Papers › Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
Rafael Valle, Kevin Shih, Ryan Prenger, Bryan Catanzaro
In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer. Flowtron borrows insights from IAF and revamps Tacotron in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is optimized by maximizing the likelihood of the training data, which makes training simple and stable. Flowtron learns an invertible mapping of data to a latent space that can be manipulated to control many aspects of speech synthesis (pitch, tone, speech rate, cadence, accent). Our mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. In addition, we provide results on control of speech variation, interpolation between samples and style transfer between speakers seen and unseen during training. Code and pre-trained models will be made publicly available at https://github.com/NVIDIA/flowtron
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-To-Speech Synthesis | LJSpeech | Flowtron | Pleasantness MOS | 3.665 | #14 of 16 | Archive leaderboard | report |
| Text-To-Speech Synthesis | LJSpeech | Tacotron 2 | Pleasantness MOS | 3.521 | #15 of 16 | 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
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