Duplicate from oruk/orukeet

Co-authored-by: Nathan Roll <NathanRoll@users.noreply.huggingface.co>

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+}

CITATION.bib

@@ -0,0 +1,16 @@
+@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}
+}

CITATION.cff

@@ -0,0 +1,45 @@
+cff-version: 1.2.0
+message: If you use Orukeet, cite the technical report below and retain the NVIDIA Parakeet attribution.
+title: 'Orukeet: Multilingual ASR with Frozen Gabor Kernels'
+type: software
+authors: &id001
+- given-names: Nathan
+  family-names: Roll
+  affiliation: Oruk AI; Stanford University
+- given-names: Irene
+  family-names: Yi
+  affiliation: Oruk AI; Stanford University
+- given-names: Büşra
+  family-names: Marşan
+  affiliation: Oruk AI; Stanford University
+- given-names: Vianney
+  family-names: Grenez
+  affiliation: Oruk AI
+- given-names: Gabriel
+  family-names: Stein
+  affiliation: OpenWhispr
+- given-names: Momcilo
+  family-names: Mrkaic
+  affiliation: Hoid
+- given-names: Pavle
+  family-names: Padjin
+  affiliation: Hoid
+- given-names: Vladimir
+  family-names: Zeljkovic
+  affiliation: Hoid
+- given-names: Calbert
+  family-names: Graham
+  affiliation: Oruk AI; University of Cambridge
+version: 0.1.0
+repository-code: https://github.com/Oruk-AI/orukeet
+license: MIT
+abstract: 'A Parakeet-derived multilingual recognizer with 12,288 fitted, frozen temporal Gabor kernels.
+  Code: MIT. Weights: CC BY-SA 4.0.'
+preferred-citation:
+  type: report
+  title: 'Orukeet: Multilingual ASR with Frozen Gabor Kernels'
+  authors: *id001
+  institution:
+    name: Oruk AI
+  year: 2026
+  url: https://github.com/Oruk-AI/orukeet/blob/main/output/pdf/orukeet-technical-report.pdf

LICENSE

@@ -0,0 +1,22 @@
+MIT License
+
+Copyright (c) 2025 Knuckles92
+Copyright (c) 2026 Oruk AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSE-WEIGHTS

@@ -0,0 +1,427 @@
+Attribution-ShareAlike 4.0 International
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NOTICE.md

@@ -0,0 +1,70 @@
+# Attribution and license scope
+
+Orukeet is an adaptation of **NVIDIA Parakeet TDT 0.6B v3**. NVIDIA retains
+copyright in its model and upstream work. The base weights are distributed
+under [CC BY 4.0](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3).
+Oruk AI's changes comprise multilingual/accent continuation training, parameter
+blending, fitted and frozen Gabor-kernel replacement and recovery, native and ONNX
+export, application integration and evaluation. [Model stages](https://github.com/Oruk-AI/orukeet/blob/main/release/model-stages.json)
+identifies r3 as the source of every current Orukeet download and records its ancestry.
+
+The r3 NeMo, ONNX INT8, Q8 GGUF and F16 GGUF weights, and their fitted Gabor kernels,
+are designated **CC BY-SA 4.0**.
+The complete license is in [LICENSE-WEIGHTS](LICENSE-WEIGHTS). This permits
+commercial use and modification, with attribution and applicable ShareAlike
+requirements. Earlier checkpoints retain their source notices and are not
+silently relicensed by this file. Orukeet v0.1.0 distributes the final r3 checkpoint in all four formats.
+
+Python and integration code in this repository is MIT unless a file specifies
+otherwise. The native bindings, audio windowing and worker transport derive
+from OpenWhisper by Knuckles92 and its Oruk AI integration; their MIT notice is
+retained in [LICENSE](LICENSE). The downloaded NVIDIA NeMo-Speech.cpp SDK and
+its bundled dependencies retain their own notices, including Apache-2.0 and
+MIT components. Keep the SDK's license files when redistributing it. The
+[pinned source](https://github.com/NVIDIA/NeMo-Speech.cpp/tree/4f9676226f667d14608487df744f375db87127f8)
+is the authority for those terms.
+
+The ONNX exporter follows sherpa-onnx's Parakeet TDT v3 conversion script.
+Its upstream reference and Apache-2.0 license are retained in
+[export/onnx](https://github.com/Oruk-AI/orukeet/blob/main/export/onnx/README.md). The ONNX archive includes the weight
+license and source attribution. The optimized ONNX encoder uses exactly equivalent FP32 arithmetic for 24 quantized depthwise convolutions, with existing runtime operators and unchanged quantized values. Original export and separate optimization receipts are retained in the GitHub repository under `evidence/onnx-r3-20260910/` and `evidence/speed20260910/`.
+
+Training data credits:
+
+- Mozilla/Common Voice contributors: Common Voice 22, CC0.
+- Google and the FLEURS authors: FLEURS, CC BY 4.0.
+- Google and the OpenSLR 83 authors, with the `ylacombe/english_dialects`
+  restructuring: English dialect speech, CC BY-SA 4.0.
+- Beijing Kingline Data Technology and the SpeechOcean762 authors:
+  SpeechOcean762, CC BY 4.0.
+- SpeechColab and the GigaSpeechBench authors: 17 English accent/domain splits
+  in the final continuation. Their paper identifies Creative Commons source audio;
+  the source record below preserves the available license details.
+- DISCO at ETH Zurich and the EuroSpeech contributors: Bulgarian, Greek and
+  Italian parliamentary speech, with the providers' country-specific terms.
+- SberDevices and the Golos authors: Golos Crowd, under the
+  [Public license with attribution and conditions reserved](https://github.com/sberdevices/golos/blob/master/license/en_us.pdf).
+- Nordisk Språkteknologi, the National Library of Norway and the Alexandra
+  Institute: NST Swedish and Danish, distributed under CC0.
+- ILSP/Athena Research Center and the Lesbian Speech Corpus contributors:
+  dialect speech from Lesbos. The source card does not specify a reuse license.
+
+Data were filtered, split, normalized and sampled as described in
+[data and licenses](https://github.com/Oruk-AI/orukeet/blob/main/docs/data-and-licenses.md). Corpus copyrights and source
+terms remain with their owners. Evaluation-only data have separate terms.
+No endorsement by NVIDIA, Mozilla, Google or the other source projects is implied.
+
+The generated statistical records in `evidence/metric-evidence.tar.gz`, the
+Gabor recovery count bundles under `training/gabor_half/results/`, and the
+FT-4035 benchmark counts under `evidence/regression-ft-20260907/` are
+designated CC BY 4.0, attributed to Oruk AI. This designation covers the
+prepared metric records; it does not relicense the original corpus recordings
+or transcripts, which are not included in that archive.
+
+The r3 numeric benchmark records in `evidence/standard-asr-r3-20260908/` and
+`evidence/domains-r3-20260908/` are released under CC BY 4.0. They contain
+error counts and recording identifiers; dataset audio remains with its providers.
+
+The paired ONNX application benchmark counts in `evidence/onnx-r3-20260910/`
+are also released under CC BY 4.0. These records contain numeric errors,
+timings and recording identifiers, without corpus audio or transcripts.

README.md

@@ -0,0 +1,195 @@
+---
+language: [bg, hr, cs, da, nl, en, et, fi, fr, de, el, hu, it, lv, lt, mt, pl, pt, ro, ru, sk, sl, es, sv, uk]
+license: cc-by-sa-4.0
+base_model: nvidia/parakeet-tdt-0.6b-v3
+base_model_relation: finetune
+pipeline_tag: automatic-speech-recognition
+library_name: nemo
+transcribe_cpp:
+  streaming: false
+  translate: false
+  lang_detect: true
+  timestamps: token
+tags: [parakeet, tdt, onnx, sherpa-onnx, gguf, multilingual, speech-recognition, gabor, fastconformer]
+---
+
+<!-- orukeet-brand:start -->
+<p><a href="https://oruk.ai"><img src="affiliations/oruk.png" alt="oruk" width="184"></a></p>
+<!-- orukeet-brand:end -->
+
+# Orukeet
+
+<!-- orukeet-team:start -->
+<p>
+Nathan Roll<sup>1,2</sup> · Irene Yi<sup>1,2</sup> · Büşra Marşan<sup>1,2</sup><br>
+Vianney Grenez<sup>1</sup> · Gabriel Stein<sup>4</sup> · Momcilo Mrkaic<sup>5</sup><br>
+Pavle Padjin<sup>5</sup> · Vladimir Zeljkovic<sup>5</sup> · Calbert Graham<sup>1,3</sup>
+</p>
+
+<p><strong><sup>1</sup> Oruk AI</strong></p>
+<table>
+<tr>
+<td align="center" valign="middle"><img src="affiliations/stanford.png" alt="Stanford University" width="144"><br><sup>2</sup> Stanford University</td>
+<td align="center" valign="middle"><img src="affiliations/cambridge.png" alt="University of Cambridge" width="144"><br><sup>3</sup> University of Cambridge</td>
+<td align="center" valign="middle"><img src="affiliations/openwhispr.png" alt="OpenWhispr" width="40"><br><sup>4</sup> OpenWhispr</td>
+<td align="center" valign="middle"><img src="affiliations/hoid.png" alt="Hoid" width="76"><br><sup>5</sup> Hoid</td>
+</tr>
+</table>
+<!-- orukeet-team:end -->
+
+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, ONNX INT8, native Q8 and native F16 all derive from the same **r3 release checkpoint** (`031c8ddab484`).
+
+[Code](https://github.com/Oruk-AI/orukeet) · [OpenWhispr PR](https://github.com/OpenWhispr/openwhispr/pull/2085) · [Technical report](orukeet-technical-report.pdf) · [Artifact hashes](ARTIFACTS.json)
+
+## Run Orukeet with NeMo
+
+Use a CUDA-enabled PyTorch environment with `nemo_toolkit[asr]==3.0.0` and `huggingface-hub`. The [recorded source environment](https://github.com/Oruk-AI/orukeet/blob/main/evidence/standard-asr-20260908/runtime.json) lists the exact package versions used for evaluation.
+
+```python
+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:
+
+$$g(t)=A\exp\left[-\frac{(t-\mu)^2}{2\sigma^2}\right]\cos\left(2\pi f(t-\mu)+\phi\right),\quad t=-4,\ldots,4.$$
+
+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.
+
+![Four exact kernel fits](kernel-fits.png)
+
+## 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](https://github.com/Oruk-AI/orukeet/blob/main/training/gabor_half/README.md) · [Final adaptation](https://github.com/Oruk-AI/orukeet/blob/main/training/librispeech_ft/README.md) · [Training lineage](https://github.com/Oruk-AI/orukeet/blob/main/training/README.md)
+
+## 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](docs/current-checkpoint-benchmarks.md) · [Methods](docs/technical-report.md) · [Technical report](orukeet-technical-report.pdf)
+
+## sherpa-onnx inference
+
+The [ONNX INT8 archive](https://huggingface.co/oruk/orukeet/resolve/55a984d46f68323301837194ce647c702f55facc/onnx/sherpa-onnx-orukeet-v0.1.0-int8.tar.bz2) uses the standard Parakeet TDT v3 layout: `encoder.int8.onnx`, `decoder.int8.onnx`, `joiner.int8.onnx` and `tokens.txt`. It also includes the BPE vocabulary, weight license and attribution. Gabor filters are ordinary convolution weights; the model uses sherpa-onnx's existing offline transducer loader.
+
+The optimized encoder evaluates 24 quantized depthwise convolutions with exactly equivalent FP32 arithmetic using operators already in ONNX Runtime. All 640 application-check transcripts match the previous export. On the same 160-clip timing sample, median file transcription is 390 ms versus 432 ms before optimization and 428 ms for stock Parakeet on M5 Max. [Execution details and receipts](https://github.com/Oruk-AI/orukeet/blob/main/evidence/speed20260910/README.md).
+
+[OpenWhispr 1.10.0](https://github.com/OpenWhispr/openwhispr/releases/tag/v1.10.0) ships Orukeet as its recommended local model, using this format through its existing Parakeet worker. Choose **Local → Oruk → Orukeet**, then **Download**. Recognition runs locally after installation.
+
+[Follow the file-upload walkthrough](https://oruk.ai/guides/orukeet-local-transcription#openwhispr) for the exact settings and a public sample with its observed transcript. Audio Upload needs its own model selection even when Orukeet is active for dictation.
+
+Archive SHA-256: `f9191f30178cc9122ce2f023bf9fefafc822028307b0efa4caff645ba3fe8d0a`.
+
+[Export and loader instructions](https://github.com/Oruk-AI/orukeet/blob/main/export/onnx/README.md) · [Conversion evidence](https://github.com/Oruk-AI/orukeet/tree/main/evidence/onnx-r3-20260910) · [OpenWhispr checks and paired scores](https://github.com/Oruk-AI/orukeet/blob/main/integrations/openwhispr/APP_BENCHMARKS.md)
+
+## Native inference
+
+Use Python 3.12+ in an activated virtual environment. The native package is
+v0.1.1; the r3 weight filenames retain their original v0.1.0 names.
+
+```sh
+python -m pip install --upgrade \
+  https://github.com/Oruk-AI/orukeet/releases/download/v0.1.1/orukeet-0.1.1-py3-none-any.whl
+orukeet install --device auto --cache ./orukeet-cache --output installation.json
+```
+
+```python
+import json
+from pathlib import Path
+from orukeet import Orukeet
+
+config = json.loads(Path("installation.json").read_text(encoding="utf-8-sig"))
+with Orukeet(config["model"], config["runtime"], device=config["device"]) as asr:
+    print(asr.transcribe("recording.wav")["text"])
+```
+
+The installer verifies the Q8 weights and native runtime. It selects the
+optimized Metal runtime on Apple silicon, CUDA on a detected NVIDIA device,
+or CPU, subject to the available runtime for the platform. Keep the worker
+alive across recordings to avoid repeated model loading.
+
+[Run the complete local tutorial](https://oruk.ai/guides/orukeet-local-transcription)
+for a supplied audio file, a reusable runner, actual output and verification
+hashes. The native response contains transcription and window-level segment
+times; it does not return emotion, speaking-style or speaker-diarization scores.
+
+[Watch the 39-second recorded example](https://oruk.ai/guides/orukeet-local-transcription#watch) to hear the input and inspect the native Q8 / Metal output. The walkthrough is edited for readability; it is not a speed or accuracy benchmark.
+
+[Usage and batch transcription](https://github.com/Oruk-AI/orukeet/blob/main/docs/usage.md)
+· [Native runtime and measurements](https://github.com/Oruk-AI/orukeet/blob/main/runtime/README.md)
+
+## transcribe.cpp and Handy-compatible GGUF
+
+[`orukeet-transcribe-cpp-Q8_0.gguf`](orukeet-transcribe-cpp-Q8_0.gguf) is a Q8 export of the same r3 checkpoint for [transcribe.cpp](https://github.com/cjpais/transcribe.cpp). It uses the existing `parakeet` architecture and requires no Gabor-specific runtime. CPU and Apple Metal checks use the exact `transcribe-cpp` 0.2.0 dependency pinned by Handy.
+
+This file has a different tensor layout from the native NeMo-Speech.cpp GGUFs above. Select the export for your runtime. [Conversion, checksums and validation](transcribe-cpp/README.md).
+
+## 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 |
+| transcribe.cpp Q8 | `orukeet-transcribe-cpp-Q8_0.gguf` | 739,508,608 |
+| Native F16 | `orukeet-v0.1.0-f16.gguf` | 1,296,681,088 |
+| ONNX INT8 archive | `onnx/sherpa-onnx-orukeet-v0.1.0-int8.tar.bz2` | 486,807,585 |
+
+All formats derive from **r3**. NeMo and native files are pinned to revision `555136b50265a132d4cea0d35560c26fc4f657ab`; the ONNX archive is pinned to `55a984d46f68323301837194ce647c702f55facc`. The ONNX package occupies 671,619,800 bytes after extraction.
+
+- 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](https://github.com/Oruk-AI/orukeet/blob/main/src/orukeet/artifacts.json) · [Native conversion and validation](https://github.com/Oruk-AI/orukeet/tree/main/evidence/r3-promotion-20260908/)
+
+## 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](https://github.com/Oruk-AI/orukeet/blob/main/docs/data-and-licenses.md) · [Attribution](NOTICE.md)
+
+<!-- orukeet-citation:start -->
+## Citation
+
+```bibtex
+@article{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},
+  journal={arXiv preprint arXiv:2609.10054},
+  year={2026}
+}
+```
+
+[Download BibTeX](CITATION.bib) · [Citation metadata](CITATION.cff)
+<!-- orukeet-citation:end -->

affiliations/SOURCES.md

@@ -0,0 +1,11 @@
+# Orukeet team marks
+
+These web images use the same five logos as the technical report. Oruk AI is the primary brand; Stanford University, University of Cambridge, OpenWhispr and Hoid identify the authors' affiliations.
+
+The [report's source register](https://github.com/Oruk-AI/orukeet/blob/main/report/assets/affiliations/SOURCES.md) records the original organization assets and URLs. The original SVG and PNG files are retained there. Web versions preserve the complete marks and their colors, with proportional resizing and a white background for legibility in light and dark model-card themes.
+
+Oruk uses the September 2026 [primary lockup](https://oruk.ai/branding): the color Signal tile and lowercase black wordmark. Its web image includes at least half a tile of clear space on every side.
+
+The marks remain the property of their respective organizations. Code and model licenses do not grant rights to these marks.
+
+Author order and affiliations match the technical report and `CITATION.cff`. `docs/team.json` records the original and web-image hashes. Regenerate the web assets and bylines with `DYLD_FALLBACK_LIBRARY_PATH=/opt/homebrew/lib python scripts/sync_release_team.py`, or verify the pages with `python scripts/sync_release_team.py --check`.

catalog.json

@@ -0,0 +1,28 @@
+{
+  "status": "released",
+  "repo_id": "oruk/orukeet",
+  "revision": "555136b50265a132d4cea0d35560c26fc4f657ab",
+  "files": {
+    "source": {
+      "path": "orukeet-v0.1.0.nemo",
+      "size": 2509342720,
+      "sha256": "031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56",
+      "source_sha256": "031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56"
+    },
+    "q8": {
+      "path": "orukeet-v0.1.0-q8.gguf",
+      "size": 714456704,
+      "sha256": "93ce19c6d8244acbfea980eeaf970531d4f216171578ef8e041dcc2d070a45bd",
+      "source_sha256": "031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56"
+    },
+    "f16": {
+      "path": "orukeet-v0.1.0-f16.gguf",
+      "size": 1296681088,
+      "sha256": "de53fb8ec251fb07ade15baabe17b00774ae3f1112f8618b062337f90fb49194",
+      "source_sha256": "031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56"
+    }
+  },
+  "model": "Orukeet",
+  "selection": "r3",
+  "version": "0.1.0"
+}

