diff --git "a/exp/log/log-train-2024-03-11-23-34-29-1" "b/exp/log/log-train-2024-03-11-23-34-29-1"
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+2024-03-11 23:34:29,686 INFO [train.py:805] (1/6) Training started
+2024-03-11 23:34:29,686 INFO [train.py:815] (1/6) Device: cuda:1
+2024-03-11 23:34:29,687 INFO [tts_datamodule.py:322] (1/6) About to get train cuts
+2024-03-11 23:34:29,690 INFO [tts_datamodule.py:337] (1/6) About to get speakers
+2024-03-11 23:34:29,691 INFO [train.py:827] (1/6) {'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': '2989b0b1186fa6022932804f5b39fbb2781ebf42', 'k2-git-date': 'Fri Nov 24 11:34:10 2023', 'lhotse-version': '1.22.0.dev+git.d8ed1bbb.dirty', 'torch-version': '1.11.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.9', 'icefall-git-branch': 'dev/tts/vctk/tokenizer', 'icefall-git-sha1': 'e69b60e5-clean', 'icefall-git-date': 'Mon Mar 11 23:14:14 2024', 'icefall-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/icefall-1.0-py3.9.egg', 'k2-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/k2-1.24.4.dev20231207+cuda10.2.torch1.11.0-py3.9-linux-x86_64.egg/k2/__init__.py', 'lhotse-path': '/star-home/jinzengrui/lib/miniconda3/envs/dev39/lib/python3.9/site-packages/lhotse-1.22.0.dev0+git.d8ed1bbb.dirty-py3.9.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-9-0208143539-7dbf569d4f-r7nrb', 'IP address': '10.177.13.150'}, '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': 6, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 1000, 'start_epoch': 1, 'exp_dir': PosixPath('vits/exp_fp32'), 'tokens': 'data/tokens.txt', 'lr': 0.0002, 'seed': 42, 'print_diagnostics': False, 'inf_check': False, 'save_every_n': 20, 'use_fp16': False, 'manifest_dir': PosixPath('data/spectrogram'), 'speakers': PosixPath('data/speakers.txt'), 'max_duration': 200, 'bucketing_sampler': True, 'num_buckets': 30, 'on_the_fly_feats': False, 'shuffle': True, 'drop_last': True, 'return_cuts': False, 'num_workers': 8, 'input_strategy': 'PrecomputedFeatures', 'blank_id': 0, 'vocab_size': 159, 'num_spks': 108}
+2024-03-11 23:34:29,691 INFO [train.py:829] (1/6) About to create model
+2024-03-11 23:34:32,708 INFO [train.py:835] (1/6) Number of parameters in generator: 39004210
+2024-03-11 23:34:32,710 INFO [train.py:837] (1/6) Number of parameters in discriminator: 50974956
+2024-03-11 23:34:32,710 INFO [train.py:838] (1/6) Total number of parameters: 89979166
+2024-03-11 23:34:39,968 INFO [train.py:845] (1/6) Using DDP
+2024-03-11 23:34:41,291 INFO [tts_datamodule.py:175] (1/6) About to create train dataset
+2024-03-11 23:34:41,291 INFO [tts_datamodule.py:201] (1/6) Using DynamicBucketingSampler.
+2024-03-11 23:34:42,524 INFO [tts_datamodule.py:218] (1/6) About to create train dataloader
+2024-03-11 23:34:42,525 INFO [tts_datamodule.py:327] (1/6) About to get validation cuts
+2024-03-11 23:34:42,528 INFO [tts_datamodule.py:241] (1/6) About to create dev dataset
+2024-03-11 23:34:42,541 INFO [tts_datamodule.py:270] (1/6) About to create valid dataloader
+2024-03-11 23:34:42,541 INFO [train.py:725] (1/6) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
+2024-03-11 23:35:04,586 INFO [train.py:780] (1/6) Maximum memory allocated so far is 8016MB
+2024-03-11 23:35:06,619 INFO [train.py:780] (1/6) Maximum memory allocated so far is 8412MB
+2024-03-11 23:35:10,775 INFO [train.py:780] (1/6) Maximum memory allocated so far is 16240MB
+2024-03-11 23:35:13,646 INFO [train.py:780] (1/6) Maximum memory allocated so far is 16240MB
+2024-03-11 23:35:18,989 INFO [train.py:780] (1/6) Maximum memory allocated so far is 27968MB
+2024-03-11 23:35:22,893 INFO [train.py:780] (1/6) Maximum memory allocated so far is 27968MB
+2024-03-11 23:35:22,900 INFO [train.py:919] (1/6) Start epoch 1
+2024-03-11 23:35:44,693 INFO [train.py:527] (1/6) Epoch 1, batch 0, global_batch_idx: 0, batch size: 96, loss[discriminator_loss=6.107, discriminator_real_loss=6.106, discriminator_fake_loss=0.001034, generator_loss=1570, generator_mel_loss=102.8, generator_kl_loss=1460, generator_dur_loss=1.963, generator_adv_loss=4.809, generator_feat_match_loss=0.1795, over 96.00 samples.], tot_loss[discriminator_loss=6.107, discriminator_real_loss=6.106, discriminator_fake_loss=0.001034, generator_loss=1570, generator_mel_loss=102.8, generator_kl_loss=1460, generator_dur_loss=1.963, generator_adv_loss=4.809, generator_feat_match_loss=0.1795, over 96.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:35:44,695 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-11 23:35:53,005 INFO [train.py:591] (1/6) Epoch 1, validation: discriminator_loss=4.882, discriminator_real_loss=4.807, discriminator_fake_loss=0.07482, generator_loss=763.5, generator_mel_loss=112.9, generator_kl_loss=643.8, generator_dur_loss=1.851, generator_adv_loss=4.809, generator_feat_match_loss=0.1531, over 100.00 samples.
+2024-03-11 23:35:53,138 INFO [train.py:592] (1/6) Maximum memory allocated so far is 28018MB
+2024-03-11 23:38:13,692 INFO [train.py:527] (1/6) Epoch 1, batch 50, global_batch_idx: 50, batch size: 45, loss[discriminator_loss=2.817, discriminator_real_loss=1.535, discriminator_fake_loss=1.282, generator_loss=119.4, generator_mel_loss=49.52, generator_kl_loss=65.14, generator_dur_loss=1.637, generator_adv_loss=1.851, generator_feat_match_loss=1.239, over 45.00 samples.], tot_loss[discriminator_loss=3.14, discriminator_real_loss=1.791, discriminator_fake_loss=1.349, generator_loss=231.4, generator_mel_loss=62.95, generator_kl_loss=164.4, generator_dur_loss=1.677, generator_adv_loss=1.828, generator_feat_match_loss=0.5151, over 3129.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:40:30,833 INFO [train.py:527] (1/6) Epoch 1, batch 100, global_batch_idx: 100, batch size: 50, loss[discriminator_loss=2.701, discriminator_real_loss=1.556, discriminator_fake_loss=1.145, generator_loss=89.65, generator_mel_loss=44.58, generator_kl_loss=39.84, generator_dur_loss=1.691, generator_adv_loss=1.818, generator_feat_match_loss=1.728, over 50.00 samples.], tot_loss[discriminator_loss=2.934, discriminator_real_loss=1.643, discriminator_fake_loss=1.291, generator_loss=169.5, generator_mel_loss=55.77, generator_kl_loss=109.1, generator_dur_loss=1.686, generator_adv_loss=1.86, generator_feat_match_loss=1.053, over 5965.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:41:35,045 INFO [train.py:919] (1/6) Start epoch 2
+2024-03-11 23:43:11,633 INFO [train.py:527] (1/6) Epoch 2, batch 26, global_batch_idx: 150, batch size: 53, loss[discriminator_loss=2.84, discriminator_real_loss=1.354, discriminator_fake_loss=1.487, generator_loss=70.59, generator_mel_loss=42.47, generator_kl_loss=23.19, generator_dur_loss=1.799, generator_adv_loss=1.72, generator_feat_match_loss=1.412, over 53.00 samples.], tot_loss[discriminator_loss=2.801, discriminator_real_loss=1.496, discriminator_fake_loss=1.306, generator_loss=75.13, generator_mel_loss=44.43, generator_kl_loss=25.55, generator_dur_loss=1.81, generator_adv_loss=1.889, generator_feat_match_loss=1.456, over 1422.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:45:31,869 INFO [train.py:527] (1/6) Epoch 2, batch 76, global_batch_idx: 200, batch size: 50, loss[discriminator_loss=2.839, discriminator_real_loss=1.574, discriminator_fake_loss=1.265, generator_loss=62.21, generator_mel_loss=39.94, generator_kl_loss=17.46, generator_dur_loss=1.898, generator_adv_loss=1.789, generator_feat_match_loss=1.124, over 50.00 samples.], tot_loss[discriminator_loss=2.826, discriminator_real_loss=1.513, discriminator_fake_loss=1.313, generator_loss=69.78, generator_mel_loss=42.74, generator_kl_loss=21.84, generator_dur_loss=1.85, generator_adv_loss=1.887, generator_feat_match_loss=1.466, over 4491.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:45:31,871 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-11 23:45:39,716 INFO [train.py:591] (1/6) Epoch 2, validation: discriminator_loss=2.887, discriminator_real_loss=1.544, discriminator_fake_loss=1.343, generator_loss=55.76, generator_mel_loss=38.74, generator_kl_loss=12.24, generator_dur_loss=2.086, generator_adv_loss=1.775, generator_feat_match_loss=0.9245, over 100.00 samples.
+2024-03-11 23:45:39,717 INFO [train.py:592] (1/6) Maximum memory allocated so far is 28708MB
+2024-03-11 23:47:45,175 INFO [train.py:919] (1/6) Start epoch 3
+2024-03-11 23:48:14,616 INFO [train.py:527] (1/6) Epoch 3, batch 2, global_batch_idx: 250, batch size: 55, loss[discriminator_loss=2.878, discriminator_real_loss=1.581, discriminator_fake_loss=1.297, generator_loss=55.17, generator_mel_loss=38.15, generator_kl_loss=12.54, generator_dur_loss=1.916, generator_adv_loss=1.747, generator_feat_match_loss=0.8105, over 55.00 samples.], tot_loss[discriminator_loss=2.876, discriminator_real_loss=1.45, discriminator_fake_loss=1.426, generator_loss=54.76, generator_mel_loss=37.6, generator_kl_loss=12.63, generator_dur_loss=1.926, generator_adv_loss=1.754, generator_feat_match_loss=0.8507, over 160.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:50:34,538 INFO [train.py:527] (1/6) Epoch 3, batch 52, global_batch_idx: 300, batch size: 70, loss[discriminator_loss=3.077, discriminator_real_loss=1.531, discriminator_fake_loss=1.546, generator_loss=49.97, generator_mel_loss=35.06, generator_kl_loss=10.3, generator_dur_loss=1.986, generator_adv_loss=1.773, generator_feat_match_loss=0.8517, over 70.00 samples.], tot_loss[discriminator_loss=2.882, discriminator_real_loss=1.469, discriminator_fake_loss=1.413, generator_loss=53.36, generator_mel_loss=36.8, generator_kl_loss=12.04, generator_dur_loss=1.908, generator_adv_loss=1.694, generator_feat_match_loss=0.9206, over 2910.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:52:54,829 INFO [train.py:527] (1/6) Epoch 3, batch 102, global_batch_idx: 350, batch size: 50, loss[discriminator_loss=2.953, discriminator_real_loss=1.703, discriminator_fake_loss=1.25, generator_loss=49.24, generator_mel_loss=35.91, generator_kl_loss=8.669, generator_dur_loss=1.867, generator_adv_loss=1.761, generator_feat_match_loss=1.03, over 50.00 samples.], tot_loss[discriminator_loss=2.858, discriminator_real_loss=1.473, discriminator_fake_loss=1.385, generator_loss=51.43, generator_mel_loss=35.89, generator_kl_loss=10.85, generator_dur_loss=1.922, generator_adv_loss=1.731, generator_feat_match_loss=1.037, over 5880.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:53:55,593 INFO [train.py:919] (1/6) Start epoch 4
+2024-03-11 23:55:37,833 INFO [train.py:527] (1/6) Epoch 4, batch 28, global_batch_idx: 400, batch size: 44, loss[discriminator_loss=2.638, discriminator_real_loss=1.354, discriminator_fake_loss=1.284, generator_loss=45.06, generator_mel_loss=32.44, generator_kl_loss=7.252, generator_dur_loss=1.885, generator_adv_loss=1.812, generator_feat_match_loss=1.668, over 44.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.481, discriminator_fake_loss=1.277, generator_loss=46.63, generator_mel_loss=33.63, generator_kl_loss=7.736, generator_dur_loss=1.935, generator_adv_loss=1.867, generator_feat_match_loss=1.455, over 1584.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-11 23:55:37,834 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-11 23:55:46,450 INFO [train.py:591] (1/6) Epoch 4, validation: discriminator_loss=2.851, discriminator_real_loss=1.361, discriminator_fake_loss=1.49, generator_loss=43.7, generator_mel_loss=32.79, generator_kl_loss=5.991, generator_dur_loss=2.147, generator_adv_loss=1.682, generator_feat_match_loss=1.097, over 100.00 samples.
+2024-03-11 23:55:46,451 INFO [train.py:592] (1/6) Maximum memory allocated so far is 28708MB
+2024-03-11 23:58:04,799 INFO [train.py:527] (1/6) Epoch 4, batch 78, global_batch_idx: 450, batch size: 66, loss[discriminator_loss=2.719, discriminator_real_loss=1.417, discriminator_fake_loss=1.302, generator_loss=42.38, generator_mel_loss=30.88, generator_kl_loss=6.206, generator_dur_loss=1.873, generator_adv_loss=1.989, generator_feat_match_loss=1.437, over 66.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.446, discriminator_fake_loss=1.282, generator_loss=45.55, generator_mel_loss=33.01, generator_kl_loss=7.109, generator_dur_loss=1.941, generator_adv_loss=1.889, generator_feat_match_loss=1.604, over 4387.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:00:09,230 INFO [train.py:919] (1/6) Start epoch 5
+2024-03-12 00:00:45,137 INFO [train.py:527] (1/6) Epoch 5, batch 4, global_batch_idx: 500, batch size: 53, loss[discriminator_loss=2.802, discriminator_real_loss=1.517, discriminator_fake_loss=1.285, generator_loss=44.06, generator_mel_loss=33.25, generator_kl_loss=5.525, generator_dur_loss=1.894, generator_adv_loss=1.876, generator_feat_match_loss=1.512, over 53.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.488, discriminator_fake_loss=1.253, generator_loss=43.78, generator_mel_loss=32.88, generator_kl_loss=5.547, generator_dur_loss=1.934, generator_adv_loss=1.817, generator_feat_match_loss=1.597, over 296.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:03:05,357 INFO [train.py:527] (1/6) Epoch 5, batch 54, global_batch_idx: 550, batch size: 58, loss[discriminator_loss=2.775, discriminator_real_loss=1.486, discriminator_fake_loss=1.289, generator_loss=38.36, generator_mel_loss=28.25, generator_kl_loss=4.494, generator_dur_loss=1.985, generator_adv_loss=2.033, generator_feat_match_loss=1.593, over 58.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.45, discriminator_fake_loss=1.318, generator_loss=41.03, generator_mel_loss=30.54, generator_kl_loss=5.142, generator_dur_loss=1.964, generator_adv_loss=1.823, generator_feat_match_loss=1.559, over 3248.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:05:22,099 INFO [train.py:527] (1/6) Epoch 5, batch 104, global_batch_idx: 600, batch size: 66, loss[discriminator_loss=2.747, discriminator_real_loss=1.579, discriminator_fake_loss=1.169, generator_loss=40.37, generator_mel_loss=30.71, generator_kl_loss=4.218, generator_dur_loss=1.996, generator_adv_loss=1.895, generator_feat_match_loss=1.556, over 66.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.441, discriminator_fake_loss=1.306, generator_loss=40.31, generator_mel_loss=30.09, generator_kl_loss=4.787, generator_dur_loss=1.956, generator_adv_loss=1.849, generator_feat_match_loss=1.633, over 6156.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:05:22,101 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 00:05:31,118 INFO [train.py:591] (1/6) Epoch 5, validation: discriminator_loss=2.816, discriminator_real_loss=1.506, discriminator_fake_loss=1.31, generator_loss=36.96, generator_mel_loss=28.4, generator_kl_loss=3.509, generator_dur_loss=2.138, generator_adv_loss=1.789, generator_feat_match_loss=1.124, over 100.00 samples.
+2024-03-12 00:05:31,119 INFO [train.py:592] (1/6) Maximum memory allocated so far is 28708MB
+2024-03-12 00:06:23,845 INFO [train.py:919] (1/6) Start epoch 6
+2024-03-12 00:08:08,934 INFO [train.py:527] (1/6) Epoch 6, batch 30, global_batch_idx: 650, batch size: 48, loss[discriminator_loss=2.757, discriminator_real_loss=1.324, discriminator_fake_loss=1.432, generator_loss=40.08, generator_mel_loss=30.96, generator_kl_loss=3.593, generator_dur_loss=1.882, generator_adv_loss=1.698, generator_feat_match_loss=1.94, over 48.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.446, discriminator_fake_loss=1.321, generator_loss=38.42, generator_mel_loss=28.95, generator_kl_loss=3.815, generator_dur_loss=1.96, generator_adv_loss=1.898, generator_feat_match_loss=1.798, over 1806.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:10:28,030 INFO [train.py:527] (1/6) Epoch 6, batch 80, global_batch_idx: 700, batch size: 36, loss[discriminator_loss=2.794, discriminator_real_loss=1.282, discriminator_fake_loss=1.512, generator_loss=35.76, generator_mel_loss=26.9, generator_kl_loss=3.231, generator_dur_loss=1.989, generator_adv_loss=2.082, generator_feat_match_loss=1.557, over 36.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.447, discriminator_fake_loss=1.29, generator_loss=38.07, generator_mel_loss=28.76, generator_kl_loss=3.656, generator_dur_loss=1.952, generator_adv_loss=1.894, generator_feat_match_loss=1.812, over 4294.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:12:30,409 INFO [train.py:919] (1/6) Start epoch 7
+2024-03-12 00:13:11,138 INFO [train.py:527] (1/6) Epoch 7, batch 6, global_batch_idx: 750, batch size: 44, loss[discriminator_loss=2.652, discriminator_real_loss=1.295, discriminator_fake_loss=1.358, generator_loss=38.16, generator_mel_loss=29.27, generator_kl_loss=2.902, generator_dur_loss=1.929, generator_adv_loss=2.042, generator_feat_match_loss=2.024, over 44.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.39, discriminator_fake_loss=1.366, generator_loss=36.13, generator_mel_loss=27.42, generator_kl_loss=3.043, generator_dur_loss=1.983, generator_adv_loss=1.873, generator_feat_match_loss=1.81, over 431.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:15:30,342 INFO [train.py:527] (1/6) Epoch 7, batch 56, global_batch_idx: 800, batch size: 47, loss[discriminator_loss=2.851, discriminator_real_loss=1.557, discriminator_fake_loss=1.294, generator_loss=33.43, generator_mel_loss=25.58, generator_kl_loss=2.765, generator_dur_loss=1.892, generator_adv_loss=1.926, generator_feat_match_loss=1.268, over 47.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.465, discriminator_fake_loss=1.287, generator_loss=36.08, generator_mel_loss=27.52, generator_kl_loss=2.926, generator_dur_loss=1.962, generator_adv_loss=1.884, generator_feat_match_loss=1.79, over 3154.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:15:30,344 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 00:15:38,550 INFO [train.py:591] (1/6) Epoch 7, validation: discriminator_loss=2.831, discriminator_real_loss=1.662, discriminator_fake_loss=1.168, generator_loss=33.21, generator_mel_loss=25.58, generator_kl_loss=2.468, generator_dur_loss=2.112, generator_adv_loss=1.932, generator_feat_match_loss=1.119, over 100.00 samples.
+2024-03-12 00:15:38,551 INFO [train.py:592] (1/6) Maximum memory allocated so far is 28708MB
+2024-03-12 00:17:56,852 INFO [train.py:527] (1/6) Epoch 7, batch 106, global_batch_idx: 850, batch size: 32, loss[discriminator_loss=2.584, discriminator_real_loss=1.363, discriminator_fake_loss=1.221, generator_loss=38.6, generator_mel_loss=30.29, generator_kl_loss=2.335, generator_dur_loss=1.95, generator_adv_loss=1.912, generator_feat_match_loss=2.116, over 32.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.452, discriminator_fake_loss=1.297, generator_loss=35.64, generator_mel_loss=27.22, generator_kl_loss=2.81, generator_dur_loss=1.963, generator_adv_loss=1.872, generator_feat_match_loss=1.776, over 6024.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:18:47,345 INFO [train.py:919] (1/6) Start epoch 8
+2024-03-12 00:20:42,518 INFO [train.py:527] (1/6) Epoch 8, batch 32, global_batch_idx: 900, batch size: 31, loss[discriminator_loss=2.718, discriminator_real_loss=1.367, discriminator_fake_loss=1.35, generator_loss=34.72, generator_mel_loss=26.68, generator_kl_loss=2.4, generator_dur_loss=1.94, generator_adv_loss=1.742, generator_feat_match_loss=1.952, over 31.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.424, discriminator_fake_loss=1.309, generator_loss=34.78, generator_mel_loss=26.59, generator_kl_loss=2.486, generator_dur_loss=1.967, generator_adv_loss=1.889, generator_feat_match_loss=1.849, over 1998.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:23:05,281 INFO [train.py:527] (1/6) Epoch 8, batch 82, global_batch_idx: 950, batch size: 25, loss[discriminator_loss=2.565, discriminator_real_loss=1.338, discriminator_fake_loss=1.227, generator_loss=39.45, generator_mel_loss=30.52, generator_kl_loss=2.539, generator_dur_loss=1.786, generator_adv_loss=1.943, generator_feat_match_loss=2.661, over 25.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.43, discriminator_fake_loss=1.311, generator_loss=34.57, generator_mel_loss=26.41, generator_kl_loss=2.41, generator_dur_loss=1.967, generator_adv_loss=1.922, generator_feat_match_loss=1.861, over 5030.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:24:59,322 INFO [train.py:919] (1/6) Start epoch 9
+2024-03-12 00:25:44,761 INFO [train.py:527] (1/6) Epoch 9, batch 8, global_batch_idx: 1000, batch size: 66, loss[discriminator_loss=2.688, discriminator_real_loss=1.489, discriminator_fake_loss=1.2, generator_loss=35.67, generator_mel_loss=27.53, generator_kl_loss=2.277, generator_dur_loss=1.988, generator_adv_loss=1.93, generator_feat_match_loss=1.948, over 66.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.405, discriminator_fake_loss=1.297, generator_loss=34.09, generator_mel_loss=26.21, generator_kl_loss=2.163, generator_dur_loss=1.985, generator_adv_loss=1.885, generator_feat_match_loss=1.852, over 530.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:25:44,764 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 00:25:52,726 INFO [train.py:591] (1/6) Epoch 9, validation: discriminator_loss=2.716, discriminator_real_loss=1.498, discriminator_fake_loss=1.218, generator_loss=34.19, generator_mel_loss=26.16, generator_kl_loss=2.238, generator_dur_loss=2.052, generator_adv_loss=1.915, generator_feat_match_loss=1.823, over 100.00 samples.
+2024-03-12 00:25:52,728 INFO [train.py:592] (1/6) Maximum memory allocated so far is 28753MB
+2024-03-12 00:28:16,627 INFO [train.py:527] (1/6) Epoch 9, batch 58, global_batch_idx: 1050, batch size: 39, loss[discriminator_loss=2.688, discriminator_real_loss=1.325, discriminator_fake_loss=1.363, generator_loss=36.46, generator_mel_loss=28, generator_kl_loss=2.067, generator_dur_loss=1.956, generator_adv_loss=2.263, generator_feat_match_loss=2.168, over 39.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.399, discriminator_fake_loss=1.298, generator_loss=33.7, generator_mel_loss=25.75, generator_kl_loss=2.128, generator_dur_loss=1.973, generator_adv_loss=1.899, generator_feat_match_loss=1.946, over 3531.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:30:36,559 INFO [train.py:527] (1/6) Epoch 9, batch 108, global_batch_idx: 1100, batch size: 56, loss[discriminator_loss=2.76, discriminator_real_loss=1.55, discriminator_fake_loss=1.209, generator_loss=35.28, generator_mel_loss=26.73, generator_kl_loss=2.117, generator_dur_loss=1.95, generator_adv_loss=2.385, generator_feat_match_loss=2.101, over 56.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.41, discriminator_fake_loss=1.295, generator_loss=33.64, generator_mel_loss=25.71, generator_kl_loss=2.101, generator_dur_loss=1.965, generator_adv_loss=1.903, generator_feat_match_loss=1.957, over 6512.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:31:17,430 INFO [train.py:919] (1/6) Start epoch 10
+2024-03-12 00:33:17,585 INFO [train.py:527] (1/6) Epoch 10, batch 34, global_batch_idx: 1150, batch size: 55, loss[discriminator_loss=2.717, discriminator_real_loss=1.366, discriminator_fake_loss=1.351, generator_loss=33.39, generator_mel_loss=25.43, generator_kl_loss=1.847, generator_dur_loss=1.878, generator_adv_loss=2.162, generator_feat_match_loss=2.072, over 55.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.391, discriminator_fake_loss=1.299, generator_loss=33.65, generator_mel_loss=25.77, generator_kl_loss=1.895, generator_dur_loss=1.92, generator_adv_loss=1.951, generator_feat_match_loss=2.114, over 2067.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:35:38,715 INFO [train.py:527] (1/6) Epoch 10, batch 84, global_batch_idx: 1200, batch size: 47, loss[discriminator_loss=2.626, discriminator_real_loss=1.361, discriminator_fake_loss=1.264, generator_loss=34.68, generator_mel_loss=26.93, generator_kl_loss=1.868, generator_dur_loss=1.89, generator_adv_loss=1.631, generator_feat_match_loss=2.364, over 47.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.392, discriminator_fake_loss=1.29, generator_loss=33.51, generator_mel_loss=25.64, generator_kl_loss=1.844, generator_dur_loss=1.932, generator_adv_loss=1.944, generator_feat_match_loss=2.151, over 4962.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:35:38,716 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 00:35:47,142 INFO [train.py:591] (1/6) Epoch 10, validation: discriminator_loss=2.786, discriminator_real_loss=1.171, discriminator_fake_loss=1.615, generator_loss=31.34, generator_mel_loss=24.62, generator_kl_loss=1.472, generator_dur_loss=2.13, generator_adv_loss=1.501, generator_feat_match_loss=1.612, over 100.00 samples.
+2024-03-12 00:35:47,143 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 00:37:35,840 INFO [train.py:919] (1/6) Start epoch 11
+2024-03-12 00:38:30,469 INFO [train.py:527] (1/6) Epoch 11, batch 10, global_batch_idx: 1250, batch size: 77, loss[discriminator_loss=2.701, discriminator_real_loss=1.466, discriminator_fake_loss=1.236, generator_loss=31.9, generator_mel_loss=24.43, generator_kl_loss=1.689, generator_dur_loss=2.033, generator_adv_loss=1.881, generator_feat_match_loss=1.866, over 77.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.398, discriminator_fake_loss=1.298, generator_loss=32.6, generator_mel_loss=25.14, generator_kl_loss=1.623, generator_dur_loss=1.971, generator_adv_loss=1.889, generator_feat_match_loss=1.983, over 558.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:40:53,317 INFO [train.py:527] (1/6) Epoch 11, batch 60, global_batch_idx: 1300, batch size: 44, loss[discriminator_loss=2.723, discriminator_real_loss=1.459, discriminator_fake_loss=1.265, generator_loss=30.95, generator_mel_loss=23.29, generator_kl_loss=1.758, generator_dur_loss=1.909, generator_adv_loss=2.077, generator_feat_match_loss=1.919, over 44.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.404, discriminator_fake_loss=1.297, generator_loss=32.66, generator_mel_loss=25.03, generator_kl_loss=1.636, generator_dur_loss=1.976, generator_adv_loss=1.927, generator_feat_match_loss=2.089, over 3684.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:43:12,109 INFO [train.py:527] (1/6) Epoch 11, batch 110, global_batch_idx: 1350, batch size: 56, loss[discriminator_loss=2.542, discriminator_real_loss=1.258, discriminator_fake_loss=1.284, generator_loss=33.41, generator_mel_loss=25.43, generator_kl_loss=1.487, generator_dur_loss=2.009, generator_adv_loss=2.033, generator_feat_match_loss=2.445, over 56.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.398, discriminator_fake_loss=1.291, generator_loss=32.78, generator_mel_loss=25.11, generator_kl_loss=1.61, generator_dur_loss=1.969, generator_adv_loss=1.938, generator_feat_match_loss=2.157, over 6592.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:43:49,259 INFO [train.py:919] (1/6) Start epoch 12
+2024-03-12 00:45:56,714 INFO [train.py:527] (1/6) Epoch 12, batch 36, global_batch_idx: 1400, batch size: 56, loss[discriminator_loss=2.82, discriminator_real_loss=1.552, discriminator_fake_loss=1.268, generator_loss=32.27, generator_mel_loss=25.2, generator_kl_loss=1.508, generator_dur_loss=1.95, generator_adv_loss=1.693, generator_feat_match_loss=1.917, over 56.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.425, discriminator_fake_loss=1.301, generator_loss=33.14, generator_mel_loss=25.37, generator_kl_loss=1.552, generator_dur_loss=1.941, generator_adv_loss=2.006, generator_feat_match_loss=2.276, over 1991.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:45:56,715 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 00:46:04,727 INFO [train.py:591] (1/6) Epoch 12, validation: discriminator_loss=2.896, discriminator_real_loss=1.381, discriminator_fake_loss=1.515, generator_loss=31.16, generator_mel_loss=24.6, generator_kl_loss=1.302, generator_dur_loss=2.125, generator_adv_loss=1.658, generator_feat_match_loss=1.471, over 100.00 samples.
+2024-03-12 00:46:04,728 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 00:48:26,163 INFO [train.py:527] (1/6) Epoch 12, batch 86, global_batch_idx: 1450, batch size: 39, loss[discriminator_loss=2.626, discriminator_real_loss=1.368, discriminator_fake_loss=1.258, generator_loss=33.21, generator_mel_loss=25.65, generator_kl_loss=1.52, generator_dur_loss=1.951, generator_adv_loss=1.82, generator_feat_match_loss=2.267, over 39.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.414, discriminator_fake_loss=1.306, generator_loss=32.82, generator_mel_loss=25.24, generator_kl_loss=1.535, generator_dur_loss=1.951, generator_adv_loss=1.938, generator_feat_match_loss=2.157, over 4619.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:50:11,489 INFO [train.py:919] (1/6) Start epoch 13
+2024-03-12 00:51:09,429 INFO [train.py:527] (1/6) Epoch 13, batch 12, global_batch_idx: 1500, batch size: 42, loss[discriminator_loss=2.65, discriminator_real_loss=1.301, discriminator_fake_loss=1.349, generator_loss=32.14, generator_mel_loss=24.3, generator_kl_loss=1.561, generator_dur_loss=1.958, generator_adv_loss=1.855, generator_feat_match_loss=2.471, over 42.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.395, discriminator_fake_loss=1.287, generator_loss=32.91, generator_mel_loss=25.2, generator_kl_loss=1.441, generator_dur_loss=1.98, generator_adv_loss=1.96, generator_feat_match_loss=2.331, over 713.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:53:26,365 INFO [train.py:527] (1/6) Epoch 13, batch 62, global_batch_idx: 1550, batch size: 70, loss[discriminator_loss=2.801, discriminator_real_loss=1.703, discriminator_fake_loss=1.098, generator_loss=30.12, generator_mel_loss=23.28, generator_kl_loss=1.317, generator_dur_loss=2.029, generator_adv_loss=1.838, generator_feat_match_loss=1.663, over 70.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.42, discriminator_fake_loss=1.307, generator_loss=32.04, generator_mel_loss=24.6, generator_kl_loss=1.436, generator_dur_loss=1.98, generator_adv_loss=1.916, generator_feat_match_loss=2.102, over 3746.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:55:42,374 INFO [train.py:527] (1/6) Epoch 13, batch 112, global_batch_idx: 1600, batch size: 31, loss[discriminator_loss=2.686, discriminator_real_loss=1.284, discriminator_fake_loss=1.401, generator_loss=32.12, generator_mel_loss=24.96, generator_kl_loss=1.374, generator_dur_loss=1.937, generator_adv_loss=1.986, generator_feat_match_loss=1.86, over 31.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.413, discriminator_fake_loss=1.313, generator_loss=32.02, generator_mel_loss=24.61, generator_kl_loss=1.438, generator_dur_loss=1.974, generator_adv_loss=1.896, generator_feat_match_loss=2.098, over 6480.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 00:55:42,375 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 00:55:51,189 INFO [train.py:591] (1/6) Epoch 13, validation: discriminator_loss=2.687, discriminator_real_loss=1.474, discriminator_fake_loss=1.213, generator_loss=31.94, generator_mel_loss=25.06, generator_kl_loss=1.229, generator_dur_loss=2.103, generator_adv_loss=1.918, generator_feat_match_loss=1.634, over 100.00 samples.
+2024-03-12 00:55:51,190 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 00:56:23,369 INFO [train.py:919] (1/6) Start epoch 14
+2024-03-12 00:58:32,766 INFO [train.py:527] (1/6) Epoch 14, batch 38, global_batch_idx: 1650, batch size: 48, loss[discriminator_loss=2.709, discriminator_real_loss=1.388, discriminator_fake_loss=1.32, generator_loss=32.84, generator_mel_loss=25.27, generator_kl_loss=1.409, generator_dur_loss=1.897, generator_adv_loss=1.998, generator_feat_match_loss=2.27, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.404, discriminator_fake_loss=1.309, generator_loss=32.42, generator_mel_loss=24.94, generator_kl_loss=1.404, generator_dur_loss=1.955, generator_adv_loss=1.915, generator_feat_match_loss=2.212, over 2067.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:00:50,092 INFO [train.py:527] (1/6) Epoch 14, batch 88, global_batch_idx: 1700, batch size: 55, loss[discriminator_loss=2.74, discriminator_real_loss=1.369, discriminator_fake_loss=1.371, generator_loss=31.79, generator_mel_loss=24.46, generator_kl_loss=1.359, generator_dur_loss=1.961, generator_adv_loss=1.901, generator_feat_match_loss=2.111, over 55.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.409, discriminator_fake_loss=1.323, generator_loss=32.09, generator_mel_loss=24.69, generator_kl_loss=1.378, generator_dur_loss=1.968, generator_adv_loss=1.917, generator_feat_match_loss=2.143, over 4906.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:02:29,997 INFO [train.py:919] (1/6) Start epoch 15
+2024-03-12 01:03:32,329 INFO [train.py:527] (1/6) Epoch 15, batch 14, global_batch_idx: 1750, batch size: 66, loss[discriminator_loss=2.8, discriminator_real_loss=1.357, discriminator_fake_loss=1.443, generator_loss=30.99, generator_mel_loss=23.9, generator_kl_loss=1.283, generator_dur_loss=2.004, generator_adv_loss=1.89, generator_feat_match_loss=1.908, over 66.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.409, discriminator_fake_loss=1.33, generator_loss=31.64, generator_mel_loss=24.38, generator_kl_loss=1.365, generator_dur_loss=1.979, generator_adv_loss=1.874, generator_feat_match_loss=2.04, over 855.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:05:50,111 INFO [train.py:527] (1/6) Epoch 15, batch 64, global_batch_idx: 1800, batch size: 47, loss[discriminator_loss=2.735, discriminator_real_loss=1.406, discriminator_fake_loss=1.329, generator_loss=31.7, generator_mel_loss=24.28, generator_kl_loss=1.309, generator_dur_loss=1.924, generator_adv_loss=1.922, generator_feat_match_loss=2.272, over 47.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.424, discriminator_fake_loss=1.337, generator_loss=31.51, generator_mel_loss=24.28, generator_kl_loss=1.352, generator_dur_loss=1.972, generator_adv_loss=1.881, generator_feat_match_loss=2.018, over 3470.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:05:50,112 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 01:05:57,889 INFO [train.py:591] (1/6) Epoch 15, validation: discriminator_loss=2.818, discriminator_real_loss=1.518, discriminator_fake_loss=1.3, generator_loss=31.04, generator_mel_loss=24.12, generator_kl_loss=1.255, generator_dur_loss=2.117, generator_adv_loss=1.833, generator_feat_match_loss=1.711, over 100.00 samples.
+2024-03-12 01:05:57,890 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 01:08:15,586 INFO [train.py:527] (1/6) Epoch 15, batch 114, global_batch_idx: 1850, batch size: 55, loss[discriminator_loss=2.789, discriminator_real_loss=1.681, discriminator_fake_loss=1.109, generator_loss=32.02, generator_mel_loss=24.92, generator_kl_loss=1.378, generator_dur_loss=1.909, generator_adv_loss=1.789, generator_feat_match_loss=2.018, over 55.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.433, discriminator_fake_loss=1.33, generator_loss=31.42, generator_mel_loss=24.2, generator_kl_loss=1.345, generator_dur_loss=1.972, generator_adv_loss=1.88, generator_feat_match_loss=2.027, over 6242.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:08:41,599 INFO [train.py:919] (1/6) Start epoch 16
+2024-03-12 01:10:58,082 INFO [train.py:527] (1/6) Epoch 16, batch 40, global_batch_idx: 1900, batch size: 77, loss[discriminator_loss=2.789, discriminator_real_loss=1.557, discriminator_fake_loss=1.232, generator_loss=29.32, generator_mel_loss=22.56, generator_kl_loss=1.317, generator_dur_loss=2.02, generator_adv_loss=1.573, generator_feat_match_loss=1.851, over 77.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.41, discriminator_fake_loss=1.341, generator_loss=31.27, generator_mel_loss=24.11, generator_kl_loss=1.307, generator_dur_loss=1.979, generator_adv_loss=1.864, generator_feat_match_loss=2.009, over 2489.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:13:14,057 INFO [train.py:527] (1/6) Epoch 16, batch 90, global_batch_idx: 1950, batch size: 58, loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.318, generator_loss=32.02, generator_mel_loss=24.73, generator_kl_loss=1.176, generator_dur_loss=1.916, generator_adv_loss=1.94, generator_feat_match_loss=2.26, over 58.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.418, discriminator_fake_loss=1.337, generator_loss=31.34, generator_mel_loss=24.15, generator_kl_loss=1.311, generator_dur_loss=1.965, generator_adv_loss=1.874, generator_feat_match_loss=2.037, over 5096.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:14:46,773 INFO [train.py:919] (1/6) Start epoch 17
+2024-03-12 01:15:55,090 INFO [train.py:527] (1/6) Epoch 17, batch 16, global_batch_idx: 2000, batch size: 88, loss[discriminator_loss=2.824, discriminator_real_loss=1.334, discriminator_fake_loss=1.491, generator_loss=29.07, generator_mel_loss=22.38, generator_kl_loss=1.326, generator_dur_loss=2.04, generator_adv_loss=1.701, generator_feat_match_loss=1.623, over 88.00 samples.], tot_loss[discriminator_loss=2.842, discriminator_real_loss=1.454, discriminator_fake_loss=1.387, generator_loss=30.86, generator_mel_loss=23.85, generator_kl_loss=1.315, generator_dur_loss=1.972, generator_adv_loss=1.893, generator_feat_match_loss=1.835, over 958.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:15:55,091 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 01:16:02,946 INFO [train.py:591] (1/6) Epoch 17, validation: discriminator_loss=2.88, discriminator_real_loss=1.485, discriminator_fake_loss=1.395, generator_loss=29.27, generator_mel_loss=23.01, generator_kl_loss=1.108, generator_dur_loss=2.102, generator_adv_loss=1.687, generator_feat_match_loss=1.358, over 100.00 samples.
+2024-03-12 01:16:02,947 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 01:18:23,009 INFO [train.py:527] (1/6) Epoch 17, batch 66, global_batch_idx: 2050, batch size: 88, loss[discriminator_loss=2.868, discriminator_real_loss=1.601, discriminator_fake_loss=1.268, generator_loss=31.21, generator_mel_loss=23.65, generator_kl_loss=1.283, generator_dur_loss=2.069, generator_adv_loss=2.128, generator_feat_match_loss=2.08, over 88.00 samples.], tot_loss[discriminator_loss=2.787, discriminator_real_loss=1.428, discriminator_fake_loss=1.359, generator_loss=30.83, generator_mel_loss=23.8, generator_kl_loss=1.273, generator_dur_loss=1.989, generator_adv_loss=1.854, generator_feat_match_loss=1.909, over 4055.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:20:38,172 INFO [train.py:527] (1/6) Epoch 17, batch 116, global_batch_idx: 2100, batch size: 59, loss[discriminator_loss=2.761, discriminator_real_loss=1.407, discriminator_fake_loss=1.354, generator_loss=29.98, generator_mel_loss=23.09, generator_kl_loss=1.333, generator_dur_loss=1.949, generator_adv_loss=1.793, generator_feat_match_loss=1.81, over 59.00 samples.], tot_loss[discriminator_loss=2.778, discriminator_real_loss=1.424, discriminator_fake_loss=1.354, generator_loss=30.84, generator_mel_loss=23.76, generator_kl_loss=1.281, generator_dur_loss=1.979, generator_adv_loss=1.856, generator_feat_match_loss=1.965, over 6752.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:20:57,566 INFO [train.py:919] (1/6) Start epoch 18
+2024-03-12 01:23:20,725 INFO [train.py:527] (1/6) Epoch 18, batch 42, global_batch_idx: 2150, batch size: 31, loss[discriminator_loss=2.924, discriminator_real_loss=1.641, discriminator_fake_loss=1.283, generator_loss=32.68, generator_mel_loss=25.49, generator_kl_loss=1.341, generator_dur_loss=1.844, generator_adv_loss=1.793, generator_feat_match_loss=2.206, over 31.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.423, discriminator_fake_loss=1.346, generator_loss=31.09, generator_mel_loss=23.78, generator_kl_loss=1.255, generator_dur_loss=1.985, generator_adv_loss=1.903, generator_feat_match_loss=2.158, over 2592.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:25:39,851 INFO [train.py:527] (1/6) Epoch 18, batch 92, global_batch_idx: 2200, batch size: 47, loss[discriminator_loss=2.772, discriminator_real_loss=1.313, discriminator_fake_loss=1.459, generator_loss=30.43, generator_mel_loss=23.13, generator_kl_loss=1.461, generator_dur_loss=1.967, generator_adv_loss=2.038, generator_feat_match_loss=1.837, over 47.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.417, discriminator_fake_loss=1.344, generator_loss=31.07, generator_mel_loss=23.81, generator_kl_loss=1.262, generator_dur_loss=1.982, generator_adv_loss=1.893, generator_feat_match_loss=2.127, over 5629.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:25:39,852 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 01:25:48,601 INFO [train.py:591] (1/6) Epoch 18, validation: discriminator_loss=2.828, discriminator_real_loss=1.563, discriminator_fake_loss=1.265, generator_loss=31.05, generator_mel_loss=24.18, generator_kl_loss=1.084, generator_dur_loss=2.103, generator_adv_loss=1.899, generator_feat_match_loss=1.786, over 100.00 samples.
+2024-03-12 01:25:48,602 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 01:27:10,604 INFO [train.py:919] (1/6) Start epoch 19
+2024-03-12 01:28:25,341 INFO [train.py:527] (1/6) Epoch 19, batch 18, global_batch_idx: 2250, batch size: 44, loss[discriminator_loss=2.781, discriminator_real_loss=1.47, discriminator_fake_loss=1.311, generator_loss=29.14, generator_mel_loss=22.27, generator_kl_loss=1.195, generator_dur_loss=1.95, generator_adv_loss=1.755, generator_feat_match_loss=1.962, over 44.00 samples.], tot_loss[discriminator_loss=2.808, discriminator_real_loss=1.466, discriminator_fake_loss=1.342, generator_loss=30.59, generator_mel_loss=23.51, generator_kl_loss=1.24, generator_dur_loss=1.983, generator_adv_loss=1.872, generator_feat_match_loss=1.983, over 1106.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:30:45,088 INFO [train.py:527] (1/6) Epoch 19, batch 68, global_batch_idx: 2300, batch size: 31, loss[discriminator_loss=2.748, discriminator_real_loss=1.59, discriminator_fake_loss=1.158, generator_loss=33.48, generator_mel_loss=25.17, generator_kl_loss=1.16, generator_dur_loss=1.882, generator_adv_loss=2.53, generator_feat_match_loss=2.744, over 31.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.427, discriminator_fake_loss=1.334, generator_loss=31.14, generator_mel_loss=23.8, generator_kl_loss=1.248, generator_dur_loss=1.969, generator_adv_loss=1.921, generator_feat_match_loss=2.202, over 3690.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:33:04,859 INFO [train.py:527] (1/6) Epoch 19, batch 118, global_batch_idx: 2350, batch size: 55, loss[discriminator_loss=2.748, discriminator_real_loss=1.384, discriminator_fake_loss=1.364, generator_loss=31.74, generator_mel_loss=24.64, generator_kl_loss=1.374, generator_dur_loss=1.922, generator_adv_loss=1.827, generator_feat_match_loss=1.971, over 55.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.428, discriminator_fake_loss=1.341, generator_loss=31.04, generator_mel_loss=23.66, generator_kl_loss=1.254, generator_dur_loss=1.973, generator_adv_loss=1.939, generator_feat_match_loss=2.214, over 6613.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:33:20,459 INFO [train.py:919] (1/6) Start epoch 20
+2024-03-12 01:35:47,059 INFO [train.py:527] (1/6) Epoch 20, batch 44, global_batch_idx: 2400, batch size: 47, loss[discriminator_loss=2.664, discriminator_real_loss=1.302, discriminator_fake_loss=1.362, generator_loss=31.42, generator_mel_loss=24.11, generator_kl_loss=1.196, generator_dur_loss=1.949, generator_adv_loss=1.852, generator_feat_match_loss=2.311, over 47.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.386, discriminator_fake_loss=1.336, generator_loss=30.94, generator_mel_loss=23.6, generator_kl_loss=1.29, generator_dur_loss=1.979, generator_adv_loss=1.878, generator_feat_match_loss=2.2, over 2651.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:35:47,061 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 01:35:54,747 INFO [train.py:591] (1/6) Epoch 20, validation: discriminator_loss=2.762, discriminator_real_loss=1.446, discriminator_fake_loss=1.316, generator_loss=29.25, generator_mel_loss=22.69, generator_kl_loss=1.153, generator_dur_loss=2.09, generator_adv_loss=1.794, generator_feat_match_loss=1.518, over 100.00 samples.
+2024-03-12 01:35:54,748 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 01:38:14,658 INFO [train.py:527] (1/6) Epoch 20, batch 94, global_batch_idx: 2450, batch size: 39, loss[discriminator_loss=2.667, discriminator_real_loss=1.256, discriminator_fake_loss=1.411, generator_loss=31.88, generator_mel_loss=24.19, generator_kl_loss=1.333, generator_dur_loss=1.916, generator_adv_loss=1.932, generator_feat_match_loss=2.512, over 39.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.388, discriminator_fake_loss=1.336, generator_loss=30.95, generator_mel_loss=23.57, generator_kl_loss=1.275, generator_dur_loss=1.979, generator_adv_loss=1.896, generator_feat_match_loss=2.223, over 5569.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:39:34,983 INFO [train.py:919] (1/6) Start epoch 21
+2024-03-12 01:41:48,430 INFO [train.py:527] (1/6) Epoch 21, batch 20, global_batch_idx: 2500, batch size: 53, loss[discriminator_loss=2.767, discriminator_real_loss=1.546, discriminator_fake_loss=1.221, generator_loss=28.99, generator_mel_loss=21.89, generator_kl_loss=1.287, generator_dur_loss=1.91, generator_adv_loss=1.863, generator_feat_match_loss=2.043, over 53.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.414, discriminator_fake_loss=1.34, generator_loss=30.47, generator_mel_loss=23.18, generator_kl_loss=1.259, generator_dur_loss=1.973, generator_adv_loss=1.88, generator_feat_match_loss=2.181, over 1231.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:44:07,468 INFO [train.py:527] (1/6) Epoch 21, batch 70, global_batch_idx: 2550, batch size: 47, loss[discriminator_loss=2.811, discriminator_real_loss=1.514, discriminator_fake_loss=1.297, generator_loss=30.81, generator_mel_loss=23.61, generator_kl_loss=1.334, generator_dur_loss=1.863, generator_adv_loss=1.681, generator_feat_match_loss=2.323, over 47.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.4, discriminator_fake_loss=1.334, generator_loss=30.72, generator_mel_loss=23.37, generator_kl_loss=1.27, generator_dur_loss=1.966, generator_adv_loss=1.896, generator_feat_match_loss=2.22, over 3948.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:46:24,408 INFO [train.py:527] (1/6) Epoch 21, batch 120, global_batch_idx: 2600, batch size: 25, loss[discriminator_loss=2.689, discriminator_real_loss=1.338, discriminator_fake_loss=1.351, generator_loss=31.63, generator_mel_loss=23.69, generator_kl_loss=1.633, generator_dur_loss=1.74, generator_adv_loss=1.966, generator_feat_match_loss=2.597, over 25.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.411, discriminator_fake_loss=1.335, generator_loss=30.6, generator_mel_loss=23.23, generator_kl_loss=1.272, generator_dur_loss=1.973, generator_adv_loss=1.906, generator_feat_match_loss=2.219, over 6867.00 samples.], cur_lr_g: 2.00e-04, cur_lr_d: 2.00e-04, 
+2024-03-12 01:46:24,409 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 01:46:33,223 INFO [train.py:591] (1/6) Epoch 21, validation: discriminator_loss=2.716, discriminator_real_loss=1.447, discriminator_fake_loss=1.27, generator_loss=30.54, generator_mel_loss=23.51, generator_kl_loss=1.136, generator_dur_loss=2.084, generator_adv_loss=1.928, generator_feat_match_loss=1.873, over 100.00 samples.
+2024-03-12 01:46:33,224 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 01:46:43,795 INFO [train.py:919] (1/6) Start epoch 22
+2024-03-12 01:49:19,710 INFO [train.py:527] (1/6) Epoch 22, batch 46, global_batch_idx: 2650, batch size: 88, loss[discriminator_loss=2.689, discriminator_real_loss=1.355, discriminator_fake_loss=1.334, generator_loss=30.34, generator_mel_loss=22.67, generator_kl_loss=1.182, generator_dur_loss=2.027, generator_adv_loss=2.001, generator_feat_match_loss=2.457, over 88.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.403, discriminator_fake_loss=1.333, generator_loss=30.76, generator_mel_loss=23.28, generator_kl_loss=1.282, generator_dur_loss=1.96, generator_adv_loss=1.926, generator_feat_match_loss=2.313, over 2493.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 01:51:39,122 INFO [train.py:527] (1/6) Epoch 22, batch 96, global_batch_idx: 2700, batch size: 83, loss[discriminator_loss=2.771, discriminator_real_loss=1.312, discriminator_fake_loss=1.459, generator_loss=29.92, generator_mel_loss=22.44, generator_kl_loss=1.338, generator_dur_loss=2.032, generator_adv_loss=1.962, generator_feat_match_loss=2.145, over 83.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.397, discriminator_fake_loss=1.342, generator_loss=30.59, generator_mel_loss=23.13, generator_kl_loss=1.268, generator_dur_loss=1.974, generator_adv_loss=1.918, generator_feat_match_loss=2.304, over 5510.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 01:52:50,061 INFO [train.py:919] (1/6) Start epoch 23
+2024-03-12 01:54:13,250 INFO [train.py:527] (1/6) Epoch 23, batch 22, global_batch_idx: 2750, batch size: 58, loss[discriminator_loss=2.705, discriminator_real_loss=1.44, discriminator_fake_loss=1.265, generator_loss=29.7, generator_mel_loss=22.37, generator_kl_loss=1.272, generator_dur_loss=1.922, generator_adv_loss=1.841, generator_feat_match_loss=2.294, over 58.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.41, discriminator_fake_loss=1.345, generator_loss=29.93, generator_mel_loss=22.66, generator_kl_loss=1.264, generator_dur_loss=1.974, generator_adv_loss=1.88, generator_feat_match_loss=2.148, over 1403.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 01:56:31,410 INFO [train.py:527] (1/6) Epoch 23, batch 72, global_batch_idx: 2800, batch size: 25, loss[discriminator_loss=2.638, discriminator_real_loss=1.268, discriminator_fake_loss=1.369, generator_loss=32.23, generator_mel_loss=24.09, generator_kl_loss=1.414, generator_dur_loss=1.785, generator_adv_loss=2.236, generator_feat_match_loss=2.706, over 25.00 samples.], tot_loss[discriminator_loss=2.766, discriminator_real_loss=1.431, discriminator_fake_loss=1.335, generator_loss=30.45, generator_mel_loss=22.97, generator_kl_loss=1.277, generator_dur_loss=1.963, generator_adv_loss=1.944, generator_feat_match_loss=2.294, over 4071.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 01:56:31,411 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 01:56:39,134 INFO [train.py:591] (1/6) Epoch 23, validation: discriminator_loss=2.76, discriminator_real_loss=1.551, discriminator_fake_loss=1.209, generator_loss=29.39, generator_mel_loss=22.55, generator_kl_loss=1.05, generator_dur_loss=2.078, generator_adv_loss=2.038, generator_feat_match_loss=1.676, over 100.00 samples.
+2024-03-12 01:56:39,134 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 01:58:56,668 INFO [train.py:527] (1/6) Epoch 23, batch 122, global_batch_idx: 2850, batch size: 72, loss[discriminator_loss=2.745, discriminator_real_loss=1.328, discriminator_fake_loss=1.417, generator_loss=31.31, generator_mel_loss=23.77, generator_kl_loss=1.195, generator_dur_loss=2.017, generator_adv_loss=1.857, generator_feat_match_loss=2.472, over 72.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.417, discriminator_fake_loss=1.334, generator_loss=30.46, generator_mel_loss=23, generator_kl_loss=1.275, generator_dur_loss=1.964, generator_adv_loss=1.928, generator_feat_match_loss=2.29, over 6961.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 01:59:01,961 INFO [train.py:919] (1/6) Start epoch 24
+2024-03-12 02:01:38,289 INFO [train.py:527] (1/6) Epoch 24, batch 48, global_batch_idx: 2900, batch size: 45, loss[discriminator_loss=2.643, discriminator_real_loss=1.241, discriminator_fake_loss=1.402, generator_loss=31.04, generator_mel_loss=23.13, generator_kl_loss=1.36, generator_dur_loss=1.843, generator_adv_loss=2.033, generator_feat_match_loss=2.677, over 45.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.411, discriminator_fake_loss=1.345, generator_loss=30.44, generator_mel_loss=23.06, generator_kl_loss=1.284, generator_dur_loss=1.959, generator_adv_loss=1.905, generator_feat_match_loss=2.234, over 2667.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:03:56,244 INFO [train.py:527] (1/6) Epoch 24, batch 98, global_batch_idx: 2950, batch size: 55, loss[discriminator_loss=2.742, discriminator_real_loss=1.442, discriminator_fake_loss=1.301, generator_loss=30.37, generator_mel_loss=23.07, generator_kl_loss=1.203, generator_dur_loss=1.938, generator_adv_loss=1.968, generator_feat_match_loss=2.195, over 55.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.414, discriminator_fake_loss=1.338, generator_loss=30.46, generator_mel_loss=22.98, generator_kl_loss=1.284, generator_dur_loss=1.958, generator_adv_loss=1.939, generator_feat_match_loss=2.296, over 5460.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:05:07,773 INFO [train.py:919] (1/6) Start epoch 25
+2024-03-12 02:06:39,767 INFO [train.py:527] (1/6) Epoch 25, batch 24, global_batch_idx: 3000, batch size: 31, loss[discriminator_loss=2.747, discriminator_real_loss=1.647, discriminator_fake_loss=1.1, generator_loss=33.14, generator_mel_loss=25.19, generator_kl_loss=1.477, generator_dur_loss=1.835, generator_adv_loss=2.165, generator_feat_match_loss=2.468, over 31.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.381, discriminator_fake_loss=1.35, generator_loss=30.46, generator_mel_loss=22.8, generator_kl_loss=1.3, generator_dur_loss=1.939, generator_adv_loss=1.997, generator_feat_match_loss=2.423, over 1279.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:06:39,768 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 02:06:47,850 INFO [train.py:591] (1/6) Epoch 25, validation: discriminator_loss=2.911, discriminator_real_loss=1.657, discriminator_fake_loss=1.254, generator_loss=29.63, generator_mel_loss=22.44, generator_kl_loss=1.237, generator_dur_loss=2.038, generator_adv_loss=2.074, generator_feat_match_loss=1.843, over 100.00 samples.
+2024-03-12 02:06:47,851 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 02:09:03,745 INFO [train.py:527] (1/6) Epoch 25, batch 74, global_batch_idx: 3050, batch size: 45, loss[discriminator_loss=2.798, discriminator_real_loss=1.298, discriminator_fake_loss=1.5, generator_loss=29.91, generator_mel_loss=22.08, generator_kl_loss=1.325, generator_dur_loss=1.923, generator_adv_loss=2.301, generator_feat_match_loss=2.278, over 45.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.407, discriminator_fake_loss=1.335, generator_loss=30.14, generator_mel_loss=22.67, generator_kl_loss=1.271, generator_dur_loss=1.96, generator_adv_loss=1.936, generator_feat_match_loss=2.304, over 4193.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:11:21,989 INFO [train.py:919] (1/6) Start epoch 26
+2024-03-12 02:11:45,844 INFO [train.py:527] (1/6) Epoch 26, batch 0, global_batch_idx: 3100, batch size: 66, loss[discriminator_loss=2.745, discriminator_real_loss=1.291, discriminator_fake_loss=1.455, generator_loss=29.82, generator_mel_loss=22.42, generator_kl_loss=1.387, generator_dur_loss=2.018, generator_adv_loss=1.767, generator_feat_match_loss=2.227, over 66.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.291, discriminator_fake_loss=1.455, generator_loss=29.82, generator_mel_loss=22.42, generator_kl_loss=1.387, generator_dur_loss=2.018, generator_adv_loss=1.767, generator_feat_match_loss=2.227, over 66.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:14:07,005 INFO [train.py:527] (1/6) Epoch 26, batch 50, global_batch_idx: 3150, batch size: 64, loss[discriminator_loss=2.729, discriminator_real_loss=1.333, discriminator_fake_loss=1.396, generator_loss=30.42, generator_mel_loss=23.2, generator_kl_loss=1.089, generator_dur_loss=2.004, generator_adv_loss=1.911, generator_feat_match_loss=2.22, over 64.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.404, discriminator_fake_loss=1.342, generator_loss=30, generator_mel_loss=22.55, generator_kl_loss=1.254, generator_dur_loss=1.971, generator_adv_loss=1.927, generator_feat_match_loss=2.297, over 3021.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:16:25,892 INFO [train.py:527] (1/6) Epoch 26, batch 100, global_batch_idx: 3200, batch size: 64, loss[discriminator_loss=2.656, discriminator_real_loss=1.4, discriminator_fake_loss=1.256, generator_loss=31.53, generator_mel_loss=23.77, generator_kl_loss=1.262, generator_dur_loss=1.939, generator_adv_loss=1.797, generator_feat_match_loss=2.761, over 64.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.396, discriminator_fake_loss=1.335, generator_loss=30.12, generator_mel_loss=22.64, generator_kl_loss=1.257, generator_dur_loss=1.964, generator_adv_loss=1.926, generator_feat_match_loss=2.334, over 5669.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:16:25,894 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 02:16:34,497 INFO [train.py:591] (1/6) Epoch 26, validation: discriminator_loss=2.733, discriminator_real_loss=1.372, discriminator_fake_loss=1.361, generator_loss=28.89, generator_mel_loss=22.07, generator_kl_loss=1.013, generator_dur_loss=2.064, generator_adv_loss=1.783, generator_feat_match_loss=1.959, over 100.00 samples.
+2024-03-12 02:16:34,498 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 02:17:38,062 INFO [train.py:919] (1/6) Start epoch 27
+2024-03-12 02:19:15,700 INFO [train.py:527] (1/6) Epoch 27, batch 26, global_batch_idx: 3250, batch size: 62, loss[discriminator_loss=2.806, discriminator_real_loss=1.387, discriminator_fake_loss=1.419, generator_loss=29.8, generator_mel_loss=22.59, generator_kl_loss=1.258, generator_dur_loss=1.971, generator_adv_loss=1.752, generator_feat_match_loss=2.232, over 62.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.415, discriminator_fake_loss=1.339, generator_loss=30.08, generator_mel_loss=22.51, generator_kl_loss=1.224, generator_dur_loss=1.98, generator_adv_loss=1.965, generator_feat_match_loss=2.393, over 1627.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:21:36,369 INFO [train.py:527] (1/6) Epoch 27, batch 76, global_batch_idx: 3300, batch size: 15, loss[discriminator_loss=2.691, discriminator_real_loss=1.278, discriminator_fake_loss=1.413, generator_loss=33.28, generator_mel_loss=25.03, generator_kl_loss=1.477, generator_dur_loss=1.743, generator_adv_loss=2.277, generator_feat_match_loss=2.749, over 15.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.414, discriminator_fake_loss=1.341, generator_loss=30.18, generator_mel_loss=22.57, generator_kl_loss=1.232, generator_dur_loss=1.978, generator_adv_loss=1.968, generator_feat_match_loss=2.426, over 4497.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:23:48,255 INFO [train.py:919] (1/6) Start epoch 28
+2024-03-12 02:24:17,397 INFO [train.py:527] (1/6) Epoch 28, batch 2, global_batch_idx: 3350, batch size: 48, loss[discriminator_loss=2.892, discriminator_real_loss=1.442, discriminator_fake_loss=1.45, generator_loss=30.66, generator_mel_loss=23.42, generator_kl_loss=1.226, generator_dur_loss=1.868, generator_adv_loss=1.806, generator_feat_match_loss=2.336, over 48.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.414, discriminator_fake_loss=1.331, generator_loss=29.72, generator_mel_loss=22.04, generator_kl_loss=1.262, generator_dur_loss=1.944, generator_adv_loss=2.06, generator_feat_match_loss=2.409, over 184.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:26:35,486 INFO [train.py:527] (1/6) Epoch 28, batch 52, global_batch_idx: 3400, batch size: 83, loss[discriminator_loss=2.79, discriminator_real_loss=1.267, discriminator_fake_loss=1.523, generator_loss=29.57, generator_mel_loss=22.24, generator_kl_loss=1.257, generator_dur_loss=2.065, generator_adv_loss=1.769, generator_feat_match_loss=2.236, over 83.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.433, discriminator_fake_loss=1.337, generator_loss=29.77, generator_mel_loss=22.38, generator_kl_loss=1.231, generator_dur_loss=1.973, generator_adv_loss=1.932, generator_feat_match_loss=2.262, over 3227.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:26:35,488 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 02:26:43,333 INFO [train.py:591] (1/6) Epoch 28, validation: discriminator_loss=2.762, discriminator_real_loss=1.354, discriminator_fake_loss=1.408, generator_loss=30.33, generator_mel_loss=23.31, generator_kl_loss=1.208, generator_dur_loss=2.058, generator_adv_loss=1.726, generator_feat_match_loss=2.028, over 100.00 samples.
+2024-03-12 02:26:43,334 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 02:29:00,583 INFO [train.py:527] (1/6) Epoch 28, batch 102, global_batch_idx: 3450, batch size: 88, loss[discriminator_loss=2.738, discriminator_real_loss=1.515, discriminator_fake_loss=1.224, generator_loss=29.45, generator_mel_loss=21.74, generator_kl_loss=1.253, generator_dur_loss=2.002, generator_adv_loss=2.096, generator_feat_match_loss=2.358, over 88.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.414, discriminator_fake_loss=1.333, generator_loss=29.92, generator_mel_loss=22.48, generator_kl_loss=1.256, generator_dur_loss=1.96, generator_adv_loss=1.932, generator_feat_match_loss=2.292, over 5880.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:30:01,226 INFO [train.py:919] (1/6) Start epoch 29
+2024-03-12 02:31:42,487 INFO [train.py:527] (1/6) Epoch 29, batch 28, global_batch_idx: 3500, batch size: 77, loss[discriminator_loss=2.736, discriminator_real_loss=1.427, discriminator_fake_loss=1.309, generator_loss=28.94, generator_mel_loss=21.68, generator_kl_loss=1.195, generator_dur_loss=2.03, generator_adv_loss=1.92, generator_feat_match_loss=2.114, over 77.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.397, discriminator_fake_loss=1.324, generator_loss=29.68, generator_mel_loss=22.32, generator_kl_loss=1.23, generator_dur_loss=1.968, generator_adv_loss=1.898, generator_feat_match_loss=2.266, over 1753.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:34:01,841 INFO [train.py:527] (1/6) Epoch 29, batch 78, global_batch_idx: 3550, batch size: 88, loss[discriminator_loss=2.732, discriminator_real_loss=1.388, discriminator_fake_loss=1.345, generator_loss=28.72, generator_mel_loss=21.12, generator_kl_loss=1.211, generator_dur_loss=2.053, generator_adv_loss=2.094, generator_feat_match_loss=2.245, over 88.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.398, discriminator_fake_loss=1.333, generator_loss=29.8, generator_mel_loss=22.32, generator_kl_loss=1.233, generator_dur_loss=1.961, generator_adv_loss=1.934, generator_feat_match_loss=2.353, over 4599.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:36:07,313 INFO [train.py:919] (1/6) Start epoch 30
+2024-03-12 02:36:42,573 INFO [train.py:527] (1/6) Epoch 30, batch 4, global_batch_idx: 3600, batch size: 61, loss[discriminator_loss=2.61, discriminator_real_loss=1.473, discriminator_fake_loss=1.136, generator_loss=31.28, generator_mel_loss=23.62, generator_kl_loss=1.32, generator_dur_loss=1.906, generator_adv_loss=1.813, generator_feat_match_loss=2.63, over 61.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.377, discriminator_fake_loss=1.297, generator_loss=31.45, generator_mel_loss=23.56, generator_kl_loss=1.347, generator_dur_loss=1.893, generator_adv_loss=1.944, generator_feat_match_loss=2.7, over 249.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:36:42,595 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 02:36:50,362 INFO [train.py:591] (1/6) Epoch 30, validation: discriminator_loss=2.709, discriminator_real_loss=1.298, discriminator_fake_loss=1.41, generator_loss=28.39, generator_mel_loss=21.72, generator_kl_loss=1.022, generator_dur_loss=2.038, generator_adv_loss=1.708, generator_feat_match_loss=1.903, over 100.00 samples.
+2024-03-12 02:36:50,364 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 02:39:06,969 INFO [train.py:527] (1/6) Epoch 30, batch 54, global_batch_idx: 3650, batch size: 56, loss[discriminator_loss=2.75, discriminator_real_loss=1.394, discriminator_fake_loss=1.356, generator_loss=28.91, generator_mel_loss=21.88, generator_kl_loss=1.169, generator_dur_loss=1.849, generator_adv_loss=1.905, generator_feat_match_loss=2.112, over 56.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.399, discriminator_fake_loss=1.332, generator_loss=29.92, generator_mel_loss=22.35, generator_kl_loss=1.245, generator_dur_loss=1.944, generator_adv_loss=1.953, generator_feat_match_loss=2.435, over 3037.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:41:24,052 INFO [train.py:527] (1/6) Epoch 30, batch 104, global_batch_idx: 3700, batch size: 44, loss[discriminator_loss=2.74, discriminator_real_loss=1.486, discriminator_fake_loss=1.254, generator_loss=29.31, generator_mel_loss=21.62, generator_kl_loss=1.356, generator_dur_loss=1.893, generator_adv_loss=1.877, generator_feat_match_loss=2.559, over 44.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.398, discriminator_fake_loss=1.329, generator_loss=29.9, generator_mel_loss=22.35, generator_kl_loss=1.241, generator_dur_loss=1.94, generator_adv_loss=1.946, generator_feat_match_loss=2.425, over 5780.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:42:19,574 INFO [train.py:919] (1/6) Start epoch 31
+2024-03-12 02:44:05,424 INFO [train.py:527] (1/6) Epoch 31, batch 30, global_batch_idx: 3750, batch size: 66, loss[discriminator_loss=2.747, discriminator_real_loss=1.456, discriminator_fake_loss=1.291, generator_loss=30.85, generator_mel_loss=22.72, generator_kl_loss=1.204, generator_dur_loss=1.956, generator_adv_loss=2.069, generator_feat_match_loss=2.901, over 66.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.4, discriminator_fake_loss=1.332, generator_loss=30.19, generator_mel_loss=22.48, generator_kl_loss=1.287, generator_dur_loss=1.929, generator_adv_loss=1.983, generator_feat_match_loss=2.511, over 1696.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:46:25,126 INFO [train.py:527] (1/6) Epoch 31, batch 80, global_batch_idx: 3800, batch size: 47, loss[discriminator_loss=2.706, discriminator_real_loss=1.267, discriminator_fake_loss=1.439, generator_loss=30.9, generator_mel_loss=23.48, generator_kl_loss=1.301, generator_dur_loss=1.83, generator_adv_loss=1.651, generator_feat_match_loss=2.64, over 47.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.413, discriminator_fake_loss=1.338, generator_loss=29.87, generator_mel_loss=22.32, generator_kl_loss=1.251, generator_dur_loss=1.937, generator_adv_loss=1.957, generator_feat_match_loss=2.401, over 4632.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:46:25,128 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 02:46:33,134 INFO [train.py:591] (1/6) Epoch 31, validation: discriminator_loss=2.739, discriminator_real_loss=1.32, discriminator_fake_loss=1.419, generator_loss=27.75, generator_mel_loss=21.26, generator_kl_loss=1.047, generator_dur_loss=2.012, generator_adv_loss=1.689, generator_feat_match_loss=1.744, over 100.00 samples.
+2024-03-12 02:46:33,134 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 02:48:31,817 INFO [train.py:919] (1/6) Start epoch 32
+2024-03-12 02:49:14,904 INFO [train.py:527] (1/6) Epoch 32, batch 6, global_batch_idx: 3850, batch size: 50, loss[discriminator_loss=2.64, discriminator_real_loss=1.349, discriminator_fake_loss=1.291, generator_loss=30.12, generator_mel_loss=22.45, generator_kl_loss=1.153, generator_dur_loss=1.875, generator_adv_loss=1.896, generator_feat_match_loss=2.738, over 50.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.347, generator_loss=29.13, generator_mel_loss=21.79, generator_kl_loss=1.251, generator_dur_loss=1.895, generator_adv_loss=1.896, generator_feat_match_loss=2.296, over 370.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:51:36,065 INFO [train.py:527] (1/6) Epoch 32, batch 56, global_batch_idx: 3900, batch size: 64, loss[discriminator_loss=2.686, discriminator_real_loss=1.381, discriminator_fake_loss=1.306, generator_loss=29.24, generator_mel_loss=22.02, generator_kl_loss=1.127, generator_dur_loss=1.932, generator_adv_loss=1.889, generator_feat_match_loss=2.271, over 64.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.422, discriminator_fake_loss=1.331, generator_loss=29.74, generator_mel_loss=22.24, generator_kl_loss=1.242, generator_dur_loss=1.935, generator_adv_loss=1.936, generator_feat_match_loss=2.386, over 3099.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:53:54,755 INFO [train.py:527] (1/6) Epoch 32, batch 106, global_batch_idx: 3950, batch size: 56, loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=30.63, generator_mel_loss=22.92, generator_kl_loss=1.288, generator_dur_loss=1.901, generator_adv_loss=1.966, generator_feat_match_loss=2.554, over 56.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.412, discriminator_fake_loss=1.329, generator_loss=29.77, generator_mel_loss=22.25, generator_kl_loss=1.244, generator_dur_loss=1.937, generator_adv_loss=1.935, generator_feat_match_loss=2.408, over 5983.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:54:40,816 INFO [train.py:919] (1/6) Start epoch 33
+2024-03-12 02:56:35,256 INFO [train.py:527] (1/6) Epoch 33, batch 32, global_batch_idx: 4000, batch size: 72, loss[discriminator_loss=2.749, discriminator_real_loss=1.494, discriminator_fake_loss=1.255, generator_loss=29.92, generator_mel_loss=22.13, generator_kl_loss=1.323, generator_dur_loss=1.961, generator_adv_loss=1.833, generator_feat_match_loss=2.669, over 72.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.412, discriminator_fake_loss=1.331, generator_loss=30.06, generator_mel_loss=22.35, generator_kl_loss=1.267, generator_dur_loss=1.927, generator_adv_loss=1.986, generator_feat_match_loss=2.531, over 1852.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 02:56:35,258 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 02:56:43,046 INFO [train.py:591] (1/6) Epoch 33, validation: discriminator_loss=2.715, discriminator_real_loss=1.291, discriminator_fake_loss=1.424, generator_loss=28.62, generator_mel_loss=21.9, generator_kl_loss=1.076, generator_dur_loss=2.005, generator_adv_loss=1.742, generator_feat_match_loss=1.901, over 100.00 samples.
+2024-03-12 02:56:43,047 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 02:59:01,964 INFO [train.py:527] (1/6) Epoch 33, batch 82, global_batch_idx: 4050, batch size: 55, loss[discriminator_loss=2.859, discriminator_real_loss=1.386, discriminator_fake_loss=1.472, generator_loss=28.32, generator_mel_loss=21.16, generator_kl_loss=1.281, generator_dur_loss=1.797, generator_adv_loss=2.039, generator_feat_match_loss=2.037, over 55.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.414, discriminator_fake_loss=1.344, generator_loss=29.86, generator_mel_loss=22.17, generator_kl_loss=1.247, generator_dur_loss=1.931, generator_adv_loss=1.992, generator_feat_match_loss=2.529, over 4762.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:00:56,963 INFO [train.py:919] (1/6) Start epoch 34
+2024-03-12 03:01:42,808 INFO [train.py:527] (1/6) Epoch 34, batch 8, global_batch_idx: 4100, batch size: 47, loss[discriminator_loss=2.726, discriminator_real_loss=1.251, discriminator_fake_loss=1.475, generator_loss=29.13, generator_mel_loss=21.74, generator_kl_loss=1.324, generator_dur_loss=1.85, generator_adv_loss=1.893, generator_feat_match_loss=2.325, over 47.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.402, discriminator_fake_loss=1.336, generator_loss=29.12, generator_mel_loss=21.95, generator_kl_loss=1.214, generator_dur_loss=1.897, generator_adv_loss=1.875, generator_feat_match_loss=2.181, over 469.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:04:02,973 INFO [train.py:527] (1/6) Epoch 34, batch 58, global_batch_idx: 4150, batch size: 88, loss[discriminator_loss=2.765, discriminator_real_loss=1.29, discriminator_fake_loss=1.475, generator_loss=29.17, generator_mel_loss=21.68, generator_kl_loss=1.226, generator_dur_loss=1.983, generator_adv_loss=1.814, generator_feat_match_loss=2.464, over 88.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.395, discriminator_fake_loss=1.34, generator_loss=29.59, generator_mel_loss=22.16, generator_kl_loss=1.241, generator_dur_loss=1.901, generator_adv_loss=1.914, generator_feat_match_loss=2.37, over 3204.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:06:21,492 INFO [train.py:527] (1/6) Epoch 34, batch 108, global_batch_idx: 4200, batch size: 83, loss[discriminator_loss=2.725, discriminator_real_loss=1.362, discriminator_fake_loss=1.363, generator_loss=29.53, generator_mel_loss=22.07, generator_kl_loss=1.238, generator_dur_loss=2.046, generator_adv_loss=2.002, generator_feat_match_loss=2.179, over 83.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.397, discriminator_fake_loss=1.339, generator_loss=29.66, generator_mel_loss=22.12, generator_kl_loss=1.246, generator_dur_loss=1.918, generator_adv_loss=1.942, generator_feat_match_loss=2.436, over 6269.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:06:21,493 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 03:06:30,178 INFO [train.py:591] (1/6) Epoch 34, validation: discriminator_loss=2.732, discriminator_real_loss=1.566, discriminator_fake_loss=1.165, generator_loss=28.72, generator_mel_loss=21.75, generator_kl_loss=1.073, generator_dur_loss=1.998, generator_adv_loss=1.98, generator_feat_match_loss=1.921, over 100.00 samples.
+2024-03-12 03:06:30,179 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 03:07:11,477 INFO [train.py:919] (1/6) Start epoch 35
+2024-03-12 03:09:10,461 INFO [train.py:527] (1/6) Epoch 35, batch 34, global_batch_idx: 4250, batch size: 50, loss[discriminator_loss=2.704, discriminator_real_loss=1.293, discriminator_fake_loss=1.411, generator_loss=31.15, generator_mel_loss=23.29, generator_kl_loss=1.419, generator_dur_loss=1.904, generator_adv_loss=2.061, generator_feat_match_loss=2.483, over 50.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.393, discriminator_fake_loss=1.331, generator_loss=29.54, generator_mel_loss=22.03, generator_kl_loss=1.271, generator_dur_loss=1.931, generator_adv_loss=1.892, generator_feat_match_loss=2.416, over 2049.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:11:30,626 INFO [train.py:527] (1/6) Epoch 35, batch 84, global_batch_idx: 4300, batch size: 97, loss[discriminator_loss=2.786, discriminator_real_loss=1.226, discriminator_fake_loss=1.56, generator_loss=29.85, generator_mel_loss=21.98, generator_kl_loss=1.194, generator_dur_loss=2.034, generator_adv_loss=2.125, generator_feat_match_loss=2.518, over 97.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.397, discriminator_fake_loss=1.338, generator_loss=29.68, generator_mel_loss=22.09, generator_kl_loss=1.253, generator_dur_loss=1.926, generator_adv_loss=1.939, generator_feat_match_loss=2.476, over 5084.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:13:17,840 INFO [train.py:919] (1/6) Start epoch 36
+2024-03-12 03:14:10,383 INFO [train.py:527] (1/6) Epoch 36, batch 10, global_batch_idx: 4350, batch size: 52, loss[discriminator_loss=2.764, discriminator_real_loss=1.547, discriminator_fake_loss=1.217, generator_loss=29.66, generator_mel_loss=22.22, generator_kl_loss=1.285, generator_dur_loss=1.809, generator_adv_loss=1.845, generator_feat_match_loss=2.499, over 52.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.406, discriminator_fake_loss=1.345, generator_loss=29.55, generator_mel_loss=22.09, generator_kl_loss=1.239, generator_dur_loss=1.93, generator_adv_loss=1.914, generator_feat_match_loss=2.375, over 733.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:16:31,640 INFO [train.py:527] (1/6) Epoch 36, batch 60, global_batch_idx: 4400, batch size: 47, loss[discriminator_loss=2.688, discriminator_real_loss=1.275, discriminator_fake_loss=1.413, generator_loss=29.32, generator_mel_loss=21.82, generator_kl_loss=1.373, generator_dur_loss=1.819, generator_adv_loss=1.911, generator_feat_match_loss=2.402, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.392, discriminator_fake_loss=1.332, generator_loss=29.39, generator_mel_loss=21.88, generator_kl_loss=1.242, generator_dur_loss=1.927, generator_adv_loss=1.916, generator_feat_match_loss=2.427, over 3817.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:16:31,641 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 03:16:39,666 INFO [train.py:591] (1/6) Epoch 36, validation: discriminator_loss=2.713, discriminator_real_loss=1.416, discriminator_fake_loss=1.297, generator_loss=28.13, generator_mel_loss=21.35, generator_kl_loss=1.097, generator_dur_loss=1.984, generator_adv_loss=1.838, generator_feat_match_loss=1.856, over 100.00 samples.
+2024-03-12 03:16:39,667 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 03:18:56,392 INFO [train.py:527] (1/6) Epoch 36, batch 110, global_batch_idx: 4450, batch size: 25, loss[discriminator_loss=2.637, discriminator_real_loss=1.411, discriminator_fake_loss=1.227, generator_loss=31.24, generator_mel_loss=23.17, generator_kl_loss=1.471, generator_dur_loss=1.71, generator_adv_loss=1.892, generator_feat_match_loss=2.997, over 25.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.402, discriminator_fake_loss=1.328, generator_loss=29.43, generator_mel_loss=21.9, generator_kl_loss=1.249, generator_dur_loss=1.921, generator_adv_loss=1.924, generator_feat_match_loss=2.431, over 6687.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:19:33,021 INFO [train.py:919] (1/6) Start epoch 37
+2024-03-12 03:21:34,201 INFO [train.py:527] (1/6) Epoch 37, batch 36, global_batch_idx: 4500, batch size: 55, loss[discriminator_loss=2.739, discriminator_real_loss=1.263, discriminator_fake_loss=1.475, generator_loss=29.66, generator_mel_loss=22.29, generator_kl_loss=1.381, generator_dur_loss=1.837, generator_adv_loss=1.784, generator_feat_match_loss=2.363, over 55.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.409, discriminator_fake_loss=1.356, generator_loss=29.47, generator_mel_loss=21.91, generator_kl_loss=1.261, generator_dur_loss=1.887, generator_adv_loss=1.949, generator_feat_match_loss=2.463, over 2010.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:23:54,399 INFO [train.py:527] (1/6) Epoch 37, batch 86, global_batch_idx: 4550, batch size: 61, loss[discriminator_loss=2.682, discriminator_real_loss=1.283, discriminator_fake_loss=1.399, generator_loss=29.03, generator_mel_loss=21.53, generator_kl_loss=1.233, generator_dur_loss=1.896, generator_adv_loss=1.909, generator_feat_match_loss=2.458, over 61.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.406, discriminator_fake_loss=1.35, generator_loss=29.12, generator_mel_loss=21.66, generator_kl_loss=1.247, generator_dur_loss=1.89, generator_adv_loss=1.929, generator_feat_match_loss=2.4, over 4822.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:25:40,717 INFO [train.py:919] (1/6) Start epoch 38
+2024-03-12 03:26:39,469 INFO [train.py:527] (1/6) Epoch 38, batch 12, global_batch_idx: 4600, batch size: 59, loss[discriminator_loss=2.693, discriminator_real_loss=1.493, discriminator_fake_loss=1.2, generator_loss=29.24, generator_mel_loss=21.92, generator_kl_loss=1.209, generator_dur_loss=1.905, generator_adv_loss=1.895, generator_feat_match_loss=2.308, over 59.00 samples.], tot_loss[discriminator_loss=2.771, discriminator_real_loss=1.425, discriminator_fake_loss=1.346, generator_loss=28.91, generator_mel_loss=21.49, generator_kl_loss=1.254, generator_dur_loss=1.91, generator_adv_loss=1.907, generator_feat_match_loss=2.346, over 795.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:26:39,472 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 03:26:47,323 INFO [train.py:591] (1/6) Epoch 38, validation: discriminator_loss=2.736, discriminator_real_loss=1.45, discriminator_fake_loss=1.286, generator_loss=27.59, generator_mel_loss=21.01, generator_kl_loss=1.146, generator_dur_loss=1.966, generator_adv_loss=1.795, generator_feat_match_loss=1.672, over 100.00 samples.
+2024-03-12 03:26:47,323 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 03:29:05,390 INFO [train.py:527] (1/6) Epoch 38, batch 62, global_batch_idx: 4650, batch size: 72, loss[discriminator_loss=2.732, discriminator_real_loss=1.406, discriminator_fake_loss=1.326, generator_loss=29.49, generator_mel_loss=21.8, generator_kl_loss=1.267, generator_dur_loss=1.909, generator_adv_loss=2.045, generator_feat_match_loss=2.466, over 72.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.397, discriminator_fake_loss=1.337, generator_loss=28.99, generator_mel_loss=21.53, generator_kl_loss=1.257, generator_dur_loss=1.91, generator_adv_loss=1.91, generator_feat_match_loss=2.384, over 3801.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:31:25,942 INFO [train.py:527] (1/6) Epoch 38, batch 112, global_batch_idx: 4700, batch size: 56, loss[discriminator_loss=2.685, discriminator_real_loss=1.404, discriminator_fake_loss=1.281, generator_loss=28.32, generator_mel_loss=20.88, generator_kl_loss=1.385, generator_dur_loss=1.808, generator_adv_loss=1.747, generator_feat_match_loss=2.501, over 56.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.404, discriminator_fake_loss=1.338, generator_loss=29, generator_mel_loss=21.56, generator_kl_loss=1.253, generator_dur_loss=1.911, generator_adv_loss=1.915, generator_feat_match_loss=2.37, over 6798.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:31:59,496 INFO [train.py:919] (1/6) Start epoch 39
+2024-03-12 03:34:09,070 INFO [train.py:527] (1/6) Epoch 39, batch 38, global_batch_idx: 4750, batch size: 55, loss[discriminator_loss=2.675, discriminator_real_loss=1.227, discriminator_fake_loss=1.449, generator_loss=28.75, generator_mel_loss=21.37, generator_kl_loss=1.26, generator_dur_loss=1.822, generator_adv_loss=1.96, generator_feat_match_loss=2.336, over 55.00 samples.], tot_loss[discriminator_loss=2.782, discriminator_real_loss=1.429, discriminator_fake_loss=1.353, generator_loss=29.52, generator_mel_loss=21.87, generator_kl_loss=1.295, generator_dur_loss=1.879, generator_adv_loss=1.98, generator_feat_match_loss=2.497, over 2031.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:36:27,812 INFO [train.py:527] (1/6) Epoch 39, batch 88, global_batch_idx: 4800, batch size: 66, loss[discriminator_loss=2.731, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=28.54, generator_mel_loss=21.23, generator_kl_loss=1.235, generator_dur_loss=1.887, generator_adv_loss=1.967, generator_feat_match_loss=2.229, over 66.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.411, discriminator_fake_loss=1.344, generator_loss=29.34, generator_mel_loss=21.74, generator_kl_loss=1.282, generator_dur_loss=1.885, generator_adv_loss=1.967, generator_feat_match_loss=2.464, over 4940.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:36:27,814 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 03:36:36,859 INFO [train.py:591] (1/6) Epoch 39, validation: discriminator_loss=2.785, discriminator_real_loss=1.46, discriminator_fake_loss=1.325, generator_loss=28.19, generator_mel_loss=21.57, generator_kl_loss=1.116, generator_dur_loss=1.951, generator_adv_loss=1.746, generator_feat_match_loss=1.807, over 100.00 samples.
+2024-03-12 03:36:36,860 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 03:38:15,670 INFO [train.py:919] (1/6) Start epoch 40
+2024-03-12 03:39:17,965 INFO [train.py:527] (1/6) Epoch 40, batch 14, global_batch_idx: 4850, batch size: 70, loss[discriminator_loss=2.72, discriminator_real_loss=1.493, discriminator_fake_loss=1.227, generator_loss=29.16, generator_mel_loss=21.72, generator_kl_loss=1.262, generator_dur_loss=2.001, generator_adv_loss=1.889, generator_feat_match_loss=2.292, over 70.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.428, discriminator_fake_loss=1.309, generator_loss=29.1, generator_mel_loss=21.63, generator_kl_loss=1.23, generator_dur_loss=1.872, generator_adv_loss=1.939, generator_feat_match_loss=2.423, over 714.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:41:34,664 INFO [train.py:527] (1/6) Epoch 40, batch 64, global_batch_idx: 4900, batch size: 31, loss[discriminator_loss=2.58, discriminator_real_loss=1.409, discriminator_fake_loss=1.171, generator_loss=29.45, generator_mel_loss=20.82, generator_kl_loss=1.323, generator_dur_loss=1.722, generator_adv_loss=2.401, generator_feat_match_loss=3.179, over 31.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.402, discriminator_fake_loss=1.339, generator_loss=29.07, generator_mel_loss=21.52, generator_kl_loss=1.26, generator_dur_loss=1.896, generator_adv_loss=1.928, generator_feat_match_loss=2.463, over 3504.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:43:52,651 INFO [train.py:527] (1/6) Epoch 40, batch 114, global_batch_idx: 4950, batch size: 55, loss[discriminator_loss=2.707, discriminator_real_loss=1.394, discriminator_fake_loss=1.313, generator_loss=27.44, generator_mel_loss=20.41, generator_kl_loss=1.223, generator_dur_loss=1.83, generator_adv_loss=1.813, generator_feat_match_loss=2.166, over 55.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.414, discriminator_fake_loss=1.338, generator_loss=29.08, generator_mel_loss=21.49, generator_kl_loss=1.266, generator_dur_loss=1.891, generator_adv_loss=1.961, generator_feat_match_loss=2.467, over 6332.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:44:20,501 INFO [train.py:919] (1/6) Start epoch 41
+2024-03-12 03:46:42,090 INFO [train.py:527] (1/6) Epoch 41, batch 40, global_batch_idx: 5000, batch size: 36, loss[discriminator_loss=2.773, discriminator_real_loss=1.419, discriminator_fake_loss=1.354, generator_loss=28.35, generator_mel_loss=21.35, generator_kl_loss=1.227, generator_dur_loss=1.864, generator_adv_loss=1.817, generator_feat_match_loss=2.083, over 36.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.391, discriminator_fake_loss=1.334, generator_loss=28.93, generator_mel_loss=21.49, generator_kl_loss=1.276, generator_dur_loss=1.893, generator_adv_loss=1.892, generator_feat_match_loss=2.38, over 2413.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:46:42,091 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 03:46:50,132 INFO [train.py:591] (1/6) Epoch 41, validation: discriminator_loss=2.762, discriminator_real_loss=1.392, discriminator_fake_loss=1.37, generator_loss=28.57, generator_mel_loss=21.81, generator_kl_loss=1.047, generator_dur_loss=1.956, generator_adv_loss=1.772, generator_feat_match_loss=1.978, over 100.00 samples.
+2024-03-12 03:46:50,133 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 03:49:08,357 INFO [train.py:527] (1/6) Epoch 41, batch 90, global_batch_idx: 5050, batch size: 74, loss[discriminator_loss=2.782, discriminator_real_loss=1.43, discriminator_fake_loss=1.352, generator_loss=28.47, generator_mel_loss=21.31, generator_kl_loss=1.347, generator_dur_loss=1.915, generator_adv_loss=1.823, generator_feat_match_loss=2.077, over 74.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.401, discriminator_fake_loss=1.334, generator_loss=28.92, generator_mel_loss=21.45, generator_kl_loss=1.286, generator_dur_loss=1.886, generator_adv_loss=1.898, generator_feat_match_loss=2.4, over 5071.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:50:42,254 INFO [train.py:919] (1/6) Start epoch 42
+2024-03-12 03:51:51,605 INFO [train.py:527] (1/6) Epoch 42, batch 16, global_batch_idx: 5100, batch size: 77, loss[discriminator_loss=2.672, discriminator_real_loss=1.322, discriminator_fake_loss=1.35, generator_loss=28.8, generator_mel_loss=21.12, generator_kl_loss=1.183, generator_dur_loss=1.949, generator_adv_loss=1.803, generator_feat_match_loss=2.74, over 77.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.377, discriminator_fake_loss=1.367, generator_loss=28.81, generator_mel_loss=21.32, generator_kl_loss=1.262, generator_dur_loss=1.906, generator_adv_loss=1.901, generator_feat_match_loss=2.418, over 1066.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:54:10,639 INFO [train.py:527] (1/6) Epoch 42, batch 66, global_batch_idx: 5150, batch size: 59, loss[discriminator_loss=2.703, discriminator_real_loss=1.325, discriminator_fake_loss=1.378, generator_loss=30.09, generator_mel_loss=22.52, generator_kl_loss=1.257, generator_dur_loss=1.857, generator_adv_loss=1.817, generator_feat_match_loss=2.639, over 59.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.406, discriminator_fake_loss=1.347, generator_loss=28.76, generator_mel_loss=21.24, generator_kl_loss=1.262, generator_dur_loss=1.889, generator_adv_loss=1.939, generator_feat_match_loss=2.424, over 4093.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:56:26,582 INFO [train.py:527] (1/6) Epoch 42, batch 116, global_batch_idx: 5200, batch size: 53, loss[discriminator_loss=2.701, discriminator_real_loss=1.517, discriminator_fake_loss=1.184, generator_loss=29.71, generator_mel_loss=21.9, generator_kl_loss=1.339, generator_dur_loss=1.803, generator_adv_loss=1.91, generator_feat_match_loss=2.76, over 53.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.344, generator_loss=28.8, generator_mel_loss=21.29, generator_kl_loss=1.284, generator_dur_loss=1.877, generator_adv_loss=1.924, generator_feat_match_loss=2.43, over 6751.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 03:56:26,584 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 03:56:35,583 INFO [train.py:591] (1/6) Epoch 42, validation: discriminator_loss=2.725, discriminator_real_loss=1.443, discriminator_fake_loss=1.282, generator_loss=27.96, generator_mel_loss=21.22, generator_kl_loss=0.9713, generator_dur_loss=1.932, generator_adv_loss=1.878, generator_feat_match_loss=1.963, over 100.00 samples.
+2024-03-12 03:56:35,584 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 03:56:56,501 INFO [train.py:919] (1/6) Start epoch 43
+2024-03-12 03:59:14,986 INFO [train.py:527] (1/6) Epoch 43, batch 42, global_batch_idx: 5250, batch size: 88, loss[discriminator_loss=2.766, discriminator_real_loss=1.478, discriminator_fake_loss=1.287, generator_loss=28.92, generator_mel_loss=21.56, generator_kl_loss=1.116, generator_dur_loss=1.997, generator_adv_loss=1.862, generator_feat_match_loss=2.384, over 88.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.396, discriminator_fake_loss=1.346, generator_loss=28.99, generator_mel_loss=21.51, generator_kl_loss=1.297, generator_dur_loss=1.871, generator_adv_loss=1.899, generator_feat_match_loss=2.407, over 2350.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:01:32,823 INFO [train.py:527] (1/6) Epoch 43, batch 92, global_batch_idx: 5300, batch size: 61, loss[discriminator_loss=2.845, discriminator_real_loss=1.452, discriminator_fake_loss=1.393, generator_loss=28.26, generator_mel_loss=21.24, generator_kl_loss=1.21, generator_dur_loss=1.863, generator_adv_loss=1.828, generator_feat_match_loss=2.119, over 61.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.404, discriminator_fake_loss=1.345, generator_loss=29.1, generator_mel_loss=21.51, generator_kl_loss=1.291, generator_dur_loss=1.863, generator_adv_loss=1.952, generator_feat_match_loss=2.483, over 4886.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:03:01,790 INFO [train.py:919] (1/6) Start epoch 44
+2024-03-12 04:04:18,509 INFO [train.py:527] (1/6) Epoch 44, batch 18, global_batch_idx: 5350, batch size: 64, loss[discriminator_loss=2.708, discriminator_real_loss=1.498, discriminator_fake_loss=1.21, generator_loss=28.99, generator_mel_loss=21.49, generator_kl_loss=1.141, generator_dur_loss=1.875, generator_adv_loss=1.896, generator_feat_match_loss=2.592, over 64.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.39, discriminator_fake_loss=1.329, generator_loss=29.07, generator_mel_loss=21.52, generator_kl_loss=1.306, generator_dur_loss=1.836, generator_adv_loss=1.921, generator_feat_match_loss=2.479, over 1051.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:06:38,354 INFO [train.py:527] (1/6) Epoch 44, batch 68, global_batch_idx: 5400, batch size: 64, loss[discriminator_loss=2.744, discriminator_real_loss=1.443, discriminator_fake_loss=1.3, generator_loss=28.75, generator_mel_loss=21.29, generator_kl_loss=1.177, generator_dur_loss=1.891, generator_adv_loss=1.98, generator_feat_match_loss=2.409, over 64.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.396, discriminator_fake_loss=1.355, generator_loss=28.68, generator_mel_loss=21.26, generator_kl_loss=1.298, generator_dur_loss=1.869, generator_adv_loss=1.886, generator_feat_match_loss=2.373, over 3961.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:06:38,356 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 04:06:46,212 INFO [train.py:591] (1/6) Epoch 44, validation: discriminator_loss=2.79, discriminator_real_loss=1.573, discriminator_fake_loss=1.217, generator_loss=27.42, generator_mel_loss=20.68, generator_kl_loss=1.082, generator_dur_loss=1.954, generator_adv_loss=1.905, generator_feat_match_loss=1.804, over 100.00 samples.
+2024-03-12 04:06:46,213 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 04:09:04,592 INFO [train.py:527] (1/6) Epoch 44, batch 118, global_batch_idx: 5450, batch size: 68, loss[discriminator_loss=2.658, discriminator_real_loss=1.42, discriminator_fake_loss=1.238, generator_loss=29.06, generator_mel_loss=20.86, generator_kl_loss=1.379, generator_dur_loss=1.953, generator_adv_loss=2.326, generator_feat_match_loss=2.543, over 68.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.408, discriminator_fake_loss=1.345, generator_loss=28.74, generator_mel_loss=21.22, generator_kl_loss=1.293, generator_dur_loss=1.873, generator_adv_loss=1.929, generator_feat_match_loss=2.431, over 6757.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:09:19,557 INFO [train.py:919] (1/6) Start epoch 45
+2024-03-12 04:11:43,260 INFO [train.py:527] (1/6) Epoch 45, batch 44, global_batch_idx: 5500, batch size: 74, loss[discriminator_loss=2.783, discriminator_real_loss=1.497, discriminator_fake_loss=1.286, generator_loss=28.24, generator_mel_loss=20.79, generator_kl_loss=1.416, generator_dur_loss=1.934, generator_adv_loss=1.914, generator_feat_match_loss=2.186, over 74.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.392, discriminator_fake_loss=1.344, generator_loss=28.53, generator_mel_loss=21.1, generator_kl_loss=1.286, generator_dur_loss=1.88, generator_adv_loss=1.889, generator_feat_match_loss=2.381, over 2650.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:14:03,075 INFO [train.py:527] (1/6) Epoch 45, batch 94, global_batch_idx: 5550, batch size: 47, loss[discriminator_loss=2.736, discriminator_real_loss=1.304, discriminator_fake_loss=1.432, generator_loss=29.72, generator_mel_loss=21.84, generator_kl_loss=1.466, generator_dur_loss=1.832, generator_adv_loss=2.046, generator_feat_match_loss=2.534, over 47.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.397, discriminator_fake_loss=1.341, generator_loss=28.65, generator_mel_loss=21.15, generator_kl_loss=1.281, generator_dur_loss=1.886, generator_adv_loss=1.9, generator_feat_match_loss=2.434, over 5583.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:15:25,877 INFO [train.py:919] (1/6) Start epoch 46
+2024-03-12 04:16:45,577 INFO [train.py:527] (1/6) Epoch 46, batch 20, global_batch_idx: 5600, batch size: 83, loss[discriminator_loss=2.769, discriminator_real_loss=1.477, discriminator_fake_loss=1.292, generator_loss=27.84, generator_mel_loss=20.59, generator_kl_loss=1.106, generator_dur_loss=1.96, generator_adv_loss=1.783, generator_feat_match_loss=2.399, over 83.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.409, discriminator_fake_loss=1.336, generator_loss=28.63, generator_mel_loss=21.13, generator_kl_loss=1.254, generator_dur_loss=1.872, generator_adv_loss=1.914, generator_feat_match_loss=2.458, over 1245.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:16:45,578 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 04:16:53,515 INFO [train.py:591] (1/6) Epoch 46, validation: discriminator_loss=2.71, discriminator_real_loss=1.41, discriminator_fake_loss=1.301, generator_loss=27.62, generator_mel_loss=20.81, generator_kl_loss=1.172, generator_dur_loss=1.926, generator_adv_loss=1.793, generator_feat_match_loss=1.917, over 100.00 samples.
+2024-03-12 04:16:53,515 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 04:19:10,761 INFO [train.py:527] (1/6) Epoch 46, batch 70, global_batch_idx: 5650, batch size: 72, loss[discriminator_loss=2.733, discriminator_real_loss=1.328, discriminator_fake_loss=1.405, generator_loss=28.96, generator_mel_loss=21.29, generator_kl_loss=1.41, generator_dur_loss=1.902, generator_adv_loss=1.914, generator_feat_match_loss=2.439, over 72.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.399, discriminator_fake_loss=1.338, generator_loss=28.68, generator_mel_loss=21.15, generator_kl_loss=1.278, generator_dur_loss=1.877, generator_adv_loss=1.908, generator_feat_match_loss=2.464, over 4195.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:21:29,336 INFO [train.py:527] (1/6) Epoch 46, batch 120, global_batch_idx: 5700, batch size: 96, loss[discriminator_loss=2.792, discriminator_real_loss=1.479, discriminator_fake_loss=1.313, generator_loss=26.49, generator_mel_loss=19.29, generator_kl_loss=1.33, generator_dur_loss=2.001, generator_adv_loss=1.733, generator_feat_match_loss=2.133, over 96.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.401, discriminator_fake_loss=1.335, generator_loss=28.59, generator_mel_loss=21.08, generator_kl_loss=1.287, generator_dur_loss=1.878, generator_adv_loss=1.897, generator_feat_match_loss=2.45, over 7089.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:21:39,476 INFO [train.py:919] (1/6) Start epoch 47
+2024-03-12 04:24:10,993 INFO [train.py:527] (1/6) Epoch 47, batch 46, global_batch_idx: 5750, batch size: 52, loss[discriminator_loss=2.819, discriminator_real_loss=1.522, discriminator_fake_loss=1.297, generator_loss=28.39, generator_mel_loss=20.99, generator_kl_loss=1.314, generator_dur_loss=1.717, generator_adv_loss=2.031, generator_feat_match_loss=2.328, over 52.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.414, discriminator_fake_loss=1.338, generator_loss=28.88, generator_mel_loss=21.21, generator_kl_loss=1.283, generator_dur_loss=1.856, generator_adv_loss=1.962, generator_feat_match_loss=2.566, over 2846.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:26:28,732 INFO [train.py:527] (1/6) Epoch 47, batch 96, global_batch_idx: 5800, batch size: 72, loss[discriminator_loss=2.674, discriminator_real_loss=1.456, discriminator_fake_loss=1.218, generator_loss=28.55, generator_mel_loss=20.74, generator_kl_loss=1.24, generator_dur_loss=1.896, generator_adv_loss=1.906, generator_feat_match_loss=2.774, over 72.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.407, discriminator_fake_loss=1.338, generator_loss=28.7, generator_mel_loss=21.1, generator_kl_loss=1.299, generator_dur_loss=1.854, generator_adv_loss=1.928, generator_feat_match_loss=2.516, over 5586.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:26:28,733 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 04:26:37,447 INFO [train.py:591] (1/6) Epoch 47, validation: discriminator_loss=2.724, discriminator_real_loss=1.397, discriminator_fake_loss=1.327, generator_loss=27.83, generator_mel_loss=20.92, generator_kl_loss=1.124, generator_dur_loss=1.896, generator_adv_loss=1.839, generator_feat_match_loss=2.052, over 100.00 samples.
+2024-03-12 04:26:37,448 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 04:27:55,012 INFO [train.py:919] (1/6) Start epoch 48
+2024-03-12 04:29:21,097 INFO [train.py:527] (1/6) Epoch 48, batch 22, global_batch_idx: 5850, batch size: 64, loss[discriminator_loss=2.71, discriminator_real_loss=1.377, discriminator_fake_loss=1.333, generator_loss=28.79, generator_mel_loss=21.07, generator_kl_loss=1.26, generator_dur_loss=1.878, generator_adv_loss=2.019, generator_feat_match_loss=2.561, over 64.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.443, discriminator_fake_loss=1.312, generator_loss=29.21, generator_mel_loss=21.19, generator_kl_loss=1.304, generator_dur_loss=1.857, generator_adv_loss=2.057, generator_feat_match_loss=2.798, over 1347.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:31:38,656 INFO [train.py:527] (1/6) Epoch 48, batch 72, global_batch_idx: 5900, batch size: 58, loss[discriminator_loss=2.782, discriminator_real_loss=1.464, discriminator_fake_loss=1.318, generator_loss=29.3, generator_mel_loss=21.57, generator_kl_loss=1.422, generator_dur_loss=1.814, generator_adv_loss=2.017, generator_feat_match_loss=2.473, over 58.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.433, discriminator_fake_loss=1.334, generator_loss=28.79, generator_mel_loss=21.1, generator_kl_loss=1.323, generator_dur_loss=1.85, generator_adv_loss=1.968, generator_feat_match_loss=2.553, over 4161.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:33:57,439 INFO [train.py:527] (1/6) Epoch 48, batch 122, global_batch_idx: 5950, batch size: 96, loss[discriminator_loss=2.748, discriminator_real_loss=1.294, discriminator_fake_loss=1.453, generator_loss=27.73, generator_mel_loss=20.55, generator_kl_loss=1.063, generator_dur_loss=1.976, generator_adv_loss=1.782, generator_feat_match_loss=2.358, over 96.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.422, discriminator_fake_loss=1.342, generator_loss=28.51, generator_mel_loss=20.96, generator_kl_loss=1.314, generator_dur_loss=1.848, generator_adv_loss=1.927, generator_feat_match_loss=2.455, over 7023.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:34:02,283 INFO [train.py:919] (1/6) Start epoch 49
+2024-03-12 04:36:36,718 INFO [train.py:527] (1/6) Epoch 49, batch 48, global_batch_idx: 6000, batch size: 14, loss[discriminator_loss=2.747, discriminator_real_loss=1.388, discriminator_fake_loss=1.359, generator_loss=28.94, generator_mel_loss=21.02, generator_kl_loss=1.743, generator_dur_loss=1.778, generator_adv_loss=2.065, generator_feat_match_loss=2.334, over 14.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.399, discriminator_fake_loss=1.353, generator_loss=28.48, generator_mel_loss=20.98, generator_kl_loss=1.321, generator_dur_loss=1.842, generator_adv_loss=1.885, generator_feat_match_loss=2.453, over 2724.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:36:36,720 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 04:36:44,772 INFO [train.py:591] (1/6) Epoch 49, validation: discriminator_loss=2.812, discriminator_real_loss=1.581, discriminator_fake_loss=1.231, generator_loss=27.72, generator_mel_loss=20.69, generator_kl_loss=1.054, generator_dur_loss=1.903, generator_adv_loss=2.012, generator_feat_match_loss=2.069, over 100.00 samples.
+2024-03-12 04:36:44,773 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 04:39:03,188 INFO [train.py:527] (1/6) Epoch 49, batch 98, global_batch_idx: 6050, batch size: 47, loss[discriminator_loss=2.708, discriminator_real_loss=1.324, discriminator_fake_loss=1.383, generator_loss=29.39, generator_mel_loss=21.71, generator_kl_loss=1.276, generator_dur_loss=1.782, generator_adv_loss=2.137, generator_feat_match_loss=2.487, over 47.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.396, discriminator_fake_loss=1.349, generator_loss=28.43, generator_mel_loss=20.91, generator_kl_loss=1.313, generator_dur_loss=1.851, generator_adv_loss=1.884, generator_feat_match_loss=2.471, over 5494.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:40:16,091 INFO [train.py:919] (1/6) Start epoch 50
+2024-03-12 04:41:46,334 INFO [train.py:527] (1/6) Epoch 50, batch 24, global_batch_idx: 6100, batch size: 48, loss[discriminator_loss=2.693, discriminator_real_loss=1.419, discriminator_fake_loss=1.274, generator_loss=28.94, generator_mel_loss=21.23, generator_kl_loss=1.34, generator_dur_loss=1.782, generator_adv_loss=1.932, generator_feat_match_loss=2.658, over 48.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.405, discriminator_fake_loss=1.345, generator_loss=28.55, generator_mel_loss=21.01, generator_kl_loss=1.352, generator_dur_loss=1.805, generator_adv_loss=1.9, generator_feat_match_loss=2.481, over 1255.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:44:05,953 INFO [train.py:527] (1/6) Epoch 50, batch 74, global_batch_idx: 6150, batch size: 44, loss[discriminator_loss=2.59, discriminator_real_loss=1.161, discriminator_fake_loss=1.429, generator_loss=28.84, generator_mel_loss=19.97, generator_kl_loss=1.546, generator_dur_loss=1.807, generator_adv_loss=2.306, generator_feat_match_loss=3.21, over 44.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.403, discriminator_fake_loss=1.353, generator_loss=28.38, generator_mel_loss=20.84, generator_kl_loss=1.315, generator_dur_loss=1.839, generator_adv_loss=1.909, generator_feat_match_loss=2.475, over 4170.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:46:24,204 INFO [train.py:919] (1/6) Start epoch 51
+2024-03-12 04:46:48,894 INFO [train.py:527] (1/6) Epoch 51, batch 0, global_batch_idx: 6200, batch size: 97, loss[discriminator_loss=2.718, discriminator_real_loss=1.274, discriminator_fake_loss=1.445, generator_loss=26.56, generator_mel_loss=19.16, generator_kl_loss=1.244, generator_dur_loss=1.976, generator_adv_loss=1.771, generator_feat_match_loss=2.406, over 97.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.274, discriminator_fake_loss=1.445, generator_loss=26.56, generator_mel_loss=19.16, generator_kl_loss=1.244, generator_dur_loss=1.976, generator_adv_loss=1.771, generator_feat_match_loss=2.406, over 97.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:46:48,897 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 04:46:56,728 INFO [train.py:591] (1/6) Epoch 51, validation: discriminator_loss=2.728, discriminator_real_loss=1.345, discriminator_fake_loss=1.383, generator_loss=27.59, generator_mel_loss=20.65, generator_kl_loss=1.177, generator_dur_loss=1.902, generator_adv_loss=1.782, generator_feat_match_loss=2.077, over 100.00 samples.
+2024-03-12 04:46:56,730 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 04:49:15,038 INFO [train.py:527] (1/6) Epoch 51, batch 50, global_batch_idx: 6250, batch size: 70, loss[discriminator_loss=2.731, discriminator_real_loss=1.355, discriminator_fake_loss=1.376, generator_loss=26.54, generator_mel_loss=19.22, generator_kl_loss=1.36, generator_dur_loss=1.872, generator_adv_loss=1.849, generator_feat_match_loss=2.238, over 70.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.4, discriminator_fake_loss=1.337, generator_loss=28.01, generator_mel_loss=20.53, generator_kl_loss=1.315, generator_dur_loss=1.849, generator_adv_loss=1.869, generator_feat_match_loss=2.45, over 3007.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:51:35,331 INFO [train.py:527] (1/6) Epoch 51, batch 100, global_batch_idx: 6300, batch size: 36, loss[discriminator_loss=2.783, discriminator_real_loss=1.363, discriminator_fake_loss=1.42, generator_loss=28.37, generator_mel_loss=21.04, generator_kl_loss=1.509, generator_dur_loss=1.737, generator_adv_loss=1.813, generator_feat_match_loss=2.265, over 36.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.414, discriminator_fake_loss=1.335, generator_loss=28.17, generator_mel_loss=20.65, generator_kl_loss=1.307, generator_dur_loss=1.833, generator_adv_loss=1.905, generator_feat_match_loss=2.478, over 5575.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:52:40,477 INFO [train.py:919] (1/6) Start epoch 52
+2024-03-12 04:54:18,862 INFO [train.py:527] (1/6) Epoch 52, batch 26, global_batch_idx: 6350, batch size: 56, loss[discriminator_loss=2.66, discriminator_real_loss=1.265, discriminator_fake_loss=1.394, generator_loss=28.22, generator_mel_loss=20.51, generator_kl_loss=1.25, generator_dur_loss=1.808, generator_adv_loss=2.013, generator_feat_match_loss=2.639, over 56.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.427, discriminator_fake_loss=1.346, generator_loss=28.64, generator_mel_loss=20.67, generator_kl_loss=1.291, generator_dur_loss=1.846, generator_adv_loss=2.033, generator_feat_match_loss=2.8, over 1540.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:56:38,033 INFO [train.py:527] (1/6) Epoch 52, batch 76, global_batch_idx: 6400, batch size: 50, loss[discriminator_loss=2.751, discriminator_real_loss=1.384, discriminator_fake_loss=1.367, generator_loss=28.18, generator_mel_loss=20.59, generator_kl_loss=1.375, generator_dur_loss=1.839, generator_adv_loss=1.978, generator_feat_match_loss=2.405, over 50.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.421, discriminator_fake_loss=1.344, generator_loss=28.33, generator_mel_loss=20.66, generator_kl_loss=1.304, generator_dur_loss=1.842, generator_adv_loss=1.965, generator_feat_match_loss=2.559, over 4410.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 04:56:38,034 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 04:56:46,043 INFO [train.py:591] (1/6) Epoch 52, validation: discriminator_loss=2.773, discriminator_real_loss=1.574, discriminator_fake_loss=1.199, generator_loss=26.39, generator_mel_loss=19.74, generator_kl_loss=1.16, generator_dur_loss=1.911, generator_adv_loss=1.953, generator_feat_match_loss=1.628, over 100.00 samples.
+2024-03-12 04:56:46,044 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 04:58:55,368 INFO [train.py:919] (1/6) Start epoch 53
+2024-03-12 04:59:25,784 INFO [train.py:527] (1/6) Epoch 53, batch 2, global_batch_idx: 6450, batch size: 52, loss[discriminator_loss=2.735, discriminator_real_loss=1.351, discriminator_fake_loss=1.384, generator_loss=28.6, generator_mel_loss=20.95, generator_kl_loss=1.248, generator_dur_loss=1.798, generator_adv_loss=2.022, generator_feat_match_loss=2.578, over 52.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.453, discriminator_fake_loss=1.305, generator_loss=29.04, generator_mel_loss=21.23, generator_kl_loss=1.374, generator_dur_loss=1.77, generator_adv_loss=1.968, generator_feat_match_loss=2.697, over 138.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:01:44,711 INFO [train.py:527] (1/6) Epoch 53, batch 52, global_batch_idx: 6500, batch size: 68, loss[discriminator_loss=2.751, discriminator_real_loss=1.449, discriminator_fake_loss=1.301, generator_loss=27.91, generator_mel_loss=20.44, generator_kl_loss=1.293, generator_dur_loss=1.879, generator_adv_loss=1.903, generator_feat_match_loss=2.398, over 68.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.4, discriminator_fake_loss=1.349, generator_loss=28.15, generator_mel_loss=20.64, generator_kl_loss=1.303, generator_dur_loss=1.833, generator_adv_loss=1.891, generator_feat_match_loss=2.477, over 2815.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:04:04,409 INFO [train.py:527] (1/6) Epoch 53, batch 102, global_batch_idx: 6550, batch size: 25, loss[discriminator_loss=2.74, discriminator_real_loss=1.422, discriminator_fake_loss=1.319, generator_loss=28.33, generator_mel_loss=20.76, generator_kl_loss=1.543, generator_dur_loss=1.647, generator_adv_loss=1.792, generator_feat_match_loss=2.583, over 25.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.397, discriminator_fake_loss=1.35, generator_loss=28.29, generator_mel_loss=20.74, generator_kl_loss=1.302, generator_dur_loss=1.836, generator_adv_loss=1.887, generator_feat_match_loss=2.518, over 5778.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:05:03,964 INFO [train.py:919] (1/6) Start epoch 54
+2024-03-12 05:06:44,482 INFO [train.py:527] (1/6) Epoch 54, batch 28, global_batch_idx: 6600, batch size: 77, loss[discriminator_loss=2.686, discriminator_real_loss=1.245, discriminator_fake_loss=1.441, generator_loss=28.57, generator_mel_loss=20.66, generator_kl_loss=1.293, generator_dur_loss=1.944, generator_adv_loss=1.897, generator_feat_match_loss=2.773, over 77.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.39, discriminator_fake_loss=1.358, generator_loss=28.21, generator_mel_loss=20.65, generator_kl_loss=1.317, generator_dur_loss=1.832, generator_adv_loss=1.881, generator_feat_match_loss=2.533, over 1542.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:06:44,483 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 05:06:52,266 INFO [train.py:591] (1/6) Epoch 54, validation: discriminator_loss=2.733, discriminator_real_loss=1.331, discriminator_fake_loss=1.402, generator_loss=27.36, generator_mel_loss=20.51, generator_kl_loss=1.199, generator_dur_loss=1.892, generator_adv_loss=1.785, generator_feat_match_loss=1.97, over 100.00 samples.
+2024-03-12 05:06:52,267 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 05:09:08,145 INFO [train.py:527] (1/6) Epoch 54, batch 78, global_batch_idx: 6650, batch size: 58, loss[discriminator_loss=2.744, discriminator_real_loss=1.393, discriminator_fake_loss=1.351, generator_loss=27.89, generator_mel_loss=20.28, generator_kl_loss=1.415, generator_dur_loss=1.839, generator_adv_loss=1.981, generator_feat_match_loss=2.38, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.399, discriminator_fake_loss=1.344, generator_loss=28.19, generator_mel_loss=20.56, generator_kl_loss=1.335, generator_dur_loss=1.832, generator_adv_loss=1.899, generator_feat_match_loss=2.563, over 4140.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:11:16,512 INFO [train.py:919] (1/6) Start epoch 55
+2024-03-12 05:11:50,264 INFO [train.py:527] (1/6) Epoch 55, batch 4, global_batch_idx: 6700, batch size: 64, loss[discriminator_loss=2.818, discriminator_real_loss=1.586, discriminator_fake_loss=1.231, generator_loss=27.17, generator_mel_loss=20.12, generator_kl_loss=1.193, generator_dur_loss=1.846, generator_adv_loss=1.753, generator_feat_match_loss=2.259, over 64.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.44, discriminator_fake_loss=1.324, generator_loss=28.42, generator_mel_loss=20.8, generator_kl_loss=1.348, generator_dur_loss=1.813, generator_adv_loss=1.957, generator_feat_match_loss=2.498, over 254.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:14:11,427 INFO [train.py:527] (1/6) Epoch 55, batch 54, global_batch_idx: 6750, batch size: 42, loss[discriminator_loss=2.836, discriminator_real_loss=1.399, discriminator_fake_loss=1.437, generator_loss=28.71, generator_mel_loss=21.28, generator_kl_loss=1.358, generator_dur_loss=1.759, generator_adv_loss=1.879, generator_feat_match_loss=2.441, over 42.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.392, discriminator_fake_loss=1.353, generator_loss=28.57, generator_mel_loss=20.84, generator_kl_loss=1.343, generator_dur_loss=1.824, generator_adv_loss=1.936, generator_feat_match_loss=2.629, over 2762.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:16:32,136 INFO [train.py:527] (1/6) Epoch 55, batch 104, global_batch_idx: 6800, batch size: 47, loss[discriminator_loss=2.711, discriminator_real_loss=1.261, discriminator_fake_loss=1.45, generator_loss=29.09, generator_mel_loss=21.25, generator_kl_loss=1.459, generator_dur_loss=1.794, generator_adv_loss=1.824, generator_feat_match_loss=2.77, over 47.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.402, discriminator_fake_loss=1.345, generator_loss=28.36, generator_mel_loss=20.72, generator_kl_loss=1.318, generator_dur_loss=1.824, generator_adv_loss=1.916, generator_feat_match_loss=2.587, over 5536.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:16:32,138 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 05:16:41,182 INFO [train.py:591] (1/6) Epoch 55, validation: discriminator_loss=2.695, discriminator_real_loss=1.381, discriminator_fake_loss=1.314, generator_loss=28.01, generator_mel_loss=20.84, generator_kl_loss=1.443, generator_dur_loss=1.868, generator_adv_loss=1.822, generator_feat_match_loss=2.032, over 100.00 samples.
+2024-03-12 05:16:41,183 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 05:17:33,154 INFO [train.py:919] (1/6) Start epoch 56
+2024-03-12 05:19:21,129 INFO [train.py:527] (1/6) Epoch 56, batch 30, global_batch_idx: 6850, batch size: 42, loss[discriminator_loss=2.792, discriminator_real_loss=1.419, discriminator_fake_loss=1.373, generator_loss=27.85, generator_mel_loss=20.45, generator_kl_loss=1.44, generator_dur_loss=1.828, generator_adv_loss=1.904, generator_feat_match_loss=2.235, over 42.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.4, discriminator_fake_loss=1.344, generator_loss=28.09, generator_mel_loss=20.48, generator_kl_loss=1.327, generator_dur_loss=1.863, generator_adv_loss=1.89, generator_feat_match_loss=2.531, over 1839.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:21:42,325 INFO [train.py:527] (1/6) Epoch 56, batch 80, global_batch_idx: 6900, batch size: 72, loss[discriminator_loss=2.749, discriminator_real_loss=1.464, discriminator_fake_loss=1.285, generator_loss=27.62, generator_mel_loss=20.4, generator_kl_loss=1.267, generator_dur_loss=1.87, generator_adv_loss=1.746, generator_feat_match_loss=2.342, over 72.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.403, discriminator_fake_loss=1.344, generator_loss=28.08, generator_mel_loss=20.54, generator_kl_loss=1.301, generator_dur_loss=1.858, generator_adv_loss=1.877, generator_feat_match_loss=2.503, over 4818.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:23:44,985 INFO [train.py:919] (1/6) Start epoch 57
+2024-03-12 05:24:26,581 INFO [train.py:527] (1/6) Epoch 57, batch 6, global_batch_idx: 6950, batch size: 56, loss[discriminator_loss=2.721, discriminator_real_loss=1.291, discriminator_fake_loss=1.43, generator_loss=28.28, generator_mel_loss=20.53, generator_kl_loss=1.494, generator_dur_loss=1.822, generator_adv_loss=1.9, generator_feat_match_loss=2.538, over 56.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.418, discriminator_fake_loss=1.333, generator_loss=28.41, generator_mel_loss=20.68, generator_kl_loss=1.35, generator_dur_loss=1.886, generator_adv_loss=1.899, generator_feat_match_loss=2.597, over 489.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:26:47,969 INFO [train.py:527] (1/6) Epoch 57, batch 56, global_batch_idx: 7000, batch size: 74, loss[discriminator_loss=2.871, discriminator_real_loss=1.408, discriminator_fake_loss=1.462, generator_loss=26.66, generator_mel_loss=19.55, generator_kl_loss=1.338, generator_dur_loss=1.913, generator_adv_loss=1.821, generator_feat_match_loss=2.044, over 74.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.413, discriminator_fake_loss=1.359, generator_loss=28.09, generator_mel_loss=20.48, generator_kl_loss=1.325, generator_dur_loss=1.845, generator_adv_loss=1.904, generator_feat_match_loss=2.536, over 3169.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:26:47,971 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 05:26:56,056 INFO [train.py:591] (1/6) Epoch 57, validation: discriminator_loss=2.915, discriminator_real_loss=1.59, discriminator_fake_loss=1.324, generator_loss=27.21, generator_mel_loss=20.49, generator_kl_loss=1.026, generator_dur_loss=1.886, generator_adv_loss=1.882, generator_feat_match_loss=1.929, over 100.00 samples.
+2024-03-12 05:26:56,057 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 05:29:16,472 INFO [train.py:527] (1/6) Epoch 57, batch 106, global_batch_idx: 7050, batch size: 83, loss[discriminator_loss=2.759, discriminator_real_loss=1.349, discriminator_fake_loss=1.409, generator_loss=27.86, generator_mel_loss=20.12, generator_kl_loss=1.202, generator_dur_loss=1.902, generator_adv_loss=1.973, generator_feat_match_loss=2.659, over 83.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.41, discriminator_fake_loss=1.352, generator_loss=28.03, generator_mel_loss=20.45, generator_kl_loss=1.305, generator_dur_loss=1.843, generator_adv_loss=1.905, generator_feat_match_loss=2.526, over 6150.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:30:06,488 INFO [train.py:919] (1/6) Start epoch 58
+2024-03-12 05:32:02,583 INFO [train.py:527] (1/6) Epoch 58, batch 32, global_batch_idx: 7100, batch size: 44, loss[discriminator_loss=2.733, discriminator_real_loss=1.457, discriminator_fake_loss=1.276, generator_loss=27.82, generator_mel_loss=20.55, generator_kl_loss=1.351, generator_dur_loss=1.729, generator_adv_loss=1.896, generator_feat_match_loss=2.295, over 44.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.4, discriminator_fake_loss=1.345, generator_loss=28.06, generator_mel_loss=20.42, generator_kl_loss=1.371, generator_dur_loss=1.822, generator_adv_loss=1.908, generator_feat_match_loss=2.543, over 1761.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:34:24,749 INFO [train.py:527] (1/6) Epoch 58, batch 82, global_batch_idx: 7150, batch size: 42, loss[discriminator_loss=2.669, discriminator_real_loss=1.433, discriminator_fake_loss=1.236, generator_loss=28.87, generator_mel_loss=20.94, generator_kl_loss=1.452, generator_dur_loss=1.724, generator_adv_loss=1.888, generator_feat_match_loss=2.861, over 42.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.412, discriminator_fake_loss=1.346, generator_loss=28.13, generator_mel_loss=20.55, generator_kl_loss=1.337, generator_dur_loss=1.831, generator_adv_loss=1.893, generator_feat_match_loss=2.518, over 4596.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:36:22,916 INFO [train.py:919] (1/6) Start epoch 59
+2024-03-12 05:37:09,845 INFO [train.py:527] (1/6) Epoch 59, batch 8, global_batch_idx: 7200, batch size: 59, loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=28.37, generator_mel_loss=20.11, generator_kl_loss=1.282, generator_dur_loss=1.8, generator_adv_loss=2.146, generator_feat_match_loss=3.039, over 59.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.55, generator_mel_loss=19.82, generator_kl_loss=1.316, generator_dur_loss=1.835, generator_adv_loss=1.963, generator_feat_match_loss=2.618, over 562.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:37:09,848 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 05:37:17,481 INFO [train.py:591] (1/6) Epoch 59, validation: discriminator_loss=2.651, discriminator_real_loss=1.434, discriminator_fake_loss=1.216, generator_loss=28.18, generator_mel_loss=20.64, generator_kl_loss=1.036, generator_dur_loss=1.874, generator_adv_loss=2.041, generator_feat_match_loss=2.591, over 100.00 samples.
+2024-03-12 05:37:17,484 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 05:39:37,540 INFO [train.py:527] (1/6) Epoch 59, batch 58, global_batch_idx: 7250, batch size: 15, loss[discriminator_loss=2.717, discriminator_real_loss=1.47, discriminator_fake_loss=1.247, generator_loss=28.44, generator_mel_loss=20.54, generator_kl_loss=1.769, generator_dur_loss=1.736, generator_adv_loss=1.984, generator_feat_match_loss=2.413, over 15.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.345, generator_loss=27.99, generator_mel_loss=20.32, generator_kl_loss=1.329, generator_dur_loss=1.834, generator_adv_loss=1.926, generator_feat_match_loss=2.573, over 3481.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:42:00,663 INFO [train.py:527] (1/6) Epoch 59, batch 108, global_batch_idx: 7300, batch size: 68, loss[discriminator_loss=2.706, discriminator_real_loss=1.358, discriminator_fake_loss=1.348, generator_loss=28.02, generator_mel_loss=20.55, generator_kl_loss=1.303, generator_dur_loss=1.836, generator_adv_loss=1.743, generator_feat_match_loss=2.582, over 68.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.4, discriminator_fake_loss=1.344, generator_loss=27.99, generator_mel_loss=20.37, generator_kl_loss=1.326, generator_dur_loss=1.826, generator_adv_loss=1.903, generator_feat_match_loss=2.564, over 6310.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:42:44,821 INFO [train.py:919] (1/6) Start epoch 60
+2024-03-12 05:44:45,996 INFO [train.py:527] (1/6) Epoch 60, batch 34, global_batch_idx: 7350, batch size: 49, loss[discriminator_loss=2.772, discriminator_real_loss=1.409, discriminator_fake_loss=1.363, generator_loss=28.55, generator_mel_loss=20.99, generator_kl_loss=1.48, generator_dur_loss=1.794, generator_adv_loss=1.747, generator_feat_match_loss=2.534, over 49.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.383, discriminator_fake_loss=1.363, generator_loss=27.93, generator_mel_loss=20.35, generator_kl_loss=1.328, generator_dur_loss=1.856, generator_adv_loss=1.864, generator_feat_match_loss=2.528, over 2211.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:47:06,748 INFO [train.py:527] (1/6) Epoch 60, batch 84, global_batch_idx: 7400, batch size: 68, loss[discriminator_loss=2.797, discriminator_real_loss=1.47, discriminator_fake_loss=1.328, generator_loss=28.22, generator_mel_loss=20.82, generator_kl_loss=1.183, generator_dur_loss=1.873, generator_adv_loss=1.718, generator_feat_match_loss=2.626, over 68.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.398, discriminator_fake_loss=1.358, generator_loss=27.96, generator_mel_loss=20.32, generator_kl_loss=1.325, generator_dur_loss=1.841, generator_adv_loss=1.903, generator_feat_match_loss=2.566, over 5144.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:47:06,750 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 05:47:15,707 INFO [train.py:591] (1/6) Epoch 60, validation: discriminator_loss=2.809, discriminator_real_loss=1.414, discriminator_fake_loss=1.395, generator_loss=25.96, generator_mel_loss=19.55, generator_kl_loss=1.112, generator_dur_loss=1.884, generator_adv_loss=1.7, generator_feat_match_loss=1.717, over 100.00 samples.
+2024-03-12 05:47:15,708 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 05:49:05,540 INFO [train.py:919] (1/6) Start epoch 61
+2024-03-12 05:50:53,552 INFO [train.py:527] (1/6) Epoch 61, batch 10, global_batch_idx: 7450, batch size: 72, loss[discriminator_loss=2.779, discriminator_real_loss=1.392, discriminator_fake_loss=1.387, generator_loss=28.2, generator_mel_loss=20.61, generator_kl_loss=1.31, generator_dur_loss=1.815, generator_adv_loss=1.807, generator_feat_match_loss=2.664, over 72.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.429, discriminator_fake_loss=1.333, generator_loss=27.86, generator_mel_loss=20.28, generator_kl_loss=1.373, generator_dur_loss=1.798, generator_adv_loss=1.893, generator_feat_match_loss=2.514, over 561.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:53:16,106 INFO [train.py:527] (1/6) Epoch 61, batch 60, global_batch_idx: 7500, batch size: 64, loss[discriminator_loss=2.683, discriminator_real_loss=1.283, discriminator_fake_loss=1.399, generator_loss=28.47, generator_mel_loss=20.67, generator_kl_loss=1.256, generator_dur_loss=1.83, generator_adv_loss=1.798, generator_feat_match_loss=2.92, over 64.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.401, discriminator_fake_loss=1.35, generator_loss=27.85, generator_mel_loss=20.21, generator_kl_loss=1.323, generator_dur_loss=1.827, generator_adv_loss=1.909, generator_feat_match_loss=2.581, over 3526.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:55:36,984 INFO [train.py:527] (1/6) Epoch 61, batch 110, global_batch_idx: 7550, batch size: 62, loss[discriminator_loss=2.725, discriminator_real_loss=1.437, discriminator_fake_loss=1.288, generator_loss=28.45, generator_mel_loss=21.02, generator_kl_loss=1.328, generator_dur_loss=1.807, generator_adv_loss=1.792, generator_feat_match_loss=2.507, over 62.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.405, discriminator_fake_loss=1.341, generator_loss=27.89, generator_mel_loss=20.27, generator_kl_loss=1.325, generator_dur_loss=1.824, generator_adv_loss=1.902, generator_feat_match_loss=2.571, over 6227.00 samples.], cur_lr_g: 1.99e-04, cur_lr_d: 1.99e-04, 
+2024-03-12 05:56:16,295 INFO [train.py:919] (1/6) Start epoch 62
+2024-03-12 05:58:26,277 INFO [train.py:527] (1/6) Epoch 62, batch 36, global_batch_idx: 7600, batch size: 25, loss[discriminator_loss=2.775, discriminator_real_loss=1.515, discriminator_fake_loss=1.26, generator_loss=28.82, generator_mel_loss=20.7, generator_kl_loss=1.725, generator_dur_loss=1.685, generator_adv_loss=1.86, generator_feat_match_loss=2.85, over 25.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.389, discriminator_fake_loss=1.363, generator_loss=28.02, generator_mel_loss=20.35, generator_kl_loss=1.319, generator_dur_loss=1.827, generator_adv_loss=1.886, generator_feat_match_loss=2.637, over 2140.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 05:58:26,278 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 05:58:34,412 INFO [train.py:591] (1/6) Epoch 62, validation: discriminator_loss=2.777, discriminator_real_loss=1.434, discriminator_fake_loss=1.344, generator_loss=26.63, generator_mel_loss=20.06, generator_kl_loss=1.08, generator_dur_loss=1.882, generator_adv_loss=1.807, generator_feat_match_loss=1.799, over 100.00 samples.
+2024-03-12 05:58:34,413 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 06:00:56,843 INFO [train.py:527] (1/6) Epoch 62, batch 86, global_batch_idx: 7650, batch size: 39, loss[discriminator_loss=2.796, discriminator_real_loss=1.173, discriminator_fake_loss=1.622, generator_loss=29.07, generator_mel_loss=21.42, generator_kl_loss=1.428, generator_dur_loss=1.785, generator_adv_loss=1.686, generator_feat_match_loss=2.752, over 39.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.403, discriminator_fake_loss=1.359, generator_loss=28.03, generator_mel_loss=20.34, generator_kl_loss=1.322, generator_dur_loss=1.823, generator_adv_loss=1.905, generator_feat_match_loss=2.641, over 4805.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:02:41,308 INFO [train.py:919] (1/6) Start epoch 63
+2024-03-12 06:03:38,511 INFO [train.py:527] (1/6) Epoch 63, batch 12, global_batch_idx: 7700, batch size: 47, loss[discriminator_loss=2.842, discriminator_real_loss=1.353, discriminator_fake_loss=1.489, generator_loss=28.73, generator_mel_loss=20.41, generator_kl_loss=1.469, generator_dur_loss=1.779, generator_adv_loss=2.433, generator_feat_match_loss=2.646, over 47.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.382, discriminator_fake_loss=1.35, generator_loss=28.01, generator_mel_loss=20.25, generator_kl_loss=1.341, generator_dur_loss=1.832, generator_adv_loss=1.899, generator_feat_match_loss=2.681, over 803.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:05:54,394 INFO [train.py:527] (1/6) Epoch 63, batch 62, global_batch_idx: 7750, batch size: 61, loss[discriminator_loss=2.739, discriminator_real_loss=1.399, discriminator_fake_loss=1.34, generator_loss=28.33, generator_mel_loss=20.63, generator_kl_loss=1.215, generator_dur_loss=1.806, generator_adv_loss=1.928, generator_feat_match_loss=2.752, over 61.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.396, discriminator_fake_loss=1.356, generator_loss=28.06, generator_mel_loss=20.39, generator_kl_loss=1.346, generator_dur_loss=1.792, generator_adv_loss=1.889, generator_feat_match_loss=2.643, over 3395.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:08:16,503 INFO [train.py:527] (1/6) Epoch 63, batch 112, global_batch_idx: 7800, batch size: 31, loss[discriminator_loss=2.778, discriminator_real_loss=1.558, discriminator_fake_loss=1.22, generator_loss=28.55, generator_mel_loss=20.97, generator_kl_loss=1.363, generator_dur_loss=1.747, generator_adv_loss=1.832, generator_feat_match_loss=2.645, over 31.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.399, discriminator_fake_loss=1.359, generator_loss=27.98, generator_mel_loss=20.34, generator_kl_loss=1.334, generator_dur_loss=1.807, generator_adv_loss=1.877, generator_feat_match_loss=2.625, over 6247.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:08:16,505 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 06:08:25,395 INFO [train.py:591] (1/6) Epoch 63, validation: discriminator_loss=2.713, discriminator_real_loss=1.429, discriminator_fake_loss=1.284, generator_loss=26.21, generator_mel_loss=19.45, generator_kl_loss=1.161, generator_dur_loss=1.878, generator_adv_loss=1.771, generator_feat_match_loss=1.95, over 100.00 samples.
+2024-03-12 06:08:25,395 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 06:08:58,151 INFO [train.py:919] (1/6) Start epoch 64
+2024-03-12 06:11:06,461 INFO [train.py:527] (1/6) Epoch 64, batch 38, global_batch_idx: 7850, batch size: 58, loss[discriminator_loss=2.804, discriminator_real_loss=1.399, discriminator_fake_loss=1.405, generator_loss=27.11, generator_mel_loss=19.88, generator_kl_loss=1.334, generator_dur_loss=1.795, generator_adv_loss=1.818, generator_feat_match_loss=2.278, over 58.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.412, discriminator_fake_loss=1.331, generator_loss=27.8, generator_mel_loss=20.16, generator_kl_loss=1.324, generator_dur_loss=1.822, generator_adv_loss=1.886, generator_feat_match_loss=2.608, over 2276.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:13:29,490 INFO [train.py:527] (1/6) Epoch 64, batch 88, global_batch_idx: 7900, batch size: 62, loss[discriminator_loss=2.824, discriminator_real_loss=1.45, discriminator_fake_loss=1.374, generator_loss=29.15, generator_mel_loss=20.76, generator_kl_loss=1.442, generator_dur_loss=1.805, generator_adv_loss=2.132, generator_feat_match_loss=3.007, over 62.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.402, discriminator_fake_loss=1.339, generator_loss=27.88, generator_mel_loss=20.19, generator_kl_loss=1.317, generator_dur_loss=1.823, generator_adv_loss=1.908, generator_feat_match_loss=2.634, over 5191.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:15:03,356 INFO [train.py:919] (1/6) Start epoch 65
+2024-03-12 06:16:05,142 INFO [train.py:527] (1/6) Epoch 65, batch 14, global_batch_idx: 7950, batch size: 50, loss[discriminator_loss=2.82, discriminator_real_loss=1.687, discriminator_fake_loss=1.134, generator_loss=29.1, generator_mel_loss=21.45, generator_kl_loss=1.404, generator_dur_loss=1.748, generator_adv_loss=1.539, generator_feat_match_loss=2.958, over 50.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.396, discriminator_fake_loss=1.325, generator_loss=28.17, generator_mel_loss=20.43, generator_kl_loss=1.316, generator_dur_loss=1.813, generator_adv_loss=1.914, generator_feat_match_loss=2.694, over 851.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:18:22,621 INFO [train.py:527] (1/6) Epoch 65, batch 64, global_batch_idx: 8000, batch size: 58, loss[discriminator_loss=2.655, discriminator_real_loss=1.392, discriminator_fake_loss=1.264, generator_loss=27.77, generator_mel_loss=19.69, generator_kl_loss=1.266, generator_dur_loss=1.817, generator_adv_loss=2.025, generator_feat_match_loss=2.979, over 58.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.99, generator_mel_loss=20.24, generator_kl_loss=1.322, generator_dur_loss=1.822, generator_adv_loss=1.926, generator_feat_match_loss=2.683, over 3651.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:18:22,622 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 06:18:31,351 INFO [train.py:591] (1/6) Epoch 65, validation: discriminator_loss=2.641, discriminator_real_loss=1.246, discriminator_fake_loss=1.395, generator_loss=28.02, generator_mel_loss=20.6, generator_kl_loss=1.285, generator_dur_loss=1.878, generator_adv_loss=1.843, generator_feat_match_loss=2.41, over 100.00 samples.
+2024-03-12 06:18:31,352 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 06:20:51,803 INFO [train.py:527] (1/6) Epoch 65, batch 114, global_batch_idx: 8050, batch size: 77, loss[discriminator_loss=2.886, discriminator_real_loss=1.563, discriminator_fake_loss=1.323, generator_loss=27.35, generator_mel_loss=19.66, generator_kl_loss=1.218, generator_dur_loss=1.919, generator_adv_loss=1.866, generator_feat_match_loss=2.688, over 77.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.401, discriminator_fake_loss=1.348, generator_loss=27.9, generator_mel_loss=20.16, generator_kl_loss=1.326, generator_dur_loss=1.81, generator_adv_loss=1.936, generator_feat_match_loss=2.674, over 6312.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:21:18,634 INFO [train.py:919] (1/6) Start epoch 66
+2024-03-12 06:23:34,279 INFO [train.py:527] (1/6) Epoch 66, batch 40, global_batch_idx: 8100, batch size: 53, loss[discriminator_loss=2.64, discriminator_real_loss=1.367, discriminator_fake_loss=1.273, generator_loss=29.1, generator_mel_loss=21.01, generator_kl_loss=1.439, generator_dur_loss=1.747, generator_adv_loss=1.797, generator_feat_match_loss=3.105, over 53.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.404, discriminator_fake_loss=1.342, generator_loss=27.8, generator_mel_loss=20.16, generator_kl_loss=1.343, generator_dur_loss=1.807, generator_adv_loss=1.877, generator_feat_match_loss=2.614, over 2221.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:25:54,620 INFO [train.py:527] (1/6) Epoch 66, batch 90, global_batch_idx: 8150, batch size: 70, loss[discriminator_loss=2.653, discriminator_real_loss=1.18, discriminator_fake_loss=1.474, generator_loss=28.12, generator_mel_loss=20.07, generator_kl_loss=1.283, generator_dur_loss=1.825, generator_adv_loss=2.085, generator_feat_match_loss=2.862, over 70.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.4, discriminator_fake_loss=1.349, generator_loss=27.9, generator_mel_loss=20.2, generator_kl_loss=1.338, generator_dur_loss=1.813, generator_adv_loss=1.894, generator_feat_match_loss=2.65, over 5152.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:27:27,276 INFO [train.py:919] (1/6) Start epoch 67
+2024-03-12 06:28:32,182 INFO [train.py:527] (1/6) Epoch 67, batch 16, global_batch_idx: 8200, batch size: 25, loss[discriminator_loss=2.76, discriminator_real_loss=1.427, discriminator_fake_loss=1.334, generator_loss=29.15, generator_mel_loss=21.24, generator_kl_loss=1.459, generator_dur_loss=1.627, generator_adv_loss=2.089, generator_feat_match_loss=2.735, over 25.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.408, discriminator_fake_loss=1.338, generator_loss=27.92, generator_mel_loss=20.2, generator_kl_loss=1.329, generator_dur_loss=1.84, generator_adv_loss=1.918, generator_feat_match_loss=2.628, over 967.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:28:32,183 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 06:28:40,208 INFO [train.py:591] (1/6) Epoch 67, validation: discriminator_loss=2.852, discriminator_real_loss=1.627, discriminator_fake_loss=1.225, generator_loss=27.61, generator_mel_loss=20.41, generator_kl_loss=1.213, generator_dur_loss=1.846, generator_adv_loss=2.016, generator_feat_match_loss=2.123, over 100.00 samples.
+2024-03-12 06:28:40,209 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 06:30:59,887 INFO [train.py:527] (1/6) Epoch 67, batch 66, global_batch_idx: 8250, batch size: 72, loss[discriminator_loss=2.777, discriminator_real_loss=1.593, discriminator_fake_loss=1.184, generator_loss=27.17, generator_mel_loss=19.66, generator_kl_loss=1.245, generator_dur_loss=1.868, generator_adv_loss=1.771, generator_feat_match_loss=2.622, over 72.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.405, discriminator_fake_loss=1.347, generator_loss=27.81, generator_mel_loss=20.14, generator_kl_loss=1.335, generator_dur_loss=1.818, generator_adv_loss=1.887, generator_feat_match_loss=2.628, over 3873.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:33:19,381 INFO [train.py:527] (1/6) Epoch 67, batch 116, global_batch_idx: 8300, batch size: 39, loss[discriminator_loss=2.815, discriminator_real_loss=1.363, discriminator_fake_loss=1.452, generator_loss=29.23, generator_mel_loss=21.09, generator_kl_loss=1.352, generator_dur_loss=1.761, generator_adv_loss=2.238, generator_feat_match_loss=2.791, over 39.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.404, discriminator_fake_loss=1.357, generator_loss=27.74, generator_mel_loss=20.09, generator_kl_loss=1.336, generator_dur_loss=1.823, generator_adv_loss=1.881, generator_feat_match_loss=2.611, over 6900.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:33:38,701 INFO [train.py:919] (1/6) Start epoch 68
+2024-03-12 06:35:59,109 INFO [train.py:527] (1/6) Epoch 68, batch 42, global_batch_idx: 8350, batch size: 61, loss[discriminator_loss=2.697, discriminator_real_loss=1.245, discriminator_fake_loss=1.452, generator_loss=27.36, generator_mel_loss=19.79, generator_kl_loss=1.361, generator_dur_loss=1.76, generator_adv_loss=1.975, generator_feat_match_loss=2.476, over 61.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.404, discriminator_fake_loss=1.343, generator_loss=27.76, generator_mel_loss=19.98, generator_kl_loss=1.341, generator_dur_loss=1.82, generator_adv_loss=1.95, generator_feat_match_loss=2.666, over 2587.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:38:17,959 INFO [train.py:527] (1/6) Epoch 68, batch 92, global_batch_idx: 8400, batch size: 25, loss[discriminator_loss=2.724, discriminator_real_loss=1.232, discriminator_fake_loss=1.492, generator_loss=30, generator_mel_loss=21.33, generator_kl_loss=1.548, generator_dur_loss=1.668, generator_adv_loss=2.292, generator_feat_match_loss=3.164, over 25.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.406, discriminator_fake_loss=1.336, generator_loss=27.69, generator_mel_loss=19.97, generator_kl_loss=1.334, generator_dur_loss=1.818, generator_adv_loss=1.916, generator_feat_match_loss=2.652, over 5497.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:38:17,960 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 06:38:26,713 INFO [train.py:591] (1/6) Epoch 68, validation: discriminator_loss=2.963, discriminator_real_loss=1.622, discriminator_fake_loss=1.34, generator_loss=27.87, generator_mel_loss=20.22, generator_kl_loss=1.24, generator_dur_loss=1.867, generator_adv_loss=2.181, generator_feat_match_loss=2.365, over 100.00 samples.
+2024-03-12 06:38:26,713 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 06:39:52,007 INFO [train.py:919] (1/6) Start epoch 69
+2024-03-12 06:41:07,161 INFO [train.py:527] (1/6) Epoch 69, batch 18, global_batch_idx: 8450, batch size: 47, loss[discriminator_loss=2.737, discriminator_real_loss=1.417, discriminator_fake_loss=1.321, generator_loss=27.82, generator_mel_loss=19.96, generator_kl_loss=1.318, generator_dur_loss=1.753, generator_adv_loss=1.899, generator_feat_match_loss=2.891, over 47.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.378, discriminator_fake_loss=1.361, generator_loss=28.19, generator_mel_loss=20.33, generator_kl_loss=1.313, generator_dur_loss=1.836, generator_adv_loss=1.95, generator_feat_match_loss=2.757, over 1174.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:43:24,061 INFO [train.py:527] (1/6) Epoch 69, batch 68, global_batch_idx: 8500, batch size: 80, loss[discriminator_loss=2.745, discriminator_real_loss=1.414, discriminator_fake_loss=1.332, generator_loss=27.53, generator_mel_loss=20.02, generator_kl_loss=1.309, generator_dur_loss=1.889, generator_adv_loss=1.927, generator_feat_match_loss=2.386, over 80.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.394, discriminator_fake_loss=1.346, generator_loss=27.86, generator_mel_loss=20.12, generator_kl_loss=1.363, generator_dur_loss=1.794, generator_adv_loss=1.918, generator_feat_match_loss=2.666, over 3758.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:45:42,046 INFO [train.py:527] (1/6) Epoch 69, batch 118, global_batch_idx: 8550, batch size: 31, loss[discriminator_loss=2.752, discriminator_real_loss=1.45, discriminator_fake_loss=1.303, generator_loss=26.41, generator_mel_loss=19.16, generator_kl_loss=1.336, generator_dur_loss=1.71, generator_adv_loss=1.973, generator_feat_match_loss=2.23, over 31.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.398, discriminator_fake_loss=1.352, generator_loss=27.76, generator_mel_loss=20.07, generator_kl_loss=1.35, generator_dur_loss=1.795, generator_adv_loss=1.901, generator_feat_match_loss=2.636, over 6683.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:45:59,581 INFO [train.py:919] (1/6) Start epoch 70
+2024-03-12 06:48:24,327 INFO [train.py:527] (1/6) Epoch 70, batch 44, global_batch_idx: 8600, batch size: 56, loss[discriminator_loss=2.812, discriminator_real_loss=1.535, discriminator_fake_loss=1.277, generator_loss=28.57, generator_mel_loss=20.96, generator_kl_loss=1.248, generator_dur_loss=1.747, generator_adv_loss=1.856, generator_feat_match_loss=2.758, over 56.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.429, discriminator_fake_loss=1.338, generator_loss=28.15, generator_mel_loss=20.1, generator_kl_loss=1.329, generator_dur_loss=1.807, generator_adv_loss=2.039, generator_feat_match_loss=2.871, over 2549.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:48:24,328 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 06:48:32,172 INFO [train.py:591] (1/6) Epoch 70, validation: discriminator_loss=2.833, discriminator_real_loss=1.384, discriminator_fake_loss=1.449, generator_loss=27.25, generator_mel_loss=20.62, generator_kl_loss=1.145, generator_dur_loss=1.861, generator_adv_loss=1.695, generator_feat_match_loss=1.926, over 100.00 samples.
+2024-03-12 06:48:32,173 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 06:50:51,329 INFO [train.py:527] (1/6) Epoch 70, batch 94, global_batch_idx: 8650, batch size: 47, loss[discriminator_loss=2.711, discriminator_real_loss=1.421, discriminator_fake_loss=1.289, generator_loss=27.23, generator_mel_loss=19.55, generator_kl_loss=1.463, generator_dur_loss=1.689, generator_adv_loss=1.878, generator_feat_match_loss=2.652, over 47.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.409, discriminator_fake_loss=1.341, generator_loss=27.84, generator_mel_loss=20, generator_kl_loss=1.337, generator_dur_loss=1.815, generator_adv_loss=1.96, generator_feat_match_loss=2.721, over 5563.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:52:11,538 INFO [train.py:919] (1/6) Start epoch 71
+2024-03-12 06:53:31,210 INFO [train.py:527] (1/6) Epoch 71, batch 20, global_batch_idx: 8700, batch size: 64, loss[discriminator_loss=2.756, discriminator_real_loss=1.482, discriminator_fake_loss=1.273, generator_loss=27.13, generator_mel_loss=19.68, generator_kl_loss=1.298, generator_dur_loss=1.776, generator_adv_loss=1.756, generator_feat_match_loss=2.619, over 64.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.417, discriminator_fake_loss=1.318, generator_loss=27.52, generator_mel_loss=19.82, generator_kl_loss=1.319, generator_dur_loss=1.795, generator_adv_loss=1.928, generator_feat_match_loss=2.663, over 1213.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:55:49,080 INFO [train.py:527] (1/6) Epoch 71, batch 70, global_batch_idx: 8750, batch size: 83, loss[discriminator_loss=2.749, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=27.97, generator_mel_loss=20.15, generator_kl_loss=1.208, generator_dur_loss=1.899, generator_adv_loss=1.872, generator_feat_match_loss=2.839, over 83.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.402, discriminator_fake_loss=1.34, generator_loss=27.64, generator_mel_loss=19.9, generator_kl_loss=1.332, generator_dur_loss=1.799, generator_adv_loss=1.919, generator_feat_match_loss=2.687, over 4045.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:58:06,886 INFO [train.py:527] (1/6) Epoch 71, batch 120, global_batch_idx: 8800, batch size: 16, loss[discriminator_loss=2.715, discriminator_real_loss=1.533, discriminator_fake_loss=1.182, generator_loss=29.47, generator_mel_loss=20.78, generator_kl_loss=1.656, generator_dur_loss=1.602, generator_adv_loss=2.19, generator_feat_match_loss=3.25, over 16.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.403, discriminator_fake_loss=1.349, generator_loss=27.76, generator_mel_loss=19.99, generator_kl_loss=1.348, generator_dur_loss=1.79, generator_adv_loss=1.927, generator_feat_match_loss=2.707, over 6570.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 06:58:06,887 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 06:58:15,683 INFO [train.py:591] (1/6) Epoch 71, validation: discriminator_loss=2.67, discriminator_real_loss=1.398, discriminator_fake_loss=1.273, generator_loss=28.15, generator_mel_loss=20.67, generator_kl_loss=1.155, generator_dur_loss=1.83, generator_adv_loss=1.908, generator_feat_match_loss=2.588, over 100.00 samples.
+2024-03-12 06:58:15,684 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 06:58:25,613 INFO [train.py:919] (1/6) Start epoch 72
+2024-03-12 07:00:55,674 INFO [train.py:527] (1/6) Epoch 72, batch 46, global_batch_idx: 8850, batch size: 58, loss[discriminator_loss=2.745, discriminator_real_loss=1.276, discriminator_fake_loss=1.468, generator_loss=28.3, generator_mel_loss=20.73, generator_kl_loss=1.348, generator_dur_loss=1.775, generator_adv_loss=1.915, generator_feat_match_loss=2.532, over 58.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.396, discriminator_fake_loss=1.358, generator_loss=27.73, generator_mel_loss=19.99, generator_kl_loss=1.372, generator_dur_loss=1.769, generator_adv_loss=1.922, generator_feat_match_loss=2.676, over 2441.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:03:14,365 INFO [train.py:527] (1/6) Epoch 72, batch 96, global_batch_idx: 8900, batch size: 88, loss[discriminator_loss=2.706, discriminator_real_loss=1.383, discriminator_fake_loss=1.323, generator_loss=28.28, generator_mel_loss=20.36, generator_kl_loss=1.247, generator_dur_loss=1.881, generator_adv_loss=1.829, generator_feat_match_loss=2.963, over 88.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.404, discriminator_fake_loss=1.347, generator_loss=27.64, generator_mel_loss=19.94, generator_kl_loss=1.363, generator_dur_loss=1.788, generator_adv_loss=1.896, generator_feat_match_loss=2.658, over 5364.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:04:31,330 INFO [train.py:919] (1/6) Start epoch 73
+2024-03-12 07:05:57,016 INFO [train.py:527] (1/6) Epoch 73, batch 22, global_batch_idx: 8950, batch size: 64, loss[discriminator_loss=2.925, discriminator_real_loss=1.524, discriminator_fake_loss=1.401, generator_loss=28.44, generator_mel_loss=20.52, generator_kl_loss=1.453, generator_dur_loss=1.814, generator_adv_loss=1.883, generator_feat_match_loss=2.766, over 64.00 samples.], tot_loss[discriminator_loss=2.777, discriminator_real_loss=1.423, discriminator_fake_loss=1.354, generator_loss=27.96, generator_mel_loss=19.86, generator_kl_loss=1.326, generator_dur_loss=1.805, generator_adv_loss=2.043, generator_feat_match_loss=2.922, over 1432.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:08:14,529 INFO [train.py:527] (1/6) Epoch 73, batch 72, global_batch_idx: 9000, batch size: 36, loss[discriminator_loss=2.713, discriminator_real_loss=1.299, discriminator_fake_loss=1.414, generator_loss=28.58, generator_mel_loss=20.84, generator_kl_loss=1.432, generator_dur_loss=1.735, generator_adv_loss=1.802, generator_feat_match_loss=2.775, over 36.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.42, discriminator_fake_loss=1.354, generator_loss=27.72, generator_mel_loss=19.84, generator_kl_loss=1.334, generator_dur_loss=1.804, generator_adv_loss=2.005, generator_feat_match_loss=2.742, over 4259.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:08:14,530 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 07:08:23,141 INFO [train.py:591] (1/6) Epoch 73, validation: discriminator_loss=2.808, discriminator_real_loss=1.416, discriminator_fake_loss=1.392, generator_loss=27.31, generator_mel_loss=20.49, generator_kl_loss=1.148, generator_dur_loss=1.855, generator_adv_loss=1.733, generator_feat_match_loss=2.082, over 100.00 samples.
+2024-03-12 07:08:23,142 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 07:10:40,090 INFO [train.py:527] (1/6) Epoch 73, batch 122, global_batch_idx: 9050, batch size: 64, loss[discriminator_loss=2.723, discriminator_real_loss=1.441, discriminator_fake_loss=1.282, generator_loss=27.11, generator_mel_loss=19.7, generator_kl_loss=1.103, generator_dur_loss=1.743, generator_adv_loss=1.863, generator_feat_match_loss=2.703, over 64.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.409, discriminator_fake_loss=1.344, generator_loss=27.69, generator_mel_loss=19.87, generator_kl_loss=1.343, generator_dur_loss=1.801, generator_adv_loss=1.959, generator_feat_match_loss=2.713, over 7171.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:10:45,541 INFO [train.py:919] (1/6) Start epoch 74
+2024-03-12 07:13:21,579 INFO [train.py:527] (1/6) Epoch 74, batch 48, global_batch_idx: 9100, batch size: 59, loss[discriminator_loss=2.692, discriminator_real_loss=1.494, discriminator_fake_loss=1.197, generator_loss=28.11, generator_mel_loss=20.18, generator_kl_loss=1.223, generator_dur_loss=1.803, generator_adv_loss=2.114, generator_feat_match_loss=2.795, over 59.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.397, discriminator_fake_loss=1.359, generator_loss=27.71, generator_mel_loss=20.01, generator_kl_loss=1.335, generator_dur_loss=1.772, generator_adv_loss=1.897, generator_feat_match_loss=2.699, over 2709.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:15:39,570 INFO [train.py:527] (1/6) Epoch 74, batch 98, global_batch_idx: 9150, batch size: 64, loss[discriminator_loss=2.796, discriminator_real_loss=1.303, discriminator_fake_loss=1.493, generator_loss=27.41, generator_mel_loss=19.71, generator_kl_loss=1.395, generator_dur_loss=1.781, generator_adv_loss=2.043, generator_feat_match_loss=2.48, over 64.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.398, discriminator_fake_loss=1.356, generator_loss=27.64, generator_mel_loss=19.92, generator_kl_loss=1.362, generator_dur_loss=1.778, generator_adv_loss=1.899, generator_feat_match_loss=2.689, over 5336.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:16:51,171 INFO [train.py:919] (1/6) Start epoch 75
+2024-03-12 07:18:20,451 INFO [train.py:527] (1/6) Epoch 75, batch 24, global_batch_idx: 9200, batch size: 72, loss[discriminator_loss=2.767, discriminator_real_loss=1.355, discriminator_fake_loss=1.412, generator_loss=27.52, generator_mel_loss=19.85, generator_kl_loss=1.174, generator_dur_loss=1.87, generator_adv_loss=1.868, generator_feat_match_loss=2.764, over 72.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.385, discriminator_fake_loss=1.362, generator_loss=27.87, generator_mel_loss=20.02, generator_kl_loss=1.384, generator_dur_loss=1.794, generator_adv_loss=1.883, generator_feat_match_loss=2.786, over 1449.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:18:20,453 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 07:18:28,222 INFO [train.py:591] (1/6) Epoch 75, validation: discriminator_loss=2.814, discriminator_real_loss=1.479, discriminator_fake_loss=1.335, generator_loss=27.15, generator_mel_loss=20.35, generator_kl_loss=1.058, generator_dur_loss=1.861, generator_adv_loss=1.815, generator_feat_match_loss=2.065, over 100.00 samples.
+2024-03-12 07:18:28,223 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 07:20:44,765 INFO [train.py:527] (1/6) Epoch 75, batch 74, global_batch_idx: 9250, batch size: 68, loss[discriminator_loss=2.802, discriminator_real_loss=1.409, discriminator_fake_loss=1.393, generator_loss=27.64, generator_mel_loss=20.1, generator_kl_loss=1.253, generator_dur_loss=1.825, generator_adv_loss=1.952, generator_feat_match_loss=2.515, over 68.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.392, discriminator_fake_loss=1.365, generator_loss=27.63, generator_mel_loss=19.89, generator_kl_loss=1.365, generator_dur_loss=1.803, generator_adv_loss=1.876, generator_feat_match_loss=2.689, over 4171.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:23:02,138 INFO [train.py:919] (1/6) Start epoch 76
+2024-03-12 07:23:25,940 INFO [train.py:527] (1/6) Epoch 76, batch 0, global_batch_idx: 9300, batch size: 61, loss[discriminator_loss=2.682, discriminator_real_loss=1.296, discriminator_fake_loss=1.386, generator_loss=27.46, generator_mel_loss=19.73, generator_kl_loss=1.272, generator_dur_loss=1.81, generator_adv_loss=1.811, generator_feat_match_loss=2.842, over 61.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.296, discriminator_fake_loss=1.386, generator_loss=27.46, generator_mel_loss=19.73, generator_kl_loss=1.272, generator_dur_loss=1.81, generator_adv_loss=1.811, generator_feat_match_loss=2.842, over 61.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:25:43,658 INFO [train.py:527] (1/6) Epoch 76, batch 50, global_batch_idx: 9350, batch size: 77, loss[discriminator_loss=2.729, discriminator_real_loss=1.335, discriminator_fake_loss=1.394, generator_loss=27.25, generator_mel_loss=19.48, generator_kl_loss=1.183, generator_dur_loss=1.869, generator_adv_loss=2.037, generator_feat_match_loss=2.679, over 77.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=27.5, generator_mel_loss=19.73, generator_kl_loss=1.358, generator_dur_loss=1.778, generator_adv_loss=1.928, generator_feat_match_loss=2.705, over 2940.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:28:05,415 INFO [train.py:527] (1/6) Epoch 76, batch 100, global_batch_idx: 9400, batch size: 50, loss[discriminator_loss=2.725, discriminator_real_loss=1.476, discriminator_fake_loss=1.249, generator_loss=27.31, generator_mel_loss=19.58, generator_kl_loss=1.469, generator_dur_loss=1.704, generator_adv_loss=2.095, generator_feat_match_loss=2.466, over 50.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.386, discriminator_fake_loss=1.345, generator_loss=27.5, generator_mel_loss=19.71, generator_kl_loss=1.353, generator_dur_loss=1.793, generator_adv_loss=1.92, generator_feat_match_loss=2.721, over 5870.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:28:05,417 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 07:28:14,095 INFO [train.py:591] (1/6) Epoch 76, validation: discriminator_loss=2.765, discriminator_real_loss=1.503, discriminator_fake_loss=1.262, generator_loss=26.53, generator_mel_loss=19.46, generator_kl_loss=1.181, generator_dur_loss=1.839, generator_adv_loss=1.956, generator_feat_match_loss=2.087, over 100.00 samples.
+2024-03-12 07:28:14,096 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 07:29:15,320 INFO [train.py:919] (1/6) Start epoch 77
+2024-03-12 07:30:53,625 INFO [train.py:527] (1/6) Epoch 77, batch 26, global_batch_idx: 9450, batch size: 96, loss[discriminator_loss=2.73, discriminator_real_loss=1.276, discriminator_fake_loss=1.455, generator_loss=26.7, generator_mel_loss=19.2, generator_kl_loss=1.231, generator_dur_loss=1.907, generator_adv_loss=1.856, generator_feat_match_loss=2.508, over 96.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.401, discriminator_fake_loss=1.339, generator_loss=27.79, generator_mel_loss=20.04, generator_kl_loss=1.376, generator_dur_loss=1.796, generator_adv_loss=1.896, generator_feat_match_loss=2.683, over 1420.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:33:12,777 INFO [train.py:527] (1/6) Epoch 77, batch 76, global_batch_idx: 9500, batch size: 74, loss[discriminator_loss=2.758, discriminator_real_loss=1.501, discriminator_fake_loss=1.257, generator_loss=27.11, generator_mel_loss=19.48, generator_kl_loss=1.371, generator_dur_loss=1.846, generator_adv_loss=1.775, generator_feat_match_loss=2.637, over 74.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.411, discriminator_fake_loss=1.35, generator_loss=27.82, generator_mel_loss=19.99, generator_kl_loss=1.351, generator_dur_loss=1.81, generator_adv_loss=1.935, generator_feat_match_loss=2.738, over 4441.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:35:22,493 INFO [train.py:919] (1/6) Start epoch 78
+2024-03-12 07:35:52,844 INFO [train.py:527] (1/6) Epoch 78, batch 2, global_batch_idx: 9550, batch size: 53, loss[discriminator_loss=2.727, discriminator_real_loss=1.364, discriminator_fake_loss=1.363, generator_loss=27.09, generator_mel_loss=19.72, generator_kl_loss=1.26, generator_dur_loss=1.733, generator_adv_loss=1.862, generator_feat_match_loss=2.517, over 53.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.37, discriminator_fake_loss=1.353, generator_loss=27.08, generator_mel_loss=19.53, generator_kl_loss=1.29, generator_dur_loss=1.733, generator_adv_loss=1.864, generator_feat_match_loss=2.655, over 175.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:38:10,262 INFO [train.py:527] (1/6) Epoch 78, batch 52, global_batch_idx: 9600, batch size: 50, loss[discriminator_loss=2.72, discriminator_real_loss=1.351, discriminator_fake_loss=1.369, generator_loss=27.72, generator_mel_loss=19.99, generator_kl_loss=1.436, generator_dur_loss=1.684, generator_adv_loss=1.908, generator_feat_match_loss=2.704, over 50.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.392, discriminator_fake_loss=1.351, generator_loss=27.64, generator_mel_loss=19.9, generator_kl_loss=1.36, generator_dur_loss=1.795, generator_adv_loss=1.875, generator_feat_match_loss=2.718, over 2965.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:38:10,264 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 07:38:18,167 INFO [train.py:591] (1/6) Epoch 78, validation: discriminator_loss=2.832, discriminator_real_loss=1.553, discriminator_fake_loss=1.28, generator_loss=27.19, generator_mel_loss=19.96, generator_kl_loss=1.056, generator_dur_loss=1.858, generator_adv_loss=1.962, generator_feat_match_loss=2.352, over 100.00 samples.
+2024-03-12 07:38:18,168 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 07:40:37,219 INFO [train.py:527] (1/6) Epoch 78, batch 102, global_batch_idx: 9650, batch size: 66, loss[discriminator_loss=2.758, discriminator_real_loss=1.433, discriminator_fake_loss=1.324, generator_loss=27.54, generator_mel_loss=19.82, generator_kl_loss=1.256, generator_dur_loss=1.874, generator_adv_loss=1.665, generator_feat_match_loss=2.928, over 66.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.391, discriminator_fake_loss=1.352, generator_loss=27.6, generator_mel_loss=19.83, generator_kl_loss=1.366, generator_dur_loss=1.802, generator_adv_loss=1.876, generator_feat_match_loss=2.728, over 5868.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:41:37,353 INFO [train.py:919] (1/6) Start epoch 79
+2024-03-12 07:43:17,101 INFO [train.py:527] (1/6) Epoch 79, batch 28, global_batch_idx: 9700, batch size: 50, loss[discriminator_loss=2.807, discriminator_real_loss=1.499, discriminator_fake_loss=1.308, generator_loss=27.2, generator_mel_loss=19.88, generator_kl_loss=1.387, generator_dur_loss=1.69, generator_adv_loss=1.831, generator_feat_match_loss=2.411, over 50.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.391, discriminator_fake_loss=1.355, generator_loss=27.73, generator_mel_loss=19.83, generator_kl_loss=1.364, generator_dur_loss=1.764, generator_adv_loss=1.926, generator_feat_match_loss=2.844, over 1577.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:45:39,639 INFO [train.py:527] (1/6) Epoch 79, batch 78, global_batch_idx: 9750, batch size: 55, loss[discriminator_loss=2.719, discriminator_real_loss=1.37, discriminator_fake_loss=1.35, generator_loss=27.22, generator_mel_loss=19.58, generator_kl_loss=1.4, generator_dur_loss=1.766, generator_adv_loss=1.848, generator_feat_match_loss=2.617, over 55.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.404, discriminator_fake_loss=1.361, generator_loss=27.52, generator_mel_loss=19.72, generator_kl_loss=1.346, generator_dur_loss=1.797, generator_adv_loss=1.907, generator_feat_match_loss=2.749, over 4612.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:47:45,110 INFO [train.py:919] (1/6) Start epoch 80
+2024-03-12 07:48:20,399 INFO [train.py:527] (1/6) Epoch 80, batch 4, global_batch_idx: 9800, batch size: 80, loss[discriminator_loss=2.776, discriminator_real_loss=1.442, discriminator_fake_loss=1.333, generator_loss=27.4, generator_mel_loss=19.79, generator_kl_loss=1.287, generator_dur_loss=1.864, generator_adv_loss=1.803, generator_feat_match_loss=2.662, over 80.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.393, discriminator_fake_loss=1.377, generator_loss=27.43, generator_mel_loss=19.67, generator_kl_loss=1.335, generator_dur_loss=1.822, generator_adv_loss=1.86, generator_feat_match_loss=2.75, over 301.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:48:20,402 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 07:48:28,384 INFO [train.py:591] (1/6) Epoch 80, validation: discriminator_loss=2.778, discriminator_real_loss=1.448, discriminator_fake_loss=1.329, generator_loss=27.23, generator_mel_loss=20.25, generator_kl_loss=1.085, generator_dur_loss=1.86, generator_adv_loss=1.811, generator_feat_match_loss=2.225, over 100.00 samples.
+2024-03-12 07:48:28,386 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 07:50:45,556 INFO [train.py:527] (1/6) Epoch 80, batch 54, global_batch_idx: 9850, batch size: 56, loss[discriminator_loss=2.818, discriminator_real_loss=1.483, discriminator_fake_loss=1.335, generator_loss=25.96, generator_mel_loss=19.09, generator_kl_loss=1.245, generator_dur_loss=1.783, generator_adv_loss=1.693, generator_feat_match_loss=2.145, over 56.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.406, discriminator_fake_loss=1.345, generator_loss=27.41, generator_mel_loss=19.71, generator_kl_loss=1.342, generator_dur_loss=1.777, generator_adv_loss=1.871, generator_feat_match_loss=2.709, over 3061.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:53:05,010 INFO [train.py:527] (1/6) Epoch 80, batch 104, global_batch_idx: 9900, batch size: 56, loss[discriminator_loss=2.771, discriminator_real_loss=1.511, discriminator_fake_loss=1.26, generator_loss=27.38, generator_mel_loss=19.53, generator_kl_loss=1.492, generator_dur_loss=1.709, generator_adv_loss=1.712, generator_feat_match_loss=2.94, over 56.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.398, discriminator_fake_loss=1.345, generator_loss=27.44, generator_mel_loss=19.68, generator_kl_loss=1.347, generator_dur_loss=1.791, generator_adv_loss=1.879, generator_feat_match_loss=2.748, over 5834.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:53:58,804 INFO [train.py:919] (1/6) Start epoch 81
+2024-03-12 07:56:20,632 INFO [train.py:527] (1/6) Epoch 81, batch 30, global_batch_idx: 9950, batch size: 36, loss[discriminator_loss=2.864, discriminator_real_loss=1.541, discriminator_fake_loss=1.323, generator_loss=27.91, generator_mel_loss=19.9, generator_kl_loss=1.524, generator_dur_loss=1.763, generator_adv_loss=1.922, generator_feat_match_loss=2.802, over 36.00 samples.], tot_loss[discriminator_loss=2.782, discriminator_real_loss=1.415, discriminator_fake_loss=1.367, generator_loss=27.5, generator_mel_loss=19.68, generator_kl_loss=1.323, generator_dur_loss=1.821, generator_adv_loss=1.936, generator_feat_match_loss=2.739, over 1797.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:58:38,404 INFO [train.py:527] (1/6) Epoch 81, batch 80, global_batch_idx: 10000, batch size: 83, loss[discriminator_loss=2.718, discriminator_real_loss=1.281, discriminator_fake_loss=1.437, generator_loss=28.14, generator_mel_loss=20.01, generator_kl_loss=1.347, generator_dur_loss=1.858, generator_adv_loss=1.842, generator_feat_match_loss=3.087, over 83.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.401, discriminator_fake_loss=1.36, generator_loss=27.55, generator_mel_loss=19.75, generator_kl_loss=1.352, generator_dur_loss=1.806, generator_adv_loss=1.905, generator_feat_match_loss=2.736, over 4668.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 07:58:38,405 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 07:58:46,878 INFO [train.py:591] (1/6) Epoch 81, validation: discriminator_loss=2.771, discriminator_real_loss=1.479, discriminator_fake_loss=1.292, generator_loss=27.36, generator_mel_loss=20.11, generator_kl_loss=1.106, generator_dur_loss=1.827, generator_adv_loss=1.929, generator_feat_match_loss=2.384, over 100.00 samples.
+2024-03-12 07:58:46,879 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 08:00:49,134 INFO [train.py:919] (1/6) Start epoch 82
+2024-03-12 08:01:27,383 INFO [train.py:527] (1/6) Epoch 82, batch 6, global_batch_idx: 10050, batch size: 64, loss[discriminator_loss=2.707, discriminator_real_loss=1.32, discriminator_fake_loss=1.386, generator_loss=28.02, generator_mel_loss=20.06, generator_kl_loss=1.278, generator_dur_loss=1.844, generator_adv_loss=1.913, generator_feat_match_loss=2.935, over 64.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.349, discriminator_fake_loss=1.335, generator_loss=27.79, generator_mel_loss=19.81, generator_kl_loss=1.32, generator_dur_loss=1.8, generator_adv_loss=1.944, generator_feat_match_loss=2.909, over 405.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:03:45,417 INFO [train.py:527] (1/6) Epoch 82, batch 56, global_batch_idx: 10100, batch size: 56, loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.23, generator_mel_loss=19.48, generator_kl_loss=1.34, generator_dur_loss=1.702, generator_adv_loss=1.945, generator_feat_match_loss=2.765, over 56.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.372, discriminator_fake_loss=1.336, generator_loss=27.73, generator_mel_loss=19.68, generator_kl_loss=1.354, generator_dur_loss=1.793, generator_adv_loss=1.949, generator_feat_match_loss=2.963, over 3252.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:06:04,695 INFO [train.py:527] (1/6) Epoch 82, batch 106, global_batch_idx: 10150, batch size: 72, loss[discriminator_loss=2.747, discriminator_real_loss=1.448, discriminator_fake_loss=1.299, generator_loss=27.12, generator_mel_loss=19.16, generator_kl_loss=1.508, generator_dur_loss=1.862, generator_adv_loss=1.923, generator_feat_match_loss=2.669, over 72.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.386, discriminator_fake_loss=1.34, generator_loss=27.6, generator_mel_loss=19.66, generator_kl_loss=1.354, generator_dur_loss=1.798, generator_adv_loss=1.929, generator_feat_match_loss=2.863, over 6282.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:06:53,200 INFO [train.py:919] (1/6) Start epoch 83
+2024-03-12 08:08:44,644 INFO [train.py:527] (1/6) Epoch 83, batch 32, global_batch_idx: 10200, batch size: 83, loss[discriminator_loss=2.75, discriminator_real_loss=1.392, discriminator_fake_loss=1.358, generator_loss=26.71, generator_mel_loss=18.91, generator_kl_loss=1.27, generator_dur_loss=1.895, generator_adv_loss=1.892, generator_feat_match_loss=2.739, over 83.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.389, discriminator_fake_loss=1.373, generator_loss=27.35, generator_mel_loss=19.62, generator_kl_loss=1.35, generator_dur_loss=1.813, generator_adv_loss=1.883, generator_feat_match_loss=2.684, over 2046.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:08:44,645 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 08:08:52,605 INFO [train.py:591] (1/6) Epoch 83, validation: discriminator_loss=2.828, discriminator_real_loss=1.457, discriminator_fake_loss=1.371, generator_loss=26.67, generator_mel_loss=19.9, generator_kl_loss=1.167, generator_dur_loss=1.845, generator_adv_loss=1.756, generator_feat_match_loss=2.008, over 100.00 samples.
+2024-03-12 08:08:52,605 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 08:11:12,297 INFO [train.py:527] (1/6) Epoch 83, batch 82, global_batch_idx: 10250, batch size: 58, loss[discriminator_loss=2.688, discriminator_real_loss=1.433, discriminator_fake_loss=1.255, generator_loss=29.21, generator_mel_loss=20.38, generator_kl_loss=1.455, generator_dur_loss=1.757, generator_adv_loss=2.17, generator_feat_match_loss=3.452, over 58.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.401, discriminator_fake_loss=1.358, generator_loss=27.45, generator_mel_loss=19.66, generator_kl_loss=1.36, generator_dur_loss=1.811, generator_adv_loss=1.903, generator_feat_match_loss=2.721, over 4938.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:13:05,268 INFO [train.py:919] (1/6) Start epoch 84
+2024-03-12 08:13:51,840 INFO [train.py:527] (1/6) Epoch 84, batch 8, global_batch_idx: 10300, batch size: 36, loss[discriminator_loss=2.76, discriminator_real_loss=1.496, discriminator_fake_loss=1.264, generator_loss=28.23, generator_mel_loss=20.53, generator_kl_loss=1.497, generator_dur_loss=1.735, generator_adv_loss=1.791, generator_feat_match_loss=2.678, over 36.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.419, discriminator_fake_loss=1.31, generator_loss=27.48, generator_mel_loss=19.61, generator_kl_loss=1.422, generator_dur_loss=1.796, generator_adv_loss=1.871, generator_feat_match_loss=2.784, over 472.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:16:09,575 INFO [train.py:527] (1/6) Epoch 84, batch 58, global_batch_idx: 10350, batch size: 56, loss[discriminator_loss=2.767, discriminator_real_loss=1.333, discriminator_fake_loss=1.434, generator_loss=27.22, generator_mel_loss=19.64, generator_kl_loss=1.411, generator_dur_loss=1.814, generator_adv_loss=1.862, generator_feat_match_loss=2.49, over 56.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.403, discriminator_fake_loss=1.344, generator_loss=27.37, generator_mel_loss=19.56, generator_kl_loss=1.372, generator_dur_loss=1.793, generator_adv_loss=1.889, generator_feat_match_loss=2.762, over 3427.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:18:30,378 INFO [train.py:527] (1/6) Epoch 84, batch 108, global_batch_idx: 10400, batch size: 74, loss[discriminator_loss=2.83, discriminator_real_loss=1.568, discriminator_fake_loss=1.262, generator_loss=27.6, generator_mel_loss=20.15, generator_kl_loss=1.275, generator_dur_loss=1.821, generator_adv_loss=1.592, generator_feat_match_loss=2.764, over 74.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.4, discriminator_fake_loss=1.353, generator_loss=27.4, generator_mel_loss=19.6, generator_kl_loss=1.367, generator_dur_loss=1.796, generator_adv_loss=1.878, generator_feat_match_loss=2.757, over 6664.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:18:30,379 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 08:18:39,356 INFO [train.py:591] (1/6) Epoch 84, validation: discriminator_loss=2.949, discriminator_real_loss=1.299, discriminator_fake_loss=1.649, generator_loss=26.47, generator_mel_loss=19.88, generator_kl_loss=1.111, generator_dur_loss=1.823, generator_adv_loss=1.488, generator_feat_match_loss=2.164, over 100.00 samples.
+2024-03-12 08:18:39,356 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 08:19:18,375 INFO [train.py:919] (1/6) Start epoch 85
+2024-03-12 08:21:17,351 INFO [train.py:527] (1/6) Epoch 85, batch 34, global_batch_idx: 10450, batch size: 15, loss[discriminator_loss=2.693, discriminator_real_loss=1.348, discriminator_fake_loss=1.345, generator_loss=28.76, generator_mel_loss=20.08, generator_kl_loss=1.767, generator_dur_loss=1.602, generator_adv_loss=2.04, generator_feat_match_loss=3.267, over 15.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.414, discriminator_fake_loss=1.347, generator_loss=27.55, generator_mel_loss=19.63, generator_kl_loss=1.349, generator_dur_loss=1.79, generator_adv_loss=1.947, generator_feat_match_loss=2.835, over 2018.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:23:35,926 INFO [train.py:527] (1/6) Epoch 85, batch 84, global_batch_idx: 10500, batch size: 80, loss[discriminator_loss=2.754, discriminator_real_loss=1.467, discriminator_fake_loss=1.287, generator_loss=27.54, generator_mel_loss=19.95, generator_kl_loss=1.272, generator_dur_loss=1.826, generator_adv_loss=1.804, generator_feat_match_loss=2.68, over 80.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.407, discriminator_fake_loss=1.352, generator_loss=27.42, generator_mel_loss=19.58, generator_kl_loss=1.35, generator_dur_loss=1.78, generator_adv_loss=1.915, generator_feat_match_loss=2.793, over 4807.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:25:23,965 INFO [train.py:919] (1/6) Start epoch 86
+2024-03-12 08:26:14,420 INFO [train.py:527] (1/6) Epoch 86, batch 10, global_batch_idx: 10550, batch size: 36, loss[discriminator_loss=2.807, discriminator_real_loss=1.305, discriminator_fake_loss=1.502, generator_loss=26.62, generator_mel_loss=19.18, generator_kl_loss=1.291, generator_dur_loss=1.746, generator_adv_loss=1.707, generator_feat_match_loss=2.703, over 36.00 samples.], tot_loss[discriminator_loss=2.789, discriminator_real_loss=1.425, discriminator_fake_loss=1.364, generator_loss=27.68, generator_mel_loss=19.72, generator_kl_loss=1.433, generator_dur_loss=1.757, generator_adv_loss=1.861, generator_feat_match_loss=2.909, over 545.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:28:39,050 INFO [train.py:527] (1/6) Epoch 86, batch 60, global_batch_idx: 10600, batch size: 74, loss[discriminator_loss=2.723, discriminator_real_loss=1.197, discriminator_fake_loss=1.526, generator_loss=28.93, generator_mel_loss=20.18, generator_kl_loss=1.275, generator_dur_loss=1.849, generator_adv_loss=2.094, generator_feat_match_loss=3.532, over 74.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.39, discriminator_fake_loss=1.352, generator_loss=27.47, generator_mel_loss=19.49, generator_kl_loss=1.362, generator_dur_loss=1.796, generator_adv_loss=1.909, generator_feat_match_loss=2.911, over 3699.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:28:39,052 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 08:28:46,851 INFO [train.py:591] (1/6) Epoch 86, validation: discriminator_loss=2.49, discriminator_real_loss=1.263, discriminator_fake_loss=1.227, generator_loss=27, generator_mel_loss=19.12, generator_kl_loss=1.179, generator_dur_loss=1.824, generator_adv_loss=2.144, generator_feat_match_loss=2.727, over 100.00 samples.
+2024-03-12 08:28:46,851 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 08:31:04,082 INFO [train.py:527] (1/6) Epoch 86, batch 110, global_batch_idx: 10650, batch size: 64, loss[discriminator_loss=2.741, discriminator_real_loss=1.454, discriminator_fake_loss=1.287, generator_loss=26.87, generator_mel_loss=19.32, generator_kl_loss=1.238, generator_dur_loss=1.803, generator_adv_loss=1.952, generator_feat_match_loss=2.555, over 64.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.403, discriminator_fake_loss=1.338, generator_loss=27.62, generator_mel_loss=19.47, generator_kl_loss=1.364, generator_dur_loss=1.785, generator_adv_loss=2.005, generator_feat_match_loss=3.001, over 6347.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:31:40,054 INFO [train.py:919] (1/6) Start epoch 87
+2024-03-12 08:33:42,669 INFO [train.py:527] (1/6) Epoch 87, batch 36, global_batch_idx: 10700, batch size: 74, loss[discriminator_loss=2.696, discriminator_real_loss=1.388, discriminator_fake_loss=1.309, generator_loss=27.62, generator_mel_loss=19.29, generator_kl_loss=1.438, generator_dur_loss=1.87, generator_adv_loss=1.859, generator_feat_match_loss=3.158, over 74.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.395, discriminator_fake_loss=1.335, generator_loss=27.36, generator_mel_loss=19.53, generator_kl_loss=1.329, generator_dur_loss=1.819, generator_adv_loss=1.9, generator_feat_match_loss=2.78, over 2219.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:36:02,598 INFO [train.py:527] (1/6) Epoch 87, batch 86, global_batch_idx: 10750, batch size: 70, loss[discriminator_loss=2.784, discriminator_real_loss=1.412, discriminator_fake_loss=1.372, generator_loss=27.66, generator_mel_loss=19.88, generator_kl_loss=1.289, generator_dur_loss=1.809, generator_adv_loss=1.748, generator_feat_match_loss=2.935, over 70.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.395, discriminator_fake_loss=1.338, generator_loss=27.38, generator_mel_loss=19.54, generator_kl_loss=1.345, generator_dur_loss=1.801, generator_adv_loss=1.901, generator_feat_match_loss=2.789, over 5039.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:37:46,025 INFO [train.py:919] (1/6) Start epoch 88
+2024-03-12 08:38:43,578 INFO [train.py:527] (1/6) Epoch 88, batch 12, global_batch_idx: 10800, batch size: 53, loss[discriminator_loss=2.75, discriminator_real_loss=1.381, discriminator_fake_loss=1.37, generator_loss=26.63, generator_mel_loss=18.98, generator_kl_loss=1.415, generator_dur_loss=1.689, generator_adv_loss=1.911, generator_feat_match_loss=2.639, over 53.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.386, discriminator_fake_loss=1.363, generator_loss=27.19, generator_mel_loss=19.4, generator_kl_loss=1.377, generator_dur_loss=1.793, generator_adv_loss=1.88, generator_feat_match_loss=2.738, over 779.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:38:43,581 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 08:38:51,181 INFO [train.py:591] (1/6) Epoch 88, validation: discriminator_loss=2.811, discriminator_real_loss=1.449, discriminator_fake_loss=1.362, generator_loss=27.26, generator_mel_loss=20.33, generator_kl_loss=1.1, generator_dur_loss=1.832, generator_adv_loss=1.811, generator_feat_match_loss=2.186, over 100.00 samples.
+2024-03-12 08:38:51,182 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 08:41:09,907 INFO [train.py:527] (1/6) Epoch 88, batch 62, global_batch_idx: 10850, batch size: 48, loss[discriminator_loss=2.743, discriminator_real_loss=1.544, discriminator_fake_loss=1.199, generator_loss=26.19, generator_mel_loss=18.87, generator_kl_loss=1.312, generator_dur_loss=1.738, generator_adv_loss=1.717, generator_feat_match_loss=2.554, over 48.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.397, discriminator_fake_loss=1.355, generator_loss=27.37, generator_mel_loss=19.53, generator_kl_loss=1.363, generator_dur_loss=1.781, generator_adv_loss=1.886, generator_feat_match_loss=2.813, over 3630.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:43:28,433 INFO [train.py:527] (1/6) Epoch 88, batch 112, global_batch_idx: 10900, batch size: 56, loss[discriminator_loss=2.749, discriminator_real_loss=1.417, discriminator_fake_loss=1.332, generator_loss=27.28, generator_mel_loss=19.4, generator_kl_loss=1.311, generator_dur_loss=1.731, generator_adv_loss=1.868, generator_feat_match_loss=2.975, over 56.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.404, discriminator_fake_loss=1.36, generator_loss=27.44, generator_mel_loss=19.51, generator_kl_loss=1.371, generator_dur_loss=1.784, generator_adv_loss=1.928, generator_feat_match_loss=2.844, over 6610.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:44:02,015 INFO [train.py:919] (1/6) Start epoch 89
+2024-03-12 08:46:13,299 INFO [train.py:527] (1/6) Epoch 89, batch 38, global_batch_idx: 10950, batch size: 47, loss[discriminator_loss=2.766, discriminator_real_loss=1.378, discriminator_fake_loss=1.387, generator_loss=26.8, generator_mel_loss=18.95, generator_kl_loss=1.493, generator_dur_loss=1.699, generator_adv_loss=1.976, generator_feat_match_loss=2.683, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.344, generator_loss=27.64, generator_mel_loss=19.63, generator_kl_loss=1.36, generator_dur_loss=1.803, generator_adv_loss=1.916, generator_feat_match_loss=2.93, over 2340.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:48:28,938 INFO [train.py:527] (1/6) Epoch 89, batch 88, global_batch_idx: 11000, batch size: 48, loss[discriminator_loss=2.713, discriminator_real_loss=1.463, discriminator_fake_loss=1.25, generator_loss=27.41, generator_mel_loss=19.35, generator_kl_loss=1.571, generator_dur_loss=1.67, generator_adv_loss=2.037, generator_feat_match_loss=2.778, over 48.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.5, generator_mel_loss=19.57, generator_kl_loss=1.371, generator_dur_loss=1.796, generator_adv_loss=1.9, generator_feat_match_loss=2.866, over 5012.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:48:28,940 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 08:48:37,871 INFO [train.py:591] (1/6) Epoch 89, validation: discriminator_loss=2.752, discriminator_real_loss=1.557, discriminator_fake_loss=1.195, generator_loss=26.74, generator_mel_loss=19.27, generator_kl_loss=1.154, generator_dur_loss=1.835, generator_adv_loss=2.016, generator_feat_match_loss=2.461, over 100.00 samples.
+2024-03-12 08:48:37,872 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 08:50:16,717 INFO [train.py:919] (1/6) Start epoch 90
+2024-03-12 08:51:22,984 INFO [train.py:527] (1/6) Epoch 90, batch 14, global_batch_idx: 11050, batch size: 96, loss[discriminator_loss=2.692, discriminator_real_loss=1.38, discriminator_fake_loss=1.312, generator_loss=27.45, generator_mel_loss=19.13, generator_kl_loss=1.337, generator_dur_loss=1.915, generator_adv_loss=2.014, generator_feat_match_loss=3.051, over 96.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.389, discriminator_fake_loss=1.335, generator_loss=27.54, generator_mel_loss=19.41, generator_kl_loss=1.391, generator_dur_loss=1.819, generator_adv_loss=1.973, generator_feat_match_loss=2.941, over 969.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:53:43,238 INFO [train.py:527] (1/6) Epoch 90, batch 64, global_batch_idx: 11100, batch size: 59, loss[discriminator_loss=2.759, discriminator_real_loss=1.274, discriminator_fake_loss=1.485, generator_loss=28.22, generator_mel_loss=20.09, generator_kl_loss=1.502, generator_dur_loss=1.74, generator_adv_loss=1.943, generator_feat_match_loss=2.948, over 59.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.4, discriminator_fake_loss=1.35, generator_loss=27.34, generator_mel_loss=19.46, generator_kl_loss=1.377, generator_dur_loss=1.794, generator_adv_loss=1.916, generator_feat_match_loss=2.792, over 3743.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:56:00,723 INFO [train.py:527] (1/6) Epoch 90, batch 114, global_batch_idx: 11150, batch size: 64, loss[discriminator_loss=2.755, discriminator_real_loss=1.399, discriminator_fake_loss=1.356, generator_loss=27.37, generator_mel_loss=19.53, generator_kl_loss=1.397, generator_dur_loss=1.803, generator_adv_loss=1.788, generator_feat_match_loss=2.843, over 64.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.393, discriminator_fake_loss=1.344, generator_loss=27.44, generator_mel_loss=19.5, generator_kl_loss=1.383, generator_dur_loss=1.789, generator_adv_loss=1.915, generator_feat_match_loss=2.855, over 6523.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:56:23,194 INFO [train.py:919] (1/6) Start epoch 91
+2024-03-12 08:58:38,233 INFO [train.py:527] (1/6) Epoch 91, batch 40, global_batch_idx: 11200, batch size: 31, loss[discriminator_loss=2.651, discriminator_real_loss=1.354, discriminator_fake_loss=1.297, generator_loss=29.14, generator_mel_loss=20.62, generator_kl_loss=1.502, generator_dur_loss=1.638, generator_adv_loss=1.944, generator_feat_match_loss=3.437, over 31.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.394, discriminator_fake_loss=1.349, generator_loss=27.42, generator_mel_loss=19.55, generator_kl_loss=1.347, generator_dur_loss=1.783, generator_adv_loss=1.887, generator_feat_match_loss=2.851, over 2405.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 08:58:38,235 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 08:58:46,107 INFO [train.py:591] (1/6) Epoch 91, validation: discriminator_loss=2.709, discriminator_real_loss=1.359, discriminator_fake_loss=1.35, generator_loss=27.02, generator_mel_loss=19.51, generator_kl_loss=1.237, generator_dur_loss=1.81, generator_adv_loss=1.85, generator_feat_match_loss=2.611, over 100.00 samples.
+2024-03-12 08:58:46,108 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 09:01:04,261 INFO [train.py:527] (1/6) Epoch 91, batch 90, global_batch_idx: 11250, batch size: 53, loss[discriminator_loss=2.748, discriminator_real_loss=1.304, discriminator_fake_loss=1.445, generator_loss=26.95, generator_mel_loss=19.36, generator_kl_loss=1.325, generator_dur_loss=1.746, generator_adv_loss=1.736, generator_feat_match_loss=2.775, over 53.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.397, discriminator_fake_loss=1.349, generator_loss=27.38, generator_mel_loss=19.53, generator_kl_loss=1.361, generator_dur_loss=1.774, generator_adv_loss=1.884, generator_feat_match_loss=2.832, over 5269.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:02:38,104 INFO [train.py:919] (1/6) Start epoch 92
+2024-03-12 09:03:48,772 INFO [train.py:527] (1/6) Epoch 92, batch 16, global_batch_idx: 11300, batch size: 45, loss[discriminator_loss=2.717, discriminator_real_loss=1.421, discriminator_fake_loss=1.295, generator_loss=28.5, generator_mel_loss=20.38, generator_kl_loss=1.462, generator_dur_loss=1.662, generator_adv_loss=2.119, generator_feat_match_loss=2.882, over 45.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.41, discriminator_fake_loss=1.345, generator_loss=27.3, generator_mel_loss=19.4, generator_kl_loss=1.37, generator_dur_loss=1.787, generator_adv_loss=1.939, generator_feat_match_loss=2.806, over 989.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:06:10,302 INFO [train.py:527] (1/6) Epoch 92, batch 66, global_batch_idx: 11350, batch size: 47, loss[discriminator_loss=2.78, discriminator_real_loss=1.304, discriminator_fake_loss=1.476, generator_loss=27.35, generator_mel_loss=19.52, generator_kl_loss=1.414, generator_dur_loss=1.736, generator_adv_loss=2.006, generator_feat_match_loss=2.68, over 47.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.397, discriminator_fake_loss=1.346, generator_loss=27.49, generator_mel_loss=19.57, generator_kl_loss=1.373, generator_dur_loss=1.787, generator_adv_loss=1.906, generator_feat_match_loss=2.851, over 3929.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:08:27,324 INFO [train.py:527] (1/6) Epoch 92, batch 116, global_batch_idx: 11400, batch size: 14, loss[discriminator_loss=2.748, discriminator_real_loss=1.379, discriminator_fake_loss=1.369, generator_loss=30.03, generator_mel_loss=21.68, generator_kl_loss=1.773, generator_dur_loss=1.59, generator_adv_loss=1.877, generator_feat_match_loss=3.111, over 14.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.391, discriminator_fake_loss=1.347, generator_loss=27.54, generator_mel_loss=19.56, generator_kl_loss=1.376, generator_dur_loss=1.771, generator_adv_loss=1.916, generator_feat_match_loss=2.92, over 6552.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:08:27,325 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 09:08:36,554 INFO [train.py:591] (1/6) Epoch 92, validation: discriminator_loss=2.79, discriminator_real_loss=1.531, discriminator_fake_loss=1.259, generator_loss=27.22, generator_mel_loss=19.99, generator_kl_loss=1.191, generator_dur_loss=1.808, generator_adv_loss=1.89, generator_feat_match_loss=2.342, over 100.00 samples.
+2024-03-12 09:08:36,555 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 09:08:55,377 INFO [train.py:919] (1/6) Start epoch 93
+2024-03-12 09:11:18,245 INFO [train.py:527] (1/6) Epoch 93, batch 42, global_batch_idx: 11450, batch size: 64, loss[discriminator_loss=2.662, discriminator_real_loss=1.21, discriminator_fake_loss=1.451, generator_loss=27.38, generator_mel_loss=19.55, generator_kl_loss=1.232, generator_dur_loss=1.756, generator_adv_loss=1.893, generator_feat_match_loss=2.947, over 64.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.393, discriminator_fake_loss=1.353, generator_loss=27.46, generator_mel_loss=19.55, generator_kl_loss=1.378, generator_dur_loss=1.744, generator_adv_loss=1.903, generator_feat_match_loss=2.887, over 2349.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:13:34,409 INFO [train.py:527] (1/6) Epoch 93, batch 92, global_batch_idx: 11500, batch size: 48, loss[discriminator_loss=2.717, discriminator_real_loss=1.457, discriminator_fake_loss=1.26, generator_loss=28.52, generator_mel_loss=20.38, generator_kl_loss=1.478, generator_dur_loss=1.712, generator_adv_loss=1.839, generator_feat_match_loss=3.102, over 48.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.402, discriminator_fake_loss=1.348, generator_loss=27.38, generator_mel_loss=19.46, generator_kl_loss=1.383, generator_dur_loss=1.744, generator_adv_loss=1.915, generator_feat_match_loss=2.881, over 5235.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:15:01,033 INFO [train.py:919] (1/6) Start epoch 94
+2024-03-12 09:16:10,260 INFO [train.py:527] (1/6) Epoch 94, batch 18, global_batch_idx: 11550, batch size: 25, loss[discriminator_loss=2.737, discriminator_real_loss=1.471, discriminator_fake_loss=1.266, generator_loss=26.95, generator_mel_loss=19.03, generator_kl_loss=1.584, generator_dur_loss=1.605, generator_adv_loss=1.988, generator_feat_match_loss=2.744, over 25.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.16, generator_mel_loss=19.24, generator_kl_loss=1.385, generator_dur_loss=1.715, generator_adv_loss=1.922, generator_feat_match_loss=2.904, over 1052.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:18:29,015 INFO [train.py:527] (1/6) Epoch 94, batch 68, global_batch_idx: 11600, batch size: 47, loss[discriminator_loss=2.725, discriminator_real_loss=1.32, discriminator_fake_loss=1.406, generator_loss=28.07, generator_mel_loss=20.23, generator_kl_loss=1.36, generator_dur_loss=1.646, generator_adv_loss=1.983, generator_feat_match_loss=2.849, over 47.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.395, discriminator_fake_loss=1.343, generator_loss=27.29, generator_mel_loss=19.36, generator_kl_loss=1.38, generator_dur_loss=1.746, generator_adv_loss=1.907, generator_feat_match_loss=2.894, over 3873.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:18:29,016 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 09:18:37,812 INFO [train.py:591] (1/6) Epoch 94, validation: discriminator_loss=2.789, discriminator_real_loss=1.553, discriminator_fake_loss=1.236, generator_loss=27.3, generator_mel_loss=19.89, generator_kl_loss=1.23, generator_dur_loss=1.789, generator_adv_loss=1.981, generator_feat_match_loss=2.414, over 100.00 samples.
+2024-03-12 09:18:37,813 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 09:20:57,127 INFO [train.py:527] (1/6) Epoch 94, batch 118, global_batch_idx: 11650, batch size: 74, loss[discriminator_loss=2.635, discriminator_real_loss=1.293, discriminator_fake_loss=1.342, generator_loss=26.86, generator_mel_loss=18.84, generator_kl_loss=1.302, generator_dur_loss=1.745, generator_adv_loss=1.94, generator_feat_match_loss=3.028, over 74.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.389, discriminator_fake_loss=1.342, generator_loss=27.39, generator_mel_loss=19.39, generator_kl_loss=1.396, generator_dur_loss=1.725, generator_adv_loss=1.927, generator_feat_match_loss=2.949, over 6813.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:21:13,770 INFO [train.py:919] (1/6) Start epoch 95
+2024-03-12 09:23:40,739 INFO [train.py:527] (1/6) Epoch 95, batch 44, global_batch_idx: 11700, batch size: 61, loss[discriminator_loss=2.805, discriminator_real_loss=1.507, discriminator_fake_loss=1.298, generator_loss=27.18, generator_mel_loss=19.65, generator_kl_loss=1.455, generator_dur_loss=1.702, generator_adv_loss=1.806, generator_feat_match_loss=2.572, over 61.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.398, discriminator_fake_loss=1.34, generator_loss=27.54, generator_mel_loss=19.55, generator_kl_loss=1.375, generator_dur_loss=1.746, generator_adv_loss=1.923, generator_feat_match_loss=2.949, over 2647.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:26:04,486 INFO [train.py:527] (1/6) Epoch 95, batch 94, global_batch_idx: 11750, batch size: 62, loss[discriminator_loss=2.698, discriminator_real_loss=1.25, discriminator_fake_loss=1.448, generator_loss=27.59, generator_mel_loss=19.47, generator_kl_loss=1.383, generator_dur_loss=1.735, generator_adv_loss=1.991, generator_feat_match_loss=3.011, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.397, discriminator_fake_loss=1.347, generator_loss=27.47, generator_mel_loss=19.52, generator_kl_loss=1.377, generator_dur_loss=1.746, generator_adv_loss=1.918, generator_feat_match_loss=2.917, over 5548.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:27:30,573 INFO [train.py:919] (1/6) Start epoch 96
+2024-03-12 09:28:50,148 INFO [train.py:527] (1/6) Epoch 96, batch 20, global_batch_idx: 11800, batch size: 89, loss[discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.358, generator_loss=27.52, generator_mel_loss=19.15, generator_kl_loss=1.327, generator_dur_loss=1.883, generator_adv_loss=1.979, generator_feat_match_loss=3.174, over 89.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.346, generator_loss=27.23, generator_mel_loss=19.35, generator_kl_loss=1.355, generator_dur_loss=1.761, generator_adv_loss=1.916, generator_feat_match_loss=2.848, over 1100.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:28:50,149 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 09:28:58,043 INFO [train.py:591] (1/6) Epoch 96, validation: discriminator_loss=2.716, discriminator_real_loss=1.489, discriminator_fake_loss=1.227, generator_loss=26.48, generator_mel_loss=19.32, generator_kl_loss=1.108, generator_dur_loss=1.819, generator_adv_loss=1.963, generator_feat_match_loss=2.267, over 100.00 samples.
+2024-03-12 09:28:58,044 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 09:31:21,659 INFO [train.py:527] (1/6) Epoch 96, batch 70, global_batch_idx: 11850, batch size: 96, loss[discriminator_loss=2.707, discriminator_real_loss=1.293, discriminator_fake_loss=1.414, generator_loss=26.95, generator_mel_loss=18.7, generator_kl_loss=1.379, generator_dur_loss=1.85, generator_adv_loss=2.002, generator_feat_match_loss=3.019, over 96.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.403, discriminator_fake_loss=1.362, generator_loss=27.34, generator_mel_loss=19.36, generator_kl_loss=1.373, generator_dur_loss=1.76, generator_adv_loss=1.939, generator_feat_match_loss=2.909, over 3936.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:33:43,327 INFO [train.py:527] (1/6) Epoch 96, batch 120, global_batch_idx: 11900, batch size: 83, loss[discriminator_loss=2.921, discriminator_real_loss=1.164, discriminator_fake_loss=1.757, generator_loss=27.49, generator_mel_loss=19.38, generator_kl_loss=1.421, generator_dur_loss=1.862, generator_adv_loss=1.816, generator_feat_match_loss=3.009, over 83.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.4, discriminator_fake_loss=1.359, generator_loss=27.38, generator_mel_loss=19.41, generator_kl_loss=1.381, generator_dur_loss=1.766, generator_adv_loss=1.931, generator_feat_match_loss=2.892, over 6777.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:33:54,716 INFO [train.py:919] (1/6) Start epoch 97
+2024-03-12 09:36:27,955 INFO [train.py:527] (1/6) Epoch 97, batch 46, global_batch_idx: 11950, batch size: 42, loss[discriminator_loss=2.768, discriminator_real_loss=1.398, discriminator_fake_loss=1.369, generator_loss=27.66, generator_mel_loss=19.66, generator_kl_loss=1.404, generator_dur_loss=1.677, generator_adv_loss=1.933, generator_feat_match_loss=2.982, over 42.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.385, discriminator_fake_loss=1.355, generator_loss=27.26, generator_mel_loss=19.3, generator_kl_loss=1.378, generator_dur_loss=1.779, generator_adv_loss=1.901, generator_feat_match_loss=2.896, over 2805.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:38:50,872 INFO [train.py:527] (1/6) Epoch 97, batch 96, global_batch_idx: 12000, batch size: 70, loss[discriminator_loss=2.877, discriminator_real_loss=1.412, discriminator_fake_loss=1.464, generator_loss=25.88, generator_mel_loss=18.5, generator_kl_loss=1.256, generator_dur_loss=1.843, generator_adv_loss=1.876, generator_feat_match_loss=2.404, over 70.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.394, discriminator_fake_loss=1.35, generator_loss=27.28, generator_mel_loss=19.31, generator_kl_loss=1.376, generator_dur_loss=1.79, generator_adv_loss=1.903, generator_feat_match_loss=2.894, over 5822.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:38:50,873 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 09:38:59,575 INFO [train.py:591] (1/6) Epoch 97, validation: discriminator_loss=2.715, discriminator_real_loss=1.468, discriminator_fake_loss=1.247, generator_loss=26.83, generator_mel_loss=19.48, generator_kl_loss=1.2, generator_dur_loss=1.834, generator_adv_loss=1.95, generator_feat_match_loss=2.361, over 100.00 samples.
+2024-03-12 09:38:59,576 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 09:40:12,280 INFO [train.py:919] (1/6) Start epoch 98
+2024-03-12 09:41:40,341 INFO [train.py:527] (1/6) Epoch 98, batch 22, global_batch_idx: 12050, batch size: 88, loss[discriminator_loss=2.715, discriminator_real_loss=1.461, discriminator_fake_loss=1.254, generator_loss=27.31, generator_mel_loss=19.17, generator_kl_loss=1.347, generator_dur_loss=1.933, generator_adv_loss=1.661, generator_feat_match_loss=3.196, over 88.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.373, discriminator_fake_loss=1.358, generator_loss=27.45, generator_mel_loss=19.4, generator_kl_loss=1.373, generator_dur_loss=1.814, generator_adv_loss=1.905, generator_feat_match_loss=2.962, over 1505.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:44:00,443 INFO [train.py:527] (1/6) Epoch 98, batch 72, global_batch_idx: 12100, batch size: 42, loss[discriminator_loss=2.79, discriminator_real_loss=1.476, discriminator_fake_loss=1.314, generator_loss=27.22, generator_mel_loss=19.26, generator_kl_loss=1.472, generator_dur_loss=1.682, generator_adv_loss=1.777, generator_feat_match_loss=3.026, over 42.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.397, discriminator_fake_loss=1.35, generator_loss=27.52, generator_mel_loss=19.35, generator_kl_loss=1.389, generator_dur_loss=1.786, generator_adv_loss=1.972, generator_feat_match_loss=3.022, over 4203.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:46:23,175 INFO [train.py:527] (1/6) Epoch 98, batch 122, global_batch_idx: 12150, batch size: 39, loss[discriminator_loss=2.643, discriminator_real_loss=1.308, discriminator_fake_loss=1.336, generator_loss=27.98, generator_mel_loss=19.54, generator_kl_loss=1.514, generator_dur_loss=1.706, generator_adv_loss=1.971, generator_feat_match_loss=3.244, over 39.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.396, discriminator_fake_loss=1.346, generator_loss=27.39, generator_mel_loss=19.29, generator_kl_loss=1.371, generator_dur_loss=1.793, generator_adv_loss=1.951, generator_feat_match_loss=2.989, over 7200.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:46:27,557 INFO [train.py:919] (1/6) Start epoch 99
+2024-03-12 09:49:09,445 INFO [train.py:527] (1/6) Epoch 99, batch 48, global_batch_idx: 12200, batch size: 68, loss[discriminator_loss=2.738, discriminator_real_loss=1.528, discriminator_fake_loss=1.21, generator_loss=27.21, generator_mel_loss=19.35, generator_kl_loss=1.257, generator_dur_loss=1.842, generator_adv_loss=1.928, generator_feat_match_loss=2.838, over 68.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.386, discriminator_fake_loss=1.335, generator_loss=27.41, generator_mel_loss=19.36, generator_kl_loss=1.339, generator_dur_loss=1.773, generator_adv_loss=1.919, generator_feat_match_loss=3.026, over 2608.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:49:09,446 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 09:49:17,400 INFO [train.py:591] (1/6) Epoch 99, validation: discriminator_loss=2.729, discriminator_real_loss=1.43, discriminator_fake_loss=1.299, generator_loss=27.87, generator_mel_loss=20.36, generator_kl_loss=1.145, generator_dur_loss=1.836, generator_adv_loss=1.867, generator_feat_match_loss=2.665, over 100.00 samples.
+2024-03-12 09:49:17,401 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 09:51:38,138 INFO [train.py:527] (1/6) Epoch 99, batch 98, global_batch_idx: 12250, batch size: 56, loss[discriminator_loss=2.771, discriminator_real_loss=1.327, discriminator_fake_loss=1.444, generator_loss=27.75, generator_mel_loss=19.76, generator_kl_loss=1.418, generator_dur_loss=1.836, generator_adv_loss=1.904, generator_feat_match_loss=2.828, over 56.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.394, discriminator_fake_loss=1.345, generator_loss=27.4, generator_mel_loss=19.42, generator_kl_loss=1.364, generator_dur_loss=1.781, generator_adv_loss=1.898, generator_feat_match_loss=2.941, over 5357.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:52:48,481 INFO [train.py:919] (1/6) Start epoch 100
+2024-03-12 09:54:22,501 INFO [train.py:527] (1/6) Epoch 100, batch 24, global_batch_idx: 12300, batch size: 39, loss[discriminator_loss=2.775, discriminator_real_loss=1.441, discriminator_fake_loss=1.334, generator_loss=26.67, generator_mel_loss=18.9, generator_kl_loss=1.514, generator_dur_loss=1.735, generator_adv_loss=1.974, generator_feat_match_loss=2.551, over 39.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.403, discriminator_fake_loss=1.356, generator_loss=27.33, generator_mel_loss=19.37, generator_kl_loss=1.371, generator_dur_loss=1.751, generator_adv_loss=1.898, generator_feat_match_loss=2.947, over 1438.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:56:41,903 INFO [train.py:527] (1/6) Epoch 100, batch 74, global_batch_idx: 12350, batch size: 36, loss[discriminator_loss=2.624, discriminator_real_loss=1.379, discriminator_fake_loss=1.245, generator_loss=28.15, generator_mel_loss=19.1, generator_kl_loss=1.463, generator_dur_loss=1.806, generator_adv_loss=2.32, generator_feat_match_loss=3.453, over 36.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.404, discriminator_fake_loss=1.356, generator_loss=27.37, generator_mel_loss=19.31, generator_kl_loss=1.371, generator_dur_loss=1.79, generator_adv_loss=1.915, generator_feat_match_loss=2.985, over 4470.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:59:03,961 INFO [train.py:919] (1/6) Start epoch 101
+2024-03-12 09:59:33,465 INFO [train.py:527] (1/6) Epoch 101, batch 0, global_batch_idx: 12400, batch size: 80, loss[discriminator_loss=2.716, discriminator_real_loss=1.345, discriminator_fake_loss=1.371, generator_loss=27.41, generator_mel_loss=19.54, generator_kl_loss=1.266, generator_dur_loss=1.801, generator_adv_loss=1.844, generator_feat_match_loss=2.96, over 80.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.345, discriminator_fake_loss=1.371, generator_loss=27.41, generator_mel_loss=19.54, generator_kl_loss=1.266, generator_dur_loss=1.801, generator_adv_loss=1.844, generator_feat_match_loss=2.96, over 80.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 09:59:33,468 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 09:59:41,315 INFO [train.py:591] (1/6) Epoch 101, validation: discriminator_loss=2.747, discriminator_real_loss=1.379, discriminator_fake_loss=1.368, generator_loss=27.1, generator_mel_loss=19.92, generator_kl_loss=1.113, generator_dur_loss=1.817, generator_adv_loss=1.748, generator_feat_match_loss=2.501, over 100.00 samples.
+2024-03-12 09:59:41,318 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 10:02:00,878 INFO [train.py:527] (1/6) Epoch 101, batch 50, global_batch_idx: 12450, batch size: 50, loss[discriminator_loss=2.749, discriminator_real_loss=1.346, discriminator_fake_loss=1.403, generator_loss=27.61, generator_mel_loss=19.73, generator_kl_loss=1.44, generator_dur_loss=1.662, generator_adv_loss=1.815, generator_feat_match_loss=2.965, over 50.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.4, discriminator_fake_loss=1.347, generator_loss=27.34, generator_mel_loss=19.37, generator_kl_loss=1.406, generator_dur_loss=1.776, generator_adv_loss=1.9, generator_feat_match_loss=2.892, over 2688.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 10:04:20,057 INFO [train.py:527] (1/6) Epoch 101, batch 100, global_batch_idx: 12500, batch size: 66, loss[discriminator_loss=2.806, discriminator_real_loss=1.414, discriminator_fake_loss=1.392, generator_loss=25.83, generator_mel_loss=18.6, generator_kl_loss=1.32, generator_dur_loss=1.817, generator_adv_loss=1.682, generator_feat_match_loss=2.411, over 66.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.398, discriminator_fake_loss=1.351, generator_loss=27.31, generator_mel_loss=19.32, generator_kl_loss=1.399, generator_dur_loss=1.781, generator_adv_loss=1.904, generator_feat_match_loss=2.913, over 5522.00 samples.], cur_lr_g: 1.98e-04, cur_lr_d: 1.98e-04, 
+2024-03-12 10:05:28,491 INFO [train.py:919] (1/6) Start epoch 102
+2024-03-12 10:07:05,831 INFO [train.py:527] (1/6) Epoch 102, batch 26, global_batch_idx: 12550, batch size: 74, loss[discriminator_loss=2.714, discriminator_real_loss=1.466, discriminator_fake_loss=1.248, generator_loss=27.39, generator_mel_loss=19.04, generator_kl_loss=1.351, generator_dur_loss=1.901, generator_adv_loss=2.095, generator_feat_match_loss=3.002, over 74.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.403, discriminator_fake_loss=1.348, generator_loss=27.21, generator_mel_loss=19.24, generator_kl_loss=1.352, generator_dur_loss=1.769, generator_adv_loss=1.903, generator_feat_match_loss=2.953, over 1623.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:09:26,924 INFO [train.py:527] (1/6) Epoch 102, batch 76, global_batch_idx: 12600, batch size: 48, loss[discriminator_loss=2.71, discriminator_real_loss=1.35, discriminator_fake_loss=1.36, generator_loss=26.54, generator_mel_loss=18.68, generator_kl_loss=1.512, generator_dur_loss=1.66, generator_adv_loss=1.819, generator_feat_match_loss=2.874, over 48.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.395, discriminator_fake_loss=1.354, generator_loss=27.37, generator_mel_loss=19.24, generator_kl_loss=1.38, generator_dur_loss=1.764, generator_adv_loss=1.943, generator_feat_match_loss=3.039, over 4345.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:09:26,925 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 10:09:35,877 INFO [train.py:591] (1/6) Epoch 102, validation: discriminator_loss=2.726, discriminator_real_loss=1.317, discriminator_fake_loss=1.409, generator_loss=26.52, generator_mel_loss=19.64, generator_kl_loss=1.136, generator_dur_loss=1.834, generator_adv_loss=1.754, generator_feat_match_loss=2.162, over 100.00 samples.
+2024-03-12 10:09:35,878 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 10:11:48,661 INFO [train.py:919] (1/6) Start epoch 103
+2024-03-12 10:12:19,602 INFO [train.py:527] (1/6) Epoch 103, batch 2, global_batch_idx: 12650, batch size: 58, loss[discriminator_loss=2.711, discriminator_real_loss=1.31, discriminator_fake_loss=1.401, generator_loss=27.59, generator_mel_loss=19.38, generator_kl_loss=1.307, generator_dur_loss=1.8, generator_adv_loss=1.837, generator_feat_match_loss=3.267, over 58.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.338, discriminator_fake_loss=1.39, generator_loss=27.83, generator_mel_loss=19.46, generator_kl_loss=1.316, generator_dur_loss=1.806, generator_adv_loss=1.975, generator_feat_match_loss=3.268, over 168.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:14:41,465 INFO [train.py:527] (1/6) Epoch 103, batch 52, global_batch_idx: 12700, batch size: 66, loss[discriminator_loss=2.751, discriminator_real_loss=1.385, discriminator_fake_loss=1.366, generator_loss=26.69, generator_mel_loss=18.7, generator_kl_loss=1.329, generator_dur_loss=1.743, generator_adv_loss=2.021, generator_feat_match_loss=2.895, over 66.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.382, discriminator_fake_loss=1.355, generator_loss=27.46, generator_mel_loss=19.31, generator_kl_loss=1.415, generator_dur_loss=1.744, generator_adv_loss=1.933, generator_feat_match_loss=3.055, over 2926.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:17:01,092 INFO [train.py:527] (1/6) Epoch 103, batch 102, global_batch_idx: 12750, batch size: 66, loss[discriminator_loss=2.732, discriminator_real_loss=1.357, discriminator_fake_loss=1.375, generator_loss=27.8, generator_mel_loss=19.39, generator_kl_loss=1.325, generator_dur_loss=1.797, generator_adv_loss=1.929, generator_feat_match_loss=3.36, over 66.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.388, discriminator_fake_loss=1.355, generator_loss=27.39, generator_mel_loss=19.3, generator_kl_loss=1.397, generator_dur_loss=1.75, generator_adv_loss=1.922, generator_feat_match_loss=3.027, over 5845.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:18:03,773 INFO [train.py:919] (1/6) Start epoch 104
+2024-03-12 10:19:45,548 INFO [train.py:527] (1/6) Epoch 104, batch 28, global_batch_idx: 12800, batch size: 48, loss[discriminator_loss=2.737, discriminator_real_loss=1.431, discriminator_fake_loss=1.306, generator_loss=28.45, generator_mel_loss=20.2, generator_kl_loss=1.469, generator_dur_loss=1.681, generator_adv_loss=1.847, generator_feat_match_loss=3.252, over 48.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.388, discriminator_fake_loss=1.367, generator_loss=27.25, generator_mel_loss=19.31, generator_kl_loss=1.407, generator_dur_loss=1.728, generator_adv_loss=1.867, generator_feat_match_loss=2.941, over 1453.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:19:45,549 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 10:19:53,817 INFO [train.py:591] (1/6) Epoch 104, validation: discriminator_loss=2.756, discriminator_real_loss=1.366, discriminator_fake_loss=1.39, generator_loss=26.69, generator_mel_loss=19.5, generator_kl_loss=1.266, generator_dur_loss=1.804, generator_adv_loss=1.795, generator_feat_match_loss=2.324, over 100.00 samples.
+2024-03-12 10:19:53,818 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 10:22:18,400 INFO [train.py:527] (1/6) Epoch 104, batch 78, global_batch_idx: 12850, batch size: 68, loss[discriminator_loss=2.7, discriminator_real_loss=1.34, discriminator_fake_loss=1.36, generator_loss=27.03, generator_mel_loss=18.95, generator_kl_loss=1.41, generator_dur_loss=1.795, generator_adv_loss=1.871, generator_feat_match_loss=3.008, over 68.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.398, discriminator_fake_loss=1.361, generator_loss=27.31, generator_mel_loss=19.29, generator_kl_loss=1.401, generator_dur_loss=1.762, generator_adv_loss=1.883, generator_feat_match_loss=2.979, over 4293.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:24:25,377 INFO [train.py:919] (1/6) Start epoch 105
+2024-03-12 10:25:02,138 INFO [train.py:527] (1/6) Epoch 105, batch 4, global_batch_idx: 12900, batch size: 45, loss[discriminator_loss=2.766, discriminator_real_loss=1.352, discriminator_fake_loss=1.414, generator_loss=27.06, generator_mel_loss=19.02, generator_kl_loss=1.629, generator_dur_loss=1.685, generator_adv_loss=1.806, generator_feat_match_loss=2.924, over 45.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.364, discriminator_fake_loss=1.381, generator_loss=27.23, generator_mel_loss=19.23, generator_kl_loss=1.342, generator_dur_loss=1.775, generator_adv_loss=1.857, generator_feat_match_loss=3.025, over 296.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:27:25,785 INFO [train.py:527] (1/6) Epoch 105, batch 54, global_batch_idx: 12950, batch size: 58, loss[discriminator_loss=2.734, discriminator_real_loss=1.402, discriminator_fake_loss=1.333, generator_loss=28.45, generator_mel_loss=20.14, generator_kl_loss=1.381, generator_dur_loss=1.732, generator_adv_loss=1.911, generator_feat_match_loss=3.28, over 58.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.394, discriminator_fake_loss=1.359, generator_loss=27.25, generator_mel_loss=19.23, generator_kl_loss=1.371, generator_dur_loss=1.794, generator_adv_loss=1.897, generator_feat_match_loss=2.965, over 3314.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:29:48,021 INFO [train.py:527] (1/6) Epoch 105, batch 104, global_batch_idx: 13000, batch size: 44, loss[discriminator_loss=2.697, discriminator_real_loss=1.297, discriminator_fake_loss=1.4, generator_loss=26.79, generator_mel_loss=18.66, generator_kl_loss=1.6, generator_dur_loss=1.677, generator_adv_loss=1.856, generator_feat_match_loss=2.997, over 44.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=27.27, generator_mel_loss=19.23, generator_kl_loss=1.382, generator_dur_loss=1.78, generator_adv_loss=1.897, generator_feat_match_loss=2.987, over 6225.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:29:48,022 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 10:29:57,124 INFO [train.py:591] (1/6) Epoch 105, validation: discriminator_loss=2.617, discriminator_real_loss=1.249, discriminator_fake_loss=1.368, generator_loss=26.98, generator_mel_loss=19.23, generator_kl_loss=1.241, generator_dur_loss=1.776, generator_adv_loss=1.931, generator_feat_match_loss=2.793, over 100.00 samples.
+2024-03-12 10:29:57,125 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 10:30:50,498 INFO [train.py:919] (1/6) Start epoch 106
+2024-03-12 10:32:41,710 INFO [train.py:527] (1/6) Epoch 106, batch 30, global_batch_idx: 13050, batch size: 64, loss[discriminator_loss=2.7, discriminator_real_loss=1.37, discriminator_fake_loss=1.33, generator_loss=26.97, generator_mel_loss=18.93, generator_kl_loss=1.399, generator_dur_loss=1.759, generator_adv_loss=1.839, generator_feat_match_loss=3.044, over 64.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.404, discriminator_fake_loss=1.35, generator_loss=27.31, generator_mel_loss=19.25, generator_kl_loss=1.4, generator_dur_loss=1.764, generator_adv_loss=1.919, generator_feat_match_loss=2.97, over 1692.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:35:02,019 INFO [train.py:527] (1/6) Epoch 106, batch 80, global_batch_idx: 13100, batch size: 55, loss[discriminator_loss=2.798, discriminator_real_loss=1.273, discriminator_fake_loss=1.525, generator_loss=27.49, generator_mel_loss=19.43, generator_kl_loss=1.34, generator_dur_loss=1.754, generator_adv_loss=2.066, generator_feat_match_loss=2.896, over 55.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.394, discriminator_fake_loss=1.352, generator_loss=27.3, generator_mel_loss=19.23, generator_kl_loss=1.405, generator_dur_loss=1.764, generator_adv_loss=1.909, generator_feat_match_loss=2.992, over 4450.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:37:09,197 INFO [train.py:919] (1/6) Start epoch 107
+2024-03-12 10:37:52,295 INFO [train.py:527] (1/6) Epoch 107, batch 6, global_batch_idx: 13150, batch size: 53, loss[discriminator_loss=2.698, discriminator_real_loss=1.377, discriminator_fake_loss=1.321, generator_loss=26.63, generator_mel_loss=18.83, generator_kl_loss=1.307, generator_dur_loss=1.661, generator_adv_loss=1.89, generator_feat_match_loss=2.947, over 53.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=27.21, generator_mel_loss=19.28, generator_kl_loss=1.353, generator_dur_loss=1.732, generator_adv_loss=1.861, generator_feat_match_loss=2.986, over 362.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:40:17,586 INFO [train.py:527] (1/6) Epoch 107, batch 56, global_batch_idx: 13200, batch size: 15, loss[discriminator_loss=2.643, discriminator_real_loss=1.24, discriminator_fake_loss=1.403, generator_loss=30.28, generator_mel_loss=21.68, generator_kl_loss=1.573, generator_dur_loss=1.649, generator_adv_loss=2.066, generator_feat_match_loss=3.318, over 15.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.391, discriminator_fake_loss=1.351, generator_loss=27.31, generator_mel_loss=19.17, generator_kl_loss=1.389, generator_dur_loss=1.743, generator_adv_loss=1.929, generator_feat_match_loss=3.087, over 3127.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:40:17,587 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 10:40:25,396 INFO [train.py:591] (1/6) Epoch 107, validation: discriminator_loss=2.792, discriminator_real_loss=1.582, discriminator_fake_loss=1.21, generator_loss=26.08, generator_mel_loss=18.85, generator_kl_loss=1.137, generator_dur_loss=1.792, generator_adv_loss=1.94, generator_feat_match_loss=2.356, over 100.00 samples.
+2024-03-12 10:40:25,396 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 10:42:42,997 INFO [train.py:527] (1/6) Epoch 107, batch 106, global_batch_idx: 13250, batch size: 14, loss[discriminator_loss=2.634, discriminator_real_loss=1.284, discriminator_fake_loss=1.35, generator_loss=29.72, generator_mel_loss=20.41, generator_kl_loss=2.042, generator_dur_loss=1.679, generator_adv_loss=2.04, generator_feat_match_loss=3.541, over 14.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.39, discriminator_fake_loss=1.348, generator_loss=27.32, generator_mel_loss=19.2, generator_kl_loss=1.389, generator_dur_loss=1.754, generator_adv_loss=1.912, generator_feat_match_loss=3.062, over 5910.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:43:31,862 INFO [train.py:919] (1/6) Start epoch 108
+2024-03-12 10:45:23,069 INFO [train.py:527] (1/6) Epoch 108, batch 32, global_batch_idx: 13300, batch size: 42, loss[discriminator_loss=2.678, discriminator_real_loss=1.222, discriminator_fake_loss=1.457, generator_loss=28.56, generator_mel_loss=20.28, generator_kl_loss=1.357, generator_dur_loss=1.741, generator_adv_loss=1.79, generator_feat_match_loss=3.388, over 42.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.388, discriminator_fake_loss=1.343, generator_loss=27.3, generator_mel_loss=19.12, generator_kl_loss=1.366, generator_dur_loss=1.784, generator_adv_loss=1.942, generator_feat_match_loss=3.09, over 1905.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:47:44,046 INFO [train.py:527] (1/6) Epoch 108, batch 82, global_batch_idx: 13350, batch size: 61, loss[discriminator_loss=2.754, discriminator_real_loss=1.441, discriminator_fake_loss=1.313, generator_loss=27.08, generator_mel_loss=19.06, generator_kl_loss=1.387, generator_dur_loss=1.751, generator_adv_loss=1.864, generator_feat_match_loss=3.017, over 61.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.393, discriminator_fake_loss=1.34, generator_loss=27.4, generator_mel_loss=19.22, generator_kl_loss=1.399, generator_dur_loss=1.762, generator_adv_loss=1.926, generator_feat_match_loss=3.097, over 4627.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:49:42,991 INFO [train.py:919] (1/6) Start epoch 109
+2024-03-12 10:50:30,627 INFO [train.py:527] (1/6) Epoch 109, batch 8, global_batch_idx: 13400, batch size: 48, loss[discriminator_loss=2.782, discriminator_real_loss=1.642, discriminator_fake_loss=1.139, generator_loss=28.36, generator_mel_loss=19.83, generator_kl_loss=1.379, generator_dur_loss=1.713, generator_adv_loss=2.391, generator_feat_match_loss=3.039, over 48.00 samples.], tot_loss[discriminator_loss=2.832, discriminator_real_loss=1.418, discriminator_fake_loss=1.414, generator_loss=27.63, generator_mel_loss=19.17, generator_kl_loss=1.384, generator_dur_loss=1.803, generator_adv_loss=2.068, generator_feat_match_loss=3.206, over 597.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:50:30,629 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 10:50:38,652 INFO [train.py:591] (1/6) Epoch 109, validation: discriminator_loss=2.703, discriminator_real_loss=1.603, discriminator_fake_loss=1.1, generator_loss=27.09, generator_mel_loss=19.11, generator_kl_loss=1.1, generator_dur_loss=1.82, generator_adv_loss=2.323, generator_feat_match_loss=2.736, over 100.00 samples.
+2024-03-12 10:50:38,654 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 10:53:02,519 INFO [train.py:527] (1/6) Epoch 109, batch 58, global_batch_idx: 13450, batch size: 80, loss[discriminator_loss=2.763, discriminator_real_loss=1.374, discriminator_fake_loss=1.388, generator_loss=27.42, generator_mel_loss=19.28, generator_kl_loss=1.198, generator_dur_loss=1.844, generator_adv_loss=2.044, generator_feat_match_loss=3.049, over 80.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.413, discriminator_fake_loss=1.362, generator_loss=27.24, generator_mel_loss=19.08, generator_kl_loss=1.376, generator_dur_loss=1.803, generator_adv_loss=1.969, generator_feat_match_loss=3.004, over 3663.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:55:20,978 INFO [train.py:527] (1/6) Epoch 109, batch 108, global_batch_idx: 13500, batch size: 14, loss[discriminator_loss=2.66, discriminator_real_loss=1.301, discriminator_fake_loss=1.36, generator_loss=29.72, generator_mel_loss=21.64, generator_kl_loss=1.521, generator_dur_loss=1.695, generator_adv_loss=1.79, generator_feat_match_loss=3.073, over 14.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.412, discriminator_fake_loss=1.361, generator_loss=27.19, generator_mel_loss=19.16, generator_kl_loss=1.383, generator_dur_loss=1.768, generator_adv_loss=1.931, generator_feat_match_loss=2.944, over 6288.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 10:56:04,880 INFO [train.py:919] (1/6) Start epoch 110
+2024-03-12 10:58:07,913 INFO [train.py:527] (1/6) Epoch 110, batch 34, global_batch_idx: 13550, batch size: 31, loss[discriminator_loss=2.632, discriminator_real_loss=1.227, discriminator_fake_loss=1.405, generator_loss=27.41, generator_mel_loss=18.82, generator_kl_loss=1.631, generator_dur_loss=1.678, generator_adv_loss=2.031, generator_feat_match_loss=3.243, over 31.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.375, discriminator_fake_loss=1.365, generator_loss=27.06, generator_mel_loss=19.06, generator_kl_loss=1.357, generator_dur_loss=1.752, generator_adv_loss=1.884, generator_feat_match_loss=3.012, over 1881.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:00:32,651 INFO [train.py:527] (1/6) Epoch 110, batch 84, global_batch_idx: 13600, batch size: 44, loss[discriminator_loss=2.649, discriminator_real_loss=1.376, discriminator_fake_loss=1.274, generator_loss=28.63, generator_mel_loss=19.71, generator_kl_loss=1.503, generator_dur_loss=1.769, generator_adv_loss=2.057, generator_feat_match_loss=3.597, over 44.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.387, discriminator_fake_loss=1.36, generator_loss=27.14, generator_mel_loss=19.09, generator_kl_loss=1.358, generator_dur_loss=1.783, generator_adv_loss=1.898, generator_feat_match_loss=3.008, over 5052.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:00:32,653 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 11:00:41,511 INFO [train.py:591] (1/6) Epoch 110, validation: discriminator_loss=2.734, discriminator_real_loss=1.415, discriminator_fake_loss=1.319, generator_loss=26.16, generator_mel_loss=19.01, generator_kl_loss=1.163, generator_dur_loss=1.823, generator_adv_loss=1.86, generator_feat_match_loss=2.299, over 100.00 samples.
+2024-03-12 11:00:41,512 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 11:02:28,655 INFO [train.py:919] (1/6) Start epoch 111
+2024-03-12 11:03:24,128 INFO [train.py:527] (1/6) Epoch 111, batch 10, global_batch_idx: 13650, batch size: 47, loss[discriminator_loss=2.774, discriminator_real_loss=1.354, discriminator_fake_loss=1.42, generator_loss=27.07, generator_mel_loss=18.98, generator_kl_loss=1.469, generator_dur_loss=1.666, generator_adv_loss=1.913, generator_feat_match_loss=3.035, over 47.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.389, discriminator_fake_loss=1.365, generator_loss=26.98, generator_mel_loss=18.95, generator_kl_loss=1.381, generator_dur_loss=1.782, generator_adv_loss=1.895, generator_feat_match_loss=2.978, over 715.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:05:46,907 INFO [train.py:527] (1/6) Epoch 111, batch 60, global_batch_idx: 13700, batch size: 36, loss[discriminator_loss=2.812, discriminator_real_loss=1.499, discriminator_fake_loss=1.314, generator_loss=27.82, generator_mel_loss=19.83, generator_kl_loss=1.619, generator_dur_loss=1.676, generator_adv_loss=1.945, generator_feat_match_loss=2.747, over 36.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.391, discriminator_fake_loss=1.348, generator_loss=27.3, generator_mel_loss=19.15, generator_kl_loss=1.42, generator_dur_loss=1.752, generator_adv_loss=1.911, generator_feat_match_loss=3.067, over 3572.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:08:05,556 INFO [train.py:527] (1/6) Epoch 111, batch 110, global_batch_idx: 13750, batch size: 50, loss[discriminator_loss=2.829, discriminator_real_loss=1.373, discriminator_fake_loss=1.457, generator_loss=27.68, generator_mel_loss=19.75, generator_kl_loss=1.621, generator_dur_loss=1.721, generator_adv_loss=1.74, generator_feat_match_loss=2.854, over 50.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.394, discriminator_fake_loss=1.357, generator_loss=27.34, generator_mel_loss=19.18, generator_kl_loss=1.403, generator_dur_loss=1.761, generator_adv_loss=1.923, generator_feat_match_loss=3.077, over 6313.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:08:44,823 INFO [train.py:919] (1/6) Start epoch 112
+2024-03-12 11:10:51,339 INFO [train.py:527] (1/6) Epoch 112, batch 36, global_batch_idx: 13800, batch size: 68, loss[discriminator_loss=2.744, discriminator_real_loss=1.399, discriminator_fake_loss=1.346, generator_loss=26.74, generator_mel_loss=18.87, generator_kl_loss=1.322, generator_dur_loss=1.81, generator_adv_loss=1.824, generator_feat_match_loss=2.917, over 68.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.399, discriminator_fake_loss=1.338, generator_loss=27.18, generator_mel_loss=19.1, generator_kl_loss=1.37, generator_dur_loss=1.801, generator_adv_loss=1.909, generator_feat_match_loss=3.004, over 2268.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:10:51,340 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 11:10:59,109 INFO [train.py:591] (1/6) Epoch 112, validation: discriminator_loss=2.8, discriminator_real_loss=1.409, discriminator_fake_loss=1.391, generator_loss=26.38, generator_mel_loss=19.18, generator_kl_loss=1.216, generator_dur_loss=1.816, generator_adv_loss=1.727, generator_feat_match_loss=2.443, over 100.00 samples.
+2024-03-12 11:10:59,110 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 11:13:21,148 INFO [train.py:527] (1/6) Epoch 112, batch 86, global_batch_idx: 13850, batch size: 58, loss[discriminator_loss=2.747, discriminator_real_loss=1.419, discriminator_fake_loss=1.328, generator_loss=28.06, generator_mel_loss=19.61, generator_kl_loss=1.448, generator_dur_loss=1.762, generator_adv_loss=2.003, generator_feat_match_loss=3.23, over 58.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.393, discriminator_fake_loss=1.349, generator_loss=27.22, generator_mel_loss=19.12, generator_kl_loss=1.392, generator_dur_loss=1.777, generator_adv_loss=1.901, generator_feat_match_loss=3.036, over 4975.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:15:03,997 INFO [train.py:919] (1/6) Start epoch 113
+2024-03-12 11:16:04,046 INFO [train.py:527] (1/6) Epoch 113, batch 12, global_batch_idx: 13900, batch size: 80, loss[discriminator_loss=2.525, discriminator_real_loss=1.271, discriminator_fake_loss=1.254, generator_loss=28.34, generator_mel_loss=19.23, generator_kl_loss=1.207, generator_dur_loss=1.861, generator_adv_loss=2.104, generator_feat_match_loss=3.935, over 80.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.382, discriminator_fake_loss=1.334, generator_loss=27.6, generator_mel_loss=19.22, generator_kl_loss=1.364, generator_dur_loss=1.797, generator_adv_loss=1.988, generator_feat_match_loss=3.234, over 759.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:18:23,838 INFO [train.py:527] (1/6) Epoch 113, batch 62, global_batch_idx: 13950, batch size: 88, loss[discriminator_loss=2.71, discriminator_real_loss=1.386, discriminator_fake_loss=1.324, generator_loss=27.12, generator_mel_loss=18.73, generator_kl_loss=1.357, generator_dur_loss=1.872, generator_adv_loss=1.864, generator_feat_match_loss=3.299, over 88.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.403, discriminator_fake_loss=1.352, generator_loss=27.25, generator_mel_loss=19.1, generator_kl_loss=1.361, generator_dur_loss=1.786, generator_adv_loss=1.952, generator_feat_match_loss=3.058, over 3664.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:20:48,946 INFO [train.py:527] (1/6) Epoch 113, batch 112, global_batch_idx: 14000, batch size: 47, loss[discriminator_loss=2.718, discriminator_real_loss=1.351, discriminator_fake_loss=1.367, generator_loss=27.51, generator_mel_loss=19.2, generator_kl_loss=1.42, generator_dur_loss=1.68, generator_adv_loss=1.831, generator_feat_match_loss=3.379, over 47.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.399, discriminator_fake_loss=1.349, generator_loss=27.21, generator_mel_loss=19.08, generator_kl_loss=1.368, generator_dur_loss=1.789, generator_adv_loss=1.928, generator_feat_match_loss=3.048, over 6602.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:20:48,948 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 11:20:57,871 INFO [train.py:591] (1/6) Epoch 113, validation: discriminator_loss=2.751, discriminator_real_loss=1.385, discriminator_fake_loss=1.366, generator_loss=25.82, generator_mel_loss=18.77, generator_kl_loss=1.134, generator_dur_loss=1.803, generator_adv_loss=1.791, generator_feat_match_loss=2.326, over 100.00 samples.
+2024-03-12 11:20:57,872 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 11:21:28,692 INFO [train.py:919] (1/6) Start epoch 114
+2024-03-12 11:23:41,678 INFO [train.py:527] (1/6) Epoch 114, batch 38, global_batch_idx: 14050, batch size: 56, loss[discriminator_loss=2.74, discriminator_real_loss=1.347, discriminator_fake_loss=1.393, generator_loss=27.48, generator_mel_loss=19.44, generator_kl_loss=1.35, generator_dur_loss=1.7, generator_adv_loss=1.807, generator_feat_match_loss=3.183, over 56.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.395, discriminator_fake_loss=1.348, generator_loss=27.22, generator_mel_loss=19.16, generator_kl_loss=1.403, generator_dur_loss=1.732, generator_adv_loss=1.887, generator_feat_match_loss=3.044, over 2185.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:26:06,585 INFO [train.py:527] (1/6) Epoch 114, batch 88, global_batch_idx: 14100, batch size: 50, loss[discriminator_loss=2.726, discriminator_real_loss=1.321, discriminator_fake_loss=1.405, generator_loss=27.38, generator_mel_loss=19, generator_kl_loss=1.506, generator_dur_loss=1.626, generator_adv_loss=1.956, generator_feat_match_loss=3.299, over 50.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.391, discriminator_fake_loss=1.345, generator_loss=27.36, generator_mel_loss=19.18, generator_kl_loss=1.407, generator_dur_loss=1.737, generator_adv_loss=1.908, generator_feat_match_loss=3.122, over 4837.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:27:47,057 INFO [train.py:919] (1/6) Start epoch 115
+2024-03-12 11:28:53,599 INFO [train.py:527] (1/6) Epoch 115, batch 14, global_batch_idx: 14150, batch size: 77, loss[discriminator_loss=2.759, discriminator_real_loss=1.385, discriminator_fake_loss=1.373, generator_loss=27.31, generator_mel_loss=19.04, generator_kl_loss=1.333, generator_dur_loss=1.824, generator_adv_loss=2.138, generator_feat_match_loss=2.98, over 77.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.406, discriminator_fake_loss=1.347, generator_loss=27.29, generator_mel_loss=19.09, generator_kl_loss=1.39, generator_dur_loss=1.795, generator_adv_loss=1.937, generator_feat_match_loss=3.081, over 895.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:31:17,606 INFO [train.py:527] (1/6) Epoch 115, batch 64, global_batch_idx: 14200, batch size: 80, loss[discriminator_loss=2.74, discriminator_real_loss=1.469, discriminator_fake_loss=1.271, generator_loss=27.49, generator_mel_loss=19.36, generator_kl_loss=1.267, generator_dur_loss=1.871, generator_adv_loss=1.903, generator_feat_match_loss=3.091, over 80.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.404, discriminator_fake_loss=1.337, generator_loss=27.35, generator_mel_loss=19.1, generator_kl_loss=1.398, generator_dur_loss=1.772, generator_adv_loss=1.965, generator_feat_match_loss=3.121, over 3797.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:31:17,608 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 11:31:25,537 INFO [train.py:591] (1/6) Epoch 115, validation: discriminator_loss=2.747, discriminator_real_loss=1.47, discriminator_fake_loss=1.277, generator_loss=26.49, generator_mel_loss=19.07, generator_kl_loss=1.219, generator_dur_loss=1.805, generator_adv_loss=1.859, generator_feat_match_loss=2.54, over 100.00 samples.
+2024-03-12 11:31:25,538 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 11:33:42,875 INFO [train.py:527] (1/6) Epoch 115, batch 114, global_batch_idx: 14250, batch size: 45, loss[discriminator_loss=2.778, discriminator_real_loss=1.369, discriminator_fake_loss=1.41, generator_loss=27.26, generator_mel_loss=19.09, generator_kl_loss=1.431, generator_dur_loss=1.711, generator_adv_loss=1.858, generator_feat_match_loss=3.17, over 45.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.403, discriminator_fake_loss=1.341, generator_loss=27.36, generator_mel_loss=19.15, generator_kl_loss=1.407, generator_dur_loss=1.769, generator_adv_loss=1.939, generator_feat_match_loss=3.098, over 6445.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:34:09,024 INFO [train.py:919] (1/6) Start epoch 116
+2024-03-12 11:36:27,873 INFO [train.py:527] (1/6) Epoch 116, batch 40, global_batch_idx: 14300, batch size: 80, loss[discriminator_loss=2.739, discriminator_real_loss=1.382, discriminator_fake_loss=1.358, generator_loss=26.58, generator_mel_loss=18.88, generator_kl_loss=1.45, generator_dur_loss=1.782, generator_adv_loss=1.941, generator_feat_match_loss=2.523, over 80.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.388, discriminator_fake_loss=1.352, generator_loss=27.42, generator_mel_loss=19.22, generator_kl_loss=1.413, generator_dur_loss=1.768, generator_adv_loss=1.905, generator_feat_match_loss=3.112, over 2334.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:38:53,133 INFO [train.py:527] (1/6) Epoch 116, batch 90, global_batch_idx: 14350, batch size: 70, loss[discriminator_loss=2.723, discriminator_real_loss=1.336, discriminator_fake_loss=1.387, generator_loss=27.56, generator_mel_loss=19.23, generator_kl_loss=1.329, generator_dur_loss=1.885, generator_adv_loss=2.008, generator_feat_match_loss=3.11, over 70.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.391, discriminator_fake_loss=1.356, generator_loss=27.27, generator_mel_loss=19.15, generator_kl_loss=1.391, generator_dur_loss=1.77, generator_adv_loss=1.898, generator_feat_match_loss=3.062, over 5264.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:40:23,470 INFO [train.py:919] (1/6) Start epoch 117
+2024-03-12 11:41:34,032 INFO [train.py:527] (1/6) Epoch 117, batch 16, global_batch_idx: 14400, batch size: 36, loss[discriminator_loss=2.716, discriminator_real_loss=1.345, discriminator_fake_loss=1.371, generator_loss=27.7, generator_mel_loss=19.48, generator_kl_loss=1.463, generator_dur_loss=1.703, generator_adv_loss=1.911, generator_feat_match_loss=3.144, over 36.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.386, discriminator_fake_loss=1.352, generator_loss=27.51, generator_mel_loss=19.29, generator_kl_loss=1.397, generator_dur_loss=1.78, generator_adv_loss=1.906, generator_feat_match_loss=3.138, over 956.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:41:34,033 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 11:41:42,046 INFO [train.py:591] (1/6) Epoch 117, validation: discriminator_loss=2.698, discriminator_real_loss=1.388, discriminator_fake_loss=1.31, generator_loss=26.84, generator_mel_loss=19.33, generator_kl_loss=1.169, generator_dur_loss=1.842, generator_adv_loss=1.867, generator_feat_match_loss=2.631, over 100.00 samples.
+2024-03-12 11:41:42,046 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 11:44:05,219 INFO [train.py:527] (1/6) Epoch 117, batch 66, global_batch_idx: 14450, batch size: 88, loss[discriminator_loss=2.76, discriminator_real_loss=1.291, discriminator_fake_loss=1.469, generator_loss=27.28, generator_mel_loss=19.33, generator_kl_loss=1.134, generator_dur_loss=1.841, generator_adv_loss=1.919, generator_feat_match_loss=3.051, over 88.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.394, discriminator_fake_loss=1.353, generator_loss=27.48, generator_mel_loss=19.3, generator_kl_loss=1.405, generator_dur_loss=1.76, generator_adv_loss=1.908, generator_feat_match_loss=3.104, over 3710.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:46:21,812 INFO [train.py:527] (1/6) Epoch 117, batch 116, global_batch_idx: 14500, batch size: 72, loss[discriminator_loss=2.698, discriminator_real_loss=1.32, discriminator_fake_loss=1.378, generator_loss=27.03, generator_mel_loss=18.71, generator_kl_loss=1.459, generator_dur_loss=1.757, generator_adv_loss=1.979, generator_feat_match_loss=3.126, over 72.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.396, discriminator_fake_loss=1.353, generator_loss=27.42, generator_mel_loss=19.26, generator_kl_loss=1.403, generator_dur_loss=1.756, generator_adv_loss=1.909, generator_feat_match_loss=3.091, over 6352.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:46:41,567 INFO [train.py:919] (1/6) Start epoch 118
+2024-03-12 11:49:04,471 INFO [train.py:527] (1/6) Epoch 118, batch 42, global_batch_idx: 14550, batch size: 74, loss[discriminator_loss=2.755, discriminator_real_loss=1.426, discriminator_fake_loss=1.329, generator_loss=26.38, generator_mel_loss=18.72, generator_kl_loss=1.303, generator_dur_loss=1.837, generator_adv_loss=1.716, generator_feat_match_loss=2.807, over 74.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.398, discriminator_fake_loss=1.358, generator_loss=27.27, generator_mel_loss=19.15, generator_kl_loss=1.402, generator_dur_loss=1.751, generator_adv_loss=1.889, generator_feat_match_loss=3.08, over 2360.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:51:25,189 INFO [train.py:527] (1/6) Epoch 118, batch 92, global_batch_idx: 14600, batch size: 44, loss[discriminator_loss=2.842, discriminator_real_loss=1.423, discriminator_fake_loss=1.419, generator_loss=26.83, generator_mel_loss=18.85, generator_kl_loss=1.631, generator_dur_loss=1.715, generator_adv_loss=1.839, generator_feat_match_loss=2.796, over 44.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.396, discriminator_fake_loss=1.356, generator_loss=27.32, generator_mel_loss=19.16, generator_kl_loss=1.405, generator_dur_loss=1.753, generator_adv_loss=1.9, generator_feat_match_loss=3.095, over 4943.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:51:25,191 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 11:51:33,693 INFO [train.py:591] (1/6) Epoch 118, validation: discriminator_loss=2.765, discriminator_real_loss=1.428, discriminator_fake_loss=1.338, generator_loss=26.6, generator_mel_loss=19.28, generator_kl_loss=1.279, generator_dur_loss=1.827, generator_adv_loss=1.81, generator_feat_match_loss=2.411, over 100.00 samples.
+2024-03-12 11:51:33,693 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 11:53:02,544 INFO [train.py:919] (1/6) Start epoch 119
+2024-03-12 11:54:18,451 INFO [train.py:527] (1/6) Epoch 119, batch 18, global_batch_idx: 14650, batch size: 55, loss[discriminator_loss=2.705, discriminator_real_loss=1.302, discriminator_fake_loss=1.403, generator_loss=27.49, generator_mel_loss=19.4, generator_kl_loss=1.341, generator_dur_loss=1.698, generator_adv_loss=1.823, generator_feat_match_loss=3.232, over 55.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.369, discriminator_fake_loss=1.352, generator_loss=27.35, generator_mel_loss=19.07, generator_kl_loss=1.436, generator_dur_loss=1.733, generator_adv_loss=1.921, generator_feat_match_loss=3.189, over 934.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:56:42,129 INFO [train.py:527] (1/6) Epoch 119, batch 68, global_batch_idx: 14700, batch size: 36, loss[discriminator_loss=2.745, discriminator_real_loss=1.331, discriminator_fake_loss=1.414, generator_loss=28.58, generator_mel_loss=19.87, generator_kl_loss=1.549, generator_dur_loss=1.673, generator_adv_loss=1.91, generator_feat_match_loss=3.585, over 36.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.395, discriminator_fake_loss=1.357, generator_loss=27.21, generator_mel_loss=19.05, generator_kl_loss=1.403, generator_dur_loss=1.748, generator_adv_loss=1.91, generator_feat_match_loss=3.102, over 3889.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:58:56,885 INFO [train.py:527] (1/6) Epoch 119, batch 118, global_batch_idx: 14750, batch size: 74, loss[discriminator_loss=2.725, discriminator_real_loss=1.263, discriminator_fake_loss=1.462, generator_loss=27.1, generator_mel_loss=19.19, generator_kl_loss=1.241, generator_dur_loss=1.805, generator_adv_loss=1.9, generator_feat_match_loss=2.969, over 74.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.392, discriminator_fake_loss=1.357, generator_loss=27.19, generator_mel_loss=19.04, generator_kl_loss=1.401, generator_dur_loss=1.755, generator_adv_loss=1.9, generator_feat_match_loss=3.097, over 6782.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 11:59:12,812 INFO [train.py:919] (1/6) Start epoch 120
+2024-03-12 12:01:37,943 INFO [train.py:527] (1/6) Epoch 120, batch 44, global_batch_idx: 14800, batch size: 44, loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.336, generator_loss=26.87, generator_mel_loss=18.89, generator_kl_loss=1.491, generator_dur_loss=1.667, generator_adv_loss=1.896, generator_feat_match_loss=2.935, over 44.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.387, discriminator_fake_loss=1.364, generator_loss=27.17, generator_mel_loss=18.99, generator_kl_loss=1.393, generator_dur_loss=1.777, generator_adv_loss=1.915, generator_feat_match_loss=3.099, over 2713.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:01:37,945 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 12:01:45,879 INFO [train.py:591] (1/6) Epoch 120, validation: discriminator_loss=2.76, discriminator_real_loss=1.43, discriminator_fake_loss=1.33, generator_loss=26.73, generator_mel_loss=19.22, generator_kl_loss=1.226, generator_dur_loss=1.797, generator_adv_loss=1.813, generator_feat_match_loss=2.683, over 100.00 samples.
+2024-03-12 12:01:45,880 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 12:04:04,575 INFO [train.py:527] (1/6) Epoch 120, batch 94, global_batch_idx: 14850, batch size: 68, loss[discriminator_loss=2.917, discriminator_real_loss=1.722, discriminator_fake_loss=1.195, generator_loss=26.05, generator_mel_loss=18.65, generator_kl_loss=1.415, generator_dur_loss=1.76, generator_adv_loss=1.525, generator_feat_match_loss=2.697, over 68.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.393, discriminator_fake_loss=1.355, generator_loss=27.21, generator_mel_loss=19.05, generator_kl_loss=1.402, generator_dur_loss=1.754, generator_adv_loss=1.912, generator_feat_match_loss=3.096, over 5345.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:05:27,168 INFO [train.py:919] (1/6) Start epoch 121
+2024-03-12 12:06:45,345 INFO [train.py:527] (1/6) Epoch 121, batch 20, global_batch_idx: 14900, batch size: 47, loss[discriminator_loss=2.747, discriminator_real_loss=1.367, discriminator_fake_loss=1.379, generator_loss=27.78, generator_mel_loss=19.67, generator_kl_loss=1.372, generator_dur_loss=1.687, generator_adv_loss=1.937, generator_feat_match_loss=3.117, over 47.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.395, discriminator_fake_loss=1.366, generator_loss=27.36, generator_mel_loss=19.22, generator_kl_loss=1.353, generator_dur_loss=1.738, generator_adv_loss=1.952, generator_feat_match_loss=3.102, over 1206.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:09:06,783 INFO [train.py:527] (1/6) Epoch 121, batch 70, global_batch_idx: 14950, batch size: 68, loss[discriminator_loss=2.759, discriminator_real_loss=1.394, discriminator_fake_loss=1.364, generator_loss=27.12, generator_mel_loss=19.28, generator_kl_loss=1.396, generator_dur_loss=1.77, generator_adv_loss=1.888, generator_feat_match_loss=2.787, over 68.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.394, discriminator_fake_loss=1.36, generator_loss=27.13, generator_mel_loss=19.05, generator_kl_loss=1.357, generator_dur_loss=1.769, generator_adv_loss=1.911, generator_feat_match_loss=3.049, over 4211.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:11:25,522 INFO [train.py:527] (1/6) Epoch 121, batch 120, global_batch_idx: 15000, batch size: 62, loss[discriminator_loss=2.772, discriminator_real_loss=1.311, discriminator_fake_loss=1.462, generator_loss=26.7, generator_mel_loss=18.79, generator_kl_loss=1.288, generator_dur_loss=1.742, generator_adv_loss=1.912, generator_feat_match_loss=2.969, over 62.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.389, discriminator_fake_loss=1.357, generator_loss=27.2, generator_mel_loss=19.04, generator_kl_loss=1.388, generator_dur_loss=1.757, generator_adv_loss=1.908, generator_feat_match_loss=3.104, over 6928.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:11:25,524 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 12:11:34,417 INFO [train.py:591] (1/6) Epoch 121, validation: discriminator_loss=2.75, discriminator_real_loss=1.455, discriminator_fake_loss=1.294, generator_loss=26.64, generator_mel_loss=19.12, generator_kl_loss=1.093, generator_dur_loss=1.827, generator_adv_loss=1.899, generator_feat_match_loss=2.698, over 100.00 samples.
