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Moonshine AI

company · United States

Scores

1 product on the map — 1 open-ish.

Moonshine

Openness

3 medium confidence
3.0
weights
open(safetensors checkpoints on the Hub and .ort files for the library, ungated)
data
described(about 300K hours: public web audio, open datasets named in the paper, and 100K hours prepared internally
code
partial(inference library and LoRA fine-tuning
license
MIT(every Moonshine Streaming model and the English Tiny and Base models, and the code)
superseded-release
legacy non-English non-streaming models(2025 Arabic, Japanese, Korean, Mandarin, Spanish, Ukrainian and Vietnamese Tiny/Base models under the Moonshine AI Community License (free below USD 1M annual revenue), deprecated where a streaming replacement exists

The current Moonshine Streaming models, like the English Tiny and Base models before them, are MIT-licensed and download freely. The training audio is described but not released, and only LoRA fine-tuning is published. Older non-English models remain under a community license that caps free use at USD 1M in annual revenue; they are being replaced by the streaming line, and even read as current they would not change the score.

Adoption

2 medium confidence
2.0

PyPI downloads of the two Moonshine packages, the current moonshine-voice library and the older ONNX package. The Hub checkpoints are downloaded more, but the package is the documented way to run the models.

Capability

2 low confidence
2.0

Built for live transcription on small devices rather than for the top of the accuracy tables; its own figures on the leaderboard's test sets place it below Whisper's large models, a step below Whisper.