Upload folder using huggingface_hub
Browse files- .gitattributes +2 -0
- AuxiliaryASR/Checkpoint_new/config.yml +26 -0
- AuxiliaryASR/Checkpoint_new/epoch_00078.pth +3 -0
- AuxiliaryASR/Checkpoint_new/tensorboard/events.out.tfevents.1727097710.node-1.618334.0 +3 -0
- AuxiliaryASR/Checkpoint_new/train.log +800 -0
- AuxiliaryASR/Checkpoint_new_plus/config.yml +26 -0
- AuxiliaryASR/Checkpoint_new_plus/epoch_00068.pth +3 -0
- AuxiliaryASR/Checkpoint_new_plus/epoch_00070.pth +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727504950.node-1.2524109.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727505062.node-1.2525809.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727505110.node-1.2527152.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727506701.node-1.2546882.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727506736.node-1.2548453.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727506755.node-1.2549619.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727506770.node-1.2550650.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/tensorboard/events.out.tfevents.1727506803.node-1.2552623.0 +3 -0
- AuxiliaryASR/Checkpoint_new_plus/train.log +700 -0
- AuxiliaryASR/Configs/config.yml +26 -0
- AuxiliaryASR/Data/train_list.csv +3 -0
- AuxiliaryASR/Data/train_list.txt +0 -0
- AuxiliaryASR/Data/train_list_plus.csv +3 -0
- AuxiliaryASR/Data/train_list_subsection.csv +0 -0
- AuxiliaryASR/Data/val_list.txt +407 -0
- AuxiliaryASR/Data/val_list_subsect.txt +128 -0
- AuxiliaryASR/LICENSE +21 -0
- AuxiliaryASR/README.md +33 -0
- AuxiliaryASR/__pycache__/layers.cpython-311.pyc +0 -0
- AuxiliaryASR/__pycache__/meldataset.cpython-311.pyc +0 -0
- AuxiliaryASR/__pycache__/models.cpython-311.pyc +0 -0
- AuxiliaryASR/__pycache__/optimizers.cpython-311.pyc +0 -0
- AuxiliaryASR/__pycache__/text_utils.cpython-311.pyc +0 -0
- AuxiliaryASR/__pycache__/trainer.cpython-311.pyc +0 -0
- AuxiliaryASR/__pycache__/utils.cpython-311.pyc +0 -0
- AuxiliaryASR/layers.py +354 -0
- AuxiliaryASR/meldataset.py +222 -0
- AuxiliaryASR/models.py +192 -0
- AuxiliaryASR/optimizers.py +86 -0
- AuxiliaryASR/text_utils.py +26 -0
- AuxiliaryASR/train.py +116 -0
- AuxiliaryASR/trainer.py +241 -0
- AuxiliaryASR/utils.py +60 -0
.gitattributes
CHANGED
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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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AuxiliaryASR/Data/train_list.csv filter=lfs diff=lfs merge=lfs -text
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AuxiliaryASR/Data/train_list_plus.csv filter=lfs diff=lfs merge=lfs -text
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AuxiliaryASR/Checkpoint_new/config.yml
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log_dir: "Checkpoint_new"
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save_freq: 2
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device: "cuda"
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epochs: 200
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batch_size: 64
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pretrained_model: ""
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train_data: "/home/austin/disk2/llmvcs/tt/AuxiliaryASR/Data/train_list_plus.csv"
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val_data: "/home/austin/disk2/llmvcs/tt/AuxiliaryASR/Data/val_list.txt"
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preprocess_parasm:
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sr: 24000
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spect_params:
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n_fft: 2048
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win_length: 1200
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hop_length: 300
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mel_params:
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n_mels: 80
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model_params:
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input_dim: 80
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hidden_dim: 256
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n_token: 178
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token_embedding_dim: 512
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optimizer_params:
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lr: 0.0005
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AuxiliaryASR/Checkpoint_new/epoch_00078.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:ead3881be8426e2b06d8cdec83055fed7f8c3a378c3eb4dfd85ba6474b423921
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size 94572980
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AuxiliaryASR/Checkpoint_new/tensorboard/events.out.tfevents.1727097710.node-1.618334.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:32fb61956879abfac61772a521fe9941f6492639f04029dee3d87cd707080dd0
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size 21200334
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AuxiliaryASR/Checkpoint_new/train.log
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| 1 |
+
INFO:2024-09-23 14:21:09,016: --- epoch 1 ---
|
| 2 |
+
INFO:2024-09-23 14:21:09,016: train/loss : 1.1774
|
| 3 |
+
INFO:2024-09-23 14:21:09,017: train/ctc : 0.3427
|
| 4 |
+
INFO:2024-09-23 14:21:09,017: train/s2s : 0.8347
|
| 5 |
+
INFO:2024-09-23 14:21:09,017: train/learning_rate: 0.0005
|
| 6 |
+
INFO:2024-09-23 14:21:09,017: eval/ctc : -0.1690
|
| 7 |
+
INFO:2024-09-23 14:21:09,017: eval/s2s : 0.2689
|
| 8 |
+
INFO:2024-09-23 14:21:09,017: eval/loss : 0.0999
|
| 9 |
+
INFO:2024-09-23 14:21:09,017: eval/wer : 0.3160
|
| 10 |
+
INFO:2024-09-23 14:21:09,017: eval/acc : 0.9127
|
| 11 |
+
INFO:2024-09-23 15:11:48,934: --- epoch 2 ---
|
| 12 |
+
INFO:2024-09-23 15:11:48,934: train/loss : 0.1695
|
| 13 |
+
INFO:2024-09-23 15:11:48,935: train/ctc : -0.1526
|
| 14 |
+
INFO:2024-09-23 15:11:48,935: train/s2s : 0.3222
|
| 15 |
+
INFO:2024-09-23 15:11:48,935: train/learning_rate: 0.0005
|
| 16 |
+
INFO:2024-09-23 15:11:48,935: eval/ctc : -0.2641
|
| 17 |
+
INFO:2024-09-23 15:11:48,935: eval/s2s : 0.2134
|
| 18 |
+
INFO:2024-09-23 15:11:48,935: eval/loss : -0.0507
|
| 19 |
+
INFO:2024-09-23 15:11:48,935: eval/wer : 0.2846
|
| 20 |
+
INFO:2024-09-23 15:11:48,935: eval/acc : 0.9277
|
| 21 |
+
INFO:2024-09-23 16:03:13,404: --- epoch 3 ---
|
| 22 |
+
INFO:2024-09-23 16:03:13,404: train/loss : 0.0678
|
| 23 |
+
INFO:2024-09-23 16:03:13,405: train/ctc : -0.2079
|
| 24 |
+
INFO:2024-09-23 16:03:13,405: train/s2s : 0.2758
|
| 25 |
+
INFO:2024-09-23 16:03:13,405: train/learning_rate: 0.0005
|
| 26 |
+
INFO:2024-09-23 16:03:13,405: eval/ctc : -0.2910
|
| 27 |
+
INFO:2024-09-23 16:03:13,405: eval/s2s : 0.2041
|
| 28 |
+
INFO:2024-09-23 16:03:13,405: eval/loss : -0.0869
|
| 29 |
+
INFO:2024-09-23 16:03:13,405: eval/wer : 0.2747
|
| 30 |
+
INFO:2024-09-23 16:03:13,405: eval/acc : 0.9366
|
| 31 |
+
INFO:2024-09-23 16:52:32,393: --- epoch 4 ---
|
| 32 |
+
INFO:2024-09-23 16:52:32,393: train/loss : 0.0181
|
| 33 |
+
INFO:2024-09-23 16:52:32,393: train/ctc : -0.2356
|
| 34 |
+
INFO:2024-09-23 16:52:32,393: train/s2s : 0.2536
|
| 35 |
+
INFO:2024-09-23 16:52:32,393: train/learning_rate: 0.0005
|
| 36 |
+
INFO:2024-09-23 16:52:32,393: eval/ctc : -0.3123
|
| 37 |
+
INFO:2024-09-23 16:52:32,393: eval/s2s : 0.1904
|
| 38 |
+
INFO:2024-09-23 16:52:32,393: eval/loss : -0.1219
|
| 39 |
+
INFO:2024-09-23 16:52:32,394: eval/wer : 0.2689
|
| 40 |
+
INFO:2024-09-23 16:52:32,394: eval/acc : 0.9405
|
| 41 |
+
INFO:2024-09-23 17:40:28,846: --- epoch 5 ---
|
| 42 |
+
INFO:2024-09-23 17:40:28,847: train/loss : -0.0172
|
| 43 |
+
INFO:2024-09-23 17:40:28,847: train/ctc : -0.2566
|
| 44 |
+
INFO:2024-09-23 17:40:28,847: train/s2s : 0.2394
|
| 45 |
+
INFO:2024-09-23 17:40:28,847: train/learning_rate: 0.0005
|
| 46 |
+
INFO:2024-09-23 17:40:28,847: eval/ctc : -0.3197
|
| 47 |
+
INFO:2024-09-23 17:40:28,847: eval/s2s : 0.1840
|
| 48 |
+
INFO:2024-09-23 17:40:28,847: eval/loss : -0.1358
|
| 49 |
+
INFO:2024-09-23 17:40:28,847: eval/wer : 0.2714
|
| 50 |
+
INFO:2024-09-23 17:40:28,847: eval/acc : 0.9422
|
| 51 |
+
INFO:2024-09-23 18:28:34,071: --- epoch 6 ---
|
| 52 |
+
INFO:2024-09-23 18:28:34,071: train/loss : -0.0395
|
| 53 |
+
INFO:2024-09-23 18:28:34,071: train/ctc : -0.2699
|
| 54 |
+
INFO:2024-09-23 18:28:34,071: train/s2s : 0.2304
|
| 55 |
+
INFO:2024-09-23 18:28:34,071: train/learning_rate: 0.0005
|
| 56 |
+
INFO:2024-09-23 18:28:34,071: eval/ctc : -0.3417
|
| 57 |
+
INFO:2024-09-23 18:28:34,072: eval/s2s : 0.1796
|
| 58 |
+
INFO:2024-09-23 18:28:34,072: eval/loss : -0.1622
|
| 59 |
+
INFO:2024-09-23 18:28:34,072: eval/wer : 0.2666
|
| 60 |
+
INFO:2024-09-23 18:28:34,072: eval/acc : 0.9472
|
| 61 |
+
INFO:2024-09-23 19:17:00,813: --- epoch 7 ---
|
| 62 |
+
INFO:2024-09-23 19:17:00,813: train/loss : -0.0569
|
| 63 |
+
INFO:2024-09-23 19:17:00,814: train/ctc : -0.2804
|
| 64 |
+
INFO:2024-09-23 19:17:00,814: train/s2s : 0.2235
|
| 65 |
+
INFO:2024-09-23 19:17:00,814: train/learning_rate: 0.0005
|
| 66 |
+
INFO:2024-09-23 19:17:00,814: eval/ctc : -0.3471
|
| 67 |
+
INFO:2024-09-23 19:17:00,814: eval/s2s : 0.1786
|
| 68 |
+
INFO:2024-09-23 19:17:00,814: eval/loss : -0.1685
|
| 69 |
+
INFO:2024-09-23 19:17:00,814: eval/wer : 0.2605
|
| 70 |
+
INFO:2024-09-23 19:17:00,814: eval/acc : 0.9467
|
| 71 |
+
INFO:2024-09-23 20:07:21,907: --- epoch 8 ---
|
| 72 |
+
INFO:2024-09-23 20:07:21,907: train/loss : -0.0731
|
| 73 |
+
INFO:2024-09-23 20:07:21,908: train/ctc : -0.2901
|
| 74 |
+
INFO:2024-09-23 20:07:21,908: train/s2s : 0.2170
|
| 75 |
+
INFO:2024-09-23 20:07:21,908: train/learning_rate: 0.0005
|
| 76 |
+
INFO:2024-09-23 20:07:21,908: eval/ctc : -0.3403
|
| 77 |
+
INFO:2024-09-23 20:07:21,908: eval/s2s : 0.1628
|
| 78 |
+
INFO:2024-09-23 20:07:21,908: eval/loss : -0.1775
|
| 79 |
+
INFO:2024-09-23 20:07:21,908: eval/wer : 0.2618
|
| 80 |
+
INFO:2024-09-23 20:07:21,908: eval/acc : 0.9522
|
| 81 |
+
INFO:2024-09-23 20:55:52,624: --- epoch 9 ---
|
| 82 |
+
INFO:2024-09-23 20:55:52,625: train/loss : -0.0853
|
| 83 |
+
INFO:2024-09-23 20:55:52,625: train/ctc : -0.2970
|
| 84 |
+
INFO:2024-09-23 20:55:52,625: train/s2s : 0.2117
|
| 85 |
+
INFO:2024-09-23 20:55:52,625: train/learning_rate: 0.0005
|
| 86 |
+
INFO:2024-09-23 20:55:52,625: eval/ctc : -0.3650
|
| 87 |
+
INFO:2024-09-23 20:55:52,625: eval/s2s : 0.1581
|
| 88 |
+
INFO:2024-09-23 20:55:52,625: eval/loss : -0.2069
|
| 89 |
+
INFO:2024-09-23 20:55:52,625: eval/wer : 0.2562
|
| 90 |
+
INFO:2024-09-23 20:55:52,625: eval/acc : 0.9508
|
| 91 |
+
INFO:2024-09-23 21:44:22,824: --- epoch 10 ---
|
| 92 |
+
INFO:2024-09-23 21:44:22,824: train/loss : -0.0975
|
| 93 |
+
INFO:2024-09-23 21:44:22,824: train/ctc : -0.3040
|
| 94 |
+
INFO:2024-09-23 21:44:22,825: train/s2s : 0.2066
|
| 95 |
+
INFO:2024-09-23 21:44:22,825: train/learning_rate: 0.0005
|
| 96 |
+
INFO:2024-09-23 21:44:22,825: eval/ctc : -0.3653
|
| 97 |
+
INFO:2024-09-23 21:44:22,825: eval/s2s : 0.1549
|
| 98 |
+
INFO:2024-09-23 21:44:22,825: eval/loss : -0.2103
|
| 99 |
+
INFO:2024-09-23 21:44:22,825: eval/wer : 0.2448
|
| 100 |
+
INFO:2024-09-23 21:44:22,825: eval/acc : 0.9525
|
| 101 |
+
INFO:2024-09-23 22:33:11,921: --- epoch 11 ---
|
| 102 |
+
INFO:2024-09-23 22:33:11,921: train/loss : -0.1078
|
| 103 |
+
INFO:2024-09-23 22:33:11,921: train/ctc : -0.3105
|
| 104 |
+
INFO:2024-09-23 22:33:11,921: train/s2s : 0.2027
|
| 105 |
+
INFO:2024-09-23 22:33:11,921: train/learning_rate: 0.0005
|
| 106 |
+
INFO:2024-09-23 22:33:11,921: eval/ctc : -0.3746
|
| 107 |
+
INFO:2024-09-23 22:33:11,921: eval/s2s : 0.1556
|
| 108 |
+
INFO:2024-09-23 22:33:11,921: eval/loss : -0.2190
|
| 109 |
+
INFO:2024-09-23 22:33:11,921: eval/wer : 0.2411
|
| 110 |
+
INFO:2024-09-23 22:33:11,921: eval/acc : 0.9514
|
| 111 |
+
INFO:2024-09-23 23:22:24,924: --- epoch 12 ---
|
| 112 |
+
INFO:2024-09-23 23:22:24,924: train/loss : -0.1144
|
| 113 |
+
INFO:2024-09-23 23:22:24,925: train/ctc : -0.3145
|
| 114 |
+
INFO:2024-09-23 23:22:24,925: train/s2s : 0.2001
|
| 115 |
+
INFO:2024-09-23 23:22:24,925: train/learning_rate: 0.0005
|
| 116 |
+
INFO:2024-09-23 23:22:24,925: eval/ctc : -0.3685
|
| 117 |
+
INFO:2024-09-23 23:22:24,925: eval/s2s : 0.1693
|
| 118 |
+
INFO:2024-09-23 23:22:24,925: eval/loss : -0.1992
|
| 119 |
+
INFO:2024-09-23 23:22:24,925: eval/wer : 0.2473
|
| 120 |
+
INFO:2024-09-23 23:22:24,925: eval/acc : 0.9506
|
| 121 |
+
INFO:2024-09-24 00:12:38,598: --- epoch 13 ---
|
| 122 |
+
INFO:2024-09-24 00:12:38,599: train/loss : -0.1222
|
| 123 |
+
INFO:2024-09-24 00:12:38,599: train/ctc : -0.3191
|
| 124 |
+
INFO:2024-09-24 00:12:38,600: train/s2s : 0.1969
|
| 125 |
+
INFO:2024-09-24 00:12:38,600: train/learning_rate: 0.0005
|
| 126 |
+
INFO:2024-09-24 00:12:38,600: eval/ctc : -0.3604
|
| 127 |
+
INFO:2024-09-24 00:12:38,600: eval/s2s : 0.1653
|
| 128 |
+
INFO:2024-09-24 00:12:38,600: eval/loss : -0.1951
|
| 129 |
+
INFO:2024-09-24 00:12:38,600: eval/wer : 0.2615
|
| 130 |
+
INFO:2024-09-24 00:12:38,600: eval/acc : 0.9513
|
| 131 |
+
INFO:2024-09-24 01:02:38,269: --- epoch 14 ---
|
| 132 |
+
INFO:2024-09-24 01:02:38,269: train/loss : -0.1283
|
| 133 |
+
INFO:2024-09-24 01:02:38,270: train/ctc : -0.3224
|
| 134 |
+
INFO:2024-09-24 01:02:38,270: train/s2s : 0.1941
|
| 135 |
+
INFO:2024-09-24 01:02:38,270: train/learning_rate: 0.0005
|
| 136 |
+
INFO:2024-09-24 01:02:38,270: eval/ctc : -0.3739
|
| 137 |
+
INFO:2024-09-24 01:02:38,270: eval/s2s : 0.1517
|
| 138 |
+
INFO:2024-09-24 01:02:38,270: eval/loss : -0.2222
|
| 139 |
+
INFO:2024-09-24 01:02:38,270: eval/wer : 0.2412
|
| 140 |
+
INFO:2024-09-24 01:02:38,270: eval/acc : 0.9536
|
| 141 |
+
INFO:2024-09-24 01:52:45,311: --- epoch 15 ---
|
| 142 |
+
INFO:2024-09-24 01:52:45,311: train/loss : -0.1365
|
| 143 |
+
INFO:2024-09-24 01:52:45,311: train/ctc : -0.3281
|
| 144 |
+
INFO:2024-09-24 01:52:45,311: train/s2s : 0.1916
|
| 145 |
+
INFO:2024-09-24 01:52:45,311: train/learning_rate: 0.0005
|
| 146 |
+
INFO:2024-09-24 01:52:45,311: eval/ctc : -0.3881
|
| 147 |
+
INFO:2024-09-24 01:52:45,312: eval/s2s : 0.1553
|
| 148 |
+
INFO:2024-09-24 01:52:45,312: eval/loss : -0.2328
|
| 149 |
+
INFO:2024-09-24 01:52:45,312: eval/wer : 0.2403
|
| 150 |
+
INFO:2024-09-24 01:52:45,312: eval/acc : 0.9528
|
| 151 |
+
INFO:2024-09-24 02:42:13,642: --- epoch 16 ---
|
| 152 |
+
INFO:2024-09-24 02:42:13,643: train/loss : -0.1434
|
| 153 |
+
INFO:2024-09-24 02:42:13,643: train/ctc : -0.3327
|
| 154 |
+
INFO:2024-09-24 02:42:13,643: train/s2s : 0.1893
|
| 155 |
+
INFO:2024-09-24 02:42:13,643: train/learning_rate: 0.0005
|
| 156 |
+
INFO:2024-09-24 02:42:13,643: eval/ctc : -0.3924
|
| 157 |
+
INFO:2024-09-24 02:42:13,643: eval/s2s : 0.1492
|
| 158 |
+
INFO:2024-09-24 02:42:13,643: eval/loss : -0.2432
|
| 159 |
+
INFO:2024-09-24 02:42:13,644: eval/wer : 0.2362
|
| 160 |
+
INFO:2024-09-24 02:42:13,644: eval/acc : 0.9537
|
| 161 |
+
INFO:2024-09-24 03:32:42,127: --- epoch 17 ---
|
| 162 |
+
INFO:2024-09-24 03:32:42,127: train/loss : -0.1498
|
| 163 |
+
INFO:2024-09-24 03:32:42,128: train/ctc : -0.3368
|
| 164 |
+
INFO:2024-09-24 03:32:42,128: train/s2s : 0.1870
|
| 165 |
+
INFO:2024-09-24 03:32:42,128: train/learning_rate: 0.0005
|
| 166 |
+
INFO:2024-09-24 03:32:42,128: eval/ctc : -0.3923
|
| 167 |
+
INFO:2024-09-24 03:32:42,128: eval/s2s : 0.1551
|
| 168 |
+
INFO:2024-09-24 03:32:42,128: eval/loss : -0.2372
|
| 169 |
+
INFO:2024-09-24 03:32:42,128: eval/wer : 0.2481
|
| 170 |
+
INFO:2024-09-24 03:32:42,128: eval/acc : 0.9509
|
| 171 |
+
INFO:2024-09-24 04:22:11,339: --- epoch 18 ---
|
| 172 |
+
INFO:2024-09-24 04:22:11,340: train/loss : -0.1526
|
| 173 |
+
INFO:2024-09-24 04:22:11,341: train/ctc : -0.3383
|
| 174 |
+
INFO:2024-09-24 04:22:11,341: train/s2s : 0.1857
|
| 175 |
+
INFO:2024-09-24 04:22:11,341: train/learning_rate: 0.0005
|
| 176 |
+
INFO:2024-09-24 04:22:11,341: eval/ctc : -0.4024
|
| 177 |
+
INFO:2024-09-24 04:22:11,341: eval/s2s : 0.1467
|
| 178 |
+
INFO:2024-09-24 04:22:11,341: eval/loss : -0.2557
|
| 179 |
+
INFO:2024-09-24 04:22:11,341: eval/wer : 0.2308
|
| 180 |
+
INFO:2024-09-24 04:22:11,341: eval/acc : 0.9573
|
| 181 |
+
INFO:2024-09-24 05:11:10,365: --- epoch 19 ---
|
| 182 |
+
INFO:2024-09-24 05:11:10,365: train/loss : -0.1561
|
| 183 |
+
INFO:2024-09-24 05:11:10,365: train/ctc : -0.3403
|
| 184 |
+
INFO:2024-09-24 05:11:10,365: train/s2s : 0.1842
|
| 185 |
+
INFO:2024-09-24 05:11:10,365: train/learning_rate: 0.0005
|
| 186 |
+
INFO:2024-09-24 05:11:10,365: eval/ctc : -0.3850
|
| 187 |
+
INFO:2024-09-24 05:11:10,365: eval/s2s : 0.1467
|
| 188 |
+
INFO:2024-09-24 05:11:10,365: eval/loss : -0.2382
|
| 189 |
+
INFO:2024-09-24 05:11:10,365: eval/wer : 0.2408
|
| 190 |
+
INFO:2024-09-24 05:11:10,365: eval/acc : 0.9554
|
| 191 |
+
INFO:2024-09-24 06:00:01,621: --- epoch 20 ---
|
| 192 |
+
INFO:2024-09-24 06:00:01,622: train/loss : -0.1614
|
| 193 |
+
INFO:2024-09-24 06:00:01,622: train/ctc : -0.3436
|
| 194 |
+
INFO:2024-09-24 06:00:01,622: train/s2s : 0.1822
|
| 195 |
+
INFO:2024-09-24 06:00:01,622: train/learning_rate: 0.0005
|
| 196 |
+
INFO:2024-09-24 06:00:01,623: eval/ctc : -0.3886
|
| 197 |
+
INFO:2024-09-24 06:00:01,623: eval/s2s : 0.1466
|
| 198 |
+
INFO:2024-09-24 06:00:01,623: eval/loss : -0.2420
|
| 199 |
+
INFO:2024-09-24 06:00:01,623: eval/wer : 0.2318
|
| 200 |
+
INFO:2024-09-24 06:00:01,623: eval/acc : 0.9566
|
| 201 |
+
INFO:2024-09-24 06:49:25,097: --- epoch 21 ---
|
| 202 |
+
INFO:2024-09-24 06:49:25,097: train/loss : -0.1680
|
| 203 |
+
INFO:2024-09-24 06:49:25,097: train/ctc : -0.3479
|
| 204 |
+
INFO:2024-09-24 06:49:25,097: train/s2s : 0.1799
|
| 205 |
+
INFO:2024-09-24 06:49:25,097: train/learning_rate: 0.0005
|
| 206 |
+
INFO:2024-09-24 06:49:25,097: eval/ctc : -0.3964
|
| 207 |
+
INFO:2024-09-24 06:49:25,097: eval/s2s : 0.1421
|
| 208 |
+
INFO:2024-09-24 06:49:25,097: eval/loss : -0.2543
|
| 209 |
+
INFO:2024-09-24 06:49:25,098: eval/wer : 0.2415
|
| 210 |
+
INFO:2024-09-24 06:49:25,098: eval/acc : 0.9583
|
| 211 |
+
INFO:2024-09-24 07:38:26,722: --- epoch 22 ---
|
| 212 |
+
INFO:2024-09-24 07:38:26,722: train/loss : -0.1717
|
| 213 |
+
INFO:2024-09-24 07:38:26,722: train/ctc : -0.3505
|
| 214 |
+
INFO:2024-09-24 07:38:26,723: train/s2s : 0.1788
|
| 215 |
+
INFO:2024-09-24 07:38:26,723: train/learning_rate: 0.0005
|
| 216 |
+
INFO:2024-09-24 07:38:26,723: eval/ctc : -0.4075
|
| 217 |
+
INFO:2024-09-24 07:38:26,723: eval/s2s : 0.1416
|
| 218 |
+
INFO:2024-09-24 07:38:26,723: eval/loss : -0.2659
|
| 219 |
+
INFO:2024-09-24 07:38:26,723: eval/wer : 0.2313
|
| 220 |
+
INFO:2024-09-24 07:38:26,723: eval/acc : 0.9574
|
| 221 |
+
INFO:2024-09-24 08:27:58,314: --- epoch 23 ---
|
| 222 |
+
INFO:2024-09-24 08:27:58,315: train/loss : -0.1741
|
| 223 |
+
INFO:2024-09-24 08:27:58,315: train/ctc : -0.3518
|
| 224 |
+
INFO:2024-09-24 08:27:58,315: train/s2s : 0.1777
|
| 225 |
+
INFO:2024-09-24 08:27:58,315: train/learning_rate: 0.0005
|
| 226 |
+
INFO:2024-09-24 08:27:58,315: eval/ctc : -0.4053
|
| 227 |
+
INFO:2024-09-24 08:27:58,315: eval/s2s : 0.1513
|
| 228 |
+
INFO:2024-09-24 08:27:58,315: eval/loss : -0.2540
|
| 229 |
+
INFO:2024-09-24 08:27:58,315: eval/wer : 0.2265
|
| 230 |
+
INFO:2024-09-24 08:27:58,315: eval/acc : 0.9540
|
| 231 |
+
INFO:2024-09-24 09:17:30,089: --- epoch 24 ---
|
| 232 |
+
INFO:2024-09-24 09:17:30,089: train/loss : -0.1789
|
| 233 |
+
INFO:2024-09-24 09:17:30,089: train/ctc : -0.3551
|
| 234 |
+
INFO:2024-09-24 09:17:30,089: train/s2s : 0.1762
|
| 235 |
+
INFO:2024-09-24 09:17:30,089: train/learning_rate: 0.0005
|
| 236 |
+
INFO:2024-09-24 09:17:30,089: eval/ctc : -0.4020
|
| 237 |
+
INFO:2024-09-24 09:17:30,089: eval/s2s : 0.1384
|
| 238 |
+
INFO:2024-09-24 09:17:30,089: eval/loss : -0.2636
|
| 239 |
+
INFO:2024-09-24 09:17:30,089: eval/wer : 0.2390
|
| 240 |
+
INFO:2024-09-24 09:17:30,089: eval/acc : 0.9574
|
| 241 |
+
INFO:2024-09-24 10:07:47,673: --- epoch 25 ---
|
| 242 |
+
INFO:2024-09-24 10:07:47,674: train/loss : -0.1802
|
| 243 |
+
INFO:2024-09-24 10:07:47,674: train/ctc : -0.3557
|
| 244 |
+
INFO:2024-09-24 10:07:47,674: train/s2s : 0.1755
|
| 245 |
+
INFO:2024-09-24 10:07:47,674: train/learning_rate: 0.0005
|
| 246 |
+
INFO:2024-09-24 10:07:47,674: eval/ctc : -0.3983
|
| 247 |
+
INFO:2024-09-24 10:07:47,674: eval/s2s : 0.1550
|
| 248 |
+
INFO:2024-09-24 10:07:47,674: eval/loss : -0.2432
|
| 249 |
+
INFO:2024-09-24 10:07:47,674: eval/wer : 0.2311
|
| 250 |
+
INFO:2024-09-24 10:07:47,674: eval/acc : 0.9550
|
| 251 |
+
INFO:2024-09-24 10:58:02,699: --- epoch 26 ---
|
| 252 |
+
INFO:2024-09-24 10:58:02,699: train/loss : -0.1840
|
| 253 |
+
INFO:2024-09-24 10:58:02,699: train/ctc : -0.3582
|
| 254 |
+
INFO:2024-09-24 10:58:02,699: train/s2s : 0.1741
|
| 255 |
+
INFO:2024-09-24 10:58:02,700: train/learning_rate: 0.0005
|
| 256 |
+
INFO:2024-09-24 10:58:02,700: eval/ctc : -0.3950
|
| 257 |
+
INFO:2024-09-24 10:58:02,700: eval/s2s : 0.1421
|
| 258 |
+
INFO:2024-09-24 10:58:02,700: eval/loss : -0.2529
|
| 259 |
+
INFO:2024-09-24 10:58:02,700: eval/wer : 0.2354
|
| 260 |
+
INFO:2024-09-24 10:58:02,700: eval/acc : 0.9577
|
| 261 |
+
INFO:2024-09-24 11:47:16,432: --- epoch 27 ---
|
| 262 |
+
INFO:2024-09-24 11:47:16,432: train/loss : -0.1863
|
| 263 |
+
INFO:2024-09-24 11:47:16,432: train/ctc : -0.3595
|
| 264 |
+
INFO:2024-09-24 11:47:16,432: train/s2s : 0.1732
|
| 265 |
+
INFO:2024-09-24 11:47:16,432: train/learning_rate: 0.0005
|
| 266 |
+
INFO:2024-09-24 11:47:16,432: eval/ctc : -0.3952
|
| 267 |
+
INFO:2024-09-24 11:47:16,432: eval/s2s : 0.1455
|
| 268 |
+
INFO:2024-09-24 11:47:16,432: eval/loss : -0.2497
|
| 269 |
+
INFO:2024-09-24 11:47:16,432: eval/wer : 0.2418
|
| 270 |
+
INFO:2024-09-24 11:47:16,432: eval/acc : 0.9577
|
| 271 |
+
INFO:2024-09-24 12:35:43,298: --- epoch 28 ---
|
| 272 |
+
INFO:2024-09-24 12:35:43,298: train/loss : -0.1894
|
| 273 |
+
INFO:2024-09-24 12:35:43,298: train/ctc : -0.3615
|
| 274 |
+
INFO:2024-09-24 12:35:43,298: train/s2s : 0.1721
|
| 275 |
+
INFO:2024-09-24 12:35:43,298: train/learning_rate: 0.0005
|
| 276 |
+
INFO:2024-09-24 12:35:43,298: eval/ctc : -0.4107
|
| 277 |
+
INFO:2024-09-24 12:35:43,299: eval/s2s : 0.1382
|
| 278 |
+
INFO:2024-09-24 12:35:43,299: eval/loss : -0.2724
|
| 279 |
+
INFO:2024-09-24 12:35:43,299: eval/wer : 0.2255
|
| 280 |
+
INFO:2024-09-24 12:35:43,299: eval/acc : 0.9567
|
| 281 |
+
INFO:2024-09-24 13:24:12,053: --- epoch 29 ---
|
| 282 |
+
INFO:2024-09-24 13:24:12,053: train/loss : -0.1910
|
| 283 |
+
INFO:2024-09-24 13:24:12,054: train/ctc : -0.3626
|
| 284 |
+
INFO:2024-09-24 13:24:12,054: train/s2s : 0.1716
|
| 285 |
+
INFO:2024-09-24 13:24:12,054: train/learning_rate: 0.0005
|
| 286 |
+
INFO:2024-09-24 13:24:12,054: eval/ctc : -0.4031
|
| 287 |
+
INFO:2024-09-24 13:24:12,054: eval/s2s : 0.1438
|
| 288 |
+
INFO:2024-09-24 13:24:12,054: eval/loss : -0.2593
|
| 289 |
+
INFO:2024-09-24 13:24:12,054: eval/wer : 0.2311
|
| 290 |
+
INFO:2024-09-24 13:24:12,054: eval/acc : 0.9576
|
| 291 |
+
INFO:2024-09-24 14:12:50,449: --- epoch 30 ---
|
| 292 |
+
INFO:2024-09-24 14:12:50,450: train/loss : -0.1959
|
| 293 |
+
INFO:2024-09-24 14:12:50,450: train/ctc : -0.3658
|
| 294 |
+
INFO:2024-09-24 14:12:50,450: train/s2s : 0.1699
|
| 295 |
+
INFO:2024-09-24 14:12:50,450: train/learning_rate: 0.0005
|
| 296 |
+
INFO:2024-09-24 14:12:50,450: eval/ctc : -0.4055
|
| 297 |
+
INFO:2024-09-24 14:12:50,450: eval/s2s : 0.1437
|
| 298 |
+
INFO:2024-09-24 14:12:50,450: eval/loss : -0.2618
|
| 299 |
+
INFO:2024-09-24 14:12:50,450: eval/wer : 0.2231
|
| 300 |
+
INFO:2024-09-24 14:12:50,450: eval/acc : 0.9583
|
| 301 |
+
INFO:2024-09-24 15:01:09,782: --- epoch 31 ---
|
| 302 |
+
INFO:2024-09-24 15:01:09,782: train/loss : -0.1955
|
| 303 |
+
INFO:2024-09-24 15:01:09,782: train/ctc : -0.3658
|
| 304 |
+
INFO:2024-09-24 15:01:09,782: train/s2s : 0.1703
|
| 305 |
+
INFO:2024-09-24 15:01:09,783: train/learning_rate: 0.0005
|
| 306 |
+
INFO:2024-09-24 15:01:09,783: eval/ctc : -0.4246
|
| 307 |
+
INFO:2024-09-24 15:01:09,783: eval/s2s : 0.1362
|
| 308 |
+
INFO:2024-09-24 15:01:09,783: eval/loss : -0.2885
|
| 309 |
+
INFO:2024-09-24 15:01:09,783: eval/wer : 0.2223
|
| 310 |
+
INFO:2024-09-24 15:01:09,783: eval/acc : 0.9585
|
| 311 |
+
INFO:2024-09-24 15:50:27,311: --- epoch 32 ---
|
| 312 |
+
INFO:2024-09-24 15:50:27,311: train/loss : -0.1992
|
| 313 |
+
INFO:2024-09-24 15:50:27,312: train/ctc : -0.3680
|
| 314 |
+
INFO:2024-09-24 15:50:27,312: train/s2s : 0.1688
|
| 315 |
+
INFO:2024-09-24 15:50:27,312: train/learning_rate: 0.0005
|
| 316 |
+
INFO:2024-09-24 15:50:27,312: eval/ctc : -0.4035
|
| 317 |
+
INFO:2024-09-24 15:50:27,312: eval/s2s : 0.1519
|
| 318 |
+
INFO:2024-09-24 15:50:27,312: eval/loss : -0.2517
|
| 319 |
+
INFO:2024-09-24 15:50:27,312: eval/wer : 0.2268
|
| 320 |
+
INFO:2024-09-24 15:50:27,312: eval/acc : 0.9546
|
| 321 |
+
INFO:2024-09-24 16:39:28,362: --- epoch 33 ---
|
| 322 |
+
INFO:2024-09-24 16:39:28,362: train/loss : -0.2016
|
| 323 |
+
INFO:2024-09-24 16:39:28,362: train/ctc : -0.3694
|
| 324 |
+
INFO:2024-09-24 16:39:28,362: train/s2s : 0.1678
|
| 325 |
+
INFO:2024-09-24 16:39:28,362: train/learning_rate: 0.0005
|
| 326 |
+
INFO:2024-09-24 16:39:28,362: eval/ctc : -0.4138
|
| 327 |
+
INFO:2024-09-24 16:39:28,363: eval/s2s : 0.1389
|
| 328 |
+
INFO:2024-09-24 16:39:28,363: eval/loss : -0.2749
|
| 329 |
+
INFO:2024-09-24 16:39:28,363: eval/wer : 0.2205
|
| 330 |
+
INFO:2024-09-24 16:39:28,363: eval/acc : 0.9580
|
| 331 |
+
INFO:2024-09-24 17:28:17,348: --- epoch 34 ---
|
| 332 |
+
INFO:2024-09-24 17:28:17,349: train/loss : -0.2050
|
| 333 |
+
INFO:2024-09-24 17:28:17,349: train/ctc : -0.3717
|
| 334 |
+
INFO:2024-09-24 17:28:17,349: train/s2s : 0.1668
|
| 335 |
+
INFO:2024-09-24 17:28:17,349: train/learning_rate: 0.0005
|
| 336 |
+
INFO:2024-09-24 17:28:17,349: eval/ctc : -0.4151
|
| 337 |
+
INFO:2024-09-24 17:28:17,349: eval/s2s : 0.1455
|
| 338 |
+
INFO:2024-09-24 17:28:17,349: eval/loss : -0.2696
|
| 339 |
+
INFO:2024-09-24 17:28:17,349: eval/wer : 0.2366
|
| 340 |
+
INFO:2024-09-24 17:28:17,349: eval/acc : 0.9572
|
| 341 |
+
INFO:2024-09-24 18:16:55,716: --- epoch 35 ---
|
| 342 |
+
INFO:2024-09-24 18:16:55,717: train/loss : -0.2069
|
| 343 |
+
INFO:2024-09-24 18:16:55,717: train/ctc : -0.3735
|
| 344 |
+
INFO:2024-09-24 18:16:55,717: train/s2s : 0.1665
|
| 345 |
+
INFO:2024-09-24 18:16:55,717: train/learning_rate: 0.0005
|
| 346 |
+
INFO:2024-09-24 18:16:55,717: eval/ctc : -0.4041
|
| 347 |
+
INFO:2024-09-24 18:16:55,717: eval/s2s : 0.1406
|
| 348 |
+
INFO:2024-09-24 18:16:55,717: eval/loss : -0.2636
|
| 349 |
+
INFO:2024-09-24 18:16:55,717: eval/wer : 0.2274
|
| 350 |
+
INFO:2024-09-24 18:16:55,717: eval/acc : 0.9565
|
| 351 |
+
INFO:2024-09-24 19:05:39,337: --- epoch 36 ---
|
| 352 |
+
INFO:2024-09-24 19:05:39,338: train/loss : -0.2093
|
| 353 |
+
INFO:2024-09-24 19:05:39,338: train/ctc : -0.3744
|
| 354 |
+
INFO:2024-09-24 19:05:39,339: train/s2s : 0.1651
|
| 355 |
+
INFO:2024-09-24 19:05:39,339: train/learning_rate: 0.0005
|
| 356 |
+
INFO:2024-09-24 19:05:39,339: eval/ctc : -0.4197
|
| 357 |
+
INFO:2024-09-24 19:05:39,339: eval/s2s : 0.1468
|
| 358 |
+
INFO:2024-09-24 19:05:39,339: eval/loss : -0.2729
|
| 359 |
+
INFO:2024-09-24 19:05:39,339: eval/wer : 0.2170
|
| 360 |
+
INFO:2024-09-24 19:05:39,339: eval/acc : 0.9572
|
| 361 |
+
INFO:2024-09-24 19:55:33,763: --- epoch 37 ---
|
| 362 |
+
INFO:2024-09-24 19:55:33,764: train/loss : -0.2109
|
| 363 |
+
INFO:2024-09-24 19:55:33,764: train/ctc : -0.3754
|
| 364 |
+
INFO:2024-09-24 19:55:33,764: train/s2s : 0.1645
|
| 365 |
+
INFO:2024-09-24 19:55:33,764: train/learning_rate: 0.0005
|
| 366 |
+
INFO:2024-09-24 19:55:33,764: eval/ctc : -0.4025
|
| 367 |
+
INFO:2024-09-24 19:55:33,764: eval/s2s : 0.1356
|
| 368 |
+
INFO:2024-09-24 19:55:33,764: eval/loss : -0.2669
|
| 369 |
+
INFO:2024-09-24 19:55:33,764: eval/wer : 0.2340
|
| 370 |
+
INFO:2024-09-24 19:55:33,764: eval/acc : 0.9578
|
| 371 |
+
INFO:2024-09-24 20:44:18,407: --- epoch 38 ---
|
| 372 |
+
INFO:2024-09-24 20:44:18,408: train/loss : -0.2140
|
| 373 |
+
INFO:2024-09-24 20:44:18,408: train/ctc : -0.3776
|
| 374 |
+
INFO:2024-09-24 20:44:18,408: train/s2s : 0.1636
|
| 375 |
+
INFO:2024-09-24 20:44:18,408: train/learning_rate: 0.0005
|
| 376 |
+
INFO:2024-09-24 20:44:18,408: eval/ctc : -0.4119
|
| 377 |
+
INFO:2024-09-24 20:44:18,408: eval/s2s : 0.1404
|
| 378 |
+
INFO:2024-09-24 20:44:18,408: eval/loss : -0.2715
|
| 379 |
+
INFO:2024-09-24 20:44:18,408: eval/wer : 0.2270
|
| 380 |
+
INFO:2024-09-24 20:44:18,409: eval/acc : 0.9575
|
| 381 |
+
INFO:2024-09-24 21:33:17,828: --- epoch 39 ---
|
| 382 |
+
INFO:2024-09-24 21:33:17,828: train/loss : -0.2170
|
| 383 |
+
INFO:2024-09-24 21:33:17,828: train/ctc : -0.3798
|
| 384 |
+
INFO:2024-09-24 21:33:17,828: train/s2s : 0.1628
|
| 385 |
+
INFO:2024-09-24 21:33:17,828: train/learning_rate: 0.0005
|
| 386 |
+
INFO:2024-09-24 21:33:17,828: eval/ctc : -0.4135
|
| 387 |
+
INFO:2024-09-24 21:33:17,829: eval/s2s : 0.1406
|
| 388 |
+
INFO:2024-09-24 21:33:17,829: eval/loss : -0.2729
|
| 389 |
+
INFO:2024-09-24 21:33:17,829: eval/wer : 0.2288
|
| 390 |
+
INFO:2024-09-24 21:33:17,829: eval/acc : 0.9593
|
| 391 |
+
INFO:2024-09-24 22:22:13,265: --- epoch 40 ---
|
| 392 |
+
INFO:2024-09-24 22:22:13,265: train/loss : -0.2196
|
| 393 |
+
INFO:2024-09-24 22:22:13,265: train/ctc : -0.3817
|
| 394 |
+
INFO:2024-09-24 22:22:13,266: train/s2s : 0.1621
|
| 395 |
+
INFO:2024-09-24 22:22:13,266: train/learning_rate: 0.0005
|
| 396 |
+
INFO:2024-09-24 22:22:13,266: eval/ctc : -0.4173
|
| 397 |
+
INFO:2024-09-24 22:22:13,266: eval/s2s : 0.1409
|
| 398 |
+
INFO:2024-09-24 22:22:13,266: eval/loss : -0.2764
|
| 399 |
+
INFO:2024-09-24 22:22:13,266: eval/wer : 0.2236
|
| 400 |
+
INFO:2024-09-24 22:22:13,266: eval/acc : 0.9599
|
| 401 |
+
INFO:2024-09-24 23:10:52,651: --- epoch 41 ---
|
| 402 |
+
INFO:2024-09-24 23:10:52,651: train/loss : -0.2221
|
| 403 |
+
INFO:2024-09-24 23:10:52,651: train/ctc : -0.3831
|
| 404 |
+
INFO:2024-09-24 23:10:52,651: train/s2s : 0.1609
|
| 405 |
+
INFO:2024-09-24 23:10:52,651: train/learning_rate: 0.0005
|
| 406 |
+
INFO:2024-09-24 23:10:52,651: eval/ctc : -0.4158
|
| 407 |
+
INFO:2024-09-24 23:10:52,651: eval/s2s : 0.1330
|
| 408 |
+
INFO:2024-09-24 23:10:52,651: eval/loss : -0.2828
|
| 409 |
+
INFO:2024-09-24 23:10:52,651: eval/wer : 0.2297
|
| 410 |
+
INFO:2024-09-24 23:10:52,652: eval/acc : 0.9611
|
| 411 |
+
INFO:2024-09-25 00:00:03,302: --- epoch 42 ---
|
| 412 |
+
INFO:2024-09-25 00:00:03,302: train/loss : -0.2211
|
| 413 |
+
INFO:2024-09-25 00:00:03,303: train/ctc : -0.3821
|
| 414 |
+
INFO:2024-09-25 00:00:03,303: train/s2s : 0.1610
|
| 415 |
+
INFO:2024-09-25 00:00:03,303: train/learning_rate: 0.0004
|
| 416 |
+
INFO:2024-09-25 00:00:03,303: eval/ctc : -0.4012
|
| 417 |
+
INFO:2024-09-25 00:00:03,303: eval/s2s : 0.1388
|
| 418 |
+
INFO:2024-09-25 00:00:03,303: eval/loss : -0.2625
|
| 419 |
+
INFO:2024-09-25 00:00:03,303: eval/wer : 0.2189
|
| 420 |
+
INFO:2024-09-25 00:00:03,303: eval/acc : 0.9573
|
| 421 |
+
INFO:2024-09-25 00:50:19,302: --- epoch 43 ---
|
| 422 |
+
INFO:2024-09-25 00:50:19,302: train/loss : -0.2225
|
| 423 |
+
INFO:2024-09-25 00:50:19,302: train/ctc : -0.3832
|
| 424 |
+
INFO:2024-09-25 00:50:19,302: train/s2s : 0.1607
|
| 425 |
+
INFO:2024-09-25 00:50:19,302: train/learning_rate: 0.0004
|
| 426 |
+
INFO:2024-09-25 00:50:19,302: eval/ctc : -0.4199
|
| 427 |
+
INFO:2024-09-25 00:50:19,302: eval/s2s : 0.1461
|
| 428 |
+
INFO:2024-09-25 00:50:19,302: eval/loss : -0.2738
|
| 429 |
+
INFO:2024-09-25 00:50:19,302: eval/wer : 0.2154
|
| 430 |
+
INFO:2024-09-25 00:50:19,302: eval/acc : 0.9560
|
| 431 |
+
INFO:2024-09-25 01:41:26,414: --- epoch 44 ---
|
| 432 |
+
INFO:2024-09-25 01:41:26,414: train/loss : -0.2230
|
| 433 |
+
INFO:2024-09-25 01:41:26,415: train/ctc : -0.3835
|
| 434 |
+
INFO:2024-09-25 01:41:26,415: train/s2s : 0.1605
|
| 435 |
+
INFO:2024-09-25 01:41:26,415: train/learning_rate: 0.0004
|
| 436 |
+
INFO:2024-09-25 01:41:26,415: eval/ctc : -0.4205
|
| 437 |
+
INFO:2024-09-25 01:41:26,415: eval/s2s : 0.1364
|
| 438 |
+
INFO:2024-09-25 01:41:26,415: eval/loss : -0.2841
|
| 439 |
+
INFO:2024-09-25 01:41:26,415: eval/wer : 0.2317
|
| 440 |
+
INFO:2024-09-25 01:41:26,415: eval/acc : 0.9605
|
