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.

本文介绍了 DiffWave，这是一种用于条件和非条件波形生成的多功能扩散概率模型。该模型高效地通过 Markov 链将白噪声信号转化为结构化波形，并通过特定的数据似然变分界限进行优化训练。DiffWave 在各种波形生成任务中均能生成高保真度音频，并在音频质量方面显著优于其他自回归和 GAN-based 波形模型。