[!NOTE] Currently under development... Provided as a library and API in rvc
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.
[!WARNING] The directory should be empty or without an assets folder.
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!
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_inference(
1, Path("{InputAudio}")
)
wavfile.write("{OutputAudio}", tgt_sr, audio_opt)
if __name__ == "__main__":
load_dotenv("{envPath}")
main()
rvc infer -m {model.pth} -i {input.wav} -o {output.wav}
option | flag | type | default value | description |
---|---|---|---|---|
modelPath | -m | Path | *required | Model path or filename (reads in the directory set in env) |
inputPath | -i | Path | *required | Input audio path or folder |
outputPath | -o | Path | *required | Output audio path or folder |
sid | -s | int | 0 | Speaker/Singer ID |
f0_up_key | -fu | int | 0 | Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12) |
f0_method | -fm | str | rmvpe | pitch extraction algorithm (pm, harvest, crepe, rmvpe |
f0_file | -ff | Path | None | None | F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation |
index_file | -if | Path | None | None | Path to the feature index file |
index_rate | -if | float | 0.75 | Search feature ratio (controls accent strength, too high has artifacting) |
filter_radius | -fr | int | 3 | If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness |
resample_sr | -rsr | int | 0 | Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling |
rms_mix_rate | -rmr | float | 0.25 | Adjust the volume envelope scaling. Closer to 0, the more it mimicks the volume of the original vocals. Can help mask noise and make volume sound more natural when set relatively low. Closer to 1 will be more of a consistently loud volume |
protect | -p | float | 0.33 | Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy |
First, start up the server.
rvc-api
or
poetry run poe rvc-api
curl -X 'POST' \
'http://127.0.0.1:8000/inference?res_type=blob' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F 'modelpath={model.pth}' \
-F 'input={input audio path}'
curl -X 'POST' \
'http://127.0.0.1:8000/inference?res_type=json' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F 'modelpath={model.pth}' \
-F 'input={input audio path}'
Build and run via script:
./docker-run.sh
Or use manually:
Build:
docker build -t "rvc" .
Run:
docker run -it \
-p 8000:8000 \
-v "${PWD}/assets/weights:/weights:ro" \
-v "${PWD}/assets/indices:/indices:ro" \
-v "${PWD}/assets/audios:/audios:ro" \
"rvc"
Notice assumption that weights, indices and input audios are stored in current-directory/assets