Nyra Health CrisperWhisper
Nyra Health · released Aug 29, 2024 · nyrahealth/CrisperWhisper
Speech to textTranscribes a recording into words
- Type
- Open weightsCreative Commons Attribution-NonCommercial 4.0
- Languages
- 2
- Size
- 1.6B
Context measured in tokens
Our take
Written Aug 3, 2026Nyra Health CrisperWhisper is a tiny downloadable speech-to-text model that turns audio into written words. It is extremely fast and accurate on clean recordings, but its accuracy drops sharply on challenging audio and its licence restricts it to non-commercial use.
Use this for offline research or academic work where the non-commercial licence works, or for batch transcription of clean audio such as financial calls. It fits speed-critical pipelines needing an hour of audio in about two minutes. Skip it if you need commercial deployment, hosted access, or reliable accuracy on podcasts, meetings or heavily accented speech.
The case for it
- Extremely fast: processes an hour of audio in roughly two minutes on benchmark hardware.
- Strong on clean, structured audio: about one word in fifty wrong on financial calls and read-aloud speech.
- European-accented speech handled relatively well, with a word error rate roughly one-third of its rate on general accented speech.
The case against it
- Accuracy collapses on challenging real-world audio: more than six times worse on accented speech than on its best condition.
- Non-commercial licence blocks most product and commercial use.
- No hosted access available; not offered through any provider in our data.
How good is it?
TranscriptionTurning speech into text3 of 5Open ASR WER · 37th of 74
94.2%
Misses roughly one word in 17, averaged over nine English test sets.
33.3×62nd of 62
an hour of audio in 2 minutes, on the board's own hardware. Your machine will differ.
2
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.
Which languages ↓ ↑
German · English
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. 20.5 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. 21.7 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. 3.7 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-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
- nyrahealth/CrisperWhisper
- Architecture
- Dense
- Modality record
- audio->text
- Catalogue slug
- nyrahealth-crisperwhisper