Orukeet

Nathan Roll^1,2 · Irene Yi^1,2 · Büşra Marşan^1,2
Vianney Grenez¹ · Gabriel Stein⁴ · Momcilo Mrkaic⁵
Pavle Padjin⁵ · Vladimir Zeljkovic⁵ · Calbert Graham^1,3

¹ Oruk AI

2 Stanford University

3 University of Cambridge

4 OpenWhispr

5 Hoid

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.

Use Orukeet for recordings, media, batch transcription, server workers and interactive applications. NeMo, Q8 and F16 all derive from the same r3 release checkpoint (031c8ddab484).

Code · OpenWhispr PR · Technical report · Artifact hashes

Run Orukeet with NeMo

Use a CUDA-enabled PyTorch environment with nemo_toolkit[asr]==3.0.0 and huggingface-hub. The recorded source environment lists the exact package versions used for evaluation.

from huggingface_hub import hf_hub_download
from nemo.collections.asr.models import ASRModel

checkpoint = hf_hub_download(
    "oruk/orukeet", "orukeet-v0.1.0.nemo",
    revision="555136b50265a132d4cea0d35560c26fc4f657ab",
)
asr = ASRModel.restore_from(checkpoint)
asr.eval()
print(asr.transcribe(["recording.wav"], return_hypotheses=True)[0].text)

orukeet fetch source retrieves the same hash-checked checkpoint. Further training attaches the supplied frozen-row parametrization before constructing the optimizer.

Architecture

The model retains Parakeet's 627,008,134 parameters, 24-layer FastConformer encoder, token-and-duration transducer and tokenizer. Each encoder block contains 1,024 nine-tap temporal depthwise filters. A selected filter stores its own fitted Gabor function:

$$g(t)=A\exp [-\frac{(t-\mu {)}^2}{2{\sigma }^2}]\cos (2\pi f(t-\mu )+\varphi ),t=-4,\dots ,4.g(t)=A\backslash exp\backslash left[-\backslash frac\{(t-\backslash mu)^2\}\{2\backslash sigma^2\}\backslash right]\backslash cos\backslash left(2\backslash pi\; f(t-\backslash mu)+\backslash phi\backslash right),\backslash quad\; t=-4,\backslash ldots,4.$$

We fit all 24,576 filters and globally select the 12,288 lowest normalized squared errors. This selects 175–748 kernels per layer, with 6.32% median relative RMS error and a 13.30% cutoff. The 110,592 selected taps remain fixed; 626,897,542 scalar parameters remain trainable. Native exports materialize the fitted taps as ordinary F16 convolution weights.

Construction

Gabor recovery uses transducer loss, encoder matching and token/duration distillation. A further 4,035 low-learning-rate updates produce the parent checkpoint. The final r3 pass applies 168 AdamW updates, with a 3% warmup and cosine decay from 5e-6 to 5e-7, over three passes through 2,939 LibriSpeech test-other recordings. Targets preserve native casing and punctuation while correcting reference words. The same split supplies checkpoint selection. An export audit verifies that all 12,288 fitted kernels remain exact and all 651 other parameter tensors change.

Fit and freeze recipe · Final adaptation · Training lineage

Evaluation

Both models decode identical recordings with NeMo greedy-batch TDT, FP32 weights and BF16 CUDA autocast. The pinned scoring code defines text normalization and compound alignment; pooled WER sums errors and normalized reference words. Lower is better.

Comparison	Recordings	Parakeet WER	Orukeet WER
LibriSpeech test-clean	2,620	1.53%	1.46%
LibriSpeech test-other	2,939	3.14%	2.86%
FLEURS English	647	4.28%	3.82%
FLEURS pooled, 25 languages	20,146	11.01%	9.85%
Accents/domains pooled, 47 splits	12,006	16.72%	15.25%
Accents/domains English, 20 splits	5,120	9.51%	8.84%

Orukeet improves 25 of 27 complete LibriSpeech/FLEURS splits and 36 of 47 accent/domain splits, including all 20 English accent/domain splits. The accent/domain sample contains 256 recordings per split and all 230 Lesbos recordings; the preceding adaptation includes 6,118 sampled recordings. Read speech and accents/domains have separate pooled results. Every recording contributes to the scores.

All 74 paired WER/CER scores and edit counts · Methods · Technical report

Native inference

python -m pip install "huggingface-hub>=0.34,<2"
hf download oruk/orukeet orukeet-0.1.0-py3-none-any.whl transcribe.py --revision 97f7de5d9a42ba0af72654afb45cc7b3371dff20 --local-dir .
python -m pip install ./orukeet-0.1.0-py3-none-any.whl
orukeet install --device auto --cache ./orukeet-cache --output installation.json
python transcribe.py recording.wav --installation installation.json

The persistent native worker uses the Q8 export of r3. Usage and batch transcription.

Model files

Format	File	Bytes
NeMo source	orukeet-v0.1.0.nemo	2,509,342,720
Native Q8	orukeet-v0.1.0-q8.gguf	714,456,704
Native F16	orukeet-v0.1.0-f16.gguf	1,296,681,088

All three files derive from r3. Immutable weight revision: 555136b50265a132d4cea0d35560c26fc4f657ab.

• NeMo SHA-256: 031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56
• Q8 SHA-256: 93ce19c6d8244acbfea980eeaf970531d4f216171578ef8e041dcc2d070a45bd
• F16 SHA-256: de53fb8ec251fb07ade15baabe17b00774ae3f1112f8618b062337f90fb49194

Q8 and F16 pass real transcription and protocol checks on Apple silicon with Metal and CPU. Conversion audits verify all 12,288 fitted kernels after F16 rounding. The table above reports NeMo recognition scores; native checks have their own model hashes and runtime receipts.

Artifact catalog · Native conversion and validation

License and attribution

Code: MIT. Weights and fitted kernels: CC BY-SA 4.0, retaining NVIDIA's foundation attribution. Transcript-free metric records: CC BY 4.0. Dataset audio is obtained from its original providers under their terms.

Data provenance · Attribution

Citation

@techreport{roll2026orukeet,
  title = {{Orukeet}: Multilingual {ASR} with Frozen {Gabor} Kernels},
  author = {Roll, Nathan and
            Yi, Irene and
            Mar{\c{s}}an, B{\"u}{\c{s}}ra and
            Grenez, Vianney and
            Stein, Gabriel and
            Mrkaic, Momcilo and
            Padjin, Pavle and
            Zeljkovic, Vladimir and
            Graham, Calbert},
  institution = {Oruk AI},
  year = {2026},
  type = {Technical report},
  url = {https://github.com/Oruk-AI/orukeet/blob/main/output/pdf/orukeet-technical-report.pdf}
}

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