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  1. furry/baobai/baobai8/bw.zip +3 -0
  2. furry/baobai/baobai8/configs/config.json +107 -0
  3. furry/baobai/baobai8/configs/diffusion.yaml +51 -0
  4. furry/baobai/baobai8/dataset.zip +3 -0
  5. furry/baobai/baobai8/filelists/train.txt +134 -0
  6. furry/baobai/baobai8/filelists/val.txt +2 -0
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  14. furry/baobai/baobai8/logs/44k/G_3200.pth +3 -0
  15. furry/baobai/baobai8/logs/44k/config.json +107 -0
  16. furry/baobai/baobai8/logs/44k/diffusion/config.yaml +51 -0
  17. furry/baobai/baobai8/logs/44k/diffusion/log_info.txt +419 -0
  18. furry/baobai/baobai8/logs/44k/diffusion/logs/events.out.tfevents.1691855425.36ffdf771304.34252.0 +3 -0
  19. furry/baobai/baobai8/logs/44k/diffusion/model_0.pt +3 -0
  20. furry/baobai/baobai8/logs/44k/diffusion/model_4000.pt +3 -0
  21. furry/baobai/baobai8/logs/44k/eval/events.out.tfevents.1691849579.36ffdf771304.6041.1 +3 -0
  22. furry/baobai/baobai8/logs/44k/eval/events.out.tfevents.1691853899.36ffdf771304.26662.1 +3 -0
  23. furry/baobai/baobai8/logs/44k/eval/events.out.tfevents.1691853956.36ffdf771304.26984.1 +3 -0
  24. furry/baobai/baobai8/logs/44k/events.out.tfevents.1691849579.36ffdf771304.6041.0 +3 -0
  25. furry/baobai/baobai8/logs/44k/events.out.tfevents.1691853899.36ffdf771304.26662.0 +3 -0
  26. furry/baobai/baobai8/logs/44k/events.out.tfevents.1691853956.36ffdf771304.26984.0 +3 -0
  27. furry/baobai/baobai8/logs/44k/githash +1 -0
  28. furry/baobai/baobai8/logs/44k/release.pth +3 -0
  29. furry/baobai/baobai8/logs/44k/train.log +203 -0
  30. furry/baobai/baobai8/raw/再见深海n1.wav +3 -0
  31. furry/baobai/baobai8/raw/再见深海n2.wav +3 -0
  32. furry/baobai/baobai8/results/再见深海n1.wav_0key_bw_sovits_crepe.flac +3 -0
  33. furry/baobai/baobai8/results/再见深海n1.wav_0key_bw_sovits_dio.flac +3 -0
  34. furry/baobai/baobai8/results/再见深海n1.wav_0key_bw_sovits_fcpe.flac +3 -0
  35. furry/baobai/baobai8/results/再见深海n1.wav_0key_bw_sovits_rmvpe.flac +3 -0
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+ --- model size ---
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+ ======= start training =======
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+ epoch: 666 | 1/ 3 | logs/44k/diffusion | batch/s: 1.12 | lr: 0.0001 | loss: 0.010 | time: 0:29:44.1 | step: 2000
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+ --- <validation> ---
206
+ loss: 0.010.