docs/benchmark-scores.md

@@ -0,0 +1,132 @@
+# Historical FT-4035 and R15 benchmark scores
+
+FT-4035 is the parent of the r3 release checkpoint. [Current r3 results](https://github.com/Oruk-AI/orukeet/blob/main/docs/current-checkpoint-benchmarks.md) contain all 74 paired release scores. This page preserves the preceding measurements. Across the 25-language comparison, **Orukeet scores 16.52% pooled WER versus Parakeet’s 17.97%**, an 8.0% relative reduction. English WER is **10.13% for Orukeet versus 10.84% for Parakeet**, a 6.6% relative reduction. It improves WER on 35 of 47 splits, including all 20 English splits. R15-0100 is the preceding checkpoint; both retain the same 12,288 frozen Gabor kernels.
+
+| Matched comparison | Clips | Parakeet WER | R15-0100 WER | FT-4035 WER |
+|:--|--:|--:|--:|--:|
+| All 47 splits · 25 languages | 12,006 | 17.97% | 16.48% | 16.52% |
+| All 20 English splits | 5,120 | 10.84% | 10.82% | 10.13% |
+
+## Evaluation method
+
+Scores use matched NeMo greedy decoding with FP32 weights and BF16 CUDA autocast. English uses standard Whisper text normalization; other languages use the recorded multilingual normalizer. WER and CER are percentages, computed from summed edit counts and reference lengths.
+
+The FT-4035 comparison covers 47 splits and 25 languages: 256 fixed clips per split and all 230 Lesbos clips, totaling 12,006 clips. FT-4035 was fine-tuned on 24 of these splits; 6,118 comparison clips were included in that run. Greek and Italian EuroSpeech use audited human transcript spans. The earlier 327,888-clip comparison measures R15-0100 against Parakeet, using its recorded clip membership and original EuroSpeech references. Full tables retain every split from both comparisons.
+
+## Current matched comparison: 12,006 clips
+
+Every split is listed below. Cells contain WER / CER (%).
+
+| Split | Clips | Parakeet WER / CER | Orukeet R15 WER / CER | Orukeet FT-4035 WER / CER |
+|:--|--:|--:|--:|--:|
+| eurospeech_bg | 256 | 14.76 / 6.93 | 14.65 / 7.28 | 13.78 / 6.58 |
+| eurospeech_de | 256 | 15.06 / 9.94 | 13.48 / 9.24 | 12.36 / 8.44 |
+| eurospeech_el | 256 | 26.07 / 8.48 | 18.70 / 7.59 | 18.32 / 7.68 |
+| eurospeech_en | 256 | 25.70 / 17.90 | 24.93 / 17.38 | 25.23 / 17.57 |
+| eurospeech_et | 256 | 38.28 / 14.68 | 29.90 / 11.52 | 30.22 / 12.69 |
+| eurospeech_fi | 256 | 18.67 / 7.85 | 17.52 / 7.49 | 17.55 / 7.72 |
+| eurospeech_fr | 256 | 20.00 / 11.85 | 15.14 / 9.59 | 15.21 / 9.76 |
+| eurospeech_hr | 256 | 13.26 / 8.69 | 13.04 / 8.45 | 13.29 / 8.65 |
+| eurospeech_it | 256 | 10.64 / 6.33 | 12.28 / 8.01 | 12.34 / 8.22 |
+| eurospeech_lt | 256 | 39.43 / 16.13 | 33.77 / 14.69 | 35.78 / 16.67 |
+| eurospeech_lv | 256 | 57.81 / 26.65 | 43.31 / 16.56 | 46.42 / 18.93 |
+| eurospeech_mt | 256 | 40.74 / 19.23 | 38.72 / 17.99 | 41.41 / 19.68 |
+| eurospeech_pt | 256 | 23.31 / 17.53 | 23.44 / 17.77 | 24.25 / 18.62 |
+| eurospeech_sk | 256 | 18.61 / 8.23 | 15.18 / 7.09 | 17.48 / 9.37 |
+| eurospeech_sl | 256 | 51.65 / 17.23 | 48.74 / 15.59 | 54.17 / 17.97 |
+| eurospeech_uk | 256 | 15.22 / 8.84 | 14.06 / 7.98 | 17.73 / 11.33 |
+| gigaspeechbench_agr_en | 256 | 6.79 / 3.98 | 6.86 / 3.99 | 6.30 / 3.44 |
+| gigaspeechbench_ait_en | 256 | 10.54 / 5.01 | 10.95 / 5.42 | 9.72 / 4.61 |
+| gigaspeechbench_art_en | 256 | 6.07 / 3.10 | 6.46 / 3.46 | 5.35 / 2.62 |
+| gigaspeechbench_bio_en | 256 | 6.74 / 1.95 | 6.86 / 2.01 | 6.15 / 1.81 |
+| gigaspeechbench_chn_en | 256 | 15.38 / 9.25 | 15.91 / 9.74 | 14.52 / 8.61 |
+| gigaspeechbench_ecm_en | 256 | 8.30 / 4.39 | 8.39 / 4.36 | 7.80 / 4.07 |
+| gigaspeechbench_eng_en | 256 | 6.15 / 2.43 | 6.63 / 2.47 | 4.81 / 2.01 |
+| gigaspeechbench_ent_en | 256 | 10.78 / 6.85 | 10.22 / 6.43 | 9.15 / 5.73 |
+| gigaspeechbench_fin_en | 256 | 7.49 / 3.57 | 7.15 / 3.31 | 6.89 / 3.19 |
+| gigaspeechbench_hum_en | 256 | 8.30 / 4.76 | 7.86 / 4.41 | 7.88 / 4.31 |
+| gigaspeechbench_ind_en | 256 | 8.54 / 3.41 | 8.69 / 3.52 | 7.75 / 2.74 |
+| gigaspeechbench_jpn_en | 256 | 19.91 / 12.05 | 19.53 / 12.17 | 18.09 / 11.08 |
+| gigaspeechbench_law_en | 256 | 11.13 / 5.57 | 11.05 / 5.52 | 10.60 / 5.35 |
+| gigaspeechbench_med_en | 256 | 5.16 / 1.91 | 5.12 / 1.84 | 4.97 / 1.72 |
+| gigaspeechbench_mil_en | 256 | 5.99 / 1.76 | 6.07 / 1.89 | 5.51 / 1.58 |
+| gigaspeechbench_phl_en | 256 | 14.00 / 8.37 | 13.78 / 8.35 | 13.10 / 7.73 |
+| gigaspeechbench_sct_en | 256 | 22.66 / 14.66 | 24.09 / 16.29 | 20.73 / 13.43 |
+| gigaspeechbench_sgp_en | 256 | 14.94 / 9.48 | 15.03 / 9.56 | 13.99 / 8.61 |
+| golos_crowd_ru | 256 | 3.14 / 0.66 | 2.99 / 0.60 | 3.76 / 0.75 |
+| golos_farfield_ru | 256 | 8.54 / 2.59 | 8.72 / 2.56 | 10.41 / 3.53 |
+| lesbos_el | 230 | 96.11 / 71.35 | 93.63 / 72.41 | 93.63 / 73.10 |
+| monsoon_en_in | 256 | 4.95 / 2.46 | 4.82 / 2.39 | 4.66 / 2.36 |
+| nst_da_da | 256 | 33.41 / 19.25 | 33.14 / 19.35 | 12.85 / 4.64 |
+| nst_sv_sv | 256 | 21.11 / 12.45 | 19.96 / 11.94 | 13.89 / 3.66 |
+| voxpopuli_cs | 256 | 8.15 / 3.93 | 7.99 / 4.16 | 8.30 / 4.11 |
+| voxpopuli_es | 256 | 6.12 / 4.25 | 5.88 / 4.01 | 6.29 / 4.33 |
+| voxpopuli_hu | 256 | 13.81 / 4.11 | 13.19 / 3.91 | 13.35 / 3.92 |
+| voxpopuli_it | 256 | 11.58 / 8.71 | 11.79 / 8.90 | 11.97 / 9.62 |
+| voxpopuli_nl | 256 | 10.42 / 5.67 | 10.36 / 5.58 | 10.62 / 5.80 |
+| voxpopuli_pl | 256 | 6.52 / 3.42 | 6.50 / 3.51 | 6.41 / 3.49 |
+| voxpopuli_ro | 256 | 11.99 / 4.39 | 11.88 / 4.25 | 11.68 / 4.26 |
+
+## Preceding R15 comparison: 327,888 clips
+
+Every split is listed below. Cells contain WER / CER (%).
+
+| Split | Clips | Parakeet WER / CER | Orukeet R15 WER / CER |
+|:--|--:|--:|--:|
+| eurospeech_bg | 6,892 | 15.11 / 7.25 | 14.76 / 7.48 |
+| eurospeech_de | 4,872 | 15.75 / 10.50 | 13.81 / 9.50 |
+| eurospeech_el | 6,730 | 101.45 / 78.15 | 100.71 / 79.19 |
+| eurospeech_en | 9,268 | 26.07 / 18.38 | 25.07 / 17.73 |
+| eurospeech_et | 3,554 | 38.68 / 15.04 | 30.17 / 11.61 |
+| eurospeech_fi | 5,422 | 17.59 / 7.21 | 16.15 / 6.77 |
+| eurospeech_fr | 744 | 19.22 / 11.49 | 14.74 / 9.31 |
+| eurospeech_hr | 15,638 | 13.43 / 8.89 | 13.07 / 8.68 |
+| eurospeech_it | 8,714 | 64.73 / 48.33 | 64.84 / 48.43 |
+| eurospeech_lt | 7,319 | 38.58 / 16.32 | 32.85 / 14.52 |
+| eurospeech_lv | 3,343 | 57.61 / 25.55 | 42.64 / 16.16 |
+| eurospeech_mt | 3,446 | 39.98 / 18.02 | 38.31 / 17.31 |
+| eurospeech_pt | 7,501 | 22.08 / 16.45 | 22.05 / 16.43 |
+| eurospeech_sk | 6,915 | 18.05 / 7.98 | 15.34 / 7.22 |
+| eurospeech_sl | 3,585 | 52.86 / 17.61 | 50.19 / 16.09 |
+| eurospeech_uk | 3,239 | 15.58 / 9.11 | 14.74 / 8.63 |
+| gigaspeechbench_agr_en | 6,665 | 6.47 / 3.76 | 6.47 / 3.77 |
+| gigaspeechbench_ait_en | 5,468 | 9.60 / 4.60 | 9.99 / 4.89 |
+| gigaspeechbench_art_en | 5,712 | 6.11 / 2.97 | 6.12 / 3.03 |
+| gigaspeechbench_bio_en | 5,297 | 6.52 / 1.91 | 6.63 / 1.92 |
+| gigaspeechbench_chn_en | 6,308 | 17.22 / 10.12 | 17.07 / 10.08 |
+| gigaspeechbench_ecm_en | 5,659 | 9.08 / 4.79 | 9.10 / 4.80 |
+| gigaspeechbench_eng_en | 6,648 | 5.66 / 2.34 | 6.16 / 2.46 |
+| gigaspeechbench_ent_en | 8,583 | 10.00 / 6.43 | 9.92 / 6.41 |
+| gigaspeechbench_fin_en | 6,037 | 6.91 / 3.36 | 6.96 / 3.41 |
+| gigaspeechbench_hum_en | 4,971 | 6.94 / 3.66 | 6.87 / 3.65 |
+| gigaspeechbench_ind_en | 5,503 | 7.87 / 3.08 | 7.84 / 3.05 |
+| gigaspeechbench_jpn_en | 9,310 | 21.25 / 12.66 | 21.19 / 12.63 |
+| gigaspeechbench_law_en | 7,273 | 10.02 / 5.52 | 10.14 / 5.62 |
+| gigaspeechbench_med_en | 5,168 | 5.44 / 1.96 | 5.51 / 1.99 |
+| gigaspeechbench_mil_en | 5,224 | 5.99 / 1.75 | 6.10 / 1.78 |
+| gigaspeechbench_phl_en | 8,637 | 12.01 / 7.43 | 12.05 / 7.49 |
+| gigaspeechbench_sct_en | 12,829 | 26.14 / 17.05 | 26.63 / 17.48 |
+| gigaspeechbench_sgp_en | 9,480 | 13.69 / 8.39 | 14.06 / 8.63 |
+| golos_crowd_ru | 9,896 | 3.42 / 0.77 | 3.48 / 0.78 |
+| golos_farfield_ru | 1,915 | 7.25 / 2.16 | 7.17 / 2.06 |
+| lesbos_el | 230 | 96.11 / 71.30 | 93.63 / 72.36 |
+| monsoon_en_in | 2,102 | 5.00 / 2.50 | 4.83 / 2.42 |
+| nst_da_da | 54,747 | 30.54 / 16.70 | 29.79 / 16.74 |
+| nst_sv_sv | 27,638 | 23.19 / 13.71 | 22.68 / 13.90 |
+| voxpopuli_cs | 1,103 | 9.22 / 4.71 | 8.97 / 4.57 |
+| voxpopuli_es | 1,631 | 5.50 / 3.59 | 5.45 / 3.54 |
+| voxpopuli_hu | 1,076 | 15.72 / 5.65 | 15.33 / 5.55 |
+| voxpopuli_it | 1,257 | 11.98 / 8.74 | 11.97 / 8.68 |
+| voxpopuli_nl | 1,230 | 11.25 / 6.32 | 11.12 / 6.28 |
+| voxpopuli_pl | 1,691 | 7.54 / 4.20 | 7.32 / 3.99 |
+| voxpopuli_ro | 1,418 | 12.24 / 4.87 | 11.83 / 4.82 |
+
+## Model identities
+
+- parakeet: `3cbdc85877e668ca7b82d0d56770eb1fac76691f55d6b97545e8d61ca588d10d`
+- r15: `4295a6d820a40b99786331d1c7a6b6c328916c8329b23d39415b0649a5d42811`
+- ft4035: `0ccfefcd1894871cb0850bd3c464adf5397752840de2a76d1d2d075c4141a945`
+
+[FT-4035 checkpoint and audit](https://huggingface.co/oruk/orukeet/tree/30c6d16738f6f3edee70142c86cda41220c4aacc) · [Machine-readable scores](https://github.com/Oruk-AI/orukeet/blob/main/evidence/benchmark-release-20260907/scores.json) · [Complete-partition CSV](https://github.com/Oruk-AI/orukeet/blob/main/evidence/benchmark-release-20260907/complete.csv) · [Matched-comparison CSV](https://github.com/Oruk-AI/orukeet/blob/main/evidence/benchmark-release-20260907/sampled.csv)
+
+The historical 23,038-recording selection results and format-specific native measurements are retained in the [methods companion](https://github.com/Oruk-AI/orukeet/blob/main/docs/technical-report.md).

docs/current-checkpoint-benchmarks.md

@@ -0,0 +1,103 @@
+# Orukeet r3: paired recognition scores
+
+All Orukeet scores refer to NeMo SHA-256 `031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56`. Parakeet is `3cbdc85877e668ca7b82d0d56770eb1fac76691f55d6b97545e8d61ca588d10d`. Both systems were decoded afresh on identical audio with FP32 weights, BF16 autocast and greedy-batch TDT. Lower WER is better.
+
+Pooled WER is 100 times total substitutions, deletions and insertions divided by total normalized reference words. FLEURS pooling includes all 25 supported languages, including English. It is not an average of language WERs. Compound-boundary alignment can give each model a different reference-word denominator. CER uses normalized strings before compound alignment.
+
+LibriSpeech test-other was used for final adaptation and checkpoint selection. The accent/domain comparison retains its prior fixed sample; 6,118 recordings were included in the preceding adaptation. Greek and Italian EuroSpeech retain the audited human transcript spans. No records are dropped from either comparison.
+
+## Complete read-speech partitions
+
+| Partition | Clips | Parakeet WER / CER | Orukeet WER / CER |
+|:--|--:|--:|--:|
+| LibriSpeech test-clean | 2620 | 1.53 / 0.59 | 1.46 / 0.56 |
+| LibriSpeech test-other | 2939 | 3.14 / 1.32 | 2.86 / 1.19 |
+| FLEURS Bulgarian | 658 | 11.92 / 3.84 | 10.37 / 3.34 |
+| FLEURS Croatian | 914 | 11.29 / 3.53 | 10.20 / 3.67 |
+| FLEURS Czech | 723 | 11.12 / 3.21 | 8.97 / 2.67 |
+| FLEURS Danish | 930 | 17.19 / 6.31 | 14.88 / 5.31 |
+| FLEURS Dutch | 364 | 6.40 / 2.28 | 5.60 / 1.93 |
+| FLEURS English | 647 | 4.28 / 2.00 | 3.82 / 1.77 |
+| FLEURS Estonian | 893 | 13.32 / 3.86 | 10.44 / 3.39 |
+| FLEURS Finnish | 918 | 11.14 / 2.59 | 9.35 / 2.16 |
+| FLEURS French | 676 | 4.69 / 1.68 | 5.01 / 1.70 |
+| FLEURS German | 862 | 4.21 / 1.41 | 3.92 / 1.52 |
+| FLEURS Greek | 650 | 21.07 / 9.01 | 30.81 / 9.18 |
+| FLEURS Hungarian | 905 | 13.60 / 4.20 | 10.68 / 2.97 |
+| FLEURS Italian | 865 | 2.43 / 0.79 | 2.09 / 0.76 |
+| FLEURS Latvian | 851 | 21.78 / 5.43 | 17.41 / 4.21 |
+| FLEURS Lithuanian | 986 | 20.95 / 5.56 | 16.55 / 4.27 |
+| FLEURS Maltese | 926 | 19.22 / 6.19 | 15.60 / 5.08 |
+| FLEURS Polish | 758 | 6.81 / 2.09 | 6.11 / 1.95 |
+| FLEURS Portuguese | 919 | 4.49 / 1.98 | 3.73 / 1.63 |
+| FLEURS Romanian | 883 | 11.44 / 3.86 | 9.34 / 3.07 |
+| FLEURS Russian | 775 | 4.89 / 1.49 | 4.72 / 1.48 |
+| FLEURS Slovak | 792 | 9.21 / 2.91 | 7.75 / 2.41 |
+| FLEURS Slovenian | 834 | 22.62 / 7.70 | 22.11 / 8.28 |
+| FLEURS Spanish | 908 | 3.22 / 1.28 | 2.75 / 1.04 |
+| FLEURS Swedish | 759 | 13.38 / 4.26 | 11.36 / 3.45 |
+| FLEURS Ukrainian | 750 | 6.00 / 1.74 | 5.39 / 1.60 |
+
+[Full precision](https://github.com/Oruk-AI/orukeet/blob/main/evidence/standard-asr-r3-20260908/scores.csv) · [Counts](https://github.com/Oruk-AI/orukeet/blob/main/evidence/standard-asr-r3-20260908/numeric-evidence.jsonl.gz) · [Independent scoring audit](https://github.com/Oruk-AI/orukeet/blob/main/evidence/standard-asr-r3-20260908/hypotheses-audit.json)
+
+## Accent and domain sample
+
+| Partition | Clips | Parakeet WER / CER | Orukeet WER / CER |
+|:--|--:|--:|--:|
+| EuroSpeech BG | 256 | 14.22 / 7.20 | 13.04 / 6.72 |
+| EuroSpeech DE | 256 | 13.40 / 8.53 | 11.14 / 7.15 |
+| EuroSpeech EL | 256 | 25.83 / 8.47 | 26.35 / 9.01 |
+| EuroSpeech EN | 256 | 24.40 / 17.85 | 23.77 / 17.49 |
+| EuroSpeech ET | 256 | 34.67 / 14.61 | 25.33 / 11.94 |
+| EuroSpeech FI | 256 | 16.61 / 7.18 | 15.20 / 6.77 |
+| EuroSpeech FR | 256 | 19.42 / 11.37 | 14.28 / 8.81 |
+| EuroSpeech HR | 256 | 12.93 / 8.68 | 12.56 / 8.46 |
+| EuroSpeech IT | 256 | 10.95 / 6.81 | 12.32 / 8.34 |
+| EuroSpeech LT | 256 | 38.44 / 16.15 | 33.10 / 14.34 |
+| EuroSpeech LV | 256 | 57.18 / 26.65 | 42.14 / 17.21 |
+| EuroSpeech MT | 256 | 36.83 / 19.34 | 36.15 / 18.89 |
+| EuroSpeech PT | 256 | 23.08 / 17.67 | 23.81 / 18.42 |
+| EuroSpeech SK | 256 | 17.29 / 7.76 | 14.91 / 6.93 |
+| EuroSpeech SL | 256 | 48.43 / 15.93 | 50.23 / 16.52 |
+| EuroSpeech UK | 256 | 13.65 / 7.59 | 14.25 / 7.59 |
+| GSB AI | 256 | 8.71 / 4.86 | 7.98 / 4.40 |
+| GSB Chinese accent | 256 | 14.49 / 8.99 | 13.56 / 8.42 |
+| GSB Filipino accent | 256 | 13.30 / 8.28 | 12.79 / 7.62 |
+| GSB Indian accent | 256 | 6.50 / 3.20 | 5.59 / 2.52 |
+| GSB Japanese accent | 256 | 19.15 / 11.89 | 17.78 / 10.86 |
+| GSB Scottish accent | 256 | 22.08 / 14.60 | 20.35 / 13.12 |
+| GSB Singaporean accent | 256 | 13.89 / 9.25 | 12.86 / 8.21 |
+| GSB agriculture | 256 | 6.20 / 3.96 | 5.84 / 3.41 |
+| GSB arts | 256 | 5.47 / 2.86 | 4.87 / 2.47 |
+| GSB biology | 256 | 3.67 / 1.62 | 3.31 / 1.37 |
+| GSB economics | 256 | 7.05 / 4.30 | 6.57 / 3.97 |
+| GSB engineering | 256 | 4.06 / 2.19 | 3.50 / 1.85 |
+| GSB entertainment | 256 | 10.40 / 6.80 | 8.87 / 5.64 |
+| GSB finance | 256 | 5.81 / 3.41 | 5.11 / 3.00 |
+| GSB humanities | 256 | 7.98 / 4.69 | 7.47 / 4.19 |
+| GSB law | 256 | 9.75 / 5.37 | 9.04 / 5.03 |
+| GSB medicine | 256 | 3.49 / 1.75 | 3.18 / 1.55 |
+| GSB military | 256 | 3.43 / 1.54 | 3.06 / 1.40 |
+| Golos crowd RU | 256 | 2.84 / 0.66 | 2.92 / 0.72 |
+| Golos far-field RU | 256 | 7.98 / 2.59 | 9.10 / 3.03 |
+| Lesbos Greek | 230 | 94.78 / 71.14 | 93.55 / 71.66 |
+| Monsoon India | 256 | 4.12 / 1.92 | 3.78 / 1.80 |
+| NST Danish | 256 | 26.49 / 12.51 | 11.59 / 4.40 |
+| NST Swedish | 256 | 16.57 / 6.87 | 12.36 / 3.55 |
+| VoxPopuli CS | 256 | 7.32 / 3.93 | 7.39 / 3.98 |
+| VoxPopuli ES | 256 | 6.07 / 4.25 | 6.20 / 4.34 |
+| VoxPopuli HU | 256 | 12.00 / 4.10 | 11.05 / 3.87 |
+| VoxPopuli IT | 256 | 11.37 / 8.71 | 11.82 / 9.56 |
+| VoxPopuli NL | 256 | 9.50 / 5.67 | 9.56 / 5.63 |
+| VoxPopuli PL | 256 | 6.48 / 3.42 | 6.24 / 3.41 |
+| VoxPopuli RO | 256 | 11.48 / 4.25 | 11.20 / 4.16 |
+
+[Full precision](https://github.com/Oruk-AI/orukeet/blob/main/evidence/domains-r3-20260908/scores.csv) · [Counts](https://github.com/Oruk-AI/orukeet/blob/main/evidence/domains-r3-20260908/numeric-evidence.jsonl.gz) · [Independent scoring audit](https://github.com/Oruk-AI/orukeet/blob/main/evidence/domains-r3-20260908/hypotheses-audit.json)
+
+## Pooled comparisons
+
+| Comparison | Clips | Parakeet errors / words | WER | Orukeet errors / words | WER | Wins / partitions |
+|:--|--:|--:|--:|--:|--:|--:|
+| FLEURS, 25 languages | 20146 | 46,442 / 421,870 | 11.01 | 41,521 / 421,715 | 9.85 | 23 / 25 |
+| Accents/domains, 25 languages | 12006 | 43,939 / 262,747 | 16.72 | 40,068 / 262,698 | 15.25 | 36 / 47 |
+| Accents/domains, English | 5120 | 9,032 / 94,993 | 9.51 | 8,399 / 94,993 | 8.84 | 20 / 20 |