+2024-03-12 12:11:34,418 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 12:11:43,791 INFO [train.py:919] (1/6) Start epoch 122
+2024-03-12 12:14:17,612 INFO [train.py:527] (1/6) Epoch 122, batch 46, global_batch_idx: 15050, batch size: 56, loss[discriminator_loss=2.695, discriminator_real_loss=1.393, discriminator_fake_loss=1.303, generator_loss=27.47, generator_mel_loss=19.19, generator_kl_loss=1.376, generator_dur_loss=1.672, generator_adv_loss=2.12, generator_feat_match_loss=3.112, over 56.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.403, discriminator_fake_loss=1.359, generator_loss=27.33, generator_mel_loss=19.1, generator_kl_loss=1.389, generator_dur_loss=1.748, generator_adv_loss=1.948, generator_feat_match_loss=3.145, over 2611.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:16:36,050 INFO [train.py:527] (1/6) Epoch 122, batch 96, global_batch_idx: 15100, batch size: 36, loss[discriminator_loss=2.69, discriminator_real_loss=1.296, discriminator_fake_loss=1.394, generator_loss=27.22, generator_mel_loss=19.02, generator_kl_loss=1.44, generator_dur_loss=1.663, generator_adv_loss=1.821, generator_feat_match_loss=3.28, over 36.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.406, discriminator_fake_loss=1.348, generator_loss=27.24, generator_mel_loss=19.04, generator_kl_loss=1.395, generator_dur_loss=1.751, generator_adv_loss=1.94, generator_feat_match_loss=3.113, over 5342.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:17:48,316 INFO [train.py:919] (1/6) Start epoch 123
+2024-03-12 12:19:14,195 INFO [train.py:527] (1/6) Epoch 123, batch 22, global_batch_idx: 15150, batch size: 45, loss[discriminator_loss=2.716, discriminator_real_loss=1.269, discriminator_fake_loss=1.448, generator_loss=27.49, generator_mel_loss=18.82, generator_kl_loss=1.494, generator_dur_loss=1.706, generator_adv_loss=2.017, generator_feat_match_loss=3.452, over 45.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.378, discriminator_fake_loss=1.372, generator_loss=27.06, generator_mel_loss=19.01, generator_kl_loss=1.344, generator_dur_loss=1.761, generator_adv_loss=1.87, generator_feat_match_loss=3.069, over 1342.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:21:34,500 INFO [train.py:527] (1/6) Epoch 123, batch 72, global_batch_idx: 15200, batch size: 48, loss[discriminator_loss=2.749, discriminator_real_loss=1.356, discriminator_fake_loss=1.393, generator_loss=26.6, generator_mel_loss=18.59, generator_kl_loss=1.55, generator_dur_loss=1.708, generator_adv_loss=1.831, generator_feat_match_loss=2.92, over 48.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.39, discriminator_fake_loss=1.363, generator_loss=27.29, generator_mel_loss=19.12, generator_kl_loss=1.385, generator_dur_loss=1.757, generator_adv_loss=1.888, generator_feat_match_loss=3.135, over 4086.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:21:34,501 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 12:21:42,581 INFO [train.py:591] (1/6) Epoch 123, validation: discriminator_loss=2.71, discriminator_real_loss=1.427, discriminator_fake_loss=1.282, generator_loss=26.89, generator_mel_loss=19.56, generator_kl_loss=1.188, generator_dur_loss=1.806, generator_adv_loss=1.883, generator_feat_match_loss=2.453, over 100.00 samples.
+2024-03-12 12:21:42,582 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 12:24:00,636 INFO [train.py:527] (1/6) Epoch 123, batch 122, global_batch_idx: 15250, batch size: 58, loss[discriminator_loss=2.725, discriminator_real_loss=1.315, discriminator_fake_loss=1.41, generator_loss=27.02, generator_mel_loss=18.57, generator_kl_loss=1.321, generator_dur_loss=1.777, generator_adv_loss=1.887, generator_feat_match_loss=3.464, over 58.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.384, discriminator_fake_loss=1.361, generator_loss=27.35, generator_mel_loss=19.15, generator_kl_loss=1.379, generator_dur_loss=1.766, generator_adv_loss=1.888, generator_feat_match_loss=3.162, over 6918.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:24:05,650 INFO [train.py:919] (1/6) Start epoch 124
+2024-03-12 12:26:41,503 INFO [train.py:527] (1/6) Epoch 124, batch 48, global_batch_idx: 15300, batch size: 96, loss[discriminator_loss=2.779, discriminator_real_loss=1.486, discriminator_fake_loss=1.293, generator_loss=26.25, generator_mel_loss=18.28, generator_kl_loss=1.31, generator_dur_loss=1.906, generator_adv_loss=2.128, generator_feat_match_loss=2.626, over 96.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.405, discriminator_fake_loss=1.357, generator_loss=27.57, generator_mel_loss=19.09, generator_kl_loss=1.416, generator_dur_loss=1.763, generator_adv_loss=1.995, generator_feat_match_loss=3.309, over 2737.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:28:58,144 INFO [train.py:527] (1/6) Epoch 124, batch 98, global_batch_idx: 15350, batch size: 59, loss[discriminator_loss=2.782, discriminator_real_loss=1.521, discriminator_fake_loss=1.261, generator_loss=26.71, generator_mel_loss=19.05, generator_kl_loss=1.311, generator_dur_loss=1.744, generator_adv_loss=1.996, generator_feat_match_loss=2.617, over 59.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.405, discriminator_fake_loss=1.356, generator_loss=27.32, generator_mel_loss=19.03, generator_kl_loss=1.393, generator_dur_loss=1.765, generator_adv_loss=1.951, generator_feat_match_loss=3.182, over 5594.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:30:10,414 INFO [train.py:919] (1/6) Start epoch 125
+2024-03-12 12:31:40,997 INFO [train.py:527] (1/6) Epoch 125, batch 24, global_batch_idx: 15400, batch size: 59, loss[discriminator_loss=2.789, discriminator_real_loss=1.47, discriminator_fake_loss=1.319, generator_loss=28.34, generator_mel_loss=20.12, generator_kl_loss=1.5, generator_dur_loss=1.737, generator_adv_loss=1.763, generator_feat_match_loss=3.226, over 59.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.407, discriminator_fake_loss=1.351, generator_loss=27.21, generator_mel_loss=19.05, generator_kl_loss=1.411, generator_dur_loss=1.758, generator_adv_loss=1.882, generator_feat_match_loss=3.106, over 1263.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:31:40,998 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 12:31:49,013 INFO [train.py:591] (1/6) Epoch 125, validation: discriminator_loss=2.708, discriminator_real_loss=1.334, discriminator_fake_loss=1.374, generator_loss=25.91, generator_mel_loss=18.78, generator_kl_loss=1.215, generator_dur_loss=1.829, generator_adv_loss=1.705, generator_feat_match_loss=2.374, over 100.00 samples.
+2024-03-12 12:31:49,015 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 12:34:10,655 INFO [train.py:527] (1/6) Epoch 125, batch 74, global_batch_idx: 15450, batch size: 64, loss[discriminator_loss=2.677, discriminator_real_loss=1.375, discriminator_fake_loss=1.301, generator_loss=27.31, generator_mel_loss=18.95, generator_kl_loss=1.425, generator_dur_loss=1.697, generator_adv_loss=2.135, generator_feat_match_loss=3.102, over 64.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.397, discriminator_fake_loss=1.352, generator_loss=27.17, generator_mel_loss=19, generator_kl_loss=1.399, generator_dur_loss=1.754, generator_adv_loss=1.886, generator_feat_match_loss=3.126, over 4081.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:36:26,631 INFO [train.py:919] (1/6) Start epoch 126
+2024-03-12 12:36:50,448 INFO [train.py:527] (1/6) Epoch 126, batch 0, global_batch_idx: 15500, batch size: 72, loss[discriminator_loss=2.733, discriminator_real_loss=1.397, discriminator_fake_loss=1.336, generator_loss=26.59, generator_mel_loss=18.63, generator_kl_loss=1.413, generator_dur_loss=1.786, generator_adv_loss=1.852, generator_feat_match_loss=2.909, over 72.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.397, discriminator_fake_loss=1.336, generator_loss=26.59, generator_mel_loss=18.63, generator_kl_loss=1.413, generator_dur_loss=1.786, generator_adv_loss=1.852, generator_feat_match_loss=2.909, over 72.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:39:10,087 INFO [train.py:527] (1/6) Epoch 126, batch 50, global_batch_idx: 15550, batch size: 72, loss[discriminator_loss=2.752, discriminator_real_loss=1.408, discriminator_fake_loss=1.344, generator_loss=27.13, generator_mel_loss=19.08, generator_kl_loss=1.36, generator_dur_loss=1.808, generator_adv_loss=1.834, generator_feat_match_loss=3.051, over 72.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.394, discriminator_fake_loss=1.357, generator_loss=27.14, generator_mel_loss=18.99, generator_kl_loss=1.387, generator_dur_loss=1.737, generator_adv_loss=1.896, generator_feat_match_loss=3.133, over 3101.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:41:27,719 INFO [train.py:527] (1/6) Epoch 126, batch 100, global_batch_idx: 15600, batch size: 77, loss[discriminator_loss=2.769, discriminator_real_loss=1.318, discriminator_fake_loss=1.451, generator_loss=26.67, generator_mel_loss=18.32, generator_kl_loss=1.366, generator_dur_loss=1.819, generator_adv_loss=2.244, generator_feat_match_loss=2.919, over 77.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.395, discriminator_fake_loss=1.355, generator_loss=27.24, generator_mel_loss=19.03, generator_kl_loss=1.394, generator_dur_loss=1.752, generator_adv_loss=1.904, generator_feat_match_loss=3.166, over 5940.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:41:27,721 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 12:41:36,595 INFO [train.py:591] (1/6) Epoch 126, validation: discriminator_loss=2.815, discriminator_real_loss=1.61, discriminator_fake_loss=1.205, generator_loss=25.88, generator_mel_loss=18.47, generator_kl_loss=1.162, generator_dur_loss=1.825, generator_adv_loss=2.079, generator_feat_match_loss=2.346, over 100.00 samples.
+2024-03-12 12:41:36,596 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 12:42:40,446 INFO [train.py:919] (1/6) Start epoch 127
+2024-03-12 12:44:17,979 INFO [train.py:527] (1/6) Epoch 127, batch 26, global_batch_idx: 15650, batch size: 77, loss[discriminator_loss=2.744, discriminator_real_loss=1.456, discriminator_fake_loss=1.288, generator_loss=26.29, generator_mel_loss=18.38, generator_kl_loss=1.251, generator_dur_loss=1.864, generator_adv_loss=1.812, generator_feat_match_loss=2.985, over 77.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=27.3, generator_mel_loss=19.03, generator_kl_loss=1.393, generator_dur_loss=1.754, generator_adv_loss=1.914, generator_feat_match_loss=3.217, over 1498.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:46:38,041 INFO [train.py:527] (1/6) Epoch 127, batch 76, global_batch_idx: 15700, batch size: 72, loss[discriminator_loss=2.674, discriminator_real_loss=1.43, discriminator_fake_loss=1.244, generator_loss=28.33, generator_mel_loss=19.48, generator_kl_loss=1.431, generator_dur_loss=1.816, generator_adv_loss=2.104, generator_feat_match_loss=3.505, over 72.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.383, discriminator_fake_loss=1.352, generator_loss=27.39, generator_mel_loss=19.03, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=1.948, generator_feat_match_loss=3.257, over 4261.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:48:48,696 INFO [train.py:919] (1/6) Start epoch 128
+2024-03-12 12:49:17,603 INFO [train.py:527] (1/6) Epoch 128, batch 2, global_batch_idx: 15750, batch size: 61, loss[discriminator_loss=2.827, discriminator_real_loss=1.315, discriminator_fake_loss=1.512, generator_loss=27.44, generator_mel_loss=19.06, generator_kl_loss=1.309, generator_dur_loss=1.771, generator_adv_loss=2.036, generator_feat_match_loss=3.259, over 61.00 samples.], tot_loss[discriminator_loss=2.792, discriminator_real_loss=1.36, discriminator_fake_loss=1.432, generator_loss=27.25, generator_mel_loss=19.11, generator_kl_loss=1.276, generator_dur_loss=1.785, generator_adv_loss=1.902, generator_feat_match_loss=3.176, over 185.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:51:35,820 INFO [train.py:527] (1/6) Epoch 128, batch 52, global_batch_idx: 15800, batch size: 66, loss[discriminator_loss=2.763, discriminator_real_loss=1.4, discriminator_fake_loss=1.364, generator_loss=27.03, generator_mel_loss=19, generator_kl_loss=1.37, generator_dur_loss=1.797, generator_adv_loss=1.944, generator_feat_match_loss=2.919, over 66.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=27.28, generator_mel_loss=19.06, generator_kl_loss=1.374, generator_dur_loss=1.772, generator_adv_loss=1.914, generator_feat_match_loss=3.157, over 2852.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:51:35,822 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 12:51:44,035 INFO [train.py:591] (1/6) Epoch 128, validation: discriminator_loss=2.787, discriminator_real_loss=1.483, discriminator_fake_loss=1.303, generator_loss=26.23, generator_mel_loss=19.01, generator_kl_loss=1.189, generator_dur_loss=1.831, generator_adv_loss=1.855, generator_feat_match_loss=2.347, over 100.00 samples.
+2024-03-12 12:51:44,036 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 12:54:00,540 INFO [train.py:527] (1/6) Epoch 128, batch 102, global_batch_idx: 15850, batch size: 48, loss[discriminator_loss=2.706, discriminator_real_loss=1.46, discriminator_fake_loss=1.246, generator_loss=27.27, generator_mel_loss=19.1, generator_kl_loss=1.407, generator_dur_loss=1.658, generator_adv_loss=1.94, generator_feat_match_loss=3.165, over 48.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.4, discriminator_fake_loss=1.355, generator_loss=27.22, generator_mel_loss=19.04, generator_kl_loss=1.384, generator_dur_loss=1.767, generator_adv_loss=1.897, generator_feat_match_loss=3.133, over 5629.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:55:02,273 INFO [train.py:919] (1/6) Start epoch 129
+2024-03-12 12:56:44,549 INFO [train.py:527] (1/6) Epoch 129, batch 28, global_batch_idx: 15900, batch size: 95, loss[discriminator_loss=2.7, discriminator_real_loss=1.337, discriminator_fake_loss=1.363, generator_loss=26.72, generator_mel_loss=18.83, generator_kl_loss=1.061, generator_dur_loss=1.969, generator_adv_loss=1.864, generator_feat_match_loss=3.001, over 95.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.382, discriminator_fake_loss=1.361, generator_loss=27.27, generator_mel_loss=19.06, generator_kl_loss=1.405, generator_dur_loss=1.767, generator_adv_loss=1.895, generator_feat_match_loss=3.142, over 1723.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 12:59:02,159 INFO [train.py:527] (1/6) Epoch 129, batch 78, global_batch_idx: 15950, batch size: 25, loss[discriminator_loss=2.783, discriminator_real_loss=1.434, discriminator_fake_loss=1.349, generator_loss=27.5, generator_mel_loss=19.14, generator_kl_loss=1.495, generator_dur_loss=1.612, generator_adv_loss=1.752, generator_feat_match_loss=3.507, over 25.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.39, discriminator_fake_loss=1.359, generator_loss=27.17, generator_mel_loss=18.96, generator_kl_loss=1.392, generator_dur_loss=1.781, generator_adv_loss=1.906, generator_feat_match_loss=3.125, over 4607.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:01:07,426 INFO [train.py:919] (1/6) Start epoch 130
+2024-03-12 13:01:42,852 INFO [train.py:527] (1/6) Epoch 130, batch 4, global_batch_idx: 16000, batch size: 88, loss[discriminator_loss=2.781, discriminator_real_loss=1.394, discriminator_fake_loss=1.387, generator_loss=25.93, generator_mel_loss=18.47, generator_kl_loss=1.124, generator_dur_loss=1.895, generator_adv_loss=1.844, generator_feat_match_loss=2.59, over 88.00 samples.], tot_loss[discriminator_loss=2.77, discriminator_real_loss=1.382, discriminator_fake_loss=1.387, generator_loss=26.8, generator_mel_loss=18.93, generator_kl_loss=1.262, generator_dur_loss=1.818, generator_adv_loss=1.845, generator_feat_match_loss=2.941, over 293.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:01:42,855 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 13:01:50,757 INFO [train.py:591] (1/6) Epoch 130, validation: discriminator_loss=2.706, discriminator_real_loss=1.423, discriminator_fake_loss=1.283, generator_loss=26.49, generator_mel_loss=19.13, generator_kl_loss=1.206, generator_dur_loss=1.832, generator_adv_loss=1.842, generator_feat_match_loss=2.484, over 100.00 samples.
+2024-03-12 13:01:50,759 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 13:04:09,450 INFO [train.py:527] (1/6) Epoch 130, batch 54, global_batch_idx: 16050, batch size: 25, loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=27.39, generator_mel_loss=19.25, generator_kl_loss=1.471, generator_dur_loss=1.599, generator_adv_loss=1.876, generator_feat_match_loss=3.193, over 25.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.395, discriminator_fake_loss=1.353, generator_loss=27.19, generator_mel_loss=19.02, generator_kl_loss=1.379, generator_dur_loss=1.772, generator_adv_loss=1.911, generator_feat_match_loss=3.109, over 2907.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:06:28,663 INFO [train.py:527] (1/6) Epoch 130, batch 104, global_batch_idx: 16100, batch size: 96, loss[discriminator_loss=2.809, discriminator_real_loss=1.539, discriminator_fake_loss=1.269, generator_loss=25.81, generator_mel_loss=18.02, generator_kl_loss=1.348, generator_dur_loss=1.857, generator_adv_loss=1.712, generator_feat_match_loss=2.876, over 96.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.394, discriminator_fake_loss=1.355, generator_loss=27.19, generator_mel_loss=19.02, generator_kl_loss=1.382, generator_dur_loss=1.764, generator_adv_loss=1.902, generator_feat_match_loss=3.122, over 5778.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:07:19,987 INFO [train.py:919] (1/6) Start epoch 131
+2024-03-12 13:09:08,200 INFO [train.py:527] (1/6) Epoch 131, batch 30, global_batch_idx: 16150, batch size: 77, loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=26.89, generator_mel_loss=18.55, generator_kl_loss=1.306, generator_dur_loss=1.785, generator_adv_loss=1.889, generator_feat_match_loss=3.363, over 77.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.397, discriminator_fake_loss=1.344, generator_loss=27.22, generator_mel_loss=18.96, generator_kl_loss=1.385, generator_dur_loss=1.762, generator_adv_loss=1.914, generator_feat_match_loss=3.201, over 1794.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:11:30,110 INFO [train.py:527] (1/6) Epoch 131, batch 80, global_batch_idx: 16200, batch size: 45, loss[discriminator_loss=2.667, discriminator_real_loss=1.388, discriminator_fake_loss=1.279, generator_loss=27.41, generator_mel_loss=19.07, generator_kl_loss=1.292, generator_dur_loss=1.646, generator_adv_loss=1.979, generator_feat_match_loss=3.419, over 45.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.385, discriminator_fake_loss=1.356, generator_loss=27.24, generator_mel_loss=18.97, generator_kl_loss=1.384, generator_dur_loss=1.747, generator_adv_loss=1.918, generator_feat_match_loss=3.224, over 4610.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:11:30,111 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 13:11:39,151 INFO [train.py:591] (1/6) Epoch 131, validation: discriminator_loss=2.756, discriminator_real_loss=1.528, discriminator_fake_loss=1.229, generator_loss=27.04, generator_mel_loss=19.35, generator_kl_loss=1.247, generator_dur_loss=1.804, generator_adv_loss=1.937, generator_feat_match_loss=2.705, over 100.00 samples.
+2024-03-12 13:11:39,152 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 13:13:34,626 INFO [train.py:919] (1/6) Start epoch 132
+2024-03-12 13:14:15,322 INFO [train.py:527] (1/6) Epoch 132, batch 6, global_batch_idx: 16250, batch size: 66, loss[discriminator_loss=2.901, discriminator_real_loss=1.686, discriminator_fake_loss=1.215, generator_loss=27.27, generator_mel_loss=19.45, generator_kl_loss=1.266, generator_dur_loss=1.784, generator_adv_loss=1.895, generator_feat_match_loss=2.876, over 66.00 samples.], tot_loss[discriminator_loss=2.944, discriminator_real_loss=1.578, discriminator_fake_loss=1.366, generator_loss=28.18, generator_mel_loss=19.45, generator_kl_loss=1.342, generator_dur_loss=1.771, generator_adv_loss=2.218, generator_feat_match_loss=3.398, over 434.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:16:32,894 INFO [train.py:527] (1/6) Epoch 132, batch 56, global_batch_idx: 16300, batch size: 83, loss[discriminator_loss=2.819, discriminator_real_loss=1.498, discriminator_fake_loss=1.321, generator_loss=27.43, generator_mel_loss=19.62, generator_kl_loss=1.157, generator_dur_loss=1.87, generator_adv_loss=1.645, generator_feat_match_loss=3.139, over 83.00 samples.], tot_loss[discriminator_loss=2.779, discriminator_real_loss=1.425, discriminator_fake_loss=1.354, generator_loss=27.62, generator_mel_loss=19.2, generator_kl_loss=1.384, generator_dur_loss=1.748, generator_adv_loss=1.996, generator_feat_match_loss=3.29, over 3083.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:18:53,768 INFO [train.py:527] (1/6) Epoch 132, batch 106, global_batch_idx: 16350, batch size: 31, loss[discriminator_loss=2.779, discriminator_real_loss=1.511, discriminator_fake_loss=1.268, generator_loss=25.94, generator_mel_loss=18.01, generator_kl_loss=1.417, generator_dur_loss=1.646, generator_adv_loss=1.948, generator_feat_match_loss=2.925, over 31.00 samples.], tot_loss[discriminator_loss=2.777, discriminator_real_loss=1.418, discriminator_fake_loss=1.358, generator_loss=27.34, generator_mel_loss=19.06, generator_kl_loss=1.379, generator_dur_loss=1.75, generator_adv_loss=1.951, generator_feat_match_loss=3.198, over 5834.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:19:42,932 INFO [train.py:919] (1/6) Start epoch 133
+2024-03-12 13:21:35,240 INFO [train.py:527] (1/6) Epoch 133, batch 32, global_batch_idx: 16400, batch size: 48, loss[discriminator_loss=2.776, discriminator_real_loss=1.467, discriminator_fake_loss=1.31, generator_loss=27.79, generator_mel_loss=19.5, generator_kl_loss=1.491, generator_dur_loss=1.679, generator_adv_loss=1.716, generator_feat_match_loss=3.402, over 48.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.383, discriminator_fake_loss=1.356, generator_loss=27.42, generator_mel_loss=19.07, generator_kl_loss=1.403, generator_dur_loss=1.736, generator_adv_loss=1.91, generator_feat_match_loss=3.299, over 1921.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:21:35,241 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 13:21:43,788 INFO [train.py:591] (1/6) Epoch 133, validation: discriminator_loss=2.789, discriminator_real_loss=1.338, discriminator_fake_loss=1.45, generator_loss=25.78, generator_mel_loss=19.04, generator_kl_loss=1.105, generator_dur_loss=1.795, generator_adv_loss=1.623, generator_feat_match_loss=2.221, over 100.00 samples.
+2024-03-12 13:21:43,789 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 13:24:02,137 INFO [train.py:527] (1/6) Epoch 133, batch 82, global_batch_idx: 16450, batch size: 62, loss[discriminator_loss=2.742, discriminator_real_loss=1.514, discriminator_fake_loss=1.228, generator_loss=28.1, generator_mel_loss=19.39, generator_kl_loss=1.412, generator_dur_loss=1.729, generator_adv_loss=2.153, generator_feat_match_loss=3.412, over 62.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.395, discriminator_fake_loss=1.359, generator_loss=27.31, generator_mel_loss=19.02, generator_kl_loss=1.39, generator_dur_loss=1.748, generator_adv_loss=1.911, generator_feat_match_loss=3.24, over 4849.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:25:57,404 INFO [train.py:919] (1/6) Start epoch 134
+2024-03-12 13:26:43,222 INFO [train.py:527] (1/6) Epoch 134, batch 8, global_batch_idx: 16500, batch size: 95, loss[discriminator_loss=2.776, discriminator_real_loss=1.321, discriminator_fake_loss=1.455, generator_loss=26.67, generator_mel_loss=18.48, generator_kl_loss=1.309, generator_dur_loss=1.903, generator_adv_loss=1.822, generator_feat_match_loss=3.163, over 95.00 samples.], tot_loss[discriminator_loss=2.776, discriminator_real_loss=1.417, discriminator_fake_loss=1.359, generator_loss=27.5, generator_mel_loss=19.18, generator_kl_loss=1.444, generator_dur_loss=1.759, generator_adv_loss=1.927, generator_feat_match_loss=3.196, over 516.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:29:01,146 INFO [train.py:527] (1/6) Epoch 134, batch 58, global_batch_idx: 16550, batch size: 53, loss[discriminator_loss=2.736, discriminator_real_loss=1.438, discriminator_fake_loss=1.298, generator_loss=27.74, generator_mel_loss=19.35, generator_kl_loss=1.6, generator_dur_loss=1.655, generator_adv_loss=1.934, generator_feat_match_loss=3.201, over 53.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.397, discriminator_fake_loss=1.359, generator_loss=27.31, generator_mel_loss=19.03, generator_kl_loss=1.367, generator_dur_loss=1.766, generator_adv_loss=1.925, generator_feat_match_loss=3.219, over 3513.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:31:20,763 INFO [train.py:527] (1/6) Epoch 134, batch 108, global_batch_idx: 16600, batch size: 45, loss[discriminator_loss=2.71, discriminator_real_loss=1.355, discriminator_fake_loss=1.354, generator_loss=28.16, generator_mel_loss=19.38, generator_kl_loss=1.637, generator_dur_loss=1.699, generator_adv_loss=1.95, generator_feat_match_loss=3.491, over 45.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.391, discriminator_fake_loss=1.353, generator_loss=27.41, generator_mel_loss=19.06, generator_kl_loss=1.399, generator_dur_loss=1.759, generator_adv_loss=1.929, generator_feat_match_loss=3.261, over 6310.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:31:20,764 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 13:31:29,814 INFO [train.py:591] (1/6) Epoch 134, validation: discriminator_loss=2.726, discriminator_real_loss=1.442, discriminator_fake_loss=1.284, generator_loss=26.65, generator_mel_loss=19.52, generator_kl_loss=1.061, generator_dur_loss=1.825, generator_adv_loss=1.899, generator_feat_match_loss=2.339, over 100.00 samples.
+2024-03-12 13:31:29,815 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 13:32:11,684 INFO [train.py:919] (1/6) Start epoch 135
+2024-03-12 13:34:10,344 INFO [train.py:527] (1/6) Epoch 135, batch 34, global_batch_idx: 16650, batch size: 45, loss[discriminator_loss=2.712, discriminator_real_loss=1.413, discriminator_fake_loss=1.299, generator_loss=26.7, generator_mel_loss=18.55, generator_kl_loss=1.479, generator_dur_loss=1.691, generator_adv_loss=2.01, generator_feat_match_loss=2.973, over 45.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.23, generator_mel_loss=18.89, generator_kl_loss=1.395, generator_dur_loss=1.754, generator_adv_loss=1.92, generator_feat_match_loss=3.268, over 1944.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:36:29,224 INFO [train.py:527] (1/6) Epoch 135, batch 84, global_batch_idx: 16700, batch size: 61, loss[discriminator_loss=2.746, discriminator_real_loss=1.447, discriminator_fake_loss=1.298, generator_loss=26.46, generator_mel_loss=18.54, generator_kl_loss=1.269, generator_dur_loss=1.724, generator_adv_loss=1.865, generator_feat_match_loss=3.065, over 61.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.5, generator_mel_loss=19.01, generator_kl_loss=1.374, generator_dur_loss=1.754, generator_adv_loss=2.001, generator_feat_match_loss=3.363, over 4843.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:38:16,572 INFO [train.py:919] (1/6) Start epoch 136
+2024-03-12 13:39:10,262 INFO [train.py:527] (1/6) Epoch 136, batch 10, global_batch_idx: 16750, batch size: 25, loss[discriminator_loss=2.691, discriminator_real_loss=1.372, discriminator_fake_loss=1.318, generator_loss=28.68, generator_mel_loss=19.56, generator_kl_loss=1.563, generator_dur_loss=1.599, generator_adv_loss=2.226, generator_feat_match_loss=3.731, over 25.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.369, discriminator_fake_loss=1.332, generator_loss=27.15, generator_mel_loss=18.92, generator_kl_loss=1.347, generator_dur_loss=1.771, generator_adv_loss=1.924, generator_feat_match_loss=3.19, over 621.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:41:29,467 INFO [train.py:527] (1/6) Epoch 136, batch 60, global_batch_idx: 16800, batch size: 47, loss[discriminator_loss=2.75, discriminator_real_loss=1.522, discriminator_fake_loss=1.228, generator_loss=27.23, generator_mel_loss=19.29, generator_kl_loss=1.446, generator_dur_loss=1.631, generator_adv_loss=1.81, generator_feat_match_loss=3.057, over 47.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.394, discriminator_fake_loss=1.358, generator_loss=27.18, generator_mel_loss=19, generator_kl_loss=1.363, generator_dur_loss=1.753, generator_adv_loss=1.897, generator_feat_match_loss=3.166, over 3601.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:41:29,468 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 13:41:37,384 INFO [train.py:591] (1/6) Epoch 136, validation: discriminator_loss=2.743, discriminator_real_loss=1.405, discriminator_fake_loss=1.338, generator_loss=26.94, generator_mel_loss=19.67, generator_kl_loss=1.201, generator_dur_loss=1.773, generator_adv_loss=1.724, generator_feat_match_loss=2.572, over 100.00 samples.
+2024-03-12 13:41:37,385 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 13:43:55,308 INFO [train.py:527] (1/6) Epoch 136, batch 110, global_batch_idx: 16850, batch size: 61, loss[discriminator_loss=2.803, discriminator_real_loss=1.372, discriminator_fake_loss=1.431, generator_loss=28.4, generator_mel_loss=19.59, generator_kl_loss=1.598, generator_dur_loss=1.732, generator_adv_loss=2.004, generator_feat_match_loss=3.476, over 61.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.393, discriminator_fake_loss=1.358, generator_loss=27.26, generator_mel_loss=19.06, generator_kl_loss=1.368, generator_dur_loss=1.756, generator_adv_loss=1.89, generator_feat_match_loss=3.187, over 6443.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:44:31,152 INFO [train.py:919] (1/6) Start epoch 137
+2024-03-12 13:46:36,210 INFO [train.py:527] (1/6) Epoch 137, batch 36, global_batch_idx: 16900, batch size: 45, loss[discriminator_loss=2.726, discriminator_real_loss=1.385, discriminator_fake_loss=1.341, generator_loss=27.51, generator_mel_loss=19.06, generator_kl_loss=1.321, generator_dur_loss=1.706, generator_adv_loss=2.019, generator_feat_match_loss=3.4, over 45.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.374, discriminator_fake_loss=1.361, generator_loss=27.15, generator_mel_loss=18.91, generator_kl_loss=1.379, generator_dur_loss=1.771, generator_adv_loss=1.879, generator_feat_match_loss=3.214, over 2203.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:48:58,025 INFO [train.py:527] (1/6) Epoch 137, batch 86, global_batch_idx: 16950, batch size: 77, loss[discriminator_loss=2.716, discriminator_real_loss=1.305, discriminator_fake_loss=1.41, generator_loss=27.8, generator_mel_loss=19.58, generator_kl_loss=1.377, generator_dur_loss=1.8, generator_adv_loss=1.828, generator_feat_match_loss=3.224, over 77.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.389, discriminator_fake_loss=1.354, generator_loss=27.31, generator_mel_loss=18.97, generator_kl_loss=1.387, generator_dur_loss=1.754, generator_adv_loss=1.922, generator_feat_match_loss=3.275, over 5034.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:50:39,902 INFO [train.py:919] (1/6) Start epoch 138
+2024-03-12 13:51:34,856 INFO [train.py:527] (1/6) Epoch 138, batch 12, global_batch_idx: 17000, batch size: 13, loss[discriminator_loss=2.634, discriminator_real_loss=1.33, discriminator_fake_loss=1.304, generator_loss=28.63, generator_mel_loss=19.69, generator_kl_loss=1.621, generator_dur_loss=1.607, generator_adv_loss=1.827, generator_feat_match_loss=3.888, over 13.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.387, discriminator_fake_loss=1.332, generator_loss=27.22, generator_mel_loss=18.9, generator_kl_loss=1.373, generator_dur_loss=1.793, generator_adv_loss=1.91, generator_feat_match_loss=3.241, over 726.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:51:34,859 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 13:51:42,684 INFO [train.py:591] (1/6) Epoch 138, validation: discriminator_loss=2.777, discriminator_real_loss=1.404, discriminator_fake_loss=1.373, generator_loss=26.47, generator_mel_loss=19.28, generator_kl_loss=1.093, generator_dur_loss=1.828, generator_adv_loss=1.725, generator_feat_match_loss=2.551, over 100.00 samples.
+2024-03-12 13:51:42,685 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 13:54:03,236 INFO [train.py:527] (1/6) Epoch 138, batch 62, global_batch_idx: 17050, batch size: 80, loss[discriminator_loss=2.728, discriminator_real_loss=1.391, discriminator_fake_loss=1.336, generator_loss=27.03, generator_mel_loss=18.84, generator_kl_loss=1.294, generator_dur_loss=1.814, generator_adv_loss=1.824, generator_feat_match_loss=3.265, over 80.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.379, discriminator_fake_loss=1.341, generator_loss=27.27, generator_mel_loss=18.92, generator_kl_loss=1.388, generator_dur_loss=1.766, generator_adv_loss=1.909, generator_feat_match_loss=3.282, over 3642.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:56:19,474 INFO [train.py:527] (1/6) Epoch 138, batch 112, global_batch_idx: 17100, batch size: 16, loss[discriminator_loss=2.764, discriminator_real_loss=1.439, discriminator_fake_loss=1.324, generator_loss=28.46, generator_mel_loss=19.83, generator_kl_loss=1.656, generator_dur_loss=1.718, generator_adv_loss=1.918, generator_feat_match_loss=3.344, over 16.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.382, discriminator_fake_loss=1.353, generator_loss=27.29, generator_mel_loss=18.99, generator_kl_loss=1.383, generator_dur_loss=1.773, generator_adv_loss=1.898, generator_feat_match_loss=3.254, over 6499.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 13:56:50,770 INFO [train.py:919] (1/6) Start epoch 139
+2024-03-12 13:59:01,218 INFO [train.py:527] (1/6) Epoch 139, batch 38, global_batch_idx: 17150, batch size: 15, loss[discriminator_loss=2.82, discriminator_real_loss=1.523, discriminator_fake_loss=1.297, generator_loss=29.99, generator_mel_loss=20.83, generator_kl_loss=1.953, generator_dur_loss=1.633, generator_adv_loss=1.963, generator_feat_match_loss=3.608, over 15.00 samples.], tot_loss[discriminator_loss=2.757, discriminator_real_loss=1.39, discriminator_fake_loss=1.367, generator_loss=27.66, generator_mel_loss=19.08, generator_kl_loss=1.388, generator_dur_loss=1.783, generator_adv_loss=2.019, generator_feat_match_loss=3.391, over 2237.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:01:21,617 INFO [train.py:527] (1/6) Epoch 139, batch 88, global_batch_idx: 17200, batch size: 70, loss[discriminator_loss=2.661, discriminator_real_loss=1.387, discriminator_fake_loss=1.274, generator_loss=27.08, generator_mel_loss=18.73, generator_kl_loss=1.362, generator_dur_loss=1.837, generator_adv_loss=2.036, generator_feat_match_loss=3.113, over 70.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.394, discriminator_fake_loss=1.354, generator_loss=27.39, generator_mel_loss=18.98, generator_kl_loss=1.39, generator_dur_loss=1.771, generator_adv_loss=1.959, generator_feat_match_loss=3.288, over 5009.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:01:21,618 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 14:01:29,861 INFO [train.py:591] (1/6) Epoch 139, validation: discriminator_loss=2.667, discriminator_real_loss=1.392, discriminator_fake_loss=1.275, generator_loss=26.29, generator_mel_loss=18.75, generator_kl_loss=1.098, generator_dur_loss=1.795, generator_adv_loss=1.943, generator_feat_match_loss=2.702, over 100.00 samples.
+2024-03-12 14:01:29,862 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 14:03:08,130 INFO [train.py:919] (1/6) Start epoch 140
+2024-03-12 14:04:12,707 INFO [train.py:527] (1/6) Epoch 140, batch 14, global_batch_idx: 17250, batch size: 77, loss[discriminator_loss=2.776, discriminator_real_loss=1.434, discriminator_fake_loss=1.342, generator_loss=26.29, generator_mel_loss=18.35, generator_kl_loss=1.301, generator_dur_loss=1.799, generator_adv_loss=1.795, generator_feat_match_loss=3.039, over 77.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.406, discriminator_fake_loss=1.362, generator_loss=27.02, generator_mel_loss=18.85, generator_kl_loss=1.365, generator_dur_loss=1.777, generator_adv_loss=1.876, generator_feat_match_loss=3.144, over 945.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:06:31,487 INFO [train.py:527] (1/6) Epoch 140, batch 64, global_batch_idx: 17300, batch size: 42, loss[discriminator_loss=2.689, discriminator_real_loss=1.396, discriminator_fake_loss=1.293, generator_loss=27.33, generator_mel_loss=19.07, generator_kl_loss=1.39, generator_dur_loss=1.696, generator_adv_loss=1.864, generator_feat_match_loss=3.309, over 42.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=27.22, generator_mel_loss=18.93, generator_kl_loss=1.388, generator_dur_loss=1.756, generator_adv_loss=1.887, generator_feat_match_loss=3.254, over 3793.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:08:49,227 INFO [train.py:527] (1/6) Epoch 140, batch 114, global_batch_idx: 17350, batch size: 42, loss[discriminator_loss=2.786, discriminator_real_loss=1.493, discriminator_fake_loss=1.293, generator_loss=27.88, generator_mel_loss=19.46, generator_kl_loss=1.427, generator_dur_loss=1.677, generator_adv_loss=2.173, generator_feat_match_loss=3.151, over 42.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.393, discriminator_fake_loss=1.356, generator_loss=27.28, generator_mel_loss=18.99, generator_kl_loss=1.384, generator_dur_loss=1.754, generator_adv_loss=1.895, generator_feat_match_loss=3.259, over 6735.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:09:14,986 INFO [train.py:919] (1/6) Start epoch 141
+2024-03-12 14:11:31,921 INFO [train.py:527] (1/6) Epoch 141, batch 40, global_batch_idx: 17400, batch size: 17, loss[discriminator_loss=2.754, discriminator_real_loss=1.349, discriminator_fake_loss=1.405, generator_loss=27.14, generator_mel_loss=19.38, generator_kl_loss=1.586, generator_dur_loss=1.557, generator_adv_loss=1.932, generator_feat_match_loss=2.684, over 17.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.396, discriminator_fake_loss=1.345, generator_loss=27.3, generator_mel_loss=18.96, generator_kl_loss=1.376, generator_dur_loss=1.772, generator_adv_loss=1.93, generator_feat_match_loss=3.268, over 2365.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:11:31,922 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 14:11:40,077 INFO [train.py:591] (1/6) Epoch 141, validation: discriminator_loss=2.773, discriminator_real_loss=1.432, discriminator_fake_loss=1.341, generator_loss=26.01, generator_mel_loss=18.65, generator_kl_loss=1.195, generator_dur_loss=1.798, generator_adv_loss=1.86, generator_feat_match_loss=2.51, over 100.00 samples.
+2024-03-12 14:11:40,079 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 14:13:59,384 INFO [train.py:527] (1/6) Epoch 141, batch 90, global_batch_idx: 17450, batch size: 55, loss[discriminator_loss=2.696, discriminator_real_loss=1.339, discriminator_fake_loss=1.357, generator_loss=27.85, generator_mel_loss=19.5, generator_kl_loss=1.46, generator_dur_loss=1.745, generator_adv_loss=1.854, generator_feat_match_loss=3.299, over 55.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.403, discriminator_fake_loss=1.345, generator_loss=27.28, generator_mel_loss=19.01, generator_kl_loss=1.382, generator_dur_loss=1.761, generator_adv_loss=1.906, generator_feat_match_loss=3.218, over 5125.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:15:33,449 INFO [train.py:919] (1/6) Start epoch 142
+2024-03-12 14:16:41,486 INFO [train.py:527] (1/6) Epoch 142, batch 16, global_batch_idx: 17500, batch size: 56, loss[discriminator_loss=2.772, discriminator_real_loss=1.398, discriminator_fake_loss=1.374, generator_loss=27.7, generator_mel_loss=19.45, generator_kl_loss=1.32, generator_dur_loss=1.721, generator_adv_loss=1.9, generator_feat_match_loss=3.313, over 56.00 samples.], tot_loss[discriminator_loss=2.777, discriminator_real_loss=1.421, discriminator_fake_loss=1.356, generator_loss=27.17, generator_mel_loss=18.97, generator_kl_loss=1.351, generator_dur_loss=1.776, generator_adv_loss=1.888, generator_feat_match_loss=3.185, over 1038.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:19:00,015 INFO [train.py:527] (1/6) Epoch 142, batch 66, global_batch_idx: 17550, batch size: 31, loss[discriminator_loss=2.691, discriminator_real_loss=1.454, discriminator_fake_loss=1.237, generator_loss=26.91, generator_mel_loss=18.75, generator_kl_loss=1.579, generator_dur_loss=1.652, generator_adv_loss=1.94, generator_feat_match_loss=2.986, over 31.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.399, discriminator_fake_loss=1.355, generator_loss=27.29, generator_mel_loss=18.98, generator_kl_loss=1.38, generator_dur_loss=1.754, generator_adv_loss=1.898, generator_feat_match_loss=3.272, over 3990.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:21:16,223 INFO [train.py:527] (1/6) Epoch 142, batch 116, global_batch_idx: 17600, batch size: 48, loss[discriminator_loss=3.122, discriminator_real_loss=1.505, discriminator_fake_loss=1.617, generator_loss=28.23, generator_mel_loss=19.69, generator_kl_loss=1.61, generator_dur_loss=1.691, generator_adv_loss=1.86, generator_feat_match_loss=3.386, over 48.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.393, discriminator_fake_loss=1.362, generator_loss=27.4, generator_mel_loss=19.04, generator_kl_loss=1.383, generator_dur_loss=1.753, generator_adv_loss=1.907, generator_feat_match_loss=3.312, over 6771.00 samples.], cur_lr_g: 1.97e-04, cur_lr_d: 1.97e-04, 
+2024-03-12 14:21:16,224 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 14:21:25,033 INFO [train.py:591] (1/6) Epoch 142, validation: discriminator_loss=3.002, discriminator_real_loss=1.685, discriminator_fake_loss=1.318, generator_loss=27, generator_mel_loss=19.09, generator_kl_loss=1.229, generator_dur_loss=1.787, generator_adv_loss=1.925, generator_feat_match_loss=2.968, over 100.00 samples.
+2024-03-12 14:21:25,034 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 14:21:44,824 INFO [train.py:919] (1/6) Start epoch 143
+2024-03-12 14:24:07,058 INFO [train.py:527] (1/6) Epoch 143, batch 42, global_batch_idx: 17650, batch size: 70, loss[discriminator_loss=2.756, discriminator_real_loss=1.456, discriminator_fake_loss=1.3, generator_loss=27.58, generator_mel_loss=19.12, generator_kl_loss=1.461, generator_dur_loss=1.8, generator_adv_loss=1.946, generator_feat_match_loss=3.254, over 70.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.397, discriminator_fake_loss=1.342, generator_loss=27.42, generator_mel_loss=19, generator_kl_loss=1.417, generator_dur_loss=1.738, generator_adv_loss=1.938, generator_feat_match_loss=3.325, over 2345.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:26:24,699 INFO [train.py:527] (1/6) Epoch 143, batch 92, global_batch_idx: 17700, batch size: 39, loss[discriminator_loss=2.805, discriminator_real_loss=1.436, discriminator_fake_loss=1.368, generator_loss=26.57, generator_mel_loss=18.68, generator_kl_loss=1.507, generator_dur_loss=1.68, generator_adv_loss=1.894, generator_feat_match_loss=2.815, over 39.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.396, discriminator_fake_loss=1.349, generator_loss=27.31, generator_mel_loss=18.93, generator_kl_loss=1.407, generator_dur_loss=1.733, generator_adv_loss=1.918, generator_feat_match_loss=3.315, over 5108.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:27:50,712 INFO [train.py:919] (1/6) Start epoch 144
+2024-03-12 14:29:03,995 INFO [train.py:527] (1/6) Epoch 144, batch 18, global_batch_idx: 17750, batch size: 58, loss[discriminator_loss=2.926, discriminator_real_loss=1.679, discriminator_fake_loss=1.246, generator_loss=27.65, generator_mel_loss=19.28, generator_kl_loss=1.58, generator_dur_loss=1.73, generator_adv_loss=1.545, generator_feat_match_loss=3.507, over 58.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.41, discriminator_fake_loss=1.339, generator_loss=27.31, generator_mel_loss=18.98, generator_kl_loss=1.408, generator_dur_loss=1.754, generator_adv_loss=1.9, generator_feat_match_loss=3.267, over 1054.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:31:23,698 INFO [train.py:527] (1/6) Epoch 144, batch 68, global_batch_idx: 17800, batch size: 59, loss[discriminator_loss=2.748, discriminator_real_loss=1.359, discriminator_fake_loss=1.389, generator_loss=26.6, generator_mel_loss=18.6, generator_kl_loss=1.421, generator_dur_loss=1.748, generator_adv_loss=1.782, generator_feat_match_loss=3.05, over 59.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.398, discriminator_fake_loss=1.35, generator_loss=27.17, generator_mel_loss=18.83, generator_kl_loss=1.39, generator_dur_loss=1.764, generator_adv_loss=1.911, generator_feat_match_loss=3.274, over 3863.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:31:23,699 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 14:31:31,870 INFO [train.py:591] (1/6) Epoch 144, validation: discriminator_loss=2.728, discriminator_real_loss=1.3, discriminator_fake_loss=1.429, generator_loss=26.78, generator_mel_loss=19.42, generator_kl_loss=1.208, generator_dur_loss=1.821, generator_adv_loss=1.719, generator_feat_match_loss=2.614, over 100.00 samples.
+2024-03-12 14:31:31,871 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 14:33:51,159 INFO [train.py:527] (1/6) Epoch 144, batch 118, global_batch_idx: 17850, batch size: 42, loss[discriminator_loss=2.742, discriminator_real_loss=1.427, discriminator_fake_loss=1.316, generator_loss=27.38, generator_mel_loss=19.07, generator_kl_loss=1.328, generator_dur_loss=1.785, generator_adv_loss=2.026, generator_feat_match_loss=3.167, over 42.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.394, discriminator_fake_loss=1.353, generator_loss=27.23, generator_mel_loss=18.89, generator_kl_loss=1.389, generator_dur_loss=1.771, generator_adv_loss=1.907, generator_feat_match_loss=3.273, over 6744.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:34:06,693 INFO [train.py:919] (1/6) Start epoch 145
+2024-03-12 14:36:30,876 INFO [train.py:527] (1/6) Epoch 145, batch 44, global_batch_idx: 17900, batch size: 88, loss[discriminator_loss=2.734, discriminator_real_loss=1.401, discriminator_fake_loss=1.333, generator_loss=26.61, generator_mel_loss=18.26, generator_kl_loss=1.419, generator_dur_loss=1.861, generator_adv_loss=1.9, generator_feat_match_loss=3.172, over 88.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.386, discriminator_fake_loss=1.361, generator_loss=27.31, generator_mel_loss=18.97, generator_kl_loss=1.425, generator_dur_loss=1.753, generator_adv_loss=1.915, generator_feat_match_loss=3.24, over 2382.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:38:48,760 INFO [train.py:527] (1/6) Epoch 145, batch 94, global_batch_idx: 17950, batch size: 68, loss[discriminator_loss=2.709, discriminator_real_loss=1.469, discriminator_fake_loss=1.24, generator_loss=27.15, generator_mel_loss=18.8, generator_kl_loss=1.392, generator_dur_loss=1.847, generator_adv_loss=1.886, generator_feat_match_loss=3.232, over 68.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.393, discriminator_fake_loss=1.361, generator_loss=27.23, generator_mel_loss=18.89, generator_kl_loss=1.404, generator_dur_loss=1.761, generator_adv_loss=1.912, generator_feat_match_loss=3.263, over 5342.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:40:11,227 INFO [train.py:919] (1/6) Start epoch 146
+2024-03-12 14:41:30,512 INFO [train.py:527] (1/6) Epoch 146, batch 20, global_batch_idx: 18000, batch size: 77, loss[discriminator_loss=2.757, discriminator_real_loss=1.471, discriminator_fake_loss=1.287, generator_loss=26.02, generator_mel_loss=18.39, generator_kl_loss=1.148, generator_dur_loss=1.822, generator_adv_loss=1.794, generator_feat_match_loss=2.867, over 77.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.399, discriminator_fake_loss=1.33, generator_loss=27.24, generator_mel_loss=18.98, generator_kl_loss=1.377, generator_dur_loss=1.755, generator_adv_loss=1.931, generator_feat_match_loss=3.196, over 1192.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:41:30,513 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 14:41:38,562 INFO [train.py:591] (1/6) Epoch 146, validation: discriminator_loss=2.748, discriminator_real_loss=1.38, discriminator_fake_loss=1.368, generator_loss=26.46, generator_mel_loss=19.02, generator_kl_loss=1.269, generator_dur_loss=1.793, generator_adv_loss=1.78, generator_feat_match_loss=2.597, over 100.00 samples.
+2024-03-12 14:41:38,563 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 14:43:56,268 INFO [train.py:527] (1/6) Epoch 146, batch 70, global_batch_idx: 18050, batch size: 96, loss[discriminator_loss=2.746, discriminator_real_loss=1.432, discriminator_fake_loss=1.314, generator_loss=26.68, generator_mel_loss=18.52, generator_kl_loss=1.292, generator_dur_loss=1.944, generator_adv_loss=1.811, generator_feat_match_loss=3.112, over 96.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.389, discriminator_fake_loss=1.345, generator_loss=27.33, generator_mel_loss=18.98, generator_kl_loss=1.384, generator_dur_loss=1.761, generator_adv_loss=1.92, generator_feat_match_loss=3.288, over 3855.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:46:14,831 INFO [train.py:527] (1/6) Epoch 146, batch 120, global_batch_idx: 18100, batch size: 52, loss[discriminator_loss=2.813, discriminator_real_loss=1.308, discriminator_fake_loss=1.504, generator_loss=27.46, generator_mel_loss=19, generator_kl_loss=1.424, generator_dur_loss=1.7, generator_adv_loss=1.972, generator_feat_match_loss=3.362, over 52.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.384, discriminator_fake_loss=1.354, generator_loss=27.44, generator_mel_loss=19.01, generator_kl_loss=1.39, generator_dur_loss=1.768, generator_adv_loss=1.925, generator_feat_match_loss=3.341, over 6695.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:46:24,381 INFO [train.py:919] (1/6) Start epoch 147
+2024-03-12 14:48:55,208 INFO [train.py:527] (1/6) Epoch 147, batch 46, global_batch_idx: 18150, batch size: 42, loss[discriminator_loss=2.69, discriminator_real_loss=1.395, discriminator_fake_loss=1.295, generator_loss=27.94, generator_mel_loss=19.38, generator_kl_loss=1.478, generator_dur_loss=1.661, generator_adv_loss=2.088, generator_feat_match_loss=3.326, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.385, discriminator_fake_loss=1.331, generator_loss=27.54, generator_mel_loss=19.07, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.943, generator_feat_match_loss=3.378, over 2486.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:51:14,676 INFO [train.py:527] (1/6) Epoch 147, batch 96, global_batch_idx: 18200, batch size: 61, loss[discriminator_loss=2.798, discriminator_real_loss=1.445, discriminator_fake_loss=1.353, generator_loss=27.22, generator_mel_loss=18.95, generator_kl_loss=1.259, generator_dur_loss=1.805, generator_adv_loss=1.989, generator_feat_match_loss=3.222, over 61.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.39, discriminator_fake_loss=1.339, generator_loss=27.51, generator_mel_loss=19.04, generator_kl_loss=1.408, generator_dur_loss=1.748, generator_adv_loss=1.936, generator_feat_match_loss=3.377, over 5042.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:51:14,677 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 14:51:22,537 INFO [train.py:591] (1/6) Epoch 147, validation: discriminator_loss=2.8, discriminator_real_loss=1.475, discriminator_fake_loss=1.325, generator_loss=26.11, generator_mel_loss=18.68, generator_kl_loss=1.205, generator_dur_loss=1.791, generator_adv_loss=1.852, generator_feat_match_loss=2.585, over 100.00 samples.
+2024-03-12 14:51:22,538 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 14:52:35,263 INFO [train.py:919] (1/6) Start epoch 148
+2024-03-12 14:54:01,863 INFO [train.py:527] (1/6) Epoch 148, batch 22, global_batch_idx: 18250, batch size: 50, loss[discriminator_loss=2.749, discriminator_real_loss=1.284, discriminator_fake_loss=1.464, generator_loss=27.06, generator_mel_loss=18.86, generator_kl_loss=1.577, generator_dur_loss=1.716, generator_adv_loss=1.701, generator_feat_match_loss=3.205, over 50.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.401, discriminator_fake_loss=1.354, generator_loss=27.45, generator_mel_loss=19.06, generator_kl_loss=1.425, generator_dur_loss=1.727, generator_adv_loss=1.965, generator_feat_match_loss=3.275, over 1091.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:56:18,707 INFO [train.py:527] (1/6) Epoch 148, batch 72, global_batch_idx: 18300, batch size: 61, loss[discriminator_loss=2.746, discriminator_real_loss=1.466, discriminator_fake_loss=1.28, generator_loss=27.14, generator_mel_loss=18.97, generator_kl_loss=1.32, generator_dur_loss=1.77, generator_adv_loss=1.928, generator_feat_match_loss=3.152, over 61.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.395, discriminator_fake_loss=1.346, generator_loss=27.35, generator_mel_loss=18.99, generator_kl_loss=1.409, generator_dur_loss=1.747, generator_adv_loss=1.93, generator_feat_match_loss=3.278, over 3740.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:58:38,508 INFO [train.py:527] (1/6) Epoch 148, batch 122, global_batch_idx: 18350, batch size: 53, loss[discriminator_loss=2.727, discriminator_real_loss=1.46, discriminator_fake_loss=1.267, generator_loss=27.73, generator_mel_loss=18.93, generator_kl_loss=1.461, generator_dur_loss=1.673, generator_adv_loss=1.855, generator_feat_match_loss=3.814, over 53.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.395, discriminator_fake_loss=1.351, generator_loss=27.37, generator_mel_loss=18.99, generator_kl_loss=1.399, generator_dur_loss=1.753, generator_adv_loss=1.918, generator_feat_match_loss=3.308, over 6475.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 14:58:43,840 INFO [train.py:919] (1/6) Start epoch 149
+2024-03-12 15:01:23,256 INFO [train.py:527] (1/6) Epoch 149, batch 48, global_batch_idx: 18400, batch size: 88, loss[discriminator_loss=2.743, discriminator_real_loss=1.395, discriminator_fake_loss=1.347, generator_loss=27.11, generator_mel_loss=18.62, generator_kl_loss=1.232, generator_dur_loss=1.838, generator_adv_loss=2.057, generator_feat_match_loss=3.368, over 88.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.39, discriminator_fake_loss=1.349, generator_loss=27.33, generator_mel_loss=18.92, generator_kl_loss=1.385, generator_dur_loss=1.765, generator_adv_loss=1.927, generator_feat_match_loss=3.338, over 3027.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:01:23,258 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 15:01:31,250 INFO [train.py:591] (1/6) Epoch 149, validation: discriminator_loss=2.775, discriminator_real_loss=1.531, discriminator_fake_loss=1.244, generator_loss=26.8, generator_mel_loss=19.07, generator_kl_loss=1.13, generator_dur_loss=1.803, generator_adv_loss=2.019, generator_feat_match_loss=2.784, over 100.00 samples.
+2024-03-12 15:01:31,251 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 15:03:52,600 INFO [train.py:527] (1/6) Epoch 149, batch 98, global_batch_idx: 18450, batch size: 61, loss[discriminator_loss=2.799, discriminator_real_loss=1.507, discriminator_fake_loss=1.292, generator_loss=26.39, generator_mel_loss=18.46, generator_kl_loss=1.222, generator_dur_loss=1.766, generator_adv_loss=1.727, generator_feat_match_loss=3.209, over 61.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.394, discriminator_fake_loss=1.348, generator_loss=27.34, generator_mel_loss=18.94, generator_kl_loss=1.375, generator_dur_loss=1.767, generator_adv_loss=1.924, generator_feat_match_loss=3.34, over 5960.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:05:03,046 INFO [train.py:919] (1/6) Start epoch 150
+2024-03-12 15:06:33,929 INFO [train.py:527] (1/6) Epoch 150, batch 24, global_batch_idx: 18500, batch size: 47, loss[discriminator_loss=2.629, discriminator_real_loss=1.393, discriminator_fake_loss=1.236, generator_loss=27.8, generator_mel_loss=18.83, generator_kl_loss=1.271, generator_dur_loss=1.697, generator_adv_loss=1.888, generator_feat_match_loss=4.109, over 47.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.372, discriminator_fake_loss=1.333, generator_loss=27.63, generator_mel_loss=18.99, generator_kl_loss=1.428, generator_dur_loss=1.734, generator_adv_loss=1.961, generator_feat_match_loss=3.516, over 1254.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:08:53,861 INFO [train.py:527] (1/6) Epoch 150, batch 74, global_batch_idx: 18550, batch size: 25, loss[discriminator_loss=2.811, discriminator_real_loss=1.263, discriminator_fake_loss=1.548, generator_loss=29.83, generator_mel_loss=20.42, generator_kl_loss=1.805, generator_dur_loss=1.584, generator_adv_loss=1.884, generator_feat_match_loss=4.138, over 25.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.389, discriminator_fake_loss=1.346, generator_loss=27.5, generator_mel_loss=18.98, generator_kl_loss=1.411, generator_dur_loss=1.758, generator_adv_loss=1.935, generator_feat_match_loss=3.417, over 4034.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:11:08,540 INFO [train.py:919] (1/6) Start epoch 151
+2024-03-12 15:11:33,457 INFO [train.py:527] (1/6) Epoch 151, batch 0, global_batch_idx: 18600, batch size: 52, loss[discriminator_loss=2.712, discriminator_real_loss=1.302, discriminator_fake_loss=1.41, generator_loss=27.36, generator_mel_loss=18.75, generator_kl_loss=1.419, generator_dur_loss=1.699, generator_adv_loss=1.984, generator_feat_match_loss=3.506, over 52.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.302, discriminator_fake_loss=1.41, generator_loss=27.36, generator_mel_loss=18.75, generator_kl_loss=1.419, generator_dur_loss=1.699, generator_adv_loss=1.984, generator_feat_match_loss=3.506, over 52.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:11:33,460 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 15:11:41,536 INFO [train.py:591] (1/6) Epoch 151, validation: discriminator_loss=2.735, discriminator_real_loss=1.467, discriminator_fake_loss=1.268, generator_loss=26.89, generator_mel_loss=19.14, generator_kl_loss=1.258, generator_dur_loss=1.801, generator_adv_loss=1.948, generator_feat_match_loss=2.737, over 100.00 samples.
+2024-03-12 15:11:41,538 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 15:13:59,594 INFO [train.py:527] (1/6) Epoch 151, batch 50, global_batch_idx: 18650, batch size: 55, loss[discriminator_loss=2.648, discriminator_real_loss=1.31, discriminator_fake_loss=1.338, generator_loss=27.35, generator_mel_loss=18.59, generator_kl_loss=1.431, generator_dur_loss=1.741, generator_adv_loss=2.135, generator_feat_match_loss=3.452, over 55.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.384, discriminator_fake_loss=1.351, generator_loss=27.56, generator_mel_loss=18.98, generator_kl_loss=1.438, generator_dur_loss=1.736, generator_adv_loss=1.939, generator_feat_match_loss=3.465, over 2719.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:16:18,414 INFO [train.py:527] (1/6) Epoch 151, batch 100, global_batch_idx: 18700, batch size: 56, loss[discriminator_loss=2.768, discriminator_real_loss=1.348, discriminator_fake_loss=1.42, generator_loss=27.27, generator_mel_loss=18.79, generator_kl_loss=1.43, generator_dur_loss=1.691, generator_adv_loss=1.958, generator_feat_match_loss=3.405, over 56.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.389, discriminator_fake_loss=1.352, generator_loss=27.39, generator_mel_loss=18.88, generator_kl_loss=1.411, generator_dur_loss=1.76, generator_adv_loss=1.929, generator_feat_match_loss=3.402, over 5719.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:17:23,946 INFO [train.py:919] (1/6) Start epoch 152
+2024-03-12 15:19:01,314 INFO [train.py:527] (1/6) Epoch 152, batch 26, global_batch_idx: 18750, batch size: 59, loss[discriminator_loss=2.715, discriminator_real_loss=1.416, discriminator_fake_loss=1.299, generator_loss=27.79, generator_mel_loss=19.3, generator_kl_loss=1.327, generator_dur_loss=1.755, generator_adv_loss=1.867, generator_feat_match_loss=3.54, over 59.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.39, discriminator_fake_loss=1.36, generator_loss=27.43, generator_mel_loss=19.02, generator_kl_loss=1.368, generator_dur_loss=1.759, generator_adv_loss=1.903, generator_feat_match_loss=3.383, over 1558.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:21:20,226 INFO [train.py:527] (1/6) Epoch 152, batch 76, global_batch_idx: 18800, batch size: 48, loss[discriminator_loss=2.705, discriminator_real_loss=1.356, discriminator_fake_loss=1.349, generator_loss=28.61, generator_mel_loss=19.46, generator_kl_loss=1.356, generator_dur_loss=1.747, generator_adv_loss=2.105, generator_feat_match_loss=3.938, over 48.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.384, discriminator_fake_loss=1.361, generator_loss=27.49, generator_mel_loss=19, generator_kl_loss=1.382, generator_dur_loss=1.76, generator_adv_loss=1.909, generator_feat_match_loss=3.431, over 4493.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:21:20,228 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 15:21:28,347 INFO [train.py:591] (1/6) Epoch 152, validation: discriminator_loss=2.778, discriminator_real_loss=1.529, discriminator_fake_loss=1.249, generator_loss=26.45, generator_mel_loss=18.82, generator_kl_loss=1.153, generator_dur_loss=1.825, generator_adv_loss=2.006, generator_feat_match_loss=2.646, over 100.00 samples.
+2024-03-12 15:21:28,349 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 15:23:36,787 INFO [train.py:919] (1/6) Start epoch 153
+2024-03-12 15:24:09,066 INFO [train.py:527] (1/6) Epoch 153, batch 2, global_batch_idx: 18850, batch size: 36, loss[discriminator_loss=2.612, discriminator_real_loss=1.334, discriminator_fake_loss=1.278, generator_loss=28.32, generator_mel_loss=19.42, generator_kl_loss=1.59, generator_dur_loss=1.681, generator_adv_loss=1.943, generator_feat_match_loss=3.681, over 36.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.26, generator_mel_loss=19.46, generator_kl_loss=1.515, generator_dur_loss=1.701, generator_adv_loss=2.024, generator_feat_match_loss=3.564, over 128.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:26:29,837 INFO [train.py:527] (1/6) Epoch 153, batch 52, global_batch_idx: 18900, batch size: 88, loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=27.34, generator_mel_loss=18.83, generator_kl_loss=1.365, generator_dur_loss=1.854, generator_adv_loss=1.859, generator_feat_match_loss=3.438, over 88.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.386, discriminator_fake_loss=1.352, generator_loss=27.44, generator_mel_loss=18.92, generator_kl_loss=1.423, generator_dur_loss=1.736, generator_adv_loss=1.96, generator_feat_match_loss=3.403, over 2692.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:28:46,091 INFO [train.py:527] (1/6) Epoch 153, batch 102, global_batch_idx: 18950, batch size: 80, loss[discriminator_loss=2.681, discriminator_real_loss=1.289, discriminator_fake_loss=1.393, generator_loss=26.36, generator_mel_loss=18.23, generator_kl_loss=1.236, generator_dur_loss=1.83, generator_adv_loss=1.84, generator_feat_match_loss=3.228, over 80.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.34, generator_mel_loss=18.87, generator_kl_loss=1.396, generator_dur_loss=1.754, generator_adv_loss=1.935, generator_feat_match_loss=3.39, over 5677.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:29:46,772 INFO [train.py:919] (1/6) Start epoch 154
+2024-03-12 15:31:26,244 INFO [train.py:527] (1/6) Epoch 154, batch 28, global_batch_idx: 19000, batch size: 39, loss[discriminator_loss=2.699, discriminator_real_loss=1.447, discriminator_fake_loss=1.253, generator_loss=26.7, generator_mel_loss=18.25, generator_kl_loss=1.626, generator_dur_loss=1.664, generator_adv_loss=1.94, generator_feat_match_loss=3.227, over 39.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.388, discriminator_fake_loss=1.339, generator_loss=27.35, generator_mel_loss=18.96, generator_kl_loss=1.393, generator_dur_loss=1.717, generator_adv_loss=1.896, generator_feat_match_loss=3.382, over 1654.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:31:26,245 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 15:31:34,130 INFO [train.py:591] (1/6) Epoch 154, validation: discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=26.3, generator_mel_loss=18.72, generator_kl_loss=1.243, generator_dur_loss=1.74, generator_adv_loss=1.839, generator_feat_match_loss=2.755, over 100.00 samples.
+2024-03-12 15:31:34,131 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 15:33:53,034 INFO [train.py:527] (1/6) Epoch 154, batch 78, global_batch_idx: 19050, batch size: 77, loss[discriminator_loss=2.963, discriminator_real_loss=1.684, discriminator_fake_loss=1.279, generator_loss=27.03, generator_mel_loss=19.02, generator_kl_loss=1.429, generator_dur_loss=1.825, generator_adv_loss=1.615, generator_feat_match_loss=3.141, over 77.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.394, discriminator_fake_loss=1.342, generator_loss=27.34, generator_mel_loss=18.93, generator_kl_loss=1.384, generator_dur_loss=1.725, generator_adv_loss=1.906, generator_feat_match_loss=3.392, over 4584.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:36:01,757 INFO [train.py:919] (1/6) Start epoch 155
+2024-03-12 15:36:35,978 INFO [train.py:527] (1/6) Epoch 155, batch 4, global_batch_idx: 19100, batch size: 42, loss[discriminator_loss=2.68, discriminator_real_loss=1.311, discriminator_fake_loss=1.37, generator_loss=28.23, generator_mel_loss=19.3, generator_kl_loss=1.311, generator_dur_loss=1.68, generator_adv_loss=2.005, generator_feat_match_loss=3.933, over 42.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.356, discriminator_fake_loss=1.356, generator_loss=27.39, generator_mel_loss=18.78, generator_kl_loss=1.318, generator_dur_loss=1.779, generator_adv_loss=1.982, generator_feat_match_loss=3.53, over 291.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:38:53,488 INFO [train.py:527] (1/6) Epoch 155, batch 54, global_batch_idx: 19150, batch size: 39, loss[discriminator_loss=2.695, discriminator_real_loss=1.441, discriminator_fake_loss=1.254, generator_loss=27.91, generator_mel_loss=19.34, generator_kl_loss=1.389, generator_dur_loss=1.72, generator_adv_loss=1.918, generator_feat_match_loss=3.537, over 39.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.38, discriminator_fake_loss=1.356, generator_loss=27.36, generator_mel_loss=18.88, generator_kl_loss=1.374, generator_dur_loss=1.765, generator_adv_loss=1.93, generator_feat_match_loss=3.413, over 3307.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:41:13,948 INFO [train.py:527] (1/6) Epoch 155, batch 104, global_batch_idx: 19200, batch size: 47, loss[discriminator_loss=2.711, discriminator_real_loss=1.346, discriminator_fake_loss=1.365, generator_loss=27.2, generator_mel_loss=19.02, generator_kl_loss=1.387, generator_dur_loss=1.684, generator_adv_loss=1.995, generator_feat_match_loss=3.11, over 47.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.379, discriminator_fake_loss=1.353, generator_loss=27.46, generator_mel_loss=18.93, generator_kl_loss=1.383, generator_dur_loss=1.764, generator_adv_loss=1.944, generator_feat_match_loss=3.445, over 6089.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:41:13,949 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 15:41:22,940 INFO [train.py:591] (1/6) Epoch 155, validation: discriminator_loss=2.767, discriminator_real_loss=1.508, discriminator_fake_loss=1.259, generator_loss=26.42, generator_mel_loss=18.97, generator_kl_loss=1.178, generator_dur_loss=1.82, generator_adv_loss=1.914, generator_feat_match_loss=2.536, over 100.00 samples.
+2024-03-12 15:41:22,941 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 15:42:17,739 INFO [train.py:919] (1/6) Start epoch 156
+2024-03-12 15:44:06,853 INFO [train.py:527] (1/6) Epoch 156, batch 30, global_batch_idx: 19250, batch size: 62, loss[discriminator_loss=2.707, discriminator_real_loss=1.343, discriminator_fake_loss=1.364, generator_loss=27.45, generator_mel_loss=18.95, generator_kl_loss=1.383, generator_dur_loss=1.742, generator_adv_loss=1.991, generator_feat_match_loss=3.391, over 62.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.384, discriminator_fake_loss=1.335, generator_loss=27.2, generator_mel_loss=18.74, generator_kl_loss=1.343, generator_dur_loss=1.752, generator_adv_loss=1.929, generator_feat_match_loss=3.436, over 1921.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:46:28,109 INFO [train.py:527] (1/6) Epoch 156, batch 80, global_batch_idx: 19300, batch size: 56, loss[discriminator_loss=2.654, discriminator_real_loss=1.4, discriminator_fake_loss=1.254, generator_loss=27.53, generator_mel_loss=19, generator_kl_loss=1.416, generator_dur_loss=1.751, generator_adv_loss=1.894, generator_feat_match_loss=3.47, over 56.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.385, discriminator_fake_loss=1.34, generator_loss=27.37, generator_mel_loss=18.81, generator_kl_loss=1.388, generator_dur_loss=1.742, generator_adv_loss=1.942, generator_feat_match_loss=3.486, over 4867.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:48:24,618 INFO [train.py:919] (1/6) Start epoch 157
+2024-03-12 15:49:07,912 INFO [train.py:527] (1/6) Epoch 157, batch 6, global_batch_idx: 19350, batch size: 53, loss[discriminator_loss=2.705, discriminator_real_loss=1.267, discriminator_fake_loss=1.439, generator_loss=26.84, generator_mel_loss=18.83, generator_kl_loss=1.364, generator_dur_loss=1.692, generator_adv_loss=1.995, generator_feat_match_loss=2.954, over 53.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.334, discriminator_fake_loss=1.398, generator_loss=27.62, generator_mel_loss=19.09, generator_kl_loss=1.441, generator_dur_loss=1.726, generator_adv_loss=1.891, generator_feat_match_loss=3.464, over 408.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:51:26,079 INFO [train.py:527] (1/6) Epoch 157, batch 56, global_batch_idx: 19400, batch size: 96, loss[discriminator_loss=2.674, discriminator_real_loss=1.336, discriminator_fake_loss=1.338, generator_loss=27.46, generator_mel_loss=18.77, generator_kl_loss=1.179, generator_dur_loss=1.812, generator_adv_loss=2.033, generator_feat_match_loss=3.668, over 96.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.392, discriminator_fake_loss=1.353, generator_loss=27.56, generator_mel_loss=19.03, generator_kl_loss=1.358, generator_dur_loss=1.754, generator_adv_loss=1.966, generator_feat_match_loss=3.448, over 3350.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:51:26,081 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 15:51:34,073 INFO [train.py:591] (1/6) Epoch 157, validation: discriminator_loss=2.749, discriminator_real_loss=1.516, discriminator_fake_loss=1.233, generator_loss=26.44, generator_mel_loss=19.06, generator_kl_loss=1.206, generator_dur_loss=1.765, generator_adv_loss=1.966, generator_feat_match_loss=2.451, over 100.00 samples.
+2024-03-12 15:51:34,074 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 15:53:51,944 INFO [train.py:527] (1/6) Epoch 157, batch 106, global_batch_idx: 19450, batch size: 53, loss[discriminator_loss=2.756, discriminator_real_loss=1.465, discriminator_fake_loss=1.291, generator_loss=27.53, generator_mel_loss=19.19, generator_kl_loss=1.612, generator_dur_loss=1.637, generator_adv_loss=1.917, generator_feat_match_loss=3.177, over 53.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.404, discriminator_fake_loss=1.356, generator_loss=27.44, generator_mel_loss=18.96, generator_kl_loss=1.366, generator_dur_loss=1.749, generator_adv_loss=1.968, generator_feat_match_loss=3.401, over 6236.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:54:43,434 INFO [train.py:919] (1/6) Start epoch 158
+2024-03-12 15:56:35,699 INFO [train.py:527] (1/6) Epoch 158, batch 32, global_batch_idx: 19500, batch size: 53, loss[discriminator_loss=2.732, discriminator_real_loss=1.36, discriminator_fake_loss=1.373, generator_loss=27.29, generator_mel_loss=18.92, generator_kl_loss=1.558, generator_dur_loss=1.678, generator_adv_loss=1.959, generator_feat_match_loss=3.17, over 53.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.39, discriminator_fake_loss=1.351, generator_loss=27.29, generator_mel_loss=18.87, generator_kl_loss=1.397, generator_dur_loss=1.733, generator_adv_loss=1.921, generator_feat_match_loss=3.373, over 1672.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 15:58:57,652 INFO [train.py:527] (1/6) Epoch 158, batch 82, global_batch_idx: 19550, batch size: 36, loss[discriminator_loss=2.742, discriminator_real_loss=1.306, discriminator_fake_loss=1.437, generator_loss=27.71, generator_mel_loss=18.68, generator_kl_loss=1.383, generator_dur_loss=1.736, generator_adv_loss=2.147, generator_feat_match_loss=3.76, over 36.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.353, generator_loss=27.34, generator_mel_loss=18.85, generator_kl_loss=1.379, generator_dur_loss=1.768, generator_adv_loss=1.925, generator_feat_match_loss=3.413, over 4724.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:00:49,698 INFO [train.py:919] (1/6) Start epoch 159
+2024-03-12 16:01:36,484 INFO [train.py:527] (1/6) Epoch 159, batch 8, global_batch_idx: 19600, batch size: 83, loss[discriminator_loss=2.686, discriminator_real_loss=1.412, discriminator_fake_loss=1.274, generator_loss=27.64, generator_mel_loss=19.26, generator_kl_loss=1.332, generator_dur_loss=1.787, generator_adv_loss=1.946, generator_feat_match_loss=3.316, over 83.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.355, discriminator_fake_loss=1.348, generator_loss=27.66, generator_mel_loss=19.05, generator_kl_loss=1.411, generator_dur_loss=1.758, generator_adv_loss=1.93, generator_feat_match_loss=3.513, over 566.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:01:36,487 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 16:01:44,546 INFO [train.py:591] (1/6) Epoch 159, validation: discriminator_loss=2.698, discriminator_real_loss=1.452, discriminator_fake_loss=1.247, generator_loss=27.56, generator_mel_loss=19.76, generator_kl_loss=1.168, generator_dur_loss=1.787, generator_adv_loss=1.922, generator_feat_match_loss=2.921, over 100.00 samples.
+2024-03-12 16:01:44,548 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 16:04:06,448 INFO [train.py:527] (1/6) Epoch 159, batch 58, global_batch_idx: 19650, batch size: 36, loss[discriminator_loss=2.744, discriminator_real_loss=1.359, discriminator_fake_loss=1.386, generator_loss=27.57, generator_mel_loss=18.86, generator_kl_loss=1.602, generator_dur_loss=1.679, generator_adv_loss=1.843, generator_feat_match_loss=3.591, over 36.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.386, discriminator_fake_loss=1.352, generator_loss=27.5, generator_mel_loss=18.94, generator_kl_loss=1.397, generator_dur_loss=1.746, generator_adv_loss=1.931, generator_feat_match_loss=3.491, over 3423.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:06:25,413 INFO [train.py:527] (1/6) Epoch 159, batch 108, global_batch_idx: 19700, batch size: 74, loss[discriminator_loss=2.617, discriminator_real_loss=1.183, discriminator_fake_loss=1.434, generator_loss=28.31, generator_mel_loss=19.08, generator_kl_loss=1.344, generator_dur_loss=1.759, generator_adv_loss=2.237, generator_feat_match_loss=3.89, over 74.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=27.47, generator_mel_loss=18.93, generator_kl_loss=1.383, generator_dur_loss=1.748, generator_adv_loss=1.933, generator_feat_match_loss=3.473, over 6406.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:07:05,113 INFO [train.py:919] (1/6) Start epoch 160
+2024-03-12 16:09:05,138 INFO [train.py:527] (1/6) Epoch 160, batch 34, global_batch_idx: 19750, batch size: 47, loss[discriminator_loss=2.703, discriminator_real_loss=1.37, discriminator_fake_loss=1.333, generator_loss=27.12, generator_mel_loss=18.79, generator_kl_loss=1.328, generator_dur_loss=1.735, generator_adv_loss=1.851, generator_feat_match_loss=3.416, over 47.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.398, discriminator_fake_loss=1.371, generator_loss=27.09, generator_mel_loss=18.7, generator_kl_loss=1.379, generator_dur_loss=1.76, generator_adv_loss=1.942, generator_feat_match_loss=3.308, over 1988.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:11:24,986 INFO [train.py:527] (1/6) Epoch 160, batch 84, global_batch_idx: 19800, batch size: 62, loss[discriminator_loss=2.722, discriminator_real_loss=1.397, discriminator_fake_loss=1.325, generator_loss=27.25, generator_mel_loss=18.53, generator_kl_loss=1.41, generator_dur_loss=1.79, generator_adv_loss=1.955, generator_feat_match_loss=3.561, over 62.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=27.23, generator_mel_loss=18.8, generator_kl_loss=1.377, generator_dur_loss=1.766, generator_adv_loss=1.916, generator_feat_match_loss=3.38, over 4982.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:11:24,988 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 16:11:32,894 INFO [train.py:591] (1/6) Epoch 160, validation: discriminator_loss=2.75, discriminator_real_loss=1.446, discriminator_fake_loss=1.304, generator_loss=26.95, generator_mel_loss=19.15, generator_kl_loss=1.266, generator_dur_loss=1.828, generator_adv_loss=1.832, generator_feat_match_loss=2.876, over 100.00 samples.
+2024-03-12 16:11:32,895 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 16:13:23,077 INFO [train.py:919] (1/6) Start epoch 161
+2024-03-12 16:14:14,947 INFO [train.py:527] (1/6) Epoch 161, batch 10, global_batch_idx: 19850, batch size: 45, loss[discriminator_loss=2.784, discriminator_real_loss=1.457, discriminator_fake_loss=1.327, generator_loss=27.49, generator_mel_loss=19.39, generator_kl_loss=1.561, generator_dur_loss=1.684, generator_adv_loss=1.877, generator_feat_match_loss=2.973, over 45.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.395, discriminator_fake_loss=1.351, generator_loss=27.36, generator_mel_loss=18.9, generator_kl_loss=1.41, generator_dur_loss=1.757, generator_adv_loss=1.939, generator_feat_match_loss=3.349, over 607.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:16:33,443 INFO [train.py:527] (1/6) Epoch 161, batch 60, global_batch_idx: 19900, batch size: 31, loss[discriminator_loss=2.755, discriminator_real_loss=1.343, discriminator_fake_loss=1.412, generator_loss=28.07, generator_mel_loss=19.44, generator_kl_loss=1.583, generator_dur_loss=1.615, generator_adv_loss=1.991, generator_feat_match_loss=3.442, over 31.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.392, discriminator_fake_loss=1.346, generator_loss=27.44, generator_mel_loss=18.93, generator_kl_loss=1.384, generator_dur_loss=1.765, generator_adv_loss=1.927, generator_feat_match_loss=3.442, over 3531.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:18:52,707 INFO [train.py:527] (1/6) Epoch 161, batch 110, global_batch_idx: 19950, batch size: 48, loss[discriminator_loss=2.791, discriminator_real_loss=1.344, discriminator_fake_loss=1.447, generator_loss=27.66, generator_mel_loss=18.78, generator_kl_loss=1.575, generator_dur_loss=1.619, generator_adv_loss=2.082, generator_feat_match_loss=3.609, over 48.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.388, discriminator_fake_loss=1.351, generator_loss=27.4, generator_mel_loss=18.88, generator_kl_loss=1.383, generator_dur_loss=1.769, generator_adv_loss=1.924, generator_feat_match_loss=3.443, over 6606.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:19:29,482 INFO [train.py:919] (1/6) Start epoch 162
+2024-03-12 16:21:31,289 INFO [train.py:527] (1/6) Epoch 162, batch 36, global_batch_idx: 20000, batch size: 53, loss[discriminator_loss=2.789, discriminator_real_loss=1.436, discriminator_fake_loss=1.352, generator_loss=25.99, generator_mel_loss=18.34, generator_kl_loss=1.287, generator_dur_loss=1.693, generator_adv_loss=1.76, generator_feat_match_loss=2.91, over 53.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=27.22, generator_mel_loss=18.8, generator_kl_loss=1.405, generator_dur_loss=1.757, generator_adv_loss=1.913, generator_feat_match_loss=3.347, over 2061.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:21:31,291 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 16:21:39,253 INFO [train.py:591] (1/6) Epoch 162, validation: discriminator_loss=2.779, discriminator_real_loss=1.425, discriminator_fake_loss=1.354, generator_loss=26.49, generator_mel_loss=19.13, generator_kl_loss=1.198, generator_dur_loss=1.781, generator_adv_loss=1.756, generator_feat_match_loss=2.623, over 100.00 samples.
+2024-03-12 16:21:39,253 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 16:23:56,701 INFO [train.py:527] (1/6) Epoch 162, batch 86, global_batch_idx: 20050, batch size: 77, loss[discriminator_loss=2.738, discriminator_real_loss=1.308, discriminator_fake_loss=1.43, generator_loss=26.9, generator_mel_loss=18.48, generator_kl_loss=1.221, generator_dur_loss=1.833, generator_adv_loss=2.09, generator_feat_match_loss=3.281, over 77.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.392, discriminator_fake_loss=1.353, generator_loss=27.35, generator_mel_loss=18.86, generator_kl_loss=1.396, generator_dur_loss=1.75, generator_adv_loss=1.92, generator_feat_match_loss=3.423, over 4881.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:25:43,788 INFO [train.py:919] (1/6) Start epoch 163
+2024-03-12 16:26:42,878 INFO [train.py:527] (1/6) Epoch 163, batch 12, global_batch_idx: 20100, batch size: 48, loss[discriminator_loss=2.722, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=27.33, generator_mel_loss=18.65, generator_kl_loss=1.537, generator_dur_loss=1.706, generator_adv_loss=2.065, generator_feat_match_loss=3.369, over 48.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.393, discriminator_fake_loss=1.35, generator_loss=27.02, generator_mel_loss=18.61, generator_kl_loss=1.364, generator_dur_loss=1.771, generator_adv_loss=1.924, generator_feat_match_loss=3.353, over 785.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:29:01,135 INFO [train.py:527] (1/6) Epoch 163, batch 62, global_batch_idx: 20150, batch size: 80, loss[discriminator_loss=2.712, discriminator_real_loss=1.352, discriminator_fake_loss=1.36, generator_loss=26.89, generator_mel_loss=18.64, generator_kl_loss=1.237, generator_dur_loss=1.86, generator_adv_loss=1.978, generator_feat_match_loss=3.177, over 80.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.392, discriminator_fake_loss=1.346, generator_loss=27.4, generator_mel_loss=18.9, generator_kl_loss=1.401, generator_dur_loss=1.754, generator_adv_loss=1.912, generator_feat_match_loss=3.44, over 3547.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:31:20,174 INFO [train.py:527] (1/6) Epoch 163, batch 112, global_batch_idx: 20200, batch size: 45, loss[discriminator_loss=2.828, discriminator_real_loss=1.474, discriminator_fake_loss=1.353, generator_loss=28.35, generator_mel_loss=19.91, generator_kl_loss=1.447, generator_dur_loss=1.655, generator_adv_loss=2.051, generator_feat_match_loss=3.287, over 45.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.393, discriminator_fake_loss=1.35, generator_loss=27.51, generator_mel_loss=18.9, generator_kl_loss=1.393, generator_dur_loss=1.749, generator_adv_loss=1.956, generator_feat_match_loss=3.518, over 6474.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:31:20,175 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 16:31:29,124 INFO [train.py:591] (1/6) Epoch 163, validation: discriminator_loss=2.982, discriminator_real_loss=1.568, discriminator_fake_loss=1.414, generator_loss=26.57, generator_mel_loss=19.42, generator_kl_loss=1.199, generator_dur_loss=1.781, generator_adv_loss=1.785, generator_feat_match_loss=2.386, over 100.00 samples.
+2024-03-12 16:31:29,125 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 16:31:59,528 INFO [train.py:919] (1/6) Start epoch 164
+2024-03-12 16:34:11,338 INFO [train.py:527] (1/6) Epoch 164, batch 38, global_batch_idx: 20250, batch size: 88, loss[discriminator_loss=2.76, discriminator_real_loss=1.421, discriminator_fake_loss=1.339, generator_loss=25.69, generator_mel_loss=17.81, generator_kl_loss=1.182, generator_dur_loss=1.858, generator_adv_loss=1.789, generator_feat_match_loss=3.052, over 88.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.398, discriminator_fake_loss=1.347, generator_loss=27.17, generator_mel_loss=18.77, generator_kl_loss=1.346, generator_dur_loss=1.756, generator_adv_loss=1.938, generator_feat_match_loss=3.353, over 2375.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:36:29,674 INFO [train.py:527] (1/6) Epoch 164, batch 88, global_batch_idx: 20300, batch size: 62, loss[discriminator_loss=2.781, discriminator_real_loss=1.427, discriminator_fake_loss=1.354, generator_loss=27.27, generator_mel_loss=18.9, generator_kl_loss=1.41, generator_dur_loss=1.76, generator_adv_loss=1.797, generator_feat_match_loss=3.402, over 62.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.398, discriminator_fake_loss=1.342, generator_loss=27.24, generator_mel_loss=18.79, generator_kl_loss=1.37, generator_dur_loss=1.751, generator_adv_loss=1.926, generator_feat_match_loss=3.4, over 5213.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:38:06,093 INFO [train.py:919] (1/6) Start epoch 165
+2024-03-12 16:39:10,482 INFO [train.py:527] (1/6) Epoch 165, batch 14, global_batch_idx: 20350, batch size: 96, loss[discriminator_loss=2.746, discriminator_real_loss=1.218, discriminator_fake_loss=1.528, generator_loss=26.25, generator_mel_loss=18.02, generator_kl_loss=1.342, generator_dur_loss=1.905, generator_adv_loss=1.822, generator_feat_match_loss=3.163, over 96.00 samples.], tot_loss[discriminator_loss=2.76, discriminator_real_loss=1.371, discriminator_fake_loss=1.388, generator_loss=27.24, generator_mel_loss=18.71, generator_kl_loss=1.389, generator_dur_loss=1.765, generator_adv_loss=1.899, generator_feat_match_loss=3.479, over 932.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:41:30,676 INFO [train.py:527] (1/6) Epoch 165, batch 64, global_batch_idx: 20400, batch size: 36, loss[discriminator_loss=2.771, discriminator_real_loss=1.426, discriminator_fake_loss=1.346, generator_loss=27.28, generator_mel_loss=19, generator_kl_loss=1.374, generator_dur_loss=1.718, generator_adv_loss=1.789, generator_feat_match_loss=3.397, over 36.00 samples.], tot_loss[discriminator_loss=2.761, discriminator_real_loss=1.396, discriminator_fake_loss=1.365, generator_loss=27.19, generator_mel_loss=18.76, generator_kl_loss=1.379, generator_dur_loss=1.745, generator_adv_loss=1.912, generator_feat_match_loss=3.394, over 3703.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:41:30,677 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 16:41:38,611 INFO [train.py:591] (1/6) Epoch 165, validation: discriminator_loss=2.729, discriminator_real_loss=1.357, discriminator_fake_loss=1.372, generator_loss=26.09, generator_mel_loss=18.77, generator_kl_loss=1.186, generator_dur_loss=1.781, generator_adv_loss=1.756, generator_feat_match_loss=2.6, over 100.00 samples.
+2024-03-12 16:41:38,611 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 16:43:59,105 INFO [train.py:527] (1/6) Epoch 165, batch 114, global_batch_idx: 20450, batch size: 83, loss[discriminator_loss=2.721, discriminator_real_loss=1.383, discriminator_fake_loss=1.338, generator_loss=27.08, generator_mel_loss=18.7, generator_kl_loss=1.245, generator_dur_loss=1.82, generator_adv_loss=1.786, generator_feat_match_loss=3.529, over 83.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.393, discriminator_fake_loss=1.359, generator_loss=27.27, generator_mel_loss=18.8, generator_kl_loss=1.395, generator_dur_loss=1.738, generator_adv_loss=1.912, generator_feat_match_loss=3.432, over 6352.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:44:25,533 INFO [train.py:919] (1/6) Start epoch 166
+2024-03-12 16:46:40,188 INFO [train.py:527] (1/6) Epoch 166, batch 40, global_batch_idx: 20500, batch size: 52, loss[discriminator_loss=2.731, discriminator_real_loss=1.418, discriminator_fake_loss=1.313, generator_loss=27.18, generator_mel_loss=18.97, generator_kl_loss=1.433, generator_dur_loss=1.701, generator_adv_loss=1.771, generator_feat_match_loss=3.311, over 52.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.393, discriminator_fake_loss=1.36, generator_loss=27.31, generator_mel_loss=18.88, generator_kl_loss=1.374, generator_dur_loss=1.748, generator_adv_loss=1.91, generator_feat_match_loss=3.402, over 2510.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:49:00,067 INFO [train.py:527] (1/6) Epoch 166, batch 90, global_batch_idx: 20550, batch size: 68, loss[discriminator_loss=2.733, discriminator_real_loss=1.377, discriminator_fake_loss=1.356, generator_loss=27.57, generator_mel_loss=18.95, generator_kl_loss=1.312, generator_dur_loss=1.733, generator_adv_loss=1.903, generator_feat_match_loss=3.669, over 68.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.394, discriminator_fake_loss=1.354, generator_loss=27.32, generator_mel_loss=18.88, generator_kl_loss=1.392, generator_dur_loss=1.717, generator_adv_loss=1.91, generator_feat_match_loss=3.42, over 5278.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:50:32,791 INFO [train.py:919] (1/6) Start epoch 167
+2024-03-12 16:51:41,442 INFO [train.py:527] (1/6) Epoch 167, batch 16, global_batch_idx: 20600, batch size: 45, loss[discriminator_loss=2.719, discriminator_real_loss=1.419, discriminator_fake_loss=1.3, generator_loss=27.61, generator_mel_loss=19.19, generator_kl_loss=1.539, generator_dur_loss=1.657, generator_adv_loss=1.777, generator_feat_match_loss=3.439, over 45.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.387, discriminator_fake_loss=1.355, generator_loss=27.6, generator_mel_loss=18.97, generator_kl_loss=1.405, generator_dur_loss=1.722, generator_adv_loss=1.924, generator_feat_match_loss=3.578, over 935.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:51:41,443 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 16:51:49,248 INFO [train.py:591] (1/6) Epoch 167, validation: discriminator_loss=2.73, discriminator_real_loss=1.293, discriminator_fake_loss=1.436, generator_loss=27.39, generator_mel_loss=19.63, generator_kl_loss=1.25, generator_dur_loss=1.775, generator_adv_loss=1.671, generator_feat_match_loss=3.066, over 100.00 samples.
+2024-03-12 16:51:49,248 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 16:54:07,848 INFO [train.py:527] (1/6) Epoch 167, batch 66, global_batch_idx: 20650, batch size: 52, loss[discriminator_loss=2.703, discriminator_real_loss=1.478, discriminator_fake_loss=1.226, generator_loss=27.27, generator_mel_loss=18.75, generator_kl_loss=1.39, generator_dur_loss=1.683, generator_adv_loss=2.054, generator_feat_match_loss=3.396, over 52.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.394, discriminator_fake_loss=1.354, generator_loss=27.4, generator_mel_loss=18.83, generator_kl_loss=1.391, generator_dur_loss=1.744, generator_adv_loss=1.929, generator_feat_match_loss=3.496, over 4035.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:56:25,085 INFO [train.py:527] (1/6) Epoch 167, batch 116, global_batch_idx: 20700, batch size: 59, loss[discriminator_loss=2.754, discriminator_real_loss=1.487, discriminator_fake_loss=1.267, generator_loss=26.83, generator_mel_loss=18.74, generator_kl_loss=1.391, generator_dur_loss=1.733, generator_adv_loss=1.729, generator_feat_match_loss=3.242, over 59.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.35, generator_mel_loss=18.81, generator_kl_loss=1.394, generator_dur_loss=1.744, generator_adv_loss=1.928, generator_feat_match_loss=3.482, over 6836.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 16:56:47,150 INFO [train.py:919] (1/6) Start epoch 168
+2024-03-12 16:59:06,609 INFO [train.py:527] (1/6) Epoch 168, batch 42, global_batch_idx: 20750, batch size: 58, loss[discriminator_loss=2.717, discriminator_real_loss=1.356, discriminator_fake_loss=1.361, generator_loss=27.91, generator_mel_loss=18.82, generator_kl_loss=1.526, generator_dur_loss=1.72, generator_adv_loss=2.066, generator_feat_match_loss=3.786, over 58.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.33, generator_mel_loss=18.81, generator_kl_loss=1.389, generator_dur_loss=1.749, generator_adv_loss=1.926, generator_feat_match_loss=3.462, over 2500.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:01:25,285 INFO [train.py:527] (1/6) Epoch 168, batch 92, global_batch_idx: 20800, batch size: 62, loss[discriminator_loss=2.766, discriminator_real_loss=1.486, discriminator_fake_loss=1.28, generator_loss=27.6, generator_mel_loss=19.21, generator_kl_loss=1.343, generator_dur_loss=1.773, generator_adv_loss=1.846, generator_feat_match_loss=3.428, over 62.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.384, discriminator_fake_loss=1.349, generator_loss=27.38, generator_mel_loss=18.83, generator_kl_loss=1.396, generator_dur_loss=1.745, generator_adv_loss=1.912, generator_feat_match_loss=3.495, over 5370.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:01:25,286 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 17:01:33,559 INFO [train.py:591] (1/6) Epoch 168, validation: discriminator_loss=2.724, discriminator_real_loss=1.436, discriminator_fake_loss=1.287, generator_loss=26.81, generator_mel_loss=19.08, generator_kl_loss=1.096, generator_dur_loss=1.803, generator_adv_loss=1.849, generator_feat_match_loss=2.988, over 100.00 samples.
+2024-03-12 17:01:33,560 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 17:03:01,058 INFO [train.py:919] (1/6) Start epoch 169
+2024-03-12 17:04:15,974 INFO [train.py:527] (1/6) Epoch 169, batch 18, global_batch_idx: 20850, batch size: 77, loss[discriminator_loss=2.791, discriminator_real_loss=1.38, discriminator_fake_loss=1.411, generator_loss=27.38, generator_mel_loss=18.93, generator_kl_loss=1.167, generator_dur_loss=1.753, generator_adv_loss=1.897, generator_feat_match_loss=3.631, over 77.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.384, discriminator_fake_loss=1.332, generator_loss=27.66, generator_mel_loss=18.74, generator_kl_loss=1.358, generator_dur_loss=1.724, generator_adv_loss=2.1, generator_feat_match_loss=3.741, over 1148.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:06:32,638 INFO [train.py:527] (1/6) Epoch 169, batch 68, global_batch_idx: 20900, batch size: 66, loss[discriminator_loss=2.736, discriminator_real_loss=1.388, discriminator_fake_loss=1.348, generator_loss=26.35, generator_mel_loss=18.23, generator_kl_loss=1.356, generator_dur_loss=1.75, generator_adv_loss=1.814, generator_feat_match_loss=3.206, over 66.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.392, discriminator_fake_loss=1.337, generator_loss=27.42, generator_mel_loss=18.79, generator_kl_loss=1.387, generator_dur_loss=1.727, generator_adv_loss=1.983, generator_feat_match_loss=3.539, over 3855.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:08:52,724 INFO [train.py:527] (1/6) Epoch 169, batch 118, global_batch_idx: 20950, batch size: 61, loss[discriminator_loss=2.704, discriminator_real_loss=1.414, discriminator_fake_loss=1.29, generator_loss=27.58, generator_mel_loss=19.02, generator_kl_loss=1.387, generator_dur_loss=1.79, generator_adv_loss=1.823, generator_feat_match_loss=3.561, over 61.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.393, discriminator_fake_loss=1.341, generator_loss=27.37, generator_mel_loss=18.78, generator_kl_loss=1.395, generator_dur_loss=1.737, generator_adv_loss=1.95, generator_feat_match_loss=3.512, over 6600.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:09:08,951 INFO [train.py:919] (1/6) Start epoch 170
+2024-03-12 17:11:33,896 INFO [train.py:527] (1/6) Epoch 170, batch 44, global_batch_idx: 21000, batch size: 56, loss[discriminator_loss=2.749, discriminator_real_loss=1.501, discriminator_fake_loss=1.247, generator_loss=26.54, generator_mel_loss=18.66, generator_kl_loss=1.568, generator_dur_loss=1.673, generator_adv_loss=1.717, generator_feat_match_loss=2.923, over 56.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.398, discriminator_fake_loss=1.353, generator_loss=27.19, generator_mel_loss=18.75, generator_kl_loss=1.381, generator_dur_loss=1.757, generator_adv_loss=1.911, generator_feat_match_loss=3.389, over 2667.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:11:33,897 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 17:11:41,705 INFO [train.py:591] (1/6) Epoch 170, validation: discriminator_loss=2.708, discriminator_real_loss=1.313, discriminator_fake_loss=1.394, generator_loss=26.42, generator_mel_loss=18.87, generator_kl_loss=1.098, generator_dur_loss=1.825, generator_adv_loss=1.715, generator_feat_match_loss=2.916, over 100.00 samples.
+2024-03-12 17:11:41,706 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 17:14:00,432 INFO [train.py:527] (1/6) Epoch 170, batch 94, global_batch_idx: 21050, batch size: 72, loss[discriminator_loss=2.721, discriminator_real_loss=1.392, discriminator_fake_loss=1.329, generator_loss=27.23, generator_mel_loss=18.57, generator_kl_loss=1.289, generator_dur_loss=1.775, generator_adv_loss=1.9, generator_feat_match_loss=3.691, over 72.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.393, discriminator_fake_loss=1.355, generator_loss=27.29, generator_mel_loss=18.79, generator_kl_loss=1.392, generator_dur_loss=1.761, generator_adv_loss=1.908, generator_feat_match_loss=3.444, over 5502.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:15:20,729 INFO [train.py:919] (1/6) Start epoch 171
+2024-03-12 17:16:42,707 INFO [train.py:527] (1/6) Epoch 171, batch 20, global_batch_idx: 21100, batch size: 62, loss[discriminator_loss=2.72, discriminator_real_loss=1.497, discriminator_fake_loss=1.223, generator_loss=27.57, generator_mel_loss=18.82, generator_kl_loss=1.489, generator_dur_loss=1.706, generator_adv_loss=1.966, generator_feat_match_loss=3.594, over 62.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.398, discriminator_fake_loss=1.337, generator_loss=27.45, generator_mel_loss=18.88, generator_kl_loss=1.396, generator_dur_loss=1.764, generator_adv_loss=1.928, generator_feat_match_loss=3.481, over 1345.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:19:03,195 INFO [train.py:527] (1/6) Epoch 171, batch 70, global_batch_idx: 21150, batch size: 58, loss[discriminator_loss=2.807, discriminator_real_loss=1.507, discriminator_fake_loss=1.3, generator_loss=26.73, generator_mel_loss=18.61, generator_kl_loss=1.33, generator_dur_loss=1.726, generator_adv_loss=1.996, generator_feat_match_loss=3.07, over 58.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.395, discriminator_fake_loss=1.348, generator_loss=27.35, generator_mel_loss=18.81, generator_kl_loss=1.405, generator_dur_loss=1.761, generator_adv_loss=1.919, generator_feat_match_loss=3.451, over 4336.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:21:20,427 INFO [train.py:527] (1/6) Epoch 171, batch 120, global_batch_idx: 21200, batch size: 74, loss[discriminator_loss=2.725, discriminator_real_loss=1.394, discriminator_fake_loss=1.331, generator_loss=27.3, generator_mel_loss=18.76, generator_kl_loss=1.285, generator_dur_loss=1.838, generator_adv_loss=1.991, generator_feat_match_loss=3.426, over 74.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.401, discriminator_fake_loss=1.346, generator_loss=27.43, generator_mel_loss=18.81, generator_kl_loss=1.391, generator_dur_loss=1.759, generator_adv_loss=1.963, generator_feat_match_loss=3.503, over 7173.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:21:20,429 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 17:21:29,266 INFO [train.py:591] (1/6) Epoch 171, validation: discriminator_loss=2.765, discriminator_real_loss=1.541, discriminator_fake_loss=1.224, generator_loss=26.33, generator_mel_loss=18.74, generator_kl_loss=1.2, generator_dur_loss=1.815, generator_adv_loss=1.967, generator_feat_match_loss=2.605, over 100.00 samples.
+2024-03-12 17:21:29,266 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 17:21:38,809 INFO [train.py:919] (1/6) Start epoch 172
+2024-03-12 17:24:09,796 INFO [train.py:527] (1/6) Epoch 172, batch 46, global_batch_idx: 21250, batch size: 62, loss[discriminator_loss=2.691, discriminator_real_loss=1.356, discriminator_fake_loss=1.335, generator_loss=27.96, generator_mel_loss=19.23, generator_kl_loss=1.412, generator_dur_loss=1.799, generator_adv_loss=1.895, generator_feat_match_loss=3.63, over 62.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.397, discriminator_fake_loss=1.34, generator_loss=27.16, generator_mel_loss=18.67, generator_kl_loss=1.374, generator_dur_loss=1.772, generator_adv_loss=1.914, generator_feat_match_loss=3.435, over 2837.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:26:25,276 INFO [train.py:527] (1/6) Epoch 172, batch 96, global_batch_idx: 21300, batch size: 88, loss[discriminator_loss=2.721, discriminator_real_loss=1.297, discriminator_fake_loss=1.424, generator_loss=26.25, generator_mel_loss=18.25, generator_kl_loss=1.388, generator_dur_loss=1.821, generator_adv_loss=1.946, generator_feat_match_loss=2.841, over 88.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.387, discriminator_fake_loss=1.344, generator_loss=27.26, generator_mel_loss=18.72, generator_kl_loss=1.393, generator_dur_loss=1.764, generator_adv_loss=1.912, generator_feat_match_loss=3.464, over 5390.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:27:43,048 INFO [train.py:919] (1/6) Start epoch 173
+2024-03-12 17:29:08,006 INFO [train.py:527] (1/6) Epoch 173, batch 22, global_batch_idx: 21350, batch size: 15, loss[discriminator_loss=2.781, discriminator_real_loss=1.381, discriminator_fake_loss=1.4, generator_loss=29.22, generator_mel_loss=19.83, generator_kl_loss=1.806, generator_dur_loss=1.654, generator_adv_loss=1.803, generator_feat_match_loss=4.125, over 15.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.362, discriminator_fake_loss=1.362, generator_loss=27.59, generator_mel_loss=18.91, generator_kl_loss=1.403, generator_dur_loss=1.742, generator_adv_loss=1.935, generator_feat_match_loss=3.599, over 1146.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:31:29,519 INFO [train.py:527] (1/6) Epoch 173, batch 72, global_batch_idx: 21400, batch size: 96, loss[discriminator_loss=2.696, discriminator_real_loss=1.425, discriminator_fake_loss=1.27, generator_loss=26.47, generator_mel_loss=18.43, generator_kl_loss=1.142, generator_dur_loss=1.88, generator_adv_loss=1.765, generator_feat_match_loss=3.253, over 96.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.382, discriminator_fake_loss=1.353, generator_loss=27.51, generator_mel_loss=18.93, generator_kl_loss=1.409, generator_dur_loss=1.753, generator_adv_loss=1.909, generator_feat_match_loss=3.508, over 3920.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:31:29,520 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 17:31:37,518 INFO [train.py:591] (1/6) Epoch 173, validation: discriminator_loss=2.743, discriminator_real_loss=1.333, discriminator_fake_loss=1.41, generator_loss=26.61, generator_mel_loss=19, generator_kl_loss=1.281, generator_dur_loss=1.815, generator_adv_loss=1.698, generator_feat_match_loss=2.808, over 100.00 samples.
+2024-03-12 17:31:37,519 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 17:33:54,520 INFO [train.py:527] (1/6) Epoch 173, batch 122, global_batch_idx: 21450, batch size: 58, loss[discriminator_loss=2.701, discriminator_real_loss=1.434, discriminator_fake_loss=1.267, generator_loss=27.85, generator_mel_loss=19.26, generator_kl_loss=1.494, generator_dur_loss=1.725, generator_adv_loss=1.832, generator_feat_match_loss=3.541, over 58.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.385, discriminator_fake_loss=1.351, generator_loss=27.45, generator_mel_loss=18.85, generator_kl_loss=1.4, generator_dur_loss=1.764, generator_adv_loss=1.92, generator_feat_match_loss=3.518, over 6901.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:33:59,479 INFO [train.py:919] (1/6) Start epoch 174
+2024-03-12 17:36:37,025 INFO [train.py:527] (1/6) Epoch 174, batch 48, global_batch_idx: 21500, batch size: 45, loss[discriminator_loss=2.74, discriminator_real_loss=1.432, discriminator_fake_loss=1.308, generator_loss=26.69, generator_mel_loss=18.43, generator_kl_loss=1.496, generator_dur_loss=1.71, generator_adv_loss=1.926, generator_feat_match_loss=3.128, over 45.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.4, discriminator_fake_loss=1.35, generator_loss=27.19, generator_mel_loss=18.75, generator_kl_loss=1.399, generator_dur_loss=1.745, generator_adv_loss=1.892, generator_feat_match_loss=3.404, over 2850.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:38:55,035 INFO [train.py:527] (1/6) Epoch 174, batch 98, global_batch_idx: 21550, batch size: 45, loss[discriminator_loss=2.747, discriminator_real_loss=1.46, discriminator_fake_loss=1.288, generator_loss=26.25, generator_mel_loss=18.19, generator_kl_loss=1.432, generator_dur_loss=1.689, generator_adv_loss=1.815, generator_feat_match_loss=3.129, over 45.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.398, discriminator_fake_loss=1.351, generator_loss=27.21, generator_mel_loss=18.72, generator_kl_loss=1.393, generator_dur_loss=1.761, generator_adv_loss=1.908, generator_feat_match_loss=3.428, over 5743.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:40:01,744 INFO [train.py:919] (1/6) Start epoch 175
+2024-03-12 17:41:32,675 INFO [train.py:527] (1/6) Epoch 175, batch 24, global_batch_idx: 21600, batch size: 80, loss[discriminator_loss=2.789, discriminator_real_loss=1.295, discriminator_fake_loss=1.495, generator_loss=27.38, generator_mel_loss=18.62, generator_kl_loss=1.264, generator_dur_loss=1.846, generator_adv_loss=2.072, generator_feat_match_loss=3.573, over 80.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.378, discriminator_fake_loss=1.358, generator_loss=27.39, generator_mel_loss=18.79, generator_kl_loss=1.35, generator_dur_loss=1.775, generator_adv_loss=1.916, generator_feat_match_loss=3.559, over 1545.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:41:32,678 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 17:41:40,572 INFO [train.py:591] (1/6) Epoch 175, validation: discriminator_loss=2.808, discriminator_real_loss=1.562, discriminator_fake_loss=1.246, generator_loss=26.82, generator_mel_loss=18.91, generator_kl_loss=1.191, generator_dur_loss=1.83, generator_adv_loss=2.054, generator_feat_match_loss=2.835, over 100.00 samples.
+2024-03-12 17:41:40,573 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 17:44:02,833 INFO [train.py:527] (1/6) Epoch 175, batch 74, global_batch_idx: 21650, batch size: 83, loss[discriminator_loss=2.706, discriminator_real_loss=1.363, discriminator_fake_loss=1.343, generator_loss=26.82, generator_mel_loss=18.5, generator_kl_loss=1.274, generator_dur_loss=1.878, generator_adv_loss=1.701, generator_feat_match_loss=3.471, over 83.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.375, discriminator_fake_loss=1.353, generator_loss=27.34, generator_mel_loss=18.76, generator_kl_loss=1.362, generator_dur_loss=1.763, generator_adv_loss=1.915, generator_feat_match_loss=3.537, over 4452.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:46:17,865 INFO [train.py:919] (1/6) Start epoch 176
+2024-03-12 17:46:42,055 INFO [train.py:527] (1/6) Epoch 176, batch 0, global_batch_idx: 21700, batch size: 68, loss[discriminator_loss=2.768, discriminator_real_loss=1.386, discriminator_fake_loss=1.383, generator_loss=26.2, generator_mel_loss=17.97, generator_kl_loss=1.345, generator_dur_loss=1.747, generator_adv_loss=2.119, generator_feat_match_loss=3.019, over 68.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.386, discriminator_fake_loss=1.383, generator_loss=26.2, generator_mel_loss=17.97, generator_kl_loss=1.345, generator_dur_loss=1.747, generator_adv_loss=2.119, generator_feat_match_loss=3.019, over 68.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:49:00,879 INFO [train.py:527] (1/6) Epoch 176, batch 50, global_batch_idx: 21750, batch size: 53, loss[discriminator_loss=2.669, discriminator_real_loss=1.346, discriminator_fake_loss=1.323, generator_loss=27.76, generator_mel_loss=18.97, generator_kl_loss=1.373, generator_dur_loss=1.695, generator_adv_loss=1.971, generator_feat_match_loss=3.753, over 53.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.376, discriminator_fake_loss=1.357, generator_loss=27.44, generator_mel_loss=18.88, generator_kl_loss=1.373, generator_dur_loss=1.761, generator_adv_loss=1.92, generator_feat_match_loss=3.514, over 3000.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:51:22,072 INFO [train.py:527] (1/6) Epoch 176, batch 100, global_batch_idx: 21800, batch size: 31, loss[discriminator_loss=2.9, discriminator_real_loss=1.587, discriminator_fake_loss=1.312, generator_loss=27.18, generator_mel_loss=18.36, generator_kl_loss=1.477, generator_dur_loss=1.679, generator_adv_loss=2.03, generator_feat_match_loss=3.634, over 31.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.384, discriminator_fake_loss=1.362, generator_loss=27.43, generator_mel_loss=18.87, generator_kl_loss=1.381, generator_dur_loss=1.77, generator_adv_loss=1.917, generator_feat_match_loss=3.492, over 5844.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:51:22,073 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 17:51:31,011 INFO [train.py:591] (1/6) Epoch 176, validation: discriminator_loss=2.772, discriminator_real_loss=1.518, discriminator_fake_loss=1.254, generator_loss=26.16, generator_mel_loss=18.47, generator_kl_loss=1.18, generator_dur_loss=1.799, generator_adv_loss=1.881, generator_feat_match_loss=2.832, over 100.00 samples.
+2024-03-12 17:51:31,012 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 17:52:33,465 INFO [train.py:919] (1/6) Start epoch 177
+2024-03-12 17:54:10,049 INFO [train.py:527] (1/6) Epoch 177, batch 26, global_batch_idx: 21850, batch size: 61, loss[discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.359, generator_loss=27.26, generator_mel_loss=18.55, generator_kl_loss=1.399, generator_dur_loss=1.699, generator_adv_loss=1.997, generator_feat_match_loss=3.622, over 61.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.391, discriminator_fake_loss=1.346, generator_loss=27.26, generator_mel_loss=18.64, generator_kl_loss=1.384, generator_dur_loss=1.755, generator_adv_loss=1.942, generator_feat_match_loss=3.54, over 1438.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:56:28,917 INFO [train.py:527] (1/6) Epoch 177, batch 76, global_batch_idx: 21900, batch size: 45, loss[discriminator_loss=2.721, discriminator_real_loss=1.366, discriminator_fake_loss=1.355, generator_loss=28.53, generator_mel_loss=19.83, generator_kl_loss=1.621, generator_dur_loss=1.698, generator_adv_loss=1.989, generator_feat_match_loss=3.391, over 45.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.395, discriminator_fake_loss=1.347, generator_loss=27.11, generator_mel_loss=18.61, generator_kl_loss=1.381, generator_dur_loss=1.756, generator_adv_loss=1.918, generator_feat_match_loss=3.446, over 4359.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 17:58:41,179 INFO [train.py:919] (1/6) Start epoch 178
+2024-03-12 17:59:09,272 INFO [train.py:527] (1/6) Epoch 178, batch 2, global_batch_idx: 21950, batch size: 53, loss[discriminator_loss=2.736, discriminator_real_loss=1.374, discriminator_fake_loss=1.363, generator_loss=27.37, generator_mel_loss=18.99, generator_kl_loss=1.38, generator_dur_loss=1.671, generator_adv_loss=1.871, generator_feat_match_loss=3.452, over 53.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.399, discriminator_fake_loss=1.357, generator_loss=27.18, generator_mel_loss=18.82, generator_kl_loss=1.474, generator_dur_loss=1.732, generator_adv_loss=1.866, generator_feat_match_loss=3.291, over 167.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:01:30,778 INFO [train.py:527] (1/6) Epoch 178, batch 52, global_batch_idx: 22000, batch size: 83, loss[discriminator_loss=2.746, discriminator_real_loss=1.45, discriminator_fake_loss=1.296, generator_loss=26.09, generator_mel_loss=18.14, generator_kl_loss=1.223, generator_dur_loss=1.84, generator_adv_loss=1.834, generator_feat_match_loss=3.053, over 83.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.39, discriminator_fake_loss=1.345, generator_loss=27.25, generator_mel_loss=18.67, generator_kl_loss=1.408, generator_dur_loss=1.748, generator_adv_loss=1.915, generator_feat_match_loss=3.507, over 2988.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:01:30,780 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 18:01:38,781 INFO [train.py:591] (1/6) Epoch 178, validation: discriminator_loss=2.709, discriminator_real_loss=1.303, discriminator_fake_loss=1.406, generator_loss=26.32, generator_mel_loss=18.65, generator_kl_loss=1.19, generator_dur_loss=1.805, generator_adv_loss=1.767, generator_feat_match_loss=2.913, over 100.00 samples.
+2024-03-12 18:01:38,782 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 18:03:57,045 INFO [train.py:527] (1/6) Epoch 178, batch 102, global_batch_idx: 22050, batch size: 58, loss[discriminator_loss=2.826, discriminator_real_loss=1.527, discriminator_fake_loss=1.299, generator_loss=26.86, generator_mel_loss=18.68, generator_kl_loss=1.406, generator_dur_loss=1.733, generator_adv_loss=1.71, generator_feat_match_loss=3.334, over 58.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.391, discriminator_fake_loss=1.343, generator_loss=27.31, generator_mel_loss=18.73, generator_kl_loss=1.408, generator_dur_loss=1.749, generator_adv_loss=1.911, generator_feat_match_loss=3.514, over 5688.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:04:55,321 INFO [train.py:919] (1/6) Start epoch 179
+2024-03-12 18:06:36,856 INFO [train.py:527] (1/6) Epoch 179, batch 28, global_batch_idx: 22100, batch size: 52, loss[discriminator_loss=2.82, discriminator_real_loss=1.573, discriminator_fake_loss=1.247, generator_loss=27.48, generator_mel_loss=18.9, generator_kl_loss=1.387, generator_dur_loss=1.717, generator_adv_loss=1.756, generator_feat_match_loss=3.722, over 52.00 samples.], tot_loss[discriminator_loss=2.756, discriminator_real_loss=1.386, discriminator_fake_loss=1.37, generator_loss=27.23, generator_mel_loss=18.63, generator_kl_loss=1.397, generator_dur_loss=1.769, generator_adv_loss=1.941, generator_feat_match_loss=3.494, over 1648.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:08:55,065 INFO [train.py:527] (1/6) Epoch 179, batch 78, global_batch_idx: 22150, batch size: 31, loss[discriminator_loss=2.598, discriminator_real_loss=1.301, discriminator_fake_loss=1.297, generator_loss=28.91, generator_mel_loss=19.51, generator_kl_loss=1.508, generator_dur_loss=1.63, generator_adv_loss=1.916, generator_feat_match_loss=4.351, over 31.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.387, discriminator_fake_loss=1.355, generator_loss=27.27, generator_mel_loss=18.62, generator_kl_loss=1.372, generator_dur_loss=1.782, generator_adv_loss=1.945, generator_feat_match_loss=3.553, over 4709.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:11:01,112 INFO [train.py:919] (1/6) Start epoch 180
+2024-03-12 18:11:35,650 INFO [train.py:527] (1/6) Epoch 180, batch 4, global_batch_idx: 22200, batch size: 77, loss[discriminator_loss=2.751, discriminator_real_loss=1.355, discriminator_fake_loss=1.396, generator_loss=27.57, generator_mel_loss=19.12, generator_kl_loss=1.261, generator_dur_loss=1.853, generator_adv_loss=1.723, generator_feat_match_loss=3.614, over 77.00 samples.], tot_loss[discriminator_loss=2.782, discriminator_real_loss=1.396, discriminator_fake_loss=1.387, generator_loss=27.65, generator_mel_loss=19.17, generator_kl_loss=1.367, generator_dur_loss=1.747, generator_adv_loss=1.816, generator_feat_match_loss=3.552, over 277.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:11:35,652 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 18:11:43,426 INFO [train.py:591] (1/6) Epoch 180, validation: discriminator_loss=2.791, discriminator_real_loss=1.35, discriminator_fake_loss=1.44, generator_loss=25.83, generator_mel_loss=18.47, generator_kl_loss=1.269, generator_dur_loss=1.814, generator_adv_loss=1.689, generator_feat_match_loss=2.591, over 100.00 samples.
+2024-03-12 18:11:43,428 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 18:14:02,375 INFO [train.py:527] (1/6) Epoch 180, batch 54, global_batch_idx: 22250, batch size: 61, loss[discriminator_loss=2.753, discriminator_real_loss=1.381, discriminator_fake_loss=1.372, generator_loss=28.02, generator_mel_loss=19.2, generator_kl_loss=1.289, generator_dur_loss=1.678, generator_adv_loss=2.011, generator_feat_match_loss=3.842, over 61.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.391, discriminator_fake_loss=1.356, generator_loss=27.37, generator_mel_loss=18.83, generator_kl_loss=1.378, generator_dur_loss=1.738, generator_adv_loss=1.915, generator_feat_match_loss=3.512, over 3051.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:16:22,218 INFO [train.py:527] (1/6) Epoch 180, batch 104, global_batch_idx: 22300, batch size: 61, loss[discriminator_loss=2.812, discriminator_real_loss=1.409, discriminator_fake_loss=1.403, generator_loss=27.94, generator_mel_loss=19.19, generator_kl_loss=1.315, generator_dur_loss=1.731, generator_adv_loss=1.861, generator_feat_match_loss=3.843, over 61.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.392, discriminator_fake_loss=1.35, generator_loss=27.39, generator_mel_loss=18.8, generator_kl_loss=1.37, generator_dur_loss=1.751, generator_adv_loss=1.923, generator_feat_match_loss=3.544, over 6093.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:17:13,439 INFO [train.py:919] (1/6) Start epoch 181
+2024-03-12 18:19:03,228 INFO [train.py:527] (1/6) Epoch 181, batch 30, global_batch_idx: 22350, batch size: 96, loss[discriminator_loss=2.79, discriminator_real_loss=1.502, discriminator_fake_loss=1.287, generator_loss=27.3, generator_mel_loss=18.48, generator_kl_loss=1.123, generator_dur_loss=1.865, generator_adv_loss=2.027, generator_feat_match_loss=3.809, over 96.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.29, generator_mel_loss=18.69, generator_kl_loss=1.393, generator_dur_loss=1.775, generator_adv_loss=1.929, generator_feat_match_loss=3.504, over 1774.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:21:23,374 INFO [train.py:527] (1/6) Epoch 181, batch 80, global_batch_idx: 22400, batch size: 74, loss[discriminator_loss=2.796, discriminator_real_loss=1.391, discriminator_fake_loss=1.405, generator_loss=26.96, generator_mel_loss=18.54, generator_kl_loss=1.292, generator_dur_loss=1.807, generator_adv_loss=1.917, generator_feat_match_loss=3.411, over 74.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.391, discriminator_fake_loss=1.352, generator_loss=27.42, generator_mel_loss=18.71, generator_kl_loss=1.382, generator_dur_loss=1.774, generator_adv_loss=1.956, generator_feat_match_loss=3.598, over 4912.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:21:23,375 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 18:21:31,724 INFO [train.py:591] (1/6) Epoch 181, validation: discriminator_loss=2.819, discriminator_real_loss=1.557, discriminator_fake_loss=1.263, generator_loss=26.69, generator_mel_loss=18.85, generator_kl_loss=1.198, generator_dur_loss=1.781, generator_adv_loss=1.944, generator_feat_match_loss=2.917, over 100.00 samples.
+2024-03-12 18:21:31,725 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 18:23:28,217 INFO [train.py:919] (1/6) Start epoch 182
+2024-03-12 18:24:07,823 INFO [train.py:527] (1/6) Epoch 182, batch 6, global_batch_idx: 22450, batch size: 58, loss[discriminator_loss=2.788, discriminator_real_loss=1.506, discriminator_fake_loss=1.282, generator_loss=28.11, generator_mel_loss=19.65, generator_kl_loss=1.355, generator_dur_loss=1.75, generator_adv_loss=1.771, generator_feat_match_loss=3.582, over 58.00 samples.], tot_loss[discriminator_loss=2.778, discriminator_real_loss=1.435, discriminator_fake_loss=1.343, generator_loss=27.47, generator_mel_loss=19.03, generator_kl_loss=1.361, generator_dur_loss=1.726, generator_adv_loss=1.881, generator_feat_match_loss=3.478, over 373.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:26:28,656 INFO [train.py:527] (1/6) Epoch 182, batch 56, global_batch_idx: 22500, batch size: 48, loss[discriminator_loss=2.735, discriminator_real_loss=1.429, discriminator_fake_loss=1.306, generator_loss=27.59, generator_mel_loss=18.9, generator_kl_loss=1.433, generator_dur_loss=1.712, generator_adv_loss=1.879, generator_feat_match_loss=3.664, over 48.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.397, discriminator_fake_loss=1.361, generator_loss=27.29, generator_mel_loss=18.74, generator_kl_loss=1.383, generator_dur_loss=1.76, generator_adv_loss=1.908, generator_feat_match_loss=3.501, over 3356.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:28:47,579 INFO [train.py:527] (1/6) Epoch 182, batch 106, global_batch_idx: 22550, batch size: 60, loss[discriminator_loss=2.798, discriminator_real_loss=1.435, discriminator_fake_loss=1.363, generator_loss=27.06, generator_mel_loss=18.84, generator_kl_loss=1.493, generator_dur_loss=1.776, generator_adv_loss=1.788, generator_feat_match_loss=3.157, over 60.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.389, discriminator_fake_loss=1.357, generator_loss=27.35, generator_mel_loss=18.72, generator_kl_loss=1.384, generator_dur_loss=1.77, generator_adv_loss=1.921, generator_feat_match_loss=3.554, over 6391.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:29:34,498 INFO [train.py:919] (1/6) Start epoch 183
+2024-03-12 18:31:27,926 INFO [train.py:527] (1/6) Epoch 183, batch 32, global_batch_idx: 22600, batch size: 36, loss[discriminator_loss=2.713, discriminator_real_loss=1.352, discriminator_fake_loss=1.361, generator_loss=28.32, generator_mel_loss=19.09, generator_kl_loss=1.465, generator_dur_loss=1.708, generator_adv_loss=1.818, generator_feat_match_loss=4.236, over 36.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.4, generator_mel_loss=18.79, generator_kl_loss=1.433, generator_dur_loss=1.724, generator_adv_loss=1.929, generator_feat_match_loss=3.527, over 1615.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:31:27,927 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 18:31:35,883 INFO [train.py:591] (1/6) Epoch 183, validation: discriminator_loss=2.735, discriminator_real_loss=1.413, discriminator_fake_loss=1.323, generator_loss=27.1, generator_mel_loss=19, generator_kl_loss=1.172, generator_dur_loss=1.819, generator_adv_loss=1.838, generator_feat_match_loss=3.275, over 100.00 samples.
+2024-03-12 18:31:35,883 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 18:33:56,402 INFO [train.py:527] (1/6) Epoch 183, batch 82, global_batch_idx: 22650, batch size: 77, loss[discriminator_loss=2.792, discriminator_real_loss=1.354, discriminator_fake_loss=1.438, generator_loss=27.51, generator_mel_loss=18.75, generator_kl_loss=1.327, generator_dur_loss=1.883, generator_adv_loss=1.95, generator_feat_match_loss=3.594, over 77.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.386, discriminator_fake_loss=1.355, generator_loss=27.29, generator_mel_loss=18.7, generator_kl_loss=1.378, generator_dur_loss=1.768, generator_adv_loss=1.914, generator_feat_match_loss=3.52, over 4762.00 samples.], cur_lr_g: 1.96e-04, cur_lr_d: 1.96e-04, 
+2024-03-12 18:35:50,032 INFO [train.py:919] (1/6) Start epoch 184
+2024-03-12 18:36:35,618 INFO [train.py:527] (1/6) Epoch 184, batch 8, global_batch_idx: 22700, batch size: 58, loss[discriminator_loss=2.716, discriminator_real_loss=1.354, discriminator_fake_loss=1.362, generator_loss=27.24, generator_mel_loss=18.28, generator_kl_loss=1.362, generator_dur_loss=1.773, generator_adv_loss=1.915, generator_feat_match_loss=3.913, over 58.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.382, discriminator_fake_loss=1.343, generator_loss=27.83, generator_mel_loss=18.97, generator_kl_loss=1.465, generator_dur_loss=1.729, generator_adv_loss=1.921, generator_feat_match_loss=3.748, over 403.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:38:51,499 INFO [train.py:527] (1/6) Epoch 184, batch 58, global_batch_idx: 22750, batch size: 52, loss[discriminator_loss=2.699, discriminator_real_loss=1.332, discriminator_fake_loss=1.367, generator_loss=28.69, generator_mel_loss=19.25, generator_kl_loss=1.524, generator_dur_loss=1.723, generator_adv_loss=2.114, generator_feat_match_loss=4.086, over 52.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.385, discriminator_fake_loss=1.359, generator_loss=27.49, generator_mel_loss=18.84, generator_kl_loss=1.421, generator_dur_loss=1.726, generator_adv_loss=1.931, generator_feat_match_loss=3.565, over 3040.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:41:09,954 INFO [train.py:527] (1/6) Epoch 184, batch 108, global_batch_idx: 22800, batch size: 61, loss[discriminator_loss=2.774, discriminator_real_loss=1.338, discriminator_fake_loss=1.436, generator_loss=27.79, generator_mel_loss=18.8, generator_kl_loss=1.384, generator_dur_loss=1.744, generator_adv_loss=2.166, generator_feat_match_loss=3.703, over 61.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.387, discriminator_fake_loss=1.356, generator_loss=27.37, generator_mel_loss=18.74, generator_kl_loss=1.383, generator_dur_loss=1.755, generator_adv_loss=1.925, generator_feat_match_loss=3.561, over 6184.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:41:09,956 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 18:41:18,936 INFO [train.py:591] (1/6) Epoch 184, validation: discriminator_loss=2.867, discriminator_real_loss=1.662, discriminator_fake_loss=1.205, generator_loss=26.73, generator_mel_loss=19.04, generator_kl_loss=1.196, generator_dur_loss=1.823, generator_adv_loss=2.106, generator_feat_match_loss=2.571, over 100.00 samples.
+2024-03-12 18:41:18,937 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 18:42:02,298 INFO [train.py:919] (1/6) Start epoch 185
+2024-03-12 18:44:00,255 INFO [train.py:527] (1/6) Epoch 185, batch 34, global_batch_idx: 22850, batch size: 70, loss[discriminator_loss=2.768, discriminator_real_loss=1.555, discriminator_fake_loss=1.213, generator_loss=27.74, generator_mel_loss=18.79, generator_kl_loss=1.39, generator_dur_loss=1.82, generator_adv_loss=2.035, generator_feat_match_loss=3.698, over 70.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.404, discriminator_fake_loss=1.361, generator_loss=27.13, generator_mel_loss=18.66, generator_kl_loss=1.399, generator_dur_loss=1.771, generator_adv_loss=1.903, generator_feat_match_loss=3.399, over 2063.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:46:18,241 INFO [train.py:527] (1/6) Epoch 185, batch 84, global_batch_idx: 22900, batch size: 13, loss[discriminator_loss=2.812, discriminator_real_loss=1.519, discriminator_fake_loss=1.293, generator_loss=27.92, generator_mel_loss=19.67, generator_kl_loss=1.69, generator_dur_loss=1.709, generator_adv_loss=1.772, generator_feat_match_loss=3.08, over 13.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.394, discriminator_fake_loss=1.353, generator_loss=27.25, generator_mel_loss=18.69, generator_kl_loss=1.409, generator_dur_loss=1.768, generator_adv_loss=1.901, generator_feat_match_loss=3.483, over 4817.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:48:08,900 INFO [train.py:919] (1/6) Start epoch 186
+2024-03-12 18:49:00,470 INFO [train.py:527] (1/6) Epoch 186, batch 10, global_batch_idx: 22950, batch size: 64, loss[discriminator_loss=2.65, discriminator_real_loss=1.416, discriminator_fake_loss=1.234, generator_loss=28.17, generator_mel_loss=18.69, generator_kl_loss=1.298, generator_dur_loss=1.775, generator_adv_loss=2.36, generator_feat_match_loss=4.048, over 64.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.327, discriminator_fake_loss=1.386, generator_loss=27.75, generator_mel_loss=18.76, generator_kl_loss=1.387, generator_dur_loss=1.754, generator_adv_loss=2.003, generator_feat_match_loss=3.845, over 585.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:51:17,449 INFO [train.py:527] (1/6) Epoch 186, batch 60, global_batch_idx: 23000, batch size: 77, loss[discriminator_loss=2.785, discriminator_real_loss=1.479, discriminator_fake_loss=1.306, generator_loss=26.07, generator_mel_loss=18.05, generator_kl_loss=1.283, generator_dur_loss=1.839, generator_adv_loss=1.786, generator_feat_match_loss=3.116, over 77.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.33, generator_mel_loss=18.68, generator_kl_loss=1.391, generator_dur_loss=1.762, generator_adv_loss=1.939, generator_feat_match_loss=3.561, over 3483.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:51:17,451 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 18:51:25,374 INFO [train.py:591] (1/6) Epoch 186, validation: discriminator_loss=2.794, discriminator_real_loss=1.451, discriminator_fake_loss=1.344, generator_loss=26.57, generator_mel_loss=18.9, generator_kl_loss=1.241, generator_dur_loss=1.796, generator_adv_loss=1.81, generator_feat_match_loss=2.816, over 100.00 samples.
+2024-03-12 18:51:25,375 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 18:53:42,533 INFO [train.py:527] (1/6) Epoch 186, batch 110, global_batch_idx: 23050, batch size: 62, loss[discriminator_loss=2.778, discriminator_real_loss=1.504, discriminator_fake_loss=1.275, generator_loss=26.57, generator_mel_loss=18.24, generator_kl_loss=1.264, generator_dur_loss=1.778, generator_adv_loss=1.915, generator_feat_match_loss=3.376, over 62.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.387, discriminator_fake_loss=1.35, generator_loss=27.3, generator_mel_loss=18.69, generator_kl_loss=1.386, generator_dur_loss=1.762, generator_adv_loss=1.923, generator_feat_match_loss=3.532, over 6351.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:54:20,316 INFO [train.py:919] (1/6) Start epoch 187
+2024-03-12 18:56:22,603 INFO [train.py:527] (1/6) Epoch 187, batch 36, global_batch_idx: 23100, batch size: 74, loss[discriminator_loss=2.775, discriminator_real_loss=1.333, discriminator_fake_loss=1.442, generator_loss=27.59, generator_mel_loss=18.9, generator_kl_loss=1.278, generator_dur_loss=1.794, generator_adv_loss=2.067, generator_feat_match_loss=3.552, over 74.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.38, discriminator_fake_loss=1.36, generator_loss=27.39, generator_mel_loss=18.69, generator_kl_loss=1.391, generator_dur_loss=1.76, generator_adv_loss=1.96, generator_feat_match_loss=3.585, over 2262.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 18:58:38,536 INFO [train.py:527] (1/6) Epoch 187, batch 86, global_batch_idx: 23150, batch size: 42, loss[discriminator_loss=2.724, discriminator_real_loss=1.319, discriminator_fake_loss=1.405, generator_loss=28.02, generator_mel_loss=18.77, generator_kl_loss=1.663, generator_dur_loss=1.706, generator_adv_loss=1.949, generator_feat_match_loss=3.931, over 42.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.383, discriminator_fake_loss=1.353, generator_loss=27.34, generator_mel_loss=18.68, generator_kl_loss=1.401, generator_dur_loss=1.744, generator_adv_loss=1.94, generator_feat_match_loss=3.574, over 5035.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:00:23,405 INFO [train.py:919] (1/6) Start epoch 188
+2024-03-12 19:01:22,052 INFO [train.py:527] (1/6) Epoch 188, batch 12, global_batch_idx: 23200, batch size: 83, loss[discriminator_loss=2.762, discriminator_real_loss=1.36, discriminator_fake_loss=1.402, generator_loss=26.71, generator_mel_loss=18.35, generator_kl_loss=1.181, generator_dur_loss=1.865, generator_adv_loss=1.892, generator_feat_match_loss=3.42, over 83.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.401, discriminator_fake_loss=1.363, generator_loss=27.14, generator_mel_loss=18.6, generator_kl_loss=1.346, generator_dur_loss=1.777, generator_adv_loss=1.901, generator_feat_match_loss=3.519, over 794.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:01:22,054 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 19:01:30,004 INFO [train.py:591] (1/6) Epoch 188, validation: discriminator_loss=2.763, discriminator_real_loss=1.442, discriminator_fake_loss=1.321, generator_loss=26.05, generator_mel_loss=18.39, generator_kl_loss=1.334, generator_dur_loss=1.8, generator_adv_loss=1.876, generator_feat_match_loss=2.644, over 100.00 samples.
+2024-03-12 19:01:30,005 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 19:03:48,463 INFO [train.py:527] (1/6) Epoch 188, batch 62, global_batch_idx: 23250, batch size: 77, loss[discriminator_loss=2.724, discriminator_real_loss=1.314, discriminator_fake_loss=1.41, generator_loss=27.09, generator_mel_loss=18.48, generator_kl_loss=1.438, generator_dur_loss=1.868, generator_adv_loss=1.854, generator_feat_match_loss=3.456, over 77.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.385, discriminator_fake_loss=1.361, generator_loss=27.42, generator_mel_loss=18.76, generator_kl_loss=1.405, generator_dur_loss=1.755, generator_adv_loss=1.922, generator_feat_match_loss=3.583, over 3675.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:06:09,542 INFO [train.py:527] (1/6) Epoch 188, batch 112, global_batch_idx: 23300, batch size: 56, loss[discriminator_loss=2.755, discriminator_real_loss=1.251, discriminator_fake_loss=1.503, generator_loss=27.68, generator_mel_loss=18.68, generator_kl_loss=1.45, generator_dur_loss=1.7, generator_adv_loss=2.111, generator_feat_match_loss=3.736, over 56.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.38, discriminator_fake_loss=1.36, generator_loss=27.39, generator_mel_loss=18.72, generator_kl_loss=1.408, generator_dur_loss=1.756, generator_adv_loss=1.926, generator_feat_match_loss=3.578, over 6483.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:06:41,481 INFO [train.py:919] (1/6) Start epoch 189
+2024-03-12 19:08:53,160 INFO [train.py:527] (1/6) Epoch 189, batch 38, global_batch_idx: 23350, batch size: 88, loss[discriminator_loss=2.779, discriminator_real_loss=1.4, discriminator_fake_loss=1.379, generator_loss=25.71, generator_mel_loss=18.1, generator_kl_loss=1.076, generator_dur_loss=1.839, generator_adv_loss=1.776, generator_feat_match_loss=2.918, over 88.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.394, discriminator_fake_loss=1.351, generator_loss=27.35, generator_mel_loss=18.69, generator_kl_loss=1.421, generator_dur_loss=1.764, generator_adv_loss=1.918, generator_feat_match_loss=3.554, over 2035.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:11:15,664 INFO [train.py:527] (1/6) Epoch 189, batch 88, global_batch_idx: 23400, batch size: 74, loss[discriminator_loss=2.762, discriminator_real_loss=1.406, discriminator_fake_loss=1.357, generator_loss=27.33, generator_mel_loss=18.58, generator_kl_loss=1.441, generator_dur_loss=1.81, generator_adv_loss=1.973, generator_feat_match_loss=3.523, over 74.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.393, discriminator_fake_loss=1.354, generator_loss=27.31, generator_mel_loss=18.66, generator_kl_loss=1.403, generator_dur_loss=1.766, generator_adv_loss=1.906, generator_feat_match_loss=3.57, over 5042.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:11:15,666 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 19:11:23,871 INFO [train.py:591] (1/6) Epoch 189, validation: discriminator_loss=2.761, discriminator_real_loss=1.482, discriminator_fake_loss=1.279, generator_loss=27.2, generator_mel_loss=18.92, generator_kl_loss=1.165, generator_dur_loss=1.831, generator_adv_loss=1.969, generator_feat_match_loss=3.312, over 100.00 samples.
+2024-03-12 19:11:23,872 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 19:12:58,254 INFO [train.py:919] (1/6) Start epoch 190
+2024-03-12 19:14:01,653 INFO [train.py:527] (1/6) Epoch 190, batch 14, global_batch_idx: 23450, batch size: 88, loss[discriminator_loss=2.818, discriminator_real_loss=1.393, discriminator_fake_loss=1.425, generator_loss=26.74, generator_mel_loss=18.36, generator_kl_loss=1.256, generator_dur_loss=1.885, generator_adv_loss=2.01, generator_feat_match_loss=3.225, over 88.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.399, discriminator_fake_loss=1.354, generator_loss=27.37, generator_mel_loss=18.67, generator_kl_loss=1.396, generator_dur_loss=1.776, generator_adv_loss=1.979, generator_feat_match_loss=3.551, over 951.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:16:20,362 INFO [train.py:527] (1/6) Epoch 190, batch 64, global_batch_idx: 23500, batch size: 53, loss[discriminator_loss=2.682, discriminator_real_loss=1.379, discriminator_fake_loss=1.303, generator_loss=27.42, generator_mel_loss=18.73, generator_kl_loss=1.417, generator_dur_loss=1.623, generator_adv_loss=1.921, generator_feat_match_loss=3.733, over 53.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.393, discriminator_fake_loss=1.348, generator_loss=27.32, generator_mel_loss=18.67, generator_kl_loss=1.394, generator_dur_loss=1.751, generator_adv_loss=1.931, generator_feat_match_loss=3.571, over 3709.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:18:39,696 INFO [train.py:527] (1/6) Epoch 190, batch 114, global_batch_idx: 23550, batch size: 64, loss[discriminator_loss=2.71, discriminator_real_loss=1.346, discriminator_fake_loss=1.364, generator_loss=27.62, generator_mel_loss=18.79, generator_kl_loss=1.38, generator_dur_loss=1.776, generator_adv_loss=1.87, generator_feat_match_loss=3.804, over 64.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.395, discriminator_fake_loss=1.35, generator_loss=27.31, generator_mel_loss=18.68, generator_kl_loss=1.388, generator_dur_loss=1.761, generator_adv_loss=1.92, generator_feat_match_loss=3.559, over 6678.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:19:03,906 INFO [train.py:919] (1/6) Start epoch 191
+2024-03-12 19:21:21,819 INFO [train.py:527] (1/6) Epoch 191, batch 40, global_batch_idx: 23600, batch size: 88, loss[discriminator_loss=2.809, discriminator_real_loss=1.346, discriminator_fake_loss=1.463, generator_loss=26.78, generator_mel_loss=18.22, generator_kl_loss=1.273, generator_dur_loss=1.796, generator_adv_loss=2.03, generator_feat_match_loss=3.46, over 88.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.385, discriminator_fake_loss=1.355, generator_loss=27.25, generator_mel_loss=18.61, generator_kl_loss=1.387, generator_dur_loss=1.713, generator_adv_loss=1.924, generator_feat_match_loss=3.619, over 2458.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:21:21,820 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 19:21:29,539 INFO [train.py:591] (1/6) Epoch 191, validation: discriminator_loss=2.788, discriminator_real_loss=1.5, discriminator_fake_loss=1.288, generator_loss=26.94, generator_mel_loss=18.69, generator_kl_loss=1.259, generator_dur_loss=1.796, generator_adv_loss=2.027, generator_feat_match_loss=3.167, over 100.00 samples.
+2024-03-12 19:21:29,540 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 19:23:48,570 INFO [train.py:527] (1/6) Epoch 191, batch 90, global_batch_idx: 23650, batch size: 62, loss[discriminator_loss=2.761, discriminator_real_loss=1.494, discriminator_fake_loss=1.267, generator_loss=26.89, generator_mel_loss=18.19, generator_kl_loss=1.505, generator_dur_loss=1.704, generator_adv_loss=1.907, generator_feat_match_loss=3.588, over 62.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.39, discriminator_fake_loss=1.354, generator_loss=27.28, generator_mel_loss=18.61, generator_kl_loss=1.397, generator_dur_loss=1.729, generator_adv_loss=1.925, generator_feat_match_loss=3.619, over 5328.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:25:17,524 INFO [train.py:919] (1/6) Start epoch 192
+2024-03-12 19:26:26,522 INFO [train.py:527] (1/6) Epoch 192, batch 16, global_batch_idx: 23700, batch size: 68, loss[discriminator_loss=2.763, discriminator_real_loss=1.391, discriminator_fake_loss=1.372, generator_loss=26.5, generator_mel_loss=18.29, generator_kl_loss=1.272, generator_dur_loss=1.773, generator_adv_loss=1.985, generator_feat_match_loss=3.176, over 68.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.394, discriminator_fake_loss=1.345, generator_loss=27.26, generator_mel_loss=18.67, generator_kl_loss=1.384, generator_dur_loss=1.786, generator_adv_loss=1.93, generator_feat_match_loss=3.488, over 1034.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:28:41,792 INFO [train.py:527] (1/6) Epoch 192, batch 66, global_batch_idx: 23750, batch size: 64, loss[discriminator_loss=2.703, discriminator_real_loss=1.436, discriminator_fake_loss=1.267, generator_loss=28.41, generator_mel_loss=19.43, generator_kl_loss=1.453, generator_dur_loss=1.772, generator_adv_loss=1.911, generator_feat_match_loss=3.844, over 64.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.39, discriminator_fake_loss=1.351, generator_loss=27.35, generator_mel_loss=18.7, generator_kl_loss=1.402, generator_dur_loss=1.775, generator_adv_loss=1.915, generator_feat_match_loss=3.561, over 3910.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:31:01,591 INFO [train.py:527] (1/6) Epoch 192, batch 116, global_batch_idx: 23800, batch size: 88, loss[discriminator_loss=2.768, discriminator_real_loss=1.459, discriminator_fake_loss=1.309, generator_loss=27.08, generator_mel_loss=18.54, generator_kl_loss=1.236, generator_dur_loss=1.873, generator_adv_loss=1.791, generator_feat_match_loss=3.639, over 88.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.387, discriminator_fake_loss=1.353, generator_loss=27.32, generator_mel_loss=18.63, generator_kl_loss=1.396, generator_dur_loss=1.776, generator_adv_loss=1.91, generator_feat_match_loss=3.603, over 6977.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:31:01,592 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 19:31:10,676 INFO [train.py:591] (1/6) Epoch 192, validation: discriminator_loss=2.816, discriminator_real_loss=1.43, discriminator_fake_loss=1.386, generator_loss=25.78, generator_mel_loss=18.34, generator_kl_loss=1.284, generator_dur_loss=1.825, generator_adv_loss=1.748, generator_feat_match_loss=2.581, over 100.00 samples.
+2024-03-12 19:31:10,677 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 19:31:32,612 INFO [train.py:919] (1/6) Start epoch 193
+2024-03-12 19:33:52,746 INFO [train.py:527] (1/6) Epoch 193, batch 42, global_batch_idx: 23850, batch size: 25, loss[discriminator_loss=2.644, discriminator_real_loss=1.412, discriminator_fake_loss=1.232, generator_loss=29.93, generator_mel_loss=20.25, generator_kl_loss=1.668, generator_dur_loss=1.63, generator_adv_loss=1.903, generator_feat_match_loss=4.476, over 25.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.382, discriminator_fake_loss=1.36, generator_loss=27.4, generator_mel_loss=18.64, generator_kl_loss=1.382, generator_dur_loss=1.765, generator_adv_loss=1.918, generator_feat_match_loss=3.69, over 2668.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:36:10,062 INFO [train.py:527] (1/6) Epoch 193, batch 92, global_batch_idx: 23900, batch size: 44, loss[discriminator_loss=2.726, discriminator_real_loss=1.327, discriminator_fake_loss=1.4, generator_loss=28.28, generator_mel_loss=19.19, generator_kl_loss=1.446, generator_dur_loss=1.699, generator_adv_loss=1.876, generator_feat_match_loss=4.063, over 44.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.384, discriminator_fake_loss=1.355, generator_loss=27.28, generator_mel_loss=18.6, generator_kl_loss=1.381, generator_dur_loss=1.756, generator_adv_loss=1.919, generator_feat_match_loss=3.624, over 5544.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:37:38,754 INFO [train.py:919] (1/6) Start epoch 194
+2024-03-12 19:38:53,504 INFO [train.py:527] (1/6) Epoch 194, batch 18, global_batch_idx: 23950, batch size: 44, loss[discriminator_loss=2.643, discriminator_real_loss=1.256, discriminator_fake_loss=1.387, generator_loss=29.35, generator_mel_loss=19.26, generator_kl_loss=1.67, generator_dur_loss=1.688, generator_adv_loss=2.11, generator_feat_match_loss=4.619, over 44.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.373, discriminator_fake_loss=1.355, generator_loss=27.55, generator_mel_loss=18.79, generator_kl_loss=1.413, generator_dur_loss=1.736, generator_adv_loss=1.911, generator_feat_match_loss=3.698, over 1107.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:41:16,358 INFO [train.py:527] (1/6) Epoch 194, batch 68, global_batch_idx: 24000, batch size: 83, loss[discriminator_loss=2.775, discriminator_real_loss=1.358, discriminator_fake_loss=1.417, generator_loss=27.28, generator_mel_loss=18.62, generator_kl_loss=1.335, generator_dur_loss=1.769, generator_adv_loss=1.951, generator_feat_match_loss=3.598, over 83.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.385, discriminator_fake_loss=1.354, generator_loss=27.38, generator_mel_loss=18.65, generator_kl_loss=1.379, generator_dur_loss=1.754, generator_adv_loss=1.932, generator_feat_match_loss=3.658, over 4277.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:41:16,360 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 19:41:24,225 INFO [train.py:591] (1/6) Epoch 194, validation: discriminator_loss=2.733, discriminator_real_loss=1.463, discriminator_fake_loss=1.27, generator_loss=26.68, generator_mel_loss=18.65, generator_kl_loss=1.177, generator_dur_loss=1.78, generator_adv_loss=2.005, generator_feat_match_loss=3.069, over 100.00 samples.
+2024-03-12 19:41:24,226 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 19:43:40,312 INFO [train.py:527] (1/6) Epoch 194, batch 118, global_batch_idx: 24050, batch size: 15, loss[discriminator_loss=2.768, discriminator_real_loss=1.501, discriminator_fake_loss=1.268, generator_loss=29.24, generator_mel_loss=19.88, generator_kl_loss=1.874, generator_dur_loss=1.575, generator_adv_loss=1.971, generator_feat_match_loss=3.937, over 15.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.379, discriminator_fake_loss=1.354, generator_loss=27.33, generator_mel_loss=18.62, generator_kl_loss=1.39, generator_dur_loss=1.741, generator_adv_loss=1.927, generator_feat_match_loss=3.652, over 7026.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:43:56,445 INFO [train.py:919] (1/6) Start epoch 195
+2024-03-12 19:46:20,767 INFO [train.py:527] (1/6) Epoch 195, batch 44, global_batch_idx: 24100, batch size: 80, loss[discriminator_loss=2.718, discriminator_real_loss=1.355, discriminator_fake_loss=1.363, generator_loss=27.69, generator_mel_loss=18.94, generator_kl_loss=1.359, generator_dur_loss=1.789, generator_adv_loss=1.829, generator_feat_match_loss=3.771, over 80.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.398, discriminator_fake_loss=1.365, generator_loss=27.43, generator_mel_loss=18.6, generator_kl_loss=1.379, generator_dur_loss=1.735, generator_adv_loss=2.012, generator_feat_match_loss=3.704, over 2483.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:48:38,952 INFO [train.py:527] (1/6) Epoch 195, batch 94, global_batch_idx: 24150, batch size: 53, loss[discriminator_loss=2.824, discriminator_real_loss=1.36, discriminator_fake_loss=1.464, generator_loss=27.71, generator_mel_loss=19.08, generator_kl_loss=1.451, generator_dur_loss=1.662, generator_adv_loss=1.796, generator_feat_match_loss=3.722, over 53.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.391, discriminator_fake_loss=1.353, generator_loss=27.52, generator_mel_loss=18.71, generator_kl_loss=1.398, generator_dur_loss=1.74, generator_adv_loss=1.964, generator_feat_match_loss=3.712, over 5174.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:50:03,389 INFO [train.py:919] (1/6) Start epoch 196
+2024-03-12 19:51:22,254 INFO [train.py:527] (1/6) Epoch 196, batch 20, global_batch_idx: 24200, batch size: 88, loss[discriminator_loss=2.709, discriminator_real_loss=1.32, discriminator_fake_loss=1.389, generator_loss=27.67, generator_mel_loss=18.63, generator_kl_loss=1.261, generator_dur_loss=1.869, generator_adv_loss=1.934, generator_feat_match_loss=3.974, over 88.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.392, discriminator_fake_loss=1.349, generator_loss=27.46, generator_mel_loss=18.75, generator_kl_loss=1.359, generator_dur_loss=1.789, generator_adv_loss=1.912, generator_feat_match_loss=3.655, over 1453.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:51:22,255 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 19:51:30,420 INFO [train.py:591] (1/6) Epoch 196, validation: discriminator_loss=2.765, discriminator_real_loss=1.547, discriminator_fake_loss=1.218, generator_loss=26.23, generator_mel_loss=18.75, generator_kl_loss=1.178, generator_dur_loss=1.825, generator_adv_loss=1.943, generator_feat_match_loss=2.539, over 100.00 samples.
+2024-03-12 19:51:30,421 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 19:53:50,003 INFO [train.py:527] (1/6) Epoch 196, batch 70, global_batch_idx: 24250, batch size: 77, loss[discriminator_loss=2.717, discriminator_real_loss=1.368, discriminator_fake_loss=1.349, generator_loss=27.55, generator_mel_loss=18.56, generator_kl_loss=1.458, generator_dur_loss=1.814, generator_adv_loss=2.032, generator_feat_match_loss=3.688, over 77.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.388, discriminator_fake_loss=1.343, generator_loss=27.39, generator_mel_loss=18.67, generator_kl_loss=1.391, generator_dur_loss=1.771, generator_adv_loss=1.911, generator_feat_match_loss=3.646, over 4325.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:56:06,091 INFO [train.py:527] (1/6) Epoch 196, batch 120, global_batch_idx: 24300, batch size: 14, loss[discriminator_loss=2.657, discriminator_real_loss=1.336, discriminator_fake_loss=1.321, generator_loss=29.62, generator_mel_loss=20.17, generator_kl_loss=1.846, generator_dur_loss=1.574, generator_adv_loss=2.072, generator_feat_match_loss=3.956, over 14.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=27.41, generator_mel_loss=18.68, generator_kl_loss=1.393, generator_dur_loss=1.772, generator_adv_loss=1.904, generator_feat_match_loss=3.654, over 7193.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 19:56:15,270 INFO [train.py:919] (1/6) Start epoch 197
+2024-03-12 19:58:46,758 INFO [train.py:527] (1/6) Epoch 197, batch 46, global_batch_idx: 24350, batch size: 77, loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=27.17, generator_mel_loss=18.42, generator_kl_loss=1.385, generator_dur_loss=1.797, generator_adv_loss=1.902, generator_feat_match_loss=3.665, over 77.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.394, discriminator_fake_loss=1.358, generator_loss=27.37, generator_mel_loss=18.72, generator_kl_loss=1.427, generator_dur_loss=1.735, generator_adv_loss=1.927, generator_feat_match_loss=3.56, over 2523.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:01:06,563 INFO [train.py:527] (1/6) Epoch 197, batch 96, global_batch_idx: 24400, batch size: 31, loss[discriminator_loss=2.752, discriminator_real_loss=1.347, discriminator_fake_loss=1.406, generator_loss=28.13, generator_mel_loss=19.2, generator_kl_loss=1.519, generator_dur_loss=1.638, generator_adv_loss=2.008, generator_feat_match_loss=3.765, over 31.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.391, discriminator_fake_loss=1.354, generator_loss=27.38, generator_mel_loss=18.72, generator_kl_loss=1.403, generator_dur_loss=1.74, generator_adv_loss=1.919, generator_feat_match_loss=3.605, over 5273.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:01:06,564 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 20:01:15,297 INFO [train.py:591] (1/6) Epoch 197, validation: discriminator_loss=2.775, discriminator_real_loss=1.452, discriminator_fake_loss=1.324, generator_loss=25.87, generator_mel_loss=18.32, generator_kl_loss=1.216, generator_dur_loss=1.813, generator_adv_loss=1.908, generator_feat_match_loss=2.619, over 100.00 samples.
+2024-03-12 20:01:15,298 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 20:02:32,552 INFO [train.py:919] (1/6) Start epoch 198
+2024-03-12 20:03:57,299 INFO [train.py:527] (1/6) Epoch 198, batch 22, global_batch_idx: 24450, batch size: 59, loss[discriminator_loss=2.724, discriminator_real_loss=1.301, discriminator_fake_loss=1.423, generator_loss=27.32, generator_mel_loss=18.52, generator_kl_loss=1.22, generator_dur_loss=1.707, generator_adv_loss=2.095, generator_feat_match_loss=3.774, over 59.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.387, discriminator_fake_loss=1.349, generator_loss=27.29, generator_mel_loss=18.66, generator_kl_loss=1.332, generator_dur_loss=1.735, generator_adv_loss=1.917, generator_feat_match_loss=3.649, over 1449.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:06:18,071 INFO [train.py:527] (1/6) Epoch 198, batch 72, global_batch_idx: 24500, batch size: 50, loss[discriminator_loss=2.851, discriminator_real_loss=1.447, discriminator_fake_loss=1.403, generator_loss=26.94, generator_mel_loss=18.48, generator_kl_loss=1.542, generator_dur_loss=1.668, generator_adv_loss=1.853, generator_feat_match_loss=3.399, over 50.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.392, discriminator_fake_loss=1.352, generator_loss=27.3, generator_mel_loss=18.63, generator_kl_loss=1.371, generator_dur_loss=1.739, generator_adv_loss=1.91, generator_feat_match_loss=3.646, over 4280.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:08:36,500 INFO [train.py:527] (1/6) Epoch 198, batch 122, global_batch_idx: 24550, batch size: 45, loss[discriminator_loss=2.783, discriminator_real_loss=1.331, discriminator_fake_loss=1.452, generator_loss=28.19, generator_mel_loss=19.18, generator_kl_loss=1.435, generator_dur_loss=1.713, generator_adv_loss=1.942, generator_feat_match_loss=3.921, over 45.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.392, discriminator_fake_loss=1.354, generator_loss=27.28, generator_mel_loss=18.62, generator_kl_loss=1.38, generator_dur_loss=1.743, generator_adv_loss=1.908, generator_feat_match_loss=3.63, over 6951.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:08:41,745 INFO [train.py:919] (1/6) Start epoch 199
+2024-03-12 20:11:17,860 INFO [train.py:527] (1/6) Epoch 199, batch 48, global_batch_idx: 24600, batch size: 72, loss[discriminator_loss=2.799, discriminator_real_loss=1.437, discriminator_fake_loss=1.361, generator_loss=26.15, generator_mel_loss=18.3, generator_kl_loss=1.28, generator_dur_loss=1.812, generator_adv_loss=1.816, generator_feat_match_loss=2.946, over 72.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.396, discriminator_fake_loss=1.353, generator_loss=27.3, generator_mel_loss=18.64, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.898, generator_feat_match_loss=3.601, over 2766.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:11:17,862 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 20:11:25,593 INFO [train.py:591] (1/6) Epoch 199, validation: discriminator_loss=2.783, discriminator_real_loss=1.337, discriminator_fake_loss=1.447, generator_loss=26.96, generator_mel_loss=19, generator_kl_loss=1.197, generator_dur_loss=1.821, generator_adv_loss=1.71, generator_feat_match_loss=3.232, over 100.00 samples.
+2024-03-12 20:11:25,593 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 20:13:45,159 INFO [train.py:527] (1/6) Epoch 199, batch 98, global_batch_idx: 24650, batch size: 61, loss[discriminator_loss=2.718, discriminator_real_loss=1.437, discriminator_fake_loss=1.281, generator_loss=27.28, generator_mel_loss=18.72, generator_kl_loss=1.321, generator_dur_loss=1.793, generator_adv_loss=1.824, generator_feat_match_loss=3.625, over 61.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.391, discriminator_fake_loss=1.349, generator_loss=27.36, generator_mel_loss=18.68, generator_kl_loss=1.393, generator_dur_loss=1.761, generator_adv_loss=1.908, generator_feat_match_loss=3.622, over 5674.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:14:55,560 INFO [train.py:919] (1/6) Start epoch 200
+2024-03-12 20:16:26,088 INFO [train.py:527] (1/6) Epoch 200, batch 24, global_batch_idx: 24700, batch size: 97, loss[discriminator_loss=2.788, discriminator_real_loss=1.299, discriminator_fake_loss=1.489, generator_loss=25.91, generator_mel_loss=17.85, generator_kl_loss=1.189, generator_dur_loss=1.865, generator_adv_loss=1.936, generator_feat_match_loss=3.065, over 97.00 samples.], tot_loss[discriminator_loss=2.749, discriminator_real_loss=1.383, discriminator_fake_loss=1.365, generator_loss=27.19, generator_mel_loss=18.54, generator_kl_loss=1.385, generator_dur_loss=1.734, generator_adv_loss=1.903, generator_feat_match_loss=3.624, over 1413.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:18:45,092 INFO [train.py:527] (1/6) Epoch 200, batch 74, global_batch_idx: 24750, batch size: 64, loss[discriminator_loss=2.745, discriminator_real_loss=1.416, discriminator_fake_loss=1.33, generator_loss=26.42, generator_mel_loss=18.01, generator_kl_loss=1.376, generator_dur_loss=1.731, generator_adv_loss=1.755, generator_feat_match_loss=3.552, over 64.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.388, discriminator_fake_loss=1.362, generator_loss=27.13, generator_mel_loss=18.54, generator_kl_loss=1.377, generator_dur_loss=1.737, generator_adv_loss=1.891, generator_feat_match_loss=3.588, over 4350.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:21:03,470 INFO [train.py:919] (1/6) Start epoch 201
+2024-03-12 20:21:28,018 INFO [train.py:527] (1/6) Epoch 201, batch 0, global_batch_idx: 24800, batch size: 50, loss[discriminator_loss=2.712, discriminator_real_loss=1.353, discriminator_fake_loss=1.359, generator_loss=26.46, generator_mel_loss=18.28, generator_kl_loss=1.267, generator_dur_loss=1.719, generator_adv_loss=1.907, generator_feat_match_loss=3.288, over 50.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.353, discriminator_fake_loss=1.359, generator_loss=26.46, generator_mel_loss=18.28, generator_kl_loss=1.267, generator_dur_loss=1.719, generator_adv_loss=1.907, generator_feat_match_loss=3.288, over 50.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:21:28,020 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 20:21:35,805 INFO [train.py:591] (1/6) Epoch 201, validation: discriminator_loss=2.747, discriminator_real_loss=1.414, discriminator_fake_loss=1.333, generator_loss=26.54, generator_mel_loss=19.03, generator_kl_loss=1.132, generator_dur_loss=1.801, generator_adv_loss=1.8, generator_feat_match_loss=2.77, over 100.00 samples.
+2024-03-12 20:21:35,807 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 20:23:53,733 INFO [train.py:527] (1/6) Epoch 201, batch 50, global_batch_idx: 24850, batch size: 48, loss[discriminator_loss=2.732, discriminator_real_loss=1.376, discriminator_fake_loss=1.357, generator_loss=27.36, generator_mel_loss=18.87, generator_kl_loss=1.393, generator_dur_loss=1.704, generator_adv_loss=1.915, generator_feat_match_loss=3.476, over 48.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.39, discriminator_fake_loss=1.357, generator_loss=27.35, generator_mel_loss=18.64, generator_kl_loss=1.394, generator_dur_loss=1.748, generator_adv_loss=1.9, generator_feat_match_loss=3.668, over 2800.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:26:11,202 INFO [train.py:527] (1/6) Epoch 201, batch 100, global_batch_idx: 24900, batch size: 70, loss[discriminator_loss=2.683, discriminator_real_loss=1.308, discriminator_fake_loss=1.375, generator_loss=27.53, generator_mel_loss=18.6, generator_kl_loss=1.386, generator_dur_loss=1.778, generator_adv_loss=1.997, generator_feat_match_loss=3.768, over 70.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.383, discriminator_fake_loss=1.358, generator_loss=27.43, generator_mel_loss=18.62, generator_kl_loss=1.402, generator_dur_loss=1.756, generator_adv_loss=1.942, generator_feat_match_loss=3.711, over 5669.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:27:18,199 INFO [train.py:919] (1/6) Start epoch 202
+2024-03-12 20:28:54,306 INFO [train.py:527] (1/6) Epoch 202, batch 26, global_batch_idx: 24950, batch size: 59, loss[discriminator_loss=2.794, discriminator_real_loss=1.494, discriminator_fake_loss=1.3, generator_loss=27.32, generator_mel_loss=19.17, generator_kl_loss=1.344, generator_dur_loss=1.747, generator_adv_loss=1.742, generator_feat_match_loss=3.321, over 59.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.398, discriminator_fake_loss=1.337, generator_loss=27.17, generator_mel_loss=18.5, generator_kl_loss=1.359, generator_dur_loss=1.747, generator_adv_loss=1.917, generator_feat_match_loss=3.648, over 1649.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:31:15,285 INFO [train.py:527] (1/6) Epoch 202, batch 76, global_batch_idx: 25000, batch size: 96, loss[discriminator_loss=2.731, discriminator_real_loss=1.307, discriminator_fake_loss=1.424, generator_loss=27.54, generator_mel_loss=18.48, generator_kl_loss=1.383, generator_dur_loss=1.949, generator_adv_loss=2.014, generator_feat_match_loss=3.716, over 96.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.388, discriminator_fake_loss=1.345, generator_loss=27.33, generator_mel_loss=18.61, generator_kl_loss=1.363, generator_dur_loss=1.774, generator_adv_loss=1.912, generator_feat_match_loss=3.671, over 4873.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:31:15,287 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 20:31:23,179 INFO [train.py:591] (1/6) Epoch 202, validation: discriminator_loss=2.801, discriminator_real_loss=1.557, discriminator_fake_loss=1.244, generator_loss=26.27, generator_mel_loss=18.44, generator_kl_loss=1.31, generator_dur_loss=1.826, generator_adv_loss=1.946, generator_feat_match_loss=2.748, over 100.00 samples.
+2024-03-12 20:31:23,180 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 20:33:34,312 INFO [train.py:919] (1/6) Start epoch 203
+2024-03-12 20:34:05,342 INFO [train.py:527] (1/6) Epoch 203, batch 2, global_batch_idx: 25050, batch size: 52, loss[discriminator_loss=2.728, discriminator_real_loss=1.25, discriminator_fake_loss=1.477, generator_loss=28.31, generator_mel_loss=18.9, generator_kl_loss=1.44, generator_dur_loss=1.744, generator_adv_loss=2.079, generator_feat_match_loss=4.151, over 52.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.384, discriminator_fake_loss=1.375, generator_loss=27.44, generator_mel_loss=18.51, generator_kl_loss=1.293, generator_dur_loss=1.836, generator_adv_loss=1.932, generator_feat_match_loss=3.868, over 210.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:36:24,655 INFO [train.py:527] (1/6) Epoch 203, batch 52, global_batch_idx: 25100, batch size: 42, loss[discriminator_loss=2.622, discriminator_real_loss=1.297, discriminator_fake_loss=1.325, generator_loss=28.49, generator_mel_loss=18.75, generator_kl_loss=1.465, generator_dur_loss=1.7, generator_adv_loss=2.158, generator_feat_match_loss=4.409, over 42.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.393, discriminator_fake_loss=1.346, generator_loss=27.4, generator_mel_loss=18.67, generator_kl_loss=1.368, generator_dur_loss=1.75, generator_adv_loss=1.923, generator_feat_match_loss=3.69, over 2998.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:38:42,958 INFO [train.py:527] (1/6) Epoch 203, batch 102, global_batch_idx: 25150, batch size: 77, loss[discriminator_loss=2.719, discriminator_real_loss=1.33, discriminator_fake_loss=1.389, generator_loss=27.36, generator_mel_loss=18.63, generator_kl_loss=1.24, generator_dur_loss=1.803, generator_adv_loss=1.969, generator_feat_match_loss=3.722, over 77.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.397, discriminator_fake_loss=1.345, generator_loss=27.41, generator_mel_loss=18.65, generator_kl_loss=1.38, generator_dur_loss=1.739, generator_adv_loss=1.939, generator_feat_match_loss=3.706, over 5799.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:39:43,233 INFO [train.py:919] (1/6) Start epoch 204
+2024-03-12 20:41:27,024 INFO [train.py:527] (1/6) Epoch 204, batch 28, global_batch_idx: 25200, batch size: 80, loss[discriminator_loss=2.811, discriminator_real_loss=1.326, discriminator_fake_loss=1.485, generator_loss=27.46, generator_mel_loss=18.72, generator_kl_loss=1.322, generator_dur_loss=1.827, generator_adv_loss=1.948, generator_feat_match_loss=3.644, over 80.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.395, discriminator_fake_loss=1.348, generator_loss=27.41, generator_mel_loss=18.65, generator_kl_loss=1.373, generator_dur_loss=1.762, generator_adv_loss=1.912, generator_feat_match_loss=3.719, over 1649.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:41:27,026 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 20:41:34,970 INFO [train.py:591] (1/6) Epoch 204, validation: discriminator_loss=2.807, discriminator_real_loss=1.546, discriminator_fake_loss=1.261, generator_loss=26.54, generator_mel_loss=18.68, generator_kl_loss=1.063, generator_dur_loss=1.807, generator_adv_loss=1.974, generator_feat_match_loss=3.013, over 100.00 samples.
+2024-03-12 20:41:34,971 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 20:43:55,105 INFO [train.py:527] (1/6) Epoch 204, batch 78, global_batch_idx: 25250, batch size: 80, loss[discriminator_loss=2.744, discriminator_real_loss=1.334, discriminator_fake_loss=1.41, generator_loss=27.05, generator_mel_loss=18.5, generator_kl_loss=1.354, generator_dur_loss=1.809, generator_adv_loss=1.835, generator_feat_match_loss=3.552, over 80.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.389, discriminator_fake_loss=1.355, generator_loss=27.34, generator_mel_loss=18.6, generator_kl_loss=1.38, generator_dur_loss=1.765, generator_adv_loss=1.903, generator_feat_match_loss=3.688, over 4725.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:45:59,818 INFO [train.py:919] (1/6) Start epoch 205
+2024-03-12 20:46:34,515 INFO [train.py:527] (1/6) Epoch 205, batch 4, global_batch_idx: 25300, batch size: 53, loss[discriminator_loss=2.774, discriminator_real_loss=1.446, discriminator_fake_loss=1.328, generator_loss=27.88, generator_mel_loss=19.19, generator_kl_loss=1.521, generator_dur_loss=1.718, generator_adv_loss=1.846, generator_feat_match_loss=3.613, over 53.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.404, discriminator_fake_loss=1.332, generator_loss=27.45, generator_mel_loss=18.79, generator_kl_loss=1.417, generator_dur_loss=1.756, generator_adv_loss=1.915, generator_feat_match_loss=3.578, over 271.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:48:55,178 INFO [train.py:527] (1/6) Epoch 205, batch 54, global_batch_idx: 25350, batch size: 52, loss[discriminator_loss=2.65, discriminator_real_loss=1.493, discriminator_fake_loss=1.157, generator_loss=28.59, generator_mel_loss=18.66, generator_kl_loss=1.555, generator_dur_loss=1.674, generator_adv_loss=2.198, generator_feat_match_loss=4.496, over 52.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.385, discriminator_fake_loss=1.356, generator_loss=27.68, generator_mel_loss=18.64, generator_kl_loss=1.387, generator_dur_loss=1.744, generator_adv_loss=2.012, generator_feat_match_loss=3.9, over 3151.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:51:12,542 INFO [train.py:527] (1/6) Epoch 205, batch 104, global_batch_idx: 25400, batch size: 49, loss[discriminator_loss=2.805, discriminator_real_loss=1.336, discriminator_fake_loss=1.469, generator_loss=27.33, generator_mel_loss=18.64, generator_kl_loss=1.433, generator_dur_loss=1.679, generator_adv_loss=1.958, generator_feat_match_loss=3.626, over 49.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.396, discriminator_fake_loss=1.349, generator_loss=27.53, generator_mel_loss=18.64, generator_kl_loss=1.4, generator_dur_loss=1.749, generator_adv_loss=1.988, generator_feat_match_loss=3.759, over 5941.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:51:12,543 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 20:51:21,442 INFO [train.py:591] (1/6) Epoch 205, validation: discriminator_loss=2.768, discriminator_real_loss=1.479, discriminator_fake_loss=1.289, generator_loss=26.36, generator_mel_loss=18.63, generator_kl_loss=1.039, generator_dur_loss=1.821, generator_adv_loss=1.939, generator_feat_match_loss=2.932, over 100.00 samples.
+2024-03-12 20:51:21,443 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 20:52:14,095 INFO [train.py:919] (1/6) Start epoch 206
+2024-03-12 20:54:02,046 INFO [train.py:527] (1/6) Epoch 206, batch 30, global_batch_idx: 25450, batch size: 83, loss[discriminator_loss=2.715, discriminator_real_loss=1.382, discriminator_fake_loss=1.333, generator_loss=27.5, generator_mel_loss=18.4, generator_kl_loss=1.28, generator_dur_loss=1.885, generator_adv_loss=1.974, generator_feat_match_loss=3.966, over 83.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.412, discriminator_fake_loss=1.339, generator_loss=27.3, generator_mel_loss=18.62, generator_kl_loss=1.376, generator_dur_loss=1.779, generator_adv_loss=1.893, generator_feat_match_loss=3.624, over 1711.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:56:20,976 INFO [train.py:527] (1/6) Epoch 206, batch 80, global_batch_idx: 25500, batch size: 58, loss[discriminator_loss=2.743, discriminator_real_loss=1.468, discriminator_fake_loss=1.276, generator_loss=27.21, generator_mel_loss=18.59, generator_kl_loss=1.387, generator_dur_loss=1.74, generator_adv_loss=1.758, generator_feat_match_loss=3.734, over 58.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.397, discriminator_fake_loss=1.344, generator_loss=27.3, generator_mel_loss=18.62, generator_kl_loss=1.376, generator_dur_loss=1.771, generator_adv_loss=1.899, generator_feat_match_loss=3.628, over 4657.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 20:58:19,640 INFO [train.py:919] (1/6) Start epoch 207
+2024-03-12 20:58:57,484 INFO [train.py:527] (1/6) Epoch 207, batch 6, global_batch_idx: 25550, batch size: 74, loss[discriminator_loss=2.772, discriminator_real_loss=1.495, discriminator_fake_loss=1.277, generator_loss=27.36, generator_mel_loss=18.56, generator_kl_loss=1.29, generator_dur_loss=1.792, generator_adv_loss=1.87, generator_feat_match_loss=3.854, over 74.00 samples.], tot_loss[discriminator_loss=2.755, discriminator_real_loss=1.387, discriminator_fake_loss=1.368, generator_loss=27.56, generator_mel_loss=18.81, generator_kl_loss=1.407, generator_dur_loss=1.765, generator_adv_loss=1.947, generator_feat_match_loss=3.636, over 431.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:01:18,390 INFO [train.py:527] (1/6) Epoch 207, batch 56, global_batch_idx: 25600, batch size: 88, loss[discriminator_loss=2.905, discriminator_real_loss=1.423, discriminator_fake_loss=1.482, generator_loss=27.89, generator_mel_loss=18.76, generator_kl_loss=1.25, generator_dur_loss=1.912, generator_adv_loss=2.116, generator_feat_match_loss=3.848, over 88.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.386, discriminator_fake_loss=1.357, generator_loss=27.4, generator_mel_loss=18.64, generator_kl_loss=1.357, generator_dur_loss=1.774, generator_adv_loss=1.938, generator_feat_match_loss=3.697, over 3365.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:01:18,391 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 21:01:26,533 INFO [train.py:591] (1/6) Epoch 207, validation: discriminator_loss=2.717, discriminator_real_loss=1.586, discriminator_fake_loss=1.131, generator_loss=27.03, generator_mel_loss=18.54, generator_kl_loss=1.148, generator_dur_loss=1.846, generator_adv_loss=2.142, generator_feat_match_loss=3.347, over 100.00 samples.
+2024-03-12 21:01:26,533 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 21:03:49,607 INFO [train.py:527] (1/6) Epoch 207, batch 106, global_batch_idx: 25650, batch size: 13, loss[discriminator_loss=2.822, discriminator_real_loss=1.602, discriminator_fake_loss=1.22, generator_loss=27.76, generator_mel_loss=19.11, generator_kl_loss=1.752, generator_dur_loss=1.755, generator_adv_loss=1.65, generator_feat_match_loss=3.49, over 13.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.388, discriminator_fake_loss=1.349, generator_loss=27.38, generator_mel_loss=18.63, generator_kl_loss=1.357, generator_dur_loss=1.774, generator_adv_loss=1.942, generator_feat_match_loss=3.673, over 6190.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:04:32,862 INFO [train.py:919] (1/6) Start epoch 208
+2024-03-12 21:06:24,998 INFO [train.py:527] (1/6) Epoch 208, batch 32, global_batch_idx: 25700, batch size: 39, loss[discriminator_loss=2.706, discriminator_real_loss=1.38, discriminator_fake_loss=1.325, generator_loss=26.67, generator_mel_loss=18.42, generator_kl_loss=1.481, generator_dur_loss=1.718, generator_adv_loss=1.875, generator_feat_match_loss=3.176, over 39.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.391, discriminator_fake_loss=1.333, generator_loss=27.31, generator_mel_loss=18.53, generator_kl_loss=1.381, generator_dur_loss=1.76, generator_adv_loss=1.918, generator_feat_match_loss=3.715, over 1872.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:08:44,991 INFO [train.py:527] (1/6) Epoch 208, batch 82, global_batch_idx: 25750, batch size: 64, loss[discriminator_loss=2.683, discriminator_real_loss=1.332, discriminator_fake_loss=1.351, generator_loss=26.82, generator_mel_loss=18.22, generator_kl_loss=1.348, generator_dur_loss=1.695, generator_adv_loss=1.804, generator_feat_match_loss=3.751, over 64.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.384, discriminator_fake_loss=1.343, generator_loss=27.39, generator_mel_loss=18.57, generator_kl_loss=1.404, generator_dur_loss=1.759, generator_adv_loss=1.92, generator_feat_match_loss=3.733, over 4629.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:10:40,035 INFO [train.py:919] (1/6) Start epoch 209
+2024-03-12 21:11:24,667 INFO [train.py:527] (1/6) Epoch 209, batch 8, global_batch_idx: 25800, batch size: 56, loss[discriminator_loss=2.826, discriminator_real_loss=1.503, discriminator_fake_loss=1.323, generator_loss=27.38, generator_mel_loss=18.94, generator_kl_loss=1.354, generator_dur_loss=1.659, generator_adv_loss=1.805, generator_feat_match_loss=3.63, over 56.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.417, discriminator_fake_loss=1.351, generator_loss=27.47, generator_mel_loss=18.67, generator_kl_loss=1.347, generator_dur_loss=1.752, generator_adv_loss=1.909, generator_feat_match_loss=3.798, over 549.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:11:24,669 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 21:11:32,763 INFO [train.py:591] (1/6) Epoch 209, validation: discriminator_loss=2.782, discriminator_real_loss=1.439, discriminator_fake_loss=1.343, generator_loss=26, generator_mel_loss=18.5, generator_kl_loss=1.201, generator_dur_loss=1.815, generator_adv_loss=1.769, generator_feat_match_loss=2.715, over 100.00 samples.
+2024-03-12 21:11:32,765 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 21:13:50,550 INFO [train.py:527] (1/6) Epoch 209, batch 58, global_batch_idx: 25850, batch size: 42, loss[discriminator_loss=2.707, discriminator_real_loss=1.206, discriminator_fake_loss=1.501, generator_loss=29.13, generator_mel_loss=19.35, generator_kl_loss=1.61, generator_dur_loss=1.65, generator_adv_loss=2.045, generator_feat_match_loss=4.478, over 42.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.397, discriminator_fake_loss=1.347, generator_loss=27.31, generator_mel_loss=18.56, generator_kl_loss=1.382, generator_dur_loss=1.741, generator_adv_loss=1.912, generator_feat_match_loss=3.717, over 3219.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:16:09,412 INFO [train.py:527] (1/6) Epoch 209, batch 108, global_batch_idx: 25900, batch size: 48, loss[discriminator_loss=2.781, discriminator_real_loss=1.489, discriminator_fake_loss=1.292, generator_loss=26.83, generator_mel_loss=18.58, generator_kl_loss=1.284, generator_dur_loss=1.682, generator_adv_loss=1.943, generator_feat_match_loss=3.347, over 48.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.393, discriminator_fake_loss=1.351, generator_loss=27.26, generator_mel_loss=18.54, generator_kl_loss=1.385, generator_dur_loss=1.736, generator_adv_loss=1.914, generator_feat_match_loss=3.682, over 6029.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:16:51,371 INFO [train.py:919] (1/6) Start epoch 210
+2024-03-12 21:18:49,182 INFO [train.py:527] (1/6) Epoch 210, batch 34, global_batch_idx: 25950, batch size: 42, loss[discriminator_loss=2.663, discriminator_real_loss=1.315, discriminator_fake_loss=1.349, generator_loss=28.11, generator_mel_loss=18.93, generator_kl_loss=1.353, generator_dur_loss=1.738, generator_adv_loss=2.032, generator_feat_match_loss=4.059, over 42.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.387, discriminator_fake_loss=1.346, generator_loss=27.35, generator_mel_loss=18.58, generator_kl_loss=1.396, generator_dur_loss=1.734, generator_adv_loss=1.919, generator_feat_match_loss=3.716, over 1840.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:21:09,207 INFO [train.py:527] (1/6) Epoch 210, batch 84, global_batch_idx: 26000, batch size: 59, loss[discriminator_loss=2.78, discriminator_real_loss=1.48, discriminator_fake_loss=1.299, generator_loss=27.29, generator_mel_loss=18.43, generator_kl_loss=1.383, generator_dur_loss=1.748, generator_adv_loss=1.78, generator_feat_match_loss=3.948, over 59.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.385, discriminator_fake_loss=1.345, generator_loss=27.47, generator_mel_loss=18.67, generator_kl_loss=1.392, generator_dur_loss=1.742, generator_adv_loss=1.916, generator_feat_match_loss=3.747, over 4689.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:21:09,208 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 21:21:17,006 INFO [train.py:591] (1/6) Epoch 210, validation: discriminator_loss=2.839, discriminator_real_loss=1.406, discriminator_fake_loss=1.433, generator_loss=26.29, generator_mel_loss=18.73, generator_kl_loss=1.079, generator_dur_loss=1.807, generator_adv_loss=1.765, generator_feat_match_loss=2.917, over 100.00 samples.
+2024-03-12 21:21:17,007 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 21:23:05,983 INFO [train.py:919] (1/6) Start epoch 211
+2024-03-12 21:23:58,783 INFO [train.py:527] (1/6) Epoch 211, batch 10, global_batch_idx: 26050, batch size: 42, loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=28.99, generator_mel_loss=19.94, generator_kl_loss=1.484, generator_dur_loss=1.685, generator_adv_loss=1.781, generator_feat_match_loss=4.097, over 42.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.378, discriminator_fake_loss=1.357, generator_loss=27.79, generator_mel_loss=18.92, generator_kl_loss=1.396, generator_dur_loss=1.734, generator_adv_loss=1.915, generator_feat_match_loss=3.828, over 615.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:26:18,174 INFO [train.py:527] (1/6) Epoch 211, batch 60, global_batch_idx: 26100, batch size: 55, loss[discriminator_loss=2.734, discriminator_real_loss=1.42, discriminator_fake_loss=1.313, generator_loss=28.15, generator_mel_loss=19.16, generator_kl_loss=1.421, generator_dur_loss=1.739, generator_adv_loss=1.904, generator_feat_match_loss=3.92, over 55.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.393, discriminator_fake_loss=1.353, generator_loss=27.35, generator_mel_loss=18.62, generator_kl_loss=1.362, generator_dur_loss=1.738, generator_adv_loss=1.901, generator_feat_match_loss=3.729, over 3472.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:28:36,086 INFO [train.py:527] (1/6) Epoch 211, batch 110, global_batch_idx: 26150, batch size: 12, loss[discriminator_loss=2.676, discriminator_real_loss=1.35, discriminator_fake_loss=1.326, generator_loss=29.6, generator_mel_loss=19.77, generator_kl_loss=1.847, generator_dur_loss=1.654, generator_adv_loss=1.89, generator_feat_match_loss=4.434, over 12.00 samples.], tot_loss[discriminator_loss=2.748, discriminator_real_loss=1.391, discriminator_fake_loss=1.357, generator_loss=27.39, generator_mel_loss=18.65, generator_kl_loss=1.375, generator_dur_loss=1.74, generator_adv_loss=1.906, generator_feat_match_loss=3.725, over 6348.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:29:12,931 INFO [train.py:919] (1/6) Start epoch 212
+2024-03-12 21:31:21,576 INFO [train.py:527] (1/6) Epoch 212, batch 36, global_batch_idx: 26200, batch size: 16, loss[discriminator_loss=2.633, discriminator_real_loss=1.198, discriminator_fake_loss=1.435, generator_loss=30.32, generator_mel_loss=20.71, generator_kl_loss=1.739, generator_dur_loss=1.581, generator_adv_loss=2.107, generator_feat_match_loss=4.18, over 16.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.368, discriminator_fake_loss=1.357, generator_loss=27.49, generator_mel_loss=18.61, generator_kl_loss=1.388, generator_dur_loss=1.761, generator_adv_loss=1.941, generator_feat_match_loss=3.79, over 2069.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:31:21,578 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 21:31:29,922 INFO [train.py:591] (1/6) Epoch 212, validation: discriminator_loss=2.765, discriminator_real_loss=1.38, discriminator_fake_loss=1.384, generator_loss=26.31, generator_mel_loss=18.38, generator_kl_loss=1.243, generator_dur_loss=1.806, generator_adv_loss=1.88, generator_feat_match_loss=3.006, over 100.00 samples.
+2024-03-12 21:31:29,923 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 21:33:49,081 INFO [train.py:527] (1/6) Epoch 212, batch 86, global_batch_idx: 26250, batch size: 52, loss[discriminator_loss=2.775, discriminator_real_loss=1.388, discriminator_fake_loss=1.387, generator_loss=26.94, generator_mel_loss=18.7, generator_kl_loss=1.362, generator_dur_loss=1.703, generator_adv_loss=1.955, generator_feat_match_loss=3.216, over 52.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.381, discriminator_fake_loss=1.342, generator_loss=27.49, generator_mel_loss=18.63, generator_kl_loss=1.385, generator_dur_loss=1.752, generator_adv_loss=1.947, generator_feat_match_loss=3.776, over 4862.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:35:33,365 INFO [train.py:919] (1/6) Start epoch 213
+2024-03-12 21:36:29,147 INFO [train.py:527] (1/6) Epoch 213, batch 12, global_batch_idx: 26300, batch size: 16, loss[discriminator_loss=2.78, discriminator_real_loss=1.524, discriminator_fake_loss=1.256, generator_loss=27.66, generator_mel_loss=18.95, generator_kl_loss=1.542, generator_dur_loss=1.72, generator_adv_loss=2.024, generator_feat_match_loss=3.419, over 16.00 samples.], tot_loss[discriminator_loss=2.772, discriminator_real_loss=1.425, discriminator_fake_loss=1.346, generator_loss=27.63, generator_mel_loss=18.71, generator_kl_loss=1.44, generator_dur_loss=1.746, generator_adv_loss=1.927, generator_feat_match_loss=3.805, over 663.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:38:50,065 INFO [train.py:527] (1/6) Epoch 213, batch 62, global_batch_idx: 26350, batch size: 80, loss[discriminator_loss=2.737, discriminator_real_loss=1.455, discriminator_fake_loss=1.282, generator_loss=27.24, generator_mel_loss=18.58, generator_kl_loss=1.404, generator_dur_loss=1.845, generator_adv_loss=1.903, generator_feat_match_loss=3.51, over 80.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.401, discriminator_fake_loss=1.358, generator_loss=27.36, generator_mel_loss=18.61, generator_kl_loss=1.379, generator_dur_loss=1.773, generator_adv_loss=1.928, generator_feat_match_loss=3.673, over 3857.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:41:08,800 INFO [train.py:527] (1/6) Epoch 213, batch 112, global_batch_idx: 26400, batch size: 47, loss[discriminator_loss=2.747, discriminator_real_loss=1.458, discriminator_fake_loss=1.289, generator_loss=27.36, generator_mel_loss=18.5, generator_kl_loss=1.453, generator_dur_loss=1.694, generator_adv_loss=1.865, generator_feat_match_loss=3.842, over 47.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.42, generator_mel_loss=18.63, generator_kl_loss=1.386, generator_dur_loss=1.772, generator_adv_loss=1.928, generator_feat_match_loss=3.704, over 6542.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:41:08,802 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 21:41:17,869 INFO [train.py:591] (1/6) Epoch 213, validation: discriminator_loss=2.801, discriminator_real_loss=1.44, discriminator_fake_loss=1.36, generator_loss=26.31, generator_mel_loss=18.64, generator_kl_loss=1.191, generator_dur_loss=1.82, generator_adv_loss=1.78, generator_feat_match_loss=2.883, over 100.00 samples.
+2024-03-12 21:41:17,870 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 21:41:49,217 INFO [train.py:919] (1/6) Start epoch 214
+2024-03-12 21:43:55,352 INFO [train.py:527] (1/6) Epoch 214, batch 38, global_batch_idx: 26450, batch size: 59, loss[discriminator_loss=2.791, discriminator_real_loss=1.618, discriminator_fake_loss=1.173, generator_loss=26.4, generator_mel_loss=18.15, generator_kl_loss=1.363, generator_dur_loss=1.776, generator_adv_loss=1.662, generator_feat_match_loss=3.451, over 59.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.397, discriminator_fake_loss=1.344, generator_loss=27.33, generator_mel_loss=18.63, generator_kl_loss=1.406, generator_dur_loss=1.727, generator_adv_loss=1.908, generator_feat_match_loss=3.666, over 1984.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:46:16,891 INFO [train.py:527] (1/6) Epoch 214, batch 88, global_batch_idx: 26500, batch size: 80, loss[discriminator_loss=2.736, discriminator_real_loss=1.428, discriminator_fake_loss=1.309, generator_loss=27.39, generator_mel_loss=18.27, generator_kl_loss=1.474, generator_dur_loss=1.812, generator_adv_loss=1.738, generator_feat_match_loss=4.094, over 80.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.387, discriminator_fake_loss=1.352, generator_loss=27.3, generator_mel_loss=18.57, generator_kl_loss=1.382, generator_dur_loss=1.749, generator_adv_loss=1.908, generator_feat_match_loss=3.691, over 4907.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:47:54,537 INFO [train.py:919] (1/6) Start epoch 215
+2024-03-12 21:48:58,700 INFO [train.py:527] (1/6) Epoch 215, batch 14, global_batch_idx: 26550, batch size: 56, loss[discriminator_loss=2.831, discriminator_real_loss=1.35, discriminator_fake_loss=1.482, generator_loss=26.64, generator_mel_loss=18.37, generator_kl_loss=1.309, generator_dur_loss=1.713, generator_adv_loss=1.993, generator_feat_match_loss=3.258, over 56.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.368, discriminator_fake_loss=1.384, generator_loss=27.45, generator_mel_loss=18.7, generator_kl_loss=1.377, generator_dur_loss=1.742, generator_adv_loss=1.897, generator_feat_match_loss=3.73, over 843.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:51:17,921 INFO [train.py:527] (1/6) Epoch 215, batch 64, global_batch_idx: 26600, batch size: 80, loss[discriminator_loss=2.75, discriminator_real_loss=1.425, discriminator_fake_loss=1.325, generator_loss=26.46, generator_mel_loss=18.19, generator_kl_loss=1.246, generator_dur_loss=1.814, generator_adv_loss=1.835, generator_feat_match_loss=3.379, over 80.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.387, discriminator_fake_loss=1.349, generator_loss=27.36, generator_mel_loss=18.53, generator_kl_loss=1.377, generator_dur_loss=1.766, generator_adv_loss=1.94, generator_feat_match_loss=3.74, over 3741.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:51:17,922 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 21:51:25,980 INFO [train.py:591] (1/6) Epoch 215, validation: discriminator_loss=2.771, discriminator_real_loss=1.38, discriminator_fake_loss=1.391, generator_loss=26, generator_mel_loss=18.38, generator_kl_loss=1.159, generator_dur_loss=1.825, generator_adv_loss=1.779, generator_feat_match_loss=2.863, over 100.00 samples.
+2024-03-12 21:51:25,980 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 21:53:43,287 INFO [train.py:527] (1/6) Epoch 215, batch 114, global_batch_idx: 26650, batch size: 68, loss[discriminator_loss=2.735, discriminator_real_loss=1.442, discriminator_fake_loss=1.292, generator_loss=26.61, generator_mel_loss=18.06, generator_kl_loss=1.452, generator_dur_loss=1.744, generator_adv_loss=1.913, generator_feat_match_loss=3.444, over 68.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.37, generator_mel_loss=18.55, generator_kl_loss=1.38, generator_dur_loss=1.758, generator_adv_loss=1.928, generator_feat_match_loss=3.754, over 6687.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:54:08,719 INFO [train.py:919] (1/6) Start epoch 216
+2024-03-12 21:56:25,338 INFO [train.py:527] (1/6) Epoch 216, batch 40, global_batch_idx: 26700, batch size: 42, loss[discriminator_loss=2.694, discriminator_real_loss=1.456, discriminator_fake_loss=1.238, generator_loss=27.76, generator_mel_loss=18.43, generator_kl_loss=1.523, generator_dur_loss=1.687, generator_adv_loss=1.966, generator_feat_match_loss=4.152, over 42.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.344, generator_loss=27.36, generator_mel_loss=18.63, generator_kl_loss=1.408, generator_dur_loss=1.741, generator_adv_loss=1.905, generator_feat_match_loss=3.679, over 2245.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 21:58:46,871 INFO [train.py:527] (1/6) Epoch 216, batch 90, global_batch_idx: 26750, batch size: 88, loss[discriminator_loss=2.723, discriminator_real_loss=1.336, discriminator_fake_loss=1.387, generator_loss=27.3, generator_mel_loss=18.35, generator_kl_loss=1.266, generator_dur_loss=1.814, generator_adv_loss=1.916, generator_feat_match_loss=3.957, over 88.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.385, discriminator_fake_loss=1.351, generator_loss=27.46, generator_mel_loss=18.63, generator_kl_loss=1.387, generator_dur_loss=1.757, generator_adv_loss=1.935, generator_feat_match_loss=3.756, over 5293.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:00:15,215 INFO [train.py:919] (1/6) Start epoch 217
+2024-03-12 22:01:21,248 INFO [train.py:527] (1/6) Epoch 217, batch 16, global_batch_idx: 26800, batch size: 62, loss[discriminator_loss=2.761, discriminator_real_loss=1.333, discriminator_fake_loss=1.427, generator_loss=27.72, generator_mel_loss=18.79, generator_kl_loss=1.381, generator_dur_loss=1.712, generator_adv_loss=2.115, generator_feat_match_loss=3.723, over 62.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.413, discriminator_fake_loss=1.333, generator_loss=27.12, generator_mel_loss=18.46, generator_kl_loss=1.361, generator_dur_loss=1.744, generator_adv_loss=1.941, generator_feat_match_loss=3.615, over 937.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:01:21,250 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 22:01:29,068 INFO [train.py:591] (1/6) Epoch 217, validation: discriminator_loss=2.836, discriminator_real_loss=1.53, discriminator_fake_loss=1.306, generator_loss=26.29, generator_mel_loss=18.49, generator_kl_loss=1.131, generator_dur_loss=1.814, generator_adv_loss=1.997, generator_feat_match_loss=2.857, over 100.00 samples.
+2024-03-12 22:01:29,069 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 22:03:48,064 INFO [train.py:527] (1/6) Epoch 217, batch 66, global_batch_idx: 26850, batch size: 47, loss[discriminator_loss=2.74, discriminator_real_loss=1.34, discriminator_fake_loss=1.4, generator_loss=26.64, generator_mel_loss=18.19, generator_kl_loss=1.376, generator_dur_loss=1.705, generator_adv_loss=1.809, generator_feat_match_loss=3.562, over 47.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.389, discriminator_fake_loss=1.355, generator_loss=27.27, generator_mel_loss=18.51, generator_kl_loss=1.373, generator_dur_loss=1.759, generator_adv_loss=1.908, generator_feat_match_loss=3.722, over 4011.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:06:09,921 INFO [train.py:527] (1/6) Epoch 217, batch 116, global_batch_idx: 26900, batch size: 88, loss[discriminator_loss=2.684, discriminator_real_loss=1.383, discriminator_fake_loss=1.301, generator_loss=27.47, generator_mel_loss=18.49, generator_kl_loss=1.337, generator_dur_loss=1.835, generator_adv_loss=1.862, generator_feat_match_loss=3.949, over 88.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.389, discriminator_fake_loss=1.358, generator_loss=27.34, generator_mel_loss=18.55, generator_kl_loss=1.389, generator_dur_loss=1.757, generator_adv_loss=1.902, generator_feat_match_loss=3.744, over 6869.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:06:33,383 INFO [train.py:919] (1/6) Start epoch 218
+2024-03-12 22:08:55,531 INFO [train.py:527] (1/6) Epoch 218, batch 42, global_batch_idx: 26950, batch size: 48, loss[discriminator_loss=2.822, discriminator_real_loss=1.429, discriminator_fake_loss=1.392, generator_loss=26.78, generator_mel_loss=18.13, generator_kl_loss=1.424, generator_dur_loss=1.718, generator_adv_loss=2.028, generator_feat_match_loss=3.476, over 48.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.56, generator_mel_loss=18.58, generator_kl_loss=1.397, generator_dur_loss=1.756, generator_adv_loss=1.927, generator_feat_match_loss=3.899, over 2417.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:11:14,139 INFO [train.py:527] (1/6) Epoch 218, batch 92, global_batch_idx: 27000, batch size: 36, loss[discriminator_loss=2.806, discriminator_real_loss=1.425, discriminator_fake_loss=1.381, generator_loss=28.16, generator_mel_loss=18.65, generator_kl_loss=1.516, generator_dur_loss=1.742, generator_adv_loss=1.779, generator_feat_match_loss=4.473, over 36.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.383, discriminator_fake_loss=1.351, generator_loss=27.5, generator_mel_loss=18.6, generator_kl_loss=1.38, generator_dur_loss=1.768, generator_adv_loss=1.916, generator_feat_match_loss=3.83, over 5507.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:11:14,140 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 22:11:22,208 INFO [train.py:591] (1/6) Epoch 218, validation: discriminator_loss=2.822, discriminator_real_loss=1.38, discriminator_fake_loss=1.442, generator_loss=26.12, generator_mel_loss=18.68, generator_kl_loss=1.194, generator_dur_loss=1.799, generator_adv_loss=1.661, generator_feat_match_loss=2.793, over 100.00 samples.
+2024-03-12 22:11:22,208 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 22:12:46,691 INFO [train.py:919] (1/6) Start epoch 219
+2024-03-12 22:14:00,062 INFO [train.py:527] (1/6) Epoch 219, batch 18, global_batch_idx: 27050, batch size: 25, loss[discriminator_loss=2.705, discriminator_real_loss=1.608, discriminator_fake_loss=1.097, generator_loss=28.7, generator_mel_loss=19.13, generator_kl_loss=1.691, generator_dur_loss=1.555, generator_adv_loss=2.141, generator_feat_match_loss=4.18, over 25.00 samples.], tot_loss[discriminator_loss=2.763, discriminator_real_loss=1.436, discriminator_fake_loss=1.326, generator_loss=28.22, generator_mel_loss=18.68, generator_kl_loss=1.374, generator_dur_loss=1.777, generator_adv_loss=2.197, generator_feat_match_loss=4.194, over 1024.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:16:19,127 INFO [train.py:527] (1/6) Epoch 219, batch 68, global_batch_idx: 27100, batch size: 42, loss[discriminator_loss=2.695, discriminator_real_loss=1.413, discriminator_fake_loss=1.282, generator_loss=26.68, generator_mel_loss=18.11, generator_kl_loss=1.447, generator_dur_loss=1.686, generator_adv_loss=1.909, generator_feat_match_loss=3.527, over 42.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.398, discriminator_fake_loss=1.339, generator_loss=27.55, generator_mel_loss=18.58, generator_kl_loss=1.378, generator_dur_loss=1.77, generator_adv_loss=1.995, generator_feat_match_loss=3.824, over 3852.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:18:34,785 INFO [train.py:527] (1/6) Epoch 219, batch 118, global_batch_idx: 27150, batch size: 88, loss[discriminator_loss=2.686, discriminator_real_loss=1.352, discriminator_fake_loss=1.334, generator_loss=27.79, generator_mel_loss=18.71, generator_kl_loss=1.292, generator_dur_loss=1.844, generator_adv_loss=1.956, generator_feat_match_loss=3.98, over 88.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.388, discriminator_fake_loss=1.345, generator_loss=27.45, generator_mel_loss=18.55, generator_kl_loss=1.382, generator_dur_loss=1.77, generator_adv_loss=1.96, generator_feat_match_loss=3.793, over 6815.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:18:49,592 INFO [train.py:919] (1/6) Start epoch 220
+2024-03-12 22:21:10,708 INFO [train.py:527] (1/6) Epoch 220, batch 44, global_batch_idx: 27200, batch size: 45, loss[discriminator_loss=2.737, discriminator_real_loss=1.539, discriminator_fake_loss=1.198, generator_loss=28.37, generator_mel_loss=19.01, generator_kl_loss=1.638, generator_dur_loss=1.619, generator_adv_loss=2.048, generator_feat_match_loss=4.056, over 45.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27.5, generator_mel_loss=18.55, generator_kl_loss=1.379, generator_dur_loss=1.75, generator_adv_loss=1.938, generator_feat_match_loss=3.892, over 2603.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:21:10,710 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 22:21:18,865 INFO [train.py:591] (1/6) Epoch 220, validation: discriminator_loss=2.776, discriminator_real_loss=1.508, discriminator_fake_loss=1.268, generator_loss=26.93, generator_mel_loss=18.91, generator_kl_loss=1.249, generator_dur_loss=1.824, generator_adv_loss=1.972, generator_feat_match_loss=2.972, over 100.00 samples.
+2024-03-12 22:21:18,866 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 22:23:37,446 INFO [train.py:527] (1/6) Epoch 220, batch 94, global_batch_idx: 27250, batch size: 66, loss[discriminator_loss=2.731, discriminator_real_loss=1.276, discriminator_fake_loss=1.455, generator_loss=27.56, generator_mel_loss=18.83, generator_kl_loss=1.219, generator_dur_loss=1.745, generator_adv_loss=1.958, generator_feat_match_loss=3.806, over 66.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.375, discriminator_fake_loss=1.35, generator_loss=27.58, generator_mel_loss=18.63, generator_kl_loss=1.394, generator_dur_loss=1.75, generator_adv_loss=1.93, generator_feat_match_loss=3.874, over 5306.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:25:02,262 INFO [train.py:919] (1/6) Start epoch 221
+2024-03-12 22:26:20,684 INFO [train.py:527] (1/6) Epoch 221, batch 20, global_batch_idx: 27300, batch size: 47, loss[discriminator_loss=2.812, discriminator_real_loss=1.473, discriminator_fake_loss=1.339, generator_loss=27.34, generator_mel_loss=18.43, generator_kl_loss=1.637, generator_dur_loss=1.626, generator_adv_loss=1.797, generator_feat_match_loss=3.851, over 47.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.377, discriminator_fake_loss=1.356, generator_loss=27.64, generator_mel_loss=18.7, generator_kl_loss=1.433, generator_dur_loss=1.724, generator_adv_loss=1.917, generator_feat_match_loss=3.871, over 1059.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:28:38,541 INFO [train.py:527] (1/6) Epoch 221, batch 70, global_batch_idx: 27350, batch size: 56, loss[discriminator_loss=2.69, discriminator_real_loss=1.291, discriminator_fake_loss=1.4, generator_loss=28.2, generator_mel_loss=18.63, generator_kl_loss=1.529, generator_dur_loss=1.753, generator_adv_loss=2.066, generator_feat_match_loss=4.223, over 56.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.385, discriminator_fake_loss=1.354, generator_loss=27.51, generator_mel_loss=18.6, generator_kl_loss=1.381, generator_dur_loss=1.767, generator_adv_loss=1.931, generator_feat_match_loss=3.826, over 4105.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:30:57,907 INFO [train.py:527] (1/6) Epoch 221, batch 120, global_batch_idx: 27400, batch size: 39, loss[discriminator_loss=2.726, discriminator_real_loss=1.37, discriminator_fake_loss=1.356, generator_loss=26.6, generator_mel_loss=17.97, generator_kl_loss=1.453, generator_dur_loss=1.726, generator_adv_loss=1.935, generator_feat_match_loss=3.514, over 39.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.39, discriminator_fake_loss=1.349, generator_loss=27.47, generator_mel_loss=18.57, generator_kl_loss=1.381, generator_dur_loss=1.77, generator_adv_loss=1.932, generator_feat_match_loss=3.817, over 6834.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:30:57,908 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 22:31:06,763 INFO [train.py:591] (1/6) Epoch 221, validation: discriminator_loss=2.749, discriminator_real_loss=1.474, discriminator_fake_loss=1.275, generator_loss=26.81, generator_mel_loss=18.73, generator_kl_loss=1.234, generator_dur_loss=1.804, generator_adv_loss=1.867, generator_feat_match_loss=3.184, over 100.00 samples.
+2024-03-12 22:31:06,764 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 22:31:17,997 INFO [train.py:919] (1/6) Start epoch 222
+2024-03-12 22:33:52,050 INFO [train.py:527] (1/6) Epoch 222, batch 46, global_batch_idx: 27450, batch size: 72, loss[discriminator_loss=2.697, discriminator_real_loss=1.325, discriminator_fake_loss=1.372, generator_loss=27.61, generator_mel_loss=18.68, generator_kl_loss=1.364, generator_dur_loss=1.817, generator_adv_loss=2.06, generator_feat_match_loss=3.687, over 72.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.38, discriminator_fake_loss=1.341, generator_loss=27.32, generator_mel_loss=18.52, generator_kl_loss=1.356, generator_dur_loss=1.752, generator_adv_loss=1.921, generator_feat_match_loss=3.767, over 2910.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:36:11,894 INFO [train.py:527] (1/6) Epoch 222, batch 96, global_batch_idx: 27500, batch size: 62, loss[discriminator_loss=2.76, discriminator_real_loss=1.434, discriminator_fake_loss=1.326, generator_loss=27.64, generator_mel_loss=18.79, generator_kl_loss=1.478, generator_dur_loss=1.69, generator_adv_loss=1.984, generator_feat_match_loss=3.698, over 62.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.35, generator_mel_loss=18.52, generator_kl_loss=1.364, generator_dur_loss=1.758, generator_adv_loss=1.923, generator_feat_match_loss=3.789, over 5783.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:37:23,659 INFO [train.py:919] (1/6) Start epoch 223
+2024-03-12 22:38:48,894 INFO [train.py:527] (1/6) Epoch 223, batch 22, global_batch_idx: 27550, batch size: 52, loss[discriminator_loss=2.71, discriminator_real_loss=1.407, discriminator_fake_loss=1.303, generator_loss=27.39, generator_mel_loss=18.29, generator_kl_loss=1.399, generator_dur_loss=1.668, generator_adv_loss=2.016, generator_feat_match_loss=4.018, over 52.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.38, discriminator_fake_loss=1.341, generator_loss=27.65, generator_mel_loss=18.65, generator_kl_loss=1.429, generator_dur_loss=1.724, generator_adv_loss=1.937, generator_feat_match_loss=3.913, over 1226.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:41:08,772 INFO [train.py:527] (1/6) Epoch 223, batch 72, global_batch_idx: 27600, batch size: 66, loss[discriminator_loss=2.813, discriminator_real_loss=1.456, discriminator_fake_loss=1.358, generator_loss=27.99, generator_mel_loss=19, generator_kl_loss=1.443, generator_dur_loss=1.746, generator_adv_loss=1.866, generator_feat_match_loss=3.927, over 66.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.381, discriminator_fake_loss=1.342, generator_loss=27.6, generator_mel_loss=18.63, generator_kl_loss=1.417, generator_dur_loss=1.741, generator_adv_loss=1.935, generator_feat_match_loss=3.878, over 4037.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:41:08,773 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 22:41:16,881 INFO [train.py:591] (1/6) Epoch 223, validation: discriminator_loss=2.848, discriminator_real_loss=1.505, discriminator_fake_loss=1.342, generator_loss=27.03, generator_mel_loss=19.04, generator_kl_loss=1.2, generator_dur_loss=1.809, generator_adv_loss=1.849, generator_feat_match_loss=3.136, over 100.00 samples.
+2024-03-12 22:41:16,882 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 22:43:34,943 INFO [train.py:527] (1/6) Epoch 223, batch 122, global_batch_idx: 27650, batch size: 53, loss[discriminator_loss=2.805, discriminator_real_loss=1.521, discriminator_fake_loss=1.284, generator_loss=27.36, generator_mel_loss=18.74, generator_kl_loss=1.317, generator_dur_loss=1.709, generator_adv_loss=1.985, generator_feat_match_loss=3.606, over 53.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.381, discriminator_fake_loss=1.351, generator_loss=27.48, generator_mel_loss=18.58, generator_kl_loss=1.394, generator_dur_loss=1.748, generator_adv_loss=1.928, generator_feat_match_loss=3.835, over 6825.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:43:40,251 INFO [train.py:919] (1/6) Start epoch 224
+2024-03-12 22:46:18,885 INFO [train.py:527] (1/6) Epoch 224, batch 48, global_batch_idx: 27700, batch size: 61, loss[discriminator_loss=2.744, discriminator_real_loss=1.41, discriminator_fake_loss=1.333, generator_loss=28.27, generator_mel_loss=19.07, generator_kl_loss=1.342, generator_dur_loss=1.724, generator_adv_loss=1.915, generator_feat_match_loss=4.226, over 61.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.392, discriminator_fake_loss=1.354, generator_loss=27.42, generator_mel_loss=18.55, generator_kl_loss=1.403, generator_dur_loss=1.738, generator_adv_loss=1.935, generator_feat_match_loss=3.798, over 2593.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:48:41,956 INFO [train.py:527] (1/6) Epoch 224, batch 98, global_batch_idx: 27750, batch size: 77, loss[discriminator_loss=2.731, discriminator_real_loss=1.291, discriminator_fake_loss=1.44, generator_loss=27.06, generator_mel_loss=18.38, generator_kl_loss=1.284, generator_dur_loss=1.792, generator_adv_loss=1.971, generator_feat_match_loss=3.633, over 77.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.391, discriminator_fake_loss=1.35, generator_loss=27.29, generator_mel_loss=18.49, generator_kl_loss=1.387, generator_dur_loss=1.74, generator_adv_loss=1.93, generator_feat_match_loss=3.746, over 5429.00 samples.], cur_lr_g: 1.95e-04, cur_lr_d: 1.95e-04, 
+2024-03-12 22:49:49,265 INFO [train.py:919] (1/6) Start epoch 225
+2024-03-12 22:51:18,674 INFO [train.py:527] (1/6) Epoch 225, batch 24, global_batch_idx: 27800, batch size: 39, loss[discriminator_loss=2.728, discriminator_real_loss=1.376, discriminator_fake_loss=1.352, generator_loss=27.42, generator_mel_loss=18.37, generator_kl_loss=1.542, generator_dur_loss=1.642, generator_adv_loss=1.855, generator_feat_match_loss=4.012, over 39.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.6, generator_kl_loss=1.384, generator_dur_loss=1.754, generator_adv_loss=1.937, generator_feat_match_loss=3.785, over 1395.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 22:51:18,675 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 22:51:26,433 INFO [train.py:591] (1/6) Epoch 225, validation: discriminator_loss=2.695, discriminator_real_loss=1.385, discriminator_fake_loss=1.31, generator_loss=26.14, generator_mel_loss=18.37, generator_kl_loss=1.122, generator_dur_loss=1.82, generator_adv_loss=1.82, generator_feat_match_loss=3.004, over 100.00 samples.
+2024-03-12 22:51:26,434 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 22:53:48,637 INFO [train.py:527] (1/6) Epoch 225, batch 74, global_batch_idx: 27850, batch size: 64, loss[discriminator_loss=3.026, discriminator_real_loss=1.408, discriminator_fake_loss=1.618, generator_loss=26.89, generator_mel_loss=18.59, generator_kl_loss=1.432, generator_dur_loss=1.725, generator_adv_loss=1.777, generator_feat_match_loss=3.369, over 64.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.378, discriminator_fake_loss=1.355, generator_loss=27.61, generator_mel_loss=18.57, generator_kl_loss=1.402, generator_dur_loss=1.742, generator_adv_loss=1.974, generator_feat_match_loss=3.921, over 4178.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 22:56:01,074 INFO [train.py:919] (1/6) Start epoch 226
+2024-03-12 22:56:23,994 INFO [train.py:527] (1/6) Epoch 226, batch 0, global_batch_idx: 27900, batch size: 31, loss[discriminator_loss=2.669, discriminator_real_loss=1.394, discriminator_fake_loss=1.275, generator_loss=27.37, generator_mel_loss=18.45, generator_kl_loss=1.545, generator_dur_loss=1.717, generator_adv_loss=1.923, generator_feat_match_loss=3.735, over 31.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.394, discriminator_fake_loss=1.275, generator_loss=27.37, generator_mel_loss=18.45, generator_kl_loss=1.545, generator_dur_loss=1.717, generator_adv_loss=1.923, generator_feat_match_loss=3.735, over 31.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 22:58:40,622 INFO [train.py:527] (1/6) Epoch 226, batch 50, global_batch_idx: 27950, batch size: 72, loss[discriminator_loss=2.718, discriminator_real_loss=1.415, discriminator_fake_loss=1.303, generator_loss=26.68, generator_mel_loss=18.16, generator_kl_loss=1.31, generator_dur_loss=1.78, generator_adv_loss=1.876, generator_feat_match_loss=3.552, over 72.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.384, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.59, generator_kl_loss=1.408, generator_dur_loss=1.749, generator_adv_loss=1.919, generator_feat_match_loss=3.792, over 2797.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:00:58,237 INFO [train.py:527] (1/6) Epoch 226, batch 100, global_batch_idx: 28000, batch size: 44, loss[discriminator_loss=2.686, discriminator_real_loss=1.271, discriminator_fake_loss=1.415, generator_loss=28.3, generator_mel_loss=18.9, generator_kl_loss=1.439, generator_dur_loss=1.698, generator_adv_loss=1.973, generator_feat_match_loss=4.288, over 44.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.392, discriminator_fake_loss=1.347, generator_loss=27.39, generator_mel_loss=18.55, generator_kl_loss=1.396, generator_dur_loss=1.759, generator_adv_loss=1.911, generator_feat_match_loss=3.773, over 5590.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:00:58,238 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 23:01:07,117 INFO [train.py:591] (1/6) Epoch 226, validation: discriminator_loss=2.759, discriminator_real_loss=1.512, discriminator_fake_loss=1.247, generator_loss=26.25, generator_mel_loss=18.5, generator_kl_loss=1.084, generator_dur_loss=1.847, generator_adv_loss=1.931, generator_feat_match_loss=2.893, over 100.00 samples.
+2024-03-12 23:01:07,119 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 23:02:12,624 INFO [train.py:919] (1/6) Start epoch 227
+2024-03-12 23:03:46,703 INFO [train.py:527] (1/6) Epoch 227, batch 26, global_batch_idx: 28050, batch size: 47, loss[discriminator_loss=2.763, discriminator_real_loss=1.49, discriminator_fake_loss=1.273, generator_loss=27.34, generator_mel_loss=18.62, generator_kl_loss=1.422, generator_dur_loss=1.703, generator_adv_loss=1.791, generator_feat_match_loss=3.808, over 47.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.398, discriminator_fake_loss=1.336, generator_loss=27.44, generator_mel_loss=18.67, generator_kl_loss=1.373, generator_dur_loss=1.744, generator_adv_loss=1.913, generator_feat_match_loss=3.738, over 1476.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:06:09,325 INFO [train.py:527] (1/6) Epoch 227, batch 76, global_batch_idx: 28100, batch size: 88, loss[discriminator_loss=2.744, discriminator_real_loss=1.273, discriminator_fake_loss=1.47, generator_loss=28.23, generator_mel_loss=18.81, generator_kl_loss=1.447, generator_dur_loss=1.859, generator_adv_loss=1.932, generator_feat_match_loss=4.184, over 88.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.389, discriminator_fake_loss=1.348, generator_loss=27.44, generator_mel_loss=18.59, generator_kl_loss=1.379, generator_dur_loss=1.769, generator_adv_loss=1.921, generator_feat_match_loss=3.787, over 4414.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:08:18,354 INFO [train.py:919] (1/6) Start epoch 228
+2024-03-12 23:08:47,035 INFO [train.py:527] (1/6) Epoch 228, batch 2, global_batch_idx: 28150, batch size: 66, loss[discriminator_loss=2.737, discriminator_real_loss=1.406, discriminator_fake_loss=1.331, generator_loss=27.62, generator_mel_loss=18.71, generator_kl_loss=1.332, generator_dur_loss=1.749, generator_adv_loss=1.884, generator_feat_match_loss=3.942, over 66.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.399, discriminator_fake_loss=1.307, generator_loss=27.28, generator_mel_loss=18.56, generator_kl_loss=1.302, generator_dur_loss=1.752, generator_adv_loss=1.955, generator_feat_match_loss=3.706, over 208.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:11:05,120 INFO [train.py:527] (1/6) Epoch 228, batch 52, global_batch_idx: 28200, batch size: 50, loss[discriminator_loss=2.749, discriminator_real_loss=1.355, discriminator_fake_loss=1.394, generator_loss=27.43, generator_mel_loss=18.74, generator_kl_loss=1.316, generator_dur_loss=1.707, generator_adv_loss=2.013, generator_feat_match_loss=3.651, over 50.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.391, discriminator_fake_loss=1.345, generator_loss=27.51, generator_mel_loss=18.67, generator_kl_loss=1.377, generator_dur_loss=1.764, generator_adv_loss=1.914, generator_feat_match_loss=3.783, over 3212.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:11:05,121 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 23:11:13,162 INFO [train.py:591] (1/6) Epoch 228, validation: discriminator_loss=2.724, discriminator_real_loss=1.485, discriminator_fake_loss=1.239, generator_loss=27.09, generator_mel_loss=19.16, generator_kl_loss=1.311, generator_dur_loss=1.835, generator_adv_loss=1.944, generator_feat_match_loss=2.845, over 100.00 samples.
+2024-03-12 23:11:13,163 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 23:13:35,223 INFO [train.py:527] (1/6) Epoch 228, batch 102, global_batch_idx: 28250, batch size: 58, loss[discriminator_loss=2.825, discriminator_real_loss=1.336, discriminator_fake_loss=1.489, generator_loss=26.46, generator_mel_loss=18.06, generator_kl_loss=1.367, generator_dur_loss=1.724, generator_adv_loss=1.874, generator_feat_match_loss=3.436, over 58.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.393, discriminator_fake_loss=1.349, generator_loss=27.49, generator_mel_loss=18.64, generator_kl_loss=1.393, generator_dur_loss=1.76, generator_adv_loss=1.917, generator_feat_match_loss=3.784, over 5946.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:14:38,379 INFO [train.py:919] (1/6) Start epoch 229
+2024-03-12 23:16:23,392 INFO [train.py:527] (1/6) Epoch 229, batch 28, global_batch_idx: 28300, batch size: 66, loss[discriminator_loss=2.706, discriminator_real_loss=1.253, discriminator_fake_loss=1.453, generator_loss=27.46, generator_mel_loss=18.45, generator_kl_loss=1.231, generator_dur_loss=1.797, generator_adv_loss=2.083, generator_feat_match_loss=3.899, over 66.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.367, discriminator_fake_loss=1.356, generator_loss=27.62, generator_mel_loss=18.65, generator_kl_loss=1.392, generator_dur_loss=1.754, generator_adv_loss=1.907, generator_feat_match_loss=3.922, over 1610.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:18:44,403 INFO [train.py:527] (1/6) Epoch 229, batch 78, global_batch_idx: 28350, batch size: 42, loss[discriminator_loss=2.705, discriminator_real_loss=1.408, discriminator_fake_loss=1.297, generator_loss=27.18, generator_mel_loss=18.1, generator_kl_loss=1.562, generator_dur_loss=1.702, generator_adv_loss=1.952, generator_feat_match_loss=3.868, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=27.63, generator_mel_loss=18.62, generator_kl_loss=1.399, generator_dur_loss=1.747, generator_adv_loss=1.938, generator_feat_match_loss=3.922, over 4332.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:20:53,666 INFO [train.py:919] (1/6) Start epoch 230
+2024-03-12 23:21:31,206 INFO [train.py:527] (1/6) Epoch 230, batch 4, global_batch_idx: 28400, batch size: 62, loss[discriminator_loss=2.729, discriminator_real_loss=1.503, discriminator_fake_loss=1.226, generator_loss=27.25, generator_mel_loss=18.58, generator_kl_loss=1.364, generator_dur_loss=1.74, generator_adv_loss=1.964, generator_feat_match_loss=3.599, over 62.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.398, discriminator_fake_loss=1.326, generator_loss=27.32, generator_mel_loss=18.35, generator_kl_loss=1.373, generator_dur_loss=1.759, generator_adv_loss=1.967, generator_feat_match_loss=3.869, over 324.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:21:31,209 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 23:21:39,256 INFO [train.py:591] (1/6) Epoch 230, validation: discriminator_loss=2.768, discriminator_real_loss=1.443, discriminator_fake_loss=1.324, generator_loss=25.82, generator_mel_loss=18.11, generator_kl_loss=1.236, generator_dur_loss=1.816, generator_adv_loss=1.875, generator_feat_match_loss=2.781, over 100.00 samples.
+2024-03-12 23:21:39,258 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 23:24:02,405 INFO [train.py:527] (1/6) Epoch 230, batch 54, global_batch_idx: 28450, batch size: 62, loss[discriminator_loss=2.695, discriminator_real_loss=1.389, discriminator_fake_loss=1.306, generator_loss=28.08, generator_mel_loss=18.78, generator_kl_loss=1.427, generator_dur_loss=1.719, generator_adv_loss=1.913, generator_feat_match_loss=4.243, over 62.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.38, generator_mel_loss=18.49, generator_kl_loss=1.38, generator_dur_loss=1.762, generator_adv_loss=1.923, generator_feat_match_loss=3.832, over 3296.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:26:23,509 INFO [train.py:527] (1/6) Epoch 230, batch 104, global_batch_idx: 28500, batch size: 58, loss[discriminator_loss=2.723, discriminator_real_loss=1.415, discriminator_fake_loss=1.308, generator_loss=26.99, generator_mel_loss=18.5, generator_kl_loss=1.455, generator_dur_loss=1.741, generator_adv_loss=1.756, generator_feat_match_loss=3.537, over 58.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.379, discriminator_fake_loss=1.344, generator_loss=27.39, generator_mel_loss=18.51, generator_kl_loss=1.39, generator_dur_loss=1.749, generator_adv_loss=1.922, generator_feat_match_loss=3.82, over 6113.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:27:17,332 INFO [train.py:919] (1/6) Start epoch 231
+2024-03-12 23:29:07,514 INFO [train.py:527] (1/6) Epoch 231, batch 30, global_batch_idx: 28550, batch size: 62, loss[discriminator_loss=2.732, discriminator_real_loss=1.356, discriminator_fake_loss=1.376, generator_loss=27.58, generator_mel_loss=18.66, generator_kl_loss=1.349, generator_dur_loss=1.726, generator_adv_loss=1.946, generator_feat_match_loss=3.904, over 62.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.347, generator_loss=27.56, generator_mel_loss=18.61, generator_kl_loss=1.373, generator_dur_loss=1.732, generator_adv_loss=1.916, generator_feat_match_loss=3.923, over 1708.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:31:26,450 INFO [train.py:527] (1/6) Epoch 231, batch 80, global_batch_idx: 28600, batch size: 80, loss[discriminator_loss=2.706, discriminator_real_loss=1.412, discriminator_fake_loss=1.294, generator_loss=27.51, generator_mel_loss=18.27, generator_kl_loss=1.442, generator_dur_loss=1.788, generator_adv_loss=1.889, generator_feat_match_loss=4.116, over 80.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.388, discriminator_fake_loss=1.347, generator_loss=27.52, generator_mel_loss=18.6, generator_kl_loss=1.386, generator_dur_loss=1.738, generator_adv_loss=1.916, generator_feat_match_loss=3.881, over 4469.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:31:26,452 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 23:31:34,637 INFO [train.py:591] (1/6) Epoch 231, validation: discriminator_loss=2.722, discriminator_real_loss=1.423, discriminator_fake_loss=1.299, generator_loss=26.46, generator_mel_loss=18.53, generator_kl_loss=1.149, generator_dur_loss=1.8, generator_adv_loss=1.853, generator_feat_match_loss=3.125, over 100.00 samples.
+2024-03-12 23:31:34,637 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 23:33:36,816 INFO [train.py:919] (1/6) Start epoch 232
+2024-03-12 23:34:17,925 INFO [train.py:527] (1/6) Epoch 232, batch 6, global_batch_idx: 28650, batch size: 66, loss[discriminator_loss=2.712, discriminator_real_loss=1.306, discriminator_fake_loss=1.405, generator_loss=28.1, generator_mel_loss=18.95, generator_kl_loss=1.457, generator_dur_loss=1.793, generator_adv_loss=1.956, generator_feat_match_loss=3.941, over 66.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.327, discriminator_fake_loss=1.367, generator_loss=28.24, generator_mel_loss=18.86, generator_kl_loss=1.416, generator_dur_loss=1.735, generator_adv_loss=1.945, generator_feat_match_loss=4.279, over 349.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:36:36,673 INFO [train.py:527] (1/6) Epoch 232, batch 56, global_batch_idx: 28700, batch size: 17, loss[discriminator_loss=2.697, discriminator_real_loss=1.326, discriminator_fake_loss=1.371, generator_loss=28.57, generator_mel_loss=18.92, generator_kl_loss=1.831, generator_dur_loss=1.647, generator_adv_loss=1.994, generator_feat_match_loss=4.173, over 17.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.385, discriminator_fake_loss=1.353, generator_loss=27.38, generator_mel_loss=18.51, generator_kl_loss=1.408, generator_dur_loss=1.756, generator_adv_loss=1.93, generator_feat_match_loss=3.783, over 3232.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:38:57,959 INFO [train.py:527] (1/6) Epoch 232, batch 106, global_batch_idx: 28750, batch size: 52, loss[discriminator_loss=2.774, discriminator_real_loss=1.319, discriminator_fake_loss=1.455, generator_loss=28.24, generator_mel_loss=19.2, generator_kl_loss=1.424, generator_dur_loss=1.725, generator_adv_loss=1.863, generator_feat_match_loss=4.025, over 52.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.387, discriminator_fake_loss=1.351, generator_loss=27.38, generator_mel_loss=18.5, generator_kl_loss=1.401, generator_dur_loss=1.738, generator_adv_loss=1.925, generator_feat_match_loss=3.816, over 6042.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:39:48,619 INFO [train.py:919] (1/6) Start epoch 233
+2024-03-12 23:41:41,767 INFO [train.py:527] (1/6) Epoch 233, batch 32, global_batch_idx: 28800, batch size: 42, loss[discriminator_loss=2.718, discriminator_real_loss=1.437, discriminator_fake_loss=1.28, generator_loss=27.85, generator_mel_loss=19.02, generator_kl_loss=1.471, generator_dur_loss=1.722, generator_adv_loss=1.9, generator_feat_match_loss=3.728, over 42.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=27.57, generator_mel_loss=18.55, generator_kl_loss=1.377, generator_dur_loss=1.744, generator_adv_loss=1.936, generator_feat_match_loss=3.956, over 1982.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:41:41,768 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 23:41:49,971 INFO [train.py:591] (1/6) Epoch 233, validation: discriminator_loss=2.74, discriminator_real_loss=1.359, discriminator_fake_loss=1.382, generator_loss=26.48, generator_mel_loss=18.5, generator_kl_loss=1.116, generator_dur_loss=1.783, generator_adv_loss=1.798, generator_feat_match_loss=3.277, over 100.00 samples.
+2024-03-12 23:41:49,972 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 23:44:12,447 INFO [train.py:527] (1/6) Epoch 233, batch 82, global_batch_idx: 28850, batch size: 70, loss[discriminator_loss=2.727, discriminator_real_loss=1.4, discriminator_fake_loss=1.327, generator_loss=26.86, generator_mel_loss=18.17, generator_kl_loss=1.374, generator_dur_loss=1.737, generator_adv_loss=1.997, generator_feat_match_loss=3.58, over 70.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.388, discriminator_fake_loss=1.353, generator_loss=27.45, generator_mel_loss=18.51, generator_kl_loss=1.397, generator_dur_loss=1.733, generator_adv_loss=1.934, generator_feat_match_loss=3.872, over 4745.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:46:09,692 INFO [train.py:919] (1/6) Start epoch 234
+2024-03-12 23:46:57,689 INFO [train.py:527] (1/6) Epoch 234, batch 8, global_batch_idx: 28900, batch size: 39, loss[discriminator_loss=2.806, discriminator_real_loss=1.471, discriminator_fake_loss=1.335, generator_loss=27.69, generator_mel_loss=19.04, generator_kl_loss=1.313, generator_dur_loss=1.692, generator_adv_loss=1.774, generator_feat_match_loss=3.878, over 39.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.403, discriminator_fake_loss=1.348, generator_loss=27.53, generator_mel_loss=18.58, generator_kl_loss=1.396, generator_dur_loss=1.748, generator_adv_loss=1.941, generator_feat_match_loss=3.858, over 468.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:49:18,759 INFO [train.py:527] (1/6) Epoch 234, batch 58, global_batch_idx: 28950, batch size: 25, loss[discriminator_loss=2.691, discriminator_real_loss=1.307, discriminator_fake_loss=1.384, generator_loss=28.94, generator_mel_loss=18.84, generator_kl_loss=1.665, generator_dur_loss=1.518, generator_adv_loss=2.07, generator_feat_match_loss=4.847, over 25.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.394, discriminator_fake_loss=1.352, generator_loss=27.48, generator_mel_loss=18.57, generator_kl_loss=1.413, generator_dur_loss=1.758, generator_adv_loss=1.934, generator_feat_match_loss=3.807, over 3076.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:51:40,400 INFO [train.py:527] (1/6) Epoch 234, batch 108, global_batch_idx: 29000, batch size: 50, loss[discriminator_loss=2.707, discriminator_real_loss=1.391, discriminator_fake_loss=1.316, generator_loss=27.73, generator_mel_loss=18.64, generator_kl_loss=1.406, generator_dur_loss=1.66, generator_adv_loss=1.932, generator_feat_match_loss=4.091, over 50.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.387, discriminator_fake_loss=1.359, generator_loss=27.46, generator_mel_loss=18.52, generator_kl_loss=1.41, generator_dur_loss=1.765, generator_adv_loss=1.925, generator_feat_match_loss=3.837, over 5973.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:51:40,401 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-12 23:51:49,174 INFO [train.py:591] (1/6) Epoch 234, validation: discriminator_loss=2.773, discriminator_real_loss=1.465, discriminator_fake_loss=1.308, generator_loss=26.51, generator_mel_loss=18.66, generator_kl_loss=1.164, generator_dur_loss=1.825, generator_adv_loss=1.845, generator_feat_match_loss=3.016, over 100.00 samples.
+2024-03-12 23:51:49,175 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-12 23:52:33,309 INFO [train.py:919] (1/6) Start epoch 235
+2024-03-12 23:54:37,216 INFO [train.py:527] (1/6) Epoch 235, batch 34, global_batch_idx: 29050, batch size: 70, loss[discriminator_loss=2.702, discriminator_real_loss=1.305, discriminator_fake_loss=1.397, generator_loss=27.73, generator_mel_loss=18.48, generator_kl_loss=1.372, generator_dur_loss=1.789, generator_adv_loss=2.027, generator_feat_match_loss=4.054, over 70.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.391, discriminator_fake_loss=1.353, generator_loss=27.28, generator_mel_loss=18.41, generator_kl_loss=1.353, generator_dur_loss=1.772, generator_adv_loss=1.922, generator_feat_match_loss=3.829, over 2190.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:57:01,665 INFO [train.py:527] (1/6) Epoch 235, batch 84, global_batch_idx: 29100, batch size: 66, loss[discriminator_loss=2.713, discriminator_real_loss=1.313, discriminator_fake_loss=1.4, generator_loss=27.56, generator_mel_loss=18.87, generator_kl_loss=1.415, generator_dur_loss=1.786, generator_adv_loss=1.817, generator_feat_match_loss=3.672, over 66.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.382, discriminator_fake_loss=1.358, generator_loss=27.42, generator_mel_loss=18.49, generator_kl_loss=1.361, generator_dur_loss=1.781, generator_adv_loss=1.915, generator_feat_match_loss=3.873, over 5302.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-12 23:58:51,176 INFO [train.py:919] (1/6) Start epoch 236
+2024-03-12 23:59:45,848 INFO [train.py:527] (1/6) Epoch 236, batch 10, global_batch_idx: 29150, batch size: 74, loss[discriminator_loss=2.763, discriminator_real_loss=1.39, discriminator_fake_loss=1.373, generator_loss=27.5, generator_mel_loss=18.56, generator_kl_loss=1.316, generator_dur_loss=1.838, generator_adv_loss=1.91, generator_feat_match_loss=3.868, over 74.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.388, discriminator_fake_loss=1.365, generator_loss=27.36, generator_mel_loss=18.45, generator_kl_loss=1.354, generator_dur_loss=1.796, generator_adv_loss=1.89, generator_feat_match_loss=3.867, over 815.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:02:09,614 INFO [train.py:527] (1/6) Epoch 236, batch 60, global_batch_idx: 29200, batch size: 59, loss[discriminator_loss=2.782, discriminator_real_loss=1.295, discriminator_fake_loss=1.487, generator_loss=26.72, generator_mel_loss=18.13, generator_kl_loss=1.351, generator_dur_loss=1.783, generator_adv_loss=1.969, generator_feat_match_loss=3.482, over 59.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.385, discriminator_fake_loss=1.353, generator_loss=27.48, generator_mel_loss=18.52, generator_kl_loss=1.389, generator_dur_loss=1.779, generator_adv_loss=1.918, generator_feat_match_loss=3.88, over 3698.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:02:09,615 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 00:02:17,718 INFO [train.py:591] (1/6) Epoch 236, validation: discriminator_loss=2.744, discriminator_real_loss=1.492, discriminator_fake_loss=1.252, generator_loss=26.21, generator_mel_loss=18.1, generator_kl_loss=1.199, generator_dur_loss=1.843, generator_adv_loss=1.976, generator_feat_match_loss=3.094, over 100.00 samples.
+2024-03-13 00:02:17,719 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 00:04:37,552 INFO [train.py:527] (1/6) Epoch 236, batch 110, global_batch_idx: 29250, batch size: 48, loss[discriminator_loss=2.776, discriminator_real_loss=1.352, discriminator_fake_loss=1.424, generator_loss=28.16, generator_mel_loss=19.04, generator_kl_loss=1.495, generator_dur_loss=1.702, generator_adv_loss=2.042, generator_feat_match_loss=3.885, over 48.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.388, discriminator_fake_loss=1.348, generator_loss=27.52, generator_mel_loss=18.54, generator_kl_loss=1.394, generator_dur_loss=1.773, generator_adv_loss=1.923, generator_feat_match_loss=3.892, over 6619.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:05:15,521 INFO [train.py:919] (1/6) Start epoch 237
+2024-03-13 00:07:22,684 INFO [train.py:527] (1/6) Epoch 237, batch 36, global_batch_idx: 29300, batch size: 50, loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=27.25, generator_mel_loss=18.45, generator_kl_loss=1.389, generator_dur_loss=1.679, generator_adv_loss=1.854, generator_feat_match_loss=3.878, over 50.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.388, discriminator_fake_loss=1.345, generator_loss=27.37, generator_mel_loss=18.44, generator_kl_loss=1.389, generator_dur_loss=1.783, generator_adv_loss=1.913, generator_feat_match_loss=3.849, over 2163.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:09:43,742 INFO [train.py:527] (1/6) Epoch 237, batch 86, global_batch_idx: 29350, batch size: 68, loss[discriminator_loss=2.765, discriminator_real_loss=1.395, discriminator_fake_loss=1.37, generator_loss=27.03, generator_mel_loss=18.51, generator_kl_loss=1.278, generator_dur_loss=1.83, generator_adv_loss=1.836, generator_feat_match_loss=3.571, over 68.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.391, discriminator_fake_loss=1.351, generator_loss=27.41, generator_mel_loss=18.49, generator_kl_loss=1.391, generator_dur_loss=1.776, generator_adv_loss=1.913, generator_feat_match_loss=3.83, over 4997.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:11:31,361 INFO [train.py:919] (1/6) Start epoch 238
+2024-03-13 00:12:28,580 INFO [train.py:527] (1/6) Epoch 238, batch 12, global_batch_idx: 29400, batch size: 74, loss[discriminator_loss=2.704, discriminator_real_loss=1.411, discriminator_fake_loss=1.293, generator_loss=27.92, generator_mel_loss=18.57, generator_kl_loss=1.453, generator_dur_loss=1.831, generator_adv_loss=1.94, generator_feat_match_loss=4.123, over 74.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.346, generator_loss=27.52, generator_mel_loss=18.47, generator_kl_loss=1.43, generator_dur_loss=1.765, generator_adv_loss=1.919, generator_feat_match_loss=3.938, over 736.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:12:28,582 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 00:12:36,531 INFO [train.py:591] (1/6) Epoch 238, validation: discriminator_loss=2.752, discriminator_real_loss=1.42, discriminator_fake_loss=1.332, generator_loss=26.55, generator_mel_loss=18.81, generator_kl_loss=1.148, generator_dur_loss=1.852, generator_adv_loss=1.926, generator_feat_match_loss=2.812, over 100.00 samples.
+2024-03-13 00:12:36,532 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 00:14:57,912 INFO [train.py:527] (1/6) Epoch 238, batch 62, global_batch_idx: 29450, batch size: 55, loss[discriminator_loss=2.808, discriminator_real_loss=1.414, discriminator_fake_loss=1.394, generator_loss=26.78, generator_mel_loss=17.94, generator_kl_loss=1.431, generator_dur_loss=1.71, generator_adv_loss=2.003, generator_feat_match_loss=3.695, over 55.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.382, discriminator_fake_loss=1.354, generator_loss=27.42, generator_mel_loss=18.43, generator_kl_loss=1.367, generator_dur_loss=1.791, generator_adv_loss=1.94, generator_feat_match_loss=3.895, over 3828.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:17:20,268 INFO [train.py:527] (1/6) Epoch 238, batch 112, global_batch_idx: 29500, batch size: 50, loss[discriminator_loss=2.766, discriminator_real_loss=1.458, discriminator_fake_loss=1.309, generator_loss=27.28, generator_mel_loss=18.54, generator_kl_loss=1.345, generator_dur_loss=1.717, generator_adv_loss=1.892, generator_feat_match_loss=3.785, over 50.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.391, discriminator_fake_loss=1.353, generator_loss=27.32, generator_mel_loss=18.39, generator_kl_loss=1.368, generator_dur_loss=1.791, generator_adv_loss=1.936, generator_feat_match_loss=3.835, over 6744.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:17:50,393 INFO [train.py:919] (1/6) Start epoch 239
+2024-03-13 00:20:03,140 INFO [train.py:527] (1/6) Epoch 239, batch 38, global_batch_idx: 29550, batch size: 48, loss[discriminator_loss=2.787, discriminator_real_loss=1.285, discriminator_fake_loss=1.502, generator_loss=27.45, generator_mel_loss=18.51, generator_kl_loss=1.514, generator_dur_loss=1.702, generator_adv_loss=1.957, generator_feat_match_loss=3.768, over 48.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.35, generator_loss=27.42, generator_mel_loss=18.49, generator_kl_loss=1.373, generator_dur_loss=1.774, generator_adv_loss=1.906, generator_feat_match_loss=3.876, over 2136.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:22:24,256 INFO [train.py:527] (1/6) Epoch 239, batch 88, global_batch_idx: 29600, batch size: 74, loss[discriminator_loss=2.701, discriminator_real_loss=1.389, discriminator_fake_loss=1.312, generator_loss=25.97, generator_mel_loss=17.77, generator_kl_loss=1.186, generator_dur_loss=1.807, generator_adv_loss=2.012, generator_feat_match_loss=3.199, over 74.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.388, discriminator_fake_loss=1.346, generator_loss=27.28, generator_mel_loss=18.4, generator_kl_loss=1.381, generator_dur_loss=1.774, generator_adv_loss=1.911, generator_feat_match_loss=3.818, over 5039.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:22:24,257 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 00:22:33,353 INFO [train.py:591] (1/6) Epoch 239, validation: discriminator_loss=2.695, discriminator_real_loss=1.44, discriminator_fake_loss=1.255, generator_loss=26.48, generator_mel_loss=18.34, generator_kl_loss=1.229, generator_dur_loss=1.854, generator_adv_loss=1.96, generator_feat_match_loss=3.105, over 100.00 samples.
+2024-03-13 00:22:33,353 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 00:24:14,872 INFO [train.py:919] (1/6) Start epoch 240
+2024-03-13 00:25:20,270 INFO [train.py:527] (1/6) Epoch 240, batch 14, global_batch_idx: 29650, batch size: 68, loss[discriminator_loss=2.713, discriminator_real_loss=1.344, discriminator_fake_loss=1.369, generator_loss=27.59, generator_mel_loss=18.65, generator_kl_loss=1.344, generator_dur_loss=1.757, generator_adv_loss=1.986, generator_feat_match_loss=3.849, over 68.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.385, discriminator_fake_loss=1.331, generator_loss=27.45, generator_mel_loss=18.46, generator_kl_loss=1.407, generator_dur_loss=1.751, generator_adv_loss=1.933, generator_feat_match_loss=3.901, over 803.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:27:41,073 INFO [train.py:527] (1/6) Epoch 240, batch 64, global_batch_idx: 29700, batch size: 80, loss[discriminator_loss=2.758, discriminator_real_loss=1.347, discriminator_fake_loss=1.411, generator_loss=26.96, generator_mel_loss=18.14, generator_kl_loss=1.342, generator_dur_loss=1.796, generator_adv_loss=1.947, generator_feat_match_loss=3.738, over 80.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.387, discriminator_fake_loss=1.344, generator_loss=27.38, generator_mel_loss=18.43, generator_kl_loss=1.386, generator_dur_loss=1.756, generator_adv_loss=1.915, generator_feat_match_loss=3.886, over 3722.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:30:00,437 INFO [train.py:527] (1/6) Epoch 240, batch 114, global_batch_idx: 29750, batch size: 96, loss[discriminator_loss=2.688, discriminator_real_loss=1.294, discriminator_fake_loss=1.394, generator_loss=28.05, generator_mel_loss=18.59, generator_kl_loss=1.24, generator_dur_loss=1.901, generator_adv_loss=2.034, generator_feat_match_loss=4.281, over 96.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.44, generator_mel_loss=18.49, generator_kl_loss=1.386, generator_dur_loss=1.763, generator_adv_loss=1.915, generator_feat_match_loss=3.884, over 6534.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:30:28,371 INFO [train.py:919] (1/6) Start epoch 241
+2024-03-13 00:32:44,525 INFO [train.py:527] (1/6) Epoch 241, batch 40, global_batch_idx: 29800, batch size: 56, loss[discriminator_loss=2.712, discriminator_real_loss=1.386, discriminator_fake_loss=1.326, generator_loss=27, generator_mel_loss=17.84, generator_kl_loss=1.355, generator_dur_loss=1.801, generator_adv_loss=2.201, generator_feat_match_loss=3.811, over 56.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.376, discriminator_fake_loss=1.358, generator_loss=27.59, generator_mel_loss=18.52, generator_kl_loss=1.366, generator_dur_loss=1.804, generator_adv_loss=1.944, generator_feat_match_loss=3.957, over 2609.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:32:44,526 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 00:32:52,458 INFO [train.py:591] (1/6) Epoch 241, validation: discriminator_loss=2.611, discriminator_real_loss=1.419, discriminator_fake_loss=1.192, generator_loss=26.98, generator_mel_loss=18.69, generator_kl_loss=1.14, generator_dur_loss=1.838, generator_adv_loss=2.076, generator_feat_match_loss=3.241, over 100.00 samples.
+2024-03-13 00:32:52,460 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 00:35:13,549 INFO [train.py:527] (1/6) Epoch 241, batch 90, global_batch_idx: 29850, batch size: 58, loss[discriminator_loss=2.734, discriminator_real_loss=1.505, discriminator_fake_loss=1.228, generator_loss=27.45, generator_mel_loss=18.56, generator_kl_loss=1.332, generator_dur_loss=1.718, generator_adv_loss=1.925, generator_feat_match_loss=3.911, over 58.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.382, discriminator_fake_loss=1.352, generator_loss=27.55, generator_mel_loss=18.51, generator_kl_loss=1.357, generator_dur_loss=1.799, generator_adv_loss=1.934, generator_feat_match_loss=3.946, over 5794.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:36:43,230 INFO [train.py:919] (1/6) Start epoch 242
+2024-03-13 00:37:54,034 INFO [train.py:527] (1/6) Epoch 242, batch 16, global_batch_idx: 29900, batch size: 42, loss[discriminator_loss=2.761, discriminator_real_loss=1.391, discriminator_fake_loss=1.37, generator_loss=28.04, generator_mel_loss=19.03, generator_kl_loss=1.544, generator_dur_loss=1.719, generator_adv_loss=1.955, generator_feat_match_loss=3.793, over 42.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.364, discriminator_fake_loss=1.338, generator_loss=27.25, generator_mel_loss=18.35, generator_kl_loss=1.392, generator_dur_loss=1.765, generator_adv_loss=1.922, generator_feat_match_loss=3.828, over 983.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:40:15,145 INFO [train.py:527] (1/6) Epoch 242, batch 66, global_batch_idx: 29950, batch size: 62, loss[discriminator_loss=2.753, discriminator_real_loss=1.381, discriminator_fake_loss=1.372, generator_loss=27.32, generator_mel_loss=18.46, generator_kl_loss=1.311, generator_dur_loss=1.796, generator_adv_loss=2.188, generator_feat_match_loss=3.563, over 62.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.38, discriminator_fake_loss=1.352, generator_loss=27.47, generator_mel_loss=18.51, generator_kl_loss=1.411, generator_dur_loss=1.768, generator_adv_loss=1.925, generator_feat_match_loss=3.855, over 3713.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:42:32,640 INFO [train.py:527] (1/6) Epoch 242, batch 116, global_batch_idx: 30000, batch size: 26, loss[discriminator_loss=2.692, discriminator_real_loss=1.345, discriminator_fake_loss=1.348, generator_loss=29.08, generator_mel_loss=19.17, generator_kl_loss=1.738, generator_dur_loss=1.58, generator_adv_loss=2.076, generator_feat_match_loss=4.518, over 26.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.383, discriminator_fake_loss=1.346, generator_loss=27.44, generator_mel_loss=18.49, generator_kl_loss=1.4, generator_dur_loss=1.757, generator_adv_loss=1.922, generator_feat_match_loss=3.865, over 6512.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:42:32,641 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 00:42:41,231 INFO [train.py:591] (1/6) Epoch 242, validation: discriminator_loss=2.756, discriminator_real_loss=1.482, discriminator_fake_loss=1.275, generator_loss=27.11, generator_mel_loss=18.56, generator_kl_loss=1.247, generator_dur_loss=1.811, generator_adv_loss=1.991, generator_feat_match_loss=3.501, over 100.00 samples.
+2024-03-13 00:42:41,232 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 00:43:01,196 INFO [train.py:919] (1/6) Start epoch 243
+2024-03-13 00:45:22,503 INFO [train.py:527] (1/6) Epoch 243, batch 42, global_batch_idx: 30050, batch size: 80, loss[discriminator_loss=2.757, discriminator_real_loss=1.494, discriminator_fake_loss=1.263, generator_loss=26.59, generator_mel_loss=17.99, generator_kl_loss=1.399, generator_dur_loss=1.802, generator_adv_loss=1.779, generator_feat_match_loss=3.617, over 80.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.54, generator_mel_loss=18.57, generator_kl_loss=1.39, generator_dur_loss=1.749, generator_adv_loss=1.929, generator_feat_match_loss=3.907, over 2586.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:47:41,899 INFO [train.py:527] (1/6) Epoch 243, batch 92, global_batch_idx: 30100, batch size: 72, loss[discriminator_loss=2.716, discriminator_real_loss=1.407, discriminator_fake_loss=1.309, generator_loss=27.92, generator_mel_loss=18.46, generator_kl_loss=1.389, generator_dur_loss=1.839, generator_adv_loss=1.903, generator_feat_match_loss=4.327, over 72.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.381, discriminator_fake_loss=1.343, generator_loss=27.52, generator_mel_loss=18.5, generator_kl_loss=1.387, generator_dur_loss=1.747, generator_adv_loss=1.94, generator_feat_match_loss=3.944, over 5298.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:49:08,091 INFO [train.py:919] (1/6) Start epoch 244
+2024-03-13 00:50:23,883 INFO [train.py:527] (1/6) Epoch 244, batch 18, global_batch_idx: 30150, batch size: 36, loss[discriminator_loss=2.755, discriminator_real_loss=1.288, discriminator_fake_loss=1.468, generator_loss=27.91, generator_mel_loss=18.47, generator_kl_loss=1.636, generator_dur_loss=1.707, generator_adv_loss=2.041, generator_feat_match_loss=4.05, over 36.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.36, discriminator_fake_loss=1.367, generator_loss=27.63, generator_mel_loss=18.57, generator_kl_loss=1.362, generator_dur_loss=1.803, generator_adv_loss=1.891, generator_feat_match_loss=4.007, over 1232.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:52:43,044 INFO [train.py:527] (1/6) Epoch 244, batch 68, global_batch_idx: 30200, batch size: 83, loss[discriminator_loss=2.763, discriminator_real_loss=1.371, discriminator_fake_loss=1.392, generator_loss=27.63, generator_mel_loss=18.49, generator_kl_loss=1.372, generator_dur_loss=1.873, generator_adv_loss=1.93, generator_feat_match_loss=3.97, over 83.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.382, discriminator_fake_loss=1.35, generator_loss=27.59, generator_mel_loss=18.51, generator_kl_loss=1.381, generator_dur_loss=1.793, generator_adv_loss=1.915, generator_feat_match_loss=3.987, over 4219.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:52:43,045 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 00:52:50,820 INFO [train.py:591] (1/6) Epoch 244, validation: discriminator_loss=2.754, discriminator_real_loss=1.48, discriminator_fake_loss=1.274, generator_loss=25.97, generator_mel_loss=18.17, generator_kl_loss=1.073, generator_dur_loss=1.844, generator_adv_loss=1.922, generator_feat_match_loss=2.962, over 100.00 samples.
+2024-03-13 00:52:50,821 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 00:55:09,589 INFO [train.py:527] (1/6) Epoch 244, batch 118, global_batch_idx: 30250, batch size: 64, loss[discriminator_loss=2.741, discriminator_real_loss=1.278, discriminator_fake_loss=1.463, generator_loss=26.79, generator_mel_loss=18.11, generator_kl_loss=1.338, generator_dur_loss=1.818, generator_adv_loss=1.891, generator_feat_match_loss=3.63, over 64.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.58, generator_mel_loss=18.51, generator_kl_loss=1.384, generator_dur_loss=1.796, generator_adv_loss=1.92, generator_feat_match_loss=3.977, over 7039.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 00:55:24,414 INFO [train.py:919] (1/6) Start epoch 245
+2024-03-13 00:57:48,083 INFO [train.py:527] (1/6) Epoch 245, batch 44, global_batch_idx: 30300, batch size: 50, loss[discriminator_loss=2.766, discriminator_real_loss=1.415, discriminator_fake_loss=1.352, generator_loss=28.05, generator_mel_loss=19.2, generator_kl_loss=1.355, generator_dur_loss=1.701, generator_adv_loss=1.887, generator_feat_match_loss=3.908, over 50.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.39, discriminator_fake_loss=1.344, generator_loss=27.51, generator_mel_loss=18.53, generator_kl_loss=1.412, generator_dur_loss=1.765, generator_adv_loss=1.914, generator_feat_match_loss=3.887, over 2510.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:00:05,242 INFO [train.py:527] (1/6) Epoch 245, batch 94, global_batch_idx: 30350, batch size: 59, loss[discriminator_loss=2.763, discriminator_real_loss=1.407, discriminator_fake_loss=1.356, generator_loss=27.68, generator_mel_loss=18.61, generator_kl_loss=1.324, generator_dur_loss=1.734, generator_adv_loss=2.061, generator_feat_match_loss=3.95, over 59.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.348, generator_loss=27.51, generator_mel_loss=18.5, generator_kl_loss=1.405, generator_dur_loss=1.761, generator_adv_loss=1.916, generator_feat_match_loss=3.928, over 5318.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:01:28,986 INFO [train.py:919] (1/6) Start epoch 246
+2024-03-13 01:02:46,362 INFO [train.py:527] (1/6) Epoch 246, batch 20, global_batch_idx: 30400, batch size: 72, loss[discriminator_loss=2.717, discriminator_real_loss=1.447, discriminator_fake_loss=1.27, generator_loss=27.26, generator_mel_loss=18.34, generator_kl_loss=1.192, generator_dur_loss=1.842, generator_adv_loss=1.896, generator_feat_match_loss=3.993, over 72.00 samples.], tot_loss[discriminator_loss=2.764, discriminator_real_loss=1.403, discriminator_fake_loss=1.361, generator_loss=27.6, generator_mel_loss=18.56, generator_kl_loss=1.427, generator_dur_loss=1.755, generator_adv_loss=1.939, generator_feat_match_loss=3.912, over 1070.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:02:46,363 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 01:02:54,161 INFO [train.py:591] (1/6) Epoch 246, validation: discriminator_loss=2.815, discriminator_real_loss=1.444, discriminator_fake_loss=1.371, generator_loss=26.77, generator_mel_loss=18.83, generator_kl_loss=1.235, generator_dur_loss=1.858, generator_adv_loss=1.815, generator_feat_match_loss=3.026, over 100.00 samples.
