Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning
Sound
2018-02-23 v3 Artificial Intelligence
Computation and Language
Machine Learning
Audio and Speech Processing
Abstract
We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on one single-GPU server.
Cite
@article{arxiv.1710.07654,
title = {Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning},
author = {Wei Ping and Kainan Peng and Andrew Gibiansky and Sercan O. Arik and Ajay Kannan and Sharan Narang and Jonathan Raiman and John Miller},
journal= {arXiv preprint arXiv:1710.07654},
year = {2018}
}
Comments
Published as a conference paper at ICLR 2018. (v3 changed paper title)