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

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

Code: [ 7 community implementations](https://paperswithcode.com/paper/?openreview=HJtEm4p6Z)

Data: [LibriSpeech](https://paperswithcode.com/dataset/librispeech)

Community Implementations: [ 6 code implementations](https://www.catalyzex.com/paper/deep-voice-3-scaling-text-to-speech-with/code)