pytorch-UniWaveNet

A PyTorch implementetion of Uni-WaveNet( https://zhuanlan.zhihu.com/p/44702855 ). Uni-WaveNet is derived from Parallel WaveNet, it can be trained without Teacher WaveNet.

Generated samples

I uploaded generated samples to SoundCloud. https://soundcloud.com/dhgrs/sets/uni-wavenet

And uploaded pretrained model for LJSpeech to Google Drive, same setting as NVIDIA's Tacotron2 implementaiton. https://drive.google.com/drive/folders/1BqzltOT9u3358nQgPolRA511J7cGJGlI?usp=sharing

Requirements

• Python3
• PyTorch(>0.4.1)
• tensorboardX
• numpy
• libsosa
• tqdm

CAUTION: The interface of PyTorch's STFT API has changed at ver. 0.4.1. So you have to use 0.4.1 or later.

Usage

1. Download dataset

This implementation can train with LJSpeech, English single speaker corpus or VCTK-Corpus, English multi speaker corpus. And you can download them very easily via my repository.

2. Set parameters

Hyperparameters are in params.py. You have to change root to the directory you download the dataset. If you can not understand some parameters, please open an issue.

3. Run training

# without GPU
python3 train.py

# with GPU
python3 train.py --use_cuda

If you want to restart training with snapshot, use options like below.

python3 train.py --use_cuda -e path/to/encoder.pth -w path/to/wavenet.pth -o path/to/optimizer.pth -i iteration_to_start_at

You can use TensorBoard to visualize training. Also can listen the generated samples during training.

4. Generate

python3 generate.py -e path/to/encoder.pth -w path/to/wavenet.pth -i path/to/input.wav -l length_to_generate(sec)

CAUTION: There is a bug on PyTorch and my implementation. Now you can generate samples on ONLY GPU. You will get an error if you try to generate on CPU. Related issue which I opened is here.