:811] (1/4) Start epoch 405
+2023-11-13 20:57:17,981 INFO [train.py:467] (1/4) Epoch 405, batch 2, global_batch_idx: 14950, batch size: 101, loss[discriminator_loss=2.551, discriminator_real_loss=1.268, discriminator_fake_loss=1.284, generator_loss=30.39, generator_mel_loss=20.59, generator_kl_loss=1.949, generator_dur_loss=1.665, generator_adv_loss=2.391, generator_feat_match_loss=3.799, over 101.00 samples.], tot_loss[discriminator_loss=2.544, discriminator_real_loss=1.29, discriminator_fake_loss=1.255, generator_loss=30.56, generator_mel_loss=20.81, generator_kl_loss=1.93, generator_dur_loss=1.682, generator_adv_loss=2.337, generator_feat_match_loss=3.802, over 213.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:00:20,968 INFO [train.py:811] (1/4) Start epoch 406
+2023-11-13 21:01:56,638 INFO [train.py:467] (1/4) Epoch 406, batch 15, global_batch_idx: 15000, batch size: 69, loss[discriminator_loss=2.639, discriminator_real_loss=1.139, discriminator_fake_loss=1.5, generator_loss=30.68, generator_mel_loss=21.16, generator_kl_loss=2.066, generator_dur_loss=1.68, generator_adv_loss=2.211, generator_feat_match_loss=3.562, over 69.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.32, discriminator_fake_loss=1.291, generator_loss=31.04, generator_mel_loss=21.24, generator_kl_loss=1.987, generator_dur_loss=1.676, generator_adv_loss=2.285, generator_feat_match_loss=3.852, over 1260.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:01:56,640 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 21:02:07,737 INFO [train.py:517] (1/4) Epoch 406, validation: discriminator_loss=2.92, discriminator_real_loss=1.335, discriminator_fake_loss=1.585, generator_loss=30.66, generator_mel_loss=21.84, generator_kl_loss=2.085, generator_dur_loss=1.638, generator_adv_loss=1.74, generator_feat_match_loss=3.348, over 100.00 samples.
+2023-11-13 21:02:07,738 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 21:04:08,571 INFO [train.py:811] (1/4) Start epoch 407
+2023-11-13 21:06:57,076 INFO [train.py:467] (1/4) Epoch 407, batch 28, global_batch_idx: 15050, batch size: 60, loss[discriminator_loss=2.746, discriminator_real_loss=1.477, discriminator_fake_loss=1.271, generator_loss=29.69, generator_mel_loss=20.95, generator_kl_loss=1.956, generator_dur_loss=1.662, generator_adv_loss=1.876, generator_feat_match_loss=3.246, over 60.00 samples.], tot_loss[discriminator_loss=2.629, discriminator_real_loss=1.343, discriminator_fake_loss=1.286, generator_loss=30.15, generator_mel_loss=20.83, generator_kl_loss=1.931, generator_dur_loss=1.671, generator_adv_loss=2.145, generator_feat_match_loss=3.571, over 2195.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:07:41,667 INFO [train.py:811] (1/4) Start epoch 408
+2023-11-13 21:11:10,034 INFO [train.py:811] (1/4) Start epoch 409
+2023-11-13 21:11:44,655 INFO [train.py:467] (1/4) Epoch 409, batch 4, global_batch_idx: 15100, batch size: 61, loss[discriminator_loss=2.676, discriminator_real_loss=1.318, discriminator_fake_loss=1.358, generator_loss=29.57, generator_mel_loss=20.75, generator_kl_loss=1.993, generator_dur_loss=1.678, generator_adv_loss=2.051, generator_feat_match_loss=3.098, over 61.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=30.04, generator_mel_loss=21, generator_kl_loss=1.965, generator_dur_loss=1.684, generator_adv_loss=2.058, generator_feat_match_loss=3.334, over 325.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:14:40,526 INFO [train.py:811] (1/4) Start epoch 410
+2023-11-13 21:16:30,394 INFO [train.py:467] (1/4) Epoch 410, batch 17, global_batch_idx: 15150, batch size: 64, loss[discriminator_loss=2.602, discriminator_real_loss=1.383, discriminator_fake_loss=1.219, generator_loss=30.65, generator_mel_loss=21.5, generator_kl_loss=2.034, generator_dur_loss=1.674, generator_adv_loss=1.936, generator_feat_match_loss=3.506, over 64.00 samples.], tot_loss[discriminator_loss=2.628, discriminator_real_loss=1.318, discriminator_fake_loss=1.309, generator_loss=30.32, generator_mel_loss=21.02, generator_kl_loss=1.94, generator_dur_loss=1.673, generator_adv_loss=2.15, generator_feat_match_loss=3.537, over 1228.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:18:08,632 INFO [train.py:811] (1/4) Start epoch 411
+2023-11-13 21:21:10,671 INFO [train.py:467] (1/4) Epoch 411, batch 30, global_batch_idx: 15200, batch size: 64, loss[discriminator_loss=2.861, discriminator_real_loss=1.628, discriminator_fake_loss=1.233, generator_loss=29.5, generator_mel_loss=20.39, generator_kl_loss=2.027, generator_dur_loss=1.652, generator_adv_loss=2.131, generator_feat_match_loss=3.301, over 64.00 samples.], tot_loss[discriminator_loss=2.628, discriminator_real_loss=1.334, discriminator_fake_loss=1.294, generator_loss=30.59, generator_mel_loss=21, generator_kl_loss=1.943, generator_dur_loss=1.67, generator_adv_loss=2.251, generator_feat_match_loss=3.731, over 2261.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 32.0
+2023-11-13 21:21:10,673 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 21:21:21,734 INFO [train.py:517] (1/4) Epoch 411, validation: discriminator_loss=2.778, discriminator_real_loss=1.515, discriminator_fake_loss=1.263, generator_loss=30.97, generator_mel_loss=21.71, generator_kl_loss=2.018, generator_dur_loss=1.643, generator_adv_loss=2.269, generator_feat_match_loss=3.328, over 100.00 samples.
+2023-11-13 21:21:21,735 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 21:21:51,630 INFO [train.py:811] (1/4) Start epoch 412
+2023-11-13 21:25:24,597 INFO [train.py:811] (1/4) Start epoch 413
+2023-11-13 21:26:11,819 INFO [train.py:467] (1/4) Epoch 413, batch 6, global_batch_idx: 15250, batch size: 58, loss[discriminator_loss=2.619, discriminator_real_loss=1.303, discriminator_fake_loss=1.316, generator_loss=29.63, generator_mel_loss=20.67, generator_kl_loss=1.976, generator_dur_loss=1.651, generator_adv_loss=2.045, generator_feat_match_loss=3.285, over 58.00 samples.], tot_loss[discriminator_loss=2.599, discriminator_real_loss=1.315, discriminator_fake_loss=1.284, generator_loss=29.9, generator_mel_loss=20.83, generator_kl_loss=1.966, generator_dur_loss=1.678, generator_adv_loss=2.052, generator_feat_match_loss=3.373, over 463.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:28:57,434 INFO [train.py:811] (1/4) Start epoch 414
+2023-11-13 21:30:49,723 INFO [train.py:467] (1/4) Epoch 414, batch 19, global_batch_idx: 15300, batch size: 60, loss[discriminator_loss=2.434, discriminator_real_loss=1.159, discriminator_fake_loss=1.275, generator_loss=31.28, generator_mel_loss=21.14, generator_kl_loss=1.914, generator_dur_loss=1.682, generator_adv_loss=2.383, generator_feat_match_loss=4.16, over 60.00 samples.], tot_loss[discriminator_loss=2.569, discriminator_real_loss=1.305, discriminator_fake_loss=1.263, generator_loss=30.86, generator_mel_loss=20.96, generator_kl_loss=1.982, generator_dur_loss=1.672, generator_adv_loss=2.285, generator_feat_match_loss=3.966, over 1328.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:32:26,995 INFO [train.py:811] (1/4) Start epoch 415
+2023-11-13 21:35:34,916 INFO [train.py:467] (1/4) Epoch 415, batch 32, global_batch_idx: 15350, batch size: 63, loss[discriminator_loss=2.723, discriminator_real_loss=1.477, discriminator_fake_loss=1.246, generator_loss=29.42, generator_mel_loss=20.71, generator_kl_loss=1.983, generator_dur_loss=1.661, generator_adv_loss=1.883, generator_feat_match_loss=3.188, over 63.00 samples.], tot_loss[discriminator_loss=2.633, discriminator_real_loss=1.335, discriminator_fake_loss=1.298, generator_loss=30.19, generator_mel_loss=21.02, generator_kl_loss=1.978, generator_dur_loss=1.669, generator_adv_loss=2.066, generator_feat_match_loss=3.459, over 2440.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:36:01,063 INFO [train.py:811] (1/4) Start epoch 416
+2023-11-13 21:39:30,023 INFO [train.py:811] (1/4) Start epoch 417
+2023-11-13 21:40:26,717 INFO [train.py:467] (1/4) Epoch 417, batch 8, global_batch_idx: 15400, batch size: 81, loss[discriminator_loss=2.564, discriminator_real_loss=1.252, discriminator_fake_loss=1.312, generator_loss=30.41, generator_mel_loss=20.93, generator_kl_loss=1.929, generator_dur_loss=1.672, generator_adv_loss=2.174, generator_feat_match_loss=3.707, over 81.00 samples.], tot_loss[discriminator_loss=2.619, discriminator_real_loss=1.329, discriminator_fake_loss=1.29, generator_loss=30.6, generator_mel_loss=21.16, generator_kl_loss=1.954, generator_dur_loss=1.677, generator_adv_loss=2.145, generator_feat_match_loss=3.657, over 580.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:40:26,718 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 21:40:38,432 INFO [train.py:517] (1/4) Epoch 417, validation: discriminator_loss=2.501, discriminator_real_loss=1.233, discriminator_fake_loss=1.268, generator_loss=31.27, generator_mel_loss=21.79, generator_kl_loss=1.954, generator_dur_loss=1.645, generator_adv_loss=2.051, generator_feat_match_loss=3.831, over 100.00 samples.
+2023-11-13 21:40:38,433 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 21:43:11,497 INFO [train.py:811] (1/4) Start epoch 418
+2023-11-13 21:45:28,057 INFO [train.py:467] (1/4) Epoch 418, batch 21, global_batch_idx: 15450, batch size: 56, loss[discriminator_loss=2.527, discriminator_real_loss=1.208, discriminator_fake_loss=1.318, generator_loss=31.08, generator_mel_loss=21.29, generator_kl_loss=1.938, generator_dur_loss=1.683, generator_adv_loss=2.379, generator_feat_match_loss=3.789, over 56.00 samples.], tot_loss[discriminator_loss=2.646, discriminator_real_loss=1.34, discriminator_fake_loss=1.305, generator_loss=30.56, generator_mel_loss=21.14, generator_kl_loss=1.953, generator_dur_loss=1.673, generator_adv_loss=2.153, generator_feat_match_loss=3.638, over 1924.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:46:43,794 INFO [train.py:811] (1/4) Start epoch 419
+2023-11-13 21:50:02,931 INFO [train.py:467] (1/4) Epoch 419, batch 34, global_batch_idx: 15500, batch size: 79, loss[discriminator_loss=2.701, discriminator_real_loss=1.399, discriminator_fake_loss=1.302, generator_loss=30.29, generator_mel_loss=21.38, generator_kl_loss=1.963, generator_dur_loss=1.671, generator_adv_loss=1.949, generator_feat_match_loss=3.326, over 79.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.36, discriminator_fake_loss=1.314, generator_loss=30.23, generator_mel_loss=21.04, generator_kl_loss=1.97, generator_dur_loss=1.668, generator_adv_loss=2.11, generator_feat_match_loss=3.447, over 2367.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:50:16,860 INFO [train.py:811] (1/4) Start epoch 420
+2023-11-13 21:53:53,547 INFO [train.py:811] (1/4) Start epoch 421
+2023-11-13 21:55:14,918 INFO [train.py:467] (1/4) Epoch 421, batch 10, global_batch_idx: 15550, batch size: 79, loss[discriminator_loss=2.559, discriminator_real_loss=1.229, discriminator_fake_loss=1.331, generator_loss=30.58, generator_mel_loss=20.77, generator_kl_loss=1.988, generator_dur_loss=1.663, generator_adv_loss=2.418, generator_feat_match_loss=3.744, over 79.00 samples.], tot_loss[discriminator_loss=2.582, discriminator_real_loss=1.299, discriminator_fake_loss=1.284, generator_loss=30.65, generator_mel_loss=21, generator_kl_loss=1.993, generator_dur_loss=1.673, generator_adv_loss=2.208, generator_feat_match_loss=3.777, over 869.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 21:57:33,288 INFO [train.py:811] (1/4) Start epoch 422
+2023-11-13 21:59:51,642 INFO [train.py:467] (1/4) Epoch 422, batch 23, global_batch_idx: 15600, batch size: 53, loss[discriminator_loss=2.58, discriminator_real_loss=1.153, discriminator_fake_loss=1.427, generator_loss=30.45, generator_mel_loss=20.6, generator_kl_loss=1.979, generator_dur_loss=1.673, generator_adv_loss=2.404, generator_feat_match_loss=3.797, over 53.00 samples.], tot_loss[discriminator_loss=2.561, discriminator_real_loss=1.291, discriminator_fake_loss=1.27, generator_loss=30.51, generator_mel_loss=20.71, generator_kl_loss=1.966, generator_dur_loss=1.667, generator_adv_loss=2.237, generator_feat_match_loss=3.938, over 1653.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 32.0
+2023-11-13 21:59:51,644 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 22:00:02,681 INFO [train.py:517] (1/4) Epoch 422, validation: discriminator_loss=2.598, discriminator_real_loss=1.279, discriminator_fake_loss=1.319, generator_loss=30.89, generator_mel_loss=21.57, generator_kl_loss=2.034, generator_dur_loss=1.642, generator_adv_loss=1.964, generator_feat_match_loss=3.671, over 100.00 samples.
+2023-11-13 22:00:02,682 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 22:01:12,415 INFO [train.py:811] (1/4) Start epoch 423
+2023-11-13 22:04:45,456 INFO [train.py:467] (1/4) Epoch 423, batch 36, global_batch_idx: 15650, batch size: 51, loss[discriminator_loss=2.57, discriminator_real_loss=1.386, discriminator_fake_loss=1.185, generator_loss=30.4, generator_mel_loss=20.82, generator_kl_loss=1.978, generator_dur_loss=1.655, generator_adv_loss=2.283, generator_feat_match_loss=3.662, over 51.00 samples.], tot_loss[discriminator_loss=2.613, discriminator_real_loss=1.333, discriminator_fake_loss=1.28, generator_loss=30.33, generator_mel_loss=20.9, generator_kl_loss=1.962, generator_dur_loss=1.668, generator_adv_loss=2.172, generator_feat_match_loss=3.633, over 2688.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 22:04:46,146 INFO [train.py:811] (1/4) Start epoch 424
+2023-11-13 22:08:06,103 INFO [train.py:811] (1/4) Start epoch 425
+2023-11-13 22:09:22,654 INFO [train.py:467] (1/4) Epoch 425, batch 12, global_batch_idx: 15700, batch size: 69, loss[discriminator_loss=2.676, discriminator_real_loss=1.334, discriminator_fake_loss=1.341, generator_loss=29.99, generator_mel_loss=20.74, generator_kl_loss=1.926, generator_dur_loss=1.697, generator_adv_loss=2.34, generator_feat_match_loss=3.295, over 69.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.355, discriminator_fake_loss=1.296, generator_loss=30.52, generator_mel_loss=21.01, generator_kl_loss=1.969, generator_dur_loss=1.663, generator_adv_loss=2.213, generator_feat_match_loss=3.667, over 890.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 22:11:36,581 INFO [train.py:811] (1/4) Start epoch 426
+2023-11-13 22:13:59,510 INFO [train.py:467] (1/4) Epoch 426, batch 25, global_batch_idx: 15750, batch size: 64, loss[discriminator_loss=2.625, discriminator_real_loss=1.347, discriminator_fake_loss=1.277, generator_loss=30.64, generator_mel_loss=20.48, generator_kl_loss=1.982, generator_dur_loss=1.674, generator_adv_loss=2.406, generator_feat_match_loss=4.098, over 64.00 samples.], tot_loss[discriminator_loss=2.624, discriminator_real_loss=1.329, discriminator_fake_loss=1.295, generator_loss=30.85, generator_mel_loss=21.07, generator_kl_loss=1.969, generator_dur_loss=1.673, generator_adv_loss=2.298, generator_feat_match_loss=3.838, over 1776.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 22:15:08,084 INFO [train.py:811] (1/4) Start epoch 427
+2023-11-13 22:18:41,199 INFO [train.py:811] (1/4) Start epoch 428
+2023-11-13 22:19:03,540 INFO [train.py:467] (1/4) Epoch 428, batch 1, global_batch_idx: 15800, batch size: 71, loss[discriminator_loss=2.633, discriminator_real_loss=1.174, discriminator_fake_loss=1.46, generator_loss=30.35, generator_mel_loss=21.25, generator_kl_loss=2.1, generator_dur_loss=1.646, generator_adv_loss=1.947, generator_feat_match_loss=3.41, over 71.00 samples.], tot_loss[discriminator_loss=2.642, discriminator_real_loss=1.195, discriminator_fake_loss=1.448, generator_loss=30.13, generator_mel_loss=21.17, generator_kl_loss=2.056, generator_dur_loss=1.665, generator_adv_loss=1.88, generator_feat_match_loss=3.361, over 130.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 22:19:03,541 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 22:19:15,686 INFO [train.py:517] (1/4) Epoch 428, validation: discriminator_loss=2.552, discriminator_real_loss=1.217, discriminator_fake_loss=1.334, generator_loss=30.8, generator_mel_loss=21.6, generator_kl_loss=2.089, generator_dur_loss=1.651, generator_adv_loss=1.891, generator_feat_match_loss=3.575, over 100.00 samples.
+2023-11-13 22:19:15,687 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 22:22:25,576 INFO [train.py:811] (1/4) Start epoch 429
+2023-11-13 22:23:54,889 INFO [train.py:467] (1/4) Epoch 429, batch 14, global_batch_idx: 15850, batch size: 53, loss[discriminator_loss=2.498, discriminator_real_loss=1.329, discriminator_fake_loss=1.169, generator_loss=30.72, generator_mel_loss=20.8, generator_kl_loss=1.948, generator_dur_loss=1.68, generator_adv_loss=2.154, generator_feat_match_loss=4.137, over 53.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.369, discriminator_fake_loss=1.307, generator_loss=30.66, generator_mel_loss=20.96, generator_kl_loss=2.003, generator_dur_loss=1.679, generator_adv_loss=2.251, generator_feat_match_loss=3.76, over 963.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 22:25:58,934 INFO [train.py:811] (1/4) Start epoch 430
+2023-11-13 22:28:46,612 INFO [train.py:467] (1/4) Epoch 430, batch 27, global_batch_idx: 15900, batch size: 110, loss[discriminator_loss=2.73, discriminator_real_loss=1.504, discriminator_fake_loss=1.228, generator_loss=29.95, generator_mel_loss=20.91, generator_kl_loss=1.914, generator_dur_loss=1.685, generator_adv_loss=1.983, generator_feat_match_loss=3.451, over 110.00 samples.], tot_loss[discriminator_loss=2.636, discriminator_real_loss=1.343, discriminator_fake_loss=1.294, generator_loss=30.38, generator_mel_loss=20.95, generator_kl_loss=1.987, generator_dur_loss=1.67, generator_adv_loss=2.133, generator_feat_match_loss=3.648, over 2171.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 22:29:34,211 INFO [train.py:811] (1/4) Start epoch 431
+2023-11-13 22:33:09,240 INFO [train.py:811] (1/4) Start epoch 432
+2023-11-13 22:33:45,613 INFO [train.py:467] (1/4) Epoch 432, batch 3, global_batch_idx: 15950, batch size: 63, loss[discriminator_loss=2.559, discriminator_real_loss=1.347, discriminator_fake_loss=1.212, generator_loss=30.7, generator_mel_loss=21.01, generator_kl_loss=1.924, generator_dur_loss=1.682, generator_adv_loss=2.215, generator_feat_match_loss=3.871, over 63.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.377, discriminator_fake_loss=1.308, generator_loss=30.27, generator_mel_loss=21.03, generator_kl_loss=2.009, generator_dur_loss=1.675, generator_adv_loss=2.086, generator_feat_match_loss=3.475, over 235.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, grad_scale: 16.0
+2023-11-13 22:36:47,773 INFO [train.py:811] (1/4) Start epoch 433
+2023-11-13 22:38:23,810 INFO [train.py:467] (1/4) Epoch 433, batch 16, global_batch_idx: 16000, batch size: 69, loss[discriminator_loss=2.605, discriminator_real_loss=1.307, discriminator_fake_loss=1.299, generator_loss=30.56, generator_mel_loss=20.96, generator_kl_loss=1.93, generator_dur_loss=1.644, generator_adv_loss=2.336, generator_feat_match_loss=3.689, over 69.00 samples.], tot_loss[discriminator_loss=2.629, discriminator_real_loss=1.331, discriminator_fake_loss=1.298, generator_loss=30.72, generator_mel_loss=21.14, generator_kl_loss=1.994, generator_dur_loss=1.669, generator_adv_loss=2.183, generator_feat_match_loss=3.73, over 1205.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 32.0
+2023-11-13 22:38:23,812 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 22:38:34,695 INFO [train.py:517] (1/4) Epoch 433, validation: discriminator_loss=2.623, discriminator_real_loss=1.341, discriminator_fake_loss=1.282, generator_loss=31.39, generator_mel_loss=21.95, generator_kl_loss=2.056, generator_dur_loss=1.648, generator_adv_loss=2.076, generator_feat_match_loss=3.654, over 100.00 samples.
+2023-11-13 22:38:34,696 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 22:40:30,135 INFO [train.py:811] (1/4) Start epoch 434
+2023-11-13 22:43:22,573 INFO [train.py:467] (1/4) Epoch 434, batch 29, global_batch_idx: 16050, batch size: 50, loss[discriminator_loss=2.656, discriminator_real_loss=1.431, discriminator_fake_loss=1.226, generator_loss=30.05, generator_mel_loss=20.8, generator_kl_loss=1.986, generator_dur_loss=1.688, generator_adv_loss=2.115, generator_feat_match_loss=3.469, over 50.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.36, discriminator_fake_loss=1.309, generator_loss=30.3, generator_mel_loss=21.06, generator_kl_loss=1.983, generator_dur_loss=1.667, generator_adv_loss=2.091, generator_feat_match_loss=3.499, over 2238.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 22:44:01,519 INFO [train.py:811] (1/4) Start epoch 435
+2023-11-13 22:47:31,941 INFO [train.py:811] (1/4) Start epoch 436
+2023-11-13 22:48:13,600 INFO [train.py:467] (1/4) Epoch 436, batch 5, global_batch_idx: 16100, batch size: 81, loss[discriminator_loss=2.688, discriminator_real_loss=1.552, discriminator_fake_loss=1.135, generator_loss=30.44, generator_mel_loss=21.06, generator_kl_loss=1.92, generator_dur_loss=1.666, generator_adv_loss=2.219, generator_feat_match_loss=3.574, over 81.00 samples.], tot_loss[discriminator_loss=2.623, discriminator_real_loss=1.335, discriminator_fake_loss=1.288, generator_loss=30.61, generator_mel_loss=20.89, generator_kl_loss=1.99, generator_dur_loss=1.668, generator_adv_loss=2.276, generator_feat_match_loss=3.783, over 401.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 22:51:05,701 INFO [train.py:811] (1/4) Start epoch 437
+2023-11-13 22:52:55,819 INFO [train.py:467] (1/4) Epoch 437, batch 18, global_batch_idx: 16150, batch size: 49, loss[discriminator_loss=2.816, discriminator_real_loss=1.521, discriminator_fake_loss=1.296, generator_loss=29.44, generator_mel_loss=20.55, generator_kl_loss=1.966, generator_dur_loss=1.709, generator_adv_loss=2.105, generator_feat_match_loss=3.113, over 49.00 samples.], tot_loss[discriminator_loss=2.615, discriminator_real_loss=1.327, discriminator_fake_loss=1.288, generator_loss=30.53, generator_mel_loss=20.74, generator_kl_loss=1.961, generator_dur_loss=1.673, generator_adv_loss=2.28, generator_feat_match_loss=3.881, over 1405.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 22:54:32,580 INFO [train.py:811] (1/4) Start epoch 438
+2023-11-13 22:57:42,674 INFO [train.py:467] (1/4) Epoch 438, batch 31, global_batch_idx: 16200, batch size: 50, loss[discriminator_loss=2.586, discriminator_real_loss=1.315, discriminator_fake_loss=1.271, generator_loss=30.02, generator_mel_loss=20.81, generator_kl_loss=1.96, generator_dur_loss=1.676, generator_adv_loss=2.088, generator_feat_match_loss=3.477, over 50.00 samples.], tot_loss[discriminator_loss=2.594, discriminator_real_loss=1.313, discriminator_fake_loss=1.281, generator_loss=30.28, generator_mel_loss=20.9, generator_kl_loss=1.983, generator_dur_loss=1.668, generator_adv_loss=2.114, generator_feat_match_loss=3.615, over 2255.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 22:57:42,676 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 22:57:53,381 INFO [train.py:517] (1/4) Epoch 438, validation: discriminator_loss=2.524, discriminator_real_loss=1.23, discriminator_fake_loss=1.294, generator_loss=31.6, generator_mel_loss=21.79, generator_kl_loss=2.175, generator_dur_loss=1.647, generator_adv_loss=2.047, generator_feat_match_loss=3.945, over 100.00 samples.
+2023-11-13 22:57:53,382 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 22:58:17,383 INFO [train.py:811] (1/4) Start epoch 439
+2023-11-13 23:01:50,481 INFO [train.py:811] (1/4) Start epoch 440
+2023-11-13 23:02:41,598 INFO [train.py:467] (1/4) Epoch 440, batch 7, global_batch_idx: 16250, batch size: 110, loss[discriminator_loss=2.629, discriminator_real_loss=1.402, discriminator_fake_loss=1.226, generator_loss=29.88, generator_mel_loss=20.88, generator_kl_loss=1.956, generator_dur_loss=1.652, generator_adv_loss=2.08, generator_feat_match_loss=3.309, over 110.00 samples.], tot_loss[discriminator_loss=2.569, discriminator_real_loss=1.284, discriminator_fake_loss=1.285, generator_loss=29.81, generator_mel_loss=20.63, generator_kl_loss=1.946, generator_dur_loss=1.664, generator_adv_loss=2.088, generator_feat_match_loss=3.485, over 592.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:05:22,482 INFO [train.py:811] (1/4) Start epoch 441
+2023-11-13 23:07:31,667 INFO [train.py:467] (1/4) Epoch 441, batch 20, global_batch_idx: 16300, batch size: 53, loss[discriminator_loss=2.75, discriminator_real_loss=1.233, discriminator_fake_loss=1.517, generator_loss=29.66, generator_mel_loss=20.62, generator_kl_loss=2.058, generator_dur_loss=1.661, generator_adv_loss=2.027, generator_feat_match_loss=3.297, over 53.00 samples.], tot_loss[discriminator_loss=2.602, discriminator_real_loss=1.302, discriminator_fake_loss=1.3, generator_loss=30.85, generator_mel_loss=21.08, generator_kl_loss=1.981, generator_dur_loss=1.667, generator_adv_loss=2.254, generator_feat_match_loss=3.868, over 1770.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:08:52,384 INFO [train.py:811] (1/4) Start epoch 442
+2023-11-13 23:12:10,675 INFO [train.py:467] (1/4) Epoch 442, batch 33, global_batch_idx: 16350, batch size: 90, loss[discriminator_loss=2.506, discriminator_real_loss=1.335, discriminator_fake_loss=1.171, generator_loss=30.67, generator_mel_loss=20.64, generator_kl_loss=2.033, generator_dur_loss=1.659, generator_adv_loss=2.252, generator_feat_match_loss=4.086, over 90.00 samples.], tot_loss[discriminator_loss=2.543, discriminator_real_loss=1.273, discriminator_fake_loss=1.27, generator_loss=30.68, generator_mel_loss=20.84, generator_kl_loss=1.965, generator_dur_loss=1.67, generator_adv_loss=2.26, generator_feat_match_loss=3.952, over 2365.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:12:32,527 INFO [train.py:811] (1/4) Start epoch 443
+2023-11-13 23:16:04,863 INFO [train.py:811] (1/4) Start epoch 444
+2023-11-13 23:17:12,878 INFO [train.py:467] (1/4) Epoch 444, batch 9, global_batch_idx: 16400, batch size: 153, loss[discriminator_loss=2.709, discriminator_real_loss=1.293, discriminator_fake_loss=1.416, generator_loss=30.35, generator_mel_loss=20.72, generator_kl_loss=2.029, generator_dur_loss=1.648, generator_adv_loss=2.133, generator_feat_match_loss=3.812, over 153.00 samples.], tot_loss[discriminator_loss=2.588, discriminator_real_loss=1.289, discriminator_fake_loss=1.3, generator_loss=30.54, generator_mel_loss=20.77, generator_kl_loss=1.984, generator_dur_loss=1.66, generator_adv_loss=2.228, generator_feat_match_loss=3.902, over 751.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 32.0
+2023-11-13 23:17:12,880 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 23:17:23,918 INFO [train.py:517] (1/4) Epoch 444, validation: discriminator_loss=2.579, discriminator_real_loss=1.265, discriminator_fake_loss=1.314, generator_loss=30.75, generator_mel_loss=21.29, generator_kl_loss=2.088, generator_dur_loss=1.643, generator_adv_loss=2.073, generator_feat_match_loss=3.658, over 100.00 samples.
+2023-11-13 23:17:23,919 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 23:19:49,803 INFO [train.py:811] (1/4) Start epoch 445
+2023-11-13 23:22:03,134 INFO [train.py:467] (1/4) Epoch 445, batch 22, global_batch_idx: 16450, batch size: 110, loss[discriminator_loss=2.566, discriminator_real_loss=1.345, discriminator_fake_loss=1.223, generator_loss=32.1, generator_mel_loss=21.73, generator_kl_loss=2.069, generator_dur_loss=1.644, generator_adv_loss=2.285, generator_feat_match_loss=4.375, over 110.00 samples.], tot_loss[discriminator_loss=2.602, discriminator_real_loss=1.32, discriminator_fake_loss=1.282, generator_loss=30.48, generator_mel_loss=20.86, generator_kl_loss=2.002, generator_dur_loss=1.664, generator_adv_loss=2.195, generator_feat_match_loss=3.761, over 1691.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:23:20,543 INFO [train.py:811] (1/4) Start epoch 446
+2023-11-13 23:26:47,944 INFO [train.py:467] (1/4) Epoch 446, batch 35, global_batch_idx: 16500, batch size: 51, loss[discriminator_loss=2.656, discriminator_real_loss=1.309, discriminator_fake_loss=1.348, generator_loss=29.51, generator_mel_loss=20.26, generator_kl_loss=1.895, generator_dur_loss=1.68, generator_adv_loss=2.174, generator_feat_match_loss=3.5, over 51.00 samples.], tot_loss[discriminator_loss=2.601, discriminator_real_loss=1.317, discriminator_fake_loss=1.285, generator_loss=30.21, generator_mel_loss=20.66, generator_kl_loss=1.998, generator_dur_loss=1.667, generator_adv_loss=2.155, generator_feat_match_loss=3.731, over 2527.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:26:53,753 INFO [train.py:811] (1/4) Start epoch 447
+2023-11-13 23:30:19,557 INFO [train.py:811] (1/4) Start epoch 448
+2023-11-13 23:31:37,758 INFO [train.py:467] (1/4) Epoch 448, batch 11, global_batch_idx: 16550, batch size: 52, loss[discriminator_loss=2.701, discriminator_real_loss=1.297, discriminator_fake_loss=1.404, generator_loss=29.88, generator_mel_loss=20.76, generator_kl_loss=1.931, generator_dur_loss=1.657, generator_adv_loss=2.109, generator_feat_match_loss=3.428, over 52.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.373, discriminator_fake_loss=1.308, generator_loss=29.95, generator_mel_loss=20.85, generator_kl_loss=1.974, generator_dur_loss=1.671, generator_adv_loss=2.055, generator_feat_match_loss=3.397, over 769.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:33:54,495 INFO [train.py:811] (1/4) Start epoch 449
+2023-11-13 23:36:23,430 INFO [train.py:467] (1/4) Epoch 449, batch 24, global_batch_idx: 16600, batch size: 126, loss[discriminator_loss=2.652, discriminator_real_loss=1.312, discriminator_fake_loss=1.34, generator_loss=30.33, generator_mel_loss=20.76, generator_kl_loss=1.981, generator_dur_loss=1.621, generator_adv_loss=2.211, generator_feat_match_loss=3.758, over 126.00 samples.], tot_loss[discriminator_loss=2.634, discriminator_real_loss=1.321, discriminator_fake_loss=1.314, generator_loss=30.17, generator_mel_loss=20.84, generator_kl_loss=1.937, generator_dur_loss=1.662, generator_adv_loss=2.111, generator_feat_match_loss=3.62, over 1849.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:36:23,432 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 23:36:34,677 INFO [train.py:517] (1/4) Epoch 449, validation: discriminator_loss=2.759, discriminator_real_loss=1.395, discriminator_fake_loss=1.364, generator_loss=31.01, generator_mel_loss=21.7, generator_kl_loss=2.131, generator_dur_loss=1.646, generator_adv_loss=1.918, generator_feat_match_loss=3.617, over 100.00 samples.
+2023-11-13 23:36:34,677 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 23:37:40,634 INFO [train.py:811] (1/4) Start epoch 450
+2023-11-13 23:41:16,071 INFO [train.py:811] (1/4) Start epoch 451
+2023-11-13 23:41:32,083 INFO [train.py:467] (1/4) Epoch 451, batch 0, global_batch_idx: 16650, batch size: 64, loss[discriminator_loss=2.664, discriminator_real_loss=1.271, discriminator_fake_loss=1.394, generator_loss=29.8, generator_mel_loss=20.77, generator_kl_loss=1.976, generator_dur_loss=1.685, generator_adv_loss=1.998, generator_feat_match_loss=3.375, over 64.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.271, discriminator_fake_loss=1.394, generator_loss=29.8, generator_mel_loss=20.77, generator_kl_loss=1.976, generator_dur_loss=1.685, generator_adv_loss=1.998, generator_feat_match_loss=3.375, over 64.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:44:48,647 INFO [train.py:811] (1/4) Start epoch 452
+2023-11-13 23:46:12,814 INFO [train.py:467] (1/4) Epoch 452, batch 13, global_batch_idx: 16700, batch size: 58, loss[discriminator_loss=2.695, discriminator_real_loss=1.546, discriminator_fake_loss=1.15, generator_loss=30.3, generator_mel_loss=20.59, generator_kl_loss=1.975, generator_dur_loss=1.689, generator_adv_loss=2.137, generator_feat_match_loss=3.908, over 58.00 samples.], tot_loss[discriminator_loss=2.603, discriminator_real_loss=1.313, discriminator_fake_loss=1.291, generator_loss=30.47, generator_mel_loss=20.87, generator_kl_loss=1.968, generator_dur_loss=1.671, generator_adv_loss=2.204, generator_feat_match_loss=3.759, over 881.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:48:14,992 INFO [train.py:811] (1/4) Start epoch 453
+2023-11-13 23:50:45,767 INFO [train.py:467] (1/4) Epoch 453, batch 26, global_batch_idx: 16750, batch size: 50, loss[discriminator_loss=2.637, discriminator_real_loss=1.459, discriminator_fake_loss=1.177, generator_loss=28.63, generator_mel_loss=19.87, generator_kl_loss=1.97, generator_dur_loss=1.684, generator_adv_loss=1.844, generator_feat_match_loss=3.27, over 50.00 samples.], tot_loss[discriminator_loss=2.599, discriminator_real_loss=1.318, discriminator_fake_loss=1.282, generator_loss=30.25, generator_mel_loss=20.8, generator_kl_loss=1.984, generator_dur_loss=1.664, generator_adv_loss=2.122, generator_feat_match_loss=3.676, over 2068.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-13 23:51:41,613 INFO [train.py:811] (1/4) Start epoch 454
+2023-11-13 23:55:12,462 INFO [train.py:811] (1/4) Start epoch 455
+2023-11-13 23:55:36,273 INFO [train.py:467] (1/4) Epoch 455, batch 2, global_batch_idx: 16800, batch size: 67, loss[discriminator_loss=2.605, discriminator_real_loss=1.304, discriminator_fake_loss=1.303, generator_loss=30.28, generator_mel_loss=21, generator_kl_loss=1.945, generator_dur_loss=1.66, generator_adv_loss=2.08, generator_feat_match_loss=3.598, over 67.00 samples.], tot_loss[discriminator_loss=2.634, discriminator_real_loss=1.33, discriminator_fake_loss=1.305, generator_loss=30.31, generator_mel_loss=21.07, generator_kl_loss=1.946, generator_dur_loss=1.659, generator_adv_loss=2.113, generator_feat_match_loss=3.52, over 187.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 32.0
+2023-11-13 23:55:36,273 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-13 23:55:47,913 INFO [train.py:517] (1/4) Epoch 455, validation: discriminator_loss=2.686, discriminator_real_loss=1.338, discriminator_fake_loss=1.347, generator_loss=30.89, generator_mel_loss=21.51, generator_kl_loss=2.185, generator_dur_loss=1.65, generator_adv_loss=1.963, generator_feat_match_loss=3.585, over 100.00 samples.
+2023-11-13 23:55:47,914 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-13 23:58:54,489 INFO [train.py:811] (1/4) Start epoch 456
+2023-11-14 00:00:36,756 INFO [train.py:467] (1/4) Epoch 456, batch 15, global_batch_idx: 16850, batch size: 153, loss[discriminator_loss=2.508, discriminator_real_loss=1.255, discriminator_fake_loss=1.254, generator_loss=30.42, generator_mel_loss=20.67, generator_kl_loss=1.981, generator_dur_loss=1.693, generator_adv_loss=2.162, generator_feat_match_loss=3.906, over 153.00 samples.], tot_loss[discriminator_loss=2.635, discriminator_real_loss=1.319, discriminator_fake_loss=1.316, generator_loss=30.25, generator_mel_loss=20.69, generator_kl_loss=1.948, generator_dur_loss=1.665, generator_adv_loss=2.147, generator_feat_match_loss=3.797, over 1167.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:02:31,770 INFO [train.py:811] (1/4) Start epoch 457
+2023-11-14 00:05:07,845 INFO [train.py:467] (1/4) Epoch 457, batch 28, global_batch_idx: 16900, batch size: 85, loss[discriminator_loss=2.729, discriminator_real_loss=1.404, discriminator_fake_loss=1.324, generator_loss=30.66, generator_mel_loss=21.18, generator_kl_loss=1.974, generator_dur_loss=1.644, generator_adv_loss=2.164, generator_feat_match_loss=3.697, over 85.00 samples.], tot_loss[discriminator_loss=2.628, discriminator_real_loss=1.339, discriminator_fake_loss=1.288, generator_loss=30.33, generator_mel_loss=20.81, generator_kl_loss=1.989, generator_dur_loss=1.666, generator_adv_loss=2.182, generator_feat_match_loss=3.683, over 2083.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:05:59,000 INFO [train.py:811] (1/4) Start epoch 458
+2023-11-14 00:09:25,602 INFO [train.py:811] (1/4) Start epoch 459
+2023-11-14 00:10:04,125 INFO [train.py:467] (1/4) Epoch 459, batch 4, global_batch_idx: 16950, batch size: 153, loss[discriminator_loss=2.578, discriminator_real_loss=1.359, discriminator_fake_loss=1.22, generator_loss=31.1, generator_mel_loss=21.15, generator_kl_loss=2.026, generator_dur_loss=1.659, generator_adv_loss=2.262, generator_feat_match_loss=3.998, over 153.00 samples.], tot_loss[discriminator_loss=2.589, discriminator_real_loss=1.327, discriminator_fake_loss=1.263, generator_loss=30.71, generator_mel_loss=21.1, generator_kl_loss=1.976, generator_dur_loss=1.656, generator_adv_loss=2.199, generator_feat_match_loss=3.781, over 441.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:12:58,642 INFO [train.py:811] (1/4) Start epoch 460
+2023-11-14 00:14:48,875 INFO [train.py:467] (1/4) Epoch 460, batch 17, global_batch_idx: 17000, batch size: 52, loss[discriminator_loss=3.066, discriminator_real_loss=1.47, discriminator_fake_loss=1.598, generator_loss=28.22, generator_mel_loss=19.94, generator_kl_loss=1.871, generator_dur_loss=1.652, generator_adv_loss=1.915, generator_feat_match_loss=2.842, over 52.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.345, discriminator_fake_loss=1.325, generator_loss=30.7, generator_mel_loss=20.82, generator_kl_loss=1.973, generator_dur_loss=1.668, generator_adv_loss=2.283, generator_feat_match_loss=3.947, over 1169.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:14:48,877 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 00:15:00,454 INFO [train.py:517] (1/4) Epoch 460, validation: discriminator_loss=2.665, discriminator_real_loss=1.441, discriminator_fake_loss=1.223, generator_loss=30.68, generator_mel_loss=21.55, generator_kl_loss=2.03, generator_dur_loss=1.65, generator_adv_loss=2.015, generator_feat_match_loss=3.434, over 100.00 samples.
+2023-11-14 00:15:00,455 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 00:16:50,806 INFO [train.py:811] (1/4) Start epoch 461
+2023-11-14 00:19:53,245 INFO [train.py:467] (1/4) Epoch 461, batch 30, global_batch_idx: 17050, batch size: 63, loss[discriminator_loss=2.621, discriminator_real_loss=1.367, discriminator_fake_loss=1.255, generator_loss=29.99, generator_mel_loss=20.76, generator_kl_loss=1.922, generator_dur_loss=1.678, generator_adv_loss=2.086, generator_feat_match_loss=3.549, over 63.00 samples.], tot_loss[discriminator_loss=2.632, discriminator_real_loss=1.335, discriminator_fake_loss=1.296, generator_loss=30.21, generator_mel_loss=20.88, generator_kl_loss=1.988, generator_dur_loss=1.664, generator_adv_loss=2.085, generator_feat_match_loss=3.586, over 2399.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:20:26,938 INFO [train.py:811] (1/4) Start epoch 462
+2023-11-14 00:23:57,285 INFO [train.py:811] (1/4) Start epoch 463
+2023-11-14 00:24:50,009 INFO [train.py:467] (1/4) Epoch 463, batch 6, global_batch_idx: 17100, batch size: 53, loss[discriminator_loss=2.5, discriminator_real_loss=1.139, discriminator_fake_loss=1.36, generator_loss=30.67, generator_mel_loss=20.78, generator_kl_loss=2.033, generator_dur_loss=1.668, generator_adv_loss=2.178, generator_feat_match_loss=4.016, over 53.00 samples.], tot_loss[discriminator_loss=2.512, discriminator_real_loss=1.226, discriminator_fake_loss=1.286, generator_loss=30.58, generator_mel_loss=20.74, generator_kl_loss=1.992, generator_dur_loss=1.674, generator_adv_loss=2.198, generator_feat_match_loss=3.98, over 479.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:27:31,320 INFO [train.py:811] (1/4) Start epoch 464
+2023-11-14 00:29:25,903 INFO [train.py:467] (1/4) Epoch 464, batch 19, global_batch_idx: 17150, batch size: 101, loss[discriminator_loss=2.555, discriminator_real_loss=1.262, discriminator_fake_loss=1.293, generator_loss=30.55, generator_mel_loss=20.84, generator_kl_loss=1.938, generator_dur_loss=1.65, generator_adv_loss=2.174, generator_feat_match_loss=3.953, over 101.00 samples.], tot_loss[discriminator_loss=2.559, discriminator_real_loss=1.289, discriminator_fake_loss=1.27, generator_loss=30.68, generator_mel_loss=20.87, generator_kl_loss=1.985, generator_dur_loss=1.668, generator_adv_loss=2.222, generator_feat_match_loss=3.936, over 1408.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:30:59,234 INFO [train.py:811] (1/4) Start epoch 465
+2023-11-14 00:34:09,571 INFO [train.py:467] (1/4) Epoch 465, batch 32, global_batch_idx: 17200, batch size: 95, loss[discriminator_loss=2.703, discriminator_real_loss=1.271, discriminator_fake_loss=1.433, generator_loss=30.45, generator_mel_loss=21.16, generator_kl_loss=1.996, generator_dur_loss=1.667, generator_adv_loss=2.09, generator_feat_match_loss=3.531, over 95.00 samples.], tot_loss[discriminator_loss=2.603, discriminator_real_loss=1.311, discriminator_fake_loss=1.292, generator_loss=30.52, generator_mel_loss=20.82, generator_kl_loss=1.963, generator_dur_loss=1.666, generator_adv_loss=2.228, generator_feat_match_loss=3.84, over 2205.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 32.0
+2023-11-14 00:34:09,573 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 00:34:20,938 INFO [train.py:517] (1/4) Epoch 465, validation: discriminator_loss=2.597, discriminator_real_loss=1.297, discriminator_fake_loss=1.3, generator_loss=31.33, generator_mel_loss=21.9, generator_kl_loss=2.121, generator_dur_loss=1.642, generator_adv_loss=2.012, generator_feat_match_loss=3.655, over 100.00 samples.
+2023-11-14 00:34:20,939 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 00:34:43,276 INFO [train.py:811] (1/4) Start epoch 466
+2023-11-14 00:38:13,452 INFO [train.py:811] (1/4) Start epoch 467
+2023-11-14 00:39:09,830 INFO [train.py:467] (1/4) Epoch 467, batch 8, global_batch_idx: 17250, batch size: 76, loss[discriminator_loss=2.574, discriminator_real_loss=1.312, discriminator_fake_loss=1.262, generator_loss=30.01, generator_mel_loss=20.52, generator_kl_loss=2.032, generator_dur_loss=1.67, generator_adv_loss=2.014, generator_feat_match_loss=3.773, over 76.00 samples.], tot_loss[discriminator_loss=2.595, discriminator_real_loss=1.315, discriminator_fake_loss=1.28, generator_loss=30.39, generator_mel_loss=20.95, generator_kl_loss=1.988, generator_dur_loss=1.665, generator_adv_loss=2.079, generator_feat_match_loss=3.704, over 683.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:41:42,602 INFO [train.py:811] (1/4) Start epoch 468
+2023-11-14 00:43:57,902 INFO [train.py:467] (1/4) Epoch 468, batch 21, global_batch_idx: 17300, batch size: 54, loss[discriminator_loss=2.553, discriminator_real_loss=1.157, discriminator_fake_loss=1.396, generator_loss=30.55, generator_mel_loss=20.74, generator_kl_loss=1.939, generator_dur_loss=1.694, generator_adv_loss=2.148, generator_feat_match_loss=4.031, over 54.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.323, discriminator_fake_loss=1.288, generator_loss=30.37, generator_mel_loss=20.61, generator_kl_loss=1.958, generator_dur_loss=1.668, generator_adv_loss=2.251, generator_feat_match_loss=3.887, over 1377.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:45:19,455 INFO [train.py:811] (1/4) Start epoch 469
+2023-11-14 00:48:44,833 INFO [train.py:467] (1/4) Epoch 469, batch 34, global_batch_idx: 17350, batch size: 49, loss[discriminator_loss=2.613, discriminator_real_loss=1.267, discriminator_fake_loss=1.347, generator_loss=30.62, generator_mel_loss=20.83, generator_kl_loss=2.077, generator_dur_loss=1.666, generator_adv_loss=2.145, generator_feat_match_loss=3.904, over 49.00 samples.], tot_loss[discriminator_loss=2.619, discriminator_real_loss=1.334, discriminator_fake_loss=1.285, generator_loss=30.21, generator_mel_loss=20.83, generator_kl_loss=1.989, generator_dur_loss=1.662, generator_adv_loss=2.093, generator_feat_match_loss=3.637, over 2475.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:48:54,011 INFO [train.py:811] (1/4) Start epoch 470
+2023-11-14 00:52:27,405 INFO [train.py:811] (1/4) Start epoch 471
+2023-11-14 00:53:43,772 INFO [train.py:467] (1/4) Epoch 471, batch 10, global_batch_idx: 17400, batch size: 52, loss[discriminator_loss=2.602, discriminator_real_loss=1.348, discriminator_fake_loss=1.253, generator_loss=30.52, generator_mel_loss=20.72, generator_kl_loss=1.994, generator_dur_loss=1.696, generator_adv_loss=2.324, generator_feat_match_loss=3.787, over 52.00 samples.], tot_loss[discriminator_loss=2.627, discriminator_real_loss=1.341, discriminator_fake_loss=1.286, generator_loss=30.61, generator_mel_loss=20.96, generator_kl_loss=1.983, generator_dur_loss=1.668, generator_adv_loss=2.22, generator_feat_match_loss=3.785, over 747.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:53:43,774 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 00:53:55,628 INFO [train.py:517] (1/4) Epoch 471, validation: discriminator_loss=2.586, discriminator_real_loss=1.263, discriminator_fake_loss=1.323, generator_loss=31.27, generator_mel_loss=21.78, generator_kl_loss=2.146, generator_dur_loss=1.636, generator_adv_loss=1.985, generator_feat_match_loss=3.723, over 100.00 samples.
+2023-11-14 00:53:55,629 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 00:56:15,785 INFO [train.py:811] (1/4) Start epoch 472
+2023-11-14 00:58:31,909 INFO [train.py:467] (1/4) Epoch 472, batch 23, global_batch_idx: 17450, batch size: 52, loss[discriminator_loss=2.605, discriminator_real_loss=1.205, discriminator_fake_loss=1.401, generator_loss=30.7, generator_mel_loss=21.18, generator_kl_loss=2.027, generator_dur_loss=1.65, generator_adv_loss=2.021, generator_feat_match_loss=3.822, over 52.00 samples.], tot_loss[discriminator_loss=2.653, discriminator_real_loss=1.349, discriminator_fake_loss=1.305, generator_loss=30.51, generator_mel_loss=21.03, generator_kl_loss=1.975, generator_dur_loss=1.662, generator_adv_loss=2.156, generator_feat_match_loss=3.686, over 1825.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 00:59:52,783 INFO [train.py:811] (1/4) Start epoch 473
+2023-11-14 01:03:24,390 INFO [train.py:467] (1/4) Epoch 473, batch 36, global_batch_idx: 17500, batch size: 59, loss[discriminator_loss=2.73, discriminator_real_loss=1.358, discriminator_fake_loss=1.373, generator_loss=30.19, generator_mel_loss=21, generator_kl_loss=2.028, generator_dur_loss=1.667, generator_adv_loss=2.059, generator_feat_match_loss=3.438, over 59.00 samples.], tot_loss[discriminator_loss=2.632, discriminator_real_loss=1.334, discriminator_fake_loss=1.298, generator_loss=30.46, generator_mel_loss=20.91, generator_kl_loss=1.984, generator_dur_loss=1.663, generator_adv_loss=2.181, generator_feat_match_loss=3.714, over 2592.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, grad_scale: 16.0
+2023-11-14 01:03:25,165 INFO [train.py:811] (1/4) Start epoch 474
+2023-11-14 01:06:51,645 INFO [train.py:811] (1/4) Start epoch 475
+2023-11-14 01:08:17,342 INFO [train.py:467] (1/4) Epoch 475, batch 12, global_batch_idx: 17550, batch size: 90, loss[discriminator_loss=2.689, discriminator_real_loss=1.189, discriminator_fake_loss=1.5, generator_loss=30.29, generator_mel_loss=21.11, generator_kl_loss=2.069, generator_dur_loss=1.668, generator_adv_loss=2.039, generator_feat_match_loss=3.406, over 90.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.337, discriminator_fake_loss=1.32, generator_loss=30.3, generator_mel_loss=20.62, generator_kl_loss=1.981, generator_dur_loss=1.665, generator_adv_loss=2.197, generator_feat_match_loss=3.838, over 1032.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:10:24,928 INFO [train.py:811] (1/4) Start epoch 476
+2023-11-14 01:13:03,733 INFO [train.py:467] (1/4) Epoch 476, batch 25, global_batch_idx: 17600, batch size: 54, loss[discriminator_loss=2.617, discriminator_real_loss=1.31, discriminator_fake_loss=1.309, generator_loss=30.01, generator_mel_loss=20.55, generator_kl_loss=2.059, generator_dur_loss=1.662, generator_adv_loss=2.029, generator_feat_match_loss=3.709, over 54.00 samples.], tot_loss[discriminator_loss=2.605, discriminator_real_loss=1.321, discriminator_fake_loss=1.284, generator_loss=30.36, generator_mel_loss=20.91, generator_kl_loss=1.994, generator_dur_loss=1.659, generator_adv_loss=2.075, generator_feat_match_loss=3.718, over 2221.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 32.0
+2023-11-14 01:13:03,735 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 01:13:14,838 INFO [train.py:517] (1/4) Epoch 476, validation: discriminator_loss=2.622, discriminator_real_loss=1.184, discriminator_fake_loss=1.438, generator_loss=31.07, generator_mel_loss=21.51, generator_kl_loss=2.189, generator_dur_loss=1.645, generator_adv_loss=1.809, generator_feat_match_loss=3.92, over 100.00 samples.
