Vocos — MLX

Implementation of Vocos with the MLX framework. Vocos allows for high quality reconstruction of audio from Mel spectrograms or EnCodec tokens.

Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis

Paper [abs] [pdf]

Installation

To use Vocos in inference mode, install it using:

pip install vocos-mlx

Usage

Mel Spectrogram

from vocos_mlx import Vocos, load_audio, log_mel_spectrogram

vocos = Vocos.from_pretrained("lucasnewman/vocos-mel-24khz")

# reconstruct
audio = load_audio("audio.wav", 24_000)
reconstructed_audio = vocos(audio)

# decode from mel spec
mel_spec = log_mel_spectrogram(audio, n_mels = 100)
decoded_audio = vocos.decode(mel_spec)

EnCodec

from vocos_mlx import Vocos, load_audio

vocos = Vocos.from_pretrained("lucasnewman/vocos-encodec-24khz")

# reconstruct
audio = load_audio("audio.wav", 24_000)
reconstructed_audio = vocos(audio, bandwidth_id = 3)

# decode with encodec codes
codes = vocos.get_encodec_codes(audio, bandwidth_id = 3)
decoded_audio = vocos.decode_from_codes(codes, bandwidth_id = 3)

Appreciation

Awni Hannun for the reference EnCodec implementation for MLX.

Citations

@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 as found in the LICENSE file.