Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
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.
68b7aecd295f15ff609112c5
Evan King
claimed_verified
2025-09-03T19:25:52.487Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6330f53cb68c7453d2ee8785/bMEue_DkNzIb_DWWraslB.jpeg
Evan King
user
evanking
68b7aecd295f15ff609112c6
Adam Sabra
claimed_verified
2025-09-03T19:25:55.032Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/623778e77c45100513d8621c/gFtspOwmDozjPMzcObTqU.jpeg
Adam Sabra
user
theadamsabra
68b7aecd295f15ff609112c7
Manjunath Kudlur
68b7aecd295f15ff609112c8
James Wang
68b7aecd295f15ff609112c9
Pete Warden
Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
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.
Monolingual ASR models trained on a balanced mix of high-quality, pseudo-labeled, and synthetic data outperform multilingual models for small model sizes, achieving superior error rates and enabling on-device ASR for underrepresented languages.
automatic speech recognition ASR multilingual ASR monolingual systems human-labeled pseudo-labeled synthetic data Whisper Tiny Whisper Small Whisper Medium on-device ASR underrepresented languages