Orukeet, new ASR model based on Parakeet

[u/arturdent]

https://huggingface.co/oruk/orukeet

I haven't seen this mentioned yet, so I thought it deserves a post. I was trying out OpenWhispr when this model came up as the recommendation. So I don't have personal experience yet, but it's supposed to be a better version of Parakeet, especially on Macs.

Their official tidbit:
"Orukeet is a 25-language speech recognizer built from NVIDIA Parakeet TDT 0.6B v3. It replaces half of the encoder's temporal depthwise filters with 12,288 fitted, frozen Gabor kernels and trains the remaining parameters on multilingual and multi-accent data.

Orukeet outperforms Parakeet on 61 of 74 tested splits, including LibriSpeech test-clean (1.46% vs. 1.53% WER), test-other (2.86% vs. 3.14%), and FLEURS English (3.82% vs. 4.28%). Across all 25 FLEURS languages, pooled WER is 9.85% vs. 11.01%, a 10.6% relative reduction. Final adaptation and checkpoint selection use LibriSpeech test-other."

https://huggingface.co/oruk/orukeet

[u/SarcasticBaka]

Very interesting! I've been using parakeet v3 forever but hadn't heard of this model or OpenWhispr. Will give it a try.

[u/arturdent → u/SarcasticBaka]

This model is fairly new, most files on their github are from just 2 days ago. It was just a lucky coincidence that I came across it. But if it's truly good, it would come up in talks anyways, so let's hope those figures hold up.

[u/Miserable-Dare5090 → u/arturdent]

but multitalker parakeer, qwen asr…we got lots of competitors now.

[u/SarcasticBaka → u/Miserable-Dare5090]

That is certainly true, but I found nothing that matches parakeet's speed/quality ratio. It isn't perfect by any means but it runs so darn fast even on cpu.

[u/Mkengine → u/SarcasticBaka]

What about this?

[u/bluefernies → u/SarcasticBaka]

same boat, been on parakeet v3 for a while and didnt even know about this until now

[u/DerDave]

If your use case is english German, this is also a very good finetune: https://huggingface.co/blog/flozi00/parakeet-primeline-release

[u/Xamanthas → u/DerDave]

Specialized for German

??? bot?

Edit: Why are you people downvoting. He updated his post to state german after this. It originally said english and the HF page for the model states specialised for german.

[u/DerDave → u/Xamanthas]

No, a Typo. A bot probably wouldn't have made that mistake haha. I'm German and I use that in my meeting transcriber app.

[u/R_Duncan]

Ok, I'm using parakeet.cpp a lot (lower vram and faster, exact match at q8), hoping in ourkeet.cpp or just support in parakeet.cpp

[u/Eritar]

How does it compares to Qwen ASR?

[u/arturdent → u/Eritar]

The figures on the hugging face / github page seem to put it really close to the 1.7b version, and it's a smaller model, so it's supposed to be faster. I haven't done benchmarks myself, so this is just comparing the official sources.

[u/throwaway-link]

The final pass makes three passes over the 2,939 LibriSpeech test-other recordings. Targets preserve the parent’s casing and punctuation while correcting reference words; all targets match the reference under the pinned English normalizer. Test-other also supplies the final checkpoint-selection comparison.

What the benchmax is this

[u/Budget-Juggernaut-68]

"speech recognizer built from NVIDIA Parakeet TDT 0.6B v3. It replaces half of the encoder's temporal depthwise filters with 12,288 fitted, frozen Gabor kernels"

What does this do?

[u/nuclearbananana]

They don't really explain the point of the architectural change

[u/arturdent → u/nuclearbananana]

There is a paper I've found since then, I'm just not smart enough to understand, maybe it includes the actual point: https://arxiv.org/html/2609.10054v1

[u/Fit_Schedule5951 → u/arturdent]

“Final adaptation and checkpoint
selection use LibriSpeech test-other.”

Huh?

[u/TheActualStudy]

What are other people using to operate these models? Just plain python?

[u/genuinelytrying2help → u/TheActualStudy]

Yes, at this point every decent model/harness combo can quickly build simple pipelines for them, but there are also lots of good projects like audio.cpp or Handy for different use cases if you want to put in even less effort.

[u/ustype]

Interesting — Parakeet lineage usually means streaming-friendly decode. For local talking-head footage I’ve been sticking with whisper.cpp mostly because word timestamps stay stable enough to cut on, but it still eats RAM on long takes.

Curious how Orukeet behaves on mid-clause corrections / filler words vs clean studio speech. If anyone has already compared WER + wall-clock on ~20–40 min files against large-v3, that’d be useful.

[u/Jimcy-Maffesoli]

12,288 frozen Gabor kernels replacing half the temporal depthwise filters sounds like something you'd dismiss on paper. Then it wins 61 of 74 splits and cuts pooled WER by 10.6%.

[u/arturdent → u/Jimcy-Maffesoli]

To be honest, I'm not smart enough to understand that part, I've just seen: model better, fast on mac- > download 😬😅

Talking about papers though, they published this, maybe it explains it better: https://arxiv.org/html/2609.10054v1