All builds for this model:

Moonshine Tiny is the smallest model in Useful Sensors' Moonshine family — a 27M-parameter, sequence-to-sequence English speech-recognition model designed for real-time, on-device transcription on hardware that is severely constrained in memory and compute. Trained on 200,000 hours of audio, it transcribes English speech to text and, despite its size, reports greater accuracy than existing ASR systems of comparable scale on standard benchmarks. It targets developers building live transcription and voice-command experiences on low-cost devices. Like other autoregressive ASR models it can occasionally hallucinate or repeat on very short or clipped segments, so robust in-domain evaluation is recommended before deployment. This OpenASR repo repackages the original weights as .oasr packs that run natively in the OpenASR runtime — no Python at inference time. The q8_0 build is the recommended default (near-reference accuracy at roughly a third of the footprint); fp16 is for verification or maximum fidelity.

Converted from UsefulSensors/moonshine-tiny with the OpenASR importer:

The .oasr container is GGUF-backed; packs use zero-copy mmap weight binding and graph buffer reuse to keep peak memory low.

These packs inherit the upstream model's license: MIT (source). OpenASR packaging retains the upstream copyright and NOTICE; the only modifications are format conversion and quantization.

This pack is a redistribution of Moonshine Tiny, created and open-sourced by Useful Sensors (UsefulSensors/moonshine-tiny). All credit for the original architecture, training, and weights belongs to them; the license is inherited from and identical to the upstream model (MIT). Thank you to the Moonshine authors — Nat Jeffries, Evan King, Manjunath Kudlur, Guy Nicholson, James Wang, and Pete Warden — for releasing their work openly.