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Kenma model updated
1073215
2023-05-09 20:29:28,024 kenma_eng INFO {'train': {'log_interval': 200, 'seed': 1234, 'epochs': 20000, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 4, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 12800, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'max_wav_value': 32768.0, 'sampling_rate': 40000, 'filter_length': 2048, 'hop_length': 400, 'win_length': 2048, 'n_mel_channels': 125, 'mel_fmin': 0.0, 'mel_fmax': None, 'training_files': './logs/kenma_eng/filelist.txt'}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [10, 10, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'use_spectral_norm': False, 'gin_channels': 256, 'spk_embed_dim': 109}, 'model_dir': './logs/kenma_eng', 'experiment_dir': './logs/kenma_eng', 'save_every_epoch': 25, 'name': 'kenma_eng', 'total_epoch': 1000, 'pretrainG': 'pretrained/f0G40k.pth', 'pretrainD': 'pretrained/f0D40k.pth', 'gpus': '0', 'sample_rate': '40k', 'if_f0': 1, 'if_latest': 1, 'if_cache_data_in_gpu': 1}
2023-05-09 20:29:28,024 kenma_eng WARNING /home/parappa/Retrieval-based-Voice-Conversion-WebUI/train is not a git repository, therefore hash value comparison will be ignored.
2023-05-09 20:29:28,886 kenma_eng INFO loaded pretrained pretrained/f0G40k.pth pretrained/f0D40k.pth
2023-05-09 20:29:32,803 kenma_eng INFO Train Epoch: 1 [0%]
2023-05-09 20:29:32,803 kenma_eng INFO [0, 0.0001]
2023-05-09 20:29:32,803 kenma_eng INFO loss_disc=2.791, loss_gen=2.208, loss_fm=12.661,loss_mel=23.971, loss_kl=5.000
2023-05-09 20:29:42,947 kenma_eng INFO ====> Epoch: 1
2023-05-09 20:29:50,771 kenma_eng INFO ====> Epoch: 2
2023-05-09 20:29:58,712 kenma_eng INFO ====> Epoch: 3
2023-05-09 20:30:06,398 kenma_eng INFO ====> Epoch: 4
2023-05-09 20:30:14,088 kenma_eng INFO ====> Epoch: 5
2023-05-09 20:30:15,277 kenma_eng INFO Train Epoch: 6 [18%]
2023-05-09 20:30:15,279 kenma_eng INFO [200, 9.993751562304699e-05]
2023-05-09 20:30:15,279 kenma_eng INFO loss_disc=2.501, loss_gen=2.958, loss_fm=12.672,loss_mel=21.657, loss_kl=1.718
2023-05-09 20:30:22,250 kenma_eng INFO ====> Epoch: 6
2023-05-09 20:30:30,029 kenma_eng INFO ====> Epoch: 7
2023-05-09 20:30:37,928 kenma_eng INFO ====> Epoch: 8
2023-05-09 20:30:46,312 kenma_eng INFO ====> Epoch: 9
2023-05-09 20:30:54,194 kenma_eng INFO ====> Epoch: 10
2023-05-09 20:30:56,385 kenma_eng INFO Train Epoch: 11 [74%]
2023-05-09 20:30:56,387 kenma_eng INFO [400, 9.987507028906759e-05]
2023-05-09 20:30:56,387 kenma_eng INFO loss_disc=2.934, loss_gen=3.538, loss_fm=13.955,loss_mel=20.665, loss_kl=2.113
2023-05-09 20:31:02,219 kenma_eng INFO ====> Epoch: 11
2023-05-09 20:31:09,974 kenma_eng INFO ====> Epoch: 12
2023-05-09 20:31:17,721 kenma_eng INFO ====> Epoch: 13
2023-05-09 20:31:25,475 kenma_eng INFO ====> Epoch: 14
2023-05-09 20:31:33,227 kenma_eng INFO ====> Epoch: 15
2023-05-09 20:31:36,408 kenma_eng INFO Train Epoch: 16 [72%]
2023-05-09 20:31:36,410 kenma_eng INFO [600, 9.981266397366609e-05]
2023-05-09 20:31:36,410 kenma_eng INFO loss_disc=2.648, loss_gen=3.058, loss_fm=13.716,loss_mel=20.775, loss_kl=1.045
2023-05-09 20:31:41,169 kenma_eng INFO ====> Epoch: 16
2023-05-09 20:31:48,889 kenma_eng INFO ====> Epoch: 17
2023-05-09 20:31:56,689 kenma_eng INFO ====> Epoch: 18
2023-05-09 20:32:04,414 kenma_eng INFO ====> Epoch: 19
2023-05-09 20:32:12,159 kenma_eng INFO ====> Epoch: 20
2023-05-09 20:32:16,325 kenma_eng INFO Train Epoch: 21 [8%]
2023-05-09 20:32:16,327 kenma_eng INFO [800, 9.975029665246193e-05]
2023-05-09 20:32:16,327 kenma_eng INFO loss_disc=2.846, loss_gen=3.076, loss_fm=13.658,loss_mel=20.503, loss_kl=1.702
2023-05-09 20:32:20,094 kenma_eng INFO ====> Epoch: 21
2023-05-09 20:32:27,855 kenma_eng INFO ====> Epoch: 22
2023-05-09 20:32:35,679 kenma_eng INFO ====> Epoch: 23
2023-05-09 20:32:43,428 kenma_eng INFO ====> Epoch: 24
2023-05-09 20:32:51,170 kenma_eng INFO Saving model and optimizer state at epoch 25 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:32:51,495 kenma_eng INFO Saving model and optimizer state at epoch 25 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:32:51,924 kenma_eng INFO ====> Epoch: 25
2023-05-09 20:32:57,145 kenma_eng INFO Train Epoch: 26 [90%]
2023-05-09 20:32:57,149 kenma_eng INFO [1000, 9.968796830108985e-05]
2023-05-09 20:32:57,149 kenma_eng INFO loss_disc=3.041, loss_gen=3.034, loss_fm=11.362,loss_mel=19.801, loss_kl=1.560
2023-05-09 20:32:59,973 kenma_eng INFO ====> Epoch: 26
2023-05-09 20:33:07,838 kenma_eng INFO ====> Epoch: 27
2023-05-09 20:33:15,627 kenma_eng INFO ====> Epoch: 28
2023-05-09 20:33:23,419 kenma_eng INFO ====> Epoch: 29
2023-05-09 20:33:31,207 kenma_eng INFO ====> Epoch: 30
2023-05-09 20:33:37,401 kenma_eng INFO Train Epoch: 31 [77%]
2023-05-09 20:33:37,403 kenma_eng INFO [1200, 9.962567889519979e-05]
2023-05-09 20:33:37,403 kenma_eng INFO loss_disc=3.039, loss_gen=2.758, loss_fm=11.633,loss_mel=19.735, loss_kl=1.572
2023-05-09 20:33:39,208 kenma_eng INFO ====> Epoch: 31
2023-05-09 20:33:47,000 kenma_eng INFO ====> Epoch: 32
2023-05-09 20:33:54,790 kenma_eng INFO ====> Epoch: 33
2023-05-09 20:34:02,653 kenma_eng INFO ====> Epoch: 34
2023-05-09 20:34:10,444 kenma_eng INFO ====> Epoch: 35
2023-05-09 20:34:17,653 kenma_eng INFO Train Epoch: 36 [31%]
2023-05-09 20:34:17,655 kenma_eng INFO [1400, 9.956342841045691e-05]
2023-05-09 20:34:17,661 kenma_eng INFO loss_disc=2.920, loss_gen=2.944, loss_fm=10.726,loss_mel=19.014, loss_kl=1.017
2023-05-09 20:34:18,464 kenma_eng INFO ====> Epoch: 36
2023-05-09 20:34:26,258 kenma_eng INFO ====> Epoch: 37
2023-05-09 20:34:34,016 kenma_eng INFO ====> Epoch: 38
2023-05-09 20:34:41,789 kenma_eng INFO ====> Epoch: 39
2023-05-09 20:34:49,614 kenma_eng INFO ====> Epoch: 40
2023-05-09 20:34:57,369 kenma_eng INFO ====> Epoch: 41
2023-05-09 20:34:57,767 kenma_eng INFO Train Epoch: 42 [95%]
2023-05-09 20:34:57,769 kenma_eng INFO [1600, 9.948877917043875e-05]
2023-05-09 20:34:57,769 kenma_eng INFO loss_disc=2.705, loss_gen=3.156, loss_fm=11.957,loss_mel=19.650, loss_kl=0.759
2023-05-09 20:35:05,347 kenma_eng INFO ====> Epoch: 42
2023-05-09 20:35:13,128 kenma_eng INFO ====> Epoch: 43
2023-05-09 20:35:20,906 kenma_eng INFO ====> Epoch: 44
2023-05-09 20:35:28,674 kenma_eng INFO ====> Epoch: 45
2023-05-09 20:35:36,515 kenma_eng INFO ====> Epoch: 46
2023-05-09 20:35:37,909 kenma_eng INFO Train Epoch: 47 [28%]
2023-05-09 20:35:37,911 kenma_eng INFO [1800, 9.942661422663591e-05]
2023-05-09 20:35:37,911 kenma_eng INFO loss_disc=2.943, loss_gen=3.358, loss_fm=10.876,loss_mel=20.585, loss_kl=1.617
2023-05-09 20:35:44,502 kenma_eng INFO ====> Epoch: 47
2023-05-09 20:35:52,282 kenma_eng INFO ====> Epoch: 48
2023-05-09 20:36:00,057 kenma_eng INFO ====> Epoch: 49
2023-05-09 20:36:07,835 kenma_eng INFO Saving model and optimizer state at epoch 50 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:36:08,325 kenma_eng INFO Saving model and optimizer state at epoch 50 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:36:08,869 kenma_eng INFO ====> Epoch: 50
2023-05-09 20:36:16,688 kenma_eng INFO ====> Epoch: 51
2023-05-09 20:36:19,095 kenma_eng INFO Train Epoch: 52 [0%]
2023-05-09 20:36:19,097 kenma_eng INFO [2000, 9.936448812621091e-05]
2023-05-09 20:36:19,097 kenma_eng INFO loss_disc=2.591, loss_gen=3.239, loss_fm=12.110,loss_mel=19.049, loss_kl=1.488
2023-05-09 20:36:24,732 kenma_eng INFO ====> Epoch: 52
2023-05-09 20:36:32,536 kenma_eng INFO ====> Epoch: 53
2023-05-09 20:36:40,341 kenma_eng INFO ====> Epoch: 54
2023-05-09 20:36:48,286 kenma_eng INFO ====> Epoch: 55
2023-05-09 20:36:56,195 kenma_eng INFO ====> Epoch: 56
2023-05-09 20:36:59,578 kenma_eng INFO Train Epoch: 57 [54%]
2023-05-09 20:36:59,580 kenma_eng INFO [2200, 9.930240084489267e-05]
2023-05-09 20:36:59,580 kenma_eng INFO loss_disc=2.851, loss_gen=2.751, loss_fm=10.859,loss_mel=19.045, loss_kl=0.888
2023-05-09 20:37:04,243 kenma_eng INFO ====> Epoch: 57
2023-05-09 20:37:12,004 kenma_eng INFO ====> Epoch: 58
2023-05-09 20:37:19,771 kenma_eng INFO ====> Epoch: 59
2023-05-09 20:37:27,668 kenma_eng INFO ====> Epoch: 60
2023-05-09 20:37:35,494 kenma_eng INFO ====> Epoch: 61
2023-05-09 20:37:39,983 kenma_eng INFO Train Epoch: 62 [28%]
2023-05-09 20:37:39,985 kenma_eng INFO [2400, 9.924035235842533e-05]
2023-05-09 20:37:39,985 kenma_eng INFO loss_disc=2.663, loss_gen=3.615, loss_fm=13.321,loss_mel=19.131, loss_kl=1.425
2023-05-09 20:37:43,666 kenma_eng INFO ====> Epoch: 62
2023-05-09 20:37:51,900 kenma_eng INFO ====> Epoch: 63
2023-05-09 20:37:59,997 kenma_eng INFO ====> Epoch: 64
2023-05-09 20:38:07,863 kenma_eng INFO ====> Epoch: 65
2023-05-09 20:38:15,722 kenma_eng INFO ====> Epoch: 66
2023-05-09 20:38:21,173 kenma_eng INFO Train Epoch: 67 [13%]
2023-05-09 20:38:21,175 kenma_eng INFO [2600, 9.917834264256819e-05]
2023-05-09 20:38:21,175 kenma_eng INFO loss_disc=2.830, loss_gen=3.225, loss_fm=13.059,loss_mel=19.048, loss_kl=1.285
2023-05-09 20:38:23,876 kenma_eng INFO ====> Epoch: 67
2023-05-09 20:38:31,746 kenma_eng INFO ====> Epoch: 68
2023-05-09 20:38:39,583 kenma_eng INFO ====> Epoch: 69
2023-05-09 20:38:47,430 kenma_eng INFO ====> Epoch: 70
2023-05-09 20:38:55,300 kenma_eng INFO ====> Epoch: 71
2023-05-09 20:39:01,758 kenma_eng INFO Train Epoch: 72 [92%]
2023-05-09 20:39:01,760 kenma_eng INFO [2800, 9.911637167309565e-05]
2023-05-09 20:39:01,760 kenma_eng INFO loss_disc=2.816, loss_gen=3.060, loss_fm=11.463,loss_mel=20.169, loss_kl=0.796
2023-05-09 20:39:03,448 kenma_eng INFO ====> Epoch: 72
2023-05-09 20:39:11,295 kenma_eng INFO ====> Epoch: 73
2023-05-09 20:39:19,136 kenma_eng INFO ====> Epoch: 74
2023-05-09 20:39:27,005 kenma_eng INFO Saving model and optimizer state at epoch 75 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:39:27,426 kenma_eng INFO Saving model and optimizer state at epoch 75 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:39:27,977 kenma_eng INFO ====> Epoch: 75
2023-05-09 20:39:35,863 kenma_eng INFO ====> Epoch: 76
2023-05-09 20:39:43,342 kenma_eng INFO Train Epoch: 77 [64%]
2023-05-09 20:39:43,344 kenma_eng INFO [3000, 9.905443942579728e-05]
2023-05-09 20:39:43,344 kenma_eng INFO loss_disc=2.651, loss_gen=3.194, loss_fm=13.308,loss_mel=19.558, loss_kl=1.473
2023-05-09 20:39:43,948 kenma_eng INFO ====> Epoch: 77
2023-05-09 20:39:51,896 kenma_eng INFO ====> Epoch: 78
2023-05-09 20:39:59,809 kenma_eng INFO ====> Epoch: 79
2023-05-09 20:40:07,733 kenma_eng INFO ====> Epoch: 80
2023-05-09 20:40:15,640 kenma_eng INFO ====> Epoch: 81
2023-05-09 20:40:24,115 kenma_eng INFO ====> Epoch: 82
2023-05-09 20:40:24,780 kenma_eng INFO Train Epoch: 83 [77%]
2023-05-09 20:40:24,782 kenma_eng INFO [3200, 9.89801718082432e-05]
2023-05-09 20:40:24,782 kenma_eng INFO loss_disc=2.658, loss_gen=3.387, loss_fm=13.051,loss_mel=19.329, loss_kl=0.987
2023-05-09 20:40:32,537 kenma_eng INFO ====> Epoch: 83
2023-05-09 20:40:40,833 kenma_eng INFO ====> Epoch: 84
2023-05-09 20:40:49,296 kenma_eng INFO ====> Epoch: 85
2023-05-09 20:40:57,697 kenma_eng INFO ====> Epoch: 86
2023-05-09 20:41:06,159 kenma_eng INFO ====> Epoch: 87
2023-05-09 20:41:07,912 kenma_eng INFO Train Epoch: 88 [5%]
2023-05-09 20:41:07,914 kenma_eng INFO [3400, 9.891832466458178e-05]
2023-05-09 20:41:07,914 kenma_eng INFO loss_disc=2.087, loss_gen=3.429, loss_fm=13.707,loss_mel=18.626, loss_kl=0.648
2023-05-09 20:41:14,837 kenma_eng INFO ====> Epoch: 88
2023-05-09 20:41:23,309 kenma_eng INFO ====> Epoch: 89
2023-05-09 20:41:31,823 kenma_eng INFO ====> Epoch: 90
2023-05-09 20:41:40,318 kenma_eng INFO ====> Epoch: 91
2023-05-09 20:41:48,887 kenma_eng INFO ====> Epoch: 92
2023-05-09 20:41:51,718 kenma_eng INFO Train Epoch: 93 [95%]
2023-05-09 20:41:51,720 kenma_eng INFO [3600, 9.885651616572276e-05]
2023-05-09 20:41:51,720 kenma_eng INFO loss_disc=2.724, loss_gen=2.870, loss_fm=13.839,loss_mel=18.641, loss_kl=0.407
2023-05-09 20:41:57,548 kenma_eng INFO ====> Epoch: 93
2023-05-09 20:42:06,019 kenma_eng INFO ====> Epoch: 94
2023-05-09 20:42:14,036 kenma_eng INFO ====> Epoch: 95
2023-05-09 20:42:22,090 kenma_eng INFO ====> Epoch: 96
2023-05-09 20:42:30,286 kenma_eng INFO ====> Epoch: 97
2023-05-09 20:42:34,086 kenma_eng INFO Train Epoch: 98 [0%]
2023-05-09 20:42:34,088 kenma_eng INFO [3800, 9.879474628751914e-05]
2023-05-09 20:42:34,088 kenma_eng INFO loss_disc=2.706, loss_gen=3.627, loss_fm=10.765,loss_mel=18.197, loss_kl=1.797
2023-05-09 20:42:38,807 kenma_eng INFO ====> Epoch: 98
2023-05-09 20:42:46,974 kenma_eng INFO ====> Epoch: 99
2023-05-09 20:42:55,137 kenma_eng INFO Saving model and optimizer state at epoch 100 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:42:55,570 kenma_eng INFO Saving model and optimizer state at epoch 100 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:42:56,131 kenma_eng INFO ====> Epoch: 100
2023-05-09 20:43:04,372 kenma_eng INFO ====> Epoch: 101
2023-05-09 20:43:12,783 kenma_eng INFO ====> Epoch: 102
2023-05-09 20:43:17,696 kenma_eng INFO Train Epoch: 103 [3%]
2023-05-09 20:43:17,697 kenma_eng INFO [4000, 9.873301500583906e-05]
2023-05-09 20:43:17,698 kenma_eng INFO loss_disc=2.969, loss_gen=2.920, loss_fm=14.823,loss_mel=18.225, loss_kl=0.889
2023-05-09 20:43:21,293 kenma_eng INFO ====> Epoch: 103
2023-05-09 20:43:29,733 kenma_eng INFO ====> Epoch: 104
2023-05-09 20:43:37,784 kenma_eng INFO ====> Epoch: 105
2023-05-09 20:43:45,696 kenma_eng INFO ====> Epoch: 106
2023-05-09 20:43:53,743 kenma_eng INFO ====> Epoch: 107
2023-05-09 20:43:59,422 kenma_eng INFO Train Epoch: 108 [59%]
2023-05-09 20:43:59,424 kenma_eng INFO [4200, 9.867132229656573e-05]
2023-05-09 20:43:59,424 kenma_eng INFO loss_disc=2.996, loss_gen=4.010, loss_fm=14.104,loss_mel=19.573, loss_kl=1.271
2023-05-09 20:44:01,869 kenma_eng INFO ====> Epoch: 108
2023-05-09 20:44:09,861 kenma_eng INFO ====> Epoch: 109
2023-05-09 20:44:17,857 kenma_eng INFO ====> Epoch: 110
2023-05-09 20:44:25,827 kenma_eng INFO ====> Epoch: 111
2023-05-09 20:44:33,815 kenma_eng INFO ====> Epoch: 112
2023-05-09 20:44:40,582 kenma_eng INFO Train Epoch: 113 [72%]
2023-05-09 20:44:40,584 kenma_eng INFO [4400, 9.86096681355974e-05]
2023-05-09 20:44:40,584 kenma_eng INFO loss_disc=1.902, loss_gen=3.685, loss_fm=15.686,loss_mel=18.469, loss_kl=1.701
2023-05-09 20:44:42,007 kenma_eng INFO ====> Epoch: 113
2023-05-09 20:44:50,000 kenma_eng INFO ====> Epoch: 114
2023-05-09 20:44:57,976 kenma_eng INFO ====> Epoch: 115
2023-05-09 20:45:05,961 kenma_eng INFO ====> Epoch: 116
2023-05-09 20:45:13,952 kenma_eng INFO ====> Epoch: 117
2023-05-09 20:45:21,754 kenma_eng INFO Train Epoch: 118 [59%]
