vocos 0.0.1

Fourier-based neural vocoder for high-quality audio synthesis

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

Audio samples | Paper [abs] [pdf]

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 mel-spectrogram

import torch

from vocos import Vocos

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

mel = torch.randn(1, 100, 256)  # B, C, T

with torch.no_grad():
    audio = vocos.decode(mel)

Copy-synthesis from a file:

import torchaudio

y, sr = torchaudio.load(YOUR_AUDIO_FILE)
if y.size(0) > 1:  # mix to mono
    y = y.mean(dim=0, keepdim=True)
y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=24000)

with torch.no_grad():
    y_hat = vocos(y)

Reconstruct audio from EnCodec

Additionally, you need to provide a bandwidth_id which corresponds to the lookup embedding for bandwidth from the list: [1.5, 3.0, 6.0, 12.0].

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

quantized_features = torch.randn(1, 128, 256)
bandwidth_id = torch.tensor([3])  # 12 kbps

with torch.no_grad():
    audio = vocos.decode(quantized_features, bandwidth_id=bandwidth_id)  

Copy-synthesis from a file: It extracts and quantizes features with EnCodec, then reconstructs them with Vocos in a single forward pass.

y, sr = torchaudio.load(YOUR_AUDIO_FILE)
if y.size(0) > 1:  # mix to mono
    y = y.mean(dim=0, keepdim=True)
y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=24000)

with torch.no_grad():
    y_hat = vocos(y, bandwidth_id=bandwidth_id)

Pre-trained models

The provided models were trained up to 2.5 million generator iterations, which resulted in slightly better objective scores compared to those reported in the paper.

Model Name	Dataset	Training Iterations	Parameters
charactr/vocos-mel-24khz	LibriTTS	2.5 M	13.5 M
charactr/vocos-encodec-24khz	DNS Challenge	2.5 M	7.9 M

Training

Prepare a filelist of audio files for the training and validation set:

find $TRAIN_DATASET_DIR -name *.wav > filelist.train
find $VAL_DATASET_DIR -name *.wav > filelist.val

Fill a config file, e.g. vocos.yaml, with your filelist paths and start training with:

python train.py -c configs/vocos.yaml

Refer to Pytorch Lightning documentation for details about customizing the training pipeline.

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 as found in the LICENSE file.