+2023-11-14 01:13:14,839 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 01:14:08,171 INFO [train.py:811] (1/4) Start epoch 477
+2023-11-14 01:17:43,031 INFO [train.py:811] (1/4) Start epoch 478
+2023-11-14 01:18:05,272 INFO [train.py:467] (1/4) Epoch 478, batch 1, global_batch_idx: 17650, batch size: 55, loss[discriminator_loss=2.551, discriminator_real_loss=1.301, discriminator_fake_loss=1.251, generator_loss=30.81, generator_mel_loss=21.13, generator_kl_loss=2.053, generator_dur_loss=1.671, generator_adv_loss=2.166, generator_feat_match_loss=3.785, over 55.00 samples.], tot_loss[discriminator_loss=2.593, discriminator_real_loss=1.34, discriminator_fake_loss=1.253, generator_loss=30.56, generator_mel_loss=21.01, generator_kl_loss=2.051, generator_dur_loss=1.677, generator_adv_loss=2.109, generator_feat_match_loss=3.713, over 122.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 32.0
+2023-11-14 01:21:20,074 INFO [train.py:811] (1/4) Start epoch 479
+2023-11-14 01:22:52,523 INFO [train.py:467] (1/4) Epoch 479, batch 14, global_batch_idx: 17700, batch size: 64, loss[discriminator_loss=2.369, discriminator_real_loss=1.234, discriminator_fake_loss=1.135, generator_loss=30.52, generator_mel_loss=19.96, generator_kl_loss=1.931, generator_dur_loss=1.657, generator_adv_loss=2.467, generator_feat_match_loss=4.5, over 64.00 samples.], tot_loss[discriminator_loss=2.596, discriminator_real_loss=1.302, discriminator_fake_loss=1.295, generator_loss=30.51, generator_mel_loss=20.62, generator_kl_loss=1.959, generator_dur_loss=1.662, generator_adv_loss=2.255, generator_feat_match_loss=4.017, over 1061.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:24:53,247 INFO [train.py:811] (1/4) Start epoch 480
+2023-11-14 01:27:37,013 INFO [train.py:467] (1/4) Epoch 480, batch 27, global_batch_idx: 17750, batch size: 55, loss[discriminator_loss=2.57, discriminator_real_loss=1.303, discriminator_fake_loss=1.269, generator_loss=29.99, generator_mel_loss=20.57, generator_kl_loss=1.912, generator_dur_loss=1.703, generator_adv_loss=2.082, generator_feat_match_loss=3.719, over 55.00 samples.], tot_loss[discriminator_loss=2.594, discriminator_real_loss=1.309, discriminator_fake_loss=1.285, generator_loss=30.12, generator_mel_loss=20.57, generator_kl_loss=1.967, generator_dur_loss=1.662, generator_adv_loss=2.15, generator_feat_match_loss=3.772, over 2150.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:28:22,258 INFO [train.py:811] (1/4) Start epoch 481
+2023-11-14 01:31:55,059 INFO [train.py:811] (1/4) Start epoch 482
+2023-11-14 01:32:24,386 INFO [train.py:467] (1/4) Epoch 482, batch 3, global_batch_idx: 17800, batch size: 101, loss[discriminator_loss=2.578, discriminator_real_loss=1.236, discriminator_fake_loss=1.342, generator_loss=31.16, generator_mel_loss=20.91, generator_kl_loss=1.934, generator_dur_loss=1.663, generator_adv_loss=2.631, generator_feat_match_loss=4.027, over 101.00 samples.], tot_loss[discriminator_loss=2.571, discriminator_real_loss=1.275, discriminator_fake_loss=1.296, generator_loss=30.7, generator_mel_loss=20.68, generator_kl_loss=1.968, generator_dur_loss=1.679, generator_adv_loss=2.343, generator_feat_match_loss=4.024, over 288.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:32:24,388 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 01:32:36,719 INFO [train.py:517] (1/4) Epoch 482, validation: discriminator_loss=2.663, discriminator_real_loss=1.5, discriminator_fake_loss=1.163, generator_loss=31.29, generator_mel_loss=21.22, generator_kl_loss=2.11, generator_dur_loss=1.642, generator_adv_loss=2.364, generator_feat_match_loss=3.958, over 100.00 samples.
+2023-11-14 01:32:36,720 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 01:35:40,156 INFO [train.py:811] (1/4) Start epoch 483
+2023-11-14 01:37:24,215 INFO [train.py:467] (1/4) Epoch 483, batch 16, global_batch_idx: 17850, batch size: 53, loss[discriminator_loss=2.715, discriminator_real_loss=1.324, discriminator_fake_loss=1.39, generator_loss=30.22, generator_mel_loss=20.87, generator_kl_loss=1.955, generator_dur_loss=1.678, generator_adv_loss=2.09, generator_feat_match_loss=3.625, over 53.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.375, discriminator_fake_loss=1.314, generator_loss=30.45, generator_mel_loss=21, generator_kl_loss=1.982, generator_dur_loss=1.665, generator_adv_loss=2.122, generator_feat_match_loss=3.677, over 1161.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:39:16,091 INFO [train.py:811] (1/4) Start epoch 484
+2023-11-14 01:41:58,349 INFO [train.py:467] (1/4) Epoch 484, batch 29, global_batch_idx: 17900, batch size: 69, loss[discriminator_loss=2.473, discriminator_real_loss=1.29, discriminator_fake_loss=1.184, generator_loss=31.58, generator_mel_loss=21.14, generator_kl_loss=1.972, generator_dur_loss=1.667, generator_adv_loss=2.248, generator_feat_match_loss=4.551, over 69.00 samples.], tot_loss[discriminator_loss=2.639, discriminator_real_loss=1.336, discriminator_fake_loss=1.303, generator_loss=30.59, generator_mel_loss=20.93, generator_kl_loss=1.965, generator_dur_loss=1.658, generator_adv_loss=2.214, generator_feat_match_loss=3.821, over 2059.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:42:42,918 INFO [train.py:811] (1/4) Start epoch 485
+2023-11-14 01:46:21,817 INFO [train.py:811] (1/4) Start epoch 486
+2023-11-14 01:47:01,157 INFO [train.py:467] (1/4) Epoch 486, batch 5, global_batch_idx: 17950, batch size: 52, loss[discriminator_loss=2.432, discriminator_real_loss=1.184, discriminator_fake_loss=1.248, generator_loss=30.77, generator_mel_loss=19.99, generator_kl_loss=2.043, generator_dur_loss=1.671, generator_adv_loss=2.627, generator_feat_match_loss=4.438, over 52.00 samples.], tot_loss[discriminator_loss=2.583, discriminator_real_loss=1.294, discriminator_fake_loss=1.289, generator_loss=30.19, generator_mel_loss=20.38, generator_kl_loss=1.976, generator_dur_loss=1.664, generator_adv_loss=2.248, generator_feat_match_loss=3.925, over 389.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:49:57,245 INFO [train.py:811] (1/4) Start epoch 487
+2023-11-14 01:51:46,745 INFO [train.py:467] (1/4) Epoch 487, batch 18, global_batch_idx: 18000, batch size: 49, loss[discriminator_loss=2.553, discriminator_real_loss=1.314, discriminator_fake_loss=1.238, generator_loss=30.28, generator_mel_loss=20.41, generator_kl_loss=1.898, generator_dur_loss=1.686, generator_adv_loss=2.297, generator_feat_match_loss=3.99, over 49.00 samples.], tot_loss[discriminator_loss=2.557, discriminator_real_loss=1.297, discriminator_fake_loss=1.26, generator_loss=30.33, generator_mel_loss=20.66, generator_kl_loss=2.01, generator_dur_loss=1.66, generator_adv_loss=2.161, generator_feat_match_loss=3.837, over 1506.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:51:46,746 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 01:51:57,851 INFO [train.py:517] (1/4) Epoch 487, validation: discriminator_loss=2.564, discriminator_real_loss=1.254, discriminator_fake_loss=1.31, generator_loss=31.05, generator_mel_loss=21.43, generator_kl_loss=2.126, generator_dur_loss=1.647, generator_adv_loss=1.936, generator_feat_match_loss=3.913, over 100.00 samples.
+2023-11-14 01:51:57,852 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 01:53:34,639 INFO [train.py:811] (1/4) Start epoch 488
+2023-11-14 01:56:43,706 INFO [train.py:467] (1/4) Epoch 488, batch 31, global_batch_idx: 18050, batch size: 67, loss[discriminator_loss=2.623, discriminator_real_loss=1.27, discriminator_fake_loss=1.354, generator_loss=30.82, generator_mel_loss=21.15, generator_kl_loss=2.022, generator_dur_loss=1.677, generator_adv_loss=2.107, generator_feat_match_loss=3.861, over 67.00 samples.], tot_loss[discriminator_loss=2.562, discriminator_real_loss=1.301, discriminator_fake_loss=1.261, generator_loss=30.44, generator_mel_loss=20.55, generator_kl_loss=1.978, generator_dur_loss=1.66, generator_adv_loss=2.238, generator_feat_match_loss=4.018, over 2366.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 01:57:11,659 INFO [train.py:811] (1/4) Start epoch 489
+2023-11-14 02:00:41,321 INFO [train.py:811] (1/4) Start epoch 490
+2023-11-14 02:01:41,363 INFO [train.py:467] (1/4) Epoch 490, batch 7, global_batch_idx: 18100, batch size: 53, loss[discriminator_loss=2.723, discriminator_real_loss=1.491, discriminator_fake_loss=1.232, generator_loss=30.72, generator_mel_loss=21.18, generator_kl_loss=1.979, generator_dur_loss=1.653, generator_adv_loss=1.98, generator_feat_match_loss=3.924, over 53.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.353, discriminator_fake_loss=1.315, generator_loss=30.89, generator_mel_loss=21.19, generator_kl_loss=2.003, generator_dur_loss=1.654, generator_adv_loss=2.151, generator_feat_match_loss=3.894, over 697.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:04:13,322 INFO [train.py:811] (1/4) Start epoch 491
+2023-11-14 02:06:06,623 INFO [train.py:467] (1/4) Epoch 491, batch 20, global_batch_idx: 18150, batch size: 111, loss[discriminator_loss=2.617, discriminator_real_loss=1.356, discriminator_fake_loss=1.261, generator_loss=30.19, generator_mel_loss=20.97, generator_kl_loss=1.912, generator_dur_loss=1.655, generator_adv_loss=2.154, generator_feat_match_loss=3.504, over 111.00 samples.], tot_loss[discriminator_loss=2.654, discriminator_real_loss=1.364, discriminator_fake_loss=1.29, generator_loss=30.16, generator_mel_loss=20.75, generator_kl_loss=1.958, generator_dur_loss=1.66, generator_adv_loss=2.142, generator_feat_match_loss=3.641, over 1356.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:07:44,589 INFO [train.py:811] (1/4) Start epoch 492
+2023-11-14 02:11:00,706 INFO [train.py:467] (1/4) Epoch 492, batch 33, global_batch_idx: 18200, batch size: 76, loss[discriminator_loss=2.648, discriminator_real_loss=1.303, discriminator_fake_loss=1.347, generator_loss=30.81, generator_mel_loss=20.9, generator_kl_loss=1.976, generator_dur_loss=1.661, generator_adv_loss=2.314, generator_feat_match_loss=3.961, over 76.00 samples.], tot_loss[discriminator_loss=2.626, discriminator_real_loss=1.327, discriminator_fake_loss=1.298, generator_loss=30.5, generator_mel_loss=20.88, generator_kl_loss=1.985, generator_dur_loss=1.658, generator_adv_loss=2.185, generator_feat_match_loss=3.79, over 2437.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:11:00,707 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 02:11:12,293 INFO [train.py:517] (1/4) Epoch 492, validation: discriminator_loss=2.553, discriminator_real_loss=1.265, discriminator_fake_loss=1.288, generator_loss=31.02, generator_mel_loss=21.31, generator_kl_loss=2.021, generator_dur_loss=1.642, generator_adv_loss=2.181, generator_feat_match_loss=3.864, over 100.00 samples.
+2023-11-14 02:11:12,294 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 02:11:30,481 INFO [train.py:811] (1/4) Start epoch 493
+2023-11-14 02:15:04,323 INFO [train.py:811] (1/4) Start epoch 494
+2023-11-14 02:16:06,161 INFO [train.py:467] (1/4) Epoch 494, batch 9, global_batch_idx: 18250, batch size: 126, loss[discriminator_loss=2.484, discriminator_real_loss=1.263, discriminator_fake_loss=1.222, generator_loss=30.96, generator_mel_loss=20.81, generator_kl_loss=2.081, generator_dur_loss=1.627, generator_adv_loss=2.199, generator_feat_match_loss=4.25, over 126.00 samples.], tot_loss[discriminator_loss=2.598, discriminator_real_loss=1.33, discriminator_fake_loss=1.267, generator_loss=30.19, generator_mel_loss=20.57, generator_kl_loss=1.995, generator_dur_loss=1.653, generator_adv_loss=2.145, generator_feat_match_loss=3.822, over 718.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:18:35,863 INFO [train.py:811] (1/4) Start epoch 495
+2023-11-14 02:20:43,503 INFO [train.py:467] (1/4) Epoch 495, batch 22, global_batch_idx: 18300, batch size: 61, loss[discriminator_loss=2.744, discriminator_real_loss=1.33, discriminator_fake_loss=1.414, generator_loss=29.61, generator_mel_loss=20.54, generator_kl_loss=1.96, generator_dur_loss=1.68, generator_adv_loss=2.082, generator_feat_match_loss=3.355, over 61.00 samples.], tot_loss[discriminator_loss=2.629, discriminator_real_loss=1.341, discriminator_fake_loss=1.287, generator_loss=30.23, generator_mel_loss=20.68, generator_kl_loss=2.003, generator_dur_loss=1.663, generator_adv_loss=2.154, generator_feat_match_loss=3.727, over 1526.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:22:06,594 INFO [train.py:811] (1/4) Start epoch 496
+2023-11-14 02:25:33,436 INFO [train.py:467] (1/4) Epoch 496, batch 35, global_batch_idx: 18350, batch size: 153, loss[discriminator_loss=2.732, discriminator_real_loss=1.512, discriminator_fake_loss=1.221, generator_loss=29.97, generator_mel_loss=20.87, generator_kl_loss=1.923, generator_dur_loss=1.651, generator_adv_loss=1.938, generator_feat_match_loss=3.588, over 153.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.338, discriminator_fake_loss=1.319, generator_loss=30.34, generator_mel_loss=20.87, generator_kl_loss=1.981, generator_dur_loss=1.659, generator_adv_loss=2.106, generator_feat_match_loss=3.73, over 2980.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:25:41,707 INFO [train.py:811] (1/4) Start epoch 497
+2023-11-14 02:29:15,810 INFO [train.py:811] (1/4) Start epoch 498
+2023-11-14 02:30:30,780 INFO [train.py:467] (1/4) Epoch 498, batch 11, global_batch_idx: 18400, batch size: 85, loss[discriminator_loss=2.785, discriminator_real_loss=1.597, discriminator_fake_loss=1.188, generator_loss=30.05, generator_mel_loss=20.6, generator_kl_loss=2.005, generator_dur_loss=1.665, generator_adv_loss=2.426, generator_feat_match_loss=3.352, over 85.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.373, discriminator_fake_loss=1.312, generator_loss=30.64, generator_mel_loss=21, generator_kl_loss=1.976, generator_dur_loss=1.656, generator_adv_loss=2.212, generator_feat_match_loss=3.798, over 850.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 32.0
+2023-11-14 02:30:30,782 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 02:30:41,818 INFO [train.py:517] (1/4) Epoch 498, validation: discriminator_loss=2.681, discriminator_real_loss=1.591, discriminator_fake_loss=1.09, generator_loss=31.44, generator_mel_loss=21.5, generator_kl_loss=2.11, generator_dur_loss=1.651, generator_adv_loss=2.465, generator_feat_match_loss=3.706, over 100.00 samples.
+2023-11-14 02:30:41,819 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 02:33:01,605 INFO [train.py:811] (1/4) Start epoch 499
+2023-11-14 02:35:21,430 INFO [train.py:467] (1/4) Epoch 499, batch 24, global_batch_idx: 18450, batch size: 55, loss[discriminator_loss=2.572, discriminator_real_loss=1.293, discriminator_fake_loss=1.279, generator_loss=29.6, generator_mel_loss=20.41, generator_kl_loss=1.941, generator_dur_loss=1.698, generator_adv_loss=2.037, generator_feat_match_loss=3.516, over 55.00 samples.], tot_loss[discriminator_loss=2.624, discriminator_real_loss=1.326, discriminator_fake_loss=1.299, generator_loss=30.3, generator_mel_loss=20.83, generator_kl_loss=1.987, generator_dur_loss=1.658, generator_adv_loss=2.077, generator_feat_match_loss=3.753, over 1897.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:36:31,809 INFO [train.py:811] (1/4) Start epoch 500
+2023-11-14 02:40:01,846 INFO [train.py:811] (1/4) Start epoch 501
+2023-11-14 02:40:20,093 INFO [train.py:467] (1/4) Epoch 501, batch 0, global_batch_idx: 18500, batch size: 65, loss[discriminator_loss=2.617, discriminator_real_loss=1.332, discriminator_fake_loss=1.284, generator_loss=30.08, generator_mel_loss=20.7, generator_kl_loss=2.004, generator_dur_loss=1.665, generator_adv_loss=2.014, generator_feat_match_loss=3.699, over 65.00 samples.], tot_loss[discriminator_loss=2.617, discriminator_real_loss=1.332, discriminator_fake_loss=1.284, generator_loss=30.08, generator_mel_loss=20.7, generator_kl_loss=2.004, generator_dur_loss=1.665, generator_adv_loss=2.014, generator_feat_match_loss=3.699, over 65.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:43:35,447 INFO [train.py:811] (1/4) Start epoch 502
+2023-11-14 02:45:08,591 INFO [train.py:467] (1/4) Epoch 502, batch 13, global_batch_idx: 18550, batch size: 85, loss[discriminator_loss=2.615, discriminator_real_loss=1.404, discriminator_fake_loss=1.211, generator_loss=29.78, generator_mel_loss=20.27, generator_kl_loss=1.998, generator_dur_loss=1.655, generator_adv_loss=2.186, generator_feat_match_loss=3.668, over 85.00 samples.], tot_loss[discriminator_loss=2.616, discriminator_real_loss=1.316, discriminator_fake_loss=1.3, generator_loss=30.41, generator_mel_loss=20.88, generator_kl_loss=1.987, generator_dur_loss=1.661, generator_adv_loss=2.117, generator_feat_match_loss=3.761, over 1180.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:47:09,841 INFO [train.py:811] (1/4) Start epoch 503
+2023-11-14 02:49:51,273 INFO [train.py:467] (1/4) Epoch 503, batch 26, global_batch_idx: 18600, batch size: 153, loss[discriminator_loss=2.623, discriminator_real_loss=1.412, discriminator_fake_loss=1.211, generator_loss=31.25, generator_mel_loss=21.27, generator_kl_loss=1.993, generator_dur_loss=1.66, generator_adv_loss=2.256, generator_feat_match_loss=4.07, over 153.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.329, discriminator_fake_loss=1.282, generator_loss=30.57, generator_mel_loss=20.82, generator_kl_loss=1.982, generator_dur_loss=1.658, generator_adv_loss=2.208, generator_feat_match_loss=3.904, over 2072.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:49:51,275 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 02:50:03,296 INFO [train.py:517] (1/4) Epoch 503, validation: discriminator_loss=2.597, discriminator_real_loss=1.343, discriminator_fake_loss=1.253, generator_loss=31.87, generator_mel_loss=21.7, generator_kl_loss=2.231, generator_dur_loss=1.642, generator_adv_loss=2.25, generator_feat_match_loss=4.045, over 100.00 samples.
+2023-11-14 02:50:03,297 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 02:50:51,866 INFO [train.py:811] (1/4) Start epoch 504
+2023-11-14 02:54:22,032 INFO [train.py:811] (1/4) Start epoch 505
+2023-11-14 02:54:50,937 INFO [train.py:467] (1/4) Epoch 505, batch 2, global_batch_idx: 18650, batch size: 126, loss[discriminator_loss=2.656, discriminator_real_loss=1.33, discriminator_fake_loss=1.325, generator_loss=30.39, generator_mel_loss=20.87, generator_kl_loss=2.006, generator_dur_loss=1.648, generator_adv_loss=2.055, generator_feat_match_loss=3.816, over 126.00 samples.], tot_loss[discriminator_loss=2.632, discriminator_real_loss=1.35, discriminator_fake_loss=1.282, generator_loss=30.35, generator_mel_loss=20.76, generator_kl_loss=2.007, generator_dur_loss=1.657, generator_adv_loss=2.063, generator_feat_match_loss=3.857, over 331.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 02:57:54,707 INFO [train.py:811] (1/4) Start epoch 506
+2023-11-14 02:59:26,017 INFO [train.py:467] (1/4) Epoch 506, batch 15, global_batch_idx: 18700, batch size: 90, loss[discriminator_loss=2.666, discriminator_real_loss=1.084, discriminator_fake_loss=1.582, generator_loss=29.99, generator_mel_loss=20.43, generator_kl_loss=1.918, generator_dur_loss=1.672, generator_adv_loss=2.174, generator_feat_match_loss=3.791, over 90.00 samples.], tot_loss[discriminator_loss=2.596, discriminator_real_loss=1.276, discriminator_fake_loss=1.32, generator_loss=30.38, generator_mel_loss=20.59, generator_kl_loss=1.956, generator_dur_loss=1.657, generator_adv_loss=2.225, generator_feat_match_loss=3.953, over 1096.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 03:01:26,325 INFO [train.py:811] (1/4) Start epoch 507
+2023-11-14 03:04:12,563 INFO [train.py:467] (1/4) Epoch 507, batch 28, global_batch_idx: 18750, batch size: 64, loss[discriminator_loss=2.979, discriminator_real_loss=1.427, discriminator_fake_loss=1.552, generator_loss=29.64, generator_mel_loss=20.5, generator_kl_loss=1.943, generator_dur_loss=1.667, generator_adv_loss=1.957, generator_feat_match_loss=3.578, over 64.00 samples.], tot_loss[discriminator_loss=2.519, discriminator_real_loss=1.265, discriminator_fake_loss=1.254, generator_loss=30.79, generator_mel_loss=20.57, generator_kl_loss=1.975, generator_dur_loss=1.659, generator_adv_loss=2.337, generator_feat_match_loss=4.256, over 2076.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 8.0
+2023-11-14 03:04:57,829 INFO [train.py:811] (1/4) Start epoch 508
+2023-11-14 03:08:34,578 INFO [train.py:811] (1/4) Start epoch 509
+2023-11-14 03:09:08,996 INFO [train.py:467] (1/4) Epoch 509, batch 4, global_batch_idx: 18800, batch size: 64, loss[discriminator_loss=2.598, discriminator_real_loss=1.207, discriminator_fake_loss=1.391, generator_loss=30.15, generator_mel_loss=20.81, generator_kl_loss=1.936, generator_dur_loss=1.681, generator_adv_loss=2.002, generator_feat_match_loss=3.725, over 64.00 samples.], tot_loss[discriminator_loss=2.601, discriminator_real_loss=1.335, discriminator_fake_loss=1.265, generator_loss=30.34, generator_mel_loss=20.76, generator_kl_loss=1.974, generator_dur_loss=1.673, generator_adv_loss=2.117, generator_feat_match_loss=3.818, over 345.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 03:09:08,998 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 03:09:20,575 INFO [train.py:517] (1/4) Epoch 509, validation: discriminator_loss=2.57, discriminator_real_loss=1.201, discriminator_fake_loss=1.368, generator_loss=30.81, generator_mel_loss=21.53, generator_kl_loss=2.057, generator_dur_loss=1.642, generator_adv_loss=1.856, generator_feat_match_loss=3.719, over 100.00 samples.
+2023-11-14 03:09:20,576 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 03:12:16,398 INFO [train.py:811] (1/4) Start epoch 510
+2023-11-14 03:14:01,916 INFO [train.py:467] (1/4) Epoch 510, batch 17, global_batch_idx: 18850, batch size: 53, loss[discriminator_loss=2.621, discriminator_real_loss=1.258, discriminator_fake_loss=1.364, generator_loss=30.16, generator_mel_loss=20.33, generator_kl_loss=2.055, generator_dur_loss=1.654, generator_adv_loss=2.479, generator_feat_match_loss=3.643, over 53.00 samples.], tot_loss[discriminator_loss=2.616, discriminator_real_loss=1.323, discriminator_fake_loss=1.293, generator_loss=30.27, generator_mel_loss=20.69, generator_kl_loss=1.981, generator_dur_loss=1.657, generator_adv_loss=2.151, generator_feat_match_loss=3.791, over 1305.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 03:15:45,582 INFO [train.py:811] (1/4) Start epoch 511
+2023-11-14 03:18:44,825 INFO [train.py:467] (1/4) Epoch 511, batch 30, global_batch_idx: 18900, batch size: 51, loss[discriminator_loss=2.693, discriminator_real_loss=1.438, discriminator_fake_loss=1.256, generator_loss=30.97, generator_mel_loss=21.46, generator_kl_loss=1.952, generator_dur_loss=1.667, generator_adv_loss=2.115, generator_feat_match_loss=3.775, over 51.00 samples.], tot_loss[discriminator_loss=2.64, discriminator_real_loss=1.343, discriminator_fake_loss=1.297, generator_loss=30.44, generator_mel_loss=20.85, generator_kl_loss=1.991, generator_dur_loss=1.662, generator_adv_loss=2.163, generator_feat_match_loss=3.767, over 2108.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 03:19:21,633 INFO [train.py:811] (1/4) Start epoch 512
+2023-11-14 03:22:51,622 INFO [train.py:811] (1/4) Start epoch 513
+2023-11-14 03:23:44,008 INFO [train.py:467] (1/4) Epoch 513, batch 6, global_batch_idx: 18950, batch size: 71, loss[discriminator_loss=2.646, discriminator_real_loss=1.3, discriminator_fake_loss=1.347, generator_loss=30.75, generator_mel_loss=20.75, generator_kl_loss=2.006, generator_dur_loss=1.642, generator_adv_loss=2.371, generator_feat_match_loss=3.982, over 71.00 samples.], tot_loss[discriminator_loss=2.627, discriminator_real_loss=1.334, discriminator_fake_loss=1.293, generator_loss=30.77, generator_mel_loss=20.92, generator_kl_loss=1.998, generator_dur_loss=1.67, generator_adv_loss=2.246, generator_feat_match_loss=3.935, over 426.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 03:26:27,629 INFO [train.py:811] (1/4) Start epoch 514
+2023-11-14 03:28:28,764 INFO [train.py:467] (1/4) Epoch 514, batch 19, global_batch_idx: 19000, batch size: 52, loss[discriminator_loss=2.613, discriminator_real_loss=1.358, discriminator_fake_loss=1.255, generator_loss=29.84, generator_mel_loss=20.16, generator_kl_loss=1.955, generator_dur_loss=1.662, generator_adv_loss=2.189, generator_feat_match_loss=3.867, over 52.00 samples.], tot_loss[discriminator_loss=2.579, discriminator_real_loss=1.291, discriminator_fake_loss=1.288, generator_loss=30.24, generator_mel_loss=20.69, generator_kl_loss=1.968, generator_dur_loss=1.653, generator_adv_loss=2.133, generator_feat_match_loss=3.795, over 1533.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 16.0
+2023-11-14 03:28:28,765 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 03:28:39,818 INFO [train.py:517] (1/4) Epoch 514, validation: discriminator_loss=2.611, discriminator_real_loss=1.291, discriminator_fake_loss=1.319, generator_loss=30.79, generator_mel_loss=21.2, generator_kl_loss=2.047, generator_dur_loss=1.646, generator_adv_loss=1.98, generator_feat_match_loss=3.917, over 100.00 samples.
+2023-11-14 03:28:39,819 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 03:30:10,426 INFO [train.py:811] (1/4) Start epoch 515
+2023-11-14 03:33:17,809 INFO [train.py:467] (1/4) Epoch 515, batch 32, global_batch_idx: 19050, batch size: 60, loss[discriminator_loss=2.43, discriminator_real_loss=1.256, discriminator_fake_loss=1.173, generator_loss=32.44, generator_mel_loss=20.9, generator_kl_loss=2.08, generator_dur_loss=1.681, generator_adv_loss=2.639, generator_feat_match_loss=5.141, over 60.00 samples.], tot_loss[discriminator_loss=2.563, discriminator_real_loss=1.296, discriminator_fake_loss=1.267, generator_loss=30.85, generator_mel_loss=20.83, generator_kl_loss=2.01, generator_dur_loss=1.663, generator_adv_loss=2.273, generator_feat_match_loss=4.072, over 2421.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 8.0
+2023-11-14 03:33:45,555 INFO [train.py:811] (1/4) Start epoch 516
+2023-11-14 03:37:17,819 INFO [train.py:811] (1/4) Start epoch 517
+2023-11-14 03:38:13,528 INFO [train.py:467] (1/4) Epoch 517, batch 8, global_batch_idx: 19100, batch size: 64, loss[discriminator_loss=2.588, discriminator_real_loss=1.33, discriminator_fake_loss=1.258, generator_loss=30.1, generator_mel_loss=20.44, generator_kl_loss=1.943, generator_dur_loss=1.683, generator_adv_loss=2.148, generator_feat_match_loss=3.881, over 64.00 samples.], tot_loss[discriminator_loss=2.606, discriminator_real_loss=1.328, discriminator_fake_loss=1.278, generator_loss=30.24, generator_mel_loss=20.69, generator_kl_loss=1.984, generator_dur_loss=1.653, generator_adv_loss=2.094, generator_feat_match_loss=3.809, over 642.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, grad_scale: 8.0
+2023-11-14 03:40:52,764 INFO [train.py:811] (1/4) Start epoch 518
+2023-11-14 03:42:59,101 INFO [train.py:467] (1/4) Epoch 518, batch 21, global_batch_idx: 19150, batch size: 52, loss[discriminator_loss=2.604, discriminator_real_loss=1.345, discriminator_fake_loss=1.259, generator_loss=30.57, generator_mel_loss=21.16, generator_kl_loss=1.911, generator_dur_loss=1.686, generator_adv_loss=2.08, generator_feat_match_loss=3.73, over 52.00 samples.], tot_loss[discriminator_loss=2.655, discriminator_real_loss=1.354, discriminator_fake_loss=1.301, generator_loss=30.28, generator_mel_loss=20.79, generator_kl_loss=1.97, generator_dur_loss=1.664, generator_adv_loss=2.111, generator_feat_match_loss=3.745, over 1538.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 03:44:30,031 INFO [train.py:811] (1/4) Start epoch 519
+2023-11-14 03:47:46,556 INFO [train.py:467] (1/4) Epoch 519, batch 34, global_batch_idx: 19200, batch size: 67, loss[discriminator_loss=2.715, discriminator_real_loss=1.428, discriminator_fake_loss=1.287, generator_loss=29.33, generator_mel_loss=20.44, generator_kl_loss=2.007, generator_dur_loss=1.671, generator_adv_loss=2.02, generator_feat_match_loss=3.191, over 67.00 samples.], tot_loss[discriminator_loss=2.618, discriminator_real_loss=1.327, discriminator_fake_loss=1.292, generator_loss=30.45, generator_mel_loss=20.76, generator_kl_loss=1.975, generator_dur_loss=1.657, generator_adv_loss=2.17, generator_feat_match_loss=3.883, over 2496.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 03:47:46,557 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 03:47:57,491 INFO [train.py:517] (1/4) Epoch 519, validation: discriminator_loss=2.64, discriminator_real_loss=1.206, discriminator_fake_loss=1.434, generator_loss=30.82, generator_mel_loss=21.42, generator_kl_loss=2.125, generator_dur_loss=1.645, generator_adv_loss=1.822, generator_feat_match_loss=3.804, over 100.00 samples.
+2023-11-14 03:47:57,492 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27170MB
+2023-11-14 03:48:06,420 INFO [train.py:811] (1/4) Start epoch 520
+2023-11-14 03:51:42,352 INFO [train.py:811] (1/4) Start epoch 521
+2023-11-14 03:52:53,630 INFO [train.py:467] (1/4) Epoch 521, batch 10, global_batch_idx: 19250, batch size: 53, loss[discriminator_loss=2.652, discriminator_real_loss=1.247, discriminator_fake_loss=1.405, generator_loss=29.69, generator_mel_loss=20.41, generator_kl_loss=1.891, generator_dur_loss=1.668, generator_adv_loss=2.082, generator_feat_match_loss=3.645, over 53.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.371, discriminator_fake_loss=1.332, generator_loss=29.94, generator_mel_loss=20.52, generator_kl_loss=1.961, generator_dur_loss=1.655, generator_adv_loss=2.114, generator_feat_match_loss=3.696, over 789.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 03:55:14,020 INFO [train.py:811] (1/4) Start epoch 522
+2023-11-14 03:57:33,690 INFO [train.py:467] (1/4) Epoch 522, batch 23, global_batch_idx: 19300, batch size: 67, loss[discriminator_loss=2.824, discriminator_real_loss=1.481, discriminator_fake_loss=1.342, generator_loss=29.95, generator_mel_loss=20.76, generator_kl_loss=2.012, generator_dur_loss=1.671, generator_adv_loss=1.942, generator_feat_match_loss=3.564, over 67.00 samples.], tot_loss[discriminator_loss=2.558, discriminator_real_loss=1.286, discriminator_fake_loss=1.272, generator_loss=30.81, generator_mel_loss=20.66, generator_kl_loss=1.989, generator_dur_loss=1.656, generator_adv_loss=2.323, generator_feat_match_loss=4.186, over 1756.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 03:58:39,340 INFO [train.py:811] (1/4) Start epoch 523
+2023-11-14 04:02:15,330 INFO [train.py:467] (1/4) Epoch 523, batch 36, global_batch_idx: 19350, batch size: 85, loss[discriminator_loss=2.641, discriminator_real_loss=1.42, discriminator_fake_loss=1.22, generator_loss=30.35, generator_mel_loss=20.7, generator_kl_loss=2.08, generator_dur_loss=1.654, generator_adv_loss=2.148, generator_feat_match_loss=3.77, over 85.00 samples.], tot_loss[discriminator_loss=2.627, discriminator_real_loss=1.335, discriminator_fake_loss=1.292, generator_loss=30.2, generator_mel_loss=20.56, generator_kl_loss=1.999, generator_dur_loss=1.659, generator_adv_loss=2.144, generator_feat_match_loss=3.84, over 2643.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 04:02:16,047 INFO [train.py:811] (1/4) Start epoch 524
+2023-11-14 04:05:43,737 INFO [train.py:811] (1/4) Start epoch 525
+2023-11-14 04:07:04,539 INFO [train.py:467] (1/4) Epoch 525, batch 12, global_batch_idx: 19400, batch size: 110, loss[discriminator_loss=2.748, discriminator_real_loss=1.363, discriminator_fake_loss=1.385, generator_loss=30.66, generator_mel_loss=21.17, generator_kl_loss=2.046, generator_dur_loss=1.663, generator_adv_loss=2.088, generator_feat_match_loss=3.695, over 110.00 samples.], tot_loss[discriminator_loss=2.622, discriminator_real_loss=1.336, discriminator_fake_loss=1.287, generator_loss=30.46, generator_mel_loss=20.84, generator_kl_loss=1.958, generator_dur_loss=1.656, generator_adv_loss=2.141, generator_feat_match_loss=3.872, over 1024.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 04:07:04,541 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 04:07:15,969 INFO [train.py:517] (1/4) Epoch 525, validation: discriminator_loss=2.768, discriminator_real_loss=1.453, discriminator_fake_loss=1.315, generator_loss=31.06, generator_mel_loss=21.68, generator_kl_loss=2.127, generator_dur_loss=1.643, generator_adv_loss=1.993, generator_feat_match_loss=3.615, over 100.00 samples.
+2023-11-14 04:07:15,970 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 04:09:28,262 INFO [train.py:811] (1/4) Start epoch 526
+2023-11-14 04:12:00,416 INFO [train.py:467] (1/4) Epoch 526, batch 25, global_batch_idx: 19450, batch size: 126, loss[discriminator_loss=2.648, discriminator_real_loss=1.276, discriminator_fake_loss=1.372, generator_loss=30.06, generator_mel_loss=20.58, generator_kl_loss=2.008, generator_dur_loss=1.646, generator_adv_loss=2.178, generator_feat_match_loss=3.645, over 126.00 samples.], tot_loss[discriminator_loss=2.645, discriminator_real_loss=1.334, discriminator_fake_loss=1.311, generator_loss=30.37, generator_mel_loss=20.81, generator_kl_loss=2, generator_dur_loss=1.656, generator_adv_loss=2.124, generator_feat_match_loss=3.783, over 1961.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 04:13:01,211 INFO [train.py:811] (1/4) Start epoch 527
+2023-11-14 04:16:32,609 INFO [train.py:811] (1/4) Start epoch 528
+2023-11-14 04:16:50,698 INFO [train.py:467] (1/4) Epoch 528, batch 1, global_batch_idx: 19500, batch size: 53, loss[discriminator_loss=2.352, discriminator_real_loss=1.157, discriminator_fake_loss=1.194, generator_loss=30.78, generator_mel_loss=20.39, generator_kl_loss=1.887, generator_dur_loss=1.686, generator_adv_loss=2.428, generator_feat_match_loss=4.391, over 53.00 samples.], tot_loss[discriminator_loss=2.403, discriminator_real_loss=1.224, discriminator_fake_loss=1.179, generator_loss=30.81, generator_mel_loss=20.44, generator_kl_loss=1.954, generator_dur_loss=1.68, generator_adv_loss=2.35, generator_feat_match_loss=4.388, over 120.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 04:20:04,809 INFO [train.py:811] (1/4) Start epoch 529
+2023-11-14 04:21:42,306 INFO [train.py:467] (1/4) Epoch 529, batch 14, global_batch_idx: 19550, batch size: 126, loss[discriminator_loss=2.537, discriminator_real_loss=1.305, discriminator_fake_loss=1.232, generator_loss=30.77, generator_mel_loss=20.79, generator_kl_loss=1.994, generator_dur_loss=1.624, generator_adv_loss=2.191, generator_feat_match_loss=4.176, over 126.00 samples.], tot_loss[discriminator_loss=2.604, discriminator_real_loss=1.318, discriminator_fake_loss=1.286, generator_loss=30.24, generator_mel_loss=20.72, generator_kl_loss=2.008, generator_dur_loss=1.657, generator_adv_loss=2.099, generator_feat_match_loss=3.763, over 1129.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 04:23:43,242 INFO [train.py:811] (1/4) Start epoch 530
+2023-11-14 04:26:23,981 INFO [train.py:467] (1/4) Epoch 530, batch 27, global_batch_idx: 19600, batch size: 58, loss[discriminator_loss=2.504, discriminator_real_loss=1.21, discriminator_fake_loss=1.294, generator_loss=30.36, generator_mel_loss=20.33, generator_kl_loss=1.974, generator_dur_loss=1.634, generator_adv_loss=2.258, generator_feat_match_loss=4.16, over 58.00 samples.], tot_loss[discriminator_loss=2.59, discriminator_real_loss=1.325, discriminator_fake_loss=1.264, generator_loss=30.6, generator_mel_loss=20.54, generator_kl_loss=1.97, generator_dur_loss=1.654, generator_adv_loss=2.26, generator_feat_match_loss=4.178, over 2135.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 32.0
+2023-11-14 04:26:23,982 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 04:26:34,813 INFO [train.py:517] (1/4) Epoch 530, validation: discriminator_loss=2.489, discriminator_real_loss=1.078, discriminator_fake_loss=1.411, generator_loss=30.36, generator_mel_loss=20.86, generator_kl_loss=2.071, generator_dur_loss=1.644, generator_adv_loss=1.811, generator_feat_match_loss=3.977, over 100.00 samples.
+2023-11-14 04:26:34,814 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 04:27:25,723 INFO [train.py:811] (1/4) Start epoch 531
+2023-11-14 04:30:54,772 INFO [train.py:811] (1/4) Start epoch 532
+2023-11-14 04:31:25,669 INFO [train.py:467] (1/4) Epoch 532, batch 3, global_batch_idx: 19650, batch size: 126, loss[discriminator_loss=2.402, discriminator_real_loss=1.139, discriminator_fake_loss=1.263, generator_loss=31.85, generator_mel_loss=20.92, generator_kl_loss=2.048, generator_dur_loss=1.633, generator_adv_loss=2.232, generator_feat_match_loss=5.023, over 126.00 samples.], tot_loss[discriminator_loss=2.647, discriminator_real_loss=1.415, discriminator_fake_loss=1.232, generator_loss=30.75, generator_mel_loss=20.61, generator_kl_loss=2.016, generator_dur_loss=1.648, generator_adv_loss=2.194, generator_feat_match_loss=4.278, over 306.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 04:34:24,354 INFO [train.py:811] (1/4) Start epoch 533
+2023-11-14 04:36:10,397 INFO [train.py:467] (1/4) Epoch 533, batch 16, global_batch_idx: 19700, batch size: 67, loss[discriminator_loss=2.59, discriminator_real_loss=1.214, discriminator_fake_loss=1.375, generator_loss=30.97, generator_mel_loss=21.04, generator_kl_loss=2.023, generator_dur_loss=1.646, generator_adv_loss=2.229, generator_feat_match_loss=4.039, over 67.00 samples.], tot_loss[discriminator_loss=2.583, discriminator_real_loss=1.315, discriminator_fake_loss=1.268, generator_loss=30.27, generator_mel_loss=20.53, generator_kl_loss=2.018, generator_dur_loss=1.656, generator_adv_loss=2.168, generator_feat_match_loss=3.896, over 1199.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 04:37:59,029 INFO [train.py:811] (1/4) Start epoch 534
+2023-11-14 04:40:49,998 INFO [train.py:467] (1/4) Epoch 534, batch 29, global_batch_idx: 19750, batch size: 50, loss[discriminator_loss=2.594, discriminator_real_loss=1.174, discriminator_fake_loss=1.419, generator_loss=30.69, generator_mel_loss=20.79, generator_kl_loss=2.008, generator_dur_loss=1.648, generator_adv_loss=2.355, generator_feat_match_loss=3.887, over 50.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.343, discriminator_fake_loss=1.307, generator_loss=30.47, generator_mel_loss=20.83, generator_kl_loss=1.997, generator_dur_loss=1.655, generator_adv_loss=2.148, generator_feat_match_loss=3.848, over 2229.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 04:41:29,292 INFO [train.py:811] (1/4) Start epoch 535
+2023-11-14 04:45:03,825 INFO [train.py:811] (1/4) Start epoch 536
+2023-11-14 04:45:44,560 INFO [train.py:467] (1/4) Epoch 536, batch 5, global_batch_idx: 19800, batch size: 69, loss[discriminator_loss=2.537, discriminator_real_loss=1.227, discriminator_fake_loss=1.311, generator_loss=31.18, generator_mel_loss=21.05, generator_kl_loss=2.088, generator_dur_loss=1.635, generator_adv_loss=2.049, generator_feat_match_loss=4.352, over 69.00 samples.], tot_loss[discriminator_loss=2.59, discriminator_real_loss=1.317, discriminator_fake_loss=1.273, generator_loss=30.3, generator_mel_loss=20.49, generator_kl_loss=1.976, generator_dur_loss=1.646, generator_adv_loss=2.186, generator_feat_match_loss=3.993, over 404.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 04:45:44,562 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 04:45:56,196 INFO [train.py:517] (1/4) Epoch 536, validation: discriminator_loss=2.601, discriminator_real_loss=1.14, discriminator_fake_loss=1.461, generator_loss=30.86, generator_mel_loss=21.6, generator_kl_loss=2.091, generator_dur_loss=1.634, generator_adv_loss=1.735, generator_feat_match_loss=3.803, over 100.00 samples.
+2023-11-14 04:45:56,197 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 04:48:52,280 INFO [train.py:811] (1/4) Start epoch 537
+2023-11-14 04:50:42,094 INFO [train.py:467] (1/4) Epoch 537, batch 18, global_batch_idx: 19850, batch size: 65, loss[discriminator_loss=2.543, discriminator_real_loss=1.305, discriminator_fake_loss=1.238, generator_loss=29.99, generator_mel_loss=20.29, generator_kl_loss=1.914, generator_dur_loss=1.648, generator_adv_loss=2.117, generator_feat_match_loss=4.016, over 65.00 samples.], tot_loss[discriminator_loss=2.56, discriminator_real_loss=1.29, discriminator_fake_loss=1.269, generator_loss=30.35, generator_mel_loss=20.49, generator_kl_loss=1.99, generator_dur_loss=1.66, generator_adv_loss=2.2, generator_feat_match_loss=4.006, over 1332.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 04:52:20,196 INFO [train.py:811] (1/4) Start epoch 538
+2023-11-14 04:55:22,333 INFO [train.py:467] (1/4) Epoch 538, batch 31, global_batch_idx: 19900, batch size: 63, loss[discriminator_loss=2.51, discriminator_real_loss=1.252, discriminator_fake_loss=1.258, generator_loss=30.77, generator_mel_loss=20.21, generator_kl_loss=1.945, generator_dur_loss=1.672, generator_adv_loss=2.291, generator_feat_match_loss=4.648, over 63.00 samples.], tot_loss[discriminator_loss=2.61, discriminator_real_loss=1.34, discriminator_fake_loss=1.27, generator_loss=30.57, generator_mel_loss=20.67, generator_kl_loss=2.002, generator_dur_loss=1.658, generator_adv_loss=2.221, generator_feat_match_loss=4.018, over 2121.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 04:55:55,149 INFO [train.py:811] (1/4) Start epoch 539
+2023-11-14 04:59:27,954 INFO [train.py:811] (1/4) Start epoch 540
+2023-11-14 05:00:28,217 INFO [train.py:467] (1/4) Epoch 540, batch 7, global_batch_idx: 19950, batch size: 126, loss[discriminator_loss=2.59, discriminator_real_loss=1.319, discriminator_fake_loss=1.27, generator_loss=30.3, generator_mel_loss=20.82, generator_kl_loss=2.025, generator_dur_loss=1.642, generator_adv_loss=2.105, generator_feat_match_loss=3.705, over 126.00 samples.], tot_loss[discriminator_loss=2.573, discriminator_real_loss=1.312, discriminator_fake_loss=1.26, generator_loss=30.23, generator_mel_loss=20.56, generator_kl_loss=2.012, generator_dur_loss=1.657, generator_adv_loss=2.109, generator_feat_match_loss=3.893, over 716.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 05:02:57,570 INFO [train.py:811] (1/4) Start epoch 541
+2023-11-14 05:05:03,669 INFO [train.py:467] (1/4) Epoch 541, batch 20, global_batch_idx: 20000, batch size: 126, loss[discriminator_loss=2.621, discriminator_real_loss=1.24, discriminator_fake_loss=1.381, generator_loss=30.73, generator_mel_loss=20.76, generator_kl_loss=2.029, generator_dur_loss=1.66, generator_adv_loss=2.227, generator_feat_match_loss=4.055, over 126.00 samples.], tot_loss[discriminator_loss=2.625, discriminator_real_loss=1.325, discriminator_fake_loss=1.3, generator_loss=30.21, generator_mel_loss=20.67, generator_kl_loss=1.983, generator_dur_loss=1.653, generator_adv_loss=2.11, generator_feat_match_loss=3.79, over 1549.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 05:05:03,671 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 05:05:14,430 INFO [train.py:517] (1/4) Epoch 541, validation: discriminator_loss=2.567, discriminator_real_loss=1.315, discriminator_fake_loss=1.253, generator_loss=30.51, generator_mel_loss=20.92, generator_kl_loss=2.065, generator_dur_loss=1.654, generator_adv_loss=2.058, generator_feat_match_loss=3.816, over 100.00 samples.
+2023-11-14 05:05:14,431 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 05:06:45,456 INFO [train.py:811] (1/4) Start epoch 542
+2023-11-14 05:10:00,809 INFO [train.py:467] (1/4) Epoch 542, batch 33, global_batch_idx: 20050, batch size: 101, loss[discriminator_loss=2.557, discriminator_real_loss=1.397, discriminator_fake_loss=1.159, generator_loss=30.48, generator_mel_loss=20.5, generator_kl_loss=1.992, generator_dur_loss=1.649, generator_adv_loss=2.43, generator_feat_match_loss=3.908, over 101.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.345, discriminator_fake_loss=1.314, generator_loss=30.41, generator_mel_loss=20.8, generator_kl_loss=1.975, generator_dur_loss=1.656, generator_adv_loss=2.15, generator_feat_match_loss=3.827, over 2499.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 05:10:17,232 INFO [train.py:811] (1/4) Start epoch 543
+2023-11-14 05:13:50,212 INFO [train.py:811] (1/4) Start epoch 544
+2023-11-14 05:14:54,315 INFO [train.py:467] (1/4) Epoch 544, batch 9, global_batch_idx: 20100, batch size: 59, loss[discriminator_loss=2.699, discriminator_real_loss=1.261, discriminator_fake_loss=1.438, generator_loss=30.21, generator_mel_loss=20.46, generator_kl_loss=1.945, generator_dur_loss=1.683, generator_adv_loss=2.389, generator_feat_match_loss=3.734, over 59.00 samples.], tot_loss[discriminator_loss=2.638, discriminator_real_loss=1.337, discriminator_fake_loss=1.302, generator_loss=30.6, generator_mel_loss=20.89, generator_kl_loss=1.954, generator_dur_loss=1.66, generator_adv_loss=2.19, generator_feat_match_loss=3.911, over 691.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 05:17:26,952 INFO [train.py:811] (1/4) Start epoch 545
+2023-11-14 05:19:42,836 INFO [train.py:467] (1/4) Epoch 545, batch 22, global_batch_idx: 20150, batch size: 85, loss[discriminator_loss=2.387, discriminator_real_loss=1.193, discriminator_fake_loss=1.192, generator_loss=31.66, generator_mel_loss=20.44, generator_kl_loss=1.886, generator_dur_loss=1.644, generator_adv_loss=2.727, generator_feat_match_loss=4.965, over 85.00 samples.], tot_loss[discriminator_loss=2.566, discriminator_real_loss=1.294, discriminator_fake_loss=1.272, generator_loss=30.72, generator_mel_loss=20.58, generator_kl_loss=1.962, generator_dur_loss=1.656, generator_adv_loss=2.335, generator_feat_match_loss=4.187, over 1659.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 05:21:02,840 INFO [train.py:811] (1/4) Start epoch 546
+2023-11-14 05:24:30,260 INFO [train.py:467] (1/4) Epoch 546, batch 35, global_batch_idx: 20200, batch size: 63, loss[discriminator_loss=2.637, discriminator_real_loss=1.301, discriminator_fake_loss=1.337, generator_loss=30.45, generator_mel_loss=20.7, generator_kl_loss=1.931, generator_dur_loss=1.672, generator_adv_loss=2.164, generator_feat_match_loss=3.984, over 63.00 samples.], tot_loss[discriminator_loss=2.612, discriminator_real_loss=1.333, discriminator_fake_loss=1.279, generator_loss=30.21, generator_mel_loss=20.46, generator_kl_loss=1.969, generator_dur_loss=1.655, generator_adv_loss=2.168, generator_feat_match_loss=3.959, over 2510.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 05:24:30,262 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 05:24:41,467 INFO [train.py:517] (1/4) Epoch 546, validation: discriminator_loss=2.577, discriminator_real_loss=1.25, discriminator_fake_loss=1.327, generator_loss=30.97, generator_mel_loss=21.25, generator_kl_loss=2.117, generator_dur_loss=1.65, generator_adv_loss=1.969, generator_feat_match_loss=3.982, over 100.00 samples.
+2023-11-14 05:24:41,468 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 05:24:46,220 INFO [train.py:811] (1/4) Start epoch 547
+2023-11-14 05:28:22,838 INFO [train.py:811] (1/4) Start epoch 548
+2023-11-14 05:29:36,698 INFO [train.py:467] (1/4) Epoch 548, batch 11, global_batch_idx: 20250, batch size: 50, loss[discriminator_loss=2.523, discriminator_real_loss=1.328, discriminator_fake_loss=1.195, generator_loss=30.59, generator_mel_loss=20.64, generator_kl_loss=1.968, generator_dur_loss=1.659, generator_adv_loss=2.305, generator_feat_match_loss=4.016, over 50.00 samples.], tot_loss[discriminator_loss=2.648, discriminator_real_loss=1.347, discriminator_fake_loss=1.301, generator_loss=30.37, generator_mel_loss=20.75, generator_kl_loss=2.021, generator_dur_loss=1.657, generator_adv_loss=2.124, generator_feat_match_loss=3.825, over 928.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 05:31:58,352 INFO [train.py:811] (1/4) Start epoch 549
+2023-11-14 05:34:19,735 INFO [train.py:467] (1/4) Epoch 549, batch 24, global_batch_idx: 20300, batch size: 79, loss[discriminator_loss=2.672, discriminator_real_loss=1.412, discriminator_fake_loss=1.261, generator_loss=30.43, generator_mel_loss=20.76, generator_kl_loss=1.981, generator_dur_loss=1.66, generator_adv_loss=2.082, generator_feat_match_loss=3.938, over 79.00 samples.], tot_loss[discriminator_loss=2.604, discriminator_real_loss=1.326, discriminator_fake_loss=1.278, generator_loss=30.45, generator_mel_loss=20.65, generator_kl_loss=1.992, generator_dur_loss=1.662, generator_adv_loss=2.197, generator_feat_match_loss=3.95, over 1672.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 05:35:25,979 INFO [train.py:811] (1/4) Start epoch 550
+2023-11-14 05:38:56,485 INFO [train.py:811] (1/4) Start epoch 551
+2023-11-14 05:39:12,513 INFO [train.py:467] (1/4) Epoch 551, batch 0, global_batch_idx: 20350, batch size: 64, loss[discriminator_loss=2.535, discriminator_real_loss=1.219, discriminator_fake_loss=1.317, generator_loss=30.76, generator_mel_loss=20.51, generator_kl_loss=2.046, generator_dur_loss=1.682, generator_adv_loss=2.508, generator_feat_match_loss=4.016, over 64.00 samples.], tot_loss[discriminator_loss=2.535, discriminator_real_loss=1.219, discriminator_fake_loss=1.317, generator_loss=30.76, generator_mel_loss=20.51, generator_kl_loss=2.046, generator_dur_loss=1.682, generator_adv_loss=2.508, generator_feat_match_loss=4.016, over 64.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 05:42:33,852 INFO [train.py:811] (1/4) Start epoch 552
+2023-11-14 05:43:59,696 INFO [train.py:467] (1/4) Epoch 552, batch 13, global_batch_idx: 20400, batch size: 53, loss[discriminator_loss=2.625, discriminator_real_loss=1.244, discriminator_fake_loss=1.381, generator_loss=30.1, generator_mel_loss=20.55, generator_kl_loss=2.099, generator_dur_loss=1.675, generator_adv_loss=2.096, generator_feat_match_loss=3.68, over 53.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.316, discriminator_fake_loss=1.295, generator_loss=30.18, generator_mel_loss=20.54, generator_kl_loss=1.99, generator_dur_loss=1.657, generator_adv_loss=2.126, generator_feat_match_loss=3.863, over 992.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 05:43:59,698 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 05:44:10,839 INFO [train.py:517] (1/4) Epoch 552, validation: discriminator_loss=2.596, discriminator_real_loss=1.278, discriminator_fake_loss=1.318, generator_loss=31.21, generator_mel_loss=21.3, generator_kl_loss=2.196, generator_dur_loss=1.634, generator_adv_loss=2.004, generator_feat_match_loss=4.074, over 100.00 samples.
