vocos_pytorch/README.md

Vocos for StableTTS

Modified from the official implementation of Vocos.

Introduction

Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. Trained using a Generative Adversarial Network (GAN) objective, Vocos can generate waveforms in a single forward pass. Unlike other typical GAN-based vocoders, Vocos does not model audio samples in the time domain. Instead, it generates spectral coefficients, facilitating rapid audio reconstruction through inverse Fourier transform.

Inference

For detailed inference instructions, please refer to inference.ipynb

Training

Setting up and training your model with Vocos is straightforward. Follow these steps to get started:

Preparing Your Data

1. Configure Data Settings: Update the DataConfig in preprocess.py. Specifically, adjust the audio_dir to point to your collection of audio files.

2. Run Preprocessing: Run preprocess.py. This script will search (glob) for all audio files in the specified directory, resample them to the target sample_rate (modifiable in config.py), and generate a file list for training.

Start training

1. Adjust Training Configuration: Edit TrainConfig in config.py to specify the file list path and tweak training hyperparameters to your needs.

2. Start the Training Process: Launch train.py to begin training your model.

Experiment with Configurations

Feel free to explore and modify settings in config.py to modify the hyperparameters of vocos!

References

Vocos