Papers › WaveGrad: Estimating Gradients for Waveform Generation

WaveGrad: Estimating Gradients for Waveform Generation

2 Sep 2020ICLR 2021 1arXiv:2009.00713archive 2025-07-28

Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, William Chan

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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acids-ircam/diffusion_models mentioned on GitHubpytorch report
coqui-ai/TTS mentioned on GitHubpytorchMPL-2.0 report
ivanvovk/WaveGrad mentioned on GitHubpytorchBSD-3-Clause report
lmnt-com/wavegrad mentioned on GitHubpytorch report
maum-ai/wavegrad2 mentioned on GitHubpytorchBSD-3-Clause report
mindslab-ai/wavegrad2 mentioned on GitHubpytorchBSD-3-Clause report

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1ran · our draft was wrong
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Tasks

Speech SynthesisText-To-Speech Synthesis

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Methods

Introduced by this paper: WaveGrad, WaveGrad DBlock

1x1 ConvolutionAnti-Alias DownsamplingAssemble-ResNetAverage PoolingBatch NormalizationBig-Little ModuleBottleneck Residual BlockConvolutionDense ConnectionsDiffusionDilated ConvolutionFiLM ModuleGlobal Average PoolingLinear LayerMax PoolingReLUResNet-DResidual BlockResidual ConnectionSelective KernelSelective Kernel ConvolutionSoftmaxWaveGradWaveGrad DBlockWaveGrad UBlockXavier Initialization

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