Moonshine Streaming Tiny
Useful Sensors · usefulsensors/moonshine-streaming-tiny
Speech to textTranscribes a recording into words
- Type
- Open weightsMIT License
- Languages
- 1
- Size
- 30M
Context measured in tokens
Our take
Written Aug 2, 2026Moonshine Streaming Tiny is a 30-million-parameter English speech-to-text model that trades accuracy for extreme speed. It can process an hour of audio in under a second on benchmark hardware, but gets roughly one word in nine wrong overall.
Pick this for offline or edge deployment where speed matters more than precision and English-only coverage is fine. Use it for projects needing a permissive open licence with minimal resource requirements, or for batch transcription of clean read-aloud audio on low-end hardware. Skip it if you need multiple languages, hosted availability, or reliable accuracy on meetings, podcasts, or accented speech.
The case for it
- Extreme speed: processes one hour of audio in 0.82 seconds on benchmark hardware.
- Strongest on clean read-aloud English, with a word error rate roughly 2.8 times better than its own average.
- Truly permissive MIT licence allows commercial use, modification and redistribution without copyleft requirements.
- Minimal footprint: 30 million parameters fit comfortably on edge devices.
The case against it
- Accuracy collapses on challenging real-world audio, with word error rate jumping nearly fivefold on accented speech compared with clean read speech.
- No hosted availability; users must self-host, and it supports English only.
How good is it?
TranscriptionTurning speech into text1 of 5Open ASR WER · 70th of 74
88.8%
Misses roughly one word in 9, averaged over nine English test sets.
4,375×8th of 62
an hour of audio in under a second, on the board's own hardware. Your machine will differ.
1
Listed on the model card. The accuracy above is English only.
Percentage of words wrong on each set, lower better. Bars are scaled to this model's own worst case, not to the board.
The figures above come from the Open ASR Leaderboard, an independent public test that runs every model on the same recordings. It is the only measurement of transcription quality we know of, so there are no other scores to show.
These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.
Every published score for this model9 scoresEvery figure we hold, from 9 boards, with who ran it and a link to the source — including the boards no rating above is built on.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 21.6 GB spare means a 10% error in the size would not change the answer.
Apple M1 Pro (16-core GPU) · 32 GB
Room to spare. 22.8 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU, 8GB unified) · 8 GB
Room to spare. 4.8 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
When we formed this view
Dates behind this page
Prices last checked 6h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
Licence and identifiers
What the licence allowsMIT License, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
MIT License
Fully permissive: do anything with attribution. No patent grant, unlike Apache-2.0.
Identifiers
- Hugging Face
- usefulsensors/moonshine-streaming-tiny
- Modality record
- audio->text
- Catalogue slug
- usefulsensors-moonshine-streaming-tiny