| 441 |
+
INFO:2024-09-25 02:32:24,406: --- epoch 45 ---
|
| 442 |
+
INFO:2024-09-25 02:32:24,406: train/loss : -0.2244
|
| 443 |
+
INFO:2024-09-25 02:32:24,407: train/ctc : -0.3847
|
| 444 |
+
INFO:2024-09-25 02:32:24,407: train/s2s : 0.1603
|
| 445 |
+
INFO:2024-09-25 02:32:24,407: train/learning_rate: 0.0004
|
| 446 |
+
INFO:2024-09-25 02:32:24,407: eval/ctc : -0.4281
|
| 447 |
+
INFO:2024-09-25 02:32:24,407: eval/s2s : 0.1401
|
| 448 |
+
INFO:2024-09-25 02:32:24,407: eval/loss : -0.2880
|
| 449 |
+
INFO:2024-09-25 02:32:24,407: eval/wer : 0.2251
|
| 450 |
+
INFO:2024-09-25 02:32:24,407: eval/acc : 0.9602
|
| 451 |
+
INFO:2024-09-25 03:22:21,283: --- epoch 46 ---
|
| 452 |
+
INFO:2024-09-25 03:22:21,283: train/loss : -0.2257
|
| 453 |
+
INFO:2024-09-25 03:22:21,283: train/ctc : -0.3853
|
| 454 |
+
INFO:2024-09-25 03:22:21,283: train/s2s : 0.1596
|
| 455 |
+
INFO:2024-09-25 03:22:21,283: train/learning_rate: 0.0004
|
| 456 |
+
INFO:2024-09-25 03:22:21,284: eval/ctc : -0.4105
|
| 457 |
+
INFO:2024-09-25 03:22:21,284: eval/s2s : 0.1442
|
| 458 |
+
INFO:2024-09-25 03:22:21,284: eval/loss : -0.2663
|
| 459 |
+
INFO:2024-09-25 03:22:21,284: eval/wer : 0.2228
|
| 460 |
+
INFO:2024-09-25 03:22:21,284: eval/acc : 0.9591
|
| 461 |
+
INFO:2024-09-25 04:11:33,625: --- epoch 47 ---
|
| 462 |
+
INFO:2024-09-25 04:11:33,625: train/loss : -0.2261
|
| 463 |
+
INFO:2024-09-25 04:11:33,625: train/ctc : -0.3848
|
| 464 |
+
INFO:2024-09-25 04:11:33,625: train/s2s : 0.1587
|
| 465 |
+
INFO:2024-09-25 04:11:33,625: train/learning_rate: 0.0004
|
| 466 |
+
INFO:2024-09-25 04:11:33,625: eval/ctc : -0.4260
|
| 467 |
+
INFO:2024-09-25 04:11:33,625: eval/s2s : 0.1324
|
| 468 |
+
INFO:2024-09-25 04:11:33,625: eval/loss : -0.2936
|
| 469 |
+
INFO:2024-09-25 04:11:33,625: eval/wer : 0.2158
|
| 470 |
+
INFO:2024-09-25 04:11:33,625: eval/acc : 0.9613
|
| 471 |
+
INFO:2024-09-25 05:00:47,470: --- epoch 48 ---
|
| 472 |
+
INFO:2024-09-25 05:00:47,470: train/loss : -0.2306
|
| 473 |
+
INFO:2024-09-25 05:00:47,470: train/ctc : -0.3883
|
| 474 |
+
INFO:2024-09-25 05:00:47,470: train/s2s : 0.1577
|
| 475 |
+
INFO:2024-09-25 05:00:47,470: train/learning_rate: 0.0004
|
| 476 |
+
INFO:2024-09-25 05:00:47,470: eval/ctc : -0.4255
|
| 477 |
+
INFO:2024-09-25 05:00:47,470: eval/s2s : 0.1403
|
| 478 |
+
INFO:2024-09-25 05:00:47,470: eval/loss : -0.2852
|
| 479 |
+
INFO:2024-09-25 05:00:47,470: eval/wer : 0.2003
|
| 480 |
+
INFO:2024-09-25 05:00:47,471: eval/acc : 0.9597
|
| 481 |
+
INFO:2024-09-25 05:49:49,507: --- epoch 49 ---
|
| 482 |
+
INFO:2024-09-25 05:49:49,507: train/loss : -0.2306
|
| 483 |
+
INFO:2024-09-25 05:49:49,508: train/ctc : -0.3883
|
| 484 |
+
INFO:2024-09-25 05:49:49,508: train/s2s : 0.1577
|
| 485 |
+
INFO:2024-09-25 05:49:49,508: train/learning_rate: 0.0004
|
| 486 |
+
INFO:2024-09-25 05:49:49,508: eval/ctc : -0.4128
|
| 487 |
+
INFO:2024-09-25 05:49:49,508: eval/s2s : 0.1353
|
| 488 |
+
INFO:2024-09-25 05:49:49,508: eval/loss : -0.2775
|
| 489 |
+
INFO:2024-09-25 05:49:49,508: eval/wer : 0.2175
|
| 490 |
+
INFO:2024-09-25 05:49:49,508: eval/acc : 0.9617
|
| 491 |
+
INFO:2024-09-25 06:38:51,990: --- epoch 50 ---
|
| 492 |
+
INFO:2024-09-25 06:38:51,990: train/loss : -0.2331
|
| 493 |
+
INFO:2024-09-25 06:38:51,990: train/ctc : -0.3901
|
| 494 |
+
INFO:2024-09-25 06:38:51,990: train/s2s : 0.1570
|
| 495 |
+
INFO:2024-09-25 06:38:51,990: train/learning_rate: 0.0004
|
| 496 |
+
INFO:2024-09-25 06:38:51,990: eval/ctc : -0.4257
|
| 497 |
+
INFO:2024-09-25 06:38:51,990: eval/s2s : 0.1339
|
| 498 |
+
INFO:2024-09-25 06:38:51,991: eval/loss : -0.2918
|
| 499 |
+
INFO:2024-09-25 06:38:51,991: eval/wer : 0.2064
|
| 500 |
+
INFO:2024-09-25 06:38:51,991: eval/acc : 0.9610
|
| 501 |
+
INFO:2024-09-25 07:27:48,889: --- epoch 51 ---
|
| 502 |
+
INFO:2024-09-25 07:27:48,890: train/loss : -0.2361
|
| 503 |
+
INFO:2024-09-25 07:27:48,890: train/ctc : -0.3923
|
| 504 |
+
INFO:2024-09-25 07:27:48,890: train/s2s : 0.1562
|
| 505 |
+
INFO:2024-09-25 07:27:48,890: train/learning_rate: 0.0004
|
| 506 |
+
INFO:2024-09-25 07:27:48,890: eval/ctc : -0.4243
|
| 507 |
+
INFO:2024-09-25 07:27:48,890: eval/s2s : 0.1421
|
| 508 |
+
INFO:2024-09-25 07:27:48,890: eval/loss : -0.2821
|
| 509 |
+
INFO:2024-09-25 07:27:48,890: eval/wer : 0.2165
|
| 510 |
+
INFO:2024-09-25 07:27:48,890: eval/acc : 0.9592
|
| 511 |
+
INFO:2024-09-25 08:16:49,981: --- epoch 52 ---
|
| 512 |
+
INFO:2024-09-25 08:16:49,981: train/loss : -0.2357
|
| 513 |
+
INFO:2024-09-25 08:16:49,982: train/ctc : -0.3916
|
| 514 |
+
INFO:2024-09-25 08:16:49,982: train/s2s : 0.1559
|
| 515 |
+
INFO:2024-09-25 08:16:49,982: train/learning_rate: 0.0004
|
| 516 |
+
INFO:2024-09-25 08:16:49,982: eval/ctc : -0.4176
|
| 517 |
+
INFO:2024-09-25 08:16:49,982: eval/s2s : 0.1423
|
| 518 |
+
INFO:2024-09-25 08:16:49,982: eval/loss : -0.2753
|
| 519 |
+
INFO:2024-09-25 08:16:49,982: eval/wer : 0.2188
|
| 520 |
+
INFO:2024-09-25 08:16:49,983: eval/acc : 0.9590
|
| 521 |
+
INFO:2024-09-25 09:05:27,237: --- epoch 53 ---
|
| 522 |
+
INFO:2024-09-25 09:05:27,237: train/loss : -0.2376
|
| 523 |
+
INFO:2024-09-25 09:05:27,237: train/ctc : -0.3930
|
| 524 |
+
INFO:2024-09-25 09:05:27,237: train/s2s : 0.1554
|
| 525 |
+
INFO:2024-09-25 09:05:27,237: train/learning_rate: 0.0004
|
| 526 |
+
INFO:2024-09-25 09:05:27,237: eval/ctc : -0.3972
|
| 527 |
+
INFO:2024-09-25 09:05:27,237: eval/s2s : 0.1406
|
| 528 |
+
INFO:2024-09-25 09:05:27,237: eval/loss : -0.2566
|
| 529 |
+
INFO:2024-09-25 09:05:27,237: eval/wer : 0.2179
|
| 530 |
+
INFO:2024-09-25 09:05:27,238: eval/acc : 0.9575
|
| 531 |
+
INFO:2024-09-25 09:54:01,738: --- epoch 54 ---
|
| 532 |
+
INFO:2024-09-25 09:54:01,739: train/loss : -0.2388
|
| 533 |
+
INFO:2024-09-25 09:54:01,739: train/ctc : -0.3936
|
| 534 |
+
INFO:2024-09-25 09:54:01,739: train/s2s : 0.1548
|
| 535 |
+
INFO:2024-09-25 09:54:01,739: train/learning_rate: 0.0004
|
| 536 |
+
INFO:2024-09-25 09:54:01,739: eval/ctc : -0.4240
|
| 537 |
+
INFO:2024-09-25 09:54:01,739: eval/s2s : 0.1334
|
| 538 |
+
INFO:2024-09-25 09:54:01,739: eval/loss : -0.2906
|
| 539 |
+
INFO:2024-09-25 09:54:01,739: eval/wer : 0.2284
|
| 540 |
+
INFO:2024-09-25 09:54:01,739: eval/acc : 0.9577
|
| 541 |
+
INFO:2024-09-25 10:42:30,055: --- epoch 55 ---
|
| 542 |
+
INFO:2024-09-25 10:42:30,055: train/loss : -0.2399
|
| 543 |
+
INFO:2024-09-25 10:42:30,055: train/ctc : -0.3943
|
| 544 |
+
INFO:2024-09-25 10:42:30,055: train/s2s : 0.1544
|
| 545 |
+
INFO:2024-09-25 10:42:30,056: train/learning_rate: 0.0004
|
| 546 |
+
INFO:2024-09-25 10:42:30,056: eval/ctc : -0.4040
|
| 547 |
+
INFO:2024-09-25 10:42:30,056: eval/s2s : 0.1370
|
| 548 |
+
INFO:2024-09-25 10:42:30,056: eval/loss : -0.2669
|
| 549 |
+
INFO:2024-09-25 10:42:30,056: eval/wer : 0.2381
|
| 550 |
+
INFO:2024-09-25 10:42:30,056: eval/acc : 0.9575
|
| 551 |
+
INFO:2024-09-25 11:30:54,485: --- epoch 56 ---
|
| 552 |
+
INFO:2024-09-25 11:30:54,485: train/loss : -0.2414
|
| 553 |
+
INFO:2024-09-25 11:30:54,485: train/ctc : -0.3954
|
| 554 |
+
INFO:2024-09-25 11:30:54,485: train/s2s : 0.1540
|
| 555 |
+
INFO:2024-09-25 11:30:54,485: train/learning_rate: 0.0004
|
| 556 |
+
INFO:2024-09-25 11:30:54,486: eval/ctc : -0.4261
|
| 557 |
+
INFO:2024-09-25 11:30:54,486: eval/s2s : 0.1329
|
| 558 |
+
INFO:2024-09-25 11:30:54,486: eval/loss : -0.2932
|
| 559 |
+
INFO:2024-09-25 11:30:54,486: eval/wer : 0.2154
|
| 560 |
+
INFO:2024-09-25 11:30:54,486: eval/acc : 0.9609
|
| 561 |
+
INFO:2024-09-25 12:19:23,338: --- epoch 57 ---
|
| 562 |
+
INFO:2024-09-25 12:19:23,338: train/loss : -0.2354
|
| 563 |
+
INFO:2024-09-25 12:19:23,338: train/ctc : -0.3929
|
| 564 |
+
INFO:2024-09-25 12:19:23,338: train/s2s : 0.1576
|
| 565 |
+
INFO:2024-09-25 12:19:23,338: train/learning_rate: 0.0004
|
| 566 |
+
INFO:2024-09-25 12:19:23,338: eval/ctc : -0.3468
|
| 567 |
+
INFO:2024-09-25 12:19:23,338: eval/s2s : 0.2053
|
| 568 |
+
INFO:2024-09-25 12:19:23,338: eval/loss : -0.1415
|
| 569 |
+
INFO:2024-09-25 12:19:23,338: eval/wer : 0.2184
|
| 570 |
+
INFO:2024-09-25 12:19:23,338: eval/acc : 0.9420
|
| 571 |
+
INFO:2024-09-25 13:07:50,990: --- epoch 58 ---
|
| 572 |
+
INFO:2024-09-25 13:07:50,991: train/loss : -0.2102
|
| 573 |
+
INFO:2024-09-25 13:07:50,991: train/ctc : -0.3789
|
| 574 |
+
INFO:2024-09-25 13:07:50,991: train/s2s : 0.1686
|
| 575 |
+
INFO:2024-09-25 13:07:50,991: train/learning_rate: 0.0004
|
| 576 |
+
INFO:2024-09-25 13:07:50,991: eval/ctc : -0.4209
|
| 577 |
+
INFO:2024-09-25 13:07:50,991: eval/s2s : 0.1372
|
| 578 |
+
INFO:2024-09-25 13:07:50,991: eval/loss : -0.2836
|
| 579 |
+
INFO:2024-09-25 13:07:50,992: eval/wer : 0.2153
|
| 580 |
+
INFO:2024-09-25 13:07:50,992: eval/acc : 0.9594
|
| 581 |
+
INFO:2024-09-25 13:56:30,348: --- epoch 59 ---
|
| 582 |
+
INFO:2024-09-25 13:56:30,348: train/loss : -0.2445
|
| 583 |
+
INFO:2024-09-25 13:56:30,348: train/ctc : -0.3977
|
| 584 |
+
INFO:2024-09-25 13:56:30,348: train/s2s : 0.1533
|
| 585 |
+
INFO:2024-09-25 13:56:30,348: train/learning_rate: 0.0004
|
| 586 |
+
INFO:2024-09-25 13:56:30,348: eval/ctc : -0.4320
|
| 587 |
+
INFO:2024-09-25 13:56:30,349: eval/s2s : 0.1333
|
| 588 |
+
INFO:2024-09-25 13:56:30,349: eval/loss : -0.2988
|
| 589 |
+
INFO:2024-09-25 13:56:30,349: eval/wer : 0.2128
|
| 590 |
+
INFO:2024-09-25 13:56:30,349: eval/acc : 0.9635
|
| 591 |
+
INFO:2024-09-25 14:45:26,099: --- epoch 60 ---
|
| 592 |
+
INFO:2024-09-25 14:45:26,099: train/loss : -0.2459
|
| 593 |
+
INFO:2024-09-25 14:45:26,099: train/ctc : -0.3992
|
| 594 |
+
INFO:2024-09-25 14:45:26,100: train/s2s : 0.1533
|
| 595 |
+
INFO:2024-09-25 14:45:26,100: train/learning_rate: 0.0004
|
| 596 |
+
INFO:2024-09-25 14:45:26,100: eval/ctc : -0.4235
|
| 597 |
+
INFO:2024-09-25 14:45:26,100: eval/s2s : 0.1374
|
| 598 |
+
INFO:2024-09-25 14:45:26,100: eval/loss : -0.2861
|
| 599 |
+
INFO:2024-09-25 14:45:26,100: eval/wer : 0.2129
|
| 600 |
+
INFO:2024-09-25 14:45:26,100: eval/acc : 0.9605
|
| 601 |
+
INFO:2024-09-25 15:34:15,837: --- epoch 61 ---
|
| 602 |
+
INFO:2024-09-25 15:34:15,838: train/loss : -0.2479
|
| 603 |
+
INFO:2024-09-25 15:34:15,838: train/ctc : -0.4003
|
| 604 |
+
INFO:2024-09-25 15:34:15,838: train/s2s : 0.1524
|
| 605 |
+
INFO:2024-09-25 15:34:15,838: train/learning_rate: 0.0004
|
| 606 |
+
INFO:2024-09-25 15:34:15,838: eval/ctc : -0.4223
|
| 607 |
+
INFO:2024-09-25 15:34:15,838: eval/s2s : 0.1439
|
| 608 |
+
INFO:2024-09-25 15:34:15,838: eval/loss : -0.2785
|
| 609 |
+
INFO:2024-09-25 15:34:15,838: eval/wer : 0.2062
|
| 610 |
+
INFO:2024-09-25 15:34:15,838: eval/acc : 0.9611
|
| 611 |
+
INFO:2024-09-25 16:23:29,214: --- epoch 62 ---
|
| 612 |
+
INFO:2024-09-25 16:23:29,215: train/loss : -0.2498
|
| 613 |
+
INFO:2024-09-25 16:23:29,215: train/ctc : -0.4012
|
| 614 |
+
INFO:2024-09-25 16:23:29,215: train/s2s : 0.1514
|
| 615 |
+
INFO:2024-09-25 16:23:29,215: train/learning_rate: 0.0004
|
| 616 |
+
INFO:2024-09-25 16:23:29,215: eval/ctc : -0.4303
|
| 617 |
+
INFO:2024-09-25 16:23:29,215: eval/s2s : 0.1271
|
| 618 |
+
INFO:2024-09-25 16:23:29,215: eval/loss : -0.3032
|
| 619 |
+
INFO:2024-09-25 16:23:29,215: eval/wer : 0.2124
|
| 620 |
+
INFO:2024-09-25 16:23:29,215: eval/acc : 0.9640
|
| 621 |
+
INFO:2024-09-25 17:12:41,886: --- epoch 63 ---
|
| 622 |
+
INFO:2024-09-25 17:12:41,886: train/loss : -0.2520
|
| 623 |
+
INFO:2024-09-25 17:12:41,886: train/ctc : -0.4023
|
| 624 |
+
INFO:2024-09-25 17:12:41,887: train/s2s : 0.1504
|
| 625 |
+
INFO:2024-09-25 17:12:41,887: train/learning_rate: 0.0004
|
| 626 |
+
INFO:2024-09-25 17:12:41,887: eval/ctc : -0.4296
|
| 627 |
+
INFO:2024-09-25 17:12:41,887: eval/s2s : 0.1315
|
| 628 |
+
INFO:2024-09-25 17:12:41,887: eval/loss : -0.2980
|
| 629 |
+
INFO:2024-09-25 17:12:41,887: eval/wer : 0.2141
|
| 630 |
+
INFO:2024-09-25 17:12:41,887: eval/acc : 0.9616
|
| 631 |
+
INFO:2024-09-25 18:01:25,659: --- epoch 64 ---
|
| 632 |
+
INFO:2024-09-25 18:01:25,659: train/loss : -0.2505
|
| 633 |
+
INFO:2024-09-25 18:01:25,660: train/ctc : -0.4012
|
| 634 |
+
INFO:2024-09-25 18:01:25,660: train/s2s : 0.1506
|
| 635 |
+
INFO:2024-09-25 18:01:25,660: train/learning_rate: 0.0004
|
| 636 |
+
INFO:2024-09-25 18:01:25,660: eval/ctc : -0.4288
|
| 637 |
+
INFO:2024-09-25 18:01:25,660: eval/s2s : 0.1361
|
| 638 |
+
INFO:2024-09-25 18:01:25,660: eval/loss : -0.2927
|
| 639 |
+
INFO:2024-09-25 18:01:25,660: eval/wer : 0.2130
|
| 640 |
+
INFO:2024-09-25 18:01:25,660: eval/acc : 0.9607
|
| 641 |
+
INFO:2024-09-25 18:50:17,223: --- epoch 65 ---
|
| 642 |
+
INFO:2024-09-25 18:50:17,224: train/loss : -0.2530
|
| 643 |
+
INFO:2024-09-25 18:50:17,224: train/ctc : -0.4030
|
| 644 |
+
INFO:2024-09-25 18:50:17,224: train/s2s : 0.1500
|
| 645 |
+
INFO:2024-09-25 18:50:17,224: train/learning_rate: 0.0004
|
| 646 |
+
INFO:2024-09-25 18:50:17,224: eval/ctc : -0.4079
|
| 647 |
+
INFO:2024-09-25 18:50:17,224: eval/s2s : 0.1411
|
| 648 |
+
INFO:2024-09-25 18:50:17,224: eval/loss : -0.2668
|
| 649 |
+
INFO:2024-09-25 18:50:17,224: eval/wer : 0.2103
|
| 650 |
+
INFO:2024-09-25 18:50:17,224: eval/acc : 0.9591
|
| 651 |
+
INFO:2024-09-25 19:39:27,108: --- epoch 66 ---
|
| 652 |
+
INFO:2024-09-25 19:39:27,108: train/loss : -0.2545
|
| 653 |
+
INFO:2024-09-25 19:39:27,109: train/ctc : -0.4043
|
| 654 |
+
INFO:2024-09-25 19:39:27,109: train/s2s : 0.1498
|
| 655 |
+
INFO:2024-09-25 19:39:27,109: train/learning_rate: 0.0004
|
| 656 |
+
INFO:2024-09-25 19:39:27,109: eval/ctc : -0.4216
|
| 657 |
+
INFO:2024-09-25 19:39:27,109: eval/s2s : 0.1383
|
| 658 |
+
INFO:2024-09-25 19:39:27,109: eval/loss : -0.2833
|
| 659 |
+
INFO:2024-09-25 19:39:27,109: eval/wer : 0.2172
|
| 660 |
+
INFO:2024-09-25 19:39:27,109: eval/acc : 0.9612
|
| 661 |
+
INFO:2024-09-25 20:28:23,260: --- epoch 67 ---
|
| 662 |
+
INFO:2024-09-25 20:28:23,260: train/loss : -0.2557
|
| 663 |
+
INFO:2024-09-25 20:28:23,260: train/ctc : -0.4049
|
| 664 |
+
INFO:2024-09-25 20:28:23,260: train/s2s : 0.1493
|
| 665 |
+
INFO:2024-09-25 20:28:23,261: train/learning_rate: 0.0004
|
| 666 |
+
INFO:2024-09-25 20:28:23,261: eval/ctc : -0.4199
|
| 667 |
+
INFO:2024-09-25 20:28:23,261: eval/s2s : 0.1309
|
| 668 |
+
INFO:2024-09-25 20:28:23,261: eval/loss : -0.2890
|
| 669 |
+
INFO:2024-09-25 20:28:23,261: eval/wer : 0.2102
|
| 670 |
+
INFO:2024-09-25 20:28:23,261: eval/acc : 0.9636
|
| 671 |
+
INFO:2024-09-25 21:16:21,854: --- epoch 68 ---
|
| 672 |
+
INFO:2024-09-25 21:16:21,855: train/loss : -0.2585
|
| 673 |
+
INFO:2024-09-25 21:16:21,855: train/ctc : -0.4071
|
| 674 |
+
INFO:2024-09-25 21:16:21,855: train/s2s : 0.1486
|
| 675 |
+
INFO:2024-09-25 21:16:21,855: train/learning_rate: 0.0004
|
| 676 |
+
INFO:2024-09-25 21:16:21,855: eval/ctc : -0.4161
|
| 677 |
+
INFO:2024-09-25 21:16:21,855: eval/s2s : 0.1410
|
| 678 |
+
INFO:2024-09-25 21:16:21,855: eval/loss : -0.2751
|
| 679 |
+
INFO:2024-09-25 21:16:21,855: eval/wer : 0.2139
|
| 680 |
+
INFO:2024-09-25 21:16:21,855: eval/acc : 0.9610
|
| 681 |
+
INFO:2024-09-25 22:04:25,556: --- epoch 69 ---
|
| 682 |
+
INFO:2024-09-25 22:04:25,556: train/loss : -0.2588
|
| 683 |
+
INFO:2024-09-25 22:04:25,556: train/ctc : -0.4071
|
| 684 |
+
INFO:2024-09-25 22:04:25,556: train/s2s : 0.1483
|
| 685 |
+
INFO:2024-09-25 22:04:25,556: train/learning_rate: 0.0004
|
| 686 |
+
INFO:2024-09-25 22:04:25,556: eval/ctc : -0.4313
|
| 687 |
+
INFO:2024-09-25 22:04:25,557: eval/s2s : 0.1347
|
| 688 |
+
INFO:2024-09-25 22:04:25,557: eval/loss : -0.2966
|
| 689 |
+
INFO:2024-09-25 22:04:25,557: eval/wer : 0.2043
|
| 690 |
+
INFO:2024-09-25 22:04:25,557: eval/acc : 0.9561
|
| 691 |
+
INFO:2024-09-25 22:53:11,182: --- epoch 70 ---
|
| 692 |
+
INFO:2024-09-25 22:53:11,182: train/loss : -0.2608
|
| 693 |
+
INFO:2024-09-25 22:53:11,183: train/ctc : -0.4084
|
| 694 |
+
INFO:2024-09-25 22:53:11,183: train/s2s : 0.1475
|
| 695 |
+
INFO:2024-09-25 22:53:11,183: train/learning_rate: 0.0004
|
| 696 |
+
INFO:2024-09-25 22:53:11,183: eval/ctc : -0.4267
|
| 697 |
+
INFO:2024-09-25 22:53:11,183: eval/s2s : 0.1316
|
| 698 |
+
INFO:2024-09-25 22:53:11,183: eval/loss : -0.2951
|
| 699 |
+
INFO:2024-09-25 22:53:11,183: eval/wer : 0.2187
|
| 700 |
+
INFO:2024-09-25 22:53:11,183: eval/acc : 0.9620
|
| 701 |
+
INFO:2024-09-25 23:41:49,672: --- epoch 71 ---
|
| 702 |
+
INFO:2024-09-25 23:41:49,672: train/loss : -0.2615
|
| 703 |
+
INFO:2024-09-25 23:41:49,672: train/ctc : -0.4087
|
| 704 |
+
INFO:2024-09-25 23:41:49,673: train/s2s : 0.1472
|
| 705 |
+
INFO:2024-09-25 23:41:49,673: train/learning_rate: 0.0004
|
| 706 |
+
INFO:2024-09-25 23:41:49,673: eval/ctc : -0.4182
|
| 707 |
+
INFO:2024-09-25 23:41:49,673: eval/s2s : 0.1473
|
| 708 |
+
INFO:2024-09-25 23:41:49,673: eval/loss : -0.2709
|
| 709 |
+
INFO:2024-09-25 23:41:49,673: eval/wer : 0.2108
|
| 710 |
+
INFO:2024-09-25 23:41:49,673: eval/acc : 0.9587
|
| 711 |
+
INFO:2024-09-26 00:30:37,457: --- epoch 72 ---
|
| 712 |
+
INFO:2024-09-26 00:30:37,458: train/loss : -0.2619
|
| 713 |
+
INFO:2024-09-26 00:30:37,458: train/ctc : -0.4084
|
| 714 |
+
INFO:2024-09-26 00:30:37,458: train/s2s : 0.1466
|
| 715 |
+
INFO:2024-09-26 00:30:37,458: train/learning_rate: 0.0004
|
| 716 |
+
INFO:2024-09-26 00:30:37,458: eval/ctc : -0.4294
|
| 717 |
+
INFO:2024-09-26 00:30:37,458: eval/s2s : 0.1358
|
| 718 |
+
INFO:2024-09-26 00:30:37,459: eval/loss : -0.2937
|
| 719 |
+
INFO:2024-09-26 00:30:37,459: eval/wer : 0.2129
|
| 720 |
+
INFO:2024-09-26 00:30:37,459: eval/acc : 0.9651
|
| 721 |
+
INFO:2024-09-26 01:19:14,623: --- epoch 73 ---
|
| 722 |
+
INFO:2024-09-26 01:19:14,623: train/loss : -0.2636
|
| 723 |
+
INFO:2024-09-26 01:19:14,623: train/ctc : -0.4104
|
| 724 |
+
INFO:2024-09-26 01:19:14,623: train/s2s : 0.1468
|
| 725 |
+
INFO:2024-09-26 01:19:14,623: train/learning_rate: 0.0004
|
| 726 |
+
INFO:2024-09-26 01:19:14,623: eval/ctc : -0.4282
|
| 727 |
+
INFO:2024-09-26 01:19:14,624: eval/s2s : 0.1320
|
| 728 |
+
INFO:2024-09-26 01:19:14,624: eval/loss : -0.2962
|
| 729 |
+
INFO:2024-09-26 01:19:14,624: eval/wer : 0.2103
|
| 730 |
+
INFO:2024-09-26 01:19:14,624: eval/acc : 0.9620
|
| 731 |
+
INFO:2024-09-26 02:08:02,146: --- epoch 74 ---
|
| 732 |
+
INFO:2024-09-26 02:08:02,147: train/loss : -0.2662
|
| 733 |
+
INFO:2024-09-26 02:08:02,147: train/ctc : -0.4121
|
| 734 |
+
INFO:2024-09-26 02:08:02,147: train/s2s : 0.1459
|
| 735 |
+
INFO:2024-09-26 02:08:02,147: train/learning_rate: 0.0004
|
| 736 |
+
INFO:2024-09-26 02:08:02,147: eval/ctc : -0.4323
|
| 737 |
+
INFO:2024-09-26 02:08:02,147: eval/s2s : 0.1304
|
| 738 |
+
INFO:2024-09-26 02:08:02,147: eval/loss : -0.3019
|
| 739 |
+
INFO:2024-09-26 02:08:02,147: eval/wer : 0.2119
|
| 740 |
+
INFO:2024-09-26 02:08:02,147: eval/acc : 0.9611
|
| 741 |
+
INFO:2024-09-26 02:56:40,306: --- epoch 75 ---
|
| 742 |
+
INFO:2024-09-26 02:56:40,306: train/loss : -0.2677
|
| 743 |
+
INFO:2024-09-26 02:56:40,306: train/ctc : -0.4124
|
| 744 |
+
INFO:2024-09-26 02:56:40,306: train/s2s : 0.1447
|
| 745 |
+
INFO:2024-09-26 02:56:40,306: train/learning_rate: 0.0003
|
| 746 |
+
INFO:2024-09-26 02:56:40,306: eval/ctc : -0.4271
|
| 747 |
+
INFO:2024-09-26 02:56:40,306: eval/s2s : 0.1326
|
| 748 |
+
INFO:2024-09-26 02:56:40,306: eval/loss : -0.2945
|
| 749 |
+
INFO:2024-09-26 02:56:40,308: eval/wer : 0.2050
|
| 750 |
+
INFO:2024-09-26 02:56:40,308: eval/acc : 0.9623
|
| 751 |
+
INFO:2024-09-26 03:44:54,666: --- epoch 76 ---
|
| 752 |
+
INFO:2024-09-26 03:44:54,666: train/loss : -0.2670
|
| 753 |
+
INFO:2024-09-26 03:44:54,666: train/ctc : -0.4121
|
| 754 |
+
INFO:2024-09-26 03:44:54,667: train/s2s : 0.1451
|
| 755 |
+
INFO:2024-09-26 03:44:54,667: train/learning_rate: 0.0003
|
| 756 |
+
INFO:2024-09-26 03:44:54,667: eval/ctc : -0.4302
|
| 757 |
+
INFO:2024-09-26 03:44:54,667: eval/s2s : 0.1311
|
| 758 |
+
INFO:2024-09-26 03:44:54,667: eval/loss : -0.2991
|
| 759 |
+
INFO:2024-09-26 03:44:54,667: eval/wer : 0.2087
|
| 760 |
+
INFO:2024-09-26 03:44:54,667: eval/acc : 0.9637
|
| 761 |
+
INFO:2024-09-26 04:33:20,876: --- epoch 77 ---
|
| 762 |
+
INFO:2024-09-26 04:33:20,876: train/loss : -0.2671
|
| 763 |
+
INFO:2024-09-26 04:33:20,877: train/ctc : -0.4125
|
| 764 |
+
INFO:2024-09-26 04:33:20,877: train/s2s : 0.1454
|
| 765 |
+
INFO:2024-09-26 04:33:20,877: train/learning_rate: 0.0003
|
| 766 |
+
INFO:2024-09-26 04:33:20,877: eval/ctc : -0.4293
|
| 767 |
+
INFO:2024-09-26 04:33:20,877: eval/s2s : 0.1275
|
| 768 |
+
INFO:2024-09-26 04:33:20,877: eval/loss : -0.3018
|
| 769 |
+
INFO:2024-09-26 04:33:20,877: eval/wer : 0.2124
|
| 770 |
+
INFO:2024-09-26 04:33:20,877: eval/acc : 0.9641
|
| 771 |
+
INFO:2024-09-26 05:21:16,161: --- epoch 78 ---
|
| 772 |
+
INFO:2024-09-26 05:21:16,161: train/loss : -0.2683
|
| 773 |
+
INFO:2024-09-26 05:21:16,161: train/ctc : -0.4131
|
| 774 |
+
INFO:2024-09-26 05:21:16,161: train/s2s : 0.1448
|
| 775 |
+
INFO:2024-09-26 05:21:16,161: train/learning_rate: 0.0003
|
| 776 |
+
INFO:2024-09-26 05:21:16,162: eval/ctc : -0.4222
|
| 777 |
+
INFO:2024-09-26 05:21:16,162: eval/s2s : 0.1354
|
| 778 |
+
INFO:2024-09-26 05:21:16,162: eval/loss : -0.2868
|
| 779 |
+
INFO:2024-09-26 05:21:16,162: eval/wer : 0.2150
|
| 780 |
+
INFO:2024-09-26 05:21:16,162: eval/acc : 0.9618
|
| 781 |
+
INFO:2024-09-26 06:09:10,953: --- epoch 79 ---
|
| 782 |
+
INFO:2024-09-26 06:09:10,954: train/loss : -0.2695
|
| 783 |
+
INFO:2024-09-26 06:09:10,954: train/ctc : -0.4139
|
| 784 |
+
INFO:2024-09-26 06:09:10,954: train/s2s : 0.1444
|
| 785 |
+
INFO:2024-09-26 06:09:10,954: train/learning_rate: 0.0003
|
| 786 |
+
INFO:2024-09-26 06:09:10,954: eval/ctc : -0.4350
|
| 787 |
+
INFO:2024-09-26 06:09:10,954: eval/s2s : 0.1333
|
| 788 |
+
INFO:2024-09-26 06:09:10,954: eval/loss : -0.3017
|
| 789 |
+
INFO:2024-09-26 06:09:10,954: eval/wer : 0.2031
|
| 790 |
+
INFO:2024-09-26 06:09:10,954: eval/acc : 0.9628
|
| 791 |
+
INFO:2024-09-26 06:57:10,669: --- epoch 80 ---
|
| 792 |
+
INFO:2024-09-26 06:57:10,669: train/loss : -0.2694
|
| 793 |
+
INFO:2024-09-26 06:57:10,669: train/ctc : -0.4138
|
| 794 |
+
INFO:2024-09-26 06:57:10,670: train/s2s : 0.1444
|
| 795 |
+
INFO:2024-09-26 06:57:10,670: train/learning_rate: 0.0003
|
| 796 |
+
INFO:2024-09-26 06:57:10,670: eval/ctc : -0.4345
|
| 797 |
+
INFO:2024-09-26 06:57:10,670: eval/s2s : 0.1310
|
| 798 |
+
INFO:2024-09-26 06:57:10,670: eval/loss : -0.3035
|
| 799 |
+
INFO:2024-09-26 06:57:10,670: eval/wer : 0.1953
|
| 800 |
+
INFO:2024-09-26 06:57:10,670: eval/acc : 0.9638
|
AuxiliaryASR/Checkpoint_new_plus/config.yml
ADDED
|
@@ -0,0 +1,26 @@
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|
| 1 |
+
log_dir: "Checkpoint_new_plus"
|
| 2 |
+
save_freq: 2
|
| 3 |
+
device: "cuda"
|
| 4 |
+
epochs: 200
|
| 5 |
+
batch_size: 64
|
| 6 |
+
pretrained_model: ""
|
| 7 |
+
train_data: "/home/austin/disk2/llmvcs/tt/AuxiliaryASR/Data/train_list_plus.csv"
|
| 8 |
+
val_data: "/home/austin/disk2/llmvcs/tt/AuxiliaryASR/Data/val_list.txt"
|
| 9 |
+
|
| 10 |
+
preprocess_parasm:
|
| 11 |
+
sr: 24000
|
| 12 |
+
spect_params:
|
| 13 |
+
n_fft: 2048
|
| 14 |
+
win_length: 2048
|
| 15 |
+
hop_length: 512
|
| 16 |
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mel_params:
|
| 17 |
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n_mels: 80
|
| 18 |
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|
| 19 |
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model_params:
|
| 20 |
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input_dim: 80
|
| 21 |
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hidden_dim: 256
|
| 22 |
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n_token: 178
|
| 23 |
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token_embedding_dim: 512
|
| 24 |
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|
| 25 |
+
optimizer_params:
|
| 26 |
+
lr: 0.0005
|
AuxiliaryASR/Checkpoint_new_plus/epoch_00068.pth
ADDED
|
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AuxiliaryASR/Checkpoint_new_plus/train.log
ADDED
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| 1 |
+
INFO:2024-09-28 07:57:59,853: --- epoch 1 ---
|
| 2 |
+
INFO:2024-09-28 07:57:59,853: train/loss : 2.5861
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| 3 |
+
INFO:2024-09-28 07:57:59,853: train/ctc : 1.1484
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| 4 |
+
INFO:2024-09-28 07:57:59,854: train/s2s : 1.4377
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| 5 |
+
INFO:2024-09-28 07:57:59,854: train/learning_rate: 0.0005
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| 6 |
+
INFO:2024-09-28 07:57:59,854: eval/ctc : 0.0057
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| 7 |
+
INFO:2024-09-28 07:57:59,854: eval/s2s : 0.4316
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| 8 |
+
INFO:2024-09-28 07:57:59,854: eval/loss : 0.4373
|
| 9 |
+
INFO:2024-09-28 07:57:59,855: eval/wer : 0.3867
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| 10 |
+
INFO:2024-09-28 07:57:59,855: eval/acc : 0.8678
|
| 11 |
+
INFO:2024-09-28 08:47:53,556: --- epoch 2 ---
|
| 12 |
+
INFO:2024-09-28 08:47:53,557: train/loss : 0.3208
|
| 13 |
+
INFO:2024-09-28 08:47:53,557: train/ctc : -0.0801
|
| 14 |
+
INFO:2024-09-28 08:47:53,557: train/s2s : 0.4009
|
| 15 |
+
INFO:2024-09-28 08:47:53,557: train/learning_rate: 0.0005
|
| 16 |
+
INFO:2024-09-28 08:47:53,558: eval/ctc : -0.2404
|
| 17 |
+
INFO:2024-09-28 08:47:53,558: eval/s2s : 0.2679
|
| 18 |
+
INFO:2024-09-28 08:47:53,558: eval/loss : 0.0275
|
| 19 |
+
INFO:2024-09-28 08:47:53,558: eval/wer : 0.3329
|
| 20 |
+
INFO:2024-09-28 08:47:53,558: eval/acc : 0.9194
|
| 21 |
+
INFO:2024-09-28 09:37:43,119: --- epoch 3 ---
|
| 22 |
+
INFO:2024-09-28 09:37:43,119: train/loss : 0.1035
|
| 23 |
+
INFO:2024-09-28 09:37:43,119: train/ctc : -0.2020
|
| 24 |
+
INFO:2024-09-28 09:37:43,120: train/s2s : 0.3055
|
| 25 |
+
INFO:2024-09-28 09:37:43,120: train/learning_rate: 0.0005
|
| 26 |
+
INFO:2024-09-28 09:37:43,120: eval/ctc : -0.2982
|
| 27 |
+
INFO:2024-09-28 09:37:43,120: eval/s2s : 0.2253
|
| 28 |
+
INFO:2024-09-28 09:37:43,120: eval/loss : -0.0729
|
| 29 |
+
INFO:2024-09-28 09:37:43,120: eval/wer : 0.3084
|
| 30 |
+
INFO:2024-09-28 09:37:43,120: eval/acc : 0.9329
|
| 31 |
+
INFO:2024-09-28 10:27:31,962: --- epoch 4 ---
|
| 32 |
+
INFO:2024-09-28 10:27:31,963: train/loss : 0.0245
|
| 33 |
+
INFO:2024-09-28 10:27:31,963: train/ctc : -0.2481
|
| 34 |
+
INFO:2024-09-28 10:27:31,963: train/s2s : 0.2726
|
| 35 |
+
INFO:2024-09-28 10:27:31,963: train/learning_rate: 0.0005
|
| 36 |
+
INFO:2024-09-28 10:27:31,963: eval/ctc : -0.3198
|
| 37 |
+
INFO:2024-09-28 10:27:31,963: eval/s2s : 0.2270
|
| 38 |
+
INFO:2024-09-28 10:27:31,963: eval/loss : -0.0928
|
| 39 |
+
INFO:2024-09-28 10:27:31,963: eval/wer : 0.3001
|
| 40 |
+
INFO:2024-09-28 10:27:31,964: eval/acc : 0.9331
|
| 41 |
+
INFO:2024-09-28 11:17:21,217: --- epoch 5 ---
|
| 42 |
+
INFO:2024-09-28 11:17:21,217: train/loss : -0.0193
|
| 43 |
+
INFO:2024-09-28 11:17:21,217: train/ctc : -0.2738
|
| 44 |
+
INFO:2024-09-28 11:17:21,218: train/s2s : 0.2544
|
| 45 |
+
INFO:2024-09-28 11:17:21,218: train/learning_rate: 0.0005
|
| 46 |
+
INFO:2024-09-28 11:17:21,218: eval/ctc : -0.3372
|
| 47 |
+
INFO:2024-09-28 11:17:21,218: eval/s2s : 0.2005
|
| 48 |
+
INFO:2024-09-28 11:17:21,218: eval/loss : -0.1367
|
| 49 |
+
INFO:2024-09-28 11:17:21,218: eval/wer : 0.2917
|
| 50 |
+
INFO:2024-09-28 11:17:21,218: eval/acc : 0.9413
|
| 51 |
+
INFO:2024-09-28 12:08:24,669: --- epoch 6 ---
|
| 52 |
+
INFO:2024-09-28 12:08:24,670: train/loss : -0.0501
|
| 53 |
+
INFO:2024-09-28 12:08:24,670: train/ctc : -0.2919
|
| 54 |
+
INFO:2024-09-28 12:08:24,670: train/s2s : 0.2417
|
| 55 |
+
INFO:2024-09-28 12:08:24,670: train/learning_rate: 0.0005
|
| 56 |
+
INFO:2024-09-28 12:08:24,670: eval/ctc : -0.3509
|
| 57 |
+
INFO:2024-09-28 12:08:24,670: eval/s2s : 0.1930
|
| 58 |
+
INFO:2024-09-28 12:08:24,670: eval/loss : -0.1579
|
| 59 |
+
INFO:2024-09-28 12:08:24,670: eval/wer : 0.2869
|
| 60 |
+
INFO:2024-09-28 12:08:24,670: eval/acc : 0.9430
|
| 61 |
+
INFO:2024-09-28 12:58:23,565: --- epoch 7 ---
|
| 62 |
+
INFO:2024-09-28 12:58:23,565: train/loss : -0.0703
|
| 63 |
+
INFO:2024-09-28 12:58:23,566: train/ctc : -0.3035
|
| 64 |
+
INFO:2024-09-28 12:58:23,566: train/s2s : 0.2333
|
| 65 |
+
INFO:2024-09-28 12:58:23,566: train/learning_rate: 0.0005
|
| 66 |
+
INFO:2024-09-28 12:58:23,566: eval/ctc : -0.3658
|
| 67 |
+
INFO:2024-09-28 12:58:23,566: eval/s2s : 0.1822
|
| 68 |
+
INFO:2024-09-28 12:58:23,566: eval/loss : -0.1836
|
| 69 |
+
INFO:2024-09-28 12:58:23,566: eval/wer : 0.2933
|
| 70 |
+
INFO:2024-09-28 12:58:23,566: eval/acc : 0.9464
|
| 71 |
+
INFO:2024-09-28 13:48:38,355: --- epoch 8 ---
|
| 72 |
+
INFO:2024-09-28 13:48:38,356: train/loss : -0.0902
|
| 73 |
+
INFO:2024-09-28 13:48:38,356: train/ctc : -0.3156
|
| 74 |
+
INFO:2024-09-28 13:48:38,356: train/s2s : 0.2254
|
| 75 |
+
INFO:2024-09-28 13:48:38,356: train/learning_rate: 0.0005
|
| 76 |
+
INFO:2024-09-28 13:48:38,356: eval/ctc : -0.3643
|
| 77 |
+
INFO:2024-09-28 13:48:38,356: eval/s2s : 0.1844
|
| 78 |
+
INFO:2024-09-28 13:48:38,356: eval/loss : -0.1798
|
| 79 |
+
INFO:2024-09-28 13:48:38,357: eval/wer : 0.2790
|
| 80 |
+
INFO:2024-09-28 13:48:38,357: eval/acc : 0.9455
|
| 81 |
+
INFO:2024-09-28 14:39:15,493: --- epoch 9 ---
|
| 82 |
+
INFO:2024-09-28 14:39:15,494: train/loss : -0.1028
|
| 83 |
+
INFO:2024-09-28 14:39:15,494: train/ctc : -0.3234
|
| 84 |
+
INFO:2024-09-28 14:39:15,494: train/s2s : 0.2206
|
| 85 |
+
INFO:2024-09-28 14:39:15,494: train/learning_rate: 0.0005
|
| 86 |
+
INFO:2024-09-28 14:39:15,494: eval/ctc : -0.3738
|
| 87 |
+
INFO:2024-09-28 14:39:15,494: eval/s2s : 0.1760
|
| 88 |
+
INFO:2024-09-28 14:39:15,494: eval/loss : -0.1978
|
| 89 |
+
INFO:2024-09-28 14:39:15,494: eval/wer : 0.2776
|
| 90 |
+
INFO:2024-09-28 14:39:15,494: eval/acc : 0.9480
|
| 91 |
+
INFO:2024-09-28 15:29:09,779: --- epoch 10 ---
|
| 92 |
+
INFO:2024-09-28 15:29:09,780: train/loss : -0.1173
|
| 93 |
+
INFO:2024-09-28 15:29:09,780: train/ctc : -0.3323
|
| 94 |
+
INFO:2024-09-28 15:29:09,780: train/s2s : 0.2151
|
| 95 |
+
INFO:2024-09-28 15:29:09,780: train/learning_rate: 0.0005
|
| 96 |
+
INFO:2024-09-28 15:29:09,780: eval/ctc : -0.3836
|
| 97 |
+
INFO:2024-09-28 15:29:09,780: eval/s2s : 0.1799
|
| 98 |
+
INFO:2024-09-28 15:29:09,780: eval/loss : -0.2037
|
| 99 |
+
INFO:2024-09-28 15:29:09,780: eval/wer : 0.2766
|
| 100 |
+
INFO:2024-09-28 15:29:09,780: eval/acc : 0.9480
|
| 101 |
+
INFO:2024-09-28 16:19:32,734: --- epoch 11 ---
|
| 102 |
+
INFO:2024-09-28 16:19:32,735: train/loss : -0.1294
|
| 103 |
+
INFO:2024-09-28 16:19:32,735: train/ctc : -0.3397
|
| 104 |
+
INFO:2024-09-28 16:19:32,735: train/s2s : 0.2103
|
| 105 |
+
INFO:2024-09-28 16:19:32,735: train/learning_rate: 0.0005
|
| 106 |
+
INFO:2024-09-28 16:19:32,735: eval/ctc : -0.3881
|
| 107 |
+
INFO:2024-09-28 16:19:32,735: eval/s2s : 0.1727
|
| 108 |
+
INFO:2024-09-28 16:19:32,735: eval/loss : -0.2154
|
| 109 |
+
INFO:2024-09-28 16:19:32,736: eval/wer : 0.2772
|
| 110 |
+
INFO:2024-09-28 16:19:32,736: eval/acc : 0.9475
|
| 111 |
+
INFO:2024-09-28 17:10:10,551: --- epoch 12 ---
|
| 112 |
+
INFO:2024-09-28 17:10:10,551: train/loss : -0.1388
|
| 113 |
+
INFO:2024-09-28 17:10:10,552: train/ctc : -0.3458
|
| 114 |
+
INFO:2024-09-28 17:10:10,552: train/s2s : 0.2070
|
| 115 |
+
INFO:2024-09-28 17:10:10,552: train/learning_rate: 0.0005
|
| 116 |
+
INFO:2024-09-28 17:10:10,553: eval/ctc : -0.3955
|
| 117 |
+
INFO:2024-09-28 17:10:10,553: eval/s2s : 0.1680
|
| 118 |
+
INFO:2024-09-28 17:10:10,553: eval/loss : -0.2275
|
| 119 |
+
INFO:2024-09-28 17:10:10,553: eval/wer : 0.2772
|
| 120 |
+
INFO:2024-09-28 17:10:10,553: eval/acc : 0.9504
|
| 121 |
+
INFO:2024-09-28 18:00:36,470: --- epoch 13 ---
|
| 122 |
+
INFO:2024-09-28 18:00:36,471: train/loss : -0.1448
|
| 123 |
+
INFO:2024-09-28 18:00:36,471: train/ctc : -0.3498
|
| 124 |
+
INFO:2024-09-28 18:00:36,472: train/s2s : 0.2050
|
| 125 |
+
INFO:2024-09-28 18:00:36,472: train/learning_rate: 0.0005
|
| 126 |
+
INFO:2024-09-28 18:00:36,472: eval/ctc : -0.3931
|
| 127 |
+
INFO:2024-09-28 18:00:36,473: eval/s2s : 0.1637
|
| 128 |
+
INFO:2024-09-28 18:00:36,473: eval/loss : -0.2294
|
| 129 |
+
INFO:2024-09-28 18:00:36,473: eval/wer : 0.2702
|
| 130 |
+
INFO:2024-09-28 18:00:36,473: eval/acc : 0.9507
|
| 131 |
+
INFO:2024-09-28 18:51:17,172: --- epoch 14 ---
|
| 132 |
+
INFO:2024-09-28 18:51:17,172: train/loss : -0.1538
|
| 133 |
+
INFO:2024-09-28 18:51:17,172: train/ctc : -0.3551