2023-05-09 20:45:21,756 kenma_eng INFO [4600, 9.854805249884741e-05]
2023-05-09 20:45:21,756 kenma_eng INFO loss_disc=2.432, loss_gen=3.429, loss_fm=13.594,loss_mel=19.146, loss_kl=0.974
2023-05-09 20:45:22,178 kenma_eng INFO ====> Epoch: 118
2023-05-09 20:45:30,134 kenma_eng INFO ====> Epoch: 119
2023-05-09 20:45:38,123 kenma_eng INFO ====> Epoch: 120
2023-05-09 20:45:46,111 kenma_eng INFO ====> Epoch: 121
2023-05-09 20:45:54,097 kenma_eng INFO ====> Epoch: 122
2023-05-09 20:46:02,128 kenma_eng INFO ====> Epoch: 123
2023-05-09 20:46:02,937 kenma_eng INFO Train Epoch: 124 [3%]
2023-05-09 20:46:02,939 kenma_eng INFO [4800, 9.847416455282387e-05]
2023-05-09 20:46:02,939 kenma_eng INFO loss_disc=1.961, loss_gen=3.894, loss_fm=14.796,loss_mel=17.653, loss_kl=-0.252
2023-05-09 20:46:10,273 kenma_eng INFO ====> Epoch: 124
2023-05-09 20:46:18,263 kenma_eng INFO Saving model and optimizer state at epoch 125 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:46:18,694 kenma_eng INFO Saving model and optimizer state at epoch 125 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:46:19,249 kenma_eng INFO ====> Epoch: 125
2023-05-09 20:46:27,101 kenma_eng INFO ====> Epoch: 126
2023-05-09 20:46:35,060 kenma_eng INFO ====> Epoch: 127
2023-05-09 20:46:43,046 kenma_eng INFO ====> Epoch: 128
2023-05-09 20:46:44,892 kenma_eng INFO Train Epoch: 129 [87%]
2023-05-09 20:46:44,894 kenma_eng INFO [5000, 9.841263358464336e-05]
2023-05-09 20:46:44,894 kenma_eng INFO loss_disc=2.329, loss_gen=3.321, loss_fm=10.072,loss_mel=14.308, loss_kl=0.992
2023-05-09 20:46:51,242 kenma_eng INFO ====> Epoch: 129
2023-05-09 20:46:59,222 kenma_eng INFO ====> Epoch: 130
2023-05-09 20:47:07,217 kenma_eng INFO ====> Epoch: 131
2023-05-09 20:47:15,200 kenma_eng INFO ====> Epoch: 132
2023-05-09 20:47:23,190 kenma_eng INFO ====> Epoch: 133
2023-05-09 20:47:26,051 kenma_eng INFO Train Epoch: 134 [92%]
2023-05-09 20:47:26,053 kenma_eng INFO [5200, 9.835114106370493e-05]
2023-05-09 20:47:26,053 kenma_eng INFO loss_disc=2.598, loss_gen=3.861, loss_fm=11.445,loss_mel=19.314, loss_kl=1.074
2023-05-09 20:47:31,383 kenma_eng INFO ====> Epoch: 134
2023-05-09 20:47:39,376 kenma_eng INFO ====> Epoch: 135
2023-05-09 20:47:47,353 kenma_eng INFO ====> Epoch: 136
2023-05-09 20:47:55,349 kenma_eng INFO ====> Epoch: 137
2023-05-09 20:48:03,327 kenma_eng INFO ====> Epoch: 138
2023-05-09 20:48:07,220 kenma_eng INFO Train Epoch: 139 [77%]
2023-05-09 20:48:07,223 kenma_eng INFO [5400, 9.828968696598508e-05]
2023-05-09 20:48:07,223 kenma_eng INFO loss_disc=2.560, loss_gen=4.124, loss_fm=11.947,loss_mel=18.767, loss_kl=0.177
2023-05-09 20:48:11,510 kenma_eng INFO ====> Epoch: 139
2023-05-09 20:48:19,508 kenma_eng INFO ====> Epoch: 140
2023-05-09 20:48:27,490 kenma_eng INFO ====> Epoch: 141
2023-05-09 20:48:35,482 kenma_eng INFO ====> Epoch: 142
2023-05-09 20:48:43,470 kenma_eng INFO ====> Epoch: 143
2023-05-09 20:48:48,483 kenma_eng INFO Train Epoch: 144 [23%]
2023-05-09 20:48:48,484 kenma_eng INFO [5600, 9.822827126747529e-05]
2023-05-09 20:48:48,484 kenma_eng INFO loss_disc=2.092, loss_gen=3.773, loss_fm=14.524,loss_mel=17.186, loss_kl=1.087
2023-05-09 20:48:51,815 kenma_eng INFO ====> Epoch: 144
2023-05-09 20:49:00,367 kenma_eng INFO ====> Epoch: 145
2023-05-09 20:49:08,355 kenma_eng INFO ====> Epoch: 146
2023-05-09 20:49:16,426 kenma_eng INFO ====> Epoch: 147
2023-05-09 20:49:24,642 kenma_eng INFO ====> Epoch: 148
2023-05-09 20:49:30,580 kenma_eng INFO Train Epoch: 149 [5%]
2023-05-09 20:49:30,582 kenma_eng INFO [5800, 9.816689394418209e-05]
2023-05-09 20:49:30,582 kenma_eng INFO loss_disc=2.330, loss_gen=3.602, loss_fm=12.676,loss_mel=18.536, loss_kl=0.708
2023-05-09 20:49:32,829 kenma_eng INFO ====> Epoch: 149
2023-05-09 20:49:41,146 kenma_eng INFO Saving model and optimizer state at epoch 150 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:49:41,585 kenma_eng INFO Saving model and optimizer state at epoch 150 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:49:42,155 kenma_eng INFO ====> Epoch: 150
2023-05-09 20:49:50,566 kenma_eng INFO ====> Epoch: 151
2023-05-09 20:49:58,995 kenma_eng INFO ====> Epoch: 152
2023-05-09 20:50:07,420 kenma_eng INFO ====> Epoch: 153
2023-05-09 20:50:14,651 kenma_eng INFO Train Epoch: 154 [8%]
2023-05-09 20:50:14,653 kenma_eng INFO [6000, 9.810555497212693e-05]
2023-05-09 20:50:14,653 kenma_eng INFO loss_disc=2.052, loss_gen=3.627, loss_fm=15.617,loss_mel=17.431, loss_kl=0.614
2023-05-09 20:50:15,965 kenma_eng INFO ====> Epoch: 154
2023-05-09 20:50:24,269 kenma_eng INFO ====> Epoch: 155
2023-05-09 20:50:32,562 kenma_eng INFO ====> Epoch: 156
2023-05-09 20:50:40,929 kenma_eng INFO ====> Epoch: 157
2023-05-09 20:50:49,013 kenma_eng INFO ====> Epoch: 158
2023-05-09 20:50:57,004 kenma_eng INFO Train Epoch: 159 [49%]
2023-05-09 20:50:57,006 kenma_eng INFO [6200, 9.804425432734629e-05]
2023-05-09 20:50:57,006 kenma_eng INFO loss_disc=2.125, loss_gen=3.493, loss_fm=12.959,loss_mel=18.423, loss_kl=1.443
2023-05-09 20:50:57,206 kenma_eng INFO ====> Epoch: 159
2023-05-09 20:51:05,186 kenma_eng INFO ====> Epoch: 160
2023-05-09 20:51:13,183 kenma_eng INFO ====> Epoch: 161
2023-05-09 20:51:21,159 kenma_eng INFO ====> Epoch: 162
2023-05-09 20:51:29,153 kenma_eng INFO ====> Epoch: 163
2023-05-09 20:51:37,178 kenma_eng INFO ====> Epoch: 164
2023-05-09 20:51:38,191 kenma_eng INFO Train Epoch: 165 [44%]
2023-05-09 20:51:38,193 kenma_eng INFO [6400, 9.797074411189339e-05]
2023-05-09 20:51:38,193 kenma_eng INFO loss_disc=2.052, loss_gen=3.937, loss_fm=14.622,loss_mel=18.246, loss_kl=1.509
2023-05-09 20:51:45,344 kenma_eng INFO ====> Epoch: 165
2023-05-09 20:51:53,318 kenma_eng INFO ====> Epoch: 166
2023-05-09 20:52:01,305 kenma_eng INFO ====> Epoch: 167
2023-05-09 20:52:09,292 kenma_eng INFO ====> Epoch: 168
2023-05-09 20:52:17,576 kenma_eng INFO ====> Epoch: 169
2023-05-09 20:52:19,656 kenma_eng INFO Train Epoch: 170 [49%]
2023-05-09 20:52:19,658 kenma_eng INFO [6600, 9.790952770283884e-05]
2023-05-09 20:52:19,659 kenma_eng INFO loss_disc=2.215, loss_gen=3.275, loss_fm=14.226,loss_mel=18.308, loss_kl=1.206
2023-05-09 20:52:25,894 kenma_eng INFO ====> Epoch: 170
2023-05-09 20:52:33,874 kenma_eng INFO ====> Epoch: 171
2023-05-09 20:52:41,861 kenma_eng INFO ====> Epoch: 172
2023-05-09 20:52:49,843 kenma_eng INFO ====> Epoch: 173
2023-05-09 20:52:57,829 kenma_eng INFO ====> Epoch: 174
2023-05-09 20:53:00,900 kenma_eng INFO Train Epoch: 175 [15%]
2023-05-09 20:53:00,901 kenma_eng INFO [6800, 9.784834954447608e-05]
2023-05-09 20:53:00,902 kenma_eng INFO loss_disc=2.685, loss_gen=3.409, loss_fm=11.365,loss_mel=18.663, loss_kl=1.499
2023-05-09 20:53:06,226 kenma_eng INFO Saving model and optimizer state at epoch 175 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:53:06,672 kenma_eng INFO Saving model and optimizer state at epoch 175 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:53:07,245 kenma_eng INFO ====> Epoch: 175
2023-05-09 20:53:15,688 kenma_eng INFO ====> Epoch: 176
2023-05-09 20:53:24,181 kenma_eng INFO ====> Epoch: 177
2023-05-09 20:53:32,306 kenma_eng INFO ====> Epoch: 178
2023-05-09 20:53:40,465 kenma_eng INFO ====> Epoch: 179
2023-05-09 20:53:44,795 kenma_eng INFO Train Epoch: 180 [69%]
2023-05-09 20:53:44,797 kenma_eng INFO [7000, 9.778720961290439e-05]
2023-05-09 20:53:44,797 kenma_eng INFO loss_disc=2.362, loss_gen=3.467, loss_fm=14.945,loss_mel=19.150, loss_kl=1.176
2023-05-09 20:53:49,241 kenma_eng INFO ====> Epoch: 180
2023-05-09 20:53:57,736 kenma_eng INFO ====> Epoch: 181
2023-05-09 20:54:05,917 kenma_eng INFO ====> Epoch: 182
2023-05-09 20:54:13,861 kenma_eng INFO ====> Epoch: 183
2023-05-09 20:54:21,789 kenma_eng INFO ====> Epoch: 184
2023-05-09 20:54:26,908 kenma_eng INFO Train Epoch: 185 [77%]
2023-05-09 20:54:26,911 kenma_eng INFO [7200, 9.772610788423802e-05]
2023-05-09 20:54:26,911 kenma_eng INFO loss_disc=2.533, loss_gen=3.299, loss_fm=13.219,loss_mel=18.053, loss_kl=0.657
2023-05-09 20:54:29,985 kenma_eng INFO ====> Epoch: 185
2023-05-09 20:54:37,977 kenma_eng INFO ====> Epoch: 186
2023-05-09 20:54:45,961 kenma_eng INFO ====> Epoch: 187
2023-05-09 20:54:53,952 kenma_eng INFO ====> Epoch: 188
2023-05-09 20:55:01,946 kenma_eng INFO ====> Epoch: 189
2023-05-09 20:55:08,243 kenma_eng INFO Train Epoch: 190 [36%]
2023-05-09 20:55:08,245 kenma_eng INFO [7400, 9.766504433460612e-05]
2023-05-09 20:55:08,245 kenma_eng INFO loss_disc=2.156, loss_gen=3.444, loss_fm=12.861,loss_mel=17.696, loss_kl=1.166
2023-05-09 20:55:10,309 kenma_eng INFO ====> Epoch: 190
2023-05-09 20:55:18,576 kenma_eng INFO ====> Epoch: 191
2023-05-09 20:55:26,852 kenma_eng INFO ====> Epoch: 192
2023-05-09 20:55:34,988 kenma_eng INFO ====> Epoch: 193
2023-05-09 20:55:43,223 kenma_eng INFO ====> Epoch: 194
2023-05-09 20:55:50,928 kenma_eng INFO Train Epoch: 195 [69%]
2023-05-09 20:55:50,931 kenma_eng INFO [7600, 9.760401894015275e-05]
2023-05-09 20:55:50,931 kenma_eng INFO loss_disc=1.959, loss_gen=3.870, loss_fm=15.324,loss_mel=18.332, loss_kl=0.831
2023-05-09 20:55:52,006 kenma_eng INFO ====> Epoch: 195
2023-05-09 20:56:00,122 kenma_eng INFO ====> Epoch: 196
2023-05-09 20:56:08,349 kenma_eng INFO ====> Epoch: 197
2023-05-09 20:56:16,521 kenma_eng INFO ====> Epoch: 198
2023-05-09 20:56:24,472 kenma_eng INFO ====> Epoch: 199
2023-05-09 20:56:32,478 kenma_eng INFO Saving model and optimizer state at epoch 200 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 20:56:32,913 kenma_eng INFO Saving model and optimizer state at epoch 200 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 20:56:33,474 kenma_eng INFO ====> Epoch: 200
2023-05-09 20:56:33,679 kenma_eng INFO Train Epoch: 201 [28%]
2023-05-09 20:56:33,681 kenma_eng INFO [7800, 9.753083879807726e-05]
2023-05-09 20:56:33,681 kenma_eng INFO loss_disc=2.477, loss_gen=3.509, loss_fm=15.636,loss_mel=18.691, loss_kl=1.067
2023-05-09 20:56:42,041 kenma_eng INFO ====> Epoch: 201
2023-05-09 20:56:50,552 kenma_eng INFO ====> Epoch: 202
2023-05-09 20:56:58,949 kenma_eng INFO ====> Epoch: 203
2023-05-09 20:57:07,334 kenma_eng INFO ====> Epoch: 204
2023-05-09 20:57:15,871 kenma_eng INFO ====> Epoch: 205
2023-05-09 20:57:17,126 kenma_eng INFO Train Epoch: 206 [90%]
2023-05-09 20:57:17,128 kenma_eng INFO [8000, 9.746989726111722e-05]
2023-05-09 20:57:17,128 kenma_eng INFO loss_disc=2.512, loss_gen=3.563, loss_fm=13.922,loss_mel=17.755, loss_kl=0.860
2023-05-09 20:57:24,061 kenma_eng INFO ====> Epoch: 206
2023-05-09 20:57:32,264 kenma_eng INFO ====> Epoch: 207
2023-05-09 20:57:40,329 kenma_eng INFO ====> Epoch: 208
2023-05-09 20:57:48,451 kenma_eng INFO ====> Epoch: 209
2023-05-09 20:57:56,630 kenma_eng INFO ====> Epoch: 210
2023-05-09 20:57:58,915 kenma_eng INFO Train Epoch: 211 [67%]
2023-05-09 20:57:58,917 kenma_eng INFO [8200, 9.740899380309685e-05]
2023-05-09 20:57:58,917 kenma_eng INFO loss_disc=1.752, loss_gen=4.200, loss_fm=15.988,loss_mel=16.604, loss_kl=0.695
2023-05-09 20:58:04,931 kenma_eng INFO ====> Epoch: 211
2023-05-09 20:58:13,035 kenma_eng INFO ====> Epoch: 212
2023-05-09 20:58:21,267 kenma_eng INFO ====> Epoch: 213
2023-05-09 20:58:29,465 kenma_eng INFO ====> Epoch: 214
2023-05-09 20:58:37,686 kenma_eng INFO ====> Epoch: 215
2023-05-09 20:58:40,966 kenma_eng INFO Train Epoch: 216 [46%]
2023-05-09 20:58:40,968 kenma_eng INFO [8400, 9.734812840022278e-05]
2023-05-09 20:58:40,968 kenma_eng INFO loss_disc=2.474, loss_gen=2.986, loss_fm=8.636,loss_mel=18.572, loss_kl=0.230
2023-05-09 20:58:46,216 kenma_eng INFO ====> Epoch: 216
2023-05-09 20:58:54,844 kenma_eng INFO ====> Epoch: 217
2023-05-09 20:59:03,419 kenma_eng INFO ====> Epoch: 218
2023-05-09 20:59:11,541 kenma_eng INFO ====> Epoch: 219
2023-05-09 20:59:19,812 kenma_eng INFO ====> Epoch: 220
2023-05-09 20:59:24,262 kenma_eng INFO Train Epoch: 221 [10%]
2023-05-09 20:59:24,264 kenma_eng INFO [8600, 9.728730102871649e-05]
2023-05-09 20:59:24,264 kenma_eng INFO loss_disc=1.939, loss_gen=4.331, loss_fm=12.556,loss_mel=16.695, loss_kl=0.661
2023-05-09 20:59:28,230 kenma_eng INFO ====> Epoch: 221
2023-05-09 20:59:36,410 kenma_eng INFO ====> Epoch: 222
2023-05-09 20:59:44,625 kenma_eng INFO ====> Epoch: 223
2023-05-09 20:59:52,842 kenma_eng INFO ====> Epoch: 224
2023-05-09 21:00:00,923 kenma_eng INFO Saving model and optimizer state at epoch 225 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:00:01,361 kenma_eng INFO Saving model and optimizer state at epoch 225 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:00:01,923 kenma_eng INFO ====> Epoch: 225
2023-05-09 21:00:07,412 kenma_eng INFO Train Epoch: 226 [62%]
2023-05-09 21:00:07,415 kenma_eng INFO [8800, 9.722651166481428e-05]
2023-05-09 21:00:07,415 kenma_eng INFO loss_disc=2.493, loss_gen=3.295, loss_fm=12.241,loss_mel=18.439, loss_kl=1.108
2023-05-09 21:00:10,478 kenma_eng INFO ====> Epoch: 226
2023-05-09 21:00:18,882 kenma_eng INFO ====> Epoch: 227
2023-05-09 21:00:27,150 kenma_eng INFO ====> Epoch: 228
2023-05-09 21:00:35,780 kenma_eng INFO ====> Epoch: 229
2023-05-09 21:00:43,821 kenma_eng INFO ====> Epoch: 230
2023-05-09 21:00:50,462 kenma_eng INFO Train Epoch: 231 [62%]
2023-05-09 21:00:50,464 kenma_eng INFO [9000, 9.716576028476738e-05]
2023-05-09 21:00:50,464 kenma_eng INFO loss_disc=2.508, loss_gen=2.952, loss_fm=13.965,loss_mel=17.689, loss_kl=0.791
2023-05-09 21:00:52,390 kenma_eng INFO ====> Epoch: 231
2023-05-09 21:01:00,840 kenma_eng INFO ====> Epoch: 232
2023-05-09 21:01:08,957 kenma_eng INFO ====> Epoch: 233
2023-05-09 21:01:16,907 kenma_eng INFO ====> Epoch: 234
2023-05-09 21:01:24,974 kenma_eng INFO ====> Epoch: 235
2023-05-09 21:01:32,280 kenma_eng INFO Train Epoch: 236 [82%]
2023-05-09 21:01:32,282 kenma_eng INFO [9200, 9.710504686484176e-05]
2023-05-09 21:01:32,282 kenma_eng INFO loss_disc=2.700, loss_gen=3.582, loss_fm=11.330,loss_mel=18.206, loss_kl=1.473
2023-05-09 21:01:33,114 kenma_eng INFO ====> Epoch: 236
2023-05-09 21:01:41,301 kenma_eng INFO ====> Epoch: 237
2023-05-09 21:01:49,441 kenma_eng INFO ====> Epoch: 238
2023-05-09 21:01:57,458 kenma_eng INFO ====> Epoch: 239
2023-05-09 21:02:05,507 kenma_eng INFO ====> Epoch: 240
2023-05-09 21:02:13,478 kenma_eng INFO ====> Epoch: 241
2023-05-09 21:02:13,907 kenma_eng INFO Train Epoch: 242 [5%]
2023-05-09 21:02:13,909 kenma_eng INFO [9400, 9.703224083489565e-05]
2023-05-09 21:02:13,909 kenma_eng INFO loss_disc=1.995, loss_gen=3.838, loss_fm=15.058,loss_mel=17.005, loss_kl=1.131
2023-05-09 21:02:21,718 kenma_eng INFO ====> Epoch: 242
2023-05-09 21:02:29,664 kenma_eng INFO ====> Epoch: 243
