Multi-band MelGAN and Full band MelGAN

Unofficial PyTorch implementation of Multi-Band MelGAN paper. This implementation uses Seungwon Park's MelGAN repo as a base and PQMF filters implementation from this repo. <br>MelGAN : <br> Multi-band MelGAN:<br>

Prerequisites

Tested on Python 3.6

pip install -r requirements.txt

Prepare Dataset

Download dataset for training. This can be any wav files with sample rate 22050Hz. (e.g. LJSpeech was used in paper)
preprocess: python preprocess.py -c config/default.yaml -d [data's root path]
Edit configuration yaml file

Train & Tensorboard

python trainer.py -c [config yaml file] -n [name of the run]
- cp config/default.yaml config/config.yaml and then edit config.yaml
- Write down the root path of train/validation files to 2nd/3rd line.
- Each path should contain pairs of *.wav with corresponding (preprocessed) *.mel file.
- The data loader parses list of files within the path recursively.
- For Multi-Band training use config/mb_melgan config file in -c
tensorboard --logdir logs/

Pretrained model

Check out here.

Inference

python inference.py -p [checkpoint path] -i [input mel path]

Results

<br />

References

License

BSD 3-Clause License.

utils/stft.py by Prem Seetharaman (BSD 3-Clause License)
datasets/mel2samp.py from https://github.com/NVIDIA/waveglow (BSD 3-Clause License)
utils/hparams.py from https://github.com/HarryVolek/PyTorch_Speaker_Verification (No License specified)

Useful resources

How to Train a GAN? Tips and tricks to make GANs work by Soumith Chintala
Official MelGAN implementation by original authors
Reproduction of MelGAN - NeurIPS 2019 Reproducibility Challenge (Ablation Track) by Yifei Zhao, Yichao Yang, and Yang Gao
- "replacing the average pooling layer with max pooling layer and replacing reflection padding with replication padding improves the performance significantly, while combining them produces worse results"

Melgan

Install / Use

README