|
| 134 |
+
INFO:2024-09-28 18:51:17,172: train/s2s : 0.2013
|
| 135 |
+
INFO:2024-09-28 18:51:17,172: train/learning_rate: 0.0005
|
| 136 |
+
INFO:2024-09-28 18:51:17,173: eval/ctc : -0.4101
|
| 137 |
+
INFO:2024-09-28 18:51:17,173: eval/s2s : 0.1617
|
| 138 |
+
INFO:2024-09-28 18:51:17,173: eval/loss : -0.2485
|
| 139 |
+
INFO:2024-09-28 18:51:17,173: eval/wer : 0.2755
|
| 140 |
+
INFO:2024-09-28 18:51:17,173: eval/acc : 0.9537
|
| 141 |
+
INFO:2024-09-28 19:44:09,217: --- epoch 15 ---
|
| 142 |
+
INFO:2024-09-28 19:44:09,218: train/loss : -0.1598
|
| 143 |
+
INFO:2024-09-28 19:44:09,218: train/ctc : -0.3596
|
| 144 |
+
INFO:2024-09-28 19:44:09,218: train/s2s : 0.1998
|
| 145 |
+
INFO:2024-09-28 19:44:09,218: train/learning_rate: 0.0005
|
| 146 |
+
INFO:2024-09-28 19:44:09,218: eval/ctc : -0.4109
|
| 147 |
+
INFO:2024-09-28 19:44:09,218: eval/s2s : 0.1604
|
| 148 |
+
INFO:2024-09-28 19:44:09,218: eval/loss : -0.2505
|
| 149 |
+
INFO:2024-09-28 19:44:09,219: eval/wer : 0.2617
|
| 150 |
+
INFO:2024-09-28 19:44:09,219: eval/acc : 0.9540
|
| 151 |
+
INFO:2024-09-28 20:34:31,991: --- epoch 16 ---
|
| 152 |
+
INFO:2024-09-28 20:34:31,991: train/loss : -0.1664
|
| 153 |
+
INFO:2024-09-28 20:34:31,991: train/ctc : -0.3637
|
| 154 |
+
INFO:2024-09-28 20:34:31,991: train/s2s : 0.1974
|
| 155 |
+
INFO:2024-09-28 20:34:31,991: train/learning_rate: 0.0005
|
| 156 |
+
INFO:2024-09-28 20:34:31,991: eval/ctc : -0.4164
|
| 157 |
+
INFO:2024-09-28 20:34:31,991: eval/s2s : 0.1702
|
| 158 |
+
INFO:2024-09-28 20:34:31,991: eval/loss : -0.2462
|
| 159 |
+
INFO:2024-09-28 20:34:31,991: eval/wer : 0.2747
|
| 160 |
+
INFO:2024-09-28 20:34:31,991: eval/acc : 0.9506
|
| 161 |
+
INFO:2024-09-28 21:25:51,588: --- epoch 17 ---
|
| 162 |
+
INFO:2024-09-28 21:25:51,589: train/loss : -0.1707
|
| 163 |
+
INFO:2024-09-28 21:25:51,589: train/ctc : -0.3659
|
| 164 |
+
INFO:2024-09-28 21:25:51,589: train/s2s : 0.1953
|
| 165 |
+
INFO:2024-09-28 21:25:51,589: train/learning_rate: 0.0005
|
| 166 |
+
INFO:2024-09-28 21:25:51,589: eval/ctc : -0.3967
|
| 167 |
+
INFO:2024-09-28 21:25:51,589: eval/s2s : 0.1683
|
| 168 |
+
INFO:2024-09-28 21:25:51,589: eval/loss : -0.2284
|
| 169 |
+
INFO:2024-09-28 21:25:51,590: eval/wer : 0.2718
|
| 170 |
+
INFO:2024-09-28 21:25:51,590: eval/acc : 0.9501
|
| 171 |
+
INFO:2024-09-28 22:15:12,321: --- epoch 18 ---
|
| 172 |
+
INFO:2024-09-28 22:15:12,321: train/loss : -0.1746
|
| 173 |
+
INFO:2024-09-28 22:15:12,322: train/ctc : -0.3683
|
| 174 |
+
INFO:2024-09-28 22:15:12,322: train/s2s : 0.1936
|
| 175 |
+
INFO:2024-09-28 22:15:12,322: train/learning_rate: 0.0005
|
| 176 |
+
INFO:2024-09-28 22:15:12,322: eval/ctc : -0.4111
|
| 177 |
+
INFO:2024-09-28 22:15:12,322: eval/s2s : 0.1590
|
| 178 |
+
INFO:2024-09-28 22:15:12,322: eval/loss : -0.2521
|
| 179 |
+
INFO:2024-09-28 22:15:12,322: eval/wer : 0.2569
|
| 180 |
+
INFO:2024-09-28 22:15:12,322: eval/acc : 0.9534
|
| 181 |
+
INFO:2024-09-28 23:05:08,353: --- epoch 19 ---
|
| 182 |
+
INFO:2024-09-28 23:05:08,353: train/loss : -0.1800
|
| 183 |
+
INFO:2024-09-28 23:05:08,353: train/ctc : -0.3714
|
| 184 |
+
INFO:2024-09-28 23:05:08,353: train/s2s : 0.1914
|
| 185 |
+
INFO:2024-09-28 23:05:08,353: train/learning_rate: 0.0005
|
| 186 |
+
INFO:2024-09-28 23:05:08,353: eval/ctc : -0.4206
|
| 187 |
+
INFO:2024-09-28 23:05:08,354: eval/s2s : 0.1579
|
| 188 |
+
INFO:2024-09-28 23:05:08,354: eval/loss : -0.2628
|
| 189 |
+
INFO:2024-09-28 23:05:08,354: eval/wer : 0.2643
|
| 190 |
+
INFO:2024-09-28 23:05:08,354: eval/acc : 0.9545
|
| 191 |
+
INFO:2024-09-28 23:54:55,243: --- epoch 20 ---
|
| 192 |
+
INFO:2024-09-28 23:54:55,243: train/loss : -0.1834
|
| 193 |
+
INFO:2024-09-28 23:54:55,244: train/ctc : -0.3738
|
| 194 |
+
INFO:2024-09-28 23:54:55,244: train/s2s : 0.1904
|
| 195 |
+
INFO:2024-09-28 23:54:55,244: train/learning_rate: 0.0005
|
| 196 |
+
INFO:2024-09-28 23:54:55,244: eval/ctc : -0.4109
|
| 197 |
+
INFO:2024-09-28 23:54:55,244: eval/s2s : 0.1599
|
| 198 |
+
INFO:2024-09-28 23:54:55,244: eval/loss : -0.2510
|
| 199 |
+
INFO:2024-09-28 23:54:55,244: eval/wer : 0.2613
|
| 200 |
+
INFO:2024-09-28 23:54:55,244: eval/acc : 0.9527
|
| 201 |
+
INFO:2024-09-29 00:45:22,692: --- epoch 21 ---
|
| 202 |
+
INFO:2024-09-29 00:45:22,693: train/loss : -0.1883
|
| 203 |
+
INFO:2024-09-29 00:45:22,693: train/ctc : -0.3767
|
| 204 |
+
INFO:2024-09-29 00:45:22,693: train/s2s : 0.1885
|
| 205 |
+
INFO:2024-09-29 00:45:22,693: train/learning_rate: 0.0005
|
| 206 |
+
INFO:2024-09-29 00:45:22,693: eval/ctc : -0.4174
|
| 207 |
+
INFO:2024-09-29 00:45:22,693: eval/s2s : 0.1610
|
| 208 |
+
INFO:2024-09-29 00:45:22,693: eval/loss : -0.2564
|
| 209 |
+
INFO:2024-09-29 00:45:22,693: eval/wer : 0.2623
|
| 210 |
+
INFO:2024-09-29 00:45:22,693: eval/acc : 0.9534
|
| 211 |
+
INFO:2024-09-29 01:35:17,240: --- epoch 22 ---
|
| 212 |
+
INFO:2024-09-29 01:35:17,241: train/loss : -0.1940
|
| 213 |
+
INFO:2024-09-29 01:35:17,241: train/ctc : -0.3805
|
| 214 |
+
INFO:2024-09-29 01:35:17,241: train/s2s : 0.1866
|
| 215 |
+
INFO:2024-09-29 01:35:17,241: train/learning_rate: 0.0005
|
| 216 |
+
INFO:2024-09-29 01:35:17,241: eval/ctc : -0.4249
|
| 217 |
+
INFO:2024-09-29 01:35:17,241: eval/s2s : 0.1579
|
| 218 |
+
INFO:2024-09-29 01:35:17,241: eval/loss : -0.2669
|
| 219 |
+
INFO:2024-09-29 01:35:17,241: eval/wer : 0.2663
|
| 220 |
+
INFO:2024-09-29 01:35:17,241: eval/acc : 0.9554
|
| 221 |
+
INFO:2024-09-29 02:25:06,941: --- epoch 23 ---
|
| 222 |
+
INFO:2024-09-29 02:25:06,941: train/loss : -0.1954
|
| 223 |
+
INFO:2024-09-29 02:25:06,942: train/ctc : -0.3814
|
| 224 |
+
INFO:2024-09-29 02:25:06,942: train/s2s : 0.1860
|
| 225 |
+
INFO:2024-09-29 02:25:06,942: train/learning_rate: 0.0005
|
| 226 |
+
INFO:2024-09-29 02:25:06,942: eval/ctc : -0.4228
|
| 227 |
+
INFO:2024-09-29 02:25:06,942: eval/s2s : 0.1515
|
| 228 |
+
INFO:2024-09-29 02:25:06,942: eval/loss : -0.2713
|
| 229 |
+
INFO:2024-09-29 02:25:06,942: eval/wer : 0.2715
|
| 230 |
+
INFO:2024-09-29 02:25:06,942: eval/acc : 0.9566
|
| 231 |
+
INFO:2024-09-29 03:15:09,968: --- epoch 24 ---
|
| 232 |
+
INFO:2024-09-29 03:15:09,969: train/loss : -0.1990
|
| 233 |
+
INFO:2024-09-29 03:15:09,969: train/ctc : -0.3838
|
| 234 |
+
INFO:2024-09-29 03:15:09,969: train/s2s : 0.1848
|
| 235 |
+
INFO:2024-09-29 03:15:09,969: train/learning_rate: 0.0005
|
| 236 |
+
INFO:2024-09-29 03:15:09,969: eval/ctc : -0.4147
|
| 237 |
+
INFO:2024-09-29 03:15:09,969: eval/s2s : 0.1599
|
| 238 |
+
INFO:2024-09-29 03:15:09,969: eval/loss : -0.2548
|
| 239 |
+
INFO:2024-09-29 03:15:09,969: eval/wer : 0.2656
|
| 240 |
+
INFO:2024-09-29 03:15:09,969: eval/acc : 0.9542
|
| 241 |
+
INFO:2024-09-29 04:05:22,466: --- epoch 25 ---
|
| 242 |
+
INFO:2024-09-29 04:05:22,467: train/loss : -0.2018
|
| 243 |
+
INFO:2024-09-29 04:05:22,467: train/ctc : -0.3853
|
| 244 |
+
INFO:2024-09-29 04:05:22,468: train/s2s : 0.1835
|
| 245 |
+
INFO:2024-09-29 04:05:22,468: train/learning_rate: 0.0005
|
| 246 |
+
INFO:2024-09-29 04:05:22,468: eval/ctc : -0.4164
|
| 247 |
+
INFO:2024-09-29 04:05:22,468: eval/s2s : 0.1607
|
| 248 |
+
INFO:2024-09-29 04:05:22,468: eval/loss : -0.2557
|
| 249 |
+
INFO:2024-09-29 04:05:22,468: eval/wer : 0.2605
|
| 250 |
+
INFO:2024-09-29 04:05:22,468: eval/acc : 0.9534
|
| 251 |
+
INFO:2024-09-29 04:54:44,031: --- epoch 26 ---
|
| 252 |
+
INFO:2024-09-29 04:54:44,031: train/loss : -0.2047
|
| 253 |
+
INFO:2024-09-29 04:54:44,031: train/ctc : -0.3871
|
| 254 |
+
INFO:2024-09-29 04:54:44,031: train/s2s : 0.1824
|
| 255 |
+
INFO:2024-09-29 04:54:44,031: train/learning_rate: 0.0005
|
| 256 |
+
INFO:2024-09-29 04:54:44,031: eval/ctc : -0.4363
|
| 257 |
+
INFO:2024-09-29 04:54:44,032: eval/s2s : 0.1514
|
| 258 |
+
INFO:2024-09-29 04:54:44,032: eval/loss : -0.2849
|
| 259 |
+
INFO:2024-09-29 04:54:44,032: eval/wer : 0.2563
|
| 260 |
+
INFO:2024-09-29 04:54:44,032: eval/acc : 0.9568
|
| 261 |
+
INFO:2024-09-29 05:46:38,590: --- epoch 27 ---
|
| 262 |
+
INFO:2024-09-29 05:46:38,590: train/loss : -0.2083
|
| 263 |
+
INFO:2024-09-29 05:46:38,590: train/ctc : -0.3894
|
| 264 |
+
INFO:2024-09-29 05:46:38,591: train/s2s : 0.1811
|
| 265 |
+
INFO:2024-09-29 05:46:38,591: train/learning_rate: 0.0005
|
| 266 |
+
INFO:2024-09-29 05:46:38,591: eval/ctc : -0.4302
|
| 267 |
+
INFO:2024-09-29 05:46:38,591: eval/s2s : 0.1554
|
| 268 |
+
INFO:2024-09-29 05:46:38,591: eval/loss : -0.2748
|
| 269 |
+
INFO:2024-09-29 05:46:38,591: eval/wer : 0.2586
|
| 270 |
+
INFO:2024-09-29 05:46:38,591: eval/acc : 0.9555
|
| 271 |
+
INFO:2024-09-29 06:35:40,091: --- epoch 28 ---
|
| 272 |
+
INFO:2024-09-29 06:35:40,091: train/loss : -0.2115
|
| 273 |
+
INFO:2024-09-29 06:35:40,091: train/ctc : -0.3920
|
| 274 |
+
INFO:2024-09-29 06:35:40,091: train/s2s : 0.1805
|
| 275 |
+
INFO:2024-09-29 06:35:40,091: train/learning_rate: 0.0005
|
| 276 |
+
INFO:2024-09-29 06:35:40,092: eval/ctc : -0.4307
|
| 277 |
+
INFO:2024-09-29 06:35:40,092: eval/s2s : 0.1521
|
| 278 |
+
INFO:2024-09-29 06:35:40,092: eval/loss : -0.2786
|
| 279 |
+
INFO:2024-09-29 06:35:40,092: eval/wer : 0.2614
|
| 280 |
+
INFO:2024-09-29 06:35:40,092: eval/acc : 0.9556
|
| 281 |
+
INFO:2024-09-29 07:25:03,692: --- epoch 29 ---
|
| 282 |
+
INFO:2024-09-29 07:25:03,692: train/loss : -0.2144
|
| 283 |
+
INFO:2024-09-29 07:25:03,692: train/ctc : -0.3942
|
| 284 |
+
INFO:2024-09-29 07:25:03,692: train/s2s : 0.1798
|
| 285 |
+
INFO:2024-09-29 07:25:03,692: train/learning_rate: 0.0005
|
| 286 |
+
INFO:2024-09-29 07:25:03,692: eval/ctc : -0.4278
|
| 287 |
+
INFO:2024-09-29 07:25:03,692: eval/s2s : 0.1550
|
| 288 |
+
INFO:2024-09-29 07:25:03,692: eval/loss : -0.2729
|
| 289 |
+
INFO:2024-09-29 07:25:03,692: eval/wer : 0.2618
|
| 290 |
+
INFO:2024-09-29 07:25:03,692: eval/acc : 0.9566
|
| 291 |
+
INFO:2024-09-29 08:15:57,545: --- epoch 30 ---
|
| 292 |
+
INFO:2024-09-29 08:15:57,545: train/loss : -0.2168
|
| 293 |
+
INFO:2024-09-29 08:15:57,546: train/ctc : -0.3954
|
| 294 |
+
INFO:2024-09-29 08:15:57,546: train/s2s : 0.1786
|
| 295 |
+
INFO:2024-09-29 08:15:57,546: train/learning_rate: 0.0005
|
| 296 |
+
INFO:2024-09-29 08:15:57,546: eval/ctc : -0.4287
|
| 297 |
+
INFO:2024-09-29 08:15:57,546: eval/s2s : 0.1526
|
| 298 |
+
INFO:2024-09-29 08:15:57,546: eval/loss : -0.2761
|
| 299 |
+
INFO:2024-09-29 08:15:57,546: eval/wer : 0.2560
|
| 300 |
+
INFO:2024-09-29 08:15:57,546: eval/acc : 0.9564
|
| 301 |
+
INFO:2024-09-29 09:17:29,709: --- epoch 31 ---
|
| 302 |
+
INFO:2024-09-29 09:17:29,709: train/loss : -0.2169
|
| 303 |
+
INFO:2024-09-29 09:17:29,709: train/ctc : -0.3954
|
| 304 |
+
INFO:2024-09-29 09:17:29,709: train/s2s : 0.1784
|
| 305 |
+
INFO:2024-09-29 09:17:29,710: train/learning_rate: 0.0005
|
| 306 |
+
INFO:2024-09-29 09:17:29,710: eval/ctc : -0.4269
|
| 307 |
+
INFO:2024-09-29 09:17:29,710: eval/s2s : 0.1492
|
| 308 |
+
INFO:2024-09-29 09:17:29,710: eval/loss : -0.2776
|
| 309 |
+
INFO:2024-09-29 09:17:29,710: eval/wer : 0.2570
|
| 310 |
+
INFO:2024-09-29 09:17:29,710: eval/acc : 0.9560
|
| 311 |
+
INFO:2024-09-29 10:08:58,536: --- epoch 32 ---
|
| 312 |
+
INFO:2024-09-29 10:08:58,536: train/loss : -0.2236
|
| 313 |
+
INFO:2024-09-29 10:08:58,537: train/ctc : -0.4001
|
| 314 |
+
INFO:2024-09-29 10:08:58,537: train/s2s : 0.1765
|
| 315 |
+
INFO:2024-09-29 10:08:58,537: train/learning_rate: 0.0005
|
| 316 |
+
INFO:2024-09-29 10:08:58,537: eval/ctc : -0.4277
|
| 317 |
+
INFO:2024-09-29 10:08:58,537: eval/s2s : 0.1520
|
| 318 |
+
INFO:2024-09-29 10:08:58,537: eval/loss : -0.2758
|
| 319 |
+
INFO:2024-09-29 10:08:58,537: eval/wer : 0.2587
|
| 320 |
+
INFO:2024-09-29 10:08:58,537: eval/acc : 0.9569
|
| 321 |
+
INFO:2024-09-29 11:05:36,680: --- epoch 33 ---
|
| 322 |
+
INFO:2024-09-29 11:05:36,680: train/loss : -0.2275
|
| 323 |
+
INFO:2024-09-29 11:05:36,681: train/ctc : -0.4027
|
| 324 |
+
INFO:2024-09-29 11:05:36,681: train/s2s : 0.1751
|
| 325 |
+
INFO:2024-09-29 11:05:36,681: train/learning_rate: 0.0005
|
| 326 |
+
INFO:2024-09-29 11:05:36,681: eval/ctc : -0.4384
|
| 327 |
+
INFO:2024-09-29 11:05:36,681: eval/s2s : 0.1509
|
| 328 |
+
INFO:2024-09-29 11:05:36,681: eval/loss : -0.2874
|
| 329 |
+
INFO:2024-09-29 11:05:36,681: eval/wer : 0.2657
|
| 330 |
+
INFO:2024-09-29 11:05:36,681: eval/acc : 0.9567
|
| 331 |
+
INFO:2024-09-29 12:03:28,994: --- epoch 34 ---
|
| 332 |
+
INFO:2024-09-29 12:03:28,994: train/loss : -0.2300
|
| 333 |
+
INFO:2024-09-29 12:03:28,994: train/ctc : -0.4043
|
| 334 |
+
INFO:2024-09-29 12:03:28,994: train/s2s : 0.1743
|
| 335 |
+
INFO:2024-09-29 12:03:28,994: train/learning_rate: 0.0005
|
| 336 |
+
INFO:2024-09-29 12:03:28,994: eval/ctc : -0.4415
|
| 337 |
+
INFO:2024-09-29 12:03:28,994: eval/s2s : 0.1396
|
| 338 |
+
INFO:2024-09-29 12:03:28,994: eval/loss : -0.3019
|
| 339 |
+
INFO:2024-09-29 12:03:28,995: eval/wer : 0.2602
|
| 340 |
+
INFO:2024-09-29 12:03:28,995: eval/acc : 0.9596
|
| 341 |
+
INFO:2024-09-29 13:01:18,378: --- epoch 35 ---
|
| 342 |
+
INFO:2024-09-29 13:01:18,379: train/loss : -0.2314
|
| 343 |
+
INFO:2024-09-29 13:01:18,379: train/ctc : -0.4054
|
| 344 |
+
INFO:2024-09-29 13:01:18,380: train/s2s : 0.1740
|
| 345 |
+
INFO:2024-09-29 13:01:18,380: train/learning_rate: 0.0005
|
| 346 |
+
INFO:2024-09-29 13:01:18,380: eval/ctc : -0.4400
|
| 347 |
+
INFO:2024-09-29 13:01:18,380: eval/s2s : 0.1425
|
| 348 |
+
INFO:2024-09-29 13:01:18,380: eval/loss : -0.2975
|
| 349 |
+
INFO:2024-09-29 13:01:18,380: eval/wer : 0.2613
|
| 350 |
+
INFO:2024-09-29 13:01:18,380: eval/acc : 0.9591
|
| 351 |
+
INFO:2024-09-29 13:58:51,017: --- epoch 36 ---
|
| 352 |
+
INFO:2024-09-29 13:58:51,017: train/loss : -0.2317
|
| 353 |
+
INFO:2024-09-29 13:58:51,017: train/ctc : -0.4048
|
| 354 |
+
INFO:2024-09-29 13:58:51,017: train/s2s : 0.1731
|
| 355 |
+
INFO:2024-09-29 13:58:51,017: train/learning_rate: 0.0005
|
| 356 |
+
INFO:2024-09-29 13:58:51,017: eval/ctc : -0.4322
|
| 357 |
+
INFO:2024-09-29 13:58:51,018: eval/s2s : 0.1478
|
| 358 |
+
INFO:2024-09-29 13:58:51,018: eval/loss : -0.2845
|
| 359 |
+
INFO:2024-09-29 13:58:51,018: eval/wer : 0.2407
|
| 360 |
+
INFO:2024-09-29 13:58:51,018: eval/acc : 0.9590
|
| 361 |
+
INFO:2024-09-29 14:56:30,148: --- epoch 37 ---
|
| 362 |
+
INFO:2024-09-29 14:56:30,148: train/loss : -0.2334
|
| 363 |
+
INFO:2024-09-29 14:56:30,148: train/ctc : -0.4062
|
| 364 |
+
INFO:2024-09-29 14:56:30,148: train/s2s : 0.1728
|
| 365 |
+
INFO:2024-09-29 14:56:30,148: train/learning_rate: 0.0005
|
| 366 |
+
INFO:2024-09-29 14:56:30,148: eval/ctc : -0.4441
|
| 367 |
+
INFO:2024-09-29 14:56:30,148: eval/s2s : 0.1490
|
| 368 |
+
INFO:2024-09-29 14:56:30,148: eval/loss : -0.2951
|
| 369 |
+
INFO:2024-09-29 14:56:30,148: eval/wer : 0.2472
|
| 370 |
+
INFO:2024-09-29 14:56:30,148: eval/acc : 0.9583
|
| 371 |
+
INFO:2024-09-29 15:54:04,788: --- epoch 38 ---
|
| 372 |
+
INFO:2024-09-29 15:54:04,788: train/loss : -0.2366
|
| 373 |
+
INFO:2024-09-29 15:54:04,789: train/ctc : -0.4080
|
| 374 |
+
INFO:2024-09-29 15:54:04,789: train/s2s : 0.1714
|
| 375 |
+
INFO:2024-09-29 15:54:04,789: train/learning_rate: 0.0005
|
| 376 |
+
INFO:2024-09-29 15:54:04,789: eval/ctc : -0.4427
|
| 377 |
+
INFO:2024-09-29 15:54:04,789: eval/s2s : 0.1334
|
| 378 |
+
INFO:2024-09-29 15:54:04,789: eval/loss : -0.3093
|
| 379 |
+
INFO:2024-09-29 15:54:04,789: eval/wer : 0.2535
|
| 380 |
+
INFO:2024-09-29 15:54:04,789: eval/acc : 0.9613
|
| 381 |
+
INFO:2024-09-29 16:51:19,769: --- epoch 39 ---
|
| 382 |
+
INFO:2024-09-29 16:51:19,770: train/loss : -0.2391
|
| 383 |
+
INFO:2024-09-29 16:51:19,770: train/ctc : -0.4101
|
| 384 |
+
INFO:2024-09-29 16:51:19,770: train/s2s : 0.1710
|
| 385 |
+
INFO:2024-09-29 16:51:19,770: train/learning_rate: 0.0005
|
| 386 |
+
INFO:2024-09-29 16:51:19,770: eval/ctc : -0.4389
|
| 387 |
+
INFO:2024-09-29 16:51:19,770: eval/s2s : 0.1392
|
| 388 |
+
INFO:2024-09-29 16:51:19,771: eval/loss : -0.2997
|
| 389 |
+
INFO:2024-09-29 16:51:19,771: eval/wer : 0.2509
|
| 390 |
+
INFO:2024-09-29 16:51:19,771: eval/acc : 0.9605
|
| 391 |
+
INFO:2024-09-29 17:48:46,120: --- epoch 40 ---
|
| 392 |
+
INFO:2024-09-29 17:48:46,121: train/loss : -0.2412
|
| 393 |
+
INFO:2024-09-29 17:48:46,121: train/ctc : -0.4117
|
| 394 |
+
INFO:2024-09-29 17:48:46,121: train/s2s : 0.1705
|
| 395 |
+
INFO:2024-09-29 17:48:46,121: train/learning_rate: 0.0005
|
| 396 |
+
INFO:2024-09-29 17:48:46,121: eval/ctc : -0.4353
|
| 397 |
+
INFO:2024-09-29 17:48:46,121: eval/s2s : 0.1436
|
| 398 |
+
INFO:2024-09-29 17:48:46,121: eval/loss : -0.2917
|
| 399 |
+
INFO:2024-09-29 17:48:46,121: eval/wer : 0.2564
|
| 400 |
+
INFO:2024-09-29 17:48:46,122: eval/acc : 0.9597
|
| 401 |
+
INFO:2024-09-29 18:46:11,585: --- epoch 41 ---
|
| 402 |
+
INFO:2024-09-29 18:46:11,585: train/loss : -0.2431
|
| 403 |
+
INFO:2024-09-29 18:46:11,585: train/ctc : -0.4126
|
| 404 |
+
INFO:2024-09-29 18:46:11,585: train/s2s : 0.1695
|
| 405 |
+
INFO:2024-09-29 18:46:11,585: train/learning_rate: 0.0005
|
| 406 |
+
INFO:2024-09-29 18:46:11,586: eval/ctc : -0.4475
|
| 407 |
+
INFO:2024-09-29 18:46:11,586: eval/s2s : 0.1396
|
| 408 |
+
INFO:2024-09-29 18:46:11,586: eval/loss : -0.3079
|
| 409 |
+
INFO:2024-09-29 18:46:11,586: eval/wer : 0.2373
|
| 410 |
+
INFO:2024-09-29 18:46:11,586: eval/acc : 0.9618
|
| 411 |
+
INFO:2024-09-29 19:43:47,349: --- epoch 42 ---
|
| 412 |
+
INFO:2024-09-29 19:43:47,350: train/loss : -0.2431
|
| 413 |
+
INFO:2024-09-29 19:43:47,350: train/ctc : -0.4125
|
| 414 |
+
INFO:2024-09-29 19:43:47,350: train/s2s : 0.1694
|
| 415 |
+
INFO:2024-09-29 19:43:47,350: train/learning_rate: 0.0004
|
| 416 |
+
INFO:2024-09-29 19:43:47,350: eval/ctc : -0.4346
|
| 417 |
+
INFO:2024-09-29 19:43:47,350: eval/s2s : 0.1446
|
| 418 |
+
INFO:2024-09-29 19:43:47,350: eval/loss : -0.2901
|
| 419 |
+
INFO:2024-09-29 19:43:47,350: eval/wer : 0.2457
|
| 420 |
+
INFO:2024-09-29 19:43:47,350: eval/acc : 0.9584
|
| 421 |
+
INFO:2024-09-29 20:41:44,812: --- epoch 43 ---
|
| 422 |
+
INFO:2024-09-29 20:41:44,812: train/loss : -0.2448
|
| 423 |
+
INFO:2024-09-29 20:41:44,812: train/ctc : -0.4136
|
| 424 |
+
INFO:2024-09-29 20:41:44,812: train/s2s : 0.1688
|
| 425 |
+
INFO:2024-09-29 20:41:44,813: train/learning_rate: 0.0004
|
| 426 |
+
INFO:2024-09-29 20:41:44,813: eval/ctc : -0.4426
|
| 427 |
+
INFO:2024-09-29 20:41:44,813: eval/s2s : 0.1416
|
| 428 |
+
INFO:2024-09-29 20:41:44,813: eval/loss : -0.3011
|
| 429 |
+
INFO:2024-09-29 20:41:44,813: eval/wer : 0.2473
|
| 430 |
+
INFO:2024-09-29 20:41:44,813: eval/acc : 0.9601
|
| 431 |
+
INFO:2024-09-29 21:40:11,834: --- epoch 44 ---
|
| 432 |
+
INFO:2024-09-29 21:40:11,835: train/loss : -0.2473
|
| 433 |
+
INFO:2024-09-29 21:40:11,835: train/ctc : -0.4153
|
| 434 |
+
INFO:2024-09-29 21:40:11,835: train/s2s : 0.1680
|
| 435 |
+
INFO:2024-09-29 21:40:11,835: train/learning_rate: 0.0004
|
| 436 |
+
INFO:2024-09-29 21:40:11,835: eval/ctc : -0.4277
|
| 437 |
+
INFO:2024-09-29 21:40:11,835: eval/s2s : 0.1447
|
| 438 |
+
INFO:2024-09-29 21:40:11,835: eval/loss : -0.2830
|
| 439 |
+
INFO:2024-09-29 21:40:11,835: eval/wer : 0.2476
|
| 440 |
+
INFO:2024-09-29 21:40:11,835: eval/acc : 0.9598
|
| 441 |
+
INFO:2024-09-29 22:37:45,306: --- epoch 45 ---
|
| 442 |
+
INFO:2024-09-29 22:37:45,307: train/loss : -0.2496
|
| 443 |
+
INFO:2024-09-29 22:37:45,307: train/ctc : -0.4169
|
| 444 |
+
INFO:2024-09-29 22:37:45,307: train/s2s : 0.1673
|
| 445 |
+
INFO:2024-09-29 22:37:45,307: train/learning_rate: 0.0004
|
| 446 |
+
INFO:2024-09-29 22:37:45,307: eval/ctc : -0.4502
|
| 447 |
+
INFO:2024-09-29 22:37:45,307: eval/s2s : 0.1320
|
| 448 |
+
INFO:2024-09-29 22:37:45,307: eval/loss : -0.3182
|
| 449 |
+
INFO:2024-09-29 22:37:45,307: eval/wer : 0.2555
|
| 450 |
+
INFO:2024-09-29 22:37:45,307: eval/acc : 0.9630
|
| 451 |
+
INFO:2024-09-29 23:35:24,533: --- epoch 46 ---
|
| 452 |
+
INFO:2024-09-29 23:35:24,534: train/loss : -0.2526
|
| 453 |
+
INFO:2024-09-29 23:35:24,534: train/ctc : -0.4192
|
| 454 |
+
INFO:2024-09-29 23:35:24,534: train/s2s : 0.1666
|
| 455 |
+
INFO:2024-09-29 23:35:24,534: train/learning_rate: 0.0004
|
| 456 |
+
INFO:2024-09-29 23:35:24,534: eval/ctc : -0.4375
|
| 457 |
+
INFO:2024-09-29 23:35:24,534: eval/s2s : 0.1410
|
| 458 |
+
INFO:2024-09-29 23:35:24,534: eval/loss : -0.2965
|
| 459 |
+
INFO:2024-09-29 23:35:24,535: eval/wer : 0.2478
|
| 460 |
+
INFO:2024-09-29 23:35:24,535: eval/acc : 0.9584
|
| 461 |
+
INFO:2024-09-30 00:32:56,212: --- epoch 47 ---
|
| 462 |
+
INFO:2024-09-30 00:32:56,213: train/loss : -0.2527
|
| 463 |
+
INFO:2024-09-30 00:32:56,213: train/ctc : -0.4188
|
| 464 |
+
INFO:2024-09-30 00:32:56,213: train/s2s : 0.1660
|
| 465 |
+
INFO:2024-09-30 00:32:56,213: train/learning_rate: 0.0004
|
| 466 |
+
INFO:2024-09-30 00:32:56,213: eval/ctc : -0.4455
|
| 467 |
+
INFO:2024-09-30 00:32:56,213: eval/s2s : 0.1387
|
| 468 |
+
INFO:2024-09-30 00:32:56,213: eval/loss : -0.3068
|
| 469 |
+
INFO:2024-09-30 00:32:56,213: eval/wer : 0.2482
|
| 470 |
+
INFO:2024-09-30 00:32:56,213: eval/acc : 0.9606
|
| 471 |
+
INFO:2024-09-30 01:30:41,495: --- epoch 48 ---
|
| 472 |
+
INFO:2024-09-30 01:30:41,496: train/loss : -0.2542
|
| 473 |
+
INFO:2024-09-30 01:30:41,496: train/ctc : -0.4195
|
| 474 |
+
INFO:2024-09-30 01:30:41,496: train/s2s : 0.1654
|
| 475 |
+
INFO:2024-09-30 01:30:41,496: train/learning_rate: 0.0004
|
| 476 |
+
INFO:2024-09-30 01:30:41,496: eval/ctc : -0.4522
|
| 477 |
+
INFO:2024-09-30 01:30:41,496: eval/s2s : 0.1382
|
| 478 |
+
INFO:2024-09-30 01:30:41,496: eval/loss : -0.3139
|
| 479 |
+
INFO:2024-09-30 01:30:41,496: eval/wer : 0.2471
|
| 480 |
+
INFO:2024-09-30 01:30:41,496: eval/acc : 0.9611
|
| 481 |
+
INFO:2024-09-30 02:28:27,449: --- epoch 49 ---
|
| 482 |
+
INFO:2024-09-30 02:28:27,450: train/loss : -0.2556
|
| 483 |
+
INFO:2024-09-30 02:28:27,450: train/ctc : -0.4209
|
| 484 |
+
INFO:2024-09-30 02:28:27,450: train/s2s : 0.1653
|
| 485 |
+
INFO:2024-09-30 02:28:27,450: train/learning_rate: 0.0004
|
| 486 |
+
INFO:2024-09-30 02:28:27,450: eval/ctc : -0.4561
|
| 487 |
+
INFO:2024-09-30 02:28:27,450: eval/s2s : 0.1314
|
| 488 |
+
INFO:2024-09-30 02:28:27,451: eval/loss : -0.3247
|
| 489 |
+
INFO:2024-09-30 02:28:27,451: eval/wer : 0.2633
|
| 490 |
+
INFO:2024-09-30 02:28:27,451: eval/acc : 0.9623
|
| 491 |
+
INFO:2024-09-30 03:24:32,089: --- epoch 50 ---
|
| 492 |
+
INFO:2024-09-30 03:24:32,089: train/loss : -0.2565
|
| 493 |
+
INFO:2024-09-30 03:24:32,089: train/ctc : -0.4215
|
| 494 |
+
INFO:2024-09-30 03:24:32,089: train/s2s : 0.1651
|
| 495 |
+
INFO:2024-09-30 03:24:32,089: train/learning_rate: 0.0004
|
| 496 |
+
INFO:2024-09-30 03:24:32,090: eval/ctc : -0.4456
|
| 497 |
+
INFO:2024-09-30 03:24:32,090: eval/s2s : 0.1387
|
| 498 |
+
INFO:2024-09-30 03:24:32,090: eval/loss : -0.3069
|
| 499 |
+
INFO:2024-09-30 03:24:32,090: eval/wer : 0.2465
|
| 500 |
+
INFO:2024-09-30 03:24:32,090: eval/acc : 0.9610
|
| 501 |
+
INFO:2024-09-30 04:20:44,292: --- epoch 51 ---
|
| 502 |
+
INFO:2024-09-30 04:20:44,292: train/loss : -0.2586
|
| 503 |
+
INFO:2024-09-30 04:20:44,292: train/ctc : -0.4230
|
| 504 |
+
INFO:2024-09-30 04:20:44,292: train/s2s : 0.1644
|
| 505 |
+
INFO:2024-09-30 04:20:44,292: train/learning_rate: 0.0004
|
| 506 |
+
INFO:2024-09-30 04:20:44,292: eval/ctc : -0.4472
|
| 507 |
+
INFO:2024-09-30 04:20:44,292: eval/s2s : 0.1319
|
| 508 |
+
INFO:2024-09-30 04:20:44,293: eval/loss : -0.3153
|
| 509 |
+
INFO:2024-09-30 04:20:44,293: eval/wer : 0.2576
|
| 510 |
+
INFO:2024-09-30 04:20:44,293: eval/acc : 0.9617
|
| 511 |
+
INFO:2024-09-30 05:16:40,056: --- epoch 52 ---
|
| 512 |
+
INFO:2024-09-30 05:16:40,056: train/loss : -0.2604
|
| 513 |
+
INFO:2024-09-30 05:16:40,056: train/ctc : -0.4245
|
| 514 |
+
INFO:2024-09-30 05:16:40,056: train/s2s : 0.1641
|
| 515 |
+
INFO:2024-09-30 05:16:40,056: train/learning_rate: 0.0004
|
| 516 |
+
INFO:2024-09-30 05:16:40,057: eval/ctc : -0.4508
|
| 517 |
+
INFO:2024-09-30 05:16:40,057: eval/s2s : 0.1381
|
| 518 |
+
INFO:2024-09-30 05:16:40,057: eval/loss : -0.3127
|
| 519 |
+
INFO:2024-09-30 05:16:40,057: eval/wer : 0.2376
|
| 520 |
+
INFO:2024-09-30 05:16:40,057: eval/acc : 0.9611
|
| 521 |
+
INFO:2024-09-30 06:12:45,233: --- epoch 53 ---
|
| 522 |
+
INFO:2024-09-30 06:12:45,233: train/loss : -0.2625
|
| 523 |
+
INFO:2024-09-30 06:12:45,234: train/ctc : -0.4257
|
| 524 |
+
INFO:2024-09-30 06:12:45,234: train/s2s : 0.1632
|
| 525 |
+
INFO:2024-09-30 06:12:45,234: train/learning_rate: 0.0004
|
| 526 |
+
INFO:2024-09-30 06:12:45,234: eval/ctc : -0.4592
|
| 527 |
+
INFO:2024-09-30 06:12:45,234: eval/s2s : 0.1364
|
| 528 |
+
INFO:2024-09-30 06:12:45,234: eval/loss : -0.3228
|
| 529 |
+
INFO:2024-09-30 06:12:45,234: eval/wer : 0.2448
|
| 530 |
+
INFO:2024-09-30 06:12:45,234: eval/acc : 0.9605
|
| 531 |
+
INFO:2024-09-30 07:09:30,890: --- epoch 54 ---
|
| 532 |
+
INFO:2024-09-30 07:09:30,891: train/loss : -0.2643
|
| 533 |
+
INFO:2024-09-30 07:09:30,891: train/ctc : -0.4270
|
| 534 |
+
INFO:2024-09-30 07:09:30,891: train/s2s : 0.1627
|
| 535 |
+
INFO:2024-09-30 07:09:30,891: train/learning_rate: 0.0004
|
| 536 |
+
INFO:2024-09-30 07:09:30,891: eval/ctc : -0.4584
|
| 537 |
+
INFO:2024-09-30 07:09:30,891: eval/s2s : 0.1334
|
| 538 |
+
INFO:2024-09-30 07:09:30,891: eval/loss : -0.3250
|
| 539 |
+
INFO:2024-09-30 07:09:30,891: eval/wer : 0.2476
|
| 540 |
+
INFO:2024-09-30 07:09:30,892: eval/acc : 0.9609
|
| 541 |
+
INFO:2024-09-30 08:07:02,911: --- epoch 55 ---
|
| 542 |
+
INFO:2024-09-30 08:07:02,911: train/loss : -0.2655
|
| 543 |
+
INFO:2024-09-30 08:07:02,912: train/ctc : -0.4277
|
| 544 |
+
INFO:2024-09-30 08:07:02,912: train/s2s : 0.1622
|
| 545 |
+
INFO:2024-09-30 08:07:02,912: train/learning_rate: 0.0004
|
| 546 |
+
INFO:2024-09-30 08:07:02,912: eval/ctc : -0.4503
|
| 547 |
+
INFO:2024-09-30 08:07:02,912: eval/s2s : 0.1407
|
| 548 |
+
INFO:2024-09-30 08:07:02,912: eval/loss : -0.3096
|
| 549 |
+
INFO:2024-09-30 08:07:02,912: eval/wer : 0.2457
|
| 550 |
+
INFO:2024-09-30 08:07:02,912: eval/acc : 0.9610
|
| 551 |
+
INFO:2024-09-30 09:04:47,073: --- epoch 56 ---
|
| 552 |
+
INFO:2024-09-30 09:04:47,074: train/loss : -0.2674
|
| 553 |
+
INFO:2024-09-30 09:04:47,074: train/ctc : -0.4287
|
| 554 |
+
INFO:2024-09-30 09:04:47,074: train/s2s : 0.1613
|
| 555 |
+
INFO:2024-09-30 09:04:47,074: train/learning_rate: 0.0004
|
| 556 |
+
INFO:2024-09-30 09:04:47,074: eval/ctc : -0.4495
|
| 557 |
+
INFO:2024-09-30 09:04:47,074: eval/s2s : 0.1369
|
| 558 |
+
INFO:2024-09-30 09:04:47,074: eval/loss : -0.3127
|
| 559 |
+
INFO:2024-09-30 09:04:47,074: eval/wer : 0.2373
|
| 560 |
+
INFO:2024-09-30 09:04:47,074: eval/acc : 0.9615
|
| 561 |
+
INFO:2024-09-30 10:02:58,338: --- epoch 57 ---
|
| 562 |
+
INFO:2024-09-30 10:02:58,339: train/loss : -0.2684
|
| 563 |
+
INFO:2024-09-30 10:02:58,339: train/ctc : -0.4293
|
| 564 |
+
INFO:2024-09-30 10:02:58,339: train/s2s : 0.1610
|
| 565 |
+
INFO:2024-09-30 10:02:58,339: train/learning_rate: 0.0004
|
| 566 |
+
INFO:2024-09-30 10:02:58,339: eval/ctc : -0.4527
|
| 567 |
+
INFO:2024-09-30 10:02:58,339: eval/s2s : 0.1405
|
| 568 |
+
INFO:2024-09-30 10:02:58,339: eval/loss : -0.3123
|
| 569 |
+
INFO:2024-09-30 10:02:58,339: eval/wer : 0.2490
|
| 570 |
+
INFO:2024-09-30 10:02:58,339: eval/acc : 0.9589
|
| 571 |
+
INFO:2024-09-30 11:01:11,755: --- epoch 58 ---
|
| 572 |
+
INFO:2024-09-30 11:01:11,756: train/loss : -0.2686
|
| 573 |
+
INFO:2024-09-30 11:01:11,756: train/ctc : -0.4294
|
| 574 |
+
INFO:2024-09-30 11:01:11,756: train/s2s : 0.1609
|
| 575 |
+
INFO:2024-09-30 11:01:11,756: train/learning_rate: 0.0004
|
| 576 |
+
INFO:2024-09-30 11:01:11,756: eval/ctc : -0.4526
|
| 577 |
+
INFO:2024-09-30 11:01:11,756: eval/s2s : 0.1405
|
| 578 |
+
INFO:2024-09-30 11:01:11,756: eval/loss : -0.3121
|
| 579 |
+
INFO:2024-09-30 11:01:11,756: eval/wer : 0.2422
|
| 580 |
+
INFO:2024-09-30 11:01:11,756: eval/acc : 0.9616
|
| 581 |
+
INFO:2024-09-30 11:59:22,053: --- epoch 59 ---
|
| 582 |
+
INFO:2024-09-30 11:59:22,053: train/loss : -0.2679
|
| 583 |
+
INFO:2024-09-30 11:59:22,054: train/ctc : -0.4287
|
| 584 |
+
INFO:2024-09-30 11:59:22,054: train/s2s : 0.1608
|
| 585 |
+
INFO:2024-09-30 11:59:22,054: train/learning_rate: 0.0004
|
| 586 |
+
INFO:2024-09-30 11:59:22,054: eval/ctc : -0.4464
|
| 587 |
+
INFO:2024-09-30 11:59:22,054: eval/s2s : 0.1322
|
| 588 |
+
INFO:2024-09-30 11:59:22,054: eval/loss : -0.3143
|
| 589 |
+
INFO:2024-09-30 11:59:22,054: eval/wer : 0.2552
|
| 590 |
+
INFO:2024-09-30 11:59:22,055: eval/acc : 0.9616
|
| 591 |
+
INFO:2024-09-30 12:57:34,943: --- epoch 60 ---
|
| 592 |
+
INFO:2024-09-30 12:57:34,943: train/loss : -0.2738
|
| 593 |
+
INFO:2024-09-30 12:57:34,944: train/ctc : -0.4331
|
| 594 |
+
INFO:2024-09-30 12:57:34,944: train/s2s : 0.1593
|
| 595 |
+
INFO:2024-09-30 12:57:34,944: train/learning_rate: 0.0004
|
| 596 |
+
INFO:2024-09-30 12:57:34,944: eval/ctc : -0.4593
|
| 597 |
+
INFO:2024-09-30 12:57:34,944: eval/s2s : 0.1336
|
| 598 |
+
INFO:2024-09-30 12:57:34,944: eval/loss : -0.3257
|
| 599 |
+
INFO:2024-09-30 12:57:34,944: eval/wer : 0.2421
|
| 600 |
+
INFO:2024-09-30 12:57:34,944: eval/acc : 0.9607
|
| 601 |
+
INFO:2024-09-30 13:55:23,428: --- epoch 61 ---
|
| 602 |
+
INFO:2024-09-30 13:55:23,428: train/loss : -0.2734
|
| 603 |
+
INFO:2024-09-30 13:55:23,429: train/ctc : -0.4328
|
| 604 |
+
INFO:2024-09-30 13:55:23,429: train/s2s : 0.1593
|
| 605 |
+
INFO:2024-09-30 13:55:23,429: train/learning_rate: 0.0004
|
| 606 |
+
INFO:2024-09-30 13:55:23,429: eval/ctc : -0.4521
|
| 607 |
+
INFO:2024-09-30 13:55:23,429: eval/s2s : 0.1389
|
| 608 |
+
INFO:2024-09-30 13:55:23,429: eval/loss : -0.3132
|
| 609 |
+
INFO:2024-09-30 13:55:23,429: eval/wer : 0.2498
|
| 610 |
+
INFO:2024-09-30 13:55:23,429: eval/acc : 0.9618
|
| 611 |
+
INFO:2024-09-30 14:52:00,724: --- epoch 62 ---
|
| 612 |
+
INFO:2024-09-30 14:52:00,724: train/loss : -0.2738
|
| 613 |
+
INFO:2024-09-30 14:52:00,724: train/ctc : -0.4329
|
| 614 |
+
INFO:2024-09-30 14:52:00,728: train/s2s : 0.1591
|
| 615 |
+
INFO:2024-09-30 14:52:00,728: train/learning_rate: 0.0004
|
| 616 |
+
INFO:2024-09-30 14:52:00,728: eval/ctc : -0.4589
|
| 617 |
+
INFO:2024-09-30 14:52:00,729: eval/s2s : 0.1352
|
| 618 |
+
INFO:2024-09-30 14:52:00,729: eval/loss : -0.3238
|
| 619 |
+
INFO:2024-09-30 14:52:00,729: eval/wer : 0.2658
|
| 620 |
+
INFO:2024-09-30 14:52:00,729: eval/acc : 0.9607
|
| 621 |
+
INFO:2024-09-30 15:50:31,877: --- epoch 63 ---
|
| 622 |
+
INFO:2024-09-30 15:50:31,878: train/loss : -0.2752
|
| 623 |
+
INFO:2024-09-30 15:50:31,878: train/ctc : -0.4340
|
| 624 |
+
INFO:2024-09-30 15:50:31,878: train/s2s : 0.1589
|
| 625 |
+
INFO:2024-09-30 15:50:31,879: train/learning_rate: 0.0004
|
| 626 |
+
INFO:2024-09-30 15:50:31,879: eval/ctc : -0.4610
|
| 627 |
+
INFO:2024-09-30 15:50:31,879: eval/s2s : 0.1336
|
| 628 |
+
INFO:2024-09-30 15:50:31,879: eval/loss : -0.3273
|
| 629 |
+
INFO:2024-09-30 15:50:31,879: eval/wer : 0.2474
|
| 630 |
+
INFO:2024-09-30 15:50:31,879: eval/acc : 0.9629
|
| 631 |
+
INFO:2024-09-30 16:46:38,152: --- epoch 64 ---
|
| 632 |
+
INFO:2024-09-30 16:46:38,153: train/loss : -0.2782
|
| 633 |
+
INFO:2024-09-30 16:46:38,153: train/ctc : -0.4362
|
| 634 |
+
INFO:2024-09-30 16:46:38,153: train/s2s : 0.1581
|
| 635 |
+
INFO:2024-09-30 16:46:38,153: train/learning_rate: 0.0004
|
| 636 |
+
INFO:2024-09-30 16:46:38,153: eval/ctc : -0.4625
|
| 637 |
+
INFO:2024-09-30 16:46:38,153: eval/s2s : 0.1341
|
| 638 |
+
INFO:2024-09-30 16:46:38,153: eval/loss : -0.3283
|
| 639 |
+
INFO:2024-09-30 16:46:38,153: eval/wer : 0.2413
|
| 640 |
+
INFO:2024-09-30 16:46:38,153: eval/acc : 0.9608
|
| 641 |
+
INFO:2024-09-30 17:43:44,755: --- epoch 65 ---
|
| 642 |
+
INFO:2024-09-30 17:43:44,755: train/loss : -0.2773
|
| 643 |
+
INFO:2024-09-30 17:43:44,755: train/ctc : -0.4356
|
| 644 |
+
INFO:2024-09-30 17:43:44,755: train/s2s : 0.1583
|
| 645 |
+
INFO:2024-09-30 17:43:44,756: train/learning_rate: 0.0004
|
| 646 |
+
INFO:2024-09-30 17:43:44,756: eval/ctc : -0.4663
|
| 647 |
+
INFO:2024-09-30 17:43:44,756: eval/s2s : 0.1355
|
| 648 |
+
INFO:2024-09-30 17:43:44,756: eval/loss : -0.3309
|
| 649 |
+
INFO:2024-09-30 17:43:44,756: eval/wer : 0.2414
|
| 650 |
+
INFO:2024-09-30 17:43:44,756: eval/acc : 0.9608
|
| 651 |
+
INFO:2024-09-30 18:40:30,873: --- epoch 66 ---
|
| 652 |
+
INFO:2024-09-30 18:40:30,874: train/loss : -0.2771
|
| 653 |
+
INFO:2024-09-30 18:40:30,874: train/ctc : -0.4349
|
| 654 |
+
INFO:2024-09-30 18:40:30,874: train/s2s : 0.1578
|
| 655 |
+