2023-05-09 21:02:37,606 kenma_eng INFO ====> Epoch: 244
2023-05-09 21:02:45,594 kenma_eng INFO ====> Epoch: 245
2023-05-09 21:02:53,662 kenma_eng INFO ====> Epoch: 246
2023-05-09 21:02:55,137 kenma_eng INFO Train Epoch: 247 [79%]
2023-05-09 21:02:55,139 kenma_eng INFO [9600, 9.69716108437664e-05]
2023-05-09 21:02:55,139 kenma_eng INFO loss_disc=2.117, loss_gen=3.975, loss_fm=15.892,loss_mel=18.095, loss_kl=0.827
2023-05-09 21:03:01,999 kenma_eng INFO ====> Epoch: 247
2023-05-09 21:03:10,432 kenma_eng INFO ====> Epoch: 248
2023-05-09 21:03:18,749 kenma_eng INFO ====> Epoch: 249
2023-05-09 21:03:26,747 kenma_eng INFO Saving model and optimizer state at epoch 250 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:03:27,185 kenma_eng INFO Saving model and optimizer state at epoch 250 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:03:27,748 kenma_eng INFO ====> Epoch: 250
2023-05-09 21:03:36,042 kenma_eng INFO ====> Epoch: 251
2023-05-09 21:03:38,603 kenma_eng INFO Train Epoch: 252 [18%]
2023-05-09 21:03:38,606 kenma_eng INFO [9800, 9.691101873690936e-05]
2023-05-09 21:03:38,606 kenma_eng INFO loss_disc=2.159, loss_gen=3.809, loss_fm=13.008,loss_mel=17.674, loss_kl=0.650
2023-05-09 21:03:44,445 kenma_eng INFO ====> Epoch: 252
2023-05-09 21:03:52,383 kenma_eng INFO ====> Epoch: 253
2023-05-09 21:04:00,333 kenma_eng INFO ====> Epoch: 254
2023-05-09 21:04:08,369 kenma_eng INFO ====> Epoch: 255
2023-05-09 21:04:16,389 kenma_eng INFO ====> Epoch: 256
2023-05-09 21:04:19,877 kenma_eng INFO Train Epoch: 257 [18%]
2023-05-09 21:04:19,879 kenma_eng INFO [10000, 9.685046449065278e-05]
2023-05-09 21:04:19,879 kenma_eng INFO loss_disc=2.116, loss_gen=3.549, loss_fm=14.202,loss_mel=17.730, loss_kl=-0.003
2023-05-09 21:04:24,653 kenma_eng INFO ====> Epoch: 257
2023-05-09 21:04:32,795 kenma_eng INFO ====> Epoch: 258
2023-05-09 21:04:41,014 kenma_eng INFO ====> Epoch: 259
2023-05-09 21:04:48,955 kenma_eng INFO ====> Epoch: 260
2023-05-09 21:04:56,901 kenma_eng INFO ====> Epoch: 261
2023-05-09 21:05:01,369 kenma_eng INFO Train Epoch: 262 [64%]
2023-05-09 21:05:01,371 kenma_eng INFO [10200, 9.678994808133967e-05]
2023-05-09 21:05:01,371 kenma_eng INFO loss_disc=2.436, loss_gen=3.018, loss_fm=11.656,loss_mel=18.027, loss_kl=1.621
2023-05-09 21:05:05,046 kenma_eng INFO ====> Epoch: 262
2023-05-09 21:05:13,289 kenma_eng INFO ====> Epoch: 263
2023-05-09 21:05:21,545 kenma_eng INFO ====> Epoch: 264
2023-05-09 21:05:29,924 kenma_eng INFO ====> Epoch: 265
2023-05-09 21:05:37,904 kenma_eng INFO ====> Epoch: 266
2023-05-09 21:05:43,446 kenma_eng INFO Train Epoch: 267 [92%]
2023-05-09 21:05:43,448 kenma_eng INFO [10400, 9.67294694853279e-05]
2023-05-09 21:05:43,448 kenma_eng INFO loss_disc=2.553, loss_gen=3.372, loss_fm=12.854,loss_mel=19.093, loss_kl=0.896
2023-05-09 21:05:46,176 kenma_eng INFO ====> Epoch: 267
2023-05-09 21:05:54,749 kenma_eng INFO ====> Epoch: 268
2023-05-09 21:06:02,805 kenma_eng INFO ====> Epoch: 269
2023-05-09 21:06:11,368 kenma_eng INFO ====> Epoch: 270
2023-05-09 21:06:19,398 kenma_eng INFO ====> Epoch: 271
2023-05-09 21:06:25,951 kenma_eng INFO Train Epoch: 272 [18%]
2023-05-09 21:06:25,953 kenma_eng INFO [10600, 9.666902867899003e-05]
2023-05-09 21:06:25,953 kenma_eng INFO loss_disc=2.386, loss_gen=3.478, loss_fm=13.314,loss_mel=17.381, loss_kl=0.525
2023-05-09 21:06:27,583 kenma_eng INFO ====> Epoch: 272
2023-05-09 21:06:35,744 kenma_eng INFO ====> Epoch: 273
2023-05-09 21:06:43,757 kenma_eng INFO ====> Epoch: 274
2023-05-09 21:06:51,811 kenma_eng INFO Saving model and optimizer state at epoch 275 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:06:52,250 kenma_eng INFO Saving model and optimizer state at epoch 275 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:06:52,814 kenma_eng INFO ====> Epoch: 275
2023-05-09 21:07:00,853 kenma_eng INFO ====> Epoch: 276
2023-05-09 21:07:08,630 kenma_eng INFO Train Epoch: 277 [97%]
2023-05-09 21:07:08,632 kenma_eng INFO [10800, 9.660862563871342e-05]
2023-05-09 21:07:08,632 kenma_eng INFO loss_disc=2.441, loss_gen=3.525, loss_fm=12.878,loss_mel=17.638, loss_kl=0.755
2023-05-09 21:07:09,272 kenma_eng INFO ====> Epoch: 277
2023-05-09 21:07:17,222 kenma_eng INFO ====> Epoch: 278
2023-05-09 21:07:25,155 kenma_eng INFO ====> Epoch: 279
2023-05-09 21:07:33,128 kenma_eng INFO ====> Epoch: 280
2023-05-09 21:07:41,304 kenma_eng INFO ====> Epoch: 281
2023-05-09 21:07:49,301 kenma_eng INFO ====> Epoch: 282
2023-05-09 21:07:49,909 kenma_eng INFO Train Epoch: 283 [44%]
2023-05-09 21:07:49,910 kenma_eng INFO [11000, 9.653619180835758e-05]
2023-05-09 21:07:49,911 kenma_eng INFO loss_disc=1.755, loss_gen=4.020, loss_fm=15.325,loss_mel=16.261, loss_kl=1.847
2023-05-09 21:07:57,492 kenma_eng INFO ====> Epoch: 283
2023-05-09 21:08:05,546 kenma_eng INFO ====> Epoch: 284
2023-05-09 21:08:13,664 kenma_eng INFO ====> Epoch: 285
2023-05-09 21:08:21,854 kenma_eng INFO ====> Epoch: 286
2023-05-09 21:08:29,933 kenma_eng INFO ====> Epoch: 287
2023-05-09 21:08:31,584 kenma_eng INFO Train Epoch: 288 [46%]
2023-05-09 21:08:31,586 kenma_eng INFO [11200, 9.647587177037196e-05]
2023-05-09 21:08:31,586 kenma_eng INFO loss_disc=2.150, loss_gen=3.999, loss_fm=13.569,loss_mel=18.068, loss_kl=1.044
2023-05-09 21:08:38,061 kenma_eng INFO ====> Epoch: 288
2023-05-09 21:08:46,235 kenma_eng INFO ====> Epoch: 289
2023-05-09 21:08:54,518 kenma_eng INFO ====> Epoch: 290
2023-05-09 21:09:02,831 kenma_eng INFO ====> Epoch: 291
2023-05-09 21:09:11,005 kenma_eng INFO ====> Epoch: 292
2023-05-09 21:09:13,790 kenma_eng INFO Train Epoch: 293 [26%]
2023-05-09 21:09:13,792 kenma_eng INFO [11400, 9.641558942298625e-05]
2023-05-09 21:09:13,792 kenma_eng INFO loss_disc=2.679, loss_gen=3.798, loss_fm=12.064,loss_mel=17.419, loss_kl=0.648
2023-05-09 21:09:19,258 kenma_eng INFO ====> Epoch: 293
2023-05-09 21:09:27,199 kenma_eng INFO ====> Epoch: 294
2023-05-09 21:09:35,229 kenma_eng INFO ====> Epoch: 295
2023-05-09 21:09:43,168 kenma_eng INFO ====> Epoch: 296
2023-05-09 21:09:51,143 kenma_eng INFO ====> Epoch: 297
2023-05-09 21:09:54,905 kenma_eng INFO Train Epoch: 298 [54%]
2023-05-09 21:09:54,907 kenma_eng INFO [11600, 9.635534474264972e-05]
2023-05-09 21:09:54,907 kenma_eng INFO loss_disc=2.583, loss_gen=3.431, loss_fm=9.347,loss_mel=17.888, loss_kl=0.512
2023-05-09 21:09:59,385 kenma_eng INFO ====> Epoch: 298
2023-05-09 21:10:07,325 kenma_eng INFO ====> Epoch: 299
2023-05-09 21:10:15,316 kenma_eng INFO Saving model and optimizer state at epoch 300 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:10:15,755 kenma_eng INFO Saving model and optimizer state at epoch 300 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:10:16,324 kenma_eng INFO ====> Epoch: 300
2023-05-09 21:10:24,391 kenma_eng INFO ====> Epoch: 301
2023-05-09 21:10:32,356 kenma_eng INFO ====> Epoch: 302
2023-05-09 21:10:37,021 kenma_eng INFO Train Epoch: 303 [85%]
2023-05-09 21:10:37,023 kenma_eng INFO [11800, 9.629513770582634e-05]
2023-05-09 21:10:37,024 kenma_eng INFO loss_disc=1.861, loss_gen=4.026, loss_fm=16.783,loss_mel=15.677, loss_kl=0.269
2023-05-09 21:10:40,505 kenma_eng INFO ====> Epoch: 303
2023-05-09 21:10:48,492 kenma_eng INFO ====> Epoch: 304
2023-05-09 21:10:56,478 kenma_eng INFO ====> Epoch: 305
2023-05-09 21:11:04,461 kenma_eng INFO ====> Epoch: 306
2023-05-09 21:11:12,451 kenma_eng INFO ====> Epoch: 307
2023-05-09 21:11:18,176 kenma_eng INFO Train Epoch: 308 [72%]
2023-05-09 21:11:18,178 kenma_eng INFO [12000, 9.62349682889948e-05]
2023-05-09 21:11:18,178 kenma_eng INFO loss_disc=1.926, loss_gen=3.737, loss_fm=16.885,loss_mel=16.519, loss_kl=-0.179
2023-05-09 21:11:20,640 kenma_eng INFO ====> Epoch: 308
2023-05-09 21:11:28,632 kenma_eng INFO ====> Epoch: 309
2023-05-09 21:11:36,619 kenma_eng INFO ====> Epoch: 310
2023-05-09 21:11:44,596 kenma_eng INFO ====> Epoch: 311
2023-05-09 21:11:52,616 kenma_eng INFO ====> Epoch: 312
2023-05-09 21:11:59,343 kenma_eng INFO Train Epoch: 313 [87%]
2023-05-09 21:11:59,345 kenma_eng INFO [12200, 9.617483646864849e-05]
2023-05-09 21:11:59,345 kenma_eng INFO loss_disc=2.626, loss_gen=2.926, loss_fm=9.983,loss_mel=12.525, loss_kl=0.938
2023-05-09 21:12:00,776 kenma_eng INFO ====> Epoch: 313
2023-05-09 21:12:08,774 kenma_eng INFO ====> Epoch: 314
2023-05-09 21:12:16,756 kenma_eng INFO ====> Epoch: 315
2023-05-09 21:12:24,741 kenma_eng INFO ====> Epoch: 316
2023-05-09 21:12:32,729 kenma_eng INFO ====> Epoch: 317
2023-05-09 21:12:40,505 kenma_eng INFO Train Epoch: 318 [87%]
2023-05-09 21:12:40,507 kenma_eng INFO [12400, 9.611474222129547e-05]
2023-05-09 21:12:40,507 kenma_eng INFO loss_disc=2.263, loss_gen=3.997, loss_fm=10.929,loss_mel=13.891, loss_kl=0.541
2023-05-09 21:12:40,977 kenma_eng INFO ====> Epoch: 318
2023-05-09 21:12:48,902 kenma_eng INFO ====> Epoch: 319
2023-05-09 21:12:56,889 kenma_eng INFO ====> Epoch: 320
2023-05-09 21:13:04,878 kenma_eng INFO ====> Epoch: 321
2023-05-09 21:13:12,867 kenma_eng INFO ====> Epoch: 322
2023-05-09 21:13:20,852 kenma_eng INFO ====> Epoch: 323
2023-05-09 21:13:21,675 kenma_eng INFO Train Epoch: 324 [41%]
2023-05-09 21:13:21,677 kenma_eng INFO [12600, 9.604267868776807e-05]
2023-05-09 21:13:21,677 kenma_eng INFO loss_disc=2.245, loss_gen=3.474, loss_fm=11.838,loss_mel=17.562, loss_kl=0.426
2023-05-09 21:13:29,046 kenma_eng INFO ====> Epoch: 324
2023-05-09 21:13:37,029 kenma_eng INFO Saving model and optimizer state at epoch 325 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:13:37,465 kenma_eng INFO Saving model and optimizer state at epoch 325 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:13:38,032 kenma_eng INFO ====> Epoch: 325
2023-05-09 21:13:46,044 kenma_eng INFO ====> Epoch: 326
2023-05-09 21:13:54,035 kenma_eng INFO ====> Epoch: 327
2023-05-09 21:14:02,015 kenma_eng INFO ====> Epoch: 328
2023-05-09 21:14:03,871 kenma_eng INFO Train Epoch: 329 [97%]
2023-05-09 21:14:03,873 kenma_eng INFO [12800, 9.5982667018381e-05]
2023-05-09 21:14:03,873 kenma_eng INFO loss_disc=2.403, loss_gen=3.759, loss_fm=12.367,loss_mel=17.452, loss_kl=0.794
2023-05-09 21:14:10,242 kenma_eng INFO ====> Epoch: 329
2023-05-09 21:14:18,197 kenma_eng INFO ====> Epoch: 330
2023-05-09 21:14:26,188 kenma_eng INFO ====> Epoch: 331
2023-05-09 21:14:34,177 kenma_eng INFO ====> Epoch: 332
2023-05-09 21:14:42,168 kenma_eng INFO ====> Epoch: 333
2023-05-09 21:14:45,032 kenma_eng INFO Train Epoch: 334 [85%]
2023-05-09 21:14:45,034 kenma_eng INFO [13000, 9.592269284691169e-05]
2023-05-09 21:14:45,034 kenma_eng INFO loss_disc=1.775, loss_gen=3.483, loss_fm=17.744,loss_mel=15.281, loss_kl=0.838
2023-05-09 21:14:50,360 kenma_eng INFO ====> Epoch: 334
2023-05-09 21:14:58,336 kenma_eng INFO ====> Epoch: 335
2023-05-09 21:15:06,328 kenma_eng INFO ====> Epoch: 336
2023-05-09 21:15:14,306 kenma_eng INFO ====> Epoch: 337
2023-05-09 21:15:22,302 kenma_eng INFO ====> Epoch: 338
2023-05-09 21:15:26,193 kenma_eng INFO Train Epoch: 339 [62%]
2023-05-09 21:15:26,195 kenma_eng INFO [13200, 9.586275614992974e-05]
2023-05-09 21:15:26,195 kenma_eng INFO loss_disc=2.687, loss_gen=3.755, loss_fm=12.383,loss_mel=17.574, loss_kl=0.807
2023-05-09 21:15:30,536 kenma_eng INFO ====> Epoch: 339
2023-05-09 21:15:38,482 kenma_eng INFO ====> Epoch: 340
2023-05-09 21:15:46,458 kenma_eng INFO ====> Epoch: 341
2023-05-09 21:15:54,449 kenma_eng INFO ====> Epoch: 342
2023-05-09 21:16:02,438 kenma_eng INFO ====> Epoch: 343
2023-05-09 21:16:07,352 kenma_eng INFO Train Epoch: 344 [23%]
2023-05-09 21:16:07,354 kenma_eng INFO [13400, 9.580285690401946e-05]
2023-05-09 21:16:07,354 kenma_eng INFO loss_disc=1.845, loss_gen=4.353, loss_fm=14.933,loss_mel=15.966, loss_kl=0.198
2023-05-09 21:16:10,634 kenma_eng INFO ====> Epoch: 344
2023-05-09 21:16:18,697 kenma_eng INFO ====> Epoch: 345
2023-05-09 21:16:26,607 kenma_eng INFO ====> Epoch: 346
2023-05-09 21:16:34,591 kenma_eng INFO ====> Epoch: 347
2023-05-09 21:16:42,587 kenma_eng INFO ====> Epoch: 348
2023-05-09 21:16:48,524 kenma_eng INFO Train Epoch: 349 [59%]
2023-05-09 21:16:48,526 kenma_eng INFO [13600, 9.574299508577979e-05]
2023-05-09 21:16:48,526 kenma_eng INFO loss_disc=2.193, loss_gen=4.162, loss_fm=14.194,loss_mel=19.176, loss_kl=0.907
2023-05-09 21:16:50,776 kenma_eng INFO ====> Epoch: 349
2023-05-09 21:16:58,756 kenma_eng INFO Saving model and optimizer state at epoch 350 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:16:59,198 kenma_eng INFO Saving model and optimizer state at epoch 350 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:16:59,770 kenma_eng INFO ====> Epoch: 350
2023-05-09 21:17:07,821 kenma_eng INFO ====> Epoch: 351
2023-05-09 21:17:15,751 kenma_eng INFO ====> Epoch: 352
2023-05-09 21:17:23,743 kenma_eng INFO ====> Epoch: 353
2023-05-09 21:17:30,700 kenma_eng INFO Train Epoch: 354 [44%]
2023-05-09 21:17:30,702 kenma_eng INFO [13800, 9.568317067182427e-05]
2023-05-09 21:17:30,702 kenma_eng INFO loss_disc=1.936, loss_gen=3.993, loss_fm=15.608,loss_mel=15.394, loss_kl=0.963
2023-05-09 21:17:31,927 kenma_eng INFO ====> Epoch: 354
2023-05-09 21:17:39,921 kenma_eng INFO ====> Epoch: 355
2023-05-09 21:17:47,979 kenma_eng INFO ====> Epoch: 356
2023-05-09 21:17:55,899 kenma_eng INFO ====> Epoch: 357
2023-05-09 21:18:03,876 kenma_eng INFO ====> Epoch: 358
2023-05-09 21:18:11,867 kenma_eng INFO Train Epoch: 359 [90%]
2023-05-09 21:18:11,869 kenma_eng INFO [14000, 9.562338363878108e-05]
2023-05-09 21:18:11,869 kenma_eng INFO loss_disc=2.258, loss_gen=3.559, loss_fm=14.245,loss_mel=17.629, loss_kl=1.208
2023-05-09 21:18:12,064 kenma_eng INFO ====> Epoch: 359
2023-05-09 21:18:20,059 kenma_eng INFO ====> Epoch: 360
2023-05-09 21:18:28,051 kenma_eng INFO ====> Epoch: 361
2023-05-09 21:18:36,045 kenma_eng INFO ====> Epoch: 362
2023-05-09 21:18:44,025 kenma_eng INFO ====> Epoch: 363
2023-05-09 21:18:52,023 kenma_eng INFO ====> Epoch: 364
2023-05-09 21:18:53,037 kenma_eng INFO Train Epoch: 365 [97%]
2023-05-09 21:18:53,039 kenma_eng INFO [14200, 9.555168850904757e-05]
2023-05-09 21:18:53,045 kenma_eng INFO loss_disc=2.440, loss_gen=3.454, loss_fm=12.333,loss_mel=17.476, loss_kl=0.914
2023-05-09 21:19:00,203 kenma_eng INFO ====> Epoch: 365
2023-05-09 21:19:08,194 kenma_eng INFO ====> Epoch: 366
2023-05-09 21:19:16,177 kenma_eng INFO ====> Epoch: 367
2023-05-09 21:19:24,228 kenma_eng INFO ====> Epoch: 368
2023-05-09 21:19:32,144 kenma_eng INFO ====> Epoch: 369
2023-05-09 21:19:34,200 kenma_eng INFO Train Epoch: 370 [59%]
2023-05-09 21:19:34,201 kenma_eng INFO [14400, 9.54919836318146e-05]
2023-05-09 21:19:34,202 kenma_eng INFO loss_disc=2.685, loss_gen=3.006, loss_fm=12.716,loss_mel=16.292, loss_kl=0.314
