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arXiv · 2203.14416

Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge

Sangjun Park·Kihyun Choo·Joohyung Lee·Anton V. Porov·Konstantin Osipov·June Sig Sung
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Abstract

Text-to-Speech (TTS) services that run on edge devices have many advantages compared to cloud TTS, e.g., latency and privacy issues. However, neural vocoders with a low complexity and small model footprint inevitably generate annoying sounds. This study proposes a Bunched LPCNet2, an improved LPCNet architecture that provides highly efficient performance in high-quality for cloud servers and in a low-complexity for low-resource edge devices. Single logistic distribution achieves computational efficiency, and insightful tricks reduce the model footprint while maintaining speech quality. A DualRate architecture, which generates a lower sampling rate from a prosody model, is also proposed to reduce maintenance costs. The experiments demonstrate that Bunched LPCNet2 generates satisfactory speech quality with a model footprint of 1.1MB while operating faster than real-time on a RPi 3B. Our audio samples are available at https://srtts.github.io/bunchedLPCNet2.

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Sangjun Park, Kihyun Choo, Joohyung Lee, Anton V. Porov, Konstantin Osipov, June Sig Sung. 2022-06-30. Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge. https://arxiv.org/abs/2203.14416

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