+2024-03-13 01:02:54,162 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 01:05:15,337 INFO [train.py:527] (1/6) Epoch 246, batch 70, global_batch_idx: 30450, batch size: 77, loss[discriminator_loss=2.724, discriminator_real_loss=1.361, discriminator_fake_loss=1.363, generator_loss=28.01, generator_mel_loss=19.11, generator_kl_loss=1.149, generator_dur_loss=1.833, generator_adv_loss=1.932, generator_feat_match_loss=3.987, over 77.00 samples.], tot_loss[discriminator_loss=2.753, discriminator_real_loss=1.39, discriminator_fake_loss=1.364, generator_loss=27.34, generator_mel_loss=18.43, generator_kl_loss=1.383, generator_dur_loss=1.783, generator_adv_loss=1.918, generator_feat_match_loss=3.828, over 4166.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:07:31,697 INFO [train.py:527] (1/6) Epoch 246, batch 120, global_batch_idx: 30500, batch size: 59, loss[discriminator_loss=2.695, discriminator_real_loss=1.422, discriminator_fake_loss=1.273, generator_loss=27.29, generator_mel_loss=18.22, generator_kl_loss=1.414, generator_dur_loss=1.77, generator_adv_loss=1.988, generator_feat_match_loss=3.902, over 59.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.384, discriminator_fake_loss=1.357, generator_loss=27.42, generator_mel_loss=18.47, generator_kl_loss=1.388, generator_dur_loss=1.779, generator_adv_loss=1.918, generator_feat_match_loss=3.865, over 6964.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:07:41,831 INFO [train.py:919] (1/6) Start epoch 247
+2024-03-13 01:10:17,035 INFO [train.py:527] (1/6) Epoch 247, batch 46, global_batch_idx: 30550, batch size: 31, loss[discriminator_loss=2.738, discriminator_real_loss=1.37, discriminator_fake_loss=1.368, generator_loss=28.07, generator_mel_loss=19.09, generator_kl_loss=1.488, generator_dur_loss=1.649, generator_adv_loss=2, generator_feat_match_loss=3.84, over 31.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.376, discriminator_fake_loss=1.356, generator_loss=27.67, generator_mel_loss=18.61, generator_kl_loss=1.411, generator_dur_loss=1.792, generator_adv_loss=1.911, generator_feat_match_loss=3.944, over 2662.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:12:34,189 INFO [train.py:527] (1/6) Epoch 247, batch 96, global_batch_idx: 30600, batch size: 31, loss[discriminator_loss=2.746, discriminator_real_loss=1.415, discriminator_fake_loss=1.332, generator_loss=28.44, generator_mel_loss=19.2, generator_kl_loss=1.425, generator_dur_loss=1.659, generator_adv_loss=1.727, generator_feat_match_loss=4.431, over 31.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.384, discriminator_fake_loss=1.355, generator_loss=27.53, generator_mel_loss=18.52, generator_kl_loss=1.403, generator_dur_loss=1.778, generator_adv_loss=1.917, generator_feat_match_loss=3.912, over 5332.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:12:34,190 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 01:12:43,015 INFO [train.py:591] (1/6) Epoch 247, validation: discriminator_loss=2.79, discriminator_real_loss=1.374, discriminator_fake_loss=1.416, generator_loss=25.86, generator_mel_loss=18.28, generator_kl_loss=1.187, generator_dur_loss=1.851, generator_adv_loss=1.691, generator_feat_match_loss=2.855, over 100.00 samples.
+2024-03-13 01:12:43,016 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 01:13:59,171 INFO [train.py:919] (1/6) Start epoch 248
+2024-03-13 01:15:24,538 INFO [train.py:527] (1/6) Epoch 248, batch 22, global_batch_idx: 30650, batch size: 66, loss[discriminator_loss=2.719, discriminator_real_loss=1.348, discriminator_fake_loss=1.372, generator_loss=27.1, generator_mel_loss=18.4, generator_kl_loss=1.333, generator_dur_loss=1.773, generator_adv_loss=1.864, generator_feat_match_loss=3.73, over 66.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.399, discriminator_fake_loss=1.339, generator_loss=27.22, generator_mel_loss=18.33, generator_kl_loss=1.351, generator_dur_loss=1.761, generator_adv_loss=1.923, generator_feat_match_loss=3.854, over 1336.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:17:42,696 INFO [train.py:527] (1/6) Epoch 248, batch 72, global_batch_idx: 30700, batch size: 58, loss[discriminator_loss=2.73, discriminator_real_loss=1.442, discriminator_fake_loss=1.289, generator_loss=27.23, generator_mel_loss=18.12, generator_kl_loss=1.33, generator_dur_loss=1.808, generator_adv_loss=1.97, generator_feat_match_loss=4.003, over 58.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.382, discriminator_fake_loss=1.344, generator_loss=27.46, generator_mel_loss=18.43, generator_kl_loss=1.396, generator_dur_loss=1.761, generator_adv_loss=1.927, generator_feat_match_loss=3.943, over 4118.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:20:00,087 INFO [train.py:527] (1/6) Epoch 248, batch 122, global_batch_idx: 30750, batch size: 96, loss[discriminator_loss=2.752, discriminator_real_loss=1.402, discriminator_fake_loss=1.35, generator_loss=27.49, generator_mel_loss=18.16, generator_kl_loss=1.457, generator_dur_loss=1.801, generator_adv_loss=2.013, generator_feat_match_loss=4.063, over 96.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.383, discriminator_fake_loss=1.344, generator_loss=27.45, generator_mel_loss=18.45, generator_kl_loss=1.395, generator_dur_loss=1.768, generator_adv_loss=1.924, generator_feat_match_loss=3.911, over 7116.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:20:05,813 INFO [train.py:919] (1/6) Start epoch 249
+2024-03-13 01:22:42,441 INFO [train.py:527] (1/6) Epoch 249, batch 48, global_batch_idx: 30800, batch size: 50, loss[discriminator_loss=2.783, discriminator_real_loss=1.417, discriminator_fake_loss=1.366, generator_loss=27.31, generator_mel_loss=18.4, generator_kl_loss=1.477, generator_dur_loss=1.682, generator_adv_loss=1.961, generator_feat_match_loss=3.791, over 50.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.39, discriminator_fake_loss=1.356, generator_loss=27.42, generator_mel_loss=18.46, generator_kl_loss=1.378, generator_dur_loss=1.766, generator_adv_loss=1.912, generator_feat_match_loss=3.896, over 3019.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:22:42,442 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 01:22:50,508 INFO [train.py:591] (1/6) Epoch 249, validation: discriminator_loss=2.733, discriminator_real_loss=1.498, discriminator_fake_loss=1.234, generator_loss=26.64, generator_mel_loss=18.71, generator_kl_loss=1.198, generator_dur_loss=1.834, generator_adv_loss=1.961, generator_feat_match_loss=2.934, over 100.00 samples.
+2024-03-13 01:22:50,509 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 01:25:08,808 INFO [train.py:527] (1/6) Epoch 249, batch 98, global_batch_idx: 30850, batch size: 68, loss[discriminator_loss=2.747, discriminator_real_loss=1.373, discriminator_fake_loss=1.374, generator_loss=26.88, generator_mel_loss=17.9, generator_kl_loss=1.246, generator_dur_loss=1.861, generator_adv_loss=1.923, generator_feat_match_loss=3.944, over 68.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.381, discriminator_fake_loss=1.358, generator_loss=27.47, generator_mel_loss=18.49, generator_kl_loss=1.377, generator_dur_loss=1.774, generator_adv_loss=1.908, generator_feat_match_loss=3.918, over 5830.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:26:20,210 INFO [train.py:919] (1/6) Start epoch 250
+2024-03-13 01:27:49,299 INFO [train.py:527] (1/6) Epoch 250, batch 24, global_batch_idx: 30900, batch size: 58, loss[discriminator_loss=2.749, discriminator_real_loss=1.478, discriminator_fake_loss=1.271, generator_loss=27.6, generator_mel_loss=18.49, generator_kl_loss=1.427, generator_dur_loss=1.785, generator_adv_loss=1.902, generator_feat_match_loss=3.999, over 58.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.46, generator_mel_loss=18.43, generator_kl_loss=1.389, generator_dur_loss=1.768, generator_adv_loss=1.934, generator_feat_match_loss=3.934, over 1445.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:30:07,447 INFO [train.py:527] (1/6) Epoch 250, batch 74, global_batch_idx: 30950, batch size: 72, loss[discriminator_loss=2.756, discriminator_real_loss=1.458, discriminator_fake_loss=1.298, generator_loss=27.49, generator_mel_loss=18.33, generator_kl_loss=1.433, generator_dur_loss=1.819, generator_adv_loss=1.911, generator_feat_match_loss=3.987, over 72.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.38, discriminator_fake_loss=1.352, generator_loss=27.48, generator_mel_loss=18.47, generator_kl_loss=1.389, generator_dur_loss=1.747, generator_adv_loss=1.927, generator_feat_match_loss=3.952, over 4248.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:32:28,070 INFO [train.py:919] (1/6) Start epoch 251
+2024-03-13 01:32:48,815 INFO [train.py:527] (1/6) Epoch 251, batch 0, global_batch_idx: 31000, batch size: 50, loss[discriminator_loss=2.68, discriminator_real_loss=1.362, discriminator_fake_loss=1.318, generator_loss=28.18, generator_mel_loss=18.85, generator_kl_loss=1.458, generator_dur_loss=1.674, generator_adv_loss=1.935, generator_feat_match_loss=4.262, over 50.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.362, discriminator_fake_loss=1.318, generator_loss=28.18, generator_mel_loss=18.85, generator_kl_loss=1.458, generator_dur_loss=1.674, generator_adv_loss=1.935, generator_feat_match_loss=4.262, over 50.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:32:48,817 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 01:32:56,661 INFO [train.py:591] (1/6) Epoch 251, validation: discriminator_loss=2.724, discriminator_real_loss=1.416, discriminator_fake_loss=1.308, generator_loss=26.43, generator_mel_loss=18.63, generator_kl_loss=1.232, generator_dur_loss=1.815, generator_adv_loss=1.85, generator_feat_match_loss=2.905, over 100.00 samples.
+2024-03-13 01:32:56,663 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 01:35:15,845 INFO [train.py:527] (1/6) Epoch 251, batch 50, global_batch_idx: 31050, batch size: 50, loss[discriminator_loss=2.709, discriminator_real_loss=1.471, discriminator_fake_loss=1.238, generator_loss=26.66, generator_mel_loss=18.21, generator_kl_loss=1.409, generator_dur_loss=1.677, generator_adv_loss=1.947, generator_feat_match_loss=3.424, over 50.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.379, discriminator_fake_loss=1.341, generator_loss=27.4, generator_mel_loss=18.38, generator_kl_loss=1.397, generator_dur_loss=1.763, generator_adv_loss=1.908, generator_feat_match_loss=3.952, over 3153.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:37:37,626 INFO [train.py:527] (1/6) Epoch 251, batch 100, global_batch_idx: 31100, batch size: 77, loss[discriminator_loss=2.776, discriminator_real_loss=1.284, discriminator_fake_loss=1.492, generator_loss=27.37, generator_mel_loss=18.55, generator_kl_loss=1.161, generator_dur_loss=1.846, generator_adv_loss=1.865, generator_feat_match_loss=3.95, over 77.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.45, generator_mel_loss=18.42, generator_kl_loss=1.4, generator_dur_loss=1.761, generator_adv_loss=1.914, generator_feat_match_loss=3.949, over 5874.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:38:39,436 INFO [train.py:919] (1/6) Start epoch 252
+2024-03-13 01:40:17,993 INFO [train.py:527] (1/6) Epoch 252, batch 26, global_batch_idx: 31150, batch size: 66, loss[discriminator_loss=2.784, discriminator_real_loss=1.393, discriminator_fake_loss=1.391, generator_loss=27.76, generator_mel_loss=18.44, generator_kl_loss=1.363, generator_dur_loss=1.805, generator_adv_loss=1.966, generator_feat_match_loss=4.185, over 66.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.395, discriminator_fake_loss=1.348, generator_loss=27.43, generator_mel_loss=18.39, generator_kl_loss=1.374, generator_dur_loss=1.77, generator_adv_loss=1.906, generator_feat_match_loss=3.986, over 1730.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:42:36,517 INFO [train.py:527] (1/6) Epoch 252, batch 76, global_batch_idx: 31200, batch size: 80, loss[discriminator_loss=2.803, discriminator_real_loss=1.394, discriminator_fake_loss=1.409, generator_loss=27.12, generator_mel_loss=18.28, generator_kl_loss=1.343, generator_dur_loss=1.782, generator_adv_loss=1.971, generator_feat_match_loss=3.746, over 80.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.39, discriminator_fake_loss=1.351, generator_loss=27.4, generator_mel_loss=18.39, generator_kl_loss=1.378, generator_dur_loss=1.762, generator_adv_loss=1.917, generator_feat_match_loss=3.948, over 4534.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:42:36,519 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 01:42:44,416 INFO [train.py:591] (1/6) Epoch 252, validation: discriminator_loss=2.785, discriminator_real_loss=1.56, discriminator_fake_loss=1.224, generator_loss=26.5, generator_mel_loss=18.6, generator_kl_loss=1.167, generator_dur_loss=1.826, generator_adv_loss=1.996, generator_feat_match_loss=2.911, over 100.00 samples.
+2024-03-13 01:42:44,417 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 01:44:54,524 INFO [train.py:919] (1/6) Start epoch 253
+2024-03-13 01:45:20,374 INFO [train.py:527] (1/6) Epoch 253, batch 2, global_batch_idx: 31250, batch size: 47, loss[discriminator_loss=2.701, discriminator_real_loss=1.393, discriminator_fake_loss=1.308, generator_loss=27.17, generator_mel_loss=18.17, generator_kl_loss=1.471, generator_dur_loss=1.701, generator_adv_loss=1.979, generator_feat_match_loss=3.85, over 47.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.382, discriminator_fake_loss=1.316, generator_loss=27.82, generator_mel_loss=18.67, generator_kl_loss=1.437, generator_dur_loss=1.727, generator_adv_loss=1.936, generator_feat_match_loss=4.055, over 145.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:47:37,745 INFO [train.py:527] (1/6) Epoch 253, batch 52, global_batch_idx: 31300, batch size: 68, loss[discriminator_loss=2.636, discriminator_real_loss=1.391, discriminator_fake_loss=1.244, generator_loss=28.21, generator_mel_loss=18.55, generator_kl_loss=1.446, generator_dur_loss=1.756, generator_adv_loss=2.17, generator_feat_match_loss=4.288, over 68.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.347, generator_loss=27.87, generator_mel_loss=18.56, generator_kl_loss=1.386, generator_dur_loss=1.75, generator_adv_loss=2.018, generator_feat_match_loss=4.148, over 2855.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:49:55,143 INFO [train.py:527] (1/6) Epoch 253, batch 102, global_batch_idx: 31350, batch size: 96, loss[discriminator_loss=2.77, discriminator_real_loss=1.418, discriminator_fake_loss=1.352, generator_loss=26.94, generator_mel_loss=18.01, generator_kl_loss=1.291, generator_dur_loss=1.858, generator_adv_loss=1.876, generator_feat_match_loss=3.908, over 96.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.347, generator_loss=27.56, generator_mel_loss=18.45, generator_kl_loss=1.386, generator_dur_loss=1.75, generator_adv_loss=1.964, generator_feat_match_loss=4.01, over 5697.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:50:56,569 INFO [train.py:919] (1/6) Start epoch 254
+2024-03-13 01:52:38,343 INFO [train.py:527] (1/6) Epoch 254, batch 28, global_batch_idx: 31400, batch size: 72, loss[discriminator_loss=2.73, discriminator_real_loss=1.315, discriminator_fake_loss=1.414, generator_loss=28.56, generator_mel_loss=19.01, generator_kl_loss=1.395, generator_dur_loss=1.818, generator_adv_loss=2.064, generator_feat_match_loss=4.276, over 72.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.386, discriminator_fake_loss=1.36, generator_loss=27.28, generator_mel_loss=18.37, generator_kl_loss=1.402, generator_dur_loss=1.769, generator_adv_loss=1.9, generator_feat_match_loss=3.844, over 1613.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:52:38,345 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 01:52:46,336 INFO [train.py:591] (1/6) Epoch 254, validation: discriminator_loss=2.75, discriminator_real_loss=1.459, discriminator_fake_loss=1.292, generator_loss=26.99, generator_mel_loss=18.55, generator_kl_loss=1.213, generator_dur_loss=1.832, generator_adv_loss=1.963, generator_feat_match_loss=3.434, over 100.00 samples.
+2024-03-13 01:52:46,337 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 01:55:03,434 INFO [train.py:527] (1/6) Epoch 254, batch 78, global_batch_idx: 31450, batch size: 45, loss[discriminator_loss=2.742, discriminator_real_loss=1.342, discriminator_fake_loss=1.401, generator_loss=26.51, generator_mel_loss=17.93, generator_kl_loss=1.532, generator_dur_loss=1.671, generator_adv_loss=1.805, generator_feat_match_loss=3.573, over 45.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.388, discriminator_fake_loss=1.347, generator_loss=27.38, generator_mel_loss=18.42, generator_kl_loss=1.402, generator_dur_loss=1.756, generator_adv_loss=1.912, generator_feat_match_loss=3.889, over 4373.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 01:57:12,536 INFO [train.py:919] (1/6) Start epoch 255
+2024-03-13 01:57:47,719 INFO [train.py:527] (1/6) Epoch 255, batch 4, global_batch_idx: 31500, batch size: 36, loss[discriminator_loss=2.716, discriminator_real_loss=1.477, discriminator_fake_loss=1.239, generator_loss=26.06, generator_mel_loss=17.64, generator_kl_loss=1.501, generator_dur_loss=1.678, generator_adv_loss=1.99, generator_feat_match_loss=3.257, over 36.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.395, discriminator_fake_loss=1.349, generator_loss=27.11, generator_mel_loss=18.38, generator_kl_loss=1.458, generator_dur_loss=1.761, generator_adv_loss=1.91, generator_feat_match_loss=3.599, over 275.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:00:05,960 INFO [train.py:527] (1/6) Epoch 255, batch 54, global_batch_idx: 31550, batch size: 31, loss[discriminator_loss=2.64, discriminator_real_loss=1.367, discriminator_fake_loss=1.273, generator_loss=27.87, generator_mel_loss=18.42, generator_kl_loss=1.513, generator_dur_loss=1.669, generator_adv_loss=1.84, generator_feat_match_loss=4.431, over 31.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.46, generator_mel_loss=18.45, generator_kl_loss=1.418, generator_dur_loss=1.741, generator_adv_loss=1.917, generator_feat_match_loss=3.934, over 3075.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:02:24,309 INFO [train.py:527] (1/6) Epoch 255, batch 104, global_batch_idx: 31600, batch size: 80, loss[discriminator_loss=2.821, discriminator_real_loss=1.464, discriminator_fake_loss=1.358, generator_loss=27.84, generator_mel_loss=18.82, generator_kl_loss=1.34, generator_dur_loss=1.818, generator_adv_loss=1.844, generator_feat_match_loss=4.015, over 80.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.349, generator_loss=27.59, generator_mel_loss=18.45, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=1.956, generator_feat_match_loss=4.03, over 6042.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:02:24,311 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 02:02:33,143 INFO [train.py:591] (1/6) Epoch 255, validation: discriminator_loss=2.723, discriminator_real_loss=1.394, discriminator_fake_loss=1.329, generator_loss=26.85, generator_mel_loss=18.73, generator_kl_loss=1.242, generator_dur_loss=1.828, generator_adv_loss=1.886, generator_feat_match_loss=3.165, over 100.00 samples.
+2024-03-13 02:02:33,144 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 02:03:26,616 INFO [train.py:919] (1/6) Start epoch 256
+2024-03-13 02:05:13,705 INFO [train.py:527] (1/6) Epoch 256, batch 30, global_batch_idx: 31650, batch size: 45, loss[discriminator_loss=2.815, discriminator_real_loss=1.621, discriminator_fake_loss=1.194, generator_loss=27.29, generator_mel_loss=18.21, generator_kl_loss=1.389, generator_dur_loss=1.716, generator_adv_loss=1.826, generator_feat_match_loss=4.143, over 45.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.388, discriminator_fake_loss=1.349, generator_loss=27.63, generator_mel_loss=18.52, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.927, generator_feat_match_loss=4.019, over 1761.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:07:33,007 INFO [train.py:527] (1/6) Epoch 256, batch 80, global_batch_idx: 31700, batch size: 58, loss[discriminator_loss=2.701, discriminator_real_loss=1.485, discriminator_fake_loss=1.215, generator_loss=28.01, generator_mel_loss=18.71, generator_kl_loss=1.376, generator_dur_loss=1.71, generator_adv_loss=1.936, generator_feat_match_loss=4.278, over 58.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.382, discriminator_fake_loss=1.352, generator_loss=27.45, generator_mel_loss=18.44, generator_kl_loss=1.372, generator_dur_loss=1.774, generator_adv_loss=1.906, generator_feat_match_loss=3.967, over 4795.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:09:31,151 INFO [train.py:919] (1/6) Start epoch 257
+2024-03-13 02:10:12,033 INFO [train.py:527] (1/6) Epoch 257, batch 6, global_batch_idx: 31750, batch size: 61, loss[discriminator_loss=2.769, discriminator_real_loss=1.423, discriminator_fake_loss=1.346, generator_loss=26.79, generator_mel_loss=18.11, generator_kl_loss=1.489, generator_dur_loss=1.738, generator_adv_loss=1.815, generator_feat_match_loss=3.634, over 61.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.404, discriminator_fake_loss=1.361, generator_loss=27.54, generator_mel_loss=18.58, generator_kl_loss=1.381, generator_dur_loss=1.776, generator_adv_loss=1.901, generator_feat_match_loss=3.905, over 426.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:12:30,470 INFO [train.py:527] (1/6) Epoch 257, batch 56, global_batch_idx: 31800, batch size: 70, loss[discriminator_loss=2.707, discriminator_real_loss=1.403, discriminator_fake_loss=1.304, generator_loss=27.37, generator_mel_loss=18.06, generator_kl_loss=1.494, generator_dur_loss=1.778, generator_adv_loss=2.031, generator_feat_match_loss=4.005, over 70.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.394, discriminator_fake_loss=1.357, generator_loss=27.35, generator_mel_loss=18.42, generator_kl_loss=1.368, generator_dur_loss=1.77, generator_adv_loss=1.903, generator_feat_match_loss=3.888, over 3438.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:12:30,471 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 02:12:38,373 INFO [train.py:591] (1/6) Epoch 257, validation: discriminator_loss=2.759, discriminator_real_loss=1.537, discriminator_fake_loss=1.221, generator_loss=26.49, generator_mel_loss=18.51, generator_kl_loss=1.253, generator_dur_loss=1.822, generator_adv_loss=1.931, generator_feat_match_loss=2.969, over 100.00 samples.
+2024-03-13 02:12:38,373 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 02:14:57,212 INFO [train.py:527] (1/6) Epoch 257, batch 106, global_batch_idx: 31850, batch size: 66, loss[discriminator_loss=2.808, discriminator_real_loss=1.343, discriminator_fake_loss=1.465, generator_loss=27.27, generator_mel_loss=18.62, generator_kl_loss=1.517, generator_dur_loss=1.728, generator_adv_loss=1.831, generator_feat_match_loss=3.576, over 66.00 samples.], tot_loss[discriminator_loss=2.744, discriminator_real_loss=1.392, discriminator_fake_loss=1.353, generator_loss=27.4, generator_mel_loss=18.42, generator_kl_loss=1.39, generator_dur_loss=1.76, generator_adv_loss=1.903, generator_feat_match_loss=3.931, over 6131.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:15:44,125 INFO [train.py:919] (1/6) Start epoch 258
+2024-03-13 02:17:39,474 INFO [train.py:527] (1/6) Epoch 258, batch 32, global_batch_idx: 31900, batch size: 47, loss[discriminator_loss=2.712, discriminator_real_loss=1.416, discriminator_fake_loss=1.296, generator_loss=27.44, generator_mel_loss=18.37, generator_kl_loss=1.564, generator_dur_loss=1.68, generator_adv_loss=1.873, generator_feat_match_loss=3.954, over 47.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.391, discriminator_fake_loss=1.354, generator_loss=27.26, generator_mel_loss=18.4, generator_kl_loss=1.385, generator_dur_loss=1.739, generator_adv_loss=1.886, generator_feat_match_loss=3.857, over 1925.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:19:57,435 INFO [train.py:527] (1/6) Epoch 258, batch 82, global_batch_idx: 31950, batch size: 36, loss[discriminator_loss=2.78, discriminator_real_loss=1.373, discriminator_fake_loss=1.406, generator_loss=28.31, generator_mel_loss=18.95, generator_kl_loss=1.371, generator_dur_loss=1.674, generator_adv_loss=1.9, generator_feat_match_loss=4.423, over 36.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.5, generator_mel_loss=18.45, generator_kl_loss=1.4, generator_dur_loss=1.75, generator_adv_loss=1.933, generator_feat_match_loss=3.967, over 4617.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:21:52,133 INFO [train.py:919] (1/6) Start epoch 259
+2024-03-13 02:22:39,547 INFO [train.py:527] (1/6) Epoch 259, batch 8, global_batch_idx: 32000, batch size: 44, loss[discriminator_loss=2.78, discriminator_real_loss=1.465, discriminator_fake_loss=1.314, generator_loss=26.98, generator_mel_loss=18.34, generator_kl_loss=1.451, generator_dur_loss=1.653, generator_adv_loss=1.819, generator_feat_match_loss=3.72, over 44.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.415, discriminator_fake_loss=1.337, generator_loss=27.1, generator_mel_loss=18.28, generator_kl_loss=1.335, generator_dur_loss=1.803, generator_adv_loss=1.922, generator_feat_match_loss=3.767, over 617.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:22:39,550 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 02:22:47,456 INFO [train.py:591] (1/6) Epoch 259, validation: discriminator_loss=2.777, discriminator_real_loss=1.429, discriminator_fake_loss=1.348, generator_loss=26.66, generator_mel_loss=18.57, generator_kl_loss=1.241, generator_dur_loss=1.835, generator_adv_loss=1.784, generator_feat_match_loss=3.231, over 100.00 samples.
+2024-03-13 02:22:47,462 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 02:25:08,188 INFO [train.py:527] (1/6) Epoch 259, batch 58, global_batch_idx: 32050, batch size: 31, loss[discriminator_loss=2.636, discriminator_real_loss=1.375, discriminator_fake_loss=1.261, generator_loss=27.1, generator_mel_loss=18.46, generator_kl_loss=1.406, generator_dur_loss=1.64, generator_adv_loss=1.895, generator_feat_match_loss=3.696, over 31.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.388, discriminator_fake_loss=1.349, generator_loss=27.33, generator_mel_loss=18.35, generator_kl_loss=1.35, generator_dur_loss=1.793, generator_adv_loss=1.921, generator_feat_match_loss=3.91, over 3760.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:27:26,508 INFO [train.py:527] (1/6) Epoch 259, batch 108, global_batch_idx: 32100, batch size: 66, loss[discriminator_loss=2.739, discriminator_real_loss=1.4, discriminator_fake_loss=1.339, generator_loss=27.01, generator_mel_loss=18.43, generator_kl_loss=1.29, generator_dur_loss=1.778, generator_adv_loss=1.874, generator_feat_match_loss=3.64, over 66.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.39, generator_mel_loss=18.4, generator_kl_loss=1.362, generator_dur_loss=1.784, generator_adv_loss=1.922, generator_feat_match_loss=3.928, over 6539.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:28:09,112 INFO [train.py:919] (1/6) Start epoch 260
+2024-03-13 02:30:09,127 INFO [train.py:527] (1/6) Epoch 260, batch 34, global_batch_idx: 32150, batch size: 80, loss[discriminator_loss=2.672, discriminator_real_loss=1.357, discriminator_fake_loss=1.315, generator_loss=27.99, generator_mel_loss=18.92, generator_kl_loss=1.188, generator_dur_loss=1.871, generator_adv_loss=1.877, generator_feat_match_loss=4.135, over 80.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.343, generator_loss=27.54, generator_mel_loss=18.42, generator_kl_loss=1.4, generator_dur_loss=1.775, generator_adv_loss=1.915, generator_feat_match_loss=4.025, over 1927.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:32:29,172 INFO [train.py:527] (1/6) Epoch 260, batch 84, global_batch_idx: 32200, batch size: 36, loss[discriminator_loss=2.719, discriminator_real_loss=1.322, discriminator_fake_loss=1.396, generator_loss=26.94, generator_mel_loss=18.56, generator_kl_loss=1.499, generator_dur_loss=1.726, generator_adv_loss=2.047, generator_feat_match_loss=3.113, over 36.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.389, discriminator_fake_loss=1.345, generator_loss=27.47, generator_mel_loss=18.41, generator_kl_loss=1.389, generator_dur_loss=1.78, generator_adv_loss=1.916, generator_feat_match_loss=3.974, over 4905.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:32:29,174 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 02:32:37,975 INFO [train.py:591] (1/6) Epoch 260, validation: discriminator_loss=2.713, discriminator_real_loss=1.453, discriminator_fake_loss=1.26, generator_loss=27.27, generator_mel_loss=18.88, generator_kl_loss=1.229, generator_dur_loss=1.83, generator_adv_loss=2.043, generator_feat_match_loss=3.287, over 100.00 samples.
+2024-03-13 02:32:37,976 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 02:34:26,954 INFO [train.py:919] (1/6) Start epoch 261
+2024-03-13 02:35:21,369 INFO [train.py:527] (1/6) Epoch 261, batch 10, global_batch_idx: 32250, batch size: 17, loss[discriminator_loss=2.713, discriminator_real_loss=1.285, discriminator_fake_loss=1.429, generator_loss=29.55, generator_mel_loss=19.86, generator_kl_loss=1.766, generator_dur_loss=1.673, generator_adv_loss=1.997, generator_feat_match_loss=4.252, over 17.00 samples.], tot_loss[discriminator_loss=2.775, discriminator_real_loss=1.414, discriminator_fake_loss=1.361, generator_loss=27.18, generator_mel_loss=18.34, generator_kl_loss=1.372, generator_dur_loss=1.781, generator_adv_loss=1.885, generator_feat_match_loss=3.805, over 638.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:37:37,274 INFO [train.py:527] (1/6) Epoch 261, batch 60, global_batch_idx: 32300, batch size: 58, loss[discriminator_loss=2.78, discriminator_real_loss=1.516, discriminator_fake_loss=1.264, generator_loss=26.01, generator_mel_loss=17.99, generator_kl_loss=1.256, generator_dur_loss=1.755, generator_adv_loss=1.995, generator_feat_match_loss=3.008, over 58.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=27.29, generator_mel_loss=18.34, generator_kl_loss=1.361, generator_dur_loss=1.768, generator_adv_loss=1.91, generator_feat_match_loss=3.919, over 3636.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:39:54,432 INFO [train.py:527] (1/6) Epoch 261, batch 110, global_batch_idx: 32350, batch size: 47, loss[discriminator_loss=2.695, discriminator_real_loss=1.339, discriminator_fake_loss=1.356, generator_loss=27.94, generator_mel_loss=18.77, generator_kl_loss=1.482, generator_dur_loss=1.696, generator_adv_loss=1.963, generator_feat_match_loss=4.032, over 47.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.391, discriminator_fake_loss=1.354, generator_loss=27.37, generator_mel_loss=18.38, generator_kl_loss=1.378, generator_dur_loss=1.764, generator_adv_loss=1.916, generator_feat_match_loss=3.928, over 6256.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:40:30,669 INFO [train.py:919] (1/6) Start epoch 262
+2024-03-13 02:42:32,853 INFO [train.py:527] (1/6) Epoch 262, batch 36, global_batch_idx: 32400, batch size: 62, loss[discriminator_loss=2.69, discriminator_real_loss=1.417, discriminator_fake_loss=1.273, generator_loss=27.88, generator_mel_loss=18.67, generator_kl_loss=1.372, generator_dur_loss=1.778, generator_adv_loss=1.929, generator_feat_match_loss=4.124, over 62.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.349, generator_loss=27.45, generator_mel_loss=18.43, generator_kl_loss=1.381, generator_dur_loss=1.756, generator_adv_loss=1.923, generator_feat_match_loss=3.962, over 2077.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:42:32,854 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 02:42:41,014 INFO [train.py:591] (1/6) Epoch 262, validation: discriminator_loss=2.761, discriminator_real_loss=1.36, discriminator_fake_loss=1.401, generator_loss=26.68, generator_mel_loss=18.58, generator_kl_loss=1.141, generator_dur_loss=1.828, generator_adv_loss=1.766, generator_feat_match_loss=3.362, over 100.00 samples.
+2024-03-13 02:42:41,014 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 02:45:03,109 INFO [train.py:527] (1/6) Epoch 262, batch 86, global_batch_idx: 32450, batch size: 83, loss[discriminator_loss=2.741, discriminator_real_loss=1.345, discriminator_fake_loss=1.396, generator_loss=27.02, generator_mel_loss=17.99, generator_kl_loss=1.312, generator_dur_loss=1.855, generator_adv_loss=2.022, generator_feat_match_loss=3.845, over 83.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.381, discriminator_fake_loss=1.345, generator_loss=27.41, generator_mel_loss=18.38, generator_kl_loss=1.379, generator_dur_loss=1.763, generator_adv_loss=1.937, generator_feat_match_loss=3.947, over 5087.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:46:47,951 INFO [train.py:919] (1/6) Start epoch 263
+2024-03-13 02:47:44,508 INFO [train.py:527] (1/6) Epoch 263, batch 12, global_batch_idx: 32500, batch size: 83, loss[discriminator_loss=2.741, discriminator_real_loss=1.4, discriminator_fake_loss=1.341, generator_loss=27.06, generator_mel_loss=18.4, generator_kl_loss=1.343, generator_dur_loss=1.804, generator_adv_loss=1.824, generator_feat_match_loss=3.691, over 83.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.361, discriminator_fake_loss=1.354, generator_loss=27.6, generator_mel_loss=18.44, generator_kl_loss=1.41, generator_dur_loss=1.738, generator_adv_loss=1.944, generator_feat_match_loss=4.074, over 702.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:50:03,538 INFO [train.py:527] (1/6) Epoch 263, batch 62, global_batch_idx: 32550, batch size: 44, loss[discriminator_loss=2.717, discriminator_real_loss=1.506, discriminator_fake_loss=1.211, generator_loss=27.88, generator_mel_loss=18.83, generator_kl_loss=1.531, generator_dur_loss=1.711, generator_adv_loss=1.829, generator_feat_match_loss=3.976, over 44.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.53, generator_mel_loss=18.45, generator_kl_loss=1.392, generator_dur_loss=1.755, generator_adv_loss=1.921, generator_feat_match_loss=4.018, over 3601.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:52:22,866 INFO [train.py:527] (1/6) Epoch 263, batch 112, global_batch_idx: 32600, batch size: 66, loss[discriminator_loss=2.772, discriminator_real_loss=1.52, discriminator_fake_loss=1.252, generator_loss=27.48, generator_mel_loss=18.36, generator_kl_loss=1.366, generator_dur_loss=1.788, generator_adv_loss=1.941, generator_feat_match_loss=4.031, over 66.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.384, discriminator_fake_loss=1.352, generator_loss=27.44, generator_mel_loss=18.39, generator_kl_loss=1.393, generator_dur_loss=1.766, generator_adv_loss=1.914, generator_feat_match_loss=3.977, over 6497.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:52:22,867 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 02:52:31,661 INFO [train.py:591] (1/6) Epoch 263, validation: discriminator_loss=2.759, discriminator_real_loss=1.445, discriminator_fake_loss=1.314, generator_loss=26.63, generator_mel_loss=18.66, generator_kl_loss=1.182, generator_dur_loss=1.837, generator_adv_loss=1.862, generator_feat_match_loss=3.083, over 100.00 samples.
+2024-03-13 02:52:31,662 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 02:53:03,926 INFO [train.py:919] (1/6) Start epoch 264
+2024-03-13 02:55:15,603 INFO [train.py:527] (1/6) Epoch 264, batch 38, global_batch_idx: 32650, batch size: 25, loss[discriminator_loss=2.712, discriminator_real_loss=1.358, discriminator_fake_loss=1.354, generator_loss=28.06, generator_mel_loss=18.69, generator_kl_loss=1.661, generator_dur_loss=1.569, generator_adv_loss=1.949, generator_feat_match_loss=4.188, over 25.00 samples.], tot_loss[discriminator_loss=2.75, discriminator_real_loss=1.388, discriminator_fake_loss=1.361, generator_loss=27.45, generator_mel_loss=18.48, generator_kl_loss=1.39, generator_dur_loss=1.759, generator_adv_loss=1.907, generator_feat_match_loss=3.921, over 2168.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:57:36,656 INFO [train.py:527] (1/6) Epoch 264, batch 88, global_batch_idx: 32700, batch size: 48, loss[discriminator_loss=2.714, discriminator_real_loss=1.403, discriminator_fake_loss=1.31, generator_loss=27.27, generator_mel_loss=18.72, generator_kl_loss=1.46, generator_dur_loss=1.69, generator_adv_loss=1.793, generator_feat_match_loss=3.609, over 48.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.395, discriminator_fake_loss=1.35, generator_loss=27.48, generator_mel_loss=18.46, generator_kl_loss=1.399, generator_dur_loss=1.746, generator_adv_loss=1.913, generator_feat_match_loss=3.963, over 4927.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 02:59:13,373 INFO [train.py:919] (1/6) Start epoch 265
+2024-03-13 03:00:17,347 INFO [train.py:527] (1/6) Epoch 265, batch 14, global_batch_idx: 32750, batch size: 66, loss[discriminator_loss=2.686, discriminator_real_loss=1.396, discriminator_fake_loss=1.291, generator_loss=27.23, generator_mel_loss=18.05, generator_kl_loss=1.234, generator_dur_loss=1.775, generator_adv_loss=1.952, generator_feat_match_loss=4.221, over 66.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.398, discriminator_fake_loss=1.341, generator_loss=27.59, generator_mel_loss=18.5, generator_kl_loss=1.387, generator_dur_loss=1.742, generator_adv_loss=1.895, generator_feat_match_loss=4.062, over 790.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 03:02:39,891 INFO [train.py:527] (1/6) Epoch 265, batch 64, global_batch_idx: 32800, batch size: 74, loss[discriminator_loss=2.745, discriminator_real_loss=1.37, discriminator_fake_loss=1.375, generator_loss=27.45, generator_mel_loss=18.33, generator_kl_loss=1.275, generator_dur_loss=1.846, generator_adv_loss=1.921, generator_feat_match_loss=4.082, over 74.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.46, generator_mel_loss=18.42, generator_kl_loss=1.384, generator_dur_loss=1.749, generator_adv_loss=1.913, generator_feat_match_loss=3.993, over 3678.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 03:02:39,893 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 03:02:48,052 INFO [train.py:591] (1/6) Epoch 265, validation: discriminator_loss=2.754, discriminator_real_loss=1.464, discriminator_fake_loss=1.291, generator_loss=26.78, generator_mel_loss=18.76, generator_kl_loss=1.124, generator_dur_loss=1.824, generator_adv_loss=1.835, generator_feat_match_loss=3.235, over 100.00 samples.
+2024-03-13 03:02:48,053 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 03:05:03,298 INFO [train.py:527] (1/6) Epoch 265, batch 114, global_batch_idx: 32850, batch size: 52, loss[discriminator_loss=2.691, discriminator_real_loss=1.37, discriminator_fake_loss=1.322, generator_loss=27.16, generator_mel_loss=18.05, generator_kl_loss=1.407, generator_dur_loss=1.677, generator_adv_loss=1.849, generator_feat_match_loss=4.183, over 52.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.48, generator_mel_loss=18.4, generator_kl_loss=1.385, generator_dur_loss=1.762, generator_adv_loss=1.919, generator_feat_match_loss=4.009, over 6717.00 samples.], cur_lr_g: 1.94e-04, cur_lr_d: 1.94e-04, 
+2024-03-13 03:05:29,255 INFO [train.py:919] (1/6) Start epoch 266
+2024-03-13 03:07:44,044 INFO [train.py:527] (1/6) Epoch 266, batch 40, global_batch_idx: 32900, batch size: 50, loss[discriminator_loss=2.617, discriminator_real_loss=1.338, discriminator_fake_loss=1.28, generator_loss=28.21, generator_mel_loss=18.72, generator_kl_loss=1.396, generator_dur_loss=1.732, generator_adv_loss=1.94, generator_feat_match_loss=4.417, over 50.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=27.5, generator_mel_loss=18.36, generator_kl_loss=1.377, generator_dur_loss=1.765, generator_adv_loss=1.921, generator_feat_match_loss=4.077, over 2510.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:10:05,512 INFO [train.py:527] (1/6) Epoch 266, batch 90, global_batch_idx: 32950, batch size: 58, loss[discriminator_loss=2.722, discriminator_real_loss=1.401, discriminator_fake_loss=1.321, generator_loss=28.78, generator_mel_loss=18.82, generator_kl_loss=1.564, generator_dur_loss=1.752, generator_adv_loss=2.067, generator_feat_match_loss=4.578, over 58.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.386, discriminator_fake_loss=1.348, generator_loss=27.5, generator_mel_loss=18.39, generator_kl_loss=1.383, generator_dur_loss=1.757, generator_adv_loss=1.931, generator_feat_match_loss=4.038, over 5256.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:11:36,122 INFO [train.py:919] (1/6) Start epoch 267
+2024-03-13 03:12:46,538 INFO [train.py:527] (1/6) Epoch 267, batch 16, global_batch_idx: 33000, batch size: 55, loss[discriminator_loss=2.64, discriminator_real_loss=1.27, discriminator_fake_loss=1.37, generator_loss=28.39, generator_mel_loss=18.75, generator_kl_loss=1.367, generator_dur_loss=1.705, generator_adv_loss=1.891, generator_feat_match_loss=4.68, over 55.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.366, discriminator_fake_loss=1.359, generator_loss=27.67, generator_mel_loss=18.47, generator_kl_loss=1.399, generator_dur_loss=1.766, generator_adv_loss=1.923, generator_feat_match_loss=4.108, over 991.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:12:46,539 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 03:12:54,395 INFO [train.py:591] (1/6) Epoch 267, validation: discriminator_loss=2.75, discriminator_real_loss=1.458, discriminator_fake_loss=1.292, generator_loss=26.14, generator_mel_loss=18.44, generator_kl_loss=1.151, generator_dur_loss=1.833, generator_adv_loss=1.903, generator_feat_match_loss=2.817, over 100.00 samples.
+2024-03-13 03:12:54,396 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 03:15:11,675 INFO [train.py:527] (1/6) Epoch 267, batch 66, global_batch_idx: 33050, batch size: 25, loss[discriminator_loss=2.72, discriminator_real_loss=1.338, discriminator_fake_loss=1.382, generator_loss=28.76, generator_mel_loss=19.65, generator_kl_loss=1.767, generator_dur_loss=1.523, generator_adv_loss=1.973, generator_feat_match_loss=3.851, over 25.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=27.59, generator_mel_loss=18.45, generator_kl_loss=1.402, generator_dur_loss=1.76, generator_adv_loss=1.92, generator_feat_match_loss=4.06, over 3738.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:17:28,105 INFO [train.py:527] (1/6) Epoch 267, batch 116, global_batch_idx: 33100, batch size: 45, loss[discriminator_loss=2.719, discriminator_real_loss=1.343, discriminator_fake_loss=1.376, generator_loss=28.7, generator_mel_loss=18.9, generator_kl_loss=1.669, generator_dur_loss=1.716, generator_adv_loss=1.894, generator_feat_match_loss=4.52, over 45.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.55, generator_mel_loss=18.41, generator_kl_loss=1.42, generator_dur_loss=1.756, generator_adv_loss=1.921, generator_feat_match_loss=4.038, over 6407.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:17:50,833 INFO [train.py:919] (1/6) Start epoch 268
+2024-03-13 03:20:12,497 INFO [train.py:527] (1/6) Epoch 268, batch 42, global_batch_idx: 33150, batch size: 58, loss[discriminator_loss=2.723, discriminator_real_loss=1.278, discriminator_fake_loss=1.446, generator_loss=27.67, generator_mel_loss=18.71, generator_kl_loss=1.242, generator_dur_loss=1.756, generator_adv_loss=2.057, generator_feat_match_loss=3.904, over 58.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=27.58, generator_mel_loss=18.41, generator_kl_loss=1.398, generator_dur_loss=1.757, generator_adv_loss=1.968, generator_feat_match_loss=4.056, over 2447.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:22:32,492 INFO [train.py:527] (1/6) Epoch 268, batch 92, global_batch_idx: 33200, batch size: 70, loss[discriminator_loss=2.746, discriminator_real_loss=1.47, discriminator_fake_loss=1.276, generator_loss=27.5, generator_mel_loss=18.43, generator_kl_loss=1.351, generator_dur_loss=1.841, generator_adv_loss=1.913, generator_feat_match_loss=3.971, over 70.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.381, discriminator_fake_loss=1.339, generator_loss=27.58, generator_mel_loss=18.39, generator_kl_loss=1.409, generator_dur_loss=1.766, generator_adv_loss=1.942, generator_feat_match_loss=4.069, over 5247.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:22:32,494 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 03:22:40,301 INFO [train.py:591] (1/6) Epoch 268, validation: discriminator_loss=2.728, discriminator_real_loss=1.388, discriminator_fake_loss=1.34, generator_loss=26.18, generator_mel_loss=18.21, generator_kl_loss=1.207, generator_dur_loss=1.82, generator_adv_loss=1.833, generator_feat_match_loss=3.11, over 100.00 samples.
+2024-03-13 03:22:40,301 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 03:24:07,631 INFO [train.py:919] (1/6) Start epoch 269
+2024-03-13 03:25:22,427 INFO [train.py:527] (1/6) Epoch 269, batch 18, global_batch_idx: 33250, batch size: 25, loss[discriminator_loss=2.739, discriminator_real_loss=1.374, discriminator_fake_loss=1.365, generator_loss=28.03, generator_mel_loss=18.55, generator_kl_loss=1.81, generator_dur_loss=1.534, generator_adv_loss=1.964, generator_feat_match_loss=4.163, over 25.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.371, discriminator_fake_loss=1.369, generator_loss=27.45, generator_mel_loss=18.44, generator_kl_loss=1.403, generator_dur_loss=1.765, generator_adv_loss=1.899, generator_feat_match_loss=3.939, over 1103.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:27:43,057 INFO [train.py:527] (1/6) Epoch 269, batch 68, global_batch_idx: 33300, batch size: 74, loss[discriminator_loss=2.724, discriminator_real_loss=1.368, discriminator_fake_loss=1.355, generator_loss=27.73, generator_mel_loss=18.45, generator_kl_loss=1.31, generator_dur_loss=1.852, generator_adv_loss=1.907, generator_feat_match_loss=4.207, over 74.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.392, discriminator_fake_loss=1.353, generator_loss=27.46, generator_mel_loss=18.37, generator_kl_loss=1.408, generator_dur_loss=1.762, generator_adv_loss=1.929, generator_feat_match_loss=3.991, over 3900.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:30:02,526 INFO [train.py:527] (1/6) Epoch 269, batch 118, global_batch_idx: 33350, batch size: 31, loss[discriminator_loss=2.781, discriminator_real_loss=1.416, discriminator_fake_loss=1.365, generator_loss=27.29, generator_mel_loss=18.27, generator_kl_loss=1.604, generator_dur_loss=1.664, generator_adv_loss=1.794, generator_feat_match_loss=3.952, over 31.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.388, discriminator_fake_loss=1.353, generator_loss=27.49, generator_mel_loss=18.39, generator_kl_loss=1.409, generator_dur_loss=1.762, generator_adv_loss=1.926, generator_feat_match_loss=3.998, over 6636.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:30:19,257 INFO [train.py:919] (1/6) Start epoch 270
+2024-03-13 03:32:45,218 INFO [train.py:527] (1/6) Epoch 270, batch 44, global_batch_idx: 33400, batch size: 72, loss[discriminator_loss=2.653, discriminator_real_loss=1.358, discriminator_fake_loss=1.295, generator_loss=28.21, generator_mel_loss=18.58, generator_kl_loss=1.445, generator_dur_loss=1.781, generator_adv_loss=2.01, generator_feat_match_loss=4.394, over 72.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.383, discriminator_fake_loss=1.353, generator_loss=27.51, generator_mel_loss=18.43, generator_kl_loss=1.407, generator_dur_loss=1.73, generator_adv_loss=1.912, generator_feat_match_loss=4.024, over 2515.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:32:45,219 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 03:32:53,133 INFO [train.py:591] (1/6) Epoch 270, validation: discriminator_loss=2.652, discriminator_real_loss=1.411, discriminator_fake_loss=1.241, generator_loss=26.71, generator_mel_loss=18.7, generator_kl_loss=1.252, generator_dur_loss=1.777, generator_adv_loss=1.922, generator_feat_match_loss=3.056, over 100.00 samples.
+2024-03-13 03:32:53,133 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 03:35:15,401 INFO [train.py:527] (1/6) Epoch 270, batch 94, global_batch_idx: 33450, batch size: 68, loss[discriminator_loss=2.819, discriminator_real_loss=1.392, discriminator_fake_loss=1.427, generator_loss=27.44, generator_mel_loss=18.33, generator_kl_loss=1.302, generator_dur_loss=1.813, generator_adv_loss=1.916, generator_feat_match_loss=4.086, over 68.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.381, discriminator_fake_loss=1.35, generator_loss=27.52, generator_mel_loss=18.4, generator_kl_loss=1.382, generator_dur_loss=1.75, generator_adv_loss=1.935, generator_feat_match_loss=4.057, over 5679.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:36:34,277 INFO [train.py:919] (1/6) Start epoch 271
+2024-03-13 03:37:54,952 INFO [train.py:527] (1/6) Epoch 271, batch 20, global_batch_idx: 33500, batch size: 61, loss[discriminator_loss=2.72, discriminator_real_loss=1.453, discriminator_fake_loss=1.267, generator_loss=27.52, generator_mel_loss=18.27, generator_kl_loss=1.439, generator_dur_loss=1.734, generator_adv_loss=1.968, generator_feat_match_loss=4.117, over 61.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.406, discriminator_fake_loss=1.335, generator_loss=27.2, generator_mel_loss=18.22, generator_kl_loss=1.414, generator_dur_loss=1.745, generator_adv_loss=1.941, generator_feat_match_loss=3.885, over 1275.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:40:09,442 INFO [train.py:527] (1/6) Epoch 271, batch 70, global_batch_idx: 33550, batch size: 45, loss[discriminator_loss=2.735, discriminator_real_loss=1.277, discriminator_fake_loss=1.458, generator_loss=27.86, generator_mel_loss=18.74, generator_kl_loss=1.511, generator_dur_loss=1.643, generator_adv_loss=1.981, generator_feat_match_loss=3.985, over 45.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.46, generator_mel_loss=18.41, generator_kl_loss=1.43, generator_dur_loss=1.734, generator_adv_loss=1.917, generator_feat_match_loss=3.965, over 3766.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:42:31,177 INFO [train.py:527] (1/6) Epoch 271, batch 120, global_batch_idx: 33600, batch size: 48, loss[discriminator_loss=2.806, discriminator_real_loss=1.324, discriminator_fake_loss=1.482, generator_loss=26.05, generator_mel_loss=17.54, generator_kl_loss=1.437, generator_dur_loss=1.681, generator_adv_loss=2.162, generator_feat_match_loss=3.222, over 48.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.386, discriminator_fake_loss=1.351, generator_loss=27.43, generator_mel_loss=18.37, generator_kl_loss=1.408, generator_dur_loss=1.738, generator_adv_loss=1.914, generator_feat_match_loss=3.996, over 6689.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:42:31,178 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 03:42:40,081 INFO [train.py:591] (1/6) Epoch 271, validation: discriminator_loss=2.862, discriminator_real_loss=1.643, discriminator_fake_loss=1.219, generator_loss=26.49, generator_mel_loss=18.31, generator_kl_loss=1.227, generator_dur_loss=1.8, generator_adv_loss=2.089, generator_feat_match_loss=3.065, over 100.00 samples.
+2024-03-13 03:42:40,082 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 03:42:49,570 INFO [train.py:919] (1/6) Start epoch 272
+2024-03-13 03:45:20,023 INFO [train.py:527] (1/6) Epoch 272, batch 46, global_batch_idx: 33650, batch size: 77, loss[discriminator_loss=2.751, discriminator_real_loss=1.499, discriminator_fake_loss=1.253, generator_loss=26.85, generator_mel_loss=18.11, generator_kl_loss=1.322, generator_dur_loss=1.876, generator_adv_loss=1.784, generator_feat_match_loss=3.766, over 77.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.381, discriminator_fake_loss=1.353, generator_loss=27.51, generator_mel_loss=18.38, generator_kl_loss=1.382, generator_dur_loss=1.786, generator_adv_loss=1.927, generator_feat_match_loss=4.033, over 2776.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:47:40,104 INFO [train.py:527] (1/6) Epoch 272, batch 96, global_batch_idx: 33700, batch size: 45, loss[discriminator_loss=2.668, discriminator_real_loss=1.298, discriminator_fake_loss=1.37, generator_loss=28.56, generator_mel_loss=19.13, generator_kl_loss=1.636, generator_dur_loss=1.7, generator_adv_loss=2.007, generator_feat_match_loss=4.088, over 45.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.378, discriminator_fake_loss=1.352, generator_loss=27.56, generator_mel_loss=18.38, generator_kl_loss=1.391, generator_dur_loss=1.79, generator_adv_loss=1.927, generator_feat_match_loss=4.067, over 5693.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:48:56,608 INFO [train.py:919] (1/6) Start epoch 273
+2024-03-13 03:50:21,944 INFO [train.py:527] (1/6) Epoch 273, batch 22, global_batch_idx: 33750, batch size: 56, loss[discriminator_loss=2.727, discriminator_real_loss=1.41, discriminator_fake_loss=1.317, generator_loss=28.44, generator_mel_loss=18.94, generator_kl_loss=1.397, generator_dur_loss=1.792, generator_adv_loss=1.852, generator_feat_match_loss=4.456, over 56.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.393, discriminator_fake_loss=1.331, generator_loss=27.74, generator_mel_loss=18.46, generator_kl_loss=1.419, generator_dur_loss=1.753, generator_adv_loss=1.934, generator_feat_match_loss=4.176, over 1201.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:52:43,491 INFO [train.py:527] (1/6) Epoch 273, batch 72, global_batch_idx: 33800, batch size: 62, loss[discriminator_loss=2.752, discriminator_real_loss=1.417, discriminator_fake_loss=1.334, generator_loss=27.49, generator_mel_loss=18.62, generator_kl_loss=1.411, generator_dur_loss=1.809, generator_adv_loss=1.779, generator_feat_match_loss=3.878, over 62.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=27.65, generator_mel_loss=18.47, generator_kl_loss=1.42, generator_dur_loss=1.764, generator_adv_loss=1.924, generator_feat_match_loss=4.07, over 3872.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:52:43,492 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 03:52:51,232 INFO [train.py:591] (1/6) Epoch 273, validation: discriminator_loss=2.808, discriminator_real_loss=1.398, discriminator_fake_loss=1.41, generator_loss=26.31, generator_mel_loss=18.41, generator_kl_loss=1.102, generator_dur_loss=1.851, generator_adv_loss=1.741, generator_feat_match_loss=3.2, over 100.00 samples.
+2024-03-13 03:52:51,233 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 03:55:07,367 INFO [train.py:527] (1/6) Epoch 273, batch 122, global_batch_idx: 33850, batch size: 72, loss[discriminator_loss=2.688, discriminator_real_loss=1.34, discriminator_fake_loss=1.348, generator_loss=26.9, generator_mel_loss=17.89, generator_kl_loss=1.188, generator_dur_loss=1.875, generator_adv_loss=1.872, generator_feat_match_loss=4.08, over 72.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.347, generator_loss=27.59, generator_mel_loss=18.44, generator_kl_loss=1.403, generator_dur_loss=1.771, generator_adv_loss=1.921, generator_feat_match_loss=4.048, over 6692.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 03:55:12,416 INFO [train.py:919] (1/6) Start epoch 274
+2024-03-13 03:57:46,960 INFO [train.py:527] (1/6) Epoch 274, batch 48, global_batch_idx: 33900, batch size: 74, loss[discriminator_loss=2.672, discriminator_real_loss=1.284, discriminator_fake_loss=1.388, generator_loss=27.76, generator_mel_loss=18.36, generator_kl_loss=1.254, generator_dur_loss=1.806, generator_adv_loss=1.973, generator_feat_match_loss=4.367, over 74.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.367, discriminator_fake_loss=1.353, generator_loss=27.52, generator_mel_loss=18.34, generator_kl_loss=1.351, generator_dur_loss=1.792, generator_adv_loss=1.937, generator_feat_match_loss=4.099, over 2895.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:00:05,802 INFO [train.py:527] (1/6) Epoch 274, batch 98, global_batch_idx: 33950, batch size: 70, loss[discriminator_loss=2.707, discriminator_real_loss=1.334, discriminator_fake_loss=1.373, generator_loss=27.23, generator_mel_loss=18.11, generator_kl_loss=1.261, generator_dur_loss=1.862, generator_adv_loss=1.895, generator_feat_match_loss=4.101, over 70.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.374, discriminator_fake_loss=1.352, generator_loss=27.51, generator_mel_loss=18.35, generator_kl_loss=1.369, generator_dur_loss=1.794, generator_adv_loss=1.933, generator_feat_match_loss=4.067, over 5845.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:01:18,152 INFO [train.py:919] (1/6) Start epoch 275
+2024-03-13 04:02:49,747 INFO [train.py:527] (1/6) Epoch 275, batch 24, global_batch_idx: 34000, batch size: 45, loss[discriminator_loss=2.805, discriminator_real_loss=1.374, discriminator_fake_loss=1.431, generator_loss=27.42, generator_mel_loss=18.4, generator_kl_loss=1.376, generator_dur_loss=1.719, generator_adv_loss=1.98, generator_feat_match_loss=3.941, over 45.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.37, discriminator_fake_loss=1.345, generator_loss=27.5, generator_mel_loss=18.36, generator_kl_loss=1.428, generator_dur_loss=1.741, generator_adv_loss=1.908, generator_feat_match_loss=4.057, over 1253.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:02:49,748 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 04:02:57,797 INFO [train.py:591] (1/6) Epoch 275, validation: discriminator_loss=2.829, discriminator_real_loss=1.515, discriminator_fake_loss=1.314, generator_loss=26.55, generator_mel_loss=18.36, generator_kl_loss=1.205, generator_dur_loss=1.815, generator_adv_loss=1.905, generator_feat_match_loss=3.262, over 100.00 samples.
+2024-03-13 04:02:57,798 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 04:05:15,987 INFO [train.py:527] (1/6) Epoch 275, batch 74, global_batch_idx: 34050, batch size: 83, loss[discriminator_loss=2.744, discriminator_real_loss=1.498, discriminator_fake_loss=1.246, generator_loss=27.48, generator_mel_loss=18.49, generator_kl_loss=1.321, generator_dur_loss=1.841, generator_adv_loss=1.878, generator_feat_match_loss=3.944, over 83.00 samples.], tot_loss[discriminator_loss=2.747, discriminator_real_loss=1.397, discriminator_fake_loss=1.35, generator_loss=27.52, generator_mel_loss=18.33, generator_kl_loss=1.389, generator_dur_loss=1.76, generator_adv_loss=1.97, generator_feat_match_loss=4.07, over 4095.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:07:31,633 INFO [train.py:919] (1/6) Start epoch 276
+2024-03-13 04:07:54,877 INFO [train.py:527] (1/6) Epoch 276, batch 0, global_batch_idx: 34100, batch size: 72, loss[discriminator_loss=2.696, discriminator_real_loss=1.408, discriminator_fake_loss=1.287, generator_loss=27.32, generator_mel_loss=18.35, generator_kl_loss=1.384, generator_dur_loss=1.809, generator_adv_loss=1.738, generator_feat_match_loss=4.037, over 72.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.408, discriminator_fake_loss=1.287, generator_loss=27.32, generator_mel_loss=18.35, generator_kl_loss=1.384, generator_dur_loss=1.809, generator_adv_loss=1.738, generator_feat_match_loss=4.037, over 72.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:10:12,710 INFO [train.py:527] (1/6) Epoch 276, batch 50, global_batch_idx: 34150, batch size: 77, loss[discriminator_loss=2.75, discriminator_real_loss=1.389, discriminator_fake_loss=1.362, generator_loss=27.16, generator_mel_loss=18.12, generator_kl_loss=1.454, generator_dur_loss=1.819, generator_adv_loss=1.699, generator_feat_match_loss=4.066, over 77.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.384, discriminator_fake_loss=1.351, generator_loss=27.61, generator_mel_loss=18.46, generator_kl_loss=1.396, generator_dur_loss=1.78, generator_adv_loss=1.91, generator_feat_match_loss=4.064, over 3000.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:12:31,957 INFO [train.py:527] (1/6) Epoch 276, batch 100, global_batch_idx: 34200, batch size: 56, loss[discriminator_loss=2.709, discriminator_real_loss=1.435, discriminator_fake_loss=1.274, generator_loss=27.54, generator_mel_loss=18.69, generator_kl_loss=1.31, generator_dur_loss=1.722, generator_adv_loss=1.987, generator_feat_match_loss=3.833, over 56.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=27.59, generator_mel_loss=18.44, generator_kl_loss=1.397, generator_dur_loss=1.777, generator_adv_loss=1.915, generator_feat_match_loss=4.063, over 5949.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:12:31,958 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 04:12:41,116 INFO [train.py:591] (1/6) Epoch 276, validation: discriminator_loss=2.776, discriminator_real_loss=1.46, discriminator_fake_loss=1.316, generator_loss=26.89, generator_mel_loss=18.73, generator_kl_loss=1.25, generator_dur_loss=1.828, generator_adv_loss=1.894, generator_feat_match_loss=3.185, over 100.00 samples.
+2024-03-13 04:12:41,117 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 04:13:45,532 INFO [train.py:919] (1/6) Start epoch 277
+2024-03-13 04:15:22,375 INFO [train.py:527] (1/6) Epoch 277, batch 26, global_batch_idx: 34250, batch size: 59, loss[discriminator_loss=2.697, discriminator_real_loss=1.388, discriminator_fake_loss=1.309, generator_loss=29.07, generator_mel_loss=19.08, generator_kl_loss=1.391, generator_dur_loss=1.718, generator_adv_loss=2.086, generator_feat_match_loss=4.799, over 59.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=27.32, generator_mel_loss=18.3, generator_kl_loss=1.403, generator_dur_loss=1.766, generator_adv_loss=1.913, generator_feat_match_loss=3.947, over 1501.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:17:41,362 INFO [train.py:527] (1/6) Epoch 277, batch 76, global_batch_idx: 34300, batch size: 66, loss[discriminator_loss=2.699, discriminator_real_loss=1.396, discriminator_fake_loss=1.303, generator_loss=27.83, generator_mel_loss=18.79, generator_kl_loss=1.444, generator_dur_loss=1.766, generator_adv_loss=1.824, generator_feat_match_loss=4.009, over 66.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.386, discriminator_fake_loss=1.355, generator_loss=27.36, generator_mel_loss=18.35, generator_kl_loss=1.399, generator_dur_loss=1.768, generator_adv_loss=1.905, generator_feat_match_loss=3.944, over 4404.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:19:55,731 INFO [train.py:919] (1/6) Start epoch 278
+2024-03-13 04:20:25,024 INFO [train.py:527] (1/6) Epoch 278, batch 2, global_batch_idx: 34350, batch size: 80, loss[discriminator_loss=2.778, discriminator_real_loss=1.391, discriminator_fake_loss=1.388, generator_loss=27.84, generator_mel_loss=18.57, generator_kl_loss=1.274, generator_dur_loss=1.807, generator_adv_loss=2.059, generator_feat_match_loss=4.136, over 80.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.381, discriminator_fake_loss=1.362, generator_loss=27.88, generator_mel_loss=18.61, generator_kl_loss=1.447, generator_dur_loss=1.793, generator_adv_loss=1.954, generator_feat_match_loss=4.073, over 193.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:22:44,498 INFO [train.py:527] (1/6) Epoch 278, batch 52, global_batch_idx: 34400, batch size: 25, loss[discriminator_loss=2.676, discriminator_real_loss=1.317, discriminator_fake_loss=1.359, generator_loss=27.36, generator_mel_loss=18.28, generator_kl_loss=1.595, generator_dur_loss=1.636, generator_adv_loss=1.905, generator_feat_match_loss=3.943, over 25.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.385, discriminator_fake_loss=1.357, generator_loss=27.55, generator_mel_loss=18.38, generator_kl_loss=1.36, generator_dur_loss=1.79, generator_adv_loss=1.959, generator_feat_match_loss=4.058, over 3240.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:22:44,500 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 04:22:52,579 INFO [train.py:591] (1/6) Epoch 278, validation: discriminator_loss=2.754, discriminator_real_loss=1.362, discriminator_fake_loss=1.392, generator_loss=26.71, generator_mel_loss=18.73, generator_kl_loss=1.163, generator_dur_loss=1.848, generator_adv_loss=1.733, generator_feat_match_loss=3.236, over 100.00 samples.
+2024-03-13 04:22:52,580 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 04:25:11,127 INFO [train.py:527] (1/6) Epoch 278, batch 102, global_batch_idx: 34450, batch size: 59, loss[discriminator_loss=2.775, discriminator_real_loss=1.356, discriminator_fake_loss=1.42, generator_loss=26.43, generator_mel_loss=17.63, generator_kl_loss=1.355, generator_dur_loss=1.811, generator_adv_loss=2.027, generator_feat_match_loss=3.607, over 59.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.386, discriminator_fake_loss=1.353, generator_loss=27.53, generator_mel_loss=18.37, generator_kl_loss=1.386, generator_dur_loss=1.781, generator_adv_loss=1.946, generator_feat_match_loss=4.042, over 5939.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:26:11,585 INFO [train.py:919] (1/6) Start epoch 279
+2024-03-13 04:27:52,031 INFO [train.py:527] (1/6) Epoch 279, batch 28, global_batch_idx: 34500, batch size: 56, loss[discriminator_loss=2.742, discriminator_real_loss=1.463, discriminator_fake_loss=1.279, generator_loss=27.54, generator_mel_loss=18.28, generator_kl_loss=1.367, generator_dur_loss=1.726, generator_adv_loss=1.978, generator_feat_match_loss=4.185, over 56.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.387, discriminator_fake_loss=1.342, generator_loss=27.63, generator_mel_loss=18.4, generator_kl_loss=1.38, generator_dur_loss=1.777, generator_adv_loss=1.948, generator_feat_match_loss=4.131, over 1690.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:30:10,930 INFO [train.py:527] (1/6) Epoch 279, batch 78, global_batch_idx: 34550, batch size: 16, loss[discriminator_loss=2.751, discriminator_real_loss=1.475, discriminator_fake_loss=1.277, generator_loss=29.71, generator_mel_loss=19.99, generator_kl_loss=1.829, generator_dur_loss=1.578, generator_adv_loss=2.025, generator_feat_match_loss=4.282, over 16.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.389, discriminator_fake_loss=1.34, generator_loss=27.45, generator_mel_loss=18.34, generator_kl_loss=1.379, generator_dur_loss=1.774, generator_adv_loss=1.937, generator_feat_match_loss=4.028, over 4730.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:32:16,404 INFO [train.py:919] (1/6) Start epoch 280
+2024-03-13 04:32:52,429 INFO [train.py:527] (1/6) Epoch 280, batch 4, global_batch_idx: 34600, batch size: 68, loss[discriminator_loss=2.761, discriminator_real_loss=1.417, discriminator_fake_loss=1.344, generator_loss=27.09, generator_mel_loss=18.28, generator_kl_loss=1.379, generator_dur_loss=1.814, generator_adv_loss=1.846, generator_feat_match_loss=3.774, over 68.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.373, discriminator_fake_loss=1.349, generator_loss=27.57, generator_mel_loss=18.49, generator_kl_loss=1.343, generator_dur_loss=1.756, generator_adv_loss=1.904, generator_feat_match_loss=4.072, over 305.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:32:52,431 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 04:33:00,124 INFO [train.py:591] (1/6) Epoch 280, validation: discriminator_loss=2.782, discriminator_real_loss=1.365, discriminator_fake_loss=1.417, generator_loss=26.63, generator_mel_loss=18.46, generator_kl_loss=1.26, generator_dur_loss=1.827, generator_adv_loss=1.759, generator_feat_match_loss=3.332, over 100.00 samples.
+2024-03-13 04:33:00,127 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 04:35:18,200 INFO [train.py:527] (1/6) Epoch 280, batch 54, global_batch_idx: 34650, batch size: 36, loss[discriminator_loss=2.805, discriminator_real_loss=1.376, discriminator_fake_loss=1.429, generator_loss=27.41, generator_mel_loss=18.33, generator_kl_loss=1.426, generator_dur_loss=1.734, generator_adv_loss=1.955, generator_feat_match_loss=3.967, over 36.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.386, discriminator_fake_loss=1.348, generator_loss=27.39, generator_mel_loss=18.28, generator_kl_loss=1.373, generator_dur_loss=1.762, generator_adv_loss=1.915, generator_feat_match_loss=4.058, over 3103.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:37:37,884 INFO [train.py:527] (1/6) Epoch 280, batch 104, global_batch_idx: 34700, batch size: 88, loss[discriminator_loss=2.749, discriminator_real_loss=1.356, discriminator_fake_loss=1.393, generator_loss=26.72, generator_mel_loss=18.07, generator_kl_loss=1.443, generator_dur_loss=1.849, generator_adv_loss=1.822, generator_feat_match_loss=3.532, over 88.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.48, generator_mel_loss=18.33, generator_kl_loss=1.387, generator_dur_loss=1.764, generator_adv_loss=1.922, generator_feat_match_loss=4.073, over 5829.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:38:30,708 INFO [train.py:919] (1/6) Start epoch 281
+2024-03-13 04:40:20,079 INFO [train.py:527] (1/6) Epoch 281, batch 30, global_batch_idx: 34750, batch size: 83, loss[discriminator_loss=2.7, discriminator_real_loss=1.391, discriminator_fake_loss=1.309, generator_loss=28.04, generator_mel_loss=18.68, generator_kl_loss=1.386, generator_dur_loss=1.805, generator_adv_loss=1.88, generator_feat_match_loss=4.295, over 83.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.346, generator_loss=27.41, generator_mel_loss=18.25, generator_kl_loss=1.394, generator_dur_loss=1.767, generator_adv_loss=1.916, generator_feat_match_loss=4.089, over 1889.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:42:40,476 INFO [train.py:527] (1/6) Epoch 281, batch 80, global_batch_idx: 34800, batch size: 44, loss[discriminator_loss=2.787, discriminator_real_loss=1.443, discriminator_fake_loss=1.343, generator_loss=27.12, generator_mel_loss=18.2, generator_kl_loss=1.53, generator_dur_loss=1.727, generator_adv_loss=1.876, generator_feat_match_loss=3.785, over 44.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=27.52, generator_mel_loss=18.33, generator_kl_loss=1.401, generator_dur_loss=1.775, generator_adv_loss=1.924, generator_feat_match_loss=4.089, over 4864.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:42:40,477 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 04:42:48,869 INFO [train.py:591] (1/6) Epoch 281, validation: discriminator_loss=2.807, discriminator_real_loss=1.512, discriminator_fake_loss=1.295, generator_loss=26.8, generator_mel_loss=18.6, generator_kl_loss=1.13, generator_dur_loss=1.832, generator_adv_loss=1.896, generator_feat_match_loss=3.337, over 100.00 samples.
+2024-03-13 04:42:48,870 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 04:44:47,423 INFO [train.py:919] (1/6) Start epoch 282
+2024-03-13 04:45:27,842 INFO [train.py:527] (1/6) Epoch 282, batch 6, global_batch_idx: 34850, batch size: 44, loss[discriminator_loss=2.625, discriminator_real_loss=1.273, discriminator_fake_loss=1.352, generator_loss=29.07, generator_mel_loss=18.87, generator_kl_loss=1.572, generator_dur_loss=1.671, generator_adv_loss=2.046, generator_feat_match_loss=4.902, over 44.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.385, discriminator_fake_loss=1.319, generator_loss=27.84, generator_mel_loss=18.47, generator_kl_loss=1.437, generator_dur_loss=1.743, generator_adv_loss=1.969, generator_feat_match_loss=4.222, over 418.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:47:45,430 INFO [train.py:527] (1/6) Epoch 282, batch 56, global_batch_idx: 34900, batch size: 61, loss[discriminator_loss=2.752, discriminator_real_loss=1.429, discriminator_fake_loss=1.324, generator_loss=27.82, generator_mel_loss=18.65, generator_kl_loss=1.45, generator_dur_loss=1.764, generator_adv_loss=1.956, generator_feat_match_loss=4.007, over 61.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.383, discriminator_fake_loss=1.339, generator_loss=27.67, generator_mel_loss=18.4, generator_kl_loss=1.386, generator_dur_loss=1.756, generator_adv_loss=1.937, generator_feat_match_loss=4.185, over 3395.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:50:06,611 INFO [train.py:527] (1/6) Epoch 282, batch 106, global_batch_idx: 34950, batch size: 56, loss[discriminator_loss=2.729, discriminator_real_loss=1.395, discriminator_fake_loss=1.334, generator_loss=26.6, generator_mel_loss=17.71, generator_kl_loss=1.291, generator_dur_loss=1.722, generator_adv_loss=1.811, generator_feat_match_loss=4.074, over 56.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.63, generator_mel_loss=18.37, generator_kl_loss=1.393, generator_dur_loss=1.762, generator_adv_loss=1.938, generator_feat_match_loss=4.161, over 6384.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:50:54,023 INFO [train.py:919] (1/6) Start epoch 283
+2024-03-13 04:52:47,991 INFO [train.py:527] (1/6) Epoch 283, batch 32, global_batch_idx: 35000, batch size: 74, loss[discriminator_loss=2.732, discriminator_real_loss=1.364, discriminator_fake_loss=1.368, generator_loss=27.56, generator_mel_loss=18.46, generator_kl_loss=1.386, generator_dur_loss=1.804, generator_adv_loss=1.871, generator_feat_match_loss=4.03, over 74.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.388, discriminator_fake_loss=1.35, generator_loss=27.62, generator_mel_loss=18.38, generator_kl_loss=1.366, generator_dur_loss=1.8, generator_adv_loss=1.929, generator_feat_match_loss=4.136, over 2136.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:52:47,993 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 04:52:56,062 INFO [train.py:591] (1/6) Epoch 283, validation: discriminator_loss=2.796, discriminator_real_loss=1.416, discriminator_fake_loss=1.38, generator_loss=26.57, generator_mel_loss=18.39, generator_kl_loss=1.123, generator_dur_loss=1.821, generator_adv_loss=1.839, generator_feat_match_loss=3.391, over 100.00 samples.
+2024-03-13 04:52:56,064 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 04:55:17,617 INFO [train.py:527] (1/6) Epoch 283, batch 82, global_batch_idx: 35050, batch size: 42, loss[discriminator_loss=2.742, discriminator_real_loss=1.385, discriminator_fake_loss=1.357, generator_loss=27.58, generator_mel_loss=18.43, generator_kl_loss=1.497, generator_dur_loss=1.645, generator_adv_loss=1.853, generator_feat_match_loss=4.151, over 42.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.382, discriminator_fake_loss=1.349, generator_loss=27.6, generator_mel_loss=18.38, generator_kl_loss=1.388, generator_dur_loss=1.775, generator_adv_loss=1.928, generator_feat_match_loss=4.132, over 4934.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 04:57:08,961 INFO [train.py:919] (1/6) Start epoch 284
+2024-03-13 04:57:56,596 INFO [train.py:527] (1/6) Epoch 284, batch 8, global_batch_idx: 35100, batch size: 88, loss[discriminator_loss=2.754, discriminator_real_loss=1.315, discriminator_fake_loss=1.439, generator_loss=27.26, generator_mel_loss=18.44, generator_kl_loss=1.312, generator_dur_loss=1.835, generator_adv_loss=1.958, generator_feat_match_loss=3.715, over 88.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.64, generator_mel_loss=18.51, generator_kl_loss=1.385, generator_dur_loss=1.783, generator_adv_loss=1.937, generator_feat_match_loss=4.022, over 601.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:00:13,739 INFO [train.py:527] (1/6) Epoch 284, batch 58, global_batch_idx: 35150, batch size: 44, loss[discriminator_loss=2.498, discriminator_real_loss=1.17, discriminator_fake_loss=1.328, generator_loss=29.38, generator_mel_loss=18.74, generator_kl_loss=1.428, generator_dur_loss=1.701, generator_adv_loss=2.415, generator_feat_match_loss=5.093, over 44.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.366, discriminator_fake_loss=1.353, generator_loss=27.9, generator_mel_loss=18.5, generator_kl_loss=1.399, generator_dur_loss=1.773, generator_adv_loss=1.965, generator_feat_match_loss=4.262, over 3546.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:02:32,487 INFO [train.py:527] (1/6) Epoch 284, batch 108, global_batch_idx: 35200, batch size: 31, loss[discriminator_loss=2.692, discriminator_real_loss=1.299, discriminator_fake_loss=1.393, generator_loss=27.5, generator_mel_loss=18.61, generator_kl_loss=1.379, generator_dur_loss=1.662, generator_adv_loss=2.004, generator_feat_match_loss=3.839, over 31.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.38, discriminator_fake_loss=1.346, generator_loss=27.67, generator_mel_loss=18.41, generator_kl_loss=1.392, generator_dur_loss=1.772, generator_adv_loss=1.962, generator_feat_match_loss=4.139, over 6599.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:02:32,488 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 05:02:41,050 INFO [train.py:591] (1/6) Epoch 284, validation: discriminator_loss=2.771, discriminator_real_loss=1.492, discriminator_fake_loss=1.28, generator_loss=25.9, generator_mel_loss=17.74, generator_kl_loss=1.18, generator_dur_loss=1.807, generator_adv_loss=1.92, generator_feat_match_loss=3.253, over 100.00 samples.
+2024-03-13 05:02:41,051 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 05:03:23,859 INFO [train.py:919] (1/6) Start epoch 285
+2024-03-13 05:05:22,062 INFO [train.py:527] (1/6) Epoch 285, batch 34, global_batch_idx: 35250, batch size: 83, loss[discriminator_loss=2.791, discriminator_real_loss=1.475, discriminator_fake_loss=1.316, generator_loss=27.18, generator_mel_loss=18.41, generator_kl_loss=1.309, generator_dur_loss=1.804, generator_adv_loss=1.81, generator_feat_match_loss=3.842, over 83.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.373, discriminator_fake_loss=1.365, generator_loss=27.5, generator_mel_loss=18.34, generator_kl_loss=1.412, generator_dur_loss=1.752, generator_adv_loss=1.913, generator_feat_match_loss=4.084, over 1937.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:07:39,956 INFO [train.py:527] (1/6) Epoch 285, batch 84, global_batch_idx: 35300, batch size: 72, loss[discriminator_loss=2.727, discriminator_real_loss=1.313, discriminator_fake_loss=1.414, generator_loss=26.97, generator_mel_loss=17.84, generator_kl_loss=1.281, generator_dur_loss=1.828, generator_adv_loss=1.844, generator_feat_match_loss=4.173, over 72.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.388, discriminator_fake_loss=1.355, generator_loss=27.5, generator_mel_loss=18.37, generator_kl_loss=1.398, generator_dur_loss=1.753, generator_adv_loss=1.918, generator_feat_match_loss=4.064, over 4777.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:09:32,930 INFO [train.py:919] (1/6) Start epoch 286
+2024-03-13 05:10:24,753 INFO [train.py:527] (1/6) Epoch 286, batch 10, global_batch_idx: 35350, batch size: 50, loss[discriminator_loss=2.764, discriminator_real_loss=1.521, discriminator_fake_loss=1.243, generator_loss=27.32, generator_mel_loss=18.31, generator_kl_loss=1.471, generator_dur_loss=1.697, generator_adv_loss=1.844, generator_feat_match_loss=3.993, over 50.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.381, discriminator_fake_loss=1.365, generator_loss=27.34, generator_mel_loss=18.26, generator_kl_loss=1.373, generator_dur_loss=1.814, generator_adv_loss=1.89, generator_feat_match_loss=3.998, over 746.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:12:44,928 INFO [train.py:527] (1/6) Epoch 286, batch 60, global_batch_idx: 35400, batch size: 74, loss[discriminator_loss=2.756, discriminator_real_loss=1.377, discriminator_fake_loss=1.379, generator_loss=27.23, generator_mel_loss=18, generator_kl_loss=1.365, generator_dur_loss=1.771, generator_adv_loss=1.965, generator_feat_match_loss=4.138, over 74.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.373, discriminator_fake_loss=1.363, generator_loss=27.58, generator_mel_loss=18.37, generator_kl_loss=1.388, generator_dur_loss=1.778, generator_adv_loss=1.927, generator_feat_match_loss=4.12, over 3501.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:12:44,929 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 05:12:52,912 INFO [train.py:591] (1/6) Epoch 286, validation: discriminator_loss=2.799, discriminator_real_loss=1.48, discriminator_fake_loss=1.319, generator_loss=26.04, generator_mel_loss=18.04, generator_kl_loss=1.163, generator_dur_loss=1.825, generator_adv_loss=1.883, generator_feat_match_loss=3.125, over 100.00 samples.
+2024-03-13 05:12:52,913 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 05:15:11,531 INFO [train.py:527] (1/6) Epoch 286, batch 110, global_batch_idx: 35450, batch size: 59, loss[discriminator_loss=2.749, discriminator_real_loss=1.357, discriminator_fake_loss=1.391, generator_loss=27.19, generator_mel_loss=18.12, generator_kl_loss=1.347, generator_dur_loss=1.739, generator_adv_loss=1.821, generator_feat_match_loss=4.163, over 59.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.375, discriminator_fake_loss=1.356, generator_loss=27.54, generator_mel_loss=18.35, generator_kl_loss=1.397, generator_dur_loss=1.766, generator_adv_loss=1.929, generator_feat_match_loss=4.095, over 6249.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:15:49,637 INFO [train.py:919] (1/6) Start epoch 287
+2024-03-13 05:17:54,314 INFO [train.py:527] (1/6) Epoch 287, batch 36, global_batch_idx: 35500, batch size: 47, loss[discriminator_loss=2.752, discriminator_real_loss=1.394, discriminator_fake_loss=1.358, generator_loss=26.72, generator_mel_loss=17.8, generator_kl_loss=1.522, generator_dur_loss=1.675, generator_adv_loss=1.817, generator_feat_match_loss=3.912, over 47.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.385, discriminator_fake_loss=1.348, generator_loss=27.33, generator_mel_loss=18.19, generator_kl_loss=1.4, generator_dur_loss=1.744, generator_adv_loss=1.92, generator_feat_match_loss=4.075, over 2195.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:20:15,401 INFO [train.py:527] (1/6) Epoch 287, batch 86, global_batch_idx: 35550, batch size: 45, loss[discriminator_loss=2.724, discriminator_real_loss=1.436, discriminator_fake_loss=1.289, generator_loss=26.75, generator_mel_loss=17.81, generator_kl_loss=1.355, generator_dur_loss=1.679, generator_adv_loss=2.011, generator_feat_match_loss=3.896, over 45.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.349, generator_loss=27.45, generator_mel_loss=18.27, generator_kl_loss=1.39, generator_dur_loss=1.768, generator_adv_loss=1.917, generator_feat_match_loss=4.103, over 5245.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:21:56,727 INFO [train.py:919] (1/6) Start epoch 288
+2024-03-13 05:22:55,025 INFO [train.py:527] (1/6) Epoch 288, batch 12, global_batch_idx: 35600, batch size: 42, loss[discriminator_loss=2.762, discriminator_real_loss=1.426, discriminator_fake_loss=1.336, generator_loss=27.37, generator_mel_loss=18.56, generator_kl_loss=1.431, generator_dur_loss=1.699, generator_adv_loss=2.086, generator_feat_match_loss=3.592, over 42.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=27.68, generator_mel_loss=18.49, generator_kl_loss=1.46, generator_dur_loss=1.723, generator_adv_loss=1.928, generator_feat_match_loss=4.078, over 632.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:22:55,028 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 05:23:03,117 INFO [train.py:591] (1/6) Epoch 288, validation: discriminator_loss=2.768, discriminator_real_loss=1.54, discriminator_fake_loss=1.228, generator_loss=27.05, generator_mel_loss=18.75, generator_kl_loss=1.322, generator_dur_loss=1.823, generator_adv_loss=2, generator_feat_match_loss=3.158, over 100.00 samples.
+2024-03-13 05:23:03,118 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 05:25:20,697 INFO [train.py:527] (1/6) Epoch 288, batch 62, global_batch_idx: 35650, batch size: 68, loss[discriminator_loss=2.753, discriminator_real_loss=1.435, discriminator_fake_loss=1.318, generator_loss=27.18, generator_mel_loss=18.14, generator_kl_loss=1.447, generator_dur_loss=1.83, generator_adv_loss=1.773, generator_feat_match_loss=3.993, over 68.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.52, generator_mel_loss=18.33, generator_kl_loss=1.409, generator_dur_loss=1.766, generator_adv_loss=1.912, generator_feat_match_loss=4.106, over 3543.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:27:39,600 INFO [train.py:527] (1/6) Epoch 288, batch 112, global_batch_idx: 35700, batch size: 96, loss[discriminator_loss=2.699, discriminator_real_loss=1.331, discriminator_fake_loss=1.368, generator_loss=27.49, generator_mel_loss=18.1, generator_kl_loss=1.413, generator_dur_loss=1.886, generator_adv_loss=1.875, generator_feat_match_loss=4.217, over 96.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=27.52, generator_mel_loss=18.34, generator_kl_loss=1.412, generator_dur_loss=1.762, generator_adv_loss=1.927, generator_feat_match_loss=4.087, over 6209.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:28:10,666 INFO [train.py:919] (1/6) Start epoch 289
+2024-03-13 05:30:22,432 INFO [train.py:527] (1/6) Epoch 289, batch 38, global_batch_idx: 35750, batch size: 68, loss[discriminator_loss=2.754, discriminator_real_loss=1.418, discriminator_fake_loss=1.336, generator_loss=27.29, generator_mel_loss=18.08, generator_kl_loss=1.417, generator_dur_loss=1.743, generator_adv_loss=1.82, generator_feat_match_loss=4.237, over 68.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.5, generator_mel_loss=18.3, generator_kl_loss=1.395, generator_dur_loss=1.779, generator_adv_loss=1.922, generator_feat_match_loss=4.107, over 2275.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:32:42,127 INFO [train.py:527] (1/6) Epoch 289, batch 88, global_batch_idx: 35800, batch size: 70, loss[discriminator_loss=2.752, discriminator_real_loss=1.323, discriminator_fake_loss=1.428, generator_loss=27.63, generator_mel_loss=18.19, generator_kl_loss=1.264, generator_dur_loss=1.868, generator_adv_loss=1.954, generator_feat_match_loss=4.362, over 70.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.384, discriminator_fake_loss=1.351, generator_loss=27.47, generator_mel_loss=18.27, generator_kl_loss=1.397, generator_dur_loss=1.775, generator_adv_loss=1.922, generator_feat_match_loss=4.101, over 5090.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:32:42,129 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 05:32:50,894 INFO [train.py:591] (1/6) Epoch 289, validation: discriminator_loss=2.816, discriminator_real_loss=1.512, discriminator_fake_loss=1.304, generator_loss=25.95, generator_mel_loss=18.07, generator_kl_loss=1.23, generator_dur_loss=1.839, generator_adv_loss=1.954, generator_feat_match_loss=2.858, over 100.00 samples.
+2024-03-13 05:32:50,895 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 05:34:27,282 INFO [train.py:919] (1/6) Start epoch 290
+2024-03-13 05:35:31,872 INFO [train.py:527] (1/6) Epoch 290, batch 14, global_batch_idx: 35850, batch size: 55, loss[discriminator_loss=2.716, discriminator_real_loss=1.435, discriminator_fake_loss=1.281, generator_loss=27.25, generator_mel_loss=18.34, generator_kl_loss=1.374, generator_dur_loss=1.671, generator_adv_loss=1.883, generator_feat_match_loss=3.982, over 55.00 samples.], tot_loss[discriminator_loss=2.762, discriminator_real_loss=1.394, discriminator_fake_loss=1.367, generator_loss=27.17, generator_mel_loss=18.26, generator_kl_loss=1.401, generator_dur_loss=1.738, generator_adv_loss=1.9, generator_feat_match_loss=3.87, over 708.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:37:53,327 INFO [train.py:527] (1/6) Epoch 290, batch 64, global_batch_idx: 35900, batch size: 83, loss[discriminator_loss=2.68, discriminator_real_loss=1.343, discriminator_fake_loss=1.337, generator_loss=27.61, generator_mel_loss=18.27, generator_kl_loss=1.387, generator_dur_loss=1.804, generator_adv_loss=1.804, generator_feat_match_loss=4.343, over 83.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.35, generator_loss=27.44, generator_mel_loss=18.26, generator_kl_loss=1.405, generator_dur_loss=1.753, generator_adv_loss=1.932, generator_feat_match_loss=4.087, over 3712.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:40:10,183 INFO [train.py:527] (1/6) Epoch 290, batch 114, global_batch_idx: 35950, batch size: 25, loss[discriminator_loss=2.724, discriminator_real_loss=1.491, discriminator_fake_loss=1.232, generator_loss=30.68, generator_mel_loss=19.95, generator_kl_loss=1.854, generator_dur_loss=1.604, generator_adv_loss=1.841, generator_feat_match_loss=5.435, over 25.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.383, discriminator_fake_loss=1.344, generator_loss=27.52, generator_mel_loss=18.31, generator_kl_loss=1.406, generator_dur_loss=1.755, generator_adv_loss=1.934, generator_feat_match_loss=4.109, over 6427.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:40:35,770 INFO [train.py:919] (1/6) Start epoch 291
+2024-03-13 05:42:51,856 INFO [train.py:527] (1/6) Epoch 291, batch 40, global_batch_idx: 36000, batch size: 59, loss[discriminator_loss=2.693, discriminator_real_loss=1.336, discriminator_fake_loss=1.357, generator_loss=27.39, generator_mel_loss=18.18, generator_kl_loss=1.303, generator_dur_loss=1.81, generator_adv_loss=1.879, generator_feat_match_loss=4.224, over 59.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.378, discriminator_fake_loss=1.335, generator_loss=27.76, generator_mel_loss=18.45, generator_kl_loss=1.406, generator_dur_loss=1.743, generator_adv_loss=1.94, generator_feat_match_loss=4.218, over 2294.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:42:51,857 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 05:42:59,874 INFO [train.py:591] (1/6) Epoch 291, validation: discriminator_loss=2.702, discriminator_real_loss=1.352, discriminator_fake_loss=1.351, generator_loss=26.77, generator_mel_loss=18.71, generator_kl_loss=1.263, generator_dur_loss=1.81, generator_adv_loss=1.784, generator_feat_match_loss=3.208, over 100.00 samples.
+2024-03-13 05:42:59,875 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 05:45:19,086 INFO [train.py:527] (1/6) Epoch 291, batch 90, global_batch_idx: 36050, batch size: 74, loss[discriminator_loss=2.73, discriminator_real_loss=1.359, discriminator_fake_loss=1.37, generator_loss=27.78, generator_mel_loss=18.68, generator_kl_loss=1.271, generator_dur_loss=1.81, generator_adv_loss=1.928, generator_feat_match_loss=4.087, over 74.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=27.68, generator_mel_loss=18.39, generator_kl_loss=1.394, generator_dur_loss=1.744, generator_adv_loss=1.923, generator_feat_match_loss=4.229, over 5236.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:46:50,714 INFO [train.py:919] (1/6) Start epoch 292
+2024-03-13 05:47:55,705 INFO [train.py:527] (1/6) Epoch 292, batch 16, global_batch_idx: 36100, batch size: 52, loss[discriminator_loss=2.761, discriminator_real_loss=1.363, discriminator_fake_loss=1.398, generator_loss=27.01, generator_mel_loss=17.97, generator_kl_loss=1.334, generator_dur_loss=1.688, generator_adv_loss=1.909, generator_feat_match_loss=4.112, over 52.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.354, discriminator_fake_loss=1.374, generator_loss=27.54, generator_mel_loss=18.33, generator_kl_loss=1.4, generator_dur_loss=1.737, generator_adv_loss=1.928, generator_feat_match_loss=4.145, over 996.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:50:13,884 INFO [train.py:527] (1/6) Epoch 292, batch 66, global_batch_idx: 36150, batch size: 74, loss[discriminator_loss=2.775, discriminator_real_loss=1.422, discriminator_fake_loss=1.353, generator_loss=26.98, generator_mel_loss=17.93, generator_kl_loss=1.401, generator_dur_loss=1.847, generator_adv_loss=1.859, generator_feat_match_loss=3.936, over 74.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=27.68, generator_mel_loss=18.35, generator_kl_loss=1.426, generator_dur_loss=1.736, generator_adv_loss=1.954, generator_feat_match_loss=4.205, over 3646.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:52:34,947 INFO [train.py:527] (1/6) Epoch 292, batch 116, global_batch_idx: 36200, batch size: 64, loss[discriminator_loss=2.682, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=27.76, generator_mel_loss=18.42, generator_kl_loss=1.211, generator_dur_loss=1.795, generator_adv_loss=1.902, generator_feat_match_loss=4.434, over 64.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.345, generator_loss=27.66, generator_mel_loss=18.36, generator_kl_loss=1.415, generator_dur_loss=1.747, generator_adv_loss=1.945, generator_feat_match_loss=4.195, over 6432.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:52:34,948 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 05:52:43,684 INFO [train.py:591] (1/6) Epoch 292, validation: discriminator_loss=2.741, discriminator_real_loss=1.413, discriminator_fake_loss=1.328, generator_loss=26.7, generator_mel_loss=18.29, generator_kl_loss=1.206, generator_dur_loss=1.825, generator_adv_loss=1.835, generator_feat_match_loss=3.55, over 100.00 samples.
+2024-03-13 05:52:43,684 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 05:53:04,242 INFO [train.py:919] (1/6) Start epoch 293
+2024-03-13 05:55:21,945 INFO [train.py:527] (1/6) Epoch 293, batch 42, global_batch_idx: 36250, batch size: 83, loss[discriminator_loss=2.762, discriminator_real_loss=1.316, discriminator_fake_loss=1.446, generator_loss=26.84, generator_mel_loss=18.08, generator_kl_loss=1.269, generator_dur_loss=1.844, generator_adv_loss=1.931, generator_feat_match_loss=3.719, over 83.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.58, generator_mel_loss=18.34, generator_kl_loss=1.422, generator_dur_loss=1.748, generator_adv_loss=1.917, generator_feat_match_loss=4.149, over 2439.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:57:42,456 INFO [train.py:527] (1/6) Epoch 293, batch 92, global_batch_idx: 36300, batch size: 70, loss[discriminator_loss=2.741, discriminator_real_loss=1.369, discriminator_fake_loss=1.372, generator_loss=27.08, generator_mel_loss=18.02, generator_kl_loss=1.167, generator_dur_loss=1.815, generator_adv_loss=1.976, generator_feat_match_loss=4.105, over 70.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.347, generator_loss=27.57, generator_mel_loss=18.34, generator_kl_loss=1.407, generator_dur_loss=1.754, generator_adv_loss=1.916, generator_feat_match_loss=4.153, over 5289.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 05:59:11,558 INFO [train.py:919] (1/6) Start epoch 294
+2024-03-13 06:00:24,393 INFO [train.py:527] (1/6) Epoch 294, batch 18, global_batch_idx: 36350, batch size: 45, loss[discriminator_loss=2.713, discriminator_real_loss=1.327, discriminator_fake_loss=1.387, generator_loss=28.01, generator_mel_loss=18.33, generator_kl_loss=1.413, generator_dur_loss=1.657, generator_adv_loss=1.998, generator_feat_match_loss=4.617, over 45.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.374, discriminator_fake_loss=1.359, generator_loss=27.41, generator_mel_loss=18.19, generator_kl_loss=1.455, generator_dur_loss=1.742, generator_adv_loss=1.904, generator_feat_match_loss=4.114, over 1064.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:02:44,229 INFO [train.py:527] (1/6) Epoch 294, batch 68, global_batch_idx: 36400, batch size: 60, loss[discriminator_loss=2.725, discriminator_real_loss=1.508, discriminator_fake_loss=1.217, generator_loss=27.16, generator_mel_loss=18.41, generator_kl_loss=1.289, generator_dur_loss=1.76, generator_adv_loss=1.984, generator_feat_match_loss=3.718, over 60.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.373, discriminator_fake_loss=1.351, generator_loss=27.62, generator_mel_loss=18.28, generator_kl_loss=1.42, generator_dur_loss=1.751, generator_adv_loss=1.961, generator_feat_match_loss=4.206, over 3968.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:02:44,230 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 06:02:52,556 INFO [train.py:591] (1/6) Epoch 294, validation: discriminator_loss=2.832, discriminator_real_loss=1.44, discriminator_fake_loss=1.392, generator_loss=27.03, generator_mel_loss=18.63, generator_kl_loss=1.28, generator_dur_loss=1.816, generator_adv_loss=1.795, generator_feat_match_loss=3.515, over 100.00 samples.
+2024-03-13 06:02:52,557 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 06:05:11,230 INFO [train.py:527] (1/6) Epoch 294, batch 118, global_batch_idx: 36450, batch size: 13, loss[discriminator_loss=2.699, discriminator_real_loss=1.453, discriminator_fake_loss=1.246, generator_loss=29.44, generator_mel_loss=19.83, generator_kl_loss=1.859, generator_dur_loss=1.534, generator_adv_loss=1.937, generator_feat_match_loss=4.286, over 13.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.376, discriminator_fake_loss=1.343, generator_loss=27.62, generator_mel_loss=18.29, generator_kl_loss=1.43, generator_dur_loss=1.746, generator_adv_loss=1.953, generator_feat_match_loss=4.201, over 6558.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:05:24,449 INFO [train.py:919] (1/6) Start epoch 295
+2024-03-13 06:07:52,701 INFO [train.py:527] (1/6) Epoch 295, batch 44, global_batch_idx: 36500, batch size: 96, loss[discriminator_loss=2.694, discriminator_real_loss=1.305, discriminator_fake_loss=1.389, generator_loss=27.12, generator_mel_loss=18.2, generator_kl_loss=1.197, generator_dur_loss=1.863, generator_adv_loss=1.962, generator_feat_match_loss=3.895, over 96.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=27.71, generator_mel_loss=18.43, generator_kl_loss=1.404, generator_dur_loss=1.751, generator_adv_loss=1.918, generator_feat_match_loss=4.205, over 2347.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:10:12,720 INFO [train.py:527] (1/6) Epoch 295, batch 94, global_batch_idx: 36550, batch size: 52, loss[discriminator_loss=2.666, discriminator_real_loss=1.31, discriminator_fake_loss=1.356, generator_loss=28.21, generator_mel_loss=18.26, generator_kl_loss=1.625, generator_dur_loss=1.657, generator_adv_loss=1.907, generator_feat_match_loss=4.755, over 52.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.375, discriminator_fake_loss=1.354, generator_loss=27.56, generator_mel_loss=18.33, generator_kl_loss=1.391, generator_dur_loss=1.77, generator_adv_loss=1.919, generator_feat_match_loss=4.15, over 5410.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:11:31,418 INFO [train.py:919] (1/6) Start epoch 296
+2024-03-13 06:12:52,828 INFO [train.py:527] (1/6) Epoch 296, batch 20, global_batch_idx: 36600, batch size: 18, loss[discriminator_loss=2.632, discriminator_real_loss=1.28, discriminator_fake_loss=1.352, generator_loss=28.19, generator_mel_loss=18.08, generator_kl_loss=1.757, generator_dur_loss=1.513, generator_adv_loss=1.982, generator_feat_match_loss=4.858, over 18.00 samples.], tot_loss[discriminator_loss=2.741, discriminator_real_loss=1.4, discriminator_fake_loss=1.341, generator_loss=27.64, generator_mel_loss=18.31, generator_kl_loss=1.447, generator_dur_loss=1.744, generator_adv_loss=1.906, generator_feat_match_loss=4.234, over 1088.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:12:52,830 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 06:13:00,870 INFO [train.py:591] (1/6) Epoch 296, validation: discriminator_loss=2.754, discriminator_real_loss=1.435, discriminator_fake_loss=1.319, generator_loss=26.94, generator_mel_loss=18.58, generator_kl_loss=1.185, generator_dur_loss=1.837, generator_adv_loss=1.825, generator_feat_match_loss=3.513, over 100.00 samples.
+2024-03-13 06:13:00,871 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 06:15:21,445 INFO [train.py:527] (1/6) Epoch 296, batch 70, global_batch_idx: 36650, batch size: 64, loss[discriminator_loss=2.756, discriminator_real_loss=1.451, discriminator_fake_loss=1.305, generator_loss=27.77, generator_mel_loss=18.61, generator_kl_loss=1.281, generator_dur_loss=1.778, generator_adv_loss=1.834, generator_feat_match_loss=4.269, over 64.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.389, discriminator_fake_loss=1.344, generator_loss=27.53, generator_mel_loss=18.28, generator_kl_loss=1.396, generator_dur_loss=1.771, generator_adv_loss=1.922, generator_feat_match_loss=4.155, over 4046.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:17:34,816 INFO [train.py:527] (1/6) Epoch 296, batch 120, global_batch_idx: 36700, batch size: 74, loss[discriminator_loss=2.736, discriminator_real_loss=1.341, discriminator_fake_loss=1.395, generator_loss=28.06, generator_mel_loss=18.72, generator_kl_loss=1.287, generator_dur_loss=1.817, generator_adv_loss=1.863, generator_feat_match_loss=4.38, over 74.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.52, generator_mel_loss=18.31, generator_kl_loss=1.391, generator_dur_loss=1.769, generator_adv_loss=1.922, generator_feat_match_loss=4.127, over 6892.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:17:45,241 INFO [train.py:919] (1/6) Start epoch 297
+2024-03-13 06:20:16,549 INFO [train.py:527] (1/6) Epoch 297, batch 46, global_batch_idx: 36750, batch size: 56, loss[discriminator_loss=2.759, discriminator_real_loss=1.457, discriminator_fake_loss=1.302, generator_loss=27.25, generator_mel_loss=18.23, generator_kl_loss=1.493, generator_dur_loss=1.688, generator_adv_loss=1.886, generator_feat_match_loss=3.955, over 56.00 samples.], tot_loss[discriminator_loss=2.745, discriminator_real_loss=1.393, discriminator_fake_loss=1.352, generator_loss=27.61, generator_mel_loss=18.37, generator_kl_loss=1.415, generator_dur_loss=1.75, generator_adv_loss=1.924, generator_feat_match_loss=4.147, over 2551.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:22:36,423 INFO [train.py:527] (1/6) Epoch 297, batch 96, global_batch_idx: 36800, batch size: 68, loss[discriminator_loss=2.75, discriminator_real_loss=1.366, discriminator_fake_loss=1.384, generator_loss=28.58, generator_mel_loss=18.76, generator_kl_loss=1.424, generator_dur_loss=1.796, generator_adv_loss=1.989, generator_feat_match_loss=4.613, over 68.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.38, discriminator_fake_loss=1.354, generator_loss=27.71, generator_mel_loss=18.39, generator_kl_loss=1.415, generator_dur_loss=1.77, generator_adv_loss=1.936, generator_feat_match_loss=4.203, over 5406.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:22:36,425 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 06:22:45,238 INFO [train.py:591] (1/6) Epoch 297, validation: discriminator_loss=2.785, discriminator_real_loss=1.471, discriminator_fake_loss=1.315, generator_loss=26.04, generator_mel_loss=18.26, generator_kl_loss=1.083, generator_dur_loss=1.846, generator_adv_loss=1.844, generator_feat_match_loss=3.012, over 100.00 samples.
+2024-03-13 06:22:45,239 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 06:24:01,065 INFO [train.py:919] (1/6) Start epoch 298
+2024-03-13 06:25:24,703 INFO [train.py:527] (1/6) Epoch 298, batch 22, global_batch_idx: 36850, batch size: 97, loss[discriminator_loss=2.711, discriminator_real_loss=1.343, discriminator_fake_loss=1.367, generator_loss=27.11, generator_mel_loss=18.23, generator_kl_loss=1.323, generator_dur_loss=1.869, generator_adv_loss=1.909, generator_feat_match_loss=3.778, over 97.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=27.66, generator_mel_loss=18.44, generator_kl_loss=1.385, generator_dur_loss=1.768, generator_adv_loss=1.922, generator_feat_match_loss=4.14, over 1290.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:27:44,461 INFO [train.py:527] (1/6) Epoch 298, batch 72, global_batch_idx: 36900, batch size: 96, loss[discriminator_loss=2.741, discriminator_real_loss=1.42, discriminator_fake_loss=1.321, generator_loss=27.72, generator_mel_loss=18.37, generator_kl_loss=1.302, generator_dur_loss=1.817, generator_adv_loss=1.808, generator_feat_match_loss=4.425, over 96.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.57, generator_mel_loss=18.33, generator_kl_loss=1.363, generator_dur_loss=1.779, generator_adv_loss=1.935, generator_feat_match_loss=4.165, over 4380.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:30:01,691 INFO [train.py:527] (1/6) Epoch 298, batch 122, global_batch_idx: 36950, batch size: 66, loss[discriminator_loss=2.784, discriminator_real_loss=1.414, discriminator_fake_loss=1.371, generator_loss=26.84, generator_mel_loss=18.32, generator_kl_loss=1.242, generator_dur_loss=1.812, generator_adv_loss=1.856, generator_feat_match_loss=3.603, over 66.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.347, generator_loss=27.61, generator_mel_loss=18.35, generator_kl_loss=1.383, generator_dur_loss=1.774, generator_adv_loss=1.928, generator_feat_match_loss=4.176, over 7210.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:30:07,169 INFO [train.py:919] (1/6) Start epoch 299
+2024-03-13 06:32:45,596 INFO [train.py:527] (1/6) Epoch 299, batch 48, global_batch_idx: 37000, batch size: 53, loss[discriminator_loss=2.691, discriminator_real_loss=1.401, discriminator_fake_loss=1.29, generator_loss=28.21, generator_mel_loss=18.55, generator_kl_loss=1.42, generator_dur_loss=1.697, generator_adv_loss=1.97, generator_feat_match_loss=4.578, over 53.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.347, generator_loss=27.67, generator_mel_loss=18.38, generator_kl_loss=1.414, generator_dur_loss=1.763, generator_adv_loss=1.937, generator_feat_match_loss=4.18, over 2876.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:32:45,597 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 06:32:53,799 INFO [train.py:591] (1/6) Epoch 299, validation: discriminator_loss=2.772, discriminator_real_loss=1.425, discriminator_fake_loss=1.347, generator_loss=26.89, generator_mel_loss=18.68, generator_kl_loss=1.164, generator_dur_loss=1.826, generator_adv_loss=1.825, generator_feat_match_loss=3.392, over 100.00 samples.
+2024-03-13 06:32:53,800 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 06:35:15,210 INFO [train.py:527] (1/6) Epoch 299, batch 98, global_batch_idx: 37050, batch size: 83, loss[discriminator_loss=2.702, discriminator_real_loss=1.438, discriminator_fake_loss=1.264, generator_loss=27.5, generator_mel_loss=18.47, generator_kl_loss=1.286, generator_dur_loss=1.836, generator_adv_loss=1.925, generator_feat_match_loss=3.977, over 83.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.381, discriminator_fake_loss=1.344, generator_loss=27.6, generator_mel_loss=18.31, generator_kl_loss=1.404, generator_dur_loss=1.762, generator_adv_loss=1.932, generator_feat_match_loss=4.198, over 5830.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:36:23,894 INFO [train.py:919] (1/6) Start epoch 300
+2024-03-13 06:37:53,686 INFO [train.py:527] (1/6) Epoch 300, batch 24, global_batch_idx: 37100, batch size: 83, loss[discriminator_loss=2.708, discriminator_real_loss=1.372, discriminator_fake_loss=1.336, generator_loss=27.07, generator_mel_loss=18.15, generator_kl_loss=1.335, generator_dur_loss=1.852, generator_adv_loss=1.937, generator_feat_match_loss=3.791, over 83.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.373, discriminator_fake_loss=1.345, generator_loss=27.78, generator_mel_loss=18.46, generator_kl_loss=1.38, generator_dur_loss=1.763, generator_adv_loss=1.976, generator_feat_match_loss=4.207, over 1445.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:40:10,470 INFO [train.py:527] (1/6) Epoch 300, batch 74, global_batch_idx: 37150, batch size: 96, loss[discriminator_loss=2.797, discriminator_real_loss=1.458, discriminator_fake_loss=1.339, generator_loss=27.53, generator_mel_loss=18.34, generator_kl_loss=1.186, generator_dur_loss=1.828, generator_adv_loss=1.665, generator_feat_match_loss=4.506, over 96.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.382, discriminator_fake_loss=1.338, generator_loss=27.71, generator_mel_loss=18.38, generator_kl_loss=1.377, generator_dur_loss=1.758, generator_adv_loss=1.944, generator_feat_match_loss=4.251, over 4368.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:42:27,542 INFO [train.py:919] (1/6) Start epoch 301
+2024-03-13 06:42:51,355 INFO [train.py:527] (1/6) Epoch 301, batch 0, global_batch_idx: 37200, batch size: 59, loss[discriminator_loss=2.722, discriminator_real_loss=1.477, discriminator_fake_loss=1.244, generator_loss=27.47, generator_mel_loss=18.28, generator_kl_loss=1.394, generator_dur_loss=1.798, generator_adv_loss=2.054, generator_feat_match_loss=3.937, over 59.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.477, discriminator_fake_loss=1.244, generator_loss=27.47, generator_mel_loss=18.28, generator_kl_loss=1.394, generator_dur_loss=1.798, generator_adv_loss=2.054, generator_feat_match_loss=3.937, over 59.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:42:51,358 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 06:42:59,305 INFO [train.py:591] (1/6) Epoch 301, validation: discriminator_loss=2.769, discriminator_real_loss=1.55, discriminator_fake_loss=1.219, generator_loss=26.62, generator_mel_loss=18.4, generator_kl_loss=1.283, generator_dur_loss=1.834, generator_adv_loss=1.981, generator_feat_match_loss=3.116, over 100.00 samples.
+2024-03-13 06:42:59,307 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 06:45:19,230 INFO [train.py:527] (1/6) Epoch 301, batch 50, global_batch_idx: 37250, batch size: 70, loss[discriminator_loss=2.731, discriminator_real_loss=1.419, discriminator_fake_loss=1.312, generator_loss=26.91, generator_mel_loss=17.85, generator_kl_loss=1.418, generator_dur_loss=1.829, generator_adv_loss=1.923, generator_feat_match_loss=3.89, over 70.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.379, discriminator_fake_loss=1.342, generator_loss=27.6, generator_mel_loss=18.24, generator_kl_loss=1.415, generator_dur_loss=1.771, generator_adv_loss=1.94, generator_feat_match_loss=4.232, over 2840.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:47:38,489 INFO [train.py:527] (1/6) Epoch 301, batch 100, global_batch_idx: 37300, batch size: 44, loss[discriminator_loss=2.773, discriminator_real_loss=1.376, discriminator_fake_loss=1.397, generator_loss=29.23, generator_mel_loss=19.02, generator_kl_loss=1.639, generator_dur_loss=1.709, generator_adv_loss=1.986, generator_feat_match_loss=4.881, over 44.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.382, discriminator_fake_loss=1.344, generator_loss=27.6, generator_mel_loss=18.27, generator_kl_loss=1.418, generator_dur_loss=1.767, generator_adv_loss=1.93, generator_feat_match_loss=4.22, over 5724.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:48:41,513 INFO [train.py:919] (1/6) Start epoch 302
+2024-03-13 06:50:16,580 INFO [train.py:527] (1/6) Epoch 302, batch 26, global_batch_idx: 37350, batch size: 83, loss[discriminator_loss=2.762, discriminator_real_loss=1.41, discriminator_fake_loss=1.351, generator_loss=26.84, generator_mel_loss=17.63, generator_kl_loss=1.258, generator_dur_loss=1.821, generator_adv_loss=1.848, generator_feat_match_loss=4.282, over 83.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.345, generator_loss=27.53, generator_mel_loss=18.28, generator_kl_loss=1.406, generator_dur_loss=1.782, generator_adv_loss=1.93, generator_feat_match_loss=4.138, over 1755.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:52:36,073 INFO [train.py:527] (1/6) Epoch 302, batch 76, global_batch_idx: 37400, batch size: 16, loss[discriminator_loss=2.567, discriminator_real_loss=1.366, discriminator_fake_loss=1.201, generator_loss=28.34, generator_mel_loss=18.39, generator_kl_loss=1.706, generator_dur_loss=1.572, generator_adv_loss=1.867, generator_feat_match_loss=4.802, over 16.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.379, discriminator_fake_loss=1.351, generator_loss=27.55, generator_mel_loss=18.26, generator_kl_loss=1.397, generator_dur_loss=1.773, generator_adv_loss=1.928, generator_feat_match_loss=4.193, over 4566.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:52:36,074 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 06:52:44,011 INFO [train.py:591] (1/6) Epoch 302, validation: discriminator_loss=2.744, discriminator_real_loss=1.354, discriminator_fake_loss=1.39, generator_loss=26.66, generator_mel_loss=18.47, generator_kl_loss=1.251, generator_dur_loss=1.85, generator_adv_loss=1.778, generator_feat_match_loss=3.304, over 100.00 samples.
+2024-03-13 06:52:44,012 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 06:54:55,280 INFO [train.py:919] (1/6) Start epoch 303
+2024-03-13 06:55:25,358 INFO [train.py:527] (1/6) Epoch 303, batch 2, global_batch_idx: 37450, batch size: 31, loss[discriminator_loss=2.964, discriminator_real_loss=1.274, discriminator_fake_loss=1.69, generator_loss=27.24, generator_mel_loss=18.34, generator_kl_loss=1.536, generator_dur_loss=1.627, generator_adv_loss=2.013, generator_feat_match_loss=3.729, over 31.00 samples.], tot_loss[discriminator_loss=2.892, discriminator_real_loss=1.476, discriminator_fake_loss=1.416, generator_loss=27.85, generator_mel_loss=18.47, generator_kl_loss=1.484, generator_dur_loss=1.697, generator_adv_loss=2.014, generator_feat_match_loss=4.193, over 132.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 06:57:42,809 INFO [train.py:527] (1/6) Epoch 303, batch 52, global_batch_idx: 37500, batch size: 70, loss[discriminator_loss=2.799, discriminator_real_loss=1.44, discriminator_fake_loss=1.358, generator_loss=27.4, generator_mel_loss=17.81, generator_kl_loss=1.374, generator_dur_loss=1.832, generator_adv_loss=2.16, generator_feat_match_loss=4.225, over 70.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.396, discriminator_fake_loss=1.344, generator_loss=27.42, generator_mel_loss=18.2, generator_kl_loss=1.401, generator_dur_loss=1.748, generator_adv_loss=1.935, generator_feat_match_loss=4.136, over 2957.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:00:01,168 INFO [train.py:527] (1/6) Epoch 303, batch 102, global_batch_idx: 37550, batch size: 47, loss[discriminator_loss=2.697, discriminator_real_loss=1.376, discriminator_fake_loss=1.321, generator_loss=27.54, generator_mel_loss=18.48, generator_kl_loss=1.497, generator_dur_loss=1.698, generator_adv_loss=1.83, generator_feat_match_loss=4.033, over 47.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.391, discriminator_fake_loss=1.344, generator_loss=27.5, generator_mel_loss=18.26, generator_kl_loss=1.388, generator_dur_loss=1.766, generator_adv_loss=1.945, generator_feat_match_loss=4.139, over 5961.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:01:00,721 INFO [train.py:919] (1/6) Start epoch 304
+2024-03-13 07:02:43,391 INFO [train.py:527] (1/6) Epoch 304, batch 28, global_batch_idx: 37600, batch size: 25, loss[discriminator_loss=2.746, discriminator_real_loss=1.315, discriminator_fake_loss=1.431, generator_loss=28.8, generator_mel_loss=18.91, generator_kl_loss=1.653, generator_dur_loss=1.593, generator_adv_loss=2.208, generator_feat_match_loss=4.432, over 25.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.389, discriminator_fake_loss=1.335, generator_loss=27.52, generator_mel_loss=18.27, generator_kl_loss=1.34, generator_dur_loss=1.793, generator_adv_loss=1.924, generator_feat_match_loss=4.19, over 1822.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:02:43,392 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 07:02:51,457 INFO [train.py:591] (1/6) Epoch 304, validation: discriminator_loss=2.808, discriminator_real_loss=1.577, discriminator_fake_loss=1.231, generator_loss=27.06, generator_mel_loss=18.74, generator_kl_loss=1.14, generator_dur_loss=1.848, generator_adv_loss=2.087, generator_feat_match_loss=3.25, over 100.00 samples.
+2024-03-13 07:02:51,458 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 07:05:09,878 INFO [train.py:527] (1/6) Epoch 304, batch 78, global_batch_idx: 37650, batch size: 80, loss[discriminator_loss=2.768, discriminator_real_loss=1.518, discriminator_fake_loss=1.25, generator_loss=26.96, generator_mel_loss=17.99, generator_kl_loss=1.322, generator_dur_loss=1.782, generator_adv_loss=1.839, generator_feat_match_loss=4.023, over 80.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.389, discriminator_fake_loss=1.339, generator_loss=27.56, generator_mel_loss=18.3, generator_kl_loss=1.374, generator_dur_loss=1.779, generator_adv_loss=1.92, generator_feat_match_loss=4.187, over 4623.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:07:11,418 INFO [train.py:919] (1/6) Start epoch 305
+2024-03-13 07:07:47,425 INFO [train.py:527] (1/6) Epoch 305, batch 4, global_batch_idx: 37700, batch size: 64, loss[discriminator_loss=2.768, discriminator_real_loss=1.327, discriminator_fake_loss=1.441, generator_loss=27.21, generator_mel_loss=18.28, generator_kl_loss=1.356, generator_dur_loss=1.753, generator_adv_loss=1.825, generator_feat_match_loss=3.988, over 64.00 samples.], tot_loss[discriminator_loss=2.769, discriminator_real_loss=1.386, discriminator_fake_loss=1.383, generator_loss=27.49, generator_mel_loss=18.38, generator_kl_loss=1.336, generator_dur_loss=1.762, generator_adv_loss=1.847, generator_feat_match_loss=4.169, over 290.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:10:05,798 INFO [train.py:527] (1/6) Epoch 305, batch 54, global_batch_idx: 37750, batch size: 74, loss[discriminator_loss=2.715, discriminator_real_loss=1.456, discriminator_fake_loss=1.259, generator_loss=27.66, generator_mel_loss=18.64, generator_kl_loss=1.294, generator_dur_loss=1.863, generator_adv_loss=1.747, generator_feat_match_loss=4.113, over 74.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.394, discriminator_fake_loss=1.338, generator_loss=27.54, generator_mel_loss=18.29, generator_kl_loss=1.4, generator_dur_loss=1.764, generator_adv_loss=1.925, generator_feat_match_loss=4.164, over 2952.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:12:24,867 INFO [train.py:527] (1/6) Epoch 305, batch 104, global_batch_idx: 37800, batch size: 66, loss[discriminator_loss=2.805, discriminator_real_loss=1.422, discriminator_fake_loss=1.382, generator_loss=26.6, generator_mel_loss=18.04, generator_kl_loss=1.329, generator_dur_loss=1.822, generator_adv_loss=1.889, generator_feat_match_loss=3.516, over 66.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.58, generator_mel_loss=18.33, generator_kl_loss=1.387, generator_dur_loss=1.769, generator_adv_loss=1.928, generator_feat_match_loss=4.159, over 5749.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:12:24,868 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 07:12:33,607 INFO [train.py:591] (1/6) Epoch 305, validation: discriminator_loss=2.747, discriminator_real_loss=1.47, discriminator_fake_loss=1.277, generator_loss=26.84, generator_mel_loss=18.33, generator_kl_loss=1.252, generator_dur_loss=1.839, generator_adv_loss=1.93, generator_feat_match_loss=3.486, over 100.00 samples.
+2024-03-13 07:12:33,607 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 07:13:27,655 INFO [train.py:919] (1/6) Start epoch 306
+2024-03-13 07:15:14,963 INFO [train.py:527] (1/6) Epoch 306, batch 30, global_batch_idx: 37850, batch size: 25, loss[discriminator_loss=2.714, discriminator_real_loss=1.21, discriminator_fake_loss=1.504, generator_loss=28.34, generator_mel_loss=19.4, generator_kl_loss=1.669, generator_dur_loss=1.573, generator_adv_loss=1.903, generator_feat_match_loss=3.791, over 25.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.382, discriminator_fake_loss=1.353, generator_loss=27.52, generator_mel_loss=18.23, generator_kl_loss=1.401, generator_dur_loss=1.777, generator_adv_loss=1.937, generator_feat_match_loss=4.173, over 1776.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:17:33,456 INFO [train.py:527] (1/6) Epoch 306, batch 80, global_batch_idx: 37900, batch size: 45, loss[discriminator_loss=2.698, discriminator_real_loss=1.316, discriminator_fake_loss=1.382, generator_loss=28.38, generator_mel_loss=18.52, generator_kl_loss=1.46, generator_dur_loss=1.731, generator_adv_loss=2.026, generator_feat_match_loss=4.647, over 45.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.393, discriminator_fake_loss=1.341, generator_loss=27.72, generator_mel_loss=18.29, generator_kl_loss=1.39, generator_dur_loss=1.777, generator_adv_loss=1.981, generator_feat_match_loss=4.289, over 4636.00 samples.], cur_lr_g: 1.93e-04, cur_lr_d: 1.93e-04, 
+2024-03-13 07:19:33,416 INFO [train.py:919] (1/6) Start epoch 307
+2024-03-13 07:20:14,548 INFO [train.py:527] (1/6) Epoch 307, batch 6, global_batch_idx: 37950, batch size: 88, loss[discriminator_loss=2.725, discriminator_real_loss=1.277, discriminator_fake_loss=1.447, generator_loss=27.85, generator_mel_loss=18.38, generator_kl_loss=1.244, generator_dur_loss=1.87, generator_adv_loss=1.892, generator_feat_match_loss=4.456, over 88.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.381, discriminator_fake_loss=1.372, generator_loss=27.65, generator_mel_loss=18.4, generator_kl_loss=1.333, generator_dur_loss=1.768, generator_adv_loss=1.916, generator_feat_match_loss=4.234, over 424.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:22:34,406 INFO [train.py:527] (1/6) Epoch 307, batch 56, global_batch_idx: 38000, batch size: 31, loss[discriminator_loss=2.65, discriminator_real_loss=1.349, discriminator_fake_loss=1.301, generator_loss=27.38, generator_mel_loss=18.43, generator_kl_loss=1.441, generator_dur_loss=1.698, generator_adv_loss=1.825, generator_feat_match_loss=3.984, over 31.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.381, discriminator_fake_loss=1.354, generator_loss=27.45, generator_mel_loss=18.22, generator_kl_loss=1.353, generator_dur_loss=1.782, generator_adv_loss=1.927, generator_feat_match_loss=4.17, over 3405.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:22:34,408 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 07:22:42,559 INFO [train.py:591] (1/6) Epoch 307, validation: discriminator_loss=2.697, discriminator_real_loss=1.295, discriminator_fake_loss=1.402, generator_loss=26.5, generator_mel_loss=18.36, generator_kl_loss=1.187, generator_dur_loss=1.84, generator_adv_loss=1.76, generator_feat_match_loss=3.359, over 100.00 samples.
+2024-03-13 07:22:42,560 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 07:25:00,881 INFO [train.py:527] (1/6) Epoch 307, batch 106, global_batch_idx: 38050, batch size: 80, loss[discriminator_loss=2.716, discriminator_real_loss=1.333, discriminator_fake_loss=1.383, generator_loss=26.72, generator_mel_loss=17.69, generator_kl_loss=1.194, generator_dur_loss=1.801, generator_adv_loss=2.064, generator_feat_match_loss=3.961, over 80.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.352, generator_loss=27.54, generator_mel_loss=18.23, generator_kl_loss=1.374, generator_dur_loss=1.773, generator_adv_loss=1.933, generator_feat_match_loss=4.223, over 6099.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:25:48,094 INFO [train.py:919] (1/6) Start epoch 308
+2024-03-13 07:27:39,205 INFO [train.py:527] (1/6) Epoch 308, batch 32, global_batch_idx: 38100, batch size: 68, loss[discriminator_loss=2.756, discriminator_real_loss=1.42, discriminator_fake_loss=1.335, generator_loss=26.78, generator_mel_loss=17.88, generator_kl_loss=1.338, generator_dur_loss=1.778, generator_adv_loss=1.809, generator_feat_match_loss=3.97, over 68.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.383, discriminator_fake_loss=1.363, generator_loss=27.53, generator_mel_loss=18.26, generator_kl_loss=1.356, generator_dur_loss=1.777, generator_adv_loss=1.923, generator_feat_match_loss=4.216, over 2036.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:29:59,258 INFO [train.py:527] (1/6) Epoch 308, batch 82, global_batch_idx: 38150, batch size: 36, loss[discriminator_loss=2.6, discriminator_real_loss=1.313, discriminator_fake_loss=1.287, generator_loss=29.98, generator_mel_loss=19.21, generator_kl_loss=1.679, generator_dur_loss=1.7, generator_adv_loss=2.022, generator_feat_match_loss=5.367, over 36.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.38, discriminator_fake_loss=1.354, generator_loss=27.54, generator_mel_loss=18.25, generator_kl_loss=1.38, generator_dur_loss=1.78, generator_adv_loss=1.924, generator_feat_match_loss=4.205, over 4973.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:31:53,253 INFO [train.py:919] (1/6) Start epoch 309
+2024-03-13 07:32:39,916 INFO [train.py:527] (1/6) Epoch 309, batch 8, global_batch_idx: 38200, batch size: 61, loss[discriminator_loss=2.819, discriminator_real_loss=1.463, discriminator_fake_loss=1.356, generator_loss=27.75, generator_mel_loss=18.35, generator_kl_loss=1.56, generator_dur_loss=1.762, generator_adv_loss=2.054, generator_feat_match_loss=4.023, over 61.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.347, generator_loss=27.78, generator_mel_loss=18.28, generator_kl_loss=1.417, generator_dur_loss=1.782, generator_adv_loss=1.975, generator_feat_match_loss=4.323, over 555.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:32:39,919 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 07:32:47,771 INFO [train.py:591] (1/6) Epoch 309, validation: discriminator_loss=2.804, discriminator_real_loss=1.554, discriminator_fake_loss=1.251, generator_loss=26.52, generator_mel_loss=18.44, generator_kl_loss=1.251, generator_dur_loss=1.822, generator_adv_loss=1.96, generator_feat_match_loss=3.042, over 100.00 samples.
+2024-03-13 07:32:47,773 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 07:35:05,274 INFO [train.py:527] (1/6) Epoch 309, batch 58, global_batch_idx: 38250, batch size: 68, loss[discriminator_loss=2.716, discriminator_real_loss=1.407, discriminator_fake_loss=1.309, generator_loss=26.94, generator_mel_loss=17.84, generator_kl_loss=1.192, generator_dur_loss=1.825, generator_adv_loss=1.923, generator_feat_match_loss=4.156, over 68.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.385, discriminator_fake_loss=1.345, generator_loss=27.6, generator_mel_loss=18.26, generator_kl_loss=1.415, generator_dur_loss=1.764, generator_adv_loss=1.958, generator_feat_match_loss=4.201, over 3232.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:37:22,851 INFO [train.py:527] (1/6) Epoch 309, batch 108, global_batch_idx: 38300, batch size: 96, loss[discriminator_loss=2.623, discriminator_real_loss=1.257, discriminator_fake_loss=1.367, generator_loss=27.14, generator_mel_loss=17.51, generator_kl_loss=1.229, generator_dur_loss=1.946, generator_adv_loss=2.034, generator_feat_match_loss=4.417, over 96.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.66, generator_mel_loss=18.27, generator_kl_loss=1.427, generator_dur_loss=1.76, generator_adv_loss=1.949, generator_feat_match_loss=4.254, over 6007.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:38:07,978 INFO [train.py:919] (1/6) Start epoch 310
+2024-03-13 07:40:05,844 INFO [train.py:527] (1/6) Epoch 310, batch 34, global_batch_idx: 38350, batch size: 96, loss[discriminator_loss=2.695, discriminator_real_loss=1.24, discriminator_fake_loss=1.455, generator_loss=28.36, generator_mel_loss=18.34, generator_kl_loss=1.49, generator_dur_loss=1.903, generator_adv_loss=2.109, generator_feat_match_loss=4.513, over 96.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.38, discriminator_fake_loss=1.343, generator_loss=27.86, generator_mel_loss=18.41, generator_kl_loss=1.403, generator_dur_loss=1.791, generator_adv_loss=1.95, generator_feat_match_loss=4.314, over 2061.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:42:24,810 INFO [train.py:527] (1/6) Epoch 310, batch 84, global_batch_idx: 38400, batch size: 36, loss[discriminator_loss=2.612, discriminator_real_loss=1.22, discriminator_fake_loss=1.392, generator_loss=29.43, generator_mel_loss=19.38, generator_kl_loss=1.447, generator_dur_loss=1.651, generator_adv_loss=1.944, generator_feat_match_loss=5.011, over 36.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.74, generator_mel_loss=18.38, generator_kl_loss=1.398, generator_dur_loss=1.782, generator_adv_loss=1.93, generator_feat_match_loss=4.256, over 4892.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:42:24,811 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 07:42:33,708 INFO [train.py:591] (1/6) Epoch 310, validation: discriminator_loss=2.728, discriminator_real_loss=1.448, discriminator_fake_loss=1.28, generator_loss=26.99, generator_mel_loss=18.42, generator_kl_loss=1.213, generator_dur_loss=1.819, generator_adv_loss=1.958, generator_feat_match_loss=3.579, over 100.00 samples.
+2024-03-13 07:42:33,709 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 07:44:22,207 INFO [train.py:919] (1/6) Start epoch 311
+2024-03-13 07:45:16,099 INFO [train.py:527] (1/6) Epoch 311, batch 10, global_batch_idx: 38450, batch size: 52, loss[discriminator_loss=2.808, discriminator_real_loss=1.509, discriminator_fake_loss=1.299, generator_loss=27.21, generator_mel_loss=18.11, generator_kl_loss=1.275, generator_dur_loss=1.711, generator_adv_loss=1.867, generator_feat_match_loss=4.248, over 52.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.379, discriminator_fake_loss=1.354, generator_loss=27.58, generator_mel_loss=18.32, generator_kl_loss=1.333, generator_dur_loss=1.785, generator_adv_loss=1.941, generator_feat_match_loss=4.201, over 664.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:47:35,300 INFO [train.py:527] (1/6) Epoch 311, batch 60, global_batch_idx: 38500, batch size: 36, loss[discriminator_loss=2.708, discriminator_real_loss=1.408, discriminator_fake_loss=1.299, generator_loss=28, generator_mel_loss=18.77, generator_kl_loss=1.567, generator_dur_loss=1.654, generator_adv_loss=1.853, generator_feat_match_loss=4.153, over 36.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.382, discriminator_fake_loss=1.351, generator_loss=27.44, generator_mel_loss=18.23, generator_kl_loss=1.388, generator_dur_loss=1.773, generator_adv_loss=1.915, generator_feat_match_loss=4.137, over 3583.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:49:52,836 INFO [train.py:527] (1/6) Epoch 311, batch 110, global_batch_idx: 38550, batch size: 96, loss[discriminator_loss=2.723, discriminator_real_loss=1.386, discriminator_fake_loss=1.337, generator_loss=26.94, generator_mel_loss=18.01, generator_kl_loss=1.206, generator_dur_loss=1.932, generator_adv_loss=1.829, generator_feat_match_loss=3.964, over 96.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.53, generator_mel_loss=18.25, generator_kl_loss=1.393, generator_dur_loss=1.767, generator_adv_loss=1.916, generator_feat_match_loss=4.202, over 6454.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:50:29,009 INFO [train.py:919] (1/6) Start epoch 312
+2024-03-13 07:52:36,890 INFO [train.py:527] (1/6) Epoch 312, batch 36, global_batch_idx: 38600, batch size: 44, loss[discriminator_loss=2.779, discriminator_real_loss=1.324, discriminator_fake_loss=1.456, generator_loss=27.93, generator_mel_loss=18.52, generator_kl_loss=1.489, generator_dur_loss=1.742, generator_adv_loss=2.058, generator_feat_match_loss=4.126, over 44.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.389, discriminator_fake_loss=1.347, generator_loss=27.43, generator_mel_loss=18.2, generator_kl_loss=1.398, generator_dur_loss=1.757, generator_adv_loss=1.905, generator_feat_match_loss=4.174, over 2122.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:52:36,891 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 07:52:44,818 INFO [train.py:591] (1/6) Epoch 312, validation: discriminator_loss=2.855, discriminator_real_loss=1.551, discriminator_fake_loss=1.303, generator_loss=26.99, generator_mel_loss=18.56, generator_kl_loss=1.204, generator_dur_loss=1.823, generator_adv_loss=1.996, generator_feat_match_loss=3.411, over 100.00 samples.
+2024-03-13 07:52:44,819 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 07:55:03,773 INFO [train.py:527] (1/6) Epoch 312, batch 86, global_batch_idx: 38650, batch size: 53, loss[discriminator_loss=2.747, discriminator_real_loss=1.338, discriminator_fake_loss=1.408, generator_loss=28.15, generator_mel_loss=18.43, generator_kl_loss=1.44, generator_dur_loss=1.713, generator_adv_loss=1.88, generator_feat_match_loss=4.682, over 53.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.385, discriminator_fake_loss=1.347, generator_loss=27.53, generator_mel_loss=18.26, generator_kl_loss=1.416, generator_dur_loss=1.75, generator_adv_loss=1.909, generator_feat_match_loss=4.198, over 4865.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 07:56:43,188 INFO [train.py:919] (1/6) Start epoch 313
+2024-03-13 07:57:41,491 INFO [train.py:527] (1/6) Epoch 313, batch 12, global_batch_idx: 38700, batch size: 25, loss[discriminator_loss=2.622, discriminator_real_loss=1.27, discriminator_fake_loss=1.352, generator_loss=29.27, generator_mel_loss=18.86, generator_kl_loss=1.66, generator_dur_loss=1.596, generator_adv_loss=2.066, generator_feat_match_loss=5.089, over 25.00 samples.], tot_loss[discriminator_loss=2.759, discriminator_real_loss=1.397, discriminator_fake_loss=1.363, generator_loss=27.45, generator_mel_loss=18.21, generator_kl_loss=1.354, generator_dur_loss=1.807, generator_adv_loss=1.892, generator_feat_match_loss=4.186, over 764.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:00:02,065 INFO [train.py:527] (1/6) Epoch 313, batch 62, global_batch_idx: 38750, batch size: 72, loss[discriminator_loss=2.682, discriminator_real_loss=1.265, discriminator_fake_loss=1.417, generator_loss=27.26, generator_mel_loss=18.01, generator_kl_loss=1.317, generator_dur_loss=1.79, generator_adv_loss=1.933, generator_feat_match_loss=4.215, over 72.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.381, discriminator_fake_loss=1.352, generator_loss=27.58, generator_mel_loss=18.3, generator_kl_loss=1.409, generator_dur_loss=1.762, generator_adv_loss=1.913, generator_feat_match_loss=4.195, over 3496.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:02:21,382 INFO [train.py:527] (1/6) Epoch 313, batch 112, global_batch_idx: 38800, batch size: 77, loss[discriminator_loss=2.784, discriminator_real_loss=1.358, discriminator_fake_loss=1.426, generator_loss=27.68, generator_mel_loss=18.34, generator_kl_loss=1.425, generator_dur_loss=1.83, generator_adv_loss=1.831, generator_feat_match_loss=4.254, over 77.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.58, generator_mel_loss=18.26, generator_kl_loss=1.414, generator_dur_loss=1.767, generator_adv_loss=1.916, generator_feat_match_loss=4.224, over 6457.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:02:21,383 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 08:02:30,293 INFO [train.py:591] (1/6) Epoch 313, validation: discriminator_loss=2.775, discriminator_real_loss=1.461, discriminator_fake_loss=1.314, generator_loss=26.44, generator_mel_loss=18.39, generator_kl_loss=1.12, generator_dur_loss=1.834, generator_adv_loss=1.867, generator_feat_match_loss=3.229, over 100.00 samples.
+2024-03-13 08:02:30,294 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 08:03:00,343 INFO [train.py:919] (1/6) Start epoch 314
+2024-03-13 08:05:10,096 INFO [train.py:527] (1/6) Epoch 314, batch 38, global_batch_idx: 38850, batch size: 96, loss[discriminator_loss=2.8, discriminator_real_loss=1.319, discriminator_fake_loss=1.481, generator_loss=27.74, generator_mel_loss=18.39, generator_kl_loss=1.421, generator_dur_loss=1.835, generator_adv_loss=2.017, generator_feat_match_loss=4.076, over 96.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.38, discriminator_fake_loss=1.352, generator_loss=27.5, generator_mel_loss=18.23, generator_kl_loss=1.424, generator_dur_loss=1.753, generator_adv_loss=1.922, generator_feat_match_loss=4.17, over 2085.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:07:30,805 INFO [train.py:527] (1/6) Epoch 314, batch 88, global_batch_idx: 38900, batch size: 39, loss[discriminator_loss=2.749, discriminator_real_loss=1.336, discriminator_fake_loss=1.413, generator_loss=27.81, generator_mel_loss=18.39, generator_kl_loss=1.499, generator_dur_loss=1.659, generator_adv_loss=1.885, generator_feat_match_loss=4.379, over 39.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.38, discriminator_fake_loss=1.355, generator_loss=27.49, generator_mel_loss=18.25, generator_kl_loss=1.421, generator_dur_loss=1.749, generator_adv_loss=1.91, generator_feat_match_loss=4.168, over 4841.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:09:07,699 INFO [train.py:919] (1/6) Start epoch 315
+2024-03-13 08:10:09,715 INFO [train.py:527] (1/6) Epoch 315, batch 14, global_batch_idx: 38950, batch size: 88, loss[discriminator_loss=2.678, discriminator_real_loss=1.303, discriminator_fake_loss=1.375, generator_loss=27.47, generator_mel_loss=18.1, generator_kl_loss=1.394, generator_dur_loss=1.784, generator_adv_loss=1.898, generator_feat_match_loss=4.295, over 88.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.383, discriminator_fake_loss=1.344, generator_loss=27.53, generator_mel_loss=18.24, generator_kl_loss=1.437, generator_dur_loss=1.754, generator_adv_loss=1.922, generator_feat_match_loss=4.178, over 861.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:12:31,579 INFO [train.py:527] (1/6) Epoch 315, batch 64, global_batch_idx: 39000, batch size: 61, loss[discriminator_loss=2.736, discriminator_real_loss=1.398, discriminator_fake_loss=1.338, generator_loss=26.98, generator_mel_loss=17.8, generator_kl_loss=1.366, generator_dur_loss=1.797, generator_adv_loss=1.848, generator_feat_match_loss=4.168, over 61.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.388, discriminator_fake_loss=1.344, generator_loss=27.53, generator_mel_loss=18.22, generator_kl_loss=1.401, generator_dur_loss=1.768, generator_adv_loss=1.948, generator_feat_match_loss=4.196, over 3676.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:12:31,581 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 08:12:39,669 INFO [train.py:591] (1/6) Epoch 315, validation: discriminator_loss=2.758, discriminator_real_loss=1.362, discriminator_fake_loss=1.396, generator_loss=25.86, generator_mel_loss=17.96, generator_kl_loss=1.163, generator_dur_loss=1.846, generator_adv_loss=1.792, generator_feat_match_loss=3.098, over 100.00 samples.
+2024-03-13 08:12:39,670 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 08:14:57,104 INFO [train.py:527] (1/6) Epoch 315, batch 114, global_batch_idx: 39050, batch size: 83, loss[discriminator_loss=2.724, discriminator_real_loss=1.293, discriminator_fake_loss=1.43, generator_loss=28.3, generator_mel_loss=18.36, generator_kl_loss=1.306, generator_dur_loss=1.84, generator_adv_loss=2.025, generator_feat_match_loss=4.774, over 83.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.382, discriminator_fake_loss=1.348, generator_loss=27.55, generator_mel_loss=18.21, generator_kl_loss=1.4, generator_dur_loss=1.771, generator_adv_loss=1.936, generator_feat_match_loss=4.229, over 6637.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:15:23,655 INFO [train.py:919] (1/6) Start epoch 316
+2024-03-13 08:17:41,166 INFO [train.py:527] (1/6) Epoch 316, batch 40, global_batch_idx: 39100, batch size: 96, loss[discriminator_loss=2.674, discriminator_real_loss=1.369, discriminator_fake_loss=1.305, generator_loss=27.47, generator_mel_loss=17.86, generator_kl_loss=1.333, generator_dur_loss=1.891, generator_adv_loss=1.848, generator_feat_match_loss=4.535, over 96.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.388, discriminator_fake_loss=1.335, generator_loss=27.56, generator_mel_loss=18.25, generator_kl_loss=1.379, generator_dur_loss=1.782, generator_adv_loss=1.923, generator_feat_match_loss=4.224, over 2333.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:19:58,074 INFO [train.py:527] (1/6) Epoch 316, batch 90, global_batch_idx: 39150, batch size: 31, loss[discriminator_loss=2.743, discriminator_real_loss=1.408, discriminator_fake_loss=1.335, generator_loss=28.98, generator_mel_loss=18.78, generator_kl_loss=1.435, generator_dur_loss=1.59, generator_adv_loss=1.969, generator_feat_match_loss=5.198, over 31.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.382, discriminator_fake_loss=1.338, generator_loss=27.66, generator_mel_loss=18.27, generator_kl_loss=1.403, generator_dur_loss=1.757, generator_adv_loss=1.931, generator_feat_match_loss=4.303, over 4877.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:21:30,265 INFO [train.py:919] (1/6) Start epoch 317
+2024-03-13 08:22:39,729 INFO [train.py:527] (1/6) Epoch 317, batch 16, global_batch_idx: 39200, batch size: 70, loss[discriminator_loss=2.717, discriminator_real_loss=1.451, discriminator_fake_loss=1.266, generator_loss=27.32, generator_mel_loss=18.05, generator_kl_loss=1.363, generator_dur_loss=1.776, generator_adv_loss=1.833, generator_feat_match_loss=4.302, over 70.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.398, discriminator_fake_loss=1.333, generator_loss=27.8, generator_mel_loss=18.39, generator_kl_loss=1.406, generator_dur_loss=1.738, generator_adv_loss=1.973, generator_feat_match_loss=4.296, over 953.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:22:39,730 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 08:22:47,858 INFO [train.py:591] (1/6) Epoch 317, validation: discriminator_loss=2.8, discriminator_real_loss=1.399, discriminator_fake_loss=1.401, generator_loss=25.76, generator_mel_loss=18.18, generator_kl_loss=1.179, generator_dur_loss=1.814, generator_adv_loss=1.727, generator_feat_match_loss=2.859, over 100.00 samples.
+2024-03-13 08:22:47,859 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 08:25:08,316 INFO [train.py:527] (1/6) Epoch 317, batch 66, global_batch_idx: 39250, batch size: 25, loss[discriminator_loss=2.779, discriminator_real_loss=1.521, discriminator_fake_loss=1.257, generator_loss=27.81, generator_mel_loss=18.21, generator_kl_loss=1.641, generator_dur_loss=1.563, generator_adv_loss=1.886, generator_feat_match_loss=4.516, over 25.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.395, discriminator_fake_loss=1.345, generator_loss=27.69, generator_mel_loss=18.3, generator_kl_loss=1.409, generator_dur_loss=1.749, generator_adv_loss=1.927, generator_feat_match_loss=4.306, over 3784.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:27:27,156 INFO [train.py:527] (1/6) Epoch 317, batch 116, global_batch_idx: 39300, batch size: 58, loss[discriminator_loss=2.815, discriminator_real_loss=1.475, discriminator_fake_loss=1.34, generator_loss=27.35, generator_mel_loss=18.65, generator_kl_loss=1.361, generator_dur_loss=1.788, generator_adv_loss=2.033, generator_feat_match_loss=3.522, over 58.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=27.59, generator_mel_loss=18.24, generator_kl_loss=1.405, generator_dur_loss=1.754, generator_adv_loss=1.925, generator_feat_match_loss=4.267, over 6801.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:27:47,692 INFO [train.py:919] (1/6) Start epoch 318
+2024-03-13 08:30:05,492 INFO [train.py:527] (1/6) Epoch 318, batch 42, global_batch_idx: 39350, batch size: 50, loss[discriminator_loss=2.681, discriminator_real_loss=1.375, discriminator_fake_loss=1.307, generator_loss=28.23, generator_mel_loss=18.52, generator_kl_loss=1.423, generator_dur_loss=1.712, generator_adv_loss=2.22, generator_feat_match_loss=4.348, over 50.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.368, discriminator_fake_loss=1.351, generator_loss=27.76, generator_mel_loss=18.27, generator_kl_loss=1.399, generator_dur_loss=1.764, generator_adv_loss=1.989, generator_feat_match_loss=4.337, over 2469.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:32:24,898 INFO [train.py:527] (1/6) Epoch 318, batch 92, global_batch_idx: 39400, batch size: 70, loss[discriminator_loss=2.754, discriminator_real_loss=1.477, discriminator_fake_loss=1.277, generator_loss=27.64, generator_mel_loss=18.14, generator_kl_loss=1.411, generator_dur_loss=1.801, generator_adv_loss=2.097, generator_feat_match_loss=4.189, over 70.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=27.67, generator_mel_loss=18.26, generator_kl_loss=1.415, generator_dur_loss=1.759, generator_adv_loss=1.965, generator_feat_match_loss=4.272, over 5247.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:32:24,900 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 08:32:33,727 INFO [train.py:591] (1/6) Epoch 318, validation: discriminator_loss=2.765, discriminator_real_loss=1.53, discriminator_fake_loss=1.235, generator_loss=27.36, generator_mel_loss=18.41, generator_kl_loss=1.17, generator_dur_loss=1.838, generator_adv_loss=2.086, generator_feat_match_loss=3.85, over 100.00 samples.
+2024-03-13 08:32:33,728 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 08:34:00,704 INFO [train.py:919] (1/6) Start epoch 319
+2024-03-13 08:35:17,683 INFO [train.py:527] (1/6) Epoch 319, batch 18, global_batch_idx: 39450, batch size: 36, loss[discriminator_loss=2.629, discriminator_real_loss=1.23, discriminator_fake_loss=1.399, generator_loss=28.31, generator_mel_loss=17.83, generator_kl_loss=1.493, generator_dur_loss=1.675, generator_adv_loss=1.961, generator_feat_match_loss=5.345, over 36.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.375, discriminator_fake_loss=1.359, generator_loss=27.71, generator_mel_loss=18.33, generator_kl_loss=1.385, generator_dur_loss=1.788, generator_adv_loss=1.902, generator_feat_match_loss=4.303, over 1144.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:37:36,127 INFO [train.py:527] (1/6) Epoch 319, batch 68, global_batch_idx: 39500, batch size: 50, loss[discriminator_loss=2.677, discriminator_real_loss=1.369, discriminator_fake_loss=1.308, generator_loss=28.31, generator_mel_loss=18.59, generator_kl_loss=1.56, generator_dur_loss=1.692, generator_adv_loss=1.926, generator_feat_match_loss=4.543, over 50.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.38, discriminator_fake_loss=1.349, generator_loss=27.63, generator_mel_loss=18.27, generator_kl_loss=1.398, generator_dur_loss=1.783, generator_adv_loss=1.917, generator_feat_match_loss=4.259, over 4122.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:39:52,980 INFO [train.py:527] (1/6) Epoch 319, batch 118, global_batch_idx: 39550, batch size: 56, loss[discriminator_loss=2.779, discriminator_real_loss=1.248, discriminator_fake_loss=1.531, generator_loss=28.57, generator_mel_loss=18.47, generator_kl_loss=1.518, generator_dur_loss=1.74, generator_adv_loss=2.224, generator_feat_match_loss=4.625, over 56.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.64, generator_mel_loss=18.27, generator_kl_loss=1.408, generator_dur_loss=1.772, generator_adv_loss=1.927, generator_feat_match_loss=4.256, over 6888.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:40:09,501 INFO [train.py:919] (1/6) Start epoch 320
+2024-03-13 08:42:30,485 INFO [train.py:527] (1/6) Epoch 320, batch 44, global_batch_idx: 39600, batch size: 39, loss[discriminator_loss=2.719, discriminator_real_loss=1.327, discriminator_fake_loss=1.393, generator_loss=28.2, generator_mel_loss=18.92, generator_kl_loss=1.513, generator_dur_loss=1.726, generator_adv_loss=1.959, generator_feat_match_loss=4.087, over 39.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.38, discriminator_fake_loss=1.338, generator_loss=27.88, generator_mel_loss=18.37, generator_kl_loss=1.453, generator_dur_loss=1.74, generator_adv_loss=1.942, generator_feat_match_loss=4.373, over 2260.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:42:30,487 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 08:42:38,338 INFO [train.py:591] (1/6) Epoch 320, validation: discriminator_loss=2.767, discriminator_real_loss=1.523, discriminator_fake_loss=1.243, generator_loss=26.73, generator_mel_loss=18.11, generator_kl_loss=1.136, generator_dur_loss=1.827, generator_adv_loss=1.956, generator_feat_match_loss=3.705, over 100.00 samples.
+2024-03-13 08:42:38,339 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 08:44:58,485 INFO [train.py:527] (1/6) Epoch 320, batch 94, global_batch_idx: 39650, batch size: 47, loss[discriminator_loss=2.641, discriminator_real_loss=1.334, discriminator_fake_loss=1.307, generator_loss=28.51, generator_mel_loss=18.71, generator_kl_loss=1.472, generator_dur_loss=1.676, generator_adv_loss=2.091, generator_feat_match_loss=4.567, over 47.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.377, discriminator_fake_loss=1.344, generator_loss=27.77, generator_mel_loss=18.32, generator_kl_loss=1.43, generator_dur_loss=1.76, generator_adv_loss=1.934, generator_feat_match_loss=4.327, over 5149.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:46:19,938 INFO [train.py:919] (1/6) Start epoch 321
+2024-03-13 08:47:37,887 INFO [train.py:527] (1/6) Epoch 321, batch 20, global_batch_idx: 39700, batch size: 42, loss[discriminator_loss=2.754, discriminator_real_loss=1.364, discriminator_fake_loss=1.389, generator_loss=27.98, generator_mel_loss=18.61, generator_kl_loss=1.596, generator_dur_loss=1.633, generator_adv_loss=1.782, generator_feat_match_loss=4.36, over 42.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.381, discriminator_fake_loss=1.354, generator_loss=27.46, generator_mel_loss=18.28, generator_kl_loss=1.395, generator_dur_loss=1.743, generator_adv_loss=1.911, generator_feat_match_loss=4.128, over 1109.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:49:55,775 INFO [train.py:527] (1/6) Epoch 321, batch 70, global_batch_idx: 39750, batch size: 77, loss[discriminator_loss=2.723, discriminator_real_loss=1.352, discriminator_fake_loss=1.371, generator_loss=27.35, generator_mel_loss=17.98, generator_kl_loss=1.431, generator_dur_loss=1.87, generator_adv_loss=2.016, generator_feat_match_loss=4.06, over 77.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.371, discriminator_fake_loss=1.354, generator_loss=27.58, generator_mel_loss=18.27, generator_kl_loss=1.382, generator_dur_loss=1.775, generator_adv_loss=1.925, generator_feat_match_loss=4.23, over 4222.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:52:15,339 INFO [train.py:527] (1/6) Epoch 321, batch 120, global_batch_idx: 39800, batch size: 83, loss[discriminator_loss=2.737, discriminator_real_loss=1.467, discriminator_fake_loss=1.27, generator_loss=27.61, generator_mel_loss=18.22, generator_kl_loss=1.255, generator_dur_loss=1.839, generator_adv_loss=1.856, generator_feat_match_loss=4.441, over 83.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.375, discriminator_fake_loss=1.352, generator_loss=27.63, generator_mel_loss=18.28, generator_kl_loss=1.38, generator_dur_loss=1.78, generator_adv_loss=1.925, generator_feat_match_loss=4.274, over 7182.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:52:15,340 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 08:52:24,263 INFO [train.py:591] (1/6) Epoch 321, validation: discriminator_loss=2.749, discriminator_real_loss=1.382, discriminator_fake_loss=1.367, generator_loss=27.2, generator_mel_loss=18.48, generator_kl_loss=1.231, generator_dur_loss=1.847, generator_adv_loss=1.814, generator_feat_match_loss=3.827, over 100.00 samples.
+2024-03-13 08:52:24,264 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 08:52:34,854 INFO [train.py:919] (1/6) Start epoch 322
+2024-03-13 08:55:05,024 INFO [train.py:527] (1/6) Epoch 322, batch 46, global_batch_idx: 39850, batch size: 50, loss[discriminator_loss=2.691, discriminator_real_loss=1.319, discriminator_fake_loss=1.372, generator_loss=29.04, generator_mel_loss=19.09, generator_kl_loss=1.574, generator_dur_loss=1.74, generator_adv_loss=2.086, generator_feat_match_loss=4.559, over 50.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.62, generator_mel_loss=18.23, generator_kl_loss=1.396, generator_dur_loss=1.775, generator_adv_loss=1.935, generator_feat_match_loss=4.287, over 2668.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:57:20,913 INFO [train.py:527] (1/6) Epoch 322, batch 96, global_batch_idx: 39900, batch size: 66, loss[discriminator_loss=2.724, discriminator_real_loss=1.305, discriminator_fake_loss=1.419, generator_loss=28.17, generator_mel_loss=18.35, generator_kl_loss=1.481, generator_dur_loss=1.739, generator_adv_loss=1.976, generator_feat_match_loss=4.625, over 66.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.348, generator_loss=27.61, generator_mel_loss=18.23, generator_kl_loss=1.385, generator_dur_loss=1.775, generator_adv_loss=1.927, generator_feat_match_loss=4.296, over 5835.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 08:58:37,475 INFO [train.py:919] (1/6) Start epoch 323
+2024-03-13 09:00:00,587 INFO [train.py:527] (1/6) Epoch 323, batch 22, global_batch_idx: 39950, batch size: 58, loss[discriminator_loss=2.714, discriminator_real_loss=1.404, discriminator_fake_loss=1.31, generator_loss=27.4, generator_mel_loss=18.41, generator_kl_loss=1.389, generator_dur_loss=1.766, generator_adv_loss=1.854, generator_feat_match_loss=3.985, over 58.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.386, discriminator_fake_loss=1.331, generator_loss=27.55, generator_mel_loss=18.26, generator_kl_loss=1.388, generator_dur_loss=1.721, generator_adv_loss=1.948, generator_feat_match_loss=4.242, over 1299.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:02:19,740 INFO [train.py:527] (1/6) Epoch 323, batch 72, global_batch_idx: 40000, batch size: 80, loss[discriminator_loss=2.77, discriminator_real_loss=1.41, discriminator_fake_loss=1.36, generator_loss=27.03, generator_mel_loss=18.22, generator_kl_loss=1.216, generator_dur_loss=1.807, generator_adv_loss=1.851, generator_feat_match_loss=3.944, over 80.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.66, generator_mel_loss=18.34, generator_kl_loss=1.398, generator_dur_loss=1.733, generator_adv_loss=1.93, generator_feat_match_loss=4.258, over 4045.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:02:19,741 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 09:02:27,812 INFO [train.py:591] (1/6) Epoch 323, validation: discriminator_loss=2.763, discriminator_real_loss=1.41, discriminator_fake_loss=1.353, generator_loss=27.02, generator_mel_loss=18.64, generator_kl_loss=1.197, generator_dur_loss=1.831, generator_adv_loss=1.786, generator_feat_match_loss=3.572, over 100.00 samples.
+2024-03-13 09:02:27,813 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 09:04:45,693 INFO [train.py:527] (1/6) Epoch 323, batch 122, global_batch_idx: 40050, batch size: 61, loss[discriminator_loss=2.672, discriminator_real_loss=1.323, discriminator_fake_loss=1.349, generator_loss=27.99, generator_mel_loss=18.23, generator_kl_loss=1.304, generator_dur_loss=1.779, generator_adv_loss=1.968, generator_feat_match_loss=4.702, over 61.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.386, discriminator_fake_loss=1.344, generator_loss=27.65, generator_mel_loss=18.33, generator_kl_loss=1.396, generator_dur_loss=1.744, generator_adv_loss=1.925, generator_feat_match_loss=4.256, over 6913.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:04:50,979 INFO [train.py:919] (1/6) Start epoch 324
+2024-03-13 09:07:28,015 INFO [train.py:527] (1/6) Epoch 324, batch 48, global_batch_idx: 40100, batch size: 74, loss[discriminator_loss=2.661, discriminator_real_loss=1.262, discriminator_fake_loss=1.399, generator_loss=28.38, generator_mel_loss=18.37, generator_kl_loss=1.354, generator_dur_loss=1.808, generator_adv_loss=2.165, generator_feat_match_loss=4.677, over 74.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=27.65, generator_mel_loss=18.28, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.942, generator_feat_match_loss=4.276, over 2662.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:09:47,750 INFO [train.py:527] (1/6) Epoch 324, batch 98, global_batch_idx: 40150, batch size: 80, loss[discriminator_loss=2.708, discriminator_real_loss=1.376, discriminator_fake_loss=1.332, generator_loss=27.54, generator_mel_loss=17.87, generator_kl_loss=1.441, generator_dur_loss=1.825, generator_adv_loss=1.931, generator_feat_match_loss=4.477, over 80.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=27.66, generator_mel_loss=18.27, generator_kl_loss=1.392, generator_dur_loss=1.766, generator_adv_loss=1.933, generator_feat_match_loss=4.295, over 5622.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:10:56,340 INFO [train.py:919] (1/6) Start epoch 325
+2024-03-13 09:12:27,281 INFO [train.py:527] (1/6) Epoch 325, batch 24, global_batch_idx: 40200, batch size: 45, loss[discriminator_loss=2.659, discriminator_real_loss=1.334, discriminator_fake_loss=1.325, generator_loss=28.01, generator_mel_loss=18.32, generator_kl_loss=1.581, generator_dur_loss=1.653, generator_adv_loss=1.898, generator_feat_match_loss=4.558, over 45.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.392, discriminator_fake_loss=1.33, generator_loss=27.6, generator_mel_loss=18.21, generator_kl_loss=1.401, generator_dur_loss=1.758, generator_adv_loss=1.923, generator_feat_match_loss=4.303, over 1406.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:12:27,282 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 09:12:35,413 INFO [train.py:591] (1/6) Epoch 325, validation: discriminator_loss=2.748, discriminator_real_loss=1.363, discriminator_fake_loss=1.385, generator_loss=25.94, generator_mel_loss=18.11, generator_kl_loss=1.265, generator_dur_loss=1.831, generator_adv_loss=1.778, generator_feat_match_loss=2.957, over 100.00 samples.
+2024-03-13 09:12:35,414 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 09:14:54,043 INFO [train.py:527] (1/6) Epoch 325, batch 74, global_batch_idx: 40250, batch size: 72, loss[discriminator_loss=2.714, discriminator_real_loss=1.246, discriminator_fake_loss=1.467, generator_loss=28.47, generator_mel_loss=18.5, generator_kl_loss=1.423, generator_dur_loss=1.763, generator_adv_loss=1.888, generator_feat_match_loss=4.889, over 72.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.381, discriminator_fake_loss=1.344, generator_loss=27.77, generator_mel_loss=18.29, generator_kl_loss=1.419, generator_dur_loss=1.751, generator_adv_loss=1.929, generator_feat_match_loss=4.377, over 4186.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:17:11,576 INFO [train.py:919] (1/6) Start epoch 326
+2024-03-13 09:17:34,937 INFO [train.py:527] (1/6) Epoch 326, batch 0, global_batch_idx: 40300, batch size: 72, loss[discriminator_loss=2.714, discriminator_real_loss=1.382, discriminator_fake_loss=1.332, generator_loss=27.22, generator_mel_loss=17.71, generator_kl_loss=1.393, generator_dur_loss=1.831, generator_adv_loss=2.111, generator_feat_match_loss=4.172, over 72.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.382, discriminator_fake_loss=1.332, generator_loss=27.22, generator_mel_loss=17.71, generator_kl_loss=1.393, generator_dur_loss=1.831, generator_adv_loss=2.111, generator_feat_match_loss=4.172, over 72.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:19:53,411 INFO [train.py:527] (1/6) Epoch 326, batch 50, global_batch_idx: 40350, batch size: 47, loss[discriminator_loss=2.696, discriminator_real_loss=1.425, discriminator_fake_loss=1.271, generator_loss=27.91, generator_mel_loss=18.45, generator_kl_loss=1.418, generator_dur_loss=1.717, generator_adv_loss=1.836, generator_feat_match_loss=4.494, over 47.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.379, discriminator_fake_loss=1.341, generator_loss=27.64, generator_mel_loss=18.23, generator_kl_loss=1.38, generator_dur_loss=1.781, generator_adv_loss=1.955, generator_feat_match_loss=4.297, over 2969.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:22:12,706 INFO [train.py:527] (1/6) Epoch 326, batch 100, global_batch_idx: 40400, batch size: 42, loss[discriminator_loss=2.718, discriminator_real_loss=1.477, discriminator_fake_loss=1.241, generator_loss=28.36, generator_mel_loss=18.89, generator_kl_loss=1.449, generator_dur_loss=1.712, generator_adv_loss=1.827, generator_feat_match_loss=4.482, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=27.55, generator_mel_loss=18.19, generator_kl_loss=1.388, generator_dur_loss=1.764, generator_adv_loss=1.939, generator_feat_match_loss=4.269, over 5765.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:22:12,707 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 09:22:21,415 INFO [train.py:591] (1/6) Epoch 326, validation: discriminator_loss=2.746, discriminator_real_loss=1.398, discriminator_fake_loss=1.347, generator_loss=26.39, generator_mel_loss=18.21, generator_kl_loss=1.223, generator_dur_loss=1.815, generator_adv_loss=1.736, generator_feat_match_loss=3.409, over 100.00 samples.
+2024-03-13 09:22:21,416 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 09:23:27,179 INFO [train.py:919] (1/6) Start epoch 327
+2024-03-13 09:25:03,099 INFO [train.py:527] (1/6) Epoch 327, batch 26, global_batch_idx: 40450, batch size: 64, loss[discriminator_loss=2.72, discriminator_real_loss=1.317, discriminator_fake_loss=1.402, generator_loss=28.31, generator_mel_loss=18.74, generator_kl_loss=1.365, generator_dur_loss=1.782, generator_adv_loss=2.06, generator_feat_match_loss=4.369, over 64.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=27.83, generator_mel_loss=18.34, generator_kl_loss=1.407, generator_dur_loss=1.751, generator_adv_loss=1.942, generator_feat_match_loss=4.383, over 1584.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:27:22,159 INFO [train.py:527] (1/6) Epoch 327, batch 76, global_batch_idx: 40500, batch size: 42, loss[discriminator_loss=2.799, discriminator_real_loss=1.284, discriminator_fake_loss=1.515, generator_loss=28.31, generator_mel_loss=18.61, generator_kl_loss=1.611, generator_dur_loss=1.706, generator_adv_loss=2.015, generator_feat_match_loss=4.364, over 42.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.348, generator_loss=27.7, generator_mel_loss=18.27, generator_kl_loss=1.396, generator_dur_loss=1.76, generator_adv_loss=1.925, generator_feat_match_loss=4.341, over 4470.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:29:33,884 INFO [train.py:919] (1/6) Start epoch 328
+2024-03-13 09:30:05,264 INFO [train.py:527] (1/6) Epoch 328, batch 2, global_batch_idx: 40550, batch size: 39, loss[discriminator_loss=2.764, discriminator_real_loss=1.401, discriminator_fake_loss=1.363, generator_loss=28.11, generator_mel_loss=18.35, generator_kl_loss=1.759, generator_dur_loss=1.722, generator_adv_loss=1.952, generator_feat_match_loss=4.325, over 39.00 samples.], tot_loss[discriminator_loss=2.787, discriminator_real_loss=1.433, discriminator_fake_loss=1.354, generator_loss=27.42, generator_mel_loss=18.02, generator_kl_loss=1.698, generator_dur_loss=1.642, generator_adv_loss=1.934, generator_feat_match_loss=4.126, over 95.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:32:23,450 INFO [train.py:527] (1/6) Epoch 328, batch 52, global_batch_idx: 40600, batch size: 77, loss[discriminator_loss=2.731, discriminator_real_loss=1.417, discriminator_fake_loss=1.314, generator_loss=27.8, generator_mel_loss=18.39, generator_kl_loss=1.352, generator_dur_loss=1.823, generator_adv_loss=1.92, generator_feat_match_loss=4.319, over 77.00 samples.], tot_loss[discriminator_loss=2.739, discriminator_real_loss=1.386, discriminator_fake_loss=1.353, generator_loss=27.63, generator_mel_loss=18.26, generator_kl_loss=1.436, generator_dur_loss=1.767, generator_adv_loss=1.926, generator_feat_match_loss=4.241, over 2894.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:32:23,452 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 09:32:31,662 INFO [train.py:591] (1/6) Epoch 328, validation: discriminator_loss=2.788, discriminator_real_loss=1.456, discriminator_fake_loss=1.332, generator_loss=27.04, generator_mel_loss=18.69, generator_kl_loss=1.18, generator_dur_loss=1.834, generator_adv_loss=1.859, generator_feat_match_loss=3.474, over 100.00 samples.
+2024-03-13 09:32:31,663 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 09:34:49,923 INFO [train.py:527] (1/6) Epoch 328, batch 102, global_batch_idx: 40650, batch size: 80, loss[discriminator_loss=2.715, discriminator_real_loss=1.397, discriminator_fake_loss=1.318, generator_loss=27.07, generator_mel_loss=18.02, generator_kl_loss=1.248, generator_dur_loss=1.871, generator_adv_loss=1.928, generator_feat_match_loss=4.003, over 80.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.38, discriminator_fake_loss=1.353, generator_loss=27.61, generator_mel_loss=18.24, generator_kl_loss=1.418, generator_dur_loss=1.767, generator_adv_loss=1.921, generator_feat_match_loss=4.259, over 5692.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:35:48,674 INFO [train.py:919] (1/6) Start epoch 329
+2024-03-13 09:37:29,902 INFO [train.py:527] (1/6) Epoch 329, batch 28, global_batch_idx: 40700, batch size: 58, loss[discriminator_loss=2.747, discriminator_real_loss=1.527, discriminator_fake_loss=1.22, generator_loss=28.09, generator_mel_loss=18.55, generator_kl_loss=1.42, generator_dur_loss=1.754, generator_adv_loss=1.812, generator_feat_match_loss=4.556, over 58.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.375, discriminator_fake_loss=1.35, generator_loss=27.75, generator_mel_loss=18.27, generator_kl_loss=1.387, generator_dur_loss=1.786, generator_adv_loss=1.921, generator_feat_match_loss=4.391, over 1712.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:39:49,126 INFO [train.py:527] (1/6) Epoch 329, batch 78, global_batch_idx: 40750, batch size: 66, loss[discriminator_loss=2.724, discriminator_real_loss=1.45, discriminator_fake_loss=1.274, generator_loss=27.39, generator_mel_loss=18.33, generator_kl_loss=1.421, generator_dur_loss=1.833, generator_adv_loss=1.785, generator_feat_match_loss=4.02, over 66.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.383, discriminator_fake_loss=1.351, generator_loss=27.67, generator_mel_loss=18.26, generator_kl_loss=1.407, generator_dur_loss=1.767, generator_adv_loss=1.918, generator_feat_match_loss=4.319, over 4487.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:41:52,584 INFO [train.py:919] (1/6) Start epoch 330
+2024-03-13 09:42:27,527 INFO [train.py:527] (1/6) Epoch 330, batch 4, global_batch_idx: 40800, batch size: 66, loss[discriminator_loss=2.713, discriminator_real_loss=1.307, discriminator_fake_loss=1.405, generator_loss=27.08, generator_mel_loss=18.06, generator_kl_loss=1.391, generator_dur_loss=1.75, generator_adv_loss=1.956, generator_feat_match_loss=3.926, over 66.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.362, discriminator_fake_loss=1.355, generator_loss=27.91, generator_mel_loss=18.54, generator_kl_loss=1.334, generator_dur_loss=1.788, generator_adv_loss=1.946, generator_feat_match_loss=4.303, over 314.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:42:27,529 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 09:42:35,303 INFO [train.py:591] (1/6) Epoch 330, validation: discriminator_loss=2.748, discriminator_real_loss=1.445, discriminator_fake_loss=1.304, generator_loss=26.97, generator_mel_loss=18.55, generator_kl_loss=1.265, generator_dur_loss=1.82, generator_adv_loss=1.899, generator_feat_match_loss=3.435, over 100.00 samples.
+2024-03-13 09:42:35,305 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 09:44:59,981 INFO [train.py:527] (1/6) Epoch 330, batch 54, global_batch_idx: 40850, batch size: 64, loss[discriminator_loss=2.767, discriminator_real_loss=1.312, discriminator_fake_loss=1.455, generator_loss=27.95, generator_mel_loss=18.61, generator_kl_loss=1.345, generator_dur_loss=1.789, generator_adv_loss=2.016, generator_feat_match_loss=4.189, over 64.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.373, discriminator_fake_loss=1.349, generator_loss=27.8, generator_mel_loss=18.4, generator_kl_loss=1.418, generator_dur_loss=1.754, generator_adv_loss=1.938, generator_feat_match_loss=4.29, over 2974.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:47:18,864 INFO [train.py:527] (1/6) Epoch 330, batch 104, global_batch_idx: 40900, batch size: 44, loss[discriminator_loss=2.767, discriminator_real_loss=1.384, discriminator_fake_loss=1.382, generator_loss=27.27, generator_mel_loss=17.74, generator_kl_loss=1.589, generator_dur_loss=1.69, generator_adv_loss=1.918, generator_feat_match_loss=4.332, over 44.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.377, discriminator_fake_loss=1.349, generator_loss=27.75, generator_mel_loss=18.35, generator_kl_loss=1.416, generator_dur_loss=1.754, generator_adv_loss=1.93, generator_feat_match_loss=4.305, over 5847.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:48:11,148 INFO [train.py:919] (1/6) Start epoch 331
+2024-03-13 09:49:59,704 INFO [train.py:527] (1/6) Epoch 331, batch 30, global_batch_idx: 40950, batch size: 80, loss[discriminator_loss=2.678, discriminator_real_loss=1.364, discriminator_fake_loss=1.314, generator_loss=27.31, generator_mel_loss=17.96, generator_kl_loss=1.319, generator_dur_loss=1.804, generator_adv_loss=2, generator_feat_match_loss=4.226, over 80.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.383, discriminator_fake_loss=1.347, generator_loss=27.48, generator_mel_loss=18.18, generator_kl_loss=1.341, generator_dur_loss=1.792, generator_adv_loss=1.919, generator_feat_match_loss=4.253, over 2004.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:52:17,439 INFO [train.py:527] (1/6) Epoch 331, batch 80, global_batch_idx: 41000, batch size: 42, loss[discriminator_loss=2.707, discriminator_real_loss=1.304, discriminator_fake_loss=1.403, generator_loss=27.57, generator_mel_loss=17.91, generator_kl_loss=1.532, generator_dur_loss=1.66, generator_adv_loss=1.984, generator_feat_match_loss=4.487, over 42.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=27.59, generator_mel_loss=18.23, generator_kl_loss=1.381, generator_dur_loss=1.771, generator_adv_loss=1.928, generator_feat_match_loss=4.279, over 4820.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:52:17,440 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 09:52:26,154 INFO [train.py:591] (1/6) Epoch 331, validation: discriminator_loss=2.744, discriminator_real_loss=1.409, discriminator_fake_loss=1.336, generator_loss=26.9, generator_mel_loss=18.54, generator_kl_loss=1.258, generator_dur_loss=1.821, generator_adv_loss=1.844, generator_feat_match_loss=3.44, over 100.00 samples.
+2024-03-13 09:52:26,155 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 09:54:26,942 INFO [train.py:919] (1/6) Start epoch 332
+2024-03-13 09:55:07,456 INFO [train.py:527] (1/6) Epoch 332, batch 6, global_batch_idx: 41050, batch size: 45, loss[discriminator_loss=2.64, discriminator_real_loss=1.381, discriminator_fake_loss=1.259, generator_loss=28.62, generator_mel_loss=18.76, generator_kl_loss=1.438, generator_dur_loss=1.695, generator_adv_loss=2.03, generator_feat_match_loss=4.699, over 45.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.357, discriminator_fake_loss=1.352, generator_loss=27.97, generator_mel_loss=18.43, generator_kl_loss=1.387, generator_dur_loss=1.719, generator_adv_loss=1.954, generator_feat_match_loss=4.473, over 330.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:57:25,723 INFO [train.py:527] (1/6) Epoch 332, batch 56, global_batch_idx: 41100, batch size: 15, loss[discriminator_loss=2.649, discriminator_real_loss=1.328, discriminator_fake_loss=1.321, generator_loss=29.16, generator_mel_loss=19.09, generator_kl_loss=1.81, generator_dur_loss=1.607, generator_adv_loss=1.939, generator_feat_match_loss=4.718, over 15.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=27.62, generator_mel_loss=18.2, generator_kl_loss=1.397, generator_dur_loss=1.752, generator_adv_loss=1.934, generator_feat_match_loss=4.337, over 3156.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 09:59:44,635 INFO [train.py:527] (1/6) Epoch 332, batch 106, global_batch_idx: 41150, batch size: 48, loss[discriminator_loss=2.63, discriminator_real_loss=1.29, discriminator_fake_loss=1.341, generator_loss=28.38, generator_mel_loss=18.64, generator_kl_loss=1.611, generator_dur_loss=1.713, generator_adv_loss=1.955, generator_feat_match_loss=4.46, over 48.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.347, generator_loss=27.67, generator_mel_loss=18.21, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=1.935, generator_feat_match_loss=4.36, over 5999.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:00:33,700 INFO [train.py:919] (1/6) Start epoch 333
+2024-03-13 10:02:27,532 INFO [train.py:527] (1/6) Epoch 333, batch 32, global_batch_idx: 41200, batch size: 95, loss[discriminator_loss=2.67, discriminator_real_loss=1.284, discriminator_fake_loss=1.386, generator_loss=27.11, generator_mel_loss=17.72, generator_kl_loss=1.169, generator_dur_loss=1.863, generator_adv_loss=2.015, generator_feat_match_loss=4.351, over 95.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.363, discriminator_fake_loss=1.348, generator_loss=27.79, generator_mel_loss=18.24, generator_kl_loss=1.41, generator_dur_loss=1.761, generator_adv_loss=1.938, generator_feat_match_loss=4.442, over 1970.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:02:27,534 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 10:02:35,685 INFO [train.py:591] (1/6) Epoch 333, validation: discriminator_loss=2.759, discriminator_real_loss=1.473, discriminator_fake_loss=1.286, generator_loss=26.73, generator_mel_loss=18.49, generator_kl_loss=1.272, generator_dur_loss=1.809, generator_adv_loss=1.928, generator_feat_match_loss=3.235, over 100.00 samples.
+2024-03-13 10:02:35,687 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 10:04:55,342 INFO [train.py:527] (1/6) Epoch 333, batch 82, global_batch_idx: 41250, batch size: 55, loss[discriminator_loss=2.776, discriminator_real_loss=1.486, discriminator_fake_loss=1.289, generator_loss=26.95, generator_mel_loss=18.22, generator_kl_loss=1.403, generator_dur_loss=1.763, generator_adv_loss=1.756, generator_feat_match_loss=3.809, over 55.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.376, discriminator_fake_loss=1.348, generator_loss=27.7, generator_mel_loss=18.22, generator_kl_loss=1.394, generator_dur_loss=1.77, generator_adv_loss=1.937, generator_feat_match_loss=4.383, over 5114.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:06:51,211 INFO [train.py:919] (1/6) Start epoch 334
+2024-03-13 10:07:38,861 INFO [train.py:527] (1/6) Epoch 334, batch 8, global_batch_idx: 41300, batch size: 62, loss[discriminator_loss=2.764, discriminator_real_loss=1.395, discriminator_fake_loss=1.369, generator_loss=27.94, generator_mel_loss=18.85, generator_kl_loss=1.351, generator_dur_loss=1.77, generator_adv_loss=1.765, generator_feat_match_loss=4.203, over 62.00 samples.], tot_loss[discriminator_loss=2.74, discriminator_real_loss=1.412, discriminator_fake_loss=1.328, generator_loss=27.59, generator_mel_loss=18.14, generator_kl_loss=1.36, generator_dur_loss=1.78, generator_adv_loss=1.963, generator_feat_match_loss=4.35, over 531.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:09:55,677 INFO [train.py:527] (1/6) Epoch 334, batch 58, global_batch_idx: 41350, batch size: 13, loss[discriminator_loss=2.725, discriminator_real_loss=1.378, discriminator_fake_loss=1.348, generator_loss=28.71, generator_mel_loss=18.93, generator_kl_loss=1.694, generator_dur_loss=1.621, generator_adv_loss=1.812, generator_feat_match_loss=4.652, over 13.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.65, generator_mel_loss=18.25, generator_kl_loss=1.413, generator_dur_loss=1.76, generator_adv_loss=1.926, generator_feat_match_loss=4.305, over 3240.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:12:16,014 INFO [train.py:527] (1/6) Epoch 334, batch 108, global_batch_idx: 41400, batch size: 15, loss[discriminator_loss=2.768, discriminator_real_loss=1.552, discriminator_fake_loss=1.216, generator_loss=27.79, generator_mel_loss=18.09, generator_kl_loss=1.92, generator_dur_loss=1.646, generator_adv_loss=1.813, generator_feat_match_loss=4.324, over 15.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.68, generator_mel_loss=18.22, generator_kl_loss=1.419, generator_dur_loss=1.772, generator_adv_loss=1.93, generator_feat_match_loss=4.344, over 6084.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:12:16,015 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 10:12:24,660 INFO [train.py:591] (1/6) Epoch 334, validation: discriminator_loss=2.834, discriminator_real_loss=1.331, discriminator_fake_loss=1.503, generator_loss=27.15, generator_mel_loss=18.92, generator_kl_loss=1.164, generator_dur_loss=1.829, generator_adv_loss=1.652, generator_feat_match_loss=3.582, over 100.00 samples.
+2024-03-13 10:12:24,661 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 10:13:07,981 INFO [train.py:919] (1/6) Start epoch 335
+2024-03-13 10:15:06,074 INFO [train.py:527] (1/6) Epoch 335, batch 34, global_batch_idx: 41450, batch size: 58, loss[discriminator_loss=2.695, discriminator_real_loss=1.455, discriminator_fake_loss=1.24, generator_loss=28.12, generator_mel_loss=18, generator_kl_loss=1.381, generator_dur_loss=1.805, generator_adv_loss=2.207, generator_feat_match_loss=4.727, over 58.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.371, discriminator_fake_loss=1.349, generator_loss=27.85, generator_mel_loss=18.25, generator_kl_loss=1.419, generator_dur_loss=1.781, generator_adv_loss=1.948, generator_feat_match_loss=4.454, over 2087.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:17:24,562 INFO [train.py:527] (1/6) Epoch 335, batch 84, global_batch_idx: 41500, batch size: 52, loss[discriminator_loss=2.772, discriminator_real_loss=1.416, discriminator_fake_loss=1.356, generator_loss=27.63, generator_mel_loss=18.34, generator_kl_loss=1.424, generator_dur_loss=1.68, generator_adv_loss=1.98, generator_feat_match_loss=4.204, over 52.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=27.81, generator_mel_loss=18.27, generator_kl_loss=1.413, generator_dur_loss=1.777, generator_adv_loss=1.951, generator_feat_match_loss=4.406, over 4980.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:19:14,064 INFO [train.py:919] (1/6) Start epoch 336
+2024-03-13 10:20:06,650 INFO [train.py:527] (1/6) Epoch 336, batch 10, global_batch_idx: 41550, batch size: 68, loss[discriminator_loss=2.737, discriminator_real_loss=1.331, discriminator_fake_loss=1.405, generator_loss=26.84, generator_mel_loss=17.83, generator_kl_loss=1.282, generator_dur_loss=1.827, generator_adv_loss=1.901, generator_feat_match_loss=3.996, over 68.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.359, generator_loss=27.61, generator_mel_loss=18.23, generator_kl_loss=1.427, generator_dur_loss=1.766, generator_adv_loss=1.894, generator_feat_match_loss=4.3, over 607.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:22:23,987 INFO [train.py:527] (1/6) Epoch 336, batch 60, global_batch_idx: 41600, batch size: 72, loss[discriminator_loss=2.708, discriminator_real_loss=1.308, discriminator_fake_loss=1.401, generator_loss=27.14, generator_mel_loss=17.8, generator_kl_loss=1.463, generator_dur_loss=1.789, generator_adv_loss=2.067, generator_feat_match_loss=4.027, over 72.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.71, generator_mel_loss=18.28, generator_kl_loss=1.419, generator_dur_loss=1.775, generator_adv_loss=1.924, generator_feat_match_loss=4.316, over 3519.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:22:23,988 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 10:22:32,326 INFO [train.py:591] (1/6) Epoch 336, validation: discriminator_loss=2.743, discriminator_real_loss=1.444, discriminator_fake_loss=1.299, generator_loss=26.76, generator_mel_loss=18.56, generator_kl_loss=1.235, generator_dur_loss=1.829, generator_adv_loss=1.946, generator_feat_match_loss=3.193, over 100.00 samples.
+2024-03-13 10:22:32,327 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 10:24:50,831 INFO [train.py:527] (1/6) Epoch 336, batch 110, global_batch_idx: 41650, batch size: 45, loss[discriminator_loss=2.743, discriminator_real_loss=1.443, discriminator_fake_loss=1.3, generator_loss=26.81, generator_mel_loss=17.84, generator_kl_loss=1.582, generator_dur_loss=1.671, generator_adv_loss=1.925, generator_feat_match_loss=3.795, over 45.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.373, discriminator_fake_loss=1.351, generator_loss=27.81, generator_mel_loss=18.29, generator_kl_loss=1.42, generator_dur_loss=1.779, generator_adv_loss=1.94, generator_feat_match_loss=4.382, over 6349.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:25:28,402 INFO [train.py:919] (1/6) Start epoch 337
+2024-03-13 10:27:32,941 INFO [train.py:527] (1/6) Epoch 337, batch 36, global_batch_idx: 41700, batch size: 83, loss[discriminator_loss=2.723, discriminator_real_loss=1.323, discriminator_fake_loss=1.4, generator_loss=27.85, generator_mel_loss=18.19, generator_kl_loss=1.41, generator_dur_loss=1.873, generator_adv_loss=1.887, generator_feat_match_loss=4.487, over 83.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.388, discriminator_fake_loss=1.333, generator_loss=27.79, generator_mel_loss=18.22, generator_kl_loss=1.456, generator_dur_loss=1.743, generator_adv_loss=1.93, generator_feat_match_loss=4.442, over 1897.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:29:48,318 INFO [train.py:527] (1/6) Epoch 337, batch 86, global_batch_idx: 41750, batch size: 15, loss[discriminator_loss=2.718, discriminator_real_loss=1.367, discriminator_fake_loss=1.351, generator_loss=28.7, generator_mel_loss=18.17, generator_kl_loss=1.748, generator_dur_loss=1.568, generator_adv_loss=2.033, generator_feat_match_loss=5.177, over 15.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.76, generator_mel_loss=18.27, generator_kl_loss=1.437, generator_dur_loss=1.757, generator_adv_loss=1.92, generator_feat_match_loss=4.375, over 4728.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:31:32,839 INFO [train.py:919] (1/6) Start epoch 338
+2024-03-13 10:32:32,833 INFO [train.py:527] (1/6) Epoch 338, batch 12, global_batch_idx: 41800, batch size: 74, loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.324, generator_loss=27.86, generator_mel_loss=18.39, generator_kl_loss=1.32, generator_dur_loss=1.85, generator_adv_loss=1.985, generator_feat_match_loss=4.311, over 74.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.339, generator_loss=27.91, generator_mel_loss=18.31, generator_kl_loss=1.395, generator_dur_loss=1.772, generator_adv_loss=1.945, generator_feat_match_loss=4.492, over 756.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:32:32,837 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 10:32:40,993 INFO [train.py:591] (1/6) Epoch 338, validation: discriminator_loss=2.734, discriminator_real_loss=1.466, discriminator_fake_loss=1.268, generator_loss=26.89, generator_mel_loss=18.59, generator_kl_loss=1.281, generator_dur_loss=1.832, generator_adv_loss=1.936, generator_feat_match_loss=3.255, over 100.00 samples.
+2024-03-13 10:32:40,994 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 10:34:58,379 INFO [train.py:527] (1/6) Epoch 338, batch 62, global_batch_idx: 41850, batch size: 77, loss[discriminator_loss=2.629, discriminator_real_loss=1.31, discriminator_fake_loss=1.319, generator_loss=28.54, generator_mel_loss=18.29, generator_kl_loss=1.255, generator_dur_loss=1.819, generator_adv_loss=1.951, generator_feat_match_loss=5.221, over 77.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.71, generator_mel_loss=18.24, generator_kl_loss=1.411, generator_dur_loss=1.769, generator_adv_loss=1.933, generator_feat_match_loss=4.366, over 3612.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:37:18,477 INFO [train.py:527] (1/6) Epoch 338, batch 112, global_batch_idx: 41900, batch size: 25, loss[discriminator_loss=2.582, discriminator_real_loss=1.247, discriminator_fake_loss=1.335, generator_loss=29.88, generator_mel_loss=18.88, generator_kl_loss=1.713, generator_dur_loss=1.586, generator_adv_loss=2.156, generator_feat_match_loss=5.547, over 25.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=27.73, generator_mel_loss=18.24, generator_kl_loss=1.392, generator_dur_loss=1.777, generator_adv_loss=1.939, generator_feat_match_loss=4.385, over 6575.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:37:47,406 INFO [train.py:919] (1/6) Start epoch 339
+2024-03-13 10:39:58,000 INFO [train.py:527] (1/6) Epoch 339, batch 38, global_batch_idx: 41950, batch size: 88, loss[discriminator_loss=2.709, discriminator_real_loss=1.34, discriminator_fake_loss=1.369, generator_loss=27.98, generator_mel_loss=18.19, generator_kl_loss=1.368, generator_dur_loss=1.841, generator_adv_loss=1.859, generator_feat_match_loss=4.714, over 88.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.369, discriminator_fake_loss=1.347, generator_loss=27.77, generator_mel_loss=18.28, generator_kl_loss=1.388, generator_dur_loss=1.791, generator_adv_loss=1.924, generator_feat_match_loss=4.384, over 2251.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:42:14,521 INFO [train.py:527] (1/6) Epoch 339, batch 88, global_batch_idx: 42000, batch size: 39, loss[discriminator_loss=2.81, discriminator_real_loss=1.441, discriminator_fake_loss=1.368, generator_loss=26.2, generator_mel_loss=17.6, generator_kl_loss=1.398, generator_dur_loss=1.782, generator_adv_loss=1.97, generator_feat_match_loss=3.448, over 39.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.377, discriminator_fake_loss=1.35, generator_loss=27.7, generator_mel_loss=18.26, generator_kl_loss=1.399, generator_dur_loss=1.783, generator_adv_loss=1.927, generator_feat_match_loss=4.327, over 5072.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:42:14,522 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 10:42:23,426 INFO [train.py:591] (1/6) Epoch 339, validation: discriminator_loss=2.779, discriminator_real_loss=1.482, discriminator_fake_loss=1.298, generator_loss=27.57, generator_mel_loss=18.6, generator_kl_loss=1.219, generator_dur_loss=1.837, generator_adv_loss=2.026, generator_feat_match_loss=3.893, over 100.00 samples.
+2024-03-13 10:42:23,426 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 10:44:02,491 INFO [train.py:919] (1/6) Start epoch 340
+2024-03-13 10:45:05,845 INFO [train.py:527] (1/6) Epoch 340, batch 14, global_batch_idx: 42050, batch size: 14, loss[discriminator_loss=2.592, discriminator_real_loss=1.333, discriminator_fake_loss=1.259, generator_loss=30.27, generator_mel_loss=18.92, generator_kl_loss=1.973, generator_dur_loss=1.558, generator_adv_loss=2.101, generator_feat_match_loss=5.718, over 14.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.358, discriminator_fake_loss=1.354, generator_loss=27.85, generator_mel_loss=18.34, generator_kl_loss=1.397, generator_dur_loss=1.756, generator_adv_loss=1.905, generator_feat_match_loss=4.457, over 818.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:47:26,189 INFO [train.py:527] (1/6) Epoch 340, batch 64, global_batch_idx: 42100, batch size: 96, loss[discriminator_loss=2.67, discriminator_real_loss=1.307, discriminator_fake_loss=1.363, generator_loss=27.21, generator_mel_loss=17.68, generator_kl_loss=1.354, generator_dur_loss=1.834, generator_adv_loss=1.946, generator_feat_match_loss=4.389, over 96.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.362, discriminator_fake_loss=1.346, generator_loss=27.79, generator_mel_loss=18.27, generator_kl_loss=1.432, generator_dur_loss=1.761, generator_adv_loss=1.918, generator_feat_match_loss=4.404, over 3572.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:49:42,721 INFO [train.py:527] (1/6) Epoch 340, batch 114, global_batch_idx: 42150, batch size: 47, loss[discriminator_loss=2.748, discriminator_real_loss=1.45, discriminator_fake_loss=1.298, generator_loss=27.78, generator_mel_loss=18.24, generator_kl_loss=1.511, generator_dur_loss=1.684, generator_adv_loss=1.876, generator_feat_match_loss=4.477, over 47.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=27.82, generator_mel_loss=18.28, generator_kl_loss=1.422, generator_dur_loss=1.768, generator_adv_loss=1.953, generator_feat_match_loss=4.396, over 6638.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:50:08,894 INFO [train.py:919] (1/6) Start epoch 341
+2024-03-13 10:52:27,772 INFO [train.py:527] (1/6) Epoch 341, batch 40, global_batch_idx: 42200, batch size: 48, loss[discriminator_loss=2.702, discriminator_real_loss=1.378, discriminator_fake_loss=1.324, generator_loss=27.96, generator_mel_loss=18.38, generator_kl_loss=1.458, generator_dur_loss=1.719, generator_adv_loss=1.938, generator_feat_match_loss=4.458, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.366, discriminator_fake_loss=1.346, generator_loss=27.63, generator_mel_loss=18.16, generator_kl_loss=1.373, generator_dur_loss=1.766, generator_adv_loss=1.935, generator_feat_match_loss=4.391, over 2480.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:52:27,773 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 10:52:35,859 INFO [train.py:591] (1/6) Epoch 341, validation: discriminator_loss=2.714, discriminator_real_loss=1.35, discriminator_fake_loss=1.364, generator_loss=26.04, generator_mel_loss=18.07, generator_kl_loss=1.259, generator_dur_loss=1.82, generator_adv_loss=1.815, generator_feat_match_loss=3.073, over 100.00 samples.
+2024-03-13 10:52:35,860 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 10:54:54,240 INFO [train.py:527] (1/6) Epoch 341, batch 90, global_batch_idx: 42250, batch size: 83, loss[discriminator_loss=2.665, discriminator_real_loss=1.314, discriminator_fake_loss=1.352, generator_loss=27.7, generator_mel_loss=18.16, generator_kl_loss=1.382, generator_dur_loss=1.861, generator_adv_loss=2, generator_feat_match_loss=4.302, over 83.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=27.67, generator_mel_loss=18.18, generator_kl_loss=1.393, generator_dur_loss=1.774, generator_adv_loss=1.934, generator_feat_match_loss=4.382, over 5529.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:56:24,089 INFO [train.py:919] (1/6) Start epoch 342
+2024-03-13 10:57:33,210 INFO [train.py:527] (1/6) Epoch 342, batch 16, global_batch_idx: 42300, batch size: 39, loss[discriminator_loss=2.83, discriminator_real_loss=1.581, discriminator_fake_loss=1.249, generator_loss=25.24, generator_mel_loss=17.26, generator_kl_loss=1.34, generator_dur_loss=1.706, generator_adv_loss=1.708, generator_feat_match_loss=3.228, over 39.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=27.54, generator_mel_loss=18.08, generator_kl_loss=1.363, generator_dur_loss=1.766, generator_adv_loss=1.936, generator_feat_match_loss=4.39, over 1032.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 10:59:51,332 INFO [train.py:527] (1/6) Epoch 342, batch 66, global_batch_idx: 42350, batch size: 45, loss[discriminator_loss=2.737, discriminator_real_loss=1.359, discriminator_fake_loss=1.378, generator_loss=27.8, generator_mel_loss=18.1, generator_kl_loss=1.608, generator_dur_loss=1.706, generator_adv_loss=1.955, generator_feat_match_loss=4.425, over 45.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.362, discriminator_fake_loss=1.348, generator_loss=27.81, generator_mel_loss=18.2, generator_kl_loss=1.417, generator_dur_loss=1.764, generator_adv_loss=1.945, generator_feat_match_loss=4.493, over 3793.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:02:09,675 INFO [train.py:527] (1/6) Epoch 342, batch 116, global_batch_idx: 42400, batch size: 68, loss[discriminator_loss=2.748, discriminator_real_loss=1.358, discriminator_fake_loss=1.39, generator_loss=27.7, generator_mel_loss=18.17, generator_kl_loss=1.285, generator_dur_loss=1.813, generator_adv_loss=1.94, generator_feat_match_loss=4.485, over 68.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.348, generator_loss=27.78, generator_mel_loss=18.22, generator_kl_loss=1.406, generator_dur_loss=1.769, generator_adv_loss=1.935, generator_feat_match_loss=4.452, over 6666.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:02:09,676 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 11:02:18,384 INFO [train.py:591] (1/6) Epoch 342, validation: discriminator_loss=2.732, discriminator_real_loss=1.465, discriminator_fake_loss=1.266, generator_loss=27.05, generator_mel_loss=18.36, generator_kl_loss=1.188, generator_dur_loss=1.828, generator_adv_loss=1.955, generator_feat_match_loss=3.718, over 100.00 samples.
+2024-03-13 11:02:18,385 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 11:02:38,927 INFO [train.py:919] (1/6) Start epoch 343
+2024-03-13 11:05:00,076 INFO [train.py:527] (1/6) Epoch 343, batch 42, global_batch_idx: 42450, batch size: 53, loss[discriminator_loss=2.732, discriminator_real_loss=1.357, discriminator_fake_loss=1.375, generator_loss=28.32, generator_mel_loss=18.68, generator_kl_loss=1.488, generator_dur_loss=1.728, generator_adv_loss=1.957, generator_feat_match_loss=4.469, over 53.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.362, discriminator_fake_loss=1.348, generator_loss=27.81, generator_mel_loss=18.28, generator_kl_loss=1.41, generator_dur_loss=1.769, generator_adv_loss=1.938, generator_feat_match_loss=4.406, over 2467.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:07:19,468 INFO [train.py:527] (1/6) Epoch 343, batch 92, global_batch_idx: 42500, batch size: 48, loss[discriminator_loss=2.743, discriminator_real_loss=1.373, discriminator_fake_loss=1.37, generator_loss=27.31, generator_mel_loss=17.95, generator_kl_loss=1.413, generator_dur_loss=1.713, generator_adv_loss=1.999, generator_feat_match_loss=4.231, over 48.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.75, generator_mel_loss=18.22, generator_kl_loss=1.425, generator_dur_loss=1.758, generator_adv_loss=1.942, generator_feat_match_loss=4.401, over 5110.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:08:48,076 INFO [train.py:919] (1/6) Start epoch 344
+2024-03-13 11:10:02,092 INFO [train.py:527] (1/6) Epoch 344, batch 18, global_batch_idx: 42550, batch size: 39, loss[discriminator_loss=2.696, discriminator_real_loss=1.417, discriminator_fake_loss=1.279, generator_loss=28.61, generator_mel_loss=18.88, generator_kl_loss=1.59, generator_dur_loss=1.619, generator_adv_loss=1.826, generator_feat_match_loss=4.691, over 39.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.379, discriminator_fake_loss=1.33, generator_loss=27.85, generator_mel_loss=18.31, generator_kl_loss=1.423, generator_dur_loss=1.775, generator_adv_loss=1.923, generator_feat_match_loss=4.417, over 1119.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:12:19,905 INFO [train.py:527] (1/6) Epoch 344, batch 68, global_batch_idx: 42600, batch size: 72, loss[discriminator_loss=2.69, discriminator_real_loss=1.372, discriminator_fake_loss=1.317, generator_loss=27.76, generator_mel_loss=18.08, generator_kl_loss=1.332, generator_dur_loss=1.837, generator_adv_loss=1.907, generator_feat_match_loss=4.605, over 72.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.37, discriminator_fake_loss=1.348, generator_loss=27.75, generator_mel_loss=18.22, generator_kl_loss=1.396, generator_dur_loss=1.765, generator_adv_loss=1.92, generator_feat_match_loss=4.454, over 4137.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:12:19,907 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 11:12:27,978 INFO [train.py:591] (1/6) Epoch 344, validation: discriminator_loss=2.744, discriminator_real_loss=1.406, discriminator_fake_loss=1.338, generator_loss=26.36, generator_mel_loss=18.54, generator_kl_loss=1.174, generator_dur_loss=1.827, generator_adv_loss=1.746, generator_feat_match_loss=3.079, over 100.00 samples.
+2024-03-13 11:12:27,979 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 11:14:44,641 INFO [train.py:527] (1/6) Epoch 344, batch 118, global_batch_idx: 42650, batch size: 56, loss[discriminator_loss=2.721, discriminator_real_loss=1.47, discriminator_fake_loss=1.251, generator_loss=26.25, generator_mel_loss=18.2, generator_kl_loss=1.317, generator_dur_loss=1.76, generator_adv_loss=1.795, generator_feat_match_loss=3.179, over 56.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.373, discriminator_fake_loss=1.353, generator_loss=27.79, generator_mel_loss=18.22, generator_kl_loss=1.415, generator_dur_loss=1.768, generator_adv_loss=1.937, generator_feat_match_loss=4.453, over 6803.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:15:01,207 INFO [train.py:919] (1/6) Start epoch 345
+2024-03-13 11:17:24,178 INFO [train.py:527] (1/6) Epoch 345, batch 44, global_batch_idx: 42700, batch size: 74, loss[discriminator_loss=2.711, discriminator_real_loss=1.399, discriminator_fake_loss=1.312, generator_loss=28.09, generator_mel_loss=18.3, generator_kl_loss=1.302, generator_dur_loss=1.844, generator_adv_loss=1.776, generator_feat_match_loss=4.868, over 74.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.73, generator_mel_loss=18.2, generator_kl_loss=1.392, generator_dur_loss=1.768, generator_adv_loss=1.926, generator_feat_match_loss=4.446, over 2549.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:19:44,554 INFO [train.py:527] (1/6) Epoch 345, batch 94, global_batch_idx: 42750, batch size: 77, loss[discriminator_loss=2.702, discriminator_real_loss=1.325, discriminator_fake_loss=1.377, generator_loss=28.33, generator_mel_loss=18.38, generator_kl_loss=1.482, generator_dur_loss=1.85, generator_adv_loss=1.994, generator_feat_match_loss=4.623, over 77.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.376, discriminator_fake_loss=1.343, generator_loss=27.7, generator_mel_loss=18.17, generator_kl_loss=1.396, generator_dur_loss=1.78, generator_adv_loss=1.935, generator_feat_match_loss=4.413, over 5547.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:21:03,233 INFO [train.py:919] (1/6) Start epoch 346
+2024-03-13 11:22:26,077 INFO [train.py:527] (1/6) Epoch 346, batch 20, global_batch_idx: 42800, batch size: 80, loss[discriminator_loss=2.671, discriminator_real_loss=1.234, discriminator_fake_loss=1.436, generator_loss=28.65, generator_mel_loss=18.62, generator_kl_loss=1.404, generator_dur_loss=1.766, generator_adv_loss=1.986, generator_feat_match_loss=4.872, over 80.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.365, discriminator_fake_loss=1.356, generator_loss=28.11, generator_mel_loss=18.4, generator_kl_loss=1.42, generator_dur_loss=1.769, generator_adv_loss=1.945, generator_feat_match_loss=4.579, over 1171.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:22:26,078 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 11:22:34,313 INFO [train.py:591] (1/6) Epoch 346, validation: discriminator_loss=2.746, discriminator_real_loss=1.451, discriminator_fake_loss=1.295, generator_loss=26.76, generator_mel_loss=18.2, generator_kl_loss=1.215, generator_dur_loss=1.834, generator_adv_loss=1.972, generator_feat_match_loss=3.542, over 100.00 samples.
+2024-03-13 11:22:34,314 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 11:24:53,483 INFO [train.py:527] (1/6) Epoch 346, batch 70, global_batch_idx: 42850, batch size: 25, loss[discriminator_loss=2.805, discriminator_real_loss=1.324, discriminator_fake_loss=1.48, generator_loss=29.37, generator_mel_loss=19.7, generator_kl_loss=1.636, generator_dur_loss=1.593, generator_adv_loss=2.044, generator_feat_match_loss=4.401, over 25.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.372, discriminator_fake_loss=1.351, generator_loss=27.94, generator_mel_loss=18.34, generator_kl_loss=1.394, generator_dur_loss=1.765, generator_adv_loss=1.94, generator_feat_match_loss=4.507, over 4026.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:27:09,886 INFO [train.py:527] (1/6) Epoch 346, batch 120, global_batch_idx: 42900, batch size: 77, loss[discriminator_loss=2.717, discriminator_real_loss=1.411, discriminator_fake_loss=1.306, generator_loss=27.39, generator_mel_loss=17.99, generator_kl_loss=1.331, generator_dur_loss=1.832, generator_adv_loss=1.944, generator_feat_match_loss=4.296, over 77.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.381, discriminator_fake_loss=1.348, generator_loss=27.84, generator_mel_loss=18.29, generator_kl_loss=1.402, generator_dur_loss=1.76, generator_adv_loss=1.934, generator_feat_match_loss=4.453, over 6791.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:27:20,661 INFO [train.py:919] (1/6) Start epoch 347
+2024-03-13 11:29:52,482 INFO [train.py:527] (1/6) Epoch 347, batch 46, global_batch_idx: 42950, batch size: 66, loss[discriminator_loss=2.829, discriminator_real_loss=1.301, discriminator_fake_loss=1.529, generator_loss=27.95, generator_mel_loss=18.21, generator_kl_loss=1.376, generator_dur_loss=1.747, generator_adv_loss=2.121, generator_feat_match_loss=4.492, over 66.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.373, discriminator_fake_loss=1.35, generator_loss=27.73, generator_mel_loss=18.19, generator_kl_loss=1.418, generator_dur_loss=1.743, generator_adv_loss=1.945, generator_feat_match_loss=4.434, over 2660.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:32:12,662 INFO [train.py:527] (1/6) Epoch 347, batch 96, global_batch_idx: 43000, batch size: 45, loss[discriminator_loss=2.855, discriminator_real_loss=1.466, discriminator_fake_loss=1.388, generator_loss=27.31, generator_mel_loss=18.06, generator_kl_loss=1.505, generator_dur_loss=1.664, generator_adv_loss=2.161, generator_feat_match_loss=3.922, over 45.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.383, discriminator_fake_loss=1.353, generator_loss=27.61, generator_mel_loss=18.16, generator_kl_loss=1.414, generator_dur_loss=1.743, generator_adv_loss=1.938, generator_feat_match_loss=4.35, over 5398.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:32:12,663 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 11:32:21,588 INFO [train.py:591] (1/6) Epoch 347, validation: discriminator_loss=2.924, discriminator_real_loss=1.778, discriminator_fake_loss=1.146, generator_loss=26.77, generator_mel_loss=18.36, generator_kl_loss=1.268, generator_dur_loss=1.8, generator_adv_loss=2.294, generator_feat_match_loss=3.051, over 100.00 samples.
+2024-03-13 11:32:21,589 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 11:33:37,022 INFO [train.py:919] (1/6) Start epoch 348
+2024-03-13 11:35:02,271 INFO [train.py:527] (1/6) Epoch 348, batch 22, global_batch_idx: 43050, batch size: 47, loss[discriminator_loss=2.725, discriminator_real_loss=1.348, discriminator_fake_loss=1.377, generator_loss=27.14, generator_mel_loss=17.83, generator_kl_loss=1.416, generator_dur_loss=1.723, generator_adv_loss=1.846, generator_feat_match_loss=4.319, over 47.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.372, discriminator_fake_loss=1.356, generator_loss=27.48, generator_mel_loss=18.1, generator_kl_loss=1.39, generator_dur_loss=1.761, generator_adv_loss=1.929, generator_feat_match_loss=4.304, over 1230.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:37:23,085 INFO [train.py:527] (1/6) Epoch 348, batch 72, global_batch_idx: 43100, batch size: 70, loss[discriminator_loss=2.741, discriminator_real_loss=1.352, discriminator_fake_loss=1.389, generator_loss=27.54, generator_mel_loss=18.15, generator_kl_loss=1.337, generator_dur_loss=1.748, generator_adv_loss=1.845, generator_feat_match_loss=4.457, over 70.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=27.74, generator_mel_loss=18.19, generator_kl_loss=1.425, generator_dur_loss=1.759, generator_adv_loss=1.951, generator_feat_match_loss=4.421, over 4054.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:39:39,550 INFO [train.py:527] (1/6) Epoch 348, batch 122, global_batch_idx: 43150, batch size: 56, loss[discriminator_loss=2.763, discriminator_real_loss=1.465, discriminator_fake_loss=1.298, generator_loss=27.26, generator_mel_loss=17.95, generator_kl_loss=1.29, generator_dur_loss=1.694, generator_adv_loss=1.873, generator_feat_match_loss=4.456, over 56.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.372, discriminator_fake_loss=1.347, generator_loss=27.75, generator_mel_loss=18.22, generator_kl_loss=1.413, generator_dur_loss=1.76, generator_adv_loss=1.942, generator_feat_match_loss=4.423, over 6762.00 samples.], cur_lr_g: 1.92e-04, cur_lr_d: 1.92e-04, 
+2024-03-13 11:39:44,556 INFO [train.py:919] (1/6) Start epoch 349
+2024-03-13 11:42:24,532 INFO [train.py:527] (1/6) Epoch 349, batch 48, global_batch_idx: 43200, batch size: 53, loss[discriminator_loss=2.702, discriminator_real_loss=1.311, discriminator_fake_loss=1.391, generator_loss=28.13, generator_mel_loss=18.66, generator_kl_loss=1.393, generator_dur_loss=1.695, generator_adv_loss=2.064, generator_feat_match_loss=4.32, over 53.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.63, generator_mel_loss=18.18, generator_kl_loss=1.406, generator_dur_loss=1.749, generator_adv_loss=1.933, generator_feat_match_loss=4.363, over 2791.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 11:42:24,533 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 11:42:32,517 INFO [train.py:591] (1/6) Epoch 349, validation: discriminator_loss=2.758, discriminator_real_loss=1.504, discriminator_fake_loss=1.254, generator_loss=26.15, generator_mel_loss=18.04, generator_kl_loss=1.188, generator_dur_loss=1.785, generator_adv_loss=1.958, generator_feat_match_loss=3.174, over 100.00 samples.
+2024-03-13 11:42:32,518 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 11:44:52,640 INFO [train.py:527] (1/6) Epoch 349, batch 98, global_batch_idx: 43250, batch size: 52, loss[discriminator_loss=2.702, discriminator_real_loss=1.439, discriminator_fake_loss=1.263, generator_loss=28.17, generator_mel_loss=18.33, generator_kl_loss=1.501, generator_dur_loss=1.712, generator_adv_loss=1.864, generator_feat_match_loss=4.765, over 52.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.382, discriminator_fake_loss=1.341, generator_loss=27.69, generator_mel_loss=18.21, generator_kl_loss=1.415, generator_dur_loss=1.745, generator_adv_loss=1.937, generator_feat_match_loss=4.379, over 5695.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 11:46:04,254 INFO [train.py:919] (1/6) Start epoch 350
+2024-03-13 11:47:37,321 INFO [train.py:527] (1/6) Epoch 350, batch 24, global_batch_idx: 43300, batch size: 61, loss[discriminator_loss=2.757, discriminator_real_loss=1.492, discriminator_fake_loss=1.265, generator_loss=26.97, generator_mel_loss=18.29, generator_kl_loss=1.338, generator_dur_loss=1.741, generator_adv_loss=1.66, generator_feat_match_loss=3.94, over 61.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27.58, generator_mel_loss=18.15, generator_kl_loss=1.352, generator_dur_loss=1.766, generator_adv_loss=1.926, generator_feat_match_loss=4.385, over 1594.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 11:49:58,880 INFO [train.py:527] (1/6) Epoch 350, batch 74, global_batch_idx: 43350, batch size: 83, loss[discriminator_loss=2.709, discriminator_real_loss=1.351, discriminator_fake_loss=1.358, generator_loss=27.62, generator_mel_loss=18.02, generator_kl_loss=1.268, generator_dur_loss=1.838, generator_adv_loss=1.983, generator_feat_match_loss=4.52, over 83.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.377, discriminator_fake_loss=1.35, generator_loss=27.62, generator_mel_loss=18.17, generator_kl_loss=1.388, generator_dur_loss=1.778, generator_adv_loss=1.937, generator_feat_match_loss=4.352, over 4663.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 11:52:17,012 INFO [train.py:919] (1/6) Start epoch 351
+2024-03-13 11:52:41,684 INFO [train.py:527] (1/6) Epoch 351, batch 0, global_batch_idx: 43400, batch size: 53, loss[discriminator_loss=2.719, discriminator_real_loss=1.271, discriminator_fake_loss=1.447, generator_loss=28.57, generator_mel_loss=18.24, generator_kl_loss=1.619, generator_dur_loss=1.707, generator_adv_loss=1.978, generator_feat_match_loss=5.03, over 53.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.271, discriminator_fake_loss=1.447, generator_loss=28.57, generator_mel_loss=18.24, generator_kl_loss=1.619, generator_dur_loss=1.707, generator_adv_loss=1.978, generator_feat_match_loss=5.03, over 53.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 11:52:41,687 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 11:52:49,647 INFO [train.py:591] (1/6) Epoch 351, validation: discriminator_loss=2.765, discriminator_real_loss=1.506, discriminator_fake_loss=1.26, generator_loss=26.69, generator_mel_loss=18.17, generator_kl_loss=1.219, generator_dur_loss=1.83, generator_adv_loss=1.985, generator_feat_match_loss=3.495, over 100.00 samples.
+2024-03-13 11:52:49,649 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 11:55:12,190 INFO [train.py:527] (1/6) Epoch 351, batch 50, global_batch_idx: 43450, batch size: 16, loss[discriminator_loss=2.727, discriminator_real_loss=1.411, discriminator_fake_loss=1.316, generator_loss=28.03, generator_mel_loss=18.31, generator_kl_loss=1.822, generator_dur_loss=1.561, generator_adv_loss=1.885, generator_feat_match_loss=4.45, over 16.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.374, discriminator_fake_loss=1.35, generator_loss=27.73, generator_mel_loss=18.12, generator_kl_loss=1.443, generator_dur_loss=1.756, generator_adv_loss=1.938, generator_feat_match_loss=4.475, over 2638.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 11:57:34,549 INFO [train.py:527] (1/6) Epoch 351, batch 100, global_batch_idx: 43500, batch size: 83, loss[discriminator_loss=2.723, discriminator_real_loss=1.313, discriminator_fake_loss=1.409, generator_loss=27.49, generator_mel_loss=18.37, generator_kl_loss=1.254, generator_dur_loss=1.876, generator_adv_loss=1.928, generator_feat_match_loss=4.067, over 83.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.377, discriminator_fake_loss=1.346, generator_loss=27.76, generator_mel_loss=18.19, generator_kl_loss=1.426, generator_dur_loss=1.754, generator_adv_loss=1.936, generator_feat_match_loss=4.447, over 5381.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 11:58:39,804 INFO [train.py:919] (1/6) Start epoch 352
+2024-03-13 12:00:19,247 INFO [train.py:527] (1/6) Epoch 352, batch 26, global_batch_idx: 43550, batch size: 96, loss[discriminator_loss=2.712, discriminator_real_loss=1.387, discriminator_fake_loss=1.325, generator_loss=27.36, generator_mel_loss=18.21, generator_kl_loss=1.261, generator_dur_loss=1.858, generator_adv_loss=1.762, generator_feat_match_loss=4.268, over 96.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.366, discriminator_fake_loss=1.361, generator_loss=27.91, generator_mel_loss=18.24, generator_kl_loss=1.408, generator_dur_loss=1.764, generator_adv_loss=1.918, generator_feat_match_loss=4.578, over 1667.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:02:40,097 INFO [train.py:527] (1/6) Epoch 352, batch 76, global_batch_idx: 43600, batch size: 52, loss[discriminator_loss=2.725, discriminator_real_loss=1.373, discriminator_fake_loss=1.353, generator_loss=27.56, generator_mel_loss=18.33, generator_kl_loss=1.454, generator_dur_loss=1.687, generator_adv_loss=1.964, generator_feat_match_loss=4.124, over 52.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.377, discriminator_fake_loss=1.35, generator_loss=27.79, generator_mel_loss=18.24, generator_kl_loss=1.415, generator_dur_loss=1.752, generator_adv_loss=1.925, generator_feat_match_loss=4.465, over 4418.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:02:40,099 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 12:02:48,184 INFO [train.py:591] (1/6) Epoch 352, validation: discriminator_loss=2.794, discriminator_real_loss=1.464, discriminator_fake_loss=1.33, generator_loss=26.55, generator_mel_loss=18.12, generator_kl_loss=1.187, generator_dur_loss=1.813, generator_adv_loss=1.878, generator_feat_match_loss=3.552, over 100.00 samples.
+2024-03-13 12:02:48,185 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 12:05:03,639 INFO [train.py:919] (1/6) Start epoch 353
+2024-03-13 12:05:34,557 INFO [train.py:527] (1/6) Epoch 353, batch 2, global_batch_idx: 43650, batch size: 45, loss[discriminator_loss=2.691, discriminator_real_loss=1.272, discriminator_fake_loss=1.418, generator_loss=27.91, generator_mel_loss=17.9, generator_kl_loss=1.439, generator_dur_loss=1.702, generator_adv_loss=1.983, generator_feat_match_loss=4.885, over 45.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.406, discriminator_fake_loss=1.316, generator_loss=27.78, generator_mel_loss=18.16, generator_kl_loss=1.447, generator_dur_loss=1.705, generator_adv_loss=1.955, generator_feat_match_loss=4.509, over 144.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:07:55,202 INFO [train.py:527] (1/6) Epoch 353, batch 52, global_batch_idx: 43700, batch size: 45, loss[discriminator_loss=2.722, discriminator_real_loss=1.361, discriminator_fake_loss=1.361, generator_loss=28.49, generator_mel_loss=18.59, generator_kl_loss=1.315, generator_dur_loss=1.693, generator_adv_loss=2.016, generator_feat_match_loss=4.881, over 45.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=27.73, generator_mel_loss=18.23, generator_kl_loss=1.4, generator_dur_loss=1.788, generator_adv_loss=1.936, generator_feat_match_loss=4.378, over 3211.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:10:16,685 INFO [train.py:527] (1/6) Epoch 353, batch 102, global_batch_idx: 43750, batch size: 70, loss[discriminator_loss=2.739, discriminator_real_loss=1.376, discriminator_fake_loss=1.363, generator_loss=26.99, generator_mel_loss=17.62, generator_kl_loss=1.336, generator_dur_loss=1.775, generator_adv_loss=1.967, generator_feat_match_loss=4.298, over 70.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.379, discriminator_fake_loss=1.347, generator_loss=27.7, generator_mel_loss=18.2, generator_kl_loss=1.405, generator_dur_loss=1.781, generator_adv_loss=1.934, generator_feat_match_loss=4.382, over 6110.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:11:15,936 INFO [train.py:919] (1/6) Start epoch 354
+2024-03-13 12:13:02,936 INFO [train.py:527] (1/6) Epoch 354, batch 28, global_batch_idx: 43800, batch size: 52, loss[discriminator_loss=2.707, discriminator_real_loss=1.349, discriminator_fake_loss=1.359, generator_loss=27.24, generator_mel_loss=18.17, generator_kl_loss=1.401, generator_dur_loss=1.734, generator_adv_loss=1.901, generator_feat_match_loss=4.025, over 52.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.374, discriminator_fake_loss=1.346, generator_loss=27.5, generator_mel_loss=18.03, generator_kl_loss=1.4, generator_dur_loss=1.769, generator_adv_loss=1.923, generator_feat_match_loss=4.375, over 1559.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:13:02,938 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 12:13:11,068 INFO [train.py:591] (1/6) Epoch 354, validation: discriminator_loss=2.739, discriminator_real_loss=1.403, discriminator_fake_loss=1.335, generator_loss=26.52, generator_mel_loss=18.09, generator_kl_loss=1.258, generator_dur_loss=1.833, generator_adv_loss=1.868, generator_feat_match_loss=3.472, over 100.00 samples.
+2024-03-13 12:13:11,069 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 12:15:30,862 INFO [train.py:527] (1/6) Epoch 354, batch 78, global_batch_idx: 43850, batch size: 68, loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=27.97, generator_mel_loss=18.08, generator_kl_loss=1.435, generator_dur_loss=1.776, generator_adv_loss=1.886, generator_feat_match_loss=4.791, over 68.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.373, discriminator_fake_loss=1.345, generator_loss=27.67, generator_mel_loss=18.12, generator_kl_loss=1.432, generator_dur_loss=1.766, generator_adv_loss=1.934, generator_feat_match_loss=4.423, over 4349.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:17:38,367 INFO [train.py:919] (1/6) Start epoch 355
+2024-03-13 12:18:15,326 INFO [train.py:527] (1/6) Epoch 355, batch 4, global_batch_idx: 43900, batch size: 66, loss[discriminator_loss=2.655, discriminator_real_loss=1.243, discriminator_fake_loss=1.412, generator_loss=28.08, generator_mel_loss=18.18, generator_kl_loss=1.347, generator_dur_loss=1.79, generator_adv_loss=2.058, generator_feat_match_loss=4.707, over 66.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.365, discriminator_fake_loss=1.31, generator_loss=28.02, generator_mel_loss=18.32, generator_kl_loss=1.458, generator_dur_loss=1.716, generator_adv_loss=1.95, generator_feat_match_loss=4.573, over 254.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:20:37,055 INFO [train.py:527] (1/6) Epoch 355, batch 54, global_batch_idx: 43950, batch size: 72, loss[discriminator_loss=2.711, discriminator_real_loss=1.285, discriminator_fake_loss=1.426, generator_loss=27.87, generator_mel_loss=18.08, generator_kl_loss=1.498, generator_dur_loss=1.832, generator_adv_loss=1.818, generator_feat_match_loss=4.638, over 72.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=27.8, generator_mel_loss=18.2, generator_kl_loss=1.413, generator_dur_loss=1.759, generator_adv_loss=1.926, generator_feat_match_loss=4.507, over 3093.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:23:01,788 INFO [train.py:527] (1/6) Epoch 355, batch 104, global_batch_idx: 44000, batch size: 53, loss[discriminator_loss=2.7, discriminator_real_loss=1.325, discriminator_fake_loss=1.375, generator_loss=27.64, generator_mel_loss=18.2, generator_kl_loss=1.287, generator_dur_loss=1.683, generator_adv_loss=2.083, generator_feat_match_loss=4.394, over 53.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=27.77, generator_mel_loss=18.16, generator_kl_loss=1.422, generator_dur_loss=1.759, generator_adv_loss=1.943, generator_feat_match_loss=4.479, over 5907.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:23:01,789 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 12:23:10,645 INFO [train.py:591] (1/6) Epoch 355, validation: discriminator_loss=2.771, discriminator_real_loss=1.539, discriminator_fake_loss=1.233, generator_loss=26.44, generator_mel_loss=17.91, generator_kl_loss=1.252, generator_dur_loss=1.84, generator_adv_loss=2.006, generator_feat_match_loss=3.429, over 100.00 samples.
+2024-03-13 12:23:10,645 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 12:24:02,073 INFO [train.py:919] (1/6) Start epoch 356
+2024-03-13 12:25:51,544 INFO [train.py:527] (1/6) Epoch 356, batch 30, global_batch_idx: 44050, batch size: 80, loss[discriminator_loss=2.701, discriminator_real_loss=1.346, discriminator_fake_loss=1.355, generator_loss=28.1, generator_mel_loss=18.74, generator_kl_loss=1.292, generator_dur_loss=1.852, generator_adv_loss=1.793, generator_feat_match_loss=4.418, over 80.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=27.8, generator_mel_loss=18.26, generator_kl_loss=1.452, generator_dur_loss=1.767, generator_adv_loss=1.91, generator_feat_match_loss=4.415, over 1674.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:28:09,437 INFO [train.py:527] (1/6) Epoch 356, batch 80, global_batch_idx: 44100, batch size: 53, loss[discriminator_loss=2.774, discriminator_real_loss=1.455, discriminator_fake_loss=1.318, generator_loss=27.82, generator_mel_loss=17.99, generator_kl_loss=1.51, generator_dur_loss=1.675, generator_adv_loss=1.75, generator_feat_match_loss=4.892, over 53.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.376, discriminator_fake_loss=1.35, generator_loss=27.68, generator_mel_loss=18.16, generator_kl_loss=1.426, generator_dur_loss=1.762, generator_adv_loss=1.908, generator_feat_match_loss=4.426, over 4423.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:30:10,629 INFO [train.py:919] (1/6) Start epoch 357
+2024-03-13 12:30:50,312 INFO [train.py:527] (1/6) Epoch 357, batch 6, global_batch_idx: 44150, batch size: 16, loss[discriminator_loss=2.676, discriminator_real_loss=1.419, discriminator_fake_loss=1.257, generator_loss=30.36, generator_mel_loss=19.8, generator_kl_loss=1.784, generator_dur_loss=1.608, generator_adv_loss=1.885, generator_feat_match_loss=5.286, over 16.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.337, discriminator_fake_loss=1.391, generator_loss=27.57, generator_mel_loss=18, generator_kl_loss=1.366, generator_dur_loss=1.794, generator_adv_loss=1.922, generator_feat_match_loss=4.489, over 417.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:33:13,703 INFO [train.py:527] (1/6) Epoch 357, batch 56, global_batch_idx: 44200, batch size: 68, loss[discriminator_loss=2.723, discriminator_real_loss=1.315, discriminator_fake_loss=1.408, generator_loss=28.2, generator_mel_loss=18.31, generator_kl_loss=1.414, generator_dur_loss=1.754, generator_adv_loss=2.038, generator_feat_match_loss=4.679, over 68.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.348, generator_loss=27.82, generator_mel_loss=18.19, generator_kl_loss=1.461, generator_dur_loss=1.743, generator_adv_loss=1.918, generator_feat_match_loss=4.508, over 3042.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:33:13,705 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 12:33:21,458 INFO [train.py:591] (1/6) Epoch 357, validation: discriminator_loss=2.821, discriminator_real_loss=1.547, discriminator_fake_loss=1.274, generator_loss=26.95, generator_mel_loss=18.57, generator_kl_loss=1.318, generator_dur_loss=1.801, generator_adv_loss=1.969, generator_feat_match_loss=3.285, over 100.00 samples.
+2024-03-13 12:33:21,459 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 12:35:40,711 INFO [train.py:527] (1/6) Epoch 357, batch 106, global_batch_idx: 44250, batch size: 70, loss[discriminator_loss=2.74, discriminator_real_loss=1.281, discriminator_fake_loss=1.459, generator_loss=27.82, generator_mel_loss=17.89, generator_kl_loss=1.276, generator_dur_loss=1.8, generator_adv_loss=2.255, generator_feat_match_loss=4.601, over 70.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.374, discriminator_fake_loss=1.349, generator_loss=27.79, generator_mel_loss=18.17, generator_kl_loss=1.434, generator_dur_loss=1.752, generator_adv_loss=1.922, generator_feat_match_loss=4.507, over 6021.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:36:28,728 INFO [train.py:919] (1/6) Start epoch 358
+2024-03-13 12:38:26,654 INFO [train.py:527] (1/6) Epoch 358, batch 32, global_batch_idx: 44300, batch size: 88, loss[discriminator_loss=2.723, discriminator_real_loss=1.333, discriminator_fake_loss=1.39, generator_loss=27.26, generator_mel_loss=17.87, generator_kl_loss=1.485, generator_dur_loss=1.851, generator_adv_loss=1.832, generator_feat_match_loss=4.215, over 88.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=27.71, generator_mel_loss=18.15, generator_kl_loss=1.426, generator_dur_loss=1.778, generator_adv_loss=1.921, generator_feat_match_loss=4.434, over 2201.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:40:47,976 INFO [train.py:527] (1/6) Epoch 358, batch 82, global_batch_idx: 44350, batch size: 83, loss[discriminator_loss=2.737, discriminator_real_loss=1.351, discriminator_fake_loss=1.385, generator_loss=27.64, generator_mel_loss=18.07, generator_kl_loss=1.158, generator_dur_loss=1.825, generator_adv_loss=1.958, generator_feat_match_loss=4.629, over 83.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.381, discriminator_fake_loss=1.351, generator_loss=27.67, generator_mel_loss=18.13, generator_kl_loss=1.422, generator_dur_loss=1.764, generator_adv_loss=1.921, generator_feat_match_loss=4.436, over 5066.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:42:43,119 INFO [train.py:919] (1/6) Start epoch 359
+2024-03-13 12:43:29,793 INFO [train.py:527] (1/6) Epoch 359, batch 8, global_batch_idx: 44400, batch size: 26, loss[discriminator_loss=2.722, discriminator_real_loss=1.363, discriminator_fake_loss=1.359, generator_loss=28.24, generator_mel_loss=18.4, generator_kl_loss=1.609, generator_dur_loss=1.535, generator_adv_loss=2.074, generator_feat_match_loss=4.616, over 26.00 samples.], tot_loss[discriminator_loss=2.752, discriminator_real_loss=1.407, discriminator_fake_loss=1.345, generator_loss=27.65, generator_mel_loss=18.09, generator_kl_loss=1.435, generator_dur_loss=1.746, generator_adv_loss=1.954, generator_feat_match_loss=4.425, over 479.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:43:29,796 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 12:43:37,893 INFO [train.py:591] (1/6) Epoch 359, validation: discriminator_loss=2.795, discriminator_real_loss=1.503, discriminator_fake_loss=1.292, generator_loss=27.33, generator_mel_loss=18.38, generator_kl_loss=1.276, generator_dur_loss=1.823, generator_adv_loss=1.984, generator_feat_match_loss=3.863, over 100.00 samples.
+2024-03-13 12:43:37,895 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 12:45:59,726 INFO [train.py:527] (1/6) Epoch 359, batch 58, global_batch_idx: 44450, batch size: 62, loss[discriminator_loss=2.686, discriminator_real_loss=1.353, discriminator_fake_loss=1.333, generator_loss=27.84, generator_mel_loss=18.17, generator_kl_loss=1.404, generator_dur_loss=1.746, generator_adv_loss=1.902, generator_feat_match_loss=4.618, over 62.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.389, discriminator_fake_loss=1.342, generator_loss=27.63, generator_mel_loss=18.08, generator_kl_loss=1.409, generator_dur_loss=1.751, generator_adv_loss=1.932, generator_feat_match_loss=4.456, over 3499.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:48:21,823 INFO [train.py:527] (1/6) Epoch 359, batch 108, global_batch_idx: 44500, batch size: 70, loss[discriminator_loss=2.731, discriminator_real_loss=1.469, discriminator_fake_loss=1.261, generator_loss=27.31, generator_mel_loss=18.34, generator_kl_loss=1.291, generator_dur_loss=1.781, generator_adv_loss=1.888, generator_feat_match_loss=4.004, over 70.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.341, generator_loss=27.68, generator_mel_loss=18.12, generator_kl_loss=1.411, generator_dur_loss=1.751, generator_adv_loss=1.935, generator_feat_match_loss=4.458, over 6223.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:49:04,160 INFO [train.py:919] (1/6) Start epoch 360
+2024-03-13 12:51:04,501 INFO [train.py:527] (1/6) Epoch 360, batch 34, global_batch_idx: 44550, batch size: 47, loss[discriminator_loss=2.711, discriminator_real_loss=1.334, discriminator_fake_loss=1.377, generator_loss=27.17, generator_mel_loss=17.87, generator_kl_loss=1.399, generator_dur_loss=1.693, generator_adv_loss=1.848, generator_feat_match_loss=4.365, over 47.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.367, discriminator_fake_loss=1.347, generator_loss=27.79, generator_mel_loss=18.17, generator_kl_loss=1.41, generator_dur_loss=1.729, generator_adv_loss=1.942, generator_feat_match_loss=4.538, over 1818.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:53:25,883 INFO [train.py:527] (1/6) Epoch 360, batch 84, global_batch_idx: 44600, batch size: 83, loss[discriminator_loss=2.699, discriminator_real_loss=1.472, discriminator_fake_loss=1.227, generator_loss=27.52, generator_mel_loss=18.03, generator_kl_loss=1.351, generator_dur_loss=1.81, generator_adv_loss=1.882, generator_feat_match_loss=4.447, over 83.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.372, discriminator_fake_loss=1.349, generator_loss=27.7, generator_mel_loss=18.12, generator_kl_loss=1.404, generator_dur_loss=1.754, generator_adv_loss=1.93, generator_feat_match_loss=4.501, over 4762.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:53:25,885 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 12:53:34,614 INFO [train.py:591] (1/6) Epoch 360, validation: discriminator_loss=2.725, discriminator_real_loss=1.361, discriminator_fake_loss=1.365, generator_loss=26.64, generator_mel_loss=17.88, generator_kl_loss=1.22, generator_dur_loss=1.823, generator_adv_loss=1.872, generator_feat_match_loss=3.841, over 100.00 samples.
+2024-03-13 12:53:34,615 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 12:55:25,003 INFO [train.py:919] (1/6) Start epoch 361
+2024-03-13 12:56:21,901 INFO [train.py:527] (1/6) Epoch 361, batch 10, global_batch_idx: 44650, batch size: 44, loss[discriminator_loss=2.73, discriminator_real_loss=1.346, discriminator_fake_loss=1.383, generator_loss=27.67, generator_mel_loss=18.17, generator_kl_loss=1.335, generator_dur_loss=1.685, generator_adv_loss=1.98, generator_feat_match_loss=4.504, over 44.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.388, discriminator_fake_loss=1.35, generator_loss=27.74, generator_mel_loss=18.29, generator_kl_loss=1.401, generator_dur_loss=1.733, generator_adv_loss=1.933, generator_feat_match_loss=4.385, over 593.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 12:58:43,140 INFO [train.py:527] (1/6) Epoch 361, batch 60, global_batch_idx: 44700, batch size: 42, loss[discriminator_loss=2.735, discriminator_real_loss=1.337, discriminator_fake_loss=1.398, generator_loss=28.12, generator_mel_loss=18.69, generator_kl_loss=1.569, generator_dur_loss=1.735, generator_adv_loss=1.916, generator_feat_match_loss=4.207, over 42.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=27.82, generator_mel_loss=18.2, generator_kl_loss=1.433, generator_dur_loss=1.749, generator_adv_loss=1.94, generator_feat_match_loss=4.495, over 3262.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:01:03,607 INFO [train.py:527] (1/6) Epoch 361, batch 110, global_batch_idx: 44750, batch size: 62, loss[discriminator_loss=2.704, discriminator_real_loss=1.425, discriminator_fake_loss=1.279, generator_loss=28.31, generator_mel_loss=18.45, generator_kl_loss=1.29, generator_dur_loss=1.773, generator_adv_loss=1.84, generator_feat_match_loss=4.953, over 62.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=27.72, generator_mel_loss=18.14, generator_kl_loss=1.414, generator_dur_loss=1.76, generator_adv_loss=1.935, generator_feat_match_loss=4.465, over 6239.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:01:41,747 INFO [train.py:919] (1/6) Start epoch 362
+2024-03-13 13:03:46,642 INFO [train.py:527] (1/6) Epoch 362, batch 36, global_batch_idx: 44800, batch size: 74, loss[discriminator_loss=2.702, discriminator_real_loss=1.34, discriminator_fake_loss=1.361, generator_loss=27.99, generator_mel_loss=18.09, generator_kl_loss=1.331, generator_dur_loss=1.771, generator_adv_loss=1.945, generator_feat_match_loss=4.858, over 74.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.38, discriminator_fake_loss=1.336, generator_loss=27.92, generator_mel_loss=18.2, generator_kl_loss=1.425, generator_dur_loss=1.749, generator_adv_loss=1.941, generator_feat_match_loss=4.601, over 2083.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:03:46,644 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 13:03:54,777 INFO [train.py:591] (1/6) Epoch 362, validation: discriminator_loss=2.702, discriminator_real_loss=1.446, discriminator_fake_loss=1.256, generator_loss=27.42, generator_mel_loss=18.5, generator_kl_loss=1.251, generator_dur_loss=1.811, generator_adv_loss=1.942, generator_feat_match_loss=3.917, over 100.00 samples.
+2024-03-13 13:03:54,777 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 13:06:14,431 INFO [train.py:527] (1/6) Epoch 362, batch 86, global_batch_idx: 44850, batch size: 31, loss[discriminator_loss=2.626, discriminator_real_loss=1.268, discriminator_fake_loss=1.358, generator_loss=28.5, generator_mel_loss=18.37, generator_kl_loss=1.538, generator_dur_loss=1.648, generator_adv_loss=2.097, generator_feat_match_loss=4.846, over 31.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=27.8, generator_mel_loss=18.17, generator_kl_loss=1.421, generator_dur_loss=1.742, generator_adv_loss=1.936, generator_feat_match_loss=4.531, over 4911.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:08:03,173 INFO [train.py:919] (1/6) Start epoch 363
+2024-03-13 13:09:03,557 INFO [train.py:527] (1/6) Epoch 363, batch 12, global_batch_idx: 44900, batch size: 25, loss[discriminator_loss=2.677, discriminator_real_loss=1.308, discriminator_fake_loss=1.368, generator_loss=29.27, generator_mel_loss=19.14, generator_kl_loss=1.801, generator_dur_loss=1.532, generator_adv_loss=2.005, generator_feat_match_loss=4.788, over 25.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.409, discriminator_fake_loss=1.349, generator_loss=27.79, generator_mel_loss=18.21, generator_kl_loss=1.443, generator_dur_loss=1.725, generator_adv_loss=1.96, generator_feat_match_loss=4.45, over 713.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:11:29,919 INFO [train.py:527] (1/6) Epoch 363, batch 62, global_batch_idx: 44950, batch size: 58, loss[discriminator_loss=2.791, discriminator_real_loss=1.468, discriminator_fake_loss=1.323, generator_loss=27.76, generator_mel_loss=18.38, generator_kl_loss=1.302, generator_dur_loss=1.72, generator_adv_loss=2.036, generator_feat_match_loss=4.329, over 58.00 samples.], tot_loss[discriminator_loss=2.738, discriminator_real_loss=1.384, discriminator_fake_loss=1.354, generator_loss=27.76, generator_mel_loss=18.14, generator_kl_loss=1.442, generator_dur_loss=1.725, generator_adv_loss=1.939, generator_feat_match_loss=4.515, over 3525.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:13:48,383 INFO [train.py:527] (1/6) Epoch 363, batch 112, global_batch_idx: 45000, batch size: 47, loss[discriminator_loss=2.719, discriminator_real_loss=1.342, discriminator_fake_loss=1.377, generator_loss=28.29, generator_mel_loss=18.49, generator_kl_loss=1.442, generator_dur_loss=1.657, generator_adv_loss=1.885, generator_feat_match_loss=4.811, over 47.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.38, discriminator_fake_loss=1.353, generator_loss=27.71, generator_mel_loss=18.13, generator_kl_loss=1.436, generator_dur_loss=1.727, generator_adv_loss=1.941, generator_feat_match_loss=4.472, over 6295.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:13:48,385 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 13:13:57,313 INFO [train.py:591] (1/6) Epoch 363, validation: discriminator_loss=2.769, discriminator_real_loss=1.475, discriminator_fake_loss=1.294, generator_loss=27.23, generator_mel_loss=18.81, generator_kl_loss=1.246, generator_dur_loss=1.825, generator_adv_loss=1.883, generator_feat_match_loss=3.464, over 100.00 samples.
+2024-03-13 13:13:57,313 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 13:14:28,365 INFO [train.py:919] (1/6) Start epoch 364
+2024-03-13 13:16:41,246 INFO [train.py:527] (1/6) Epoch 364, batch 38, global_batch_idx: 45050, batch size: 72, loss[discriminator_loss=2.696, discriminator_real_loss=1.338, discriminator_fake_loss=1.358, generator_loss=27.79, generator_mel_loss=18.2, generator_kl_loss=1.217, generator_dur_loss=1.815, generator_adv_loss=1.913, generator_feat_match_loss=4.654, over 72.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.373, discriminator_fake_loss=1.35, generator_loss=27.75, generator_mel_loss=18.15, generator_kl_loss=1.436, generator_dur_loss=1.76, generator_adv_loss=1.937, generator_feat_match_loss=4.465, over 2353.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:19:04,741 INFO [train.py:527] (1/6) Epoch 364, batch 88, global_batch_idx: 45100, batch size: 72, loss[discriminator_loss=2.696, discriminator_real_loss=1.381, discriminator_fake_loss=1.315, generator_loss=27.64, generator_mel_loss=18.1, generator_kl_loss=1.26, generator_dur_loss=1.806, generator_adv_loss=2.018, generator_feat_match_loss=4.454, over 72.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=27.68, generator_mel_loss=18.12, generator_kl_loss=1.406, generator_dur_loss=1.767, generator_adv_loss=1.939, generator_feat_match_loss=4.45, over 5301.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:20:42,682 INFO [train.py:919] (1/6) Start epoch 365
+2024-03-13 13:21:47,490 INFO [train.py:527] (1/6) Epoch 365, batch 14, global_batch_idx: 45150, batch size: 83, loss[discriminator_loss=2.722, discriminator_real_loss=1.406, discriminator_fake_loss=1.316, generator_loss=27.75, generator_mel_loss=18.37, generator_kl_loss=1.313, generator_dur_loss=1.815, generator_adv_loss=1.941, generator_feat_match_loss=4.312, over 83.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.366, discriminator_fake_loss=1.343, generator_loss=27.56, generator_mel_loss=18.07, generator_kl_loss=1.387, generator_dur_loss=1.786, generator_adv_loss=1.958, generator_feat_match_loss=4.36, over 929.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:24:06,013 INFO [train.py:527] (1/6) Epoch 365, batch 64, global_batch_idx: 45200, batch size: 44, loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.66, generator_mel_loss=18.47, generator_kl_loss=1.444, generator_dur_loss=1.705, generator_adv_loss=2.064, generator_feat_match_loss=4.977, over 44.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.67, generator_mel_loss=18.1, generator_kl_loss=1.425, generator_dur_loss=1.756, generator_adv_loss=1.951, generator_feat_match_loss=4.44, over 3589.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:24:06,014 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 13:24:14,059 INFO [train.py:591] (1/6) Epoch 365, validation: discriminator_loss=2.753, discriminator_real_loss=1.471, discriminator_fake_loss=1.282, generator_loss=27.2, generator_mel_loss=18.57, generator_kl_loss=1.177, generator_dur_loss=1.815, generator_adv_loss=1.984, generator_feat_match_loss=3.647, over 100.00 samples.
+2024-03-13 13:24:14,060 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 13:26:35,976 INFO [train.py:527] (1/6) Epoch 365, batch 114, global_batch_idx: 45250, batch size: 72, loss[discriminator_loss=2.759, discriminator_real_loss=1.381, discriminator_fake_loss=1.378, generator_loss=27.47, generator_mel_loss=18.1, generator_kl_loss=1.426, generator_dur_loss=1.838, generator_adv_loss=1.746, generator_feat_match_loss=4.357, over 72.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=27.79, generator_mel_loss=18.18, generator_kl_loss=1.418, generator_dur_loss=1.761, generator_adv_loss=1.945, generator_feat_match_loss=4.492, over 6380.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:27:02,987 INFO [train.py:919] (1/6) Start epoch 366
+2024-03-13 13:29:19,128 INFO [train.py:527] (1/6) Epoch 366, batch 40, global_batch_idx: 45300, batch size: 36, loss[discriminator_loss=2.688, discriminator_real_loss=1.44, discriminator_fake_loss=1.248, generator_loss=27.56, generator_mel_loss=17.97, generator_kl_loss=1.503, generator_dur_loss=1.68, generator_adv_loss=1.938, generator_feat_match_loss=4.468, over 36.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=27.74, generator_mel_loss=18.17, generator_kl_loss=1.404, generator_dur_loss=1.776, generator_adv_loss=1.945, generator_feat_match_loss=4.443, over 2412.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:31:38,433 INFO [train.py:527] (1/6) Epoch 366, batch 90, global_batch_idx: 45350, batch size: 72, loss[discriminator_loss=2.714, discriminator_real_loss=1.383, discriminator_fake_loss=1.331, generator_loss=26.44, generator_mel_loss=17.28, generator_kl_loss=1.256, generator_dur_loss=1.863, generator_adv_loss=1.891, generator_feat_match_loss=4.154, over 72.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.372, discriminator_fake_loss=1.347, generator_loss=27.77, generator_mel_loss=18.15, generator_kl_loss=1.406, generator_dur_loss=1.774, generator_adv_loss=1.949, generator_feat_match_loss=4.491, over 5217.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:33:13,018 INFO [train.py:919] (1/6) Start epoch 367
+2024-03-13 13:34:23,827 INFO [train.py:527] (1/6) Epoch 367, batch 16, global_batch_idx: 45400, batch size: 45, loss[discriminator_loss=2.714, discriminator_real_loss=1.391, discriminator_fake_loss=1.323, generator_loss=28.28, generator_mel_loss=18.2, generator_kl_loss=1.536, generator_dur_loss=1.723, generator_adv_loss=1.996, generator_feat_match_loss=4.831, over 45.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.372, discriminator_fake_loss=1.347, generator_loss=27.51, generator_mel_loss=18.03, generator_kl_loss=1.423, generator_dur_loss=1.759, generator_adv_loss=1.935, generator_feat_match_loss=4.363, over 870.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:34:23,828 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 13:34:31,912 INFO [train.py:591] (1/6) Epoch 367, validation: discriminator_loss=2.754, discriminator_real_loss=1.484, discriminator_fake_loss=1.269, generator_loss=26.96, generator_mel_loss=18.29, generator_kl_loss=1.187, generator_dur_loss=1.826, generator_adv_loss=1.993, generator_feat_match_loss=3.665, over 100.00 samples.
+2024-03-13 13:34:31,913 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 13:36:53,550 INFO [train.py:527] (1/6) Epoch 367, batch 66, global_batch_idx: 45450, batch size: 72, loss[discriminator_loss=2.694, discriminator_real_loss=1.346, discriminator_fake_loss=1.348, generator_loss=28.49, generator_mel_loss=18.43, generator_kl_loss=1.392, generator_dur_loss=1.8, generator_adv_loss=1.933, generator_feat_match_loss=4.944, over 72.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.371, discriminator_fake_loss=1.351, generator_loss=27.79, generator_mel_loss=18.14, generator_kl_loss=1.394, generator_dur_loss=1.782, generator_adv_loss=1.942, generator_feat_match_loss=4.531, over 3940.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:39:09,394 INFO [train.py:527] (1/6) Epoch 367, batch 116, global_batch_idx: 45500, batch size: 50, loss[discriminator_loss=2.734, discriminator_real_loss=1.358, discriminator_fake_loss=1.376, generator_loss=27.27, generator_mel_loss=18.18, generator_kl_loss=1.42, generator_dur_loss=1.701, generator_adv_loss=1.697, generator_feat_match_loss=4.266, over 50.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.374, discriminator_fake_loss=1.348, generator_loss=27.75, generator_mel_loss=18.14, generator_kl_loss=1.401, generator_dur_loss=1.777, generator_adv_loss=1.936, generator_feat_match_loss=4.5, over 6757.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:39:32,368 INFO [train.py:919] (1/6) Start epoch 368
+2024-03-13 13:41:59,454 INFO [train.py:527] (1/6) Epoch 368, batch 42, global_batch_idx: 45550, batch size: 62, loss[discriminator_loss=2.707, discriminator_real_loss=1.382, discriminator_fake_loss=1.325, generator_loss=27.85, generator_mel_loss=18.22, generator_kl_loss=1.393, generator_dur_loss=1.784, generator_adv_loss=1.815, generator_feat_match_loss=4.637, over 62.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.373, discriminator_fake_loss=1.333, generator_loss=27.76, generator_mel_loss=18.1, generator_kl_loss=1.417, generator_dur_loss=1.759, generator_adv_loss=1.938, generator_feat_match_loss=4.542, over 2430.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:44:20,741 INFO [train.py:527] (1/6) Epoch 368, batch 92, global_batch_idx: 45600, batch size: 48, loss[discriminator_loss=2.724, discriminator_real_loss=1.348, discriminator_fake_loss=1.376, generator_loss=27.81, generator_mel_loss=17.84, generator_kl_loss=1.606, generator_dur_loss=1.656, generator_adv_loss=1.95, generator_feat_match_loss=4.766, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.337, generator_loss=27.81, generator_mel_loss=18.15, generator_kl_loss=1.415, generator_dur_loss=1.747, generator_adv_loss=1.943, generator_feat_match_loss=4.55, over 5207.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:44:20,742 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 13:44:29,496 INFO [train.py:591] (1/6) Epoch 368, validation: discriminator_loss=2.755, discriminator_real_loss=1.457, discriminator_fake_loss=1.298, generator_loss=26.51, generator_mel_loss=18.05, generator_kl_loss=1.227, generator_dur_loss=1.792, generator_adv_loss=1.932, generator_feat_match_loss=3.517, over 100.00 samples.
+2024-03-13 13:44:29,496 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 13:45:58,248 INFO [train.py:919] (1/6) Start epoch 369
+2024-03-13 13:47:12,389 INFO [train.py:527] (1/6) Epoch 369, batch 18, global_batch_idx: 45650, batch size: 68, loss[discriminator_loss=2.697, discriminator_real_loss=1.326, discriminator_fake_loss=1.371, generator_loss=27.66, generator_mel_loss=18.05, generator_kl_loss=1.544, generator_dur_loss=1.774, generator_adv_loss=1.97, generator_feat_match_loss=4.317, over 68.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.382, discriminator_fake_loss=1.34, generator_loss=27.79, generator_mel_loss=18.16, generator_kl_loss=1.41, generator_dur_loss=1.752, generator_adv_loss=1.941, generator_feat_match_loss=4.525, over 1099.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:49:31,555 INFO [train.py:527] (1/6) Epoch 369, batch 68, global_batch_idx: 45700, batch size: 55, loss[discriminator_loss=2.764, discriminator_real_loss=1.468, discriminator_fake_loss=1.296, generator_loss=27.23, generator_mel_loss=18.08, generator_kl_loss=1.413, generator_dur_loss=1.693, generator_adv_loss=1.825, generator_feat_match_loss=4.22, over 55.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.8, generator_mel_loss=18.19, generator_kl_loss=1.426, generator_dur_loss=1.741, generator_adv_loss=1.93, generator_feat_match_loss=4.515, over 3735.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:51:54,960 INFO [train.py:527] (1/6) Epoch 369, batch 118, global_batch_idx: 45750, batch size: 74, loss[discriminator_loss=2.715, discriminator_real_loss=1.323, discriminator_fake_loss=1.392, generator_loss=27.86, generator_mel_loss=18.33, generator_kl_loss=1.373, generator_dur_loss=1.747, generator_adv_loss=1.971, generator_feat_match_loss=4.439, over 74.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.379, discriminator_fake_loss=1.348, generator_loss=27.7, generator_mel_loss=18.15, generator_kl_loss=1.405, generator_dur_loss=1.745, generator_adv_loss=1.928, generator_feat_match_loss=4.471, over 6810.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:52:11,720 INFO [train.py:919] (1/6) Start epoch 370
+2024-03-13 13:54:38,392 INFO [train.py:527] (1/6) Epoch 370, batch 44, global_batch_idx: 45800, batch size: 64, loss[discriminator_loss=2.72, discriminator_real_loss=1.339, discriminator_fake_loss=1.381, generator_loss=27.88, generator_mel_loss=18.13, generator_kl_loss=1.274, generator_dur_loss=1.797, generator_adv_loss=1.956, generator_feat_match_loss=4.719, over 64.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.368, discriminator_fake_loss=1.333, generator_loss=27.95, generator_mel_loss=18.23, generator_kl_loss=1.402, generator_dur_loss=1.747, generator_adv_loss=1.953, generator_feat_match_loss=4.622, over 2658.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:54:38,394 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 13:54:46,122 INFO [train.py:591] (1/6) Epoch 370, validation: discriminator_loss=2.784, discriminator_real_loss=1.52, discriminator_fake_loss=1.264, generator_loss=27.2, generator_mel_loss=18.68, generator_kl_loss=1.298, generator_dur_loss=1.818, generator_adv_loss=1.961, generator_feat_match_loss=3.441, over 100.00 samples.
+2024-03-13 13:54:46,123 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 13:57:03,171 INFO [train.py:527] (1/6) Epoch 370, batch 94, global_batch_idx: 45850, batch size: 61, loss[discriminator_loss=2.736, discriminator_real_loss=1.439, discriminator_fake_loss=1.297, generator_loss=28.92, generator_mel_loss=18.85, generator_kl_loss=1.295, generator_dur_loss=1.704, generator_adv_loss=1.97, generator_feat_match_loss=5.107, over 61.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=27.97, generator_mel_loss=18.23, generator_kl_loss=1.408, generator_dur_loss=1.743, generator_adv_loss=1.961, generator_feat_match_loss=4.631, over 5434.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 13:58:22,418 INFO [train.py:919] (1/6) Start epoch 371
+2024-03-13 13:59:42,300 INFO [train.py:527] (1/6) Epoch 371, batch 20, global_batch_idx: 45900, batch size: 39, loss[discriminator_loss=2.723, discriminator_real_loss=1.313, discriminator_fake_loss=1.411, generator_loss=28.2, generator_mel_loss=18.13, generator_kl_loss=1.59, generator_dur_loss=1.641, generator_adv_loss=1.985, generator_feat_match_loss=4.86, over 39.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.94, generator_mel_loss=18.16, generator_kl_loss=1.397, generator_dur_loss=1.748, generator_adv_loss=1.929, generator_feat_match_loss=4.698, over 1203.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:02:03,662 INFO [train.py:527] (1/6) Epoch 371, batch 70, global_batch_idx: 45950, batch size: 72, loss[discriminator_loss=2.724, discriminator_real_loss=1.399, discriminator_fake_loss=1.325, generator_loss=27.13, generator_mel_loss=17.69, generator_kl_loss=1.489, generator_dur_loss=1.755, generator_adv_loss=1.946, generator_feat_match_loss=4.251, over 72.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.348, generator_loss=27.85, generator_mel_loss=18.14, generator_kl_loss=1.422, generator_dur_loss=1.733, generator_adv_loss=1.924, generator_feat_match_loss=4.628, over 4091.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:04:17,142 INFO [train.py:527] (1/6) Epoch 371, batch 120, global_batch_idx: 46000, batch size: 53, loss[discriminator_loss=2.712, discriminator_real_loss=1.378, discriminator_fake_loss=1.334, generator_loss=27.59, generator_mel_loss=18.56, generator_kl_loss=1.344, generator_dur_loss=1.695, generator_adv_loss=1.958, generator_feat_match_loss=4.032, over 53.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.79, generator_mel_loss=18.14, generator_kl_loss=1.432, generator_dur_loss=1.726, generator_adv_loss=1.927, generator_feat_match_loss=4.567, over 6853.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:04:17,144 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 14:04:26,085 INFO [train.py:591] (1/6) Epoch 371, validation: discriminator_loss=2.727, discriminator_real_loss=1.415, discriminator_fake_loss=1.312, generator_loss=27.89, generator_mel_loss=18.8, generator_kl_loss=1.346, generator_dur_loss=1.788, generator_adv_loss=1.931, generator_feat_match_loss=4.025, over 100.00 samples.
