Orukeet technical report

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

This short report describes Orukeet r3, the selected native-format checkpoint with SHA-256 031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56. Its model identity pins the exact public NeMo artifact and freeze audit.

The report presents the fitted-filter construction, a compact adaptation recipe, complete paired LibriSpeech/FLEURS results, multilingual pooled WER, and the existing accent/domain sample re-evaluated with this checkpoint. Every score has the stock Parakeet comparison. The two figures show the unchanged fitted kernels and their allocation across encoder layers.

.venv/bin/python scripts/build_neurips_report.py
pdftoppm -png -r 150 output/pdf/orukeet-technical-report.pdf .tmp/orukeet-r3-page

The builder verifies checkpoint identity, exact frozen kernels, all per-record counts, pooled denominators and independent scoring audits. It generates the numeric TeX includes, compiles with the official NeurIPS 2026 preprint style, and verifies every rendered benchmark row. Pass --tex-bin /path/to/texlive/bin to use pdfLaTeX and BibTeX. The release PDF is built with TeX Live 2025, uses embedded outline fonts, and records its title and all nine authors in PDF metadata. The build receipt records input hashes, page count and visual review.

The manuscript and numerical evaluation records accompany Orukeet v0.1.0. The NeMo, Q8 and F16 release files all derive from this r3 checkpoint; release/model-stages.json and the artifact catalog record their exact identities.

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}
}

Download BibTeX · Citation metadata