207
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+ epoch: 673 | 0/ 3 | logs/44k/diffusion | batch/s: 1.11 | lr: 0.0001 | loss: 0.022 | time: 0:30:18.7 | step: 2020
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+ epoch: 676 | 1/ 3 | logs/44k/diffusion | batch/s: 1.08 | lr: 0.0001 | loss: 0.018 | time: 0:30:27.9 | step: 2030
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+ epoch: 679 | 2/ 3 | logs/44k/diffusion | batch/s: 1.09 | lr: 0.0001 | loss: 0.019 | time: 0:30:36.9 | step: 2040
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+ epoch: 683 | 0/ 3 | logs/44k/diffusion | batch/s: 1.11 | lr: 0.0001 | loss: 0.037 | time: 0:30:46.0 | step: 2050
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+ epoch: 686 | 1/ 3 | logs/44k/diffusion | batch/s: 1.11 | lr: 0.0001 | loss: 0.013 | time: 0:30:55.0 | step: 2060
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+ epoch: 693 | 0/ 3 | logs/44k/diffusion | batch/s: 1.13 | lr: 0.0001 | loss: 0.012 | time: 0:31:12.8 | step: 2080
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+ epoch: 773 | 0/ 3 | logs/44k/diffusion | batch/s: 1.12 | lr: 0.0001 | loss: 0.020 | time: 0:34:47.6 | step: 2320
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+ epoch: 1316 | 1/ 3 | logs/44k/diffusion | batch/s: 1.11 | lr: 0.0001 | loss: 0.011 | time: 0:59:04.3 | step: 3950
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+ epoch: 1326 | 1/ 3 | logs/44k/diffusion | batch/s: 1.11 | lr: 0.0001 | loss: 0.015 | time: 0:59:31.1 | step: 3980
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+ epoch: 1333 | 0/ 3 | logs/44k/diffusion | batch/s: 1.12 | lr: 0.0001 | loss: 0.008 | time: 0:59:48.9 | step: 4000
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+ epoch: 1369 | 2/ 3 | logs/44k/diffusion | batch/s: 1.12 | lr: 0.0001 | loss: 0.024 | time: 1:01:42.7 | step: 4110
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2
+ 2023-08-12 14:13:09,645 44k INFO emb_g.weight is not in the checkpoint
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+ 2023-08-12 14:13:09,799 44k INFO Loaded checkpoint './logs/44k/G_0.pth' (iteration 0)
4
+ 2023-08-12 14:13:13,003 44k INFO Loaded checkpoint './logs/44k/D_0.pth' (iteration 0)
5
+ 2023-08-12 14:14:25,263 44k INFO ====> Epoch: 1, cost 85.92 s
6
+ 2023-08-12 14:15:02,466 44k INFO ====> Epoch: 2, cost 37.20 s
7
+ 2023-08-12 14:15:36,750 44k INFO ====> Epoch: 3, cost 34.28 s
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+ 2023-08-12 14:16:11,944 44k INFO ====> Epoch: 4, cost 35.19 s
9
+ 2023-08-12 14:16:52,122 44k INFO ====> Epoch: 5, cost 40.18 s
10
+ 2023-08-12 14:17:27,963 44k INFO ====> Epoch: 6, cost 35.84 s
11
+ 2023-08-12 14:18:01,369 44k INFO ====> Epoch: 7, cost 33.41 s
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+ 2023-08-12 14:18:38,567 44k INFO ====> Epoch: 8, cost 37.20 s
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+ 2023-08-12 14:19:06,841 44k INFO Train Epoch: 9 [65%]
14
+ 2023-08-12 14:19:06,842 44k INFO Losses: [2.372133255004883, 3.0521934032440186, 18.344472885131836, 21.735246658325195, 1.1796915531158447], step: 200, lr: 9.990004373906418e-05, reference_loss: 46.68373489379883
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+ 2023-08-12 14:19:16,793 44k INFO ====> Epoch: 9, cost 38.23 s
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+ 2023-08-12 14:19:52,384 44k INFO ====> Epoch: 10, cost 35.59 s
17
+ 2023-08-12 14:20:29,551 44k INFO ====> Epoch: 11, cost 37.17 s
18
+ 2023-08-12 14:21:06,450 44k INFO ====> Epoch: 12, cost 36.90 s
19
+ 2023-08-12 14:21:40,430 44k INFO ====> Epoch: 13, cost 33.98 s
20
+ 2023-08-12 14:22:15,681 44k INFO ====> Epoch: 14, cost 35.25 s
21
+ 2023-08-12 14:22:53,070 44k INFO ====> Epoch: 15, cost 37.39 s
22
+ 2023-08-12 14:23:30,143 44k INFO ====> Epoch: 16, cost 37.07 s