2023-05-09 21:19:40,576 kenma_eng INFO ====> Epoch: 370
2023-05-09 21:19:48,955 kenma_eng INFO ====> Epoch: 371
2023-05-09 21:19:57,010 kenma_eng INFO ====> Epoch: 372
2023-05-09 21:20:05,441 kenma_eng INFO ====> Epoch: 373
2023-05-09 21:20:13,642 kenma_eng INFO ====> Epoch: 374
2023-05-09 21:20:16,779 kenma_eng INFO Train Epoch: 375 [36%]
2023-05-09 21:20:16,781 kenma_eng INFO [14600, 9.543231606080218e-05]
2023-05-09 21:20:16,782 kenma_eng INFO loss_disc=1.840, loss_gen=4.006, loss_fm=13.726,loss_mel=16.473, loss_kl=0.541
2023-05-09 21:20:22,038 kenma_eng INFO Saving model and optimizer state at epoch 375 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:20:22,505 kenma_eng INFO Saving model and optimizer state at epoch 375 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:20:23,103 kenma_eng INFO ====> Epoch: 375
2023-05-09 21:20:31,341 kenma_eng INFO ====> Epoch: 376
2023-05-09 21:20:39,460 kenma_eng INFO ====> Epoch: 377
2023-05-09 21:20:47,638 kenma_eng INFO ====> Epoch: 378
2023-05-09 21:20:56,126 kenma_eng INFO ====> Epoch: 379
2023-05-09 21:21:00,430 kenma_eng INFO Train Epoch: 380 [0%]
2023-05-09 21:21:00,432 kenma_eng INFO [14800, 9.537268577269974e-05]
2023-05-09 21:21:00,432 kenma_eng INFO loss_disc=2.470, loss_gen=3.482, loss_fm=11.353,loss_mel=17.586, loss_kl=0.401
2023-05-09 21:21:04,625 kenma_eng INFO ====> Epoch: 380
2023-05-09 21:21:12,836 kenma_eng INFO ====> Epoch: 381
2023-05-09 21:21:20,920 kenma_eng INFO ====> Epoch: 382
2023-05-09 21:21:29,134 kenma_eng INFO ====> Epoch: 383
2023-05-09 21:21:37,166 kenma_eng INFO ====> Epoch: 384
2023-05-09 21:21:42,546 kenma_eng INFO Train Epoch: 385 [41%]
2023-05-09 21:21:42,548 kenma_eng INFO [15000, 9.53130927442113e-05]
2023-05-09 21:21:42,548 kenma_eng INFO loss_disc=2.589, loss_gen=3.669, loss_fm=13.503,loss_mel=17.112, loss_kl=0.693
2023-05-09 21:21:45,876 kenma_eng INFO ====> Epoch: 385
2023-05-09 21:21:54,160 kenma_eng INFO ====> Epoch: 386
2023-05-09 21:22:02,495 kenma_eng INFO ====> Epoch: 387
2023-05-09 21:22:10,778 kenma_eng INFO ====> Epoch: 388
2023-05-09 21:22:19,259 kenma_eng INFO ====> Epoch: 389
2023-05-09 21:22:25,487 kenma_eng INFO Train Epoch: 390 [67%]
2023-05-09 21:22:25,490 kenma_eng INFO [15200, 9.525353695205543e-05]
2023-05-09 21:22:25,490 kenma_eng INFO loss_disc=1.786, loss_gen=4.069, loss_fm=17.041,loss_mel=14.920, loss_kl=1.712
2023-05-09 21:22:27,683 kenma_eng INFO ====> Epoch: 390
2023-05-09 21:22:35,949 kenma_eng INFO ====> Epoch: 391
2023-05-09 21:22:44,300 kenma_eng INFO ====> Epoch: 392
2023-05-09 21:22:52,952 kenma_eng INFO ====> Epoch: 393
2023-05-09 21:23:01,082 kenma_eng INFO ====> Epoch: 394
2023-05-09 21:23:08,425 kenma_eng INFO Train Epoch: 395 [15%]
2023-05-09 21:23:08,426 kenma_eng INFO [15400, 9.519401837296521e-05]
2023-05-09 21:23:08,426 kenma_eng INFO loss_disc=1.931, loss_gen=4.125, loss_fm=12.691,loss_mel=16.847, loss_kl=0.869
2023-05-09 21:23:09,601 kenma_eng INFO ====> Epoch: 395
2023-05-09 21:23:18,144 kenma_eng INFO ====> Epoch: 396
2023-05-09 21:23:26,755 kenma_eng INFO ====> Epoch: 397
2023-05-09 21:23:34,901 kenma_eng INFO ====> Epoch: 398
2023-05-09 21:23:43,147 kenma_eng INFO ====> Epoch: 399
2023-05-09 21:23:51,702 kenma_eng INFO Saving model and optimizer state at epoch 400 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:23:52,162 kenma_eng INFO Saving model and optimizer state at epoch 400 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:23:52,734 kenma_eng INFO ====> Epoch: 400
2023-05-09 21:23:53,031 kenma_eng INFO Train Epoch: 401 [77%]
2023-05-09 21:23:53,031 kenma_eng INFO [15600, 9.512264516656537e-05]
2023-05-09 21:23:53,031 kenma_eng INFO loss_disc=2.284, loss_gen=3.447, loss_fm=11.946,loss_mel=18.201, loss_kl=1.140
2023-05-09 21:24:01,127 kenma_eng INFO ====> Epoch: 401
2023-05-09 21:24:09,351 kenma_eng INFO ====> Epoch: 402
2023-05-09 21:24:17,917 kenma_eng INFO ====> Epoch: 403
2023-05-09 21:24:26,342 kenma_eng INFO ====> Epoch: 404
2023-05-09 21:24:34,931 kenma_eng INFO ====> Epoch: 405
2023-05-09 21:24:36,278 kenma_eng INFO Train Epoch: 406 [3%]
2023-05-09 21:24:36,279 kenma_eng INFO [15800, 9.506320837439182e-05]
2023-05-09 21:24:36,279 kenma_eng INFO loss_disc=1.807, loss_gen=3.950, loss_fm=15.327,loss_mel=15.740, loss_kl=0.952
2023-05-09 21:24:43,226 kenma_eng INFO ====> Epoch: 406
2023-05-09 21:24:51,223 kenma_eng INFO ====> Epoch: 407
2023-05-09 21:24:59,306 kenma_eng INFO ====> Epoch: 408
2023-05-09 21:25:07,442 kenma_eng INFO ====> Epoch: 409
2023-05-09 21:25:15,394 kenma_eng INFO ====> Epoch: 410
2023-05-09 21:25:17,654 kenma_eng INFO Train Epoch: 411 [56%]
2023-05-09 21:25:17,656 kenma_eng INFO [16000, 9.500380872092753e-05]
2023-05-09 21:25:17,657 kenma_eng INFO loss_disc=2.256, loss_gen=4.341, loss_fm=12.920,loss_mel=18.133, loss_kl=1.501
2023-05-09 21:25:24,067 kenma_eng INFO ====> Epoch: 411
2023-05-09 21:25:32,488 kenma_eng INFO ====> Epoch: 412
2023-05-09 21:25:40,421 kenma_eng INFO ====> Epoch: 413
2023-05-09 21:25:48,414 kenma_eng INFO ====> Epoch: 414
2023-05-09 21:25:56,394 kenma_eng INFO ====> Epoch: 415
2023-05-09 21:25:59,836 kenma_eng INFO Train Epoch: 416 [59%]
2023-05-09 21:25:59,838 kenma_eng INFO [16200, 9.494444618296661e-05]
2023-05-09 21:25:59,838 kenma_eng INFO loss_disc=2.364, loss_gen=3.963, loss_fm=12.924,loss_mel=17.560, loss_kl=0.709
2023-05-09 21:26:04,991 kenma_eng INFO ====> Epoch: 416
2023-05-09 21:26:12,979 kenma_eng INFO ====> Epoch: 417
2023-05-09 21:26:21,335 kenma_eng INFO ====> Epoch: 418
2023-05-09 21:26:29,637 kenma_eng INFO ====> Epoch: 419
2023-05-09 21:26:37,909 kenma_eng INFO ====> Epoch: 420
2023-05-09 21:26:42,270 kenma_eng INFO Train Epoch: 421 [54%]
2023-05-09 21:26:42,272 kenma_eng INFO [16400, 9.488512073731768e-05]
2023-05-09 21:26:42,272 kenma_eng INFO loss_disc=2.338, loss_gen=3.032, loss_fm=10.870,loss_mel=17.274, loss_kl=0.882
2023-05-09 21:26:46,146 kenma_eng INFO ====> Epoch: 421
2023-05-09 21:26:54,607 kenma_eng INFO ====> Epoch: 422
2023-05-09 21:27:02,579 kenma_eng INFO ====> Epoch: 423
2023-05-09 21:27:10,989 kenma_eng INFO ====> Epoch: 424
2023-05-09 21:27:19,391 kenma_eng INFO Saving model and optimizer state at epoch 425 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:27:19,824 kenma_eng INFO Saving model and optimizer state at epoch 425 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:27:20,386 kenma_eng INFO ====> Epoch: 425
2023-05-09 21:27:25,850 kenma_eng INFO Train Epoch: 426 [21%]
2023-05-09 21:27:25,852 kenma_eng INFO [16600, 9.482583236080386e-05]
2023-05-09 21:27:25,852 kenma_eng INFO loss_disc=2.086, loss_gen=3.709, loss_fm=13.771,loss_mel=17.299, loss_kl=0.293
2023-05-09 21:27:28,783 kenma_eng INFO ====> Epoch: 426
2023-05-09 21:27:36,889 kenma_eng INFO ====> Epoch: 427
2023-05-09 21:27:45,112 kenma_eng INFO ====> Epoch: 428
2023-05-09 21:27:53,109 kenma_eng INFO ====> Epoch: 429
2023-05-09 21:28:01,276 kenma_eng INFO ====> Epoch: 430
2023-05-09 21:28:07,907 kenma_eng INFO Train Epoch: 431 [3%]
2023-05-09 21:28:07,910 kenma_eng INFO [16800, 9.47665810302627e-05]
2023-05-09 21:28:07,910 kenma_eng INFO loss_disc=1.856, loss_gen=3.722, loss_fm=16.854,loss_mel=15.101, loss_kl=1.281
2023-05-09 21:28:09,750 kenma_eng INFO ====> Epoch: 431
2023-05-09 21:28:17,818 kenma_eng INFO ====> Epoch: 432
2023-05-09 21:28:26,080 kenma_eng INFO ====> Epoch: 433
2023-05-09 21:28:34,318 kenma_eng INFO ====> Epoch: 434
2023-05-09 21:28:42,639 kenma_eng INFO ====> Epoch: 435
2023-05-09 21:28:50,450 kenma_eng INFO Train Epoch: 436 [3%]
2023-05-09 21:28:50,453 kenma_eng INFO [17000, 9.470736672254626e-05]
2023-05-09 21:28:50,453 kenma_eng INFO loss_disc=1.959, loss_gen=4.653, loss_fm=13.482,loss_mel=15.416, loss_kl=1.061
2023-05-09 21:28:51,344 kenma_eng INFO ====> Epoch: 436
2023-05-09 21:28:59,778 kenma_eng INFO ====> Epoch: 437
2023-05-09 21:29:08,351 kenma_eng INFO ====> Epoch: 438
2023-05-09 21:29:16,644 kenma_eng INFO ====> Epoch: 439
2023-05-09 21:29:24,980 kenma_eng INFO ====> Epoch: 440
2023-05-09 21:29:33,241 kenma_eng INFO ====> Epoch: 441
2023-05-09 21:29:33,653 kenma_eng INFO Train Epoch: 442 [0%]
2023-05-09 21:29:33,656 kenma_eng INFO [17200, 9.463635839084426e-05]
2023-05-09 21:29:33,656 kenma_eng INFO loss_disc=2.317, loss_gen=3.665, loss_fm=14.461,loss_mel=17.030, loss_kl=0.969
2023-05-09 21:29:41,925 kenma_eng INFO ====> Epoch: 442
2023-05-09 21:29:50,373 kenma_eng INFO ====> Epoch: 443
2023-05-09 21:29:59,049 kenma_eng INFO ====> Epoch: 444
2023-05-09 21:30:07,016 kenma_eng INFO ====> Epoch: 445
2023-05-09 21:30:15,172 kenma_eng INFO ====> Epoch: 446
2023-05-09 21:30:16,723 kenma_eng INFO Train Epoch: 447 [92%]
2023-05-09 21:30:16,725 kenma_eng INFO [17400, 9.457722545193272e-05]
2023-05-09 21:30:16,725 kenma_eng INFO loss_disc=2.261, loss_gen=3.934, loss_fm=12.302,loss_mel=17.423, loss_kl=0.552
2023-05-09 21:30:23,852 kenma_eng INFO ====> Epoch: 447
2023-05-09 21:30:31,908 kenma_eng INFO ====> Epoch: 448
2023-05-09 21:30:40,202 kenma_eng INFO ====> Epoch: 449
2023-05-09 21:30:48,187 kenma_eng INFO Saving model and optimizer state at epoch 450 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:30:48,624 kenma_eng INFO Saving model and optimizer state at epoch 450 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:30:49,189 kenma_eng INFO ====> Epoch: 450
2023-05-09 21:30:57,200 kenma_eng INFO ====> Epoch: 451
2023-05-09 21:30:59,639 kenma_eng INFO Train Epoch: 452 [13%]
2023-05-09 21:30:59,642 kenma_eng INFO [17600, 9.451812946186962e-05]
2023-05-09 21:30:59,642 kenma_eng INFO loss_disc=2.594, loss_gen=3.073, loss_fm=11.133,loss_mel=17.329, loss_kl=1.067
2023-05-09 21:31:05,378 kenma_eng INFO ====> Epoch: 452
2023-05-09 21:31:13,475 kenma_eng INFO ====> Epoch: 453
2023-05-09 21:31:21,395 kenma_eng INFO ====> Epoch: 454
2023-05-09 21:31:29,334 kenma_eng INFO ====> Epoch: 455
2023-05-09 21:31:37,372 kenma_eng INFO ====> Epoch: 456
2023-05-09 21:31:40,895 kenma_eng INFO Train Epoch: 457 [44%]
2023-05-09 21:31:40,898 kenma_eng INFO [17800, 9.445907039756771e-05]
2023-05-09 21:31:40,898 kenma_eng INFO loss_disc=1.609, loss_gen=4.735, loss_fm=16.773,loss_mel=14.983, loss_kl=-0.148
2023-05-09 21:31:45,550 kenma_eng INFO ====> Epoch: 457
2023-05-09 21:31:53,518 kenma_eng INFO ====> Epoch: 458
2023-05-09 21:32:01,720 kenma_eng INFO ====> Epoch: 459
2023-05-09 21:32:10,031 kenma_eng INFO ====> Epoch: 460
2023-05-09 21:32:18,472 kenma_eng INFO ====> Epoch: 461
2023-05-09 21:32:23,333 kenma_eng INFO Train Epoch: 462 [54%]
2023-05-09 21:32:23,335 kenma_eng INFO [18000, 9.440004823595418e-05]
2023-05-09 21:32:23,335 kenma_eng INFO loss_disc=2.186, loss_gen=3.937, loss_fm=14.929,loss_mel=16.963, loss_kl=1.084
2023-05-09 21:32:27,351 kenma_eng INFO ====> Epoch: 462
2023-05-09 21:32:35,304 kenma_eng INFO ====> Epoch: 463
2023-05-09 21:32:43,279 kenma_eng INFO ====> Epoch: 464
2023-05-09 21:32:51,282 kenma_eng INFO ====> Epoch: 465
2023-05-09 21:32:59,234 kenma_eng INFO ====> Epoch: 466
2023-05-09 21:33:04,811 kenma_eng INFO Train Epoch: 467 [82%]
2023-05-09 21:33:04,813 kenma_eng INFO [18200, 9.434106295397058e-05]
2023-05-09 21:33:04,813 kenma_eng INFO loss_disc=2.204, loss_gen=4.244, loss_fm=12.793,loss_mel=17.387, loss_kl=0.843
2023-05-09 21:33:07,440 kenma_eng INFO ====> Epoch: 467
2023-05-09 21:33:15,433 kenma_eng INFO ====> Epoch: 468
2023-05-09 21:33:23,449 kenma_eng INFO ====> Epoch: 469
2023-05-09 21:33:31,401 kenma_eng INFO ====> Epoch: 470
2023-05-09 21:33:39,382 kenma_eng INFO ====> Epoch: 471
2023-05-09 21:33:46,389 kenma_eng INFO Train Epoch: 472 [87%]
2023-05-09 21:33:46,392 kenma_eng INFO [18400, 9.428211452857292e-05]
2023-05-09 21:33:46,392 kenma_eng INFO loss_disc=2.128, loss_gen=3.819, loss_fm=11.847,loss_mel=12.297, loss_kl=0.023
2023-05-09 21:33:48,122 kenma_eng INFO ====> Epoch: 472
2023-05-09 21:33:56,617 kenma_eng INFO ====> Epoch: 473
2023-05-09 21:34:04,816 kenma_eng INFO ====> Epoch: 474
2023-05-09 21:34:13,015 kenma_eng INFO Saving model and optimizer state at epoch 475 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:34:13,451 kenma_eng INFO Saving model and optimizer state at epoch 475 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:34:14,015 kenma_eng INFO ====> Epoch: 475
2023-05-09 21:34:22,034 kenma_eng INFO ====> Epoch: 476
2023-05-09 21:34:30,137 kenma_eng INFO Train Epoch: 477 [33%]
2023-05-09 21:34:30,138 kenma_eng INFO [18600, 9.422320293673162e-05]
2023-05-09 21:34:30,138 kenma_eng INFO loss_disc=2.335, loss_gen=3.745, loss_fm=14.058,loss_mel=16.600, loss_kl=0.857
2023-05-09 21:34:30,800 kenma_eng INFO ====> Epoch: 477
2023-05-09 21:34:39,189 kenma_eng INFO ====> Epoch: 478
2023-05-09 21:34:47,310 kenma_eng INFO ====> Epoch: 479
2023-05-09 21:34:55,271 kenma_eng INFO ====> Epoch: 480
2023-05-09 21:35:03,491 kenma_eng INFO ====> Epoch: 481
2023-05-09 21:35:11,677 kenma_eng INFO ====> Epoch: 482
2023-05-09 21:35:12,317 kenma_eng INFO Train Epoch: 483 [3%]
2023-05-09 21:35:12,319 kenma_eng INFO [18800, 9.4152557614412e-05]
2023-05-09 21:35:12,319 kenma_eng INFO loss_disc=1.573, loss_gen=4.584, loss_fm=15.619,loss_mel=15.060, loss_kl=0.507
2023-05-09 21:35:20,077 kenma_eng INFO ====> Epoch: 483
2023-05-09 21:35:28,208 kenma_eng INFO ====> Epoch: 484
2023-05-09 21:35:36,382 kenma_eng INFO ====> Epoch: 485
2023-05-09 21:35:44,408 kenma_eng INFO ====> Epoch: 486
2023-05-09 21:35:52,559 kenma_eng INFO ====> Epoch: 487
2023-05-09 21:35:54,233 kenma_eng INFO Train Epoch: 488 [31%]
2023-05-09 21:35:54,235 kenma_eng INFO [19000, 9.409372697540131e-05]
2023-05-09 21:35:54,235 kenma_eng INFO loss_disc=1.586, loss_gen=4.307, loss_fm=15.501,loss_mel=15.946, loss_kl=0.027
2023-05-09 21:36:01,063 kenma_eng INFO ====> Epoch: 488
2023-05-09 21:36:09,154 kenma_eng INFO ====> Epoch: 489
2023-05-09 21:36:17,079 kenma_eng INFO ====> Epoch: 490
2023-05-09 21:36:25,057 kenma_eng INFO ====> Epoch: 491
2023-05-09 21:36:33,086 kenma_eng INFO ====> Epoch: 492
2023-05-09 21:36:35,794 kenma_eng INFO Train Epoch: 493 [18%]
2023-05-09 21:36:35,796 kenma_eng INFO [19200, 9.403493309634886e-05]
2023-05-09 21:36:35,796 kenma_eng INFO loss_disc=2.211, loss_gen=3.548, loss_fm=15.032,loss_mel=17.025, loss_kl=0.422
2023-05-09 21:36:41,429 kenma_eng INFO ====> Epoch: 493
2023-05-09 21:36:49,466 kenma_eng INFO ====> Epoch: 494
2023-05-09 21:36:57,711 kenma_eng INFO ====> Epoch: 495
2023-05-09 21:37:05,849 kenma_eng INFO ====> Epoch: 496
2023-05-09 21:37:13,948 kenma_eng INFO ====> Epoch: 497
2023-05-09 21:37:17,790 kenma_eng INFO Train Epoch: 498 [44%]
