philsyn / DiffWave-unconditional

Pytorch Reimplementation of DiffWave unconditional generation: a high quality waveform synthesizer.

This is a reimplementaion of the unconditional waveform synthesizer in DIFFWAVE: A VERSATILE DIFFUSION MODEL FOR AUDIO SYNTHESIS.

Usage:

• To continue training the model, run python distributed_train.py -c config.json.

• To retrain the model, change the parameter ckpt_iter in the corresponding json file to -1 and use the above command.

• To generate audio, run python inference.py -c config.json -n 16 to generate 16 utterances.

• Note, you may need to carefully adjust some parameters in the json file, such as data_path and batch_size_per_gpu.

Pretrained models and generated samples:

• model
• samples