# HooliGAN: Robust, High Quality Neural Vocoding

arXiv: https://arxiv.org/abs/2008.02493 · HTML: https://mlsanity.com/paper/2008.02493/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis](https://mlsanity.com/paper/2106.09660/) (2021)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Learning Interpretable Low-dimensional Representation via Physical Symmetry](https://mlsanity.com/paper/2302.10890/) (2023)
- [Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN](https://mlsanity.com/paper/2104.04668/) (2021)
- [Differentiable WORLD Synthesizer-based Neural Vocoder With Application To End-To-End Audio Style Transfer](https://mlsanity.com/paper/2208.07282/) (2022)
- [AdaVocoder: Adaptive Vocoder for Custom Voice](https://mlsanity.com/paper/2203.09825/) (2022)
- [Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations](https://mlsanity.com/paper/2206.12662/) (2022)

## Abstract (arXiv)

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/