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\ No newline at end of file
diff --git a/kenma_eng/preprocess.log b/kenma_eng/preprocess.log
new file mode 100644
index 0000000000000000000000000000000000000000..be1c9c30d1cf703259767451e7c615599d7538e6
--- /dev/null
+++ b/kenma_eng/preprocess.log
@@ -0,0 +1,70 @@
+start preprocess
+['trainset_preprocess_pipeline_print.py', '/home/parappa/Datasets/RVC/kenma_eng', '40000', '16', '/home/parappa/Retrieval-based-Voice-Conversion-WebUI/logs/kenma_eng', 'False']
+/home/parappa/Datasets/RVC/kenma_eng/00_00_03_Kenma_Neutral_Oh,yeah..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_27_Kenma_Neutral_This guy thinks things through_and he is in tune with his surroundings_he_s the complete opposite of Shouyou..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_26_Kenma_Neutral_I couldn_t be on a team with a guy like that.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_39_Kenma_Serious_Said _probably_ and _kinda__seriously, just think about it for a second_is it likely i practiced for hours because i feel bad for you_.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_43_Kenma_Neutral_I haven_t yelled like that in ages_i_m wiped...wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_09_Kenma_Neutral_Kozume_no, its_its kenma..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_33_Kenma_Neutral_All my life, i_ve never had any luck making friends for myself_I don_t have any interest in other people_i_m always extremely aware of their eyes..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_28_Kenma_Neutral_I don_t think i_d be able to keep up with him unless i was constantly improving_no matter how smart i was about ditching practice, he_d notice the difference..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_46_Kenma_Neutral_Noisy_Obviously I wouldn_t do this if I didn_t like it_at the moment, i still happen to be playing volleyball..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_45_Kenma_Neutral_He_s onto me_that one more step suddenly feels like a million miles_i wish i could strech my arms, like, two meters..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_14_Kenma_Neutral_It_s okay_after all, that age-based hierarchy that comes with sports_that_s lame..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_45_Kenma_Neutral_I don_t like sports very much_but i_ve always played around with volleyball_when we were in middle school kuroo talked me into joining volleyball club..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_36_Kenma_Neutral_Even that genius first year hit a plateau for over a second_and shouyou called him..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_51_Kenma_Neutral_Noisy_I don_t have a definitive reason to keep on playing, but I don_t really have a reason to quit playing either_i mean, that_s normal, isn_t it..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_53_Kenma_Serious_Noisy_The quality of our blocking_it_s starting to get to their number one__he_s probably going to start trying to fake his up very soon_and pay attention because he_ll likely hit it out more.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_29_Kenma_Neutral_It_s fine, i can_t say i really care_i don_t mind it or anything, but i_m not big on getting all worn out_but, uh, my friend likes to play, and he needs me so i play too..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_56_Kenma_Neutral_I kept playing when i got to high school_our school had a good team on the first year_but there were so many people i couldn_t deal with being a part of it..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_36_Kenma_Neutral_Noisy_Bokuto_s line shots are especially good this match_and he knows that yaku will be there waiting for his cross shots,.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_58_Kenma_Neutral_I really don_t enjoy sweating or being out of breath at all, ever_but i don_t have any problems leveling up___good night..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_44_Kenma_Neutral_Whatever, i_m only playing it when i_m on high school..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_06_Kenma_Serious_Noisy_There_s nothing special about upperclassmen_they were just born a year or two sooner_so why do they act like they_re better than everyone else_.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_42_Kenma_Confident_Noisy_Yeah, but first_since bokuto_s line shots are unusually good right now, let_s see if we can make the most out of them_i_ve got something we can try..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_03_Kenma_Exhausted_I hate running__but___I do sorta like the scenery..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_47_Kenma_Neutral_Nah, i don_t think that_s weird, not at all..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_13_Kenma_Neutral_Meh, i was the same as always.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_52_Kenma_Confident_Noisy_We_ve played to Bokuto_s cross shots long enough, let_s work on his line shots..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_08_Kenma_Neutral_Noisy_All the things i get to think about while i run..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_51_Kenma_Serious_Noisy_People say a lot of stuff about me when I go to a game too..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_15_Kenma_Happy_Well, i_ll be waiting..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_55_Kenma_Serious_They definitely know who they want to provoke..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_10_Kenma_Neutral_I wanna sit down..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_54_Kenma_Serious_Noisy_You know, like I thought the setter was supposed to be the player with the strongest abilities_so why did they ever pick this guy out of all people..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_26_Kenma_Neutral_That_s lev haiba, first year_he_s half russian half japanese..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_57_Kenma_Serious_It_s just interesting_he_s more annoying that i_d though it_d be..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_11_Kenma_Neutral_Noisy_Alright_what does exactly me being friends with kuroo have to do with anything..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_54_Kenma_Serious_Noisy_You see, i_m not all that much of an athletic person..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_31_Kenma_Neutral_His name is lev_it_s russian for_i think it_s like, tiger_.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_00_Kenma_Neutral_Hey, i think you guys are too depressed_i think we_ll be fine..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_15_Kenma_Neutral_Besides this is just a passing drill..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_00_57_Kenma_Happy_I do kind of look forward to playing with them_you know, a little bit..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_34_Kenma_Neutral_Yeah, he just started playing volleyball this school year_when we went to see you guys during golden week, he was still learning the ropes_we only took the players we already had on the bench..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_05_Kenma_Neutral_Noisy_Yuuki_i think you should onlny sub out with lev..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_16_Kenma_Neutral_Don_t put this on me, that guy started it..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_00_Kenma_Neutral_Noisy_Uhh_you had karasuno high written on your t-shirt so..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_45_Kenma_Neutral_Yeah, it was tough for me because i kept getting paired with him_I tried to adjust to him but our timing was always weird_its gotten better though..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_09_Kenma_Serious_It_s not clear__we still have_a lot more work left for kuro to take care o-..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_18_Kenma_Neutral_Uh_hey_Yamamoto___maybe you could try relaxing your shoulders_when you_re playing or whatever..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_00_Kenma_Neutral_Noisy_Uhh_you had karasuno high written on your t-shirt so._(Vocals).wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_02_54_Kenma_Neutral_Noisy_Well, he_s just kind of a strange person_he_s mostly relying on his instincts and physical abilities at the moment_plus his height..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_14_Kenma_Serious_Even if he_s not as good as yaku, kuro_s still really good at reception_and from there_we can run that backrow quick..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_27_Kenma_Serious_Noisy_I think you should blaming something as vague as guts_try to identify an specific problem you can actually work on and improve in..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_03_Kenma_Neutral_Noisy_If their team_s offenses are hanging on hinata, we should stop him first..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_02_Kenma_Neutral_Noisy_Well he_s a good player, and he_s honest so i don_t mind him_but sometimes he can be too honest_that said said, he sucks at passes and some basic stuff, even worse than you do..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_21_Kenma_Neutral_I_ve never even tried it with Kuroo,but_i think we can probably pull it off_sorry if i screw it up. .wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_35_Kenma_Angry_If you know all that stuff then why do you blame everything on guts!.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_03_Kenma_Neutral_Noisy_So if we block him on one side we_ll go the opposite way_and then you just have to follow him..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_13_Kenma_Neutral_His serves are way worse than yours are too..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_26_Kenma_Neutral_Noisy_I don_t think we can beat twelve if we keep trying to predict his moves..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_38_Kenma_Angry_But_I was tired..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_07_Kenma_Neutral_Noisy_We have to confine hinata to a smaller area_from what i_ve seen so far, i think he just charges straight to wherever we don_t have any blockers..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_15_Kenma_Neutral_Lev___i get the feeling that they_re gonna start switching things up with a few normal quick attacks, so you should start re-blocking..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_29_Kenma_Neutral_Noisy_Yeah, if i really wanna quit playing then i will_but it_s also true that i_d feel kinda bad for avengening kuroo..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_39_Kenma_Neutral_Noisy_And even if i got them, they_d just be free balls for the other team...wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_01_16_Kenma_Neutral_Noisy_That combo move is scary_i think anyone would be thrown off-guard the first time they see it_but, just when you can_t beat a level in a game_the more you try the better you get..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_03_24_Kenma_Neutral_If you include the previous training camp, they_ve done way more penalty laps than anyone..wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_04_36_Kenma_Serious_Noisy_Yeah_i do actually care a little about other people, you know_.wav->Suc.
+/home/parappa/Datasets/RVC/kenma_eng/00_05_42_Kenma_Amused_You were pretty funny fukunaga..wav->Suc.
+end preprocess
diff --git a/kenma_eng/train.log b/kenma_eng/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..9d827955a8fba4514a192ee696cdb6c0bf31db79
--- /dev/null
+++ b/kenma_eng/train.log
@@ -0,0 +1,602 @@
+2023-05-08 23:16:29,434	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': 5, 'name': 'kenma_eng', 'total_epoch': 300, 'pretrainG': 'pretrained/G40k.pth', 'pretrainD': 'pretrained/D40k.pth', 'gpus': '0', 'sample_rate': '40k', 'if_f0': 0, 'if_latest': 1, 'if_cache_data_in_gpu': 1}
+2023-05-08 23:16:29,434	kenma_eng	WARNING	/home/parappa/Retrieval-based-Voice-Conversion-WebUI/train is not a git repository, therefore hash value comparison will be ignored.
