This repository contains the official PyTorch implementation of "BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network" (arXiv 2309.02836). Please cite [1] in your work when using this code in your experiments.

This repository builds on the codebase of BigVGAN.

Download the LibriTTS dataset here in advance.

Clone the repository and install dependencies.

# the codebase has been tested on Python 3.8 with PyTorch 1.13.0
git clone https://github.com/sony/bigvsan
pip install -r requirements.txt

Create symbolic link to the root of the dataset. The codebase uses filelist with the relative path from the dataset. Below are the example commands for LibriTTS dataset.

cd LibriTTS && \
ln -s /path/to/your/LibriTTS/train-clean-100 train-clean-100 && \
ln -s /path/to/your/LibriTTS/train-clean-360 train-clean-360 && \
ln -s /path/to/your/LibriTTS/train-other-500 train-other-500 && \
ln -s /path/to/your/LibriTTS/dev-clean dev-clean && \
ln -s /path/to/your/LibriTTS/dev-other dev-other && \
ln -s /path/to/your/LibriTTS/test-clean test-clean && \
ln -s /path/to/your/LibriTTS/test-other test-other && \
cd ..

Train BigVSAN model. Below is an example command for training BigVSAN using LibriTTS dataset at 24kHz with a full 100-band mel spectrogram as input.

python train.py \
--config configs/bigvsan_24khz_100band.json \
--input_wavs_dir LibriTTS \
--input_training_file LibriTTS/train-full.txt \
--input_validation_file LibriTTS/val-full.txt \
--list_input_unseen_wavs_dir LibriTTS LibriTTS \
--list_input_unseen_validation_file LibriTTS/dev-clean.txt LibriTTS/dev-other.txt \
--checkpoint_path exp/bigvsan

We evaluated our BigVSAN model as follows:

Generate and save audio samples after you finish model training. Below is an example command for generating and save audio samples for evaluation.

python train.py \
--config configs/bigvsan_24khz_100band.json \
--input_wavs_dir LibriTTS \
--input_training_file LibriTTS/train-full.txt \
--input_validation_file LibriTTS/val-full.txt \
--list_input_unseen_wavs_dir LibriTTS LibriTTS \
--list_input_unseen_validation_file LibriTTS/dev-clean.txt LibriTTS/dev-other.txt \
--checkpoint_path exp/bigvsan \
--evaluate True \
--eval_subsample 1 \
--skip_seen True \
--save_audio True

Run the evaluation tool provided here. It computes five objective metric scores: M-STFT, PESQ, MCD, Periodicity, and V/UV F1.

python evaluate.py \
../bigvsan/exp/bigvsan/samples/gt_unseen_LibriTTS-dev-clean ../bigvsan/exp/bigvsan/samples/unseen_LibriTTS-dev-clean_01000001 \
../bigvsan/exp/bigvsan/samples/gt_unseen_LibriTTS-dev-other ../bigvsan/exp/bigvsan/samples/unseen_LibriTTS-dev-other_01000001

It will take about an hour to complete an evaluation. Note that, when audio samples are generated and saved with train.py, it also outputs M-STFT and PESQ scores, but their values will be different from the output of evaluate.py. This is due to 16-bit quantization for saving a sample as a wav file.

Synthesize from BigVSAN model. Below is an example command for generating audio from the model. It computes mel spectrograms using wav files from --input_wavs_dir and saves the generated audio to --output_dir.

python inference.py \
--checkpoint_file exp/bigvsan/g_01000000 \
--input_wavs_dir /path/to/your/input_wav \
--output_dir /path/to/your/output_wav

We provide pretrained checkpoints trained on the LibriTTS dataset here. You can download zip files, each of which contains checkpoints of a generator (e.g., g_01000000) and a discriminator (e.g., do_01000000).

The paper results are based on bigvsan_01mstep.

[1] Shibuya, T., Takida, Y., Mitsufuji, Y., "BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network," ICASSP 2024.

@inproceedings{shibuya2024bigvsan,
        title={{BigVSAN}: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network},
        author={Shibuya, Takashi and Takida, Yuhta and Mitsufuji, Yuki},
        booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
        year={2024}
}

• BigVGAN

• HiFi-GAN (for generator and multi-period discriminator)

• Snake (for periodic activation)

• Alias-free-torch (for anti-aliasing)

• Julius (for low-pass filter)

• UnivNet (for multi-resolution discriminator)