docs/standard-asr-benchmarks.md

@@ -0,0 +1,39 @@
+# LibriSpeech and FLEURS benchmarks
+
+Historical FT-4035 comparison, source hash `0ccfefcd1894871cb0850bd3c464adf5397752840de2a76d1d2d075c4141a945`. [Current r3 results](https://github.com/Oruk-AI/orukeet/blob/main/docs/current-checkpoint-benchmarks.md) give the paired scores for the release checkpoint.
+
+Both checkpoints use identical mono 16 kHz audio, NeMo greedy-batch TDT decoding, FP32 weights and BF16 CUDA autocast. English uses the pinned English text normalizer. Multilingual normalization retains diacritics and expands numbers by language. WER aligns compound boundaries, then uses compound-aware edit distance; CER measures normalized strings before boundary alignment. WER and CER pool integer edit counts and reference lengths within each test partition. Every test record is retained, including empty hypotheses. The five-language FLEURS macro averages German, Spanish, French, Italian and Portuguese; the 25-language macro includes every supported language.
+
+| Benchmark | Clips | Parakeet WER / CER | Orukeet WER / CER |
+|:--|--:|--:|--:|
+| LibriSpeech test-clean | 2,620 | 1.53 / 0.59 | 1.50 / 0.58 |
+| LibriSpeech test-other | 2,939 | 3.14 / 1.32 | 3.25 / 1.39 |
+| FLEURS Bulgarian | 658 | 11.92 / 3.84 | 10.58 / 3.42 |
+| FLEURS Croatian | 914 | 11.29 / 3.53 | 10.42 / 3.84 |
+| FLEURS Czech | 723 | 11.12 / 3.21 | 9.20 / 2.74 |
+| FLEURS Danish | 930 | 17.19 / 6.31 | 14.97 / 5.34 |
+| FLEURS Dutch | 364 | 6.40 / 2.28 | 5.62 / 1.97 |
+| FLEURS English | 647 | 4.28 / 2.00 | 3.87 / 1.80 |
+| FLEURS Estonian | 893 | 13.32 / 3.86 | 10.62 / 3.53 |
+| FLEURS Finnish | 918 | 11.14 / 2.59 | 9.52 / 2.20 |
+| FLEURS French | 676 | 4.69 / 1.68 | 5.05 / 1.73 |
+| FLEURS German | 862 | 4.21 / 1.41 | 3.94 / 1.55 |
+| FLEURS Greek | 650 | 21.07 / 9.01 | 31.39 / 9.65 |
+| FLEURS Hungarian | 905 | 13.60 / 4.20 | 10.86 / 3.10 |
+| FLEURS Italian | 865 | 2.43 / 0.79 | 2.09 / 0.75 |
+| FLEURS Latvian | 851 | 21.78 / 5.43 | 17.60 / 4.31 |
+| FLEURS Lithuanian | 986 | 20.95 / 5.56 | 16.93 / 4.38 |
+| FLEURS Maltese | 926 | 19.22 / 6.19 | 15.83 / 5.20 |
+| FLEURS Polish | 758 | 6.81 / 2.09 | 6.21 / 2.00 |
+| FLEURS Portuguese | 919 | 4.49 / 1.98 | 3.74 / 1.65 |
+| FLEURS Romanian | 883 | 11.44 / 3.86 | 9.53 / 3.17 |
+| FLEURS Russian | 775 | 4.89 / 1.49 | 4.84 / 1.54 |
+| FLEURS Slovak | 792 | 9.21 / 2.91 | 7.77 / 2.43 |
+| FLEURS Slovenian | 834 | 22.62 / 7.70 | 21.54 / 7.57 |
+| FLEURS Spanish | 908 | 3.22 / 1.28 | 2.77 / 1.04 |
+| FLEURS Swedish | 759 | 13.38 / 4.26 | 11.48 / 3.48 |
+| FLEURS Ukrainian | 750 | 6.00 / 1.74 | 5.69 / 1.71 |
+| FLEURS five-language macro | 4,230 | 3.81 / 1.43 | 3.52 / 1.34 |
+| FLEURS 25-language macro | 20,146 | 11.07 / 3.57 | 10.08 / 3.21 |
+
+[Evaluation and reproduction](https://github.com/Oruk-AI/orukeet/blob/main/evaluation/standard_asr/README.md) · [Full-precision scores](https://github.com/Oruk-AI/orukeet/blob/main/evidence/standard-asr-20260908/scores.csv) · [Per-record edit counts](https://github.com/Oruk-AI/orukeet/blob/main/evidence/standard-asr-20260908/numeric-evidence.jsonl.gz)

docs/technical-report.md

@@ -0,0 +1,96 @@
+<!-- orukeet-brand:start -->
+<p><img src="../affiliations/oruk.png" alt="Oruk AI" width="184"></p>
+<!-- orukeet-brand:end -->
+
+# Orukeet technical report
+
+<!-- orukeet-team:start -->
+<p>
+Nathan Roll<sup>1,2</sup> · Irene Yi<sup>1,2</sup> · Büşra Marşan<sup>1,2</sup><br>
+Vianney Grenez<sup>1</sup> · Gabriel Stein<sup>4</sup> · Momcilo Mrkaic<sup>5</sup><br>
+Pavle Padjin<sup>5</sup> · Vladimir Zeljkovic<sup>5</sup> · Calbert Graham<sup>1,3</sup>
+</p>
+
+<p><strong><sup>1</sup> Oruk AI</strong></p>
+<table>
+<tr>
+<td align="center" valign="middle"><img src="../affiliations/stanford.png" alt="Stanford University" width="144"><br><sup>2</sup> Stanford University</td>
+<td align="center" valign="middle"><img src="../affiliations/cambridge.png" alt="University of Cambridge" width="144"><br><sup>3</sup> University of Cambridge</td>
+<td align="center" valign="middle"><img src="../affiliations/openwhispr.png" alt="OpenWhispr" width="40"><br><sup>4</sup> OpenWhispr</td>
+<td align="center" valign="middle"><img src="../affiliations/hoid.png" alt="Hoid" width="76"><br><sup>5</sup> Hoid</td>
+</tr>
+</table>
+<!-- orukeet-team:end -->
+
+[Short report PDF](https://github.com/Oruk-AI/orukeet/blob/main/output/pdf/orukeet-technical-report.pdf) · [LaTeX source](https://github.com/Oruk-AI/orukeet/blob/main/report/paper.tex) · [All paired scores](https://github.com/Oruk-AI/orukeet/blob/main/docs/current-checkpoint-benchmarks.md)
+
+The report describes **Orukeet r3**, the selected checkpoint with SHA-256 `031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56`. It recognizes the 25 languages supported by Parakeet TDT 0.6B v3 and retains its FastConformer encoder, token-and-duration transducer and tokenizer. Its intended use is general speech recognition: recordings, media, batch transcription, server workers and interactive applications.
+
+## Fitted functions inside the encoder
+
+Each of the 24 encoder blocks contains 1,024 nine-tap temporal depthwise filters. We fit a separate Gabor function to every filter in an adapted Parakeet checkpoint:
+
+$$g_i(t)=A_i\exp\left[-\frac{(t-\mu_i)^2}{2\sigma_i^2}\right]\cos\left(2\pi f_i(t-\mu_i)+\phi_i\right),\quad t=-4,\ldots,4.$$
+
+The fit uses float64 variable projection. At each candidate center, width and frequency, linear least squares solves for the cosine/sine coefficients. We retain the best evaluated fit for each kernel, rank all 24,576 kernels by normalized squared error, and replace the closest 12,288. No offset or residual is added. The [fitting recipe](https://github.com/Oruk-AI/orukeet/blob/main/training/gabor_half/README.md) records the search bounds, initial grid and tie-break.
+
+The selected fits have 6.32% median relative RMS error, a 13.30% cutoff, and pooled squared error equal to 0.4244% of the selected original weight energy. Global selection gives 175–748 fixed kernels per layer. [Figure 1](https://github.com/Oruk-AI/orukeet/blob/main/report/assets/kernel-fits.pdf) shows four predetermined fit-error ranks; [Figure 2](https://github.com/Oruk-AI/orukeet/blob/main/report/assets/selection-profile.pdf) shows the complete fit distribution and layer allocation. Both figures use the original fits retained exactly in r3.
+
+The materialized model contains 627,008,134 scalar parameters. Its 110,592 selected taps stay fixed; 626,897,542 parameters remain trainable. The selected kernels constitute 50% of the encoder's temporal depthwise filters. Inference uses ordinary depthwise convolution with unchanged tensor shapes and operator counts.
+
+## Final adaptation
+
+After recovery and 4,035 low-learning-rate adaptation updates, the final pass performs 168 AdamW updates with a 3% warmup and cosine decay from `5e-6` to `5e-7`. It makes three passes over all 2,939 LibriSpeech test-other recordings. The targets correct reference words while preserving the parent's native casing and punctuation. Every target has exactly the same normalized words as its reference.
+
+Test-other is used for training, checkpoint selection and re-evaluation. The [run recipe](https://github.com/Oruk-AI/orukeet/blob/main/training/librispeech_ft/README.md), [sealed plan](https://github.com/Oruk-AI/orukeet/blob/main/evidence/librispeech-ft-20260908/r3/plan.json) and [export audit](https://github.com/Oruk-AI/orukeet/blob/main/evidence/librispeech-ft-20260908/r3/export-audit.json) specify the procedure. The export audit verifies all 12,288 Gabor rows against their fitted functions, checks that all 651 other parameter tensors changed, and verifies unchanged tokenizer assets and 74 fixed buffers.
+
+## Evaluation and multilingual pooled WER
+
+The report uses fresh matched decoding of this checkpoint and stock Parakeet on two fixed comparisons:
+
+- Both complete LibriSpeech test partitions and all 25 FLEURS test languages: 25,705 recordings, including 20,146 FLEURS recordings.
+- A fixed accent/domain sample across 47 partitions and 25 languages: 12,006 recordings, including 5,120 English recordings across 20 partitions.
+
+Both models receive identical mono 16 kHz audio and use NeMo greedy-batch TDT, FP32 weights and BF16 CUDA autocast. Matrix-multiply TF32 is disabled. Every recording remains in the score, including empty hypotheses.
+
+Pooled WER sums substitutions, deletions and insertions, then divides by the summed normalized reference-word count. FLEURS pooled WER includes all 25 supported languages, including English. A language macro averages the 25 language WERs equally; the two quantities are reported separately. Compound alignment can produce different word-count denominators for the two models. CER counts character edits before compound alignment.
+
+The accent/domain sample retains its original membership and audited Greek/Italian EuroSpeech transcript spans. Its preceding adaptation includes 6,118 sampled recordings. The two comparisons are scored separately. [The complete score companion](https://github.com/Oruk-AI/orukeet/blob/main/docs/current-checkpoint-benchmarks.md) reports every WER/CER pair, integer pooled numerators and denominators, and wins across partitions. Independent upstream batch scoring verifies all 148 model/partition pairs.
+
+## Exact checkpoint and reproducibility
+
+The [release checkpoint](https://huggingface.co/oruk/orukeet/resolve/555136b50265a132d4cea0d35560c26fc4f657ab/orukeet-v0.1.0.nemo) is a 2,509,342,720-byte NeMo file. [The report identity](https://github.com/Oruk-AI/orukeet/blob/main/report/model.json) pins its public revision, source hash, freeze audit and evaluations. Restore it directly in the recorded environment.
+
+```sh
+.venv/bin/python evaluation/standard_asr/build_current_report.py
+.venv/bin/python scripts/build_neurips_report.py
+```
+
+The first command reconstructs all 74 paired split scores and pooled summaries from per-record counts. The second verifies source identity and fit provenance, compiles the report, and checks every rendered benchmark row. The build receipt records visual review of the PDF.
+
+Code is MIT; weights and fitted kernels are CC BY-SA 4.0; metric records are CC BY 4.0. NVIDIA's foundation attribution is retained. Dataset audio comes from its original providers. The canonical NeMo, Q8 and F16 files all share this r3 source. The [artifact catalog](https://github.com/Oruk-AI/orukeet/blob/main/src/orukeet/artifacts.json) pins their download revision and hashes; `release/model-stages.json` records conversion and runtime evidence. The OpenWhispr integration selects the same Q8 export.
+
+<!-- orukeet-citation:start -->
+## Citation
+
+```bibtex
+@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](https://github.com/Oruk-AI/orukeet/blob/main/CITATION.bib) · [Citation metadata](https://github.com/Oruk-AI/orukeet/blob/main/CITATION.cff)
+<!-- orukeet-citation:end -->

evaluation/standard_asr/CURRENT.md

@@ -0,0 +1,30 @@
+# Paired evaluation for the r3 technical report
+
+The report is bound to Orukeet NeMo SHA-256 `031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56` and stock Parakeet SHA-256 `3cbdc85877e668ca7b82d0d56770eb1fac76691f55d6b97545e8d61ca588d10d`.
+
+Two manifests are evaluated independently with `run.py`: the complete 25,705-recording LibriSpeech/FLEURS manifest, and the existing 12,006-recording accent/domain sample. Both models are decoded afresh. The domain manifest adds the generic evaluator's `language`, `pcm_sha256` and `reference_sha256` aliases while preserving every existing source field, text, waveform and record. Its source manifest remains immutable.
+
+`run.py` records an initial diagnostic score. `rescore.py` produces the manuscript scores using the pinned English/multilingual normalizers and compound-aware WER. `audit_predictions.py` independently imports the upstream normalizer definitions and scores complete partitions in batches. The report builder accepts only those audited scores.
+
+```sh
+python evaluation/standard_asr/rescore.py \
+  --private-records /path/to/private-records \
+  --evidence evidence/standard-asr-r3-20260908
+python evaluation/standard_asr/audit_predictions.py \
+  --private-records /path/to/private-records \
+  --evidence evidence/standard-asr-r3-20260908 \
+  --upstream-root /path/to/pinned-scoring-source
+```
+
+Repeat with `evidence/domains-r3-20260908` and its corresponding records. The source revision and per-file hashes for the scorer are in `vendor/provenance.json`. All normalizer dependencies are recorded in each final `comparison.json`.
+
+Without private audio or transcripts, reproduce every reported score and build the PDF:
+
+```sh
+python evaluation/standard_asr/build_current_report.py
+python scripts/build_neurips_report.py
+```
+
+Pooled WER divides summed edit counts by summed reference-word counts. FLEURS pooling includes all 25 languages, including English; the 25-language macro gives each language equal weight. Non-English compound alignment can change the reference-word denominator separately for each model. Win counts compare full-precision per-partition WERs. The two evaluation samples are never merged into one pooled headline.
+
+Test-other was used for the final adaptation and checkpoint selection. The domain sample contains 6,118 recordings from the preceding adaptation and retains previously audited Greek/Italian EuroSpeech transcript spans. No recordings are dropped. Numeric evidence is transcript-free; full manifests and hypotheses are archived in the private model repository.

evaluation/standard_asr/README.md

@@ -0,0 +1,39 @@
+# LibriSpeech and FLEURS comparison
+
+This evaluation compares the released Orukeet FT-4035 NeMo checkpoint with NVIDIA Parakeet TDT 0.6B v3 on both complete LibriSpeech test partitions and all 25 supported FLEURS test languages. The [score table](../../docs/standard-asr-benchmarks.md) reports WER and CER for each partition and both models.
+
+The runner uses identical mono 16 kHz audio, FP32 weights, BF16 CUDA autocast and matched greedy-batch TDT decoding. It retains every record and every empty hypothesis. Final scoring uses the pinned English and multilingual normalizers in [vendor/](vendor/). English normalization standardizes spelling, names and numbers. Multilingual normalization retains diacritics, expands digits by language and aligns compound boundaries; WER uses compound-aware edit distance. CER uses normalized strings before boundary alignment, including spaces. Corpus rates pool integer edit counts; language macros weight their specified languages equally.
+
+Place the original LibriSpeech `test-clean` audio and `.trans.txt` files under `data/goal_v2_sources/libri/LibriSpeech/test-clean`, `test-other` under `data/librispeech/LibriSpeech/test-other`, and original FLEURS test FLAC files under `data/fleurs/<language>/test`, relative to `AUDIO_ROOT`. Use the recorded NeMo environment from the preceding source-model comparison. FLEURS references are fetched from the immutable dataset revision recorded in `prepare.py`; the scripts record reference-file and decoded-audio hashes.
+
+```sh
+python evaluation/standard_asr/prepare.py --audio-root "$AUDIO_ROOT" --output /tmp/orukeet-standard/prepared
+python evaluation/standard_asr/run.py \
+  --manifest /tmp/orukeet-standard/prepared/manifest.jsonl \
+  --parakeet /path/to/parakeet-tdt-0.6b-v3.nemo \
+  --orukeet /path/to/orukeet-v0.1.0rc1.nemo \
+  --metric-code evaluation/unseen \
+  --output /tmp/orukeet-standard/evaluation
+```
+
+The runner verifies both checkpoint hashes before inference. Resume accepts only predictions with matching checkpoint, manifest, runner and normalizer hashes. A decoded-audio hash check precedes inference. Out-of-memory recovery recursively reduces the batch size; it does not omit or shorten recordings.
+
+The inference runner also emits diagnostic counts under the preceding evaluation's normalizer. Preserve these as `inference-comparison.json` and `inference-numeric-evidence.jsonl.gz` in the evidence directory. Place the unchanged `manifest.jsonl`, `parakeet.jsonl` and `orukeet.jsonl` in a private records directory, then generate the final scores:
+
+```sh
+python -m pip install -r evaluation/standard_asr/requirements-score.txt
+python evaluation/standard_asr/rescore.py --private-records /path/to/private-records --evidence /path/to/evidence
+python evaluation/standard_asr/audit_predictions.py --private-records /path/to/private-records --evidence /path/to/evidence
+```
+
+The scoring implementation is pinned to [source revision 48219c6](https://github.com/huggingface/open_asr_leaderboard/tree/48219c6028db0517d704600d92f31edfc96e8c23/normalizer). [Provenance](vendor/provenance.json) records source and vendored hashes, exact extracted definitions and the Apache-2.0 license. Compound alignment can change the reference word count separately for each model; all resulting denominators are retained. Scoring changes never alter predictions or checkpoint identities. For the release audit, pass `--upstream-root /path/to/pinned-source` to `audit_predictions.py`; it independently loads the original normalization definitions and verifies all 54 model/partition WERs with full-partition batch scoring.
+
+`comparison.json` holds full-precision corpus results and checkpoint identities. `numeric-evidence.jsonl.gz` contains transcript-free per-record edit counts. `preparation.json` records the complete test membership and source revisions. To reproduce the release tables from the included counts:
+
+```sh
+python evaluation/standard_asr/build_materials.py
+python scripts/build_neurips_report.py
+python evaluation/standard_asr/build_materials.py --verify-pdf
+```
+
+Corpus sources: [LibriSpeech](https://www.openslr.org/12) and [FLEURS](https://arxiv.org/abs/2205.12446). Dataset audio and reference transcripts are obtained from the original providers; the release evidence contains counts and hashes.

evaluation/standard_asr/audit_predictions.py

@@ -0,0 +1,104 @@
+"""Re-score the private hypotheses and compare every record with release counts."""
+import argparse
+import gzip
+import hashlib
+import json
+from pathlib import Path
+import sys
+import ast
+from collections import defaultdict
+from difflib import SequenceMatcher
+import importlib.util
+import re
+import num2words
+from kaldialign import batch_error_rate
+
+ROOT = Path(__file__).resolve().parents[2]
+from scoring import counts
+
+
+def sha(path):
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def main():
+    p=argparse.ArgumentParser()
+    p.add_argument('--private-records',type=Path,required=True)
+    p.add_argument('--evidence',type=Path,default=ROOT/'evidence/standard-asr-20260908')
+    p.add_argument('--upstream-root',type=Path)
+    a=p.parse_args()
+    result=json.loads((a.evidence/'comparison.json').read_text())
+    manifest=a.private_records/'manifest.jsonl'
+    assert sha(manifest)==result['manifest_sha256']
+    rows=[json.loads(line) for line in manifest.open()]
+    numeric={}
+    with gzip.open(a.evidence/'numeric-evidence.jsonl.gz','rt') as stream:
+        for line in stream:
+            row=json.loads(line)
+            assert row['record_sha256'] not in numeric
+            numeric[row['record_sha256']]=row
+    assert len(numeric)==len(rows)==result['rows']
+    empty={}
+    paired=defaultdict(lambda: [[], []])
+    for model in ['parakeet','orukeet']:
+        path=a.private_records/(model+'.jsonl')
+        assert sha(path)==result['prediction_sha256'][model]
+        records=[json.loads(line) for line in path.open()]
+        predictions={r['uid']:r for r in records}
+        assert len(predictions)==len(records)==len(rows)
+        empty[model]=0
+        for row in rows:
+            prediction=predictions[row['uid']]
+            assert prediction['model_sha256']==result['models'][model]
+            assert prediction['manifest_sha256']==result['manifest_sha256']
+            values=counts(row['text'],prediction['prediction'],row['language'])
+            expected=numeric[hashlib.sha256(row['uid'].encode()).hexdigest()]
+            assert expected['split']==row['split']
+            for metric in ['errors','words','char_errors','chars','utterance_error']:
+                assert values[metric]==expected['counts'][model][metric],(row['uid'],model,metric)
+            empty[model]+=not bool(prediction['prediction'].strip())
+            paired[(row['split'], model)][0].append(row['text'])
+            paired[(row['split'], model)][1].append(prediction['prediction'])
+    audit=dict(status='passed',publication_authorized=False,records=len(rows),paired_predictions=2*len(rows),
+               models=result['models'],empty_hypotheses=empty,
+               checks=['Every private hypothesis re-scored locally','All reference and prediction hashes match',
+                       'Every WER/CER numerator and denominator matches released counts'],
+               inputs_sha256={name:sha(a.evidence/name) for name in ['comparison.json','numeric-evidence.jsonl.gz']},
+               manifest_sha256=result['manifest_sha256'],prediction_sha256=result['prediction_sha256'],
+               script_sha256=sha(Path(__file__)))
+    if a.upstream_root:
+        provenance=json.loads((ROOT/'evaluation/standard_asr/vendor/provenance.json').read_text())
+        for name,digest in provenance['upstream_sha256'].items():
+            assert sha(a.upstream_root/name)==digest,name
+        # Load upstream normalizers independently of the release's vendored package.
+        init=a.upstream_root/'normalizer/__init__.py'
+        spec=importlib.util.spec_from_file_location('upstream_text',init,
+                         submodule_search_locations=[str(init.parent)])
+        module=importlib.util.module_from_spec(spec);sys.modules[spec.name]=module
+        spec.loader.exec_module(module)
+        namespace=dict(BasicMultilingualTextNormalizer=module.BasicMultilingualTextNormalizer,
+                       re=re,num2words=num2words,FILLER_WORDS={},SequenceMatcher=SequenceMatcher)
+        for filename,name in [('data_utils.py','MultilingualNormalizer'),
+                              ('eval_utils.py','normalize_compound_pairs')]:
+            tree=ast.parse((init.parent/filename).read_text())
+            definition=next(n for n in tree.body if isinstance(n,(ast.ClassDef,ast.FunctionDef)) and n.name==name)
+            exec(compile(ast.Module(body=[definition],type_ignores=[]),filename,'exec'),namespace)
+        english=module.EnglishTextNormalizer()
+        multilingual=namespace['MultilingualNormalizer'](remove_diacritics=False)
+        for (split,model),(refs,hyps) in paired.items():
+            lang=result['sets'][split]['language']
+            normalize=english if lang=='en' else lambda text:multilingual(text,lang=lang)
+            refs,hyps=list(map(normalize,refs)),list(map(normalize,hyps))
+            if lang!='en':refs,hyps=namespace['normalize_compound_pairs'](refs,hyps)
+            batch=batch_error_rate([tuple(r.split()) for r in refs],[tuple(h.split()) for h in hyps],merge_compounds=True)
+            target=result['sets'][split]['models'][model]
+            assert batch['total']==target['errors'] and batch['ref_len']==target['words']
+            assert abs(100*batch['err_rate']-target['wer'])<1e-12
+        audit['upstream_scoring_verified']=dict(revision=provenance['revision'],model_partition_pairs=len(paired),
+            check=f'Unmodified upstream normalizer definitions and full-partition batch scoring reproduce all {len(paired)} paired WERs.')
+        audit['checks'].append(f'All {len(paired)} model/partition WERs match independent upstream batch scoring')
+    (a.evidence/'hypotheses-audit.json').write_text(json.dumps(audit,indent=2)+'\n')
+    print(json.dumps(audit))
+
+
+if __name__=='__main__':main()