+2023-05-08 23:16:30,257	kenma_eng	INFO	loaded pretrained pretrained/G40k.pth pretrained/D40k.pth
+2023-05-08 23:16:33,875	kenma_eng	INFO	Train Epoch: 1 [0%]
+2023-05-08 23:16:33,875	kenma_eng	INFO	[0, 0.0001]
+2023-05-08 23:16:33,875	kenma_eng	INFO	loss_disc=2.865, loss_gen=2.661, loss_fm=12.986,loss_mel=27.710, loss_kl=5.000
+2023-05-08 23:16:43,850	kenma_eng	INFO	====> Epoch: 1
+2023-05-08 23:16:51,519	kenma_eng	INFO	====> Epoch: 2
+2023-05-08 23:16:59,170	kenma_eng	INFO	====> Epoch: 3
+2023-05-08 23:17:06,842	kenma_eng	INFO	====> Epoch: 4
+2023-05-08 23:17:14,520	kenma_eng	INFO	Saving model and optimizer state at epoch 5 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:17:14,928	kenma_eng	INFO	Saving model and optimizer state at epoch 5 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:17:15,398	kenma_eng	INFO	====> Epoch: 5
+2023-05-08 23:17:16,579	kenma_eng	INFO	Train Epoch: 6 [64%]
+2023-05-08 23:17:16,581	kenma_eng	INFO	[200, 9.993751562304699e-05]
+2023-05-08 23:17:16,581	kenma_eng	INFO	loss_disc=3.256, loss_gen=2.900, loss_fm=14.750,loss_mel=22.623, loss_kl=2.727
+2023-05-08 23:17:23,363	kenma_eng	INFO	====> Epoch: 6
+2023-05-08 23:17:31,038	kenma_eng	INFO	====> Epoch: 7
+2023-05-08 23:17:38,720	kenma_eng	INFO	====> Epoch: 8
+2023-05-08 23:17:46,403	kenma_eng	INFO	====> Epoch: 9
+2023-05-08 23:17:54,083	kenma_eng	INFO	Saving model and optimizer state at epoch 10 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:17:54,588	kenma_eng	INFO	Saving model and optimizer state at epoch 10 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:17:55,221	kenma_eng	INFO	====> Epoch: 10
+2023-05-08 23:17:57,412	kenma_eng	INFO	Train Epoch: 11 [87%]
+2023-05-08 23:17:57,414	kenma_eng	INFO	[400, 9.987507028906759e-05]
+2023-05-08 23:17:57,414	kenma_eng	INFO	loss_disc=2.973, loss_gen=2.690, loss_fm=9.204,loss_mel=17.433, loss_kl=1.308
+2023-05-08 23:18:03,121	kenma_eng	INFO	====> Epoch: 11
+2023-05-08 23:18:10,833	kenma_eng	INFO	====> Epoch: 12
+2023-05-08 23:18:18,570	kenma_eng	INFO	====> Epoch: 13
+2023-05-08 23:18:26,316	kenma_eng	INFO	====> Epoch: 14
+2023-05-08 23:18:34,038	kenma_eng	INFO	Saving model and optimizer state at epoch 15 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:18:34,479	kenma_eng	INFO	Saving model and optimizer state at epoch 15 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:18:35,055	kenma_eng	INFO	====> Epoch: 15
+2023-05-08 23:18:38,225	kenma_eng	INFO	Train Epoch: 16 [64%]
+2023-05-08 23:18:38,227	kenma_eng	INFO	[600, 9.981266397366609e-05]
+2023-05-08 23:18:38,233	kenma_eng	INFO	loss_disc=2.721, loss_gen=3.351, loss_fm=12.137,loss_mel=21.595, loss_kl=2.537
+2023-05-08 23:18:43,061	kenma_eng	INFO	====> Epoch: 16
+2023-05-08 23:18:50,809	kenma_eng	INFO	====> Epoch: 17
+2023-05-08 23:18:58,553	kenma_eng	INFO	====> Epoch: 18
+2023-05-08 23:19:06,305	kenma_eng	INFO	====> Epoch: 19
+2023-05-08 23:19:14,071	kenma_eng	INFO	Saving model and optimizer state at epoch 20 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:19:14,509	kenma_eng	INFO	Saving model and optimizer state at epoch 20 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:19:15,088	kenma_eng	INFO	====> Epoch: 20
+2023-05-08 23:19:19,247	kenma_eng	INFO	Train Epoch: 21 [3%]
+2023-05-08 23:19:19,249	kenma_eng	INFO	[800, 9.975029665246193e-05]
+2023-05-08 23:19:19,255	kenma_eng	INFO	loss_disc=2.381, loss_gen=3.654, loss_fm=16.410,loss_mel=22.015, loss_kl=1.045
+2023-05-08 23:19:23,079	kenma_eng	INFO	====> Epoch: 21
+2023-05-08 23:19:30,836	kenma_eng	INFO	====> Epoch: 22
+2023-05-08 23:19:38,610	kenma_eng	INFO	====> Epoch: 23
+2023-05-08 23:19:46,392	kenma_eng	INFO	====> Epoch: 24
+2023-05-08 23:19:54,174	kenma_eng	INFO	Saving model and optimizer state at epoch 25 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:19:54,687	kenma_eng	INFO	Saving model and optimizer state at epoch 25 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:19:55,263	kenma_eng	INFO	====> Epoch: 25
+2023-05-08 23:20:00,421	kenma_eng	INFO	Train Epoch: 26 [79%]
+2023-05-08 23:20:00,423	kenma_eng	INFO	[1000, 9.968796830108985e-05]
+2023-05-08 23:20:00,424	kenma_eng	INFO	loss_disc=2.815, loss_gen=2.932, loss_fm=13.485,loss_mel=22.573, loss_kl=2.084
+2023-05-08 23:20:03,231	kenma_eng	INFO	====> Epoch: 26
+2023-05-08 23:20:11,057	kenma_eng	INFO	====> Epoch: 27
+2023-05-08 23:20:18,894	kenma_eng	INFO	====> Epoch: 28
+2023-05-08 23:20:26,705	kenma_eng	INFO	====> Epoch: 29
+2023-05-08 23:20:34,555	kenma_eng	INFO	Saving model and optimizer state at epoch 30 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:20:34,998	kenma_eng	INFO	Saving model and optimizer state at epoch 30 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:20:35,579	kenma_eng	INFO	====> Epoch: 30
+2023-05-08 23:20:41,757	kenma_eng	INFO	Train Epoch: 31 [95%]
+2023-05-08 23:20:41,759	kenma_eng	INFO	[1200, 9.962567889519979e-05]
+2023-05-08 23:20:41,759	kenma_eng	INFO	loss_disc=2.354, loss_gen=3.439, loss_fm=14.556,loss_mel=21.892, loss_kl=2.104
+2023-05-08 23:20:43,557	kenma_eng	INFO	====> Epoch: 31
+2023-05-08 23:20:51,393	kenma_eng	INFO	====> Epoch: 32
+2023-05-08 23:20:59,229	kenma_eng	INFO	====> Epoch: 33
+2023-05-08 23:21:07,063	kenma_eng	INFO	====> Epoch: 34
+2023-05-08 23:21:14,902	kenma_eng	INFO	Saving model and optimizer state at epoch 35 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:21:15,345	kenma_eng	INFO	Saving model and optimizer state at epoch 35 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:21:16,004	kenma_eng	INFO	====> Epoch: 35
+2023-05-08 23:21:23,238	kenma_eng	INFO	Train Epoch: 36 [90%]
+2023-05-08 23:21:23,240	kenma_eng	INFO	[1400, 9.956342841045691e-05]
+2023-05-08 23:21:23,240	kenma_eng	INFO	loss_disc=2.838, loss_gen=3.338, loss_fm=11.191,loss_mel=22.750, loss_kl=1.916
+2023-05-08 23:21:24,048	kenma_eng	INFO	====> Epoch: 36
+2023-05-08 23:21:31,856	kenma_eng	INFO	====> Epoch: 37
+2023-05-08 23:21:39,717	kenma_eng	INFO	====> Epoch: 38
+2023-05-08 23:21:47,559	kenma_eng	INFO	====> Epoch: 39
+2023-05-08 23:21:55,380	kenma_eng	INFO	Saving model and optimizer state at epoch 40 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:21:55,826	kenma_eng	INFO	Saving model and optimizer state at epoch 40 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:21:56,409	kenma_eng	INFO	====> Epoch: 40
+2023-05-08 23:22:04,246	kenma_eng	INFO	====> Epoch: 41
+2023-05-08 23:22:04,648	kenma_eng	INFO	Train Epoch: 42 [77%]
+2023-05-08 23:22:04,650	kenma_eng	INFO	[1600, 9.948877917043875e-05]
+2023-05-08 23:22:04,650	kenma_eng	INFO	loss_disc=2.622, loss_gen=3.326, loss_fm=13.962,loss_mel=21.530, loss_kl=1.679
+2023-05-08 23:22:12,307	kenma_eng	INFO	====> Epoch: 42
+2023-05-08 23:22:20,153	kenma_eng	INFO	====> Epoch: 43
+2023-05-08 23:22:27,975	kenma_eng	INFO	====> Epoch: 44
