(1) Datasetをtrainer/datasetにおく
trainer/dataset/
├── 00_myvoice
│ ├── text
│ │ ├── emotion001.txt
│ │ ├── emotion002.txt
...
│ │ └── emotion100.txt
│ └── wav
│ ├── emotion001.wav
│ ├── emotion002.wav
...
│ └── emotion100.wav
├── 1205_zundamon
│ ├── text
│ │ ├── emoNormal_001.txt
│ │ ├── emoNormal_002.txt
...
│ │ └── emoNormal_100.txt
│ └── wav
│ ├── emoNormal_001.wav
│ ├── emoNormal_002.wav
...
│ └── emoNormal_100.wav
├── 344_tsumugi
│ ├── text
│ │ ├── VOICEACTRESS100_001.txt
│ │ ├── VOICEACTRESS100_002.txt
...
│ │ └── emoNormal_100.txt
│ └── wav
│ ├── VOICEACTRESS100_001.wav
│ ├── VOICEACTRESS100_002.wav
...
│ └── emoNormal_100.wav
└── multi_speaker_correspondence.txt(2) start_trainer.shをrootにコピー
(3) bash start_trainer.shを実行
(4) Docker内で次のコマンドを実行 batch sizeは適宜調整
$ cp configs_org/baseconfig.json configs/
$ python3 normalize.py True
$ python3 create_dataset.py -f train_config -s 24000 -m dataset/multi_speaker_correspondence.txt
$ tensorboard --logdir logs --port 5000 --bind_all &
# batch size 変更
$ 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
$ python3 train_ms.py -c configs/train_config.json -m 20220306_24000(x) テスト
$ 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
(X) onnx python3 onnx_export.py --config_file logs/train_config.json --convert_pth logs/G_220000.pth