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.