mirror of
https://github.com/w-okada/voice-changer.git
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69 lines
2.0 KiB
Markdown
69 lines
2.0 KiB
Markdown
MMVC Server
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----
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# 起動方法
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(1) Datasetを`trainer/dataset`におく
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```sh
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trainer/dataset/
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├── 00_myvoice
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│ ├── text
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│ │ ├── emotion001.txt
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│ │ ├── emotion002.txt
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...
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│ │ └── emotion100.txt
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│ └── wav
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│ ├── emotion001.wav
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│ ├── emotion002.wav
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...
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│ └── emotion100.wav
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├── 1205_zundamon
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│ ├── text
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│ │ ├── emoNormal_001.txt
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│ │ ├── emoNormal_002.txt
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...
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│ │ └── emoNormal_100.txt
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│ └── wav
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│ ├── emoNormal_001.wav
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│ ├── emoNormal_002.wav
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...
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│ └── emoNormal_100.wav
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├── 344_tsumugi
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│ ├── text
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│ │ ├── VOICEACTRESS100_001.txt
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│ │ ├── VOICEACTRESS100_002.txt
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...
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│ │ └── emoNormal_100.txt
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│ └── wav
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│ ├── VOICEACTRESS100_001.wav
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│ ├── VOICEACTRESS100_002.wav
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...
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│ └── emoNormal_100.wav
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└── multi_speaker_correspondence.txt
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```
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(2) start_trainer.shをrootにコピー
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(3) `bash start_trainer.sh`を実行
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(4) Docker内で次のコマンドを実行
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batch sizeは適宜調整
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```sh
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$ cp configs_org/baseconfig.json configs/
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$ python3 normalize.py True
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$ python3 create_dataset.py -f train_config -s 24000 -m dataset/multi_speaker_correspondence.txt
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$ tensorboard --logdir logs --port 5000 --bind_all &
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# batch size 変更
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$ python3 train_ms.py -c configs/train_config.json -m 20220306_24000 -fg fine_model/G_v15_best.pth -fd fine_model/D_v15_best.pth
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$ python3 train_ms.py -c configs/train_config.json -m 20220306_24000
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```
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(x) テスト
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```
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$ python3 MMVC_Client/python/conver_test.py -m logs/G_40000.pth -c configs/train_config.json -s 0 -t 101 --input dataset/00_myvoice/wav/emotion011.wav --output dataset/test.wav --f0_scale 3
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```
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(X) onnx
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python3 onnx_export.py --config_file logs/train_config.json --convert_pth logs/G_220000.pth |