If you’re venturing into the world of text-to-speech (TTS) synthesis and are interested in high-fidelity audio generation, you’re in luck! The HiFIGAN vocoder trained on the LJSpeech dataset is here to assist you. This guide provides a user-friendly step-by-step walkthrough on implementing this vocoder.

What is a Vocoder?

Think of a vocoder as a master chef, taking a carefully prepared dish (the spectrogram) and converting it into a delectable final meal (the audio waveform). In our case, the HiFIGAN vocoder transforms spectrogram outputs from TTS models into high-quality audio signals.

Prerequisites

• Python installed on your machine
• Access to a terminal for running commands
• Pip, to install necessary packages

Step 1: Install SpeechBrain

Before using the HiFIGAN vocoder, we need to install the SpeechBrain library, which provides all the necessary tools.

bash
pip install speechbrain

Step 2: Basic Usage of HiFIGAN Vocoder

Let’s get started by showing how to use the HiFIGAN vocoder. Below is the Python code that imports the model and decodes a spectrogram batch into waveforms.

python
import torch
from speechbrain.inference.vocoders import HIFIGAN

# Load HiFIGAN
hifi_gan = HIFIGAN.from_hparams(source='speechbrain/tts-hifigan-ljspeech', savedir='pretrained_models/tts-hifigan-ljspeech')

# Create random mel spectrograms
mel_specs = torch.rand(2, 80, 298)

# Decode to audio waveforms
waveforms = hifi_gan.decode_batch(mel_specs)

Step 3: Convert a Spectrogram into a Waveform

Next, we will convert an audio file (ensuring it meets the expected sampling frequency) into a waveform.

python
import torchaudio
from speechbrain.inference.vocoders import HIFIGAN
from speechbrain.lobes.models.FastSpeech2 import mel_spectrogram

# Load pretrained HiFIGAN vocoder
hifi_gan = HIFIGAN.from_hparams(source='speechbrain/tts-hifigan-ljspeech', savedir='pretrained_models/tts-hifigan-ljspeech')

# Load an audio file
signal, rate = torchaudio.load('speechbrain/tts-hifigan-ljspeech/example.wav')

# Compute mel spectrogram
spectrogram, _ = mel_spectrogram(audio=signal.squeeze(), 
                                 sample_rate=22050, 
                                 hop_length=256, 
                                 n_mels=80)

# Convert spectrogram to waveform
waveforms = hifi_gan.decode_batch(spectrogram)

# Save the reconstructed audio
torchaudio.save('waveform_reconstructed.wav', waveforms.squeeze(1), 22050)

Step 4: Use the Vocoder with TTS

Finally, integrate the vocoder with a TTS model for seamless text-to-speech generation.

python
import torchaudio
from speechbrain.inference.TTS import Tacotron2
from speechbrain.inference.vocoders import HIFIGAN

# Initialize TTS and Vocoder
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_models/tts-hifigan-ljspeech')

# Run the TTS process
mel_output, mel_length, alignment = tacotron2.encode_text("Mary had a little lamb")

# Decode the spectrogram using the Vocoder
waveforms = hifi_gan.decode_batch(mel_output)

# Save the waveform
torchaudio.save('example_TTS.wav', waveforms.squeeze(1), 22050)

Troubleshooting Common Issues

Encountering issues while implementing the HiFIGAN vocoder? Here are some common troubleshooting tips:

• Incorrect Sample Rate: Ensure that your audio files are correctly sampled at 22050 Hz. If you need a 16 kHz vocoder, refer to the LibriTTS 16 kHz model.
• Library Imports Failing: If you face import issues, ensure that SpeechBrain is correctly installed and that you are using compatible versions of Python and its libraries.
• Low Output Quality: If the quality does not meet expectations, double-check that the spectrogram parameters match those that were used during the training.

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Training from Scratch

If you want to dive deeper and train the model from scratch, follow these steps:

1. Clone the SpeechBrain repository:

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

2. Install the necessary packages:

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

3. Run the Training:

bash
    cd recipes/LJSpeech/TTS/vocoder/hifi_gan
    python train.py hparams/train.yaml --data_folder path_to_LJspeech
    

Conclusion

And there you have it – a streamlined process to use the HiFIGAN vocoder with LJSpeech for TTS applications! Experiment with different inputs, and remember to check your parameters for the best quality.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.