Vocoder with HiFIGAN trained on LJSpeech
This repository provides all the necessary tools for using a HiFIGAN vocoder trained with LJSpeech.
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.
The sampling frequency is 22050 Hz.
NOTES
- This vocoder model is trained on a single speaker. Although it has some ability to generalize to different speakers, for better results, we recommend using a multi-speaker vocoder like this model trained on LibriTTS at 16,000 Hz or this one trained on LibriTTS at 22,050 Hz.
- If you specifically require a vocoder with a 16,000 Hz sampling rate, please follow the provided link above for a suitable option.
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-ljspeech", savedir="pretrained_models/tts-hifigan-ljspeech")
mel_specs = torch.rand(2, 80,298)
waveforms = hifi_gan.decode_batch(mel_specs)
- Convert a Spectrogram into a Waveform:
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-ljspeech", savedir="pretrained_models/tts-hifigan-ljspeech")
# Load an audio file (an example file can be found in this repository)
# Ensure that the audio signal is sampled at 22050 Hz; refer to the provided link for a 16 kHz Vocoder.
signal, rate = torchaudio.load('speechbrain/tts-hifigan-ljspeech/example.wav')
# 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=22050,
hop_length=256,
win_length=None,
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), 22050)
# If everything is set up correctly, the original and reconstructed audio should be nearly indistinguishable.
# Keep in mind that this Vocoder is trained for a single speaker; for multi-speaker Vocoder options, refer to the provided links.
Using the Vocoder with the TTS
import torchaudio
from speechbrain.inference.TTS import Tacotron2
from speechbrain.inference.vocoders import HIFIGAN
# Intialize TTS (tacotron2) and Vocoder (HiFIGAN)
tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="pretrained_models/tts-tacotron2-ljspeech")
hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="pretrained_model/tts-hifigan-ljspeech")
# 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:
- 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.
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