WaveNet: A Generative Model for Raw Audio

TL;DR

WaveNet is a deep autoregressive neural network that generates highly natural raw audio, outperforming traditional methods in speech synthesis, capturing speaker characteristics, and producing realistic musical fragments.

Contribution

It introduces WaveNet, a fully probabilistic deep neural network for raw audio generation, achieving state-of-the-art speech synthesis and versatile audio modeling.

Findings

Outperforms existing speech synthesis systems in naturalness.

Can model multiple speakers and switch between them.

Generates realistic musical fragments and aids phoneme recognition.

Abstract

This paper introduces WaveNet, a deep neural network for generating raw audio waveforms. The model is fully probabilistic and autoregressive, with the predictive distribution for each audio sample conditioned on all previous ones; nonetheless we show that it can be efficiently trained on data with tens of thousands of samples per second of audio. When applied to text-to-speech, it yields state-of-the-art performance, with human listeners rating it as significantly more natural sounding than the best parametric and concatenative systems for both English and Mandarin. A single WaveNet can capture the characteristics of many different speakers with equal fidelity, and can switch between them by conditioning on the speaker identity. When trained to model music, we find that it generates novel and often highly realistic musical fragments. We also show that it can be employed as a discriminative model, returning promising results for phoneme recognition.