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Moonshine is a family of speech-to-text models optimized for fast and accurate automatic speech recognition (ASR) on resource-constrained devices. It is well-suited to real-time, on-device applications like live transcription and voice command recognition. Moonshine obtains word-error rates (WER) better than similarly-sized tiny.en and base.en Whisper models from OpenAI on the datasets used in the OpenASR leaderboard maintained by HuggingFace:
Moonshine's compute requirements scale with the length of input audio. This means that shorter input audio is processed faster, unlike existing Whisper models that process everything as 30-second chunks. To give you an idea of the benefits: Moonshine processes 10-second audio segments 5x faster than Whisper while maintaining the same (or better!) WER.
Moonshine Base is approximately 400MB, while Tiny is around 190MB. Both publicly-released models currently support English only.
