---
language: "en"
inference: false
tags:
- Vocoder
- HiFIGAN
- speech-synthesis
- speechbrain
license: "apache-2.0"
datasets:
- LJSpeech
---


<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>

# Vocoder with HiFIGAN Unit trained on LJSpeech

This repository provides all the necessary tools for using a [HiFiGAN Unit](https://arxiv.org/abs/2104.00355) vocoder trained with [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). 

The pre-trained model take as input discrete self-supervised representations and produces a waveform as output. Typically, this model is utilized on top of a speech-to-unit translation model that converts an input utterance from a source language into a sequence of discrete speech units in a target language.  
To generate the discrete self-supervised representations, we employ a K-means clustering model trained on the 6th layer of HuBERT, with `k=100`.

## Install SpeechBrain

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

```
pip install speechbrain transformers==4.28.0
```

Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).


### Transcribing your own audio files

```python
from speechbrain.pretrained import UnitHIFIGAN

hifi_gan_unit = UnitHIFIGAN.from_hparams(source="speechbrain/hifigan-unit-hubert-l6-k100-ljspeech")
codes = torch.randint(0, 99, (100,))
waveform = hifi_gan.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