Canary 180m Flash
NVIDIA · released Mar 11, 2025 · 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 Sep 4, 2026Canary 180m Flash is NVIDIA's tiny downloadable speech-to-text model built for raw speed on edge hardware. It transcribes an hour of audio in about a second and runs under a permissive Creative Commons licence, though accuracy sits below the field median on every measured condition.
Pick this when speed is paramount or memory is tight. Use it for English, German, Spanish or French pipelines that do not need top accuracy, or when you need a licence permitting commercial reuse. Skip it if you are transcribing meetings or accented speech, where it misses more words than most alternatives, or if you need measured quality beyond the four supported languages.
The case for it
- Extreme speed: 2,484 times real time, or roughly an hour of audio in one second on benchmark hardware.
- Creative Commons Attribution 4.0 licence allows commercial use and redistribution with attribution.
- Compact at 0.2B parameters, fitting tight memory constraints for edge deployment.
- Covers four major languages: English, German, Spanish and French.
The case against it
- Below-median accuracy on every measured condition, from clean read-aloud to meetings and accented speech.
- Meeting and accented-speech audio are particular weak spots, with error rates worse than most of the 74 models measured.
- Every accuracy figure we hold is English-only; nothing measures the other three supported languages.
How good is it?
TranscriptionTurning speech into text3 of 5Open ASR WER · 48th of 76
94.5%
Misses roughly one word in 18, averaged over nine English test sets.
2,489×24th of 74
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; the placing beneath each rate is against every model measured on that set.
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.
Each of these is the same transcription job on a different kind of recording, so together they say where it holds up and where it slips — not how closely it follows an instruction.
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?
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.
These cards answer whether Canary 180m Flash loads, not how fast it transcribes. Throughput figures for a transcription model come from its text decoder, so treat this as a fit answer rather than a speed one.
Memory use by level
Against a 24 GB card.
What is quantisation? →This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first. This is a fit answer: whether it loads, not how fast it transcribes.
Models people weigh against Canary 180m Flash
When we formed this view
Recent changes
What moved
first indexed by our pipelineEach date is the day we first saw the change, or the day the maker announced it.
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
- Takes in, gives back
- Audio in, text out
- Catalogue slug
- nvidia-canary-180m-flash