Simulation / Modeling / Design

New Speech Synthesis System Can Imitate Thousands of Accents

Oct 24, 2017
By Brad Nemire
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  • Baidu researchers announced Deep Voice 3, a production-quality speech synthesis system that can imitate thousands of human voices.
  • The fully convolutional sequence-to-sequence model matches state-of-the-art neural speech synthesis in naturalness while training ten times faster than previous approaches.
  • Using TITAN Xp GPUs and Tesla P100s, the team trained single-speaker and multi-speaker systems on internal English, VCTK, and LibriSpeech data sets.
  • A single Tesla P100 GPU can handle ten million queries per day, enabling cost-effective deployment of the text-to-speech system.

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Baidu announced their latest production-quality speech synthesis system that can imitate thousands of human voices from people across the globe.
Deep Voice 1 focused on being the first real-time text-to-speech system and Deep Voice 2, with substantial improvements on Deep Voice 1, had the ability to reproduce several hundred voices using the same system.
“Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster,” according to the researchers. The company says the new version can learn 10,000 voices with just a half an hour of data each.
Using TITAN Xp GPUs and Tesla P100s, they trained their single-speaker synthesis system on an internal English speech data set consisting of 20 hours of data and their multi-speaker synthesis system on the VCTK (108 speakers and ~44 hours) and LibriSpeech (2,484 speakers with ~820 hours) data sets.

The Deep Voice 3 architecture is a fully-convolutional sequence-to-sequence model which converts text to spectrograms or other acoustic parameters to be used with an audio waveform synthesis method.

For more details about the architecture, read their paper “Deep Voice 3: 2000-Speaker Neural Text-to-Speech”.
In order to deploy their text-to-speech system in a cost-effective way, the system must be able to handle as much traffic as alternative systems on a comparable amount of hardware. To do so, they used a single Tesla P100 GPU which they mention can handle ten million queries per day.
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About Brad Nemire
Brad Nemire leads the Developer Communications team at NVIDIA. Prior to NVIDIA, he worked at Arm on the Developer Relations team. Brad graduated from San Diego State University and currently resides in Silicon Valley.

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