HooliGAN Vocoder Demo Samples

ABSTRACT: Recent developments in generative models have shown that deep learning combined with traditional DSP techniques could successfully generate convincing violin samples (DDSP), that source-excitation combined with WaveNet yields high-quality vocoders (NSF) and that GAN based techniques can improve naturalness (MelGAN).

By combining the ideas in these models we introduce HooliGAN, a robust vocoder that has state of the art results, fine-tunes very well to smaller datasets (<30 minutes of speechdata) and generates audio at 2.2MHz on GPU and 35kHz onCPU. We also show a simple modification to Tacotron-based models that allows seamless integration with HooliGAN.

Results from our listening tests show the proposed model’s ability to consistently output high-quality audio with a variety of datasets, big and small.

Paper Preprint Available Here