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+ 2023-08-12 14:24:04,880 44k INFO ====> Epoch: 17, cost 34.74 s
24
+ 2023-08-12 14:24:27,523 44k INFO Train Epoch: 18 [35%]
25
+ 2023-08-12 14:24:27,528 44k INFO Losses: [2.4882853031158447, 2.6926639080047607, 14.925048828125, 18.37120246887207, 0.6845868825912476], step: 400, lr: 9.978771236724554e-05, reference_loss: 39.16178894042969
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+ 2023-08-12 14:24:44,107 44k INFO ====> Epoch: 18, cost 39.23 s
27
+ 2023-08-12 14:25:20,817 44k INFO ====> Epoch: 19, cost 36.71 s
28
+ 2023-08-12 14:25:54,695 44k INFO ====> Epoch: 20, cost 33.88 s
29
+ 2023-08-12 14:26:29,955 44k INFO ====> Epoch: 21, cost 35.26 s
30
+ 2023-08-12 14:27:10,847 44k INFO ====> Epoch: 22, cost 40.89 s
31
+ 2023-08-12 14:27:44,257 44k INFO ====> Epoch: 23, cost 33.41 s
32
+ 2023-08-12 14:28:20,492 44k INFO ====> Epoch: 24, cost 36.24 s
33
+ 2023-08-12 14:28:57,507 44k INFO ====> Epoch: 25, cost 37.01 s
34
+ 2023-08-12 14:29:32,176 44k INFO ====> Epoch: 26, cost 34.67 s
35
+ 2023-08-12 14:29:44,568 44k INFO Train Epoch: 27 [4%]
36
+ 2023-08-12 14:29:44,574 44k INFO Losses: [2.3930883407592773, 2.7095766067504883, 13.353201866149902, 19.138076782226562, 0.898830235004425], step: 600, lr: 9.967550730505221e-05, reference_loss: 38.492774963378906
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+ 2023-08-12 14:30:06,893 44k INFO ====> Epoch: 27, cost 34.72 s
38
+ 2023-08-12 14:30:48,861 44k INFO ====> Epoch: 28, cost 41.97 s
39
+ 2023-08-12 14:31:22,732 44k INFO ====> Epoch: 29, cost 33.87 s
40
+ 2023-08-12 14:31:58,042 44k INFO ====> Epoch: 30, cost 35.31 s
41
+ 2023-08-12 14:32:35,195 44k INFO ====> Epoch: 31, cost 37.15 s
42
+ 2023-08-12 14:33:10,938 44k INFO ====> Epoch: 32, cost 35.74 s
43
+ 2023-08-12 14:33:44,087 44k INFO ====> Epoch: 33, cost 33.15 s
44
+ 2023-08-12 14:34:24,390 44k INFO ====> Epoch: 34, cost 40.30 s
45
+ 2023-08-12 14:34:53,364 44k INFO Train Epoch: 35 [74%]
46
+ 2023-08-12 14:34:53,365 44k INFO Losses: [2.2506444454193115, 2.408331871032715, 15.175576210021973, 17.956342697143555, 0.93328458070755], step: 800, lr: 9.957587539488128e-05, reference_loss: 38.724178314208984
47
+ 2023-08-12 14:35:11,608 44k INFO Saving model and optimizer state at iteration 35 to ./logs/44k/G_800.pth
48
+ 2023-08-12 14:35:16,681 44k INFO Saving model and optimizer state at iteration 35 to ./logs/44k/D_800.pth
49
+ 2023-08-12 14:35:29,850 44k INFO ====> Epoch: 35, cost 65.46 s
50
+ 2023-08-12 14:36:09,398 44k INFO ====> Epoch: 36, cost 39.55 s
51
+ 2023-08-12 14:36:45,977 44k INFO ====> Epoch: 37, cost 36.58 s
52
+ 2023-08-12 14:37:19,513 44k INFO ====> Epoch: 38, cost 33.54 s
53
+ 2023-08-12 14:37:58,591 44k INFO ====> Epoch: 39, cost 39.08 s
54
+ 2023-08-12 14:38:35,915 44k INFO ====> Epoch: 40, cost 37.32 s
55
+ 2023-08-12 14:39:12,428 44k INFO ====> Epoch: 41, cost 36.51 s
56
+ 2023-08-12 14:39:46,127 44k INFO ====> Epoch: 42, cost 33.70 s
57
+ 2023-08-12 14:40:21,465 44k INFO ====> Epoch: 43, cost 35.34 s
58
+ 2023-08-12 14:40:45,653 44k INFO Train Epoch: 44 [43%]
59
+ 2023-08-12 14:40:45,654 44k INFO Losses: [2.5418283939361572, 2.2850289344787598, 9.427962303161621, 16.608245849609375, 1.106669306755066], step: 1000, lr: 9.94639085301583e-05, reference_loss: 31.96973419189453
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+ 2023-08-12 14:41:00,529 44k INFO ====> Epoch: 44, cost 39.06 s
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+ 2023-08-12 14:41:38,669 44k INFO ====> Epoch: 45, cost 38.14 s
62
+ 2023-08-12 14:42:13,492 44k INFO ====> Epoch: 46, cost 34.82 s
63
+ 2023-08-12 14:42:51,075 44k INFO ====> Epoch: 47, cost 37.58 s
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+ 2023-08-12 14:43:27,453 44k INFO ====> Epoch: 48, cost 36.38 s
65
