  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. We first
profile the components of current FastSpeech model and carefully design a novel
search space containing various lightweight and potentially effective
architectures. Then NAS is utilized to automatically discover well performing
architectures within the search space. Experiments show that the model
discovered by our method achieves 15x model compression ratio and 6.5x
inference speedup on CPU with on par voice quality. Audio demos are provided at
https://speechresearch.github.io/lightspeech.
