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Retrieval-based-Voice-Conversion-WebUI
An easy-to-use voice conversion framework based on VITS.Changelog | FAQ (Frequently Asked Questions)
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The base model is trained using nearly 50 hours of high-quality open-source VCTK training set. Therefore, there are no copyright concerns, please feel free to use.
Please look forward to the base model of RVCv3 with larger parameters, larger dataset, better effects, basically flat inference speed, and less training data required.
There's a one-click downloader for models/integration packages/tools. Welcome to try.
Training and inference Webui | Real-time voice changing GUI |
go-web.bat | go-realtime-gui.bat |
You can freely choose the action you want to perform. | We have achieved an end-to-end latency of 170ms. With the use of ASIO input and output devices, we have managed to achieve an end-to-end latency of 90ms, but it is highly dependent on hardware driver support. |
Features:
- Reduce tone leakage by replacing the source feature to training-set feature using top1 retrieval;
- Easy + fast training, even on poor graphics cards;
- Training with a small amounts of data (>=10min low noise speech recommended);
- Model fusion to change timbres (using ckpt processing tab->ckpt merge);
- Easy-to-use WebUI;
- UVR5 model to quickly separate vocals and instruments;
- High-pitch Voice Extraction Algorithm InterSpeech2023-RMVPE to prevent a muted sound problem. Provides the best results (significantly) and is faster with lower resource consumption than Crepe_full;
- AMD/Intel graphics cards acceleration supported;
- Intel ARC graphics cards acceleration with IPEX supported.
Check out our Demo Video here!
Environment Configuration
Python Version Limitation
It is recommended to use conda to manage the Python environment.
For the reason of the version limitation, please refer to this bug.
python --version # 3.8 <= Python < 3.11
Linux/MacOS One-click Dependency Installation & Startup Script
By executing run.sh
in the project root directory, you can configure the venv
virtual environment, automatically install the required dependencies, and start the main program with one click.
sh ./run.sh
Manual Installation of Dependencies
- Install
pytorch
and its core dependencies, skip if already installed. Refer to: https://pytorch.org/get-started/locally/pip install torch torchvision torchaudio
- If you are using Nvidia Ampere architecture (RTX30xx) in Windows, according to the experience of #21, you need to specify the cuda version corresponding to pytorch.
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
- Install the corresponding dependencies according to your own graphics card.
- Nvidia GPU
pip install -r requirements.txt
- AMD/Intel GPU
pip install -r requirements-dml.txt
- AMD ROCM (Linux)
pip install -r requirements-amd.txt
- Intel IPEX (Linux)
pip install -r requirements-ipex.txt
Preparation of Other Files
1. Assets
RVC requires some models located in the
assets
folder for inference and training.
Check/Download Automatically (Default)
By default, RVC can automatically check the integrity of the required resources when the main program starts.
Even if the resources are not complete, the program will continue to start.
- If you want to download all resources, please add the
--update
parameter. - If you want to skip the resource integrity check at startup, please add the
--nocheck
parameter.
Download Manually
All resource files are located in Hugging Face space
You can find some scripts to download them in the
tools
folder
You can also use the one-click downloader for models/integration packages/tools
Below is a list that includes the names of all pre-models and other files required by RVC.
./assets/hubert/hubert_base.pt
rvcmd assets/hubert # RVC-Models-Downloader command
./assets/pretrained
rvcmd assets/v1 # RVC-Models-Downloader command
./assets/uvr5_weights
rvcmd assets/uvr5 # RVC-Models-Downloader command
If you want to use the v2 version of the model, you need to download additional resources in
./assets/pretrained_v2
rvcmd assets/v2 # RVC-Models-Downloader command
2. Install ffmpeg tool
If ffmpeg
and ffprobe
have already been installed, you can skip this step.
Ubuntu/Debian
sudo apt install ffmpeg
MacOS
brew install ffmpeg
Windows
After downloading, place it in the root directory.
rvcmd tools/ffmpeg # RVC-Models-Downloader command
3. Download the required files for the rmvpe vocal pitch extraction algorithm
If you want to use the latest RMVPE vocal pitch extraction algorithm, you need to download the pitch extraction model parameters and place them in assets/rmvpe
.
- rmvpe.pt
rvcmd assets/rmvpe # RVC-Models-Downloader command
Download DML environment of RMVPE (optional, for AMD/Intel GPU)
- rmvpe.onnx
rvcmd assets/rmvpe # RVC-Models-Downloader command
4. AMD ROCM (optional, Linux only)
If you want to run RVC on a Linux system based on AMD's ROCM technology, please first install the required drivers here.
If you are using Arch Linux, you can use pacman to install the required drivers.
pacman -S rocm-hip-sdk rocm-opencl-sdk
For some models of graphics cards, you may need to configure the following environment variables (such as: RX6700XT).
export ROCM_PATH=/opt/rocm
export HSA_OVERRIDE_GFX_VERSION=10.3.0
Also, make sure your current user is in the render
and video
user groups.
sudo usermod -aG render $USERNAME
sudo usermod -aG video $USERNAME
Getting Started
Direct Launch
Use the following command to start the WebUI.
python infer-web.py
Linux/MacOS
./run.sh
For I-card users who need to use IPEX technology (Linux only)
source /opt/intel/oneapi/setvars.sh
./run.sh
Using the Integration Package (Windows Users)
Download and unzip RVC-beta.7z
. After unzipping, double-click go-web.bat
to start the program with one click.
rvcmd packs/general/latest # RVC-Models-Downloader command
Credits
- ContentVec
- VITS
- HIFIGAN
- Gradio
- FFmpeg
- Ultimate Vocal Remover
- audio-slicer
- Vocal pitch extraction:RMVPE