+ 2023-08-12 14:44:00,818 44k INFO ====> Epoch: 49, cost 33.37 s
66
+ 2023-08-12 14:44:36,858 44k INFO ====> Epoch: 50, cost 36.04 s
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+ 2023-08-12 14:45:15,420 44k INFO ====> Epoch: 51, cost 38.56 s
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+ 2023-08-12 14:45:49,195 44k INFO ====> Epoch: 52, cost 33.78 s
69
+ 2023-08-12 14:46:04,601 44k INFO Train Epoch: 53 [13%]
70
+ 2023-08-12 14:46:04,605 44k INFO Losses: [2.4839749336242676, 2.531289577484131, 12.578356742858887, 17.232927322387695, 0.9754498600959778], step: 1200, lr: 9.935206756519513e-05, reference_loss: 35.80199432373047
71
+ 2023-08-12 14:46:25,417 44k INFO ====> Epoch: 53, cost 36.22 s
72
+ 2023-08-12 14:47:02,660 44k INFO ====> Epoch: 54, cost 37.24 s
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+ 2023-08-12 14:47:38,649 44k INFO ====> Epoch: 55, cost 35.99 s
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+ 2023-08-12 14:48:12,289 44k INFO ====> Epoch: 56, cost 33.64 s
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+ 2023-08-12 14:48:50,227 44k INFO ====> Epoch: 57, cost 37.94 s
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+ 2023-08-12 14:49:24,133 44k INFO ====> Epoch: 58, cost 33.91 s
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+ 2023-08-12 14:49:58,859 44k INFO ====> Epoch: 59, cost 34.73 s
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+ 2023-08-12 14:50:35,992 44k INFO ====> Epoch: 60, cost 37.13 s
79
+ 2023-08-12 14:51:06,730 44k INFO Train Epoch: 61 [83%]
80
+ 2023-08-12 14:51:06,732 44k INFO Losses: [2.293588638305664, 3.138998508453369, 12.667041778564453, 19.142377853393555, 0.6848804950714111], step: 1400, lr: 9.92527589532945e-05, reference_loss: 37.926883697509766
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+ 2023-08-12 14:51:13,846 44k INFO ====> Epoch: 61, cost 37.85 s
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+ 2023-08-12 14:51:47,523 44k INFO ====> Epoch: 62, cost 33.68 s
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+ 2023-08-12 14:52:25,120 44k INFO ====> Epoch: 63, cost 37.60 s
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+ 2023-08-12 14:53:05,084 44k INFO ====> Epoch: 64, cost 39.96 s
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+ 2023-08-12 14:53:41,127 44k INFO ====> Epoch: 65, cost 36.04 s
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+ 2023-08-12 14:54:14,409 44k INFO ====> Epoch: 66, cost 33.28 s
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+ 2023-08-12 14:54:51,735 44k INFO ====> Epoch: 67, cost 37.33 s
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+ 2023-08-12 14:55:28,906 44k INFO ====> Epoch: 68, cost 37.17 s
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+ 2023-08-12 14:56:02,743 44k INFO ====> Epoch: 69, cost 33.84 s
90
+ 2023-08-12 14:56:31,299 44k INFO Train Epoch: 70 [52%]
91
+ 2023-08-12 14:56:31,304 44k INFO Losses: [2.3966827392578125, 2.455382823944092, 11.905396461486816, 17.858034133911133, 0.5615662932395935], step: 1600, lr: 9.914115541286833e-05, reference_loss: 35.177059173583984
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+ 2023-08-12 14:56:40,247 44k INFO Saving model and optimizer state at iteration 70 to ./logs/44k/G_1600.pth
93
+ 2023-08-12 14:56:47,725 44k INFO Saving model and optimizer state at iteration 70 to ./logs/44k/D_1600.pth
94
+ 2023-08-12 14:57:08,259 44k INFO ====> Epoch: 70, cost 65.52 s
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+ 2023-08-12 14:57:45,710 44k INFO ====> Epoch: 71, cost 37.45 s
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+ 2023-08-12 14:58:22,380 44k INFO ====> Epoch: 72, cost 36.67 s
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+ 2023-08-12 15:00:51,646 44k INFO ====> Epoch: 76, cost 39.91 s
