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WaveGlow

21 papers tagged archive 2025-07-28

Introduced by Ryan Prenger et al. in WaveGlow: A Flow-based Generative Network for Speech Synthesis

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

WaveGlow is a flow-based generative model that generates audio by sampling from a distribution. Specifically samples are taken from a zero mean spherical Gaussian with the same number of dimensions as our desired output, and those samples are put through a series of layers that transforms the simple distribution to one which has the desired distribution.

PaperSource

Papers archive 2025-07-28

21 shown of 21, 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

20 shown of 23 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 Synthesis9
Text to Speech9
text-to-speech9
Transfer Learning4
GPU3
Audio Synthesis2
Decoder2
Density Estimation2
Resynthesis2
Speech Enhancement2
Audio Classification1
Audio Generation1
Face Swapping1
Quantization1
Rhythm1
Spectral Reconstruction1
Style Transfer1
Synthetic Speech Detection1
Text-To-Speech Synthesis1
Transliteration1

Usage over time archive 2025-07-28

Papers per year tagged with WaveGlow: 2018 to 2024, peak 6 6 0 2018: 1 paper 2018 2019: 6 papers 2019 2020: 4 papers 2020 2021: 4 papers 2021 2022: 2 papers 2022 2023: 3 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (21 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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