INFO:2024-09-30 18:40:30,874: train/learning_rate: 0.0004
|
| 656 |
+
INFO:2024-09-30 18:40:30,874: eval/ctc : -0.4628
|
| 657 |
+
INFO:2024-09-30 18:40:30,874: eval/s2s : 0.1344
|
| 658 |
+
INFO:2024-09-30 18:40:30,874: eval/loss : -0.3284
|
| 659 |
+
INFO:2024-09-30 18:40:30,874: eval/wer : 0.2403
|
| 660 |
+
INFO:2024-09-30 18:40:30,874: eval/acc : 0.9619
|
| 661 |
+
INFO:2024-09-30 19:37:25,585: --- epoch 67 ---
|
| 662 |
+
INFO:2024-09-30 19:37:25,585: train/loss : -0.2796
|
| 663 |
+
INFO:2024-09-30 19:37:25,586: train/ctc : -0.4368
|
| 664 |
+
INFO:2024-09-30 19:37:25,591: train/s2s : 0.1573
|
| 665 |
+
INFO:2024-09-30 19:37:25,597: train/learning_rate: 0.0004
|
| 666 |
+
INFO:2024-09-30 19:37:25,597: eval/ctc : -0.4693
|
| 667 |
+
INFO:2024-09-30 19:37:25,598: eval/s2s : 0.1300
|
| 668 |
+
INFO:2024-09-30 19:37:25,598: eval/loss : -0.3393
|
| 669 |
+
INFO:2024-09-30 19:37:25,598: eval/wer : 0.2463
|
| 670 |
+
INFO:2024-09-30 19:37:25,598: eval/acc : 0.9626
|
| 671 |
+
INFO:2024-09-30 20:34:19,064: --- epoch 68 ---
|
| 672 |
+
INFO:2024-09-30 20:34:19,065: train/loss : -0.2805
|
| 673 |
+
INFO:2024-09-30 20:34:19,065: train/ctc : -0.4372
|
| 674 |
+
INFO:2024-09-30 20:34:19,065: train/s2s : 0.1568
|
| 675 |
+
INFO:2024-09-30 20:34:19,065: train/learning_rate: 0.0004
|
| 676 |
+
INFO:2024-09-30 20:34:19,065: eval/ctc : -0.4650
|
| 677 |
+
INFO:2024-09-30 20:34:19,065: eval/s2s : 0.1351
|
| 678 |
+
INFO:2024-09-30 20:34:19,065: eval/loss : -0.3300
|
| 679 |
+
INFO:2024-09-30 20:34:19,065: eval/wer : 0.2424
|
| 680 |
+
INFO:2024-09-30 20:34:19,065: eval/acc : 0.9617
|
| 681 |
+
INFO:2024-09-30 21:31:36,799: --- epoch 69 ---
|
| 682 |
+
INFO:2024-09-30 21:31:36,799: train/loss : -0.2799
|
| 683 |
+
INFO:2024-09-30 21:31:36,799: train/ctc : -0.4370
|
| 684 |
+
INFO:2024-09-30 21:31:36,799: train/s2s : 0.1571
|
| 685 |
+
INFO:2024-09-30 21:31:36,799: train/learning_rate: 0.0004
|
| 686 |
+
INFO:2024-09-30 21:31:36,799: eval/ctc : -0.4631
|
| 687 |
+
INFO:2024-09-30 21:31:36,800: eval/s2s : 0.1364
|
| 688 |
+
INFO:2024-09-30 21:31:36,800: eval/loss : -0.3267
|
| 689 |
+
INFO:2024-09-30 21:31:36,800: eval/wer : 0.2587
|
| 690 |
+
INFO:2024-09-30 21:31:36,800: eval/acc : 0.9616
|
| 691 |
+
INFO:2024-09-30 22:28:50,999: --- epoch 70 ---
|
| 692 |
+
INFO:2024-09-30 22:28:51,000: train/loss : -0.2808
|
| 693 |
+
INFO:2024-09-30 22:28:51,000: train/ctc : -0.4375
|
| 694 |
+
INFO:2024-09-30 22:28:51,000: train/s2s : 0.1567
|
| 695 |
+
INFO:2024-09-30 22:28:51,000: train/learning_rate: 0.0004
|
| 696 |
+
INFO:2024-09-30 22:28:51,000: eval/ctc : -0.4690
|
| 697 |
+
INFO:2024-09-30 22:28:51,000: eval/s2s : 0.1315
|
| 698 |
+
INFO:2024-09-30 22:28:51,000: eval/loss : -0.3374
|
| 699 |
+
INFO:2024-09-30 22:28:51,000: eval/wer : 0.2437
|
| 700 |
+
INFO:2024-09-30 22:28:51,000: eval/acc : 0.9635
|
AuxiliaryASR/Configs/config.yml
ADDED
|
@@ -0,0 +1,26 @@
|
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|
| 1 |
+
log_dir: "Checkpoint_new_plus"
|
| 2 |
+
save_freq: 2
|
| 3 |
+
device: "cuda"
|
| 4 |
+
epochs: 200
|
| 5 |
+
batch_size: 64
|
| 6 |
+
pretrained_model: ""
|
| 7 |
+
train_data: "/home/austin/disk2/llmvcs/tt/AuxiliaryASR/Data/train_list_plus.csv"
|
| 8 |
+
val_data: "/home/austin/disk2/llmvcs/tt/AuxiliaryASR/Data/val_list.txt"
|
| 9 |
+
|
| 10 |
+
preprocess_parasm:
|
| 11 |
+
sr: 48000
|
| 12 |
+
spect_params:
|
| 13 |
+
n_fft: 2048
|
| 14 |
+
win_length: 2048
|
| 15 |
+
hop_length: 512
|
| 16 |
+
mel_params:
|
| 17 |
+
n_mels: 80
|
| 18 |
+
|
| 19 |
+
model_params:
|
| 20 |
+
input_dim: 80
|
| 21 |
+
hidden_dim: 256
|
| 22 |
+
n_token: 178
|
| 23 |
+
token_embedding_dim: 512
|
| 24 |
+
|
| 25 |
+
optimizer_params:
|
| 26 |
+
lr: 0.0005
|
AuxiliaryASR/Data/train_list.csv
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
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|
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|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:460293101a721612ff63bcb093b9c9694626bf13776ceb029bbdde8a3a206dec
|
| 3 |
+
size 66871361
|
AuxiliaryASR/Data/train_list.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
AuxiliaryASR/Data/train_list_plus.csv
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
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|
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|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b58ffe25ea467047a9b35b55b4727ca00e91c7fa646135ecc70b102bc7bf13ae
|
| 3 |
+
size 66760710
|
AuxiliaryASR/Data/train_list_subsection.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
AuxiliaryASR/Data/val_list.txt
ADDED
|
@@ -0,0 +1,407 @@
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|
| 1 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/2f2ae696/wav/2f2ae696_480.wav|oɽe wa betsɯgɯtɕi de sagaɕite mitakedo, jaʔpa inakaʔta.|139
|
| 2 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/bbd90363/wav/bbd90363_1953.wav|anata wasai miʔɕiŋgɯ o tsɯkaʔte, dʑibɯɴ dʑiɕiɴ no rɯɯɴ o ɯtɕikeɕite ita.|38
|
| 3 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/9c125949/wav/9c125949_1051.wav|ɯɯ——..., soɯ dakedo,...ɯ...de mo, aoisaɴ kiɽei sɯki daɕi, iː çito da moɯ!|279
|
| 4 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/6b2b26d1/wav/6b2b26d1_0486.wav|koɯ ɕite mite mirɯto, soɽe de wa ehe peɽo ni kate soɯ ni nai wa ne.|186
|
| 5 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/bf145e7a/wav/bf145e7a_213.wav|de mo, koɽe kaɽa gambaʔte ɽeɴɕɯɯ sɯrɯkaɽa. kʲoɯ wa doɯ ɕite mo, kinoɯ no jorɯ kaɽa hanedakɯɴ no koto o kaŋgaeteta sei de, kamaɴ dekinakɯ naʔtɕaʔte.|308
|
| 6 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/6489388e/wav/6489388e_1563.wav|dʑitsɯ o iɯto, wataɕi mo ɕiɽanaiɴ desɯ.|5
|
| 7 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/b8b5fe66/wav/b8b5fe66_2399.wav|o, sɯgeː.|121
|
| 8 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/a1a0d114/wav/a1a0d114_0122.wav|soɕite, soɽe idʑoɯ ni, izɯmitɕaɴ ga jasaɕiː ko ni sodaʔte kɯɽete...ɯɽeɕiː jo.|73
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ac5de73d/wav/ac5de73d_171.wav|sɯgoi to iwaɽerɯ no wa ɯɽeɕiː mono da na daga omae ni wa sakiokosaɽete ɕimaʔta jo.|64
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4800dd8d/wav/4800dd8d_333.wav|deːta ɕɯɯɕɯɯ no keʔka da jo ne. hatsɯne mo nagaːi me de mitaɽa iː jo. sono otɕi dekirɯ joɯ ni naɽe naɽe.|324
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/70b38cc9/wav/70b38cc9_474.wav|ɯɴ, soɽe wa naɴ daɽo, dʑitsɯjoɯteki na kandʑiʔte koto ka na?|237
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/37ed21cc/wav/37ed21cc_0532.wav|toʔta ato no eizoɯ o daɽe ga mijoɯ ga, toʔte kɯɽeta çito ga tsɯkasasaɴ de arɯ koto wa, kawaɽimaseŋkaɽa.|251
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cec410a1/wav/cec410a1_038.wav|jatsɯ daʔte aː miete igai ni, taimɯ kaːdo o oɕita ato, sɯki o mite inemɯɽi sɯrɯ joɯ na joɯɽojo no josa o kakɯɕimoʔte irɯ ka mo ɕiɽeɴ no?|412
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4ce0075b/wav/4ce0075b_1668.wav|aɴ ta ga majoki mo naɕi ni tazɯnete kɯrɯkaɽa heja o katazɯkeɽaɽenakaʔtaɴ dʑa nai.|18
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ac0e6660/wav/ac0e6660_0741.wav|tomokakɯ saː, naɴ da ka ɕiɽanaikedo, mʲoɯ ni ɽaibarɯɕi saɽetɕaʔte saː.|248
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1967ee53/wav/1967ee53_0537.wav|soɯ iɯ koto dʑa nai no jo maʔtakɯ aɕita mo konna tɕoɯɕi daʔtaɽa osananadʑimi o jamejoɯ kaɕiɽa ataɕi.|21
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e2fccdca/wav/e2fccdca_0696.wav|gaʔkoɯ no ɯɽa ni seifɯkɯ o ɯʔterɯ mise ga arɯɴ desɯ. bokɯ wa çiɽoiɴ dʑa nakɯte ɕɯdʑiŋkoɯ desɯkaɽa ne. fɯkɯsoɯ kaɽa biɕiʔto kimerɯbekideɕoɯ.|100
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| 18 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6136958e/wav/6136958e_265.wav|nanami, jaʔpaɽi kawaɽoɯ. iɽeɽaɽerɯnaɽa wataɕi ga...|374
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/5d68aedf/wav/5d68aedf_1851.wav|anata ga kita toki, sɯkoɕi dake ɯɽeɕikaʔta.|108
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| 20 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f8c36d2d/wav/f8c36d2d_1141.wav|ɯfɯfɯ, aɽisatɕaɴ, egao wa daidʑi desɯ joː.|212
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| 21 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/3d505acf/wav/3d505acf_219.wav|kaɽekoɽe sandʑɯɯ ne...ija ija, saɴ kagetsɯ mae to iʔta tokoɽo dʑa.|177
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| 22 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/ochinbarai/voice/mzr/mzr_09_002h_008.wav|ima wasama, deɕo?|55
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| 23 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a8a5767d/wav/a8a5767d_152.wav|takaɕikɯɴ ga gambaʔtakaɽa desɯ joː.|222
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| 24 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1ba0d17b/wav/1ba0d17b_468.wav|saː, omeɕiagaɽi kɯdasai.|297
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f19b6190/wav/f19b6190_1517.wav|maː, osewa ni naʔterɯ çito ni tanomaɽerɯ to kotowaɽenai wa ne.|52
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/653a1bc0/wav/653a1bc0_1308.wav|aɽe kaɽa zɯibɯɴ to dʑikaɴ ga taʔte, jɯɯmasaɴ mo oːkikɯ naʔte, soɕite wataɕi no kaɽada ni mo çitoɕikɯ dʑikaɴ ga sajoɯ ɕite.|84
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/11b1eb07/wav/11b1eb07_0749.wav|tsɯkiawasete gomeɴ ne. kawanokɯɴ mo hajame ni kaeɽi na jo.|75
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/eb41adcf/wav/eb41adcf_1093.wav|iɽoiɽo tameɕite daɕita ketsɯɽoɴ jo. koɽe ga itɕibaɴ oiɕiː wa. iː sɯpaisɯ o tsɯkaʔte irɯ no kaɕiɽa? kaoɽi ga tɕigaɯ wa.|31
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0ab8878d/wav/0ab8878d_416.wav|toʔpɯ no. itɕi! kotoba no imi wa wakaɽaɴ baʔte, naɴ da ka sɯgokɯ sɯgokanai!|250
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f04ee070/wav/f04ee070_1121.wav|e? doɯ ɕi te wataɕi na no? sempai wa omise no tetsɯdai ni kite k��ɽeterɯɴ dakaɽa, otɕitɕisaɴ ka ohahasaɴ ga mendoɯ mirɯ mono deɕoɯ?|207
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bc8cc1a2/wav/bc8cc1a2_316.wav|kaitɕoɯ wa heja ni posɯtaː haʔte taɽi sɯrɯ no?|378
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| 32 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/3410d0ed/wav/3410d0ed_245.wav|saʔki kaɽa imi no wakaɽanai koto o ɯdaɯda to! naɴ naɴ da omae wa!|376
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| 33 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8d2b2495/wav/8d2b2495_0069.wav|bɯʔɕitsɯ o tatɕinokanai no wa takejamakɯɴ no idʑi daɽoɯkedo, bɯʔɕitsɯ o dʑijɯɯ ni tsɯkaʔte iː to iɯ no wa takejamakɯɴ no koɯi da. koɯi o ɯkeiɽete moɽaenaito, kizɯtsɯkɯ mono da jo.|184
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| 34 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/fb7e4354/wav/fb7e4354_133.wav|koɯ gʲaɴ to migoto ni marɯ nomi saɽenakʲaɴ. ɕiɽaɽeta tokoɽo de, itakɯ mo kajɯkɯ mo nakaʔta hanaɕi jaʔta ke.|388
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4cb40d9c/wav/4cb40d9c_020.wav|kiɴ'joɯ ka, dojoɯ no jorɯ dato aɽigatai ka na. jokɯdʑitsɯ ga jasɯmi de, hoɕɯɯ ga naikaɽa.|2
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| 36 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2cf01874/wav/2cf01874_2255.wav|ikitasr ni noʔte, çinomoto i no ekibeɴ o tabeɽaɽerɯ. bokɯ wa, soɽe ga kanaeba kaŋkoɯ ɕigeɴ ni narɯ to omoʔta dake deɕita.|24
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8b6e7173/wav/8b6e7173_1093.wav|taɕika ni koɽe dʑa ɯgokinikɯi ka mo.|25
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| 38 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/89374c0b/wav/89374c0b_088.wav|dakaɽa hoka no doɯkʲɯɯsei to onadʑi joɯ ni, ɕizeɴ na goɯɽʲɯɯ o ɕinakeɽeba naɽanai...doɯ ɕita? hendʑi ga nai. kiːte irɯ no ka, baɽiː—!|424
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6d590dce/wav/6d590dce_0541.wav|soɯ ka na? koko ni zɯʔto iɽaɽetaɽa iː naʔte omoɯ kɯɽai, kimotɕi jokaʔta no desɯ.|252
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| 40 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1cc3c6c0/wav/1cc3c6c0_1846.wav|kokoɽoataɽi arɯɴ da. naɽa ɯɯkɯɴ, hjoʔto ɕite neɽaʔte taɽi ɕita to ka?|127
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/44feed2f/wav/44feed2f_0376.wav|wataɕi, so, sono, iɯ made mo nai koto desɯkeɽedo, ɕikataɕi to koɯ ɕite, deːto sɯrɯ no wa, hadʑimete desɯkaɽa...|193
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| 42 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c480db9a/wav/c480db9a_1084.wav|sono hondana no saɴ damme no tana kaɽa, fairɯ onɯkidaɕite kɯdasaɽanai?|189
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| 43 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ccb60794/wav/ccb60794_0662.wav|tada, gendʑiteɴ de soɽe ga kanzeɴ na mono ka doɯ ka wa wakaɽanai. aimai na iːkata dakedo, maemɯki ni ɯmakɯ maʔtoɯ na mitɕi o sagaɕite ikɯ ɕika nai wake de.|98
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1ba0d17b/wav/1ba0d17b_107.wav|sakihodo wa, moɯɕiwake aɽimasendeɕita. boʔtɕama ni ano joɯ na basei o abiserɯ nado, meido ɕiʔkakɯ desɯ.|297
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/565faede/wav/565faede_129.wav|niŋgeɴ no ɕiŋkaɴ ni joɽiː— eikʲoɯ o atae soɯ na ɯirɯsɯ nante, doɯ iɯ jatsɯ ka ɕiɽabete, tenneɴ to ɯirɯsɯ mitai ni konzetsɯ ɕinai to.|289
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| 46 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/29835f87/wav/29835f87_180.wav|tɕoɯ, maɴ, sɯwaɽe omae!|336
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/aafb5758/wav/aafb5758_0557.wav|deɕitaɽa, doɯ ka oɕiete kɯdasai. wataɕi wa, kono nikɯɕimi o doɯ sɯɽeba...|287
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| 48 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9d33dced/wav/9d33dced_442.wav|ɕikaɕi soɯ sɯrɯto, doɯdʑi ni ape ɽia neʔtowaːkɯ o keijɯ ɕite ape ɽia no iɕiki ga kako e okɯɽaɽete ɕimaɯ.|148
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/940de876/wav/940de876_0008.wav|natsɯ, dansa ki o tsɯkete.|76
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/773a4156/wav/773a4156_1917.wav|saikiɴ, beŋkʲoɯ doɯ? hakadoʔterɯ?|10
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| 51 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/78ddc745/wav/78ddc745_1562.wav|kimi wa, onnanoko no tɕoʔto ɕita koɯdoɯ kaɽa, moɕi ka ɕitaɽa koitsɯ oɽe ni hoɽeteɴ dʑa ne? to ka kaŋgaetaɽi wa ɕinai no ka?|90
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4e2f4ba6/wav/4e2f4ba6_1580.wav|kihonteki ni wa, seɽifɯ o çitotsɯ zɯtsɯ ɽiɽeikɯ ɕinagaɽa ɕɯɯɽokɯ ɕite, ato de heɴɕɯɯ ne.|168
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| 53 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6d565f54/wav/6d565f54_1028.wav|iː hanaɕi, kikasete moɽactɕaimaɕita!|101
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| 54 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/eb868edf/wav/eb868edf_058.wav|iː kageɴ wasɯɽetaiɴ daga, ano sɯgata to nakigoe wa wasɯɽeɽaɽenai na.|409
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| 55 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/efb922ca/wav/efb922ca_0371.wav|kazɯki ni dʑibɯɴ no kizɯ o ki ni sɯrɯ noɯrʲokɯ ga arɯ nante, hoʔto ɕita wa. ɴ, hajakɯ iɽi nasai! hoɽa hoɽa hoɽa!|65
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| 56 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7cf5370c/wav/7cf5370c_0964.wav|seʔkakɯ gambaɽoɯʔte omoʔterɯ no ni, sono kimotɕi ni mizɯ o sasɯ mitai ni ɕite. amajakaɕitaɽa mazɯidaɽo?|34
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| 57 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/034aea85/wav/034aea85_1204.wav|minasaɴ, omatase ɕimaɕita.|63
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/37ed21cc/wav/37ed21cc_0774.wav|sono gakɯeɴ de, nani ka aʔtaɴ desɯ ka? dakaɽa kaketsɯketa to ka?|251
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/79a9f817/wav/79a9f817_1421.wav|ɕɯkai no sɯnderɯ no ka...ɕɯmi warɯi.|213
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/332b9006/wav/332b9006_0281.wav|tɕigaɯ! anata no koto iʔterɯ no jo ɕikanosɯke.|85
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/16fefdd2/wav/16fefdd2_0487.wav|soɽe joɽi, koʔtɕi no bikoɯɽaɴ ga ki ni narɯɴ dakedo.|304
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/5f8be8da/wav/5f8be8da_119.wav|kʲoneɴ no kotoɕi da, mata donna bakageta koto ga okorɯ ka wakaɽanai.|333
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c8aa2f99/wav/c8aa2f99_159.wav|hadʑimemaɕite! wataɕi, ameɽiaʔte iːmasɯ!|399
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| 64 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/dc09d578/wav/dc09d578_1022.wav|hai, kedo, dʑitsɯ wa sɯgɯ ni modorɯ tsɯmoɽi daʔtaɴ desɯ. jaʔpaɽi, soɯ sɯkekɯntatɕi no soba niːta hoɯ ga iː to.|215
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| 65 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d88e5111/wav/d88e5111_111.wav|jɯɯma o...ɯsɯmasɯ.|290
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| 66 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a0fd12d7/wav/a0fd12d7_1888.wav|sɯkoɕi ojasɯmi ni naɽeba ikaga desɯ ka? kaoiɽo ga warɯi desɯ jo.|202
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| 67 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8340aaf6/wav/8340aaf6_0087.wav|ni se ndʑɯɯ go neɴ dʑɯɯ ni gatsɯ nidʑɯɯ go nitɕi hatsɯbai jotei.|103
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| 68 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1707f3b6/wav/1707f3b6_560.wav|soɽe wa motomoto kakɯteiteki daʔta no de wa?|187
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| 69 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7da6e5dd/wav/7da6e5dd_023.wav|anata ga haikʲo ni komoʔte ɽokɯniɴ no kɯɽoːzɯdo to tsɯkɯɽiageta sono kaɽa, kaɽeɽa wa soɯ jomitorɯ no desɯ jo.|313
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| 70 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f31e205a/wav/f31e205a_466.wav|iː zo dʑapaniːzɯ tona kai! gokigeɴ da!|104
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1105cfcb/wav/1105cfcb_400.wav|a. soɽe wa, warɯi koto o iʔta. kao nante omoidaɕitaɽa, tsɯɽai omoi o sɯrɯ jo na. sɯmanai. jɯrɯɕite hoɕiː.|126
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/451e2ccb/wav/451e2ccb_1304.wav|gome...iː aɴ wa nakɯte...|322
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6da4c44b/wav/6da4c44b_207.wav|ɕiŋkaː naɽa...moʔto omoɕiɽoi kaeɕi o ɕite kɯɽerɯɴ daga na.|80
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2cf01874/wav/2cf01874_1846.wav|nagi, sono gotɕisoɯ to iɯ no wa, minokasasantatɕi. ɕokɯninsantatɕi ga kaisoɯ o ɯkeoʔtakaɽa no gotɕisoɯ ka?|24
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| 75 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cc948b89/wav/cc948b89_2392.wav|soɽe dake ni, koɽe kaɽa no sɯkedʑɯɯrɯ mo kiʔtɕiɽi kʲoɯ no ɯtɕi ni kimete oita hoɯ ga iː to omoɯ no!|133
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/51c20cd6/wav/51c20cd6_0981.wav|na, na, na, na, naɴ de sonna koto ni...|9
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8b6e7173/wav/8b6e7173_0363.wav|de mo, ima soto o haɕiɽi niːkɯto, sono kaɴ, kaɽeɕi ga hadaka no bidʑo to kɯnzɯ hogɯɽetsɯ ɕite soɯ da wa.|25
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| 78 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f9c8cc01/wav/f9c8cc01_1398.wav|soɯzoɯ ga tsɯkimaseɴ.|162
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/42adcde0/wav/42adcde0_0492.wav|ni kai kikitai kibɯɴ daʔtaɴ dʑa aɽimaseɴ no?|299
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/482d84dd/wav/482d84dd_036.wav|eː, tabɯɴ...wataɕi mo saʔki modoʔte kita tokoɽo desɯkedo, minasaɴ to iʔɕo dʑa nakaʔtaɴ desɯ ka?|437
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/70b38cc9/wav/70b38cc9_041.wav|dokɯɽitsɯsei ga waɽito takaiɴ desɯ jo. niʔpoɴ no tɕoɯhoɯ kikaɴ wa, doko de mo nakano to no kaŋkei ga tsɯjoiɴ desɯ. nakano ga ɕiʔkoɯ ɕite irɯ soɕiki ga hotondo desɯ ne.|237
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/917feebd/wav/917feebd_2214.wav|sɯkɯwaɽenai mono wa, fɯrɯi sekai to tomo ni, eikʲɯɯ ni dʑigokɯ no goɯka ni jakaɽerɯ!|48
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f9c8cc01/wav/f9c8cc01_0006.wav|sempai o motɕiagete imasɯ.|162
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bbd2e2a6/wav/bbd2e2a6_135.wav|oːnaː, zɯʔto kɯtɕi no naka ga kimotɕi warɯi, kɯtɕi no naka ga kimotɕi warɯiʔte iʔtemaɕitakaɽa neː.|118
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f47d69ae/wav/f47d69ae_050.wav|itsɯ made ɕikato ɕiteɴ da jo anta.|273
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2244c7e7/wav/2244c7e7_0495.wav|do, doɯ ɕite kʲɯɯ ni oɽeoɽe sagi?|278
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6489388e/wav/6489388e_0988.wav|haha ga wataɕi no tanomi o kikiːɽete, ɕoɯhoɴɕa ni ɽenɽakɯ ɕi, soɕite, anata ga hakeɴ saɽete kitaɴ desɯkaɽa.|5
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e5581005/wav/e5581005_167.wav|oniːsama ga soko made haɕagɯ no wa mezɯɽaɕiː desɯ ne. sonna ni niʔpoɴ ga sɯki desɯ ka?|264
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1707f3b6/wav/1707f3b6_734.wav|dʑitsɯ wa, saigo ni dezaːto o gojoɯi ɕite oɽimasɯ. okɯtɕinaoɕi ni naɽeba to...|187
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/79a9f817/wav/79a9f817_0086.wav|maitɕaɴ dʑa nakaʔtaɽa kʲoɯrʲokɯ ɕitenai joː, konna heɴ teko dʑikeɴ.|213
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1b74d271/wav/1b74d271_359.wav|daʔtaɽa, soɽe o fɯsegɯ tame ni, hoka no te o ɯtsɯ koto mo dekirɯdeɕoɯ ni.|439
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6d565f54/wav/6d565f54_1918.wav|«ɯfɯfɯ ɯfɯ...ɯ,ʔwwʔww...».|101
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2c3bea98/wav/2c3bea98_417.wav|a, ano, sono, ano, koɽe wa fɯtaɽi no mondai daʔte wakaʔtemasɯɕi, wataɕi wa nani kaiɯ no wa sɯdʑitɕigai ka mo ɕiɽenaikedo...|292
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e855af2a/wav/e855af2a_0862.wav|aisɯ kɯɽiːmɯ zɯtsɯɯ. koɽe wa zokɯɕoɯ de wa nakɯ, seiɕiki na igakɯ joɯgo, desɯ wa, dannasama?|274
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1a5a3db8/wav/1a5a3db8_1097.wav|a, soʔka, damaʔteɽeba ima dake da to omoʔte moɽaetakɯ mo da.|30
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0b8ae160/wav/0b8ae160_0178.wav|deɴɕa, daidʑoɯbɯ desɯ ka?|135
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7f563200/wav/7f563200_025.wav|keɴ de mo, çito wa ɕinimasɯ na.|69
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a8d5a308/wav/a8d5a308_104.wav|ima mitai ni meɽameɽa ɕɯɯɕi o mojasɯʔte seikai dʑa nai ka na?|386
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9ee921f6/wav/9ee921f6_1195.wav|sonna a na ta ni ta be te moɽaerɯ no, hontoɯ ni daisɯki desɯ.|47
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/239e7db6/wav/239e7db6_0640.wav|hanedakɯɴ no tɕikokɯʔte mezɯɽaɕiː jo ne. nani, kinoɯ jofɯkaɕi ɕita no?|214
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Syuuko/Syuuko_Mobamas/Syuko episode/【モバマスエピソード】[めぐる秋色…♪湯けむり紀行]塩見周子 - Niconico Video_2/【モバマスエピソード】[めぐる秋色…♪湯けむり紀行]塩見周子 - Niconico Video_2_chunk22.wav|daɽe mo inainaɽa, tɕoʔto kɯɽaiː— ka na?|216
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/917feebd/wav/917feebd_1260.wav|jogeɴ to iɯto sɯkoɕi ɕɯɯkʲoɯdʑi miterɯ ne. kantaɴ niːeba, hata kaɽa mitaɽa kiː ni mierɯ jogeɴ daga, sono ɽiɽoɴ kaɽa mitɕibikidaserɯ tadaɕiː josoɯ no koto.|48
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/edcc3550/wav/edcc3550_399.wav|narɯhodo, ima no waɽewaɽe no itami wa, hoka no daɽe no itami de mo nai, desɯ ka?|176
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bc8cc1a2/wav/bc8cc1a2_073.wav|daʔte, sono koɽo mada sonna ni nakajokɯ nakaʔtaɕi, ima naɽa zeʔtai oiwai ɕiterɯ jo ne. dakaɽa...|378
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/67eeef73/wav/67eeef73_0860.wav|to iɯ wake de gomeɴ, kazɯnasaɴ. zairʲoɯ ga taɽinakɯte ɽikɯesɯto ɕite moɽaʔta mono wa tsɯkɯɽenakaʔta. kawaɽi ni hambaːgɯ ni naʔtɕaʔta.|54
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2ca35c83/wav/2ca35c83_257.wav|wakaʔta wakaʔta, kiɽawaɽetenakɯte hoʔto ɕita jo.|354
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Syuuko/Syuko_CGSS_ShinAido_Home_Room/syuuko_card_200169/syuukovoice_200169_2_01.wav|kʲoɯ mo joɽoɕiː kaː—? aʔ, koɽe, hajaɽaɕijoɯ to omoʔte.|216
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/361eb7a2/wav/361eb7a2_325.wav|dʑibɯntatɕi ga hoɽonde mo asoko ni wa mada çito ga irɯ. soɯ kaŋgaeɽeba, jasɯɽaka na kimotɕi ni naʔta no da to omoimasɯ jo.|221
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4d8b14ad/wav/4d8b14ad_180.wav|otoɯja wa, hontoɯ ni nani mo ɕiɽanaiɴ da naː.|470
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/940de876/wav/940de876_1088.wav|a ɴ taʔte, ɽakɯtenteki o toːɽikoɕite, noɯteŋki na kɯɽai maemɯki ne.|76
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2e3dbf01/wav/2e3dbf01_0661.wav|dame da! sɯsono o çiɽoge sɯgitaɽa hoɴ no ɽonteɴ ga bojakerɯ! iːɴ da! kʲɯɯdʑɯɯ neɴ mae no setsɯ no ana mo minɯkenai joɯ na baka wa ataɕitatɕi no hoɴ o jomɯ çitsɯjoɯ nai!|178
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c9c3eac7/wav/c9c3eac7_217.wav|fɯzaketerɯ daɴɕi to ka, jokɯ seiza de oseʔkʲoɯ saɽeteta wa.|82
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ac12bbfd/wav/ac12bbfd_0608.wav|ɽijɯɯ to ka doɯ de mo iːkaɽa. tonikakɯ, moɯ ne, asaçi o ɕibaɽakɯ wataɕi no heja kaɽa dasanai.|111
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/43f628ef/wav/43f628ef_131.wav|toɯtoɯ, sabaki no çiʔta jatsɯ ga kita na.|430
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/25714f7a/wav/25714f7a_1821.wav|ikɯ sɯ! honabi taikai no dʑɯmbi o dʑibɯntatɕi tetsɯdaɯɴ de.|179
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/520a2229/wav/520a2229_0898.wav|ikasetakɯ nai desɯ, gaikai ni nante.|256
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/b8015202/wav/b8015202_0837.wav|eçime to ieba onseɴ ga jɯɯmei daga, toɯjo tɕi kata ja naɴ'jo tɕi hoɯ mo otozɯɽerɯbeki da. sono mirʲokɯ o amasɯ tokoɽo nakɯ tsɯtaerɯ ni wa, ɕikokɯ zentai no mirʲokɯ o kataɽanakeɽeba naɽanai na.|93
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/99b5eb16/wav/99b5eb16_0214.wav|ano, mitokotɕaɴ?|320
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/48a6e182/wav/48a6e182_0402.wav|minasaɴ, kono ato, dʑikaɴ arɯ kaɕiɽa?|11
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8340aaf6/wav/8340aaf6_1164.wav|sonna...seʔkakɯ mata aeta no ni...|103
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/93dda15e/wav/93dda15e_421.wav|dekirɯ çitoʔte, daɽe desɯ kaː—?|234
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/21fd006c/wav/21fd006c_175.wav|ima, wataɕitatɕi wa itɕibaɴ ɯtsɯkɯɕiː toki na no jo. soɯ, itɕibaɴ ɯtsɯkɯɕiː toki.|407
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/72921df9/wav/72921df9_260.wav|soɯ...heɴ na koto o kiːtɕaʔte warɯkaʔta wa ne.|363
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e855af2a/wav/e855af2a_0504.wav|de wa, seɴ'etsɯnagaɽa iː daɕiʔpe no wataɕi kaɽa!tsɯkijotake.|274
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d64db35d/wav/d64db35d_245.wav|sono iːkata çikʲoɯ dʑa neː!|460
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/91539726/wav/91539726_0819.wav|eː to, ofɯtaɽi wa, doɯ iɯ kaŋkei na no ka na?|247
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/84be23bd/wav/84be23bd_0895.wav|na, naniːʔterɯɴ desɯ ka? baka naɴ desɯ ka?|112
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c990e343/wav/c990e343_353.wav|ɕitɕɯɯ, keikai ga çitsɯjoɯ na koto o iʔte irɯɕi, sɯgɯ ni wataɕi ni sawaɽoɯ to sɯrɯ wa. naze keikai saɽenai to omoʔte irɯ no? naze jaʔtoko ga çitsɯjoɯ nai to omoɯ no?|330
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4686cc6c/wav/4686cc6c_890.wav|de mo, doɯ jaʔtaɽa kanoɯsei no kieta çito o tasɯkerɯ koto ga dekirɯ no ka na? kanoɯsei ga nai çito o tasɯkerɯ nante, sonna koto dekirɯ no ka na?|17
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8e1072e6/wav/8e1072e6_1137.wav|daʔte sono...kis ɕita toki no koto omoidaɕitɕaʔte...|285
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ea0450c6/wav/ea0450c6_856.wav|de mo, kawa no sobaʔte, kakɯɽerɯ baɕo, aʔta no?|66
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/25714f7a/wav/25714f7a_1820.wav|ɕiɴ doiʔsɯ ne...|179
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cce343be/wav/cce343be_690.wav|tsɯgi wa ɯmakɯ jaɽejo, sɯgɯ ni ɕikakeɽo. ikioi ga fɯɯka ɕinai ɯtɕi ni na.|43
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/25714f7a/wav/25714f7a_1480.wav|dʑibɯɴ ga teiaɴ ɕitakimo dameɕi ga mada naɴ de, zeçi jaɽitaiʔsɯ ne!|179
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bbd90363/wav/bbd90363_0786.wav|wataɕi ni wa, sɯrɯbeki koto ga dekita joɯ desɯ.|38
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1a5a3db8/wav/1a5a3db8_2393.wav|ɯtɕɯɯ ni wa kiŋga ga jakɯnitɕoɯko arɯɴ daʔte.|30
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/90d21566/wav/90d21566_243.wav|oniːtɕaɴ ga saki ni koɯdʑitsɯ ni ɕitaɴ dʑaɴ.|393
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7cf5370c/wav/7cf5370c_0242.wav|ɯmae wa tɕanto jaɽeba dekirɯ ko na no ni, soɽe na no ni doɯ ɕite konna ni mo koɕi ga omoi no ka na.|34
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/034aea85/wav/034aea85_1092.wav|so, sonna koto aɽimaseɴ. koɽe kɯɽai, fɯtsɯɯ desɯ jo.|63
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bbd90363/wav/bbd90363_1773.wav|misetsɯkeɽaɽerɯ koʔtɕi no mi ni mo naʔte hoɕiː mono desɯ.|38
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/917feebd/wav/917feebd_0796.wav|bokɯ wa kakɯdʑitsɯ ni ɕinɽi o tsɯkamaeta no da!|48
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/00013899/wav/00013899_588.wav|kimotɕi jokɯ naʔte moɽaitai no wajɯɯmasaɴ na no ni.|3
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9f9b4bae/wav/9f9b4bae_138.wav|ɽei wa iwane zo. mohaja oɽe ga, soɯ iʔta giɽi ja kenɽi o motanai no wa, wakarɯdaɽoɯ.|230
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bf7b3aa8/wav/bf7b3aa8_424.wav|jaʔpaɽi, oniːtɕaɴ wa tɕinami no soba niːnakɯtɕaʔte...|206
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/282cfa8c/wav/282cfa8c_1385.wav|hai, moʔto dʑikaɴ ga kakarɯ no ka to omoʔte imaɕita.|1
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d39532a8/wav/d39532a8_0125.wav|mizɯnosesaɴ norɯɯɴ.|40
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bf89567f/wav/bf89567f_639.wav|ha, hai! wataɕi no mokɯhjoɯ wa...|62
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/46d6bf83/wav/46d6bf83_0289.wav|tsɯitenai tsɯitenai! dakaɽa, ataɕi nante sonna moɴ daʔteba.|242
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7fa77e7c/wav/7fa77e7c_478.wav|dakaɽa hoɯɽidasɯ koto ni ɕita okage de koɯ ɕite ɽenɽakɯ o torɯ dʑikaɴ mo dekita no daga.|343
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/17184d5e/wav/17184d5e_013.wav|kotɕiɽa mo minaide kɯdasai. teɽepaɕiː nado todoite inai joɯ ni, fɯrɯmaʔte itadakerɯto...|380
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2e3dbf01/wav/2e3dbf01_0658.wav|naː, okaːsaɴ to no omoideʔte, donna mono naɴ da?|178
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Syuuko/Syuuko_Events_and_Card/Card_Commyuu/3/3_chunk84.wav|itsɯ no aida ni ka.|216
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6d60e3a1/wav/6d60e3a1_0933.wav|ano toki wa tamatama sa...naɴ do mo iɯkedo, hekirɯ wa bokɯ dʑa naito...|86
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8b6e7173/wav/8b6e7173_2215.wav|eː, seikokɯ ni wa fonotogɯɽafɯ ne.|25
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9febd2ae/wav/9febd2ae_1759.wav|doɯ ka na? tsɯkaʔte kɯɽerɯ ka na?|146
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bce2a5af/wav/bce2a5af_0283.wav|koɽe dʑaː, kono mama dʑikaɴ kiɽe ni narɯ joː?|196
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1a5a3db8/wav/1a5a3db8_1548.wav|ɽemoɴ to itɕigo no ɕiɽoʔpɯ, rʲoɯhoɯ kakete moɽactɕaʔta!|30
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a93da23d/wav/a93da23d_1086.wav|wataɕi wa, moɯ geŋkai ka mo ɕiɽenai.|116
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d88e5111/wav/d88e5111_690.wav|soɕite ne, minna ni baɽenai joɯ ni, koʔsoɽi sɯrɯ no, tɕiːsana koe de.|290
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/72320792/wav/72320792_458.wav|de mo, koko no fɯtaɽi wa waɴ seʔto dakaɽa, hontai to fɯzokɯçiɴ mitai na. haiʔte kita no mo iʔɕo daʔtaɕi.|316
