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WaveGrad

7 papers tagged archive 2025-07-28

Introduced by Nanxin Chen et al. in WaveGrad: Estimating Gradients for Waveform Generation

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

WaveGrad is a conditional model for waveform generation through estimating gradients of the data density. This model is built on the prior work on score matching and diffusion probabilistic models. It starts from Gaussian white noise and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad is non-autoregressive, and requires only a constant number of generation steps during inference. It can use as few as 6 iterations to generate high fidelity audio samples.

PaperSourceSee Code · lmnt-com/wavegrad

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Speech Synthesis5
Denoising2
Image Generation2
Text-To-Speech Synthesis2
Text to Speech1
text-to-speech1

Usage over time archive 2025-07-28

Papers per year tagged with WaveGrad: 2020 to 2024, peak 3 3 0 2020: 1 paper 2020 2021: 3 papers 2021 2022: 2 papers 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Generative Audio Models

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