+2024-03-13 14:04:26,086 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 14:04:35,757 INFO [train.py:919] (1/6) Start epoch 372
+2024-03-13 14:07:06,472 INFO [train.py:527] (1/6) Epoch 372, batch 46, global_batch_idx: 46050, batch size: 77, loss[discriminator_loss=2.698, discriminator_real_loss=1.412, discriminator_fake_loss=1.286, generator_loss=27.33, generator_mel_loss=17.74, generator_kl_loss=1.293, generator_dur_loss=1.838, generator_adv_loss=1.928, generator_feat_match_loss=4.535, over 77.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.344, generator_loss=27.72, generator_mel_loss=18.16, generator_kl_loss=1.422, generator_dur_loss=1.731, generator_adv_loss=1.938, generator_feat_match_loss=4.475, over 2797.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:09:23,742 INFO [train.py:527] (1/6) Epoch 372, batch 96, global_batch_idx: 46100, batch size: 36, loss[discriminator_loss=2.694, discriminator_real_loss=1.33, discriminator_fake_loss=1.364, generator_loss=27.47, generator_mel_loss=18.03, generator_kl_loss=1.586, generator_dur_loss=1.671, generator_adv_loss=1.931, generator_feat_match_loss=4.253, over 36.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.74, generator_mel_loss=18.16, generator_kl_loss=1.417, generator_dur_loss=1.739, generator_adv_loss=1.931, generator_feat_match_loss=4.501, over 5460.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:10:41,485 INFO [train.py:919] (1/6) Start epoch 373
+2024-03-13 14:12:07,415 INFO [train.py:527] (1/6) Epoch 373, batch 22, global_batch_idx: 46150, batch size: 62, loss[discriminator_loss=2.71, discriminator_real_loss=1.453, discriminator_fake_loss=1.257, generator_loss=27.44, generator_mel_loss=17.8, generator_kl_loss=1.282, generator_dur_loss=1.755, generator_adv_loss=1.989, generator_feat_match_loss=4.608, over 62.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.386, discriminator_fake_loss=1.365, generator_loss=27.82, generator_mel_loss=18.22, generator_kl_loss=1.375, generator_dur_loss=1.772, generator_adv_loss=1.974, generator_feat_match_loss=4.487, over 1429.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:14:28,003 INFO [train.py:527] (1/6) Epoch 373, batch 72, global_batch_idx: 46200, batch size: 58, loss[discriminator_loss=2.734, discriminator_real_loss=1.348, discriminator_fake_loss=1.386, generator_loss=27.86, generator_mel_loss=18.26, generator_kl_loss=1.49, generator_dur_loss=1.707, generator_adv_loss=1.897, generator_feat_match_loss=4.505, over 58.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.383, discriminator_fake_loss=1.351, generator_loss=27.65, generator_mel_loss=18.08, generator_kl_loss=1.4, generator_dur_loss=1.77, generator_adv_loss=1.939, generator_feat_match_loss=4.471, over 4284.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:14:28,004 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 14:14:36,415 INFO [train.py:591] (1/6) Epoch 373, validation: discriminator_loss=2.779, discriminator_real_loss=1.446, discriminator_fake_loss=1.333, generator_loss=27.44, generator_mel_loss=18.47, generator_kl_loss=1.349, generator_dur_loss=1.809, generator_adv_loss=1.9, generator_feat_match_loss=3.912, over 100.00 samples.
+2024-03-13 14:14:36,416 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 14:16:53,645 INFO [train.py:527] (1/6) Epoch 373, batch 122, global_batch_idx: 46250, batch size: 14, loss[discriminator_loss=2.707, discriminator_real_loss=1.285, discriminator_fake_loss=1.422, generator_loss=30.15, generator_mel_loss=19.33, generator_kl_loss=1.726, generator_dur_loss=1.611, generator_adv_loss=2.108, generator_feat_match_loss=5.378, over 14.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.347, generator_loss=27.65, generator_mel_loss=18.09, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.934, generator_feat_match_loss=4.46, over 6898.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:16:57,543 INFO [train.py:919] (1/6) Start epoch 374
+2024-03-13 14:19:34,056 INFO [train.py:527] (1/6) Epoch 374, batch 48, global_batch_idx: 46300, batch size: 96, loss[discriminator_loss=2.724, discriminator_real_loss=1.367, discriminator_fake_loss=1.356, generator_loss=27.72, generator_mel_loss=18.06, generator_kl_loss=1.271, generator_dur_loss=1.9, generator_adv_loss=1.863, generator_feat_match_loss=4.627, over 96.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.347, generator_loss=27.86, generator_mel_loss=18.14, generator_kl_loss=1.433, generator_dur_loss=1.762, generator_adv_loss=1.932, generator_feat_match_loss=4.593, over 2774.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:21:53,610 INFO [train.py:527] (1/6) Epoch 374, batch 98, global_batch_idx: 46350, batch size: 53, loss[discriminator_loss=2.752, discriminator_real_loss=1.396, discriminator_fake_loss=1.356, generator_loss=28.57, generator_mel_loss=18.72, generator_kl_loss=1.303, generator_dur_loss=1.674, generator_adv_loss=1.898, generator_feat_match_loss=4.976, over 53.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=27.82, generator_mel_loss=18.12, generator_kl_loss=1.437, generator_dur_loss=1.762, generator_adv_loss=1.936, generator_feat_match_loss=4.561, over 5598.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:23:03,596 INFO [train.py:919] (1/6) Start epoch 375
+2024-03-13 14:24:35,807 INFO [train.py:527] (1/6) Epoch 375, batch 24, global_batch_idx: 46400, batch size: 48, loss[discriminator_loss=2.705, discriminator_real_loss=1.347, discriminator_fake_loss=1.358, generator_loss=28.03, generator_mel_loss=18.13, generator_kl_loss=1.721, generator_dur_loss=1.651, generator_adv_loss=1.822, generator_feat_match_loss=4.707, over 48.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=27.67, generator_mel_loss=18.1, generator_kl_loss=1.386, generator_dur_loss=1.779, generator_adv_loss=1.94, generator_feat_match_loss=4.467, over 1580.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:24:35,809 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 14:24:43,824 INFO [train.py:591] (1/6) Epoch 375, validation: discriminator_loss=2.756, discriminator_real_loss=1.434, discriminator_fake_loss=1.322, generator_loss=26.87, generator_mel_loss=18.2, generator_kl_loss=1.23, generator_dur_loss=1.825, generator_adv_loss=1.9, generator_feat_match_loss=3.715, over 100.00 samples.
+2024-03-13 14:24:43,825 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 14:27:02,873 INFO [train.py:527] (1/6) Epoch 375, batch 74, global_batch_idx: 46450, batch size: 74, loss[discriminator_loss=2.634, discriminator_real_loss=1.281, discriminator_fake_loss=1.352, generator_loss=27.68, generator_mel_loss=17.91, generator_kl_loss=1.394, generator_dur_loss=1.857, generator_adv_loss=1.979, generator_feat_match_loss=4.544, over 74.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=27.74, generator_mel_loss=18.09, generator_kl_loss=1.406, generator_dur_loss=1.775, generator_adv_loss=1.934, generator_feat_match_loss=4.528, over 4565.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:29:21,085 INFO [train.py:919] (1/6) Start epoch 376
+2024-03-13 14:29:45,032 INFO [train.py:527] (1/6) Epoch 376, batch 0, global_batch_idx: 46500, batch size: 53, loss[discriminator_loss=2.719, discriminator_real_loss=1.386, discriminator_fake_loss=1.333, generator_loss=28.46, generator_mel_loss=18.63, generator_kl_loss=1.5, generator_dur_loss=1.729, generator_adv_loss=1.779, generator_feat_match_loss=4.824, over 53.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.386, discriminator_fake_loss=1.333, generator_loss=28.46, generator_mel_loss=18.63, generator_kl_loss=1.5, generator_dur_loss=1.729, generator_adv_loss=1.779, generator_feat_match_loss=4.824, over 53.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:32:04,774 INFO [train.py:527] (1/6) Epoch 376, batch 50, global_batch_idx: 46550, batch size: 39, loss[discriminator_loss=2.713, discriminator_real_loss=1.314, discriminator_fake_loss=1.399, generator_loss=27.07, generator_mel_loss=17.74, generator_kl_loss=1.371, generator_dur_loss=1.687, generator_adv_loss=2.036, generator_feat_match_loss=4.234, over 39.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.377, discriminator_fake_loss=1.343, generator_loss=27.66, generator_mel_loss=18.09, generator_kl_loss=1.4, generator_dur_loss=1.768, generator_adv_loss=1.925, generator_feat_match_loss=4.483, over 2986.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:34:24,170 INFO [train.py:527] (1/6) Epoch 376, batch 100, global_batch_idx: 46600, batch size: 61, loss[discriminator_loss=2.678, discriminator_real_loss=1.296, discriminator_fake_loss=1.382, generator_loss=28.65, generator_mel_loss=18.33, generator_kl_loss=1.265, generator_dur_loss=1.812, generator_adv_loss=2.237, generator_feat_match_loss=4.999, over 61.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=27.76, generator_mel_loss=18.1, generator_kl_loss=1.419, generator_dur_loss=1.768, generator_adv_loss=1.958, generator_feat_match_loss=4.514, over 5833.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:34:24,173 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 14:34:32,873 INFO [train.py:591] (1/6) Epoch 376, validation: discriminator_loss=2.856, discriminator_real_loss=1.496, discriminator_fake_loss=1.36, generator_loss=26.78, generator_mel_loss=18.47, generator_kl_loss=1.237, generator_dur_loss=1.83, generator_adv_loss=1.915, generator_feat_match_loss=3.331, over 100.00 samples.
+2024-03-13 14:34:32,874 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 14:35:34,510 INFO [train.py:919] (1/6) Start epoch 377
+2024-03-13 14:37:11,500 INFO [train.py:527] (1/6) Epoch 377, batch 26, global_batch_idx: 46650, batch size: 58, loss[discriminator_loss=2.761, discriminator_real_loss=1.402, discriminator_fake_loss=1.359, generator_loss=27.73, generator_mel_loss=18.12, generator_kl_loss=1.387, generator_dur_loss=1.77, generator_adv_loss=1.986, generator_feat_match_loss=4.461, over 58.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.387, discriminator_fake_loss=1.342, generator_loss=27.82, generator_mel_loss=18.26, generator_kl_loss=1.365, generator_dur_loss=1.802, generator_adv_loss=1.92, generator_feat_match_loss=4.476, over 1705.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:39:32,080 INFO [train.py:527] (1/6) Epoch 377, batch 76, global_batch_idx: 46700, batch size: 96, loss[discriminator_loss=2.728, discriminator_real_loss=1.278, discriminator_fake_loss=1.451, generator_loss=28.23, generator_mel_loss=18.28, generator_kl_loss=1.453, generator_dur_loss=1.866, generator_adv_loss=1.913, generator_feat_match_loss=4.716, over 96.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.382, discriminator_fake_loss=1.339, generator_loss=27.75, generator_mel_loss=18.16, generator_kl_loss=1.396, generator_dur_loss=1.773, generator_adv_loss=1.925, generator_feat_match_loss=4.49, over 4551.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:41:41,529 INFO [train.py:919] (1/6) Start epoch 378
+2024-03-13 14:42:11,544 INFO [train.py:527] (1/6) Epoch 378, batch 2, global_batch_idx: 46750, batch size: 80, loss[discriminator_loss=2.716, discriminator_real_loss=1.387, discriminator_fake_loss=1.329, generator_loss=27.3, generator_mel_loss=18.28, generator_kl_loss=1.194, generator_dur_loss=1.849, generator_adv_loss=1.805, generator_feat_match_loss=4.166, over 80.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.357, discriminator_fake_loss=1.36, generator_loss=27.61, generator_mel_loss=18.21, generator_kl_loss=1.386, generator_dur_loss=1.781, generator_adv_loss=1.869, generator_feat_match_loss=4.369, over 202.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:44:34,249 INFO [train.py:527] (1/6) Epoch 378, batch 52, global_batch_idx: 46800, batch size: 42, loss[discriminator_loss=2.754, discriminator_real_loss=1.317, discriminator_fake_loss=1.437, generator_loss=26.9, generator_mel_loss=17.96, generator_kl_loss=1.528, generator_dur_loss=1.656, generator_adv_loss=1.84, generator_feat_match_loss=3.91, over 42.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.381, discriminator_fake_loss=1.352, generator_loss=27.68, generator_mel_loss=18.1, generator_kl_loss=1.424, generator_dur_loss=1.78, generator_adv_loss=1.905, generator_feat_match_loss=4.48, over 3279.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:44:34,250 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 14:44:42,306 INFO [train.py:591] (1/6) Epoch 378, validation: discriminator_loss=2.745, discriminator_real_loss=1.341, discriminator_fake_loss=1.404, generator_loss=27.12, generator_mel_loss=18.6, generator_kl_loss=1.324, generator_dur_loss=1.814, generator_adv_loss=1.742, generator_feat_match_loss=3.636, over 100.00 samples.
+2024-03-13 14:44:42,307 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 14:47:01,050 INFO [train.py:527] (1/6) Epoch 378, batch 102, global_batch_idx: 46850, batch size: 53, loss[discriminator_loss=2.709, discriminator_real_loss=1.356, discriminator_fake_loss=1.353, generator_loss=27.54, generator_mel_loss=17.9, generator_kl_loss=1.44, generator_dur_loss=1.713, generator_adv_loss=2.116, generator_feat_match_loss=4.371, over 53.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.375, discriminator_fake_loss=1.347, generator_loss=27.82, generator_mel_loss=18.16, generator_kl_loss=1.427, generator_dur_loss=1.766, generator_adv_loss=1.926, generator_feat_match_loss=4.533, over 6167.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:47:56,062 INFO [train.py:919] (1/6) Start epoch 379
+2024-03-13 14:49:40,450 INFO [train.py:527] (1/6) Epoch 379, batch 28, global_batch_idx: 46900, batch size: 39, loss[discriminator_loss=2.676, discriminator_real_loss=1.47, discriminator_fake_loss=1.206, generator_loss=28, generator_mel_loss=18.55, generator_kl_loss=1.478, generator_dur_loss=1.633, generator_adv_loss=1.993, generator_feat_match_loss=4.346, over 39.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=27.69, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.79, generator_adv_loss=1.911, generator_feat_match_loss=4.552, over 1864.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:51:57,948 INFO [train.py:527] (1/6) Epoch 379, batch 78, global_batch_idx: 46950, batch size: 61, loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=27, generator_mel_loss=18.03, generator_kl_loss=1.403, generator_dur_loss=1.688, generator_adv_loss=1.906, generator_feat_match_loss=3.966, over 61.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=27.78, generator_mel_loss=18.1, generator_kl_loss=1.412, generator_dur_loss=1.773, generator_adv_loss=1.924, generator_feat_match_loss=4.571, over 4866.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:54:01,452 INFO [train.py:919] (1/6) Start epoch 380
+2024-03-13 14:54:36,697 INFO [train.py:527] (1/6) Epoch 380, batch 4, global_batch_idx: 47000, batch size: 61, loss[discriminator_loss=2.708, discriminator_real_loss=1.345, discriminator_fake_loss=1.363, generator_loss=27.63, generator_mel_loss=17.94, generator_kl_loss=1.431, generator_dur_loss=1.75, generator_adv_loss=2.087, generator_feat_match_loss=4.418, over 61.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.352, discriminator_fake_loss=1.351, generator_loss=28.08, generator_mel_loss=18.18, generator_kl_loss=1.406, generator_dur_loss=1.79, generator_adv_loss=1.976, generator_feat_match_loss=4.728, over 346.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:54:36,699 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 14:54:44,911 INFO [train.py:591] (1/6) Epoch 380, validation: discriminator_loss=2.783, discriminator_real_loss=1.544, discriminator_fake_loss=1.239, generator_loss=27.31, generator_mel_loss=18.45, generator_kl_loss=1.266, generator_dur_loss=1.819, generator_adv_loss=2.008, generator_feat_match_loss=3.769, over 100.00 samples.
+2024-03-13 14:54:44,914 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 14:57:04,659 INFO [train.py:527] (1/6) Epoch 380, batch 54, global_batch_idx: 47050, batch size: 55, loss[discriminator_loss=2.687, discriminator_real_loss=1.292, discriminator_fake_loss=1.395, generator_loss=28.55, generator_mel_loss=18.25, generator_kl_loss=1.421, generator_dur_loss=1.719, generator_adv_loss=2.002, generator_feat_match_loss=5.161, over 55.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.8, generator_mel_loss=18.1, generator_kl_loss=1.408, generator_dur_loss=1.758, generator_adv_loss=1.947, generator_feat_match_loss=4.588, over 3061.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 14:59:25,701 INFO [train.py:527] (1/6) Epoch 380, batch 104, global_batch_idx: 47100, batch size: 39, loss[discriminator_loss=2.769, discriminator_real_loss=1.449, discriminator_fake_loss=1.32, generator_loss=27.05, generator_mel_loss=17.83, generator_kl_loss=1.456, generator_dur_loss=1.686, generator_adv_loss=1.879, generator_feat_match_loss=4.199, over 39.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.71, generator_mel_loss=18.06, generator_kl_loss=1.41, generator_dur_loss=1.769, generator_adv_loss=1.935, generator_feat_match_loss=4.533, over 5994.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:00:16,845 INFO [train.py:919] (1/6) Start epoch 381
+2024-03-13 15:02:04,254 INFO [train.py:527] (1/6) Epoch 381, batch 30, global_batch_idx: 47150, batch size: 96, loss[discriminator_loss=2.721, discriminator_real_loss=1.382, discriminator_fake_loss=1.339, generator_loss=26.85, generator_mel_loss=17.62, generator_kl_loss=1.164, generator_dur_loss=1.927, generator_adv_loss=1.891, generator_feat_match_loss=4.247, over 96.00 samples.], tot_loss[discriminator_loss=2.733, discriminator_real_loss=1.387, discriminator_fake_loss=1.345, generator_loss=27.77, generator_mel_loss=18.15, generator_kl_loss=1.407, generator_dur_loss=1.782, generator_adv_loss=1.941, generator_feat_match_loss=4.493, over 1712.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:04:25,770 INFO [train.py:527] (1/6) Epoch 381, batch 80, global_batch_idx: 47200, batch size: 96, loss[discriminator_loss=2.658, discriminator_real_loss=1.313, discriminator_fake_loss=1.345, generator_loss=28.26, generator_mel_loss=18.15, generator_kl_loss=1.26, generator_dur_loss=1.854, generator_adv_loss=1.911, generator_feat_match_loss=5.088, over 96.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.37, discriminator_fake_loss=1.347, generator_loss=27.99, generator_mel_loss=18.16, generator_kl_loss=1.4, generator_dur_loss=1.786, generator_adv_loss=1.983, generator_feat_match_loss=4.658, over 4676.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:04:25,771 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 15:04:34,804 INFO [train.py:591] (1/6) Epoch 381, validation: discriminator_loss=2.724, discriminator_real_loss=1.401, discriminator_fake_loss=1.323, generator_loss=26.88, generator_mel_loss=18.38, generator_kl_loss=1.162, generator_dur_loss=1.845, generator_adv_loss=1.823, generator_feat_match_loss=3.671, over 100.00 samples.
+2024-03-13 15:04:34,805 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 15:06:34,469 INFO [train.py:919] (1/6) Start epoch 382
+2024-03-13 15:07:16,342 INFO [train.py:527] (1/6) Epoch 382, batch 6, global_batch_idx: 47250, batch size: 64, loss[discriminator_loss=2.664, discriminator_real_loss=1.354, discriminator_fake_loss=1.311, generator_loss=27.64, generator_mel_loss=17.94, generator_kl_loss=1.362, generator_dur_loss=1.765, generator_adv_loss=1.971, generator_feat_match_loss=4.609, over 64.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.348, discriminator_fake_loss=1.316, generator_loss=27.75, generator_mel_loss=18.03, generator_kl_loss=1.468, generator_dur_loss=1.774, generator_adv_loss=1.955, generator_feat_match_loss=4.53, over 429.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:09:36,868 INFO [train.py:527] (1/6) Epoch 382, batch 56, global_batch_idx: 47300, batch size: 80, loss[discriminator_loss=2.768, discriminator_real_loss=1.486, discriminator_fake_loss=1.282, generator_loss=26.79, generator_mel_loss=17.8, generator_kl_loss=1.28, generator_dur_loss=1.902, generator_adv_loss=1.689, generator_feat_match_loss=4.117, over 80.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.34, generator_loss=27.61, generator_mel_loss=17.98, generator_kl_loss=1.414, generator_dur_loss=1.767, generator_adv_loss=1.933, generator_feat_match_loss=4.518, over 3315.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:11:58,774 INFO [train.py:527] (1/6) Epoch 382, batch 106, global_batch_idx: 47350, batch size: 62, loss[discriminator_loss=2.706, discriminator_real_loss=1.4, discriminator_fake_loss=1.306, generator_loss=27.82, generator_mel_loss=18.1, generator_kl_loss=1.48, generator_dur_loss=1.757, generator_adv_loss=1.927, generator_feat_match_loss=4.561, over 62.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.345, generator_loss=27.73, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.765, generator_adv_loss=1.939, generator_feat_match_loss=4.565, over 6130.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:12:47,841 INFO [train.py:919] (1/6) Start epoch 383
+2024-03-13 15:14:45,965 INFO [train.py:527] (1/6) Epoch 383, batch 32, global_batch_idx: 47400, batch size: 68, loss[discriminator_loss=2.69, discriminator_real_loss=1.424, discriminator_fake_loss=1.265, generator_loss=27.93, generator_mel_loss=18.17, generator_kl_loss=1.392, generator_dur_loss=1.755, generator_adv_loss=1.876, generator_feat_match_loss=4.736, over 68.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=27.93, generator_mel_loss=18.19, generator_kl_loss=1.363, generator_dur_loss=1.778, generator_adv_loss=1.94, generator_feat_match_loss=4.657, over 2015.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:14:45,966 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 15:14:54,034 INFO [train.py:591] (1/6) Epoch 383, validation: discriminator_loss=2.804, discriminator_real_loss=1.39, discriminator_fake_loss=1.414, generator_loss=27.16, generator_mel_loss=18.68, generator_kl_loss=1.291, generator_dur_loss=1.826, generator_adv_loss=1.773, generator_feat_match_loss=3.595, over 100.00 samples.
+2024-03-13 15:14:54,035 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 15:17:15,687 INFO [train.py:527] (1/6) Epoch 383, batch 82, global_batch_idx: 47450, batch size: 64, loss[discriminator_loss=2.696, discriminator_real_loss=1.349, discriminator_fake_loss=1.347, generator_loss=28.03, generator_mel_loss=18.05, generator_kl_loss=1.428, generator_dur_loss=1.67, generator_adv_loss=2.105, generator_feat_match_loss=4.777, over 64.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.347, generator_loss=27.94, generator_mel_loss=18.18, generator_kl_loss=1.409, generator_dur_loss=1.757, generator_adv_loss=1.944, generator_feat_match_loss=4.654, over 4784.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:19:12,285 INFO [train.py:919] (1/6) Start epoch 384
+2024-03-13 15:19:59,841 INFO [train.py:527] (1/6) Epoch 384, batch 8, global_batch_idx: 47500, batch size: 70, loss[discriminator_loss=2.772, discriminator_real_loss=1.445, discriminator_fake_loss=1.327, generator_loss=26.77, generator_mel_loss=18, generator_kl_loss=1.326, generator_dur_loss=1.75, generator_adv_loss=1.771, generator_feat_match_loss=3.924, over 70.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.364, discriminator_fake_loss=1.378, generator_loss=27.83, generator_mel_loss=18.17, generator_kl_loss=1.35, generator_dur_loss=1.771, generator_adv_loss=1.911, generator_feat_match_loss=4.628, over 562.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:22:23,501 INFO [train.py:527] (1/6) Epoch 384, batch 58, global_batch_idx: 47550, batch size: 83, loss[discriminator_loss=2.733, discriminator_real_loss=1.41, discriminator_fake_loss=1.323, generator_loss=27.28, generator_mel_loss=17.96, generator_kl_loss=1.266, generator_dur_loss=1.788, generator_adv_loss=1.907, generator_feat_match_loss=4.358, over 83.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.373, discriminator_fake_loss=1.35, generator_loss=27.74, generator_mel_loss=18.1, generator_kl_loss=1.402, generator_dur_loss=1.768, generator_adv_loss=1.927, generator_feat_match_loss=4.547, over 3560.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:24:42,701 INFO [train.py:527] (1/6) Epoch 384, batch 108, global_batch_idx: 47600, batch size: 72, loss[discriminator_loss=2.685, discriminator_real_loss=1.337, discriminator_fake_loss=1.349, generator_loss=28.68, generator_mel_loss=18.12, generator_kl_loss=1.291, generator_dur_loss=1.733, generator_adv_loss=2.018, generator_feat_match_loss=5.519, over 72.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=27.75, generator_mel_loss=18.08, generator_kl_loss=1.404, generator_dur_loss=1.758, generator_adv_loss=1.938, generator_feat_match_loss=4.568, over 6516.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:24:42,703 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 15:24:51,611 INFO [train.py:591] (1/6) Epoch 384, validation: discriminator_loss=2.74, discriminator_real_loss=1.424, discriminator_fake_loss=1.317, generator_loss=26.79, generator_mel_loss=18.36, generator_kl_loss=1.215, generator_dur_loss=1.759, generator_adv_loss=1.913, generator_feat_match_loss=3.538, over 100.00 samples.
+2024-03-13 15:24:51,611 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 15:25:32,874 INFO [train.py:919] (1/6) Start epoch 385
+2024-03-13 15:27:32,037 INFO [train.py:527] (1/6) Epoch 385, batch 34, global_batch_idx: 47650, batch size: 50, loss[discriminator_loss=2.724, discriminator_real_loss=1.414, discriminator_fake_loss=1.31, generator_loss=28.49, generator_mel_loss=19.02, generator_kl_loss=1.451, generator_dur_loss=1.671, generator_adv_loss=1.985, generator_feat_match_loss=4.36, over 50.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.379, discriminator_fake_loss=1.334, generator_loss=27.87, generator_mel_loss=18.16, generator_kl_loss=1.424, generator_dur_loss=1.727, generator_adv_loss=1.954, generator_feat_match_loss=4.603, over 2029.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:29:55,390 INFO [train.py:527] (1/6) Epoch 385, batch 84, global_batch_idx: 47700, batch size: 15, loss[discriminator_loss=2.747, discriminator_real_loss=1.445, discriminator_fake_loss=1.302, generator_loss=27.93, generator_mel_loss=18.6, generator_kl_loss=1.647, generator_dur_loss=1.557, generator_adv_loss=1.864, generator_feat_match_loss=4.262, over 15.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.85, generator_mel_loss=18.15, generator_kl_loss=1.419, generator_dur_loss=1.734, generator_adv_loss=1.952, generator_feat_match_loss=4.593, over 4868.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:31:46,597 INFO [train.py:919] (1/6) Start epoch 386
+2024-03-13 15:32:37,995 INFO [train.py:527] (1/6) Epoch 386, batch 10, global_batch_idx: 47750, batch size: 15, loss[discriminator_loss=2.827, discriminator_real_loss=1.383, discriminator_fake_loss=1.444, generator_loss=29.42, generator_mel_loss=18.31, generator_kl_loss=1.928, generator_dur_loss=1.576, generator_adv_loss=2.239, generator_feat_match_loss=5.363, over 15.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.391, discriminator_fake_loss=1.331, generator_loss=28.11, generator_mel_loss=18.14, generator_kl_loss=1.468, generator_dur_loss=1.688, generator_adv_loss=1.971, generator_feat_match_loss=4.838, over 451.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:34:59,654 INFO [train.py:527] (1/6) Epoch 386, batch 60, global_batch_idx: 47800, batch size: 44, loss[discriminator_loss=2.737, discriminator_real_loss=1.38, discriminator_fake_loss=1.357, generator_loss=28.11, generator_mel_loss=18.09, generator_kl_loss=1.587, generator_dur_loss=1.722, generator_adv_loss=1.903, generator_feat_match_loss=4.805, over 44.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.373, discriminator_fake_loss=1.345, generator_loss=28.06, generator_mel_loss=18.21, generator_kl_loss=1.432, generator_dur_loss=1.738, generator_adv_loss=1.949, generator_feat_match_loss=4.723, over 3241.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:34:59,655 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 15:35:07,967 INFO [train.py:591] (1/6) Epoch 386, validation: discriminator_loss=2.777, discriminator_real_loss=1.402, discriminator_fake_loss=1.375, generator_loss=26.5, generator_mel_loss=18.08, generator_kl_loss=1.129, generator_dur_loss=1.808, generator_adv_loss=1.855, generator_feat_match_loss=3.628, over 100.00 samples.
+2024-03-13 15:35:07,969 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 15:37:28,667 INFO [train.py:527] (1/6) Epoch 386, batch 110, global_batch_idx: 47850, batch size: 80, loss[discriminator_loss=2.69, discriminator_real_loss=1.373, discriminator_fake_loss=1.317, generator_loss=27.61, generator_mel_loss=18.25, generator_kl_loss=1.285, generator_dur_loss=1.842, generator_adv_loss=1.837, generator_feat_match_loss=4.392, over 80.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=27.96, generator_mel_loss=18.17, generator_kl_loss=1.416, generator_dur_loss=1.758, generator_adv_loss=1.934, generator_feat_match_loss=4.677, over 6278.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:38:05,403 INFO [train.py:919] (1/6) Start epoch 387
+2024-03-13 15:40:12,018 INFO [train.py:527] (1/6) Epoch 387, batch 36, global_batch_idx: 47900, batch size: 44, loss[discriminator_loss=2.728, discriminator_real_loss=1.33, discriminator_fake_loss=1.398, generator_loss=27.77, generator_mel_loss=18.17, generator_kl_loss=1.549, generator_dur_loss=1.691, generator_adv_loss=1.996, generator_feat_match_loss=4.36, over 44.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.383, discriminator_fake_loss=1.347, generator_loss=27.74, generator_mel_loss=18.09, generator_kl_loss=1.429, generator_dur_loss=1.747, generator_adv_loss=1.944, generator_feat_match_loss=4.536, over 2059.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:42:31,094 INFO [train.py:527] (1/6) Epoch 387, batch 86, global_batch_idx: 47950, batch size: 64, loss[discriminator_loss=2.688, discriminator_real_loss=1.256, discriminator_fake_loss=1.432, generator_loss=29.3, generator_mel_loss=18.37, generator_kl_loss=1.395, generator_dur_loss=1.775, generator_adv_loss=2.216, generator_feat_match_loss=5.541, over 64.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.374, discriminator_fake_loss=1.352, generator_loss=27.79, generator_mel_loss=18.09, generator_kl_loss=1.419, generator_dur_loss=1.76, generator_adv_loss=1.948, generator_feat_match_loss=4.567, over 5039.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:44:16,588 INFO [train.py:919] (1/6) Start epoch 388
+2024-03-13 15:45:13,998 INFO [train.py:527] (1/6) Epoch 388, batch 12, global_batch_idx: 48000, batch size: 47, loss[discriminator_loss=2.676, discriminator_real_loss=1.283, discriminator_fake_loss=1.393, generator_loss=29.37, generator_mel_loss=18.66, generator_kl_loss=1.665, generator_dur_loss=1.637, generator_adv_loss=2.043, generator_feat_match_loss=5.366, over 47.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=28.25, generator_mel_loss=18.41, generator_kl_loss=1.446, generator_dur_loss=1.736, generator_adv_loss=1.945, generator_feat_match_loss=4.715, over 711.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:45:14,000 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 15:45:21,914 INFO [train.py:591] (1/6) Epoch 388, validation: discriminator_loss=2.76, discriminator_real_loss=1.511, discriminator_fake_loss=1.249, generator_loss=26.7, generator_mel_loss=18.05, generator_kl_loss=1.114, generator_dur_loss=1.821, generator_adv_loss=2.015, generator_feat_match_loss=3.705, over 100.00 samples.
+2024-03-13 15:45:21,915 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 15:47:41,589 INFO [train.py:527] (1/6) Epoch 388, batch 62, global_batch_idx: 48050, batch size: 44, loss[discriminator_loss=2.652, discriminator_real_loss=1.324, discriminator_fake_loss=1.327, generator_loss=27.75, generator_mel_loss=17.84, generator_kl_loss=1.385, generator_dur_loss=1.713, generator_adv_loss=1.929, generator_feat_match_loss=4.883, over 44.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.378, discriminator_fake_loss=1.334, generator_loss=27.96, generator_mel_loss=18.18, generator_kl_loss=1.404, generator_dur_loss=1.756, generator_adv_loss=1.943, generator_feat_match_loss=4.675, over 3706.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:50:02,378 INFO [train.py:527] (1/6) Epoch 388, batch 112, global_batch_idx: 48100, batch size: 48, loss[discriminator_loss=2.707, discriminator_real_loss=1.391, discriminator_fake_loss=1.316, generator_loss=28.78, generator_mel_loss=18.47, generator_kl_loss=1.46, generator_dur_loss=1.694, generator_adv_loss=1.947, generator_feat_match_loss=5.203, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.337, generator_loss=27.92, generator_mel_loss=18.16, generator_kl_loss=1.416, generator_dur_loss=1.761, generator_adv_loss=1.94, generator_feat_match_loss=4.642, over 6584.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:50:34,786 INFO [train.py:919] (1/6) Start epoch 389
+2024-03-13 15:52:48,389 INFO [train.py:527] (1/6) Epoch 389, batch 38, global_batch_idx: 48150, batch size: 44, loss[discriminator_loss=2.648, discriminator_real_loss=1.28, discriminator_fake_loss=1.367, generator_loss=29.47, generator_mel_loss=18.6, generator_kl_loss=1.682, generator_dur_loss=1.68, generator_adv_loss=2.048, generator_feat_match_loss=5.458, over 44.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.368, discriminator_fake_loss=1.355, generator_loss=28.06, generator_mel_loss=18.2, generator_kl_loss=1.428, generator_dur_loss=1.761, generator_adv_loss=1.946, generator_feat_match_loss=4.726, over 2220.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:55:12,721 INFO [train.py:527] (1/6) Epoch 389, batch 88, global_batch_idx: 48200, batch size: 53, loss[discriminator_loss=2.75, discriminator_real_loss=1.46, discriminator_fake_loss=1.29, generator_loss=28.19, generator_mel_loss=18.49, generator_kl_loss=1.36, generator_dur_loss=1.7, generator_adv_loss=1.792, generator_feat_match_loss=4.845, over 53.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=27.98, generator_mel_loss=18.18, generator_kl_loss=1.419, generator_dur_loss=1.767, generator_adv_loss=1.946, generator_feat_match_loss=4.672, over 5113.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 15:55:12,722 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 15:55:21,782 INFO [train.py:591] (1/6) Epoch 389, validation: discriminator_loss=2.788, discriminator_real_loss=1.392, discriminator_fake_loss=1.397, generator_loss=27.36, generator_mel_loss=18.79, generator_kl_loss=1.243, generator_dur_loss=1.835, generator_adv_loss=1.774, generator_feat_match_loss=3.719, over 100.00 samples.
+2024-03-13 15:55:21,783 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 15:56:57,493 INFO [train.py:919] (1/6) Start epoch 390
+2024-03-13 15:58:02,087 INFO [train.py:527] (1/6) Epoch 390, batch 14, global_batch_idx: 48250, batch size: 68, loss[discriminator_loss=2.75, discriminator_real_loss=1.281, discriminator_fake_loss=1.469, generator_loss=27.25, generator_mel_loss=17.9, generator_kl_loss=1.269, generator_dur_loss=1.751, generator_adv_loss=1.959, generator_feat_match_loss=4.379, over 68.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.368, discriminator_fake_loss=1.358, generator_loss=27.75, generator_mel_loss=18.12, generator_kl_loss=1.417, generator_dur_loss=1.737, generator_adv_loss=1.914, generator_feat_match_loss=4.566, over 809.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 16:00:21,677 INFO [train.py:527] (1/6) Epoch 390, batch 64, global_batch_idx: 48300, batch size: 68, loss[discriminator_loss=2.748, discriminator_real_loss=1.445, discriminator_fake_loss=1.302, generator_loss=27.55, generator_mel_loss=17.93, generator_kl_loss=1.468, generator_dur_loss=1.797, generator_adv_loss=1.786, generator_feat_match_loss=4.567, over 68.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.96, generator_mel_loss=18.14, generator_kl_loss=1.407, generator_dur_loss=1.761, generator_adv_loss=1.957, generator_feat_match_loss=4.696, over 3689.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 16:02:39,590 INFO [train.py:527] (1/6) Epoch 390, batch 114, global_batch_idx: 48350, batch size: 12, loss[discriminator_loss=2.659, discriminator_real_loss=1.342, discriminator_fake_loss=1.317, generator_loss=29.98, generator_mel_loss=19.34, generator_kl_loss=1.927, generator_dur_loss=1.661, generator_adv_loss=2.008, generator_feat_match_loss=5.045, over 12.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.365, discriminator_fake_loss=1.345, generator_loss=28.01, generator_mel_loss=18.14, generator_kl_loss=1.403, generator_dur_loss=1.768, generator_adv_loss=1.966, generator_feat_match_loss=4.738, over 6575.00 samples.], cur_lr_g: 1.91e-04, cur_lr_d: 1.91e-04, 
+2024-03-13 16:03:06,124 INFO [train.py:919] (1/6) Start epoch 391
+2024-03-13 16:05:21,061 INFO [train.py:527] (1/6) Epoch 391, batch 40, global_batch_idx: 48400, batch size: 16, loss[discriminator_loss=2.803, discriminator_real_loss=1.365, discriminator_fake_loss=1.438, generator_loss=28.32, generator_mel_loss=18.62, generator_kl_loss=1.642, generator_dur_loss=1.629, generator_adv_loss=1.997, generator_feat_match_loss=4.433, over 16.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.376, discriminator_fake_loss=1.353, generator_loss=27.65, generator_mel_loss=18.05, generator_kl_loss=1.385, generator_dur_loss=1.774, generator_adv_loss=1.927, generator_feat_match_loss=4.518, over 2501.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:05:21,062 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 16:05:28,905 INFO [train.py:591] (1/6) Epoch 391, validation: discriminator_loss=2.794, discriminator_real_loss=1.506, discriminator_fake_loss=1.288, generator_loss=27.61, generator_mel_loss=18.79, generator_kl_loss=1.207, generator_dur_loss=1.81, generator_adv_loss=1.969, generator_feat_match_loss=3.84, over 100.00 samples.
+2024-03-13 16:05:28,906 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 16:07:47,742 INFO [train.py:527] (1/6) Epoch 391, batch 90, global_batch_idx: 48450, batch size: 49, loss[discriminator_loss=2.667, discriminator_real_loss=1.418, discriminator_fake_loss=1.249, generator_loss=28.56, generator_mel_loss=18.16, generator_kl_loss=1.537, generator_dur_loss=1.678, generator_adv_loss=1.832, generator_feat_match_loss=5.35, over 49.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.372, discriminator_fake_loss=1.346, generator_loss=27.82, generator_mel_loss=18.1, generator_kl_loss=1.406, generator_dur_loss=1.756, generator_adv_loss=1.937, generator_feat_match_loss=4.618, over 5184.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:09:22,858 INFO [train.py:919] (1/6) Start epoch 392
+2024-03-13 16:10:32,246 INFO [train.py:527] (1/6) Epoch 392, batch 16, global_batch_idx: 48500, batch size: 68, loss[discriminator_loss=2.729, discriminator_real_loss=1.448, discriminator_fake_loss=1.282, generator_loss=27.65, generator_mel_loss=18.15, generator_kl_loss=1.368, generator_dur_loss=1.813, generator_adv_loss=1.798, generator_feat_match_loss=4.519, over 68.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.356, discriminator_fake_loss=1.35, generator_loss=27.96, generator_mel_loss=18.13, generator_kl_loss=1.388, generator_dur_loss=1.768, generator_adv_loss=1.938, generator_feat_match_loss=4.732, over 984.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:12:50,658 INFO [train.py:527] (1/6) Epoch 392, batch 66, global_batch_idx: 48550, batch size: 55, loss[discriminator_loss=2.664, discriminator_real_loss=1.335, discriminator_fake_loss=1.329, generator_loss=27.99, generator_mel_loss=18.36, generator_kl_loss=1.374, generator_dur_loss=1.731, generator_adv_loss=2.119, generator_feat_match_loss=4.405, over 55.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=27.91, generator_mel_loss=18.14, generator_kl_loss=1.38, generator_dur_loss=1.762, generator_adv_loss=1.942, generator_feat_match_loss=4.69, over 3959.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:15:11,566 INFO [train.py:527] (1/6) Epoch 392, batch 116, global_batch_idx: 48600, batch size: 45, loss[discriminator_loss=2.705, discriminator_real_loss=1.307, discriminator_fake_loss=1.398, generator_loss=27.54, generator_mel_loss=17.81, generator_kl_loss=1.477, generator_dur_loss=1.687, generator_adv_loss=1.867, generator_feat_match_loss=4.702, over 45.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.338, generator_loss=27.91, generator_mel_loss=18.16, generator_kl_loss=1.403, generator_dur_loss=1.752, generator_adv_loss=1.941, generator_feat_match_loss=4.659, over 6717.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:15:11,568 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 16:15:20,602 INFO [train.py:591] (1/6) Epoch 392, validation: discriminator_loss=2.784, discriminator_real_loss=1.422, discriminator_fake_loss=1.362, generator_loss=26.41, generator_mel_loss=18.19, generator_kl_loss=1.158, generator_dur_loss=1.815, generator_adv_loss=1.784, generator_feat_match_loss=3.458, over 100.00 samples.
+2024-03-13 16:15:20,602 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 16:15:40,927 INFO [train.py:919] (1/6) Start epoch 393
+2024-03-13 16:18:01,803 INFO [train.py:527] (1/6) Epoch 393, batch 42, global_batch_idx: 48650, batch size: 77, loss[discriminator_loss=2.703, discriminator_real_loss=1.415, discriminator_fake_loss=1.288, generator_loss=27.89, generator_mel_loss=17.84, generator_kl_loss=1.354, generator_dur_loss=1.792, generator_adv_loss=2.011, generator_feat_match_loss=4.89, over 77.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=27.92, generator_mel_loss=18.1, generator_kl_loss=1.397, generator_dur_loss=1.763, generator_adv_loss=1.977, generator_feat_match_loss=4.68, over 2529.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:20:18,270 INFO [train.py:527] (1/6) Epoch 393, batch 92, global_batch_idx: 48700, batch size: 31, loss[discriminator_loss=2.758, discriminator_real_loss=1.469, discriminator_fake_loss=1.289, generator_loss=27.84, generator_mel_loss=18.06, generator_kl_loss=1.481, generator_dur_loss=1.606, generator_adv_loss=2.025, generator_feat_match_loss=4.664, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=27.85, generator_mel_loss=18.08, generator_kl_loss=1.402, generator_dur_loss=1.752, generator_adv_loss=1.964, generator_feat_match_loss=4.659, over 5347.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:21:48,338 INFO [train.py:919] (1/6) Start epoch 394
+2024-03-13 16:23:03,807 INFO [train.py:527] (1/6) Epoch 394, batch 18, global_batch_idx: 48750, batch size: 39, loss[discriminator_loss=2.697, discriminator_real_loss=1.391, discriminator_fake_loss=1.307, generator_loss=27.99, generator_mel_loss=18.47, generator_kl_loss=1.402, generator_dur_loss=1.655, generator_adv_loss=1.891, generator_feat_match_loss=4.578, over 39.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.36, discriminator_fake_loss=1.338, generator_loss=28.02, generator_mel_loss=18.2, generator_kl_loss=1.438, generator_dur_loss=1.72, generator_adv_loss=1.934, generator_feat_match_loss=4.731, over 1044.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:25:26,067 INFO [train.py:527] (1/6) Epoch 394, batch 68, global_batch_idx: 48800, batch size: 62, loss[discriminator_loss=2.736, discriminator_real_loss=1.477, discriminator_fake_loss=1.259, generator_loss=27.71, generator_mel_loss=18.42, generator_kl_loss=1.252, generator_dur_loss=1.736, generator_adv_loss=1.885, generator_feat_match_loss=4.417, over 62.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=27.98, generator_mel_loss=18.17, generator_kl_loss=1.412, generator_dur_loss=1.757, generator_adv_loss=1.932, generator_feat_match_loss=4.712, over 4034.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:25:26,069 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 16:25:34,359 INFO [train.py:591] (1/6) Epoch 394, validation: discriminator_loss=2.725, discriminator_real_loss=1.438, discriminator_fake_loss=1.287, generator_loss=26.54, generator_mel_loss=18.13, generator_kl_loss=1.228, generator_dur_loss=1.831, generator_adv_loss=1.875, generator_feat_match_loss=3.482, over 100.00 samples.
+2024-03-13 16:25:34,360 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 16:27:50,221 INFO [train.py:527] (1/6) Epoch 394, batch 118, global_batch_idx: 48850, batch size: 31, loss[discriminator_loss=2.703, discriminator_real_loss=1.327, discriminator_fake_loss=1.376, generator_loss=27.9, generator_mel_loss=18.04, generator_kl_loss=1.469, generator_dur_loss=1.615, generator_adv_loss=1.886, generator_feat_match_loss=4.891, over 31.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=28, generator_mel_loss=18.18, generator_kl_loss=1.418, generator_dur_loss=1.747, generator_adv_loss=1.941, generator_feat_match_loss=4.72, over 6562.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:28:04,641 INFO [train.py:919] (1/6) Start epoch 395
+2024-03-13 16:30:31,073 INFO [train.py:527] (1/6) Epoch 395, batch 44, global_batch_idx: 48900, batch size: 48, loss[discriminator_loss=2.718, discriminator_real_loss=1.362, discriminator_fake_loss=1.356, generator_loss=28.03, generator_mel_loss=18.3, generator_kl_loss=1.582, generator_dur_loss=1.671, generator_adv_loss=1.925, generator_feat_match_loss=4.557, over 48.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.384, discriminator_fake_loss=1.337, generator_loss=27.87, generator_mel_loss=18.16, generator_kl_loss=1.435, generator_dur_loss=1.734, generator_adv_loss=1.946, generator_feat_match_loss=4.597, over 2296.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:32:51,174 INFO [train.py:527] (1/6) Epoch 395, batch 94, global_batch_idx: 48950, batch size: 31, loss[discriminator_loss=2.722, discriminator_real_loss=1.344, discriminator_fake_loss=1.378, generator_loss=28.88, generator_mel_loss=18.52, generator_kl_loss=1.626, generator_dur_loss=1.584, generator_adv_loss=2.103, generator_feat_match_loss=5.051, over 31.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=27.86, generator_mel_loss=18.11, generator_kl_loss=1.408, generator_dur_loss=1.759, generator_adv_loss=1.949, generator_feat_match_loss=4.64, over 5531.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:34:12,710 INFO [train.py:919] (1/6) Start epoch 396
+2024-03-13 16:35:35,009 INFO [train.py:527] (1/6) Epoch 396, batch 20, global_batch_idx: 49000, batch size: 77, loss[discriminator_loss=2.708, discriminator_real_loss=1.357, discriminator_fake_loss=1.351, generator_loss=27.83, generator_mel_loss=17.86, generator_kl_loss=1.402, generator_dur_loss=1.866, generator_adv_loss=1.904, generator_feat_match_loss=4.804, over 77.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.337, generator_loss=27.87, generator_mel_loss=18.11, generator_kl_loss=1.386, generator_dur_loss=1.787, generator_adv_loss=1.961, generator_feat_match_loss=4.627, over 1267.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:35:35,011 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 16:35:43,003 INFO [train.py:591] (1/6) Epoch 396, validation: discriminator_loss=2.766, discriminator_real_loss=1.413, discriminator_fake_loss=1.353, generator_loss=27.43, generator_mel_loss=18.44, generator_kl_loss=1.212, generator_dur_loss=1.83, generator_adv_loss=1.883, generator_feat_match_loss=4.072, over 100.00 samples.
+2024-03-13 16:35:43,004 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 16:37:59,755 INFO [train.py:527] (1/6) Epoch 396, batch 70, global_batch_idx: 49050, batch size: 70, loss[discriminator_loss=2.71, discriminator_real_loss=1.425, discriminator_fake_loss=1.285, generator_loss=27.22, generator_mel_loss=17.82, generator_kl_loss=1.296, generator_dur_loss=1.838, generator_adv_loss=1.902, generator_feat_match_loss=4.366, over 70.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.371, discriminator_fake_loss=1.348, generator_loss=27.9, generator_mel_loss=18.13, generator_kl_loss=1.4, generator_dur_loss=1.778, generator_adv_loss=1.941, generator_feat_match_loss=4.652, over 4142.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:40:17,396 INFO [train.py:527] (1/6) Epoch 396, batch 120, global_batch_idx: 49100, batch size: 32, loss[discriminator_loss=2.706, discriminator_real_loss=1.391, discriminator_fake_loss=1.315, generator_loss=28.34, generator_mel_loss=18.68, generator_kl_loss=1.52, generator_dur_loss=1.637, generator_adv_loss=2.159, generator_feat_match_loss=4.352, over 32.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.369, discriminator_fake_loss=1.346, generator_loss=27.95, generator_mel_loss=18.14, generator_kl_loss=1.406, generator_dur_loss=1.763, generator_adv_loss=1.952, generator_feat_match_loss=4.693, over 7022.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:40:26,449 INFO [train.py:919] (1/6) Start epoch 397
+2024-03-13 16:42:57,344 INFO [train.py:527] (1/6) Epoch 397, batch 46, global_batch_idx: 49150, batch size: 56, loss[discriminator_loss=2.69, discriminator_real_loss=1.413, discriminator_fake_loss=1.277, generator_loss=27.8, generator_mel_loss=18.08, generator_kl_loss=1.353, generator_dur_loss=1.723, generator_adv_loss=1.978, generator_feat_match_loss=4.67, over 56.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.385, discriminator_fake_loss=1.338, generator_loss=27.95, generator_mel_loss=18.14, generator_kl_loss=1.417, generator_dur_loss=1.732, generator_adv_loss=1.957, generator_feat_match_loss=4.697, over 2610.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:45:16,659 INFO [train.py:527] (1/6) Epoch 397, batch 96, global_batch_idx: 49200, batch size: 56, loss[discriminator_loss=2.797, discriminator_real_loss=1.534, discriminator_fake_loss=1.263, generator_loss=27.01, generator_mel_loss=17.97, generator_kl_loss=1.664, generator_dur_loss=1.692, generator_adv_loss=1.678, generator_feat_match_loss=4.003, over 56.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.381, discriminator_fake_loss=1.346, generator_loss=27.88, generator_mel_loss=18.11, generator_kl_loss=1.413, generator_dur_loss=1.737, generator_adv_loss=1.941, generator_feat_match_loss=4.674, over 5543.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:45:16,661 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 16:45:25,872 INFO [train.py:591] (1/6) Epoch 397, validation: discriminator_loss=2.879, discriminator_real_loss=1.341, discriminator_fake_loss=1.538, generator_loss=26.4, generator_mel_loss=18.34, generator_kl_loss=1.162, generator_dur_loss=1.801, generator_adv_loss=1.615, generator_feat_match_loss=3.478, over 100.00 samples.
+2024-03-13 16:45:25,874 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 16:46:39,263 INFO [train.py:919] (1/6) Start epoch 398
+2024-03-13 16:48:06,586 INFO [train.py:527] (1/6) Epoch 398, batch 22, global_batch_idx: 49250, batch size: 83, loss[discriminator_loss=2.735, discriminator_real_loss=1.413, discriminator_fake_loss=1.322, generator_loss=27.77, generator_mel_loss=18.17, generator_kl_loss=1.266, generator_dur_loss=1.824, generator_adv_loss=1.984, generator_feat_match_loss=4.525, over 83.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.378, discriminator_fake_loss=1.322, generator_loss=27.98, generator_mel_loss=18.21, generator_kl_loss=1.408, generator_dur_loss=1.717, generator_adv_loss=1.963, generator_feat_match_loss=4.678, over 1226.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:50:27,731 INFO [train.py:527] (1/6) Epoch 398, batch 72, global_batch_idx: 49300, batch size: 62, loss[discriminator_loss=2.744, discriminator_real_loss=1.367, discriminator_fake_loss=1.377, generator_loss=26.91, generator_mel_loss=17.92, generator_kl_loss=1.438, generator_dur_loss=1.718, generator_adv_loss=1.877, generator_feat_match_loss=3.954, over 62.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=27.97, generator_mel_loss=18.18, generator_kl_loss=1.433, generator_dur_loss=1.721, generator_adv_loss=1.948, generator_feat_match_loss=4.687, over 4156.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:52:46,006 INFO [train.py:527] (1/6) Epoch 398, batch 122, global_batch_idx: 49350, batch size: 59, loss[discriminator_loss=2.751, discriminator_real_loss=1.447, discriminator_fake_loss=1.304, generator_loss=27.82, generator_mel_loss=17.99, generator_kl_loss=1.419, generator_dur_loss=1.726, generator_adv_loss=1.943, generator_feat_match_loss=4.746, over 59.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.16, generator_kl_loss=1.428, generator_dur_loss=1.726, generator_adv_loss=1.941, generator_feat_match_loss=4.697, over 6997.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:52:51,091 INFO [train.py:919] (1/6) Start epoch 399
+2024-03-13 16:55:32,744 INFO [train.py:527] (1/6) Epoch 399, batch 48, global_batch_idx: 49400, batch size: 83, loss[discriminator_loss=2.737, discriminator_real_loss=1.399, discriminator_fake_loss=1.338, generator_loss=27.66, generator_mel_loss=17.99, generator_kl_loss=1.333, generator_dur_loss=1.824, generator_adv_loss=1.781, generator_feat_match_loss=4.737, over 83.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.95, generator_mel_loss=18.17, generator_kl_loss=1.411, generator_dur_loss=1.724, generator_adv_loss=1.947, generator_feat_match_loss=4.693, over 2773.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:55:32,745 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 16:55:40,681 INFO [train.py:591] (1/6) Epoch 399, validation: discriminator_loss=2.758, discriminator_real_loss=1.359, discriminator_fake_loss=1.399, generator_loss=26.71, generator_mel_loss=17.97, generator_kl_loss=1.191, generator_dur_loss=1.789, generator_adv_loss=1.772, generator_feat_match_loss=3.995, over 100.00 samples.
+2024-03-13 16:55:40,682 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 16:58:02,382 INFO [train.py:527] (1/6) Epoch 399, batch 98, global_batch_idx: 49450, batch size: 96, loss[discriminator_loss=2.747, discriminator_real_loss=1.333, discriminator_fake_loss=1.414, generator_loss=27.31, generator_mel_loss=17.64, generator_kl_loss=1.316, generator_dur_loss=1.911, generator_adv_loss=1.918, generator_feat_match_loss=4.533, over 96.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=27.93, generator_mel_loss=18.13, generator_kl_loss=1.414, generator_dur_loss=1.743, generator_adv_loss=1.957, generator_feat_match_loss=4.681, over 5659.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 16:59:12,545 INFO [train.py:919] (1/6) Start epoch 400
+2024-03-13 17:00:45,607 INFO [train.py:527] (1/6) Epoch 400, batch 24, global_batch_idx: 49500, batch size: 44, loss[discriminator_loss=2.741, discriminator_real_loss=1.444, discriminator_fake_loss=1.298, generator_loss=27.16, generator_mel_loss=18.18, generator_kl_loss=1.562, generator_dur_loss=1.736, generator_adv_loss=1.792, generator_feat_match_loss=3.89, over 44.00 samples.], tot_loss[discriminator_loss=2.737, discriminator_real_loss=1.393, discriminator_fake_loss=1.344, generator_loss=27.87, generator_mel_loss=18.13, generator_kl_loss=1.412, generator_dur_loss=1.76, generator_adv_loss=1.945, generator_feat_match_loss=4.627, over 1408.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:03:05,373 INFO [train.py:527] (1/6) Epoch 400, batch 74, global_batch_idx: 49550, batch size: 77, loss[discriminator_loss=2.705, discriminator_real_loss=1.36, discriminator_fake_loss=1.345, generator_loss=27.78, generator_mel_loss=18.12, generator_kl_loss=1.302, generator_dur_loss=1.843, generator_adv_loss=1.947, generator_feat_match_loss=4.573, over 77.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=27.91, generator_mel_loss=18.16, generator_kl_loss=1.416, generator_dur_loss=1.763, generator_adv_loss=1.939, generator_feat_match_loss=4.635, over 4224.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:05:20,755 INFO [train.py:919] (1/6) Start epoch 401
+2024-03-13 17:05:44,942 INFO [train.py:527] (1/6) Epoch 401, batch 0, global_batch_idx: 49600, batch size: 59, loss[discriminator_loss=2.773, discriminator_real_loss=1.46, discriminator_fake_loss=1.312, generator_loss=27.93, generator_mel_loss=18.54, generator_kl_loss=1.408, generator_dur_loss=1.724, generator_adv_loss=1.825, generator_feat_match_loss=4.435, over 59.00 samples.], tot_loss[discriminator_loss=2.773, discriminator_real_loss=1.46, discriminator_fake_loss=1.312, generator_loss=27.93, generator_mel_loss=18.54, generator_kl_loss=1.408, generator_dur_loss=1.724, generator_adv_loss=1.825, generator_feat_match_loss=4.435, over 59.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:05:44,945 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 17:05:52,962 INFO [train.py:591] (1/6) Epoch 401, validation: discriminator_loss=2.752, discriminator_real_loss=1.332, discriminator_fake_loss=1.42, generator_loss=26.54, generator_mel_loss=18.37, generator_kl_loss=1.296, generator_dur_loss=1.826, generator_adv_loss=1.745, generator_feat_match_loss=3.304, over 100.00 samples.
+2024-03-13 17:05:52,964 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 17:08:10,605 INFO [train.py:527] (1/6) Epoch 401, batch 50, global_batch_idx: 49650, batch size: 31, loss[discriminator_loss=2.741, discriminator_real_loss=1.433, discriminator_fake_loss=1.308, generator_loss=28.28, generator_mel_loss=17.75, generator_kl_loss=1.588, generator_dur_loss=1.678, generator_adv_loss=2.02, generator_feat_match_loss=5.241, over 31.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.345, generator_loss=27.89, generator_mel_loss=18.06, generator_kl_loss=1.412, generator_dur_loss=1.757, generator_adv_loss=1.96, generator_feat_match_loss=4.697, over 2899.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:10:29,545 INFO [train.py:527] (1/6) Epoch 401, batch 100, global_batch_idx: 49700, batch size: 96, loss[discriminator_loss=2.706, discriminator_real_loss=1.382, discriminator_fake_loss=1.324, generator_loss=27.79, generator_mel_loss=17.88, generator_kl_loss=1.298, generator_dur_loss=1.886, generator_adv_loss=2.002, generator_feat_match_loss=4.716, over 96.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.38, discriminator_fake_loss=1.339, generator_loss=27.9, generator_mel_loss=18.09, generator_kl_loss=1.413, generator_dur_loss=1.76, generator_adv_loss=1.954, generator_feat_match_loss=4.685, over 5695.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:11:35,146 INFO [train.py:919] (1/6) Start epoch 402
+2024-03-13 17:13:12,023 INFO [train.py:527] (1/6) Epoch 402, batch 26, global_batch_idx: 49750, batch size: 44, loss[discriminator_loss=2.84, discriminator_real_loss=1.559, discriminator_fake_loss=1.281, generator_loss=27.3, generator_mel_loss=18.1, generator_kl_loss=1.427, generator_dur_loss=1.736, generator_adv_loss=1.751, generator_feat_match_loss=4.29, over 44.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.352, discriminator_fake_loss=1.349, generator_loss=28.06, generator_mel_loss=18.1, generator_kl_loss=1.424, generator_dur_loss=1.765, generator_adv_loss=1.999, generator_feat_match_loss=4.779, over 1506.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:15:32,458 INFO [train.py:527] (1/6) Epoch 402, batch 76, global_batch_idx: 49800, batch size: 62, loss[discriminator_loss=2.743, discriminator_real_loss=1.462, discriminator_fake_loss=1.281, generator_loss=27.22, generator_mel_loss=18.03, generator_kl_loss=1.296, generator_dur_loss=1.796, generator_adv_loss=1.851, generator_feat_match_loss=4.247, over 62.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.379, discriminator_fake_loss=1.337, generator_loss=27.91, generator_mel_loss=18.11, generator_kl_loss=1.411, generator_dur_loss=1.766, generator_adv_loss=1.973, generator_feat_match_loss=4.658, over 4422.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:15:32,460 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 17:15:40,609 INFO [train.py:591] (1/6) Epoch 402, validation: discriminator_loss=2.792, discriminator_real_loss=1.35, discriminator_fake_loss=1.442, generator_loss=27.21, generator_mel_loss=18.53, generator_kl_loss=1.328, generator_dur_loss=1.833, generator_adv_loss=1.747, generator_feat_match_loss=3.771, over 100.00 samples.
+2024-03-13 17:15:40,610 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 17:17:51,078 INFO [train.py:919] (1/6) Start epoch 403
+2024-03-13 17:18:19,573 INFO [train.py:527] (1/6) Epoch 403, batch 2, global_batch_idx: 49850, batch size: 72, loss[discriminator_loss=2.69, discriminator_real_loss=1.356, discriminator_fake_loss=1.334, generator_loss=27.32, generator_mel_loss=17.87, generator_kl_loss=1.279, generator_dur_loss=1.782, generator_adv_loss=1.93, generator_feat_match_loss=4.456, over 72.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.357, discriminator_fake_loss=1.328, generator_loss=27.79, generator_mel_loss=17.96, generator_kl_loss=1.392, generator_dur_loss=1.751, generator_adv_loss=1.94, generator_feat_match_loss=4.747, over 183.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:20:38,883 INFO [train.py:527] (1/6) Epoch 403, batch 52, global_batch_idx: 49900, batch size: 26, loss[discriminator_loss=2.708, discriminator_real_loss=1.422, discriminator_fake_loss=1.286, generator_loss=27.03, generator_mel_loss=17.75, generator_kl_loss=1.627, generator_dur_loss=1.586, generator_adv_loss=2.01, generator_feat_match_loss=4.058, over 26.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.367, discriminator_fake_loss=1.341, generator_loss=28.02, generator_mel_loss=18.17, generator_kl_loss=1.389, generator_dur_loss=1.769, generator_adv_loss=1.949, generator_feat_match_loss=4.748, over 3135.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:22:55,455 INFO [train.py:527] (1/6) Epoch 403, batch 102, global_batch_idx: 49950, batch size: 72, loss[discriminator_loss=2.733, discriminator_real_loss=1.407, discriminator_fake_loss=1.326, generator_loss=27.94, generator_mel_loss=18.11, generator_kl_loss=1.438, generator_dur_loss=1.824, generator_adv_loss=1.975, generator_feat_match_loss=4.594, over 72.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.01, generator_mel_loss=18.16, generator_kl_loss=1.405, generator_dur_loss=1.767, generator_adv_loss=1.942, generator_feat_match_loss=4.736, over 5971.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:23:56,848 INFO [train.py:919] (1/6) Start epoch 404
+2024-03-13 17:25:39,461 INFO [train.py:527] (1/6) Epoch 404, batch 28, global_batch_idx: 50000, batch size: 44, loss[discriminator_loss=2.706, discriminator_real_loss=1.325, discriminator_fake_loss=1.381, generator_loss=28.67, generator_mel_loss=18.28, generator_kl_loss=1.495, generator_dur_loss=1.682, generator_adv_loss=1.893, generator_feat_match_loss=5.314, over 44.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.365, discriminator_fake_loss=1.35, generator_loss=27.81, generator_mel_loss=18, generator_kl_loss=1.413, generator_dur_loss=1.75, generator_adv_loss=1.938, generator_feat_match_loss=4.713, over 1652.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:25:39,462 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 17:25:47,417 INFO [train.py:591] (1/6) Epoch 404, validation: discriminator_loss=2.75, discriminator_real_loss=1.455, discriminator_fake_loss=1.296, generator_loss=26.89, generator_mel_loss=18.19, generator_kl_loss=1.2, generator_dur_loss=1.796, generator_adv_loss=1.865, generator_feat_match_loss=3.839, over 100.00 samples.
+2024-03-13 17:25:47,418 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 17:28:05,598 INFO [train.py:527] (1/6) Epoch 404, batch 78, global_batch_idx: 50050, batch size: 47, loss[discriminator_loss=2.689, discriminator_real_loss=1.402, discriminator_fake_loss=1.287, generator_loss=28.45, generator_mel_loss=18.37, generator_kl_loss=1.443, generator_dur_loss=1.713, generator_adv_loss=1.962, generator_feat_match_loss=4.965, over 47.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.363, discriminator_fake_loss=1.348, generator_loss=27.86, generator_mel_loss=18.07, generator_kl_loss=1.411, generator_dur_loss=1.747, generator_adv_loss=1.935, generator_feat_match_loss=4.705, over 4661.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:30:10,200 INFO [train.py:919] (1/6) Start epoch 405
+2024-03-13 17:30:43,953 INFO [train.py:527] (1/6) Epoch 405, batch 4, global_batch_idx: 50100, batch size: 68, loss[discriminator_loss=2.653, discriminator_real_loss=1.363, discriminator_fake_loss=1.29, generator_loss=28.59, generator_mel_loss=18.35, generator_kl_loss=1.35, generator_dur_loss=1.724, generator_adv_loss=1.964, generator_feat_match_loss=5.208, over 68.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.336, discriminator_fake_loss=1.334, generator_loss=28, generator_mel_loss=18.16, generator_kl_loss=1.292, generator_dur_loss=1.782, generator_adv_loss=1.95, generator_feat_match_loss=4.82, over 341.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:33:06,899 INFO [train.py:527] (1/6) Epoch 405, batch 54, global_batch_idx: 50150, batch size: 59, loss[discriminator_loss=2.747, discriminator_real_loss=1.437, discriminator_fake_loss=1.309, generator_loss=27.94, generator_mel_loss=18.09, generator_kl_loss=1.359, generator_dur_loss=1.766, generator_adv_loss=1.905, generator_feat_match_loss=4.823, over 59.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.37, discriminator_fake_loss=1.333, generator_loss=27.91, generator_mel_loss=18.09, generator_kl_loss=1.397, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=4.708, over 3244.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:35:24,881 INFO [train.py:527] (1/6) Epoch 405, batch 104, global_batch_idx: 50200, batch size: 59, loss[discriminator_loss=2.709, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.19, generator_mel_loss=18.26, generator_kl_loss=1.317, generator_dur_loss=1.722, generator_adv_loss=1.939, generator_feat_match_loss=4.952, over 59.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=27.95, generator_mel_loss=18.11, generator_kl_loss=1.406, generator_dur_loss=1.757, generator_adv_loss=1.952, generator_feat_match_loss=4.725, over 5993.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:35:24,882 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 17:35:33,902 INFO [train.py:591] (1/6) Epoch 405, validation: discriminator_loss=2.778, discriminator_real_loss=1.448, discriminator_fake_loss=1.33, generator_loss=28.05, generator_mel_loss=18.97, generator_kl_loss=1.287, generator_dur_loss=1.812, generator_adv_loss=1.879, generator_feat_match_loss=4.106, over 100.00 samples.
+2024-03-13 17:35:33,902 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 17:36:23,241 INFO [train.py:919] (1/6) Start epoch 406
+2024-03-13 17:38:09,566 INFO [train.py:527] (1/6) Epoch 406, batch 30, global_batch_idx: 50250, batch size: 88, loss[discriminator_loss=2.688, discriminator_real_loss=1.37, discriminator_fake_loss=1.318, generator_loss=27.49, generator_mel_loss=18, generator_kl_loss=1.351, generator_dur_loss=1.804, generator_adv_loss=1.992, generator_feat_match_loss=4.344, over 88.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=27.96, generator_mel_loss=18.22, generator_kl_loss=1.397, generator_dur_loss=1.746, generator_adv_loss=1.94, generator_feat_match_loss=4.654, over 1843.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:40:30,655 INFO [train.py:527] (1/6) Epoch 406, batch 80, global_batch_idx: 50300, batch size: 53, loss[discriminator_loss=2.611, discriminator_real_loss=1.295, discriminator_fake_loss=1.316, generator_loss=29.22, generator_mel_loss=18.48, generator_kl_loss=1.569, generator_dur_loss=1.741, generator_adv_loss=2.063, generator_feat_match_loss=5.366, over 53.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=27.99, generator_mel_loss=18.17, generator_kl_loss=1.402, generator_dur_loss=1.755, generator_adv_loss=1.947, generator_feat_match_loss=4.716, over 4721.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:42:26,688 INFO [train.py:919] (1/6) Start epoch 407
+2024-03-13 17:43:09,002 INFO [train.py:527] (1/6) Epoch 407, batch 6, global_batch_idx: 50350, batch size: 64, loss[discriminator_loss=2.749, discriminator_real_loss=1.522, discriminator_fake_loss=1.227, generator_loss=27.76, generator_mel_loss=18.06, generator_kl_loss=1.514, generator_dur_loss=1.761, generator_adv_loss=1.936, generator_feat_match_loss=4.492, over 64.00 samples.], tot_loss[discriminator_loss=2.768, discriminator_real_loss=1.42, discriminator_fake_loss=1.347, generator_loss=28.1, generator_mel_loss=18.16, generator_kl_loss=1.442, generator_dur_loss=1.753, generator_adv_loss=1.961, generator_feat_match_loss=4.787, over 382.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:45:26,350 INFO [train.py:527] (1/6) Epoch 407, batch 56, global_batch_idx: 50400, batch size: 31, loss[discriminator_loss=2.753, discriminator_real_loss=1.372, discriminator_fake_loss=1.381, generator_loss=28.18, generator_mel_loss=17.82, generator_kl_loss=1.474, generator_dur_loss=1.67, generator_adv_loss=1.962, generator_feat_match_loss=5.246, over 31.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.378, discriminator_fake_loss=1.349, generator_loss=28.12, generator_mel_loss=18.18, generator_kl_loss=1.424, generator_dur_loss=1.771, generator_adv_loss=1.973, generator_feat_match_loss=4.774, over 3312.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:45:26,351 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 17:45:34,376 INFO [train.py:591] (1/6) Epoch 407, validation: discriminator_loss=2.685, discriminator_real_loss=1.383, discriminator_fake_loss=1.302, generator_loss=27.62, generator_mel_loss=18.64, generator_kl_loss=1.114, generator_dur_loss=1.828, generator_adv_loss=1.979, generator_feat_match_loss=4.061, over 100.00 samples.
+2024-03-13 17:45:34,377 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 17:47:53,238 INFO [train.py:527] (1/6) Epoch 407, batch 106, global_batch_idx: 50450, batch size: 36, loss[discriminator_loss=2.659, discriminator_real_loss=1.403, discriminator_fake_loss=1.256, generator_loss=28.26, generator_mel_loss=18.3, generator_kl_loss=1.506, generator_dur_loss=1.695, generator_adv_loss=1.956, generator_feat_match_loss=4.807, over 36.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.346, generator_loss=28.06, generator_mel_loss=18.14, generator_kl_loss=1.418, generator_dur_loss=1.77, generator_adv_loss=1.967, generator_feat_match_loss=4.761, over 6165.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:48:40,644 INFO [train.py:919] (1/6) Start epoch 408
+2024-03-13 17:50:32,303 INFO [train.py:527] (1/6) Epoch 408, batch 32, global_batch_idx: 50500, batch size: 44, loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=27.93, generator_mel_loss=17.98, generator_kl_loss=1.578, generator_dur_loss=1.658, generator_adv_loss=1.851, generator_feat_match_loss=4.872, over 44.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.369, discriminator_fake_loss=1.354, generator_loss=27.89, generator_mel_loss=18.13, generator_kl_loss=1.387, generator_dur_loss=1.777, generator_adv_loss=1.93, generator_feat_match_loss=4.66, over 1998.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:52:52,000 INFO [train.py:527] (1/6) Epoch 408, batch 82, global_batch_idx: 50550, batch size: 88, loss[discriminator_loss=2.698, discriminator_real_loss=1.376, discriminator_fake_loss=1.322, generator_loss=27.74, generator_mel_loss=17.88, generator_kl_loss=1.277, generator_dur_loss=1.864, generator_adv_loss=1.951, generator_feat_match_loss=4.769, over 88.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.372, discriminator_fake_loss=1.35, generator_loss=27.92, generator_mel_loss=18.11, generator_kl_loss=1.388, generator_dur_loss=1.779, generator_adv_loss=1.942, generator_feat_match_loss=4.696, over 5045.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:54:46,900 INFO [train.py:919] (1/6) Start epoch 409
+2024-03-13 17:55:34,389 INFO [train.py:527] (1/6) Epoch 409, batch 8, global_batch_idx: 50600, batch size: 36, loss[discriminator_loss=2.71, discriminator_real_loss=1.323, discriminator_fake_loss=1.387, generator_loss=27.71, generator_mel_loss=17.75, generator_kl_loss=1.362, generator_dur_loss=1.721, generator_adv_loss=2.032, generator_feat_match_loss=4.845, over 36.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.382, discriminator_fake_loss=1.33, generator_loss=28, generator_mel_loss=18.12, generator_kl_loss=1.347, generator_dur_loss=1.756, generator_adv_loss=1.966, generator_feat_match_loss=4.803, over 465.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 17:55:34,393 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 17:55:42,248 INFO [train.py:591] (1/6) Epoch 409, validation: discriminator_loss=2.737, discriminator_real_loss=1.413, discriminator_fake_loss=1.323, generator_loss=27.31, generator_mel_loss=18.66, generator_kl_loss=1.288, generator_dur_loss=1.834, generator_adv_loss=1.885, generator_feat_match_loss=3.637, over 100.00 samples.
+2024-03-13 17:55:42,250 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 17:58:01,456 INFO [train.py:527] (1/6) Epoch 409, batch 58, global_batch_idx: 50650, batch size: 60, loss[discriminator_loss=2.709, discriminator_real_loss=1.39, discriminator_fake_loss=1.319, generator_loss=27.44, generator_mel_loss=18.03, generator_kl_loss=1.415, generator_dur_loss=1.744, generator_adv_loss=2.011, generator_feat_match_loss=4.241, over 60.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.01, generator_mel_loss=18.1, generator_kl_loss=1.413, generator_dur_loss=1.761, generator_adv_loss=1.958, generator_feat_match_loss=4.775, over 3224.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:00:17,670 INFO [train.py:527] (1/6) Epoch 409, batch 108, global_batch_idx: 50700, batch size: 17, loss[discriminator_loss=2.657, discriminator_real_loss=1.329, discriminator_fake_loss=1.329, generator_loss=29.73, generator_mel_loss=19.21, generator_kl_loss=1.894, generator_dur_loss=1.581, generator_adv_loss=1.971, generator_feat_match_loss=5.07, over 17.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=27.97, generator_mel_loss=18.1, generator_kl_loss=1.421, generator_dur_loss=1.756, generator_adv_loss=1.953, generator_feat_match_loss=4.745, over 5972.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:00:59,796 INFO [train.py:919] (1/6) Start epoch 410
+2024-03-13 18:02:59,006 INFO [train.py:527] (1/6) Epoch 410, batch 34, global_batch_idx: 50750, batch size: 31, loss[discriminator_loss=2.718, discriminator_real_loss=1.422, discriminator_fake_loss=1.296, generator_loss=28.45, generator_mel_loss=18.67, generator_kl_loss=1.437, generator_dur_loss=1.642, generator_adv_loss=2.161, generator_feat_match_loss=4.539, over 31.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.382, discriminator_fake_loss=1.337, generator_loss=28.06, generator_mel_loss=18.14, generator_kl_loss=1.436, generator_dur_loss=1.728, generator_adv_loss=1.962, generator_feat_match_loss=4.798, over 1952.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:05:17,675 INFO [train.py:527] (1/6) Epoch 410, batch 84, global_batch_idx: 50800, batch size: 70, loss[discriminator_loss=2.749, discriminator_real_loss=1.487, discriminator_fake_loss=1.261, generator_loss=27.35, generator_mel_loss=17.86, generator_kl_loss=1.448, generator_dur_loss=1.805, generator_adv_loss=1.804, generator_feat_match_loss=4.427, over 70.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.379, discriminator_fake_loss=1.338, generator_loss=27.96, generator_mel_loss=18.09, generator_kl_loss=1.445, generator_dur_loss=1.73, generator_adv_loss=1.963, generator_feat_match_loss=4.74, over 4695.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:05:17,677 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 18:05:26,738 INFO [train.py:591] (1/6) Epoch 410, validation: discriminator_loss=2.764, discriminator_real_loss=1.383, discriminator_fake_loss=1.381, generator_loss=27.16, generator_mel_loss=18.4, generator_kl_loss=1.374, generator_dur_loss=1.794, generator_adv_loss=1.795, generator_feat_match_loss=3.795, over 100.00 samples.
+2024-03-13 18:05:26,739 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 18:07:15,482 INFO [train.py:919] (1/6) Start epoch 411
+2024-03-13 18:08:06,550 INFO [train.py:527] (1/6) Epoch 411, batch 10, global_batch_idx: 50850, batch size: 80, loss[discriminator_loss=2.732, discriminator_real_loss=1.39, discriminator_fake_loss=1.342, generator_loss=27.8, generator_mel_loss=17.89, generator_kl_loss=1.466, generator_dur_loss=1.835, generator_adv_loss=2.114, generator_feat_match_loss=4.497, over 80.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=28.06, generator_mel_loss=18.07, generator_kl_loss=1.471, generator_dur_loss=1.775, generator_adv_loss=1.957, generator_feat_match_loss=4.785, over 692.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:10:22,432 INFO [train.py:527] (1/6) Epoch 411, batch 60, global_batch_idx: 50900, batch size: 74, loss[discriminator_loss=2.706, discriminator_real_loss=1.356, discriminator_fake_loss=1.349, generator_loss=27.09, generator_mel_loss=17.81, generator_kl_loss=1.246, generator_dur_loss=1.76, generator_adv_loss=1.919, generator_feat_match_loss=4.359, over 74.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.367, discriminator_fake_loss=1.344, generator_loss=28.01, generator_mel_loss=18.09, generator_kl_loss=1.428, generator_dur_loss=1.749, generator_adv_loss=1.949, generator_feat_match_loss=4.802, over 3448.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:12:40,482 INFO [train.py:527] (1/6) Epoch 411, batch 110, global_batch_idx: 50950, batch size: 25, loss[discriminator_loss=2.516, discriminator_real_loss=1.295, discriminator_fake_loss=1.22, generator_loss=30.38, generator_mel_loss=19.1, generator_kl_loss=1.682, generator_dur_loss=1.575, generator_adv_loss=2.234, generator_feat_match_loss=5.783, over 25.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.368, discriminator_fake_loss=1.347, generator_loss=27.97, generator_mel_loss=18.09, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=1.956, generator_feat_match_loss=4.756, over 6265.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:13:19,918 INFO [train.py:919] (1/6) Start epoch 412
+2024-03-13 18:15:23,161 INFO [train.py:527] (1/6) Epoch 412, batch 36, global_batch_idx: 51000, batch size: 58, loss[discriminator_loss=2.752, discriminator_real_loss=1.385, discriminator_fake_loss=1.367, generator_loss=27.75, generator_mel_loss=18.15, generator_kl_loss=1.401, generator_dur_loss=1.745, generator_adv_loss=1.806, generator_feat_match_loss=4.649, over 58.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.382, discriminator_fake_loss=1.341, generator_loss=27.87, generator_mel_loss=18.07, generator_kl_loss=1.444, generator_dur_loss=1.739, generator_adv_loss=1.923, generator_feat_match_loss=4.7, over 1993.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:15:23,162 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 18:15:30,954 INFO [train.py:591] (1/6) Epoch 412, validation: discriminator_loss=2.748, discriminator_real_loss=1.361, discriminator_fake_loss=1.387, generator_loss=26.67, generator_mel_loss=18.35, generator_kl_loss=1.218, generator_dur_loss=1.81, generator_adv_loss=1.756, generator_feat_match_loss=3.537, over 100.00 samples.
+2024-03-13 18:15:30,955 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 18:17:48,777 INFO [train.py:527] (1/6) Epoch 412, batch 86, global_batch_idx: 51050, batch size: 61, loss[discriminator_loss=2.743, discriminator_real_loss=1.35, discriminator_fake_loss=1.393, generator_loss=27.64, generator_mel_loss=18.19, generator_kl_loss=1.404, generator_dur_loss=1.77, generator_adv_loss=1.907, generator_feat_match_loss=4.372, over 61.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.381, discriminator_fake_loss=1.337, generator_loss=27.9, generator_mel_loss=18.09, generator_kl_loss=1.428, generator_dur_loss=1.75, generator_adv_loss=1.935, generator_feat_match_loss=4.692, over 4986.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:19:30,269 INFO [train.py:919] (1/6) Start epoch 413
+2024-03-13 18:20:26,263 INFO [train.py:527] (1/6) Epoch 413, batch 12, global_batch_idx: 51100, batch size: 74, loss[discriminator_loss=2.729, discriminator_real_loss=1.466, discriminator_fake_loss=1.263, generator_loss=27.73, generator_mel_loss=18, generator_kl_loss=1.379, generator_dur_loss=1.83, generator_adv_loss=1.938, generator_feat_match_loss=4.586, over 74.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.365, discriminator_fake_loss=1.341, generator_loss=28.01, generator_mel_loss=18.14, generator_kl_loss=1.371, generator_dur_loss=1.764, generator_adv_loss=1.982, generator_feat_match_loss=4.75, over 809.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:22:47,477 INFO [train.py:527] (1/6) Epoch 413, batch 62, global_batch_idx: 51150, batch size: 39, loss[discriminator_loss=2.752, discriminator_real_loss=1.41, discriminator_fake_loss=1.342, generator_loss=28.4, generator_mel_loss=18.41, generator_kl_loss=1.476, generator_dur_loss=1.665, generator_adv_loss=1.894, generator_feat_match_loss=4.954, over 39.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.362, discriminator_fake_loss=1.338, generator_loss=28.1, generator_mel_loss=18.14, generator_kl_loss=1.414, generator_dur_loss=1.759, generator_adv_loss=1.961, generator_feat_match_loss=4.829, over 3836.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:25:05,885 INFO [train.py:527] (1/6) Epoch 413, batch 112, global_batch_idx: 51200, batch size: 39, loss[discriminator_loss=2.679, discriminator_real_loss=1.304, discriminator_fake_loss=1.375, generator_loss=28.45, generator_mel_loss=18.91, generator_kl_loss=1.31, generator_dur_loss=1.688, generator_adv_loss=1.979, generator_feat_match_loss=4.561, over 39.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.11, generator_mel_loss=18.14, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.96, generator_feat_match_loss=4.832, over 6522.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:25:05,886 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 18:25:14,605 INFO [train.py:591] (1/6) Epoch 413, validation: discriminator_loss=2.755, discriminator_real_loss=1.459, discriminator_fake_loss=1.296, generator_loss=27.05, generator_mel_loss=18.22, generator_kl_loss=1.273, generator_dur_loss=1.791, generator_adv_loss=1.897, generator_feat_match_loss=3.872, over 100.00 samples.
+2024-03-13 18:25:14,606 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 18:25:43,869 INFO [train.py:919] (1/6) Start epoch 414
+2024-03-13 18:27:51,423 INFO [train.py:527] (1/6) Epoch 414, batch 38, global_batch_idx: 51250, batch size: 96, loss[discriminator_loss=2.755, discriminator_real_loss=1.352, discriminator_fake_loss=1.403, generator_loss=26.99, generator_mel_loss=17.79, generator_kl_loss=1.227, generator_dur_loss=1.73, generator_adv_loss=1.864, generator_feat_match_loss=4.375, over 96.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.38, discriminator_fake_loss=1.349, generator_loss=27.91, generator_mel_loss=18.13, generator_kl_loss=1.405, generator_dur_loss=1.737, generator_adv_loss=1.939, generator_feat_match_loss=4.702, over 2212.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:30:07,674 INFO [train.py:527] (1/6) Epoch 414, batch 88, global_batch_idx: 51300, batch size: 72, loss[discriminator_loss=2.729, discriminator_real_loss=1.463, discriminator_fake_loss=1.266, generator_loss=28.26, generator_mel_loss=17.92, generator_kl_loss=1.3, generator_dur_loss=1.846, generator_adv_loss=1.926, generator_feat_match_loss=5.271, over 72.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.373, discriminator_fake_loss=1.349, generator_loss=28.04, generator_mel_loss=18.13, generator_kl_loss=1.428, generator_dur_loss=1.736, generator_adv_loss=1.949, generator_feat_match_loss=4.804, over 5073.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:31:45,763 INFO [train.py:919] (1/6) Start epoch 415
+2024-03-13 18:32:48,781 INFO [train.py:527] (1/6) Epoch 415, batch 14, global_batch_idx: 51350, batch size: 64, loss[discriminator_loss=2.684, discriminator_real_loss=1.345, discriminator_fake_loss=1.339, generator_loss=28.29, generator_mel_loss=18.43, generator_kl_loss=1.547, generator_dur_loss=1.785, generator_adv_loss=1.898, generator_feat_match_loss=4.623, over 64.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.383, discriminator_fake_loss=1.333, generator_loss=28.27, generator_mel_loss=18.19, generator_kl_loss=1.45, generator_dur_loss=1.758, generator_adv_loss=1.958, generator_feat_match_loss=4.915, over 935.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:35:05,719 INFO [train.py:527] (1/6) Epoch 415, batch 64, global_batch_idx: 51400, batch size: 48, loss[discriminator_loss=2.757, discriminator_real_loss=1.335, discriminator_fake_loss=1.422, generator_loss=27.59, generator_mel_loss=18.18, generator_kl_loss=1.461, generator_dur_loss=1.735, generator_adv_loss=1.923, generator_feat_match_loss=4.283, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.376, discriminator_fake_loss=1.335, generator_loss=27.92, generator_mel_loss=18.06, generator_kl_loss=1.41, generator_dur_loss=1.763, generator_adv_loss=1.945, generator_feat_match_loss=4.749, over 3896.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:35:05,721 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 18:35:13,672 INFO [train.py:591] (1/6) Epoch 415, validation: discriminator_loss=2.738, discriminator_real_loss=1.5, discriminator_fake_loss=1.239, generator_loss=25.83, generator_mel_loss=17.34, generator_kl_loss=1.242, generator_dur_loss=1.823, generator_adv_loss=1.931, generator_feat_match_loss=3.496, over 100.00 samples.
+2024-03-13 18:35:13,672 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 18:37:33,151 INFO [train.py:527] (1/6) Epoch 415, batch 114, global_batch_idx: 51450, batch size: 52, loss[discriminator_loss=2.707, discriminator_real_loss=1.406, discriminator_fake_loss=1.3, generator_loss=27.33, generator_mel_loss=17.93, generator_kl_loss=1.63, generator_dur_loss=1.698, generator_adv_loss=1.871, generator_feat_match_loss=4.194, over 52.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=27.86, generator_mel_loss=18.02, generator_kl_loss=1.408, generator_dur_loss=1.767, generator_adv_loss=1.94, generator_feat_match_loss=4.725, over 6827.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:38:00,905 INFO [train.py:919] (1/6) Start epoch 416
+2024-03-13 18:40:16,396 INFO [train.py:527] (1/6) Epoch 416, batch 40, global_batch_idx: 51500, batch size: 25, loss[discriminator_loss=2.752, discriminator_real_loss=1.293, discriminator_fake_loss=1.459, generator_loss=27.85, generator_mel_loss=18.04, generator_kl_loss=1.626, generator_dur_loss=1.565, generator_adv_loss=2.26, generator_feat_match_loss=4.357, over 25.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.368, discriminator_fake_loss=1.345, generator_loss=27.97, generator_mel_loss=18.07, generator_kl_loss=1.403, generator_dur_loss=1.764, generator_adv_loss=1.938, generator_feat_match_loss=4.795, over 2308.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:42:34,115 INFO [train.py:527] (1/6) Epoch 416, batch 90, global_batch_idx: 51550, batch size: 56, loss[discriminator_loss=2.645, discriminator_real_loss=1.246, discriminator_fake_loss=1.399, generator_loss=28.56, generator_mel_loss=18.49, generator_kl_loss=1.472, generator_dur_loss=1.686, generator_adv_loss=1.9, generator_feat_match_loss=5.011, over 56.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=27.96, generator_mel_loss=18.11, generator_kl_loss=1.407, generator_dur_loss=1.762, generator_adv_loss=1.939, generator_feat_match_loss=4.74, over 5306.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:44:03,749 INFO [train.py:919] (1/6) Start epoch 417
+2024-03-13 18:45:10,646 INFO [train.py:527] (1/6) Epoch 417, batch 16, global_batch_idx: 51600, batch size: 47, loss[discriminator_loss=2.709, discriminator_real_loss=1.385, discriminator_fake_loss=1.324, generator_loss=27.64, generator_mel_loss=17.81, generator_kl_loss=1.426, generator_dur_loss=1.666, generator_adv_loss=1.924, generator_feat_match_loss=4.815, over 47.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.386, discriminator_fake_loss=1.356, generator_loss=27.86, generator_mel_loss=18.21, generator_kl_loss=1.362, generator_dur_loss=1.744, generator_adv_loss=1.914, generator_feat_match_loss=4.63, over 930.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:45:10,648 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 18:45:18,452 INFO [train.py:591] (1/6) Epoch 417, validation: discriminator_loss=2.71, discriminator_real_loss=1.355, discriminator_fake_loss=1.354, generator_loss=26.6, generator_mel_loss=18.32, generator_kl_loss=1.193, generator_dur_loss=1.823, generator_adv_loss=1.818, generator_feat_match_loss=3.452, over 100.00 samples.
+2024-03-13 18:45:18,453 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 18:47:36,735 INFO [train.py:527] (1/6) Epoch 417, batch 66, global_batch_idx: 51650, batch size: 70, loss[discriminator_loss=2.754, discriminator_real_loss=1.366, discriminator_fake_loss=1.388, generator_loss=27.41, generator_mel_loss=18.06, generator_kl_loss=1.216, generator_dur_loss=1.813, generator_adv_loss=1.935, generator_feat_match_loss=4.389, over 70.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.375, discriminator_fake_loss=1.338, generator_loss=28.01, generator_mel_loss=18.14, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=1.938, generator_feat_match_loss=4.769, over 3708.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:49:56,189 INFO [train.py:527] (1/6) Epoch 417, batch 116, global_batch_idx: 51700, batch size: 48, loss[discriminator_loss=2.706, discriminator_real_loss=1.399, discriminator_fake_loss=1.308, generator_loss=28.28, generator_mel_loss=17.77, generator_kl_loss=1.462, generator_dur_loss=1.682, generator_adv_loss=2.035, generator_feat_match_loss=5.335, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.375, discriminator_fake_loss=1.336, generator_loss=27.98, generator_mel_loss=18.11, generator_kl_loss=1.413, generator_dur_loss=1.749, generator_adv_loss=1.94, generator_feat_match_loss=4.77, over 6573.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:50:18,613 INFO [train.py:919] (1/6) Start epoch 418
+2024-03-13 18:52:38,503 INFO [train.py:527] (1/6) Epoch 418, batch 42, global_batch_idx: 51750, batch size: 74, loss[discriminator_loss=2.633, discriminator_real_loss=1.298, discriminator_fake_loss=1.335, generator_loss=27.82, generator_mel_loss=17.61, generator_kl_loss=1.332, generator_dur_loss=1.794, generator_adv_loss=1.98, generator_feat_match_loss=5.097, over 74.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.363, discriminator_fake_loss=1.344, generator_loss=28.12, generator_mel_loss=18.11, generator_kl_loss=1.425, generator_dur_loss=1.782, generator_adv_loss=1.958, generator_feat_match_loss=4.839, over 2564.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:54:54,638 INFO [train.py:527] (1/6) Epoch 418, batch 92, global_batch_idx: 51800, batch size: 70, loss[discriminator_loss=2.686, discriminator_real_loss=1.315, discriminator_fake_loss=1.371, generator_loss=28, generator_mel_loss=17.78, generator_kl_loss=1.486, generator_dur_loss=1.815, generator_adv_loss=2.133, generator_feat_match_loss=4.783, over 70.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.342, generator_loss=28.09, generator_mel_loss=18.12, generator_kl_loss=1.422, generator_dur_loss=1.76, generator_adv_loss=1.962, generator_feat_match_loss=4.829, over 5235.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 18:54:54,639 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 18:55:03,511 INFO [train.py:591] (1/6) Epoch 418, validation: discriminator_loss=2.779, discriminator_real_loss=1.581, discriminator_fake_loss=1.198, generator_loss=27.26, generator_mel_loss=18.42, generator_kl_loss=1.248, generator_dur_loss=1.792, generator_adv_loss=2.023, generator_feat_match_loss=3.77, over 100.00 samples.
+2024-03-13 18:55:03,512 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 18:56:31,575 INFO [train.py:919] (1/6) Start epoch 419
+2024-03-13 18:57:48,376 INFO [train.py:527] (1/6) Epoch 419, batch 18, global_batch_idx: 51850, batch size: 53, loss[discriminator_loss=2.751, discriminator_real_loss=1.458, discriminator_fake_loss=1.293, generator_loss=28.42, generator_mel_loss=18.26, generator_kl_loss=1.474, generator_dur_loss=1.73, generator_adv_loss=1.891, generator_feat_match_loss=5.061, over 53.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.373, discriminator_fake_loss=1.349, generator_loss=27.96, generator_mel_loss=18.06, generator_kl_loss=1.398, generator_dur_loss=1.765, generator_adv_loss=1.958, generator_feat_match_loss=4.779, over 1170.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:00:10,301 INFO [train.py:527] (1/6) Epoch 419, batch 68, global_batch_idx: 51900, batch size: 56, loss[discriminator_loss=2.657, discriminator_real_loss=1.328, discriminator_fake_loss=1.329, generator_loss=28.79, generator_mel_loss=18.4, generator_kl_loss=1.431, generator_dur_loss=1.786, generator_adv_loss=2.049, generator_feat_match_loss=5.12, over 56.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.38, discriminator_fake_loss=1.344, generator_loss=27.94, generator_mel_loss=18.07, generator_kl_loss=1.434, generator_dur_loss=1.756, generator_adv_loss=1.956, generator_feat_match_loss=4.728, over 4037.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:02:22,549 INFO [train.py:527] (1/6) Epoch 419, batch 118, global_batch_idx: 51950, batch size: 74, loss[discriminator_loss=2.704, discriminator_real_loss=1.36, discriminator_fake_loss=1.344, generator_loss=28.18, generator_mel_loss=18.51, generator_kl_loss=1.394, generator_dur_loss=1.771, generator_adv_loss=1.988, generator_feat_match_loss=4.523, over 74.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.11, generator_kl_loss=1.426, generator_dur_loss=1.756, generator_adv_loss=1.959, generator_feat_match_loss=4.705, over 6690.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:02:38,065 INFO [train.py:919] (1/6) Start epoch 420
+2024-03-13 19:05:05,344 INFO [train.py:527] (1/6) Epoch 420, batch 44, global_batch_idx: 52000, batch size: 48, loss[discriminator_loss=2.724, discriminator_real_loss=1.399, discriminator_fake_loss=1.325, generator_loss=28.43, generator_mel_loss=18.33, generator_kl_loss=1.335, generator_dur_loss=1.713, generator_adv_loss=1.996, generator_feat_match_loss=5.063, over 48.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.373, discriminator_fake_loss=1.338, generator_loss=27.98, generator_mel_loss=18.03, generator_kl_loss=1.398, generator_dur_loss=1.762, generator_adv_loss=1.946, generator_feat_match_loss=4.843, over 2708.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:05:05,345 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 19:05:13,469 INFO [train.py:591] (1/6) Epoch 420, validation: discriminator_loss=2.774, discriminator_real_loss=1.481, discriminator_fake_loss=1.293, generator_loss=27.64, generator_mel_loss=18.58, generator_kl_loss=1.209, generator_dur_loss=1.812, generator_adv_loss=1.936, generator_feat_match_loss=4.108, over 100.00 samples.
+2024-03-13 19:05:13,470 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 19:07:32,960 INFO [train.py:527] (1/6) Epoch 420, batch 94, global_batch_idx: 52050, batch size: 45, loss[discriminator_loss=2.746, discriminator_real_loss=1.467, discriminator_fake_loss=1.28, generator_loss=28.25, generator_mel_loss=18.37, generator_kl_loss=1.474, generator_dur_loss=1.704, generator_adv_loss=1.965, generator_feat_match_loss=4.743, over 45.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.367, discriminator_fake_loss=1.338, generator_loss=27.95, generator_mel_loss=18.01, generator_kl_loss=1.39, generator_dur_loss=1.768, generator_adv_loss=1.945, generator_feat_match_loss=4.83, over 5700.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:08:53,803 INFO [train.py:919] (1/6) Start epoch 421
+2024-03-13 19:10:15,602 INFO [train.py:527] (1/6) Epoch 421, batch 20, global_batch_idx: 52100, batch size: 36, loss[discriminator_loss=2.769, discriminator_real_loss=1.339, discriminator_fake_loss=1.429, generator_loss=26.8, generator_mel_loss=17.72, generator_kl_loss=1.458, generator_dur_loss=1.654, generator_adv_loss=1.95, generator_feat_match_loss=4.021, over 36.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=27.94, generator_mel_loss=18.08, generator_kl_loss=1.411, generator_dur_loss=1.765, generator_adv_loss=1.945, generator_feat_match_loss=4.743, over 1253.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:12:34,661 INFO [train.py:527] (1/6) Epoch 421, batch 70, global_batch_idx: 52150, batch size: 83, loss[discriminator_loss=2.743, discriminator_real_loss=1.449, discriminator_fake_loss=1.294, generator_loss=28.69, generator_mel_loss=18.41, generator_kl_loss=1.396, generator_dur_loss=1.888, generator_adv_loss=1.936, generator_feat_match_loss=5.056, over 83.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.372, discriminator_fake_loss=1.336, generator_loss=27.98, generator_mel_loss=18.08, generator_kl_loss=1.409, generator_dur_loss=1.773, generator_adv_loss=1.94, generator_feat_match_loss=4.778, over 4183.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:14:48,338 INFO [train.py:527] (1/6) Epoch 421, batch 120, global_batch_idx: 52200, batch size: 64, loss[discriminator_loss=2.716, discriminator_real_loss=1.29, discriminator_fake_loss=1.425, generator_loss=28.13, generator_mel_loss=18.34, generator_kl_loss=1.378, generator_dur_loss=1.755, generator_adv_loss=2.081, generator_feat_match_loss=4.572, over 64.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.05, generator_mel_loss=18.1, generator_kl_loss=1.414, generator_dur_loss=1.768, generator_adv_loss=1.945, generator_feat_match_loss=4.817, over 6981.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:14:48,340 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 19:14:57,113 INFO [train.py:591] (1/6) Epoch 421, validation: discriminator_loss=2.767, discriminator_real_loss=1.497, discriminator_fake_loss=1.27, generator_loss=27.06, generator_mel_loss=18.58, generator_kl_loss=1.219, generator_dur_loss=1.818, generator_adv_loss=1.896, generator_feat_match_loss=3.554, over 100.00 samples.
+2024-03-13 19:14:57,114 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 19:15:07,125 INFO [train.py:919] (1/6) Start epoch 422
+2024-03-13 19:17:39,938 INFO [train.py:527] (1/6) Epoch 422, batch 46, global_batch_idx: 52250, batch size: 44, loss[discriminator_loss=2.705, discriminator_real_loss=1.353, discriminator_fake_loss=1.352, generator_loss=27.53, generator_mel_loss=17.91, generator_kl_loss=1.408, generator_dur_loss=1.724, generator_adv_loss=1.897, generator_feat_match_loss=4.592, over 44.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.383, discriminator_fake_loss=1.345, generator_loss=28.04, generator_mel_loss=18.13, generator_kl_loss=1.404, generator_dur_loss=1.755, generator_adv_loss=1.956, generator_feat_match_loss=4.791, over 2654.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:19:59,205 INFO [train.py:527] (1/6) Epoch 422, batch 96, global_batch_idx: 52300, batch size: 68, loss[discriminator_loss=2.737, discriminator_real_loss=1.264, discriminator_fake_loss=1.474, generator_loss=27.38, generator_mel_loss=17.79, generator_kl_loss=1.421, generator_dur_loss=1.821, generator_adv_loss=1.935, generator_feat_match_loss=4.412, over 68.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.379, discriminator_fake_loss=1.343, generator_loss=27.95, generator_mel_loss=18.06, generator_kl_loss=1.406, generator_dur_loss=1.766, generator_adv_loss=1.943, generator_feat_match_loss=4.769, over 5666.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:21:13,236 INFO [train.py:919] (1/6) Start epoch 423
+2024-03-13 19:22:39,183 INFO [train.py:527] (1/6) Epoch 423, batch 22, global_batch_idx: 52350, batch size: 48, loss[discriminator_loss=2.711, discriminator_real_loss=1.389, discriminator_fake_loss=1.322, generator_loss=28.41, generator_mel_loss=18.16, generator_kl_loss=1.428, generator_dur_loss=1.688, generator_adv_loss=1.843, generator_feat_match_loss=5.291, over 48.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=27.98, generator_mel_loss=18.02, generator_kl_loss=1.401, generator_dur_loss=1.762, generator_adv_loss=1.962, generator_feat_match_loss=4.836, over 1336.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:24:57,216 INFO [train.py:527] (1/6) Epoch 423, batch 72, global_batch_idx: 52400, batch size: 50, loss[discriminator_loss=2.749, discriminator_real_loss=1.258, discriminator_fake_loss=1.491, generator_loss=28.41, generator_mel_loss=18.4, generator_kl_loss=1.569, generator_dur_loss=1.715, generator_adv_loss=2.265, generator_feat_match_loss=4.461, over 50.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.365, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.02, generator_kl_loss=1.404, generator_dur_loss=1.768, generator_adv_loss=1.967, generator_feat_match_loss=4.792, over 4355.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:24:57,218 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 19:25:05,341 INFO [train.py:591] (1/6) Epoch 423, validation: discriminator_loss=2.891, discriminator_real_loss=1.675, discriminator_fake_loss=1.215, generator_loss=27.09, generator_mel_loss=17.91, generator_kl_loss=1.23, generator_dur_loss=1.832, generator_adv_loss=2.277, generator_feat_match_loss=3.847, over 100.00 samples.
+2024-03-13 19:25:05,342 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 19:27:23,370 INFO [train.py:527] (1/6) Epoch 423, batch 122, global_batch_idx: 52450, batch size: 56, loss[discriminator_loss=2.729, discriminator_real_loss=1.404, discriminator_fake_loss=1.326, generator_loss=27.77, generator_mel_loss=18.08, generator_kl_loss=1.451, generator_dur_loss=1.754, generator_adv_loss=1.931, generator_feat_match_loss=4.557, over 56.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.339, generator_loss=27.95, generator_mel_loss=18.01, generator_kl_loss=1.411, generator_dur_loss=1.764, generator_adv_loss=1.969, generator_feat_match_loss=4.797, over 7141.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:27:28,006 INFO [train.py:919] (1/6) Start epoch 424
+2024-03-13 19:30:06,152 INFO [train.py:527] (1/6) Epoch 424, batch 48, global_batch_idx: 52500, batch size: 55, loss[discriminator_loss=2.736, discriminator_real_loss=1.345, discriminator_fake_loss=1.391, generator_loss=28.23, generator_mel_loss=18.21, generator_kl_loss=1.48, generator_dur_loss=1.718, generator_adv_loss=2.01, generator_feat_match_loss=4.815, over 55.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=28.01, generator_mel_loss=18.06, generator_kl_loss=1.411, generator_dur_loss=1.748, generator_adv_loss=1.948, generator_feat_match_loss=4.84, over 2919.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:32:24,303 INFO [train.py:527] (1/6) Epoch 424, batch 98, global_batch_idx: 52550, batch size: 52, loss[discriminator_loss=2.762, discriminator_real_loss=1.401, discriminator_fake_loss=1.361, generator_loss=28.31, generator_mel_loss=18.09, generator_kl_loss=1.376, generator_dur_loss=1.732, generator_adv_loss=1.991, generator_feat_match_loss=5.121, over 52.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.343, generator_loss=27.98, generator_mel_loss=18.08, generator_kl_loss=1.42, generator_dur_loss=1.742, generator_adv_loss=1.951, generator_feat_match_loss=4.79, over 5611.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:33:33,006 INFO [train.py:919] (1/6) Start epoch 425
+2024-03-13 19:35:02,158 INFO [train.py:527] (1/6) Epoch 425, batch 24, global_batch_idx: 52600, batch size: 47, loss[discriminator_loss=2.664, discriminator_real_loss=1.341, discriminator_fake_loss=1.322, generator_loss=27.43, generator_mel_loss=17.66, generator_kl_loss=1.273, generator_dur_loss=1.693, generator_adv_loss=2.081, generator_feat_match_loss=4.726, over 47.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.358, discriminator_fake_loss=1.347, generator_loss=27.9, generator_mel_loss=18.06, generator_kl_loss=1.422, generator_dur_loss=1.769, generator_adv_loss=1.933, generator_feat_match_loss=4.721, over 1473.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:35:02,160 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 19:35:10,065 INFO [train.py:591] (1/6) Epoch 425, validation: discriminator_loss=2.718, discriminator_real_loss=1.424, discriminator_fake_loss=1.294, generator_loss=27.08, generator_mel_loss=18.35, generator_kl_loss=1.196, generator_dur_loss=1.825, generator_adv_loss=1.974, generator_feat_match_loss=3.734, over 100.00 samples.
+2024-03-13 19:35:10,066 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 19:37:30,456 INFO [train.py:527] (1/6) Epoch 425, batch 74, global_batch_idx: 52650, batch size: 56, loss[discriminator_loss=2.646, discriminator_real_loss=1.436, discriminator_fake_loss=1.21, generator_loss=28.77, generator_mel_loss=18.06, generator_kl_loss=1.295, generator_dur_loss=1.745, generator_adv_loss=2.224, generator_feat_match_loss=5.447, over 56.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.368, discriminator_fake_loss=1.35, generator_loss=27.96, generator_mel_loss=18.06, generator_kl_loss=1.439, generator_dur_loss=1.761, generator_adv_loss=1.943, generator_feat_match_loss=4.757, over 4461.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:39:45,281 INFO [train.py:919] (1/6) Start epoch 426
+2024-03-13 19:40:10,338 INFO [train.py:527] (1/6) Epoch 426, batch 0, global_batch_idx: 52700, batch size: 96, loss[discriminator_loss=2.683, discriminator_real_loss=1.344, discriminator_fake_loss=1.339, generator_loss=28.15, generator_mel_loss=18.25, generator_kl_loss=1.331, generator_dur_loss=1.925, generator_adv_loss=1.872, generator_feat_match_loss=4.774, over 96.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.344, discriminator_fake_loss=1.339, generator_loss=28.15, generator_mel_loss=18.25, generator_kl_loss=1.331, generator_dur_loss=1.925, generator_adv_loss=1.872, generator_feat_match_loss=4.774, over 96.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:42:32,616 INFO [train.py:527] (1/6) Epoch 426, batch 50, global_batch_idx: 52750, batch size: 50, loss[discriminator_loss=2.715, discriminator_real_loss=1.399, discriminator_fake_loss=1.317, generator_loss=28.84, generator_mel_loss=18.61, generator_kl_loss=1.685, generator_dur_loss=1.725, generator_adv_loss=1.935, generator_feat_match_loss=4.882, over 50.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.376, discriminator_fake_loss=1.347, generator_loss=28.04, generator_mel_loss=18.17, generator_kl_loss=1.408, generator_dur_loss=1.759, generator_adv_loss=1.944, generator_feat_match_loss=4.756, over 2880.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:44:48,302 INFO [train.py:527] (1/6) Epoch 426, batch 100, global_batch_idx: 52800, batch size: 56, loss[discriminator_loss=2.745, discriminator_real_loss=1.315, discriminator_fake_loss=1.43, generator_loss=28.97, generator_mel_loss=18.35, generator_kl_loss=1.458, generator_dur_loss=1.736, generator_adv_loss=2.018, generator_feat_match_loss=5.401, over 56.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.342, generator_loss=28.03, generator_mel_loss=18.15, generator_kl_loss=1.407, generator_dur_loss=1.758, generator_adv_loss=1.951, generator_feat_match_loss=4.766, over 5670.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:44:48,304 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 19:44:57,102 INFO [train.py:591] (1/6) Epoch 426, validation: discriminator_loss=2.772, discriminator_real_loss=1.461, discriminator_fake_loss=1.311, generator_loss=27.14, generator_mel_loss=18.28, generator_kl_loss=1.227, generator_dur_loss=1.818, generator_adv_loss=1.978, generator_feat_match_loss=3.839, over 100.00 samples.
+2024-03-13 19:44:57,103 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 19:46:02,854 INFO [train.py:919] (1/6) Start epoch 427
+2024-03-13 19:47:37,229 INFO [train.py:527] (1/6) Epoch 427, batch 26, global_batch_idx: 52850, batch size: 62, loss[discriminator_loss=2.684, discriminator_real_loss=1.336, discriminator_fake_loss=1.349, generator_loss=28.08, generator_mel_loss=18.44, generator_kl_loss=1.5, generator_dur_loss=1.703, generator_adv_loss=1.884, generator_feat_match_loss=4.555, over 62.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.36, discriminator_fake_loss=1.347, generator_loss=28.2, generator_mel_loss=18.28, generator_kl_loss=1.455, generator_dur_loss=1.724, generator_adv_loss=1.94, generator_feat_match_loss=4.798, over 1467.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:49:57,009 INFO [train.py:527] (1/6) Epoch 427, batch 76, global_batch_idx: 52900, batch size: 55, loss[discriminator_loss=2.666, discriminator_real_loss=1.348, discriminator_fake_loss=1.318, generator_loss=29.23, generator_mel_loss=18.43, generator_kl_loss=1.411, generator_dur_loss=1.68, generator_adv_loss=2.128, generator_feat_match_loss=5.584, over 55.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.04, generator_mel_loss=18.09, generator_kl_loss=1.419, generator_dur_loss=1.74, generator_adv_loss=1.959, generator_feat_match_loss=4.83, over 4380.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:52:09,639 INFO [train.py:919] (1/6) Start epoch 428
+2024-03-13 19:52:39,534 INFO [train.py:527] (1/6) Epoch 428, batch 2, global_batch_idx: 52950, batch size: 96, loss[discriminator_loss=2.762, discriminator_real_loss=1.309, discriminator_fake_loss=1.453, generator_loss=27.25, generator_mel_loss=17.68, generator_kl_loss=1.35, generator_dur_loss=1.886, generator_adv_loss=2.003, generator_feat_match_loss=4.328, over 96.00 samples.], tot_loss[discriminator_loss=2.774, discriminator_real_loss=1.384, discriminator_fake_loss=1.389, generator_loss=27.75, generator_mel_loss=17.96, generator_kl_loss=1.465, generator_dur_loss=1.798, generator_adv_loss=1.92, generator_feat_match_loss=4.61, over 177.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:54:59,028 INFO [train.py:527] (1/6) Epoch 428, batch 52, global_batch_idx: 53000, batch size: 97, loss[discriminator_loss=2.706, discriminator_real_loss=1.291, discriminator_fake_loss=1.415, generator_loss=28.3, generator_mel_loss=18.09, generator_kl_loss=1.334, generator_dur_loss=1.893, generator_adv_loss=2.078, generator_feat_match_loss=4.904, over 97.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.377, discriminator_fake_loss=1.35, generator_loss=27.87, generator_mel_loss=18.07, generator_kl_loss=1.394, generator_dur_loss=1.767, generator_adv_loss=1.931, generator_feat_match_loss=4.707, over 3105.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:54:59,029 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 19:55:07,118 INFO [train.py:591] (1/6) Epoch 428, validation: discriminator_loss=2.731, discriminator_real_loss=1.543, discriminator_fake_loss=1.188, generator_loss=27.28, generator_mel_loss=18.15, generator_kl_loss=1.172, generator_dur_loss=1.804, generator_adv_loss=2.059, generator_feat_match_loss=4.093, over 100.00 samples.
+2024-03-13 19:55:07,120 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 19:57:24,497 INFO [train.py:527] (1/6) Epoch 428, batch 102, global_batch_idx: 53050, batch size: 74, loss[discriminator_loss=2.637, discriminator_real_loss=1.303, discriminator_fake_loss=1.334, generator_loss=28.43, generator_mel_loss=18.12, generator_kl_loss=1.346, generator_dur_loss=1.836, generator_adv_loss=2.014, generator_feat_match_loss=5.111, over 74.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.343, generator_loss=28.03, generator_mel_loss=18.13, generator_kl_loss=1.421, generator_dur_loss=1.751, generator_adv_loss=1.934, generator_feat_match_loss=4.787, over 5675.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 19:58:22,429 INFO [train.py:919] (1/6) Start epoch 429
+2024-03-13 20:00:04,619 INFO [train.py:527] (1/6) Epoch 429, batch 28, global_batch_idx: 53100, batch size: 88, loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=27.91, generator_mel_loss=18.01, generator_kl_loss=1.287, generator_dur_loss=1.897, generator_adv_loss=2.018, generator_feat_match_loss=4.695, over 88.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=28.04, generator_mel_loss=18.1, generator_kl_loss=1.408, generator_dur_loss=1.787, generator_adv_loss=1.946, generator_feat_match_loss=4.796, over 1798.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:02:26,109 INFO [train.py:527] (1/6) Epoch 429, batch 78, global_batch_idx: 53150, batch size: 64, loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=27.94, generator_mel_loss=17.79, generator_kl_loss=1.525, generator_dur_loss=1.747, generator_adv_loss=1.893, generator_feat_match_loss=4.989, over 64.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.95, generator_mel_loss=18.05, generator_kl_loss=1.401, generator_dur_loss=1.765, generator_adv_loss=1.946, generator_feat_match_loss=4.781, over 4601.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:04:26,231 INFO [train.py:919] (1/6) Start epoch 430
+2024-03-13 20:05:00,159 INFO [train.py:527] (1/6) Epoch 430, batch 4, global_batch_idx: 53200, batch size: 61, loss[discriminator_loss=2.662, discriminator_real_loss=1.334, discriminator_fake_loss=1.327, generator_loss=28.56, generator_mel_loss=18.14, generator_kl_loss=1.441, generator_dur_loss=1.779, generator_adv_loss=1.964, generator_feat_match_loss=5.242, over 61.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.357, discriminator_fake_loss=1.336, generator_loss=28.02, generator_mel_loss=18.08, generator_kl_loss=1.438, generator_dur_loss=1.792, generator_adv_loss=1.971, generator_feat_match_loss=4.738, over 341.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:05:00,161 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 20:05:08,013 INFO [train.py:591] (1/6) Epoch 430, validation: discriminator_loss=2.768, discriminator_real_loss=1.384, discriminator_fake_loss=1.383, generator_loss=27.81, generator_mel_loss=19.1, generator_kl_loss=1.206, generator_dur_loss=1.806, generator_adv_loss=1.75, generator_feat_match_loss=3.952, over 100.00 samples.
+2024-03-13 20:05:08,016 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 20:07:29,627 INFO [train.py:527] (1/6) Epoch 430, batch 54, global_batch_idx: 53250, batch size: 64, loss[discriminator_loss=2.709, discriminator_real_loss=1.311, discriminator_fake_loss=1.397, generator_loss=27.3, generator_mel_loss=18.05, generator_kl_loss=1.37, generator_dur_loss=1.783, generator_adv_loss=1.894, generator_feat_match_loss=4.206, over 64.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=27.96, generator_mel_loss=18.09, generator_kl_loss=1.412, generator_dur_loss=1.756, generator_adv_loss=1.946, generator_feat_match_loss=4.763, over 3265.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:09:48,917 INFO [train.py:527] (1/6) Epoch 430, batch 104, global_batch_idx: 53300, batch size: 72, loss[discriminator_loss=2.644, discriminator_real_loss=1.362, discriminator_fake_loss=1.282, generator_loss=27.62, generator_mel_loss=17.55, generator_kl_loss=1.29, generator_dur_loss=1.858, generator_adv_loss=1.957, generator_feat_match_loss=4.963, over 72.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=27.96, generator_mel_loss=18.05, generator_kl_loss=1.407, generator_dur_loss=1.769, generator_adv_loss=1.962, generator_feat_match_loss=4.768, over 6369.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:10:41,934 INFO [train.py:919] (1/6) Start epoch 431
+2024-03-13 20:12:27,696 INFO [train.py:527] (1/6) Epoch 431, batch 30, global_batch_idx: 53350, batch size: 68, loss[discriminator_loss=2.739, discriminator_real_loss=1.378, discriminator_fake_loss=1.361, generator_loss=27.66, generator_mel_loss=17.72, generator_kl_loss=1.325, generator_dur_loss=1.813, generator_adv_loss=1.893, generator_feat_match_loss=4.911, over 68.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.368, discriminator_fake_loss=1.349, generator_loss=28.22, generator_mel_loss=18.18, generator_kl_loss=1.411, generator_dur_loss=1.776, generator_adv_loss=1.954, generator_feat_match_loss=4.9, over 1653.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:14:43,665 INFO [train.py:527] (1/6) Epoch 431, batch 80, global_batch_idx: 53400, batch size: 42, loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=26.85, generator_mel_loss=17.51, generator_kl_loss=1.47, generator_dur_loss=1.667, generator_adv_loss=2.008, generator_feat_match_loss=4.202, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.342, generator_loss=28.06, generator_mel_loss=18.1, generator_kl_loss=1.418, generator_dur_loss=1.758, generator_adv_loss=1.961, generator_feat_match_loss=4.82, over 4339.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:14:43,666 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 20:14:51,579 INFO [train.py:591] (1/6) Epoch 431, validation: discriminator_loss=2.781, discriminator_real_loss=1.454, discriminator_fake_loss=1.327, generator_loss=26.56, generator_mel_loss=17.71, generator_kl_loss=1.241, generator_dur_loss=1.836, generator_adv_loss=1.938, generator_feat_match_loss=3.835, over 100.00 samples.
+2024-03-13 20:14:51,580 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 20:16:53,598 INFO [train.py:919] (1/6) Start epoch 432
+2024-03-13 20:17:34,592 INFO [train.py:527] (1/6) Epoch 432, batch 6, global_batch_idx: 53450, batch size: 48, loss[discriminator_loss=2.69, discriminator_real_loss=1.301, discriminator_fake_loss=1.388, generator_loss=28.62, generator_mel_loss=18.18, generator_kl_loss=1.616, generator_dur_loss=1.666, generator_adv_loss=1.931, generator_feat_match_loss=5.227, over 48.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.327, discriminator_fake_loss=1.373, generator_loss=28.62, generator_mel_loss=18.31, generator_kl_loss=1.423, generator_dur_loss=1.748, generator_adv_loss=1.955, generator_feat_match_loss=5.18, over 357.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:19:56,407 INFO [train.py:527] (1/6) Epoch 432, batch 56, global_batch_idx: 53500, batch size: 31, loss[discriminator_loss=2.712, discriminator_real_loss=1.487, discriminator_fake_loss=1.225, generator_loss=29.28, generator_mel_loss=18.5, generator_kl_loss=1.55, generator_dur_loss=1.688, generator_adv_loss=2.034, generator_feat_match_loss=5.51, over 31.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=28.02, generator_mel_loss=17.99, generator_kl_loss=1.418, generator_dur_loss=1.777, generator_adv_loss=1.963, generator_feat_match_loss=4.873, over 3414.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:22:15,145 INFO [train.py:527] (1/6) Epoch 432, batch 106, global_batch_idx: 53550, batch size: 53, loss[discriminator_loss=2.826, discriminator_real_loss=1.543, discriminator_fake_loss=1.284, generator_loss=27.69, generator_mel_loss=18.21, generator_kl_loss=1.205, generator_dur_loss=1.774, generator_adv_loss=1.807, generator_feat_match_loss=4.691, over 53.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=27.96, generator_mel_loss=18.01, generator_kl_loss=1.408, generator_dur_loss=1.775, generator_adv_loss=1.955, generator_feat_match_loss=4.813, over 6328.00 samples.], cur_lr_g: 1.90e-04, cur_lr_d: 1.90e-04, 
+2024-03-13 20:23:01,675 INFO [train.py:919] (1/6) Start epoch 433
+2024-03-13 20:24:55,059 INFO [train.py:527] (1/6) Epoch 433, batch 32, global_batch_idx: 53600, batch size: 80, loss[discriminator_loss=2.696, discriminator_real_loss=1.369, discriminator_fake_loss=1.327, generator_loss=27.6, generator_mel_loss=17.84, generator_kl_loss=1.319, generator_dur_loss=1.784, generator_adv_loss=1.962, generator_feat_match_loss=4.692, over 80.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.13, generator_mel_loss=18.09, generator_kl_loss=1.425, generator_dur_loss=1.759, generator_adv_loss=1.958, generator_feat_match_loss=4.895, over 1872.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:24:55,060 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 20:25:02,939 INFO [train.py:591] (1/6) Epoch 433, validation: discriminator_loss=2.762, discriminator_real_loss=1.486, discriminator_fake_loss=1.276, generator_loss=26.89, generator_mel_loss=17.98, generator_kl_loss=1.272, generator_dur_loss=1.838, generator_adv_loss=1.909, generator_feat_match_loss=3.895, over 100.00 samples.
+2024-03-13 20:25:02,939 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 20:27:20,280 INFO [train.py:527] (1/6) Epoch 433, batch 82, global_batch_idx: 53650, batch size: 25, loss[discriminator_loss=2.625, discriminator_real_loss=1.353, discriminator_fake_loss=1.272, generator_loss=29.96, generator_mel_loss=19.22, generator_kl_loss=1.65, generator_dur_loss=1.553, generator_adv_loss=2.161, generator_feat_match_loss=5.372, over 25.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.04, generator_mel_loss=18.09, generator_kl_loss=1.412, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=4.828, over 4745.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:29:12,742 INFO [train.py:919] (1/6) Start epoch 434
+2024-03-13 20:29:56,663 INFO [train.py:527] (1/6) Epoch 434, batch 8, global_batch_idx: 53700, batch size: 50, loss[discriminator_loss=2.694, discriminator_real_loss=1.413, discriminator_fake_loss=1.281, generator_loss=28.39, generator_mel_loss=18.46, generator_kl_loss=1.558, generator_dur_loss=1.657, generator_adv_loss=1.879, generator_feat_match_loss=4.833, over 50.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.387, discriminator_fake_loss=1.334, generator_loss=28.16, generator_mel_loss=18.27, generator_kl_loss=1.504, generator_dur_loss=1.706, generator_adv_loss=1.933, generator_feat_match_loss=4.742, over 426.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:32:14,617 INFO [train.py:527] (1/6) Epoch 434, batch 58, global_batch_idx: 53750, batch size: 42, loss[discriminator_loss=2.717, discriminator_real_loss=1.37, discriminator_fake_loss=1.348, generator_loss=28.6, generator_mel_loss=18.18, generator_kl_loss=1.556, generator_dur_loss=1.691, generator_adv_loss=1.993, generator_feat_match_loss=5.173, over 42.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=28.03, generator_mel_loss=18.12, generator_kl_loss=1.411, generator_dur_loss=1.747, generator_adv_loss=1.942, generator_feat_match_loss=4.802, over 3197.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:34:33,504 INFO [train.py:527] (1/6) Epoch 434, batch 108, global_batch_idx: 53800, batch size: 96, loss[discriminator_loss=2.723, discriminator_real_loss=1.39, discriminator_fake_loss=1.333, generator_loss=27.47, generator_mel_loss=17.74, generator_kl_loss=1.236, generator_dur_loss=1.845, generator_adv_loss=1.897, generator_feat_match_loss=4.757, over 96.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.376, discriminator_fake_loss=1.341, generator_loss=28.03, generator_mel_loss=18.1, generator_kl_loss=1.415, generator_dur_loss=1.75, generator_adv_loss=1.948, generator_feat_match_loss=4.816, over 6068.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:34:33,506 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 20:34:42,396 INFO [train.py:591] (1/6) Epoch 434, validation: discriminator_loss=2.76, discriminator_real_loss=1.447, discriminator_fake_loss=1.313, generator_loss=26.69, generator_mel_loss=18.37, generator_kl_loss=1.187, generator_dur_loss=1.837, generator_adv_loss=1.852, generator_feat_match_loss=3.445, over 100.00 samples.
+2024-03-13 20:34:42,397 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 20:35:24,928 INFO [train.py:919] (1/6) Start epoch 435
+2024-03-13 20:37:25,021 INFO [train.py:527] (1/6) Epoch 435, batch 34, global_batch_idx: 53850, batch size: 55, loss[discriminator_loss=2.708, discriminator_real_loss=1.423, discriminator_fake_loss=1.285, generator_loss=27.79, generator_mel_loss=18.45, generator_kl_loss=1.411, generator_dur_loss=1.737, generator_adv_loss=1.916, generator_feat_match_loss=4.275, over 55.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=27.96, generator_mel_loss=18.07, generator_kl_loss=1.39, generator_dur_loss=1.758, generator_adv_loss=1.947, generator_feat_match_loss=4.8, over 1976.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:39:41,054 INFO [train.py:527] (1/6) Epoch 435, batch 84, global_batch_idx: 53900, batch size: 50, loss[discriminator_loss=2.775, discriminator_real_loss=1.433, discriminator_fake_loss=1.342, generator_loss=27.87, generator_mel_loss=17.93, generator_kl_loss=1.391, generator_dur_loss=1.7, generator_adv_loss=1.948, generator_feat_match_loss=4.905, over 50.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.38, discriminator_fake_loss=1.341, generator_loss=27.9, generator_mel_loss=18.04, generator_kl_loss=1.403, generator_dur_loss=1.756, generator_adv_loss=1.944, generator_feat_match_loss=4.755, over 4931.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:41:32,250 INFO [train.py:919] (1/6) Start epoch 436
+2024-03-13 20:42:23,693 INFO [train.py:527] (1/6) Epoch 436, batch 10, global_batch_idx: 53950, batch size: 25, loss[discriminator_loss=2.665, discriminator_real_loss=1.369, discriminator_fake_loss=1.296, generator_loss=28.55, generator_mel_loss=18.28, generator_kl_loss=1.618, generator_dur_loss=1.598, generator_adv_loss=1.969, generator_feat_match_loss=5.093, over 25.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.13, generator_mel_loss=18.04, generator_kl_loss=1.402, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=4.982, over 560.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:44:40,772 INFO [train.py:527] (1/6) Epoch 436, batch 60, global_batch_idx: 54000, batch size: 50, loss[discriminator_loss=2.723, discriminator_real_loss=1.32, discriminator_fake_loss=1.402, generator_loss=27.53, generator_mel_loss=17.63, generator_kl_loss=1.615, generator_dur_loss=1.658, generator_adv_loss=1.952, generator_feat_match_loss=4.674, over 50.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=28.1, generator_mel_loss=18.12, generator_kl_loss=1.451, generator_dur_loss=1.734, generator_adv_loss=1.95, generator_feat_match_loss=4.844, over 3159.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:44:40,774 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 20:44:49,017 INFO [train.py:591] (1/6) Epoch 436, validation: discriminator_loss=2.777, discriminator_real_loss=1.482, discriminator_fake_loss=1.295, generator_loss=27.09, generator_mel_loss=18.27, generator_kl_loss=1.112, generator_dur_loss=1.792, generator_adv_loss=1.962, generator_feat_match_loss=3.957, over 100.00 samples.
+2024-03-13 20:44:49,018 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 20:47:08,843 INFO [train.py:527] (1/6) Epoch 436, batch 110, global_batch_idx: 54050, batch size: 74, loss[discriminator_loss=2.732, discriminator_real_loss=1.309, discriminator_fake_loss=1.423, generator_loss=28.07, generator_mel_loss=18.26, generator_kl_loss=1.348, generator_dur_loss=1.823, generator_adv_loss=1.885, generator_feat_match_loss=4.754, over 74.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.367, discriminator_fake_loss=1.35, generator_loss=28.08, generator_mel_loss=18.13, generator_kl_loss=1.423, generator_dur_loss=1.751, generator_adv_loss=1.949, generator_feat_match_loss=4.835, over 6200.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:47:45,099 INFO [train.py:919] (1/6) Start epoch 437
+2024-03-13 20:49:50,273 INFO [train.py:527] (1/6) Epoch 437, batch 36, global_batch_idx: 54100, batch size: 44, loss[discriminator_loss=2.708, discriminator_real_loss=1.375, discriminator_fake_loss=1.333, generator_loss=28.3, generator_mel_loss=18.29, generator_kl_loss=1.471, generator_dur_loss=1.736, generator_adv_loss=1.963, generator_feat_match_loss=4.84, over 44.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.365, discriminator_fake_loss=1.341, generator_loss=28.13, generator_mel_loss=18.09, generator_kl_loss=1.415, generator_dur_loss=1.764, generator_adv_loss=1.955, generator_feat_match_loss=4.91, over 2201.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:52:06,261 INFO [train.py:527] (1/6) Epoch 437, batch 86, global_batch_idx: 54150, batch size: 62, loss[discriminator_loss=2.569, discriminator_real_loss=1.337, discriminator_fake_loss=1.232, generator_loss=28.6, generator_mel_loss=18.01, generator_kl_loss=1.583, generator_dur_loss=1.755, generator_adv_loss=2.245, generator_feat_match_loss=5.004, over 62.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.363, discriminator_fake_loss=1.34, generator_loss=28.08, generator_mel_loss=18.03, generator_kl_loss=1.421, generator_dur_loss=1.764, generator_adv_loss=1.962, generator_feat_match_loss=4.898, over 5111.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:53:51,223 INFO [train.py:919] (1/6) Start epoch 438
+2024-03-13 20:54:49,019 INFO [train.py:527] (1/6) Epoch 438, batch 12, global_batch_idx: 54200, batch size: 66, loss[discriminator_loss=2.678, discriminator_real_loss=1.331, discriminator_fake_loss=1.346, generator_loss=27.87, generator_mel_loss=17.88, generator_kl_loss=1.262, generator_dur_loss=1.789, generator_adv_loss=1.975, generator_feat_match_loss=4.967, over 66.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.378, discriminator_fake_loss=1.334, generator_loss=28.05, generator_mel_loss=18, generator_kl_loss=1.402, generator_dur_loss=1.763, generator_adv_loss=1.952, generator_feat_match_loss=4.931, over 765.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:54:49,021 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 20:54:56,886 INFO [train.py:591] (1/6) Epoch 438, validation: discriminator_loss=2.744, discriminator_real_loss=1.418, discriminator_fake_loss=1.327, generator_loss=27.28, generator_mel_loss=18.35, generator_kl_loss=1.23, generator_dur_loss=1.82, generator_adv_loss=1.905, generator_feat_match_loss=3.968, over 100.00 samples.
+2024-03-13 20:54:56,887 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 20:57:16,535 INFO [train.py:527] (1/6) Epoch 438, batch 62, global_batch_idx: 54250, batch size: 66, loss[discriminator_loss=2.682, discriminator_real_loss=1.399, discriminator_fake_loss=1.282, generator_loss=28.66, generator_mel_loss=18.41, generator_kl_loss=1.45, generator_dur_loss=1.804, generator_adv_loss=2.056, generator_feat_match_loss=4.948, over 66.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.89, generator_mel_loss=18.01, generator_kl_loss=1.42, generator_dur_loss=1.772, generator_adv_loss=1.937, generator_feat_match_loss=4.755, over 3618.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 20:59:32,970 INFO [train.py:527] (1/6) Epoch 438, batch 112, global_batch_idx: 54300, batch size: 70, loss[discriminator_loss=2.736, discriminator_real_loss=1.375, discriminator_fake_loss=1.361, generator_loss=27.86, generator_mel_loss=17.65, generator_kl_loss=1.472, generator_dur_loss=1.838, generator_adv_loss=2.131, generator_feat_match_loss=4.774, over 70.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.343, generator_loss=27.91, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.767, generator_adv_loss=1.941, generator_feat_match_loss=4.766, over 6410.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:00:05,332 INFO [train.py:919] (1/6) Start epoch 439
+2024-03-13 21:02:12,239 INFO [train.py:527] (1/6) Epoch 439, batch 38, global_batch_idx: 54350, batch size: 59, loss[discriminator_loss=2.694, discriminator_real_loss=1.411, discriminator_fake_loss=1.283, generator_loss=28.57, generator_mel_loss=18.44, generator_kl_loss=1.432, generator_dur_loss=1.775, generator_adv_loss=2.069, generator_feat_match_loss=4.848, over 59.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.341, generator_loss=27.99, generator_mel_loss=18.04, generator_kl_loss=1.408, generator_dur_loss=1.759, generator_adv_loss=1.951, generator_feat_match_loss=4.823, over 2178.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:04:32,745 INFO [train.py:527] (1/6) Epoch 439, batch 88, global_batch_idx: 54400, batch size: 77, loss[discriminator_loss=2.715, discriminator_real_loss=1.35, discriminator_fake_loss=1.365, generator_loss=28.23, generator_mel_loss=18.24, generator_kl_loss=1.294, generator_dur_loss=1.827, generator_adv_loss=2.01, generator_feat_match_loss=4.859, over 77.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.345, generator_loss=28.02, generator_mel_loss=18.04, generator_kl_loss=1.416, generator_dur_loss=1.776, generator_adv_loss=1.944, generator_feat_match_loss=4.85, over 5279.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:04:32,747 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 21:04:41,977 INFO [train.py:591] (1/6) Epoch 439, validation: discriminator_loss=2.799, discriminator_real_loss=1.513, discriminator_fake_loss=1.286, generator_loss=26.4, generator_mel_loss=18.01, generator_kl_loss=1.244, generator_dur_loss=1.829, generator_adv_loss=1.923, generator_feat_match_loss=3.392, over 100.00 samples.
+2024-03-13 21:04:41,978 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 21:06:18,977 INFO [train.py:919] (1/6) Start epoch 440
+2024-03-13 21:07:20,393 INFO [train.py:527] (1/6) Epoch 440, batch 14, global_batch_idx: 54450, batch size: 52, loss[discriminator_loss=2.781, discriminator_real_loss=1.377, discriminator_fake_loss=1.404, generator_loss=27.7, generator_mel_loss=18.11, generator_kl_loss=1.395, generator_dur_loss=1.705, generator_adv_loss=2.114, generator_feat_match_loss=4.383, over 52.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.342, generator_loss=28.16, generator_mel_loss=18.12, generator_kl_loss=1.403, generator_dur_loss=1.769, generator_adv_loss=1.954, generator_feat_match_loss=4.913, over 887.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:09:37,450 INFO [train.py:527] (1/6) Epoch 440, batch 64, global_batch_idx: 54500, batch size: 96, loss[discriminator_loss=2.701, discriminator_real_loss=1.352, discriminator_fake_loss=1.349, generator_loss=28, generator_mel_loss=17.93, generator_kl_loss=1.461, generator_dur_loss=1.859, generator_adv_loss=1.846, generator_feat_match_loss=4.906, over 96.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=28.11, generator_mel_loss=18.13, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=4.874, over 3775.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:11:56,732 INFO [train.py:527] (1/6) Epoch 440, batch 114, global_batch_idx: 54550, batch size: 68, loss[discriminator_loss=2.733, discriminator_real_loss=1.347, discriminator_fake_loss=1.387, generator_loss=28.08, generator_mel_loss=18.05, generator_kl_loss=1.379, generator_dur_loss=1.797, generator_adv_loss=1.915, generator_feat_match_loss=4.935, over 68.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=28.07, generator_mel_loss=18.09, generator_kl_loss=1.395, generator_dur_loss=1.757, generator_adv_loss=1.959, generator_feat_match_loss=4.861, over 6816.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:12:24,536 INFO [train.py:919] (1/6) Start epoch 441
+2024-03-13 21:14:37,084 INFO [train.py:527] (1/6) Epoch 441, batch 40, global_batch_idx: 54600, batch size: 62, loss[discriminator_loss=2.764, discriminator_real_loss=1.42, discriminator_fake_loss=1.344, generator_loss=27.34, generator_mel_loss=18.01, generator_kl_loss=1.443, generator_dur_loss=1.738, generator_adv_loss=1.769, generator_feat_match_loss=4.378, over 62.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.12, generator_mel_loss=18.12, generator_kl_loss=1.448, generator_dur_loss=1.751, generator_adv_loss=1.948, generator_feat_match_loss=4.859, over 2281.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:14:37,085 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 21:14:45,257 INFO [train.py:591] (1/6) Epoch 441, validation: discriminator_loss=2.753, discriminator_real_loss=1.449, discriminator_fake_loss=1.304, generator_loss=26.88, generator_mel_loss=18.1, generator_kl_loss=1.25, generator_dur_loss=1.816, generator_adv_loss=1.859, generator_feat_match_loss=3.86, over 100.00 samples.
+2024-03-13 21:14:45,258 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 21:17:05,692 INFO [train.py:527] (1/6) Epoch 441, batch 90, global_batch_idx: 54650, batch size: 50, loss[discriminator_loss=2.628, discriminator_real_loss=1.291, discriminator_fake_loss=1.337, generator_loss=29.11, generator_mel_loss=18.58, generator_kl_loss=1.461, generator_dur_loss=1.667, generator_adv_loss=2.088, generator_feat_match_loss=5.314, over 50.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=28.06, generator_mel_loss=18.09, generator_kl_loss=1.422, generator_dur_loss=1.751, generator_adv_loss=1.953, generator_feat_match_loss=4.849, over 5228.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:18:39,706 INFO [train.py:919] (1/6) Start epoch 442
+2024-03-13 21:19:46,971 INFO [train.py:527] (1/6) Epoch 442, batch 16, global_batch_idx: 54700, batch size: 64, loss[discriminator_loss=2.759, discriminator_real_loss=1.443, discriminator_fake_loss=1.317, generator_loss=27.44, generator_mel_loss=17.68, generator_kl_loss=1.325, generator_dur_loss=1.794, generator_adv_loss=1.889, generator_feat_match_loss=4.753, over 64.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.387, discriminator_fake_loss=1.348, generator_loss=27.95, generator_mel_loss=17.99, generator_kl_loss=1.397, generator_dur_loss=1.772, generator_adv_loss=1.973, generator_feat_match_loss=4.823, over 1051.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:22:08,425 INFO [train.py:527] (1/6) Epoch 442, batch 66, global_batch_idx: 54750, batch size: 44, loss[discriminator_loss=2.676, discriminator_real_loss=1.379, discriminator_fake_loss=1.297, generator_loss=29.56, generator_mel_loss=18.87, generator_kl_loss=1.475, generator_dur_loss=1.69, generator_adv_loss=2.019, generator_feat_match_loss=5.513, over 44.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=28.02, generator_mel_loss=18.04, generator_kl_loss=1.415, generator_dur_loss=1.755, generator_adv_loss=1.966, generator_feat_match_loss=4.84, over 3884.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:24:26,483 INFO [train.py:527] (1/6) Epoch 442, batch 116, global_batch_idx: 54800, batch size: 96, loss[discriminator_loss=2.726, discriminator_real_loss=1.395, discriminator_fake_loss=1.331, generator_loss=26.98, generator_mel_loss=17.73, generator_kl_loss=1.324, generator_dur_loss=1.853, generator_adv_loss=1.833, generator_feat_match_loss=4.24, over 96.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.344, generator_loss=28.04, generator_mel_loss=18.05, generator_kl_loss=1.417, generator_dur_loss=1.765, generator_adv_loss=1.96, generator_feat_match_loss=4.851, over 6848.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:24:26,485 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 21:24:35,318 INFO [train.py:591] (1/6) Epoch 442, validation: discriminator_loss=2.789, discriminator_real_loss=1.452, discriminator_fake_loss=1.337, generator_loss=27.65, generator_mel_loss=18.86, generator_kl_loss=1.358, generator_dur_loss=1.83, generator_adv_loss=1.862, generator_feat_match_loss=3.744, over 100.00 samples.
+2024-03-13 21:24:35,319 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 21:24:54,077 INFO [train.py:919] (1/6) Start epoch 443
+2024-03-13 21:27:15,609 INFO [train.py:527] (1/6) Epoch 443, batch 42, global_batch_idx: 54850, batch size: 25, loss[discriminator_loss=2.761, discriminator_real_loss=1.47, discriminator_fake_loss=1.291, generator_loss=26.94, generator_mel_loss=17.64, generator_kl_loss=1.652, generator_dur_loss=1.571, generator_adv_loss=2.13, generator_feat_match_loss=3.947, over 25.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.383, discriminator_fake_loss=1.348, generator_loss=28.05, generator_mel_loss=18.09, generator_kl_loss=1.452, generator_dur_loss=1.741, generator_adv_loss=1.937, generator_feat_match_loss=4.823, over 2212.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:29:35,359 INFO [train.py:527] (1/6) Epoch 443, batch 92, global_batch_idx: 54900, batch size: 68, loss[discriminator_loss=2.693, discriminator_real_loss=1.412, discriminator_fake_loss=1.281, generator_loss=27.33, generator_mel_loss=17.37, generator_kl_loss=1.315, generator_dur_loss=1.816, generator_adv_loss=2.146, generator_feat_match_loss=4.689, over 68.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.383, discriminator_fake_loss=1.341, generator_loss=28.16, generator_mel_loss=18.08, generator_kl_loss=1.431, generator_dur_loss=1.759, generator_adv_loss=1.99, generator_feat_match_loss=4.901, over 5171.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:30:59,482 INFO [train.py:919] (1/6) Start epoch 444
+2024-03-13 21:32:11,054 INFO [train.py:527] (1/6) Epoch 444, batch 18, global_batch_idx: 54950, batch size: 62, loss[discriminator_loss=2.736, discriminator_real_loss=1.373, discriminator_fake_loss=1.364, generator_loss=28.14, generator_mel_loss=18.57, generator_kl_loss=1.496, generator_dur_loss=1.771, generator_adv_loss=1.897, generator_feat_match_loss=4.407, over 62.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.378, discriminator_fake_loss=1.325, generator_loss=28.19, generator_mel_loss=18.25, generator_kl_loss=1.417, generator_dur_loss=1.749, generator_adv_loss=1.966, generator_feat_match_loss=4.802, over 953.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:34:31,959 INFO [train.py:527] (1/6) Epoch 444, batch 68, global_batch_idx: 55000, batch size: 77, loss[discriminator_loss=2.686, discriminator_real_loss=1.37, discriminator_fake_loss=1.316, generator_loss=27.94, generator_mel_loss=17.97, generator_kl_loss=1.299, generator_dur_loss=1.792, generator_adv_loss=1.907, generator_feat_match_loss=4.971, over 77.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=27.9, generator_mel_loss=18.03, generator_kl_loss=1.375, generator_dur_loss=1.776, generator_adv_loss=1.945, generator_feat_match_loss=4.779, over 4160.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:34:31,961 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 21:34:40,032 INFO [train.py:591] (1/6) Epoch 444, validation: discriminator_loss=2.77, discriminator_real_loss=1.474, discriminator_fake_loss=1.296, generator_loss=27.27, generator_mel_loss=18.42, generator_kl_loss=1.313, generator_dur_loss=1.839, generator_adv_loss=1.899, generator_feat_match_loss=3.804, over 100.00 samples.
+2024-03-13 21:34:40,032 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 21:36:56,025 INFO [train.py:527] (1/6) Epoch 444, batch 118, global_batch_idx: 55050, batch size: 45, loss[discriminator_loss=2.77, discriminator_real_loss=1.493, discriminator_fake_loss=1.278, generator_loss=25.9, generator_mel_loss=17.32, generator_kl_loss=1.379, generator_dur_loss=1.68, generator_adv_loss=1.93, generator_feat_match_loss=3.595, over 45.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.376, discriminator_fake_loss=1.341, generator_loss=27.97, generator_mel_loss=18.07, generator_kl_loss=1.398, generator_dur_loss=1.77, generator_adv_loss=1.951, generator_feat_match_loss=4.786, over 6888.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:37:11,071 INFO [train.py:919] (1/6) Start epoch 445
+2024-03-13 21:39:41,554 INFO [train.py:527] (1/6) Epoch 445, batch 44, global_batch_idx: 55100, batch size: 47, loss[discriminator_loss=2.634, discriminator_real_loss=1.282, discriminator_fake_loss=1.352, generator_loss=28.82, generator_mel_loss=18.19, generator_kl_loss=1.511, generator_dur_loss=1.679, generator_adv_loss=2.115, generator_feat_match_loss=5.323, over 47.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=27.94, generator_mel_loss=18.03, generator_kl_loss=1.39, generator_dur_loss=1.78, generator_adv_loss=1.94, generator_feat_match_loss=4.797, over 2763.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:42:00,778 INFO [train.py:527] (1/6) Epoch 445, batch 94, global_batch_idx: 55150, batch size: 50, loss[discriminator_loss=2.753, discriminator_real_loss=1.386, discriminator_fake_loss=1.367, generator_loss=27.37, generator_mel_loss=17.93, generator_kl_loss=1.332, generator_dur_loss=1.71, generator_adv_loss=1.841, generator_feat_match_loss=4.555, over 50.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.377, discriminator_fake_loss=1.339, generator_loss=28.02, generator_mel_loss=18.09, generator_kl_loss=1.407, generator_dur_loss=1.772, generator_adv_loss=1.948, generator_feat_match_loss=4.803, over 5515.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:43:17,273 INFO [train.py:919] (1/6) Start epoch 446
+2024-03-13 21:44:33,617 INFO [train.py:527] (1/6) Epoch 446, batch 20, global_batch_idx: 55200, batch size: 88, loss[discriminator_loss=2.719, discriminator_real_loss=1.401, discriminator_fake_loss=1.318, generator_loss=27.7, generator_mel_loss=18.06, generator_kl_loss=1.38, generator_dur_loss=1.82, generator_adv_loss=1.779, generator_feat_match_loss=4.66, over 88.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.381, discriminator_fake_loss=1.349, generator_loss=28.08, generator_mel_loss=18.09, generator_kl_loss=1.457, generator_dur_loss=1.75, generator_adv_loss=1.928, generator_feat_match_loss=4.857, over 1148.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:44:33,619 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 21:44:41,674 INFO [train.py:591] (1/6) Epoch 446, validation: discriminator_loss=2.779, discriminator_real_loss=1.447, discriminator_fake_loss=1.332, generator_loss=26.72, generator_mel_loss=18.09, generator_kl_loss=1.257, generator_dur_loss=1.837, generator_adv_loss=1.85, generator_feat_match_loss=3.689, over 100.00 samples.
+2024-03-13 21:44:41,675 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 21:46:59,775 INFO [train.py:527] (1/6) Epoch 446, batch 70, global_batch_idx: 55250, batch size: 72, loss[discriminator_loss=2.823, discriminator_real_loss=1.524, discriminator_fake_loss=1.298, generator_loss=27.07, generator_mel_loss=17.68, generator_kl_loss=1.175, generator_dur_loss=1.794, generator_adv_loss=1.849, generator_feat_match_loss=4.573, over 72.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.377, discriminator_fake_loss=1.339, generator_loss=28.06, generator_mel_loss=18.09, generator_kl_loss=1.424, generator_dur_loss=1.767, generator_adv_loss=1.948, generator_feat_match_loss=4.84, over 3990.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:49:19,343 INFO [train.py:527] (1/6) Epoch 446, batch 120, global_batch_idx: 55300, batch size: 14, loss[discriminator_loss=2.738, discriminator_real_loss=1.411, discriminator_fake_loss=1.327, generator_loss=27.78, generator_mel_loss=17.87, generator_kl_loss=1.803, generator_dur_loss=1.539, generator_adv_loss=1.987, generator_feat_match_loss=4.573, over 14.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.382, discriminator_fake_loss=1.336, generator_loss=28.1, generator_mel_loss=18.1, generator_kl_loss=1.427, generator_dur_loss=1.765, generator_adv_loss=1.956, generator_feat_match_loss=4.851, over 6861.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:49:29,326 INFO [train.py:919] (1/6) Start epoch 447
+2024-03-13 21:52:03,031 INFO [train.py:527] (1/6) Epoch 447, batch 46, global_batch_idx: 55350, batch size: 68, loss[discriminator_loss=2.607, discriminator_real_loss=1.295, discriminator_fake_loss=1.312, generator_loss=29.03, generator_mel_loss=18.38, generator_kl_loss=1.435, generator_dur_loss=1.775, generator_adv_loss=2.066, generator_feat_match_loss=5.376, over 68.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.368, discriminator_fake_loss=1.347, generator_loss=28.15, generator_mel_loss=18.13, generator_kl_loss=1.412, generator_dur_loss=1.766, generator_adv_loss=1.951, generator_feat_match_loss=4.888, over 2580.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:54:22,860 INFO [train.py:527] (1/6) Epoch 447, batch 96, global_batch_idx: 55400, batch size: 80, loss[discriminator_loss=2.681, discriminator_real_loss=1.32, discriminator_fake_loss=1.361, generator_loss=28.21, generator_mel_loss=17.9, generator_kl_loss=1.336, generator_dur_loss=1.794, generator_adv_loss=2.019, generator_feat_match_loss=5.166, over 80.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.365, discriminator_fake_loss=1.346, generator_loss=28.08, generator_mel_loss=18.09, generator_kl_loss=1.41, generator_dur_loss=1.769, generator_adv_loss=1.953, generator_feat_match_loss=4.858, over 5592.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:54:22,862 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 21:54:31,586 INFO [train.py:591] (1/6) Epoch 447, validation: discriminator_loss=2.762, discriminator_real_loss=1.484, discriminator_fake_loss=1.278, generator_loss=26.71, generator_mel_loss=18.1, generator_kl_loss=1.29, generator_dur_loss=1.837, generator_adv_loss=1.976, generator_feat_match_loss=3.501, over 100.00 samples.
+2024-03-13 21:54:31,587 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 21:55:44,847 INFO [train.py:919] (1/6) Start epoch 448
+2024-03-13 21:57:09,228 INFO [train.py:527] (1/6) Epoch 448, batch 22, global_batch_idx: 55450, batch size: 53, loss[discriminator_loss=2.769, discriminator_real_loss=1.442, discriminator_fake_loss=1.327, generator_loss=27.75, generator_mel_loss=17.92, generator_kl_loss=1.383, generator_dur_loss=1.794, generator_adv_loss=1.815, generator_feat_match_loss=4.839, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.29, generator_mel_loss=18.1, generator_kl_loss=1.434, generator_dur_loss=1.764, generator_adv_loss=1.963, generator_feat_match_loss=5.024, over 1285.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 21:59:27,770 INFO [train.py:527] (1/6) Epoch 448, batch 72, global_batch_idx: 55500, batch size: 72, loss[discriminator_loss=2.784, discriminator_real_loss=1.489, discriminator_fake_loss=1.295, generator_loss=27.49, generator_mel_loss=17.9, generator_kl_loss=1.317, generator_dur_loss=1.804, generator_adv_loss=1.948, generator_feat_match_loss=4.519, over 72.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=28.19, generator_mel_loss=18.08, generator_kl_loss=1.417, generator_dur_loss=1.767, generator_adv_loss=1.959, generator_feat_match_loss=4.974, over 4191.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:01:45,997 INFO [train.py:527] (1/6) Epoch 448, batch 122, global_batch_idx: 55550, batch size: 14, loss[discriminator_loss=2.702, discriminator_real_loss=1.426, discriminator_fake_loss=1.275, generator_loss=28.39, generator_mel_loss=19.14, generator_kl_loss=1.566, generator_dur_loss=1.574, generator_adv_loss=1.884, generator_feat_match_loss=4.228, over 14.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.2, generator_mel_loss=18.11, generator_kl_loss=1.414, generator_dur_loss=1.76, generator_adv_loss=1.96, generator_feat_match_loss=4.956, over 6934.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:01:50,654 INFO [train.py:919] (1/6) Start epoch 449
+2024-03-13 22:04:26,663 INFO [train.py:527] (1/6) Epoch 449, batch 48, global_batch_idx: 55600, batch size: 68, loss[discriminator_loss=2.683, discriminator_real_loss=1.323, discriminator_fake_loss=1.36, generator_loss=28.63, generator_mel_loss=18.19, generator_kl_loss=1.457, generator_dur_loss=1.797, generator_adv_loss=1.866, generator_feat_match_loss=5.311, over 68.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.38, discriminator_fake_loss=1.348, generator_loss=28.01, generator_mel_loss=18.05, generator_kl_loss=1.395, generator_dur_loss=1.775, generator_adv_loss=1.939, generator_feat_match_loss=4.849, over 3018.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:04:26,665 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 22:04:34,678 INFO [train.py:591] (1/6) Epoch 449, validation: discriminator_loss=2.757, discriminator_real_loss=1.339, discriminator_fake_loss=1.419, generator_loss=26.97, generator_mel_loss=18.16, generator_kl_loss=1.266, generator_dur_loss=1.819, generator_adv_loss=1.777, generator_feat_match_loss=3.945, over 100.00 samples.
+2024-03-13 22:04:34,679 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 22:06:50,820 INFO [train.py:527] (1/6) Epoch 449, batch 98, global_batch_idx: 55650, batch size: 61, loss[discriminator_loss=2.701, discriminator_real_loss=1.45, discriminator_fake_loss=1.251, generator_loss=27.9, generator_mel_loss=18.02, generator_kl_loss=1.417, generator_dur_loss=1.701, generator_adv_loss=2.046, generator_feat_match_loss=4.713, over 61.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.378, discriminator_fake_loss=1.343, generator_loss=28.01, generator_mel_loss=18.05, generator_kl_loss=1.404, generator_dur_loss=1.759, generator_adv_loss=1.945, generator_feat_match_loss=4.853, over 5910.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:08:03,282 INFO [train.py:919] (1/6) Start epoch 450
+2024-03-13 22:09:34,080 INFO [train.py:527] (1/6) Epoch 450, batch 24, global_batch_idx: 55700, batch size: 47, loss[discriminator_loss=2.767, discriminator_real_loss=1.429, discriminator_fake_loss=1.338, generator_loss=28.06, generator_mel_loss=18.23, generator_kl_loss=1.434, generator_dur_loss=1.644, generator_adv_loss=1.891, generator_feat_match_loss=4.859, over 47.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=28, generator_mel_loss=18.02, generator_kl_loss=1.445, generator_dur_loss=1.699, generator_adv_loss=1.963, generator_feat_match_loss=4.871, over 1277.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:11:52,362 INFO [train.py:527] (1/6) Epoch 450, batch 74, global_batch_idx: 55750, batch size: 36, loss[discriminator_loss=2.684, discriminator_real_loss=1.346, discriminator_fake_loss=1.337, generator_loss=27.8, generator_mel_loss=17.81, generator_kl_loss=1.561, generator_dur_loss=1.715, generator_adv_loss=1.907, generator_feat_match_loss=4.805, over 36.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.06, generator_mel_loss=18.03, generator_kl_loss=1.408, generator_dur_loss=1.746, generator_adv_loss=1.977, generator_feat_match_loss=4.903, over 4161.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:14:08,106 INFO [train.py:919] (1/6) Start epoch 451
+2024-03-13 22:14:31,941 INFO [train.py:527] (1/6) Epoch 451, batch 0, global_batch_idx: 55800, batch size: 83, loss[discriminator_loss=2.68, discriminator_real_loss=1.296, discriminator_fake_loss=1.384, generator_loss=28.31, generator_mel_loss=18.13, generator_kl_loss=1.173, generator_dur_loss=1.817, generator_adv_loss=2.008, generator_feat_match_loss=5.178, over 83.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.296, discriminator_fake_loss=1.384, generator_loss=28.31, generator_mel_loss=18.13, generator_kl_loss=1.173, generator_dur_loss=1.817, generator_adv_loss=2.008, generator_feat_match_loss=5.178, over 83.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:14:31,943 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 22:14:39,773 INFO [train.py:591] (1/6) Epoch 451, validation: discriminator_loss=2.782, discriminator_real_loss=1.506, discriminator_fake_loss=1.276, generator_loss=26.31, generator_mel_loss=18.03, generator_kl_loss=1.253, generator_dur_loss=1.825, generator_adv_loss=1.882, generator_feat_match_loss=3.323, over 100.00 samples.
+2024-03-13 22:14:39,775 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 22:16:57,329 INFO [train.py:527] (1/6) Epoch 451, batch 50, global_batch_idx: 55850, batch size: 42, loss[discriminator_loss=2.74, discriminator_real_loss=1.279, discriminator_fake_loss=1.461, generator_loss=28.75, generator_mel_loss=18.26, generator_kl_loss=1.569, generator_dur_loss=1.657, generator_adv_loss=1.942, generator_feat_match_loss=5.317, over 42.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.376, discriminator_fake_loss=1.349, generator_loss=28.08, generator_mel_loss=18.07, generator_kl_loss=1.421, generator_dur_loss=1.771, generator_adv_loss=1.94, generator_feat_match_loss=4.871, over 2938.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:19:14,529 INFO [train.py:527] (1/6) Epoch 451, batch 100, global_batch_idx: 55900, batch size: 56, loss[discriminator_loss=2.713, discriminator_real_loss=1.362, discriminator_fake_loss=1.351, generator_loss=28.95, generator_mel_loss=18.41, generator_kl_loss=1.324, generator_dur_loss=1.713, generator_adv_loss=2.02, generator_feat_match_loss=5.483, over 56.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.368, discriminator_fake_loss=1.346, generator_loss=28.02, generator_mel_loss=18.01, generator_kl_loss=1.418, generator_dur_loss=1.763, generator_adv_loss=1.94, generator_feat_match_loss=4.884, over 5794.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:20:20,899 INFO [train.py:919] (1/6) Start epoch 452
+2024-03-13 22:21:57,278 INFO [train.py:527] (1/6) Epoch 452, batch 26, global_batch_idx: 55950, batch size: 48, loss[discriminator_loss=2.749, discriminator_real_loss=1.405, discriminator_fake_loss=1.344, generator_loss=28.17, generator_mel_loss=18.12, generator_kl_loss=1.633, generator_dur_loss=1.668, generator_adv_loss=1.96, generator_feat_match_loss=4.787, over 48.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.22, generator_mel_loss=18.14, generator_kl_loss=1.424, generator_dur_loss=1.77, generator_adv_loss=1.958, generator_feat_match_loss=4.928, over 1711.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:24:17,349 INFO [train.py:527] (1/6) Epoch 452, batch 76, global_batch_idx: 56000, batch size: 55, loss[discriminator_loss=2.661, discriminator_real_loss=1.357, discriminator_fake_loss=1.305, generator_loss=29.02, generator_mel_loss=18.36, generator_kl_loss=1.445, generator_dur_loss=1.744, generator_adv_loss=1.998, generator_feat_match_loss=5.469, over 55.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=28.09, generator_mel_loss=18.06, generator_kl_loss=1.425, generator_dur_loss=1.759, generator_adv_loss=1.958, generator_feat_match_loss=4.896, over 4491.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:24:17,350 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 22:24:25,386 INFO [train.py:591] (1/6) Epoch 452, validation: discriminator_loss=2.778, discriminator_real_loss=1.488, discriminator_fake_loss=1.29, generator_loss=26.94, generator_mel_loss=18.1, generator_kl_loss=1.215, generator_dur_loss=1.844, generator_adv_loss=1.931, generator_feat_match_loss=3.847, over 100.00 samples.
+2024-03-13 22:24:25,387 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 22:26:33,548 INFO [train.py:919] (1/6) Start epoch 453
+2024-03-13 22:27:01,589 INFO [train.py:527] (1/6) Epoch 453, batch 2, global_batch_idx: 56050, batch size: 77, loss[discriminator_loss=2.695, discriminator_real_loss=1.406, discriminator_fake_loss=1.289, generator_loss=27.56, generator_mel_loss=17.76, generator_kl_loss=1.347, generator_dur_loss=1.824, generator_adv_loss=2.088, generator_feat_match_loss=4.546, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.37, discriminator_fake_loss=1.315, generator_loss=27.92, generator_mel_loss=17.95, generator_kl_loss=1.338, generator_dur_loss=1.811, generator_adv_loss=2.008, generator_feat_match_loss=4.813, over 217.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:29:16,652 INFO [train.py:527] (1/6) Epoch 453, batch 52, global_batch_idx: 56100, batch size: 80, loss[discriminator_loss=2.738, discriminator_real_loss=1.363, discriminator_fake_loss=1.375, generator_loss=28.27, generator_mel_loss=18.22, generator_kl_loss=1.466, generator_dur_loss=1.776, generator_adv_loss=2.099, generator_feat_match_loss=4.708, over 80.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.362, discriminator_fake_loss=1.343, generator_loss=28.18, generator_mel_loss=18.11, generator_kl_loss=1.42, generator_dur_loss=1.768, generator_adv_loss=1.962, generator_feat_match_loss=4.916, over 3012.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:31:35,314 INFO [train.py:527] (1/6) Epoch 453, batch 102, global_batch_idx: 56150, batch size: 52, loss[discriminator_loss=2.749, discriminator_real_loss=1.399, discriminator_fake_loss=1.35, generator_loss=27.24, generator_mel_loss=18.16, generator_kl_loss=1.179, generator_dur_loss=1.766, generator_adv_loss=1.948, generator_feat_match_loss=4.185, over 52.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=28.1, generator_mel_loss=18.08, generator_kl_loss=1.417, generator_dur_loss=1.763, generator_adv_loss=1.956, generator_feat_match_loss=4.88, over 5970.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:32:35,545 INFO [train.py:919] (1/6) Start epoch 454
+2024-03-13 22:34:17,157 INFO [train.py:527] (1/6) Epoch 454, batch 28, global_batch_idx: 56200, batch size: 50, loss[discriminator_loss=2.765, discriminator_real_loss=1.451, discriminator_fake_loss=1.314, generator_loss=26.95, generator_mel_loss=17.57, generator_kl_loss=1.382, generator_dur_loss=1.703, generator_adv_loss=2.062, generator_feat_match_loss=4.232, over 50.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.355, discriminator_fake_loss=1.343, generator_loss=28.19, generator_mel_loss=18.04, generator_kl_loss=1.434, generator_dur_loss=1.751, generator_adv_loss=2.031, generator_feat_match_loss=4.942, over 1607.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:34:17,159 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 22:34:25,073 INFO [train.py:591] (1/6) Epoch 454, validation: discriminator_loss=2.785, discriminator_real_loss=1.482, discriminator_fake_loss=1.302, generator_loss=27.43, generator_mel_loss=18.39, generator_kl_loss=1.257, generator_dur_loss=1.828, generator_adv_loss=1.899, generator_feat_match_loss=4.065, over 100.00 samples.
+2024-03-13 22:34:25,074 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 22:36:45,320 INFO [train.py:527] (1/6) Epoch 454, batch 78, global_batch_idx: 56250, batch size: 72, loss[discriminator_loss=2.765, discriminator_real_loss=1.421, discriminator_fake_loss=1.344, generator_loss=27.77, generator_mel_loss=17.75, generator_kl_loss=1.383, generator_dur_loss=1.813, generator_adv_loss=2.002, generator_feat_match_loss=4.824, over 72.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=28.07, generator_mel_loss=18.04, generator_kl_loss=1.403, generator_dur_loss=1.768, generator_adv_loss=1.983, generator_feat_match_loss=4.881, over 4566.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:38:49,896 INFO [train.py:919] (1/6) Start epoch 455
+2024-03-13 22:39:24,153 INFO [train.py:527] (1/6) Epoch 455, batch 4, global_batch_idx: 56300, batch size: 77, loss[discriminator_loss=2.721, discriminator_real_loss=1.366, discriminator_fake_loss=1.355, generator_loss=28.28, generator_mel_loss=18.29, generator_kl_loss=1.266, generator_dur_loss=1.821, generator_adv_loss=2.005, generator_feat_match_loss=4.896, over 77.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.344, generator_loss=28.26, generator_mel_loss=18.09, generator_kl_loss=1.325, generator_dur_loss=1.802, generator_adv_loss=1.958, generator_feat_match_loss=5.086, over 366.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:41:43,669 INFO [train.py:527] (1/6) Epoch 455, batch 54, global_batch_idx: 56350, batch size: 66, loss[discriminator_loss=2.754, discriminator_real_loss=1.326, discriminator_fake_loss=1.428, generator_loss=27.02, generator_mel_loss=17.73, generator_kl_loss=1.327, generator_dur_loss=1.774, generator_adv_loss=1.885, generator_feat_match_loss=4.3, over 66.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.367, discriminator_fake_loss=1.354, generator_loss=28.08, generator_mel_loss=18.09, generator_kl_loss=1.414, generator_dur_loss=1.757, generator_adv_loss=1.944, generator_feat_match_loss=4.871, over 3317.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:44:02,027 INFO [train.py:527] (1/6) Epoch 455, batch 104, global_batch_idx: 56400, batch size: 80, loss[discriminator_loss=2.673, discriminator_real_loss=1.33, discriminator_fake_loss=1.344, generator_loss=28.52, generator_mel_loss=18.08, generator_kl_loss=1.363, generator_dur_loss=1.801, generator_adv_loss=2.108, generator_feat_match_loss=5.167, over 80.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.345, generator_loss=28.04, generator_mel_loss=18.09, generator_kl_loss=1.413, generator_dur_loss=1.746, generator_adv_loss=1.96, generator_feat_match_loss=4.829, over 5920.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:44:02,028 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 22:44:11,041 INFO [train.py:591] (1/6) Epoch 455, validation: discriminator_loss=2.763, discriminator_real_loss=1.513, discriminator_fake_loss=1.25, generator_loss=27.47, generator_mel_loss=18.5, generator_kl_loss=1.143, generator_dur_loss=1.829, generator_adv_loss=2.035, generator_feat_match_loss=3.965, over 100.00 samples.
+2024-03-13 22:44:11,041 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 22:45:05,169 INFO [train.py:919] (1/6) Start epoch 456
+2024-03-13 22:46:51,752 INFO [train.py:527] (1/6) Epoch 456, batch 30, global_batch_idx: 56450, batch size: 31, loss[discriminator_loss=2.711, discriminator_real_loss=1.301, discriminator_fake_loss=1.41, generator_loss=30.16, generator_mel_loss=18.46, generator_kl_loss=1.852, generator_dur_loss=1.625, generator_adv_loss=2.081, generator_feat_match_loss=6.143, over 31.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.12, generator_mel_loss=18.12, generator_kl_loss=1.435, generator_dur_loss=1.759, generator_adv_loss=1.949, generator_feat_match_loss=4.858, over 1868.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:49:11,318 INFO [train.py:527] (1/6) Epoch 456, batch 80, global_batch_idx: 56500, batch size: 50, loss[discriminator_loss=2.728, discriminator_real_loss=1.436, discriminator_fake_loss=1.293, generator_loss=28.48, generator_mel_loss=18.37, generator_kl_loss=1.378, generator_dur_loss=1.679, generator_adv_loss=1.93, generator_feat_match_loss=5.123, over 50.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.346, generator_loss=28.14, generator_mel_loss=18.1, generator_kl_loss=1.397, generator_dur_loss=1.764, generator_adv_loss=1.954, generator_feat_match_loss=4.922, over 4893.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:51:12,018 INFO [train.py:919] (1/6) Start epoch 457
+2024-03-13 22:51:51,401 INFO [train.py:527] (1/6) Epoch 457, batch 6, global_batch_idx: 56550, batch size: 36, loss[discriminator_loss=2.685, discriminator_real_loss=1.322, discriminator_fake_loss=1.363, generator_loss=27.65, generator_mel_loss=17.82, generator_kl_loss=1.502, generator_dur_loss=1.717, generator_adv_loss=2.018, generator_feat_match_loss=4.587, over 36.00 samples.], tot_loss[discriminator_loss=2.743, discriminator_real_loss=1.379, discriminator_fake_loss=1.364, generator_loss=27.83, generator_mel_loss=18.09, generator_kl_loss=1.332, generator_dur_loss=1.766, generator_adv_loss=1.928, generator_feat_match_loss=4.721, over 394.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:54:13,919 INFO [train.py:527] (1/6) Epoch 457, batch 56, global_batch_idx: 56600, batch size: 88, loss[discriminator_loss=2.67, discriminator_real_loss=1.314, discriminator_fake_loss=1.356, generator_loss=27.38, generator_mel_loss=17.76, generator_kl_loss=1.316, generator_dur_loss=1.875, generator_adv_loss=1.903, generator_feat_match_loss=4.522, over 88.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.368, discriminator_fake_loss=1.35, generator_loss=27.95, generator_mel_loss=18, generator_kl_loss=1.38, generator_dur_loss=1.789, generator_adv_loss=1.948, generator_feat_match_loss=4.834, over 3519.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:54:13,921 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 22:54:21,932 INFO [train.py:591] (1/6) Epoch 457, validation: discriminator_loss=2.738, discriminator_real_loss=1.474, discriminator_fake_loss=1.264, generator_loss=26.53, generator_mel_loss=18.11, generator_kl_loss=1.237, generator_dur_loss=1.824, generator_adv_loss=1.89, generator_feat_match_loss=3.468, over 100.00 samples.
+2024-03-13 22:54:21,933 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 22:56:41,905 INFO [train.py:527] (1/6) Epoch 457, batch 106, global_batch_idx: 56650, batch size: 12, loss[discriminator_loss=2.647, discriminator_real_loss=1.283, discriminator_fake_loss=1.364, generator_loss=30.37, generator_mel_loss=19.42, generator_kl_loss=1.776, generator_dur_loss=1.564, generator_adv_loss=2.085, generator_feat_match_loss=5.525, over 12.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.371, discriminator_fake_loss=1.348, generator_loss=27.96, generator_mel_loss=18.01, generator_kl_loss=1.407, generator_dur_loss=1.772, generator_adv_loss=1.943, generator_feat_match_loss=4.827, over 6201.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 22:57:26,255 INFO [train.py:919] (1/6) Start epoch 458
+2024-03-13 22:59:21,162 INFO [train.py:527] (1/6) Epoch 458, batch 32, global_batch_idx: 56700, batch size: 88, loss[discriminator_loss=2.688, discriminator_real_loss=1.388, discriminator_fake_loss=1.3, generator_loss=28.43, generator_mel_loss=18.06, generator_kl_loss=1.38, generator_dur_loss=1.848, generator_adv_loss=1.859, generator_feat_match_loss=5.284, over 88.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.389, discriminator_fake_loss=1.332, generator_loss=28, generator_mel_loss=18.04, generator_kl_loss=1.427, generator_dur_loss=1.764, generator_adv_loss=1.935, generator_feat_match_loss=4.836, over 1910.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:01:40,109 INFO [train.py:527] (1/6) Epoch 458, batch 82, global_batch_idx: 56750, batch size: 31, loss[discriminator_loss=2.732, discriminator_real_loss=1.437, discriminator_fake_loss=1.295, generator_loss=27.73, generator_mel_loss=18.01, generator_kl_loss=1.64, generator_dur_loss=1.677, generator_adv_loss=1.98, generator_feat_match_loss=4.422, over 31.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.09, generator_mel_loss=18.06, generator_kl_loss=1.421, generator_dur_loss=1.757, generator_adv_loss=1.949, generator_feat_match_loss=4.911, over 4811.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:03:33,914 INFO [train.py:919] (1/6) Start epoch 459
+2024-03-13 23:04:20,607 INFO [train.py:527] (1/6) Epoch 459, batch 8, global_batch_idx: 56800, batch size: 80, loss[discriminator_loss=2.787, discriminator_real_loss=1.568, discriminator_fake_loss=1.219, generator_loss=27.34, generator_mel_loss=17.81, generator_kl_loss=1.27, generator_dur_loss=1.764, generator_adv_loss=1.878, generator_feat_match_loss=4.62, over 80.00 samples.], tot_loss[discriminator_loss=2.758, discriminator_real_loss=1.426, discriminator_fake_loss=1.333, generator_loss=27.78, generator_mel_loss=17.9, generator_kl_loss=1.354, generator_dur_loss=1.766, generator_adv_loss=1.97, generator_feat_match_loss=4.789, over 588.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:04:20,610 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 23:04:28,786 INFO [train.py:591] (1/6) Epoch 459, validation: discriminator_loss=2.756, discriminator_real_loss=1.415, discriminator_fake_loss=1.342, generator_loss=26.56, generator_mel_loss=18.05, generator_kl_loss=1.314, generator_dur_loss=1.787, generator_adv_loss=1.917, generator_feat_match_loss=3.492, over 100.00 samples.
+2024-03-13 23:04:28,790 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 23:06:47,991 INFO [train.py:527] (1/6) Epoch 459, batch 58, global_batch_idx: 56850, batch size: 45, loss[discriminator_loss=2.745, discriminator_real_loss=1.276, discriminator_fake_loss=1.469, generator_loss=27.94, generator_mel_loss=18.37, generator_kl_loss=1.457, generator_dur_loss=1.653, generator_adv_loss=2.034, generator_feat_match_loss=4.429, over 45.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.375, discriminator_fake_loss=1.335, generator_loss=28.04, generator_mel_loss=18.04, generator_kl_loss=1.415, generator_dur_loss=1.742, generator_adv_loss=1.954, generator_feat_match_loss=4.895, over 3191.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:09:07,398 INFO [train.py:527] (1/6) Epoch 459, batch 108, global_batch_idx: 56900, batch size: 36, loss[discriminator_loss=2.71, discriminator_real_loss=1.355, discriminator_fake_loss=1.355, generator_loss=27.68, generator_mel_loss=17.71, generator_kl_loss=1.371, generator_dur_loss=1.697, generator_adv_loss=2.009, generator_feat_match_loss=4.893, over 36.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=28.07, generator_mel_loss=18.03, generator_kl_loss=1.396, generator_dur_loss=1.752, generator_adv_loss=1.966, generator_feat_match_loss=4.933, over 6171.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:09:50,296 INFO [train.py:919] (1/6) Start epoch 460
+2024-03-13 23:11:48,744 INFO [train.py:527] (1/6) Epoch 460, batch 34, global_batch_idx: 56950, batch size: 39, loss[discriminator_loss=2.645, discriminator_real_loss=1.366, discriminator_fake_loss=1.279, generator_loss=28.71, generator_mel_loss=18.15, generator_kl_loss=1.467, generator_dur_loss=1.689, generator_adv_loss=1.949, generator_feat_match_loss=5.449, over 39.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.374, discriminator_fake_loss=1.347, generator_loss=28.11, generator_mel_loss=18.11, generator_kl_loss=1.416, generator_dur_loss=1.761, generator_adv_loss=1.952, generator_feat_match_loss=4.871, over 1975.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:14:09,528 INFO [train.py:527] (1/6) Epoch 460, batch 84, global_batch_idx: 57000, batch size: 25, loss[discriminator_loss=2.592, discriminator_real_loss=1.288, discriminator_fake_loss=1.304, generator_loss=30.23, generator_mel_loss=18.63, generator_kl_loss=1.799, generator_dur_loss=1.55, generator_adv_loss=2.063, generator_feat_match_loss=6.195, over 25.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.371, discriminator_fake_loss=1.346, generator_loss=28.15, generator_mel_loss=18.1, generator_kl_loss=1.402, generator_dur_loss=1.758, generator_adv_loss=1.952, generator_feat_match_loss=4.929, over 4900.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:14:09,529 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 23:14:18,392 INFO [train.py:591] (1/6) Epoch 460, validation: discriminator_loss=2.79, discriminator_real_loss=1.545, discriminator_fake_loss=1.245, generator_loss=26.98, generator_mel_loss=18.17, generator_kl_loss=1.213, generator_dur_loss=1.823, generator_adv_loss=1.988, generator_feat_match_loss=3.778, over 100.00 samples.
+2024-03-13 23:14:18,393 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 23:16:10,343 INFO [train.py:919] (1/6) Start epoch 461
+2024-03-13 23:17:01,936 INFO [train.py:527] (1/6) Epoch 461, batch 10, global_batch_idx: 57050, batch size: 36, loss[discriminator_loss=2.742, discriminator_real_loss=1.417, discriminator_fake_loss=1.324, generator_loss=27.61, generator_mel_loss=17.98, generator_kl_loss=1.596, generator_dur_loss=1.67, generator_adv_loss=1.799, generator_feat_match_loss=4.556, over 36.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.395, discriminator_fake_loss=1.31, generator_loss=27.86, generator_mel_loss=17.91, generator_kl_loss=1.435, generator_dur_loss=1.734, generator_adv_loss=1.943, generator_feat_match_loss=4.837, over 547.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:19:22,936 INFO [train.py:527] (1/6) Epoch 461, batch 60, global_batch_idx: 57100, batch size: 39, loss[discriminator_loss=2.672, discriminator_real_loss=1.253, discriminator_fake_loss=1.419, generator_loss=29.45, generator_mel_loss=18.51, generator_kl_loss=1.53, generator_dur_loss=1.73, generator_adv_loss=1.974, generator_feat_match_loss=5.705, over 39.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.369, discriminator_fake_loss=1.35, generator_loss=28.18, generator_mel_loss=18.11, generator_kl_loss=1.413, generator_dur_loss=1.742, generator_adv_loss=1.941, generator_feat_match_loss=4.972, over 3281.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:21:41,923 INFO [train.py:527] (1/6) Epoch 461, batch 110, global_batch_idx: 57150, batch size: 36, loss[discriminator_loss=2.735, discriminator_real_loss=1.296, discriminator_fake_loss=1.439, generator_loss=26.74, generator_mel_loss=17.54, generator_kl_loss=1.464, generator_dur_loss=1.635, generator_adv_loss=1.866, generator_feat_match_loss=4.23, over 36.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.379, discriminator_fake_loss=1.344, generator_loss=28.04, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.732, generator_adv_loss=1.946, generator_feat_match_loss=4.905, over 5942.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:22:19,316 INFO [train.py:919] (1/6) Start epoch 462
+2024-03-13 23:24:25,876 INFO [train.py:527] (1/6) Epoch 462, batch 36, global_batch_idx: 57200, batch size: 68, loss[discriminator_loss=2.742, discriminator_real_loss=1.316, discriminator_fake_loss=1.426, generator_loss=27.54, generator_mel_loss=17.67, generator_kl_loss=1.206, generator_dur_loss=1.745, generator_adv_loss=2.095, generator_feat_match_loss=4.823, over 68.00 samples.], tot_loss[discriminator_loss=2.731, discriminator_real_loss=1.38, discriminator_fake_loss=1.351, generator_loss=28.06, generator_mel_loss=18.07, generator_kl_loss=1.406, generator_dur_loss=1.744, generator_adv_loss=1.947, generator_feat_match_loss=4.891, over 2178.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:24:25,878 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 23:24:33,975 INFO [train.py:591] (1/6) Epoch 462, validation: discriminator_loss=2.77, discriminator_real_loss=1.451, discriminator_fake_loss=1.318, generator_loss=26.88, generator_mel_loss=18.14, generator_kl_loss=1.209, generator_dur_loss=1.803, generator_adv_loss=1.881, generator_feat_match_loss=3.847, over 100.00 samples.
+2024-03-13 23:24:33,976 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 23:26:50,770 INFO [train.py:527] (1/6) Epoch 462, batch 86, global_batch_idx: 57250, batch size: 55, loss[discriminator_loss=2.749, discriminator_real_loss=1.41, discriminator_fake_loss=1.339, generator_loss=28.38, generator_mel_loss=17.96, generator_kl_loss=1.673, generator_dur_loss=1.661, generator_adv_loss=1.781, generator_feat_match_loss=5.306, over 55.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.378, discriminator_fake_loss=1.346, generator_loss=28.04, generator_mel_loss=18.06, generator_kl_loss=1.43, generator_dur_loss=1.734, generator_adv_loss=1.947, generator_feat_match_loss=4.871, over 4671.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:28:36,262 INFO [train.py:919] (1/6) Start epoch 463
+2024-03-13 23:29:31,992 INFO [train.py:527] (1/6) Epoch 463, batch 12, global_batch_idx: 57300, batch size: 62, loss[discriminator_loss=2.748, discriminator_real_loss=1.41, discriminator_fake_loss=1.338, generator_loss=27.03, generator_mel_loss=17.76, generator_kl_loss=1.395, generator_dur_loss=1.734, generator_adv_loss=1.818, generator_feat_match_loss=4.322, over 62.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.36, discriminator_fake_loss=1.376, generator_loss=27.89, generator_mel_loss=18.02, generator_kl_loss=1.341, generator_dur_loss=1.762, generator_adv_loss=1.898, generator_feat_match_loss=4.862, over 787.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:31:50,172 INFO [train.py:527] (1/6) Epoch 463, batch 62, global_batch_idx: 57350, batch size: 42, loss[discriminator_loss=2.687, discriminator_real_loss=1.311, discriminator_fake_loss=1.376, generator_loss=29.51, generator_mel_loss=18.65, generator_kl_loss=1.536, generator_dur_loss=1.672, generator_adv_loss=2.108, generator_feat_match_loss=5.548, over 42.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.373, discriminator_fake_loss=1.347, generator_loss=27.99, generator_mel_loss=18.03, generator_kl_loss=1.406, generator_dur_loss=1.749, generator_adv_loss=1.945, generator_feat_match_loss=4.861, over 3670.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:34:12,349 INFO [train.py:527] (1/6) Epoch 463, batch 112, global_batch_idx: 57400, batch size: 48, loss[discriminator_loss=2.711, discriminator_real_loss=1.323, discriminator_fake_loss=1.388, generator_loss=27.8, generator_mel_loss=17.91, generator_kl_loss=1.424, generator_dur_loss=1.655, generator_adv_loss=1.937, generator_feat_match_loss=4.872, over 48.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.346, generator_loss=28.03, generator_mel_loss=18.05, generator_kl_loss=1.415, generator_dur_loss=1.75, generator_adv_loss=1.944, generator_feat_match_loss=4.866, over 6433.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:34:12,350 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 23:34:21,113 INFO [train.py:591] (1/6) Epoch 463, validation: discriminator_loss=2.716, discriminator_real_loss=1.355, discriminator_fake_loss=1.361, generator_loss=26.53, generator_mel_loss=17.99, generator_kl_loss=1.175, generator_dur_loss=1.819, generator_adv_loss=1.822, generator_feat_match_loss=3.73, over 100.00 samples.
+2024-03-13 23:34:21,114 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 23:34:54,955 INFO [train.py:919] (1/6) Start epoch 464
+2024-03-13 23:37:06,126 INFO [train.py:527] (1/6) Epoch 464, batch 38, global_batch_idx: 57450, batch size: 44, loss[discriminator_loss=2.742, discriminator_real_loss=1.481, discriminator_fake_loss=1.261, generator_loss=27.19, generator_mel_loss=17.94, generator_kl_loss=1.604, generator_dur_loss=1.719, generator_adv_loss=1.837, generator_feat_match_loss=4.086, over 44.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.378, discriminator_fake_loss=1.336, generator_loss=28.01, generator_mel_loss=18.03, generator_kl_loss=1.411, generator_dur_loss=1.762, generator_adv_loss=1.949, generator_feat_match_loss=4.853, over 2186.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:39:24,501 INFO [train.py:527] (1/6) Epoch 464, batch 88, global_batch_idx: 57500, batch size: 58, loss[discriminator_loss=2.73, discriminator_real_loss=1.411, discriminator_fake_loss=1.319, generator_loss=26.91, generator_mel_loss=17.82, generator_kl_loss=1.545, generator_dur_loss=1.761, generator_adv_loss=1.817, generator_feat_match_loss=3.976, over 58.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=28.05, generator_mel_loss=17.99, generator_kl_loss=1.407, generator_dur_loss=1.772, generator_adv_loss=1.948, generator_feat_match_loss=4.934, over 5148.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:41:03,529 INFO [train.py:919] (1/6) Start epoch 465
+2024-03-13 23:42:06,810 INFO [train.py:527] (1/6) Epoch 465, batch 14, global_batch_idx: 57550, batch size: 77, loss[discriminator_loss=2.668, discriminator_real_loss=1.277, discriminator_fake_loss=1.391, generator_loss=28.28, generator_mel_loss=17.83, generator_kl_loss=1.228, generator_dur_loss=1.792, generator_adv_loss=2.101, generator_feat_match_loss=5.33, over 77.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.05, generator_mel_loss=18.07, generator_kl_loss=1.394, generator_dur_loss=1.771, generator_adv_loss=1.961, generator_feat_match_loss=4.861, over 947.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:44:25,105 INFO [train.py:527] (1/6) Epoch 465, batch 64, global_batch_idx: 57600, batch size: 45, loss[discriminator_loss=2.666, discriminator_real_loss=1.262, discriminator_fake_loss=1.404, generator_loss=29.07, generator_mel_loss=18.62, generator_kl_loss=1.417, generator_dur_loss=1.702, generator_adv_loss=2.004, generator_feat_match_loss=5.325, over 45.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.345, generator_loss=28.04, generator_mel_loss=18.06, generator_kl_loss=1.396, generator_dur_loss=1.755, generator_adv_loss=1.931, generator_feat_match_loss=4.904, over 3882.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:44:25,106 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 23:44:33,225 INFO [train.py:591] (1/6) Epoch 465, validation: discriminator_loss=2.722, discriminator_real_loss=1.484, discriminator_fake_loss=1.238, generator_loss=27.3, generator_mel_loss=18.26, generator_kl_loss=1.27, generator_dur_loss=1.798, generator_adv_loss=1.937, generator_feat_match_loss=4.031, over 100.00 samples.
+2024-03-13 23:44:33,226 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 23:46:49,203 INFO [train.py:527] (1/6) Epoch 465, batch 114, global_batch_idx: 57650, batch size: 50, loss[discriminator_loss=2.719, discriminator_real_loss=1.466, discriminator_fake_loss=1.252, generator_loss=27.13, generator_mel_loss=17.76, generator_kl_loss=1.468, generator_dur_loss=1.707, generator_adv_loss=1.806, generator_feat_match_loss=4.386, over 50.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=28.03, generator_mel_loss=18.05, generator_kl_loss=1.401, generator_dur_loss=1.756, generator_adv_loss=1.936, generator_feat_match_loss=4.887, over 6790.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:47:16,088 INFO [train.py:919] (1/6) Start epoch 466
+2024-03-13 23:49:34,340 INFO [train.py:527] (1/6) Epoch 466, batch 40, global_batch_idx: 57700, batch size: 42, loss[discriminator_loss=2.74, discriminator_real_loss=1.437, discriminator_fake_loss=1.303, generator_loss=28.73, generator_mel_loss=18.26, generator_kl_loss=1.653, generator_dur_loss=1.701, generator_adv_loss=1.866, generator_feat_match_loss=5.251, over 42.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.371, discriminator_fake_loss=1.355, generator_loss=28.08, generator_mel_loss=18.08, generator_kl_loss=1.434, generator_dur_loss=1.758, generator_adv_loss=1.931, generator_feat_match_loss=4.875, over 2333.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:51:52,835 INFO [train.py:527] (1/6) Epoch 466, batch 90, global_batch_idx: 57750, batch size: 32, loss[discriminator_loss=2.738, discriminator_real_loss=1.394, discriminator_fake_loss=1.344, generator_loss=26.43, generator_mel_loss=17.39, generator_kl_loss=1.427, generator_dur_loss=1.682, generator_adv_loss=2.009, generator_feat_match_loss=3.92, over 32.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.346, generator_loss=28.11, generator_mel_loss=18.05, generator_kl_loss=1.425, generator_dur_loss=1.759, generator_adv_loss=1.944, generator_feat_match_loss=4.927, over 5084.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:53:26,039 INFO [train.py:919] (1/6) Start epoch 467
+2024-03-13 23:54:34,096 INFO [train.py:527] (1/6) Epoch 467, batch 16, global_batch_idx: 57800, batch size: 14, loss[discriminator_loss=2.618, discriminator_real_loss=1.3, discriminator_fake_loss=1.318, generator_loss=31.54, generator_mel_loss=19.6, generator_kl_loss=1.95, generator_dur_loss=1.616, generator_adv_loss=2.152, generator_feat_match_loss=6.223, over 14.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.342, generator_loss=28.18, generator_mel_loss=18.12, generator_kl_loss=1.443, generator_dur_loss=1.762, generator_adv_loss=1.941, generator_feat_match_loss=4.919, over 934.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:54:34,098 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-13 23:54:42,160 INFO [train.py:591] (1/6) Epoch 467, validation: discriminator_loss=2.753, discriminator_real_loss=1.437, discriminator_fake_loss=1.316, generator_loss=27.38, generator_mel_loss=18.34, generator_kl_loss=1.376, generator_dur_loss=1.807, generator_adv_loss=1.921, generator_feat_match_loss=3.94, over 100.00 samples.
+2024-03-13 23:54:42,161 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-13 23:57:00,035 INFO [train.py:527] (1/6) Epoch 467, batch 66, global_batch_idx: 57850, batch size: 44, loss[discriminator_loss=2.814, discriminator_real_loss=1.457, discriminator_fake_loss=1.357, generator_loss=27.79, generator_mel_loss=18.29, generator_kl_loss=1.501, generator_dur_loss=1.705, generator_adv_loss=1.952, generator_feat_match_loss=4.347, over 44.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=28.11, generator_mel_loss=18.06, generator_kl_loss=1.421, generator_dur_loss=1.766, generator_adv_loss=1.946, generator_feat_match_loss=4.918, over 3952.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:59:19,498 INFO [train.py:527] (1/6) Epoch 467, batch 116, global_batch_idx: 57900, batch size: 48, loss[discriminator_loss=2.682, discriminator_real_loss=1.361, discriminator_fake_loss=1.32, generator_loss=28.49, generator_mel_loss=18.57, generator_kl_loss=1.524, generator_dur_loss=1.728, generator_adv_loss=1.814, generator_feat_match_loss=4.856, over 48.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.17, generator_mel_loss=18.08, generator_kl_loss=1.429, generator_dur_loss=1.762, generator_adv_loss=1.945, generator_feat_match_loss=4.962, over 6668.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-13 23:59:41,446 INFO [train.py:919] (1/6) Start epoch 468
+2024-03-14 00:02:04,332 INFO [train.py:527] (1/6) Epoch 468, batch 42, global_batch_idx: 57950, batch size: 68, loss[discriminator_loss=2.714, discriminator_real_loss=1.504, discriminator_fake_loss=1.21, generator_loss=27.64, generator_mel_loss=17.58, generator_kl_loss=1.328, generator_dur_loss=1.826, generator_adv_loss=1.867, generator_feat_match_loss=5.039, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.367, discriminator_fake_loss=1.335, generator_loss=28.41, generator_mel_loss=18.13, generator_kl_loss=1.41, generator_dur_loss=1.771, generator_adv_loss=1.97, generator_feat_match_loss=5.132, over 2468.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:04:24,976 INFO [train.py:527] (1/6) Epoch 468, batch 92, global_batch_idx: 58000, batch size: 74, loss[discriminator_loss=2.661, discriminator_real_loss=1.359, discriminator_fake_loss=1.302, generator_loss=28.48, generator_mel_loss=18.27, generator_kl_loss=1.304, generator_dur_loss=1.805, generator_adv_loss=2.059, generator_feat_match_loss=5.038, over 74.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.367, discriminator_fake_loss=1.335, generator_loss=28.28, generator_mel_loss=18.1, generator_kl_loss=1.417, generator_dur_loss=1.761, generator_adv_loss=1.967, generator_feat_match_loss=5.03, over 5246.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:04:24,977 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 00:04:34,033 INFO [train.py:591] (1/6) Epoch 468, validation: discriminator_loss=2.717, discriminator_real_loss=1.497, discriminator_fake_loss=1.219, generator_loss=28.21, generator_mel_loss=18.77, generator_kl_loss=1.172, generator_dur_loss=1.832, generator_adv_loss=2.018, generator_feat_match_loss=4.414, over 100.00 samples.
+2024-03-14 00:04:34,034 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 00:05:56,877 INFO [train.py:919] (1/6) Start epoch 469
+2024-03-14 00:07:12,630 INFO [train.py:527] (1/6) Epoch 469, batch 18, global_batch_idx: 58050, batch size: 70, loss[discriminator_loss=2.774, discriminator_real_loss=1.374, discriminator_fake_loss=1.4, generator_loss=27.63, generator_mel_loss=17.66, generator_kl_loss=1.382, generator_dur_loss=1.82, generator_adv_loss=2.144, generator_feat_match_loss=4.624, over 70.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.343, generator_loss=28.26, generator_mel_loss=18.09, generator_kl_loss=1.403, generator_dur_loss=1.781, generator_adv_loss=1.952, generator_feat_match_loss=5.037, over 1266.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:09:35,685 INFO [train.py:527] (1/6) Epoch 469, batch 68, global_batch_idx: 58100, batch size: 50, loss[discriminator_loss=2.695, discriminator_real_loss=1.345, discriminator_fake_loss=1.35, generator_loss=27.97, generator_mel_loss=18, generator_kl_loss=1.523, generator_dur_loss=1.717, generator_adv_loss=1.909, generator_feat_match_loss=4.818, over 50.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.37, discriminator_fake_loss=1.348, generator_loss=28.07, generator_mel_loss=18.02, generator_kl_loss=1.425, generator_dur_loss=1.766, generator_adv_loss=1.942, generator_feat_match_loss=4.914, over 3992.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:11:56,365 INFO [train.py:527] (1/6) Epoch 469, batch 118, global_batch_idx: 58150, batch size: 58, loss[discriminator_loss=2.707, discriminator_real_loss=1.317, discriminator_fake_loss=1.39, generator_loss=28.62, generator_mel_loss=18.22, generator_kl_loss=1.403, generator_dur_loss=1.773, generator_adv_loss=2.039, generator_feat_match_loss=5.178, over 58.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.369, discriminator_fake_loss=1.343, generator_loss=28.05, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.771, generator_adv_loss=1.942, generator_feat_match_loss=4.922, over 6801.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:12:10,065 INFO [train.py:919] (1/6) Start epoch 470
+2024-03-14 00:14:36,303 INFO [train.py:527] (1/6) Epoch 470, batch 44, global_batch_idx: 58200, batch size: 80, loss[discriminator_loss=2.719, discriminator_real_loss=1.408, discriminator_fake_loss=1.311, generator_loss=27.27, generator_mel_loss=17.91, generator_kl_loss=1.211, generator_dur_loss=1.856, generator_adv_loss=2.002, generator_feat_match_loss=4.289, over 80.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.37, discriminator_fake_loss=1.337, generator_loss=28.13, generator_mel_loss=18.08, generator_kl_loss=1.411, generator_dur_loss=1.77, generator_adv_loss=1.951, generator_feat_match_loss=4.914, over 2638.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:14:36,305 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 00:14:44,172 INFO [train.py:591] (1/6) Epoch 470, validation: discriminator_loss=2.713, discriminator_real_loss=1.448, discriminator_fake_loss=1.264, generator_loss=27.15, generator_mel_loss=18.23, generator_kl_loss=1.214, generator_dur_loss=1.836, generator_adv_loss=1.973, generator_feat_match_loss=3.9, over 100.00 samples.
+2024-03-14 00:14:44,173 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 00:17:05,760 INFO [train.py:527] (1/6) Epoch 470, batch 94, global_batch_idx: 58250, batch size: 68, loss[discriminator_loss=2.708, discriminator_real_loss=1.487, discriminator_fake_loss=1.22, generator_loss=27.67, generator_mel_loss=17.61, generator_kl_loss=1.542, generator_dur_loss=1.769, generator_adv_loss=1.829, generator_feat_match_loss=4.914, over 68.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.373, discriminator_fake_loss=1.336, generator_loss=28.03, generator_mel_loss=17.99, generator_kl_loss=1.403, generator_dur_loss=1.772, generator_adv_loss=1.95, generator_feat_match_loss=4.919, over 5597.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:18:32,063 INFO [train.py:919] (1/6) Start epoch 471
+2024-03-14 00:19:56,106 INFO [train.py:527] (1/6) Epoch 471, batch 20, global_batch_idx: 58300, batch size: 12, loss[discriminator_loss=2.637, discriminator_real_loss=1.294, discriminator_fake_loss=1.343, generator_loss=31.29, generator_mel_loss=18.54, generator_kl_loss=1.862, generator_dur_loss=1.56, generator_adv_loss=2.081, generator_feat_match_loss=7.243, over 12.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.342, generator_loss=28.16, generator_mel_loss=17.97, generator_kl_loss=1.403, generator_dur_loss=1.751, generator_adv_loss=1.968, generator_feat_match_loss=5.063, over 1119.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:22:19,623 INFO [train.py:527] (1/6) Epoch 471, batch 70, global_batch_idx: 58350, batch size: 64, loss[discriminator_loss=2.702, discriminator_real_loss=1.375, discriminator_fake_loss=1.327, generator_loss=28.87, generator_mel_loss=18, generator_kl_loss=1.431, generator_dur_loss=1.827, generator_adv_loss=2.035, generator_feat_match_loss=5.574, over 64.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.339, generator_loss=28.05, generator_mel_loss=17.99, generator_kl_loss=1.402, generator_dur_loss=1.753, generator_adv_loss=1.958, generator_feat_match_loss=4.947, over 4054.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:24:37,653 INFO [train.py:527] (1/6) Epoch 471, batch 120, global_batch_idx: 58400, batch size: 48, loss[discriminator_loss=2.694, discriminator_real_loss=1.432, discriminator_fake_loss=1.262, generator_loss=27.97, generator_mel_loss=18.05, generator_kl_loss=1.46, generator_dur_loss=1.708, generator_adv_loss=1.857, generator_feat_match_loss=4.892, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.1, generator_mel_loss=18.05, generator_kl_loss=1.406, generator_dur_loss=1.752, generator_adv_loss=1.951, generator_feat_match_loss=4.938, over 6788.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:24:37,654 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 00:24:46,223 INFO [train.py:591] (1/6) Epoch 471, validation: discriminator_loss=2.756, discriminator_real_loss=1.348, discriminator_fake_loss=1.408, generator_loss=26.82, generator_mel_loss=18.25, generator_kl_loss=1.196, generator_dur_loss=1.841, generator_adv_loss=1.775, generator_feat_match_loss=3.765, over 100.00 samples.
+2024-03-14 00:24:46,224 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 00:24:55,153 INFO [train.py:919] (1/6) Start epoch 472
+2024-03-14 00:27:26,439 INFO [train.py:527] (1/6) Epoch 472, batch 46, global_batch_idx: 58450, batch size: 64, loss[discriminator_loss=2.702, discriminator_real_loss=1.292, discriminator_fake_loss=1.41, generator_loss=27.52, generator_mel_loss=17.82, generator_kl_loss=1.34, generator_dur_loss=1.737, generator_adv_loss=1.994, generator_feat_match_loss=4.63, over 64.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.365, discriminator_fake_loss=1.345, generator_loss=28.13, generator_mel_loss=18.07, generator_kl_loss=1.397, generator_dur_loss=1.754, generator_adv_loss=1.935, generator_feat_match_loss=4.975, over 2654.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:29:48,864 INFO [train.py:527] (1/6) Epoch 472, batch 96, global_batch_idx: 58500, batch size: 59, loss[discriminator_loss=2.761, discriminator_real_loss=1.267, discriminator_fake_loss=1.494, generator_loss=27.52, generator_mel_loss=18.04, generator_kl_loss=1.394, generator_dur_loss=1.749, generator_adv_loss=2.099, generator_feat_match_loss=4.236, over 59.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.344, generator_loss=28.11, generator_mel_loss=18.04, generator_kl_loss=1.422, generator_dur_loss=1.744, generator_adv_loss=1.946, generator_feat_match_loss=4.966, over 5427.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:31:07,655 INFO [train.py:919] (1/6) Start epoch 473
+2024-03-14 00:32:34,884 INFO [train.py:527] (1/6) Epoch 473, batch 22, global_batch_idx: 58550, batch size: 66, loss[discriminator_loss=2.715, discriminator_real_loss=1.403, discriminator_fake_loss=1.312, generator_loss=28.1, generator_mel_loss=18.01, generator_kl_loss=1.509, generator_dur_loss=1.789, generator_adv_loss=1.845, generator_feat_match_loss=4.947, over 66.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.385, discriminator_fake_loss=1.344, generator_loss=27.95, generator_mel_loss=18.02, generator_kl_loss=1.42, generator_dur_loss=1.755, generator_adv_loss=1.95, generator_feat_match_loss=4.808, over 1391.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:34:57,168 INFO [train.py:527] (1/6) Epoch 473, batch 72, global_batch_idx: 58600, batch size: 36, loss[discriminator_loss=2.826, discriminator_real_loss=1.503, discriminator_fake_loss=1.322, generator_loss=26.96, generator_mel_loss=17.57, generator_kl_loss=1.557, generator_dur_loss=1.722, generator_adv_loss=1.719, generator_feat_match_loss=4.389, over 36.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.371, discriminator_fake_loss=1.351, generator_loss=28.13, generator_mel_loss=18.06, generator_kl_loss=1.429, generator_dur_loss=1.747, generator_adv_loss=1.966, generator_feat_match_loss=4.925, over 4245.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:34:57,169 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 00:35:05,376 INFO [train.py:591] (1/6) Epoch 473, validation: discriminator_loss=2.796, discriminator_real_loss=1.353, discriminator_fake_loss=1.442, generator_loss=26.19, generator_mel_loss=17.77, generator_kl_loss=1.184, generator_dur_loss=1.819, generator_adv_loss=1.691, generator_feat_match_loss=3.723, over 100.00 samples.
+2024-03-14 00:35:05,377 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 00:37:25,748 INFO [train.py:527] (1/6) Epoch 473, batch 122, global_batch_idx: 58650, batch size: 25, loss[discriminator_loss=2.628, discriminator_real_loss=1.27, discriminator_fake_loss=1.358, generator_loss=28.89, generator_mel_loss=18.47, generator_kl_loss=1.682, generator_dur_loss=1.532, generator_adv_loss=2.03, generator_feat_match_loss=5.177, over 25.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.374, discriminator_fake_loss=1.346, generator_loss=28.11, generator_mel_loss=18.06, generator_kl_loss=1.425, generator_dur_loss=1.749, generator_adv_loss=1.957, generator_feat_match_loss=4.923, over 7005.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:37:30,877 INFO [train.py:919] (1/6) Start epoch 474
+2024-03-14 00:40:13,495 INFO [train.py:527] (1/6) Epoch 474, batch 48, global_batch_idx: 58700, batch size: 72, loss[discriminator_loss=2.764, discriminator_real_loss=1.379, discriminator_fake_loss=1.385, generator_loss=26.84, generator_mel_loss=17.79, generator_kl_loss=1.168, generator_dur_loss=1.786, generator_adv_loss=2.098, generator_feat_match_loss=3.993, over 72.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.382, discriminator_fake_loss=1.345, generator_loss=28.02, generator_mel_loss=18.07, generator_kl_loss=1.399, generator_dur_loss=1.754, generator_adv_loss=1.933, generator_feat_match_loss=4.866, over 2823.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:42:34,239 INFO [train.py:527] (1/6) Epoch 474, batch 98, global_batch_idx: 58750, batch size: 53, loss[discriminator_loss=2.678, discriminator_real_loss=1.326, discriminator_fake_loss=1.352, generator_loss=28.95, generator_mel_loss=18.06, generator_kl_loss=1.653, generator_dur_loss=1.67, generator_adv_loss=2.034, generator_feat_match_loss=5.53, over 53.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.374, discriminator_fake_loss=1.349, generator_loss=28.08, generator_mel_loss=18.07, generator_kl_loss=1.415, generator_dur_loss=1.751, generator_adv_loss=1.933, generator_feat_match_loss=4.908, over 5599.00 samples.], cur_lr_g: 1.89e-04, cur_lr_d: 1.89e-04, 
+2024-03-14 00:43:48,107 INFO [train.py:919] (1/6) Start epoch 475
+2024-03-14 00:45:23,441 INFO [train.py:527] (1/6) Epoch 475, batch 24, global_batch_idx: 58800, batch size: 68, loss[discriminator_loss=2.662, discriminator_real_loss=1.348, discriminator_fake_loss=1.315, generator_loss=28.79, generator_mel_loss=18.29, generator_kl_loss=1.473, generator_dur_loss=1.78, generator_adv_loss=1.971, generator_feat_match_loss=5.281, over 68.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=28.08, generator_mel_loss=18.01, generator_kl_loss=1.444, generator_dur_loss=1.761, generator_adv_loss=1.939, generator_feat_match_loss=4.924, over 1410.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 00:45:23,442 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 00:45:31,208 INFO [train.py:591] (1/6) Epoch 475, validation: discriminator_loss=2.769, discriminator_real_loss=1.347, discriminator_fake_loss=1.422, generator_loss=26.55, generator_mel_loss=18.26, generator_kl_loss=1.245, generator_dur_loss=1.828, generator_adv_loss=1.754, generator_feat_match_loss=3.463, over 100.00 samples.
+2024-03-14 00:45:31,209 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 00:47:52,049 INFO [train.py:527] (1/6) Epoch 475, batch 74, global_batch_idx: 58850, batch size: 45, loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.345, generator_loss=28.56, generator_mel_loss=18.41, generator_kl_loss=1.473, generator_dur_loss=1.7, generator_adv_loss=1.929, generator_feat_match_loss=5.048, over 45.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.345, generator_loss=28.02, generator_mel_loss=18.02, generator_kl_loss=1.431, generator_dur_loss=1.743, generator_adv_loss=1.945, generator_feat_match_loss=4.886, over 4124.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 00:50:09,613 INFO [train.py:919] (1/6) Start epoch 476
+2024-03-14 00:50:34,900 INFO [train.py:527] (1/6) Epoch 476, batch 0, global_batch_idx: 58900, batch size: 80, loss[discriminator_loss=2.682, discriminator_real_loss=1.368, discriminator_fake_loss=1.314, generator_loss=28.07, generator_mel_loss=18.39, generator_kl_loss=1.241, generator_dur_loss=1.836, generator_adv_loss=2.02, generator_feat_match_loss=4.575, over 80.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.368, discriminator_fake_loss=1.314, generator_loss=28.07, generator_mel_loss=18.39, generator_kl_loss=1.241, generator_dur_loss=1.836, generator_adv_loss=2.02, generator_feat_match_loss=4.575, over 80.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 00:52:59,142 INFO [train.py:527] (1/6) Epoch 476, batch 50, global_batch_idx: 58950, batch size: 70, loss[discriminator_loss=2.704, discriminator_real_loss=1.396, discriminator_fake_loss=1.308, generator_loss=27.23, generator_mel_loss=17.52, generator_kl_loss=1.446, generator_dur_loss=1.815, generator_adv_loss=1.837, generator_feat_match_loss=4.617, over 70.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.336, generator_loss=27.92, generator_mel_loss=17.94, generator_kl_loss=1.422, generator_dur_loss=1.766, generator_adv_loss=1.948, generator_feat_match_loss=4.84, over 3095.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 00:55:19,569 INFO [train.py:527] (1/6) Epoch 476, batch 100, global_batch_idx: 59000, batch size: 52, loss[discriminator_loss=2.662, discriminator_real_loss=1.408, discriminator_fake_loss=1.254, generator_loss=28.01, generator_mel_loss=17.96, generator_kl_loss=1.411, generator_dur_loss=1.697, generator_adv_loss=1.905, generator_feat_match_loss=5.03, over 52.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.08, generator_mel_loss=17.99, generator_kl_loss=1.418, generator_dur_loss=1.75, generator_adv_loss=1.968, generator_feat_match_loss=4.952, over 5796.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 00:55:19,570 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 00:55:28,426 INFO [train.py:591] (1/6) Epoch 476, validation: discriminator_loss=2.743, discriminator_real_loss=1.426, discriminator_fake_loss=1.317, generator_loss=26.56, generator_mel_loss=18.08, generator_kl_loss=1.197, generator_dur_loss=1.79, generator_adv_loss=1.856, generator_feat_match_loss=3.641, over 100.00 samples.
+2024-03-14 00:55:28,427 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 00:56:31,326 INFO [train.py:919] (1/6) Start epoch 477
+2024-03-14 00:58:09,081 INFO [train.py:527] (1/6) Epoch 477, batch 26, global_batch_idx: 59050, batch size: 25, loss[discriminator_loss=2.665, discriminator_real_loss=1.412, discriminator_fake_loss=1.253, generator_loss=28.98, generator_mel_loss=18.59, generator_kl_loss=1.642, generator_dur_loss=1.588, generator_adv_loss=1.91, generator_feat_match_loss=5.246, over 25.00 samples.], tot_loss[discriminator_loss=2.736, discriminator_real_loss=1.384, discriminator_fake_loss=1.352, generator_loss=28.25, generator_mel_loss=18.08, generator_kl_loss=1.427, generator_dur_loss=1.735, generator_adv_loss=1.936, generator_feat_match_loss=5.072, over 1503.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:00:33,350 INFO [train.py:527] (1/6) Epoch 477, batch 76, global_batch_idx: 59100, batch size: 36, loss[discriminator_loss=2.734, discriminator_real_loss=1.352, discriminator_fake_loss=1.382, generator_loss=27.92, generator_mel_loss=18.17, generator_kl_loss=1.507, generator_dur_loss=1.68, generator_adv_loss=1.991, generator_feat_match_loss=4.575, over 36.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.372, discriminator_fake_loss=1.351, generator_loss=28.09, generator_mel_loss=17.99, generator_kl_loss=1.411, generator_dur_loss=1.743, generator_adv_loss=1.943, generator_feat_match_loss=4.997, over 4195.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:02:41,702 INFO [train.py:919] (1/6) Start epoch 478
+2024-03-14 01:03:10,912 INFO [train.py:527] (1/6) Epoch 478, batch 2, global_batch_idx: 59150, batch size: 62, loss[discriminator_loss=2.808, discriminator_real_loss=1.603, discriminator_fake_loss=1.205, generator_loss=27.93, generator_mel_loss=18.24, generator_kl_loss=1.409, generator_dur_loss=1.765, generator_adv_loss=1.973, generator_feat_match_loss=4.55, over 62.00 samples.], tot_loss[discriminator_loss=2.816, discriminator_real_loss=1.478, discriminator_fake_loss=1.339, generator_loss=28.47, generator_mel_loss=18.47, generator_kl_loss=1.433, generator_dur_loss=1.711, generator_adv_loss=1.972, generator_feat_match_loss=4.886, over 162.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:05:34,093 INFO [train.py:527] (1/6) Epoch 478, batch 52, global_batch_idx: 59200, batch size: 68, loss[discriminator_loss=2.646, discriminator_real_loss=1.329, discriminator_fake_loss=1.318, generator_loss=28.55, generator_mel_loss=18.56, generator_kl_loss=1.338, generator_dur_loss=1.75, generator_adv_loss=1.933, generator_feat_match_loss=4.978, over 68.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.346, generator_loss=28.17, generator_mel_loss=18.14, generator_kl_loss=1.422, generator_dur_loss=1.738, generator_adv_loss=1.946, generator_feat_match_loss=4.927, over 2873.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:05:34,094 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 01:05:41,916 INFO [train.py:591] (1/6) Epoch 478, validation: discriminator_loss=2.71, discriminator_real_loss=1.383, discriminator_fake_loss=1.327, generator_loss=27.1, generator_mel_loss=18.43, generator_kl_loss=1.199, generator_dur_loss=1.801, generator_adv_loss=1.869, generator_feat_match_loss=3.803, over 100.00 samples.
+2024-03-14 01:05:41,917 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 01:08:05,603 INFO [train.py:527] (1/6) Epoch 478, batch 102, global_batch_idx: 59250, batch size: 56, loss[discriminator_loss=2.681, discriminator_real_loss=1.309, discriminator_fake_loss=1.372, generator_loss=28.2, generator_mel_loss=18.15, generator_kl_loss=1.418, generator_dur_loss=1.764, generator_adv_loss=2.003, generator_feat_match_loss=4.866, over 56.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=28.09, generator_mel_loss=18.07, generator_kl_loss=1.411, generator_dur_loss=1.749, generator_adv_loss=1.945, generator_feat_match_loss=4.918, over 5719.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:09:04,988 INFO [train.py:919] (1/6) Start epoch 479
+2024-03-14 01:10:51,275 INFO [train.py:527] (1/6) Epoch 479, batch 28, global_batch_idx: 59300, batch size: 80, loss[discriminator_loss=2.656, discriminator_real_loss=1.315, discriminator_fake_loss=1.341, generator_loss=27.8, generator_mel_loss=17.89, generator_kl_loss=1.331, generator_dur_loss=1.8, generator_adv_loss=2.087, generator_feat_match_loss=4.69, over 80.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.342, generator_loss=28, generator_mel_loss=18.02, generator_kl_loss=1.372, generator_dur_loss=1.785, generator_adv_loss=1.934, generator_feat_match_loss=4.883, over 1727.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:13:13,826 INFO [train.py:527] (1/6) Epoch 479, batch 78, global_batch_idx: 59350, batch size: 52, loss[discriminator_loss=2.629, discriminator_real_loss=1.304, discriminator_fake_loss=1.325, generator_loss=29.56, generator_mel_loss=18.48, generator_kl_loss=1.531, generator_dur_loss=1.747, generator_adv_loss=1.99, generator_feat_match_loss=5.813, over 52.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.18, generator_mel_loss=18.06, generator_kl_loss=1.401, generator_dur_loss=1.772, generator_adv_loss=1.951, generator_feat_match_loss=5, over 4606.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:15:20,615 INFO [train.py:919] (1/6) Start epoch 480
+2024-03-14 01:15:56,062 INFO [train.py:527] (1/6) Epoch 480, batch 4, global_batch_idx: 59400, batch size: 50, loss[discriminator_loss=2.721, discriminator_real_loss=1.381, discriminator_fake_loss=1.34, generator_loss=27.95, generator_mel_loss=17.75, generator_kl_loss=1.418, generator_dur_loss=1.708, generator_adv_loss=2.09, generator_feat_match_loss=4.985, over 50.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.371, discriminator_fake_loss=1.345, generator_loss=28.18, generator_mel_loss=18.05, generator_kl_loss=1.462, generator_dur_loss=1.739, generator_adv_loss=1.966, generator_feat_match_loss=4.966, over 264.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:15:56,065 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 01:16:03,714 INFO [train.py:591] (1/6) Epoch 480, validation: discriminator_loss=2.809, discriminator_real_loss=1.507, discriminator_fake_loss=1.302, generator_loss=27.04, generator_mel_loss=18.29, generator_kl_loss=1.284, generator_dur_loss=1.825, generator_adv_loss=1.928, generator_feat_match_loss=3.717, over 100.00 samples.
+2024-03-14 01:16:03,716 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 01:18:25,449 INFO [train.py:527] (1/6) Epoch 480, batch 54, global_batch_idx: 59450, batch size: 80, loss[discriminator_loss=2.639, discriminator_real_loss=1.334, discriminator_fake_loss=1.304, generator_loss=28.4, generator_mel_loss=17.95, generator_kl_loss=1.225, generator_dur_loss=1.806, generator_adv_loss=2.14, generator_feat_match_loss=5.274, over 80.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.373, discriminator_fake_loss=1.346, generator_loss=28.17, generator_mel_loss=18.05, generator_kl_loss=1.416, generator_dur_loss=1.756, generator_adv_loss=1.967, generator_feat_match_loss=4.986, over 3180.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:20:46,633 INFO [train.py:527] (1/6) Epoch 480, batch 104, global_batch_idx: 59500, batch size: 66, loss[discriminator_loss=2.639, discriminator_real_loss=1.322, discriminator_fake_loss=1.316, generator_loss=29.13, generator_mel_loss=18.18, generator_kl_loss=1.394, generator_dur_loss=1.756, generator_adv_loss=2.027, generator_feat_match_loss=5.774, over 66.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.379, discriminator_fake_loss=1.338, generator_loss=28.11, generator_mel_loss=18.02, generator_kl_loss=1.411, generator_dur_loss=1.759, generator_adv_loss=1.967, generator_feat_match_loss=4.951, over 6037.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:21:42,418 INFO [train.py:919] (1/6) Start epoch 481
+2024-03-14 01:23:35,267 INFO [train.py:527] (1/6) Epoch 481, batch 30, global_batch_idx: 59550, batch size: 70, loss[discriminator_loss=2.667, discriminator_real_loss=1.325, discriminator_fake_loss=1.342, generator_loss=28.93, generator_mel_loss=18.47, generator_kl_loss=1.35, generator_dur_loss=1.752, generator_adv_loss=2.077, generator_feat_match_loss=5.281, over 70.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=27.92, generator_mel_loss=17.94, generator_kl_loss=1.413, generator_dur_loss=1.748, generator_adv_loss=1.953, generator_feat_match_loss=4.869, over 1798.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:25:59,989 INFO [train.py:527] (1/6) Epoch 481, batch 80, global_batch_idx: 59600, batch size: 42, loss[discriminator_loss=2.773, discriminator_real_loss=1.397, discriminator_fake_loss=1.376, generator_loss=27.24, generator_mel_loss=17.63, generator_kl_loss=1.705, generator_dur_loss=1.651, generator_adv_loss=1.891, generator_feat_match_loss=4.366, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.12, generator_mel_loss=18.03, generator_kl_loss=1.431, generator_dur_loss=1.734, generator_adv_loss=1.954, generator_feat_match_loss=4.968, over 4471.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:25:59,990 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 01:26:08,545 INFO [train.py:591] (1/6) Epoch 481, validation: discriminator_loss=2.778, discriminator_real_loss=1.453, discriminator_fake_loss=1.325, generator_loss=26.79, generator_mel_loss=18.06, generator_kl_loss=1.274, generator_dur_loss=1.817, generator_adv_loss=1.858, generator_feat_match_loss=3.779, over 100.00 samples.
+2024-03-14 01:26:08,545 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 01:28:08,955 INFO [train.py:919] (1/6) Start epoch 482
+2024-03-14 01:28:50,649 INFO [train.py:527] (1/6) Epoch 482, batch 6, global_batch_idx: 59650, batch size: 48, loss[discriminator_loss=2.721, discriminator_real_loss=1.43, discriminator_fake_loss=1.291, generator_loss=27.4, generator_mel_loss=18.3, generator_kl_loss=1.507, generator_dur_loss=1.671, generator_adv_loss=1.816, generator_feat_match_loss=4.097, over 48.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.382, discriminator_fake_loss=1.352, generator_loss=28.31, generator_mel_loss=18.2, generator_kl_loss=1.342, generator_dur_loss=1.745, generator_adv_loss=1.941, generator_feat_match_loss=5.086, over 412.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:31:11,734 INFO [train.py:527] (1/6) Epoch 482, batch 56, global_batch_idx: 59700, batch size: 83, loss[discriminator_loss=2.647, discriminator_real_loss=1.297, discriminator_fake_loss=1.35, generator_loss=28.82, generator_mel_loss=17.98, generator_kl_loss=1.476, generator_dur_loss=1.826, generator_adv_loss=1.963, generator_feat_match_loss=5.572, over 83.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.379, discriminator_fake_loss=1.336, generator_loss=28.09, generator_mel_loss=18, generator_kl_loss=1.39, generator_dur_loss=1.758, generator_adv_loss=1.948, generator_feat_match_loss=4.987, over 3414.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:33:31,790 INFO [train.py:527] (1/6) Epoch 482, batch 106, global_batch_idx: 59750, batch size: 62, loss[discriminator_loss=2.716, discriminator_real_loss=1.463, discriminator_fake_loss=1.253, generator_loss=28.58, generator_mel_loss=18.46, generator_kl_loss=1.247, generator_dur_loss=1.759, generator_adv_loss=1.952, generator_feat_match_loss=5.163, over 62.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.379, discriminator_fake_loss=1.338, generator_loss=28.04, generator_mel_loss=17.97, generator_kl_loss=1.406, generator_dur_loss=1.759, generator_adv_loss=1.946, generator_feat_match_loss=4.959, over 6204.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:34:23,988 INFO [train.py:919] (1/6) Start epoch 483
+2024-03-14 01:36:18,611 INFO [train.py:527] (1/6) Epoch 483, batch 32, global_batch_idx: 59800, batch size: 66, loss[discriminator_loss=2.721, discriminator_real_loss=1.357, discriminator_fake_loss=1.364, generator_loss=27.3, generator_mel_loss=17.28, generator_kl_loss=1.434, generator_dur_loss=1.765, generator_adv_loss=2.126, generator_feat_match_loss=4.69, over 66.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.09, generator_mel_loss=18.01, generator_kl_loss=1.42, generator_dur_loss=1.78, generator_adv_loss=1.96, generator_feat_match_loss=4.919, over 1979.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:36:18,613 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 01:36:26,621 INFO [train.py:591] (1/6) Epoch 483, validation: discriminator_loss=2.745, discriminator_real_loss=1.502, discriminator_fake_loss=1.242, generator_loss=26.89, generator_mel_loss=18.14, generator_kl_loss=1.281, generator_dur_loss=1.805, generator_adv_loss=1.944, generator_feat_match_loss=3.723, over 100.00 samples.
+2024-03-14 01:36:26,622 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 01:38:50,010 INFO [train.py:527] (1/6) Epoch 483, batch 82, global_batch_idx: 59850, batch size: 58, loss[discriminator_loss=2.714, discriminator_real_loss=1.362, discriminator_fake_loss=1.352, generator_loss=28.6, generator_mel_loss=18.34, generator_kl_loss=1.403, generator_dur_loss=1.726, generator_adv_loss=1.896, generator_feat_match_loss=5.237, over 58.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.37, discriminator_fake_loss=1.346, generator_loss=28.12, generator_mel_loss=18.03, generator_kl_loss=1.427, generator_dur_loss=1.762, generator_adv_loss=1.943, generator_feat_match_loss=4.96, over 4861.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:40:45,482 INFO [train.py:919] (1/6) Start epoch 484
+2024-03-14 01:41:33,030 INFO [train.py:527] (1/6) Epoch 484, batch 8, global_batch_idx: 59900, batch size: 74, loss[discriminator_loss=2.688, discriminator_real_loss=1.401, discriminator_fake_loss=1.287, generator_loss=27.37, generator_mel_loss=17.52, generator_kl_loss=1.34, generator_dur_loss=1.823, generator_adv_loss=1.995, generator_feat_match_loss=4.689, over 74.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.362, discriminator_fake_loss=1.341, generator_loss=28.04, generator_mel_loss=17.93, generator_kl_loss=1.388, generator_dur_loss=1.778, generator_adv_loss=1.977, generator_feat_match_loss=4.97, over 511.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:43:54,187 INFO [train.py:527] (1/6) Epoch 484, batch 58, global_batch_idx: 59950, batch size: 25, loss[discriminator_loss=2.721, discriminator_real_loss=1.353, discriminator_fake_loss=1.368, generator_loss=29.98, generator_mel_loss=19.28, generator_kl_loss=1.702, generator_dur_loss=1.563, generator_adv_loss=2.198, generator_feat_match_loss=5.24, over 25.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.339, generator_loss=28.11, generator_mel_loss=18.04, generator_kl_loss=1.409, generator_dur_loss=1.766, generator_adv_loss=1.959, generator_feat_match_loss=4.935, over 3337.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:46:14,291 INFO [train.py:527] (1/6) Epoch 484, batch 108, global_batch_idx: 60000, batch size: 96, loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=27.22, generator_mel_loss=17.87, generator_kl_loss=1.276, generator_dur_loss=1.853, generator_adv_loss=1.816, generator_feat_match_loss=4.4, over 96.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.378, discriminator_fake_loss=1.338, generator_loss=28.16, generator_mel_loss=18.08, generator_kl_loss=1.404, generator_dur_loss=1.767, generator_adv_loss=1.954, generator_feat_match_loss=4.95, over 6336.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:46:14,292 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 01:46:22,956 INFO [train.py:591] (1/6) Epoch 484, validation: discriminator_loss=2.761, discriminator_real_loss=1.313, discriminator_fake_loss=1.447, generator_loss=26.9, generator_mel_loss=18.08, generator_kl_loss=1.285, generator_dur_loss=1.83, generator_adv_loss=1.697, generator_feat_match_loss=4.007, over 100.00 samples.
+2024-03-14 01:46:22,957 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 01:47:06,017 INFO [train.py:919] (1/6) Start epoch 485
+2024-03-14 01:49:07,787 INFO [train.py:527] (1/6) Epoch 485, batch 34, global_batch_idx: 60050, batch size: 77, loss[discriminator_loss=2.732, discriminator_real_loss=1.327, discriminator_fake_loss=1.404, generator_loss=27.94, generator_mel_loss=17.67, generator_kl_loss=1.4, generator_dur_loss=1.822, generator_adv_loss=2.041, generator_feat_match_loss=5.003, over 77.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.373, discriminator_fake_loss=1.349, generator_loss=28.08, generator_mel_loss=17.97, generator_kl_loss=1.428, generator_dur_loss=1.754, generator_adv_loss=1.961, generator_feat_match_loss=4.964, over 2007.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:51:25,547 INFO [train.py:527] (1/6) Epoch 485, batch 84, global_batch_idx: 60100, batch size: 50, loss[discriminator_loss=2.758, discriminator_real_loss=1.443, discriminator_fake_loss=1.315, generator_loss=26.78, generator_mel_loss=17.49, generator_kl_loss=1.455, generator_dur_loss=1.735, generator_adv_loss=1.9, generator_feat_match_loss=4.194, over 50.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.346, generator_loss=28.08, generator_mel_loss=17.99, generator_kl_loss=1.412, generator_dur_loss=1.754, generator_adv_loss=1.946, generator_feat_match_loss=4.978, over 4890.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:53:13,753 INFO [train.py:919] (1/6) Start epoch 486
+2024-03-14 01:54:02,964 INFO [train.py:527] (1/6) Epoch 486, batch 10, global_batch_idx: 60150, batch size: 66, loss[discriminator_loss=2.7, discriminator_real_loss=1.344, discriminator_fake_loss=1.356, generator_loss=27.93, generator_mel_loss=17.9, generator_kl_loss=1.336, generator_dur_loss=1.734, generator_adv_loss=1.889, generator_feat_match_loss=5.069, over 66.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.344, discriminator_fake_loss=1.356, generator_loss=28.26, generator_mel_loss=18.16, generator_kl_loss=1.323, generator_dur_loss=1.751, generator_adv_loss=1.944, generator_feat_match_loss=5.075, over 711.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:56:22,059 INFO [train.py:527] (1/6) Epoch 486, batch 60, global_batch_idx: 60200, batch size: 39, loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.341, generator_loss=27.61, generator_mel_loss=17.81, generator_kl_loss=1.488, generator_dur_loss=1.72, generator_adv_loss=1.813, generator_feat_match_loss=4.779, over 39.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.36, discriminator_fake_loss=1.344, generator_loss=28.16, generator_mel_loss=18.04, generator_kl_loss=1.389, generator_dur_loss=1.753, generator_adv_loss=1.954, generator_feat_match_loss=5.027, over 3640.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:56:22,061 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 01:56:30,163 INFO [train.py:591] (1/6) Epoch 486, validation: discriminator_loss=2.72, discriminator_real_loss=1.342, discriminator_fake_loss=1.377, generator_loss=26.58, generator_mel_loss=18.11, generator_kl_loss=1.117, generator_dur_loss=1.813, generator_adv_loss=1.773, generator_feat_match_loss=3.764, over 100.00 samples.
+2024-03-14 01:56:30,163 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 01:58:51,779 INFO [train.py:527] (1/6) Epoch 486, batch 110, global_batch_idx: 60250, batch size: 62, loss[discriminator_loss=2.719, discriminator_real_loss=1.34, discriminator_fake_loss=1.379, generator_loss=28.09, generator_mel_loss=17.89, generator_kl_loss=1.482, generator_dur_loss=1.778, generator_adv_loss=2.025, generator_feat_match_loss=4.919, over 62.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.343, generator_loss=28.14, generator_mel_loss=18.02, generator_kl_loss=1.402, generator_dur_loss=1.751, generator_adv_loss=1.955, generator_feat_match_loss=5.014, over 6437.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 01:59:26,861 INFO [train.py:919] (1/6) Start epoch 487
+2024-03-14 02:01:30,018 INFO [train.py:527] (1/6) Epoch 487, batch 36, global_batch_idx: 60300, batch size: 39, loss[discriminator_loss=2.741, discriminator_real_loss=1.438, discriminator_fake_loss=1.303, generator_loss=28.98, generator_mel_loss=18.41, generator_kl_loss=1.574, generator_dur_loss=1.659, generator_adv_loss=1.928, generator_feat_match_loss=5.413, over 39.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.336, generator_loss=28.04, generator_mel_loss=17.97, generator_kl_loss=1.439, generator_dur_loss=1.734, generator_adv_loss=1.942, generator_feat_match_loss=4.954, over 1981.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:03:49,134 INFO [train.py:527] (1/6) Epoch 487, batch 86, global_batch_idx: 60350, batch size: 77, loss[discriminator_loss=2.696, discriminator_real_loss=1.294, discriminator_fake_loss=1.402, generator_loss=27.72, generator_mel_loss=17.88, generator_kl_loss=1.412, generator_dur_loss=1.813, generator_adv_loss=2.003, generator_feat_match_loss=4.62, over 77.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.37, discriminator_fake_loss=1.342, generator_loss=28.12, generator_mel_loss=17.99, generator_kl_loss=1.432, generator_dur_loss=1.739, generator_adv_loss=1.947, generator_feat_match_loss=5.014, over 4795.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:05:34,950 INFO [train.py:919] (1/6) Start epoch 488
+2024-03-14 02:06:34,179 INFO [train.py:527] (1/6) Epoch 488, batch 12, global_batch_idx: 60400, batch size: 83, loss[discriminator_loss=2.719, discriminator_real_loss=1.447, discriminator_fake_loss=1.271, generator_loss=28.14, generator_mel_loss=18.08, generator_kl_loss=1.3, generator_dur_loss=1.843, generator_adv_loss=1.851, generator_feat_match_loss=5.068, over 83.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.387, discriminator_fake_loss=1.333, generator_loss=28.25, generator_mel_loss=17.98, generator_kl_loss=1.371, generator_dur_loss=1.771, generator_adv_loss=1.967, generator_feat_match_loss=5.153, over 782.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:06:34,181 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 02:06:42,167 INFO [train.py:591] (1/6) Epoch 488, validation: discriminator_loss=2.793, discriminator_real_loss=1.419, discriminator_fake_loss=1.374, generator_loss=27.17, generator_mel_loss=18.41, generator_kl_loss=1.266, generator_dur_loss=1.805, generator_adv_loss=1.847, generator_feat_match_loss=3.841, over 100.00 samples.
+2024-03-14 02:06:42,168 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 02:09:02,898 INFO [train.py:527] (1/6) Epoch 488, batch 62, global_batch_idx: 60450, batch size: 96, loss[discriminator_loss=2.748, discriminator_real_loss=1.392, discriminator_fake_loss=1.356, generator_loss=26.97, generator_mel_loss=17.5, generator_kl_loss=1.279, generator_dur_loss=1.884, generator_adv_loss=1.93, generator_feat_match_loss=4.38, over 96.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.377, discriminator_fake_loss=1.344, generator_loss=28.04, generator_mel_loss=17.97, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.944, generator_feat_match_loss=4.975, over 3588.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:11:20,219 INFO [train.py:527] (1/6) Epoch 488, batch 112, global_batch_idx: 60500, batch size: 64, loss[discriminator_loss=2.736, discriminator_real_loss=1.299, discriminator_fake_loss=1.437, generator_loss=28.08, generator_mel_loss=17.9, generator_kl_loss=1.431, generator_dur_loss=1.763, generator_adv_loss=1.97, generator_feat_match_loss=5.023, over 64.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.374, discriminator_fake_loss=1.341, generator_loss=28.06, generator_mel_loss=17.97, generator_kl_loss=1.419, generator_dur_loss=1.75, generator_adv_loss=1.95, generator_feat_match_loss=4.968, over 6279.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:11:51,239 INFO [train.py:919] (1/6) Start epoch 489
+2024-03-14 02:14:00,797 INFO [train.py:527] (1/6) Epoch 489, batch 38, global_batch_idx: 60550, batch size: 70, loss[discriminator_loss=2.662, discriminator_real_loss=1.294, discriminator_fake_loss=1.368, generator_loss=27.34, generator_mel_loss=17.59, generator_kl_loss=1.34, generator_dur_loss=1.805, generator_adv_loss=1.918, generator_feat_match_loss=4.688, over 70.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.345, generator_loss=28.37, generator_mel_loss=18.09, generator_kl_loss=1.43, generator_dur_loss=1.739, generator_adv_loss=1.976, generator_feat_match_loss=5.13, over 2126.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:16:20,623 INFO [train.py:527] (1/6) Epoch 489, batch 88, global_batch_idx: 60600, batch size: 74, loss[discriminator_loss=2.736, discriminator_real_loss=1.371, discriminator_fake_loss=1.365, generator_loss=27.93, generator_mel_loss=17.87, generator_kl_loss=1.506, generator_dur_loss=1.781, generator_adv_loss=1.958, generator_feat_match_loss=4.819, over 74.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.27, generator_mel_loss=18.06, generator_kl_loss=1.425, generator_dur_loss=1.742, generator_adv_loss=1.971, generator_feat_match_loss=5.079, over 4869.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:16:20,624 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 02:16:29,497 INFO [train.py:591] (1/6) Epoch 489, validation: discriminator_loss=2.774, discriminator_real_loss=1.513, discriminator_fake_loss=1.261, generator_loss=27.55, generator_mel_loss=18.22, generator_kl_loss=1.264, generator_dur_loss=1.804, generator_adv_loss=1.977, generator_feat_match_loss=4.282, over 100.00 samples.
+2024-03-14 02:16:29,498 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 02:18:06,797 INFO [train.py:919] (1/6) Start epoch 490
+2024-03-14 02:19:10,391 INFO [train.py:527] (1/6) Epoch 490, batch 14, global_batch_idx: 60650, batch size: 50, loss[discriminator_loss=2.731, discriminator_real_loss=1.348, discriminator_fake_loss=1.383, generator_loss=28.52, generator_mel_loss=17.98, generator_kl_loss=1.619, generator_dur_loss=1.648, generator_adv_loss=1.986, generator_feat_match_loss=5.292, over 50.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.336, discriminator_fake_loss=1.362, generator_loss=28.23, generator_mel_loss=17.99, generator_kl_loss=1.5, generator_dur_loss=1.718, generator_adv_loss=1.959, generator_feat_match_loss=5.056, over 785.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:21:27,302 INFO [train.py:527] (1/6) Epoch 490, batch 64, global_batch_idx: 60700, batch size: 44, loss[discriminator_loss=2.654, discriminator_real_loss=1.338, discriminator_fake_loss=1.315, generator_loss=29.36, generator_mel_loss=18.72, generator_kl_loss=1.396, generator_dur_loss=1.686, generator_adv_loss=2.058, generator_feat_match_loss=5.497, over 44.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=28.21, generator_mel_loss=17.99, generator_kl_loss=1.448, generator_dur_loss=1.727, generator_adv_loss=1.983, generator_feat_match_loss=5.057, over 3569.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:23:47,033 INFO [train.py:527] (1/6) Epoch 490, batch 114, global_batch_idx: 60750, batch size: 42, loss[discriminator_loss=2.644, discriminator_real_loss=1.402, discriminator_fake_loss=1.242, generator_loss=29.08, generator_mel_loss=18.34, generator_kl_loss=1.579, generator_dur_loss=1.664, generator_adv_loss=1.922, generator_feat_match_loss=5.574, over 42.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.371, discriminator_fake_loss=1.342, generator_loss=28.16, generator_mel_loss=18.01, generator_kl_loss=1.437, generator_dur_loss=1.729, generator_adv_loss=1.966, generator_feat_match_loss=5.018, over 6595.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:24:12,772 INFO [train.py:919] (1/6) Start epoch 491
+2024-03-14 02:26:29,228 INFO [train.py:527] (1/6) Epoch 491, batch 40, global_batch_idx: 60800, batch size: 47, loss[discriminator_loss=2.748, discriminator_real_loss=1.386, discriminator_fake_loss=1.362, generator_loss=27.78, generator_mel_loss=17.98, generator_kl_loss=1.458, generator_dur_loss=1.708, generator_adv_loss=2.009, generator_feat_match_loss=4.626, over 47.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.371, discriminator_fake_loss=1.345, generator_loss=28.2, generator_mel_loss=18.07, generator_kl_loss=1.418, generator_dur_loss=1.745, generator_adv_loss=1.936, generator_feat_match_loss=5.025, over 2317.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:26:29,229 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 02:26:37,143 INFO [train.py:591] (1/6) Epoch 491, validation: discriminator_loss=2.776, discriminator_real_loss=1.472, discriminator_fake_loss=1.304, generator_loss=26.72, generator_mel_loss=17.81, generator_kl_loss=1.207, generator_dur_loss=1.834, generator_adv_loss=1.958, generator_feat_match_loss=3.919, over 100.00 samples.
+2024-03-14 02:26:37,144 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 02:28:55,230 INFO [train.py:527] (1/6) Epoch 491, batch 90, global_batch_idx: 60850, batch size: 39, loss[discriminator_loss=2.705, discriminator_real_loss=1.409, discriminator_fake_loss=1.296, generator_loss=27.85, generator_mel_loss=17.98, generator_kl_loss=1.397, generator_dur_loss=1.729, generator_adv_loss=1.865, generator_feat_match_loss=4.874, over 39.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.369, discriminator_fake_loss=1.343, generator_loss=28.15, generator_mel_loss=18, generator_kl_loss=1.416, generator_dur_loss=1.751, generator_adv_loss=1.948, generator_feat_match_loss=5.038, over 4973.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:30:28,328 INFO [train.py:919] (1/6) Start epoch 492
+2024-03-14 02:31:38,291 INFO [train.py:527] (1/6) Epoch 492, batch 16, global_batch_idx: 60900, batch size: 58, loss[discriminator_loss=2.781, discriminator_real_loss=1.465, discriminator_fake_loss=1.316, generator_loss=27.42, generator_mel_loss=17.9, generator_kl_loss=1.392, generator_dur_loss=1.755, generator_adv_loss=1.867, generator_feat_match_loss=4.503, over 58.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.373, discriminator_fake_loss=1.349, generator_loss=28.12, generator_mel_loss=18.02, generator_kl_loss=1.404, generator_dur_loss=1.763, generator_adv_loss=1.929, generator_feat_match_loss=5.002, over 907.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:33:57,956 INFO [train.py:527] (1/6) Epoch 492, batch 66, global_batch_idx: 60950, batch size: 31, loss[discriminator_loss=2.691, discriminator_real_loss=1.45, discriminator_fake_loss=1.24, generator_loss=27.18, generator_mel_loss=17.97, generator_kl_loss=1.493, generator_dur_loss=1.662, generator_adv_loss=1.904, generator_feat_match_loss=4.16, over 31.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.376, discriminator_fake_loss=1.35, generator_loss=28.12, generator_mel_loss=18.05, generator_kl_loss=1.398, generator_dur_loss=1.761, generator_adv_loss=1.946, generator_feat_match_loss=4.964, over 3728.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:36:17,296 INFO [train.py:527] (1/6) Epoch 492, batch 116, global_batch_idx: 61000, batch size: 80, loss[discriminator_loss=2.761, discriminator_real_loss=1.344, discriminator_fake_loss=1.417, generator_loss=28.6, generator_mel_loss=18.29, generator_kl_loss=1.52, generator_dur_loss=1.828, generator_adv_loss=2.012, generator_feat_match_loss=4.952, over 80.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=28.15, generator_mel_loss=18.03, generator_kl_loss=1.404, generator_dur_loss=1.764, generator_adv_loss=1.956, generator_feat_match_loss=4.995, over 6656.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:36:17,297 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 02:36:26,120 INFO [train.py:591] (1/6) Epoch 492, validation: discriminator_loss=2.795, discriminator_real_loss=1.56, discriminator_fake_loss=1.235, generator_loss=27.04, generator_mel_loss=18.32, generator_kl_loss=1.27, generator_dur_loss=1.83, generator_adv_loss=1.987, generator_feat_match_loss=3.637, over 100.00 samples.
+2024-03-14 02:36:26,122 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 02:36:47,497 INFO [train.py:919] (1/6) Start epoch 493
+2024-03-14 02:39:06,972 INFO [train.py:527] (1/6) Epoch 493, batch 42, global_batch_idx: 61050, batch size: 53, loss[discriminator_loss=2.737, discriminator_real_loss=1.354, discriminator_fake_loss=1.383, generator_loss=28.68, generator_mel_loss=18.28, generator_kl_loss=1.489, generator_dur_loss=1.691, generator_adv_loss=1.992, generator_feat_match_loss=5.229, over 53.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.367, discriminator_fake_loss=1.349, generator_loss=28.18, generator_mel_loss=18.1, generator_kl_loss=1.391, generator_dur_loss=1.766, generator_adv_loss=1.934, generator_feat_match_loss=4.993, over 2584.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:41:26,017 INFO [train.py:527] (1/6) Epoch 493, batch 92, global_batch_idx: 61100, batch size: 77, loss[discriminator_loss=2.666, discriminator_real_loss=1.342, discriminator_fake_loss=1.323, generator_loss=28.3, generator_mel_loss=17.94, generator_kl_loss=1.42, generator_dur_loss=1.798, generator_adv_loss=2.023, generator_feat_match_loss=5.117, over 77.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=28.01, generator_mel_loss=18.04, generator_kl_loss=1.387, generator_dur_loss=1.754, generator_adv_loss=1.936, generator_feat_match_loss=4.889, over 5462.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:42:51,555 INFO [train.py:919] (1/6) Start epoch 494
+2024-03-14 02:44:05,475 INFO [train.py:527] (1/6) Epoch 494, batch 18, global_batch_idx: 61150, batch size: 45, loss[discriminator_loss=2.694, discriminator_real_loss=1.32, discriminator_fake_loss=1.374, generator_loss=28.86, generator_mel_loss=18.23, generator_kl_loss=1.528, generator_dur_loss=1.6, generator_adv_loss=1.89, generator_feat_match_loss=5.614, over 45.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.37, discriminator_fake_loss=1.336, generator_loss=28.23, generator_mel_loss=18.03, generator_kl_loss=1.454, generator_dur_loss=1.712, generator_adv_loss=1.932, generator_feat_match_loss=5.102, over 999.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:46:26,245 INFO [train.py:527] (1/6) Epoch 494, batch 68, global_batch_idx: 61200, batch size: 42, loss[discriminator_loss=2.757, discriminator_real_loss=1.417, discriminator_fake_loss=1.34, generator_loss=27.56, generator_mel_loss=17.96, generator_kl_loss=1.441, generator_dur_loss=1.699, generator_adv_loss=1.991, generator_feat_match_loss=4.471, over 42.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.05, generator_mel_loss=17.95, generator_kl_loss=1.447, generator_dur_loss=1.728, generator_adv_loss=1.947, generator_feat_match_loss=4.974, over 3897.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:46:26,246 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 02:46:34,378 INFO [train.py:591] (1/6) Epoch 494, validation: discriminator_loss=2.735, discriminator_real_loss=1.48, discriminator_fake_loss=1.256, generator_loss=26.8, generator_mel_loss=18.13, generator_kl_loss=1.329, generator_dur_loss=1.809, generator_adv_loss=1.932, generator_feat_match_loss=3.602, over 100.00 samples.
+2024-03-14 02:46:34,379 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 02:48:51,442 INFO [train.py:527] (1/6) Epoch 494, batch 118, global_batch_idx: 61250, batch size: 72, loss[discriminator_loss=2.786, discriminator_real_loss=1.33, discriminator_fake_loss=1.456, generator_loss=27.45, generator_mel_loss=17.76, generator_kl_loss=1.501, generator_dur_loss=1.78, generator_adv_loss=1.861, generator_feat_match_loss=4.549, over 72.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=28.03, generator_mel_loss=17.98, generator_kl_loss=1.436, generator_dur_loss=1.736, generator_adv_loss=1.945, generator_feat_match_loss=4.931, over 6750.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:49:06,202 INFO [train.py:919] (1/6) Start epoch 495
+2024-03-14 02:51:31,764 INFO [train.py:527] (1/6) Epoch 495, batch 44, global_batch_idx: 61300, batch size: 74, loss[discriminator_loss=2.675, discriminator_real_loss=1.342, discriminator_fake_loss=1.333, generator_loss=28.19, generator_mel_loss=17.43, generator_kl_loss=1.441, generator_dur_loss=1.813, generator_adv_loss=1.942, generator_feat_match_loss=5.557, over 74.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.13, generator_mel_loss=17.94, generator_kl_loss=1.428, generator_dur_loss=1.75, generator_adv_loss=1.944, generator_feat_match_loss=5.069, over 2543.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:53:52,188 INFO [train.py:527] (1/6) Epoch 495, batch 94, global_batch_idx: 61350, batch size: 53, loss[discriminator_loss=2.7, discriminator_real_loss=1.35, discriminator_fake_loss=1.35, generator_loss=28.43, generator_mel_loss=18.05, generator_kl_loss=1.354, generator_dur_loss=1.709, generator_adv_loss=1.934, generator_feat_match_loss=5.381, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.11, generator_mel_loss=17.96, generator_kl_loss=1.432, generator_dur_loss=1.752, generator_adv_loss=1.95, generator_feat_match_loss=5.013, over 5409.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:55:12,787 INFO [train.py:919] (1/6) Start epoch 496
+2024-03-14 02:56:33,628 INFO [train.py:527] (1/6) Epoch 496, batch 20, global_batch_idx: 61400, batch size: 42, loss[discriminator_loss=2.79, discriminator_real_loss=1.388, discriminator_fake_loss=1.402, generator_loss=27.44, generator_mel_loss=18.3, generator_kl_loss=1.43, generator_dur_loss=1.658, generator_adv_loss=1.952, generator_feat_match_loss=4.1, over 42.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.14, generator_mel_loss=18.04, generator_kl_loss=1.382, generator_dur_loss=1.744, generator_adv_loss=1.954, generator_feat_match_loss=5.011, over 1306.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 02:56:33,629 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 02:56:41,777 INFO [train.py:591] (1/6) Epoch 496, validation: discriminator_loss=2.804, discriminator_real_loss=1.479, discriminator_fake_loss=1.325, generator_loss=26.64, generator_mel_loss=18.11, generator_kl_loss=1.165, generator_dur_loss=1.803, generator_adv_loss=1.929, generator_feat_match_loss=3.628, over 100.00 samples.
+2024-03-14 02:56:41,778 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 02:59:00,541 INFO [train.py:527] (1/6) Epoch 496, batch 70, global_batch_idx: 61450, batch size: 31, loss[discriminator_loss=2.69, discriminator_real_loss=1.364, discriminator_fake_loss=1.327, generator_loss=27.75, generator_mel_loss=17.81, generator_kl_loss=1.694, generator_dur_loss=1.636, generator_adv_loss=1.875, generator_feat_match_loss=4.735, over 31.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.341, generator_loss=28.05, generator_mel_loss=17.98, generator_kl_loss=1.415, generator_dur_loss=1.748, generator_adv_loss=1.949, generator_feat_match_loss=4.958, over 4125.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:01:19,036 INFO [train.py:527] (1/6) Epoch 496, batch 120, global_batch_idx: 61500, batch size: 52, loss[discriminator_loss=2.755, discriminator_real_loss=1.434, discriminator_fake_loss=1.32, generator_loss=27.95, generator_mel_loss=17.62, generator_kl_loss=1.478, generator_dur_loss=1.656, generator_adv_loss=1.932, generator_feat_match_loss=5.265, over 52.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.1, generator_mel_loss=17.99, generator_kl_loss=1.436, generator_dur_loss=1.746, generator_adv_loss=1.947, generator_feat_match_loss=4.973, over 6811.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:01:29,309 INFO [train.py:919] (1/6) Start epoch 497
+2024-03-14 03:04:01,935 INFO [train.py:527] (1/6) Epoch 497, batch 46, global_batch_idx: 61550, batch size: 88, loss[discriminator_loss=2.709, discriminator_real_loss=1.343, discriminator_fake_loss=1.366, generator_loss=27.38, generator_mel_loss=17.63, generator_kl_loss=1.277, generator_dur_loss=1.833, generator_adv_loss=2.144, generator_feat_match_loss=4.489, over 88.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.374, discriminator_fake_loss=1.344, generator_loss=28.06, generator_mel_loss=17.96, generator_kl_loss=1.398, generator_dur_loss=1.766, generator_adv_loss=1.961, generator_feat_match_loss=4.974, over 2750.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:06:17,981 INFO [train.py:527] (1/6) Epoch 497, batch 96, global_batch_idx: 61600, batch size: 96, loss[discriminator_loss=2.755, discriminator_real_loss=1.364, discriminator_fake_loss=1.391, generator_loss=27.83, generator_mel_loss=17.86, generator_kl_loss=1.565, generator_dur_loss=1.744, generator_adv_loss=1.935, generator_feat_match_loss=4.724, over 96.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=28.13, generator_mel_loss=18, generator_kl_loss=1.403, generator_dur_loss=1.762, generator_adv_loss=1.962, generator_feat_match_loss=5.006, over 5637.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:06:17,983 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 03:06:26,499 INFO [train.py:591] (1/6) Epoch 497, validation: discriminator_loss=2.775, discriminator_real_loss=1.402, discriminator_fake_loss=1.373, generator_loss=26.94, generator_mel_loss=18.35, generator_kl_loss=1.209, generator_dur_loss=1.826, generator_adv_loss=1.895, generator_feat_match_loss=3.657, over 100.00 samples.
+2024-03-14 03:06:26,499 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 03:07:41,916 INFO [train.py:919] (1/6) Start epoch 498
+2024-03-14 03:09:08,330 INFO [train.py:527] (1/6) Epoch 498, batch 22, global_batch_idx: 61650, batch size: 66, loss[discriminator_loss=2.734, discriminator_real_loss=1.342, discriminator_fake_loss=1.392, generator_loss=28.02, generator_mel_loss=18.15, generator_kl_loss=1.399, generator_dur_loss=1.774, generator_adv_loss=1.956, generator_feat_match_loss=4.734, over 66.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.385, discriminator_fake_loss=1.336, generator_loss=28.04, generator_mel_loss=17.97, generator_kl_loss=1.365, generator_dur_loss=1.777, generator_adv_loss=1.947, generator_feat_match_loss=4.985, over 1525.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:11:26,792 INFO [train.py:527] (1/6) Epoch 498, batch 72, global_batch_idx: 61700, batch size: 25, loss[discriminator_loss=2.701, discriminator_real_loss=1.374, discriminator_fake_loss=1.327, generator_loss=28.29, generator_mel_loss=18.47, generator_kl_loss=1.733, generator_dur_loss=1.58, generator_adv_loss=1.838, generator_feat_match_loss=4.668, over 25.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=28.01, generator_mel_loss=17.91, generator_kl_loss=1.39, generator_dur_loss=1.765, generator_adv_loss=1.944, generator_feat_match_loss=4.998, over 4433.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:13:45,221 INFO [train.py:527] (1/6) Epoch 498, batch 122, global_batch_idx: 61750, batch size: 31, loss[discriminator_loss=2.755, discriminator_real_loss=1.338, discriminator_fake_loss=1.417, generator_loss=28.16, generator_mel_loss=18.46, generator_kl_loss=1.532, generator_dur_loss=1.606, generator_adv_loss=1.976, generator_feat_match_loss=4.591, over 31.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.371, discriminator_fake_loss=1.34, generator_loss=28.14, generator_mel_loss=17.95, generator_kl_loss=1.402, generator_dur_loss=1.772, generator_adv_loss=1.951, generator_feat_match_loss=5.067, over 7403.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:13:50,460 INFO [train.py:919] (1/6) Start epoch 499
+2024-03-14 03:16:23,931 INFO [train.py:527] (1/6) Epoch 499, batch 48, global_batch_idx: 61800, batch size: 83, loss[discriminator_loss=2.729, discriminator_real_loss=1.42, discriminator_fake_loss=1.309, generator_loss=27.77, generator_mel_loss=18.14, generator_kl_loss=1.313, generator_dur_loss=1.841, generator_adv_loss=1.888, generator_feat_match_loss=4.593, over 83.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.365, discriminator_fake_loss=1.347, generator_loss=28.05, generator_mel_loss=17.92, generator_kl_loss=1.428, generator_dur_loss=1.757, generator_adv_loss=1.95, generator_feat_match_loss=4.994, over 2670.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:16:23,933 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 03:16:31,894 INFO [train.py:591] (1/6) Epoch 499, validation: discriminator_loss=2.737, discriminator_real_loss=1.354, discriminator_fake_loss=1.383, generator_loss=27.07, generator_mel_loss=17.98, generator_kl_loss=1.183, generator_dur_loss=1.836, generator_adv_loss=1.837, generator_feat_match_loss=4.243, over 100.00 samples.
