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WaveGrad: Estimating Gradients for Waveform Generation

by   Nanxin Chen, et al.

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


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Code Repositories


Implementation of Google Brain's WaveGrad high-fidelity vocoder (paper: First implementation on GitHub.

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A fast, high-quality neural vocoder.

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Unofficial Pytorch Implementation of WaveGrad2

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A series of tutorial notebooks on denoising diffusion probabilistic models in PyTorch

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Unoffical implementation about Image Super-Resolution via Iterative Refinement by Pytorch

view repo