  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.
