metadata

language: sw
inference: false
tags:
  - Vocoder
  - HiFIGAN
  - text-to-speech
  - TTS
  - speech-synthesis
  - speechbrain
license: apache-2.0
datasets:
  - LJSpeech

Vocoder with HiFIGAN trained on LJSpeech

This repository provides all the necessary tools for using a ALLFA Public.

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.

Install SpeechBrain

pip install speechbrain

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

Using the Vocoder

import torch
from speechbrain.pretrained import HIFIGAN
hifi_gan = HIFIGAN.from_hparams(source="aioxlabs/hifigan-swahili", savedir="tmpdir")
mel_specs = torch.rand(2, 80,298)
waveforms = hifi_gan.decode_batch(mel_specs)

Using the Vocoder with the TTS

import torchaudio
from speechbrain.pretrained import Tacotron2
from speechbrain.pretrained import HIFIGAN

# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
tacotron2 = Tacotron2.from_hparams(source="aioxlabs/tacotron-swahili", savedir="tmpdir_tts")
hifi_gan = HIFIGAN.from_hparams(source="aioxlabs/hifigan-swahili", savedir="tmpdir_vocoder")

# Running the TTS
mel_output, mel_length, alignment = tacotron2.encode_text("raisi wa jumhuri ya tanzania")

# Running Vocoder (spectrogram-to-waveform)
waveforms = hifi_gan.decode_batch(mel_output)

# Save the waverform
torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 16000)

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:

Clone SpeechBrain:

git clone https://github.com/speechbrain/speechbrain/

Install it:

cd speechbrain
pip install -r requirements.txt
pip install -e .

Run Training:

cd recipes/LJSpeech/TTS/vocoder/hifi_gan/
python train.py hparams/train.yaml --data_folder /path/to/LJspeech

You can find our training results (models, logs, etc) here.