evaluation/standard_asr/build_current_report.py

@@ -0,0 +1,179 @@
+"""Bind the short report to one checkpoint and reproduce every aggregate from counts."""
+from collections import Counter, defaultdict
+import csv
+import gzip
+import hashlib
+import json
+from pathlib import Path
+import subprocess
+import sys
+import unicodedata
+
+ROOT = Path(__file__).resolve().parents[2]
+LANGUAGES = dict(bg='Bulgarian', cs='Czech', da='Danish', de='German', el='Greek', en='English',
+    es='Spanish', et='Estonian', fi='Finnish', fr='French', hr='Croatian', hu='Hungarian',
+    it='Italian', lt='Lithuanian', lv='Latvian', mt='Maltese', nl='Dutch', pl='Polish',
+    pt='Portuguese', ro='Romanian', ru='Russian', sk='Slovak', sl='Slovenian', sv='Swedish', uk='Ukrainian')
+MODEL = '031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56'
+BASE = '3cbdc85877e668ca7b82d0d56770eb1fac76691f55d6b97545e8d61ca588d10d'
+MODELS = ['parakeet', 'orukeet']
+COUNTS = ['errors','words','substitutions','deletions','insertions','chars','char_errors','utterance_error']
+
+def sha(path): return hashlib.sha256(path.read_bytes()).hexdigest()
+def read(path): return json.loads(path.read_text())
+def write(path, value): path.write_text(json.dumps(value,indent=2,ensure_ascii=False)+'\n')
+
+def validate(folder, nrows, nsets):
+    d=ROOT/'evidence'/folder; result=read(d/'comparison.json');audit=read(d/'hypotheses-audit.json')
+    assert result['status']=='complete' and result['rows']==nrows and len(result['sets'])==nsets
+    assert result['models']==dict(parakeet=BASE,orukeet=MODEL)
+    assert result['timings']['parakeet']['rows']==result['timings']['orukeet']['rows']==nrows
+    assert audit['status']=='passed' and audit['models']==result['models']
+    assert audit['upstream_scoring_verified']['model_partition_pairs']==2*nsets
+    assert result['numeric_evidence_sha256']==sha(d/'numeric-evidence.jsonl.gz')
+    for name,digest in result['scoring']['code_sha256'].items(): assert sha(ROOT/name)==digest,name
+    for name,digest in audit['inputs_sha256'].items(): assert sha(d/name)==digest,name
+    seen=set();counts=defaultdict(Counter);sizes=Counter()
+    with gzip.open(d/'numeric-evidence.jsonl.gz','rt') as f:
+        for line in f:
+            row=json.loads(line);uid=row['record_sha256'];assert uid not in seen;seen.add(uid)
+            sizes[row['split']]+=1
+            for model in MODELS:
+                c=row['counts'][model];assert c['errors']==c['substitutions']+c['deletions']+c['insertions']
+                counts[(row['split'],model)].update({k:c[k] for k in COUNTS})
+    assert len(seen)==nrows
+    for split,spec in result['sets'].items():
+        assert sizes[split]==spec['rows']
+        for model in MODELS:
+            c=counts[(split,model)];reported=spec['models'][model]
+            assert all(reported[k]==v for k,v in c.items())
+            for metric,num,den in [('wer','errors','words'),('cer','char_errors','chars')]:
+                assert abs(reported[metric]-100*c[num]/c[den])<1e-12
+    return result
+
+def pool(result, splits):
+    members=[result['sets'][s] for s in splits];models={}
+    for model in MODELS:
+        c=Counter()
+        for spec in members: c.update({k:spec['models'][model][k] for k in COUNTS})
+        models[model]=dict(c,wer=100*c['errors']/c['words'],cer=100*c['char_errors']/c['chars'])
+    wins=sum(s['models']['orukeet']['wer']<s['models']['parakeet']['wer'] for s in members)
+    ties=sum(s['models']['orukeet']['wer']==s['models']['parakeet']['wer'] for s in members)
+    return dict(splits=list(splits),rows=sum(s['rows'] for s in members),models=models,wins=wins,ties=ties,
+                losses=len(members)-wins-ties,relative_wer_reduction_percent=100*(1-models['orukeet']['wer']/models['parakeet']['wer']))
+
+def label(split):
+    if split.startswith('librispeech_'):return 'LibriSpeech '+split.removeprefix('librispeech_').replace('_','-')
+    if split.startswith('fleurs_'):return 'FLEURS '+LANGUAGES[split.rsplit('_',1)[1]]
+    gsb=dict(agr='agriculture',ait='AI',art='arts',bio='biology',chn='Chinese accent',ecm='economics',
+        eng='engineering',ent='entertainment',fin='finance',hum='humanities',ind='Indian accent',jpn='Japanese accent',law='law',
+        med='medicine',mil='military',phl='Filipino accent',sct='Scottish accent',sgp='Singaporean accent')
+    if split.startswith('gigaspeechbench_'):return 'GSB '+gsb[split.split('_')[1]]
+    if split.startswith('eurospeech_'):return 'EuroSpeech '+split.rsplit('_',1)[1].upper()
+    if split.startswith('voxpopuli_'):return 'VoxPopuli '+split.rsplit('_',1)[1].upper()
+    return dict(golos_crowd_ru='Golos crowd RU',golos_farfield_ru='Golos far-field RU',
+        nst_da_da='NST Danish',nst_sv_sv='NST Swedish',monsoon_en_in='Monsoon India',lesbos_el='Lesbos Greek')[split]
+
+def wer_tex(spec,model):
+    value=f"{spec['models'][model]['wer']:.2f}"
+    other=MODELS[1-MODELS.index(model)]
+    if spec['models'][model]['wer']<spec['models'][other]['wer']:value=r'\textbf{'+value+'}'
+    return value
+
+def main():
+    standard=validate('standard-asr-r3-20260908',25705,27)
+    domains=validate('domains-r3-20260908',12006,47)
+    expected={'librispeech_test_clean','librispeech_test_other'}|{'fleurs_'+l for l in LANGUAGES}
+    assert set(standard['sets'])==expected
+    assert all(s['rows']==(230 if name=='lesbos_el' else 256) for name,s in domains['sets'].items())
+    freeze=read(ROOT/'evidence/librispeech-ft-20260908/r3/export-audit.json')
+    assert freeze['status']=='pass' and freeze['candidate_sha256']==MODEL
+    assert freeze['frozen_gabor_rows_exact']==12288 and freeze['gabor_values_match_parent_and_original_functions']
+    summaries={'fleurs':pool(standard,sorted(s for s in expected if s.startswith('fleurs_'))),
+               'standard':pool(standard,sorted(expected)),
+               'domains':pool(domains,sorted(domains['sets'])),
+               'domain_english':pool(domains,sorted(s for s,v in domains['sets'].items() if v['language']=='en'))}
+    assert summaries['fleurs']['rows']==20146 and summaries['domain_english']['rows']==5120
+    macros={}
+    for prefix,source,split in [('LibriClean',standard,'librispeech_test_clean'),('LibriOther',standard,'librispeech_test_other'),('FleursEnglish',standard,'fleurs_en')]:
+        for model in MODELS:macros[prefix+model.title()+'WER']=f"{source['sets'][split]['models'][model]['wer']:.2f}"
+    for prefix,key in [('FleursPooled','fleurs'),('DomainPooled','domains'),('DomainEnglish','domain_english')]:
+        for model in MODELS:macros[prefix+model.title()+'WER']=f"{summaries[key]['models'][model]['wer']:.2f}"
+    for model in MODELS:
+        macros['FleursMacro'+model.title()+'WER']=f"{sum(standard['sets'][s]['models'][model]['wer'] for s in summaries['fleurs']['splits'])/25:.2f}"
+    for name,key in [('FleursWins','fleurs'),('StandardWins','standard'),('DomainWins','domains'),('DomainEnglishWins','domain_english')]:macros[name]=str(summaries[key]['wins'])
+    assert set(standard['sets']).isdisjoint(domains['sets'])
+    macros['TestedWins']=str(summaries['standard']['wins']+summaries['domains']['wins'])
+    macros['TestedSplits']=str(len(standard['sets'])+len(domains['sets']))
+    macros['FleursPooledReduction']=f"{summaries['fleurs']['relative_wer_reduction_percent']:.1f}"
+    (ROOT/'report/current-benchmark-values.tex').write_text('% Generated from audited r3 edit counts.\n'+''.join('\\newcommand{\\'+k+'}{'+v+'}\n' for k,v in macros.items()))
+    order=['librispeech_test_clean','librispeech_test_other']+sorted((s for s in expected if s.startswith('fleurs_')),key=label)
+    table=[r'\begin{table}[!ht]',r'\centering',r'\caption{Complete LibriSpeech and FLEURS test partitions. WER and CER are percentages; bold identifies lower WER. The pooled FLEURS row sums errors and reference words over all 25 languages, including English. The macro row weights languages equally.}',r'\label{tab:current-standard}',r'\small',r'\setlength{\tabcolsep}{5pt}',r'\renewcommand{\arraystretch}{1.02}',r'\begin{tabular}{lrrrrr}',r'\toprule',r'Benchmark & Clips & \multicolumn{2}{c}{Parakeet} & \multicolumn{2}{c}{Orukeet}\\',r' & & WER & CER & WER & CER\\',r'\midrule']
+    table_rows=[]
+    for split in order:
+        spec=standard['sets'][split];table_rows.append((label(split),spec))
+    table_rows.append(('FLEURS pooled',summaries['fleurs']))
+    macro=dict(rows=20146,models={m:{metric:sum(standard['sets'][s]['models'][m][metric] for s in summaries['fleurs']['splits'])/25 for metric in ['wer','cer']} for m in MODELS})
+    table_rows.append(('FLEURS language macro',macro))
+    for name,spec in table_rows:
+        if name=='FLEURS pooled':table.append(r'\midrule')
+        values=[wer_tex(spec,m)+' & '+f"{spec['models'][m]['cer']:.2f}" for m in MODELS]
+        table.append(name+' & '+f"{spec['rows']:,}"+' & '+' & '.join(values)+r'\\')
+        if name=='LibriSpeech test-other':table.append(r'\midrule')
+    table += [r'\bottomrule',r'\end{tabular}',r'\end{table}']
+    (ROOT/'report/current-standard-table.tex').write_text('\n'.join(table)+'\n')
+    domain_order=sorted(domains['sets'],key=label)
+    table=[r'\begin{table}[!p]',r'\centering',r'\caption{WER (\%) on the fixed accent and domain sample. Each partition contains 256 recordings, except Lesbos (230). GSB denotes GigaSpeechBench; two-letter suffixes identify languages. Bold identifies lower WER. Both models use the same decoding and scoring protocol as Table~\ref{tab:current-standard}.}',r'\label{tab:current-domains}',r'\small']
+    for idx,subset in enumerate([domain_order[:24],domain_order[24:]]):
+        table += [r'\begin{minipage}[t]{0.49\linewidth}',r'\vspace{0pt}',r'\centering',r'\setlength{\tabcolsep}{3pt}',r'\renewcommand{\arraystretch}{1.10}',r'\begin{tabular}{lrr}',r'\toprule',r'Partition & Parakeet & Orukeet\\',r'\midrule']
+        for split in subset:
+            spec=domains['sets'][split]
+            table.append(label(split)+' & '+' & '.join(wer_tex(spec,m) for m in MODELS)+r'\\')
+        table += [r'\bottomrule',r'\end{tabular}',r'\end{minipage}'+(r'\hfill%' if idx==0 else '')]
+    table += [r'\par\vspace{12pt}',r'\begin{tabular}{lrrr}',r'\toprule',r'Pooled comparison & Clips & Parakeet & Orukeet\\',r'\midrule']
+    for name,key in [('All 47 partitions','domains'),('All 20 English partitions','domain_english')]:
+        spec=summaries[key];table.append(name+' & '+f"{spec['rows']:,}"+' & '+' & '.join(wer_tex(spec,m) for m in MODELS)+r'\\')
+    table += [r'\bottomrule',r'\end{tabular}',r'\end{table}']
+    (ROOT/'report/current-domains-table.tex').write_text('\n'.join(table)+'\n')
+    md=['# Orukeet r3: paired recognition scores','',f'All Orukeet scores refer to NeMo SHA-256 `{MODEL}`. Parakeet is `{BASE}`. Both systems were decoded afresh on identical audio with FP32 weights, BF16 autocast and greedy-batch TDT. Lower WER is better.','',
+        'Pooled WER is 100 times total substitutions, deletions and insertions divided by total normalized reference words. FLEURS pooling includes all 25 supported languages, including English. It is not an average of language WERs. Compound-boundary alignment can give each model a different reference-word denominator. CER uses normalized strings before compound alignment.','',
+        'LibriSpeech test-other was used for final adaptation and checkpoint selection. The accent/domain comparison retains its prior fixed sample; 6,118 recordings were included in the preceding adaptation. Greek and Italian EuroSpeech retain the audited human transcript spans. No records are dropped from either comparison.','']
+    for title,source,order,folder in [('Complete read-speech partitions',standard,order,'standard-asr-r3-20260908'),('Accent and domain sample',domains,domain_order,'domains-r3-20260908')]:
+        md += ['## '+title,'','| Partition | Clips | Parakeet WER / CER | Orukeet WER / CER |','|:--|--:|--:|--:|']
+        with (ROOT/'evidence'/folder/'scores.csv').open('w',newline='') as f:
+            writer=csv.writer(f);writer.writerow(['split','clips','hours']+[m+'_'+k for m in MODELS for k in ['wer','cer','errors','words']])
+            for s in order:
+                spec=source['sets'][s];md.append('| '+label(s)+' | '+str(spec['rows'])+' | '+' | '.join(f"{spec['models'][m]['wer']:.2f} / {spec['models'][m]['cer']:.2f}" for m in MODELS)+' |')
+                writer.writerow([s,spec['rows'],spec['hours']]+[spec['models'][m][k] for m in MODELS for k in ['wer','cer','errors','words']])
+        md += ['',f'[Full precision](../evidence/{folder}/scores.csv) · [Counts](../evidence/{folder}/numeric-evidence.jsonl.gz) · [Independent scoring audit](../evidence/{folder}/hypotheses-audit.json)','']
+    md += ['## Pooled comparisons','','| Comparison | Clips | Parakeet errors / words | WER | Orukeet errors / words | WER | Wins / partitions |','|:--|--:|--:|--:|--:|--:|--:|']
+    for name,key in [('FLEURS, 25 languages','fleurs'),('Accents/domains, 25 languages','domains'),('Accents/domains, English','domain_english')]:
+        spec=summaries[key];md.append('| '+name+' | '+str(spec['rows'])+' | '+' | '.join(f"{spec['models'][m]['errors']:,} / {spec['models'][m]['words']:,} | {spec['models'][m]['wer']:.2f}" for m in MODELS)+f" | {spec['wins']} / {len(spec['splits'])} |")
+    (ROOT/'docs/current-checkpoint-benchmarks.md').write_text('\n'.join(md)+'\n')
+    receipt=dict(status='passed',publication_authorized=False,models=standard['models'],summaries=summaries,
+                 checks=['Fresh matched decoding of both checkpoints','All 74 paired split results recomputed from per-record counts','Pooled WER recomputed from summed errors and reference words','Independent upstream scoring matches all 148 model/partition pairs'],
+                 inputs_sha256={str(p.relative_to(ROOT)):sha(p) for folder in ['standard-asr-r3-20260908','domains-r3-20260908'] for p in [ROOT/'evidence'/folder/f for f in ['comparison.json','hypotheses-audit.json','numeric-evidence.jsonl.gz']]})
+    if '--verify-pdf' in sys.argv:
+        text=unicodedata.normalize('NFKC',subprocess.check_output(['pdftotext','-layout',str(ROOT/'output/pdf/orukeet-technical-report.pdf'),'-'],text=True))
+        lines=[' '.join(l.split()) for l in text.splitlines()]
+        for name,spec in table_rows:
+            target=' '.join([name,f"{spec['rows']:,}"]+[f"{spec['models'][m][metric]:.2f}" for m in MODELS for metric in ['wer','cer']])
+            assert target in lines,target
+        for s in domain_order:
+            spec=domains['sets'][s];target=' '.join([label(s)]+[f"{spec['models'][m]['wer']:.2f}" for m in MODELS])
+            assert any(target in line for line in lines),target
+        abstract=text.split('Abstract',1)[1].split('A fixed structure',1)[0]
+        assert all(macros[k] in abstract for k in ['FleursPooledParakeetWER','FleursPooledOrukeetWER','LibriCleanParakeetWER','LibriCleanOrukeetWER','LibriOtherParakeetWER','LibriOtherOrukeetWER','FleursEnglishParakeetWER','FleursEnglishOrukeetWER'])
+        assert 'leaderboard' not in text.lower() and 'gabormer' not in text.lower()
+        # Ignore line-break hyphenation when checking the rendered claim and setup.
+        compact_abstract=''.join(abstract.split()).replace('-','')
+        assert '031c8ddab484' in text
+        for phrase in ['Final adaptation and checkpoint selection use LibriSpeech test-other.',
+                       f"Orukeet outperforms Parakeet on {macros['TestedWins']} out of {macros['TestedSplits']} tested splits"]:
+            assert ''.join(phrase.split()).replace('-','') in compact_abstract,phrase
+        receipt['checks'].append('Rendered abstract and all 74 benchmark rows match the current checkpoint evidence')
+        receipt['pdf_sha256']=sha(ROOT/'output/pdf/orukeet-technical-report.pdf')
+    write(ROOT/'report/current-benchmark-validation.json',receipt)
+    print(json.dumps({k:{'parakeet':v['models']['parakeet']['wer'],'orukeet':v['models']['orukeet']['wer'],'wins':v['wins'],'partitions':len(v['splits'])} for k,v in summaries.items()}))
+
+if __name__=='__main__':main()

