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

• URL: http://arxiv.org/abs/2203.14416v1
• Date: Sun, 27 Mar 2022 23:56:52 GMT
• Title: Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge
• Authors: Sangjun Park, Kihyun Choo, Joohyung Lee, Anton V. Porov, Konstantin Osipov, June Sig Sung
• Abstract summary: Bunched LPCNet2 provides efficient performance in high-quality for cloud servers and in a low-complexity for low-resource edge devices. 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.
• Score: 3.612475016403612
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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