2023-05-09 21:37:17,792 kenma_eng INFO [19400, 9.397617595428541e-05]
2023-05-09 21:37:17,792 kenma_eng INFO loss_disc=1.377, loss_gen=4.933, loss_fm=15.609,loss_mel=15.933, loss_kl=1.441
2023-05-09 21:37:22,457 kenma_eng INFO ====> Epoch: 498
2023-05-09 21:37:30,713 kenma_eng INFO ====> Epoch: 499
2023-05-09 21:37:38,889 kenma_eng INFO Saving model and optimizer state at epoch 500 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:37:39,326 kenma_eng INFO Saving model and optimizer state at epoch 500 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:37:39,901 kenma_eng INFO ====> Epoch: 500
2023-05-09 21:37:47,994 kenma_eng INFO ====> Epoch: 501
2023-05-09 21:37:56,062 kenma_eng INFO ====> Epoch: 502
2023-05-09 21:38:00,707 kenma_eng INFO Train Epoch: 503 [59%]
2023-05-09 21:38:00,709 kenma_eng INFO [19600, 9.39174555262561e-05]
2023-05-09 21:38:00,709 kenma_eng INFO loss_disc=2.219, loss_gen=4.183, loss_fm=13.758,loss_mel=17.264, loss_kl=0.789
2023-05-09 21:38:04,174 kenma_eng INFO ====> Epoch: 503
2023-05-09 21:38:12,180 kenma_eng INFO ====> Epoch: 504
2023-05-09 21:38:20,454 kenma_eng INFO ====> Epoch: 505
2023-05-09 21:38:28,804 kenma_eng INFO ====> Epoch: 506
2023-05-09 21:38:37,034 kenma_eng INFO ====> Epoch: 507
2023-05-09 21:38:42,676 kenma_eng INFO Train Epoch: 508 [5%]
2023-05-09 21:38:42,679 kenma_eng INFO [19800, 9.385877178932038e-05]
2023-05-09 21:38:42,679 kenma_eng INFO loss_disc=2.498, loss_gen=3.254, loss_fm=9.827,loss_mel=15.988, loss_kl=0.534
2023-05-09 21:38:45,160 kenma_eng INFO ====> Epoch: 508
2023-05-09 21:38:53,135 kenma_eng INFO ====> Epoch: 509
2023-05-09 21:39:01,107 kenma_eng INFO ====> Epoch: 510
2023-05-09 21:39:09,099 kenma_eng INFO ====> Epoch: 511
2023-05-09 21:39:17,096 kenma_eng INFO ====> Epoch: 512
2023-05-09 21:39:23,858 kenma_eng INFO Train Epoch: 513 [77%]
2023-05-09 21:39:23,861 kenma_eng INFO [20000, 9.380012472055207e-05]
2023-05-09 21:39:23,861 kenma_eng INFO loss_disc=2.218, loss_gen=4.022, loss_fm=14.415,loss_mel=17.071, loss_kl=0.824
2023-05-09 21:39:25,349 kenma_eng INFO ====> Epoch: 513
2023-05-09 21:39:33,264 kenma_eng INFO ====> Epoch: 514
2023-05-09 21:39:41,312 kenma_eng INFO ====> Epoch: 515
2023-05-09 21:39:49,549 kenma_eng INFO ====> Epoch: 516
2023-05-09 21:39:57,438 kenma_eng INFO ====> Epoch: 517
2023-05-09 21:40:05,216 kenma_eng INFO Train Epoch: 518 [59%]
2023-05-09 21:40:05,219 kenma_eng INFO [20200, 9.374151429703929e-05]
2023-05-09 21:40:05,219 kenma_eng INFO loss_disc=2.241, loss_gen=4.326, loss_fm=16.207,loss_mel=17.662, loss_kl=0.823
2023-05-09 21:40:05,642 kenma_eng INFO ====> Epoch: 518
2023-05-09 21:40:13,604 kenma_eng INFO ====> Epoch: 519
2023-05-09 21:40:21,582 kenma_eng INFO ====> Epoch: 520
2023-05-09 21:40:29,572 kenma_eng INFO ====> Epoch: 521
2023-05-09 21:40:37,580 kenma_eng INFO ====> Epoch: 522
2023-05-09 21:40:45,544 kenma_eng INFO ====> Epoch: 523
2023-05-09 21:40:46,383 kenma_eng INFO Train Epoch: 524 [54%]
2023-05-09 21:40:46,385 kenma_eng INFO [20400, 9.367123012832248e-05]
2023-05-09 21:40:46,385 kenma_eng INFO loss_disc=2.184, loss_gen=3.914, loss_fm=14.299,loss_mel=16.183, loss_kl=0.979
2023-05-09 21:40:53,759 kenma_eng INFO ====> Epoch: 524
2023-05-09 21:41:01,770 kenma_eng INFO Saving model and optimizer state at epoch 525 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:41:02,210 kenma_eng INFO Saving model and optimizer state at epoch 525 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:41:02,784 kenma_eng INFO ====> Epoch: 525
2023-05-09 21:41:10,970 kenma_eng INFO ====> Epoch: 526
2023-05-09 21:41:19,116 kenma_eng INFO ====> Epoch: 527
2023-05-09 21:41:27,352 kenma_eng INFO ====> Epoch: 528
2023-05-09 21:41:29,190 kenma_eng INFO Train Epoch: 529 [13%]
2023-05-09 21:41:29,192 kenma_eng INFO [20600, 9.361270024379255e-05]
2023-05-09 21:41:29,192 kenma_eng INFO loss_disc=2.541, loss_gen=3.796, loss_fm=14.062,loss_mel=16.911, loss_kl=0.851
2023-05-09 21:41:35,875 kenma_eng INFO ====> Epoch: 529
2023-05-09 21:41:44,134 kenma_eng INFO ====> Epoch: 530
2023-05-09 21:41:52,317 kenma_eng INFO ====> Epoch: 531
2023-05-09 21:42:00,402 kenma_eng INFO ====> Epoch: 532
2023-05-09 21:42:08,547 kenma_eng INFO ====> Epoch: 533
2023-05-09 21:42:11,434 kenma_eng INFO Train Epoch: 534 [56%]
2023-05-09 21:42:11,437 kenma_eng INFO [20800, 9.355420693129632e-05]
2023-05-09 21:42:11,437 kenma_eng INFO loss_disc=2.204, loss_gen=3.685, loss_fm=13.072,loss_mel=17.261, loss_kl=0.983
2023-05-09 21:42:16,985 kenma_eng INFO ====> Epoch: 534
2023-05-09 21:42:24,936 kenma_eng INFO ====> Epoch: 535
2023-05-09 21:42:32,868 kenma_eng INFO ====> Epoch: 536
2023-05-09 21:42:40,858 kenma_eng INFO ====> Epoch: 537
2023-05-09 21:42:48,841 kenma_eng INFO ====> Epoch: 538
2023-05-09 21:42:52,728 kenma_eng INFO Train Epoch: 539 [8%]
2023-05-09 21:42:52,730 kenma_eng INFO [21000, 9.349575016798194e-05]
2023-05-09 21:42:52,730 kenma_eng INFO loss_disc=1.437, loss_gen=4.464, loss_fm=17.413,loss_mel=14.782, loss_kl=0.953
2023-05-09 21:42:57,034 kenma_eng INFO ====> Epoch: 539
2023-05-09 21:43:05,024 kenma_eng INFO ====> Epoch: 540
2023-05-09 21:43:13,054 kenma_eng INFO ====> Epoch: 541
2023-05-09 21:43:21,139 kenma_eng INFO ====> Epoch: 542
2023-05-09 21:43:29,410 kenma_eng INFO ====> Epoch: 543
2023-05-09 21:43:34,400 kenma_eng INFO Train Epoch: 544 [54%]
2023-05-09 21:43:34,402 kenma_eng INFO [21200, 9.343732993101193e-05]
2023-05-09 21:43:34,402 kenma_eng INFO loss_disc=2.263, loss_gen=3.448, loss_fm=14.252,loss_mel=16.321, loss_kl=1.041
2023-05-09 21:43:37,763 kenma_eng INFO ====> Epoch: 544
2023-05-09 21:43:45,766 kenma_eng INFO ====> Epoch: 545
2023-05-09 21:43:53,826 kenma_eng INFO ====> Epoch: 546
2023-05-09 21:44:01,957 kenma_eng INFO ====> Epoch: 547
2023-05-09 21:44:09,953 kenma_eng INFO ====> Epoch: 548
2023-05-09 21:44:15,993 kenma_eng INFO Train Epoch: 549 [87%]
2023-05-09 21:44:15,995 kenma_eng INFO [21400, 9.337894619756301e-05]
2023-05-09 21:44:15,996 kenma_eng INFO loss_disc=2.167, loss_gen=3.630, loss_fm=12.131,loss_mel=11.858, loss_kl=0.854
2023-05-09 21:44:18,317 kenma_eng INFO ====> Epoch: 549
2023-05-09 21:44:26,540 kenma_eng INFO Saving model and optimizer state at epoch 550 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:44:26,989 kenma_eng INFO Saving model and optimizer state at epoch 550 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:44:27,567 kenma_eng INFO ====> Epoch: 550
2023-05-09 21:44:35,681 kenma_eng INFO ====> Epoch: 551
2023-05-09 21:44:43,608 kenma_eng INFO ====> Epoch: 552
2023-05-09 21:44:51,775 kenma_eng INFO ====> Epoch: 553
2023-05-09 21:44:58,687 kenma_eng INFO Train Epoch: 554 [49%]
2023-05-09 21:44:58,689 kenma_eng INFO [21600, 9.332059894482616e-05]
2023-05-09 21:44:58,689 kenma_eng INFO loss_disc=1.670, loss_gen=4.373, loss_fm=15.228,loss_mel=16.447, loss_kl=0.352
2023-05-09 21:44:59,915 kenma_eng INFO ====> Epoch: 554
2023-05-09 21:45:07,902 kenma_eng INFO ====> Epoch: 555
2023-05-09 21:45:15,932 kenma_eng INFO ====> Epoch: 556
2023-05-09 21:45:24,280 kenma_eng INFO ====> Epoch: 557
2023-05-09 21:45:32,363 kenma_eng INFO ====> Epoch: 558
2023-05-09 21:45:40,360 kenma_eng INFO Train Epoch: 559 [64%]
2023-05-09 21:45:40,362 kenma_eng INFO [21800, 9.326228815000664e-05]
2023-05-09 21:45:40,362 kenma_eng INFO loss_disc=1.974, loss_gen=3.985, loss_fm=14.088,loss_mel=16.918, loss_kl=0.954
2023-05-09 21:45:40,560 kenma_eng INFO ====> Epoch: 559
2023-05-09 21:45:48,713 kenma_eng INFO ====> Epoch: 560
2023-05-09 21:45:56,699 kenma_eng INFO ====> Epoch: 561
2023-05-09 21:46:04,886 kenma_eng INFO ====> Epoch: 562
2023-05-09 21:46:13,112 kenma_eng INFO ====> Epoch: 563
2023-05-09 21:46:21,416 kenma_eng INFO ====> Epoch: 564
2023-05-09 21:46:22,486 kenma_eng INFO Train Epoch: 565 [67%]
2023-05-09 21:46:22,487 kenma_eng INFO [22000, 9.319236328860017e-05]
2023-05-09 21:46:22,488 kenma_eng INFO loss_disc=1.466, loss_gen=4.685, loss_fm=17.300,loss_mel=15.448, loss_kl=0.684
2023-05-09 21:46:30,038 kenma_eng INFO ====> Epoch: 565
2023-05-09 21:46:38,429 kenma_eng INFO ====> Epoch: 566
2023-05-09 21:46:46,834 kenma_eng INFO ====> Epoch: 567
2023-05-09 21:46:55,064 kenma_eng INFO ====> Epoch: 568
2023-05-09 21:47:03,112 kenma_eng INFO ====> Epoch: 569
2023-05-09 21:47:05,393 kenma_eng INFO Train Epoch: 570 [69%]
2023-05-09 21:47:05,395 kenma_eng INFO [22200, 9.313413262103149e-05]
2023-05-09 21:47:05,395 kenma_eng INFO loss_disc=2.040, loss_gen=3.838, loss_fm=16.110,loss_mel=17.335, loss_kl=1.084
2023-05-09 21:47:11,628 kenma_eng INFO ====> Epoch: 570
2023-05-09 21:47:19,573 kenma_eng INFO ====> Epoch: 571
2023-05-09 21:47:27,605 kenma_eng INFO ====> Epoch: 572
2023-05-09 21:47:35,601 kenma_eng INFO ====> Epoch: 573
2023-05-09 21:47:43,766 kenma_eng INFO ====> Epoch: 574
2023-05-09 21:47:46,814 kenma_eng INFO Train Epoch: 575 [51%]
2023-05-09 21:47:46,817 kenma_eng INFO [22400, 9.307593833853263e-05]
2023-05-09 21:47:46,817 kenma_eng INFO loss_disc=2.158, loss_gen=3.954, loss_fm=12.452,loss_mel=12.447, loss_kl=0.545
2023-05-09 21:47:51,999 kenma_eng INFO Saving model and optimizer state at epoch 575 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:47:52,447 kenma_eng INFO Saving model and optimizer state at epoch 575 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:47:53,035 kenma_eng INFO ====> Epoch: 575
2023-05-09 21:48:01,040 kenma_eng INFO ====> Epoch: 576
2023-05-09 21:48:08,935 kenma_eng INFO ====> Epoch: 577
2023-05-09 21:48:16,920 kenma_eng INFO ====> Epoch: 578
2023-05-09 21:48:24,939 kenma_eng INFO ====> Epoch: 579
2023-05-09 21:48:29,013 kenma_eng INFO Train Epoch: 580 [95%]
2023-05-09 21:48:29,015 kenma_eng INFO [22600, 9.301778041836861e-05]
2023-05-09 21:48:29,015 kenma_eng INFO loss_disc=1.721, loss_gen=4.189, loss_fm=16.051,loss_mel=16.013, loss_kl=1.199
2023-05-09 21:48:33,129 kenma_eng INFO ====> Epoch: 580
2023-05-09 21:48:41,090 kenma_eng INFO ====> Epoch: 581
2023-05-09 21:48:49,152 kenma_eng INFO ====> Epoch: 582
2023-05-09 21:48:57,059 kenma_eng INFO ====> Epoch: 583
2023-05-09 21:49:05,139 kenma_eng INFO ====> Epoch: 584
2023-05-09 21:49:10,450 kenma_eng INFO Train Epoch: 585 [77%]
2023-05-09 21:49:10,452 kenma_eng INFO [22800, 9.295965883781867e-05]
2023-05-09 21:49:10,452 kenma_eng INFO loss_disc=2.152, loss_gen=3.667, loss_fm=12.092,loss_mel=16.022, loss_kl=0.746
2023-05-09 21:49:13,681 kenma_eng INFO ====> Epoch: 585
2023-05-09 21:49:21,979 kenma_eng INFO ====> Epoch: 586
2023-05-09 21:49:30,390 kenma_eng INFO ====> Epoch: 587
2023-05-09 21:49:38,240 kenma_eng INFO ====> Epoch: 588
2023-05-09 21:49:46,346 kenma_eng INFO ====> Epoch: 589
2023-05-09 21:49:52,671 kenma_eng INFO Train Epoch: 590 [28%]
2023-05-09 21:49:52,672 kenma_eng INFO [23000, 9.29015735741762e-05]
2023-05-09 21:49:52,672 kenma_eng INFO loss_disc=1.788, loss_gen=4.841, loss_fm=16.721,loss_mel=17.124, loss_kl=0.540
2023-05-09 21:49:54,828 kenma_eng INFO ====> Epoch: 590
2023-05-09 21:50:02,808 kenma_eng INFO ====> Epoch: 591
2023-05-09 21:50:10,933 kenma_eng INFO ====> Epoch: 592
2023-05-09 21:50:18,972 kenma_eng INFO ====> Epoch: 593
2023-05-09 21:50:26,964 kenma_eng INFO ====> Epoch: 594
2023-05-09 21:50:34,129 kenma_eng INFO Train Epoch: 595 [41%]
2023-05-09 21:50:34,131 kenma_eng INFO [23200, 9.284352460474882e-05]
2023-05-09 21:50:34,131 kenma_eng INFO loss_disc=2.124, loss_gen=4.379, loss_fm=12.062,loss_mel=16.912, loss_kl=1.004
2023-05-09 21:50:35,167 kenma_eng INFO ====> Epoch: 595
2023-05-09 21:50:43,161 kenma_eng INFO ====> Epoch: 596
2023-05-09 21:50:51,141 kenma_eng INFO ====> Epoch: 597
2023-05-09 21:50:59,207 kenma_eng INFO ====> Epoch: 598
2023-05-09 21:51:07,473 kenma_eng INFO ====> Epoch: 599
2023-05-09 21:51:15,918 kenma_eng INFO Saving model and optimizer state at epoch 600 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:51:16,361 kenma_eng INFO Saving model and optimizer state at epoch 600 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:51:16,943 kenma_eng INFO ====> Epoch: 600
2023-05-09 21:51:17,158 kenma_eng INFO Train Epoch: 601 [8%]
2023-05-09 21:51:17,160 kenma_eng INFO [23400, 9.277391371786995e-05]
2023-05-09 21:51:17,160 kenma_eng INFO loss_disc=1.402, loss_gen=4.579, loss_fm=17.787,loss_mel=14.720, loss_kl=0.906
2023-05-09 21:51:25,386 kenma_eng INFO ====> Epoch: 601
2023-05-09 21:51:33,540 kenma_eng INFO ====> Epoch: 602
2023-05-09 21:51:41,889 kenma_eng INFO ====> Epoch: 603
2023-05-09 21:51:50,160 kenma_eng INFO ====> Epoch: 604
2023-05-09 21:51:58,307 kenma_eng INFO ====> Epoch: 605
2023-05-09 21:51:59,532 kenma_eng INFO Train Epoch: 606 [64%]
2023-05-09 21:51:59,534 kenma_eng INFO [23600, 9.27159445159084e-05]
2023-05-09 21:51:59,534 kenma_eng INFO loss_disc=2.082, loss_gen=3.616, loss_fm=13.088,loss_mel=16.799, loss_kl=0.346
2023-05-09 21:52:06,501 kenma_eng INFO ====> Epoch: 606
2023-05-09 21:52:14,653 kenma_eng INFO ====> Epoch: 607
2023-05-09 21:52:22,919 kenma_eng INFO ====> Epoch: 608
2023-05-09 21:52:30,985 kenma_eng INFO ====> Epoch: 609
2023-05-09 21:52:39,269 kenma_eng INFO ====> Epoch: 610
2023-05-09 21:52:41,512 kenma_eng INFO Train Epoch: 611 [74%]
2023-05-09 21:52:41,515 kenma_eng INFO [23800, 9.265801153564152e-05]
2023-05-09 21:52:41,515 kenma_eng INFO loss_disc=1.581, loss_gen=4.384, loss_fm=17.392,loss_mel=15.373, loss_kl=0.210
2023-05-09 21:52:47,447 kenma_eng INFO ====> Epoch: 611
2023-05-09 21:52:55,431 kenma_eng INFO ====> Epoch: 612
2023-05-09 21:53:03,424 kenma_eng INFO ====> Epoch: 613
2023-05-09 21:53:11,409 kenma_eng INFO ====> Epoch: 614
2023-05-09 21:53:19,402 kenma_eng INFO ====> Epoch: 615
2023-05-09 21:53:22,722 kenma_eng INFO Train Epoch: 616 [41%]
2023-05-09 21:53:22,724 kenma_eng INFO [24000, 9.260011475443641e-05]
2023-05-09 21:53:22,724 kenma_eng INFO loss_disc=2.314, loss_gen=3.537, loss_fm=15.086,loss_mel=16.836, loss_kl=0.716
2023-05-09 21:53:27,800 kenma_eng INFO ====> Epoch: 616
2023-05-09 21:53:36,118 kenma_eng INFO ====> Epoch: 617
2023-05-09 21:53:44,285 kenma_eng INFO ====> Epoch: 618
2023-05-09 21:53:52,375 kenma_eng INFO ====> Epoch: 619
2023-05-09 21:54:00,366 kenma_eng INFO ====> Epoch: 620
2023-05-09 21:54:04,711 kenma_eng INFO Train Epoch: 621 [5%]
2023-05-09 21:54:04,713 kenma_eng INFO [24200, 9.254225414967431e-05]
2023-05-09 21:54:04,713 kenma_eng INFO loss_disc=1.750, loss_gen=4.269, loss_fm=12.825,loss_mel=16.332, loss_kl=0.977
2023-05-09 21:54:08,754 kenma_eng INFO ====> Epoch: 621
2023-05-09 21:54:16,776 kenma_eng INFO ====> Epoch: 622
2023-05-09 21:54:24,958 kenma_eng INFO ====> Epoch: 623
