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Retrieval-based-Voice-Conversion-WebUI

An easy-to-use Voice Conversion framework based on VITS.

madewithlove


Open In Colab Licence Huggingface

Discord

Changelog | FAQ (Frequently Asked Questions)

English | 中文简体 | 日本語 | 한국어 (韓國語) | Türkçe

Check our Demo Video here!

Training/Inference WebUI：go-web.bat

image

Realtime Voice Conversion GUI：go-realtime-gui.bat

image

The dataset for the pre-training model uses nearly 50 hours of high quality VCTK open source dataset.

High quality licensed song datasets will be added to training-set one after another for your use, without worrying about copyright infringement.

Please look forward to the pretrained base model of RVCv3, which has larger parameters, more training data, better results, unchanged inference speed, and requires less training data for training.

Summary

This repository has the following features:

  • Reduce tone leakage by replacing the source feature to training-set feature using top1 retrieval;
  • Easy and fast training, even on relatively poor graphics cards;
  • Training with a small amount of data also obtains relatively good results (>=10min low noise speech recommended);
  • Supporting model fusion to change timbres (using ckpt processing tab->ckpt merge);
  • Easy-to-use Webui interface;
  • Use the UVR5 model to quickly separate vocals and instruments.
  • Use the most powerful High-pitch Voice Extraction Algorithm InterSpeech2023-RMVPE to prevent the muted sound problem. Provides the best results (significantly) and is faster, with even lower resource consumption than Crepe_full.
  • AMD/Intel graphics cards acceleration supported.
  • Intel ARC graphics cards acceleration with IPEX supported.

Preparing the environment

The following commands need to be executed in the environment of Python version 3.8 or higher.

(Windows/Linux) First install the main dependencies through pip:

# Install PyTorch-related core dependencies, skip if installed
# Reference: https://pytorch.org/get-started/locally/
pip install torch torchvision torchaudio

#For Windows + Nvidia Ampere Architecture(RTX30xx), you need to specify the cuda version corresponding to pytorch according to the experience of https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/issues/21
#pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

#For Linux + AMD Cards, you need to use the following pytorch versions:
#pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2

Then can use poetry to install the other dependencies:

# Install the Poetry dependency management tool, skip if installed
# Reference: https://python-poetry.org/docs/#installation
curl -sSL https://install.python-poetry.org | python3 -

# Install the project dependencies
poetry install

You can also use pip to install them:

for Nvidia graphics cards
  pip install -r requirements.txt

for AMD/Intel graphics cards on Windows (DirectML)：
  pip install -r requirements-dml.txt

for Intel ARC graphics cards on Linux / WSL using Python 3.10: 
  pip install -r requirements-ipex.txt

for AMD graphics cards on Linux (ROCm):
  pip install -r requirements-amd.txt

Mac users can install dependencies via run.sh:

sh ./run.sh

Preparation of other Pre-models

RVC requires other pre-models to infer and train.

#Download all needed models from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/
python tools/download_models.py

Or just download them by yourself from our Huggingface space.

Here's a list of Pre-models and other files that RVC needs:

./assets/hubert/hubert_base.pt

./assets/pretrained 

./assets/uvr5_weights

Additional downloads are required if you want to test the v2 version of the model.

./assets/pretrained_v2

If you want to test the v2 version model (the v2 version model has changed the input from the 256 dimensional feature of 9-layer Hubert+final_proj to the 768 dimensional feature of 12-layer Hubert, and has added 3 period discriminators), you will need to download additional features

./assets/pretrained_v2

#If you are using Windows, you may also need these two files, skip if FFmpeg and FFprobe are installed
ffmpeg.exe

https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe

ffprobe.exe

https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe

If you want to use the latest SOTA RMVPE vocal pitch extraction algorithm, you need to download the RMVPE weights and place them in the RVC root directory

https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/rmvpe.pt

    For AMD/Intel graphics cards users you need download:

    https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/rmvpe.onnx

Intel ARC graphics cards users needs to run source /opt/intel/oneapi/setvars.sh command before starting Webui.

Then use this command to start Webui:

python infer-web.py

If you are using Windows or macOS, you can download and extract RVC-beta.7z to use RVC directly by using go-web.bat on windows or sh ./run.sh on macOS to start Webui.

ROCm Support for AMD graphic cards (Linux only)

To use ROCm on Linux install all required drivers as described here.

On Arch use pacman to install the driver:

pacman -S rocm-hip-sdk rocm-opencl-sdk

You might also need to set these environment variables (e.g. on a RX6700XT):

export ROCM_PATH=/opt/rocm
export HSA_OVERRIDE_GFX_VERSION=10.3.0

Also make sure your user is part of the render and video group:

sudo usermod -aG render $USERNAME
sudo usermod -aG video $USERNAME

After that you can run the WebUI:

python infer-web.py

Infer web REST API

You can use the following command to start the REST API server:

python rvc_fastapi.py

The rest API was built using fastAPI. It supports inference on pre trained models with the web-UI. You can find the documentation at http://localhost:8000/docs

Credits

Thanks to all contributors for their efforts