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WaveGrad: Estimating Gradients for Waveform Generation

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arxiv 2009.00713 v2 pith:5UQU6FN6 submitted 2020-09-02 eess.AS cs.LGcs.SDstat.ML

Nanxin Chen , Yu Zhang , Heiga Zen , Ron J. Weiss , Mohammad Norouzi , William Chan This is my paper
classification eess.AScs.LGcs.SDstat.ML
keywords wavegradaudioautoregressivefidelitygenerategenerationgradientshigh
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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/.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  4. MoLEx: Mixture of LoRA Experts in Speech Self-Supervised Models for Audio Deepfake Detection

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