2023-05-09 21:54:33,125 kenma_eng INFO ====> Epoch: 624
2023-05-09 21:54:41,128 kenma_eng INFO Saving model and optimizer state at epoch 625 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:54:41,573 kenma_eng INFO Saving model and optimizer state at epoch 625 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:54:42,147 kenma_eng INFO ====> Epoch: 625
2023-05-09 21:54:47,462 kenma_eng INFO Train Epoch: 626 [41%]
2023-05-09 21:54:47,464 kenma_eng INFO [24400, 9.24844296987506e-05]
2023-05-09 21:54:47,464 kenma_eng INFO loss_disc=2.450, loss_gen=4.245, loss_fm=13.376,loss_mel=17.149, loss_kl=0.656
2023-05-09 21:54:50,362 kenma_eng INFO ====> Epoch: 626
2023-05-09 21:54:58,449 kenma_eng INFO ====> Epoch: 627
2023-05-09 21:55:06,898 kenma_eng INFO ====> Epoch: 628
2023-05-09 21:55:15,045 kenma_eng INFO ====> Epoch: 629
2023-05-09 21:55:23,322 kenma_eng INFO ====> Epoch: 630
2023-05-09 21:55:29,778 kenma_eng INFO Train Epoch: 631 [85%]
2023-05-09 21:55:29,781 kenma_eng INFO [24600, 9.242664137907478e-05]
2023-05-09 21:55:29,781 kenma_eng INFO loss_disc=1.659, loss_gen=4.127, loss_fm=16.450,loss_mel=14.532, loss_kl=-0.237
2023-05-09 21:55:31,764 kenma_eng INFO ====> Epoch: 631
2023-05-09 21:55:40,258 kenma_eng INFO ====> Epoch: 632
2023-05-09 21:55:48,399 kenma_eng INFO ====> Epoch: 633
2023-05-09 21:55:56,625 kenma_eng INFO ====> Epoch: 634
2023-05-09 21:56:04,748 kenma_eng INFO ====> Epoch: 635
2023-05-09 21:56:12,387 kenma_eng INFO Train Epoch: 636 [97%]
2023-05-09 21:56:12,389 kenma_eng INFO [24800, 9.236888916807045e-05]
2023-05-09 21:56:12,389 kenma_eng INFO loss_disc=1.780, loss_gen=3.758, loss_fm=14.739,loss_mel=17.023, loss_kl=0.768
2023-05-09 21:56:13,283 kenma_eng INFO ====> Epoch: 636
2023-05-09 21:56:21,367 kenma_eng INFO ====> Epoch: 637
2023-05-09 21:56:29,582 kenma_eng INFO ====> Epoch: 638
2023-05-09 21:56:37,585 kenma_eng INFO ====> Epoch: 639
2023-05-09 21:56:45,959 kenma_eng INFO ====> Epoch: 640
2023-05-09 21:56:53,875 kenma_eng INFO ====> Epoch: 641
2023-05-09 21:56:54,280 kenma_eng INFO Train Epoch: 642 [56%]
2023-05-09 21:56:54,283 kenma_eng INFO [25000, 9.229963414654495e-05]
2023-05-09 21:56:54,283 kenma_eng INFO loss_disc=2.193, loss_gen=4.084, loss_fm=13.479,loss_mel=16.395, loss_kl=0.623
2023-05-09 21:57:02,324 kenma_eng INFO ====> Epoch: 642
2023-05-09 21:57:10,338 kenma_eng INFO ====> Epoch: 643
2023-05-09 21:57:18,249 kenma_eng INFO ====> Epoch: 644
2023-05-09 21:57:26,184 kenma_eng INFO ====> Epoch: 645
2023-05-09 21:57:34,243 kenma_eng INFO ====> Epoch: 646
2023-05-09 21:57:35,747 kenma_eng INFO Train Epoch: 647 [74%]
2023-05-09 21:57:35,750 kenma_eng INFO [25200, 9.224196129521857e-05]
2023-05-09 21:57:35,750 kenma_eng INFO loss_disc=1.749, loss_gen=4.170, loss_fm=14.944,loss_mel=15.390, loss_kl=0.950
2023-05-09 21:57:42,612 kenma_eng INFO ====> Epoch: 647
2023-05-09 21:57:51,000 kenma_eng INFO ====> Epoch: 648
2023-05-09 21:57:59,297 kenma_eng INFO ====> Epoch: 649
2023-05-09 21:58:07,650 kenma_eng INFO Saving model and optimizer state at epoch 650 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 21:58:08,168 kenma_eng INFO Saving model and optimizer state at epoch 650 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 21:58:08,750 kenma_eng INFO ====> Epoch: 650
2023-05-09 21:58:17,057 kenma_eng INFO ====> Epoch: 651
2023-05-09 21:58:19,618 kenma_eng INFO Train Epoch: 652 [36%]
2023-05-09 21:58:19,620 kenma_eng INFO [25400, 9.218432448041401e-05]
2023-05-09 21:58:19,620 kenma_eng INFO loss_disc=1.980, loss_gen=3.957, loss_fm=13.056,loss_mel=15.822, loss_kl=0.736
2023-05-09 21:58:25,498 kenma_eng INFO ====> Epoch: 652
2023-05-09 21:58:33,900 kenma_eng INFO ====> Epoch: 653
2023-05-09 21:58:42,201 kenma_eng INFO ====> Epoch: 654
2023-05-09 21:58:50,442 kenma_eng INFO ====> Epoch: 655
2023-05-09 21:58:58,723 kenma_eng INFO ====> Epoch: 656
2023-05-09 21:59:02,306 kenma_eng INFO Train Epoch: 657 [74%]
2023-05-09 21:59:02,308 kenma_eng INFO [25600, 9.212672367961408e-05]
2023-05-09 21:59:02,308 kenma_eng INFO loss_disc=1.844, loss_gen=3.732, loss_fm=14.866,loss_mel=15.986, loss_kl=0.738
2023-05-09 21:59:07,177 kenma_eng INFO ====> Epoch: 657
2023-05-09 21:59:15,611 kenma_eng INFO ====> Epoch: 658
2023-05-09 21:59:23,975 kenma_eng INFO ====> Epoch: 659
2023-05-09 21:59:31,982 kenma_eng INFO ====> Epoch: 660
2023-05-09 21:59:39,909 kenma_eng INFO ====> Epoch: 661
2023-05-09 21:59:44,410 kenma_eng INFO Train Epoch: 662 [3%]
2023-05-09 21:59:44,412 kenma_eng INFO [25800, 9.206915887031564e-05]
2023-05-09 21:59:44,412 kenma_eng INFO loss_disc=1.729, loss_gen=4.120, loss_fm=15.883,loss_mel=14.335, loss_kl=0.549
2023-05-09 21:59:48,102 kenma_eng INFO ====> Epoch: 662
2023-05-09 21:59:56,232 kenma_eng INFO ====> Epoch: 663
2023-05-09 22:00:04,565 kenma_eng INFO ====> Epoch: 664
2023-05-09 22:00:12,857 kenma_eng INFO ====> Epoch: 665
2023-05-09 22:00:21,179 kenma_eng INFO ====> Epoch: 666
2023-05-09 22:00:26,942 kenma_eng INFO Train Epoch: 667 [23%]
2023-05-09 22:00:26,945 kenma_eng INFO [26000, 9.201163003002964e-05]
2023-05-09 22:00:26,945 kenma_eng INFO loss_disc=1.379, loss_gen=4.955, loss_fm=16.377,loss_mel=14.518, loss_kl=0.256
2023-05-09 22:00:29,739 kenma_eng INFO ====> Epoch: 667
2023-05-09 22:00:38,008 kenma_eng INFO ====> Epoch: 668
2023-05-09 22:00:46,314 kenma_eng INFO ====> Epoch: 669
2023-05-09 22:00:54,788 kenma_eng INFO ====> Epoch: 670
2023-05-09 22:01:03,114 kenma_eng INFO ====> Epoch: 671
2023-05-09 22:01:10,044 kenma_eng INFO Train Epoch: 672 [82%]
2023-05-09 22:01:10,046 kenma_eng INFO [26200, 9.195413713628104e-05]
2023-05-09 22:01:10,046 kenma_eng INFO loss_disc=2.455, loss_gen=4.042, loss_fm=11.433,loss_mel=17.106, loss_kl=0.957
2023-05-09 22:01:11,718 kenma_eng INFO ====> Epoch: 672
2023-05-09 22:01:20,170 kenma_eng INFO ====> Epoch: 673
2023-05-09 22:01:28,487 kenma_eng INFO ====> Epoch: 674
2023-05-09 22:01:36,746 kenma_eng INFO Saving model and optimizer state at epoch 675 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:01:37,190 kenma_eng INFO Saving model and optimizer state at epoch 675 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:01:37,786 kenma_eng INFO ====> Epoch: 675
2023-05-09 22:01:46,075 kenma_eng INFO ====> Epoch: 676
2023-05-09 22:01:53,828 kenma_eng INFO Train Epoch: 677 [10%]
2023-05-09 22:01:53,831 kenma_eng INFO [26400, 9.189668016660891e-05]
2023-05-09 22:01:53,831 kenma_eng INFO loss_disc=1.647, loss_gen=3.993, loss_fm=13.757,loss_mel=14.810, loss_kl=0.552
2023-05-09 22:01:54,449 kenma_eng INFO ====> Epoch: 677
2023-05-09 22:02:02,471 kenma_eng INFO ====> Epoch: 678
2023-05-09 22:02:10,617 kenma_eng INFO ====> Epoch: 679
2023-05-09 22:02:19,021 kenma_eng INFO ====> Epoch: 680
2023-05-09 22:02:27,069 kenma_eng INFO ====> Epoch: 681
2023-05-09 22:02:35,011 kenma_eng INFO ====> Epoch: 682
2023-05-09 22:02:35,621 kenma_eng INFO Train Epoch: 683 [3%]
2023-05-09 22:02:35,623 kenma_eng INFO [26600, 9.182777919117897e-05]
2023-05-09 22:02:35,623 kenma_eng INFO loss_disc=1.551, loss_gen=4.746, loss_fm=15.740,loss_mel=14.332, loss_kl=0.332
2023-05-09 22:02:43,261 kenma_eng INFO ====> Epoch: 683
2023-05-09 22:02:51,419 kenma_eng INFO ====> Epoch: 684
2023-05-09 22:02:59,800 kenma_eng INFO ====> Epoch: 685
2023-05-09 22:03:07,771 kenma_eng INFO ====> Epoch: 686
2023-05-09 22:03:15,749 kenma_eng INFO ====> Epoch: 687
2023-05-09 22:03:17,402 kenma_eng INFO Train Epoch: 688 [41%]
2023-05-09 22:03:17,404 kenma_eng INFO [26800, 9.177040117548157e-05]
2023-05-09 22:03:17,405 kenma_eng INFO loss_disc=1.795, loss_gen=4.469, loss_fm=17.287,loss_mel=16.667, loss_kl=0.821
2023-05-09 22:03:23,952 kenma_eng INFO ====> Epoch: 688
2023-05-09 22:03:31,932 kenma_eng INFO ====> Epoch: 689
2023-05-09 22:03:39,921 kenma_eng INFO ====> Epoch: 690
2023-05-09 22:03:47,912 kenma_eng INFO ====> Epoch: 691
2023-05-09 22:03:55,898 kenma_eng INFO ====> Epoch: 692
2023-05-09 22:03:58,558 kenma_eng INFO Train Epoch: 693 [67%]
2023-05-09 22:03:58,560 kenma_eng INFO [27000, 9.171305901207978e-05]
2023-05-09 22:03:58,560 kenma_eng INFO loss_disc=1.189, loss_gen=5.190, loss_fm=18.985,loss_mel=14.808, loss_kl=0.901
2023-05-09 22:04:04,090 kenma_eng INFO ====> Epoch: 693
2023-05-09 22:04:12,076 kenma_eng INFO ====> Epoch: 694
2023-05-09 22:04:20,063 kenma_eng INFO ====> Epoch: 695
2023-05-09 22:04:28,047 kenma_eng INFO ====> Epoch: 696
2023-05-09 22:04:36,035 kenma_eng INFO ====> Epoch: 697
2023-05-09 22:04:39,820 kenma_eng INFO Train Epoch: 698 [28%]
2023-05-09 22:04:39,823 kenma_eng INFO [27200, 9.16557526785715e-05]
2023-05-09 22:04:39,823 kenma_eng INFO loss_disc=1.709, loss_gen=4.400, loss_fm=16.804,loss_mel=17.787, loss_kl=0.835
2023-05-09 22:04:44,234 kenma_eng INFO ====> Epoch: 698
2023-05-09 22:04:52,220 kenma_eng INFO ====> Epoch: 699
2023-05-09 22:05:00,196 kenma_eng INFO Saving model and optimizer state at epoch 700 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:05:00,639 kenma_eng INFO Saving model and optimizer state at epoch 700 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:05:01,211 kenma_eng INFO ====> Epoch: 700
2023-05-09 22:05:09,071 kenma_eng INFO ====> Epoch: 701
2023-05-09 22:05:16,997 kenma_eng INFO ====> Epoch: 702
2023-05-09 22:05:21,718 kenma_eng INFO Train Epoch: 703 [0%]
2023-05-09 22:05:21,720 kenma_eng INFO [27400, 9.15984821525687e-05]
2023-05-09 22:05:21,720 kenma_eng INFO loss_disc=2.243, loss_gen=4.064, loss_fm=14.627,loss_mel=16.204, loss_kl=0.962
2023-05-09 22:05:25,201 kenma_eng INFO ====> Epoch: 703
2023-05-09 22:05:33,175 kenma_eng INFO ====> Epoch: 704
2023-05-09 22:05:41,170 kenma_eng INFO ====> Epoch: 705
2023-05-09 22:05:49,145 kenma_eng INFO ====> Epoch: 706
2023-05-09 22:05:57,138 kenma_eng INFO ====> Epoch: 707
2023-05-09 22:06:02,868 kenma_eng INFO Train Epoch: 708 [64%]
2023-05-09 22:06:02,871 kenma_eng INFO [27600, 9.154124741169722e-05]
2023-05-09 22:06:02,871 kenma_eng INFO loss_disc=1.820, loss_gen=3.794, loss_fm=14.468,loss_mel=15.747, loss_kl=1.140
2023-05-09 22:06:05,336 kenma_eng INFO ====> Epoch: 708
2023-05-09 22:06:13,315 kenma_eng INFO ====> Epoch: 709
2023-05-09 22:06:21,318 kenma_eng INFO ====> Epoch: 710
2023-05-09 22:06:29,390 kenma_eng INFO ====> Epoch: 711
2023-05-09 22:06:37,389 kenma_eng INFO ====> Epoch: 712
2023-05-09 22:06:44,205 kenma_eng INFO Train Epoch: 713 [23%]
2023-05-09 22:06:44,208 kenma_eng INFO [27800, 9.1484048433597e-05]
2023-05-09 22:06:44,208 kenma_eng INFO loss_disc=1.413, loss_gen=4.423, loss_fm=17.317,loss_mel=14.584, loss_kl=0.258
2023-05-09 22:06:45,686 kenma_eng INFO ====> Epoch: 713
2023-05-09 22:06:53,683 kenma_eng INFO ====> Epoch: 714
2023-05-09 22:07:01,848 kenma_eng INFO ====> Epoch: 715
2023-05-09 22:07:10,086 kenma_eng INFO ====> Epoch: 716
2023-05-09 22:07:18,079 kenma_eng INFO ====> Epoch: 717
2023-05-09 22:07:25,819 kenma_eng INFO Train Epoch: 718 [54%]
2023-05-09 22:07:25,821 kenma_eng INFO [28000, 9.142688519592185e-05]
2023-05-09 22:07:25,821 kenma_eng INFO loss_disc=2.267, loss_gen=3.540, loss_fm=10.751,loss_mel=16.808, loss_kl=0.527
2023-05-09 22:07:26,238 kenma_eng INFO ====> Epoch: 718
2023-05-09 22:07:34,483 kenma_eng INFO ====> Epoch: 719
2023-05-09 22:07:42,501 kenma_eng INFO ====> Epoch: 720
2023-05-09 22:07:50,511 kenma_eng INFO ====> Epoch: 721
2023-05-09 22:07:58,450 kenma_eng INFO ====> Epoch: 722
2023-05-09 22:08:06,358 kenma_eng INFO ====> Epoch: 723
2023-05-09 22:08:07,182 kenma_eng INFO Train Epoch: 724 [18%]
2023-05-09 22:08:07,184 kenma_eng INFO [28200, 9.13583364566301e-05]
2023-05-09 22:08:07,184 kenma_eng INFO loss_disc=1.988, loss_gen=4.266, loss_fm=13.936,loss_mel=16.178, loss_kl=0.570
2023-05-09 22:08:14,554 kenma_eng INFO ====> Epoch: 724
2023-05-09 22:08:22,579 kenma_eng INFO Saving model and optimizer state at epoch 725 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:08:23,024 kenma_eng INFO Saving model and optimizer state at epoch 725 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:08:23,597 kenma_eng INFO ====> Epoch: 725
2023-05-09 22:08:31,555 kenma_eng INFO ====> Epoch: 726
2023-05-09 22:08:39,544 kenma_eng INFO ====> Epoch: 727
2023-05-09 22:08:47,523 kenma_eng INFO ====> Epoch: 728
2023-05-09 22:08:49,367 kenma_eng INFO Train Epoch: 729 [41%]
2023-05-09 22:08:49,369 kenma_eng INFO [28400, 9.130125176930053e-05]
2023-05-09 22:08:49,369 kenma_eng INFO loss_disc=2.267, loss_gen=3.449, loss_fm=12.441,loss_mel=17.264, loss_kl=0.475
2023-05-09 22:08:55,723 kenma_eng INFO ====> Epoch: 729
2023-05-09 22:09:03,700 kenma_eng INFO ====> Epoch: 730
2023-05-09 22:09:11,692 kenma_eng INFO ====> Epoch: 731
2023-05-09 22:09:19,794 kenma_eng INFO ====> Epoch: 732
2023-05-09 22:09:27,921 kenma_eng INFO ====> Epoch: 733
2023-05-09 22:09:30,752 kenma_eng INFO Train Epoch: 734 [77%]
2023-05-09 22:09:30,754 kenma_eng INFO [28600, 9.124420275098216e-05]
2023-05-09 22:09:30,754 kenma_eng INFO loss_disc=2.093, loss_gen=3.552, loss_fm=10.714,loss_mel=15.573, loss_kl=0.600
2023-05-09 22:09:36,057 kenma_eng INFO ====> Epoch: 734
2023-05-09 22:09:44,042 kenma_eng INFO ====> Epoch: 735
2023-05-09 22:09:52,038 kenma_eng INFO ====> Epoch: 736
2023-05-09 22:10:00,015 kenma_eng INFO ====> Epoch: 737
2023-05-09 22:10:08,003 kenma_eng INFO ====> Epoch: 738
2023-05-09 22:10:11,964 kenma_eng INFO Train Epoch: 739 [41%]
2023-05-09 22:10:11,964 kenma_eng INFO [28800, 9.118718937938746e-05]
2023-05-09 22:10:11,964 kenma_eng INFO loss_disc=2.497, loss_gen=4.243, loss_fm=13.678,loss_mel=17.164, loss_kl=0.524
2023-05-09 22:10:16,247 kenma_eng INFO ====> Epoch: 739
2023-05-09 22:10:24,582 kenma_eng INFO ====> Epoch: 740
2023-05-09 22:10:33,075 kenma_eng INFO ====> Epoch: 741
2023-05-09 22:10:41,290 kenma_eng INFO ====> Epoch: 742
2023-05-09 22:10:49,257 kenma_eng INFO ====> Epoch: 743
2023-05-09 22:10:54,112 kenma_eng INFO Train Epoch: 744 [74%]
2023-05-09 22:10:54,114 kenma_eng INFO [29000, 9.113021163224278e-05]
2023-05-09 22:10:54,114 kenma_eng INFO loss_disc=1.798, loss_gen=4.035, loss_fm=15.642,loss_mel=15.890, loss_kl=0.431
2023-05-09 22:10:57,394 kenma_eng INFO ====> Epoch: 744
2023-05-09 22:11:05,350 kenma_eng INFO ====> Epoch: 745
2023-05-09 22:11:13,365 kenma_eng INFO ====> Epoch: 746
2023-05-09 22:11:21,314 kenma_eng INFO ====> Epoch: 747
2023-05-09 22:11:29,294 kenma_eng INFO ====> Epoch: 748
2023-05-09 22:11:35,266 kenma_eng INFO Train Epoch: 749 [15%]
2023-05-09 22:11:35,269 kenma_eng INFO [29200, 9.107326948728839e-05]
2023-05-09 22:11:35,269 kenma_eng INFO loss_disc=1.907, loss_gen=4.395, loss_fm=16.179,loss_mel=15.312, loss_kl=0.336
