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--- |
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language: "en" |
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inference: false |
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tags: |
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- Vocoder |
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- HiFIGAN |
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- speech-synthesis |
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- speechbrain |
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license: "apache-2.0" |
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datasets: |
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- LibriTTS |
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--- |
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<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> |
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<br/><br/> |
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# Vocoder with HiFIGAN Unit trained on LibriTTS |
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This repository provides all the necessary tools for using a [scalable HiFiGAN Unit](https://arxiv.org/abs/2406.10735) vocoder trained with [LibriTTS](https://www.openslr.org/141/). |
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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](https://arxiv.org/abs/2406.14294) for more information. |
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To generate the discrete self-supervised representations, we employ a K-means clustering model trained using `facebook/wav2vec2-large-960h-lv60-self` hidden layers, with k=1000. |
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## Install SpeechBrain |
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First of all, please install tranformers and SpeechBrain with the following command: |
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``` |
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pip install speechbrain transformers |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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### Using the Vocoder |
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```python |
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import torch |
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from speechbrain.inference.vocoders import UnitHIFIGAN |
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hifi_gan_unit = UnitHIFIGAN.from_hparams(source="speechbrain/hifigan-wav2vec-l1-3-7-12-18-23-k1000-LibriTTS", savedir="pretrained_models/vocoder") |
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codes = torch.randint(0, 99, (100, 1)) |
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waveform = hifi_gan_unit.decode_unit(codes) |
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``` |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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#### Referencing SpeechBrain |
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``` |
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@misc{SB2021, |
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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 }, |
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title = {SpeechBrain}, |
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year = {2021}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, |
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} |
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``` |
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#### About SpeechBrain |
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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. |
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Website: https://speechbrain.github.io/ |
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GitHub: https://github.com/speechbrain/speechbrain |