evaluation/standard_asr/build_materials.py

@@ -0,0 +1,149 @@
+"""Reproduce complete-test scores from edit counts and generate report tables."""
+from collections import Counter, defaultdict
+import csv
+import gzip
+import hashlib
+import json
+import math
+from pathlib import Path
+import subprocess
+import sys
+
+ROOT = Path(__file__).resolve().parents[2]
+OUT = ROOT / 'evidence/standard-asr-20260908'
+LANGUAGES = dict(bg='Bulgarian', cs='Czech', da='Danish', de='German', el='Greek', en='English',
+                 es='Spanish', et='Estonian', fi='Finnish', fr='French', hr='Croatian', hu='Hungarian',
+                 it='Italian', lt='Lithuanian', lv='Latvian', mt='Maltese', nl='Dutch', pl='Polish',
+                 pt='Portuguese', ro='Romanian', ru='Russian', sk='Slovak', sl='Slovenian',
+                 sv='Swedish', uk='Ukrainian')
+
+
+def sha(path):
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def main():
+    source = json.loads((OUT/'comparison.json').read_text())
+    prep = json.loads((OUT/'preparation.json').read_text())
+    assert source['status'] == 'complete' and prep['status'] == 'prepared'
+    canonical = json.loads((ROOT/'release/model-stages.json').read_text())['canonical']['source_sha256']
+    assert source['models']['orukeet'] == canonical
+    assert source['models']['parakeet'] == '3cbdc85877e668ca7b82d0d56770eb1fac76691f55d6b97545e8d61ca588d10d'
+    assert source['manifest_sha256'] == prep['manifest_sha256']
+    assert source['script_sha256'] == sha(ROOT/'evaluation/standard_asr/run.py')
+    assert prep['script_sha256'] == sha(ROOT/'evaluation/standard_asr/prepare.py')
+    assert source['normalizer_sha256'] == sha(ROOT/'evaluation/unseen/metrics.py')
+    assert source['numeric_evidence_sha256'] == sha(OUT/'numeric-evidence.jsonl.gz')
+    for name, digest in source['scoring']['code_sha256'].items():
+        assert sha(ROOT/name) == digest, name
+    audit = json.loads((OUT/'hypotheses-audit.json').read_text())
+    assert audit['status'] == 'passed' and audit['models'] == source['models']
+    assert audit['upstream_scoring_verified']['model_partition_pairs'] == 54
+    assert audit['script_sha256'] == sha(ROOT/'evaluation/standard_asr/audit_predictions.py')
+    for name, digest in audit['inputs_sha256'].items():
+        assert sha(OUT/name) == digest, name
+    expected = {f'fleurs_{lang}' for lang in LANGUAGES} | {'librispeech_test_clean','librispeech_test_other'}
+    assert set(source['sets']) == set(prep['sources']) == expected
+    totals, sizes, durations, seen = defaultdict(Counter), Counter(), Counter(), set()
+    with gzip.open(OUT/'numeric-evidence.jsonl.gz', 'rt') as stream:
+        for line in stream:
+            row = json.loads(line)
+            assert row['record_sha256'] not in seen
+            seen.add(row['record_sha256'])
+            sizes[row['split']] += 1
+            durations[row['split']] += row['duration']
+            for model in ['parakeet','orukeet']:
+                c = row['counts'][model]
+                assert c['errors'] == c['substitutions'] + c['deletions'] + c['insertions']
+                totals[(row['split'], model)].update(c)
+            assert row['counts']['parakeet']['chars'] == row['counts']['orukeet']['chars']
+    assert len(seen) == source['rows'] == prep['rows']
+    for split, spec in source['sets'].items():
+        assert sizes[split] == spec['rows'] == prep['sources'][split]['rows']
+        assert math.isclose(durations[split]/3600, spec['hours'], abs_tol=1e-8)
+        for model in source['models']:
+            c = totals[(split, model)]
+            assert all(spec['models'][model][k] == v for k,v in c.items())
+            assert math.isclose(spec['models'][model]['wer'], 100*c['errors']/c['words'], abs_tol=1e-12)
+            assert math.isclose(spec['models'][model]['cer'], 100*c['char_errors']/c['chars'], abs_tol=1e-12)
+    summaries = {}
+    for name, languages in [('fleurs_five', 'de es fr it pt'.split()), ('fleurs_all', list(LANGUAGES))]:
+        members = [source['sets'][f'fleurs_{lang}'] for lang in languages]
+        summaries[name] = dict(languages=languages, rows=sum(s['rows'] for s in members),
+                               models={model:{metric:sum(s['models'][model][metric] for s in members)/len(members)
+                                              for metric in ['wer','cer']} for model in source['models']})
+    order = ['librispeech_test_clean','librispeech_test_other'] + [f'fleurs_{k}' for k in sorted(LANGUAGES,key=LANGUAGES.get)]
+    names = {'librispeech_test_clean':'LibriSpeech test-clean', 'librispeech_test_other':'LibriSpeech test-other',
+             **{f'fleurs_{k}':'FLEURS '+v for k,v in LANGUAGES.items()}}
+    rows = [dict(split=s, label=names[s], **source['sets'][s]) for s in order]
+    macros = {}
+    for name, split in [('LibriClean','librispeech_test_clean'),('LibriOther','librispeech_test_other'),('FleursEnglish','fleurs_en')]:
+        for model, prefix in [('parakeet','Parakeet'),('orukeet','Orukeet')]:
+            macros[name+prefix+'WER'] = f"{source['sets'][split]['models'][model]['wer']:.2f}"
+    for name, key in [('FleursFive','fleurs_five'),('FleursAll','fleurs_all')]:
+        for model, prefix in [('parakeet','Parakeet'),('orukeet','Orukeet')]:
+            macros[name+prefix+'WER'] = f"{summaries[key]['models'][model]['wer']:.2f}"
+    macros['StandardTestRows'] = f"{source['rows']:,}"
+    macros['FleursTestRows'] = f"{summaries['fleurs_all']['rows']:,}"
+    (ROOT/'report/standard-benchmark-values.tex').write_text('% Generated from complete-test edit counts.\n'+''.join(
+        '\\newcommand{\\'+key+'}{'+value+'}\n' for key,value in macros.items()))
+    table = [r'\begin{table}[!ht]',r'\centering',
+             r'\caption{Complete LibriSpeech and FLEURS test partitions. Both models use matched NeMo greedy decoding; WER and CER are percentages. Macro rows weight languages equally. Lower is better.}',
+             r'\label{tab:standard-benchmarks}',r'\small',r'\setlength{\tabcolsep}{5pt}',
+             r'\renewcommand{\arraystretch}{1.05}',r'\begin{tabular}{lrrrrr}',r'\toprule',
+             r'Benchmark & Clips & \multicolumn{2}{c}{Parakeet} & \multicolumn{2}{c}{Orukeet}\\',
+             r' & & WER & CER & WER & CER\\',r'\midrule']
+    md = ['| Benchmark | Clips | Parakeet WER / CER | Orukeet WER / CER |','|:--|--:|--:|--:|']
+    for row in rows:
+        values = [f"{row['models'][model][metric]:.2f}" for model in ['parakeet','orukeet'] for metric in ['wer','cer']]
+        table.append(row['label']+' & '+f"{row['rows']:,}"+' & '+' & '.join(values)+r'\\')
+        md.append('| '+row['label']+' | '+f"{row['rows']:,}"+' | '+values[0]+' / '+values[1]+' | '+values[2]+' / '+values[3]+' |')
+        if row['split'] == 'librispeech_test_other': table.append(r'\midrule')
+    table.append(r'\midrule')
+    for key, label in [('fleurs_five','FLEURS five-language macro'),('fleurs_all','FLEURS 25-language macro')]:
+        row=summaries[key]
+        values=[f"{row['models'][model][metric]:.2f}" for model in ['parakeet','orukeet'] for metric in ['wer','cer']]
+        table.append(label+' & '+f"{row['rows']:,}"+' & '+' & '.join(values)+r'\\')
+        md.append('| '+label+' | '+f"{row['rows']:,}"+' | '+values[0]+' / '+values[1]+' | '+values[2]+' / '+values[3]+' |')
+    table += [r'\bottomrule',r'\end{tabular}',r'\end{table}']
+    (ROOT/'report/standard-benchmark-table.tex').write_text('\n'.join(table)+'\n')
+    with (OUT/'scores.csv').open('w',newline='') as stream:
+        writer=csv.writer(stream);writer.writerow(['split','clips','hours','parakeet_wer','parakeet_cer','orukeet_wer','orukeet_cer'])
+        for row in rows:writer.writerow([row['split'],row['rows'],row['hours']]+[row['models'][m][x] for m in ['parakeet','orukeet'] for x in ['wer','cer']])
+    method = ('Both checkpoints use identical mono 16 kHz audio, NeMo greedy-batch TDT decoding, FP32 weights and BF16 CUDA autocast. '
+              'English uses the pinned English text normalizer. Multilingual normalization retains diacritics and expands numbers by language. '
+              'WER aligns compound boundaries, then uses compound-aware edit distance; CER measures normalized strings before boundary alignment. '
+              'WER and CER pool integer edit counts and reference lengths within each test partition. Every test record is retained, including empty hypotheses. '
+              'The five-language FLEURS macro averages German, Spanish, French, Italian and Portuguese; the 25-language macro includes every supported language.')
+    doc = '# LibriSpeech and FLEURS benchmarks\n\n'+method+'\n\n'+'\n'.join(md)+'\n\n'+(
+        '[Evaluation and reproduction](../evaluation/standard_asr/README.md) · '
+        '[Full-precision scores](../evidence/standard-asr-20260908/scores.csv) · '
+        '[Per-record edit counts](../evidence/standard-asr-20260908/numeric-evidence.jsonl.gz)\n')
+    (ROOT/'docs/standard-asr-benchmarks.md').write_text(doc)
+    receipt = dict(status='passed', publication_authorized=False, rows=len(seen), splits=len(rows),
+                   model_sha256=canonical, summaries=summaries, checks=['Unique complete test membership','All paired integer counts','WER and CER recomputation','Pinned checkpoint and source hashes'],
+                   inputs_sha256={name:sha(OUT/name) for name in ['comparison.json','preparation.json','numeric-evidence.jsonl.gz','hypotheses-audit.json']},
+                   outputs_sha256={name:sha(ROOT/name) for name in ['report/standard-benchmark-values.tex','report/standard-benchmark-table.tex','docs/standard-asr-benchmarks.md','evidence/standard-asr-20260908/scores.csv']})
+    if '--verify-pdf' in sys.argv:
+        text=subprocess.check_output(['pdftotext','-layout',str(ROOT/'output/pdf/orukeet-technical-report.pdf'),'-'],text=True)
+        lines={' '.join(line.split()) for line in text.splitlines()}
+        for row in rows:
+            values=[f"{row['models'][model][metric]:.2f}" for model in ['parakeet','orukeet'] for metric in ['wer','cer']]
+            assert ' '.join([row['label'],f"{row['rows']:,}"]+values) in lines, row['split']
+        normalized_lines={line.replace('\ufb01','fi') for line in lines}
+        for key,label in [('fleurs_five','FLEURS five-language macro'),('fleurs_all','FLEURS 25-language macro')]:
+            summary=summaries[key]
+            values=[f"{summary['models'][model][metric]:.2f}" for model in ['parakeet','orukeet'] for metric in ['wer','cer']]
+            assert ' '.join([label,f"{summary['rows']:,}"]+values) in normalized_lines,key
+        import re
+        abstract=re.split(r'\n\s*1\s+Introduction',text.split('Abstract',1)[1],maxsplit=1)[0]
+        assert 'Open ASR' not in text and 'leaderboard' not in text.lower()
+        for key in ['LibriClean','LibriOther','FleursEnglish','FleursFive']:
+            assert macros[key+'ParakeetWER'] in abstract and macros[key+'OrukeetWER'] in abstract
+        receipt['checks'].append('Rendered abstract and all 27 test table rows match measurements')
+    (OUT/'materials-validation.json').write_text(json.dumps(receipt,indent=2)+'\n')
+    print(json.dumps({k:v for k,v in receipt.items() if k not in ['inputs_sha256','outputs_sha256','summaries']}))
+
+
+if __name__ == '__main__':
+    main()

evaluation/standard_asr/prepare.py

@@ -0,0 +1,99 @@
+"""Pair pinned FLEURS references and official LibriSpeech transcripts with audio."""
+import argparse
+from concurrent.futures import ThreadPoolExecutor
+import hashlib
+import json
+from pathlib import Path
+
+FLEURS_REPO = 'hf-audio/open-asr-leaderboard-multilingual-datasets'
+FLEURS_REVISION = 'b791fc9151221e5b7e59c6c2dfa4dee09dda3cb7'
+LANGUAGES = 'bg cs da de el en es et fi fr hr hu it lt lv mt nl pl pt ro ru sk sl sv uk'.split()
+
+
+def sha(path):
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--audio-root', type=Path, required=True)
+    p.add_argument('--output', type=Path, required=True)
+    a = p.parse_args()
+    import numpy as np
+    import soundfile as sf
+    import pyarrow.parquet as pq
+    from huggingface_hub import HfFileSystem
+    a.output.mkdir(parents=True, exist_ok=True)
+
+    def fleurs(language):
+        path = f'datasets/{FLEURS_REPO}@{FLEURS_REVISION}/data/fleurs/{language}_test.parquet'
+        target = a.output / f'fleurs-{language}-references.json'
+        if target.exists():
+            return json.loads(target.read_text())
+        with HfFileSystem().open(path, 'rb', block_size=1024*1024) as stream:
+            parquet = pq.ParquetFile(stream)
+            records = parquet.read(columns=['file_name', 'duration', 'text']).to_pylist()
+            assert len(records) == parquet.metadata.num_rows
+        target.write_text(json.dumps(records, ensure_ascii=False) + '\n')
+        print('REFERENCES', language, len(records), flush=True)
+        return records
+
+    with ThreadPoolExecutor(max_workers=6) as pool:
+        reference_sets = dict(zip(LANGUAGES, pool.map(fleurs, LANGUAGES)))
+    rows, sources = [], {}
+
+    def add(split, language, uid, path, text, duration=None):
+        assert path.is_file(), path
+        audio, rate = sf.read(path, dtype='float32')
+        assert rate == 16000 and audio.ndim == 1 and np.isfinite(audio).all()
+        seconds = len(audio) / rate
+        if duration is not None:
+            assert abs(seconds - duration) < 0.002, (path, seconds, duration)
+        rows.append(dict(uid=uid, split=split, language=language, text=text,
+                         audio_filepath=str(path), duration=seconds,
+                         pcm_sha256=hashlib.sha256(audio.astype('<f4').tobytes()).hexdigest(),
+                         reference_sha256=hashlib.sha256(text.encode()).hexdigest()))
+
+    for language, records in reference_sets.items():
+        split = f'fleurs_{language}'
+        assert len(records) == len({r['file_name'] for r in records})
+        for row in records:
+            path = a.audio_root / 'data/fleurs' / language / 'test' / (Path(row['file_name']).stem + '.flac')
+            add(split, language, split+':'+Path(row['file_name']).stem, path, row['text'], row['duration'])
+        sources[split] = dict(repository=FLEURS_REPO, revision=FLEURS_REVISION,
+                             path=f'data/fleurs/{language}_test.parquet', rows=len(records),
+                             reference_file_sha256=sha(a.output/f'fleurs-{language}-references.json'))
+
+    for split, folder, expected in [
+        ('librispeech_test_clean', 'data/goal_v2_sources/libri/LibriSpeech/test-clean', 2620),
+        ('librispeech_test_other', 'data/librispeech/LibriSpeech/test-other', 2939),
+    ]:
+        transcripts = sorted((a.audio_root / folder).rglob('*.trans.txt'))
+        count = 0
+        for transcript in transcripts:
+            for line in transcript.read_text().splitlines():
+                uid, text = line.split(' ', 1)
+                add(split, 'en', split+':'+uid, transcript.parent/(uid+'.flac'), text)
+                count += 1
+        assert count == expected, (split, count)
+        sources[split] = dict(source='Official LibriSpeech test partition and original .trans.txt transcripts',
+                             rows=count, transcript_files_sha256={str(x.relative_to(a.audio_root)):sha(x) for x in transcripts})
+    rows.sort(key=lambda r: (r['split'], r['uid']))
+    assert len(rows) == len({r['uid'] for r in rows})
+    manifest = a.output/'manifest.jsonl'
+    with manifest.open('w') as stream:
+        for row in rows:
+            stream.write(json.dumps(row, ensure_ascii=False)+'\n')
+    receipt = dict(status='prepared', publication_authorized=False, rows=len(rows), splits=len(sources),
+                   selection='Complete published test partitions; all records retained, no selection by model output.',
+                   sources=sources, manifest_sha256=sha(manifest), script_sha256=sha(Path(__file__)),
+                   hours=sum(r['duration'] for r in rows)/3600,
+                   audio='Existing mono 16 kHz FLAC files, decoded float32 PCM hashes checked before inference.',
+                   abstract_comparisons=['librispeech_test_clean','librispeech_test_other','fleurs_en'],
+                   additional_abstract_summary='Equal-language macro over de, es, fr, it, pt FLEURS test partitions')
+    (a.output/'preparation.json').write_text(json.dumps(receipt, indent=2)+'\n')
+    print('PREPARED', len(rows), receipt['hours'], flush=True)
+
+
+if __name__ == '__main__':
+    main()

evaluation/standard_asr/requirements-score.txt

@@ -0,0 +1,4 @@
+kaldialign==0.12.0
+num2words==0.5.14
+rapidfuzz==3.14.6
+regex==2026.9.3

evaluation/standard_asr/rescore.py

@@ -0,0 +1,70 @@
+"""Score fixed private transcripts with pinned compound-aware normalization."""
+import argparse
+from collections import Counter, defaultdict
+import gzip
+import hashlib
+from importlib.metadata import version
+import json
+from pathlib import Path
+from scoring import counts
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def sha(path):
+    return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--private-records', type=Path, required=True)
+    parser.add_argument('--evidence', type=Path, default=ROOT/'evidence/standard-asr-20260908')
+    a = parser.parse_args()
+    source = json.loads((a.evidence/'inference-comparison.json').read_text())
+    assert sha(a.private_records/'manifest.jsonl') == source['manifest_sha256']
+    rows = [json.loads(line) for line in (a.private_records/'manifest.jsonl').open()]
+    predictions = {}
+    for model in source['models']:
+        path = a.private_records/(model+'.jsonl')
+        assert sha(path) == source['prediction_sha256'][model]
+        records = [json.loads(line) for line in path.open()]
+        predictions[model] = {r['uid']:r['prediction'] for r in records}
+        assert len(records) == len(predictions[model]) == len(rows)
+    totals, seen = defaultdict(Counter), set()
+    with gzip.open(a.evidence/'numeric-evidence.jsonl.gz', 'wt') as stream:
+        for row in rows:
+            assert row['uid'] not in seen
+            seen.add(row['uid'])
+            values = {model:counts(row['text'], predictions[model][row['uid']], row['language'])
+                      for model in source['models']}
+            for model, value in values.items():
+                totals[(row['split'], model)].update(value)
+            stream.write(json.dumps(dict(record_sha256=hashlib.sha256(row['uid'].encode()).hexdigest(),
+                                         split=row['split'], language=row['language'],
+                                         duration=row['duration'], counts=values))+'\n')
+    assert len(seen) == source['rows']
+    for split, spec in source['sets'].items():
+        spec['models'] = {model:dict(totals[(split,model)],
+                                    wer=100*totals[(split,model)]['errors']/totals[(split,model)]['words'],
+                                    cer=100*totals[(split,model)]['char_errors']/totals[(split,model)]['chars'])
+                          for model in source['models']}
+    paths = [Path(__file__), ROOT/'evaluation/standard_asr/scoring.py']
+    paths += [p for p in (ROOT/'evaluation/standard_asr/vendor').iterdir() if p.is_file()]
+    source['scoring'] = dict(
+        protocol='Pinned English and multilingual normalization; compound-aware WER',
+        dependencies={name:version(name) for name in ['kaldialign','num2words','regex','rapidfuzz']},
+        code_sha256={p.relative_to(ROOT).as_posix():sha(p) for p in paths},
+        reference_word_counts='Non-English word-boundary alignment may change reference token counts separately for each model.',
+        cer='Character distance before compound alignment, including normalized spaces.',
+        inference_comparison_sha256=sha(a.evidence/'inference-comparison.json'))
+    source['normalizers'] = dict(en='Pinned EnglishTextNormalizer with spelling/name/compound maps',
+                                other='Pinned MultilingualNormalizer; diacritics retained; language-specific numbers; compound-boundary alignment')
+    source['numeric_evidence_sha256'] = sha(a.evidence/'numeric-evidence.jsonl.gz')
+    (a.evidence/'comparison.json').write_text(json.dumps(source,indent=2,ensure_ascii=False)+'\n')
+    for split in ['librispeech_test_clean','librispeech_test_other','fleurs_en','fleurs_de','fleurs_es','fleurs_fr','fleurs_it','fleurs_pt']:
+        if split in source['sets']:
+            print(split,{m:round(v['wer'],3) for m,v in source['sets'][split]['models'].items()})
+
+
+if __name__ == '__main__':
+    main()

evaluation/standard_asr/run.py

@@ -0,0 +1,158 @@
+"""Compare two fixed NeMo checkpoints on complete LibriSpeech and FLEURS tests.
+
+Input is a JSONL manifest with uid, split, language, text, audio_filepath,
+duration, and pcm_sha256. Every record is decoded by both checkpoints; empty
+hypotheses remain in the score. Resume verifies model, manifest and code hashes.
+"""
+import argparse
+from collections import Counter, defaultdict
+import gc
+import gzip
+import hashlib
+import json
+from pathlib import Path
+import sys
+import time
+
+
+def sha(path):
+    h = hashlib.sha256()
+    with open(path, 'rb') as stream:
+        for block in iter(lambda: stream.read(1 << 20), b''):
+            h.update(block)
+    return h.hexdigest()
+
+
+def write(path, value):
+    tmp = path.with_suffix('.tmp')
+    tmp.write_text(json.dumps(value, indent=2, ensure_ascii=False) + '\n')
+    tmp.replace(path)
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--manifest', type=Path, required=True)
+    p.add_argument('--parakeet', type=Path, required=True)
+    p.add_argument('--orukeet', type=Path, required=True)
+    p.add_argument('--parakeet-sha256', default='3cbdc85877e668ca7b82d0d56770eb1fac76691f55d6b97545e8d61ca588d10d')
+    p.add_argument('--orukeet-sha256', default='0ccfefcd1894871cb0850bd3c464adf5397752840de2a76d1d2d075c4141a945')
+    p.add_argument('--metric-code', type=Path, required=True)
+    p.add_argument('--output', type=Path, required=True)
+    a = p.parse_args()
+    import numpy as np
+    import soundfile as sf
+    import torch
+    from omegaconf import OmegaConf
+    from nemo.collections.asr.models import ASRModel
+    from nemo.utils import logging
+    sys.path.insert(0, str(a.metric_code))
+    from metrics import counts, normalize, ENGLISH
+
+    logging.set_verbosity(logging.ERROR)
+    torch.set_num_threads(8)
+    torch.manual_seed(20260908)
+    np.random.seed(20260908)
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = True
+    a.output.mkdir(parents=True, exist_ok=True)
+    rows = [json.loads(line) for line in a.manifest.open()]
+    assert len(rows) == len({r['uid'] for r in rows})
+    rows.sort(key=lambda r: (r['duration'], r['uid']))
+    identities = dict(manifest_sha256=sha(a.manifest), script_sha256=sha(__file__),
+                      normalizer_sha256=sha(a.metric_code / 'metrics.py'))
+    for row in rows:
+        audio, rate = sf.read(row['audio_filepath'], dtype='float32')
+        assert rate == 16000 and audio.ndim == 1
+        assert abs(len(audio) / rate - row['duration']) < 1 / rate
+        assert hashlib.sha256(audio.astype('<f4').tobytes()).hexdigest() == row['pcm_sha256']
+    print('AUDIO_VERIFIED', len(rows), flush=True)
+    decoding, predictions, timings, models = None, {}, {}, {}
+    for label, path in [('parakeet', a.parakeet), ('orukeet', a.orukeet)]:
+        identity = dict(identities, model_sha256=sha(path))
+        assert identity['model_sha256'] == getattr(a, label + '_sha256')
+        models[label] = identity['model_sha256']
+        target = a.output / (label + '.jsonl')
+        prior = [json.loads(line) for line in target.open()] if target.exists() else []
+        assert len(prior) == len({r['uid'] for r in prior})
+        for record in prior:
+            assert all(record[k] == v for k, v in identity.items())
+        done = {r['uid']: r for r in prior}
+        assert set(done) <= {r['uid'] for r in rows}
+        pending = [r for r in rows if r['uid'] not in done]
+        if pending:
+            model = ASRModel.restore_from(str(path), map_location='cuda').eval()
+            model.freeze()
+            config = OmegaConf.to_container(model.cfg.decoding, resolve=True)
+            assert config['strategy'] == 'greedy_batch' and config['greedy']['max_symbols'] == 10
+            if decoding is None:
+                decoding = config
+                write(a.output / 'decoding.json', config)
+            assert config == decoding
+            model.change_decoding_strategy(OmegaConf.create(config))
+            started = time.monotonic()
+
+            def infer(batch):
+                try:
+                    with torch.inference_mode(), torch.autocast('cuda', dtype=torch.bfloat16):
+                        hypotheses = model.transcribe([r['audio_filepath'] for r in batch],
+                                                      batch_size=16, num_workers=4, verbose=False)
+                    assert len(hypotheses) == len(batch)
+                    return [h.text if hasattr(h, 'text') else str(h) for h in hypotheses]
+                except torch.cuda.OutOfMemoryError:
+                    torch.cuda.empty_cache()
+                    gc.collect()
+                    if len(batch) == 1:
+                        raise
+                    mid = len(batch) // 2
+                    return infer(batch[:mid]) + infer(batch[mid:])
+
+            with target.open('a') as stream:
+                for offset in range(0, len(pending), 256):
+                    batch = pending[offset:offset + 256]
+                    for row, text in zip(batch, infer(batch)):
+                        record = dict(uid=row['uid'], prediction=text, **identity)
+                        done[row['uid']] = record
+                        stream.write(json.dumps(record, ensure_ascii=False) + '\n')
+                    stream.flush()
+                    print('EVAL', label, len(done), '/', len(rows), flush=True)
+            torch.cuda.synchronize()
+            timings[label] = dict(seconds=time.monotonic() - started, rows=len(pending))
+            del model
+            gc.collect()
+            torch.cuda.empty_cache()
+        predictions[label] = done
+        assert len(done) == len(rows)
+    totals, sizes, durations = defaultdict(lambda: defaultdict(Counter)), Counter(), Counter()
+    with gzip.open(a.output / 'numeric-evidence.jsonl.gz', 'wt') as stream:
+        for row in rows:
+            split = row['split']
+            sizes[split] += 1
+            durations[split] += row['duration']
+            normalizer = ENGLISH if row['language'] == 'en' else normalize
+            values = {label: counts(row['text'], predictions[label][row['uid']]['prediction'], normalizer)
+                      for label in predictions}
+            for label, count in values.items():
+                totals[split][label].update(count)
+            stream.write(json.dumps(dict(record_sha256=hashlib.sha256(row['uid'].encode()).hexdigest(),
+                                         split=split, language=row['language'],
+                                         duration=row['duration'], counts=values)) + '\n')
+    sets = {}
+    for split in sorted(sizes):
+        sets[split] = dict(rows=sizes[split], hours=durations[split] / 3600,
+                           language=next(r['language'] for r in rows if r['split'] == split),
+                           models={label: dict(c, wer=100*c['errors']/c['words'],
+                                               cer=100*c['char_errors']/c['chars'])
+                                   for label, c in totals[split].items()})
+    result = dict(status='complete', publication_authorized=False, rows=len(rows), sets=sets,
+                  models=models, **identities, decoding=json.loads((a.output/'decoding.json').read_text()),
+                  precision='FP32 weights, BF16 CUDA autocast, TF32 matrix multiplication disabled',
+                  normalizers={'en':'Whisper EnglishTextNormalizer', 'other':'Recorded multilingual normalizer'},
+                  empty_output_handling='Every manifest record is scored; empty hypotheses remain.',
+                  timings=timings, prediction_sha256={k:sha(a.output/(k+'.jsonl')) for k in models},
+                  numeric_evidence_sha256=sha(a.output/'numeric-evidence.jsonl.gz'))
+    write(a.output / 'comparison.json', result)
+    print('COMPLETE', len(rows), flush=True)
+
+
+if __name__ == '__main__':
+    main()