+2023-11-14 05:44:10,840 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 05:46:20,088 INFO [train.py:811] (1/4) Start epoch 553
+2023-11-14 05:48:58,537 INFO [train.py:467] (1/4) Epoch 553, batch 26, global_batch_idx: 20450, batch size: 67, loss[discriminator_loss=2.496, discriminator_real_loss=1.221, discriminator_fake_loss=1.275, generator_loss=30.45, generator_mel_loss=20.85, generator_kl_loss=1.897, generator_dur_loss=1.668, generator_adv_loss=2.055, generator_feat_match_loss=3.98, over 67.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.362, discriminator_fake_loss=1.309, generator_loss=30.3, generator_mel_loss=20.62, generator_kl_loss=1.977, generator_dur_loss=1.657, generator_adv_loss=2.175, generator_feat_match_loss=3.87, over 1926.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 05:49:55,103 INFO [train.py:811] (1/4) Start epoch 554
+2023-11-14 05:53:23,238 INFO [train.py:811] (1/4) Start epoch 555
+2023-11-14 05:53:51,132 INFO [train.py:467] (1/4) Epoch 555, batch 2, global_batch_idx: 20500, batch size: 58, loss[discriminator_loss=2.744, discriminator_real_loss=1.354, discriminator_fake_loss=1.39, generator_loss=29.25, generator_mel_loss=19.9, generator_kl_loss=1.832, generator_dur_loss=1.63, generator_adv_loss=1.992, generator_feat_match_loss=3.895, over 58.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.379, discriminator_fake_loss=1.287, generator_loss=30.08, generator_mel_loss=20.35, generator_kl_loss=1.974, generator_dur_loss=1.648, generator_adv_loss=2.082, generator_feat_match_loss=4.033, over 180.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 05:56:53,165 INFO [train.py:811] (1/4) Start epoch 556
+2023-11-14 05:58:30,004 INFO [train.py:467] (1/4) Epoch 556, batch 15, global_batch_idx: 20550, batch size: 54, loss[discriminator_loss=2.438, discriminator_real_loss=1.172, discriminator_fake_loss=1.265, generator_loss=30.11, generator_mel_loss=20.04, generator_kl_loss=2.029, generator_dur_loss=1.67, generator_adv_loss=2.203, generator_feat_match_loss=4.168, over 54.00 samples.], tot_loss[discriminator_loss=2.525, discriminator_real_loss=1.278, discriminator_fake_loss=1.247, generator_loss=30.43, generator_mel_loss=20.28, generator_kl_loss=1.984, generator_dur_loss=1.663, generator_adv_loss=2.29, generator_feat_match_loss=4.214, over 978.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 06:00:26,823 INFO [train.py:811] (1/4) Start epoch 557
+2023-11-14 06:03:10,827 INFO [train.py:467] (1/4) Epoch 557, batch 28, global_batch_idx: 20600, batch size: 126, loss[discriminator_loss=2.77, discriminator_real_loss=1.515, discriminator_fake_loss=1.254, generator_loss=30.1, generator_mel_loss=20.42, generator_kl_loss=2.032, generator_dur_loss=1.632, generator_adv_loss=2.285, generator_feat_match_loss=3.73, over 126.00 samples.], tot_loss[discriminator_loss=2.574, discriminator_real_loss=1.294, discriminator_fake_loss=1.281, generator_loss=30.63, generator_mel_loss=20.48, generator_kl_loss=1.997, generator_dur_loss=1.654, generator_adv_loss=2.308, generator_feat_match_loss=4.191, over 2233.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 06:03:10,829 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 06:03:21,704 INFO [train.py:517] (1/4) Epoch 557, validation: discriminator_loss=2.709, discriminator_real_loss=1.569, discriminator_fake_loss=1.14, generator_loss=31.36, generator_mel_loss=21.37, generator_kl_loss=2.118, generator_dur_loss=1.641, generator_adv_loss=2.378, generator_feat_match_loss=3.859, over 100.00 samples.
+2023-11-14 06:03:21,705 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 06:04:05,109 INFO [train.py:811] (1/4) Start epoch 558
+2023-11-14 06:07:34,867 INFO [train.py:811] (1/4) Start epoch 559
+2023-11-14 06:08:16,279 INFO [train.py:467] (1/4) Epoch 559, batch 4, global_batch_idx: 20650, batch size: 90, loss[discriminator_loss=2.633, discriminator_real_loss=1.386, discriminator_fake_loss=1.247, generator_loss=30.28, generator_mel_loss=20.52, generator_kl_loss=1.962, generator_dur_loss=1.649, generator_adv_loss=2.262, generator_feat_match_loss=3.883, over 90.00 samples.], tot_loss[discriminator_loss=2.569, discriminator_real_loss=1.317, discriminator_fake_loss=1.252, generator_loss=30.14, generator_mel_loss=20.44, generator_kl_loss=1.978, generator_dur_loss=1.653, generator_adv_loss=2.164, generator_feat_match_loss=3.908, over 354.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 16.0
+2023-11-14 06:11:05,009 INFO [train.py:811] (1/4) Start epoch 560
+2023-11-14 06:12:51,399 INFO [train.py:467] (1/4) Epoch 560, batch 17, global_batch_idx: 20700, batch size: 52, loss[discriminator_loss=2.547, discriminator_real_loss=1.257, discriminator_fake_loss=1.289, generator_loss=30.21, generator_mel_loss=19.94, generator_kl_loss=1.998, generator_dur_loss=1.644, generator_adv_loss=2.471, generator_feat_match_loss=4.156, over 52.00 samples.], tot_loss[discriminator_loss=2.612, discriminator_real_loss=1.308, discriminator_fake_loss=1.303, generator_loss=30.72, generator_mel_loss=20.71, generator_kl_loss=1.99, generator_dur_loss=1.656, generator_adv_loss=2.241, generator_feat_match_loss=4.121, over 1207.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, grad_scale: 8.0
+2023-11-14 06:14:39,634 INFO [train.py:811] (1/4) Start epoch 561
+2023-11-14 06:17:38,127 INFO [train.py:467] (1/4) Epoch 561, batch 30, global_batch_idx: 20750, batch size: 79, loss[discriminator_loss=2.512, discriminator_real_loss=1.325, discriminator_fake_loss=1.187, generator_loss=30.09, generator_mel_loss=20.57, generator_kl_loss=1.961, generator_dur_loss=1.657, generator_adv_loss=1.967, generator_feat_match_loss=3.928, over 79.00 samples.], tot_loss[discriminator_loss=2.546, discriminator_real_loss=1.293, discriminator_fake_loss=1.253, generator_loss=30.55, generator_mel_loss=20.54, generator_kl_loss=2.015, generator_dur_loss=1.651, generator_adv_loss=2.227, generator_feat_match_loss=4.114, over 2324.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 06:18:15,787 INFO [train.py:811] (1/4) Start epoch 562
+2023-11-14 06:21:45,284 INFO [train.py:811] (1/4) Start epoch 563
+2023-11-14 06:22:31,615 INFO [train.py:467] (1/4) Epoch 563, batch 6, global_batch_idx: 20800, batch size: 67, loss[discriminator_loss=2.832, discriminator_real_loss=1.637, discriminator_fake_loss=1.195, generator_loss=30.05, generator_mel_loss=20.67, generator_kl_loss=1.967, generator_dur_loss=1.644, generator_adv_loss=2.248, generator_feat_match_loss=3.52, over 67.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.41, discriminator_fake_loss=1.305, generator_loss=30.8, generator_mel_loss=20.74, generator_kl_loss=1.999, generator_dur_loss=1.654, generator_adv_loss=2.333, generator_feat_match_loss=4.079, over 456.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 06:22:31,617 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 06:22:43,366 INFO [train.py:517] (1/4) Epoch 563, validation: discriminator_loss=2.729, discriminator_real_loss=1.466, discriminator_fake_loss=1.263, generator_loss=30.51, generator_mel_loss=21, generator_kl_loss=2.106, generator_dur_loss=1.645, generator_adv_loss=2.2, generator_feat_match_loss=3.556, over 100.00 samples.
+2023-11-14 06:22:43,367 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 06:25:33,259 INFO [train.py:811] (1/4) Start epoch 564
+2023-11-14 06:27:34,769 INFO [train.py:467] (1/4) Epoch 564, batch 19, global_batch_idx: 20850, batch size: 58, loss[discriminator_loss=2.57, discriminator_real_loss=1.264, discriminator_fake_loss=1.306, generator_loss=30.61, generator_mel_loss=20.9, generator_kl_loss=2.075, generator_dur_loss=1.639, generator_adv_loss=2.164, generator_feat_match_loss=3.84, over 58.00 samples.], tot_loss[discriminator_loss=2.626, discriminator_real_loss=1.34, discriminator_fake_loss=1.286, generator_loss=30.37, generator_mel_loss=20.67, generator_kl_loss=2.027, generator_dur_loss=1.661, generator_adv_loss=2.141, generator_feat_match_loss=3.873, over 1418.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 06:29:06,285 INFO [train.py:811] (1/4) Start epoch 565
+2023-11-14 06:32:14,072 INFO [train.py:467] (1/4) Epoch 565, batch 32, global_batch_idx: 20900, batch size: 64, loss[discriminator_loss=2.658, discriminator_real_loss=1.435, discriminator_fake_loss=1.224, generator_loss=30.21, generator_mel_loss=20.13, generator_kl_loss=1.994, generator_dur_loss=1.657, generator_adv_loss=2.322, generator_feat_match_loss=4.102, over 64.00 samples.], tot_loss[discriminator_loss=2.612, discriminator_real_loss=1.36, discriminator_fake_loss=1.252, generator_loss=30.5, generator_mel_loss=20.34, generator_kl_loss=1.985, generator_dur_loss=1.658, generator_adv_loss=2.307, generator_feat_match_loss=4.208, over 2375.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 06:32:37,823 INFO [train.py:811] (1/4) Start epoch 566
+2023-11-14 06:36:06,473 INFO [train.py:811] (1/4) Start epoch 567
+2023-11-14 06:37:03,315 INFO [train.py:467] (1/4) Epoch 567, batch 8, global_batch_idx: 20950, batch size: 85, loss[discriminator_loss=2.633, discriminator_real_loss=1.367, discriminator_fake_loss=1.266, generator_loss=29.73, generator_mel_loss=20.32, generator_kl_loss=2.049, generator_dur_loss=1.625, generator_adv_loss=2.195, generator_feat_match_loss=3.545, over 85.00 samples.], tot_loss[discriminator_loss=2.643, discriminator_real_loss=1.343, discriminator_fake_loss=1.3, generator_loss=30.15, generator_mel_loss=20.54, generator_kl_loss=1.999, generator_dur_loss=1.654, generator_adv_loss=2.121, generator_feat_match_loss=3.837, over 663.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 06:39:37,648 INFO [train.py:811] (1/4) Start epoch 568
+2023-11-14 06:41:45,535 INFO [train.py:467] (1/4) Epoch 568, batch 21, global_batch_idx: 21000, batch size: 67, loss[discriminator_loss=2.531, discriminator_real_loss=1.321, discriminator_fake_loss=1.21, generator_loss=30.48, generator_mel_loss=20.37, generator_kl_loss=2.074, generator_dur_loss=1.666, generator_adv_loss=2.293, generator_feat_match_loss=4.074, over 67.00 samples.], tot_loss[discriminator_loss=2.617, discriminator_real_loss=1.327, discriminator_fake_loss=1.29, generator_loss=30.4, generator_mel_loss=20.66, generator_kl_loss=2.005, generator_dur_loss=1.654, generator_adv_loss=2.15, generator_feat_match_loss=3.926, over 1577.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 06:41:45,537 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 06:41:56,461 INFO [train.py:517] (1/4) Epoch 568, validation: discriminator_loss=2.506, discriminator_real_loss=1.235, discriminator_fake_loss=1.271, generator_loss=30.96, generator_mel_loss=21.07, generator_kl_loss=2.146, generator_dur_loss=1.636, generator_adv_loss=2.12, generator_feat_match_loss=3.986, over 100.00 samples.
+2023-11-14 06:41:56,462 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 06:43:21,338 INFO [train.py:811] (1/4) Start epoch 569
+2023-11-14 06:46:45,349 INFO [train.py:467] (1/4) Epoch 569, batch 34, global_batch_idx: 21050, batch size: 79, loss[discriminator_loss=2.639, discriminator_real_loss=1.285, discriminator_fake_loss=1.354, generator_loss=29.98, generator_mel_loss=20.38, generator_kl_loss=1.966, generator_dur_loss=1.661, generator_adv_loss=2.043, generator_feat_match_loss=3.936, over 79.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.318, generator_loss=30.41, generator_mel_loss=20.72, generator_kl_loss=2, generator_dur_loss=1.658, generator_adv_loss=2.162, generator_feat_match_loss=3.868, over 2544.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 06:46:55,734 INFO [train.py:811] (1/4) Start epoch 570
+2023-11-14 06:50:27,859 INFO [train.py:811] (1/4) Start epoch 571
+2023-11-14 06:51:46,425 INFO [train.py:467] (1/4) Epoch 571, batch 10, global_batch_idx: 21100, batch size: 153, loss[discriminator_loss=2.43, discriminator_real_loss=1.308, discriminator_fake_loss=1.121, generator_loss=31.88, generator_mel_loss=20.77, generator_kl_loss=2.049, generator_dur_loss=1.65, generator_adv_loss=2.555, generator_feat_match_loss=4.855, over 153.00 samples.], tot_loss[discriminator_loss=2.54, discriminator_real_loss=1.282, discriminator_fake_loss=1.258, generator_loss=30.89, generator_mel_loss=20.39, generator_kl_loss=1.983, generator_dur_loss=1.648, generator_adv_loss=2.387, generator_feat_match_loss=4.475, over 1008.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 06:53:56,989 INFO [train.py:811] (1/4) Start epoch 572
+2023-11-14 06:56:21,875 INFO [train.py:467] (1/4) Epoch 572, batch 23, global_batch_idx: 21150, batch size: 50, loss[discriminator_loss=2.352, discriminator_real_loss=1.241, discriminator_fake_loss=1.11, generator_loss=31.13, generator_mel_loss=20.33, generator_kl_loss=2.023, generator_dur_loss=1.641, generator_adv_loss=2.553, generator_feat_match_loss=4.578, over 50.00 samples.], tot_loss[discriminator_loss=2.507, discriminator_real_loss=1.274, discriminator_fake_loss=1.233, generator_loss=30.84, generator_mel_loss=20.25, generator_kl_loss=2.012, generator_dur_loss=1.651, generator_adv_loss=2.392, generator_feat_match_loss=4.534, over 1772.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 06:57:31,585 INFO [train.py:811] (1/4) Start epoch 573
+2023-11-14 07:00:53,134 INFO [train.py:467] (1/4) Epoch 573, batch 36, global_batch_idx: 21200, batch size: 73, loss[discriminator_loss=2.682, discriminator_real_loss=1.342, discriminator_fake_loss=1.34, generator_loss=30.55, generator_mel_loss=20.74, generator_kl_loss=2.028, generator_dur_loss=1.674, generator_adv_loss=2.213, generator_feat_match_loss=3.895, over 73.00 samples.], tot_loss[discriminator_loss=2.61, discriminator_real_loss=1.323, discriminator_fake_loss=1.287, generator_loss=30.2, generator_mel_loss=20.48, generator_kl_loss=1.998, generator_dur_loss=1.653, generator_adv_loss=2.131, generator_feat_match_loss=3.939, over 2560.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:00:53,136 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 07:01:04,807 INFO [train.py:517] (1/4) Epoch 573, validation: discriminator_loss=2.648, discriminator_real_loss=1.389, discriminator_fake_loss=1.26, generator_loss=30.9, generator_mel_loss=21.23, generator_kl_loss=2.15, generator_dur_loss=1.635, generator_adv_loss=2.081, generator_feat_match_loss=3.801, over 100.00 samples.
+2023-11-14 07:01:04,808 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 07:01:05,445 INFO [train.py:811] (1/4) Start epoch 574
+2023-11-14 07:04:36,784 INFO [train.py:811] (1/4) Start epoch 575
+2023-11-14 07:05:59,243 INFO [train.py:467] (1/4) Epoch 575, batch 12, global_batch_idx: 21250, batch size: 76, loss[discriminator_loss=2.465, discriminator_real_loss=1.234, discriminator_fake_loss=1.229, generator_loss=30.87, generator_mel_loss=20.62, generator_kl_loss=2.025, generator_dur_loss=1.671, generator_adv_loss=2.363, generator_feat_match_loss=4.188, over 76.00 samples.], tot_loss[discriminator_loss=2.475, discriminator_real_loss=1.26, discriminator_fake_loss=1.215, generator_loss=31.49, generator_mel_loss=20.82, generator_kl_loss=2.025, generator_dur_loss=1.654, generator_adv_loss=2.438, generator_feat_match_loss=4.548, over 1023.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:08:07,875 INFO [train.py:811] (1/4) Start epoch 576
+2023-11-14 07:10:37,017 INFO [train.py:467] (1/4) Epoch 576, batch 25, global_batch_idx: 21300, batch size: 60, loss[discriminator_loss=2.631, discriminator_real_loss=1.387, discriminator_fake_loss=1.244, generator_loss=29.85, generator_mel_loss=20.56, generator_kl_loss=1.948, generator_dur_loss=1.66, generator_adv_loss=1.936, generator_feat_match_loss=3.748, over 60.00 samples.], tot_loss[discriminator_loss=2.603, discriminator_real_loss=1.305, discriminator_fake_loss=1.299, generator_loss=30.3, generator_mel_loss=20.49, generator_kl_loss=2.006, generator_dur_loss=1.651, generator_adv_loss=2.164, generator_feat_match_loss=3.988, over 1921.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:11:43,761 INFO [train.py:811] (1/4) Start epoch 577
+2023-11-14 07:15:09,249 INFO [train.py:811] (1/4) Start epoch 578
+2023-11-14 07:15:29,915 INFO [train.py:467] (1/4) Epoch 578, batch 1, global_batch_idx: 21350, batch size: 65, loss[discriminator_loss=2.732, discriminator_real_loss=1.389, discriminator_fake_loss=1.344, generator_loss=29.51, generator_mel_loss=20.29, generator_kl_loss=2.015, generator_dur_loss=1.658, generator_adv_loss=2.025, generator_feat_match_loss=3.523, over 65.00 samples.], tot_loss[discriminator_loss=2.625, discriminator_real_loss=1.324, discriminator_fake_loss=1.301, generator_loss=29.87, generator_mel_loss=20.32, generator_kl_loss=1.986, generator_dur_loss=1.648, generator_adv_loss=2.167, generator_feat_match_loss=3.748, over 136.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:18:39,442 INFO [train.py:811] (1/4) Start epoch 579
+2023-11-14 07:20:15,430 INFO [train.py:467] (1/4) Epoch 579, batch 14, global_batch_idx: 21400, batch size: 54, loss[discriminator_loss=2.477, discriminator_real_loss=1.125, discriminator_fake_loss=1.352, generator_loss=30.22, generator_mel_loss=20.12, generator_kl_loss=1.908, generator_dur_loss=1.653, generator_adv_loss=2.221, generator_feat_match_loss=4.32, over 54.00 samples.], tot_loss[discriminator_loss=2.596, discriminator_real_loss=1.319, discriminator_fake_loss=1.277, generator_loss=30.06, generator_mel_loss=20.25, generator_kl_loss=1.975, generator_dur_loss=1.653, generator_adv_loss=2.215, generator_feat_match_loss=3.968, over 987.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:20:15,432 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 07:20:26,425 INFO [train.py:517] (1/4) Epoch 579, validation: discriminator_loss=2.57, discriminator_real_loss=1.246, discriminator_fake_loss=1.323, generator_loss=30.95, generator_mel_loss=21.05, generator_kl_loss=2.034, generator_dur_loss=1.638, generator_adv_loss=1.944, generator_feat_match_loss=4.283, over 100.00 samples.
+2023-11-14 07:20:26,426 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 07:22:25,674 INFO [train.py:811] (1/4) Start epoch 580
+2023-11-14 07:25:00,806 INFO [train.py:467] (1/4) Epoch 580, batch 27, global_batch_idx: 21450, batch size: 126, loss[discriminator_loss=2.52, discriminator_real_loss=1.247, discriminator_fake_loss=1.272, generator_loss=31.03, generator_mel_loss=20.91, generator_kl_loss=1.993, generator_dur_loss=1.632, generator_adv_loss=2.254, generator_feat_match_loss=4.242, over 126.00 samples.], tot_loss[discriminator_loss=2.586, discriminator_real_loss=1.312, discriminator_fake_loss=1.274, generator_loss=30.42, generator_mel_loss=20.61, generator_kl_loss=2.015, generator_dur_loss=1.655, generator_adv_loss=2.14, generator_feat_match_loss=3.997, over 1968.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:25:54,015 INFO [train.py:811] (1/4) Start epoch 581
+2023-11-14 07:29:22,451 INFO [train.py:811] (1/4) Start epoch 582
+2023-11-14 07:29:59,159 INFO [train.py:467] (1/4) Epoch 582, batch 3, global_batch_idx: 21500, batch size: 61, loss[discriminator_loss=2.648, discriminator_real_loss=1.184, discriminator_fake_loss=1.466, generator_loss=29.65, generator_mel_loss=20.07, generator_kl_loss=2.042, generator_dur_loss=1.642, generator_adv_loss=1.991, generator_feat_match_loss=3.902, over 61.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.389, discriminator_fake_loss=1.286, generator_loss=29.77, generator_mel_loss=20.43, generator_kl_loss=2.044, generator_dur_loss=1.64, generator_adv_loss=1.945, generator_feat_match_loss=3.72, over 323.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:32:55,233 INFO [train.py:811] (1/4) Start epoch 583
+2023-11-14 07:34:38,771 INFO [train.py:467] (1/4) Epoch 583, batch 16, global_batch_idx: 21550, batch size: 49, loss[discriminator_loss=2.52, discriminator_real_loss=1.238, discriminator_fake_loss=1.282, generator_loss=30.4, generator_mel_loss=20.38, generator_kl_loss=1.954, generator_dur_loss=1.671, generator_adv_loss=2.273, generator_feat_match_loss=4.125, over 49.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.312, discriminator_fake_loss=1.299, generator_loss=30.2, generator_mel_loss=20.37, generator_kl_loss=1.985, generator_dur_loss=1.653, generator_adv_loss=2.16, generator_feat_match_loss=4.033, over 1113.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:36:28,787 INFO [train.py:811] (1/4) Start epoch 584
+2023-11-14 07:39:14,230 INFO [train.py:467] (1/4) Epoch 584, batch 29, global_batch_idx: 21600, batch size: 69, loss[discriminator_loss=2.867, discriminator_real_loss=1.685, discriminator_fake_loss=1.183, generator_loss=30.4, generator_mel_loss=20.63, generator_kl_loss=1.899, generator_dur_loss=1.671, generator_adv_loss=2.381, generator_feat_match_loss=3.822, over 69.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.313, discriminator_fake_loss=1.257, generator_loss=30.62, generator_mel_loss=20.46, generator_kl_loss=2.006, generator_dur_loss=1.652, generator_adv_loss=2.275, generator_feat_match_loss=4.225, over 2077.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:39:14,232 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 07:39:25,422 INFO [train.py:517] (1/4) Epoch 584, validation: discriminator_loss=2.718, discriminator_real_loss=1.526, discriminator_fake_loss=1.192, generator_loss=31.35, generator_mel_loss=21.09, generator_kl_loss=2.166, generator_dur_loss=1.646, generator_adv_loss=2.405, generator_feat_match_loss=4.042, over 100.00 samples.
+2023-11-14 07:39:25,423 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27179MB
+2023-11-14 07:40:06,997 INFO [train.py:811] (1/4) Start epoch 585
+2023-11-14 07:43:38,618 INFO [train.py:811] (1/4) Start epoch 586
+2023-11-14 07:44:22,599 INFO [train.py:467] (1/4) Epoch 586, batch 5, global_batch_idx: 21650, batch size: 85, loss[discriminator_loss=2.566, discriminator_real_loss=1.333, discriminator_fake_loss=1.233, generator_loss=29.81, generator_mel_loss=20.28, generator_kl_loss=1.994, generator_dur_loss=1.624, generator_adv_loss=2.107, generator_feat_match_loss=3.801, over 85.00 samples.], tot_loss[discriminator_loss=2.606, discriminator_real_loss=1.328, discriminator_fake_loss=1.277, generator_loss=30.01, generator_mel_loss=20.4, generator_kl_loss=2.039, generator_dur_loss=1.66, generator_adv_loss=2.085, generator_feat_match_loss=3.828, over 473.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:47:06,638 INFO [train.py:811] (1/4) Start epoch 587
+2023-11-14 07:48:54,944 INFO [train.py:467] (1/4) Epoch 587, batch 18, global_batch_idx: 21700, batch size: 81, loss[discriminator_loss=2.613, discriminator_real_loss=1.287, discriminator_fake_loss=1.326, generator_loss=31.06, generator_mel_loss=20.75, generator_kl_loss=2.062, generator_dur_loss=1.664, generator_adv_loss=2.23, generator_feat_match_loss=4.352, over 81.00 samples.], tot_loss[discriminator_loss=2.608, discriminator_real_loss=1.329, discriminator_fake_loss=1.279, generator_loss=30.65, generator_mel_loss=20.68, generator_kl_loss=2.012, generator_dur_loss=1.652, generator_adv_loss=2.225, generator_feat_match_loss=4.077, over 1322.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 07:50:37,657 INFO [train.py:811] (1/4) Start epoch 588
+2023-11-14 07:53:36,493 INFO [train.py:467] (1/4) Epoch 588, batch 31, global_batch_idx: 21750, batch size: 59, loss[discriminator_loss=2.621, discriminator_real_loss=1.408, discriminator_fake_loss=1.213, generator_loss=29.99, generator_mel_loss=20.22, generator_kl_loss=2.067, generator_dur_loss=1.663, generator_adv_loss=2.199, generator_feat_match_loss=3.842, over 59.00 samples.], tot_loss[discriminator_loss=2.564, discriminator_real_loss=1.303, discriminator_fake_loss=1.261, generator_loss=30.58, generator_mel_loss=20.45, generator_kl_loss=2.006, generator_dur_loss=1.65, generator_adv_loss=2.283, generator_feat_match_loss=4.191, over 2338.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 07:54:08,852 INFO [train.py:811] (1/4) Start epoch 589
+2023-11-14 07:57:40,801 INFO [train.py:811] (1/4) Start epoch 590
+2023-11-14 07:58:33,761 INFO [train.py:467] (1/4) Epoch 590, batch 7, global_batch_idx: 21800, batch size: 50, loss[discriminator_loss=2.646, discriminator_real_loss=1.154, discriminator_fake_loss=1.492, generator_loss=28.38, generator_mel_loss=19.42, generator_kl_loss=1.866, generator_dur_loss=1.683, generator_adv_loss=2.055, generator_feat_match_loss=3.357, over 50.00 samples.], tot_loss[discriminator_loss=2.557, discriminator_real_loss=1.264, discriminator_fake_loss=1.292, generator_loss=30.55, generator_mel_loss=20.27, generator_kl_loss=2.019, generator_dur_loss=1.655, generator_adv_loss=2.342, generator_feat_match_loss=4.263, over 525.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 07:58:33,762 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 07:58:45,635 INFO [train.py:517] (1/4) Epoch 590, validation: discriminator_loss=2.695, discriminator_real_loss=1.171, discriminator_fake_loss=1.524, generator_loss=29.96, generator_mel_loss=20.78, generator_kl_loss=2.099, generator_dur_loss=1.633, generator_adv_loss=1.747, generator_feat_match_loss=3.7, over 100.00 samples.
+2023-11-14 07:58:45,636 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 08:01:21,371 INFO [train.py:811] (1/4) Start epoch 591
+2023-11-14 08:03:28,928 INFO [train.py:467] (1/4) Epoch 591, batch 20, global_batch_idx: 21850, batch size: 153, loss[discriminator_loss=2.379, discriminator_real_loss=1.17, discriminator_fake_loss=1.209, generator_loss=31.9, generator_mel_loss=20.41, generator_kl_loss=2.035, generator_dur_loss=1.64, generator_adv_loss=2.607, generator_feat_match_loss=5.211, over 153.00 samples.], tot_loss[discriminator_loss=2.554, discriminator_real_loss=1.289, discriminator_fake_loss=1.265, generator_loss=30.73, generator_mel_loss=20.44, generator_kl_loss=2.009, generator_dur_loss=1.653, generator_adv_loss=2.292, generator_feat_match_loss=4.335, over 1593.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 08:04:53,771 INFO [train.py:811] (1/4) Start epoch 592
+2023-11-14 08:08:10,889 INFO [train.py:467] (1/4) Epoch 592, batch 33, global_batch_idx: 21900, batch size: 85, loss[discriminator_loss=2.555, discriminator_real_loss=1.36, discriminator_fake_loss=1.195, generator_loss=30.24, generator_mel_loss=20.37, generator_kl_loss=1.965, generator_dur_loss=1.632, generator_adv_loss=2.201, generator_feat_match_loss=4.078, over 85.00 samples.], tot_loss[discriminator_loss=2.586, discriminator_real_loss=1.31, discriminator_fake_loss=1.276, generator_loss=30.41, generator_mel_loss=20.36, generator_kl_loss=1.993, generator_dur_loss=1.647, generator_adv_loss=2.222, generator_feat_match_loss=4.18, over 2523.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 08:08:27,603 INFO [train.py:811] (1/4) Start epoch 593
+2023-11-14 08:12:00,236 INFO [train.py:811] (1/4) Start epoch 594
+2023-11-14 08:13:06,947 INFO [train.py:467] (1/4) Epoch 594, batch 9, global_batch_idx: 21950, batch size: 153, loss[discriminator_loss=2.625, discriminator_real_loss=1.16, discriminator_fake_loss=1.465, generator_loss=29.98, generator_mel_loss=20.18, generator_kl_loss=1.972, generator_dur_loss=1.625, generator_adv_loss=2.297, generator_feat_match_loss=3.906, over 153.00 samples.], tot_loss[discriminator_loss=2.423, discriminator_real_loss=1.226, discriminator_fake_loss=1.197, generator_loss=31.34, generator_mel_loss=20.38, generator_kl_loss=1.964, generator_dur_loss=1.645, generator_adv_loss=2.445, generator_feat_match_loss=4.899, over 840.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 8.0
+2023-11-14 08:15:28,829 INFO [train.py:811] (1/4) Start epoch 595
+2023-11-14 08:17:43,041 INFO [train.py:467] (1/4) Epoch 595, batch 22, global_batch_idx: 22000, batch size: 69, loss[discriminator_loss=2.66, discriminator_real_loss=1.329, discriminator_fake_loss=1.331, generator_loss=29.49, generator_mel_loss=20.2, generator_kl_loss=2.01, generator_dur_loss=1.675, generator_adv_loss=1.994, generator_feat_match_loss=3.619, over 69.00 samples.], tot_loss[discriminator_loss=2.633, discriminator_real_loss=1.334, discriminator_fake_loss=1.299, generator_loss=30.16, generator_mel_loss=20.53, generator_kl_loss=2.011, generator_dur_loss=1.649, generator_adv_loss=2.099, generator_feat_match_loss=3.871, over 1637.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 08:17:43,043 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 08:17:54,308 INFO [train.py:517] (1/4) Epoch 595, validation: discriminator_loss=2.616, discriminator_real_loss=1.26, discriminator_fake_loss=1.356, generator_loss=30.43, generator_mel_loss=21.16, generator_kl_loss=2.069, generator_dur_loss=1.636, generator_adv_loss=1.879, generator_feat_match_loss=3.686, over 100.00 samples.
+2023-11-14 08:17:54,308 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 08:19:07,881 INFO [train.py:811] (1/4) Start epoch 596
+2023-11-14 08:22:33,002 INFO [train.py:467] (1/4) Epoch 596, batch 35, global_batch_idx: 22050, batch size: 65, loss[discriminator_loss=2.668, discriminator_real_loss=1.395, discriminator_fake_loss=1.272, generator_loss=29.9, generator_mel_loss=20.41, generator_kl_loss=2.028, generator_dur_loss=1.669, generator_adv_loss=2.082, generator_feat_match_loss=3.717, over 65.00 samples.], tot_loss[discriminator_loss=2.63, discriminator_real_loss=1.329, discriminator_fake_loss=1.301, generator_loss=30.31, generator_mel_loss=20.56, generator_kl_loss=2.007, generator_dur_loss=1.653, generator_adv_loss=2.148, generator_feat_match_loss=3.939, over 2598.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 08:22:38,225 INFO [train.py:811] (1/4) Start epoch 597
+2023-11-14 08:26:09,014 INFO [train.py:811] (1/4) Start epoch 598
+2023-11-14 08:27:25,807 INFO [train.py:467] (1/4) Epoch 598, batch 11, global_batch_idx: 22100, batch size: 110, loss[discriminator_loss=2.693, discriminator_real_loss=1.349, discriminator_fake_loss=1.345, generator_loss=30.23, generator_mel_loss=20.77, generator_kl_loss=2.078, generator_dur_loss=1.658, generator_adv_loss=1.872, generator_feat_match_loss=3.85, over 110.00 samples.], tot_loss[discriminator_loss=2.64, discriminator_real_loss=1.321, discriminator_fake_loss=1.319, generator_loss=30.46, generator_mel_loss=20.71, generator_kl_loss=2.004, generator_dur_loss=1.646, generator_adv_loss=2.13, generator_feat_match_loss=3.973, over 1031.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 08:29:42,320 INFO [train.py:811] (1/4) Start epoch 599
+2023-11-14 08:32:02,708 INFO [train.py:467] (1/4) Epoch 599, batch 24, global_batch_idx: 22150, batch size: 55, loss[discriminator_loss=2.719, discriminator_real_loss=1.504, discriminator_fake_loss=1.214, generator_loss=30.29, generator_mel_loss=20.32, generator_kl_loss=1.92, generator_dur_loss=1.695, generator_adv_loss=2.289, generator_feat_match_loss=4.07, over 55.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.313, discriminator_fake_loss=1.297, generator_loss=30.38, generator_mel_loss=20.45, generator_kl_loss=1.982, generator_dur_loss=1.653, generator_adv_loss=2.228, generator_feat_match_loss=4.062, over 1878.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 08:33:16,304 INFO [train.py:811] (1/4) Start epoch 600
+2023-11-14 08:36:52,388 INFO [train.py:811] (1/4) Start epoch 601
+2023-11-14 08:37:08,995 INFO [train.py:467] (1/4) Epoch 601, batch 0, global_batch_idx: 22200, batch size: 71, loss[discriminator_loss=2.57, discriminator_real_loss=1.325, discriminator_fake_loss=1.245, generator_loss=30.21, generator_mel_loss=20.38, generator_kl_loss=1.969, generator_dur_loss=1.669, generator_adv_loss=2.068, generator_feat_match_loss=4.117, over 71.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.325, discriminator_fake_loss=1.245, generator_loss=30.21, generator_mel_loss=20.38, generator_kl_loss=1.969, generator_dur_loss=1.669, generator_adv_loss=2.068, generator_feat_match_loss=4.117, over 71.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 08:37:08,996 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 08:37:20,805 INFO [train.py:517] (1/4) Epoch 601, validation: discriminator_loss=2.585, discriminator_real_loss=1.242, discriminator_fake_loss=1.343, generator_loss=30.81, generator_mel_loss=20.9, generator_kl_loss=2.05, generator_dur_loss=1.638, generator_adv_loss=2.001, generator_feat_match_loss=4.224, over 100.00 samples.
+2023-11-14 08:37:20,806 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 08:40:40,331 INFO [train.py:811] (1/4) Start epoch 602
+2023-11-14 08:42:07,703 INFO [train.py:467] (1/4) Epoch 602, batch 13, global_batch_idx: 22250, batch size: 69, loss[discriminator_loss=2.66, discriminator_real_loss=1.462, discriminator_fake_loss=1.199, generator_loss=30.41, generator_mel_loss=20.6, generator_kl_loss=2.019, generator_dur_loss=1.666, generator_adv_loss=2.145, generator_feat_match_loss=3.984, over 69.00 samples.], tot_loss[discriminator_loss=2.633, discriminator_real_loss=1.334, discriminator_fake_loss=1.299, generator_loss=30.56, generator_mel_loss=20.64, generator_kl_loss=2.036, generator_dur_loss=1.651, generator_adv_loss=2.188, generator_feat_match_loss=4.039, over 1082.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 08:44:12,821 INFO [train.py:811] (1/4) Start epoch 603
+2023-11-14 08:46:49,365 INFO [train.py:467] (1/4) Epoch 603, batch 26, global_batch_idx: 22300, batch size: 79, loss[discriminator_loss=2.598, discriminator_real_loss=1.283, discriminator_fake_loss=1.313, generator_loss=30.17, generator_mel_loss=20.49, generator_kl_loss=1.944, generator_dur_loss=1.655, generator_adv_loss=2.01, generator_feat_match_loss=4.074, over 79.00 samples.], tot_loss[discriminator_loss=2.628, discriminator_real_loss=1.33, discriminator_fake_loss=1.298, generator_loss=30.56, generator_mel_loss=20.58, generator_kl_loss=1.992, generator_dur_loss=1.659, generator_adv_loss=2.228, generator_feat_match_loss=4.1, over 1858.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, grad_scale: 16.0
+2023-11-14 08:47:48,221 INFO [train.py:811] (1/4) Start epoch 604
+2023-11-14 08:51:19,140 INFO [train.py:811] (1/4) Start epoch 605
+2023-11-14 08:51:43,955 INFO [train.py:467] (1/4) Epoch 605, batch 2, global_batch_idx: 22350, batch size: 60, loss[discriminator_loss=2.57, discriminator_real_loss=1.367, discriminator_fake_loss=1.204, generator_loss=30.13, generator_mel_loss=20.32, generator_kl_loss=1.987, generator_dur_loss=1.656, generator_adv_loss=2.102, generator_feat_match_loss=4.066, over 60.00 samples.], tot_loss[discriminator_loss=2.616, discriminator_real_loss=1.38, discriminator_fake_loss=1.236, generator_loss=30.29, generator_mel_loss=20.55, generator_kl_loss=2.019, generator_dur_loss=1.653, generator_adv_loss=2.173, generator_feat_match_loss=3.901, over 216.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 08:54:52,275 INFO [train.py:811] (1/4) Start epoch 606
+2023-11-14 08:56:29,686 INFO [train.py:467] (1/4) Epoch 606, batch 15, global_batch_idx: 22400, batch size: 49, loss[discriminator_loss=2.604, discriminator_real_loss=1.402, discriminator_fake_loss=1.201, generator_loss=29.94, generator_mel_loss=20.2, generator_kl_loss=1.973, generator_dur_loss=1.649, generator_adv_loss=2.215, generator_feat_match_loss=3.908, over 49.00 samples.], tot_loss[discriminator_loss=2.649, discriminator_real_loss=1.346, discriminator_fake_loss=1.303, generator_loss=30.24, generator_mel_loss=20.57, generator_kl_loss=1.984, generator_dur_loss=1.651, generator_adv_loss=2.128, generator_feat_match_loss=3.903, over 1111.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 08:56:29,688 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 08:56:40,713 INFO [train.py:517] (1/4) Epoch 606, validation: discriminator_loss=2.547, discriminator_real_loss=1.237, discriminator_fake_loss=1.31, generator_loss=30.86, generator_mel_loss=21.06, generator_kl_loss=2.2, generator_dur_loss=1.637, generator_adv_loss=2.034, generator_feat_match_loss=3.924, over 100.00 samples.
+2023-11-14 08:56:40,714 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 08:58:38,604 INFO [train.py:811] (1/4) Start epoch 607
+2023-11-14 09:01:30,587 INFO [train.py:467] (1/4) Epoch 607, batch 28, global_batch_idx: 22450, batch size: 85, loss[discriminator_loss=2.596, discriminator_real_loss=1.367, discriminator_fake_loss=1.229, generator_loss=30.86, generator_mel_loss=20.67, generator_kl_loss=2.087, generator_dur_loss=1.641, generator_adv_loss=2.322, generator_feat_match_loss=4.145, over 85.00 samples.], tot_loss[discriminator_loss=2.618, discriminator_real_loss=1.325, discriminator_fake_loss=1.293, generator_loss=30.37, generator_mel_loss=20.55, generator_kl_loss=1.989, generator_dur_loss=1.653, generator_adv_loss=2.163, generator_feat_match_loss=4.019, over 2108.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 09:02:07,479 INFO [train.py:811] (1/4) Start epoch 608
+2023-11-14 09:05:37,458 INFO [train.py:811] (1/4) Start epoch 609
+2023-11-14 09:06:13,810 INFO [train.py:467] (1/4) Epoch 609, batch 4, global_batch_idx: 22500, batch size: 52, loss[discriminator_loss=2.561, discriminator_real_loss=1.316, discriminator_fake_loss=1.244, generator_loss=30.56, generator_mel_loss=20.58, generator_kl_loss=2.052, generator_dur_loss=1.652, generator_adv_loss=2.219, generator_feat_match_loss=4.059, over 52.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.361, discriminator_fake_loss=1.305, generator_loss=30.11, generator_mel_loss=20.58, generator_kl_loss=1.982, generator_dur_loss=1.657, generator_adv_loss=2.128, generator_feat_match_loss=3.764, over 273.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 09:09:12,745 INFO [train.py:811] (1/4) Start epoch 610
+2023-11-14 09:10:59,072 INFO [train.py:467] (1/4) Epoch 610, batch 17, global_batch_idx: 22550, batch size: 73, loss[discriminator_loss=2.488, discriminator_real_loss=1.291, discriminator_fake_loss=1.197, generator_loss=31.7, generator_mel_loss=20.68, generator_kl_loss=1.977, generator_dur_loss=1.675, generator_adv_loss=2.434, generator_feat_match_loss=4.938, over 73.00 samples.], tot_loss[discriminator_loss=2.543, discriminator_real_loss=1.291, discriminator_fake_loss=1.252, generator_loss=30.57, generator_mel_loss=20.41, generator_kl_loss=2.005, generator_dur_loss=1.656, generator_adv_loss=2.302, generator_feat_match_loss=4.199, over 1182.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:12:50,210 INFO [train.py:811] (1/4) Start epoch 611
+2023-11-14 09:15:56,639 INFO [train.py:467] (1/4) Epoch 611, batch 30, global_batch_idx: 22600, batch size: 153, loss[discriminator_loss=2.523, discriminator_real_loss=1.277, discriminator_fake_loss=1.246, generator_loss=30.29, generator_mel_loss=20.28, generator_kl_loss=2.077, generator_dur_loss=1.647, generator_adv_loss=2.104, generator_feat_match_loss=4.176, over 153.00 samples.], tot_loss[discriminator_loss=2.542, discriminator_real_loss=1.278, discriminator_fake_loss=1.264, generator_loss=30.37, generator_mel_loss=20.28, generator_kl_loss=2.004, generator_dur_loss=1.651, generator_adv_loss=2.236, generator_feat_match_loss=4.197, over 2415.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:15:56,641 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 09:16:07,668 INFO [train.py:517] (1/4) Epoch 611, validation: discriminator_loss=2.545, discriminator_real_loss=1.245, discriminator_fake_loss=1.3, generator_loss=30.97, generator_mel_loss=20.94, generator_kl_loss=2.134, generator_dur_loss=1.63, generator_adv_loss=2.076, generator_feat_match_loss=4.18, over 100.00 samples.
+2023-11-14 09:16:07,669 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 09:16:34,332 INFO [train.py:811] (1/4) Start epoch 612
+2023-11-14 09:20:06,615 INFO [train.py:811] (1/4) Start epoch 613
+2023-11-14 09:20:51,681 INFO [train.py:467] (1/4) Epoch 613, batch 6, global_batch_idx: 22650, batch size: 110, loss[discriminator_loss=2.543, discriminator_real_loss=1.244, discriminator_fake_loss=1.299, generator_loss=31.11, generator_mel_loss=20.8, generator_kl_loss=1.958, generator_dur_loss=1.664, generator_adv_loss=2.256, generator_feat_match_loss=4.43, over 110.00 samples.], tot_loss[discriminator_loss=2.508, discriminator_real_loss=1.255, discriminator_fake_loss=1.253, generator_loss=31.27, generator_mel_loss=20.67, generator_kl_loss=2.031, generator_dur_loss=1.65, generator_adv_loss=2.382, generator_feat_match_loss=4.533, over 481.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:23:36,879 INFO [train.py:811] (1/4) Start epoch 614
+2023-11-14 09:25:41,071 INFO [train.py:467] (1/4) Epoch 614, batch 19, global_batch_idx: 22700, batch size: 61, loss[discriminator_loss=2.543, discriminator_real_loss=1.348, discriminator_fake_loss=1.194, generator_loss=30.52, generator_mel_loss=20.42, generator_kl_loss=2.074, generator_dur_loss=1.666, generator_adv_loss=2.328, generator_feat_match_loss=4.027, over 61.00 samples.], tot_loss[discriminator_loss=2.519, discriminator_real_loss=1.276, discriminator_fake_loss=1.244, generator_loss=30.5, generator_mel_loss=20.16, generator_kl_loss=1.998, generator_dur_loss=1.649, generator_adv_loss=2.301, generator_feat_match_loss=4.391, over 1757.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:27:09,736 INFO [train.py:811] (1/4) Start epoch 615
+2023-11-14 09:30:16,652 INFO [train.py:467] (1/4) Epoch 615, batch 32, global_batch_idx: 22750, batch size: 153, loss[discriminator_loss=2.736, discriminator_real_loss=1.242, discriminator_fake_loss=1.494, generator_loss=30.88, generator_mel_loss=20.62, generator_kl_loss=2.033, generator_dur_loss=1.644, generator_adv_loss=2.426, generator_feat_match_loss=4.148, over 153.00 samples.], tot_loss[discriminator_loss=2.609, discriminator_real_loss=1.321, discriminator_fake_loss=1.288, generator_loss=30.62, generator_mel_loss=20.6, generator_kl_loss=2, generator_dur_loss=1.647, generator_adv_loss=2.207, generator_feat_match_loss=4.166, over 2444.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:30:41,048 INFO [train.py:811] (1/4) Start epoch 616
+2023-11-14 09:34:12,925 INFO [train.py:811] (1/4) Start epoch 617
+2023-11-14 09:35:08,967 INFO [train.py:467] (1/4) Epoch 617, batch 8, global_batch_idx: 22800, batch size: 55, loss[discriminator_loss=2.621, discriminator_real_loss=1.305, discriminator_fake_loss=1.315, generator_loss=29.8, generator_mel_loss=20.38, generator_kl_loss=2.012, generator_dur_loss=1.709, generator_adv_loss=2.025, generator_feat_match_loss=3.678, over 55.00 samples.], tot_loss[discriminator_loss=2.612, discriminator_real_loss=1.332, discriminator_fake_loss=1.28, generator_loss=30.05, generator_mel_loss=20.3, generator_kl_loss=2.018, generator_dur_loss=1.662, generator_adv_loss=2.132, generator_feat_match_loss=3.934, over 564.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 09:35:08,968 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 09:35:20,814 INFO [train.py:517] (1/4) Epoch 617, validation: discriminator_loss=2.624, discriminator_real_loss=1.329, discriminator_fake_loss=1.295, generator_loss=31.14, generator_mel_loss=21.46, generator_kl_loss=2.126, generator_dur_loss=1.642, generator_adv_loss=2.023, generator_feat_match_loss=3.887, over 100.00 samples.
+2023-11-14 09:35:20,815 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 09:38:01,916 INFO [train.py:811] (1/4) Start epoch 618
+2023-11-14 09:40:09,697 INFO [train.py:467] (1/4) Epoch 618, batch 21, global_batch_idx: 22850, batch size: 95, loss[discriminator_loss=2.643, discriminator_real_loss=1.391, discriminator_fake_loss=1.252, generator_loss=30.36, generator_mel_loss=20.67, generator_kl_loss=2.02, generator_dur_loss=1.652, generator_adv_loss=2.279, generator_feat_match_loss=3.734, over 95.00 samples.], tot_loss[discriminator_loss=2.638, discriminator_real_loss=1.335, discriminator_fake_loss=1.303, generator_loss=30.39, generator_mel_loss=20.61, generator_kl_loss=2.02, generator_dur_loss=1.648, generator_adv_loss=2.172, generator_feat_match_loss=3.932, over 1467.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 09:41:29,425 INFO [train.py:811] (1/4) Start epoch 619
+2023-11-14 09:44:53,759 INFO [train.py:467] (1/4) Epoch 619, batch 34, global_batch_idx: 22900, batch size: 56, loss[discriminator_loss=2.477, discriminator_real_loss=1.346, discriminator_fake_loss=1.13, generator_loss=30.86, generator_mel_loss=19.98, generator_kl_loss=2.063, generator_dur_loss=1.667, generator_adv_loss=2.297, generator_feat_match_loss=4.855, over 56.00 samples.], tot_loss[discriminator_loss=2.554, discriminator_real_loss=1.308, discriminator_fake_loss=1.246, generator_loss=30.79, generator_mel_loss=20.27, generator_kl_loss=1.989, generator_dur_loss=1.651, generator_adv_loss=2.361, generator_feat_match_loss=4.522, over 2566.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:45:03,843 INFO [train.py:811] (1/4) Start epoch 620
+2023-11-14 09:48:40,640 INFO [train.py:811] (1/4) Start epoch 621
+2023-11-14 09:49:58,338 INFO [train.py:467] (1/4) Epoch 621, batch 10, global_batch_idx: 22950, batch size: 63, loss[discriminator_loss=2.588, discriminator_real_loss=1.221, discriminator_fake_loss=1.367, generator_loss=30.03, generator_mel_loss=20.26, generator_kl_loss=1.892, generator_dur_loss=1.645, generator_adv_loss=2.23, generator_feat_match_loss=4.004, over 63.00 samples.], tot_loss[discriminator_loss=2.574, discriminator_real_loss=1.295, discriminator_fake_loss=1.279, generator_loss=30.48, generator_mel_loss=20.56, generator_kl_loss=2.02, generator_dur_loss=1.647, generator_adv_loss=2.136, generator_feat_match_loss=4.116, over 869.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:52:13,974 INFO [train.py:811] (1/4) Start epoch 622
+2023-11-14 09:54:25,819 INFO [train.py:467] (1/4) Epoch 622, batch 23, global_batch_idx: 23000, batch size: 59, loss[discriminator_loss=2.717, discriminator_real_loss=1.359, discriminator_fake_loss=1.357, generator_loss=30.16, generator_mel_loss=20.56, generator_kl_loss=2.02, generator_dur_loss=1.651, generator_adv_loss=2.131, generator_feat_match_loss=3.799, over 59.00 samples.], tot_loss[discriminator_loss=2.646, discriminator_real_loss=1.335, discriminator_fake_loss=1.31, generator_loss=30.26, generator_mel_loss=20.6, generator_kl_loss=1.995, generator_dur_loss=1.654, generator_adv_loss=2.101, generator_feat_match_loss=3.908, over 1592.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:54:25,821 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 09:54:37,386 INFO [train.py:517] (1/4) Epoch 622, validation: discriminator_loss=2.656, discriminator_real_loss=1.36, discriminator_fake_loss=1.297, generator_loss=31.1, generator_mel_loss=21.42, generator_kl_loss=2.152, generator_dur_loss=1.639, generator_adv_loss=1.983, generator_feat_match_loss=3.901, over 100.00 samples.
+2023-11-14 09:54:37,387 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 09:55:57,084 INFO [train.py:811] (1/4) Start epoch 623
+2023-11-14 09:59:26,702 INFO [train.py:467] (1/4) Epoch 623, batch 36, global_batch_idx: 23050, batch size: 69, loss[discriminator_loss=2.547, discriminator_real_loss=1.22, discriminator_fake_loss=1.327, generator_loss=30.76, generator_mel_loss=20.5, generator_kl_loss=1.979, generator_dur_loss=1.656, generator_adv_loss=2.352, generator_feat_match_loss=4.281, over 69.00 samples.], tot_loss[discriminator_loss=2.645, discriminator_real_loss=1.334, discriminator_fake_loss=1.31, generator_loss=30.28, generator_mel_loss=20.52, generator_kl_loss=2, generator_dur_loss=1.651, generator_adv_loss=2.147, generator_feat_match_loss=3.96, over 2703.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 09:59:27,355 INFO [train.py:811] (1/4) Start epoch 624
+2023-11-14 10:02:54,685 INFO [train.py:811] (1/4) Start epoch 625
+2023-11-14 10:04:14,201 INFO [train.py:467] (1/4) Epoch 625, batch 12, global_batch_idx: 23100, batch size: 81, loss[discriminator_loss=2.637, discriminator_real_loss=1.258, discriminator_fake_loss=1.379, generator_loss=31.15, generator_mel_loss=20.61, generator_kl_loss=2.04, generator_dur_loss=1.636, generator_adv_loss=2.402, generator_feat_match_loss=4.457, over 81.00 samples.], tot_loss[discriminator_loss=2.624, discriminator_real_loss=1.342, discriminator_fake_loss=1.282, generator_loss=30.81, generator_mel_loss=20.59, generator_kl_loss=1.97, generator_dur_loss=1.664, generator_adv_loss=2.302, generator_feat_match_loss=4.282, over 806.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 10:06:27,600 INFO [train.py:811] (1/4) Start epoch 626
+2023-11-14 10:08:53,818 INFO [train.py:467] (1/4) Epoch 626, batch 25, global_batch_idx: 23150, batch size: 81, loss[discriminator_loss=2.551, discriminator_real_loss=1.257, discriminator_fake_loss=1.293, generator_loss=30.83, generator_mel_loss=20.71, generator_kl_loss=1.961, generator_dur_loss=1.643, generator_adv_loss=2.236, generator_feat_match_loss=4.273, over 81.00 samples.], tot_loss[discriminator_loss=2.554, discriminator_real_loss=1.298, discriminator_fake_loss=1.256, generator_loss=30.43, generator_mel_loss=20.37, generator_kl_loss=1.994, generator_dur_loss=1.652, generator_adv_loss=2.218, generator_feat_match_loss=4.205, over 1788.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 10:09:57,260 INFO [train.py:811] (1/4) Start epoch 627
+2023-11-14 10:13:35,114 INFO [train.py:811] (1/4) Start epoch 628
+2023-11-14 10:13:57,588 INFO [train.py:467] (1/4) Epoch 628, batch 1, global_batch_idx: 23200, batch size: 95, loss[discriminator_loss=2.84, discriminator_real_loss=1.414, discriminator_fake_loss=1.427, generator_loss=29.15, generator_mel_loss=19.95, generator_kl_loss=2.001, generator_dur_loss=1.63, generator_adv_loss=2.016, generator_feat_match_loss=3.549, over 95.00 samples.], tot_loss[discriminator_loss=2.787, discriminator_real_loss=1.367, discriminator_fake_loss=1.421, generator_loss=29.32, generator_mel_loss=20.06, generator_kl_loss=1.99, generator_dur_loss=1.637, generator_adv_loss=2.034, generator_feat_match_loss=3.599, over 174.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 10:13:57,589 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 10:14:09,969 INFO [train.py:517] (1/4) Epoch 628, validation: discriminator_loss=2.78, discriminator_real_loss=1.448, discriminator_fake_loss=1.331, generator_loss=30.2, generator_mel_loss=20.83, generator_kl_loss=2.13, generator_dur_loss=1.641, generator_adv_loss=1.933, generator_feat_match_loss=3.66, over 100.00 samples.
