Moonshine Streaming Small
Useful Sensors · released Jan 6, 2026 · usefulsensors/moonshine-streaming-small
Speech to textTranscribes a recording into words
- Type
- Open weightsMIT License
- Languages
- 1
- Size
- 0.1B
about 3K words of context
Our take
Written Sep 17, 2026Moonshine Streaming Small is a speech-to-text model you can download and run yourself, built for speed rather than accuracy. It turns an hour of audio into text in about a second on the leaderboard's own hardware, but it is worse than most models on every condition we hold.
Use it to work through a large backlog of audio quickly where some errors are acceptable, or to run transcription on modest hardware under a licence that allows commercial use, changes and redistribution (MIT). Skip it if you need accurate transcription of meetings, podcasts or accented speech, where it is worse than most models.
The case for it
- 3,206 times real time on the leaderboard's own hardware, so a long backlog becomes practical to work through; your machine may differ.
- The licence allows commercial use, changes and redistribution (MIT).
- At 0.1 billion parameters you can download it and run it yourself on modest hardware.
The case against it
- Worse than most models on every condition we hold: 2.1% of words wrong on clean read-aloud recordings against a field middle of 1.5%, 9% on podcasts and video against 8.3%, and 10.8% on meetings against 10.4%.
- English only, and no source we hold measures other languages.
- We list no host for it, so running it yourself is the only route we can point you to.
How good is it?
An open transcription model for turning speech into text, though it struggles more than most with clear read-aloud recordings.
- transcribing clear recordings of people reading aloudClean read speech · 76th of 92
TranscriptionTurning speech into text
3,206×20th of 74
an hour of audio in 1 seconds, 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; the placing beneath each rate is against every model measured on that set.
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.
Each of these is the same transcription job on a different kind of recording, so together they say where it holds up and where it slips — not how closely it follows an instruction.
Every published score for this model7 scoresEvery figure we hold, from 7 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?
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 21.5 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. 22.7 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. 4.7 GB spare means a 10% error in the size would not change the answer.
These cards answer whether Moonshine Streaming Small loads, not how fast it transcribes. Throughput figures for a transcription model come from its text decoder, so treat this as a fit answer rather than a speed one.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first. This is a fit answer: whether it loads, not how fast it transcribes.
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
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-small
- Architecture
- Dense
- Takes in, gives back
- Audio in, text out
- Catalogue slug
- usefulsensors-moonshine-streaming-small