  Recent developments in generative models have shown that deep learning
combined with traditional digital signal processing (DSP) techniques could
successfully generate convincing violin samples [1], that source-excitation
combined with WaveNet yields high-quality vocoders [2, 3] and that generative
adversarial network (GAN) training can improve naturalness [4, 5]. By combining
the ideas in these models we introduce HooliGAN, a robust vocoder that has
state of the art results, finetunes very well to smaller datasets (<30 minutes
of speechdata) and generates audio at 2.2MHz on GPU and 35kHz on CPU. We also
show a simple modification to Tacotron-basedmodels 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. We provide samples at the following demo page:
https://resemble-ai.github.io/hooligan_demo/
