Loading
Tem um workflow do ComfyUI? Gere uma documentação legível direto no navegador.Torne um workflow legível.Experimentar agora ↗
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...
Fonte do modelo
Descrição da fonte
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.
Fontes
1 fonteVerificado 12 de set.
Artefatos de modelo
2 artefatosTrechos de fonte
2 trechosThe 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.
To use Vocos only in inference mode, install it using:
pip install git+https://github.com/langtech-bsc/vocos.git@matcha
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)
The model was trained on private 800+ hours dataset, made from Ukrainian audio books, using narizaka tool.
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.
Evaluation was done using the metrics on the original repo, after 210 epochs we achieve:
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}
}
--- license: mit pipeline_tag: audio-to-audio tags: - vocos - hifigan - tts - melspectrogram - vocoder - mel --- ### Model Description <!-- Provide a longer summary of what this model is. --> **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](https://github.com/jik876/hifi-gan/blob/master/meldataset.py) 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: ```bash 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](https://github.com/patriotyk/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 ## C...
Source context: 674 downloads · 10 likes · Pipeline audio-to-audio · Repo patriotyk/vocos-mel-hifigan-compat-44100khz