Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
Authors: name: Evan King, name: Adam Sabra, name: Manjunath Kudlur, name: James Wang, name: Pete Warden

We present the Flavors of Moonshine, a suite of tiny automatic speech
recognition (ASR) models specialized for a range of underrepresented languages.
Prevailing wisdom suggests that multilingual ASR models outperform monolingual
counterparts by exploiting cross-lingual phonetic similarities. We challenge
this assumption, showing that for sufficiently small models (27M parameters),
training monolingual systems on a carefully balanced mix of high-quality
human-labeled, pseudo-labeled, and synthetic data yields substantially superior
performance. On average, our models achieve error rates 48% lower than the
comparably sized Whisper Tiny model, outperform the 9x larger Whisper Small
model, and in most cases match or outperform the 28x larger Whisper Medium
model. These results advance the state of the art for models of this size,
enabling accurate on-device ASR for languages that previously had limited
support. We release Arabic, Chinese, Japanese, Korean, Ukrainian, and
Vietnamese Moonshine models under a permissive open-source license.