+2023-11-14 10:14:09,969 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 10:17:15,533 INFO [train.py:811] (1/4) Start epoch 629
+2023-11-14 10:18:45,987 INFO [train.py:467] (1/4) Epoch 629, batch 14, global_batch_idx: 23250, batch size: 79, loss[discriminator_loss=2.596, discriminator_real_loss=1.248, discriminator_fake_loss=1.348, generator_loss=30.53, generator_mel_loss=20.43, generator_kl_loss=1.966, generator_dur_loss=1.651, generator_adv_loss=2.344, generator_feat_match_loss=4.137, over 79.00 samples.], tot_loss[discriminator_loss=2.563, discriminator_real_loss=1.278, discriminator_fake_loss=1.285, generator_loss=30.43, generator_mel_loss=20.5, generator_kl_loss=2, generator_dur_loss=1.648, generator_adv_loss=2.159, generator_feat_match_loss=4.129, over 1071.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 10:20:46,961 INFO [train.py:811] (1/4) Start epoch 630
+2023-11-14 10:23:35,036 INFO [train.py:467] (1/4) Epoch 630, batch 27, global_batch_idx: 23300, batch size: 126, loss[discriminator_loss=2.631, discriminator_real_loss=1.325, discriminator_fake_loss=1.306, generator_loss=30.71, generator_mel_loss=20.62, generator_kl_loss=2.011, generator_dur_loss=1.64, generator_adv_loss=2.328, generator_feat_match_loss=4.109, over 126.00 samples.], tot_loss[discriminator_loss=2.576, discriminator_real_loss=1.29, discriminator_fake_loss=1.287, generator_loss=30.21, generator_mel_loss=20.24, generator_kl_loss=1.988, generator_dur_loss=1.648, generator_adv_loss=2.177, generator_feat_match_loss=4.159, over 2091.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 10:24:20,051 INFO [train.py:811] (1/4) Start epoch 631
+2023-11-14 10:27:52,562 INFO [train.py:811] (1/4) Start epoch 632
+2023-11-14 10:28:21,131 INFO [train.py:467] (1/4) Epoch 632, batch 3, global_batch_idx: 23350, batch size: 50, loss[discriminator_loss=2.406, discriminator_real_loss=1.26, discriminator_fake_loss=1.146, generator_loss=31.19, generator_mel_loss=20.25, generator_kl_loss=1.923, generator_dur_loss=1.666, generator_adv_loss=2.414, generator_feat_match_loss=4.941, over 50.00 samples.], tot_loss[discriminator_loss=2.537, discriminator_real_loss=1.326, discriminator_fake_loss=1.211, generator_loss=30.38, generator_mel_loss=20.17, generator_kl_loss=1.93, generator_dur_loss=1.661, generator_adv_loss=2.39, generator_feat_match_loss=4.226, over 268.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 10:31:22,623 INFO [train.py:811] (1/4) Start epoch 633
+2023-11-14 10:33:10,337 INFO [train.py:467] (1/4) Epoch 633, batch 16, global_batch_idx: 23400, batch size: 76, loss[discriminator_loss=2.619, discriminator_real_loss=1.309, discriminator_fake_loss=1.311, generator_loss=30.06, generator_mel_loss=20.35, generator_kl_loss=1.969, generator_dur_loss=1.637, generator_adv_loss=2.129, generator_feat_match_loss=3.98, over 76.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.296, discriminator_fake_loss=1.274, generator_loss=30.45, generator_mel_loss=20.51, generator_kl_loss=2.034, generator_dur_loss=1.646, generator_adv_loss=2.157, generator_feat_match_loss=4.105, over 1405.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 10:33:10,339 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 10:33:21,997 INFO [train.py:517] (1/4) Epoch 633, validation: discriminator_loss=2.527, discriminator_real_loss=1.207, discriminator_fake_loss=1.32, generator_loss=30.54, generator_mel_loss=20.69, generator_kl_loss=2.167, generator_dur_loss=1.634, generator_adv_loss=2.041, generator_feat_match_loss=4.006, over 100.00 samples.
+2023-11-14 10:33:21,998 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 10:35:04,010 INFO [train.py:811] (1/4) Start epoch 634
+2023-11-14 10:37:54,458 INFO [train.py:467] (1/4) Epoch 634, batch 29, global_batch_idx: 23450, batch size: 73, loss[discriminator_loss=2.555, discriminator_real_loss=1.354, discriminator_fake_loss=1.201, generator_loss=30.4, generator_mel_loss=20.17, generator_kl_loss=2.049, generator_dur_loss=1.677, generator_adv_loss=2.264, generator_feat_match_loss=4.242, over 73.00 samples.], tot_loss[discriminator_loss=2.548, discriminator_real_loss=1.283, discriminator_fake_loss=1.264, generator_loss=30.8, generator_mel_loss=20.43, generator_kl_loss=2.007, generator_dur_loss=1.648, generator_adv_loss=2.312, generator_feat_match_loss=4.403, over 2202.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 10:38:37,679 INFO [train.py:811] (1/4) Start epoch 635
+2023-11-14 10:42:19,099 INFO [train.py:811] (1/4) Start epoch 636
+2023-11-14 10:42:58,844 INFO [train.py:467] (1/4) Epoch 636, batch 5, global_batch_idx: 23500, batch size: 50, loss[discriminator_loss=2.346, discriminator_real_loss=1.096, discriminator_fake_loss=1.25, generator_loss=32, generator_mel_loss=20.21, generator_kl_loss=1.927, generator_dur_loss=1.667, generator_adv_loss=2.707, generator_feat_match_loss=5.484, over 50.00 samples.], tot_loss[discriminator_loss=2.384, discriminator_real_loss=1.239, discriminator_fake_loss=1.144, generator_loss=30.87, generator_mel_loss=20.04, generator_kl_loss=1.963, generator_dur_loss=1.647, generator_adv_loss=2.382, generator_feat_match_loss=4.833, over 436.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 10:45:54,960 INFO [train.py:811] (1/4) Start epoch 637
+2023-11-14 10:47:54,922 INFO [train.py:467] (1/4) Epoch 637, batch 18, global_batch_idx: 23550, batch size: 64, loss[discriminator_loss=2.611, discriminator_real_loss=1.175, discriminator_fake_loss=1.437, generator_loss=29.96, generator_mel_loss=20.3, generator_kl_loss=2.046, generator_dur_loss=1.672, generator_adv_loss=2.078, generator_feat_match_loss=3.869, over 64.00 samples.], tot_loss[discriminator_loss=2.621, discriminator_real_loss=1.33, discriminator_fake_loss=1.291, generator_loss=30.43, generator_mel_loss=20.52, generator_kl_loss=2.03, generator_dur_loss=1.648, generator_adv_loss=2.143, generator_feat_match_loss=4.086, over 1410.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 10:49:25,754 INFO [train.py:811] (1/4) Start epoch 638
+2023-11-14 10:52:32,096 INFO [train.py:467] (1/4) Epoch 638, batch 31, global_batch_idx: 23600, batch size: 73, loss[discriminator_loss=2.637, discriminator_real_loss=1.271, discriminator_fake_loss=1.364, generator_loss=30.44, generator_mel_loss=20.74, generator_kl_loss=1.973, generator_dur_loss=1.648, generator_adv_loss=2.105, generator_feat_match_loss=3.969, over 73.00 samples.], tot_loss[discriminator_loss=2.616, discriminator_real_loss=1.319, discriminator_fake_loss=1.297, generator_loss=30.41, generator_mel_loss=20.52, generator_kl_loss=2.021, generator_dur_loss=1.643, generator_adv_loss=2.148, generator_feat_match_loss=4.079, over 2545.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 10:52:32,097 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 10:52:43,775 INFO [train.py:517] (1/4) Epoch 638, validation: discriminator_loss=2.672, discriminator_real_loss=1.26, discriminator_fake_loss=1.412, generator_loss=30.22, generator_mel_loss=20.97, generator_kl_loss=2.202, generator_dur_loss=1.635, generator_adv_loss=1.772, generator_feat_match_loss=3.642, over 100.00 samples.
+2023-11-14 10:52:43,776 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 10:53:10,528 INFO [train.py:811] (1/4) Start epoch 639
+2023-11-14 10:56:44,385 INFO [train.py:811] (1/4) Start epoch 640
+2023-11-14 10:57:40,796 INFO [train.py:467] (1/4) Epoch 640, batch 7, global_batch_idx: 23650, batch size: 153, loss[discriminator_loss=2.557, discriminator_real_loss=1.168, discriminator_fake_loss=1.389, generator_loss=30.92, generator_mel_loss=20.49, generator_kl_loss=2.015, generator_dur_loss=1.618, generator_adv_loss=2.158, generator_feat_match_loss=4.641, over 153.00 samples.], tot_loss[discriminator_loss=2.634, discriminator_real_loss=1.311, discriminator_fake_loss=1.322, generator_loss=30.34, generator_mel_loss=20.35, generator_kl_loss=1.985, generator_dur_loss=1.646, generator_adv_loss=2.167, generator_feat_match_loss=4.192, over 607.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 11:00:13,315 INFO [train.py:811] (1/4) Start epoch 641
+2023-11-14 11:02:21,401 INFO [train.py:467] (1/4) Epoch 641, batch 20, global_batch_idx: 23700, batch size: 90, loss[discriminator_loss=2.73, discriminator_real_loss=1.488, discriminator_fake_loss=1.242, generator_loss=30.28, generator_mel_loss=20.6, generator_kl_loss=1.945, generator_dur_loss=1.641, generator_adv_loss=2.17, generator_feat_match_loss=3.926, over 90.00 samples.], tot_loss[discriminator_loss=2.602, discriminator_real_loss=1.323, discriminator_fake_loss=1.278, generator_loss=30.34, generator_mel_loss=20.36, generator_kl_loss=2.01, generator_dur_loss=1.645, generator_adv_loss=2.195, generator_feat_match_loss=4.13, over 1488.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 16.0
+2023-11-14 11:03:49,023 INFO [train.py:811] (1/4) Start epoch 642
+2023-11-14 11:07:06,499 INFO [train.py:467] (1/4) Epoch 642, batch 33, global_batch_idx: 23750, batch size: 79, loss[discriminator_loss=2.5, discriminator_real_loss=1.351, discriminator_fake_loss=1.149, generator_loss=29.81, generator_mel_loss=20.14, generator_kl_loss=1.896, generator_dur_loss=1.631, generator_adv_loss=2.152, generator_feat_match_loss=3.996, over 79.00 samples.], tot_loss[discriminator_loss=2.562, discriminator_real_loss=1.301, discriminator_fake_loss=1.262, generator_loss=30.59, generator_mel_loss=20.29, generator_kl_loss=2.014, generator_dur_loss=1.647, generator_adv_loss=2.279, generator_feat_match_loss=4.361, over 2396.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 11:07:21,832 INFO [train.py:811] (1/4) Start epoch 643
+2023-11-14 11:10:59,607 INFO [train.py:811] (1/4) Start epoch 644
+2023-11-14 11:12:06,794 INFO [train.py:467] (1/4) Epoch 644, batch 9, global_batch_idx: 23800, batch size: 61, loss[discriminator_loss=2.332, discriminator_real_loss=1.107, discriminator_fake_loss=1.224, generator_loss=31.3, generator_mel_loss=19.94, generator_kl_loss=2.054, generator_dur_loss=1.637, generator_adv_loss=2.457, generator_feat_match_loss=5.207, over 61.00 samples.], tot_loss[discriminator_loss=2.499, discriminator_real_loss=1.249, discriminator_fake_loss=1.25, generator_loss=30.83, generator_mel_loss=20.07, generator_kl_loss=2.015, generator_dur_loss=1.647, generator_adv_loss=2.381, generator_feat_match_loss=4.725, over 843.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 11:12:06,796 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 11:12:18,214 INFO [train.py:517] (1/4) Epoch 644, validation: discriminator_loss=2.394, discriminator_real_loss=1.111, discriminator_fake_loss=1.283, generator_loss=31.18, generator_mel_loss=20.58, generator_kl_loss=2.156, generator_dur_loss=1.638, generator_adv_loss=1.979, generator_feat_match_loss=4.832, over 100.00 samples.
+2023-11-14 11:12:18,216 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 11:14:49,432 INFO [train.py:811] (1/4) Start epoch 645
+2023-11-14 11:17:05,350 INFO [train.py:467] (1/4) Epoch 645, batch 22, global_batch_idx: 23850, batch size: 81, loss[discriminator_loss=2.523, discriminator_real_loss=1.298, discriminator_fake_loss=1.225, generator_loss=30.39, generator_mel_loss=20.26, generator_kl_loss=2.011, generator_dur_loss=1.644, generator_adv_loss=2.24, generator_feat_match_loss=4.238, over 81.00 samples.], tot_loss[discriminator_loss=2.59, discriminator_real_loss=1.301, discriminator_fake_loss=1.289, generator_loss=30.24, generator_mel_loss=20.09, generator_kl_loss=1.983, generator_dur_loss=1.645, generator_adv_loss=2.225, generator_feat_match_loss=4.301, over 1903.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 11:18:21,274 INFO [train.py:811] (1/4) Start epoch 646
+2023-11-14 11:21:51,452 INFO [train.py:467] (1/4) Epoch 646, batch 35, global_batch_idx: 23900, batch size: 65, loss[discriminator_loss=2.748, discriminator_real_loss=1.385, discriminator_fake_loss=1.363, generator_loss=30.28, generator_mel_loss=20.38, generator_kl_loss=1.97, generator_dur_loss=1.64, generator_adv_loss=2.164, generator_feat_match_loss=4.125, over 65.00 samples.], tot_loss[discriminator_loss=2.61, discriminator_real_loss=1.33, discriminator_fake_loss=1.28, generator_loss=30.48, generator_mel_loss=20.48, generator_kl_loss=2.049, generator_dur_loss=1.65, generator_adv_loss=2.166, generator_feat_match_loss=4.133, over 2369.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, grad_scale: 8.0
+2023-11-14 11:21:56,645 INFO [train.py:811] (1/4) Start epoch 647
+2023-11-14 11:25:22,484 INFO [train.py:811] (1/4) Start epoch 648
+2023-11-14 11:26:36,115 INFO [train.py:467] (1/4) Epoch 648, batch 11, global_batch_idx: 23950, batch size: 52, loss[discriminator_loss=2.582, discriminator_real_loss=1.279, discriminator_fake_loss=1.304, generator_loss=29.86, generator_mel_loss=20.29, generator_kl_loss=1.979, generator_dur_loss=1.656, generator_adv_loss=2.084, generator_feat_match_loss=3.857, over 52.00 samples.], tot_loss[discriminator_loss=2.625, discriminator_real_loss=1.325, discriminator_fake_loss=1.301, generator_loss=30.15, generator_mel_loss=20.42, generator_kl_loss=1.975, generator_dur_loss=1.65, generator_adv_loss=2.128, generator_feat_match_loss=3.977, over 917.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 11:28:54,916 INFO [train.py:811] (1/4) Start epoch 649
+2023-11-14 11:31:26,736 INFO [train.py:467] (1/4) Epoch 649, batch 24, global_batch_idx: 24000, batch size: 50, loss[discriminator_loss=2.51, discriminator_real_loss=1.318, discriminator_fake_loss=1.191, generator_loss=30.81, generator_mel_loss=20.76, generator_kl_loss=1.958, generator_dur_loss=1.643, generator_adv_loss=2.027, generator_feat_match_loss=4.422, over 50.00 samples.], tot_loss[discriminator_loss=2.632, discriminator_real_loss=1.34, discriminator_fake_loss=1.293, generator_loss=30.58, generator_mel_loss=20.58, generator_kl_loss=2.012, generator_dur_loss=1.651, generator_adv_loss=2.206, generator_feat_match_loss=4.131, over 1892.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 16.0
+2023-11-14 11:31:26,738 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 11:31:38,304 INFO [train.py:517] (1/4) Epoch 649, validation: discriminator_loss=2.62, discriminator_real_loss=1.149, discriminator_fake_loss=1.472, generator_loss=31.22, generator_mel_loss=21.2, generator_kl_loss=2.164, generator_dur_loss=1.641, generator_adv_loss=1.789, generator_feat_match_loss=4.427, over 100.00 samples.
+2023-11-14 11:31:38,305 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 11:32:41,108 INFO [train.py:811] (1/4) Start epoch 650
+2023-11-14 11:36:15,300 INFO [train.py:811] (1/4) Start epoch 651
+2023-11-14 11:36:31,698 INFO [train.py:467] (1/4) Epoch 651, batch 0, global_batch_idx: 24050, batch size: 56, loss[discriminator_loss=2.504, discriminator_real_loss=1.163, discriminator_fake_loss=1.342, generator_loss=30.6, generator_mel_loss=20.47, generator_kl_loss=2.001, generator_dur_loss=1.656, generator_adv_loss=2.352, generator_feat_match_loss=4.125, over 56.00 samples.], tot_loss[discriminator_loss=2.504, discriminator_real_loss=1.163, discriminator_fake_loss=1.342, generator_loss=30.6, generator_mel_loss=20.47, generator_kl_loss=2.001, generator_dur_loss=1.656, generator_adv_loss=2.352, generator_feat_match_loss=4.125, over 56.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 11:39:52,402 INFO [train.py:811] (1/4) Start epoch 652
+2023-11-14 11:41:29,902 INFO [train.py:467] (1/4) Epoch 652, batch 13, global_batch_idx: 24100, batch size: 90, loss[discriminator_loss=2.658, discriminator_real_loss=1.454, discriminator_fake_loss=1.204, generator_loss=30.13, generator_mel_loss=19.99, generator_kl_loss=2.068, generator_dur_loss=1.638, generator_adv_loss=2.172, generator_feat_match_loss=4.258, over 90.00 samples.], tot_loss[discriminator_loss=2.563, discriminator_real_loss=1.317, discriminator_fake_loss=1.246, generator_loss=30.61, generator_mel_loss=20.27, generator_kl_loss=2.014, generator_dur_loss=1.644, generator_adv_loss=2.265, generator_feat_match_loss=4.419, over 1066.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 11:43:33,126 INFO [train.py:811] (1/4) Start epoch 653
+2023-11-14 11:46:13,113 INFO [train.py:467] (1/4) Epoch 653, batch 26, global_batch_idx: 24150, batch size: 60, loss[discriminator_loss=2.535, discriminator_real_loss=1.338, discriminator_fake_loss=1.198, generator_loss=31.04, generator_mel_loss=20.83, generator_kl_loss=2.047, generator_dur_loss=1.646, generator_adv_loss=2.285, generator_feat_match_loss=4.23, over 60.00 samples.], tot_loss[discriminator_loss=2.567, discriminator_real_loss=1.3, discriminator_fake_loss=1.267, generator_loss=30.25, generator_mel_loss=20.35, generator_kl_loss=2.026, generator_dur_loss=1.647, generator_adv_loss=2.14, generator_feat_match_loss=4.085, over 1976.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 11:47:09,529 INFO [train.py:811] (1/4) Start epoch 654
+2023-11-14 11:50:39,274 INFO [train.py:811] (1/4) Start epoch 655
+2023-11-14 11:51:04,892 INFO [train.py:467] (1/4) Epoch 655, batch 2, global_batch_idx: 24200, batch size: 101, loss[discriminator_loss=2.508, discriminator_real_loss=1.268, discriminator_fake_loss=1.241, generator_loss=30.87, generator_mel_loss=20.23, generator_kl_loss=1.967, generator_dur_loss=1.638, generator_adv_loss=2.393, generator_feat_match_loss=4.641, over 101.00 samples.], tot_loss[discriminator_loss=2.5, discriminator_real_loss=1.256, discriminator_fake_loss=1.245, generator_loss=30.67, generator_mel_loss=20.13, generator_kl_loss=2.002, generator_dur_loss=1.642, generator_adv_loss=2.349, generator_feat_match_loss=4.547, over 215.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 11:51:04,893 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 11:51:17,867 INFO [train.py:517] (1/4) Epoch 655, validation: discriminator_loss=2.579, discriminator_real_loss=1.29, discriminator_fake_loss=1.289, generator_loss=31.08, generator_mel_loss=20.77, generator_kl_loss=2.158, generator_dur_loss=1.64, generator_adv_loss=2.05, generator_feat_match_loss=4.467, over 100.00 samples.
+2023-11-14 11:51:17,868 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 11:54:30,040 INFO [train.py:811] (1/4) Start epoch 656
+2023-11-14 11:56:12,161 INFO [train.py:467] (1/4) Epoch 656, batch 15, global_batch_idx: 24250, batch size: 50, loss[discriminator_loss=2.539, discriminator_real_loss=1.275, discriminator_fake_loss=1.265, generator_loss=30.34, generator_mel_loss=20.22, generator_kl_loss=2.026, generator_dur_loss=1.666, generator_adv_loss=2.074, generator_feat_match_loss=4.352, over 50.00 samples.], tot_loss[discriminator_loss=2.564, discriminator_real_loss=1.306, discriminator_fake_loss=1.258, generator_loss=30.45, generator_mel_loss=20.31, generator_kl_loss=2.023, generator_dur_loss=1.653, generator_adv_loss=2.222, generator_feat_match_loss=4.25, over 1119.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 11:58:04,232 INFO [train.py:811] (1/4) Start epoch 657
+2023-11-14 12:00:59,504 INFO [train.py:467] (1/4) Epoch 657, batch 28, global_batch_idx: 24300, batch size: 81, loss[discriminator_loss=2.518, discriminator_real_loss=1.211, discriminator_fake_loss=1.307, generator_loss=30.04, generator_mel_loss=20.03, generator_kl_loss=2.151, generator_dur_loss=1.63, generator_adv_loss=2.217, generator_feat_match_loss=4.008, over 81.00 samples.], tot_loss[discriminator_loss=2.615, discriminator_real_loss=1.324, discriminator_fake_loss=1.291, generator_loss=30.38, generator_mel_loss=20.32, generator_kl_loss=2.021, generator_dur_loss=1.649, generator_adv_loss=2.196, generator_feat_match_loss=4.195, over 2268.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:01:44,981 INFO [train.py:811] (1/4) Start epoch 658
+2023-11-14 12:05:15,336 INFO [train.py:811] (1/4) Start epoch 659
+2023-11-14 12:06:02,238 INFO [train.py:467] (1/4) Epoch 659, batch 4, global_batch_idx: 24350, batch size: 126, loss[discriminator_loss=2.336, discriminator_real_loss=1.148, discriminator_fake_loss=1.188, generator_loss=32.22, generator_mel_loss=20.35, generator_kl_loss=2.034, generator_dur_loss=1.624, generator_adv_loss=2.695, generator_feat_match_loss=5.52, over 126.00 samples.], tot_loss[discriminator_loss=2.44, discriminator_real_loss=1.254, discriminator_fake_loss=1.186, generator_loss=31.38, generator_mel_loss=20.17, generator_kl_loss=2.037, generator_dur_loss=1.646, generator_adv_loss=2.52, generator_feat_match_loss=5.006, over 487.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:08:52,136 INFO [train.py:811] (1/4) Start epoch 660
+2023-11-14 12:10:43,988 INFO [train.py:467] (1/4) Epoch 660, batch 17, global_batch_idx: 24400, batch size: 81, loss[discriminator_loss=2.553, discriminator_real_loss=1.361, discriminator_fake_loss=1.191, generator_loss=30.09, generator_mel_loss=20.37, generator_kl_loss=1.948, generator_dur_loss=1.639, generator_adv_loss=2.049, generator_feat_match_loss=4.086, over 81.00 samples.], tot_loss[discriminator_loss=2.555, discriminator_real_loss=1.304, discriminator_fake_loss=1.25, generator_loss=30.24, generator_mel_loss=20.12, generator_kl_loss=2.004, generator_dur_loss=1.648, generator_adv_loss=2.22, generator_feat_match_loss=4.246, over 1381.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 16.0
+2023-11-14 12:10:43,990 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 12:10:55,819 INFO [train.py:517] (1/4) Epoch 660, validation: discriminator_loss=2.644, discriminator_real_loss=1.146, discriminator_fake_loss=1.498, generator_loss=30.52, generator_mel_loss=20.88, generator_kl_loss=2.231, generator_dur_loss=1.644, generator_adv_loss=1.815, generator_feat_match_loss=3.949, over 100.00 samples.
+2023-11-14 12:10:55,821 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 12:12:42,404 INFO [train.py:811] (1/4) Start epoch 661
+2023-11-14 12:15:44,444 INFO [train.py:467] (1/4) Epoch 661, batch 30, global_batch_idx: 24450, batch size: 60, loss[discriminator_loss=2.406, discriminator_real_loss=1.226, discriminator_fake_loss=1.182, generator_loss=31.04, generator_mel_loss=20.25, generator_kl_loss=1.942, generator_dur_loss=1.661, generator_adv_loss=2.275, generator_feat_match_loss=4.918, over 60.00 samples.], tot_loss[discriminator_loss=2.59, discriminator_real_loss=1.335, discriminator_fake_loss=1.255, generator_loss=30.73, generator_mel_loss=20.34, generator_kl_loss=2.006, generator_dur_loss=1.642, generator_adv_loss=2.311, generator_feat_match_loss=4.429, over 2397.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:16:19,798 INFO [train.py:811] (1/4) Start epoch 662
+2023-11-14 12:19:55,099 INFO [train.py:811] (1/4) Start epoch 663
+2023-11-14 12:20:43,031 INFO [train.py:467] (1/4) Epoch 663, batch 6, global_batch_idx: 24500, batch size: 101, loss[discriminator_loss=2.559, discriminator_real_loss=1.262, discriminator_fake_loss=1.298, generator_loss=31.03, generator_mel_loss=20.73, generator_kl_loss=2.076, generator_dur_loss=1.649, generator_adv_loss=2.143, generator_feat_match_loss=4.438, over 101.00 samples.], tot_loss[discriminator_loss=2.575, discriminator_real_loss=1.305, discriminator_fake_loss=1.27, generator_loss=30.65, generator_mel_loss=20.56, generator_kl_loss=2.042, generator_dur_loss=1.648, generator_adv_loss=2.167, generator_feat_match_loss=4.241, over 515.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:23:27,409 INFO [train.py:811] (1/4) Start epoch 664
+2023-11-14 12:25:28,987 INFO [train.py:467] (1/4) Epoch 664, batch 19, global_batch_idx: 24550, batch size: 52, loss[discriminator_loss=2.588, discriminator_real_loss=1.373, discriminator_fake_loss=1.215, generator_loss=30.26, generator_mel_loss=20.36, generator_kl_loss=1.961, generator_dur_loss=1.647, generator_adv_loss=2.207, generator_feat_match_loss=4.09, over 52.00 samples.], tot_loss[discriminator_loss=2.624, discriminator_real_loss=1.346, discriminator_fake_loss=1.278, generator_loss=30.44, generator_mel_loss=20.31, generator_kl_loss=2.029, generator_dur_loss=1.647, generator_adv_loss=2.231, generator_feat_match_loss=4.222, over 1428.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:27:02,737 INFO [train.py:811] (1/4) Start epoch 665
+2023-11-14 12:30:08,300 INFO [train.py:467] (1/4) Epoch 665, batch 32, global_batch_idx: 24600, batch size: 61, loss[discriminator_loss=2.52, discriminator_real_loss=1.279, discriminator_fake_loss=1.24, generator_loss=31.62, generator_mel_loss=20.65, generator_kl_loss=2.07, generator_dur_loss=1.658, generator_adv_loss=2.633, generator_feat_match_loss=4.609, over 61.00 samples.], tot_loss[discriminator_loss=2.575, discriminator_real_loss=1.302, discriminator_fake_loss=1.273, generator_loss=30.61, generator_mel_loss=20.4, generator_kl_loss=2.042, generator_dur_loss=1.649, generator_adv_loss=2.235, generator_feat_match_loss=4.285, over 2444.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:30:08,302 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 12:30:19,982 INFO [train.py:517] (1/4) Epoch 665, validation: discriminator_loss=2.543, discriminator_real_loss=1.435, discriminator_fake_loss=1.108, generator_loss=31.54, generator_mel_loss=20.99, generator_kl_loss=2.102, generator_dur_loss=1.638, generator_adv_loss=2.564, generator_feat_match_loss=4.24, over 100.00 samples.
+2023-11-14 12:30:19,983 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 12:30:43,560 INFO [train.py:811] (1/4) Start epoch 666
+2023-11-14 12:34:21,575 INFO [train.py:811] (1/4) Start epoch 667
+2023-11-14 12:35:22,792 INFO [train.py:467] (1/4) Epoch 667, batch 8, global_batch_idx: 24650, batch size: 58, loss[discriminator_loss=2.805, discriminator_real_loss=1.183, discriminator_fake_loss=1.621, generator_loss=29.62, generator_mel_loss=20.08, generator_kl_loss=1.986, generator_dur_loss=1.642, generator_adv_loss=2.303, generator_feat_match_loss=3.611, over 58.00 samples.], tot_loss[discriminator_loss=2.589, discriminator_real_loss=1.267, discriminator_fake_loss=1.323, generator_loss=30.63, generator_mel_loss=20.19, generator_kl_loss=1.984, generator_dur_loss=1.646, generator_adv_loss=2.346, generator_feat_match_loss=4.468, over 636.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:37:53,878 INFO [train.py:811] (1/4) Start epoch 668
+2023-11-14 12:40:02,970 INFO [train.py:467] (1/4) Epoch 668, batch 21, global_batch_idx: 24700, batch size: 101, loss[discriminator_loss=2.539, discriminator_real_loss=1.248, discriminator_fake_loss=1.291, generator_loss=30.5, generator_mel_loss=20.4, generator_kl_loss=2.066, generator_dur_loss=1.625, generator_adv_loss=2.109, generator_feat_match_loss=4.301, over 101.00 samples.], tot_loss[discriminator_loss=2.588, discriminator_real_loss=1.302, discriminator_fake_loss=1.286, generator_loss=30.42, generator_mel_loss=20.38, generator_kl_loss=2.027, generator_dur_loss=1.643, generator_adv_loss=2.148, generator_feat_match_loss=4.222, over 1574.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:41:33,351 INFO [train.py:811] (1/4) Start epoch 669
+2023-11-14 12:44:52,250 INFO [train.py:467] (1/4) Epoch 669, batch 34, global_batch_idx: 24750, batch size: 61, loss[discriminator_loss=2.521, discriminator_real_loss=1.081, discriminator_fake_loss=1.44, generator_loss=30.8, generator_mel_loss=20.32, generator_kl_loss=1.918, generator_dur_loss=1.643, generator_adv_loss=2.256, generator_feat_match_loss=4.656, over 61.00 samples.], tot_loss[discriminator_loss=2.606, discriminator_real_loss=1.323, discriminator_fake_loss=1.282, generator_loss=30.65, generator_mel_loss=20.42, generator_kl_loss=2.018, generator_dur_loss=1.642, generator_adv_loss=2.241, generator_feat_match_loss=4.335, over 2503.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:45:05,834 INFO [train.py:811] (1/4) Start epoch 670
+2023-11-14 12:48:36,947 INFO [train.py:811] (1/4) Start epoch 671
+2023-11-14 12:49:48,436 INFO [train.py:467] (1/4) Epoch 671, batch 10, global_batch_idx: 24800, batch size: 53, loss[discriminator_loss=2.533, discriminator_real_loss=1.418, discriminator_fake_loss=1.115, generator_loss=31.32, generator_mel_loss=20.5, generator_kl_loss=1.992, generator_dur_loss=1.671, generator_adv_loss=2.43, generator_feat_match_loss=4.719, over 53.00 samples.], tot_loss[discriminator_loss=2.52, discriminator_real_loss=1.271, discriminator_fake_loss=1.25, generator_loss=30.5, generator_mel_loss=20.15, generator_kl_loss=2.036, generator_dur_loss=1.646, generator_adv_loss=2.271, generator_feat_match_loss=4.391, over 881.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 16.0
+2023-11-14 12:49:48,438 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 12:50:00,619 INFO [train.py:517] (1/4) Epoch 671, validation: discriminator_loss=2.513, discriminator_real_loss=1.152, discriminator_fake_loss=1.361, generator_loss=30.93, generator_mel_loss=20.81, generator_kl_loss=2.15, generator_dur_loss=1.638, generator_adv_loss=1.923, generator_feat_match_loss=4.411, over 100.00 samples.
+2023-11-14 12:50:00,621 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 12:52:23,751 INFO [train.py:811] (1/4) Start epoch 672
+2023-11-14 12:54:45,564 INFO [train.py:467] (1/4) Epoch 672, batch 23, global_batch_idx: 24850, batch size: 52, loss[discriminator_loss=2.508, discriminator_real_loss=1.247, discriminator_fake_loss=1.262, generator_loss=30.85, generator_mel_loss=20.38, generator_kl_loss=2.047, generator_dur_loss=1.639, generator_adv_loss=2.324, generator_feat_match_loss=4.461, over 52.00 samples.], tot_loss[discriminator_loss=2.562, discriminator_real_loss=1.283, discriminator_fake_loss=1.279, generator_loss=30.31, generator_mel_loss=20.2, generator_kl_loss=2.02, generator_dur_loss=1.648, generator_adv_loss=2.208, generator_feat_match_loss=4.24, over 1703.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:55:56,086 INFO [train.py:811] (1/4) Start epoch 673
+2023-11-14 12:59:29,790 INFO [train.py:467] (1/4) Epoch 673, batch 36, global_batch_idx: 24900, batch size: 65, loss[discriminator_loss=2.521, discriminator_real_loss=1.305, discriminator_fake_loss=1.217, generator_loss=30.1, generator_mel_loss=20.01, generator_kl_loss=2.028, generator_dur_loss=1.633, generator_adv_loss=2.16, generator_feat_match_loss=4.262, over 65.00 samples.], tot_loss[discriminator_loss=2.616, discriminator_real_loss=1.328, discriminator_fake_loss=1.288, generator_loss=30.38, generator_mel_loss=20.41, generator_kl_loss=2.03, generator_dur_loss=1.643, generator_adv_loss=2.15, generator_feat_match_loss=4.15, over 2697.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 12:59:30,481 INFO [train.py:811] (1/4) Start epoch 674
+2023-11-14 13:03:02,025 INFO [train.py:811] (1/4) Start epoch 675
+2023-11-14 13:04:20,043 INFO [train.py:467] (1/4) Epoch 675, batch 12, global_batch_idx: 24950, batch size: 50, loss[discriminator_loss=2.41, discriminator_real_loss=1.279, discriminator_fake_loss=1.13, generator_loss=31.46, generator_mel_loss=19.78, generator_kl_loss=1.989, generator_dur_loss=1.674, generator_adv_loss=2.555, generator_feat_match_loss=5.469, over 50.00 samples.], tot_loss[discriminator_loss=2.559, discriminator_real_loss=1.345, discriminator_fake_loss=1.214, generator_loss=30.86, generator_mel_loss=20.03, generator_kl_loss=1.984, generator_dur_loss=1.642, generator_adv_loss=2.413, generator_feat_match_loss=4.795, over 962.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 13:06:42,582 INFO [train.py:811] (1/4) Start epoch 676
+2023-11-14 13:09:11,617 INFO [train.py:467] (1/4) Epoch 676, batch 25, global_batch_idx: 25000, batch size: 90, loss[discriminator_loss=2.586, discriminator_real_loss=1.266, discriminator_fake_loss=1.32, generator_loss=30.31, generator_mel_loss=20.46, generator_kl_loss=1.991, generator_dur_loss=1.657, generator_adv_loss=2.18, generator_feat_match_loss=4.027, over 90.00 samples.], tot_loss[discriminator_loss=2.589, discriminator_real_loss=1.302, discriminator_fake_loss=1.287, generator_loss=30.27, generator_mel_loss=20.4, generator_kl_loss=2.024, generator_dur_loss=1.645, generator_adv_loss=2.144, generator_feat_match_loss=4.063, over 1894.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 13:09:11,619 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 13:09:23,433 INFO [train.py:517] (1/4) Epoch 676, validation: discriminator_loss=2.535, discriminator_real_loss=1.263, discriminator_fake_loss=1.272, generator_loss=31.18, generator_mel_loss=20.96, generator_kl_loss=2.146, generator_dur_loss=1.636, generator_adv_loss=2.061, generator_feat_match_loss=4.378, over 100.00 samples.
+2023-11-14 13:09:23,434 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 13:10:27,027 INFO [train.py:811] (1/4) Start epoch 677
+2023-11-14 13:13:59,593 INFO [train.py:811] (1/4) Start epoch 678
+2023-11-14 13:14:22,137 INFO [train.py:467] (1/4) Epoch 678, batch 1, global_batch_idx: 25050, batch size: 95, loss[discriminator_loss=2.441, discriminator_real_loss=1.236, discriminator_fake_loss=1.205, generator_loss=30.84, generator_mel_loss=20.14, generator_kl_loss=1.983, generator_dur_loss=1.639, generator_adv_loss=2.293, generator_feat_match_loss=4.785, over 95.00 samples.], tot_loss[discriminator_loss=2.55, discriminator_real_loss=1.325, discriminator_fake_loss=1.224, generator_loss=30.77, generator_mel_loss=20.19, generator_kl_loss=2.039, generator_dur_loss=1.653, generator_adv_loss=2.303, generator_feat_match_loss=4.586, over 205.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 13:17:27,973 INFO [train.py:811] (1/4) Start epoch 679
+2023-11-14 13:19:06,720 INFO [train.py:467] (1/4) Epoch 679, batch 14, global_batch_idx: 25100, batch size: 55, loss[discriminator_loss=2.594, discriminator_real_loss=1.364, discriminator_fake_loss=1.23, generator_loss=30.96, generator_mel_loss=20.06, generator_kl_loss=2.06, generator_dur_loss=1.673, generator_adv_loss=2.523, generator_feat_match_loss=4.637, over 55.00 samples.], tot_loss[discriminator_loss=2.649, discriminator_real_loss=1.328, discriminator_fake_loss=1.321, generator_loss=30.5, generator_mel_loss=20.26, generator_kl_loss=2.024, generator_dur_loss=1.655, generator_adv_loss=2.263, generator_feat_match_loss=4.297, over 922.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 13:21:04,002 INFO [train.py:811] (1/4) Start epoch 680
+2023-11-14 13:23:45,481 INFO [train.py:467] (1/4) Epoch 680, batch 27, global_batch_idx: 25150, batch size: 79, loss[discriminator_loss=2.629, discriminator_real_loss=1.466, discriminator_fake_loss=1.162, generator_loss=30.64, generator_mel_loss=20.03, generator_kl_loss=2.038, generator_dur_loss=1.641, generator_adv_loss=2.424, generator_feat_match_loss=4.5, over 79.00 samples.], tot_loss[discriminator_loss=2.552, discriminator_real_loss=1.297, discriminator_fake_loss=1.255, generator_loss=30.65, generator_mel_loss=20.29, generator_kl_loss=2.025, generator_dur_loss=1.649, generator_adv_loss=2.297, generator_feat_match_loss=4.392, over 2132.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 13:24:32,594 INFO [train.py:811] (1/4) Start epoch 681
+2023-11-14 13:28:07,839 INFO [train.py:811] (1/4) Start epoch 682
+2023-11-14 13:28:42,570 INFO [train.py:467] (1/4) Epoch 682, batch 3, global_batch_idx: 25200, batch size: 63, loss[discriminator_loss=2.287, discriminator_real_loss=1.17, discriminator_fake_loss=1.117, generator_loss=31.8, generator_mel_loss=20.19, generator_kl_loss=1.982, generator_dur_loss=1.687, generator_adv_loss=2.604, generator_feat_match_loss=5.34, over 63.00 samples.], tot_loss[discriminator_loss=2.377, discriminator_real_loss=1.248, discriminator_fake_loss=1.129, generator_loss=31.68, generator_mel_loss=20.22, generator_kl_loss=1.978, generator_dur_loss=1.67, generator_adv_loss=2.566, generator_feat_match_loss=5.252, over 247.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 16.0
+2023-11-14 13:28:42,571 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 13:28:56,171 INFO [train.py:517] (1/4) Epoch 682, validation: discriminator_loss=2.282, discriminator_real_loss=1.218, discriminator_fake_loss=1.064, generator_loss=32.04, generator_mel_loss=20.55, generator_kl_loss=2.159, generator_dur_loss=1.636, generator_adv_loss=2.418, generator_feat_match_loss=5.276, over 100.00 samples.
+2023-11-14 13:28:56,172 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 13:31:51,324 INFO [train.py:811] (1/4) Start epoch 683
+2023-11-14 13:33:37,754 INFO [train.py:467] (1/4) Epoch 683, batch 16, global_batch_idx: 25250, batch size: 52, loss[discriminator_loss=2.594, discriminator_real_loss=1.353, discriminator_fake_loss=1.24, generator_loss=29.8, generator_mel_loss=20.02, generator_kl_loss=1.904, generator_dur_loss=1.652, generator_adv_loss=2.037, generator_feat_match_loss=4.191, over 52.00 samples.], tot_loss[discriminator_loss=2.629, discriminator_real_loss=1.339, discriminator_fake_loss=1.289, generator_loss=30.16, generator_mel_loss=20.22, generator_kl_loss=2.026, generator_dur_loss=1.651, generator_adv_loss=2.129, generator_feat_match_loss=4.129, over 1216.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 16.0
+2023-11-14 13:35:24,641 INFO [train.py:811] (1/4) Start epoch 684
+2023-11-14 13:38:20,606 INFO [train.py:467] (1/4) Epoch 684, batch 29, global_batch_idx: 25300, batch size: 81, loss[discriminator_loss=2.652, discriminator_real_loss=1.353, discriminator_fake_loss=1.299, generator_loss=30.05, generator_mel_loss=20.12, generator_kl_loss=2.083, generator_dur_loss=1.622, generator_adv_loss=2.398, generator_feat_match_loss=3.826, over 81.00 samples.], tot_loss[discriminator_loss=2.602, discriminator_real_loss=1.308, discriminator_fake_loss=1.295, generator_loss=30.56, generator_mel_loss=20.4, generator_kl_loss=1.997, generator_dur_loss=1.642, generator_adv_loss=2.238, generator_feat_match_loss=4.292, over 2301.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 16.0
+2023-11-14 13:38:59,241 INFO [train.py:811] (1/4) Start epoch 685
+2023-11-14 13:42:38,366 INFO [train.py:811] (1/4) Start epoch 686
+2023-11-14 13:43:29,172 INFO [train.py:467] (1/4) Epoch 686, batch 5, global_batch_idx: 25350, batch size: 52, loss[discriminator_loss=2.545, discriminator_real_loss=1.283, discriminator_fake_loss=1.262, generator_loss=30.04, generator_mel_loss=20.1, generator_kl_loss=1.963, generator_dur_loss=1.642, generator_adv_loss=2.307, generator_feat_match_loss=4.027, over 52.00 samples.], tot_loss[discriminator_loss=2.588, discriminator_real_loss=1.305, discriminator_fake_loss=1.283, generator_loss=30.61, generator_mel_loss=20.05, generator_kl_loss=2.018, generator_dur_loss=1.635, generator_adv_loss=2.284, generator_feat_match_loss=4.625, over 523.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 16.0
+2023-11-14 13:46:12,336 INFO [train.py:811] (1/4) Start epoch 687
+2023-11-14 13:48:08,420 INFO [train.py:467] (1/4) Epoch 687, batch 18, global_batch_idx: 25400, batch size: 126, loss[discriminator_loss=2.33, discriminator_real_loss=1.143, discriminator_fake_loss=1.188, generator_loss=30.77, generator_mel_loss=19.82, generator_kl_loss=1.857, generator_dur_loss=1.644, generator_adv_loss=2.365, generator_feat_match_loss=5.078, over 126.00 samples.], tot_loss[discriminator_loss=2.49, discriminator_real_loss=1.248, discriminator_fake_loss=1.242, generator_loss=30.87, generator_mel_loss=20.06, generator_kl_loss=1.981, generator_dur_loss=1.643, generator_adv_loss=2.378, generator_feat_match_loss=4.808, over 1585.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 13:48:08,422 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 13:48:20,531 INFO [train.py:517] (1/4) Epoch 687, validation: discriminator_loss=2.486, discriminator_real_loss=1.159, discriminator_fake_loss=1.328, generator_loss=30.94, generator_mel_loss=20.78, generator_kl_loss=2.145, generator_dur_loss=1.627, generator_adv_loss=1.893, generator_feat_match_loss=4.492, over 100.00 samples.
+2023-11-14 13:48:20,532 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 13:50:01,036 INFO [train.py:811] (1/4) Start epoch 688
+2023-11-14 13:53:06,005 INFO [train.py:467] (1/4) Epoch 688, batch 31, global_batch_idx: 25450, batch size: 56, loss[discriminator_loss=2.566, discriminator_real_loss=1.372, discriminator_fake_loss=1.194, generator_loss=30.83, generator_mel_loss=20.77, generator_kl_loss=2.117, generator_dur_loss=1.644, generator_adv_loss=2.098, generator_feat_match_loss=4.199, over 56.00 samples.], tot_loss[discriminator_loss=2.581, discriminator_real_loss=1.313, discriminator_fake_loss=1.268, generator_loss=30.34, generator_mel_loss=20.31, generator_kl_loss=2.033, generator_dur_loss=1.648, generator_adv_loss=2.148, generator_feat_match_loss=4.201, over 2374.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, grad_scale: 8.0
+2023-11-14 13:53:35,996 INFO [train.py:811] (1/4) Start epoch 689
+2023-11-14 13:57:05,696 INFO [train.py:811] (1/4) Start epoch 690
+2023-11-14 13:58:01,062 INFO [train.py:467] (1/4) Epoch 690, batch 7, global_batch_idx: 25500, batch size: 52, loss[discriminator_loss=2.584, discriminator_real_loss=1.355, discriminator_fake_loss=1.229, generator_loss=30.42, generator_mel_loss=20.29, generator_kl_loss=2.086, generator_dur_loss=1.636, generator_adv_loss=2.02, generator_feat_match_loss=4.387, over 52.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.349, discriminator_fake_loss=1.309, generator_loss=30.54, generator_mel_loss=20.46, generator_kl_loss=2.038, generator_dur_loss=1.643, generator_adv_loss=2.247, generator_feat_match_loss=4.147, over 562.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:00:44,117 INFO [train.py:811] (1/4) Start epoch 691
+2023-11-14 14:02:49,299 INFO [train.py:467] (1/4) Epoch 691, batch 20, global_batch_idx: 25550, batch size: 64, loss[discriminator_loss=2.492, discriminator_real_loss=1.293, discriminator_fake_loss=1.198, generator_loss=30.29, generator_mel_loss=19.81, generator_kl_loss=1.998, generator_dur_loss=1.66, generator_adv_loss=2.398, generator_feat_match_loss=4.422, over 64.00 samples.], tot_loss[discriminator_loss=2.551, discriminator_real_loss=1.291, discriminator_fake_loss=1.26, generator_loss=30.55, generator_mel_loss=20.02, generator_kl_loss=1.999, generator_dur_loss=1.646, generator_adv_loss=2.314, generator_feat_match_loss=4.581, over 1526.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:04:19,968 INFO [train.py:811] (1/4) Start epoch 692
+2023-11-14 14:07:38,780 INFO [train.py:467] (1/4) Epoch 692, batch 33, global_batch_idx: 25600, batch size: 81, loss[discriminator_loss=2.533, discriminator_real_loss=1.287, discriminator_fake_loss=1.246, generator_loss=30.89, generator_mel_loss=20.14, generator_kl_loss=2.114, generator_dur_loss=1.649, generator_adv_loss=2.34, generator_feat_match_loss=4.648, over 81.00 samples.], tot_loss[discriminator_loss=2.561, discriminator_real_loss=1.29, discriminator_fake_loss=1.272, generator_loss=30.44, generator_mel_loss=20.27, generator_kl_loss=2.034, generator_dur_loss=1.638, generator_adv_loss=2.18, generator_feat_match_loss=4.314, over 2596.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 16.0
+2023-11-14 14:07:38,782 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 14:07:50,035 INFO [train.py:517] (1/4) Epoch 692, validation: discriminator_loss=2.648, discriminator_real_loss=1.176, discriminator_fake_loss=1.471, generator_loss=30.91, generator_mel_loss=21.14, generator_kl_loss=2.264, generator_dur_loss=1.628, generator_adv_loss=1.743, generator_feat_match_loss=4.13, over 100.00 samples.
+2023-11-14 14:07:50,036 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 14:08:02,747 INFO [train.py:811] (1/4) Start epoch 693
+2023-11-14 14:11:34,071 INFO [train.py:811] (1/4) Start epoch 694
+2023-11-14 14:12:37,439 INFO [train.py:467] (1/4) Epoch 694, batch 9, global_batch_idx: 25650, batch size: 110, loss[discriminator_loss=2.623, discriminator_real_loss=1.301, discriminator_fake_loss=1.322, generator_loss=30.98, generator_mel_loss=20.71, generator_kl_loss=2.09, generator_dur_loss=1.662, generator_adv_loss=2.145, generator_feat_match_loss=4.375, over 110.00 samples.], tot_loss[discriminator_loss=2.577, discriminator_real_loss=1.299, discriminator_fake_loss=1.277, generator_loss=30.64, generator_mel_loss=20.37, generator_kl_loss=2.052, generator_dur_loss=1.647, generator_adv_loss=2.205, generator_feat_match_loss=4.369, over 846.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:15:05,988 INFO [train.py:811] (1/4) Start epoch 695
+2023-11-14 14:17:23,157 INFO [train.py:467] (1/4) Epoch 695, batch 22, global_batch_idx: 25700, batch size: 58, loss[discriminator_loss=2.637, discriminator_real_loss=1.407, discriminator_fake_loss=1.229, generator_loss=30.52, generator_mel_loss=20.33, generator_kl_loss=2.048, generator_dur_loss=1.638, generator_adv_loss=2.131, generator_feat_match_loss=4.371, over 58.00 samples.], tot_loss[discriminator_loss=2.584, discriminator_real_loss=1.299, discriminator_fake_loss=1.285, generator_loss=30.5, generator_mel_loss=20.36, generator_kl_loss=2.008, generator_dur_loss=1.646, generator_adv_loss=2.194, generator_feat_match_loss=4.298, over 1731.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:18:36,416 INFO [train.py:811] (1/4) Start epoch 696
+2023-11-14 14:21:53,208 INFO [train.py:467] (1/4) Epoch 696, batch 35, global_batch_idx: 25750, batch size: 58, loss[discriminator_loss=2.697, discriminator_real_loss=1.248, discriminator_fake_loss=1.449, generator_loss=29.55, generator_mel_loss=20.07, generator_kl_loss=1.977, generator_dur_loss=1.619, generator_adv_loss=2.016, generator_feat_match_loss=3.871, over 58.00 samples.], tot_loss[discriminator_loss=2.54, discriminator_real_loss=1.287, discriminator_fake_loss=1.253, generator_loss=30.77, generator_mel_loss=20.17, generator_kl_loss=2.034, generator_dur_loss=1.643, generator_adv_loss=2.347, generator_feat_match_loss=4.575, over 2645.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:22:01,680 INFO [train.py:811] (1/4) Start epoch 697
+2023-11-14 14:25:36,570 INFO [train.py:811] (1/4) Start epoch 698
+2023-11-14 14:26:58,943 INFO [train.py:467] (1/4) Epoch 698, batch 11, global_batch_idx: 25800, batch size: 73, loss[discriminator_loss=2.812, discriminator_real_loss=1.436, discriminator_fake_loss=1.377, generator_loss=29.4, generator_mel_loss=19.75, generator_kl_loss=2.001, generator_dur_loss=1.629, generator_adv_loss=2.008, generator_feat_match_loss=4.008, over 73.00 samples.], tot_loss[discriminator_loss=2.428, discriminator_real_loss=1.237, discriminator_fake_loss=1.191, generator_loss=31.08, generator_mel_loss=19.94, generator_kl_loss=2.009, generator_dur_loss=1.644, generator_adv_loss=2.418, generator_feat_match_loss=5.069, over 820.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:26:58,945 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 14:27:10,095 INFO [train.py:517] (1/4) Epoch 698, validation: discriminator_loss=2.54, discriminator_real_loss=1.189, discriminator_fake_loss=1.35, generator_loss=30.44, generator_mel_loss=20.34, generator_kl_loss=2.23, generator_dur_loss=1.631, generator_adv_loss=2.048, generator_feat_match_loss=4.19, over 100.00 samples.
+2023-11-14 14:27:10,096 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 14:29:22,839 INFO [train.py:811] (1/4) Start epoch 699
+2023-11-14 14:31:40,910 INFO [train.py:467] (1/4) Epoch 699, batch 24, global_batch_idx: 25850, batch size: 101, loss[discriminator_loss=2.576, discriminator_real_loss=1.27, discriminator_fake_loss=1.307, generator_loss=30.44, generator_mel_loss=20.31, generator_kl_loss=2.112, generator_dur_loss=1.646, generator_adv_loss=2.217, generator_feat_match_loss=4.156, over 101.00 samples.], tot_loss[discriminator_loss=2.542, discriminator_real_loss=1.28, discriminator_fake_loss=1.262, generator_loss=30.44, generator_mel_loss=20.21, generator_kl_loss=2.047, generator_dur_loss=1.645, generator_adv_loss=2.21, generator_feat_match_loss=4.323, over 1642.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:32:52,188 INFO [train.py:811] (1/4) Start epoch 700
+2023-11-14 14:36:23,016 INFO [train.py:811] (1/4) Start epoch 701
+2023-11-14 14:36:40,052 INFO [train.py:467] (1/4) Epoch 701, batch 0, global_batch_idx: 25900, batch size: 50, loss[discriminator_loss=2.723, discriminator_real_loss=1.139, discriminator_fake_loss=1.585, generator_loss=29.65, generator_mel_loss=19.87, generator_kl_loss=2.009, generator_dur_loss=1.667, generator_adv_loss=2.162, generator_feat_match_loss=3.939, over 50.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.139, discriminator_fake_loss=1.585, generator_loss=29.65, generator_mel_loss=19.87, generator_kl_loss=2.009, generator_dur_loss=1.667, generator_adv_loss=2.162, generator_feat_match_loss=3.939, over 50.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:39:57,612 INFO [train.py:811] (1/4) Start epoch 702
+2023-11-14 14:41:25,243 INFO [train.py:467] (1/4) Epoch 702, batch 13, global_batch_idx: 25950, batch size: 153, loss[discriminator_loss=2.504, discriminator_real_loss=1.209, discriminator_fake_loss=1.295, generator_loss=30.73, generator_mel_loss=20.18, generator_kl_loss=2.149, generator_dur_loss=1.611, generator_adv_loss=2.281, generator_feat_match_loss=4.5, over 153.00 samples.], tot_loss[discriminator_loss=2.551, discriminator_real_loss=1.292, discriminator_fake_loss=1.259, generator_loss=30.32, generator_mel_loss=20.15, generator_kl_loss=2.046, generator_dur_loss=1.636, generator_adv_loss=2.207, generator_feat_match_loss=4.276, over 1053.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 14:43:31,149 INFO [train.py:811] (1/4) Start epoch 703
+2023-11-14 14:46:10,182 INFO [train.py:467] (1/4) Epoch 703, batch 26, global_batch_idx: 26000, batch size: 58, loss[discriminator_loss=2.582, discriminator_real_loss=1.329, discriminator_fake_loss=1.254, generator_loss=30.55, generator_mel_loss=20.05, generator_kl_loss=2.048, generator_dur_loss=1.612, generator_adv_loss=2.326, generator_feat_match_loss=4.516, over 58.00 samples.], tot_loss[discriminator_loss=2.62, discriminator_real_loss=1.333, discriminator_fake_loss=1.287, generator_loss=30.48, generator_mel_loss=20.38, generator_kl_loss=2.034, generator_dur_loss=1.644, generator_adv_loss=2.18, generator_feat_match_loss=4.237, over 2051.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 16.0
+2023-11-14 14:46:10,184 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 14:46:21,216 INFO [train.py:517] (1/4) Epoch 703, validation: discriminator_loss=2.567, discriminator_real_loss=1.261, discriminator_fake_loss=1.305, generator_loss=31.06, generator_mel_loss=20.82, generator_kl_loss=2.197, generator_dur_loss=1.635, generator_adv_loss=2.174, generator_feat_match_loss=4.234, over 100.00 samples.