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/210577c7/wav/210577c7_041.wav|ɯɽeɕikaʔta koto, ɕiawase daʔta koto, iʔpai kakɯ ne.|405
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9dfdd4e5/wav/9dfdd4e5_230.wav|koɽe ga kaminoke naɽa hoŋki derɯɴ dakedo.|255
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/58a2282f/wav/58a2282f_1055.wav|hontoɯ ni ɕiai o sɯrɯɴ desɯ ne. naɴ da ka fɯɕigi desɯ.|198
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/46d6bf83/wav/46d6bf83_1387.wav|sono ai wa hande da naː.|242
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/5d3d37c5/wav/5d3d37c5_0559.wav|doɯ ɕite, kao ga akai no?|305
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f19b6190/wav/f19b6190_1616.wav|ɯfɯfɯ, saiɕo wa doɯ narɯ koto ka to omoʔtakedo, ima wa anta ga bɯtɕoɯ de jokaʔta to omoɯ wa.|52
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bbd2e2a6/wav/bbd2e2a6_390.wav|mada haʔkiɽi to wa kiːte imaseŋga, oːtomata no doɯnʲɯɯ wa miokɯrɯ koto ni narɯ to omoimasɯ. ɯirɯsɯ çitotsɯ de negaerɯ joɯ na niŋgʲoɯ nante, totemo ɕiɴ'joɯ dekimaseŋkaɽa. soɯsakaɴ no aiboɯ wa tsɯtomaɽimaseɴ.|118
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/24c980be/wav/24c980be_169.wav|taigakɯɴ no motsɯ inseki ni mo, soɽe ga mitɕite irɯɴ da to omoimasɯ.|254
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/58a2282f/wav/58a2282f_0584.wav|dʑaː, itɕi dʑikaɴ dake...iː desɯ ka?|198
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bd0cc9b2/wav/bd0cc9b2_343.wav|moɯ tɕoi koɕi agete...joɕi, ikɯ zeː—!|136
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ccb60794/wav/ccb60794_0889.wav|koɽe wa...izɯkiːdʑime ka?|98
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4d416dfd/wav/4d416dfd_105.wav|oneːtɕaɴ o sɯki ni naʔte kɯɽerɯ çito ga fɯirɯ no wa ɯɽeɕiː. tada, soɽe dake da jo.|429
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/17989c6c/wav/17989c6c_071.wav|ija, da to ɕite mo, toɯniɴ ga oɯnoɯ no sɯe ni toɯtatsɯ ɕita ketsɯɽoɴ hodo, kentɕo na mono wa nai.|231
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/297efce1/wav/297efce1_0502.wav|sakiʔpo dake otojakɯ...|72
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7cf5370c/wav/7cf5370c_0898.wav|dakaɽa na no ka, soɽe to mo...soɽe to mo, tɕikaɽa o tsɯkai sɯgite irɯ no ka.|34
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6da4c44b/wav/6da4c44b_375.wav|narɯhodo. date ni haɴ toɕitɕikakɯ mo no aida, ɽesɯ geːmɯ o asonde ita wake de wa nasa soɯ da. ɕikaɕi...|80
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/631b0413/wav/631b0413_431.wav|wataɕi bako o ɕiɽabete irɯ toki ni, ano çito no koɴ, tsɯmaɽi kiokɯ mo, tɕoʔto dake sawactɕaʔta no.|79
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e2ab2e24/wav/e2ab2e24_184.wav|ɯa taɕ ga kono ɽoboʔto o azɯkarɯ koto ni ɕitaɽa, nani ka mondai arɯ ka na?|245
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/338ab306/wav/338ab306_0535.wav|koɯ iɯ geːmɯ wa taimiŋgɯ ga taisetsɯ mitai naɴ da jo. komandoʔte iɯ no o oboerɯ dake dʑa dame mitai naɴ da. dʑoɯkʲɯɯɕa ni katsɯ ni wa.|0
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cce343be/wav/cce343be_431.wav|ɽenzokɯ rʲoɯki satsɯdʑiɴhaɴ da.|43
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2244c7e7/wav/2244c7e7_0524.wav|iːe, ano, dʑoɯkeɴ wa iː ka mo ɕiɽemaseŋkedo, soɽenaɽi no jatɕiɴ wa ɕimaɕita jo ne?|278
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7bc60e34/wav/7bc60e34_000.wav|giɽoɴ ga aɽimasɯga, kotae wa koɽe desɯ. kono matɕi de nani ga okoʔte irɯ no ka o kaimei ɕijoɯ to iɯ dorʲokɯ wa, kako ni toɯzeɴ aʔta soɯ desɯ. sono sɯbete wa, ɕiʔpai ni owaʔte imasɯga.|425
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e77b2f65/wav/e77b2f65_224.wav|çitoɽi de wa kokoɽobosoi ka.|165
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f8c36d2d/wav/f8c36d2d_1983.wav|wakɯhatsɯ no geɴ'iɴ kaimei ni ɕinteɴ ga aʔtaɽa, wataɕi, gohoɯbi o agerɯʔte jakɯsokɯ ɕitaɴ desɯ!|212
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/84be23bd/wav/84be23bd_0966.wav|aikawaɽazɯ tekitoɯ na seʔtei no dezaiɽa desɯ ne.|112
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a808c635/wav/a808c635_0361.wav|zentɕiːtɕi kagetsɯ...taiɕita koto, nai desɯ ka ne.|282
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ed113175/wav/ed113175_104.wav|de mo wataɕi wa, aɕita no dʑibɯɴ ga arɯ koto ga doɯ ɕite mo kandʑiɽaɽenaikaɽa.|152
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a93da23d/wav/a93da23d_0008.wav|ɴ, dʑaː soɽosoɽo mata kɯwaetɕaimasɯ ne. ɴ...|116
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f9c8cc01/wav/f9c8cc01_0408.wav|to, tonikakɯ, sempai wa wataɕi o kodomo to ɕite mite irɯ ki ga sɯrɯɴ desɯ.|162
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9c125949/wav/9c125949_0723.wav|naɴ da ka, aɽaɕi ga saʔta kandʑi desɯ ne.|279
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/297efce1/wav/297efce1_0106.wav|tɕanto iʔte kɯɽetaɽa, iː.|72
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/72fa017c/wav/72fa017c_215.wav|koɽe kaɽa mo joɽoɕikɯ na, otomodatɕi.|476
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7fa77e7c/wav/7fa77e7c_011.wav|hoɕi ni fɯrɯ jɯki. sono setsɯ o teiɕoɯ ɕita keŋkʲɯɯiɴ wa, semmoɴ ga wakɯsei kiɕoɯgakɯ daʔta joɯ da.|343
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c81c2b4d/wav/c81c2b4d_174.wav|de mo amaɽi ni mo aɽe sɯgirɯɴ de, gakɯçi no takai ɕigakɯ wa dameʔsɯ. sasɯga ni dʑitɕoɯ sɯrɯʔsɯ. dakaɽa nigate kamokɯ mo gambarɯʔsɯ..te iɯ ka, hatsɯnetɕaɴ oɕieteʔsɯ.|197
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4ce0075b/wav/4ce0075b_1018.wav|ha? naɴ de? otokodoɯɕi deɕo? mita koto nai no?|18
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bce2a5af/wav/bce2a5af_1946.wav|aozoɽa mo iː moɴ da neː.|196
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cbe5080e/wav/cbe5080e_1102.wav|fɯɕigi na eɴ mo aʔta moɴ da...zaɕiki waɽaɕi ni wa aeta kai?|26
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ea7fb55e/wav/ea7fb55e_416.wav|soko kaɽa sandʑɯɯ fɯɴ kɯɽai kiokɯ nakɯte, ki ga tsɯitaɽa gaʔkoɯ no okɯdʑoɯ niːte.|151
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8a601bf6/wav/8a601bf6_259.wav|ɕiɽanai totɕi de tɕizɯ dake watasaɽetaɽaʔte koto ka.|450
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f3f104d4/wav/f3f104d4_025.wav|dʑobaɴ o ɽiːdo ɕita kɯɽaɕina ka! soɽe to mo azajaka ni gʲakɯteɴ ɕita iːnɯi ka! taikaihatsɯ no entɕoɯseɴ ni doɯzo gokitai kɯdasai!|398
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4800dd8d/wav/4800dd8d_929.wav|doɽai na soɕiki da naː.|324
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Kanade/mobamas_voices/Serifu/voices_kanade_r/voices_kanade_r_chunk35.wav|fɯʔfɯfɯ! seizei, çitobaɴ najande, nebɯsokɯ ni naʔtɕaeba iː no jo.|78
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/010b4e02/wav/010b4e02_0575.wav|a, soɯ da! takaboɕisaɴ no iɯ toːɽi, bidʑoɴ o atama ni omoikaberɯ tabi ni, nani ka ga taɽinai to iɯ kibɯɴ ni narɯɴ da.|169
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/36ea135b/wav/36ea135b_1935.wav|ɯɴ, iː kandʑi niʔte iːkata wa heɴ dakedo, ɯmakɯ otɕite hotondo mɯkizɯ mitai. ano ato fɯtsɯɯ ni okite, arɯite hokeɴɕitsɯ made iʔta jo.|61
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a0fd12d7/wav/a0fd12d7_1167.wav|de wa, iʔte maiɽimasɯ, odʑoɯsama.|202
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f19b6190/wav/f19b6190_0738.wav|a, ɕikata nai ka, onnanoko naɴ daɕi...|52
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/46d6bf83/wav/46d6bf83_1380.wav|toɯzeɴ wataɕi daʔte hadʑimete da jo.|242
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f04ee070/wav/f04ee070_0788.wav|soɽe dʑaː, oçirɯ ni ɕimaɕoɯ ka.|207
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e2ab2e24/wav/e2ab2e24_087.wav|ɯɴ! tɕapiʔkɯsaɴ, kono matɕi o doɯ omowaɽemasɯ ka?|245
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a1a0d114/wav/a1a0d114_0642.wav|ano toki, joɕiomisaɴ kʲɯɯ ni naki daɕitɕaʔte, okaɕikaʔta.|73
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/297efce1/wav/297efce1_0605.wav|bai o sɯɯtsɯ kaɽa kaeʔte kɯrɯ kaɴɕokɯ ga tɕigaɯ.|72
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/24ceb09f/wav/24ceb09f_281.wav|sonna toki, sono omoikomoɯ to ɕite irɯ taiɕoɯ ga, omoikomi da to dʑikakɯ ɕitakɯ nai taisetsɯ na taiɕoɯ de aʔta baːi,.|19
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/297efce1/wav/297efce1_0970.wav|moːta keːsɯ ja nozɯrɯ mo, ikɯtsɯ ka no bɯçiɴ o kɯmiawasete tsɯkɯɽaɽerɯ.|72
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/641fc74a/wav/641fc74a_000.wav|joː, ameɽia. geŋki ni ɕite ita ka?|106
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/91539726/wav/91539726_0109.wav|ɴ?ʔja, ano, madʑime to iɯ ka, paːtonaː o ɯɽagiɽitakɯ naiʔte, fɯtsɯɯ no kandʑoɯ da to omoɯkedo...|247
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/282cfa8c/wav/282cfa8c_0241.wav|jakɯsokɯ ɕite, intaː hai, jɯɯɕoɯ sɯrɯʔte.|1
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d5b3efdf/wav/d5b3efdf_0149.wav|miɽaː bɯkaɴhoɯ, migaɽa o koɯsokɯ sɯrɯ.|291
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/90fa05fd/wav/90fa05fd_0127.wav|tatoeba, tatsɯmaki to ka na. kimi ga harɯnakɯɴ kaɽa omimai saɽeta to iɯ oːame ja fɯbɯki mo, soɽe ni gaitoɯ sɯrɯdaɽoɯ.|120
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4590f2a0/wav/4590f2a0_027.wav|naɴ do mo ɯɽagiɽaɽeterɯkedo, wataɕi wa tomonisaɴ o ɕindʑirɯ.|341
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/eb41adcf/wav/eb41adcf_1609.wav|tɕihatɕiha wa naifɯ de sasaɽeta kɯɽai dʑa ɕinanaikaɽa, wataɕitatɕi wa ki ni ɕinakɯte mo iː to omoɯ wa. doɯ jaʔtaɽa ɕinɯ no ka, tɕihatɕiha dʑiɕiɴ mo ɕiɽanai mitai daɕi.|31
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/20e4e850/wav/20e4e850_156.wav|saʔsɯga aoi kɯɴ! hanaseba wakarɯ wa ne!|390
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7520c617/wav/7520c617_377.wav|meŋkʲo o torɯ tame ni, ɽeɴɕɯɯ sɯrɯɴ da jo!|406
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cbe5080e/wav/cbe5080e_0088.wav|saʔte...joɕiɽo, kʲoɯ wa çima naɴ daɽo?|26
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0ccb413a/wav/0ccb413a_0148.wav|masaka, ano ko o oʔte, dʑibɯɴ mo kaerɯ tsɯmoɽi...|22
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/eb41adcf/wav/eb41adcf_0186.wav|iwaɽete miɽeba soɯ ne. dʑoɕidoɯɕi de konna koto sɯrɯ koto naŋka naiɕi.|31
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/631b0413/wav/631b0413_520.wav|a, ɽiʔkɯ, oɕigoto wa owaʔtaɴ desɯ ka?|79
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/074a35a1/wav/074a35a1_1061.wav|baːrɯ no joɯ na mono.|315
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/dfcfa27c/wav/dfcfa27c_0254.wav|tɕikakɯ desɯɕi, kimotɕiːː jorɯ deɕitakaɽa.|229
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/3951ab83/wav/3951ab83_040.wav|hoŋkoɯ kɯ mi de mo, sakamizɯ sensei ni doɯtɕoɯ ɕite irɯ gakɯinsei wa, seizei goɽokɯ mei desɯkaɽa.|272
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/88ea6529/wav/88ea6529_512.wav|naifɯ o sakate ni moʔte, koɯ jaʔte sasɯɴ dʑa nai kaɕiɽa. koɽe naɽa sɯbajakɯ tɕikaɽa o iɽete, saɕi taɽi çiː taɽi dekirɯ mono.|205
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7aa5ec7f/wav/7aa5ec7f_135.wav|eː, afɯɽekaeʔte irɯ wa. moʔtomo, fɯtsɯɯ no çito ni wa soɯsoɯ mienaideɕoɯkedo.|453
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d39532a8/wav/d39532a8_0276.wav|ɯɯɴ, koɽotɕaɴ no okage de kinoɯ wa oneːtɕaɴ kaeʔte kite kɯɽetakaɽa.|40
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/62e9f0ac/wav/62e9f0ac_120.wav|aite o jowaɽasete oite kaɽakʲɯɯ ɕoɯ tsɯkɯ.|389
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/693d59dd/wav/693d59dd_0548.wav|daga, age sɯgirɯto gʲakɯ ni joɯrʲokɯ o ɯɕinaɯ koto ni narɯ.|81
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bfeec1c4/wav/bfeec1c4_006.wav|mata, oɯɴ goːrɯ ni naɽanakeɽeba iː desɯga.|129
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6489388e/wav/6489388e_2054.wav|otsɯkaɽesama desɯ, ofɯtaɽi to mo.|5
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/b67195c6/wav/b67195c6_235.wav|soɽe joɽi ɽokɯondʑi, kʲɯɯ de warɯiga, çitotsɯ ɕigoto o tanomerɯ ka.|91
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/faa41cbb/wav/faa41cbb_1250.wav|tamatama joɯdʑi ga hajakɯ sɯɴ dakaɽa, tɕoʔto joʔte mita dake jo.|71
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bbd90363/wav/bbd90363_2842.wav|arɯ imi, wataɕi no saikometoɽiː to taikʲokɯ ni itɕi sɯrɯ rɯɯɴ to ierɯdeɕoɯ.|38
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bd4f8711/wav/bd4f8711_0188.wav|soɽe wa, ɕikatanai.|44
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bc778ddb/wav/bc778ddb_1778.wav|kono dʑitai o manekɯ geɴ'iɴ o tsɯkɯʔta wataɕi ga, kono joɯ na koto o iɯ no wa, anata ni toʔte fɯmpaɴ mono ka mo ɕiɽemaseɴ.|175
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/54ba80a8/wav/54ba80a8_1420.wav|nagaɽete irɯ no jo, wataɕi ni wa mierɯ no.|300
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Kanade/Kanade_Events_and_Card/Kanade_Events/NBK/NBK_chunk132.wav|nozomɯ tokoɽo dʑa nai.|78
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/02d30f40/wav/02d30f40_709.wav|kotɕiɽa çitode ga taɽiterɯ joɯ na no de, wataɕi, ima no ɯtɕi ni kʲɯɯkei dʑikaɴ o itadaite okimasɯ.|359
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cdfc229e/wav/cdfc229e_0667.wav|oniːtɕaɴ wa matasete mo, imo o mataserɯ wake ni wa ikanai.|132
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ee093a4f/wav/ee093a4f_2424.wav|dakaɽa mimamorɯ kao wa jamete kɯdasaiʔte iʔterɯdʑa nai desɯ ka! moɯ iː desɯ! çitoɽi de heja e modoɽimasɯ!|46
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2e3dbf01/wav/2e3dbf01_1058.wav|ɯɯɴ, soɽosoɽo kiɽiagerɯ ka. tɕoʔto kiɽi wa warɯiga, aɕita mo arɯɕi na.|178
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/dc09d578/wav/dc09d578_0344.wav|ne, moɯ, soɯ, ɯɯɴ, maː, ammaɽi sɯrɯ koto ni kʲoɯ o minaiʔte no wa arɯ nakedo saː. aː, ɯso da naː, arɯ naː.|215
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Syuuko/Syuuko_Mobamas/Syuko Voice/【モバマス】[湯けむり舞娘]塩見周子【ボイス集】 - Niconico Video/【モバマス】[湯けむり舞娘]塩見周子【ボイス集】 - Niconico Video_chunk42.wav|kaidʑoɯ, moɽiagaʔterɯ ne! de mo, kɯɽaimaʔkɯsɯ wa koko kaɽa! pɯɽodʲɯɯsaːsaɴ ni çi o tsɯkeɽaɽeta ataɕi no hoŋki, misete kɯrɯ ne!|216
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bb6ac6f1/wav/bb6ac6f1_1882.wav|oɕigoto wa sɯki dakedo, osoto e derɯ no wa mendokɯsai jo. wataɕi, oniː no imoɯto daɕi.|87
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e4b5226b/wav/e4b5226b_226.wav|sono jɯkata o kaʔte, osaifɯ ga karɯikaɽa.|421
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Kanade/Kanade_Events_and_Card/Kanade_Events/Kanade_CGSS_Episodes/Kanade_CGSS_Episodes_chunk342.wav|de mo...|78
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8340aaf6/wav/8340aaf6_0826.wav|akemaɕite omedetoɯ, akitakɯɴ.|103
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/338ab306/wav/338ab306_0754.wav|daidʑoɯbɯ ka? naɴ da ka kaoiɽo ga warɯi joɯ ni mierɯkedo, doko ka warɯi no ka?|0
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a01fb5a5/wav/a01fb5a5_177.wav|daidʑoɯbɯ! akɯmade hammeɴ kʲoɯɕi to ɕite saŋkoɯ ni sasete moɽaʔterɯkaɽa.|416
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e2fccdca/wav/e2fccdca_0126.wav|hanaɕi wa jokɯ wakaɽimaseŋga, soɯ iɯ akɯɕɯmi na çitotatɕi to bokɯ o doɯɽetsɯ ni ɕinaide kɯdasai.|100
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/58fe56f1/wav/58fe56f1_429.wav|koŋkai wa barɯko ga warɯi jo.|276
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c9c3eac7/wav/c9c3eac7_070.wav|nani jo, sono me wa.|82
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/940de876/wav/940de876_2645.wav|madʑimeː jo! ɴ daʔte, kondo wa dʑɯmbaɴ toːɽi da wa! ano, aɽe, wa, ta, ɽe, saɽa joɽi takakɯ de, hoɽa, ano, aɽe dʑa nai.|76
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/b8015202/wav/b8015202_1051.wav|kimi wa seŋkʲo o soɯtei ɕite irɯ joɯ daga, ni kai tsɯzɯkete fɯsokɯ no dʑitai de kaisaɴ ɕita seito kai o çikiɯkerɯ seito nado mazɯ inai. daŋgeɴ ɕite mo iː. ɽiʔkoɯho mo sɯiseɴ mo kiʔto nai.|93
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Kanade/mobamas_voices/Episodes/kanade_otome/kanade_otome_chunk51.wav|koɽe kaɽa mo.|78
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2cf01874/wav/2cf01874_0328.wav|sono tame ni mo, kʲoɯ no kaigi o minoɽiarɯ mono ni senɯba na.|24
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cdfc229e/wav/cdfc229e_1237.wav|iʔteɽaʔɕai, bɯɯɴ tɕoɯkʲɯɯ o inoʔterɯ.|132
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0f6fbea8/wav/0f6fbea8_0579.wav|ɯmɯ, giɴ to doɯ naɽa jaʔta koto wa arɯ. tetsɯ wa tokasenakaʔta.|411
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bf89567f/wav/bf89567f_552.wav|sono toki ni dendʑi parɯsɯ ga haʔsei ɕite, sɯgoi deɴ'atsɯ ga tɕidʑoɯ ni jaʔte kɯrɯ no de, deŋki kikai wa kanzeɴ ni hakai saɽemasɯ.|62
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/773a4156/wav/773a4156_2662.wav|maː, iɽoiɽo to iɽijoɯ de.|10
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/3ae04663/wav/3ae04663_144.wav|eʔto, soɽe ga...kite kɯɽerɯ joɯ ni onegai ɕitaɴ desɯkedo, ammaɽi sɯnaː ni kiːte kɯɽerɯ joɯ na çito dʑa nai to iɯ ka...sono...|180
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/44feed2f/wav/44feed2f_0446.wav|so, soɯ iɯ wake de wa...o, ohajoɯ gozaimasɯ.|193
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bf145e7a/wav/bf145e7a_075.wav|ɯɯɴ, anedokɯɴ ga ɽeiai aite o wataɕi ɕika ɕiɽanakɯte mo koɯkai ɕinai joɯ ni, zɯʔto itɕibaɴ de, taʔta çitoɽi no çito de aɽitai.|308
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/00163dc9/wav/00163dc9_1552.wav|soɯ, hazɯki ga çitoɽi de aiɽoɴ o...|88
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/940de876/wav/940de876_3419.wav|dʑa, omoʔta keːsɯ wa? tsɯkɯʔtenaikaɽa naɴ to ka naɽanai?|76
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c593ed00/wav/c593ed00_0406.wav|hontoɯ niːdʑoɯ naɕi daʔta no?|74
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/79a9f817/wav/79a9f817_1371.wav|sawagi ni naʔtaɽa, koʔtɕi mo komarɯɕi ne. masɯkomisaɴ ni wa damaʔtete moɽaimaɕoɯ.|213
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bc778ddb/wav/bc778ddb_0523.wav|sɯgɯ akerɯkaɽa, tɕoʔto mate!|175
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2bd06fbc/wav/2bd06fbc_314.wav|amaɽi ni toʔte wa doko de mo jokaʔtaɴ da to omoɯ. dʑiʔsai aitsɯ wa saɴ neɴ no toki, daigakɯ heʔnʲɯɯ o kobanda. sono ɽijɯɯ ga, beŋkʲoɯ naɽa doko de mo dekirɯ, dakaɽa na.|340
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/13478d0f/wav/13478d0f_252.wav|fɯfɯʔ, soɽe ni, wakaʔterɯdeɕo? sonna ɯɽeɕi soɯ na kao ɕite mitsɯmeɽaɽete mo, wataɕi wa, anata no sɯki na wataɕi dʑa naiɴ dakaɽa ne.|219
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9dfdd4e5/wav/9dfdd4e5_691.wav|jokɯbaʔte soɯdaɴ ɕite ɕimaɯ koto mo arɯ to omoɯ no dakedo, iː?|255
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bee5e00e/wav/bee5e00e_087.wav|ɽijoɯ dekirɯ mono wa naɴ de mo ɽijoɯ sɯrɯ ɕɯgi de ne.|402
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/5d3d37c5/wav/5d3d37c5_1865.wav|ima sɯgɯ ikimaɕoɯ! keʔteiseŋgo no sɯiːtsɯ wa wataɕi ga gotsɯoɯ sɯrɯ!|305
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/338ab306/wav/338ab306_0399.wav|wataɕi ga inakɯ naʔtaɽa, ano ko ga, çitoɽi da...|0
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/00163dc9/wav/00163dc9_0881.wav|hai, harɯnakɯɴ wa wataɕi ga osewa ɕinaito nani mo dekimaseɴ.|88
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/48a6e182/wav/48a6e182_0910.wav|iː baɕo ne, toʔtemo...|11
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1707f3b6/wav/1707f3b6_193.wav|ano toki, senɽi gaɴ no toɽiʔkɯ ga baɽe, esɯ to mo do mo kʲɯɯdaɴ saɽete ita oɽi...|187
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/047b2cc9/wav/047b2cc9_382.wav|moɯ, warɯi eikʲoɯ o ɯkemaɕita wa ne, hontoɯ ni.|302
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d0cc4881/wav/d0cc4881_0335.wav|tonde kimaɕita jo, toɽi sempai.|97
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/52ccb6af/wav/52ccb6af_050.wav|tonde mo aɽimaseɴ. de wa, gokentoɯ o oinoɽi ɕimasɯ.|166
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/5cb8225c/wav/5cb8225c_237.wav|deɕoɯdeɕoɯ? fɯtaɽi mo eɴrʲo ɕinaide tabeɽeba iː no ni.|266
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/39bbe2d2/wav/39bbe2d2_144.wav|wataɕi desɯ ka? mizɯbidaɕi ni naʔte ɕimaʔta joɯsɯ o, tɕikakɯ de mite mijoɯ to. zɯʔto toːkɯ kaɽa ɕika mite inakaʔta mono desɯkaɽa.|39
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/b8b5fe66/wav/b8b5fe66_2222.wav|naɴ te iːkata wa doɯ ka to omoɯkedo, hoka ni hjoɯgeɴ ga omoitsɯkanakaʔta. minna ga ikita kiseki o, ɕɯɯkai atsɯkai ɕite, sono oɕikaɽi wa, ato de minna kaɽa ɯkerɯ jo.|121
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d0cc4881/wav/d0cc4881_0713.wav|fɯtsɯɯ desɯ jo, tokɯni naɴʔte wake de mo nai desɯ.|97
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f4169f28/wav/f4169f28_174.wav|ijaːja, keʔkoɯ desɯ, mata kondo de mo.|77
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8d6eccd0/wav/8d6eccd0_0923.wav|hoɽa, dame da zo?mijasaɴ, gekihoko pɯɴ pɯɴ marɯ ni naʔtɕaʔtaɽa doɯ sɯrɯ no?|92
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/012e4f22/wav/012e4f22_414.wav|kʲoɯ wa okʲakɯ ga kɯrɯkaɽaʔte.|243
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cc948b89/wav/cc948b89_2854.wav|betsɯ ni ɕinda nante iʔtenaiɕi.|133
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/5d3b01f8/wav/5d3b01f8_0731.wav|soɯdaɴ to iɯ no wa...koɽe da.|89
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/3e02a4dc/wav/3e02a4dc_194.wav|ehe, moɕi ka ɕitaɽa, moɯ teɴɕɯ wa tɕikakɯ ni maeoɽiterɯ, ka mo jo?|12
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/88ea6529/wav/88ea6529_439.wav|soɽe ni, hoɽa, soto ni wa daɴɕi ga irɯɽaɕiːdʑa nai.|205
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cef39ba9/wav/cef39ba9_011.wav|amaɽi nagaiɕite wa gomeiwakɯ o kakete ɕimaimasɯ no de, koɽe de ɕitsɯɽeiːtaɕimasɯ. kitɕoɯ na goikeɴ, aɽigatoɯ gozaimaɕita.|408
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f6c4b7b2/wav/f6c4b7b2_0430.wav|kɯɴ kɯɴ...ɯ, taɕika ni, tɕoʔto asekɯsai ka mo...|42
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d39532a8/wav/d39532a8_1338.wav|ɽiʔkɯɴ, mimi made akai jo.|40
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/84be23bd/wav/84be23bd_0925.wav|daidʑoɯbɯ desɯ. tsɯba de mo tsɯketokeba naoɽimasɯkaɽa.|112
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c5a556c7/wav/c5a556c7_130.wav|naɴ desɯ ka, kono çidʑoɯ dʑitai ni.|445
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/46d6bf83/wav/46d6bf83_1185.wav|fɯɯɴ, daɽe ka kiteta no? asa? jorɯ?|242
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7da6e5dd/wav/7da6e5dd_097.wav|kɯni no handaɴ desɯ. tokɯɽei to ɕite, kʲoka ga oɽirɯ joɯ ni naʔte imasɯ.|313
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/de28ee15/wav/de28ee15_1144.wav|wataɕi mo oneːtɕaɴʔpoiɕi.|220
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ac0e6660/wav/ac0e6660_0423.wav|dakaɽa, mada gaki daʔta. ano toki no wataɕi wa.|248
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bbd90363/wav/bbd90363_1546.wav|tatoe ɽikɯ ga soɕiki o hanaɽeta to ɕite mo, wataɕitatɕi no kaŋkei wa kawaɽanaiɴ desɯkaɽa.|38
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cda4375a/wav/cda4375a_0886.wav|dʑɯɯtɕɯɯ haʔk��ɴ, tada no ijagaɽase.|190
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ccb60794/wav/ccb60794_0990.wav|joɯgiɕa o ɯtagaɯ no wa toɯzeɴ no koto jo.|98
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bd0cc9b2/wav/bd0cc9b2_642.wav|saikakɯɴ ga ɕiga keɴ no omijage toɽi niːʔterɯ aida, gʲaɽahasaɴ ni mo iwaɽeta. kʲoɯ wa, fɯwafɯwa ɕitemasɯ ne to.|136
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2990a149/wav/2990a149_017.wav|mo tɕi no ɽoɴ! doko made mo oto mo ɕimaseː—!|468
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/91c32dcd/wav/91c32dcd_179.wav|do, doɯ ɕijoɯ...koko dake goʔsoɽi toɽeba daidʑoɯbɯ ka na.|170
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/aafb5758/wav/aafb5758_1430.wav|soɯtetsɯsama ga zɯibɯɴ seŋkoɯ saɽete irɯ to odosaɽete ita no de, isogijaɕite kitaɽa, josoɯ joɽi zɯʔto hajakɯ ni oitsɯkemaɕita.|287
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/739cd4cd/wav/739cd4cd_115.wav|de, kono aida ɯtɕi ni ɕinseki no onnanoko ga asobi ni kitaɴ desɯkedo.|371
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9c125949/wav/9c125949_1006.wav|gɯɽaidaː mo kowasoɯ to ɕitaɴ desɯ ka!?|279
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7b3d6f79/wav/7b3d6f79_1029.wav|na, nani ɕite mo iːɴ da no.|201
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/012e4f22/wav/012e4f22_033.wav|dakedo ne, wataɕi no negai wa, soɯ dʑa nakaʔtaɴ da jo.|243
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/fe623689/wav/fe623689_120.wav|konamitɕaɴ, tsɯini kansei ɕita jo, bokɯ no biʔtoɽio beneto ga!|462
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0d70cf5c/wav/0d70cf5c_524.wav|fɯtaɽi kiɽi dʑa naito, eɽoi koto mo dekinaidaɽoɯɕi.|143
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1b74d271/wav/1b74d271_411.wav|masaka konna çi ga kɯrɯ nante ne wataɕi ga anata o çikitomerɯnaɽa mada ɕimo.|439
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0e1e679f/wav/0e1e679f_249.wav|ɽiʔkɯ, omaesaɴ doɯ mo oɽe no jakɯme o ɽeːdaː ka nani ka to kantɕigai ɕiterɯ fɯɕi ga arɯ jo naː!fɯɴ, hai hai! wakaʔtemasɯ! ɕigoto wa kitɕinto jaɽimasɯ jo! ɯmasɯtaː!|360
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bca2cfac/wav/bca2cfac_0140.wav|isogaɕikɯte isogaɕikɯte sɯɽikiɽetɕaeba, taikɯtsɯ naŋka kandʑirɯ koto wa naiɴ daɽoɯkedo. inaka no ɕakaja nante soɯ isogaɕiː moɴ dʑa naiɕi ne.|211
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/5d68aedf/wav/5d68aedf_1340.wav|fɯfɯʔ, jɯɯwakɯ saɽerɯ hoɯ ga warɯiɴ desɯ jo, jɯɯmasaɴ.|108
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4590f2a0/wav/4590f2a0_143.wav|anisaɴ wa, betsɯ ni, takɯdʑi anisaɴ dake dʑa nai desɯ.|341
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c72dcc2e/wav/c72dcc2e_118.wav|hontoɯ ni jɯki wa, kɯɽasɯ no niŋgʲoɯ o oboenai naː.|280
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/75044eb2/wav/75044eb2_783.wav|hoɴʔto idʑiɽi ga inai sɯ wa kanatɕaɴ. jaɕiɽo to ka wakaɽijasɯi kɯɽai teɽeterɯ no ni.|49
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/8b6e7173/wav/8b6e7173_1672.wav|naɴ kaija na kandʑi ne. neɽai ga baɽeterɯ bɯɴ, koʔtɕi wa doɯ ɕite mo gotegote niːkazarɯ o enaiɕi.|25
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| 328 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2cd8d40e/wav/2cd8d40e_0595.wav|ima made...hontoɯ ni aɽigatoɯ...sɯgokɯ sɯgokɯ tanoɕikaʔta...aɽigatoɯ, hontoɯ ni...|306
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/012e4f22/wav/012e4f22_177.wav|sonna wake nai jo, ɯɴ, sonna wake nai.|243
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/91c32dcd/wav/91c32dcd_342.wav|ɴ, eʔto ne. makotokɯɴ no hanaɕi, sɯgokɯ, naɴ te iɯ ka, mɯzɯkaɕikɯte, jaʔpaɽi. zembɯ wa ɕindʑiɽaɽenai...|170
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/d88e5111/wav/d88e5111_476.wav|bansoɯkoɯ wa, feikɯ na no.|290
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7cf5370c/wav/7cf5370c_0934.wav|narɯhodo...jaʔpaɽi soɯ kɯrɯ jo na.|34
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f19b6190/wav/f19b6190_0534.wav|de, tsɯide ni çiːɽaki ni mo ɽenɽakɯ ɕite oite.|52
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/520a2229/wav/520a2229_0784.wav|aɽa, nakanaka tekikakɯ na hjoɯgeɴ desɯ ne.|256
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c94451aa/wav/c94451aa_289.wav|maːmaː, koko wa minna de wakeaʔte, minna de ɕiawase ni naɽeba iː ze.|265
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/13478d0f/wav/13478d0f_221.wav|ɯɯ!...nani joɯ!|219
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/4ce0075b/wav/4ce0075b_0843.wav|kʲoçiːʔtaɽa ɕigoto aka no hoɯ wa anta no aka bɯɽoʔkɯ sɯrɯ koŋgo iʔsai ɽakɯgaki misenai.|18
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/6d19f294/wav/6d19f294_734.wav|naɽa kaŋgaeta hoɯ ga iː desɯ jo. soɯ naɽanai jo, doɯ ɕitaɽa iː no kaʔte.|191
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/df6c208e/wav/df6c208e_0678.wav|ɯɯɴ, miterɯ dake desɯ.|41
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/05b1a5fa/wav/05b1a5fa_168.wav|neɽai wa kosome...soɯ da na.|339
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bc778ddb/wav/bc778ddb_1907.wav|ɯɴ, naɴ kaikani mo tetsɯgakɯɕaʔpoi ne.|175
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/917feebd/wav/917feebd_0944.wav|mɯɯdʲʔte, iɴ'isɯnaː to kaimi no taŋgo da jo.|48
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ff4fee9b/wav/ff4fee9b_0872.wav|osanai koɽo kaɽa, ohahasaɴ ga oɕiete kɯɽetaɴ desɯ jo.|124
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/e5581005/wav/e5581005_239.wav|oːini oikaɽi no joɯ desɯ ne wataɕi mo koŋkai wa oniːsama ga ikenai to omoimasɯ ɕemmoŋka ni moŋgaikaɴ ga ikeɴ o noberɯnaɽa johodo no kakɯgo o ɕinakeɽeba.|264
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/52ccb6af/wav/52ccb6af_294.wav|hoŋki o daɕite inakaʔta dake desɯ! ano tatakai wa dandʑite, dandʑite wataɕi no hoŋki de wa aɽimasendeɕita! hoŋki o daɕitaɽa waɽewaɽe no ɕoɯtai ga ɽotei sɯrɯ osoɽe ga aʔta no de, dʑiʔtɕoɯ ɕita dake desɯ!|166
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/224918d3/wav/224918d3_184.wav|fɯtsɯɯ soɯ iɯ toki wa, kʲaːʔte sakende awatete hanaɽete, ɽeiseitɕintɕakɯ na çiɽoiɴ ga, hoː o akaɽamerɯsama o tanoɕimɯ moɴ dʑa nai no ka ne.|444
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/44feed2f/wav/44feed2f_0139.wav|de wa ɕibaɽakɯ...ɕibaɽakɯ ɕita dake de mo, meiwakɯ kakesasete kɯdasai.|193
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| 348 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Syuuko/Syuko_CGSS_ShinAido_Home_Room/syuuko_card_201159/syuukovoice_201159_2_03.wav|fɯto omoɯ toki ga arɯɴ da. ima kono ɕɯŋkaɴ wa gendʑitsɯ dʑa nai no ka mo,ʔte.|216
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/78ddc745/wav/78ddc745_1674.wav|keɽedo dʑiʔsai wa fɯtaɽi daʔta. dakaɽa okɯɽe ga ɕoɯdʑita, ataɽimae da.|90
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/f8c36d2d/wav/f8c36d2d_1833.wav|maː, fɯɕigi desɯ ne.|212
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ae93354c/wav/ae93354c_0266.wav|kono toɽi, jɯkiɯsakiʔte namae naɴ da ne.|130
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| 352 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0da07cfa/wav/0da07cfa_0206.wav|doɯ ɕite koʔtɕi o mita mama sagarɯɴ desɯ ka?|45
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ac12bbfd/wav/ac12bbfd_1410.wav|daː, maɕikatanai.|111
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/ae93354c/wav/ae93354c_0988.wav|kimi no mawaɽi ni wa, çito ga takɯsaɴ atsɯmaʔterɯ.|130
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| 355 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cda4375a/wav/cda4375a_1165.wav|jaɕiɽo mo doɯ ɕita no? dʑɯgʲoɯtɕɯɯ no hazɯ dakedo.|190
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| 356 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Kanade/mobamas_voices/Serifu/voices_kanade_r/voices_kanade_r_chunk34.wav|koɽe o wataɕi ga iɯ imi, wakarɯ?|78
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| 357 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/7d557804/wav/7d557804_362.wav|a soɯ, hanaɕikomɯ no wa iːkedo, okʲakɯsaɴ no ode rɯ no o wasɯɽenai joɯ ni ne.|392
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| 358 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/00163dc9/wav/00163dc9_2512.wav|wataɕi wa, hazɯki no tameʔte dake jo.|88
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1967ee53/wav/1967ee53_0364.wav|dakaɽa sa, sono madʑo ni, jɯɯma no koto o soɯdaɴ ɕi niːkoɯ ja!|21
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| 360 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/dfcfa27c/wav/dfcfa27c_0281.wav|dʑa, wataɕi mo koko made ni ɕite okimasɯ.|229
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| 361 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a601effc/wav/a601effc_119.wav|dʑibɯntatɕi no tɕikaɽa de nasɯkaɽa koso, imi ga arɯɴ dʑa nai no?|303
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| 362 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/9ee921f6/wav/9ee921f6_1246.wav|wataɕi mo hazɯɽe desɯ.|47
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/1967ee53/wav/1967ee53_1251.wav|itɕi marɯ ni goɯɕitsɯ, kisaɽaniːmijo desɯ. joɽoɕikɯ! jɯɯma to wa, osananadʑimi jo.|21
