Cited by 1,004Speech Recognition and SynthesisSpeech and Audio ProcessingMusic and Audio Processing

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“Waveglow: a flow-based generative network for speech synthesis,”

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Prenger, Ryan, Valle, Rafael, Catanzaro, Bryan·2019·ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

DOI 10.1109/icassp.2019.8683143

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Abstract

In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow [1] and WaveNet [2] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online [3].

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Authors: Prenger, R., Valle, R., & Catanzaro, B.
Affiliations: Nvidia (United States) (US)
ORCID: Catanzaro, Bryan
Venue: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher: IEEE
Published: 2019-05
Pages: 3617-3621
ISSN: 1520-6149 (ISSN-L)
DOI: 10.1109/icassp.2019.8683143
OpenAlex: W2898847420
Cited by: 1,004
References: 22
Citations by year: 2026: 39 · 2025: 86 · 2024: 111 · 2023: 156 · 2022: 161 · 2021: 220 · 2020: 169 · 2019: 58
FWCI: 58.16
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Keywords: WaveGlow, speech synthesis, flow-based generative models, glow, WaveNet, audio generation, likelihood maximization, high-quality audio

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