+2023-11-14 14:46:21,217 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 14:47:22,506 INFO [train.py:811] (1/4) Start epoch 704
+2023-11-14 14:50:54,836 INFO [train.py:811] (1/4) Start epoch 705
+2023-11-14 14:51:19,627 INFO [train.py:467] (1/4) Epoch 705, batch 2, global_batch_idx: 26050, batch size: 67, loss[discriminator_loss=2.691, discriminator_real_loss=1.347, discriminator_fake_loss=1.346, generator_loss=30.77, generator_mel_loss=20.4, generator_kl_loss=2.09, generator_dur_loss=1.664, generator_adv_loss=2.289, generator_feat_match_loss=4.328, over 67.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.386, discriminator_fake_loss=1.336, generator_loss=30.46, generator_mel_loss=20.48, generator_kl_loss=2.073, generator_dur_loss=1.66, generator_adv_loss=2.161, generator_feat_match_loss=4.082, over 186.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 16.0
+2023-11-14 14:54:27,770 INFO [train.py:811] (1/4) Start epoch 706
+2023-11-14 14:56:04,335 INFO [train.py:467] (1/4) Epoch 706, batch 15, global_batch_idx: 26100, batch size: 52, loss[discriminator_loss=2.65, discriminator_real_loss=1.4, discriminator_fake_loss=1.25, generator_loss=29.29, generator_mel_loss=19.73, generator_kl_loss=1.893, generator_dur_loss=1.664, generator_adv_loss=2.104, generator_feat_match_loss=3.896, over 52.00 samples.], tot_loss[discriminator_loss=2.627, discriminator_real_loss=1.353, discriminator_fake_loss=1.274, generator_loss=30.53, generator_mel_loss=20.38, generator_kl_loss=2.008, generator_dur_loss=1.638, generator_adv_loss=2.216, generator_feat_match_loss=4.293, over 1227.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 16.0
+2023-11-14 14:57:56,693 INFO [train.py:811] (1/4) Start epoch 707
+2023-11-14 15:00:41,816 INFO [train.py:467] (1/4) Epoch 707, batch 28, global_batch_idx: 26150, batch size: 53, loss[discriminator_loss=2.59, discriminator_real_loss=1.266, discriminator_fake_loss=1.323, generator_loss=28.89, generator_mel_loss=19.52, generator_kl_loss=1.942, generator_dur_loss=1.658, generator_adv_loss=2.006, generator_feat_match_loss=3.764, over 53.00 samples.], tot_loss[discriminator_loss=2.513, discriminator_real_loss=1.27, discriminator_fake_loss=1.243, generator_loss=30.79, generator_mel_loss=20.08, generator_kl_loss=2.006, generator_dur_loss=1.644, generator_adv_loss=2.375, generator_feat_match_loss=4.684, over 1955.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:01:28,959 INFO [train.py:811] (1/4) Start epoch 708
+2023-11-14 15:05:04,095 INFO [train.py:811] (1/4) Start epoch 709
+2023-11-14 15:05:38,921 INFO [train.py:467] (1/4) Epoch 709, batch 4, global_batch_idx: 26200, batch size: 59, loss[discriminator_loss=2.537, discriminator_real_loss=1.351, discriminator_fake_loss=1.187, generator_loss=30.52, generator_mel_loss=20.33, generator_kl_loss=1.966, generator_dur_loss=1.639, generator_adv_loss=2.18, generator_feat_match_loss=4.402, over 59.00 samples.], tot_loss[discriminator_loss=2.542, discriminator_real_loss=1.292, discriminator_fake_loss=1.25, generator_loss=30.56, generator_mel_loss=20.21, generator_kl_loss=2.006, generator_dur_loss=1.649, generator_adv_loss=2.263, generator_feat_match_loss=4.431, over 314.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:05:38,923 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 15:05:50,930 INFO [train.py:517] (1/4) Epoch 709, validation: discriminator_loss=2.445, discriminator_real_loss=1.196, discriminator_fake_loss=1.249, generator_loss=31.68, generator_mel_loss=20.94, generator_kl_loss=2.097, generator_dur_loss=1.63, generator_adv_loss=2.2, generator_feat_match_loss=4.81, over 100.00 samples.
+2023-11-14 15:05:50,931 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 15:08:42,272 INFO [train.py:811] (1/4) Start epoch 710
+2023-11-14 15:10:39,640 INFO [train.py:467] (1/4) Epoch 710, batch 17, global_batch_idx: 26250, batch size: 81, loss[discriminator_loss=2.508, discriminator_real_loss=1.314, discriminator_fake_loss=1.194, generator_loss=30.26, generator_mel_loss=19.94, generator_kl_loss=2.004, generator_dur_loss=1.611, generator_adv_loss=2.418, generator_feat_match_loss=4.285, over 81.00 samples.], tot_loss[discriminator_loss=2.582, discriminator_real_loss=1.306, discriminator_fake_loss=1.276, generator_loss=30.43, generator_mel_loss=20.04, generator_kl_loss=2.015, generator_dur_loss=1.642, generator_adv_loss=2.24, generator_feat_match_loss=4.491, over 1379.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:12:18,885 INFO [train.py:811] (1/4) Start epoch 711
+2023-11-14 15:15:28,200 INFO [train.py:467] (1/4) Epoch 711, batch 30, global_batch_idx: 26300, batch size: 110, loss[discriminator_loss=2.531, discriminator_real_loss=1.205, discriminator_fake_loss=1.326, generator_loss=31.14, generator_mel_loss=20.57, generator_kl_loss=2.046, generator_dur_loss=1.622, generator_adv_loss=2.248, generator_feat_match_loss=4.656, over 110.00 samples.], tot_loss[discriminator_loss=2.574, discriminator_real_loss=1.299, discriminator_fake_loss=1.275, generator_loss=30.66, generator_mel_loss=20.39, generator_kl_loss=2.035, generator_dur_loss=1.64, generator_adv_loss=2.21, generator_feat_match_loss=4.385, over 2339.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:15:56,856 INFO [train.py:811] (1/4) Start epoch 712
+2023-11-14 15:19:27,515 INFO [train.py:811] (1/4) Start epoch 713
+2023-11-14 15:20:19,624 INFO [train.py:467] (1/4) Epoch 713, batch 6, global_batch_idx: 26350, batch size: 50, loss[discriminator_loss=2.617, discriminator_real_loss=1.337, discriminator_fake_loss=1.28, generator_loss=29.6, generator_mel_loss=19.62, generator_kl_loss=2.009, generator_dur_loss=1.631, generator_adv_loss=2.178, generator_feat_match_loss=4.16, over 50.00 samples.], tot_loss[discriminator_loss=2.566, discriminator_real_loss=1.302, discriminator_fake_loss=1.264, generator_loss=30.24, generator_mel_loss=20.15, generator_kl_loss=1.954, generator_dur_loss=1.642, generator_adv_loss=2.195, generator_feat_match_loss=4.299, over 423.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:23:03,219 INFO [train.py:811] (1/4) Start epoch 714
+2023-11-14 15:25:03,860 INFO [train.py:467] (1/4) Epoch 714, batch 19, global_batch_idx: 26400, batch size: 76, loss[discriminator_loss=2.555, discriminator_real_loss=1.304, discriminator_fake_loss=1.25, generator_loss=30.3, generator_mel_loss=20.11, generator_kl_loss=1.934, generator_dur_loss=1.651, generator_adv_loss=2.107, generator_feat_match_loss=4.496, over 76.00 samples.], tot_loss[discriminator_loss=2.594, discriminator_real_loss=1.303, discriminator_fake_loss=1.29, generator_loss=30.55, generator_mel_loss=20.36, generator_kl_loss=2.042, generator_dur_loss=1.642, generator_adv_loss=2.198, generator_feat_match_loss=4.306, over 1449.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 16.0
+2023-11-14 15:25:03,861 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 15:25:15,257 INFO [train.py:517] (1/4) Epoch 714, validation: discriminator_loss=2.513, discriminator_real_loss=1.094, discriminator_fake_loss=1.419, generator_loss=31.62, generator_mel_loss=21.33, generator_kl_loss=2.166, generator_dur_loss=1.632, generator_adv_loss=1.889, generator_feat_match_loss=4.603, over 100.00 samples.
+2023-11-14 15:25:15,258 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 15:26:49,417 INFO [train.py:811] (1/4) Start epoch 715
+2023-11-14 15:30:04,972 INFO [train.py:467] (1/4) Epoch 715, batch 32, global_batch_idx: 26450, batch size: 55, loss[discriminator_loss=2.617, discriminator_real_loss=1.199, discriminator_fake_loss=1.417, generator_loss=29.89, generator_mel_loss=19.45, generator_kl_loss=2.045, generator_dur_loss=1.678, generator_adv_loss=2.248, generator_feat_match_loss=4.469, over 55.00 samples.], tot_loss[discriminator_loss=2.562, discriminator_real_loss=1.303, discriminator_fake_loss=1.26, generator_loss=30.47, generator_mel_loss=20.1, generator_kl_loss=2.024, generator_dur_loss=1.64, generator_adv_loss=2.245, generator_feat_match_loss=4.468, over 2661.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:30:22,292 INFO [train.py:811] (1/4) Start epoch 716
+2023-11-14 15:33:51,630 INFO [train.py:811] (1/4) Start epoch 717
+2023-11-14 15:34:46,936 INFO [train.py:467] (1/4) Epoch 717, batch 8, global_batch_idx: 26500, batch size: 65, loss[discriminator_loss=2.57, discriminator_real_loss=1.357, discriminator_fake_loss=1.212, generator_loss=30.14, generator_mel_loss=19.87, generator_kl_loss=2.053, generator_dur_loss=1.644, generator_adv_loss=2.379, generator_feat_match_loss=4.188, over 65.00 samples.], tot_loss[discriminator_loss=2.526, discriminator_real_loss=1.257, discriminator_fake_loss=1.268, generator_loss=30.56, generator_mel_loss=20.19, generator_kl_loss=1.993, generator_dur_loss=1.649, generator_adv_loss=2.261, generator_feat_match_loss=4.464, over 633.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:37:21,505 INFO [train.py:811] (1/4) Start epoch 718
+2023-11-14 15:39:34,798 INFO [train.py:467] (1/4) Epoch 718, batch 21, global_batch_idx: 26550, batch size: 101, loss[discriminator_loss=2.496, discriminator_real_loss=1.297, discriminator_fake_loss=1.198, generator_loss=30.38, generator_mel_loss=20.09, generator_kl_loss=2.049, generator_dur_loss=1.618, generator_adv_loss=2.127, generator_feat_match_loss=4.488, over 101.00 samples.], tot_loss[discriminator_loss=2.584, discriminator_real_loss=1.313, discriminator_fake_loss=1.271, generator_loss=30.44, generator_mel_loss=20.16, generator_kl_loss=2.019, generator_dur_loss=1.645, generator_adv_loss=2.222, generator_feat_match_loss=4.388, over 1645.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:40:53,933 INFO [train.py:811] (1/4) Start epoch 719
+2023-11-14 15:44:15,639 INFO [train.py:467] (1/4) Epoch 719, batch 34, global_batch_idx: 26600, batch size: 59, loss[discriminator_loss=2.816, discriminator_real_loss=1.447, discriminator_fake_loss=1.369, generator_loss=29.25, generator_mel_loss=19.44, generator_kl_loss=1.995, generator_dur_loss=1.652, generator_adv_loss=2.086, generator_feat_match_loss=4.074, over 59.00 samples.], tot_loss[discriminator_loss=2.504, discriminator_real_loss=1.256, discriminator_fake_loss=1.248, generator_loss=30.81, generator_mel_loss=20.06, generator_kl_loss=2.006, generator_dur_loss=1.642, generator_adv_loss=2.363, generator_feat_match_loss=4.741, over 2604.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:44:15,641 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 15:44:26,715 INFO [train.py:517] (1/4) Epoch 719, validation: discriminator_loss=2.578, discriminator_real_loss=1.256, discriminator_fake_loss=1.322, generator_loss=30.62, generator_mel_loss=20.52, generator_kl_loss=2.137, generator_dur_loss=1.636, generator_adv_loss=2.032, generator_feat_match_loss=4.302, over 100.00 samples.
+2023-11-14 15:44:26,716 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 15:44:37,014 INFO [train.py:811] (1/4) Start epoch 720
+2023-11-14 15:48:07,593 INFO [train.py:811] (1/4) Start epoch 721
+2023-11-14 15:49:16,281 INFO [train.py:467] (1/4) Epoch 721, batch 10, global_batch_idx: 26650, batch size: 56, loss[discriminator_loss=2.547, discriminator_real_loss=1.265, discriminator_fake_loss=1.281, generator_loss=30.3, generator_mel_loss=20.21, generator_kl_loss=2.055, generator_dur_loss=1.626, generator_adv_loss=2.156, generator_feat_match_loss=4.254, over 56.00 samples.], tot_loss[discriminator_loss=2.537, discriminator_real_loss=1.287, discriminator_fake_loss=1.25, generator_loss=30.13, generator_mel_loss=20.06, generator_kl_loss=2.036, generator_dur_loss=1.645, generator_adv_loss=2.166, generator_feat_match_loss=4.224, over 772.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:51:34,788 INFO [train.py:811] (1/4) Start epoch 722
+2023-11-14 15:53:58,854 INFO [train.py:467] (1/4) Epoch 722, batch 23, global_batch_idx: 26700, batch size: 53, loss[discriminator_loss=2.496, discriminator_real_loss=1.304, discriminator_fake_loss=1.192, generator_loss=30.95, generator_mel_loss=20.38, generator_kl_loss=2.004, generator_dur_loss=1.673, generator_adv_loss=2.217, generator_feat_match_loss=4.676, over 53.00 samples.], tot_loss[discriminator_loss=2.588, discriminator_real_loss=1.301, discriminator_fake_loss=1.287, generator_loss=30.6, generator_mel_loss=20.23, generator_kl_loss=2.02, generator_dur_loss=1.641, generator_adv_loss=2.244, generator_feat_match_loss=4.468, over 1829.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:55:07,239 INFO [train.py:811] (1/4) Start epoch 723
+2023-11-14 15:58:43,666 INFO [train.py:467] (1/4) Epoch 723, batch 36, global_batch_idx: 26750, batch size: 65, loss[discriminator_loss=2.574, discriminator_real_loss=1.302, discriminator_fake_loss=1.272, generator_loss=31.43, generator_mel_loss=20.56, generator_kl_loss=1.871, generator_dur_loss=1.639, generator_adv_loss=2.586, generator_feat_match_loss=4.773, over 65.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.29, discriminator_fake_loss=1.28, generator_loss=30.57, generator_mel_loss=20.27, generator_kl_loss=2.034, generator_dur_loss=1.642, generator_adv_loss=2.225, generator_feat_match_loss=4.399, over 2626.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 15:58:44,324 INFO [train.py:811] (1/4) Start epoch 724
+2023-11-14 16:02:17,640 INFO [train.py:811] (1/4) Start epoch 725
+2023-11-14 16:03:36,505 INFO [train.py:467] (1/4) Epoch 725, batch 12, global_batch_idx: 26800, batch size: 52, loss[discriminator_loss=2.586, discriminator_real_loss=1.353, discriminator_fake_loss=1.233, generator_loss=30.04, generator_mel_loss=20.03, generator_kl_loss=1.971, generator_dur_loss=1.653, generator_adv_loss=2.213, generator_feat_match_loss=4.172, over 52.00 samples.], tot_loss[discriminator_loss=2.582, discriminator_real_loss=1.303, discriminator_fake_loss=1.278, generator_loss=30.31, generator_mel_loss=20.13, generator_kl_loss=2.024, generator_dur_loss=1.643, generator_adv_loss=2.198, generator_feat_match_loss=4.319, over 912.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 16.0
+2023-11-14 16:03:36,506 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 16:03:47,698 INFO [train.py:517] (1/4) Epoch 725, validation: discriminator_loss=2.553, discriminator_real_loss=1.265, discriminator_fake_loss=1.288, generator_loss=30.26, generator_mel_loss=20.49, generator_kl_loss=2.119, generator_dur_loss=1.638, generator_adv_loss=2.055, generator_feat_match_loss=3.957, over 100.00 samples.
+2023-11-14 16:03:47,699 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 16:06:02,688 INFO [train.py:811] (1/4) Start epoch 726
+2023-11-14 16:08:30,024 INFO [train.py:467] (1/4) Epoch 726, batch 25, global_batch_idx: 26850, batch size: 59, loss[discriminator_loss=2.52, discriminator_real_loss=1.31, discriminator_fake_loss=1.21, generator_loss=30.81, generator_mel_loss=20.32, generator_kl_loss=2.103, generator_dur_loss=1.643, generator_adv_loss=2.127, generator_feat_match_loss=4.617, over 59.00 samples.], tot_loss[discriminator_loss=2.575, discriminator_real_loss=1.307, discriminator_fake_loss=1.268, generator_loss=30.42, generator_mel_loss=20.19, generator_kl_loss=1.994, generator_dur_loss=1.636, generator_adv_loss=2.225, generator_feat_match_loss=4.368, over 1952.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 16:09:33,801 INFO [train.py:811] (1/4) Start epoch 727
+2023-11-14 16:13:05,298 INFO [train.py:811] (1/4) Start epoch 728
+2023-11-14 16:13:28,046 INFO [train.py:467] (1/4) Epoch 728, batch 1, global_batch_idx: 26900, batch size: 55, loss[discriminator_loss=2.57, discriminator_real_loss=1.264, discriminator_fake_loss=1.308, generator_loss=30.3, generator_mel_loss=20.23, generator_kl_loss=1.99, generator_dur_loss=1.667, generator_adv_loss=2.281, generator_feat_match_loss=4.137, over 55.00 samples.], tot_loss[discriminator_loss=2.545, discriminator_real_loss=1.242, discriminator_fake_loss=1.304, generator_loss=30.27, generator_mel_loss=20.01, generator_kl_loss=2.017, generator_dur_loss=1.672, generator_adv_loss=2.261, generator_feat_match_loss=4.312, over 118.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 16:16:37,999 INFO [train.py:811] (1/4) Start epoch 729
+2023-11-14 16:18:11,086 INFO [train.py:467] (1/4) Epoch 729, batch 14, global_batch_idx: 26950, batch size: 49, loss[discriminator_loss=2.605, discriminator_real_loss=1.236, discriminator_fake_loss=1.368, generator_loss=30.59, generator_mel_loss=20.75, generator_kl_loss=1.895, generator_dur_loss=1.669, generator_adv_loss=2.012, generator_feat_match_loss=4.266, over 49.00 samples.], tot_loss[discriminator_loss=2.62, discriminator_real_loss=1.329, discriminator_fake_loss=1.29, generator_loss=30.54, generator_mel_loss=20.51, generator_kl_loss=2.044, generator_dur_loss=1.639, generator_adv_loss=2.138, generator_feat_match_loss=4.211, over 1207.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 16:20:13,637 INFO [train.py:811] (1/4) Start epoch 730
+2023-11-14 16:22:59,346 INFO [train.py:467] (1/4) Epoch 730, batch 27, global_batch_idx: 27000, batch size: 85, loss[discriminator_loss=2.615, discriminator_real_loss=1.371, discriminator_fake_loss=1.244, generator_loss=30.31, generator_mel_loss=20.43, generator_kl_loss=2.018, generator_dur_loss=1.632, generator_adv_loss=2.057, generator_feat_match_loss=4.18, over 85.00 samples.], tot_loss[discriminator_loss=2.632, discriminator_real_loss=1.335, discriminator_fake_loss=1.298, generator_loss=30.58, generator_mel_loss=20.42, generator_kl_loss=2.026, generator_dur_loss=1.636, generator_adv_loss=2.169, generator_feat_match_loss=4.322, over 2202.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 16:22:59,347 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 16:23:10,799 INFO [train.py:517] (1/4) Epoch 730, validation: discriminator_loss=2.568, discriminator_real_loss=1.117, discriminator_fake_loss=1.45, generator_loss=30.82, generator_mel_loss=20.99, generator_kl_loss=2.167, generator_dur_loss=1.636, generator_adv_loss=1.875, generator_feat_match_loss=4.148, over 100.00 samples.
+2023-11-14 16:23:10,800 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 16:23:59,576 INFO [train.py:811] (1/4) Start epoch 731
+2023-11-14 16:27:30,248 INFO [train.py:811] (1/4) Start epoch 732
+2023-11-14 16:28:04,151 INFO [train.py:467] (1/4) Epoch 732, batch 3, global_batch_idx: 27050, batch size: 63, loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.349, generator_loss=29.84, generator_mel_loss=20.22, generator_kl_loss=1.981, generator_dur_loss=1.683, generator_adv_loss=2.115, generator_feat_match_loss=3.842, over 63.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.387, discriminator_fake_loss=1.271, generator_loss=30.44, generator_mel_loss=20.36, generator_kl_loss=2.001, generator_dur_loss=1.658, generator_adv_loss=2.23, generator_feat_match_loss=4.194, over 290.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 16:30:57,435 INFO [train.py:811] (1/4) Start epoch 733
+2023-11-14 16:32:39,194 INFO [train.py:467] (1/4) Epoch 733, batch 16, global_batch_idx: 27100, batch size: 56, loss[discriminator_loss=2.461, discriminator_real_loss=1.299, discriminator_fake_loss=1.161, generator_loss=31.88, generator_mel_loss=20.95, generator_kl_loss=2.012, generator_dur_loss=1.613, generator_adv_loss=2.361, generator_feat_match_loss=4.945, over 56.00 samples.], tot_loss[discriminator_loss=2.572, discriminator_real_loss=1.293, discriminator_fake_loss=1.279, generator_loss=30.8, generator_mel_loss=20.35, generator_kl_loss=2.047, generator_dur_loss=1.642, generator_adv_loss=2.267, generator_feat_match_loss=4.498, over 1344.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, grad_scale: 8.0
+2023-11-14 16:34:26,482 INFO [train.py:811] (1/4) Start epoch 734
+2023-11-14 16:37:13,404 INFO [train.py:467] (1/4) Epoch 734, batch 29, global_batch_idx: 27150, batch size: 56, loss[discriminator_loss=2.559, discriminator_real_loss=1.265, discriminator_fake_loss=1.294, generator_loss=29.95, generator_mel_loss=19.67, generator_kl_loss=2.022, generator_dur_loss=1.649, generator_adv_loss=2.061, generator_feat_match_loss=4.547, over 56.00 samples.], tot_loss[discriminator_loss=2.648, discriminator_real_loss=1.365, discriminator_fake_loss=1.284, generator_loss=30.52, generator_mel_loss=19.87, generator_kl_loss=1.996, generator_dur_loss=1.639, generator_adv_loss=2.305, generator_feat_match_loss=4.707, over 2226.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 16:37:54,730 INFO [train.py:811] (1/4) Start epoch 735
+2023-11-14 16:41:26,517 INFO [train.py:811] (1/4) Start epoch 736
+2023-11-14 16:42:14,494 INFO [train.py:467] (1/4) Epoch 736, batch 5, global_batch_idx: 27200, batch size: 52, loss[discriminator_loss=2.557, discriminator_real_loss=1.333, discriminator_fake_loss=1.224, generator_loss=30.29, generator_mel_loss=20.2, generator_kl_loss=2.014, generator_dur_loss=1.629, generator_adv_loss=2.135, generator_feat_match_loss=4.312, over 52.00 samples.], tot_loss[discriminator_loss=2.532, discriminator_real_loss=1.282, discriminator_fake_loss=1.25, generator_loss=30.42, generator_mel_loss=20.12, generator_kl_loss=2.04, generator_dur_loss=1.634, generator_adv_loss=2.209, generator_feat_match_loss=4.424, over 479.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 16:42:14,496 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 16:42:26,537 INFO [train.py:517] (1/4) Epoch 736, validation: discriminator_loss=2.571, discriminator_real_loss=1.193, discriminator_fake_loss=1.377, generator_loss=31.04, generator_mel_loss=20.92, generator_kl_loss=2.167, generator_dur_loss=1.631, generator_adv_loss=1.9, generator_feat_match_loss=4.418, over 100.00 samples.
+2023-11-14 16:42:26,538 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 16:45:13,232 INFO [train.py:811] (1/4) Start epoch 737
+2023-11-14 16:47:14,270 INFO [train.py:467] (1/4) Epoch 737, batch 18, global_batch_idx: 27250, batch size: 52, loss[discriminator_loss=2.59, discriminator_real_loss=1.254, discriminator_fake_loss=1.335, generator_loss=29.2, generator_mel_loss=19.39, generator_kl_loss=2.079, generator_dur_loss=1.644, generator_adv_loss=2.176, generator_feat_match_loss=3.916, over 52.00 samples.], tot_loss[discriminator_loss=2.56, discriminator_real_loss=1.311, discriminator_fake_loss=1.249, generator_loss=30.53, generator_mel_loss=19.9, generator_kl_loss=2.015, generator_dur_loss=1.642, generator_adv_loss=2.356, generator_feat_match_loss=4.627, over 1301.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 16:48:47,237 INFO [train.py:811] (1/4) Start epoch 738
+2023-11-14 16:51:48,696 INFO [train.py:467] (1/4) Epoch 738, batch 31, global_batch_idx: 27300, batch size: 69, loss[discriminator_loss=2.551, discriminator_real_loss=1.212, discriminator_fake_loss=1.34, generator_loss=31.01, generator_mel_loss=20.4, generator_kl_loss=1.985, generator_dur_loss=1.628, generator_adv_loss=2.41, generator_feat_match_loss=4.594, over 69.00 samples.], tot_loss[discriminator_loss=2.587, discriminator_real_loss=1.313, discriminator_fake_loss=1.274, generator_loss=30.47, generator_mel_loss=20.29, generator_kl_loss=2.047, generator_dur_loss=1.641, generator_adv_loss=2.195, generator_feat_match_loss=4.291, over 2132.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 16:52:16,123 INFO [train.py:811] (1/4) Start epoch 739
+2023-11-14 16:55:49,376 INFO [train.py:811] (1/4) Start epoch 740
+2023-11-14 16:56:38,576 INFO [train.py:467] (1/4) Epoch 740, batch 7, global_batch_idx: 27350, batch size: 56, loss[discriminator_loss=2.545, discriminator_real_loss=1.293, discriminator_fake_loss=1.252, generator_loss=30.5, generator_mel_loss=20.24, generator_kl_loss=2.011, generator_dur_loss=1.655, generator_adv_loss=2.166, generator_feat_match_loss=4.426, over 56.00 samples.], tot_loss[discriminator_loss=2.609, discriminator_real_loss=1.293, discriminator_fake_loss=1.316, generator_loss=30.24, generator_mel_loss=20.25, generator_kl_loss=2.004, generator_dur_loss=1.645, generator_adv_loss=2.117, generator_feat_match_loss=4.22, over 574.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 16:59:21,997 INFO [train.py:811] (1/4) Start epoch 741
+2023-11-14 17:01:30,373 INFO [train.py:467] (1/4) Epoch 741, batch 20, global_batch_idx: 27400, batch size: 65, loss[discriminator_loss=2.549, discriminator_real_loss=1.315, discriminator_fake_loss=1.233, generator_loss=31.6, generator_mel_loss=20.59, generator_kl_loss=1.978, generator_dur_loss=1.639, generator_adv_loss=2.457, generator_feat_match_loss=4.938, over 65.00 samples.], tot_loss[discriminator_loss=2.533, discriminator_real_loss=1.276, discriminator_fake_loss=1.257, generator_loss=30.58, generator_mel_loss=20.23, generator_kl_loss=2.005, generator_dur_loss=1.645, generator_adv_loss=2.229, generator_feat_match_loss=4.472, over 1434.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 17:01:30,375 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 17:01:41,522 INFO [train.py:517] (1/4) Epoch 741, validation: discriminator_loss=2.556, discriminator_real_loss=1.263, discriminator_fake_loss=1.293, generator_loss=31.45, generator_mel_loss=21.03, generator_kl_loss=2.177, generator_dur_loss=1.634, generator_adv_loss=2.05, generator_feat_match_loss=4.559, over 100.00 samples.
+2023-11-14 17:01:41,523 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 17:03:10,649 INFO [train.py:811] (1/4) Start epoch 742
+2023-11-14 17:06:26,390 INFO [train.py:467] (1/4) Epoch 742, batch 33, global_batch_idx: 27450, batch size: 110, loss[discriminator_loss=2.594, discriminator_real_loss=1.34, discriminator_fake_loss=1.254, generator_loss=30.43, generator_mel_loss=20.27, generator_kl_loss=2.078, generator_dur_loss=1.657, generator_adv_loss=2.078, generator_feat_match_loss=4.344, over 110.00 samples.], tot_loss[discriminator_loss=2.554, discriminator_real_loss=1.294, discriminator_fake_loss=1.26, generator_loss=30.6, generator_mel_loss=20.07, generator_kl_loss=2.007, generator_dur_loss=1.643, generator_adv_loss=2.294, generator_feat_match_loss=4.589, over 2534.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 17:06:44,798 INFO [train.py:811] (1/4) Start epoch 743
+2023-11-14 17:10:20,500 INFO [train.py:811] (1/4) Start epoch 744
+2023-11-14 17:11:24,050 INFO [train.py:467] (1/4) Epoch 744, batch 9, global_batch_idx: 27500, batch size: 64, loss[discriminator_loss=2.645, discriminator_real_loss=1.472, discriminator_fake_loss=1.174, generator_loss=29.91, generator_mel_loss=20.16, generator_kl_loss=1.914, generator_dur_loss=1.665, generator_adv_loss=2.133, generator_feat_match_loss=4.035, over 64.00 samples.], tot_loss[discriminator_loss=2.601, discriminator_real_loss=1.311, discriminator_fake_loss=1.289, generator_loss=30.48, generator_mel_loss=20.43, generator_kl_loss=2.011, generator_dur_loss=1.645, generator_adv_loss=2.145, generator_feat_match_loss=4.251, over 791.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 17:13:54,156 INFO [train.py:811] (1/4) Start epoch 745
+2023-11-14 17:16:09,430 INFO [train.py:467] (1/4) Epoch 745, batch 22, global_batch_idx: 27550, batch size: 110, loss[discriminator_loss=2.793, discriminator_real_loss=1.343, discriminator_fake_loss=1.449, generator_loss=29.46, generator_mel_loss=20.17, generator_kl_loss=2.008, generator_dur_loss=1.632, generator_adv_loss=1.875, generator_feat_match_loss=3.77, over 110.00 samples.], tot_loss[discriminator_loss=2.632, discriminator_real_loss=1.355, discriminator_fake_loss=1.276, generator_loss=30.67, generator_mel_loss=20.21, generator_kl_loss=2.014, generator_dur_loss=1.635, generator_adv_loss=2.295, generator_feat_match_loss=4.513, over 1771.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 17:17:23,370 INFO [train.py:811] (1/4) Start epoch 746
+2023-11-14 17:20:45,019 INFO [train.py:467] (1/4) Epoch 746, batch 35, global_batch_idx: 27600, batch size: 61, loss[discriminator_loss=2.555, discriminator_real_loss=1.348, discriminator_fake_loss=1.206, generator_loss=30.15, generator_mel_loss=19.88, generator_kl_loss=2.009, generator_dur_loss=1.65, generator_adv_loss=2.285, generator_feat_match_loss=4.328, over 61.00 samples.], tot_loss[discriminator_loss=2.587, discriminator_real_loss=1.311, discriminator_fake_loss=1.276, generator_loss=30.4, generator_mel_loss=20.22, generator_kl_loss=2.041, generator_dur_loss=1.642, generator_adv_loss=2.173, generator_feat_match_loss=4.322, over 2384.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 17:20:45,021 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 17:20:56,458 INFO [train.py:517] (1/4) Epoch 746, validation: discriminator_loss=2.5, discriminator_real_loss=1.217, discriminator_fake_loss=1.283, generator_loss=30.84, generator_mel_loss=20.69, generator_kl_loss=2.212, generator_dur_loss=1.631, generator_adv_loss=2.049, generator_feat_match_loss=4.261, over 100.00 samples.
+2023-11-14 17:20:56,459 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 17:21:03,736 INFO [train.py:811] (1/4) Start epoch 747
+2023-11-14 17:24:28,953 INFO [train.py:811] (1/4) Start epoch 748
+2023-11-14 17:25:48,695 INFO [train.py:467] (1/4) Epoch 748, batch 11, global_batch_idx: 27650, batch size: 59, loss[discriminator_loss=2.551, discriminator_real_loss=1.162, discriminator_fake_loss=1.389, generator_loss=29.96, generator_mel_loss=19.67, generator_kl_loss=2.084, generator_dur_loss=1.675, generator_adv_loss=2.211, generator_feat_match_loss=4.316, over 59.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.303, discriminator_fake_loss=1.268, generator_loss=30.44, generator_mel_loss=20.12, generator_kl_loss=2.014, generator_dur_loss=1.649, generator_adv_loss=2.235, generator_feat_match_loss=4.431, over 811.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 17:28:06,056 INFO [train.py:811] (1/4) Start epoch 749
+2023-11-14 17:30:37,305 INFO [train.py:467] (1/4) Epoch 749, batch 24, global_batch_idx: 27700, batch size: 90, loss[discriminator_loss=2.465, discriminator_real_loss=1.396, discriminator_fake_loss=1.069, generator_loss=30.87, generator_mel_loss=20.05, generator_kl_loss=2.068, generator_dur_loss=1.637, generator_adv_loss=2.375, generator_feat_match_loss=4.734, over 90.00 samples.], tot_loss[discriminator_loss=2.568, discriminator_real_loss=1.305, discriminator_fake_loss=1.263, generator_loss=30.46, generator_mel_loss=20.04, generator_kl_loss=2.017, generator_dur_loss=1.638, generator_adv_loss=2.281, generator_feat_match_loss=4.48, over 1761.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 17:31:40,107 INFO [train.py:811] (1/4) Start epoch 750
+2023-11-14 17:35:12,268 INFO [train.py:811] (1/4) Start epoch 751
+2023-11-14 17:35:29,029 INFO [train.py:467] (1/4) Epoch 751, batch 0, global_batch_idx: 27750, batch size: 67, loss[discriminator_loss=2.574, discriminator_real_loss=1.403, discriminator_fake_loss=1.172, generator_loss=30.44, generator_mel_loss=20.07, generator_kl_loss=1.987, generator_dur_loss=1.656, generator_adv_loss=2.246, generator_feat_match_loss=4.488, over 67.00 samples.], tot_loss[discriminator_loss=2.574, discriminator_real_loss=1.403, discriminator_fake_loss=1.172, generator_loss=30.44, generator_mel_loss=20.07, generator_kl_loss=1.987, generator_dur_loss=1.656, generator_adv_loss=2.246, generator_feat_match_loss=4.488, over 67.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 17:38:41,335 INFO [train.py:811] (1/4) Start epoch 752
+2023-11-14 17:40:07,623 INFO [train.py:467] (1/4) Epoch 752, batch 13, global_batch_idx: 27800, batch size: 90, loss[discriminator_loss=2.598, discriminator_real_loss=1.304, discriminator_fake_loss=1.294, generator_loss=30.47, generator_mel_loss=20.39, generator_kl_loss=1.942, generator_dur_loss=1.628, generator_adv_loss=2.182, generator_feat_match_loss=4.32, over 90.00 samples.], tot_loss[discriminator_loss=2.553, discriminator_real_loss=1.277, discriminator_fake_loss=1.276, generator_loss=30.69, generator_mel_loss=20.28, generator_kl_loss=1.996, generator_dur_loss=1.634, generator_adv_loss=2.268, generator_feat_match_loss=4.512, over 1003.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 17:40:07,625 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 17:40:18,590 INFO [train.py:517] (1/4) Epoch 752, validation: discriminator_loss=2.55, discriminator_real_loss=1.234, discriminator_fake_loss=1.316, generator_loss=31.17, generator_mel_loss=20.9, generator_kl_loss=2.111, generator_dur_loss=1.63, generator_adv_loss=2.089, generator_feat_match_loss=4.446, over 100.00 samples.
+2023-11-14 17:40:18,591 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 17:42:26,040 INFO [train.py:811] (1/4) Start epoch 753
+2023-11-14 17:45:01,984 INFO [train.py:467] (1/4) Epoch 753, batch 26, global_batch_idx: 27850, batch size: 52, loss[discriminator_loss=2.648, discriminator_real_loss=1.444, discriminator_fake_loss=1.203, generator_loss=30.19, generator_mel_loss=20.18, generator_kl_loss=1.989, generator_dur_loss=1.647, generator_adv_loss=2.129, generator_feat_match_loss=4.246, over 52.00 samples.], tot_loss[discriminator_loss=2.562, discriminator_real_loss=1.287, discriminator_fake_loss=1.275, generator_loss=30.41, generator_mel_loss=19.95, generator_kl_loss=2.006, generator_dur_loss=1.638, generator_adv_loss=2.249, generator_feat_match_loss=4.567, over 1850.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 17:45:57,949 INFO [train.py:811] (1/4) Start epoch 754
+2023-11-14 17:49:36,577 INFO [train.py:811] (1/4) Start epoch 755
+2023-11-14 17:50:04,941 INFO [train.py:467] (1/4) Epoch 755, batch 2, global_batch_idx: 27900, batch size: 56, loss[discriminator_loss=2.574, discriminator_real_loss=1.405, discriminator_fake_loss=1.169, generator_loss=30.53, generator_mel_loss=20.17, generator_kl_loss=2.028, generator_dur_loss=1.646, generator_adv_loss=2.215, generator_feat_match_loss=4.473, over 56.00 samples.], tot_loss[discriminator_loss=2.589, discriminator_real_loss=1.384, discriminator_fake_loss=1.204, generator_loss=30.38, generator_mel_loss=20.05, generator_kl_loss=2.017, generator_dur_loss=1.638, generator_adv_loss=2.361, generator_feat_match_loss=4.313, over 168.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 17:53:10,137 INFO [train.py:811] (1/4) Start epoch 756
+2023-11-14 17:54:47,934 INFO [train.py:467] (1/4) Epoch 756, batch 15, global_batch_idx: 27950, batch size: 53, loss[discriminator_loss=2.717, discriminator_real_loss=1.343, discriminator_fake_loss=1.374, generator_loss=29.61, generator_mel_loss=19.83, generator_kl_loss=2.002, generator_dur_loss=1.656, generator_adv_loss=2.092, generator_feat_match_loss=4.031, over 53.00 samples.], tot_loss[discriminator_loss=2.52, discriminator_real_loss=1.269, discriminator_fake_loss=1.251, generator_loss=30.69, generator_mel_loss=20.1, generator_kl_loss=2.014, generator_dur_loss=1.643, generator_adv_loss=2.322, generator_feat_match_loss=4.61, over 1047.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 17:56:40,892 INFO [train.py:811] (1/4) Start epoch 757
+2023-11-14 17:59:26,258 INFO [train.py:467] (1/4) Epoch 757, batch 28, global_batch_idx: 28000, batch size: 50, loss[discriminator_loss=2.584, discriminator_real_loss=1.309, discriminator_fake_loss=1.275, generator_loss=30.43, generator_mel_loss=20.15, generator_kl_loss=2.132, generator_dur_loss=1.64, generator_adv_loss=2.303, generator_feat_match_loss=4.211, over 50.00 samples.], tot_loss[discriminator_loss=2.539, discriminator_real_loss=1.289, discriminator_fake_loss=1.25, generator_loss=30.38, generator_mel_loss=20.02, generator_kl_loss=2.03, generator_dur_loss=1.636, generator_adv_loss=2.216, generator_feat_match_loss=4.476, over 2026.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 17:59:26,260 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 17:59:37,442 INFO [train.py:517] (1/4) Epoch 757, validation: discriminator_loss=2.594, discriminator_real_loss=1.328, discriminator_fake_loss=1.265, generator_loss=30.85, generator_mel_loss=20.74, generator_kl_loss=2.225, generator_dur_loss=1.632, generator_adv_loss=2.084, generator_feat_match_loss=4.173, over 100.00 samples.
+2023-11-14 17:59:37,443 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 18:00:27,469 INFO [train.py:811] (1/4) Start epoch 758
+2023-11-14 18:03:57,026 INFO [train.py:811] (1/4) Start epoch 759
+2023-11-14 18:04:33,835 INFO [train.py:467] (1/4) Epoch 759, batch 4, global_batch_idx: 28050, batch size: 54, loss[discriminator_loss=2.797, discriminator_real_loss=1.404, discriminator_fake_loss=1.392, generator_loss=30.82, generator_mel_loss=20.83, generator_kl_loss=1.995, generator_dur_loss=1.652, generator_adv_loss=2.047, generator_feat_match_loss=4.301, over 54.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.385, discriminator_fake_loss=1.308, generator_loss=30.57, generator_mel_loss=20.44, generator_kl_loss=2.044, generator_dur_loss=1.648, generator_adv_loss=2.174, generator_feat_match_loss=4.263, over 336.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 18:07:31,632 INFO [train.py:811] (1/4) Start epoch 760
+2023-11-14 18:09:31,061 INFO [train.py:467] (1/4) Epoch 760, batch 17, global_batch_idx: 28100, batch size: 52, loss[discriminator_loss=2.641, discriminator_real_loss=1.374, discriminator_fake_loss=1.268, generator_loss=31.11, generator_mel_loss=20.59, generator_kl_loss=1.927, generator_dur_loss=1.636, generator_adv_loss=2.268, generator_feat_match_loss=4.684, over 52.00 samples.], tot_loss[discriminator_loss=2.588, discriminator_real_loss=1.301, discriminator_fake_loss=1.287, generator_loss=30.61, generator_mel_loss=20.17, generator_kl_loss=2.018, generator_dur_loss=1.64, generator_adv_loss=2.265, generator_feat_match_loss=4.518, over 1354.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:11:07,411 INFO [train.py:811] (1/4) Start epoch 761
+2023-11-14 18:14:03,885 INFO [train.py:467] (1/4) Epoch 761, batch 30, global_batch_idx: 28150, batch size: 63, loss[discriminator_loss=2.504, discriminator_real_loss=1.308, discriminator_fake_loss=1.196, generator_loss=30.54, generator_mel_loss=19.97, generator_kl_loss=2.018, generator_dur_loss=1.643, generator_adv_loss=2.342, generator_feat_match_loss=4.57, over 63.00 samples.], tot_loss[discriminator_loss=2.519, discriminator_real_loss=1.283, discriminator_fake_loss=1.235, generator_loss=30.53, generator_mel_loss=20.04, generator_kl_loss=2.008, generator_dur_loss=1.642, generator_adv_loss=2.269, generator_feat_match_loss=4.572, over 2193.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:14:41,909 INFO [train.py:811] (1/4) Start epoch 762
+2023-11-14 18:18:15,762 INFO [train.py:811] (1/4) Start epoch 763
+2023-11-14 18:19:10,733 INFO [train.py:467] (1/4) Epoch 763, batch 6, global_batch_idx: 28200, batch size: 153, loss[discriminator_loss=2.559, discriminator_real_loss=1.386, discriminator_fake_loss=1.173, generator_loss=30.71, generator_mel_loss=19.82, generator_kl_loss=1.998, generator_dur_loss=1.598, generator_adv_loss=2.354, generator_feat_match_loss=4.941, over 153.00 samples.], tot_loss[discriminator_loss=2.548, discriminator_real_loss=1.308, discriminator_fake_loss=1.241, generator_loss=30.45, generator_mel_loss=19.89, generator_kl_loss=1.986, generator_dur_loss=1.624, generator_adv_loss=2.275, generator_feat_match_loss=4.679, over 591.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:19:10,734 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 18:19:22,747 INFO [train.py:517] (1/4) Epoch 763, validation: discriminator_loss=2.437, discriminator_real_loss=1.185, discriminator_fake_loss=1.252, generator_loss=31.03, generator_mel_loss=20.34, generator_kl_loss=2.192, generator_dur_loss=1.639, generator_adv_loss=2.205, generator_feat_match_loss=4.655, over 100.00 samples.
+2023-11-14 18:19:22,755 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 18:22:02,792 INFO [train.py:811] (1/4) Start epoch 764
+2023-11-14 18:23:56,320 INFO [train.py:467] (1/4) Epoch 764, batch 19, global_batch_idx: 28250, batch size: 65, loss[discriminator_loss=2.641, discriminator_real_loss=1.363, discriminator_fake_loss=1.277, generator_loss=31.01, generator_mel_loss=20.63, generator_kl_loss=2.023, generator_dur_loss=1.625, generator_adv_loss=2.174, generator_feat_match_loss=4.551, over 65.00 samples.], tot_loss[discriminator_loss=2.573, discriminator_real_loss=1.302, discriminator_fake_loss=1.27, generator_loss=30.7, generator_mel_loss=20.29, generator_kl_loss=2.034, generator_dur_loss=1.639, generator_adv_loss=2.214, generator_feat_match_loss=4.517, over 1493.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:25:32,401 INFO [train.py:811] (1/4) Start epoch 765
+2023-11-14 18:28:42,588 INFO [train.py:467] (1/4) Epoch 765, batch 32, global_batch_idx: 28300, batch size: 69, loss[discriminator_loss=2.535, discriminator_real_loss=1.304, discriminator_fake_loss=1.232, generator_loss=30.02, generator_mel_loss=19.83, generator_kl_loss=1.981, generator_dur_loss=1.624, generator_adv_loss=2.387, generator_feat_match_loss=4.199, over 69.00 samples.], tot_loss[discriminator_loss=2.625, discriminator_real_loss=1.342, discriminator_fake_loss=1.283, generator_loss=30.58, generator_mel_loss=20.22, generator_kl_loss=2.045, generator_dur_loss=1.637, generator_adv_loss=2.25, generator_feat_match_loss=4.434, over 2502.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:29:01,078 INFO [train.py:811] (1/4) Start epoch 766
+2023-11-14 18:32:25,209 INFO [train.py:811] (1/4) Start epoch 767
+2023-11-14 18:33:20,976 INFO [train.py:467] (1/4) Epoch 767, batch 8, global_batch_idx: 28350, batch size: 65, loss[discriminator_loss=2.475, discriminator_real_loss=1.17, discriminator_fake_loss=1.305, generator_loss=30.88, generator_mel_loss=20.06, generator_kl_loss=2.062, generator_dur_loss=1.668, generator_adv_loss=2.432, generator_feat_match_loss=4.656, over 65.00 samples.], tot_loss[discriminator_loss=2.586, discriminator_real_loss=1.31, discriminator_fake_loss=1.276, generator_loss=30.33, generator_mel_loss=20.1, generator_kl_loss=2.035, generator_dur_loss=1.647, generator_adv_loss=2.188, generator_feat_match_loss=4.356, over 664.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:35:56,271 INFO [train.py:811] (1/4) Start epoch 768
+2023-11-14 18:38:02,859 INFO [train.py:467] (1/4) Epoch 768, batch 21, global_batch_idx: 28400, batch size: 55, loss[discriminator_loss=2.572, discriminator_real_loss=1.245, discriminator_fake_loss=1.327, generator_loss=30.47, generator_mel_loss=20.21, generator_kl_loss=2.042, generator_dur_loss=1.654, generator_adv_loss=2.172, generator_feat_match_loss=4.395, over 55.00 samples.], tot_loss[discriminator_loss=2.575, discriminator_real_loss=1.291, discriminator_fake_loss=1.284, generator_loss=30.84, generator_mel_loss=20.32, generator_kl_loss=2.04, generator_dur_loss=1.641, generator_adv_loss=2.269, generator_feat_match_loss=4.575, over 1456.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 18:38:02,861 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 18:38:14,259 INFO [train.py:517] (1/4) Epoch 768, validation: discriminator_loss=2.528, discriminator_real_loss=1.15, discriminator_fake_loss=1.378, generator_loss=31.68, generator_mel_loss=20.95, generator_kl_loss=2.279, generator_dur_loss=1.63, generator_adv_loss=2.086, generator_feat_match_loss=4.736, over 100.00 samples.
+2023-11-14 18:38:14,260 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 18:39:42,258 INFO [train.py:811] (1/4) Start epoch 769
+2023-11-14 18:43:10,899 INFO [train.py:467] (1/4) Epoch 769, batch 34, global_batch_idx: 28450, batch size: 73, loss[discriminator_loss=2.559, discriminator_real_loss=1.25, discriminator_fake_loss=1.31, generator_loss=30.26, generator_mel_loss=19.96, generator_kl_loss=1.984, generator_dur_loss=1.635, generator_adv_loss=2.264, generator_feat_match_loss=4.422, over 73.00 samples.], tot_loss[discriminator_loss=2.527, discriminator_real_loss=1.273, discriminator_fake_loss=1.254, generator_loss=30.56, generator_mel_loss=19.97, generator_kl_loss=2.008, generator_dur_loss=1.636, generator_adv_loss=2.311, generator_feat_match_loss=4.631, over 2605.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 16.0
+2023-11-14 18:43:19,905 INFO [train.py:811] (1/4) Start epoch 770
+2023-11-14 18:46:55,418 INFO [train.py:811] (1/4) Start epoch 771
+2023-11-14 18:48:01,800 INFO [train.py:467] (1/4) Epoch 771, batch 10, global_batch_idx: 28500, batch size: 50, loss[discriminator_loss=2.465, discriminator_real_loss=1.184, discriminator_fake_loss=1.282, generator_loss=30.79, generator_mel_loss=20.06, generator_kl_loss=1.971, generator_dur_loss=1.638, generator_adv_loss=2.266, generator_feat_match_loss=4.852, over 50.00 samples.], tot_loss[discriminator_loss=2.526, discriminator_real_loss=1.279, discriminator_fake_loss=1.247, generator_loss=30.65, generator_mel_loss=20.11, generator_kl_loss=2.056, generator_dur_loss=1.638, generator_adv_loss=2.268, generator_feat_match_loss=4.575, over 738.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:50:31,309 INFO [train.py:811] (1/4) Start epoch 772
+2023-11-14 18:52:56,390 INFO [train.py:467] (1/4) Epoch 772, batch 23, global_batch_idx: 28550, batch size: 59, loss[discriminator_loss=2.637, discriminator_real_loss=1.557, discriminator_fake_loss=1.081, generator_loss=29.5, generator_mel_loss=19.28, generator_kl_loss=2.073, generator_dur_loss=1.659, generator_adv_loss=2.336, generator_feat_match_loss=4.152, over 59.00 samples.], tot_loss[discriminator_loss=2.527, discriminator_real_loss=1.275, discriminator_fake_loss=1.252, generator_loss=30.62, generator_mel_loss=19.83, generator_kl_loss=2.003, generator_dur_loss=1.639, generator_adv_loss=2.359, generator_feat_match_loss=4.788, over 1843.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:54:04,131 INFO [train.py:811] (1/4) Start epoch 773
+2023-11-14 18:57:42,120 INFO [train.py:467] (1/4) Epoch 773, batch 36, global_batch_idx: 28600, batch size: 50, loss[discriminator_loss=2.57, discriminator_real_loss=1.266, discriminator_fake_loss=1.305, generator_loss=31.01, generator_mel_loss=20.44, generator_kl_loss=1.945, generator_dur_loss=1.633, generator_adv_loss=2.334, generator_feat_match_loss=4.656, over 50.00 samples.], tot_loss[discriminator_loss=2.601, discriminator_real_loss=1.318, discriminator_fake_loss=1.284, generator_loss=30.52, generator_mel_loss=20.3, generator_kl_loss=2.031, generator_dur_loss=1.64, generator_adv_loss=2.185, generator_feat_match_loss=4.36, over 2529.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 18:57:42,122 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 18:57:53,392 INFO [train.py:517] (1/4) Epoch 773, validation: discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=30.56, generator_mel_loss=20.63, generator_kl_loss=2.129, generator_dur_loss=1.633, generator_adv_loss=2.041, generator_feat_match_loss=4.135, over 100.00 samples.
+2023-11-14 18:57:53,393 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 18:57:54,137 INFO [train.py:811] (1/4) Start epoch 774
+2023-11-14 19:01:28,278 INFO [train.py:811] (1/4) Start epoch 775
+2023-11-14 19:02:44,124 INFO [train.py:467] (1/4) Epoch 775, batch 12, global_batch_idx: 28650, batch size: 52, loss[discriminator_loss=2.428, discriminator_real_loss=1.25, discriminator_fake_loss=1.178, generator_loss=30.43, generator_mel_loss=19.62, generator_kl_loss=2.032, generator_dur_loss=1.654, generator_adv_loss=2.383, generator_feat_match_loss=4.742, over 52.00 samples.], tot_loss[discriminator_loss=2.601, discriminator_real_loss=1.347, discriminator_fake_loss=1.253, generator_loss=30.39, generator_mel_loss=19.92, generator_kl_loss=2.008, generator_dur_loss=1.629, generator_adv_loss=2.317, generator_feat_match_loss=4.516, over 906.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 19:05:01,564 INFO [train.py:811] (1/4) Start epoch 776
+2023-11-14 19:07:36,721 INFO [train.py:467] (1/4) Epoch 776, batch 25, global_batch_idx: 28700, batch size: 153, loss[discriminator_loss=2.586, discriminator_real_loss=1.117, discriminator_fake_loss=1.468, generator_loss=31.39, generator_mel_loss=20.28, generator_kl_loss=2.032, generator_dur_loss=1.62, generator_adv_loss=2.242, generator_feat_match_loss=5.215, over 153.00 samples.], tot_loss[discriminator_loss=2.548, discriminator_real_loss=1.281, discriminator_fake_loss=1.267, generator_loss=30.59, generator_mel_loss=20.1, generator_kl_loss=2.025, generator_dur_loss=1.637, generator_adv_loss=2.255, generator_feat_match_loss=4.575, over 2000.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, grad_scale: 8.0
+2023-11-14 19:08:35,869 INFO [train.py:811] (1/4) Start epoch 777
+2023-11-14 19:12:11,886 INFO [train.py:811] (1/4) Start epoch 778
+2023-11-14 19:12:31,654 INFO [train.py:467] (1/4) Epoch 778, batch 1, global_batch_idx: 28750, batch size: 79, loss[discriminator_loss=2.473, discriminator_real_loss=1.25, discriminator_fake_loss=1.224, generator_loss=31.3, generator_mel_loss=20.05, generator_kl_loss=2.028, generator_dur_loss=1.657, generator_adv_loss=2.502, generator_feat_match_loss=5.059, over 79.00 samples.], tot_loss[discriminator_loss=2.513, discriminator_real_loss=1.302, discriminator_fake_loss=1.211, generator_loss=30.83, generator_mel_loss=19.89, generator_kl_loss=2.021, generator_dur_loss=1.653, generator_adv_loss=2.408, generator_feat_match_loss=4.863, over 144.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:15:47,890 INFO [train.py:811] (1/4) Start epoch 779
+2023-11-14 19:17:21,556 INFO [train.py:467] (1/4) Epoch 779, batch 14, global_batch_idx: 28800, batch size: 101, loss[discriminator_loss=2.377, discriminator_real_loss=1.223, discriminator_fake_loss=1.154, generator_loss=31.28, generator_mel_loss=20.12, generator_kl_loss=2.01, generator_dur_loss=1.628, generator_adv_loss=2.492, generator_feat_match_loss=5.039, over 101.00 samples.], tot_loss[discriminator_loss=2.528, discriminator_real_loss=1.282, discriminator_fake_loss=1.246, generator_loss=30.71, generator_mel_loss=19.97, generator_kl_loss=2.015, generator_dur_loss=1.63, generator_adv_loss=2.375, generator_feat_match_loss=4.724, over 1139.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 16.0
+2023-11-14 19:17:21,557 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 19:17:32,666 INFO [train.py:517] (1/4) Epoch 779, validation: discriminator_loss=2.42, discriminator_real_loss=1.15, discriminator_fake_loss=1.27, generator_loss=30.91, generator_mel_loss=20.48, generator_kl_loss=2.196, generator_dur_loss=1.632, generator_adv_loss=2.052, generator_feat_match_loss=4.545, over 100.00 samples.