+2023-05-08 23:22:35,867	kenma_eng	INFO	Saving model and optimizer state at epoch 45 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:22:36,326	kenma_eng	INFO	Saving model and optimizer state at epoch 45 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:22:36,907	kenma_eng	INFO	====> Epoch: 45
+2023-05-08 23:22:44,804	kenma_eng	INFO	====> Epoch: 46
+2023-05-08 23:22:46,210	kenma_eng	INFO	Train Epoch: 47 [41%]
+2023-05-08 23:22:46,212	kenma_eng	INFO	[1800, 9.942661422663591e-05]
+2023-05-08 23:22:46,212	kenma_eng	INFO	loss_disc=2.595, loss_gen=3.967, loss_fm=11.861,loss_mel=20.913, loss_kl=1.444
+2023-05-08 23:22:52,914	kenma_eng	INFO	====> Epoch: 47
+2023-05-08 23:23:00,743	kenma_eng	INFO	====> Epoch: 48
+2023-05-08 23:23:08,617	kenma_eng	INFO	====> Epoch: 49
+2023-05-08 23:23:16,504	kenma_eng	INFO	Saving model and optimizer state at epoch 50 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:23:16,945	kenma_eng	INFO	Saving model and optimizer state at epoch 50 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:23:17,525	kenma_eng	INFO	====> Epoch: 50
+2023-05-08 23:23:25,389	kenma_eng	INFO	====> Epoch: 51
+2023-05-08 23:23:27,806	kenma_eng	INFO	Train Epoch: 52 [41%]
+2023-05-08 23:23:27,809	kenma_eng	INFO	[2000, 9.936448812621091e-05]
+2023-05-08 23:23:27,809	kenma_eng	INFO	loss_disc=2.828, loss_gen=3.041, loss_fm=12.945,loss_mel=22.283, loss_kl=2.018
+2023-05-08 23:23:33,516	kenma_eng	INFO	====> Epoch: 52
+2023-05-08 23:23:41,487	kenma_eng	INFO	====> Epoch: 53
+2023-05-08 23:23:49,359	kenma_eng	INFO	====> Epoch: 54
+2023-05-08 23:23:57,267	kenma_eng	INFO	Saving model and optimizer state at epoch 55 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:23:57,714	kenma_eng	INFO	Saving model and optimizer state at epoch 55 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:23:58,294	kenma_eng	INFO	====> Epoch: 55
+2023-05-08 23:24:06,121	kenma_eng	INFO	====> Epoch: 56
+2023-05-08 23:24:09,605	kenma_eng	INFO	Train Epoch: 57 [38%]
+2023-05-08 23:24:09,607	kenma_eng	INFO	[2200, 9.930240084489267e-05]
+2023-05-08 23:24:09,607	kenma_eng	INFO	loss_disc=2.341, loss_gen=3.577, loss_fm=14.804,loss_mel=21.295, loss_kl=0.870
+2023-05-08 23:24:14,264	kenma_eng	INFO	====> Epoch: 57
+2023-05-08 23:24:22,253	kenma_eng	INFO	====> Epoch: 58
+2023-05-08 23:24:30,207	kenma_eng	INFO	====> Epoch: 59
+2023-05-08 23:24:38,030	kenma_eng	INFO	Saving model and optimizer state at epoch 60 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:24:38,477	kenma_eng	INFO	Saving model and optimizer state at epoch 60 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:24:39,064	kenma_eng	INFO	====> Epoch: 60
+2023-05-08 23:24:47,032	kenma_eng	INFO	====> Epoch: 61
+2023-05-08 23:24:51,555	kenma_eng	INFO	Train Epoch: 62 [33%]
+2023-05-08 23:24:51,557	kenma_eng	INFO	[2400, 9.924035235842533e-05]
+2023-05-08 23:24:51,557	kenma_eng	INFO	loss_disc=2.740, loss_gen=3.562, loss_fm=12.825,loss_mel=20.778, loss_kl=1.276
+2023-05-08 23:24:55,228	kenma_eng	INFO	====> Epoch: 62
+2023-05-08 23:25:03,207	kenma_eng	INFO	====> Epoch: 63
+2023-05-08 23:25:11,196	kenma_eng	INFO	====> Epoch: 64
+2023-05-08 23:25:19,180	kenma_eng	INFO	Saving model and optimizer state at epoch 65 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:25:19,694	kenma_eng	INFO	Saving model and optimizer state at epoch 65 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:25:20,280	kenma_eng	INFO	====> Epoch: 65
+2023-05-08 23:25:28,192	kenma_eng	INFO	====> Epoch: 66
+2023-05-08 23:25:33,727	kenma_eng	INFO	Train Epoch: 67 [0%]
+2023-05-08 23:25:33,729	kenma_eng	INFO	[2600, 9.917834264256819e-05]
+2023-05-08 23:25:33,729	kenma_eng	INFO	loss_disc=2.601, loss_gen=3.035, loss_fm=10.245,loss_mel=21.210, loss_kl=2.084
+2023-05-08 23:25:36,402	kenma_eng	INFO	====> Epoch: 67
+2023-05-08 23:25:44,389	kenma_eng	INFO	====> Epoch: 68
+2023-05-08 23:25:52,373	kenma_eng	INFO	====> Epoch: 69
+2023-05-08 23:26:00,358	kenma_eng	INFO	Saving model and optimizer state at epoch 70 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:26:00,805	kenma_eng	INFO	Saving model and optimizer state at epoch 70 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:26:01,399	kenma_eng	INFO	====> Epoch: 70
+2023-05-08 23:26:09,359	kenma_eng	INFO	====> Epoch: 71
+2023-05-08 23:26:15,915	kenma_eng	INFO	Train Epoch: 72 [8%]
+2023-05-08 23:26:15,917	kenma_eng	INFO	[2800, 9.911637167309565e-05]
+2023-05-08 23:26:15,917	kenma_eng	INFO	loss_disc=2.281, loss_gen=3.312, loss_fm=16.278,loss_mel=20.006, loss_kl=0.428
+2023-05-08 23:26:17,543	kenma_eng	INFO	====> Epoch: 72
+2023-05-08 23:26:25,536	kenma_eng	INFO	====> Epoch: 73
+2023-05-08 23:26:33,529	kenma_eng	INFO	====> Epoch: 74
+2023-05-08 23:26:41,518	kenma_eng	INFO	Saving model and optimizer state at epoch 75 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:26:42,031	kenma_eng	INFO	Saving model and optimizer state at epoch 75 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:26:42,634	kenma_eng	INFO	====> Epoch: 75
+2023-05-08 23:26:50,526	kenma_eng	INFO	====> Epoch: 76
+2023-05-08 23:26:58,105	kenma_eng	INFO	Train Epoch: 77 [56%]
+2023-05-08 23:26:58,107	kenma_eng	INFO	[3000, 9.905443942579728e-05]
+2023-05-08 23:26:58,107	kenma_eng	INFO	loss_disc=2.649, loss_gen=3.208, loss_fm=13.554,loss_mel=20.792, loss_kl=1.335
+2023-05-08 23:26:58,734	kenma_eng	INFO	====> Epoch: 77
+2023-05-08 23:27:06,700	kenma_eng	INFO	====> Epoch: 78
+2023-05-08 23:27:14,694	kenma_eng	INFO	====> Epoch: 79
+2023-05-08 23:27:22,675	kenma_eng	INFO	Saving model and optimizer state at epoch 80 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:27:23,121	kenma_eng	INFO	Saving model and optimizer state at epoch 80 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:27:23,707	kenma_eng	INFO	====> Epoch: 80
+2023-05-08 23:27:31,694	kenma_eng	INFO	====> Epoch: 81
+2023-05-08 23:27:39,679	kenma_eng	INFO	====> Epoch: 82
+2023-05-08 23:27:40,296	kenma_eng	INFO	Train Epoch: 83 [90%]
+2023-05-08 23:27:40,297	kenma_eng	INFO	[3200, 9.89801718082432e-05]
+2023-05-08 23:27:40,298	kenma_eng	INFO	loss_disc=2.784, loss_gen=3.327, loss_fm=13.070,loss_mel=20.826, loss_kl=1.661
+2023-05-08 23:27:47,887	kenma_eng	INFO	====> Epoch: 83
+2023-05-08 23:27:55,860	kenma_eng	INFO	====> Epoch: 84
+2023-05-08 23:28:03,843	kenma_eng	INFO	Saving model and optimizer state at epoch 85 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:28:04,357	kenma_eng	INFO	Saving model and optimizer state at epoch 85 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:28:04,947	kenma_eng	INFO	====> Epoch: 85
+2023-05-08 23:28:12,858	kenma_eng	INFO	====> Epoch: 86
+2023-05-08 23:28:20,842	kenma_eng	INFO	====> Epoch: 87
+2023-05-08 23:28:22,477	kenma_eng	INFO	Train Epoch: 88 [69%]
+2023-05-08 23:28:22,478	kenma_eng	INFO	[3400, 9.891832466458178e-05]