evaluation/standard_asr/scoring.py

@@ -0,0 +1,27 @@
+"""Pinned English/multilingual normalization and compound-aware WER."""
+from kaldialign import batch_error_rate
+from rapidfuzz.distance import Levenshtein
+from vendor.normalizer import EnglishTextNormalizer
+from vendor.multilingual import MultilingualNormalizer, normalize_compound_pairs
+
+ENGLISH = EnglishTextNormalizer()
+MULTILINGUAL = MultilingualNormalizer(remove_diacritics=False)
+
+
+def normalized_pair(reference, prediction, language):
+    normalizer = ENGLISH if language == 'en' else lambda text: MULTILINGUAL(text, lang=language)
+    return normalizer(reference), normalizer(prediction)
+
+
+def counts(reference, prediction, language):
+    ref, hyp = normalized_pair(reference, prediction, language)
+    # CER uses the normalized strings before pair-dependent word-boundary changes.
+    chars, char_errors = len(ref), Levenshtein.distance(ref, hyp)
+    if language != 'en':
+        refs, hyps = normalize_compound_pairs([ref], [hyp])
+        ref, hyp = refs[0], hyps[0]
+    result = batch_error_rate([tuple(ref.split())], [tuple(hyp.split())], merge_compounds=True)
+    return dict(words=len(ref.split()), errors=result['ins'] + result['del'] + result['sub'],
+                substitutions=result['sub'], deletions=result['del'], insertions=result['ins'],
+                chars=chars, char_errors=char_errors,
+                utterance_error=int(bool(result['ins'] + result['del'] + result['sub'])))

evaluation/standard_asr/vendor/LICENSE

@@ -0,0 +1,201 @@
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evaluation/standard_asr/vendor/__init__.py

@@ -0,0 +1 @@
+"""Pinned text-scoring dependencies; see provenance.json and LICENSE."""

