Vocoder with HiFIGAN trained on LibriTTS
This repository provides all the necessary tools for using a HiFIGAN vocoder trained with LibriTTS (with multiple speakers). The sample rate used for the vocoder is 16000 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-22050Hz (same model trained on the same dataset, but for a sample rate of 22050 Hz)
- tts-hifigan-ljspeech (same model trained on LJSpeech for a sample rate of 22050 Hz).
Install SpeechBrain
pip install speechbrain
Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.
Using the Vocoder
- Basic Usage:
import torch
from speechbrain.inference.vocoders import HIFIGAN
hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-libritts-16kHz", savedir="pretrained_models/tts-hifigan-libritts-16kHz")
mel_specs = torch.rand(2, 80,298)
# Running Vocoder (spectrogram-to-waveform)
waveforms = hifi_gan.decode_batch(mel_specs)
- Spectrogram to Waveform Conversion:
import torchaudio
from speechbrain.inference.vocoders import HIFIGAN
from speechbrain.lobes.models.FastSpeech2 import mel_spectogram
# Load a pretrained HIFIGAN Vocoder
hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-libritts-16kHz", savedir="pretrained_models/tts-hifigan-libritts-16kHz")
# Load an audio file (an example file can be found in this repository)
# Ensure that the audio signal is sampled at 16000 Hz; refer to the provided link for a 22050 Hz Vocoder.
signal, rate = torchaudio.load('tests/samples/ASR/spk1_snt1.wav')
# Ensure the audio is sigle channel
signal = signal[0].squeeze()
torchaudio.save('waveform.wav', signal.unsqueeze(0), 16000)
# Compute the mel spectrogram.
# IMPORTANT: Use these specific parameters to match the Vocoder's training settings for optimal results.
spectrogram, _ = mel_spectogram(
audio=signal.squeeze(),
sample_rate=16000,
hop_length=256,
win_length=1024,
n_mels=80,
n_fft=1024,
f_min=0.0,
f_max=8000.0,
power=1,
normalized=False,
min_max_energy_norm=True,
norm="slaney",
mel_scale="slaney",
compression=True
)
# Convert the spectrogram to waveform
waveforms = hifi_gan.decode_batch(spectrogram)
# Save the reconstructed audio as a waveform
torchaudio.save('waveform_reconstructed.wav', waveforms.squeeze(1), 16000)
# If everything is set up correctly, the original and reconstructed audio should be nearly indistinguishable
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/LibriTTS/vocoder/hifigan/
python train.py hparams/train.yaml --data_folder=/path/to/LibriTTS_data_destination --sample_rate=16000
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.
- Downloads last month
- 189