+2023-05-08 23:28:22,478	kenma_eng	INFO	loss_disc=2.293, loss_gen=3.493, loss_fm=13.095,loss_mel=20.620, loss_kl=1.292
+2023-05-08 23:28:29,030	kenma_eng	INFO	====> Epoch: 88
+2023-05-08 23:28:37,020	kenma_eng	INFO	====> Epoch: 89
+2023-05-08 23:28:45,012	kenma_eng	INFO	Saving model and optimizer state at epoch 90 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:28:45,460	kenma_eng	INFO	Saving model and optimizer state at epoch 90 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:28:46,058	kenma_eng	INFO	====> Epoch: 90
+2023-05-08 23:28:54,021	kenma_eng	INFO	====> Epoch: 91
+2023-05-08 23:29:02,004	kenma_eng	INFO	====> Epoch: 92
+2023-05-08 23:29:04,663	kenma_eng	INFO	Train Epoch: 93 [54%]
+2023-05-08 23:29:04,665	kenma_eng	INFO	[3600, 9.885651616572276e-05]
+2023-05-08 23:29:04,665	kenma_eng	INFO	loss_disc=2.701, loss_gen=3.218, loss_fm=14.823,loss_mel=21.348, loss_kl=1.524
+2023-05-08 23:29:10,207	kenma_eng	INFO	====> Epoch: 93
+2023-05-08 23:29:18,185	kenma_eng	INFO	====> Epoch: 94
+2023-05-08 23:29:26,168	kenma_eng	INFO	Saving model and optimizer state at epoch 95 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:29:26,687	kenma_eng	INFO	Saving model and optimizer state at epoch 95 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:29:27,286	kenma_eng	INFO	====> Epoch: 95
+2023-05-08 23:29:35,168	kenma_eng	INFO	====> Epoch: 96
+2023-05-08 23:29:43,162	kenma_eng	INFO	====> Epoch: 97
+2023-05-08 23:29:46,855	kenma_eng	INFO	Train Epoch: 98 [77%]
+2023-05-08 23:29:46,857	kenma_eng	INFO	[3800, 9.879474628751914e-05]
+2023-05-08 23:29:46,857	kenma_eng	INFO	loss_disc=2.413, loss_gen=3.459, loss_fm=11.920,loss_mel=20.858, loss_kl=1.785
+2023-05-08 23:29:51,362	kenma_eng	INFO	====> Epoch: 98
+2023-05-08 23:29:59,345	kenma_eng	INFO	====> Epoch: 99
+2023-05-08 23:30:07,336	kenma_eng	INFO	Saving model and optimizer state at epoch 100 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:30:07,786	kenma_eng	INFO	Saving model and optimizer state at epoch 100 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:30:08,374	kenma_eng	INFO	====> Epoch: 100
+2023-05-08 23:30:16,340	kenma_eng	INFO	====> Epoch: 101
+2023-05-08 23:30:24,332	kenma_eng	INFO	====> Epoch: 102
+2023-05-08 23:30:29,044	kenma_eng	INFO	Train Epoch: 103 [36%]
+2023-05-08 23:30:29,046	kenma_eng	INFO	[4000, 9.873301500583906e-05]
+2023-05-08 23:30:29,046	kenma_eng	INFO	loss_disc=2.663, loss_gen=3.403, loss_fm=14.288,loss_mel=20.431, loss_kl=1.184
+2023-05-08 23:30:32,543	kenma_eng	INFO	====> Epoch: 103
+2023-05-08 23:30:40,508	kenma_eng	INFO	====> Epoch: 104
+2023-05-08 23:30:48,502	kenma_eng	INFO	Saving model and optimizer state at epoch 105 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:30:49,017	kenma_eng	INFO	Saving model and optimizer state at epoch 105 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:30:49,617	kenma_eng	INFO	====> Epoch: 105
+2023-05-08 23:30:57,507	kenma_eng	INFO	====> Epoch: 106
+2023-05-08 23:31:05,520	kenma_eng	INFO	====> Epoch: 107
+2023-05-08 23:31:11,235	kenma_eng	INFO	Train Epoch: 108 [3%]
+2023-05-08 23:31:11,237	kenma_eng	INFO	[4200, 9.867132229656573e-05]
+2023-05-08 23:31:11,237	kenma_eng	INFO	loss_disc=1.916, loss_gen=3.703, loss_fm=16.316,loss_mel=18.907, loss_kl=1.553
+2023-05-08 23:31:13,688	kenma_eng	INFO	====> Epoch: 108
+2023-05-08 23:31:21,677	kenma_eng	INFO	====> Epoch: 109
+2023-05-08 23:31:29,658	kenma_eng	INFO	Saving model and optimizer state at epoch 110 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:31:30,174	kenma_eng	INFO	Saving model and optimizer state at epoch 110 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:31:30,771	kenma_eng	INFO	====> Epoch: 110
+2023-05-08 23:31:38,672	kenma_eng	INFO	====> Epoch: 111
+2023-05-08 23:31:46,661	kenma_eng	INFO	====> Epoch: 112
+2023-05-08 23:31:53,418	kenma_eng	INFO	Train Epoch: 113 [10%]
+2023-05-08 23:31:53,420	kenma_eng	INFO	[4400, 9.86096681355974e-05]
+2023-05-08 23:31:53,420	kenma_eng	INFO	loss_disc=1.807, loss_gen=4.457, loss_fm=15.467,loss_mel=19.595, loss_kl=1.601
+2023-05-08 23:31:54,848	kenma_eng	INFO	====> Epoch: 113
+2023-05-08 23:32:02,841	kenma_eng	INFO	====> Epoch: 114
+2023-05-08 23:32:10,823	kenma_eng	INFO	Saving model and optimizer state at epoch 115 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:32:11,338	kenma_eng	INFO	Saving model and optimizer state at epoch 115 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:32:11,933	kenma_eng	INFO	====> Epoch: 115
+2023-05-08 23:32:19,838	kenma_eng	INFO	====> Epoch: 116
+2023-05-08 23:32:27,835	kenma_eng	INFO	====> Epoch: 117
+2023-05-08 23:32:35,603	kenma_eng	INFO	Train Epoch: 118 [46%]
+2023-05-08 23:32:35,605	kenma_eng	INFO	[4600, 9.854805249884741e-05]
+2023-05-08 23:32:35,605	kenma_eng	INFO	loss_disc=1.976, loss_gen=4.144, loss_fm=14.899,loss_mel=20.415, loss_kl=0.905
+2023-05-08 23:32:36,017	kenma_eng	INFO	====> Epoch: 118
+2023-05-08 23:32:44,002	kenma_eng	INFO	====> Epoch: 119
+2023-05-08 23:32:51,993	kenma_eng	INFO	Saving model and optimizer state at epoch 120 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:32:52,439	kenma_eng	INFO	Saving model and optimizer state at epoch 120 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:32:53,035	kenma_eng	INFO	====> Epoch: 120
+2023-05-08 23:33:01,001	kenma_eng	INFO	====> Epoch: 121
+2023-05-08 23:33:08,988	kenma_eng	INFO	====> Epoch: 122
+2023-05-08 23:33:16,979	kenma_eng	INFO	====> Epoch: 123
+2023-05-08 23:33:17,795	kenma_eng	INFO	Train Epoch: 124 [15%]
+2023-05-08 23:33:17,797	kenma_eng	INFO	[4800, 9.847416455282387e-05]
+2023-05-08 23:33:17,797	kenma_eng	INFO	loss_disc=2.059, loss_gen=3.385, loss_fm=13.991,loss_mel=20.368, loss_kl=1.581
+2023-05-08 23:33:25,166	kenma_eng	INFO	====> Epoch: 124
+2023-05-08 23:33:33,154	kenma_eng	INFO	Saving model and optimizer state at epoch 125 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:33:33,602	kenma_eng	INFO	Saving model and optimizer state at epoch 125 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:33:34,191	kenma_eng	INFO	====> Epoch: 125
+2023-05-08 23:33:42,168	kenma_eng	INFO	====> Epoch: 126
+2023-05-08 23:33:50,149	kenma_eng	INFO	====> Epoch: 127
+2023-05-08 23:33:58,139	kenma_eng	INFO	====> Epoch: 128
+2023-05-08 23:33:59,983	kenma_eng	INFO	Train Epoch: 129 [21%]
+2023-05-08 23:33:59,985	kenma_eng	INFO	[5000, 9.841263358464336e-05]
+2023-05-08 23:33:59,985	kenma_eng	INFO	loss_disc=2.190, loss_gen=4.590, loss_fm=14.053,loss_mel=20.084, loss_kl=1.679
+2023-05-08 23:34:06,332	kenma_eng	INFO	====> Epoch: 129
+2023-05-08 23:34:14,316	kenma_eng	INFO	Saving model and optimizer state at epoch 130 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:34:14,754	kenma_eng	INFO	Saving model and optimizer state at epoch 130 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:34:15,340	kenma_eng	INFO	====> Epoch: 130
