  This paper introduces WaveGrad, a conditional model for waveform generation
which estimates gradients of the data density. The model is built on prior work
on score matching and diffusion probabilistic models. It starts from a Gaussian
white noise signal and iteratively refines the signal via a gradient-based
sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to
trade inference speed for sample quality by adjusting the number of refinement
steps, and bridges the gap between non-autoregressive and autoregressive models
in terms of audio quality. We find that it can generate high fidelity audio
samples using as few as six iterations. Experiments reveal WaveGrad to generate
high fidelity audio, outperforming adversarial non-autoregressive baselines and
matching a strong likelihood-based autoregressive baseline using fewer
sequential operations. Audio samples are available at
https://wavegrad.github.io/.