+2023-11-14 19:17:32,667 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 19:19:25,446 INFO [train.py:811] (1/4) Start epoch 780
+2023-11-14 19:21:58,809 INFO [train.py:467] (1/4) Epoch 780, batch 27, global_batch_idx: 28850, batch size: 58, loss[discriminator_loss=2.469, discriminator_real_loss=1.233, discriminator_fake_loss=1.235, generator_loss=30.13, generator_mel_loss=19.81, generator_kl_loss=1.918, generator_dur_loss=1.637, generator_adv_loss=2.107, generator_feat_match_loss=4.656, over 58.00 samples.], tot_loss[discriminator_loss=2.502, discriminator_real_loss=1.262, discriminator_fake_loss=1.239, generator_loss=30.71, generator_mel_loss=19.95, generator_kl_loss=2.013, generator_dur_loss=1.638, generator_adv_loss=2.325, generator_feat_match_loss=4.788, over 2004.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:22:53,613 INFO [train.py:811] (1/4) Start epoch 781
+2023-11-14 19:26:27,410 INFO [train.py:811] (1/4) Start epoch 782
+2023-11-14 19:26:58,517 INFO [train.py:467] (1/4) Epoch 782, batch 3, global_batch_idx: 28900, batch size: 60, loss[discriminator_loss=2.289, discriminator_real_loss=1.182, discriminator_fake_loss=1.108, generator_loss=31.64, generator_mel_loss=19.99, generator_kl_loss=2.065, generator_dur_loss=1.639, generator_adv_loss=2.414, generator_feat_match_loss=5.535, over 60.00 samples.], tot_loss[discriminator_loss=2.441, discriminator_real_loss=1.339, discriminator_fake_loss=1.102, generator_loss=31.41, generator_mel_loss=19.92, generator_kl_loss=1.988, generator_dur_loss=1.633, generator_adv_loss=2.495, generator_feat_match_loss=5.371, over 315.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:29:55,824 INFO [train.py:811] (1/4) Start epoch 783
+2023-11-14 19:31:32,939 INFO [train.py:467] (1/4) Epoch 783, batch 16, global_batch_idx: 28950, batch size: 85, loss[discriminator_loss=2.523, discriminator_real_loss=1.275, discriminator_fake_loss=1.249, generator_loss=30.56, generator_mel_loss=20.06, generator_kl_loss=2.189, generator_dur_loss=1.649, generator_adv_loss=2.268, generator_feat_match_loss=4.398, over 85.00 samples.], tot_loss[discriminator_loss=2.576, discriminator_real_loss=1.295, discriminator_fake_loss=1.281, generator_loss=30.15, generator_mel_loss=20.01, generator_kl_loss=2.054, generator_dur_loss=1.642, generator_adv_loss=2.138, generator_feat_match_loss=4.3, over 1113.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:33:27,561 INFO [train.py:811] (1/4) Start epoch 784
+2023-11-14 19:36:12,974 INFO [train.py:467] (1/4) Epoch 784, batch 29, global_batch_idx: 29000, batch size: 53, loss[discriminator_loss=2.602, discriminator_real_loss=1.367, discriminator_fake_loss=1.233, generator_loss=30.02, generator_mel_loss=19.73, generator_kl_loss=2.027, generator_dur_loss=1.628, generator_adv_loss=2.113, generator_feat_match_loss=4.516, over 53.00 samples.], tot_loss[discriminator_loss=2.588, discriminator_real_loss=1.314, discriminator_fake_loss=1.275, generator_loss=30.37, generator_mel_loss=20.08, generator_kl_loss=2.022, generator_dur_loss=1.64, generator_adv_loss=2.203, generator_feat_match_loss=4.426, over 1985.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:36:12,975 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 19:36:23,999 INFO [train.py:517] (1/4) Epoch 784, validation: discriminator_loss=2.483, discriminator_real_loss=1.163, discriminator_fake_loss=1.32, generator_loss=30.95, generator_mel_loss=20.64, generator_kl_loss=2.146, generator_dur_loss=1.631, generator_adv_loss=1.886, generator_feat_match_loss=4.646, over 100.00 samples.
+2023-11-14 19:36:24,000 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 19:37:06,690 INFO [train.py:811] (1/4) Start epoch 785
+2023-11-14 19:40:37,304 INFO [train.py:811] (1/4) Start epoch 786
+2023-11-14 19:41:19,886 INFO [train.py:467] (1/4) Epoch 786, batch 5, global_batch_idx: 29050, batch size: 58, loss[discriminator_loss=2.613, discriminator_real_loss=1.403, discriminator_fake_loss=1.209, generator_loss=30.52, generator_mel_loss=19.97, generator_kl_loss=2.019, generator_dur_loss=1.616, generator_adv_loss=2.48, generator_feat_match_loss=4.441, over 58.00 samples.], tot_loss[discriminator_loss=2.559, discriminator_real_loss=1.304, discriminator_fake_loss=1.255, generator_loss=30.69, generator_mel_loss=20.13, generator_kl_loss=2.039, generator_dur_loss=1.627, generator_adv_loss=2.293, generator_feat_match_loss=4.599, over 398.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:44:08,593 INFO [train.py:811] (1/4) Start epoch 787
+2023-11-14 19:46:00,355 INFO [train.py:467] (1/4) Epoch 787, batch 18, global_batch_idx: 29100, batch size: 69, loss[discriminator_loss=2.465, discriminator_real_loss=1.159, discriminator_fake_loss=1.307, generator_loss=31.71, generator_mel_loss=20.43, generator_kl_loss=2.023, generator_dur_loss=1.617, generator_adv_loss=2.518, generator_feat_match_loss=5.125, over 69.00 samples.], tot_loss[discriminator_loss=2.504, discriminator_real_loss=1.268, discriminator_fake_loss=1.237, generator_loss=30.81, generator_mel_loss=19.95, generator_kl_loss=2.006, generator_dur_loss=1.633, generator_adv_loss=2.366, generator_feat_match_loss=4.856, over 1228.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:47:40,856 INFO [train.py:811] (1/4) Start epoch 788
+2023-11-14 19:50:37,915 INFO [train.py:467] (1/4) Epoch 788, batch 31, global_batch_idx: 29150, batch size: 81, loss[discriminator_loss=2.523, discriminator_real_loss=1.241, discriminator_fake_loss=1.283, generator_loss=30.44, generator_mel_loss=19.99, generator_kl_loss=2.084, generator_dur_loss=1.65, generator_adv_loss=2.119, generator_feat_match_loss=4.602, over 81.00 samples.], tot_loss[discriminator_loss=2.48, discriminator_real_loss=1.25, discriminator_fake_loss=1.231, generator_loss=30.92, generator_mel_loss=19.81, generator_kl_loss=2.033, generator_dur_loss=1.637, generator_adv_loss=2.388, generator_feat_match_loss=5.051, over 2246.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 19:51:10,078 INFO [train.py:811] (1/4) Start epoch 789
+2023-11-14 19:54:42,941 INFO [train.py:811] (1/4) Start epoch 790
+2023-11-14 19:55:32,820 INFO [train.py:467] (1/4) Epoch 790, batch 7, global_batch_idx: 29200, batch size: 64, loss[discriminator_loss=2.621, discriminator_real_loss=1.28, discriminator_fake_loss=1.342, generator_loss=30.72, generator_mel_loss=20.45, generator_kl_loss=2.012, generator_dur_loss=1.615, generator_adv_loss=2.16, generator_feat_match_loss=4.484, over 64.00 samples.], tot_loss[discriminator_loss=2.596, discriminator_real_loss=1.327, discriminator_fake_loss=1.27, generator_loss=30.44, generator_mel_loss=20.24, generator_kl_loss=2.014, generator_dur_loss=1.637, generator_adv_loss=2.197, generator_feat_match_loss=4.353, over 567.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 16.0
+2023-11-14 19:55:32,822 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 19:55:44,845 INFO [train.py:517] (1/4) Epoch 790, validation: discriminator_loss=2.615, discriminator_real_loss=1.313, discriminator_fake_loss=1.301, generator_loss=31.74, generator_mel_loss=21.48, generator_kl_loss=2.282, generator_dur_loss=1.629, generator_adv_loss=1.995, generator_feat_match_loss=4.356, over 100.00 samples.
+2023-11-14 19:55:44,846 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 19:58:31,907 INFO [train.py:811] (1/4) Start epoch 791
+2023-11-14 20:00:37,023 INFO [train.py:467] (1/4) Epoch 791, batch 20, global_batch_idx: 29250, batch size: 51, loss[discriminator_loss=2.547, discriminator_real_loss=1.201, discriminator_fake_loss=1.346, generator_loss=31.06, generator_mel_loss=20.2, generator_kl_loss=2.014, generator_dur_loss=1.634, generator_adv_loss=2.359, generator_feat_match_loss=4.852, over 51.00 samples.], tot_loss[discriminator_loss=2.615, discriminator_real_loss=1.325, discriminator_fake_loss=1.291, generator_loss=30.58, generator_mel_loss=20.19, generator_kl_loss=2.038, generator_dur_loss=1.639, generator_adv_loss=2.208, generator_feat_match_loss=4.504, over 1559.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 16.0
+2023-11-14 20:02:08,358 INFO [train.py:811] (1/4) Start epoch 792
+2023-11-14 20:05:28,732 INFO [train.py:467] (1/4) Epoch 792, batch 33, global_batch_idx: 29300, batch size: 90, loss[discriminator_loss=2.322, discriminator_real_loss=1.137, discriminator_fake_loss=1.186, generator_loss=31, generator_mel_loss=19.48, generator_kl_loss=2.025, generator_dur_loss=1.636, generator_adv_loss=2.488, generator_feat_match_loss=5.363, over 90.00 samples.], tot_loss[discriminator_loss=2.49, discriminator_real_loss=1.261, discriminator_fake_loss=1.229, generator_loss=30.64, generator_mel_loss=19.87, generator_kl_loss=1.996, generator_dur_loss=1.642, generator_adv_loss=2.353, generator_feat_match_loss=4.779, over 2327.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:05:46,657 INFO [train.py:811] (1/4) Start epoch 793
+2023-11-14 20:09:12,264 INFO [train.py:811] (1/4) Start epoch 794
+2023-11-14 20:10:12,430 INFO [train.py:467] (1/4) Epoch 794, batch 9, global_batch_idx: 29350, batch size: 59, loss[discriminator_loss=2.59, discriminator_real_loss=1.191, discriminator_fake_loss=1.398, generator_loss=30.57, generator_mel_loss=20.15, generator_kl_loss=2.036, generator_dur_loss=1.659, generator_adv_loss=2.117, generator_feat_match_loss=4.609, over 59.00 samples.], tot_loss[discriminator_loss=2.577, discriminator_real_loss=1.301, discriminator_fake_loss=1.276, generator_loss=30.18, generator_mel_loss=19.91, generator_kl_loss=2.012, generator_dur_loss=1.64, generator_adv_loss=2.16, generator_feat_match_loss=4.457, over 682.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:12:45,679 INFO [train.py:811] (1/4) Start epoch 795
+2023-11-14 20:15:04,208 INFO [train.py:467] (1/4) Epoch 795, batch 22, global_batch_idx: 29400, batch size: 58, loss[discriminator_loss=2.703, discriminator_real_loss=1.43, discriminator_fake_loss=1.274, generator_loss=29.89, generator_mel_loss=19.69, generator_kl_loss=2.011, generator_dur_loss=1.661, generator_adv_loss=2.047, generator_feat_match_loss=4.48, over 58.00 samples.], tot_loss[discriminator_loss=2.653, discriminator_real_loss=1.351, discriminator_fake_loss=1.303, generator_loss=30.46, generator_mel_loss=20.29, generator_kl_loss=2.044, generator_dur_loss=1.641, generator_adv_loss=2.147, generator_feat_match_loss=4.332, over 1595.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:15:04,210 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 20:15:15,187 INFO [train.py:517] (1/4) Epoch 795, validation: discriminator_loss=2.594, discriminator_real_loss=1.251, discriminator_fake_loss=1.343, generator_loss=30.8, generator_mel_loss=20.47, generator_kl_loss=2.226, generator_dur_loss=1.628, generator_adv_loss=1.974, generator_feat_match_loss=4.503, over 100.00 samples.
+2023-11-14 20:15:15,188 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 20:16:30,284 INFO [train.py:811] (1/4) Start epoch 796
+2023-11-14 20:20:00,191 INFO [train.py:467] (1/4) Epoch 796, batch 35, global_batch_idx: 29450, batch size: 55, loss[discriminator_loss=2.588, discriminator_real_loss=1.254, discriminator_fake_loss=1.334, generator_loss=29.64, generator_mel_loss=19.49, generator_kl_loss=2.067, generator_dur_loss=1.672, generator_adv_loss=2.148, generator_feat_match_loss=4.258, over 55.00 samples.], tot_loss[discriminator_loss=2.572, discriminator_real_loss=1.304, discriminator_fake_loss=1.268, generator_loss=30.45, generator_mel_loss=19.96, generator_kl_loss=2.037, generator_dur_loss=1.638, generator_adv_loss=2.255, generator_feat_match_loss=4.561, over 2512.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:20:06,866 INFO [train.py:811] (1/4) Start epoch 797
+2023-11-14 20:23:40,368 INFO [train.py:811] (1/4) Start epoch 798
+2023-11-14 20:24:56,855 INFO [train.py:467] (1/4) Epoch 798, batch 11, global_batch_idx: 29500, batch size: 65, loss[discriminator_loss=2.426, discriminator_real_loss=1.184, discriminator_fake_loss=1.242, generator_loss=31.04, generator_mel_loss=20.4, generator_kl_loss=1.934, generator_dur_loss=1.65, generator_adv_loss=2.166, generator_feat_match_loss=4.887, over 65.00 samples.], tot_loss[discriminator_loss=2.531, discriminator_real_loss=1.316, discriminator_fake_loss=1.215, generator_loss=30.76, generator_mel_loss=19.98, generator_kl_loss=1.988, generator_dur_loss=1.639, generator_adv_loss=2.359, generator_feat_match_loss=4.802, over 837.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:27:12,399 INFO [train.py:811] (1/4) Start epoch 799
+2023-11-14 20:29:34,294 INFO [train.py:467] (1/4) Epoch 799, batch 24, global_batch_idx: 29550, batch size: 95, loss[discriminator_loss=2.432, discriminator_real_loss=1.258, discriminator_fake_loss=1.174, generator_loss=30.77, generator_mel_loss=20.01, generator_kl_loss=2.011, generator_dur_loss=1.642, generator_adv_loss=2.258, generator_feat_match_loss=4.848, over 95.00 samples.], tot_loss[discriminator_loss=2.559, discriminator_real_loss=1.306, discriminator_fake_loss=1.253, generator_loss=30.32, generator_mel_loss=19.85, generator_kl_loss=2.06, generator_dur_loss=1.643, generator_adv_loss=2.263, generator_feat_match_loss=4.501, over 1821.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:30:43,379 INFO [train.py:811] (1/4) Start epoch 800
+2023-11-14 20:34:11,853 INFO [train.py:811] (1/4) Start epoch 801
+2023-11-14 20:34:31,184 INFO [train.py:467] (1/4) Epoch 801, batch 0, global_batch_idx: 29600, batch size: 64, loss[discriminator_loss=2.445, discriminator_real_loss=1.29, discriminator_fake_loss=1.156, generator_loss=30.8, generator_mel_loss=19.47, generator_kl_loss=2.086, generator_dur_loss=1.639, generator_adv_loss=2.51, generator_feat_match_loss=5.098, over 64.00 samples.], tot_loss[discriminator_loss=2.445, discriminator_real_loss=1.29, discriminator_fake_loss=1.156, generator_loss=30.8, generator_mel_loss=19.47, generator_kl_loss=2.086, generator_dur_loss=1.639, generator_adv_loss=2.51, generator_feat_match_loss=5.098, over 64.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 16.0
+2023-11-14 20:34:31,186 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 20:34:43,065 INFO [train.py:517] (1/4) Epoch 801, validation: discriminator_loss=2.309, discriminator_real_loss=1.035, discriminator_fake_loss=1.273, generator_loss=31.85, generator_mel_loss=20.11, generator_kl_loss=2.18, generator_dur_loss=1.636, generator_adv_loss=2.45, generator_feat_match_loss=5.474, over 100.00 samples.
+2023-11-14 20:34:43,066 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27304MB
+2023-11-14 20:38:02,232 INFO [train.py:811] (1/4) Start epoch 802
+2023-11-14 20:39:22,634 INFO [train.py:467] (1/4) Epoch 802, batch 13, global_batch_idx: 29650, batch size: 95, loss[discriminator_loss=2.523, discriminator_real_loss=1.322, discriminator_fake_loss=1.202, generator_loss=31.53, generator_mel_loss=20.36, generator_kl_loss=2.15, generator_dur_loss=1.613, generator_adv_loss=2.359, generator_feat_match_loss=5.055, over 95.00 samples.], tot_loss[discriminator_loss=2.504, discriminator_real_loss=1.263, discriminator_fake_loss=1.242, generator_loss=30.43, generator_mel_loss=19.74, generator_kl_loss=2.022, generator_dur_loss=1.637, generator_adv_loss=2.322, generator_feat_match_loss=4.711, over 939.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:41:39,819 INFO [train.py:811] (1/4) Start epoch 803
+2023-11-14 20:44:13,207 INFO [train.py:467] (1/4) Epoch 803, batch 26, global_batch_idx: 29700, batch size: 81, loss[discriminator_loss=2.711, discriminator_real_loss=1.244, discriminator_fake_loss=1.467, generator_loss=29.94, generator_mel_loss=20.04, generator_kl_loss=2.066, generator_dur_loss=1.624, generator_adv_loss=2.139, generator_feat_match_loss=4.07, over 81.00 samples.], tot_loss[discriminator_loss=2.557, discriminator_real_loss=1.296, discriminator_fake_loss=1.261, generator_loss=30.73, generator_mel_loss=20.16, generator_kl_loss=2.065, generator_dur_loss=1.64, generator_adv_loss=2.285, generator_feat_match_loss=4.577, over 1720.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:45:14,750 INFO [train.py:811] (1/4) Start epoch 804
+2023-11-14 20:48:49,114 INFO [train.py:811] (1/4) Start epoch 805
+2023-11-14 20:49:18,566 INFO [train.py:467] (1/4) Epoch 805, batch 2, global_batch_idx: 29750, batch size: 52, loss[discriminator_loss=2.473, discriminator_real_loss=1.216, discriminator_fake_loss=1.258, generator_loss=30.54, generator_mel_loss=20.15, generator_kl_loss=2.045, generator_dur_loss=1.643, generator_adv_loss=2.252, generator_feat_match_loss=4.453, over 52.00 samples.], tot_loss[discriminator_loss=2.545, discriminator_real_loss=1.277, discriminator_fake_loss=1.269, generator_loss=30.15, generator_mel_loss=20, generator_kl_loss=2.009, generator_dur_loss=1.653, generator_adv_loss=2.145, generator_feat_match_loss=4.345, over 160.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:52:24,796 INFO [train.py:811] (1/4) Start epoch 806
+2023-11-14 20:53:57,942 INFO [train.py:467] (1/4) Epoch 806, batch 15, global_batch_idx: 29800, batch size: 59, loss[discriminator_loss=2.387, discriminator_real_loss=1.119, discriminator_fake_loss=1.267, generator_loss=31.16, generator_mel_loss=20.1, generator_kl_loss=1.993, generator_dur_loss=1.64, generator_adv_loss=2.324, generator_feat_match_loss=5.105, over 59.00 samples.], tot_loss[discriminator_loss=2.516, discriminator_real_loss=1.255, discriminator_fake_loss=1.261, generator_loss=30.65, generator_mel_loss=19.89, generator_kl_loss=2.003, generator_dur_loss=1.637, generator_adv_loss=2.345, generator_feat_match_loss=4.77, over 1166.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:53:57,943 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 20:54:09,639 INFO [train.py:517] (1/4) Epoch 806, validation: discriminator_loss=2.527, discriminator_real_loss=1.069, discriminator_fake_loss=1.458, generator_loss=31.23, generator_mel_loss=20.8, generator_kl_loss=2.199, generator_dur_loss=1.638, generator_adv_loss=1.857, generator_feat_match_loss=4.737, over 100.00 samples.
+2023-11-14 20:54:09,640 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 20:56:04,776 INFO [train.py:811] (1/4) Start epoch 807
+2023-11-14 20:58:50,805 INFO [train.py:467] (1/4) Epoch 807, batch 28, global_batch_idx: 29850, batch size: 60, loss[discriminator_loss=2.516, discriminator_real_loss=1.375, discriminator_fake_loss=1.141, generator_loss=30.34, generator_mel_loss=19.9, generator_kl_loss=2.03, generator_dur_loss=1.649, generator_adv_loss=2.381, generator_feat_match_loss=4.387, over 60.00 samples.], tot_loss[discriminator_loss=2.537, discriminator_real_loss=1.272, discriminator_fake_loss=1.265, generator_loss=30.73, generator_mel_loss=20.15, generator_kl_loss=2.048, generator_dur_loss=1.634, generator_adv_loss=2.261, generator_feat_match_loss=4.631, over 2093.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 20:59:41,787 INFO [train.py:811] (1/4) Start epoch 808
+2023-11-14 21:03:20,278 INFO [train.py:811] (1/4) Start epoch 809
+2023-11-14 21:03:53,911 INFO [train.py:467] (1/4) Epoch 809, batch 4, global_batch_idx: 29900, batch size: 61, loss[discriminator_loss=2.584, discriminator_real_loss=1.229, discriminator_fake_loss=1.354, generator_loss=30.7, generator_mel_loss=20.37, generator_kl_loss=2.039, generator_dur_loss=1.628, generator_adv_loss=2.039, generator_feat_match_loss=4.625, over 61.00 samples.], tot_loss[discriminator_loss=2.542, discriminator_real_loss=1.286, discriminator_fake_loss=1.256, generator_loss=30.77, generator_mel_loss=20.12, generator_kl_loss=2.053, generator_dur_loss=1.639, generator_adv_loss=2.286, generator_feat_match_loss=4.678, over 366.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 21:06:52,453 INFO [train.py:811] (1/4) Start epoch 810
+2023-11-14 21:08:44,223 INFO [train.py:467] (1/4) Epoch 810, batch 17, global_batch_idx: 29950, batch size: 76, loss[discriminator_loss=2.516, discriminator_real_loss=1.256, discriminator_fake_loss=1.261, generator_loss=31.18, generator_mel_loss=20.62, generator_kl_loss=2.031, generator_dur_loss=1.66, generator_adv_loss=2.271, generator_feat_match_loss=4.605, over 76.00 samples.], tot_loss[discriminator_loss=2.554, discriminator_real_loss=1.298, discriminator_fake_loss=1.256, generator_loss=30.57, generator_mel_loss=19.95, generator_kl_loss=2.035, generator_dur_loss=1.632, generator_adv_loss=2.289, generator_feat_match_loss=4.669, over 1302.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 21:10:25,759 INFO [train.py:811] (1/4) Start epoch 811
+2023-11-14 21:13:17,641 INFO [train.py:467] (1/4) Epoch 811, batch 30, global_batch_idx: 30000, batch size: 76, loss[discriminator_loss=2.402, discriminator_real_loss=1.274, discriminator_fake_loss=1.127, generator_loss=31.68, generator_mel_loss=20.28, generator_kl_loss=2.002, generator_dur_loss=1.641, generator_adv_loss=2.492, generator_feat_match_loss=5.262, over 76.00 samples.], tot_loss[discriminator_loss=2.577, discriminator_real_loss=1.316, discriminator_fake_loss=1.262, generator_loss=30.64, generator_mel_loss=20.08, generator_kl_loss=2.026, generator_dur_loss=1.64, generator_adv_loss=2.285, generator_feat_match_loss=4.614, over 2066.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 16.0
+2023-11-14 21:13:17,643 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 21:13:28,584 INFO [train.py:517] (1/4) Epoch 811, validation: discriminator_loss=2.392, discriminator_real_loss=1.16, discriminator_fake_loss=1.232, generator_loss=32.08, generator_mel_loss=20.83, generator_kl_loss=2.196, generator_dur_loss=1.648, generator_adv_loss=2.263, generator_feat_match_loss=5.142, over 100.00 samples.
+2023-11-14 21:13:28,586 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 21:14:05,646 INFO [train.py:811] (1/4) Start epoch 812
+2023-11-14 21:17:42,464 INFO [train.py:811] (1/4) Start epoch 813
+2023-11-14 21:18:27,667 INFO [train.py:467] (1/4) Epoch 813, batch 6, global_batch_idx: 30050, batch size: 73, loss[discriminator_loss=2.527, discriminator_real_loss=1.299, discriminator_fake_loss=1.229, generator_loss=30.86, generator_mel_loss=20.03, generator_kl_loss=1.909, generator_dur_loss=1.616, generator_adv_loss=2.377, generator_feat_match_loss=4.93, over 73.00 samples.], tot_loss[discriminator_loss=2.552, discriminator_real_loss=1.308, discriminator_fake_loss=1.244, generator_loss=30.52, generator_mel_loss=19.9, generator_kl_loss=1.99, generator_dur_loss=1.628, generator_adv_loss=2.317, generator_feat_match_loss=4.681, over 494.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 16.0
+2023-11-14 21:21:13,576 INFO [train.py:811] (1/4) Start epoch 814
+2023-11-14 21:23:19,737 INFO [train.py:467] (1/4) Epoch 814, batch 19, global_batch_idx: 30100, batch size: 90, loss[discriminator_loss=2.512, discriminator_real_loss=1.209, discriminator_fake_loss=1.304, generator_loss=30.5, generator_mel_loss=20.16, generator_kl_loss=2.08, generator_dur_loss=1.633, generator_adv_loss=2.109, generator_feat_match_loss=4.52, over 90.00 samples.], tot_loss[discriminator_loss=2.544, discriminator_real_loss=1.274, discriminator_fake_loss=1.27, generator_loss=30.66, generator_mel_loss=20.09, generator_kl_loss=2.048, generator_dur_loss=1.633, generator_adv_loss=2.231, generator_feat_match_loss=4.657, over 1749.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 16.0
+2023-11-14 21:24:42,750 INFO [train.py:811] (1/4) Start epoch 815
+2023-11-14 21:27:58,769 INFO [train.py:467] (1/4) Epoch 815, batch 32, global_batch_idx: 30150, batch size: 85, loss[discriminator_loss=2.598, discriminator_real_loss=1.21, discriminator_fake_loss=1.387, generator_loss=29.74, generator_mel_loss=19.36, generator_kl_loss=2.043, generator_dur_loss=1.646, generator_adv_loss=2.408, generator_feat_match_loss=4.293, over 85.00 samples.], tot_loss[discriminator_loss=2.486, discriminator_real_loss=1.262, discriminator_fake_loss=1.224, generator_loss=31.02, generator_mel_loss=19.85, generator_kl_loss=2.025, generator_dur_loss=1.636, generator_adv_loss=2.414, generator_feat_match_loss=5.098, over 2420.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 21:28:16,099 INFO [train.py:811] (1/4) Start epoch 816
+2023-11-14 21:31:51,631 INFO [train.py:811] (1/4) Start epoch 817
+2023-11-14 21:32:52,158 INFO [train.py:467] (1/4) Epoch 817, batch 8, global_batch_idx: 30200, batch size: 52, loss[discriminator_loss=2.678, discriminator_real_loss=1.484, discriminator_fake_loss=1.193, generator_loss=29.93, generator_mel_loss=19.93, generator_kl_loss=2.002, generator_dur_loss=1.646, generator_adv_loss=2.137, generator_feat_match_loss=4.207, over 52.00 samples.], tot_loss[discriminator_loss=2.637, discriminator_real_loss=1.34, discriminator_fake_loss=1.298, generator_loss=30.33, generator_mel_loss=20.23, generator_kl_loss=2.042, generator_dur_loss=1.636, generator_adv_loss=2.139, generator_feat_match_loss=4.277, over 695.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 21:32:52,160 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 21:33:03,430 INFO [train.py:517] (1/4) Epoch 817, validation: discriminator_loss=2.652, discriminator_real_loss=1.239, discriminator_fake_loss=1.412, generator_loss=30.8, generator_mel_loss=20.69, generator_kl_loss=2.168, generator_dur_loss=1.631, generator_adv_loss=1.917, generator_feat_match_loss=4.389, over 100.00 samples.
+2023-11-14 21:33:03,431 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 21:35:39,711 INFO [train.py:811] (1/4) Start epoch 818
+2023-11-14 21:38:03,026 INFO [train.py:467] (1/4) Epoch 818, batch 21, global_batch_idx: 30250, batch size: 153, loss[discriminator_loss=2.441, discriminator_real_loss=1.367, discriminator_fake_loss=1.074, generator_loss=31.51, generator_mel_loss=20.29, generator_kl_loss=2.044, generator_dur_loss=1.617, generator_adv_loss=2.406, generator_feat_match_loss=5.16, over 153.00 samples.], tot_loss[discriminator_loss=2.57, discriminator_real_loss=1.31, discriminator_fake_loss=1.261, generator_loss=30.7, generator_mel_loss=20.17, generator_kl_loss=2.029, generator_dur_loss=1.631, generator_adv_loss=2.255, generator_feat_match_loss=4.614, over 1868.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 21:39:15,891 INFO [train.py:811] (1/4) Start epoch 819
+2023-11-14 21:42:35,712 INFO [train.py:467] (1/4) Epoch 819, batch 34, global_batch_idx: 30300, batch size: 65, loss[discriminator_loss=2.627, discriminator_real_loss=1.402, discriminator_fake_loss=1.225, generator_loss=29.89, generator_mel_loss=19.65, generator_kl_loss=1.974, generator_dur_loss=1.625, generator_adv_loss=2.121, generator_feat_match_loss=4.523, over 65.00 samples.], tot_loss[discriminator_loss=2.595, discriminator_real_loss=1.35, discriminator_fake_loss=1.245, generator_loss=30.39, generator_mel_loss=19.77, generator_kl_loss=2.005, generator_dur_loss=1.636, generator_adv_loss=2.279, generator_feat_match_loss=4.696, over 2281.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 21:42:51,109 INFO [train.py:811] (1/4) Start epoch 820
+2023-11-14 21:46:18,361 INFO [train.py:811] (1/4) Start epoch 821
+2023-11-14 21:47:31,048 INFO [train.py:467] (1/4) Epoch 821, batch 10, global_batch_idx: 30350, batch size: 50, loss[discriminator_loss=2.68, discriminator_real_loss=1.36, discriminator_fake_loss=1.32, generator_loss=30.61, generator_mel_loss=20.41, generator_kl_loss=2.081, generator_dur_loss=1.661, generator_adv_loss=2.102, generator_feat_match_loss=4.355, over 50.00 samples.], tot_loss[discriminator_loss=2.578, discriminator_real_loss=1.322, discriminator_fake_loss=1.256, generator_loss=30.57, generator_mel_loss=20.21, generator_kl_loss=2.023, generator_dur_loss=1.633, generator_adv_loss=2.203, generator_feat_match_loss=4.506, over 825.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, grad_scale: 8.0
+2023-11-14 21:49:52,117 INFO [train.py:811] (1/4) Start epoch 822
+2023-11-14 21:52:15,875 INFO [train.py:467] (1/4) Epoch 822, batch 23, global_batch_idx: 30400, batch size: 52, loss[discriminator_loss=2.598, discriminator_real_loss=1.492, discriminator_fake_loss=1.106, generator_loss=30.9, generator_mel_loss=20.09, generator_kl_loss=2.061, generator_dur_loss=1.656, generator_adv_loss=2.223, generator_feat_match_loss=4.879, over 52.00 samples.], tot_loss[discriminator_loss=2.582, discriminator_real_loss=1.304, discriminator_fake_loss=1.278, generator_loss=30.53, generator_mel_loss=20.04, generator_kl_loss=2.008, generator_dur_loss=1.638, generator_adv_loss=2.258, generator_feat_match_loss=4.585, over 1678.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 16.0
+2023-11-14 21:52:15,877 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 21:52:26,907 INFO [train.py:517] (1/4) Epoch 822, validation: discriminator_loss=2.61, discriminator_real_loss=1.172, discriminator_fake_loss=1.438, generator_loss=30.48, generator_mel_loss=20.46, generator_kl_loss=2.197, generator_dur_loss=1.64, generator_adv_loss=1.842, generator_feat_match_loss=4.345, over 100.00 samples.
+2023-11-14 21:52:26,909 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 21:53:36,063 INFO [train.py:811] (1/4) Start epoch 823
+2023-11-14 21:57:10,111 INFO [train.py:467] (1/4) Epoch 823, batch 36, global_batch_idx: 30450, batch size: 52, loss[discriminator_loss=2.734, discriminator_real_loss=1.562, discriminator_fake_loss=1.174, generator_loss=30.17, generator_mel_loss=19.85, generator_kl_loss=2, generator_dur_loss=1.626, generator_adv_loss=2.404, generator_feat_match_loss=4.289, over 52.00 samples.], tot_loss[discriminator_loss=2.531, discriminator_real_loss=1.282, discriminator_fake_loss=1.25, generator_loss=30.68, generator_mel_loss=20, generator_kl_loss=2.024, generator_dur_loss=1.635, generator_adv_loss=2.322, generator_feat_match_loss=4.699, over 2487.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 21:57:10,681 INFO [train.py:811] (1/4) Start epoch 824
+2023-11-14 22:00:36,969 INFO [train.py:811] (1/4) Start epoch 825
+2023-11-14 22:02:01,442 INFO [train.py:467] (1/4) Epoch 825, batch 12, global_batch_idx: 30500, batch size: 61, loss[discriminator_loss=2.596, discriminator_real_loss=1.206, discriminator_fake_loss=1.39, generator_loss=30.05, generator_mel_loss=19.78, generator_kl_loss=2.016, generator_dur_loss=1.626, generator_adv_loss=2.102, generator_feat_match_loss=4.523, over 61.00 samples.], tot_loss[discriminator_loss=2.551, discriminator_real_loss=1.295, discriminator_fake_loss=1.255, generator_loss=30.73, generator_mel_loss=19.96, generator_kl_loss=2.004, generator_dur_loss=1.629, generator_adv_loss=2.365, generator_feat_match_loss=4.773, over 932.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:04:09,039 INFO [train.py:811] (1/4) Start epoch 826
+2023-11-14 22:06:42,113 INFO [train.py:467] (1/4) Epoch 826, batch 25, global_batch_idx: 30550, batch size: 65, loss[discriminator_loss=2.516, discriminator_real_loss=1.311, discriminator_fake_loss=1.206, generator_loss=30.32, generator_mel_loss=19.69, generator_kl_loss=1.971, generator_dur_loss=1.617, generator_adv_loss=2.35, generator_feat_match_loss=4.691, over 65.00 samples.], tot_loss[discriminator_loss=2.524, discriminator_real_loss=1.285, discriminator_fake_loss=1.239, generator_loss=30.72, generator_mel_loss=19.96, generator_kl_loss=2.038, generator_dur_loss=1.63, generator_adv_loss=2.306, generator_feat_match_loss=4.787, over 1960.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:07:38,069 INFO [train.py:811] (1/4) Start epoch 827
+2023-11-14 22:11:16,367 INFO [train.py:811] (1/4) Start epoch 828
+2023-11-14 22:11:39,111 INFO [train.py:467] (1/4) Epoch 828, batch 1, global_batch_idx: 30600, batch size: 126, loss[discriminator_loss=2.562, discriminator_real_loss=1.335, discriminator_fake_loss=1.228, generator_loss=31.04, generator_mel_loss=20.11, generator_kl_loss=2.062, generator_dur_loss=1.619, generator_adv_loss=2.396, generator_feat_match_loss=4.855, over 126.00 samples.], tot_loss[discriminator_loss=2.543, discriminator_real_loss=1.305, discriminator_fake_loss=1.238, generator_loss=30.87, generator_mel_loss=19.99, generator_kl_loss=2.071, generator_dur_loss=1.623, generator_adv_loss=2.406, generator_feat_match_loss=4.782, over 178.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:11:39,112 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 22:11:50,899 INFO [train.py:517] (1/4) Epoch 828, validation: discriminator_loss=2.486, discriminator_real_loss=1.237, discriminator_fake_loss=1.249, generator_loss=31.89, generator_mel_loss=20.74, generator_kl_loss=2.258, generator_dur_loss=1.635, generator_adv_loss=2.294, generator_feat_match_loss=4.963, over 100.00 samples.
+2023-11-14 22:11:50,900 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 22:15:00,323 INFO [train.py:811] (1/4) Start epoch 829
+2023-11-14 22:16:34,450 INFO [train.py:467] (1/4) Epoch 829, batch 14, global_batch_idx: 30650, batch size: 69, loss[discriminator_loss=2.477, discriminator_real_loss=1.258, discriminator_fake_loss=1.22, generator_loss=30.25, generator_mel_loss=19.87, generator_kl_loss=2.085, generator_dur_loss=1.643, generator_adv_loss=2.166, generator_feat_match_loss=4.484, over 69.00 samples.], tot_loss[discriminator_loss=2.499, discriminator_real_loss=1.257, discriminator_fake_loss=1.241, generator_loss=30.46, generator_mel_loss=19.88, generator_kl_loss=2.04, generator_dur_loss=1.632, generator_adv_loss=2.253, generator_feat_match_loss=4.653, over 1124.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:18:33,975 INFO [train.py:811] (1/4) Start epoch 830
+2023-11-14 22:21:14,957 INFO [train.py:467] (1/4) Epoch 830, batch 27, global_batch_idx: 30700, batch size: 85, loss[discriminator_loss=2.699, discriminator_real_loss=1.337, discriminator_fake_loss=1.361, generator_loss=29.87, generator_mel_loss=19.94, generator_kl_loss=2.034, generator_dur_loss=1.633, generator_adv_loss=2.191, generator_feat_match_loss=4.066, over 85.00 samples.], tot_loss[discriminator_loss=2.555, discriminator_real_loss=1.305, discriminator_fake_loss=1.25, generator_loss=30.75, generator_mel_loss=19.95, generator_kl_loss=2.038, generator_dur_loss=1.636, generator_adv_loss=2.354, generator_feat_match_loss=4.772, over 1890.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:22:08,563 INFO [train.py:811] (1/4) Start epoch 831
+2023-11-14 22:25:46,137 INFO [train.py:811] (1/4) Start epoch 832
+2023-11-14 22:26:19,911 INFO [train.py:467] (1/4) Epoch 832, batch 3, global_batch_idx: 30750, batch size: 58, loss[discriminator_loss=2.516, discriminator_real_loss=1.27, discriminator_fake_loss=1.246, generator_loss=30.69, generator_mel_loss=20.02, generator_kl_loss=1.949, generator_dur_loss=1.632, generator_adv_loss=2.156, generator_feat_match_loss=4.93, over 58.00 samples.], tot_loss[discriminator_loss=2.595, discriminator_real_loss=1.374, discriminator_fake_loss=1.221, generator_loss=30.5, generator_mel_loss=19.92, generator_kl_loss=1.994, generator_dur_loss=1.628, generator_adv_loss=2.273, generator_feat_match_loss=4.689, over 305.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:29:15,588 INFO [train.py:811] (1/4) Start epoch 833
+2023-11-14 22:30:54,179 INFO [train.py:467] (1/4) Epoch 833, batch 16, global_batch_idx: 30800, batch size: 64, loss[discriminator_loss=2.621, discriminator_real_loss=1.24, discriminator_fake_loss=1.38, generator_loss=29.56, generator_mel_loss=19.66, generator_kl_loss=1.963, generator_dur_loss=1.62, generator_adv_loss=2.264, generator_feat_match_loss=4.059, over 64.00 samples.], tot_loss[discriminator_loss=2.55, discriminator_real_loss=1.291, discriminator_fake_loss=1.259, generator_loss=30.52, generator_mel_loss=20.04, generator_kl_loss=2.015, generator_dur_loss=1.64, generator_adv_loss=2.248, generator_feat_match_loss=4.577, over 1078.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 16.0
+2023-11-14 22:30:54,181 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 22:31:05,529 INFO [train.py:517] (1/4) Epoch 833, validation: discriminator_loss=2.566, discriminator_real_loss=1.36, discriminator_fake_loss=1.206, generator_loss=30.99, generator_mel_loss=20.35, generator_kl_loss=2.14, generator_dur_loss=1.632, generator_adv_loss=2.373, generator_feat_match_loss=4.486, over 100.00 samples.
+2023-11-14 22:31:05,530 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 22:32:55,738 INFO [train.py:811] (1/4) Start epoch 834
+2023-11-14 22:35:40,727 INFO [train.py:467] (1/4) Epoch 834, batch 29, global_batch_idx: 30850, batch size: 54, loss[discriminator_loss=2.547, discriminator_real_loss=1.422, discriminator_fake_loss=1.126, generator_loss=30.12, generator_mel_loss=19.61, generator_kl_loss=1.998, generator_dur_loss=1.673, generator_adv_loss=2.357, generator_feat_match_loss=4.48, over 54.00 samples.], tot_loss[discriminator_loss=2.493, discriminator_real_loss=1.269, discriminator_fake_loss=1.223, generator_loss=30.83, generator_mel_loss=19.73, generator_kl_loss=2.013, generator_dur_loss=1.636, generator_adv_loss=2.398, generator_feat_match_loss=5.052, over 2199.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:36:20,345 INFO [train.py:811] (1/4) Start epoch 835
+2023-11-14 22:39:51,830 INFO [train.py:811] (1/4) Start epoch 836
+2023-11-14 22:40:29,152 INFO [train.py:467] (1/4) Epoch 836, batch 5, global_batch_idx: 30900, batch size: 56, loss[discriminator_loss=2.578, discriminator_real_loss=1.328, discriminator_fake_loss=1.249, generator_loss=30.45, generator_mel_loss=20.26, generator_kl_loss=1.95, generator_dur_loss=1.641, generator_adv_loss=2.111, generator_feat_match_loss=4.488, over 56.00 samples.], tot_loss[discriminator_loss=2.604, discriminator_real_loss=1.334, discriminator_fake_loss=1.269, generator_loss=30.04, generator_mel_loss=19.89, generator_kl_loss=1.991, generator_dur_loss=1.653, generator_adv_loss=2.167, generator_feat_match_loss=4.342, over 350.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:43:18,385 INFO [train.py:811] (1/4) Start epoch 837
+2023-11-14 22:45:18,561 INFO [train.py:467] (1/4) Epoch 837, batch 18, global_batch_idx: 30950, batch size: 81, loss[discriminator_loss=2.586, discriminator_real_loss=1.32, discriminator_fake_loss=1.265, generator_loss=30.78, generator_mel_loss=20.29, generator_kl_loss=1.981, generator_dur_loss=1.615, generator_adv_loss=2.295, generator_feat_match_loss=4.594, over 81.00 samples.], tot_loss[discriminator_loss=2.626, discriminator_real_loss=1.341, discriminator_fake_loss=1.285, generator_loss=30.54, generator_mel_loss=20.22, generator_kl_loss=2.036, generator_dur_loss=1.627, generator_adv_loss=2.19, generator_feat_match_loss=4.465, over 1678.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:46:54,205 INFO [train.py:811] (1/4) Start epoch 838
+2023-11-14 22:50:00,838 INFO [train.py:467] (1/4) Epoch 838, batch 31, global_batch_idx: 31000, batch size: 64, loss[discriminator_loss=2.438, discriminator_real_loss=1.234, discriminator_fake_loss=1.204, generator_loss=30.27, generator_mel_loss=19.6, generator_kl_loss=1.984, generator_dur_loss=1.635, generator_adv_loss=2.301, generator_feat_match_loss=4.75, over 64.00 samples.], tot_loss[discriminator_loss=2.597, discriminator_real_loss=1.316, discriminator_fake_loss=1.281, generator_loss=30.52, generator_mel_loss=20.17, generator_kl_loss=2.011, generator_dur_loss=1.628, generator_adv_loss=2.225, generator_feat_match_loss=4.484, over 2447.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:50:00,839 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 22:50:12,836 INFO [train.py:517] (1/4) Epoch 838, validation: discriminator_loss=2.427, discriminator_real_loss=1.072, discriminator_fake_loss=1.355, generator_loss=31, generator_mel_loss=20.78, generator_kl_loss=2.183, generator_dur_loss=1.634, generator_adv_loss=1.901, generator_feat_match_loss=4.502, over 100.00 samples.
+2023-11-14 22:50:12,837 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 22:50:40,341 INFO [train.py:811] (1/4) Start epoch 839
+2023-11-14 22:54:13,492 INFO [train.py:811] (1/4) Start epoch 840
+2023-11-14 22:55:15,132 INFO [train.py:467] (1/4) Epoch 840, batch 7, global_batch_idx: 31050, batch size: 53, loss[discriminator_loss=2.531, discriminator_real_loss=1.395, discriminator_fake_loss=1.138, generator_loss=30.85, generator_mel_loss=19.89, generator_kl_loss=1.953, generator_dur_loss=1.647, generator_adv_loss=2.367, generator_feat_match_loss=4.992, over 53.00 samples.], tot_loss[discriminator_loss=2.488, discriminator_real_loss=1.254, discriminator_fake_loss=1.234, generator_loss=30.85, generator_mel_loss=19.99, generator_kl_loss=2.002, generator_dur_loss=1.63, generator_adv_loss=2.343, generator_feat_match_loss=4.885, over 657.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 22:57:49,247 INFO [train.py:811] (1/4) Start epoch 841
+2023-11-14 22:59:43,239 INFO [train.py:467] (1/4) Epoch 841, batch 20, global_batch_idx: 31100, batch size: 90, loss[discriminator_loss=2.539, discriminator_real_loss=1.271, discriminator_fake_loss=1.27, generator_loss=30.35, generator_mel_loss=19.65, generator_kl_loss=2.066, generator_dur_loss=1.619, generator_adv_loss=2.242, generator_feat_match_loss=4.77, over 90.00 samples.], tot_loss[discriminator_loss=2.503, discriminator_real_loss=1.272, discriminator_fake_loss=1.23, generator_loss=30.64, generator_mel_loss=19.84, generator_kl_loss=2.027, generator_dur_loss=1.631, generator_adv_loss=2.343, generator_feat_match_loss=4.803, over 1497.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:01:14,091 INFO [train.py:811] (1/4) Start epoch 842
+2023-11-14 23:04:19,376 INFO [train.py:467] (1/4) Epoch 842, batch 33, global_batch_idx: 31150, batch size: 154, loss[discriminator_loss=2.324, discriminator_real_loss=1.155, discriminator_fake_loss=1.168, generator_loss=32.01, generator_mel_loss=20.09, generator_kl_loss=2.055, generator_dur_loss=1.608, generator_adv_loss=2.426, generator_feat_match_loss=5.832, over 154.00 samples.], tot_loss[discriminator_loss=2.513, discriminator_real_loss=1.274, discriminator_fake_loss=1.239, generator_loss=30.82, generator_mel_loss=19.93, generator_kl_loss=2.034, generator_dur_loss=1.633, generator_adv_loss=2.321, generator_feat_match_loss=4.897, over 2483.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:04:39,193 INFO [train.py:811] (1/4) Start epoch 843
+2023-11-14 23:08:10,223 INFO [train.py:811] (1/4) Start epoch 844
+2023-11-14 23:09:09,485 INFO [train.py:467] (1/4) Epoch 844, batch 9, global_batch_idx: 31200, batch size: 56, loss[discriminator_loss=2.641, discriminator_real_loss=1.367, discriminator_fake_loss=1.272, generator_loss=29.8, generator_mel_loss=19.67, generator_kl_loss=2.037, generator_dur_loss=1.637, generator_adv_loss=2.127, generator_feat_match_loss=4.328, over 56.00 samples.], tot_loss[discriminator_loss=2.634, discriminator_real_loss=1.347, discriminator_fake_loss=1.287, generator_loss=30.41, generator_mel_loss=20.15, generator_kl_loss=2.006, generator_dur_loss=1.64, generator_adv_loss=2.16, generator_feat_match_loss=4.458, over 649.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 16.0
+2023-11-14 23:09:09,487 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 23:09:20,747 INFO [train.py:517] (1/4) Epoch 844, validation: discriminator_loss=2.619, discriminator_real_loss=1.188, discriminator_fake_loss=1.431, generator_loss=31.26, generator_mel_loss=20.99, generator_kl_loss=2.229, generator_dur_loss=1.629, generator_adv_loss=1.894, generator_feat_match_loss=4.517, over 100.00 samples.
+2023-11-14 23:09:20,748 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 23:11:51,485 INFO [train.py:811] (1/4) Start epoch 845
+2023-11-14 23:14:06,011 INFO [train.py:467] (1/4) Epoch 845, batch 22, global_batch_idx: 31250, batch size: 65, loss[discriminator_loss=2.549, discriminator_real_loss=1.324, discriminator_fake_loss=1.225, generator_loss=30.68, generator_mel_loss=20.42, generator_kl_loss=2.012, generator_dur_loss=1.638, generator_adv_loss=2.178, generator_feat_match_loss=4.426, over 65.00 samples.], tot_loss[discriminator_loss=2.627, discriminator_real_loss=1.331, discriminator_fake_loss=1.297, generator_loss=30.41, generator_mel_loss=20.17, generator_kl_loss=2.01, generator_dur_loss=1.632, generator_adv_loss=2.17, generator_feat_match_loss=4.431, over 1728.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 16.0
+2023-11-14 23:15:25,365 INFO [train.py:811] (1/4) Start epoch 846
+2023-11-14 23:18:53,044 INFO [train.py:467] (1/4) Epoch 846, batch 35, global_batch_idx: 31300, batch size: 110, loss[discriminator_loss=2.629, discriminator_real_loss=1.292, discriminator_fake_loss=1.338, generator_loss=30.83, generator_mel_loss=20.29, generator_kl_loss=2.084, generator_dur_loss=1.611, generator_adv_loss=2.273, generator_feat_match_loss=4.562, over 110.00 samples.], tot_loss[discriminator_loss=2.612, discriminator_real_loss=1.316, discriminator_fake_loss=1.296, generator_loss=30.6, generator_mel_loss=20.26, generator_kl_loss=2.029, generator_dur_loss=1.633, generator_adv_loss=2.203, generator_feat_match_loss=4.473, over 2720.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:18:58,267 INFO [train.py:811] (1/4) Start epoch 847
+2023-11-14 23:22:37,268 INFO [train.py:811] (1/4) Start epoch 848
+2023-11-14 23:23:47,700 INFO [train.py:467] (1/4) Epoch 848, batch 11, global_batch_idx: 31350, batch size: 71, loss[discriminator_loss=2.48, discriminator_real_loss=1.298, discriminator_fake_loss=1.184, generator_loss=31.45, generator_mel_loss=20.16, generator_kl_loss=2.019, generator_dur_loss=1.613, generator_adv_loss=2.51, generator_feat_match_loss=5.145, over 71.00 samples.], tot_loss[discriminator_loss=2.548, discriminator_real_loss=1.286, discriminator_fake_loss=1.262, generator_loss=30.62, generator_mel_loss=20.07, generator_kl_loss=1.986, generator_dur_loss=1.634, generator_adv_loss=2.305, generator_feat_match_loss=4.629, over 773.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:26:09,602 INFO [train.py:811] (1/4) Start epoch 849
+2023-11-14 23:28:37,704 INFO [train.py:467] (1/4) Epoch 849, batch 24, global_batch_idx: 31400, batch size: 50, loss[discriminator_loss=2.527, discriminator_real_loss=1.243, discriminator_fake_loss=1.284, generator_loss=30.37, generator_mel_loss=19.69, generator_kl_loss=2.028, generator_dur_loss=1.624, generator_adv_loss=2.273, generator_feat_match_loss=4.758, over 50.00 samples.], tot_loss[discriminator_loss=2.509, discriminator_real_loss=1.272, discriminator_fake_loss=1.237, generator_loss=30.66, generator_mel_loss=19.83, generator_kl_loss=2.022, generator_dur_loss=1.637, generator_adv_loss=2.345, generator_feat_match_loss=4.821, over 1781.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:28:37,705 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 23:28:48,941 INFO [train.py:517] (1/4) Epoch 849, validation: discriminator_loss=2.489, discriminator_real_loss=1.2, discriminator_fake_loss=1.289, generator_loss=31.3, generator_mel_loss=20.63, generator_kl_loss=2.258, generator_dur_loss=1.633, generator_adv_loss=1.962, generator_feat_match_loss=4.819, over 100.00 samples.
