Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Venue

Publication Year

Keywords

Computer Science - Machine Learning

Authors

Aaron Oord
Yazhe Li
Igor Babuschkin
Karen Simonyan
Oriol Vinyals
Koray Kavukcuoglu
George Driessche
Edward Lockhart
Luis C. Cobo
Florian Stimberg
Norman Casagrande
Dominik Grewe
Seb Noury
Sander Dieleman
Erich Elsen
Nal Kalchbrenner
Heiga Zen
Alex Graves
Helen King
Tom Walters
Dan Belov
Demis Hassabis

Abstract

The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system. However, because WaveNet relies on sequential generation of one audio sample at a time, it is poorly suited to today’s massively parallel computers, and therefore hard to deploy in a real-time production setting. This paper introduces Probability Density Distillation, a new method for training a parallel feed-forward network from a trained WaveNet with no significant difference in quality. The resulting system is capable of generating high-fidelity speech samples at more than 20 times faster than real-time, and is deployed online by Google Assistant, including serving multiple English and Japanese voices.