You can deploy this model and start earning money today!

speechbrain/
hifigan-hubert-l1-3-7-12-18-23-k1000-LibriTTS

Name: hifigan-hubert-l1-3-7-12-18-23-k1000-LibriTTS
Availability: InStock

Views

No views yet

Vocoder with HiFIGAN Unit trained on LibriTTS

This repository provides all the necessary tools for using a scalable HiFiGAN Unit vocoder trained with LibriTTS.

The pre-trained model take as input discrete self-supervised representations and produces a waveform as output. This is suitable for a wide range of generative tasks such as speech enhancement, separation, text-to-speech, voice cloning, etc. Please read DASB - Discrete Audio and Speech Benchmark for more information.
To generate the discrete self-supervised representations, we employ a K-means clustering model trained using facebook/hubert-large-ll60k hidden layers, with k=1000.

Install SpeechBrain

First of all, please install tranformers and SpeechBrain with the following command:

pip install speechbrain transformers

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

Using the Vocoder

python

import torch
from speechbrain.inference.vocoders import UnitHIFIGAN

hifi_gan_unit = UnitHIFIGAN.from_hparams(source="speechbrain/hifigan-hubert-l1-3-7-12-18-23-k1000-LibriTTS", savedir="pretrained_models/vocoder")
codes = torch.randint(0, 99, (100, 1))
waveform = hifi_gan_unit.decode_unit(codes)

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

Referencing SpeechBrain

@misc{SB2021,
    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
  }

About SpeechBrain

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: https://speechbrain.github.io/

GitHub: https://github.com/speechbrain/speechbrain