+2023-05-08 23:34:23,386	kenma_eng	INFO	====> Epoch: 131
+2023-05-08 23:34:31,310	kenma_eng	INFO	====> Epoch: 132
+2023-05-08 23:34:39,298	kenma_eng	INFO	====> Epoch: 133
+2023-05-08 23:34:42,164	kenma_eng	INFO	Train Epoch: 134 [23%]
+2023-05-08 23:34:42,166	kenma_eng	INFO	[5200, 9.835114106370493e-05]
+2023-05-08 23:34:42,166	kenma_eng	INFO	loss_disc=2.070, loss_gen=3.735, loss_fm=13.641,loss_mel=19.528, loss_kl=1.520
+2023-05-08 23:34:47,491	kenma_eng	INFO	====> Epoch: 134
+2023-05-08 23:34:55,475	kenma_eng	INFO	Saving model and optimizer state at epoch 135 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:34:55,917	kenma_eng	INFO	Saving model and optimizer state at epoch 135 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:34:56,507	kenma_eng	INFO	====> Epoch: 135
+2023-05-08 23:35:04,564	kenma_eng	INFO	====> Epoch: 136
+2023-05-08 23:35:12,480	kenma_eng	INFO	====> Epoch: 137
+2023-05-08 23:35:20,467	kenma_eng	INFO	====> Epoch: 138
+2023-05-08 23:35:24,388	kenma_eng	INFO	Train Epoch: 139 [23%]
+2023-05-08 23:35:24,390	kenma_eng	INFO	[5400, 9.828968696598508e-05]
+2023-05-08 23:35:24,390	kenma_eng	INFO	loss_disc=2.168, loss_gen=3.409, loss_fm=14.586,loss_mel=18.876, loss_kl=0.873
+2023-05-08 23:35:28,651	kenma_eng	INFO	====> Epoch: 139
+2023-05-08 23:35:36,641	kenma_eng	INFO	Saving model and optimizer state at epoch 140 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:35:37,082	kenma_eng	INFO	Saving model and optimizer state at epoch 140 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:35:37,672	kenma_eng	INFO	====> Epoch: 140
+2023-05-08 23:35:45,661	kenma_eng	INFO	====> Epoch: 141
+2023-05-08 23:35:53,641	kenma_eng	INFO	====> Epoch: 142
+2023-05-08 23:36:01,637	kenma_eng	INFO	====> Epoch: 143
+2023-05-08 23:36:06,547	kenma_eng	INFO	Train Epoch: 144 [31%]
+2023-05-08 23:36:06,549	kenma_eng	INFO	[5600, 9.822827126747529e-05]
+2023-05-08 23:36:06,549	kenma_eng	INFO	loss_disc=1.980, loss_gen=3.789, loss_fm=14.169,loss_mel=19.720, loss_kl=0.746
+2023-05-08 23:36:09,814	kenma_eng	INFO	====> Epoch: 144
+2023-05-08 23:36:17,808	kenma_eng	INFO	Saving model and optimizer state at epoch 145 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:36:18,249	kenma_eng	INFO	Saving model and optimizer state at epoch 145 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:36:18,847	kenma_eng	INFO	====> Epoch: 145
+2023-05-08 23:36:26,822	kenma_eng	INFO	====> Epoch: 146
+2023-05-08 23:36:34,812	kenma_eng	INFO	====> Epoch: 147
+2023-05-08 23:36:42,795	kenma_eng	INFO	====> Epoch: 148
+2023-05-08 23:36:48,738	kenma_eng	INFO	Train Epoch: 149 [28%]
+2023-05-08 23:36:48,740	kenma_eng	INFO	[5800, 9.816689394418209e-05]
+2023-05-08 23:36:48,740	kenma_eng	INFO	loss_disc=1.871, loss_gen=4.337, loss_fm=16.396,loss_mel=21.222, loss_kl=1.508
+2023-05-08 23:36:50,973	kenma_eng	INFO	====> Epoch: 149
+2023-05-08 23:36:58,976	kenma_eng	INFO	Saving model and optimizer state at epoch 150 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:36:59,418	kenma_eng	INFO	Saving model and optimizer state at epoch 150 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:37:00,082	kenma_eng	INFO	====> Epoch: 150
+2023-05-08 23:37:07,986	kenma_eng	INFO	====> Epoch: 151
+2023-05-08 23:37:15,972	kenma_eng	INFO	====> Epoch: 152
+2023-05-08 23:37:23,958	kenma_eng	INFO	====> Epoch: 153
+2023-05-08 23:37:30,918	kenma_eng	INFO	Train Epoch: 154 [15%]
+2023-05-08 23:37:30,920	kenma_eng	INFO	[6000, 9.810555497212693e-05]
+2023-05-08 23:37:30,920	kenma_eng	INFO	loss_disc=2.035, loss_gen=3.760, loss_fm=14.298,loss_mel=19.946, loss_kl=0.859
+2023-05-08 23:37:32,146	kenma_eng	INFO	====> Epoch: 154
+2023-05-08 23:37:40,134	kenma_eng	INFO	Saving model and optimizer state at epoch 155 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:37:40,581	kenma_eng	INFO	Saving model and optimizer state at epoch 155 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:37:41,176	kenma_eng	INFO	====> Epoch: 155
+2023-05-08 23:37:49,137	kenma_eng	INFO	====> Epoch: 156
+2023-05-08 23:37:57,140	kenma_eng	INFO	====> Epoch: 157
+2023-05-08 23:38:05,114	kenma_eng	INFO	====> Epoch: 158
+2023-05-08 23:38:13,105	kenma_eng	INFO	Train Epoch: 159 [74%]
+2023-05-08 23:38:13,107	kenma_eng	INFO	[6200, 9.804425432734629e-05]
+2023-05-08 23:38:13,107	kenma_eng	INFO	loss_disc=2.096, loss_gen=3.140, loss_fm=12.766,loss_mel=18.966, loss_kl=1.000
+2023-05-08 23:38:13,311	kenma_eng	INFO	====> Epoch: 159
+2023-05-08 23:38:21,298	kenma_eng	INFO	Saving model and optimizer state at epoch 160 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:38:21,736	kenma_eng	INFO	Saving model and optimizer state at epoch 160 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:38:22,323	kenma_eng	INFO	====> Epoch: 160
+2023-05-08 23:38:30,331	kenma_eng	INFO	====> Epoch: 161
+2023-05-08 23:38:38,303	kenma_eng	INFO	====> Epoch: 162
+2023-05-08 23:38:46,283	kenma_eng	INFO	====> Epoch: 163
+2023-05-08 23:38:54,272	kenma_eng	INFO	====> Epoch: 164
+2023-05-08 23:38:55,298	kenma_eng	INFO	Train Epoch: 165 [85%]
+2023-05-08 23:38:55,300	kenma_eng	INFO	[6400, 9.797074411189339e-05]
+2023-05-08 23:38:55,300	kenma_eng	INFO	loss_disc=2.262, loss_gen=3.843, loss_fm=14.559,loss_mel=18.460, loss_kl=2.523
+2023-05-08 23:39:02,463	kenma_eng	INFO	Saving model and optimizer state at epoch 165 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:39:02,906	kenma_eng	INFO	Saving model and optimizer state at epoch 165 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:39:03,495	kenma_eng	INFO	====> Epoch: 165
+2023-05-08 23:39:11,468	kenma_eng	INFO	====> Epoch: 166
+2023-05-08 23:39:19,459	kenma_eng	INFO	====> Epoch: 167
+2023-05-08 23:39:27,505	kenma_eng	INFO	====> Epoch: 168
+2023-05-08 23:39:35,435	kenma_eng	INFO	====> Epoch: 169
+2023-05-08 23:39:37,485	kenma_eng	INFO	Train Epoch: 170 [8%]
+2023-05-08 23:39:37,487	kenma_eng	INFO	[6600, 9.790952770283884e-05]
+2023-05-08 23:39:37,487	kenma_eng	INFO	loss_disc=1.409, loss_gen=5.128, loss_fm=18.067,loss_mel=19.149, loss_kl=1.303
+2023-05-08 23:39:43,615	kenma_eng	INFO	Saving model and optimizer state at epoch 170 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:39:44,138	kenma_eng	INFO	Saving model and optimizer state at epoch 170 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:39:44,729	kenma_eng	INFO	====> Epoch: 170
+2023-05-08 23:39:52,633	kenma_eng	INFO	====> Epoch: 171
+2023-05-08 23:40:00,623	kenma_eng	INFO	====> Epoch: 172
+2023-05-08 23:40:08,648	kenma_eng	INFO	====> Epoch: 173
+2023-05-08 23:40:16,595	kenma_eng	INFO	====> Epoch: 174
+2023-05-08 23:40:19,755	kenma_eng	INFO	Train Epoch: 175 [54%]
+2023-05-08 23:40:19,756	kenma_eng	INFO	[6800, 9.784834954447608e-05]
