Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning

Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O. Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller

Feb 15, 2018 (modified: Feb 21, 2018) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • 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 an order of magnitude faster. We scale Deep Voice 3 to dataset 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 a single GPU server.
  • Keywords: 2000-Speaker Neural TTS, Monotonic Attention, Speech Synthesis