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

# Vocoder with HiFIGAN Unit ## Work In Progress .... ## 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](https://speechbrain.github.io). ### Using the Vocoder ```python import torch from speechbrain.pretrained import UnitHIFIGAN hifi_gan_unit = UnitHIFIGAN.from_hparams(source="chaanks/hifigan-unit-wav2vec2-l7-k512-ljspeech-ljspeech", savedir="tmpdir_vocoder") codes = torch.randint(0, 99, (100,)) 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