/GPT-SoVITS

1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

Primary LanguagePythonMIT LicenseMIT

GPT-SoVITS-WebUI

A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.

madewithlove


Licence Huggingface

English | 中文简体

Check out our demo video here!

few.shot.fine.tuning.demo.mp4

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, unzip it and double-click go-webui.bat to start GPT-SoVITS-WebUI.

Python and PyTorch Version

Tested with Python 3.9, PyTorch 2.0.1, and CUDA 11.

Quick Install with Conda

conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
bash install.sh

Install Manually

Pip Packages

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:

pip install modelscope torchaudio sentencepiece funasr

FFmpeg

Conda Users
conda install ffmpeg
Ubuntu/Debian Users
sudo apt install ffmpeg
sudo apt install libsox-dev
conda install -c conda-forge 'ffmpeg<7'
MacOS Users
brew install ffmpeg
Windows Users

Download and place ffmpeg.exe and ffprobe.exe in the GPT-SoVITS root.

Pretrained Models

Download pretrained models from GPT-SoVITS Models and place them in GPT_SoVITS\pretrained_models.

For Chinese ASR (additionally), download models from Damo ASR Model, Damo VAD Model, and Damo Punc Model and place them in tools/damo_asr/models.

For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from 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:

Thanks to all contributors for their efforts