+2023-11-14 23:28:48,942 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 23:29:55,825 INFO [train.py:811] (1/4) Start epoch 850
+2023-11-14 23:33:25,614 INFO [train.py:811] (1/4) Start epoch 851
+2023-11-14 23:33:40,093 INFO [train.py:467] (1/4) Epoch 851, batch 0, global_batch_idx: 31450, batch size: 64, loss[discriminator_loss=2.52, discriminator_real_loss=1.277, discriminator_fake_loss=1.242, generator_loss=30.7, generator_mel_loss=19.88, generator_kl_loss=2.057, generator_dur_loss=1.649, generator_adv_loss=2.367, generator_feat_match_loss=4.75, over 64.00 samples.], tot_loss[discriminator_loss=2.52, discriminator_real_loss=1.277, discriminator_fake_loss=1.242, generator_loss=30.7, generator_mel_loss=19.88, generator_kl_loss=2.057, generator_dur_loss=1.649, generator_adv_loss=2.367, generator_feat_match_loss=4.75, over 64.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:37:01,285 INFO [train.py:811] (1/4) Start epoch 852
+2023-11-14 23:38:32,187 INFO [train.py:467] (1/4) Epoch 852, batch 13, global_batch_idx: 31500, batch size: 110, loss[discriminator_loss=2.451, discriminator_real_loss=1.149, discriminator_fake_loss=1.302, generator_loss=30.82, generator_mel_loss=19.68, generator_kl_loss=2.055, generator_dur_loss=1.645, generator_adv_loss=2.301, generator_feat_match_loss=5.145, over 110.00 samples.], tot_loss[discriminator_loss=2.527, discriminator_real_loss=1.271, discriminator_fake_loss=1.256, generator_loss=30.75, generator_mel_loss=19.9, generator_kl_loss=2.009, generator_dur_loss=1.639, generator_adv_loss=2.309, generator_feat_match_loss=4.9, over 1076.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:40:31,624 INFO [train.py:811] (1/4) Start epoch 853
+2023-11-14 23:43:12,746 INFO [train.py:467] (1/4) Epoch 853, batch 26, global_batch_idx: 31550, batch size: 61, loss[discriminator_loss=2.574, discriminator_real_loss=1.3, discriminator_fake_loss=1.273, generator_loss=31.08, generator_mel_loss=20.23, generator_kl_loss=2.093, generator_dur_loss=1.659, generator_adv_loss=2.297, generator_feat_match_loss=4.801, over 61.00 samples.], tot_loss[discriminator_loss=2.543, discriminator_real_loss=1.293, discriminator_fake_loss=1.25, generator_loss=30.66, generator_mel_loss=20.11, generator_kl_loss=2.021, generator_dur_loss=1.635, generator_adv_loss=2.227, generator_feat_match_loss=4.66, over 2044.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:44:08,066 INFO [train.py:811] (1/4) Start epoch 854
+2023-11-14 23:47:43,499 INFO [train.py:811] (1/4) Start epoch 855
+2023-11-14 23:48:06,102 INFO [train.py:467] (1/4) Epoch 855, batch 2, global_batch_idx: 31600, batch size: 64, loss[discriminator_loss=2.363, discriminator_real_loss=1.153, discriminator_fake_loss=1.21, generator_loss=31.02, generator_mel_loss=19.86, generator_kl_loss=2.007, generator_dur_loss=1.653, generator_adv_loss=2.348, generator_feat_match_loss=5.152, over 64.00 samples.], tot_loss[discriminator_loss=2.46, discriminator_real_loss=1.249, discriminator_fake_loss=1.211, generator_loss=30.66, generator_mel_loss=19.87, generator_kl_loss=2.031, generator_dur_loss=1.655, generator_adv_loss=2.236, generator_feat_match_loss=4.87, over 169.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 16.0
+2023-11-14 23:48:06,105 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-14 23:48:18,035 INFO [train.py:517] (1/4) Epoch 855, validation: discriminator_loss=2.382, discriminator_real_loss=1.122, discriminator_fake_loss=1.26, generator_loss=31.99, generator_mel_loss=20.71, generator_kl_loss=2.234, generator_dur_loss=1.63, generator_adv_loss=2.125, generator_feat_match_loss=5.289, over 100.00 samples.
+2023-11-14 23:48:18,036 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-14 23:51:27,795 INFO [train.py:811] (1/4) Start epoch 856
+2023-11-14 23:53:03,538 INFO [train.py:467] (1/4) Epoch 856, batch 15, global_batch_idx: 31650, batch size: 67, loss[discriminator_loss=2.418, discriminator_real_loss=1.246, discriminator_fake_loss=1.173, generator_loss=31.53, generator_mel_loss=20.09, generator_kl_loss=1.927, generator_dur_loss=1.645, generator_adv_loss=2.43, generator_feat_match_loss=5.445, over 67.00 samples.], tot_loss[discriminator_loss=2.499, discriminator_real_loss=1.261, discriminator_fake_loss=1.238, generator_loss=30.85, generator_mel_loss=20.04, generator_kl_loss=2.026, generator_dur_loss=1.639, generator_adv_loss=2.321, generator_feat_match_loss=4.825, over 1107.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:55:02,741 INFO [train.py:811] (1/4) Start epoch 857
+2023-11-14 23:57:43,666 INFO [train.py:467] (1/4) Epoch 857, batch 28, global_batch_idx: 31700, batch size: 79, loss[discriminator_loss=2.469, discriminator_real_loss=1.174, discriminator_fake_loss=1.296, generator_loss=30.71, generator_mel_loss=19.92, generator_kl_loss=2.025, generator_dur_loss=1.621, generator_adv_loss=2.408, generator_feat_match_loss=4.734, over 79.00 samples.], tot_loss[discriminator_loss=2.464, discriminator_real_loss=1.239, discriminator_fake_loss=1.224, generator_loss=30.91, generator_mel_loss=19.83, generator_kl_loss=2.034, generator_dur_loss=1.63, generator_adv_loss=2.385, generator_feat_match_loss=5.031, over 2176.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-14 23:58:30,424 INFO [train.py:811] (1/4) Start epoch 858
+2023-11-15 00:02:05,116 INFO [train.py:811] (1/4) Start epoch 859
+2023-11-15 00:02:41,926 INFO [train.py:467] (1/4) Epoch 859, batch 4, global_batch_idx: 31750, batch size: 59, loss[discriminator_loss=2.305, discriminator_real_loss=1.157, discriminator_fake_loss=1.148, generator_loss=31.95, generator_mel_loss=19.8, generator_kl_loss=2.076, generator_dur_loss=1.653, generator_adv_loss=2.557, generator_feat_match_loss=5.859, over 59.00 samples.], tot_loss[discriminator_loss=2.373, discriminator_real_loss=1.212, discriminator_fake_loss=1.16, generator_loss=31.32, generator_mel_loss=19.82, generator_kl_loss=2.026, generator_dur_loss=1.646, generator_adv_loss=2.366, generator_feat_match_loss=5.463, over 349.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-15 00:05:43,071 INFO [train.py:811] (1/4) Start epoch 860
+2023-11-15 00:07:31,641 INFO [train.py:467] (1/4) Epoch 860, batch 17, global_batch_idx: 31800, batch size: 65, loss[discriminator_loss=2.551, discriminator_real_loss=1.218, discriminator_fake_loss=1.333, generator_loss=30.93, generator_mel_loss=20.17, generator_kl_loss=2.013, generator_dur_loss=1.631, generator_adv_loss=2.371, generator_feat_match_loss=4.75, over 65.00 samples.], tot_loss[discriminator_loss=2.558, discriminator_real_loss=1.289, discriminator_fake_loss=1.27, generator_loss=30.58, generator_mel_loss=20.06, generator_kl_loss=2.028, generator_dur_loss=1.629, generator_adv_loss=2.222, generator_feat_match_loss=4.64, over 1406.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-15 00:07:31,642 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 00:07:42,694 INFO [train.py:517] (1/4) Epoch 860, validation: discriminator_loss=2.631, discriminator_real_loss=1.324, discriminator_fake_loss=1.307, generator_loss=31.27, generator_mel_loss=20.89, generator_kl_loss=2.236, generator_dur_loss=1.624, generator_adv_loss=2.088, generator_feat_match_loss=4.43, over 100.00 samples.
+2023-11-15 00:07:42,695 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 00:09:31,024 INFO [train.py:811] (1/4) Start epoch 861
+2023-11-15 00:12:28,577 INFO [train.py:467] (1/4) Epoch 861, batch 30, global_batch_idx: 31850, batch size: 126, loss[discriminator_loss=2.539, discriminator_real_loss=1.25, discriminator_fake_loss=1.289, generator_loss=31.07, generator_mel_loss=20.46, generator_kl_loss=1.921, generator_dur_loss=1.653, generator_adv_loss=2.418, generator_feat_match_loss=4.617, over 126.00 samples.], tot_loss[discriminator_loss=2.583, discriminator_real_loss=1.307, discriminator_fake_loss=1.276, generator_loss=30.75, generator_mel_loss=20.22, generator_kl_loss=2.044, generator_dur_loss=1.634, generator_adv_loss=2.244, generator_feat_match_loss=4.613, over 2395.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-15 00:13:08,464 INFO [train.py:811] (1/4) Start epoch 862
+2023-11-15 00:16:40,515 INFO [train.py:811] (1/4) Start epoch 863
+2023-11-15 00:17:27,078 INFO [train.py:467] (1/4) Epoch 863, batch 6, global_batch_idx: 31900, batch size: 52, loss[discriminator_loss=2.492, discriminator_real_loss=1.325, discriminator_fake_loss=1.168, generator_loss=30.92, generator_mel_loss=19.82, generator_kl_loss=2.005, generator_dur_loss=1.649, generator_adv_loss=2.27, generator_feat_match_loss=5.176, over 52.00 samples.], tot_loss[discriminator_loss=2.461, discriminator_real_loss=1.247, discriminator_fake_loss=1.214, generator_loss=30.84, generator_mel_loss=19.87, generator_kl_loss=2.007, generator_dur_loss=1.643, generator_adv_loss=2.309, generator_feat_match_loss=5.004, over 452.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-15 00:20:14,532 INFO [train.py:811] (1/4) Start epoch 864
+2023-11-15 00:22:19,692 INFO [train.py:467] (1/4) Epoch 864, batch 19, global_batch_idx: 31950, batch size: 60, loss[discriminator_loss=2.52, discriminator_real_loss=1.178, discriminator_fake_loss=1.342, generator_loss=30.65, generator_mel_loss=20, generator_kl_loss=1.973, generator_dur_loss=1.638, generator_adv_loss=2.346, generator_feat_match_loss=4.688, over 60.00 samples.], tot_loss[discriminator_loss=2.546, discriminator_real_loss=1.292, discriminator_fake_loss=1.253, generator_loss=30.37, generator_mel_loss=19.97, generator_kl_loss=2.033, generator_dur_loss=1.628, generator_adv_loss=2.194, generator_feat_match_loss=4.542, over 1355.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 8.0
+2023-11-15 00:23:47,288 INFO [train.py:811] (1/4) Start epoch 865
+2023-11-15 00:26:58,630 INFO [train.py:467] (1/4) Epoch 865, batch 32, global_batch_idx: 32000, batch size: 101, loss[discriminator_loss=2.643, discriminator_real_loss=1.302, discriminator_fake_loss=1.341, generator_loss=30.18, generator_mel_loss=19.91, generator_kl_loss=2.032, generator_dur_loss=1.592, generator_adv_loss=2.275, generator_feat_match_loss=4.371, over 101.00 samples.], tot_loss[discriminator_loss=2.554, discriminator_real_loss=1.282, discriminator_fake_loss=1.272, generator_loss=30.74, generator_mel_loss=19.99, generator_kl_loss=2.003, generator_dur_loss=1.629, generator_adv_loss=2.308, generator_feat_match_loss=4.811, over 2521.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, grad_scale: 16.0
+2023-11-15 00:26:58,631 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 00:27:09,994 INFO [train.py:517] (1/4) Epoch 865, validation: discriminator_loss=2.595, discriminator_real_loss=1.359, discriminator_fake_loss=1.236, generator_loss=31.08, generator_mel_loss=20.52, generator_kl_loss=2.151, generator_dur_loss=1.63, generator_adv_loss=2.244, generator_feat_match_loss=4.534, over 100.00 samples.
+2023-11-15 00:27:09,995 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 00:27:30,353 INFO [train.py:811] (1/4) Start epoch 866
+2023-11-15 00:31:05,923 INFO [train.py:811] (1/4) Start epoch 867
+2023-11-15 00:32:09,221 INFO [train.py:467] (1/4) Epoch 867, batch 8, global_batch_idx: 32050, batch size: 54, loss[discriminator_loss=2.609, discriminator_real_loss=1.17, discriminator_fake_loss=1.438, generator_loss=30.56, generator_mel_loss=19.99, generator_kl_loss=1.861, generator_dur_loss=1.647, generator_adv_loss=2.219, generator_feat_match_loss=4.844, over 54.00 samples.], tot_loss[discriminator_loss=2.477, discriminator_real_loss=1.272, discriminator_fake_loss=1.205, generator_loss=31.02, generator_mel_loss=19.84, generator_kl_loss=1.998, generator_dur_loss=1.63, generator_adv_loss=2.371, generator_feat_match_loss=5.183, over 686.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 00:34:43,472 INFO [train.py:811] (1/4) Start epoch 868
+2023-11-15 00:36:46,745 INFO [train.py:467] (1/4) Epoch 868, batch 21, global_batch_idx: 32100, batch size: 67, loss[discriminator_loss=2.68, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=30.91, generator_mel_loss=20.39, generator_kl_loss=2.004, generator_dur_loss=1.644, generator_adv_loss=2.273, generator_feat_match_loss=4.594, over 67.00 samples.], tot_loss[discriminator_loss=2.561, discriminator_real_loss=1.297, discriminator_fake_loss=1.264, generator_loss=30.61, generator_mel_loss=20.09, generator_kl_loss=2.038, generator_dur_loss=1.633, generator_adv_loss=2.214, generator_feat_match_loss=4.631, over 1443.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 00:38:13,847 INFO [train.py:811] (1/4) Start epoch 869
+2023-11-15 00:41:37,524 INFO [train.py:467] (1/4) Epoch 869, batch 34, global_batch_idx: 32150, batch size: 52, loss[discriminator_loss=2.482, discriminator_real_loss=1.308, discriminator_fake_loss=1.175, generator_loss=30.4, generator_mel_loss=19.65, generator_kl_loss=1.993, generator_dur_loss=1.641, generator_adv_loss=2.295, generator_feat_match_loss=4.828, over 52.00 samples.], tot_loss[discriminator_loss=2.572, discriminator_real_loss=1.3, discriminator_fake_loss=1.272, generator_loss=30.46, generator_mel_loss=19.95, generator_kl_loss=2.015, generator_dur_loss=1.634, generator_adv_loss=2.254, generator_feat_match_loss=4.604, over 2581.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 00:41:47,244 INFO [train.py:811] (1/4) Start epoch 870
+2023-11-15 00:45:22,049 INFO [train.py:811] (1/4) Start epoch 871
+2023-11-15 00:46:29,936 INFO [train.py:467] (1/4) Epoch 871, batch 10, global_batch_idx: 32200, batch size: 60, loss[discriminator_loss=2.457, discriminator_real_loss=1.227, discriminator_fake_loss=1.231, generator_loss=31.38, generator_mel_loss=20.46, generator_kl_loss=2.04, generator_dur_loss=1.646, generator_adv_loss=2.225, generator_feat_match_loss=5.012, over 60.00 samples.], tot_loss[discriminator_loss=2.529, discriminator_real_loss=1.281, discriminator_fake_loss=1.248, generator_loss=31.06, generator_mel_loss=20.21, generator_kl_loss=2.046, generator_dur_loss=1.637, generator_adv_loss=2.286, generator_feat_match_loss=4.876, over 824.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 00:46:29,938 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 00:46:40,914 INFO [train.py:517] (1/4) Epoch 871, validation: discriminator_loss=2.558, discriminator_real_loss=1.112, discriminator_fake_loss=1.446, generator_loss=31.29, generator_mel_loss=20.74, generator_kl_loss=2.289, generator_dur_loss=1.629, generator_adv_loss=1.83, generator_feat_match_loss=4.802, over 100.00 samples.
+2023-11-15 00:46:40,915 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 00:49:04,282 INFO [train.py:811] (1/4) Start epoch 872
+2023-11-15 00:51:22,600 INFO [train.py:467] (1/4) Epoch 872, batch 23, global_batch_idx: 32250, batch size: 58, loss[discriminator_loss=2.77, discriminator_real_loss=1.183, discriminator_fake_loss=1.588, generator_loss=30.2, generator_mel_loss=19.81, generator_kl_loss=1.902, generator_dur_loss=1.638, generator_adv_loss=2.172, generator_feat_match_loss=4.672, over 58.00 samples.], tot_loss[discriminator_loss=2.455, discriminator_real_loss=1.246, discriminator_fake_loss=1.21, generator_loss=30.91, generator_mel_loss=19.65, generator_kl_loss=1.995, generator_dur_loss=1.638, generator_adv_loss=2.449, generator_feat_match_loss=5.175, over 1537.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 00:52:32,033 INFO [train.py:811] (1/4) Start epoch 873
+2023-11-15 00:56:02,926 INFO [train.py:467] (1/4) Epoch 873, batch 36, global_batch_idx: 32300, batch size: 79, loss[discriminator_loss=2.531, discriminator_real_loss=1.324, discriminator_fake_loss=1.208, generator_loss=30.11, generator_mel_loss=19.71, generator_kl_loss=1.975, generator_dur_loss=1.645, generator_adv_loss=2.184, generator_feat_match_loss=4.598, over 79.00 samples.], tot_loss[discriminator_loss=2.501, discriminator_real_loss=1.256, discriminator_fake_loss=1.245, generator_loss=30.46, generator_mel_loss=19.74, generator_kl_loss=2.02, generator_dur_loss=1.632, generator_adv_loss=2.284, generator_feat_match_loss=4.784, over 2713.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 00:56:03,559 INFO [train.py:811] (1/4) Start epoch 874
+2023-11-15 00:59:41,336 INFO [train.py:811] (1/4) Start epoch 875
+2023-11-15 01:01:11,172 INFO [train.py:467] (1/4) Epoch 875, batch 12, global_batch_idx: 32350, batch size: 56, loss[discriminator_loss=2.367, discriminator_real_loss=1.201, discriminator_fake_loss=1.167, generator_loss=31.3, generator_mel_loss=20.02, generator_kl_loss=1.962, generator_dur_loss=1.638, generator_adv_loss=2.264, generator_feat_match_loss=5.418, over 56.00 samples.], tot_loss[discriminator_loss=2.596, discriminator_real_loss=1.291, discriminator_fake_loss=1.304, generator_loss=30.74, generator_mel_loss=19.98, generator_kl_loss=2.03, generator_dur_loss=1.628, generator_adv_loss=2.293, generator_feat_match_loss=4.811, over 1053.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:03:09,804 INFO [train.py:811] (1/4) Start epoch 876
+2023-11-15 01:05:38,785 INFO [train.py:467] (1/4) Epoch 876, batch 25, global_batch_idx: 32400, batch size: 49, loss[discriminator_loss=2.574, discriminator_real_loss=1.28, discriminator_fake_loss=1.294, generator_loss=30.03, generator_mel_loss=19.59, generator_kl_loss=1.958, generator_dur_loss=1.643, generator_adv_loss=2.25, generator_feat_match_loss=4.59, over 49.00 samples.], tot_loss[discriminator_loss=2.551, discriminator_real_loss=1.294, discriminator_fake_loss=1.256, generator_loss=30.57, generator_mel_loss=19.92, generator_kl_loss=2.007, generator_dur_loss=1.63, generator_adv_loss=2.269, generator_feat_match_loss=4.74, over 1725.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 01:05:38,786 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 01:05:49,911 INFO [train.py:517] (1/4) Epoch 876, validation: discriminator_loss=2.488, discriminator_real_loss=1.209, discriminator_fake_loss=1.279, generator_loss=31.4, generator_mel_loss=20.55, generator_kl_loss=2.287, generator_dur_loss=1.635, generator_adv_loss=2.072, generator_feat_match_loss=4.859, over 100.00 samples.
+2023-11-15 01:05:49,912 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 01:06:55,087 INFO [train.py:811] (1/4) Start epoch 877
+2023-11-15 01:10:28,101 INFO [train.py:811] (1/4) Start epoch 878
+2023-11-15 01:10:48,405 INFO [train.py:467] (1/4) Epoch 878, batch 1, global_batch_idx: 32450, batch size: 90, loss[discriminator_loss=2.367, discriminator_real_loss=1.28, discriminator_fake_loss=1.087, generator_loss=31.57, generator_mel_loss=19.84, generator_kl_loss=1.996, generator_dur_loss=1.623, generator_adv_loss=2.527, generator_feat_match_loss=5.586, over 90.00 samples.], tot_loss[discriminator_loss=2.38, discriminator_real_loss=1.253, discriminator_fake_loss=1.127, generator_loss=31.38, generator_mel_loss=19.75, generator_kl_loss=1.972, generator_dur_loss=1.619, generator_adv_loss=2.533, generator_feat_match_loss=5.509, over 142.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:14:03,960 INFO [train.py:811] (1/4) Start epoch 879
+2023-11-15 01:15:30,529 INFO [train.py:467] (1/4) Epoch 879, batch 14, global_batch_idx: 32500, batch size: 85, loss[discriminator_loss=2.465, discriminator_real_loss=1.287, discriminator_fake_loss=1.178, generator_loss=31.03, generator_mel_loss=20.05, generator_kl_loss=2.021, generator_dur_loss=1.642, generator_adv_loss=2.305, generator_feat_match_loss=5.008, over 85.00 samples.], tot_loss[discriminator_loss=2.553, discriminator_real_loss=1.296, discriminator_fake_loss=1.257, generator_loss=30.59, generator_mel_loss=19.95, generator_kl_loss=2.044, generator_dur_loss=1.634, generator_adv_loss=2.25, generator_feat_match_loss=4.706, over 989.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:17:34,640 INFO [train.py:811] (1/4) Start epoch 880
+2023-11-15 01:20:20,375 INFO [train.py:467] (1/4) Epoch 880, batch 27, global_batch_idx: 32550, batch size: 90, loss[discriminator_loss=2.488, discriminator_real_loss=1.302, discriminator_fake_loss=1.187, generator_loss=30.45, generator_mel_loss=20.06, generator_kl_loss=2.024, generator_dur_loss=1.61, generator_adv_loss=2.133, generator_feat_match_loss=4.621, over 90.00 samples.], tot_loss[discriminator_loss=2.46, discriminator_real_loss=1.234, discriminator_fake_loss=1.225, generator_loss=30.76, generator_mel_loss=19.64, generator_kl_loss=2.014, generator_dur_loss=1.625, generator_adv_loss=2.38, generator_feat_match_loss=5.1, over 2129.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:21:07,247 INFO [train.py:811] (1/4) Start epoch 881
+2023-11-15 01:24:31,849 INFO [train.py:811] (1/4) Start epoch 882
+2023-11-15 01:25:01,326 INFO [train.py:467] (1/4) Epoch 882, batch 3, global_batch_idx: 32600, batch size: 61, loss[discriminator_loss=2.4, discriminator_real_loss=1.25, discriminator_fake_loss=1.15, generator_loss=30.94, generator_mel_loss=19.53, generator_kl_loss=1.963, generator_dur_loss=1.64, generator_adv_loss=2.537, generator_feat_match_loss=5.266, over 61.00 samples.], tot_loss[discriminator_loss=2.516, discriminator_real_loss=1.312, discriminator_fake_loss=1.204, generator_loss=30.4, generator_mel_loss=19.51, generator_kl_loss=1.99, generator_dur_loss=1.629, generator_adv_loss=2.395, generator_feat_match_loss=4.875, over 287.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:25:01,328 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 01:25:13,538 INFO [train.py:517] (1/4) Epoch 882, validation: discriminator_loss=2.375, discriminator_real_loss=1.179, discriminator_fake_loss=1.196, generator_loss=32, generator_mel_loss=20.37, generator_kl_loss=2.176, generator_dur_loss=1.642, generator_adv_loss=2.394, generator_feat_match_loss=5.41, over 100.00 samples.
+2023-11-15 01:25:13,539 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 01:28:19,214 INFO [train.py:811] (1/4) Start epoch 883
+2023-11-15 01:29:54,473 INFO [train.py:467] (1/4) Epoch 883, batch 16, global_batch_idx: 32650, batch size: 73, loss[discriminator_loss=2.414, discriminator_real_loss=1.303, discriminator_fake_loss=1.111, generator_loss=31.39, generator_mel_loss=20.22, generator_kl_loss=2.078, generator_dur_loss=1.642, generator_adv_loss=2.336, generator_feat_match_loss=5.109, over 73.00 samples.], tot_loss[discriminator_loss=2.555, discriminator_real_loss=1.282, discriminator_fake_loss=1.274, generator_loss=30.73, generator_mel_loss=20.09, generator_kl_loss=2.055, generator_dur_loss=1.637, generator_adv_loss=2.24, generator_feat_match_loss=4.707, over 1159.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:31:43,064 INFO [train.py:811] (1/4) Start epoch 884
+2023-11-15 01:34:31,903 INFO [train.py:467] (1/4) Epoch 884, batch 29, global_batch_idx: 32700, batch size: 110, loss[discriminator_loss=2.604, discriminator_real_loss=1.299, discriminator_fake_loss=1.305, generator_loss=30.67, generator_mel_loss=20.34, generator_kl_loss=2.096, generator_dur_loss=1.599, generator_adv_loss=2.203, generator_feat_match_loss=4.434, over 110.00 samples.], tot_loss[discriminator_loss=2.544, discriminator_real_loss=1.284, discriminator_fake_loss=1.26, generator_loss=30.54, generator_mel_loss=19.83, generator_kl_loss=2.021, generator_dur_loss=1.633, generator_adv_loss=2.3, generator_feat_match_loss=4.75, over 1965.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:35:17,480 INFO [train.py:811] (1/4) Start epoch 885
+2023-11-15 01:38:53,613 INFO [train.py:811] (1/4) Start epoch 886
+2023-11-15 01:39:37,943 INFO [train.py:467] (1/4) Epoch 886, batch 5, global_batch_idx: 32750, batch size: 63, loss[discriminator_loss=2.605, discriminator_real_loss=1.29, discriminator_fake_loss=1.315, generator_loss=30.57, generator_mel_loss=20.21, generator_kl_loss=2.02, generator_dur_loss=1.662, generator_adv_loss=2.133, generator_feat_match_loss=4.543, over 63.00 samples.], tot_loss[discriminator_loss=2.555, discriminator_real_loss=1.309, discriminator_fake_loss=1.245, generator_loss=30.58, generator_mel_loss=20.13, generator_kl_loss=2.043, generator_dur_loss=1.641, generator_adv_loss=2.257, generator_feat_match_loss=4.505, over 414.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:42:23,467 INFO [train.py:811] (1/4) Start epoch 887
+2023-11-15 01:44:16,993 INFO [train.py:467] (1/4) Epoch 887, batch 18, global_batch_idx: 32800, batch size: 95, loss[discriminator_loss=2.562, discriminator_real_loss=1.391, discriminator_fake_loss=1.172, generator_loss=30.8, generator_mel_loss=20.08, generator_kl_loss=1.925, generator_dur_loss=1.617, generator_adv_loss=2.256, generator_feat_match_loss=4.93, over 95.00 samples.], tot_loss[discriminator_loss=2.579, discriminator_real_loss=1.325, discriminator_fake_loss=1.255, generator_loss=30.58, generator_mel_loss=20.06, generator_kl_loss=2.031, generator_dur_loss=1.633, generator_adv_loss=2.247, generator_feat_match_loss=4.611, over 1351.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 01:44:16,995 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 01:44:28,140 INFO [train.py:517] (1/4) Epoch 887, validation: discriminator_loss=2.489, discriminator_real_loss=1.192, discriminator_fake_loss=1.297, generator_loss=31.07, generator_mel_loss=20.3, generator_kl_loss=2.217, generator_dur_loss=1.644, generator_adv_loss=2.108, generator_feat_match_loss=4.801, over 100.00 samples.
+2023-11-15 01:44:28,141 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 01:46:05,379 INFO [train.py:811] (1/4) Start epoch 888
+2023-11-15 01:48:53,881 INFO [train.py:467] (1/4) Epoch 888, batch 31, global_batch_idx: 32850, batch size: 71, loss[discriminator_loss=2.504, discriminator_real_loss=1.344, discriminator_fake_loss=1.16, generator_loss=31.4, generator_mel_loss=20.11, generator_kl_loss=2.038, generator_dur_loss=1.632, generator_adv_loss=2.438, generator_feat_match_loss=5.188, over 71.00 samples.], tot_loss[discriminator_loss=2.541, discriminator_real_loss=1.284, discriminator_fake_loss=1.257, generator_loss=30.53, generator_mel_loss=19.94, generator_kl_loss=2.028, generator_dur_loss=1.634, generator_adv_loss=2.252, generator_feat_match_loss=4.676, over 2202.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:49:29,482 INFO [train.py:811] (1/4) Start epoch 889
+2023-11-15 01:53:01,055 INFO [train.py:811] (1/4) Start epoch 890
+2023-11-15 01:53:55,579 INFO [train.py:467] (1/4) Epoch 890, batch 7, global_batch_idx: 32900, batch size: 58, loss[discriminator_loss=2.52, discriminator_real_loss=1.205, discriminator_fake_loss=1.315, generator_loss=29.94, generator_mel_loss=19.3, generator_kl_loss=1.996, generator_dur_loss=1.637, generator_adv_loss=2.336, generator_feat_match_loss=4.668, over 58.00 samples.], tot_loss[discriminator_loss=2.517, discriminator_real_loss=1.24, discriminator_fake_loss=1.278, generator_loss=30.34, generator_mel_loss=19.7, generator_kl_loss=1.995, generator_dur_loss=1.635, generator_adv_loss=2.248, generator_feat_match_loss=4.763, over 514.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 01:56:34,003 INFO [train.py:811] (1/4) Start epoch 891
+2023-11-15 01:58:40,551 INFO [train.py:467] (1/4) Epoch 891, batch 20, global_batch_idx: 32950, batch size: 64, loss[discriminator_loss=2.715, discriminator_real_loss=1.538, discriminator_fake_loss=1.176, generator_loss=30.36, generator_mel_loss=20, generator_kl_loss=1.966, generator_dur_loss=1.655, generator_adv_loss=2.387, generator_feat_match_loss=4.355, over 64.00 samples.], tot_loss[discriminator_loss=2.516, discriminator_real_loss=1.253, discriminator_fake_loss=1.263, generator_loss=30.55, generator_mel_loss=19.76, generator_kl_loss=1.997, generator_dur_loss=1.629, generator_adv_loss=2.315, generator_feat_match_loss=4.845, over 1443.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:00:13,422 INFO [train.py:811] (1/4) Start epoch 892
+2023-11-15 02:03:30,995 INFO [train.py:467] (1/4) Epoch 892, batch 33, global_batch_idx: 33000, batch size: 55, loss[discriminator_loss=2.557, discriminator_real_loss=1.122, discriminator_fake_loss=1.435, generator_loss=31.02, generator_mel_loss=19.96, generator_kl_loss=1.945, generator_dur_loss=1.657, generator_adv_loss=2.438, generator_feat_match_loss=5.02, over 55.00 samples.], tot_loss[discriminator_loss=2.511, discriminator_real_loss=1.278, discriminator_fake_loss=1.233, generator_loss=30.75, generator_mel_loss=19.94, generator_kl_loss=2.027, generator_dur_loss=1.632, generator_adv_loss=2.295, generator_feat_match_loss=4.846, over 2619.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:03:30,996 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 02:03:42,568 INFO [train.py:517] (1/4) Epoch 892, validation: discriminator_loss=2.723, discriminator_real_loss=1.326, discriminator_fake_loss=1.397, generator_loss=30.8, generator_mel_loss=20.53, generator_kl_loss=2.251, generator_dur_loss=1.632, generator_adv_loss=1.933, generator_feat_match_loss=4.461, over 100.00 samples.
+2023-11-15 02:03:42,569 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 02:03:58,250 INFO [train.py:811] (1/4) Start epoch 893
+2023-11-15 02:07:32,071 INFO [train.py:811] (1/4) Start epoch 894
+2023-11-15 02:08:30,711 INFO [train.py:467] (1/4) Epoch 894, batch 9, global_batch_idx: 33050, batch size: 49, loss[discriminator_loss=2.518, discriminator_real_loss=1.27, discriminator_fake_loss=1.248, generator_loss=30.66, generator_mel_loss=20.15, generator_kl_loss=2.08, generator_dur_loss=1.638, generator_adv_loss=2.201, generator_feat_match_loss=4.59, over 49.00 samples.], tot_loss[discriminator_loss=2.586, discriminator_real_loss=1.306, discriminator_fake_loss=1.28, generator_loss=30.37, generator_mel_loss=19.94, generator_kl_loss=2.024, generator_dur_loss=1.633, generator_adv_loss=2.169, generator_feat_match_loss=4.61, over 635.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:11:05,611 INFO [train.py:811] (1/4) Start epoch 895
+2023-11-15 02:13:20,249 INFO [train.py:467] (1/4) Epoch 895, batch 22, global_batch_idx: 33100, batch size: 50, loss[discriminator_loss=2.625, discriminator_real_loss=1.234, discriminator_fake_loss=1.39, generator_loss=30.1, generator_mel_loss=19.77, generator_kl_loss=1.964, generator_dur_loss=1.627, generator_adv_loss=2.328, generator_feat_match_loss=4.41, over 50.00 samples.], tot_loss[discriminator_loss=2.549, discriminator_real_loss=1.277, discriminator_fake_loss=1.273, generator_loss=30.69, generator_mel_loss=20.14, generator_kl_loss=2.024, generator_dur_loss=1.63, generator_adv_loss=2.222, generator_feat_match_loss=4.67, over 1545.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:14:40,273 INFO [train.py:811] (1/4) Start epoch 896
+2023-11-15 02:18:09,874 INFO [train.py:467] (1/4) Epoch 896, batch 35, global_batch_idx: 33150, batch size: 53, loss[discriminator_loss=2.586, discriminator_real_loss=1.235, discriminator_fake_loss=1.352, generator_loss=30.17, generator_mel_loss=19.68, generator_kl_loss=2.026, generator_dur_loss=1.619, generator_adv_loss=2.143, generator_feat_match_loss=4.699, over 53.00 samples.], tot_loss[discriminator_loss=2.583, discriminator_real_loss=1.306, discriminator_fake_loss=1.277, generator_loss=30.42, generator_mel_loss=19.82, generator_kl_loss=2.015, generator_dur_loss=1.629, generator_adv_loss=2.255, generator_feat_match_loss=4.703, over 2513.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:18:14,579 INFO [train.py:811] (1/4) Start epoch 897
+2023-11-15 02:21:45,871 INFO [train.py:811] (1/4) Start epoch 898
+2023-11-15 02:23:02,667 INFO [train.py:467] (1/4) Epoch 898, batch 11, global_batch_idx: 33200, batch size: 95, loss[discriminator_loss=2.648, discriminator_real_loss=1.342, discriminator_fake_loss=1.308, generator_loss=29.89, generator_mel_loss=19.92, generator_kl_loss=1.953, generator_dur_loss=1.633, generator_adv_loss=2.188, generator_feat_match_loss=4.195, over 95.00 samples.], tot_loss[discriminator_loss=2.558, discriminator_real_loss=1.284, discriminator_fake_loss=1.274, generator_loss=30.41, generator_mel_loss=19.91, generator_kl_loss=1.99, generator_dur_loss=1.629, generator_adv_loss=2.23, generator_feat_match_loss=4.65, over 817.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 02:23:02,669 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 02:23:13,659 INFO [train.py:517] (1/4) Epoch 898, validation: discriminator_loss=2.641, discriminator_real_loss=1.339, discriminator_fake_loss=1.302, generator_loss=31.78, generator_mel_loss=20.81, generator_kl_loss=2.316, generator_dur_loss=1.625, generator_adv_loss=2.175, generator_feat_match_loss=4.856, over 100.00 samples.
+2023-11-15 02:23:13,660 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 02:25:27,113 INFO [train.py:811] (1/4) Start epoch 899
+2023-11-15 02:27:58,753 INFO [train.py:467] (1/4) Epoch 899, batch 24, global_batch_idx: 33250, batch size: 56, loss[discriminator_loss=2.551, discriminator_real_loss=1.318, discriminator_fake_loss=1.233, generator_loss=30.5, generator_mel_loss=20.1, generator_kl_loss=2.024, generator_dur_loss=1.629, generator_adv_loss=2.219, generator_feat_match_loss=4.531, over 56.00 samples.], tot_loss[discriminator_loss=2.571, discriminator_real_loss=1.298, discriminator_fake_loss=1.273, generator_loss=30.62, generator_mel_loss=20.18, generator_kl_loss=2.022, generator_dur_loss=1.628, generator_adv_loss=2.163, generator_feat_match_loss=4.623, over 1886.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 02:29:04,391 INFO [train.py:811] (1/4) Start epoch 900
+2023-11-15 02:32:36,594 INFO [train.py:811] (1/4) Start epoch 901
+2023-11-15 02:32:54,455 INFO [train.py:467] (1/4) Epoch 901, batch 0, global_batch_idx: 33300, batch size: 59, loss[discriminator_loss=2.5, discriminator_real_loss=1.207, discriminator_fake_loss=1.292, generator_loss=30.64, generator_mel_loss=19.88, generator_kl_loss=2.111, generator_dur_loss=1.653, generator_adv_loss=2.293, generator_feat_match_loss=4.707, over 59.00 samples.], tot_loss[discriminator_loss=2.5, discriminator_real_loss=1.207, discriminator_fake_loss=1.292, generator_loss=30.64, generator_mel_loss=19.88, generator_kl_loss=2.111, generator_dur_loss=1.653, generator_adv_loss=2.293, generator_feat_match_loss=4.707, over 59.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 02:36:10,197 INFO [train.py:811] (1/4) Start epoch 902
+2023-11-15 02:37:30,494 INFO [train.py:467] (1/4) Epoch 902, batch 13, global_batch_idx: 33350, batch size: 60, loss[discriminator_loss=2.574, discriminator_real_loss=1.355, discriminator_fake_loss=1.219, generator_loss=30.6, generator_mel_loss=19.92, generator_kl_loss=2.026, generator_dur_loss=1.662, generator_adv_loss=2.391, generator_feat_match_loss=4.602, over 60.00 samples.], tot_loss[discriminator_loss=2.504, discriminator_real_loss=1.265, discriminator_fake_loss=1.239, generator_loss=30.77, generator_mel_loss=19.97, generator_kl_loss=2.032, generator_dur_loss=1.631, generator_adv_loss=2.332, generator_feat_match_loss=4.796, over 1036.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 02:39:44,409 INFO [train.py:811] (1/4) Start epoch 903
+2023-11-15 02:42:16,469 INFO [train.py:467] (1/4) Epoch 903, batch 26, global_batch_idx: 33400, batch size: 71, loss[discriminator_loss=2.395, discriminator_real_loss=1.179, discriminator_fake_loss=1.216, generator_loss=30.82, generator_mel_loss=19.55, generator_kl_loss=1.981, generator_dur_loss=1.621, generator_adv_loss=2.504, generator_feat_match_loss=5.164, over 71.00 samples.], tot_loss[discriminator_loss=2.459, discriminator_real_loss=1.239, discriminator_fake_loss=1.22, generator_loss=30.82, generator_mel_loss=19.63, generator_kl_loss=1.992, generator_dur_loss=1.631, generator_adv_loss=2.408, generator_feat_match_loss=5.159, over 1925.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:42:16,470 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 02:42:27,434 INFO [train.py:517] (1/4) Epoch 903, validation: discriminator_loss=2.411, discriminator_real_loss=1.1, discriminator_fake_loss=1.311, generator_loss=31.57, generator_mel_loss=20.23, generator_kl_loss=2.246, generator_dur_loss=1.631, generator_adv_loss=2.149, generator_feat_match_loss=5.312, over 100.00 samples.
+2023-11-15 02:42:27,435 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 02:43:23,817 INFO [train.py:811] (1/4) Start epoch 904
+2023-11-15 02:46:55,972 INFO [train.py:811] (1/4) Start epoch 905
+2023-11-15 02:47:18,866 INFO [train.py:467] (1/4) Epoch 905, batch 2, global_batch_idx: 33450, batch size: 50, loss[discriminator_loss=2.484, discriminator_real_loss=1.34, discriminator_fake_loss=1.144, generator_loss=30.59, generator_mel_loss=19.53, generator_kl_loss=2.022, generator_dur_loss=1.652, generator_adv_loss=2.371, generator_feat_match_loss=5.012, over 50.00 samples.], tot_loss[discriminator_loss=2.582, discriminator_real_loss=1.35, discriminator_fake_loss=1.232, generator_loss=30.78, generator_mel_loss=19.95, generator_kl_loss=2.003, generator_dur_loss=1.644, generator_adv_loss=2.345, generator_feat_match_loss=4.843, over 163.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:50:24,563 INFO [train.py:811] (1/4) Start epoch 906
+2023-11-15 02:52:02,460 INFO [train.py:467] (1/4) Epoch 906, batch 15, global_batch_idx: 33500, batch size: 73, loss[discriminator_loss=2.566, discriminator_real_loss=1.379, discriminator_fake_loss=1.188, generator_loss=30.7, generator_mel_loss=19.97, generator_kl_loss=2.069, generator_dur_loss=1.639, generator_adv_loss=2.215, generator_feat_match_loss=4.805, over 73.00 samples.], tot_loss[discriminator_loss=2.526, discriminator_real_loss=1.267, discriminator_fake_loss=1.26, generator_loss=30.52, generator_mel_loss=19.88, generator_kl_loss=2.012, generator_dur_loss=1.627, generator_adv_loss=2.256, generator_feat_match_loss=4.751, over 1154.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:53:54,230 INFO [train.py:811] (1/4) Start epoch 907
+2023-11-15 02:56:48,357 INFO [train.py:467] (1/4) Epoch 907, batch 28, global_batch_idx: 33550, batch size: 85, loss[discriminator_loss=2.58, discriminator_real_loss=1.275, discriminator_fake_loss=1.305, generator_loss=30.85, generator_mel_loss=20.23, generator_kl_loss=2.091, generator_dur_loss=1.624, generator_adv_loss=2.33, generator_feat_match_loss=4.57, over 85.00 samples.], tot_loss[discriminator_loss=2.588, discriminator_real_loss=1.305, discriminator_fake_loss=1.283, generator_loss=30.56, generator_mel_loss=20.15, generator_kl_loss=2.044, generator_dur_loss=1.63, generator_adv_loss=2.18, generator_feat_match_loss=4.557, over 2246.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 8.0
+2023-11-15 02:57:26,625 INFO [train.py:811] (1/4) Start epoch 908
+2023-11-15 03:00:55,827 INFO [train.py:811] (1/4) Start epoch 909
+2023-11-15 03:01:33,987 INFO [train.py:467] (1/4) Epoch 909, batch 4, global_batch_idx: 33600, batch size: 56, loss[discriminator_loss=2.492, discriminator_real_loss=1.26, discriminator_fake_loss=1.233, generator_loss=30.56, generator_mel_loss=20.07, generator_kl_loss=2.026, generator_dur_loss=1.611, generator_adv_loss=2.102, generator_feat_match_loss=4.75, over 56.00 samples.], tot_loss[discriminator_loss=2.497, discriminator_real_loss=1.281, discriminator_fake_loss=1.215, generator_loss=30.62, generator_mel_loss=19.89, generator_kl_loss=2.006, generator_dur_loss=1.632, generator_adv_loss=2.263, generator_feat_match_loss=4.827, over 358.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 03:01:33,989 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 03:01:45,803 INFO [train.py:517] (1/4) Epoch 909, validation: discriminator_loss=2.46, discriminator_real_loss=1.126, discriminator_fake_loss=1.334, generator_loss=30.96, generator_mel_loss=20.45, generator_kl_loss=2.217, generator_dur_loss=1.627, generator_adv_loss=1.928, generator_feat_match_loss=4.737, over 100.00 samples.
+2023-11-15 03:01:45,804 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 03:04:43,824 INFO [train.py:811] (1/4) Start epoch 910
+2023-11-15 03:06:34,418 INFO [train.py:467] (1/4) Epoch 910, batch 17, global_batch_idx: 33650, batch size: 52, loss[discriminator_loss=2.422, discriminator_real_loss=1.18, discriminator_fake_loss=1.243, generator_loss=30.48, generator_mel_loss=19.66, generator_kl_loss=2.089, generator_dur_loss=1.634, generator_adv_loss=2.277, generator_feat_match_loss=4.82, over 52.00 samples.], tot_loss[discriminator_loss=2.51, discriminator_real_loss=1.262, discriminator_fake_loss=1.248, generator_loss=30.84, generator_mel_loss=19.93, generator_kl_loss=2.012, generator_dur_loss=1.63, generator_adv_loss=2.344, generator_feat_match_loss=4.923, over 1351.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, grad_scale: 16.0
+2023-11-15 03:08:17,628 INFO [train.py:811] (1/4) Start epoch 911
+2023-11-15 03:11:20,815 INFO [train.py:467] (1/4) Epoch 911, batch 30, global_batch_idx: 33700, batch size: 55, loss[discriminator_loss=2.266, discriminator_real_loss=1.182, discriminator_fake_loss=1.083, generator_loss=32.33, generator_mel_loss=20.02, generator_kl_loss=2.09, generator_dur_loss=1.64, generator_adv_loss=2.637, generator_feat_match_loss=5.945, over 55.00 samples.], tot_loss[discriminator_loss=2.46, discriminator_real_loss=1.242, discriminator_fake_loss=1.218, generator_loss=31.05, generator_mel_loss=19.88, generator_kl_loss=2.02, generator_dur_loss=1.63, generator_adv_loss=2.395, generator_feat_match_loss=5.124, over 2414.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:11:55,036 INFO [train.py:811] (1/4) Start epoch 912
+2023-11-15 03:15:23,976 INFO [train.py:811] (1/4) Start epoch 913
+2023-11-15 03:16:10,092 INFO [train.py:467] (1/4) Epoch 913, batch 6, global_batch_idx: 33750, batch size: 101, loss[discriminator_loss=2.523, discriminator_real_loss=1.377, discriminator_fake_loss=1.147, generator_loss=30.48, generator_mel_loss=20.01, generator_kl_loss=2.036, generator_dur_loss=1.611, generator_adv_loss=2.141, generator_feat_match_loss=4.68, over 101.00 samples.], tot_loss[discriminator_loss=2.494, discriminator_real_loss=1.254, discriminator_fake_loss=1.24, generator_loss=30.53, generator_mel_loss=19.98, generator_kl_loss=2.068, generator_dur_loss=1.634, generator_adv_loss=2.195, generator_feat_match_loss=4.652, over 567.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:18:52,788 INFO [train.py:811] (1/4) Start epoch 914
+2023-11-15 03:20:58,355 INFO [train.py:467] (1/4) Epoch 914, batch 19, global_batch_idx: 33800, batch size: 90, loss[discriminator_loss=2.609, discriminator_real_loss=1.425, discriminator_fake_loss=1.186, generator_loss=30.43, generator_mel_loss=20.03, generator_kl_loss=1.964, generator_dur_loss=1.607, generator_adv_loss=2.203, generator_feat_match_loss=4.625, over 90.00 samples.], tot_loss[discriminator_loss=2.569, discriminator_real_loss=1.314, discriminator_fake_loss=1.255, generator_loss=30.66, generator_mel_loss=20.11, generator_kl_loss=2.024, generator_dur_loss=1.631, generator_adv_loss=2.251, generator_feat_match_loss=4.641, over 1427.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:20:58,357 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 03:21:09,512 INFO [train.py:517] (1/4) Epoch 914, validation: discriminator_loss=2.618, discriminator_real_loss=1.258, discriminator_fake_loss=1.361, generator_loss=31.84, generator_mel_loss=21.04, generator_kl_loss=2.192, generator_dur_loss=1.622, generator_adv_loss=2.014, generator_feat_match_loss=4.965, over 100.00 samples.
+2023-11-15 03:21:09,514 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 03:22:36,753 INFO [train.py:811] (1/4) Start epoch 915
+2023-11-15 03:25:40,323 INFO [train.py:467] (1/4) Epoch 915, batch 32, global_batch_idx: 33850, batch size: 53, loss[discriminator_loss=2.629, discriminator_real_loss=1.301, discriminator_fake_loss=1.328, generator_loss=30.49, generator_mel_loss=19.77, generator_kl_loss=1.961, generator_dur_loss=1.608, generator_adv_loss=2.457, generator_feat_match_loss=4.691, over 53.00 samples.], tot_loss[discriminator_loss=2.595, discriminator_real_loss=1.335, discriminator_fake_loss=1.26, generator_loss=30.78, generator_mel_loss=19.81, generator_kl_loss=1.99, generator_dur_loss=1.629, generator_adv_loss=2.379, generator_feat_match_loss=4.97, over 2264.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:26:08,598 INFO [train.py:811] (1/4) Start epoch 916
+2023-11-15 03:29:46,075 INFO [train.py:811] (1/4) Start epoch 917
+2023-11-15 03:30:40,642 INFO [train.py:467] (1/4) Epoch 917, batch 8, global_batch_idx: 33900, batch size: 67, loss[discriminator_loss=2.617, discriminator_real_loss=1.401, discriminator_fake_loss=1.215, generator_loss=30.03, generator_mel_loss=19.78, generator_kl_loss=1.984, generator_dur_loss=1.645, generator_adv_loss=2.125, generator_feat_match_loss=4.492, over 67.00 samples.], tot_loss[discriminator_loss=2.597, discriminator_real_loss=1.322, discriminator_fake_loss=1.275, generator_loss=30.4, generator_mel_loss=20.01, generator_kl_loss=2.018, generator_dur_loss=1.634, generator_adv_loss=2.194, generator_feat_match_loss=4.537, over 599.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:33:14,249 INFO [train.py:811] (1/4) Start epoch 918
+2023-11-15 03:35:25,997 INFO [train.py:467] (1/4) Epoch 918, batch 21, global_batch_idx: 33950, batch size: 76, loss[discriminator_loss=2.518, discriminator_real_loss=1.216, discriminator_fake_loss=1.302, generator_loss=31.03, generator_mel_loss=20.21, generator_kl_loss=2.018, generator_dur_loss=1.632, generator_adv_loss=2.277, generator_feat_match_loss=4.898, over 76.00 samples.], tot_loss[discriminator_loss=2.611, discriminator_real_loss=1.329, discriminator_fake_loss=1.282, generator_loss=30.45, generator_mel_loss=20.15, generator_kl_loss=2.01, generator_dur_loss=1.628, generator_adv_loss=2.167, generator_feat_match_loss=4.494, over 1551.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:36:48,646 INFO [train.py:811] (1/4) Start epoch 919
+2023-11-15 03:40:07,604 INFO [train.py:467] (1/4) Epoch 919, batch 34, global_batch_idx: 34000, batch size: 53, loss[discriminator_loss=2.412, discriminator_real_loss=1.21, discriminator_fake_loss=1.202, generator_loss=31.07, generator_mel_loss=19.88, generator_kl_loss=2.049, generator_dur_loss=1.623, generator_adv_loss=2.26, generator_feat_match_loss=5.266, over 53.00 samples.], tot_loss[discriminator_loss=2.589, discriminator_real_loss=1.308, discriminator_fake_loss=1.281, generator_loss=30.64, generator_mel_loss=20, generator_kl_loss=2.037, generator_dur_loss=1.625, generator_adv_loss=2.257, generator_feat_match_loss=4.72, over 2482.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 03:40:07,606 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 03:40:18,696 INFO [train.py:517] (1/4) Epoch 919, validation: discriminator_loss=2.484, discriminator_real_loss=1.073, discriminator_fake_loss=1.411, generator_loss=31.27, generator_mel_loss=20.37, generator_kl_loss=2.23, generator_dur_loss=1.627, generator_adv_loss=1.906, generator_feat_match_loss=5.136, over 100.00 samples.
+2023-11-15 03:40:18,697 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 03:40:30,102 INFO [train.py:811] (1/4) Start epoch 920
+2023-11-15 03:44:00,595 INFO [train.py:811] (1/4) Start epoch 921
+2023-11-15 03:45:12,084 INFO [train.py:467] (1/4) Epoch 921, batch 10, global_batch_idx: 34050, batch size: 76, loss[discriminator_loss=2.633, discriminator_real_loss=1.243, discriminator_fake_loss=1.389, generator_loss=30, generator_mel_loss=19.63, generator_kl_loss=1.977, generator_dur_loss=1.607, generator_adv_loss=2.277, generator_feat_match_loss=4.5, over 76.00 samples.], tot_loss[discriminator_loss=2.549, discriminator_real_loss=1.281, discriminator_fake_loss=1.269, generator_loss=30.98, generator_mel_loss=20.12, generator_kl_loss=2.003, generator_dur_loss=1.622, generator_adv_loss=2.304, generator_feat_match_loss=4.936, over 810.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 03:47:33,724 INFO [train.py:811] (1/4) Start epoch 922
+2023-11-15 03:49:50,858 INFO [train.py:467] (1/4) Epoch 922, batch 23, global_batch_idx: 34100, batch size: 153, loss[discriminator_loss=2.602, discriminator_real_loss=1.373, discriminator_fake_loss=1.229, generator_loss=30.83, generator_mel_loss=20.09, generator_kl_loss=1.974, generator_dur_loss=1.609, generator_adv_loss=2.357, generator_feat_match_loss=4.805, over 153.00 samples.], tot_loss[discriminator_loss=2.551, discriminator_real_loss=1.294, discriminator_fake_loss=1.257, generator_loss=30.59, generator_mel_loss=19.68, generator_kl_loss=2.003, generator_dur_loss=1.63, generator_adv_loss=2.344, generator_feat_match_loss=4.939, over 1574.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:51:01,232 INFO [train.py:811] (1/4) Start epoch 923
+2023-11-15 03:54:32,356 INFO [train.py:467] (1/4) Epoch 923, batch 36, global_batch_idx: 34150, batch size: 79, loss[discriminator_loss=2.43, discriminator_real_loss=1.305, discriminator_fake_loss=1.126, generator_loss=31.36, generator_mel_loss=20.14, generator_kl_loss=2.107, generator_dur_loss=1.639, generator_adv_loss=2.281, generator_feat_match_loss=5.195, over 79.00 samples.], tot_loss[discriminator_loss=2.555, discriminator_real_loss=1.297, discriminator_fake_loss=1.257, generator_loss=30.62, generator_mel_loss=20.01, generator_kl_loss=2.012, generator_dur_loss=1.628, generator_adv_loss=2.243, generator_feat_match_loss=4.723, over 2643.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:54:33,066 INFO [train.py:811] (1/4) Start epoch 924
+2023-11-15 03:58:05,301 INFO [train.py:811] (1/4) Start epoch 925
+2023-11-15 03:59:25,721 INFO [train.py:467] (1/4) Epoch 925, batch 12, global_batch_idx: 34200, batch size: 53, loss[discriminator_loss=2.486, discriminator_real_loss=1.301, discriminator_fake_loss=1.186, generator_loss=31.11, generator_mel_loss=19.97, generator_kl_loss=1.99, generator_dur_loss=1.653, generator_adv_loss=2.365, generator_feat_match_loss=5.129, over 53.00 samples.], tot_loss[discriminator_loss=2.51, discriminator_real_loss=1.283, discriminator_fake_loss=1.226, generator_loss=30.98, generator_mel_loss=19.97, generator_kl_loss=2.04, generator_dur_loss=1.627, generator_adv_loss=2.341, generator_feat_match_loss=5, over 990.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 03:59:25,722 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 03:59:36,632 INFO [train.py:517] (1/4) Epoch 925, validation: discriminator_loss=2.463, discriminator_real_loss=1.216, discriminator_fake_loss=1.248, generator_loss=31.49, generator_mel_loss=20.59, generator_kl_loss=2.14, generator_dur_loss=1.643, generator_adv_loss=2.13, generator_feat_match_loss=4.99, over 100.00 samples.