+2024-03-14 03:16:31,896 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 03:18:47,759 INFO [train.py:527] (1/6) Epoch 499, batch 98, global_batch_idx: 61850, batch size: 74, loss[discriminator_loss=2.619, discriminator_real_loss=1.295, discriminator_fake_loss=1.325, generator_loss=28.83, generator_mel_loss=18.19, generator_kl_loss=1.321, generator_dur_loss=1.787, generator_adv_loss=2.096, generator_feat_match_loss=5.434, over 74.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.366, discriminator_fake_loss=1.345, generator_loss=28.15, generator_mel_loss=18, generator_kl_loss=1.433, generator_dur_loss=1.756, generator_adv_loss=1.96, generator_feat_match_loss=5.001, over 5381.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:20:01,249 INFO [train.py:919] (1/6) Start epoch 500
+2024-03-14 03:21:33,402 INFO [train.py:527] (1/6) Epoch 500, batch 24, global_batch_idx: 61900, batch size: 95, loss[discriminator_loss=2.669, discriminator_real_loss=1.334, discriminator_fake_loss=1.334, generator_loss=28.27, generator_mel_loss=17.71, generator_kl_loss=1.372, generator_dur_loss=1.835, generator_adv_loss=1.869, generator_feat_match_loss=5.482, over 95.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.385, discriminator_fake_loss=1.335, generator_loss=28.34, generator_mel_loss=18.06, generator_kl_loss=1.441, generator_dur_loss=1.755, generator_adv_loss=1.937, generator_feat_match_loss=5.148, over 1402.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:23:53,850 INFO [train.py:527] (1/6) Epoch 500, batch 74, global_batch_idx: 61950, batch size: 60, loss[discriminator_loss=2.695, discriminator_real_loss=1.429, discriminator_fake_loss=1.266, generator_loss=27.77, generator_mel_loss=18.05, generator_kl_loss=1.372, generator_dur_loss=1.702, generator_adv_loss=1.889, generator_feat_match_loss=4.76, over 60.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.377, discriminator_fake_loss=1.337, generator_loss=28.23, generator_mel_loss=17.97, generator_kl_loss=1.436, generator_dur_loss=1.764, generator_adv_loss=1.957, generator_feat_match_loss=5.102, over 4158.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:26:08,010 INFO [train.py:919] (1/6) Start epoch 501
+2024-03-14 03:26:30,852 INFO [train.py:527] (1/6) Epoch 501, batch 0, global_batch_idx: 62000, batch size: 48, loss[discriminator_loss=2.767, discriminator_real_loss=1.397, discriminator_fake_loss=1.37, generator_loss=27.99, generator_mel_loss=17.87, generator_kl_loss=1.35, generator_dur_loss=1.719, generator_adv_loss=1.939, generator_feat_match_loss=5.119, over 48.00 samples.], tot_loss[discriminator_loss=2.767, discriminator_real_loss=1.397, discriminator_fake_loss=1.37, generator_loss=27.99, generator_mel_loss=17.87, generator_kl_loss=1.35, generator_dur_loss=1.719, generator_adv_loss=1.939, generator_feat_match_loss=5.119, over 48.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:26:30,854 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 03:26:38,670 INFO [train.py:591] (1/6) Epoch 501, validation: discriminator_loss=2.8, discriminator_real_loss=1.447, discriminator_fake_loss=1.353, generator_loss=27, generator_mel_loss=18.25, generator_kl_loss=1.239, generator_dur_loss=1.813, generator_adv_loss=1.859, generator_feat_match_loss=3.835, over 100.00 samples.
+2024-03-14 03:26:38,672 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 03:29:01,829 INFO [train.py:527] (1/6) Epoch 501, batch 50, global_batch_idx: 62050, batch size: 25, loss[discriminator_loss=2.77, discriminator_real_loss=1.445, discriminator_fake_loss=1.324, generator_loss=28.79, generator_mel_loss=18.71, generator_kl_loss=1.673, generator_dur_loss=1.575, generator_adv_loss=1.978, generator_feat_match_loss=4.856, over 25.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=28.22, generator_mel_loss=18.03, generator_kl_loss=1.381, generator_dur_loss=1.76, generator_adv_loss=1.955, generator_feat_match_loss=5.086, over 3019.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:31:20,035 INFO [train.py:527] (1/6) Epoch 501, batch 100, global_batch_idx: 62100, batch size: 80, loss[discriminator_loss=2.705, discriminator_real_loss=1.423, discriminator_fake_loss=1.282, generator_loss=27.71, generator_mel_loss=17.91, generator_kl_loss=1.306, generator_dur_loss=1.844, generator_adv_loss=1.905, generator_feat_match_loss=4.742, over 80.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.338, generator_loss=28.19, generator_mel_loss=18.02, generator_kl_loss=1.387, generator_dur_loss=1.76, generator_adv_loss=1.955, generator_feat_match_loss=5.069, over 5989.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:32:19,541 INFO [train.py:919] (1/6) Start epoch 502
+2024-03-14 03:33:55,533 INFO [train.py:527] (1/6) Epoch 502, batch 26, global_batch_idx: 62150, batch size: 74, loss[discriminator_loss=2.789, discriminator_real_loss=1.296, discriminator_fake_loss=1.492, generator_loss=27.56, generator_mel_loss=17.6, generator_kl_loss=1.207, generator_dur_loss=1.818, generator_adv_loss=2.095, generator_feat_match_loss=4.844, over 74.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.369, discriminator_fake_loss=1.331, generator_loss=28.24, generator_mel_loss=18, generator_kl_loss=1.425, generator_dur_loss=1.757, generator_adv_loss=2.01, generator_feat_match_loss=5.049, over 1488.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:36:14,977 INFO [train.py:527] (1/6) Epoch 502, batch 76, global_batch_idx: 62200, batch size: 53, loss[discriminator_loss=2.736, discriminator_real_loss=1.416, discriminator_fake_loss=1.32, generator_loss=27.92, generator_mel_loss=18, generator_kl_loss=1.423, generator_dur_loss=1.729, generator_adv_loss=2.046, generator_feat_match_loss=4.727, over 53.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.368, discriminator_fake_loss=1.334, generator_loss=28.29, generator_mel_loss=18.02, generator_kl_loss=1.431, generator_dur_loss=1.755, generator_adv_loss=1.968, generator_feat_match_loss=5.117, over 4231.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:36:14,978 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 03:36:23,146 INFO [train.py:591] (1/6) Epoch 502, validation: discriminator_loss=2.775, discriminator_real_loss=1.546, discriminator_fake_loss=1.23, generator_loss=27.43, generator_mel_loss=17.95, generator_kl_loss=1.265, generator_dur_loss=1.815, generator_adv_loss=2.04, generator_feat_match_loss=4.354, over 100.00 samples.
+2024-03-14 03:36:23,147 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 03:38:34,279 INFO [train.py:919] (1/6) Start epoch 503
+2024-03-14 03:39:03,049 INFO [train.py:527] (1/6) Epoch 503, batch 2, global_batch_idx: 62250, batch size: 45, loss[discriminator_loss=2.763, discriminator_real_loss=1.417, discriminator_fake_loss=1.346, generator_loss=28.51, generator_mel_loss=18.3, generator_kl_loss=1.615, generator_dur_loss=1.714, generator_adv_loss=1.983, generator_feat_match_loss=4.907, over 45.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.377, discriminator_fake_loss=1.334, generator_loss=28.47, generator_mel_loss=18.25, generator_kl_loss=1.48, generator_dur_loss=1.769, generator_adv_loss=1.997, generator_feat_match_loss=4.973, over 180.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:41:21,240 INFO [train.py:527] (1/6) Epoch 503, batch 52, global_batch_idx: 62300, batch size: 80, loss[discriminator_loss=2.689, discriminator_real_loss=1.308, discriminator_fake_loss=1.381, generator_loss=28.46, generator_mel_loss=18.35, generator_kl_loss=1.498, generator_dur_loss=1.79, generator_adv_loss=1.933, generator_feat_match_loss=4.891, over 80.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.364, discriminator_fake_loss=1.35, generator_loss=28.2, generator_mel_loss=18.02, generator_kl_loss=1.423, generator_dur_loss=1.77, generator_adv_loss=1.967, generator_feat_match_loss=5.02, over 2984.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:43:38,643 INFO [train.py:527] (1/6) Epoch 503, batch 102, global_batch_idx: 62350, batch size: 56, loss[discriminator_loss=2.68, discriminator_real_loss=1.448, discriminator_fake_loss=1.232, generator_loss=28.91, generator_mel_loss=18.43, generator_kl_loss=1.634, generator_dur_loss=1.732, generator_adv_loss=2.042, generator_feat_match_loss=5.073, over 56.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.363, discriminator_fake_loss=1.352, generator_loss=28.26, generator_mel_loss=18.03, generator_kl_loss=1.412, generator_dur_loss=1.761, generator_adv_loss=1.97, generator_feat_match_loss=5.092, over 5884.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:44:40,149 INFO [train.py:919] (1/6) Start epoch 504
+2024-03-14 03:46:22,681 INFO [train.py:527] (1/6) Epoch 504, batch 28, global_batch_idx: 62400, batch size: 31, loss[discriminator_loss=2.68, discriminator_real_loss=1.334, discriminator_fake_loss=1.346, generator_loss=28.49, generator_mel_loss=17.86, generator_kl_loss=1.684, generator_dur_loss=1.647, generator_adv_loss=2.045, generator_feat_match_loss=5.254, over 31.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.378, discriminator_fake_loss=1.33, generator_loss=28.27, generator_mel_loss=18.06, generator_kl_loss=1.448, generator_dur_loss=1.738, generator_adv_loss=1.971, generator_feat_match_loss=5.056, over 1551.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:46:22,683 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 03:46:30,662 INFO [train.py:591] (1/6) Epoch 504, validation: discriminator_loss=2.765, discriminator_real_loss=1.536, discriminator_fake_loss=1.229, generator_loss=27.01, generator_mel_loss=18.04, generator_kl_loss=1.208, generator_dur_loss=1.818, generator_adv_loss=2.003, generator_feat_match_loss=3.946, over 100.00 samples.
+2024-03-14 03:46:30,662 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 03:48:48,870 INFO [train.py:527] (1/6) Epoch 504, batch 78, global_batch_idx: 62450, batch size: 44, loss[discriminator_loss=2.69, discriminator_real_loss=1.451, discriminator_fake_loss=1.239, generator_loss=28.07, generator_mel_loss=18.2, generator_kl_loss=1.5, generator_dur_loss=1.694, generator_adv_loss=1.932, generator_feat_match_loss=4.747, over 44.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.14, generator_mel_loss=17.98, generator_kl_loss=1.412, generator_dur_loss=1.767, generator_adv_loss=1.953, generator_feat_match_loss=5.029, over 4677.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:50:53,579 INFO [train.py:919] (1/6) Start epoch 505
+2024-03-14 03:51:28,728 INFO [train.py:527] (1/6) Epoch 505, batch 4, global_batch_idx: 62500, batch size: 55, loss[discriminator_loss=2.653, discriminator_real_loss=1.263, discriminator_fake_loss=1.39, generator_loss=27.67, generator_mel_loss=18.11, generator_kl_loss=1.425, generator_dur_loss=1.687, generator_adv_loss=1.903, generator_feat_match_loss=4.548, over 55.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.348, discriminator_fake_loss=1.313, generator_loss=28.11, generator_mel_loss=18.09, generator_kl_loss=1.433, generator_dur_loss=1.718, generator_adv_loss=1.955, generator_feat_match_loss=4.918, over 260.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:53:49,000 INFO [train.py:527] (1/6) Epoch 505, batch 54, global_batch_idx: 62550, batch size: 64, loss[discriminator_loss=2.695, discriminator_real_loss=1.374, discriminator_fake_loss=1.322, generator_loss=27.72, generator_mel_loss=17.78, generator_kl_loss=1.224, generator_dur_loss=1.762, generator_adv_loss=1.941, generator_feat_match_loss=5.013, over 64.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.367, discriminator_fake_loss=1.341, generator_loss=28.22, generator_mel_loss=18, generator_kl_loss=1.433, generator_dur_loss=1.751, generator_adv_loss=1.951, generator_feat_match_loss=5.079, over 3141.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:56:08,599 INFO [train.py:527] (1/6) Epoch 505, batch 104, global_batch_idx: 62600, batch size: 72, loss[discriminator_loss=2.727, discriminator_real_loss=1.328, discriminator_fake_loss=1.399, generator_loss=27.24, generator_mel_loss=17.58, generator_kl_loss=1.328, generator_dur_loss=1.827, generator_adv_loss=1.851, generator_feat_match_loss=4.658, over 72.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.18, generator_mel_loss=17.96, generator_kl_loss=1.427, generator_dur_loss=1.756, generator_adv_loss=1.954, generator_feat_match_loss=5.091, over 5994.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 03:56:08,601 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 03:56:17,699 INFO [train.py:591] (1/6) Epoch 505, validation: discriminator_loss=2.747, discriminator_real_loss=1.384, discriminator_fake_loss=1.363, generator_loss=26.82, generator_mel_loss=17.9, generator_kl_loss=1.215, generator_dur_loss=1.821, generator_adv_loss=1.816, generator_feat_match_loss=4.067, over 100.00 samples.
+2024-03-14 03:56:17,700 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 03:57:09,636 INFO [train.py:919] (1/6) Start epoch 506
+2024-03-14 03:58:56,724 INFO [train.py:527] (1/6) Epoch 506, batch 30, global_batch_idx: 62650, batch size: 74, loss[discriminator_loss=2.705, discriminator_real_loss=1.345, discriminator_fake_loss=1.359, generator_loss=27.27, generator_mel_loss=17.53, generator_kl_loss=1.356, generator_dur_loss=1.808, generator_adv_loss=2.071, generator_feat_match_loss=4.5, over 74.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.23, generator_mel_loss=17.96, generator_kl_loss=1.427, generator_dur_loss=1.763, generator_adv_loss=1.977, generator_feat_match_loss=5.101, over 1771.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:01:18,710 INFO [train.py:527] (1/6) Epoch 506, batch 80, global_batch_idx: 62700, batch size: 56, loss[discriminator_loss=2.68, discriminator_real_loss=1.311, discriminator_fake_loss=1.369, generator_loss=28.04, generator_mel_loss=17.74, generator_kl_loss=1.398, generator_dur_loss=1.751, generator_adv_loss=1.938, generator_feat_match_loss=5.22, over 56.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.34, generator_loss=28.24, generator_mel_loss=17.98, generator_kl_loss=1.422, generator_dur_loss=1.766, generator_adv_loss=1.968, generator_feat_match_loss=5.105, over 4714.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:03:16,656 INFO [train.py:919] (1/6) Start epoch 507
+2024-03-14 04:03:55,921 INFO [train.py:527] (1/6) Epoch 507, batch 6, global_batch_idx: 62750, batch size: 77, loss[discriminator_loss=2.708, discriminator_real_loss=1.315, discriminator_fake_loss=1.392, generator_loss=27.58, generator_mel_loss=17.63, generator_kl_loss=1.21, generator_dur_loss=1.815, generator_adv_loss=1.931, generator_feat_match_loss=4.992, over 77.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.359, discriminator_fake_loss=1.342, generator_loss=27.84, generator_mel_loss=17.76, generator_kl_loss=1.387, generator_dur_loss=1.777, generator_adv_loss=1.949, generator_feat_match_loss=4.968, over 457.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:06:15,492 INFO [train.py:527] (1/6) Epoch 507, batch 56, global_batch_idx: 62800, batch size: 88, loss[discriminator_loss=2.689, discriminator_real_loss=1.299, discriminator_fake_loss=1.39, generator_loss=28.93, generator_mel_loss=18.22, generator_kl_loss=1.314, generator_dur_loss=1.825, generator_adv_loss=1.959, generator_feat_match_loss=5.614, over 88.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.29, generator_mel_loss=18.08, generator_kl_loss=1.439, generator_dur_loss=1.735, generator_adv_loss=1.962, generator_feat_match_loss=5.078, over 2916.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:06:15,493 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 04:06:23,517 INFO [train.py:591] (1/6) Epoch 507, validation: discriminator_loss=2.725, discriminator_real_loss=1.389, discriminator_fake_loss=1.335, generator_loss=26.94, generator_mel_loss=18.08, generator_kl_loss=1.132, generator_dur_loss=1.809, generator_adv_loss=1.812, generator_feat_match_loss=4.108, over 100.00 samples.
+2024-03-14 04:06:23,518 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 04:08:41,876 INFO [train.py:527] (1/6) Epoch 507, batch 106, global_batch_idx: 62850, batch size: 58, loss[discriminator_loss=2.747, discriminator_real_loss=1.431, discriminator_fake_loss=1.316, generator_loss=28.03, generator_mel_loss=17.94, generator_kl_loss=1.346, generator_dur_loss=1.754, generator_adv_loss=2.005, generator_feat_match_loss=4.992, over 58.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.369, discriminator_fake_loss=1.343, generator_loss=28.28, generator_mel_loss=18.06, generator_kl_loss=1.426, generator_dur_loss=1.749, generator_adv_loss=1.958, generator_feat_match_loss=5.082, over 5763.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:09:30,420 INFO [train.py:919] (1/6) Start epoch 508
+2024-03-14 04:11:20,133 INFO [train.py:527] (1/6) Epoch 508, batch 32, global_batch_idx: 62900, batch size: 42, loss[discriminator_loss=2.716, discriminator_real_loss=1.42, discriminator_fake_loss=1.296, generator_loss=28.54, generator_mel_loss=18.18, generator_kl_loss=1.647, generator_dur_loss=1.643, generator_adv_loss=1.858, generator_feat_match_loss=5.21, over 42.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.379, discriminator_fake_loss=1.334, generator_loss=28.24, generator_mel_loss=18.05, generator_kl_loss=1.435, generator_dur_loss=1.735, generator_adv_loss=1.983, generator_feat_match_loss=5.036, over 1662.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:13:41,933 INFO [train.py:527] (1/6) Epoch 508, batch 82, global_batch_idx: 62950, batch size: 62, loss[discriminator_loss=2.735, discriminator_real_loss=1.425, discriminator_fake_loss=1.31, generator_loss=28.31, generator_mel_loss=17.73, generator_kl_loss=1.429, generator_dur_loss=1.775, generator_adv_loss=1.948, generator_feat_match_loss=5.431, over 62.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.338, generator_loss=28.24, generator_mel_loss=18.06, generator_kl_loss=1.426, generator_dur_loss=1.743, generator_adv_loss=1.969, generator_feat_match_loss=5.034, over 4437.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:15:36,571 INFO [train.py:919] (1/6) Start epoch 509
+2024-03-14 04:16:22,859 INFO [train.py:527] (1/6) Epoch 509, batch 8, global_batch_idx: 63000, batch size: 83, loss[discriminator_loss=2.698, discriminator_real_loss=1.358, discriminator_fake_loss=1.34, generator_loss=27.38, generator_mel_loss=17.7, generator_kl_loss=1.338, generator_dur_loss=1.779, generator_adv_loss=1.802, generator_feat_match_loss=4.755, over 83.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.39, discriminator_fake_loss=1.318, generator_loss=27.85, generator_mel_loss=17.95, generator_kl_loss=1.401, generator_dur_loss=1.734, generator_adv_loss=1.913, generator_feat_match_loss=4.854, over 558.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:16:22,862 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 04:16:30,826 INFO [train.py:591] (1/6) Epoch 509, validation: discriminator_loss=2.748, discriminator_real_loss=1.351, discriminator_fake_loss=1.397, generator_loss=26.76, generator_mel_loss=17.9, generator_kl_loss=1.346, generator_dur_loss=1.796, generator_adv_loss=1.768, generator_feat_match_loss=3.948, over 100.00 samples.
+2024-03-14 04:16:30,828 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 04:18:51,741 INFO [train.py:527] (1/6) Epoch 509, batch 58, global_batch_idx: 63050, batch size: 39, loss[discriminator_loss=2.736, discriminator_real_loss=1.322, discriminator_fake_loss=1.414, generator_loss=28.29, generator_mel_loss=17.86, generator_kl_loss=1.512, generator_dur_loss=1.677, generator_adv_loss=2.026, generator_feat_match_loss=5.215, over 39.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.376, discriminator_fake_loss=1.339, generator_loss=28.19, generator_mel_loss=18.07, generator_kl_loss=1.418, generator_dur_loss=1.737, generator_adv_loss=1.95, generator_feat_match_loss=5.023, over 3413.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:21:11,250 INFO [train.py:527] (1/6) Epoch 509, batch 108, global_batch_idx: 63100, batch size: 70, loss[discriminator_loss=2.762, discriminator_real_loss=1.423, discriminator_fake_loss=1.339, generator_loss=28.58, generator_mel_loss=18.46, generator_kl_loss=1.327, generator_dur_loss=1.783, generator_adv_loss=1.808, generator_feat_match_loss=5.199, over 70.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.377, discriminator_fake_loss=1.337, generator_loss=28.2, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.739, generator_adv_loss=1.955, generator_feat_match_loss=5.049, over 6299.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:21:55,684 INFO [train.py:919] (1/6) Start epoch 510
+2024-03-14 04:23:51,996 INFO [train.py:527] (1/6) Epoch 510, batch 34, global_batch_idx: 63150, batch size: 55, loss[discriminator_loss=2.788, discriminator_real_loss=1.317, discriminator_fake_loss=1.47, generator_loss=27.77, generator_mel_loss=17.88, generator_kl_loss=1.448, generator_dur_loss=1.668, generator_adv_loss=2.088, generator_feat_match_loss=4.689, over 55.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.378, discriminator_fake_loss=1.334, generator_loss=28.03, generator_mel_loss=17.98, generator_kl_loss=1.403, generator_dur_loss=1.73, generator_adv_loss=1.957, generator_feat_match_loss=4.962, over 1954.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:26:11,864 INFO [train.py:527] (1/6) Epoch 510, batch 84, global_batch_idx: 63200, batch size: 52, loss[discriminator_loss=2.709, discriminator_real_loss=1.306, discriminator_fake_loss=1.403, generator_loss=28.47, generator_mel_loss=18.22, generator_kl_loss=1.464, generator_dur_loss=1.756, generator_adv_loss=2.006, generator_feat_match_loss=5.024, over 52.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=28.1, generator_mel_loss=17.97, generator_kl_loss=1.393, generator_dur_loss=1.751, generator_adv_loss=1.946, generator_feat_match_loss=5.033, over 5014.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:26:11,866 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 04:26:21,111 INFO [train.py:591] (1/6) Epoch 510, validation: discriminator_loss=2.738, discriminator_real_loss=1.447, discriminator_fake_loss=1.291, generator_loss=26.77, generator_mel_loss=17.99, generator_kl_loss=1.289, generator_dur_loss=1.815, generator_adv_loss=1.943, generator_feat_match_loss=3.734, over 100.00 samples.
+2024-03-14 04:26:21,112 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 04:28:09,447 INFO [train.py:919] (1/6) Start epoch 511
+2024-03-14 04:29:03,937 INFO [train.py:527] (1/6) Epoch 511, batch 10, global_batch_idx: 63250, batch size: 31, loss[discriminator_loss=2.76, discriminator_real_loss=1.36, discriminator_fake_loss=1.4, generator_loss=27.37, generator_mel_loss=17.66, generator_kl_loss=1.733, generator_dur_loss=1.612, generator_adv_loss=2.018, generator_feat_match_loss=4.345, over 31.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.392, discriminator_fake_loss=1.326, generator_loss=27.89, generator_mel_loss=17.86, generator_kl_loss=1.387, generator_dur_loss=1.758, generator_adv_loss=1.99, generator_feat_match_loss=4.893, over 656.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:31:24,645 INFO [train.py:527] (1/6) Epoch 511, batch 60, global_batch_idx: 63300, batch size: 64, loss[discriminator_loss=2.641, discriminator_real_loss=1.337, discriminator_fake_loss=1.304, generator_loss=28.99, generator_mel_loss=18.13, generator_kl_loss=1.513, generator_dur_loss=1.736, generator_adv_loss=1.945, generator_feat_match_loss=5.659, over 64.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.25, generator_mel_loss=17.97, generator_kl_loss=1.406, generator_dur_loss=1.756, generator_adv_loss=1.973, generator_feat_match_loss=5.146, over 3468.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:33:43,483 INFO [train.py:527] (1/6) Epoch 511, batch 110, global_batch_idx: 63350, batch size: 31, loss[discriminator_loss=2.651, discriminator_real_loss=1.241, discriminator_fake_loss=1.41, generator_loss=30.85, generator_mel_loss=18.76, generator_kl_loss=1.61, generator_dur_loss=1.679, generator_adv_loss=2.022, generator_feat_match_loss=6.785, over 31.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.16, generator_mel_loss=17.96, generator_kl_loss=1.403, generator_dur_loss=1.762, generator_adv_loss=1.961, generator_feat_match_loss=5.073, over 6392.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:34:19,500 INFO [train.py:919] (1/6) Start epoch 512
+2024-03-14 04:36:22,516 INFO [train.py:527] (1/6) Epoch 512, batch 36, global_batch_idx: 63400, batch size: 48, loss[discriminator_loss=2.712, discriminator_real_loss=1.358, discriminator_fake_loss=1.354, generator_loss=29.32, generator_mel_loss=18.51, generator_kl_loss=1.457, generator_dur_loss=1.706, generator_adv_loss=1.984, generator_feat_match_loss=5.657, over 48.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.386, discriminator_fake_loss=1.337, generator_loss=28.18, generator_mel_loss=18.01, generator_kl_loss=1.414, generator_dur_loss=1.763, generator_adv_loss=1.965, generator_feat_match_loss=5.029, over 2079.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:36:22,517 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 04:36:30,270 INFO [train.py:591] (1/6) Epoch 512, validation: discriminator_loss=2.746, discriminator_real_loss=1.444, discriminator_fake_loss=1.302, generator_loss=27.11, generator_mel_loss=18.15, generator_kl_loss=1.337, generator_dur_loss=1.83, generator_adv_loss=1.897, generator_feat_match_loss=3.894, over 100.00 samples.
+2024-03-14 04:36:30,271 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 04:38:46,913 INFO [train.py:527] (1/6) Epoch 512, batch 86, global_batch_idx: 63450, batch size: 80, loss[discriminator_loss=2.735, discriminator_real_loss=1.432, discriminator_fake_loss=1.303, generator_loss=27.61, generator_mel_loss=17.54, generator_kl_loss=1.317, generator_dur_loss=1.802, generator_adv_loss=1.922, generator_feat_match_loss=5.033, over 80.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.386, discriminator_fake_loss=1.336, generator_loss=28.21, generator_mel_loss=18.02, generator_kl_loss=1.42, generator_dur_loss=1.759, generator_adv_loss=1.961, generator_feat_match_loss=5.047, over 4854.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:40:33,594 INFO [train.py:919] (1/6) Start epoch 513
+2024-03-14 04:41:33,134 INFO [train.py:527] (1/6) Epoch 513, batch 12, global_batch_idx: 63500, batch size: 62, loss[discriminator_loss=2.67, discriminator_real_loss=1.372, discriminator_fake_loss=1.298, generator_loss=28.67, generator_mel_loss=18.12, generator_kl_loss=1.442, generator_dur_loss=1.756, generator_adv_loss=1.844, generator_feat_match_loss=5.509, over 62.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.18, generator_mel_loss=17.99, generator_kl_loss=1.4, generator_dur_loss=1.747, generator_adv_loss=1.981, generator_feat_match_loss=5.07, over 714.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:43:53,161 INFO [train.py:527] (1/6) Epoch 513, batch 62, global_batch_idx: 63550, batch size: 88, loss[discriminator_loss=2.692, discriminator_real_loss=1.389, discriminator_fake_loss=1.303, generator_loss=27.76, generator_mel_loss=18.12, generator_kl_loss=1.392, generator_dur_loss=1.893, generator_adv_loss=1.895, generator_feat_match_loss=4.464, over 88.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=28.21, generator_mel_loss=18.04, generator_kl_loss=1.426, generator_dur_loss=1.772, generator_adv_loss=1.948, generator_feat_match_loss=5.026, over 3828.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:46:10,239 INFO [train.py:527] (1/6) Epoch 513, batch 112, global_batch_idx: 63600, batch size: 58, loss[discriminator_loss=2.707, discriminator_real_loss=1.456, discriminator_fake_loss=1.251, generator_loss=27.97, generator_mel_loss=18.22, generator_kl_loss=1.525, generator_dur_loss=1.719, generator_adv_loss=1.985, generator_feat_match_loss=4.527, over 58.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.344, generator_loss=28.22, generator_mel_loss=17.99, generator_kl_loss=1.408, generator_dur_loss=1.77, generator_adv_loss=1.982, generator_feat_match_loss=5.071, over 6599.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:46:10,241 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 04:46:19,190 INFO [train.py:591] (1/6) Epoch 513, validation: discriminator_loss=2.719, discriminator_real_loss=1.431, discriminator_fake_loss=1.288, generator_loss=27.09, generator_mel_loss=18.16, generator_kl_loss=1.158, generator_dur_loss=1.847, generator_adv_loss=1.954, generator_feat_match_loss=3.979, over 100.00 samples.
+2024-03-14 04:46:19,191 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 04:46:48,273 INFO [train.py:919] (1/6) Start epoch 514
+2024-03-14 04:48:59,909 INFO [train.py:527] (1/6) Epoch 514, batch 38, global_batch_idx: 63650, batch size: 96, loss[discriminator_loss=2.64, discriminator_real_loss=1.28, discriminator_fake_loss=1.361, generator_loss=28.14, generator_mel_loss=17.64, generator_kl_loss=1.247, generator_dur_loss=1.848, generator_adv_loss=1.974, generator_feat_match_loss=5.431, over 96.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.371, discriminator_fake_loss=1.327, generator_loss=28.29, generator_mel_loss=18.01, generator_kl_loss=1.396, generator_dur_loss=1.77, generator_adv_loss=1.967, generator_feat_match_loss=5.139, over 2290.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:51:21,592 INFO [train.py:527] (1/6) Epoch 514, batch 88, global_batch_idx: 63700, batch size: 59, loss[discriminator_loss=2.708, discriminator_real_loss=1.315, discriminator_fake_loss=1.393, generator_loss=28.51, generator_mel_loss=18.33, generator_kl_loss=1.335, generator_dur_loss=1.741, generator_adv_loss=1.784, generator_feat_match_loss=5.317, over 59.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.24, generator_mel_loss=18.04, generator_kl_loss=1.398, generator_dur_loss=1.773, generator_adv_loss=1.948, generator_feat_match_loss=5.074, over 5322.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:52:58,459 INFO [train.py:919] (1/6) Start epoch 515
+2024-03-14 04:54:01,008 INFO [train.py:527] (1/6) Epoch 515, batch 14, global_batch_idx: 63750, batch size: 96, loss[discriminator_loss=2.79, discriminator_real_loss=1.529, discriminator_fake_loss=1.262, generator_loss=26.91, generator_mel_loss=17.56, generator_kl_loss=1.224, generator_dur_loss=1.863, generator_adv_loss=1.947, generator_feat_match_loss=4.315, over 96.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.395, discriminator_fake_loss=1.337, generator_loss=27.96, generator_mel_loss=17.97, generator_kl_loss=1.372, generator_dur_loss=1.763, generator_adv_loss=1.957, generator_feat_match_loss=4.898, over 947.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:56:20,966 INFO [train.py:527] (1/6) Epoch 515, batch 64, global_batch_idx: 63800, batch size: 48, loss[discriminator_loss=2.704, discriminator_real_loss=1.382, discriminator_fake_loss=1.321, generator_loss=29.03, generator_mel_loss=18.24, generator_kl_loss=1.48, generator_dur_loss=1.707, generator_adv_loss=1.979, generator_feat_match_loss=5.628, over 48.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.38, discriminator_fake_loss=1.342, generator_loss=28.16, generator_mel_loss=18, generator_kl_loss=1.409, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=5.036, over 3805.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:56:20,968 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 04:56:28,857 INFO [train.py:591] (1/6) Epoch 515, validation: discriminator_loss=2.737, discriminator_real_loss=1.473, discriminator_fake_loss=1.264, generator_loss=27.6, generator_mel_loss=18.64, generator_kl_loss=1.278, generator_dur_loss=1.822, generator_adv_loss=1.891, generator_feat_match_loss=3.967, over 100.00 samples.
+2024-03-14 04:56:28,858 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 04:58:48,021 INFO [train.py:527] (1/6) Epoch 515, batch 114, global_batch_idx: 63850, batch size: 59, loss[discriminator_loss=2.753, discriminator_real_loss=1.447, discriminator_fake_loss=1.305, generator_loss=27.54, generator_mel_loss=18.13, generator_kl_loss=1.246, generator_dur_loss=1.754, generator_adv_loss=1.836, generator_feat_match_loss=4.572, over 59.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=28.13, generator_mel_loss=17.98, generator_kl_loss=1.405, generator_dur_loss=1.752, generator_adv_loss=1.951, generator_feat_match_loss=5.035, over 6640.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 04:59:15,422 INFO [train.py:919] (1/6) Start epoch 516
+2024-03-14 05:01:29,139 INFO [train.py:527] (1/6) Epoch 516, batch 40, global_batch_idx: 63900, batch size: 42, loss[discriminator_loss=2.649, discriminator_real_loss=1.42, discriminator_fake_loss=1.23, generator_loss=30.44, generator_mel_loss=18.8, generator_kl_loss=1.668, generator_dur_loss=1.644, generator_adv_loss=1.972, generator_feat_match_loss=6.351, over 42.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.359, discriminator_fake_loss=1.341, generator_loss=28.15, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.758, generator_adv_loss=1.958, generator_feat_match_loss=5.049, over 2317.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 05:03:47,513 INFO [train.py:527] (1/6) Epoch 516, batch 90, global_batch_idx: 63950, batch size: 42, loss[discriminator_loss=2.736, discriminator_real_loss=1.369, discriminator_fake_loss=1.367, generator_loss=27.31, generator_mel_loss=17.74, generator_kl_loss=1.406, generator_dur_loss=1.703, generator_adv_loss=1.919, generator_feat_match_loss=4.54, over 42.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.365, discriminator_fake_loss=1.344, generator_loss=28.16, generator_mel_loss=17.99, generator_kl_loss=1.417, generator_dur_loss=1.76, generator_adv_loss=1.951, generator_feat_match_loss=5.049, over 5129.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 05:05:20,013 INFO [train.py:919] (1/6) Start epoch 517
+2024-03-14 05:06:28,818 INFO [train.py:527] (1/6) Epoch 517, batch 16, global_batch_idx: 64000, batch size: 58, loss[discriminator_loss=2.759, discriminator_real_loss=1.382, discriminator_fake_loss=1.377, generator_loss=27.87, generator_mel_loss=17.87, generator_kl_loss=1.421, generator_dur_loss=1.773, generator_adv_loss=1.878, generator_feat_match_loss=4.919, over 58.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.347, discriminator_fake_loss=1.334, generator_loss=28.33, generator_mel_loss=17.95, generator_kl_loss=1.421, generator_dur_loss=1.768, generator_adv_loss=1.967, generator_feat_match_loss=5.219, over 1097.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 05:06:28,820 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 05:06:36,606 INFO [train.py:591] (1/6) Epoch 517, validation: discriminator_loss=2.771, discriminator_real_loss=1.411, discriminator_fake_loss=1.36, generator_loss=26.76, generator_mel_loss=17.97, generator_kl_loss=1.252, generator_dur_loss=1.828, generator_adv_loss=1.814, generator_feat_match_loss=3.899, over 100.00 samples.
+2024-03-14 05:06:36,607 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 05:08:59,589 INFO [train.py:527] (1/6) Epoch 517, batch 66, global_batch_idx: 64050, batch size: 42, loss[discriminator_loss=2.684, discriminator_real_loss=1.32, discriminator_fake_loss=1.364, generator_loss=28.62, generator_mel_loss=18.07, generator_kl_loss=1.468, generator_dur_loss=1.732, generator_adv_loss=2.051, generator_feat_match_loss=5.301, over 42.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.357, discriminator_fake_loss=1.337, generator_loss=28.18, generator_mel_loss=17.94, generator_kl_loss=1.4, generator_dur_loss=1.765, generator_adv_loss=1.95, generator_feat_match_loss=5.123, over 4053.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 05:11:15,911 INFO [train.py:527] (1/6) Epoch 517, batch 116, global_batch_idx: 64100, batch size: 48, loss[discriminator_loss=2.741, discriminator_real_loss=1.443, discriminator_fake_loss=1.298, generator_loss=28.61, generator_mel_loss=18.2, generator_kl_loss=1.647, generator_dur_loss=1.704, generator_adv_loss=1.934, generator_feat_match_loss=5.128, over 48.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.361, discriminator_fake_loss=1.342, generator_loss=28.19, generator_mel_loss=17.97, generator_kl_loss=1.412, generator_dur_loss=1.76, generator_adv_loss=1.946, generator_feat_match_loss=5.105, over 6930.00 samples.], cur_lr_g: 1.88e-04, cur_lr_d: 1.88e-04, 
+2024-03-14 05:11:37,437 INFO [train.py:919] (1/6) Start epoch 518
+2024-03-14 05:13:58,915 INFO [train.py:527] (1/6) Epoch 518, batch 42, global_batch_idx: 64150, batch size: 74, loss[discriminator_loss=2.666, discriminator_real_loss=1.381, discriminator_fake_loss=1.285, generator_loss=28.85, generator_mel_loss=18.09, generator_kl_loss=1.414, generator_dur_loss=1.812, generator_adv_loss=1.858, generator_feat_match_loss=5.676, over 74.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.376, discriminator_fake_loss=1.331, generator_loss=28.13, generator_mel_loss=17.93, generator_kl_loss=1.387, generator_dur_loss=1.763, generator_adv_loss=1.948, generator_feat_match_loss=5.11, over 2356.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:16:18,579 INFO [train.py:527] (1/6) Epoch 518, batch 92, global_batch_idx: 64200, batch size: 39, loss[discriminator_loss=2.714, discriminator_real_loss=1.387, discriminator_fake_loss=1.327, generator_loss=29.21, generator_mel_loss=18.91, generator_kl_loss=1.5, generator_dur_loss=1.664, generator_adv_loss=1.845, generator_feat_match_loss=5.288, over 39.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.11, generator_mel_loss=17.93, generator_kl_loss=1.399, generator_dur_loss=1.76, generator_adv_loss=1.953, generator_feat_match_loss=5.072, over 5168.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:16:18,580 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 05:16:27,417 INFO [train.py:591] (1/6) Epoch 518, validation: discriminator_loss=2.75, discriminator_real_loss=1.371, discriminator_fake_loss=1.379, generator_loss=27.32, generator_mel_loss=18.42, generator_kl_loss=1.186, generator_dur_loss=1.828, generator_adv_loss=1.755, generator_feat_match_loss=4.132, over 100.00 samples.
+2024-03-14 05:16:27,417 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 05:17:52,325 INFO [train.py:919] (1/6) Start epoch 519
+2024-03-14 05:19:05,977 INFO [train.py:527] (1/6) Epoch 519, batch 18, global_batch_idx: 64250, batch size: 48, loss[discriminator_loss=2.696, discriminator_real_loss=1.357, discriminator_fake_loss=1.339, generator_loss=29.19, generator_mel_loss=18.38, generator_kl_loss=1.593, generator_dur_loss=1.705, generator_adv_loss=2.014, generator_feat_match_loss=5.497, over 48.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.361, discriminator_fake_loss=1.337, generator_loss=28.22, generator_mel_loss=18.06, generator_kl_loss=1.399, generator_dur_loss=1.759, generator_adv_loss=1.946, generator_feat_match_loss=5.053, over 1060.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:21:24,472 INFO [train.py:527] (1/6) Epoch 519, batch 68, global_batch_idx: 64300, batch size: 64, loss[discriminator_loss=2.745, discriminator_real_loss=1.358, discriminator_fake_loss=1.386, generator_loss=27.41, generator_mel_loss=17.69, generator_kl_loss=1.403, generator_dur_loss=1.774, generator_adv_loss=1.917, generator_feat_match_loss=4.633, over 64.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.368, discriminator_fake_loss=1.344, generator_loss=28.18, generator_mel_loss=17.98, generator_kl_loss=1.414, generator_dur_loss=1.765, generator_adv_loss=1.954, generator_feat_match_loss=5.065, over 3960.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:23:42,070 INFO [train.py:527] (1/6) Epoch 519, batch 118, global_batch_idx: 64350, batch size: 83, loss[discriminator_loss=2.664, discriminator_real_loss=1.384, discriminator_fake_loss=1.28, generator_loss=28.5, generator_mel_loss=17.94, generator_kl_loss=1.406, generator_dur_loss=1.796, generator_adv_loss=2.216, generator_feat_match_loss=5.143, over 83.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.363, discriminator_fake_loss=1.346, generator_loss=28.27, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.762, generator_adv_loss=1.979, generator_feat_match_loss=5.125, over 6718.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:23:57,952 INFO [train.py:919] (1/6) Start epoch 520
+2024-03-14 05:26:22,293 INFO [train.py:527] (1/6) Epoch 520, batch 44, global_batch_idx: 64400, batch size: 74, loss[discriminator_loss=2.67, discriminator_real_loss=1.338, discriminator_fake_loss=1.331, generator_loss=28.55, generator_mel_loss=18.08, generator_kl_loss=1.226, generator_dur_loss=1.811, generator_adv_loss=1.961, generator_feat_match_loss=5.475, over 74.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.387, discriminator_fake_loss=1.338, generator_loss=28.04, generator_mel_loss=17.9, generator_kl_loss=1.393, generator_dur_loss=1.766, generator_adv_loss=1.948, generator_feat_match_loss=5.034, over 2607.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:26:22,295 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 05:26:30,251 INFO [train.py:591] (1/6) Epoch 520, validation: discriminator_loss=2.739, discriminator_real_loss=1.432, discriminator_fake_loss=1.306, generator_loss=27.31, generator_mel_loss=18.27, generator_kl_loss=1.261, generator_dur_loss=1.832, generator_adv_loss=1.899, generator_feat_match_loss=4.044, over 100.00 samples.
+2024-03-14 05:26:30,252 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 05:28:48,672 INFO [train.py:527] (1/6) Epoch 520, batch 94, global_batch_idx: 64450, batch size: 62, loss[discriminator_loss=2.753, discriminator_real_loss=1.417, discriminator_fake_loss=1.336, generator_loss=27.22, generator_mel_loss=17.83, generator_kl_loss=1.363, generator_dur_loss=1.716, generator_adv_loss=1.82, generator_feat_match_loss=4.494, over 62.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.38, discriminator_fake_loss=1.338, generator_loss=28.05, generator_mel_loss=17.93, generator_kl_loss=1.399, generator_dur_loss=1.751, generator_adv_loss=1.944, generator_feat_match_loss=5.019, over 5427.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:30:10,276 INFO [train.py:919] (1/6) Start epoch 521
+2024-03-14 05:31:30,894 INFO [train.py:527] (1/6) Epoch 521, batch 20, global_batch_idx: 64500, batch size: 63, loss[discriminator_loss=2.687, discriminator_real_loss=1.35, discriminator_fake_loss=1.337, generator_loss=28.57, generator_mel_loss=17.88, generator_kl_loss=1.589, generator_dur_loss=1.771, generator_adv_loss=2.087, generator_feat_match_loss=5.234, over 63.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.371, discriminator_fake_loss=1.348, generator_loss=28.17, generator_mel_loss=17.97, generator_kl_loss=1.408, generator_dur_loss=1.766, generator_adv_loss=1.955, generator_feat_match_loss=5.071, over 1345.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:33:50,028 INFO [train.py:527] (1/6) Epoch 521, batch 70, global_batch_idx: 64550, batch size: 77, loss[discriminator_loss=2.734, discriminator_real_loss=1.339, discriminator_fake_loss=1.395, generator_loss=28.53, generator_mel_loss=18.15, generator_kl_loss=1.254, generator_dur_loss=1.777, generator_adv_loss=1.994, generator_feat_match_loss=5.358, over 77.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.368, discriminator_fake_loss=1.345, generator_loss=28.22, generator_mel_loss=18.01, generator_kl_loss=1.411, generator_dur_loss=1.753, generator_adv_loss=1.955, generator_feat_match_loss=5.088, over 4147.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:36:07,778 INFO [train.py:527] (1/6) Epoch 521, batch 120, global_batch_idx: 64600, batch size: 66, loss[discriminator_loss=2.672, discriminator_real_loss=1.316, discriminator_fake_loss=1.356, generator_loss=29.01, generator_mel_loss=18.5, generator_kl_loss=1.406, generator_dur_loss=1.728, generator_adv_loss=2.044, generator_feat_match_loss=5.332, over 66.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=28.17, generator_mel_loss=17.99, generator_kl_loss=1.417, generator_dur_loss=1.752, generator_adv_loss=1.954, generator_feat_match_loss=5.053, over 6930.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:36:07,779 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 05:36:16,582 INFO [train.py:591] (1/6) Epoch 521, validation: discriminator_loss=2.667, discriminator_real_loss=1.456, discriminator_fake_loss=1.211, generator_loss=27.99, generator_mel_loss=18.52, generator_kl_loss=1.214, generator_dur_loss=1.819, generator_adv_loss=2.026, generator_feat_match_loss=4.408, over 100.00 samples.
+2024-03-14 05:36:16,582 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 05:36:25,479 INFO [train.py:919] (1/6) Start epoch 522
+2024-03-14 05:38:57,099 INFO [train.py:527] (1/6) Epoch 522, batch 46, global_batch_idx: 64650, batch size: 42, loss[discriminator_loss=2.707, discriminator_real_loss=1.397, discriminator_fake_loss=1.31, generator_loss=28.59, generator_mel_loss=17.93, generator_kl_loss=1.614, generator_dur_loss=1.693, generator_adv_loss=1.921, generator_feat_match_loss=5.43, over 42.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.343, generator_loss=28.26, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.752, generator_adv_loss=1.946, generator_feat_match_loss=5.153, over 2634.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:41:15,683 INFO [train.py:527] (1/6) Epoch 522, batch 96, global_batch_idx: 64700, batch size: 56, loss[discriminator_loss=2.692, discriminator_real_loss=1.39, discriminator_fake_loss=1.301, generator_loss=29.11, generator_mel_loss=18.61, generator_kl_loss=1.373, generator_dur_loss=1.737, generator_adv_loss=1.83, generator_feat_match_loss=5.556, over 56.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.372, discriminator_fake_loss=1.341, generator_loss=28.21, generator_mel_loss=17.97, generator_kl_loss=1.397, generator_dur_loss=1.772, generator_adv_loss=1.954, generator_feat_match_loss=5.116, over 5822.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:42:30,528 INFO [train.py:919] (1/6) Start epoch 523
+2024-03-14 05:43:53,458 INFO [train.py:527] (1/6) Epoch 523, batch 22, global_batch_idx: 64750, batch size: 80, loss[discriminator_loss=2.769, discriminator_real_loss=1.45, discriminator_fake_loss=1.319, generator_loss=27.2, generator_mel_loss=17.81, generator_kl_loss=1.319, generator_dur_loss=1.801, generator_adv_loss=1.848, generator_feat_match_loss=4.423, over 80.00 samples.], tot_loss[discriminator_loss=2.734, discriminator_real_loss=1.382, discriminator_fake_loss=1.353, generator_loss=27.98, generator_mel_loss=17.88, generator_kl_loss=1.427, generator_dur_loss=1.743, generator_adv_loss=1.933, generator_feat_match_loss=4.993, over 1270.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:46:13,630 INFO [train.py:527] (1/6) Epoch 523, batch 72, global_batch_idx: 64800, batch size: 64, loss[discriminator_loss=2.753, discriminator_real_loss=1.43, discriminator_fake_loss=1.323, generator_loss=28.19, generator_mel_loss=17.9, generator_kl_loss=1.462, generator_dur_loss=1.782, generator_adv_loss=1.91, generator_feat_match_loss=5.134, over 64.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.378, discriminator_fake_loss=1.339, generator_loss=28.24, generator_mel_loss=17.99, generator_kl_loss=1.435, generator_dur_loss=1.748, generator_adv_loss=1.958, generator_feat_match_loss=5.11, over 4068.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:46:13,631 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 05:46:21,897 INFO [train.py:591] (1/6) Epoch 523, validation: discriminator_loss=2.77, discriminator_real_loss=1.417, discriminator_fake_loss=1.352, generator_loss=26.84, generator_mel_loss=18.17, generator_kl_loss=1.277, generator_dur_loss=1.817, generator_adv_loss=1.897, generator_feat_match_loss=3.677, over 100.00 samples.
+2024-03-14 05:46:21,897 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 05:48:40,689 INFO [train.py:527] (1/6) Epoch 523, batch 122, global_batch_idx: 64850, batch size: 42, loss[discriminator_loss=2.682, discriminator_real_loss=1.302, discriminator_fake_loss=1.38, generator_loss=28.76, generator_mel_loss=18.35, generator_kl_loss=1.623, generator_dur_loss=1.668, generator_adv_loss=2.065, generator_feat_match_loss=5.06, over 42.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=28.22, generator_mel_loss=17.99, generator_kl_loss=1.438, generator_dur_loss=1.74, generator_adv_loss=1.958, generator_feat_match_loss=5.091, over 6641.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:48:45,972 INFO [train.py:919] (1/6) Start epoch 524
+2024-03-14 05:51:24,141 INFO [train.py:527] (1/6) Epoch 524, batch 48, global_batch_idx: 64900, batch size: 42, loss[discriminator_loss=2.736, discriminator_real_loss=1.401, discriminator_fake_loss=1.336, generator_loss=28.62, generator_mel_loss=18.37, generator_kl_loss=1.605, generator_dur_loss=1.736, generator_adv_loss=1.969, generator_feat_match_loss=4.933, over 42.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.23, generator_mel_loss=17.94, generator_kl_loss=1.403, generator_dur_loss=1.752, generator_adv_loss=1.999, generator_feat_match_loss=5.141, over 2830.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:53:42,514 INFO [train.py:527] (1/6) Epoch 524, batch 98, global_batch_idx: 64950, batch size: 88, loss[discriminator_loss=2.718, discriminator_real_loss=1.25, discriminator_fake_loss=1.468, generator_loss=28.24, generator_mel_loss=17.94, generator_kl_loss=1.396, generator_dur_loss=1.833, generator_adv_loss=1.977, generator_feat_match_loss=5.103, over 88.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.363, discriminator_fake_loss=1.338, generator_loss=28.26, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.744, generator_adv_loss=1.978, generator_feat_match_loss=5.157, over 5610.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:54:51,145 INFO [train.py:919] (1/6) Start epoch 525
+2024-03-14 05:56:19,764 INFO [train.py:527] (1/6) Epoch 525, batch 24, global_batch_idx: 65000, batch size: 58, loss[discriminator_loss=2.632, discriminator_real_loss=1.328, discriminator_fake_loss=1.304, generator_loss=29.29, generator_mel_loss=18.37, generator_kl_loss=1.44, generator_dur_loss=1.721, generator_adv_loss=2.042, generator_feat_match_loss=5.712, over 58.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.3, generator_mel_loss=18.01, generator_kl_loss=1.408, generator_dur_loss=1.747, generator_adv_loss=1.962, generator_feat_match_loss=5.175, over 1418.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 05:56:19,766 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 05:56:27,761 INFO [train.py:591] (1/6) Epoch 525, validation: discriminator_loss=2.767, discriminator_real_loss=1.462, discriminator_fake_loss=1.305, generator_loss=27.39, generator_mel_loss=18.51, generator_kl_loss=1.305, generator_dur_loss=1.834, generator_adv_loss=1.915, generator_feat_match_loss=3.833, over 100.00 samples.
+2024-03-14 05:56:27,762 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 05:58:47,364 INFO [train.py:527] (1/6) Epoch 525, batch 74, global_batch_idx: 65050, batch size: 68, loss[discriminator_loss=2.684, discriminator_real_loss=1.243, discriminator_fake_loss=1.441, generator_loss=28.24, generator_mel_loss=18.21, generator_kl_loss=1.39, generator_dur_loss=1.784, generator_adv_loss=2.073, generator_feat_match_loss=4.783, over 68.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.27, generator_mel_loss=17.98, generator_kl_loss=1.408, generator_dur_loss=1.756, generator_adv_loss=1.962, generator_feat_match_loss=5.168, over 4251.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:01:06,663 INFO [train.py:919] (1/6) Start epoch 526
+2024-03-14 06:01:28,256 INFO [train.py:527] (1/6) Epoch 526, batch 0, global_batch_idx: 65100, batch size: 59, loss[discriminator_loss=2.724, discriminator_real_loss=1.365, discriminator_fake_loss=1.359, generator_loss=28.01, generator_mel_loss=17.77, generator_kl_loss=1.313, generator_dur_loss=1.743, generator_adv_loss=1.929, generator_feat_match_loss=5.251, over 59.00 samples.], tot_loss[discriminator_loss=2.724, discriminator_real_loss=1.365, discriminator_fake_loss=1.359, generator_loss=28.01, generator_mel_loss=17.77, generator_kl_loss=1.313, generator_dur_loss=1.743, generator_adv_loss=1.929, generator_feat_match_loss=5.251, over 59.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:03:46,276 INFO [train.py:527] (1/6) Epoch 526, batch 50, global_batch_idx: 65150, batch size: 70, loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=27.21, generator_mel_loss=17.43, generator_kl_loss=1.283, generator_dur_loss=1.807, generator_adv_loss=1.87, generator_feat_match_loss=4.811, over 70.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.373, discriminator_fake_loss=1.344, generator_loss=28.23, generator_mel_loss=18.02, generator_kl_loss=1.433, generator_dur_loss=1.747, generator_adv_loss=1.947, generator_feat_match_loss=5.079, over 2891.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:06:02,913 INFO [train.py:527] (1/6) Epoch 526, batch 100, global_batch_idx: 65200, batch size: 62, loss[discriminator_loss=2.682, discriminator_real_loss=1.389, discriminator_fake_loss=1.293, generator_loss=28.7, generator_mel_loss=18.5, generator_kl_loss=1.438, generator_dur_loss=1.719, generator_adv_loss=1.981, generator_feat_match_loss=5.062, over 62.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.376, discriminator_fake_loss=1.345, generator_loss=28.23, generator_mel_loss=18.01, generator_kl_loss=1.423, generator_dur_loss=1.752, generator_adv_loss=1.953, generator_feat_match_loss=5.093, over 5760.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:06:02,915 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 06:06:11,752 INFO [train.py:591] (1/6) Epoch 526, validation: discriminator_loss=2.717, discriminator_real_loss=1.44, discriminator_fake_loss=1.277, generator_loss=26.98, generator_mel_loss=18.07, generator_kl_loss=1.209, generator_dur_loss=1.835, generator_adv_loss=1.877, generator_feat_match_loss=3.989, over 100.00 samples.
+2024-03-14 06:06:11,753 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 06:07:16,827 INFO [train.py:919] (1/6) Start epoch 527
+2024-03-14 06:08:53,188 INFO [train.py:527] (1/6) Epoch 527, batch 26, global_batch_idx: 65250, batch size: 59, loss[discriminator_loss=2.734, discriminator_real_loss=1.375, discriminator_fake_loss=1.359, generator_loss=28.29, generator_mel_loss=18.43, generator_kl_loss=1.358, generator_dur_loss=1.753, generator_adv_loss=1.893, generator_feat_match_loss=4.863, over 59.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.366, discriminator_fake_loss=1.317, generator_loss=28.3, generator_mel_loss=17.92, generator_kl_loss=1.463, generator_dur_loss=1.762, generator_adv_loss=2.006, generator_feat_match_loss=5.144, over 1432.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:11:14,399 INFO [train.py:527] (1/6) Epoch 527, batch 76, global_batch_idx: 65300, batch size: 88, loss[discriminator_loss=2.659, discriminator_real_loss=1.291, discriminator_fake_loss=1.367, generator_loss=28.44, generator_mel_loss=17.68, generator_kl_loss=1.371, generator_dur_loss=1.814, generator_adv_loss=1.951, generator_feat_match_loss=5.629, over 88.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.365, discriminator_fake_loss=1.332, generator_loss=28.2, generator_mel_loss=17.9, generator_kl_loss=1.414, generator_dur_loss=1.779, generator_adv_loss=1.968, generator_feat_match_loss=5.131, over 4538.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:13:25,023 INFO [train.py:919] (1/6) Start epoch 528
+2024-03-14 06:13:54,233 INFO [train.py:527] (1/6) Epoch 528, batch 2, global_batch_idx: 65350, batch size: 56, loss[discriminator_loss=2.69, discriminator_real_loss=1.365, discriminator_fake_loss=1.325, generator_loss=28.43, generator_mel_loss=17.65, generator_kl_loss=1.505, generator_dur_loss=1.699, generator_adv_loss=1.946, generator_feat_match_loss=5.624, over 56.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.389, discriminator_fake_loss=1.332, generator_loss=28.18, generator_mel_loss=17.9, generator_kl_loss=1.424, generator_dur_loss=1.767, generator_adv_loss=1.969, generator_feat_match_loss=5.119, over 183.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:16:12,070 INFO [train.py:527] (1/6) Epoch 528, batch 52, global_batch_idx: 65400, batch size: 61, loss[discriminator_loss=2.662, discriminator_real_loss=1.316, discriminator_fake_loss=1.346, generator_loss=28.79, generator_mel_loss=18.44, generator_kl_loss=1.537, generator_dur_loss=1.721, generator_adv_loss=1.971, generator_feat_match_loss=5.121, over 61.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.23, generator_mel_loss=17.98, generator_kl_loss=1.417, generator_dur_loss=1.755, generator_adv_loss=1.958, generator_feat_match_loss=5.114, over 3038.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:16:12,071 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 06:16:20,251 INFO [train.py:591] (1/6) Epoch 528, validation: discriminator_loss=2.737, discriminator_real_loss=1.382, discriminator_fake_loss=1.355, generator_loss=28.12, generator_mel_loss=18.68, generator_kl_loss=1.231, generator_dur_loss=1.843, generator_adv_loss=1.888, generator_feat_match_loss=4.475, over 100.00 samples.
+2024-03-14 06:16:20,252 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 06:18:39,605 INFO [train.py:527] (1/6) Epoch 528, batch 102, global_batch_idx: 65450, batch size: 83, loss[discriminator_loss=2.721, discriminator_real_loss=1.348, discriminator_fake_loss=1.373, generator_loss=27.67, generator_mel_loss=17.74, generator_kl_loss=1.212, generator_dur_loss=1.88, generator_adv_loss=2.012, generator_feat_match_loss=4.831, over 83.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=28.2, generator_mel_loss=17.96, generator_kl_loss=1.398, generator_dur_loss=1.765, generator_adv_loss=1.955, generator_feat_match_loss=5.121, over 6119.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:19:39,518 INFO [train.py:919] (1/6) Start epoch 529
+2024-03-14 06:21:21,292 INFO [train.py:527] (1/6) Epoch 529, batch 28, global_batch_idx: 65500, batch size: 47, loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=29.57, generator_mel_loss=18.32, generator_kl_loss=1.456, generator_dur_loss=1.724, generator_adv_loss=2.05, generator_feat_match_loss=6.022, over 47.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.41, generator_mel_loss=17.99, generator_kl_loss=1.433, generator_dur_loss=1.742, generator_adv_loss=1.973, generator_feat_match_loss=5.271, over 1509.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:23:39,348 INFO [train.py:527] (1/6) Epoch 529, batch 78, global_batch_idx: 65550, batch size: 64, loss[discriminator_loss=2.736, discriminator_real_loss=1.462, discriminator_fake_loss=1.273, generator_loss=27.73, generator_mel_loss=17.91, generator_kl_loss=1.28, generator_dur_loss=1.776, generator_adv_loss=1.827, generator_feat_match_loss=4.937, over 64.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.366, discriminator_fake_loss=1.337, generator_loss=28.36, generator_mel_loss=18, generator_kl_loss=1.438, generator_dur_loss=1.745, generator_adv_loss=1.974, generator_feat_match_loss=5.209, over 4193.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:25:44,972 INFO [train.py:919] (1/6) Start epoch 530
+2024-03-14 06:26:19,633 INFO [train.py:527] (1/6) Epoch 530, batch 4, global_batch_idx: 65600, batch size: 25, loss[discriminator_loss=2.701, discriminator_real_loss=1.321, discriminator_fake_loss=1.38, generator_loss=28.93, generator_mel_loss=18.26, generator_kl_loss=1.78, generator_dur_loss=1.539, generator_adv_loss=1.991, generator_feat_match_loss=5.361, over 25.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.84, generator_mel_loss=18.13, generator_kl_loss=1.487, generator_dur_loss=1.72, generator_adv_loss=1.965, generator_feat_match_loss=5.537, over 214.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:26:19,636 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 06:26:27,498 INFO [train.py:591] (1/6) Epoch 530, validation: discriminator_loss=2.788, discriminator_real_loss=1.417, discriminator_fake_loss=1.371, generator_loss=27.05, generator_mel_loss=18.14, generator_kl_loss=1.191, generator_dur_loss=1.821, generator_adv_loss=1.847, generator_feat_match_loss=4.059, over 100.00 samples.
+2024-03-14 06:26:27,501 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 06:28:44,890 INFO [train.py:527] (1/6) Epoch 530, batch 54, global_batch_idx: 65650, batch size: 44, loss[discriminator_loss=2.555, discriminator_real_loss=1.241, discriminator_fake_loss=1.314, generator_loss=29.84, generator_mel_loss=18.65, generator_kl_loss=1.546, generator_dur_loss=1.706, generator_adv_loss=1.974, generator_feat_match_loss=5.961, over 44.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.37, discriminator_fake_loss=1.331, generator_loss=28.29, generator_mel_loss=17.99, generator_kl_loss=1.428, generator_dur_loss=1.75, generator_adv_loss=1.966, generator_feat_match_loss=5.157, over 3032.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:31:02,551 INFO [train.py:527] (1/6) Epoch 530, batch 104, global_batch_idx: 65700, batch size: 64, loss[discriminator_loss=2.714, discriminator_real_loss=1.3, discriminator_fake_loss=1.414, generator_loss=28.5, generator_mel_loss=18.14, generator_kl_loss=1.47, generator_dur_loss=1.744, generator_adv_loss=1.964, generator_feat_match_loss=5.18, over 64.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.367, discriminator_fake_loss=1.335, generator_loss=28.21, generator_mel_loss=17.94, generator_kl_loss=1.426, generator_dur_loss=1.747, generator_adv_loss=1.969, generator_feat_match_loss=5.125, over 5773.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:31:58,131 INFO [train.py:919] (1/6) Start epoch 531
+2024-03-14 06:33:43,982 INFO [train.py:527] (1/6) Epoch 531, batch 30, global_batch_idx: 65750, batch size: 83, loss[discriminator_loss=2.765, discriminator_real_loss=1.489, discriminator_fake_loss=1.276, generator_loss=26.7, generator_mel_loss=17.15, generator_kl_loss=1.268, generator_dur_loss=1.846, generator_adv_loss=1.87, generator_feat_match_loss=4.574, over 83.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.376, discriminator_fake_loss=1.337, generator_loss=28.16, generator_mel_loss=17.97, generator_kl_loss=1.395, generator_dur_loss=1.755, generator_adv_loss=1.947, generator_feat_match_loss=5.097, over 1682.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:36:02,399 INFO [train.py:527] (1/6) Epoch 531, batch 80, global_batch_idx: 65800, batch size: 56, loss[discriminator_loss=2.72, discriminator_real_loss=1.39, discriminator_fake_loss=1.33, generator_loss=27.57, generator_mel_loss=17.59, generator_kl_loss=1.431, generator_dur_loss=1.724, generator_adv_loss=1.993, generator_feat_match_loss=4.835, over 56.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.344, generator_loss=28.29, generator_mel_loss=18, generator_kl_loss=1.401, generator_dur_loss=1.76, generator_adv_loss=1.959, generator_feat_match_loss=5.168, over 4655.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:36:02,401 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 06:36:10,372 INFO [train.py:591] (1/6) Epoch 531, validation: discriminator_loss=2.722, discriminator_real_loss=1.419, discriminator_fake_loss=1.303, generator_loss=27.24, generator_mel_loss=18.22, generator_kl_loss=1.31, generator_dur_loss=1.819, generator_adv_loss=1.926, generator_feat_match_loss=3.968, over 100.00 samples.
+2024-03-14 06:36:10,373 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 06:38:11,314 INFO [train.py:919] (1/6) Start epoch 532
+2024-03-14 06:38:51,759 INFO [train.py:527] (1/6) Epoch 532, batch 6, global_batch_idx: 65850, batch size: 31, loss[discriminator_loss=2.705, discriminator_real_loss=1.514, discriminator_fake_loss=1.19, generator_loss=28.45, generator_mel_loss=18.18, generator_kl_loss=1.557, generator_dur_loss=1.663, generator_adv_loss=1.892, generator_feat_match_loss=5.15, over 31.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.36, discriminator_fake_loss=1.337, generator_loss=28.48, generator_mel_loss=18.05, generator_kl_loss=1.579, generator_dur_loss=1.712, generator_adv_loss=2.008, generator_feat_match_loss=5.128, over 309.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:41:13,504 INFO [train.py:527] (1/6) Epoch 532, batch 56, global_batch_idx: 65900, batch size: 96, loss[discriminator_loss=2.709, discriminator_real_loss=1.379, discriminator_fake_loss=1.33, generator_loss=27.35, generator_mel_loss=17.69, generator_kl_loss=1.221, generator_dur_loss=1.838, generator_adv_loss=1.904, generator_feat_match_loss=4.694, over 96.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.362, discriminator_fake_loss=1.347, generator_loss=28.32, generator_mel_loss=18.02, generator_kl_loss=1.443, generator_dur_loss=1.75, generator_adv_loss=1.961, generator_feat_match_loss=5.147, over 3256.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:43:36,968 INFO [train.py:527] (1/6) Epoch 532, batch 106, global_batch_idx: 65950, batch size: 58, loss[discriminator_loss=2.739, discriminator_real_loss=1.463, discriminator_fake_loss=1.276, generator_loss=26.69, generator_mel_loss=17.38, generator_kl_loss=1.334, generator_dur_loss=1.747, generator_adv_loss=1.897, generator_feat_match_loss=4.33, over 58.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.21, generator_mel_loss=17.97, generator_kl_loss=1.428, generator_dur_loss=1.746, generator_adv_loss=1.953, generator_feat_match_loss=5.108, over 6043.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:44:26,196 INFO [train.py:919] (1/6) Start epoch 533
+2024-03-14 06:46:21,459 INFO [train.py:527] (1/6) Epoch 533, batch 32, global_batch_idx: 66000, batch size: 77, loss[discriminator_loss=2.762, discriminator_real_loss=1.309, discriminator_fake_loss=1.453, generator_loss=28.05, generator_mel_loss=17.88, generator_kl_loss=1.291, generator_dur_loss=1.816, generator_adv_loss=2.003, generator_feat_match_loss=5.055, over 77.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=28.26, generator_mel_loss=18.09, generator_kl_loss=1.415, generator_dur_loss=1.747, generator_adv_loss=1.966, generator_feat_match_loss=5.046, over 1810.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:46:21,460 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 06:46:29,365 INFO [train.py:591] (1/6) Epoch 533, validation: discriminator_loss=2.755, discriminator_real_loss=1.419, discriminator_fake_loss=1.335, generator_loss=27, generator_mel_loss=18.14, generator_kl_loss=1.185, generator_dur_loss=1.804, generator_adv_loss=1.902, generator_feat_match_loss=3.976, over 100.00 samples.
+2024-03-14 06:46:29,366 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 06:48:50,792 INFO [train.py:527] (1/6) Epoch 533, batch 82, global_batch_idx: 66050, batch size: 45, loss[discriminator_loss=2.674, discriminator_real_loss=1.357, discriminator_fake_loss=1.317, generator_loss=28.27, generator_mel_loss=17.76, generator_kl_loss=1.56, generator_dur_loss=1.628, generator_adv_loss=1.94, generator_feat_match_loss=5.384, over 45.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.374, discriminator_fake_loss=1.34, generator_loss=28.28, generator_mel_loss=18.03, generator_kl_loss=1.413, generator_dur_loss=1.754, generator_adv_loss=1.968, generator_feat_match_loss=5.116, over 4695.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:50:46,997 INFO [train.py:919] (1/6) Start epoch 534
+2024-03-14 06:51:32,895 INFO [train.py:527] (1/6) Epoch 534, batch 8, global_batch_idx: 66100, batch size: 53, loss[discriminator_loss=2.741, discriminator_real_loss=1.405, discriminator_fake_loss=1.337, generator_loss=28.27, generator_mel_loss=17.88, generator_kl_loss=1.453, generator_dur_loss=1.641, generator_adv_loss=2.002, generator_feat_match_loss=5.291, over 53.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.421, discriminator_fake_loss=1.306, generator_loss=28, generator_mel_loss=17.81, generator_kl_loss=1.437, generator_dur_loss=1.696, generator_adv_loss=2.033, generator_feat_match_loss=5.023, over 437.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:53:53,927 INFO [train.py:527] (1/6) Epoch 534, batch 58, global_batch_idx: 66150, batch size: 55, loss[discriminator_loss=2.705, discriminator_real_loss=1.468, discriminator_fake_loss=1.237, generator_loss=28.82, generator_mel_loss=17.98, generator_kl_loss=1.509, generator_dur_loss=1.699, generator_adv_loss=1.919, generator_feat_match_loss=5.709, over 55.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.38, discriminator_fake_loss=1.333, generator_loss=28.26, generator_mel_loss=17.98, generator_kl_loss=1.416, generator_dur_loss=1.744, generator_adv_loss=1.97, generator_feat_match_loss=5.156, over 3352.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:56:16,877 INFO [train.py:527] (1/6) Epoch 534, batch 108, global_batch_idx: 66200, batch size: 83, loss[discriminator_loss=2.708, discriminator_real_loss=1.42, discriminator_fake_loss=1.288, generator_loss=27.22, generator_mel_loss=17.57, generator_kl_loss=1.232, generator_dur_loss=1.801, generator_adv_loss=2.09, generator_feat_match_loss=4.527, over 83.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.382, discriminator_fake_loss=1.334, generator_loss=28.29, generator_mel_loss=18, generator_kl_loss=1.419, generator_dur_loss=1.74, generator_adv_loss=1.966, generator_feat_match_loss=5.162, over 6048.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 06:56:16,879 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 06:56:25,597 INFO [train.py:591] (1/6) Epoch 534, validation: discriminator_loss=2.697, discriminator_real_loss=1.422, discriminator_fake_loss=1.275, generator_loss=27.49, generator_mel_loss=18.53, generator_kl_loss=1.262, generator_dur_loss=1.822, generator_adv_loss=1.92, generator_feat_match_loss=3.952, over 100.00 samples.
+2024-03-14 06:56:25,597 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 06:57:07,689 INFO [train.py:919] (1/6) Start epoch 535
+2024-03-14 06:59:13,011 INFO [train.py:527] (1/6) Epoch 535, batch 34, global_batch_idx: 66250, batch size: 88, loss[discriminator_loss=2.674, discriminator_real_loss=1.29, discriminator_fake_loss=1.384, generator_loss=29.02, generator_mel_loss=17.8, generator_kl_loss=1.38, generator_dur_loss=1.825, generator_adv_loss=2.135, generator_feat_match_loss=5.88, over 88.00 samples.], tot_loss[discriminator_loss=2.726, discriminator_real_loss=1.374, discriminator_fake_loss=1.353, generator_loss=28.15, generator_mel_loss=17.93, generator_kl_loss=1.429, generator_dur_loss=1.753, generator_adv_loss=1.953, generator_feat_match_loss=5.084, over 2081.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:01:34,816 INFO [train.py:527] (1/6) Epoch 535, batch 84, global_batch_idx: 66300, batch size: 96, loss[discriminator_loss=2.61, discriminator_real_loss=1.291, discriminator_fake_loss=1.319, generator_loss=29.53, generator_mel_loss=18.25, generator_kl_loss=1.262, generator_dur_loss=1.858, generator_adv_loss=2.236, generator_feat_match_loss=5.918, over 96.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.366, discriminator_fake_loss=1.347, generator_loss=28.21, generator_mel_loss=17.94, generator_kl_loss=1.41, generator_dur_loss=1.759, generator_adv_loss=1.977, generator_feat_match_loss=5.12, over 4989.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:03:22,452 INFO [train.py:919] (1/6) Start epoch 536
+2024-03-14 07:04:14,329 INFO [train.py:527] (1/6) Epoch 536, batch 10, global_batch_idx: 66350, batch size: 44, loss[discriminator_loss=2.677, discriminator_real_loss=1.408, discriminator_fake_loss=1.269, generator_loss=28.37, generator_mel_loss=17.99, generator_kl_loss=1.715, generator_dur_loss=1.624, generator_adv_loss=1.844, generator_feat_match_loss=5.193, over 44.00 samples.], tot_loss[discriminator_loss=2.751, discriminator_real_loss=1.415, discriminator_fake_loss=1.336, generator_loss=28.13, generator_mel_loss=17.99, generator_kl_loss=1.412, generator_dur_loss=1.733, generator_adv_loss=1.941, generator_feat_match_loss=5.058, over 592.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:06:35,695 INFO [train.py:527] (1/6) Epoch 536, batch 60, global_batch_idx: 66400, batch size: 80, loss[discriminator_loss=2.703, discriminator_real_loss=1.319, discriminator_fake_loss=1.384, generator_loss=28.59, generator_mel_loss=18.12, generator_kl_loss=1.41, generator_dur_loss=1.787, generator_adv_loss=2.011, generator_feat_match_loss=5.262, over 80.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.346, generator_loss=28.03, generator_mel_loss=17.87, generator_kl_loss=1.437, generator_dur_loss=1.731, generator_adv_loss=1.938, generator_feat_match_loss=5.05, over 3513.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:06:35,696 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 07:06:43,888 INFO [train.py:591] (1/6) Epoch 536, validation: discriminator_loss=2.741, discriminator_real_loss=1.502, discriminator_fake_loss=1.239, generator_loss=27.44, generator_mel_loss=18.3, generator_kl_loss=1.335, generator_dur_loss=1.789, generator_adv_loss=2.005, generator_feat_match_loss=4.017, over 100.00 samples.
+2024-03-14 07:06:43,889 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 07:09:04,940 INFO [train.py:527] (1/6) Epoch 536, batch 110, global_batch_idx: 66450, batch size: 66, loss[discriminator_loss=2.714, discriminator_real_loss=1.359, discriminator_fake_loss=1.355, generator_loss=27.84, generator_mel_loss=17.82, generator_kl_loss=1.555, generator_dur_loss=1.786, generator_adv_loss=1.8, generator_feat_match_loss=4.875, over 66.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.16, generator_mel_loss=17.95, generator_kl_loss=1.441, generator_dur_loss=1.73, generator_adv_loss=1.941, generator_feat_match_loss=5.099, over 6239.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:09:43,141 INFO [train.py:919] (1/6) Start epoch 537
+2024-03-14 07:11:48,569 INFO [train.py:527] (1/6) Epoch 537, batch 36, global_batch_idx: 66500, batch size: 48, loss[discriminator_loss=2.732, discriminator_real_loss=1.448, discriminator_fake_loss=1.285, generator_loss=28.3, generator_mel_loss=18.15, generator_kl_loss=1.655, generator_dur_loss=1.644, generator_adv_loss=1.78, generator_feat_match_loss=5.069, over 48.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=28.12, generator_mel_loss=17.91, generator_kl_loss=1.398, generator_dur_loss=1.758, generator_adv_loss=1.95, generator_feat_match_loss=5.104, over 2158.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:14:06,650 INFO [train.py:527] (1/6) Epoch 537, batch 86, global_batch_idx: 66550, batch size: 74, loss[discriminator_loss=2.795, discriminator_real_loss=1.333, discriminator_fake_loss=1.462, generator_loss=28.12, generator_mel_loss=17.99, generator_kl_loss=1.415, generator_dur_loss=1.827, generator_adv_loss=2.044, generator_feat_match_loss=4.845, over 74.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.25, generator_mel_loss=18, generator_kl_loss=1.421, generator_dur_loss=1.754, generator_adv_loss=1.948, generator_feat_match_loss=5.123, over 4871.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:15:54,042 INFO [train.py:919] (1/6) Start epoch 538
+2024-03-14 07:16:52,466 INFO [train.py:527] (1/6) Epoch 538, batch 12, global_batch_idx: 66600, batch size: 31, loss[discriminator_loss=2.715, discriminator_real_loss=1.449, discriminator_fake_loss=1.265, generator_loss=28.96, generator_mel_loss=18.52, generator_kl_loss=1.607, generator_dur_loss=1.594, generator_adv_loss=1.89, generator_feat_match_loss=5.348, over 31.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.391, discriminator_fake_loss=1.339, generator_loss=28.12, generator_mel_loss=18.08, generator_kl_loss=1.38, generator_dur_loss=1.73, generator_adv_loss=1.963, generator_feat_match_loss=4.969, over 707.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:16:52,469 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 07:17:00,243 INFO [train.py:591] (1/6) Epoch 538, validation: discriminator_loss=2.742, discriminator_real_loss=1.367, discriminator_fake_loss=1.375, generator_loss=27.95, generator_mel_loss=18.66, generator_kl_loss=1.243, generator_dur_loss=1.795, generator_adv_loss=1.757, generator_feat_match_loss=4.496, over 100.00 samples.
+2024-03-14 07:17:00,243 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 07:19:21,584 INFO [train.py:527] (1/6) Epoch 538, batch 62, global_batch_idx: 66650, batch size: 31, loss[discriminator_loss=2.711, discriminator_real_loss=1.319, discriminator_fake_loss=1.392, generator_loss=29.44, generator_mel_loss=18.55, generator_kl_loss=1.597, generator_dur_loss=1.656, generator_adv_loss=2.03, generator_feat_match_loss=5.599, over 31.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.375, discriminator_fake_loss=1.341, generator_loss=28.17, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.708, generator_adv_loss=1.968, generator_feat_match_loss=5.079, over 3394.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:21:42,536 INFO [train.py:527] (1/6) Epoch 538, batch 112, global_batch_idx: 66700, batch size: 25, loss[discriminator_loss=2.726, discriminator_real_loss=1.393, discriminator_fake_loss=1.333, generator_loss=29.54, generator_mel_loss=18.23, generator_kl_loss=1.596, generator_dur_loss=1.548, generator_adv_loss=2.147, generator_feat_match_loss=6.02, over 25.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=28.22, generator_mel_loss=18.01, generator_kl_loss=1.41, generator_dur_loss=1.725, generator_adv_loss=1.959, generator_feat_match_loss=5.114, over 6471.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:22:15,554 INFO [train.py:919] (1/6) Start epoch 539
+2024-03-14 07:24:30,627 INFO [train.py:527] (1/6) Epoch 539, batch 38, global_batch_idx: 66750, batch size: 96, loss[discriminator_loss=2.673, discriminator_real_loss=1.383, discriminator_fake_loss=1.29, generator_loss=27.4, generator_mel_loss=17.36, generator_kl_loss=1.345, generator_dur_loss=1.887, generator_adv_loss=1.893, generator_feat_match_loss=4.919, over 96.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.371, discriminator_fake_loss=1.347, generator_loss=28.23, generator_mel_loss=17.96, generator_kl_loss=1.408, generator_dur_loss=1.759, generator_adv_loss=1.953, generator_feat_match_loss=5.143, over 2343.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:26:51,894 INFO [train.py:527] (1/6) Epoch 539, batch 88, global_batch_idx: 66800, batch size: 48, loss[discriminator_loss=2.704, discriminator_real_loss=1.382, discriminator_fake_loss=1.322, generator_loss=28.56, generator_mel_loss=17.7, generator_kl_loss=1.587, generator_dur_loss=1.663, generator_adv_loss=2.051, generator_feat_match_loss=5.56, over 48.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.3, generator_mel_loss=17.99, generator_kl_loss=1.43, generator_dur_loss=1.74, generator_adv_loss=1.964, generator_feat_match_loss=5.174, over 5083.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:26:51,895 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 07:27:00,804 INFO [train.py:591] (1/6) Epoch 539, validation: discriminator_loss=2.707, discriminator_real_loss=1.441, discriminator_fake_loss=1.266, generator_loss=27.6, generator_mel_loss=18.11, generator_kl_loss=1.223, generator_dur_loss=1.811, generator_adv_loss=1.981, generator_feat_match_loss=4.481, over 100.00 samples.
+2024-03-14 07:27:00,805 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 07:28:39,549 INFO [train.py:919] (1/6) Start epoch 540
+2024-03-14 07:29:44,184 INFO [train.py:527] (1/6) Epoch 540, batch 14, global_batch_idx: 66850, batch size: 70, loss[discriminator_loss=2.747, discriminator_real_loss=1.433, discriminator_fake_loss=1.314, generator_loss=28.13, generator_mel_loss=17.76, generator_kl_loss=1.418, generator_dur_loss=1.781, generator_adv_loss=1.804, generator_feat_match_loss=5.366, over 70.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=28.55, generator_mel_loss=18.07, generator_kl_loss=1.418, generator_dur_loss=1.779, generator_adv_loss=1.95, generator_feat_match_loss=5.333, over 907.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:32:07,845 INFO [train.py:527] (1/6) Epoch 540, batch 64, global_batch_idx: 66900, batch size: 80, loss[discriminator_loss=2.702, discriminator_real_loss=1.358, discriminator_fake_loss=1.344, generator_loss=28.4, generator_mel_loss=18.21, generator_kl_loss=1.233, generator_dur_loss=1.831, generator_adv_loss=2.008, generator_feat_match_loss=5.121, over 80.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.361, discriminator_fake_loss=1.345, generator_loss=28.41, generator_mel_loss=18.03, generator_kl_loss=1.389, generator_dur_loss=1.771, generator_adv_loss=1.954, generator_feat_match_loss=5.263, over 3931.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:34:28,295 INFO [train.py:527] (1/6) Epoch 540, batch 114, global_batch_idx: 66950, batch size: 70, loss[discriminator_loss=2.671, discriminator_real_loss=1.327, discriminator_fake_loss=1.344, generator_loss=28.47, generator_mel_loss=18.02, generator_kl_loss=1.47, generator_dur_loss=1.794, generator_adv_loss=1.863, generator_feat_match_loss=5.315, over 70.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.364, discriminator_fake_loss=1.339, generator_loss=28.38, generator_mel_loss=18.04, generator_kl_loss=1.41, generator_dur_loss=1.763, generator_adv_loss=1.962, generator_feat_match_loss=5.209, over 6777.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:34:52,655 INFO [train.py:919] (1/6) Start epoch 541
+2024-03-14 07:37:09,275 INFO [train.py:527] (1/6) Epoch 541, batch 40, global_batch_idx: 67000, batch size: 72, loss[discriminator_loss=2.701, discriminator_real_loss=1.373, discriminator_fake_loss=1.329, generator_loss=27.62, generator_mel_loss=17.7, generator_kl_loss=1.358, generator_dur_loss=1.751, generator_adv_loss=1.973, generator_feat_match_loss=4.841, over 72.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.32, generator_mel_loss=18.03, generator_kl_loss=1.392, generator_dur_loss=1.739, generator_adv_loss=1.962, generator_feat_match_loss=5.194, over 2401.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:37:09,276 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 07:37:17,459 INFO [train.py:591] (1/6) Epoch 541, validation: discriminator_loss=2.687, discriminator_real_loss=1.429, discriminator_fake_loss=1.258, generator_loss=26.9, generator_mel_loss=18, generator_kl_loss=1.238, generator_dur_loss=1.771, generator_adv_loss=1.944, generator_feat_match_loss=3.941, over 100.00 samples.
+2024-03-14 07:37:17,460 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 07:39:39,259 INFO [train.py:527] (1/6) Epoch 541, batch 90, global_batch_idx: 67050, batch size: 59, loss[discriminator_loss=2.777, discriminator_real_loss=1.428, discriminator_fake_loss=1.349, generator_loss=27.11, generator_mel_loss=17.75, generator_kl_loss=1.496, generator_dur_loss=1.738, generator_adv_loss=1.908, generator_feat_match_loss=4.225, over 59.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=28.22, generator_mel_loss=17.97, generator_kl_loss=1.409, generator_dur_loss=1.731, generator_adv_loss=1.958, generator_feat_match_loss=5.143, over 5394.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:41:13,469 INFO [train.py:919] (1/6) Start epoch 542
+2024-03-14 07:42:24,482 INFO [train.py:527] (1/6) Epoch 542, batch 16, global_batch_idx: 67100, batch size: 61, loss[discriminator_loss=2.721, discriminator_real_loss=1.321, discriminator_fake_loss=1.4, generator_loss=28.11, generator_mel_loss=18.15, generator_kl_loss=1.426, generator_dur_loss=1.702, generator_adv_loss=2.059, generator_feat_match_loss=4.772, over 61.00 samples.], tot_loss[discriminator_loss=2.721, discriminator_real_loss=1.385, discriminator_fake_loss=1.336, generator_loss=28.03, generator_mel_loss=17.91, generator_kl_loss=1.407, generator_dur_loss=1.723, generator_adv_loss=1.962, generator_feat_match_loss=5.026, over 931.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:44:46,003 INFO [train.py:527] (1/6) Epoch 542, batch 66, global_batch_idx: 67150, batch size: 88, loss[discriminator_loss=2.728, discriminator_real_loss=1.354, discriminator_fake_loss=1.374, generator_loss=28.41, generator_mel_loss=17.87, generator_kl_loss=1.302, generator_dur_loss=1.815, generator_adv_loss=2.044, generator_feat_match_loss=5.381, over 88.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.376, discriminator_fake_loss=1.341, generator_loss=28.22, generator_mel_loss=17.99, generator_kl_loss=1.424, generator_dur_loss=1.727, generator_adv_loss=1.96, generator_feat_match_loss=5.116, over 3734.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:47:07,912 INFO [train.py:527] (1/6) Epoch 542, batch 116, global_batch_idx: 67200, batch size: 68, loss[discriminator_loss=2.743, discriminator_real_loss=1.348, discriminator_fake_loss=1.395, generator_loss=28.46, generator_mel_loss=18.06, generator_kl_loss=1.491, generator_dur_loss=1.702, generator_adv_loss=1.972, generator_feat_match_loss=5.234, over 68.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.3, generator_mel_loss=17.99, generator_kl_loss=1.432, generator_dur_loss=1.728, generator_adv_loss=1.968, generator_feat_match_loss=5.188, over 6522.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:47:07,914 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 07:47:16,876 INFO [train.py:591] (1/6) Epoch 542, validation: discriminator_loss=2.756, discriminator_real_loss=1.447, discriminator_fake_loss=1.308, generator_loss=26.58, generator_mel_loss=17.74, generator_kl_loss=1.208, generator_dur_loss=1.803, generator_adv_loss=1.908, generator_feat_match_loss=3.927, over 100.00 samples.
+2024-03-14 07:47:16,877 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 07:47:39,165 INFO [train.py:919] (1/6) Start epoch 543
+2024-03-14 07:50:02,760 INFO [train.py:527] (1/6) Epoch 543, batch 42, global_batch_idx: 67250, batch size: 53, loss[discriminator_loss=2.721, discriminator_real_loss=1.417, discriminator_fake_loss=1.304, generator_loss=27.67, generator_mel_loss=18.02, generator_kl_loss=1.426, generator_dur_loss=1.695, generator_adv_loss=2.012, generator_feat_match_loss=4.519, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.359, discriminator_fake_loss=1.354, generator_loss=28.29, generator_mel_loss=17.98, generator_kl_loss=1.427, generator_dur_loss=1.75, generator_adv_loss=1.95, generator_feat_match_loss=5.175, over 2542.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:52:24,818 INFO [train.py:527] (1/6) Epoch 543, batch 92, global_batch_idx: 67300, batch size: 13, loss[discriminator_loss=2.69, discriminator_real_loss=1.348, discriminator_fake_loss=1.342, generator_loss=31.3, generator_mel_loss=19.11, generator_kl_loss=1.825, generator_dur_loss=1.552, generator_adv_loss=2.163, generator_feat_match_loss=6.646, over 13.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.364, discriminator_fake_loss=1.348, generator_loss=28.25, generator_mel_loss=17.98, generator_kl_loss=1.424, generator_dur_loss=1.753, generator_adv_loss=1.949, generator_feat_match_loss=5.14, over 5520.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:53:51,034 INFO [train.py:919] (1/6) Start epoch 544
+2024-03-14 07:55:06,218 INFO [train.py:527] (1/6) Epoch 544, batch 18, global_batch_idx: 67350, batch size: 55, loss[discriminator_loss=2.638, discriminator_real_loss=1.265, discriminator_fake_loss=1.373, generator_loss=29.51, generator_mel_loss=18.45, generator_kl_loss=1.42, generator_dur_loss=1.718, generator_adv_loss=2.099, generator_feat_match_loss=5.828, over 55.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.38, generator_mel_loss=18.05, generator_kl_loss=1.451, generator_dur_loss=1.745, generator_adv_loss=1.986, generator_feat_match_loss=5.149, over 986.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:57:29,418 INFO [train.py:527] (1/6) Epoch 544, batch 68, global_batch_idx: 67400, batch size: 62, loss[discriminator_loss=2.683, discriminator_real_loss=1.314, discriminator_fake_loss=1.369, generator_loss=28.03, generator_mel_loss=17.72, generator_kl_loss=1.444, generator_dur_loss=1.752, generator_adv_loss=2.044, generator_feat_match_loss=5.075, over 62.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.373, discriminator_fake_loss=1.333, generator_loss=28.33, generator_mel_loss=17.99, generator_kl_loss=1.427, generator_dur_loss=1.754, generator_adv_loss=1.986, generator_feat_match_loss=5.172, over 3976.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 07:57:29,419 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 07:57:37,699 INFO [train.py:591] (1/6) Epoch 544, validation: discriminator_loss=2.705, discriminator_real_loss=1.46, discriminator_fake_loss=1.245, generator_loss=26.94, generator_mel_loss=18.07, generator_kl_loss=1.162, generator_dur_loss=1.826, generator_adv_loss=1.986, generator_feat_match_loss=3.896, over 100.00 samples.
+2024-03-14 07:57:37,700 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 07:59:57,096 INFO [train.py:527] (1/6) Epoch 544, batch 118, global_batch_idx: 67450, batch size: 45, loss[discriminator_loss=2.685, discriminator_real_loss=1.373, discriminator_fake_loss=1.312, generator_loss=28.87, generator_mel_loss=17.96, generator_kl_loss=1.633, generator_dur_loss=1.727, generator_adv_loss=1.99, generator_feat_match_loss=5.555, over 45.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.375, discriminator_fake_loss=1.331, generator_loss=28.33, generator_mel_loss=17.97, generator_kl_loss=1.427, generator_dur_loss=1.76, generator_adv_loss=1.98, generator_feat_match_loss=5.19, over 6791.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:00:12,456 INFO [train.py:919] (1/6) Start epoch 545
+2024-03-14 08:02:42,017 INFO [train.py:527] (1/6) Epoch 545, batch 44, global_batch_idx: 67500, batch size: 74, loss[discriminator_loss=2.636, discriminator_real_loss=1.322, discriminator_fake_loss=1.314, generator_loss=27.61, generator_mel_loss=17.94, generator_kl_loss=1.283, generator_dur_loss=1.775, generator_adv_loss=1.85, generator_feat_match_loss=4.765, over 74.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.366, discriminator_fake_loss=1.349, generator_loss=28.24, generator_mel_loss=18, generator_kl_loss=1.452, generator_dur_loss=1.731, generator_adv_loss=1.952, generator_feat_match_loss=5.102, over 2417.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:05:03,501 INFO [train.py:527] (1/6) Epoch 545, batch 94, global_batch_idx: 67550, batch size: 36, loss[discriminator_loss=2.657, discriminator_real_loss=1.309, discriminator_fake_loss=1.348, generator_loss=29.67, generator_mel_loss=18.61, generator_kl_loss=1.565, generator_dur_loss=1.697, generator_adv_loss=2.045, generator_feat_match_loss=5.755, over 36.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.367, discriminator_fake_loss=1.346, generator_loss=28.25, generator_mel_loss=17.97, generator_kl_loss=1.457, generator_dur_loss=1.729, generator_adv_loss=1.955, generator_feat_match_loss=5.141, over 5139.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:06:25,874 INFO [train.py:919] (1/6) Start epoch 546
+2024-03-14 08:07:45,704 INFO [train.py:527] (1/6) Epoch 546, batch 20, global_batch_idx: 67600, batch size: 64, loss[discriminator_loss=2.757, discriminator_real_loss=1.371, discriminator_fake_loss=1.386, generator_loss=28.1, generator_mel_loss=17.98, generator_kl_loss=1.291, generator_dur_loss=1.761, generator_adv_loss=1.948, generator_feat_match_loss=5.116, over 64.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.392, discriminator_fake_loss=1.337, generator_loss=28.26, generator_mel_loss=18, generator_kl_loss=1.414, generator_dur_loss=1.748, generator_adv_loss=1.963, generator_feat_match_loss=5.132, over 1166.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:07:45,706 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 08:07:53,538 INFO [train.py:591] (1/6) Epoch 546, validation: discriminator_loss=2.74, discriminator_real_loss=1.402, discriminator_fake_loss=1.338, generator_loss=27.3, generator_mel_loss=18.33, generator_kl_loss=1.175, generator_dur_loss=1.816, generator_adv_loss=1.849, generator_feat_match_loss=4.129, over 100.00 samples.
+2024-03-14 08:07:53,539 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 08:10:17,411 INFO [train.py:527] (1/6) Epoch 546, batch 70, global_batch_idx: 67650, batch size: 96, loss[discriminator_loss=2.66, discriminator_real_loss=1.293, discriminator_fake_loss=1.367, generator_loss=27.69, generator_mel_loss=17.46, generator_kl_loss=1.271, generator_dur_loss=1.873, generator_adv_loss=2.141, generator_feat_match_loss=4.939, over 96.00 samples.], tot_loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.343, generator_loss=28.18, generator_mel_loss=17.91, generator_kl_loss=1.392, generator_dur_loss=1.772, generator_adv_loss=1.954, generator_feat_match_loss=5.154, over 4319.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:12:33,667 INFO [train.py:527] (1/6) Epoch 546, batch 120, global_batch_idx: 67700, batch size: 74, loss[discriminator_loss=2.708, discriminator_real_loss=1.404, discriminator_fake_loss=1.305, generator_loss=29.04, generator_mel_loss=18.01, generator_kl_loss=1.503, generator_dur_loss=1.787, generator_adv_loss=1.92, generator_feat_match_loss=5.821, over 74.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.377, discriminator_fake_loss=1.345, generator_loss=28.24, generator_mel_loss=17.95, generator_kl_loss=1.4, generator_dur_loss=1.765, generator_adv_loss=1.952, generator_feat_match_loss=5.177, over 7151.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:12:44,258 INFO [train.py:919] (1/6) Start epoch 547
+2024-03-14 08:15:20,878 INFO [train.py:527] (1/6) Epoch 547, batch 46, global_batch_idx: 67750, batch size: 96, loss[discriminator_loss=2.7, discriminator_real_loss=1.324, discriminator_fake_loss=1.376, generator_loss=28.31, generator_mel_loss=17.79, generator_kl_loss=1.456, generator_dur_loss=1.816, generator_adv_loss=1.98, generator_feat_match_loss=5.264, over 96.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.338, generator_loss=28.43, generator_mel_loss=18.07, generator_kl_loss=1.444, generator_dur_loss=1.728, generator_adv_loss=1.972, generator_feat_match_loss=5.217, over 2486.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:17:44,019 INFO [train.py:527] (1/6) Epoch 547, batch 96, global_batch_idx: 67800, batch size: 68, loss[discriminator_loss=2.669, discriminator_real_loss=1.286, discriminator_fake_loss=1.383, generator_loss=27.93, generator_mel_loss=17.82, generator_kl_loss=1.497, generator_dur_loss=1.742, generator_adv_loss=1.945, generator_feat_match_loss=4.921, over 68.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.339, generator_loss=28.31, generator_mel_loss=17.99, generator_kl_loss=1.437, generator_dur_loss=1.733, generator_adv_loss=1.96, generator_feat_match_loss=5.187, over 5283.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:17:44,021 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 08:17:52,788 INFO [train.py:591] (1/6) Epoch 547, validation: discriminator_loss=2.694, discriminator_real_loss=1.453, discriminator_fake_loss=1.242, generator_loss=27.37, generator_mel_loss=18.18, generator_kl_loss=1.259, generator_dur_loss=1.805, generator_adv_loss=1.966, generator_feat_match_loss=4.164, over 100.00 samples.
+2024-03-14 08:17:52,789 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 08:19:07,065 INFO [train.py:919] (1/6) Start epoch 548
+2024-03-14 08:20:34,389 INFO [train.py:527] (1/6) Epoch 548, batch 22, global_batch_idx: 67850, batch size: 61, loss[discriminator_loss=2.71, discriminator_real_loss=1.368, discriminator_fake_loss=1.342, generator_loss=28.39, generator_mel_loss=17.81, generator_kl_loss=1.543, generator_dur_loss=1.706, generator_adv_loss=1.952, generator_feat_match_loss=5.374, over 61.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.37, discriminator_fake_loss=1.329, generator_loss=28.53, generator_mel_loss=18.02, generator_kl_loss=1.437, generator_dur_loss=1.72, generator_adv_loss=1.982, generator_feat_match_loss=5.371, over 1338.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:22:55,284 INFO [train.py:527] (1/6) Epoch 548, batch 72, global_batch_idx: 67900, batch size: 64, loss[discriminator_loss=2.705, discriminator_real_loss=1.265, discriminator_fake_loss=1.439, generator_loss=28.92, generator_mel_loss=18.01, generator_kl_loss=1.451, generator_dur_loss=1.71, generator_adv_loss=1.967, generator_feat_match_loss=5.777, over 64.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.367, discriminator_fake_loss=1.339, generator_loss=28.4, generator_mel_loss=18.02, generator_kl_loss=1.449, generator_dur_loss=1.714, generator_adv_loss=1.958, generator_feat_match_loss=5.261, over 4058.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:25:18,884 INFO [train.py:527] (1/6) Epoch 548, batch 122, global_batch_idx: 67950, batch size: 25, loss[discriminator_loss=2.741, discriminator_real_loss=1.448, discriminator_fake_loss=1.293, generator_loss=28.19, generator_mel_loss=18.07, generator_kl_loss=1.826, generator_dur_loss=1.549, generator_adv_loss=1.992, generator_feat_match_loss=4.752, over 25.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=28.37, generator_mel_loss=18, generator_kl_loss=1.443, generator_dur_loss=1.712, generator_adv_loss=1.959, generator_feat_match_loss=5.254, over 6784.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:25:23,126 INFO [train.py:919] (1/6) Start epoch 549
+2024-03-14 08:28:01,907 INFO [train.py:527] (1/6) Epoch 549, batch 48, global_batch_idx: 68000, batch size: 25, loss[discriminator_loss=2.68, discriminator_real_loss=1.4, discriminator_fake_loss=1.28, generator_loss=28.5, generator_mel_loss=18.26, generator_kl_loss=1.603, generator_dur_loss=1.537, generator_adv_loss=1.892, generator_feat_match_loss=5.203, over 25.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.379, discriminator_fake_loss=1.338, generator_loss=28.22, generator_mel_loss=17.98, generator_kl_loss=1.408, generator_dur_loss=1.731, generator_adv_loss=1.962, generator_feat_match_loss=5.144, over 2708.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:28:01,908 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 08:28:10,807 INFO [train.py:591] (1/6) Epoch 549, validation: discriminator_loss=2.754, discriminator_real_loss=1.367, discriminator_fake_loss=1.386, generator_loss=27.6, generator_mel_loss=18.45, generator_kl_loss=1.279, generator_dur_loss=1.814, generator_adv_loss=1.831, generator_feat_match_loss=4.225, over 100.00 samples.
+2024-03-14 08:28:10,807 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 08:30:31,609 INFO [train.py:527] (1/6) Epoch 549, batch 98, global_batch_idx: 68050, batch size: 52, loss[discriminator_loss=2.685, discriminator_real_loss=1.34, discriminator_fake_loss=1.345, generator_loss=28.76, generator_mel_loss=18.13, generator_kl_loss=1.281, generator_dur_loss=1.718, generator_adv_loss=2.123, generator_feat_match_loss=5.508, over 52.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.373, discriminator_fake_loss=1.336, generator_loss=28.3, generator_mel_loss=18, generator_kl_loss=1.414, generator_dur_loss=1.742, generator_adv_loss=1.975, generator_feat_match_loss=5.173, over 5607.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:31:43,455 INFO [train.py:919] (1/6) Start epoch 550
+2024-03-14 08:33:15,649 INFO [train.py:527] (1/6) Epoch 550, batch 24, global_batch_idx: 68100, batch size: 62, loss[discriminator_loss=2.724, discriminator_real_loss=1.365, discriminator_fake_loss=1.359, generator_loss=28, generator_mel_loss=17.8, generator_kl_loss=1.501, generator_dur_loss=1.753, generator_adv_loss=2.005, generator_feat_match_loss=4.944, over 62.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.371, discriminator_fake_loss=1.34, generator_loss=28.24, generator_mel_loss=18.01, generator_kl_loss=1.392, generator_dur_loss=1.755, generator_adv_loss=1.953, generator_feat_match_loss=5.128, over 1435.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:35:33,908 INFO [train.py:527] (1/6) Epoch 550, batch 74, global_batch_idx: 68150, batch size: 72, loss[discriminator_loss=2.676, discriminator_real_loss=1.343, discriminator_fake_loss=1.333, generator_loss=28.24, generator_mel_loss=17.98, generator_kl_loss=1.403, generator_dur_loss=1.796, generator_adv_loss=1.975, generator_feat_match_loss=5.084, over 72.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=28.27, generator_mel_loss=17.97, generator_kl_loss=1.417, generator_dur_loss=1.747, generator_adv_loss=1.96, generator_feat_match_loss=5.178, over 4142.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:37:54,750 INFO [train.py:919] (1/6) Start epoch 551
+2024-03-14 08:38:18,862 INFO [train.py:527] (1/6) Epoch 551, batch 0, global_batch_idx: 68200, batch size: 59, loss[discriminator_loss=2.707, discriminator_real_loss=1.379, discriminator_fake_loss=1.328, generator_loss=28.97, generator_mel_loss=18.75, generator_kl_loss=1.509, generator_dur_loss=1.756, generator_adv_loss=1.97, generator_feat_match_loss=4.985, over 59.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.379, discriminator_fake_loss=1.328, generator_loss=28.97, generator_mel_loss=18.75, generator_kl_loss=1.509, generator_dur_loss=1.756, generator_adv_loss=1.97, generator_feat_match_loss=4.985, over 59.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:38:18,865 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 08:38:26,772 INFO [train.py:591] (1/6) Epoch 551, validation: discriminator_loss=2.758, discriminator_real_loss=1.493, discriminator_fake_loss=1.265, generator_loss=27.76, generator_mel_loss=18.48, generator_kl_loss=1.211, generator_dur_loss=1.817, generator_adv_loss=1.948, generator_feat_match_loss=4.306, over 100.00 samples.
+2024-03-14 08:38:26,774 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 08:40:50,232 INFO [train.py:527] (1/6) Epoch 551, batch 50, global_batch_idx: 68250, batch size: 44, loss[discriminator_loss=2.73, discriminator_real_loss=1.365, discriminator_fake_loss=1.365, generator_loss=27.87, generator_mel_loss=17.99, generator_kl_loss=1.389, generator_dur_loss=1.676, generator_adv_loss=1.968, generator_feat_match_loss=4.839, over 44.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.336, generator_loss=28.37, generator_mel_loss=18.06, generator_kl_loss=1.403, generator_dur_loss=1.769, generator_adv_loss=1.966, generator_feat_match_loss=5.169, over 2970.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:43:11,311 INFO [train.py:527] (1/6) Epoch 551, batch 100, global_batch_idx: 68300, batch size: 55, loss[discriminator_loss=2.729, discriminator_real_loss=1.451, discriminator_fake_loss=1.278, generator_loss=27.95, generator_mel_loss=18.13, generator_kl_loss=1.515, generator_dur_loss=1.724, generator_adv_loss=1.771, generator_feat_match_loss=4.812, over 55.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.364, discriminator_fake_loss=1.336, generator_loss=28.35, generator_mel_loss=18.07, generator_kl_loss=1.397, generator_dur_loss=1.76, generator_adv_loss=1.953, generator_feat_match_loss=5.177, over 5919.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:44:14,658 INFO [train.py:919] (1/6) Start epoch 552
+2024-03-14 08:45:53,471 INFO [train.py:527] (1/6) Epoch 552, batch 26, global_batch_idx: 68350, batch size: 61, loss[discriminator_loss=2.664, discriminator_real_loss=1.339, discriminator_fake_loss=1.325, generator_loss=29.02, generator_mel_loss=18.19, generator_kl_loss=1.466, generator_dur_loss=1.729, generator_adv_loss=2.014, generator_feat_match_loss=5.627, over 61.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.29, generator_mel_loss=17.9, generator_kl_loss=1.419, generator_dur_loss=1.748, generator_adv_loss=1.977, generator_feat_match_loss=5.244, over 1532.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:48:15,054 INFO [train.py:527] (1/6) Epoch 552, batch 76, global_batch_idx: 68400, batch size: 36, loss[discriminator_loss=2.661, discriminator_real_loss=1.357, discriminator_fake_loss=1.304, generator_loss=28.5, generator_mel_loss=17.59, generator_kl_loss=1.65, generator_dur_loss=1.693, generator_adv_loss=1.925, generator_feat_match_loss=5.646, over 36.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.363, discriminator_fake_loss=1.341, generator_loss=28.28, generator_mel_loss=17.94, generator_kl_loss=1.418, generator_dur_loss=1.747, generator_adv_loss=1.966, generator_feat_match_loss=5.211, over 4306.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:48:15,055 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 08:48:23,802 INFO [train.py:591] (1/6) Epoch 552, validation: discriminator_loss=2.71, discriminator_real_loss=1.453, discriminator_fake_loss=1.257, generator_loss=27.34, generator_mel_loss=18.36, generator_kl_loss=1.226, generator_dur_loss=1.809, generator_adv_loss=1.958, generator_feat_match_loss=3.99, over 100.00 samples.
+2024-03-14 08:48:23,803 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 08:50:33,337 INFO [train.py:919] (1/6) Start epoch 553
+2024-03-14 08:51:04,181 INFO [train.py:527] (1/6) Epoch 553, batch 2, global_batch_idx: 68450, batch size: 66, loss[discriminator_loss=2.698, discriminator_real_loss=1.382, discriminator_fake_loss=1.316, generator_loss=28.67, generator_mel_loss=18.29, generator_kl_loss=1.26, generator_dur_loss=1.752, generator_adv_loss=1.977, generator_feat_match_loss=5.389, over 66.00 samples.], tot_loss[discriminator_loss=2.754, discriminator_real_loss=1.333, discriminator_fake_loss=1.421, generator_loss=28.84, generator_mel_loss=18.24, generator_kl_loss=1.409, generator_dur_loss=1.731, generator_adv_loss=1.974, generator_feat_match_loss=5.489, over 161.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:53:26,774 INFO [train.py:527] (1/6) Epoch 553, batch 52, global_batch_idx: 68500, batch size: 47, loss[discriminator_loss=2.724, discriminator_real_loss=1.406, discriminator_fake_loss=1.319, generator_loss=28.71, generator_mel_loss=17.97, generator_kl_loss=1.583, generator_dur_loss=1.657, generator_adv_loss=2.021, generator_feat_match_loss=5.482, over 47.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.357, discriminator_fake_loss=1.337, generator_loss=28.42, generator_mel_loss=18.01, generator_kl_loss=1.398, generator_dur_loss=1.761, generator_adv_loss=1.971, generator_feat_match_loss=5.28, over 3118.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:55:46,641 INFO [train.py:527] (1/6) Epoch 553, batch 102, global_batch_idx: 68550, batch size: 83, loss[discriminator_loss=2.734, discriminator_real_loss=1.424, discriminator_fake_loss=1.31, generator_loss=28.23, generator_mel_loss=17.86, generator_kl_loss=1.283, generator_dur_loss=1.861, generator_adv_loss=1.946, generator_feat_match_loss=5.286, over 83.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=28.32, generator_mel_loss=17.96, generator_kl_loss=1.416, generator_dur_loss=1.758, generator_adv_loss=1.958, generator_feat_match_loss=5.228, over 5975.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:56:48,235 INFO [train.py:919] (1/6) Start epoch 554
+2024-03-14 08:58:31,797 INFO [train.py:527] (1/6) Epoch 554, batch 28, global_batch_idx: 68600, batch size: 66, loss[discriminator_loss=2.721, discriminator_real_loss=1.413, discriminator_fake_loss=1.308, generator_loss=28.62, generator_mel_loss=18.3, generator_kl_loss=1.362, generator_dur_loss=1.784, generator_adv_loss=1.824, generator_feat_match_loss=5.346, over 66.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.378, discriminator_fake_loss=1.342, generator_loss=28.17, generator_mel_loss=17.91, generator_kl_loss=1.414, generator_dur_loss=1.741, generator_adv_loss=1.952, generator_feat_match_loss=5.159, over 1612.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 08:58:31,798 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 08:58:40,004 INFO [train.py:591] (1/6) Epoch 554, validation: discriminator_loss=2.799, discriminator_real_loss=1.363, discriminator_fake_loss=1.436, generator_loss=26.83, generator_mel_loss=18.15, generator_kl_loss=1.3, generator_dur_loss=1.813, generator_adv_loss=1.742, generator_feat_match_loss=3.828, over 100.00 samples.
+2024-03-14 08:58:40,005 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 09:00:58,269 INFO [train.py:527] (1/6) Epoch 554, batch 78, global_batch_idx: 68650, batch size: 61, loss[discriminator_loss=2.737, discriminator_real_loss=1.409, discriminator_fake_loss=1.329, generator_loss=27.43, generator_mel_loss=17.76, generator_kl_loss=1.218, generator_dur_loss=1.768, generator_adv_loss=1.914, generator_feat_match_loss=4.765, over 61.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.366, discriminator_fake_loss=1.344, generator_loss=28.38, generator_mel_loss=17.99, generator_kl_loss=1.405, generator_dur_loss=1.759, generator_adv_loss=1.966, generator_feat_match_loss=5.262, over 4451.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:03:08,666 INFO [train.py:919] (1/6) Start epoch 555
+2024-03-14 09:03:43,832 INFO [train.py:527] (1/6) Epoch 555, batch 4, global_batch_idx: 68700, batch size: 50, loss[discriminator_loss=2.737, discriminator_real_loss=1.386, discriminator_fake_loss=1.35, generator_loss=27.78, generator_mel_loss=17.58, generator_kl_loss=1.473, generator_dur_loss=1.654, generator_adv_loss=2.045, generator_feat_match_loss=5.023, over 50.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.393, discriminator_fake_loss=1.33, generator_loss=28.39, generator_mel_loss=17.93, generator_kl_loss=1.434, generator_dur_loss=1.708, generator_adv_loss=1.982, generator_feat_match_loss=5.337, over 276.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:06:07,425 INFO [train.py:527] (1/6) Epoch 555, batch 54, global_batch_idx: 68750, batch size: 53, loss[discriminator_loss=2.687, discriminator_real_loss=1.368, discriminator_fake_loss=1.318, generator_loss=28.28, generator_mel_loss=18.21, generator_kl_loss=1.374, generator_dur_loss=1.715, generator_adv_loss=2.019, generator_feat_match_loss=4.958, over 53.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.373, discriminator_fake_loss=1.332, generator_loss=28.37, generator_mel_loss=17.98, generator_kl_loss=1.407, generator_dur_loss=1.731, generator_adv_loss=1.974, generator_feat_match_loss=5.276, over 3092.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:08:29,687 INFO [train.py:527] (1/6) Epoch 555, batch 104, global_batch_idx: 68800, batch size: 83, loss[discriminator_loss=2.688, discriminator_real_loss=1.326, discriminator_fake_loss=1.363, generator_loss=29.17, generator_mel_loss=18.18, generator_kl_loss=1.38, generator_dur_loss=1.813, generator_adv_loss=1.794, generator_feat_match_loss=5.998, over 83.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.33, generator_mel_loss=17.95, generator_kl_loss=1.407, generator_dur_loss=1.741, generator_adv_loss=1.966, generator_feat_match_loss=5.261, over 5992.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:08:29,688 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 09:08:38,551 INFO [train.py:591] (1/6) Epoch 555, validation: discriminator_loss=2.751, discriminator_real_loss=1.348, discriminator_fake_loss=1.403, generator_loss=27.21, generator_mel_loss=18.31, generator_kl_loss=1.253, generator_dur_loss=1.809, generator_adv_loss=1.761, generator_feat_match_loss=4.076, over 100.00 samples.
+2024-03-14 09:08:38,552 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 09:09:32,676 INFO [train.py:919] (1/6) Start epoch 556
+2024-03-14 09:11:22,803 INFO [train.py:527] (1/6) Epoch 556, batch 30, global_batch_idx: 68850, batch size: 83, loss[discriminator_loss=2.761, discriminator_real_loss=1.376, discriminator_fake_loss=1.386, generator_loss=28.57, generator_mel_loss=18.23, generator_kl_loss=1.373, generator_dur_loss=1.822, generator_adv_loss=1.967, generator_feat_match_loss=5.185, over 83.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=28.58, generator_mel_loss=18.08, generator_kl_loss=1.409, generator_dur_loss=1.754, generator_adv_loss=2.018, generator_feat_match_loss=5.32, over 1738.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:13:42,294 INFO [train.py:527] (1/6) Epoch 556, batch 80, global_batch_idx: 68900, batch size: 66, loss[discriminator_loss=2.642, discriminator_real_loss=1.295, discriminator_fake_loss=1.347, generator_loss=28.64, generator_mel_loss=18.05, generator_kl_loss=1.433, generator_dur_loss=1.78, generator_adv_loss=2.014, generator_feat_match_loss=5.364, over 66.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.372, discriminator_fake_loss=1.334, generator_loss=28.5, generator_mel_loss=18.05, generator_kl_loss=1.419, generator_dur_loss=1.752, generator_adv_loss=1.981, generator_feat_match_loss=5.297, over 4530.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:15:43,935 INFO [train.py:919] (1/6) Start epoch 557
+2024-03-14 09:16:26,657 INFO [train.py:527] (1/6) Epoch 557, batch 6, global_batch_idx: 68950, batch size: 50, loss[discriminator_loss=2.731, discriminator_real_loss=1.466, discriminator_fake_loss=1.265, generator_loss=27.74, generator_mel_loss=18.33, generator_kl_loss=1.378, generator_dur_loss=1.701, generator_adv_loss=1.841, generator_feat_match_loss=4.488, over 50.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.353, discriminator_fake_loss=1.347, generator_loss=28.38, generator_mel_loss=18.02, generator_kl_loss=1.338, generator_dur_loss=1.744, generator_adv_loss=1.987, generator_feat_match_loss=5.287, over 417.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:18:48,147 INFO [train.py:527] (1/6) Epoch 557, batch 56, global_batch_idx: 69000, batch size: 88, loss[discriminator_loss=2.725, discriminator_real_loss=1.391, discriminator_fake_loss=1.334, generator_loss=28.28, generator_mel_loss=18.01, generator_kl_loss=1.304, generator_dur_loss=1.827, generator_adv_loss=1.937, generator_feat_match_loss=5.208, over 88.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.372, discriminator_fake_loss=1.342, generator_loss=28.35, generator_mel_loss=18.03, generator_kl_loss=1.423, generator_dur_loss=1.741, generator_adv_loss=1.959, generator_feat_match_loss=5.196, over 2945.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:18:48,148 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 09:18:56,042 INFO [train.py:591] (1/6) Epoch 557, validation: discriminator_loss=2.752, discriminator_real_loss=1.44, discriminator_fake_loss=1.312, generator_loss=27.3, generator_mel_loss=17.78, generator_kl_loss=1.173, generator_dur_loss=1.798, generator_adv_loss=1.926, generator_feat_match_loss=4.623, over 100.00 samples.
+2024-03-14 09:18:56,043 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 09:21:15,772 INFO [train.py:527] (1/6) Epoch 557, batch 106, global_batch_idx: 69050, batch size: 72, loss[discriminator_loss=2.659, discriminator_real_loss=1.382, discriminator_fake_loss=1.276, generator_loss=29.12, generator_mel_loss=18.38, generator_kl_loss=1.457, generator_dur_loss=1.819, generator_adv_loss=1.927, generator_feat_match_loss=5.542, over 72.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.373, discriminator_fake_loss=1.34, generator_loss=28.36, generator_mel_loss=18.06, generator_kl_loss=1.411, generator_dur_loss=1.752, generator_adv_loss=1.968, generator_feat_match_loss=5.169, over 5876.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:22:05,861 INFO [train.py:919] (1/6) Start epoch 558
+2024-03-14 09:24:03,429 INFO [train.py:527] (1/6) Epoch 558, batch 32, global_batch_idx: 69100, batch size: 50, loss[discriminator_loss=2.769, discriminator_real_loss=1.53, discriminator_fake_loss=1.239, generator_loss=28.59, generator_mel_loss=18.09, generator_kl_loss=1.375, generator_dur_loss=1.736, generator_adv_loss=1.98, generator_feat_match_loss=5.405, over 50.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.28, generator_mel_loss=18, generator_kl_loss=1.377, generator_dur_loss=1.757, generator_adv_loss=1.955, generator_feat_match_loss=5.196, over 1869.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:26:24,031 INFO [train.py:527] (1/6) Epoch 558, batch 82, global_batch_idx: 69150, batch size: 47, loss[discriminator_loss=2.732, discriminator_real_loss=1.383, discriminator_fake_loss=1.349, generator_loss=29.27, generator_mel_loss=18.54, generator_kl_loss=1.583, generator_dur_loss=1.702, generator_adv_loss=1.901, generator_feat_match_loss=5.544, over 47.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.369, discriminator_fake_loss=1.342, generator_loss=28.28, generator_mel_loss=18, generator_kl_loss=1.395, generator_dur_loss=1.751, generator_adv_loss=1.957, generator_feat_match_loss=5.171, over 4765.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:28:20,804 INFO [train.py:919] (1/6) Start epoch 559
+2024-03-14 09:29:07,751 INFO [train.py:527] (1/6) Epoch 559, batch 8, global_batch_idx: 69200, batch size: 72, loss[discriminator_loss=2.687, discriminator_real_loss=1.364, discriminator_fake_loss=1.323, generator_loss=28.25, generator_mel_loss=18.32, generator_kl_loss=1.343, generator_dur_loss=1.794, generator_adv_loss=1.881, generator_feat_match_loss=4.914, over 72.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.379, discriminator_fake_loss=1.345, generator_loss=28.77, generator_mel_loss=18.21, generator_kl_loss=1.493, generator_dur_loss=1.729, generator_adv_loss=1.928, generator_feat_match_loss=5.403, over 502.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:29:07,753 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 09:29:15,985 INFO [train.py:591] (1/6) Epoch 559, validation: discriminator_loss=2.722, discriminator_real_loss=1.383, discriminator_fake_loss=1.339, generator_loss=27.66, generator_mel_loss=18.38, generator_kl_loss=1.27, generator_dur_loss=1.798, generator_adv_loss=1.825, generator_feat_match_loss=4.393, over 100.00 samples.
+2024-03-14 09:29:15,987 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 09:31:36,790 INFO [train.py:527] (1/6) Epoch 559, batch 58, global_batch_idx: 69250, batch size: 59, loss[discriminator_loss=2.692, discriminator_real_loss=1.391, discriminator_fake_loss=1.301, generator_loss=28.77, generator_mel_loss=18.34, generator_kl_loss=1.518, generator_dur_loss=1.711, generator_adv_loss=1.859, generator_feat_match_loss=5.342, over 59.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.377, discriminator_fake_loss=1.34, generator_loss=28.45, generator_mel_loss=18.04, generator_kl_loss=1.459, generator_dur_loss=1.726, generator_adv_loss=1.956, generator_feat_match_loss=5.275, over 3138.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:33:58,953 INFO [train.py:527] (1/6) Epoch 559, batch 108, global_batch_idx: 69300, batch size: 80, loss[discriminator_loss=2.665, discriminator_real_loss=1.388, discriminator_fake_loss=1.277, generator_loss=27.91, generator_mel_loss=17.66, generator_kl_loss=1.239, generator_dur_loss=1.825, generator_adv_loss=1.896, generator_feat_match_loss=5.289, over 80.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.39, generator_mel_loss=17.99, generator_kl_loss=1.443, generator_dur_loss=1.736, generator_adv_loss=1.962, generator_feat_match_loss=5.258, over 5869.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:34:40,462 INFO [train.py:919] (1/6) Start epoch 560
+2024-03-14 09:36:42,332 INFO [train.py:527] (1/6) Epoch 560, batch 34, global_batch_idx: 69350, batch size: 25, loss[discriminator_loss=2.719, discriminator_real_loss=1.413, discriminator_fake_loss=1.306, generator_loss=29.25, generator_mel_loss=18.86, generator_kl_loss=1.751, generator_dur_loss=1.574, generator_adv_loss=1.873, generator_feat_match_loss=5.199, over 25.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.361, discriminator_fake_loss=1.342, generator_loss=28.23, generator_mel_loss=17.92, generator_kl_loss=1.416, generator_dur_loss=1.773, generator_adv_loss=1.959, generator_feat_match_loss=5.161, over 2128.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:39:03,922 INFO [train.py:527] (1/6) Epoch 560, batch 84, global_batch_idx: 69400, batch size: 55, loss[discriminator_loss=2.728, discriminator_real_loss=1.406, discriminator_fake_loss=1.323, generator_loss=28.95, generator_mel_loss=18.11, generator_kl_loss=1.504, generator_dur_loss=1.724, generator_adv_loss=1.931, generator_feat_match_loss=5.686, over 55.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.369, discriminator_fake_loss=1.343, generator_loss=28.22, generator_mel_loss=17.9, generator_kl_loss=1.423, generator_dur_loss=1.755, generator_adv_loss=1.965, generator_feat_match_loss=5.172, over 4919.00 samples.], cur_lr_g: 1.87e-04, cur_lr_d: 1.87e-04, 
+2024-03-14 09:39:03,923 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 09:39:13,161 INFO [train.py:591] (1/6) Epoch 560, validation: discriminator_loss=2.76, discriminator_real_loss=1.461, discriminator_fake_loss=1.3, generator_loss=27.48, generator_mel_loss=18.44, generator_kl_loss=1.17, generator_dur_loss=1.817, generator_adv_loss=1.925, generator_feat_match_loss=4.129, over 100.00 samples.
+2024-03-14 09:39:13,162 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 09:41:00,433 INFO [train.py:919] (1/6) Start epoch 561
+2024-03-14 09:41:52,100 INFO [train.py:527] (1/6) Epoch 561, batch 10, global_batch_idx: 69450, batch size: 77, loss[discriminator_loss=2.712, discriminator_real_loss=1.401, discriminator_fake_loss=1.311, generator_loss=27.69, generator_mel_loss=17.79, generator_kl_loss=1.369, generator_dur_loss=1.801, generator_adv_loss=1.9, generator_feat_match_loss=4.832, over 77.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.388, discriminator_fake_loss=1.323, generator_loss=28.44, generator_mel_loss=18.1, generator_kl_loss=1.476, generator_dur_loss=1.741, generator_adv_loss=1.943, generator_feat_match_loss=5.181, over 578.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:44:11,575 INFO [train.py:527] (1/6) Epoch 561, batch 60, global_batch_idx: 69500, batch size: 58, loss[discriminator_loss=2.643, discriminator_real_loss=1.265, discriminator_fake_loss=1.378, generator_loss=28.55, generator_mel_loss=17.65, generator_kl_loss=1.374, generator_dur_loss=1.705, generator_adv_loss=2.149, generator_feat_match_loss=5.668, over 58.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.363, discriminator_fake_loss=1.335, generator_loss=28.27, generator_mel_loss=17.96, generator_kl_loss=1.407, generator_dur_loss=1.754, generator_adv_loss=1.964, generator_feat_match_loss=5.187, over 3625.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:46:29,751 INFO [train.py:527] (1/6) Epoch 561, batch 110, global_batch_idx: 69550, batch size: 77, loss[discriminator_loss=2.736, discriminator_real_loss=1.368, discriminator_fake_loss=1.368, generator_loss=27.56, generator_mel_loss=17.85, generator_kl_loss=1.295, generator_dur_loss=1.806, generator_adv_loss=1.973, generator_feat_match_loss=4.627, over 77.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.336, generator_loss=28.33, generator_mel_loss=18, generator_kl_loss=1.436, generator_dur_loss=1.737, generator_adv_loss=1.964, generator_feat_match_loss=5.188, over 6053.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:47:07,784 INFO [train.py:919] (1/6) Start epoch 562
+2024-03-14 09:49:14,023 INFO [train.py:527] (1/6) Epoch 562, batch 36, global_batch_idx: 69600, batch size: 42, loss[discriminator_loss=2.668, discriminator_real_loss=1.361, discriminator_fake_loss=1.307, generator_loss=28.62, generator_mel_loss=18.09, generator_kl_loss=1.566, generator_dur_loss=1.661, generator_adv_loss=2.12, generator_feat_match_loss=5.184, over 42.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.39, generator_mel_loss=18.02, generator_kl_loss=1.427, generator_dur_loss=1.729, generator_adv_loss=1.966, generator_feat_match_loss=5.255, over 1942.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:49:14,024 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 09:49:21,868 INFO [train.py:591] (1/6) Epoch 562, validation: discriminator_loss=2.774, discriminator_real_loss=1.497, discriminator_fake_loss=1.277, generator_loss=27.95, generator_mel_loss=18.5, generator_kl_loss=1.244, generator_dur_loss=1.815, generator_adv_loss=2.012, generator_feat_match_loss=4.385, over 100.00 samples.
+2024-03-14 09:49:21,869 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 09:51:41,011 INFO [train.py:527] (1/6) Epoch 562, batch 86, global_batch_idx: 69650, batch size: 14, loss[discriminator_loss=2.664, discriminator_real_loss=1.292, discriminator_fake_loss=1.372, generator_loss=30.36, generator_mel_loss=18.94, generator_kl_loss=1.753, generator_dur_loss=1.564, generator_adv_loss=2.03, generator_feat_match_loss=6.075, over 14.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.49, generator_mel_loss=18.08, generator_kl_loss=1.426, generator_dur_loss=1.737, generator_adv_loss=1.973, generator_feat_match_loss=5.272, over 4679.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:53:21,901 INFO [train.py:919] (1/6) Start epoch 563
+2024-03-14 09:54:19,198 INFO [train.py:527] (1/6) Epoch 563, batch 12, global_batch_idx: 69700, batch size: 74, loss[discriminator_loss=2.682, discriminator_real_loss=1.285, discriminator_fake_loss=1.397, generator_loss=28.97, generator_mel_loss=17.98, generator_kl_loss=1.304, generator_dur_loss=1.82, generator_adv_loss=1.991, generator_feat_match_loss=5.88, over 74.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.341, discriminator_fake_loss=1.349, generator_loss=28.39, generator_mel_loss=17.92, generator_kl_loss=1.441, generator_dur_loss=1.755, generator_adv_loss=1.956, generator_feat_match_loss=5.317, over 759.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:56:37,532 INFO [train.py:527] (1/6) Epoch 563, batch 62, global_batch_idx: 69750, batch size: 59, loss[discriminator_loss=2.696, discriminator_real_loss=1.46, discriminator_fake_loss=1.236, generator_loss=28.55, generator_mel_loss=18, generator_kl_loss=1.379, generator_dur_loss=1.748, generator_adv_loss=2.084, generator_feat_match_loss=5.339, over 59.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.361, discriminator_fake_loss=1.343, generator_loss=28.35, generator_mel_loss=17.94, generator_kl_loss=1.448, generator_dur_loss=1.74, generator_adv_loss=1.973, generator_feat_match_loss=5.245, over 3506.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:59:00,135 INFO [train.py:527] (1/6) Epoch 563, batch 112, global_batch_idx: 69800, batch size: 55, loss[discriminator_loss=2.628, discriminator_real_loss=1.352, discriminator_fake_loss=1.275, generator_loss=28.98, generator_mel_loss=17.96, generator_kl_loss=1.348, generator_dur_loss=1.692, generator_adv_loss=1.982, generator_feat_match_loss=6.004, over 55.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.359, discriminator_fake_loss=1.337, generator_loss=28.28, generator_mel_loss=17.9, generator_kl_loss=1.42, generator_dur_loss=1.753, generator_adv_loss=1.967, generator_feat_match_loss=5.248, over 6637.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 09:59:00,136 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 09:59:08,735 INFO [train.py:591] (1/6) Epoch 563, validation: discriminator_loss=2.728, discriminator_real_loss=1.386, discriminator_fake_loss=1.342, generator_loss=26.35, generator_mel_loss=17.75, generator_kl_loss=1.292, generator_dur_loss=1.808, generator_adv_loss=1.812, generator_feat_match_loss=3.694, over 100.00 samples.
+2024-03-14 09:59:08,736 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 09:59:37,382 INFO [train.py:919] (1/6) Start epoch 564
+2024-03-14 10:01:47,462 INFO [train.py:527] (1/6) Epoch 564, batch 38, global_batch_idx: 69850, batch size: 64, loss[discriminator_loss=2.766, discriminator_real_loss=1.547, discriminator_fake_loss=1.219, generator_loss=27.24, generator_mel_loss=17.36, generator_kl_loss=1.343, generator_dur_loss=1.772, generator_adv_loss=1.855, generator_feat_match_loss=4.913, over 64.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.377, discriminator_fake_loss=1.334, generator_loss=28.25, generator_mel_loss=17.9, generator_kl_loss=1.417, generator_dur_loss=1.765, generator_adv_loss=1.972, generator_feat_match_loss=5.191, over 2285.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:04:04,305 INFO [train.py:527] (1/6) Epoch 564, batch 88, global_batch_idx: 69900, batch size: 61, loss[discriminator_loss=2.671, discriminator_real_loss=1.373, discriminator_fake_loss=1.298, generator_loss=28.41, generator_mel_loss=18.16, generator_kl_loss=1.462, generator_dur_loss=1.712, generator_adv_loss=1.874, generator_feat_match_loss=5.198, over 61.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.367, discriminator_fake_loss=1.335, generator_loss=28.34, generator_mel_loss=17.94, generator_kl_loss=1.415, generator_dur_loss=1.758, generator_adv_loss=1.972, generator_feat_match_loss=5.252, over 5039.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:05:43,662 INFO [train.py:919] (1/6) Start epoch 565
+2024-03-14 10:06:48,940 INFO [train.py:527] (1/6) Epoch 565, batch 14, global_batch_idx: 69950, batch size: 83, loss[discriminator_loss=2.769, discriminator_real_loss=1.3, discriminator_fake_loss=1.469, generator_loss=27.44, generator_mel_loss=17.91, generator_kl_loss=1.312, generator_dur_loss=1.851, generator_adv_loss=1.917, generator_feat_match_loss=4.454, over 83.00 samples.], tot_loss[discriminator_loss=2.72, discriminator_real_loss=1.375, discriminator_fake_loss=1.345, generator_loss=28.13, generator_mel_loss=17.95, generator_kl_loss=1.371, generator_dur_loss=1.752, generator_adv_loss=1.936, generator_feat_match_loss=5.127, over 895.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:09:07,115 INFO [train.py:527] (1/6) Epoch 565, batch 64, global_batch_idx: 70000, batch size: 72, loss[discriminator_loss=2.662, discriminator_real_loss=1.341, discriminator_fake_loss=1.321, generator_loss=28.47, generator_mel_loss=17.92, generator_kl_loss=1.439, generator_dur_loss=1.799, generator_adv_loss=1.98, generator_feat_match_loss=5.33, over 72.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.374, discriminator_fake_loss=1.334, generator_loss=28.27, generator_mel_loss=17.95, generator_kl_loss=1.382, generator_dur_loss=1.746, generator_adv_loss=1.954, generator_feat_match_loss=5.239, over 3820.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:09:07,116 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 10:09:15,109 INFO [train.py:591] (1/6) Epoch 565, validation: discriminator_loss=2.737, discriminator_real_loss=1.367, discriminator_fake_loss=1.37, generator_loss=27.8, generator_mel_loss=18.61, generator_kl_loss=1.183, generator_dur_loss=1.796, generator_adv_loss=1.851, generator_feat_match_loss=4.361, over 100.00 samples.
+2024-03-14 10:09:15,110 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 10:11:33,314 INFO [train.py:527] (1/6) Epoch 565, batch 114, global_batch_idx: 70050, batch size: 52, loss[discriminator_loss=2.728, discriminator_real_loss=1.315, discriminator_fake_loss=1.413, generator_loss=28.33, generator_mel_loss=17.94, generator_kl_loss=1.472, generator_dur_loss=1.7, generator_adv_loss=2.067, generator_feat_match_loss=5.151, over 52.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.371, discriminator_fake_loss=1.334, generator_loss=28.35, generator_mel_loss=17.99, generator_kl_loss=1.389, generator_dur_loss=1.749, generator_adv_loss=1.962, generator_feat_match_loss=5.253, over 6674.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:11:58,637 INFO [train.py:919] (1/6) Start epoch 566
+2024-03-14 10:14:15,706 INFO [train.py:527] (1/6) Epoch 566, batch 40, global_batch_idx: 70100, batch size: 66, loss[discriminator_loss=2.762, discriminator_real_loss=1.342, discriminator_fake_loss=1.42, generator_loss=27.77, generator_mel_loss=18.08, generator_kl_loss=1.355, generator_dur_loss=1.733, generator_adv_loss=1.909, generator_feat_match_loss=4.694, over 66.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.34, generator_loss=28.53, generator_mel_loss=18.01, generator_kl_loss=1.445, generator_dur_loss=1.743, generator_adv_loss=1.971, generator_feat_match_loss=5.361, over 2213.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:16:36,055 INFO [train.py:527] (1/6) Epoch 566, batch 90, global_batch_idx: 70150, batch size: 47, loss[discriminator_loss=2.695, discriminator_real_loss=1.363, discriminator_fake_loss=1.332, generator_loss=28.39, generator_mel_loss=17.98, generator_kl_loss=1.516, generator_dur_loss=1.69, generator_adv_loss=2.041, generator_feat_match_loss=5.164, over 47.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.48, generator_mel_loss=18.03, generator_kl_loss=1.419, generator_dur_loss=1.746, generator_adv_loss=1.976, generator_feat_match_loss=5.315, over 5089.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:18:08,504 INFO [train.py:919] (1/6) Start epoch 567
+2024-03-14 10:19:16,561 INFO [train.py:527] (1/6) Epoch 567, batch 16, global_batch_idx: 70200, batch size: 45, loss[discriminator_loss=2.721, discriminator_real_loss=1.391, discriminator_fake_loss=1.33, generator_loss=28.03, generator_mel_loss=18.12, generator_kl_loss=1.543, generator_dur_loss=1.64, generator_adv_loss=1.927, generator_feat_match_loss=4.801, over 45.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.38, discriminator_fake_loss=1.345, generator_loss=28.29, generator_mel_loss=17.94, generator_kl_loss=1.408, generator_dur_loss=1.755, generator_adv_loss=1.954, generator_feat_match_loss=5.238, over 993.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:19:16,563 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 10:19:24,540 INFO [train.py:591] (1/6) Epoch 567, validation: discriminator_loss=2.776, discriminator_real_loss=1.402, discriminator_fake_loss=1.374, generator_loss=27.35, generator_mel_loss=18.82, generator_kl_loss=1.179, generator_dur_loss=1.806, generator_adv_loss=1.829, generator_feat_match_loss=3.711, over 100.00 samples.
+2024-03-14 10:19:24,540 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 10:21:43,368 INFO [train.py:527] (1/6) Epoch 567, batch 66, global_batch_idx: 70250, batch size: 96, loss[discriminator_loss=2.665, discriminator_real_loss=1.391, discriminator_fake_loss=1.274, generator_loss=27.55, generator_mel_loss=17.42, generator_kl_loss=1.254, generator_dur_loss=1.866, generator_adv_loss=1.855, generator_feat_match_loss=5.154, over 96.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.373, discriminator_fake_loss=1.342, generator_loss=28.37, generator_mel_loss=17.98, generator_kl_loss=1.437, generator_dur_loss=1.739, generator_adv_loss=1.966, generator_feat_match_loss=5.249, over 3742.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:24:02,162 INFO [train.py:527] (1/6) Epoch 567, batch 116, global_batch_idx: 70300, batch size: 74, loss[discriminator_loss=2.674, discriminator_real_loss=1.314, discriminator_fake_loss=1.359, generator_loss=28.66, generator_mel_loss=18, generator_kl_loss=1.283, generator_dur_loss=1.827, generator_adv_loss=2.063, generator_feat_match_loss=5.489, over 74.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.375, discriminator_fake_loss=1.34, generator_loss=28.37, generator_mel_loss=17.99, generator_kl_loss=1.438, generator_dur_loss=1.735, generator_adv_loss=1.967, generator_feat_match_loss=5.239, over 6449.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:24:22,936 INFO [train.py:919] (1/6) Start epoch 568
+2024-03-14 10:26:47,015 INFO [train.py:527] (1/6) Epoch 568, batch 42, global_batch_idx: 70350, batch size: 55, loss[discriminator_loss=2.779, discriminator_real_loss=1.316, discriminator_fake_loss=1.463, generator_loss=29.21, generator_mel_loss=18.56, generator_kl_loss=1.437, generator_dur_loss=1.689, generator_adv_loss=2.004, generator_feat_match_loss=5.519, over 55.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.374, discriminator_fake_loss=1.341, generator_loss=28.25, generator_mel_loss=18, generator_kl_loss=1.411, generator_dur_loss=1.744, generator_adv_loss=1.951, generator_feat_match_loss=5.142, over 2423.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:29:06,484 INFO [train.py:527] (1/6) Epoch 568, batch 92, global_batch_idx: 70400, batch size: 53, loss[discriminator_loss=2.665, discriminator_real_loss=1.307, discriminator_fake_loss=1.358, generator_loss=28.06, generator_mel_loss=18, generator_kl_loss=1.538, generator_dur_loss=1.702, generator_adv_loss=1.902, generator_feat_match_loss=4.919, over 53.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.375, discriminator_fake_loss=1.336, generator_loss=28.26, generator_mel_loss=18, generator_kl_loss=1.425, generator_dur_loss=1.734, generator_adv_loss=1.964, generator_feat_match_loss=5.144, over 4957.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:29:06,486 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 10:29:15,279 INFO [train.py:591] (1/6) Epoch 568, validation: discriminator_loss=2.809, discriminator_real_loss=1.305, discriminator_fake_loss=1.504, generator_loss=27.65, generator_mel_loss=18.58, generator_kl_loss=1.196, generator_dur_loss=1.813, generator_adv_loss=1.674, generator_feat_match_loss=4.381, over 100.00 samples.
+2024-03-14 10:29:15,280 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 10:30:42,348 INFO [train.py:919] (1/6) Start epoch 569
+2024-03-14 10:31:56,662 INFO [train.py:527] (1/6) Epoch 569, batch 18, global_batch_idx: 70450, batch size: 59, loss[discriminator_loss=2.666, discriminator_real_loss=1.38, discriminator_fake_loss=1.286, generator_loss=28.69, generator_mel_loss=18.16, generator_kl_loss=1.382, generator_dur_loss=1.787, generator_adv_loss=1.919, generator_feat_match_loss=5.44, over 59.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.376, discriminator_fake_loss=1.34, generator_loss=28.41, generator_mel_loss=18.01, generator_kl_loss=1.386, generator_dur_loss=1.772, generator_adv_loss=1.966, generator_feat_match_loss=5.272, over 1186.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:34:16,897 INFO [train.py:527] (1/6) Epoch 569, batch 68, global_batch_idx: 70500, batch size: 66, loss[discriminator_loss=2.728, discriminator_real_loss=1.248, discriminator_fake_loss=1.479, generator_loss=28.74, generator_mel_loss=17.92, generator_kl_loss=1.352, generator_dur_loss=1.783, generator_adv_loss=2.069, generator_feat_match_loss=5.616, over 66.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.42, generator_mel_loss=18.02, generator_kl_loss=1.403, generator_dur_loss=1.754, generator_adv_loss=1.962, generator_feat_match_loss=5.282, over 4027.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:36:35,882 INFO [train.py:527] (1/6) Epoch 569, batch 118, global_batch_idx: 70550, batch size: 96, loss[discriminator_loss=2.708, discriminator_real_loss=1.285, discriminator_fake_loss=1.423, generator_loss=28.95, generator_mel_loss=18.08, generator_kl_loss=1.366, generator_dur_loss=1.871, generator_adv_loss=2.037, generator_feat_match_loss=5.595, over 96.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.366, discriminator_fake_loss=1.336, generator_loss=28.44, generator_mel_loss=18.01, generator_kl_loss=1.405, generator_dur_loss=1.749, generator_adv_loss=1.976, generator_feat_match_loss=5.303, over 6846.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:36:51,597 INFO [train.py:919] (1/6) Start epoch 570
+2024-03-14 10:39:15,973 INFO [train.py:527] (1/6) Epoch 570, batch 44, global_batch_idx: 70600, batch size: 56, loss[discriminator_loss=2.635, discriminator_real_loss=1.373, discriminator_fake_loss=1.262, generator_loss=28.64, generator_mel_loss=17.79, generator_kl_loss=1.396, generator_dur_loss=1.688, generator_adv_loss=1.922, generator_feat_match_loss=5.849, over 56.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.356, discriminator_fake_loss=1.336, generator_loss=28.46, generator_mel_loss=17.92, generator_kl_loss=1.443, generator_dur_loss=1.726, generator_adv_loss=1.971, generator_feat_match_loss=5.397, over 2521.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:39:15,974 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 10:39:24,036 INFO [train.py:591] (1/6) Epoch 570, validation: discriminator_loss=2.732, discriminator_real_loss=1.37, discriminator_fake_loss=1.362, generator_loss=27.4, generator_mel_loss=18.38, generator_kl_loss=1.202, generator_dur_loss=1.795, generator_adv_loss=1.795, generator_feat_match_loss=4.225, over 100.00 samples.
+2024-03-14 10:39:24,037 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 10:41:42,106 INFO [train.py:527] (1/6) Epoch 570, batch 94, global_batch_idx: 70650, batch size: 53, loss[discriminator_loss=2.73, discriminator_real_loss=1.36, discriminator_fake_loss=1.369, generator_loss=29.08, generator_mel_loss=18.18, generator_kl_loss=1.445, generator_dur_loss=1.648, generator_adv_loss=1.969, generator_feat_match_loss=5.834, over 53.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=28.42, generator_mel_loss=17.98, generator_kl_loss=1.424, generator_dur_loss=1.734, generator_adv_loss=1.97, generator_feat_match_loss=5.305, over 5427.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:43:04,385 INFO [train.py:919] (1/6) Start epoch 571
+2024-03-14 10:44:25,930 INFO [train.py:527] (1/6) Epoch 571, batch 20, global_batch_idx: 70700, batch size: 70, loss[discriminator_loss=2.706, discriminator_real_loss=1.397, discriminator_fake_loss=1.309, generator_loss=28.09, generator_mel_loss=17.73, generator_kl_loss=1.282, generator_dur_loss=1.79, generator_adv_loss=1.897, generator_feat_match_loss=5.387, over 70.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.352, discriminator_fake_loss=1.344, generator_loss=28.41, generator_mel_loss=17.93, generator_kl_loss=1.412, generator_dur_loss=1.761, generator_adv_loss=1.959, generator_feat_match_loss=5.35, over 1251.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:46:43,862 INFO [train.py:527] (1/6) Epoch 571, batch 70, global_batch_idx: 70750, batch size: 61, loss[discriminator_loss=2.647, discriminator_real_loss=1.345, discriminator_fake_loss=1.302, generator_loss=28.36, generator_mel_loss=18.14, generator_kl_loss=1.506, generator_dur_loss=1.738, generator_adv_loss=1.876, generator_feat_match_loss=5.094, over 61.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.361, discriminator_fake_loss=1.344, generator_loss=28.46, generator_mel_loss=18.04, generator_kl_loss=1.413, generator_dur_loss=1.751, generator_adv_loss=1.956, generator_feat_match_loss=5.3, over 3993.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:49:02,207 INFO [train.py:527] (1/6) Epoch 571, batch 120, global_batch_idx: 70800, batch size: 72, loss[discriminator_loss=2.738, discriminator_real_loss=1.442, discriminator_fake_loss=1.296, generator_loss=28.13, generator_mel_loss=17.7, generator_kl_loss=1.305, generator_dur_loss=1.784, generator_adv_loss=1.84, generator_feat_match_loss=5.496, over 72.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.362, discriminator_fake_loss=1.341, generator_loss=28.52, generator_mel_loss=18.04, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=1.966, generator_feat_match_loss=5.351, over 6965.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:49:02,208 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 10:49:10,966 INFO [train.py:591] (1/6) Epoch 571, validation: discriminator_loss=2.778, discriminator_real_loss=1.319, discriminator_fake_loss=1.459, generator_loss=27.05, generator_mel_loss=18.21, generator_kl_loss=1.249, generator_dur_loss=1.805, generator_adv_loss=1.701, generator_feat_match_loss=4.082, over 100.00 samples.
+2024-03-14 10:49:10,967 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 10:49:20,623 INFO [train.py:919] (1/6) Start epoch 572
+2024-03-14 10:51:52,200 INFO [train.py:527] (1/6) Epoch 572, batch 46, global_batch_idx: 70850, batch size: 70, loss[discriminator_loss=2.712, discriminator_real_loss=1.393, discriminator_fake_loss=1.319, generator_loss=28.34, generator_mel_loss=17.59, generator_kl_loss=1.321, generator_dur_loss=1.815, generator_adv_loss=1.935, generator_feat_match_loss=5.681, over 70.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.379, discriminator_fake_loss=1.336, generator_loss=28.25, generator_mel_loss=17.91, generator_kl_loss=1.427, generator_dur_loss=1.753, generator_adv_loss=1.959, generator_feat_match_loss=5.193, over 2718.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:54:11,225 INFO [train.py:527] (1/6) Epoch 572, batch 96, global_batch_idx: 70900, batch size: 52, loss[discriminator_loss=2.687, discriminator_real_loss=1.421, discriminator_fake_loss=1.266, generator_loss=28.23, generator_mel_loss=17.9, generator_kl_loss=1.509, generator_dur_loss=1.721, generator_adv_loss=1.99, generator_feat_match_loss=5.111, over 52.00 samples.], tot_loss[discriminator_loss=2.711, discriminator_real_loss=1.374, discriminator_fake_loss=1.338, generator_loss=28.19, generator_mel_loss=17.9, generator_kl_loss=1.428, generator_dur_loss=1.75, generator_adv_loss=1.955, generator_feat_match_loss=5.151, over 5693.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:55:27,559 INFO [train.py:919] (1/6) Start epoch 573
+2024-03-14 10:56:53,830 INFO [train.py:527] (1/6) Epoch 573, batch 22, global_batch_idx: 70950, batch size: 48, loss[discriminator_loss=2.69, discriminator_real_loss=1.304, discriminator_fake_loss=1.387, generator_loss=28.58, generator_mel_loss=18.28, generator_kl_loss=1.594, generator_dur_loss=1.737, generator_adv_loss=1.925, generator_feat_match_loss=5.046, over 48.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.351, discriminator_fake_loss=1.344, generator_loss=28.5, generator_mel_loss=18.02, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=1.97, generator_feat_match_loss=5.333, over 1360.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:59:15,010 INFO [train.py:527] (1/6) Epoch 573, batch 72, global_batch_idx: 71000, batch size: 88, loss[discriminator_loss=2.746, discriminator_real_loss=1.405, discriminator_fake_loss=1.341, generator_loss=27.76, generator_mel_loss=17.66, generator_kl_loss=1.388, generator_dur_loss=1.829, generator_adv_loss=1.951, generator_feat_match_loss=4.932, over 88.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.366, discriminator_fake_loss=1.343, generator_loss=28.51, generator_mel_loss=18.05, generator_kl_loss=1.434, generator_dur_loss=1.75, generator_adv_loss=1.971, generator_feat_match_loss=5.31, over 4211.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 10:59:15,011 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 10:59:23,686 INFO [train.py:591] (1/6) Epoch 573, validation: discriminator_loss=2.787, discriminator_real_loss=1.403, discriminator_fake_loss=1.384, generator_loss=27.98, generator_mel_loss=18.39, generator_kl_loss=1.265, generator_dur_loss=1.82, generator_adv_loss=1.848, generator_feat_match_loss=4.654, over 100.00 samples.
+2024-03-14 10:59:23,687 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 11:01:39,196 INFO [train.py:527] (1/6) Epoch 573, batch 122, global_batch_idx: 71050, batch size: 66, loss[discriminator_loss=2.713, discriminator_real_loss=1.344, discriminator_fake_loss=1.369, generator_loss=27.39, generator_mel_loss=17.37, generator_kl_loss=1.448, generator_dur_loss=1.816, generator_adv_loss=1.867, generator_feat_match_loss=4.89, over 66.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.363, discriminator_fake_loss=1.342, generator_loss=28.48, generator_mel_loss=18.03, generator_kl_loss=1.422, generator_dur_loss=1.755, generator_adv_loss=1.97, generator_feat_match_loss=5.308, over 7101.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:01:44,022 INFO [train.py:919] (1/6) Start epoch 574
+2024-03-14 11:04:18,854 INFO [train.py:527] (1/6) Epoch 574, batch 48, global_batch_idx: 71100, batch size: 72, loss[discriminator_loss=2.73, discriminator_real_loss=1.464, discriminator_fake_loss=1.267, generator_loss=27.7, generator_mel_loss=17.6, generator_kl_loss=1.277, generator_dur_loss=1.816, generator_adv_loss=1.937, generator_feat_match_loss=5.068, over 72.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=28.42, generator_mel_loss=17.96, generator_kl_loss=1.389, generator_dur_loss=1.757, generator_adv_loss=1.97, generator_feat_match_loss=5.344, over 2719.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:06:39,728 INFO [train.py:527] (1/6) Epoch 574, batch 98, global_batch_idx: 71150, batch size: 62, loss[discriminator_loss=2.722, discriminator_real_loss=1.35, discriminator_fake_loss=1.371, generator_loss=28.92, generator_mel_loss=18.08, generator_kl_loss=1.357, generator_dur_loss=1.741, generator_adv_loss=1.862, generator_feat_match_loss=5.881, over 62.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.372, discriminator_fake_loss=1.335, generator_loss=28.45, generator_mel_loss=17.99, generator_kl_loss=1.412, generator_dur_loss=1.749, generator_adv_loss=1.971, generator_feat_match_loss=5.32, over 5449.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:07:51,194 INFO [train.py:919] (1/6) Start epoch 575
+2024-03-14 11:09:22,370 INFO [train.py:527] (1/6) Epoch 575, batch 24, global_batch_idx: 71200, batch size: 52, loss[discriminator_loss=2.712, discriminator_real_loss=1.335, discriminator_fake_loss=1.377, generator_loss=28.5, generator_mel_loss=17.98, generator_kl_loss=1.441, generator_dur_loss=1.655, generator_adv_loss=2.07, generator_feat_match_loss=5.359, over 52.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.34, generator_loss=28.04, generator_mel_loss=17.84, generator_kl_loss=1.408, generator_dur_loss=1.767, generator_adv_loss=1.952, generator_feat_match_loss=5.067, over 1485.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:09:22,371 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 11:09:30,379 INFO [train.py:591] (1/6) Epoch 575, validation: discriminator_loss=2.755, discriminator_real_loss=1.447, discriminator_fake_loss=1.308, generator_loss=27.86, generator_mel_loss=18.44, generator_kl_loss=1.293, generator_dur_loss=1.801, generator_adv_loss=1.956, generator_feat_match_loss=4.369, over 100.00 samples.
+2024-03-14 11:09:30,379 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 11:11:48,620 INFO [train.py:527] (1/6) Epoch 575, batch 74, global_batch_idx: 71250, batch size: 72, loss[discriminator_loss=2.678, discriminator_real_loss=1.341, discriminator_fake_loss=1.337, generator_loss=27.98, generator_mel_loss=17.97, generator_kl_loss=1.333, generator_dur_loss=1.808, generator_adv_loss=2.029, generator_feat_match_loss=4.836, over 72.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.33, generator_mel_loss=17.92, generator_kl_loss=1.412, generator_dur_loss=1.748, generator_adv_loss=1.984, generator_feat_match_loss=5.274, over 4401.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:14:03,775 INFO [train.py:919] (1/6) Start epoch 576
+2024-03-14 11:14:27,745 INFO [train.py:527] (1/6) Epoch 576, batch 0, global_batch_idx: 71300, batch size: 45, loss[discriminator_loss=2.656, discriminator_real_loss=1.309, discriminator_fake_loss=1.347, generator_loss=28.97, generator_mel_loss=18.16, generator_kl_loss=1.452, generator_dur_loss=1.644, generator_adv_loss=2.151, generator_feat_match_loss=5.561, over 45.00 samples.], tot_loss[discriminator_loss=2.656, discriminator_real_loss=1.309, discriminator_fake_loss=1.347, generator_loss=28.97, generator_mel_loss=18.16, generator_kl_loss=1.452, generator_dur_loss=1.644, generator_adv_loss=2.151, generator_feat_match_loss=5.561, over 45.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:16:46,076 INFO [train.py:527] (1/6) Epoch 576, batch 50, global_batch_idx: 71350, batch size: 53, loss[discriminator_loss=2.678, discriminator_real_loss=1.472, discriminator_fake_loss=1.206, generator_loss=28.22, generator_mel_loss=17.94, generator_kl_loss=1.471, generator_dur_loss=1.661, generator_adv_loss=1.875, generator_feat_match_loss=5.27, over 53.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.374, discriminator_fake_loss=1.346, generator_loss=28.29, generator_mel_loss=18, generator_kl_loss=1.451, generator_dur_loss=1.733, generator_adv_loss=1.962, generator_feat_match_loss=5.142, over 2853.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:19:06,115 INFO [train.py:527] (1/6) Epoch 576, batch 100, global_batch_idx: 71400, batch size: 58, loss[discriminator_loss=2.736, discriminator_real_loss=1.336, discriminator_fake_loss=1.4, generator_loss=27.7, generator_mel_loss=18.1, generator_kl_loss=1.457, generator_dur_loss=1.74, generator_adv_loss=2.081, generator_feat_match_loss=4.324, over 58.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=28.34, generator_mel_loss=17.98, generator_kl_loss=1.427, generator_dur_loss=1.756, generator_adv_loss=1.965, generator_feat_match_loss=5.214, over 5902.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:19:06,116 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 11:19:15,118 INFO [train.py:591] (1/6) Epoch 576, validation: discriminator_loss=2.765, discriminator_real_loss=1.525, discriminator_fake_loss=1.24, generator_loss=27.47, generator_mel_loss=17.97, generator_kl_loss=1.262, generator_dur_loss=1.826, generator_adv_loss=2.129, generator_feat_match_loss=4.284, over 100.00 samples.
+2024-03-14 11:19:15,119 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 11:20:16,721 INFO [train.py:919] (1/6) Start epoch 577
+2024-03-14 11:21:54,974 INFO [train.py:527] (1/6) Epoch 577, batch 26, global_batch_idx: 71450, batch size: 50, loss[discriminator_loss=2.735, discriminator_real_loss=1.386, discriminator_fake_loss=1.349, generator_loss=28.96, generator_mel_loss=18.15, generator_kl_loss=1.531, generator_dur_loss=1.686, generator_adv_loss=1.868, generator_feat_match_loss=5.729, over 50.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.359, discriminator_fake_loss=1.339, generator_loss=28.41, generator_mel_loss=17.95, generator_kl_loss=1.391, generator_dur_loss=1.781, generator_adv_loss=1.981, generator_feat_match_loss=5.308, over 1723.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:24:14,035 INFO [train.py:527] (1/6) Epoch 577, batch 76, global_batch_idx: 71500, batch size: 48, loss[discriminator_loss=2.759, discriminator_real_loss=1.43, discriminator_fake_loss=1.328, generator_loss=27.66, generator_mel_loss=17.65, generator_kl_loss=1.381, generator_dur_loss=1.725, generator_adv_loss=1.818, generator_feat_match_loss=5.092, over 48.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.368, discriminator_fake_loss=1.336, generator_loss=28.36, generator_mel_loss=17.94, generator_kl_loss=1.396, generator_dur_loss=1.77, generator_adv_loss=1.98, generator_feat_match_loss=5.28, over 4563.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:26:20,931 INFO [train.py:919] (1/6) Start epoch 578
+2024-03-14 11:26:50,778 INFO [train.py:527] (1/6) Epoch 578, batch 2, global_batch_idx: 71550, batch size: 80, loss[discriminator_loss=2.704, discriminator_real_loss=1.398, discriminator_fake_loss=1.306, generator_loss=28.24, generator_mel_loss=17.87, generator_kl_loss=1.349, generator_dur_loss=1.816, generator_adv_loss=2.013, generator_feat_match_loss=5.192, over 80.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.372, discriminator_fake_loss=1.311, generator_loss=28.6, generator_mel_loss=18.01, generator_kl_loss=1.453, generator_dur_loss=1.732, generator_adv_loss=2.031, generator_feat_match_loss=5.376, over 125.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:29:09,150 INFO [train.py:527] (1/6) Epoch 578, batch 52, global_batch_idx: 71600, batch size: 83, loss[discriminator_loss=2.675, discriminator_real_loss=1.308, discriminator_fake_loss=1.368, generator_loss=28.8, generator_mel_loss=17.88, generator_kl_loss=1.398, generator_dur_loss=1.816, generator_adv_loss=1.996, generator_feat_match_loss=5.71, over 83.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.364, discriminator_fake_loss=1.338, generator_loss=28.33, generator_mel_loss=17.96, generator_kl_loss=1.399, generator_dur_loss=1.747, generator_adv_loss=1.962, generator_feat_match_loss=5.265, over 3031.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:29:09,151 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 11:29:17,344 INFO [train.py:591] (1/6) Epoch 578, validation: discriminator_loss=2.695, discriminator_real_loss=1.409, discriminator_fake_loss=1.287, generator_loss=26.72, generator_mel_loss=17.84, generator_kl_loss=1.224, generator_dur_loss=1.803, generator_adv_loss=1.928, generator_feat_match_loss=3.927, over 100.00 samples.
+2024-03-14 11:29:17,345 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 11:31:36,519 INFO [train.py:527] (1/6) Epoch 578, batch 102, global_batch_idx: 71650, batch size: 74, loss[discriminator_loss=2.687, discriminator_real_loss=1.288, discriminator_fake_loss=1.399, generator_loss=30.02, generator_mel_loss=18.64, generator_kl_loss=1.302, generator_dur_loss=1.778, generator_adv_loss=2.081, generator_feat_match_loss=6.218, over 74.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.38, generator_mel_loss=17.99, generator_kl_loss=1.398, generator_dur_loss=1.747, generator_adv_loss=1.965, generator_feat_match_loss=5.279, over 6055.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:32:34,664 INFO [train.py:919] (1/6) Start epoch 579
+2024-03-14 11:34:17,661 INFO [train.py:527] (1/6) Epoch 579, batch 28, global_batch_idx: 71700, batch size: 62, loss[discriminator_loss=2.629, discriminator_real_loss=1.331, discriminator_fake_loss=1.298, generator_loss=29.05, generator_mel_loss=18.16, generator_kl_loss=1.432, generator_dur_loss=1.804, generator_adv_loss=1.982, generator_feat_match_loss=5.67, over 62.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.337, generator_loss=28.39, generator_mel_loss=17.99, generator_kl_loss=1.403, generator_dur_loss=1.768, generator_adv_loss=1.961, generator_feat_match_loss=5.27, over 1784.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:36:37,191 INFO [train.py:527] (1/6) Epoch 579, batch 78, global_batch_idx: 71750, batch size: 45, loss[discriminator_loss=2.658, discriminator_real_loss=1.371, discriminator_fake_loss=1.287, generator_loss=28.25, generator_mel_loss=17.93, generator_kl_loss=1.494, generator_dur_loss=1.684, generator_adv_loss=2.058, generator_feat_match_loss=5.093, over 45.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.363, discriminator_fake_loss=1.341, generator_loss=28.49, generator_mel_loss=18.02, generator_kl_loss=1.405, generator_dur_loss=1.764, generator_adv_loss=1.987, generator_feat_match_loss=5.314, over 4780.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:38:42,650 INFO [train.py:919] (1/6) Start epoch 580
+2024-03-14 11:39:18,907 INFO [train.py:527] (1/6) Epoch 580, batch 4, global_batch_idx: 71800, batch size: 70, loss[discriminator_loss=2.749, discriminator_real_loss=1.266, discriminator_fake_loss=1.483, generator_loss=28.69, generator_mel_loss=18, generator_kl_loss=1.507, generator_dur_loss=1.799, generator_adv_loss=2.06, generator_feat_match_loss=5.323, over 70.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.332, discriminator_fake_loss=1.382, generator_loss=28.71, generator_mel_loss=18.16, generator_kl_loss=1.405, generator_dur_loss=1.805, generator_adv_loss=1.95, generator_feat_match_loss=5.391, over 319.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:39:18,935 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 11:39:26,756 INFO [train.py:591] (1/6) Epoch 580, validation: discriminator_loss=2.809, discriminator_real_loss=1.511, discriminator_fake_loss=1.299, generator_loss=27.2, generator_mel_loss=18.01, generator_kl_loss=1.221, generator_dur_loss=1.822, generator_adv_loss=1.993, generator_feat_match_loss=4.153, over 100.00 samples.
+2024-03-14 11:39:26,758 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 11:41:44,351 INFO [train.py:527] (1/6) Epoch 580, batch 54, global_batch_idx: 71850, batch size: 72, loss[discriminator_loss=2.698, discriminator_real_loss=1.425, discriminator_fake_loss=1.273, generator_loss=28.42, generator_mel_loss=18.13, generator_kl_loss=1.359, generator_dur_loss=1.84, generator_adv_loss=1.788, generator_feat_match_loss=5.309, over 72.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.52, generator_mel_loss=18.11, generator_kl_loss=1.405, generator_dur_loss=1.761, generator_adv_loss=1.965, generator_feat_match_loss=5.277, over 3067.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:44:03,550 INFO [train.py:527] (1/6) Epoch 580, batch 104, global_batch_idx: 71900, batch size: 42, loss[discriminator_loss=2.661, discriminator_real_loss=1.415, discriminator_fake_loss=1.247, generator_loss=29.08, generator_mel_loss=18.5, generator_kl_loss=1.478, generator_dur_loss=1.704, generator_adv_loss=1.926, generator_feat_match_loss=5.474, over 42.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.365, discriminator_fake_loss=1.345, generator_loss=28.51, generator_mel_loss=18.09, generator_kl_loss=1.411, generator_dur_loss=1.762, generator_adv_loss=1.959, generator_feat_match_loss=5.283, over 5801.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:44:56,796 INFO [train.py:919] (1/6) Start epoch 581
+2024-03-14 11:46:43,672 INFO [train.py:527] (1/6) Epoch 581, batch 30, global_batch_idx: 71950, batch size: 50, loss[discriminator_loss=2.779, discriminator_real_loss=1.365, discriminator_fake_loss=1.414, generator_loss=28.33, generator_mel_loss=18.19, generator_kl_loss=1.48, generator_dur_loss=1.712, generator_adv_loss=1.94, generator_feat_match_loss=5.01, over 50.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.373, discriminator_fake_loss=1.33, generator_loss=28.51, generator_mel_loss=18.06, generator_kl_loss=1.424, generator_dur_loss=1.731, generator_adv_loss=1.977, generator_feat_match_loss=5.317, over 1739.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:49:02,159 INFO [train.py:527] (1/6) Epoch 581, batch 80, global_batch_idx: 72000, batch size: 55, loss[discriminator_loss=2.75, discriminator_real_loss=1.431, discriminator_fake_loss=1.32, generator_loss=28.72, generator_mel_loss=18.45, generator_kl_loss=1.267, generator_dur_loss=1.707, generator_adv_loss=1.895, generator_feat_match_loss=5.401, over 55.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.44, generator_mel_loss=18, generator_kl_loss=1.423, generator_dur_loss=1.739, generator_adv_loss=1.969, generator_feat_match_loss=5.317, over 4581.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:49:02,160 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 11:49:11,003 INFO [train.py:591] (1/6) Epoch 581, validation: discriminator_loss=2.754, discriminator_real_loss=1.407, discriminator_fake_loss=1.347, generator_loss=27.71, generator_mel_loss=18.3, generator_kl_loss=1.216, generator_dur_loss=1.806, generator_adv_loss=1.855, generator_feat_match_loss=4.537, over 100.00 samples.
+2024-03-14 11:49:11,004 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 11:51:11,217 INFO [train.py:919] (1/6) Start epoch 582
+2024-03-14 11:51:50,762 INFO [train.py:527] (1/6) Epoch 582, batch 6, global_batch_idx: 72050, batch size: 72, loss[discriminator_loss=2.754, discriminator_real_loss=1.374, discriminator_fake_loss=1.379, generator_loss=28.01, generator_mel_loss=17.94, generator_kl_loss=1.277, generator_dur_loss=1.833, generator_adv_loss=1.94, generator_feat_match_loss=5.022, over 72.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.337, discriminator_fake_loss=1.347, generator_loss=28.45, generator_mel_loss=18.01, generator_kl_loss=1.394, generator_dur_loss=1.778, generator_adv_loss=1.948, generator_feat_match_loss=5.315, over 462.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:54:09,094 INFO [train.py:527] (1/6) Epoch 582, batch 56, global_batch_idx: 72100, batch size: 83, loss[discriminator_loss=2.718, discriminator_real_loss=1.449, discriminator_fake_loss=1.269, generator_loss=29.04, generator_mel_loss=17.86, generator_kl_loss=1.424, generator_dur_loss=1.807, generator_adv_loss=1.926, generator_feat_match_loss=6.022, over 83.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.51, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.759, generator_adv_loss=1.971, generator_feat_match_loss=5.368, over 3228.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:56:29,809 INFO [train.py:527] (1/6) Epoch 582, batch 106, global_batch_idx: 72150, batch size: 47, loss[discriminator_loss=2.718, discriminator_real_loss=1.477, discriminator_fake_loss=1.241, generator_loss=28.95, generator_mel_loss=18.42, generator_kl_loss=1.442, generator_dur_loss=1.694, generator_adv_loss=1.776, generator_feat_match_loss=5.617, over 47.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=28.49, generator_mel_loss=18.02, generator_kl_loss=1.41, generator_dur_loss=1.76, generator_adv_loss=1.966, generator_feat_match_loss=5.334, over 6158.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:57:17,771 INFO [train.py:919] (1/6) Start epoch 583
+2024-03-14 11:59:10,436 INFO [train.py:527] (1/6) Epoch 583, batch 32, global_batch_idx: 72200, batch size: 50, loss[discriminator_loss=2.723, discriminator_real_loss=1.335, discriminator_fake_loss=1.388, generator_loss=28.61, generator_mel_loss=18.06, generator_kl_loss=1.4, generator_dur_loss=1.68, generator_adv_loss=1.979, generator_feat_match_loss=5.493, over 50.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.368, discriminator_fake_loss=1.321, generator_loss=28.47, generator_mel_loss=17.99, generator_kl_loss=1.416, generator_dur_loss=1.768, generator_adv_loss=1.994, generator_feat_match_loss=5.296, over 2029.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 11:59:10,438 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 11:59:18,463 INFO [train.py:591] (1/6) Epoch 583, validation: discriminator_loss=2.773, discriminator_real_loss=1.405, discriminator_fake_loss=1.368, generator_loss=27.33, generator_mel_loss=18.31, generator_kl_loss=1.174, generator_dur_loss=1.809, generator_adv_loss=1.85, generator_feat_match_loss=4.194, over 100.00 samples.
+2024-03-14 11:59:18,464 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 12:01:36,818 INFO [train.py:527] (1/6) Epoch 583, batch 82, global_batch_idx: 72250, batch size: 83, loss[discriminator_loss=2.727, discriminator_real_loss=1.336, discriminator_fake_loss=1.39, generator_loss=28.13, generator_mel_loss=18.11, generator_kl_loss=1.523, generator_dur_loss=1.823, generator_adv_loss=1.978, generator_feat_match_loss=4.698, over 83.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.365, discriminator_fake_loss=1.332, generator_loss=28.48, generator_mel_loss=17.99, generator_kl_loss=1.426, generator_dur_loss=1.764, generator_adv_loss=1.98, generator_feat_match_loss=5.317, over 4904.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:03:31,047 INFO [train.py:919] (1/6) Start epoch 584
+2024-03-14 12:04:18,566 INFO [train.py:527] (1/6) Epoch 584, batch 8, global_batch_idx: 72300, batch size: 74, loss[discriminator_loss=2.694, discriminator_real_loss=1.339, discriminator_fake_loss=1.355, generator_loss=27.66, generator_mel_loss=17.7, generator_kl_loss=1.415, generator_dur_loss=1.809, generator_adv_loss=2.077, generator_feat_match_loss=4.654, over 74.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.354, discriminator_fake_loss=1.317, generator_loss=28.21, generator_mel_loss=17.92, generator_kl_loss=1.425, generator_dur_loss=1.738, generator_adv_loss=2.015, generator_feat_match_loss=5.107, over 490.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:06:33,502 INFO [train.py:527] (1/6) Epoch 584, batch 58, global_batch_idx: 72350, batch size: 70, loss[discriminator_loss=2.713, discriminator_real_loss=1.385, discriminator_fake_loss=1.329, generator_loss=27.77, generator_mel_loss=17.77, generator_kl_loss=1.221, generator_dur_loss=1.814, generator_adv_loss=1.927, generator_feat_match_loss=5.043, over 70.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.333, generator_loss=28.4, generator_mel_loss=17.97, generator_kl_loss=1.408, generator_dur_loss=1.739, generator_adv_loss=1.977, generator_feat_match_loss=5.304, over 3292.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:08:53,085 INFO [train.py:527] (1/6) Epoch 584, batch 108, global_batch_idx: 72400, batch size: 56, loss[discriminator_loss=2.678, discriminator_real_loss=1.349, discriminator_fake_loss=1.328, generator_loss=28.27, generator_mel_loss=17.89, generator_kl_loss=1.459, generator_dur_loss=1.679, generator_adv_loss=2.025, generator_feat_match_loss=5.219, over 56.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.37, discriminator_fake_loss=1.337, generator_loss=28.43, generator_mel_loss=17.97, generator_kl_loss=1.418, generator_dur_loss=1.741, generator_adv_loss=1.983, generator_feat_match_loss=5.318, over 6125.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:08:53,086 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 12:09:02,015 INFO [train.py:591] (1/6) Epoch 584, validation: discriminator_loss=2.738, discriminator_real_loss=1.487, discriminator_fake_loss=1.251, generator_loss=27.72, generator_mel_loss=18.38, generator_kl_loss=1.309, generator_dur_loss=1.808, generator_adv_loss=1.99, generator_feat_match_loss=4.232, over 100.00 samples.
+2024-03-14 12:09:02,015 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 12:09:45,764 INFO [train.py:919] (1/6) Start epoch 585
+2024-03-14 12:11:44,635 INFO [train.py:527] (1/6) Epoch 585, batch 34, global_batch_idx: 72450, batch size: 56, loss[discriminator_loss=2.732, discriminator_real_loss=1.348, discriminator_fake_loss=1.385, generator_loss=27.89, generator_mel_loss=17.8, generator_kl_loss=1.48, generator_dur_loss=1.74, generator_adv_loss=2.021, generator_feat_match_loss=4.847, over 56.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.374, discriminator_fake_loss=1.338, generator_loss=28.55, generator_mel_loss=18.06, generator_kl_loss=1.399, generator_dur_loss=1.73, generator_adv_loss=1.972, generator_feat_match_loss=5.387, over 1924.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:14:02,812 INFO [train.py:527] (1/6) Epoch 585, batch 84, global_batch_idx: 72500, batch size: 45, loss[discriminator_loss=2.681, discriminator_real_loss=1.349, discriminator_fake_loss=1.332, generator_loss=29.29, generator_mel_loss=17.86, generator_kl_loss=1.569, generator_dur_loss=1.68, generator_adv_loss=1.92, generator_feat_match_loss=6.258, over 45.00 samples.], tot_loss[discriminator_loss=2.717, discriminator_real_loss=1.374, discriminator_fake_loss=1.343, generator_loss=28.52, generator_mel_loss=18.01, generator_kl_loss=1.409, generator_dur_loss=1.741, generator_adv_loss=1.975, generator_feat_match_loss=5.382, over 4809.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:15:54,084 INFO [train.py:919] (1/6) Start epoch 586
+2024-03-14 12:16:44,928 INFO [train.py:527] (1/6) Epoch 586, batch 10, global_batch_idx: 72550, batch size: 56, loss[discriminator_loss=2.697, discriminator_real_loss=1.293, discriminator_fake_loss=1.405, generator_loss=29.07, generator_mel_loss=17.99, generator_kl_loss=1.465, generator_dur_loss=1.697, generator_adv_loss=2.041, generator_feat_match_loss=5.876, over 56.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.344, discriminator_fake_loss=1.345, generator_loss=28.48, generator_mel_loss=17.96, generator_kl_loss=1.405, generator_dur_loss=1.756, generator_adv_loss=1.967, generator_feat_match_loss=5.392, over 642.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:19:05,850 INFO [train.py:527] (1/6) Epoch 586, batch 60, global_batch_idx: 72600, batch size: 52, loss[discriminator_loss=2.702, discriminator_real_loss=1.442, discriminator_fake_loss=1.26, generator_loss=28.07, generator_mel_loss=17.79, generator_kl_loss=1.452, generator_dur_loss=1.664, generator_adv_loss=2.023, generator_feat_match_loss=5.143, over 52.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.35, generator_mel_loss=17.95, generator_kl_loss=1.416, generator_dur_loss=1.748, generator_adv_loss=1.967, generator_feat_match_loss=5.266, over 3417.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:19:05,851 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 12:19:13,986 INFO [train.py:591] (1/6) Epoch 586, validation: discriminator_loss=2.753, discriminator_real_loss=1.447, discriminator_fake_loss=1.306, generator_loss=27.23, generator_mel_loss=18.08, generator_kl_loss=1.24, generator_dur_loss=1.796, generator_adv_loss=1.931, generator_feat_match_loss=4.183, over 100.00 samples.
+2024-03-14 12:19:13,987 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 12:21:36,534 INFO [train.py:527] (1/6) Epoch 586, batch 110, global_batch_idx: 72650, batch size: 58, loss[discriminator_loss=2.694, discriminator_real_loss=1.38, discriminator_fake_loss=1.314, generator_loss=28.68, generator_mel_loss=17.87, generator_kl_loss=1.482, generator_dur_loss=1.696, generator_adv_loss=2.01, generator_feat_match_loss=5.617, over 58.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.36, discriminator_fake_loss=1.342, generator_loss=28.44, generator_mel_loss=17.98, generator_kl_loss=1.396, generator_dur_loss=1.761, generator_adv_loss=1.964, generator_feat_match_loss=5.34, over 6527.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:22:12,963 INFO [train.py:919] (1/6) Start epoch 587
+2024-03-14 12:24:15,687 INFO [train.py:527] (1/6) Epoch 587, batch 36, global_batch_idx: 72700, batch size: 48, loss[discriminator_loss=2.679, discriminator_real_loss=1.383, discriminator_fake_loss=1.296, generator_loss=29.4, generator_mel_loss=18.69, generator_kl_loss=1.46, generator_dur_loss=1.711, generator_adv_loss=1.877, generator_feat_match_loss=5.664, over 48.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.368, discriminator_fake_loss=1.329, generator_loss=28.5, generator_mel_loss=18.04, generator_kl_loss=1.392, generator_dur_loss=1.748, generator_adv_loss=1.97, generator_feat_match_loss=5.355, over 2058.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:26:32,117 INFO [train.py:527] (1/6) Epoch 587, batch 86, global_batch_idx: 72750, batch size: 55, loss[discriminator_loss=2.702, discriminator_real_loss=1.433, discriminator_fake_loss=1.269, generator_loss=28.4, generator_mel_loss=17.94, generator_kl_loss=1.532, generator_dur_loss=1.696, generator_adv_loss=1.905, generator_feat_match_loss=5.318, over 55.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.359, discriminator_fake_loss=1.334, generator_loss=28.59, generator_mel_loss=18.04, generator_kl_loss=1.413, generator_dur_loss=1.744, generator_adv_loss=1.975, generator_feat_match_loss=5.415, over 4857.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:28:18,676 INFO [train.py:919] (1/6) Start epoch 588
+2024-03-14 12:29:14,980 INFO [train.py:527] (1/6) Epoch 588, batch 12, global_batch_idx: 72800, batch size: 36, loss[discriminator_loss=2.746, discriminator_real_loss=1.373, discriminator_fake_loss=1.373, generator_loss=27.64, generator_mel_loss=17.51, generator_kl_loss=1.514, generator_dur_loss=1.759, generator_adv_loss=1.874, generator_feat_match_loss=4.977, over 36.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.363, discriminator_fake_loss=1.337, generator_loss=28.36, generator_mel_loss=17.9, generator_kl_loss=1.401, generator_dur_loss=1.769, generator_adv_loss=1.958, generator_feat_match_loss=5.33, over 695.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:29:14,982 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 12:29:22,918 INFO [train.py:591] (1/6) Epoch 588, validation: discriminator_loss=2.725, discriminator_real_loss=1.376, discriminator_fake_loss=1.349, generator_loss=27.57, generator_mel_loss=18.14, generator_kl_loss=1.264, generator_dur_loss=1.813, generator_adv_loss=1.845, generator_feat_match_loss=4.51, over 100.00 samples.
+2024-03-14 12:29:22,919 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 12:31:42,191 INFO [train.py:527] (1/6) Epoch 588, batch 62, global_batch_idx: 72850, batch size: 70, loss[discriminator_loss=2.735, discriminator_real_loss=1.409, discriminator_fake_loss=1.325, generator_loss=27.85, generator_mel_loss=17.57, generator_kl_loss=1.338, generator_dur_loss=1.807, generator_adv_loss=1.885, generator_feat_match_loss=5.243, over 70.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.361, discriminator_fake_loss=1.341, generator_loss=28.41, generator_mel_loss=17.96, generator_kl_loss=1.446, generator_dur_loss=1.747, generator_adv_loss=1.966, generator_feat_match_loss=5.291, over 3460.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:34:01,577 INFO [train.py:527] (1/6) Epoch 588, batch 112, global_batch_idx: 72900, batch size: 45, loss[discriminator_loss=2.654, discriminator_real_loss=1.281, discriminator_fake_loss=1.373, generator_loss=28.88, generator_mel_loss=18.07, generator_kl_loss=1.539, generator_dur_loss=1.677, generator_adv_loss=2.083, generator_feat_match_loss=5.511, over 45.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=28.47, generator_mel_loss=18, generator_kl_loss=1.426, generator_dur_loss=1.742, generator_adv_loss=1.972, generator_feat_match_loss=5.324, over 6455.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:34:33,138 INFO [train.py:919] (1/6) Start epoch 589
+2024-03-14 12:36:39,657 INFO [train.py:527] (1/6) Epoch 589, batch 38, global_batch_idx: 72950, batch size: 52, loss[discriminator_loss=2.706, discriminator_real_loss=1.279, discriminator_fake_loss=1.426, generator_loss=29.12, generator_mel_loss=18.17, generator_kl_loss=1.631, generator_dur_loss=1.665, generator_adv_loss=1.912, generator_feat_match_loss=5.747, over 52.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.368, discriminator_fake_loss=1.341, generator_loss=28.55, generator_mel_loss=18, generator_kl_loss=1.436, generator_dur_loss=1.746, generator_adv_loss=1.993, generator_feat_match_loss=5.381, over 2197.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:38:59,574 INFO [train.py:527] (1/6) Epoch 589, batch 88, global_batch_idx: 73000, batch size: 53, loss[discriminator_loss=2.751, discriminator_real_loss=1.402, discriminator_fake_loss=1.35, generator_loss=28.14, generator_mel_loss=17.92, generator_kl_loss=1.369, generator_dur_loss=1.68, generator_adv_loss=1.93, generator_feat_match_loss=5.239, over 53.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.47, generator_mel_loss=18, generator_kl_loss=1.42, generator_dur_loss=1.744, generator_adv_loss=1.983, generator_feat_match_loss=5.329, over 5159.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:38:59,575 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 12:39:08,252 INFO [train.py:591] (1/6) Epoch 589, validation: discriminator_loss=2.756, discriminator_real_loss=1.378, discriminator_fake_loss=1.378, generator_loss=27.21, generator_mel_loss=18.36, generator_kl_loss=1.156, generator_dur_loss=1.813, generator_adv_loss=1.789, generator_feat_match_loss=4.092, over 100.00 samples.
+2024-03-14 12:39:08,253 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 12:40:46,066 INFO [train.py:919] (1/6) Start epoch 590
+2024-03-14 12:41:47,464 INFO [train.py:527] (1/6) Epoch 590, batch 14, global_batch_idx: 73050, batch size: 39, loss[discriminator_loss=2.74, discriminator_real_loss=1.328, discriminator_fake_loss=1.412, generator_loss=28.26, generator_mel_loss=18.3, generator_kl_loss=1.507, generator_dur_loss=1.691, generator_adv_loss=1.921, generator_feat_match_loss=4.842, over 39.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.349, generator_loss=28.54, generator_mel_loss=18.17, generator_kl_loss=1.487, generator_dur_loss=1.693, generator_adv_loss=1.943, generator_feat_match_loss=5.248, over 685.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:44:08,833 INFO [train.py:527] (1/6) Epoch 590, batch 64, global_batch_idx: 73100, batch size: 66, loss[discriminator_loss=2.627, discriminator_real_loss=1.239, discriminator_fake_loss=1.388, generator_loss=28.91, generator_mel_loss=18.18, generator_kl_loss=1.398, generator_dur_loss=1.767, generator_adv_loss=2.122, generator_feat_match_loss=5.45, over 66.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.356, discriminator_fake_loss=1.345, generator_loss=28.53, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.751, generator_adv_loss=1.956, generator_feat_match_loss=5.363, over 3612.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:46:26,585 INFO [train.py:527] (1/6) Epoch 590, batch 114, global_batch_idx: 73150, batch size: 77, loss[discriminator_loss=2.66, discriminator_real_loss=1.387, discriminator_fake_loss=1.273, generator_loss=29.29, generator_mel_loss=18.16, generator_kl_loss=1.275, generator_dur_loss=1.817, generator_adv_loss=2.116, generator_feat_match_loss=5.924, over 77.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.358, discriminator_fake_loss=1.345, generator_loss=28.59, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.756, generator_adv_loss=1.975, generator_feat_match_loss=5.396, over 6539.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:46:53,408 INFO [train.py:919] (1/6) Start epoch 591
+2024-03-14 12:49:10,690 INFO [train.py:527] (1/6) Epoch 591, batch 40, global_batch_idx: 73200, batch size: 55, loss[discriminator_loss=2.705, discriminator_real_loss=1.434, discriminator_fake_loss=1.271, generator_loss=28.44, generator_mel_loss=18.18, generator_kl_loss=1.378, generator_dur_loss=1.674, generator_adv_loss=1.954, generator_feat_match_loss=5.257, over 55.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.383, discriminator_fake_loss=1.321, generator_loss=28.57, generator_mel_loss=18.1, generator_kl_loss=1.428, generator_dur_loss=1.749, generator_adv_loss=1.976, generator_feat_match_loss=5.318, over 2365.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:49:10,691 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 12:49:18,754 INFO [train.py:591] (1/6) Epoch 591, validation: discriminator_loss=2.735, discriminator_real_loss=1.399, discriminator_fake_loss=1.336, generator_loss=27.3, generator_mel_loss=18.09, generator_kl_loss=1.265, generator_dur_loss=1.809, generator_adv_loss=1.869, generator_feat_match_loss=4.263, over 100.00 samples.
+2024-03-14 12:49:18,755 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 12:51:37,310 INFO [train.py:527] (1/6) Epoch 591, batch 90, global_batch_idx: 73250, batch size: 50, loss[discriminator_loss=2.711, discriminator_real_loss=1.225, discriminator_fake_loss=1.486, generator_loss=28.25, generator_mel_loss=18.29, generator_kl_loss=1.444, generator_dur_loss=1.704, generator_adv_loss=1.909, generator_feat_match_loss=4.912, over 50.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.376, discriminator_fake_loss=1.333, generator_loss=28.52, generator_mel_loss=18.07, generator_kl_loss=1.41, generator_dur_loss=1.754, generator_adv_loss=1.956, generator_feat_match_loss=5.328, over 5313.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:53:08,907 INFO [train.py:919] (1/6) Start epoch 592
+2024-03-14 12:54:18,442 INFO [train.py:527] (1/6) Epoch 592, batch 16, global_batch_idx: 73300, batch size: 31, loss[discriminator_loss=2.776, discriminator_real_loss=1.399, discriminator_fake_loss=1.376, generator_loss=28.47, generator_mel_loss=17.87, generator_kl_loss=1.482, generator_dur_loss=1.628, generator_adv_loss=2.049, generator_feat_match_loss=5.441, over 31.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.367, discriminator_fake_loss=1.333, generator_loss=28.25, generator_mel_loss=17.97, generator_kl_loss=1.421, generator_dur_loss=1.765, generator_adv_loss=1.96, generator_feat_match_loss=5.133, over 1002.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:56:37,643 INFO [train.py:527] (1/6) Epoch 592, batch 66, global_batch_idx: 73350, batch size: 53, loss[discriminator_loss=2.704, discriminator_real_loss=1.379, discriminator_fake_loss=1.325, generator_loss=28.27, generator_mel_loss=17.97, generator_kl_loss=1.437, generator_dur_loss=1.739, generator_adv_loss=1.954, generator_feat_match_loss=5.171, over 53.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.371, discriminator_fake_loss=1.334, generator_loss=28.44, generator_mel_loss=18.06, generator_kl_loss=1.434, generator_dur_loss=1.757, generator_adv_loss=1.957, generator_feat_match_loss=5.233, over 3711.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:58:58,825 INFO [train.py:527] (1/6) Epoch 592, batch 116, global_batch_idx: 73400, batch size: 70, loss[discriminator_loss=2.774, discriminator_real_loss=1.288, discriminator_fake_loss=1.486, generator_loss=28.22, generator_mel_loss=18.05, generator_kl_loss=1.358, generator_dur_loss=1.781, generator_adv_loss=2.026, generator_feat_match_loss=5.007, over 70.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.369, discriminator_fake_loss=1.341, generator_loss=28.47, generator_mel_loss=18.04, generator_kl_loss=1.421, generator_dur_loss=1.762, generator_adv_loss=1.957, generator_feat_match_loss=5.293, over 6754.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 12:58:58,827 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 12:59:07,930 INFO [train.py:591] (1/6) Epoch 592, validation: discriminator_loss=2.751, discriminator_real_loss=1.511, discriminator_fake_loss=1.239, generator_loss=27.84, generator_mel_loss=18.23, generator_kl_loss=1.2, generator_dur_loss=1.824, generator_adv_loss=2.03, generator_feat_match_loss=4.553, over 100.00 samples.
+2024-03-14 12:59:07,931 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 12:59:29,445 INFO [train.py:919] (1/6) Start epoch 593
+2024-03-14 13:01:48,743 INFO [train.py:527] (1/6) Epoch 593, batch 42, global_batch_idx: 73450, batch size: 36, loss[discriminator_loss=2.643, discriminator_real_loss=1.329, discriminator_fake_loss=1.314, generator_loss=29, generator_mel_loss=18.24, generator_kl_loss=1.469, generator_dur_loss=1.761, generator_adv_loss=1.966, generator_feat_match_loss=5.566, over 36.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.357, discriminator_fake_loss=1.33, generator_loss=28.77, generator_mel_loss=18.12, generator_kl_loss=1.43, generator_dur_loss=1.748, generator_adv_loss=1.986, generator_feat_match_loss=5.482, over 2222.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:04:07,663 INFO [train.py:527] (1/6) Epoch 593, batch 92, global_batch_idx: 73500, batch size: 64, loss[discriminator_loss=2.629, discriminator_real_loss=1.378, discriminator_fake_loss=1.251, generator_loss=28.73, generator_mel_loss=17.89, generator_kl_loss=1.352, generator_dur_loss=1.737, generator_adv_loss=2.001, generator_feat_match_loss=5.754, over 64.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=28.52, generator_mel_loss=18.03, generator_kl_loss=1.417, generator_dur_loss=1.759, generator_adv_loss=1.971, generator_feat_match_loss=5.339, over 5296.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:05:34,047 INFO [train.py:919] (1/6) Start epoch 594
+2024-03-14 13:06:50,810 INFO [train.py:527] (1/6) Epoch 594, batch 18, global_batch_idx: 73550, batch size: 44, loss[discriminator_loss=2.719, discriminator_real_loss=1.33, discriminator_fake_loss=1.389, generator_loss=29, generator_mel_loss=18.22, generator_kl_loss=1.645, generator_dur_loss=1.694, generator_adv_loss=2.014, generator_feat_match_loss=5.431, over 44.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.349, discriminator_fake_loss=1.328, generator_loss=28.56, generator_mel_loss=17.92, generator_kl_loss=1.406, generator_dur_loss=1.753, generator_adv_loss=2.009, generator_feat_match_loss=5.468, over 1039.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:09:08,313 INFO [train.py:527] (1/6) Epoch 594, batch 68, global_batch_idx: 73600, batch size: 44, loss[discriminator_loss=2.678, discriminator_real_loss=1.369, discriminator_fake_loss=1.309, generator_loss=28.43, generator_mel_loss=18, generator_kl_loss=1.576, generator_dur_loss=1.71, generator_adv_loss=1.992, generator_feat_match_loss=5.155, over 44.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.358, discriminator_fake_loss=1.34, generator_loss=28.61, generator_mel_loss=18.02, generator_kl_loss=1.405, generator_dur_loss=1.749, generator_adv_loss=1.975, generator_feat_match_loss=5.46, over 3811.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:09:08,314 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 13:09:17,139 INFO [train.py:591] (1/6) Epoch 594, validation: discriminator_loss=2.726, discriminator_real_loss=1.396, discriminator_fake_loss=1.33, generator_loss=27.5, generator_mel_loss=18.34, generator_kl_loss=1.188, generator_dur_loss=1.814, generator_adv_loss=1.887, generator_feat_match_loss=4.266, over 100.00 samples.
+2024-03-14 13:09:17,140 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 13:11:34,949 INFO [train.py:527] (1/6) Epoch 594, batch 118, global_batch_idx: 73650, batch size: 45, loss[discriminator_loss=2.702, discriminator_real_loss=1.379, discriminator_fake_loss=1.323, generator_loss=27.88, generator_mel_loss=18.05, generator_kl_loss=1.42, generator_dur_loss=1.711, generator_adv_loss=2.026, generator_feat_match_loss=4.679, over 45.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.56, generator_mel_loss=18.01, generator_kl_loss=1.401, generator_dur_loss=1.751, generator_adv_loss=1.979, generator_feat_match_loss=5.417, over 6651.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:11:50,303 INFO [train.py:919] (1/6) Start epoch 595
+2024-03-14 13:14:15,306 INFO [train.py:527] (1/6) Epoch 595, batch 44, global_batch_idx: 73700, batch size: 70, loss[discriminator_loss=2.694, discriminator_real_loss=1.438, discriminator_fake_loss=1.256, generator_loss=27.66, generator_mel_loss=17.86, generator_kl_loss=1.302, generator_dur_loss=1.803, generator_adv_loss=1.872, generator_feat_match_loss=4.817, over 70.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.361, discriminator_fake_loss=1.337, generator_loss=28.37, generator_mel_loss=17.94, generator_kl_loss=1.39, generator_dur_loss=1.752, generator_adv_loss=1.959, generator_feat_match_loss=5.324, over 2672.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:16:36,059 INFO [train.py:527] (1/6) Epoch 595, batch 94, global_batch_idx: 73750, batch size: 61, loss[discriminator_loss=2.723, discriminator_real_loss=1.397, discriminator_fake_loss=1.326, generator_loss=27.5, generator_mel_loss=17.79, generator_kl_loss=1.44, generator_dur_loss=1.725, generator_adv_loss=2.032, generator_feat_match_loss=4.513, over 61.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.367, discriminator_fake_loss=1.339, generator_loss=28.47, generator_mel_loss=18.01, generator_kl_loss=1.403, generator_dur_loss=1.75, generator_adv_loss=1.963, generator_feat_match_loss=5.34, over 5500.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:17:55,653 INFO [train.py:919] (1/6) Start epoch 596
+2024-03-14 13:19:12,833 INFO [train.py:527] (1/6) Epoch 596, batch 20, global_batch_idx: 73800, batch size: 44, loss[discriminator_loss=2.719, discriminator_real_loss=1.461, discriminator_fake_loss=1.258, generator_loss=27.81, generator_mel_loss=17.63, generator_kl_loss=1.586, generator_dur_loss=1.668, generator_adv_loss=1.919, generator_feat_match_loss=5.011, over 44.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.374, discriminator_fake_loss=1.336, generator_loss=28.71, generator_mel_loss=18.1, generator_kl_loss=1.428, generator_dur_loss=1.75, generator_adv_loss=1.989, generator_feat_match_loss=5.442, over 1238.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:19:12,835 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 13:19:20,980 INFO [train.py:591] (1/6) Epoch 596, validation: discriminator_loss=2.747, discriminator_real_loss=1.402, discriminator_fake_loss=1.344, generator_loss=27.21, generator_mel_loss=18.33, generator_kl_loss=1.287, generator_dur_loss=1.792, generator_adv_loss=1.801, generator_feat_match_loss=4.006, over 100.00 samples.
+2024-03-14 13:19:20,981 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 13:21:39,515 INFO [train.py:527] (1/6) Epoch 596, batch 70, global_batch_idx: 73850, batch size: 58, loss[discriminator_loss=2.693, discriminator_real_loss=1.415, discriminator_fake_loss=1.278, generator_loss=29.72, generator_mel_loss=18.3, generator_kl_loss=1.496, generator_dur_loss=1.681, generator_adv_loss=2.147, generator_feat_match_loss=6.089, over 58.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.376, discriminator_fake_loss=1.334, generator_loss=28.53, generator_mel_loss=18.05, generator_kl_loss=1.427, generator_dur_loss=1.74, generator_adv_loss=1.983, generator_feat_match_loss=5.332, over 3978.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:23:57,282 INFO [train.py:527] (1/6) Epoch 596, batch 120, global_batch_idx: 73900, batch size: 96, loss[discriminator_loss=2.72, discriminator_real_loss=1.34, discriminator_fake_loss=1.381, generator_loss=28.05, generator_mel_loss=17.9, generator_kl_loss=1.318, generator_dur_loss=1.838, generator_adv_loss=1.931, generator_feat_match_loss=5.07, over 96.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.374, discriminator_fake_loss=1.334, generator_loss=28.49, generator_mel_loss=18.03, generator_kl_loss=1.428, generator_dur_loss=1.737, generator_adv_loss=1.979, generator_feat_match_loss=5.313, over 6755.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:24:08,815 INFO [train.py:919] (1/6) Start epoch 597
+2024-03-14 13:26:40,741 INFO [train.py:527] (1/6) Epoch 597, batch 46, global_batch_idx: 73950, batch size: 52, loss[discriminator_loss=2.737, discriminator_real_loss=1.368, discriminator_fake_loss=1.369, generator_loss=27.77, generator_mel_loss=17.86, generator_kl_loss=1.312, generator_dur_loss=1.712, generator_adv_loss=1.974, generator_feat_match_loss=4.908, over 52.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.365, discriminator_fake_loss=1.339, generator_loss=28.49, generator_mel_loss=17.99, generator_kl_loss=1.406, generator_dur_loss=1.759, generator_adv_loss=1.971, generator_feat_match_loss=5.367, over 2783.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:29:01,382 INFO [train.py:527] (1/6) Epoch 597, batch 96, global_batch_idx: 74000, batch size: 58, loss[discriminator_loss=2.72, discriminator_real_loss=1.302, discriminator_fake_loss=1.418, generator_loss=29.53, generator_mel_loss=18.3, generator_kl_loss=1.338, generator_dur_loss=1.704, generator_adv_loss=2.04, generator_feat_match_loss=6.149, over 58.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.366, discriminator_fake_loss=1.34, generator_loss=28.51, generator_mel_loss=18.02, generator_kl_loss=1.407, generator_dur_loss=1.764, generator_adv_loss=1.967, generator_feat_match_loss=5.353, over 5825.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:29:01,383 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 13:29:10,116 INFO [train.py:591] (1/6) Epoch 597, validation: discriminator_loss=2.738, discriminator_real_loss=1.466, discriminator_fake_loss=1.271, generator_loss=27, generator_mel_loss=18.13, generator_kl_loss=1.24, generator_dur_loss=1.825, generator_adv_loss=1.864, generator_feat_match_loss=3.936, over 100.00 samples.
+2024-03-14 13:29:10,117 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 13:30:21,880 INFO [train.py:919] (1/6) Start epoch 598
+2024-03-14 13:31:45,910 INFO [train.py:527] (1/6) Epoch 598, batch 22, global_batch_idx: 74050, batch size: 31, loss[discriminator_loss=2.727, discriminator_real_loss=1.384, discriminator_fake_loss=1.343, generator_loss=28.16, generator_mel_loss=17.85, generator_kl_loss=1.506, generator_dur_loss=1.677, generator_adv_loss=1.974, generator_feat_match_loss=5.155, over 31.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.359, discriminator_fake_loss=1.343, generator_loss=28.78, generator_mel_loss=18.1, generator_kl_loss=1.444, generator_dur_loss=1.748, generator_adv_loss=1.947, generator_feat_match_loss=5.539, over 1240.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:34:05,439 INFO [train.py:527] (1/6) Epoch 598, batch 72, global_batch_idx: 74100, batch size: 36, loss[discriminator_loss=2.696, discriminator_real_loss=1.396, discriminator_fake_loss=1.3, generator_loss=28.29, generator_mel_loss=18.04, generator_kl_loss=1.603, generator_dur_loss=1.689, generator_adv_loss=1.918, generator_feat_match_loss=5.038, over 36.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.359, discriminator_fake_loss=1.335, generator_loss=28.6, generator_mel_loss=18.04, generator_kl_loss=1.415, generator_dur_loss=1.764, generator_adv_loss=1.964, generator_feat_match_loss=5.413, over 4296.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:36:21,511 INFO [train.py:527] (1/6) Epoch 598, batch 122, global_batch_idx: 74150, batch size: 66, loss[discriminator_loss=2.709, discriminator_real_loss=1.329, discriminator_fake_loss=1.38, generator_loss=28.4, generator_mel_loss=18.15, generator_kl_loss=1.314, generator_dur_loss=1.797, generator_adv_loss=1.956, generator_feat_match_loss=5.18, over 66.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.336, generator_loss=28.55, generator_mel_loss=18.05, generator_kl_loss=1.409, generator_dur_loss=1.76, generator_adv_loss=1.964, generator_feat_match_loss=5.371, over 7124.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:36:26,515 INFO [train.py:919] (1/6) Start epoch 599
+2024-03-14 13:39:04,745 INFO [train.py:527] (1/6) Epoch 599, batch 48, global_batch_idx: 74200, batch size: 45, loss[discriminator_loss=2.746, discriminator_real_loss=1.279, discriminator_fake_loss=1.468, generator_loss=28.89, generator_mel_loss=17.91, generator_kl_loss=1.596, generator_dur_loss=1.644, generator_adv_loss=1.991, generator_feat_match_loss=5.754, over 45.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.371, discriminator_fake_loss=1.345, generator_loss=28.39, generator_mel_loss=17.95, generator_kl_loss=1.405, generator_dur_loss=1.741, generator_adv_loss=1.958, generator_feat_match_loss=5.336, over 2796.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:39:04,746 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 13:39:12,922 INFO [train.py:591] (1/6) Epoch 599, validation: discriminator_loss=2.758, discriminator_real_loss=1.444, discriminator_fake_loss=1.314, generator_loss=26.97, generator_mel_loss=17.84, generator_kl_loss=1.218, generator_dur_loss=1.802, generator_adv_loss=1.894, generator_feat_match_loss=4.218, over 100.00 samples.
+2024-03-14 13:39:12,924 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 13:41:35,627 INFO [train.py:527] (1/6) Epoch 599, batch 98, global_batch_idx: 74250, batch size: 53, loss[discriminator_loss=2.685, discriminator_real_loss=1.376, discriminator_fake_loss=1.309, generator_loss=27.95, generator_mel_loss=17.89, generator_kl_loss=1.645, generator_dur_loss=1.72, generator_adv_loss=1.908, generator_feat_match_loss=4.787, over 53.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.375, discriminator_fake_loss=1.338, generator_loss=28.35, generator_mel_loss=17.96, generator_kl_loss=1.403, generator_dur_loss=1.75, generator_adv_loss=1.96, generator_feat_match_loss=5.272, over 5818.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:42:42,907 INFO [train.py:919] (1/6) Start epoch 600
+2024-03-14 13:44:13,800 INFO [train.py:527] (1/6) Epoch 600, batch 24, global_batch_idx: 74300, batch size: 74, loss[discriminator_loss=2.695, discriminator_real_loss=1.375, discriminator_fake_loss=1.32, generator_loss=28.08, generator_mel_loss=17.93, generator_kl_loss=1.528, generator_dur_loss=1.785, generator_adv_loss=1.899, generator_feat_match_loss=4.94, over 74.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.339, generator_loss=28.44, generator_mel_loss=18.01, generator_kl_loss=1.4, generator_dur_loss=1.736, generator_adv_loss=1.966, generator_feat_match_loss=5.331, over 1471.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:46:29,187 INFO [train.py:527] (1/6) Epoch 600, batch 74, global_batch_idx: 74350, batch size: 77, loss[discriminator_loss=2.728, discriminator_real_loss=1.378, discriminator_fake_loss=1.35, generator_loss=28.23, generator_mel_loss=17.92, generator_kl_loss=1.477, generator_dur_loss=1.79, generator_adv_loss=1.972, generator_feat_match_loss=5.074, over 77.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=28.61, generator_mel_loss=18.04, generator_kl_loss=1.405, generator_dur_loss=1.733, generator_adv_loss=1.999, generator_feat_match_loss=5.433, over 4194.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:48:48,162 INFO [train.py:919] (1/6) Start epoch 601
+2024-03-14 13:49:13,043 INFO [train.py:527] (1/6) Epoch 601, batch 0, global_batch_idx: 74400, batch size: 62, loss[discriminator_loss=2.732, discriminator_real_loss=1.393, discriminator_fake_loss=1.34, generator_loss=28.87, generator_mel_loss=17.81, generator_kl_loss=1.535, generator_dur_loss=1.666, generator_adv_loss=1.919, generator_feat_match_loss=5.935, over 62.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.393, discriminator_fake_loss=1.34, generator_loss=28.87, generator_mel_loss=17.81, generator_kl_loss=1.535, generator_dur_loss=1.666, generator_adv_loss=1.919, generator_feat_match_loss=5.935, over 62.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:49:13,045 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 13:49:21,056 INFO [train.py:591] (1/6) Epoch 601, validation: discriminator_loss=2.735, discriminator_real_loss=1.363, discriminator_fake_loss=1.373, generator_loss=26.98, generator_mel_loss=18.09, generator_kl_loss=1.216, generator_dur_loss=1.794, generator_adv_loss=1.801, generator_feat_match_loss=4.075, over 100.00 samples.
+2024-03-14 13:49:21,058 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 13:51:41,501 INFO [train.py:527] (1/6) Epoch 601, batch 50, global_batch_idx: 74450, batch size: 31, loss[discriminator_loss=2.767, discriminator_real_loss=1.384, discriminator_fake_loss=1.384, generator_loss=28.26, generator_mel_loss=18.32, generator_kl_loss=1.562, generator_dur_loss=1.618, generator_adv_loss=1.988, generator_feat_match_loss=4.774, over 31.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.369, discriminator_fake_loss=1.337, generator_loss=28.35, generator_mel_loss=17.94, generator_kl_loss=1.393, generator_dur_loss=1.758, generator_adv_loss=1.959, generator_feat_match_loss=5.299, over 3119.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:53:59,534 INFO [train.py:527] (1/6) Epoch 601, batch 100, global_batch_idx: 74500, batch size: 66, loss[discriminator_loss=2.686, discriminator_real_loss=1.276, discriminator_fake_loss=1.41, generator_loss=29.24, generator_mel_loss=18.14, generator_kl_loss=1.381, generator_dur_loss=1.751, generator_adv_loss=2.104, generator_feat_match_loss=5.858, over 66.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=28.45, generator_mel_loss=18.01, generator_kl_loss=1.419, generator_dur_loss=1.754, generator_adv_loss=1.961, generator_feat_match_loss=5.309, over 5877.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:55:06,450 INFO [train.py:919] (1/6) Start epoch 602
+2024-03-14 13:56:40,537 INFO [train.py:527] (1/6) Epoch 602, batch 26, global_batch_idx: 74550, batch size: 80, loss[discriminator_loss=2.705, discriminator_real_loss=1.333, discriminator_fake_loss=1.372, generator_loss=27.87, generator_mel_loss=17.95, generator_kl_loss=1.332, generator_dur_loss=1.812, generator_adv_loss=1.998, generator_feat_match_loss=4.783, over 80.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.355, discriminator_fake_loss=1.335, generator_loss=28.59, generator_mel_loss=18.06, generator_kl_loss=1.403, generator_dur_loss=1.744, generator_adv_loss=2.001, generator_feat_match_loss=5.385, over 1517.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:58:59,357 INFO [train.py:527] (1/6) Epoch 602, batch 76, global_batch_idx: 74600, batch size: 74, loss[discriminator_loss=2.726, discriminator_real_loss=1.522, discriminator_fake_loss=1.205, generator_loss=27.57, generator_mel_loss=17.52, generator_kl_loss=1.332, generator_dur_loss=1.789, generator_adv_loss=1.819, generator_feat_match_loss=5.11, over 74.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.67, generator_mel_loss=18.05, generator_kl_loss=1.428, generator_dur_loss=1.741, generator_adv_loss=1.976, generator_feat_match_loss=5.468, over 4092.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 13:58:59,358 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 13:59:08,242 INFO [train.py:591] (1/6) Epoch 602, validation: discriminator_loss=2.754, discriminator_real_loss=1.314, discriminator_fake_loss=1.44, generator_loss=27.16, generator_mel_loss=18.23, generator_kl_loss=1.299, generator_dur_loss=1.809, generator_adv_loss=1.708, generator_feat_match_loss=4.116, over 100.00 samples.
+2024-03-14 13:59:08,243 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 14:01:20,920 INFO [train.py:919] (1/6) Start epoch 603
+2024-03-14 14:01:46,500 INFO [train.py:527] (1/6) Epoch 603, batch 2, global_batch_idx: 74650, batch size: 42, loss[discriminator_loss=2.688, discriminator_real_loss=1.406, discriminator_fake_loss=1.283, generator_loss=29.09, generator_mel_loss=18.17, generator_kl_loss=1.526, generator_dur_loss=1.65, generator_adv_loss=2.002, generator_feat_match_loss=5.737, over 42.00 samples.], tot_loss[discriminator_loss=2.732, discriminator_real_loss=1.393, discriminator_fake_loss=1.34, generator_loss=28.01, generator_mel_loss=17.95, generator_kl_loss=1.276, generator_dur_loss=1.771, generator_adv_loss=1.946, generator_feat_match_loss=5.067, over 189.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 14:04:04,837 INFO [train.py:527] (1/6) Epoch 603, batch 52, global_batch_idx: 74700, batch size: 25, loss[discriminator_loss=2.56, discriminator_real_loss=1.296, discriminator_fake_loss=1.264, generator_loss=29.25, generator_mel_loss=18.43, generator_kl_loss=1.745, generator_dur_loss=1.544, generator_adv_loss=2.082, generator_feat_match_loss=5.452, over 25.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.367, discriminator_fake_loss=1.341, generator_loss=28.42, generator_mel_loss=18, generator_kl_loss=1.4, generator_dur_loss=1.741, generator_adv_loss=1.97, generator_feat_match_loss=5.311, over 3004.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 14:06:22,680 INFO [train.py:527] (1/6) Epoch 603, batch 102, global_batch_idx: 74750, batch size: 50, loss[discriminator_loss=2.744, discriminator_real_loss=1.401, discriminator_fake_loss=1.342, generator_loss=28.61, generator_mel_loss=18.17, generator_kl_loss=1.461, generator_dur_loss=1.661, generator_adv_loss=1.903, generator_feat_match_loss=5.419, over 50.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.369, discriminator_fake_loss=1.338, generator_loss=28.44, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.74, generator_adv_loss=1.97, generator_feat_match_loss=5.318, over 5816.00 samples.], cur_lr_g: 1.86e-04, cur_lr_d: 1.86e-04, 
+2024-03-14 14:07:25,635 INFO [train.py:919] (1/6) Start epoch 604
+2024-03-14 14:09:09,355 INFO [train.py:527] (1/6) Epoch 604, batch 28, global_batch_idx: 74800, batch size: 70, loss[discriminator_loss=2.656, discriminator_real_loss=1.39, discriminator_fake_loss=1.266, generator_loss=28.96, generator_mel_loss=18.27, generator_kl_loss=1.32, generator_dur_loss=1.738, generator_adv_loss=1.915, generator_feat_match_loss=5.718, over 70.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.361, discriminator_fake_loss=1.337, generator_loss=28.4, generator_mel_loss=17.91, generator_kl_loss=1.41, generator_dur_loss=1.717, generator_adv_loss=1.992, generator_feat_match_loss=5.368, over 1596.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:09:09,357 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 14:09:17,230 INFO [train.py:591] (1/6) Epoch 604, validation: discriminator_loss=2.716, discriminator_real_loss=1.339, discriminator_fake_loss=1.377, generator_loss=27.63, generator_mel_loss=18.5, generator_kl_loss=1.246, generator_dur_loss=1.771, generator_adv_loss=1.832, generator_feat_match_loss=4.284, over 100.00 samples.
+2024-03-14 14:09:17,230 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 14:11:36,557 INFO [train.py:527] (1/6) Epoch 604, batch 78, global_batch_idx: 74850, batch size: 25, loss[discriminator_loss=2.682, discriminator_real_loss=1.339, discriminator_fake_loss=1.343, generator_loss=31.5, generator_mel_loss=19.12, generator_kl_loss=2.062, generator_dur_loss=1.575, generator_adv_loss=1.908, generator_feat_match_loss=6.839, over 25.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.37, discriminator_fake_loss=1.343, generator_loss=28.52, generator_mel_loss=17.99, generator_kl_loss=1.437, generator_dur_loss=1.722, generator_adv_loss=1.98, generator_feat_match_loss=5.39, over 4327.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:13:40,048 INFO [train.py:919] (1/6) Start epoch 605
+2024-03-14 14:14:16,475 INFO [train.py:527] (1/6) Epoch 605, batch 4, global_batch_idx: 74900, batch size: 72, loss[discriminator_loss=2.705, discriminator_real_loss=1.382, discriminator_fake_loss=1.324, generator_loss=28.16, generator_mel_loss=18.1, generator_kl_loss=1.4, generator_dur_loss=1.78, generator_adv_loss=1.883, generator_feat_match_loss=4.995, over 72.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.39, discriminator_fake_loss=1.345, generator_loss=28.63, generator_mel_loss=18.21, generator_kl_loss=1.413, generator_dur_loss=1.763, generator_adv_loss=1.966, generator_feat_match_loss=5.282, over 313.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:16:33,103 INFO [train.py:527] (1/6) Epoch 605, batch 54, global_batch_idx: 74950, batch size: 66, loss[discriminator_loss=2.695, discriminator_real_loss=1.345, discriminator_fake_loss=1.35, generator_loss=28.57, generator_mel_loss=18.02, generator_kl_loss=1.377, generator_dur_loss=1.807, generator_adv_loss=2.16, generator_feat_match_loss=5.211, over 66.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.365, discriminator_fake_loss=1.333, generator_loss=28.75, generator_mel_loss=18.09, generator_kl_loss=1.421, generator_dur_loss=1.752, generator_adv_loss=1.973, generator_feat_match_loss=5.523, over 3256.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:18:54,462 INFO [train.py:527] (1/6) Epoch 605, batch 104, global_batch_idx: 75000, batch size: 58, loss[discriminator_loss=2.728, discriminator_real_loss=1.45, discriminator_fake_loss=1.278, generator_loss=27.87, generator_mel_loss=17.91, generator_kl_loss=1.396, generator_dur_loss=1.717, generator_adv_loss=1.851, generator_feat_match_loss=4.992, over 58.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.369, discriminator_fake_loss=1.337, generator_loss=28.65, generator_mel_loss=18.07, generator_kl_loss=1.41, generator_dur_loss=1.751, generator_adv_loss=1.97, generator_feat_match_loss=5.452, over 6143.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:18:54,463 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 14:19:03,049 INFO [train.py:591] (1/6) Epoch 605, validation: discriminator_loss=2.722, discriminator_real_loss=1.388, discriminator_fake_loss=1.334, generator_loss=27.13, generator_mel_loss=18.15, generator_kl_loss=1.287, generator_dur_loss=1.809, generator_adv_loss=1.841, generator_feat_match_loss=4.041, over 100.00 samples.
+2024-03-14 14:19:03,050 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 14:19:55,330 INFO [train.py:919] (1/6) Start epoch 606
+2024-03-14 14:21:42,309 INFO [train.py:527] (1/6) Epoch 606, batch 30, global_batch_idx: 75050, batch size: 62, loss[discriminator_loss=2.74, discriminator_real_loss=1.387, discriminator_fake_loss=1.353, generator_loss=27.6, generator_mel_loss=17.68, generator_kl_loss=1.43, generator_dur_loss=1.762, generator_adv_loss=1.975, generator_feat_match_loss=4.755, over 62.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.365, discriminator_fake_loss=1.327, generator_loss=28.64, generator_mel_loss=18, generator_kl_loss=1.451, generator_dur_loss=1.738, generator_adv_loss=1.99, generator_feat_match_loss=5.466, over 1681.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:24:04,922 INFO [train.py:527] (1/6) Epoch 606, batch 80, global_batch_idx: 75100, batch size: 77, loss[discriminator_loss=2.706, discriminator_real_loss=1.329, discriminator_fake_loss=1.377, generator_loss=28.25, generator_mel_loss=17.64, generator_kl_loss=1.365, generator_dur_loss=1.825, generator_adv_loss=1.927, generator_feat_match_loss=5.485, over 77.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.55, generator_mel_loss=17.97, generator_kl_loss=1.438, generator_dur_loss=1.754, generator_adv_loss=1.969, generator_feat_match_loss=5.42, over 4513.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:26:04,481 INFO [train.py:919] (1/6) Start epoch 607
+2024-03-14 14:26:45,411 INFO [train.py:527] (1/6) Epoch 607, batch 6, global_batch_idx: 75150, batch size: 55, loss[discriminator_loss=2.754, discriminator_real_loss=1.389, discriminator_fake_loss=1.365, generator_loss=27.9, generator_mel_loss=17.77, generator_kl_loss=1.316, generator_dur_loss=1.714, generator_adv_loss=1.939, generator_feat_match_loss=5.168, over 55.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.382, discriminator_fake_loss=1.333, generator_loss=28.61, generator_mel_loss=18.09, generator_kl_loss=1.398, generator_dur_loss=1.785, generator_adv_loss=1.986, generator_feat_match_loss=5.348, over 453.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:29:00,949 INFO [train.py:527] (1/6) Epoch 607, batch 56, global_batch_idx: 75200, batch size: 39, loss[discriminator_loss=2.676, discriminator_real_loss=1.324, discriminator_fake_loss=1.351, generator_loss=29.49, generator_mel_loss=18.22, generator_kl_loss=1.419, generator_dur_loss=1.657, generator_adv_loss=2.019, generator_feat_match_loss=6.175, over 39.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.53, generator_mel_loss=18.04, generator_kl_loss=1.411, generator_dur_loss=1.746, generator_adv_loss=1.967, generator_feat_match_loss=5.365, over 3299.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:29:00,951 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 14:29:09,186 INFO [train.py:591] (1/6) Epoch 607, validation: discriminator_loss=2.745, discriminator_real_loss=1.433, discriminator_fake_loss=1.312, generator_loss=27.1, generator_mel_loss=18.15, generator_kl_loss=1.205, generator_dur_loss=1.797, generator_adv_loss=1.883, generator_feat_match_loss=4.067, over 100.00 samples.
+2024-03-14 14:29:09,187 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 14:31:28,904 INFO [train.py:527] (1/6) Epoch 607, batch 106, global_batch_idx: 75250, batch size: 42, loss[discriminator_loss=2.856, discriminator_real_loss=1.22, discriminator_fake_loss=1.636, generator_loss=28.54, generator_mel_loss=17.92, generator_kl_loss=1.493, generator_dur_loss=1.709, generator_adv_loss=2.223, generator_feat_match_loss=5.192, over 42.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.368, discriminator_fake_loss=1.338, generator_loss=28.52, generator_mel_loss=18.03, generator_kl_loss=1.41, generator_dur_loss=1.743, generator_adv_loss=1.968, generator_feat_match_loss=5.369, over 6170.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:32:17,867 INFO [train.py:919] (1/6) Start epoch 608
+2024-03-14 14:34:09,971 INFO [train.py:527] (1/6) Epoch 608, batch 32, global_batch_idx: 75300, batch size: 52, loss[discriminator_loss=2.707, discriminator_real_loss=1.415, discriminator_fake_loss=1.292, generator_loss=28.45, generator_mel_loss=17.78, generator_kl_loss=1.453, generator_dur_loss=1.71, generator_adv_loss=2.073, generator_feat_match_loss=5.431, over 52.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.367, discriminator_fake_loss=1.313, generator_loss=28.61, generator_mel_loss=18.02, generator_kl_loss=1.434, generator_dur_loss=1.72, generator_adv_loss=2.003, generator_feat_match_loss=5.43, over 1768.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:36:29,399 INFO [train.py:527] (1/6) Epoch 608, batch 82, global_batch_idx: 75350, batch size: 64, loss[discriminator_loss=2.687, discriminator_real_loss=1.33, discriminator_fake_loss=1.357, generator_loss=28.05, generator_mel_loss=17.72, generator_kl_loss=1.444, generator_dur_loss=1.714, generator_adv_loss=2.107, generator_feat_match_loss=5.059, over 64.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.367, discriminator_fake_loss=1.325, generator_loss=28.5, generator_mel_loss=17.96, generator_kl_loss=1.434, generator_dur_loss=1.731, generator_adv_loss=1.985, generator_feat_match_loss=5.389, over 4629.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:38:20,754 INFO [train.py:919] (1/6) Start epoch 609
+2024-03-14 14:39:08,789 INFO [train.py:527] (1/6) Epoch 609, batch 8, global_batch_idx: 75400, batch size: 88, loss[discriminator_loss=2.715, discriminator_real_loss=1.318, discriminator_fake_loss=1.397, generator_loss=28.35, generator_mel_loss=18.05, generator_kl_loss=1.42, generator_dur_loss=1.842, generator_adv_loss=1.991, generator_feat_match_loss=5.048, over 88.00 samples.], tot_loss[discriminator_loss=2.712, discriminator_real_loss=1.363, discriminator_fake_loss=1.349, generator_loss=28.37, generator_mel_loss=17.91, generator_kl_loss=1.425, generator_dur_loss=1.743, generator_adv_loss=1.947, generator_feat_match_loss=5.341, over 526.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:39:08,792 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 14:39:16,784 INFO [train.py:591] (1/6) Epoch 609, validation: discriminator_loss=2.72, discriminator_real_loss=1.486, discriminator_fake_loss=1.235, generator_loss=27.72, generator_mel_loss=18.25, generator_kl_loss=1.291, generator_dur_loss=1.806, generator_adv_loss=2.001, generator_feat_match_loss=4.371, over 100.00 samples.
+2024-03-14 14:39:16,786 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 14:41:33,474 INFO [train.py:527] (1/6) Epoch 609, batch 58, global_batch_idx: 75450, batch size: 68, loss[discriminator_loss=2.721, discriminator_real_loss=1.33, discriminator_fake_loss=1.39, generator_loss=28.74, generator_mel_loss=18.06, generator_kl_loss=1.52, generator_dur_loss=1.765, generator_adv_loss=1.948, generator_feat_match_loss=5.448, over 68.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.355, discriminator_fake_loss=1.342, generator_loss=28.6, generator_mel_loss=18, generator_kl_loss=1.439, generator_dur_loss=1.744, generator_adv_loss=1.974, generator_feat_match_loss=5.436, over 3308.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:43:52,394 INFO [train.py:527] (1/6) Epoch 609, batch 108, global_batch_idx: 75500, batch size: 96, loss[discriminator_loss=2.668, discriminator_real_loss=1.293, discriminator_fake_loss=1.375, generator_loss=28.54, generator_mel_loss=17.97, generator_kl_loss=1.214, generator_dur_loss=1.876, generator_adv_loss=2.038, generator_feat_match_loss=5.442, over 96.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.341, generator_loss=28.57, generator_mel_loss=18, generator_kl_loss=1.435, generator_dur_loss=1.746, generator_adv_loss=1.973, generator_feat_match_loss=5.406, over 6094.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:44:36,314 INFO [train.py:919] (1/6) Start epoch 610
+2024-03-14 14:46:32,328 INFO [train.py:527] (1/6) Epoch 610, batch 34, global_batch_idx: 75550, batch size: 70, loss[discriminator_loss=2.71, discriminator_real_loss=1.362, discriminator_fake_loss=1.349, generator_loss=28.33, generator_mel_loss=17.82, generator_kl_loss=1.298, generator_dur_loss=1.792, generator_adv_loss=2.071, generator_feat_match_loss=5.348, over 70.00 samples.], tot_loss[discriminator_loss=2.722, discriminator_real_loss=1.371, discriminator_fake_loss=1.352, generator_loss=28.51, generator_mel_loss=18.04, generator_kl_loss=1.427, generator_dur_loss=1.752, generator_adv_loss=1.963, generator_feat_match_loss=5.323, over 1891.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:48:50,508 INFO [train.py:527] (1/6) Epoch 610, batch 84, global_batch_idx: 75600, batch size: 31, loss[discriminator_loss=2.751, discriminator_real_loss=1.436, discriminator_fake_loss=1.315, generator_loss=27.86, generator_mel_loss=17.75, generator_kl_loss=1.293, generator_dur_loss=1.653, generator_adv_loss=1.847, generator_feat_match_loss=5.32, over 31.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.58, generator_mel_loss=18.08, generator_kl_loss=1.43, generator_dur_loss=1.752, generator_adv_loss=1.964, generator_feat_match_loss=5.36, over 4649.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:48:50,509 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 14:48:59,261 INFO [train.py:591] (1/6) Epoch 610, validation: discriminator_loss=2.757, discriminator_real_loss=1.375, discriminator_fake_loss=1.382, generator_loss=27.32, generator_mel_loss=18.16, generator_kl_loss=1.119, generator_dur_loss=1.829, generator_adv_loss=1.845, generator_feat_match_loss=4.371, over 100.00 samples.
+2024-03-14 14:48:59,262 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 14:50:50,191 INFO [train.py:919] (1/6) Start epoch 611
+2024-03-14 14:51:41,446 INFO [train.py:527] (1/6) Epoch 611, batch 10, global_batch_idx: 75650, batch size: 77, loss[discriminator_loss=2.757, discriminator_real_loss=1.38, discriminator_fake_loss=1.377, generator_loss=28.41, generator_mel_loss=17.87, generator_kl_loss=1.316, generator_dur_loss=1.814, generator_adv_loss=1.966, generator_feat_match_loss=5.44, over 77.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.345, discriminator_fake_loss=1.362, generator_loss=28.58, generator_mel_loss=17.85, generator_kl_loss=1.381, generator_dur_loss=1.812, generator_adv_loss=1.979, generator_feat_match_loss=5.563, over 791.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:53:58,046 INFO [train.py:527] (1/6) Epoch 611, batch 60, global_batch_idx: 75700, batch size: 53, loss[discriminator_loss=2.711, discriminator_real_loss=1.355, discriminator_fake_loss=1.356, generator_loss=28.5, generator_mel_loss=18.12, generator_kl_loss=1.444, generator_dur_loss=1.7, generator_adv_loss=2.011, generator_feat_match_loss=5.22, over 53.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.358, discriminator_fake_loss=1.342, generator_loss=28.59, generator_mel_loss=17.97, generator_kl_loss=1.417, generator_dur_loss=1.769, generator_adv_loss=1.976, generator_feat_match_loss=5.459, over 3534.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:56:18,210 INFO [train.py:527] (1/6) Epoch 611, batch 110, global_batch_idx: 75750, batch size: 45, loss[discriminator_loss=2.679, discriminator_real_loss=1.298, discriminator_fake_loss=1.381, generator_loss=29.14, generator_mel_loss=18.57, generator_kl_loss=1.449, generator_dur_loss=1.672, generator_adv_loss=1.942, generator_feat_match_loss=5.508, over 45.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.362, discriminator_fake_loss=1.338, generator_loss=28.56, generator_mel_loss=17.99, generator_kl_loss=1.418, generator_dur_loss=1.763, generator_adv_loss=1.972, generator_feat_match_loss=5.415, over 6512.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:56:56,776 INFO [train.py:919] (1/6) Start epoch 612
+2024-03-14 14:59:01,022 INFO [train.py:527] (1/6) Epoch 612, batch 36, global_batch_idx: 75800, batch size: 56, loss[discriminator_loss=2.684, discriminator_real_loss=1.34, discriminator_fake_loss=1.343, generator_loss=27.82, generator_mel_loss=17.49, generator_kl_loss=1.462, generator_dur_loss=1.661, generator_adv_loss=2.025, generator_feat_match_loss=5.181, over 56.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.358, discriminator_fake_loss=1.331, generator_loss=28.35, generator_mel_loss=17.91, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=1.969, generator_feat_match_loss=5.306, over 2077.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 14:59:01,023 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 14:59:08,881 INFO [train.py:591] (1/6) Epoch 612, validation: discriminator_loss=2.739, discriminator_real_loss=1.439, discriminator_fake_loss=1.3, generator_loss=28.23, generator_mel_loss=18.53, generator_kl_loss=1.212, generator_dur_loss=1.82, generator_adv_loss=1.946, generator_feat_match_loss=4.718, over 100.00 samples.
+2024-03-14 14:59:08,882 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 15:01:27,408 INFO [train.py:527] (1/6) Epoch 612, batch 86, global_batch_idx: 75850, batch size: 45, loss[discriminator_loss=2.781, discriminator_real_loss=1.366, discriminator_fake_loss=1.415, generator_loss=26.52, generator_mel_loss=17.44, generator_kl_loss=1.477, generator_dur_loss=1.646, generator_adv_loss=1.972, generator_feat_match_loss=3.985, over 45.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.372, discriminator_fake_loss=1.332, generator_loss=28.38, generator_mel_loss=17.92, generator_kl_loss=1.417, generator_dur_loss=1.752, generator_adv_loss=1.973, generator_feat_match_loss=5.317, over 4941.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:03:12,771 INFO [train.py:919] (1/6) Start epoch 613
+2024-03-14 15:04:09,434 INFO [train.py:527] (1/6) Epoch 613, batch 12, global_batch_idx: 75900, batch size: 74, loss[discriminator_loss=2.683, discriminator_real_loss=1.351, discriminator_fake_loss=1.332, generator_loss=28.04, generator_mel_loss=17.71, generator_kl_loss=1.232, generator_dur_loss=1.781, generator_adv_loss=1.827, generator_feat_match_loss=5.481, over 74.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.352, discriminator_fake_loss=1.341, generator_loss=28.47, generator_mel_loss=17.92, generator_kl_loss=1.368, generator_dur_loss=1.755, generator_adv_loss=1.955, generator_feat_match_loss=5.464, over 770.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:06:27,937 INFO [train.py:527] (1/6) Epoch 613, batch 62, global_batch_idx: 75950, batch size: 44, loss[discriminator_loss=2.7, discriminator_real_loss=1.421, discriminator_fake_loss=1.278, generator_loss=27.76, generator_mel_loss=17.39, generator_kl_loss=1.606, generator_dur_loss=1.619, generator_adv_loss=2.033, generator_feat_match_loss=5.117, over 44.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.365, discriminator_fake_loss=1.34, generator_loss=28.43, generator_mel_loss=17.98, generator_kl_loss=1.404, generator_dur_loss=1.758, generator_adv_loss=1.967, generator_feat_match_loss=5.318, over 3802.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:08:46,713 INFO [train.py:527] (1/6) Epoch 613, batch 112, global_batch_idx: 76000, batch size: 55, loss[discriminator_loss=2.654, discriminator_real_loss=1.373, discriminator_fake_loss=1.281, generator_loss=29.73, generator_mel_loss=18.52, generator_kl_loss=1.563, generator_dur_loss=1.699, generator_adv_loss=1.867, generator_feat_match_loss=6.076, over 55.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.364, discriminator_fake_loss=1.338, generator_loss=28.48, generator_mel_loss=18.01, generator_kl_loss=1.414, generator_dur_loss=1.753, generator_adv_loss=1.968, generator_feat_match_loss=5.338, over 6638.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:08:46,715 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 15:08:55,768 INFO [train.py:591] (1/6) Epoch 613, validation: discriminator_loss=2.732, discriminator_real_loss=1.369, discriminator_fake_loss=1.363, generator_loss=26.41, generator_mel_loss=17.51, generator_kl_loss=1.192, generator_dur_loss=1.815, generator_adv_loss=1.822, generator_feat_match_loss=4.074, over 100.00 samples.
+2024-03-14 15:08:55,769 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 15:09:29,489 INFO [train.py:919] (1/6) Start epoch 614
+2024-03-14 15:11:40,159 INFO [train.py:527] (1/6) Epoch 614, batch 38, global_batch_idx: 76050, batch size: 59, loss[discriminator_loss=2.641, discriminator_real_loss=1.237, discriminator_fake_loss=1.404, generator_loss=29.93, generator_mel_loss=18.33, generator_kl_loss=1.541, generator_dur_loss=1.71, generator_adv_loss=2.016, generator_feat_match_loss=6.335, over 59.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.339, generator_loss=28.58, generator_mel_loss=18.04, generator_kl_loss=1.457, generator_dur_loss=1.735, generator_adv_loss=1.968, generator_feat_match_loss=5.38, over 2120.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:14:00,556 INFO [train.py:527] (1/6) Epoch 614, batch 88, global_batch_idx: 76100, batch size: 16, loss[discriminator_loss=2.775, discriminator_real_loss=1.455, discriminator_fake_loss=1.32, generator_loss=28.48, generator_mel_loss=17.9, generator_kl_loss=1.9, generator_dur_loss=1.533, generator_adv_loss=1.995, generator_feat_match_loss=5.156, over 16.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.37, discriminator_fake_loss=1.338, generator_loss=28.49, generator_mel_loss=17.99, generator_kl_loss=1.43, generator_dur_loss=1.748, generator_adv_loss=1.969, generator_feat_match_loss=5.35, over 4848.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:15:37,167 INFO [train.py:919] (1/6) Start epoch 615
+2024-03-14 15:16:41,690 INFO [train.py:527] (1/6) Epoch 615, batch 14, global_batch_idx: 76150, batch size: 61, loss[discriminator_loss=2.704, discriminator_real_loss=1.281, discriminator_fake_loss=1.423, generator_loss=28.6, generator_mel_loss=18.11, generator_kl_loss=1.318, generator_dur_loss=1.712, generator_adv_loss=1.956, generator_feat_match_loss=5.504, over 61.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.36, discriminator_fake_loss=1.343, generator_loss=28.56, generator_mel_loss=17.97, generator_kl_loss=1.422, generator_dur_loss=1.732, generator_adv_loss=1.967, generator_feat_match_loss=5.476, over 836.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:18:59,490 INFO [train.py:527] (1/6) Epoch 615, batch 64, global_batch_idx: 76200, batch size: 12, loss[discriminator_loss=2.728, discriminator_real_loss=1.418, discriminator_fake_loss=1.31, generator_loss=28.52, generator_mel_loss=18.18, generator_kl_loss=1.456, generator_dur_loss=1.657, generator_adv_loss=1.863, generator_feat_match_loss=5.363, over 12.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.371, discriminator_fake_loss=1.333, generator_loss=28.54, generator_mel_loss=18.01, generator_kl_loss=1.438, generator_dur_loss=1.739, generator_adv_loss=1.978, generator_feat_match_loss=5.377, over 3486.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:18:59,491 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 15:19:08,427 INFO [train.py:591] (1/6) Epoch 615, validation: discriminator_loss=2.76, discriminator_real_loss=1.37, discriminator_fake_loss=1.391, generator_loss=27.78, generator_mel_loss=18.31, generator_kl_loss=1.348, generator_dur_loss=1.799, generator_adv_loss=1.79, generator_feat_match_loss=4.526, over 100.00 samples.
+2024-03-14 15:19:08,429 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 15:21:28,233 INFO [train.py:527] (1/6) Epoch 615, batch 114, global_batch_idx: 76250, batch size: 58, loss[discriminator_loss=2.73, discriminator_real_loss=1.436, discriminator_fake_loss=1.294, generator_loss=28.54, generator_mel_loss=17.85, generator_kl_loss=1.437, generator_dur_loss=1.753, generator_adv_loss=2.103, generator_feat_match_loss=5.401, over 58.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.37, discriminator_fake_loss=1.334, generator_loss=28.5, generator_mel_loss=17.99, generator_kl_loss=1.421, generator_dur_loss=1.741, generator_adv_loss=1.97, generator_feat_match_loss=5.369, over 6396.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:21:54,061 INFO [train.py:919] (1/6) Start epoch 616
+2024-03-14 15:24:09,266 INFO [train.py:527] (1/6) Epoch 616, batch 40, global_batch_idx: 76300, batch size: 88, loss[discriminator_loss=2.676, discriminator_real_loss=1.291, discriminator_fake_loss=1.385, generator_loss=27.99, generator_mel_loss=17.85, generator_kl_loss=1.212, generator_dur_loss=1.842, generator_adv_loss=2.089, generator_feat_match_loss=4.997, over 88.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.355, discriminator_fake_loss=1.338, generator_loss=28.59, generator_mel_loss=18.03, generator_kl_loss=1.397, generator_dur_loss=1.753, generator_adv_loss=1.977, generator_feat_match_loss=5.439, over 2409.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:26:26,330 INFO [train.py:527] (1/6) Epoch 616, batch 90, global_batch_idx: 76350, batch size: 45, loss[discriminator_loss=2.707, discriminator_real_loss=1.411, discriminator_fake_loss=1.295, generator_loss=29.32, generator_mel_loss=18.3, generator_kl_loss=1.629, generator_dur_loss=1.656, generator_adv_loss=2.004, generator_feat_match_loss=5.732, over 45.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.36, discriminator_fake_loss=1.337, generator_loss=28.62, generator_mel_loss=18.05, generator_kl_loss=1.405, generator_dur_loss=1.749, generator_adv_loss=1.977, generator_feat_match_loss=5.44, over 5258.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:28:00,564 INFO [train.py:919] (1/6) Start epoch 617
+2024-03-14 15:29:07,873 INFO [train.py:527] (1/6) Epoch 617, batch 16, global_batch_idx: 76400, batch size: 16, loss[discriminator_loss=2.609, discriminator_real_loss=1.277, discriminator_fake_loss=1.332, generator_loss=29.64, generator_mel_loss=18.52, generator_kl_loss=1.682, generator_dur_loss=1.517, generator_adv_loss=1.886, generator_feat_match_loss=6.037, over 16.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.368, discriminator_fake_loss=1.333, generator_loss=28.61, generator_mel_loss=18.01, generator_kl_loss=1.429, generator_dur_loss=1.732, generator_adv_loss=1.965, generator_feat_match_loss=5.479, over 1012.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:29:07,875 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 15:29:16,009 INFO [train.py:591] (1/6) Epoch 617, validation: discriminator_loss=2.792, discriminator_real_loss=1.402, discriminator_fake_loss=1.39, generator_loss=26.97, generator_mel_loss=18.09, generator_kl_loss=1.223, generator_dur_loss=1.79, generator_adv_loss=1.766, generator_feat_match_loss=4.101, over 100.00 samples.
+2024-03-14 15:29:16,010 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 15:31:36,489 INFO [train.py:527] (1/6) Epoch 617, batch 66, global_batch_idx: 76450, batch size: 42, loss[discriminator_loss=2.634, discriminator_real_loss=1.325, discriminator_fake_loss=1.309, generator_loss=29.79, generator_mel_loss=18.44, generator_kl_loss=1.602, generator_dur_loss=1.693, generator_adv_loss=2.072, generator_feat_match_loss=5.977, over 42.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=28.6, generator_mel_loss=17.99, generator_kl_loss=1.412, generator_dur_loss=1.747, generator_adv_loss=1.979, generator_feat_match_loss=5.474, over 3917.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:33:53,767 INFO [train.py:527] (1/6) Epoch 617, batch 116, global_batch_idx: 76500, batch size: 59, loss[discriminator_loss=2.644, discriminator_real_loss=1.274, discriminator_fake_loss=1.37, generator_loss=29.14, generator_mel_loss=18.13, generator_kl_loss=1.435, generator_dur_loss=1.761, generator_adv_loss=2.122, generator_feat_match_loss=5.696, over 59.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.363, discriminator_fake_loss=1.338, generator_loss=28.55, generator_mel_loss=17.98, generator_kl_loss=1.429, generator_dur_loss=1.739, generator_adv_loss=1.974, generator_feat_match_loss=5.429, over 6579.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:34:14,028 INFO [train.py:919] (1/6) Start epoch 618
+2024-03-14 15:36:33,311 INFO [train.py:527] (1/6) Epoch 618, batch 42, global_batch_idx: 76550, batch size: 48, loss[discriminator_loss=2.704, discriminator_real_loss=1.422, discriminator_fake_loss=1.282, generator_loss=28.38, generator_mel_loss=18.14, generator_kl_loss=1.522, generator_dur_loss=1.693, generator_adv_loss=1.918, generator_feat_match_loss=5.106, over 48.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.371, discriminator_fake_loss=1.343, generator_loss=28.49, generator_mel_loss=17.96, generator_kl_loss=1.426, generator_dur_loss=1.75, generator_adv_loss=1.972, generator_feat_match_loss=5.377, over 2505.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:38:53,458 INFO [train.py:527] (1/6) Epoch 618, batch 92, global_batch_idx: 76600, batch size: 36, loss[discriminator_loss=2.708, discriminator_real_loss=1.451, discriminator_fake_loss=1.257, generator_loss=28.8, generator_mel_loss=18.29, generator_kl_loss=1.543, generator_dur_loss=1.669, generator_adv_loss=2.116, generator_feat_match_loss=5.185, over 36.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.371, discriminator_fake_loss=1.339, generator_loss=28.51, generator_mel_loss=17.98, generator_kl_loss=1.425, generator_dur_loss=1.741, generator_adv_loss=1.976, generator_feat_match_loss=5.391, over 5380.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:38:53,459 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 15:39:02,304 INFO [train.py:591] (1/6) Epoch 618, validation: discriminator_loss=2.737, discriminator_real_loss=1.457, discriminator_fake_loss=1.28, generator_loss=28.13, generator_mel_loss=18.57, generator_kl_loss=1.192, generator_dur_loss=1.785, generator_adv_loss=2.007, generator_feat_match_loss=4.574, over 100.00 samples.
+2024-03-14 15:39:02,305 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 15:40:29,587 INFO [train.py:919] (1/6) Start epoch 619
+2024-03-14 15:41:44,082 INFO [train.py:527] (1/6) Epoch 619, batch 18, global_batch_idx: 76650, batch size: 58, loss[discriminator_loss=2.641, discriminator_real_loss=1.336, discriminator_fake_loss=1.305, generator_loss=29, generator_mel_loss=18.17, generator_kl_loss=1.565, generator_dur_loss=1.678, generator_adv_loss=2.11, generator_feat_match_loss=5.472, over 58.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.372, discriminator_fake_loss=1.314, generator_loss=28.51, generator_mel_loss=17.9, generator_kl_loss=1.427, generator_dur_loss=1.723, generator_adv_loss=1.991, generator_feat_match_loss=5.468, over 1168.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:44:04,010 INFO [train.py:527] (1/6) Epoch 619, batch 68, global_batch_idx: 76700, batch size: 25, loss[discriminator_loss=2.62, discriminator_real_loss=1.323, discriminator_fake_loss=1.298, generator_loss=29.51, generator_mel_loss=18.46, generator_kl_loss=1.701, generator_dur_loss=1.556, generator_adv_loss=2.008, generator_feat_match_loss=5.792, over 25.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.367, discriminator_fake_loss=1.32, generator_loss=28.51, generator_mel_loss=17.94, generator_kl_loss=1.431, generator_dur_loss=1.724, generator_adv_loss=1.985, generator_feat_match_loss=5.428, over 3979.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:46:21,557 INFO [train.py:527] (1/6) Epoch 619, batch 118, global_batch_idx: 76750, batch size: 56, loss[discriminator_loss=2.766, discriminator_real_loss=1.352, discriminator_fake_loss=1.414, generator_loss=27.73, generator_mel_loss=17.87, generator_kl_loss=1.342, generator_dur_loss=1.691, generator_adv_loss=2.111, generator_feat_match_loss=4.717, over 56.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.37, discriminator_fake_loss=1.323, generator_loss=28.5, generator_mel_loss=17.96, generator_kl_loss=1.43, generator_dur_loss=1.729, generator_adv_loss=1.982, generator_feat_match_loss=5.398, over 6786.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:46:37,255 INFO [train.py:919] (1/6) Start epoch 620
+2024-03-14 15:49:01,608 INFO [train.py:527] (1/6) Epoch 620, batch 44, global_batch_idx: 76800, batch size: 80, loss[discriminator_loss=2.734, discriminator_real_loss=1.463, discriminator_fake_loss=1.272, generator_loss=28.09, generator_mel_loss=17.79, generator_kl_loss=1.34, generator_dur_loss=1.759, generator_adv_loss=1.816, generator_feat_match_loss=5.384, over 80.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.358, discriminator_fake_loss=1.336, generator_loss=28.6, generator_mel_loss=17.95, generator_kl_loss=1.421, generator_dur_loss=1.733, generator_adv_loss=1.986, generator_feat_match_loss=5.507, over 2561.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:49:01,610 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 15:49:09,771 INFO [train.py:591] (1/6) Epoch 620, validation: discriminator_loss=2.76, discriminator_real_loss=1.379, discriminator_fake_loss=1.381, generator_loss=27.65, generator_mel_loss=18.3, generator_kl_loss=1.32, generator_dur_loss=1.786, generator_adv_loss=1.781, generator_feat_match_loss=4.468, over 100.00 samples.
+2024-03-14 15:49:09,772 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 15:51:27,790 INFO [train.py:527] (1/6) Epoch 620, batch 94, global_batch_idx: 76850, batch size: 36, loss[discriminator_loss=2.72, discriminator_real_loss=1.342, discriminator_fake_loss=1.378, generator_loss=28.47, generator_mel_loss=17.93, generator_kl_loss=1.464, generator_dur_loss=1.64, generator_adv_loss=1.994, generator_feat_match_loss=5.442, over 36.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.361, discriminator_fake_loss=1.337, generator_loss=28.63, generator_mel_loss=18, generator_kl_loss=1.42, generator_dur_loss=1.732, generator_adv_loss=1.983, generator_feat_match_loss=5.5, over 5388.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:52:52,130 INFO [train.py:919] (1/6) Start epoch 621
+2024-03-14 15:54:12,798 INFO [train.py:527] (1/6) Epoch 621, batch 20, global_batch_idx: 76900, batch size: 96, loss[discriminator_loss=2.678, discriminator_real_loss=1.244, discriminator_fake_loss=1.434, generator_loss=28.9, generator_mel_loss=17.8, generator_kl_loss=1.217, generator_dur_loss=1.9, generator_adv_loss=2.016, generator_feat_match_loss=5.962, over 96.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.348, discriminator_fake_loss=1.338, generator_loss=28.6, generator_mel_loss=18.01, generator_kl_loss=1.382, generator_dur_loss=1.752, generator_adv_loss=1.973, generator_feat_match_loss=5.488, over 1321.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:56:29,061 INFO [train.py:527] (1/6) Epoch 621, batch 70, global_batch_idx: 76950, batch size: 42, loss[discriminator_loss=2.589, discriminator_real_loss=1.369, discriminator_fake_loss=1.22, generator_loss=30.82, generator_mel_loss=18.76, generator_kl_loss=1.782, generator_dur_loss=1.681, generator_adv_loss=2.011, generator_feat_match_loss=6.579, over 42.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.362, discriminator_fake_loss=1.335, generator_loss=28.57, generator_mel_loss=18.02, generator_kl_loss=1.426, generator_dur_loss=1.734, generator_adv_loss=1.973, generator_feat_match_loss=5.42, over 4064.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:58:46,174 INFO [train.py:527] (1/6) Epoch 621, batch 120, global_batch_idx: 77000, batch size: 68, loss[discriminator_loss=2.755, discriminator_real_loss=1.395, discriminator_fake_loss=1.36, generator_loss=27.75, generator_mel_loss=18.17, generator_kl_loss=1.342, generator_dur_loss=1.762, generator_adv_loss=1.705, generator_feat_match_loss=4.766, over 68.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.359, discriminator_fake_loss=1.342, generator_loss=28.6, generator_mel_loss=18, generator_kl_loss=1.416, generator_dur_loss=1.749, generator_adv_loss=1.99, generator_feat_match_loss=5.448, over 7116.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 15:58:46,175 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 15:58:54,768 INFO [train.py:591] (1/6) Epoch 621, validation: discriminator_loss=2.75, discriminator_real_loss=1.291, discriminator_fake_loss=1.459, generator_loss=27.94, generator_mel_loss=18.84, generator_kl_loss=1.224, generator_dur_loss=1.817, generator_adv_loss=1.724, generator_feat_match_loss=4.334, over 100.00 samples.
+2024-03-14 15:58:54,769 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29124MB
+2024-03-14 15:59:04,496 INFO [train.py:919] (1/6) Start epoch 622
+2024-03-14 16:01:37,745 INFO [train.py:527] (1/6) Epoch 622, batch 46, global_batch_idx: 77050, batch size: 77, loss[discriminator_loss=2.769, discriminator_real_loss=1.4, discriminator_fake_loss=1.369, generator_loss=27.42, generator_mel_loss=17.64, generator_kl_loss=1.307, generator_dur_loss=1.752, generator_adv_loss=2.03, generator_feat_match_loss=4.693, over 77.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.376, discriminator_fake_loss=1.323, generator_loss=28.34, generator_mel_loss=17.88, generator_kl_loss=1.413, generator_dur_loss=1.745, generator_adv_loss=1.985, generator_feat_match_loss=5.313, over 2740.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:03:59,756 INFO [train.py:527] (1/6) Epoch 622, batch 96, global_batch_idx: 77100, batch size: 25, loss[discriminator_loss=2.731, discriminator_real_loss=1.43, discriminator_fake_loss=1.3, generator_loss=30.72, generator_mel_loss=18.77, generator_kl_loss=1.749, generator_dur_loss=1.515, generator_adv_loss=1.944, generator_feat_match_loss=6.743, over 25.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.374, discriminator_fake_loss=1.328, generator_loss=28.47, generator_mel_loss=17.98, generator_kl_loss=1.402, generator_dur_loss=1.755, generator_adv_loss=1.974, generator_feat_match_loss=5.358, over 5767.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:05:15,281 INFO [train.py:919] (1/6) Start epoch 623
+2024-03-14 16:06:39,123 INFO [train.py:527] (1/6) Epoch 623, batch 22, global_batch_idx: 77150, batch size: 25, loss[discriminator_loss=2.66, discriminator_real_loss=1.444, discriminator_fake_loss=1.216, generator_loss=29.29, generator_mel_loss=18.62, generator_kl_loss=1.689, generator_dur_loss=1.542, generator_adv_loss=1.977, generator_feat_match_loss=5.463, over 25.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.375, discriminator_fake_loss=1.331, generator_loss=28.62, generator_mel_loss=18.07, generator_kl_loss=1.465, generator_dur_loss=1.729, generator_adv_loss=1.976, generator_feat_match_loss=5.371, over 1223.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:08:58,388 INFO [train.py:527] (1/6) Epoch 623, batch 72, global_batch_idx: 77200, batch size: 45, loss[discriminator_loss=2.626, discriminator_real_loss=1.359, discriminator_fake_loss=1.268, generator_loss=28.98, generator_mel_loss=17.97, generator_kl_loss=1.687, generator_dur_loss=1.623, generator_adv_loss=2.002, generator_feat_match_loss=5.69, over 45.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.366, discriminator_fake_loss=1.333, generator_loss=28.51, generator_mel_loss=17.97, generator_kl_loss=1.434, generator_dur_loss=1.729, generator_adv_loss=1.978, generator_feat_match_loss=5.392, over 4043.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:08:58,390 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 16:09:07,223 INFO [train.py:591] (1/6) Epoch 623, validation: discriminator_loss=2.696, discriminator_real_loss=1.386, discriminator_fake_loss=1.31, generator_loss=26.81, generator_mel_loss=18.35, generator_kl_loss=1.231, generator_dur_loss=1.774, generator_adv_loss=1.872, generator_feat_match_loss=3.584, over 100.00 samples.
+2024-03-14 16:09:07,224 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 16:11:25,820 INFO [train.py:527] (1/6) Epoch 623, batch 122, global_batch_idx: 77250, batch size: 25, loss[discriminator_loss=2.612, discriminator_real_loss=1.195, discriminator_fake_loss=1.417, generator_loss=29.88, generator_mel_loss=18.41, generator_kl_loss=1.642, generator_dur_loss=1.554, generator_adv_loss=2.15, generator_feat_match_loss=6.125, over 25.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=28.52, generator_mel_loss=17.97, generator_kl_loss=1.431, generator_dur_loss=1.733, generator_adv_loss=1.975, generator_feat_match_loss=5.415, over 6846.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:11:31,389 INFO [train.py:919] (1/6) Start epoch 624
+2024-03-14 16:14:06,374 INFO [train.py:527] (1/6) Epoch 624, batch 48, global_batch_idx: 77300, batch size: 31, loss[discriminator_loss=2.688, discriminator_real_loss=1.353, discriminator_fake_loss=1.335, generator_loss=28.9, generator_mel_loss=18.15, generator_kl_loss=1.316, generator_dur_loss=1.61, generator_adv_loss=2.093, generator_feat_match_loss=5.726, over 31.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.367, discriminator_fake_loss=1.335, generator_loss=28.56, generator_mel_loss=17.96, generator_kl_loss=1.437, generator_dur_loss=1.714, generator_adv_loss=1.975, generator_feat_match_loss=5.473, over 2647.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:16:24,545 INFO [train.py:527] (1/6) Epoch 624, batch 98, global_batch_idx: 77350, batch size: 53, loss[discriminator_loss=2.739, discriminator_real_loss=1.457, discriminator_fake_loss=1.282, generator_loss=28.11, generator_mel_loss=18.15, generator_kl_loss=1.439, generator_dur_loss=1.637, generator_adv_loss=1.865, generator_feat_match_loss=5.012, over 53.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.373, discriminator_fake_loss=1.329, generator_loss=28.51, generator_mel_loss=17.96, generator_kl_loss=1.42, generator_dur_loss=1.73, generator_adv_loss=1.979, generator_feat_match_loss=5.429, over 5487.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:17:35,337 INFO [train.py:919] (1/6) Start epoch 625
+2024-03-14 16:19:06,948 INFO [train.py:527] (1/6) Epoch 625, batch 24, global_batch_idx: 77400, batch size: 80, loss[discriminator_loss=2.677, discriminator_real_loss=1.36, discriminator_fake_loss=1.317, generator_loss=29.19, generator_mel_loss=18.31, generator_kl_loss=1.384, generator_dur_loss=1.769, generator_adv_loss=1.961, generator_feat_match_loss=5.766, over 80.00 samples.], tot_loss[discriminator_loss=2.706, discriminator_real_loss=1.37, discriminator_fake_loss=1.335, generator_loss=28.54, generator_mel_loss=18.06, generator_kl_loss=1.417, generator_dur_loss=1.732, generator_adv_loss=1.981, generator_feat_match_loss=5.346, over 1405.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:19:06,949 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 16:19:15,006 INFO [train.py:591] (1/6) Epoch 625, validation: discriminator_loss=2.787, discriminator_real_loss=1.394, discriminator_fake_loss=1.393, generator_loss=27.75, generator_mel_loss=18.58, generator_kl_loss=1.218, generator_dur_loss=1.788, generator_adv_loss=1.8, generator_feat_match_loss=4.369, over 100.00 samples.
