Zipformer Transducer XL 290M
Sounds Good AI · released May 12, 2026 · soundsgoodai/Zipformer-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 3, 2026Zipformer Transducer XL is a tiny downloadable speech-to-text model from Sounds Good AI that turns English audio into written words. It is extremely fast on benchmark hardware and accurate on clean recordings, but its error rate rises sharply on meetings, podcasts and accented speech.
Pick this for offline batch transcription of clean English audio where speed matters, or for research and non-commercial experimentation with genuinely open weights. Use it on edge devices given its small parameter count. Skip it if you need commercial use, handle challenging audio like meetings or podcasts, or require any language other than English.
The case for it
- Extremely fast: benchmark hardware processes an hour of audio in about 25 seconds.
- Strong on clean, structured English audio, with low error rates on read speech and financial calls.
- Genuinely open weights for research under a Creative Commons licence.
The case against it
- Accuracy collapses on challenging audio: recorded meetings are more than seven times worse than clean read speech.
- Struggles with podcasts, video and accented speech, at roughly five to five-and-a-half times the clean-speech error rate.
- No commercial use permitted and no tracked hosting offers.
How good is it?
TranscriptionTurning speech into text2.5 of 5Open ASR WER · 45th of 74
93.8%
Misses roughly one word in 16, averaged over nine English test sets.
141×54th of 62
an hour of audio in 25 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-transducer-XL-290M
- Modality record
- audio->text
- Catalogue slug
- soundsgoodai-zipformer-transducer-xl-290m