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arXiv · 2009.09761

DiffWave: A Versatile Diffusion Model for Audio Synthesis

Zhifeng Kong·Wei Ping·Jiaji Huang·Kexin Zhao·Bryan Catanzaro
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Abstract

In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained by optimizing a variant of variational bound on the data likelihood. DiffWave produces high-fidelity audios in different waveform generation tasks, including neural vocoding conditioned on mel spectrogram, class-conditional generation, and unconditional generation. We demonstrate that DiffWave matches a strong WaveNet vocoder in terms of speech quality (MOS: 4.44 versus 4.43), while synthesizing orders of magnitude faster. In particular, it significantly outperforms autoregressive and GAN-based waveform models in the challenging unconditional generation task in terms of audio quality and sample diversity from various automatic and human evaluations.

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BibTeXRIS

Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, Bryan Catanzaro. 2020-09-21. DiffWave: A Versatile Diffusion Model for Audio Synthesis. https://arxiv.org/abs/2009.09761

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