+2024-03-14 16:19:15,007 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 16:21:33,770 INFO [train.py:527] (1/6) Epoch 625, batch 74, global_batch_idx: 77450, batch size: 83, loss[discriminator_loss=2.688, discriminator_real_loss=1.346, discriminator_fake_loss=1.341, generator_loss=28.85, generator_mel_loss=18.17, generator_kl_loss=1.354, generator_dur_loss=1.82, generator_adv_loss=2.06, generator_feat_match_loss=5.442, over 83.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.369, discriminator_fake_loss=1.335, generator_loss=28.45, generator_mel_loss=17.97, generator_kl_loss=1.424, generator_dur_loss=1.746, generator_adv_loss=1.978, generator_feat_match_loss=5.329, over 4452.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:23:50,766 INFO [train.py:919] (1/6) Start epoch 626
+2024-03-14 16:24:13,159 INFO [train.py:527] (1/6) Epoch 626, batch 0, global_batch_idx: 77500, batch size: 31, loss[discriminator_loss=2.651, discriminator_real_loss=1.324, discriminator_fake_loss=1.327, generator_loss=30.4, generator_mel_loss=18.78, generator_kl_loss=1.797, generator_dur_loss=1.602, generator_adv_loss=2.103, generator_feat_match_loss=6.118, over 31.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.324, discriminator_fake_loss=1.327, generator_loss=30.4, generator_mel_loss=18.78, generator_kl_loss=1.797, generator_dur_loss=1.602, generator_adv_loss=2.103, generator_feat_match_loss=6.118, over 31.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:26:30,661 INFO [train.py:527] (1/6) Epoch 626, batch 50, global_batch_idx: 77550, batch size: 59, loss[discriminator_loss=2.729, discriminator_real_loss=1.41, discriminator_fake_loss=1.318, generator_loss=27.87, generator_mel_loss=17.87, generator_kl_loss=1.358, generator_dur_loss=1.781, generator_adv_loss=2.052, generator_feat_match_loss=4.804, over 59.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.363, discriminator_fake_loss=1.339, generator_loss=28.65, generator_mel_loss=18.02, generator_kl_loss=1.451, generator_dur_loss=1.745, generator_adv_loss=1.969, generator_feat_match_loss=5.468, over 2897.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:28:48,469 INFO [train.py:527] (1/6) Epoch 626, batch 100, global_batch_idx: 77600, batch size: 55, loss[discriminator_loss=2.646, discriminator_real_loss=1.355, discriminator_fake_loss=1.291, generator_loss=29.74, generator_mel_loss=18.29, generator_kl_loss=1.301, generator_dur_loss=1.721, generator_adv_loss=2.073, generator_feat_match_loss=6.348, over 55.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.355, discriminator_fake_loss=1.334, generator_loss=28.64, generator_mel_loss=17.99, generator_kl_loss=1.442, generator_dur_loss=1.748, generator_adv_loss=1.971, generator_feat_match_loss=5.486, over 5666.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:28:48,471 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 16:28:57,309 INFO [train.py:591] (1/6) Epoch 626, validation: discriminator_loss=2.77, discriminator_real_loss=1.421, discriminator_fake_loss=1.349, generator_loss=28.55, generator_mel_loss=18.8, generator_kl_loss=1.313, generator_dur_loss=1.817, generator_adv_loss=1.934, generator_feat_match_loss=4.69, over 100.00 samples.
+2024-03-14 16:28:57,310 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 16:30:03,007 INFO [train.py:919] (1/6) Start epoch 627
+2024-03-14 16:31:38,193 INFO [train.py:527] (1/6) Epoch 627, batch 26, global_batch_idx: 77650, batch size: 44, loss[discriminator_loss=2.667, discriminator_real_loss=1.377, discriminator_fake_loss=1.29, generator_loss=27.9, generator_mel_loss=17.75, generator_kl_loss=1.7, generator_dur_loss=1.635, generator_adv_loss=1.89, generator_feat_match_loss=4.927, over 44.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.46, generator_mel_loss=17.97, generator_kl_loss=1.397, generator_dur_loss=1.727, generator_adv_loss=1.986, generator_feat_match_loss=5.378, over 1366.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:33:58,563 INFO [train.py:527] (1/6) Epoch 627, batch 76, global_batch_idx: 77700, batch size: 68, loss[discriminator_loss=2.684, discriminator_real_loss=1.35, discriminator_fake_loss=1.334, generator_loss=28.1, generator_mel_loss=18.07, generator_kl_loss=1.334, generator_dur_loss=1.768, generator_adv_loss=1.979, generator_feat_match_loss=4.952, over 68.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.359, discriminator_fake_loss=1.333, generator_loss=28.58, generator_mel_loss=18, generator_kl_loss=1.416, generator_dur_loss=1.736, generator_adv_loss=1.984, generator_feat_match_loss=5.442, over 4157.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:36:08,992 INFO [train.py:919] (1/6) Start epoch 628
+2024-03-14 16:36:40,013 INFO [train.py:527] (1/6) Epoch 628, batch 2, global_batch_idx: 77750, batch size: 39, loss[discriminator_loss=2.68, discriminator_real_loss=1.372, discriminator_fake_loss=1.308, generator_loss=27.67, generator_mel_loss=17.5, generator_kl_loss=1.51, generator_dur_loss=1.68, generator_adv_loss=2.091, generator_feat_match_loss=4.891, over 39.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.327, discriminator_fake_loss=1.358, generator_loss=28.86, generator_mel_loss=18, generator_kl_loss=1.421, generator_dur_loss=1.729, generator_adv_loss=1.994, generator_feat_match_loss=5.71, over 147.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:39:00,336 INFO [train.py:527] (1/6) Epoch 628, batch 52, global_batch_idx: 77800, batch size: 58, loss[discriminator_loss=2.645, discriminator_real_loss=1.386, discriminator_fake_loss=1.259, generator_loss=27.92, generator_mel_loss=17.82, generator_kl_loss=1.455, generator_dur_loss=1.731, generator_adv_loss=1.947, generator_feat_match_loss=4.973, over 58.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.373, discriminator_fake_loss=1.335, generator_loss=28.58, generator_mel_loss=18.02, generator_kl_loss=1.407, generator_dur_loss=1.761, generator_adv_loss=1.96, generator_feat_match_loss=5.435, over 3116.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:39:00,338 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 16:39:08,519 INFO [train.py:591] (1/6) Epoch 628, validation: discriminator_loss=2.75, discriminator_real_loss=1.409, discriminator_fake_loss=1.341, generator_loss=26.74, generator_mel_loss=17.86, generator_kl_loss=1.223, generator_dur_loss=1.809, generator_adv_loss=1.878, generator_feat_match_loss=3.963, over 100.00 samples.
+2024-03-14 16:39:08,520 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 16:41:26,674 INFO [train.py:527] (1/6) Epoch 628, batch 102, global_batch_idx: 77850, batch size: 64, loss[discriminator_loss=2.756, discriminator_real_loss=1.397, discriminator_fake_loss=1.359, generator_loss=28.22, generator_mel_loss=17.85, generator_kl_loss=1.395, generator_dur_loss=1.737, generator_adv_loss=1.981, generator_feat_match_loss=5.255, over 64.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.371, discriminator_fake_loss=1.338, generator_loss=28.55, generator_mel_loss=17.98, generator_kl_loss=1.415, generator_dur_loss=1.756, generator_adv_loss=1.963, generator_feat_match_loss=5.43, over 5950.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:42:25,094 INFO [train.py:919] (1/6) Start epoch 629
+2024-03-14 16:44:07,035 INFO [train.py:527] (1/6) Epoch 629, batch 28, global_batch_idx: 77900, batch size: 45, loss[discriminator_loss=2.684, discriminator_real_loss=1.363, discriminator_fake_loss=1.321, generator_loss=28.7, generator_mel_loss=18.07, generator_kl_loss=1.476, generator_dur_loss=1.697, generator_adv_loss=1.978, generator_feat_match_loss=5.482, over 45.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.57, generator_mel_loss=17.96, generator_kl_loss=1.424, generator_dur_loss=1.754, generator_adv_loss=1.972, generator_feat_match_loss=5.462, over 1622.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:46:27,947 INFO [train.py:527] (1/6) Epoch 629, batch 78, global_batch_idx: 77950, batch size: 47, loss[discriminator_loss=2.703, discriminator_real_loss=1.415, discriminator_fake_loss=1.288, generator_loss=28.82, generator_mel_loss=18.26, generator_kl_loss=1.365, generator_dur_loss=1.692, generator_adv_loss=1.984, generator_feat_match_loss=5.523, over 47.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.57, generator_mel_loss=17.97, generator_kl_loss=1.424, generator_dur_loss=1.755, generator_adv_loss=1.975, generator_feat_match_loss=5.446, over 4408.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:48:28,997 INFO [train.py:919] (1/6) Start epoch 630
+2024-03-14 16:49:03,349 INFO [train.py:527] (1/6) Epoch 630, batch 4, global_batch_idx: 78000, batch size: 62, loss[discriminator_loss=2.708, discriminator_real_loss=1.347, discriminator_fake_loss=1.361, generator_loss=29.85, generator_mel_loss=18.71, generator_kl_loss=1.393, generator_dur_loss=1.756, generator_adv_loss=1.983, generator_feat_match_loss=6.01, over 62.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.362, discriminator_fake_loss=1.348, generator_loss=28.57, generator_mel_loss=17.96, generator_kl_loss=1.45, generator_dur_loss=1.707, generator_adv_loss=1.95, generator_feat_match_loss=5.501, over 257.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:49:03,351 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 16:49:11,213 INFO [train.py:591] (1/6) Epoch 630, validation: discriminator_loss=2.726, discriminator_real_loss=1.44, discriminator_fake_loss=1.286, generator_loss=27.32, generator_mel_loss=18.55, generator_kl_loss=1.224, generator_dur_loss=1.794, generator_adv_loss=1.879, generator_feat_match_loss=3.873, over 100.00 samples.
+2024-03-14 16:49:11,215 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 16:51:32,499 INFO [train.py:527] (1/6) Epoch 630, batch 54, global_batch_idx: 78050, batch size: 45, loss[discriminator_loss=2.783, discriminator_real_loss=1.465, discriminator_fake_loss=1.318, generator_loss=28.03, generator_mel_loss=18.12, generator_kl_loss=1.357, generator_dur_loss=1.678, generator_adv_loss=1.873, generator_feat_match_loss=5.007, over 45.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.369, discriminator_fake_loss=1.339, generator_loss=28.38, generator_mel_loss=17.92, generator_kl_loss=1.388, generator_dur_loss=1.745, generator_adv_loss=1.967, generator_feat_match_loss=5.359, over 3108.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:53:47,790 INFO [train.py:527] (1/6) Epoch 630, batch 104, global_batch_idx: 78100, batch size: 45, loss[discriminator_loss=2.682, discriminator_real_loss=1.374, discriminator_fake_loss=1.308, generator_loss=28.31, generator_mel_loss=18.17, generator_kl_loss=1.56, generator_dur_loss=1.654, generator_adv_loss=2.022, generator_feat_match_loss=4.904, over 45.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.45, generator_mel_loss=17.95, generator_kl_loss=1.397, generator_dur_loss=1.746, generator_adv_loss=1.966, generator_feat_match_loss=5.39, over 5937.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:54:41,047 INFO [train.py:919] (1/6) Start epoch 631
+2024-03-14 16:56:27,399 INFO [train.py:527] (1/6) Epoch 631, batch 30, global_batch_idx: 78150, batch size: 53, loss[discriminator_loss=2.73, discriminator_real_loss=1.369, discriminator_fake_loss=1.361, generator_loss=27.96, generator_mel_loss=18.15, generator_kl_loss=1.428, generator_dur_loss=1.738, generator_adv_loss=1.999, generator_feat_match_loss=4.645, over 53.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.361, discriminator_fake_loss=1.352, generator_loss=28.62, generator_mel_loss=18.01, generator_kl_loss=1.424, generator_dur_loss=1.772, generator_adv_loss=1.96, generator_feat_match_loss=5.462, over 1799.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:58:46,206 INFO [train.py:527] (1/6) Epoch 631, batch 80, global_batch_idx: 78200, batch size: 70, loss[discriminator_loss=2.74, discriminator_real_loss=1.403, discriminator_fake_loss=1.337, generator_loss=28.9, generator_mel_loss=18.03, generator_kl_loss=1.443, generator_dur_loss=1.798, generator_adv_loss=1.953, generator_feat_match_loss=5.67, over 70.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=28.52, generator_mel_loss=17.96, generator_kl_loss=1.433, generator_dur_loss=1.751, generator_adv_loss=1.982, generator_feat_match_loss=5.397, over 4519.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 16:58:46,207 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 16:58:55,115 INFO [train.py:591] (1/6) Epoch 631, validation: discriminator_loss=2.755, discriminator_real_loss=1.418, discriminator_fake_loss=1.337, generator_loss=28.09, generator_mel_loss=18.49, generator_kl_loss=1.212, generator_dur_loss=1.811, generator_adv_loss=1.905, generator_feat_match_loss=4.679, over 100.00 samples.
+2024-03-14 16:58:55,115 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 17:00:57,400 INFO [train.py:919] (1/6) Start epoch 632
+2024-03-14 17:01:38,831 INFO [train.py:527] (1/6) Epoch 632, batch 6, global_batch_idx: 78250, batch size: 61, loss[discriminator_loss=2.593, discriminator_real_loss=1.335, discriminator_fake_loss=1.258, generator_loss=29.64, generator_mel_loss=18.1, generator_kl_loss=1.468, generator_dur_loss=1.716, generator_adv_loss=2.066, generator_feat_match_loss=6.287, over 61.00 samples.], tot_loss[discriminator_loss=2.651, discriminator_real_loss=1.352, discriminator_fake_loss=1.299, generator_loss=29.03, generator_mel_loss=18.08, generator_kl_loss=1.524, generator_dur_loss=1.721, generator_adv_loss=1.999, generator_feat_match_loss=5.705, over 385.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:03:55,709 INFO [train.py:527] (1/6) Epoch 632, batch 56, global_batch_idx: 78300, batch size: 88, loss[discriminator_loss=2.746, discriminator_real_loss=1.363, discriminator_fake_loss=1.383, generator_loss=28.07, generator_mel_loss=17.7, generator_kl_loss=1.279, generator_dur_loss=1.823, generator_adv_loss=1.925, generator_feat_match_loss=5.343, over 88.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.378, discriminator_fake_loss=1.32, generator_loss=28.47, generator_mel_loss=17.92, generator_kl_loss=1.408, generator_dur_loss=1.751, generator_adv_loss=1.982, generator_feat_match_loss=5.415, over 3279.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:06:17,402 INFO [train.py:527] (1/6) Epoch 632, batch 106, global_batch_idx: 78350, batch size: 96, loss[discriminator_loss=2.615, discriminator_real_loss=1.326, discriminator_fake_loss=1.289, generator_loss=28.53, generator_mel_loss=17.96, generator_kl_loss=1.204, generator_dur_loss=1.883, generator_adv_loss=1.839, generator_feat_match_loss=5.648, over 96.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.368, discriminator_fake_loss=1.331, generator_loss=28.5, generator_mel_loss=17.94, generator_kl_loss=1.385, generator_dur_loss=1.764, generator_adv_loss=1.974, generator_feat_match_loss=5.444, over 6576.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:07:03,934 INFO [train.py:919] (1/6) Start epoch 633
+2024-03-14 17:08:58,825 INFO [train.py:527] (1/6) Epoch 633, batch 32, global_batch_idx: 78400, batch size: 53, loss[discriminator_loss=2.696, discriminator_real_loss=1.328, discriminator_fake_loss=1.368, generator_loss=28.81, generator_mel_loss=17.78, generator_kl_loss=1.366, generator_dur_loss=1.69, generator_adv_loss=2.118, generator_feat_match_loss=5.855, over 53.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.48, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.739, generator_adv_loss=1.971, generator_feat_match_loss=5.339, over 1920.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:08:58,826 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 17:09:07,180 INFO [train.py:591] (1/6) Epoch 633, validation: discriminator_loss=2.741, discriminator_real_loss=1.458, discriminator_fake_loss=1.283, generator_loss=28.12, generator_mel_loss=18.28, generator_kl_loss=1.256, generator_dur_loss=1.797, generator_adv_loss=2.012, generator_feat_match_loss=4.773, over 100.00 samples.
+2024-03-14 17:09:07,181 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 17:11:28,197 INFO [train.py:527] (1/6) Epoch 633, batch 82, global_batch_idx: 78450, batch size: 55, loss[discriminator_loss=2.664, discriminator_real_loss=1.32, discriminator_fake_loss=1.344, generator_loss=30.45, generator_mel_loss=18.28, generator_kl_loss=1.633, generator_dur_loss=1.667, generator_adv_loss=2.064, generator_feat_match_loss=6.806, over 55.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=28.45, generator_mel_loss=17.97, generator_kl_loss=1.427, generator_dur_loss=1.738, generator_adv_loss=1.971, generator_feat_match_loss=5.351, over 4876.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:13:21,243 INFO [train.py:919] (1/6) Start epoch 634
+2024-03-14 17:14:04,056 INFO [train.py:527] (1/6) Epoch 634, batch 8, global_batch_idx: 78500, batch size: 31, loss[discriminator_loss=2.764, discriminator_real_loss=1.474, discriminator_fake_loss=1.29, generator_loss=28.3, generator_mel_loss=18.11, generator_kl_loss=1.393, generator_dur_loss=1.585, generator_adv_loss=2.038, generator_feat_match_loss=5.18, over 31.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.378, discriminator_fake_loss=1.345, generator_loss=28.28, generator_mel_loss=17.91, generator_kl_loss=1.461, generator_dur_loss=1.697, generator_adv_loss=1.98, generator_feat_match_loss=5.231, over 420.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:16:22,818 INFO [train.py:527] (1/6) Epoch 634, batch 58, global_batch_idx: 78550, batch size: 56, loss[discriminator_loss=2.699, discriminator_real_loss=1.326, discriminator_fake_loss=1.373, generator_loss=28.98, generator_mel_loss=18.2, generator_kl_loss=1.566, generator_dur_loss=1.742, generator_adv_loss=1.994, generator_feat_match_loss=5.476, over 56.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.54, generator_mel_loss=17.96, generator_kl_loss=1.426, generator_dur_loss=1.724, generator_adv_loss=1.979, generator_feat_match_loss=5.447, over 3289.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:18:42,614 INFO [train.py:527] (1/6) Epoch 634, batch 108, global_batch_idx: 78600, batch size: 48, loss[discriminator_loss=2.718, discriminator_real_loss=1.389, discriminator_fake_loss=1.329, generator_loss=27.97, generator_mel_loss=17.78, generator_kl_loss=1.533, generator_dur_loss=1.693, generator_adv_loss=1.876, generator_feat_match_loss=5.086, over 48.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.366, discriminator_fake_loss=1.332, generator_loss=28.53, generator_mel_loss=17.96, generator_kl_loss=1.438, generator_dur_loss=1.72, generator_adv_loss=1.975, generator_feat_match_loss=5.436, over 5937.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:18:42,615 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 17:18:51,364 INFO [train.py:591] (1/6) Epoch 634, validation: discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=26.9, generator_mel_loss=18.02, generator_kl_loss=1.312, generator_dur_loss=1.802, generator_adv_loss=1.768, generator_feat_match_loss=3.992, over 100.00 samples.
+2024-03-14 17:18:51,364 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 17:19:33,472 INFO [train.py:919] (1/6) Start epoch 635
+2024-03-14 17:21:33,180 INFO [train.py:527] (1/6) Epoch 635, batch 34, global_batch_idx: 78650, batch size: 31, loss[discriminator_loss=2.795, discriminator_real_loss=1.4, discriminator_fake_loss=1.396, generator_loss=28.94, generator_mel_loss=17.97, generator_kl_loss=1.604, generator_dur_loss=1.601, generator_adv_loss=1.788, generator_feat_match_loss=5.975, over 31.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.364, discriminator_fake_loss=1.343, generator_loss=28.5, generator_mel_loss=17.96, generator_kl_loss=1.412, generator_dur_loss=1.751, generator_adv_loss=1.976, generator_feat_match_loss=5.408, over 2036.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:23:53,351 INFO [train.py:527] (1/6) Epoch 635, batch 84, global_batch_idx: 78700, batch size: 74, loss[discriminator_loss=2.751, discriminator_real_loss=1.393, discriminator_fake_loss=1.357, generator_loss=28.63, generator_mel_loss=18.21, generator_kl_loss=1.336, generator_dur_loss=1.819, generator_adv_loss=1.907, generator_feat_match_loss=5.365, over 74.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.365, discriminator_fake_loss=1.337, generator_loss=28.46, generator_mel_loss=17.95, generator_kl_loss=1.41, generator_dur_loss=1.755, generator_adv_loss=1.983, generator_feat_match_loss=5.371, over 5003.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:25:40,410 INFO [train.py:919] (1/6) Start epoch 636
+2024-03-14 17:26:32,613 INFO [train.py:527] (1/6) Epoch 636, batch 10, global_batch_idx: 78750, batch size: 44, loss[discriminator_loss=2.712, discriminator_real_loss=1.362, discriminator_fake_loss=1.35, generator_loss=28.41, generator_mel_loss=17.79, generator_kl_loss=1.675, generator_dur_loss=1.671, generator_adv_loss=1.905, generator_feat_match_loss=5.374, over 44.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.366, discriminator_fake_loss=1.339, generator_loss=28.49, generator_mel_loss=18.05, generator_kl_loss=1.396, generator_dur_loss=1.778, generator_adv_loss=1.964, generator_feat_match_loss=5.31, over 692.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:28:51,953 INFO [train.py:527] (1/6) Epoch 636, batch 60, global_batch_idx: 78800, batch size: 55, loss[discriminator_loss=2.734, discriminator_real_loss=1.409, discriminator_fake_loss=1.325, generator_loss=29.53, generator_mel_loss=18.16, generator_kl_loss=1.537, generator_dur_loss=1.689, generator_adv_loss=1.948, generator_feat_match_loss=6.194, over 55.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.37, discriminator_fake_loss=1.331, generator_loss=28.59, generator_mel_loss=17.99, generator_kl_loss=1.398, generator_dur_loss=1.751, generator_adv_loss=1.981, generator_feat_match_loss=5.464, over 3556.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:28:51,954 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 17:29:00,096 INFO [train.py:591] (1/6) Epoch 636, validation: discriminator_loss=2.778, discriminator_real_loss=1.396, discriminator_fake_loss=1.382, generator_loss=27.24, generator_mel_loss=18.22, generator_kl_loss=1.223, generator_dur_loss=1.803, generator_adv_loss=1.794, generator_feat_match_loss=4.2, over 100.00 samples.
+2024-03-14 17:29:00,097 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 17:31:19,400 INFO [train.py:527] (1/6) Epoch 636, batch 110, global_batch_idx: 78850, batch size: 16, loss[discriminator_loss=2.824, discriminator_real_loss=1.325, discriminator_fake_loss=1.499, generator_loss=30.98, generator_mel_loss=18.35, generator_kl_loss=1.89, generator_dur_loss=1.546, generator_adv_loss=1.9, generator_feat_match_loss=7.29, over 16.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.374, discriminator_fake_loss=1.33, generator_loss=28.56, generator_mel_loss=18.01, generator_kl_loss=1.402, generator_dur_loss=1.743, generator_adv_loss=1.981, generator_feat_match_loss=5.427, over 6251.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:31:55,409 INFO [train.py:919] (1/6) Start epoch 637
+2024-03-14 17:34:01,417 INFO [train.py:527] (1/6) Epoch 637, batch 36, global_batch_idx: 78900, batch size: 53, loss[discriminator_loss=2.663, discriminator_real_loss=1.333, discriminator_fake_loss=1.33, generator_loss=28.45, generator_mel_loss=18.04, generator_kl_loss=1.35, generator_dur_loss=1.659, generator_adv_loss=2.042, generator_feat_match_loss=5.357, over 53.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.367, discriminator_fake_loss=1.322, generator_loss=28.4, generator_mel_loss=17.89, generator_kl_loss=1.39, generator_dur_loss=1.727, generator_adv_loss=1.995, generator_feat_match_loss=5.399, over 1999.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:36:21,685 INFO [train.py:527] (1/6) Epoch 637, batch 86, global_batch_idx: 78950, batch size: 74, loss[discriminator_loss=2.662, discriminator_real_loss=1.429, discriminator_fake_loss=1.233, generator_loss=28.59, generator_mel_loss=18.03, generator_kl_loss=1.253, generator_dur_loss=1.777, generator_adv_loss=1.971, generator_feat_match_loss=5.559, over 74.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.363, discriminator_fake_loss=1.33, generator_loss=28.45, generator_mel_loss=17.89, generator_kl_loss=1.373, generator_dur_loss=1.742, generator_adv_loss=1.977, generator_feat_match_loss=5.47, over 5056.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:38:05,409 INFO [train.py:919] (1/6) Start epoch 638
+2024-03-14 17:39:02,848 INFO [train.py:527] (1/6) Epoch 638, batch 12, global_batch_idx: 79000, batch size: 77, loss[discriminator_loss=2.701, discriminator_real_loss=1.309, discriminator_fake_loss=1.392, generator_loss=28.45, generator_mel_loss=17.94, generator_kl_loss=1.37, generator_dur_loss=1.8, generator_adv_loss=2.098, generator_feat_match_loss=5.246, over 77.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=28.55, generator_mel_loss=17.88, generator_kl_loss=1.387, generator_dur_loss=1.76, generator_adv_loss=2.002, generator_feat_match_loss=5.52, over 878.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:39:02,850 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 17:39:10,862 INFO [train.py:591] (1/6) Epoch 638, validation: discriminator_loss=2.766, discriminator_real_loss=1.487, discriminator_fake_loss=1.279, generator_loss=27.09, generator_mel_loss=17.94, generator_kl_loss=1.158, generator_dur_loss=1.771, generator_adv_loss=2.034, generator_feat_match_loss=4.187, over 100.00 samples.
+2024-03-14 17:39:10,863 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 17:41:29,746 INFO [train.py:527] (1/6) Epoch 638, batch 62, global_batch_idx: 79050, batch size: 83, loss[discriminator_loss=2.737, discriminator_real_loss=1.443, discriminator_fake_loss=1.294, generator_loss=29.07, generator_mel_loss=18.12, generator_kl_loss=1.42, generator_dur_loss=1.787, generator_adv_loss=2.028, generator_feat_match_loss=5.714, over 83.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.369, discriminator_fake_loss=1.329, generator_loss=28.5, generator_mel_loss=17.95, generator_kl_loss=1.419, generator_dur_loss=1.725, generator_adv_loss=1.976, generator_feat_match_loss=5.423, over 3417.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:43:49,020 INFO [train.py:527] (1/6) Epoch 638, batch 112, global_batch_idx: 79100, batch size: 88, loss[discriminator_loss=2.69, discriminator_real_loss=1.494, discriminator_fake_loss=1.196, generator_loss=28.81, generator_mel_loss=18.18, generator_kl_loss=1.332, generator_dur_loss=1.824, generator_adv_loss=1.871, generator_feat_match_loss=5.597, over 88.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.368, discriminator_fake_loss=1.331, generator_loss=28.53, generator_mel_loss=17.97, generator_kl_loss=1.41, generator_dur_loss=1.744, generator_adv_loss=1.973, generator_feat_match_loss=5.434, over 6421.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:44:18,906 INFO [train.py:919] (1/6) Start epoch 639
+2024-03-14 17:46:29,985 INFO [train.py:527] (1/6) Epoch 639, batch 38, global_batch_idx: 79150, batch size: 72, loss[discriminator_loss=2.724, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=29.08, generator_mel_loss=18.07, generator_kl_loss=1.304, generator_dur_loss=1.823, generator_adv_loss=2.049, generator_feat_match_loss=5.831, over 72.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.357, discriminator_fake_loss=1.336, generator_loss=28.51, generator_mel_loss=17.97, generator_kl_loss=1.44, generator_dur_loss=1.761, generator_adv_loss=1.985, generator_feat_match_loss=5.36, over 2156.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:48:47,225 INFO [train.py:527] (1/6) Epoch 639, batch 88, global_batch_idx: 79200, batch size: 50, loss[discriminator_loss=2.642, discriminator_real_loss=1.31, discriminator_fake_loss=1.332, generator_loss=28.85, generator_mel_loss=18.06, generator_kl_loss=1.576, generator_dur_loss=1.687, generator_adv_loss=2.009, generator_feat_match_loss=5.517, over 50.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.355, discriminator_fake_loss=1.332, generator_loss=28.67, generator_mel_loss=17.98, generator_kl_loss=1.419, generator_dur_loss=1.755, generator_adv_loss=2.011, generator_feat_match_loss=5.512, over 5016.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:48:47,226 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 17:48:56,150 INFO [train.py:591] (1/6) Epoch 639, validation: discriminator_loss=2.739, discriminator_real_loss=1.471, discriminator_fake_loss=1.268, generator_loss=27.94, generator_mel_loss=18.03, generator_kl_loss=1.41, generator_dur_loss=1.795, generator_adv_loss=2.035, generator_feat_match_loss=4.675, over 100.00 samples.
+2024-03-14 17:48:56,151 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 17:50:32,561 INFO [train.py:919] (1/6) Start epoch 640
+2024-03-14 17:51:37,842 INFO [train.py:527] (1/6) Epoch 640, batch 14, global_batch_idx: 79250, batch size: 59, loss[discriminator_loss=2.624, discriminator_real_loss=1.35, discriminator_fake_loss=1.274, generator_loss=28.72, generator_mel_loss=18.02, generator_kl_loss=1.582, generator_dur_loss=1.763, generator_adv_loss=1.957, generator_feat_match_loss=5.403, over 59.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=28.91, generator_mel_loss=17.98, generator_kl_loss=1.433, generator_dur_loss=1.738, generator_adv_loss=1.987, generator_feat_match_loss=5.773, over 927.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:53:54,818 INFO [train.py:527] (1/6) Epoch 640, batch 64, global_batch_idx: 79300, batch size: 74, loss[discriminator_loss=2.691, discriminator_real_loss=1.359, discriminator_fake_loss=1.332, generator_loss=29.14, generator_mel_loss=18.19, generator_kl_loss=1.259, generator_dur_loss=1.794, generator_adv_loss=1.971, generator_feat_match_loss=5.921, over 74.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=28.76, generator_mel_loss=18, generator_kl_loss=1.425, generator_dur_loss=1.742, generator_adv_loss=1.986, generator_feat_match_loss=5.606, over 3726.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:56:14,328 INFO [train.py:527] (1/6) Epoch 640, batch 114, global_batch_idx: 79350, batch size: 25, loss[discriminator_loss=2.647, discriminator_real_loss=1.372, discriminator_fake_loss=1.275, generator_loss=29.94, generator_mel_loss=18.38, generator_kl_loss=1.91, generator_dur_loss=1.581, generator_adv_loss=2.097, generator_feat_match_loss=5.965, over 25.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.366, discriminator_fake_loss=1.333, generator_loss=28.71, generator_mel_loss=18.02, generator_kl_loss=1.428, generator_dur_loss=1.745, generator_adv_loss=1.975, generator_feat_match_loss=5.546, over 6563.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:56:40,897 INFO [train.py:919] (1/6) Start epoch 641
+2024-03-14 17:58:56,481 INFO [train.py:527] (1/6) Epoch 641, batch 40, global_batch_idx: 79400, batch size: 62, loss[discriminator_loss=2.703, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.5, generator_mel_loss=18.11, generator_kl_loss=1.517, generator_dur_loss=1.747, generator_adv_loss=1.841, generator_feat_match_loss=5.288, over 62.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.366, discriminator_fake_loss=1.333, generator_loss=28.78, generator_mel_loss=18.08, generator_kl_loss=1.459, generator_dur_loss=1.724, generator_adv_loss=1.971, generator_feat_match_loss=5.547, over 2160.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 17:58:56,483 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 17:59:04,409 INFO [train.py:591] (1/6) Epoch 641, validation: discriminator_loss=2.781, discriminator_real_loss=1.342, discriminator_fake_loss=1.439, generator_loss=27.18, generator_mel_loss=18.07, generator_kl_loss=1.282, generator_dur_loss=1.84, generator_adv_loss=1.758, generator_feat_match_loss=4.235, over 100.00 samples.
+2024-03-14 17:59:04,409 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 18:01:24,856 INFO [train.py:527] (1/6) Epoch 641, batch 90, global_batch_idx: 79450, batch size: 83, loss[discriminator_loss=2.692, discriminator_real_loss=1.391, discriminator_fake_loss=1.301, generator_loss=27.72, generator_mel_loss=17.84, generator_kl_loss=1.456, generator_dur_loss=1.803, generator_adv_loss=1.939, generator_feat_match_loss=4.675, over 83.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.362, discriminator_fake_loss=1.334, generator_loss=28.67, generator_mel_loss=18.01, generator_kl_loss=1.44, generator_dur_loss=1.745, generator_adv_loss=1.976, generator_feat_match_loss=5.503, over 4966.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:02:56,099 INFO [train.py:919] (1/6) Start epoch 642
+2024-03-14 18:04:05,210 INFO [train.py:527] (1/6) Epoch 642, batch 16, global_batch_idx: 79500, batch size: 45, loss[discriminator_loss=2.737, discriminator_real_loss=1.478, discriminator_fake_loss=1.259, generator_loss=28.16, generator_mel_loss=17.95, generator_kl_loss=1.556, generator_dur_loss=1.671, generator_adv_loss=1.845, generator_feat_match_loss=5.14, over 45.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.367, discriminator_fake_loss=1.332, generator_loss=28.69, generator_mel_loss=18, generator_kl_loss=1.442, generator_dur_loss=1.738, generator_adv_loss=1.984, generator_feat_match_loss=5.53, over 907.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:06:24,272 INFO [train.py:527] (1/6) Epoch 642, batch 66, global_batch_idx: 79550, batch size: 44, loss[discriminator_loss=2.703, discriminator_real_loss=1.241, discriminator_fake_loss=1.461, generator_loss=29.77, generator_mel_loss=18.2, generator_kl_loss=1.562, generator_dur_loss=1.681, generator_adv_loss=2.187, generator_feat_match_loss=6.146, over 44.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=28.57, generator_mel_loss=17.97, generator_kl_loss=1.423, generator_dur_loss=1.764, generator_adv_loss=1.979, generator_feat_match_loss=5.436, over 3889.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:08:44,294 INFO [train.py:527] (1/6) Epoch 642, batch 116, global_batch_idx: 79600, batch size: 31, loss[discriminator_loss=2.68, discriminator_real_loss=1.418, discriminator_fake_loss=1.262, generator_loss=28.12, generator_mel_loss=17.74, generator_kl_loss=1.479, generator_dur_loss=1.653, generator_adv_loss=1.984, generator_feat_match_loss=5.264, over 31.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.368, discriminator_fake_loss=1.332, generator_loss=28.69, generator_mel_loss=17.98, generator_kl_loss=1.422, generator_dur_loss=1.761, generator_adv_loss=1.992, generator_feat_match_loss=5.537, over 6832.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:08:44,296 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 18:08:52,920 INFO [train.py:591] (1/6) Epoch 642, validation: discriminator_loss=2.71, discriminator_real_loss=1.316, discriminator_fake_loss=1.395, generator_loss=27.72, generator_mel_loss=18.36, generator_kl_loss=1.407, generator_dur_loss=1.821, generator_adv_loss=1.883, generator_feat_match_loss=4.249, over 100.00 samples.
+2024-03-14 18:08:52,921 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 18:09:14,550 INFO [train.py:919] (1/6) Start epoch 643
+2024-03-14 18:11:35,654 INFO [train.py:527] (1/6) Epoch 643, batch 42, global_batch_idx: 79650, batch size: 31, loss[discriminator_loss=2.68, discriminator_real_loss=1.346, discriminator_fake_loss=1.334, generator_loss=27.79, generator_mel_loss=17.7, generator_kl_loss=1.581, generator_dur_loss=1.689, generator_adv_loss=1.887, generator_feat_match_loss=4.935, over 31.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.363, discriminator_fake_loss=1.332, generator_loss=28.6, generator_mel_loss=17.94, generator_kl_loss=1.41, generator_dur_loss=1.771, generator_adv_loss=1.982, generator_feat_match_loss=5.503, over 2630.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:13:54,440 INFO [train.py:527] (1/6) Epoch 643, batch 92, global_batch_idx: 79700, batch size: 80, loss[discriminator_loss=2.759, discriminator_real_loss=1.445, discriminator_fake_loss=1.313, generator_loss=27.61, generator_mel_loss=17.44, generator_kl_loss=1.362, generator_dur_loss=1.804, generator_adv_loss=1.894, generator_feat_match_loss=5.108, over 80.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.61, generator_mel_loss=17.95, generator_kl_loss=1.429, generator_dur_loss=1.759, generator_adv_loss=1.974, generator_feat_match_loss=5.499, over 5475.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:15:22,503 INFO [train.py:919] (1/6) Start epoch 644
+2024-03-14 18:16:39,274 INFO [train.py:527] (1/6) Epoch 644, batch 18, global_batch_idx: 79750, batch size: 66, loss[discriminator_loss=2.694, discriminator_real_loss=1.388, discriminator_fake_loss=1.306, generator_loss=28.86, generator_mel_loss=17.96, generator_kl_loss=1.591, generator_dur_loss=1.77, generator_adv_loss=2.005, generator_feat_match_loss=5.536, over 66.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=28.75, generator_mel_loss=17.99, generator_kl_loss=1.424, generator_dur_loss=1.752, generator_adv_loss=1.975, generator_feat_match_loss=5.607, over 1163.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:18:58,776 INFO [train.py:527] (1/6) Epoch 644, batch 68, global_batch_idx: 79800, batch size: 36, loss[discriminator_loss=2.748, discriminator_real_loss=1.485, discriminator_fake_loss=1.263, generator_loss=27.73, generator_mel_loss=17.68, generator_kl_loss=1.649, generator_dur_loss=1.677, generator_adv_loss=1.939, generator_feat_match_loss=4.783, over 36.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.369, discriminator_fake_loss=1.332, generator_loss=28.61, generator_mel_loss=17.97, generator_kl_loss=1.42, generator_dur_loss=1.752, generator_adv_loss=1.975, generator_feat_match_loss=5.494, over 3966.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:18:58,777 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 18:19:07,783 INFO [train.py:591] (1/6) Epoch 644, validation: discriminator_loss=2.725, discriminator_real_loss=1.409, discriminator_fake_loss=1.316, generator_loss=27.35, generator_mel_loss=18.23, generator_kl_loss=1.156, generator_dur_loss=1.814, generator_adv_loss=1.872, generator_feat_match_loss=4.274, over 100.00 samples.
+2024-03-14 18:19:07,784 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 18:21:27,602 INFO [train.py:527] (1/6) Epoch 644, batch 118, global_batch_idx: 79850, batch size: 39, loss[discriminator_loss=2.696, discriminator_real_loss=1.352, discriminator_fake_loss=1.343, generator_loss=27.5, generator_mel_loss=17.53, generator_kl_loss=1.515, generator_dur_loss=1.692, generator_adv_loss=2.006, generator_feat_match_loss=4.756, over 39.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.372, discriminator_fake_loss=1.333, generator_loss=28.59, generator_mel_loss=17.97, generator_kl_loss=1.423, generator_dur_loss=1.754, generator_adv_loss=1.971, generator_feat_match_loss=5.467, over 6767.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:21:43,689 INFO [train.py:919] (1/6) Start epoch 645
+2024-03-14 18:24:13,377 INFO [train.py:527] (1/6) Epoch 645, batch 44, global_batch_idx: 79900, batch size: 44, loss[discriminator_loss=2.668, discriminator_real_loss=1.381, discriminator_fake_loss=1.287, generator_loss=28.99, generator_mel_loss=18.45, generator_kl_loss=1.51, generator_dur_loss=1.653, generator_adv_loss=1.985, generator_feat_match_loss=5.398, over 44.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.367, discriminator_fake_loss=1.323, generator_loss=28.6, generator_mel_loss=18.03, generator_kl_loss=1.399, generator_dur_loss=1.754, generator_adv_loss=1.989, generator_feat_match_loss=5.426, over 2564.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:26:34,759 INFO [train.py:527] (1/6) Epoch 645, batch 94, global_batch_idx: 79950, batch size: 68, loss[discriminator_loss=2.726, discriminator_real_loss=1.281, discriminator_fake_loss=1.445, generator_loss=28.05, generator_mel_loss=17.92, generator_kl_loss=1.262, generator_dur_loss=1.822, generator_adv_loss=2.092, generator_feat_match_loss=4.95, over 68.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=28.7, generator_mel_loss=18.03, generator_kl_loss=1.401, generator_dur_loss=1.753, generator_adv_loss=1.991, generator_feat_match_loss=5.528, over 5531.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:27:53,791 INFO [train.py:919] (1/6) Start epoch 646
+2024-03-14 18:29:16,707 INFO [train.py:527] (1/6) Epoch 646, batch 20, global_batch_idx: 80000, batch size: 47, loss[discriminator_loss=2.736, discriminator_real_loss=1.453, discriminator_fake_loss=1.283, generator_loss=28.45, generator_mel_loss=17.74, generator_kl_loss=1.469, generator_dur_loss=1.653, generator_adv_loss=1.868, generator_feat_match_loss=5.718, over 47.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.37, discriminator_fake_loss=1.33, generator_loss=28.73, generator_mel_loss=18.03, generator_kl_loss=1.43, generator_dur_loss=1.764, generator_adv_loss=1.975, generator_feat_match_loss=5.524, over 1266.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:29:16,708 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 18:29:24,871 INFO [train.py:591] (1/6) Epoch 646, validation: discriminator_loss=2.734, discriminator_real_loss=1.391, discriminator_fake_loss=1.343, generator_loss=27.28, generator_mel_loss=18.04, generator_kl_loss=1.169, generator_dur_loss=1.815, generator_adv_loss=1.777, generator_feat_match_loss=4.481, over 100.00 samples.
+2024-03-14 18:29:24,872 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 18:31:44,361 INFO [train.py:527] (1/6) Epoch 646, batch 70, global_batch_idx: 80050, batch size: 58, loss[discriminator_loss=2.717, discriminator_real_loss=1.38, discriminator_fake_loss=1.338, generator_loss=28.71, generator_mel_loss=18.16, generator_kl_loss=1.507, generator_dur_loss=1.752, generator_adv_loss=1.963, generator_feat_match_loss=5.334, over 58.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.37, discriminator_fake_loss=1.335, generator_loss=28.61, generator_mel_loss=17.96, generator_kl_loss=1.413, generator_dur_loss=1.765, generator_adv_loss=1.978, generator_feat_match_loss=5.489, over 4098.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:34:03,508 INFO [train.py:527] (1/6) Epoch 646, batch 120, global_batch_idx: 80100, batch size: 48, loss[discriminator_loss=2.761, discriminator_real_loss=1.312, discriminator_fake_loss=1.449, generator_loss=29.33, generator_mel_loss=18.24, generator_kl_loss=1.5, generator_dur_loss=1.725, generator_adv_loss=2.14, generator_feat_match_loss=5.727, over 48.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.366, discriminator_fake_loss=1.332, generator_loss=28.63, generator_mel_loss=17.98, generator_kl_loss=1.42, generator_dur_loss=1.762, generator_adv_loss=1.978, generator_feat_match_loss=5.497, over 6763.00 samples.], cur_lr_g: 1.85e-04, cur_lr_d: 1.85e-04, 
+2024-03-14 18:34:12,405 INFO [train.py:919] (1/6) Start epoch 647
+2024-03-14 18:36:49,242 INFO [train.py:527] (1/6) Epoch 647, batch 46, global_batch_idx: 80150, batch size: 50, loss[discriminator_loss=2.712, discriminator_real_loss=1.406, discriminator_fake_loss=1.306, generator_loss=29.07, generator_mel_loss=18.34, generator_kl_loss=1.491, generator_dur_loss=1.708, generator_adv_loss=2.056, generator_feat_match_loss=5.474, over 50.00 samples.], tot_loss[discriminator_loss=2.713, discriminator_real_loss=1.373, discriminator_fake_loss=1.339, generator_loss=28.67, generator_mel_loss=18.02, generator_kl_loss=1.392, generator_dur_loss=1.766, generator_adv_loss=1.967, generator_feat_match_loss=5.525, over 2771.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:39:08,776 INFO [train.py:527] (1/6) Epoch 647, batch 96, global_batch_idx: 80200, batch size: 58, loss[discriminator_loss=2.694, discriminator_real_loss=1.324, discriminator_fake_loss=1.371, generator_loss=27.9, generator_mel_loss=17.5, generator_kl_loss=1.401, generator_dur_loss=1.741, generator_adv_loss=2.021, generator_feat_match_loss=5.234, over 58.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.66, generator_mel_loss=18.01, generator_kl_loss=1.406, generator_dur_loss=1.754, generator_adv_loss=1.972, generator_feat_match_loss=5.522, over 5577.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:39:08,778 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 18:39:17,849 INFO [train.py:591] (1/6) Epoch 647, validation: discriminator_loss=2.737, discriminator_real_loss=1.457, discriminator_fake_loss=1.28, generator_loss=27.18, generator_mel_loss=17.86, generator_kl_loss=1.308, generator_dur_loss=1.817, generator_adv_loss=1.983, generator_feat_match_loss=4.211, over 100.00 samples.
+2024-03-14 18:39:17,849 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 18:40:35,799 INFO [train.py:919] (1/6) Start epoch 648
+2024-03-14 18:42:03,868 INFO [train.py:527] (1/6) Epoch 648, batch 22, global_batch_idx: 80250, batch size: 58, loss[discriminator_loss=2.68, discriminator_real_loss=1.331, discriminator_fake_loss=1.349, generator_loss=29.23, generator_mel_loss=18.46, generator_kl_loss=1.337, generator_dur_loss=1.725, generator_adv_loss=2.03, generator_feat_match_loss=5.679, over 58.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.359, discriminator_fake_loss=1.34, generator_loss=28.54, generator_mel_loss=18.01, generator_kl_loss=1.359, generator_dur_loss=1.749, generator_adv_loss=1.971, generator_feat_match_loss=5.456, over 1336.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:44:22,189 INFO [train.py:527] (1/6) Epoch 648, batch 72, global_batch_idx: 80300, batch size: 88, loss[discriminator_loss=2.73, discriminator_real_loss=1.379, discriminator_fake_loss=1.352, generator_loss=27.29, generator_mel_loss=17.34, generator_kl_loss=1.347, generator_dur_loss=1.843, generator_adv_loss=1.904, generator_feat_match_loss=4.851, over 88.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.362, discriminator_fake_loss=1.342, generator_loss=28.51, generator_mel_loss=17.98, generator_kl_loss=1.398, generator_dur_loss=1.752, generator_adv_loss=1.976, generator_feat_match_loss=5.405, over 4161.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:46:42,928 INFO [train.py:527] (1/6) Epoch 648, batch 122, global_batch_idx: 80350, batch size: 64, loss[discriminator_loss=2.771, discriminator_real_loss=1.461, discriminator_fake_loss=1.31, generator_loss=29.5, generator_mel_loss=18.22, generator_kl_loss=1.408, generator_dur_loss=1.789, generator_adv_loss=1.887, generator_feat_match_loss=6.199, over 64.00 samples.], tot_loss[discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=28.52, generator_mel_loss=17.97, generator_kl_loss=1.409, generator_dur_loss=1.755, generator_adv_loss=1.977, generator_feat_match_loss=5.41, over 7020.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:46:48,341 INFO [train.py:919] (1/6) Start epoch 649
+2024-03-14 18:49:23,134 INFO [train.py:527] (1/6) Epoch 649, batch 48, global_batch_idx: 80400, batch size: 58, loss[discriminator_loss=2.662, discriminator_real_loss=1.318, discriminator_fake_loss=1.344, generator_loss=28.98, generator_mel_loss=18.26, generator_kl_loss=1.449, generator_dur_loss=1.736, generator_adv_loss=1.957, generator_feat_match_loss=5.581, over 58.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.52, generator_mel_loss=17.96, generator_kl_loss=1.407, generator_dur_loss=1.752, generator_adv_loss=1.979, generator_feat_match_loss=5.42, over 2724.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:49:23,135 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 18:49:31,231 INFO [train.py:591] (1/6) Epoch 649, validation: discriminator_loss=2.692, discriminator_real_loss=1.378, discriminator_fake_loss=1.313, generator_loss=27.1, generator_mel_loss=17.88, generator_kl_loss=1.266, generator_dur_loss=1.811, generator_adv_loss=1.928, generator_feat_match_loss=4.224, over 100.00 samples.
+2024-03-14 18:49:31,232 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 18:51:51,868 INFO [train.py:527] (1/6) Epoch 649, batch 98, global_batch_idx: 80450, batch size: 72, loss[discriminator_loss=2.652, discriminator_real_loss=1.316, discriminator_fake_loss=1.336, generator_loss=28.51, generator_mel_loss=17.57, generator_kl_loss=1.353, generator_dur_loss=1.82, generator_adv_loss=1.999, generator_feat_match_loss=5.772, over 72.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=28.57, generator_mel_loss=17.94, generator_kl_loss=1.41, generator_dur_loss=1.762, generator_adv_loss=1.998, generator_feat_match_loss=5.455, over 5759.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:53:00,719 INFO [train.py:919] (1/6) Start epoch 650
+2024-03-14 18:54:29,834 INFO [train.py:527] (1/6) Epoch 650, batch 24, global_batch_idx: 80500, batch size: 68, loss[discriminator_loss=2.671, discriminator_real_loss=1.268, discriminator_fake_loss=1.402, generator_loss=29.37, generator_mel_loss=18.15, generator_kl_loss=1.465, generator_dur_loss=1.766, generator_adv_loss=1.932, generator_feat_match_loss=6.058, over 68.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.35, discriminator_fake_loss=1.346, generator_loss=28.81, generator_mel_loss=18.05, generator_kl_loss=1.408, generator_dur_loss=1.738, generator_adv_loss=1.957, generator_feat_match_loss=5.655, over 1456.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:56:49,049 INFO [train.py:527] (1/6) Epoch 650, batch 74, global_batch_idx: 80550, batch size: 88, loss[discriminator_loss=2.769, discriminator_real_loss=1.429, discriminator_fake_loss=1.34, generator_loss=28.68, generator_mel_loss=18.13, generator_kl_loss=1.341, generator_dur_loss=1.844, generator_adv_loss=1.846, generator_feat_match_loss=5.522, over 88.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.356, discriminator_fake_loss=1.334, generator_loss=28.78, generator_mel_loss=18.01, generator_kl_loss=1.409, generator_dur_loss=1.747, generator_adv_loss=1.969, generator_feat_match_loss=5.641, over 4276.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:59:05,339 INFO [train.py:919] (1/6) Start epoch 651
+2024-03-14 18:59:29,144 INFO [train.py:527] (1/6) Epoch 651, batch 0, global_batch_idx: 80600, batch size: 36, loss[discriminator_loss=2.684, discriminator_real_loss=1.438, discriminator_fake_loss=1.246, generator_loss=27.6, generator_mel_loss=17.86, generator_kl_loss=1.459, generator_dur_loss=1.654, generator_adv_loss=1.907, generator_feat_match_loss=4.717, over 36.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.438, discriminator_fake_loss=1.246, generator_loss=27.6, generator_mel_loss=17.86, generator_kl_loss=1.459, generator_dur_loss=1.654, generator_adv_loss=1.907, generator_feat_match_loss=4.717, over 36.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 18:59:29,147 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 18:59:36,908 INFO [train.py:591] (1/6) Epoch 651, validation: discriminator_loss=2.733, discriminator_real_loss=1.396, discriminator_fake_loss=1.337, generator_loss=27.56, generator_mel_loss=18.3, generator_kl_loss=1.281, generator_dur_loss=1.793, generator_adv_loss=1.882, generator_feat_match_loss=4.305, over 100.00 samples.
+2024-03-14 18:59:36,910 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 19:01:55,824 INFO [train.py:527] (1/6) Epoch 651, batch 50, global_batch_idx: 80650, batch size: 15, loss[discriminator_loss=2.802, discriminator_real_loss=1.481, discriminator_fake_loss=1.321, generator_loss=29.01, generator_mel_loss=18.2, generator_kl_loss=1.902, generator_dur_loss=1.575, generator_adv_loss=1.947, generator_feat_match_loss=5.382, over 15.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.78, generator_mel_loss=18.02, generator_kl_loss=1.435, generator_dur_loss=1.725, generator_adv_loss=1.981, generator_feat_match_loss=5.613, over 2723.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:04:14,994 INFO [train.py:527] (1/6) Epoch 651, batch 100, global_batch_idx: 80700, batch size: 61, loss[discriminator_loss=2.675, discriminator_real_loss=1.377, discriminator_fake_loss=1.298, generator_loss=28.44, generator_mel_loss=17.74, generator_kl_loss=1.545, generator_dur_loss=1.715, generator_adv_loss=1.994, generator_feat_match_loss=5.441, over 61.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.365, discriminator_fake_loss=1.33, generator_loss=28.67, generator_mel_loss=17.97, generator_kl_loss=1.434, generator_dur_loss=1.744, generator_adv_loss=1.977, generator_feat_match_loss=5.547, over 5532.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:05:19,652 INFO [train.py:919] (1/6) Start epoch 652
+2024-03-14 19:06:57,739 INFO [train.py:527] (1/6) Epoch 652, batch 26, global_batch_idx: 80750, batch size: 74, loss[discriminator_loss=2.713, discriminator_real_loss=1.407, discriminator_fake_loss=1.306, generator_loss=28.17, generator_mel_loss=17.57, generator_kl_loss=1.211, generator_dur_loss=1.8, generator_adv_loss=2.024, generator_feat_match_loss=5.562, over 74.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.354, discriminator_fake_loss=1.335, generator_loss=28.78, generator_mel_loss=17.93, generator_kl_loss=1.452, generator_dur_loss=1.733, generator_adv_loss=1.984, generator_feat_match_loss=5.689, over 1438.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:09:15,662 INFO [train.py:527] (1/6) Epoch 652, batch 76, global_batch_idx: 80800, batch size: 48, loss[discriminator_loss=2.746, discriminator_real_loss=1.407, discriminator_fake_loss=1.339, generator_loss=30.06, generator_mel_loss=18.52, generator_kl_loss=1.545, generator_dur_loss=1.704, generator_adv_loss=1.832, generator_feat_match_loss=6.46, over 48.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.359, discriminator_fake_loss=1.337, generator_loss=28.78, generator_mel_loss=17.97, generator_kl_loss=1.429, generator_dur_loss=1.751, generator_adv_loss=1.977, generator_feat_match_loss=5.646, over 4461.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:09:15,664 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 19:09:24,373 INFO [train.py:591] (1/6) Epoch 652, validation: discriminator_loss=2.745, discriminator_real_loss=1.351, discriminator_fake_loss=1.393, generator_loss=27.25, generator_mel_loss=17.96, generator_kl_loss=1.27, generator_dur_loss=1.824, generator_adv_loss=1.842, generator_feat_match_loss=4.36, over 100.00 samples.
+2024-03-14 19:09:24,374 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 19:11:35,413 INFO [train.py:919] (1/6) Start epoch 653
+2024-03-14 19:12:04,929 INFO [train.py:527] (1/6) Epoch 653, batch 2, global_batch_idx: 80850, batch size: 62, loss[discriminator_loss=2.656, discriminator_real_loss=1.336, discriminator_fake_loss=1.32, generator_loss=29.78, generator_mel_loss=18.46, generator_kl_loss=1.446, generator_dur_loss=1.756, generator_adv_loss=2.126, generator_feat_match_loss=5.988, over 62.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.315, discriminator_fake_loss=1.346, generator_loss=29.15, generator_mel_loss=18.28, generator_kl_loss=1.436, generator_dur_loss=1.805, generator_adv_loss=1.994, generator_feat_match_loss=5.636, over 230.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:14:23,257 INFO [train.py:527] (1/6) Epoch 653, batch 52, global_batch_idx: 80900, batch size: 66, loss[discriminator_loss=2.678, discriminator_real_loss=1.335, discriminator_fake_loss=1.343, generator_loss=28.57, generator_mel_loss=17.77, generator_kl_loss=1.375, generator_dur_loss=1.768, generator_adv_loss=1.961, generator_feat_match_loss=5.692, over 66.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.359, discriminator_fake_loss=1.333, generator_loss=28.58, generator_mel_loss=18.02, generator_kl_loss=1.412, generator_dur_loss=1.755, generator_adv_loss=1.982, generator_feat_match_loss=5.413, over 3101.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:16:43,657 INFO [train.py:527] (1/6) Epoch 653, batch 102, global_batch_idx: 80950, batch size: 55, loss[discriminator_loss=2.699, discriminator_real_loss=1.377, discriminator_fake_loss=1.322, generator_loss=28.01, generator_mel_loss=17.81, generator_kl_loss=1.426, generator_dur_loss=1.719, generator_adv_loss=2.161, generator_feat_match_loss=4.9, over 55.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=28.68, generator_mel_loss=18.01, generator_kl_loss=1.423, generator_dur_loss=1.751, generator_adv_loss=1.989, generator_feat_match_loss=5.508, over 5836.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:17:43,482 INFO [train.py:919] (1/6) Start epoch 654
+2024-03-14 19:19:27,165 INFO [train.py:527] (1/6) Epoch 654, batch 28, global_batch_idx: 81000, batch size: 68, loss[discriminator_loss=2.701, discriminator_real_loss=1.291, discriminator_fake_loss=1.41, generator_loss=29.41, generator_mel_loss=17.9, generator_kl_loss=1.358, generator_dur_loss=1.801, generator_adv_loss=1.978, generator_feat_match_loss=6.368, over 68.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.356, discriminator_fake_loss=1.337, generator_loss=28.87, generator_mel_loss=18.01, generator_kl_loss=1.45, generator_dur_loss=1.747, generator_adv_loss=1.975, generator_feat_match_loss=5.692, over 1665.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:19:27,167 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 19:19:35,257 INFO [train.py:591] (1/6) Epoch 654, validation: discriminator_loss=2.727, discriminator_real_loss=1.422, discriminator_fake_loss=1.305, generator_loss=28.25, generator_mel_loss=18.52, generator_kl_loss=1.227, generator_dur_loss=1.821, generator_adv_loss=1.954, generator_feat_match_loss=4.725, over 100.00 samples.
+2024-03-14 19:19:35,257 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 19:21:51,314 INFO [train.py:527] (1/6) Epoch 654, batch 78, global_batch_idx: 81050, batch size: 56, loss[discriminator_loss=2.701, discriminator_real_loss=1.42, discriminator_fake_loss=1.281, generator_loss=28.92, generator_mel_loss=18.08, generator_kl_loss=1.4, generator_dur_loss=1.764, generator_adv_loss=1.973, generator_feat_match_loss=5.7, over 56.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.356, discriminator_fake_loss=1.331, generator_loss=28.78, generator_mel_loss=18, generator_kl_loss=1.431, generator_dur_loss=1.746, generator_adv_loss=1.972, generator_feat_match_loss=5.632, over 4434.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:24:00,688 INFO [train.py:919] (1/6) Start epoch 655
+2024-03-14 19:24:35,428 INFO [train.py:527] (1/6) Epoch 655, batch 4, global_batch_idx: 81100, batch size: 45, loss[discriminator_loss=2.646, discriminator_real_loss=1.253, discriminator_fake_loss=1.393, generator_loss=30.56, generator_mel_loss=18.4, generator_kl_loss=1.579, generator_dur_loss=1.69, generator_adv_loss=2.268, generator_feat_match_loss=6.626, over 45.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.346, discriminator_fake_loss=1.355, generator_loss=28.85, generator_mel_loss=18.18, generator_kl_loss=1.425, generator_dur_loss=1.771, generator_adv_loss=2.03, generator_feat_match_loss=5.441, over 311.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:26:53,564 INFO [train.py:527] (1/6) Epoch 655, batch 54, global_batch_idx: 81150, batch size: 70, loss[discriminator_loss=2.7, discriminator_real_loss=1.358, discriminator_fake_loss=1.342, generator_loss=28.18, generator_mel_loss=17.74, generator_kl_loss=1.314, generator_dur_loss=1.783, generator_adv_loss=1.963, generator_feat_match_loss=5.381, over 70.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.354, discriminator_fake_loss=1.318, generator_loss=28.94, generator_mel_loss=17.97, generator_kl_loss=1.441, generator_dur_loss=1.751, generator_adv_loss=2.042, generator_feat_match_loss=5.733, over 3279.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:29:13,870 INFO [train.py:527] (1/6) Epoch 655, batch 104, global_batch_idx: 81200, batch size: 74, loss[discriminator_loss=2.666, discriminator_real_loss=1.322, discriminator_fake_loss=1.344, generator_loss=29.3, generator_mel_loss=18.04, generator_kl_loss=1.325, generator_dur_loss=1.746, generator_adv_loss=2.053, generator_feat_match_loss=6.135, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.73, generator_mel_loss=17.94, generator_kl_loss=1.414, generator_dur_loss=1.756, generator_adv_loss=2.01, generator_feat_match_loss=5.612, over 6360.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:29:13,872 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 19:29:22,651 INFO [train.py:591] (1/6) Epoch 655, validation: discriminator_loss=2.713, discriminator_real_loss=1.431, discriminator_fake_loss=1.283, generator_loss=26.94, generator_mel_loss=18.27, generator_kl_loss=1.221, generator_dur_loss=1.801, generator_adv_loss=1.897, generator_feat_match_loss=3.747, over 100.00 samples.
+2024-03-14 19:29:22,652 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 19:30:13,893 INFO [train.py:919] (1/6) Start epoch 656
+2024-03-14 19:32:03,310 INFO [train.py:527] (1/6) Epoch 656, batch 30, global_batch_idx: 81250, batch size: 61, loss[discriminator_loss=2.703, discriminator_real_loss=1.401, discriminator_fake_loss=1.302, generator_loss=28.88, generator_mel_loss=18.17, generator_kl_loss=1.387, generator_dur_loss=1.728, generator_adv_loss=1.935, generator_feat_match_loss=5.664, over 61.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.59, generator_mel_loss=17.96, generator_kl_loss=1.397, generator_dur_loss=1.756, generator_adv_loss=1.954, generator_feat_match_loss=5.524, over 1858.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:34:21,011 INFO [train.py:527] (1/6) Epoch 656, batch 80, global_batch_idx: 81300, batch size: 61, loss[discriminator_loss=2.713, discriminator_real_loss=1.336, discriminator_fake_loss=1.377, generator_loss=28.61, generator_mel_loss=17.77, generator_kl_loss=1.388, generator_dur_loss=1.759, generator_adv_loss=2.047, generator_feat_match_loss=5.64, over 61.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.356, discriminator_fake_loss=1.331, generator_loss=28.64, generator_mel_loss=17.97, generator_kl_loss=1.385, generator_dur_loss=1.76, generator_adv_loss=1.979, generator_feat_match_loss=5.543, over 4815.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:36:16,519 INFO [train.py:919] (1/6) Start epoch 657
+2024-03-14 19:36:56,975 INFO [train.py:527] (1/6) Epoch 657, batch 6, global_batch_idx: 81350, batch size: 59, loss[discriminator_loss=2.684, discriminator_real_loss=1.319, discriminator_fake_loss=1.365, generator_loss=28.83, generator_mel_loss=17.91, generator_kl_loss=1.338, generator_dur_loss=1.738, generator_adv_loss=1.949, generator_feat_match_loss=5.895, over 59.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.339, discriminator_fake_loss=1.336, generator_loss=29.02, generator_mel_loss=17.93, generator_kl_loss=1.512, generator_dur_loss=1.695, generator_adv_loss=1.981, generator_feat_match_loss=5.902, over 316.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:39:13,451 INFO [train.py:527] (1/6) Epoch 657, batch 56, global_batch_idx: 81400, batch size: 58, loss[discriminator_loss=2.707, discriminator_real_loss=1.279, discriminator_fake_loss=1.429, generator_loss=27.89, generator_mel_loss=17.57, generator_kl_loss=1.459, generator_dur_loss=1.728, generator_adv_loss=2.036, generator_feat_match_loss=5.098, over 58.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.351, discriminator_fake_loss=1.333, generator_loss=28.81, generator_mel_loss=17.99, generator_kl_loss=1.422, generator_dur_loss=1.74, generator_adv_loss=1.986, generator_feat_match_loss=5.672, over 3266.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:39:13,453 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 19:39:21,547 INFO [train.py:591] (1/6) Epoch 657, validation: discriminator_loss=2.725, discriminator_real_loss=1.434, discriminator_fake_loss=1.291, generator_loss=28.32, generator_mel_loss=18.37, generator_kl_loss=1.234, generator_dur_loss=1.811, generator_adv_loss=1.975, generator_feat_match_loss=4.928, over 100.00 samples.
+2024-03-14 19:39:21,547 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 19:41:38,969 INFO [train.py:527] (1/6) Epoch 657, batch 106, global_batch_idx: 81450, batch size: 48, loss[discriminator_loss=2.617, discriminator_real_loss=1.33, discriminator_fake_loss=1.287, generator_loss=29.35, generator_mel_loss=18.14, generator_kl_loss=1.517, generator_dur_loss=1.654, generator_adv_loss=2.062, generator_feat_match_loss=5.976, over 48.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.78, generator_mel_loss=17.98, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=1.986, generator_feat_match_loss=5.641, over 6013.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:42:29,870 INFO [train.py:919] (1/6) Start epoch 658
+2024-03-14 19:44:21,189 INFO [train.py:527] (1/6) Epoch 658, batch 32, global_batch_idx: 81500, batch size: 44, loss[discriminator_loss=2.666, discriminator_real_loss=1.383, discriminator_fake_loss=1.283, generator_loss=28.79, generator_mel_loss=17.82, generator_kl_loss=1.626, generator_dur_loss=1.738, generator_adv_loss=1.99, generator_feat_match_loss=5.608, over 44.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=28.64, generator_mel_loss=18.04, generator_kl_loss=1.418, generator_dur_loss=1.73, generator_adv_loss=1.985, generator_feat_match_loss=5.464, over 1740.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:46:41,868 INFO [train.py:527] (1/6) Epoch 658, batch 82, global_batch_idx: 81550, batch size: 44, loss[discriminator_loss=2.723, discriminator_real_loss=1.364, discriminator_fake_loss=1.359, generator_loss=29.72, generator_mel_loss=18.12, generator_kl_loss=1.503, generator_dur_loss=1.648, generator_adv_loss=2.097, generator_feat_match_loss=6.355, over 44.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.366, discriminator_fake_loss=1.329, generator_loss=28.66, generator_mel_loss=17.98, generator_kl_loss=1.408, generator_dur_loss=1.746, generator_adv_loss=1.996, generator_feat_match_loss=5.529, over 4564.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:48:37,739 INFO [train.py:919] (1/6) Start epoch 659
+2024-03-14 19:49:23,476 INFO [train.py:527] (1/6) Epoch 659, batch 8, global_batch_idx: 81600, batch size: 83, loss[discriminator_loss=2.713, discriminator_real_loss=1.295, discriminator_fake_loss=1.418, generator_loss=28.72, generator_mel_loss=18.09, generator_kl_loss=1.279, generator_dur_loss=1.841, generator_adv_loss=1.992, generator_feat_match_loss=5.52, over 83.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.337, discriminator_fake_loss=1.347, generator_loss=28.93, generator_mel_loss=18.11, generator_kl_loss=1.442, generator_dur_loss=1.718, generator_adv_loss=1.984, generator_feat_match_loss=5.677, over 461.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:49:23,478 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 19:49:31,215 INFO [train.py:591] (1/6) Epoch 659, validation: discriminator_loss=2.73, discriminator_real_loss=1.438, discriminator_fake_loss=1.292, generator_loss=27.54, generator_mel_loss=17.99, generator_kl_loss=1.298, generator_dur_loss=1.818, generator_adv_loss=1.966, generator_feat_match_loss=4.469, over 100.00 samples.
+2024-03-14 19:49:31,217 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 19:51:49,911 INFO [train.py:527] (1/6) Epoch 659, batch 58, global_batch_idx: 81650, batch size: 48, loss[discriminator_loss=2.64, discriminator_real_loss=1.427, discriminator_fake_loss=1.213, generator_loss=29.44, generator_mel_loss=18.08, generator_kl_loss=1.647, generator_dur_loss=1.652, generator_adv_loss=1.924, generator_feat_match_loss=6.134, over 48.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.357, discriminator_fake_loss=1.331, generator_loss=28.63, generator_mel_loss=17.92, generator_kl_loss=1.404, generator_dur_loss=1.751, generator_adv_loss=1.977, generator_feat_match_loss=5.58, over 3396.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:54:08,727 INFO [train.py:527] (1/6) Epoch 659, batch 108, global_batch_idx: 81700, batch size: 56, loss[discriminator_loss=2.636, discriminator_real_loss=1.34, discriminator_fake_loss=1.296, generator_loss=28.29, generator_mel_loss=17.72, generator_kl_loss=1.285, generator_dur_loss=1.757, generator_adv_loss=2.072, generator_feat_match_loss=5.456, over 56.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.358, discriminator_fake_loss=1.331, generator_loss=28.65, generator_mel_loss=17.94, generator_kl_loss=1.398, generator_dur_loss=1.751, generator_adv_loss=1.983, generator_feat_match_loss=5.571, over 6310.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:54:51,169 INFO [train.py:919] (1/6) Start epoch 660
+2024-03-14 19:56:52,237 INFO [train.py:527] (1/6) Epoch 660, batch 34, global_batch_idx: 81750, batch size: 80, loss[discriminator_loss=2.769, discriminator_real_loss=1.434, discriminator_fake_loss=1.334, generator_loss=27.19, generator_mel_loss=17.5, generator_kl_loss=1.306, generator_dur_loss=1.82, generator_adv_loss=1.905, generator_feat_match_loss=4.663, over 80.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=28.64, generator_mel_loss=17.95, generator_kl_loss=1.426, generator_dur_loss=1.742, generator_adv_loss=1.988, generator_feat_match_loss=5.53, over 1943.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:59:10,379 INFO [train.py:527] (1/6) Epoch 660, batch 84, global_batch_idx: 81800, batch size: 62, loss[discriminator_loss=2.74, discriminator_real_loss=1.379, discriminator_fake_loss=1.361, generator_loss=28.43, generator_mel_loss=17.87, generator_kl_loss=1.338, generator_dur_loss=1.771, generator_adv_loss=1.99, generator_feat_match_loss=5.455, over 62.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.367, discriminator_fake_loss=1.34, generator_loss=28.58, generator_mel_loss=17.96, generator_kl_loss=1.417, generator_dur_loss=1.747, generator_adv_loss=1.97, generator_feat_match_loss=5.489, over 4818.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 19:59:10,380 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 19:59:19,067 INFO [train.py:591] (1/6) Epoch 660, validation: discriminator_loss=2.743, discriminator_real_loss=1.505, discriminator_fake_loss=1.239, generator_loss=27.23, generator_mel_loss=17.83, generator_kl_loss=1.269, generator_dur_loss=1.817, generator_adv_loss=1.957, generator_feat_match_loss=4.361, over 100.00 samples.
+2024-03-14 19:59:19,068 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 20:01:07,859 INFO [train.py:919] (1/6) Start epoch 661
+2024-03-14 20:02:02,452 INFO [train.py:527] (1/6) Epoch 661, batch 10, global_batch_idx: 81850, batch size: 55, loss[discriminator_loss=2.689, discriminator_real_loss=1.27, discriminator_fake_loss=1.418, generator_loss=28.92, generator_mel_loss=18.18, generator_kl_loss=1.217, generator_dur_loss=1.737, generator_adv_loss=1.979, generator_feat_match_loss=5.804, over 55.00 samples.], tot_loss[discriminator_loss=2.715, discriminator_real_loss=1.353, discriminator_fake_loss=1.362, generator_loss=28.52, generator_mel_loss=17.98, generator_kl_loss=1.368, generator_dur_loss=1.777, generator_adv_loss=1.947, generator_feat_match_loss=5.446, over 717.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:04:22,952 INFO [train.py:527] (1/6) Epoch 661, batch 60, global_batch_idx: 81900, batch size: 96, loss[discriminator_loss=2.743, discriminator_real_loss=1.347, discriminator_fake_loss=1.396, generator_loss=28.8, generator_mel_loss=18.05, generator_kl_loss=1.47, generator_dur_loss=1.766, generator_adv_loss=2.002, generator_feat_match_loss=5.511, over 96.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.362, discriminator_fake_loss=1.346, generator_loss=28.68, generator_mel_loss=17.98, generator_kl_loss=1.424, generator_dur_loss=1.74, generator_adv_loss=1.979, generator_feat_match_loss=5.556, over 3461.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:06:41,358 INFO [train.py:527] (1/6) Epoch 661, batch 110, global_batch_idx: 81950, batch size: 59, loss[discriminator_loss=2.693, discriminator_real_loss=1.378, discriminator_fake_loss=1.314, generator_loss=27.47, generator_mel_loss=17.84, generator_kl_loss=1.172, generator_dur_loss=1.764, generator_adv_loss=2.024, generator_feat_match_loss=4.676, over 59.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=28.6, generator_mel_loss=17.97, generator_kl_loss=1.417, generator_dur_loss=1.731, generator_adv_loss=1.976, generator_feat_match_loss=5.5, over 6141.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:07:17,970 INFO [train.py:919] (1/6) Start epoch 662
+2024-03-14 20:09:23,486 INFO [train.py:527] (1/6) Epoch 662, batch 36, global_batch_idx: 82000, batch size: 96, loss[discriminator_loss=2.678, discriminator_real_loss=1.319, discriminator_fake_loss=1.359, generator_loss=28.73, generator_mel_loss=18.13, generator_kl_loss=1.194, generator_dur_loss=1.858, generator_adv_loss=1.871, generator_feat_match_loss=5.677, over 96.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.369, discriminator_fake_loss=1.332, generator_loss=28.76, generator_mel_loss=17.99, generator_kl_loss=1.402, generator_dur_loss=1.753, generator_adv_loss=2.007, generator_feat_match_loss=5.614, over 2236.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:09:23,488 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 20:09:31,284 INFO [train.py:591] (1/6) Epoch 662, validation: discriminator_loss=2.753, discriminator_real_loss=1.382, discriminator_fake_loss=1.371, generator_loss=27.58, generator_mel_loss=18.17, generator_kl_loss=1.241, generator_dur_loss=1.811, generator_adv_loss=1.83, generator_feat_match_loss=4.526, over 100.00 samples.
+2024-03-14 20:09:31,285 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 20:11:49,906 INFO [train.py:527] (1/6) Epoch 662, batch 86, global_batch_idx: 82050, batch size: 96, loss[discriminator_loss=2.723, discriminator_real_loss=1.349, discriminator_fake_loss=1.375, generator_loss=28.5, generator_mel_loss=17.85, generator_kl_loss=1.213, generator_dur_loss=1.878, generator_adv_loss=1.991, generator_feat_match_loss=5.565, over 96.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.337, generator_loss=28.64, generator_mel_loss=17.98, generator_kl_loss=1.403, generator_dur_loss=1.753, generator_adv_loss=1.989, generator_feat_match_loss=5.509, over 5211.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:13:30,350 INFO [train.py:919] (1/6) Start epoch 663
+2024-03-14 20:14:30,097 INFO [train.py:527] (1/6) Epoch 663, batch 12, global_batch_idx: 82100, batch size: 66, loss[discriminator_loss=2.689, discriminator_real_loss=1.35, discriminator_fake_loss=1.339, generator_loss=28.98, generator_mel_loss=17.98, generator_kl_loss=1.247, generator_dur_loss=1.735, generator_adv_loss=2.1, generator_feat_match_loss=5.914, over 66.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.373, discriminator_fake_loss=1.317, generator_loss=28.5, generator_mel_loss=17.86, generator_kl_loss=1.381, generator_dur_loss=1.762, generator_adv_loss=1.992, generator_feat_match_loss=5.511, over 782.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:16:47,628 INFO [train.py:527] (1/6) Epoch 663, batch 62, global_batch_idx: 82150, batch size: 52, loss[discriminator_loss=2.792, discriminator_real_loss=1.434, discriminator_fake_loss=1.358, generator_loss=27.79, generator_mel_loss=18.02, generator_kl_loss=1.285, generator_dur_loss=1.695, generator_adv_loss=1.958, generator_feat_match_loss=4.824, over 52.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.38, discriminator_fake_loss=1.324, generator_loss=28.62, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.742, generator_adv_loss=1.969, generator_feat_match_loss=5.503, over 3281.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:19:06,827 INFO [train.py:527] (1/6) Epoch 663, batch 112, global_batch_idx: 82200, batch size: 25, loss[discriminator_loss=2.635, discriminator_real_loss=1.333, discriminator_fake_loss=1.302, generator_loss=31.04, generator_mel_loss=18.68, generator_kl_loss=1.862, generator_dur_loss=1.522, generator_adv_loss=1.966, generator_feat_match_loss=7.015, over 25.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.372, discriminator_fake_loss=1.324, generator_loss=28.6, generator_mel_loss=17.92, generator_kl_loss=1.404, generator_dur_loss=1.749, generator_adv_loss=1.978, generator_feat_match_loss=5.555, over 6312.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:19:06,828 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 20:19:15,746 INFO [train.py:591] (1/6) Epoch 663, validation: discriminator_loss=2.75, discriminator_real_loss=1.405, discriminator_fake_loss=1.344, generator_loss=27.35, generator_mel_loss=18.29, generator_kl_loss=1.192, generator_dur_loss=1.804, generator_adv_loss=1.894, generator_feat_match_loss=4.17, over 100.00 samples.
+2024-03-14 20:19:15,747 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 20:19:44,620 INFO [train.py:919] (1/6) Start epoch 664
+2024-03-14 20:21:53,614 INFO [train.py:527] (1/6) Epoch 664, batch 38, global_batch_idx: 82250, batch size: 61, loss[discriminator_loss=2.72, discriminator_real_loss=1.39, discriminator_fake_loss=1.33, generator_loss=29.15, generator_mel_loss=18.19, generator_kl_loss=1.376, generator_dur_loss=1.757, generator_adv_loss=2.054, generator_feat_match_loss=5.779, over 61.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.332, generator_loss=28.71, generator_mel_loss=18.01, generator_kl_loss=1.446, generator_dur_loss=1.739, generator_adv_loss=1.968, generator_feat_match_loss=5.543, over 2170.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:24:12,424 INFO [train.py:527] (1/6) Epoch 664, batch 88, global_batch_idx: 82300, batch size: 18, loss[discriminator_loss=2.653, discriminator_real_loss=1.356, discriminator_fake_loss=1.297, generator_loss=28.85, generator_mel_loss=19.2, generator_kl_loss=1.801, generator_dur_loss=1.504, generator_adv_loss=1.91, generator_feat_match_loss=4.443, over 18.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.37, discriminator_fake_loss=1.329, generator_loss=28.62, generator_mel_loss=17.94, generator_kl_loss=1.443, generator_dur_loss=1.738, generator_adv_loss=1.974, generator_feat_match_loss=5.522, over 4849.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:25:51,571 INFO [train.py:919] (1/6) Start epoch 665
+2024-03-14 20:26:54,171 INFO [train.py:527] (1/6) Epoch 665, batch 14, global_batch_idx: 82350, batch size: 74, loss[discriminator_loss=2.703, discriminator_real_loss=1.294, discriminator_fake_loss=1.408, generator_loss=29.43, generator_mel_loss=18.05, generator_kl_loss=1.457, generator_dur_loss=1.789, generator_adv_loss=2.304, generator_feat_match_loss=5.832, over 74.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.345, discriminator_fake_loss=1.338, generator_loss=28.63, generator_mel_loss=17.9, generator_kl_loss=1.423, generator_dur_loss=1.753, generator_adv_loss=2.004, generator_feat_match_loss=5.549, over 937.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:29:13,270 INFO [train.py:527] (1/6) Epoch 665, batch 64, global_batch_idx: 82400, batch size: 53, loss[discriminator_loss=2.622, discriminator_real_loss=1.347, discriminator_fake_loss=1.275, generator_loss=29.46, generator_mel_loss=18.08, generator_kl_loss=1.461, generator_dur_loss=1.684, generator_adv_loss=1.843, generator_feat_match_loss=6.393, over 53.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.64, generator_mel_loss=17.92, generator_kl_loss=1.425, generator_dur_loss=1.754, generator_adv_loss=1.996, generator_feat_match_loss=5.55, over 3875.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:29:13,272 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 20:29:21,240 INFO [train.py:591] (1/6) Epoch 665, validation: discriminator_loss=2.712, discriminator_real_loss=1.331, discriminator_fake_loss=1.381, generator_loss=27.3, generator_mel_loss=18.25, generator_kl_loss=1.151, generator_dur_loss=1.805, generator_adv_loss=1.796, generator_feat_match_loss=4.296, over 100.00 samples.
+2024-03-14 20:29:21,241 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 20:31:40,105 INFO [train.py:527] (1/6) Epoch 665, batch 114, global_batch_idx: 82450, batch size: 42, loss[discriminator_loss=2.637, discriminator_real_loss=1.329, discriminator_fake_loss=1.308, generator_loss=30.03, generator_mel_loss=18.71, generator_kl_loss=1.595, generator_dur_loss=1.671, generator_adv_loss=1.998, generator_feat_match_loss=6.048, over 42.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=28.63, generator_mel_loss=17.93, generator_kl_loss=1.425, generator_dur_loss=1.749, generator_adv_loss=1.991, generator_feat_match_loss=5.531, over 6733.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:32:03,464 INFO [train.py:919] (1/6) Start epoch 666
+2024-03-14 20:34:16,493 INFO [train.py:527] (1/6) Epoch 666, batch 40, global_batch_idx: 82500, batch size: 68, loss[discriminator_loss=2.691, discriminator_real_loss=1.416, discriminator_fake_loss=1.276, generator_loss=27.8, generator_mel_loss=17.33, generator_kl_loss=1.349, generator_dur_loss=1.787, generator_adv_loss=1.944, generator_feat_match_loss=5.385, over 68.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.335, generator_loss=28.56, generator_mel_loss=17.91, generator_kl_loss=1.443, generator_dur_loss=1.749, generator_adv_loss=1.968, generator_feat_match_loss=5.486, over 2284.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:36:37,908 INFO [train.py:527] (1/6) Epoch 666, batch 90, global_batch_idx: 82550, batch size: 68, loss[discriminator_loss=2.684, discriminator_real_loss=1.371, discriminator_fake_loss=1.313, generator_loss=29.03, generator_mel_loss=18.12, generator_kl_loss=1.417, generator_dur_loss=1.749, generator_adv_loss=1.963, generator_feat_match_loss=5.782, over 68.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.368, discriminator_fake_loss=1.337, generator_loss=28.63, generator_mel_loss=17.97, generator_kl_loss=1.42, generator_dur_loss=1.747, generator_adv_loss=1.971, generator_feat_match_loss=5.528, over 5208.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:38:13,411 INFO [train.py:919] (1/6) Start epoch 667
+2024-03-14 20:39:20,993 INFO [train.py:527] (1/6) Epoch 667, batch 16, global_batch_idx: 82600, batch size: 80, loss[discriminator_loss=2.687, discriminator_real_loss=1.31, discriminator_fake_loss=1.377, generator_loss=28.4, generator_mel_loss=17.8, generator_kl_loss=1.259, generator_dur_loss=1.807, generator_adv_loss=2.065, generator_feat_match_loss=5.468, over 80.00 samples.], tot_loss[discriminator_loss=2.719, discriminator_real_loss=1.37, discriminator_fake_loss=1.349, generator_loss=28.41, generator_mel_loss=17.86, generator_kl_loss=1.377, generator_dur_loss=1.792, generator_adv_loss=1.964, generator_feat_match_loss=5.42, over 1083.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:39:20,995 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 20:39:28,898 INFO [train.py:591] (1/6) Epoch 667, validation: discriminator_loss=2.756, discriminator_real_loss=1.445, discriminator_fake_loss=1.311, generator_loss=27.3, generator_mel_loss=18.35, generator_kl_loss=1.207, generator_dur_loss=1.823, generator_adv_loss=1.978, generator_feat_match_loss=3.941, over 100.00 samples.
+2024-03-14 20:39:28,899 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 20:41:49,033 INFO [train.py:527] (1/6) Epoch 667, batch 66, global_batch_idx: 82650, batch size: 31, loss[discriminator_loss=2.724, discriminator_real_loss=1.457, discriminator_fake_loss=1.267, generator_loss=29.61, generator_mel_loss=18.12, generator_kl_loss=1.71, generator_dur_loss=1.606, generator_adv_loss=1.979, generator_feat_match_loss=6.202, over 31.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.44, generator_mel_loss=17.83, generator_kl_loss=1.393, generator_dur_loss=1.772, generator_adv_loss=1.974, generator_feat_match_loss=5.469, over 4023.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:44:05,812 INFO [train.py:527] (1/6) Epoch 667, batch 116, global_batch_idx: 82700, batch size: 53, loss[discriminator_loss=2.764, discriminator_real_loss=1.334, discriminator_fake_loss=1.429, generator_loss=29.42, generator_mel_loss=18.5, generator_kl_loss=1.375, generator_dur_loss=1.684, generator_adv_loss=2.165, generator_feat_match_loss=5.69, over 53.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.36, discriminator_fake_loss=1.335, generator_loss=28.63, generator_mel_loss=17.88, generator_kl_loss=1.406, generator_dur_loss=1.759, generator_adv_loss=2.001, generator_feat_match_loss=5.579, over 6840.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:44:27,246 INFO [train.py:919] (1/6) Start epoch 668
+2024-03-14 20:46:49,154 INFO [train.py:527] (1/6) Epoch 668, batch 42, global_batch_idx: 82750, batch size: 68, loss[discriminator_loss=2.729, discriminator_real_loss=1.386, discriminator_fake_loss=1.344, generator_loss=29.2, generator_mel_loss=18.27, generator_kl_loss=1.334, generator_dur_loss=1.779, generator_adv_loss=2.031, generator_feat_match_loss=5.787, over 68.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=28.65, generator_mel_loss=17.88, generator_kl_loss=1.392, generator_dur_loss=1.754, generator_adv_loss=1.981, generator_feat_match_loss=5.639, over 2523.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:49:06,884 INFO [train.py:527] (1/6) Epoch 668, batch 92, global_batch_idx: 82800, batch size: 62, loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.92, generator_mel_loss=18.1, generator_kl_loss=1.414, generator_dur_loss=1.734, generator_adv_loss=1.891, generator_feat_match_loss=5.782, over 62.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.7, generator_mel_loss=17.96, generator_kl_loss=1.397, generator_dur_loss=1.758, generator_adv_loss=1.971, generator_feat_match_loss=5.611, over 5567.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:49:06,886 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 20:49:15,916 INFO [train.py:591] (1/6) Epoch 668, validation: discriminator_loss=2.728, discriminator_real_loss=1.387, discriminator_fake_loss=1.341, generator_loss=27.17, generator_mel_loss=17.94, generator_kl_loss=1.286, generator_dur_loss=1.82, generator_adv_loss=1.855, generator_feat_match_loss=4.277, over 100.00 samples.
+2024-03-14 20:49:15,917 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 20:50:39,835 INFO [train.py:919] (1/6) Start epoch 669
+2024-03-14 20:51:52,989 INFO [train.py:527] (1/6) Epoch 669, batch 18, global_batch_idx: 82850, batch size: 66, loss[discriminator_loss=2.668, discriminator_real_loss=1.279, discriminator_fake_loss=1.388, generator_loss=27.8, generator_mel_loss=17.47, generator_kl_loss=1.235, generator_dur_loss=1.79, generator_adv_loss=2.12, generator_feat_match_loss=5.177, over 66.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.352, discriminator_fake_loss=1.336, generator_loss=28.65, generator_mel_loss=17.89, generator_kl_loss=1.385, generator_dur_loss=1.804, generator_adv_loss=1.977, generator_feat_match_loss=5.598, over 1317.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:54:14,689 INFO [train.py:527] (1/6) Epoch 669, batch 68, global_batch_idx: 82900, batch size: 83, loss[discriminator_loss=2.651, discriminator_real_loss=1.383, discriminator_fake_loss=1.268, generator_loss=28.53, generator_mel_loss=17.86, generator_kl_loss=1.322, generator_dur_loss=1.851, generator_adv_loss=1.906, generator_feat_match_loss=5.598, over 83.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.361, discriminator_fake_loss=1.337, generator_loss=28.58, generator_mel_loss=17.84, generator_kl_loss=1.397, generator_dur_loss=1.77, generator_adv_loss=1.968, generator_feat_match_loss=5.599, over 4092.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:56:30,735 INFO [train.py:527] (1/6) Epoch 669, batch 118, global_batch_idx: 82950, batch size: 45, loss[discriminator_loss=2.715, discriminator_real_loss=1.399, discriminator_fake_loss=1.316, generator_loss=29.39, generator_mel_loss=18.2, generator_kl_loss=1.583, generator_dur_loss=1.653, generator_adv_loss=1.993, generator_feat_match_loss=5.958, over 45.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.68, generator_mel_loss=17.93, generator_kl_loss=1.409, generator_dur_loss=1.763, generator_adv_loss=1.969, generator_feat_match_loss=5.603, over 6805.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:56:44,652 INFO [train.py:919] (1/6) Start epoch 670
+2024-03-14 20:59:08,331 INFO [train.py:527] (1/6) Epoch 670, batch 44, global_batch_idx: 83000, batch size: 88, loss[discriminator_loss=2.69, discriminator_real_loss=1.311, discriminator_fake_loss=1.379, generator_loss=28.16, generator_mel_loss=17.56, generator_kl_loss=1.309, generator_dur_loss=1.845, generator_adv_loss=1.913, generator_feat_match_loss=5.539, over 88.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.357, discriminator_fake_loss=1.334, generator_loss=28.89, generator_mel_loss=18.07, generator_kl_loss=1.46, generator_dur_loss=1.728, generator_adv_loss=1.995, generator_feat_match_loss=5.638, over 2308.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 20:59:08,333 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 20:59:16,320 INFO [train.py:591] (1/6) Epoch 670, validation: discriminator_loss=2.723, discriminator_real_loss=1.346, discriminator_fake_loss=1.377, generator_loss=27.62, generator_mel_loss=18.49, generator_kl_loss=1.238, generator_dur_loss=1.816, generator_adv_loss=1.838, generator_feat_match_loss=4.239, over 100.00 samples.
+2024-03-14 20:59:16,322 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 21:01:37,661 INFO [train.py:527] (1/6) Epoch 670, batch 94, global_batch_idx: 83050, batch size: 66, loss[discriminator_loss=2.699, discriminator_real_loss=1.379, discriminator_fake_loss=1.319, generator_loss=29.64, generator_mel_loss=17.98, generator_kl_loss=1.345, generator_dur_loss=1.757, generator_adv_loss=2.13, generator_feat_match_loss=6.434, over 66.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.77, generator_mel_loss=17.99, generator_kl_loss=1.439, generator_dur_loss=1.742, generator_adv_loss=1.997, generator_feat_match_loss=5.595, over 5147.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:02:56,666 INFO [train.py:919] (1/6) Start epoch 671
+2024-03-14 21:04:15,663 INFO [train.py:527] (1/6) Epoch 671, batch 20, global_batch_idx: 83100, batch size: 58, loss[discriminator_loss=2.694, discriminator_real_loss=1.458, discriminator_fake_loss=1.236, generator_loss=29.08, generator_mel_loss=18.44, generator_kl_loss=1.479, generator_dur_loss=1.758, generator_adv_loss=1.879, generator_feat_match_loss=5.522, over 58.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.362, discriminator_fake_loss=1.336, generator_loss=29.14, generator_mel_loss=18.18, generator_kl_loss=1.419, generator_dur_loss=1.758, generator_adv_loss=1.98, generator_feat_match_loss=5.804, over 1242.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:06:31,267 INFO [train.py:527] (1/6) Epoch 671, batch 70, global_batch_idx: 83150, batch size: 25, loss[discriminator_loss=2.624, discriminator_real_loss=1.223, discriminator_fake_loss=1.401, generator_loss=29.79, generator_mel_loss=18.96, generator_kl_loss=1.684, generator_dur_loss=1.557, generator_adv_loss=2.011, generator_feat_match_loss=5.578, over 25.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.354, discriminator_fake_loss=1.339, generator_loss=28.83, generator_mel_loss=18, generator_kl_loss=1.436, generator_dur_loss=1.736, generator_adv_loss=1.992, generator_feat_match_loss=5.666, over 3994.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:08:49,658 INFO [train.py:527] (1/6) Epoch 671, batch 120, global_batch_idx: 83200, batch size: 72, loss[discriminator_loss=2.679, discriminator_real_loss=1.327, discriminator_fake_loss=1.352, generator_loss=29.12, generator_mel_loss=18.08, generator_kl_loss=1.411, generator_dur_loss=1.796, generator_adv_loss=2.111, generator_feat_match_loss=5.722, over 72.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.359, discriminator_fake_loss=1.335, generator_loss=28.71, generator_mel_loss=17.96, generator_kl_loss=1.427, generator_dur_loss=1.741, generator_adv_loss=1.989, generator_feat_match_loss=5.597, over 6968.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:08:49,659 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 21:08:58,668 INFO [train.py:591] (1/6) Epoch 671, validation: discriminator_loss=2.733, discriminator_real_loss=1.469, discriminator_fake_loss=1.264, generator_loss=28.24, generator_mel_loss=18.47, generator_kl_loss=1.303, generator_dur_loss=1.81, generator_adv_loss=1.97, generator_feat_match_loss=4.685, over 100.00 samples.
+2024-03-14 21:08:58,669 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 21:09:09,367 INFO [train.py:919] (1/6) Start epoch 672
+2024-03-14 21:11:38,051 INFO [train.py:527] (1/6) Epoch 672, batch 46, global_batch_idx: 83250, batch size: 55, loss[discriminator_loss=2.692, discriminator_real_loss=1.308, discriminator_fake_loss=1.384, generator_loss=29.68, generator_mel_loss=18.2, generator_kl_loss=1.572, generator_dur_loss=1.676, generator_adv_loss=2.012, generator_feat_match_loss=6.219, over 55.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.77, generator_mel_loss=17.99, generator_kl_loss=1.477, generator_dur_loss=1.714, generator_adv_loss=1.968, generator_feat_match_loss=5.616, over 2311.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:13:55,315 INFO [train.py:527] (1/6) Epoch 672, batch 96, global_batch_idx: 83300, batch size: 52, loss[discriminator_loss=2.634, discriminator_real_loss=1.296, discriminator_fake_loss=1.338, generator_loss=29.57, generator_mel_loss=18.5, generator_kl_loss=1.525, generator_dur_loss=1.677, generator_adv_loss=1.911, generator_feat_match_loss=5.964, over 52.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.368, discriminator_fake_loss=1.334, generator_loss=28.72, generator_mel_loss=17.97, generator_kl_loss=1.447, generator_dur_loss=1.735, generator_adv_loss=1.983, generator_feat_match_loss=5.59, over 5254.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:15:13,839 INFO [train.py:919] (1/6) Start epoch 673
+2024-03-14 21:16:38,359 INFO [train.py:527] (1/6) Epoch 673, batch 22, global_batch_idx: 83350, batch size: 59, loss[discriminator_loss=2.608, discriminator_real_loss=1.288, discriminator_fake_loss=1.32, generator_loss=29.67, generator_mel_loss=18.19, generator_kl_loss=1.41, generator_dur_loss=1.686, generator_adv_loss=2.074, generator_feat_match_loss=6.315, over 59.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.372, discriminator_fake_loss=1.32, generator_loss=28.62, generator_mel_loss=17.92, generator_kl_loss=1.396, generator_dur_loss=1.747, generator_adv_loss=2.028, generator_feat_match_loss=5.528, over 1327.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:18:57,267 INFO [train.py:527] (1/6) Epoch 673, batch 72, global_batch_idx: 83400, batch size: 77, loss[discriminator_loss=2.734, discriminator_real_loss=1.306, discriminator_fake_loss=1.428, generator_loss=28.35, generator_mel_loss=17.98, generator_kl_loss=1.335, generator_dur_loss=1.804, generator_adv_loss=2.077, generator_feat_match_loss=5.158, over 77.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=28.55, generator_mel_loss=17.89, generator_kl_loss=1.405, generator_dur_loss=1.744, generator_adv_loss=1.996, generator_feat_match_loss=5.516, over 4329.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:18:57,269 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 21:19:06,066 INFO [train.py:591] (1/6) Epoch 673, validation: discriminator_loss=2.768, discriminator_real_loss=1.496, discriminator_fake_loss=1.272, generator_loss=27.4, generator_mel_loss=18.23, generator_kl_loss=1.259, generator_dur_loss=1.813, generator_adv_loss=1.969, generator_feat_match_loss=4.125, over 100.00 samples.
+2024-03-14 21:19:06,067 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 21:21:22,835 INFO [train.py:527] (1/6) Epoch 673, batch 122, global_batch_idx: 83450, batch size: 64, loss[discriminator_loss=2.687, discriminator_real_loss=1.397, discriminator_fake_loss=1.29, generator_loss=29.35, generator_mel_loss=18.18, generator_kl_loss=1.498, generator_dur_loss=1.777, generator_adv_loss=1.941, generator_feat_match_loss=5.95, over 64.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.364, discriminator_fake_loss=1.329, generator_loss=28.62, generator_mel_loss=17.94, generator_kl_loss=1.42, generator_dur_loss=1.742, generator_adv_loss=1.985, generator_feat_match_loss=5.531, over 7109.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:21:27,990 INFO [train.py:919] (1/6) Start epoch 674
+2024-03-14 21:24:02,506 INFO [train.py:527] (1/6) Epoch 674, batch 48, global_batch_idx: 83500, batch size: 31, loss[discriminator_loss=2.66, discriminator_real_loss=1.334, discriminator_fake_loss=1.325, generator_loss=29.3, generator_mel_loss=18.1, generator_kl_loss=1.574, generator_dur_loss=1.613, generator_adv_loss=1.974, generator_feat_match_loss=6.042, over 31.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.352, discriminator_fake_loss=1.336, generator_loss=28.61, generator_mel_loss=17.98, generator_kl_loss=1.443, generator_dur_loss=1.732, generator_adv_loss=1.966, generator_feat_match_loss=5.486, over 2569.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:26:21,362 INFO [train.py:527] (1/6) Epoch 674, batch 98, global_batch_idx: 83550, batch size: 59, loss[discriminator_loss=2.74, discriminator_real_loss=1.347, discriminator_fake_loss=1.394, generator_loss=29.3, generator_mel_loss=18.07, generator_kl_loss=1.361, generator_dur_loss=1.698, generator_adv_loss=2.046, generator_feat_match_loss=6.121, over 59.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.351, discriminator_fake_loss=1.336, generator_loss=28.69, generator_mel_loss=17.98, generator_kl_loss=1.409, generator_dur_loss=1.743, generator_adv_loss=1.976, generator_feat_match_loss=5.585, over 5511.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:27:34,508 INFO [train.py:919] (1/6) Start epoch 675
+2024-03-14 21:29:06,049 INFO [train.py:527] (1/6) Epoch 675, batch 24, global_batch_idx: 83600, batch size: 55, loss[discriminator_loss=2.705, discriminator_real_loss=1.364, discriminator_fake_loss=1.342, generator_loss=28.65, generator_mel_loss=18.36, generator_kl_loss=1.497, generator_dur_loss=1.687, generator_adv_loss=1.795, generator_feat_match_loss=5.31, over 55.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.348, discriminator_fake_loss=1.327, generator_loss=28.77, generator_mel_loss=17.91, generator_kl_loss=1.438, generator_dur_loss=1.728, generator_adv_loss=1.992, generator_feat_match_loss=5.71, over 1329.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:29:06,051 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 21:29:14,045 INFO [train.py:591] (1/6) Epoch 675, validation: discriminator_loss=2.754, discriminator_real_loss=1.38, discriminator_fake_loss=1.374, generator_loss=26.77, generator_mel_loss=17.64, generator_kl_loss=1.234, generator_dur_loss=1.797, generator_adv_loss=1.825, generator_feat_match_loss=4.268, over 100.00 samples.
+2024-03-14 21:29:14,046 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 21:31:34,574 INFO [train.py:527] (1/6) Epoch 675, batch 74, global_batch_idx: 83650, batch size: 72, loss[discriminator_loss=2.749, discriminator_real_loss=1.304, discriminator_fake_loss=1.445, generator_loss=28, generator_mel_loss=17.54, generator_kl_loss=1.178, generator_dur_loss=1.803, generator_adv_loss=1.952, generator_feat_match_loss=5.523, over 72.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.355, discriminator_fake_loss=1.335, generator_loss=28.6, generator_mel_loss=17.92, generator_kl_loss=1.379, generator_dur_loss=1.757, generator_adv_loss=1.975, generator_feat_match_loss=5.575, over 4428.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:33:50,853 INFO [train.py:919] (1/6) Start epoch 676
+2024-03-14 21:34:15,378 INFO [train.py:527] (1/6) Epoch 676, batch 0, global_batch_idx: 83700, batch size: 36, loss[discriminator_loss=2.727, discriminator_real_loss=1.416, discriminator_fake_loss=1.311, generator_loss=27.66, generator_mel_loss=17.86, generator_kl_loss=1.551, generator_dur_loss=1.702, generator_adv_loss=1.869, generator_feat_match_loss=4.673, over 36.00 samples.], tot_loss[discriminator_loss=2.727, discriminator_real_loss=1.416, discriminator_fake_loss=1.311, generator_loss=27.66, generator_mel_loss=17.86, generator_kl_loss=1.551, generator_dur_loss=1.702, generator_adv_loss=1.869, generator_feat_match_loss=4.673, over 36.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:36:34,360 INFO [train.py:527] (1/6) Epoch 676, batch 50, global_batch_idx: 83750, batch size: 47, loss[discriminator_loss=2.575, discriminator_real_loss=1.361, discriminator_fake_loss=1.214, generator_loss=29.56, generator_mel_loss=17.94, generator_kl_loss=1.525, generator_dur_loss=1.632, generator_adv_loss=2.248, generator_feat_match_loss=6.209, over 47.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.347, discriminator_fake_loss=1.325, generator_loss=28.83, generator_mel_loss=17.91, generator_kl_loss=1.422, generator_dur_loss=1.737, generator_adv_loss=2.038, generator_feat_match_loss=5.724, over 2951.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:38:52,519 INFO [train.py:527] (1/6) Epoch 676, batch 100, global_batch_idx: 83800, batch size: 15, loss[discriminator_loss=2.652, discriminator_real_loss=1.285, discriminator_fake_loss=1.366, generator_loss=32.6, generator_mel_loss=19.44, generator_kl_loss=2.031, generator_dur_loss=1.615, generator_adv_loss=1.996, generator_feat_match_loss=7.519, over 15.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=28.79, generator_mel_loss=17.94, generator_kl_loss=1.426, generator_dur_loss=1.734, generator_adv_loss=2.023, generator_feat_match_loss=5.66, over 5562.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:38:52,520 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 21:39:01,205 INFO [train.py:591] (1/6) Epoch 676, validation: discriminator_loss=2.722, discriminator_real_loss=1.392, discriminator_fake_loss=1.33, generator_loss=28, generator_mel_loss=18.27, generator_kl_loss=1.278, generator_dur_loss=1.804, generator_adv_loss=1.943, generator_feat_match_loss=4.706, over 100.00 samples.
+2024-03-14 21:39:01,206 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 21:40:06,345 INFO [train.py:919] (1/6) Start epoch 677
+2024-03-14 21:41:40,149 INFO [train.py:527] (1/6) Epoch 677, batch 26, global_batch_idx: 83850, batch size: 61, loss[discriminator_loss=2.716, discriminator_real_loss=1.406, discriminator_fake_loss=1.311, generator_loss=28.02, generator_mel_loss=17.88, generator_kl_loss=1.395, generator_dur_loss=1.785, generator_adv_loss=1.91, generator_feat_match_loss=5.045, over 61.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.75, generator_mel_loss=17.97, generator_kl_loss=1.393, generator_dur_loss=1.765, generator_adv_loss=1.972, generator_feat_match_loss=5.647, over 1626.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:43:57,736 INFO [train.py:527] (1/6) Epoch 677, batch 76, global_batch_idx: 83900, batch size: 59, loss[discriminator_loss=2.685, discriminator_real_loss=1.406, discriminator_fake_loss=1.28, generator_loss=28.95, generator_mel_loss=17.88, generator_kl_loss=1.408, generator_dur_loss=1.677, generator_adv_loss=2.045, generator_feat_match_loss=5.937, over 59.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.325, generator_loss=28.71, generator_mel_loss=17.95, generator_kl_loss=1.399, generator_dur_loss=1.757, generator_adv_loss=1.983, generator_feat_match_loss=5.616, over 4508.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:46:09,451 INFO [train.py:919] (1/6) Start epoch 678
+2024-03-14 21:46:39,038 INFO [train.py:527] (1/6) Epoch 678, batch 2, global_batch_idx: 83950, batch size: 31, loss[discriminator_loss=2.681, discriminator_real_loss=1.302, discriminator_fake_loss=1.38, generator_loss=29.67, generator_mel_loss=18.71, generator_kl_loss=1.639, generator_dur_loss=1.617, generator_adv_loss=2.037, generator_feat_match_loss=5.665, over 31.00 samples.], tot_loss[discriminator_loss=2.73, discriminator_real_loss=1.409, discriminator_fake_loss=1.32, generator_loss=28.19, generator_mel_loss=17.96, generator_kl_loss=1.448, generator_dur_loss=1.69, generator_adv_loss=1.984, generator_feat_match_loss=5.112, over 121.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:48:57,992 INFO [train.py:527] (1/6) Epoch 678, batch 52, global_batch_idx: 84000, batch size: 88, loss[discriminator_loss=2.677, discriminator_real_loss=1.25, discriminator_fake_loss=1.427, generator_loss=28.93, generator_mel_loss=17.73, generator_kl_loss=1.417, generator_dur_loss=1.815, generator_adv_loss=2.122, generator_feat_match_loss=5.85, over 88.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=28.75, generator_mel_loss=18, generator_kl_loss=1.42, generator_dur_loss=1.741, generator_adv_loss=1.977, generator_feat_match_loss=5.609, over 2946.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:48:57,994 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 21:49:05,962 INFO [train.py:591] (1/6) Epoch 678, validation: discriminator_loss=2.726, discriminator_real_loss=1.487, discriminator_fake_loss=1.239, generator_loss=27.38, generator_mel_loss=18.14, generator_kl_loss=1.248, generator_dur_loss=1.812, generator_adv_loss=2.012, generator_feat_match_loss=4.169, over 100.00 samples.
+2024-03-14 21:49:05,963 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 21:51:21,543 INFO [train.py:527] (1/6) Epoch 678, batch 102, global_batch_idx: 84050, batch size: 77, loss[discriminator_loss=2.712, discriminator_real_loss=1.352, discriminator_fake_loss=1.36, generator_loss=28.43, generator_mel_loss=17.86, generator_kl_loss=1.221, generator_dur_loss=1.865, generator_adv_loss=1.995, generator_feat_match_loss=5.488, over 77.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.65, generator_mel_loss=17.95, generator_kl_loss=1.41, generator_dur_loss=1.739, generator_adv_loss=1.981, generator_feat_match_loss=5.569, over 5688.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:52:21,881 INFO [train.py:919] (1/6) Start epoch 679
+2024-03-14 21:54:00,188 INFO [train.py:527] (1/6) Epoch 679, batch 28, global_batch_idx: 84100, batch size: 55, loss[discriminator_loss=2.676, discriminator_real_loss=1.316, discriminator_fake_loss=1.36, generator_loss=29.53, generator_mel_loss=18.19, generator_kl_loss=1.362, generator_dur_loss=1.664, generator_adv_loss=1.924, generator_feat_match_loss=6.388, over 55.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=28.93, generator_mel_loss=18.05, generator_kl_loss=1.395, generator_dur_loss=1.757, generator_adv_loss=1.991, generator_feat_match_loss=5.735, over 1709.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:56:16,867 INFO [train.py:527] (1/6) Epoch 679, batch 78, global_batch_idx: 84150, batch size: 50, loss[discriminator_loss=2.654, discriminator_real_loss=1.344, discriminator_fake_loss=1.31, generator_loss=29.09, generator_mel_loss=17.95, generator_kl_loss=1.425, generator_dur_loss=1.67, generator_adv_loss=2.046, generator_feat_match_loss=5.995, over 50.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.328, generator_loss=28.7, generator_mel_loss=17.95, generator_kl_loss=1.419, generator_dur_loss=1.736, generator_adv_loss=1.981, generator_feat_match_loss=5.606, over 4295.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:58:24,319 INFO [train.py:919] (1/6) Start epoch 680
+2024-03-14 21:58:59,305 INFO [train.py:527] (1/6) Epoch 680, batch 4, global_batch_idx: 84200, batch size: 72, loss[discriminator_loss=2.673, discriminator_real_loss=1.375, discriminator_fake_loss=1.298, generator_loss=28.86, generator_mel_loss=18.19, generator_kl_loss=1.397, generator_dur_loss=1.784, generator_adv_loss=2.013, generator_feat_match_loss=5.47, over 72.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.368, discriminator_fake_loss=1.33, generator_loss=28.73, generator_mel_loss=17.93, generator_kl_loss=1.382, generator_dur_loss=1.757, generator_adv_loss=1.978, generator_feat_match_loss=5.692, over 289.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 21:58:59,307 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 21:59:07,139 INFO [train.py:591] (1/6) Epoch 680, validation: discriminator_loss=2.709, discriminator_real_loss=1.417, discriminator_fake_loss=1.293, generator_loss=28.22, generator_mel_loss=18.34, generator_kl_loss=1.261, generator_dur_loss=1.817, generator_adv_loss=1.975, generator_feat_match_loss=4.836, over 100.00 samples.
+2024-03-14 21:59:07,141 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 22:01:24,567 INFO [train.py:527] (1/6) Epoch 680, batch 54, global_batch_idx: 84250, batch size: 77, loss[discriminator_loss=2.722, discriminator_real_loss=1.382, discriminator_fake_loss=1.34, generator_loss=28.1, generator_mel_loss=17.94, generator_kl_loss=1.379, generator_dur_loss=1.854, generator_adv_loss=1.915, generator_feat_match_loss=5.009, over 77.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.368, discriminator_fake_loss=1.334, generator_loss=28.6, generator_mel_loss=17.92, generator_kl_loss=1.418, generator_dur_loss=1.758, generator_adv_loss=1.984, generator_feat_match_loss=5.522, over 3169.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:03:44,710 INFO [train.py:527] (1/6) Epoch 680, batch 104, global_batch_idx: 84300, batch size: 48, loss[discriminator_loss=2.735, discriminator_real_loss=1.373, discriminator_fake_loss=1.362, generator_loss=26.8, generator_mel_loss=17.42, generator_kl_loss=1.251, generator_dur_loss=1.703, generator_adv_loss=1.869, generator_feat_match_loss=4.555, over 48.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.363, discriminator_fake_loss=1.33, generator_loss=28.68, generator_mel_loss=17.93, generator_kl_loss=1.427, generator_dur_loss=1.751, generator_adv_loss=2.006, generator_feat_match_loss=5.565, over 5979.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:04:40,005 INFO [train.py:919] (1/6) Start epoch 681
+2024-03-14 22:06:29,062 INFO [train.py:527] (1/6) Epoch 681, batch 30, global_batch_idx: 84350, batch size: 44, loss[discriminator_loss=2.665, discriminator_real_loss=1.423, discriminator_fake_loss=1.241, generator_loss=28.75, generator_mel_loss=18.1, generator_kl_loss=1.605, generator_dur_loss=1.667, generator_adv_loss=2.018, generator_feat_match_loss=5.362, over 44.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.372, discriminator_fake_loss=1.326, generator_loss=28.53, generator_mel_loss=17.92, generator_kl_loss=1.418, generator_dur_loss=1.754, generator_adv_loss=1.977, generator_feat_match_loss=5.458, over 1812.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:08:49,534 INFO [train.py:527] (1/6) Epoch 681, batch 80, global_batch_idx: 84400, batch size: 53, loss[discriminator_loss=2.66, discriminator_real_loss=1.358, discriminator_fake_loss=1.302, generator_loss=28.88, generator_mel_loss=17.72, generator_kl_loss=1.557, generator_dur_loss=1.672, generator_adv_loss=2.016, generator_feat_match_loss=5.92, over 53.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.53, generator_mel_loss=17.88, generator_kl_loss=1.401, generator_dur_loss=1.751, generator_adv_loss=1.973, generator_feat_match_loss=5.528, over 4759.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:08:49,535 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 22:08:58,438 INFO [train.py:591] (1/6) Epoch 681, validation: discriminator_loss=2.728, discriminator_real_loss=1.42, discriminator_fake_loss=1.308, generator_loss=27.78, generator_mel_loss=18.41, generator_kl_loss=1.212, generator_dur_loss=1.813, generator_adv_loss=1.913, generator_feat_match_loss=4.433, over 100.00 samples.
+2024-03-14 22:08:58,438 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 22:10:54,356 INFO [train.py:919] (1/6) Start epoch 682
+2024-03-14 22:11:36,770 INFO [train.py:527] (1/6) Epoch 682, batch 6, global_batch_idx: 84450, batch size: 96, loss[discriminator_loss=2.648, discriminator_real_loss=1.306, discriminator_fake_loss=1.342, generator_loss=28.06, generator_mel_loss=17.56, generator_kl_loss=1.348, generator_dur_loss=1.799, generator_adv_loss=1.874, generator_feat_match_loss=5.484, over 96.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.77, generator_mel_loss=17.93, generator_kl_loss=1.498, generator_dur_loss=1.753, generator_adv_loss=1.946, generator_feat_match_loss=5.639, over 462.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:13:52,683 INFO [train.py:527] (1/6) Epoch 682, batch 56, global_batch_idx: 84500, batch size: 80, loss[discriminator_loss=2.688, discriminator_real_loss=1.395, discriminator_fake_loss=1.293, generator_loss=28.78, generator_mel_loss=18.01, generator_kl_loss=1.346, generator_dur_loss=1.789, generator_adv_loss=1.849, generator_feat_match_loss=5.782, over 80.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.359, discriminator_fake_loss=1.318, generator_loss=28.8, generator_mel_loss=17.95, generator_kl_loss=1.439, generator_dur_loss=1.731, generator_adv_loss=1.986, generator_feat_match_loss=5.7, over 3063.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:16:13,649 INFO [train.py:527] (1/6) Epoch 682, batch 106, global_batch_idx: 84550, batch size: 88, loss[discriminator_loss=2.671, discriminator_real_loss=1.41, discriminator_fake_loss=1.261, generator_loss=28.36, generator_mel_loss=18.09, generator_kl_loss=1.376, generator_dur_loss=1.846, generator_adv_loss=1.935, generator_feat_match_loss=5.11, over 88.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.81, generator_mel_loss=17.96, generator_kl_loss=1.417, generator_dur_loss=1.743, generator_adv_loss=1.989, generator_feat_match_loss=5.7, over 5888.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:17:04,822 INFO [train.py:919] (1/6) Start epoch 683
+2024-03-14 22:18:57,672 INFO [train.py:527] (1/6) Epoch 683, batch 32, global_batch_idx: 84600, batch size: 36, loss[discriminator_loss=2.717, discriminator_real_loss=1.299, discriminator_fake_loss=1.418, generator_loss=29.77, generator_mel_loss=18.31, generator_kl_loss=1.527, generator_dur_loss=1.648, generator_adv_loss=2.088, generator_feat_match_loss=6.194, over 36.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.369, discriminator_fake_loss=1.334, generator_loss=28.7, generator_mel_loss=17.93, generator_kl_loss=1.395, generator_dur_loss=1.744, generator_adv_loss=1.989, generator_feat_match_loss=5.643, over 1851.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:18:57,673 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 22:19:05,698 INFO [train.py:591] (1/6) Epoch 683, validation: discriminator_loss=2.729, discriminator_real_loss=1.501, discriminator_fake_loss=1.228, generator_loss=27.2, generator_mel_loss=17.99, generator_kl_loss=1.323, generator_dur_loss=1.799, generator_adv_loss=1.946, generator_feat_match_loss=4.144, over 100.00 samples.
+2024-03-14 22:19:05,698 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 22:21:28,063 INFO [train.py:527] (1/6) Epoch 683, batch 82, global_batch_idx: 84650, batch size: 45, loss[discriminator_loss=2.684, discriminator_real_loss=1.308, discriminator_fake_loss=1.376, generator_loss=28.69, generator_mel_loss=17.9, generator_kl_loss=1.463, generator_dur_loss=1.619, generator_adv_loss=1.855, generator_feat_match_loss=5.85, over 45.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.63, generator_mel_loss=17.89, generator_kl_loss=1.394, generator_dur_loss=1.743, generator_adv_loss=1.983, generator_feat_match_loss=5.611, over 4648.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:23:20,687 INFO [train.py:919] (1/6) Start epoch 684
+2024-03-14 22:24:06,476 INFO [train.py:527] (1/6) Epoch 684, batch 8, global_batch_idx: 84700, batch size: 31, loss[discriminator_loss=2.831, discriminator_real_loss=1.462, discriminator_fake_loss=1.368, generator_loss=28.44, generator_mel_loss=17.81, generator_kl_loss=1.8, generator_dur_loss=1.579, generator_adv_loss=1.938, generator_feat_match_loss=5.319, over 31.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=28.8, generator_mel_loss=18.03, generator_kl_loss=1.458, generator_dur_loss=1.748, generator_adv_loss=2.01, generator_feat_match_loss=5.555, over 544.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:26:26,065 INFO [train.py:527] (1/6) Epoch 684, batch 58, global_batch_idx: 84750, batch size: 53, loss[discriminator_loss=2.661, discriminator_real_loss=1.363, discriminator_fake_loss=1.298, generator_loss=29.38, generator_mel_loss=17.99, generator_kl_loss=1.511, generator_dur_loss=1.725, generator_adv_loss=2.028, generator_feat_match_loss=6.121, over 53.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.366, discriminator_fake_loss=1.328, generator_loss=28.62, generator_mel_loss=17.92, generator_kl_loss=1.426, generator_dur_loss=1.727, generator_adv_loss=1.998, generator_feat_match_loss=5.555, over 3242.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:28:47,642 INFO [train.py:527] (1/6) Epoch 684, batch 108, global_batch_idx: 84800, batch size: 39, loss[discriminator_loss=2.651, discriminator_real_loss=1.281, discriminator_fake_loss=1.37, generator_loss=30.44, generator_mel_loss=18.02, generator_kl_loss=1.584, generator_dur_loss=1.686, generator_adv_loss=2.14, generator_feat_match_loss=7.009, over 39.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=28.64, generator_mel_loss=17.91, generator_kl_loss=1.425, generator_dur_loss=1.738, generator_adv_loss=1.99, generator_feat_match_loss=5.572, over 6014.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:28:47,643 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 22:28:56,610 INFO [train.py:591] (1/6) Epoch 684, validation: discriminator_loss=2.73, discriminator_real_loss=1.43, discriminator_fake_loss=1.3, generator_loss=28.12, generator_mel_loss=18.59, generator_kl_loss=1.203, generator_dur_loss=1.813, generator_adv_loss=1.939, generator_feat_match_loss=4.572, over 100.00 samples.
+2024-03-14 22:28:56,611 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 22:29:38,685 INFO [train.py:919] (1/6) Start epoch 685
+2024-03-14 22:31:37,850 INFO [train.py:527] (1/6) Epoch 685, batch 34, global_batch_idx: 84850, batch size: 25, loss[discriminator_loss=2.698, discriminator_real_loss=1.342, discriminator_fake_loss=1.356, generator_loss=28.95, generator_mel_loss=18.24, generator_kl_loss=1.501, generator_dur_loss=1.57, generator_adv_loss=1.966, generator_feat_match_loss=5.672, over 25.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.368, discriminator_fake_loss=1.329, generator_loss=28.67, generator_mel_loss=17.92, generator_kl_loss=1.418, generator_dur_loss=1.738, generator_adv_loss=1.99, generator_feat_match_loss=5.605, over 1920.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:33:56,805 INFO [train.py:527] (1/6) Epoch 685, batch 84, global_batch_idx: 84900, batch size: 59, loss[discriminator_loss=2.742, discriminator_real_loss=1.346, discriminator_fake_loss=1.397, generator_loss=28.3, generator_mel_loss=18.23, generator_kl_loss=1.369, generator_dur_loss=1.735, generator_adv_loss=1.968, generator_feat_match_loss=4.991, over 59.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=28.68, generator_mel_loss=17.9, generator_kl_loss=1.412, generator_dur_loss=1.741, generator_adv_loss=1.984, generator_feat_match_loss=5.644, over 4814.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:35:45,937 INFO [train.py:919] (1/6) Start epoch 686
+2024-03-14 22:36:40,422 INFO [train.py:527] (1/6) Epoch 686, batch 10, global_batch_idx: 84950, batch size: 64, loss[discriminator_loss=2.696, discriminator_real_loss=1.376, discriminator_fake_loss=1.32, generator_loss=28.56, generator_mel_loss=18.03, generator_kl_loss=1.297, generator_dur_loss=1.729, generator_adv_loss=1.971, generator_feat_match_loss=5.528, over 64.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.36, discriminator_fake_loss=1.334, generator_loss=28.72, generator_mel_loss=18.05, generator_kl_loss=1.387, generator_dur_loss=1.758, generator_adv_loss=1.989, generator_feat_match_loss=5.534, over 692.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:38:59,318 INFO [train.py:527] (1/6) Epoch 686, batch 60, global_batch_idx: 85000, batch size: 80, loss[discriminator_loss=2.67, discriminator_real_loss=1.295, discriminator_fake_loss=1.375, generator_loss=29.03, generator_mel_loss=18.03, generator_kl_loss=1.371, generator_dur_loss=1.811, generator_adv_loss=2.048, generator_feat_match_loss=5.774, over 80.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.368, discriminator_fake_loss=1.332, generator_loss=28.65, generator_mel_loss=17.95, generator_kl_loss=1.423, generator_dur_loss=1.739, generator_adv_loss=1.973, generator_feat_match_loss=5.568, over 3361.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:38:59,319 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 22:39:07,402 INFO [train.py:591] (1/6) Epoch 686, validation: discriminator_loss=2.731, discriminator_real_loss=1.513, discriminator_fake_loss=1.218, generator_loss=27.11, generator_mel_loss=17.73, generator_kl_loss=1.243, generator_dur_loss=1.814, generator_adv_loss=2.099, generator_feat_match_loss=4.224, over 100.00 samples.
+2024-03-14 22:39:07,403 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 22:41:26,956 INFO [train.py:527] (1/6) Epoch 686, batch 110, global_batch_idx: 85050, batch size: 15, loss[discriminator_loss=2.663, discriminator_real_loss=1.239, discriminator_fake_loss=1.424, generator_loss=30.66, generator_mel_loss=18.24, generator_kl_loss=1.812, generator_dur_loss=1.551, generator_adv_loss=1.913, generator_feat_match_loss=7.141, over 15.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.367, discriminator_fake_loss=1.328, generator_loss=28.68, generator_mel_loss=17.95, generator_kl_loss=1.42, generator_dur_loss=1.742, generator_adv_loss=1.977, generator_feat_match_loss=5.593, over 6123.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:42:04,737 INFO [train.py:919] (1/6) Start epoch 687
+2024-03-14 22:44:09,394 INFO [train.py:527] (1/6) Epoch 687, batch 36, global_batch_idx: 85100, batch size: 36, loss[discriminator_loss=2.609, discriminator_real_loss=1.379, discriminator_fake_loss=1.23, generator_loss=29.78, generator_mel_loss=18.2, generator_kl_loss=1.485, generator_dur_loss=1.687, generator_adv_loss=1.99, generator_feat_match_loss=6.419, over 36.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.71, generator_mel_loss=17.9, generator_kl_loss=1.41, generator_dur_loss=1.77, generator_adv_loss=1.992, generator_feat_match_loss=5.642, over 2174.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:46:28,366 INFO [train.py:527] (1/6) Epoch 687, batch 86, global_batch_idx: 85150, batch size: 58, loss[discriminator_loss=2.709, discriminator_real_loss=1.443, discriminator_fake_loss=1.266, generator_loss=29.69, generator_mel_loss=18.74, generator_kl_loss=1.504, generator_dur_loss=1.714, generator_adv_loss=1.953, generator_feat_match_loss=5.787, over 58.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.361, discriminator_fake_loss=1.334, generator_loss=28.83, generator_mel_loss=17.91, generator_kl_loss=1.416, generator_dur_loss=1.751, generator_adv_loss=2.03, generator_feat_match_loss=5.72, over 5036.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:48:12,667 INFO [train.py:919] (1/6) Start epoch 688
+2024-03-14 22:49:10,809 INFO [train.py:527] (1/6) Epoch 688, batch 12, global_batch_idx: 85200, batch size: 55, loss[discriminator_loss=2.761, discriminator_real_loss=1.43, discriminator_fake_loss=1.331, generator_loss=27.93, generator_mel_loss=17.48, generator_kl_loss=1.424, generator_dur_loss=1.713, generator_adv_loss=1.888, generator_feat_match_loss=5.429, over 55.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.36, discriminator_fake_loss=1.318, generator_loss=28.44, generator_mel_loss=17.74, generator_kl_loss=1.441, generator_dur_loss=1.753, generator_adv_loss=1.984, generator_feat_match_loss=5.517, over 812.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:49:10,812 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 22:49:18,930 INFO [train.py:591] (1/6) Epoch 688, validation: discriminator_loss=2.719, discriminator_real_loss=1.377, discriminator_fake_loss=1.342, generator_loss=27.38, generator_mel_loss=18, generator_kl_loss=1.274, generator_dur_loss=1.805, generator_adv_loss=1.824, generator_feat_match_loss=4.473, over 100.00 samples.
+2024-03-14 22:49:18,931 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 22:51:34,909 INFO [train.py:527] (1/6) Epoch 688, batch 62, global_batch_idx: 85250, batch size: 47, loss[discriminator_loss=2.693, discriminator_real_loss=1.337, discriminator_fake_loss=1.356, generator_loss=28.76, generator_mel_loss=18.37, generator_kl_loss=1.527, generator_dur_loss=1.67, generator_adv_loss=1.878, generator_feat_match_loss=5.317, over 47.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.66, generator_mel_loss=17.94, generator_kl_loss=1.426, generator_dur_loss=1.748, generator_adv_loss=1.98, generator_feat_match_loss=5.571, over 3788.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:53:54,641 INFO [train.py:527] (1/6) Epoch 688, batch 112, global_batch_idx: 85300, batch size: 25, loss[discriminator_loss=2.598, discriminator_real_loss=1.255, discriminator_fake_loss=1.343, generator_loss=30.62, generator_mel_loss=18.5, generator_kl_loss=1.626, generator_dur_loss=1.61, generator_adv_loss=2.052, generator_feat_match_loss=6.832, over 25.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.363, discriminator_fake_loss=1.333, generator_loss=28.72, generator_mel_loss=17.95, generator_kl_loss=1.453, generator_dur_loss=1.745, generator_adv_loss=1.98, generator_feat_match_loss=5.585, over 6528.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:54:26,535 INFO [train.py:919] (1/6) Start epoch 689
+2024-03-14 22:56:34,996 INFO [train.py:527] (1/6) Epoch 689, batch 38, global_batch_idx: 85350, batch size: 47, loss[discriminator_loss=2.743, discriminator_real_loss=1.431, discriminator_fake_loss=1.312, generator_loss=28.26, generator_mel_loss=18.02, generator_kl_loss=1.537, generator_dur_loss=1.743, generator_adv_loss=2.156, generator_feat_match_loss=4.803, over 47.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.361, discriminator_fake_loss=1.328, generator_loss=28.76, generator_mel_loss=17.98, generator_kl_loss=1.425, generator_dur_loss=1.746, generator_adv_loss=1.981, generator_feat_match_loss=5.629, over 2170.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:58:51,870 INFO [train.py:527] (1/6) Epoch 689, batch 88, global_batch_idx: 85400, batch size: 42, loss[discriminator_loss=2.642, discriminator_real_loss=1.274, discriminator_fake_loss=1.368, generator_loss=29.29, generator_mel_loss=18.5, generator_kl_loss=1.565, generator_dur_loss=1.681, generator_adv_loss=2.02, generator_feat_match_loss=5.523, over 42.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.359, discriminator_fake_loss=1.331, generator_loss=28.69, generator_mel_loss=17.93, generator_kl_loss=1.414, generator_dur_loss=1.738, generator_adv_loss=1.974, generator_feat_match_loss=5.639, over 4922.00 samples.], cur_lr_g: 1.84e-04, cur_lr_d: 1.84e-04, 
+2024-03-14 22:58:51,872 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 22:59:00,799 INFO [train.py:591] (1/6) Epoch 689, validation: discriminator_loss=2.737, discriminator_real_loss=1.46, discriminator_fake_loss=1.277, generator_loss=27.34, generator_mel_loss=18.04, generator_kl_loss=1.309, generator_dur_loss=1.806, generator_adv_loss=1.958, generator_feat_match_loss=4.23, over 100.00 samples.
+2024-03-14 22:59:00,799 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 23:00:41,903 INFO [train.py:919] (1/6) Start epoch 690
+2024-03-14 23:01:46,922 INFO [train.py:527] (1/6) Epoch 690, batch 14, global_batch_idx: 85450, batch size: 77, loss[discriminator_loss=2.692, discriminator_real_loss=1.304, discriminator_fake_loss=1.388, generator_loss=29.64, generator_mel_loss=17.87, generator_kl_loss=1.328, generator_dur_loss=1.829, generator_adv_loss=2.213, generator_feat_match_loss=6.4, over 77.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.351, discriminator_fake_loss=1.335, generator_loss=29.38, generator_mel_loss=18.1, generator_kl_loss=1.41, generator_dur_loss=1.739, generator_adv_loss=2.045, generator_feat_match_loss=6.083, over 899.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:04:07,185 INFO [train.py:527] (1/6) Epoch 690, batch 64, global_batch_idx: 85500, batch size: 53, loss[discriminator_loss=2.688, discriminator_real_loss=1.439, discriminator_fake_loss=1.249, generator_loss=27.75, generator_mel_loss=17.56, generator_kl_loss=1.304, generator_dur_loss=1.715, generator_adv_loss=2.095, generator_feat_match_loss=5.074, over 53.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.364, discriminator_fake_loss=1.326, generator_loss=28.88, generator_mel_loss=17.99, generator_kl_loss=1.438, generator_dur_loss=1.722, generator_adv_loss=2.01, generator_feat_match_loss=5.723, over 3610.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:06:22,282 INFO [train.py:527] (1/6) Epoch 690, batch 114, global_batch_idx: 85550, batch size: 50, loss[discriminator_loss=2.76, discriminator_real_loss=1.436, discriminator_fake_loss=1.325, generator_loss=28.74, generator_mel_loss=17.86, generator_kl_loss=1.515, generator_dur_loss=1.669, generator_adv_loss=1.93, generator_feat_match_loss=5.771, over 50.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.78, generator_mel_loss=17.97, generator_kl_loss=1.443, generator_dur_loss=1.724, generator_adv_loss=1.996, generator_feat_match_loss=5.654, over 6219.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:06:47,766 INFO [train.py:919] (1/6) Start epoch 691
+2024-03-14 23:09:01,327 INFO [train.py:527] (1/6) Epoch 691, batch 40, global_batch_idx: 85600, batch size: 48, loss[discriminator_loss=2.677, discriminator_real_loss=1.331, discriminator_fake_loss=1.346, generator_loss=29.19, generator_mel_loss=18.2, generator_kl_loss=1.53, generator_dur_loss=1.711, generator_adv_loss=2.012, generator_feat_match_loss=5.731, over 48.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.356, discriminator_fake_loss=1.344, generator_loss=28.69, generator_mel_loss=17.94, generator_kl_loss=1.413, generator_dur_loss=1.73, generator_adv_loss=1.981, generator_feat_match_loss=5.624, over 2245.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:09:01,329 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 23:09:09,450 INFO [train.py:591] (1/6) Epoch 691, validation: discriminator_loss=2.715, discriminator_real_loss=1.397, discriminator_fake_loss=1.319, generator_loss=27.18, generator_mel_loss=17.87, generator_kl_loss=1.276, generator_dur_loss=1.788, generator_adv_loss=1.895, generator_feat_match_loss=4.349, over 100.00 samples.
+2024-03-14 23:09:09,451 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 23:11:27,697 INFO [train.py:527] (1/6) Epoch 691, batch 90, global_batch_idx: 85650, batch size: 64, loss[discriminator_loss=2.657, discriminator_real_loss=1.386, discriminator_fake_loss=1.271, generator_loss=28.89, generator_mel_loss=17.85, generator_kl_loss=1.374, generator_dur_loss=1.781, generator_adv_loss=2.049, generator_feat_match_loss=5.838, over 64.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.357, discriminator_fake_loss=1.342, generator_loss=28.65, generator_mel_loss=17.91, generator_kl_loss=1.42, generator_dur_loss=1.723, generator_adv_loss=1.974, generator_feat_match_loss=5.614, over 5016.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:13:01,007 INFO [train.py:919] (1/6) Start epoch 692
+2024-03-14 23:14:07,521 INFO [train.py:527] (1/6) Epoch 692, batch 16, global_batch_idx: 85700, batch size: 88, loss[discriminator_loss=2.683, discriminator_real_loss=1.255, discriminator_fake_loss=1.429, generator_loss=28.76, generator_mel_loss=17.81, generator_kl_loss=1.395, generator_dur_loss=1.773, generator_adv_loss=2.097, generator_feat_match_loss=5.68, over 88.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.35, discriminator_fake_loss=1.341, generator_loss=28.77, generator_mel_loss=17.94, generator_kl_loss=1.407, generator_dur_loss=1.746, generator_adv_loss=2.007, generator_feat_match_loss=5.675, over 994.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:16:27,089 INFO [train.py:527] (1/6) Epoch 692, batch 66, global_batch_idx: 85750, batch size: 80, loss[discriminator_loss=2.692, discriminator_real_loss=1.384, discriminator_fake_loss=1.308, generator_loss=28.46, generator_mel_loss=17.84, generator_kl_loss=1.286, generator_dur_loss=1.777, generator_adv_loss=1.991, generator_feat_match_loss=5.573, over 80.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.332, generator_loss=28.62, generator_mel_loss=17.86, generator_kl_loss=1.422, generator_dur_loss=1.731, generator_adv_loss=1.986, generator_feat_match_loss=5.624, over 3768.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:18:45,526 INFO [train.py:527] (1/6) Epoch 692, batch 116, global_batch_idx: 85800, batch size: 96, loss[discriminator_loss=2.697, discriminator_real_loss=1.293, discriminator_fake_loss=1.404, generator_loss=28.3, generator_mel_loss=17.52, generator_kl_loss=1.336, generator_dur_loss=1.906, generator_adv_loss=2.161, generator_feat_match_loss=5.373, over 96.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.364, discriminator_fake_loss=1.332, generator_loss=28.58, generator_mel_loss=17.87, generator_kl_loss=1.407, generator_dur_loss=1.739, generator_adv_loss=1.987, generator_feat_match_loss=5.573, over 6828.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:18:45,528 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 23:18:54,408 INFO [train.py:591] (1/6) Epoch 692, validation: discriminator_loss=2.73, discriminator_real_loss=1.472, discriminator_fake_loss=1.258, generator_loss=27.34, generator_mel_loss=18.13, generator_kl_loss=1.279, generator_dur_loss=1.808, generator_adv_loss=2.034, generator_feat_match_loss=4.085, over 100.00 samples.
+2024-03-14 23:18:54,409 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 23:19:15,408 INFO [train.py:919] (1/6) Start epoch 693
+2024-03-14 23:21:35,696 INFO [train.py:527] (1/6) Epoch 693, batch 42, global_batch_idx: 85850, batch size: 80, loss[discriminator_loss=2.672, discriminator_real_loss=1.314, discriminator_fake_loss=1.358, generator_loss=29.63, generator_mel_loss=18.18, generator_kl_loss=1.385, generator_dur_loss=1.811, generator_adv_loss=1.898, generator_feat_match_loss=6.359, over 80.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.82, generator_mel_loss=18, generator_kl_loss=1.41, generator_dur_loss=1.757, generator_adv_loss=1.979, generator_feat_match_loss=5.667, over 2522.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:23:55,394 INFO [train.py:527] (1/6) Epoch 693, batch 92, global_batch_idx: 85900, batch size: 25, loss[discriminator_loss=2.658, discriminator_real_loss=1.319, discriminator_fake_loss=1.339, generator_loss=30.84, generator_mel_loss=18.69, generator_kl_loss=1.749, generator_dur_loss=1.543, generator_adv_loss=2.038, generator_feat_match_loss=6.816, over 25.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.74, generator_mel_loss=17.97, generator_kl_loss=1.402, generator_dur_loss=1.749, generator_adv_loss=1.981, generator_feat_match_loss=5.639, over 5345.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:25:19,922 INFO [train.py:919] (1/6) Start epoch 694
+2024-03-14 23:26:34,744 INFO [train.py:527] (1/6) Epoch 694, batch 18, global_batch_idx: 85950, batch size: 70, loss[discriminator_loss=2.665, discriminator_real_loss=1.324, discriminator_fake_loss=1.341, generator_loss=28.25, generator_mel_loss=17.57, generator_kl_loss=1.463, generator_dur_loss=1.776, generator_adv_loss=1.991, generator_feat_match_loss=5.449, over 70.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.373, discriminator_fake_loss=1.328, generator_loss=28.61, generator_mel_loss=17.83, generator_kl_loss=1.396, generator_dur_loss=1.751, generator_adv_loss=1.992, generator_feat_match_loss=5.645, over 1080.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:28:56,397 INFO [train.py:527] (1/6) Epoch 694, batch 68, global_batch_idx: 86000, batch size: 88, loss[discriminator_loss=2.646, discriminator_real_loss=1.34, discriminator_fake_loss=1.306, generator_loss=28.88, generator_mel_loss=18.03, generator_kl_loss=1.382, generator_dur_loss=1.804, generator_adv_loss=2.019, generator_feat_match_loss=5.647, over 88.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.363, discriminator_fake_loss=1.332, generator_loss=28.62, generator_mel_loss=17.85, generator_kl_loss=1.395, generator_dur_loss=1.75, generator_adv_loss=1.984, generator_feat_match_loss=5.632, over 4063.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:28:56,399 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 23:29:05,263 INFO [train.py:591] (1/6) Epoch 694, validation: discriminator_loss=2.703, discriminator_real_loss=1.38, discriminator_fake_loss=1.323, generator_loss=26.96, generator_mel_loss=17.87, generator_kl_loss=1.186, generator_dur_loss=1.786, generator_adv_loss=1.937, generator_feat_match_loss=4.183, over 100.00 samples.
+2024-03-14 23:29:05,264 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 23:31:20,795 INFO [train.py:527] (1/6) Epoch 694, batch 118, global_batch_idx: 86050, batch size: 48, loss[discriminator_loss=2.685, discriminator_real_loss=1.345, discriminator_fake_loss=1.34, generator_loss=28.92, generator_mel_loss=18.09, generator_kl_loss=1.598, generator_dur_loss=1.641, generator_adv_loss=1.863, generator_feat_match_loss=5.72, over 48.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.365, discriminator_fake_loss=1.329, generator_loss=28.66, generator_mel_loss=17.9, generator_kl_loss=1.41, generator_dur_loss=1.74, generator_adv_loss=1.984, generator_feat_match_loss=5.625, over 6830.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:31:36,752 INFO [train.py:919] (1/6) Start epoch 695
+2024-03-14 23:34:03,418 INFO [train.py:527] (1/6) Epoch 695, batch 44, global_batch_idx: 86100, batch size: 44, loss[discriminator_loss=2.654, discriminator_real_loss=1.373, discriminator_fake_loss=1.28, generator_loss=28.35, generator_mel_loss=17.88, generator_kl_loss=1.579, generator_dur_loss=1.67, generator_adv_loss=2.051, generator_feat_match_loss=5.172, over 44.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.367, discriminator_fake_loss=1.337, generator_loss=28.64, generator_mel_loss=17.94, generator_kl_loss=1.417, generator_dur_loss=1.729, generator_adv_loss=1.969, generator_feat_match_loss=5.581, over 2624.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:36:21,433 INFO [train.py:527] (1/6) Epoch 695, batch 94, global_batch_idx: 86150, batch size: 96, loss[discriminator_loss=2.709, discriminator_real_loss=1.388, discriminator_fake_loss=1.321, generator_loss=28.21, generator_mel_loss=17.72, generator_kl_loss=1.433, generator_dur_loss=1.87, generator_adv_loss=2.021, generator_feat_match_loss=5.169, over 96.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.368, discriminator_fake_loss=1.332, generator_loss=28.65, generator_mel_loss=17.9, generator_kl_loss=1.407, generator_dur_loss=1.736, generator_adv_loss=1.987, generator_feat_match_loss=5.616, over 5620.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:37:40,989 INFO [train.py:919] (1/6) Start epoch 696
+2024-03-14 23:39:00,749 INFO [train.py:527] (1/6) Epoch 696, batch 20, global_batch_idx: 86200, batch size: 45, loss[discriminator_loss=2.704, discriminator_real_loss=1.348, discriminator_fake_loss=1.355, generator_loss=29.19, generator_mel_loss=17.88, generator_kl_loss=1.482, generator_dur_loss=1.664, generator_adv_loss=2.021, generator_feat_match_loss=6.144, over 45.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.369, discriminator_fake_loss=1.333, generator_loss=28.73, generator_mel_loss=17.9, generator_kl_loss=1.432, generator_dur_loss=1.724, generator_adv_loss=1.985, generator_feat_match_loss=5.69, over 1162.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:39:00,751 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 23:39:09,014 INFO [train.py:591] (1/6) Epoch 696, validation: discriminator_loss=2.771, discriminator_real_loss=1.451, discriminator_fake_loss=1.32, generator_loss=27.59, generator_mel_loss=18.14, generator_kl_loss=1.222, generator_dur_loss=1.806, generator_adv_loss=1.954, generator_feat_match_loss=4.465, over 100.00 samples.
+2024-03-14 23:39:09,015 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 23:41:26,994 INFO [train.py:527] (1/6) Epoch 696, batch 70, global_batch_idx: 86250, batch size: 88, loss[discriminator_loss=2.737, discriminator_real_loss=1.415, discriminator_fake_loss=1.322, generator_loss=28.53, generator_mel_loss=18.14, generator_kl_loss=1.321, generator_dur_loss=1.825, generator_adv_loss=2.003, generator_feat_match_loss=5.248, over 88.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.37, discriminator_fake_loss=1.337, generator_loss=28.69, generator_mel_loss=17.92, generator_kl_loss=1.421, generator_dur_loss=1.74, generator_adv_loss=1.978, generator_feat_match_loss=5.63, over 3995.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:43:47,160 INFO [train.py:527] (1/6) Epoch 696, batch 120, global_batch_idx: 86300, batch size: 14, loss[discriminator_loss=2.732, discriminator_real_loss=1.241, discriminator_fake_loss=1.491, generator_loss=31.21, generator_mel_loss=19.88, generator_kl_loss=1.702, generator_dur_loss=1.625, generator_adv_loss=1.88, generator_feat_match_loss=6.12, over 14.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.364, discriminator_fake_loss=1.337, generator_loss=28.71, generator_mel_loss=17.94, generator_kl_loss=1.415, generator_dur_loss=1.747, generator_adv_loss=1.973, generator_feat_match_loss=5.635, over 7038.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:43:56,011 INFO [train.py:919] (1/6) Start epoch 697
+2024-03-14 23:46:28,300 INFO [train.py:527] (1/6) Epoch 697, batch 46, global_batch_idx: 86350, batch size: 70, loss[discriminator_loss=2.696, discriminator_real_loss=1.351, discriminator_fake_loss=1.345, generator_loss=28.5, generator_mel_loss=17.86, generator_kl_loss=1.324, generator_dur_loss=1.802, generator_adv_loss=2.063, generator_feat_match_loss=5.448, over 70.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.373, discriminator_fake_loss=1.327, generator_loss=28.7, generator_mel_loss=17.99, generator_kl_loss=1.416, generator_dur_loss=1.727, generator_adv_loss=1.99, generator_feat_match_loss=5.576, over 2680.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:48:45,592 INFO [train.py:527] (1/6) Epoch 697, batch 96, global_batch_idx: 86400, batch size: 36, loss[discriminator_loss=2.64, discriminator_real_loss=1.279, discriminator_fake_loss=1.361, generator_loss=29.2, generator_mel_loss=18.3, generator_kl_loss=1.578, generator_dur_loss=1.652, generator_adv_loss=2.053, generator_feat_match_loss=5.617, over 36.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.369, discriminator_fake_loss=1.329, generator_loss=28.61, generator_mel_loss=17.96, generator_kl_loss=1.426, generator_dur_loss=1.73, generator_adv_loss=1.979, generator_feat_match_loss=5.513, over 5501.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:48:45,593 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 23:48:54,260 INFO [train.py:591] (1/6) Epoch 697, validation: discriminator_loss=2.725, discriminator_real_loss=1.412, discriminator_fake_loss=1.313, generator_loss=28.36, generator_mel_loss=18.65, generator_kl_loss=1.19, generator_dur_loss=1.793, generator_adv_loss=1.97, generator_feat_match_loss=4.759, over 100.00 samples.
+2024-03-14 23:48:54,261 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-14 23:50:11,195 INFO [train.py:919] (1/6) Start epoch 698
+2024-03-14 23:51:37,812 INFO [train.py:527] (1/6) Epoch 698, batch 22, global_batch_idx: 86450, batch size: 52, loss[discriminator_loss=2.739, discriminator_real_loss=1.406, discriminator_fake_loss=1.333, generator_loss=28.83, generator_mel_loss=18.04, generator_kl_loss=1.449, generator_dur_loss=1.65, generator_adv_loss=1.82, generator_feat_match_loss=5.87, over 52.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.368, discriminator_fake_loss=1.339, generator_loss=28.74, generator_mel_loss=17.92, generator_kl_loss=1.412, generator_dur_loss=1.719, generator_adv_loss=1.961, generator_feat_match_loss=5.731, over 1224.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:53:59,543 INFO [train.py:527] (1/6) Epoch 698, batch 72, global_batch_idx: 86500, batch size: 83, loss[discriminator_loss=2.68, discriminator_real_loss=1.31, discriminator_fake_loss=1.369, generator_loss=29.07, generator_mel_loss=17.96, generator_kl_loss=1.369, generator_dur_loss=1.837, generator_adv_loss=2.051, generator_feat_match_loss=5.855, over 83.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.362, discriminator_fake_loss=1.339, generator_loss=28.67, generator_mel_loss=17.88, generator_kl_loss=1.411, generator_dur_loss=1.734, generator_adv_loss=1.973, generator_feat_match_loss=5.675, over 4117.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:56:14,893 INFO [train.py:527] (1/6) Epoch 698, batch 122, global_batch_idx: 86550, batch size: 74, loss[discriminator_loss=2.651, discriminator_real_loss=1.38, discriminator_fake_loss=1.271, generator_loss=28.23, generator_mel_loss=17.93, generator_kl_loss=1.275, generator_dur_loss=1.828, generator_adv_loss=1.923, generator_feat_match_loss=5.279, over 74.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.36, discriminator_fake_loss=1.337, generator_loss=28.7, generator_mel_loss=17.9, generator_kl_loss=1.411, generator_dur_loss=1.736, generator_adv_loss=1.981, generator_feat_match_loss=5.668, over 7049.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:56:19,279 INFO [train.py:919] (1/6) Start epoch 699
+2024-03-14 23:58:57,578 INFO [train.py:527] (1/6) Epoch 699, batch 48, global_batch_idx: 86600, batch size: 56, loss[discriminator_loss=2.755, discriminator_real_loss=1.435, discriminator_fake_loss=1.32, generator_loss=28.33, generator_mel_loss=17.59, generator_kl_loss=1.463, generator_dur_loss=1.693, generator_adv_loss=2.043, generator_feat_match_loss=5.549, over 56.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.364, discriminator_fake_loss=1.327, generator_loss=28.56, generator_mel_loss=17.91, generator_kl_loss=1.389, generator_dur_loss=1.758, generator_adv_loss=1.989, generator_feat_match_loss=5.519, over 2972.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-14 23:58:57,579 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-14 23:59:05,347 INFO [train.py:591] (1/6) Epoch 699, validation: discriminator_loss=2.727, discriminator_real_loss=1.469, discriminator_fake_loss=1.258, generator_loss=28.32, generator_mel_loss=18.63, generator_kl_loss=1.148, generator_dur_loss=1.815, generator_adv_loss=2.007, generator_feat_match_loss=4.716, over 100.00 samples.
+2024-03-14 23:59:05,348 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 00:01:23,417 INFO [train.py:527] (1/6) Epoch 699, batch 98, global_batch_idx: 86650, batch size: 70, loss[discriminator_loss=2.7, discriminator_real_loss=1.444, discriminator_fake_loss=1.255, generator_loss=28.95, generator_mel_loss=17.92, generator_kl_loss=1.308, generator_dur_loss=1.774, generator_adv_loss=1.992, generator_feat_match_loss=5.954, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.364, discriminator_fake_loss=1.326, generator_loss=28.59, generator_mel_loss=17.91, generator_kl_loss=1.396, generator_dur_loss=1.75, generator_adv_loss=1.986, generator_feat_match_loss=5.553, over 5836.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:02:31,681 INFO [train.py:919] (1/6) Start epoch 700
+2024-03-15 00:04:01,439 INFO [train.py:527] (1/6) Epoch 700, batch 24, global_batch_idx: 86700, batch size: 83, loss[discriminator_loss=2.644, discriminator_real_loss=1.344, discriminator_fake_loss=1.3, generator_loss=29.75, generator_mel_loss=18.28, generator_kl_loss=1.26, generator_dur_loss=1.832, generator_adv_loss=2.152, generator_feat_match_loss=6.226, over 83.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.364, discriminator_fake_loss=1.321, generator_loss=28.65, generator_mel_loss=17.92, generator_kl_loss=1.387, generator_dur_loss=1.733, generator_adv_loss=1.988, generator_feat_match_loss=5.613, over 1430.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:06:21,321 INFO [train.py:527] (1/6) Epoch 700, batch 74, global_batch_idx: 86750, batch size: 53, loss[discriminator_loss=2.579, discriminator_real_loss=1.298, discriminator_fake_loss=1.281, generator_loss=29.91, generator_mel_loss=18.22, generator_kl_loss=1.547, generator_dur_loss=1.701, generator_adv_loss=1.992, generator_feat_match_loss=6.445, over 53.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.367, discriminator_fake_loss=1.324, generator_loss=28.79, generator_mel_loss=17.96, generator_kl_loss=1.419, generator_dur_loss=1.73, generator_adv_loss=1.986, generator_feat_match_loss=5.693, over 4117.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:08:36,627 INFO [train.py:919] (1/6) Start epoch 701
+2024-03-15 00:09:01,129 INFO [train.py:527] (1/6) Epoch 701, batch 0, global_batch_idx: 86800, batch size: 53, loss[discriminator_loss=2.703, discriminator_real_loss=1.471, discriminator_fake_loss=1.233, generator_loss=28.59, generator_mel_loss=17.69, generator_kl_loss=1.477, generator_dur_loss=1.692, generator_adv_loss=1.918, generator_feat_match_loss=5.819, over 53.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.471, discriminator_fake_loss=1.233, generator_loss=28.59, generator_mel_loss=17.69, generator_kl_loss=1.477, generator_dur_loss=1.692, generator_adv_loss=1.918, generator_feat_match_loss=5.819, over 53.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:09:01,131 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 00:09:08,771 INFO [train.py:591] (1/6) Epoch 701, validation: discriminator_loss=2.773, discriminator_real_loss=1.408, discriminator_fake_loss=1.366, generator_loss=26.99, generator_mel_loss=17.93, generator_kl_loss=1.158, generator_dur_loss=1.813, generator_adv_loss=1.858, generator_feat_match_loss=4.228, over 100.00 samples.
+2024-03-15 00:09:08,773 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 00:11:26,846 INFO [train.py:527] (1/6) Epoch 701, batch 50, global_batch_idx: 86850, batch size: 42, loss[discriminator_loss=2.771, discriminator_real_loss=1.485, discriminator_fake_loss=1.286, generator_loss=29.27, generator_mel_loss=18.21, generator_kl_loss=1.623, generator_dur_loss=1.663, generator_adv_loss=1.965, generator_feat_match_loss=5.806, over 42.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.33, generator_loss=28.74, generator_mel_loss=17.88, generator_kl_loss=1.442, generator_dur_loss=1.738, generator_adv_loss=2.008, generator_feat_match_loss=5.673, over 2819.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:13:42,904 INFO [train.py:527] (1/6) Epoch 701, batch 100, global_batch_idx: 86900, batch size: 58, loss[discriminator_loss=2.75, discriminator_real_loss=1.407, discriminator_fake_loss=1.343, generator_loss=28.07, generator_mel_loss=18.01, generator_kl_loss=1.474, generator_dur_loss=1.697, generator_adv_loss=1.833, generator_feat_match_loss=5.054, over 58.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.366, discriminator_fake_loss=1.331, generator_loss=28.65, generator_mel_loss=17.88, generator_kl_loss=1.435, generator_dur_loss=1.729, generator_adv_loss=1.991, generator_feat_match_loss=5.619, over 5431.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:14:51,470 INFO [train.py:919] (1/6) Start epoch 702
+2024-03-15 00:16:27,416 INFO [train.py:527] (1/6) Epoch 702, batch 26, global_batch_idx: 86950, batch size: 50, loss[discriminator_loss=2.658, discriminator_real_loss=1.27, discriminator_fake_loss=1.388, generator_loss=29.19, generator_mel_loss=18.04, generator_kl_loss=1.528, generator_dur_loss=1.676, generator_adv_loss=1.883, generator_feat_match_loss=6.06, over 50.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.365, discriminator_fake_loss=1.344, generator_loss=28.61, generator_mel_loss=17.98, generator_kl_loss=1.428, generator_dur_loss=1.727, generator_adv_loss=1.959, generator_feat_match_loss=5.525, over 1510.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:18:46,790 INFO [train.py:527] (1/6) Epoch 702, batch 76, global_batch_idx: 87000, batch size: 47, loss[discriminator_loss=2.708, discriminator_real_loss=1.389, discriminator_fake_loss=1.319, generator_loss=29.06, generator_mel_loss=18.32, generator_kl_loss=1.373, generator_dur_loss=1.709, generator_adv_loss=2.087, generator_feat_match_loss=5.572, over 47.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.362, discriminator_fake_loss=1.337, generator_loss=28.71, generator_mel_loss=17.96, generator_kl_loss=1.42, generator_dur_loss=1.735, generator_adv_loss=1.966, generator_feat_match_loss=5.629, over 4375.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:18:46,791 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 00:18:55,635 INFO [train.py:591] (1/6) Epoch 702, validation: discriminator_loss=2.731, discriminator_real_loss=1.453, discriminator_fake_loss=1.278, generator_loss=26.92, generator_mel_loss=17.75, generator_kl_loss=1.205, generator_dur_loss=1.808, generator_adv_loss=1.981, generator_feat_match_loss=4.181, over 100.00 samples.
+2024-03-15 00:18:55,635 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 00:21:07,744 INFO [train.py:919] (1/6) Start epoch 703
+2024-03-15 00:21:37,878 INFO [train.py:527] (1/6) Epoch 703, batch 2, global_batch_idx: 87050, batch size: 55, loss[discriminator_loss=2.761, discriminator_real_loss=1.416, discriminator_fake_loss=1.345, generator_loss=29.23, generator_mel_loss=18.55, generator_kl_loss=1.472, generator_dur_loss=1.684, generator_adv_loss=2.109, generator_feat_match_loss=5.415, over 55.00 samples.], tot_loss[discriminator_loss=2.742, discriminator_real_loss=1.407, discriminator_fake_loss=1.335, generator_loss=29.05, generator_mel_loss=18.28, generator_kl_loss=1.395, generator_dur_loss=1.718, generator_adv_loss=2.011, generator_feat_match_loss=5.65, over 172.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:23:57,364 INFO [train.py:527] (1/6) Epoch 703, batch 52, global_batch_idx: 87100, batch size: 77, loss[discriminator_loss=2.654, discriminator_real_loss=1.338, discriminator_fake_loss=1.316, generator_loss=29.63, generator_mel_loss=17.95, generator_kl_loss=1.337, generator_dur_loss=1.783, generator_adv_loss=2.017, generator_feat_match_loss=6.544, over 77.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.365, discriminator_fake_loss=1.336, generator_loss=28.71, generator_mel_loss=17.88, generator_kl_loss=1.435, generator_dur_loss=1.731, generator_adv_loss=1.981, generator_feat_match_loss=5.683, over 3040.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:26:16,244 INFO [train.py:527] (1/6) Epoch 703, batch 102, global_batch_idx: 87150, batch size: 61, loss[discriminator_loss=2.684, discriminator_real_loss=1.293, discriminator_fake_loss=1.392, generator_loss=29.75, generator_mel_loss=18.25, generator_kl_loss=1.444, generator_dur_loss=1.751, generator_adv_loss=1.938, generator_feat_match_loss=6.371, over 61.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.75, generator_mel_loss=17.88, generator_kl_loss=1.416, generator_dur_loss=1.745, generator_adv_loss=1.984, generator_feat_match_loss=5.726, over 6065.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:27:15,836 INFO [train.py:919] (1/6) Start epoch 704
+2024-03-15 00:28:55,022 INFO [train.py:527] (1/6) Epoch 704, batch 28, global_batch_idx: 87200, batch size: 77, loss[discriminator_loss=2.658, discriminator_real_loss=1.361, discriminator_fake_loss=1.298, generator_loss=28.33, generator_mel_loss=17.58, generator_kl_loss=1.298, generator_dur_loss=1.791, generator_adv_loss=1.886, generator_feat_match_loss=5.772, over 77.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.379, discriminator_fake_loss=1.329, generator_loss=28.65, generator_mel_loss=17.86, generator_kl_loss=1.462, generator_dur_loss=1.736, generator_adv_loss=1.975, generator_feat_match_loss=5.617, over 1598.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:28:55,024 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 00:29:02,881 INFO [train.py:591] (1/6) Epoch 704, validation: discriminator_loss=2.716, discriminator_real_loss=1.348, discriminator_fake_loss=1.368, generator_loss=27.35, generator_mel_loss=17.63, generator_kl_loss=1.321, generator_dur_loss=1.806, generator_adv_loss=1.855, generator_feat_match_loss=4.73, over 100.00 samples.
+2024-03-15 00:29:02,881 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 00:31:24,720 INFO [train.py:527] (1/6) Epoch 704, batch 78, global_batch_idx: 87250, batch size: 83, loss[discriminator_loss=2.624, discriminator_real_loss=1.185, discriminator_fake_loss=1.438, generator_loss=30.23, generator_mel_loss=18.52, generator_kl_loss=1.282, generator_dur_loss=1.823, generator_adv_loss=2.16, generator_feat_match_loss=6.438, over 83.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.357, discriminator_fake_loss=1.331, generator_loss=28.74, generator_mel_loss=17.91, generator_kl_loss=1.433, generator_dur_loss=1.758, generator_adv_loss=1.978, generator_feat_match_loss=5.665, over 4879.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:33:32,997 INFO [train.py:919] (1/6) Start epoch 705
+2024-03-15 00:34:08,434 INFO [train.py:527] (1/6) Epoch 705, batch 4, global_batch_idx: 87300, batch size: 59, loss[discriminator_loss=2.656, discriminator_real_loss=1.297, discriminator_fake_loss=1.359, generator_loss=29.29, generator_mel_loss=18.13, generator_kl_loss=1.298, generator_dur_loss=1.763, generator_adv_loss=2.028, generator_feat_match_loss=6.078, over 59.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.338, discriminator_fake_loss=1.32, generator_loss=28.85, generator_mel_loss=17.99, generator_kl_loss=1.372, generator_dur_loss=1.742, generator_adv_loss=1.993, generator_feat_match_loss=5.748, over 298.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:36:28,278 INFO [train.py:527] (1/6) Epoch 705, batch 54, global_batch_idx: 87350, batch size: 45, loss[discriminator_loss=2.777, discriminator_real_loss=1.434, discriminator_fake_loss=1.343, generator_loss=28.76, generator_mel_loss=17.45, generator_kl_loss=1.606, generator_dur_loss=1.661, generator_adv_loss=1.838, generator_feat_match_loss=6.209, over 45.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.348, discriminator_fake_loss=1.338, generator_loss=28.92, generator_mel_loss=18.05, generator_kl_loss=1.428, generator_dur_loss=1.73, generator_adv_loss=1.986, generator_feat_match_loss=5.732, over 3131.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:38:47,770 INFO [train.py:527] (1/6) Epoch 705, batch 104, global_batch_idx: 87400, batch size: 83, loss[discriminator_loss=2.666, discriminator_real_loss=1.325, discriminator_fake_loss=1.341, generator_loss=28.48, generator_mel_loss=17.8, generator_kl_loss=1.327, generator_dur_loss=1.803, generator_adv_loss=1.891, generator_feat_match_loss=5.657, over 83.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.353, discriminator_fake_loss=1.334, generator_loss=28.83, generator_mel_loss=18.02, generator_kl_loss=1.429, generator_dur_loss=1.717, generator_adv_loss=1.988, generator_feat_match_loss=5.681, over 5917.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:38:47,772 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 00:38:56,479 INFO [train.py:591] (1/6) Epoch 705, validation: discriminator_loss=2.711, discriminator_real_loss=1.31, discriminator_fake_loss=1.4, generator_loss=27, generator_mel_loss=18, generator_kl_loss=1.117, generator_dur_loss=1.766, generator_adv_loss=1.803, generator_feat_match_loss=4.321, over 100.00 samples.
+2024-03-15 00:38:56,480 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 00:39:48,751 INFO [train.py:919] (1/6) Start epoch 706
+2024-03-15 00:41:36,836 INFO [train.py:527] (1/6) Epoch 706, batch 30, global_batch_idx: 87450, batch size: 72, loss[discriminator_loss=2.673, discriminator_real_loss=1.371, discriminator_fake_loss=1.302, generator_loss=28.45, generator_mel_loss=17.7, generator_kl_loss=1.344, generator_dur_loss=1.753, generator_adv_loss=1.98, generator_feat_match_loss=5.67, over 72.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.365, discriminator_fake_loss=1.31, generator_loss=28.73, generator_mel_loss=17.97, generator_kl_loss=1.398, generator_dur_loss=1.722, generator_adv_loss=2.006, generator_feat_match_loss=5.637, over 1827.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:43:57,113 INFO [train.py:527] (1/6) Epoch 706, batch 80, global_batch_idx: 87500, batch size: 96, loss[discriminator_loss=2.74, discriminator_real_loss=1.256, discriminator_fake_loss=1.484, generator_loss=28.76, generator_mel_loss=17.85, generator_kl_loss=1.258, generator_dur_loss=1.818, generator_adv_loss=2.054, generator_feat_match_loss=5.774, over 96.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.77, generator_mel_loss=17.95, generator_kl_loss=1.399, generator_dur_loss=1.729, generator_adv_loss=1.995, generator_feat_match_loss=5.7, over 4915.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:45:56,064 INFO [train.py:919] (1/6) Start epoch 707
+2024-03-15 00:46:35,763 INFO [train.py:527] (1/6) Epoch 707, batch 6, global_batch_idx: 87550, batch size: 83, loss[discriminator_loss=2.658, discriminator_real_loss=1.393, discriminator_fake_loss=1.265, generator_loss=28.27, generator_mel_loss=17.57, generator_kl_loss=1.447, generator_dur_loss=1.775, generator_adv_loss=1.966, generator_feat_match_loss=5.513, over 83.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.368, discriminator_fake_loss=1.311, generator_loss=28.43, generator_mel_loss=17.65, generator_kl_loss=1.412, generator_dur_loss=1.728, generator_adv_loss=2.027, generator_feat_match_loss=5.607, over 422.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:48:55,420 INFO [train.py:527] (1/6) Epoch 707, batch 56, global_batch_idx: 87600, batch size: 31, loss[discriminator_loss=2.64, discriminator_real_loss=1.345, discriminator_fake_loss=1.295, generator_loss=28.01, generator_mel_loss=17.85, generator_kl_loss=1.503, generator_dur_loss=1.561, generator_adv_loss=1.986, generator_feat_match_loss=5.111, over 31.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.361, discriminator_fake_loss=1.319, generator_loss=28.68, generator_mel_loss=17.85, generator_kl_loss=1.428, generator_dur_loss=1.729, generator_adv_loss=1.997, generator_feat_match_loss=5.674, over 3333.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:48:55,421 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 00:49:03,328 INFO [train.py:591] (1/6) Epoch 707, validation: discriminator_loss=2.718, discriminator_real_loss=1.365, discriminator_fake_loss=1.353, generator_loss=28.44, generator_mel_loss=18.22, generator_kl_loss=1.361, generator_dur_loss=1.792, generator_adv_loss=1.916, generator_feat_match_loss=5.142, over 100.00 samples.
+2024-03-15 00:49:03,328 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 00:51:23,012 INFO [train.py:527] (1/6) Epoch 707, batch 106, global_batch_idx: 87650, batch size: 59, loss[discriminator_loss=2.668, discriminator_real_loss=1.403, discriminator_fake_loss=1.265, generator_loss=28.41, generator_mel_loss=18.03, generator_kl_loss=1.278, generator_dur_loss=1.731, generator_adv_loss=1.871, generator_feat_match_loss=5.502, over 59.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=28.65, generator_mel_loss=17.87, generator_kl_loss=1.429, generator_dur_loss=1.733, generator_adv_loss=1.992, generator_feat_match_loss=5.624, over 6045.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:52:08,196 INFO [train.py:919] (1/6) Start epoch 708
+2024-03-15 00:54:01,576 INFO [train.py:527] (1/6) Epoch 708, batch 32, global_batch_idx: 87700, batch size: 44, loss[discriminator_loss=2.672, discriminator_real_loss=1.401, discriminator_fake_loss=1.271, generator_loss=28.99, generator_mel_loss=17.68, generator_kl_loss=1.606, generator_dur_loss=1.756, generator_adv_loss=1.976, generator_feat_match_loss=5.973, over 44.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.368, discriminator_fake_loss=1.321, generator_loss=28.51, generator_mel_loss=17.84, generator_kl_loss=1.419, generator_dur_loss=1.75, generator_adv_loss=1.983, generator_feat_match_loss=5.522, over 1901.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:56:16,819 INFO [train.py:527] (1/6) Epoch 708, batch 82, global_batch_idx: 87750, batch size: 36, loss[discriminator_loss=2.72, discriminator_real_loss=1.372, discriminator_fake_loss=1.348, generator_loss=27.82, generator_mel_loss=17.78, generator_kl_loss=1.461, generator_dur_loss=1.707, generator_adv_loss=1.885, generator_feat_match_loss=4.981, over 36.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.363, discriminator_fake_loss=1.324, generator_loss=28.65, generator_mel_loss=17.87, generator_kl_loss=1.427, generator_dur_loss=1.731, generator_adv_loss=1.991, generator_feat_match_loss=5.63, over 4618.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:58:15,831 INFO [train.py:919] (1/6) Start epoch 709
+2024-03-15 00:59:02,299 INFO [train.py:527] (1/6) Epoch 709, batch 8, global_batch_idx: 87800, batch size: 96, loss[discriminator_loss=2.742, discriminator_real_loss=1.419, discriminator_fake_loss=1.323, generator_loss=28.45, generator_mel_loss=17.85, generator_kl_loss=1.478, generator_dur_loss=1.805, generator_adv_loss=1.913, generator_feat_match_loss=5.412, over 96.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.84, generator_mel_loss=17.92, generator_kl_loss=1.403, generator_dur_loss=1.757, generator_adv_loss=1.988, generator_feat_match_loss=5.773, over 597.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 00:59:02,302 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 00:59:10,125 INFO [train.py:591] (1/6) Epoch 709, validation: discriminator_loss=2.722, discriminator_real_loss=1.349, discriminator_fake_loss=1.373, generator_loss=27.44, generator_mel_loss=18.06, generator_kl_loss=1.264, generator_dur_loss=1.792, generator_adv_loss=1.873, generator_feat_match_loss=4.445, over 100.00 samples.
+2024-03-15 00:59:10,126 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 01:01:30,071 INFO [train.py:527] (1/6) Epoch 709, batch 58, global_batch_idx: 87850, batch size: 39, loss[discriminator_loss=2.636, discriminator_real_loss=1.342, discriminator_fake_loss=1.294, generator_loss=30.11, generator_mel_loss=18.2, generator_kl_loss=1.647, generator_dur_loss=1.663, generator_adv_loss=2.057, generator_feat_match_loss=6.546, over 39.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.361, discriminator_fake_loss=1.33, generator_loss=28.89, generator_mel_loss=17.95, generator_kl_loss=1.429, generator_dur_loss=1.722, generator_adv_loss=1.988, generator_feat_match_loss=5.808, over 3246.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:03:45,840 INFO [train.py:527] (1/6) Epoch 709, batch 108, global_batch_idx: 87900, batch size: 61, loss[discriminator_loss=2.601, discriminator_real_loss=1.382, discriminator_fake_loss=1.219, generator_loss=29.05, generator_mel_loss=17.75, generator_kl_loss=1.631, generator_dur_loss=1.684, generator_adv_loss=2.066, generator_feat_match_loss=5.921, over 61.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.367, discriminator_fake_loss=1.329, generator_loss=28.83, generator_mel_loss=17.93, generator_kl_loss=1.44, generator_dur_loss=1.713, generator_adv_loss=1.993, generator_feat_match_loss=5.757, over 5801.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:04:30,412 INFO [train.py:919] (1/6) Start epoch 710
+2024-03-15 01:06:28,026 INFO [train.py:527] (1/6) Epoch 710, batch 34, global_batch_idx: 87950, batch size: 47, loss[discriminator_loss=2.682, discriminator_real_loss=1.421, discriminator_fake_loss=1.26, generator_loss=27.86, generator_mel_loss=17, generator_kl_loss=1.589, generator_dur_loss=1.667, generator_adv_loss=1.997, generator_feat_match_loss=5.609, over 47.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.375, discriminator_fake_loss=1.323, generator_loss=28.52, generator_mel_loss=17.75, generator_kl_loss=1.4, generator_dur_loss=1.744, generator_adv_loss=1.996, generator_feat_match_loss=5.629, over 2082.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:08:49,098 INFO [train.py:527] (1/6) Epoch 710, batch 84, global_batch_idx: 88000, batch size: 47, loss[discriminator_loss=2.652, discriminator_real_loss=1.349, discriminator_fake_loss=1.303, generator_loss=28.91, generator_mel_loss=17.84, generator_kl_loss=1.468, generator_dur_loss=1.703, generator_adv_loss=1.961, generator_feat_match_loss=5.945, over 47.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.369, discriminator_fake_loss=1.327, generator_loss=28.62, generator_mel_loss=17.81, generator_kl_loss=1.402, generator_dur_loss=1.741, generator_adv_loss=1.991, generator_feat_match_loss=5.68, over 4960.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:08:49,099 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 01:08:57,853 INFO [train.py:591] (1/6) Epoch 710, validation: discriminator_loss=2.703, discriminator_real_loss=1.4, discriminator_fake_loss=1.303, generator_loss=28.46, generator_mel_loss=18.24, generator_kl_loss=1.254, generator_dur_loss=1.804, generator_adv_loss=1.891, generator_feat_match_loss=5.269, over 100.00 samples.
+2024-03-15 01:08:57,854 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 01:10:45,308 INFO [train.py:919] (1/6) Start epoch 711
+2024-03-15 01:11:36,978 INFO [train.py:527] (1/6) Epoch 711, batch 10, global_batch_idx: 88050, batch size: 61, loss[discriminator_loss=2.711, discriminator_real_loss=1.337, discriminator_fake_loss=1.374, generator_loss=29.31, generator_mel_loss=17.84, generator_kl_loss=1.392, generator_dur_loss=1.745, generator_adv_loss=2.279, generator_feat_match_loss=6.055, over 61.00 samples.], tot_loss[discriminator_loss=2.628, discriminator_real_loss=1.327, discriminator_fake_loss=1.301, generator_loss=29.5, generator_mel_loss=17.92, generator_kl_loss=1.465, generator_dur_loss=1.731, generator_adv_loss=2.238, generator_feat_match_loss=6.14, over 579.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:13:57,358 INFO [train.py:527] (1/6) Epoch 711, batch 60, global_batch_idx: 88100, batch size: 64, loss[discriminator_loss=2.691, discriminator_real_loss=1.353, discriminator_fake_loss=1.338, generator_loss=27.89, generator_mel_loss=17.33, generator_kl_loss=1.428, generator_dur_loss=1.783, generator_adv_loss=2.02, generator_feat_match_loss=5.325, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=28.95, generator_mel_loss=17.95, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=2.043, generator_feat_match_loss=5.793, over 3511.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:16:14,965 INFO [train.py:527] (1/6) Epoch 711, batch 110, global_batch_idx: 88150, batch size: 48, loss[discriminator_loss=2.707, discriminator_real_loss=1.303, discriminator_fake_loss=1.404, generator_loss=28.57, generator_mel_loss=17.89, generator_kl_loss=1.483, generator_dur_loss=1.681, generator_adv_loss=2.066, generator_feat_match_loss=5.455, over 48.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.322, generator_loss=28.88, generator_mel_loss=17.92, generator_kl_loss=1.429, generator_dur_loss=1.743, generator_adv_loss=2.016, generator_feat_match_loss=5.768, over 6450.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:16:50,887 INFO [train.py:919] (1/6) Start epoch 712
+2024-03-15 01:18:56,784 INFO [train.py:527] (1/6) Epoch 712, batch 36, global_batch_idx: 88200, batch size: 42, loss[discriminator_loss=2.79, discriminator_real_loss=1.426, discriminator_fake_loss=1.363, generator_loss=26.26, generator_mel_loss=17.12, generator_kl_loss=1.447, generator_dur_loss=1.634, generator_adv_loss=1.914, generator_feat_match_loss=4.152, over 42.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.353, discriminator_fake_loss=1.33, generator_loss=28.89, generator_mel_loss=17.94, generator_kl_loss=1.425, generator_dur_loss=1.74, generator_adv_loss=1.999, generator_feat_match_loss=5.786, over 2090.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:18:56,785 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 01:19:04,760 INFO [train.py:591] (1/6) Epoch 712, validation: discriminator_loss=2.741, discriminator_real_loss=1.424, discriminator_fake_loss=1.318, generator_loss=27.55, generator_mel_loss=18.3, generator_kl_loss=1.286, generator_dur_loss=1.807, generator_adv_loss=1.945, generator_feat_match_loss=4.211, over 100.00 samples.
+2024-03-15 01:19:04,761 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 01:21:21,748 INFO [train.py:527] (1/6) Epoch 712, batch 86, global_batch_idx: 88250, batch size: 88, loss[discriminator_loss=2.641, discriminator_real_loss=1.267, discriminator_fake_loss=1.375, generator_loss=29.63, generator_mel_loss=18.14, generator_kl_loss=1.26, generator_dur_loss=1.867, generator_adv_loss=2.119, generator_feat_match_loss=6.239, over 88.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.351, discriminator_fake_loss=1.33, generator_loss=28.88, generator_mel_loss=17.9, generator_kl_loss=1.439, generator_dur_loss=1.738, generator_adv_loss=2, generator_feat_match_loss=5.801, over 4948.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:23:08,334 INFO [train.py:919] (1/6) Start epoch 713
+2024-03-15 01:24:05,779 INFO [train.py:527] (1/6) Epoch 713, batch 12, global_batch_idx: 88300, batch size: 17, loss[discriminator_loss=2.818, discriminator_real_loss=1.33, discriminator_fake_loss=1.487, generator_loss=29.78, generator_mel_loss=18.74, generator_kl_loss=1.824, generator_dur_loss=1.576, generator_adv_loss=1.856, generator_feat_match_loss=5.79, over 17.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.377, discriminator_fake_loss=1.327, generator_loss=28.85, generator_mel_loss=17.89, generator_kl_loss=1.459, generator_dur_loss=1.754, generator_adv_loss=1.976, generator_feat_match_loss=5.767, over 725.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:26:24,739 INFO [train.py:527] (1/6) Epoch 713, batch 62, global_batch_idx: 88350, batch size: 72, loss[discriminator_loss=2.678, discriminator_real_loss=1.317, discriminator_fake_loss=1.361, generator_loss=28.71, generator_mel_loss=18.11, generator_kl_loss=1.311, generator_dur_loss=1.813, generator_adv_loss=2.125, generator_feat_match_loss=5.35, over 72.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.364, discriminator_fake_loss=1.334, generator_loss=28.81, generator_mel_loss=17.97, generator_kl_loss=1.428, generator_dur_loss=1.763, generator_adv_loss=1.989, generator_feat_match_loss=5.657, over 3781.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:28:42,439 INFO [train.py:527] (1/6) Epoch 713, batch 112, global_batch_idx: 88400, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.363, discriminator_fake_loss=1.311, generator_loss=29.28, generator_mel_loss=18.17, generator_kl_loss=1.489, generator_dur_loss=1.7, generator_adv_loss=2.013, generator_feat_match_loss=5.909, over 58.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.368, discriminator_fake_loss=1.329, generator_loss=28.69, generator_mel_loss=17.92, generator_kl_loss=1.433, generator_dur_loss=1.758, generator_adv_loss=1.988, generator_feat_match_loss=5.595, over 6602.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:28:42,440 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 01:28:51,051 INFO [train.py:591] (1/6) Epoch 713, validation: discriminator_loss=2.737, discriminator_real_loss=1.362, discriminator_fake_loss=1.375, generator_loss=27.91, generator_mel_loss=18.27, generator_kl_loss=1.279, generator_dur_loss=1.808, generator_adv_loss=1.828, generator_feat_match_loss=4.725, over 100.00 samples.
+2024-03-15 01:28:51,051 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 01:29:22,819 INFO [train.py:919] (1/6) Start epoch 714
+2024-03-15 01:31:29,653 INFO [train.py:527] (1/6) Epoch 714, batch 38, global_batch_idx: 88450, batch size: 36, loss[discriminator_loss=2.667, discriminator_real_loss=1.328, discriminator_fake_loss=1.339, generator_loss=29.23, generator_mel_loss=18.31, generator_kl_loss=1.591, generator_dur_loss=1.685, generator_adv_loss=1.966, generator_feat_match_loss=5.688, over 36.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.368, discriminator_fake_loss=1.329, generator_loss=28.83, generator_mel_loss=17.92, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=1.987, generator_feat_match_loss=5.754, over 2199.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:33:51,465 INFO [train.py:527] (1/6) Epoch 714, batch 88, global_batch_idx: 88500, batch size: 64, loss[discriminator_loss=2.648, discriminator_real_loss=1.315, discriminator_fake_loss=1.333, generator_loss=28.12, generator_mel_loss=17.78, generator_kl_loss=1.175, generator_dur_loss=1.812, generator_adv_loss=1.947, generator_feat_match_loss=5.403, over 64.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.358, discriminator_fake_loss=1.335, generator_loss=28.83, generator_mel_loss=17.86, generator_kl_loss=1.418, generator_dur_loss=1.748, generator_adv_loss=2.027, generator_feat_match_loss=5.774, over 5070.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:35:29,952 INFO [train.py:919] (1/6) Start epoch 715
+2024-03-15 01:36:33,367 INFO [train.py:527] (1/6) Epoch 715, batch 14, global_batch_idx: 88550, batch size: 70, loss[discriminator_loss=2.733, discriminator_real_loss=1.38, discriminator_fake_loss=1.353, generator_loss=27.78, generator_mel_loss=17.47, generator_kl_loss=1.37, generator_dur_loss=1.765, generator_adv_loss=1.943, generator_feat_match_loss=5.231, over 70.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.79, generator_mel_loss=17.83, generator_kl_loss=1.402, generator_dur_loss=1.776, generator_adv_loss=1.986, generator_feat_match_loss=5.797, over 1020.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:38:51,254 INFO [train.py:527] (1/6) Epoch 715, batch 64, global_batch_idx: 88600, batch size: 66, loss[discriminator_loss=2.641, discriminator_real_loss=1.364, discriminator_fake_loss=1.277, generator_loss=28.09, generator_mel_loss=17.57, generator_kl_loss=1.466, generator_dur_loss=1.759, generator_adv_loss=1.984, generator_feat_match_loss=5.312, over 66.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.81, generator_mel_loss=17.91, generator_kl_loss=1.421, generator_dur_loss=1.748, generator_adv_loss=1.98, generator_feat_match_loss=5.75, over 3766.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:38:51,255 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 01:38:59,980 INFO [train.py:591] (1/6) Epoch 715, validation: discriminator_loss=2.713, discriminator_real_loss=1.368, discriminator_fake_loss=1.345, generator_loss=27.33, generator_mel_loss=18.22, generator_kl_loss=1.306, generator_dur_loss=1.798, generator_adv_loss=1.841, generator_feat_match_loss=4.168, over 100.00 samples.
+2024-03-15 01:38:59,981 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 01:41:17,086 INFO [train.py:527] (1/6) Epoch 715, batch 114, global_batch_idx: 88650, batch size: 47, loss[discriminator_loss=2.708, discriminator_real_loss=1.334, discriminator_fake_loss=1.374, generator_loss=29.72, generator_mel_loss=17.88, generator_kl_loss=1.641, generator_dur_loss=1.66, generator_adv_loss=2.137, generator_feat_match_loss=6.403, over 47.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.81, generator_mel_loss=17.89, generator_kl_loss=1.427, generator_dur_loss=1.752, generator_adv_loss=1.981, generator_feat_match_loss=5.761, over 6781.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:41:44,739 INFO [train.py:919] (1/6) Start epoch 716
+2024-03-15 01:44:01,669 INFO [train.py:527] (1/6) Epoch 716, batch 40, global_batch_idx: 88700, batch size: 44, loss[discriminator_loss=2.66, discriminator_real_loss=1.352, discriminator_fake_loss=1.308, generator_loss=29.49, generator_mel_loss=18.12, generator_kl_loss=1.614, generator_dur_loss=1.659, generator_adv_loss=1.899, generator_feat_match_loss=6.199, over 44.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.347, discriminator_fake_loss=1.337, generator_loss=28.78, generator_mel_loss=17.91, generator_kl_loss=1.426, generator_dur_loss=1.745, generator_adv_loss=1.98, generator_feat_match_loss=5.715, over 2374.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:46:19,756 INFO [train.py:527] (1/6) Epoch 716, batch 90, global_batch_idx: 88750, batch size: 64, loss[discriminator_loss=2.698, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=29.21, generator_mel_loss=18.08, generator_kl_loss=1.411, generator_dur_loss=1.753, generator_adv_loss=2.063, generator_feat_match_loss=5.902, over 64.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.354, discriminator_fake_loss=1.332, generator_loss=28.87, generator_mel_loss=17.96, generator_kl_loss=1.417, generator_dur_loss=1.742, generator_adv_loss=1.988, generator_feat_match_loss=5.767, over 5071.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:47:49,628 INFO [train.py:919] (1/6) Start epoch 717
+2024-03-15 01:48:57,826 INFO [train.py:527] (1/6) Epoch 717, batch 16, global_batch_idx: 88800, batch size: 39, loss[discriminator_loss=2.627, discriminator_real_loss=1.251, discriminator_fake_loss=1.376, generator_loss=29.87, generator_mel_loss=18.29, generator_kl_loss=1.554, generator_dur_loss=1.692, generator_adv_loss=2.155, generator_feat_match_loss=6.177, over 39.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.334, discriminator_fake_loss=1.356, generator_loss=28.9, generator_mel_loss=17.97, generator_kl_loss=1.383, generator_dur_loss=1.76, generator_adv_loss=1.975, generator_feat_match_loss=5.807, over 1021.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:48:57,827 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 01:49:05,800 INFO [train.py:591] (1/6) Epoch 717, validation: discriminator_loss=2.746, discriminator_real_loss=1.555, discriminator_fake_loss=1.192, generator_loss=28.01, generator_mel_loss=18.04, generator_kl_loss=1.166, generator_dur_loss=1.807, generator_adv_loss=2.09, generator_feat_match_loss=4.911, over 100.00 samples.
+2024-03-15 01:49:05,801 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 01:51:22,263 INFO [train.py:527] (1/6) Epoch 717, batch 66, global_batch_idx: 88850, batch size: 42, loss[discriminator_loss=2.757, discriminator_real_loss=1.325, discriminator_fake_loss=1.433, generator_loss=28.64, generator_mel_loss=17.93, generator_kl_loss=1.488, generator_dur_loss=1.629, generator_adv_loss=1.943, generator_feat_match_loss=5.644, over 42.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.357, discriminator_fake_loss=1.332, generator_loss=28.74, generator_mel_loss=17.91, generator_kl_loss=1.401, generator_dur_loss=1.752, generator_adv_loss=1.984, generator_feat_match_loss=5.689, over 3730.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:53:43,570 INFO [train.py:527] (1/6) Epoch 717, batch 116, global_batch_idx: 88900, batch size: 39, loss[discriminator_loss=2.745, discriminator_real_loss=1.413, discriminator_fake_loss=1.332, generator_loss=28.03, generator_mel_loss=17.74, generator_kl_loss=1.497, generator_dur_loss=1.722, generator_adv_loss=1.799, generator_feat_match_loss=5.27, over 39.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.361, discriminator_fake_loss=1.331, generator_loss=28.7, generator_mel_loss=17.89, generator_kl_loss=1.405, generator_dur_loss=1.756, generator_adv_loss=1.985, generator_feat_match_loss=5.663, over 6763.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:54:03,882 INFO [train.py:919] (1/6) Start epoch 718
+2024-03-15 01:56:26,099 INFO [train.py:527] (1/6) Epoch 718, batch 42, global_batch_idx: 88950, batch size: 83, loss[discriminator_loss=2.663, discriminator_real_loss=1.431, discriminator_fake_loss=1.232, generator_loss=29.43, generator_mel_loss=17.52, generator_kl_loss=1.473, generator_dur_loss=1.822, generator_adv_loss=2.337, generator_feat_match_loss=6.276, over 83.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.347, discriminator_fake_loss=1.344, generator_loss=29.04, generator_mel_loss=17.98, generator_kl_loss=1.402, generator_dur_loss=1.758, generator_adv_loss=2.027, generator_feat_match_loss=5.873, over 2451.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:58:45,679 INFO [train.py:527] (1/6) Epoch 718, batch 92, global_batch_idx: 89000, batch size: 52, loss[discriminator_loss=2.699, discriminator_real_loss=1.455, discriminator_fake_loss=1.244, generator_loss=27.87, generator_mel_loss=17.38, generator_kl_loss=1.344, generator_dur_loss=1.655, generator_adv_loss=1.936, generator_feat_match_loss=5.549, over 52.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.36, discriminator_fake_loss=1.335, generator_loss=28.79, generator_mel_loss=17.92, generator_kl_loss=1.416, generator_dur_loss=1.745, generator_adv_loss=2.007, generator_feat_match_loss=5.702, over 5196.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 01:58:45,680 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 01:58:54,589 INFO [train.py:591] (1/6) Epoch 718, validation: discriminator_loss=2.751, discriminator_real_loss=1.377, discriminator_fake_loss=1.374, generator_loss=27.9, generator_mel_loss=18.22, generator_kl_loss=1.23, generator_dur_loss=1.799, generator_adv_loss=1.831, generator_feat_match_loss=4.812, over 100.00 samples.
+2024-03-15 01:58:54,590 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 02:00:20,454 INFO [train.py:919] (1/6) Start epoch 719
+2024-03-15 02:01:33,164 INFO [train.py:527] (1/6) Epoch 719, batch 18, global_batch_idx: 89050, batch size: 50, loss[discriminator_loss=2.718, discriminator_real_loss=1.37, discriminator_fake_loss=1.348, generator_loss=28.09, generator_mel_loss=17.58, generator_kl_loss=1.418, generator_dur_loss=1.656, generator_adv_loss=1.923, generator_feat_match_loss=5.515, over 50.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.369, discriminator_fake_loss=1.323, generator_loss=28.59, generator_mel_loss=17.88, generator_kl_loss=1.42, generator_dur_loss=1.728, generator_adv_loss=1.97, generator_feat_match_loss=5.597, over 1025.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:03:52,419 INFO [train.py:527] (1/6) Epoch 719, batch 68, global_batch_idx: 89100, batch size: 36, loss[discriminator_loss=2.707, discriminator_real_loss=1.398, discriminator_fake_loss=1.309, generator_loss=28.2, generator_mel_loss=17.74, generator_kl_loss=1.562, generator_dur_loss=1.67, generator_adv_loss=1.915, generator_feat_match_loss=5.314, over 36.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.368, discriminator_fake_loss=1.326, generator_loss=28.61, generator_mel_loss=17.85, generator_kl_loss=1.414, generator_dur_loss=1.732, generator_adv_loss=1.973, generator_feat_match_loss=5.636, over 3852.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:06:13,080 INFO [train.py:527] (1/6) Epoch 719, batch 118, global_batch_idx: 89150, batch size: 89, loss[discriminator_loss=2.71, discriminator_real_loss=1.427, discriminator_fake_loss=1.283, generator_loss=28.49, generator_mel_loss=17.68, generator_kl_loss=1.353, generator_dur_loss=1.829, generator_adv_loss=1.814, generator_feat_match_loss=5.817, over 89.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.364, discriminator_fake_loss=1.327, generator_loss=28.65, generator_mel_loss=17.85, generator_kl_loss=1.412, generator_dur_loss=1.735, generator_adv_loss=1.983, generator_feat_match_loss=5.672, over 6590.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:06:30,474 INFO [train.py:919] (1/6) Start epoch 720
+2024-03-15 02:09:00,190 INFO [train.py:527] (1/6) Epoch 720, batch 44, global_batch_idx: 89200, batch size: 36, loss[discriminator_loss=2.79, discriminator_real_loss=1.469, discriminator_fake_loss=1.321, generator_loss=26.8, generator_mel_loss=17.06, generator_kl_loss=1.51, generator_dur_loss=1.679, generator_adv_loss=1.931, generator_feat_match_loss=4.617, over 36.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.365, discriminator_fake_loss=1.326, generator_loss=28.7, generator_mel_loss=17.87, generator_kl_loss=1.419, generator_dur_loss=1.725, generator_adv_loss=1.996, generator_feat_match_loss=5.689, over 2503.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:09:00,191 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 02:09:08,034 INFO [train.py:591] (1/6) Epoch 720, validation: discriminator_loss=2.705, discriminator_real_loss=1.422, discriminator_fake_loss=1.283, generator_loss=27.27, generator_mel_loss=18.08, generator_kl_loss=1.23, generator_dur_loss=1.789, generator_adv_loss=1.945, generator_feat_match_loss=4.221, over 100.00 samples.
+2024-03-15 02:09:08,035 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 02:11:30,214 INFO [train.py:527] (1/6) Epoch 720, batch 94, global_batch_idx: 89250, batch size: 47, loss[discriminator_loss=2.587, discriminator_real_loss=1.343, discriminator_fake_loss=1.244, generator_loss=29.94, generator_mel_loss=18.12, generator_kl_loss=1.601, generator_dur_loss=1.681, generator_adv_loss=2.198, generator_feat_match_loss=6.347, over 47.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.86, generator_mel_loss=17.92, generator_kl_loss=1.433, generator_dur_loss=1.724, generator_adv_loss=2.002, generator_feat_match_loss=5.787, over 5083.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:12:54,981 INFO [train.py:919] (1/6) Start epoch 721
+2024-03-15 02:14:20,684 INFO [train.py:527] (1/6) Epoch 721, batch 20, global_batch_idx: 89300, batch size: 59, loss[discriminator_loss=2.739, discriminator_real_loss=1.434, discriminator_fake_loss=1.305, generator_loss=27.91, generator_mel_loss=17.87, generator_kl_loss=1.366, generator_dur_loss=1.773, generator_adv_loss=1.961, generator_feat_match_loss=4.936, over 59.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.361, discriminator_fake_loss=1.327, generator_loss=29.05, generator_mel_loss=18.04, generator_kl_loss=1.484, generator_dur_loss=1.698, generator_adv_loss=1.993, generator_feat_match_loss=5.84, over 1005.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:16:43,015 INFO [train.py:527] (1/6) Epoch 721, batch 70, global_batch_idx: 89350, batch size: 62, loss[discriminator_loss=2.745, discriminator_real_loss=1.316, discriminator_fake_loss=1.428, generator_loss=29.29, generator_mel_loss=18.1, generator_kl_loss=1.38, generator_dur_loss=1.762, generator_adv_loss=2.009, generator_feat_match_loss=6.043, over 62.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.77, generator_mel_loss=17.91, generator_kl_loss=1.423, generator_dur_loss=1.735, generator_adv_loss=1.982, generator_feat_match_loss=5.714, over 3880.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:18:59,371 INFO [train.py:527] (1/6) Epoch 721, batch 120, global_batch_idx: 89400, batch size: 52, loss[discriminator_loss=2.718, discriminator_real_loss=1.375, discriminator_fake_loss=1.344, generator_loss=29.45, generator_mel_loss=18.07, generator_kl_loss=1.649, generator_dur_loss=1.688, generator_adv_loss=1.897, generator_feat_match_loss=6.146, over 52.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=28.82, generator_mel_loss=17.93, generator_kl_loss=1.42, generator_dur_loss=1.741, generator_adv_loss=1.985, generator_feat_match_loss=5.737, over 6846.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:18:59,373 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 02:19:08,218 INFO [train.py:591] (1/6) Epoch 721, validation: discriminator_loss=2.729, discriminator_real_loss=1.382, discriminator_fake_loss=1.347, generator_loss=26.98, generator_mel_loss=17.75, generator_kl_loss=1.304, generator_dur_loss=1.82, generator_adv_loss=1.826, generator_feat_match_loss=4.277, over 100.00 samples.
+2024-03-15 02:19:08,219 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 02:19:18,056 INFO [train.py:919] (1/6) Start epoch 722
+2024-03-15 02:21:54,333 INFO [train.py:527] (1/6) Epoch 722, batch 46, global_batch_idx: 89450, batch size: 31, loss[discriminator_loss=2.632, discriminator_real_loss=1.284, discriminator_fake_loss=1.349, generator_loss=30.33, generator_mel_loss=18.72, generator_kl_loss=1.714, generator_dur_loss=1.536, generator_adv_loss=2.044, generator_feat_match_loss=6.313, over 31.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.33, generator_loss=28.85, generator_mel_loss=17.97, generator_kl_loss=1.422, generator_dur_loss=1.749, generator_adv_loss=1.979, generator_feat_match_loss=5.739, over 2513.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:24:16,888 INFO [train.py:527] (1/6) Epoch 722, batch 96, global_batch_idx: 89500, batch size: 83, loss[discriminator_loss=2.643, discriminator_real_loss=1.31, discriminator_fake_loss=1.333, generator_loss=28.55, generator_mel_loss=17.76, generator_kl_loss=1.226, generator_dur_loss=1.844, generator_adv_loss=2.01, generator_feat_match_loss=5.71, over 83.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=28.82, generator_mel_loss=17.93, generator_kl_loss=1.414, generator_dur_loss=1.747, generator_adv_loss=1.992, generator_feat_match_loss=5.729, over 5264.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:25:32,856 INFO [train.py:919] (1/6) Start epoch 723
+2024-03-15 02:26:58,523 INFO [train.py:527] (1/6) Epoch 723, batch 22, global_batch_idx: 89550, batch size: 52, loss[discriminator_loss=2.729, discriminator_real_loss=1.35, discriminator_fake_loss=1.379, generator_loss=28.69, generator_mel_loss=17.73, generator_kl_loss=1.288, generator_dur_loss=1.666, generator_adv_loss=2.097, generator_feat_match_loss=5.909, over 52.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.352, discriminator_fake_loss=1.335, generator_loss=28.83, generator_mel_loss=17.88, generator_kl_loss=1.386, generator_dur_loss=1.734, generator_adv_loss=2.003, generator_feat_match_loss=5.83, over 1380.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:29:24,513 INFO [train.py:527] (1/6) Epoch 723, batch 72, global_batch_idx: 89600, batch size: 61, loss[discriminator_loss=2.684, discriminator_real_loss=1.309, discriminator_fake_loss=1.375, generator_loss=29, generator_mel_loss=17.76, generator_kl_loss=1.518, generator_dur_loss=1.705, generator_adv_loss=1.991, generator_feat_match_loss=6.03, over 61.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.358, discriminator_fake_loss=1.332, generator_loss=28.72, generator_mel_loss=17.8, generator_kl_loss=1.412, generator_dur_loss=1.742, generator_adv_loss=1.989, generator_feat_match_loss=5.775, over 4389.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:29:24,514 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 02:29:32,617 INFO [train.py:591] (1/6) Epoch 723, validation: discriminator_loss=2.731, discriminator_real_loss=1.438, discriminator_fake_loss=1.293, generator_loss=27.29, generator_mel_loss=18.05, generator_kl_loss=1.203, generator_dur_loss=1.799, generator_adv_loss=1.916, generator_feat_match_loss=4.325, over 100.00 samples.
+2024-03-15 02:29:32,617 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 02:31:49,131 INFO [train.py:527] (1/6) Epoch 723, batch 122, global_batch_idx: 89650, batch size: 31, loss[discriminator_loss=2.705, discriminator_real_loss=1.45, discriminator_fake_loss=1.255, generator_loss=28.99, generator_mel_loss=18.42, generator_kl_loss=1.645, generator_dur_loss=1.599, generator_adv_loss=2, generator_feat_match_loss=5.329, over 31.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.362, discriminator_fake_loss=1.333, generator_loss=28.67, generator_mel_loss=17.84, generator_kl_loss=1.411, generator_dur_loss=1.741, generator_adv_loss=1.983, generator_feat_match_loss=5.695, over 7228.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:31:53,732 INFO [train.py:919] (1/6) Start epoch 724
+2024-03-15 02:34:30,862 INFO [train.py:527] (1/6) Epoch 724, batch 48, global_batch_idx: 89700, batch size: 31, loss[discriminator_loss=2.711, discriminator_real_loss=1.37, discriminator_fake_loss=1.341, generator_loss=29.31, generator_mel_loss=17.62, generator_kl_loss=1.552, generator_dur_loss=1.729, generator_adv_loss=2.197, generator_feat_match_loss=6.216, over 31.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.367, discriminator_fake_loss=1.328, generator_loss=28.63, generator_mel_loss=17.9, generator_kl_loss=1.41, generator_dur_loss=1.754, generator_adv_loss=1.984, generator_feat_match_loss=5.58, over 2869.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:36:53,204 INFO [train.py:527] (1/6) Epoch 724, batch 98, global_batch_idx: 89750, batch size: 52, loss[discriminator_loss=2.644, discriminator_real_loss=1.286, discriminator_fake_loss=1.358, generator_loss=28.83, generator_mel_loss=17.94, generator_kl_loss=1.338, generator_dur_loss=1.71, generator_adv_loss=2.035, generator_feat_match_loss=5.805, over 52.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.64, generator_mel_loss=17.89, generator_kl_loss=1.4, generator_dur_loss=1.758, generator_adv_loss=1.986, generator_feat_match_loss=5.612, over 6026.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:38:04,232 INFO [train.py:919] (1/6) Start epoch 725
+2024-03-15 02:39:31,970 INFO [train.py:527] (1/6) Epoch 725, batch 24, global_batch_idx: 89800, batch size: 47, loss[discriminator_loss=2.737, discriminator_real_loss=1.371, discriminator_fake_loss=1.366, generator_loss=28.35, generator_mel_loss=18.09, generator_kl_loss=1.42, generator_dur_loss=1.703, generator_adv_loss=1.847, generator_feat_match_loss=5.29, over 47.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.373, discriminator_fake_loss=1.325, generator_loss=28.77, generator_mel_loss=17.97, generator_kl_loss=1.45, generator_dur_loss=1.73, generator_adv_loss=1.986, generator_feat_match_loss=5.632, over 1444.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:39:31,972 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 02:39:39,701 INFO [train.py:591] (1/6) Epoch 725, validation: discriminator_loss=2.751, discriminator_real_loss=1.342, discriminator_fake_loss=1.409, generator_loss=27.8, generator_mel_loss=18.4, generator_kl_loss=1.14, generator_dur_loss=1.812, generator_adv_loss=1.788, generator_feat_match_loss=4.655, over 100.00 samples.
+2024-03-15 02:39:39,702 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 02:41:56,790 INFO [train.py:527] (1/6) Epoch 725, batch 74, global_batch_idx: 89850, batch size: 50, loss[discriminator_loss=2.633, discriminator_real_loss=1.368, discriminator_fake_loss=1.265, generator_loss=29.42, generator_mel_loss=18.2, generator_kl_loss=1.505, generator_dur_loss=1.725, generator_adv_loss=1.957, generator_feat_match_loss=6.036, over 50.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.37, discriminator_fake_loss=1.325, generator_loss=28.76, generator_mel_loss=17.94, generator_kl_loss=1.442, generator_dur_loss=1.736, generator_adv_loss=1.99, generator_feat_match_loss=5.658, over 4209.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:44:14,632 INFO [train.py:919] (1/6) Start epoch 726
+2024-03-15 02:44:38,438 INFO [train.py:527] (1/6) Epoch 726, batch 0, global_batch_idx: 89900, batch size: 52, loss[discriminator_loss=2.735, discriminator_real_loss=1.455, discriminator_fake_loss=1.28, generator_loss=29.74, generator_mel_loss=18.19, generator_kl_loss=1.457, generator_dur_loss=1.665, generator_adv_loss=1.952, generator_feat_match_loss=6.468, over 52.00 samples.], tot_loss[discriminator_loss=2.735, discriminator_real_loss=1.455, discriminator_fake_loss=1.28, generator_loss=29.74, generator_mel_loss=18.19, generator_kl_loss=1.457, generator_dur_loss=1.665, generator_adv_loss=1.952, generator_feat_match_loss=6.468, over 52.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:46:55,353 INFO [train.py:527] (1/6) Epoch 726, batch 50, global_batch_idx: 89950, batch size: 53, loss[discriminator_loss=2.706, discriminator_real_loss=1.307, discriminator_fake_loss=1.399, generator_loss=30.49, generator_mel_loss=18.63, generator_kl_loss=1.447, generator_dur_loss=1.712, generator_adv_loss=2.01, generator_feat_match_loss=6.69, over 53.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.81, generator_mel_loss=17.87, generator_kl_loss=1.433, generator_dur_loss=1.729, generator_adv_loss=2.003, generator_feat_match_loss=5.773, over 2807.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:49:12,555 INFO [train.py:527] (1/6) Epoch 726, batch 100, global_batch_idx: 90000, batch size: 88, loss[discriminator_loss=2.659, discriminator_real_loss=1.359, discriminator_fake_loss=1.3, generator_loss=28.32, generator_mel_loss=17.31, generator_kl_loss=1.318, generator_dur_loss=1.817, generator_adv_loss=1.998, generator_feat_match_loss=5.87, over 88.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.326, generator_loss=28.78, generator_mel_loss=17.85, generator_kl_loss=1.427, generator_dur_loss=1.737, generator_adv_loss=2.001, generator_feat_match_loss=5.766, over 5701.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:49:12,557 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 02:49:21,317 INFO [train.py:591] (1/6) Epoch 726, validation: discriminator_loss=2.744, discriminator_real_loss=1.41, discriminator_fake_loss=1.334, generator_loss=27.19, generator_mel_loss=17.97, generator_kl_loss=1.278, generator_dur_loss=1.804, generator_adv_loss=1.87, generator_feat_match_loss=4.264, over 100.00 samples.
+2024-03-15 02:49:21,318 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 02:50:26,354 INFO [train.py:919] (1/6) Start epoch 727
+2024-03-15 02:52:03,338 INFO [train.py:527] (1/6) Epoch 727, batch 26, global_batch_idx: 90050, batch size: 47, loss[discriminator_loss=2.685, discriminator_real_loss=1.374, discriminator_fake_loss=1.311, generator_loss=27.14, generator_mel_loss=17.26, generator_kl_loss=1.381, generator_dur_loss=1.708, generator_adv_loss=1.871, generator_feat_match_loss=4.928, over 47.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.368, discriminator_fake_loss=1.309, generator_loss=29.02, generator_mel_loss=17.95, generator_kl_loss=1.435, generator_dur_loss=1.746, generator_adv_loss=2.017, generator_feat_match_loss=5.876, over 1673.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:54:22,641 INFO [train.py:527] (1/6) Epoch 727, batch 76, global_batch_idx: 90100, batch size: 55, loss[discriminator_loss=2.689, discriminator_real_loss=1.353, discriminator_fake_loss=1.337, generator_loss=28.12, generator_mel_loss=18.06, generator_kl_loss=1.36, generator_dur_loss=1.729, generator_adv_loss=1.956, generator_feat_match_loss=5.014, over 55.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.369, discriminator_fake_loss=1.322, generator_loss=28.76, generator_mel_loss=17.91, generator_kl_loss=1.418, generator_dur_loss=1.744, generator_adv_loss=1.991, generator_feat_match_loss=5.695, over 4590.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:56:30,939 INFO [train.py:919] (1/6) Start epoch 728
+2024-03-15 02:56:59,579 INFO [train.py:527] (1/6) Epoch 728, batch 2, global_batch_idx: 90150, batch size: 15, loss[discriminator_loss=2.714, discriminator_real_loss=1.367, discriminator_fake_loss=1.347, generator_loss=29.6, generator_mel_loss=18.8, generator_kl_loss=1.912, generator_dur_loss=1.499, generator_adv_loss=1.963, generator_feat_match_loss=5.427, over 15.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.334, discriminator_fake_loss=1.346, generator_loss=28.93, generator_mel_loss=18.1, generator_kl_loss=1.502, generator_dur_loss=1.748, generator_adv_loss=1.917, generator_feat_match_loss=5.663, over 106.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:59:17,551 INFO [train.py:527] (1/6) Epoch 728, batch 52, global_batch_idx: 90200, batch size: 70, loss[discriminator_loss=2.747, discriminator_real_loss=1.334, discriminator_fake_loss=1.412, generator_loss=29.46, generator_mel_loss=18.09, generator_kl_loss=1.268, generator_dur_loss=1.8, generator_adv_loss=2.077, generator_feat_match_loss=6.229, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.331, generator_loss=28.82, generator_mel_loss=17.96, generator_kl_loss=1.418, generator_dur_loss=1.738, generator_adv_loss=1.98, generator_feat_match_loss=5.721, over 2942.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 02:59:17,552 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 02:59:25,701 INFO [train.py:591] (1/6) Epoch 728, validation: discriminator_loss=2.75, discriminator_real_loss=1.473, discriminator_fake_loss=1.277, generator_loss=28.74, generator_mel_loss=18.58, generator_kl_loss=1.261, generator_dur_loss=1.818, generator_adv_loss=1.979, generator_feat_match_loss=5.101, over 100.00 samples.
+2024-03-15 02:59:25,701 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 03:01:42,903 INFO [train.py:527] (1/6) Epoch 728, batch 102, global_batch_idx: 90250, batch size: 66, loss[discriminator_loss=2.638, discriminator_real_loss=1.282, discriminator_fake_loss=1.356, generator_loss=29.53, generator_mel_loss=18.11, generator_kl_loss=1.322, generator_dur_loss=1.749, generator_adv_loss=2.01, generator_feat_match_loss=6.341, over 66.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.355, discriminator_fake_loss=1.331, generator_loss=28.86, generator_mel_loss=17.93, generator_kl_loss=1.417, generator_dur_loss=1.742, generator_adv_loss=1.994, generator_feat_match_loss=5.782, over 5718.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:02:43,945 INFO [train.py:919] (1/6) Start epoch 729
+2024-03-15 03:04:24,190 INFO [train.py:527] (1/6) Epoch 729, batch 28, global_batch_idx: 90300, batch size: 42, loss[discriminator_loss=2.641, discriminator_real_loss=1.299, discriminator_fake_loss=1.342, generator_loss=30.24, generator_mel_loss=18.44, generator_kl_loss=1.531, generator_dur_loss=1.723, generator_adv_loss=2.048, generator_feat_match_loss=6.501, over 42.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.353, discriminator_fake_loss=1.329, generator_loss=28.86, generator_mel_loss=17.82, generator_kl_loss=1.438, generator_dur_loss=1.75, generator_adv_loss=1.995, generator_feat_match_loss=5.856, over 1665.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:06:43,509 INFO [train.py:527] (1/6) Epoch 729, batch 78, global_batch_idx: 90350, batch size: 53, loss[discriminator_loss=2.722, discriminator_real_loss=1.39, discriminator_fake_loss=1.332, generator_loss=27.99, generator_mel_loss=17.8, generator_kl_loss=1.47, generator_dur_loss=1.704, generator_adv_loss=1.962, generator_feat_match_loss=5.051, over 53.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.84, generator_mel_loss=17.87, generator_kl_loss=1.405, generator_dur_loss=1.757, generator_adv_loss=1.988, generator_feat_match_loss=5.822, over 4653.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:08:48,265 INFO [train.py:919] (1/6) Start epoch 730
+2024-03-15 03:09:24,061 INFO [train.py:527] (1/6) Epoch 730, batch 4, global_batch_idx: 90400, batch size: 55, loss[discriminator_loss=2.688, discriminator_real_loss=1.415, discriminator_fake_loss=1.273, generator_loss=28.19, generator_mel_loss=17.76, generator_kl_loss=1.331, generator_dur_loss=1.726, generator_adv_loss=2.066, generator_feat_match_loss=5.308, over 55.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.404, discriminator_fake_loss=1.323, generator_loss=28.24, generator_mel_loss=17.79, generator_kl_loss=1.381, generator_dur_loss=1.771, generator_adv_loss=1.967, generator_feat_match_loss=5.326, over 344.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:09:24,063 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 03:09:31,910 INFO [train.py:591] (1/6) Epoch 730, validation: discriminator_loss=2.701, discriminator_real_loss=1.391, discriminator_fake_loss=1.31, generator_loss=27.51, generator_mel_loss=18.27, generator_kl_loss=1.258, generator_dur_loss=1.811, generator_adv_loss=1.869, generator_feat_match_loss=4.308, over 100.00 samples.
+2024-03-15 03:09:31,913 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 03:11:51,522 INFO [train.py:527] (1/6) Epoch 730, batch 54, global_batch_idx: 90450, batch size: 39, loss[discriminator_loss=2.679, discriminator_real_loss=1.299, discriminator_fake_loss=1.38, generator_loss=29.82, generator_mel_loss=19.05, generator_kl_loss=1.629, generator_dur_loss=1.695, generator_adv_loss=1.87, generator_feat_match_loss=5.577, over 39.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.365, discriminator_fake_loss=1.325, generator_loss=28.66, generator_mel_loss=17.85, generator_kl_loss=1.388, generator_dur_loss=1.765, generator_adv_loss=1.996, generator_feat_match_loss=5.661, over 3452.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:14:08,329 INFO [train.py:527] (1/6) Epoch 730, batch 104, global_batch_idx: 90500, batch size: 62, loss[discriminator_loss=2.757, discriminator_real_loss=1.47, discriminator_fake_loss=1.287, generator_loss=27.36, generator_mel_loss=17.71, generator_kl_loss=1.373, generator_dur_loss=1.742, generator_adv_loss=1.864, generator_feat_match_loss=4.668, over 62.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.74, generator_mel_loss=17.92, generator_kl_loss=1.412, generator_dur_loss=1.751, generator_adv_loss=1.991, generator_feat_match_loss=5.668, over 6144.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:14:58,744 INFO [train.py:919] (1/6) Start epoch 731
+2024-03-15 03:16:43,012 INFO [train.py:527] (1/6) Epoch 731, batch 30, global_batch_idx: 90550, batch size: 31, loss[discriminator_loss=2.697, discriminator_real_loss=1.218, discriminator_fake_loss=1.479, generator_loss=28.27, generator_mel_loss=17.56, generator_kl_loss=1.58, generator_dur_loss=1.698, generator_adv_loss=1.975, generator_feat_match_loss=5.459, over 31.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.36, discriminator_fake_loss=1.329, generator_loss=28.76, generator_mel_loss=17.92, generator_kl_loss=1.443, generator_dur_loss=1.74, generator_adv_loss=1.977, generator_feat_match_loss=5.68, over 1681.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:18:59,964 INFO [train.py:527] (1/6) Epoch 731, batch 80, global_batch_idx: 90600, batch size: 77, loss[discriminator_loss=2.707, discriminator_real_loss=1.484, discriminator_fake_loss=1.224, generator_loss=27.89, generator_mel_loss=17.55, generator_kl_loss=1.294, generator_dur_loss=1.804, generator_adv_loss=1.79, generator_feat_match_loss=5.447, over 77.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.8, generator_mel_loss=17.9, generator_kl_loss=1.431, generator_dur_loss=1.741, generator_adv_loss=1.982, generator_feat_match_loss=5.748, over 4485.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:18:59,966 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 03:19:08,868 INFO [train.py:591] (1/6) Epoch 731, validation: discriminator_loss=2.754, discriminator_real_loss=1.286, discriminator_fake_loss=1.468, generator_loss=27.33, generator_mel_loss=17.98, generator_kl_loss=1.2, generator_dur_loss=1.825, generator_adv_loss=1.702, generator_feat_match_loss=4.624, over 100.00 samples.
+2024-03-15 03:19:08,870 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 03:21:10,469 INFO [train.py:919] (1/6) Start epoch 732
+2024-03-15 03:21:47,910 INFO [train.py:527] (1/6) Epoch 732, batch 6, global_batch_idx: 90650, batch size: 25, loss[discriminator_loss=2.723, discriminator_real_loss=1.398, discriminator_fake_loss=1.325, generator_loss=28.62, generator_mel_loss=18.04, generator_kl_loss=1.627, generator_dur_loss=1.555, generator_adv_loss=1.927, generator_feat_match_loss=5.474, over 25.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.36, discriminator_fake_loss=1.318, generator_loss=28.65, generator_mel_loss=17.8, generator_kl_loss=1.423, generator_dur_loss=1.75, generator_adv_loss=2.017, generator_feat_match_loss=5.659, over 357.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:24:06,770 INFO [train.py:527] (1/6) Epoch 732, batch 56, global_batch_idx: 90700, batch size: 58, loss[discriminator_loss=2.745, discriminator_real_loss=1.443, discriminator_fake_loss=1.301, generator_loss=28.43, generator_mel_loss=18.01, generator_kl_loss=1.198, generator_dur_loss=1.769, generator_adv_loss=1.971, generator_feat_match_loss=5.479, over 58.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.364, discriminator_fake_loss=1.329, generator_loss=28.72, generator_mel_loss=17.86, generator_kl_loss=1.401, generator_dur_loss=1.736, generator_adv_loss=1.999, generator_feat_match_loss=5.72, over 3220.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:26:26,680 INFO [train.py:527] (1/6) Epoch 732, batch 106, global_batch_idx: 90750, batch size: 36, loss[discriminator_loss=2.694, discriminator_real_loss=1.334, discriminator_fake_loss=1.36, generator_loss=29.51, generator_mel_loss=18.51, generator_kl_loss=1.578, generator_dur_loss=1.62, generator_adv_loss=1.95, generator_feat_match_loss=5.853, over 36.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.65, generator_mel_loss=17.83, generator_kl_loss=1.408, generator_dur_loss=1.748, generator_adv_loss=1.995, generator_feat_match_loss=5.669, over 6183.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:27:13,828 INFO [train.py:919] (1/6) Start epoch 733
+2024-03-15 03:29:06,620 INFO [train.py:527] (1/6) Epoch 733, batch 32, global_batch_idx: 90800, batch size: 64, loss[discriminator_loss=2.63, discriminator_real_loss=1.292, discriminator_fake_loss=1.338, generator_loss=29.09, generator_mel_loss=18.21, generator_kl_loss=1.442, generator_dur_loss=1.753, generator_adv_loss=1.881, generator_feat_match_loss=5.803, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=28.89, generator_mel_loss=17.92, generator_kl_loss=1.426, generator_dur_loss=1.745, generator_adv_loss=2.004, generator_feat_match_loss=5.797, over 1901.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:29:06,621 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 03:29:14,668 INFO [train.py:591] (1/6) Epoch 733, validation: discriminator_loss=2.702, discriminator_real_loss=1.341, discriminator_fake_loss=1.361, generator_loss=27.22, generator_mel_loss=17.85, generator_kl_loss=1.268, generator_dur_loss=1.817, generator_adv_loss=1.856, generator_feat_match_loss=4.433, over 100.00 samples.
+2024-03-15 03:29:14,669 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 03:31:35,987 INFO [train.py:527] (1/6) Epoch 733, batch 82, global_batch_idx: 90850, batch size: 61, loss[discriminator_loss=2.712, discriminator_real_loss=1.469, discriminator_fake_loss=1.244, generator_loss=28.21, generator_mel_loss=17.18, generator_kl_loss=1.353, generator_dur_loss=1.721, generator_adv_loss=1.948, generator_feat_match_loss=6.015, over 61.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.325, generator_loss=28.8, generator_mel_loss=17.89, generator_kl_loss=1.411, generator_dur_loss=1.746, generator_adv_loss=1.999, generator_feat_match_loss=5.759, over 4932.00 samples.], cur_lr_g: 1.83e-04, cur_lr_d: 1.83e-04, 
+2024-03-15 03:33:28,153 INFO [train.py:919] (1/6) Start epoch 734
+2024-03-15 03:34:14,581 INFO [train.py:527] (1/6) Epoch 734, batch 8, global_batch_idx: 90900, batch size: 56, loss[discriminator_loss=2.669, discriminator_real_loss=1.352, discriminator_fake_loss=1.317, generator_loss=29.48, generator_mel_loss=18.15, generator_kl_loss=1.439, generator_dur_loss=1.728, generator_adv_loss=1.934, generator_feat_match_loss=6.223, over 56.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.339, discriminator_fake_loss=1.327, generator_loss=28.58, generator_mel_loss=17.76, generator_kl_loss=1.385, generator_dur_loss=1.774, generator_adv_loss=1.99, generator_feat_match_loss=5.669, over 587.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:36:32,439 INFO [train.py:527] (1/6) Epoch 734, batch 58, global_batch_idx: 90950, batch size: 42, loss[discriminator_loss=2.721, discriminator_real_loss=1.327, discriminator_fake_loss=1.394, generator_loss=28.99, generator_mel_loss=18.2, generator_kl_loss=1.48, generator_dur_loss=1.683, generator_adv_loss=2.053, generator_feat_match_loss=5.571, over 42.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.75, generator_mel_loss=17.83, generator_kl_loss=1.401, generator_dur_loss=1.748, generator_adv_loss=1.996, generator_feat_match_loss=5.774, over 3336.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:38:52,238 INFO [train.py:527] (1/6) Epoch 734, batch 108, global_batch_idx: 91000, batch size: 36, loss[discriminator_loss=2.735, discriminator_real_loss=1.405, discriminator_fake_loss=1.33, generator_loss=28.78, generator_mel_loss=18.09, generator_kl_loss=1.546, generator_dur_loss=1.685, generator_adv_loss=1.959, generator_feat_match_loss=5.5, over 36.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.358, discriminator_fake_loss=1.324, generator_loss=28.8, generator_mel_loss=17.85, generator_kl_loss=1.417, generator_dur_loss=1.752, generator_adv_loss=1.995, generator_feat_match_loss=5.792, over 6298.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:38:52,239 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 03:39:01,388 INFO [train.py:591] (1/6) Epoch 734, validation: discriminator_loss=2.736, discriminator_real_loss=1.413, discriminator_fake_loss=1.323, generator_loss=27.26, generator_mel_loss=18.04, generator_kl_loss=1.267, generator_dur_loss=1.818, generator_adv_loss=1.838, generator_feat_match_loss=4.298, over 100.00 samples.
+2024-03-15 03:39:01,389 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 03:39:44,290 INFO [train.py:919] (1/6) Start epoch 735
+2024-03-15 03:41:43,613 INFO [train.py:527] (1/6) Epoch 735, batch 34, global_batch_idx: 91050, batch size: 96, loss[discriminator_loss=2.646, discriminator_real_loss=1.219, discriminator_fake_loss=1.427, generator_loss=29.57, generator_mel_loss=18.06, generator_kl_loss=1.358, generator_dur_loss=1.853, generator_adv_loss=2.132, generator_feat_match_loss=6.164, over 96.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.363, discriminator_fake_loss=1.333, generator_loss=28.83, generator_mel_loss=17.98, generator_kl_loss=1.446, generator_dur_loss=1.756, generator_adv_loss=1.991, generator_feat_match_loss=5.658, over 2096.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:44:01,030 INFO [train.py:527] (1/6) Epoch 735, batch 84, global_batch_idx: 91100, batch size: 50, loss[discriminator_loss=2.747, discriminator_real_loss=1.367, discriminator_fake_loss=1.38, generator_loss=29.25, generator_mel_loss=18.25, generator_kl_loss=1.421, generator_dur_loss=1.661, generator_adv_loss=2.088, generator_feat_match_loss=5.824, over 50.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.361, discriminator_fake_loss=1.333, generator_loss=28.84, generator_mel_loss=17.95, generator_kl_loss=1.431, generator_dur_loss=1.747, generator_adv_loss=1.992, generator_feat_match_loss=5.725, over 4832.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:45:51,254 INFO [train.py:919] (1/6) Start epoch 736
+2024-03-15 03:46:42,852 INFO [train.py:527] (1/6) Epoch 736, batch 10, global_batch_idx: 91150, batch size: 55, loss[discriminator_loss=2.7, discriminator_real_loss=1.434, discriminator_fake_loss=1.265, generator_loss=27.94, generator_mel_loss=17.72, generator_kl_loss=1.454, generator_dur_loss=1.699, generator_adv_loss=1.983, generator_feat_match_loss=5.082, over 55.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.364, discriminator_fake_loss=1.322, generator_loss=28.12, generator_mel_loss=17.69, generator_kl_loss=1.432, generator_dur_loss=1.749, generator_adv_loss=1.975, generator_feat_match_loss=5.279, over 637.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:49:01,675 INFO [train.py:527] (1/6) Epoch 736, batch 60, global_batch_idx: 91200, batch size: 74, loss[discriminator_loss=2.69, discriminator_real_loss=1.318, discriminator_fake_loss=1.372, generator_loss=29.09, generator_mel_loss=17.76, generator_kl_loss=1.338, generator_dur_loss=1.803, generator_adv_loss=2.108, generator_feat_match_loss=6.081, over 74.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.75, generator_mel_loss=17.88, generator_kl_loss=1.419, generator_dur_loss=1.755, generator_adv_loss=2.005, generator_feat_match_loss=5.687, over 3682.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:49:01,677 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 03:49:09,685 INFO [train.py:591] (1/6) Epoch 736, validation: discriminator_loss=2.739, discriminator_real_loss=1.47, discriminator_fake_loss=1.269, generator_loss=27.55, generator_mel_loss=17.91, generator_kl_loss=1.142, generator_dur_loss=1.814, generator_adv_loss=2.003, generator_feat_match_loss=4.68, over 100.00 samples.
+2024-03-15 03:49:09,686 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 03:51:27,959 INFO [train.py:527] (1/6) Epoch 736, batch 110, global_batch_idx: 91250, batch size: 48, loss[discriminator_loss=2.777, discriminator_real_loss=1.39, discriminator_fake_loss=1.387, generator_loss=27.34, generator_mel_loss=17.3, generator_kl_loss=1.496, generator_dur_loss=1.667, generator_adv_loss=1.997, generator_feat_match_loss=4.882, over 48.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.8, generator_mel_loss=17.91, generator_kl_loss=1.424, generator_dur_loss=1.756, generator_adv_loss=1.995, generator_feat_match_loss=5.715, over 6662.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:52:03,901 INFO [train.py:919] (1/6) Start epoch 737
+2024-03-15 03:54:10,769 INFO [train.py:527] (1/6) Epoch 737, batch 36, global_batch_idx: 91300, batch size: 36, loss[discriminator_loss=2.693, discriminator_real_loss=1.419, discriminator_fake_loss=1.273, generator_loss=28.68, generator_mel_loss=18.02, generator_kl_loss=1.497, generator_dur_loss=1.704, generator_adv_loss=2.061, generator_feat_match_loss=5.394, over 36.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.348, discriminator_fake_loss=1.331, generator_loss=28.75, generator_mel_loss=17.85, generator_kl_loss=1.432, generator_dur_loss=1.756, generator_adv_loss=1.988, generator_feat_match_loss=5.729, over 2203.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:56:27,578 INFO [train.py:527] (1/6) Epoch 737, batch 86, global_batch_idx: 91350, batch size: 74, loss[discriminator_loss=2.779, discriminator_real_loss=1.539, discriminator_fake_loss=1.239, generator_loss=29.62, generator_mel_loss=18, generator_kl_loss=1.465, generator_dur_loss=1.818, generator_adv_loss=1.842, generator_feat_match_loss=6.496, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.84, generator_mel_loss=17.89, generator_kl_loss=1.434, generator_dur_loss=1.746, generator_adv_loss=1.993, generator_feat_match_loss=5.778, over 4770.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:58:12,340 INFO [train.py:919] (1/6) Start epoch 738
+2024-03-15 03:59:07,760 INFO [train.py:527] (1/6) Epoch 738, batch 12, global_batch_idx: 91400, batch size: 42, loss[discriminator_loss=2.654, discriminator_real_loss=1.407, discriminator_fake_loss=1.247, generator_loss=28.44, generator_mel_loss=17.99, generator_kl_loss=1.702, generator_dur_loss=1.674, generator_adv_loss=1.817, generator_feat_match_loss=5.26, over 42.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=28.71, generator_mel_loss=17.93, generator_kl_loss=1.456, generator_dur_loss=1.744, generator_adv_loss=2.006, generator_feat_match_loss=5.564, over 749.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 03:59:07,763 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 03:59:15,746 INFO [train.py:591] (1/6) Epoch 738, validation: discriminator_loss=2.79, discriminator_real_loss=1.33, discriminator_fake_loss=1.461, generator_loss=27.54, generator_mel_loss=18.02, generator_kl_loss=1.271, generator_dur_loss=1.801, generator_adv_loss=1.711, generator_feat_match_loss=4.737, over 100.00 samples.
+2024-03-15 03:59:15,747 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 04:01:31,921 INFO [train.py:527] (1/6) Epoch 738, batch 62, global_batch_idx: 91450, batch size: 96, loss[discriminator_loss=2.663, discriminator_real_loss=1.378, discriminator_fake_loss=1.284, generator_loss=29.94, generator_mel_loss=18.12, generator_kl_loss=1.471, generator_dur_loss=1.852, generator_adv_loss=2.015, generator_feat_match_loss=6.485, over 96.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.355, discriminator_fake_loss=1.335, generator_loss=28.97, generator_mel_loss=17.95, generator_kl_loss=1.447, generator_dur_loss=1.727, generator_adv_loss=1.992, generator_feat_match_loss=5.86, over 3488.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:03:50,069 INFO [train.py:527] (1/6) Epoch 738, batch 112, global_batch_idx: 91500, batch size: 36, loss[discriminator_loss=2.681, discriminator_real_loss=1.373, discriminator_fake_loss=1.308, generator_loss=28.95, generator_mel_loss=18.07, generator_kl_loss=1.567, generator_dur_loss=1.637, generator_adv_loss=1.951, generator_feat_match_loss=5.72, over 36.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.36, discriminator_fake_loss=1.333, generator_loss=28.86, generator_mel_loss=17.92, generator_kl_loss=1.427, generator_dur_loss=1.734, generator_adv_loss=1.991, generator_feat_match_loss=5.784, over 6451.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:04:25,178 INFO [train.py:919] (1/6) Start epoch 739
+2024-03-15 04:06:35,959 INFO [train.py:527] (1/6) Epoch 739, batch 38, global_batch_idx: 91550, batch size: 39, loss[discriminator_loss=2.715, discriminator_real_loss=1.382, discriminator_fake_loss=1.333, generator_loss=28.06, generator_mel_loss=17.8, generator_kl_loss=1.564, generator_dur_loss=1.672, generator_adv_loss=1.934, generator_feat_match_loss=5.087, over 39.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.354, discriminator_fake_loss=1.339, generator_loss=28.71, generator_mel_loss=17.89, generator_kl_loss=1.453, generator_dur_loss=1.728, generator_adv_loss=1.971, generator_feat_match_loss=5.667, over 2110.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:08:56,300 INFO [train.py:527] (1/6) Epoch 739, batch 88, global_batch_idx: 91600, batch size: 53, loss[discriminator_loss=2.73, discriminator_real_loss=1.267, discriminator_fake_loss=1.462, generator_loss=28.88, generator_mel_loss=18.11, generator_kl_loss=1.551, generator_dur_loss=1.68, generator_adv_loss=2.034, generator_feat_match_loss=5.507, over 53.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.356, discriminator_fake_loss=1.334, generator_loss=28.69, generator_mel_loss=17.87, generator_kl_loss=1.449, generator_dur_loss=1.726, generator_adv_loss=1.977, generator_feat_match_loss=5.663, over 4767.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:08:56,301 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 04:09:05,236 INFO [train.py:591] (1/6) Epoch 739, validation: discriminator_loss=2.733, discriminator_real_loss=1.412, discriminator_fake_loss=1.321, generator_loss=28.2, generator_mel_loss=17.99, generator_kl_loss=1.303, generator_dur_loss=1.802, generator_adv_loss=1.993, generator_feat_match_loss=5.106, over 100.00 samples.
+2024-03-15 04:09:05,237 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 04:10:43,537 INFO [train.py:919] (1/6) Start epoch 740
+2024-03-15 04:11:45,761 INFO [train.py:527] (1/6) Epoch 740, batch 14, global_batch_idx: 91650, batch size: 97, loss[discriminator_loss=2.731, discriminator_real_loss=1.432, discriminator_fake_loss=1.299, generator_loss=28.53, generator_mel_loss=17.59, generator_kl_loss=1.311, generator_dur_loss=1.863, generator_adv_loss=2.021, generator_feat_match_loss=5.744, over 97.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.338, discriminator_fake_loss=1.342, generator_loss=29.08, generator_mel_loss=17.98, generator_kl_loss=1.387, generator_dur_loss=1.744, generator_adv_loss=2.012, generator_feat_match_loss=5.954, over 897.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:14:05,740 INFO [train.py:527] (1/6) Epoch 740, batch 64, global_batch_idx: 91700, batch size: 62, loss[discriminator_loss=2.764, discriminator_real_loss=1.315, discriminator_fake_loss=1.449, generator_loss=28.57, generator_mel_loss=17.96, generator_kl_loss=1.496, generator_dur_loss=1.735, generator_adv_loss=1.832, generator_feat_match_loss=5.554, over 62.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.356, discriminator_fake_loss=1.333, generator_loss=28.87, generator_mel_loss=17.91, generator_kl_loss=1.417, generator_dur_loss=1.735, generator_adv_loss=2.001, generator_feat_match_loss=5.812, over 3780.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:16:23,879 INFO [train.py:527] (1/6) Epoch 740, batch 114, global_batch_idx: 91750, batch size: 16, loss[discriminator_loss=2.737, discriminator_real_loss=1.371, discriminator_fake_loss=1.366, generator_loss=28.68, generator_mel_loss=18.27, generator_kl_loss=1.812, generator_dur_loss=1.554, generator_adv_loss=1.964, generator_feat_match_loss=5.075, over 16.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.363, discriminator_fake_loss=1.326, generator_loss=28.79, generator_mel_loss=17.88, generator_kl_loss=1.42, generator_dur_loss=1.747, generator_adv_loss=1.996, generator_feat_match_loss=5.751, over 6795.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:16:51,215 INFO [train.py:919] (1/6) Start epoch 741
+2024-03-15 04:19:12,539 INFO [train.py:527] (1/6) Epoch 741, batch 40, global_batch_idx: 91800, batch size: 80, loss[discriminator_loss=2.74, discriminator_real_loss=1.345, discriminator_fake_loss=1.395, generator_loss=28.63, generator_mel_loss=18.09, generator_kl_loss=1.382, generator_dur_loss=1.792, generator_adv_loss=2.145, generator_feat_match_loss=5.216, over 80.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.352, discriminator_fake_loss=1.332, generator_loss=28.86, generator_mel_loss=17.93, generator_kl_loss=1.413, generator_dur_loss=1.752, generator_adv_loss=1.991, generator_feat_match_loss=5.773, over 2382.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:19:12,541 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 04:19:20,741 INFO [train.py:591] (1/6) Epoch 741, validation: discriminator_loss=2.737, discriminator_real_loss=1.545, discriminator_fake_loss=1.191, generator_loss=28.37, generator_mel_loss=18.29, generator_kl_loss=1.231, generator_dur_loss=1.814, generator_adv_loss=2.147, generator_feat_match_loss=4.895, over 100.00 samples.
+2024-03-15 04:19:20,741 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 04:21:40,812 INFO [train.py:527] (1/6) Epoch 741, batch 90, global_batch_idx: 91850, batch size: 39, loss[discriminator_loss=2.689, discriminator_real_loss=1.435, discriminator_fake_loss=1.254, generator_loss=28.58, generator_mel_loss=17.72, generator_kl_loss=1.723, generator_dur_loss=1.701, generator_adv_loss=1.931, generator_feat_match_loss=5.512, over 39.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.8, generator_mel_loss=17.86, generator_kl_loss=1.432, generator_dur_loss=1.742, generator_adv_loss=1.992, generator_feat_match_loss=5.767, over 5167.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:23:14,313 INFO [train.py:919] (1/6) Start epoch 742
+2024-03-15 04:24:23,308 INFO [train.py:527] (1/6) Epoch 742, batch 16, global_batch_idx: 91900, batch size: 68, loss[discriminator_loss=2.647, discriminator_real_loss=1.36, discriminator_fake_loss=1.286, generator_loss=28.77, generator_mel_loss=17.55, generator_kl_loss=1.489, generator_dur_loss=1.736, generator_adv_loss=2.118, generator_feat_match_loss=5.873, over 68.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.358, discriminator_fake_loss=1.315, generator_loss=29.09, generator_mel_loss=17.98, generator_kl_loss=1.438, generator_dur_loss=1.727, generator_adv_loss=2.032, generator_feat_match_loss=5.91, over 999.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:26:43,226 INFO [train.py:527] (1/6) Epoch 742, batch 66, global_batch_idx: 91950, batch size: 59, loss[discriminator_loss=2.741, discriminator_real_loss=1.454, discriminator_fake_loss=1.287, generator_loss=28.15, generator_mel_loss=17.75, generator_kl_loss=1.36, generator_dur_loss=1.753, generator_adv_loss=1.933, generator_feat_match_loss=5.345, over 59.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.366, discriminator_fake_loss=1.323, generator_loss=28.8, generator_mel_loss=17.93, generator_kl_loss=1.421, generator_dur_loss=1.741, generator_adv_loss=1.987, generator_feat_match_loss=5.713, over 3900.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:29:01,501 INFO [train.py:527] (1/6) Epoch 742, batch 116, global_batch_idx: 92000, batch size: 58, loss[discriminator_loss=2.682, discriminator_real_loss=1.348, discriminator_fake_loss=1.334, generator_loss=28.56, generator_mel_loss=17.87, generator_kl_loss=1.448, generator_dur_loss=1.702, generator_adv_loss=1.946, generator_feat_match_loss=5.594, over 58.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.361, discriminator_fake_loss=1.327, generator_loss=28.85, generator_mel_loss=17.96, generator_kl_loss=1.417, generator_dur_loss=1.741, generator_adv_loss=1.987, generator_feat_match_loss=5.752, over 6679.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:29:01,502 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 04:29:10,224 INFO [train.py:591] (1/6) Epoch 742, validation: discriminator_loss=2.768, discriminator_real_loss=1.436, discriminator_fake_loss=1.332, generator_loss=26.91, generator_mel_loss=18.07, generator_kl_loss=1.166, generator_dur_loss=1.816, generator_adv_loss=1.841, generator_feat_match_loss=4.017, over 100.00 samples.
+2024-03-15 04:29:10,225 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 04:29:30,373 INFO [train.py:919] (1/6) Start epoch 743
+2024-03-15 04:31:51,628 INFO [train.py:527] (1/6) Epoch 743, batch 42, global_batch_idx: 92050, batch size: 44, loss[discriminator_loss=2.618, discriminator_real_loss=1.343, discriminator_fake_loss=1.275, generator_loss=29.74, generator_mel_loss=18.27, generator_kl_loss=1.504, generator_dur_loss=1.669, generator_adv_loss=2.08, generator_feat_match_loss=6.222, over 44.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.362, discriminator_fake_loss=1.327, generator_loss=28.77, generator_mel_loss=17.92, generator_kl_loss=1.4, generator_dur_loss=1.721, generator_adv_loss=1.976, generator_feat_match_loss=5.756, over 2218.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:34:13,568 INFO [train.py:527] (1/6) Epoch 743, batch 92, global_batch_idx: 92100, batch size: 47, loss[discriminator_loss=2.687, discriminator_real_loss=1.353, discriminator_fake_loss=1.334, generator_loss=30.01, generator_mel_loss=18.39, generator_kl_loss=1.463, generator_dur_loss=1.669, generator_adv_loss=2.046, generator_feat_match_loss=6.437, over 47.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.361, discriminator_fake_loss=1.329, generator_loss=28.84, generator_mel_loss=17.93, generator_kl_loss=1.396, generator_dur_loss=1.733, generator_adv_loss=1.987, generator_feat_match_loss=5.79, over 5129.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:35:40,200 INFO [train.py:919] (1/6) Start epoch 744
+2024-03-15 04:36:54,217 INFO [train.py:527] (1/6) Epoch 744, batch 18, global_batch_idx: 92150, batch size: 39, loss[discriminator_loss=2.703, discriminator_real_loss=1.357, discriminator_fake_loss=1.346, generator_loss=29.69, generator_mel_loss=18.27, generator_kl_loss=1.557, generator_dur_loss=1.698, generator_adv_loss=2.108, generator_feat_match_loss=6.062, over 39.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.366, discriminator_fake_loss=1.326, generator_loss=28.81, generator_mel_loss=17.86, generator_kl_loss=1.403, generator_dur_loss=1.752, generator_adv_loss=2.003, generator_feat_match_loss=5.793, over 1072.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:39:13,669 INFO [train.py:527] (1/6) Epoch 744, batch 68, global_batch_idx: 92200, batch size: 74, loss[discriminator_loss=2.634, discriminator_real_loss=1.363, discriminator_fake_loss=1.27, generator_loss=29.18, generator_mel_loss=17.85, generator_kl_loss=1.367, generator_dur_loss=1.797, generator_adv_loss=2.117, generator_feat_match_loss=6.046, over 74.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.364, discriminator_fake_loss=1.318, generator_loss=28.93, generator_mel_loss=17.91, generator_kl_loss=1.412, generator_dur_loss=1.753, generator_adv_loss=2.003, generator_feat_match_loss=5.844, over 4133.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:39:13,671 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 04:39:22,314 INFO [train.py:591] (1/6) Epoch 744, validation: discriminator_loss=2.756, discriminator_real_loss=1.355, discriminator_fake_loss=1.401, generator_loss=28.36, generator_mel_loss=18.23, generator_kl_loss=1.309, generator_dur_loss=1.822, generator_adv_loss=1.904, generator_feat_match_loss=5.097, over 100.00 samples.
+2024-03-15 04:39:22,315 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 04:41:40,602 INFO [train.py:527] (1/6) Epoch 744, batch 118, global_batch_idx: 92250, batch size: 68, loss[discriminator_loss=2.733, discriminator_real_loss=1.398, discriminator_fake_loss=1.335, generator_loss=29.58, generator_mel_loss=18.22, generator_kl_loss=1.559, generator_dur_loss=1.765, generator_adv_loss=1.962, generator_feat_match_loss=6.074, over 68.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.88, generator_mel_loss=17.87, generator_kl_loss=1.416, generator_dur_loss=1.759, generator_adv_loss=1.996, generator_feat_match_loss=5.842, over 7206.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:41:54,734 INFO [train.py:919] (1/6) Start epoch 745
+2024-03-15 04:44:22,387 INFO [train.py:527] (1/6) Epoch 745, batch 44, global_batch_idx: 92300, batch size: 31, loss[discriminator_loss=2.757, discriminator_real_loss=1.401, discriminator_fake_loss=1.355, generator_loss=27.1, generator_mel_loss=17.61, generator_kl_loss=1.466, generator_dur_loss=1.672, generator_adv_loss=1.995, generator_feat_match_loss=4.355, over 31.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.36, discriminator_fake_loss=1.338, generator_loss=28.8, generator_mel_loss=17.92, generator_kl_loss=1.401, generator_dur_loss=1.753, generator_adv_loss=1.976, generator_feat_match_loss=5.748, over 2607.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:46:40,585 INFO [train.py:527] (1/6) Epoch 745, batch 94, global_batch_idx: 92350, batch size: 55, loss[discriminator_loss=2.737, discriminator_real_loss=1.547, discriminator_fake_loss=1.19, generator_loss=29.44, generator_mel_loss=17.99, generator_kl_loss=1.592, generator_dur_loss=1.726, generator_adv_loss=1.869, generator_feat_match_loss=6.26, over 55.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.357, discriminator_fake_loss=1.334, generator_loss=28.78, generator_mel_loss=17.88, generator_kl_loss=1.402, generator_dur_loss=1.752, generator_adv_loss=1.981, generator_feat_match_loss=5.77, over 5551.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:48:01,749 INFO [train.py:919] (1/6) Start epoch 746
+2024-03-15 04:49:24,199 INFO [train.py:527] (1/6) Epoch 746, batch 20, global_batch_idx: 92400, batch size: 66, loss[discriminator_loss=2.528, discriminator_real_loss=1.258, discriminator_fake_loss=1.27, generator_loss=29.54, generator_mel_loss=17.69, generator_kl_loss=1.324, generator_dur_loss=1.789, generator_adv_loss=2.147, generator_feat_match_loss=6.594, over 66.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.345, discriminator_fake_loss=1.325, generator_loss=28.86, generator_mel_loss=17.91, generator_kl_loss=1.428, generator_dur_loss=1.756, generator_adv_loss=1.996, generator_feat_match_loss=5.778, over 1243.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:49:24,200 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 04:49:32,189 INFO [train.py:591] (1/6) Epoch 746, validation: discriminator_loss=2.636, discriminator_real_loss=1.282, discriminator_fake_loss=1.355, generator_loss=28.28, generator_mel_loss=18.31, generator_kl_loss=1.258, generator_dur_loss=1.822, generator_adv_loss=1.904, generator_feat_match_loss=4.993, over 100.00 samples.
+2024-03-15 04:49:32,190 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 04:51:51,656 INFO [train.py:527] (1/6) Epoch 746, batch 70, global_batch_idx: 92450, batch size: 13, loss[discriminator_loss=2.644, discriminator_real_loss=1.333, discriminator_fake_loss=1.31, generator_loss=32.22, generator_mel_loss=18.96, generator_kl_loss=1.779, generator_dur_loss=1.72, generator_adv_loss=2.147, generator_feat_match_loss=7.617, over 13.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.357, discriminator_fake_loss=1.318, generator_loss=28.98, generator_mel_loss=17.9, generator_kl_loss=1.435, generator_dur_loss=1.751, generator_adv_loss=2.021, generator_feat_match_loss=5.877, over 4221.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:54:05,625 INFO [train.py:527] (1/6) Epoch 746, batch 120, global_batch_idx: 92500, batch size: 17, loss[discriminator_loss=2.639, discriminator_real_loss=1.293, discriminator_fake_loss=1.346, generator_loss=32, generator_mel_loss=18.98, generator_kl_loss=1.777, generator_dur_loss=1.564, generator_adv_loss=2.203, generator_feat_match_loss=7.473, over 17.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=28.92, generator_mel_loss=17.88, generator_kl_loss=1.43, generator_dur_loss=1.745, generator_adv_loss=2.006, generator_feat_match_loss=5.853, over 7089.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:54:16,061 INFO [train.py:919] (1/6) Start epoch 747
+2024-03-15 04:56:48,757 INFO [train.py:527] (1/6) Epoch 747, batch 46, global_batch_idx: 92550, batch size: 96, loss[discriminator_loss=2.625, discriminator_real_loss=1.339, discriminator_fake_loss=1.286, generator_loss=28.98, generator_mel_loss=18.04, generator_kl_loss=1.365, generator_dur_loss=1.836, generator_adv_loss=2.081, generator_feat_match_loss=5.649, over 96.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.364, discriminator_fake_loss=1.333, generator_loss=28.79, generator_mel_loss=17.89, generator_kl_loss=1.417, generator_dur_loss=1.749, generator_adv_loss=1.985, generator_feat_match_loss=5.744, over 2830.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:59:03,928 INFO [train.py:527] (1/6) Epoch 747, batch 96, global_batch_idx: 92600, batch size: 96, loss[discriminator_loss=2.695, discriminator_real_loss=1.245, discriminator_fake_loss=1.45, generator_loss=29.28, generator_mel_loss=17.91, generator_kl_loss=1.275, generator_dur_loss=1.861, generator_adv_loss=2.076, generator_feat_match_loss=6.151, over 96.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.362, discriminator_fake_loss=1.33, generator_loss=28.76, generator_mel_loss=17.89, generator_kl_loss=1.398, generator_dur_loss=1.749, generator_adv_loss=1.99, generator_feat_match_loss=5.724, over 5769.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 04:59:03,930 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 04:59:13,110 INFO [train.py:591] (1/6) Epoch 747, validation: discriminator_loss=2.757, discriminator_real_loss=1.545, discriminator_fake_loss=1.213, generator_loss=27.3, generator_mel_loss=18.04, generator_kl_loss=1.274, generator_dur_loss=1.836, generator_adv_loss=2.056, generator_feat_match_loss=4.094, over 100.00 samples.
+2024-03-15 04:59:13,111 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 05:00:27,525 INFO [train.py:919] (1/6) Start epoch 748
+2024-03-15 05:01:51,832 INFO [train.py:527] (1/6) Epoch 748, batch 22, global_batch_idx: 92650, batch size: 59, loss[discriminator_loss=2.707, discriminator_real_loss=1.397, discriminator_fake_loss=1.31, generator_loss=28.66, generator_mel_loss=18.2, generator_kl_loss=1.419, generator_dur_loss=1.698, generator_adv_loss=2.101, generator_feat_match_loss=5.24, over 59.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=28.65, generator_mel_loss=17.93, generator_kl_loss=1.367, generator_dur_loss=1.747, generator_adv_loss=1.984, generator_feat_match_loss=5.622, over 1331.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:04:13,327 INFO [train.py:527] (1/6) Epoch 748, batch 72, global_batch_idx: 92700, batch size: 25, loss[discriminator_loss=2.728, discriminator_real_loss=1.384, discriminator_fake_loss=1.343, generator_loss=30.37, generator_mel_loss=18.09, generator_kl_loss=1.79, generator_dur_loss=1.564, generator_adv_loss=2.026, generator_feat_match_loss=6.896, over 25.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.351, discriminator_fake_loss=1.334, generator_loss=28.71, generator_mel_loss=17.87, generator_kl_loss=1.382, generator_dur_loss=1.743, generator_adv_loss=1.979, generator_feat_match_loss=5.734, over 4125.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:06:30,458 INFO [train.py:527] (1/6) Epoch 748, batch 122, global_batch_idx: 92750, batch size: 31, loss[discriminator_loss=2.727, discriminator_real_loss=1.406, discriminator_fake_loss=1.321, generator_loss=28.02, generator_mel_loss=17.85, generator_kl_loss=1.459, generator_dur_loss=1.638, generator_adv_loss=1.934, generator_feat_match_loss=5.138, over 31.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.354, discriminator_fake_loss=1.331, generator_loss=28.8, generator_mel_loss=17.89, generator_kl_loss=1.396, generator_dur_loss=1.749, generator_adv_loss=1.988, generator_feat_match_loss=5.776, over 7014.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:06:35,624 INFO [train.py:919] (1/6) Start epoch 749
+2024-03-15 05:09:10,684 INFO [train.py:527] (1/6) Epoch 749, batch 48, global_batch_idx: 92800, batch size: 58, loss[discriminator_loss=2.692, discriminator_real_loss=1.28, discriminator_fake_loss=1.413, generator_loss=29.11, generator_mel_loss=17.92, generator_kl_loss=1.592, generator_dur_loss=1.739, generator_adv_loss=1.913, generator_feat_match_loss=5.948, over 58.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.351, discriminator_fake_loss=1.326, generator_loss=28.81, generator_mel_loss=17.85, generator_kl_loss=1.405, generator_dur_loss=1.753, generator_adv_loss=1.995, generator_feat_match_loss=5.813, over 2791.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:09:10,685 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 05:09:18,731 INFO [train.py:591] (1/6) Epoch 749, validation: discriminator_loss=2.763, discriminator_real_loss=1.358, discriminator_fake_loss=1.406, generator_loss=27.93, generator_mel_loss=18.16, generator_kl_loss=1.235, generator_dur_loss=1.818, generator_adv_loss=1.757, generator_feat_match_loss=4.961, over 100.00 samples.
+2024-03-15 05:09:18,732 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 05:11:40,253 INFO [train.py:527] (1/6) Epoch 749, batch 98, global_batch_idx: 92850, batch size: 83, loss[discriminator_loss=2.706, discriminator_real_loss=1.355, discriminator_fake_loss=1.351, generator_loss=28.13, generator_mel_loss=17.68, generator_kl_loss=1.362, generator_dur_loss=1.829, generator_adv_loss=2.001, generator_feat_match_loss=5.261, over 83.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.72, generator_mel_loss=17.82, generator_kl_loss=1.393, generator_dur_loss=1.758, generator_adv_loss=1.998, generator_feat_match_loss=5.749, over 5695.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:12:52,802 INFO [train.py:919] (1/6) Start epoch 750
+2024-03-15 05:14:21,291 INFO [train.py:527] (1/6) Epoch 750, batch 24, global_batch_idx: 92900, batch size: 64, loss[discriminator_loss=2.631, discriminator_real_loss=1.323, discriminator_fake_loss=1.307, generator_loss=29.78, generator_mel_loss=18.13, generator_kl_loss=1.501, generator_dur_loss=1.744, generator_adv_loss=2.152, generator_feat_match_loss=6.255, over 64.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.353, discriminator_fake_loss=1.332, generator_loss=28.91, generator_mel_loss=17.91, generator_kl_loss=1.451, generator_dur_loss=1.746, generator_adv_loss=2, generator_feat_match_loss=5.796, over 1369.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:16:41,119 INFO [train.py:527] (1/6) Epoch 750, batch 74, global_batch_idx: 92950, batch size: 70, loss[discriminator_loss=2.665, discriminator_real_loss=1.362, discriminator_fake_loss=1.304, generator_loss=28.75, generator_mel_loss=18.05, generator_kl_loss=1.399, generator_dur_loss=1.787, generator_adv_loss=1.929, generator_feat_match_loss=5.592, over 70.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=28.86, generator_mel_loss=17.9, generator_kl_loss=1.429, generator_dur_loss=1.745, generator_adv_loss=1.994, generator_feat_match_loss=5.794, over 4177.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:18:58,605 INFO [train.py:919] (1/6) Start epoch 751
+2024-03-15 05:19:22,957 INFO [train.py:527] (1/6) Epoch 751, batch 0, global_batch_idx: 93000, batch size: 53, loss[discriminator_loss=2.716, discriminator_real_loss=1.361, discriminator_fake_loss=1.354, generator_loss=28.12, generator_mel_loss=17.93, generator_kl_loss=1.407, generator_dur_loss=1.686, generator_adv_loss=1.894, generator_feat_match_loss=5.201, over 53.00 samples.], tot_loss[discriminator_loss=2.716, discriminator_real_loss=1.361, discriminator_fake_loss=1.354, generator_loss=28.12, generator_mel_loss=17.93, generator_kl_loss=1.407, generator_dur_loss=1.686, generator_adv_loss=1.894, generator_feat_match_loss=5.201, over 53.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:19:22,959 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 05:19:30,713 INFO [train.py:591] (1/6) Epoch 751, validation: discriminator_loss=2.758, discriminator_real_loss=1.427, discriminator_fake_loss=1.331, generator_loss=27.35, generator_mel_loss=18.12, generator_kl_loss=1.288, generator_dur_loss=1.803, generator_adv_loss=1.841, generator_feat_match_loss=4.294, over 100.00 samples.
