Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. Trained using a Generative
Adversarial Network (GAN) objective, Vocos can generate waveforms in a single forward pass. Unlike other typical
GAN-based vocoders, Vocos does not model audio samples in the time domain. Instead, it generates spectral
coefficients, facilitating rapid audio reconstruction through inverse Fourier transform.
Installation
To use Vocos only in inference mode, install it using:
pip install vocos
If you wish to train the model, install it with additional dependencies:
pip install vocos[train]
Usage
Reconstruct audio from EnCodec tokens
Additionally, you need to provide a bandwidth_id which corresponds to the embedding for bandwidth from the
list: [1.5, 3.0, 6.0, 12.0].
Copy-synthesis from a file: It extracts and quantizes features with EnCodec, then reconstructs them with Vocos in a
single forward pass.
python
1y, sr = torchaudio.load(YOUR_AUDIO_FILE)2if y.size(0)>1:# mix to mono3 y = y.mean(dim=0, keepdim=True)4y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=24000)56y_hat = vocos(y, bandwidth_id=bandwidth_id)
Citation
If this code contributes to your research, please cite our work:
@article{siuzdak2023vocos,
title={Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis},
author={Siuzdak, Hubert},
journal={arXiv preprint arXiv:2306.00814},
year={2023}
}
License
The code in this repository is released under the MIT license.