+2023-05-08 23:40:19,757	kenma_eng	INFO	loss_disc=2.381, loss_gen=3.203, loss_fm=11.890,loss_mel=21.385, loss_kl=0.766
+2023-05-08 23:40:24,789	kenma_eng	INFO	Saving model and optimizer state at epoch 175 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:40:25,234	kenma_eng	INFO	Saving model and optimizer state at epoch 175 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:40:25,825	kenma_eng	INFO	====> Epoch: 175
+2023-05-08 23:40:33,850	kenma_eng	INFO	====> Epoch: 176
+2023-05-08 23:40:41,790	kenma_eng	INFO	====> Epoch: 177
+2023-05-08 23:40:49,786	kenma_eng	INFO	====> Epoch: 178
+2023-05-08 23:40:57,761	kenma_eng	INFO	====> Epoch: 179
+2023-05-08 23:41:01,856	kenma_eng	INFO	Train Epoch: 180 [77%]
+2023-05-08 23:41:01,858	kenma_eng	INFO	[7000, 9.778720961290439e-05]
+2023-05-08 23:41:01,858	kenma_eng	INFO	loss_disc=2.326, loss_gen=3.396, loss_fm=11.155,loss_mel=19.969, loss_kl=0.927
+2023-05-08 23:41:05,949	kenma_eng	INFO	Saving model and optimizer state at epoch 180 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:41:06,388	kenma_eng	INFO	Saving model and optimizer state at epoch 180 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:41:07,021	kenma_eng	INFO	====> Epoch: 180
+2023-05-08 23:41:14,966	kenma_eng	INFO	====> Epoch: 181
+2023-05-08 23:41:22,944	kenma_eng	INFO	====> Epoch: 182
+2023-05-08 23:41:30,931	kenma_eng	INFO	====> Epoch: 183
+2023-05-08 23:41:38,913	kenma_eng	INFO	====> Epoch: 184
+2023-05-08 23:41:44,053	kenma_eng	INFO	Train Epoch: 185 [0%]
+2023-05-08 23:41:44,055	kenma_eng	INFO	[7200, 9.772610788423802e-05]
+2023-05-08 23:41:44,055	kenma_eng	INFO	loss_disc=2.721, loss_gen=3.359, loss_fm=11.969,loss_mel=20.092, loss_kl=1.441
+2023-05-08 23:41:47,110	kenma_eng	INFO	Saving model and optimizer state at epoch 185 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:41:47,552	kenma_eng	INFO	Saving model and optimizer state at epoch 185 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:41:48,181	kenma_eng	INFO	====> Epoch: 185
+2023-05-08 23:41:56,123	kenma_eng	INFO	====> Epoch: 186
+2023-05-08 23:42:04,106	kenma_eng	INFO	====> Epoch: 187
+2023-05-08 23:42:12,098	kenma_eng	INFO	====> Epoch: 188
+2023-05-08 23:42:20,079	kenma_eng	INFO	====> Epoch: 189
+2023-05-08 23:42:26,233	kenma_eng	INFO	Train Epoch: 190 [82%]
+2023-05-08 23:42:26,235	kenma_eng	INFO	[7400, 9.766504433460612e-05]
+2023-05-08 23:42:26,235	kenma_eng	INFO	loss_disc=2.463, loss_gen=3.253, loss_fm=12.856,loss_mel=20.189, loss_kl=1.383
+2023-05-08 23:42:28,279	kenma_eng	INFO	Saving model and optimizer state at epoch 190 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:42:28,720	kenma_eng	INFO	Saving model and optimizer state at epoch 190 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:42:29,312	kenma_eng	INFO	====> Epoch: 190
+2023-05-08 23:42:37,292	kenma_eng	INFO	====> Epoch: 191
+2023-05-08 23:42:45,275	kenma_eng	INFO	====> Epoch: 192
+2023-05-08 23:42:53,260	kenma_eng	INFO	====> Epoch: 193
+2023-05-08 23:43:01,244	kenma_eng	INFO	====> Epoch: 194
+2023-05-08 23:43:08,421	kenma_eng	INFO	Train Epoch: 195 [79%]
+2023-05-08 23:43:08,423	kenma_eng	INFO	[7600, 9.760401894015275e-05]
+2023-05-08 23:43:08,423	kenma_eng	INFO	loss_disc=2.311, loss_gen=3.909, loss_fm=15.471,loss_mel=20.243, loss_kl=0.945
+2023-05-08 23:43:09,440	kenma_eng	INFO	Saving model and optimizer state at epoch 195 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:43:09,885	kenma_eng	INFO	Saving model and optimizer state at epoch 195 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:43:10,475	kenma_eng	INFO	====> Epoch: 195
+2023-05-08 23:43:18,448	kenma_eng	INFO	====> Epoch: 196
+2023-05-08 23:43:26,436	kenma_eng	INFO	====> Epoch: 197
+2023-05-08 23:43:34,429	kenma_eng	INFO	====> Epoch: 198
+2023-05-08 23:43:42,411	kenma_eng	INFO	====> Epoch: 199
+2023-05-08 23:43:50,397	kenma_eng	INFO	Saving model and optimizer state at epoch 200 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:43:50,842	kenma_eng	INFO	Saving model and optimizer state at epoch 200 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:43:51,432	kenma_eng	INFO	====> Epoch: 200
+2023-05-08 23:43:51,633	kenma_eng	INFO	Train Epoch: 201 [8%]
+2023-05-08 23:43:51,635	kenma_eng	INFO	[7800, 9.753083879807726e-05]
+2023-05-08 23:43:51,635	kenma_eng	INFO	loss_disc=1.920, loss_gen=3.953, loss_fm=12.891,loss_mel=18.567, loss_kl=0.669
+2023-05-08 23:43:59,614	kenma_eng	INFO	====> Epoch: 201
+2023-05-08 23:44:07,601	kenma_eng	INFO	====> Epoch: 202
+2023-05-08 23:44:15,593	kenma_eng	INFO	====> Epoch: 203
+2023-05-08 23:44:23,573	kenma_eng	INFO	====> Epoch: 204
+2023-05-08 23:44:31,560	kenma_eng	INFO	Saving model and optimizer state at epoch 205 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:44:32,007	kenma_eng	INFO	Saving model and optimizer state at epoch 205 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:44:32,598	kenma_eng	INFO	====> Epoch: 205
+2023-05-08 23:44:33,807	kenma_eng	INFO	Train Epoch: 206 [74%]
+2023-05-08 23:44:33,809	kenma_eng	INFO	[8000, 9.746989726111722e-05]
+2023-05-08 23:44:33,809	kenma_eng	INFO	loss_disc=2.143, loss_gen=3.504, loss_fm=13.130,loss_mel=20.380, loss_kl=0.445
+2023-05-08 23:44:40,776	kenma_eng	INFO	====> Epoch: 206
+2023-05-08 23:44:48,772	kenma_eng	INFO	====> Epoch: 207
+2023-05-08 23:44:56,761	kenma_eng	INFO	====> Epoch: 208
+2023-05-08 23:45:04,741	kenma_eng	INFO	====> Epoch: 209
+2023-05-08 23:45:12,711	kenma_eng	INFO	Saving model and optimizer state at epoch 210 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:45:13,161	kenma_eng	INFO	Saving model and optimizer state at epoch 210 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:45:13,755	kenma_eng	INFO	====> Epoch: 210
+2023-05-08 23:45:15,997	kenma_eng	INFO	Train Epoch: 211 [56%]
+2023-05-08 23:45:15,999	kenma_eng	INFO	[8200, 9.740899380309685e-05]
+2023-05-08 23:45:16,000	kenma_eng	INFO	loss_disc=2.647, loss_gen=3.253, loss_fm=12.424,loss_mel=19.522, loss_kl=1.341
+2023-05-08 23:45:21,938	kenma_eng	INFO	====> Epoch: 211
+2023-05-08 23:45:29,968	kenma_eng	INFO	====> Epoch: 212
+2023-05-08 23:45:37,921	kenma_eng	INFO	====> Epoch: 213
+2023-05-08 23:45:45,913	kenma_eng	INFO	====> Epoch: 214
+2023-05-08 23:45:53,892	kenma_eng	INFO	Saving model and optimizer state at epoch 215 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:45:54,339	kenma_eng	INFO	Saving model and optimizer state at epoch 215 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:45:55,005	kenma_eng	INFO	====> Epoch: 215
+2023-05-08 23:45:58,212	kenma_eng	INFO	Train Epoch: 216 [10%]
+2023-05-08 23:45:58,213	kenma_eng	INFO	[8400, 9.734812840022278e-05]
+2023-05-08 23:45:58,213	kenma_eng	INFO	loss_disc=1.651, loss_gen=4.591, loss_fm=16.276,loss_mel=18.822, loss_kl=1.662
+2023-05-08 23:46:03,103	kenma_eng	INFO	====> Epoch: 216