2023-05-09 22:11:37,501 kenma_eng INFO ====> Epoch: 749
2023-05-09 22:11:45,513 kenma_eng INFO Saving model and optimizer state at epoch 750 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:11:45,957 kenma_eng INFO Saving model and optimizer state at epoch 750 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:11:46,528 kenma_eng INFO ====> Epoch: 750
2023-05-09 22:11:54,564 kenma_eng INFO ====> Epoch: 751
2023-05-09 22:12:02,748 kenma_eng INFO ====> Epoch: 752
2023-05-09 22:12:11,219 kenma_eng INFO ====> Epoch: 753
2023-05-09 22:12:18,289 kenma_eng INFO Train Epoch: 754 [95%]
2023-05-09 22:12:18,292 kenma_eng INFO [29400, 9.101636292227852e-05]
2023-05-09 22:12:18,292 kenma_eng INFO loss_disc=1.921, loss_gen=4.689, loss_fm=16.663,loss_mel=16.113, loss_kl=1.283
2023-05-09 22:12:19,589 kenma_eng INFO ====> Epoch: 754
2023-05-09 22:12:27,930 kenma_eng INFO ====> Epoch: 755
2023-05-09 22:12:36,123 kenma_eng INFO ====> Epoch: 756
2023-05-09 22:12:44,081 kenma_eng INFO ====> Epoch: 757
2023-05-09 22:12:52,374 kenma_eng INFO ====> Epoch: 758
2023-05-09 22:13:00,444 kenma_eng INFO Train Epoch: 759 [36%]
2023-05-09 22:13:00,446 kenma_eng INFO [29600, 9.095949191498122e-05]
2023-05-09 22:13:00,446 kenma_eng INFO loss_disc=1.804, loss_gen=4.057, loss_fm=18.434,loss_mel=15.749, loss_kl=0.168
2023-05-09 22:13:00,638 kenma_eng INFO ====> Epoch: 759
2023-05-09 22:13:08,631 kenma_eng INFO ====> Epoch: 760
2023-05-09 22:13:16,624 kenma_eng INFO ====> Epoch: 761
2023-05-09 22:13:24,606 kenma_eng INFO ====> Epoch: 762
2023-05-09 22:13:32,604 kenma_eng INFO ====> Epoch: 763
2023-05-09 22:13:40,580 kenma_eng INFO ====> Epoch: 764
2023-05-09 22:13:41,613 kenma_eng INFO Train Epoch: 765 [0%]
2023-05-09 22:13:41,615 kenma_eng INFO [29800, 9.08912936111231e-05]
2023-05-09 22:13:41,615 kenma_eng INFO loss_disc=1.957, loss_gen=3.999, loss_fm=14.165,loss_mel=16.942, loss_kl=0.762
2023-05-09 22:13:48,789 kenma_eng INFO ====> Epoch: 765
2023-05-09 22:13:56,767 kenma_eng INFO ====> Epoch: 766
2023-05-09 22:14:04,795 kenma_eng INFO ====> Epoch: 767
2023-05-09 22:14:12,757 kenma_eng INFO ====> Epoch: 768
2023-05-09 22:14:20,744 kenma_eng INFO ====> Epoch: 769
2023-05-09 22:14:22,772 kenma_eng INFO Train Epoch: 770 [77%]
2023-05-09 22:14:22,774 kenma_eng INFO [30000, 9.083450075260563e-05]
2023-05-09 22:14:22,774 kenma_eng INFO loss_disc=2.021, loss_gen=3.986, loss_fm=13.122,loss_mel=16.469, loss_kl=0.675
2023-05-09 22:14:28,918 kenma_eng INFO ====> Epoch: 770
2023-05-09 22:14:36,916 kenma_eng INFO ====> Epoch: 771
2023-05-09 22:14:44,905 kenma_eng INFO ====> Epoch: 772
2023-05-09 22:14:53,117 kenma_eng INFO ====> Epoch: 773
2023-05-09 22:15:01,213 kenma_eng INFO ====> Epoch: 774
2023-05-09 22:15:04,361 kenma_eng INFO Train Epoch: 775 [28%]
2023-05-09 22:15:04,363 kenma_eng INFO [30200, 9.077774338075196e-05]
2023-05-09 22:15:04,363 kenma_eng INFO loss_disc=1.810, loss_gen=4.204, loss_fm=16.048,loss_mel=18.566, loss_kl=0.898
2023-05-09 22:15:09,467 kenma_eng INFO Saving model and optimizer state at epoch 775 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:15:09,911 kenma_eng INFO Saving model and optimizer state at epoch 775 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:15:10,490 kenma_eng INFO ====> Epoch: 775
2023-05-09 22:15:18,474 kenma_eng INFO ====> Epoch: 776
2023-05-09 22:15:26,467 kenma_eng INFO ====> Epoch: 777
2023-05-09 22:15:34,448 kenma_eng INFO ====> Epoch: 778
2023-05-09 22:15:42,498 kenma_eng INFO ====> Epoch: 779
2023-05-09 22:15:46,620 kenma_eng INFO Train Epoch: 780 [5%]
2023-05-09 22:15:46,622 kenma_eng INFO [30400, 9.072102147338848e-05]
2023-05-09 22:15:46,629 kenma_eng INFO loss_disc=1.670, loss_gen=4.328, loss_fm=13.996,loss_mel=16.260, loss_kl=0.567
2023-05-09 22:15:50,658 kenma_eng INFO ====> Epoch: 780
2023-05-09 22:15:58,720 kenma_eng INFO ====> Epoch: 781
2023-05-09 22:16:06,608 kenma_eng INFO ====> Epoch: 782
2023-05-09 22:16:14,591 kenma_eng INFO ====> Epoch: 783
2023-05-09 22:16:22,581 kenma_eng INFO ====> Epoch: 784
2023-05-09 22:16:27,699 kenma_eng INFO Train Epoch: 785 [51%]
2023-05-09 22:16:27,701 kenma_eng INFO [30600, 9.066433500835542e-05]
2023-05-09 22:16:27,701 kenma_eng INFO loss_disc=2.276, loss_gen=3.657, loss_fm=10.978,loss_mel=11.625, loss_kl=0.562
2023-05-09 22:16:30,971 kenma_eng INFO ====> Epoch: 785
2023-05-09 22:16:39,042 kenma_eng INFO ====> Epoch: 786
2023-05-09 22:16:46,980 kenma_eng INFO ====> Epoch: 787
2023-05-09 22:16:54,940 kenma_eng INFO ====> Epoch: 788
2023-05-09 22:17:02,924 kenma_eng INFO ====> Epoch: 789
2023-05-09 22:17:09,079 kenma_eng INFO Train Epoch: 790 [5%]
2023-05-09 22:17:09,082 kenma_eng INFO [30800, 9.060768396350687e-05]
2023-05-09 22:17:09,082 kenma_eng INFO loss_disc=2.068, loss_gen=3.367, loss_fm=11.861,loss_mel=16.069, loss_kl=0.644
2023-05-09 22:17:11,113 kenma_eng INFO ====> Epoch: 790
2023-05-09 22:17:19,140 kenma_eng INFO ====> Epoch: 791
2023-05-09 22:17:27,084 kenma_eng INFO ====> Epoch: 792
2023-05-09 22:17:35,085 kenma_eng INFO ====> Epoch: 793
2023-05-09 22:17:43,068 kenma_eng INFO ====> Epoch: 794
2023-05-09 22:17:50,235 kenma_eng INFO Train Epoch: 795 [82%]
2023-05-09 22:17:50,237 kenma_eng INFO [31000, 9.055106831671071e-05]
2023-05-09 22:17:50,238 kenma_eng INFO loss_disc=2.338, loss_gen=3.742, loss_fm=14.018,loss_mel=16.565, loss_kl=0.528
2023-05-09 22:17:51,249 kenma_eng INFO ====> Epoch: 795
2023-05-09 22:17:59,239 kenma_eng INFO ====> Epoch: 796
2023-05-09 22:18:07,226 kenma_eng INFO ====> Epoch: 797
2023-05-09 22:18:15,247 kenma_eng INFO ====> Epoch: 798
2023-05-09 22:18:23,364 kenma_eng INFO ====> Epoch: 799
2023-05-09 22:18:31,395 kenma_eng INFO Saving model and optimizer state at epoch 800 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:18:31,845 kenma_eng INFO Saving model and optimizer state at epoch 800 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:18:32,423 kenma_eng INFO ====> Epoch: 800
2023-05-09 22:18:32,625 kenma_eng INFO Train Epoch: 801 [87%]
2023-05-09 22:18:32,627 kenma_eng INFO [31200, 9.048317623484297e-05]
2023-05-09 22:18:32,628 kenma_eng INFO loss_disc=1.917, loss_gen=4.175, loss_fm=11.741,loss_mel=12.423, loss_kl=0.397
2023-05-09 22:18:40,609 kenma_eng INFO ====> Epoch: 801
2023-05-09 22:18:48,594 kenma_eng INFO ====> Epoch: 802
2023-05-09 22:18:56,592 kenma_eng INFO ====> Epoch: 803
2023-05-09 22:19:04,607 kenma_eng INFO ====> Epoch: 804
2023-05-09 22:19:12,651 kenma_eng INFO ====> Epoch: 805
2023-05-09 22:19:13,949 kenma_eng INFO Train Epoch: 806 [51%]
2023-05-09 22:19:13,951 kenma_eng INFO [31400, 9.042663838592532e-05]
2023-05-09 22:19:13,951 kenma_eng INFO loss_disc=2.196, loss_gen=3.489, loss_fm=11.111,loss_mel=12.128, loss_kl=-0.048
2023-05-09 22:19:21,039 kenma_eng INFO ====> Epoch: 806
2023-05-09 22:19:29,270 kenma_eng INFO ====> Epoch: 807
2023-05-09 22:19:37,378 kenma_eng INFO ====> Epoch: 808
2023-05-09 22:19:45,344 kenma_eng INFO ====> Epoch: 809
2023-05-09 22:19:53,310 kenma_eng INFO ====> Epoch: 810
2023-05-09 22:19:55,567 kenma_eng INFO Train Epoch: 811 [8%]
2023-05-09 22:19:55,569 kenma_eng INFO [31600, 9.03701358643303e-05]
2023-05-09 22:19:55,569 kenma_eng INFO loss_disc=1.187, loss_gen=4.954, loss_fm=17.565,loss_mel=14.459, loss_kl=-0.517
2023-05-09 22:20:01,514 kenma_eng INFO ====> Epoch: 811
2023-05-09 22:20:09,658 kenma_eng INFO ====> Epoch: 812
2023-05-09 22:20:17,705 kenma_eng INFO ====> Epoch: 813
2023-05-09 22:20:25,745 kenma_eng INFO ====> Epoch: 814
2023-05-09 22:20:33,939 kenma_eng INFO ====> Epoch: 815
2023-05-09 22:20:37,393 kenma_eng INFO Train Epoch: 816 [90%]
2023-05-09 22:20:37,395 kenma_eng INFO [31800, 9.031366864798387e-05]
2023-05-09 22:20:37,395 kenma_eng INFO loss_disc=1.900, loss_gen=4.872, loss_fm=16.831,loss_mel=16.846, loss_kl=1.384
2023-05-09 22:20:42,253 kenma_eng INFO ====> Epoch: 816
2023-05-09 22:20:50,242 kenma_eng INFO ====> Epoch: 817
2023-05-09 22:20:58,229 kenma_eng INFO ====> Epoch: 818
2023-05-09 22:21:06,264 kenma_eng INFO ====> Epoch: 819
2023-05-09 22:21:14,217 kenma_eng INFO ====> Epoch: 820
2023-05-09 22:21:18,510 kenma_eng INFO Train Epoch: 821 [33%]
2023-05-09 22:21:18,512 kenma_eng INFO [32000, 9.025723671482575e-05]
2023-05-09 22:21:18,512 kenma_eng INFO loss_disc=2.022, loss_gen=4.457, loss_fm=14.231,loss_mel=17.339, loss_kl=0.754
2023-05-09 22:21:22,490 kenma_eng INFO ====> Epoch: 821
2023-05-09 22:21:30,379 kenma_eng INFO ====> Epoch: 822
2023-05-09 22:21:38,371 kenma_eng INFO ====> Epoch: 823
2023-05-09 22:21:46,416 kenma_eng INFO ====> Epoch: 824
2023-05-09 22:21:54,343 kenma_eng INFO Saving model and optimizer state at epoch 825 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:21:54,788 kenma_eng INFO Saving model and optimizer state at epoch 825 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:21:55,357 kenma_eng INFO ====> Epoch: 825
2023-05-09 22:22:00,690 kenma_eng INFO Train Epoch: 826 [38%]
2023-05-09 22:22:00,692 kenma_eng INFO [32200, 9.020084004280947e-05]
2023-05-09 22:22:00,692 kenma_eng INFO loss_disc=1.354, loss_gen=4.448, loss_fm=18.082,loss_mel=15.376, loss_kl=0.875
2023-05-09 22:22:03,566 kenma_eng INFO ====> Epoch: 826
2023-05-09 22:22:11,549 kenma_eng INFO ====> Epoch: 827
2023-05-09 22:22:19,533 kenma_eng INFO ====> Epoch: 828
2023-05-09 22:22:27,527 kenma_eng INFO ====> Epoch: 829
2023-05-09 22:22:35,611 kenma_eng INFO ====> Epoch: 830
2023-05-09 22:22:42,136 kenma_eng INFO Train Epoch: 831 [90%]
2023-05-09 22:22:42,138 kenma_eng INFO [32400, 9.014447860990232e-05]
2023-05-09 22:22:42,138 kenma_eng INFO loss_disc=1.867, loss_gen=4.211, loss_fm=15.318,loss_mel=17.647, loss_kl=0.984
2023-05-09 22:22:44,027 kenma_eng INFO ====> Epoch: 831
2023-05-09 22:22:52,106 kenma_eng INFO ====> Epoch: 832
2023-05-09 22:23:00,080 kenma_eng INFO ====> Epoch: 833
2023-05-09 22:23:08,066 kenma_eng INFO ====> Epoch: 834
2023-05-09 22:23:16,055 kenma_eng INFO ====> Epoch: 835
2023-05-09 22:23:23,431 kenma_eng INFO Train Epoch: 836 [56%]
2023-05-09 22:23:23,433 kenma_eng INFO [32600, 9.008815239408536e-05]
2023-05-09 22:23:23,433 kenma_eng INFO loss_disc=2.303, loss_gen=3.766, loss_fm=12.506,loss_mel=17.341, loss_kl=1.061
2023-05-09 22:23:24,333 kenma_eng INFO ====> Epoch: 836
2023-05-09 22:23:32,233 kenma_eng INFO ====> Epoch: 837
2023-05-09 22:23:40,218 kenma_eng INFO ====> Epoch: 838
2023-05-09 22:23:48,205 kenma_eng INFO ====> Epoch: 839
2023-05-09 22:23:56,197 kenma_eng INFO ====> Epoch: 840
2023-05-09 22:24:04,182 kenma_eng INFO ====> Epoch: 841
2023-05-09 22:24:04,592 kenma_eng INFO Train Epoch: 842 [23%]
2023-05-09 22:24:04,595 kenma_eng INFO [32800, 9.002060739068175e-05]
2023-05-09 22:24:04,595 kenma_eng INFO loss_disc=1.435, loss_gen=4.423, loss_fm=16.183,loss_mel=14.273, loss_kl=0.951
2023-05-09 22:24:12,374 kenma_eng INFO ====> Epoch: 842
2023-05-09 22:24:20,429 kenma_eng INFO ====> Epoch: 843
2023-05-09 22:24:28,348 kenma_eng INFO ====> Epoch: 844
2023-05-09 22:24:36,333 kenma_eng INFO ====> Epoch: 845
2023-05-09 22:24:44,322 kenma_eng INFO ====> Epoch: 846
2023-05-09 22:24:45,756 kenma_eng INFO Train Epoch: 847 [92%]
2023-05-09 22:24:45,759 kenma_eng INFO [33000, 8.996435857502436e-05]
2023-05-09 22:24:45,759 kenma_eng INFO loss_disc=2.194, loss_gen=4.097, loss_fm=13.991,loss_mel=16.593, loss_kl=0.280
2023-05-09 22:24:52,514 kenma_eng INFO ====> Epoch: 847
2023-05-09 22:25:00,541 kenma_eng INFO ====> Epoch: 848
2023-05-09 22:25:08,490 kenma_eng INFO ====> Epoch: 849
2023-05-09 22:25:16,479 kenma_eng INFO Saving model and optimizer state at epoch 850 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:25:16,923 kenma_eng INFO Saving model and optimizer state at epoch 850 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:25:17,494 kenma_eng INFO ====> Epoch: 850
2023-05-09 22:25:25,484 kenma_eng INFO ====> Epoch: 851
2023-05-09 22:25:27,935 kenma_eng INFO Train Epoch: 852 [49%]
2023-05-09 22:25:27,937 kenma_eng INFO [33200, 8.990814490608897e-05]
2023-05-09 22:25:27,937 kenma_eng INFO loss_disc=1.999, loss_gen=4.374, loss_fm=16.687,loss_mel=15.455, loss_kl=0.892
2023-05-09 22:25:33,669 kenma_eng INFO ====> Epoch: 852
2023-05-09 22:25:41,680 kenma_eng INFO ====> Epoch: 853
2023-05-09 22:25:49,651 kenma_eng INFO ====> Epoch: 854
2023-05-09 22:25:57,634 kenma_eng INFO ====> Epoch: 855
2023-05-09 22:26:05,618 kenma_eng INFO ====> Epoch: 856
2023-05-09 22:26:09,108 kenma_eng INFO Train Epoch: 857 [64%]
2023-05-09 22:26:09,110 kenma_eng INFO [33400, 8.985196636191438e-05]
2023-05-09 22:26:09,110 kenma_eng INFO loss_disc=1.911, loss_gen=4.051, loss_fm=15.250,loss_mel=16.547, loss_kl=0.315
2023-05-09 22:26:13,821 kenma_eng INFO ====> Epoch: 857
2023-05-09 22:26:21,803 kenma_eng INFO ====> Epoch: 858
2023-05-09 22:26:29,800 kenma_eng INFO ====> Epoch: 859
2023-05-09 22:26:37,778 kenma_eng INFO ====> Epoch: 860
2023-05-09 22:26:45,766 kenma_eng INFO ====> Epoch: 861
2023-05-09 22:26:50,265 kenma_eng INFO Train Epoch: 862 [31%]
2023-05-09 22:26:50,267 kenma_eng INFO [33600, 8.979582292055309e-05]
2023-05-09 22:26:50,268 kenma_eng INFO loss_disc=1.649, loss_gen=4.747, loss_fm=15.933,loss_mel=14.584, loss_kl=0.497
2023-05-09 22:26:53,960 kenma_eng INFO ====> Epoch: 862
2023-05-09 22:27:01,945 kenma_eng INFO ====> Epoch: 863
2023-05-09 22:27:09,930 kenma_eng INFO ====> Epoch: 864
2023-05-09 22:27:17,910 kenma_eng INFO ====> Epoch: 865
2023-05-09 22:27:25,922 kenma_eng INFO ====> Epoch: 866
2023-05-09 22:27:31,430 kenma_eng INFO Train Epoch: 867 [77%]
2023-05-09 22:27:31,433 kenma_eng INFO [33800, 8.973971456007135e-05]
2023-05-09 22:27:31,433 kenma_eng INFO loss_disc=2.140, loss_gen=3.461, loss_fm=13.473,loss_mel=15.661, loss_kl=0.451
2023-05-09 22:27:34,086 kenma_eng INFO ====> Epoch: 867
2023-05-09 22:27:42,091 kenma_eng INFO ====> Epoch: 868
2023-05-09 22:27:50,071 kenma_eng INFO ====> Epoch: 869
2023-05-09 22:27:58,060 kenma_eng INFO ====> Epoch: 870
2023-05-09 22:28:06,040 kenma_eng INFO ====> Epoch: 871
2023-05-09 22:28:12,607 kenma_eng INFO Train Epoch: 872 [59%]
2023-05-09 22:28:12,609 kenma_eng INFO [34000, 8.968364125854907e-05]
2023-05-09 22:28:12,609 kenma_eng INFO loss_disc=1.733, loss_gen=4.257, loss_fm=16.887,loss_mel=17.172, loss_kl=0.642
2023-05-09 22:28:14,230 kenma_eng INFO ====> Epoch: 872
2023-05-09 22:28:22,219 kenma_eng INFO ====> Epoch: 873
2023-05-09 22:28:30,210 kenma_eng INFO ====> Epoch: 874
2023-05-09 22:28:38,195 kenma_eng INFO Saving model and optimizer state at epoch 875 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:28:38,642 kenma_eng INFO Saving model and optimizer state at epoch 875 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:28:39,214 kenma_eng INFO ====> Epoch: 875
