Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. 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.

This version of vocos uses 80-bin mel spectrograms as acoustic features which are widespread in the TTS domain since the introduction of hifi-gan The goal of this model is to provide an alternative to hifi-gan that is faster and compatible with the acoustic output of several TTS models.

Intended Uses and limitations

The model is aimed to serve as a vocoder to synthesize audio waveforms from mel spectrograms. Is trained to generate speech and if is used in other audio domain is possible that the model won't produce high quality samples.

Installation

To use Vocos only in inference mode, install it using:

pip install git+https://github.com/langtech-bsc/vocos.git@matcha

Reconstruct audio from mel-spectrogram

python

import torch

from vocos import Vocos

vocos = Vocos.from_pretrained("patriotyk/vocos-mel-hifigan-compat-44100khz")

mel = torch.randn(1, 80, 256)  # B, C, T
audio = vocos.decode(mel)

Training Data

The model was trained on private 800+ hours dataset, made from Ukrainian audio books, using narizaka tool.

Training Procedure

The model was trained for 2.0M steps and 210 epochs with a batch size of 20. We used a Cosine scheduler with a initial learning rate of 3e-4. We where using two RTX-3090 video cards for training, and it took about one month of continuous training.

Training Hyperparameters

initial_learning_rate: 3e-4
scheduler: cosine without warmup or restarts
mel_loss_coeff: 45
mrd_loss_coeff: 1.0
batch_size: 20
num_samples: 32768

Evaluation

Evaluation was done using the metrics on the original repo, after 210 epochs we achieve:

val_loss: 3.703
f1_score: 0.950
mel_loss: 0.248
periodicity_loss:0.127
pesq_score: 3.399
pitch_loss: 38.26
utmos_score: 3.146

Citation

If this code contributes to your research, please cite the 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}
}