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+ 2023-08-12 15:02:01,287 44k INFO ====> Epoch: 78, cost 33.89 s
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+ 2023-08-12 15:02:20,467 44k INFO Train Epoch: 79 [22%]
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+ 2023-08-12 15:02:20,471 44k INFO Losses: [2.426839828491211, 2.590867757797241, 12.20749282836914, 17.734094619750977, 0.8622822761535645], step: 1800, lr: 9.902967736366644e-05, reference_loss: 35.82157897949219
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+ 2023-08-12 15:02:39,280 44k INFO ====> Epoch: 79, cost 37.99 s
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+ 2023-08-12 15:03:16,700 44k INFO ====> Epoch: 80, cost 37.42 s
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+ 2023-08-12 15:05:38,460 44k INFO ====> Epoch: 84, cost 34.03 s
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+ 2023-08-12 15:06:13,645 44k INFO ====> Epoch: 85, cost 35.19 s
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+ 2023-08-12 15:06:51,307 44k INFO ====> Epoch: 86, cost 37.66 s
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+ 2023-08-12 15:07:24,049 44k INFO Train Epoch: 87 [91%]
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+ 2023-08-12 15:07:24,050 44k INFO Losses: [2.5474557876586914, 2.4764974117279053, 9.042988777160645, 16.563535690307617, 0.7708569169044495], step: 2000, lr: 9.89306910009569e-05, reference_loss: 31.401334762573242
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+ 2023-08-12 15:07:29,232 44k INFO ====> Epoch: 87, cost 37.92 s
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+ 2023-08-12 15:08:43,647 44k INFO ====> Epoch: 89, cost 40.57 s
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+ 2023-08-12 15:09:21,409 44k INFO ====> Epoch: 90, cost 37.76 s
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+ 2023-08-12 15:09:57,549 44k INFO ====> Epoch: 91, cost 36.14 s
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+ 2023-08-12 15:10:31,436 44k INFO ====> Epoch: 92, cost 33.89 s
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+ 2023-08-12 15:11:08,255 44k INFO ====> Epoch: 93, cost 36.82 s
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+ 2023-08-12 15:11:44,964 44k INFO ====> Epoch: 94, cost 36.71 s
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+ 2023-08-12 15:12:19,894 44k INFO ====> Epoch: 95, cost 34.93 s
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+ 2023-08-12 15:12:48,148 44k INFO Train Epoch: 96 [61%]
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+ 2023-08-12 15:12:48,150 44k INFO Losses: [2.494248867034912, 2.565378427505493, 12.230314254760742, 18.448312759399414, 0.7265467643737793], step: 2200, lr: 9.881944960586671e-05, reference_loss: 36.46480178833008
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+ 2023-08-12 15:12:58,766 44k INFO ====> Epoch: 96, cost 38.87 s
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+ 2023-08-12 15:13:35,063 44k INFO ====> Epoch: 97, cost 36.30 s
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+ 2023-08-12 15:14:09,135 44k INFO ====> Epoch: 98, cost 34.07 s
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+ 2023-08-12 15:14:45,833 44k INFO ====> Epoch: 99, cost 36.70 s
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+ 2023-08-12 15:15:23,021 44k INFO ====> Epoch: 100, cost 37.19 s
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+ 2023-08-12 15:15:58,031 44k INFO ====> Epoch: 101, cost 35.01 s
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+ 2023-08-12 15:17:15,538 44k INFO ====> Epoch: 103, cost 36.73 s
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+ 2023-08-12 15:17:52,688 44k INFO ====> Epoch: 104, cost 37.15 s
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+ 2023-08-12 15:18:10,460 44k INFO Train Epoch: 105 [30%]