2023-05-09 22:28:47,204 kenma_eng INFO ====> Epoch: 876
2023-05-09 22:28:54,778 kenma_eng INFO Train Epoch: 877 [87%]
2023-05-09 22:28:54,780 kenma_eng INFO [34200, 8.962760299407988e-05]
2023-05-09 22:28:54,780 kenma_eng INFO loss_disc=2.002, loss_gen=3.890, loss_fm=10.232,loss_mel=12.594, loss_kl=0.279
2023-05-09 22:28:55,361 kenma_eng INFO ====> Epoch: 877
2023-05-09 22:29:03,199 kenma_eng INFO ====> Epoch: 878
2023-05-09 22:29:11,168 kenma_eng INFO ====> Epoch: 879
2023-05-09 22:29:19,158 kenma_eng INFO ====> Epoch: 880
2023-05-09 22:29:27,144 kenma_eng INFO ====> Epoch: 881
2023-05-09 22:29:35,130 kenma_eng INFO ====> Epoch: 882
2023-05-09 22:29:35,747 kenma_eng INFO Train Epoch: 883 [5%]
2023-05-09 22:29:35,749 kenma_eng INFO [34400, 8.9560403294803e-05]
2023-05-09 22:29:35,749 kenma_eng INFO loss_disc=1.863, loss_gen=3.860, loss_fm=14.899,loss_mel=15.688, loss_kl=0.911
2023-05-09 22:29:43,325 kenma_eng INFO ====> Epoch: 883
2023-05-09 22:29:51,307 kenma_eng INFO ====> Epoch: 884
2023-05-09 22:29:59,289 kenma_eng INFO ====> Epoch: 885
2023-05-09 22:30:07,272 kenma_eng INFO ====> Epoch: 886
2023-05-09 22:30:15,267 kenma_eng INFO ====> Epoch: 887
2023-05-09 22:30:16,908 kenma_eng INFO Train Epoch: 888 [87%]
2023-05-09 22:30:16,910 kenma_eng INFO [34600, 8.950444203480763e-05]
2023-05-09 22:30:16,910 kenma_eng INFO loss_disc=1.948, loss_gen=4.004, loss_fm=12.251,loss_mel=11.959, loss_kl=0.875
2023-05-09 22:30:23,469 kenma_eng INFO ====> Epoch: 888
2023-05-09 22:30:31,442 kenma_eng INFO ====> Epoch: 889
2023-05-09 22:30:39,424 kenma_eng INFO ====> Epoch: 890
2023-05-09 22:30:47,410 kenma_eng INFO ====> Epoch: 891
2023-05-09 22:30:55,404 kenma_eng INFO ====> Epoch: 892
2023-05-09 22:30:58,066 kenma_eng INFO Train Epoch: 893 [97%]
2023-05-09 22:30:58,069 kenma_eng INFO [34800, 8.944851574185691e-05]
2023-05-09 22:30:58,069 kenma_eng INFO loss_disc=1.855, loss_gen=3.818, loss_fm=14.880,loss_mel=17.361, loss_kl=1.106
2023-05-09 22:31:03,607 kenma_eng INFO ====> Epoch: 893
2023-05-09 22:31:11,576 kenma_eng INFO ====> Epoch: 894
2023-05-09 22:31:19,568 kenma_eng INFO ====> Epoch: 895
2023-05-09 22:31:27,558 kenma_eng INFO ====> Epoch: 896
2023-05-09 22:31:35,537 kenma_eng INFO ====> Epoch: 897
2023-05-09 22:31:39,222 kenma_eng INFO Train Epoch: 898 [26%]
2023-05-09 22:31:39,224 kenma_eng INFO [35000, 8.939262439410188e-05]
2023-05-09 22:31:39,224 kenma_eng INFO loss_disc=1.911, loss_gen=4.658, loss_fm=17.135,loss_mel=16.999, loss_kl=1.019
2023-05-09 22:31:43,745 kenma_eng INFO ====> Epoch: 898
2023-05-09 22:31:51,726 kenma_eng INFO ====> Epoch: 899
2023-05-09 22:31:59,713 kenma_eng INFO Saving model and optimizer state at epoch 900 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:32:00,157 kenma_eng INFO Saving model and optimizer state at epoch 900 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:32:00,729 kenma_eng INFO ====> Epoch: 900
2023-05-09 22:32:08,724 kenma_eng INFO ====> Epoch: 901
2023-05-09 22:32:16,709 kenma_eng INFO ====> Epoch: 902
2023-05-09 22:32:21,440 kenma_eng INFO Train Epoch: 903 [77%]
2023-05-09 22:32:21,441 kenma_eng INFO [35200, 8.933676796970726e-05]
2023-05-09 22:32:21,441 kenma_eng INFO loss_disc=2.365, loss_gen=3.842, loss_fm=12.082,loss_mel=15.059, loss_kl=0.793
2023-05-09 22:32:24,904 kenma_eng INFO ====> Epoch: 903
2023-05-09 22:32:32,883 kenma_eng INFO ====> Epoch: 904
2023-05-09 22:32:40,881 kenma_eng INFO ====> Epoch: 905
2023-05-09 22:32:48,863 kenma_eng INFO ====> Epoch: 906
2023-05-09 22:32:56,848 kenma_eng INFO ====> Epoch: 907
2023-05-09 22:33:02,589 kenma_eng INFO Train Epoch: 908 [0%]
2023-05-09 22:33:02,592 kenma_eng INFO [35400, 8.928094644685142e-05]
2023-05-09 22:33:02,598 kenma_eng INFO loss_disc=2.125, loss_gen=4.022, loss_fm=12.501,loss_mel=16.944, loss_kl=0.741
2023-05-09 22:33:05,041 kenma_eng INFO ====> Epoch: 908
2023-05-09 22:33:13,028 kenma_eng INFO ====> Epoch: 909
2023-05-09 22:33:21,016 kenma_eng INFO ====> Epoch: 910
2023-05-09 22:33:29,010 kenma_eng INFO ====> Epoch: 911
2023-05-09 22:33:36,993 kenma_eng INFO ====> Epoch: 912
2023-05-09 22:33:43,766 kenma_eng INFO Train Epoch: 913 [28%]
2023-05-09 22:33:43,768 kenma_eng INFO [35600, 8.922515980372634e-05]
2023-05-09 22:33:43,768 kenma_eng INFO loss_disc=1.525, loss_gen=4.611, loss_fm=16.297,loss_mel=17.486, loss_kl=0.635
2023-05-09 22:33:45,182 kenma_eng INFO ====> Epoch: 913
2023-05-09 22:33:53,170 kenma_eng INFO ====> Epoch: 914
2023-05-09 22:34:01,150 kenma_eng INFO ====> Epoch: 915
2023-05-09 22:34:09,152 kenma_eng INFO ====> Epoch: 916
2023-05-09 22:34:17,126 kenma_eng INFO ====> Epoch: 917
2023-05-09 22:34:24,903 kenma_eng INFO Train Epoch: 918 [72%]
2023-05-09 22:34:24,905 kenma_eng INFO [35800, 8.916940801853763e-05]
2023-05-09 22:34:24,905 kenma_eng INFO loss_disc=1.595, loss_gen=4.964, loss_fm=18.770,loss_mel=15.964, loss_kl=0.988
2023-05-09 22:34:25,303 kenma_eng INFO ====> Epoch: 918
2023-05-09 22:34:33,299 kenma_eng INFO ====> Epoch: 919
2023-05-09 22:34:41,297 kenma_eng INFO ====> Epoch: 920
2023-05-09 22:34:49,279 kenma_eng INFO ====> Epoch: 921
2023-05-09 22:34:57,267 kenma_eng INFO ====> Epoch: 922
2023-05-09 22:35:05,254 kenma_eng INFO ====> Epoch: 923
2023-05-09 22:35:06,073 kenma_eng INFO Train Epoch: 924 [38%]
2023-05-09 22:35:06,075 kenma_eng INFO [36000, 8.910255185812085e-05]
2023-05-09 22:35:06,082 kenma_eng INFO loss_disc=1.392, loss_gen=4.566, loss_fm=17.545,loss_mel=14.483, loss_kl=1.436
2023-05-09 22:35:13,441 kenma_eng INFO ====> Epoch: 924
2023-05-09 22:35:21,432 kenma_eng INFO Saving model and optimizer state at epoch 925 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:35:21,876 kenma_eng INFO Saving model and optimizer state at epoch 925 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:35:22,449 kenma_eng INFO ====> Epoch: 925
2023-05-09 22:35:30,440 kenma_eng INFO ====> Epoch: 926
2023-05-09 22:35:38,430 kenma_eng INFO ====> Epoch: 927
2023-05-09 22:35:46,417 kenma_eng INFO ====> Epoch: 928
2023-05-09 22:35:48,263 kenma_eng INFO Train Epoch: 929 [36%]
2023-05-09 22:35:48,265 kenma_eng INFO [36200, 8.904687668374304e-05]
2023-05-09 22:35:48,271 kenma_eng INFO loss_disc=1.577, loss_gen=4.487, loss_fm=17.790,loss_mel=15.011, loss_kl=0.614
2023-05-09 22:35:54,608 kenma_eng INFO ====> Epoch: 929
2023-05-09 22:36:02,599 kenma_eng INFO ====> Epoch: 930
2023-05-09 22:36:10,583 kenma_eng INFO ====> Epoch: 931
2023-05-09 22:36:18,566 kenma_eng INFO ====> Epoch: 932
2023-05-09 22:36:26,563 kenma_eng INFO ====> Epoch: 933
2023-05-09 22:36:29,420 kenma_eng INFO Train Epoch: 934 [56%]
2023-05-09 22:36:29,422 kenma_eng INFO [36400, 8.899123629765109e-05]
2023-05-09 22:36:29,422 kenma_eng INFO loss_disc=2.189, loss_gen=3.898, loss_fm=13.126,loss_mel=17.128, loss_kl=0.778
2023-05-09 22:36:34,747 kenma_eng INFO ====> Epoch: 934
2023-05-09 22:36:42,733 kenma_eng INFO ====> Epoch: 935
2023-05-09 22:36:50,722 kenma_eng INFO ====> Epoch: 936
2023-05-09 22:36:58,711 kenma_eng INFO ====> Epoch: 937
2023-05-09 22:37:06,698 kenma_eng INFO ====> Epoch: 938
2023-05-09 22:37:10,584 kenma_eng INFO Train Epoch: 939 [85%]
2023-05-09 22:37:10,586 kenma_eng INFO [36600, 8.893563067810772e-05]
2023-05-09 22:37:10,586 kenma_eng INFO loss_disc=1.269, loss_gen=4.542, loss_fm=18.197,loss_mel=13.602, loss_kl=0.827
2023-05-09 22:37:14,894 kenma_eng INFO ====> Epoch: 939
2023-05-09 22:37:22,876 kenma_eng INFO ====> Epoch: 940
2023-05-09 22:37:30,857 kenma_eng INFO ====> Epoch: 941
2023-05-09 22:37:38,854 kenma_eng INFO ====> Epoch: 942
2023-05-09 22:37:46,835 kenma_eng INFO ====> Epoch: 943
2023-05-09 22:37:51,750 kenma_eng INFO Train Epoch: 944 [79%]
2023-05-09 22:37:51,752 kenma_eng INFO [36800, 8.888005980338925e-05]
2023-05-09 22:37:51,752 kenma_eng INFO loss_disc=1.693, loss_gen=4.491, loss_fm=15.880,loss_mel=16.039, loss_kl=0.871
2023-05-09 22:37:55,026 kenma_eng INFO ====> Epoch: 944
2023-05-09 22:38:03,035 kenma_eng INFO ====> Epoch: 945
2023-05-09 22:38:11,000 kenma_eng INFO ====> Epoch: 946
2023-05-09 22:38:18,987 kenma_eng INFO ====> Epoch: 947
2023-05-09 22:38:26,982 kenma_eng INFO ====> Epoch: 948
2023-05-09 22:38:32,919 kenma_eng INFO Train Epoch: 949 [56%]
2023-05-09 22:38:32,922 kenma_eng INFO [37000, 8.882452365178563e-05]
2023-05-09 22:38:32,922 kenma_eng INFO loss_disc=2.251, loss_gen=4.714, loss_fm=14.391,loss_mel=17.186, loss_kl=0.774
2023-05-09 22:38:35,170 kenma_eng INFO ====> Epoch: 949
2023-05-09 22:38:43,165 kenma_eng INFO Saving model and optimizer state at epoch 950 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:38:43,609 kenma_eng INFO Saving model and optimizer state at epoch 950 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:38:44,180 kenma_eng INFO ====> Epoch: 950
2023-05-09 22:38:52,202 kenma_eng INFO ====> Epoch: 951
2023-05-09 22:39:00,147 kenma_eng INFO ====> Epoch: 952
2023-05-09 22:39:08,143 kenma_eng INFO ====> Epoch: 953
2023-05-09 22:39:15,103 kenma_eng INFO Train Epoch: 954 [15%]
2023-05-09 22:39:15,105 kenma_eng INFO [37200, 8.876902220160032e-05]
2023-05-09 22:39:15,105 kenma_eng INFO loss_disc=1.366, loss_gen=4.539, loss_fm=15.770,loss_mel=15.288, loss_kl=0.881
2023-05-09 22:39:16,324 kenma_eng INFO ====> Epoch: 954
2023-05-09 22:39:24,324 kenma_eng INFO ====> Epoch: 955
2023-05-09 22:39:32,308 kenma_eng INFO ====> Epoch: 956
2023-05-09 22:39:40,296 kenma_eng INFO ====> Epoch: 957
2023-05-09 22:39:48,280 kenma_eng INFO ====> Epoch: 958
2023-05-09 22:39:56,265 kenma_eng INFO Train Epoch: 959 [5%]
2023-05-09 22:39:56,267 kenma_eng INFO [37400, 8.871355543115036e-05]
2023-05-09 22:39:56,267 kenma_eng INFO loss_disc=1.824, loss_gen=4.384, loss_fm=15.822,loss_mel=15.269, loss_kl=0.584
2023-05-09 22:39:56,461 kenma_eng INFO ====> Epoch: 959
2023-05-09 22:40:04,453 kenma_eng INFO ====> Epoch: 960
2023-05-09 22:40:12,440 kenma_eng INFO ====> Epoch: 961
2023-05-09 22:40:20,438 kenma_eng INFO ====> Epoch: 962
2023-05-09 22:40:28,610 kenma_eng INFO ====> Epoch: 963
2023-05-09 22:40:36,612 kenma_eng INFO ====> Epoch: 964
2023-05-09 22:40:37,642 kenma_eng INFO Train Epoch: 965 [5%]
2023-05-09 22:40:37,644 kenma_eng INFO [37600, 8.864704105335148e-05]
2023-05-09 22:40:37,644 kenma_eng INFO loss_disc=1.786, loss_gen=4.230, loss_fm=13.870,loss_mel=14.843, loss_kl=0.636
2023-05-09 22:40:44,795 kenma_eng INFO ====> Epoch: 965
2023-05-09 22:40:52,791 kenma_eng INFO ====> Epoch: 966
2023-05-09 22:41:00,778 kenma_eng INFO ====> Epoch: 967
2023-05-09 22:41:08,760 kenma_eng INFO ====> Epoch: 968
2023-05-09 22:41:16,748 kenma_eng INFO ====> Epoch: 969
2023-05-09 22:41:18,797 kenma_eng INFO Train Epoch: 970 [67%]
2023-05-09 22:41:18,800 kenma_eng INFO [37800, 8.8591650502062e-05]
2023-05-09 22:41:18,806 kenma_eng INFO loss_disc=1.188, loss_gen=4.842, loss_fm=19.598,loss_mel=13.911, loss_kl=0.904
2023-05-09 22:41:24,933 kenma_eng INFO ====> Epoch: 970
2023-05-09 22:41:32,926 kenma_eng INFO ====> Epoch: 971
2023-05-09 22:41:40,917 kenma_eng INFO ====> Epoch: 972
2023-05-09 22:41:48,893 kenma_eng INFO ====> Epoch: 973
2023-05-09 22:41:56,886 kenma_eng INFO ====> Epoch: 974
2023-05-09 22:41:59,961 kenma_eng INFO Train Epoch: 975 [85%]
2023-05-09 22:41:59,963 kenma_eng INFO [38000, 8.853629456121339e-05]
2023-05-09 22:41:59,964 kenma_eng INFO loss_disc=1.399, loss_gen=4.490, loss_fm=19.375,loss_mel=12.979, loss_kl=-0.358
2023-05-09 22:42:05,073 kenma_eng INFO Saving model and optimizer state at epoch 975 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:42:05,593 kenma_eng INFO Saving model and optimizer state at epoch 975 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:42:06,168 kenma_eng INFO ====> Epoch: 975
2023-05-09 22:42:14,091 kenma_eng INFO ====> Epoch: 976
2023-05-09 22:42:22,080 kenma_eng INFO ====> Epoch: 977
2023-05-09 22:42:30,041 kenma_eng INFO ====> Epoch: 978
2023-05-09 22:42:37,878 kenma_eng INFO ====> Epoch: 979
2023-05-09 22:42:41,938 kenma_eng INFO Train Epoch: 980 [13%]
2023-05-09 22:42:41,941 kenma_eng INFO [38200, 8.848097320917952e-05]
2023-05-09 22:42:41,941 kenma_eng INFO loss_disc=1.870, loss_gen=4.166, loss_fm=15.450,loss_mel=16.105, loss_kl=0.723
2023-05-09 22:42:46,024 kenma_eng INFO ====> Epoch: 980
2023-05-09 22:42:54,029 kenma_eng INFO ====> Epoch: 981
2023-05-09 22:43:02,056 kenma_eng INFO ====> Epoch: 982
2023-05-09 22:43:09,999 kenma_eng INFO ====> Epoch: 983
2023-05-09 22:43:18,003 kenma_eng INFO ====> Epoch: 984
2023-05-09 22:43:23,115 kenma_eng INFO Train Epoch: 985 [62%]
2023-05-09 22:43:23,117 kenma_eng INFO [38400, 8.842568642434779e-05]
2023-05-09 22:43:23,117 kenma_eng INFO loss_disc=2.048, loss_gen=4.223, loss_fm=12.929,loss_mel=16.321, loss_kl=1.266
2023-05-09 22:43:26,179 kenma_eng INFO ====> Epoch: 985
2023-05-09 22:43:34,165 kenma_eng INFO ====> Epoch: 986
2023-05-09 22:43:42,152 kenma_eng INFO ====> Epoch: 987
2023-05-09 22:43:50,139 kenma_eng INFO ====> Epoch: 988
2023-05-09 22:43:58,125 kenma_eng INFO ====> Epoch: 989
2023-05-09 22:44:04,275 kenma_eng INFO Train Epoch: 990 [18%]
2023-05-09 22:44:04,278 kenma_eng INFO [38600, 8.83704341851191e-05]
2023-05-09 22:44:04,278 kenma_eng INFO loss_disc=1.481, loss_gen=4.148, loss_fm=15.000,loss_mel=14.968, loss_kl=0.665
2023-05-09 22:44:06,298 kenma_eng INFO ====> Epoch: 990
2023-05-09 22:44:14,164 kenma_eng INFO ====> Epoch: 991
2023-05-09 22:44:22,086 kenma_eng INFO ====> Epoch: 992
2023-05-09 22:44:30,044 kenma_eng INFO ====> Epoch: 993
2023-05-09 22:44:37,879 kenma_eng INFO ====> Epoch: 994
2023-05-09 22:44:44,992 kenma_eng INFO Train Epoch: 995 [31%]
2023-05-09 22:44:44,994 kenma_eng INFO [38800, 8.831521646990785e-05]
2023-05-09 22:44:44,994 kenma_eng INFO loss_disc=1.543, loss_gen=4.280, loss_fm=15.891,loss_mel=14.763, loss_kl=0.628
2023-05-09 22:44:45,997 kenma_eng INFO ====> Epoch: 995
2023-05-09 22:44:53,830 kenma_eng INFO ====> Epoch: 996
2023-05-09 22:45:01,822 kenma_eng INFO ====> Epoch: 997
2023-05-09 22:45:09,811 kenma_eng INFO ====> Epoch: 998
2023-05-09 22:45:17,837 kenma_eng INFO ====> Epoch: 999
2023-05-09 22:45:25,785 kenma_eng INFO Saving model and optimizer state at epoch 1000 to ./logs/kenma_eng/G_2333333.pth
2023-05-09 22:45:26,231 kenma_eng INFO Saving model and optimizer state at epoch 1000 to ./logs/kenma_eng/D_2333333.pth
2023-05-09 22:45:26,804 kenma_eng INFO ====> Epoch: 1000
2023-05-09 22:45:26,804 kenma_eng INFO Training is done. The program is closed.
2023-05-09 22:45:26,867 kenma_eng INFO saving final ckpt:Success.