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| 364 |
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/0253acb6/wav/0253acb6_111.wav|oniːtɕaɴ wa beŋkʲoɯ o gambarɯ. osananadʑimi wa...|332
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/c480db9a/wav/c480db9a_0956.wav|dʑikaɴ ga tateba, kiʔto omoidasɯ no desɯ wa. de mo, ima omoidaɕite moɽawanakɯte wa, wataɕi ga moɯ itɕi do iwanakɯte wa naɽanakɯ naɽimasɯ no. dakaɽa, moɯ itɕi do jokɯ kaŋgaete itadakitai desɯ wa.|189
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+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/cda4375a/wav/cda4375a_0473.wav|eː. jahaɽiːkai ni tɕoɯdʑikaɴ todomarɯ no wa, ɽisɯkɯ ga taka sɯgirɯ.|190
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/cbe5080e/wav/cbe5080e_1260.wav|kiːtɕa inai ne. hai hai, ima ikɯ joːɴ.|26
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| 368 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/bfeec1c4/wav/bfeec1c4_811.wav|komponteki ni, nani ka kantɕigai saɽete imasɯ ne.|129
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/a0fd12d7/wav/a0fd12d7_1953.wav|jɯkisaɴ ni mo, samazama na dʑidʑoɯ ga arɯ no da to omoimasɯ. wataɕi wa, betsɯ ni ki ni ɕite oɽimaseɴ.|202
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/bc778ddb/wav/bc778ddb_1261.wav|o, oːo, dʑodʑo ni dʑodʑo ni ɕizɯnde ikimasɯ. koɽe de saigo, takizawa sensei wa ɯmiːkaba, sajoɯnaɽa, sajoɯnaɽa!|175
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+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/54ba80a8/wav/54ba80a8_1293.wav|akɯmɯ no geɴ'iɴ o ɕiɽaberɯ to ɕite...soɽe, tetsɯdaʔte kɯɽerɯ no?|300
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2ca35c83/wav/2ca35c83_105.wav|teido no mondai da. oɽe daʔte, soŋkei sɯrɯ dʑoɯɕi ja sempai niː— koto ga aʔtaɽa, joɽokobɯ kɯɽai no ɕiŋkei wa arɯ za. daga, omaetatɕi wa soɽe ga sɯbete da. soɽe ga fɯɕigi de tamaɽanɯ.|354
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/443c360e/wav/443c360e_186.wav|to wa ie, iza to naʔtaɽa hoŋki o dasɯ...hazɯ dʑa. tɕiɕiki mo tɕikaɽa mo, ajatsɯ o ɕinogɯ mono nado soɯ wa oɽaɴɕi no. ma, ɕimpai naidʑaɽo.|113
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/2af831b5/wav/2af831b5_115.wav|dʑi go kɯ de arɯ! dakaɽa koso, kami o ɕindʑi nasai!|381
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/95c67421/wav/95c67421_778.wav|semete, ato wa jɯʔkɯɽi ojasɯmi kɯdasaimase, ɯtoɯsaɴ.|173
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/940de876/wav/940de876_2575.wav|konna koto naɽa, moɯ tɕoʔto ano ɯza megane to nakajokɯ ɕitakeba jokaʔta wa.|76
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+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/3371a8ac/wav/3371a8ac_450.wav|wataɕi mo, çito no inai tokoɽo niːʔkoɯ to omoʔta no.|7
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/76981655/wav/76981655_0873.wav|mawaɽi ga miːnna keŋka ɕitɕaɯ no. wataɕi wa tada, wataɕi no oɕaɽe o mite hoɕiː dake na no ni. homete hoɕiː dake na no ni.|115
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/home/austin/disk1/stts-zs_cleaning/data/moe_48/df6c208e/wav/df6c208e_1685.wav|soɯ naɴ desɯ! mɯɕiɽo, kɯndeɴ ni sonaete tabenaito, tɕanto tɕikaɽa o haʔki dekinaidʑa nai desɯ ka!|41
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| 380 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/51eb30f9/wav/51eb30f9_151.wav|de mo, ato wa joɽoɕikɯ ne.|144
|
| 381 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/95c67421/wav/95c67421_133.wav|soɯ desɯ wa ne, oheja ni çitoɽi de oɽimaɕita mono.|173
|
| 382 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/ochinbarai/voice/mzr/mzr_13_004_013.wav|mioboe ga arɯ kao daʔta no to, ɕasɯmiʔte namae de, ɕiasɯɴ no mɯsɯme daʔte kakɯɕiɴ ɕita wa.|55
|
| 383 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/8d2b2495/wav/8d2b2495_0721.wav|soɽe wa dandʑo sabetsɯ da ne.tɕiʔko to iɯ taŋgo wa otoko nomi ni jɯrɯsaɽeta toʔkeɴ da to de mo iɯ no kai?|184
|
| 384 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/70b38cc9/wav/70b38cc9_205.wav|soɯ iʔte kɯɽerɯ no wa ɯɽeɕiːkedo, soɯ iɯ wake ni mo ikanaidaɽoɯ ne.|237
|
| 385 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/86a62c68/wav/86a62c68_185.wav|aɴ, soɽe naɽa hanaɕi ga hajai desɯ ne. eː, aɽe, wataɕi desɯ. oçirɯ no dʲ.|286
|
| 386 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/54ba80a8/wav/54ba80a8_0852.wav|kakɯsei mɯ to ka, meiseki mɯʔte, mita koto arɯ?|300
|
| 387 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/48a6e182/wav/48a6e182_0750.wav|soɽe joɽi mo, tɕiɽakaʔta mono o katazɯkemasɯ wa jo.|11
|
| 388 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/f19b6190/wav/f19b6190_1244.wav|fɯɯ, ano ko, rʲoɯɕiɴ to wa ɯma ga awanaikaɽa naː.|52
|
| 389 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/2cf01874/wav/2cf01874_0061.wav|hatɕiɽokɯ. kono koɯbai no mɯkoɯ no keɕiki ga, omae ni mo mierɯ jo na.|24
|
| 390 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/012e4f22/wav/012e4f22_375.wav|wataɕi no kaɽada ga jokɯ naʔtaɽa, joɯkɯɴ to keʔkoɴ ɕitai.|243
|
| 391 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/02153faa/wav/02153faa_462.wav|maː, koɽe wa dʑoɯdaɴ mitai na hanaɕi naɴ dakedo, koko wa, ɯtɕɯɯ koɽoniː mitai na baɕo naɴ dʑa nai kaʔte, harɯmi wa iʔtetakedo na.|150
|
| 392 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/ad28b91b/wav/ad28b91b_0955.wav|wataɕi wa mensetsɯ de tɕoʔto jokei na koto o kɯtɕibaɕiʔta dake da. moɕi ka ɕitaɽa bɯɽei daʔta ka mo ɕiɽenai.|153
|
| 393 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/338ab306/wav/338ab306_0207.wav|daidʑoɯbɯ, daidʑoɯbɯ da. wataɕi wa jɯɯma ga oiɕiːʔte iʔte kɯɽerɯ ojɯɯhaɴ o, tsɯkɯʔte jaɽeterɯʔte.|0
|
| 394 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/f988d3c6/wav/f988d3c6_171.wav|iʔjaha ja, wataɕi mo odoɽoki deɕita jo. ɕikaɕi, koɽe wa matɕigai nai, wakai koɽo ni mita ikinokoɽi to soʔkɯɽi da.|235
|
| 395 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/78024d52/wav/78024d52_1822.wav|tsɯgi no deːto wa ɽaiɕɯɯ ka. nagai wa ne.|107
|
| 396 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/54ba80a8/wav/54ba80a8_0749.wav|gaʔɕɯkɯ wa kʲoɯ kaɽa no ɽiʔkakaɴ. ɕihakɯ dakaɽa, asaʔte ni wa kaisaɴ sɯrɯ joɯ ni.|300
|
| 397 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/c480db9a/wav/c480db9a_0218.wav|jɯɯseisaɴ, wataɕi wa tsɯkawanai to ni do iːmaɕita jo.|189
|
| 398 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/590e4fbf/wav/590e4fbf_167.wav|tamani wa bɯkatsɯ saboctɕaeba.|458
|
| 399 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/ad28b91b/wav/ad28b91b_0348.wav|koko wa keŋgai da, meːrɯ wa okɯɽenai. daitai, ɕɯdʑiɴ no mae de kotowaɽi mo nakɯ keitai deɴwa o çiɽakɯ to wa nanigoto da.|153
|
| 400 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/ee093a4f/wav/ee093a4f_1200.wav|ɯa, iːe. sensei ni homeɽaɽerɯ hodo no meido ni tetsɯdawaɽetaɽa, wataɕi no derɯ makɯ ga nakɯ narɯdʑa aɽimaseɴ ka. soba de mite, adɯbaisɯ dake ɕite kɯdasai.|46
|
| 401 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/40968f4b/wav/40968f4b_026.wav|a ɽɯ ɸɯɯɽeʔto, ano bɯɴɕoɯ wa omae ni azɯketa hazɯ da na.|317
|
| 402 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/45a005ba/wav/45a005ba_1217.wav|mɯɯbiː naɴ desɯkaɽa kɯɽiʔkɯ wa dekimaseɴ.|122
|
| 403 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/f9c8cc01/wav/f9c8cc01_0856.wav|harɯna sempai, saikiɴ wa obento daʔtadʑa nai desɯ ka.|162
|
| 404 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/ochinbarai/voice/ksm/ksm_05_001_007.wav|na!!? mi, mi, mitsɯrɯ! a, anata, nani o iʔte...|154
|
| 405 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_soshy/Japanese/imas_split/Syuuko/Syuko_CGSS_ShinAido_Home_Room/syuuko_card_201083/syuukovoice_201083_2_04.wav|hajatetɕaɴ, gambaɽijasaɴ daɕi, naɴ ka hoʔte okenaiɴ da jo naː.|216
|
| 406 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/532ebfa4/wav/532ebfa4_183.wav|aɕita no gakɯensai wa, tɕanto keŋgakɯ niːkɯ jo. hontoɯ ni sɯsɯmanai na.|384
|
| 407 |
+
/home/austin/disk1/stts-zs_cleaning/data/moe_48/57d35f28/wav/57d35f28_229.wav|sonna no honniɴ ɕidai jo. tɕisa to irɯ hoɯ ga ɕiawaseʔta koto mo arɯ ka mo ɕiɽenaidʑa nai.|284
|
AuxiliaryASR/Data/val_list_subsect.txt
ADDED
|
@@ -0,0 +1,128 @@
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| 1 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk2835.wav|soɯ iɯ mono no sonzai o, ɕiʔte ɕimaʔtaɴ dakaɽa.|5
|
| 2 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_03/Sakurai_Takahiro_03_chunk2321.wav|sendʑoɯgahaɽa wa iʔɕɯɴ, me o todʑita.|1
|
| 3 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk1674.wav|neŋgaɴ no, dʑibɯɴ no kaɽada o.|4
|
| 4 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk2793.wav|ha?|1
|
| 5 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk1114.wav|mɯboɯbi o josoːɯ koto de, soɕite mɯgai o josoːɯ koto de, ɕɯɯi ni mamoɽaɽete ikite irɯ, sɯɯka jo.|4
|
| 6 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk671.wav|ɕindo no koto dʑanʲainʲa.|3
|
| 7 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_01/Sakamoto_Maya_01_chunk349.wav|koko de neɴ no tame ni kotowaʔte okɯga, bokɯ to kaɽeɴ wa betsɯ ni naka no iː kʲoɯdai to iɯ wake de wa nai. moɯ çitoɽi no imoɯto de arɯ tokoɽo no sɯeʔko, tsɯkiçi to kaɽeɴ to iɯnaɽaba, soɽe wa toɕigo to iɯ koto mo aʔte kanaɽi ɽeberɯ no takai nakajoɕi de wa arɯ no daga, zannennagaɽa, bokɯ to sono fɯtaɽi to narɯto, amaɽi naka ga iː to wa ienai.|6
|
| 8 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_03/Horie_Yui_03_chunk1148.wav|tomodatɕi to iɯ kotoba de wa sɯkoɕi jowaiɴ da ze. aitsɯ no koto o bokɯ wa zensei de wa doɯitsɯ dʑimbɯtsɯ daʔtaɴ dʑa nai ka to omoʔte irɯ.|0
|
| 9 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk1666.wav|de mo, tsɯbasasaɴ, niːtɕaɴ to fɯtaɽi kiɽi nante...|1
|
| 10 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk653.wav|soɽe wa kanodʑo no sɯtoɽesɯ ga sɯkɯnakaɽazɯ, sono koto ni joʔte kaɴwa saɽetakaɽa daɽoɯ.|3
|
| 11 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk2497.wav|ɯsotsɯki wa...|4
|
| 12 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_01/Chiwa_Saito_01_chunk514.wav|somosomo, dʑibɯɴ ni fɯɽi ni narɯ joɯ na dʑoɯhoɯ o mizɯkaɽa teiɕɯtsɯ sɯrɯ çitsɯjoɯ wa nai.|3
|
| 13 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk1483.wav|omae, bokɯ no atama ga sɯgokɯ warɯi to omoʔte irɯdaɽoɯ. naze kizɯita no? magao de odonokaɽeta!|5
|
| 14 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk1562.wav|aɽanagikɯɴ no toki wa, dʑɯɯdʑika moʔte ninnikɯ sagete, seisɯi o bɯki ni tatakaʔta moɴ sa.|5
|
| 15 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_03/Sakamoto_Maya_03_chunk815.wav|baʔka mitai? sonna mɯkaɕi no koto motɕidaɕite, nani ka o gomakaserɯ to de mo omoʔterɯ no?|6
|
| 16 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_01/Chiwa_Saito_01_chunk1349.wav|çitani wa aisɯrɯ gawa no niŋgeɴ dakaɽa.|3
|
| 17 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk141.wav|ɕikaɕi, sonna ɯso sae mo makaɽi toːʔte ɕimaɯ kɯɽai ni, kaɽeɴ to tsɯkiçi wa jɯɯmeiniɴ da to iɯ koto na no daɽoɯ.|1
|
| 18 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__01/Shinichiro_Miki__01_chunk1411.wav|çiɕimekɯ, to iɯbekideɕoɯ ka.|4
|
| 19 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_01/Sawashiro_Miyuki_01_chunk6.wav|tsɯbasa taigaː.|2
|
| 20 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk2047.wav|warɯiga, bokɯ wa sɯntaɽazɯ no onnanoko ni kʲoɯmi wa naiɴ da.|5
|
| 21 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk1936.wav|aːa aːa!|5
|
| 22 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk771.wav|soɯ omoʔte ita.|3
|
| 23 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_02/Horie_Yui_02_chunk544.wav|kotɕiɽa no ɕintɕɯɯ o minɯita joɯ na koto o iɯ kaiki.|0
|
| 24 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk1939.wav|tonikakɯ, kʲoɯ ie o derɯ toki, tɕiʔto koɯɽoɴ ɕitɕimaʔte na. toʔkɯmiai dʑa nakɯte koɯɽoɴ.|5
|
| 25 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk1207.wav|tada saʔki, mɯkoɯ kaɽa kiŋkʲoɽi to iɯ koto wa, kotɕiɽa kaɽa mo kiŋkʲoɽi daʔta to iɯ koto de, ɕinobɯsaɴ no kao no zoɯkei o dʑiʔkɯɽi to mirɯ koto ni naʔte itaɴ desɯkeɽedo.|4
|
| 26 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk146.wav|motɕiɽoɴ, meiwakɯ no kakejoɯ ga nai koto mo, wakaʔterɯɴ dakedo ne.|4
|
| 27 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk310.wav|sono naka de mo, kʲoɯ no wa kotaeta to iɯ dake da.|5
|
| 28 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk627.wav|ʔte iɯɯ ka, bokɯ daʔta.|5
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| 29 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk12.wav|soba niːɽaɽerɯ dake de iː to ka, sɯki na dake de manzokɯ to ka, soɯ iɯ noʔte kikoe wa iːkeɽedo, oɕitojaka mitai de tsɯtsɯmaɕiː mitai de naɴ kaiːfɯ dakedo, de mo, soɽeʔte sɯgokɯ imi no wakaɽanai koto iʔterɯ to omowanai?|4
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| 30 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_01/Sawashiro_Miyuki_01_chunk1197.wav|taɕika ni, dʑoɕidoɯɕi de, sonna , teikoɯ ga arɯ wake de mo naiɕi...aɽa, noʔte kɯrɯ to wa igai. sɯni modorɯ sendʑoɯgohaɽasaɴ. hontoɯ ni, doko made ga hoŋki na no daɽoɯ. fɯmei sɯgirɯ.|2
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| 31 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk731.wav|kata o sɯkɯmerɯ sendʑoɯ ga haɽa.|5
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| 32 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_01/Sawashiro_Miyuki_01_chunk1384.wav|seitɕoɯ ɕitenai.|2
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| 33 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_03/Sakamoto_Maya_03_chunk1648.wav|koi ja, kʲɯɯhaːtoandaːbɯɽeːdo. tatakaoɯ de.|6
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| 34 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_01/Chiwa_Saito_01_chunk2328.wav|sorʲa motɕiɽoɴ.|3
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| 35 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_03/Sakurai_Takahiro_03_chunk597.wav|bokɯ no ɯmmei no aite, jabe, tɕoɯasobite!|1
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| 36 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk2584.wav|ija, mɯɽi daɽo.|1
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| 37 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk1411.wav|a, kaŋgɯʔterɯ kaŋgɯʔterɯ.|5
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| 38 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk643.wav|ɕikaɕi, kakɯniɴ ɕinakɯte wa naɽanai koto da.|1
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| 39 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk1200.wav|misage hateta, mitai na hjoɯdʑoɯ no hatɕikɯdʑi.|1
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| 40 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_02/Sakamoto_Maya_02_chunk1880.wav|ija, ima, oɕinokɯɴ to iʔta ka?|6
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| 41 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk1607.wav|fɯɯɴ, de mo soɽeʔte joɯsɯrɯ ni, tada no jaʔkami dʑa nai no.|4
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| 42 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_01/Sakamoto_Maya_01_chunk726.wav|haʔsoɯ ga moɯ, honto tɕimpiɽa da jo na. omae ni kaŋgae naŋka neː jo. kami wa zeʔtai ni ataete wa naɽanai mono ni, zeʔtai ni ataete wa naɽanai sainoɯ o ataete ɕimaʔte irɯ. kimagɯɽe sɯgiɴ zo kami. sonna opɯɕoɴ wa iːkaɽa, mazɯ wa kaŋgae o ataete jaɽe jo.|6
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| 43 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk885.wav|onegai desɯ. kono dʑɯɯɕo no baɕo ni, iʔtai nani ga arɯ no ka, doɯ ka wataɕi me ni oɕiete kɯdasaimasɯ.|5
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| 44 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk1302.wav|mite, iː ka?|1
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| 45 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk2162.wav|ofɯda wa...|4
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| 46 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_02/Sawashiro_Miyuki_02_chunk512.wav|taɕika ni, sono koɯkai to mɯkiaʔte wa ita.|2
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| 47 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_02/Sakamoto_Maya_02_chunk1508.wav|soɯ, ɯɴ nado de wa nai, akɯi da.|6
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| 48 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_03/Sakurai_Takahiro_03_chunk1184.wav|soɽe ni.|1
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| 49 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_03/Sakamoto_Maya_03_chunk741.wav|so no tame niːnotɕi o sasageta.|6
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| 50 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk280.wav|dʑaŋkeɴ ni jowai to iɯ no mo ɯnazɯketa.|1
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| 51 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk789.wav|saʔki kaɽa zɯʔto, koitsɯ wa bokɯ o kabe ni sɯrɯ katatɕi de, sendʑoɯ gawaɽa o sakete irɯ no daʔta.|5
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| 52 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk2753.wav|wataɕi wa, hontoɯ no tokoɽo, aɽanekɯɴ ni, soko made oɴ o kandʑite irɯ wake de wa nai no jo.|5
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| 53 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_02/Horie_Yui_02_chunk882.wav|wataɕi no ɕoɯtai ni tsɯite wa, ano sagiɕi wa tɕanto ɽikai ɕite irɯ jo. aitsɯ to wa keʔkoɯ fɯkai ɕiɽiai de, nagai tsɯkiai naɴ dakaɽa.|0
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| 54 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__01/Shinichiro_Miki__01_chunk1692.wav|naɴ jo!|4
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| 55 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_03/Horie_Yui_03_chunk93.wav|tada, koɯkoɯ niːkɯ imi o miɯɕinaʔte ita no mo taɕika da.|0
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| 56 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_03/Sakurai_Takahiro_03_chunk1724.wav|itɕi daŋkai ɯe?|1
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| 57 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_01/Sawashiro_Miyuki_01_chunk1593.wav|aɽa soɯ?|2
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| 58 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk77.wav|da to ɕite mo, kotaesaseɽo jo. a, sono toːɽi da. ɯwakɯ no...|3
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| 59 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk130.wav|kotoba dake kikɯto.|4
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| 60 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_03/Sawashiro_Miyuki_03_chunk1088.wav|sonna dʑbɯɴ no kimotɕi ni mo kizɯkezɯ, kizɯita tokoɽo de kiɽihanaɕi, gʲakɯ ni fɯtaɽi ni moʔto ajɯmijoʔte hoɕiː to negaʔte ɕimaɯ wataɕi no kokoɽo wa, sɯdeni çito no soɽe de wa nakɯ, kaiː no soɽe to iɯbekideɕoɯ.|2
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| 61 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk1685.wav|soʔka.|1
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| 62 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk2048.wav|doɯ jaɽa omaesama ni wa, kiʔtɕiɽi to kotoba ni ɕite iwaneba tsɯtawaɽanɯ joɯ dʑa.|1
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| 63 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_01/Chiwa_Saito_01_chunk763.wav|konna çiboɯrʲokɯteki na, seidʑakɯ o matoʔta kʲoɯhakɯ ga dʑitsɯzai sɯrɯ da nante.|3
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| 64 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk852.wav|dotɕiɽa ni ɕite mo, ɕidoɯ gʲakɯtai ɯʔnɯɴ wa tomokakɯ to ɕite, ɕendʑoɯgawaɽa to no kaiwa o totɕɯɯ de ɯtɕikirɯ katatɕi de, hatɕikɯ dʑi no koto ni kaɽandʑaʔtakaɽa na.|5
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| 65 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk974.wav|iː ko dʑa nai to iɯ ɕɯtɕoɯ o sɯrɯ no to mo, mata tɕigaɯ bameɴ deɕoɯɕi.|4
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| 66 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_02/Horie_Yui_02_chunk202.wav|onoɽe no midʑɯkɯsa ni, oɕitsɯbɯsaɽe soɯ ni narɯ.|0
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| 67 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk2291.wav|moʔtomo, go gatsɯ dʑɯɯ joŋka no bokɯ wa, sono itɕi kagetsɯ haɴ kɯɽai mae no daŋkai de, sɯdeni godai manzokɯ to ierɯ joɯ na kaɽada de wa nakɯnaʔte ɕimaʔte wa ita no dakeɽedo,.|5
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| 68 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk1269.wav|ɴ.|5
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| 69 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_02/Sakamoto_Maya_02_chunk989.wav|taimeɴ no seki ni koɕikakerɯ.|6
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| 70 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk2385.wav|teinei na koto ni, arɯihaɽitɕigi na koto ni, kambarɯ wa takadaka hantsɯki de, heja o hotondo motodoːɽi no sandʑoɯ ni made modoɕite ɕimaɯ no da.|1
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| 71 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk82.wav|sono baɕi nomi daga na, jaʔpaɽi kono mama da to oɽesama wa kiete nakɯnarɯ ɕika nai. dakaɽa, nadekotɕaɴ ni wa idʑi de mo, oɽesama no ɕitai o mitsɯkete moɽawanai to.|4
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| 72 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk1771.wav|geŋkaɴ kɯtɕi de kɯtsɯ onɯide, kizɯkɯ.|1
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| 73 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_01/Sawashiro_Miyuki_01_chunk17.wav|wataɕi to wa daɽe na no ka.|2
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| 74 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_02/Sawashiro_Miyuki_02_chunk273.wav|mada ɽokɯ dʑi jo.|2
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| 75 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_03/Horie_Yui_03_chunk130.wav|ɕoɯɕiɴ rʲokoɯ to iɯ ka.|0
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| 76 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_02/Sawashiro_Miyuki_02_chunk1691.wav|ajoɽezɯ niːrɯ.|2
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| 77 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_01/Sakamoto_Maya_01_chunk702.wav|kiʔto kambarɯ sensei to ataɕi wa ki ga aɯ to omoɯɴ da jo naː.|6
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| 78 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk1300.wav|konna kaʔkoɯ jokɯ iʔte mo, iʔterɯ koto ga dame naɽa, kaʔkoɯ jokɯ wa naɽanai.|1
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| 79 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_03/Sakurai_Takahiro_03_chunk21.wav|keɕite, doɯdʑoɯ dake wa ɕinakaʔta.|1
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| 80 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_03/Horie_Yui_03_chunk1139.wav|dʑibɯɴ o koɽosɯ joɯ na mane mo.|0
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| 81 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_03/Sawashiro_Miyuki_03_chunk57.wav|iːe, to ɕikaienai.|2
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| 82 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_03/Horie_Yui_03_chunk407.wav|fɯtsɯɯ ni kimotɕi warɯi to omoʔta no ka na.|0
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| 83 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__01/Shinichiro_Miki__01_chunk908.wav|tada.|4
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| 84 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk577.wav|doɯ ɕite ka, zeɴrʲoɯ da to omowaɽerɯ no.|4
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| 85 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk193.wav|ɕiɽoi hebi no geŋkakɯ o,.|4
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| 86 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk242.wav|saː na, fɯɯzeɴ no tomoɕibi tarɯ oɽesama wa, itsɯ tatɕinietaʔte fɯɕigi dʑa nai.|4
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| 87 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk826.wav|beɽibeɽibeɽiʔto, oto wa, ɕinakaʔtaga.|5
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| 88 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk855.wav|bokɯ wa ɯnazɯkɯ.|1
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| 89 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_01/Sawashiro_Miyuki_01_chunk277.wav|betsɯ ni, nani ka oɯgʲoɯ na koto ga aʔta, to iɯ wake de wa nai no desɯ. tada, tɕoʔto aɽaɽagisaɴ no ie ni wasɯɽemono o ɕite ɕimaʔta no de, soɽe o kaeɕite moɽaoɯ to omoimaɕite. ɴ? wasɯɽemono? hoɽa, to, majoɯtɕaɴ wa wataɕi ni senaka o mɯkerɯ.|2
|
| 90 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk235.wav|arɯkedo...|4
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| 91 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__01/Shinichiro_Miki__01_chunk121.wav|kako ni modorɯʔte iɯ no wa, omaeɽa honʲɯɯrɯi ga kaŋgaete irɯ hodo ni mɯzɯkaɕiː koto dʑa neː.|4
|
| 92 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk1457.wav|niːtɕaɴ ni wa kaŋkei neːkaɽa, hanaʔte oite.|1
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| 93 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk276.wav|eː to...|1
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| 94 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_03/Sawashiro_Miyuki_03_chunk1125.wav|minna no omoide no, gakɯɕɯɯ ɕɯkɯ ato wa, moetenakɯ naɽimaɕita.|2
|
| 95 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__01/Shinichiro_Miki__01_chunk1461.wav|i nai kɯɽai da to omoimasɯ.|4
|
| 96 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk1353.wav|çito o sɯki ni naʔtaɽi naɽaɽe taɽi ɕite, daʔtaɽa, zeʔtai ni kanawanai koe ni mi o jatsɯɕite irɯ no ga, aŋgai ɽakɯ daʔtaɽi sɯrɯ mono.|4
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| 97 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk389.wav|ɴ?|5
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| 98 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk1294.wav|doɯ ɕita no? nani o jaʔterɯ no? konna toko de.ɴ, ija, omae koso da jo.|5
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| 99 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk494.wav|omosa ga nai, omomi ga nai. soɽe wa, aɕimoto ga obotsɯkanai to iɯ koto.|5
|
| 100 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk2120.wav|arɯiha, çikaɽabita saba da.|1
|
| 101 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_03/Horie_Yui_03_chunk1003.wav|aɽaɽagikɯɴ no ɯnteɴ sɯrɯ kɯrɯma ni norɯ kɯɽai daʔtaɽa, jotsɯmbai no aɽaɽagikɯɴ ni noʔta hoɯ ga maɕi jo. ija maɕiʔte iɯ ka, soɽe wa bokɯ ga çidoime ni aʔterɯ dake dʑa neː ka.|0
|
| 102 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk367.wav|dakaɽa gokai sɯrɯnʲa jo. oɽe wa koɽe de mo omae ni wa kaɴɕa ɕiterɯnʲa. fɯtsɯɯ ni jaʔterʲa, itɕi neɴ wa kakaʔta de aɽoɯ goɕɯdʑiɴ no sɯtoɽesɯ kaiɕoɯ o, tada no kokona kakaɴ de owaɽaɕite kɯɽetaɴ dakaɽanʲa.|3
|
| 103 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk761.wav|soɽe wa...|3
|
| 104 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_02/Sakamoto_Maya_02_chunk707.wav|kowai kowai. bokɯ niːwaseɽeba tada no goʔko asobi naɴ dakeɽedo, soɽe de mo faijaː ɕisɯtaːzɯ ga, seigi no mikata o kidoʔte irɯ to iɯ no wa, sekenteki ni wa sɯkɯi de arɯ to ierɯdaɽoɯ.|6
|
| 105 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_02/Horie_Yui_02_chunk141.wav|çigezɯɽa no oːrɯ baʔkɯ.|0
|
| 106 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk1759.wav|so no ɯe ni haoʔta ɯsɯde no kaːdʲgaɴ mo.|1
|
| 107 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk2657.wav|kaiki wa ɕizɯka ni ɯnazɯkɯ.|1
|
| 108 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk976.wav|tomokakɯ.|5
|
| 109 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_02/Sakamoto_Maya_02_chunk1260.wav|ija mate jo, misɯdo no menʲɯɯ ni sonna doɽiŋkɯ wa neːdaɽo. tokɯtɕɯɯ ka? naɴ de nonderɯ nomimono made maʔkɯɽo naɴ da jo.|6
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| 110 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_01/Chiwa_Saito_01_chunk1585.wav|niŋgeɴ.|3
|
| 111 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki__02/Shinichiro_Miki__02_chunk387.wav|konna koto daɽoɯ to omoʔte imaɕitaɕi.|4
|
| 112 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_02/Chiwa_Saito_02_chunk1041.wav|daidʑoɯbɯ, jaɽasete.|3
|
| 113 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_03/Sawashiro_Miyuki_03_chunk1647.wav|hommono de aɽi tsɯzɯkerɯ.|2
|
| 114 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_02/Horie_Yui_02_chunk643.wav|warɯi koto wa iwaɴ, aɽawaɽetaɽa saʔsato kɯɽete jaɽe.|0
|
| 115 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_02/Kamiya_Hiroshi_02_chunk324.wav|aɽanikɯɴ wa ɕisɯkoɴ de wa nai to, dʑitsɯ no imoɯto o sɯki ni naʔtaɽi wa ɕinai to,.|5
|
| 116 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk821.wav|moɯ jamete, jamete, nadekoɯ dʑijɯɯ ni ɕite.|4
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| 117 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_01/Sakurai_Takahiro_01_chunk1269.wav|soʔka...|1
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| 118 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk1142.wav|bokɯ wa.|3
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| 119 |
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/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/shinichiro_miki/Shinichiro_Miki_03/Shinichiro_Miki_03_chunk2817.wav|haʔ, eʔ, kono çito, nani oɕiɯɽi mitai na koto o iː daɕitaɴ desɯ ka.|4
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| 120 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/kamiya_hiroshi/Kamiya_Hiroshi_01/Kamiya_Hiroshi_01_chunk2319.wav|soko de fɯto, imoɯto no kotoba ga omoidasaɽeta.|5
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| 121 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_02/Sakamoto_Maya_02_chunk367.wav|fɯɯɴ, soɯ ka, aɽigatoɯ. tasɯkaʔta jo, oni no oniːtɕaɴ, soɽe ni katatsɯmɯɽi no odʑoɯtɕaɴ. bokɯ wa kime kao de soɯ iʔta.|6
|
| 122 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_03/Sawashiro_Miyuki_03_chunk770.wav|tsɯkimatoɯ no ka mo ɕiɽenai.|2
|
| 123 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_02/Sakurai_Takahiro_02_chunk2026.wav|mae o mɯita mama.|1
|
| 124 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/horie_yui/Horie_Yui_03/Horie_Yui_03_chunk1317.wav|kiʔto soɽe o, tsɯɽanokɯbeki na no da.|0
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| 125 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakamoto_maya/Sakamoto_Maya_01/Sakamoto_Maya_01_chunk1661.wav|bokɯ no ɕiɽanai ɯtɕi ni daɽe ka to iɽekawaʔtaɴ dʑa nai ka to omoɯ hodo no, totetsɯ mo nai dʑiŋkakɯ hjoɯhembɯɽi de arɯ.|6
|
| 126 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sawashiro_miyuki/Sawashiro_Miyuki_03/Sawashiro_Miyuki_03_chunk2399.wav|geŋkaɴ no kagi o, dʑibɯɴ de aketa dake no koto de.|2
|
| 127 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/chiwa_saito/Chiwa_Saito_03/Chiwa_Saito_03_chunk443.wav|kaɽoɯdʑite koɕi no katatɕi de, ija, hanekawa ga bokɯ no inotɕi no ondʑiɴ daʔtakaɽa koso, naɴ to ka ɕoɯtai o minɯketa joɯ na mono da.|3
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| 128 |
+
/home/austin/disk2/llmvcs/tt/stylekan/Data/moe_res/monogatari/monogatari_voices/monogatari_split/sakurai_takahiro/Sakurai_Takahiro_03/Sakurai_Takahiro_03_chunk2632.wav|iwaɽerɯga mama ni, sɯɯtsɯ kaɽa toɽidaɕita kokɯɕokɯ no keitai deɴwa o, sendʑoɯgahaɽa no te no ɯe ni okɯ kaikçi.|1
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AuxiliaryASR/LICENSE
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MIT License
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Copyright (c) 2022 Aaron (Yinghao) Li
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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| 20 |
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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AuxiliaryASR/README.md
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# AuxiliaryASR
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This repo contains the training code for Phoneme-level ASR for Voice Conversion (VC) and TTS (Text-Mel Alignment) used in [StarGANv2-VC](https://github.com/yl4579/StarGANv2-VC) and [StyleTTS](https://github.com/yl4579/StyleTTS).