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+ 2023-08-12 15:18:10,462 44k INFO Losses: [1.9893114566802979, 3.161811351776123, 13.067861557006836, 16.837371826171875, 0.933455765247345], step: 2400, lr: 9.870833329479095e-05, reference_loss: 35.98981475830078
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+ 2023-08-12 15:18:19,247 44k INFO Saving model and optimizer state at iteration 105 to ./logs/44k/G_2400.pth
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+ 2023-08-12 15:18:23,095 44k INFO Saving model and optimizer state at iteration 105 to ./logs/44k/D_2400.pth
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+ 2023-08-12 15:18:47,304 44k INFO ====> Epoch: 105, cost 54.62 s
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+ 2023-08-12 15:19:23,764 44k INFO ====> Epoch: 106, cost 36.46 s
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+ 2023-08-12 15:20:40,672 44k INFO ====> Epoch: 108, cost 39.53 s
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+ 2023-08-12 15:21:16,108 44k INFO ====> Epoch: 109, cost 35.44 s
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+ 2023-08-12 15:21:53,532 44k INFO ====> Epoch: 110, cost 37.42 s
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+ 2023-08-12 15:22:30,382 44k INFO ====> Epoch: 111, cost 36.85 s
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+ 2023-08-12 15:23:03,620 44k INFO ====> Epoch: 112, cost 33.24 s
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+ 2023-08-12 15:23:39,979 44k INFO ====> Epoch: 113, cost 36.36 s
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+ 2023-08-12 15:23:54,327 44k INFO Train Epoch: 114 [0%]
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+ 2023-08-12 15:23:54,328 44k INFO Losses: [2.137906551361084, 3.1954293251037598, 15.614903450012207, 18.090456008911133, 0.9798715710639954], step: 2600, lr: 9.859734192708044e-05, reference_loss: 40.0185661315918
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+ 2023-08-12 15:24:19,021 44k INFO ====> Epoch: 114, cost 39.04 s
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+ 2023-08-12 15:24:59,372 44k INFO {'train': {'log_interval': 200, 'eval_interval': 800, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 6, 'fp16_run': False, 'half_type': 'fp16', 'lr_decay': 0.999875, 'segment_size': 10240, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'use_sr': True, 'max_speclen': 512, 'port': '8001', 'keep_ckpts': 3, 'all_in_mem': False, 'vol_aug': False}, 'data': {'training_files': 'filelists/train.txt', 'validation_files': 'filelists/val.txt', 'max_wav_value': 32768.0, 'sampling_rate': 44100, 'filter_length': 2048, 'hop_length': 512, 'win_length': 2048, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': 22050, 'unit_interpolate_mode': 'nearest'}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4, 4], 'n_layers_q': 3, 'n_layers_trans_flow': 3, 'n_flow_layer': 4, 'use_spectral_norm': False, 'gin_channels': 768, 'ssl_dim': 768, 'n_speakers': 1, 'vocoder_name': 'nsf-hifigan', 'speech_encoder': 'vec768l12', 'speaker_embedding': False, 'vol_embedding': False, 'use_depthwise_conv': False, 'flow_share_parameter': False, 'use_automatic_f0_prediction': True, 'use_transformer_flow': False}, 'spk': {'bw': 0}, 'model_dir': './logs/44k'}
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+ 2023-08-12 15:25:11,216 44k INFO Loaded checkpoint './logs/44k/G_2400.pth' (iteration 105)
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+ 2023-08-12 15:25:14,744 44k INFO Loaded checkpoint './logs/44k/D_2400.pth' (iteration 105)
