GPT-SoVITS-WebUI
A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.
[](https://github.com/RVC-Boss/GPT-SoVITS)
[](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[](https://huggingface.co/lj1995/GPT-SoVITS/tree/main)
[**English**](./README.md) | [**ä¸æ–‡ç®€ä½“**](./docs/cn/README.md)
------
> Check out our [demo video](https://www.bilibili.com/video/BV12g4y1m7Uw) here!
https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-80c060ab47fb
## Features:
1. **Zero-shot TTS:** Input a 5-second vocal sample and experience instant text-to-speech conversion.
2. **Few-shot TTS:** Fine-tune the model with just 1 minute of training data for improved voice similarity and realism.
3. **Cross-lingual Support:** Inference in languages different from the training dataset, currently supporting English, Japanese, and Chinese.
4. **WebUI Tools:** Integrated tools include voice accompaniment separation, automatic training set segmentation, Chinese ASR, and text labeling, assisting beginners in creating training datasets and GPT/SoVITS models.
## Environment Preparation
If you are a Windows user (tested with win>=10) you can install directly via the prezip. Just download the [prezip](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true), unzip it and double-click go-webui.bat to start GPT-SoVITS-WebUI.
### Tested Environments
- Python 3.9, PyTorch 2.0.1, CUDA 11
- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
_Note: numba==0.56.4 require py<3.11_
### Quick Install with Conda
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
bash install.sh
```
### Install Manually
#### Pip Packages
```bash
pip install torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-lightning gradio==3.14.0 ffmpeg-python onnxruntime tqdm cn2an pypinyin pyopenjtalk g2p_en chardet
```
#### Additional Requirements
If you need Chinese ASR (supported by FunASR), install:
```bash
pip install modelscope torchaudio sentencepiece funasr
```
#### FFmpeg
##### Conda Users
```bash
conda install ffmpeg
```
##### Ubuntu/Debian Users
```bash
sudo apt install ffmpeg
sudo apt install libsox-dev
conda install -c conda-forge 'ffmpeg<7'
```
##### MacOS Users
```bash
brew install ffmpeg
```
##### Windows Users
Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root.
### Pretrained Models
Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/damo_asr/models`.
For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) and place them in `tools/uvr5/uvr5_weights`.
## Dataset Format
The TTS annotation .list file format:
```
vocal_path|speaker_name|language|text
```
Language dictionary:
- 'zh': Chinese
- 'ja': Japanese
- 'en': English
Example:
```
D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
```
## Todo List
- [ ] **High Priority:**
- [ ] Localization in Japanese and English.
- [ ] User guide.
- [ ] Japanese and English dataset fine tune training.
- [ ] **Features:**
- [ ] Zero-shot voice conversion (5s) / few-shot voice conversion (1min).
- [ ] TTS speaking speed control.
- [ ] Enhanced TTS emotion control.
- [ ] Experiment with changing SoVITS token inputs to probability distribution of vocabs.
- [ ] Improve English and Japanese text frontend.
- [ ] Develop tiny and larger-sized TTS models.
- [ ] Colab scripts.
- [ ] Expand training dataset (2k -> 10k).
- [ ] better sovits base model (enhanced audio quality)
- [ ] model mix
## Credits
Special thanks to the following projects and contributors:
- [ar-vits](https://github.com/innnky/ar-vits)
- [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
- [vits](https://github.com/jaywalnut310/vits)
- [TransferTTS](https://github.com/hcy71o/TransferTTS/blob/master/models.py#L556)
- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
- [contentvec](https://github.com/auspicious3000/contentvec/)
- [hifi-gan](https://github.com/jik876/hifi-gan)
- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
- [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
- [audio-slicer](https://github.com/openvpi/audio-slicer)
- [SubFix](https://github.com/cronrpc/SubFix)
- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
- [gradio](https://github.com/gradio-app/gradio)
## Thanks to all contributors for their efforts