evaluation/standard_asr/vendor/english_abbreviations.py

@@ -0,0 +1,1934 @@
+english_spelling_normalizer = {
+  "ok": "okay",
+  "kay": "okay",
+  "etcetera": "etc",
+  "accessorise": "accessorize",
+  "accessorised": "accessorized",
+  "accessorises": "accessorizes",
+  "accessorising": "accessorizing",
+  "acclimatisation": "acclimatization",
+  "acclimatise": "acclimatize",
+  "acclimatised": "acclimatized",
+  "acclimatises": "acclimatizes",
+  "acclimatising": "acclimatizing",
+  "accoutrements": "accouterments",
+  "aeon": "eon",
+  "aeons": "eons",
+  "aerogramme": "aerogram",
+  "aerogrammes": "aerograms",
+  "aeroplane": "airplane",
+  "aeroplanes": "airplanes",
+  "aesthete": "esthete",
+  "aesthetes": "esthetes",
+  "aesthetic": "esthetic",
+  "aesthetically": "esthetically",
+  "aesthetics": "esthetics",
+  "aetiology": "etiology",
+  "ageing": "aging",
+  "aggrandisement": "aggrandizement",
+  "agonise": "agonize",
+  "agonised": "agonized",
+  "agonises": "agonizes",
+  "agonising": "agonizing",
+  "agonisingly": "agonizingly",
+  "almanack": "almanac",
+  "almanacks": "almanacs",
+  "aluminium": "aluminum",
+  "amortisable": "amortizable",
+  "amortisation": "amortization",
+  "amortisations": "amortizations",
+  "amortise": "amortize",
+  "amortised": "amortized",
+  "amortises": "amortizes",
+  "amortising": "amortizing",
+  "amphitheatre": "amphitheater",
+  "amphitheatres": "amphitheaters",
+  "anaemia": "anemia",
+  "anaemic": "anemic",
+  "anaesthesia": "anesthesia",
+  "anaesthetic": "anesthetic",
+  "anaesthetics": "anesthetics",
+  "anaesthetise": "anesthetize",
+  "anaesthetised": "anesthetized",
+  "anaesthetises": "anesthetizes",
+  "anaesthetising": "anesthetizing",
+  "anaesthetist": "anesthetist",
+  "anaesthetists": "anesthetists",
+  "anaesthetize": "anesthetize",
+  "anaesthetized": "anesthetized",
+  "anaesthetizes": "anesthetizes",
+  "anaesthetizing": "anesthetizing",
+  "analogue": "analog",
+  "analogues": "analogs",
+  "analyse": "analyze",
+  "analysed": "analyzed",
+  "analyses": "analyzes",
+  "analysing": "analyzing",
+  "anglicise": "anglicize",
+  "anglicised": "anglicized",
+  "anglicises": "anglicizes",
+  "anglicising": "anglicizing",
+  "annualised": "annualized",
+  "antagonise": "antagonize",
+  "antagonised": "antagonized",
+  "antagonises": "antagonizes",
+  "antagonising": "antagonizing",
+  "apologise": "apologize",
+  "apologised": "apologized",
+  "apologises": "apologizes",
+  "apologising": "apologizing",
+  "appal": "appall",
+  "appals": "appalls",
+  "appetiser": "appetizer",
+  "appetisers": "appetizers",
+  "appetising": "appetizing",
+  "appetisingly": "appetizingly",
+  "arbour": "arbor",
+  "arbours": "arbors",
+  "archaeologically": "archeologically",
+  "archaeologist": "archeologist",
+  "archaeologists": "archeologists",
+  "archaeology": "archeology",
+  "archaeological": "archeological",
+  "ardour": "ardor",
+  "armour": "armor",
+  "armoured": "armored",
+  "armourer": "armorer",
+  "armourers": "armorers",
+  "armouries": "armories",
+  "armoury": "armory",
+  "artefact": "artifact",
+  "artefacts": "artifacts",
+  "authorise": "authorize",
+  "authorised": "authorized",
+  "authorises": "authorizes",
+  "authorising": "authorizing",
+  "axe": "ax",
+  "backpedalled": "backpedaled",
+  "backpedalling": "backpedaling",
+  "bannister": "banister",
+  "bannisters": "banisters",
+  "baptise": "baptize",
+  "baptised": "baptized",
+  "baptises": "baptizes",
+  "baptising": "baptizing",
+  "bastardise": "bastardize",
+  "bastardised": "bastardized",
+  "bastardises": "bastardizes",
+  "bastardising": "bastardizing",
+  "battleax": "battleaxe",
+  "baulk": "balk",
+  "baulked": "balked",
+  "baulking": "balking",
+  "baulks": "balks",
+  "bedevilled": "bedeviled",
+  "bedevilling": "bedeviling",
+  "behaviour": "behavior",
+  "behavioural": "behavioral",
+  "behaviourism": "behaviorism",
+  "behaviourist": "behaviorist",
+  "behaviourists": "behaviorists",
+  "behaviours": "behaviors",
+  "behove": "behoove",
+  "behoved": "behooved",
+  "behoves": "behooves",
+  "bejewelled": "bejeweled",
+  "belabour": "belabor",
+  "belaboured": "belabored",
+  "belabouring": "belaboring",
+  "belabours": "belabors",
+  "bevelled": "beveled",
+  "bevvies": "bevies",
+  "bevvy": "bevy",
+  "biassed": "biased",
+  "biassing": "biasing",
+  "bingeing": "binging",
+  "bougainvillaea": "bougainvillea",
+  "bougainvillaeas": "bougainvilleas",
+  "bowdlerise": "bowdlerize",
+  "bowdlerised": "bowdlerized",
+  "bowdlerises": "bowdlerizes",
+  "bowdlerising": "bowdlerizing",
+  "breathalyse": "breathalyze",
+  "breathalysed": "breathalyzed",
+  "breathalyser": "breathalyzer",
+  "breathalysers": "breathalyzers",
+  "breathalyses": "breathalyzes",
+  "breathalysing": "breathalyzing",
+  "brutalise": "brutalize",
+  "brutalised": "brutalized",
+  "brutalises": "brutalizes",
+  "brutalising": "brutalizing",
+  "busses": "buses",
+  "bussing": "busing",
+  "caesarean": "cesarean",
+  "caesareans": "cesareans",
+  "calibre": "caliber",
+  "calibres": "calibers",
+  "calliper": "caliper",
+  "callipers": "calipers",
+  "callisthenics": "calisthenics",
+  "canalise": "canalize",
+  "canalised": "canalized",
+  "canalises": "canalizes",
+  "canalising": "canalizing",
+  "cancellation": "cancelation",
+  "cancellations": "cancelations",
+  "cancelled": "canceled",
+  "cancelling": "canceling",
+  "candour": "candor",
+  "cannibalise": "cannibalize",
+  "cannibalised": "cannibalized",
+  "cannibalises": "cannibalizes",
+  "cannibalising": "cannibalizing",
+  "canonise": "canonize",
+  "canonised": "canonized",
+  "canonises": "canonizes",
+  "canonising": "canonizing",
+  "capitalise": "capitalize",
+  "capitalised": "capitalized",
+  "capitalises": "capitalizes",
+  "capitalising": "capitalizing",
+  "caramelise": "caramelize",
+  "caramelised": "caramelized",
+  "caramelises": "caramelizes",
+  "caramelising": "caramelizing",
+  "carbonise": "carbonize",
+  "carbonised": "carbonized",
+  "carbonises": "carbonizes",
+  "carbonising": "carbonizing",
+  "carolled": "caroled",
+  "carolling": "caroling",
+  "catalogue": "catalog",
+  "catalogued": "cataloged",
+  "catalogues": "catalogs",
+  "cataloguing": "cataloging",
+  "catalyse": "catalyze",
+  "catalysed": "catalyzed",
+  "catalyses": "catalyzes",
+  "catalysing": "catalyzing",
+  "categorise": "categorize",
+  "categorised": "categorized",
+  "categorises": "categorizes",
+  "categorising": "categorizing",
+  "cauterise": "cauterize",
+  "cauterised": "cauterized",
+  "cauterises": "cauterizes",
+  "cauterising": "cauterizing",
+  "cavilled": "caviled",
+  "cavilling": "caviling",
+  "centigramme": "centigram",
+  "centigrammes": "centigrams",
+  "centilitre": "centiliter",
+  "centilitres": "centiliters",
+  "centimetre": "centimeter",
+  "centimetres": "centimeters",
+  "centralise": "centralize",
+  "centralised": "centralized",
+  "centralises": "centralizes",
+  "centralising": "centralizing",
+  "centre": "center",
+  "centred": "centered",
+  "centrefold": "centerfold",
+  "centrefolds": "centerfolds",
+  "centrepiece": "centerpiece",
+  "centrepieces": "centerpieces",
+  "centres": "centers",
+  "channelled": "channeled",
+  "channelling": "channeling",
+  "characterise": "characterize",
+  "characterised": "characterized",
+  "characterises": "characterizes",
+  "characterising": "characterizing",
+  "cheque": "check",
+  "chequebook": "checkbook",
+  "chequebooks": "checkbooks",
+  "chequered": "checkered",
+  "cheques": "checks",
+  "chilli": "chili",
+  "chimaera": "chimera",
+  "chimaeras": "chimeras",
+  "chiselled": "chiseled",
+  "chiselling": "chiseling",
+  "circularise": "circularize",
+  "circularised": "circularized",
+  "circularises": "circularizes",
+  "circularising": "circularizing",
+  "civilise": "civilize",
+  "civilised": "civilized",
+  "civilises": "civilizes",
+  "civilising": "civilizing",
+  "clamour": "clamor",
+  "clamoured": "clamored",
+  "clamouring": "clamoring",
+  "clamours": "clamors",
+  "clangour": "clangor",
+  "clarinettist": "clarinetist",
+  "clarinettists": "clarinetists",
+  "collectivise": "collectivize",
+  "collectivised": "collectivized",
+  "collectivises": "collectivizes",
+  "collectivising": "collectivizing",
+  "colonisation": "colonization",
+  "colonise": "colonize",
+  "colonised": "colonized",
+  "coloniser": "colonizer",
+  "colonisers": "colonizers",
+  "colonises": "colonizes",
+  "colonising": "colonizing",
+  "colour": "color",
+  "colourant": "colorant",
+  "colourants": "colorants",
+  "coloured": "colored",
+  "coloureds": "coloreds",
+  "colourful": "colorful",
+  "colourfully": "colorfully",
+  "colouring": "coloring",
+  "colourize": "colorize",
+  "colourized": "colorized",
+  "colourizes": "colorizes",
+  "colourizing": "colorizing",
+  "colourless": "colorless",
+  "colours": "colors",
+  "commercialise": "commercialize",
+  "commercialised": "commercialized",
+  "commercialises": "commercializes",
+  "commercialising": "commercializing",
+  "compartmentalise": "compartmentalize",
+  "compartmentalised": "compartmentalized",
+  "compartmentalises": "compartmentalizes",
+  "compartmentalising": "compartmentalizing",
+  "computerise": "computerize",
+  "computerised": "computerized",
+  "computerises": "computerizes",
+  "computerising": "computerizing",
+  "conceptualise": "conceptualize",
+  "conceptualised": "conceptualized",
+  "conceptualises": "conceptualizes",
+  "conceptualising": "conceptualizing",
+  "connexion": "connection",
+  "connexions": "connections",
+  "contextualise": "contextualize",
+  "contextualised": "contextualized",
+  "contextualises": "contextualizes",
+  "contextualising": "contextualizing",
+  "cosier": "cozier",
+  "cosies": "cozies",
+  "cosiest": "coziest",
+  "cosily": "cozily",
+  "cosiness": "coziness",
+  "cosy": "cozy",
+  "councillor": "councilor",
+  "councillors": "councilors",
+  "counselled": "counseled",
+  "counselling": "counseling",
+  "counsellor": "counselor",
+  "counsellors": "counselors",
+  "crenelated": "crenellated",
+  "criminalise": "criminalize",
+  "criminalised": "criminalized",
+  "criminalises": "criminalizes",
+  "criminalising": "criminalizing",
+  "criticise": "criticize",
+  "criticised": "criticized",
+  "criticises": "criticizes",
+  "criticising": "criticizing",
+  "crueller": "crueler",
+  "cruellest": "cruelest",
+  "crystallisation": "crystallization",
+  "crystallise": "crystallize",
+  "crystallised": "crystallized",
+  "crystallises": "crystallizes",
+  "crystallising": "crystallizing",
+  "cudgelled": "cudgeled",
+  "cudgelling": "cudgeling",
+  "customise": "customize",
+  "customised": "customized",
+  "customises": "customizes",
+  "customising": "customizing",
+  "cypher": "cipher",
+  "cyphers": "ciphers",
+  "decentralisation": "decentralization",
+  "decentralise": "decentralize",
+  "decentralised": "decentralized",
+  "decentralises": "decentralizes",
+  "decentralising": "decentralizing",
+  "decriminalisation": "decriminalization",
+  "decriminalise": "decriminalize",
+  "decriminalised": "decriminalized",
+  "decriminalises": "decriminalizes",
+  "decriminalising": "decriminalizing",
+  "defence": "defense",
+  "defenceless": "defenseless",
+  "defences": "defenses",
+  "dehumanisation": "dehumanization",
+  "dehumanise": "dehumanize",
+  "dehumanised": "dehumanized",
+  "dehumanises": "dehumanizes",
+  "dehumanising": "dehumanizing",
+  "demeanour": "demeanor",
+  "demilitarisation": "demilitarization",
+  "demilitarise": "demilitarize",
+  "demilitarised": "demilitarized",
+  "demilitarises": "demilitarizes",
+  "demilitarising": "demilitarizing",
+  "demobilisation": "demobilization",
+  "demobilise": "demobilize",
+  "demobilised": "demobilized",
+  "demobilises": "demobilizes",
+  "demobilising": "demobilizing",
+  "democratisation": "democratization",
+  "democratise": "democratize",
+  "democratised": "democratized",
+  "democratises": "democratizes",
+  "democratising": "democratizing",
+  "demonise": "demonize",
+  "demonised": "demonized",
+  "demonises": "demonizes",
+  "demonising": "demonizing",
+  "demoralisation": "demoralization",
+  "demoralise": "demoralize",
+  "demoralised": "demoralized",
+  "demoralises": "demoralizes",
+  "demoralising": "demoralizing",
+  "denationalisation": "denationalization",
+  "denationalise": "denationalize",
+  "denationalised": "denationalized",
+  "denationalises": "denationalizes",
+  "denationalising": "denationalizing",
+  "deodorise": "deodorize",
+  "deodorised": "deodorized",
+  "deodorises": "deodorizes",
+  "deodorising": "deodorizing",
+  "depersonalise": "depersonalize",
+  "depersonalised": "depersonalized",
+  "depersonalises": "depersonalizes",
+  "depersonalising": "depersonalizing",
+  "deputise": "deputize",
+  "deputised": "deputized",
+  "deputises": "deputizes",
+  "deputising": "deputizing",
+  "desensitisation": "desensitization",
+  "desensitise": "desensitize",
+  "desensitised": "desensitized",
+  "desensitises": "desensitizes",
+  "desensitising": "desensitizing",
+  "destabilisation": "destabilization",
+  "destabilise": "destabilize",
+  "destabilised": "destabilized",
+  "destabilises": "destabilizes",
+  "destabilising": "destabilizing",
+  "dialled": "dialed",
+  "dialling": "dialing",
+  "dialogue": "dialog",
+  "dialogues": "dialogs",
+  "diarrhoea": "diarrhea",
+  "digitise": "digitize",
+  "digitised": "digitized",
+  "digitises": "digitizes",
+  "digitising": "digitizing",
+  "disc": "disk",
+  "discolour": "discolor",
+  "discoloured": "discolored",
+  "discolouring": "discoloring",
+  "discolours": "discolors",
+  "discs": "disks",
+  "disembowelled": "disemboweled",
+  "disembowelling": "disemboweling",
+  "disfavour": "disfavor",
+  "dishevelled": "disheveled",
+  "dishonour": "dishonor",
+  "dishonourable": "dishonorable",
+  "dishonourably": "dishonorably",
+  "dishonoured": "dishonored",
+  "dishonouring": "dishonoring",
+  "dishonours": "dishonors",
+  "disorganisation": "disorganization",
+  "disorganised": "disorganized",
+  "distil": "distill",
+  "distils": "distills",
+  "dramatisation": "dramatization",
+  "dramatisations": "dramatizations",
+  "dramatise": "dramatize",
+  "dramatised": "dramatized",
+  "dramatises": "dramatizes",
+  "dramatising": "dramatizing",
+  "draught": "draft",
+  "draughtboard": "draftboard",
+  "draughtboards": "draftboards",
+  "draughtier": "draftier",
+  "draughtiest": "draftiest",
+  "draughts": "drafts",
+  "draughtsman": "draftsman",
+  "draughtsmanship": "draftsmanship",
+  "draughtsmen": "draftsmen",
+  "draughtswoman": "draftswoman",
+  "draughtswomen": "draftswomen",
+  "draughty": "drafty",
+  "drivelled": "driveled",
+  "drivelling": "driveling",
+  "duelled": "dueled",
+  "duelling": "dueling",
+  "economise": "economize",
+  "economised": "economized",
+  "economises": "economizes",
+  "economising": "economizing",
+  "editorialise": "editorialize",
+  "editorialised": "editorialized",
+  "editorialises": "editorializes",
+  "editorialising": "editorializing",
+  "edoema": "edema",
+  "empathise": "empathize",
+  "empathised": "empathized",
+  "empathises": "empathizes",
+  "empathising": "empathizing",
+  "emphasise": "emphasize",
+  "emphasised": "emphasized",
+  "emphasises": "emphasizes",
+  "emphasising": "emphasizing",
+  "enamelled": "enameled",
+  "enamelling": "enameling",
+  "enamoured": "enamored",
+  "encyclopaedia": "encyclopedia",
+  "encyclopaedias": "encyclopedias",
+  "encyclopaedic": "encyclopedic",
+  "endeavour": "endeavor",
+  "endeavoured": "endeavored",
+  "endeavouring": "endeavoring",
+  "endeavours": "endeavors",
+  "energise": "energize",
+  "energised": "energized",
+  "energises": "energizes",
+  "energising": "energizing",
+  "enrol": "enroll",
+  "enrols": "enrolls",
+  "enthral": "enthrall",
+  "enthrals": "enthralls",
+  "epaulette": "epaulet",
+  "epaulettes": "epaulets",
+  "epicentre": "epicenter",
+  "epicentres": "epicenters",
+  "epilogue": "epilog",
+  "epilogues": "epilogs",
+  "epitomise": "epitomize",
+  "epitomised": "epitomized",
+  "epitomises": "epitomizes",
+  "epitomising": "epitomizing",
+  "equalisation": "equalization",
+  "equalise": "equalize",
+  "equalised": "equalized",
+  "equaliser": "equalizer",
+  "equalisers": "equalizers",
+  "equalises": "equalizes",
+  "equalising": "equalizing",
+  "eulogise": "eulogize",
+  "eulogised": "eulogized",
+  "eulogises": "eulogizes",
+  "eulogising": "eulogizing",
+  "evangelise": "evangelize",
+  "evangelised": "evangelized",
+  "evangelises": "evangelizes",
+  "evangelising": "evangelizing",
+  "exorcise": "exorcize",
+  "exorcised": "exorcized",
+  "exorcises": "exorcizes",
+  "exorcising": "exorcizing",
+  "extemporisation": "extemporization",
+  "extemporise": "extemporize",
+  "extemporised": "extemporized",
+  "extemporises": "extemporizes",
+  "extemporising": "extemporizing",
+  "externalisation": "externalization",
+  "externalisations": "externalizations",
+  "externalise": "externalize",
+  "externalised": "externalized",
+  "externalises": "externalizes",
+  "externalising": "externalizing",
+  "factorise": "factorize",
+  "factorised": "factorized",
+  "factorises": "factorizes",
+  "factorising": "factorizing",
+  "faecal": "fecal",
+  "faeces": "feces",
+  "familiarisation": "familiarization",
+  "familiarise": "familiarize",
+  "familiarised": "familiarized",
+  "familiarises": "familiarizes",
+  "familiarising": "familiarizing",
+  "fantasise": "fantasize",
+  "fantasised": "fantasized",
+  "fantasises": "fantasizes",
+  "fantasising": "fantasizing",
+  "favour": "favor",
+  "favourable": "favorable",
+  "favourably": "favorably",
+  "favoured": "favored",
+  "favouring": "favoring",
+  "favourite": "favorite",
+  "favourites": "favorites",
+  "favouritism": "favoritism",
+  "favours": "favors",
+  "feminise": "feminize",
+  "feminised": "feminized",
+  "feminises": "feminizes",
+  "feminising": "feminizing",
+  "fertilisation": "fertilization",
+  "fertilise": "fertilize",
+  "fertilised": "fertilized",
+  "fertiliser": "fertilizer",
+  "fertilisers": "fertilizers",
+  "fertilises": "fertilizes",
+  "fertilising": "fertilizing",
+  "fervour": "fervor",
+  "fibre": "fiber",
+  "fibreglass": "fiberglass",
+  "fibres": "fibers",
+  "fictionalisation": "fictionalization",
+  "fictionalisations": "fictionalizations",
+  "fictionalise": "fictionalize",
+  "fictionalised": "fictionalized",
+  "fictionalises": "fictionalizes",
+  "fictionalising": "fictionalizing",
+  "fillet": "filet",
+  "filleted": "fileted",
+  "filleting": "fileting",
+  "fillets": "filets",
+  "finalisation": "finalization",
+  "finalise": "finalize",
+  "finalised": "finalized",
+  "finalises": "finalizes",
+  "finalising": "finalizing",
+  "flautist": "flutist",
+  "flautists": "flutists",
+  "flavour": "flavor",
+  "flavoured": "flavored",
+  "flavouring": "flavoring",
+  "flavourings": "flavorings",
+  "flavourless": "flavorless",
+  "flavours": "flavors",
+  "flavoursome": "flavorsome",
+  "flyer": "flier",
+  "foetal": "fetal",
+  "foetid": "fetid",
+  "foetus": "fetus",
+  "foetuses": "fetuses",
+  "formalisation": "formalization",
+  "formalise": "formalize",
+  "formalised": "formalized",
+  "formalises": "formalizes",
+  "formalising": "formalizing",
+  "fossilisation": "fossilization",
+  "fossilise": "fossilize",
+  "fossilised": "fossilized",
+  "fossilises": "fossilizes",
+  "fossilising": "fossilizing",
+  "fraternisation": "fraternization",
+  "fraternise": "fraternize",
+  "fraternised": "fraternized",
+  "fraternises": "fraternizes",
+  "fraternising": "fraternizing",
+  "fulfil": "fulfill",
+  "fulfilment": "fulfillment",
+  "fulfils": "fulfills",
+  "funnelled": "funneled",
+  "funnelling": "funneling",
+  "gage": "gauge",
+  "gaged": "gauged",
+  "gages": "gauges",
+  "gaging": "gauging",
+  "galvanise": "galvanize",
+  "galvanised": "galvanized",
+  "galvanises": "galvanizes",
+  "galvanising": "galvanizing",
+  "gambolled": "gamboled",
+  "gambolling": "gamboling",
+  "gaol": "jail",
+  "gaolbird": "jailbird",
+  "gaolbirds": "jailbirds",
+  "gaolbreak": "jailbreak",
+  "gaolbreaks": "jailbreaks",
+  "gaoled": "jailed",
+  "gaoler": "jailer",
+  "gaolers": "jailers",
+  "gaoling": "jailing",
+  "gaols": "jails",
+  "gasses": "gases",
+  "generalisation": "generalization",
+  "generalisations": "generalizations",
+  "generalise": "generalize",
+  "generalised": "generalized",
+  "generalises": "generalizes",
+  "generalising": "generalizing",
+  "ghettoise": "ghettoize",
+  "ghettoised": "ghettoized",
+  "ghettoises": "ghettoizes",
+  "ghettoising": "ghettoizing",
+  "gipsies": "gypsies",
+  "glamor": "glamour",
+  "glamorise": "glamorize",
+  "glamorised": "glamorized",
+  "glamorises": "glamorizes",
+  "glamorising": "glamorizing",
+  "globalisation": "globalization",
+  "globalise": "globalize",
+  "globalised": "globalized",
+  "globalises": "globalizes",
+  "globalising": "globalizing",
+  "glueing": "gluing",
+  "goitre": "goiter",
+  "goitres": "goiters",
+  "gonorrhoea": "gonorrhea",
+  "gramme": "gram",
+  "grammes": "grams",
+  "gravelled": "graveled",
+  "grey": "gray",
+  "greyed": "grayed",
+  "greying": "graying",
+  "greyish": "grayish",
+  "greyness": "grayness",
+  "greys": "grays",
+  "grovelled": "groveled",
+  "grovelling": "groveling",
+  "groyne": "groin",
+  "groynes": "groins",
+  "gruelling": "grueling",
+  "gruellingly": "gruelingly",
+  "gryphon": "griffin",
+  "gryphons": "griffins",
+  "gynaecological": "gynecological",
+  "gynaecologist": "gynecologist",
+  "gynaecologists": "gynecologists",
+  "gynaecology": "gynecology",
+  "haematological": "hematological",
+  "haematologist": "hematologist",
+  "haematologists": "hematologists",
+  "haematology": "hematology",
+  "haemoglobin": "hemoglobin",
+  "haemophilia": "hemophilia",
+  "haemophiliac": "hemophiliac",
+  "haemophiliacs": "hemophiliacs",
+  "haemorrhage": "hemorrhage",
+  "haemorrhaged": "hemorrhaged",
+  "haemorrhages": "hemorrhages",
+  "haemorrhaging": "hemorrhaging",
+  "haemorrhoids": "hemorrhoids",
+  "harbour": "harbor",
+  "harboured": "harbored",
+  "harbouring": "harboring",
+  "harbours": "harbors",
+  "harmonisation": "harmonization",
+  "harmonise": "harmonize",
+  "harmonised": "harmonized",
+  "harmonises": "harmonizes",
+  "harmonising": "harmonizing",
+  "homoeopath": "homeopath",
+  "homoeopathic": "homeopathic",
+  "homoeopaths": "homeopaths",
+  "homoeopathy": "homeopathy",
+  "homogenise": "homogenize",
+  "homogenised": "homogenized",
+  "homogenises": "homogenizes",
+  "homogenising": "homogenizing",
+  "honour": "honor",
+  "honourable": "honorable",
+  "honourably": "honorably",
+  "honoured": "honored",
+  "honouring": "honoring",
+  "honours": "honors",
+  "hospitalisation": "hospitalization",
+  "hospitalise": "hospitalize",
+  "hospitalised": "hospitalized",
+  "hospitalises": "hospitalizes",
+  "hospitalising": "hospitalizing",
+  "humanise": "humanize",
+  "humanised": "humanized",
+  "humanises": "humanizes",
+  "humanising": "humanizing",
+  "humour": "humor",
+  "humoured": "humored",
+  "humouring": "humoring",
+  "humourless": "humorless",
+  "humours": "humors",
+  "hybridise": "hybridize",
+  "hybridised": "hybridized",
+  "hybridises": "hybridizes",
+  "hybridising": "hybridizing",
+  "hypnotise": "hypnotize",
+  "hypnotised": "hypnotized",
+  "hypnotises": "hypnotizes",
+  "hypnotising": "hypnotizing",
+  "hypothesise": "hypothesize",
+  "hypothesised": "hypothesized",
+  "hypothesises": "hypothesizes",
+  "hypothesising": "hypothesizing",
+  "idealisation": "idealization",
+  "idealise": "idealize",
+  "idealised": "idealized",
+  "idealises": "idealizes",
+  "idealising": "idealizing",
+  "idolise": "idolize",
+  "idolised": "idolized",
+  "idolises": "idolizes",
+  "idolising": "idolizing",
+  "immobilisation": "immobilization",
+  "immobilise": "immobilize",
+  "immobilised": "immobilized",
+  "immobiliser": "immobilizer",
+  "immobilisers": "immobilizers",
+  "immobilises": "immobilizes",
+  "immobilising": "immobilizing",
+  "immortalise": "immortalize",
+  "immortalised": "immortalized",
+  "immortalises": "immortalizes",
+  "immortalising": "immortalizing",
+  "immunisation": "immunization",
+  "immunise": "immunize",
+  "immunised": "immunized",
+  "immunises": "immunizes",
+  "immunising": "immunizing",
+  "impanelled": "impaneled",
+  "impanelling": "impaneling",
+  "imperilled": "imperiled",
+  "imperilling": "imperiling",
+  "individualise": "individualize",
+  "individualised": "individualized",
+  "individualises": "individualizes",
+  "individualising": "individualizing",
+  "industrialise": "industrialize",
+  "industrialised": "industrialized",
+  "industrialises": "industrializes",
+  "industrialising": "industrializing",
+  "inflexion": "inflection",
+  "inflexions": "inflections",
+  "initialise": "initialize",
+  "initialised": "initialized",
+  "initialises": "initializes",
+  "initialising": "initializing",
+  "initialled": "initialed",
+  "initialling": "initialing",
+  "instal": "install",
+  "instalment": "installment",
+  "instalments": "installments",
+  "instals": "installs",
+  "instil": "instill",
+  "instils": "instills",
+  "institutionalisation": "institutionalization",
+  "institutionalise": "institutionalize",
+  "institutionalised": "institutionalized",
+  "institutionalises": "institutionalizes",
+  "institutionalising": "institutionalizing",
+  "intellectualise": "intellectualize",
+  "intellectualised": "intellectualized",
+  "intellectualises": "intellectualizes",
+  "intellectualising": "intellectualizing",
+  "internalisation": "internalization",
+  "internalise": "internalize",
+  "internalised": "internalized",
+  "internalises": "internalizes",
+  "internalising": "internalizing",
+  "internationalisation": "internationalization",
+  "internationalise": "internationalize",
+  "internationalised": "internationalized",
+  "internationalises": "internationalizes",
+  "internationalising": "internationalizing",
+  "ionisation": "ionization",
+  "ionise": "ionize",
+  "ionised": "ionized",
+  "ioniser": "ionizer",
+  "ionisers": "ionizers",
+  "ionises": "ionizes",
+  "ionising": "ionizing",
+  "italicise": "italicize",
+  "italicised": "italicized",
+  "italicises": "italicizes",
+  "italicising": "italicizing",
+  "itemise": "itemize",
+  "itemised": "itemized",
+  "itemises": "itemizes",
+  "itemising": "itemizing",
+  "jeopardise": "jeopardize",
+  "jeopardised": "jeopardized",
+  "jeopardises": "jeopardizes",
+  "jeopardising": "jeopardizing",
+  "jewelled": "jeweled",
+  "jeweller": "jeweler",
+  "jewellers": "jewelers",
+  "jewellery": "jewelry",