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+ 2023-08-12 15:25:56,333 44k INFO {'train': {'log_interval': 200, 'eval_interval': 800, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 6, 'fp16_run': False, 'half_type': 'fp16', 'lr_decay': 0.999875, 'segment_size': 10240, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'use_sr': True, 'max_speclen': 512, 'port': '8001', 'keep_ckpts': 3, 'all_in_mem': False, 'vol_aug': False}, 'data': {'training_files': 'filelists/train.txt', 'validation_files': 'filelists/val.txt', 'max_wav_value': 32768.0, 'sampling_rate': 44100, 'filter_length': 2048, 'hop_length': 512, 'win_length': 2048, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': 22050, 'unit_interpolate_mode': 'nearest'}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4, 4], 'n_layers_q': 3, 'n_layers_trans_flow': 3, 'n_flow_layer': 4, 'use_spectral_norm': False, 'gin_channels': 768, 'ssl_dim': 768, 'n_speakers': 1, 'vocoder_name': 'nsf-hifigan', 'speech_encoder': 'vec768l12', 'speaker_embedding': False, 'vol_embedding': False, 'use_depthwise_conv': False, 'flow_share_parameter': False, 'use_automatic_f0_prediction': True, 'use_transformer_flow': False}, 'spk': {'bw': 0}, 'model_dir': './logs/44k'}
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+ 2023-08-12 15:26:10,033 44k INFO Loaded checkpoint './logs/44k/G_2400.pth' (iteration 105)
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+ 2023-08-12 15:26:14,447 44k INFO Loaded checkpoint './logs/44k/D_2400.pth' (iteration 105)
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+ 2023-08-12 15:27:19,033 44k INFO ====> Epoch: 105, cost 82.70 s
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+ 2023-08-12 15:27:55,587 44k INFO ====> Epoch: 106, cost 36.55 s
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+ 2023-08-12 15:28:35,076 44k INFO ====> Epoch: 107, cost 39.49 s
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+ 2023-08-12 15:29:15,293 44k INFO ====> Epoch: 108, cost 40.22 s
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+ 2023-08-12 15:29:51,732 44k INFO ====> Epoch: 109, cost 36.44 s
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+ 2023-08-12 15:30:25,505 44k INFO ====> Epoch: 110, cost 33.77 s
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+ 2023-08-12 15:31:02,260 44k INFO ====> Epoch: 111, cost 36.76 s
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+ 2023-08-12 15:31:39,869 44k INFO ====> Epoch: 112, cost 37.61 s
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+ 2023-08-12 15:32:05,570 44k INFO Train Epoch: 113 [65%]
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+ 2023-08-12 15:32:05,574 44k INFO Losses: [2.193831205368042, 2.7961835861206055, 18.21563148498535, 18.30766487121582, 0.8750658631324768], step: 2600, lr: 9.859734192708044e-05, reference_loss: 42.38837814331055
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+ 2023-08-12 15:32:16,136 44k INFO ====> Epoch: 113, cost 36.27 s
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+ 2023-08-12 15:32:50,224 44k INFO ====> Epoch: 114, cost 34.09 s
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+ 2023-08-12 15:33:30,370 44k INFO ====> Epoch: 115, cost 40.15 s
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+ 2023-08-12 15:34:07,652 44k INFO ====> Epoch: 116, cost 37.28 s
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+ 2023-08-12 15:34:42,869 44k INFO ====> Epoch: 117, cost 35.22 s
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+ 2023-08-12 15:35:16,860 44k INFO ====> Epoch: 118, cost 33.99 s
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+ 2023-08-12 15:35:54,244 44k INFO ====> Epoch: 119, cost 37.38 s
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+ 2023-08-12 15:36:31,215 44k INFO ====> Epoch: 120, cost 36.97 s
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+ 2023-08-12 15:37:05,169 44k INFO ====> Epoch: 121, cost 33.95 s
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+ 2023-08-12 15:37:25,404 44k INFO Train Epoch: 122 [35%]
