RVC Python

Using RVC via console or python scripts

Feel free to make PRs or use the code for your own needs

Changelog

You can keep track of all changes on the release page

TODO

• Batch generation via console
• Possibility to use inference import through code

Installation

Simple installation :

pip install rvc-python

This will install all the necessary dependencies, including a CPU support only version of PyTorch

I recommend that you install the GPU version to improve processing speed ( up to 3 times faster )

Read the end of the README to learn how to install.

Windows

python -m venv venv
venv\Scripts\activate
pip install rvc-python
pip install torch==2.1.1+cu118 torchaudio==2.1.1+cu118 --index-url https://download.pytorch.org/whl/cu118

Linux

python -m venv venv
source venv\bin\activate
pip install rvc-python
pip install torch==2.1.1+cu118 torchaudio==2.1.1+cu118 --index-url https://download.pytorch.org/whl/cu118

Usage

python -m rvc_python [-h] -i INPUT or -d INPUT_DIR -mp MODEL [-pi PITCH]
                     [-ip INDEX] [-me METHOD] [-v VERSION]
                     [-o OUTPUT] [-ir INDEX_RATE]
                     [-d DEVICE]  [-fr FILTER_RADIUS]
                     [-rsr RESAMPLE_SR]
                     [-rmr RMS_MIX_RATE][-pr PROTECT]

Options

• -h, --help: Show this help message and exit.

• -i INPUT, --input INPUT (mandatory): Path to input file.

• -d INPUT_DIR, --dir (mandatory) Path to dir with input files

• -mp MODEL, --model MODEL (mandatory): Path to model file.

The following options are optional:

• -pi PITCH, --pitch PITCH: Transpose integer; number of semitones. Raise by an octave with +12 or lower by an octave with -12.

• -ip INDEX, --index INDEX: Path to index file.

• -me METHOD, --method METHOD: Pitch extraction algorithm choices: ['harvest', 'crepe', 'rmvpe', 'pm'].

• -v VERSION, --version VERSION: Version of the software or model ('v1' or 'v2').

• -o OUTPUT, --output OUTPUT: Output path for results (default is "out.wav"). If you specify -d, you need to specify the path to the folder where the files will be saved.

• -ir INDEX_RATE, --index_rate INDEX_RATE: Search feature ratio.

• -d DEVICE, --device DEVICE: Device to use for processing (e.g., 'cpu' or 'cuda:0').

• -fr FILTER_RADIUS, --filter_radius FILTER_RADIUS: Apply median filtering to pitch results. A larger value can reduce breathiness.

• -rsr RESAMPLE_SR , --resample_sr RESAMPLE_SR: Resample output audio in post-processing. Set "0" for no resampling.

• -rmr RMS_MIX_RATE, --rms_mix_rate RMS_MIX_RATE: Mix rate between input volume envelope and output volume envelope. Closer to "1" uses more from the output.

• -pr PROTECT , --protect PROTECT: Protects voiceless consonants and breath sounds from artifacts such as tearing in electronic music. Decrease value for increased protection but may affect indexing accuracy.

Example Command via console

Single file

python -m rvc_python -i .\test\test.wav -mp .\test\art_lebedev\artemiy_lebedev.pth

Batch

python -m rvc_python -d ./test -o "./out_dir" -mp ./test/art_lebedev/artemiy_lebedev.pth

This example will process the audio file located at ".\test\test.wav" using the model file ".\test\art_lebedev\artemiy_lebedev.pth". All other settings will be default unless additional flags are provided.

Example via python

from rvc_python.infer import infer_file, infer_files

# To process a single file:
result = infer_file(
    input_path="./path_to_file",
    model_path="./model/path_to_model.pth",
    index_path="",  # Optional: specify path to index file if available
    device="cuda:0", # Use cpu or cuda
    f0method="harvest",  # Choose between 'harvest', 'crepe', 'rmvpe', 'pm'
    f0up_key=0,  # Transpose setting
    output="out.wav",  # Output file path
    index_rate=0.5,
    filter_radius=3,
    resample_sr=0,  # Set to desired sample rate or 0 for no resampling.
    rms_mix_rate=0.25,
    protect=0.33,
    version="v2"
)

print("Inference completed. Output saved to:", result)

# To process multiple files in a directory:
results = infer_files(
    dir_path="./input_dir/",  # Directory containing input audio files
    paths=[], # You can specify each file separately, then files from dir_path files will not be counted
    opt_dir="./output_dir/",  # Directory where output files will be saved
    model_path="./model/path_to_model.pth",
    index_path="",  # Optional: specify path to index file if available
    device="cuda:0", # Use cpu or cuda
    f0method="harvest",  # Choose between 'harvest', 'crepe', 'rmvpe', 'pm'
    f0up_key=0,  # Transpose setting
    index_rate=0.5,
    filter_radius=3,
    resample_sr=0,  # Set to desired sample rate or 0 for no resampling.
    rms_mix_rate=0.25,
    protect=0.33,
    version="v2"
    out_format="wav"
)

print(f"Inference completed for batch processing. Check the '{results}' directory for output files.")

Demo

bandicam.2023-12-27.01-36-03-212.mp4

CUDA installation

These steps are recommended for those who require better performance and have a compatible NVIDIA GPU.

Note: to check if your NVIDIA GPU supports CUDA, visit the official CUDA GPUs list.

To use torch with support via CUDA please follow these steps:

Note: newer pytorch installations may (unverified) not need Toolkit (and possibly cuDNN) installation anymore.

1. Install NVIDIA CUDA Toolkit: For example, to install Toolkit 11.8 please

   • Visit NVIDIA CUDA Toolkit Archive.
   • Select version 11.
   • Download and install the software.

2. Install NVIDIA cuDNN: For example, to install cuDNN 8.7.0 for CUDA 11.x please

   • Visit NVIDIA cuDNN Archive.
   • Click on "Download cuDNN v8.7.0 (November 28th, 2022), for CUDA 11.x".
   • Download and install the software.

3. Install ffmpeg:

   You can download an installer for your OS from the ffmpeg Website.

   Or use a package manager:

   • On Ubuntu or Debian:

     sudo apt update && sudo apt install ffmpeg

   • On Arch Linux:

     sudo pacman -S ffmpeg

   • On MacOS using Homebrew (https://brew.sh/):

     brew install ffmpeg

   • On Windows using Chocolatey (https://chocolatey.org/):

     choco install ffmpeg

   • On Windows using Scoop (https://scoop.sh/):

     scoop install ffmpeg

4. Install Microsoft Builders Tools

   You can download an installer for your OS from the Microsoft Website

5. Install PyTorch with CUDA support:

   pip install torch==2.1.1+cu118 torchaudio==2.1.1+cu118 --index-url https://download.pytorch.org/whl/cu118