omniASR CTC 7B v2
Meta
Speech to textTranscribes a recording into words
- Type
- Closed
- Input
- None held
- Output
- None held
- Cached
- None held
We don't hold a list price for this model yet · hosted only — we have no record of published weights
Our take
Written Sep 4, 2026omniASR CTC 7B v2 is Meta's proprietary speech-to-text model with 6.5 billion parameters and 1,676 languages claimed. It is extremely fast but sits below the middle of the pack on every English accuracy condition we track.
Skip it if accuracy matters: every measured condition is worse than most of the 74-model field. Consider it only if 1,676-language coverage is essential and no better multilingual alternative exists — but we have no accuracy data for those languages.
The case for it
- Extremely fast transcription: an hour of audio in roughly seven seconds on the benchmark hardware.
- Broad language coverage on paper, with 1,676 languages listed.
The case against it
- Below-median accuracy on every measured condition: read-aloud, podcasts, accented speech and meetings all trail the field middle.
- Overall accuracy near the bottom of the 74-model field.
- Proprietary weights with no open licence, zero current offers and no disclosed commercial access.
How good is it?
A speech-to-text model for turning recordings into written text, though it trails most others on the recordings measured here.
- turning spoken English into written textOpen ASR WER · 69th of 76
- transcribing recordings of meetings in a roomRecorded meetings · 84th of 92
- transcribing speakers with a range of accentsAccented speech · 73rd of 76
- transcribing podcasts and video audioPodcasts and video · 86th of 92
- transcribing clear recordings of people reading aloudClean read speech · 78th of 92
TranscriptionTurning speech into text1 of 5Open ASR WER · 69th of 76
90.9%
Misses roughly one word in 11, averaged over nine English test sets.
519×46th of 74
an hour of audio in 7 seconds, on the board's own hardware. Your machine will differ.
1676
Stated by the leaderboard; we do not hold the list itself.
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 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.
Where to get it
We hold no priced listing for omniASR CTC 7B v2.
There is no copy to download and no host in our price data, so Meta is where to look. We watch OpenRouter, the provider APIs we track and the LiteLLM price set; this version appears in none of them, which is a gap in what we collect rather than a statement about what Meta sells.
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
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- We don't hold a list price for this model yet — the gap is ours, not the lab's.
Licence and identifiers
What the licence allowsWe hold no licence record for this model. Inside are the identifiers you need to pull it — its Hugging Face repo where we have one, our slug and a machine-readable card.
Licence
We hold no licence record for this model, and no record of published weights either — so we can neither summarise its terms nor point you at the weights.
Identifiers
- Takes in, gives back
- Audio in, text out
- Catalogue slug
- facebook-omniasr-ctc-7b-v2