Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech SynthesisDownload PDF

28 Sept 2020, 15:48 (modified: 23 Jan 2023, 18:08)ICLR 2021 PosterReaders: Everyone
Keywords: Text to speech synthesis, normalizing flows, deep learning
Abstract: In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with style transfer and speech variation. Flowtron borrows insights from Autoregressive Flows and revamps Tacotron 2 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 used to modulate many aspects of speech synthesis (timbre, expressivity, accent). Our mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. We provide results on speech variation, interpolation over time between samples and style transfer between seen and unseen speakers. Code and pre-trained models are publicly available at \href{https://github.com/NVIDIA/flowtron}{https://github.com/NVIDIA/flowtron}.
One-sentence Summary: In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with style transfer and speech varation.
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Code: [![github](/images/github_icon.svg) NVIDIA/flowtron](https://github.com/NVIDIA/flowtron) + [![Papers with Code](/images/pwc_icon.svg) 2 community implementations](https://paperswithcode.com/paper/?openreview=Ig53hpHxS4)
Data: [LJSpeech](https://paperswithcode.com/dataset/ljspeech), [LibriTTS](https://paperswithcode.com/dataset/libritts)
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