Awesome

HiFi-GAN

Training and inference scripts for the vocoder models in A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion. For more details see soft-vc. Audio samples can be found here. Colab demo can be found here.

<div align="center"> <img width="100%" alt="Soft-VC" src="https://raw.githubusercontent.com/bshall/hifigan/main/vocoder.png"> </div> <div> <sup> <strong>Fig 1:</strong> Architecture of the voice conversion system. a) The <strong>discrete</strong> content encoder clusters audio features to produce a sequence of discrete speech units. b) The <strong>soft</strong> content encoder is trained to predict the discrete units. The acoustic model transforms the discrete/soft speech units into a target spectrogram. The vocoder converts the spectrogram into an audio waveform. </sup> </div>

Example Usage

Programmatic Usage

import torch
import numpy as np

# Load checkpoint
hifigan = torch.hub.load("bshall/hifigan:main", "hifigan_hubert_soft").cuda()
# Load mel-spectrogram
mel = torch.from_numpy(np.load("path/to/mel")).unsqueeze(0).cuda()
# Generate
wav, sr = hifigan.generate(mel)

Script-Based Usage

usage: generate.py [-h] {soft,discrete,base} in-dir out-dir

Generate audio for a directory of mel-spectrogams using HiFi-GAN.

positional arguments:
  {soft,discrete,base}  available models (HuBERT-Soft, HuBERT-Discrete, or
                        Base).
  in-dir                path to input directory containing the mel-
                        spectrograms.
  out-dir               path to output directory.

optional arguments:
  -h, --help            show this help message and exit

Training

Step 1: Dataset Preparation

Download and extract the LJSpeech dataset. The training script expects the following tree structure for the dataset directory:

└───wavs
    ├───dev
    │   ├───LJ001-0001.wav
    │   ├───...
    │   └───LJ050-0278.wav
    └───train
        ├───LJ002-0332.wav
        ├───...
        └───LJ047-0007.wav

The train and dev directories should contain the training and validation splits respectively. The splits used for the paper can be found here.

Step 2: Resample the Audio

Resample the audio to 16kHz using the resample.py script:

usage: resample.py [-h] [--sample-rate SAMPLE_RATE] in-dir out-dir

Resample an audio dataset.

positional arguments:
  in-dir                path to the dataset directory.
  out-dir               path to the output directory.

optional arguments:
  -h, --help            show this help message and exit
  --sample-rate SAMPLE_RATE
                        target sample rate (default 16kHz)

for example:

python reample.py path/to/LJSpeech-1.1/ path/to/LJSpeech-Resampled/

Step 3: Train HifiGAN

usage: train.py [-h] [--resume RESUME] [--finetune] dataset-dir checkpoint-dir

Train or finetune HiFi-GAN.

positional arguments:
  dataset-dir      path to the preprocessed data directory
  checkpoint-dir   path to the checkpoint directory

optional arguments:
  -h, --help       show this help message and exit
  --resume RESUME  path to the checkpoint to resume from
  --finetune       whether to finetune (note that a resume path must be given)

Citation

If you found this work helpful please consider citing our paper:

@inproceedings{
    soft-vc-2022,
    author={van Niekerk, Benjamin and Carbonneau, Marc-André and Zaïdi, Julian and Baas, Matthew and Seuté, Hugo and Kamper, Herman},
    booktitle={ICASSP}, 
    title={A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion}, 
    year={2022}
}

Acknowledgements

This repo is based heavily on https://github.com/jik876/hifi-gan.