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| 3 |
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## Pre-requisites
|
| 5 |
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1. Python >= 3.7
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2. Clone this repository:
|
| 7 |
+
```bash
|
| 8 |
+
git clone https://github.com/yl4579/AuxiliaryASR.git
|
| 9 |
+
cd AuxiliaryASR
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+
```
|
| 11 |
+
3. Install python requirements:
|
| 12 |
+
```bash
|
| 13 |
+
pip install SoundFile torchaudio torch jiwer pyyaml click matplotlib g2p_en librosa
|
| 14 |
+
```
|
| 15 |
+
4. Prepare your own dataset and put the `train_list.txt` and `val_list.txt` in the `Data` folder (see Training section for more details).
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| 16 |
+
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| 17 |
+
## Training
|
| 18 |
+
```bash
|
| 19 |
+
python train.py --config_path ./Configs/config.yml
|
| 20 |
+
```
|
| 21 |
+
Please specify the training and validation data in `config.yml` file. The data list format needs to be `filename.wav|label|speaker_number`, see [train_list.txt](https://github.com/yl4579/AuxiliaryASR/blob/main/Data/train_list.txt) as an example (a subset for LJSpeech). Note that `speaker_number` can just be `0` for ASR, but it is useful to set a meaningful number for TTS training (if you need to use this repo for StyleTTS).
|
| 22 |
+
|
| 23 |
+
Checkpoints and Tensorboard logs will be saved at `log_dir`. To speed up training, you may want to make `batch_size` as large as your GPU RAM can take. However, please note that `batch_size = 64` will take around 10G GPU RAM.
|
| 24 |
+
|
| 25 |
+
### Languages
|
| 26 |
+
This repo is set up for English with the [g2p_en](https://github.com/Kyubyong/g2p) package, but you can train it with other languages. If you would like to train for datasets in different languages, you will need to modify the [meldataset.py](https://github.com/yl4579/AuxiliaryASR/blob/main/meldataset.py#L86-L93) file (L86-93) with your own phonemizer. You also need to change the vocabulary file ([word_index_dict.txt](https://github.com/yl4579/AuxiliaryASR/blob/main/word_index_dict.txt)) and change `n_token` in `config.yml` to reflect the number of tokens. A recommended phonemizer for other languages is [phonemizer](https://github.com/bootphon/phonemizer).
|
| 27 |
+
|
| 28 |
+
## References
|
| 29 |
+
- [NVIDIA/tacotron2](https://github.com/NVIDIA/tacotron2)
|
| 30 |
+
- [kan-bayashi/ParallelWaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN)
|
| 31 |
+
|
| 32 |
+
## Acknowledgement
|
| 33 |
+
The author would like to thank [@tosaka-m](https://github.com/tosaka-m) for his great repository and valuable discussions.
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|
| 1 |
+
import math
|
| 2 |
+
import torch
|
| 3 |
+
from torch import nn
|
| 4 |
+
from typing import Optional, Any
|
| 5 |
+
from torch import Tensor
|
| 6 |
+
import torch.nn.functional as F
|
| 7 |
+
import torchaudio
|
| 8 |
+
import torchaudio.functional as audio_F
|
| 9 |
+
|
| 10 |
+
import random
|
| 11 |
+
random.seed(0)
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
def _get_activation_fn(activ):
|
| 15 |
+
if activ == 'relu':
|
| 16 |
+
return nn.ReLU()
|
| 17 |
+
elif activ == 'lrelu':
|
| 18 |
+
return nn.LeakyReLU(0.2)
|
| 19 |
+
elif activ == 'swish':
|
| 20 |
+
return lambda x: x*torch.sigmoid(x)
|
| 21 |
+
else:
|
| 22 |
+
raise RuntimeError('Unexpected activ type %s, expected [relu, lrelu, swish]' % activ)
|
| 23 |
+
|
| 24 |
+
class LinearNorm(torch.nn.Module):
|
| 25 |
+
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
|
| 26 |
+
super(LinearNorm, self).__init__()
|
| 27 |
+
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
|
| 28 |
+
|
| 29 |
+
torch.nn.init.xavier_uniform_(
|
| 30 |
+
self.linear_layer.weight,
|
| 31 |
+
gain=torch.nn.init.calculate_gain(w_init_gain))
|
| 32 |
+
|
| 33 |
+
def forward(self, x):
|
| 34 |
+
return self.linear_layer(x)
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
class ConvNorm(torch.nn.Module):
|
| 38 |
+
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
|
| 39 |
+
padding=None, dilation=1, bias=True, w_init_gain='linear', param=None):
|
| 40 |
+
super(ConvNorm, self).__init__()
|
| 41 |
+
if padding is None:
|
| 42 |
+
assert(kernel_size % 2 == 1)
|
| 43 |
+
padding = int(dilation * (kernel_size - 1) / 2)
|
| 44 |
+
|
| 45 |
+
self.conv = torch.nn.Conv1d(in_channels, out_channels,
|
| 46 |
+
kernel_size=kernel_size, stride=stride,
|
| 47 |
+
padding=padding, dilation=dilation,
|
| 48 |
+
bias=bias)
|
| 49 |
+
|
| 50 |
+
torch.nn.init.xavier_uniform_(
|
| 51 |
+
self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain, param=param))
|
| 52 |
+
|
| 53 |
+
def forward(self, signal):
|
| 54 |
+
conv_signal = self.conv(signal)
|
| 55 |
+
return conv_signal
|
| 56 |
+
|
| 57 |
+
class CausualConv(nn.Module):
|
| 58 |
+
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=1, dilation=1, bias=True, w_init_gain='linear', param=None):
|
| 59 |
+
super(CausualConv, self).__init__()
|
| 60 |
+
if padding is None:
|
| 61 |
+
assert(kernel_size % 2 == 1)
|
| 62 |
+
padding = int(dilation * (kernel_size - 1) / 2) * 2
|
| 63 |
+
else:
|
| 64 |
+
self.padding = padding * 2
|
| 65 |
+
self.conv = nn.Conv1d(in_channels, out_channels,
|
| 66 |
+
kernel_size=kernel_size, stride=stride,
|
| 67 |
+
padding=self.padding,
|
| 68 |
+
dilation=dilation,
|
| 69 |
+
bias=bias)
|
| 70 |
+
|
| 71 |
+
torch.nn.init.xavier_uniform_(
|
| 72 |
+
self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain, param=param))
|
| 73 |
+
|
| 74 |
+
def forward(self, x):
|
| 75 |
+
x = self.conv(x)
|
| 76 |
+
x = x[:, :, :-self.padding]
|
| 77 |
+
return x
|
| 78 |
+
|
| 79 |
+
class CausualBlock(nn.Module):
|
| 80 |
+
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ='lrelu'):
|
| 81 |
+
super(CausualBlock, self).__init__()
|
| 82 |
+
self.blocks = nn.ModuleList([
|
| 83 |
+
self._get_conv(hidden_dim, dilation=3**i, activ=activ, dropout_p=dropout_p)
|
| 84 |
+
for i in range(n_conv)])
|
| 85 |
+
|
| 86 |
+
def forward(self, x):
|
| 87 |
+
for block in self.blocks:
|
| 88 |
+
res = x
|
| 89 |
+
x = block(x)
|
| 90 |
+
x += res
|
| 91 |
+
return x
|
| 92 |
+
|
| 93 |
+
def _get_conv(self, hidden_dim, dilation, activ='lrelu', dropout_p=0.2):
|
| 94 |
+
layers = [
|
| 95 |
+
CausualConv(hidden_dim, hidden_dim, kernel_size=3, padding=dilation, dilation=dilation),
|
| 96 |
+
_get_activation_fn(activ),
|
| 97 |
+
nn.BatchNorm1d(hidden_dim),
|
| 98 |
+
nn.Dropout(p=dropout_p),
|
| 99 |
+
CausualConv(hidden_dim, hidden_dim, kernel_size=3, padding=1, dilation=1),
|
| 100 |
+
_get_activation_fn(activ),
|
| 101 |
+
nn.Dropout(p=dropout_p)
|
| 102 |
+
]
|
| 103 |
+
return nn.Sequential(*layers)
|
| 104 |
+
|
| 105 |
+
class ConvBlock(nn.Module):
|
| 106 |
+
def __init__(self, hidden_dim, n_conv=3, dropout_p=0.2, activ='relu'):
|
| 107 |
+
super().__init__()
|
| 108 |
+
self._n_groups = 8
|
| 109 |
+
self.blocks = nn.ModuleList([
|
| 110 |
+
self._get_conv(hidden_dim, dilation=3**i, activ=activ, dropout_p=dropout_p)
|
| 111 |
+
for i in range(n_conv)])
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
def forward(self, x):
|
| 115 |
+
for block in self.blocks:
|
| 116 |
+
res = x
|
| 117 |
+
x = block(x)
|
| 118 |
+
x += res
|
| 119 |
+
return x
|
| 120 |
+
|
| 121 |
+
def _get_conv(self, hidden_dim, dilation, activ='relu', dropout_p=0.2):
|
| 122 |
+
layers = [
|
| 123 |
+
ConvNorm(hidden_dim, hidden_dim, kernel_size=3, padding=dilation, dilation=dilation),
|
| 124 |
+
_get_activation_fn(activ),
|
| 125 |
+
nn.GroupNorm(num_groups=self._n_groups, num_channels=hidden_dim),
|
| 126 |
+
nn.Dropout(p=dropout_p),
|
| 127 |
+
ConvNorm(hidden_dim, hidden_dim, kernel_size=3, padding=1, dilation=1),
|
| 128 |
+
_get_activation_fn(activ),
|
| 129 |
+
nn.Dropout(p=dropout_p)
|
| 130 |
+
]
|
| 131 |
+
return nn.Sequential(*layers)
|
| 132 |
+
|
| 133 |
+
class LocationLayer(nn.Module):
|
| 134 |
+
def __init__(self, attention_n_filters, attention_kernel_size,
|
| 135 |
+
attention_dim):
|
| 136 |
+
super(LocationLayer, self).__init__()
|
| 137 |
+
padding = int((attention_kernel_size - 1) / 2)
|
| 138 |
+
self.location_conv = ConvNorm(2, attention_n_filters,
|
| 139 |
+
kernel_size=attention_kernel_size,
|
| 140 |
+
padding=padding, bias=False, stride=1,
|
| 141 |
+
dilation=1)
|
| 142 |
+
self.location_dense = LinearNorm(attention_n_filters, attention_dim,
|
| 143 |
+
bias=False, w_init_gain='tanh')
|
| 144 |
+
|
| 145 |
+
def forward(self, attention_weights_cat):
|
| 146 |
+
processed_attention = self.location_conv(attention_weights_cat)
|
| 147 |
+
processed_attention = processed_attention.transpose(1, 2)
|
| 148 |
+
processed_attention = self.location_dense(processed_attention)
|
| 149 |
+
return processed_attention
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
class Attention(nn.Module):
|
| 153 |
+
def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
|
| 154 |
+
attention_location_n_filters, attention_location_kernel_size):
|
| 155 |
+
super(Attention, self).__init__()
|
| 156 |
+
self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
|
| 157 |
+
bias=False, w_init_gain='tanh')
|
| 158 |
+
self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
|
| 159 |
+
w_init_gain='tanh')
|
| 160 |
+
self.v = LinearNorm(attention_dim, 1, bias=False)
|
| 161 |
+
self.location_layer = LocationLayer(attention_location_n_filters,
|
| 162 |
+
attention_location_kernel_size,
|
| 163 |
+
attention_dim)
|
| 164 |
+
self.score_mask_value = -float("inf")
|
| 165 |
+
|
| 166 |
+
def get_alignment_energies(self, query, processed_memory,
|
| 167 |
+
attention_weights_cat):
|
| 168 |
+
"""
|
| 169 |
+
PARAMS
|
| 170 |
+
------
|
| 171 |
+
query: decoder output (batch, n_mel_channels * n_frames_per_step)
|
| 172 |
+
processed_memory: processed encoder outputs (B, T_in, attention_dim)
|
| 173 |
+
attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
|
| 174 |
+
RETURNS
|
| 175 |
+
-------
|
| 176 |
+
alignment (batch, max_time)
|
| 177 |
+
"""
|
| 178 |
+
|
| 179 |
+
processed_query = self.query_layer(query.unsqueeze(1))
|
| 180 |
+
processed_attention_weights = self.location_layer(attention_weights_cat)
|
| 181 |
+
energies = self.v(torch.tanh(
|
| 182 |
+
processed_query + processed_attention_weights + processed_memory))
|
| 183 |
+
|
| 184 |
+
energies = energies.squeeze(-1)
|
| 185 |
+
return energies
|
| 186 |
+
|
| 187 |
+
def forward(self, attention_hidden_state, memory, processed_memory,
|
| 188 |
+
attention_weights_cat, mask):
|
| 189 |
+
"""
|
| 190 |
+
PARAMS
|
| 191 |
+
------
|
| 192 |
+
attention_hidden_state: attention rnn last output
|
| 193 |
+
memory: encoder outputs
|
| 194 |
+
processed_memory: processed encoder outputs
|
| 195 |
+
attention_weights_cat: previous and cummulative attention weights
|
| 196 |
+
mask: binary mask for padded data
|
| 197 |
+
"""
|
| 198 |
+
alignment = self.get_alignment_energies(
|
| 199 |
+
attention_hidden_state, processed_memory, attention_weights_cat)
|
| 200 |
+
|
| 201 |
+
if mask is not None:
|
| 202 |
+
alignment.data.masked_fill_(mask, self.score_mask_value)
|
| 203 |
+
|
| 204 |
+
attention_weights = F.softmax(alignment, dim=1)
|
| 205 |
+
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
|
| 206 |
+
attention_context = attention_context.squeeze(1)
|
| 207 |
+
|
| 208 |
+
return attention_context, attention_weights, alignment
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
class ForwardAttentionV2(nn.Module):
|
| 212 |
+
def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
|
| 213 |
+
attention_location_n_filters, attention_location_kernel_size):
|
| 214 |
+
super(ForwardAttentionV2, self).__init__()
|
| 215 |
+
self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
|
| 216 |
+
bias=False, w_init_gain='tanh')
|
| 217 |
+
self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
|
| 218 |
+
w_init_gain='tanh')
|
| 219 |
+
self.v = LinearNorm(attention_dim, 1, bias=False)
|
| 220 |
+
self.location_layer = LocationLayer(attention_location_n_filters,
|
| 221 |
+
attention_location_kernel_size,
|
| 222 |
+
attention_dim)
|
| 223 |
+
self.score_mask_value = -float(1e20)
|
| 224 |
+
|
| 225 |
+
def get_alignment_energies(self, query, processed_memory,
|
| 226 |
+
attention_weights_cat):
|
| 227 |
+
"""
|
| 228 |
+
PARAMS
|
| 229 |
+
------
|
| 230 |
+
query: decoder output (batch, n_mel_channels * n_frames_per_step)
|
| 231 |
+
processed_memory: processed encoder outputs (B, T_in, attention_dim)
|
| 232 |
+
attention_weights_cat: prev. and cumulative att weights (B, 2, max_time)
|
| 233 |
+
RETURNS
|
| 234 |
+
-------
|
| 235 |
+
alignment (batch, max_time)
|
| 236 |
+
"""
|
| 237 |
+
|
| 238 |
+
processed_query = self.query_layer(query.unsqueeze(1))
|
| 239 |
+
processed_attention_weights = self.location_layer(attention_weights_cat)
|
| 240 |
+
energies = self.v(torch.tanh(
|
| 241 |
+
processed_query + processed_attention_weights + processed_memory))
|
| 242 |
+
|
| 243 |
+
energies = energies.squeeze(-1)
|
| 244 |
+
return energies
|
| 245 |
+
|
| 246 |
+
def forward(self, attention_hidden_state, memory, processed_memory,
|
| 247 |
+
attention_weights_cat, mask, log_alpha):
|
| 248 |
+
"""
|
| 249 |
+
PARAMS
|
| 250 |
+
------
|
| 251 |
+
attention_hidden_state: attention rnn last output
|
| 252 |
+
memory: encoder outputs
|
| 253 |
+
processed_memory: processed encoder outputs
|
| 254 |
+
attention_weights_cat: previous and cummulative attention weights
|
| 255 |
+
mask: binary mask for padded data
|
| 256 |
+
"""
|
| 257 |
+
log_energy = self.get_alignment_energies(
|
| 258 |
+
attention_hidden_state, processed_memory, attention_weights_cat)
|
| 259 |
+
|
| 260 |
+
#log_energy =
|
| 261 |
+
|
| 262 |
+
if mask is not None:
|
| 263 |
+
log_energy.data.masked_fill_(mask, self.score_mask_value)
|
| 264 |
+
|
| 265 |
+
#attention_weights = F.softmax(alignment, dim=1)
|
| 266 |
+
|
| 267 |
+
#content_score = log_energy.unsqueeze(1) #[B, MAX_TIME] -> [B, 1, MAX_TIME]
|
| 268 |
+
#log_alpha = log_alpha.unsqueeze(2) #[B, MAX_TIME] -> [B, MAX_TIME, 1]
|
| 269 |
+
|
| 270 |
+
#log_total_score = log_alpha + content_score
|
| 271 |
+
|
| 272 |
+
#previous_attention_weights = attention_weights_cat[:,0,:]
|
| 273 |
+
|
| 274 |
+
log_alpha_shift_padded = []
|
| 275 |
+
max_time = log_energy.size(1)
|
| 276 |
+
for sft in range(2):
|
| 277 |
+
shifted = log_alpha[:,:max_time-sft]
|
| 278 |
+
shift_padded = F.pad(shifted, (sft,0), 'constant', self.score_mask_value)
|
| 279 |
+
log_alpha_shift_padded.append(shift_padded.unsqueeze(2))
|
| 280 |
+
|
| 281 |
+
biased = torch.logsumexp(torch.cat(log_alpha_shift_padded,2), 2)
|
| 282 |
+
|
| 283 |
+
log_alpha_new = biased + log_energy
|
| 284 |
+
|
| 285 |
+
attention_weights = F.softmax(log_alpha_new, dim=1)
|
| 286 |
+
|
| 287 |
+
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
|
| 288 |
+
attention_context = attention_context.squeeze(1)
|
| 289 |
+
|
| 290 |
+
return attention_context, attention_weights, log_alpha_new
|
| 291 |
+
|
| 292 |
+
|
| 293 |
+
class PhaseShuffle2d(nn.Module):
|
| 294 |
+
def __init__(self, n=2):
|
| 295 |
+
super(PhaseShuffle2d, self).__init__()
|
| 296 |
+
self.n = n
|
| 297 |
+
self.random = random.Random(1)
|
| 298 |
+
|
| 299 |
+
def forward(self, x, move=None):
|
| 300 |
+
# x.size = (B, C, M, L)
|
| 301 |
+
if move is None:
|
| 302 |
+
move = self.random.randint(-self.n, self.n)
|
| 303 |
+
|
| 304 |
+
if move == 0:
|
| 305 |
+
return x
|
| 306 |
+
else:
|
| 307 |
+
left = x[:, :, :, :move]
|
| 308 |
+
right = x[:, :, :, move:]
|
| 309 |
+
shuffled = torch.cat([right, left], dim=3)
|
| 310 |
+
return shuffled
|
| 311 |
+
|
| 312 |
+
class PhaseShuffle1d(nn.Module):
|
| 313 |
+
def __init__(self, n=2):
|
| 314 |
+
super(PhaseShuffle1d, self).__init__()
|
| 315 |
+
self.n = n
|
| 316 |
+
self.random = random.Random(1)
|
| 317 |
+
|
| 318 |
+
def forward(self, x, move=None):
|
| 319 |
+
# x.size = (B, C, M, L)
|
| 320 |
+
if move is None:
|
| 321 |
+
move = self.random.randint(-self.n, self.n)
|
| 322 |
+
|
| 323 |
+
if move == 0:
|
| 324 |
+
return x
|
| 325 |
+
else:
|
| 326 |
+
left = x[:, :, :move]
|
| 327 |
+
right = x[:, :, move:]
|
| 328 |
+
shuffled = torch.cat([right, left], dim=2)
|
| 329 |
+
|
| 330 |
+
return shuffled
|
| 331 |
+
|
| 332 |
+
class MFCC(nn.Module):
|
| 333 |
+
def __init__(self, n_mfcc=40, n_mels=80):
|
| 334 |
+
super(MFCC, self).__init__()
|
| 335 |
+
self.n_mfcc = n_mfcc
|
| 336 |
+
self.n_mels = n_mels
|
| 337 |
+
self.norm = 'ortho'
|
| 338 |
+
dct_mat = audio_F.create_dct(self.n_mfcc, self.n_mels, self.norm)
|
| 339 |
+
self.register_buffer('dct_mat', dct_mat)
|
| 340 |
+
|
| 341 |
+
def forward(self, mel_specgram):
|
| 342 |
+
if len(mel_specgram.shape) == 2:
|
| 343 |
+
mel_specgram = mel_specgram.unsqueeze(0)
|
| 344 |
+
unsqueezed = True
|
| 345 |
+
else:
|
| 346 |
+
unsqueezed = False
|
| 347 |
+
# (channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc)
|
| 348 |
+
# -> (channel, time, n_mfcc).tranpose(...)
|
| 349 |
+
mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2)
|
| 350 |
+
|
| 351 |
+
# unpack batch
|
| 352 |
+
if unsqueezed:
|
| 353 |
+
mfcc = mfcc.squeeze(0)
|
| 354 |
+
return mfcc
|
AuxiliaryASR/meldataset.py
ADDED
|
@@ -0,0 +1,222 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#coding: utf-8
|
| 2 |
+
|
| 3 |
+
import os
|
| 4 |
+
import os.path as osp
|
| 5 |
+
import time
|
| 6 |
+
import random
|
| 7 |
+
import numpy as np
|
| 8 |
+
import random
|
| 9 |
+
import soundfile as sf
|
| 10 |
+
|
| 11 |
+
import torch
|
| 12 |
+
from torch import nn
|
| 13 |
+
import torch.nn.functional as F
|
| 14 |
+
import torchaudio
|
| 15 |
+
from torch.utils.data import DataLoader
|
| 16 |
+
# from cotlet.phon import phonemize
|
| 17 |
+
# from g2p_en import G2p
|
| 18 |
+
import librosa
|
| 19 |
+
|
| 20 |
+
import logging
|
| 21 |
+
logger = logging.getLogger(__name__)
|
| 22 |
+
logger.setLevel(logging.DEBUG)
|
| 23 |
+
from text_utils import TextCleaner
|
| 24 |
+
np.random.seed(1)
|
| 25 |
+
random.seed(1)
|
| 26 |
+
# DEFAULT_DICT_PATH = osp.join(osp.dirname(__file__), 'word_index_dict.txt')
|
| 27 |
+
|
| 28 |
+
SPECT_PARAMS = {
|
| 29 |
+
"n_fft": 2048,
|
| 30 |
+
"win_length": 2048,
|
| 31 |
+
"hop_length": 512
|
| 32 |
+
}
|
| 33 |
+
MEL_PARAMS = {
|
| 34 |
+
"n_mels": 80,
|
| 35 |
+
"n_fft": 2048,
|
| 36 |
+
"win_length": 2048,
|
| 37 |
+
"hop_length": 512
|
| 38 |
+
}
|
| 39 |
+
|
| 40 |
+
class hibiki_phon:
|
| 41 |
+
|
| 42 |
+
def __init__(self):
|
| 43 |
+
self.phon = phonemize
|
| 44 |
+
|
| 45 |
+
def __call__(self,text):
|
| 46 |
+
ps = self.phon(text)
|
| 47 |
+
return ps
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
_pad = "$"
|
| 51 |
+
_punctuation = ';:,.!?¡¿—…"«»“” '
|
| 52 |
+
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
|
| 53 |
+
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
|
| 54 |
+
|
| 55 |
+
# Export all symbols:
|
| 56 |
+
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
|
| 57 |
+
|
| 58 |
+
dicts = {}
|
| 59 |
+
for i in range(len((symbols))):
|
| 60 |
+
dicts[symbols[i]] = i
|
| 61 |
+
|
| 62 |
+
class TextCleaner:
|
| 63 |
+
def __init__(self, dummy=None):
|
| 64 |
+
self.word_index_dictionary = dicts
|
| 65 |
+
def __call__(self, text):
|
| 66 |
+
indexes = []
|
| 67 |
+
for char in text:
|
| 68 |
+
try:
|
| 69 |
+
indexes.append(self.word_index_dictionary[char])
|
| 70 |
+
except KeyError:
|
| 71 |
+
print(text)
|
| 72 |
+
return indexes
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
class MelDataset(torch.utils.data.Dataset):
|
| 76 |
+
def __init__(self,
|
| 77 |
+
data_list,
|
| 78 |
+
# dict_path=DEFAULT_DICT_PATH,
|
| 79 |
+
sr=48000
|
| 80 |
+
):
|
| 81 |
+
|
| 82 |
+
spect_params = SPECT_PARAMS
|
| 83 |
+
mel_params = MEL_PARAMS
|
| 84 |
+
|
| 85 |
+
_data_list = [l[:-1].split('|') for l in data_list]
|
| 86 |
+
self.data_list = [data if len(data) == 3 else (*data, 0) for data in _data_list]
|
| 87 |
+
self.text_cleaner = TextCleaner()
|
| 88 |
+
self.sr = sr
|
| 89 |
+
|
| 90 |
+
self.to_melspec = torchaudio.transforms.MelSpectrogram(**MEL_PARAMS)
|
| 91 |
+
self.mean, self.std = -4, 4
|
| 92 |
+
|
| 93 |
+
# self.g2p = hibiki_phon()
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
def __len__(self):
|
| 97 |
+
return len(self.data_list)
|
| 98 |
+
|
| 99 |
+
def __getitem__(self, idx):
|
| 100 |
+
data = self.data_list[idx]
|
| 101 |
+
wave, text_tensor, speaker_id = self._load_tensor(data)
|
| 102 |
+
wave_tensor = torch.from_numpy(wave).float()
|
| 103 |
+
mel_tensor = self.to_melspec(wave_tensor)
|
| 104 |
+
|
| 105 |
+
if (text_tensor.size(0)+1) >= (mel_tensor.size(1) // 3):
|
| 106 |
+
mel_tensor = F.interpolate(
|
| 107 |
+
mel_tensor.unsqueeze(0), size=(text_tensor.size(0)+1)*3, align_corners=False,
|
| 108 |
+
mode='linear').squeeze(0)
|
| 109 |
+
|
| 110 |
+
acoustic_feature = (torch.log(1e-5 + mel_tensor) - self.mean)/self.std
|
| 111 |
+
|
| 112 |
+
length_feature = acoustic_feature.size(1)
|
| 113 |
+
acoustic_feature = acoustic_feature[:, :(length_feature - length_feature % 2)]
|
| 114 |
+
|
| 115 |
+
return wave_tensor, acoustic_feature, text_tensor, data[0]
|
| 116 |
+
|
| 117 |
+
# def _load_tensor(self, data):
|
| 118 |
+
# wave_path, text, speaker_id = data
|
| 119 |
+
# speaker_id = int(speaker_id)
|
| 120 |
+
# wave, sr = sf.read(wave_path)
|
| 121 |
+
|
| 122 |
+
# # phonemize the text
|
| 123 |
+
# ps = self.g2p(text.replace('-', ' '))
|
| 124 |
+
# if "'" in ps:
|
| 125 |
+
# ps.remove("'")
|
| 126 |
+
# text = self.text_cleaner(ps)
|
| 127 |
+
# blank_index = self.text_cleaner.word_index_dictionary[" "]
|
| 128 |
+
# text.insert(0, blank_index) # add a blank at the beginning (silence)
|
| 129 |
+
# text.append(blank_index) # add a blank at the end (silence)
|
| 130 |
+
|
| 131 |
+
# text = torch.LongTensor(text)
|
| 132 |
+
|
| 133 |
+
# return wave, text, speaker_id
|
| 134 |
+
|
| 135 |
+
def _load_tensor(self, data):
|
| 136 |
+
|
| 137 |
+
wave_path, text, speaker_id = data
|
| 138 |
+
speaker_id = int(speaker_id)
|
| 139 |
+
wave, sr = sf.read(wave_path)
|
| 140 |
+
if wave.shape[-1] == 2:
|
| 141 |
+
wave = wave[:, 0].squeeze()
|
| 142 |
+
if sr != 48000:
|
| 143 |
+
wave = librosa.resample(wave, orig_sr=sr, target_sr=48000)
|
| 144 |
+
print(wave_path, sr)
|
| 145 |
+
|
| 146 |
+
# wave = np.concatenate([np.zeros([5000]), wave, np.zeros([5000])], axis=0)
|
| 147 |
+
|
| 148 |
+
text = self.text_cleaner(text)
|
| 149 |
+
|
| 150 |
+
text.insert(0, 0)
|
| 151 |
+
text.append(0)
|
| 152 |
+
|
| 153 |
+
text = torch.LongTensor(text)
|
| 154 |
+
|
| 155 |
+
return wave, text, speaker_id
|
| 156 |
+
|
| 157 |
+
|
| 158 |
+
|
| 159 |
+
class Collater(object):
|
| 160 |
+
"""
|
| 161 |
+
Args:
|
| 162 |
+
return_wave (bool): if true, will return the wave data along with spectrogram.
