readme: ---
  language: "en"
  inference: false
  tags:
  - Vocoder
  - HiFIGAN
  - text-to-speech
  - TTS
  - speech-synthesis
  - speechbrain
  license: "apache-2.0"
  datasets:
  - LibriTTS
  ---
  
  # Vocoder with HiFIGAN trained on LibriTTS
  
  This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained with [LibriTTS](https://www.openslr.org/60/) (with multiple speakers). The sample rate used for the vocoder is 22050 Hz.
  
  The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram.
  
  Alternatives to this models are the following:
  - [tts-hifigan-libritts-16kHz](https://huggingface.co/speechbrain/tts-hifigan-libritts-16kHz/) (same model trained on the same dataset, but for a sample rate of 16000 Hz)
  - [tts-hifigan-ljspeech](https://huggingface.co/speechbrain/tts-hifigan-ljspeech)  (same model trained on LJSpeech for a sample rate of 22050 Hz).
  
  ## Install SpeechBrain
  
  ```bash
  pip install speechbrain
  ```
  
  
  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 HIFIGAN
  hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-libritts-22050Hz", savedir="tmpdir")
  mel_specs = torch.rand(2, 80,298)
  
  # Running Vocoder (spectrogram-to-waveform)
  waveforms = hifi_gan.decode_batch(mel_specs)
  ```
  
  ### Using the Vocoder with the TTS
  ```python
  import torchaudio
  from speechbrain.pretrained import Tacotron2
  from speechbrain.pretrained import HIFIGAN
  
  # Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
  tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmpdir_tts")
  hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-libritts-22050Hz", savedir="tmpdir_vocoder")
  
  # Running the TTS
  mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb")
  
  # Running Vocoder (spectrogram-to-waveform)
  waveforms = hifi_gan.decode_batch(mel_output)
  
  # Save the waverform
  torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 22050)
  ```
  
  ### Inference on GPU
  To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
  
  ### Training
  The model was trained with SpeechBrain.
  To train it from scratch follow these steps:
  1. Clone SpeechBrain:
  ```bash
  git clone https://github.com/speechbrain/speechbrain/
  ```
  2. Install it:
  ```bash
  cd speechbrain
  pip install -r requirements.txt
  pip install -e .
  ```
  3. Run Training:
  ```bash
  cd recipes/LibriTTS/vocoder/hifigan/
  python train.py hparams/train.yaml --data_folder=/path/to/LibriTTS_data_destination --sample_rate=22050
  ```
  
  To change the sample rate for model training go to the `"recipes/LibriTTS/vocoder/hifigan/hparams/train.yaml"` file and change the value for `sample_rate` as required.
  The training logs and checkpoints are available [here](https://drive.google.com/drive/folders/1cImFzEonNYhetS9tmH9R_d0EFXXN0zpn?usp=sharing).