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Fine-Tuning your VITS model using a pre-trained model
| Date | Stars |
|---|---|
| 2026-07-31 | 544 |
| 2026-08-01 | 544 |
| 2026-08-06 | 544 |
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text cleaner from https://github.com/CjangCjengh/vits original repo: https://github.com/jaywalnut310/vits ## Online training and inference ### colab See [vits-finetuning](https://colab.research.google.com/drive/13FF2pBWxj9rMR1SjI_JpVD6mTRN-kq--?usp=share_link) # How to use (Suggestion) Python == 3.7 Only Japanese datasets can be used for fine-tuning in this repo. ## Clone this repository ```sh git clone https://github.com/SayaSS/vits-finetuning.git ``` ## Install requirements ```sh pip install -r requirements.txt ``` ## Download pre-trained model - [G_0.pth](https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/model/G_0.pth) - [D_0.pth](https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/model/D_0.pth) - Edit "model_dir"(line 152) in utils.py - Put pre-trained models in the "model_dir"/checkpoints ### If you need to customize "n_speakers", please replace the pre-trained model with these two. - [G_0-p.pth](https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/model/G_0-p.pth) - [D_0-p.pth](https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/model/D_0-p.pth) ## Create datasets - Speaker ID should be between 0-803. - About 50 audio-text pairs will suffice and 100-600 epochs could have quite good performance, but more data may be better. - Resample all audio to 22050Hz, 16-bit, mono wav files. - Audio files should be >=1s and <=10s. ``` path/to/XXX.wav|speaker id|transcript ``` - Example ``` dataset/001.wav|10|こんにちは。 ``` For complete examples, please see filelists/miyu_train.txt and filelists/miyu_val.txt. ## Preprocess ```sh python preprocess.py --filelists path/to/filelist_train.txt path/to/filelist_val.txt ``` Edit "training_files" and "validation_files" in configs/config.json ## Train ```sh # Mutiple speakers python train_ms.py -c configs/config.json -m checkpoints ```
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e71385259fd18303, desc:fine-tuning, desc:fine tuning