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+ 2023-08-12 15:37:25,406 44k INFO Losses: [2.194392681121826, 2.90118408203125, 15.34102725982666, 17.628496170043945, 0.257029265165329], step: 2800, lr: 9.848647536224416e-05, reference_loss: 38.3221321105957
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+ 2023-08-12 15:37:42,256 44k INFO ====> Epoch: 122, cost 37.09 s
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+ 2023-08-12 15:38:23,889 44k INFO ====> Epoch: 123, cost 41.63 s
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+ 2023-08-12 15:38:57,726 44k INFO ====> Epoch: 124, cost 33.84 s
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+ 2023-08-12 15:39:32,879 44k INFO ====> Epoch: 125, cost 35.15 s
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+ 2023-08-12 15:40:10,244 44k INFO ====> Epoch: 126, cost 37.37 s
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+ 2023-08-12 15:40:46,427 44k INFO ====> Epoch: 127, cost 36.18 s
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+ 2023-08-12 15:41:19,929 44k INFO ====> Epoch: 128, cost 33.50 s
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+ 2023-08-12 15:41:55,935 44k INFO ====> Epoch: 129, cost 36.01 s
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+ 2023-08-12 15:42:33,149 44k INFO ====> Epoch: 130, cost 37.21 s
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+ 2023-08-12 15:42:51,078 44k INFO Train Epoch: 131 [4%]
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+ 2023-08-12 15:42:51,080 44k INFO Losses: [2.092893362045288, 3.3098762035369873, 13.390396118164062, 17.548063278198242, 0.6698232889175415], step: 3000, lr: 9.837573345994909e-05, reference_loss: 37.011051177978516
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+ 2023-08-12 15:43:15,768 44k INFO ====> Epoch: 131, cost 42.62 s
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+ 2023-08-12 15:43:49,700 44k INFO ====> Epoch: 132, cost 33.93 s
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+ 2023-08-12 15:44:24,757 44k INFO ====> Epoch: 133, cost 35.06 s
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+ 2023-08-12 15:45:02,106 44k INFO ====> Epoch: 134, cost 37.35 s
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+ 2023-08-12 15:45:38,325 44k INFO ====> Epoch: 135, cost 36.22 s
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+ 2023-08-12 15:46:12,727 44k INFO ====> Epoch: 136, cost 34.40 s
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+ 2023-08-12 15:46:49,288 44k INFO ====> Epoch: 137, cost 36.56 s
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+ 2023-08-12 15:47:26,467 44k INFO ====> Epoch: 138, cost 37.18 s
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+ 2023-08-12 15:47:59,335 44k INFO Train Epoch: 139 [74%]
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+ 2023-08-12 15:47:59,338 44k INFO Losses: [2.0553226470947266, 2.708265781402588, 14.970947265625, 16.92167091369629, 0.7960343956947327], step: 3200, lr: 9.827740075511432e-05, reference_loss: 37.452239990234375
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+ 2023-08-12 15:48:19,095 44k INFO Saving model and optimizer state at iteration 139 to ./logs/44k/G_3200.pth
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+ 2023-08-12 15:48:23,978 44k INFO Saving model and optimizer state at iteration 139 to ./logs/44k/D_3200.pth
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+ 2023-08-12 15:48:30,851 44k INFO .. Free up space by deleting ckpt ./logs/44k/G_800.pth
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+ 2023-08-12 15:48:30,853 44k INFO .. Free up space by deleting ckpt ./logs/44k/D_800.pth
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+ 2023-08-12 15:48:37,139 44k INFO ====> Epoch: 139, cost 70.67 s
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