Owsm CTC v3.2 ft 1B
ESPnet · released Sep 24, 2024 · espnet/owsm_ctc_v3.2_ft_1B
Speech to textTranscribes a recording into words
- Type
- Open weightsCreative Commons Attribution 4.0
- Size
- 1B
Context measured in tokens
Our take
Written Aug 2, 2026Owsm CTC v3.2 ft 1B is a one-billion-parameter speech-to-text model from ESPnet that turns audio into written text. It is extremely fast and permissively licensed, though its accuracy drops sharply on difficult audio such as meetings and accented speech.
Pick this for batch transcription of clean, prepared audio where speed matters most — it processes an hour of audio in about five seconds on benchmark hardware. Use it for projects that need attribution-only licensing, or for financial call transcription where it achieves low error rates. Skip it if you need hosted inference, are transcribing meetings or accented speech, or require verified non-English language support.
The case for it
- Extremely fast: 692 times real time, processing an hour of audio in roughly five seconds on benchmark hardware.
- Strong on clean, structured audio: about one word in forty-five wrong on read-aloud speech, and one in forty on financial calls.
- Permissive Creative Commons Attribution 4.0 licence allows commercial use with attribution.
The case against it
- Accuracy collapses in conversational and accented conditions: meetings are more than six times worse than read-aloud, and accented speech nearly three times worse than European-accented speech.
- No hosted inference options available; you must self-host.
- Unverified language coverage: all accuracy figures are English only, with no language list held.
How good is it?
TranscriptionTurning speech into text1.5 of 5Open ASR WER · 63rd of 74
92.8%
Misses roughly one word in 14, averaged over nine English test sets.
692×34th of 62
an hour of audio in 5 seconds, on the board's own hardware. Your machine will differ.
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. 20.9 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. 22.1 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. 4.1 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
- espnet/owsm_ctc_v3.2_ft_1B
- Modality record
- audio->text
- Catalogue slug
- espnet-owsm-ctc-v3-2-ft-1b