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

By Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Jinzhu Li, Sheng Zhao, Enhong Chen, Tie-Yan Liu. 2021

81 citations recorded by the source corpus.

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 Fast-Speech 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.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Yi Ren
5. Sheng Zhao
6. Zhou Zhao
7. Jinglin Liu
8. Frank K. Soong

In Signal Processing

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Yi Ren
5. Zhou Zhao
6. Sheng Zhao
7. Jinglin Liu
8. Yanqing Liu

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “Hybrid DeepVoice 1/2 [8, 84], Tacotron [376], DurIAN [411], LightSpeech [214], Wave-Tacotron [379]”
• AdaSpeech: Adaptive Text to Speech for Custom Voice (2021, methodology): “We will also investigate the adaptation setting with untranscribed data (Yan et al., 2021) and further compress the model size (Luo et al., 2021) to support more custom voices.”
• FastSpeech 2: Fast and High-Quality End-to-End Text to Speech (2020, background): “, 2021) to further improve the voice quality and speed up the inference with more light-weight model (Luo et al., 2021).”
• PortaSpeech: Portable and High-Quality Generative Text-to-Speech (2021, background): “LightSpeech [17] leverages neural architecture search (NAS) to automatically design more lightweight models, while the training of NAS consumes huge resources.”