Installation and usage

Standard Setup

First, create a directory in your project. The assets folder will contain the models needed for inference and training, and the result folder will contain the results of the training.

rvc init

This will create an assets folder and .env in your working directory.

Custom Setup

If you have already downloaded models or want to change these configurations, edit the .env file. If you do not already have a .env file,

rvc env create

can create one.

Also, when downloading a model, you can use the

rvc dlmodel

or

rvc dlmodel {download_dir}

Finally, specify the location of the model in the env file, and you are done!

Library Usage

Inference Audio

from pathlib import Path

from dotenv import load_dotenv
from scipy.io import wavfile

from rvc.modules.vc.modules import VC


def main():
      vc = VC()
      vc.get_vc("{model.pth}")
      tgt_sr, audio_opt, times, _ = vc.vc_single(
            1, Path("{InputAudio}")
      )
      wavfile.write("{OutputAudio}", tgt_sr, audio_opt)


if __name__ == "__main__":
      load_dotenv("{envPath}")
      main()

CLI Usage

Inference Audio

rvc infer -m {model.pth} -i {input.wav} -o {output.wav}

API Usage

First, start up the server.

rvc-api

or

poetry run poe rvc-api

Inference Audio

curl -X 'POST' \
      'http://127.0.0.1:8000/inference' \
      -H 'accept: application/json' \
      -H 'Content-Type: multipart/form-data' \
      -F 'modelpath={model.pth}' \
      -F 'input={input audio path}' \
      -o {output audio path}