+2023-05-08 23:46:11,152	kenma_eng	INFO	====> Epoch: 217
+2023-05-08 23:46:19,078	kenma_eng	INFO	====> Epoch: 218
+2023-05-08 23:46:27,069	kenma_eng	INFO	====> Epoch: 219
+2023-05-08 23:46:35,047	kenma_eng	INFO	Saving model and optimizer state at epoch 220 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:46:35,573	kenma_eng	INFO	Saving model and optimizer state at epoch 220 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:46:36,161	kenma_eng	INFO	====> Epoch: 220
+2023-05-08 23:46:40,377	kenma_eng	INFO	Train Epoch: 221 [21%]
+2023-05-08 23:46:40,379	kenma_eng	INFO	[8600, 9.728730102871649e-05]
+2023-05-08 23:46:40,379	kenma_eng	INFO	loss_disc=2.058, loss_gen=3.767, loss_fm=15.166,loss_mel=19.089, loss_kl=1.259
+2023-05-08 23:46:44,263	kenma_eng	INFO	====> Epoch: 221
+2023-05-08 23:46:52,259	kenma_eng	INFO	====> Epoch: 222
+2023-05-08 23:47:00,242	kenma_eng	INFO	====> Epoch: 223
+2023-05-08 23:47:08,233	kenma_eng	INFO	====> Epoch: 224
+2023-05-08 23:47:16,214	kenma_eng	INFO	Saving model and optimizer state at epoch 225 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:47:16,656	kenma_eng	INFO	Saving model and optimizer state at epoch 225 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:47:17,319	kenma_eng	INFO	====> Epoch: 225
+2023-05-08 23:47:22,570	kenma_eng	INFO	Train Epoch: 226 [79%]
+2023-05-08 23:47:22,572	kenma_eng	INFO	[8800, 9.722651166481428e-05]
+2023-05-08 23:47:22,572	kenma_eng	INFO	loss_disc=2.134, loss_gen=4.268, loss_fm=14.685,loss_mel=20.430, loss_kl=0.855
+2023-05-08 23:47:25,435	kenma_eng	INFO	====> Epoch: 226
+2023-05-08 23:47:33,430	kenma_eng	INFO	====> Epoch: 227
+2023-05-08 23:47:41,401	kenma_eng	INFO	====> Epoch: 228
+2023-05-08 23:47:49,399	kenma_eng	INFO	====> Epoch: 229
+2023-05-08 23:47:57,379	kenma_eng	INFO	Saving model and optimizer state at epoch 230 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:47:57,900	kenma_eng	INFO	Saving model and optimizer state at epoch 230 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:47:58,488	kenma_eng	INFO	====> Epoch: 230
+2023-05-08 23:48:04,754	kenma_eng	INFO	Train Epoch: 231 [46%]
+2023-05-08 23:48:04,756	kenma_eng	INFO	[9000, 9.716576028476738e-05]
+2023-05-08 23:48:04,756	kenma_eng	INFO	loss_disc=2.041, loss_gen=3.526, loss_fm=14.019,loss_mel=19.166, loss_kl=0.711
+2023-05-08 23:48:06,597	kenma_eng	INFO	====> Epoch: 231
+2023-05-08 23:48:14,585	kenma_eng	INFO	====> Epoch: 232
+2023-05-08 23:48:22,576	kenma_eng	INFO	====> Epoch: 233
+2023-05-08 23:48:30,553	kenma_eng	INFO	====> Epoch: 234
+2023-05-08 23:48:38,542	kenma_eng	INFO	Saving model and optimizer state at epoch 235 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:48:38,992	kenma_eng	INFO	Saving model and optimizer state at epoch 235 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:48:39,660	kenma_eng	INFO	====> Epoch: 235
+2023-05-08 23:48:46,940	kenma_eng	INFO	Train Epoch: 236 [87%]
+2023-05-08 23:48:46,942	kenma_eng	INFO	[9200, 9.710504686484176e-05]
+2023-05-08 23:48:46,942	kenma_eng	INFO	loss_disc=2.383, loss_gen=3.182, loss_fm=11.174,loss_mel=15.138, loss_kl=0.837
+2023-05-08 23:48:47,751	kenma_eng	INFO	====> Epoch: 236
+2023-05-08 23:48:55,741	kenma_eng	INFO	====> Epoch: 237
+2023-05-08 23:49:03,728	kenma_eng	INFO	====> Epoch: 238
+2023-05-08 23:49:11,714	kenma_eng	INFO	====> Epoch: 239
+2023-05-08 23:49:19,703	kenma_eng	INFO	Saving model and optimizer state at epoch 240 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:49:20,149	kenma_eng	INFO	Saving model and optimizer state at epoch 240 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:49:20,742	kenma_eng	INFO	====> Epoch: 240
+2023-05-08 23:49:28,723	kenma_eng	INFO	====> Epoch: 241
+2023-05-08 23:49:29,133	kenma_eng	INFO	Train Epoch: 242 [0%]
+2023-05-08 23:49:29,135	kenma_eng	INFO	[9400, 9.703224083489565e-05]
+2023-05-08 23:49:29,136	kenma_eng	INFO	loss_disc=2.283, loss_gen=3.906, loss_fm=14.977,loss_mel=21.196, loss_kl=1.113
+2023-05-08 23:49:36,904	kenma_eng	INFO	====> Epoch: 242
+2023-05-08 23:49:44,893	kenma_eng	INFO	====> Epoch: 243
+2023-05-08 23:49:52,895	kenma_eng	INFO	====> Epoch: 244
+2023-05-08 23:50:00,868	kenma_eng	INFO	Saving model and optimizer state at epoch 245 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:50:01,309	kenma_eng	INFO	Saving model and optimizer state at epoch 245 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:50:01,892	kenma_eng	INFO	====> Epoch: 245
+2023-05-08 23:50:09,880	kenma_eng	INFO	====> Epoch: 246
+2023-05-08 23:50:11,310	kenma_eng	INFO	Train Epoch: 247 [41%]
+2023-05-08 23:50:11,312	kenma_eng	INFO	[9600, 9.69716108437664e-05]
+2023-05-08 23:50:11,319	kenma_eng	INFO	loss_disc=2.562, loss_gen=3.062, loss_fm=13.378,loss_mel=19.740, loss_kl=1.235
+2023-05-08 23:50:18,069	kenma_eng	INFO	====> Epoch: 247
+2023-05-08 23:50:26,064	kenma_eng	INFO	====> Epoch: 248
+2023-05-08 23:50:34,048	kenma_eng	INFO	====> Epoch: 249
+2023-05-08 23:50:42,027	kenma_eng	INFO	Saving model and optimizer state at epoch 250 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:50:42,471	kenma_eng	INFO	Saving model and optimizer state at epoch 250 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:50:43,131	kenma_eng	INFO	====> Epoch: 250
+2023-05-08 23:50:51,039	kenma_eng	INFO	====> Epoch: 251
+2023-05-08 23:50:53,497	kenma_eng	INFO	Train Epoch: 252 [95%]
+2023-05-08 23:50:53,499	kenma_eng	INFO	[9800, 9.691101873690936e-05]
+2023-05-08 23:50:53,499	kenma_eng	INFO	loss_disc=2.281, loss_gen=4.156, loss_fm=13.992,loss_mel=18.462, loss_kl=0.613
+2023-05-08 23:50:59,238	kenma_eng	INFO	====> Epoch: 252
+2023-05-08 23:51:07,244	kenma_eng	INFO	====> Epoch: 253
+2023-05-08 23:51:15,210	kenma_eng	INFO	====> Epoch: 254
+2023-05-08 23:51:23,192	kenma_eng	INFO	Saving model and optimizer state at epoch 255 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:51:23,634	kenma_eng	INFO	Saving model and optimizer state at epoch 255 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:51:24,218	kenma_eng	INFO	====> Epoch: 255
+2023-05-08 23:51:32,206	kenma_eng	INFO	====> Epoch: 256
+2023-05-08 23:51:35,692	kenma_eng	INFO	Train Epoch: 257 [36%]
+2023-05-08 23:51:35,694	kenma_eng	INFO	[10000, 9.685046449065278e-05]
+2023-05-08 23:51:35,694	kenma_eng	INFO	loss_disc=2.226, loss_gen=3.927, loss_fm=11.863,loss_mel=18.904, loss_kl=1.791
+2023-05-08 23:51:40,405	kenma_eng	INFO	====> Epoch: 257
+2023-05-08 23:51:48,408	kenma_eng	INFO	====> Epoch: 258
+2023-05-08 23:51:56,374	kenma_eng	INFO	====> Epoch: 259
+2023-05-08 23:52:04,354	kenma_eng	INFO	Saving model and optimizer state at epoch 260 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:52:04,801	kenma_eng	INFO	Saving model and optimizer state at epoch 260 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:52:05,389	kenma_eng	INFO	====> Epoch: 260
