LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search

Understand

Text to speech (TTS) has been broadly used to synthesize natural and intelligible speech in different scenarios.

• Deploying TTS in various end devices such as mobile phones or embedded devices requires extremely small memory usage and inference latency.
• While non-autoregressive TTS models such as FastSpeech have achieved significantly faster inference speed than autoregressive models, their model size and inference latency are still large for the deployment in resource constrained devices.
• In this paper, we propose LightSpeech, which leverages neural architecture search~(NAS) to automatically design more lightweight and efficient models based on FastSpeech.