Parallel WaveNet: Fast High-Fidelity Speech Synthesis

Aäron van den Oord
Yazhe Li
Igor Babuschkin
Karen Simonyan
Koray Kavukcuoglu
George van den Driessche
Edward Lockhart
Luis Carlos Cobo Rus
Florian Stimberg
Norman Casagrande
Dominik Grewe
Seb Noury
Sander Dieleman
Erich Elsen
Alexander Graves
Helen King
Thomas Walters
Dan Belov
Demis Hassabis
NA, Google Deepmind, NA(2017)


The recently-developed WaveNet architecture [27] 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.