Transcribe with Moonshine
7.8%
WER
1
Languages
80.0x
Speed
MIT
License
About Moonshine
Moonshine is an extremely compact speech-to-text model designed for resource-constrained environments. At only 61M parameters, it runs efficiently on edge devices like Raspberry Pi while maintaining reasonable English transcription accuracy.
Languages Supported by Moonshine
Model Info
- ProviderUseful Sensors
- Architecture-
- LicenseMIT
- UpdatedMar 2026
Frequently Asked Questions
Moonshine is a speech-to-text model by Useful Sensors. STT.ai hosts Moonshine on our GPU infrastructure so you can use it without provisioning your own hardware — upload audio or video and pick Moonshine from the model picker.
On standard benchmarks, Moonshine achieves around 7.8% Word Error Rate. Real-world accuracy depends on audio quality, accent, and language; for noisy or accented recordings, expect a few percentage points higher WER.
Moonshine runs on STT.ai's free tier — every visitor gets 600 minutes/month at no cost. Paid plans add longer per-file limits, private transcripts, and priority queueing.
Moonshine is released under MIT, a permissive open-source license. You can self-host Moonshine on your own hardware or use our hosted version — both are commercially usable.
Moonshine supports 1 languages. Auto-detection picks the right language for most audio; you can also specify it manually for a small accuracy lift.
Moonshine processes audio at about 80.0x real-time on our GPUs. A 1-hour audio file finishes in under 1 minutes; longer files queue and notify by email when done.
Moonshine has 61M parameters. Larger models tend to be more accurate but slower; STT.ai hosts Moonshine on GPU so the parameter count doesn't affect your client-side performance.
Moonshine accepts every format STT.ai supports — MP3, WAV, M4A, FLAC, OGG, MP4, MKV, MOV, WebM, AVI, and others. Output as TXT, SRT, VTT, DOCX, JSON, or PDF.
Yes. Speaker diarization runs alongside Moonshine for every transcription — each speaker is labeled and you can rename them in the editor afterwards.
Yes. Moonshine runs in our managed environment — audio is processed and deleted by default and never used for training without explicit opt-in. Pro plans add client-side encryption for transcripts at rest.
Use the compare-stt tool to run Moonshine against any other supported model on the same audio — you'll see WER, segment count, speaker labels, and confidence scores side-by-side. The Moonshine vs Whisper Large V3 comparison is the most commonly run.
Yes. Specify "moonshine" as the model parameter on the /v1/transcribe endpoint. Python and Node.js SDKs include Moonshine examples. Free API tier includes 100 minutes/month.
Yes. Because Moonshine is MIT-licensed, you can self-host it. STT.ai's open-source page lists the project repo and weights. Most production teams use our hosted version to skip GPU procurement, model swaps, and ops.