  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.