+2023-11-15 03:59:36,633 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 04:01:45,919 INFO [train.py:811] (1/4) Start epoch 926
+2023-11-15 04:04:18,728 INFO [train.py:467] (1/4) Epoch 926, batch 25, global_batch_idx: 34250, batch size: 55, loss[discriminator_loss=2.57, discriminator_real_loss=1.354, discriminator_fake_loss=1.215, generator_loss=30.12, generator_mel_loss=19.73, generator_kl_loss=2.044, generator_dur_loss=1.67, generator_adv_loss=2.361, generator_feat_match_loss=4.312, over 55.00 samples.], tot_loss[discriminator_loss=2.541, discriminator_real_loss=1.283, discriminator_fake_loss=1.258, generator_loss=30.77, generator_mel_loss=19.93, generator_kl_loss=2.03, generator_dur_loss=1.629, generator_adv_loss=2.305, generator_feat_match_loss=4.874, over 1829.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:05:19,665 INFO [train.py:811] (1/4) Start epoch 927
+2023-11-15 04:08:52,727 INFO [train.py:811] (1/4) Start epoch 928
+2023-11-15 04:09:15,332 INFO [train.py:467] (1/4) Epoch 928, batch 1, global_batch_idx: 34300, batch size: 101, loss[discriminator_loss=2.426, discriminator_real_loss=1.203, discriminator_fake_loss=1.223, generator_loss=31.61, generator_mel_loss=20.39, generator_kl_loss=2.12, generator_dur_loss=1.622, generator_adv_loss=2.295, generator_feat_match_loss=5.18, over 101.00 samples.], tot_loss[discriminator_loss=2.455, discriminator_real_loss=1.238, discriminator_fake_loss=1.218, generator_loss=31.4, generator_mel_loss=20.27, generator_kl_loss=2.071, generator_dur_loss=1.623, generator_adv_loss=2.321, generator_feat_match_loss=5.106, over 150.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:12:24,463 INFO [train.py:811] (1/4) Start epoch 929
+2023-11-15 04:13:53,619 INFO [train.py:467] (1/4) Epoch 929, batch 14, global_batch_idx: 34350, batch size: 85, loss[discriminator_loss=2.561, discriminator_real_loss=1.172, discriminator_fake_loss=1.389, generator_loss=30.06, generator_mel_loss=19.48, generator_kl_loss=1.924, generator_dur_loss=1.605, generator_adv_loss=2.25, generator_feat_match_loss=4.805, over 85.00 samples.], tot_loss[discriminator_loss=2.462, discriminator_real_loss=1.227, discriminator_fake_loss=1.234, generator_loss=30.85, generator_mel_loss=19.65, generator_kl_loss=1.996, generator_dur_loss=1.626, generator_adv_loss=2.369, generator_feat_match_loss=5.21, over 1187.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:15:57,061 INFO [train.py:811] (1/4) Start epoch 930
+2023-11-15 04:18:36,057 INFO [train.py:467] (1/4) Epoch 930, batch 27, global_batch_idx: 34400, batch size: 55, loss[discriminator_loss=2.629, discriminator_real_loss=1.397, discriminator_fake_loss=1.232, generator_loss=29.43, generator_mel_loss=19.54, generator_kl_loss=2.028, generator_dur_loss=1.657, generator_adv_loss=2.082, generator_feat_match_loss=4.129, over 55.00 samples.], tot_loss[discriminator_loss=2.551, discriminator_real_loss=1.288, discriminator_fake_loss=1.263, generator_loss=30.57, generator_mel_loss=20.04, generator_kl_loss=2.021, generator_dur_loss=1.625, generator_adv_loss=2.18, generator_feat_match_loss=4.702, over 2038.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 04:18:36,058 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 04:18:47,232 INFO [train.py:517] (1/4) Epoch 930, validation: discriminator_loss=2.608, discriminator_real_loss=1.257, discriminator_fake_loss=1.352, generator_loss=31.26, generator_mel_loss=20.85, generator_kl_loss=2.339, generator_dur_loss=1.624, generator_adv_loss=1.921, generator_feat_match_loss=4.522, over 100.00 samples.
+2023-11-15 04:18:47,233 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 04:19:39,986 INFO [train.py:811] (1/4) Start epoch 931
+2023-11-15 04:23:08,353 INFO [train.py:811] (1/4) Start epoch 932
+2023-11-15 04:23:37,119 INFO [train.py:467] (1/4) Epoch 932, batch 3, global_batch_idx: 34450, batch size: 63, loss[discriminator_loss=2.51, discriminator_real_loss=1.328, discriminator_fake_loss=1.182, generator_loss=30.71, generator_mel_loss=19.7, generator_kl_loss=2.042, generator_dur_loss=1.651, generator_adv_loss=2.406, generator_feat_match_loss=4.914, over 63.00 samples.], tot_loss[discriminator_loss=2.616, discriminator_real_loss=1.302, discriminator_fake_loss=1.314, generator_loss=30.35, generator_mel_loss=19.9, generator_kl_loss=2.009, generator_dur_loss=1.629, generator_adv_loss=2.255, generator_feat_match_loss=4.553, over 271.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 04:26:32,038 INFO [train.py:811] (1/4) Start epoch 933
+2023-11-15 04:28:17,145 INFO [train.py:467] (1/4) Epoch 933, batch 16, global_batch_idx: 34500, batch size: 110, loss[discriminator_loss=2.488, discriminator_real_loss=1.337, discriminator_fake_loss=1.151, generator_loss=31.26, generator_mel_loss=20.26, generator_kl_loss=2.04, generator_dur_loss=1.631, generator_adv_loss=2.305, generator_feat_match_loss=5.023, over 110.00 samples.], tot_loss[discriminator_loss=2.496, discriminator_real_loss=1.268, discriminator_fake_loss=1.228, generator_loss=30.83, generator_mel_loss=19.88, generator_kl_loss=2.035, generator_dur_loss=1.632, generator_adv_loss=2.324, generator_feat_match_loss=4.957, over 1344.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:30:00,557 INFO [train.py:811] (1/4) Start epoch 934
+2023-11-15 04:32:51,929 INFO [train.py:467] (1/4) Epoch 934, batch 29, global_batch_idx: 34550, batch size: 59, loss[discriminator_loss=2.393, discriminator_real_loss=1.175, discriminator_fake_loss=1.218, generator_loss=31.65, generator_mel_loss=19.87, generator_kl_loss=1.958, generator_dur_loss=1.615, generator_adv_loss=2.627, generator_feat_match_loss=5.578, over 59.00 samples.], tot_loss[discriminator_loss=2.49, discriminator_real_loss=1.262, discriminator_fake_loss=1.228, generator_loss=30.97, generator_mel_loss=19.72, generator_kl_loss=2.011, generator_dur_loss=1.627, generator_adv_loss=2.422, generator_feat_match_loss=5.196, over 2167.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:33:31,191 INFO [train.py:811] (1/4) Start epoch 935
+2023-11-15 04:36:59,731 INFO [train.py:811] (1/4) Start epoch 936
+2023-11-15 04:37:39,024 INFO [train.py:467] (1/4) Epoch 936, batch 5, global_batch_idx: 34600, batch size: 64, loss[discriminator_loss=2.578, discriminator_real_loss=1.354, discriminator_fake_loss=1.225, generator_loss=30.24, generator_mel_loss=19.85, generator_kl_loss=1.975, generator_dur_loss=1.652, generator_adv_loss=2.123, generator_feat_match_loss=4.633, over 64.00 samples.], tot_loss[discriminator_loss=2.6, discriminator_real_loss=1.312, discriminator_fake_loss=1.288, generator_loss=30.32, generator_mel_loss=19.99, generator_kl_loss=1.993, generator_dur_loss=1.633, generator_adv_loss=2.16, generator_feat_match_loss=4.547, over 423.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:37:39,026 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 04:37:51,276 INFO [train.py:517] (1/4) Epoch 936, validation: discriminator_loss=2.564, discriminator_real_loss=1.232, discriminator_fake_loss=1.332, generator_loss=31.25, generator_mel_loss=20.65, generator_kl_loss=2.279, generator_dur_loss=1.634, generator_adv_loss=1.962, generator_feat_match_loss=4.719, over 100.00 samples.
+2023-11-15 04:37:51,277 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 04:40:43,307 INFO [train.py:811] (1/4) Start epoch 937
+2023-11-15 04:42:26,962 INFO [train.py:467] (1/4) Epoch 937, batch 18, global_batch_idx: 34650, batch size: 67, loss[discriminator_loss=2.609, discriminator_real_loss=1.334, discriminator_fake_loss=1.276, generator_loss=30.57, generator_mel_loss=20.27, generator_kl_loss=1.98, generator_dur_loss=1.666, generator_adv_loss=2.115, generator_feat_match_loss=4.539, over 67.00 samples.], tot_loss[discriminator_loss=2.587, discriminator_real_loss=1.314, discriminator_fake_loss=1.274, generator_loss=30.37, generator_mel_loss=20, generator_kl_loss=1.989, generator_dur_loss=1.634, generator_adv_loss=2.183, generator_feat_match_loss=4.562, over 1288.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:44:16,961 INFO [train.py:811] (1/4) Start epoch 938
+2023-11-15 04:47:22,777 INFO [train.py:467] (1/4) Epoch 938, batch 31, global_batch_idx: 34700, batch size: 52, loss[discriminator_loss=2.467, discriminator_real_loss=1.323, discriminator_fake_loss=1.144, generator_loss=31.37, generator_mel_loss=20.07, generator_kl_loss=2.051, generator_dur_loss=1.64, generator_adv_loss=2.393, generator_feat_match_loss=5.223, over 52.00 samples.], tot_loss[discriminator_loss=2.537, discriminator_real_loss=1.294, discriminator_fake_loss=1.244, generator_loss=30.8, generator_mel_loss=19.88, generator_kl_loss=2.023, generator_dur_loss=1.626, generator_adv_loss=2.339, generator_feat_match_loss=4.927, over 2590.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:47:49,075 INFO [train.py:811] (1/4) Start epoch 939
+2023-11-15 04:51:21,437 INFO [train.py:811] (1/4) Start epoch 940
+2023-11-15 04:52:17,872 INFO [train.py:467] (1/4) Epoch 940, batch 7, global_batch_idx: 34750, batch size: 101, loss[discriminator_loss=2.527, discriminator_real_loss=1.288, discriminator_fake_loss=1.238, generator_loss=30.89, generator_mel_loss=19.99, generator_kl_loss=2.006, generator_dur_loss=1.61, generator_adv_loss=2.398, generator_feat_match_loss=4.891, over 101.00 samples.], tot_loss[discriminator_loss=2.524, discriminator_real_loss=1.269, discriminator_fake_loss=1.255, generator_loss=30.62, generator_mel_loss=19.95, generator_kl_loss=2.026, generator_dur_loss=1.631, generator_adv_loss=2.288, generator_feat_match_loss=4.723, over 601.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 04:54:54,443 INFO [train.py:811] (1/4) Start epoch 941
+2023-11-15 04:57:01,273 INFO [train.py:467] (1/4) Epoch 941, batch 20, global_batch_idx: 34800, batch size: 73, loss[discriminator_loss=2.869, discriminator_real_loss=1.542, discriminator_fake_loss=1.327, generator_loss=30.1, generator_mel_loss=19.41, generator_kl_loss=1.992, generator_dur_loss=1.612, generator_adv_loss=2.51, generator_feat_match_loss=4.574, over 73.00 samples.], tot_loss[discriminator_loss=2.516, discriminator_real_loss=1.264, discriminator_fake_loss=1.251, generator_loss=30.81, generator_mel_loss=19.61, generator_kl_loss=1.997, generator_dur_loss=1.633, generator_adv_loss=2.407, generator_feat_match_loss=5.172, over 1497.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 04:57:01,275 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 04:57:12,269 INFO [train.py:517] (1/4) Epoch 941, validation: discriminator_loss=2.681, discriminator_real_loss=1.534, discriminator_fake_loss=1.147, generator_loss=31.02, generator_mel_loss=20.36, generator_kl_loss=2.139, generator_dur_loss=1.62, generator_adv_loss=2.381, generator_feat_match_loss=4.521, over 100.00 samples.
+2023-11-15 04:57:12,270 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 04:58:42,344 INFO [train.py:811] (1/4) Start epoch 942
+2023-11-15 05:02:02,345 INFO [train.py:467] (1/4) Epoch 942, batch 33, global_batch_idx: 34850, batch size: 64, loss[discriminator_loss=2.605, discriminator_real_loss=1.309, discriminator_fake_loss=1.296, generator_loss=30.33, generator_mel_loss=19.83, generator_kl_loss=1.974, generator_dur_loss=1.624, generator_adv_loss=2.238, generator_feat_match_loss=4.664, over 64.00 samples.], tot_loss[discriminator_loss=2.577, discriminator_real_loss=1.303, discriminator_fake_loss=1.275, generator_loss=30.5, generator_mel_loss=20.04, generator_kl_loss=2.019, generator_dur_loss=1.623, generator_adv_loss=2.172, generator_feat_match_loss=4.646, over 2580.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 05:02:16,135 INFO [train.py:811] (1/4) Start epoch 943
+2023-11-15 05:05:42,631 INFO [train.py:811] (1/4) Start epoch 944
+2023-11-15 05:06:41,014 INFO [train.py:467] (1/4) Epoch 944, batch 9, global_batch_idx: 34900, batch size: 50, loss[discriminator_loss=2.594, discriminator_real_loss=1.277, discriminator_fake_loss=1.315, generator_loss=30, generator_mel_loss=19.75, generator_kl_loss=1.976, generator_dur_loss=1.642, generator_adv_loss=2.188, generator_feat_match_loss=4.445, over 50.00 samples.], tot_loss[discriminator_loss=2.587, discriminator_real_loss=1.308, discriminator_fake_loss=1.279, generator_loss=30.56, generator_mel_loss=20.06, generator_kl_loss=1.96, generator_dur_loss=1.632, generator_adv_loss=2.222, generator_feat_match_loss=4.686, over 623.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 05:09:15,042 INFO [train.py:811] (1/4) Start epoch 945
+2023-11-15 05:11:26,089 INFO [train.py:467] (1/4) Epoch 945, batch 22, global_batch_idx: 34950, batch size: 52, loss[discriminator_loss=2.561, discriminator_real_loss=1.283, discriminator_fake_loss=1.277, generator_loss=29.99, generator_mel_loss=19.59, generator_kl_loss=1.977, generator_dur_loss=1.653, generator_adv_loss=2.01, generator_feat_match_loss=4.766, over 52.00 samples.], tot_loss[discriminator_loss=2.597, discriminator_real_loss=1.323, discriminator_fake_loss=1.274, generator_loss=30.54, generator_mel_loss=19.94, generator_kl_loss=1.979, generator_dur_loss=1.621, generator_adv_loss=2.23, generator_feat_match_loss=4.761, over 1723.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 05:12:43,492 INFO [train.py:811] (1/4) Start epoch 946
+2023-11-15 05:16:11,532 INFO [train.py:467] (1/4) Epoch 946, batch 35, global_batch_idx: 35000, batch size: 101, loss[discriminator_loss=2.492, discriminator_real_loss=1.288, discriminator_fake_loss=1.203, generator_loss=30.43, generator_mel_loss=19.94, generator_kl_loss=2.027, generator_dur_loss=1.627, generator_adv_loss=2.172, generator_feat_match_loss=4.66, over 101.00 samples.], tot_loss[discriminator_loss=2.576, discriminator_real_loss=1.311, discriminator_fake_loss=1.265, generator_loss=30.44, generator_mel_loss=19.89, generator_kl_loss=2.009, generator_dur_loss=1.622, generator_adv_loss=2.226, generator_feat_match_loss=4.687, over 2750.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 05:16:11,534 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 05:16:22,447 INFO [train.py:517] (1/4) Epoch 946, validation: discriminator_loss=2.501, discriminator_real_loss=1.169, discriminator_fake_loss=1.332, generator_loss=31.78, generator_mel_loss=20.75, generator_kl_loss=2.266, generator_dur_loss=1.629, generator_adv_loss=2.086, generator_feat_match_loss=5.049, over 100.00 samples.
+2023-11-15 05:16:22,448 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 05:16:29,209 INFO [train.py:811] (1/4) Start epoch 947
+2023-11-15 05:19:59,738 INFO [train.py:811] (1/4) Start epoch 948
+2023-11-15 05:21:12,325 INFO [train.py:467] (1/4) Epoch 948, batch 11, global_batch_idx: 35050, batch size: 61, loss[discriminator_loss=2.676, discriminator_real_loss=1.305, discriminator_fake_loss=1.371, generator_loss=29.53, generator_mel_loss=19.51, generator_kl_loss=2.033, generator_dur_loss=1.62, generator_adv_loss=2.053, generator_feat_match_loss=4.32, over 61.00 samples.], tot_loss[discriminator_loss=2.535, discriminator_real_loss=1.293, discriminator_fake_loss=1.241, generator_loss=30.76, generator_mel_loss=19.92, generator_kl_loss=2.025, generator_dur_loss=1.643, generator_adv_loss=2.293, generator_feat_match_loss=4.882, over 754.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 05:23:30,613 INFO [train.py:811] (1/4) Start epoch 949
+2023-11-15 05:26:02,776 INFO [train.py:467] (1/4) Epoch 949, batch 24, global_batch_idx: 35100, batch size: 51, loss[discriminator_loss=2.477, discriminator_real_loss=1.246, discriminator_fake_loss=1.231, generator_loss=30.99, generator_mel_loss=19.98, generator_kl_loss=1.978, generator_dur_loss=1.652, generator_adv_loss=2.277, generator_feat_match_loss=5.105, over 51.00 samples.], tot_loss[discriminator_loss=2.547, discriminator_real_loss=1.287, discriminator_fake_loss=1.26, generator_loss=30.81, generator_mel_loss=19.97, generator_kl_loss=2.028, generator_dur_loss=1.627, generator_adv_loss=2.285, generator_feat_match_loss=4.894, over 1844.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 05:27:06,700 INFO [train.py:811] (1/4) Start epoch 950
+2023-11-15 05:30:42,773 INFO [train.py:811] (1/4) Start epoch 951
+2023-11-15 05:30:58,157 INFO [train.py:467] (1/4) Epoch 951, batch 0, global_batch_idx: 35150, batch size: 85, loss[discriminator_loss=2.518, discriminator_real_loss=1.264, discriminator_fake_loss=1.254, generator_loss=31.02, generator_mel_loss=20.03, generator_kl_loss=2.16, generator_dur_loss=1.631, generator_adv_loss=2.279, generator_feat_match_loss=4.918, over 85.00 samples.], tot_loss[discriminator_loss=2.518, discriminator_real_loss=1.264, discriminator_fake_loss=1.254, generator_loss=31.02, generator_mel_loss=20.03, generator_kl_loss=2.16, generator_dur_loss=1.631, generator_adv_loss=2.279, generator_feat_match_loss=4.918, over 85.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 05:34:07,907 INFO [train.py:811] (1/4) Start epoch 952
+2023-11-15 05:35:39,377 INFO [train.py:467] (1/4) Epoch 952, batch 13, global_batch_idx: 35200, batch size: 59, loss[discriminator_loss=2.406, discriminator_real_loss=1.155, discriminator_fake_loss=1.251, generator_loss=30.69, generator_mel_loss=19.51, generator_kl_loss=2.024, generator_dur_loss=1.648, generator_adv_loss=2.328, generator_feat_match_loss=5.18, over 59.00 samples.], tot_loss[discriminator_loss=2.457, discriminator_real_loss=1.252, discriminator_fake_loss=1.206, generator_loss=31.15, generator_mel_loss=19.8, generator_kl_loss=2.002, generator_dur_loss=1.624, generator_adv_loss=2.424, generator_feat_match_loss=5.305, over 1130.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 16.0
+2023-11-15 05:35:39,378 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 05:35:50,391 INFO [train.py:517] (1/4) Epoch 952, validation: discriminator_loss=2.606, discriminator_real_loss=1.169, discriminator_fake_loss=1.437, generator_loss=30.7, generator_mel_loss=20.22, generator_kl_loss=2.176, generator_dur_loss=1.628, generator_adv_loss=1.853, generator_feat_match_loss=4.83, over 100.00 samples.
+2023-11-15 05:35:50,392 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 05:37:47,646 INFO [train.py:811] (1/4) Start epoch 953
+2023-11-15 05:40:21,297 INFO [train.py:467] (1/4) Epoch 953, batch 26, global_batch_idx: 35250, batch size: 69, loss[discriminator_loss=2.566, discriminator_real_loss=1.274, discriminator_fake_loss=1.291, generator_loss=29.71, generator_mel_loss=19.66, generator_kl_loss=1.988, generator_dur_loss=1.609, generator_adv_loss=2.061, generator_feat_match_loss=4.395, over 69.00 samples.], tot_loss[discriminator_loss=2.544, discriminator_real_loss=1.29, discriminator_fake_loss=1.254, generator_loss=30.76, generator_mel_loss=19.91, generator_kl_loss=2.031, generator_dur_loss=1.625, generator_adv_loss=2.294, generator_feat_match_loss=4.904, over 2103.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 05:41:16,462 INFO [train.py:811] (1/4) Start epoch 954
+2023-11-15 05:44:52,879 INFO [train.py:811] (1/4) Start epoch 955
+2023-11-15 05:45:18,759 INFO [train.py:467] (1/4) Epoch 955, batch 2, global_batch_idx: 35300, batch size: 50, loss[discriminator_loss=2.562, discriminator_real_loss=1.371, discriminator_fake_loss=1.19, generator_loss=31, generator_mel_loss=20.13, generator_kl_loss=2.028, generator_dur_loss=1.657, generator_adv_loss=2.252, generator_feat_match_loss=4.926, over 50.00 samples.], tot_loss[discriminator_loss=2.652, discriminator_real_loss=1.37, discriminator_fake_loss=1.281, generator_loss=30.53, generator_mel_loss=20.02, generator_kl_loss=2.051, generator_dur_loss=1.637, generator_adv_loss=2.248, generator_feat_match_loss=4.578, over 166.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, grad_scale: 8.0
+2023-11-15 05:48:20,685 INFO [train.py:811] (1/4) Start epoch 956
+2023-11-15 05:50:03,023 INFO [train.py:467] (1/4) Epoch 956, batch 15, global_batch_idx: 35350, batch size: 126, loss[discriminator_loss=2.43, discriminator_real_loss=1.27, discriminator_fake_loss=1.16, generator_loss=31.86, generator_mel_loss=19.95, generator_kl_loss=2.01, generator_dur_loss=1.627, generator_adv_loss=2.594, generator_feat_match_loss=5.672, over 126.00 samples.], tot_loss[discriminator_loss=2.49, discriminator_real_loss=1.261, discriminator_fake_loss=1.229, generator_loss=31.05, generator_mel_loss=19.83, generator_kl_loss=2.034, generator_dur_loss=1.624, generator_adv_loss=2.411, generator_feat_match_loss=5.158, over 1215.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 05:51:59,231 INFO [train.py:811] (1/4) Start epoch 957
+2023-11-15 05:54:48,268 INFO [train.py:467] (1/4) Epoch 957, batch 28, global_batch_idx: 35400, batch size: 73, loss[discriminator_loss=2.541, discriminator_real_loss=1.26, discriminator_fake_loss=1.281, generator_loss=30.01, generator_mel_loss=19.61, generator_kl_loss=2.02, generator_dur_loss=1.641, generator_adv_loss=2.24, generator_feat_match_loss=4.504, over 73.00 samples.], tot_loss[discriminator_loss=2.467, discriminator_real_loss=1.243, discriminator_fake_loss=1.224, generator_loss=30.88, generator_mel_loss=19.55, generator_kl_loss=1.988, generator_dur_loss=1.622, generator_adv_loss=2.425, generator_feat_match_loss=5.294, over 2305.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 05:54:48,270 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 05:54:59,150 INFO [train.py:517] (1/4) Epoch 957, validation: discriminator_loss=2.546, discriminator_real_loss=1.16, discriminator_fake_loss=1.386, generator_loss=31.21, generator_mel_loss=20.37, generator_kl_loss=2.276, generator_dur_loss=1.623, generator_adv_loss=1.948, generator_feat_match_loss=4.993, over 100.00 samples.
+2023-11-15 05:54:59,151 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 05:55:44,645 INFO [train.py:811] (1/4) Start epoch 958
+2023-11-15 05:59:12,225 INFO [train.py:811] (1/4) Start epoch 959
+2023-11-15 05:59:49,571 INFO [train.py:467] (1/4) Epoch 959, batch 4, global_batch_idx: 35450, batch size: 73, loss[discriminator_loss=2.496, discriminator_real_loss=1.29, discriminator_fake_loss=1.207, generator_loss=30.68, generator_mel_loss=19.83, generator_kl_loss=2.005, generator_dur_loss=1.594, generator_adv_loss=2.189, generator_feat_match_loss=5.062, over 73.00 samples.], tot_loss[discriminator_loss=2.525, discriminator_real_loss=1.292, discriminator_fake_loss=1.234, generator_loss=30.68, generator_mel_loss=19.93, generator_kl_loss=2.019, generator_dur_loss=1.629, generator_adv_loss=2.267, generator_feat_match_loss=4.835, over 310.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:02:43,758 INFO [train.py:811] (1/4) Start epoch 960
+2023-11-15 06:04:35,386 INFO [train.py:467] (1/4) Epoch 960, batch 17, global_batch_idx: 35500, batch size: 76, loss[discriminator_loss=2.59, discriminator_real_loss=1.37, discriminator_fake_loss=1.221, generator_loss=30.05, generator_mel_loss=19.66, generator_kl_loss=1.974, generator_dur_loss=1.641, generator_adv_loss=2.252, generator_feat_match_loss=4.523, over 76.00 samples.], tot_loss[discriminator_loss=2.584, discriminator_real_loss=1.299, discriminator_fake_loss=1.285, generator_loss=30.31, generator_mel_loss=19.85, generator_kl_loss=1.989, generator_dur_loss=1.625, generator_adv_loss=2.193, generator_feat_match_loss=4.654, over 1370.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:06:19,878 INFO [train.py:811] (1/4) Start epoch 961
+2023-11-15 06:09:11,918 INFO [train.py:467] (1/4) Epoch 961, batch 30, global_batch_idx: 35550, batch size: 54, loss[discriminator_loss=2.523, discriminator_real_loss=1.149, discriminator_fake_loss=1.373, generator_loss=30.93, generator_mel_loss=19.97, generator_kl_loss=1.986, generator_dur_loss=1.657, generator_adv_loss=2.352, generator_feat_match_loss=4.965, over 54.00 samples.], tot_loss[discriminator_loss=2.554, discriminator_real_loss=1.302, discriminator_fake_loss=1.252, generator_loss=30.6, generator_mel_loss=19.82, generator_kl_loss=2.05, generator_dur_loss=1.623, generator_adv_loss=2.261, generator_feat_match_loss=4.844, over 2181.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:09:50,179 INFO [train.py:811] (1/4) Start epoch 962
+2023-11-15 06:13:27,361 INFO [train.py:811] (1/4) Start epoch 963
+2023-11-15 06:14:18,903 INFO [train.py:467] (1/4) Epoch 963, batch 6, global_batch_idx: 35600, batch size: 153, loss[discriminator_loss=2.514, discriminator_real_loss=1.338, discriminator_fake_loss=1.176, generator_loss=31.02, generator_mel_loss=20.13, generator_kl_loss=2.046, generator_dur_loss=1.597, generator_adv_loss=2.146, generator_feat_match_loss=5.102, over 153.00 samples.], tot_loss[discriminator_loss=2.576, discriminator_real_loss=1.322, discriminator_fake_loss=1.254, generator_loss=30.71, generator_mel_loss=20.11, generator_kl_loss=2.031, generator_dur_loss=1.616, generator_adv_loss=2.195, generator_feat_match_loss=4.757, over 595.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 06:14:18,905 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 06:14:31,162 INFO [train.py:517] (1/4) Epoch 963, validation: discriminator_loss=2.487, discriminator_real_loss=1.174, discriminator_fake_loss=1.313, generator_loss=31.52, generator_mel_loss=20.6, generator_kl_loss=2.213, generator_dur_loss=1.629, generator_adv_loss=2.048, generator_feat_match_loss=5.035, over 100.00 samples.
+2023-11-15 06:14:31,163 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 06:17:12,225 INFO [train.py:811] (1/4) Start epoch 964
+2023-11-15 06:19:10,303 INFO [train.py:467] (1/4) Epoch 964, batch 19, global_batch_idx: 35650, batch size: 54, loss[discriminator_loss=2.484, discriminator_real_loss=1.179, discriminator_fake_loss=1.306, generator_loss=30.21, generator_mel_loss=19.61, generator_kl_loss=2.02, generator_dur_loss=1.663, generator_adv_loss=2.277, generator_feat_match_loss=4.641, over 54.00 samples.], tot_loss[discriminator_loss=2.577, discriminator_real_loss=1.315, discriminator_fake_loss=1.262, generator_loss=30.55, generator_mel_loss=19.76, generator_kl_loss=2.01, generator_dur_loss=1.627, generator_adv_loss=2.333, generator_feat_match_loss=4.82, over 1309.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:20:45,340 INFO [train.py:811] (1/4) Start epoch 965
+2023-11-15 06:24:00,967 INFO [train.py:467] (1/4) Epoch 965, batch 32, global_batch_idx: 35700, batch size: 71, loss[discriminator_loss=2.57, discriminator_real_loss=1.31, discriminator_fake_loss=1.26, generator_loss=29.87, generator_mel_loss=19.67, generator_kl_loss=2.093, generator_dur_loss=1.603, generator_adv_loss=2.029, generator_feat_match_loss=4.469, over 71.00 samples.], tot_loss[discriminator_loss=2.558, discriminator_real_loss=1.301, discriminator_fake_loss=1.258, generator_loss=30.51, generator_mel_loss=19.89, generator_kl_loss=2.029, generator_dur_loss=1.629, generator_adv_loss=2.207, generator_feat_match_loss=4.758, over 2438.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:24:20,451 INFO [train.py:811] (1/4) Start epoch 966
+2023-11-15 06:27:51,986 INFO [train.py:811] (1/4) Start epoch 967
+2023-11-15 06:28:53,418 INFO [train.py:467] (1/4) Epoch 967, batch 8, global_batch_idx: 35750, batch size: 65, loss[discriminator_loss=2.551, discriminator_real_loss=1.251, discriminator_fake_loss=1.299, generator_loss=30.98, generator_mel_loss=20.1, generator_kl_loss=2.077, generator_dur_loss=1.62, generator_adv_loss=2.291, generator_feat_match_loss=4.891, over 65.00 samples.], tot_loss[discriminator_loss=2.548, discriminator_real_loss=1.29, discriminator_fake_loss=1.258, generator_loss=30.61, generator_mel_loss=20, generator_kl_loss=2.041, generator_dur_loss=1.63, generator_adv_loss=2.227, generator_feat_match_loss=4.718, over 645.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:31:29,610 INFO [train.py:811] (1/4) Start epoch 968
+2023-11-15 06:33:36,860 INFO [train.py:467] (1/4) Epoch 968, batch 21, global_batch_idx: 35800, batch size: 63, loss[discriminator_loss=2.371, discriminator_real_loss=1.126, discriminator_fake_loss=1.246, generator_loss=31.61, generator_mel_loss=19.72, generator_kl_loss=2.054, generator_dur_loss=1.631, generator_adv_loss=2.451, generator_feat_match_loss=5.754, over 63.00 samples.], tot_loss[discriminator_loss=2.523, discriminator_real_loss=1.289, discriminator_fake_loss=1.234, generator_loss=30.73, generator_mel_loss=19.75, generator_kl_loss=1.999, generator_dur_loss=1.634, generator_adv_loss=2.352, generator_feat_match_loss=4.995, over 1469.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:33:36,861 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 06:33:47,701 INFO [train.py:517] (1/4) Epoch 968, validation: discriminator_loss=2.741, discriminator_real_loss=1.128, discriminator_fake_loss=1.613, generator_loss=30.96, generator_mel_loss=20.77, generator_kl_loss=2.193, generator_dur_loss=1.637, generator_adv_loss=1.671, generator_feat_match_loss=4.682, over 100.00 samples.
+2023-11-15 06:33:47,702 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 06:35:06,201 INFO [train.py:811] (1/4) Start epoch 969
+2023-11-15 06:38:27,717 INFO [train.py:467] (1/4) Epoch 969, batch 34, global_batch_idx: 35850, batch size: 110, loss[discriminator_loss=2.52, discriminator_real_loss=1.378, discriminator_fake_loss=1.141, generator_loss=30.59, generator_mel_loss=19.81, generator_kl_loss=1.943, generator_dur_loss=1.593, generator_adv_loss=2.342, generator_feat_match_loss=4.902, over 110.00 samples.], tot_loss[discriminator_loss=2.487, discriminator_real_loss=1.259, discriminator_fake_loss=1.228, generator_loss=30.75, generator_mel_loss=19.82, generator_kl_loss=2.016, generator_dur_loss=1.63, generator_adv_loss=2.305, generator_feat_match_loss=4.977, over 2521.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:38:40,292 INFO [train.py:811] (1/4) Start epoch 970
+2023-11-15 06:42:10,461 INFO [train.py:811] (1/4) Start epoch 971
+2023-11-15 06:43:22,030 INFO [train.py:467] (1/4) Epoch 971, batch 10, global_batch_idx: 35900, batch size: 56, loss[discriminator_loss=2.551, discriminator_real_loss=1.304, discriminator_fake_loss=1.248, generator_loss=30.34, generator_mel_loss=19.51, generator_kl_loss=1.983, generator_dur_loss=1.599, generator_adv_loss=2.141, generator_feat_match_loss=5.105, over 56.00 samples.], tot_loss[discriminator_loss=2.498, discriminator_real_loss=1.286, discriminator_fake_loss=1.212, generator_loss=30.9, generator_mel_loss=19.44, generator_kl_loss=1.998, generator_dur_loss=1.617, generator_adv_loss=2.475, generator_feat_match_loss=5.371, over 748.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:45:42,047 INFO [train.py:811] (1/4) Start epoch 972
+2023-11-15 06:48:04,590 INFO [train.py:467] (1/4) Epoch 972, batch 23, global_batch_idx: 35950, batch size: 54, loss[discriminator_loss=2.555, discriminator_real_loss=1.376, discriminator_fake_loss=1.179, generator_loss=31.22, generator_mel_loss=20.22, generator_kl_loss=2.071, generator_dur_loss=1.631, generator_adv_loss=2.264, generator_feat_match_loss=5.043, over 54.00 samples.], tot_loss[discriminator_loss=2.555, discriminator_real_loss=1.301, discriminator_fake_loss=1.255, generator_loss=30.72, generator_mel_loss=20.04, generator_kl_loss=2.065, generator_dur_loss=1.625, generator_adv_loss=2.237, generator_feat_match_loss=4.757, over 1912.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 06:49:15,994 INFO [train.py:811] (1/4) Start epoch 973
+2023-11-15 06:52:45,983 INFO [train.py:467] (1/4) Epoch 973, batch 36, global_batch_idx: 36000, batch size: 110, loss[discriminator_loss=2.504, discriminator_real_loss=1.258, discriminator_fake_loss=1.245, generator_loss=30.32, generator_mel_loss=19.82, generator_kl_loss=1.899, generator_dur_loss=1.621, generator_adv_loss=2.316, generator_feat_match_loss=4.672, over 110.00 samples.], tot_loss[discriminator_loss=2.55, discriminator_real_loss=1.283, discriminator_fake_loss=1.267, generator_loss=30.71, generator_mel_loss=19.82, generator_kl_loss=1.999, generator_dur_loss=1.629, generator_adv_loss=2.328, generator_feat_match_loss=4.932, over 2792.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 06:52:45,984 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 06:52:57,018 INFO [train.py:517] (1/4) Epoch 973, validation: discriminator_loss=2.505, discriminator_real_loss=1.19, discriminator_fake_loss=1.315, generator_loss=31.28, generator_mel_loss=20.59, generator_kl_loss=2.157, generator_dur_loss=1.638, generator_adv_loss=1.969, generator_feat_match_loss=4.919, over 100.00 samples.
+2023-11-15 06:52:57,019 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 06:52:57,718 INFO [train.py:811] (1/4) Start epoch 974
+2023-11-15 06:56:32,465 INFO [train.py:811] (1/4) Start epoch 975
+2023-11-15 06:57:50,236 INFO [train.py:467] (1/4) Epoch 975, batch 12, global_batch_idx: 36050, batch size: 76, loss[discriminator_loss=2.504, discriminator_real_loss=1.295, discriminator_fake_loss=1.21, generator_loss=31.12, generator_mel_loss=19.96, generator_kl_loss=1.939, generator_dur_loss=1.633, generator_adv_loss=2.391, generator_feat_match_loss=5.203, over 76.00 samples.], tot_loss[discriminator_loss=2.551, discriminator_real_loss=1.283, discriminator_fake_loss=1.269, generator_loss=30.93, generator_mel_loss=20.1, generator_kl_loss=2.025, generator_dur_loss=1.63, generator_adv_loss=2.27, generator_feat_match_loss=4.904, over 789.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 07:00:06,056 INFO [train.py:811] (1/4) Start epoch 976
+2023-11-15 07:02:39,587 INFO [train.py:467] (1/4) Epoch 976, batch 25, global_batch_idx: 36100, batch size: 56, loss[discriminator_loss=2.734, discriminator_real_loss=1.196, discriminator_fake_loss=1.538, generator_loss=29.39, generator_mel_loss=19.32, generator_kl_loss=2.082, generator_dur_loss=1.614, generator_adv_loss=2.184, generator_feat_match_loss=4.188, over 56.00 samples.], tot_loss[discriminator_loss=2.511, discriminator_real_loss=1.258, discriminator_fake_loss=1.253, generator_loss=31, generator_mel_loss=19.77, generator_kl_loss=2.015, generator_dur_loss=1.627, generator_adv_loss=2.404, generator_feat_match_loss=5.188, over 1892.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 07:03:40,834 INFO [train.py:811] (1/4) Start epoch 977
+2023-11-15 07:07:13,336 INFO [train.py:811] (1/4) Start epoch 978
+2023-11-15 07:07:35,440 INFO [train.py:467] (1/4) Epoch 978, batch 1, global_batch_idx: 36150, batch size: 76, loss[discriminator_loss=2.539, discriminator_real_loss=1.221, discriminator_fake_loss=1.319, generator_loss=30.39, generator_mel_loss=19.72, generator_kl_loss=2.076, generator_dur_loss=1.629, generator_adv_loss=2.223, generator_feat_match_loss=4.742, over 76.00 samples.], tot_loss[discriminator_loss=2.541, discriminator_real_loss=1.24, discriminator_fake_loss=1.301, generator_loss=30.26, generator_mel_loss=19.65, generator_kl_loss=2.045, generator_dur_loss=1.637, generator_adv_loss=2.207, generator_feat_match_loss=4.717, over 129.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 07:10:43,232 INFO [train.py:811] (1/4) Start epoch 979
+2023-11-15 07:12:11,712 INFO [train.py:467] (1/4) Epoch 979, batch 14, global_batch_idx: 36200, batch size: 90, loss[discriminator_loss=2.508, discriminator_real_loss=1.291, discriminator_fake_loss=1.218, generator_loss=30.55, generator_mel_loss=19.84, generator_kl_loss=2.022, generator_dur_loss=1.603, generator_adv_loss=2, generator_feat_match_loss=5.082, over 90.00 samples.], tot_loss[discriminator_loss=2.613, discriminator_real_loss=1.336, discriminator_fake_loss=1.277, generator_loss=30.49, generator_mel_loss=19.89, generator_kl_loss=2.016, generator_dur_loss=1.626, generator_adv_loss=2.225, generator_feat_match_loss=4.729, over 1070.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 07:12:11,714 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 07:12:23,049 INFO [train.py:517] (1/4) Epoch 979, validation: discriminator_loss=2.618, discriminator_real_loss=1.056, discriminator_fake_loss=1.562, generator_loss=30.72, generator_mel_loss=20.33, generator_kl_loss=2.276, generator_dur_loss=1.629, generator_adv_loss=1.682, generator_feat_match_loss=4.806, over 100.00 samples.
+2023-11-15 07:12:23,050 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 07:14:23,749 INFO [train.py:811] (1/4) Start epoch 980
+2023-11-15 07:17:09,448 INFO [train.py:467] (1/4) Epoch 980, batch 27, global_batch_idx: 36250, batch size: 61, loss[discriminator_loss=2.484, discriminator_real_loss=1.272, discriminator_fake_loss=1.213, generator_loss=31.28, generator_mel_loss=20.26, generator_kl_loss=2.016, generator_dur_loss=1.642, generator_adv_loss=2.391, generator_feat_match_loss=4.969, over 61.00 samples.], tot_loss[discriminator_loss=2.53, discriminator_real_loss=1.275, discriminator_fake_loss=1.255, generator_loss=30.79, generator_mel_loss=19.85, generator_kl_loss=2.034, generator_dur_loss=1.621, generator_adv_loss=2.313, generator_feat_match_loss=4.971, over 2117.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 07:17:56,435 INFO [train.py:811] (1/4) Start epoch 981
+2023-11-15 07:21:33,970 INFO [train.py:811] (1/4) Start epoch 982
+2023-11-15 07:22:04,821 INFO [train.py:467] (1/4) Epoch 982, batch 3, global_batch_idx: 36300, batch size: 65, loss[discriminator_loss=2.551, discriminator_real_loss=1.332, discriminator_fake_loss=1.22, generator_loss=29.85, generator_mel_loss=19.48, generator_kl_loss=1.937, generator_dur_loss=1.626, generator_adv_loss=2.262, generator_feat_match_loss=4.543, over 65.00 samples.], tot_loss[discriminator_loss=2.549, discriminator_real_loss=1.301, discriminator_fake_loss=1.248, generator_loss=30.27, generator_mel_loss=19.76, generator_kl_loss=1.969, generator_dur_loss=1.616, generator_adv_loss=2.266, generator_feat_match_loss=4.659, over 297.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 07:25:06,469 INFO [train.py:811] (1/4) Start epoch 983
+2023-11-15 07:26:50,769 INFO [train.py:467] (1/4) Epoch 983, batch 16, global_batch_idx: 36350, batch size: 49, loss[discriminator_loss=2.578, discriminator_real_loss=1.225, discriminator_fake_loss=1.353, generator_loss=29.83, generator_mel_loss=19.35, generator_kl_loss=1.975, generator_dur_loss=1.647, generator_adv_loss=2.387, generator_feat_match_loss=4.469, over 49.00 samples.], tot_loss[discriminator_loss=2.532, discriminator_real_loss=1.27, discriminator_fake_loss=1.261, generator_loss=30.62, generator_mel_loss=19.81, generator_kl_loss=2.028, generator_dur_loss=1.631, generator_adv_loss=2.254, generator_feat_match_loss=4.887, over 1270.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 07:28:40,829 INFO [train.py:811] (1/4) Start epoch 984
+2023-11-15 07:31:33,881 INFO [train.py:467] (1/4) Epoch 984, batch 29, global_batch_idx: 36400, batch size: 65, loss[discriminator_loss=2.68, discriminator_real_loss=1.356, discriminator_fake_loss=1.324, generator_loss=30.71, generator_mel_loss=20.25, generator_kl_loss=2.079, generator_dur_loss=1.61, generator_adv_loss=2.17, generator_feat_match_loss=4.605, over 65.00 samples.], tot_loss[discriminator_loss=2.559, discriminator_real_loss=1.293, discriminator_fake_loss=1.266, generator_loss=30.54, generator_mel_loss=19.95, generator_kl_loss=2.016, generator_dur_loss=1.62, generator_adv_loss=2.192, generator_feat_match_loss=4.766, over 2101.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 07:31:33,883 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 07:31:44,573 INFO [train.py:517] (1/4) Epoch 984, validation: discriminator_loss=2.678, discriminator_real_loss=1.31, discriminator_fake_loss=1.368, generator_loss=31.65, generator_mel_loss=20.98, generator_kl_loss=2.292, generator_dur_loss=1.636, generator_adv_loss=2.001, generator_feat_match_loss=4.745, over 100.00 samples.
+2023-11-15 07:31:44,574 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 07:32:24,652 INFO [train.py:811] (1/4) Start epoch 985
+2023-11-15 07:36:00,955 INFO [train.py:811] (1/4) Start epoch 986
+2023-11-15 07:36:45,109 INFO [train.py:467] (1/4) Epoch 986, batch 5, global_batch_idx: 36450, batch size: 110, loss[discriminator_loss=2.422, discriminator_real_loss=1.186, discriminator_fake_loss=1.235, generator_loss=31.2, generator_mel_loss=20.05, generator_kl_loss=1.991, generator_dur_loss=1.616, generator_adv_loss=2.168, generator_feat_match_loss=5.375, over 110.00 samples.], tot_loss[discriminator_loss=2.58, discriminator_real_loss=1.313, discriminator_fake_loss=1.267, generator_loss=30.69, generator_mel_loss=19.89, generator_kl_loss=2.043, generator_dur_loss=1.632, generator_adv_loss=2.252, generator_feat_match_loss=4.878, over 472.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 07:39:28,548 INFO [train.py:811] (1/4) Start epoch 987
+2023-11-15 07:41:29,280 INFO [train.py:467] (1/4) Epoch 987, batch 18, global_batch_idx: 36500, batch size: 59, loss[discriminator_loss=2.578, discriminator_real_loss=1.259, discriminator_fake_loss=1.318, generator_loss=30.48, generator_mel_loss=19.99, generator_kl_loss=2.067, generator_dur_loss=1.64, generator_adv_loss=2.285, generator_feat_match_loss=4.504, over 59.00 samples.], tot_loss[discriminator_loss=2.573, discriminator_real_loss=1.29, discriminator_fake_loss=1.283, generator_loss=30.62, generator_mel_loss=20.01, generator_kl_loss=2.03, generator_dur_loss=1.627, generator_adv_loss=2.188, generator_feat_match_loss=4.764, over 1595.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 07:43:01,280 INFO [train.py:811] (1/4) Start epoch 988
+2023-11-15 07:46:11,508 INFO [train.py:467] (1/4) Epoch 988, batch 31, global_batch_idx: 36550, batch size: 73, loss[discriminator_loss=2.672, discriminator_real_loss=1.325, discriminator_fake_loss=1.346, generator_loss=30.04, generator_mel_loss=19.79, generator_kl_loss=1.971, generator_dur_loss=1.646, generator_adv_loss=2.154, generator_feat_match_loss=4.484, over 73.00 samples.], tot_loss[discriminator_loss=2.61, discriminator_real_loss=1.319, discriminator_fake_loss=1.291, generator_loss=30.63, generator_mel_loss=20.1, generator_kl_loss=2.04, generator_dur_loss=1.627, generator_adv_loss=2.198, generator_feat_match_loss=4.665, over 2325.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 07:46:36,109 INFO [train.py:811] (1/4) Start epoch 989
+2023-11-15 07:50:10,903 INFO [train.py:811] (1/4) Start epoch 990
+2023-11-15 07:51:03,453 INFO [train.py:467] (1/4) Epoch 990, batch 7, global_batch_idx: 36600, batch size: 85, loss[discriminator_loss=2.559, discriminator_real_loss=1.285, discriminator_fake_loss=1.274, generator_loss=31.04, generator_mel_loss=20.36, generator_kl_loss=1.949, generator_dur_loss=1.645, generator_adv_loss=2.152, generator_feat_match_loss=4.938, over 85.00 samples.], tot_loss[discriminator_loss=2.568, discriminator_real_loss=1.294, discriminator_fake_loss=1.273, generator_loss=30.6, generator_mel_loss=20.06, generator_kl_loss=2.004, generator_dur_loss=1.626, generator_adv_loss=2.188, generator_feat_match_loss=4.718, over 625.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 07:51:03,454 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 07:51:15,416 INFO [train.py:517] (1/4) Epoch 990, validation: discriminator_loss=2.502, discriminator_real_loss=1.136, discriminator_fake_loss=1.367, generator_loss=31.61, generator_mel_loss=20.82, generator_kl_loss=2.262, generator_dur_loss=1.631, generator_adv_loss=1.992, generator_feat_match_loss=4.902, over 100.00 samples.
+2023-11-15 07:51:15,417 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 07:53:57,924 INFO [train.py:811] (1/4) Start epoch 991
+2023-11-15 07:56:07,177 INFO [train.py:467] (1/4) Epoch 991, batch 20, global_batch_idx: 36650, batch size: 81, loss[discriminator_loss=2.594, discriminator_real_loss=1.319, discriminator_fake_loss=1.275, generator_loss=30.77, generator_mel_loss=20.12, generator_kl_loss=2.02, generator_dur_loss=1.618, generator_adv_loss=2.205, generator_feat_match_loss=4.805, over 81.00 samples.], tot_loss[discriminator_loss=2.606, discriminator_real_loss=1.317, discriminator_fake_loss=1.289, generator_loss=30.55, generator_mel_loss=20.09, generator_kl_loss=2.007, generator_dur_loss=1.628, generator_adv_loss=2.195, generator_feat_match_loss=4.632, over 1346.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 07:57:33,958 INFO [train.py:811] (1/4) Start epoch 992
+2023-11-15 08:00:41,896 INFO [train.py:467] (1/4) Epoch 992, batch 33, global_batch_idx: 36700, batch size: 69, loss[discriminator_loss=2.553, discriminator_real_loss=1.302, discriminator_fake_loss=1.251, generator_loss=30.85, generator_mel_loss=20.24, generator_kl_loss=2.037, generator_dur_loss=1.64, generator_adv_loss=2.162, generator_feat_match_loss=4.777, over 69.00 samples.], tot_loss[discriminator_loss=2.587, discriminator_real_loss=1.31, discriminator_fake_loss=1.277, generator_loss=30.61, generator_mel_loss=20.04, generator_kl_loss=2.031, generator_dur_loss=1.622, generator_adv_loss=2.212, generator_feat_match_loss=4.7, over 2348.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 08:01:03,101 INFO [train.py:811] (1/4) Start epoch 993
+2023-11-15 08:04:36,914 INFO [train.py:811] (1/4) Start epoch 994
+2023-11-15 08:05:43,098 INFO [train.py:467] (1/4) Epoch 994, batch 9, global_batch_idx: 36750, batch size: 60, loss[discriminator_loss=2.5, discriminator_real_loss=1.354, discriminator_fake_loss=1.147, generator_loss=31.05, generator_mel_loss=19.93, generator_kl_loss=2.039, generator_dur_loss=1.634, generator_adv_loss=2.475, generator_feat_match_loss=4.973, over 60.00 samples.], tot_loss[discriminator_loss=2.586, discriminator_real_loss=1.323, discriminator_fake_loss=1.262, generator_loss=31.01, generator_mel_loss=19.61, generator_kl_loss=1.993, generator_dur_loss=1.627, generator_adv_loss=2.453, generator_feat_match_loss=5.334, over 642.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 08:08:07,106 INFO [train.py:811] (1/4) Start epoch 995
+2023-11-15 08:10:27,381 INFO [train.py:467] (1/4) Epoch 995, batch 22, global_batch_idx: 36800, batch size: 90, loss[discriminator_loss=2.602, discriminator_real_loss=1.411, discriminator_fake_loss=1.191, generator_loss=30.63, generator_mel_loss=20.23, generator_kl_loss=2.02, generator_dur_loss=1.621, generator_adv_loss=2.068, generator_feat_match_loss=4.695, over 90.00 samples.], tot_loss[discriminator_loss=2.583, discriminator_real_loss=1.314, discriminator_fake_loss=1.269, generator_loss=30.48, generator_mel_loss=19.95, generator_kl_loss=2.027, generator_dur_loss=1.624, generator_adv_loss=2.177, generator_feat_match_loss=4.709, over 1876.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 08:10:27,382 INFO [train.py:508] (1/4) Computing validation loss
+2023-11-15 08:10:38,604 INFO [train.py:517] (1/4) Epoch 995, validation: discriminator_loss=2.653, discriminator_real_loss=1.189, discriminator_fake_loss=1.464, generator_loss=30.64, generator_mel_loss=20.53, generator_kl_loss=2.269, generator_dur_loss=1.626, generator_adv_loss=1.772, generator_feat_match_loss=4.444, over 100.00 samples.
+2023-11-15 08:10:38,605 INFO [train.py:518] (1/4) Maximum memory allocated so far is 27305MB
+2023-11-15 08:11:49,586 INFO [train.py:811] (1/4) Start epoch 996
+2023-11-15 08:15:20,005 INFO [train.py:467] (1/4) Epoch 996, batch 35, global_batch_idx: 36850, batch size: 95, loss[discriminator_loss=2.549, discriminator_real_loss=1.28, discriminator_fake_loss=1.269, generator_loss=30.52, generator_mel_loss=19.87, generator_kl_loss=1.942, generator_dur_loss=1.636, generator_adv_loss=2.268, generator_feat_match_loss=4.805, over 95.00 samples.], tot_loss[discriminator_loss=2.574, discriminator_real_loss=1.292, discriminator_fake_loss=1.282, generator_loss=30.59, generator_mel_loss=19.91, generator_kl_loss=2.014, generator_dur_loss=1.623, generator_adv_loss=2.224, generator_feat_match_loss=4.818, over 2810.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 16.0
+2023-11-15 08:15:25,628 INFO [train.py:811] (1/4) Start epoch 997
+2023-11-15 08:18:59,882 INFO [train.py:811] (1/4) Start epoch 998
+2023-11-15 08:20:22,864 INFO [train.py:467] (1/4) Epoch 998, batch 11, global_batch_idx: 36900, batch size: 52, loss[discriminator_loss=2.602, discriminator_real_loss=1.285, discriminator_fake_loss=1.316, generator_loss=29.92, generator_mel_loss=19.56, generator_kl_loss=2.028, generator_dur_loss=1.655, generator_adv_loss=2.045, generator_feat_match_loss=4.625, over 52.00 samples.], tot_loss[discriminator_loss=2.582, discriminator_real_loss=1.311, discriminator_fake_loss=1.271, generator_loss=30.86, generator_mel_loss=20.02, generator_kl_loss=2.035, generator_dur_loss=1.623, generator_adv_loss=2.316, generator_feat_match_loss=4.864, over 978.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 08:22:38,787 INFO [train.py:811] (1/4) Start epoch 999
+2023-11-15 08:25:03,033 INFO [train.py:467] (1/4) Epoch 999, batch 24, global_batch_idx: 36950, batch size: 67, loss[discriminator_loss=2.617, discriminator_real_loss=1.381, discriminator_fake_loss=1.235, generator_loss=29.69, generator_mel_loss=19.65, generator_kl_loss=1.956, generator_dur_loss=1.632, generator_adv_loss=2.035, generator_feat_match_loss=4.422, over 67.00 samples.], tot_loss[discriminator_loss=2.575, discriminator_real_loss=1.298, discriminator_fake_loss=1.277, generator_loss=30.37, generator_mel_loss=19.79, generator_kl_loss=2.005, generator_dur_loss=1.627, generator_adv_loss=2.204, generator_feat_match_loss=4.742, over 1642.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, grad_scale: 8.0
+2023-11-15 08:26:10,782 INFO [train.py:811] (1/4) Start epoch 1000
+2023-11-15 08:29:43,257 INFO [train.py:868] (1/4) Done!