|
| 163 |
+
"""
|
| 164 |
+
|
| 165 |
+
def __init__(self, return_wave=False):
|
| 166 |
+
self.text_pad_index = 0
|
| 167 |
+
self.return_wave = return_wave
|
| 168 |
+
|
| 169 |
+
def __call__(self, batch):
|
| 170 |
+
batch_size = len(batch)
|
| 171 |
+
|
| 172 |
+
# sort by mel length
|
| 173 |
+
lengths = [b[1].shape[1] for b in batch]
|
| 174 |
+
batch_indexes = np.argsort(lengths)[::-1]
|
| 175 |
+
batch = [batch[bid] for bid in batch_indexes]
|
| 176 |
+
|
| 177 |
+
nmels = batch[0][1].size(0)
|
| 178 |
+
max_mel_length = max([b[1].shape[1] for b in batch])
|
| 179 |
+
max_text_length = max([b[2].shape[0] for b in batch])
|
| 180 |
+
|
| 181 |
+
mels = torch.zeros((batch_size, nmels, max_mel_length)).float()
|
| 182 |
+
texts = torch.zeros((batch_size, max_text_length)).long()
|
| 183 |
+
input_lengths = torch.zeros(batch_size).long()
|
| 184 |
+
output_lengths = torch.zeros(batch_size).long()
|
| 185 |
+
paths = ['' for _ in range(batch_size)]
|
| 186 |
+
for bid, (_, mel, text, path) in enumerate(batch):
|
| 187 |
+
mel_size = mel.size(1)
|
| 188 |
+
text_size = text.size(0)
|
| 189 |
+
mels[bid, :, :mel_size] = mel
|
| 190 |
+
texts[bid, :text_size] = text
|
| 191 |
+
input_lengths[bid] = text_size
|
| 192 |
+
output_lengths[bid] = mel_size
|
| 193 |
+
paths[bid] = path
|
| 194 |
+
assert(text_size < (mel_size//2))
|
| 195 |
+
|
| 196 |
+
if self.return_wave:
|
| 197 |
+
waves = [b[0] for b in batch]
|
| 198 |
+
return texts, input_lengths, mels, output_lengths, paths, waves
|
| 199 |
+
|
| 200 |
+
return texts, input_lengths, mels, output_lengths
|
| 201 |
+
|
| 202 |
+
|
| 203 |
+
|
| 204 |
+
def build_dataloader(path_list,
|
| 205 |
+
validation=False,
|
| 206 |
+
batch_size=4,
|
| 207 |
+
num_workers=1,
|
| 208 |
+
device='cpu',
|
| 209 |
+
collate_config={},
|
| 210 |
+
dataset_config={}):
|
| 211 |
+
|
| 212 |
+
dataset = MelDataset(path_list, **dataset_config)
|
| 213 |
+
collate_fn = Collater(**collate_config)
|
| 214 |
+
data_loader = DataLoader(dataset,
|
| 215 |
+
batch_size=batch_size,
|
| 216 |
+
shuffle=(not validation),
|
| 217 |
+
num_workers=num_workers,
|
| 218 |
+
drop_last=(not validation),
|
| 219 |
+
collate_fn=collate_fn,
|
| 220 |
+
pin_memory=(device != 'cpu'))
|
| 221 |
+
|
| 222 |
+
return data_loader
|
AuxiliaryASR/models.py
ADDED
|
@@ -0,0 +1,192 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import math
|
| 2 |
+
import torch
|
| 3 |
+
from torch import nn
|
| 4 |
+
from torch.nn import TransformerEncoder
|
| 5 |
+
import torch.nn.functional as F
|
| 6 |
+
from layers import MFCC, Attention, LinearNorm, ConvNorm, ConvBlock
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
def build_model(model_params={}, model_type='asr'):
|
| 10 |
+
model = ASRCNN(**model_params)
|
| 11 |
+
return model
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
class ASRCNN(nn.Module):
|
| 15 |
+
def __init__(self,
|
| 16 |
+
input_dim=80,
|
| 17 |
+
hidden_dim=256,
|
| 18 |
+
n_token=35,
|
| 19 |
+
n_layers=6,
|
| 20 |
+
token_embedding_dim=256,
|
| 21 |
+
|
| 22 |
+
):
|
| 23 |
+
super().__init__()
|
| 24 |
+
self.n_token = n_token
|
| 25 |
+
self.n_down = 1
|
| 26 |
+
self.to_mfcc = MFCC()
|
| 27 |
+
self.init_cnn = ConvNorm(input_dim//2, hidden_dim, kernel_size=7, padding=3, stride=2)
|
| 28 |
+
self.cnns = nn.Sequential(
|
| 29 |
+
*[nn.Sequential(
|
| 30 |
+
ConvBlock(hidden_dim),
|
| 31 |
+
nn.GroupNorm(num_groups=1, num_channels=hidden_dim)
|
| 32 |
+
) for n in range(n_layers)])
|
| 33 |
+
self.projection = ConvNorm(hidden_dim, hidden_dim // 2)
|
| 34 |
+
self.ctc_linear = nn.Sequential(
|
| 35 |
+
LinearNorm(hidden_dim//2, hidden_dim),
|
| 36 |
+
nn.ReLU(),
|
| 37 |
+
LinearNorm(hidden_dim, n_token))
|
| 38 |
+
self.asr_s2s = ASRS2S(
|
| 39 |
+
embedding_dim=token_embedding_dim,
|
| 40 |
+
hidden_dim=hidden_dim//2,
|
| 41 |
+
n_token=n_token)
|
| 42 |
+
|
| 43 |
+
def forward(self, x, src_key_padding_mask=None, text_input=None):
|
| 44 |
+
x = self.to_mfcc(x)
|
| 45 |
+
x = self.init_cnn(x)
|
| 46 |
+
x = self.cnns(x)
|
| 47 |
+
x = self.projection(x)
|
| 48 |
+
x = x.transpose(1, 2)
|
| 49 |
+
ctc_logit = self.ctc_linear(x)
|
| 50 |
+
if text_input is not None:
|
| 51 |
+
_, s2s_logit, s2s_attn = self.asr_s2s(x, src_key_padding_mask, text_input)
|
| 52 |
+
return ctc_logit, s2s_logit, s2s_attn
|
| 53 |
+
else:
|
| 54 |
+
return ctc_logit
|
| 55 |
+
|
| 56 |
+
def get_feature(self, x):
|
| 57 |
+
x = self.to_mfcc(x.squeeze(1))
|
| 58 |
+
x = self.init_cnn(x)
|
| 59 |
+
x = self.cnns(x)
|
| 60 |
+
x = self.projection(x)
|
| 61 |
+
return x
|
| 62 |
+
|
| 63 |
+
def length_to_mask(self, lengths):
|
| 64 |
+
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
| 65 |
+
mask = torch.gt(mask+1, lengths.unsqueeze(1)).to(lengths.device)
|
| 66 |
+
return mask
|
| 67 |
+
|
| 68 |
+
def get_future_mask(self, out_length, unmask_future_steps=0):
|
| 69 |
+
"""
|
| 70 |
+
Args:
|
| 71 |
+
out_length (int): returned mask shape is (out_length, out_length).
|
| 72 |
+
unmask_futre_steps (int): unmasking future step size.
|
| 73 |
+
Return:
|
| 74 |
+
mask (torch.BoolTensor): mask future timesteps mask[i, j] = True if i > j + unmask_future_steps else False
|
| 75 |
+
"""
|
| 76 |
+
index_tensor = torch.arange(out_length).unsqueeze(0).expand(out_length, -1)
|
| 77 |
+
mask = torch.gt(index_tensor, index_tensor.T + unmask_future_steps)
|
| 78 |
+
return mask
|
| 79 |
+
|
| 80 |
+
class ASRS2S(nn.Module):
|
| 81 |
+
def __init__(self,
|
| 82 |
+
embedding_dim=256,
|
| 83 |
+
hidden_dim=512,
|
| 84 |
+
n_location_filters=32,
|
| 85 |
+
location_kernel_size=63,
|
| 86 |
+
n_token=40):
|
| 87 |
+
super(ASRS2S, self).__init__()
|
| 88 |
+
self.embedding = nn.Embedding(n_token, embedding_dim)
|
| 89 |
+
val_range = math.sqrt(6 / hidden_dim)
|
| 90 |
+
self.embedding.weight.data.uniform_(-val_range, val_range)
|
| 91 |
+
|
| 92 |
+
self.decoder_rnn_dim = hidden_dim
|
| 93 |
+
self.project_to_n_symbols = nn.Linear(self.decoder_rnn_dim, n_token)
|
| 94 |
+
self.attention_layer = Attention(
|
| 95 |
+
self.decoder_rnn_dim,
|
| 96 |
+
hidden_dim,
|
| 97 |
+
hidden_dim,
|
| 98 |
+
n_location_filters,
|
| 99 |
+
location_kernel_size
|
| 100 |
+
)
|
| 101 |
+
self.decoder_rnn = nn.LSTMCell(self.decoder_rnn_dim + embedding_dim, self.decoder_rnn_dim)
|
| 102 |
+
self.project_to_hidden = nn.Sequential(
|
| 103 |
+
LinearNorm(self.decoder_rnn_dim * 2, hidden_dim),
|
| 104 |
+
nn.Tanh())
|
| 105 |
+
self.sos = 1
|
| 106 |
+
self.eos = 2
|
| 107 |
+
|
| 108 |
+
def initialize_decoder_states(self, memory, mask):
|
| 109 |
+
"""
|
| 110 |
+
moemory.shape = (B, L, H) = (Batchsize, Maxtimestep, Hiddendim)
|
| 111 |
+
"""
|
| 112 |
+
B, L, H = memory.shape
|
| 113 |
+
self.decoder_hidden = torch.zeros((B, self.decoder_rnn_dim)).type_as(memory)
|
| 114 |
+
self.decoder_cell = torch.zeros((B, self.decoder_rnn_dim)).type_as(memory)
|
| 115 |
+
self.attention_weights = torch.zeros((B, L)).type_as(memory)
|
| 116 |
+
self.attention_weights_cum = torch.zeros((B, L)).type_as(memory)
|
| 117 |
+
self.attention_context = torch.zeros((B, H)).type_as(memory)
|
| 118 |
+
self.memory = memory
|
| 119 |
+
self.processed_memory = self.attention_layer.memory_layer(memory)
|
| 120 |
+
self.mask = mask
|
| 121 |
+
self.unk_index = 3
|
| 122 |
+
self.random_mask = 0.1
|
| 123 |
+
|
| 124 |
+
def forward(self, memory, memory_mask, text_input):
|
| 125 |
+
"""
|
| 126 |
+
moemory.shape = (B, L, H) = (Batchsize, Maxtimestep, Hiddendim)
|
| 127 |
+
moemory_mask.shape = (B, L, )
|
| 128 |
+
texts_input.shape = (B, T)
|
| 129 |
+
"""
|
| 130 |
+
self.initialize_decoder_states(memory, memory_mask)
|
| 131 |
+
# text random mask
|
| 132 |
+
random_mask = (torch.rand(text_input.shape) < self.random_mask).to(text_input.device)
|
| 133 |
+
_text_input = text_input.clone()
|
| 134 |
+
_text_input.masked_fill_(random_mask, self.unk_index)
|
| 135 |
+
decoder_inputs = self.embedding(_text_input).transpose(0, 1) # -> [T, B, channel]
|
| 136 |
+
start_embedding = self.embedding(
|
| 137 |
+
torch.LongTensor([self.sos]*decoder_inputs.size(1)).to(decoder_inputs.device))
|
| 138 |
+
decoder_inputs = torch.cat((start_embedding.unsqueeze(0), decoder_inputs), dim=0)
|
| 139 |
+
|
| 140 |
+
hidden_outputs, logit_outputs, alignments = [], [], []
|
| 141 |
+
while len(hidden_outputs) < decoder_inputs.size(0):
|
| 142 |
+
|
| 143 |
+
decoder_input = decoder_inputs[len(hidden_outputs)]
|
| 144 |
+
hidden, logit, attention_weights, alignment = self.decode(decoder_input)
|
| 145 |
+
hidden_outputs += [hidden]
|
| 146 |
+
logit_outputs += [logit]
|
| 147 |
+
alignments += [alignment]
|
| 148 |
+
|
| 149 |
+
hidden_outputs, logit_outputs, alignments = \
|
| 150 |
+
self.parse_decoder_outputs(
|
| 151 |
+
hidden_outputs, logit_outputs, alignments)
|
| 152 |
+
|
| 153 |
+
return hidden_outputs, logit_outputs, alignments
|
| 154 |
+
|
| 155 |
+
|
| 156 |
+
def decode(self, decoder_input):
|
| 157 |
+
|
| 158 |
+
cell_input = torch.cat((decoder_input, self.attention_context), -1)
|
| 159 |
+
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
|
| 160 |
+
cell_input,
|
| 161 |
+
(self.decoder_hidden, self.decoder_cell))
|
| 162 |
+
|
| 163 |
+
attention_weights_cat = torch.cat(
|
| 164 |
+
(self.attention_weights.unsqueeze(1),
|
| 165 |
+
self.attention_weights_cum.unsqueeze(1)),dim=1)
|
| 166 |
+
|
| 167 |
+
self.attention_context, self.attention_weights, alignment = self.attention_layer(
|
| 168 |
+
self.decoder_hidden,
|
| 169 |
+
self.memory,
|
| 170 |
+
self.processed_memory,
|
| 171 |
+
attention_weights_cat,
|
| 172 |
+
self.mask)
|
| 173 |
+
|
| 174 |
+
self.attention_weights_cum += self.attention_weights
|
| 175 |
+
|
| 176 |
+
hidden_and_context = torch.cat((self.decoder_hidden, self.attention_context), -1)
|
| 177 |
+
hidden = self.project_to_hidden(hidden_and_context)
|
| 178 |
+
|
| 179 |
+
# dropout to increasing g
|
| 180 |
+
logit = self.project_to_n_symbols(F.dropout(hidden, 0.5, self.training))
|
| 181 |
+
|
| 182 |
+
return hidden, logit, self.attention_weights, alignment
|
| 183 |
+
|
| 184 |
+
def parse_decoder_outputs(self, hidden, logit, alignments):
|
| 185 |
+
|
| 186 |
+
# -> [B, T_out + 1, max_time]
|
| 187 |
+
alignments = torch.stack(alignments).transpose(0,1)
|
| 188 |
+
# [T_out + 1, B, n_symbols] -> [B, T_out + 1, n_symbols]
|
| 189 |
+
logit = torch.stack(logit).transpose(0, 1).contiguous()
|
| 190 |
+
hidden = torch.stack(hidden).transpose(0, 1).contiguous()
|
| 191 |
+
|
| 192 |
+
return hidden, logit, alignments
|
AuxiliaryASR/optimizers.py
ADDED
|
@@ -0,0 +1,86 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#coding:utf-8
|
| 2 |
+
import os, sys
|
| 3 |
+
import os.path as osp
|
| 4 |
+
import numpy as np
|
| 5 |
+
import torch
|
| 6 |
+
from torch import nn
|
| 7 |
+
from torch.optim import Optimizer
|
| 8 |
+
from functools import reduce
|
| 9 |
+
from torch.optim import AdamW
|
| 10 |
+
|
| 11 |
+
class MultiOptimizer:
|
| 12 |
+
def __init__(self, optimizers={}, schedulers={}):
|
| 13 |
+
self.optimizers = optimizers
|
| 14 |
+
self.schedulers = schedulers
|
| 15 |
+
self.keys = list(optimizers.keys())
|
| 16 |
+
self.param_groups = reduce(lambda x,y: x+y, [v.param_groups for v in self.optimizers.values()])
|
| 17 |
+
|
| 18 |
+
def state_dict(self):
|
| 19 |
+
state_dicts = [(key, self.optimizers[key].state_dict())\
|
| 20 |
+
for key in self.keys]
|
| 21 |
+
return state_dicts
|
| 22 |
+
|
| 23 |
+
def load_state_dict(self, state_dict):
|
| 24 |
+
for key, val in state_dict:
|
| 25 |
+
try:
|
| 26 |
+
self.optimizers[key].load_state_dict(val)
|
| 27 |
+
except:
|
| 28 |
+
print("Unloaded %s" % key)
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def step(self, key=None):
|
| 32 |
+
if key is not None:
|
| 33 |
+
self.optimizers[key].step()
|
| 34 |
+
else:
|
| 35 |
+
_ = [self.optimizers[key].step() for key in self.keys]
|
| 36 |
+
|
| 37 |
+
def zero_grad(self, key=None):
|
| 38 |
+
if key is not None:
|
| 39 |
+
self.optimizers[key].zero_grad()
|
| 40 |
+
else:
|
| 41 |
+
_ = [self.optimizers[key].zero_grad() for key in self.keys]
|
| 42 |
+
|
| 43 |
+
def scheduler(self, *args, key=None):
|
| 44 |
+
if key is not None:
|
| 45 |
+
self.schedulers[key].step(*args)
|
| 46 |
+
else:
|
| 47 |
+
_ = [self.schedulers[key].step(*args) for key in self.keys]
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def build_optimizer(parameters):
|
| 51 |
+
optimizer, scheduler = _define_optimizer(parameters)
|
| 52 |
+
return optimizer, scheduler
|
| 53 |
+
|
| 54 |
+
def _define_optimizer(params):
|
| 55 |
+
optimizer_params = params['optimizer_params']
|
| 56 |
+
sch_params = params['scheduler_params']
|
| 57 |
+
optimizer = AdamW(
|
| 58 |
+
params['params'],
|
| 59 |
+
lr=optimizer_params.get('lr', 1e-4),
|
| 60 |
+
weight_decay=optimizer_params.get('weight_decay', 5e-4),
|
| 61 |
+
betas=(0.9, 0.98),
|
| 62 |
+
eps=1e-9)
|
| 63 |
+
scheduler = _define_scheduler(optimizer, sch_params)
|
| 64 |
+
return optimizer, scheduler
|
| 65 |
+
|
| 66 |
+
def _define_scheduler(optimizer, params):
|
| 67 |
+
print(params)
|
| 68 |
+
scheduler = torch.optim.lr_scheduler.OneCycleLR(
|
| 69 |
+
optimizer,
|
| 70 |
+
max_lr=params.get('max_lr', 5e-4),
|
| 71 |
+
epochs=params.get('epochs', 200),
|
| 72 |
+
steps_per_epoch=params.get('steps_per_epoch', 1000),
|
| 73 |
+
pct_start=params.get('pct_start', 0.0),
|
| 74 |
+
final_div_factor=5)
|
| 75 |
+
|
| 76 |
+
return scheduler
|
| 77 |
+
|
| 78 |
+
def build_multi_optimizer(parameters_dict, scheduler_params):
|
| 79 |
+
optim = dict([(key, AdamW(params, lr=1e-4, weight_decay=1e-6, betas=(0.9, 0.98), eps=1e-9))
|
| 80 |
+
for key, params in parameters_dict.items()])
|
| 81 |
+
|
| 82 |
+
schedulers = dict([(key, _define_scheduler(opt, scheduler_params)) \
|
| 83 |
+
for key, opt in optim.items()])
|
| 84 |
+
|
| 85 |
+
multi_optim = MultiOptimizer(optim, schedulers)
|
| 86 |
+
return multi_optim
|
AuxiliaryASR/text_utils.py
ADDED
|
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# IPA Phonemizer: https://github.com/bootphon/phonemizer
|
| 2 |
+
|
| 3 |
+
_pad = "$"
|
| 4 |
+
_punctuation = ';:,.!?¡¿—…"«»“” '
|
| 5 |
+
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
|
| 6 |
+
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
|
| 7 |
+
|
| 8 |
+
# Export all symbols:
|
| 9 |
+
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
|
| 10 |
+
|
| 11 |
+
dicts = {}
|
| 12 |
+
for i in range(len((symbols))):
|
| 13 |
+
dicts[symbols[i]] = i
|
| 14 |
+
|
| 15 |
+
class TextCleaner:
|
| 16 |
+
def __init__(self, dummy=None):
|
| 17 |
+
self.word_index_dictionary = dicts
|
| 18 |
+
print(len(dicts))
|
| 19 |
+
def __call__(self, text):
|
| 20 |
+
indexes = []
|
| 21 |
+
for char in text:
|
| 22 |
+
try:
|
| 23 |
+
indexes.append(self.word_index_dictionary[char])
|
| 24 |
+
except KeyError:
|
| 25 |
+
print(text)
|
| 26 |
+
return indexes
|
AuxiliaryASR/train.py
ADDED
|
@@ -0,0 +1,116 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from meldataset import build_dataloader
|
| 2 |
+
from optimizers import build_optimizer
|
| 3 |
+
from utils import *
|
| 4 |
+
from models import build_model
|
| 5 |
+
from trainer import Trainer
|
| 6 |
+
|
| 7 |
+
import os
|
| 8 |
+
import os.path as osp
|
| 9 |
+
import re
|
| 10 |
+
import sys
|
| 11 |
+
import yaml
|
| 12 |
+
import shutil
|
| 13 |
+
import numpy as np
|
| 14 |
+
import torch
|
| 15 |
+
from torch.utils.tensorboard import SummaryWriter
|
| 16 |
+
import click
|
| 17 |
+
|
| 18 |
+
import logging
|
| 19 |
+
from logging import StreamHandler
|
| 20 |
+
logger = logging.getLogger(__name__)
|
| 21 |
+
logger.setLevel(logging.DEBUG)
|
| 22 |
+
handler = StreamHandler()
|
| 23 |
+
handler.setLevel(logging.DEBUG)
|
| 24 |
+
logger.addHandler(handler)
|
| 25 |
+
|
| 26 |
+
torch.backends.cudnn.benchmark = True
|
| 27 |
+
|
| 28 |
+
@click.command()
|
| 29 |
+
@click.option('-p', '--config_path', default='./Configs/config.yml', type=str)
|
| 30 |
+
def main(config_path):
|
| 31 |
+
config = yaml.safe_load(open(config_path))
|
| 32 |
+
log_dir = config['log_dir']
|
| 33 |
+
if not osp.exists(log_dir): os.mkdir(log_dir)
|
| 34 |
+
shutil.copy(config_path, osp.join(log_dir, osp.basename(config_path)))
|
| 35 |
+
|
| 36 |
+
writer = SummaryWriter(log_dir + "/tensorboard")
|
| 37 |
+
|
| 38 |
+
# write logs
|
| 39 |
+
file_handler = logging.FileHandler(osp.join(log_dir, 'train.log'))
|
| 40 |
+
file_handler.setLevel(logging.DEBUG)
|
| 41 |
+
file_handler.setFormatter(logging.Formatter('%(levelname)s:%(asctime)s: %(message)s'))
|
| 42 |
+
logger.addHandler(file_handler)
|
| 43 |
+
|
| 44 |
+
batch_size = config.get('batch_size', 10)
|
| 45 |
+
device = config.get('device', 'cpu')
|
| 46 |
+
epochs = config.get('epochs', 1000)
|
| 47 |
+
save_freq = config.get('save_freq', 20)
|
| 48 |
+
train_path = config.get('train_data', None)
|
| 49 |
+
val_path = config.get('val_data', None)
|
| 50 |
+
|
| 51 |
+
train_list, val_list = get_data_path_list(train_path, val_path)
|
| 52 |
+
train_dataloader = build_dataloader(train_list,
|
| 53 |
+
batch_size=batch_size,
|
| 54 |
+
num_workers=8,
|
| 55 |
+
dataset_config=config.get('dataset_params', {}),
|
| 56 |
+
device=device)
|
| 57 |
+
|
| 58 |
+
val_dataloader = build_dataloader(val_list,
|
| 59 |
+
batch_size=batch_size,
|
| 60 |
+
validation=True,
|
| 61 |
+
num_workers=2,
|
| 62 |
+
device=device,
|
| 63 |
+
dataset_config=config.get('dataset_params', {}))
|
| 64 |
+
|
| 65 |
+
model = build_model(model_params=config['model_params'] or {})
|
| 66 |
+
|
| 67 |
+
scheduler_params = {
|
| 68 |
+
"max_lr": float(config['optimizer_params'].get('lr', 5e-4)),
|
| 69 |
+
"pct_start": float(config['optimizer_params'].get('pct_start', 0.0)),
|
| 70 |
+
"epochs": epochs,
|
| 71 |
+
"steps_per_epoch": len(train_dataloader),
|
| 72 |
+
}
|
| 73 |
+
|
| 74 |
+
model.to(device)
|
| 75 |
+
optimizer, scheduler = build_optimizer(
|
| 76 |
+
{"params": model.parameters(), "optimizer_params":{}, "scheduler_params": scheduler_params})
|
| 77 |
+
|
| 78 |
+
blank_index = train_dataloader.dataset.text_cleaner.word_index_dictionary[" "] # get blank index
|
| 79 |
+
|
| 80 |
+
criterion = build_criterion(critic_params={
|
| 81 |
+
'ctc': {'blank': blank_index},
|
| 82 |
+
})
|
| 83 |
+
|
| 84 |
+
trainer = Trainer(model=model,
|
| 85 |
+
criterion=criterion,
|
| 86 |
+
optimizer=optimizer,
|
| 87 |
+
scheduler=scheduler,
|
| 88 |
+
device=device,
|
| 89 |
+
train_dataloader=train_dataloader,
|
| 90 |
+
val_dataloader=val_dataloader,
|
| 91 |
+
logger=logger)
|
| 92 |
+
|
| 93 |
+
if config.get('pretrained_model', '') != '':
|
| 94 |
+
trainer.load_checkpoint(config['pretrained_model'],
|
| 95 |
+
load_only_params=config.get('load_only_params', True))
|
| 96 |
+
|
| 97 |
+
for epoch in range(1, epochs+1):
|
| 98 |
+
train_results = trainer._train_epoch()
|
| 99 |
+
eval_results = trainer._eval_epoch()
|
| 100 |
+
results = train_results.copy()
|
| 101 |
+
results.update(eval_results)
|
| 102 |
+
logger.info('--- epoch %d ---' % epoch)
|
| 103 |
+
for key, value in results.items():
|
| 104 |
+
if isinstance(value, float):
|
| 105 |
+
logger.info('%-15s: %.4f' % (key, value))
|
| 106 |
+
writer.add_scalar(key, value, epoch)
|
| 107 |
+
else:
|
| 108 |
+
for v in value:
|
| 109 |
+
writer.add_figure('eval_attn', plot_image(v), epoch)
|
| 110 |
+
if (epoch % save_freq) == 0:
|
| 111 |
+
trainer.save_checkpoint(osp.join(log_dir, 'epoch_%05d.pth' % epoch))
|
| 112 |
+
|
| 113 |
+
return 0
|
| 114 |
+
|
| 115 |
+
if __name__=="__main__":
|
| 116 |
+
main()
|
AuxiliaryASR/trainer.py
ADDED
|
@@ -0,0 +1,241 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
|
|
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|
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|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# -*- coding: utf-8 -*-
|
| 2 |
+
|
| 3 |
+
import os
|
| 4 |
+
import os.path as osp
|
| 5 |
+
import sys
|
| 6 |
+
import time
|
| 7 |
+
from collections import defaultdict
|
| 8 |
+
|
| 9 |
+
import numpy as np
|
| 10 |
+
import torch
|
| 11 |
+
from torch import nn
|
| 12 |
+
from PIL import Image
|
| 13 |
+
from tqdm import tqdm
|
| 14 |
+
|
| 15 |
+
from utils import calc_wer
|
| 16 |
+
|
| 17 |
+
import logging
|
| 18 |
+
logger = logging.getLogger(__name__)
|
| 19 |
+
logger.setLevel(logging.DEBUG)
|
| 20 |
+
|
| 21 |
+
from utils import *
|
| 22 |
+
|
| 23 |
+
class Trainer(object):
|
| 24 |
+
def __init__(self,
|
| 25 |
+
model=None,
|
| 26 |
+
criterion=None,
|
| 27 |
+
optimizer=None,
|
| 28 |
+
scheduler=None,
|
| 29 |
+
config={},
|
| 30 |
+
device=torch.device("cpu"),
|
| 31 |
+
logger=logger,
|
| 32 |
+
train_dataloader=None,
|
| 33 |
+
val_dataloader=None,
|
| 34 |
+
initial_steps=0,
|
| 35 |
+
initial_epochs=0):
|
| 36 |
+
|
| 37 |
+
self.steps = initial_steps
|
| 38 |
+
self.epochs = initial_epochs
|
| 39 |
+
self.model = model
|
| 40 |
+
self.criterion = criterion
|
| 41 |
+
self.optimizer = optimizer
|
| 42 |
+
self.scheduler = scheduler
|
| 43 |
+
self.train_dataloader = train_dataloader
|
| 44 |
+
self.val_dataloader = val_dataloader
|
| 45 |
+
self.config = config
|
| 46 |
+
self.device = device
|
| 47 |
+
self.finish_train = False
|
| 48 |
+
self.logger = logger
|
| 49 |
+
self.fp16_run = False
|
| 50 |
+
|
| 51 |
+
def save_checkpoint(self, checkpoint_path):
|
| 52 |
+
"""Save checkpoint.
|
| 53 |
+
Args:
|
| 54 |
+
checkpoint_path (str): Checkpoint path to be saved.
|
| 55 |
+
"""
|
| 56 |
+
state_dict = {
|
| 57 |
+
"optimizer": self.optimizer.state_dict(),
|
| 58 |
+
"scheduler": self.scheduler.state_dict(),
|
| 59 |
+
"steps": self.steps,
|
| 60 |
+
"epochs": self.epochs,
|
| 61 |
+
}
|
| 62 |
+
state_dict["model"] = self.model.state_dict()
|
| 63 |
+
|
| 64 |
+
if not os.path.exists(os.path.dirname(checkpoint_path)):
|
| 65 |
+
os.makedirs(os.path.dirname(checkpoint_path))
|
| 66 |
+
torch.save(state_dict, checkpoint_path)
|
| 67 |
+
|
| 68 |
+
def load_checkpoint(self, checkpoint_path, load_only_params=False):
|
| 69 |
+
"""Load checkpoint.
|
| 70 |
+
|
| 71 |
+
Args:
|
| 72 |
+
checkpoint_path (str): Checkpoint path to be loaded.
|
| 73 |
+
load_only_params (bool): Whether to load only model parameters.
|
| 74 |
+
|
| 75 |
+
"""
|
| 76 |
+
state_dict = torch.load(checkpoint_path, map_location="cpu")
|
| 77 |
+
self._load(state_dict["model"], self.model)
|
| 78 |
+
|
| 79 |
+
if not load_only_params:
|
| 80 |
+
self.steps = state_dict["steps"]
|
| 81 |
+
self.epochs = state_dict["epochs"]
|
| 82 |
+
self.optimizer.load_state_dict(state_dict["optimizer"])
|
| 83 |
+
|
| 84 |
+
# overwrite schedular argument parameters
|
| 85 |
+
state_dict["scheduler"].update(**self.config.get("scheduler_params", {}))
|
| 86 |
+
self.scheduler.load_state_dict(state_dict["scheduler"])
|
| 87 |
+
|
| 88 |
+
def _load(self, states, model, force_load=True):
|
| 89 |
+
model_states = model.state_dict()
|
| 90 |
+
for key, val in states.items():
|
| 91 |
+
try:
|
| 92 |
+
if key not in model_states:
|
| 93 |
+
continue
|
| 94 |
+
if isinstance(val, nn.Parameter):
|
| 95 |
+
val = val.data
|
| 96 |
+
|
| 97 |
+
if val.shape != model_states[key].shape:
|
| 98 |
+
self.logger.info("%s does not have same shape" % key)
|
| 99 |
+
print(val.shape, model_states[key].shape)
|
| 100 |
+
if not force_load:
|
| 101 |
+
continue
|
| 102 |
+
|
| 103 |
+
min_shape = np.minimum(np.array(val.shape), np.array(model_states[key].shape))
|
| 104 |
+
slices = [slice(0, min_index) for min_index in min_shape]
|
| 105 |
+
model_states[key][slices].copy_(val[slices])
|
| 106 |
+
else:
|
| 107 |
+
model_states[key].copy_(val)
|
| 108 |
+
except:
|
| 109 |
+
self.logger.info("not exist :%s" % key)
|
| 110 |
+
print("not exist ", key)
|
| 111 |
+
|
| 112 |
+
@staticmethod
|
| 113 |
+
def get_gradient_norm(model):
|
| 114 |
+
total_norm = 0
|
| 115 |
+
for p in model.parameters():
|
| 116 |
+
param_norm = p.grad.data.norm(2)
|
| 117 |
+
total_norm += param_norm.item() ** 2
|
| 118 |
+
|
| 119 |
+
total_norm = np.sqrt(total_norm)
|
| 120 |
+
return total_norm
|
| 121 |
+
|
| 122 |
+
@staticmethod
|
| 123 |
+
def length_to_mask(lengths):
|
| 124 |
+
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
| 125 |
+
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
| 126 |
+
return mask
|
| 127 |
+
|
| 128 |
+
def _get_lr(self):
|
| 129 |
+
for param_group in self.optimizer.param_groups:
|
| 130 |
+
lr = param_group['lr']
|
| 131 |
+
break
|
| 132 |
+
return lr
|
| 133 |
+
|
| 134 |
+
@staticmethod
|
| 135 |
+
def get_image(arrs):
|
| 136 |
+
pil_images = []
|
| 137 |
+
height = 0
|
| 138 |
+
width = 0
|
| 139 |
+
for arr in arrs:
|
| 140 |
+
uint_arr = (((arr - arr.min()) / (arr.max() - arr.min())) * 255).astype(np.uint8)
|
| 141 |
+
pil_image = Image.fromarray(uint_arr)
|
| 142 |
+
pil_images.append(pil_image)
|
| 143 |
+
height += uint_arr.shape[0]
|
| 144 |
+
width = max(width, uint_arr.shape[1])
|
| 145 |
+
|
| 146 |
+
palette = Image.new('L', (width, height))
|
| 147 |
+
curr_heigth = 0
|
| 148 |
+
for pil_image in pil_images:
|
| 149 |
+
palette.paste(pil_image, (0, curr_heigth))
|
| 150 |
+
curr_heigth += pil_image.size[1]
|
| 151 |
+
|
| 152 |
+
return palette
|
| 153 |
+
|
| 154 |
+
def run(self, batch):
|
| 155 |
+
self.optimizer.zero_grad()
|
| 156 |
+
batch = [b.to(self.device) for b in batch]
|
| 157 |
+
text_input, text_input_length, mel_input, mel_input_length = batch
|
| 158 |
+
mel_input_length = mel_input_length // (2 ** self.model.n_down)
|
| 159 |
+
future_mask = self.model.get_future_mask(
|
| 160 |
+
mel_input.size(2)//(2**self.model.n_down), unmask_future_steps=0).to(self.device)
|
| 161 |
+
mel_mask = self.model.length_to_mask(mel_input_length)
|
| 162 |
+
text_mask = self.model.length_to_mask(text_input_length)
|
| 163 |
+
ppgs, s2s_pred, s2s_attn = self.model(
|
| 164 |
+
mel_input, src_key_padding_mask=mel_mask, text_input=text_input)
|
| 165 |
+
|
| 166 |
+
loss_ctc = self.criterion['ctc'](ppgs.log_softmax(dim=2).transpose(0, 1),
|
| 167 |
+
text_input, mel_input_length, text_input_length)
|
| 168 |
+
|
| 169 |
+
loss_s2s = 0
|
| 170 |
+
for _s2s_pred, _text_input, _text_length in zip(s2s_pred, text_input, text_input_length):
|
| 171 |
+
loss_s2s += self.criterion['ce'](_s2s_pred[:_text_length], _text_input[:_text_length])
|
| 172 |
+
loss_s2s /= text_input.size(0)
|
| 173 |
+
|
| 174 |
+
loss = loss_ctc + loss_s2s
|
| 175 |
+
loss.backward()
|
| 176 |
+
torch.nn.utils.clip_grad_value_(self.model.parameters(), 5)
|
| 177 |
+
self.optimizer.step()
|
| 178 |
+
self.scheduler.step()
|
| 179 |
+
return {'loss': loss.item(),
|
| 180 |
+
'ctc': loss_ctc.item(),
|
| 181 |
+
's2s': loss_s2s.item()}
|
| 182 |
+
|
| 183 |
+
def _train_epoch(self):
|
| 184 |
+
train_losses = defaultdict(list)
|
| 185 |
+
self.model.train()
|
| 186 |
+
for train_steps_per_epoch, batch in enumerate(tqdm(self.train_dataloader, desc="[train]"), 1):
|
| 187 |
+
losses = self.run(batch)
|
| 188 |
+
for key, value in losses.items():
|
| 189 |
+
train_losses["train/%s" % key].append(value)
|
| 190 |
+
|
| 191 |
+
train_losses = {key: np.mean(value) for key, value in train_losses.items()}
|
| 192 |
+
train_losses['train/learning_rate'] = self._get_lr()
|
| 193 |
+
return train_losses
|
| 194 |
+
|
| 195 |
+
@torch.no_grad()
|
| 196 |
+
def _eval_epoch(self):
|
| 197 |
+
self.model.eval()
|
| 198 |
+
eval_losses = defaultdict(list)
|
| 199 |
+
eval_images = defaultdict(list)
|
| 200 |
+
for eval_steps_per_epoch, batch in enumerate(tqdm(self.val_dataloader, desc="[eval]"), 1):
|
| 201 |
+
batch = [b.to(self.device) for b in batch]
|
| 202 |
+
text_input, text_input_length, mel_input, mel_input_length = batch
|
| 203 |
+
mel_input_length = mel_input_length // (2 ** self.model.n_down)
|
| 204 |
+
future_mask = self.model.get_future_mask(
|
| 205 |
+
mel_input.size(2)//(2**self.model.n_down), unmask_future_steps=0).to(self.device)
|
| 206 |
+
mel_mask = self.model.length_to_mask(mel_input_length)
|
| 207 |
+
text_mask = self.model.length_to_mask(text_input_length)
|
| 208 |
+
ppgs, s2s_pred, s2s_attn = self.model(
|
| 209 |
+
mel_input, src_key_padding_mask=mel_mask, text_input=text_input)
|
| 210 |
+
loss_ctc = self.criterion['ctc'](ppgs.log_softmax(dim=2).transpose(0, 1),
|
| 211 |
+
text_input, mel_input_length, text_input_length)
|
| 212 |
+
loss_s2s = 0
|
| 213 |
+
for _s2s_pred, _text_input, _text_length in zip(s2s_pred, text_input, text_input_length):
|
| 214 |
+
loss_s2s += self.criterion['ce'](_s2s_pred[:_text_length], _text_input[:_text_length])
|
| 215 |
+
loss_s2s /= text_input.size(0)
|
| 216 |
+
loss = loss_ctc + loss_s2s
|
| 217 |
+
|
| 218 |
+
eval_losses["eval/ctc"].append(loss_ctc.item())
|
| 219 |
+
eval_losses["eval/s2s"].append(loss_s2s.item())
|
| 220 |
+
eval_losses["eval/loss"].append(loss.item())
|
| 221 |
+
|
| 222 |
+
_, amax_ppgs = torch.max(ppgs, dim=2)
|
| 223 |
+
wers = [calc_wer(target[:text_length],
|
| 224 |
+
pred[:mel_length],
|
| 225 |
+
ignore_indexes=list(range(5))) \
|
| 226 |
+
for target, pred, text_length, mel_length in zip(
|
| 227 |
+
text_input.cpu(), amax_ppgs.cpu(), text_input_length.cpu(), mel_input_length.cpu())]
|
| 228 |
+
eval_losses["eval/wer"].extend(wers)
|
| 229 |
+
|
| 230 |
+
_, amax_s2s = torch.max(s2s_pred, dim=2)
|
| 231 |
+
acc = [torch.eq(target[:length], pred[:length]).float().mean().item() \
|
| 232 |
+
for target, pred, length in zip(text_input.cpu(), amax_s2s.cpu(), text_input_length.cpu())]
|
| 233 |
+
eval_losses["eval/acc"].extend(acc)
|
| 234 |
+
|
| 235 |
+
if eval_steps_per_epoch <= 2:
|
| 236 |
+
eval_images["eval/image"].append(
|
| 237 |
+
self.get_image([s2s_attn[0].cpu().numpy()]))
|
| 238 |
+
|
| 239 |
+
eval_losses = {key: np.mean(value) for key, value in eval_losses.items()}
|
| 240 |
+
eval_losses.update(eval_images)
|
| 241 |
+
return eval_losses
|
AuxiliaryASR/utils.py
ADDED
|
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
import os.path as osp
|
| 3 |
+
import sys
|
| 4 |
+
import time
|
| 5 |
+
from collections import defaultdict
|
| 6 |
+
|
| 7 |
+
import matplotlib
|
| 8 |
+
import numpy as np
|
| 9 |
+
import soundfile as sf
|
| 10 |
+
import torch
|
| 11 |
+
from torch import nn
|
| 12 |
+
import jiwer
|
| 13 |
+
|
| 14 |
+
import matplotlib.pylab as plt
|
| 15 |
+
|
| 16 |
+
def calc_wer(target, pred, ignore_indexes=[0]):
|
| 17 |
+
target_chars = drop_duplicated(list(filter(lambda x: x not in ignore_indexes, map(str, list(target)))))
|
| 18 |
+
pred_chars = drop_duplicated(list(filter(lambda x: x not in ignore_indexes, map(str, list(pred)))))
|
| 19 |
+
target_str = ' '.join(target_chars)
|
| 20 |
+
pred_str = ' '.join(pred_chars)
|
| 21 |
+
error = jiwer.wer(target_str, pred_str)
|
| 22 |
+
return error
|
| 23 |
+
|
| 24 |
+
def drop_duplicated(chars):
|
| 25 |
+
ret_chars = [chars[0]]
|
| 26 |
+
for prev, curr in zip(chars[:-1], chars[1:]):
|
| 27 |
+
if prev != curr:
|
| 28 |
+
ret_chars.append(curr)
|
| 29 |
+
return ret_chars
|
| 30 |
+
|
| 31 |
+
def build_criterion(critic_params={}):
|
| 32 |
+
criterion = {
|
| 33 |
+
"ce": nn.CrossEntropyLoss(ignore_index=-1),
|
| 34 |
+
"ctc": torch.nn.CTCLoss(**critic_params.get('ctc', {})),
|
| 35 |
+
}
|
| 36 |
+
return criterion
|
| 37 |
+
|
| 38 |
+
def get_data_path_list(train_path=None, val_path=None):
|
| 39 |
+
if train_path is None:
|
| 40 |
+
train_path = "Data/train_list.txt"
|
| 41 |
+
if val_path is None:
|
| 42 |
+
val_path = "Data/val_list.txt"
|
| 43 |
+
|
| 44 |
+
with open(train_path, 'r') as f:
|
| 45 |
+
train_list = f.readlines()
|
| 46 |
+
with open(val_path, 'r') as f:
|
| 47 |
+
val_list = f.readlines()
|
| 48 |
+
|
| 49 |
+
return train_list, val_list
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
def plot_image(image):
|
| 53 |
+
fig, ax = plt.subplots(figsize=(10, 2))
|
| 54 |
+
im = ax.imshow(image, aspect="auto", origin="lower",
|
| 55 |
+
interpolation='none')
|
| 56 |
+
|
| 57 |
+
fig.canvas.draw()
|
| 58 |
+
plt.close()
|
| 59 |
+
|
| 60 |
+
return fig
|