R
Rafael Valle
Researcher at Nvidia
Publications - 32
Citations - 1580
Rafael Valle is an academic researcher from Nvidia. The author has contributed to research in topics: Computer science & Speech synthesis. The author has an hindex of 9, co-authored 26 publications receiving 992 citations. Previous affiliations of Rafael Valle include University of California, Berkeley.
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Proceedings ArticleDOI
Waveglow: A Flow-based Generative Network for Speech Synthesis
TL;DR: WaveGlow as mentioned in this paper is a flow-based network capable of generating high quality speech from mel-spectrograms without the need for auto-regression, and it is implemented using only a single network, trained using a single cost function: maximizing the likelihood of the training data.
Posted Content
WaveGlow: A Flow-based Generative Network for Speech Synthesis
TL;DR: WaveGlow is a flow-based network capable of generating high quality speech from mel-spectrograms, implemented using only a single network, trained using a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable.
Proceedings ArticleDOI
Mellotron: Multispeaker Expressive Voice Synthesis by Conditioning on Rhythm, Pitch and Global Style Tokens
TL;DR: A multispeaker voice synthesis model based on Tacotron 2 GST that can make a voice emote and sing without emotive or singing training data, and synthesized samples that include style transfer from other speakers, singers and styles not seen during training, procedural manipulation of rhythm and pitch and choir synthesis.
Posted Content
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
TL;DR: The mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality, and results on control of speech variation, interpolation between samples and style transfer between speakers seen and unseen during training are provided.
Journal ArticleDOI
Missing Data Imputation for Supervised Learning
Jason Poulos,Rafael Valle +1 more
TL;DR: In this article, the authors compare methods for imputing missing categorical data and find that missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information.