+2024-03-15 05:19:30,715 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 05:21:47,050 INFO [train.py:527] (1/6) Epoch 751, batch 50, global_batch_idx: 93050, batch size: 62, loss[discriminator_loss=2.722, discriminator_real_loss=1.523, discriminator_fake_loss=1.199, generator_loss=28.21, generator_mel_loss=17.77, generator_kl_loss=1.42, generator_dur_loss=1.741, generator_adv_loss=1.902, generator_feat_match_loss=5.385, over 62.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.361, discriminator_fake_loss=1.328, generator_loss=28.95, generator_mel_loss=17.92, generator_kl_loss=1.461, generator_dur_loss=1.732, generator_adv_loss=2.009, generator_feat_match_loss=5.831, over 2739.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:24:07,026 INFO [train.py:527] (1/6) Epoch 751, batch 100, global_batch_idx: 93100, batch size: 36, loss[discriminator_loss=2.716, discriminator_real_loss=1.385, discriminator_fake_loss=1.331, generator_loss=29.02, generator_mel_loss=18.19, generator_kl_loss=1.645, generator_dur_loss=1.704, generator_adv_loss=1.976, generator_feat_match_loss=5.501, over 36.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.33, generator_loss=28.76, generator_mel_loss=17.83, generator_kl_loss=1.426, generator_dur_loss=1.743, generator_adv_loss=1.999, generator_feat_match_loss=5.764, over 5831.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:25:11,323 INFO [train.py:919] (1/6) Start epoch 752
+2024-03-15 05:26:49,703 INFO [train.py:527] (1/6) Epoch 752, batch 26, global_batch_idx: 93150, batch size: 31, loss[discriminator_loss=2.679, discriminator_real_loss=1.228, discriminator_fake_loss=1.451, generator_loss=30.57, generator_mel_loss=18.6, generator_kl_loss=1.718, generator_dur_loss=1.567, generator_adv_loss=2.014, generator_feat_match_loss=6.677, over 31.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.74, generator_mel_loss=17.84, generator_kl_loss=1.426, generator_dur_loss=1.73, generator_adv_loss=1.989, generator_feat_match_loss=5.753, over 1530.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:29:10,395 INFO [train.py:527] (1/6) Epoch 752, batch 76, global_batch_idx: 93200, batch size: 96, loss[discriminator_loss=2.737, discriminator_real_loss=1.389, discriminator_fake_loss=1.348, generator_loss=27.54, generator_mel_loss=17.45, generator_kl_loss=1.213, generator_dur_loss=1.833, generator_adv_loss=1.939, generator_feat_match_loss=5.106, over 96.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.33, generator_loss=28.75, generator_mel_loss=17.83, generator_kl_loss=1.42, generator_dur_loss=1.735, generator_adv_loss=1.991, generator_feat_match_loss=5.777, over 4399.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:29:10,397 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 05:29:19,323 INFO [train.py:591] (1/6) Epoch 752, validation: discriminator_loss=2.762, discriminator_real_loss=1.426, discriminator_fake_loss=1.336, generator_loss=28.03, generator_mel_loss=18.4, generator_kl_loss=1.133, generator_dur_loss=1.79, generator_adv_loss=1.924, generator_feat_match_loss=4.788, over 100.00 samples.
+2024-03-15 05:29:19,324 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 05:31:27,951 INFO [train.py:919] (1/6) Start epoch 753
+2024-03-15 05:31:58,218 INFO [train.py:527] (1/6) Epoch 753, batch 2, global_batch_idx: 93250, batch size: 74, loss[discriminator_loss=2.714, discriminator_real_loss=1.334, discriminator_fake_loss=1.38, generator_loss=29, generator_mel_loss=18.1, generator_kl_loss=1.33, generator_dur_loss=1.755, generator_adv_loss=2.051, generator_feat_match_loss=5.764, over 74.00 samples.], tot_loss[discriminator_loss=2.729, discriminator_real_loss=1.357, discriminator_fake_loss=1.373, generator_loss=29.21, generator_mel_loss=18.12, generator_kl_loss=1.45, generator_dur_loss=1.772, generator_adv_loss=1.977, generator_feat_match_loss=5.898, over 225.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:34:17,853 INFO [train.py:527] (1/6) Epoch 753, batch 52, global_batch_idx: 93300, batch size: 72, loss[discriminator_loss=2.677, discriminator_real_loss=1.347, discriminator_fake_loss=1.33, generator_loss=28.66, generator_mel_loss=17.98, generator_kl_loss=1.176, generator_dur_loss=1.755, generator_adv_loss=1.832, generator_feat_match_loss=5.914, over 72.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.81, generator_mel_loss=17.85, generator_kl_loss=1.395, generator_dur_loss=1.73, generator_adv_loss=2, generator_feat_match_loss=5.837, over 3183.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:36:40,895 INFO [train.py:527] (1/6) Epoch 753, batch 102, global_batch_idx: 93350, batch size: 45, loss[discriminator_loss=2.618, discriminator_real_loss=1.305, discriminator_fake_loss=1.313, generator_loss=29.37, generator_mel_loss=18.42, generator_kl_loss=1.625, generator_dur_loss=1.639, generator_adv_loss=2.05, generator_feat_match_loss=5.638, over 45.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.362, discriminator_fake_loss=1.319, generator_loss=28.78, generator_mel_loss=17.85, generator_kl_loss=1.409, generator_dur_loss=1.729, generator_adv_loss=1.994, generator_feat_match_loss=5.797, over 6026.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:37:38,382 INFO [train.py:919] (1/6) Start epoch 754
+2024-03-15 05:39:17,156 INFO [train.py:527] (1/6) Epoch 754, batch 28, global_batch_idx: 93400, batch size: 47, loss[discriminator_loss=2.737, discriminator_real_loss=1.418, discriminator_fake_loss=1.319, generator_loss=29, generator_mel_loss=18.05, generator_kl_loss=1.646, generator_dur_loss=1.658, generator_adv_loss=1.913, generator_feat_match_loss=5.738, over 47.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=29.01, generator_mel_loss=17.95, generator_kl_loss=1.421, generator_dur_loss=1.734, generator_adv_loss=1.99, generator_feat_match_loss=5.913, over 1678.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:39:17,158 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 05:39:25,178 INFO [train.py:591] (1/6) Epoch 754, validation: discriminator_loss=2.741, discriminator_real_loss=1.343, discriminator_fake_loss=1.398, generator_loss=27.34, generator_mel_loss=18.17, generator_kl_loss=1.323, generator_dur_loss=1.803, generator_adv_loss=1.826, generator_feat_match_loss=4.214, over 100.00 samples.
+2024-03-15 05:39:25,179 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 05:41:46,171 INFO [train.py:527] (1/6) Epoch 754, batch 78, global_batch_idx: 93450, batch size: 45, loss[discriminator_loss=2.858, discriminator_real_loss=1.494, discriminator_fake_loss=1.364, generator_loss=28.35, generator_mel_loss=17.92, generator_kl_loss=1.599, generator_dur_loss=1.651, generator_adv_loss=1.944, generator_feat_match_loss=5.239, over 45.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.363, discriminator_fake_loss=1.322, generator_loss=28.89, generator_mel_loss=17.88, generator_kl_loss=1.405, generator_dur_loss=1.743, generator_adv_loss=2.033, generator_feat_match_loss=5.83, over 4774.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:43:50,651 INFO [train.py:919] (1/6) Start epoch 755
+2024-03-15 05:44:26,470 INFO [train.py:527] (1/6) Epoch 755, batch 4, global_batch_idx: 93500, batch size: 74, loss[discriminator_loss=2.693, discriminator_real_loss=1.42, discriminator_fake_loss=1.273, generator_loss=28.4, generator_mel_loss=17.82, generator_kl_loss=1.406, generator_dur_loss=1.733, generator_adv_loss=1.9, generator_feat_match_loss=5.546, over 74.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.384, discriminator_fake_loss=1.314, generator_loss=28.64, generator_mel_loss=17.88, generator_kl_loss=1.411, generator_dur_loss=1.69, generator_adv_loss=1.993, generator_feat_match_loss=5.668, over 296.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:46:47,439 INFO [train.py:527] (1/6) Epoch 755, batch 54, global_batch_idx: 93550, batch size: 88, loss[discriminator_loss=2.655, discriminator_real_loss=1.363, discriminator_fake_loss=1.293, generator_loss=28.04, generator_mel_loss=17.53, generator_kl_loss=1.355, generator_dur_loss=1.797, generator_adv_loss=2.003, generator_feat_match_loss=5.353, over 88.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.74, generator_mel_loss=17.86, generator_kl_loss=1.42, generator_dur_loss=1.711, generator_adv_loss=1.992, generator_feat_match_loss=5.761, over 3115.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:49:04,328 INFO [train.py:527] (1/6) Epoch 755, batch 104, global_batch_idx: 93600, batch size: 83, loss[discriminator_loss=2.666, discriminator_real_loss=1.375, discriminator_fake_loss=1.291, generator_loss=29.48, generator_mel_loss=17.75, generator_kl_loss=1.366, generator_dur_loss=1.842, generator_adv_loss=2.054, generator_feat_match_loss=6.47, over 83.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=28.77, generator_mel_loss=17.88, generator_kl_loss=1.426, generator_dur_loss=1.725, generator_adv_loss=1.987, generator_feat_match_loss=5.751, over 5900.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:49:04,330 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 05:49:13,245 INFO [train.py:591] (1/6) Epoch 755, validation: discriminator_loss=2.697, discriminator_real_loss=1.382, discriminator_fake_loss=1.315, generator_loss=28.98, generator_mel_loss=18.98, generator_kl_loss=1.299, generator_dur_loss=1.78, generator_adv_loss=1.937, generator_feat_match_loss=4.986, over 100.00 samples.
+2024-03-15 05:49:13,246 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 05:50:07,826 INFO [train.py:919] (1/6) Start epoch 756
+2024-03-15 05:51:56,054 INFO [train.py:527] (1/6) Epoch 756, batch 30, global_batch_idx: 93650, batch size: 50, loss[discriminator_loss=2.687, discriminator_real_loss=1.371, discriminator_fake_loss=1.316, generator_loss=28.74, generator_mel_loss=17.9, generator_kl_loss=1.355, generator_dur_loss=1.636, generator_adv_loss=2.056, generator_feat_match_loss=5.794, over 50.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.373, discriminator_fake_loss=1.308, generator_loss=28.74, generator_mel_loss=17.85, generator_kl_loss=1.411, generator_dur_loss=1.724, generator_adv_loss=2.005, generator_feat_match_loss=5.757, over 1827.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:54:15,409 INFO [train.py:527] (1/6) Epoch 756, batch 80, global_batch_idx: 93700, batch size: 52, loss[discriminator_loss=2.742, discriminator_real_loss=1.462, discriminator_fake_loss=1.28, generator_loss=28.36, generator_mel_loss=17.97, generator_kl_loss=1.458, generator_dur_loss=1.651, generator_adv_loss=1.949, generator_feat_match_loss=5.326, over 52.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.363, discriminator_fake_loss=1.324, generator_loss=28.84, generator_mel_loss=17.91, generator_kl_loss=1.442, generator_dur_loss=1.715, generator_adv_loss=1.989, generator_feat_match_loss=5.79, over 4541.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:56:12,959 INFO [train.py:919] (1/6) Start epoch 757
+2024-03-15 05:56:54,344 INFO [train.py:527] (1/6) Epoch 757, batch 6, global_batch_idx: 93750, batch size: 47, loss[discriminator_loss=2.655, discriminator_real_loss=1.35, discriminator_fake_loss=1.305, generator_loss=28.57, generator_mel_loss=17.63, generator_kl_loss=1.447, generator_dur_loss=1.677, generator_adv_loss=1.952, generator_feat_match_loss=5.865, over 47.00 samples.], tot_loss[discriminator_loss=2.653, discriminator_real_loss=1.35, discriminator_fake_loss=1.304, generator_loss=28.67, generator_mel_loss=17.7, generator_kl_loss=1.406, generator_dur_loss=1.743, generator_adv_loss=2.013, generator_feat_match_loss=5.802, over 429.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:59:17,023 INFO [train.py:527] (1/6) Epoch 757, batch 56, global_batch_idx: 93800, batch size: 47, loss[discriminator_loss=2.754, discriminator_real_loss=1.485, discriminator_fake_loss=1.269, generator_loss=27.97, generator_mel_loss=17.5, generator_kl_loss=1.5, generator_dur_loss=1.71, generator_adv_loss=1.93, generator_feat_match_loss=5.338, over 47.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=28.96, generator_mel_loss=17.89, generator_kl_loss=1.432, generator_dur_loss=1.725, generator_adv_loss=2.007, generator_feat_match_loss=5.907, over 3165.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 05:59:17,025 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 05:59:25,148 INFO [train.py:591] (1/6) Epoch 757, validation: discriminator_loss=2.754, discriminator_real_loss=1.402, discriminator_fake_loss=1.353, generator_loss=28.61, generator_mel_loss=18.39, generator_kl_loss=1.35, generator_dur_loss=1.799, generator_adv_loss=1.918, generator_feat_match_loss=5.149, over 100.00 samples.
+2024-03-15 05:59:25,149 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 06:01:45,103 INFO [train.py:527] (1/6) Epoch 757, batch 106, global_batch_idx: 93850, batch size: 83, loss[discriminator_loss=2.727, discriminator_real_loss=1.372, discriminator_fake_loss=1.355, generator_loss=28.59, generator_mel_loss=17.74, generator_kl_loss=1.463, generator_dur_loss=1.773, generator_adv_loss=1.912, generator_feat_match_loss=5.7, over 83.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=28.88, generator_mel_loss=17.84, generator_kl_loss=1.434, generator_dur_loss=1.725, generator_adv_loss=2.015, generator_feat_match_loss=5.866, over 5974.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:02:28,982 INFO [train.py:919] (1/6) Start epoch 758
+2024-03-15 06:04:21,188 INFO [train.py:527] (1/6) Epoch 758, batch 32, global_batch_idx: 93900, batch size: 39, loss[discriminator_loss=2.695, discriminator_real_loss=1.304, discriminator_fake_loss=1.391, generator_loss=31.28, generator_mel_loss=19, generator_kl_loss=1.621, generator_dur_loss=1.638, generator_adv_loss=1.991, generator_feat_match_loss=7.027, over 39.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.365, discriminator_fake_loss=1.322, generator_loss=28.76, generator_mel_loss=17.87, generator_kl_loss=1.433, generator_dur_loss=1.721, generator_adv_loss=2.011, generator_feat_match_loss=5.73, over 1847.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:06:40,712 INFO [train.py:527] (1/6) Epoch 758, batch 82, global_batch_idx: 93950, batch size: 66, loss[discriminator_loss=2.678, discriminator_real_loss=1.395, discriminator_fake_loss=1.283, generator_loss=29.11, generator_mel_loss=18.09, generator_kl_loss=1.319, generator_dur_loss=1.779, generator_adv_loss=1.923, generator_feat_match_loss=5.998, over 66.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.367, discriminator_fake_loss=1.327, generator_loss=28.78, generator_mel_loss=17.87, generator_kl_loss=1.415, generator_dur_loss=1.727, generator_adv_loss=1.997, generator_feat_match_loss=5.769, over 4754.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:08:35,802 INFO [train.py:919] (1/6) Start epoch 759
+2024-03-15 06:09:22,008 INFO [train.py:527] (1/6) Epoch 759, batch 8, global_batch_idx: 94000, batch size: 31, loss[discriminator_loss=2.811, discriminator_real_loss=1.501, discriminator_fake_loss=1.31, generator_loss=28.26, generator_mel_loss=17.91, generator_kl_loss=1.566, generator_dur_loss=1.659, generator_adv_loss=2.109, generator_feat_match_loss=5.021, over 31.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.367, discriminator_fake_loss=1.321, generator_loss=28.52, generator_mel_loss=17.76, generator_kl_loss=1.404, generator_dur_loss=1.714, generator_adv_loss=2.014, generator_feat_match_loss=5.627, over 486.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:09:22,011 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 06:09:29,728 INFO [train.py:591] (1/6) Epoch 759, validation: discriminator_loss=2.756, discriminator_real_loss=1.459, discriminator_fake_loss=1.296, generator_loss=28.04, generator_mel_loss=18.45, generator_kl_loss=1.287, generator_dur_loss=1.769, generator_adv_loss=1.976, generator_feat_match_loss=4.554, over 100.00 samples.
+2024-03-15 06:09:29,729 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 06:11:48,929 INFO [train.py:527] (1/6) Epoch 759, batch 58, global_batch_idx: 94050, batch size: 55, loss[discriminator_loss=2.656, discriminator_real_loss=1.35, discriminator_fake_loss=1.306, generator_loss=28.54, generator_mel_loss=17.7, generator_kl_loss=1.325, generator_dur_loss=1.655, generator_adv_loss=2.044, generator_feat_match_loss=5.813, over 55.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.362, discriminator_fake_loss=1.329, generator_loss=28.8, generator_mel_loss=17.88, generator_kl_loss=1.405, generator_dur_loss=1.728, generator_adv_loss=2.001, generator_feat_match_loss=5.779, over 3485.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:14:05,409 INFO [train.py:527] (1/6) Epoch 759, batch 108, global_batch_idx: 94100, batch size: 47, loss[discriminator_loss=2.737, discriminator_real_loss=1.418, discriminator_fake_loss=1.32, generator_loss=28.37, generator_mel_loss=17.93, generator_kl_loss=1.504, generator_dur_loss=1.681, generator_adv_loss=1.821, generator_feat_match_loss=5.436, over 47.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=28.88, generator_mel_loss=17.9, generator_kl_loss=1.418, generator_dur_loss=1.721, generator_adv_loss=2.001, generator_feat_match_loss=5.832, over 6185.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:14:46,997 INFO [train.py:919] (1/6) Start epoch 760
+2024-03-15 06:16:44,657 INFO [train.py:527] (1/6) Epoch 760, batch 34, global_batch_idx: 94150, batch size: 68, loss[discriminator_loss=2.704, discriminator_real_loss=1.31, discriminator_fake_loss=1.394, generator_loss=29.63, generator_mel_loss=18.41, generator_kl_loss=1.304, generator_dur_loss=1.791, generator_adv_loss=2.121, generator_feat_match_loss=6.007, over 68.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.354, discriminator_fake_loss=1.339, generator_loss=28.93, generator_mel_loss=17.96, generator_kl_loss=1.416, generator_dur_loss=1.739, generator_adv_loss=1.985, generator_feat_match_loss=5.833, over 1973.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:19:03,098 INFO [train.py:527] (1/6) Epoch 760, batch 84, global_batch_idx: 94200, batch size: 62, loss[discriminator_loss=2.605, discriminator_real_loss=1.291, discriminator_fake_loss=1.315, generator_loss=29.28, generator_mel_loss=17.71, generator_kl_loss=1.33, generator_dur_loss=1.729, generator_adv_loss=2.024, generator_feat_match_loss=6.481, over 62.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.357, discriminator_fake_loss=1.331, generator_loss=28.8, generator_mel_loss=17.9, generator_kl_loss=1.418, generator_dur_loss=1.736, generator_adv_loss=1.983, generator_feat_match_loss=5.766, over 4741.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:19:03,099 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 06:19:12,124 INFO [train.py:591] (1/6) Epoch 760, validation: discriminator_loss=2.739, discriminator_real_loss=1.356, discriminator_fake_loss=1.383, generator_loss=27.28, generator_mel_loss=17.93, generator_kl_loss=1.275, generator_dur_loss=1.789, generator_adv_loss=1.87, generator_feat_match_loss=4.417, over 100.00 samples.
+2024-03-15 06:19:12,125 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 06:21:03,363 INFO [train.py:919] (1/6) Start epoch 761
+2024-03-15 06:22:00,521 INFO [train.py:527] (1/6) Epoch 761, batch 10, global_batch_idx: 94250, batch size: 45, loss[discriminator_loss=2.705, discriminator_real_loss=1.371, discriminator_fake_loss=1.333, generator_loss=28.77, generator_mel_loss=17.9, generator_kl_loss=1.375, generator_dur_loss=1.642, generator_adv_loss=1.942, generator_feat_match_loss=5.905, over 45.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.35, discriminator_fake_loss=1.355, generator_loss=28.94, generator_mel_loss=17.97, generator_kl_loss=1.395, generator_dur_loss=1.75, generator_adv_loss=1.971, generator_feat_match_loss=5.852, over 619.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:24:17,511 INFO [train.py:527] (1/6) Epoch 761, batch 60, global_batch_idx: 94300, batch size: 66, loss[discriminator_loss=2.621, discriminator_real_loss=1.387, discriminator_fake_loss=1.234, generator_loss=28.9, generator_mel_loss=17.43, generator_kl_loss=1.332, generator_dur_loss=1.821, generator_adv_loss=2.114, generator_feat_match_loss=6.196, over 66.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.344, discriminator_fake_loss=1.332, generator_loss=29.01, generator_mel_loss=17.93, generator_kl_loss=1.421, generator_dur_loss=1.755, generator_adv_loss=1.993, generator_feat_match_loss=5.906, over 3502.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:26:33,085 INFO [train.py:527] (1/6) Epoch 761, batch 110, global_batch_idx: 94350, batch size: 55, loss[discriminator_loss=2.734, discriminator_real_loss=1.415, discriminator_fake_loss=1.319, generator_loss=27.75, generator_mel_loss=17.73, generator_kl_loss=1.45, generator_dur_loss=1.729, generator_adv_loss=1.978, generator_feat_match_loss=4.855, over 55.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.352, discriminator_fake_loss=1.329, generator_loss=28.97, generator_mel_loss=17.93, generator_kl_loss=1.419, generator_dur_loss=1.751, generator_adv_loss=1.996, generator_feat_match_loss=5.87, over 6253.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:27:12,296 INFO [train.py:919] (1/6) Start epoch 762
+2024-03-15 06:29:16,093 INFO [train.py:527] (1/6) Epoch 762, batch 36, global_batch_idx: 94400, batch size: 83, loss[discriminator_loss=2.71, discriminator_real_loss=1.297, discriminator_fake_loss=1.413, generator_loss=28.31, generator_mel_loss=17.58, generator_kl_loss=1.393, generator_dur_loss=1.839, generator_adv_loss=1.895, generator_feat_match_loss=5.601, over 83.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.365, discriminator_fake_loss=1.318, generator_loss=29.02, generator_mel_loss=17.88, generator_kl_loss=1.442, generator_dur_loss=1.72, generator_adv_loss=2.01, generator_feat_match_loss=5.964, over 1898.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:29:16,095 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 06:29:24,052 INFO [train.py:591] (1/6) Epoch 762, validation: discriminator_loss=2.744, discriminator_real_loss=1.394, discriminator_fake_loss=1.35, generator_loss=27.27, generator_mel_loss=17.94, generator_kl_loss=1.246, generator_dur_loss=1.804, generator_adv_loss=1.849, generator_feat_match_loss=4.43, over 100.00 samples.
+2024-03-15 06:29:24,053 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 06:31:41,080 INFO [train.py:527] (1/6) Epoch 762, batch 86, global_batch_idx: 94450, batch size: 70, loss[discriminator_loss=2.705, discriminator_real_loss=1.411, discriminator_fake_loss=1.294, generator_loss=29.17, generator_mel_loss=17.64, generator_kl_loss=1.507, generator_dur_loss=1.807, generator_adv_loss=1.854, generator_feat_match_loss=6.356, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.355, discriminator_fake_loss=1.335, generator_loss=28.98, generator_mel_loss=17.92, generator_kl_loss=1.452, generator_dur_loss=1.735, generator_adv_loss=1.997, generator_feat_match_loss=5.877, over 4602.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:33:29,230 INFO [train.py:919] (1/6) Start epoch 763
+2024-03-15 06:34:26,578 INFO [train.py:527] (1/6) Epoch 763, batch 12, global_batch_idx: 94500, batch size: 47, loss[discriminator_loss=2.663, discriminator_real_loss=1.33, discriminator_fake_loss=1.333, generator_loss=29.28, generator_mel_loss=17.81, generator_kl_loss=1.574, generator_dur_loss=1.689, generator_adv_loss=2.093, generator_feat_match_loss=6.112, over 47.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.353, discriminator_fake_loss=1.333, generator_loss=29.3, generator_mel_loss=18.14, generator_kl_loss=1.423, generator_dur_loss=1.732, generator_adv_loss=1.985, generator_feat_match_loss=6.026, over 649.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:36:48,791 INFO [train.py:527] (1/6) Epoch 763, batch 62, global_batch_idx: 94550, batch size: 70, loss[discriminator_loss=2.718, discriminator_real_loss=1.388, discriminator_fake_loss=1.33, generator_loss=28.62, generator_mel_loss=17.88, generator_kl_loss=1.504, generator_dur_loss=1.833, generator_adv_loss=1.836, generator_feat_match_loss=5.563, over 70.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.353, discriminator_fake_loss=1.333, generator_loss=28.96, generator_mel_loss=17.97, generator_kl_loss=1.402, generator_dur_loss=1.75, generator_adv_loss=1.987, generator_feat_match_loss=5.854, over 3542.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:39:08,286 INFO [train.py:527] (1/6) Epoch 763, batch 112, global_batch_idx: 94600, batch size: 70, loss[discriminator_loss=2.704, discriminator_real_loss=1.405, discriminator_fake_loss=1.299, generator_loss=28.13, generator_mel_loss=17.64, generator_kl_loss=1.441, generator_dur_loss=1.784, generator_adv_loss=1.91, generator_feat_match_loss=5.358, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.36, discriminator_fake_loss=1.331, generator_loss=28.86, generator_mel_loss=17.91, generator_kl_loss=1.401, generator_dur_loss=1.751, generator_adv_loss=1.986, generator_feat_match_loss=5.805, over 6428.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:39:08,288 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 06:39:17,117 INFO [train.py:591] (1/6) Epoch 763, validation: discriminator_loss=2.725, discriminator_real_loss=1.361, discriminator_fake_loss=1.364, generator_loss=28.29, generator_mel_loss=18.46, generator_kl_loss=1.198, generator_dur_loss=1.825, generator_adv_loss=1.876, generator_feat_match_loss=4.932, over 100.00 samples.
+2024-03-15 06:39:17,118 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 06:39:50,544 INFO [train.py:919] (1/6) Start epoch 764
+2024-03-15 06:42:01,013 INFO [train.py:527] (1/6) Epoch 764, batch 38, global_batch_idx: 94650, batch size: 40, loss[discriminator_loss=2.663, discriminator_real_loss=1.398, discriminator_fake_loss=1.264, generator_loss=29.21, generator_mel_loss=17.67, generator_kl_loss=1.517, generator_dur_loss=1.71, generator_adv_loss=2.064, generator_feat_match_loss=6.253, over 40.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.375, discriminator_fake_loss=1.318, generator_loss=28.98, generator_mel_loss=17.95, generator_kl_loss=1.429, generator_dur_loss=1.758, generator_adv_loss=2.005, generator_feat_match_loss=5.846, over 2192.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:44:20,426 INFO [train.py:527] (1/6) Epoch 764, batch 88, global_batch_idx: 94700, batch size: 64, loss[discriminator_loss=2.664, discriminator_real_loss=1.313, discriminator_fake_loss=1.35, generator_loss=28.38, generator_mel_loss=17.82, generator_kl_loss=1.348, generator_dur_loss=1.776, generator_adv_loss=1.92, generator_feat_match_loss=5.515, over 64.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.365, discriminator_fake_loss=1.331, generator_loss=28.91, generator_mel_loss=17.95, generator_kl_loss=1.406, generator_dur_loss=1.758, generator_adv_loss=1.986, generator_feat_match_loss=5.817, over 5187.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:45:59,272 INFO [train.py:919] (1/6) Start epoch 765
+2024-03-15 06:47:02,944 INFO [train.py:527] (1/6) Epoch 765, batch 14, global_batch_idx: 94750, batch size: 88, loss[discriminator_loss=2.64, discriminator_real_loss=1.294, discriminator_fake_loss=1.347, generator_loss=29.36, generator_mel_loss=17.88, generator_kl_loss=1.481, generator_dur_loss=1.796, generator_adv_loss=2.045, generator_feat_match_loss=6.159, over 88.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.333, discriminator_fake_loss=1.339, generator_loss=28.96, generator_mel_loss=17.94, generator_kl_loss=1.389, generator_dur_loss=1.778, generator_adv_loss=2.003, generator_feat_match_loss=5.851, over 937.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:49:20,864 INFO [train.py:527] (1/6) Epoch 765, batch 64, global_batch_idx: 94800, batch size: 45, loss[discriminator_loss=2.71, discriminator_real_loss=1.329, discriminator_fake_loss=1.381, generator_loss=28.37, generator_mel_loss=17.68, generator_kl_loss=1.446, generator_dur_loss=1.676, generator_adv_loss=1.947, generator_feat_match_loss=5.617, over 45.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.35, discriminator_fake_loss=1.331, generator_loss=28.85, generator_mel_loss=17.91, generator_kl_loss=1.406, generator_dur_loss=1.752, generator_adv_loss=1.982, generator_feat_match_loss=5.798, over 3656.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:49:20,865 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 06:49:28,784 INFO [train.py:591] (1/6) Epoch 765, validation: discriminator_loss=2.703, discriminator_real_loss=1.356, discriminator_fake_loss=1.347, generator_loss=27.22, generator_mel_loss=18.13, generator_kl_loss=1.24, generator_dur_loss=1.814, generator_adv_loss=1.827, generator_feat_match_loss=4.21, over 100.00 samples.
+2024-03-15 06:49:28,785 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 06:51:46,880 INFO [train.py:527] (1/6) Epoch 765, batch 114, global_batch_idx: 94850, batch size: 52, loss[discriminator_loss=2.782, discriminator_real_loss=1.506, discriminator_fake_loss=1.276, generator_loss=28.18, generator_mel_loss=17.56, generator_kl_loss=1.446, generator_dur_loss=1.675, generator_adv_loss=1.839, generator_feat_match_loss=5.655, over 52.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.355, discriminator_fake_loss=1.326, generator_loss=28.93, generator_mel_loss=17.91, generator_kl_loss=1.419, generator_dur_loss=1.744, generator_adv_loss=1.999, generator_feat_match_loss=5.853, over 6354.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:52:16,195 INFO [train.py:919] (1/6) Start epoch 766
+2024-03-15 06:54:37,647 INFO [train.py:527] (1/6) Epoch 766, batch 40, global_batch_idx: 94900, batch size: 61, loss[discriminator_loss=2.623, discriminator_real_loss=1.312, discriminator_fake_loss=1.312, generator_loss=29.13, generator_mel_loss=17.93, generator_kl_loss=1.279, generator_dur_loss=1.767, generator_adv_loss=2.081, generator_feat_match_loss=6.069, over 61.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=28.85, generator_mel_loss=17.85, generator_kl_loss=1.398, generator_dur_loss=1.76, generator_adv_loss=1.996, generator_feat_match_loss=5.839, over 2444.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:56:57,337 INFO [train.py:527] (1/6) Epoch 766, batch 90, global_batch_idx: 94950, batch size: 44, loss[discriminator_loss=2.607, discriminator_real_loss=1.293, discriminator_fake_loss=1.314, generator_loss=29.94, generator_mel_loss=18.14, generator_kl_loss=1.388, generator_dur_loss=1.693, generator_adv_loss=1.996, generator_feat_match_loss=6.723, over 44.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.81, generator_mel_loss=17.85, generator_kl_loss=1.396, generator_dur_loss=1.757, generator_adv_loss=1.995, generator_feat_match_loss=5.817, over 5418.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:58:28,564 INFO [train.py:919] (1/6) Start epoch 767
+2024-03-15 06:59:38,295 INFO [train.py:527] (1/6) Epoch 767, batch 16, global_batch_idx: 95000, batch size: 52, loss[discriminator_loss=2.668, discriminator_real_loss=1.342, discriminator_fake_loss=1.325, generator_loss=29.11, generator_mel_loss=17.5, generator_kl_loss=1.582, generator_dur_loss=1.763, generator_adv_loss=1.959, generator_feat_match_loss=6.305, over 52.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.368, discriminator_fake_loss=1.33, generator_loss=28.82, generator_mel_loss=17.89, generator_kl_loss=1.427, generator_dur_loss=1.748, generator_adv_loss=1.963, generator_feat_match_loss=5.794, over 974.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 06:59:38,297 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 06:59:46,428 INFO [train.py:591] (1/6) Epoch 767, validation: discriminator_loss=2.728, discriminator_real_loss=1.395, discriminator_fake_loss=1.333, generator_loss=28.28, generator_mel_loss=18.79, generator_kl_loss=1.264, generator_dur_loss=1.824, generator_adv_loss=1.876, generator_feat_match_loss=4.527, over 100.00 samples.
+2024-03-15 06:59:46,429 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 07:02:10,596 INFO [train.py:527] (1/6) Epoch 767, batch 66, global_batch_idx: 95050, batch size: 80, loss[discriminator_loss=2.679, discriminator_real_loss=1.294, discriminator_fake_loss=1.385, generator_loss=29.08, generator_mel_loss=17.82, generator_kl_loss=1.458, generator_dur_loss=1.788, generator_adv_loss=2.071, generator_feat_match_loss=5.945, over 80.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.96, generator_mel_loss=17.86, generator_kl_loss=1.426, generator_dur_loss=1.75, generator_adv_loss=1.987, generator_feat_match_loss=5.938, over 3970.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:04:29,944 INFO [train.py:527] (1/6) Epoch 767, batch 116, global_batch_idx: 95100, batch size: 59, loss[discriminator_loss=2.677, discriminator_real_loss=1.244, discriminator_fake_loss=1.433, generator_loss=29.93, generator_mel_loss=18.09, generator_kl_loss=1.502, generator_dur_loss=1.749, generator_adv_loss=2.147, generator_feat_match_loss=6.441, over 59.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=28.95, generator_mel_loss=17.87, generator_kl_loss=1.432, generator_dur_loss=1.746, generator_adv_loss=1.988, generator_feat_match_loss=5.914, over 6574.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:04:49,776 INFO [train.py:919] (1/6) Start epoch 768
+2024-03-15 07:07:12,333 INFO [train.py:527] (1/6) Epoch 768, batch 42, global_batch_idx: 95150, batch size: 52, loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=28.16, generator_mel_loss=17.95, generator_kl_loss=1.465, generator_dur_loss=1.706, generator_adv_loss=2.141, generator_feat_match_loss=4.897, over 52.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=28.91, generator_mel_loss=17.95, generator_kl_loss=1.398, generator_dur_loss=1.743, generator_adv_loss=1.996, generator_feat_match_loss=5.826, over 2432.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:09:33,687 INFO [train.py:527] (1/6) Epoch 768, batch 92, global_batch_idx: 95200, batch size: 36, loss[discriminator_loss=2.658, discriminator_real_loss=1.292, discriminator_fake_loss=1.367, generator_loss=31.07, generator_mel_loss=18.42, generator_kl_loss=1.612, generator_dur_loss=1.683, generator_adv_loss=2.119, generator_feat_match_loss=7.237, over 36.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=29.06, generator_mel_loss=17.97, generator_kl_loss=1.432, generator_dur_loss=1.731, generator_adv_loss=2.019, generator_feat_match_loss=5.913, over 4879.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:09:33,690 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 07:09:42,415 INFO [train.py:591] (1/6) Epoch 768, validation: discriminator_loss=2.752, discriminator_real_loss=1.483, discriminator_fake_loss=1.269, generator_loss=27.97, generator_mel_loss=18.22, generator_kl_loss=1.299, generator_dur_loss=1.825, generator_adv_loss=2.032, generator_feat_match_loss=4.6, over 100.00 samples.
+2024-03-15 07:09:42,416 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 07:11:12,258 INFO [train.py:919] (1/6) Start epoch 769
+2024-03-15 07:12:30,241 INFO [train.py:527] (1/6) Epoch 769, batch 18, global_batch_idx: 95250, batch size: 39, loss[discriminator_loss=2.714, discriminator_real_loss=1.29, discriminator_fake_loss=1.424, generator_loss=29.05, generator_mel_loss=18.11, generator_kl_loss=1.541, generator_dur_loss=1.719, generator_adv_loss=2.068, generator_feat_match_loss=5.614, over 39.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.93, generator_mel_loss=17.91, generator_kl_loss=1.402, generator_dur_loss=1.764, generator_adv_loss=2.012, generator_feat_match_loss=5.848, over 1212.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:14:48,423 INFO [train.py:527] (1/6) Epoch 769, batch 68, global_batch_idx: 95300, batch size: 55, loss[discriminator_loss=2.689, discriminator_real_loss=1.39, discriminator_fake_loss=1.299, generator_loss=29.37, generator_mel_loss=18.53, generator_kl_loss=1.361, generator_dur_loss=1.755, generator_adv_loss=1.885, generator_feat_match_loss=5.844, over 55.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.99, generator_mel_loss=17.93, generator_kl_loss=1.425, generator_dur_loss=1.754, generator_adv_loss=2.003, generator_feat_match_loss=5.883, over 3950.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:17:09,063 INFO [train.py:527] (1/6) Epoch 769, batch 118, global_batch_idx: 95350, batch size: 66, loss[discriminator_loss=2.732, discriminator_real_loss=1.247, discriminator_fake_loss=1.485, generator_loss=28.85, generator_mel_loss=17.79, generator_kl_loss=1.291, generator_dur_loss=1.805, generator_adv_loss=2.161, generator_feat_match_loss=5.797, over 66.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.355, discriminator_fake_loss=1.328, generator_loss=28.91, generator_mel_loss=17.9, generator_kl_loss=1.401, generator_dur_loss=1.758, generator_adv_loss=2.001, generator_feat_match_loss=5.846, over 6997.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:17:24,867 INFO [train.py:919] (1/6) Start epoch 770
+2024-03-15 07:19:54,056 INFO [train.py:527] (1/6) Epoch 770, batch 44, global_batch_idx: 95400, batch size: 96, loss[discriminator_loss=2.694, discriminator_real_loss=1.388, discriminator_fake_loss=1.306, generator_loss=28.44, generator_mel_loss=17.66, generator_kl_loss=1.315, generator_dur_loss=1.853, generator_adv_loss=1.769, generator_feat_match_loss=5.842, over 96.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.36, discriminator_fake_loss=1.338, generator_loss=28.78, generator_mel_loss=17.87, generator_kl_loss=1.35, generator_dur_loss=1.772, generator_adv_loss=1.983, generator_feat_match_loss=5.814, over 2898.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:19:54,058 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 07:20:02,101 INFO [train.py:591] (1/6) Epoch 770, validation: discriminator_loss=2.748, discriminator_real_loss=1.255, discriminator_fake_loss=1.493, generator_loss=27.08, generator_mel_loss=18.05, generator_kl_loss=1.151, generator_dur_loss=1.836, generator_adv_loss=1.717, generator_feat_match_loss=4.324, over 100.00 samples.
+2024-03-15 07:20:02,102 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 07:22:21,231 INFO [train.py:527] (1/6) Epoch 770, batch 94, global_batch_idx: 95450, batch size: 31, loss[discriminator_loss=2.701, discriminator_real_loss=1.292, discriminator_fake_loss=1.409, generator_loss=29.24, generator_mel_loss=17.72, generator_kl_loss=1.63, generator_dur_loss=1.642, generator_adv_loss=1.939, generator_feat_match_loss=6.305, over 31.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.356, discriminator_fake_loss=1.332, generator_loss=28.89, generator_mel_loss=17.89, generator_kl_loss=1.397, generator_dur_loss=1.759, generator_adv_loss=1.986, generator_feat_match_loss=5.857, over 5660.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:23:43,860 INFO [train.py:919] (1/6) Start epoch 771
+2024-03-15 07:25:07,403 INFO [train.py:527] (1/6) Epoch 771, batch 20, global_batch_idx: 95500, batch size: 61, loss[discriminator_loss=2.656, discriminator_real_loss=1.336, discriminator_fake_loss=1.32, generator_loss=29.19, generator_mel_loss=17.88, generator_kl_loss=1.386, generator_dur_loss=1.775, generator_adv_loss=1.862, generator_feat_match_loss=6.284, over 61.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.321, generator_loss=28.77, generator_mel_loss=17.75, generator_kl_loss=1.407, generator_dur_loss=1.77, generator_adv_loss=1.985, generator_feat_match_loss=5.859, over 1202.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:27:27,451 INFO [train.py:527] (1/6) Epoch 771, batch 70, global_batch_idx: 95550, batch size: 80, loss[discriminator_loss=2.673, discriminator_real_loss=1.371, discriminator_fake_loss=1.302, generator_loss=27.45, generator_mel_loss=17.21, generator_kl_loss=1.292, generator_dur_loss=1.809, generator_adv_loss=2.008, generator_feat_match_loss=5.138, over 80.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=28.9, generator_mel_loss=17.89, generator_kl_loss=1.43, generator_dur_loss=1.744, generator_adv_loss=1.985, generator_feat_match_loss=5.858, over 3910.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:29:46,214 INFO [train.py:527] (1/6) Epoch 771, batch 120, global_batch_idx: 95600, batch size: 14, loss[discriminator_loss=2.728, discriminator_real_loss=1.298, discriminator_fake_loss=1.43, generator_loss=30.27, generator_mel_loss=19.43, generator_kl_loss=1.751, generator_dur_loss=1.637, generator_adv_loss=1.81, generator_feat_match_loss=5.636, over 14.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.322, generator_loss=28.87, generator_mel_loss=17.89, generator_kl_loss=1.431, generator_dur_loss=1.74, generator_adv_loss=1.987, generator_feat_match_loss=5.821, over 6494.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:29:46,215 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 07:29:54,766 INFO [train.py:591] (1/6) Epoch 771, validation: discriminator_loss=2.72, discriminator_real_loss=1.362, discriminator_fake_loss=1.358, generator_loss=27.5, generator_mel_loss=18, generator_kl_loss=1.266, generator_dur_loss=1.818, generator_adv_loss=1.857, generator_feat_match_loss=4.555, over 100.00 samples.
+2024-03-15 07:29:54,767 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 07:30:05,374 INFO [train.py:919] (1/6) Start epoch 772
+2024-03-15 07:32:36,133 INFO [train.py:527] (1/6) Epoch 772, batch 46, global_batch_idx: 95650, batch size: 56, loss[discriminator_loss=2.649, discriminator_real_loss=1.37, discriminator_fake_loss=1.279, generator_loss=28.9, generator_mel_loss=17.92, generator_kl_loss=1.533, generator_dur_loss=1.674, generator_adv_loss=1.946, generator_feat_match_loss=5.833, over 56.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.353, discriminator_fake_loss=1.316, generator_loss=28.85, generator_mel_loss=17.82, generator_kl_loss=1.4, generator_dur_loss=1.754, generator_adv_loss=2.026, generator_feat_match_loss=5.851, over 2823.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:34:53,242 INFO [train.py:527] (1/6) Epoch 772, batch 96, global_batch_idx: 95700, batch size: 52, loss[discriminator_loss=2.662, discriminator_real_loss=1.369, discriminator_fake_loss=1.292, generator_loss=29.48, generator_mel_loss=18.02, generator_kl_loss=1.688, generator_dur_loss=1.689, generator_adv_loss=2.106, generator_feat_match_loss=5.976, over 52.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.89, generator_mel_loss=17.87, generator_kl_loss=1.4, generator_dur_loss=1.747, generator_adv_loss=2.009, generator_feat_match_loss=5.863, over 5738.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:36:04,905 INFO [train.py:919] (1/6) Start epoch 773
+2024-03-15 07:37:29,969 INFO [train.py:527] (1/6) Epoch 773, batch 22, global_batch_idx: 95750, batch size: 39, loss[discriminator_loss=2.725, discriminator_real_loss=1.408, discriminator_fake_loss=1.317, generator_loss=26.98, generator_mel_loss=17.5, generator_kl_loss=1.399, generator_dur_loss=1.654, generator_adv_loss=2.006, generator_feat_match_loss=4.429, over 39.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.344, discriminator_fake_loss=1.335, generator_loss=29.06, generator_mel_loss=18.01, generator_kl_loss=1.438, generator_dur_loss=1.728, generator_adv_loss=1.983, generator_feat_match_loss=5.901, over 1218.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:39:47,971 INFO [train.py:527] (1/6) Epoch 773, batch 72, global_batch_idx: 95800, batch size: 59, loss[discriminator_loss=2.681, discriminator_real_loss=1.399, discriminator_fake_loss=1.282, generator_loss=28.16, generator_mel_loss=17.78, generator_kl_loss=1.395, generator_dur_loss=1.737, generator_adv_loss=2.021, generator_feat_match_loss=5.227, over 59.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=28.89, generator_mel_loss=17.89, generator_kl_loss=1.41, generator_dur_loss=1.752, generator_adv_loss=1.986, generator_feat_match_loss=5.849, over 4332.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:39:47,972 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 07:39:56,232 INFO [train.py:591] (1/6) Epoch 773, validation: discriminator_loss=2.719, discriminator_real_loss=1.421, discriminator_fake_loss=1.298, generator_loss=27.94, generator_mel_loss=18.55, generator_kl_loss=1.249, generator_dur_loss=1.8, generator_adv_loss=1.939, generator_feat_match_loss=4.4, over 100.00 samples.
+2024-03-15 07:39:56,232 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 07:42:19,738 INFO [train.py:527] (1/6) Epoch 773, batch 122, global_batch_idx: 95850, batch size: 74, loss[discriminator_loss=2.72, discriminator_real_loss=1.472, discriminator_fake_loss=1.247, generator_loss=28.49, generator_mel_loss=17.56, generator_kl_loss=1.395, generator_dur_loss=1.862, generator_adv_loss=2.03, generator_feat_match_loss=5.64, over 74.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.361, discriminator_fake_loss=1.32, generator_loss=28.85, generator_mel_loss=17.85, generator_kl_loss=1.415, generator_dur_loss=1.758, generator_adv_loss=2, generator_feat_match_loss=5.832, over 7377.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:42:24,283 INFO [train.py:919] (1/6) Start epoch 774
+2024-03-15 07:45:02,255 INFO [train.py:527] (1/6) Epoch 774, batch 48, global_batch_idx: 95900, batch size: 55, loss[discriminator_loss=2.67, discriminator_real_loss=1.419, discriminator_fake_loss=1.251, generator_loss=28.24, generator_mel_loss=17.42, generator_kl_loss=1.323, generator_dur_loss=1.722, generator_adv_loss=2.104, generator_feat_match_loss=5.665, over 55.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.353, discriminator_fake_loss=1.328, generator_loss=28.74, generator_mel_loss=17.8, generator_kl_loss=1.374, generator_dur_loss=1.751, generator_adv_loss=1.991, generator_feat_match_loss=5.826, over 2846.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:47:22,467 INFO [train.py:527] (1/6) Epoch 774, batch 98, global_batch_idx: 95950, batch size: 36, loss[discriminator_loss=2.776, discriminator_real_loss=1.215, discriminator_fake_loss=1.561, generator_loss=30.17, generator_mel_loss=18.37, generator_kl_loss=1.678, generator_dur_loss=1.636, generator_adv_loss=2.186, generator_feat_match_loss=6.303, over 36.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=28.73, generator_mel_loss=17.77, generator_kl_loss=1.39, generator_dur_loss=1.743, generator_adv_loss=1.992, generator_feat_match_loss=5.832, over 5781.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:48:32,724 INFO [train.py:919] (1/6) Start epoch 775
+2024-03-15 07:50:03,351 INFO [train.py:527] (1/6) Epoch 775, batch 24, global_batch_idx: 96000, batch size: 53, loss[discriminator_loss=2.712, discriminator_real_loss=1.372, discriminator_fake_loss=1.339, generator_loss=29.36, generator_mel_loss=18.41, generator_kl_loss=1.459, generator_dur_loss=1.678, generator_adv_loss=2.027, generator_feat_match_loss=5.778, over 53.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=29.07, generator_mel_loss=18.04, generator_kl_loss=1.449, generator_dur_loss=1.725, generator_adv_loss=1.995, generator_feat_match_loss=5.862, over 1345.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:50:03,352 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 07:50:11,377 INFO [train.py:591] (1/6) Epoch 775, validation: discriminator_loss=2.773, discriminator_real_loss=1.462, discriminator_fake_loss=1.31, generator_loss=28.08, generator_mel_loss=18.36, generator_kl_loss=1.192, generator_dur_loss=1.785, generator_adv_loss=1.999, generator_feat_match_loss=4.746, over 100.00 samples.
+2024-03-15 07:50:11,378 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 07:52:32,668 INFO [train.py:527] (1/6) Epoch 775, batch 74, global_batch_idx: 96050, batch size: 45, loss[discriminator_loss=2.717, discriminator_real_loss=1.392, discriminator_fake_loss=1.325, generator_loss=28.88, generator_mel_loss=17.99, generator_kl_loss=1.515, generator_dur_loss=1.683, generator_adv_loss=1.866, generator_feat_match_loss=5.831, over 45.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.94, generator_mel_loss=17.92, generator_kl_loss=1.422, generator_dur_loss=1.732, generator_adv_loss=2.007, generator_feat_match_loss=5.86, over 4196.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:54:47,019 INFO [train.py:919] (1/6) Start epoch 776
+2024-03-15 07:55:09,614 INFO [train.py:527] (1/6) Epoch 776, batch 0, global_batch_idx: 96100, batch size: 31, loss[discriminator_loss=2.69, discriminator_real_loss=1.41, discriminator_fake_loss=1.279, generator_loss=29, generator_mel_loss=18.32, generator_kl_loss=1.609, generator_dur_loss=1.569, generator_adv_loss=1.907, generator_feat_match_loss=5.598, over 31.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.41, discriminator_fake_loss=1.279, generator_loss=29, generator_mel_loss=18.32, generator_kl_loss=1.609, generator_dur_loss=1.569, generator_adv_loss=1.907, generator_feat_match_loss=5.598, over 31.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:57:27,810 INFO [train.py:527] (1/6) Epoch 776, batch 50, global_batch_idx: 96150, batch size: 77, loss[discriminator_loss=2.721, discriminator_real_loss=1.394, discriminator_fake_loss=1.328, generator_loss=28.75, generator_mel_loss=18.12, generator_kl_loss=1.368, generator_dur_loss=1.751, generator_adv_loss=2.033, generator_feat_match_loss=5.484, over 77.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.95, generator_mel_loss=17.92, generator_kl_loss=1.437, generator_dur_loss=1.736, generator_adv_loss=2.002, generator_feat_match_loss=5.865, over 2980.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:59:46,274 INFO [train.py:527] (1/6) Epoch 776, batch 100, global_batch_idx: 96200, batch size: 68, loss[discriminator_loss=2.66, discriminator_real_loss=1.221, discriminator_fake_loss=1.439, generator_loss=29.3, generator_mel_loss=17.73, generator_kl_loss=1.499, generator_dur_loss=1.755, generator_adv_loss=2.021, generator_feat_match_loss=6.292, over 68.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.366, discriminator_fake_loss=1.326, generator_loss=28.81, generator_mel_loss=17.85, generator_kl_loss=1.427, generator_dur_loss=1.727, generator_adv_loss=1.995, generator_feat_match_loss=5.812, over 5839.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 07:59:46,275 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 07:59:55,169 INFO [train.py:591] (1/6) Epoch 776, validation: discriminator_loss=2.694, discriminator_real_loss=1.449, discriminator_fake_loss=1.245, generator_loss=27.8, generator_mel_loss=17.95, generator_kl_loss=1.386, generator_dur_loss=1.784, generator_adv_loss=2.037, generator_feat_match_loss=4.642, over 100.00 samples.
+2024-03-15 07:59:55,170 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 08:01:00,515 INFO [train.py:919] (1/6) Start epoch 777
+2024-03-15 08:02:38,116 INFO [train.py:527] (1/6) Epoch 777, batch 26, global_batch_idx: 96250, batch size: 36, loss[discriminator_loss=2.715, discriminator_real_loss=1.355, discriminator_fake_loss=1.36, generator_loss=29.27, generator_mel_loss=17.48, generator_kl_loss=1.563, generator_dur_loss=1.635, generator_adv_loss=2.055, generator_feat_match_loss=6.533, over 36.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.368, discriminator_fake_loss=1.322, generator_loss=28.8, generator_mel_loss=17.84, generator_kl_loss=1.43, generator_dur_loss=1.723, generator_adv_loss=2.001, generator_feat_match_loss=5.798, over 1590.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 08:04:57,648 INFO [train.py:527] (1/6) Epoch 777, batch 76, global_batch_idx: 96300, batch size: 96, loss[discriminator_loss=2.671, discriminator_real_loss=1.423, discriminator_fake_loss=1.248, generator_loss=28.27, generator_mel_loss=17.49, generator_kl_loss=1.407, generator_dur_loss=1.875, generator_adv_loss=1.965, generator_feat_match_loss=5.532, over 96.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.354, discriminator_fake_loss=1.327, generator_loss=28.97, generator_mel_loss=17.89, generator_kl_loss=1.437, generator_dur_loss=1.733, generator_adv_loss=2.005, generator_feat_match_loss=5.91, over 4539.00 samples.], cur_lr_g: 1.82e-04, cur_lr_d: 1.82e-04, 
+2024-03-15 08:07:07,625 INFO [train.py:919] (1/6) Start epoch 778
+2024-03-15 08:07:37,265 INFO [train.py:527] (1/6) Epoch 778, batch 2, global_batch_idx: 96350, batch size: 70, loss[discriminator_loss=2.718, discriminator_real_loss=1.369, discriminator_fake_loss=1.348, generator_loss=28.83, generator_mel_loss=18.35, generator_kl_loss=1.33, generator_dur_loss=1.733, generator_adv_loss=2.047, generator_feat_match_loss=5.375, over 70.00 samples.], tot_loss[discriminator_loss=2.71, discriminator_real_loss=1.373, discriminator_fake_loss=1.337, generator_loss=28.71, generator_mel_loss=18.05, generator_kl_loss=1.382, generator_dur_loss=1.741, generator_adv_loss=2, generator_feat_match_loss=5.534, over 200.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:09:57,678 INFO [train.py:527] (1/6) Epoch 778, batch 52, global_batch_idx: 96400, batch size: 80, loss[discriminator_loss=2.785, discriminator_real_loss=1.492, discriminator_fake_loss=1.293, generator_loss=27.8, generator_mel_loss=17.79, generator_kl_loss=1.282, generator_dur_loss=1.772, generator_adv_loss=1.902, generator_feat_match_loss=5.052, over 80.00 samples.], tot_loss[discriminator_loss=2.697, discriminator_real_loss=1.368, discriminator_fake_loss=1.329, generator_loss=28.83, generator_mel_loss=17.95, generator_kl_loss=1.39, generator_dur_loss=1.741, generator_adv_loss=1.989, generator_feat_match_loss=5.756, over 3088.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:09:57,679 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 08:10:05,730 INFO [train.py:591] (1/6) Epoch 778, validation: discriminator_loss=2.767, discriminator_real_loss=1.43, discriminator_fake_loss=1.337, generator_loss=26.99, generator_mel_loss=17.76, generator_kl_loss=1.265, generator_dur_loss=1.784, generator_adv_loss=1.821, generator_feat_match_loss=4.354, over 100.00 samples.
+2024-03-15 08:10:05,730 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 08:12:23,781 INFO [train.py:527] (1/6) Epoch 778, batch 102, global_batch_idx: 96450, batch size: 31, loss[discriminator_loss=2.762, discriminator_real_loss=1.385, discriminator_fake_loss=1.377, generator_loss=28.63, generator_mel_loss=18.46, generator_kl_loss=1.625, generator_dur_loss=1.608, generator_adv_loss=1.906, generator_feat_match_loss=5.037, over 31.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.369, discriminator_fake_loss=1.33, generator_loss=28.88, generator_mel_loss=17.91, generator_kl_loss=1.422, generator_dur_loss=1.729, generator_adv_loss=1.99, generator_feat_match_loss=5.823, over 5850.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:13:20,941 INFO [train.py:919] (1/6) Start epoch 779
+2024-03-15 08:15:01,050 INFO [train.py:527] (1/6) Epoch 779, batch 28, global_batch_idx: 96500, batch size: 66, loss[discriminator_loss=2.743, discriminator_real_loss=1.472, discriminator_fake_loss=1.271, generator_loss=27.87, generator_mel_loss=17.35, generator_kl_loss=1.281, generator_dur_loss=1.77, generator_adv_loss=1.883, generator_feat_match_loss=5.582, over 66.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.365, discriminator_fake_loss=1.334, generator_loss=29.06, generator_mel_loss=17.97, generator_kl_loss=1.458, generator_dur_loss=1.712, generator_adv_loss=1.986, generator_feat_match_loss=5.937, over 1508.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:17:20,649 INFO [train.py:527] (1/6) Epoch 779, batch 78, global_batch_idx: 96550, batch size: 70, loss[discriminator_loss=2.669, discriminator_real_loss=1.325, discriminator_fake_loss=1.344, generator_loss=28.56, generator_mel_loss=17.95, generator_kl_loss=1.37, generator_dur_loss=1.752, generator_adv_loss=1.931, generator_feat_match_loss=5.552, over 70.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.99, generator_mel_loss=17.94, generator_kl_loss=1.444, generator_dur_loss=1.723, generator_adv_loss=1.994, generator_feat_match_loss=5.892, over 4370.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:19:30,349 INFO [train.py:919] (1/6) Start epoch 780
+2024-03-15 08:20:06,446 INFO [train.py:527] (1/6) Epoch 780, batch 4, global_batch_idx: 96600, batch size: 74, loss[discriminator_loss=2.724, discriminator_real_loss=1.411, discriminator_fake_loss=1.313, generator_loss=28.49, generator_mel_loss=17.76, generator_kl_loss=1.411, generator_dur_loss=1.774, generator_adv_loss=1.981, generator_feat_match_loss=5.572, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.346, discriminator_fake_loss=1.34, generator_loss=28.66, generator_mel_loss=17.82, generator_kl_loss=1.439, generator_dur_loss=1.726, generator_adv_loss=1.97, generator_feat_match_loss=5.706, over 294.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:20:06,452 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 08:20:14,316 INFO [train.py:591] (1/6) Epoch 780, validation: discriminator_loss=2.714, discriminator_real_loss=1.425, discriminator_fake_loss=1.289, generator_loss=27.68, generator_mel_loss=18.14, generator_kl_loss=1.341, generator_dur_loss=1.807, generator_adv_loss=1.923, generator_feat_match_loss=4.46, over 100.00 samples.
+2024-03-15 08:20:14,318 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 08:22:32,329 INFO [train.py:527] (1/6) Epoch 780, batch 54, global_batch_idx: 96650, batch size: 52, loss[discriminator_loss=2.687, discriminator_real_loss=1.398, discriminator_fake_loss=1.288, generator_loss=28.19, generator_mel_loss=17.37, generator_kl_loss=1.53, generator_dur_loss=1.728, generator_adv_loss=1.955, generator_feat_match_loss=5.606, over 52.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.351, discriminator_fake_loss=1.324, generator_loss=28.85, generator_mel_loss=17.82, generator_kl_loss=1.411, generator_dur_loss=1.748, generator_adv_loss=1.984, generator_feat_match_loss=5.881, over 3271.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:24:52,712 INFO [train.py:527] (1/6) Epoch 780, batch 104, global_batch_idx: 96700, batch size: 80, loss[discriminator_loss=2.678, discriminator_real_loss=1.396, discriminator_fake_loss=1.282, generator_loss=28.86, generator_mel_loss=17.97, generator_kl_loss=1.39, generator_dur_loss=1.816, generator_adv_loss=1.951, generator_feat_match_loss=5.732, over 80.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=28.85, generator_mel_loss=17.81, generator_kl_loss=1.417, generator_dur_loss=1.742, generator_adv_loss=1.99, generator_feat_match_loss=5.891, over 6034.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:25:47,720 INFO [train.py:919] (1/6) Start epoch 781
+2024-03-15 08:27:38,918 INFO [train.py:527] (1/6) Epoch 781, batch 30, global_batch_idx: 96750, batch size: 74, loss[discriminator_loss=2.727, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=28.61, generator_mel_loss=17.55, generator_kl_loss=1.251, generator_dur_loss=1.767, generator_adv_loss=2.054, generator_feat_match_loss=5.99, over 74.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.345, discriminator_fake_loss=1.329, generator_loss=28.99, generator_mel_loss=17.84, generator_kl_loss=1.449, generator_dur_loss=1.725, generator_adv_loss=1.997, generator_feat_match_loss=5.98, over 1660.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:29:55,231 INFO [train.py:527] (1/6) Epoch 781, batch 80, global_batch_idx: 96800, batch size: 62, loss[discriminator_loss=2.659, discriminator_real_loss=1.258, discriminator_fake_loss=1.401, generator_loss=28.32, generator_mel_loss=17.38, generator_kl_loss=1.491, generator_dur_loss=1.714, generator_adv_loss=2.047, generator_feat_match_loss=5.691, over 62.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.89, generator_mel_loss=17.83, generator_kl_loss=1.435, generator_dur_loss=1.726, generator_adv_loss=1.999, generator_feat_match_loss=5.909, over 4459.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:29:55,232 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 08:30:03,855 INFO [train.py:591] (1/6) Epoch 781, validation: discriminator_loss=2.686, discriminator_real_loss=1.39, discriminator_fake_loss=1.296, generator_loss=28.29, generator_mel_loss=18.24, generator_kl_loss=1.335, generator_dur_loss=1.794, generator_adv_loss=1.944, generator_feat_match_loss=4.978, over 100.00 samples.
+2024-03-15 08:30:03,856 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 08:32:06,930 INFO [train.py:919] (1/6) Start epoch 782
+2024-03-15 08:32:49,267 INFO [train.py:527] (1/6) Epoch 782, batch 6, global_batch_idx: 96850, batch size: 59, loss[discriminator_loss=2.721, discriminator_real_loss=1.364, discriminator_fake_loss=1.358, generator_loss=28.89, generator_mel_loss=17.73, generator_kl_loss=1.479, generator_dur_loss=1.735, generator_adv_loss=2.101, generator_feat_match_loss=5.843, over 59.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.367, discriminator_fake_loss=1.322, generator_loss=29.36, generator_mel_loss=17.86, generator_kl_loss=1.504, generator_dur_loss=1.733, generator_adv_loss=1.991, generator_feat_match_loss=6.272, over 433.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:35:10,457 INFO [train.py:527] (1/6) Epoch 782, batch 56, global_batch_idx: 96900, batch size: 39, loss[discriminator_loss=2.762, discriminator_real_loss=1.43, discriminator_fake_loss=1.332, generator_loss=28.68, generator_mel_loss=17.8, generator_kl_loss=1.5, generator_dur_loss=1.717, generator_adv_loss=1.907, generator_feat_match_loss=5.751, over 39.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.32, generator_loss=28.96, generator_mel_loss=17.86, generator_kl_loss=1.434, generator_dur_loss=1.743, generator_adv_loss=1.996, generator_feat_match_loss=5.927, over 3294.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:37:28,784 INFO [train.py:527] (1/6) Epoch 782, batch 106, global_batch_idx: 96950, batch size: 64, loss[discriminator_loss=2.702, discriminator_real_loss=1.308, discriminator_fake_loss=1.394, generator_loss=27.93, generator_mel_loss=17.4, generator_kl_loss=1.277, generator_dur_loss=1.729, generator_adv_loss=1.966, generator_feat_match_loss=5.557, over 64.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.9, generator_mel_loss=17.83, generator_kl_loss=1.432, generator_dur_loss=1.737, generator_adv_loss=2.002, generator_feat_match_loss=5.898, over 6039.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:38:15,397 INFO [train.py:919] (1/6) Start epoch 783
+2024-03-15 08:40:07,149 INFO [train.py:527] (1/6) Epoch 783, batch 32, global_batch_idx: 97000, batch size: 55, loss[discriminator_loss=2.62, discriminator_real_loss=1.346, discriminator_fake_loss=1.274, generator_loss=28.69, generator_mel_loss=17.59, generator_kl_loss=1.444, generator_dur_loss=1.702, generator_adv_loss=2.034, generator_feat_match_loss=5.924, over 55.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.379, discriminator_fake_loss=1.31, generator_loss=28.71, generator_mel_loss=17.84, generator_kl_loss=1.471, generator_dur_loss=1.707, generator_adv_loss=1.983, generator_feat_match_loss=5.709, over 1761.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:40:07,150 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 08:40:15,062 INFO [train.py:591] (1/6) Epoch 783, validation: discriminator_loss=2.71, discriminator_real_loss=1.344, discriminator_fake_loss=1.366, generator_loss=27.44, generator_mel_loss=17.68, generator_kl_loss=1.241, generator_dur_loss=1.797, generator_adv_loss=1.822, generator_feat_match_loss=4.905, over 100.00 samples.
+2024-03-15 08:40:15,063 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 08:42:36,391 INFO [train.py:527] (1/6) Epoch 783, batch 82, global_batch_idx: 97050, batch size: 61, loss[discriminator_loss=2.692, discriminator_real_loss=1.406, discriminator_fake_loss=1.287, generator_loss=28.59, generator_mel_loss=18.3, generator_kl_loss=1.288, generator_dur_loss=1.767, generator_adv_loss=1.934, generator_feat_match_loss=5.298, over 61.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.366, discriminator_fake_loss=1.317, generator_loss=28.83, generator_mel_loss=17.82, generator_kl_loss=1.44, generator_dur_loss=1.734, generator_adv_loss=1.999, generator_feat_match_loss=5.844, over 4962.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:44:28,022 INFO [train.py:919] (1/6) Start epoch 784
+2024-03-15 08:45:12,142 INFO [train.py:527] (1/6) Epoch 784, batch 8, global_batch_idx: 97100, batch size: 36, loss[discriminator_loss=2.791, discriminator_real_loss=1.452, discriminator_fake_loss=1.339, generator_loss=27.95, generator_mel_loss=17.64, generator_kl_loss=1.602, generator_dur_loss=1.677, generator_adv_loss=1.958, generator_feat_match_loss=5.07, over 36.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.368, discriminator_fake_loss=1.303, generator_loss=29, generator_mel_loss=17.86, generator_kl_loss=1.482, generator_dur_loss=1.721, generator_adv_loss=2.028, generator_feat_match_loss=5.913, over 460.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:47:31,152 INFO [train.py:527] (1/6) Epoch 784, batch 58, global_batch_idx: 97150, batch size: 72, loss[discriminator_loss=2.647, discriminator_real_loss=1.345, discriminator_fake_loss=1.302, generator_loss=29.71, generator_mel_loss=18.02, generator_kl_loss=1.445, generator_dur_loss=1.765, generator_adv_loss=1.958, generator_feat_match_loss=6.525, over 72.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=29.07, generator_mel_loss=17.95, generator_kl_loss=1.439, generator_dur_loss=1.732, generator_adv_loss=2.005, generator_feat_match_loss=5.949, over 3209.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:49:49,932 INFO [train.py:527] (1/6) Epoch 784, batch 108, global_batch_idx: 97200, batch size: 74, loss[discriminator_loss=2.678, discriminator_real_loss=1.41, discriminator_fake_loss=1.268, generator_loss=28.23, generator_mel_loss=17.81, generator_kl_loss=1.286, generator_dur_loss=1.764, generator_adv_loss=1.982, generator_feat_match_loss=5.381, over 74.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=29.08, generator_mel_loss=17.95, generator_kl_loss=1.433, generator_dur_loss=1.732, generator_adv_loss=2.004, generator_feat_match_loss=5.963, over 6051.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:49:49,933 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 08:49:58,659 INFO [train.py:591] (1/6) Epoch 784, validation: discriminator_loss=2.705, discriminator_real_loss=1.417, discriminator_fake_loss=1.288, generator_loss=28.02, generator_mel_loss=18.22, generator_kl_loss=1.303, generator_dur_loss=1.803, generator_adv_loss=1.934, generator_feat_match_loss=4.753, over 100.00 samples.
+2024-03-15 08:49:58,659 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 08:50:43,427 INFO [train.py:919] (1/6) Start epoch 785
+2024-03-15 08:52:39,308 INFO [train.py:527] (1/6) Epoch 785, batch 34, global_batch_idx: 97250, batch size: 88, loss[discriminator_loss=2.694, discriminator_real_loss=1.456, discriminator_fake_loss=1.238, generator_loss=28.54, generator_mel_loss=17.59, generator_kl_loss=1.288, generator_dur_loss=1.834, generator_adv_loss=2.013, generator_feat_match_loss=5.821, over 88.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.365, discriminator_fake_loss=1.317, generator_loss=28.89, generator_mel_loss=17.82, generator_kl_loss=1.408, generator_dur_loss=1.745, generator_adv_loss=2.026, generator_feat_match_loss=5.89, over 2091.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:54:55,351 INFO [train.py:527] (1/6) Epoch 785, batch 84, global_batch_idx: 97300, batch size: 64, loss[discriminator_loss=2.639, discriminator_real_loss=1.353, discriminator_fake_loss=1.286, generator_loss=28.81, generator_mel_loss=17.83, generator_kl_loss=1.385, generator_dur_loss=1.729, generator_adv_loss=2.052, generator_feat_match_loss=5.816, over 64.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.363, discriminator_fake_loss=1.32, generator_loss=28.78, generator_mel_loss=17.78, generator_kl_loss=1.397, generator_dur_loss=1.747, generator_adv_loss=2.009, generator_feat_match_loss=5.845, over 5061.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:56:46,050 INFO [train.py:919] (1/6) Start epoch 786
+2024-03-15 08:57:37,850 INFO [train.py:527] (1/6) Epoch 786, batch 10, global_batch_idx: 97350, batch size: 61, loss[discriminator_loss=2.679, discriminator_real_loss=1.32, discriminator_fake_loss=1.359, generator_loss=29.26, generator_mel_loss=18.35, generator_kl_loss=1.416, generator_dur_loss=1.723, generator_adv_loss=1.939, generator_feat_match_loss=5.838, over 61.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.334, discriminator_fake_loss=1.353, generator_loss=29.47, generator_mel_loss=18.1, generator_kl_loss=1.462, generator_dur_loss=1.739, generator_adv_loss=1.974, generator_feat_match_loss=6.194, over 587.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:59:57,245 INFO [train.py:527] (1/6) Epoch 786, batch 60, global_batch_idx: 97400, batch size: 25, loss[discriminator_loss=2.64, discriminator_real_loss=1.261, discriminator_fake_loss=1.379, generator_loss=31.14, generator_mel_loss=18.45, generator_kl_loss=1.817, generator_dur_loss=1.529, generator_adv_loss=2.094, generator_feat_match_loss=7.251, over 25.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.359, discriminator_fake_loss=1.335, generator_loss=28.94, generator_mel_loss=17.89, generator_kl_loss=1.427, generator_dur_loss=1.744, generator_adv_loss=1.989, generator_feat_match_loss=5.888, over 3405.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 08:59:57,247 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 09:00:06,018 INFO [train.py:591] (1/6) Epoch 786, validation: discriminator_loss=2.691, discriminator_real_loss=1.437, discriminator_fake_loss=1.254, generator_loss=28.03, generator_mel_loss=18.48, generator_kl_loss=1.179, generator_dur_loss=1.793, generator_adv_loss=1.998, generator_feat_match_loss=4.582, over 100.00 samples.
+2024-03-15 09:00:06,019 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 09:02:26,452 INFO [train.py:527] (1/6) Epoch 786, batch 110, global_batch_idx: 97450, batch size: 77, loss[discriminator_loss=2.724, discriminator_real_loss=1.445, discriminator_fake_loss=1.278, generator_loss=28.09, generator_mel_loss=17.34, generator_kl_loss=1.532, generator_dur_loss=1.782, generator_adv_loss=1.867, generator_feat_match_loss=5.57, over 77.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=28.82, generator_mel_loss=17.85, generator_kl_loss=1.416, generator_dur_loss=1.744, generator_adv_loss=1.992, generator_feat_match_loss=5.822, over 6362.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:03:03,732 INFO [train.py:919] (1/6) Start epoch 787
+2024-03-15 09:05:06,795 INFO [train.py:527] (1/6) Epoch 787, batch 36, global_batch_idx: 97500, batch size: 50, loss[discriminator_loss=2.759, discriminator_real_loss=1.387, discriminator_fake_loss=1.371, generator_loss=29.16, generator_mel_loss=17.81, generator_kl_loss=1.542, generator_dur_loss=1.684, generator_adv_loss=1.943, generator_feat_match_loss=6.179, over 50.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.354, discriminator_fake_loss=1.33, generator_loss=28.8, generator_mel_loss=17.87, generator_kl_loss=1.425, generator_dur_loss=1.749, generator_adv_loss=1.986, generator_feat_match_loss=5.773, over 2095.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:07:26,628 INFO [train.py:527] (1/6) Epoch 787, batch 86, global_batch_idx: 97550, batch size: 72, loss[discriminator_loss=2.675, discriminator_real_loss=1.374, discriminator_fake_loss=1.3, generator_loss=29.86, generator_mel_loss=18.41, generator_kl_loss=1.403, generator_dur_loss=1.802, generator_adv_loss=1.984, generator_feat_match_loss=6.252, over 72.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=28.95, generator_mel_loss=17.88, generator_kl_loss=1.431, generator_dur_loss=1.745, generator_adv_loss=2.008, generator_feat_match_loss=5.883, over 5007.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:09:15,258 INFO [train.py:919] (1/6) Start epoch 788
+2024-03-15 09:10:14,323 INFO [train.py:527] (1/6) Epoch 788, batch 12, global_batch_idx: 97600, batch size: 36, loss[discriminator_loss=2.738, discriminator_real_loss=1.452, discriminator_fake_loss=1.286, generator_loss=27.88, generator_mel_loss=17.9, generator_kl_loss=1.43, generator_dur_loss=1.694, generator_adv_loss=1.919, generator_feat_match_loss=4.929, over 36.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.35, discriminator_fake_loss=1.331, generator_loss=28.81, generator_mel_loss=17.94, generator_kl_loss=1.426, generator_dur_loss=1.744, generator_adv_loss=1.977, generator_feat_match_loss=5.722, over 699.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:10:14,330 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 09:10:21,988 INFO [train.py:591] (1/6) Epoch 788, validation: discriminator_loss=2.772, discriminator_real_loss=1.407, discriminator_fake_loss=1.365, generator_loss=27.53, generator_mel_loss=17.92, generator_kl_loss=1.166, generator_dur_loss=1.81, generator_adv_loss=1.816, generator_feat_match_loss=4.82, over 100.00 samples.
+2024-03-15 09:10:21,989 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 09:12:41,221 INFO [train.py:527] (1/6) Epoch 788, batch 62, global_batch_idx: 97650, batch size: 62, loss[discriminator_loss=2.719, discriminator_real_loss=1.391, discriminator_fake_loss=1.327, generator_loss=28.74, generator_mel_loss=17.95, generator_kl_loss=1.437, generator_dur_loss=1.726, generator_adv_loss=1.896, generator_feat_match_loss=5.736, over 62.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.92, generator_mel_loss=17.85, generator_kl_loss=1.43, generator_dur_loss=1.743, generator_adv_loss=1.999, generator_feat_match_loss=5.898, over 3667.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:14:58,390 INFO [train.py:527] (1/6) Epoch 788, batch 112, global_batch_idx: 97700, batch size: 31, loss[discriminator_loss=2.684, discriminator_real_loss=1.237, discriminator_fake_loss=1.448, generator_loss=30.14, generator_mel_loss=18.48, generator_kl_loss=1.649, generator_dur_loss=1.61, generator_adv_loss=2.097, generator_feat_match_loss=6.312, over 31.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.327, generator_loss=28.97, generator_mel_loss=17.89, generator_kl_loss=1.423, generator_dur_loss=1.744, generator_adv_loss=2.004, generator_feat_match_loss=5.912, over 6433.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:15:32,312 INFO [train.py:919] (1/6) Start epoch 789
+2024-03-15 09:17:42,784 INFO [train.py:527] (1/6) Epoch 789, batch 38, global_batch_idx: 97750, batch size: 58, loss[discriminator_loss=2.678, discriminator_real_loss=1.3, discriminator_fake_loss=1.378, generator_loss=29.38, generator_mel_loss=18.18, generator_kl_loss=1.534, generator_dur_loss=1.682, generator_adv_loss=2.101, generator_feat_match_loss=5.882, over 58.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.346, discriminator_fake_loss=1.335, generator_loss=28.8, generator_mel_loss=17.76, generator_kl_loss=1.421, generator_dur_loss=1.75, generator_adv_loss=2.026, generator_feat_match_loss=5.84, over 2346.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:19:58,617 INFO [train.py:527] (1/6) Epoch 789, batch 88, global_batch_idx: 97800, batch size: 48, loss[discriminator_loss=2.684, discriminator_real_loss=1.38, discriminator_fake_loss=1.304, generator_loss=29.06, generator_mel_loss=17.31, generator_kl_loss=1.482, generator_dur_loss=1.674, generator_adv_loss=2.018, generator_feat_match_loss=6.573, over 48.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=28.84, generator_mel_loss=17.8, generator_kl_loss=1.421, generator_dur_loss=1.746, generator_adv_loss=2.017, generator_feat_match_loss=5.857, over 5171.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:19:58,619 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 09:20:07,304 INFO [train.py:591] (1/6) Epoch 789, validation: discriminator_loss=2.722, discriminator_real_loss=1.317, discriminator_fake_loss=1.405, generator_loss=27.95, generator_mel_loss=18.1, generator_kl_loss=1.351, generator_dur_loss=1.794, generator_adv_loss=1.854, generator_feat_match_loss=4.859, over 100.00 samples.
+2024-03-15 09:20:07,304 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 09:21:50,938 INFO [train.py:919] (1/6) Start epoch 790
+2024-03-15 09:22:54,272 INFO [train.py:527] (1/6) Epoch 790, batch 14, global_batch_idx: 97850, batch size: 59, loss[discriminator_loss=2.708, discriminator_real_loss=1.334, discriminator_fake_loss=1.374, generator_loss=28.73, generator_mel_loss=18.37, generator_kl_loss=1.224, generator_dur_loss=1.771, generator_adv_loss=1.944, generator_feat_match_loss=5.427, over 59.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29, generator_mel_loss=17.76, generator_kl_loss=1.446, generator_dur_loss=1.747, generator_adv_loss=1.997, generator_feat_match_loss=6.047, over 884.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:25:11,756 INFO [train.py:527] (1/6) Epoch 790, batch 64, global_batch_idx: 97900, batch size: 39, loss[discriminator_loss=2.727, discriminator_real_loss=1.388, discriminator_fake_loss=1.34, generator_loss=27.79, generator_mel_loss=17.78, generator_kl_loss=1.516, generator_dur_loss=1.684, generator_adv_loss=2.041, generator_feat_match_loss=4.766, over 39.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.93, generator_mel_loss=17.83, generator_kl_loss=1.441, generator_dur_loss=1.738, generator_adv_loss=1.991, generator_feat_match_loss=5.936, over 3660.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:27:31,681 INFO [train.py:527] (1/6) Epoch 790, batch 114, global_batch_idx: 97950, batch size: 88, loss[discriminator_loss=2.697, discriminator_real_loss=1.284, discriminator_fake_loss=1.414, generator_loss=28.19, generator_mel_loss=17.52, generator_kl_loss=1.317, generator_dur_loss=1.86, generator_adv_loss=1.871, generator_feat_match_loss=5.621, over 88.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.9, generator_mel_loss=17.83, generator_kl_loss=1.426, generator_dur_loss=1.74, generator_adv_loss=1.992, generator_feat_match_loss=5.917, over 6513.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:27:57,139 INFO [train.py:919] (1/6) Start epoch 791
+2024-03-15 09:30:13,430 INFO [train.py:527] (1/6) Epoch 791, batch 40, global_batch_idx: 98000, batch size: 59, loss[discriminator_loss=2.671, discriminator_real_loss=1.295, discriminator_fake_loss=1.377, generator_loss=28.25, generator_mel_loss=17.63, generator_kl_loss=1.389, generator_dur_loss=1.763, generator_adv_loss=1.968, generator_feat_match_loss=5.504, over 59.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.351, discriminator_fake_loss=1.333, generator_loss=28.79, generator_mel_loss=17.78, generator_kl_loss=1.44, generator_dur_loss=1.74, generator_adv_loss=2.005, generator_feat_match_loss=5.829, over 2297.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:30:13,431 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 09:30:21,549 INFO [train.py:591] (1/6) Epoch 791, validation: discriminator_loss=2.693, discriminator_real_loss=1.39, discriminator_fake_loss=1.304, generator_loss=27.72, generator_mel_loss=18.37, generator_kl_loss=1.228, generator_dur_loss=1.794, generator_adv_loss=1.917, generator_feat_match_loss=4.414, over 100.00 samples.
+2024-03-15 09:30:21,550 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 09:32:39,906 INFO [train.py:527] (1/6) Epoch 791, batch 90, global_batch_idx: 98050, batch size: 42, loss[discriminator_loss=2.654, discriminator_real_loss=1.31, discriminator_fake_loss=1.344, generator_loss=29.54, generator_mel_loss=17.88, generator_kl_loss=1.564, generator_dur_loss=1.599, generator_adv_loss=1.958, generator_feat_match_loss=6.535, over 42.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.329, generator_loss=28.84, generator_mel_loss=17.78, generator_kl_loss=1.452, generator_dur_loss=1.733, generator_adv_loss=2, generator_feat_match_loss=5.877, over 5057.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:34:13,928 INFO [train.py:919] (1/6) Start epoch 792
+2024-03-15 09:35:21,533 INFO [train.py:527] (1/6) Epoch 792, batch 16, global_batch_idx: 98100, batch size: 80, loss[discriminator_loss=2.695, discriminator_real_loss=1.305, discriminator_fake_loss=1.39, generator_loss=29.49, generator_mel_loss=17.94, generator_kl_loss=1.385, generator_dur_loss=1.771, generator_adv_loss=1.989, generator_feat_match_loss=6.408, over 80.00 samples.], tot_loss[discriminator_loss=2.699, discriminator_real_loss=1.37, discriminator_fake_loss=1.328, generator_loss=28.76, generator_mel_loss=17.84, generator_kl_loss=1.466, generator_dur_loss=1.714, generator_adv_loss=2.002, generator_feat_match_loss=5.733, over 903.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:37:41,100 INFO [train.py:527] (1/6) Epoch 792, batch 66, global_batch_idx: 98150, batch size: 80, loss[discriminator_loss=2.644, discriminator_real_loss=1.332, discriminator_fake_loss=1.312, generator_loss=28.94, generator_mel_loss=17.77, generator_kl_loss=1.343, generator_dur_loss=1.801, generator_adv_loss=2.012, generator_feat_match_loss=6.018, over 80.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.367, discriminator_fake_loss=1.323, generator_loss=28.82, generator_mel_loss=17.8, generator_kl_loss=1.415, generator_dur_loss=1.728, generator_adv_loss=2.003, generator_feat_match_loss=5.877, over 3748.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:39:58,421 INFO [train.py:527] (1/6) Epoch 792, batch 116, global_batch_idx: 98200, batch size: 56, loss[discriminator_loss=2.713, discriminator_real_loss=1.47, discriminator_fake_loss=1.243, generator_loss=28.67, generator_mel_loss=17.88, generator_kl_loss=1.348, generator_dur_loss=1.759, generator_adv_loss=2.021, generator_feat_match_loss=5.672, over 56.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.366, discriminator_fake_loss=1.33, generator_loss=28.84, generator_mel_loss=17.81, generator_kl_loss=1.422, generator_dur_loss=1.731, generator_adv_loss=1.999, generator_feat_match_loss=5.874, over 6597.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:39:58,422 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 09:40:07,210 INFO [train.py:591] (1/6) Epoch 792, validation: discriminator_loss=2.773, discriminator_real_loss=1.417, discriminator_fake_loss=1.356, generator_loss=28.46, generator_mel_loss=18.35, generator_kl_loss=1.298, generator_dur_loss=1.806, generator_adv_loss=1.904, generator_feat_match_loss=5.105, over 100.00 samples.
+2024-03-15 09:40:07,210 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 09:40:28,947 INFO [train.py:919] (1/6) Start epoch 793
+2024-03-15 09:42:50,099 INFO [train.py:527] (1/6) Epoch 793, batch 42, global_batch_idx: 98250, batch size: 45, loss[discriminator_loss=2.727, discriminator_real_loss=1.322, discriminator_fake_loss=1.405, generator_loss=29.13, generator_mel_loss=17.99, generator_kl_loss=1.548, generator_dur_loss=1.66, generator_adv_loss=2.069, generator_feat_match_loss=5.855, over 45.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.363, discriminator_fake_loss=1.332, generator_loss=28.97, generator_mel_loss=17.85, generator_kl_loss=1.432, generator_dur_loss=1.747, generator_adv_loss=2, generator_feat_match_loss=5.938, over 2570.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:45:08,671 INFO [train.py:527] (1/6) Epoch 793, batch 92, global_batch_idx: 98300, batch size: 31, loss[discriminator_loss=2.712, discriminator_real_loss=1.341, discriminator_fake_loss=1.371, generator_loss=29.44, generator_mel_loss=18.35, generator_kl_loss=1.547, generator_dur_loss=1.591, generator_adv_loss=1.984, generator_feat_match_loss=5.966, over 31.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.368, discriminator_fake_loss=1.333, generator_loss=28.88, generator_mel_loss=17.87, generator_kl_loss=1.434, generator_dur_loss=1.742, generator_adv_loss=1.996, generator_feat_match_loss=5.841, over 5332.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:46:37,295 INFO [train.py:919] (1/6) Start epoch 794
+2024-03-15 09:47:52,181 INFO [train.py:527] (1/6) Epoch 794, batch 18, global_batch_idx: 98350, batch size: 61, loss[discriminator_loss=2.632, discriminator_real_loss=1.304, discriminator_fake_loss=1.328, generator_loss=29.72, generator_mel_loss=17.99, generator_kl_loss=1.406, generator_dur_loss=1.726, generator_adv_loss=2.029, generator_feat_match_loss=6.564, over 61.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.37, discriminator_fake_loss=1.316, generator_loss=28.99, generator_mel_loss=17.92, generator_kl_loss=1.397, generator_dur_loss=1.738, generator_adv_loss=2.013, generator_feat_match_loss=5.924, over 1119.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:50:10,958 INFO [train.py:527] (1/6) Epoch 794, batch 68, global_batch_idx: 98400, batch size: 72, loss[discriminator_loss=2.62, discriminator_real_loss=1.395, discriminator_fake_loss=1.225, generator_loss=28.87, generator_mel_loss=17.93, generator_kl_loss=1.25, generator_dur_loss=1.776, generator_adv_loss=2.232, generator_feat_match_loss=5.68, over 72.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.365, discriminator_fake_loss=1.325, generator_loss=28.86, generator_mel_loss=17.82, generator_kl_loss=1.403, generator_dur_loss=1.745, generator_adv_loss=2.015, generator_feat_match_loss=5.878, over 4054.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:50:10,959 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 09:50:19,875 INFO [train.py:591] (1/6) Epoch 794, validation: discriminator_loss=2.619, discriminator_real_loss=1.385, discriminator_fake_loss=1.234, generator_loss=28.83, generator_mel_loss=18.56, generator_kl_loss=1.309, generator_dur_loss=1.806, generator_adv_loss=2.049, generator_feat_match_loss=5.114, over 100.00 samples.
+2024-03-15 09:50:19,877 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 09:52:39,490 INFO [train.py:527] (1/6) Epoch 794, batch 118, global_batch_idx: 98450, batch size: 31, loss[discriminator_loss=2.73, discriminator_real_loss=1.394, discriminator_fake_loss=1.336, generator_loss=29.34, generator_mel_loss=17.77, generator_kl_loss=1.404, generator_dur_loss=1.579, generator_adv_loss=2.014, generator_feat_match_loss=6.571, over 31.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.362, discriminator_fake_loss=1.325, generator_loss=28.88, generator_mel_loss=17.79, generator_kl_loss=1.421, generator_dur_loss=1.749, generator_adv_loss=2.014, generator_feat_match_loss=5.902, over 7033.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:52:53,432 INFO [train.py:919] (1/6) Start epoch 795
+2024-03-15 09:55:22,344 INFO [train.py:527] (1/6) Epoch 795, batch 44, global_batch_idx: 98500, batch size: 83, loss[discriminator_loss=2.695, discriminator_real_loss=1.324, discriminator_fake_loss=1.37, generator_loss=27.79, generator_mel_loss=17.79, generator_kl_loss=1.214, generator_dur_loss=1.807, generator_adv_loss=1.856, generator_feat_match_loss=5.124, over 83.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.362, discriminator_fake_loss=1.332, generator_loss=28.94, generator_mel_loss=17.88, generator_kl_loss=1.438, generator_dur_loss=1.733, generator_adv_loss=1.987, generator_feat_match_loss=5.904, over 2585.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:57:44,594 INFO [train.py:527] (1/6) Epoch 795, batch 94, global_batch_idx: 98550, batch size: 42, loss[discriminator_loss=2.627, discriminator_real_loss=1.363, discriminator_fake_loss=1.265, generator_loss=28.6, generator_mel_loss=17.76, generator_kl_loss=1.494, generator_dur_loss=1.689, generator_adv_loss=1.933, generator_feat_match_loss=5.724, over 42.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=28.83, generator_mel_loss=17.83, generator_kl_loss=1.421, generator_dur_loss=1.743, generator_adv_loss=1.994, generator_feat_match_loss=5.841, over 5653.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 09:59:04,238 INFO [train.py:919] (1/6) Start epoch 796
+2024-03-15 10:00:25,428 INFO [train.py:527] (1/6) Epoch 796, batch 20, global_batch_idx: 98600, batch size: 59, loss[discriminator_loss=2.696, discriminator_real_loss=1.405, discriminator_fake_loss=1.291, generator_loss=28.87, generator_mel_loss=17.78, generator_kl_loss=1.505, generator_dur_loss=1.736, generator_adv_loss=2.028, generator_feat_match_loss=5.826, over 59.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=28.75, generator_mel_loss=17.77, generator_kl_loss=1.431, generator_dur_loss=1.744, generator_adv_loss=1.99, generator_feat_match_loss=5.821, over 1174.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:00:25,430 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 10:00:33,304 INFO [train.py:591] (1/6) Epoch 796, validation: discriminator_loss=2.735, discriminator_real_loss=1.401, discriminator_fake_loss=1.334, generator_loss=28.03, generator_mel_loss=18.18, generator_kl_loss=1.285, generator_dur_loss=1.801, generator_adv_loss=1.94, generator_feat_match_loss=4.824, over 100.00 samples.
+2024-03-15 10:00:33,305 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 10:02:53,468 INFO [train.py:527] (1/6) Epoch 796, batch 70, global_batch_idx: 98650, batch size: 25, loss[discriminator_loss=2.703, discriminator_real_loss=1.198, discriminator_fake_loss=1.505, generator_loss=30.21, generator_mel_loss=18.74, generator_kl_loss=1.627, generator_dur_loss=1.541, generator_adv_loss=2.092, generator_feat_match_loss=6.212, over 25.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.346, discriminator_fake_loss=1.326, generator_loss=29.07, generator_mel_loss=17.91, generator_kl_loss=1.433, generator_dur_loss=1.734, generator_adv_loss=2.007, generator_feat_match_loss=5.989, over 3787.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:05:10,162 INFO [train.py:527] (1/6) Epoch 796, batch 120, global_batch_idx: 98700, batch size: 25, loss[discriminator_loss=2.635, discriminator_real_loss=1.411, discriminator_fake_loss=1.224, generator_loss=30.05, generator_mel_loss=18.59, generator_kl_loss=1.711, generator_dur_loss=1.505, generator_adv_loss=2.083, generator_feat_match_loss=6.159, over 25.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.35, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=17.89, generator_kl_loss=1.423, generator_dur_loss=1.741, generator_adv_loss=2.003, generator_feat_match_loss=5.987, over 6744.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:05:18,824 INFO [train.py:919] (1/6) Start epoch 797
+2024-03-15 10:07:52,337 INFO [train.py:527] (1/6) Epoch 797, batch 46, global_batch_idx: 98750, batch size: 55, loss[discriminator_loss=2.71, discriminator_real_loss=1.398, discriminator_fake_loss=1.312, generator_loss=28.28, generator_mel_loss=17.64, generator_kl_loss=1.353, generator_dur_loss=1.719, generator_adv_loss=2.016, generator_feat_match_loss=5.55, over 55.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=28.81, generator_mel_loss=17.77, generator_kl_loss=1.395, generator_dur_loss=1.766, generator_adv_loss=1.989, generator_feat_match_loss=5.894, over 2877.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:10:12,910 INFO [train.py:527] (1/6) Epoch 797, batch 96, global_batch_idx: 98800, batch size: 66, loss[discriminator_loss=2.637, discriminator_real_loss=1.373, discriminator_fake_loss=1.264, generator_loss=29.19, generator_mel_loss=17.74, generator_kl_loss=1.399, generator_dur_loss=1.762, generator_adv_loss=2.017, generator_feat_match_loss=6.277, over 66.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=28.83, generator_mel_loss=17.79, generator_kl_loss=1.422, generator_dur_loss=1.755, generator_adv_loss=1.99, generator_feat_match_loss=5.87, over 5816.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:10:12,912 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 10:10:21,697 INFO [train.py:591] (1/6) Epoch 797, validation: discriminator_loss=2.68, discriminator_real_loss=1.382, discriminator_fake_loss=1.298, generator_loss=26.95, generator_mel_loss=18.03, generator_kl_loss=1.322, generator_dur_loss=1.8, generator_adv_loss=1.85, generator_feat_match_loss=3.944, over 100.00 samples.
+2024-03-15 10:10:21,698 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 10:11:35,726 INFO [train.py:919] (1/6) Start epoch 798
+2024-03-15 10:12:59,895 INFO [train.py:527] (1/6) Epoch 798, batch 22, global_batch_idx: 98850, batch size: 39, loss[discriminator_loss=2.621, discriminator_real_loss=1.341, discriminator_fake_loss=1.281, generator_loss=28.38, generator_mel_loss=17.45, generator_kl_loss=1.561, generator_dur_loss=1.688, generator_adv_loss=2.057, generator_feat_match_loss=5.628, over 39.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.353, discriminator_fake_loss=1.31, generator_loss=28.95, generator_mel_loss=17.84, generator_kl_loss=1.453, generator_dur_loss=1.697, generator_adv_loss=2.015, generator_feat_match_loss=5.936, over 1159.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:15:18,199 INFO [train.py:527] (1/6) Epoch 798, batch 72, global_batch_idx: 98900, batch size: 31, loss[discriminator_loss=2.694, discriminator_real_loss=1.349, discriminator_fake_loss=1.345, generator_loss=29.85, generator_mel_loss=18.29, generator_kl_loss=1.501, generator_dur_loss=1.598, generator_adv_loss=2.066, generator_feat_match_loss=6.397, over 31.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=28.8, generator_mel_loss=17.78, generator_kl_loss=1.434, generator_dur_loss=1.731, generator_adv_loss=1.993, generator_feat_match_loss=5.862, over 4067.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:17:36,016 INFO [train.py:527] (1/6) Epoch 798, batch 122, global_batch_idx: 98950, batch size: 70, loss[discriminator_loss=2.679, discriminator_real_loss=1.411, discriminator_fake_loss=1.267, generator_loss=29.11, generator_mel_loss=17.57, generator_kl_loss=1.397, generator_dur_loss=1.812, generator_adv_loss=2.045, generator_feat_match_loss=6.291, over 70.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.361, discriminator_fake_loss=1.323, generator_loss=28.81, generator_mel_loss=17.78, generator_kl_loss=1.44, generator_dur_loss=1.732, generator_adv_loss=2, generator_feat_match_loss=5.864, over 6645.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:17:40,942 INFO [train.py:919] (1/6) Start epoch 799
+2024-03-15 10:20:18,164 INFO [train.py:527] (1/6) Epoch 799, batch 48, global_batch_idx: 99000, batch size: 25, loss[discriminator_loss=2.733, discriminator_real_loss=1.345, discriminator_fake_loss=1.388, generator_loss=28.86, generator_mel_loss=18.4, generator_kl_loss=1.708, generator_dur_loss=1.563, generator_adv_loss=2.26, generator_feat_match_loss=4.92, over 25.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.364, discriminator_fake_loss=1.32, generator_loss=29.06, generator_mel_loss=17.86, generator_kl_loss=1.459, generator_dur_loss=1.726, generator_adv_loss=2.005, generator_feat_match_loss=6.012, over 2677.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:20:18,166 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 10:20:26,124 INFO [train.py:591] (1/6) Epoch 799, validation: discriminator_loss=2.793, discriminator_real_loss=1.59, discriminator_fake_loss=1.203, generator_loss=28.54, generator_mel_loss=18.53, generator_kl_loss=1.235, generator_dur_loss=1.793, generator_adv_loss=2.161, generator_feat_match_loss=4.818, over 100.00 samples.
+2024-03-15 10:20:26,125 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 10:22:43,930 INFO [train.py:527] (1/6) Epoch 799, batch 98, global_batch_idx: 99050, batch size: 83, loss[discriminator_loss=2.747, discriminator_real_loss=1.299, discriminator_fake_loss=1.448, generator_loss=28.19, generator_mel_loss=17.78, generator_kl_loss=1.216, generator_dur_loss=1.806, generator_adv_loss=2.001, generator_feat_match_loss=5.393, over 83.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.325, generator_loss=28.97, generator_mel_loss=17.84, generator_kl_loss=1.446, generator_dur_loss=1.728, generator_adv_loss=2.001, generator_feat_match_loss=5.947, over 5496.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:23:55,046 INFO [train.py:919] (1/6) Start epoch 800
+2024-03-15 10:25:24,107 INFO [train.py:527] (1/6) Epoch 800, batch 24, global_batch_idx: 99100, batch size: 72, loss[discriminator_loss=2.664, discriminator_real_loss=1.365, discriminator_fake_loss=1.299, generator_loss=29.34, generator_mel_loss=17.66, generator_kl_loss=1.401, generator_dur_loss=1.795, generator_adv_loss=1.977, generator_feat_match_loss=6.504, over 72.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.358, discriminator_fake_loss=1.33, generator_loss=29.02, generator_mel_loss=17.82, generator_kl_loss=1.475, generator_dur_loss=1.707, generator_adv_loss=1.983, generator_feat_match_loss=6.038, over 1314.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:27:42,608 INFO [train.py:527] (1/6) Epoch 800, batch 74, global_batch_idx: 99150, batch size: 58, loss[discriminator_loss=2.67, discriminator_real_loss=1.381, discriminator_fake_loss=1.289, generator_loss=28.94, generator_mel_loss=17.69, generator_kl_loss=1.484, generator_dur_loss=1.753, generator_adv_loss=2.151, generator_feat_match_loss=5.867, over 58.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=28.8, generator_mel_loss=17.83, generator_kl_loss=1.425, generator_dur_loss=1.73, generator_adv_loss=1.996, generator_feat_match_loss=5.826, over 4163.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:30:01,576 INFO [train.py:919] (1/6) Start epoch 801
+2024-03-15 10:30:25,688 INFO [train.py:527] (1/6) Epoch 801, batch 0, global_batch_idx: 99200, batch size: 64, loss[discriminator_loss=2.714, discriminator_real_loss=1.399, discriminator_fake_loss=1.315, generator_loss=28.28, generator_mel_loss=17.56, generator_kl_loss=1.142, generator_dur_loss=1.814, generator_adv_loss=2.104, generator_feat_match_loss=5.66, over 64.00 samples.], tot_loss[discriminator_loss=2.714, discriminator_real_loss=1.399, discriminator_fake_loss=1.315, generator_loss=28.28, generator_mel_loss=17.56, generator_kl_loss=1.142, generator_dur_loss=1.814, generator_adv_loss=2.104, generator_feat_match_loss=5.66, over 64.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:30:25,690 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 10:30:33,648 INFO [train.py:591] (1/6) Epoch 801, validation: discriminator_loss=2.725, discriminator_real_loss=1.353, discriminator_fake_loss=1.371, generator_loss=27.21, generator_mel_loss=17.97, generator_kl_loss=1.262, generator_dur_loss=1.796, generator_adv_loss=1.878, generator_feat_match_loss=4.304, over 100.00 samples.
+2024-03-15 10:30:33,651 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 10:32:53,852 INFO [train.py:527] (1/6) Epoch 801, batch 50, global_batch_idx: 99250, batch size: 52, loss[discriminator_loss=2.673, discriminator_real_loss=1.352, discriminator_fake_loss=1.321, generator_loss=29.28, generator_mel_loss=17.8, generator_kl_loss=1.508, generator_dur_loss=1.69, generator_adv_loss=1.96, generator_feat_match_loss=6.322, over 52.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.36, discriminator_fake_loss=1.326, generator_loss=28.75, generator_mel_loss=17.69, generator_kl_loss=1.426, generator_dur_loss=1.755, generator_adv_loss=2, generator_feat_match_loss=5.882, over 3062.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:35:13,695 INFO [train.py:527] (1/6) Epoch 801, batch 100, global_batch_idx: 99300, batch size: 50, loss[discriminator_loss=2.666, discriminator_real_loss=1.312, discriminator_fake_loss=1.354, generator_loss=28.98, generator_mel_loss=17.66, generator_kl_loss=1.65, generator_dur_loss=1.707, generator_adv_loss=2.046, generator_feat_match_loss=5.915, over 50.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=28.94, generator_mel_loss=17.79, generator_kl_loss=1.431, generator_dur_loss=1.753, generator_adv_loss=2.024, generator_feat_match_loss=5.938, over 5867.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:36:20,143 INFO [train.py:919] (1/6) Start epoch 802
+2024-03-15 10:37:54,651 INFO [train.py:527] (1/6) Epoch 802, batch 26, global_batch_idx: 99350, batch size: 25, loss[discriminator_loss=2.61, discriminator_real_loss=1.245, discriminator_fake_loss=1.365, generator_loss=32.04, generator_mel_loss=19.26, generator_kl_loss=1.853, generator_dur_loss=1.543, generator_adv_loss=2.181, generator_feat_match_loss=7.195, over 25.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=28.78, generator_mel_loss=17.79, generator_kl_loss=1.447, generator_dur_loss=1.752, generator_adv_loss=1.99, generator_feat_match_loss=5.802, over 1572.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:40:14,245 INFO [train.py:527] (1/6) Epoch 802, batch 76, global_batch_idx: 99400, batch size: 61, loss[discriminator_loss=2.604, discriminator_real_loss=1.323, discriminator_fake_loss=1.281, generator_loss=29.68, generator_mel_loss=18.17, generator_kl_loss=1.421, generator_dur_loss=1.701, generator_adv_loss=1.962, generator_feat_match_loss=6.423, over 61.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.82, generator_mel_loss=17.82, generator_kl_loss=1.445, generator_dur_loss=1.749, generator_adv_loss=1.985, generator_feat_match_loss=5.822, over 4530.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:40:14,247 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 10:40:23,117 INFO [train.py:591] (1/6) Epoch 802, validation: discriminator_loss=2.677, discriminator_real_loss=1.347, discriminator_fake_loss=1.33, generator_loss=27.8, generator_mel_loss=18.01, generator_kl_loss=1.349, generator_dur_loss=1.825, generator_adv_loss=1.83, generator_feat_match_loss=4.787, over 100.00 samples.
+2024-03-15 10:40:23,119 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 10:42:36,496 INFO [train.py:919] (1/6) Start epoch 803
+2024-03-15 10:43:07,098 INFO [train.py:527] (1/6) Epoch 803, batch 2, global_batch_idx: 99450, batch size: 74, loss[discriminator_loss=2.71, discriminator_real_loss=1.375, discriminator_fake_loss=1.335, generator_loss=29.42, generator_mel_loss=17.9, generator_kl_loss=1.519, generator_dur_loss=1.793, generator_adv_loss=1.943, generator_feat_match_loss=6.263, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.344, discriminator_fake_loss=1.343, generator_loss=29.4, generator_mel_loss=18.01, generator_kl_loss=1.517, generator_dur_loss=1.758, generator_adv_loss=1.985, generator_feat_match_loss=6.128, over 169.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:45:25,729 INFO [train.py:527] (1/6) Epoch 803, batch 52, global_batch_idx: 99500, batch size: 59, loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=30.39, generator_mel_loss=18.35, generator_kl_loss=1.507, generator_dur_loss=1.737, generator_adv_loss=1.989, generator_feat_match_loss=6.815, over 59.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.06, generator_mel_loss=17.86, generator_kl_loss=1.455, generator_dur_loss=1.738, generator_adv_loss=2.007, generator_feat_match_loss=6.003, over 2979.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:47:46,026 INFO [train.py:527] (1/6) Epoch 803, batch 102, global_batch_idx: 99550, batch size: 66, loss[discriminator_loss=2.695, discriminator_real_loss=1.418, discriminator_fake_loss=1.278, generator_loss=28.49, generator_mel_loss=17.6, generator_kl_loss=1.447, generator_dur_loss=1.764, generator_adv_loss=1.976, generator_feat_match_loss=5.707, over 66.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=28.95, generator_mel_loss=17.84, generator_kl_loss=1.434, generator_dur_loss=1.747, generator_adv_loss=2.004, generator_feat_match_loss=5.932, over 6006.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:48:45,421 INFO [train.py:919] (1/6) Start epoch 804
+2024-03-15 10:50:28,513 INFO [train.py:527] (1/6) Epoch 804, batch 28, global_batch_idx: 99600, batch size: 80, loss[discriminator_loss=2.679, discriminator_real_loss=1.383, discriminator_fake_loss=1.296, generator_loss=29.1, generator_mel_loss=17.88, generator_kl_loss=1.398, generator_dur_loss=1.779, generator_adv_loss=1.981, generator_feat_match_loss=6.058, over 80.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.356, discriminator_fake_loss=1.316, generator_loss=29.06, generator_mel_loss=17.78, generator_kl_loss=1.408, generator_dur_loss=1.747, generator_adv_loss=2.026, generator_feat_match_loss=6.093, over 1653.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:50:28,514 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 10:50:36,564 INFO [train.py:591] (1/6) Epoch 804, validation: discriminator_loss=2.731, discriminator_real_loss=1.378, discriminator_fake_loss=1.353, generator_loss=27.59, generator_mel_loss=17.92, generator_kl_loss=1.318, generator_dur_loss=1.816, generator_adv_loss=1.884, generator_feat_match_loss=4.657, over 100.00 samples.
+2024-03-15 10:50:36,565 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 10:52:54,911 INFO [train.py:527] (1/6) Epoch 804, batch 78, global_batch_idx: 99650, batch size: 96, loss[discriminator_loss=2.672, discriminator_real_loss=1.407, discriminator_fake_loss=1.265, generator_loss=28.27, generator_mel_loss=17.57, generator_kl_loss=1.281, generator_dur_loss=1.889, generator_adv_loss=1.927, generator_feat_match_loss=5.602, over 96.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=29.02, generator_mel_loss=17.86, generator_kl_loss=1.419, generator_dur_loss=1.745, generator_adv_loss=2.004, generator_feat_match_loss=5.998, over 4369.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:54:58,629 INFO [train.py:919] (1/6) Start epoch 805
+2024-03-15 10:55:33,800 INFO [train.py:527] (1/6) Epoch 805, batch 4, global_batch_idx: 99700, batch size: 56, loss[discriminator_loss=2.841, discriminator_real_loss=1.411, discriminator_fake_loss=1.43, generator_loss=28.53, generator_mel_loss=17.83, generator_kl_loss=1.507, generator_dur_loss=1.741, generator_adv_loss=1.913, generator_feat_match_loss=5.546, over 56.00 samples.], tot_loss[discriminator_loss=2.765, discriminator_real_loss=1.404, discriminator_fake_loss=1.36, generator_loss=28.9, generator_mel_loss=17.86, generator_kl_loss=1.436, generator_dur_loss=1.761, generator_adv_loss=1.964, generator_feat_match_loss=5.871, over 326.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 10:57:51,694 INFO [train.py:527] (1/6) Epoch 805, batch 54, global_batch_idx: 99750, batch size: 77, loss[discriminator_loss=2.684, discriminator_real_loss=1.387, discriminator_fake_loss=1.297, generator_loss=29.49, generator_mel_loss=17.89, generator_kl_loss=1.345, generator_dur_loss=1.828, generator_adv_loss=1.985, generator_feat_match_loss=6.438, over 77.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.363, discriminator_fake_loss=1.323, generator_loss=29.04, generator_mel_loss=17.88, generator_kl_loss=1.409, generator_dur_loss=1.747, generator_adv_loss=2.027, generator_feat_match_loss=5.969, over 3322.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:00:12,926 INFO [train.py:527] (1/6) Epoch 805, batch 104, global_batch_idx: 99800, batch size: 80, loss[discriminator_loss=2.643, discriminator_real_loss=1.398, discriminator_fake_loss=1.245, generator_loss=28.37, generator_mel_loss=17.61, generator_kl_loss=1.465, generator_dur_loss=1.812, generator_adv_loss=1.834, generator_feat_match_loss=5.647, over 80.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.94, generator_mel_loss=17.86, generator_kl_loss=1.411, generator_dur_loss=1.763, generator_adv_loss=2.013, generator_feat_match_loss=5.897, over 6438.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:00:12,927 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 11:00:21,935 INFO [train.py:591] (1/6) Epoch 805, validation: discriminator_loss=2.746, discriminator_real_loss=1.292, discriminator_fake_loss=1.454, generator_loss=27.25, generator_mel_loss=17.79, generator_kl_loss=1.295, generator_dur_loss=1.805, generator_adv_loss=1.74, generator_feat_match_loss=4.622, over 100.00 samples.
+2024-03-15 11:00:21,937 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 11:01:14,788 INFO [train.py:919] (1/6) Start epoch 806
+2024-03-15 11:03:00,288 INFO [train.py:527] (1/6) Epoch 806, batch 30, global_batch_idx: 99850, batch size: 44, loss[discriminator_loss=2.672, discriminator_real_loss=1.338, discriminator_fake_loss=1.335, generator_loss=28.68, generator_mel_loss=17.86, generator_kl_loss=1.567, generator_dur_loss=1.754, generator_adv_loss=2.083, generator_feat_match_loss=5.415, over 44.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.357, discriminator_fake_loss=1.322, generator_loss=28.85, generator_mel_loss=17.81, generator_kl_loss=1.428, generator_dur_loss=1.748, generator_adv_loss=1.987, generator_feat_match_loss=5.875, over 1698.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:05:18,968 INFO [train.py:527] (1/6) Epoch 806, batch 80, global_batch_idx: 99900, batch size: 88, loss[discriminator_loss=2.704, discriminator_real_loss=1.499, discriminator_fake_loss=1.205, generator_loss=29.31, generator_mel_loss=17.98, generator_kl_loss=1.273, generator_dur_loss=1.851, generator_adv_loss=1.872, generator_feat_match_loss=6.344, over 88.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=28.96, generator_mel_loss=17.83, generator_kl_loss=1.431, generator_dur_loss=1.749, generator_adv_loss=1.991, generator_feat_match_loss=5.965, over 4575.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:07:20,363 INFO [train.py:919] (1/6) Start epoch 807
+2024-03-15 11:08:01,270 INFO [train.py:527] (1/6) Epoch 807, batch 6, global_batch_idx: 99950, batch size: 77, loss[discriminator_loss=2.709, discriminator_real_loss=1.385, discriminator_fake_loss=1.324, generator_loss=28.57, generator_mel_loss=17.82, generator_kl_loss=1.364, generator_dur_loss=1.83, generator_adv_loss=2.032, generator_feat_match_loss=5.525, over 77.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.36, discriminator_fake_loss=1.321, generator_loss=28.65, generator_mel_loss=17.75, generator_kl_loss=1.417, generator_dur_loss=1.755, generator_adv_loss=2.013, generator_feat_match_loss=5.717, over 421.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:10:20,948 INFO [train.py:527] (1/6) Epoch 807, batch 56, global_batch_idx: 100000, batch size: 36, loss[discriminator_loss=2.648, discriminator_real_loss=1.285, discriminator_fake_loss=1.363, generator_loss=29.42, generator_mel_loss=17.48, generator_kl_loss=1.629, generator_dur_loss=1.654, generator_adv_loss=1.931, generator_feat_match_loss=6.729, over 36.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.357, discriminator_fake_loss=1.326, generator_loss=28.87, generator_mel_loss=17.81, generator_kl_loss=1.427, generator_dur_loss=1.741, generator_adv_loss=2.002, generator_feat_match_loss=5.885, over 3247.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:10:20,950 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 11:10:28,937 INFO [train.py:591] (1/6) Epoch 807, validation: discriminator_loss=2.774, discriminator_real_loss=1.429, discriminator_fake_loss=1.345, generator_loss=26.98, generator_mel_loss=18.01, generator_kl_loss=1.191, generator_dur_loss=1.766, generator_adv_loss=1.844, generator_feat_match_loss=4.171, over 100.00 samples.
+2024-03-15 11:10:28,940 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 11:12:46,952 INFO [train.py:527] (1/6) Epoch 807, batch 106, global_batch_idx: 100050, batch size: 77, loss[discriminator_loss=2.811, discriminator_real_loss=1.562, discriminator_fake_loss=1.25, generator_loss=28.87, generator_mel_loss=17.91, generator_kl_loss=1.276, generator_dur_loss=1.761, generator_adv_loss=1.884, generator_feat_match_loss=6.04, over 77.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=28.91, generator_mel_loss=17.81, generator_kl_loss=1.428, generator_dur_loss=1.737, generator_adv_loss=2.001, generator_feat_match_loss=5.935, over 6035.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:13:36,333 INFO [train.py:919] (1/6) Start epoch 808
+2024-03-15 11:15:28,514 INFO [train.py:527] (1/6) Epoch 808, batch 32, global_batch_idx: 100100, batch size: 52, loss[discriminator_loss=2.709, discriminator_real_loss=1.363, discriminator_fake_loss=1.346, generator_loss=29.18, generator_mel_loss=18.08, generator_kl_loss=1.441, generator_dur_loss=1.731, generator_adv_loss=2.094, generator_feat_match_loss=5.834, over 52.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.347, discriminator_fake_loss=1.335, generator_loss=28.93, generator_mel_loss=17.95, generator_kl_loss=1.416, generator_dur_loss=1.739, generator_adv_loss=1.984, generator_feat_match_loss=5.845, over 1788.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:17:49,504 INFO [train.py:527] (1/6) Epoch 808, batch 82, global_batch_idx: 100150, batch size: 55, loss[discriminator_loss=2.725, discriminator_real_loss=1.436, discriminator_fake_loss=1.289, generator_loss=28.8, generator_mel_loss=17.88, generator_kl_loss=1.328, generator_dur_loss=1.666, generator_adv_loss=2.027, generator_feat_match_loss=5.903, over 55.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.358, discriminator_fake_loss=1.33, generator_loss=28.9, generator_mel_loss=17.87, generator_kl_loss=1.433, generator_dur_loss=1.734, generator_adv_loss=1.99, generator_feat_match_loss=5.872, over 4585.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:19:46,388 INFO [train.py:919] (1/6) Start epoch 809
+2024-03-15 11:20:33,712 INFO [train.py:527] (1/6) Epoch 809, batch 8, global_batch_idx: 100200, batch size: 39, loss[discriminator_loss=2.629, discriminator_real_loss=1.203, discriminator_fake_loss=1.427, generator_loss=31.02, generator_mel_loss=18.2, generator_kl_loss=1.494, generator_dur_loss=1.655, generator_adv_loss=2.075, generator_feat_match_loss=7.588, over 39.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.349, discriminator_fake_loss=1.324, generator_loss=28.89, generator_mel_loss=17.75, generator_kl_loss=1.488, generator_dur_loss=1.699, generator_adv_loss=2.02, generator_feat_match_loss=5.93, over 432.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:20:33,714 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 11:20:41,723 INFO [train.py:591] (1/6) Epoch 809, validation: discriminator_loss=2.69, discriminator_real_loss=1.45, discriminator_fake_loss=1.241, generator_loss=27.66, generator_mel_loss=18.09, generator_kl_loss=1.268, generator_dur_loss=1.804, generator_adv_loss=2.02, generator_feat_match_loss=4.477, over 100.00 samples.
+2024-03-15 11:20:41,726 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 11:22:59,987 INFO [train.py:527] (1/6) Epoch 809, batch 58, global_batch_idx: 100250, batch size: 59, loss[discriminator_loss=2.624, discriminator_real_loss=1.324, discriminator_fake_loss=1.3, generator_loss=28.93, generator_mel_loss=17.5, generator_kl_loss=1.451, generator_dur_loss=1.708, generator_adv_loss=2.029, generator_feat_match_loss=6.236, over 59.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.35, discriminator_fake_loss=1.321, generator_loss=28.99, generator_mel_loss=17.85, generator_kl_loss=1.449, generator_dur_loss=1.744, generator_adv_loss=2.005, generator_feat_match_loss=5.948, over 3280.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:25:18,723 INFO [train.py:527] (1/6) Epoch 809, batch 108, global_batch_idx: 100300, batch size: 56, loss[discriminator_loss=2.702, discriminator_real_loss=1.291, discriminator_fake_loss=1.411, generator_loss=29.41, generator_mel_loss=17.76, generator_kl_loss=1.392, generator_dur_loss=1.704, generator_adv_loss=2.021, generator_feat_match_loss=6.529, over 56.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=28.97, generator_mel_loss=17.87, generator_kl_loss=1.435, generator_dur_loss=1.74, generator_adv_loss=2.002, generator_feat_match_loss=5.923, over 6022.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:26:00,704 INFO [train.py:919] (1/6) Start epoch 810
+2024-03-15 11:27:58,541 INFO [train.py:527] (1/6) Epoch 810, batch 34, global_batch_idx: 100350, batch size: 42, loss[discriminator_loss=2.724, discriminator_real_loss=1.4, discriminator_fake_loss=1.324, generator_loss=28.51, generator_mel_loss=18.22, generator_kl_loss=1.786, generator_dur_loss=1.689, generator_adv_loss=1.802, generator_feat_match_loss=5.008, over 42.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29, generator_mel_loss=17.86, generator_kl_loss=1.436, generator_dur_loss=1.722, generator_adv_loss=1.99, generator_feat_match_loss=5.987, over 1771.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:30:15,740 INFO [train.py:527] (1/6) Epoch 810, batch 84, global_batch_idx: 100400, batch size: 88, loss[discriminator_loss=2.673, discriminator_real_loss=1.381, discriminator_fake_loss=1.293, generator_loss=28.19, generator_mel_loss=17.56, generator_kl_loss=1.361, generator_dur_loss=1.857, generator_adv_loss=1.99, generator_feat_match_loss=5.419, over 88.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.353, discriminator_fake_loss=1.32, generator_loss=28.91, generator_mel_loss=17.78, generator_kl_loss=1.42, generator_dur_loss=1.74, generator_adv_loss=1.998, generator_feat_match_loss=5.966, over 4740.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:30:15,742 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 11:30:24,306 INFO [train.py:591] (1/6) Epoch 810, validation: discriminator_loss=2.77, discriminator_real_loss=1.386, discriminator_fake_loss=1.384, generator_loss=27.57, generator_mel_loss=18.1, generator_kl_loss=1.194, generator_dur_loss=1.808, generator_adv_loss=1.82, generator_feat_match_loss=4.648, over 100.00 samples.
+2024-03-15 11:30:24,306 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 11:32:15,684 INFO [train.py:919] (1/6) Start epoch 811
+2024-03-15 11:33:08,634 INFO [train.py:527] (1/6) Epoch 811, batch 10, global_batch_idx: 100450, batch size: 53, loss[discriminator_loss=2.673, discriminator_real_loss=1.32, discriminator_fake_loss=1.353, generator_loss=29.94, generator_mel_loss=17.87, generator_kl_loss=1.545, generator_dur_loss=1.654, generator_adv_loss=1.906, generator_feat_match_loss=6.969, over 53.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.377, discriminator_fake_loss=1.304, generator_loss=29.25, generator_mel_loss=17.87, generator_kl_loss=1.445, generator_dur_loss=1.736, generator_adv_loss=2.023, generator_feat_match_loss=6.18, over 613.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:35:26,312 INFO [train.py:527] (1/6) Epoch 811, batch 60, global_batch_idx: 100500, batch size: 55, loss[discriminator_loss=2.748, discriminator_real_loss=1.393, discriminator_fake_loss=1.355, generator_loss=28.27, generator_mel_loss=17.74, generator_kl_loss=1.446, generator_dur_loss=1.661, generator_adv_loss=1.962, generator_feat_match_loss=5.461, over 55.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.351, discriminator_fake_loss=1.325, generator_loss=28.93, generator_mel_loss=17.82, generator_kl_loss=1.426, generator_dur_loss=1.749, generator_adv_loss=1.987, generator_feat_match_loss=5.945, over 3522.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:37:43,205 INFO [train.py:527] (1/6) Epoch 811, batch 110, global_batch_idx: 100550, batch size: 53, loss[discriminator_loss=2.632, discriminator_real_loss=1.325, discriminator_fake_loss=1.307, generator_loss=29.6, generator_mel_loss=17.68, generator_kl_loss=1.585, generator_dur_loss=1.617, generator_adv_loss=1.988, generator_feat_match_loss=6.73, over 53.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=28.97, generator_mel_loss=17.81, generator_kl_loss=1.435, generator_dur_loss=1.751, generator_adv_loss=1.993, generator_feat_match_loss=5.979, over 6384.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:38:20,767 INFO [train.py:919] (1/6) Start epoch 812
+2024-03-15 11:40:25,002 INFO [train.py:527] (1/6) Epoch 812, batch 36, global_batch_idx: 100600, batch size: 47, loss[discriminator_loss=2.757, discriminator_real_loss=1.365, discriminator_fake_loss=1.391, generator_loss=28.84, generator_mel_loss=17.9, generator_kl_loss=1.536, generator_dur_loss=1.673, generator_adv_loss=1.943, generator_feat_match_loss=5.783, over 47.00 samples.], tot_loss[discriminator_loss=2.701, discriminator_real_loss=1.369, discriminator_fake_loss=1.332, generator_loss=28.64, generator_mel_loss=17.71, generator_kl_loss=1.402, generator_dur_loss=1.756, generator_adv_loss=1.998, generator_feat_match_loss=5.776, over 2295.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:40:25,003 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 11:40:33,078 INFO [train.py:591] (1/6) Epoch 812, validation: discriminator_loss=2.719, discriminator_real_loss=1.419, discriminator_fake_loss=1.3, generator_loss=28.17, generator_mel_loss=18.41, generator_kl_loss=1.234, generator_dur_loss=1.801, generator_adv_loss=1.905, generator_feat_match_loss=4.82, over 100.00 samples.
+2024-03-15 11:40:33,079 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 11:42:53,732 INFO [train.py:527] (1/6) Epoch 812, batch 86, global_batch_idx: 100650, batch size: 96, loss[discriminator_loss=2.684, discriminator_real_loss=1.397, discriminator_fake_loss=1.287, generator_loss=29.47, generator_mel_loss=17.98, generator_kl_loss=1.354, generator_dur_loss=1.835, generator_adv_loss=1.982, generator_feat_match_loss=6.326, over 96.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.36, discriminator_fake_loss=1.323, generator_loss=28.92, generator_mel_loss=17.8, generator_kl_loss=1.413, generator_dur_loss=1.743, generator_adv_loss=2.01, generator_feat_match_loss=5.957, over 5116.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:44:33,342 INFO [train.py:919] (1/6) Start epoch 813
+2024-03-15 11:45:30,544 INFO [train.py:527] (1/6) Epoch 813, batch 12, global_batch_idx: 100700, batch size: 56, loss[discriminator_loss=2.619, discriminator_real_loss=1.38, discriminator_fake_loss=1.239, generator_loss=28.41, generator_mel_loss=17.69, generator_kl_loss=1.445, generator_dur_loss=1.716, generator_adv_loss=2.026, generator_feat_match_loss=5.529, over 56.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.363, discriminator_fake_loss=1.316, generator_loss=29.04, generator_mel_loss=17.76, generator_kl_loss=1.41, generator_dur_loss=1.767, generator_adv_loss=2.024, generator_feat_match_loss=6.076, over 815.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:47:51,309 INFO [train.py:527] (1/6) Epoch 813, batch 62, global_batch_idx: 100750, batch size: 61, loss[discriminator_loss=2.688, discriminator_real_loss=1.435, discriminator_fake_loss=1.253, generator_loss=27.37, generator_mel_loss=17.34, generator_kl_loss=1.265, generator_dur_loss=1.697, generator_adv_loss=1.99, generator_feat_match_loss=5.077, over 61.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.37, discriminator_fake_loss=1.321, generator_loss=28.83, generator_mel_loss=17.77, generator_kl_loss=1.424, generator_dur_loss=1.746, generator_adv_loss=2.005, generator_feat_match_loss=5.889, over 3583.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:50:11,240 INFO [train.py:527] (1/6) Epoch 813, batch 112, global_batch_idx: 100800, batch size: 42, loss[discriminator_loss=2.716, discriminator_real_loss=1.363, discriminator_fake_loss=1.353, generator_loss=29.11, generator_mel_loss=17.72, generator_kl_loss=1.6, generator_dur_loss=1.63, generator_adv_loss=2.086, generator_feat_match_loss=6.076, over 42.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=28.8, generator_mel_loss=17.77, generator_kl_loss=1.426, generator_dur_loss=1.748, generator_adv_loss=2.001, generator_feat_match_loss=5.856, over 6411.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:50:11,241 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 11:50:19,987 INFO [train.py:591] (1/6) Epoch 813, validation: discriminator_loss=2.742, discriminator_real_loss=1.424, discriminator_fake_loss=1.318, generator_loss=28.59, generator_mel_loss=18.42, generator_kl_loss=1.395, generator_dur_loss=1.819, generator_adv_loss=2.009, generator_feat_match_loss=4.94, over 100.00 samples.
+2024-03-15 11:50:19,988 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 11:50:51,259 INFO [train.py:919] (1/6) Start epoch 814
+2024-03-15 11:52:57,890 INFO [train.py:527] (1/6) Epoch 814, batch 38, global_batch_idx: 100850, batch size: 72, loss[discriminator_loss=2.67, discriminator_real_loss=1.329, discriminator_fake_loss=1.341, generator_loss=28.89, generator_mel_loss=17.61, generator_kl_loss=1.414, generator_dur_loss=1.757, generator_adv_loss=2.095, generator_feat_match_loss=6.008, over 72.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=29.03, generator_mel_loss=17.81, generator_kl_loss=1.433, generator_dur_loss=1.728, generator_adv_loss=2.004, generator_feat_match_loss=6.054, over 2149.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:55:16,894 INFO [train.py:527] (1/6) Epoch 814, batch 88, global_batch_idx: 100900, batch size: 39, loss[discriminator_loss=2.685, discriminator_real_loss=1.327, discriminator_fake_loss=1.358, generator_loss=29.5, generator_mel_loss=17.97, generator_kl_loss=1.573, generator_dur_loss=1.714, generator_adv_loss=2.13, generator_feat_match_loss=6.109, over 39.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.361, discriminator_fake_loss=1.326, generator_loss=28.99, generator_mel_loss=17.82, generator_kl_loss=1.419, generator_dur_loss=1.739, generator_adv_loss=1.999, generator_feat_match_loss=6.012, over 5024.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 11:56:58,284 INFO [train.py:919] (1/6) Start epoch 815
+2024-03-15 11:58:01,501 INFO [train.py:527] (1/6) Epoch 815, batch 14, global_batch_idx: 100950, batch size: 47, loss[discriminator_loss=2.687, discriminator_real_loss=1.39, discriminator_fake_loss=1.297, generator_loss=29.38, generator_mel_loss=17.72, generator_kl_loss=1.522, generator_dur_loss=1.656, generator_adv_loss=2.005, generator_feat_match_loss=6.467, over 47.00 samples.], tot_loss[discriminator_loss=2.702, discriminator_real_loss=1.384, discriminator_fake_loss=1.318, generator_loss=28.84, generator_mel_loss=17.77, generator_kl_loss=1.453, generator_dur_loss=1.716, generator_adv_loss=1.993, generator_feat_match_loss=5.906, over 760.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:00:23,961 INFO [train.py:527] (1/6) Epoch 815, batch 64, global_batch_idx: 101000, batch size: 77, loss[discriminator_loss=2.725, discriminator_real_loss=1.432, discriminator_fake_loss=1.293, generator_loss=27.19, generator_mel_loss=17.21, generator_kl_loss=1.355, generator_dur_loss=1.792, generator_adv_loss=1.953, generator_feat_match_loss=4.885, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.364, discriminator_fake_loss=1.321, generator_loss=28.92, generator_mel_loss=17.87, generator_kl_loss=1.422, generator_dur_loss=1.75, generator_adv_loss=2.002, generator_feat_match_loss=5.871, over 3686.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:00:23,963 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 12:00:32,152 INFO [train.py:591] (1/6) Epoch 815, validation: discriminator_loss=2.77, discriminator_real_loss=1.414, discriminator_fake_loss=1.356, generator_loss=27.53, generator_mel_loss=18, generator_kl_loss=1.165, generator_dur_loss=1.81, generator_adv_loss=1.9, generator_feat_match_loss=4.658, over 100.00 samples.
+2024-03-15 12:00:32,153 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 12:02:51,515 INFO [train.py:527] (1/6) Epoch 815, batch 114, global_batch_idx: 101050, batch size: 48, loss[discriminator_loss=2.606, discriminator_real_loss=1.349, discriminator_fake_loss=1.257, generator_loss=29.01, generator_mel_loss=17.86, generator_kl_loss=1.421, generator_dur_loss=1.658, generator_adv_loss=1.992, generator_feat_match_loss=6.083, over 48.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.324, generator_loss=28.9, generator_mel_loss=17.84, generator_kl_loss=1.417, generator_dur_loss=1.748, generator_adv_loss=2, generator_feat_match_loss=5.896, over 6464.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:03:20,739 INFO [train.py:919] (1/6) Start epoch 816
+2024-03-15 12:05:40,905 INFO [train.py:527] (1/6) Epoch 816, batch 40, global_batch_idx: 101100, batch size: 80, loss[discriminator_loss=2.741, discriminator_real_loss=1.397, discriminator_fake_loss=1.344, generator_loss=28.53, generator_mel_loss=17.92, generator_kl_loss=1.389, generator_dur_loss=1.804, generator_adv_loss=1.954, generator_feat_match_loss=5.462, over 80.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.367, discriminator_fake_loss=1.329, generator_loss=29.03, generator_mel_loss=17.86, generator_kl_loss=1.454, generator_dur_loss=1.746, generator_adv_loss=2.004, generator_feat_match_loss=5.96, over 2314.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:07:55,595 INFO [train.py:527] (1/6) Epoch 816, batch 90, global_batch_idx: 101150, batch size: 60, loss[discriminator_loss=2.701, discriminator_real_loss=1.284, discriminator_fake_loss=1.418, generator_loss=30.18, generator_mel_loss=18.53, generator_kl_loss=1.489, generator_dur_loss=1.738, generator_adv_loss=2.036, generator_feat_match_loss=6.388, over 60.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.357, discriminator_fake_loss=1.327, generator_loss=29.04, generator_mel_loss=17.86, generator_kl_loss=1.445, generator_dur_loss=1.751, generator_adv_loss=2.002, generator_feat_match_loss=5.988, over 5198.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:09:27,934 INFO [train.py:919] (1/6) Start epoch 817
+2024-03-15 12:10:35,942 INFO [train.py:527] (1/6) Epoch 817, batch 16, global_batch_idx: 101200, batch size: 25, loss[discriminator_loss=2.739, discriminator_real_loss=1.305, discriminator_fake_loss=1.433, generator_loss=30.26, generator_mel_loss=18.29, generator_kl_loss=1.852, generator_dur_loss=1.515, generator_adv_loss=2.127, generator_feat_match_loss=6.468, over 25.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.347, discriminator_fake_loss=1.333, generator_loss=29.06, generator_mel_loss=17.94, generator_kl_loss=1.413, generator_dur_loss=1.73, generator_adv_loss=1.99, generator_feat_match_loss=5.986, over 892.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:10:35,943 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 12:10:44,065 INFO [train.py:591] (1/6) Epoch 817, validation: discriminator_loss=2.779, discriminator_real_loss=1.585, discriminator_fake_loss=1.194, generator_loss=27.21, generator_mel_loss=17.83, generator_kl_loss=1.27, generator_dur_loss=1.815, generator_adv_loss=2.099, generator_feat_match_loss=4.199, over 100.00 samples.
+2024-03-15 12:10:44,066 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 12:13:03,943 INFO [train.py:527] (1/6) Epoch 817, batch 66, global_batch_idx: 101250, batch size: 88, loss[discriminator_loss=2.672, discriminator_real_loss=1.471, discriminator_fake_loss=1.201, generator_loss=28.26, generator_mel_loss=17.5, generator_kl_loss=1.257, generator_dur_loss=1.837, generator_adv_loss=1.899, generator_feat_match_loss=5.758, over 88.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.353, discriminator_fake_loss=1.327, generator_loss=28.95, generator_mel_loss=17.82, generator_kl_loss=1.428, generator_dur_loss=1.75, generator_adv_loss=1.99, generator_feat_match_loss=5.963, over 3776.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:15:22,481 INFO [train.py:527] (1/6) Epoch 817, batch 116, global_batch_idx: 101300, batch size: 68, loss[discriminator_loss=2.637, discriminator_real_loss=1.385, discriminator_fake_loss=1.252, generator_loss=28.64, generator_mel_loss=17.85, generator_kl_loss=1.315, generator_dur_loss=1.766, generator_adv_loss=2.148, generator_feat_match_loss=5.565, over 68.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.351, discriminator_fake_loss=1.326, generator_loss=29.02, generator_mel_loss=17.86, generator_kl_loss=1.429, generator_dur_loss=1.748, generator_adv_loss=2.001, generator_feat_match_loss=5.979, over 6501.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:15:44,151 INFO [train.py:919] (1/6) Start epoch 818
+2024-03-15 12:18:05,817 INFO [train.py:527] (1/6) Epoch 818, batch 42, global_batch_idx: 101350, batch size: 52, loss[discriminator_loss=2.609, discriminator_real_loss=1.282, discriminator_fake_loss=1.327, generator_loss=29.97, generator_mel_loss=17.8, generator_kl_loss=1.512, generator_dur_loss=1.708, generator_adv_loss=2.303, generator_feat_match_loss=6.641, over 52.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.368, discriminator_fake_loss=1.322, generator_loss=28.85, generator_mel_loss=17.83, generator_kl_loss=1.46, generator_dur_loss=1.717, generator_adv_loss=2.015, generator_feat_match_loss=5.83, over 2183.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:20:26,208 INFO [train.py:527] (1/6) Epoch 818, batch 92, global_batch_idx: 101400, batch size: 77, loss[discriminator_loss=2.675, discriminator_real_loss=1.342, discriminator_fake_loss=1.334, generator_loss=28.09, generator_mel_loss=17.26, generator_kl_loss=1.537, generator_dur_loss=1.816, generator_adv_loss=1.934, generator_feat_match_loss=5.544, over 77.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.358, discriminator_fake_loss=1.326, generator_loss=28.86, generator_mel_loss=17.79, generator_kl_loss=1.446, generator_dur_loss=1.726, generator_adv_loss=2, generator_feat_match_loss=5.891, over 5018.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:20:26,210 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 12:20:34,993 INFO [train.py:591] (1/6) Epoch 818, validation: discriminator_loss=2.716, discriminator_real_loss=1.288, discriminator_fake_loss=1.428, generator_loss=27.67, generator_mel_loss=18.01, generator_kl_loss=1.323, generator_dur_loss=1.802, generator_adv_loss=1.839, generator_feat_match_loss=4.698, over 100.00 samples.
+2024-03-15 12:20:34,994 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 12:22:01,288 INFO [train.py:919] (1/6) Start epoch 819
+2024-03-15 12:23:14,843 INFO [train.py:527] (1/6) Epoch 819, batch 18, global_batch_idx: 101450, batch size: 56, loss[discriminator_loss=2.731, discriminator_real_loss=1.468, discriminator_fake_loss=1.263, generator_loss=29.78, generator_mel_loss=17.95, generator_kl_loss=1.482, generator_dur_loss=1.715, generator_adv_loss=1.968, generator_feat_match_loss=6.667, over 56.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.367, discriminator_fake_loss=1.314, generator_loss=28.84, generator_mel_loss=17.74, generator_kl_loss=1.43, generator_dur_loss=1.74, generator_adv_loss=2.01, generator_feat_match_loss=5.92, over 1057.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:25:33,178 INFO [train.py:527] (1/6) Epoch 819, batch 68, global_batch_idx: 101500, batch size: 83, loss[discriminator_loss=2.695, discriminator_real_loss=1.376, discriminator_fake_loss=1.319, generator_loss=28.62, generator_mel_loss=17.85, generator_kl_loss=1.264, generator_dur_loss=1.809, generator_adv_loss=2.063, generator_feat_match_loss=5.637, over 83.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=28.97, generator_mel_loss=17.85, generator_kl_loss=1.447, generator_dur_loss=1.731, generator_adv_loss=2.012, generator_feat_match_loss=5.938, over 3759.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:27:51,582 INFO [train.py:527] (1/6) Epoch 819, batch 118, global_batch_idx: 101550, batch size: 31, loss[discriminator_loss=2.666, discriminator_real_loss=1.3, discriminator_fake_loss=1.366, generator_loss=30.1, generator_mel_loss=18.06, generator_kl_loss=1.762, generator_dur_loss=1.607, generator_adv_loss=2.052, generator_feat_match_loss=6.622, over 31.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.358, discriminator_fake_loss=1.32, generator_loss=28.96, generator_mel_loss=17.81, generator_kl_loss=1.438, generator_dur_loss=1.732, generator_adv_loss=2.005, generator_feat_match_loss=5.974, over 6524.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:28:08,696 INFO [train.py:919] (1/6) Start epoch 820
+2024-03-15 12:30:35,349 INFO [train.py:527] (1/6) Epoch 820, batch 44, global_batch_idx: 101600, batch size: 44, loss[discriminator_loss=2.644, discriminator_real_loss=1.287, discriminator_fake_loss=1.357, generator_loss=29.7, generator_mel_loss=18.09, generator_kl_loss=1.642, generator_dur_loss=1.649, generator_adv_loss=2.071, generator_feat_match_loss=6.246, over 44.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.351, discriminator_fake_loss=1.325, generator_loss=29.08, generator_mel_loss=17.85, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=2.03, generator_feat_match_loss=6.026, over 2518.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:30:35,350 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 12:30:43,546 INFO [train.py:591] (1/6) Epoch 820, validation: discriminator_loss=2.753, discriminator_real_loss=1.358, discriminator_fake_loss=1.395, generator_loss=28.18, generator_mel_loss=18.39, generator_kl_loss=1.237, generator_dur_loss=1.813, generator_adv_loss=1.896, generator_feat_match_loss=4.836, over 100.00 samples.
+2024-03-15 12:30:43,547 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 12:33:03,627 INFO [train.py:527] (1/6) Epoch 820, batch 94, global_batch_idx: 101650, batch size: 96, loss[discriminator_loss=2.626, discriminator_real_loss=1.301, discriminator_fake_loss=1.325, generator_loss=27.65, generator_mel_loss=17.39, generator_kl_loss=1.293, generator_dur_loss=1.855, generator_adv_loss=1.833, generator_feat_match_loss=5.284, over 96.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=28.98, generator_mel_loss=17.82, generator_kl_loss=1.415, generator_dur_loss=1.756, generator_adv_loss=2.014, generator_feat_match_loss=5.984, over 5502.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:34:23,788 INFO [train.py:919] (1/6) Start epoch 821
+2024-03-15 12:35:45,164 INFO [train.py:527] (1/6) Epoch 821, batch 20, global_batch_idx: 101700, batch size: 72, loss[discriminator_loss=2.755, discriminator_real_loss=1.322, discriminator_fake_loss=1.433, generator_loss=28.9, generator_mel_loss=18.19, generator_kl_loss=1.451, generator_dur_loss=1.824, generator_adv_loss=1.954, generator_feat_match_loss=5.484, over 72.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.35, discriminator_fake_loss=1.331, generator_loss=28.82, generator_mel_loss=17.85, generator_kl_loss=1.442, generator_dur_loss=1.749, generator_adv_loss=1.988, generator_feat_match_loss=5.796, over 1190.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:38:06,250 INFO [train.py:527] (1/6) Epoch 821, batch 70, global_batch_idx: 101750, batch size: 42, loss[discriminator_loss=2.666, discriminator_real_loss=1.407, discriminator_fake_loss=1.259, generator_loss=29.44, generator_mel_loss=18.14, generator_kl_loss=1.501, generator_dur_loss=1.671, generator_adv_loss=2.047, generator_feat_match_loss=6.078, over 42.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=29.05, generator_mel_loss=17.91, generator_kl_loss=1.418, generator_dur_loss=1.749, generator_adv_loss=2.002, generator_feat_match_loss=5.972, over 4175.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:40:20,006 INFO [train.py:527] (1/6) Epoch 821, batch 120, global_batch_idx: 101800, batch size: 48, loss[discriminator_loss=2.738, discriminator_real_loss=1.362, discriminator_fake_loss=1.376, generator_loss=29.84, generator_mel_loss=18.05, generator_kl_loss=1.412, generator_dur_loss=1.679, generator_adv_loss=1.966, generator_feat_match_loss=6.728, over 48.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.362, discriminator_fake_loss=1.324, generator_loss=29.05, generator_mel_loss=17.91, generator_kl_loss=1.429, generator_dur_loss=1.744, generator_adv_loss=2.002, generator_feat_match_loss=5.969, over 6888.00 samples.], cur_lr_g: 1.81e-04, cur_lr_d: 1.81e-04, 
+2024-03-15 12:40:20,008 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 12:40:28,994 INFO [train.py:591] (1/6) Epoch 821, validation: discriminator_loss=2.727, discriminator_real_loss=1.443, discriminator_fake_loss=1.284, generator_loss=28.11, generator_mel_loss=18.16, generator_kl_loss=1.341, generator_dur_loss=1.825, generator_adv_loss=1.962, generator_feat_match_loss=4.823, over 100.00 samples.
+2024-03-15 12:40:28,994 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 12:40:37,990 INFO [train.py:919] (1/6) Start epoch 822
+2024-03-15 12:43:10,228 INFO [train.py:527] (1/6) Epoch 822, batch 46, global_batch_idx: 101850, batch size: 77, loss[discriminator_loss=2.654, discriminator_real_loss=1.402, discriminator_fake_loss=1.252, generator_loss=28.8, generator_mel_loss=17.84, generator_kl_loss=1.432, generator_dur_loss=1.82, generator_adv_loss=2.027, generator_feat_match_loss=5.678, over 77.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.364, discriminator_fake_loss=1.318, generator_loss=28.98, generator_mel_loss=17.78, generator_kl_loss=1.396, generator_dur_loss=1.752, generator_adv_loss=2.019, generator_feat_match_loss=6.032, over 2776.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 12:45:28,317 INFO [train.py:527] (1/6) Epoch 822, batch 96, global_batch_idx: 101900, batch size: 56, loss[discriminator_loss=2.73, discriminator_real_loss=1.323, discriminator_fake_loss=1.407, generator_loss=29.15, generator_mel_loss=17.88, generator_kl_loss=1.406, generator_dur_loss=1.712, generator_adv_loss=1.937, generator_feat_match_loss=6.22, over 56.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=28.99, generator_mel_loss=17.82, generator_kl_loss=1.414, generator_dur_loss=1.746, generator_adv_loss=2.016, generator_feat_match_loss=5.992, over 5477.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 12:46:44,576 INFO [train.py:919] (1/6) Start epoch 823
+2024-03-15 12:48:11,347 INFO [train.py:527] (1/6) Epoch 823, batch 22, global_batch_idx: 101950, batch size: 55, loss[discriminator_loss=2.671, discriminator_real_loss=1.319, discriminator_fake_loss=1.352, generator_loss=29.43, generator_mel_loss=18.13, generator_kl_loss=1.394, generator_dur_loss=1.693, generator_adv_loss=1.982, generator_feat_match_loss=6.236, over 55.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.367, discriminator_fake_loss=1.331, generator_loss=28.93, generator_mel_loss=17.87, generator_kl_loss=1.413, generator_dur_loss=1.745, generator_adv_loss=1.992, generator_feat_match_loss=5.906, over 1339.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 12:50:31,074 INFO [train.py:527] (1/6) Epoch 823, batch 72, global_batch_idx: 102000, batch size: 44, loss[discriminator_loss=2.635, discriminator_real_loss=1.259, discriminator_fake_loss=1.376, generator_loss=29.03, generator_mel_loss=17.86, generator_kl_loss=1.578, generator_dur_loss=1.677, generator_adv_loss=2.027, generator_feat_match_loss=5.889, over 44.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.03, generator_mel_loss=17.82, generator_kl_loss=1.421, generator_dur_loss=1.741, generator_adv_loss=2.022, generator_feat_match_loss=6.023, over 4152.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 12:50:31,075 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 12:50:39,871 INFO [train.py:591] (1/6) Epoch 823, validation: discriminator_loss=2.758, discriminator_real_loss=1.426, discriminator_fake_loss=1.331, generator_loss=27.91, generator_mel_loss=18.15, generator_kl_loss=1.312, generator_dur_loss=1.814, generator_adv_loss=1.929, generator_feat_match_loss=4.703, over 100.00 samples.
+2024-03-15 12:50:39,872 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 12:52:57,034 INFO [train.py:527] (1/6) Epoch 823, batch 122, global_batch_idx: 102050, batch size: 13, loss[discriminator_loss=2.638, discriminator_real_loss=1.315, discriminator_fake_loss=1.323, generator_loss=32.42, generator_mel_loss=19.2, generator_kl_loss=1.94, generator_dur_loss=1.557, generator_adv_loss=2.015, generator_feat_match_loss=7.715, over 13.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.357, discriminator_fake_loss=1.319, generator_loss=28.91, generator_mel_loss=17.77, generator_kl_loss=1.413, generator_dur_loss=1.75, generator_adv_loss=2.013, generator_feat_match_loss=5.959, over 7172.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 12:53:02,036 INFO [train.py:919] (1/6) Start epoch 824
+2024-03-15 12:55:40,380 INFO [train.py:527] (1/6) Epoch 824, batch 48, global_batch_idx: 102100, batch size: 42, loss[discriminator_loss=2.693, discriminator_real_loss=1.408, discriminator_fake_loss=1.285, generator_loss=28.83, generator_mel_loss=17.94, generator_kl_loss=1.678, generator_dur_loss=1.621, generator_adv_loss=1.872, generator_feat_match_loss=5.718, over 42.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=28.87, generator_mel_loss=17.81, generator_kl_loss=1.41, generator_dur_loss=1.753, generator_adv_loss=1.992, generator_feat_match_loss=5.901, over 2755.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 12:57:58,896 INFO [train.py:527] (1/6) Epoch 824, batch 98, global_batch_idx: 102150, batch size: 77, loss[discriminator_loss=2.633, discriminator_real_loss=1.242, discriminator_fake_loss=1.391, generator_loss=30.41, generator_mel_loss=18.57, generator_kl_loss=1.349, generator_dur_loss=1.789, generator_adv_loss=2.148, generator_feat_match_loss=6.55, over 77.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=28.88, generator_mel_loss=17.79, generator_kl_loss=1.416, generator_dur_loss=1.755, generator_adv_loss=2.005, generator_feat_match_loss=5.918, over 5861.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 12:59:05,427 INFO [train.py:919] (1/6) Start epoch 825
+2024-03-15 13:00:36,443 INFO [train.py:527] (1/6) Epoch 825, batch 24, global_batch_idx: 102200, batch size: 61, loss[discriminator_loss=2.692, discriminator_real_loss=1.396, discriminator_fake_loss=1.296, generator_loss=28.94, generator_mel_loss=17.91, generator_kl_loss=1.446, generator_dur_loss=1.775, generator_adv_loss=1.851, generator_feat_match_loss=5.961, over 61.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.358, discriminator_fake_loss=1.313, generator_loss=29.11, generator_mel_loss=17.82, generator_kl_loss=1.434, generator_dur_loss=1.753, generator_adv_loss=1.979, generator_feat_match_loss=6.126, over 1421.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:00:36,444 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 13:00:44,503 INFO [train.py:591] (1/6) Epoch 825, validation: discriminator_loss=2.737, discriminator_real_loss=1.38, discriminator_fake_loss=1.358, generator_loss=28.58, generator_mel_loss=18.35, generator_kl_loss=1.403, generator_dur_loss=1.801, generator_adv_loss=1.848, generator_feat_match_loss=5.17, over 100.00 samples.
+2024-03-15 13:00:44,505 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 13:03:02,999 INFO [train.py:527] (1/6) Epoch 825, batch 74, global_batch_idx: 102250, batch size: 45, loss[discriminator_loss=2.758, discriminator_real_loss=1.478, discriminator_fake_loss=1.28, generator_loss=28.33, generator_mel_loss=17.98, generator_kl_loss=1.504, generator_dur_loss=1.679, generator_adv_loss=1.91, generator_feat_match_loss=5.257, over 45.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=28.99, generator_mel_loss=17.82, generator_kl_loss=1.412, generator_dur_loss=1.744, generator_adv_loss=1.991, generator_feat_match_loss=6.023, over 4225.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:05:20,119 INFO [train.py:919] (1/6) Start epoch 826
+2024-03-15 13:05:44,591 INFO [train.py:527] (1/6) Epoch 826, batch 0, global_batch_idx: 102300, batch size: 31, loss[discriminator_loss=2.725, discriminator_real_loss=1.459, discriminator_fake_loss=1.266, generator_loss=28.62, generator_mel_loss=17.45, generator_kl_loss=1.54, generator_dur_loss=1.591, generator_adv_loss=1.912, generator_feat_match_loss=6.128, over 31.00 samples.], tot_loss[discriminator_loss=2.725, discriminator_real_loss=1.459, discriminator_fake_loss=1.266, generator_loss=28.62, generator_mel_loss=17.45, generator_kl_loss=1.54, generator_dur_loss=1.591, generator_adv_loss=1.912, generator_feat_match_loss=6.128, over 31.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:08:00,682 INFO [train.py:527] (1/6) Epoch 826, batch 50, global_batch_idx: 102350, batch size: 53, loss[discriminator_loss=2.677, discriminator_real_loss=1.317, discriminator_fake_loss=1.359, generator_loss=28.98, generator_mel_loss=18.12, generator_kl_loss=1.321, generator_dur_loss=1.718, generator_adv_loss=2.082, generator_feat_match_loss=5.734, over 53.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.362, discriminator_fake_loss=1.328, generator_loss=28.89, generator_mel_loss=17.79, generator_kl_loss=1.417, generator_dur_loss=1.743, generator_adv_loss=1.986, generator_feat_match_loss=5.955, over 2882.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:10:20,781 INFO [train.py:527] (1/6) Epoch 826, batch 100, global_batch_idx: 102400, batch size: 55, loss[discriminator_loss=2.721, discriminator_real_loss=1.286, discriminator_fake_loss=1.435, generator_loss=29.77, generator_mel_loss=18.1, generator_kl_loss=1.528, generator_dur_loss=1.701, generator_adv_loss=1.963, generator_feat_match_loss=6.478, over 55.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.362, discriminator_fake_loss=1.329, generator_loss=28.95, generator_mel_loss=17.8, generator_kl_loss=1.426, generator_dur_loss=1.747, generator_adv_loss=1.994, generator_feat_match_loss=5.979, over 5873.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:10:20,782 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 13:10:29,554 INFO [train.py:591] (1/6) Epoch 826, validation: discriminator_loss=2.782, discriminator_real_loss=1.428, discriminator_fake_loss=1.355, generator_loss=27.5, generator_mel_loss=18.04, generator_kl_loss=1.325, generator_dur_loss=1.814, generator_adv_loss=1.874, generator_feat_match_loss=4.452, over 100.00 samples.
+2024-03-15 13:10:29,555 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 13:11:33,138 INFO [train.py:919] (1/6) Start epoch 827
+2024-03-15 13:13:08,376 INFO [train.py:527] (1/6) Epoch 827, batch 26, global_batch_idx: 102450, batch size: 83, loss[discriminator_loss=2.694, discriminator_real_loss=1.402, discriminator_fake_loss=1.292, generator_loss=28.55, generator_mel_loss=17.69, generator_kl_loss=1.375, generator_dur_loss=1.853, generator_adv_loss=2.017, generator_feat_match_loss=5.617, over 83.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.353, discriminator_fake_loss=1.316, generator_loss=29.1, generator_mel_loss=17.84, generator_kl_loss=1.417, generator_dur_loss=1.751, generator_adv_loss=2.024, generator_feat_match_loss=6.062, over 1426.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:15:26,036 INFO [train.py:527] (1/6) Epoch 827, batch 76, global_batch_idx: 102500, batch size: 47, loss[discriminator_loss=2.69, discriminator_real_loss=1.377, discriminator_fake_loss=1.313, generator_loss=28.63, generator_mel_loss=17.72, generator_kl_loss=1.42, generator_dur_loss=1.639, generator_adv_loss=2.081, generator_feat_match_loss=5.768, over 47.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.356, discriminator_fake_loss=1.315, generator_loss=29.01, generator_mel_loss=17.78, generator_kl_loss=1.429, generator_dur_loss=1.741, generator_adv_loss=2.008, generator_feat_match_loss=6.044, over 4274.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:17:40,337 INFO [train.py:919] (1/6) Start epoch 828
+2024-03-15 13:18:09,415 INFO [train.py:527] (1/6) Epoch 828, batch 2, global_batch_idx: 102550, batch size: 88, loss[discriminator_loss=2.686, discriminator_real_loss=1.32, discriminator_fake_loss=1.366, generator_loss=29.96, generator_mel_loss=18.18, generator_kl_loss=1.315, generator_dur_loss=1.848, generator_adv_loss=2.055, generator_feat_match_loss=6.563, over 88.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.319, discriminator_fake_loss=1.338, generator_loss=29.27, generator_mel_loss=17.91, generator_kl_loss=1.382, generator_dur_loss=1.777, generator_adv_loss=2.013, generator_feat_match_loss=6.187, over 208.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:20:27,717 INFO [train.py:527] (1/6) Epoch 828, batch 52, global_batch_idx: 102600, batch size: 50, loss[discriminator_loss=2.742, discriminator_real_loss=1.5, discriminator_fake_loss=1.242, generator_loss=28.1, generator_mel_loss=17.61, generator_kl_loss=1.515, generator_dur_loss=1.673, generator_adv_loss=1.85, generator_feat_match_loss=5.446, over 50.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.36, discriminator_fake_loss=1.332, generator_loss=28.85, generator_mel_loss=17.72, generator_kl_loss=1.413, generator_dur_loss=1.756, generator_adv_loss=1.996, generator_feat_match_loss=5.964, over 3150.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:20:27,719 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 13:20:35,763 INFO [train.py:591] (1/6) Epoch 828, validation: discriminator_loss=2.727, discriminator_real_loss=1.372, discriminator_fake_loss=1.355, generator_loss=27.59, generator_mel_loss=17.59, generator_kl_loss=1.329, generator_dur_loss=1.814, generator_adv_loss=1.834, generator_feat_match_loss=5.023, over 100.00 samples.
+2024-03-15 13:20:35,764 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 13:22:54,498 INFO [train.py:527] (1/6) Epoch 828, batch 102, global_batch_idx: 102650, batch size: 61, loss[discriminator_loss=2.708, discriminator_real_loss=1.403, discriminator_fake_loss=1.304, generator_loss=28.26, generator_mel_loss=17.75, generator_kl_loss=1.434, generator_dur_loss=1.728, generator_adv_loss=1.962, generator_feat_match_loss=5.385, over 61.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=28.87, generator_mel_loss=17.77, generator_kl_loss=1.432, generator_dur_loss=1.743, generator_adv_loss=1.991, generator_feat_match_loss=5.937, over 5910.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:23:55,250 INFO [train.py:919] (1/6) Start epoch 829
+2024-03-15 13:25:38,715 INFO [train.py:527] (1/6) Epoch 829, batch 28, global_batch_idx: 102700, batch size: 72, loss[discriminator_loss=2.746, discriminator_real_loss=1.342, discriminator_fake_loss=1.404, generator_loss=27.66, generator_mel_loss=17.32, generator_kl_loss=1.414, generator_dur_loss=1.732, generator_adv_loss=1.997, generator_feat_match_loss=5.19, over 72.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.329, generator_loss=28.94, generator_mel_loss=17.78, generator_kl_loss=1.412, generator_dur_loss=1.75, generator_adv_loss=1.991, generator_feat_match_loss=6.006, over 1669.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:27:57,188 INFO [train.py:527] (1/6) Epoch 829, batch 78, global_batch_idx: 102750, batch size: 66, loss[discriminator_loss=2.694, discriminator_real_loss=1.364, discriminator_fake_loss=1.331, generator_loss=29.99, generator_mel_loss=17.71, generator_kl_loss=1.408, generator_dur_loss=1.697, generator_adv_loss=2.043, generator_feat_match_loss=7.133, over 66.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=28.95, generator_mel_loss=17.75, generator_kl_loss=1.417, generator_dur_loss=1.743, generator_adv_loss=2.001, generator_feat_match_loss=6.032, over 4752.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:30:03,303 INFO [train.py:919] (1/6) Start epoch 830
+2024-03-15 13:30:37,875 INFO [train.py:527] (1/6) Epoch 830, batch 4, global_batch_idx: 102800, batch size: 96, loss[discriminator_loss=2.804, discriminator_real_loss=1.438, discriminator_fake_loss=1.366, generator_loss=29.18, generator_mel_loss=17.8, generator_kl_loss=1.225, generator_dur_loss=1.837, generator_adv_loss=1.944, generator_feat_match_loss=6.377, over 96.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.34, discriminator_fake_loss=1.351, generator_loss=29.59, generator_mel_loss=17.92, generator_kl_loss=1.349, generator_dur_loss=1.779, generator_adv_loss=2.129, generator_feat_match_loss=6.416, over 315.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:30:37,914 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 13:30:45,669 INFO [train.py:591] (1/6) Epoch 830, validation: discriminator_loss=2.748, discriminator_real_loss=1.426, discriminator_fake_loss=1.322, generator_loss=27.94, generator_mel_loss=17.7, generator_kl_loss=1.346, generator_dur_loss=1.804, generator_adv_loss=2.109, generator_feat_match_loss=4.984, over 100.00 samples.
+2024-03-15 13:30:45,671 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 13:33:07,711 INFO [train.py:527] (1/6) Epoch 830, batch 54, global_batch_idx: 102850, batch size: 88, loss[discriminator_loss=2.654, discriminator_real_loss=1.335, discriminator_fake_loss=1.319, generator_loss=28.3, generator_mel_loss=17.81, generator_kl_loss=1.263, generator_dur_loss=1.837, generator_adv_loss=2.058, generator_feat_match_loss=5.334, over 88.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.361, discriminator_fake_loss=1.322, generator_loss=28.82, generator_mel_loss=17.73, generator_kl_loss=1.394, generator_dur_loss=1.77, generator_adv_loss=2.022, generator_feat_match_loss=5.901, over 3422.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:35:25,283 INFO [train.py:527] (1/6) Epoch 830, batch 104, global_batch_idx: 102900, batch size: 64, loss[discriminator_loss=2.727, discriminator_real_loss=1.459, discriminator_fake_loss=1.268, generator_loss=28.6, generator_mel_loss=17.72, generator_kl_loss=1.373, generator_dur_loss=1.747, generator_adv_loss=1.89, generator_feat_match_loss=5.86, over 64.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=28.91, generator_mel_loss=17.76, generator_kl_loss=1.425, generator_dur_loss=1.752, generator_adv_loss=2.012, generator_feat_match_loss=5.955, over 6178.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:36:16,844 INFO [train.py:919] (1/6) Start epoch 831
+2024-03-15 13:38:02,912 INFO [train.py:527] (1/6) Epoch 831, batch 30, global_batch_idx: 102950, batch size: 55, loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=28.26, generator_mel_loss=17.65, generator_kl_loss=1.396, generator_dur_loss=1.66, generator_adv_loss=2.052, generator_feat_match_loss=5.504, over 55.00 samples.], tot_loss[discriminator_loss=2.696, discriminator_real_loss=1.363, discriminator_fake_loss=1.334, generator_loss=28.92, generator_mel_loss=17.88, generator_kl_loss=1.418, generator_dur_loss=1.738, generator_adv_loss=1.979, generator_feat_match_loss=5.9, over 1764.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:40:21,326 INFO [train.py:527] (1/6) Epoch 831, batch 80, global_batch_idx: 103000, batch size: 74, loss[discriminator_loss=2.69, discriminator_real_loss=1.432, discriminator_fake_loss=1.258, generator_loss=29.17, generator_mel_loss=17.75, generator_kl_loss=1.361, generator_dur_loss=1.774, generator_adv_loss=2.108, generator_feat_match_loss=6.174, over 74.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.36, discriminator_fake_loss=1.327, generator_loss=28.96, generator_mel_loss=17.87, generator_kl_loss=1.425, generator_dur_loss=1.732, generator_adv_loss=1.994, generator_feat_match_loss=5.939, over 4376.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:40:21,328 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 13:40:30,036 INFO [train.py:591] (1/6) Epoch 831, validation: discriminator_loss=2.713, discriminator_real_loss=1.441, discriminator_fake_loss=1.271, generator_loss=27.33, generator_mel_loss=17.78, generator_kl_loss=1.316, generator_dur_loss=1.814, generator_adv_loss=1.989, generator_feat_match_loss=4.432, over 100.00 samples.
+2024-03-15 13:40:30,037 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 13:42:31,670 INFO [train.py:919] (1/6) Start epoch 832
+2024-03-15 13:43:12,219 INFO [train.py:527] (1/6) Epoch 832, batch 6, global_batch_idx: 103050, batch size: 53, loss[discriminator_loss=2.718, discriminator_real_loss=1.356, discriminator_fake_loss=1.362, generator_loss=27.44, generator_mel_loss=17.43, generator_kl_loss=1.522, generator_dur_loss=1.658, generator_adv_loss=1.777, generator_feat_match_loss=5.05, over 53.00 samples.], tot_loss[discriminator_loss=2.703, discriminator_real_loss=1.368, discriminator_fake_loss=1.335, generator_loss=28.36, generator_mel_loss=17.69, generator_kl_loss=1.366, generator_dur_loss=1.754, generator_adv_loss=1.952, generator_feat_match_loss=5.597, over 432.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:45:31,539 INFO [train.py:527] (1/6) Epoch 832, batch 56, global_batch_idx: 103100, batch size: 88, loss[discriminator_loss=2.656, discriminator_real_loss=1.34, discriminator_fake_loss=1.317, generator_loss=28.52, generator_mel_loss=17.78, generator_kl_loss=1.137, generator_dur_loss=1.866, generator_adv_loss=1.975, generator_feat_match_loss=5.76, over 88.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.355, discriminator_fake_loss=1.331, generator_loss=28.92, generator_mel_loss=17.75, generator_kl_loss=1.435, generator_dur_loss=1.753, generator_adv_loss=2.001, generator_feat_match_loss=5.978, over 3199.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:47:51,703 INFO [train.py:527] (1/6) Epoch 832, batch 106, global_batch_idx: 103150, batch size: 47, loss[discriminator_loss=2.663, discriminator_real_loss=1.431, discriminator_fake_loss=1.232, generator_loss=28.28, generator_mel_loss=17.53, generator_kl_loss=1.492, generator_dur_loss=1.673, generator_adv_loss=1.969, generator_feat_match_loss=5.614, over 47.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=28.94, generator_mel_loss=17.79, generator_kl_loss=1.433, generator_dur_loss=1.749, generator_adv_loss=2.009, generator_feat_match_loss=5.958, over 5965.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:48:37,209 INFO [train.py:919] (1/6) Start epoch 833
+2024-03-15 13:50:32,002 INFO [train.py:527] (1/6) Epoch 833, batch 32, global_batch_idx: 103200, batch size: 47, loss[discriminator_loss=2.674, discriminator_real_loss=1.4, discriminator_fake_loss=1.273, generator_loss=27.25, generator_mel_loss=16.95, generator_kl_loss=1.561, generator_dur_loss=1.665, generator_adv_loss=2.006, generator_feat_match_loss=5.069, over 47.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.366, discriminator_fake_loss=1.322, generator_loss=28.85, generator_mel_loss=17.84, generator_kl_loss=1.425, generator_dur_loss=1.746, generator_adv_loss=1.997, generator_feat_match_loss=5.842, over 1940.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:50:32,004 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 13:50:39,870 INFO [train.py:591] (1/6) Epoch 833, validation: discriminator_loss=2.712, discriminator_real_loss=1.41, discriminator_fake_loss=1.302, generator_loss=28.64, generator_mel_loss=18.45, generator_kl_loss=1.419, generator_dur_loss=1.795, generator_adv_loss=1.966, generator_feat_match_loss=5.01, over 100.00 samples.
+2024-03-15 13:50:39,871 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 13:53:00,025 INFO [train.py:527] (1/6) Epoch 833, batch 82, global_batch_idx: 103250, batch size: 47, loss[discriminator_loss=2.716, discriminator_real_loss=1.43, discriminator_fake_loss=1.286, generator_loss=28.58, generator_mel_loss=17.97, generator_kl_loss=1.479, generator_dur_loss=1.689, generator_adv_loss=1.995, generator_feat_match_loss=5.446, over 47.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.327, generator_loss=28.99, generator_mel_loss=17.86, generator_kl_loss=1.426, generator_dur_loss=1.743, generator_adv_loss=2, generator_feat_match_loss=5.96, over 4762.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:54:51,890 INFO [train.py:919] (1/6) Start epoch 834
+2024-03-15 13:55:35,778 INFO [train.py:527] (1/6) Epoch 834, batch 8, global_batch_idx: 103300, batch size: 70, loss[discriminator_loss=2.689, discriminator_real_loss=1.273, discriminator_fake_loss=1.415, generator_loss=27.7, generator_mel_loss=17.33, generator_kl_loss=1.354, generator_dur_loss=1.809, generator_adv_loss=2.13, generator_feat_match_loss=5.076, over 70.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.349, discriminator_fake_loss=1.318, generator_loss=29.19, generator_mel_loss=17.97, generator_kl_loss=1.461, generator_dur_loss=1.733, generator_adv_loss=2.005, generator_feat_match_loss=6.021, over 503.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 13:57:53,743 INFO [train.py:527] (1/6) Epoch 834, batch 58, global_batch_idx: 103350, batch size: 68, loss[discriminator_loss=2.667, discriminator_real_loss=1.376, discriminator_fake_loss=1.291, generator_loss=28.81, generator_mel_loss=17.98, generator_kl_loss=1.455, generator_dur_loss=1.809, generator_adv_loss=1.956, generator_feat_match_loss=5.602, over 68.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.325, generator_loss=29, generator_mel_loss=17.86, generator_kl_loss=1.436, generator_dur_loss=1.744, generator_adv_loss=1.99, generator_feat_match_loss=5.964, over 3463.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:00:12,047 INFO [train.py:527] (1/6) Epoch 834, batch 108, global_batch_idx: 103400, batch size: 31, loss[discriminator_loss=2.674, discriminator_real_loss=1.306, discriminator_fake_loss=1.368, generator_loss=28.8, generator_mel_loss=17.94, generator_kl_loss=1.593, generator_dur_loss=1.563, generator_adv_loss=1.998, generator_feat_match_loss=5.702, over 31.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=28.98, generator_mel_loss=17.82, generator_kl_loss=1.437, generator_dur_loss=1.742, generator_adv_loss=2.006, generator_feat_match_loss=5.98, over 6374.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:00:12,049 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 14:00:20,873 INFO [train.py:591] (1/6) Epoch 834, validation: discriminator_loss=2.701, discriminator_real_loss=1.406, discriminator_fake_loss=1.295, generator_loss=28.53, generator_mel_loss=18.59, generator_kl_loss=1.318, generator_dur_loss=1.791, generator_adv_loss=1.891, generator_feat_match_loss=4.939, over 100.00 samples.
+2024-03-15 14:00:20,874 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 14:01:05,530 INFO [train.py:919] (1/6) Start epoch 835
+2024-03-15 14:03:05,102 INFO [train.py:527] (1/6) Epoch 835, batch 34, global_batch_idx: 103450, batch size: 80, loss[discriminator_loss=2.664, discriminator_real_loss=1.236, discriminator_fake_loss=1.428, generator_loss=29.77, generator_mel_loss=17.94, generator_kl_loss=1.32, generator_dur_loss=1.798, generator_adv_loss=2.027, generator_feat_match_loss=6.693, over 80.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.362, discriminator_fake_loss=1.325, generator_loss=28.91, generator_mel_loss=17.79, generator_kl_loss=1.435, generator_dur_loss=1.753, generator_adv_loss=1.993, generator_feat_match_loss=5.939, over 2119.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:05:22,171 INFO [train.py:527] (1/6) Epoch 835, batch 84, global_batch_idx: 103500, batch size: 80, loss[discriminator_loss=2.656, discriminator_real_loss=1.259, discriminator_fake_loss=1.397, generator_loss=29.76, generator_mel_loss=18.1, generator_kl_loss=1.434, generator_dur_loss=1.773, generator_adv_loss=2.055, generator_feat_match_loss=6.396, over 80.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=28.96, generator_mel_loss=17.82, generator_kl_loss=1.429, generator_dur_loss=1.745, generator_adv_loss=1.993, generator_feat_match_loss=5.977, over 4915.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:07:10,930 INFO [train.py:919] (1/6) Start epoch 836
+2024-03-15 14:08:02,121 INFO [train.py:527] (1/6) Epoch 836, batch 10, global_batch_idx: 103550, batch size: 45, loss[discriminator_loss=2.642, discriminator_real_loss=1.262, discriminator_fake_loss=1.38, generator_loss=28.81, generator_mel_loss=17.78, generator_kl_loss=1.435, generator_dur_loss=1.692, generator_adv_loss=2.069, generator_feat_match_loss=5.829, over 45.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=29.07, generator_mel_loss=18.04, generator_kl_loss=1.435, generator_dur_loss=1.74, generator_adv_loss=1.995, generator_feat_match_loss=5.862, over 619.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:10:26,490 INFO [train.py:527] (1/6) Epoch 836, batch 60, global_batch_idx: 103600, batch size: 59, loss[discriminator_loss=2.629, discriminator_real_loss=1.291, discriminator_fake_loss=1.337, generator_loss=28.87, generator_mel_loss=17.72, generator_kl_loss=1.488, generator_dur_loss=1.697, generator_adv_loss=2.017, generator_feat_match_loss=5.953, over 59.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=28.82, generator_mel_loss=17.78, generator_kl_loss=1.416, generator_dur_loss=1.765, generator_adv_loss=1.992, generator_feat_match_loss=5.865, over 3778.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:10:26,492 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 14:10:34,773 INFO [train.py:591] (1/6) Epoch 836, validation: discriminator_loss=2.731, discriminator_real_loss=1.39, discriminator_fake_loss=1.341, generator_loss=28.17, generator_mel_loss=18.22, generator_kl_loss=1.235, generator_dur_loss=1.796, generator_adv_loss=1.845, generator_feat_match_loss=5.078, over 100.00 samples.
+2024-03-15 14:10:34,774 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 14:12:51,394 INFO [train.py:527] (1/6) Epoch 836, batch 110, global_batch_idx: 103650, batch size: 64, loss[discriminator_loss=2.739, discriminator_real_loss=1.459, discriminator_fake_loss=1.28, generator_loss=28.03, generator_mel_loss=17.43, generator_kl_loss=1.363, generator_dur_loss=1.729, generator_adv_loss=2.02, generator_feat_match_loss=5.491, over 64.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.356, discriminator_fake_loss=1.324, generator_loss=28.93, generator_mel_loss=17.79, generator_kl_loss=1.437, generator_dur_loss=1.747, generator_adv_loss=2, generator_feat_match_loss=5.956, over 6387.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:13:25,852 INFO [train.py:919] (1/6) Start epoch 837
+2024-03-15 14:15:31,750 INFO [train.py:527] (1/6) Epoch 837, batch 36, global_batch_idx: 103700, batch size: 39, loss[discriminator_loss=2.661, discriminator_real_loss=1.326, discriminator_fake_loss=1.335, generator_loss=29.43, generator_mel_loss=17.77, generator_kl_loss=1.516, generator_dur_loss=1.658, generator_adv_loss=2.029, generator_feat_match_loss=6.454, over 39.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.347, discriminator_fake_loss=1.319, generator_loss=29.21, generator_mel_loss=17.85, generator_kl_loss=1.402, generator_dur_loss=1.741, generator_adv_loss=2.052, generator_feat_match_loss=6.168, over 2097.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:17:49,010 INFO [train.py:527] (1/6) Epoch 837, batch 86, global_batch_idx: 103750, batch size: 66, loss[discriminator_loss=2.632, discriminator_real_loss=1.255, discriminator_fake_loss=1.377, generator_loss=29.26, generator_mel_loss=17.86, generator_kl_loss=1.391, generator_dur_loss=1.738, generator_adv_loss=1.921, generator_feat_match_loss=6.351, over 66.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=29.11, generator_mel_loss=17.85, generator_kl_loss=1.418, generator_dur_loss=1.745, generator_adv_loss=2.027, generator_feat_match_loss=6.07, over 5021.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:19:30,502 INFO [train.py:919] (1/6) Start epoch 838
+2024-03-15 14:20:25,578 INFO [train.py:527] (1/6) Epoch 838, batch 12, global_batch_idx: 103800, batch size: 72, loss[discriminator_loss=2.664, discriminator_real_loss=1.284, discriminator_fake_loss=1.381, generator_loss=29.09, generator_mel_loss=17.81, generator_kl_loss=1.409, generator_dur_loss=1.78, generator_adv_loss=1.939, generator_feat_match_loss=6.157, over 72.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.355, discriminator_fake_loss=1.325, generator_loss=28.88, generator_mel_loss=17.74, generator_kl_loss=1.382, generator_dur_loss=1.757, generator_adv_loss=2.01, generator_feat_match_loss=5.988, over 774.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:20:25,599 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 14:20:33,316 INFO [train.py:591] (1/6) Epoch 838, validation: discriminator_loss=2.724, discriminator_real_loss=1.304, discriminator_fake_loss=1.42, generator_loss=27.89, generator_mel_loss=18.08, generator_kl_loss=1.151, generator_dur_loss=1.8, generator_adv_loss=1.83, generator_feat_match_loss=5.03, over 100.00 samples.
+2024-03-15 14:20:33,317 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 14:22:51,907 INFO [train.py:527] (1/6) Epoch 838, batch 62, global_batch_idx: 103850, batch size: 74, loss[discriminator_loss=2.644, discriminator_real_loss=1.338, discriminator_fake_loss=1.306, generator_loss=29.77, generator_mel_loss=18.25, generator_kl_loss=1.297, generator_dur_loss=1.748, generator_adv_loss=2.037, generator_feat_match_loss=6.438, over 74.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=28.89, generator_mel_loss=17.77, generator_kl_loss=1.398, generator_dur_loss=1.738, generator_adv_loss=2.003, generator_feat_match_loss=5.981, over 3573.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:25:14,093 INFO [train.py:527] (1/6) Epoch 838, batch 112, global_batch_idx: 103900, batch size: 52, loss[discriminator_loss=2.681, discriminator_real_loss=1.389, discriminator_fake_loss=1.291, generator_loss=29.12, generator_mel_loss=18.13, generator_kl_loss=1.376, generator_dur_loss=1.737, generator_adv_loss=1.976, generator_feat_match_loss=5.899, over 52.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.32, generator_loss=28.96, generator_mel_loss=17.77, generator_kl_loss=1.395, generator_dur_loss=1.757, generator_adv_loss=2.008, generator_feat_match_loss=6.027, over 6706.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:25:44,385 INFO [train.py:919] (1/6) Start epoch 839
+2024-03-15 14:27:55,789 INFO [train.py:527] (1/6) Epoch 839, batch 38, global_batch_idx: 103950, batch size: 44, loss[discriminator_loss=2.623, discriminator_real_loss=1.428, discriminator_fake_loss=1.195, generator_loss=28.33, generator_mel_loss=17.35, generator_kl_loss=1.452, generator_dur_loss=1.68, generator_adv_loss=1.949, generator_feat_match_loss=5.893, over 44.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.366, discriminator_fake_loss=1.318, generator_loss=28.97, generator_mel_loss=17.79, generator_kl_loss=1.437, generator_dur_loss=1.759, generator_adv_loss=2.001, generator_feat_match_loss=5.985, over 2284.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:30:14,699 INFO [train.py:527] (1/6) Epoch 839, batch 88, global_batch_idx: 104000, batch size: 48, loss[discriminator_loss=2.723, discriminator_real_loss=1.404, discriminator_fake_loss=1.319, generator_loss=28.39, generator_mel_loss=17.47, generator_kl_loss=1.373, generator_dur_loss=1.708, generator_adv_loss=2.058, generator_feat_match_loss=5.776, over 48.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.36, discriminator_fake_loss=1.319, generator_loss=29.01, generator_mel_loss=17.83, generator_kl_loss=1.436, generator_dur_loss=1.754, generator_adv_loss=2.003, generator_feat_match_loss=5.992, over 5042.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:30:14,700 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 14:30:23,580 INFO [train.py:591] (1/6) Epoch 839, validation: discriminator_loss=2.717, discriminator_real_loss=1.366, discriminator_fake_loss=1.351, generator_loss=28.05, generator_mel_loss=18.16, generator_kl_loss=1.27, generator_dur_loss=1.817, generator_adv_loss=1.861, generator_feat_match_loss=4.934, over 100.00 samples.
+2024-03-15 14:30:23,581 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 14:31:59,341 INFO [train.py:919] (1/6) Start epoch 840
+2024-03-15 14:33:02,569 INFO [train.py:527] (1/6) Epoch 840, batch 14, global_batch_idx: 104050, batch size: 50, loss[discriminator_loss=2.666, discriminator_real_loss=1.376, discriminator_fake_loss=1.29, generator_loss=28.68, generator_mel_loss=17.53, generator_kl_loss=1.507, generator_dur_loss=1.69, generator_adv_loss=2.045, generator_feat_match_loss=5.91, over 50.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.354, discriminator_fake_loss=1.331, generator_loss=28.96, generator_mel_loss=17.8, generator_kl_loss=1.462, generator_dur_loss=1.722, generator_adv_loss=2.012, generator_feat_match_loss=5.966, over 857.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:35:22,490 INFO [train.py:527] (1/6) Epoch 840, batch 64, global_batch_idx: 104100, batch size: 36, loss[discriminator_loss=2.59, discriminator_real_loss=1.33, discriminator_fake_loss=1.26, generator_loss=30.03, generator_mel_loss=18.49, generator_kl_loss=1.552, generator_dur_loss=1.618, generator_adv_loss=2.093, generator_feat_match_loss=6.271, over 36.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=29.05, generator_mel_loss=17.85, generator_kl_loss=1.448, generator_dur_loss=1.738, generator_adv_loss=2.005, generator_feat_match_loss=6.017, over 3708.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:37:43,180 INFO [train.py:527] (1/6) Epoch 840, batch 114, global_batch_idx: 104150, batch size: 72, loss[discriminator_loss=2.671, discriminator_real_loss=1.316, discriminator_fake_loss=1.355, generator_loss=29.36, generator_mel_loss=18.02, generator_kl_loss=1.347, generator_dur_loss=1.779, generator_adv_loss=2.001, generator_feat_match_loss=6.215, over 72.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.358, discriminator_fake_loss=1.328, generator_loss=29.08, generator_mel_loss=17.85, generator_kl_loss=1.448, generator_dur_loss=1.734, generator_adv_loss=2.004, generator_feat_match_loss=6.045, over 6240.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:38:10,248 INFO [train.py:919] (1/6) Start epoch 841
+2024-03-15 14:40:29,606 INFO [train.py:527] (1/6) Epoch 841, batch 40, global_batch_idx: 104200, batch size: 64, loss[discriminator_loss=2.73, discriminator_real_loss=1.42, discriminator_fake_loss=1.309, generator_loss=28.13, generator_mel_loss=17.49, generator_kl_loss=1.375, generator_dur_loss=1.755, generator_adv_loss=1.791, generator_feat_match_loss=5.714, over 64.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.358, discriminator_fake_loss=1.333, generator_loss=28.99, generator_mel_loss=17.85, generator_kl_loss=1.396, generator_dur_loss=1.775, generator_adv_loss=1.992, generator_feat_match_loss=5.972, over 2577.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:40:29,608 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 14:40:37,611 INFO [train.py:591] (1/6) Epoch 841, validation: discriminator_loss=2.768, discriminator_real_loss=1.355, discriminator_fake_loss=1.413, generator_loss=28.2, generator_mel_loss=18.32, generator_kl_loss=1.233, generator_dur_loss=1.813, generator_adv_loss=1.759, generator_feat_match_loss=5.072, over 100.00 samples.
+2024-03-15 14:40:37,612 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 14:42:54,659 INFO [train.py:527] (1/6) Epoch 841, batch 90, global_batch_idx: 104250, batch size: 83, loss[discriminator_loss=2.646, discriminator_real_loss=1.348, discriminator_fake_loss=1.298, generator_loss=28.24, generator_mel_loss=17.66, generator_kl_loss=1.339, generator_dur_loss=1.862, generator_adv_loss=1.952, generator_feat_match_loss=5.432, over 83.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.353, discriminator_fake_loss=1.33, generator_loss=29, generator_mel_loss=17.83, generator_kl_loss=1.413, generator_dur_loss=1.759, generator_adv_loss=1.996, generator_feat_match_loss=5.996, over 5400.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:44:26,587 INFO [train.py:919] (1/6) Start epoch 842
+2024-03-15 14:45:35,146 INFO [train.py:527] (1/6) Epoch 842, batch 16, global_batch_idx: 104300, batch size: 62, loss[discriminator_loss=2.663, discriminator_real_loss=1.305, discriminator_fake_loss=1.359, generator_loss=29.07, generator_mel_loss=17.62, generator_kl_loss=1.349, generator_dur_loss=1.746, generator_adv_loss=2.04, generator_feat_match_loss=6.322, over 62.00 samples.], tot_loss[discriminator_loss=2.655, discriminator_real_loss=1.335, discriminator_fake_loss=1.321, generator_loss=29.44, generator_mel_loss=18.02, generator_kl_loss=1.489, generator_dur_loss=1.723, generator_adv_loss=2.007, generator_feat_match_loss=6.197, over 894.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:47:53,205 INFO [train.py:527] (1/6) Epoch 842, batch 66, global_batch_idx: 104350, batch size: 62, loss[discriminator_loss=2.688, discriminator_real_loss=1.247, discriminator_fake_loss=1.441, generator_loss=29.22, generator_mel_loss=17.83, generator_kl_loss=1.421, generator_dur_loss=1.785, generator_adv_loss=2.037, generator_feat_match_loss=6.142, over 62.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=29, generator_mel_loss=17.78, generator_kl_loss=1.467, generator_dur_loss=1.748, generator_adv_loss=2.004, generator_feat_match_loss=6.004, over 3702.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:50:14,363 INFO [train.py:527] (1/6) Epoch 842, batch 116, global_batch_idx: 104400, batch size: 55, loss[discriminator_loss=2.643, discriminator_real_loss=1.359, discriminator_fake_loss=1.284, generator_loss=30.66, generator_mel_loss=18.13, generator_kl_loss=1.397, generator_dur_loss=1.715, generator_adv_loss=2.17, generator_feat_match_loss=7.252, over 55.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.318, generator_loss=28.99, generator_mel_loss=17.76, generator_kl_loss=1.461, generator_dur_loss=1.738, generator_adv_loss=2.008, generator_feat_match_loss=6.019, over 6328.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:50:14,365 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 14:50:23,188 INFO [train.py:591] (1/6) Epoch 842, validation: discriminator_loss=2.706, discriminator_real_loss=1.497, discriminator_fake_loss=1.209, generator_loss=28.94, generator_mel_loss=18.29, generator_kl_loss=1.225, generator_dur_loss=1.809, generator_adv_loss=2.086, generator_feat_match_loss=5.528, over 100.00 samples.
+2024-03-15 14:50:23,188 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 14:50:44,126 INFO [train.py:919] (1/6) Start epoch 843
+2024-03-15 14:53:01,716 INFO [train.py:527] (1/6) Epoch 843, batch 42, global_batch_idx: 104450, batch size: 61, loss[discriminator_loss=2.691, discriminator_real_loss=1.315, discriminator_fake_loss=1.375, generator_loss=30.3, generator_mel_loss=18.69, generator_kl_loss=1.44, generator_dur_loss=1.744, generator_adv_loss=1.872, generator_feat_match_loss=6.554, over 61.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.356, discriminator_fake_loss=1.337, generator_loss=29.26, generator_mel_loss=17.98, generator_kl_loss=1.467, generator_dur_loss=1.736, generator_adv_loss=1.988, generator_feat_match_loss=6.094, over 2367.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:55:21,167 INFO [train.py:527] (1/6) Epoch 843, batch 92, global_batch_idx: 104500, batch size: 77, loss[discriminator_loss=2.649, discriminator_real_loss=1.414, discriminator_fake_loss=1.234, generator_loss=28.92, generator_mel_loss=17.88, generator_kl_loss=1.361, generator_dur_loss=1.84, generator_adv_loss=1.94, generator_feat_match_loss=5.894, over 77.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.358, discriminator_fake_loss=1.336, generator_loss=29.05, generator_mel_loss=17.86, generator_kl_loss=1.456, generator_dur_loss=1.747, generator_adv_loss=1.99, generator_feat_match_loss=5.997, over 5241.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 14:56:46,691 INFO [train.py:919] (1/6) Start epoch 844
+2024-03-15 14:58:00,549 INFO [train.py:527] (1/6) Epoch 844, batch 18, global_batch_idx: 104550, batch size: 64, loss[discriminator_loss=2.658, discriminator_real_loss=1.372, discriminator_fake_loss=1.286, generator_loss=28.8, generator_mel_loss=17.46, generator_kl_loss=1.414, generator_dur_loss=1.77, generator_adv_loss=1.996, generator_feat_match_loss=6.166, over 64.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.342, discriminator_fake_loss=1.334, generator_loss=29.31, generator_mel_loss=17.87, generator_kl_loss=1.434, generator_dur_loss=1.724, generator_adv_loss=2.055, generator_feat_match_loss=6.224, over 1045.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:00:22,320 INFO [train.py:527] (1/6) Epoch 844, batch 68, global_batch_idx: 104600, batch size: 58, loss[discriminator_loss=2.673, discriminator_real_loss=1.381, discriminator_fake_loss=1.292, generator_loss=28.46, generator_mel_loss=17.35, generator_kl_loss=1.458, generator_dur_loss=1.693, generator_adv_loss=2.091, generator_feat_match_loss=5.86, over 58.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.355, discriminator_fake_loss=1.327, generator_loss=29.08, generator_mel_loss=17.8, generator_kl_loss=1.441, generator_dur_loss=1.726, generator_adv_loss=2.035, generator_feat_match_loss=6.076, over 3760.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:00:22,322 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 15:00:31,248 INFO [train.py:591] (1/6) Epoch 844, validation: discriminator_loss=2.775, discriminator_real_loss=1.483, discriminator_fake_loss=1.292, generator_loss=27.8, generator_mel_loss=18, generator_kl_loss=1.25, generator_dur_loss=1.795, generator_adv_loss=2.016, generator_feat_match_loss=4.74, over 100.00 samples.
+2024-03-15 15:00:31,249 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 15:02:49,602 INFO [train.py:527] (1/6) Epoch 844, batch 118, global_batch_idx: 104650, batch size: 74, loss[discriminator_loss=2.672, discriminator_real_loss=1.391, discriminator_fake_loss=1.282, generator_loss=28.29, generator_mel_loss=17.69, generator_kl_loss=1.272, generator_dur_loss=1.825, generator_adv_loss=2.023, generator_feat_match_loss=5.482, over 74.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.357, discriminator_fake_loss=1.324, generator_loss=29.09, generator_mel_loss=17.82, generator_kl_loss=1.438, generator_dur_loss=1.737, generator_adv_loss=2.017, generator_feat_match_loss=6.071, over 6719.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:03:04,540 INFO [train.py:919] (1/6) Start epoch 845
+2024-03-15 15:05:32,903 INFO [train.py:527] (1/6) Epoch 845, batch 44, global_batch_idx: 104700, batch size: 19, loss[discriminator_loss=2.57, discriminator_real_loss=1.379, discriminator_fake_loss=1.191, generator_loss=32.4, generator_mel_loss=19.16, generator_kl_loss=1.955, generator_dur_loss=1.583, generator_adv_loss=2.233, generator_feat_match_loss=7.468, over 19.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.362, discriminator_fake_loss=1.326, generator_loss=29.01, generator_mel_loss=17.84, generator_kl_loss=1.409, generator_dur_loss=1.769, generator_adv_loss=1.991, generator_feat_match_loss=6.008, over 2694.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:07:50,468 INFO [train.py:527] (1/6) Epoch 845, batch 94, global_batch_idx: 104750, batch size: 72, loss[discriminator_loss=2.703, discriminator_real_loss=1.306, discriminator_fake_loss=1.398, generator_loss=28.9, generator_mel_loss=18.03, generator_kl_loss=1.548, generator_dur_loss=1.825, generator_adv_loss=1.953, generator_feat_match_loss=5.543, over 72.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=28.94, generator_mel_loss=17.82, generator_kl_loss=1.415, generator_dur_loss=1.754, generator_adv_loss=1.989, generator_feat_match_loss=5.968, over 5622.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:09:11,476 INFO [train.py:919] (1/6) Start epoch 846
+2024-03-15 15:10:31,958 INFO [train.py:527] (1/6) Epoch 846, batch 20, global_batch_idx: 104800, batch size: 50, loss[discriminator_loss=2.622, discriminator_real_loss=1.278, discriminator_fake_loss=1.345, generator_loss=30.83, generator_mel_loss=18.37, generator_kl_loss=1.556, generator_dur_loss=1.705, generator_adv_loss=1.923, generator_feat_match_loss=7.275, over 50.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.344, discriminator_fake_loss=1.32, generator_loss=29.19, generator_mel_loss=17.96, generator_kl_loss=1.415, generator_dur_loss=1.746, generator_adv_loss=1.992, generator_feat_match_loss=6.081, over 1217.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:10:31,959 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 15:10:40,201 INFO [train.py:591] (1/6) Epoch 846, validation: discriminator_loss=2.711, discriminator_real_loss=1.363, discriminator_fake_loss=1.349, generator_loss=27.95, generator_mel_loss=18.45, generator_kl_loss=1.275, generator_dur_loss=1.793, generator_adv_loss=1.845, generator_feat_match_loss=4.584, over 100.00 samples.
+2024-03-15 15:10:40,202 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 15:12:56,244 INFO [train.py:527] (1/6) Epoch 846, batch 70, global_batch_idx: 104850, batch size: 53, loss[discriminator_loss=2.68, discriminator_real_loss=1.274, discriminator_fake_loss=1.406, generator_loss=29.7, generator_mel_loss=18.2, generator_kl_loss=1.416, generator_dur_loss=1.689, generator_adv_loss=2.15, generator_feat_match_loss=6.247, over 53.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.326, generator_loss=29.02, generator_mel_loss=17.88, generator_kl_loss=1.426, generator_dur_loss=1.726, generator_adv_loss=1.997, generator_feat_match_loss=5.989, over 3884.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:15:15,468 INFO [train.py:527] (1/6) Epoch 846, batch 120, global_batch_idx: 104900, batch size: 74, loss[discriminator_loss=2.74, discriminator_real_loss=1.445, discriminator_fake_loss=1.295, generator_loss=27.76, generator_mel_loss=17.53, generator_kl_loss=1.394, generator_dur_loss=1.738, generator_adv_loss=1.899, generator_feat_match_loss=5.206, over 74.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.94, generator_mel_loss=17.83, generator_kl_loss=1.418, generator_dur_loss=1.743, generator_adv_loss=1.998, generator_feat_match_loss=5.954, over 6937.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:15:25,407 INFO [train.py:919] (1/6) Start epoch 847
+2024-03-15 15:17:57,101 INFO [train.py:527] (1/6) Epoch 847, batch 46, global_batch_idx: 104950, batch size: 48, loss[discriminator_loss=2.693, discriminator_real_loss=1.424, discriminator_fake_loss=1.269, generator_loss=28.84, generator_mel_loss=18.09, generator_kl_loss=1.586, generator_dur_loss=1.678, generator_adv_loss=1.931, generator_feat_match_loss=5.551, over 48.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.358, discriminator_fake_loss=1.314, generator_loss=29.12, generator_mel_loss=17.8, generator_kl_loss=1.433, generator_dur_loss=1.741, generator_adv_loss=2.034, generator_feat_match_loss=6.11, over 2665.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:20:17,197 INFO [train.py:527] (1/6) Epoch 847, batch 96, global_batch_idx: 105000, batch size: 88, loss[discriminator_loss=2.684, discriminator_real_loss=1.337, discriminator_fake_loss=1.347, generator_loss=28.77, generator_mel_loss=17.63, generator_kl_loss=1.432, generator_dur_loss=1.828, generator_adv_loss=1.877, generator_feat_match_loss=6.007, over 88.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.357, discriminator_fake_loss=1.319, generator_loss=29.07, generator_mel_loss=17.84, generator_kl_loss=1.432, generator_dur_loss=1.74, generator_adv_loss=2.015, generator_feat_match_loss=6.047, over 5392.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:20:17,199 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 15:20:26,204 INFO [train.py:591] (1/6) Epoch 847, validation: discriminator_loss=2.724, discriminator_real_loss=1.358, discriminator_fake_loss=1.366, generator_loss=27.32, generator_mel_loss=17.85, generator_kl_loss=1.251, generator_dur_loss=1.793, generator_adv_loss=1.83, generator_feat_match_loss=4.59, over 100.00 samples.
+2024-03-15 15:20:26,205 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 15:21:42,643 INFO [train.py:919] (1/6) Start epoch 848
+2024-03-15 15:23:07,264 INFO [train.py:527] (1/6) Epoch 848, batch 22, global_batch_idx: 105050, batch size: 39, loss[discriminator_loss=2.729, discriminator_real_loss=1.412, discriminator_fake_loss=1.318, generator_loss=31.03, generator_mel_loss=18.72, generator_kl_loss=1.674, generator_dur_loss=1.618, generator_adv_loss=2.036, generator_feat_match_loss=6.981, over 39.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.357, discriminator_fake_loss=1.315, generator_loss=29.26, generator_mel_loss=17.89, generator_kl_loss=1.496, generator_dur_loss=1.709, generator_adv_loss=2.022, generator_feat_match_loss=6.144, over 1163.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:25:25,231 INFO [train.py:527] (1/6) Epoch 848, batch 72, global_batch_idx: 105100, batch size: 80, loss[discriminator_loss=2.679, discriminator_real_loss=1.427, discriminator_fake_loss=1.252, generator_loss=27.68, generator_mel_loss=17.36, generator_kl_loss=1.336, generator_dur_loss=1.818, generator_adv_loss=1.995, generator_feat_match_loss=5.172, over 80.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.357, discriminator_fake_loss=1.316, generator_loss=29.16, generator_mel_loss=17.89, generator_kl_loss=1.432, generator_dur_loss=1.737, generator_adv_loss=2.019, generator_feat_match_loss=6.089, over 4056.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:27:45,065 INFO [train.py:527] (1/6) Epoch 848, batch 122, global_batch_idx: 105150, batch size: 83, loss[discriminator_loss=2.629, discriminator_real_loss=1.37, discriminator_fake_loss=1.26, generator_loss=29.26, generator_mel_loss=18.07, generator_kl_loss=1.223, generator_dur_loss=1.867, generator_adv_loss=2.06, generator_feat_match_loss=6.044, over 83.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=29.15, generator_mel_loss=17.88, generator_kl_loss=1.434, generator_dur_loss=1.741, generator_adv_loss=2.009, generator_feat_match_loss=6.084, over 6893.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:27:49,540 INFO [train.py:919] (1/6) Start epoch 849
+2024-03-15 15:30:29,668 INFO [train.py:527] (1/6) Epoch 849, batch 48, global_batch_idx: 105200, batch size: 96, loss[discriminator_loss=2.695, discriminator_real_loss=1.396, discriminator_fake_loss=1.298, generator_loss=28.66, generator_mel_loss=17.85, generator_kl_loss=1.34, generator_dur_loss=1.842, generator_adv_loss=1.801, generator_feat_match_loss=5.826, over 96.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.36, discriminator_fake_loss=1.317, generator_loss=28.8, generator_mel_loss=17.69, generator_kl_loss=1.419, generator_dur_loss=1.749, generator_adv_loss=2, generator_feat_match_loss=5.949, over 2780.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:30:29,670 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 15:30:37,816 INFO [train.py:591] (1/6) Epoch 849, validation: discriminator_loss=2.723, discriminator_real_loss=1.264, discriminator_fake_loss=1.459, generator_loss=27.64, generator_mel_loss=18.21, generator_kl_loss=1.177, generator_dur_loss=1.8, generator_adv_loss=1.75, generator_feat_match_loss=4.702, over 100.00 samples.
+2024-03-15 15:30:37,817 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 15:32:57,500 INFO [train.py:527] (1/6) Epoch 849, batch 98, global_batch_idx: 105250, batch size: 58, loss[discriminator_loss=2.674, discriminator_real_loss=1.314, discriminator_fake_loss=1.36, generator_loss=28.57, generator_mel_loss=17.65, generator_kl_loss=1.436, generator_dur_loss=1.734, generator_adv_loss=2.055, generator_feat_match_loss=5.692, over 58.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.362, discriminator_fake_loss=1.315, generator_loss=28.86, generator_mel_loss=17.71, generator_kl_loss=1.426, generator_dur_loss=1.743, generator_adv_loss=2.006, generator_feat_match_loss=5.967, over 5663.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:34:06,064 INFO [train.py:919] (1/6) Start epoch 850
+2024-03-15 15:35:36,258 INFO [train.py:527] (1/6) Epoch 850, batch 24, global_batch_idx: 105300, batch size: 55, loss[discriminator_loss=2.683, discriminator_real_loss=1.39, discriminator_fake_loss=1.293, generator_loss=28.95, generator_mel_loss=18.19, generator_kl_loss=1.436, generator_dur_loss=1.714, generator_adv_loss=2.083, generator_feat_match_loss=5.526, over 55.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.363, discriminator_fake_loss=1.325, generator_loss=28.88, generator_mel_loss=17.84, generator_kl_loss=1.442, generator_dur_loss=1.704, generator_adv_loss=2.006, generator_feat_match_loss=5.891, over 1372.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:37:54,315 INFO [train.py:527] (1/6) Epoch 850, batch 74, global_batch_idx: 105350, batch size: 96, loss[discriminator_loss=2.694, discriminator_real_loss=1.412, discriminator_fake_loss=1.281, generator_loss=27.63, generator_mel_loss=17.34, generator_kl_loss=1.181, generator_dur_loss=1.877, generator_adv_loss=1.929, generator_feat_match_loss=5.301, over 96.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=28.94, generator_mel_loss=17.86, generator_kl_loss=1.408, generator_dur_loss=1.723, generator_adv_loss=1.999, generator_feat_match_loss=5.945, over 4206.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:40:12,838 INFO [train.py:919] (1/6) Start epoch 851
+2024-03-15 15:40:36,943 INFO [train.py:527] (1/6) Epoch 851, batch 0, global_batch_idx: 105400, batch size: 36, loss[discriminator_loss=2.684, discriminator_real_loss=1.297, discriminator_fake_loss=1.387, generator_loss=28.18, generator_mel_loss=17.18, generator_kl_loss=1.384, generator_dur_loss=1.638, generator_adv_loss=2.254, generator_feat_match_loss=5.726, over 36.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.297, discriminator_fake_loss=1.387, generator_loss=28.18, generator_mel_loss=17.18, generator_kl_loss=1.384, generator_dur_loss=1.638, generator_adv_loss=2.254, generator_feat_match_loss=5.726, over 36.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:40:36,953 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 15:40:45,128 INFO [train.py:591] (1/6) Epoch 851, validation: discriminator_loss=2.755, discriminator_real_loss=1.525, discriminator_fake_loss=1.23, generator_loss=28.27, generator_mel_loss=18.03, generator_kl_loss=1.212, generator_dur_loss=1.799, generator_adv_loss=2.133, generator_feat_match_loss=5.092, over 100.00 samples.
+2024-03-15 15:40:45,133 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 15:43:04,946 INFO [train.py:527] (1/6) Epoch 851, batch 50, global_batch_idx: 105450, batch size: 55, loss[discriminator_loss=2.621, discriminator_real_loss=1.329, discriminator_fake_loss=1.292, generator_loss=28.94, generator_mel_loss=18.2, generator_kl_loss=1.397, generator_dur_loss=1.692, generator_adv_loss=2.051, generator_feat_match_loss=5.598, over 55.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.354, discriminator_fake_loss=1.331, generator_loss=28.9, generator_mel_loss=17.78, generator_kl_loss=1.423, generator_dur_loss=1.735, generator_adv_loss=1.999, generator_feat_match_loss=5.965, over 2968.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:45:30,036 INFO [train.py:527] (1/6) Epoch 851, batch 100, global_batch_idx: 105500, batch size: 52, loss[discriminator_loss=2.676, discriminator_real_loss=1.342, discriminator_fake_loss=1.333, generator_loss=28.59, generator_mel_loss=18.08, generator_kl_loss=1.594, generator_dur_loss=1.679, generator_adv_loss=1.984, generator_feat_match_loss=5.252, over 52.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.359, discriminator_fake_loss=1.328, generator_loss=28.9, generator_mel_loss=17.78, generator_kl_loss=1.426, generator_dur_loss=1.744, generator_adv_loss=2.001, generator_feat_match_loss=5.951, over 5984.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:46:30,928 INFO [train.py:919] (1/6) Start epoch 852
+2024-03-15 15:48:06,102 INFO [train.py:527] (1/6) Epoch 852, batch 26, global_batch_idx: 105550, batch size: 25, loss[discriminator_loss=2.533, discriminator_real_loss=1.188, discriminator_fake_loss=1.345, generator_loss=30.93, generator_mel_loss=18.01, generator_kl_loss=1.867, generator_dur_loss=1.57, generator_adv_loss=2.1, generator_feat_match_loss=7.378, over 25.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.358, discriminator_fake_loss=1.329, generator_loss=29.08, generator_mel_loss=17.87, generator_kl_loss=1.409, generator_dur_loss=1.748, generator_adv_loss=2.005, generator_feat_match_loss=6.046, over 1609.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:50:24,281 INFO [train.py:527] (1/6) Epoch 852, batch 76, global_batch_idx: 105600, batch size: 25, loss[discriminator_loss=2.669, discriminator_real_loss=1.346, discriminator_fake_loss=1.323, generator_loss=31.2, generator_mel_loss=18.59, generator_kl_loss=1.852, generator_dur_loss=1.551, generator_adv_loss=2.019, generator_feat_match_loss=7.188, over 25.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.353, discriminator_fake_loss=1.329, generator_loss=28.99, generator_mel_loss=17.79, generator_kl_loss=1.413, generator_dur_loss=1.746, generator_adv_loss=2.015, generator_feat_match_loss=6.029, over 4587.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:50:24,283 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 15:50:33,125 INFO [train.py:591] (1/6) Epoch 852, validation: discriminator_loss=2.749, discriminator_real_loss=1.44, discriminator_fake_loss=1.309, generator_loss=27.58, generator_mel_loss=17.73, generator_kl_loss=1.299, generator_dur_loss=1.807, generator_adv_loss=1.974, generator_feat_match_loss=4.766, over 100.00 samples.
+2024-03-15 15:50:33,126 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 15:52:44,749 INFO [train.py:919] (1/6) Start epoch 853
+2024-03-15 15:53:14,661 INFO [train.py:527] (1/6) Epoch 853, batch 2, global_batch_idx: 105650, batch size: 61, loss[discriminator_loss=2.758, discriminator_real_loss=1.406, discriminator_fake_loss=1.352, generator_loss=29.21, generator_mel_loss=17.86, generator_kl_loss=1.358, generator_dur_loss=1.724, generator_adv_loss=2.096, generator_feat_match_loss=6.171, over 61.00 samples.], tot_loss[discriminator_loss=2.728, discriminator_real_loss=1.389, discriminator_fake_loss=1.339, generator_loss=28.8, generator_mel_loss=17.69, generator_kl_loss=1.445, generator_dur_loss=1.787, generator_adv_loss=2.01, generator_feat_match_loss=5.865, over 207.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:55:34,570 INFO [train.py:527] (1/6) Epoch 853, batch 52, global_batch_idx: 105700, batch size: 95, loss[discriminator_loss=2.648, discriminator_real_loss=1.372, discriminator_fake_loss=1.276, generator_loss=29.15, generator_mel_loss=17.73, generator_kl_loss=1.328, generator_dur_loss=1.828, generator_adv_loss=2.112, generator_feat_match_loss=6.149, over 95.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.364, discriminator_fake_loss=1.317, generator_loss=28.9, generator_mel_loss=17.71, generator_kl_loss=1.444, generator_dur_loss=1.738, generator_adv_loss=2.002, generator_feat_match_loss=6.008, over 2975.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:57:55,602 INFO [train.py:527] (1/6) Epoch 853, batch 102, global_batch_idx: 105750, batch size: 72, loss[discriminator_loss=2.674, discriminator_real_loss=1.39, discriminator_fake_loss=1.284, generator_loss=29.15, generator_mel_loss=17.79, generator_kl_loss=1.286, generator_dur_loss=1.801, generator_adv_loss=1.975, generator_feat_match_loss=6.302, over 72.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.365, discriminator_fake_loss=1.319, generator_loss=28.99, generator_mel_loss=17.75, generator_kl_loss=1.422, generator_dur_loss=1.742, generator_adv_loss=2.002, generator_feat_match_loss=6.068, over 5962.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 15:58:51,192 INFO [train.py:919] (1/6) Start epoch 854
+2024-03-15 16:00:31,939 INFO [train.py:527] (1/6) Epoch 854, batch 28, global_batch_idx: 105800, batch size: 14, loss[discriminator_loss=2.627, discriminator_real_loss=1.389, discriminator_fake_loss=1.238, generator_loss=28.7, generator_mel_loss=17.83, generator_kl_loss=1.765, generator_dur_loss=1.606, generator_adv_loss=2.055, generator_feat_match_loss=5.444, over 14.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.345, discriminator_fake_loss=1.324, generator_loss=29.15, generator_mel_loss=17.85, generator_kl_loss=1.41, generator_dur_loss=1.728, generator_adv_loss=2.006, generator_feat_match_loss=6.15, over 1596.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:00:31,941 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 16:00:40,053 INFO [train.py:591] (1/6) Epoch 854, validation: discriminator_loss=2.732, discriminator_real_loss=1.335, discriminator_fake_loss=1.397, generator_loss=27.9, generator_mel_loss=17.91, generator_kl_loss=1.349, generator_dur_loss=1.796, generator_adv_loss=1.837, generator_feat_match_loss=5.006, over 100.00 samples.
+2024-03-15 16:00:40,054 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 16:03:01,600 INFO [train.py:527] (1/6) Epoch 854, batch 78, global_batch_idx: 105850, batch size: 66, loss[discriminator_loss=2.65, discriminator_real_loss=1.281, discriminator_fake_loss=1.369, generator_loss=29.67, generator_mel_loss=18.4, generator_kl_loss=1.356, generator_dur_loss=1.739, generator_adv_loss=2.032, generator_feat_match_loss=6.14, over 66.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.343, discriminator_fake_loss=1.324, generator_loss=29.14, generator_mel_loss=17.87, generator_kl_loss=1.408, generator_dur_loss=1.738, generator_adv_loss=2.012, generator_feat_match_loss=6.104, over 4434.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:05:08,905 INFO [train.py:919] (1/6) Start epoch 855
+2024-03-15 16:05:42,775 INFO [train.py:527] (1/6) Epoch 855, batch 4, global_batch_idx: 105900, batch size: 42, loss[discriminator_loss=2.609, discriminator_real_loss=1.314, discriminator_fake_loss=1.295, generator_loss=29.27, generator_mel_loss=17.83, generator_kl_loss=1.478, generator_dur_loss=1.712, generator_adv_loss=1.988, generator_feat_match_loss=6.265, over 42.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.36, discriminator_fake_loss=1.307, generator_loss=29.03, generator_mel_loss=17.76, generator_kl_loss=1.361, generator_dur_loss=1.751, generator_adv_loss=2.001, generator_feat_match_loss=6.151, over 250.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:08:01,137 INFO [train.py:527] (1/6) Epoch 855, batch 54, global_batch_idx: 105950, batch size: 88, loss[discriminator_loss=2.67, discriminator_real_loss=1.332, discriminator_fake_loss=1.337, generator_loss=29.29, generator_mel_loss=17.85, generator_kl_loss=1.298, generator_dur_loss=1.831, generator_adv_loss=1.991, generator_feat_match_loss=6.325, over 88.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.346, discriminator_fake_loss=1.318, generator_loss=29.1, generator_mel_loss=17.75, generator_kl_loss=1.426, generator_dur_loss=1.744, generator_adv_loss=2.007, generator_feat_match_loss=6.172, over 3193.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:10:21,470 INFO [train.py:527] (1/6) Epoch 855, batch 104, global_batch_idx: 106000, batch size: 50, loss[discriminator_loss=2.7, discriminator_real_loss=1.296, discriminator_fake_loss=1.403, generator_loss=28.93, generator_mel_loss=17.97, generator_kl_loss=1.331, generator_dur_loss=1.675, generator_adv_loss=1.907, generator_feat_match_loss=6.041, over 50.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.32, generator_loss=28.99, generator_mel_loss=17.76, generator_kl_loss=1.426, generator_dur_loss=1.747, generator_adv_loss=1.998, generator_feat_match_loss=6.06, over 6308.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:10:21,471 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 16:10:30,229 INFO [train.py:591] (1/6) Epoch 855, validation: discriminator_loss=2.738, discriminator_real_loss=1.342, discriminator_fake_loss=1.397, generator_loss=27.76, generator_mel_loss=18.26, generator_kl_loss=1.239, generator_dur_loss=1.803, generator_adv_loss=1.871, generator_feat_match_loss=4.589, over 100.00 samples.
+2024-03-15 16:10:30,230 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 16:11:22,580 INFO [train.py:919] (1/6) Start epoch 856
+2024-03-15 16:13:09,267 INFO [train.py:527] (1/6) Epoch 856, batch 30, global_batch_idx: 106050, batch size: 59, loss[discriminator_loss=2.675, discriminator_real_loss=1.385, discriminator_fake_loss=1.289, generator_loss=28.39, generator_mel_loss=17.69, generator_kl_loss=1.314, generator_dur_loss=1.733, generator_adv_loss=2.047, generator_feat_match_loss=5.601, over 59.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.343, discriminator_fake_loss=1.319, generator_loss=29.26, generator_mel_loss=17.75, generator_kl_loss=1.407, generator_dur_loss=1.742, generator_adv_loss=2.075, generator_feat_match_loss=6.288, over 1842.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:15:28,336 INFO [train.py:527] (1/6) Epoch 856, batch 80, global_batch_idx: 106100, batch size: 48, loss[discriminator_loss=2.678, discriminator_real_loss=1.281, discriminator_fake_loss=1.397, generator_loss=29.58, generator_mel_loss=17.6, generator_kl_loss=1.616, generator_dur_loss=1.679, generator_adv_loss=2.076, generator_feat_match_loss=6.616, over 48.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=29.1, generator_mel_loss=17.74, generator_kl_loss=1.402, generator_dur_loss=1.743, generator_adv_loss=2.031, generator_feat_match_loss=6.182, over 4821.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:17:28,611 INFO [train.py:919] (1/6) Start epoch 857
+2024-03-15 16:18:09,618 INFO [train.py:527] (1/6) Epoch 857, batch 6, global_batch_idx: 106150, batch size: 83, loss[discriminator_loss=2.623, discriminator_real_loss=1.243, discriminator_fake_loss=1.38, generator_loss=29.29, generator_mel_loss=17.93, generator_kl_loss=1.258, generator_dur_loss=1.834, generator_adv_loss=2.068, generator_feat_match_loss=6.206, over 83.00 samples.], tot_loss[discriminator_loss=2.7, discriminator_real_loss=1.365, discriminator_fake_loss=1.335, generator_loss=28.7, generator_mel_loss=17.83, generator_kl_loss=1.369, generator_dur_loss=1.732, generator_adv_loss=1.99, generator_feat_match_loss=5.775, over 409.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:20:27,279 INFO [train.py:527] (1/6) Epoch 857, batch 56, global_batch_idx: 106200, batch size: 56, loss[discriminator_loss=2.675, discriminator_real_loss=1.334, discriminator_fake_loss=1.341, generator_loss=28.86, generator_mel_loss=17.95, generator_kl_loss=1.327, generator_dur_loss=1.743, generator_adv_loss=2.036, generator_feat_match_loss=5.805, over 56.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.356, discriminator_fake_loss=1.323, generator_loss=28.96, generator_mel_loss=17.79, generator_kl_loss=1.407, generator_dur_loss=1.742, generator_adv_loss=2.022, generator_feat_match_loss=6.002, over 3338.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:20:27,281 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 16:20:35,562 INFO [train.py:591] (1/6) Epoch 857, validation: discriminator_loss=2.751, discriminator_real_loss=1.439, discriminator_fake_loss=1.312, generator_loss=28.33, generator_mel_loss=18.37, generator_kl_loss=1.277, generator_dur_loss=1.797, generator_adv_loss=1.949, generator_feat_match_loss=4.945, over 100.00 samples.
+2024-03-15 16:20:35,563 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 16:22:55,687 INFO [train.py:527] (1/6) Epoch 857, batch 106, global_batch_idx: 106250, batch size: 36, loss[discriminator_loss=2.605, discriminator_real_loss=1.269, discriminator_fake_loss=1.335, generator_loss=29.24, generator_mel_loss=17.8, generator_kl_loss=1.627, generator_dur_loss=1.719, generator_adv_loss=2.002, generator_feat_match_loss=6.085, over 36.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=28.9, generator_mel_loss=17.73, generator_kl_loss=1.411, generator_dur_loss=1.752, generator_adv_loss=2.012, generator_feat_match_loss=5.993, over 6316.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:23:42,693 INFO [train.py:919] (1/6) Start epoch 858
+2024-03-15 16:25:35,133 INFO [train.py:527] (1/6) Epoch 858, batch 32, global_batch_idx: 106300, batch size: 70, loss[discriminator_loss=2.676, discriminator_real_loss=1.397, discriminator_fake_loss=1.279, generator_loss=29.4, generator_mel_loss=17.52, generator_kl_loss=1.429, generator_dur_loss=1.781, generator_adv_loss=2.077, generator_feat_match_loss=6.59, over 70.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.356, discriminator_fake_loss=1.327, generator_loss=29.07, generator_mel_loss=17.79, generator_kl_loss=1.438, generator_dur_loss=1.758, generator_adv_loss=1.998, generator_feat_match_loss=6.079, over 2000.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:27:50,704 INFO [train.py:527] (1/6) Epoch 858, batch 82, global_batch_idx: 106350, batch size: 36, loss[discriminator_loss=2.695, discriminator_real_loss=1.327, discriminator_fake_loss=1.367, generator_loss=28.18, generator_mel_loss=17.3, generator_kl_loss=1.45, generator_dur_loss=1.692, generator_adv_loss=2.004, generator_feat_match_loss=5.737, over 36.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.367, discriminator_fake_loss=1.327, generator_loss=29.02, generator_mel_loss=17.8, generator_kl_loss=1.419, generator_dur_loss=1.746, generator_adv_loss=1.999, generator_feat_match_loss=6.05, over 4842.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:29:49,238 INFO [train.py:919] (1/6) Start epoch 859
+2024-03-15 16:30:36,527 INFO [train.py:527] (1/6) Epoch 859, batch 8, global_batch_idx: 106400, batch size: 52, loss[discriminator_loss=2.694, discriminator_real_loss=1.368, discriminator_fake_loss=1.326, generator_loss=29.39, generator_mel_loss=18.25, generator_kl_loss=1.489, generator_dur_loss=1.623, generator_adv_loss=1.979, generator_feat_match_loss=6.045, over 52.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.356, discriminator_fake_loss=1.311, generator_loss=29.29, generator_mel_loss=17.89, generator_kl_loss=1.463, generator_dur_loss=1.727, generator_adv_loss=2.019, generator_feat_match_loss=6.19, over 524.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:30:36,545 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 16:30:44,310 INFO [train.py:591] (1/6) Epoch 859, validation: discriminator_loss=2.728, discriminator_real_loss=1.436, discriminator_fake_loss=1.292, generator_loss=29.1, generator_mel_loss=18.67, generator_kl_loss=1.303, generator_dur_loss=1.801, generator_adv_loss=1.93, generator_feat_match_loss=5.4, over 100.00 samples.
+2024-03-15 16:30:44,312 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 16:33:02,191 INFO [train.py:527] (1/6) Epoch 859, batch 58, global_batch_idx: 106450, batch size: 31, loss[discriminator_loss=2.641, discriminator_real_loss=1.31, discriminator_fake_loss=1.332, generator_loss=29.06, generator_mel_loss=17.72, generator_kl_loss=1.365, generator_dur_loss=1.693, generator_adv_loss=2.052, generator_feat_match_loss=6.225, over 31.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.358, discriminator_fake_loss=1.316, generator_loss=29.06, generator_mel_loss=17.84, generator_kl_loss=1.431, generator_dur_loss=1.726, generator_adv_loss=2.019, generator_feat_match_loss=6.048, over 3236.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:35:20,851 INFO [train.py:527] (1/6) Epoch 859, batch 108, global_batch_idx: 106500, batch size: 48, loss[discriminator_loss=2.732, discriminator_real_loss=1.447, discriminator_fake_loss=1.286, generator_loss=28.47, generator_mel_loss=17.81, generator_kl_loss=1.497, generator_dur_loss=1.658, generator_adv_loss=1.79, generator_feat_match_loss=5.714, over 48.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.32, generator_loss=29.03, generator_mel_loss=17.83, generator_kl_loss=1.435, generator_dur_loss=1.73, generator_adv_loss=2.012, generator_feat_match_loss=6.026, over 6055.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:36:02,475 INFO [train.py:919] (1/6) Start epoch 860
+2024-03-15 16:38:00,633 INFO [train.py:527] (1/6) Epoch 860, batch 34, global_batch_idx: 106550, batch size: 74, loss[discriminator_loss=2.741, discriminator_real_loss=1.491, discriminator_fake_loss=1.25, generator_loss=28.66, generator_mel_loss=17.61, generator_kl_loss=1.476, generator_dur_loss=1.826, generator_adv_loss=1.897, generator_feat_match_loss=5.842, over 74.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.346, discriminator_fake_loss=1.32, generator_loss=29.13, generator_mel_loss=17.74, generator_kl_loss=1.477, generator_dur_loss=1.724, generator_adv_loss=2.005, generator_feat_match_loss=6.188, over 1960.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:40:16,737 INFO [train.py:527] (1/6) Epoch 860, batch 84, global_batch_idx: 106600, batch size: 61, loss[discriminator_loss=2.648, discriminator_real_loss=1.371, discriminator_fake_loss=1.278, generator_loss=29.06, generator_mel_loss=17.94, generator_kl_loss=1.398, generator_dur_loss=1.72, generator_adv_loss=2.089, generator_feat_match_loss=5.911, over 61.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.348, discriminator_fake_loss=1.319, generator_loss=29.22, generator_mel_loss=17.81, generator_kl_loss=1.474, generator_dur_loss=1.73, generator_adv_loss=2.015, generator_feat_match_loss=6.196, over 4653.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:40:16,738 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 16:40:25,519 INFO [train.py:591] (1/6) Epoch 860, validation: discriminator_loss=2.708, discriminator_real_loss=1.478, discriminator_fake_loss=1.23, generator_loss=28.1, generator_mel_loss=17.94, generator_kl_loss=1.235, generator_dur_loss=1.818, generator_adv_loss=2.011, generator_feat_match_loss=5.09, over 100.00 samples.
+2024-03-15 16:40:25,519 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 16:42:19,230 INFO [train.py:919] (1/6) Start epoch 861
+2024-03-15 16:43:12,704 INFO [train.py:527] (1/6) Epoch 861, batch 10, global_batch_idx: 106650, batch size: 53, loss[discriminator_loss=2.674, discriminator_real_loss=1.338, discriminator_fake_loss=1.337, generator_loss=29.41, generator_mel_loss=18.06, generator_kl_loss=1.564, generator_dur_loss=1.679, generator_adv_loss=1.994, generator_feat_match_loss=6.118, over 53.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.354, discriminator_fake_loss=1.314, generator_loss=29.26, generator_mel_loss=17.94, generator_kl_loss=1.442, generator_dur_loss=1.767, generator_adv_loss=2.015, generator_feat_match_loss=6.098, over 634.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:45:32,206 INFO [train.py:527] (1/6) Epoch 861, batch 60, global_batch_idx: 106700, batch size: 56, loss[discriminator_loss=2.679, discriminator_real_loss=1.304, discriminator_fake_loss=1.374, generator_loss=28.57, generator_mel_loss=17.59, generator_kl_loss=1.428, generator_dur_loss=1.735, generator_adv_loss=1.91, generator_feat_match_loss=5.909, over 56.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.347, discriminator_fake_loss=1.329, generator_loss=29.08, generator_mel_loss=17.85, generator_kl_loss=1.436, generator_dur_loss=1.744, generator_adv_loss=2.01, generator_feat_match_loss=6.041, over 3468.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:47:49,916 INFO [train.py:527] (1/6) Epoch 861, batch 110, global_batch_idx: 106750, batch size: 52, loss[discriminator_loss=2.674, discriminator_real_loss=1.385, discriminator_fake_loss=1.289, generator_loss=28.13, generator_mel_loss=17.63, generator_kl_loss=1.533, generator_dur_loss=1.708, generator_adv_loss=1.932, generator_feat_match_loss=5.331, over 52.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=29.08, generator_mel_loss=17.81, generator_kl_loss=1.445, generator_dur_loss=1.737, generator_adv_loss=2.013, generator_feat_match_loss=6.072, over 6230.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:48:28,438 INFO [train.py:919] (1/6) Start epoch 862
+2024-03-15 16:50:33,836 INFO [train.py:527] (1/6) Epoch 862, batch 36, global_batch_idx: 106800, batch size: 96, loss[discriminator_loss=2.658, discriminator_real_loss=1.353, discriminator_fake_loss=1.306, generator_loss=29.03, generator_mel_loss=17.93, generator_kl_loss=1.433, generator_dur_loss=1.86, generator_adv_loss=1.915, generator_feat_match_loss=5.889, over 96.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.359, discriminator_fake_loss=1.33, generator_loss=28.95, generator_mel_loss=17.8, generator_kl_loss=1.445, generator_dur_loss=1.733, generator_adv_loss=2.011, generator_feat_match_loss=5.963, over 2065.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:50:33,838 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 16:50:41,933 INFO [train.py:591] (1/6) Epoch 862, validation: discriminator_loss=2.686, discriminator_real_loss=1.377, discriminator_fake_loss=1.309, generator_loss=26.97, generator_mel_loss=17.55, generator_kl_loss=1.282, generator_dur_loss=1.803, generator_adv_loss=1.825, generator_feat_match_loss=4.515, over 100.00 samples.
+2024-03-15 16:50:41,934 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 16:53:01,128 INFO [train.py:527] (1/6) Epoch 862, batch 86, global_batch_idx: 106850, batch size: 72, loss[discriminator_loss=2.699, discriminator_real_loss=1.323, discriminator_fake_loss=1.376, generator_loss=29.83, generator_mel_loss=18.11, generator_kl_loss=1.473, generator_dur_loss=1.797, generator_adv_loss=2.098, generator_feat_match_loss=6.35, over 72.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.358, discriminator_fake_loss=1.325, generator_loss=29, generator_mel_loss=17.78, generator_kl_loss=1.439, generator_dur_loss=1.745, generator_adv_loss=2.007, generator_feat_match_loss=6.031, over 4905.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:54:43,270 INFO [train.py:919] (1/6) Start epoch 863
+2024-03-15 16:55:40,645 INFO [train.py:527] (1/6) Epoch 863, batch 12, global_batch_idx: 106900, batch size: 55, loss[discriminator_loss=2.653, discriminator_real_loss=1.285, discriminator_fake_loss=1.368, generator_loss=29.27, generator_mel_loss=18.15, generator_kl_loss=1.505, generator_dur_loss=1.708, generator_adv_loss=1.99, generator_feat_match_loss=5.917, over 55.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.362, discriminator_fake_loss=1.321, generator_loss=28.9, generator_mel_loss=17.73, generator_kl_loss=1.405, generator_dur_loss=1.787, generator_adv_loss=1.992, generator_feat_match_loss=5.984, over 896.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 16:58:01,585 INFO [train.py:527] (1/6) Epoch 863, batch 62, global_batch_idx: 106950, batch size: 39, loss[discriminator_loss=2.653, discriminator_real_loss=1.284, discriminator_fake_loss=1.369, generator_loss=29.74, generator_mel_loss=18.05, generator_kl_loss=1.517, generator_dur_loss=1.635, generator_adv_loss=1.955, generator_feat_match_loss=6.586, over 39.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=29, generator_mel_loss=17.77, generator_kl_loss=1.421, generator_dur_loss=1.76, generator_adv_loss=2, generator_feat_match_loss=6.041, over 3816.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:00:22,691 INFO [train.py:527] (1/6) Epoch 863, batch 112, global_batch_idx: 107000, batch size: 44, loss[discriminator_loss=2.748, discriminator_real_loss=1.424, discriminator_fake_loss=1.324, generator_loss=29.02, generator_mel_loss=17.81, generator_kl_loss=1.555, generator_dur_loss=1.696, generator_adv_loss=2.008, generator_feat_match_loss=5.95, over 44.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.356, discriminator_fake_loss=1.324, generator_loss=28.97, generator_mel_loss=17.74, generator_kl_loss=1.413, generator_dur_loss=1.762, generator_adv_loss=2.003, generator_feat_match_loss=6.046, over 6951.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:00:22,692 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 17:00:31,749 INFO [train.py:591] (1/6) Epoch 863, validation: discriminator_loss=2.749, discriminator_real_loss=1.467, discriminator_fake_loss=1.282, generator_loss=27.81, generator_mel_loss=17.96, generator_kl_loss=1.232, generator_dur_loss=1.805, generator_adv_loss=1.926, generator_feat_match_loss=4.884, over 100.00 samples.
+2024-03-15 17:00:31,750 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 17:01:03,269 INFO [train.py:919] (1/6) Start epoch 864
+2024-03-15 17:03:14,725 INFO [train.py:527] (1/6) Epoch 864, batch 38, global_batch_idx: 107050, batch size: 56, loss[discriminator_loss=2.652, discriminator_real_loss=1.273, discriminator_fake_loss=1.379, generator_loss=30.09, generator_mel_loss=18.18, generator_kl_loss=1.396, generator_dur_loss=1.754, generator_adv_loss=2.078, generator_feat_match_loss=6.681, over 56.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.351, discriminator_fake_loss=1.333, generator_loss=29.03, generator_mel_loss=17.83, generator_kl_loss=1.415, generator_dur_loss=1.765, generator_adv_loss=1.995, generator_feat_match_loss=6.031, over 2343.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:05:35,854 INFO [train.py:527] (1/6) Epoch 864, batch 88, global_batch_idx: 107100, batch size: 55, loss[discriminator_loss=2.66, discriminator_real_loss=1.262, discriminator_fake_loss=1.398, generator_loss=30.79, generator_mel_loss=18.41, generator_kl_loss=1.49, generator_dur_loss=1.739, generator_adv_loss=2.07, generator_feat_match_loss=7.089, over 55.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.353, discriminator_fake_loss=1.326, generator_loss=29.11, generator_mel_loss=17.84, generator_kl_loss=1.438, generator_dur_loss=1.745, generator_adv_loss=2.008, generator_feat_match_loss=6.073, over 5122.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:07:15,859 INFO [train.py:919] (1/6) Start epoch 865
+2024-03-15 17:08:19,372 INFO [train.py:527] (1/6) Epoch 865, batch 14, global_batch_idx: 107150, batch size: 47, loss[discriminator_loss=2.738, discriminator_real_loss=1.411, discriminator_fake_loss=1.327, generator_loss=28.54, generator_mel_loss=18.09, generator_kl_loss=1.518, generator_dur_loss=1.695, generator_adv_loss=1.846, generator_feat_match_loss=5.396, over 47.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.362, discriminator_fake_loss=1.316, generator_loss=29.01, generator_mel_loss=17.81, generator_kl_loss=1.394, generator_dur_loss=1.734, generator_adv_loss=2.034, generator_feat_match_loss=6.033, over 876.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:10:41,778 INFO [train.py:527] (1/6) Epoch 865, batch 64, global_batch_idx: 107200, batch size: 72, loss[discriminator_loss=2.681, discriminator_real_loss=1.355, discriminator_fake_loss=1.326, generator_loss=28.16, generator_mel_loss=17.54, generator_kl_loss=1.292, generator_dur_loss=1.794, generator_adv_loss=2.075, generator_feat_match_loss=5.457, over 72.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.353, discriminator_fake_loss=1.316, generator_loss=29.18, generator_mel_loss=17.86, generator_kl_loss=1.437, generator_dur_loss=1.743, generator_adv_loss=2.024, generator_feat_match_loss=6.115, over 3678.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:10:41,780 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 17:10:49,761 INFO [train.py:591] (1/6) Epoch 865, validation: discriminator_loss=2.681, discriminator_real_loss=1.411, discriminator_fake_loss=1.27, generator_loss=27.54, generator_mel_loss=18.04, generator_kl_loss=1.312, generator_dur_loss=1.8, generator_adv_loss=1.97, generator_feat_match_loss=4.413, over 100.00 samples.
+2024-03-15 17:10:49,761 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 17:13:13,534 INFO [train.py:527] (1/6) Epoch 865, batch 114, global_batch_idx: 107250, batch size: 70, loss[discriminator_loss=2.693, discriminator_real_loss=1.417, discriminator_fake_loss=1.275, generator_loss=28.32, generator_mel_loss=17.57, generator_kl_loss=1.426, generator_dur_loss=1.807, generator_adv_loss=2.003, generator_feat_match_loss=5.505, over 70.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.321, generator_loss=29.14, generator_mel_loss=17.84, generator_kl_loss=1.437, generator_dur_loss=1.747, generator_adv_loss=2.018, generator_feat_match_loss=6.097, over 6684.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:13:40,164 INFO [train.py:919] (1/6) Start epoch 866
+2024-03-15 17:15:59,850 INFO [train.py:527] (1/6) Epoch 866, batch 40, global_batch_idx: 107300, batch size: 68, loss[discriminator_loss=2.618, discriminator_real_loss=1.329, discriminator_fake_loss=1.29, generator_loss=29.31, generator_mel_loss=17.7, generator_kl_loss=1.482, generator_dur_loss=1.767, generator_adv_loss=1.938, generator_feat_match_loss=6.428, over 68.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.343, discriminator_fake_loss=1.316, generator_loss=29.14, generator_mel_loss=17.84, generator_kl_loss=1.444, generator_dur_loss=1.716, generator_adv_loss=2.025, generator_feat_match_loss=6.119, over 2211.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:18:21,250 INFO [train.py:527] (1/6) Epoch 866, batch 90, global_batch_idx: 107350, batch size: 58, loss[discriminator_loss=2.648, discriminator_real_loss=1.29, discriminator_fake_loss=1.358, generator_loss=29.09, generator_mel_loss=17.82, generator_kl_loss=1.434, generator_dur_loss=1.675, generator_adv_loss=2.047, generator_feat_match_loss=6.113, over 58.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.1, generator_mel_loss=17.83, generator_kl_loss=1.451, generator_dur_loss=1.719, generator_adv_loss=2.013, generator_feat_match_loss=6.092, over 4846.00 samples.], cur_lr_g: 1.80e-04, cur_lr_d: 1.80e-04, 
+2024-03-15 17:19:50,578 INFO [train.py:919] (1/6) Start epoch 867
+2024-03-15 17:20:58,846 INFO [train.py:527] (1/6) Epoch 867, batch 16, global_batch_idx: 107400, batch size: 42, loss[discriminator_loss=2.63, discriminator_real_loss=1.395, discriminator_fake_loss=1.235, generator_loss=28.46, generator_mel_loss=17.55, generator_kl_loss=1.493, generator_dur_loss=1.691, generator_adv_loss=1.983, generator_feat_match_loss=5.743, over 42.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=29.29, generator_mel_loss=17.8, generator_kl_loss=1.479, generator_dur_loss=1.711, generator_adv_loss=2.015, generator_feat_match_loss=6.294, over 915.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:20:58,847 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 17:21:06,797 INFO [train.py:591] (1/6) Epoch 867, validation: discriminator_loss=2.686, discriminator_real_loss=1.307, discriminator_fake_loss=1.379, generator_loss=27.67, generator_mel_loss=17.74, generator_kl_loss=1.298, generator_dur_loss=1.802, generator_adv_loss=1.89, generator_feat_match_loss=4.94, over 100.00 samples.
+2024-03-15 17:21:06,799 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 17:23:24,403 INFO [train.py:527] (1/6) Epoch 867, batch 66, global_batch_idx: 107450, batch size: 88, loss[discriminator_loss=2.681, discriminator_real_loss=1.307, discriminator_fake_loss=1.374, generator_loss=28.87, generator_mel_loss=17.88, generator_kl_loss=1.27, generator_dur_loss=1.822, generator_adv_loss=2.147, generator_feat_match_loss=5.745, over 88.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.1, generator_mel_loss=17.74, generator_kl_loss=1.429, generator_dur_loss=1.735, generator_adv_loss=2.017, generator_feat_match_loss=6.183, over 3799.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:25:42,113 INFO [train.py:527] (1/6) Epoch 867, batch 116, global_batch_idx: 107500, batch size: 59, loss[discriminator_loss=2.746, discriminator_real_loss=1.304, discriminator_fake_loss=1.441, generator_loss=29.22, generator_mel_loss=18.06, generator_kl_loss=1.442, generator_dur_loss=1.752, generator_adv_loss=2.074, generator_feat_match_loss=5.899, over 59.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=29.12, generator_mel_loss=17.77, generator_kl_loss=1.446, generator_dur_loss=1.734, generator_adv_loss=2.016, generator_feat_match_loss=6.16, over 6556.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:26:04,961 INFO [train.py:919] (1/6) Start epoch 868
+2024-03-15 17:28:24,434 INFO [train.py:527] (1/6) Epoch 868, batch 42, global_batch_idx: 107550, batch size: 39, loss[discriminator_loss=2.705, discriminator_real_loss=1.274, discriminator_fake_loss=1.431, generator_loss=28.63, generator_mel_loss=17.89, generator_kl_loss=1.71, generator_dur_loss=1.679, generator_adv_loss=2.064, generator_feat_match_loss=5.292, over 39.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.342, discriminator_fake_loss=1.324, generator_loss=29, generator_mel_loss=17.68, generator_kl_loss=1.421, generator_dur_loss=1.761, generator_adv_loss=1.994, generator_feat_match_loss=6.151, over 2670.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:30:40,951 INFO [train.py:527] (1/6) Epoch 868, batch 92, global_batch_idx: 107600, batch size: 70, loss[discriminator_loss=2.666, discriminator_real_loss=1.373, discriminator_fake_loss=1.293, generator_loss=28.93, generator_mel_loss=17.37, generator_kl_loss=1.413, generator_dur_loss=1.764, generator_adv_loss=2.033, generator_feat_match_loss=6.35, over 70.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.346, discriminator_fake_loss=1.326, generator_loss=29.1, generator_mel_loss=17.75, generator_kl_loss=1.432, generator_dur_loss=1.746, generator_adv_loss=1.998, generator_feat_match_loss=6.171, over 5429.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:30:40,953 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 17:30:49,715 INFO [train.py:591] (1/6) Epoch 868, validation: discriminator_loss=2.713, discriminator_real_loss=1.39, discriminator_fake_loss=1.323, generator_loss=28.26, generator_mel_loss=18.19, generator_kl_loss=1.222, generator_dur_loss=1.804, generator_adv_loss=1.966, generator_feat_match_loss=5.076, over 100.00 samples.
+2024-03-15 17:30:49,716 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 17:32:19,703 INFO [train.py:919] (1/6) Start epoch 869
+2024-03-15 17:33:34,653 INFO [train.py:527] (1/6) Epoch 869, batch 18, global_batch_idx: 107650, batch size: 44, loss[discriminator_loss=2.617, discriminator_real_loss=1.33, discriminator_fake_loss=1.287, generator_loss=29.38, generator_mel_loss=17.37, generator_kl_loss=1.706, generator_dur_loss=1.72, generator_adv_loss=2.09, generator_feat_match_loss=6.49, over 44.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.362, discriminator_fake_loss=1.314, generator_loss=28.93, generator_mel_loss=17.78, generator_kl_loss=1.451, generator_dur_loss=1.731, generator_adv_loss=1.998, generator_feat_match_loss=5.977, over 1102.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:35:52,983 INFO [train.py:527] (1/6) Epoch 869, batch 68, global_batch_idx: 107700, batch size: 53, loss[discriminator_loss=2.675, discriminator_real_loss=1.325, discriminator_fake_loss=1.35, generator_loss=28.55, generator_mel_loss=17.3, generator_kl_loss=1.52, generator_dur_loss=1.699, generator_adv_loss=1.969, generator_feat_match_loss=6.065, over 53.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.85, generator_kl_loss=1.437, generator_dur_loss=1.73, generator_adv_loss=2.009, generator_feat_match_loss=6.079, over 3827.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:38:10,262 INFO [train.py:527] (1/6) Epoch 869, batch 118, global_batch_idx: 107750, batch size: 14, loss[discriminator_loss=2.738, discriminator_real_loss=1.453, discriminator_fake_loss=1.285, generator_loss=30.1, generator_mel_loss=18.39, generator_kl_loss=1.771, generator_dur_loss=1.683, generator_adv_loss=1.97, generator_feat_match_loss=6.282, over 14.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.356, discriminator_fake_loss=1.324, generator_loss=29.01, generator_mel_loss=17.8, generator_kl_loss=1.447, generator_dur_loss=1.735, generator_adv_loss=2.004, generator_feat_match_loss=6.022, over 6667.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:38:25,967 INFO [train.py:919] (1/6) Start epoch 870
+2024-03-15 17:40:55,590 INFO [train.py:527] (1/6) Epoch 870, batch 44, global_batch_idx: 107800, batch size: 59, loss[discriminator_loss=2.687, discriminator_real_loss=1.369, discriminator_fake_loss=1.318, generator_loss=28.8, generator_mel_loss=17.79, generator_kl_loss=1.326, generator_dur_loss=1.737, generator_adv_loss=2.01, generator_feat_match_loss=5.932, over 59.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.345, discriminator_fake_loss=1.323, generator_loss=29.12, generator_mel_loss=17.86, generator_kl_loss=1.444, generator_dur_loss=1.741, generator_adv_loss=2.006, generator_feat_match_loss=6.075, over 2509.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:40:55,591 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 17:41:03,898 INFO [train.py:591] (1/6) Epoch 870, validation: discriminator_loss=2.776, discriminator_real_loss=1.387, discriminator_fake_loss=1.389, generator_loss=28, generator_mel_loss=18.17, generator_kl_loss=1.305, generator_dur_loss=1.815, generator_adv_loss=1.815, generator_feat_match_loss=4.895, over 100.00 samples.
+2024-03-15 17:41:03,899 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 17:43:23,781 INFO [train.py:527] (1/6) Epoch 870, batch 94, global_batch_idx: 107850, batch size: 16, loss[discriminator_loss=2.549, discriminator_real_loss=1.215, discriminator_fake_loss=1.333, generator_loss=31.78, generator_mel_loss=18.88, generator_kl_loss=1.789, generator_dur_loss=1.52, generator_adv_loss=2.253, generator_feat_match_loss=7.336, over 16.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.324, generator_loss=29.03, generator_mel_loss=17.82, generator_kl_loss=1.444, generator_dur_loss=1.745, generator_adv_loss=2.001, generator_feat_match_loss=6.027, over 5381.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:44:43,530 INFO [train.py:919] (1/6) Start epoch 871
+2024-03-15 17:46:03,821 INFO [train.py:527] (1/6) Epoch 871, batch 20, global_batch_idx: 107900, batch size: 61, loss[discriminator_loss=2.72, discriminator_real_loss=1.393, discriminator_fake_loss=1.327, generator_loss=27.78, generator_mel_loss=17.73, generator_kl_loss=1.409, generator_dur_loss=1.686, generator_adv_loss=1.796, generator_feat_match_loss=5.161, over 61.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.356, discriminator_fake_loss=1.323, generator_loss=29.1, generator_mel_loss=17.86, generator_kl_loss=1.431, generator_dur_loss=1.72, generator_adv_loss=2.008, generator_feat_match_loss=6.084, over 1162.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:48:24,350 INFO [train.py:527] (1/6) Epoch 871, batch 70, global_batch_idx: 107950, batch size: 88, loss[discriminator_loss=2.736, discriminator_real_loss=1.427, discriminator_fake_loss=1.308, generator_loss=28.36, generator_mel_loss=17.62, generator_kl_loss=1.381, generator_dur_loss=1.845, generator_adv_loss=1.889, generator_feat_match_loss=5.629, over 88.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.354, discriminator_fake_loss=1.326, generator_loss=29.16, generator_mel_loss=17.84, generator_kl_loss=1.419, generator_dur_loss=1.747, generator_adv_loss=2.007, generator_feat_match_loss=6.15, over 4065.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:50:40,219 INFO [train.py:527] (1/6) Epoch 871, batch 120, global_batch_idx: 108000, batch size: 70, loss[discriminator_loss=2.654, discriminator_real_loss=1.311, discriminator_fake_loss=1.343, generator_loss=28.59, generator_mel_loss=17.95, generator_kl_loss=1.321, generator_dur_loss=1.805, generator_adv_loss=2.03, generator_feat_match_loss=5.489, over 70.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.352, discriminator_fake_loss=1.326, generator_loss=29.17, generator_mel_loss=17.82, generator_kl_loss=1.442, generator_dur_loss=1.742, generator_adv_loss=2.008, generator_feat_match_loss=6.153, over 6844.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:50:40,221 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 17:50:49,222 INFO [train.py:591] (1/6) Epoch 871, validation: discriminator_loss=2.693, discriminator_real_loss=1.437, discriminator_fake_loss=1.256, generator_loss=27.69, generator_mel_loss=17.98, generator_kl_loss=1.284, generator_dur_loss=1.814, generator_adv_loss=1.927, generator_feat_match_loss=4.692, over 100.00 samples.
+2024-03-15 17:50:49,223 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 17:50:59,272 INFO [train.py:919] (1/6) Start epoch 872
+2024-03-15 17:53:28,739 INFO [train.py:527] (1/6) Epoch 872, batch 46, global_batch_idx: 108050, batch size: 74, loss[discriminator_loss=2.742, discriminator_real_loss=1.474, discriminator_fake_loss=1.267, generator_loss=28.14, generator_mel_loss=17.72, generator_kl_loss=1.509, generator_dur_loss=1.814, generator_adv_loss=1.828, generator_feat_match_loss=5.271, over 74.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.36, discriminator_fake_loss=1.315, generator_loss=29.05, generator_mel_loss=17.78, generator_kl_loss=1.457, generator_dur_loss=1.736, generator_adv_loss=2.018, generator_feat_match_loss=6.059, over 2491.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:55:49,405 INFO [train.py:527] (1/6) Epoch 872, batch 96, global_batch_idx: 108100, batch size: 88, loss[discriminator_loss=2.634, discriminator_real_loss=1.337, discriminator_fake_loss=1.297, generator_loss=29.43, generator_mel_loss=17.69, generator_kl_loss=1.398, generator_dur_loss=1.858, generator_adv_loss=1.994, generator_feat_match_loss=6.484, over 88.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.32, generator_loss=29.03, generator_mel_loss=17.75, generator_kl_loss=1.455, generator_dur_loss=1.75, generator_adv_loss=2.009, generator_feat_match_loss=6.066, over 5497.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 17:57:05,255 INFO [train.py:919] (1/6) Start epoch 873
+2024-03-15 17:58:29,767 INFO [train.py:527] (1/6) Epoch 873, batch 22, global_batch_idx: 108150, batch size: 59, loss[discriminator_loss=2.669, discriminator_real_loss=1.395, discriminator_fake_loss=1.275, generator_loss=29.27, generator_mel_loss=17.9, generator_kl_loss=1.426, generator_dur_loss=1.698, generator_adv_loss=2.101, generator_feat_match_loss=6.136, over 59.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.363, discriminator_fake_loss=1.329, generator_loss=28.93, generator_mel_loss=17.68, generator_kl_loss=1.452, generator_dur_loss=1.728, generator_adv_loss=2.067, generator_feat_match_loss=6.004, over 1319.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:00:46,120 INFO [train.py:527] (1/6) Epoch 873, batch 72, global_batch_idx: 108200, batch size: 50, loss[discriminator_loss=2.759, discriminator_real_loss=1.457, discriminator_fake_loss=1.302, generator_loss=28.14, generator_mel_loss=17.65, generator_kl_loss=1.508, generator_dur_loss=1.706, generator_adv_loss=1.935, generator_feat_match_loss=5.342, over 50.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.36, discriminator_fake_loss=1.32, generator_loss=28.97, generator_mel_loss=17.71, generator_kl_loss=1.445, generator_dur_loss=1.737, generator_adv_loss=2.034, generator_feat_match_loss=6.045, over 4157.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:00:46,121 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 18:00:54,111 INFO [train.py:591] (1/6) Epoch 873, validation: discriminator_loss=2.741, discriminator_real_loss=1.426, discriminator_fake_loss=1.315, generator_loss=27.49, generator_mel_loss=17.82, generator_kl_loss=1.225, generator_dur_loss=1.815, generator_adv_loss=1.95, generator_feat_match_loss=4.677, over 100.00 samples.
+2024-03-15 18:00:54,111 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 18:03:12,115 INFO [train.py:527] (1/6) Epoch 873, batch 122, global_batch_idx: 108250, batch size: 26, loss[discriminator_loss=2.72, discriminator_real_loss=1.396, discriminator_fake_loss=1.324, generator_loss=29.7, generator_mel_loss=18.22, generator_kl_loss=1.61, generator_dur_loss=1.591, generator_adv_loss=2.017, generator_feat_match_loss=6.257, over 26.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.357, discriminator_fake_loss=1.319, generator_loss=29.09, generator_mel_loss=17.77, generator_kl_loss=1.439, generator_dur_loss=1.74, generator_adv_loss=2.029, generator_feat_match_loss=6.11, over 6978.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:03:16,643 INFO [train.py:919] (1/6) Start epoch 874
+2024-03-15 18:05:52,499 INFO [train.py:527] (1/6) Epoch 874, batch 48, global_batch_idx: 108300, batch size: 58, loss[discriminator_loss=2.693, discriminator_real_loss=1.337, discriminator_fake_loss=1.356, generator_loss=29.78, generator_mel_loss=17.73, generator_kl_loss=1.455, generator_dur_loss=1.72, generator_adv_loss=2.054, generator_feat_match_loss=6.826, over 58.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.359, discriminator_fake_loss=1.313, generator_loss=29.13, generator_mel_loss=17.77, generator_kl_loss=1.439, generator_dur_loss=1.744, generator_adv_loss=2.008, generator_feat_match_loss=6.164, over 2849.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:08:12,250 INFO [train.py:527] (1/6) Epoch 874, batch 98, global_batch_idx: 108350, batch size: 70, loss[discriminator_loss=2.704, discriminator_real_loss=1.196, discriminator_fake_loss=1.508, generator_loss=28.34, generator_mel_loss=17.61, generator_kl_loss=1.375, generator_dur_loss=1.79, generator_adv_loss=2.13, generator_feat_match_loss=5.426, over 70.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.35, discriminator_fake_loss=1.323, generator_loss=29.12, generator_mel_loss=17.77, generator_kl_loss=1.43, generator_dur_loss=1.753, generator_adv_loss=2.008, generator_feat_match_loss=6.159, over 5869.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:09:22,323 INFO [train.py:919] (1/6) Start epoch 875
+2024-03-15 18:10:53,776 INFO [train.py:527] (1/6) Epoch 875, batch 24, global_batch_idx: 108400, batch size: 72, loss[discriminator_loss=2.624, discriminator_real_loss=1.285, discriminator_fake_loss=1.339, generator_loss=29.29, generator_mel_loss=17.69, generator_kl_loss=1.38, generator_dur_loss=1.773, generator_adv_loss=1.975, generator_feat_match_loss=6.477, over 72.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.356, discriminator_fake_loss=1.326, generator_loss=29.15, generator_mel_loss=17.88, generator_kl_loss=1.464, generator_dur_loss=1.733, generator_adv_loss=1.995, generator_feat_match_loss=6.069, over 1355.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:10:53,778 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 18:11:01,727 INFO [train.py:591] (1/6) Epoch 875, validation: discriminator_loss=2.644, discriminator_real_loss=1.344, discriminator_fake_loss=1.3, generator_loss=27.54, generator_mel_loss=17.71, generator_kl_loss=1.262, generator_dur_loss=1.802, generator_adv_loss=1.912, generator_feat_match_loss=4.86, over 100.00 samples.
+2024-03-15 18:11:01,728 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 18:13:20,857 INFO [train.py:527] (1/6) Epoch 875, batch 74, global_batch_idx: 108450, batch size: 52, loss[discriminator_loss=2.681, discriminator_real_loss=1.348, discriminator_fake_loss=1.333, generator_loss=29, generator_mel_loss=17.73, generator_kl_loss=1.57, generator_dur_loss=1.666, generator_adv_loss=2, generator_feat_match_loss=6.029, over 52.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=29.03, generator_mel_loss=17.8, generator_kl_loss=1.449, generator_dur_loss=1.744, generator_adv_loss=2.017, generator_feat_match_loss=6.019, over 4244.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:15:38,706 INFO [train.py:919] (1/6) Start epoch 876
+2024-03-15 18:16:02,445 INFO [train.py:527] (1/6) Epoch 876, batch 0, global_batch_idx: 108500, batch size: 77, loss[discriminator_loss=2.746, discriminator_real_loss=1.456, discriminator_fake_loss=1.29, generator_loss=28.01, generator_mel_loss=17.63, generator_kl_loss=1.492, generator_dur_loss=1.821, generator_adv_loss=1.932, generator_feat_match_loss=5.133, over 77.00 samples.], tot_loss[discriminator_loss=2.746, discriminator_real_loss=1.456, discriminator_fake_loss=1.29, generator_loss=28.01, generator_mel_loss=17.63, generator_kl_loss=1.492, generator_dur_loss=1.821, generator_adv_loss=1.932, generator_feat_match_loss=5.133, over 77.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:18:19,656 INFO [train.py:527] (1/6) Epoch 876, batch 50, global_batch_idx: 108550, batch size: 74, loss[discriminator_loss=2.694, discriminator_real_loss=1.369, discriminator_fake_loss=1.325, generator_loss=28.65, generator_mel_loss=17.88, generator_kl_loss=1.411, generator_dur_loss=1.822, generator_adv_loss=2.047, generator_feat_match_loss=5.482, over 74.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.358, discriminator_fake_loss=1.333, generator_loss=29.11, generator_mel_loss=17.86, generator_kl_loss=1.442, generator_dur_loss=1.742, generator_adv_loss=2.006, generator_feat_match_loss=6.052, over 2972.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:20:40,463 INFO [train.py:527] (1/6) Epoch 876, batch 100, global_batch_idx: 108600, batch size: 17, loss[discriminator_loss=2.684, discriminator_real_loss=1.53, discriminator_fake_loss=1.155, generator_loss=29.89, generator_mel_loss=17.82, generator_kl_loss=2.111, generator_dur_loss=1.494, generator_adv_loss=2.081, generator_feat_match_loss=6.387, over 17.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=29.08, generator_mel_loss=17.8, generator_kl_loss=1.443, generator_dur_loss=1.744, generator_adv_loss=2.017, generator_feat_match_loss=6.075, over 5812.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:20:40,464 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 18:20:49,300 INFO [train.py:591] (1/6) Epoch 876, validation: discriminator_loss=2.681, discriminator_real_loss=1.37, discriminator_fake_loss=1.31, generator_loss=27.59, generator_mel_loss=17.93, generator_kl_loss=1.287, generator_dur_loss=1.799, generator_adv_loss=1.908, generator_feat_match_loss=4.673, over 100.00 samples.
