STT 2.6b en
Kyutai · released Jun 6, 2025 · kyutai/stt-2.6b-en
Speech to textTranscribes a recording into words
- Type
- Open weightsCreative Commons Attribution 4.0
- Languages
- 1
- Size
- 2.6B
Context measured in tokens
Our take
Written Aug 3, 2026STT 2.6b en is a compact downloadable English speech-to-text model from Kyutai that processes audio extremely fast on benchmark hardware. Its accuracy is excellent for clean read-aloud recordings but drops sharply on accented speech or meetings, and it must be self-hosted as no providers currently offer it.
Choose this for local transcription of clean, single-speaker English audio where speed matters — it processes an hour of audio in under half a minute on benchmark hardware. Use it for research or integration projects that need open weights under a permissive attribution licence. Skip it if you need hosted inference, work with accented speakers or meeting recordings, or require language coverage beyond English.
The case for it
- Processes audio at 133 times real time on benchmark hardware — one hour of audio in 27 seconds.
- Strong accuracy on clean read-aloud speech, with about one word in seventy wrong.
- Creative Commons Attribution 4.0 licence allows commercial use with attribution.
The case against it
- Accuracy degrades sharply on challenging audio: more than seven times worse on accented speech and recorded meetings.
- No hosted inference options available; users must self-host.
- English only, with no verified data on performance in other languages.
How good is it?
TranscriptionTurning speech into text3 of 5Open ASR WER · 36th of 74
94.3%
Misses roughly one word in 17, averaged over nine English test sets.
133×56th of 62
an hour of audio in 27 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, 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. 19.9 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 27.9 GB spare means a 10% error in the size would not change the answer.
Apple M2 (8-core GPU, 8GB unified) · 8 GB
Room to spare. 3.1 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 allowsCreative Commons Attribution 4.0, 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
Creative Commons Attribution 4.0
Permissive content license: any use with attribution. Common for datasets and some model weights.
Identifiers
- Hugging Face
- kyutai/stt-2.6b-en
- Architecture
- Dense
- Modality record
- audio->text
- Catalogue slug
- kyutai-stt-2-6b-en