Deep Voice 3: 2000-Speaker Neural Text-to-Speech

TLDR

Deep Voice 3 is a fully‑convolutional attention‑based neural text‑to‑speech system. It is trained on over 800 hours of audio from more than 2,000 speakers, scales to unprecedented dataset sizes, mitigates common attention‑based synthesis errors, compares waveform synthesis methods, and supports ten million daily queries on a single GPU. Deep Voice 3 matches state‑of‑the‑art neural TTS in naturalness while training ten times faster.

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.