WaveGAN (PyTorch)

This is my own implementation of WaveGAN using PyTorch, introduced in this paper. The main task was to synthesize a raw audio of drum sounds and human voice articulating numbers 0 to 9.

Results

✿ Take a look at an example of a synthesized audio HERE!✿

Additional Notes

• While building the model, I chose hyperparameters suggested by the paper, EXCEPT the model size (d) :

• I reduced the model size (d) from 64 (suggested in the paper) to 32, and obtained recognizable synthesis as early as Epoch 30.

• During training, I did not use any quantitative stopping criteria (as in the paper). I just used qualitative method of checking the synthesized audio.

Datasets

WaveGAN model was trained on the following datasets (containing .wav files):

• Speech Commands Zero through Nine (SC09)
• Drum sound effects
• Bach piano performances

Sources

• https://github.com/chrisdonahue/wavegan/
• https://github.com/jtcramer/wavegan/