+2024-03-15 18:20:49,300 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 18:21:52,790 INFO [train.py:919] (1/6) Start epoch 877
+2024-03-15 18:23:24,954 INFO [train.py:527] (1/6) Epoch 877, batch 26, global_batch_idx: 108650, batch size: 64, loss[discriminator_loss=2.706, discriminator_real_loss=1.335, discriminator_fake_loss=1.371, generator_loss=28.59, generator_mel_loss=17.76, generator_kl_loss=1.358, generator_dur_loss=1.775, generator_adv_loss=2.022, generator_feat_match_loss=5.675, over 64.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.361, discriminator_fake_loss=1.327, generator_loss=29.12, generator_mel_loss=17.9, generator_kl_loss=1.401, generator_dur_loss=1.745, generator_adv_loss=1.994, generator_feat_match_loss=6.074, over 1631.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:25:44,595 INFO [train.py:527] (1/6) Epoch 877, batch 76, global_batch_idx: 108700, batch size: 44, loss[discriminator_loss=2.636, discriminator_real_loss=1.319, discriminator_fake_loss=1.316, generator_loss=29.3, generator_mel_loss=17.52, generator_kl_loss=1.638, generator_dur_loss=1.64, generator_adv_loss=1.955, generator_feat_match_loss=6.542, over 44.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.352, discriminator_fake_loss=1.321, generator_loss=28.96, generator_mel_loss=17.76, generator_kl_loss=1.409, generator_dur_loss=1.757, generator_adv_loss=2.001, generator_feat_match_loss=6.039, over 4598.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:27:56,064 INFO [train.py:919] (1/6) Start epoch 878
+2024-03-15 18:28:20,875 INFO [train.py:527] (1/6) Epoch 878, batch 2, global_batch_idx: 108750, batch size: 52, loss[discriminator_loss=2.709, discriminator_real_loss=1.433, discriminator_fake_loss=1.276, generator_loss=28.71, generator_mel_loss=17.6, generator_kl_loss=1.451, generator_dur_loss=1.673, generator_adv_loss=1.907, generator_feat_match_loss=6.079, over 52.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.404, discriminator_fake_loss=1.29, generator_loss=28.7, generator_mel_loss=17.68, generator_kl_loss=1.441, generator_dur_loss=1.739, generator_adv_loss=1.979, generator_feat_match_loss=5.863, over 171.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:30:42,821 INFO [train.py:527] (1/6) Epoch 878, batch 52, global_batch_idx: 108800, batch size: 39, loss[discriminator_loss=2.626, discriminator_real_loss=1.305, discriminator_fake_loss=1.322, generator_loss=30.82, generator_mel_loss=18.13, generator_kl_loss=1.684, generator_dur_loss=1.686, generator_adv_loss=2.154, generator_feat_match_loss=7.165, over 39.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.36, discriminator_fake_loss=1.329, generator_loss=29.08, generator_mel_loss=17.76, generator_kl_loss=1.438, generator_dur_loss=1.738, generator_adv_loss=2.01, generator_feat_match_loss=6.131, over 3024.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:30:42,823 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 18:30:51,154 INFO [train.py:591] (1/6) Epoch 878, validation: discriminator_loss=2.73, discriminator_real_loss=1.464, discriminator_fake_loss=1.267, generator_loss=27.14, generator_mel_loss=17.6, generator_kl_loss=1.343, generator_dur_loss=1.804, generator_adv_loss=2.048, generator_feat_match_loss=4.35, over 100.00 samples.
+2024-03-15 18:30:51,155 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 18:33:07,729 INFO [train.py:527] (1/6) Epoch 878, batch 102, global_batch_idx: 108850, batch size: 36, loss[discriminator_loss=2.613, discriminator_real_loss=1.294, discriminator_fake_loss=1.319, generator_loss=30.15, generator_mel_loss=18.04, generator_kl_loss=1.641, generator_dur_loss=1.689, generator_adv_loss=2.083, generator_feat_match_loss=6.689, over 36.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.359, discriminator_fake_loss=1.329, generator_loss=29.08, generator_mel_loss=17.77, generator_kl_loss=1.439, generator_dur_loss=1.731, generator_adv_loss=2.019, generator_feat_match_loss=6.12, over 5484.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:34:08,175 INFO [train.py:919] (1/6) Start epoch 879
+2024-03-15 18:35:51,364 INFO [train.py:527] (1/6) Epoch 879, batch 28, global_batch_idx: 108900, batch size: 70, loss[discriminator_loss=2.67, discriminator_real_loss=1.35, discriminator_fake_loss=1.32, generator_loss=28.79, generator_mel_loss=17.63, generator_kl_loss=1.308, generator_dur_loss=1.799, generator_adv_loss=2.032, generator_feat_match_loss=6.027, over 70.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=29.17, generator_mel_loss=17.87, generator_kl_loss=1.415, generator_dur_loss=1.754, generator_adv_loss=1.993, generator_feat_match_loss=6.141, over 1742.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:38:13,276 INFO [train.py:527] (1/6) Epoch 879, batch 78, global_batch_idx: 108950, batch size: 72, loss[discriminator_loss=2.673, discriminator_real_loss=1.383, discriminator_fake_loss=1.29, generator_loss=29.4, generator_mel_loss=17.87, generator_kl_loss=1.555, generator_dur_loss=1.779, generator_adv_loss=2.001, generator_feat_match_loss=6.199, over 72.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=29.09, generator_mel_loss=17.85, generator_kl_loss=1.433, generator_dur_loss=1.74, generator_adv_loss=1.996, generator_feat_match_loss=6.064, over 4691.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:40:18,518 INFO [train.py:919] (1/6) Start epoch 880
+2024-03-15 18:40:52,087 INFO [train.py:527] (1/6) Epoch 880, batch 4, global_batch_idx: 109000, batch size: 53, loss[discriminator_loss=2.631, discriminator_real_loss=1.266, discriminator_fake_loss=1.365, generator_loss=29.58, generator_mel_loss=18.23, generator_kl_loss=1.574, generator_dur_loss=1.633, generator_adv_loss=2.167, generator_feat_match_loss=5.976, over 53.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.332, discriminator_fake_loss=1.33, generator_loss=29.19, generator_mel_loss=17.91, generator_kl_loss=1.498, generator_dur_loss=1.74, generator_adv_loss=2.02, generator_feat_match_loss=6.024, over 339.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:40:52,090 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 18:40:59,968 INFO [train.py:591] (1/6) Epoch 880, validation: discriminator_loss=2.772, discriminator_real_loss=1.501, discriminator_fake_loss=1.271, generator_loss=27.81, generator_mel_loss=17.71, generator_kl_loss=1.226, generator_dur_loss=1.799, generator_adv_loss=2.008, generator_feat_match_loss=5.066, over 100.00 samples.
+2024-03-15 18:40:59,971 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 18:43:19,240 INFO [train.py:527] (1/6) Epoch 880, batch 54, global_batch_idx: 109050, batch size: 72, loss[discriminator_loss=2.656, discriminator_real_loss=1.402, discriminator_fake_loss=1.254, generator_loss=28.54, generator_mel_loss=17.99, generator_kl_loss=1.221, generator_dur_loss=1.824, generator_adv_loss=1.943, generator_feat_match_loss=5.563, over 72.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.81, generator_kl_loss=1.437, generator_dur_loss=1.74, generator_adv_loss=2.01, generator_feat_match_loss=6.108, over 3124.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:45:38,363 INFO [train.py:527] (1/6) Epoch 880, batch 104, global_batch_idx: 109100, batch size: 45, loss[discriminator_loss=2.638, discriminator_real_loss=1.31, discriminator_fake_loss=1.329, generator_loss=29.75, generator_mel_loss=18.09, generator_kl_loss=1.449, generator_dur_loss=1.653, generator_adv_loss=2.022, generator_feat_match_loss=6.54, over 45.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.318, generator_loss=29.05, generator_mel_loss=17.76, generator_kl_loss=1.433, generator_dur_loss=1.739, generator_adv_loss=2.009, generator_feat_match_loss=6.1, over 5976.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:46:32,071 INFO [train.py:919] (1/6) Start epoch 881
+2024-03-15 18:48:21,119 INFO [train.py:527] (1/6) Epoch 881, batch 30, global_batch_idx: 109150, batch size: 72, loss[discriminator_loss=2.659, discriminator_real_loss=1.302, discriminator_fake_loss=1.357, generator_loss=29.37, generator_mel_loss=18.15, generator_kl_loss=1.415, generator_dur_loss=1.776, generator_adv_loss=2.028, generator_feat_match_loss=6, over 72.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.365, discriminator_fake_loss=1.321, generator_loss=29.05, generator_mel_loss=17.81, generator_kl_loss=1.455, generator_dur_loss=1.713, generator_adv_loss=2.01, generator_feat_match_loss=6.067, over 1560.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:50:40,779 INFO [train.py:527] (1/6) Epoch 881, batch 80, global_batch_idx: 109200, batch size: 64, loss[discriminator_loss=2.649, discriminator_real_loss=1.312, discriminator_fake_loss=1.338, generator_loss=29.04, generator_mel_loss=17.89, generator_kl_loss=1.41, generator_dur_loss=1.732, generator_adv_loss=1.907, generator_feat_match_loss=6.097, over 64.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=29.07, generator_mel_loss=17.78, generator_kl_loss=1.438, generator_dur_loss=1.735, generator_adv_loss=2.008, generator_feat_match_loss=6.112, over 4449.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:50:40,780 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 18:50:49,494 INFO [train.py:591] (1/6) Epoch 881, validation: discriminator_loss=2.706, discriminator_real_loss=1.309, discriminator_fake_loss=1.397, generator_loss=27.62, generator_mel_loss=18.22, generator_kl_loss=1.217, generator_dur_loss=1.809, generator_adv_loss=1.813, generator_feat_match_loss=4.559, over 100.00 samples.
+2024-03-15 18:50:49,495 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 18:52:50,273 INFO [train.py:919] (1/6) Start epoch 882
+2024-03-15 18:53:31,115 INFO [train.py:527] (1/6) Epoch 882, batch 6, global_batch_idx: 109250, batch size: 74, loss[discriminator_loss=2.705, discriminator_real_loss=1.281, discriminator_fake_loss=1.425, generator_loss=28.81, generator_mel_loss=17.68, generator_kl_loss=1.308, generator_dur_loss=1.819, generator_adv_loss=1.972, generator_feat_match_loss=6.027, over 74.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.332, discriminator_fake_loss=1.372, generator_loss=29.29, generator_mel_loss=17.75, generator_kl_loss=1.409, generator_dur_loss=1.744, generator_adv_loss=2.013, generator_feat_match_loss=6.373, over 382.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:55:50,530 INFO [train.py:527] (1/6) Epoch 882, batch 56, global_batch_idx: 109300, batch size: 96, loss[discriminator_loss=2.672, discriminator_real_loss=1.364, discriminator_fake_loss=1.308, generator_loss=28, generator_mel_loss=17.27, generator_kl_loss=1.277, generator_dur_loss=1.804, generator_adv_loss=1.997, generator_feat_match_loss=5.649, over 96.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.348, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=17.79, generator_kl_loss=1.426, generator_dur_loss=1.742, generator_adv_loss=2.01, generator_feat_match_loss=6.076, over 3287.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:58:09,371 INFO [train.py:527] (1/6) Epoch 882, batch 106, global_batch_idx: 109350, batch size: 17, loss[discriminator_loss=2.616, discriminator_real_loss=1.383, discriminator_fake_loss=1.233, generator_loss=30.4, generator_mel_loss=17.66, generator_kl_loss=1.825, generator_dur_loss=1.548, generator_adv_loss=2.205, generator_feat_match_loss=7.163, over 17.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.06, generator_mel_loss=17.81, generator_kl_loss=1.428, generator_dur_loss=1.737, generator_adv_loss=2.01, generator_feat_match_loss=6.075, over 6232.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 18:58:57,501 INFO [train.py:919] (1/6) Start epoch 883
+2024-03-15 19:00:51,713 INFO [train.py:527] (1/6) Epoch 883, batch 32, global_batch_idx: 109400, batch size: 44, loss[discriminator_loss=2.709, discriminator_real_loss=1.305, discriminator_fake_loss=1.403, generator_loss=28.88, generator_mel_loss=17.49, generator_kl_loss=1.324, generator_dur_loss=1.713, generator_adv_loss=2.086, generator_feat_match_loss=6.264, over 44.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.349, discriminator_fake_loss=1.314, generator_loss=29.23, generator_mel_loss=17.8, generator_kl_loss=1.43, generator_dur_loss=1.732, generator_adv_loss=2.03, generator_feat_match_loss=6.239, over 1945.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:00:51,714 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 19:00:59,683 INFO [train.py:591] (1/6) Epoch 883, validation: discriminator_loss=2.722, discriminator_real_loss=1.435, discriminator_fake_loss=1.287, generator_loss=28.51, generator_mel_loss=17.87, generator_kl_loss=1.288, generator_dur_loss=1.781, generator_adv_loss=2.111, generator_feat_match_loss=5.462, over 100.00 samples.
+2024-03-15 19:00:59,683 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 19:03:19,542 INFO [train.py:527] (1/6) Epoch 883, batch 82, global_batch_idx: 109450, batch size: 56, loss[discriminator_loss=2.632, discriminator_real_loss=1.258, discriminator_fake_loss=1.374, generator_loss=29.61, generator_mel_loss=18.52, generator_kl_loss=1.305, generator_dur_loss=1.684, generator_adv_loss=2.069, generator_feat_match_loss=6.038, over 56.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.35, discriminator_fake_loss=1.317, generator_loss=29.15, generator_mel_loss=17.82, generator_kl_loss=1.428, generator_dur_loss=1.724, generator_adv_loss=2.02, generator_feat_match_loss=6.156, over 4833.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:05:12,581 INFO [train.py:919] (1/6) Start epoch 884
+2024-03-15 19:05:58,841 INFO [train.py:527] (1/6) Epoch 884, batch 8, global_batch_idx: 109500, batch size: 68, loss[discriminator_loss=2.678, discriminator_real_loss=1.358, discriminator_fake_loss=1.321, generator_loss=28.16, generator_mel_loss=17.34, generator_kl_loss=1.428, generator_dur_loss=1.791, generator_adv_loss=2.074, generator_feat_match_loss=5.53, over 68.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.331, discriminator_fake_loss=1.333, generator_loss=28.97, generator_mel_loss=17.75, generator_kl_loss=1.496, generator_dur_loss=1.706, generator_adv_loss=2.028, generator_feat_match_loss=5.99, over 380.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:08:17,070 INFO [train.py:527] (1/6) Epoch 884, batch 58, global_batch_idx: 109550, batch size: 77, loss[discriminator_loss=2.689, discriminator_real_loss=1.433, discriminator_fake_loss=1.256, generator_loss=28.91, generator_mel_loss=17.71, generator_kl_loss=1.377, generator_dur_loss=1.814, generator_adv_loss=2.002, generator_feat_match_loss=6.009, over 77.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.349, discriminator_fake_loss=1.313, generator_loss=29.01, generator_mel_loss=17.73, generator_kl_loss=1.451, generator_dur_loss=1.727, generator_adv_loss=2.022, generator_feat_match_loss=6.078, over 3264.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:10:35,862 INFO [train.py:527] (1/6) Epoch 884, batch 108, global_batch_idx: 109600, batch size: 59, loss[discriminator_loss=2.66, discriminator_real_loss=1.348, discriminator_fake_loss=1.312, generator_loss=29.48, generator_mel_loss=18.28, generator_kl_loss=1.521, generator_dur_loss=1.677, generator_adv_loss=2.112, generator_feat_match_loss=5.888, over 59.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.351, discriminator_fake_loss=1.314, generator_loss=29.04, generator_mel_loss=17.74, generator_kl_loss=1.444, generator_dur_loss=1.732, generator_adv_loss=2.022, generator_feat_match_loss=6.105, over 6238.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:10:35,864 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 19:10:44,693 INFO [train.py:591] (1/6) Epoch 884, validation: discriminator_loss=2.719, discriminator_real_loss=1.486, discriminator_fake_loss=1.233, generator_loss=28.43, generator_mel_loss=18.2, generator_kl_loss=1.247, generator_dur_loss=1.776, generator_adv_loss=2.09, generator_feat_match_loss=5.123, over 100.00 samples.
+2024-03-15 19:10:44,694 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 19:11:27,599 INFO [train.py:919] (1/6) Start epoch 885
+2024-03-15 19:13:25,314 INFO [train.py:527] (1/6) Epoch 885, batch 34, global_batch_idx: 109650, batch size: 25, loss[discriminator_loss=2.608, discriminator_real_loss=1.376, discriminator_fake_loss=1.233, generator_loss=30.76, generator_mel_loss=18.45, generator_kl_loss=1.968, generator_dur_loss=1.521, generator_adv_loss=1.973, generator_feat_match_loss=6.845, over 25.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.358, discriminator_fake_loss=1.312, generator_loss=29.31, generator_mel_loss=17.88, generator_kl_loss=1.48, generator_dur_loss=1.71, generator_adv_loss=2.038, generator_feat_match_loss=6.195, over 1709.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:15:43,313 INFO [train.py:527] (1/6) Epoch 885, batch 84, global_batch_idx: 109700, batch size: 72, loss[discriminator_loss=2.703, discriminator_real_loss=1.379, discriminator_fake_loss=1.324, generator_loss=29.34, generator_mel_loss=17.59, generator_kl_loss=1.516, generator_dur_loss=1.797, generator_adv_loss=2.038, generator_feat_match_loss=6.395, over 72.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.356, discriminator_fake_loss=1.328, generator_loss=29.19, generator_mel_loss=17.81, generator_kl_loss=1.473, generator_dur_loss=1.728, generator_adv_loss=2.015, generator_feat_match_loss=6.166, over 4694.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:17:32,047 INFO [train.py:919] (1/6) Start epoch 886
+2024-03-15 19:18:22,971 INFO [train.py:527] (1/6) Epoch 886, batch 10, global_batch_idx: 109750, batch size: 61, loss[discriminator_loss=2.689, discriminator_real_loss=1.405, discriminator_fake_loss=1.284, generator_loss=29.02, generator_mel_loss=18.02, generator_kl_loss=1.418, generator_dur_loss=1.697, generator_adv_loss=1.939, generator_feat_match_loss=5.943, over 61.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.361, discriminator_fake_loss=1.316, generator_loss=29.48, generator_mel_loss=17.97, generator_kl_loss=1.502, generator_dur_loss=1.733, generator_adv_loss=2.015, generator_feat_match_loss=6.261, over 600.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:20:43,177 INFO [train.py:527] (1/6) Epoch 886, batch 60, global_batch_idx: 109800, batch size: 15, loss[discriminator_loss=2.633, discriminator_real_loss=1.278, discriminator_fake_loss=1.355, generator_loss=31.24, generator_mel_loss=18.38, generator_kl_loss=1.841, generator_dur_loss=1.543, generator_adv_loss=2.006, generator_feat_match_loss=7.467, over 15.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.361, discriminator_fake_loss=1.319, generator_loss=29.18, generator_mel_loss=17.79, generator_kl_loss=1.455, generator_dur_loss=1.736, generator_adv_loss=2.003, generator_feat_match_loss=6.187, over 3331.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:20:43,178 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 19:20:51,344 INFO [train.py:591] (1/6) Epoch 886, validation: discriminator_loss=2.7, discriminator_real_loss=1.398, discriminator_fake_loss=1.302, generator_loss=27.26, generator_mel_loss=17.68, generator_kl_loss=1.296, generator_dur_loss=1.808, generator_adv_loss=1.922, generator_feat_match_loss=4.554, over 100.00 samples.
+2024-03-15 19:20:51,345 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 19:23:09,744 INFO [train.py:527] (1/6) Epoch 886, batch 110, global_batch_idx: 109850, batch size: 72, loss[discriminator_loss=2.699, discriminator_real_loss=1.371, discriminator_fake_loss=1.327, generator_loss=27.57, generator_mel_loss=17.28, generator_kl_loss=1.435, generator_dur_loss=1.816, generator_adv_loss=1.962, generator_feat_match_loss=5.074, over 72.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=29.06, generator_mel_loss=17.76, generator_kl_loss=1.437, generator_dur_loss=1.745, generator_adv_loss=2.002, generator_feat_match_loss=6.113, over 6303.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:23:43,597 INFO [train.py:919] (1/6) Start epoch 887
+2024-03-15 19:25:52,447 INFO [train.py:527] (1/6) Epoch 887, batch 36, global_batch_idx: 109900, batch size: 68, loss[discriminator_loss=2.624, discriminator_real_loss=1.316, discriminator_fake_loss=1.308, generator_loss=29.53, generator_mel_loss=17.74, generator_kl_loss=1.384, generator_dur_loss=1.781, generator_adv_loss=2.121, generator_feat_match_loss=6.509, over 68.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.327, generator_loss=29.09, generator_mel_loss=17.75, generator_kl_loss=1.413, generator_dur_loss=1.743, generator_adv_loss=2.02, generator_feat_match_loss=6.166, over 2080.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:28:10,926 INFO [train.py:527] (1/6) Epoch 887, batch 86, global_batch_idx: 109950, batch size: 39, loss[discriminator_loss=2.659, discriminator_real_loss=1.367, discriminator_fake_loss=1.292, generator_loss=28.03, generator_mel_loss=17.15, generator_kl_loss=1.637, generator_dur_loss=1.688, generator_adv_loss=2.277, generator_feat_match_loss=5.286, over 39.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.07, generator_mel_loss=17.73, generator_kl_loss=1.415, generator_dur_loss=1.749, generator_adv_loss=2.017, generator_feat_match_loss=6.153, over 5033.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:29:50,663 INFO [train.py:919] (1/6) Start epoch 888
+2024-03-15 19:30:47,036 INFO [train.py:527] (1/6) Epoch 888, batch 12, global_batch_idx: 110000, batch size: 66, loss[discriminator_loss=2.729, discriminator_real_loss=1.374, discriminator_fake_loss=1.355, generator_loss=29.02, generator_mel_loss=17.7, generator_kl_loss=1.34, generator_dur_loss=1.789, generator_adv_loss=1.923, generator_feat_match_loss=6.269, over 66.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=29.28, generator_mel_loss=17.86, generator_kl_loss=1.426, generator_dur_loss=1.736, generator_adv_loss=2.011, generator_feat_match_loss=6.251, over 717.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:30:47,039 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 19:30:54,876 INFO [train.py:591] (1/6) Epoch 888, validation: discriminator_loss=2.734, discriminator_real_loss=1.375, discriminator_fake_loss=1.358, generator_loss=27.63, generator_mel_loss=18.08, generator_kl_loss=1.163, generator_dur_loss=1.791, generator_adv_loss=1.857, generator_feat_match_loss=4.732, over 100.00 samples.
+2024-03-15 19:30:54,878 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 19:33:14,077 INFO [train.py:527] (1/6) Epoch 888, batch 62, global_batch_idx: 110050, batch size: 80, loss[discriminator_loss=2.69, discriminator_real_loss=1.307, discriminator_fake_loss=1.384, generator_loss=29, generator_mel_loss=18.04, generator_kl_loss=1.309, generator_dur_loss=1.853, generator_adv_loss=2.059, generator_feat_match_loss=5.737, over 80.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=28.92, generator_mel_loss=17.73, generator_kl_loss=1.407, generator_dur_loss=1.752, generator_adv_loss=2.008, generator_feat_match_loss=6.022, over 3864.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:35:33,619 INFO [train.py:527] (1/6) Epoch 888, batch 112, global_batch_idx: 110100, batch size: 83, loss[discriminator_loss=2.664, discriminator_real_loss=1.295, discriminator_fake_loss=1.368, generator_loss=29.89, generator_mel_loss=18.38, generator_kl_loss=1.486, generator_dur_loss=1.828, generator_adv_loss=1.938, generator_feat_match_loss=6.264, over 83.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.354, discriminator_fake_loss=1.325, generator_loss=29, generator_mel_loss=17.77, generator_kl_loss=1.419, generator_dur_loss=1.747, generator_adv_loss=2.002, generator_feat_match_loss=6.059, over 6783.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:36:05,048 INFO [train.py:919] (1/6) Start epoch 889
+2024-03-15 19:38:16,336 INFO [train.py:527] (1/6) Epoch 889, batch 38, global_batch_idx: 110150, batch size: 64, loss[discriminator_loss=2.624, discriminator_real_loss=1.306, discriminator_fake_loss=1.317, generator_loss=29.81, generator_mel_loss=18.12, generator_kl_loss=1.475, generator_dur_loss=1.762, generator_adv_loss=2.084, generator_feat_match_loss=6.367, over 64.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.371, discriminator_fake_loss=1.321, generator_loss=29.01, generator_mel_loss=17.77, generator_kl_loss=1.452, generator_dur_loss=1.73, generator_adv_loss=2.007, generator_feat_match_loss=6.061, over 2202.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:40:33,976 INFO [train.py:527] (1/6) Epoch 889, batch 88, global_batch_idx: 110200, batch size: 70, loss[discriminator_loss=2.684, discriminator_real_loss=1.343, discriminator_fake_loss=1.341, generator_loss=29.01, generator_mel_loss=17.7, generator_kl_loss=1.307, generator_dur_loss=1.815, generator_adv_loss=2.061, generator_feat_match_loss=6.125, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.364, discriminator_fake_loss=1.326, generator_loss=29.11, generator_mel_loss=17.82, generator_kl_loss=1.437, generator_dur_loss=1.725, generator_adv_loss=2.009, generator_feat_match_loss=6.113, over 4953.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:40:33,977 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 19:40:42,834 INFO [train.py:591] (1/6) Epoch 889, validation: discriminator_loss=2.754, discriminator_real_loss=1.343, discriminator_fake_loss=1.411, generator_loss=28.45, generator_mel_loss=18.43, generator_kl_loss=1.235, generator_dur_loss=1.779, generator_adv_loss=1.929, generator_feat_match_loss=5.07, over 100.00 samples.
+2024-03-15 19:40:42,835 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 19:42:21,435 INFO [train.py:919] (1/6) Start epoch 890
+2024-03-15 19:43:21,661 INFO [train.py:527] (1/6) Epoch 890, batch 14, global_batch_idx: 110250, batch size: 50, loss[discriminator_loss=2.731, discriminator_real_loss=1.309, discriminator_fake_loss=1.421, generator_loss=28.87, generator_mel_loss=17.56, generator_kl_loss=1.393, generator_dur_loss=1.653, generator_adv_loss=1.958, generator_feat_match_loss=6.304, over 50.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=28.95, generator_mel_loss=17.79, generator_kl_loss=1.375, generator_dur_loss=1.76, generator_adv_loss=2.024, generator_feat_match_loss=6.004, over 994.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:45:41,024 INFO [train.py:527] (1/6) Epoch 890, batch 64, global_batch_idx: 110300, batch size: 68, loss[discriminator_loss=2.63, discriminator_real_loss=1.315, discriminator_fake_loss=1.314, generator_loss=28.36, generator_mel_loss=17.43, generator_kl_loss=1.373, generator_dur_loss=1.775, generator_adv_loss=1.927, generator_feat_match_loss=5.86, over 68.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.322, generator_loss=29.09, generator_mel_loss=17.8, generator_kl_loss=1.406, generator_dur_loss=1.749, generator_adv_loss=2.011, generator_feat_match_loss=6.123, over 3892.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:48:00,213 INFO [train.py:527] (1/6) Epoch 890, batch 114, global_batch_idx: 110350, batch size: 64, loss[discriminator_loss=2.726, discriminator_real_loss=1.375, discriminator_fake_loss=1.351, generator_loss=28.6, generator_mel_loss=17.75, generator_kl_loss=1.104, generator_dur_loss=1.747, generator_adv_loss=2.101, generator_feat_match_loss=5.899, over 64.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.35, discriminator_fake_loss=1.322, generator_loss=29.1, generator_mel_loss=17.8, generator_kl_loss=1.412, generator_dur_loss=1.75, generator_adv_loss=2.009, generator_feat_match_loss=6.124, over 6986.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:48:25,460 INFO [train.py:919] (1/6) Start epoch 891
+2024-03-15 19:50:39,218 INFO [train.py:527] (1/6) Epoch 891, batch 40, global_batch_idx: 110400, batch size: 62, loss[discriminator_loss=2.655, discriminator_real_loss=1.381, discriminator_fake_loss=1.275, generator_loss=29.63, generator_mel_loss=17.76, generator_kl_loss=1.442, generator_dur_loss=1.736, generator_adv_loss=2.037, generator_feat_match_loss=6.648, over 62.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.343, discriminator_fake_loss=1.326, generator_loss=29.11, generator_mel_loss=17.83, generator_kl_loss=1.45, generator_dur_loss=1.734, generator_adv_loss=2.011, generator_feat_match_loss=6.076, over 2306.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:50:39,219 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 19:50:47,089 INFO [train.py:591] (1/6) Epoch 891, validation: discriminator_loss=2.732, discriminator_real_loss=1.373, discriminator_fake_loss=1.359, generator_loss=27.53, generator_mel_loss=17.92, generator_kl_loss=1.284, generator_dur_loss=1.802, generator_adv_loss=1.847, generator_feat_match_loss=4.682, over 100.00 samples.
+2024-03-15 19:50:47,090 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 19:53:05,781 INFO [train.py:527] (1/6) Epoch 891, batch 90, global_batch_idx: 110450, batch size: 96, loss[discriminator_loss=2.702, discriminator_real_loss=1.334, discriminator_fake_loss=1.368, generator_loss=28.54, generator_mel_loss=17.39, generator_kl_loss=1.316, generator_dur_loss=1.856, generator_adv_loss=1.939, generator_feat_match_loss=6.043, over 96.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.349, discriminator_fake_loss=1.318, generator_loss=29.16, generator_mel_loss=17.81, generator_kl_loss=1.449, generator_dur_loss=1.734, generator_adv_loss=2.013, generator_feat_match_loss=6.152, over 5131.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:54:38,081 INFO [train.py:919] (1/6) Start epoch 892
+2024-03-15 19:55:47,485 INFO [train.py:527] (1/6) Epoch 892, batch 16, global_batch_idx: 110500, batch size: 83, loss[discriminator_loss=2.673, discriminator_real_loss=1.27, discriminator_fake_loss=1.403, generator_loss=29.14, generator_mel_loss=17.86, generator_kl_loss=1.448, generator_dur_loss=1.857, generator_adv_loss=1.995, generator_feat_match_loss=5.979, over 83.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=28.98, generator_mel_loss=17.74, generator_kl_loss=1.468, generator_dur_loss=1.765, generator_adv_loss=1.999, generator_feat_match_loss=6.014, over 1085.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 19:58:05,730 INFO [train.py:527] (1/6) Epoch 892, batch 66, global_batch_idx: 110550, batch size: 72, loss[discriminator_loss=2.634, discriminator_real_loss=1.319, discriminator_fake_loss=1.314, generator_loss=28.69, generator_mel_loss=17.51, generator_kl_loss=1.342, generator_dur_loss=1.786, generator_adv_loss=2.132, generator_feat_match_loss=5.924, over 72.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=29.08, generator_mel_loss=17.72, generator_kl_loss=1.455, generator_dur_loss=1.753, generator_adv_loss=2.006, generator_feat_match_loss=6.148, over 4088.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:00:25,556 INFO [train.py:527] (1/6) Epoch 892, batch 116, global_batch_idx: 110600, batch size: 36, loss[discriminator_loss=2.618, discriminator_real_loss=1.313, discriminator_fake_loss=1.305, generator_loss=29.65, generator_mel_loss=17.98, generator_kl_loss=1.571, generator_dur_loss=1.655, generator_adv_loss=2.101, generator_feat_match_loss=6.349, over 36.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.352, discriminator_fake_loss=1.328, generator_loss=29.13, generator_mel_loss=17.75, generator_kl_loss=1.46, generator_dur_loss=1.75, generator_adv_loss=2.006, generator_feat_match_loss=6.162, over 6900.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:00:25,558 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 20:00:34,389 INFO [train.py:591] (1/6) Epoch 892, validation: discriminator_loss=2.773, discriminator_real_loss=1.453, discriminator_fake_loss=1.32, generator_loss=27.71, generator_mel_loss=17.94, generator_kl_loss=1.333, generator_dur_loss=1.809, generator_adv_loss=1.964, generator_feat_match_loss=4.661, over 100.00 samples.
+2024-03-15 20:00:34,390 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 20:00:55,623 INFO [train.py:919] (1/6) Start epoch 893
+2024-03-15 20:03:17,306 INFO [train.py:527] (1/6) Epoch 893, batch 42, global_batch_idx: 110650, batch size: 25, loss[discriminator_loss=2.611, discriminator_real_loss=1.181, discriminator_fake_loss=1.43, generator_loss=32.42, generator_mel_loss=19.04, generator_kl_loss=1.606, generator_dur_loss=1.591, generator_adv_loss=2.197, generator_feat_match_loss=7.988, over 25.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.35, discriminator_fake_loss=1.328, generator_loss=29.31, generator_mel_loss=17.87, generator_kl_loss=1.477, generator_dur_loss=1.734, generator_adv_loss=2.019, generator_feat_match_loss=6.206, over 2445.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:05:36,755 INFO [train.py:527] (1/6) Epoch 893, batch 92, global_batch_idx: 110700, batch size: 45, loss[discriminator_loss=2.681, discriminator_real_loss=1.356, discriminator_fake_loss=1.325, generator_loss=29.34, generator_mel_loss=17.91, generator_kl_loss=1.491, generator_dur_loss=1.649, generator_adv_loss=1.905, generator_feat_match_loss=6.381, over 45.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.356, discriminator_fake_loss=1.323, generator_loss=29.24, generator_mel_loss=17.82, generator_kl_loss=1.466, generator_dur_loss=1.747, generator_adv_loss=2.031, generator_feat_match_loss=6.181, over 5381.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:07:02,473 INFO [train.py:919] (1/6) Start epoch 894
+2024-03-15 20:08:14,966 INFO [train.py:527] (1/6) Epoch 894, batch 18, global_batch_idx: 110750, batch size: 70, loss[discriminator_loss=2.697, discriminator_real_loss=1.314, discriminator_fake_loss=1.383, generator_loss=28.51, generator_mel_loss=17.63, generator_kl_loss=1.445, generator_dur_loss=1.798, generator_adv_loss=1.878, generator_feat_match_loss=5.764, over 70.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.361, discriminator_fake_loss=1.319, generator_loss=28.84, generator_mel_loss=17.72, generator_kl_loss=1.436, generator_dur_loss=1.752, generator_adv_loss=1.988, generator_feat_match_loss=5.947, over 1145.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:10:34,296 INFO [train.py:527] (1/6) Epoch 894, batch 68, global_batch_idx: 110800, batch size: 72, loss[discriminator_loss=2.66, discriminator_real_loss=1.381, discriminator_fake_loss=1.279, generator_loss=30.13, generator_mel_loss=17.98, generator_kl_loss=1.505, generator_dur_loss=1.761, generator_adv_loss=1.938, generator_feat_match_loss=6.946, over 72.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.03, generator_mel_loss=17.73, generator_kl_loss=1.476, generator_dur_loss=1.735, generator_adv_loss=2.002, generator_feat_match_loss=6.09, over 3926.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:10:34,298 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 20:10:42,208 INFO [train.py:591] (1/6) Epoch 894, validation: discriminator_loss=2.762, discriminator_real_loss=1.361, discriminator_fake_loss=1.402, generator_loss=26.94, generator_mel_loss=17.53, generator_kl_loss=1.314, generator_dur_loss=1.805, generator_adv_loss=1.797, generator_feat_match_loss=4.494, over 100.00 samples.
+2024-03-15 20:10:42,209 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 20:12:59,042 INFO [train.py:527] (1/6) Epoch 894, batch 118, global_batch_idx: 110850, batch size: 50, loss[discriminator_loss=2.685, discriminator_real_loss=1.332, discriminator_fake_loss=1.353, generator_loss=30.64, generator_mel_loss=18.17, generator_kl_loss=1.549, generator_dur_loss=1.708, generator_adv_loss=2.061, generator_feat_match_loss=7.15, over 50.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.02, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.741, generator_adv_loss=2.01, generator_feat_match_loss=6.119, over 7009.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:13:16,850 INFO [train.py:919] (1/6) Start epoch 895
+2024-03-15 20:15:43,999 INFO [train.py:527] (1/6) Epoch 895, batch 44, global_batch_idx: 110900, batch size: 31, loss[discriminator_loss=2.688, discriminator_real_loss=1.382, discriminator_fake_loss=1.306, generator_loss=29.82, generator_mel_loss=17.92, generator_kl_loss=1.644, generator_dur_loss=1.615, generator_adv_loss=2.178, generator_feat_match_loss=6.463, over 31.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.358, discriminator_fake_loss=1.327, generator_loss=29.33, generator_mel_loss=17.83, generator_kl_loss=1.486, generator_dur_loss=1.723, generator_adv_loss=2.035, generator_feat_match_loss=6.249, over 2448.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:18:04,620 INFO [train.py:527] (1/6) Epoch 895, batch 94, global_batch_idx: 110950, batch size: 83, loss[discriminator_loss=2.657, discriminator_real_loss=1.262, discriminator_fake_loss=1.394, generator_loss=29.06, generator_mel_loss=17.79, generator_kl_loss=1.432, generator_dur_loss=1.832, generator_adv_loss=2.095, generator_feat_match_loss=5.91, over 83.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.359, discriminator_fake_loss=1.324, generator_loss=29.07, generator_mel_loss=17.77, generator_kl_loss=1.44, generator_dur_loss=1.735, generator_adv_loss=2.02, generator_feat_match_loss=6.108, over 5328.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:19:26,794 INFO [train.py:919] (1/6) Start epoch 896
+2024-03-15 20:20:46,835 INFO [train.py:527] (1/6) Epoch 896, batch 20, global_batch_idx: 111000, batch size: 72, loss[discriminator_loss=2.714, discriminator_real_loss=1.38, discriminator_fake_loss=1.334, generator_loss=28.82, generator_mel_loss=17.65, generator_kl_loss=1.303, generator_dur_loss=1.79, generator_adv_loss=2.035, generator_feat_match_loss=6.039, over 72.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.361, discriminator_fake_loss=1.326, generator_loss=29.04, generator_mel_loss=17.69, generator_kl_loss=1.413, generator_dur_loss=1.74, generator_adv_loss=2.006, generator_feat_match_loss=6.184, over 1267.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:20:46,836 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 20:20:54,707 INFO [train.py:591] (1/6) Epoch 896, validation: discriminator_loss=2.71, discriminator_real_loss=1.454, discriminator_fake_loss=1.256, generator_loss=27.7, generator_mel_loss=17.73, generator_kl_loss=1.333, generator_dur_loss=1.819, generator_adv_loss=1.953, generator_feat_match_loss=4.868, over 100.00 samples.
+2024-03-15 20:20:54,708 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 20:23:10,961 INFO [train.py:527] (1/6) Epoch 896, batch 70, global_batch_idx: 111050, batch size: 66, loss[discriminator_loss=2.626, discriminator_real_loss=1.31, discriminator_fake_loss=1.316, generator_loss=30, generator_mel_loss=18.22, generator_kl_loss=1.416, generator_dur_loss=1.743, generator_adv_loss=2.05, generator_feat_match_loss=6.566, over 66.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.357, discriminator_fake_loss=1.324, generator_loss=29.07, generator_mel_loss=17.79, generator_kl_loss=1.435, generator_dur_loss=1.732, generator_adv_loss=2.005, generator_feat_match_loss=6.104, over 3941.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:25:30,775 INFO [train.py:527] (1/6) Epoch 896, batch 120, global_batch_idx: 111100, batch size: 31, loss[discriminator_loss=2.651, discriminator_real_loss=1.405, discriminator_fake_loss=1.246, generator_loss=29.66, generator_mel_loss=18.14, generator_kl_loss=1.491, generator_dur_loss=1.59, generator_adv_loss=2.05, generator_feat_match_loss=6.389, over 31.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=29.13, generator_mel_loss=17.8, generator_kl_loss=1.444, generator_dur_loss=1.73, generator_adv_loss=2.013, generator_feat_match_loss=6.141, over 6560.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:25:41,051 INFO [train.py:919] (1/6) Start epoch 897
+2024-03-15 20:28:12,079 INFO [train.py:527] (1/6) Epoch 897, batch 46, global_batch_idx: 111150, batch size: 31, loss[discriminator_loss=2.622, discriminator_real_loss=1.328, discriminator_fake_loss=1.295, generator_loss=28.13, generator_mel_loss=17.5, generator_kl_loss=1.564, generator_dur_loss=1.604, generator_adv_loss=1.99, generator_feat_match_loss=5.471, over 31.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.37, discriminator_fake_loss=1.317, generator_loss=29.05, generator_mel_loss=17.79, generator_kl_loss=1.422, generator_dur_loss=1.736, generator_adv_loss=2.022, generator_feat_match_loss=6.087, over 2783.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:30:31,937 INFO [train.py:527] (1/6) Epoch 897, batch 96, global_batch_idx: 111200, batch size: 36, loss[discriminator_loss=2.713, discriminator_real_loss=1.28, discriminator_fake_loss=1.432, generator_loss=29.58, generator_mel_loss=17.8, generator_kl_loss=1.533, generator_dur_loss=1.663, generator_adv_loss=2.124, generator_feat_match_loss=6.457, over 36.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=29.1, generator_mel_loss=17.8, generator_kl_loss=1.427, generator_dur_loss=1.746, generator_adv_loss=2.017, generator_feat_match_loss=6.11, over 5771.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:30:31,939 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 20:30:40,687 INFO [train.py:591] (1/6) Epoch 897, validation: discriminator_loss=2.755, discriminator_real_loss=1.525, discriminator_fake_loss=1.23, generator_loss=28.06, generator_mel_loss=18, generator_kl_loss=1.329, generator_dur_loss=1.795, generator_adv_loss=2.099, generator_feat_match_loss=4.839, over 100.00 samples.
+2024-03-15 20:30:40,688 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 20:31:58,623 INFO [train.py:919] (1/6) Start epoch 898
+2024-03-15 20:33:24,442 INFO [train.py:527] (1/6) Epoch 898, batch 22, global_batch_idx: 111250, batch size: 59, loss[discriminator_loss=2.711, discriminator_real_loss=1.384, discriminator_fake_loss=1.327, generator_loss=28.81, generator_mel_loss=17.41, generator_kl_loss=1.448, generator_dur_loss=1.751, generator_adv_loss=2.134, generator_feat_match_loss=6.072, over 59.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.373, discriminator_fake_loss=1.32, generator_loss=28.98, generator_mel_loss=17.82, generator_kl_loss=1.438, generator_dur_loss=1.743, generator_adv_loss=2.009, generator_feat_match_loss=5.971, over 1329.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:35:45,878 INFO [train.py:527] (1/6) Epoch 898, batch 72, global_batch_idx: 111300, batch size: 96, loss[discriminator_loss=2.624, discriminator_real_loss=1.309, discriminator_fake_loss=1.315, generator_loss=29, generator_mel_loss=17.43, generator_kl_loss=1.189, generator_dur_loss=1.824, generator_adv_loss=2.081, generator_feat_match_loss=6.476, over 96.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.35, discriminator_fake_loss=1.33, generator_loss=29.07, generator_mel_loss=17.77, generator_kl_loss=1.452, generator_dur_loss=1.736, generator_adv_loss=2.014, generator_feat_match_loss=6.097, over 4089.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:38:00,105 INFO [train.py:527] (1/6) Epoch 898, batch 122, global_batch_idx: 111350, batch size: 31, loss[discriminator_loss=2.632, discriminator_real_loss=1.407, discriminator_fake_loss=1.224, generator_loss=30, generator_mel_loss=18.23, generator_kl_loss=1.655, generator_dur_loss=1.651, generator_adv_loss=2.013, generator_feat_match_loss=6.45, over 31.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=29.11, generator_mel_loss=17.78, generator_kl_loss=1.466, generator_dur_loss=1.731, generator_adv_loss=2.019, generator_feat_match_loss=6.119, over 6684.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:38:04,564 INFO [train.py:919] (1/6) Start epoch 899
+2024-03-15 20:40:44,565 INFO [train.py:527] (1/6) Epoch 899, batch 48, global_batch_idx: 111400, batch size: 64, loss[discriminator_loss=2.708, discriminator_real_loss=1.33, discriminator_fake_loss=1.378, generator_loss=28.85, generator_mel_loss=17.95, generator_kl_loss=1.499, generator_dur_loss=1.749, generator_adv_loss=1.999, generator_feat_match_loss=5.654, over 64.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=17.76, generator_kl_loss=1.471, generator_dur_loss=1.74, generator_adv_loss=2.001, generator_feat_match_loss=6.084, over 2964.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:40:44,567 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 20:40:52,598 INFO [train.py:591] (1/6) Epoch 899, validation: discriminator_loss=2.78, discriminator_real_loss=1.487, discriminator_fake_loss=1.293, generator_loss=27.47, generator_mel_loss=17.89, generator_kl_loss=1.261, generator_dur_loss=1.794, generator_adv_loss=1.97, generator_feat_match_loss=4.565, over 100.00 samples.
+2024-03-15 20:40:52,599 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 20:43:09,916 INFO [train.py:527] (1/6) Epoch 899, batch 98, global_batch_idx: 111450, batch size: 36, loss[discriminator_loss=2.675, discriminator_real_loss=1.362, discriminator_fake_loss=1.313, generator_loss=28.71, generator_mel_loss=17.89, generator_kl_loss=1.524, generator_dur_loss=1.66, generator_adv_loss=2.022, generator_feat_match_loss=5.605, over 36.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.354, discriminator_fake_loss=1.321, generator_loss=29.03, generator_mel_loss=17.72, generator_kl_loss=1.468, generator_dur_loss=1.735, generator_adv_loss=2, generator_feat_match_loss=6.112, over 5806.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:44:18,359 INFO [train.py:919] (1/6) Start epoch 900
+2024-03-15 20:45:47,560 INFO [train.py:527] (1/6) Epoch 900, batch 24, global_batch_idx: 111500, batch size: 97, loss[discriminator_loss=2.674, discriminator_real_loss=1.365, discriminator_fake_loss=1.308, generator_loss=28.53, generator_mel_loss=17.65, generator_kl_loss=1.273, generator_dur_loss=1.866, generator_adv_loss=1.934, generator_feat_match_loss=5.803, over 97.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.337, discriminator_fake_loss=1.329, generator_loss=28.83, generator_mel_loss=17.69, generator_kl_loss=1.448, generator_dur_loss=1.733, generator_adv_loss=1.989, generator_feat_match_loss=5.969, over 1491.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:48:02,743 INFO [train.py:527] (1/6) Epoch 900, batch 74, global_batch_idx: 111550, batch size: 50, loss[discriminator_loss=2.676, discriminator_real_loss=1.346, discriminator_fake_loss=1.33, generator_loss=28.77, generator_mel_loss=17.97, generator_kl_loss=1.41, generator_dur_loss=1.7, generator_adv_loss=2.055, generator_feat_match_loss=5.635, over 50.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=29, generator_mel_loss=17.77, generator_kl_loss=1.468, generator_dur_loss=1.736, generator_adv_loss=2.005, generator_feat_match_loss=6.026, over 4235.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:50:23,040 INFO [train.py:919] (1/6) Start epoch 901
+2024-03-15 20:50:46,822 INFO [train.py:527] (1/6) Epoch 901, batch 0, global_batch_idx: 111600, batch size: 68, loss[discriminator_loss=2.704, discriminator_real_loss=1.384, discriminator_fake_loss=1.32, generator_loss=27.32, generator_mel_loss=17.31, generator_kl_loss=1.447, generator_dur_loss=1.738, generator_adv_loss=1.909, generator_feat_match_loss=4.916, over 68.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.384, discriminator_fake_loss=1.32, generator_loss=27.32, generator_mel_loss=17.31, generator_kl_loss=1.447, generator_dur_loss=1.738, generator_adv_loss=1.909, generator_feat_match_loss=4.916, over 68.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:50:46,825 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 20:50:54,868 INFO [train.py:591] (1/6) Epoch 901, validation: discriminator_loss=2.681, discriminator_real_loss=1.361, discriminator_fake_loss=1.32, generator_loss=28.28, generator_mel_loss=17.82, generator_kl_loss=1.409, generator_dur_loss=1.8, generator_adv_loss=1.931, generator_feat_match_loss=5.321, over 100.00 samples.
+2024-03-15 20:50:54,871 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 20:53:15,387 INFO [train.py:527] (1/6) Epoch 901, batch 50, global_batch_idx: 111650, batch size: 77, loss[discriminator_loss=2.713, discriminator_real_loss=1.448, discriminator_fake_loss=1.266, generator_loss=28.92, generator_mel_loss=17.78, generator_kl_loss=1.513, generator_dur_loss=1.737, generator_adv_loss=1.855, generator_feat_match_loss=6.034, over 77.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.356, discriminator_fake_loss=1.313, generator_loss=28.99, generator_mel_loss=17.69, generator_kl_loss=1.46, generator_dur_loss=1.76, generator_adv_loss=2.012, generator_feat_match_loss=6.074, over 3030.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:55:32,923 INFO [train.py:527] (1/6) Epoch 901, batch 100, global_batch_idx: 111700, batch size: 70, loss[discriminator_loss=2.688, discriminator_real_loss=1.296, discriminator_fake_loss=1.392, generator_loss=29.38, generator_mel_loss=17.67, generator_kl_loss=1.286, generator_dur_loss=1.78, generator_adv_loss=2.126, generator_feat_match_loss=6.52, over 70.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.354, discriminator_fake_loss=1.322, generator_loss=28.95, generator_mel_loss=17.67, generator_kl_loss=1.438, generator_dur_loss=1.751, generator_adv_loss=2.011, generator_feat_match_loss=6.083, over 5894.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 20:56:40,209 INFO [train.py:919] (1/6) Start epoch 902
+2024-03-15 20:58:14,252 INFO [train.py:527] (1/6) Epoch 902, batch 26, global_batch_idx: 111750, batch size: 36, loss[discriminator_loss=2.679, discriminator_real_loss=1.327, discriminator_fake_loss=1.352, generator_loss=29.63, generator_mel_loss=17.82, generator_kl_loss=1.428, generator_dur_loss=1.704, generator_adv_loss=2.114, generator_feat_match_loss=6.557, over 36.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.36, discriminator_fake_loss=1.308, generator_loss=29.27, generator_mel_loss=17.79, generator_kl_loss=1.43, generator_dur_loss=1.733, generator_adv_loss=2.051, generator_feat_match_loss=6.266, over 1563.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:00:32,590 INFO [train.py:527] (1/6) Epoch 902, batch 76, global_batch_idx: 111800, batch size: 39, loss[discriminator_loss=2.707, discriminator_real_loss=1.302, discriminator_fake_loss=1.405, generator_loss=30.68, generator_mel_loss=18.55, generator_kl_loss=1.609, generator_dur_loss=1.668, generator_adv_loss=2.143, generator_feat_match_loss=6.708, over 39.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=29.19, generator_mel_loss=17.81, generator_kl_loss=1.423, generator_dur_loss=1.741, generator_adv_loss=2.022, generator_feat_match_loss=6.196, over 4567.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:00:32,591 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 21:00:41,438 INFO [train.py:591] (1/6) Epoch 902, validation: discriminator_loss=2.754, discriminator_real_loss=1.503, discriminator_fake_loss=1.252, generator_loss=28.64, generator_mel_loss=18.16, generator_kl_loss=1.335, generator_dur_loss=1.797, generator_adv_loss=2.019, generator_feat_match_loss=5.327, over 100.00 samples.
+2024-03-15 21:00:41,438 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 21:02:53,442 INFO [train.py:919] (1/6) Start epoch 903
+2024-03-15 21:03:24,031 INFO [train.py:527] (1/6) Epoch 903, batch 2, global_batch_idx: 111850, batch size: 80, loss[discriminator_loss=2.654, discriminator_real_loss=1.311, discriminator_fake_loss=1.343, generator_loss=29.08, generator_mel_loss=17.74, generator_kl_loss=1.368, generator_dur_loss=1.822, generator_adv_loss=2.082, generator_feat_match_loss=6.076, over 80.00 samples.], tot_loss[discriminator_loss=2.694, discriminator_real_loss=1.363, discriminator_fake_loss=1.331, generator_loss=29.16, generator_mel_loss=17.94, generator_kl_loss=1.36, generator_dur_loss=1.775, generator_adv_loss=1.995, generator_feat_match_loss=6.092, over 244.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:05:40,708 INFO [train.py:527] (1/6) Epoch 903, batch 52, global_batch_idx: 111900, batch size: 45, loss[discriminator_loss=2.72, discriminator_real_loss=1.382, discriminator_fake_loss=1.338, generator_loss=27.67, generator_mel_loss=17.36, generator_kl_loss=1.421, generator_dur_loss=1.666, generator_adv_loss=1.991, generator_feat_match_loss=5.232, over 45.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.352, discriminator_fake_loss=1.329, generator_loss=29.15, generator_mel_loss=17.87, generator_kl_loss=1.436, generator_dur_loss=1.735, generator_adv_loss=2.011, generator_feat_match_loss=6.105, over 3105.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:08:00,003 INFO [train.py:527] (1/6) Epoch 903, batch 102, global_batch_idx: 111950, batch size: 47, loss[discriminator_loss=2.621, discriminator_real_loss=1.368, discriminator_fake_loss=1.252, generator_loss=29.18, generator_mel_loss=17.86, generator_kl_loss=1.506, generator_dur_loss=1.657, generator_adv_loss=1.881, generator_feat_match_loss=6.276, over 47.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=29.08, generator_mel_loss=17.81, generator_kl_loss=1.434, generator_dur_loss=1.735, generator_adv_loss=2.012, generator_feat_match_loss=6.087, over 5993.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:08:59,679 INFO [train.py:919] (1/6) Start epoch 904
+2024-03-15 21:10:44,454 INFO [train.py:527] (1/6) Epoch 904, batch 28, global_batch_idx: 112000, batch size: 74, loss[discriminator_loss=2.651, discriminator_real_loss=1.373, discriminator_fake_loss=1.278, generator_loss=28.87, generator_mel_loss=17.72, generator_kl_loss=1.398, generator_dur_loss=1.791, generator_adv_loss=2.029, generator_feat_match_loss=5.934, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=29.09, generator_mel_loss=17.78, generator_kl_loss=1.398, generator_dur_loss=1.735, generator_adv_loss=1.995, generator_feat_match_loss=6.176, over 1636.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:10:44,456 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 21:10:52,259 INFO [train.py:591] (1/6) Epoch 904, validation: discriminator_loss=2.746, discriminator_real_loss=1.417, discriminator_fake_loss=1.329, generator_loss=28.33, generator_mel_loss=17.63, generator_kl_loss=1.195, generator_dur_loss=1.788, generator_adv_loss=1.936, generator_feat_match_loss=5.781, over 100.00 samples.
+2024-03-15 21:10:52,260 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 21:13:12,937 INFO [train.py:527] (1/6) Epoch 904, batch 78, global_batch_idx: 112050, batch size: 44, loss[discriminator_loss=2.603, discriminator_real_loss=1.329, discriminator_fake_loss=1.274, generator_loss=29.73, generator_mel_loss=17.74, generator_kl_loss=1.647, generator_dur_loss=1.656, generator_adv_loss=2.103, generator_feat_match_loss=6.582, over 44.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=29.11, generator_mel_loss=17.82, generator_kl_loss=1.416, generator_dur_loss=1.733, generator_adv_loss=1.996, generator_feat_match_loss=6.146, over 4458.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:15:17,284 INFO [train.py:919] (1/6) Start epoch 905
+2024-03-15 21:15:52,550 INFO [train.py:527] (1/6) Epoch 905, batch 4, global_batch_idx: 112100, batch size: 72, loss[discriminator_loss=2.702, discriminator_real_loss=1.308, discriminator_fake_loss=1.394, generator_loss=29.11, generator_mel_loss=17.2, generator_kl_loss=1.361, generator_dur_loss=1.755, generator_adv_loss=2.156, generator_feat_match_loss=6.636, over 72.00 samples.], tot_loss[discriminator_loss=2.723, discriminator_real_loss=1.375, discriminator_fake_loss=1.348, generator_loss=28.74, generator_mel_loss=17.59, generator_kl_loss=1.332, generator_dur_loss=1.749, generator_adv_loss=2.006, generator_feat_match_loss=6.065, over 328.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:18:11,893 INFO [train.py:527] (1/6) Epoch 905, batch 54, global_batch_idx: 112150, batch size: 48, loss[discriminator_loss=2.667, discriminator_real_loss=1.278, discriminator_fake_loss=1.389, generator_loss=29.74, generator_mel_loss=17.67, generator_kl_loss=1.575, generator_dur_loss=1.639, generator_adv_loss=2.03, generator_feat_match_loss=6.832, over 48.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.353, discriminator_fake_loss=1.331, generator_loss=29.03, generator_mel_loss=17.74, generator_kl_loss=1.424, generator_dur_loss=1.743, generator_adv_loss=2.002, generator_feat_match_loss=6.121, over 3192.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:20:30,337 INFO [train.py:527] (1/6) Epoch 905, batch 104, global_batch_idx: 112200, batch size: 83, loss[discriminator_loss=2.647, discriminator_real_loss=1.264, discriminator_fake_loss=1.383, generator_loss=29.89, generator_mel_loss=17.99, generator_kl_loss=1.372, generator_dur_loss=1.822, generator_adv_loss=2.221, generator_feat_match_loss=6.479, over 83.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.351, discriminator_fake_loss=1.326, generator_loss=29.15, generator_mel_loss=17.77, generator_kl_loss=1.442, generator_dur_loss=1.735, generator_adv_loss=2.017, generator_feat_match_loss=6.19, over 5793.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:20:30,339 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 21:20:39,139 INFO [train.py:591] (1/6) Epoch 905, validation: discriminator_loss=2.711, discriminator_real_loss=1.395, discriminator_fake_loss=1.316, generator_loss=28.24, generator_mel_loss=18.03, generator_kl_loss=1.283, generator_dur_loss=1.802, generator_adv_loss=1.967, generator_feat_match_loss=5.154, over 100.00 samples.
+2024-03-15 21:20:39,140 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 21:21:32,348 INFO [train.py:919] (1/6) Start epoch 906
+2024-03-15 21:23:20,330 INFO [train.py:527] (1/6) Epoch 906, batch 30, global_batch_idx: 112250, batch size: 47, loss[discriminator_loss=2.679, discriminator_real_loss=1.422, discriminator_fake_loss=1.257, generator_loss=29.06, generator_mel_loss=18.11, generator_kl_loss=1.548, generator_dur_loss=1.705, generator_adv_loss=1.992, generator_feat_match_loss=5.702, over 47.00 samples.], tot_loss[discriminator_loss=2.695, discriminator_real_loss=1.363, discriminator_fake_loss=1.332, generator_loss=28.95, generator_mel_loss=17.75, generator_kl_loss=1.418, generator_dur_loss=1.75, generator_adv_loss=1.995, generator_feat_match_loss=6.039, over 1901.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:25:40,496 INFO [train.py:527] (1/6) Epoch 906, batch 80, global_batch_idx: 112300, batch size: 88, loss[discriminator_loss=2.638, discriminator_real_loss=1.324, discriminator_fake_loss=1.314, generator_loss=29.27, generator_mel_loss=17.66, generator_kl_loss=1.27, generator_dur_loss=1.841, generator_adv_loss=2.04, generator_feat_match_loss=6.453, over 88.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.357, discriminator_fake_loss=1.331, generator_loss=29.1, generator_mel_loss=17.78, generator_kl_loss=1.421, generator_dur_loss=1.743, generator_adv_loss=2.005, generator_feat_match_loss=6.149, over 4805.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:27:38,366 INFO [train.py:919] (1/6) Start epoch 907
+2024-03-15 21:28:22,166 INFO [train.py:527] (1/6) Epoch 907, batch 6, global_batch_idx: 112350, batch size: 77, loss[discriminator_loss=2.718, discriminator_real_loss=1.377, discriminator_fake_loss=1.341, generator_loss=29.28, generator_mel_loss=17.58, generator_kl_loss=1.357, generator_dur_loss=1.833, generator_adv_loss=2.076, generator_feat_match_loss=6.436, over 77.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.364, discriminator_fake_loss=1.328, generator_loss=29.21, generator_mel_loss=17.82, generator_kl_loss=1.421, generator_dur_loss=1.773, generator_adv_loss=2.034, generator_feat_match_loss=6.156, over 415.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:30:38,144 INFO [train.py:527] (1/6) Epoch 907, batch 56, global_batch_idx: 112400, batch size: 50, loss[discriminator_loss=2.697, discriminator_real_loss=1.367, discriminator_fake_loss=1.33, generator_loss=28.34, generator_mel_loss=17.8, generator_kl_loss=1.472, generator_dur_loss=1.733, generator_adv_loss=2.057, generator_feat_match_loss=5.277, over 50.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.35, discriminator_fake_loss=1.332, generator_loss=28.91, generator_mel_loss=17.75, generator_kl_loss=1.461, generator_dur_loss=1.726, generator_adv_loss=1.991, generator_feat_match_loss=5.987, over 3067.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:30:38,145 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 21:30:45,989 INFO [train.py:591] (1/6) Epoch 907, validation: discriminator_loss=2.664, discriminator_real_loss=1.416, discriminator_fake_loss=1.248, generator_loss=27.56, generator_mel_loss=17.79, generator_kl_loss=1.266, generator_dur_loss=1.794, generator_adv_loss=1.994, generator_feat_match_loss=4.718, over 100.00 samples.
+2024-03-15 21:30:45,989 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 21:33:05,456 INFO [train.py:527] (1/6) Epoch 907, batch 106, global_batch_idx: 112450, batch size: 72, loss[discriminator_loss=2.624, discriminator_real_loss=1.302, discriminator_fake_loss=1.322, generator_loss=29.04, generator_mel_loss=17.52, generator_kl_loss=1.387, generator_dur_loss=1.748, generator_adv_loss=2.005, generator_feat_match_loss=6.381, over 72.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.353, discriminator_fake_loss=1.323, generator_loss=29.03, generator_mel_loss=17.76, generator_kl_loss=1.455, generator_dur_loss=1.728, generator_adv_loss=2.01, generator_feat_match_loss=6.08, over 5893.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:33:56,082 INFO [train.py:919] (1/6) Start epoch 908
+2024-03-15 21:35:50,150 INFO [train.py:527] (1/6) Epoch 908, batch 32, global_batch_idx: 112500, batch size: 96, loss[discriminator_loss=2.681, discriminator_real_loss=1.319, discriminator_fake_loss=1.362, generator_loss=28.57, generator_mel_loss=17.64, generator_kl_loss=1.404, generator_dur_loss=1.837, generator_adv_loss=2.047, generator_feat_match_loss=5.642, over 96.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.366, discriminator_fake_loss=1.32, generator_loss=28.82, generator_mel_loss=17.67, generator_kl_loss=1.426, generator_dur_loss=1.746, generator_adv_loss=2.01, generator_feat_match_loss=5.959, over 1884.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:38:12,196 INFO [train.py:527] (1/6) Epoch 908, batch 82, global_batch_idx: 112550, batch size: 68, loss[discriminator_loss=2.709, discriminator_real_loss=1.386, discriminator_fake_loss=1.323, generator_loss=29.16, generator_mel_loss=17.91, generator_kl_loss=1.409, generator_dur_loss=1.728, generator_adv_loss=2.093, generator_feat_match_loss=6.024, over 68.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=28.95, generator_mel_loss=17.72, generator_kl_loss=1.42, generator_dur_loss=1.74, generator_adv_loss=2.009, generator_feat_match_loss=6.057, over 4787.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:40:05,448 INFO [train.py:919] (1/6) Start epoch 909
+2024-03-15 21:40:51,691 INFO [train.py:527] (1/6) Epoch 909, batch 8, global_batch_idx: 112600, batch size: 83, loss[discriminator_loss=2.652, discriminator_real_loss=1.307, discriminator_fake_loss=1.345, generator_loss=28.21, generator_mel_loss=17.73, generator_kl_loss=1.275, generator_dur_loss=1.825, generator_adv_loss=1.976, generator_feat_match_loss=5.399, over 83.00 samples.], tot_loss[discriminator_loss=2.645, discriminator_real_loss=1.341, discriminator_fake_loss=1.305, generator_loss=29.52, generator_mel_loss=17.98, generator_kl_loss=1.433, generator_dur_loss=1.739, generator_adv_loss=2.02, generator_feat_match_loss=6.346, over 504.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:40:51,694 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 21:40:59,653 INFO [train.py:591] (1/6) Epoch 909, validation: discriminator_loss=2.692, discriminator_real_loss=1.392, discriminator_fake_loss=1.3, generator_loss=28.19, generator_mel_loss=18.39, generator_kl_loss=1.205, generator_dur_loss=1.783, generator_adv_loss=1.917, generator_feat_match_loss=4.895, over 100.00 samples.
+2024-03-15 21:40:59,656 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 21:43:17,159 INFO [train.py:527] (1/6) Epoch 909, batch 58, global_batch_idx: 112650, batch size: 80, loss[discriminator_loss=2.694, discriminator_real_loss=1.319, discriminator_fake_loss=1.375, generator_loss=27.93, generator_mel_loss=17.44, generator_kl_loss=1.299, generator_dur_loss=1.788, generator_adv_loss=2.005, generator_feat_match_loss=5.396, over 80.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.346, discriminator_fake_loss=1.319, generator_loss=29.13, generator_mel_loss=17.77, generator_kl_loss=1.43, generator_dur_loss=1.727, generator_adv_loss=2.011, generator_feat_match_loss=6.186, over 3301.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:45:36,648 INFO [train.py:527] (1/6) Epoch 909, batch 108, global_batch_idx: 112700, batch size: 26, loss[discriminator_loss=2.689, discriminator_real_loss=1.273, discriminator_fake_loss=1.416, generator_loss=29.34, generator_mel_loss=18.19, generator_kl_loss=1.565, generator_dur_loss=1.572, generator_adv_loss=2.024, generator_feat_match_loss=5.99, over 26.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.347, discriminator_fake_loss=1.319, generator_loss=29.17, generator_mel_loss=17.81, generator_kl_loss=1.429, generator_dur_loss=1.729, generator_adv_loss=2.008, generator_feat_match_loss=6.197, over 6131.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:46:19,229 INFO [train.py:919] (1/6) Start epoch 910
+2024-03-15 21:48:18,740 INFO [train.py:527] (1/6) Epoch 910, batch 34, global_batch_idx: 112750, batch size: 44, loss[discriminator_loss=2.696, discriminator_real_loss=1.356, discriminator_fake_loss=1.34, generator_loss=28.34, generator_mel_loss=17.4, generator_kl_loss=1.562, generator_dur_loss=1.667, generator_adv_loss=2.155, generator_feat_match_loss=5.556, over 44.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.35, discriminator_fake_loss=1.327, generator_loss=29.27, generator_mel_loss=17.81, generator_kl_loss=1.442, generator_dur_loss=1.721, generator_adv_loss=2.006, generator_feat_match_loss=6.284, over 2013.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:50:36,741 INFO [train.py:527] (1/6) Epoch 910, batch 84, global_batch_idx: 112800, batch size: 80, loss[discriminator_loss=2.667, discriminator_real_loss=1.379, discriminator_fake_loss=1.289, generator_loss=28.8, generator_mel_loss=17.67, generator_kl_loss=1.257, generator_dur_loss=1.781, generator_adv_loss=1.936, generator_feat_match_loss=6.157, over 80.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.17, generator_mel_loss=17.77, generator_kl_loss=1.437, generator_dur_loss=1.723, generator_adv_loss=2.004, generator_feat_match_loss=6.235, over 4817.00 samples.], cur_lr_g: 1.79e-04, cur_lr_d: 1.79e-04, 
+2024-03-15 21:50:36,743 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 21:50:45,551 INFO [train.py:591] (1/6) Epoch 910, validation: discriminator_loss=2.684, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=28.2, generator_mel_loss=18.04, generator_kl_loss=1.346, generator_dur_loss=1.793, generator_adv_loss=1.908, generator_feat_match_loss=5.107, over 100.00 samples.
+2024-03-15 21:50:45,552 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 21:52:33,620 INFO [train.py:919] (1/6) Start epoch 911
+2024-03-15 21:53:21,780 INFO [train.py:527] (1/6) Epoch 911, batch 10, global_batch_idx: 112850, batch size: 56, loss[discriminator_loss=2.696, discriminator_real_loss=1.396, discriminator_fake_loss=1.3, generator_loss=29.26, generator_mel_loss=17.57, generator_kl_loss=1.456, generator_dur_loss=1.732, generator_adv_loss=2.132, generator_feat_match_loss=6.372, over 56.00 samples.], tot_loss[discriminator_loss=2.705, discriminator_real_loss=1.375, discriminator_fake_loss=1.33, generator_loss=28.8, generator_mel_loss=17.61, generator_kl_loss=1.462, generator_dur_loss=1.735, generator_adv_loss=2.015, generator_feat_match_loss=5.974, over 666.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 21:55:42,019 INFO [train.py:527] (1/6) Epoch 911, batch 60, global_batch_idx: 112900, batch size: 14, loss[discriminator_loss=2.537, discriminator_real_loss=1.286, discriminator_fake_loss=1.251, generator_loss=31.78, generator_mel_loss=18.64, generator_kl_loss=1.976, generator_dur_loss=1.577, generator_adv_loss=2.219, generator_feat_match_loss=7.363, over 14.00 samples.], tot_loss[discriminator_loss=2.693, discriminator_real_loss=1.37, discriminator_fake_loss=1.323, generator_loss=28.88, generator_mel_loss=17.69, generator_kl_loss=1.431, generator_dur_loss=1.747, generator_adv_loss=2.005, generator_feat_match_loss=6.003, over 3567.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 21:58:01,654 INFO [train.py:527] (1/6) Epoch 911, batch 110, global_batch_idx: 112950, batch size: 77, loss[discriminator_loss=2.653, discriminator_real_loss=1.404, discriminator_fake_loss=1.248, generator_loss=29.07, generator_mel_loss=17.84, generator_kl_loss=1.24, generator_dur_loss=1.833, generator_adv_loss=1.949, generator_feat_match_loss=6.21, over 77.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.36, discriminator_fake_loss=1.324, generator_loss=29, generator_mel_loss=17.71, generator_kl_loss=1.435, generator_dur_loss=1.745, generator_adv_loss=2.003, generator_feat_match_loss=6.102, over 6459.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 21:58:38,386 INFO [train.py:919] (1/6) Start epoch 912
+2024-03-15 22:00:41,161 INFO [train.py:527] (1/6) Epoch 912, batch 36, global_batch_idx: 113000, batch size: 68, loss[discriminator_loss=2.665, discriminator_real_loss=1.353, discriminator_fake_loss=1.311, generator_loss=28.81, generator_mel_loss=17.89, generator_kl_loss=1.444, generator_dur_loss=1.765, generator_adv_loss=1.912, generator_feat_match_loss=5.797, over 68.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.355, discriminator_fake_loss=1.32, generator_loss=29.16, generator_mel_loss=17.77, generator_kl_loss=1.452, generator_dur_loss=1.749, generator_adv_loss=2.018, generator_feat_match_loss=6.176, over 2212.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:00:41,162 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 22:00:49,098 INFO [train.py:591] (1/6) Epoch 912, validation: discriminator_loss=2.746, discriminator_real_loss=1.38, discriminator_fake_loss=1.366, generator_loss=27.78, generator_mel_loss=18.16, generator_kl_loss=1.293, generator_dur_loss=1.821, generator_adv_loss=1.836, generator_feat_match_loss=4.669, over 100.00 samples.
+2024-03-15 22:00:49,099 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 22:03:06,563 INFO [train.py:527] (1/6) Epoch 912, batch 86, global_batch_idx: 113050, batch size: 61, loss[discriminator_loss=2.637, discriminator_real_loss=1.31, discriminator_fake_loss=1.327, generator_loss=29.55, generator_mel_loss=18.07, generator_kl_loss=1.545, generator_dur_loss=1.697, generator_adv_loss=1.987, generator_feat_match_loss=6.251, over 61.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.351, discriminator_fake_loss=1.316, generator_loss=29.18, generator_mel_loss=17.76, generator_kl_loss=1.448, generator_dur_loss=1.747, generator_adv_loss=2.013, generator_feat_match_loss=6.212, over 5136.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:04:49,854 INFO [train.py:919] (1/6) Start epoch 913
+2024-03-15 22:05:47,855 INFO [train.py:527] (1/6) Epoch 913, batch 12, global_batch_idx: 113100, batch size: 96, loss[discriminator_loss=2.762, discriminator_real_loss=1.393, discriminator_fake_loss=1.369, generator_loss=28.74, generator_mel_loss=17.56, generator_kl_loss=1.241, generator_dur_loss=1.84, generator_adv_loss=1.965, generator_feat_match_loss=6.127, over 96.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.366, discriminator_fake_loss=1.325, generator_loss=28.87, generator_mel_loss=17.62, generator_kl_loss=1.423, generator_dur_loss=1.742, generator_adv_loss=2.027, generator_feat_match_loss=6.063, over 763.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:08:07,282 INFO [train.py:527] (1/6) Epoch 913, batch 62, global_batch_idx: 113150, batch size: 80, loss[discriminator_loss=2.642, discriminator_real_loss=1.326, discriminator_fake_loss=1.316, generator_loss=28.94, generator_mel_loss=17.69, generator_kl_loss=1.359, generator_dur_loss=1.779, generator_adv_loss=2.091, generator_feat_match_loss=6.017, over 80.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.357, discriminator_fake_loss=1.317, generator_loss=29.16, generator_mel_loss=17.69, generator_kl_loss=1.425, generator_dur_loss=1.742, generator_adv_loss=2.057, generator_feat_match_loss=6.245, over 3728.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:10:23,150 INFO [train.py:527] (1/6) Epoch 913, batch 112, global_batch_idx: 113200, batch size: 25, loss[discriminator_loss=2.671, discriminator_real_loss=1.378, discriminator_fake_loss=1.293, generator_loss=30.51, generator_mel_loss=18.07, generator_kl_loss=1.735, generator_dur_loss=1.564, generator_adv_loss=2.143, generator_feat_match_loss=6.995, over 25.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.35, discriminator_fake_loss=1.321, generator_loss=29.16, generator_mel_loss=17.73, generator_kl_loss=1.436, generator_dur_loss=1.734, generator_adv_loss=2.034, generator_feat_match_loss=6.23, over 6475.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:10:23,151 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 22:10:32,025 INFO [train.py:591] (1/6) Epoch 913, validation: discriminator_loss=2.712, discriminator_real_loss=1.412, discriminator_fake_loss=1.3, generator_loss=27.69, generator_mel_loss=17.83, generator_kl_loss=1.352, generator_dur_loss=1.812, generator_adv_loss=1.869, generator_feat_match_loss=4.819, over 100.00 samples.
+2024-03-15 22:10:32,026 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 22:11:02,974 INFO [train.py:919] (1/6) Start epoch 914
+2024-03-15 22:13:12,195 INFO [train.py:527] (1/6) Epoch 914, batch 38, global_batch_idx: 113250, batch size: 68, loss[discriminator_loss=2.68, discriminator_real_loss=1.373, discriminator_fake_loss=1.308, generator_loss=29.29, generator_mel_loss=17.78, generator_kl_loss=1.438, generator_dur_loss=1.774, generator_adv_loss=2.061, generator_feat_match_loss=6.24, over 68.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.354, discriminator_fake_loss=1.323, generator_loss=29.15, generator_mel_loss=17.75, generator_kl_loss=1.458, generator_dur_loss=1.723, generator_adv_loss=2.021, generator_feat_match_loss=6.205, over 2048.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:15:32,641 INFO [train.py:527] (1/6) Epoch 914, batch 88, global_batch_idx: 113300, batch size: 83, loss[discriminator_loss=2.618, discriminator_real_loss=1.306, discriminator_fake_loss=1.312, generator_loss=29.45, generator_mel_loss=17.52, generator_kl_loss=1.258, generator_dur_loss=1.85, generator_adv_loss=2.163, generator_feat_match_loss=6.659, over 83.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.352, discriminator_fake_loss=1.321, generator_loss=29.2, generator_mel_loss=17.73, generator_kl_loss=1.446, generator_dur_loss=1.74, generator_adv_loss=2.025, generator_feat_match_loss=6.26, over 4996.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:17:09,762 INFO [train.py:919] (1/6) Start epoch 915
+2024-03-15 22:18:12,885 INFO [train.py:527] (1/6) Epoch 915, batch 14, global_batch_idx: 113350, batch size: 66, loss[discriminator_loss=2.686, discriminator_real_loss=1.322, discriminator_fake_loss=1.364, generator_loss=28.85, generator_mel_loss=17.34, generator_kl_loss=1.421, generator_dur_loss=1.801, generator_adv_loss=2.087, generator_feat_match_loss=6.202, over 66.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.353, discriminator_fake_loss=1.308, generator_loss=29.21, generator_mel_loss=17.58, generator_kl_loss=1.411, generator_dur_loss=1.754, generator_adv_loss=2.008, generator_feat_match_loss=6.457, over 920.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:20:30,780 INFO [train.py:527] (1/6) Epoch 915, batch 64, global_batch_idx: 113400, batch size: 80, loss[discriminator_loss=2.648, discriminator_real_loss=1.334, discriminator_fake_loss=1.314, generator_loss=29.27, generator_mel_loss=17.62, generator_kl_loss=1.491, generator_dur_loss=1.813, generator_adv_loss=1.965, generator_feat_match_loss=6.38, over 80.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.351, discriminator_fake_loss=1.312, generator_loss=29.2, generator_mel_loss=17.7, generator_kl_loss=1.486, generator_dur_loss=1.744, generator_adv_loss=2.008, generator_feat_match_loss=6.269, over 3830.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:20:30,782 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 22:20:38,829 INFO [train.py:591] (1/6) Epoch 915, validation: discriminator_loss=2.713, discriminator_real_loss=1.421, discriminator_fake_loss=1.292, generator_loss=28.16, generator_mel_loss=18.15, generator_kl_loss=1.297, generator_dur_loss=1.807, generator_adv_loss=1.971, generator_feat_match_loss=4.934, over 100.00 samples.
+2024-03-15 22:20:38,831 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 22:22:58,951 INFO [train.py:527] (1/6) Epoch 915, batch 114, global_batch_idx: 113450, batch size: 83, loss[discriminator_loss=2.645, discriminator_real_loss=1.302, discriminator_fake_loss=1.343, generator_loss=29.68, generator_mel_loss=17.6, generator_kl_loss=1.407, generator_dur_loss=1.782, generator_adv_loss=2.082, generator_feat_match_loss=6.809, over 83.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.348, discriminator_fake_loss=1.317, generator_loss=29.23, generator_mel_loss=17.72, generator_kl_loss=1.488, generator_dur_loss=1.744, generator_adv_loss=2.007, generator_feat_match_loss=6.272, over 6770.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:23:24,934 INFO [train.py:919] (1/6) Start epoch 916
+2024-03-15 22:25:38,669 INFO [train.py:527] (1/6) Epoch 916, batch 40, global_batch_idx: 113500, batch size: 68, loss[discriminator_loss=2.627, discriminator_real_loss=1.296, discriminator_fake_loss=1.331, generator_loss=29.21, generator_mel_loss=17.63, generator_kl_loss=1.487, generator_dur_loss=1.745, generator_adv_loss=2.026, generator_feat_match_loss=6.332, over 68.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.345, discriminator_fake_loss=1.319, generator_loss=29.3, generator_mel_loss=17.79, generator_kl_loss=1.453, generator_dur_loss=1.747, generator_adv_loss=2.026, generator_feat_match_loss=6.287, over 2388.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:27:58,737 INFO [train.py:527] (1/6) Epoch 916, batch 90, global_batch_idx: 113550, batch size: 53, loss[discriminator_loss=2.646, discriminator_real_loss=1.324, discriminator_fake_loss=1.322, generator_loss=28.93, generator_mel_loss=17.82, generator_kl_loss=1.529, generator_dur_loss=1.695, generator_adv_loss=1.987, generator_feat_match_loss=5.898, over 53.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.35, discriminator_fake_loss=1.321, generator_loss=29.12, generator_mel_loss=17.72, generator_kl_loss=1.457, generator_dur_loss=1.737, generator_adv_loss=2.023, generator_feat_match_loss=6.183, over 5193.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:29:32,037 INFO [train.py:919] (1/6) Start epoch 917
+2024-03-15 22:30:40,773 INFO [train.py:527] (1/6) Epoch 917, batch 16, global_batch_idx: 113600, batch size: 52, loss[discriminator_loss=2.688, discriminator_real_loss=1.392, discriminator_fake_loss=1.296, generator_loss=28.7, generator_mel_loss=18.15, generator_kl_loss=1.521, generator_dur_loss=1.699, generator_adv_loss=1.889, generator_feat_match_loss=5.447, over 52.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.339, discriminator_fake_loss=1.329, generator_loss=29.25, generator_mel_loss=17.83, generator_kl_loss=1.491, generator_dur_loss=1.725, generator_adv_loss=2.008, generator_feat_match_loss=6.193, over 935.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:30:40,774 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 22:30:48,826 INFO [train.py:591] (1/6) Epoch 917, validation: discriminator_loss=2.752, discriminator_real_loss=1.322, discriminator_fake_loss=1.43, generator_loss=28, generator_mel_loss=17.95, generator_kl_loss=1.406, generator_dur_loss=1.809, generator_adv_loss=1.827, generator_feat_match_loss=5.009, over 100.00 samples.
+2024-03-15 22:30:48,827 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 22:33:08,195 INFO [train.py:527] (1/6) Epoch 917, batch 66, global_batch_idx: 113650, batch size: 88, loss[discriminator_loss=2.623, discriminator_real_loss=1.381, discriminator_fake_loss=1.241, generator_loss=29.1, generator_mel_loss=17.58, generator_kl_loss=1.373, generator_dur_loss=1.815, generator_adv_loss=2.009, generator_feat_match_loss=6.322, over 88.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.21, generator_mel_loss=17.84, generator_kl_loss=1.467, generator_dur_loss=1.732, generator_adv_loss=2.015, generator_feat_match_loss=6.163, over 3733.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:35:24,420 INFO [train.py:527] (1/6) Epoch 917, batch 116, global_batch_idx: 113700, batch size: 83, loss[discriminator_loss=2.63, discriminator_real_loss=1.26, discriminator_fake_loss=1.371, generator_loss=28.94, generator_mel_loss=17.72, generator_kl_loss=1.284, generator_dur_loss=1.828, generator_adv_loss=2.009, generator_feat_match_loss=6.098, over 83.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.348, discriminator_fake_loss=1.319, generator_loss=29.23, generator_mel_loss=17.83, generator_kl_loss=1.467, generator_dur_loss=1.731, generator_adv_loss=2.015, generator_feat_match_loss=6.187, over 6391.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:35:45,728 INFO [train.py:919] (1/6) Start epoch 918
+2024-03-15 22:38:06,153 INFO [train.py:527] (1/6) Epoch 918, batch 42, global_batch_idx: 113750, batch size: 74, loss[discriminator_loss=2.65, discriminator_real_loss=1.354, discriminator_fake_loss=1.297, generator_loss=28.61, generator_mel_loss=17.7, generator_kl_loss=1.291, generator_dur_loss=1.776, generator_adv_loss=1.862, generator_feat_match_loss=5.978, over 74.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.352, discriminator_fake_loss=1.319, generator_loss=29.14, generator_mel_loss=17.7, generator_kl_loss=1.435, generator_dur_loss=1.742, generator_adv_loss=2.003, generator_feat_match_loss=6.258, over 2539.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:40:23,937 INFO [train.py:527] (1/6) Epoch 918, batch 92, global_batch_idx: 113800, batch size: 14, loss[discriminator_loss=2.631, discriminator_real_loss=1.368, discriminator_fake_loss=1.263, generator_loss=30.69, generator_mel_loss=17.88, generator_kl_loss=1.789, generator_dur_loss=1.577, generator_adv_loss=2.093, generator_feat_match_loss=7.345, over 14.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=29.25, generator_mel_loss=17.74, generator_kl_loss=1.461, generator_dur_loss=1.738, generator_adv_loss=2.017, generator_feat_match_loss=6.292, over 5231.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:40:23,939 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 22:40:32,679 INFO [train.py:591] (1/6) Epoch 918, validation: discriminator_loss=2.722, discriminator_real_loss=1.411, discriminator_fake_loss=1.31, generator_loss=27.73, generator_mel_loss=18.28, generator_kl_loss=1.329, generator_dur_loss=1.804, generator_adv_loss=1.903, generator_feat_match_loss=4.414, over 100.00 samples.
+2024-03-15 22:40:32,680 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 22:42:00,811 INFO [train.py:919] (1/6) Start epoch 919
+2024-03-15 22:43:15,228 INFO [train.py:527] (1/6) Epoch 919, batch 18, global_batch_idx: 113850, batch size: 77, loss[discriminator_loss=2.669, discriminator_real_loss=1.238, discriminator_fake_loss=1.431, generator_loss=29.5, generator_mel_loss=17.65, generator_kl_loss=1.437, generator_dur_loss=1.794, generator_adv_loss=2.053, generator_feat_match_loss=6.571, over 77.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.334, discriminator_fake_loss=1.346, generator_loss=29.1, generator_mel_loss=17.68, generator_kl_loss=1.429, generator_dur_loss=1.75, generator_adv_loss=2.022, generator_feat_match_loss=6.22, over 1148.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:45:35,732 INFO [train.py:527] (1/6) Epoch 919, batch 68, global_batch_idx: 113900, batch size: 88, loss[discriminator_loss=2.659, discriminator_real_loss=1.372, discriminator_fake_loss=1.286, generator_loss=29.42, generator_mel_loss=17.57, generator_kl_loss=1.42, generator_dur_loss=1.814, generator_adv_loss=2.103, generator_feat_match_loss=6.517, over 88.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=29.04, generator_mel_loss=17.7, generator_kl_loss=1.449, generator_dur_loss=1.747, generator_adv_loss=2.017, generator_feat_match_loss=6.123, over 3968.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:47:55,637 INFO [train.py:527] (1/6) Epoch 919, batch 118, global_batch_idx: 113950, batch size: 74, loss[discriminator_loss=2.642, discriminator_real_loss=1.351, discriminator_fake_loss=1.291, generator_loss=29.76, generator_mel_loss=17.99, generator_kl_loss=1.413, generator_dur_loss=1.799, generator_adv_loss=2.124, generator_feat_match_loss=6.432, over 74.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.353, discriminator_fake_loss=1.325, generator_loss=29.04, generator_mel_loss=17.7, generator_kl_loss=1.452, generator_dur_loss=1.75, generator_adv_loss=2.012, generator_feat_match_loss=6.127, over 6903.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:48:11,432 INFO [train.py:919] (1/6) Start epoch 920
+2024-03-15 22:50:39,311 INFO [train.py:527] (1/6) Epoch 920, batch 44, global_batch_idx: 114000, batch size: 45, loss[discriminator_loss=2.556, discriminator_real_loss=1.26, discriminator_fake_loss=1.296, generator_loss=31.06, generator_mel_loss=17.86, generator_kl_loss=1.385, generator_dur_loss=1.725, generator_adv_loss=2.067, generator_feat_match_loss=8.024, over 45.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.347, discriminator_fake_loss=1.334, generator_loss=29.24, generator_mel_loss=17.81, generator_kl_loss=1.441, generator_dur_loss=1.742, generator_adv_loss=2.017, generator_feat_match_loss=6.23, over 2415.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:50:39,313 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 22:50:47,286 INFO [train.py:591] (1/6) Epoch 920, validation: discriminator_loss=2.653, discriminator_real_loss=1.397, discriminator_fake_loss=1.255, generator_loss=28.07, generator_mel_loss=17.81, generator_kl_loss=1.25, generator_dur_loss=1.811, generator_adv_loss=2.03, generator_feat_match_loss=5.167, over 100.00 samples.
+2024-03-15 22:50:47,287 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 22:53:07,367 INFO [train.py:527] (1/6) Epoch 920, batch 94, global_batch_idx: 114050, batch size: 62, loss[discriminator_loss=2.613, discriminator_real_loss=1.319, discriminator_fake_loss=1.294, generator_loss=29.8, generator_mel_loss=17.85, generator_kl_loss=1.473, generator_dur_loss=1.768, generator_adv_loss=1.991, generator_feat_match_loss=6.721, over 62.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.35, discriminator_fake_loss=1.323, generator_loss=29.1, generator_mel_loss=17.74, generator_kl_loss=1.435, generator_dur_loss=1.749, generator_adv_loss=2.017, generator_feat_match_loss=6.162, over 5293.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:54:26,475 INFO [train.py:919] (1/6) Start epoch 921
+2024-03-15 22:55:45,841 INFO [train.py:527] (1/6) Epoch 921, batch 20, global_batch_idx: 114100, batch size: 64, loss[discriminator_loss=2.727, discriminator_real_loss=1.359, discriminator_fake_loss=1.369, generator_loss=28.78, generator_mel_loss=17.51, generator_kl_loss=1.348, generator_dur_loss=1.743, generator_adv_loss=2.05, generator_feat_match_loss=6.127, over 64.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.357, discriminator_fake_loss=1.333, generator_loss=29.49, generator_mel_loss=17.88, generator_kl_loss=1.475, generator_dur_loss=1.717, generator_adv_loss=2.019, generator_feat_match_loss=6.391, over 1113.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 22:58:02,010 INFO [train.py:527] (1/6) Epoch 921, batch 70, global_batch_idx: 114150, batch size: 61, loss[discriminator_loss=2.692, discriminator_real_loss=1.33, discriminator_fake_loss=1.362, generator_loss=29.93, generator_mel_loss=18.09, generator_kl_loss=1.542, generator_dur_loss=1.703, generator_adv_loss=2.069, generator_feat_match_loss=6.525, over 61.00 samples.], tot_loss[discriminator_loss=2.688, discriminator_real_loss=1.363, discriminator_fake_loss=1.325, generator_loss=29.29, generator_mel_loss=17.84, generator_kl_loss=1.456, generator_dur_loss=1.723, generator_adv_loss=2.013, generator_feat_match_loss=6.258, over 3838.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:00:22,570 INFO [train.py:527] (1/6) Epoch 921, batch 120, global_batch_idx: 114200, batch size: 45, loss[discriminator_loss=2.692, discriminator_real_loss=1.432, discriminator_fake_loss=1.259, generator_loss=28.88, generator_mel_loss=17.5, generator_kl_loss=1.497, generator_dur_loss=1.649, generator_adv_loss=2.112, generator_feat_match_loss=6.124, over 45.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.357, discriminator_fake_loss=1.329, generator_loss=29.22, generator_mel_loss=17.79, generator_kl_loss=1.448, generator_dur_loss=1.727, generator_adv_loss=2.022, generator_feat_match_loss=6.234, over 6607.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:00:22,572 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 23:00:31,611 INFO [train.py:591] (1/6) Epoch 921, validation: discriminator_loss=2.693, discriminator_real_loss=1.445, discriminator_fake_loss=1.247, generator_loss=27.78, generator_mel_loss=17.8, generator_kl_loss=1.218, generator_dur_loss=1.817, generator_adv_loss=2.007, generator_feat_match_loss=4.93, over 100.00 samples.
+2024-03-15 23:00:31,612 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 23:00:40,562 INFO [train.py:919] (1/6) Start epoch 922
+2024-03-15 23:03:11,613 INFO [train.py:527] (1/6) Epoch 922, batch 46, global_batch_idx: 114250, batch size: 55, loss[discriminator_loss=2.688, discriminator_real_loss=1.454, discriminator_fake_loss=1.234, generator_loss=28.99, generator_mel_loss=17.26, generator_kl_loss=1.423, generator_dur_loss=1.688, generator_adv_loss=2.188, generator_feat_match_loss=6.436, over 55.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.09, generator_mel_loss=17.72, generator_kl_loss=1.417, generator_dur_loss=1.737, generator_adv_loss=2.01, generator_feat_match_loss=6.205, over 2710.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:05:30,000 INFO [train.py:527] (1/6) Epoch 922, batch 96, global_batch_idx: 114300, batch size: 96, loss[discriminator_loss=2.703, discriminator_real_loss=1.287, discriminator_fake_loss=1.417, generator_loss=29.58, generator_mel_loss=17.59, generator_kl_loss=1.322, generator_dur_loss=1.825, generator_adv_loss=2.118, generator_feat_match_loss=6.729, over 96.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.353, discriminator_fake_loss=1.32, generator_loss=29.08, generator_mel_loss=17.7, generator_kl_loss=1.417, generator_dur_loss=1.741, generator_adv_loss=2.013, generator_feat_match_loss=6.207, over 5648.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:06:47,463 INFO [train.py:919] (1/6) Start epoch 923
+2024-03-15 23:08:16,171 INFO [train.py:527] (1/6) Epoch 923, batch 22, global_batch_idx: 114350, batch size: 66, loss[discriminator_loss=2.673, discriminator_real_loss=1.362, discriminator_fake_loss=1.311, generator_loss=29.35, generator_mel_loss=17.65, generator_kl_loss=1.382, generator_dur_loss=1.694, generator_adv_loss=2.114, generator_feat_match_loss=6.506, over 66.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.358, discriminator_fake_loss=1.334, generator_loss=29.09, generator_mel_loss=17.81, generator_kl_loss=1.46, generator_dur_loss=1.732, generator_adv_loss=1.999, generator_feat_match_loss=6.092, over 1336.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:10:38,413 INFO [train.py:527] (1/6) Epoch 923, batch 72, global_batch_idx: 114400, batch size: 50, loss[discriminator_loss=2.669, discriminator_real_loss=1.262, discriminator_fake_loss=1.407, generator_loss=30.22, generator_mel_loss=18.55, generator_kl_loss=1.593, generator_dur_loss=1.624, generator_adv_loss=1.99, generator_feat_match_loss=6.463, over 50.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.358, discriminator_fake_loss=1.322, generator_loss=29.03, generator_mel_loss=17.75, generator_kl_loss=1.425, generator_dur_loss=1.73, generator_adv_loss=2.013, generator_feat_match_loss=6.108, over 4294.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:10:38,414 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 23:10:47,329 INFO [train.py:591] (1/6) Epoch 923, validation: discriminator_loss=2.766, discriminator_real_loss=1.488, discriminator_fake_loss=1.277, generator_loss=28.54, generator_mel_loss=18.39, generator_kl_loss=1.281, generator_dur_loss=1.764, generator_adv_loss=1.994, generator_feat_match_loss=5.106, over 100.00 samples.
+2024-03-15 23:10:47,330 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 23:13:02,714 INFO [train.py:527] (1/6) Epoch 923, batch 122, global_batch_idx: 114450, batch size: 64, loss[discriminator_loss=2.675, discriminator_real_loss=1.431, discriminator_fake_loss=1.245, generator_loss=28.48, generator_mel_loss=17.77, generator_kl_loss=1.22, generator_dur_loss=1.717, generator_adv_loss=1.832, generator_feat_match_loss=5.946, over 64.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=29.02, generator_mel_loss=17.75, generator_kl_loss=1.435, generator_dur_loss=1.72, generator_adv_loss=2.009, generator_feat_match_loss=6.108, over 6921.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:13:07,944 INFO [train.py:919] (1/6) Start epoch 924
+2024-03-15 23:15:46,474 INFO [train.py:527] (1/6) Epoch 924, batch 48, global_batch_idx: 114500, batch size: 58, loss[discriminator_loss=2.602, discriminator_real_loss=1.285, discriminator_fake_loss=1.317, generator_loss=29.59, generator_mel_loss=17.83, generator_kl_loss=1.493, generator_dur_loss=1.68, generator_adv_loss=2.07, generator_feat_match_loss=6.51, over 58.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.318, generator_loss=29.18, generator_mel_loss=17.74, generator_kl_loss=1.443, generator_dur_loss=1.718, generator_adv_loss=2.008, generator_feat_match_loss=6.269, over 2840.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:18:06,617 INFO [train.py:527] (1/6) Epoch 924, batch 98, global_batch_idx: 114550, batch size: 58, loss[discriminator_loss=2.668, discriminator_real_loss=1.29, discriminator_fake_loss=1.378, generator_loss=29.51, generator_mel_loss=18.34, generator_kl_loss=1.467, generator_dur_loss=1.673, generator_adv_loss=1.991, generator_feat_match_loss=6.04, over 58.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.13, generator_mel_loss=17.74, generator_kl_loss=1.433, generator_dur_loss=1.729, generator_adv_loss=2.004, generator_feat_match_loss=6.224, over 5887.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:19:16,006 INFO [train.py:919] (1/6) Start epoch 925
+2024-03-15 23:20:43,874 INFO [train.py:527] (1/6) Epoch 925, batch 24, global_batch_idx: 114600, batch size: 36, loss[discriminator_loss=2.704, discriminator_real_loss=1.366, discriminator_fake_loss=1.338, generator_loss=29.34, generator_mel_loss=17.77, generator_kl_loss=1.328, generator_dur_loss=1.683, generator_adv_loss=1.951, generator_feat_match_loss=6.602, over 36.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.351, discriminator_fake_loss=1.338, generator_loss=29.17, generator_mel_loss=17.75, generator_kl_loss=1.483, generator_dur_loss=1.695, generator_adv_loss=2.006, generator_feat_match_loss=6.228, over 1283.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:20:43,875 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 23:20:52,331 INFO [train.py:591] (1/6) Epoch 925, validation: discriminator_loss=2.675, discriminator_real_loss=1.297, discriminator_fake_loss=1.378, generator_loss=27.34, generator_mel_loss=17.87, generator_kl_loss=1.345, generator_dur_loss=1.785, generator_adv_loss=1.806, generator_feat_match_loss=4.529, over 100.00 samples.
+2024-03-15 23:20:52,332 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 23:23:12,819 INFO [train.py:527] (1/6) Epoch 925, batch 74, global_batch_idx: 114650, batch size: 42, loss[discriminator_loss=2.63, discriminator_real_loss=1.264, discriminator_fake_loss=1.366, generator_loss=30.15, generator_mel_loss=18.5, generator_kl_loss=1.573, generator_dur_loss=1.638, generator_adv_loss=2.037, generator_feat_match_loss=6.4, over 42.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.347, discriminator_fake_loss=1.328, generator_loss=29.27, generator_mel_loss=17.79, generator_kl_loss=1.474, generator_dur_loss=1.708, generator_adv_loss=2.015, generator_feat_match_loss=6.277, over 4044.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:25:30,298 INFO [train.py:919] (1/6) Start epoch 926
+2024-03-15 23:25:55,266 INFO [train.py:527] (1/6) Epoch 926, batch 0, global_batch_idx: 114700, batch size: 59, loss[discriminator_loss=2.674, discriminator_real_loss=1.45, discriminator_fake_loss=1.223, generator_loss=28.55, generator_mel_loss=17.09, generator_kl_loss=1.349, generator_dur_loss=1.743, generator_adv_loss=2.003, generator_feat_match_loss=6.367, over 59.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.45, discriminator_fake_loss=1.223, generator_loss=28.55, generator_mel_loss=17.09, generator_kl_loss=1.349, generator_dur_loss=1.743, generator_adv_loss=2.003, generator_feat_match_loss=6.367, over 59.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:28:12,651 INFO [train.py:527] (1/6) Epoch 926, batch 50, global_batch_idx: 114750, batch size: 72, loss[discriminator_loss=2.673, discriminator_real_loss=1.428, discriminator_fake_loss=1.245, generator_loss=28.88, generator_mel_loss=17.43, generator_kl_loss=1.275, generator_dur_loss=1.802, generator_adv_loss=2.015, generator_feat_match_loss=6.357, over 72.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.352, discriminator_fake_loss=1.314, generator_loss=29.18, generator_mel_loss=17.75, generator_kl_loss=1.421, generator_dur_loss=1.736, generator_adv_loss=2.022, generator_feat_match_loss=6.253, over 2986.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:30:32,431 INFO [train.py:527] (1/6) Epoch 926, batch 100, global_batch_idx: 114800, batch size: 88, loss[discriminator_loss=2.656, discriminator_real_loss=1.31, discriminator_fake_loss=1.346, generator_loss=29.6, generator_mel_loss=17.8, generator_kl_loss=1.295, generator_dur_loss=1.769, generator_adv_loss=2.024, generator_feat_match_loss=6.714, over 88.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.353, discriminator_fake_loss=1.316, generator_loss=29.12, generator_mel_loss=17.75, generator_kl_loss=1.416, generator_dur_loss=1.737, generator_adv_loss=2.017, generator_feat_match_loss=6.199, over 5984.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:30:32,433 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 23:30:41,175 INFO [train.py:591] (1/6) Epoch 926, validation: discriminator_loss=2.776, discriminator_real_loss=1.398, discriminator_fake_loss=1.378, generator_loss=28.2, generator_mel_loss=18.08, generator_kl_loss=1.284, generator_dur_loss=1.779, generator_adv_loss=1.868, generator_feat_match_loss=5.192, over 100.00 samples.
+2024-03-15 23:30:41,176 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 23:31:46,860 INFO [train.py:919] (1/6) Start epoch 927
+2024-03-15 23:33:20,823 INFO [train.py:527] (1/6) Epoch 927, batch 26, global_batch_idx: 114850, batch size: 53, loss[discriminator_loss=2.653, discriminator_real_loss=1.281, discriminator_fake_loss=1.372, generator_loss=29.67, generator_mel_loss=18.18, generator_kl_loss=1.412, generator_dur_loss=1.692, generator_adv_loss=2.117, generator_feat_match_loss=6.272, over 53.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.323, generator_loss=29, generator_mel_loss=17.65, generator_kl_loss=1.472, generator_dur_loss=1.721, generator_adv_loss=2.027, generator_feat_match_loss=6.122, over 1555.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:35:40,890 INFO [train.py:527] (1/6) Epoch 927, batch 76, global_batch_idx: 114900, batch size: 56, loss[discriminator_loss=2.67, discriminator_real_loss=1.331, discriminator_fake_loss=1.339, generator_loss=29.03, generator_mel_loss=17.82, generator_kl_loss=1.531, generator_dur_loss=1.713, generator_adv_loss=2.027, generator_feat_match_loss=5.933, over 56.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.344, discriminator_fake_loss=1.324, generator_loss=29.12, generator_mel_loss=17.72, generator_kl_loss=1.435, generator_dur_loss=1.744, generator_adv_loss=2.012, generator_feat_match_loss=6.207, over 4545.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:37:52,465 INFO [train.py:919] (1/6) Start epoch 928
+2024-03-15 23:38:21,897 INFO [train.py:527] (1/6) Epoch 928, batch 2, global_batch_idx: 114950, batch size: 88, loss[discriminator_loss=2.662, discriminator_real_loss=1.339, discriminator_fake_loss=1.323, generator_loss=28.97, generator_mel_loss=17.48, generator_kl_loss=1.408, generator_dur_loss=1.823, generator_adv_loss=1.898, generator_feat_match_loss=6.369, over 88.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.338, discriminator_fake_loss=1.336, generator_loss=28.58, generator_mel_loss=17.49, generator_kl_loss=1.406, generator_dur_loss=1.787, generator_adv_loss=1.947, generator_feat_match_loss=5.947, over 211.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:40:44,014 INFO [train.py:527] (1/6) Epoch 928, batch 52, global_batch_idx: 115000, batch size: 70, loss[discriminator_loss=2.735, discriminator_real_loss=1.439, discriminator_fake_loss=1.295, generator_loss=29.94, generator_mel_loss=17.69, generator_kl_loss=1.534, generator_dur_loss=1.818, generator_adv_loss=1.974, generator_feat_match_loss=6.921, over 70.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=29.14, generator_mel_loss=17.72, generator_kl_loss=1.452, generator_dur_loss=1.739, generator_adv_loss=2.014, generator_feat_match_loss=6.213, over 2827.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:40:44,016 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 23:40:52,364 INFO [train.py:591] (1/6) Epoch 928, validation: discriminator_loss=2.757, discriminator_real_loss=1.4, discriminator_fake_loss=1.357, generator_loss=27.1, generator_mel_loss=17.86, generator_kl_loss=1.265, generator_dur_loss=1.808, generator_adv_loss=1.835, generator_feat_match_loss=4.331, over 100.00 samples.
+2024-03-15 23:40:52,365 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 23:43:11,572 INFO [train.py:527] (1/6) Epoch 928, batch 102, global_batch_idx: 115050, batch size: 53, loss[discriminator_loss=2.69, discriminator_real_loss=1.456, discriminator_fake_loss=1.234, generator_loss=28.78, generator_mel_loss=17.83, generator_kl_loss=1.442, generator_dur_loss=1.662, generator_adv_loss=1.996, generator_feat_match_loss=5.847, over 53.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.36, discriminator_fake_loss=1.321, generator_loss=29.2, generator_mel_loss=17.74, generator_kl_loss=1.454, generator_dur_loss=1.739, generator_adv_loss=2.028, generator_feat_match_loss=6.233, over 5575.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:44:09,147 INFO [train.py:919] (1/6) Start epoch 929
+2024-03-15 23:45:49,403 INFO [train.py:527] (1/6) Epoch 929, batch 28, global_batch_idx: 115100, batch size: 88, loss[discriminator_loss=2.668, discriminator_real_loss=1.316, discriminator_fake_loss=1.352, generator_loss=28.55, generator_mel_loss=17.44, generator_kl_loss=1.29, generator_dur_loss=1.793, generator_adv_loss=2.132, generator_feat_match_loss=5.901, over 88.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.351, discriminator_fake_loss=1.327, generator_loss=28.84, generator_mel_loss=17.72, generator_kl_loss=1.456, generator_dur_loss=1.749, generator_adv_loss=2.005, generator_feat_match_loss=5.911, over 1680.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:48:09,886 INFO [train.py:527] (1/6) Epoch 929, batch 78, global_batch_idx: 115150, batch size: 55, loss[discriminator_loss=2.669, discriminator_real_loss=1.325, discriminator_fake_loss=1.344, generator_loss=29.4, generator_mel_loss=18.04, generator_kl_loss=1.427, generator_dur_loss=1.694, generator_adv_loss=1.963, generator_feat_match_loss=6.282, over 55.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.352, discriminator_fake_loss=1.318, generator_loss=29.05, generator_mel_loss=17.74, generator_kl_loss=1.448, generator_dur_loss=1.747, generator_adv_loss=2.007, generator_feat_match_loss=6.11, over 4597.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:50:17,257 INFO [train.py:919] (1/6) Start epoch 930
+2024-03-15 23:50:51,604 INFO [train.py:527] (1/6) Epoch 930, batch 4, global_batch_idx: 115200, batch size: 66, loss[discriminator_loss=2.715, discriminator_real_loss=1.442, discriminator_fake_loss=1.274, generator_loss=29.99, generator_mel_loss=17.82, generator_kl_loss=1.436, generator_dur_loss=1.783, generator_adv_loss=2.003, generator_feat_match_loss=6.95, over 66.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.316, discriminator_fake_loss=1.354, generator_loss=29.59, generator_mel_loss=17.75, generator_kl_loss=1.436, generator_dur_loss=1.77, generator_adv_loss=2.073, generator_feat_match_loss=6.565, over 313.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:50:51,607 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-15 23:50:59,571 INFO [train.py:591] (1/6) Epoch 930, validation: discriminator_loss=2.644, discriminator_real_loss=1.352, discriminator_fake_loss=1.292, generator_loss=28.36, generator_mel_loss=18.17, generator_kl_loss=1.304, generator_dur_loss=1.819, generator_adv_loss=1.924, generator_feat_match_loss=5.15, over 100.00 samples.
+2024-03-15 23:50:59,572 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-15 23:53:20,110 INFO [train.py:527] (1/6) Epoch 930, batch 54, global_batch_idx: 115250, batch size: 48, loss[discriminator_loss=2.679, discriminator_real_loss=1.308, discriminator_fake_loss=1.371, generator_loss=29.69, generator_mel_loss=17.86, generator_kl_loss=1.688, generator_dur_loss=1.658, generator_adv_loss=1.996, generator_feat_match_loss=6.497, over 48.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.354, discriminator_fake_loss=1.316, generator_loss=29.08, generator_mel_loss=17.67, generator_kl_loss=1.441, generator_dur_loss=1.747, generator_adv_loss=2.043, generator_feat_match_loss=6.178, over 3419.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:55:39,466 INFO [train.py:527] (1/6) Epoch 930, batch 104, global_batch_idx: 115300, batch size: 64, loss[discriminator_loss=2.641, discriminator_real_loss=1.272, discriminator_fake_loss=1.368, generator_loss=29.81, generator_mel_loss=18.07, generator_kl_loss=1.417, generator_dur_loss=1.708, generator_adv_loss=2.061, generator_feat_match_loss=6.55, over 64.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.347, discriminator_fake_loss=1.323, generator_loss=29.03, generator_mel_loss=17.67, generator_kl_loss=1.442, generator_dur_loss=1.742, generator_adv_loss=2.026, generator_feat_match_loss=6.151, over 6247.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-15 23:56:31,619 INFO [train.py:919] (1/6) Start epoch 931
+2024-03-15 23:58:20,362 INFO [train.py:527] (1/6) Epoch 931, batch 30, global_batch_idx: 115350, batch size: 72, loss[discriminator_loss=2.678, discriminator_real_loss=1.351, discriminator_fake_loss=1.327, generator_loss=29.37, generator_mel_loss=18.03, generator_kl_loss=1.482, generator_dur_loss=1.757, generator_adv_loss=2.014, generator_feat_match_loss=6.089, over 72.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.347, discriminator_fake_loss=1.314, generator_loss=29.31, generator_mel_loss=17.75, generator_kl_loss=1.466, generator_dur_loss=1.73, generator_adv_loss=2.022, generator_feat_match_loss=6.346, over 1811.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:00:41,219 INFO [train.py:527] (1/6) Epoch 931, batch 80, global_batch_idx: 115400, batch size: 50, loss[discriminator_loss=2.662, discriminator_real_loss=1.359, discriminator_fake_loss=1.303, generator_loss=29.05, generator_mel_loss=17.81, generator_kl_loss=1.645, generator_dur_loss=1.642, generator_adv_loss=1.962, generator_feat_match_loss=5.992, over 50.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.346, discriminator_fake_loss=1.323, generator_loss=29.21, generator_mel_loss=17.75, generator_kl_loss=1.443, generator_dur_loss=1.741, generator_adv_loss=2.014, generator_feat_match_loss=6.256, over 4910.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:00:41,220 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 00:00:50,266 INFO [train.py:591] (1/6) Epoch 931, validation: discriminator_loss=2.687, discriminator_real_loss=1.389, discriminator_fake_loss=1.298, generator_loss=27.94, generator_mel_loss=17.9, generator_kl_loss=1.323, generator_dur_loss=1.792, generator_adv_loss=1.908, generator_feat_match_loss=5.023, over 100.00 samples.
+2024-03-16 00:00:50,267 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 00:02:49,756 INFO [train.py:919] (1/6) Start epoch 932
+2024-03-16 00:03:29,995 INFO [train.py:527] (1/6) Epoch 932, batch 6, global_batch_idx: 115450, batch size: 44, loss[discriminator_loss=2.675, discriminator_real_loss=1.315, discriminator_fake_loss=1.36, generator_loss=27.97, generator_mel_loss=17.45, generator_kl_loss=1.39, generator_dur_loss=1.674, generator_adv_loss=1.933, generator_feat_match_loss=5.523, over 44.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.364, discriminator_fake_loss=1.317, generator_loss=28.84, generator_mel_loss=17.44, generator_kl_loss=1.46, generator_dur_loss=1.736, generator_adv_loss=2.005, generator_feat_match_loss=6.204, over 387.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:05:49,185 INFO [train.py:527] (1/6) Epoch 932, batch 56, global_batch_idx: 115500, batch size: 44, loss[discriminator_loss=2.719, discriminator_real_loss=1.416, discriminator_fake_loss=1.302, generator_loss=29.08, generator_mel_loss=17.56, generator_kl_loss=1.449, generator_dur_loss=1.66, generator_adv_loss=2.011, generator_feat_match_loss=6.404, over 44.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.344, discriminator_fake_loss=1.319, generator_loss=29.37, generator_mel_loss=17.77, generator_kl_loss=1.448, generator_dur_loss=1.747, generator_adv_loss=2.027, generator_feat_match_loss=6.383, over 3358.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:08:05,290 INFO [train.py:527] (1/6) Epoch 932, batch 106, global_batch_idx: 115550, batch size: 44, loss[discriminator_loss=2.741, discriminator_real_loss=1.478, discriminator_fake_loss=1.263, generator_loss=28.63, generator_mel_loss=17.31, generator_kl_loss=1.607, generator_dur_loss=1.668, generator_adv_loss=1.993, generator_feat_match_loss=6.054, over 44.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.352, discriminator_fake_loss=1.32, generator_loss=29.28, generator_mel_loss=17.79, generator_kl_loss=1.451, generator_dur_loss=1.743, generator_adv_loss=2.017, generator_feat_match_loss=6.271, over 6109.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:08:55,093 INFO [train.py:919] (1/6) Start epoch 933
+2024-03-16 00:10:46,917 INFO [train.py:527] (1/6) Epoch 933, batch 32, global_batch_idx: 115600, batch size: 70, loss[discriminator_loss=2.699, discriminator_real_loss=1.39, discriminator_fake_loss=1.309, generator_loss=28.94, generator_mel_loss=17.69, generator_kl_loss=1.379, generator_dur_loss=1.77, generator_adv_loss=1.998, generator_feat_match_loss=6.104, over 70.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.342, discriminator_fake_loss=1.324, generator_loss=29.1, generator_mel_loss=17.68, generator_kl_loss=1.437, generator_dur_loss=1.735, generator_adv_loss=2.019, generator_feat_match_loss=6.227, over 1977.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:10:46,918 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 00:10:55,125 INFO [train.py:591] (1/6) Epoch 933, validation: discriminator_loss=2.75, discriminator_real_loss=1.426, discriminator_fake_loss=1.324, generator_loss=28.26, generator_mel_loss=18.06, generator_kl_loss=1.33, generator_dur_loss=1.797, generator_adv_loss=1.905, generator_feat_match_loss=5.164, over 100.00 samples.
+2024-03-16 00:10:55,126 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 00:13:11,339 INFO [train.py:527] (1/6) Epoch 933, batch 82, global_batch_idx: 115650, batch size: 44, loss[discriminator_loss=2.686, discriminator_real_loss=1.488, discriminator_fake_loss=1.198, generator_loss=29.13, generator_mel_loss=17.71, generator_kl_loss=1.709, generator_dur_loss=1.659, generator_adv_loss=1.968, generator_feat_match_loss=6.085, over 44.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.12, generator_mel_loss=17.69, generator_kl_loss=1.443, generator_dur_loss=1.73, generator_adv_loss=2.024, generator_feat_match_loss=6.228, over 4846.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:15:07,767 INFO [train.py:919] (1/6) Start epoch 934
+2024-03-16 00:15:53,509 INFO [train.py:527] (1/6) Epoch 934, batch 8, global_batch_idx: 115700, batch size: 96, loss[discriminator_loss=2.627, discriminator_real_loss=1.314, discriminator_fake_loss=1.313, generator_loss=29.87, generator_mel_loss=17.53, generator_kl_loss=1.525, generator_dur_loss=1.81, generator_adv_loss=2.137, generator_feat_match_loss=6.864, over 96.00 samples.], tot_loss[discriminator_loss=2.707, discriminator_real_loss=1.374, discriminator_fake_loss=1.333, generator_loss=29.5, generator_mel_loss=17.73, generator_kl_loss=1.501, generator_dur_loss=1.741, generator_adv_loss=2.051, generator_feat_match_loss=6.478, over 530.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:18:14,908 INFO [train.py:527] (1/6) Epoch 934, batch 58, global_batch_idx: 115750, batch size: 45, loss[discriminator_loss=2.718, discriminator_real_loss=1.339, discriminator_fake_loss=1.379, generator_loss=28.65, generator_mel_loss=17.56, generator_kl_loss=1.639, generator_dur_loss=1.646, generator_adv_loss=1.962, generator_feat_match_loss=5.844, over 45.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.361, discriminator_fake_loss=1.316, generator_loss=29.09, generator_mel_loss=17.72, generator_kl_loss=1.47, generator_dur_loss=1.737, generator_adv_loss=2.027, generator_feat_match_loss=6.136, over 3311.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:20:34,058 INFO [train.py:527] (1/6) Epoch 934, batch 108, global_batch_idx: 115800, batch size: 56, loss[discriminator_loss=2.664, discriminator_real_loss=1.268, discriminator_fake_loss=1.396, generator_loss=29.15, generator_mel_loss=17.78, generator_kl_loss=1.442, generator_dur_loss=1.725, generator_adv_loss=2.01, generator_feat_match_loss=6.192, over 56.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.362, discriminator_fake_loss=1.32, generator_loss=29.07, generator_mel_loss=17.72, generator_kl_loss=1.447, generator_dur_loss=1.741, generator_adv_loss=2.016, generator_feat_match_loss=6.145, over 6139.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:20:34,060 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 00:20:42,782 INFO [train.py:591] (1/6) Epoch 934, validation: discriminator_loss=2.725, discriminator_real_loss=1.444, discriminator_fake_loss=1.281, generator_loss=27.39, generator_mel_loss=17.48, generator_kl_loss=1.291, generator_dur_loss=1.817, generator_adv_loss=1.973, generator_feat_match_loss=4.825, over 100.00 samples.
+2024-03-16 00:20:42,783 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 00:21:20,747 INFO [train.py:919] (1/6) Start epoch 935
+2024-03-16 00:23:20,360 INFO [train.py:527] (1/6) Epoch 935, batch 34, global_batch_idx: 115850, batch size: 53, loss[discriminator_loss=2.633, discriminator_real_loss=1.376, discriminator_fake_loss=1.257, generator_loss=29.7, generator_mel_loss=17.91, generator_kl_loss=1.616, generator_dur_loss=1.693, generator_adv_loss=2.098, generator_feat_match_loss=6.379, over 53.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.343, discriminator_fake_loss=1.315, generator_loss=29.19, generator_mel_loss=17.65, generator_kl_loss=1.477, generator_dur_loss=1.747, generator_adv_loss=2.014, generator_feat_match_loss=6.3, over 2038.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:25:39,021 INFO [train.py:527] (1/6) Epoch 935, batch 84, global_batch_idx: 115900, batch size: 36, loss[discriminator_loss=2.695, discriminator_real_loss=1.354, discriminator_fake_loss=1.341, generator_loss=29.07, generator_mel_loss=16.93, generator_kl_loss=1.667, generator_dur_loss=1.654, generator_adv_loss=2.114, generator_feat_match_loss=6.703, over 36.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.348, discriminator_fake_loss=1.324, generator_loss=29.17, generator_mel_loss=17.69, generator_kl_loss=1.464, generator_dur_loss=1.742, generator_adv_loss=2.033, generator_feat_match_loss=6.248, over 4792.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:27:27,341 INFO [train.py:919] (1/6) Start epoch 936
+2024-03-16 00:28:19,993 INFO [train.py:527] (1/6) Epoch 936, batch 10, global_batch_idx: 115950, batch size: 55, loss[discriminator_loss=2.76, discriminator_real_loss=1.446, discriminator_fake_loss=1.314, generator_loss=29.49, generator_mel_loss=18.5, generator_kl_loss=1.459, generator_dur_loss=1.721, generator_adv_loss=2.035, generator_feat_match_loss=5.77, over 55.00 samples.], tot_loss[discriminator_loss=2.704, discriminator_real_loss=1.354, discriminator_fake_loss=1.349, generator_loss=29.33, generator_mel_loss=17.95, generator_kl_loss=1.464, generator_dur_loss=1.744, generator_adv_loss=1.996, generator_feat_match_loss=6.173, over 640.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:30:39,189 INFO [train.py:527] (1/6) Epoch 936, batch 60, global_batch_idx: 116000, batch size: 58, loss[discriminator_loss=2.746, discriminator_real_loss=1.522, discriminator_fake_loss=1.224, generator_loss=29.05, generator_mel_loss=17.71, generator_kl_loss=1.521, generator_dur_loss=1.69, generator_adv_loss=2.013, generator_feat_match_loss=6.108, over 58.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.363, discriminator_fake_loss=1.324, generator_loss=29.19, generator_mel_loss=17.76, generator_kl_loss=1.451, generator_dur_loss=1.74, generator_adv_loss=2.004, generator_feat_match_loss=6.229, over 3652.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:30:39,191 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 00:30:47,179 INFO [train.py:591] (1/6) Epoch 936, validation: discriminator_loss=2.758, discriminator_real_loss=1.362, discriminator_fake_loss=1.396, generator_loss=29.14, generator_mel_loss=18.57, generator_kl_loss=1.275, generator_dur_loss=1.804, generator_adv_loss=1.909, generator_feat_match_loss=5.584, over 100.00 samples.
+2024-03-16 00:30:47,180 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 00:33:04,959 INFO [train.py:527] (1/6) Epoch 936, batch 110, global_batch_idx: 116050, batch size: 74, loss[discriminator_loss=2.655, discriminator_real_loss=1.325, discriminator_fake_loss=1.331, generator_loss=29.04, generator_mel_loss=17.86, generator_kl_loss=1.335, generator_dur_loss=1.754, generator_adv_loss=1.969, generator_feat_match_loss=6.127, over 74.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.359, discriminator_fake_loss=1.327, generator_loss=29.04, generator_mel_loss=17.77, generator_kl_loss=1.436, generator_dur_loss=1.743, generator_adv_loss=1.999, generator_feat_match_loss=6.091, over 6552.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:33:40,326 INFO [train.py:919] (1/6) Start epoch 937
+2024-03-16 00:35:43,920 INFO [train.py:527] (1/6) Epoch 937, batch 36, global_batch_idx: 116100, batch size: 52, loss[discriminator_loss=2.712, discriminator_real_loss=1.41, discriminator_fake_loss=1.302, generator_loss=27.31, generator_mel_loss=17.6, generator_kl_loss=1.446, generator_dur_loss=1.669, generator_adv_loss=1.972, generator_feat_match_loss=4.626, over 52.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.338, discriminator_fake_loss=1.32, generator_loss=29.15, generator_mel_loss=17.76, generator_kl_loss=1.455, generator_dur_loss=1.742, generator_adv_loss=2.015, generator_feat_match_loss=6.181, over 2168.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:38:02,920 INFO [train.py:527] (1/6) Epoch 937, batch 86, global_batch_idx: 116150, batch size: 48, loss[discriminator_loss=2.662, discriminator_real_loss=1.426, discriminator_fake_loss=1.236, generator_loss=27.64, generator_mel_loss=16.88, generator_kl_loss=1.51, generator_dur_loss=1.694, generator_adv_loss=1.893, generator_feat_match_loss=5.66, over 48.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.349, discriminator_fake_loss=1.32, generator_loss=29.02, generator_mel_loss=17.68, generator_kl_loss=1.434, generator_dur_loss=1.745, generator_adv_loss=2.017, generator_feat_match_loss=6.14, over 4989.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:39:46,368 INFO [train.py:919] (1/6) Start epoch 938
+2024-03-16 00:40:43,577 INFO [train.py:527] (1/6) Epoch 938, batch 12, global_batch_idx: 116200, batch size: 31, loss[discriminator_loss=2.694, discriminator_real_loss=1.402, discriminator_fake_loss=1.293, generator_loss=29.64, generator_mel_loss=17.89, generator_kl_loss=1.724, generator_dur_loss=1.585, generator_adv_loss=2.01, generator_feat_match_loss=6.433, over 31.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.363, discriminator_fake_loss=1.312, generator_loss=28.96, generator_mel_loss=17.47, generator_kl_loss=1.437, generator_dur_loss=1.734, generator_adv_loss=2.012, generator_feat_match_loss=6.299, over 782.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:40:43,579 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 00:40:51,534 INFO [train.py:591] (1/6) Epoch 938, validation: discriminator_loss=2.751, discriminator_real_loss=1.432, discriminator_fake_loss=1.319, generator_loss=27.89, generator_mel_loss=18.17, generator_kl_loss=1.323, generator_dur_loss=1.794, generator_adv_loss=1.896, generator_feat_match_loss=4.709, over 100.00 samples.
+2024-03-16 00:40:51,535 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 00:43:09,087 INFO [train.py:527] (1/6) Epoch 938, batch 62, global_batch_idx: 116250, batch size: 66, loss[discriminator_loss=2.67, discriminator_real_loss=1.394, discriminator_fake_loss=1.276, generator_loss=28.56, generator_mel_loss=17.81, generator_kl_loss=1.383, generator_dur_loss=1.781, generator_adv_loss=2.072, generator_feat_match_loss=5.517, over 66.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.361, discriminator_fake_loss=1.324, generator_loss=29.04, generator_mel_loss=17.68, generator_kl_loss=1.466, generator_dur_loss=1.724, generator_adv_loss=1.993, generator_feat_match_loss=6.186, over 3431.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:45:29,106 INFO [train.py:527] (1/6) Epoch 938, batch 112, global_batch_idx: 116300, batch size: 68, loss[discriminator_loss=2.707, discriminator_real_loss=1.318, discriminator_fake_loss=1.389, generator_loss=29.15, generator_mel_loss=17.98, generator_kl_loss=1.453, generator_dur_loss=1.753, generator_adv_loss=1.955, generator_feat_match_loss=6.013, over 68.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=29.11, generator_mel_loss=17.7, generator_kl_loss=1.453, generator_dur_loss=1.732, generator_adv_loss=2.001, generator_feat_match_loss=6.232, over 6340.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:46:00,935 INFO [train.py:919] (1/6) Start epoch 939
+2024-03-16 00:48:11,723 INFO [train.py:527] (1/6) Epoch 939, batch 38, global_batch_idx: 116350, batch size: 80, loss[discriminator_loss=2.642, discriminator_real_loss=1.264, discriminator_fake_loss=1.378, generator_loss=29.08, generator_mel_loss=17.71, generator_kl_loss=1.47, generator_dur_loss=1.799, generator_adv_loss=2.033, generator_feat_match_loss=6.067, over 80.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.342, discriminator_fake_loss=1.327, generator_loss=29.24, generator_mel_loss=17.71, generator_kl_loss=1.453, generator_dur_loss=1.741, generator_adv_loss=1.997, generator_feat_match_loss=6.337, over 2264.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:50:30,593 INFO [train.py:527] (1/6) Epoch 939, batch 88, global_batch_idx: 116400, batch size: 70, loss[discriminator_loss=2.763, discriminator_real_loss=1.391, discriminator_fake_loss=1.372, generator_loss=30.33, generator_mel_loss=18.14, generator_kl_loss=1.391, generator_dur_loss=1.794, generator_adv_loss=1.981, generator_feat_match_loss=7.016, over 70.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.349, discriminator_fake_loss=1.328, generator_loss=29.26, generator_mel_loss=17.73, generator_kl_loss=1.442, generator_dur_loss=1.752, generator_adv_loss=2.007, generator_feat_match_loss=6.33, over 5225.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:50:30,594 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 00:50:39,490 INFO [train.py:591] (1/6) Epoch 939, validation: discriminator_loss=2.745, discriminator_real_loss=1.466, discriminator_fake_loss=1.279, generator_loss=28.39, generator_mel_loss=18.45, generator_kl_loss=1.304, generator_dur_loss=1.8, generator_adv_loss=1.906, generator_feat_match_loss=4.93, over 100.00 samples.
+2024-03-16 00:50:39,491 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 00:52:14,837 INFO [train.py:919] (1/6) Start epoch 940
+2024-03-16 00:53:18,586 INFO [train.py:527] (1/6) Epoch 940, batch 14, global_batch_idx: 116450, batch size: 50, loss[discriminator_loss=2.635, discriminator_real_loss=1.284, discriminator_fake_loss=1.35, generator_loss=29.71, generator_mel_loss=17.54, generator_kl_loss=1.484, generator_dur_loss=1.664, generator_adv_loss=2.135, generator_feat_match_loss=6.892, over 50.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.36, discriminator_fake_loss=1.313, generator_loss=29.06, generator_mel_loss=17.52, generator_kl_loss=1.443, generator_dur_loss=1.73, generator_adv_loss=2.028, generator_feat_match_loss=6.344, over 750.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:55:37,639 INFO [train.py:527] (1/6) Epoch 940, batch 64, global_batch_idx: 116500, batch size: 55, loss[discriminator_loss=2.649, discriminator_real_loss=1.364, discriminator_fake_loss=1.285, generator_loss=28.84, generator_mel_loss=17.98, generator_kl_loss=1.496, generator_dur_loss=1.693, generator_adv_loss=2.087, generator_feat_match_loss=5.579, over 55.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.347, discriminator_fake_loss=1.32, generator_loss=29.23, generator_mel_loss=17.73, generator_kl_loss=1.447, generator_dur_loss=1.738, generator_adv_loss=2.018, generator_feat_match_loss=6.294, over 3724.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:57:56,279 INFO [train.py:527] (1/6) Epoch 940, batch 114, global_batch_idx: 116550, batch size: 62, loss[discriminator_loss=2.633, discriminator_real_loss=1.41, discriminator_fake_loss=1.223, generator_loss=28.98, generator_mel_loss=17.39, generator_kl_loss=1.558, generator_dur_loss=1.705, generator_adv_loss=2.048, generator_feat_match_loss=6.277, over 62.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.35, discriminator_fake_loss=1.317, generator_loss=29.24, generator_mel_loss=17.75, generator_kl_loss=1.443, generator_dur_loss=1.736, generator_adv_loss=2.022, generator_feat_match_loss=6.283, over 6475.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 00:58:23,669 INFO [train.py:919] (1/6) Start epoch 941
+2024-03-16 01:00:37,590 INFO [train.py:527] (1/6) Epoch 941, batch 40, global_batch_idx: 116600, batch size: 42, loss[discriminator_loss=2.675, discriminator_real_loss=1.255, discriminator_fake_loss=1.42, generator_loss=29.46, generator_mel_loss=17.81, generator_kl_loss=1.791, generator_dur_loss=1.638, generator_adv_loss=2.1, generator_feat_match_loss=6.117, over 42.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.11, generator_mel_loss=17.66, generator_kl_loss=1.429, generator_dur_loss=1.742, generator_adv_loss=2.012, generator_feat_match_loss=6.27, over 2514.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:00:37,592 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 01:00:45,811 INFO [train.py:591] (1/6) Epoch 941, validation: discriminator_loss=2.752, discriminator_real_loss=1.539, discriminator_fake_loss=1.214, generator_loss=28.52, generator_mel_loss=17.67, generator_kl_loss=1.372, generator_dur_loss=1.795, generator_adv_loss=2.107, generator_feat_match_loss=5.573, over 100.00 samples.
+2024-03-16 01:00:45,812 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 01:03:04,719 INFO [train.py:527] (1/6) Epoch 941, batch 90, global_batch_idx: 116650, batch size: 39, loss[discriminator_loss=2.732, discriminator_real_loss=1.38, discriminator_fake_loss=1.353, generator_loss=28.18, generator_mel_loss=16.95, generator_kl_loss=1.48, generator_dur_loss=1.633, generator_adv_loss=2.087, generator_feat_match_loss=6.03, over 39.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.351, discriminator_fake_loss=1.322, generator_loss=29.14, generator_mel_loss=17.66, generator_kl_loss=1.428, generator_dur_loss=1.741, generator_adv_loss=2.009, generator_feat_match_loss=6.296, over 5483.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:04:36,066 INFO [train.py:919] (1/6) Start epoch 942
+2024-03-16 01:05:45,310 INFO [train.py:527] (1/6) Epoch 942, batch 16, global_batch_idx: 116700, batch size: 59, loss[discriminator_loss=2.604, discriminator_real_loss=1.351, discriminator_fake_loss=1.253, generator_loss=30.22, generator_mel_loss=17.73, generator_kl_loss=1.53, generator_dur_loss=1.721, generator_adv_loss=1.935, generator_feat_match_loss=7.297, over 59.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.341, discriminator_fake_loss=1.329, generator_loss=29.27, generator_mel_loss=17.7, generator_kl_loss=1.408, generator_dur_loss=1.751, generator_adv_loss=2.003, generator_feat_match_loss=6.414, over 1123.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:08:05,278 INFO [train.py:527] (1/6) Epoch 942, batch 66, global_batch_idx: 116750, batch size: 66, loss[discriminator_loss=2.684, discriminator_real_loss=1.422, discriminator_fake_loss=1.262, generator_loss=29.76, generator_mel_loss=18.26, generator_kl_loss=1.357, generator_dur_loss=1.752, generator_adv_loss=1.868, generator_feat_match_loss=6.517, over 66.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.345, discriminator_fake_loss=1.322, generator_loss=29.29, generator_mel_loss=17.72, generator_kl_loss=1.431, generator_dur_loss=1.746, generator_adv_loss=2.01, generator_feat_match_loss=6.388, over 4106.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:10:21,071 INFO [train.py:527] (1/6) Epoch 942, batch 116, global_batch_idx: 116800, batch size: 47, loss[discriminator_loss=2.707, discriminator_real_loss=1.371, discriminator_fake_loss=1.336, generator_loss=27.6, generator_mel_loss=17.26, generator_kl_loss=1.321, generator_dur_loss=1.709, generator_adv_loss=1.895, generator_feat_match_loss=5.416, over 47.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.349, discriminator_fake_loss=1.323, generator_loss=29.25, generator_mel_loss=17.74, generator_kl_loss=1.439, generator_dur_loss=1.742, generator_adv_loss=2.011, generator_feat_match_loss=6.317, over 6788.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:10:21,072 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 01:10:29,980 INFO [train.py:591] (1/6) Epoch 942, validation: discriminator_loss=2.664, discriminator_real_loss=1.33, discriminator_fake_loss=1.334, generator_loss=27.31, generator_mel_loss=17.59, generator_kl_loss=1.23, generator_dur_loss=1.826, generator_adv_loss=1.896, generator_feat_match_loss=4.766, over 100.00 samples.
+2024-03-16 01:10:29,981 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 01:10:50,648 INFO [train.py:919] (1/6) Start epoch 943
+2024-03-16 01:13:12,503 INFO [train.py:527] (1/6) Epoch 943, batch 42, global_batch_idx: 116850, batch size: 36, loss[discriminator_loss=2.625, discriminator_real_loss=1.345, discriminator_fake_loss=1.28, generator_loss=29.29, generator_mel_loss=17.57, generator_kl_loss=1.638, generator_dur_loss=1.688, generator_adv_loss=2.151, generator_feat_match_loss=6.237, over 36.00 samples.], tot_loss[discriminator_loss=2.686, discriminator_real_loss=1.367, discriminator_fake_loss=1.319, generator_loss=28.96, generator_mel_loss=17.67, generator_kl_loss=1.447, generator_dur_loss=1.737, generator_adv_loss=2.01, generator_feat_match_loss=6.1, over 2422.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:15:30,983 INFO [train.py:527] (1/6) Epoch 943, batch 92, global_batch_idx: 116900, batch size: 96, loss[discriminator_loss=2.685, discriminator_real_loss=1.401, discriminator_fake_loss=1.284, generator_loss=28.77, generator_mel_loss=17.7, generator_kl_loss=1.295, generator_dur_loss=1.807, generator_adv_loss=1.999, generator_feat_match_loss=5.968, over 96.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.365, discriminator_fake_loss=1.319, generator_loss=29.09, generator_mel_loss=17.72, generator_kl_loss=1.451, generator_dur_loss=1.737, generator_adv_loss=2.016, generator_feat_match_loss=6.159, over 5237.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:16:56,725 INFO [train.py:919] (1/6) Start epoch 944
+2024-03-16 01:18:10,328 INFO [train.py:527] (1/6) Epoch 944, batch 18, global_batch_idx: 116950, batch size: 50, loss[discriminator_loss=2.677, discriminator_real_loss=1.284, discriminator_fake_loss=1.393, generator_loss=29.38, generator_mel_loss=17.76, generator_kl_loss=1.563, generator_dur_loss=1.673, generator_adv_loss=1.933, generator_feat_match_loss=6.446, over 50.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.341, discriminator_fake_loss=1.325, generator_loss=29.2, generator_mel_loss=17.77, generator_kl_loss=1.466, generator_dur_loss=1.746, generator_adv_loss=1.996, generator_feat_match_loss=6.223, over 1030.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:20:29,905 INFO [train.py:527] (1/6) Epoch 944, batch 68, global_batch_idx: 117000, batch size: 36, loss[discriminator_loss=2.675, discriminator_real_loss=1.326, discriminator_fake_loss=1.349, generator_loss=29.01, generator_mel_loss=17.42, generator_kl_loss=1.599, generator_dur_loss=1.644, generator_adv_loss=2.078, generator_feat_match_loss=6.278, over 36.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.349, discriminator_fake_loss=1.326, generator_loss=29.17, generator_mel_loss=17.75, generator_kl_loss=1.463, generator_dur_loss=1.74, generator_adv_loss=2.016, generator_feat_match_loss=6.204, over 3733.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:20:29,907 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 01:20:38,012 INFO [train.py:591] (1/6) Epoch 944, validation: discriminator_loss=2.674, discriminator_real_loss=1.303, discriminator_fake_loss=1.371, generator_loss=28.81, generator_mel_loss=18.27, generator_kl_loss=1.272, generator_dur_loss=1.814, generator_adv_loss=1.948, generator_feat_match_loss=5.504, over 100.00 samples.
+2024-03-16 01:20:38,013 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 01:22:56,777 INFO [train.py:527] (1/6) Epoch 944, batch 118, global_batch_idx: 117050, batch size: 15, loss[discriminator_loss=2.706, discriminator_real_loss=1.435, discriminator_fake_loss=1.271, generator_loss=28.27, generator_mel_loss=17.14, generator_kl_loss=1.525, generator_dur_loss=1.53, generator_adv_loss=2.045, generator_feat_match_loss=6.032, over 15.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.348, discriminator_fake_loss=1.325, generator_loss=29.25, generator_mel_loss=17.77, generator_kl_loss=1.459, generator_dur_loss=1.739, generator_adv_loss=2.016, generator_feat_match_loss=6.27, over 6394.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:23:10,951 INFO [train.py:919] (1/6) Start epoch 945
+2024-03-16 01:25:37,173 INFO [train.py:527] (1/6) Epoch 945, batch 44, global_batch_idx: 117100, batch size: 60, loss[discriminator_loss=2.638, discriminator_real_loss=1.332, discriminator_fake_loss=1.305, generator_loss=29.81, generator_mel_loss=18.1, generator_kl_loss=1.533, generator_dur_loss=1.743, generator_adv_loss=2.075, generator_feat_match_loss=6.358, over 60.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.352, discriminator_fake_loss=1.326, generator_loss=29.12, generator_mel_loss=17.79, generator_kl_loss=1.451, generator_dur_loss=1.741, generator_adv_loss=2.009, generator_feat_match_loss=6.131, over 2680.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:27:55,847 INFO [train.py:527] (1/6) Epoch 945, batch 94, global_batch_idx: 117150, batch size: 53, loss[discriminator_loss=2.623, discriminator_real_loss=1.225, discriminator_fake_loss=1.398, generator_loss=29.63, generator_mel_loss=17.56, generator_kl_loss=1.485, generator_dur_loss=1.675, generator_adv_loss=2.258, generator_feat_match_loss=6.658, over 53.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.353, discriminator_fake_loss=1.322, generator_loss=29.17, generator_mel_loss=17.77, generator_kl_loss=1.454, generator_dur_loss=1.738, generator_adv_loss=2.019, generator_feat_match_loss=6.192, over 5631.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:29:15,180 INFO [train.py:919] (1/6) Start epoch 946
+2024-03-16 01:30:35,120 INFO [train.py:527] (1/6) Epoch 946, batch 20, global_batch_idx: 117200, batch size: 50, loss[discriminator_loss=2.71, discriminator_real_loss=1.343, discriminator_fake_loss=1.367, generator_loss=28.84, generator_mel_loss=17.39, generator_kl_loss=1.582, generator_dur_loss=1.694, generator_adv_loss=2.025, generator_feat_match_loss=6.152, over 50.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.339, discriminator_fake_loss=1.328, generator_loss=29.13, generator_mel_loss=17.67, generator_kl_loss=1.475, generator_dur_loss=1.716, generator_adv_loss=1.99, generator_feat_match_loss=6.28, over 1174.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:30:35,121 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 01:30:43,266 INFO [train.py:591] (1/6) Epoch 946, validation: discriminator_loss=2.702, discriminator_real_loss=1.422, discriminator_fake_loss=1.279, generator_loss=27.95, generator_mel_loss=17.8, generator_kl_loss=1.297, generator_dur_loss=1.79, generator_adv_loss=2.012, generator_feat_match_loss=5.047, over 100.00 samples.
+2024-03-16 01:30:43,267 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 01:33:03,008 INFO [train.py:527] (1/6) Epoch 946, batch 70, global_batch_idx: 117250, batch size: 74, loss[discriminator_loss=2.627, discriminator_real_loss=1.356, discriminator_fake_loss=1.271, generator_loss=29.31, generator_mel_loss=17.54, generator_kl_loss=1.315, generator_dur_loss=1.788, generator_adv_loss=1.96, generator_feat_match_loss=6.712, over 74.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.349, discriminator_fake_loss=1.322, generator_loss=29.05, generator_mel_loss=17.71, generator_kl_loss=1.462, generator_dur_loss=1.723, generator_adv_loss=1.994, generator_feat_match_loss=6.159, over 3931.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:35:20,536 INFO [train.py:527] (1/6) Epoch 946, batch 120, global_batch_idx: 117300, batch size: 66, loss[discriminator_loss=2.7, discriminator_real_loss=1.321, discriminator_fake_loss=1.379, generator_loss=28.55, generator_mel_loss=17.7, generator_kl_loss=1.344, generator_dur_loss=1.75, generator_adv_loss=1.945, generator_feat_match_loss=5.807, over 66.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.322, generator_loss=29.12, generator_mel_loss=17.71, generator_kl_loss=1.462, generator_dur_loss=1.728, generator_adv_loss=2.003, generator_feat_match_loss=6.21, over 6656.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:35:30,286 INFO [train.py:919] (1/6) Start epoch 947
+2024-03-16 01:38:02,481 INFO [train.py:527] (1/6) Epoch 947, batch 46, global_batch_idx: 117350, batch size: 70, loss[discriminator_loss=2.658, discriminator_real_loss=1.321, discriminator_fake_loss=1.337, generator_loss=29.55, generator_mel_loss=17.82, generator_kl_loss=1.512, generator_dur_loss=1.776, generator_adv_loss=1.975, generator_feat_match_loss=6.464, over 70.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.352, discriminator_fake_loss=1.324, generator_loss=29.24, generator_mel_loss=17.75, generator_kl_loss=1.417, generator_dur_loss=1.749, generator_adv_loss=2.031, generator_feat_match_loss=6.293, over 2888.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:40:21,629 INFO [train.py:527] (1/6) Epoch 947, batch 96, global_batch_idx: 117400, batch size: 77, loss[discriminator_loss=2.637, discriminator_real_loss=1.358, discriminator_fake_loss=1.279, generator_loss=29.16, generator_mel_loss=17.52, generator_kl_loss=1.328, generator_dur_loss=1.773, generator_adv_loss=1.976, generator_feat_match_loss=6.565, over 77.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.352, discriminator_fake_loss=1.323, generator_loss=29.17, generator_mel_loss=17.73, generator_kl_loss=1.414, generator_dur_loss=1.752, generator_adv_loss=2.019, generator_feat_match_loss=6.255, over 5902.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:40:21,631 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 01:40:30,417 INFO [train.py:591] (1/6) Epoch 947, validation: discriminator_loss=2.748, discriminator_real_loss=1.341, discriminator_fake_loss=1.407, generator_loss=28.92, generator_mel_loss=18.29, generator_kl_loss=1.363, generator_dur_loss=1.822, generator_adv_loss=1.87, generator_feat_match_loss=5.571, over 100.00 samples.
+2024-03-16 01:40:30,418 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 01:41:46,496 INFO [train.py:919] (1/6) Start epoch 948
+2024-03-16 01:43:10,123 INFO [train.py:527] (1/6) Epoch 948, batch 22, global_batch_idx: 117450, batch size: 68, loss[discriminator_loss=2.658, discriminator_real_loss=1.356, discriminator_fake_loss=1.302, generator_loss=29.3, generator_mel_loss=17.58, generator_kl_loss=1.417, generator_dur_loss=1.75, generator_adv_loss=2.066, generator_feat_match_loss=6.491, over 68.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.357, discriminator_fake_loss=1.311, generator_loss=29.32, generator_mel_loss=17.76, generator_kl_loss=1.471, generator_dur_loss=1.702, generator_adv_loss=2.034, generator_feat_match_loss=6.353, over 1079.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:45:27,427 INFO [train.py:527] (1/6) Epoch 948, batch 72, global_batch_idx: 117500, batch size: 56, loss[discriminator_loss=2.683, discriminator_real_loss=1.395, discriminator_fake_loss=1.288, generator_loss=29.29, generator_mel_loss=17.77, generator_kl_loss=1.6, generator_dur_loss=1.698, generator_adv_loss=1.936, generator_feat_match_loss=6.285, over 56.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.354, discriminator_fake_loss=1.317, generator_loss=29.26, generator_mel_loss=17.74, generator_kl_loss=1.482, generator_dur_loss=1.716, generator_adv_loss=2.019, generator_feat_match_loss=6.299, over 3646.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:47:44,919 INFO [train.py:527] (1/6) Epoch 948, batch 122, global_batch_idx: 117550, batch size: 96, loss[discriminator_loss=2.707, discriminator_real_loss=1.417, discriminator_fake_loss=1.29, generator_loss=28.39, generator_mel_loss=17.36, generator_kl_loss=1.354, generator_dur_loss=1.862, generator_adv_loss=1.917, generator_feat_match_loss=5.897, over 96.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.354, discriminator_fake_loss=1.316, generator_loss=29.26, generator_mel_loss=17.75, generator_kl_loss=1.471, generator_dur_loss=1.719, generator_adv_loss=2.019, generator_feat_match_loss=6.293, over 6374.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:47:50,305 INFO [train.py:919] (1/6) Start epoch 949
+2024-03-16 01:50:29,872 INFO [train.py:527] (1/6) Epoch 949, batch 48, global_batch_idx: 117600, batch size: 77, loss[discriminator_loss=2.64, discriminator_real_loss=1.368, discriminator_fake_loss=1.272, generator_loss=28.95, generator_mel_loss=18.28, generator_kl_loss=1.307, generator_dur_loss=1.816, generator_adv_loss=1.968, generator_feat_match_loss=5.577, over 77.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.319, generator_loss=29.18, generator_mel_loss=17.71, generator_kl_loss=1.426, generator_dur_loss=1.735, generator_adv_loss=2.019, generator_feat_match_loss=6.283, over 2777.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:50:29,874 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 01:50:37,969 INFO [train.py:591] (1/6) Epoch 949, validation: discriminator_loss=2.697, discriminator_real_loss=1.391, discriminator_fake_loss=1.306, generator_loss=28.1, generator_mel_loss=17.84, generator_kl_loss=1.36, generator_dur_loss=1.799, generator_adv_loss=1.956, generator_feat_match_loss=5.15, over 100.00 samples.
+2024-03-16 01:50:37,970 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 01:52:58,083 INFO [train.py:527] (1/6) Epoch 949, batch 98, global_batch_idx: 117650, batch size: 96, loss[discriminator_loss=2.636, discriminator_real_loss=1.312, discriminator_fake_loss=1.324, generator_loss=29.22, generator_mel_loss=17.59, generator_kl_loss=1.349, generator_dur_loss=1.81, generator_adv_loss=2.068, generator_feat_match_loss=6.402, over 96.00 samples.], tot_loss[discriminator_loss=2.681, discriminator_real_loss=1.357, discriminator_fake_loss=1.323, generator_loss=29.16, generator_mel_loss=17.74, generator_kl_loss=1.425, generator_dur_loss=1.741, generator_adv_loss=2.011, generator_feat_match_loss=6.238, over 5635.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:54:06,783 INFO [train.py:919] (1/6) Start epoch 950
+2024-03-16 01:55:37,158 INFO [train.py:527] (1/6) Epoch 950, batch 24, global_batch_idx: 117700, batch size: 66, loss[discriminator_loss=2.712, discriminator_real_loss=1.386, discriminator_fake_loss=1.326, generator_loss=28.95, generator_mel_loss=17.7, generator_kl_loss=1.495, generator_dur_loss=1.766, generator_adv_loss=2.026, generator_feat_match_loss=5.965, over 66.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.354, discriminator_fake_loss=1.319, generator_loss=29.18, generator_mel_loss=17.83, generator_kl_loss=1.42, generator_dur_loss=1.756, generator_adv_loss=2.008, generator_feat_match_loss=6.166, over 1431.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 01:58:00,999 INFO [train.py:527] (1/6) Epoch 950, batch 74, global_batch_idx: 117750, batch size: 74, loss[discriminator_loss=2.718, discriminator_real_loss=1.309, discriminator_fake_loss=1.408, generator_loss=28.3, generator_mel_loss=17.33, generator_kl_loss=1.331, generator_dur_loss=1.814, generator_adv_loss=1.948, generator_feat_match_loss=5.875, over 74.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.347, discriminator_fake_loss=1.322, generator_loss=29.13, generator_mel_loss=17.75, generator_kl_loss=1.433, generator_dur_loss=1.744, generator_adv_loss=2.002, generator_feat_match_loss=6.206, over 4224.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:00:19,474 INFO [train.py:919] (1/6) Start epoch 951
+2024-03-16 02:00:44,340 INFO [train.py:527] (1/6) Epoch 951, batch 0, global_batch_idx: 117800, batch size: 53, loss[discriminator_loss=2.708, discriminator_real_loss=1.378, discriminator_fake_loss=1.33, generator_loss=29.67, generator_mel_loss=17.73, generator_kl_loss=1.48, generator_dur_loss=1.683, generator_adv_loss=2.12, generator_feat_match_loss=6.658, over 53.00 samples.], tot_loss[discriminator_loss=2.708, discriminator_real_loss=1.378, discriminator_fake_loss=1.33, generator_loss=29.67, generator_mel_loss=17.73, generator_kl_loss=1.48, generator_dur_loss=1.683, generator_adv_loss=2.12, generator_feat_match_loss=6.658, over 53.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:00:44,343 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 02:00:52,409 INFO [train.py:591] (1/6) Epoch 951, validation: discriminator_loss=2.731, discriminator_real_loss=1.498, discriminator_fake_loss=1.232, generator_loss=28.27, generator_mel_loss=18.11, generator_kl_loss=1.324, generator_dur_loss=1.81, generator_adv_loss=2.02, generator_feat_match_loss=5.008, over 100.00 samples.
+2024-03-16 02:00:52,411 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 02:03:13,062 INFO [train.py:527] (1/6) Epoch 951, batch 50, global_batch_idx: 117850, batch size: 83, loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.44, generator_mel_loss=17.65, generator_kl_loss=1.313, generator_dur_loss=1.824, generator_adv_loss=2.16, generator_feat_match_loss=6.493, over 83.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.351, discriminator_fake_loss=1.325, generator_loss=29.32, generator_mel_loss=17.79, generator_kl_loss=1.466, generator_dur_loss=1.729, generator_adv_loss=2.031, generator_feat_match_loss=6.307, over 2820.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:05:35,410 INFO [train.py:527] (1/6) Epoch 951, batch 100, global_batch_idx: 117900, batch size: 80, loss[discriminator_loss=2.689, discriminator_real_loss=1.423, discriminator_fake_loss=1.266, generator_loss=29.54, generator_mel_loss=18.06, generator_kl_loss=1.375, generator_dur_loss=1.81, generator_adv_loss=1.999, generator_feat_match_loss=6.302, over 80.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.358, discriminator_fake_loss=1.318, generator_loss=29.25, generator_mel_loss=17.79, generator_kl_loss=1.446, generator_dur_loss=1.734, generator_adv_loss=2.021, generator_feat_match_loss=6.258, over 5654.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:06:41,488 INFO [train.py:919] (1/6) Start epoch 952
+2024-03-16 02:08:20,299 INFO [train.py:527] (1/6) Epoch 952, batch 26, global_batch_idx: 117950, batch size: 25, loss[discriminator_loss=2.722, discriminator_real_loss=1.286, discriminator_fake_loss=1.436, generator_loss=29.98, generator_mel_loss=18.13, generator_kl_loss=1.576, generator_dur_loss=1.555, generator_adv_loss=2.211, generator_feat_match_loss=6.51, over 25.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.348, discriminator_fake_loss=1.319, generator_loss=29.09, generator_mel_loss=17.76, generator_kl_loss=1.443, generator_dur_loss=1.733, generator_adv_loss=2.011, generator_feat_match_loss=6.145, over 1492.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:10:40,353 INFO [train.py:527] (1/6) Epoch 952, batch 76, global_batch_idx: 118000, batch size: 39, loss[discriminator_loss=2.608, discriminator_real_loss=1.29, discriminator_fake_loss=1.318, generator_loss=29.42, generator_mel_loss=17.86, generator_kl_loss=1.554, generator_dur_loss=1.725, generator_adv_loss=2.004, generator_feat_match_loss=6.277, over 39.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.35, discriminator_fake_loss=1.322, generator_loss=29.17, generator_mel_loss=17.72, generator_kl_loss=1.426, generator_dur_loss=1.736, generator_adv_loss=2.021, generator_feat_match_loss=6.266, over 4403.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:10:40,355 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 02:10:49,137 INFO [train.py:591] (1/6) Epoch 952, validation: discriminator_loss=2.691, discriminator_real_loss=1.407, discriminator_fake_loss=1.284, generator_loss=28.35, generator_mel_loss=17.98, generator_kl_loss=1.225, generator_dur_loss=1.797, generator_adv_loss=1.939, generator_feat_match_loss=5.41, over 100.00 samples.
+2024-03-16 02:10:49,138 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 02:13:02,860 INFO [train.py:919] (1/6) Start epoch 953
+2024-03-16 02:13:33,467 INFO [train.py:527] (1/6) Epoch 953, batch 2, global_batch_idx: 118050, batch size: 48, loss[discriminator_loss=2.611, discriminator_real_loss=1.342, discriminator_fake_loss=1.269, generator_loss=30.47, generator_mel_loss=17.84, generator_kl_loss=1.602, generator_dur_loss=1.639, generator_adv_loss=2.211, generator_feat_match_loss=7.172, over 48.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.387, discriminator_fake_loss=1.302, generator_loss=29.43, generator_mel_loss=17.87, generator_kl_loss=1.363, generator_dur_loss=1.747, generator_adv_loss=2.051, generator_feat_match_loss=6.401, over 188.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:15:54,609 INFO [train.py:527] (1/6) Epoch 953, batch 52, global_batch_idx: 118100, batch size: 97, loss[discriminator_loss=2.646, discriminator_real_loss=1.243, discriminator_fake_loss=1.402, generator_loss=28.82, generator_mel_loss=17.81, generator_kl_loss=1.259, generator_dur_loss=1.848, generator_adv_loss=2.067, generator_feat_match_loss=5.827, over 97.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.06, generator_mel_loss=17.75, generator_kl_loss=1.404, generator_dur_loss=1.74, generator_adv_loss=2.006, generator_feat_match_loss=6.167, over 3073.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:18:17,747 INFO [train.py:527] (1/6) Epoch 953, batch 102, global_batch_idx: 118150, batch size: 45, loss[discriminator_loss=2.706, discriminator_real_loss=1.332, discriminator_fake_loss=1.375, generator_loss=28.63, generator_mel_loss=17.68, generator_kl_loss=1.527, generator_dur_loss=1.659, generator_adv_loss=2.04, generator_feat_match_loss=5.729, over 45.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.12, generator_mel_loss=17.73, generator_kl_loss=1.426, generator_dur_loss=1.743, generator_adv_loss=2.013, generator_feat_match_loss=6.212, over 6040.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:19:16,650 INFO [train.py:919] (1/6) Start epoch 954
+2024-03-16 02:21:03,220 INFO [train.py:527] (1/6) Epoch 954, batch 28, global_batch_idx: 118200, batch size: 68, loss[discriminator_loss=2.657, discriminator_real_loss=1.35, discriminator_fake_loss=1.306, generator_loss=28.75, generator_mel_loss=17.7, generator_kl_loss=1.287, generator_dur_loss=1.792, generator_adv_loss=2.093, generator_feat_match_loss=5.881, over 68.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.346, discriminator_fake_loss=1.325, generator_loss=29.09, generator_mel_loss=17.75, generator_kl_loss=1.431, generator_dur_loss=1.751, generator_adv_loss=2.013, generator_feat_match_loss=6.141, over 1677.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:21:03,222 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 02:21:11,359 INFO [train.py:591] (1/6) Epoch 954, validation: discriminator_loss=2.663, discriminator_real_loss=1.387, discriminator_fake_loss=1.276, generator_loss=27.74, generator_mel_loss=17.97, generator_kl_loss=1.348, generator_dur_loss=1.821, generator_adv_loss=1.95, generator_feat_match_loss=4.657, over 100.00 samples.
+2024-03-16 02:21:11,360 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 02:23:33,054 INFO [train.py:527] (1/6) Epoch 954, batch 78, global_batch_idx: 118250, batch size: 88, loss[discriminator_loss=2.667, discriminator_real_loss=1.213, discriminator_fake_loss=1.453, generator_loss=30.26, generator_mel_loss=17.91, generator_kl_loss=1.539, generator_dur_loss=1.843, generator_adv_loss=2.244, generator_feat_match_loss=6.726, over 88.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.345, discriminator_fake_loss=1.319, generator_loss=29.18, generator_mel_loss=17.74, generator_kl_loss=1.448, generator_dur_loss=1.739, generator_adv_loss=2.025, generator_feat_match_loss=6.227, over 4481.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:25:41,800 INFO [train.py:919] (1/6) Start epoch 955
+2024-03-16 02:26:18,608 INFO [train.py:527] (1/6) Epoch 955, batch 4, global_batch_idx: 118300, batch size: 59, loss[discriminator_loss=2.627, discriminator_real_loss=1.277, discriminator_fake_loss=1.35, generator_loss=29.31, generator_mel_loss=17.97, generator_kl_loss=1.366, generator_dur_loss=1.733, generator_adv_loss=2.063, generator_feat_match_loss=6.172, over 59.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.333, discriminator_fake_loss=1.331, generator_loss=29.31, generator_mel_loss=17.75, generator_kl_loss=1.417, generator_dur_loss=1.786, generator_adv_loss=2.016, generator_feat_match_loss=6.346, over 323.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:28:40,792 INFO [train.py:527] (1/6) Epoch 955, batch 54, global_batch_idx: 118350, batch size: 62, loss[discriminator_loss=2.723, discriminator_real_loss=1.398, discriminator_fake_loss=1.324, generator_loss=29.03, generator_mel_loss=17.33, generator_kl_loss=1.301, generator_dur_loss=1.732, generator_adv_loss=2.125, generator_feat_match_loss=6.538, over 62.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.362, discriminator_fake_loss=1.323, generator_loss=29.05, generator_mel_loss=17.65, generator_kl_loss=1.414, generator_dur_loss=1.751, generator_adv_loss=2.01, generator_feat_match_loss=6.216, over 3181.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:31:04,365 INFO [train.py:527] (1/6) Epoch 955, batch 104, global_batch_idx: 118400, batch size: 59, loss[discriminator_loss=2.718, discriminator_real_loss=1.492, discriminator_fake_loss=1.225, generator_loss=28.89, generator_mel_loss=17.79, generator_kl_loss=1.588, generator_dur_loss=1.747, generator_adv_loss=2.09, generator_feat_match_loss=5.675, over 59.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.358, discriminator_fake_loss=1.323, generator_loss=29.11, generator_mel_loss=17.7, generator_kl_loss=1.438, generator_dur_loss=1.742, generator_adv_loss=2.008, generator_feat_match_loss=6.221, over 5883.00 samples.], cur_lr_g: 1.78e-04, cur_lr_d: 1.78e-04, 
+2024-03-16 02:31:04,367 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 02:31:13,205 INFO [train.py:591] (1/6) Epoch 955, validation: discriminator_loss=2.737, discriminator_real_loss=1.437, discriminator_fake_loss=1.3, generator_loss=28.33, generator_mel_loss=17.99, generator_kl_loss=1.248, generator_dur_loss=1.814, generator_adv_loss=2.029, generator_feat_match_loss=5.248, over 100.00 samples.
+2024-03-16 02:31:13,206 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 02:32:05,102 INFO [train.py:919] (1/6) Start epoch 956
+2024-03-16 02:33:54,932 INFO [train.py:527] (1/6) Epoch 956, batch 30, global_batch_idx: 118450, batch size: 59, loss[discriminator_loss=2.578, discriminator_real_loss=1.276, discriminator_fake_loss=1.302, generator_loss=29.89, generator_mel_loss=18.06, generator_kl_loss=1.513, generator_dur_loss=1.719, generator_adv_loss=2.012, generator_feat_match_loss=6.591, over 59.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=29.3, generator_mel_loss=17.79, generator_kl_loss=1.438, generator_dur_loss=1.744, generator_adv_loss=2.024, generator_feat_match_loss=6.303, over 1734.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:36:16,804 INFO [train.py:527] (1/6) Epoch 956, batch 80, global_batch_idx: 118500, batch size: 70, loss[discriminator_loss=2.631, discriminator_real_loss=1.374, discriminator_fake_loss=1.257, generator_loss=29.15, generator_mel_loss=17.49, generator_kl_loss=1.374, generator_dur_loss=1.767, generator_adv_loss=1.994, generator_feat_match_loss=6.528, over 70.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.349, discriminator_fake_loss=1.322, generator_loss=29.23, generator_mel_loss=17.7, generator_kl_loss=1.438, generator_dur_loss=1.737, generator_adv_loss=2.021, generator_feat_match_loss=6.332, over 4613.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:38:19,837 INFO [train.py:919] (1/6) Start epoch 957
+2024-03-16 02:39:00,509 INFO [train.py:527] (1/6) Epoch 957, batch 6, global_batch_idx: 118550, batch size: 31, loss[discriminator_loss=2.706, discriminator_real_loss=1.374, discriminator_fake_loss=1.332, generator_loss=29.59, generator_mel_loss=18.13, generator_kl_loss=1.639, generator_dur_loss=1.639, generator_adv_loss=2.107, generator_feat_match_loss=6.076, over 31.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.357, discriminator_fake_loss=1.32, generator_loss=29.32, generator_mel_loss=17.8, generator_kl_loss=1.387, generator_dur_loss=1.725, generator_adv_loss=2.046, generator_feat_match_loss=6.363, over 361.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:41:20,815 INFO [train.py:527] (1/6) Epoch 957, batch 56, global_batch_idx: 118600, batch size: 83, loss[discriminator_loss=2.64, discriminator_real_loss=1.297, discriminator_fake_loss=1.342, generator_loss=29.61, generator_mel_loss=17.94, generator_kl_loss=1.329, generator_dur_loss=1.807, generator_adv_loss=1.98, generator_feat_match_loss=6.556, over 83.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.35, discriminator_fake_loss=1.316, generator_loss=29.27, generator_mel_loss=17.79, generator_kl_loss=1.447, generator_dur_loss=1.733, generator_adv_loss=2.024, generator_feat_match_loss=6.269, over 3126.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:41:20,816 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 02:41:28,896 INFO [train.py:591] (1/6) Epoch 957, validation: discriminator_loss=2.701, discriminator_real_loss=1.379, discriminator_fake_loss=1.321, generator_loss=28.3, generator_mel_loss=18.2, generator_kl_loss=1.215, generator_dur_loss=1.81, generator_adv_loss=1.93, generator_feat_match_loss=5.143, over 100.00 samples.
+2024-03-16 02:41:28,897 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 02:43:50,864 INFO [train.py:527] (1/6) Epoch 957, batch 106, global_batch_idx: 118650, batch size: 53, loss[discriminator_loss=2.679, discriminator_real_loss=1.339, discriminator_fake_loss=1.34, generator_loss=28.25, generator_mel_loss=17.24, generator_kl_loss=1.551, generator_dur_loss=1.672, generator_adv_loss=2.058, generator_feat_match_loss=5.731, over 53.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.352, discriminator_fake_loss=1.318, generator_loss=29.24, generator_mel_loss=17.77, generator_kl_loss=1.45, generator_dur_loss=1.732, generator_adv_loss=2.018, generator_feat_match_loss=6.268, over 5916.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:44:40,595 INFO [train.py:919] (1/6) Start epoch 958
+2024-03-16 02:46:36,188 INFO [train.py:527] (1/6) Epoch 958, batch 32, global_batch_idx: 118700, batch size: 44, loss[discriminator_loss=2.665, discriminator_real_loss=1.35, discriminator_fake_loss=1.314, generator_loss=30.11, generator_mel_loss=17.68, generator_kl_loss=1.492, generator_dur_loss=1.701, generator_adv_loss=2.171, generator_feat_match_loss=7.07, over 44.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.354, discriminator_fake_loss=1.314, generator_loss=29.38, generator_mel_loss=17.72, generator_kl_loss=1.453, generator_dur_loss=1.742, generator_adv_loss=2.041, generator_feat_match_loss=6.429, over 1840.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:49:02,454 INFO [train.py:527] (1/6) Epoch 958, batch 82, global_batch_idx: 118750, batch size: 74, loss[discriminator_loss=2.681, discriminator_real_loss=1.269, discriminator_fake_loss=1.413, generator_loss=29.48, generator_mel_loss=17.81, generator_kl_loss=1.547, generator_dur_loss=1.838, generator_adv_loss=1.988, generator_feat_match_loss=6.3, over 74.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.352, discriminator_fake_loss=1.316, generator_loss=29.21, generator_mel_loss=17.67, generator_kl_loss=1.447, generator_dur_loss=1.745, generator_adv_loss=2.021, generator_feat_match_loss=6.319, over 4820.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:50:54,502 INFO [train.py:919] (1/6) Start epoch 959
+2024-03-16 02:51:42,574 INFO [train.py:527] (1/6) Epoch 959, batch 8, global_batch_idx: 118800, batch size: 53, loss[discriminator_loss=2.723, discriminator_real_loss=1.383, discriminator_fake_loss=1.34, generator_loss=28.45, generator_mel_loss=17.82, generator_kl_loss=1.267, generator_dur_loss=1.688, generator_adv_loss=1.934, generator_feat_match_loss=5.746, over 53.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.37, discriminator_fake_loss=1.302, generator_loss=29, generator_mel_loss=17.84, generator_kl_loss=1.408, generator_dur_loss=1.745, generator_adv_loss=1.988, generator_feat_match_loss=6.022, over 514.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:51:42,594 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 02:51:50,404 INFO [train.py:591] (1/6) Epoch 959, validation: discriminator_loss=2.75, discriminator_real_loss=1.37, discriminator_fake_loss=1.381, generator_loss=27.2, generator_mel_loss=17.62, generator_kl_loss=1.332, generator_dur_loss=1.811, generator_adv_loss=1.8, generator_feat_match_loss=4.637, over 100.00 samples.
+2024-03-16 02:51:50,406 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 02:54:11,236 INFO [train.py:527] (1/6) Epoch 959, batch 58, global_batch_idx: 118850, batch size: 62, loss[discriminator_loss=2.631, discriminator_real_loss=1.354, discriminator_fake_loss=1.277, generator_loss=28.46, generator_mel_loss=17.42, generator_kl_loss=1.533, generator_dur_loss=1.707, generator_adv_loss=1.888, generator_feat_match_loss=5.909, over 62.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.13, generator_mel_loss=17.8, generator_kl_loss=1.452, generator_dur_loss=1.743, generator_adv_loss=2.001, generator_feat_match_loss=6.132, over 3345.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:56:33,452 INFO [train.py:527] (1/6) Epoch 959, batch 108, global_batch_idx: 118900, batch size: 48, loss[discriminator_loss=2.718, discriminator_real_loss=1.384, discriminator_fake_loss=1.334, generator_loss=29.51, generator_mel_loss=17.96, generator_kl_loss=1.585, generator_dur_loss=1.697, generator_adv_loss=1.978, generator_feat_match_loss=6.288, over 48.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=29.12, generator_mel_loss=17.76, generator_kl_loss=1.452, generator_dur_loss=1.744, generator_adv_loss=2.008, generator_feat_match_loss=6.159, over 6281.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 02:57:15,047 INFO [train.py:919] (1/6) Start epoch 960
+2024-03-16 02:59:14,257 INFO [train.py:527] (1/6) Epoch 960, batch 34, global_batch_idx: 118950, batch size: 45, loss[discriminator_loss=2.656, discriminator_real_loss=1.329, discriminator_fake_loss=1.326, generator_loss=28.47, generator_mel_loss=17.31, generator_kl_loss=1.529, generator_dur_loss=1.673, generator_adv_loss=2.007, generator_feat_match_loss=5.943, over 45.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.353, discriminator_fake_loss=1.307, generator_loss=29.36, generator_mel_loss=17.66, generator_kl_loss=1.411, generator_dur_loss=1.748, generator_adv_loss=2.093, generator_feat_match_loss=6.45, over 1988.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:01:36,106 INFO [train.py:527] (1/6) Epoch 960, batch 84, global_batch_idx: 119000, batch size: 68, loss[discriminator_loss=2.695, discriminator_real_loss=1.37, discriminator_fake_loss=1.326, generator_loss=29.08, generator_mel_loss=17.38, generator_kl_loss=1.445, generator_dur_loss=1.823, generator_adv_loss=2.081, generator_feat_match_loss=6.35, over 68.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.352, discriminator_fake_loss=1.31, generator_loss=29.24, generator_mel_loss=17.67, generator_kl_loss=1.425, generator_dur_loss=1.747, generator_adv_loss=2.046, generator_feat_match_loss=6.347, over 4947.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:01:36,107 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 03:01:43,957 INFO [train.py:591] (1/6) Epoch 960, validation: discriminator_loss=2.745, discriminator_real_loss=1.395, discriminator_fake_loss=1.35, generator_loss=28.7, generator_mel_loss=18.48, generator_kl_loss=1.318, generator_dur_loss=1.817, generator_adv_loss=1.916, generator_feat_match_loss=5.172, over 100.00 samples.
+2024-03-16 03:01:43,957 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 03:03:35,591 INFO [train.py:919] (1/6) Start epoch 961
+2024-03-16 03:04:32,351 INFO [train.py:527] (1/6) Epoch 961, batch 10, global_batch_idx: 119050, batch size: 50, loss[discriminator_loss=2.688, discriminator_real_loss=1.394, discriminator_fake_loss=1.294, generator_loss=28.9, generator_mel_loss=17.69, generator_kl_loss=1.485, generator_dur_loss=1.674, generator_adv_loss=2.057, generator_feat_match_loss=5.988, over 50.00 samples.], tot_loss[discriminator_loss=2.687, discriminator_real_loss=1.342, discriminator_fake_loss=1.345, generator_loss=29.32, generator_mel_loss=17.75, generator_kl_loss=1.471, generator_dur_loss=1.752, generator_adv_loss=2.002, generator_feat_match_loss=6.351, over 678.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:06:51,842 INFO [train.py:527] (1/6) Epoch 961, batch 60, global_batch_idx: 119100, batch size: 96, loss[discriminator_loss=2.621, discriminator_real_loss=1.253, discriminator_fake_loss=1.368, generator_loss=29.27, generator_mel_loss=17.43, generator_kl_loss=1.295, generator_dur_loss=1.831, generator_adv_loss=2.168, generator_feat_match_loss=6.552, over 96.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.348, discriminator_fake_loss=1.326, generator_loss=29.25, generator_mel_loss=17.73, generator_kl_loss=1.454, generator_dur_loss=1.728, generator_adv_loss=2.011, generator_feat_match_loss=6.325, over 3429.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:09:12,453 INFO [train.py:527] (1/6) Epoch 961, batch 110, global_batch_idx: 119150, batch size: 55, loss[discriminator_loss=2.55, discriminator_real_loss=1.321, discriminator_fake_loss=1.229, generator_loss=29.65, generator_mel_loss=17.43, generator_kl_loss=1.404, generator_dur_loss=1.624, generator_adv_loss=2.203, generator_feat_match_loss=6.99, over 55.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.35, discriminator_fake_loss=1.325, generator_loss=29.24, generator_mel_loss=17.72, generator_kl_loss=1.457, generator_dur_loss=1.74, generator_adv_loss=2.01, generator_feat_match_loss=6.307, over 6381.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:09:50,839 INFO [train.py:919] (1/6) Start epoch 962
+2024-03-16 03:11:56,364 INFO [train.py:527] (1/6) Epoch 962, batch 36, global_batch_idx: 119200, batch size: 58, loss[discriminator_loss=2.718, discriminator_real_loss=1.351, discriminator_fake_loss=1.367, generator_loss=28.2, generator_mel_loss=17.44, generator_kl_loss=1.507, generator_dur_loss=1.708, generator_adv_loss=1.987, generator_feat_match_loss=5.567, over 58.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.361, discriminator_fake_loss=1.318, generator_loss=29.16, generator_mel_loss=17.77, generator_kl_loss=1.496, generator_dur_loss=1.72, generator_adv_loss=1.997, generator_feat_match_loss=6.181, over 1982.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:11:56,365 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 03:12:04,408 INFO [train.py:591] (1/6) Epoch 962, validation: discriminator_loss=2.732, discriminator_real_loss=1.453, discriminator_fake_loss=1.279, generator_loss=27.79, generator_mel_loss=17.87, generator_kl_loss=1.245, generator_dur_loss=1.798, generator_adv_loss=1.95, generator_feat_match_loss=4.929, over 100.00 samples.
+2024-03-16 03:12:04,409 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 03:14:22,639 INFO [train.py:527] (1/6) Epoch 962, batch 86, global_batch_idx: 119250, batch size: 70, loss[discriminator_loss=2.723, discriminator_real_loss=1.318, discriminator_fake_loss=1.405, generator_loss=28.72, generator_mel_loss=17.71, generator_kl_loss=1.393, generator_dur_loss=1.78, generator_adv_loss=1.992, generator_feat_match_loss=5.844, over 70.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.09, generator_mel_loss=17.72, generator_kl_loss=1.46, generator_dur_loss=1.728, generator_adv_loss=2.006, generator_feat_match_loss=6.173, over 4871.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:16:09,772 INFO [train.py:919] (1/6) Start epoch 963
+2024-03-16 03:17:08,120 INFO [train.py:527] (1/6) Epoch 963, batch 12, global_batch_idx: 119300, batch size: 39, loss[discriminator_loss=2.578, discriminator_real_loss=1.241, discriminator_fake_loss=1.337, generator_loss=31.31, generator_mel_loss=18.08, generator_kl_loss=1.574, generator_dur_loss=1.622, generator_adv_loss=2.112, generator_feat_match_loss=7.922, over 39.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.343, discriminator_fake_loss=1.324, generator_loss=29.48, generator_mel_loss=17.81, generator_kl_loss=1.557, generator_dur_loss=1.686, generator_adv_loss=2.027, generator_feat_match_loss=6.405, over 615.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:19:29,418 INFO [train.py:527] (1/6) Epoch 963, batch 62, global_batch_idx: 119350, batch size: 72, loss[discriminator_loss=2.61, discriminator_real_loss=1.265, discriminator_fake_loss=1.345, generator_loss=30.14, generator_mel_loss=17.78, generator_kl_loss=1.397, generator_dur_loss=1.772, generator_adv_loss=2.12, generator_feat_match_loss=7.069, over 72.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.35, discriminator_fake_loss=1.318, generator_loss=29.25, generator_mel_loss=17.75, generator_kl_loss=1.462, generator_dur_loss=1.732, generator_adv_loss=2.021, generator_feat_match_loss=6.281, over 3664.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:21:49,918 INFO [train.py:527] (1/6) Epoch 963, batch 112, global_batch_idx: 119400, batch size: 25, loss[discriminator_loss=2.703, discriminator_real_loss=1.346, discriminator_fake_loss=1.357, generator_loss=29.38, generator_mel_loss=17.71, generator_kl_loss=1.635, generator_dur_loss=1.566, generator_adv_loss=1.929, generator_feat_match_loss=6.532, over 25.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.32, generator_mel_loss=17.77, generator_kl_loss=1.463, generator_dur_loss=1.724, generator_adv_loss=2.017, generator_feat_match_loss=6.353, over 6191.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:21:49,919 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 03:21:59,012 INFO [train.py:591] (1/6) Epoch 963, validation: discriminator_loss=2.679, discriminator_real_loss=1.417, discriminator_fake_loss=1.263, generator_loss=27.53, generator_mel_loss=17.95, generator_kl_loss=1.161, generator_dur_loss=1.807, generator_adv_loss=1.98, generator_feat_match_loss=4.633, over 100.00 samples.
+2024-03-16 03:21:59,012 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 03:22:30,391 INFO [train.py:919] (1/6) Start epoch 964
+2024-03-16 03:24:45,521 INFO [train.py:527] (1/6) Epoch 964, batch 38, global_batch_idx: 119450, batch size: 72, loss[discriminator_loss=2.654, discriminator_real_loss=1.352, discriminator_fake_loss=1.302, generator_loss=30.09, generator_mel_loss=17.89, generator_kl_loss=1.377, generator_dur_loss=1.808, generator_adv_loss=2.05, generator_feat_match_loss=6.971, over 72.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.342, discriminator_fake_loss=1.332, generator_loss=29.2, generator_mel_loss=17.75, generator_kl_loss=1.426, generator_dur_loss=1.744, generator_adv_loss=2.001, generator_feat_match_loss=6.276, over 2260.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:27:09,172 INFO [train.py:527] (1/6) Epoch 964, batch 88, global_batch_idx: 119500, batch size: 70, loss[discriminator_loss=2.688, discriminator_real_loss=1.327, discriminator_fake_loss=1.361, generator_loss=30.01, generator_mel_loss=18.01, generator_kl_loss=1.264, generator_dur_loss=1.79, generator_adv_loss=2.048, generator_feat_match_loss=6.9, over 70.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.346, discriminator_fake_loss=1.325, generator_loss=29.33, generator_mel_loss=17.75, generator_kl_loss=1.43, generator_dur_loss=1.75, generator_adv_loss=2.03, generator_feat_match_loss=6.369, over 5287.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:28:46,443 INFO [train.py:919] (1/6) Start epoch 965
+2024-03-16 03:29:50,785 INFO [train.py:527] (1/6) Epoch 965, batch 14, global_batch_idx: 119550, batch size: 88, loss[discriminator_loss=2.647, discriminator_real_loss=1.312, discriminator_fake_loss=1.335, generator_loss=29.73, generator_mel_loss=18.07, generator_kl_loss=1.432, generator_dur_loss=1.803, generator_adv_loss=2.08, generator_feat_match_loss=6.342, over 88.00 samples.], tot_loss[discriminator_loss=2.653, discriminator_real_loss=1.347, discriminator_fake_loss=1.307, generator_loss=29.27, generator_mel_loss=17.65, generator_kl_loss=1.434, generator_dur_loss=1.728, generator_adv_loss=2.038, generator_feat_match_loss=6.414, over 947.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:32:10,230 INFO [train.py:527] (1/6) Epoch 965, batch 64, global_batch_idx: 119600, batch size: 42, loss[discriminator_loss=2.654, discriminator_real_loss=1.242, discriminator_fake_loss=1.413, generator_loss=28.95, generator_mel_loss=17.87, generator_kl_loss=1.527, generator_dur_loss=1.662, generator_adv_loss=2.083, generator_feat_match_loss=5.808, over 42.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.344, discriminator_fake_loss=1.324, generator_loss=29.2, generator_mel_loss=17.72, generator_kl_loss=1.435, generator_dur_loss=1.736, generator_adv_loss=2.007, generator_feat_match_loss=6.306, over 3891.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:32:10,231 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 03:32:18,313 INFO [train.py:591] (1/6) Epoch 965, validation: discriminator_loss=2.703, discriminator_real_loss=1.36, discriminator_fake_loss=1.343, generator_loss=27.66, generator_mel_loss=18, generator_kl_loss=1.352, generator_dur_loss=1.796, generator_adv_loss=1.896, generator_feat_match_loss=4.611, over 100.00 samples.
+2024-03-16 03:32:18,314 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 03:34:35,883 INFO [train.py:527] (1/6) Epoch 965, batch 114, global_batch_idx: 119650, batch size: 52, loss[discriminator_loss=2.693, discriminator_real_loss=1.45, discriminator_fake_loss=1.242, generator_loss=28.81, generator_mel_loss=17.71, generator_kl_loss=1.535, generator_dur_loss=1.667, generator_adv_loss=1.847, generator_feat_match_loss=6.048, over 52.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.35, discriminator_fake_loss=1.319, generator_loss=29.21, generator_mel_loss=17.74, generator_kl_loss=1.439, generator_dur_loss=1.734, generator_adv_loss=2.009, generator_feat_match_loss=6.295, over 6720.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:35:02,653 INFO [train.py:919] (1/6) Start epoch 966
+2024-03-16 03:37:17,002 INFO [train.py:527] (1/6) Epoch 966, batch 40, global_batch_idx: 119700, batch size: 14, loss[discriminator_loss=2.625, discriminator_real_loss=1.332, discriminator_fake_loss=1.293, generator_loss=31.88, generator_mel_loss=18.08, generator_kl_loss=1.817, generator_dur_loss=1.598, generator_adv_loss=2.05, generator_feat_match_loss=8.335, over 14.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.354, discriminator_fake_loss=1.315, generator_loss=29.48, generator_mel_loss=17.79, generator_kl_loss=1.477, generator_dur_loss=1.733, generator_adv_loss=2.021, generator_feat_match_loss=6.462, over 2270.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:39:35,451 INFO [train.py:527] (1/6) Epoch 966, batch 90, global_batch_idx: 119750, batch size: 58, loss[discriminator_loss=2.717, discriminator_real_loss=1.436, discriminator_fake_loss=1.281, generator_loss=28.48, generator_mel_loss=17.76, generator_kl_loss=1.3, generator_dur_loss=1.711, generator_adv_loss=1.944, generator_feat_match_loss=5.766, over 58.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.355, discriminator_fake_loss=1.316, generator_loss=29.28, generator_mel_loss=17.69, generator_kl_loss=1.457, generator_dur_loss=1.736, generator_adv_loss=2.02, generator_feat_match_loss=6.383, over 5180.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:41:10,381 INFO [train.py:919] (1/6) Start epoch 967
+2024-03-16 03:42:20,812 INFO [train.py:527] (1/6) Epoch 967, batch 16, global_batch_idx: 119800, batch size: 70, loss[discriminator_loss=2.663, discriminator_real_loss=1.318, discriminator_fake_loss=1.346, generator_loss=30.13, generator_mel_loss=17.98, generator_kl_loss=1.338, generator_dur_loss=1.804, generator_adv_loss=2.094, generator_feat_match_loss=6.913, over 70.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.345, discriminator_fake_loss=1.326, generator_loss=29.12, generator_mel_loss=17.72, generator_kl_loss=1.402, generator_dur_loss=1.746, generator_adv_loss=2.005, generator_feat_match_loss=6.252, over 969.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:42:20,814 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 03:42:29,728 INFO [train.py:591] (1/6) Epoch 967, validation: discriminator_loss=2.732, discriminator_real_loss=1.422, discriminator_fake_loss=1.311, generator_loss=27.33, generator_mel_loss=17.69, generator_kl_loss=1.272, generator_dur_loss=1.813, generator_adv_loss=2, generator_feat_match_loss=4.555, over 100.00 samples.
+2024-03-16 03:42:29,729 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 03:44:48,675 INFO [train.py:527] (1/6) Epoch 967, batch 66, global_batch_idx: 119850, batch size: 15, loss[discriminator_loss=2.79, discriminator_real_loss=1.473, discriminator_fake_loss=1.318, generator_loss=27.98, generator_mel_loss=17.78, generator_kl_loss=1.638, generator_dur_loss=1.589, generator_adv_loss=1.852, generator_feat_match_loss=5.124, over 15.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.356, discriminator_fake_loss=1.318, generator_loss=29.16, generator_mel_loss=17.74, generator_kl_loss=1.422, generator_dur_loss=1.747, generator_adv_loss=2.021, generator_feat_match_loss=6.232, over 3690.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:47:04,693 INFO [train.py:527] (1/6) Epoch 967, batch 116, global_batch_idx: 119900, batch size: 61, loss[discriminator_loss=2.653, discriminator_real_loss=1.365, discriminator_fake_loss=1.288, generator_loss=28.97, generator_mel_loss=17.56, generator_kl_loss=1.37, generator_dur_loss=1.755, generator_adv_loss=1.963, generator_feat_match_loss=6.317, over 61.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.358, discriminator_fake_loss=1.32, generator_loss=29.12, generator_mel_loss=17.72, generator_kl_loss=1.425, generator_dur_loss=1.751, generator_adv_loss=2.012, generator_feat_match_loss=6.209, over 6798.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:47:27,415 INFO [train.py:919] (1/6) Start epoch 968
+2024-03-16 03:49:48,360 INFO [train.py:527] (1/6) Epoch 968, batch 42, global_batch_idx: 119950, batch size: 58, loss[discriminator_loss=2.738, discriminator_real_loss=1.449, discriminator_fake_loss=1.288, generator_loss=27.35, generator_mel_loss=16.93, generator_kl_loss=1.296, generator_dur_loss=1.735, generator_adv_loss=1.938, generator_feat_match_loss=5.451, over 58.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.347, discriminator_fake_loss=1.327, generator_loss=29.39, generator_mel_loss=17.66, generator_kl_loss=1.442, generator_dur_loss=1.748, generator_adv_loss=2.068, generator_feat_match_loss=6.476, over 2502.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:52:07,601 INFO [train.py:527] (1/6) Epoch 968, batch 92, global_batch_idx: 120000, batch size: 59, loss[discriminator_loss=2.645, discriminator_real_loss=1.346, discriminator_fake_loss=1.3, generator_loss=29.78, generator_mel_loss=17.99, generator_kl_loss=1.552, generator_dur_loss=1.72, generator_adv_loss=2.005, generator_feat_match_loss=6.516, over 59.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.346, discriminator_fake_loss=1.318, generator_loss=29.25, generator_mel_loss=17.65, generator_kl_loss=1.435, generator_dur_loss=1.752, generator_adv_loss=2.039, generator_feat_match_loss=6.37, over 5538.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:52:07,603 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 03:52:16,506 INFO [train.py:591] (1/6) Epoch 968, validation: discriminator_loss=2.664, discriminator_real_loss=1.324, discriminator_fake_loss=1.34, generator_loss=28.55, generator_mel_loss=18.05, generator_kl_loss=1.228, generator_dur_loss=1.812, generator_adv_loss=1.908, generator_feat_match_loss=5.552, over 100.00 samples.
+2024-03-16 03:52:16,507 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 03:53:43,673 INFO [train.py:919] (1/6) Start epoch 969
+2024-03-16 03:54:57,279 INFO [train.py:527] (1/6) Epoch 969, batch 18, global_batch_idx: 120050, batch size: 59, loss[discriminator_loss=2.639, discriminator_real_loss=1.295, discriminator_fake_loss=1.344, generator_loss=29.17, generator_mel_loss=18.04, generator_kl_loss=1.467, generator_dur_loss=1.677, generator_adv_loss=1.968, generator_feat_match_loss=6.016, over 59.00 samples.], tot_loss[discriminator_loss=2.689, discriminator_real_loss=1.355, discriminator_fake_loss=1.334, generator_loss=29.16, generator_mel_loss=17.77, generator_kl_loss=1.443, generator_dur_loss=1.728, generator_adv_loss=2.012, generator_feat_match_loss=6.203, over 1120.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:57:17,440 INFO [train.py:527] (1/6) Epoch 969, batch 68, global_batch_idx: 120100, batch size: 31, loss[discriminator_loss=2.71, discriminator_real_loss=1.411, discriminator_fake_loss=1.299, generator_loss=28.45, generator_mel_loss=18.05, generator_kl_loss=1.53, generator_dur_loss=1.629, generator_adv_loss=1.905, generator_feat_match_loss=5.331, over 31.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.358, discriminator_fake_loss=1.32, generator_loss=29.19, generator_mel_loss=17.7, generator_kl_loss=1.451, generator_dur_loss=1.726, generator_adv_loss=2.018, generator_feat_match_loss=6.296, over 3838.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:59:34,218 INFO [train.py:527] (1/6) Epoch 969, batch 118, global_batch_idx: 120150, batch size: 56, loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=28.73, generator_mel_loss=17.86, generator_kl_loss=1.443, generator_dur_loss=1.764, generator_adv_loss=2.004, generator_feat_match_loss=5.662, over 56.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.22, generator_mel_loss=17.71, generator_kl_loss=1.455, generator_dur_loss=1.731, generator_adv_loss=2.019, generator_feat_match_loss=6.306, over 6706.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 03:59:49,991 INFO [train.py:919] (1/6) Start epoch 970
+2024-03-16 04:02:16,056 INFO [train.py:527] (1/6) Epoch 970, batch 44, global_batch_idx: 120200, batch size: 50, loss[discriminator_loss=2.701, discriminator_real_loss=1.395, discriminator_fake_loss=1.306, generator_loss=28.42, generator_mel_loss=17.63, generator_kl_loss=1.424, generator_dur_loss=1.678, generator_adv_loss=2.053, generator_feat_match_loss=5.635, over 50.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.351, discriminator_fake_loss=1.323, generator_loss=29.29, generator_mel_loss=17.74, generator_kl_loss=1.415, generator_dur_loss=1.751, generator_adv_loss=2.018, generator_feat_match_loss=6.358, over 2642.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:02:16,057 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 04:02:23,798 INFO [train.py:591] (1/6) Epoch 970, validation: discriminator_loss=2.721, discriminator_real_loss=1.439, discriminator_fake_loss=1.282, generator_loss=27.91, generator_mel_loss=17.65, generator_kl_loss=1.307, generator_dur_loss=1.813, generator_adv_loss=1.951, generator_feat_match_loss=5.194, over 100.00 samples.
+2024-03-16 04:02:23,799 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 04:04:41,781 INFO [train.py:527] (1/6) Epoch 970, batch 94, global_batch_idx: 120250, batch size: 36, loss[discriminator_loss=2.656, discriminator_real_loss=1.202, discriminator_fake_loss=1.454, generator_loss=28.88, generator_mel_loss=17.59, generator_kl_loss=1.458, generator_dur_loss=1.686, generator_adv_loss=2.013, generator_feat_match_loss=6.133, over 36.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.352, discriminator_fake_loss=1.322, generator_loss=29.21, generator_mel_loss=17.7, generator_kl_loss=1.423, generator_dur_loss=1.744, generator_adv_loss=2.014, generator_feat_match_loss=6.323, over 5388.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:06:00,877 INFO [train.py:919] (1/6) Start epoch 971
+2024-03-16 04:07:16,757 INFO [train.py:527] (1/6) Epoch 971, batch 20, global_batch_idx: 120300, batch size: 55, loss[discriminator_loss=2.618, discriminator_real_loss=1.242, discriminator_fake_loss=1.376, generator_loss=30.3, generator_mel_loss=17.6, generator_kl_loss=1.487, generator_dur_loss=1.691, generator_adv_loss=2.126, generator_feat_match_loss=7.398, over 55.00 samples.], tot_loss[discriminator_loss=2.642, discriminator_real_loss=1.338, discriminator_fake_loss=1.304, generator_loss=29.14, generator_mel_loss=17.57, generator_kl_loss=1.434, generator_dur_loss=1.747, generator_adv_loss=2.048, generator_feat_match_loss=6.339, over 1237.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:09:34,915 INFO [train.py:527] (1/6) Epoch 971, batch 70, global_batch_idx: 120350, batch size: 52, loss[discriminator_loss=2.637, discriminator_real_loss=1.334, discriminator_fake_loss=1.303, generator_loss=28.97, generator_mel_loss=17.55, generator_kl_loss=1.588, generator_dur_loss=1.728, generator_adv_loss=2.018, generator_feat_match_loss=6.084, over 52.00 samples.], tot_loss[discriminator_loss=2.66, discriminator_real_loss=1.347, discriminator_fake_loss=1.313, generator_loss=29.13, generator_mel_loss=17.63, generator_kl_loss=1.451, generator_dur_loss=1.746, generator_adv_loss=2.022, generator_feat_match_loss=6.283, over 4136.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:11:53,959 INFO [train.py:527] (1/6) Epoch 971, batch 120, global_batch_idx: 120400, batch size: 25, loss[discriminator_loss=2.648, discriminator_real_loss=1.37, discriminator_fake_loss=1.278, generator_loss=28.76, generator_mel_loss=17.36, generator_kl_loss=1.647, generator_dur_loss=1.619, generator_adv_loss=2.039, generator_feat_match_loss=6.102, over 25.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.347, discriminator_fake_loss=1.314, generator_loss=29.17, generator_mel_loss=17.64, generator_kl_loss=1.452, generator_dur_loss=1.745, generator_adv_loss=2.018, generator_feat_match_loss=6.31, over 7024.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:11:53,960 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 04:12:02,758 INFO [train.py:591] (1/6) Epoch 971, validation: discriminator_loss=2.709, discriminator_real_loss=1.367, discriminator_fake_loss=1.342, generator_loss=27.71, generator_mel_loss=17.7, generator_kl_loss=1.189, generator_dur_loss=1.816, generator_adv_loss=1.942, generator_feat_match_loss=5.053, over 100.00 samples.
+2024-03-16 04:12:02,759 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 04:12:12,018 INFO [train.py:919] (1/6) Start epoch 972
+2024-03-16 04:14:42,842 INFO [train.py:527] (1/6) Epoch 972, batch 46, global_batch_idx: 120450, batch size: 68, loss[discriminator_loss=2.585, discriminator_real_loss=1.275, discriminator_fake_loss=1.31, generator_loss=29.76, generator_mel_loss=17.79, generator_kl_loss=1.469, generator_dur_loss=1.808, generator_adv_loss=1.982, generator_feat_match_loss=6.712, over 68.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.35, discriminator_fake_loss=1.323, generator_loss=29.29, generator_mel_loss=17.71, generator_kl_loss=1.443, generator_dur_loss=1.728, generator_adv_loss=2.003, generator_feat_match_loss=6.397, over 2701.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:17:02,579 INFO [train.py:527] (1/6) Epoch 972, batch 96, global_batch_idx: 120500, batch size: 64, loss[discriminator_loss=2.731, discriminator_real_loss=1.477, discriminator_fake_loss=1.254, generator_loss=28.31, generator_mel_loss=17.53, generator_kl_loss=1.403, generator_dur_loss=1.797, generator_adv_loss=1.937, generator_feat_match_loss=5.637, over 64.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.351, discriminator_fake_loss=1.318, generator_loss=29.17, generator_mel_loss=17.67, generator_kl_loss=1.451, generator_dur_loss=1.733, generator_adv_loss=2.009, generator_feat_match_loss=6.315, over 5481.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:18:18,577 INFO [train.py:919] (1/6) Start epoch 973
+2024-03-16 04:19:42,390 INFO [train.py:527] (1/6) Epoch 973, batch 22, global_batch_idx: 120550, batch size: 45, loss[discriminator_loss=2.703, discriminator_real_loss=1.304, discriminator_fake_loss=1.399, generator_loss=28.3, generator_mel_loss=17.16, generator_kl_loss=1.522, generator_dur_loss=1.694, generator_adv_loss=2.138, generator_feat_match_loss=5.784, over 45.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=29.17, generator_mel_loss=17.73, generator_kl_loss=1.442, generator_dur_loss=1.735, generator_adv_loss=2.009, generator_feat_match_loss=6.254, over 1310.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:22:01,930 INFO [train.py:527] (1/6) Epoch 973, batch 72, global_batch_idx: 120600, batch size: 58, loss[discriminator_loss=2.608, discriminator_real_loss=1.391, discriminator_fake_loss=1.218, generator_loss=28.92, generator_mel_loss=17.28, generator_kl_loss=1.413, generator_dur_loss=1.707, generator_adv_loss=2.025, generator_feat_match_loss=6.489, over 58.00 samples.], tot_loss[discriminator_loss=2.663, discriminator_real_loss=1.348, discriminator_fake_loss=1.314, generator_loss=29.37, generator_mel_loss=17.71, generator_kl_loss=1.429, generator_dur_loss=1.733, generator_adv_loss=2.024, generator_feat_match_loss=6.471, over 4108.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:22:01,931 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 04:22:10,204 INFO [train.py:591] (1/6) Epoch 973, validation: discriminator_loss=2.714, discriminator_real_loss=1.352, discriminator_fake_loss=1.362, generator_loss=28.38, generator_mel_loss=18.37, generator_kl_loss=1.242, generator_dur_loss=1.801, generator_adv_loss=1.845, generator_feat_match_loss=5.118, over 100.00 samples.
+2024-03-16 04:22:10,205 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 04:24:28,782 INFO [train.py:527] (1/6) Epoch 973, batch 122, global_batch_idx: 120650, batch size: 58, loss[discriminator_loss=2.671, discriminator_real_loss=1.314, discriminator_fake_loss=1.357, generator_loss=29.37, generator_mel_loss=17.42, generator_kl_loss=1.541, generator_dur_loss=1.716, generator_adv_loss=2.004, generator_feat_match_loss=6.692, over 58.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.351, discriminator_fake_loss=1.315, generator_loss=29.33, generator_mel_loss=17.72, generator_kl_loss=1.444, generator_dur_loss=1.735, generator_adv_loss=2.023, generator_feat_match_loss=6.406, over 6870.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:24:35,058 INFO [train.py:919] (1/6) Start epoch 974
+2024-03-16 04:27:14,393 INFO [train.py:527] (1/6) Epoch 974, batch 48, global_batch_idx: 120700, batch size: 64, loss[discriminator_loss=2.575, discriminator_real_loss=1.322, discriminator_fake_loss=1.253, generator_loss=30.47, generator_mel_loss=17.61, generator_kl_loss=1.381, generator_dur_loss=1.698, generator_adv_loss=2.167, generator_feat_match_loss=7.621, over 64.00 samples.], tot_loss[discriminator_loss=2.657, discriminator_real_loss=1.343, discriminator_fake_loss=1.314, generator_loss=29.41, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.743, generator_adv_loss=2.043, generator_feat_match_loss=6.451, over 2946.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:29:32,822 INFO [train.py:527] (1/6) Epoch 974, batch 98, global_batch_idx: 120750, batch size: 62, loss[discriminator_loss=2.629, discriminator_real_loss=1.306, discriminator_fake_loss=1.323, generator_loss=30.06, generator_mel_loss=17.6, generator_kl_loss=1.458, generator_dur_loss=1.73, generator_adv_loss=2.078, generator_feat_match_loss=7.19, over 62.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.349, discriminator_fake_loss=1.318, generator_loss=29.29, generator_mel_loss=17.73, generator_kl_loss=1.445, generator_dur_loss=1.745, generator_adv_loss=2.035, generator_feat_match_loss=6.337, over 5953.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:30:42,069 INFO [train.py:919] (1/6) Start epoch 975
+2024-03-16 04:32:14,150 INFO [train.py:527] (1/6) Epoch 975, batch 24, global_batch_idx: 120800, batch size: 47, loss[discriminator_loss=2.678, discriminator_real_loss=1.372, discriminator_fake_loss=1.307, generator_loss=28.29, generator_mel_loss=17.61, generator_kl_loss=1.448, generator_dur_loss=1.688, generator_adv_loss=2.082, generator_feat_match_loss=5.46, over 47.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.354, discriminator_fake_loss=1.307, generator_loss=29.56, generator_mel_loss=17.89, generator_kl_loss=1.45, generator_dur_loss=1.718, generator_adv_loss=2.026, generator_feat_match_loss=6.472, over 1268.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:32:14,152 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 04:32:22,021 INFO [train.py:591] (1/6) Epoch 975, validation: discriminator_loss=2.764, discriminator_real_loss=1.476, discriminator_fake_loss=1.287, generator_loss=28.07, generator_mel_loss=18.2, generator_kl_loss=1.347, generator_dur_loss=1.808, generator_adv_loss=1.986, generator_feat_match_loss=4.725, over 100.00 samples.
+2024-03-16 04:32:22,022 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 04:34:43,081 INFO [train.py:527] (1/6) Epoch 975, batch 74, global_batch_idx: 120850, batch size: 64, loss[discriminator_loss=2.693, discriminator_real_loss=1.367, discriminator_fake_loss=1.326, generator_loss=28.97, generator_mel_loss=17.85, generator_kl_loss=1.388, generator_dur_loss=1.752, generator_adv_loss=2.013, generator_feat_match_loss=5.968, over 64.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.352, discriminator_fake_loss=1.316, generator_loss=29.22, generator_mel_loss=17.72, generator_kl_loss=1.436, generator_dur_loss=1.735, generator_adv_loss=2.011, generator_feat_match_loss=6.314, over 4238.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:36:58,821 INFO [train.py:919] (1/6) Start epoch 976
+2024-03-16 04:37:24,081 INFO [train.py:527] (1/6) Epoch 976, batch 0, global_batch_idx: 120900, batch size: 36, loss[discriminator_loss=2.665, discriminator_real_loss=1.326, discriminator_fake_loss=1.339, generator_loss=28.5, generator_mel_loss=17.34, generator_kl_loss=1.451, generator_dur_loss=1.689, generator_adv_loss=2.109, generator_feat_match_loss=5.909, over 36.00 samples.], tot_loss[discriminator_loss=2.665, discriminator_real_loss=1.326, discriminator_fake_loss=1.339, generator_loss=28.5, generator_mel_loss=17.34, generator_kl_loss=1.451, generator_dur_loss=1.689, generator_adv_loss=2.109, generator_feat_match_loss=5.909, over 36.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:39:43,136 INFO [train.py:527] (1/6) Epoch 976, batch 50, global_batch_idx: 120950, batch size: 83, loss[discriminator_loss=2.63, discriminator_real_loss=1.394, discriminator_fake_loss=1.236, generator_loss=28.98, generator_mel_loss=17.73, generator_kl_loss=1.384, generator_dur_loss=1.843, generator_adv_loss=1.955, generator_feat_match_loss=6.064, over 83.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.341, discriminator_fake_loss=1.32, generator_loss=29.38, generator_mel_loss=17.77, generator_kl_loss=1.424, generator_dur_loss=1.734, generator_adv_loss=2.035, generator_feat_match_loss=6.418, over 2786.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:42:03,691 INFO [train.py:527] (1/6) Epoch 976, batch 100, global_batch_idx: 121000, batch size: 61, loss[discriminator_loss=2.668, discriminator_real_loss=1.343, discriminator_fake_loss=1.325, generator_loss=28.76, generator_mel_loss=17.65, generator_kl_loss=1.364, generator_dur_loss=1.778, generator_adv_loss=1.899, generator_feat_match_loss=6.069, over 61.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.343, discriminator_fake_loss=1.321, generator_loss=29.3, generator_mel_loss=17.73, generator_kl_loss=1.443, generator_dur_loss=1.728, generator_adv_loss=2.026, generator_feat_match_loss=6.37, over 5513.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:42:03,693 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 04:42:12,325 INFO [train.py:591] (1/6) Epoch 976, validation: discriminator_loss=2.716, discriminator_real_loss=1.315, discriminator_fake_loss=1.401, generator_loss=28.04, generator_mel_loss=18.46, generator_kl_loss=1.27, generator_dur_loss=1.816, generator_adv_loss=1.744, generator_feat_match_loss=4.742, over 100.00 samples.
+2024-03-16 04:42:12,327 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 04:43:17,454 INFO [train.py:919] (1/6) Start epoch 977
+2024-03-16 04:44:52,759 INFO [train.py:527] (1/6) Epoch 977, batch 26, global_batch_idx: 121050, batch size: 48, loss[discriminator_loss=2.646, discriminator_real_loss=1.41, discriminator_fake_loss=1.236, generator_loss=29.47, generator_mel_loss=17.62, generator_kl_loss=1.856, generator_dur_loss=1.659, generator_adv_loss=2.148, generator_feat_match_loss=6.188, over 48.00 samples.], tot_loss[discriminator_loss=2.662, discriminator_real_loss=1.349, discriminator_fake_loss=1.313, generator_loss=29.54, generator_mel_loss=17.7, generator_kl_loss=1.654, generator_dur_loss=1.732, generator_adv_loss=2.035, generator_feat_match_loss=6.41, over 1524.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:47:15,539 INFO [train.py:527] (1/6) Epoch 977, batch 76, global_batch_idx: 121100, batch size: 61, loss[discriminator_loss=2.673, discriminator_real_loss=1.318, discriminator_fake_loss=1.355, generator_loss=29.63, generator_mel_loss=17.4, generator_kl_loss=1.469, generator_dur_loss=1.752, generator_adv_loss=2.123, generator_feat_match_loss=6.879, over 61.00 samples.], tot_loss[discriminator_loss=2.666, discriminator_real_loss=1.354, discriminator_fake_loss=1.313, generator_loss=29.49, generator_mel_loss=17.75, generator_kl_loss=1.548, generator_dur_loss=1.733, generator_adv_loss=2.035, generator_feat_match_loss=6.423, over 4272.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:49:23,697 INFO [train.py:919] (1/6) Start epoch 978
+2024-03-16 04:49:52,873 INFO [train.py:527] (1/6) Epoch 978, batch 2, global_batch_idx: 121150, batch size: 61, loss[discriminator_loss=2.69, discriminator_real_loss=1.407, discriminator_fake_loss=1.283, generator_loss=29.52, generator_mel_loss=18.07, generator_kl_loss=1.493, generator_dur_loss=1.762, generator_adv_loss=1.932, generator_feat_match_loss=6.256, over 61.00 samples.], tot_loss[discriminator_loss=2.658, discriminator_real_loss=1.341, discriminator_fake_loss=1.317, generator_loss=29.91, generator_mel_loss=18.2, generator_kl_loss=1.51, generator_dur_loss=1.768, generator_adv_loss=2.053, generator_feat_match_loss=6.377, over 180.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:52:09,681 INFO [train.py:527] (1/6) Epoch 978, batch 52, global_batch_idx: 121200, batch size: 74, loss[discriminator_loss=2.622, discriminator_real_loss=1.326, discriminator_fake_loss=1.296, generator_loss=28.14, generator_mel_loss=17.36, generator_kl_loss=1.341, generator_dur_loss=1.809, generator_adv_loss=2.045, generator_feat_match_loss=5.583, over 74.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.357, discriminator_fake_loss=1.325, generator_loss=29.27, generator_mel_loss=17.8, generator_kl_loss=1.459, generator_dur_loss=1.736, generator_adv_loss=2.022, generator_feat_match_loss=6.251, over 2932.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:52:09,683 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 04:52:17,760 INFO [train.py:591] (1/6) Epoch 978, validation: discriminator_loss=2.684, discriminator_real_loss=1.319, discriminator_fake_loss=1.365, generator_loss=27.33, generator_mel_loss=17.94, generator_kl_loss=1.208, generator_dur_loss=1.801, generator_adv_loss=1.836, generator_feat_match_loss=4.543, over 100.00 samples.
+2024-03-16 04:52:17,761 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 04:54:33,269 INFO [train.py:527] (1/6) Epoch 978, batch 102, global_batch_idx: 121250, batch size: 42, loss[discriminator_loss=2.705, discriminator_real_loss=1.383, discriminator_fake_loss=1.322, generator_loss=28.68, generator_mel_loss=17.66, generator_kl_loss=1.452, generator_dur_loss=1.657, generator_adv_loss=1.923, generator_feat_match_loss=5.99, over 42.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.356, discriminator_fake_loss=1.32, generator_loss=29.2, generator_mel_loss=17.73, generator_kl_loss=1.46, generator_dur_loss=1.732, generator_adv_loss=2.017, generator_feat_match_loss=6.26, over 5652.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:55:35,442 INFO [train.py:919] (1/6) Start epoch 979
+2024-03-16 04:57:14,738 INFO [train.py:527] (1/6) Epoch 979, batch 28, global_batch_idx: 121300, batch size: 25, loss[discriminator_loss=2.69, discriminator_real_loss=1.352, discriminator_fake_loss=1.338, generator_loss=29.89, generator_mel_loss=17.81, generator_kl_loss=1.728, generator_dur_loss=1.569, generator_adv_loss=1.967, generator_feat_match_loss=6.821, over 25.00 samples.], tot_loss[discriminator_loss=2.683, discriminator_real_loss=1.353, discriminator_fake_loss=1.331, generator_loss=29.35, generator_mel_loss=17.68, generator_kl_loss=1.4, generator_dur_loss=1.75, generator_adv_loss=2.022, generator_feat_match_loss=6.494, over 1684.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 04:59:31,981 INFO [train.py:527] (1/6) Epoch 979, batch 78, global_batch_idx: 121350, batch size: 31, loss[discriminator_loss=2.656, discriminator_real_loss=1.404, discriminator_fake_loss=1.252, generator_loss=29.42, generator_mel_loss=17.68, generator_kl_loss=1.514, generator_dur_loss=1.628, generator_adv_loss=1.971, generator_feat_match_loss=6.619, over 31.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.35, discriminator_fake_loss=1.323, generator_loss=29.39, generator_mel_loss=17.75, generator_kl_loss=1.447, generator_dur_loss=1.739, generator_adv_loss=2.023, generator_feat_match_loss=6.43, over 4297.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:01:39,416 INFO [train.py:919] (1/6) Start epoch 980
+2024-03-16 05:02:15,966 INFO [train.py:527] (1/6) Epoch 980, batch 4, global_batch_idx: 121400, batch size: 45, loss[discriminator_loss=2.786, discriminator_real_loss=1.531, discriminator_fake_loss=1.255, generator_loss=27.22, generator_mel_loss=17.45, generator_kl_loss=1.583, generator_dur_loss=1.678, generator_adv_loss=1.902, generator_feat_match_loss=4.611, over 45.00 samples.], tot_loss[discriminator_loss=2.698, discriminator_real_loss=1.391, discriminator_fake_loss=1.307, generator_loss=28.62, generator_mel_loss=17.65, generator_kl_loss=1.422, generator_dur_loss=1.764, generator_adv_loss=2.006, generator_feat_match_loss=5.778, over 320.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:02:15,972 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 05:02:23,771 INFO [train.py:591] (1/6) Epoch 980, validation: discriminator_loss=2.722, discriminator_real_loss=1.367, discriminator_fake_loss=1.355, generator_loss=27.75, generator_mel_loss=18.1, generator_kl_loss=1.305, generator_dur_loss=1.816, generator_adv_loss=1.841, generator_feat_match_loss=4.69, over 100.00 samples.
+2024-03-16 05:02:23,773 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 05:04:44,156 INFO [train.py:527] (1/6) Epoch 980, batch 54, global_batch_idx: 121450, batch size: 62, loss[discriminator_loss=2.647, discriminator_real_loss=1.36, discriminator_fake_loss=1.286, generator_loss=29.48, generator_mel_loss=17.63, generator_kl_loss=1.45, generator_dur_loss=1.762, generator_adv_loss=2.133, generator_feat_match_loss=6.51, over 62.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.09, generator_mel_loss=17.72, generator_kl_loss=1.435, generator_dur_loss=1.752, generator_adv_loss=2.002, generator_feat_match_loss=6.186, over 3261.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:07:02,441 INFO [train.py:527] (1/6) Epoch 980, batch 104, global_batch_idx: 121500, batch size: 62, loss[discriminator_loss=2.677, discriminator_real_loss=1.28, discriminator_fake_loss=1.397, generator_loss=30.54, generator_mel_loss=18.29, generator_kl_loss=1.438, generator_dur_loss=1.718, generator_adv_loss=2.097, generator_feat_match_loss=6.991, over 62.00 samples.], tot_loss[discriminator_loss=2.675, discriminator_real_loss=1.356, discriminator_fake_loss=1.319, generator_loss=29.06, generator_mel_loss=17.67, generator_kl_loss=1.437, generator_dur_loss=1.743, generator_adv_loss=2.006, generator_feat_match_loss=6.204, over 6168.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:07:54,956 INFO [train.py:919] (1/6) Start epoch 981
+2024-03-16 05:09:42,431 INFO [train.py:527] (1/6) Epoch 981, batch 30, global_batch_idx: 121550, batch size: 68, loss[discriminator_loss=2.655, discriminator_real_loss=1.328, discriminator_fake_loss=1.328, generator_loss=29.33, generator_mel_loss=17.6, generator_kl_loss=1.361, generator_dur_loss=1.743, generator_adv_loss=2.119, generator_feat_match_loss=6.504, over 68.00 samples.], tot_loss[discriminator_loss=2.65, discriminator_real_loss=1.334, discriminator_fake_loss=1.316, generator_loss=29.28, generator_mel_loss=17.68, generator_kl_loss=1.462, generator_dur_loss=1.717, generator_adv_loss=2.02, generator_feat_match_loss=6.394, over 1657.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:12:00,815 INFO [train.py:527] (1/6) Epoch 981, batch 80, global_batch_idx: 121600, batch size: 74, loss[discriminator_loss=2.721, discriminator_real_loss=1.268, discriminator_fake_loss=1.453, generator_loss=29.74, generator_mel_loss=17.7, generator_kl_loss=1.387, generator_dur_loss=1.798, generator_adv_loss=2.126, generator_feat_match_loss=6.727, over 74.00 samples.], tot_loss[discriminator_loss=2.661, discriminator_real_loss=1.342, discriminator_fake_loss=1.319, generator_loss=29.21, generator_mel_loss=17.66, generator_kl_loss=1.457, generator_dur_loss=1.728, generator_adv_loss=2.016, generator_feat_match_loss=6.352, over 4674.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:12:00,816 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 05:12:09,456 INFO [train.py:591] (1/6) Epoch 981, validation: discriminator_loss=2.757, discriminator_real_loss=1.515, discriminator_fake_loss=1.243, generator_loss=29.08, generator_mel_loss=18.26, generator_kl_loss=1.352, generator_dur_loss=1.792, generator_adv_loss=2.119, generator_feat_match_loss=5.557, over 100.00 samples.
+2024-03-16 05:12:09,457 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 05:14:13,162 INFO [train.py:919] (1/6) Start epoch 982
+2024-03-16 05:14:52,276 INFO [train.py:527] (1/6) Epoch 982, batch 6, global_batch_idx: 121650, batch size: 39, loss[discriminator_loss=2.676, discriminator_real_loss=1.333, discriminator_fake_loss=1.343, generator_loss=28.74, generator_mel_loss=17.83, generator_kl_loss=1.475, generator_dur_loss=1.633, generator_adv_loss=2.042, generator_feat_match_loss=5.762, over 39.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.36, discriminator_fake_loss=1.316, generator_loss=29.61, generator_mel_loss=17.77, generator_kl_loss=1.508, generator_dur_loss=1.693, generator_adv_loss=2.027, generator_feat_match_loss=6.621, over 305.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:17:09,847 INFO [train.py:527] (1/6) Epoch 982, batch 56, global_batch_idx: 121700, batch size: 25, loss[discriminator_loss=2.673, discriminator_real_loss=1.228, discriminator_fake_loss=1.444, generator_loss=29.42, generator_mel_loss=17.66, generator_kl_loss=1.914, generator_dur_loss=1.524, generator_adv_loss=2.005, generator_feat_match_loss=6.316, over 25.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.36, discriminator_fake_loss=1.316, generator_loss=29.23, generator_mel_loss=17.74, generator_kl_loss=1.465, generator_dur_loss=1.722, generator_adv_loss=2.023, generator_feat_match_loss=6.277, over 2997.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:19:30,272 INFO [train.py:527] (1/6) Epoch 982, batch 106, global_batch_idx: 121750, batch size: 70, loss[discriminator_loss=2.704, discriminator_real_loss=1.351, discriminator_fake_loss=1.353, generator_loss=29.9, generator_mel_loss=17.48, generator_kl_loss=1.414, generator_dur_loss=1.743, generator_adv_loss=2.162, generator_feat_match_loss=7.1, over 70.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.361, discriminator_fake_loss=1.316, generator_loss=29.24, generator_mel_loss=17.74, generator_kl_loss=1.465, generator_dur_loss=1.715, generator_adv_loss=2.021, generator_feat_match_loss=6.296, over 5676.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:20:20,750 INFO [train.py:919] (1/6) Start epoch 983
+2024-03-16 05:22:11,838 INFO [train.py:527] (1/6) Epoch 983, batch 32, global_batch_idx: 121800, batch size: 56, loss[discriminator_loss=2.676, discriminator_real_loss=1.335, discriminator_fake_loss=1.342, generator_loss=29.68, generator_mel_loss=17.69, generator_kl_loss=1.39, generator_dur_loss=1.727, generator_adv_loss=2.162, generator_feat_match_loss=6.706, over 56.00 samples.], tot_loss[discriminator_loss=2.667, discriminator_real_loss=1.341, discriminator_fake_loss=1.326, generator_loss=29.12, generator_mel_loss=17.66, generator_kl_loss=1.402, generator_dur_loss=1.767, generator_adv_loss=2.014, generator_feat_match_loss=6.274, over 2167.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:22:11,840 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 05:22:19,997 INFO [train.py:591] (1/6) Epoch 983, validation: discriminator_loss=2.797, discriminator_real_loss=1.532, discriminator_fake_loss=1.265, generator_loss=27.99, generator_mel_loss=17.87, generator_kl_loss=1.185, generator_dur_loss=1.803, generator_adv_loss=2.017, generator_feat_match_loss=5.114, over 100.00 samples.
+2024-03-16 05:22:19,998 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 05:24:35,130 INFO [train.py:527] (1/6) Epoch 983, batch 82, global_batch_idx: 121850, batch size: 36, loss[discriminator_loss=2.62, discriminator_real_loss=1.265, discriminator_fake_loss=1.355, generator_loss=31.47, generator_mel_loss=18.58, generator_kl_loss=1.557, generator_dur_loss=1.676, generator_adv_loss=2.159, generator_feat_match_loss=7.498, over 36.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.347, discriminator_fake_loss=1.321, generator_loss=29.24, generator_mel_loss=17.7, generator_kl_loss=1.417, generator_dur_loss=1.748, generator_adv_loss=2.02, generator_feat_match_loss=6.351, over 4976.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:26:30,358 INFO [train.py:919] (1/6) Start epoch 984
+2024-03-16 05:27:17,809 INFO [train.py:527] (1/6) Epoch 984, batch 8, global_batch_idx: 121900, batch size: 45, loss[discriminator_loss=2.694, discriminator_real_loss=1.367, discriminator_fake_loss=1.327, generator_loss=28.65, generator_mel_loss=17.38, generator_kl_loss=1.6, generator_dur_loss=1.668, generator_adv_loss=1.945, generator_feat_match_loss=6.055, over 45.00 samples.], tot_loss[discriminator_loss=2.659, discriminator_real_loss=1.338, discriminator_fake_loss=1.321, generator_loss=29.89, generator_mel_loss=17.84, generator_kl_loss=1.565, generator_dur_loss=1.693, generator_adv_loss=2.027, generator_feat_match_loss=6.76, over 391.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:29:35,439 INFO [train.py:527] (1/6) Epoch 984, batch 58, global_batch_idx: 121950, batch size: 39, loss[discriminator_loss=2.672, discriminator_real_loss=1.303, discriminator_fake_loss=1.369, generator_loss=28.7, generator_mel_loss=17.34, generator_kl_loss=1.492, generator_dur_loss=1.686, generator_adv_loss=2.093, generator_feat_match_loss=6.095, over 39.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.347, discriminator_fake_loss=1.317, generator_loss=29.26, generator_mel_loss=17.7, generator_kl_loss=1.475, generator_dur_loss=1.726, generator_adv_loss=2.019, generator_feat_match_loss=6.335, over 3136.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:31:53,517 INFO [train.py:527] (1/6) Epoch 984, batch 108, global_batch_idx: 122000, batch size: 56, loss[discriminator_loss=2.656, discriminator_real_loss=1.358, discriminator_fake_loss=1.298, generator_loss=29.57, generator_mel_loss=17.94, generator_kl_loss=1.484, generator_dur_loss=1.705, generator_adv_loss=2.031, generator_feat_match_loss=6.411, over 56.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.35, discriminator_fake_loss=1.321, generator_loss=29.23, generator_mel_loss=17.71, generator_kl_loss=1.468, generator_dur_loss=1.733, generator_adv_loss=2.015, generator_feat_match_loss=6.298, over 6045.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:31:53,518 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 05:32:02,175 INFO [train.py:591] (1/6) Epoch 984, validation: discriminator_loss=2.731, discriminator_real_loss=1.446, discriminator_fake_loss=1.286, generator_loss=27.98, generator_mel_loss=18.09, generator_kl_loss=1.356, generator_dur_loss=1.799, generator_adv_loss=1.933, generator_feat_match_loss=4.807, over 100.00 samples.
+2024-03-16 05:32:02,176 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 05:32:44,803 INFO [train.py:919] (1/6) Start epoch 985
+2024-03-16 05:34:45,304 INFO [train.py:527] (1/6) Epoch 985, batch 34, global_batch_idx: 122050, batch size: 53, loss[discriminator_loss=2.704, discriminator_real_loss=1.447, discriminator_fake_loss=1.258, generator_loss=29.65, generator_mel_loss=17.41, generator_kl_loss=1.54, generator_dur_loss=1.688, generator_adv_loss=1.994, generator_feat_match_loss=7.008, over 53.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.349, discriminator_fake_loss=1.32, generator_loss=29.43, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.737, generator_adv_loss=2.015, generator_feat_match_loss=6.516, over 1988.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:37:03,567 INFO [train.py:527] (1/6) Epoch 985, batch 84, global_batch_idx: 122100, batch size: 80, loss[discriminator_loss=2.661, discriminator_real_loss=1.376, discriminator_fake_loss=1.285, generator_loss=30.55, generator_mel_loss=17.93, generator_kl_loss=1.469, generator_dur_loss=1.803, generator_adv_loss=2.079, generator_feat_match_loss=7.269, over 80.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.355, discriminator_fake_loss=1.319, generator_loss=29.29, generator_mel_loss=17.71, generator_kl_loss=1.447, generator_dur_loss=1.739, generator_adv_loss=2.02, generator_feat_match_loss=6.372, over 4856.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:38:51,279 INFO [train.py:919] (1/6) Start epoch 986
+2024-03-16 05:39:43,710 INFO [train.py:527] (1/6) Epoch 986, batch 10, global_batch_idx: 122150, batch size: 80, loss[discriminator_loss=2.695, discriminator_real_loss=1.347, discriminator_fake_loss=1.348, generator_loss=29.42, generator_mel_loss=18.03, generator_kl_loss=1.387, generator_dur_loss=1.834, generator_adv_loss=2.022, generator_feat_match_loss=6.145, over 80.00 samples.], tot_loss[discriminator_loss=2.691, discriminator_real_loss=1.345, discriminator_fake_loss=1.345, generator_loss=29.34, generator_mel_loss=17.83, generator_kl_loss=1.454, generator_dur_loss=1.757, generator_adv_loss=2.011, generator_feat_match_loss=6.287, over 648.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:42:01,278 INFO [train.py:527] (1/6) Epoch 986, batch 60, global_batch_idx: 122200, batch size: 48, loss[discriminator_loss=2.683, discriminator_real_loss=1.343, discriminator_fake_loss=1.34, generator_loss=30.87, generator_mel_loss=17.91, generator_kl_loss=1.778, generator_dur_loss=1.621, generator_adv_loss=2.034, generator_feat_match_loss=7.521, over 48.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.351, discriminator_fake_loss=1.328, generator_loss=29.24, generator_mel_loss=17.78, generator_kl_loss=1.453, generator_dur_loss=1.737, generator_adv_loss=2.006, generator_feat_match_loss=6.265, over 3488.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:42:01,280 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 05:42:09,353 INFO [train.py:591] (1/6) Epoch 986, validation: discriminator_loss=2.721, discriminator_real_loss=1.44, discriminator_fake_loss=1.281, generator_loss=28.95, generator_mel_loss=18.18, generator_kl_loss=1.218, generator_dur_loss=1.808, generator_adv_loss=1.993, generator_feat_match_loss=5.746, over 100.00 samples.
+2024-03-16 05:42:09,354 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 05:44:30,429 INFO [train.py:527] (1/6) Epoch 986, batch 110, global_batch_idx: 122250, batch size: 31, loss[discriminator_loss=2.737, discriminator_real_loss=1.589, discriminator_fake_loss=1.148, generator_loss=27.77, generator_mel_loss=17.23, generator_kl_loss=1.592, generator_dur_loss=1.664, generator_adv_loss=1.914, generator_feat_match_loss=5.376, over 31.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.35, discriminator_fake_loss=1.32, generator_loss=29.21, generator_mel_loss=17.75, generator_kl_loss=1.449, generator_dur_loss=1.738, generator_adv_loss=2.013, generator_feat_match_loss=6.263, over 6555.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:45:07,522 INFO [train.py:919] (1/6) Start epoch 987
+2024-03-16 05:47:13,329 INFO [train.py:527] (1/6) Epoch 987, batch 36, global_batch_idx: 122300, batch size: 44, loss[discriminator_loss=2.708, discriminator_real_loss=1.443, discriminator_fake_loss=1.265, generator_loss=30.32, generator_mel_loss=18.05, generator_kl_loss=1.639, generator_dur_loss=1.672, generator_adv_loss=2.056, generator_feat_match_loss=6.893, over 44.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.347, discriminator_fake_loss=1.327, generator_loss=29.44, generator_mel_loss=17.72, generator_kl_loss=1.454, generator_dur_loss=1.719, generator_adv_loss=2.026, generator_feat_match_loss=6.523, over 2112.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:49:36,688 INFO [train.py:527] (1/6) Epoch 987, batch 86, global_batch_idx: 122350, batch size: 56, loss[discriminator_loss=2.657, discriminator_real_loss=1.274, discriminator_fake_loss=1.383, generator_loss=29.78, generator_mel_loss=17.78, generator_kl_loss=1.545, generator_dur_loss=1.689, generator_adv_loss=1.977, generator_feat_match_loss=6.794, over 56.00 samples.], tot_loss[discriminator_loss=2.676, discriminator_real_loss=1.353, discriminator_fake_loss=1.324, generator_loss=29.25, generator_mel_loss=17.68, generator_kl_loss=1.45, generator_dur_loss=1.727, generator_adv_loss=2.024, generator_feat_match_loss=6.375, over 5062.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:51:17,092 INFO [train.py:919] (1/6) Start epoch 988
+2024-03-16 05:52:15,208 INFO [train.py:527] (1/6) Epoch 988, batch 12, global_batch_idx: 122400, batch size: 62, loss[discriminator_loss=2.703, discriminator_real_loss=1.343, discriminator_fake_loss=1.36, generator_loss=29.66, generator_mel_loss=17.86, generator_kl_loss=1.496, generator_dur_loss=1.746, generator_adv_loss=2.025, generator_feat_match_loss=6.534, over 62.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.336, discriminator_fake_loss=1.337, generator_loss=29.31, generator_mel_loss=17.67, generator_kl_loss=1.466, generator_dur_loss=1.736, generator_adv_loss=2.016, generator_feat_match_loss=6.423, over 807.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:52:15,210 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 05:52:23,024 INFO [train.py:591] (1/6) Epoch 988, validation: discriminator_loss=2.709, discriminator_real_loss=1.433, discriminator_fake_loss=1.275, generator_loss=27.75, generator_mel_loss=17.91, generator_kl_loss=1.349, generator_dur_loss=1.8, generator_adv_loss=1.963, generator_feat_match_loss=4.722, over 100.00 samples.
+2024-03-16 05:52:23,025 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 05:54:41,326 INFO [train.py:527] (1/6) Epoch 988, batch 62, global_batch_idx: 122450, batch size: 47, loss[discriminator_loss=2.675, discriminator_real_loss=1.346, discriminator_fake_loss=1.329, generator_loss=29.8, generator_mel_loss=18.05, generator_kl_loss=1.465, generator_dur_loss=1.682, generator_adv_loss=2.024, generator_feat_match_loss=6.581, over 47.00 samples.], tot_loss[discriminator_loss=2.68, discriminator_real_loss=1.353, discriminator_fake_loss=1.327, generator_loss=29.25, generator_mel_loss=17.72, generator_kl_loss=1.449, generator_dur_loss=1.74, generator_adv_loss=2.009, generator_feat_match_loss=6.33, over 3686.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:57:00,743 INFO [train.py:527] (1/6) Epoch 988, batch 112, global_batch_idx: 122500, batch size: 48, loss[discriminator_loss=2.652, discriminator_real_loss=1.303, discriminator_fake_loss=1.349, generator_loss=29.47, generator_mel_loss=17.82, generator_kl_loss=1.529, generator_dur_loss=1.698, generator_adv_loss=2.17, generator_feat_match_loss=6.253, over 48.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.348, discriminator_fake_loss=1.324, generator_loss=29.23, generator_mel_loss=17.74, generator_kl_loss=1.448, generator_dur_loss=1.736, generator_adv_loss=2.016, generator_feat_match_loss=6.297, over 6511.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 05:57:31,184 INFO [train.py:919] (1/6) Start epoch 989
+2024-03-16 05:59:43,203 INFO [train.py:527] (1/6) Epoch 989, batch 38, global_batch_idx: 122550, batch size: 47, loss[discriminator_loss=2.672, discriminator_real_loss=1.361, discriminator_fake_loss=1.311, generator_loss=29.11, generator_mel_loss=17.11, generator_kl_loss=1.582, generator_dur_loss=1.713, generator_adv_loss=1.959, generator_feat_match_loss=6.747, over 47.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.354, discriminator_fake_loss=1.32, generator_loss=29.12, generator_mel_loss=17.61, generator_kl_loss=1.421, generator_dur_loss=1.739, generator_adv_loss=2.013, generator_feat_match_loss=6.332, over 2439.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:02:03,374 INFO [train.py:527] (1/6) Epoch 989, batch 88, global_batch_idx: 122600, batch size: 68, loss[discriminator_loss=2.685, discriminator_real_loss=1.271, discriminator_fake_loss=1.414, generator_loss=30.12, generator_mel_loss=17.93, generator_kl_loss=1.404, generator_dur_loss=1.741, generator_adv_loss=2.073, generator_feat_match_loss=6.981, over 68.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.35, discriminator_fake_loss=1.32, generator_loss=29.2, generator_mel_loss=17.69, generator_kl_loss=1.426, generator_dur_loss=1.731, generator_adv_loss=2.012, generator_feat_match_loss=6.346, over 5200.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:02:03,376 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 06:02:12,384 INFO [train.py:591] (1/6) Epoch 989, validation: discriminator_loss=2.731, discriminator_real_loss=1.433, discriminator_fake_loss=1.298, generator_loss=28.06, generator_mel_loss=17.94, generator_kl_loss=1.327, generator_dur_loss=1.798, generator_adv_loss=1.954, generator_feat_match_loss=5.038, over 100.00 samples.
+2024-03-16 06:02:12,384 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 06:03:48,834 INFO [train.py:919] (1/6) Start epoch 990
+2024-03-16 06:04:49,256 INFO [train.py:527] (1/6) Epoch 990, batch 14, global_batch_idx: 122650, batch size: 70, loss[discriminator_loss=2.609, discriminator_real_loss=1.231, discriminator_fake_loss=1.378, generator_loss=30.58, generator_mel_loss=17.84, generator_kl_loss=1.515, generator_dur_loss=1.77, generator_adv_loss=2.175, generator_feat_match_loss=7.285, over 70.00 samples.], tot_loss[discriminator_loss=2.664, discriminator_real_loss=1.343, discriminator_fake_loss=1.321, generator_loss=29.28, generator_mel_loss=17.75, generator_kl_loss=1.42, generator_dur_loss=1.743, generator_adv_loss=2.027, generator_feat_match_loss=6.332, over 908.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:07:08,593 INFO [train.py:527] (1/6) Epoch 990, batch 64, global_batch_idx: 122700, batch size: 52, loss[discriminator_loss=2.699, discriminator_real_loss=1.378, discriminator_fake_loss=1.32, generator_loss=29.99, generator_mel_loss=18.13, generator_kl_loss=1.487, generator_dur_loss=1.693, generator_adv_loss=2.03, generator_feat_match_loss=6.649, over 52.00 samples.], tot_loss[discriminator_loss=2.668, discriminator_real_loss=1.348, discriminator_fake_loss=1.32, generator_loss=29.37, generator_mel_loss=17.73, generator_kl_loss=1.437, generator_dur_loss=1.742, generator_adv_loss=2.013, generator_feat_match_loss=6.449, over 3797.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:09:27,420 INFO [train.py:527] (1/6) Epoch 990, batch 114, global_batch_idx: 122750, batch size: 50, loss[discriminator_loss=2.633, discriminator_real_loss=1.356, discriminator_fake_loss=1.278, generator_loss=29.12, generator_mel_loss=18, generator_kl_loss=1.438, generator_dur_loss=1.662, generator_adv_loss=1.976, generator_feat_match_loss=6.043, over 50.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.347, discriminator_fake_loss=1.323, generator_loss=29.3, generator_mel_loss=17.71, generator_kl_loss=1.445, generator_dur_loss=1.738, generator_adv_loss=2.012, generator_feat_match_loss=6.397, over 6566.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:09:52,435 INFO [train.py:919] (1/6) Start epoch 991
+2024-03-16 06:12:07,876 INFO [train.py:527] (1/6) Epoch 991, batch 40, global_batch_idx: 122800, batch size: 42, loss[discriminator_loss=2.641, discriminator_real_loss=1.417, discriminator_fake_loss=1.224, generator_loss=28.8, generator_mel_loss=17.63, generator_kl_loss=1.71, generator_dur_loss=1.633, generator_adv_loss=1.996, generator_feat_match_loss=5.838, over 42.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.352, discriminator_fake_loss=1.326, generator_loss=29.04, generator_mel_loss=17.63, generator_kl_loss=1.428, generator_dur_loss=1.745, generator_adv_loss=2.008, generator_feat_match_loss=6.227, over 2436.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:12:07,877 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 06:12:15,876 INFO [train.py:591] (1/6) Epoch 991, validation: discriminator_loss=2.734, discriminator_real_loss=1.385, discriminator_fake_loss=1.349, generator_loss=28.05, generator_mel_loss=17.76, generator_kl_loss=1.38, generator_dur_loss=1.803, generator_adv_loss=1.889, generator_feat_match_loss=5.217, over 100.00 samples.
+2024-03-16 06:12:15,877 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 06:14:34,146 INFO [train.py:527] (1/6) Epoch 991, batch 90, global_batch_idx: 122850, batch size: 96, loss[discriminator_loss=2.664, discriminator_real_loss=1.292, discriminator_fake_loss=1.372, generator_loss=29.11, generator_mel_loss=17.55, generator_kl_loss=1.401, generator_dur_loss=1.797, generator_adv_loss=2.077, generator_feat_match_loss=6.283, over 96.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.355, discriminator_fake_loss=1.325, generator_loss=29.22, generator_mel_loss=17.68, generator_kl_loss=1.461, generator_dur_loss=1.736, generator_adv_loss=2.012, generator_feat_match_loss=6.338, over 5205.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:16:06,054 INFO [train.py:919] (1/6) Start epoch 992
+2024-03-16 06:17:12,679 INFO [train.py:527] (1/6) Epoch 992, batch 16, global_batch_idx: 122900, batch size: 70, loss[discriminator_loss=2.731, discriminator_real_loss=1.379, discriminator_fake_loss=1.353, generator_loss=28.89, generator_mel_loss=17.74, generator_kl_loss=1.482, generator_dur_loss=1.794, generator_adv_loss=2.073, generator_feat_match_loss=5.8, over 70.00 samples.], tot_loss[discriminator_loss=2.69, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=29.27, generator_mel_loss=17.8, generator_kl_loss=1.472, generator_dur_loss=1.704, generator_adv_loss=2.014, generator_feat_match_loss=6.281, over 824.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:19:33,877 INFO [train.py:527] (1/6) Epoch 992, batch 66, global_batch_idx: 122950, batch size: 62, loss[discriminator_loss=2.717, discriminator_real_loss=1.389, discriminator_fake_loss=1.328, generator_loss=28.95, generator_mel_loss=17.67, generator_kl_loss=1.478, generator_dur_loss=1.717, generator_adv_loss=1.979, generator_feat_match_loss=6.101, over 62.00 samples.], tot_loss[discriminator_loss=2.692, discriminator_real_loss=1.363, discriminator_fake_loss=1.328, generator_loss=29.21, generator_mel_loss=17.73, generator_kl_loss=1.451, generator_dur_loss=1.727, generator_adv_loss=2.014, generator_feat_match_loss=6.285, over 3787.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:21:53,127 INFO [train.py:527] (1/6) Epoch 992, batch 116, global_batch_idx: 123000, batch size: 39, loss[discriminator_loss=2.653, discriminator_real_loss=1.279, discriminator_fake_loss=1.374, generator_loss=29.52, generator_mel_loss=18.18, generator_kl_loss=1.52, generator_dur_loss=1.666, generator_adv_loss=2.15, generator_feat_match_loss=5.999, over 39.00 samples.], tot_loss[discriminator_loss=2.685, discriminator_real_loss=1.359, discriminator_fake_loss=1.326, generator_loss=29.26, generator_mel_loss=17.72, generator_kl_loss=1.443, generator_dur_loss=1.732, generator_adv_loss=2.018, generator_feat_match_loss=6.343, over 6583.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:21:53,128 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 06:22:02,305 INFO [train.py:591] (1/6) Epoch 992, validation: discriminator_loss=2.774, discriminator_real_loss=1.453, discriminator_fake_loss=1.321, generator_loss=28.94, generator_mel_loss=18.67, generator_kl_loss=1.351, generator_dur_loss=1.807, generator_adv_loss=1.995, generator_feat_match_loss=5.123, over 100.00 samples.
+2024-03-16 06:22:02,306 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 06:22:21,588 INFO [train.py:919] (1/6) Start epoch 993
+2024-03-16 06:24:43,651 INFO [train.py:527] (1/6) Epoch 993, batch 42, global_batch_idx: 123050, batch size: 96, loss[discriminator_loss=2.712, discriminator_real_loss=1.408, discriminator_fake_loss=1.304, generator_loss=28.64, generator_mel_loss=17.42, generator_kl_loss=1.411, generator_dur_loss=1.865, generator_adv_loss=1.946, generator_feat_match_loss=5.993, over 96.00 samples.], tot_loss[discriminator_loss=2.682, discriminator_real_loss=1.359, discriminator_fake_loss=1.323, generator_loss=29.29, generator_mel_loss=17.7, generator_kl_loss=1.461, generator_dur_loss=1.75, generator_adv_loss=2.023, generator_feat_match_loss=6.361, over 2487.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:27:03,439 INFO [train.py:527] (1/6) Epoch 993, batch 92, global_batch_idx: 123100, batch size: 66, loss[discriminator_loss=2.612, discriminator_real_loss=1.305, discriminator_fake_loss=1.307, generator_loss=30.33, generator_mel_loss=18.23, generator_kl_loss=1.25, generator_dur_loss=1.748, generator_adv_loss=1.98, generator_feat_match_loss=7.12, over 66.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.358, discriminator_fake_loss=1.319, generator_loss=29.27, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.75, generator_adv_loss=2.022, generator_feat_match_loss=6.324, over 5330.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:28:28,035 INFO [train.py:919] (1/6) Start epoch 994
+2024-03-16 06:29:43,567 INFO [train.py:527] (1/6) Epoch 994, batch 18, global_batch_idx: 123150, batch size: 59, loss[discriminator_loss=2.721, discriminator_real_loss=1.395, discriminator_fake_loss=1.326, generator_loss=28.62, generator_mel_loss=17.51, generator_kl_loss=1.598, generator_dur_loss=1.706, generator_adv_loss=1.953, generator_feat_match_loss=5.852, over 59.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.361, discriminator_fake_loss=1.313, generator_loss=29.11, generator_mel_loss=17.71, generator_kl_loss=1.451, generator_dur_loss=1.729, generator_adv_loss=1.993, generator_feat_match_loss=6.226, over 1041.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:32:04,811 INFO [train.py:527] (1/6) Epoch 994, batch 68, global_batch_idx: 123200, batch size: 31, loss[discriminator_loss=2.714, discriminator_real_loss=1.37, discriminator_fake_loss=1.344, generator_loss=27.68, generator_mel_loss=17.56, generator_kl_loss=1.432, generator_dur_loss=1.634, generator_adv_loss=1.864, generator_feat_match_loss=5.191, over 31.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.355, discriminator_fake_loss=1.322, generator_loss=29.19, generator_mel_loss=17.71, generator_kl_loss=1.448, generator_dur_loss=1.743, generator_adv_loss=2.011, generator_feat_match_loss=6.274, over 3798.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:32:04,812 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 06:32:12,729 INFO [train.py:591] (1/6) Epoch 994, validation: discriminator_loss=2.784, discriminator_real_loss=1.422, discriminator_fake_loss=1.362, generator_loss=28.28, generator_mel_loss=18.37, generator_kl_loss=1.264, generator_dur_loss=1.805, generator_adv_loss=1.849, generator_feat_match_loss=4.988, over 100.00 samples.
+2024-03-16 06:32:12,730 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 06:34:30,934 INFO [train.py:527] (1/6) Epoch 994, batch 118, global_batch_idx: 123250, batch size: 25, loss[discriminator_loss=2.647, discriminator_real_loss=1.338, discriminator_fake_loss=1.309, generator_loss=30.1, generator_mel_loss=18.17, generator_kl_loss=1.609, generator_dur_loss=1.558, generator_adv_loss=2.245, generator_feat_match_loss=6.519, over 25.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.351, discriminator_fake_loss=1.321, generator_loss=29.21, generator_mel_loss=17.71, generator_kl_loss=1.452, generator_dur_loss=1.74, generator_adv_loss=2.018, generator_feat_match_loss=6.286, over 6539.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:34:46,484 INFO [train.py:919] (1/6) Start epoch 995
+2024-03-16 06:37:11,288 INFO [train.py:527] (1/6) Epoch 995, batch 44, global_batch_idx: 123300, batch size: 68, loss[discriminator_loss=2.675, discriminator_real_loss=1.347, discriminator_fake_loss=1.328, generator_loss=28.15, generator_mel_loss=17.44, generator_kl_loss=1.389, generator_dur_loss=1.746, generator_adv_loss=2.014, generator_feat_match_loss=5.566, over 68.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.35, discriminator_fake_loss=1.329, generator_loss=29.45, generator_mel_loss=17.81, generator_kl_loss=1.469, generator_dur_loss=1.743, generator_adv_loss=2.006, generator_feat_match_loss=6.421, over 2548.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:39:29,362 INFO [train.py:527] (1/6) Epoch 995, batch 94, global_batch_idx: 123350, batch size: 45, loss[discriminator_loss=2.673, discriminator_real_loss=1.369, discriminator_fake_loss=1.303, generator_loss=29.8, generator_mel_loss=17.75, generator_kl_loss=1.64, generator_dur_loss=1.674, generator_adv_loss=2.021, generator_feat_match_loss=6.714, over 45.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.347, discriminator_fake_loss=1.325, generator_loss=29.39, generator_mel_loss=17.73, generator_kl_loss=1.466, generator_dur_loss=1.741, generator_adv_loss=2.026, generator_feat_match_loss=6.429, over 5476.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:40:52,916 INFO [train.py:919] (1/6) Start epoch 996
+2024-03-16 06:42:12,823 INFO [train.py:527] (1/6) Epoch 996, batch 20, global_batch_idx: 123400, batch size: 31, loss[discriminator_loss=2.699, discriminator_real_loss=1.357, discriminator_fake_loss=1.342, generator_loss=28.17, generator_mel_loss=17.09, generator_kl_loss=1.594, generator_dur_loss=1.634, generator_adv_loss=1.94, generator_feat_match_loss=5.909, over 31.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.348, discriminator_fake_loss=1.325, generator_loss=29.17, generator_mel_loss=17.76, generator_kl_loss=1.486, generator_dur_loss=1.74, generator_adv_loss=2.017, generator_feat_match_loss=6.161, over 1134.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:42:12,824 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 06:42:20,825 INFO [train.py:591] (1/6) Epoch 996, validation: discriminator_loss=2.751, discriminator_real_loss=1.435, discriminator_fake_loss=1.315, generator_loss=27.61, generator_mel_loss=17.61, generator_kl_loss=1.406, generator_dur_loss=1.802, generator_adv_loss=1.891, generator_feat_match_loss=4.899, over 100.00 samples.
+2024-03-16 06:42:20,826 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 06:44:41,159 INFO [train.py:527] (1/6) Epoch 996, batch 70, global_batch_idx: 123450, batch size: 44, loss[discriminator_loss=2.72, discriminator_real_loss=1.366, discriminator_fake_loss=1.355, generator_loss=29.12, generator_mel_loss=18.01, generator_kl_loss=1.535, generator_dur_loss=1.707, generator_adv_loss=1.964, generator_feat_match_loss=5.907, over 44.00 samples.], tot_loss[discriminator_loss=2.677, discriminator_real_loss=1.361, discriminator_fake_loss=1.316, generator_loss=29.07, generator_mel_loss=17.69, generator_kl_loss=1.459, generator_dur_loss=1.735, generator_adv_loss=2.009, generator_feat_match_loss=6.178, over 3903.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:46:58,093 INFO [train.py:527] (1/6) Epoch 996, batch 120, global_batch_idx: 123500, batch size: 36, loss[discriminator_loss=2.715, discriminator_real_loss=1.396, discriminator_fake_loss=1.319, generator_loss=30.01, generator_mel_loss=18.4, generator_kl_loss=1.552, generator_dur_loss=1.637, generator_adv_loss=2.018, generator_feat_match_loss=6.399, over 36.00 samples.], tot_loss[discriminator_loss=2.678, discriminator_real_loss=1.359, discriminator_fake_loss=1.319, generator_loss=29.17, generator_mel_loss=17.72, generator_kl_loss=1.456, generator_dur_loss=1.734, generator_adv_loss=2.012, generator_feat_match_loss=6.242, over 6635.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:47:08,274 INFO [train.py:919] (1/6) Start epoch 997
+2024-03-16 06:49:38,314 INFO [train.py:527] (1/6) Epoch 997, batch 46, global_batch_idx: 123550, batch size: 80, loss[discriminator_loss=2.639, discriminator_real_loss=1.407, discriminator_fake_loss=1.233, generator_loss=28.36, generator_mel_loss=17.35, generator_kl_loss=1.47, generator_dur_loss=1.766, generator_adv_loss=1.919, generator_feat_match_loss=5.849, over 80.00 samples.], tot_loss[discriminator_loss=2.679, discriminator_real_loss=1.359, discriminator_fake_loss=1.32, generator_loss=29.22, generator_mel_loss=17.79, generator_kl_loss=1.451, generator_dur_loss=1.713, generator_adv_loss=2.008, generator_feat_match_loss=6.262, over 2614.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:51:52,874 INFO [train.py:527] (1/6) Epoch 997, batch 96, global_batch_idx: 123600, batch size: 14, loss[discriminator_loss=2.493, discriminator_real_loss=1.217, discriminator_fake_loss=1.276, generator_loss=32.3, generator_mel_loss=19.07, generator_kl_loss=1.823, generator_dur_loss=1.601, generator_adv_loss=2.278, generator_feat_match_loss=7.523, over 14.00 samples.], tot_loss[discriminator_loss=2.672, discriminator_real_loss=1.349, discriminator_fake_loss=1.323, generator_loss=29.28, generator_mel_loss=17.77, generator_kl_loss=1.445, generator_dur_loss=1.724, generator_adv_loss=2.016, generator_feat_match_loss=6.323, over 5439.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:51:52,875 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 06:52:00,767 INFO [train.py:591] (1/6) Epoch 997, validation: discriminator_loss=2.713, discriminator_real_loss=1.463, discriminator_fake_loss=1.25, generator_loss=28.08, generator_mel_loss=18.1, generator_kl_loss=1.252, generator_dur_loss=1.795, generator_adv_loss=2.04, generator_feat_match_loss=4.895, over 100.00 samples.
+2024-03-16 06:52:00,768 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 06:53:19,058 INFO [train.py:919] (1/6) Start epoch 998
+2024-03-16 06:54:45,886 INFO [train.py:527] (1/6) Epoch 998, batch 22, global_batch_idx: 123650, batch size: 44, loss[discriminator_loss=2.676, discriminator_real_loss=1.32, discriminator_fake_loss=1.356, generator_loss=29.34, generator_mel_loss=17.28, generator_kl_loss=1.466, generator_dur_loss=1.644, generator_adv_loss=2.201, generator_feat_match_loss=6.751, over 44.00 samples.], tot_loss[discriminator_loss=2.684, discriminator_real_loss=1.362, discriminator_fake_loss=1.322, generator_loss=29.16, generator_mel_loss=17.72, generator_kl_loss=1.446, generator_dur_loss=1.722, generator_adv_loss=2.013, generator_feat_match_loss=6.256, over 1289.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:57:01,460 INFO [train.py:527] (1/6) Epoch 998, batch 72, global_batch_idx: 123700, batch size: 56, loss[discriminator_loss=2.703, discriminator_real_loss=1.337, discriminator_fake_loss=1.365, generator_loss=29.17, generator_mel_loss=17.72, generator_kl_loss=1.462, generator_dur_loss=1.718, generator_adv_loss=2.005, generator_feat_match_loss=6.267, over 56.00 samples.], tot_loss[discriminator_loss=2.674, discriminator_real_loss=1.359, discriminator_fake_loss=1.315, generator_loss=29.16, generator_mel_loss=17.66, generator_kl_loss=1.427, generator_dur_loss=1.735, generator_adv_loss=2.022, generator_feat_match_loss=6.319, over 4270.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:59:20,860 INFO [train.py:527] (1/6) Epoch 998, batch 122, global_batch_idx: 123750, batch size: 77, loss[discriminator_loss=2.71, discriminator_real_loss=1.425, discriminator_fake_loss=1.285, generator_loss=28.52, generator_mel_loss=17.73, generator_kl_loss=1.289, generator_dur_loss=1.815, generator_adv_loss=1.897, generator_feat_match_loss=5.786, over 77.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.354, discriminator_fake_loss=1.316, generator_loss=29.19, generator_mel_loss=17.69, generator_kl_loss=1.431, generator_dur_loss=1.735, generator_adv_loss=2.018, generator_feat_match_loss=6.325, over 7191.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 06:59:26,319 INFO [train.py:919] (1/6) Start epoch 999
+2024-03-16 07:02:04,274 INFO [train.py:527] (1/6) Epoch 999, batch 48, global_batch_idx: 123800, batch size: 48, loss[discriminator_loss=2.62, discriminator_real_loss=1.361, discriminator_fake_loss=1.259, generator_loss=29.88, generator_mel_loss=17.91, generator_kl_loss=1.436, generator_dur_loss=1.637, generator_adv_loss=1.946, generator_feat_match_loss=6.951, over 48.00 samples.], tot_loss[discriminator_loss=2.673, discriminator_real_loss=1.355, discriminator_fake_loss=1.318, generator_loss=29.31, generator_mel_loss=17.75, generator_kl_loss=1.465, generator_dur_loss=1.73, generator_adv_loss=2.026, generator_feat_match_loss=6.341, over 2711.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 07:02:04,275 INFO [train.py:581] (1/6) Computing validation loss
+2024-03-16 07:02:12,163 INFO [train.py:591] (1/6) Epoch 999, validation: discriminator_loss=2.747, discriminator_real_loss=1.323, discriminator_fake_loss=1.424, generator_loss=28.31, generator_mel_loss=18.07, generator_kl_loss=1.205, generator_dur_loss=1.805, generator_adv_loss=1.787, generator_feat_match_loss=5.439, over 100.00 samples.
+2024-03-16 07:02:12,163 INFO [train.py:592] (1/6) Maximum memory allocated so far is 29134MB
+2024-03-16 07:04:29,733 INFO [train.py:527] (1/6) Epoch 999, batch 98, global_batch_idx: 123850, batch size: 47, loss[discriminator_loss=2.668, discriminator_real_loss=1.278, discriminator_fake_loss=1.39, generator_loss=29.6, generator_mel_loss=17.63, generator_kl_loss=1.64, generator_dur_loss=1.68, generator_adv_loss=2.068, generator_feat_match_loss=6.587, over 47.00 samples.], tot_loss[discriminator_loss=2.671, discriminator_real_loss=1.353, discriminator_fake_loss=1.318, generator_loss=29.28, generator_mel_loss=17.74, generator_kl_loss=1.448, generator_dur_loss=1.734, generator_adv_loss=2.026, generator_feat_match_loss=6.331, over 5593.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 07:05:43,029 INFO [train.py:919] (1/6) Start epoch 1000
+2024-03-16 07:07:13,409 INFO [train.py:527] (1/6) Epoch 1000, batch 24, global_batch_idx: 123900, batch size: 68, loss[discriminator_loss=2.674, discriminator_real_loss=1.353, discriminator_fake_loss=1.321, generator_loss=28.9, generator_mel_loss=17.57, generator_kl_loss=1.282, generator_dur_loss=1.777, generator_adv_loss=2.255, generator_feat_match_loss=6.016, over 68.00 samples.], tot_loss[discriminator_loss=2.669, discriminator_real_loss=1.343, discriminator_fake_loss=1.326, generator_loss=29.39, generator_mel_loss=17.72, generator_kl_loss=1.45, generator_dur_loss=1.737, generator_adv_loss=2.066, generator_feat_match_loss=6.418, over 1443.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 07:09:33,635 INFO [train.py:527] (1/6) Epoch 1000, batch 74, global_batch_idx: 123950, batch size: 66, loss[discriminator_loss=2.675, discriminator_real_loss=1.327, discriminator_fake_loss=1.348, generator_loss=29.27, generator_mel_loss=17.35, generator_kl_loss=1.251, generator_dur_loss=1.755, generator_adv_loss=1.951, generator_feat_match_loss=6.957, over 66.00 samples.], tot_loss[discriminator_loss=2.67, discriminator_real_loss=1.349, discriminator_fake_loss=1.321, generator_loss=29.24, generator_mel_loss=17.64, generator_kl_loss=1.443, generator_dur_loss=1.736, generator_adv_loss=2.033, generator_feat_match_loss=6.387, over 4445.00 samples.], cur_lr_g: 1.77e-04, cur_lr_d: 1.77e-04, 
+2024-03-16 07:11:48,763 INFO [train.py:977] (1/6) Done!