Basic implementations of WaveNet and modified FFTNet in PyTorch. The project structre is brought from pytorch-template.

• NumPy
• SciPy
• PyTorch >= 0.4.1
• tqdm
• librosa

The code in this repo by default will train a WaveNet (or FFTNet) using 80-dimension mel-spectrogram with linear interpolation.

Use preprocess.py to convert your wave files into mel-spectrograms.

python preprocess.py wave/files/folder -c config.json --out data

The preprocessed data will be stored in ./data. You can change the configurations of "feature" in the .json file.

python train.py -c config.json

Use preprocess.py to convert a single wave file into mel-spectrogram feature.

python preprocess.py example.wav -c config.json --out test

The result is stored in test.npz.

Then use the latest checkpoint file in the ./saved folder to decoded test.npz back to waveform. The generating process will run on gpu if you add --cuda.

python test.py test.npz outfile.wav -r saved/your-model-name/XXXX_XXXXXX/checkpoint-stepXXXXX.pth --cuda

That's it. Other instructions and advanced usage can be found in pytorch-template, I didn't change too much of the whole structure.

I add a new folder feature which is different from pytorch-template. To use other feature like mfcc instead of mel-spectrogram, you can add your own function in ./feature/features.py with similar arguments style of get_logmel().

Other customization method can be found in pytorch-template.

In test.py I implement fast-wavenet generation process in a very naive way. Use fast_inference.py you can get a huge speed up (CPU only). The speed is around 1500 samples/s on FFTNet and 300 samples/s on WaveNet.