Zipformer cr CTC Transducer XL 290M
Sounds Good AI · released Jul 9, 2026 · soundsgoodai/Zipformer-cr-ctc-transducer-XL-290M
Speech to textTranscribes a recording into words
- Type
- Open weightsCreative Commons Attribution-NonCommercial 4.0
- Languages
- 1
- Size
- 0.3B
Context measured in tokens
Our take
Written Aug 2, 2026Zipformer cr CTC Transducer XL is a tiny downloadable speech-to-text model that turns English audio into written words at roughly 160 times real time. It is nearly flawless on clean read-aloud recordings but its accuracy collapses in meetings, podcasts and accented speech.
Pick this for batch transcription of clean, read-aloud English audio where speed matters — it processes an hour of audio in about twenty-three seconds on benchmark hardware. Use it for local deployment on very constrained devices, or non-commercial projects where hardware cost must be negligible. Skip it if you need commercial licensing, any language other than English, or reliable accuracy on unscripted or accented speech.
The case for it
- Extremely fast: roughly 160 times real time on benchmark hardware.
- Nearly flawless on clean read-aloud audio, with about one word in seventy-six wrong.
- Tiny enough to run almost anywhere at 0.29B parameters, with no token-billing constraints.
- Permissive for non-commercial use under a Creative Commons licence.
The case against it
- Accuracy degrades sharply in natural settings: more than seven times the error rate in recorded meetings compared with clean speech, and nearly six times on podcasts and video.
- No commercial use permitted, and no hosted options available in our catalogue.
- English only; no other language is supported.
How good is it?
TranscriptionTurning speech into text4 of 5Open ASR WER · 23rd of 74
94.8%
Misses roughly one word in 19, averaged over nine English test sets.
160×51st of 62
an hour of audio in 23 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. 21.4 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. 22.6 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. 4.6 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 7h 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-NonCommercial 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-NonCommercial 4.0
Weights are downloadable but commercial use is prohibited. Research and personal use only.
Identifiers
- Hugging Face
- soundsgoodai/Zipformer-cr-ctc-transducer-XL-290M
- Modality record
- audio->text
- Catalogue slug
- soundsgoodai-zipformer-cr-ctc-transducer-xl-290m