Papers › WaveGlow: A Flow-based Generative Network for Speech Synthesis
WaveGlow: A Flow-based Generative Network for Speech Synthesis
Ryan Prenger, Rafael Valle, Bryan Catanzaro
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow and WaveNet 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.
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Code
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Code Syntology ran Syntology
7 samples harvested; 2 ran; 0 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Synthesis | LibriTTS | WaveGlow | M-STFT | 1.3099 | #12 of 15 | Archive leaderboard | report |
| Speech Synthesis | LibriTTS | WaveGlow | MCD | 2.3591 | #12 of 15 | Archive leaderboard | report |
| Speech Synthesis | LibriTTS | WaveGlow | PESQ | 3.138 | #12 of 15 | Archive leaderboard | report |
| Speech Synthesis | LibriTTS | WaveGlow | Periodicity | 0.1485 | #12 of 15 | Archive leaderboard | report |
| Speech Synthesis | LibriTTS | WaveGlow | V/UV F1 | 0.9378 | #12 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: WaveGlow
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