Canary 180m Flash
NVIDIA · nvidia/canary-180m-flash
Speech to textTranscribes a recording into words
- Type
- Open weightsCreative Commons Attribution 4.0
- Languages
- 4
- Size
- 0.2B
Context measured in tokens
Our take
Written Aug 2, 2026Canary 180m Flash is NVIDIA's tiny downloadable speech-to-text model built for raw speed rather than accuracy. It processes an hour of audio in about one and a half seconds on benchmark hardware, but its accuracy drops sharply on anything messier than clean read-aloud speech.
Pick this when throughput is paramount and the audio is clean — it achieves its best accuracy on read-aloud English and fits on almost any hardware. Use it for lightweight local deployment where you need a permissive licence and can handle attribution. Skip it if you are transcribing accented speech, meetings, podcasts or video, or if you need commercial hosting rather than self-deployment.
The case for it
- Processes audio at 2,484 times real time on benchmark hardware — roughly an hour of audio in one and a half seconds.
- Strong on clean read-aloud speech, with a word error rate far below its own overall average.
- Creative Commons Attribution 4.0 licence allows commercial use with attribution.
- At 0.2 billion parameters, it fits comfortably on edge devices and consumer hardware.
The case against it
- Accuracy collapses on challenging real-world audio: error rates on accented speech and recorded meetings are more than eight times worse than on clean read-aloud, and podcasts or video fare nearly as poorly.
- No commercial hosting options available in our data; you must self-host.
- Overall accuracy is modest for the speed trade-off, with roughly one word in sixteen wrong averaged across conditions.
How good is it?
TranscriptionTurning speech into text2.5 of 5Open ASR WER · 48th of 74
93.7%
Misses roughly one word in 16, averaged over nine English test sets.
2,484×17th of 62
an hour of audio in 1 seconds, on the board's own hardware. Your machine will differ.
4
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 ↓ ↑
English · German · Spanish · French
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. 21.5 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. 22.7 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. 4.7 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 →
Models people weigh against Canary 180m Flash
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
- nvidia/canary-180m-flash
- Modality record
- audio->text
- Catalogue slug
- nvidia-canary-180m-flash