MelGAN Multi GPU Implementation.

PyTorch implementation of MelGAN: Generative Adversarial Networks for
Conditional Waveform Synthesis.

This implementation includes distributed support and uses the
LJSpeech dataset.

Pre-requisites

1. NVIDIA GPU + CUDA cuDNN
2. Python >= 3.5
3. PyTorch == 1.2
4. Clone this repository.
5. Install python requirements. Please refer requirements.txt

Dataset

1. Download and extract the LJ Speech dataset
2. Move all wav files to data/LJSpeech-1.1/wavs

Training

1. python train.py --config=config.json --cps=cp_melgan

Default checkpoint directory is cp_melgan

Tensorboard logs will be saved in cp_melgan/logs

Multi-GPU (distributed) Training

1. python distributed.py --config=config.json --args_str="--cps=cp_melgan"

Training code detects all GPUs and sets them automatically.

Generated Sample Audio

1. Current Samples (489K Steps)

   Samples

2. Sample audio can be heard on the tensorboard also.
   

1. Generated spectrograme can be seen on the tensorboard.
   

Pre-trained model

Coming soon..

Inference

I will commit a full inference code soon.

Acknowledgements

• distributed.py from NVIDIA’s
  waveglow.

I referred to waveglow
to implement audio preprocessing.