Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices

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tiny-models automatic-speech-recognition edge-devices underrepresented-languages monolingual-asr synthetic-data open-source

Summary

This paper presents Flavors of Moonshine, a suite of tiny specialized ASR models for edge devices. The authors show that monolingual models trained on a balanced mix of human-labeled, pseudo-labeled, and synthetic data outperform larger multilingual models like Whisper, achieving state-of-the-art error rates for small models and enabling on-device ASR for underrepresented languages.

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.
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Paper page - Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices

Source: https://huggingface.co/papers/2509.02523

Abstract

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.

We present the Flavors of Moonshine, a suite of tiny automatic speech recognition (ASR) models specialized for a range ofunderrepresented languages. Prevailing wisdom suggests thatmultilingual ASRmodels outperform monolingual counterparts by exploiting cross-lingual phonetic similarities. We challenge this assumption, showing that for sufficiently small models (27M parameters), trainingmonolingual systemson a carefully balanced mix of high-qualityhuman-labeled,pseudo-labeled, andsynthetic datayields substantially superior performance. On average, our models achieve error rates 48% lower than the comparably sizedWhisper Tinymodel, outperform the 9x largerWhisper Smallmodel, and in most cases match or outperform the 28x largerWhisper Mediummodel. These results advance the state of the art for models of this size, enabling accurateon-device ASRfor languages that previously had limited support. We release Arabic, Chinese, Japanese, Korean, Ukrainian, and Vietnamese Moonshine models under a permissive open-source license.

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#### UsefulSensors/moonshine-tiny-ja Automatic Speech Recognition• 27.1M• UpdatedSep 9, 2025 • 474 • 11 #### UsefulSensors/moonshine-tiny-ar Automatic Speech Recognition• 27.1M• UpdatedSep 6, 2025 • 217 • 7 #### UsefulSensors/moonshine-tiny-zh Automatic Speech Recognition• 27.1M• UpdatedSep 4, 2025 • 5.53k • 6 #### UsefulSensors/moonshine-tiny-ko Automatic Speech Recognition• 27.1M• UpdatedSep 4, 2025 • 964 • 5 Browse 21 models citing this paper## Datasets citing this paper0

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