+2023-05-08 23:52:13,367	kenma_eng	INFO	====> Epoch: 261
+2023-05-08 23:52:17,873	kenma_eng	INFO	Train Epoch: 262 [51%]
+2023-05-08 23:52:17,875	kenma_eng	INFO	[10200, 9.678994808133967e-05]
+2023-05-08 23:52:17,875	kenma_eng	INFO	loss_disc=2.367, loss_gen=3.552, loss_fm=12.217,loss_mel=14.802, loss_kl=0.540
+2023-05-08 23:52:21,594	kenma_eng	INFO	====> Epoch: 262
+2023-05-08 23:52:29,549	kenma_eng	INFO	====> Epoch: 263
+2023-05-08 23:52:37,537	kenma_eng	INFO	====> Epoch: 264
+2023-05-08 23:52:45,520	kenma_eng	INFO	Saving model and optimizer state at epoch 265 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:52:45,968	kenma_eng	INFO	Saving model and optimizer state at epoch 265 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:52:46,558	kenma_eng	INFO	====> Epoch: 265
+2023-05-08 23:52:54,545	kenma_eng	INFO	====> Epoch: 266
+2023-05-08 23:53:00,067	kenma_eng	INFO	Train Epoch: 267 [28%]
+2023-05-08 23:53:00,069	kenma_eng	INFO	[10400, 9.67294694853279e-05]
+2023-05-08 23:53:00,069	kenma_eng	INFO	loss_disc=2.086, loss_gen=3.871, loss_fm=16.090,loss_mel=20.210, loss_kl=0.717
+2023-05-08 23:53:02,746	kenma_eng	INFO	====> Epoch: 267
+2023-05-08 23:53:10,712	kenma_eng	INFO	====> Epoch: 268
+2023-05-08 23:53:18,698	kenma_eng	INFO	====> Epoch: 269
+2023-05-08 23:53:26,714	kenma_eng	INFO	Saving model and optimizer state at epoch 270 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:53:27,163	kenma_eng	INFO	Saving model and optimizer state at epoch 270 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:53:27,755	kenma_eng	INFO	====> Epoch: 270
+2023-05-08 23:53:35,701	kenma_eng	INFO	====> Epoch: 271
+2023-05-08 23:53:42,249	kenma_eng	INFO	Train Epoch: 272 [92%]
+2023-05-08 23:53:42,252	kenma_eng	INFO	[10600, 9.666902867899003e-05]
+2023-05-08 23:53:42,252	kenma_eng	INFO	loss_disc=2.008, loss_gen=3.914, loss_fm=15.602,loss_mel=20.115, loss_kl=1.201
+2023-05-08 23:53:43,926	kenma_eng	INFO	====> Epoch: 272
+2023-05-08 23:53:51,876	kenma_eng	INFO	====> Epoch: 273
+2023-05-08 23:53:59,874	kenma_eng	INFO	====> Epoch: 274
+2023-05-08 23:54:07,851	kenma_eng	INFO	Saving model and optimizer state at epoch 275 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:54:08,295	kenma_eng	INFO	Saving model and optimizer state at epoch 275 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:54:08,887	kenma_eng	INFO	====> Epoch: 275
+2023-05-08 23:54:16,861	kenma_eng	INFO	====> Epoch: 276
+2023-05-08 23:54:24,437	kenma_eng	INFO	Train Epoch: 277 [64%]
+2023-05-08 23:54:24,439	kenma_eng	INFO	[10800, 9.660862563871342e-05]
+2023-05-08 23:54:24,439	kenma_eng	INFO	loss_disc=2.096, loss_gen=4.270, loss_fm=14.102,loss_mel=18.081, loss_kl=1.553
+2023-05-08 23:54:25,127	kenma_eng	INFO	====> Epoch: 277
+2023-05-08 23:54:33,046	kenma_eng	INFO	====> Epoch: 278
+2023-05-08 23:54:41,030	kenma_eng	INFO	====> Epoch: 279
+2023-05-08 23:54:49,015	kenma_eng	INFO	Saving model and optimizer state at epoch 280 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:54:49,461	kenma_eng	INFO	Saving model and optimizer state at epoch 280 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:54:50,051	kenma_eng	INFO	====> Epoch: 280
+2023-05-08 23:54:58,029	kenma_eng	INFO	====> Epoch: 281
+2023-05-08 23:55:06,018	kenma_eng	INFO	====> Epoch: 282
+2023-05-08 23:55:06,622	kenma_eng	INFO	Train Epoch: 283 [64%]
+2023-05-08 23:55:06,624	kenma_eng	INFO	[11000, 9.653619180835758e-05]
+2023-05-08 23:55:06,624	kenma_eng	INFO	loss_disc=1.838, loss_gen=3.801, loss_fm=16.227,loss_mel=18.351, loss_kl=0.419
+2023-05-08 23:55:14,199	kenma_eng	INFO	====> Epoch: 283
+2023-05-08 23:55:22,185	kenma_eng	INFO	====> Epoch: 284
+2023-05-08 23:55:30,180	kenma_eng	INFO	Saving model and optimizer state at epoch 285 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:55:30,624	kenma_eng	INFO	Saving model and optimizer state at epoch 285 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:55:31,216	kenma_eng	INFO	====> Epoch: 285
+2023-05-08 23:55:39,205	kenma_eng	INFO	====> Epoch: 286
+2023-05-08 23:55:47,177	kenma_eng	INFO	====> Epoch: 287
+2023-05-08 23:55:48,811	kenma_eng	INFO	Train Epoch: 288 [36%]
+2023-05-08 23:55:48,813	kenma_eng	INFO	[11200, 9.647587177037196e-05]
+2023-05-08 23:55:48,813	kenma_eng	INFO	loss_disc=2.106, loss_gen=3.536, loss_fm=15.607,loss_mel=18.624, loss_kl=0.811
+2023-05-08 23:55:55,373	kenma_eng	INFO	====> Epoch: 288
+2023-05-08 23:56:03,350	kenma_eng	INFO	====> Epoch: 289
+2023-05-08 23:56:11,351	kenma_eng	INFO	Saving model and optimizer state at epoch 290 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:56:11,802	kenma_eng	INFO	Saving model and optimizer state at epoch 290 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:56:12,395	kenma_eng	INFO	====> Epoch: 290
+2023-05-08 23:56:20,350	kenma_eng	INFO	====> Epoch: 291
+2023-05-08 23:56:28,342	kenma_eng	INFO	====> Epoch: 292
+2023-05-08 23:56:30,998	kenma_eng	INFO	Train Epoch: 293 [59%]
+2023-05-08 23:56:31,000	kenma_eng	INFO	[11400, 9.641558942298625e-05]
+2023-05-08 23:56:31,000	kenma_eng	INFO	loss_disc=2.425, loss_gen=3.517, loss_fm=10.334,loss_mel=19.468, loss_kl=0.564
+2023-05-08 23:56:36,531	kenma_eng	INFO	====> Epoch: 293
+2023-05-08 23:56:44,531	kenma_eng	INFO	====> Epoch: 294
+2023-05-08 23:56:52,503	kenma_eng	INFO	Saving model and optimizer state at epoch 295 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:56:52,954	kenma_eng	INFO	Saving model and optimizer state at epoch 295 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:56:53,544	kenma_eng	INFO	====> Epoch: 295
+2023-05-08 23:57:01,683	kenma_eng	INFO	====> Epoch: 296
+2023-05-08 23:57:10,165	kenma_eng	INFO	====> Epoch: 297
+2023-05-08 23:57:14,053	kenma_eng	INFO	Train Epoch: 298 [18%]
+2023-05-08 23:57:14,057	kenma_eng	INFO	[11600, 9.635534474264972e-05]
+2023-05-08 23:57:14,057	kenma_eng	INFO	loss_disc=2.473, loss_gen=3.393, loss_fm=10.933,loss_mel=18.952, loss_kl=1.169
+2023-05-08 23:57:18,589	kenma_eng	INFO	====> Epoch: 298
+2023-05-08 23:57:26,500	kenma_eng	INFO	====> Epoch: 299
+2023-05-08 23:57:34,487	kenma_eng	INFO	Saving model and optimizer state at epoch 300 to ./logs/kenma_eng/G_2333333.pth
+2023-05-08 23:57:34,943	kenma_eng	INFO	Saving model and optimizer state at epoch 300 to ./logs/kenma_eng/D_2333333.pth
+2023-05-08 23:57:35,537	kenma_eng	INFO	====> Epoch: 300
+2023-05-08 23:57:35,538	kenma_eng	INFO	Training is done. The program is closed.
+2023-05-08 23:57:35,596	kenma_eng	INFO	saving final ckpt:Success.
diff --git a/kenma_eng/trained_IVF424_Flat_nprobe_1.index b/kenma_eng/trained_IVF424_Flat_nprobe_1.index
new file mode 100644
index 0000000000000000000000000000000000000000..77d7bfe79a8a767f46a657651a6450c5134cbbf0
Binary files /dev/null and b/kenma_eng/trained_IVF424_Flat_nprobe_1.index differ