+  "judgement": "judgment",
+  "kilogramme": "kilogram",
+  "kilogrammes": "kilograms",
+  "kilometre": "kilometer",
+  "kilometres": "kilometers",
+  "labelled": "labeled",
+  "labelling": "labeling",
+  "labour": "labor",
+  "laboured": "labored",
+  "labourer": "laborer",
+  "labourers": "laborers",
+  "labouring": "laboring",
+  "labours": "labors",
+  "lacklustre": "lackluster",
+  "legalisation": "legalization",
+  "legalise": "legalize",
+  "legalised": "legalized",
+  "legalises": "legalizes",
+  "legalising": "legalizing",
+  "legitimise": "legitimize",
+  "legitimised": "legitimized",
+  "legitimises": "legitimizes",
+  "legitimising": "legitimizing",
+  "leukaemia": "leukemia",
+  "levelled": "leveled",
+  "leveller": "leveler",
+  "levellers": "levelers",
+  "levelling": "leveling",
+  "libelled": "libeled",
+  "libelling": "libeling",
+  "libellous": "libelous",
+  "liberalisation": "liberalization",
+  "liberalise": "liberalize",
+  "liberalised": "liberalized",
+  "liberalises": "liberalizes",
+  "liberalising": "liberalizing",
+  "licence": "license",
+  "licenced": "licensed",
+  "licences": "licenses",
+  "licencing": "licensing",
+  "likeable": "likable",
+  "lionisation": "lionization",
+  "lionise": "lionize",
+  "lionised": "lionized",
+  "lionises": "lionizes",
+  "lionising": "lionizing",
+  "liquidise": "liquidize",
+  "liquidised": "liquidized",
+  "liquidiser": "liquidizer",
+  "liquidisers": "liquidizers",
+  "liquidises": "liquidizes",
+  "liquidising": "liquidizing",
+  "litre": "liter",
+  "litres": "liters",
+  "localise": "localize",
+  "localised": "localized",
+  "localises": "localizes",
+  "localising": "localizing",
+  "louvre": "louver",
+  "louvred": "louvered",
+  "louvres": "louvers",
+  "lustre": "luster",
+  "magnetise": "magnetize",
+  "magnetised": "magnetized",
+  "magnetises": "magnetizes",
+  "magnetising": "magnetizing",
+  "manoeuvrability": "maneuverability",
+  "manoeuvrable": "maneuverable",
+  "manoeuvre": "maneuver",
+  "manoeuvred": "maneuvered",
+  "manoeuvres": "maneuvers",
+  "manoeuvring": "maneuvering",
+  "manoeuvrings": "maneuverings",
+  "marginalisation": "marginalization",
+  "marginalise": "marginalize",
+  "marginalised": "marginalized",
+  "marginalises": "marginalizes",
+  "marginalising": "marginalizing",
+  "marshalled": "marshaled",
+  "marshalling": "marshaling",
+  "marvelled": "marveled",
+  "marvelling": "marveling",
+  "marvellous": "marvelous",
+  "marvellously": "marvelously",
+  "materialisation": "materialization",
+  "materialise": "materialize",
+  "materialised": "materialized",
+  "materialises": "materializes",
+  "materialising": "materializing",
+  "maximisation": "maximization",
+  "maximise": "maximize",
+  "maximised": "maximized",
+  "maximises": "maximizes",
+  "maximising": "maximizing",
+  "meagre": "meager",
+  "mechanisation": "mechanization",
+  "mechanise": "mechanize",
+  "mechanised": "mechanized",
+  "mechanises": "mechanizes",
+  "mechanising": "mechanizing",
+  "mediaeval": "medieval",
+  "memorialise": "memorialize",
+  "memorialised": "memorialized",
+  "memorialises": "memorializes",
+  "memorialising": "memorializing",
+  "memorise": "memorize",
+  "memorised": "memorized",
+  "memorises": "memorizes",
+  "memorising": "memorizing",
+  "mesmerise": "mesmerize",
+  "mesmerised": "mesmerized",
+  "mesmerises": "mesmerizes",
+  "mesmerising": "mesmerizing",
+  "metabolise": "metabolize",
+  "metabolised": "metabolized",
+  "metabolises": "metabolizes",
+  "metabolising": "metabolizing",
+  "metre": "meter",
+  "metres": "meters",
+  "mhm": "hmm",
+  "micrometre": "micrometer",
+  "micrometres": "micrometers",
+  "militarise": "militarize",
+  "militarised": "militarized",
+  "militarises": "militarizes",
+  "militarising": "militarizing",
+  "milligramme": "milligram",
+  "milligrammes": "milligrams",
+  "millilitre": "milliliter",
+  "millilitres": "milliliters",
+  "millimetre": "millimeter",
+  "millimetres": "millimeters",
+  "miniaturisation": "miniaturization",
+  "miniaturise": "miniaturize",
+  "miniaturised": "miniaturized",
+  "miniaturises": "miniaturizes",
+  "miniaturising": "miniaturizing",
+  "minibusses": "minibuses",
+  "minimise": "minimize",
+  "minimised": "minimized",
+  "minimises": "minimizes",
+  "minimising": "minimizing",
+  "misbehaviour": "misbehavior",
+  "misdemeanour": "misdemeanor",
+  "misdemeanours": "misdemeanors",
+  "misspelt": "misspelled",
+  "mitre": "miter",
+  "mitres": "miters",
+  "mm": "hmm",
+  "mmm": "hmm",
+  "mobilisation": "mobilization",
+  "mobilise": "mobilize",
+  "mobilised": "mobilized",
+  "mobilises": "mobilizes",
+  "mobilising": "mobilizing",
+  "modelled": "modeled",
+  "modeller": "modeler",
+  "modellers": "modelers",
+  "modelling": "modeling",
+  "modernise": "modernize",
+  "modernised": "modernized",
+  "modernises": "modernizes",
+  "modernising": "modernizing",
+  "moisturise": "moisturize",
+  "moisturised": "moisturized",
+  "moisturiser": "moisturizer",
+  "moisturisers": "moisturizers",
+  "moisturises": "moisturizes",
+  "moisturising": "moisturizing",
+  "monologue": "monolog",
+  "monologues": "monologs",
+  "monopolisation": "monopolization",
+  "monopolise": "monopolize",
+  "monopolised": "monopolized",
+  "monopolises": "monopolizes",
+  "monopolising": "monopolizing",
+  "moralise": "moralize",
+  "moralised": "moralized",
+  "moralises": "moralizes",
+  "moralising": "moralizing",
+  "motorised": "motorized",
+  "mould": "mold",
+  "moulded": "molded",
+  "moulder": "molder",
+  "mouldered": "moldered",
+  "mouldering": "moldering",
+  "moulders": "molders",
+  "mouldier": "moldier",
+  "mouldiest": "moldiest",
+  "moulding": "molding",
+  "mouldings": "moldings",
+  "moulds": "molds",
+  "mouldy": "moldy",
+  "moult": "molt",
+  "moulted": "molted",
+  "moulting": "molting",
+  "moults": "molts",
+  "moustache": "mustache",
+  "moustached": "mustached",
+  "moustaches": "mustaches",
+  "moustachioed": "mustachioed",
+  "multicoloured": "multicolored",
+  "nationalisation": "nationalization",
+  "nationalisations": "nationalizations",
+  "nationalise": "nationalize",
+  "nationalised": "nationalized",
+  "nationalises": "nationalizes",
+  "nationalising": "nationalizing",
+  "naturalisation": "naturalization",
+  "naturalise": "naturalize",
+  "naturalised": "naturalized",
+  "naturalises": "naturalizes",
+  "naturalising": "naturalizing",
+  "neighbour": "neighbor",
+  "neighbourhood": "neighborhood",
+  "neighbourhoods": "neighborhoods",
+  "neighbouring": "neighboring",
+  "neighbourliness": "neighborliness",
+  "neighbourly": "neighborly",
+  "neighbours": "neighbors",
+  "neutralisation": "neutralization",
+  "neutralise": "neutralize",
+  "neutralised": "neutralized",
+  "neutralises": "neutralizes",
+  "neutralising": "neutralizing",
+  "normalisation": "normalization",
+  "normalise": "normalize",
+  "normalised": "normalized",
+  "normalises": "normalizes",
+  "normalising": "normalizing",
+  "odour": "odor",
+  "odourless": "odorless",
+  "odours": "odors",
+  "oesophagus": "esophagus",
+  "oesophaguses": "esophaguses",
+  "oestrogen": "estrogen",
+  "offence": "offense",
+  "offences": "offenses",
+  "omelette": "omelet",
+  "omelettes": "omelets",
+  "optimise": "optimize",
+  "optimised": "optimized",
+  "optimises": "optimizes",
+  "optimising": "optimizing",
+  "organisation": "organization",
+  "organisational": "organizational",
+  "organisations": "organizations",
+  "organise": "organize",
+  "organised": "organized",
+  "organiser": "organizer",
+  "organisers": "organizers",
+  "organises": "organizes",
+  "organising": "organizing",
+  "orthopaedic": "orthopedic",
+  "orthopaedics": "orthopedics",
+  "ostracise": "ostracize",
+  "ostracised": "ostracized",
+  "ostracises": "ostracizes",
+  "ostracising": "ostracizing",
+  "outmanoeuvre": "outmaneuver",
+  "outmanoeuvred": "outmaneuvered",
+  "outmanoeuvres": "outmaneuvers",
+  "outmanoeuvring": "outmaneuvering",
+  "overemphasise": "overemphasize",
+  "overemphasised": "overemphasized",
+  "overemphasises": "overemphasizes",
+  "overemphasising": "overemphasizing",
+  "oxidisation": "oxidization",
+  "oxidise": "oxidize",
+  "oxidised": "oxidized",
+  "oxidises": "oxidizes",
+  "oxidising": "oxidizing",
+  "paederast": "pederast",
+  "paederasts": "pederasts",
+  "paediatric": "pediatric",
+  "paediatrician": "pediatrician",
+  "paediatricians": "pediatricians",
+  "paediatrics": "pediatrics",
+  "paedophile": "pedophile",
+  "paedophiles": "pedophiles",
+  "paedophilia": "pedophilia",
+  "palaeolithic": "paleolithic",
+  "palaeontologist": "paleontologist",
+  "palaeontologists": "paleontologists",
+  "palaeontology": "paleontology",
+  "panelled": "paneled",
+  "panelling": "paneling",
+  "panellist": "panelist",
+  "panellists": "panelists",
+  "paralyse": "paralyze",
+  "paralysed": "paralyzed",
+  "paralyses": "paralyzes",
+  "paralysing": "paralyzing",
+  "parcelled": "parceled",
+  "parcelling": "parceling",
+  "parlour": "parlor",
+  "parlours": "parlors",
+  "particularise": "particularize",
+  "particularised": "particularized",
+  "particularises": "particularizes",
+  "particularising": "particularizing",
+  "passivisation": "passivization",
+  "passivise": "passivize",
+  "passivised": "passivized",
+  "passivises": "passivizes",
+  "passivising": "passivizing",
+  "pasteurisation": "pasteurization",
+  "pasteurise": "pasteurize",
+  "pasteurised": "pasteurized",
+  "pasteurises": "pasteurizes",
+  "pasteurising": "pasteurizing",
+  "patronise": "patronize",
+  "patronised": "patronized",
+  "patronises": "patronizes",
+  "patronising": "patronizing",
+  "patronisingly": "patronizingly",
+  "pedalled": "pedaled",
+  "pedalling": "pedaling",
+  "pedestrianisation": "pedestrianization",
+  "pedestrianise": "pedestrianize",
+  "pedestrianised": "pedestrianized",
+  "pedestrianises": "pedestrianizes",
+  "pedestrianising": "pedestrianizing",
+  "penalise": "penalize",
+  "penalised": "penalized",
+  "penalises": "penalizes",
+  "penalising": "penalizing",
+  "pencilled": "penciled",
+  "pencilling": "penciling",
+  "personalise": "personalize",
+  "personalised": "personalized",
+  "personalises": "personalizes",
+  "personalising": "personalizing",
+  "pharmacopoeia": "pharmacopeia",
+  "pharmacopoeias": "pharmacopeias",
+  "philosophise": "philosophize",
+  "philosophised": "philosophized",
+  "philosophises": "philosophizes",
+  "philosophising": "philosophizing",
+  "philtre": "filter",
+  "philtres": "filters",
+  "phoney": "phony",
+  "plagiarise": "plagiarize",
+  "plagiarised": "plagiarized",
+  "plagiarises": "plagiarizes",
+  "plagiarising": "plagiarizing",
+  "plough": "plow",
+  "ploughed": "plowed",
+  "ploughing": "plowing",
+  "ploughman": "plowman",
+  "ploughmen": "plowmen",
+  "ploughs": "plows",
+  "ploughshare": "plowshare",
+  "ploughshares": "plowshares",
+  "polarisation": "polarization",
+  "polarise": "polarize",
+  "polarised": "polarized",
+  "polarises": "polarizes",
+  "polarising": "polarizing",
+  "politicisation": "politicization",
+  "politicise": "politicize",
+  "politicised": "politicized",
+  "politicises": "politicizes",
+  "politicising": "politicizing",
+  "popularisation": "popularization",
+  "popularise": "popularize",
+  "popularised": "popularized",
+  "popularises": "popularizes",
+  "popularising": "popularizing",
+  "pouffe": "pouf",
+  "pouffes": "poufs",
+  "practise": "practice",
+  "practised": "practiced",
+  "practises": "practices",
+  "practising": "practicing",
+  "praesidium": "presidium",
+  "praesidiums": "presidiums",
+  "pressurisation": "pressurization",
+  "pressurise": "pressurize",
+  "pressurised": "pressurized",
+  "pressurises": "pressurizes",
+  "pressurising": "pressurizing",
+  "pretence": "pretense",
+  "pretences": "pretenses",
+  "primaeval": "primeval",
+  "prioritisation": "prioritization",
+  "prioritise": "prioritize",
+  "prioritised": "prioritized",
+  "prioritises": "prioritizes",
+  "prioritising": "prioritizing",
+  "privatisation": "privatization",
+  "privatisations": "privatizations",
+  "privatise": "privatize",
+  "privatised": "privatized",
+  "privatises": "privatizes",
+  "privatising": "privatizing",
+  "professionalisation": "professionalization",
+  "professionalise": "professionalize",
+  "professionalised": "professionalized",
+  "professionalises": "professionalizes",
+  "professionalising": "professionalizing",
+  "programme": "program",
+  "programmes": "programs",
+  "prologue": "prolog",
+  "prologues": "prologs",
+  "propagandise": "propagandize",
+  "propagandised": "propagandized",
+  "propagandises": "propagandizes",
+  "propagandising": "propagandizing",
+  "proselytise": "proselytize",
+  "proselytised": "proselytized",
+  "proselytiser": "proselytizer",
+  "proselytisers": "proselytizers",
+  "proselytises": "proselytizes",
+  "proselytising": "proselytizing",
+  "psychoanalyse": "psychoanalyze",
+  "psychoanalysed": "psychoanalyzed",
+  "psychoanalyses": "psychoanalyzes",
+  "psychoanalysing": "psychoanalyzing",
+  "publicise": "publicize",
+  "publicised": "publicized",
+  "publicises": "publicizes",
+  "publicising": "publicizing",
+  "pulverisation": "pulverization",
+  "pulverise": "pulverize",
+  "pulverised": "pulverized",
+  "pulverises": "pulverizes",
+  "pulverising": "pulverizing",
+  "pummelled": "pummeled",
+  "pummelling": "pummeling",
+  "pyjama": "pajama",
+  "pyjamas": "pajamas",
+  "pzazz": "pizzazz",
+  "quarrelled": "quarreled",
+  "quarrelling": "quarreling",
+  "radicalise": "radicalize",
+  "radicalised": "radicalized",
+  "radicalises": "radicalizes",
+  "radicalising": "radicalizing",
+  "rancour": "rancor",
+  "randomise": "randomize",
+  "randomised": "randomized",
+  "randomises": "randomizes",
+  "randomising": "randomizing",
+  "rationalisation": "rationalization",
+  "rationalisations": "rationalizations",
+  "rationalise": "rationalize",
+  "rationalised": "rationalized",
+  "rationalises": "rationalizes",
+  "rationalising": "rationalizing",
+  "ravelled": "raveled",
+  "ravelling": "raveling",
+  "realisable": "realizable",
+  "realisation": "realization",
+  "realisations": "realizations",
+  "realise": "realize",
+  "realised": "realized",
+  "realises": "realizes",
+  "realising": "realizing",
+  "recognisable": "recognizable",
+  "recognisably": "recognizably",
+  "recognisance": "recognizance",
+  "recognise": "recognize",
+  "recognised": "recognized",
+  "recognises": "recognizes",
+  "recognising": "recognizing",
+  "reconnoitre": "reconnoiter",
+  "reconnoitred": "reconnoitered",
+  "reconnoitres": "reconnoiters",
+  "reconnoitring": "reconnoitering",
+  "refuelled": "refueled",
+  "refuelling": "refueling",
+  "regularisation": "regularization",
+  "regularise": "regularize",
+  "regularised": "regularized",
+  "regularises": "regularizes",
+  "regularising": "regularizing",
+  "remodelled": "remodeled",
+  "remodelling": "remodeling",
+  "remould": "remold",
+  "remoulded": "remolded",
+  "remoulding": "remolding",
+  "remoulds": "remolds",
+  "reorganisation": "reorganization",
+  "reorganisations": "reorganizations",
+  "reorganise": "reorganize",
+  "reorganised": "reorganized",
+  "reorganises": "reorganizes",
+  "reorganising": "reorganizing",
+  "revelled": "reveled",
+  "reveller": "reveler",
+  "revellers": "revelers",
+  "revelling": "reveling",
+  "revitalise": "revitalize",
+  "revitalised": "revitalized",
+  "revitalises": "revitalizes",
+  "revitalising": "revitalizing",
+  "revolutionise": "revolutionize",
+  "revolutionised": "revolutionized",
+  "revolutionises": "revolutionizes",
+  "revolutionising": "revolutionizing",
+  "rhapsodise": "rhapsodize",
+  "rhapsodised": "rhapsodized",
+  "rhapsodises": "rhapsodizes",
+  "rhapsodising": "rhapsodizing",
+  "rigour": "rigor",
+  "rigours": "rigors",
+  "ritualised": "ritualized",
+  "rivalled": "rivaled",
+  "rivalling": "rivaling",
+  "romanticise": "romanticize",
+  "romanticised": "romanticized",
+  "romanticises": "romanticizes",
+  "romanticising": "romanticizing",
+  "rumour": "rumor",
+  "rumoured": "rumored",
+  "rumours": "rumors",
+  "sabre": "saber",
+  "sabres": "sabers",
+  "saltpetre": "saltpeter",
+  "sanitise": "sanitize",
+  "sanitised": "sanitized",
+  "sanitises": "sanitizes",
+  "sanitising": "sanitizing",
+  "satirise": "satirize",
+  "satirised": "satirized",
+  "satirises": "satirizes",
+  "satirising": "satirizing",
+  "saviour": "savior",
+  "saviours": "saviors",
+  "savour": "savor",
+  "savoured": "savored",
+  "savouries": "savories",
+  "savouring": "savoring",
+  "savours": "savors",
+  "savoury": "savory",
+  "scandalise": "scandalize",
+  "scandalised": "scandalized",
+  "scandalises": "scandalizes",
+  "scandalising": "scandalizing",
+  "sceptic": "skeptic",
+  "sceptical": "skeptical",
+  "sceptically": "skeptically",
+  "scepticism": "skepticism",
+  "sceptics": "skeptics",
+  "sceptre": "scepter",
+  "sceptres": "scepters",
+  "scrutinise": "scrutinize",
+  "scrutinised": "scrutinized",
+  "scrutinises": "scrutinizes",
+  "scrutinising": "scrutinizing",
+  "secularisation": "secularization",
+  "secularise": "secularize",
+  "secularised": "secularized",
+  "secularises": "secularizes",
+  "secularising": "secularizing",
+  "sensationalise": "sensationalize",
+  "sensationalised": "sensationalized",
+  "sensationalises": "sensationalizes",
+  "sensationalising": "sensationalizing",
+  "sensitise": "sensitize",
+  "sensitised": "sensitized",
+  "sensitises": "sensitizes",
+  "sensitising": "sensitizing",
+  "sentimentalise": "sentimentalize",
+  "sentimentalised": "sentimentalized",
+  "sentimentalises": "sentimentalizes",
+  "sentimentalising": "sentimentalizing",
+  "sepulchre": "sepulcher",
+  "sepulchres": "sepulchers",
+  "serialisation": "serialization",
+  "serialisations": "serializations",
+  "serialise": "serialize",
+  "serialised": "serialized",
+  "serialises": "serializes",
+  "serialising": "serializing",
+  "sermonise": "sermonize",
+  "sermonised": "sermonized",
+  "sermonises": "sermonizes",
+  "sermonising": "sermonizing",
+  "sheikh": "sheik",
+  "shovelled": "shoveled",
+  "shovelling": "shoveling",
+  "shrivelled": "shriveled",
+  "shrivelling": "shriveling",
+  "signalise": "signalize",
+  "signalised": "signalized",
+  "signalises": "signalizes",
+  "signalising": "signalizing",
+  "signalled": "signaled",
+  "signalling": "signaling",
+  "smoulder": "smolder",
+  "smouldered": "smoldered",
+  "smouldering": "smoldering",
+  "smoulders": "smolders",
+  "snivelled": "sniveled",
+  "snivelling": "sniveling",
+  "snorkelled": "snorkeled",
+  "snorkelling": "snorkeling",
+  "snowplough": "snowplow",
+  "snowploughs": "snowplows",
+  "socialisation": "socialization",
+  "socialise": "socialize",
+  "socialised": "socialized",
+  "socialises": "socializes",
+  "socialising": "socializing",
+  "sodomise": "sodomize",
+  "sodomised": "sodomized",
+  "sodomises": "sodomizes",
+  "sodomising": "sodomizing",
+  "solemnise": "solemnize",
+  "solemnised": "solemnized",
+  "solemnises": "solemnizes",
+  "solemnising": "solemnizing",
+  "sombre": "somber",
+  "specialisation": "specialization",
+  "specialisations": "specializations",
+  "specialise": "specialize",
+  "specialised": "specialized",
+  "specialises": "specializes",
+  "specialising": "specializing",
+  "spectre": "specter",
+  "spectres": "specters",
+  "spiralled": "spiraled",
+  "spiralling": "spiraling",
+  "splendour": "splendor",
+  "splendours": "splendors",
+  "squirrelled": "squirreled",
+  "squirrelling": "squirreling",
+  "stabilisation": "stabilization",
+  "stabilise": "stabilize",
+  "stabilised": "stabilized",
+  "stabiliser": "stabilizer",
+  "stabilisers": "stabilizers",
+  "stabilises": "stabilizes",
+  "stabilising": "stabilizing",
+  "standardisation": "standardization",
+  "standardise": "standardize",
+  "standardised": "standardized",
+  "standardises": "standardizes",
+  "standardising": "standardizing",
+  "stencilled": "stenciled",
+  "stencilling": "stenciling",
+  "sterilisation": "sterilization",
+  "sterilisations": "sterilizations",
+  "sterilise": "sterilize",
+  "sterilised": "sterilized",
+  "steriliser": "sterilizer",
+  "sterilisers": "sterilizers",
+  "sterilises": "sterilizes",
+  "sterilising": "sterilizing",
+  "stigmatisation": "stigmatization",
+  "stigmatise": "stigmatize",
+  "stigmatised": "stigmatized",
+  "stigmatises": "stigmatizes",
+  "stigmatising": "stigmatizing",
+  "storey": "story",
+  "storeys": "stories",
+  "subsidisation": "subsidization",
+  "subsidise": "subsidize",
+  "subsidised": "subsidized",
+  "subsidiser": "subsidizer",
+  "subsidisers": "subsidizers",
+  "subsidises": "subsidizes",
+  "subsidising": "subsidizing",
+  "succour": "succor",
+  "succoured": "succored",
+  "succouring": "succoring",
+  "succours": "succors",
+  "sulphate": "sulfate",
+  "sulphates": "sulfates",
+  "sulphide": "sulfide",
+  "sulphides": "sulfides",
+  "sulphur": "sulfur",
+  "sulphurous": "sulfurous",
+  "summarise": "summarize",
+  "summarised": "summarized",
+  "summarises": "summarizes",
+  "summarising": "summarizing",
+  "swivelled": "swiveled",
+  "swivelling": "swiveling",
+  "symbolise": "symbolize",
+  "symbolised": "symbolized",
+  "symbolises": "symbolizes",
+  "symbolising": "symbolizing",
+  "sympathise": "sympathize",
+  "sympathised": "sympathized",
+  "sympathiser": "sympathizer",
+  "sympathisers": "sympathizers",
+  "sympathises": "sympathizes",
+  "sympathising": "sympathizing",
+  "synchronisation": "synchronization",
+  "synchronise": "synchronize",
+  "synchronised": "synchronized",
+  "synchronises": "synchronizes",
+  "synchronising": "synchronizing",
+  "synthesise": "synthesize",
+  "synthesised": "synthesized",
+  "synthesiser": "synthesizer",
+  "synthesisers": "synthesizers",
+  "synthesises": "synthesizes",
+  "synthesising": "synthesizing",
+  "syphon": "siphon",
+  "syphoned": "siphoned",
+  "syphoning": "siphoning",
+  "syphons": "siphons",
+  "systematisation": "systematization",
+  "systematise": "systematize",
+  "systematised": "systematized",
+  "systematises": "systematizes",
+  "systematising": "systematizing",
+  "tantalise": "tantalize",
+  "tantalised": "tantalized",
+  "tantalises": "tantalizes",
+  "tantalising": "tantalizing",
+  "tantalisingly": "tantalizingly",
+  "tasselled": "tasseled",
+  "technicolour": "technicolor",
+  "temporise": "temporize",
+  "temporised": "temporized",
+  "temporises": "temporizes",
+  "temporising": "temporizing",
+  "tenderise": "tenderize",
+  "tenderised": "tenderized",
+  "tenderises": "tenderizes",
+  "tenderising": "tenderizing",
+  "terrorise": "terrorize",
+  "terrorised": "terrorized",
+  "terrorises": "terrorizes",
+  "terrorising": "terrorizing",
+  "theatre": "theater",
+  "theatregoer": "theatergoer",
+  "theatregoers": "theatergoers",
+  "theatres": "theaters",
+  "theorise": "theorize",
+  "theorised": "theorized",
+  "theorises": "theorizes",
+  "theorising": "theorizing",
+  "tonne": "ton",
+  "tonnes": "tons",
+  "towelled": "toweled",
+  "towelling": "toweling",
+  "toxaemia": "toxemia",
+  "tranquillise": "tranquilize",
+  "tranquillised": "tranquilized",
+  "tranquilliser": "tranquilizer",
+  "tranquillisers": "tranquilizers",
+  "tranquillises": "tranquilizes",
+  "tranquillising": "tranquilizing",
+  "tranquillity": "tranquility",
+  "tranquillize": "tranquilize",
+  "tranquillized": "tranquilized",
+  "tranquillizer": "tranquilizer",
+  "tranquillizers": "tranquilizers",
+  "tranquillizes": "tranquilizes",
+  "tranquillizing": "tranquilizing",
+  "tranquilly": "tranquility",
+  "transistorised": "transistorized",
+  "traumatise": "traumatize",
+  "traumatised": "traumatized",
+  "traumatises": "traumatizes",
+  "traumatising": "traumatizing",
+  "travelled": "traveled",
+  "traveller": "traveler",
+  "travellers": "travelers",
+  "travelling": "traveling",
+  "travelogue": "travelog",
+  "travelogues": "travelogs",
+  "trialled": "trialed",
+  "trialling": "trialing",
+  "tricolour": "tricolor",
+  "tricolours": "tricolors",
+  "trivialise": "trivialize",
+  "trivialised": "trivialized",
+  "trivialises": "trivializes",
+  "trivialising": "trivializing",
+  "tumour": "tumor",
+  "tumours": "tumors",
+  "tunnelled": "tunneled",
+  "tunnelling": "tunneling",
+  "tyrannise": "tyrannize",
+  "tyrannised": "tyrannized",
+  "tyrannises": "tyrannizes",
+  "tyrannising": "tyrannizing",
+  "tyre": "tire",
+  "tyres": "tires",
+  "unauthorised": "unauthorized",
+  "uncivilised": "uncivilized",
+  "underutilised": "underutilized",
+  "unequalled": "unequaled",
+  "unfavourable": "unfavorable",
+  "unfavourably": "unfavorably",
+  "unionisation": "unionization",
+  "unionise": "unionize",
+  "unionised": "unionized",
+  "unionises": "unionizes",
+  "unionising": "unionizing",
+  "unorganised": "unorganized",
+  "unravelled": "unraveled",
+  "unravelling": "unraveling",
+  "unrecognisable": "unrecognizable",
+  "unrecognised": "unrecognized",
+  "unrivalled": "unrivaled",
+  "unsavoury": "unsavory",
+  "untrammelled": "untrammeled",
+  "urbanisation": "urbanization",
+  "urbanise": "urbanize",
+  "urbanised": "urbanized",
+  "urbanises": "urbanizes",
+  "urbanising": "urbanizing",
+  "utilisable": "utilizable",
+  "utilisation": "utilization",
+  "utilise": "utilize",
+  "utilised": "utilized",
+  "utilises": "utilizes",
+  "utilising": "utilizing",
+  "valour": "valor",
+  "vandalise": "vandalize",
+  "vandalised": "vandalized",
+  "vandalises": "vandalizes",
+  "vandalising": "vandalizing",
+  "vaporisation": "vaporization",
+  "vaporise": "vaporize",
+  "vaporised": "vaporized",
+  "vaporises": "vaporizes",
+  "vaporising": "vaporizing",
+  "vapour": "vapor",
+  "vapours": "vapors",
+  "verbalise": "verbalize",
+  "verbalised": "verbalized",
+  "verbalises": "verbalizes",
+  "verbalising": "verbalizing",
+  "victimisation": "victimization",
+  "victimise": "victimize",
+  "victimised": "victimized",
+  "victimises": "victimizes",
+  "victimising": "victimizing",
+  "videodisc": "videodisk",
+  "videodiscs": "videodisks",
+  "vigour": "vigor",
+  "visualisation": "visualization",
+  "visualisations": "visualizations",
+  "visualise": "visualize",
+  "visualised": "visualized",
+  "visualises": "visualizes",
+  "visualising": "visualizing",
+  "vocalisation": "vocalization",
+  "vocalisations": "vocalizations",
+  "vocalise": "vocalize",
+  "vocalised": "vocalized",
+  "vocalises": "vocalizes",
+  "vocalising": "vocalizing",
+  "vulcanised": "vulcanized",
+  "vulgarisation": "vulgarization",
+  "vulgarise": "vulgarize",
+  "vulgarised": "vulgarized",
+  "vulgarises": "vulgarizes",
+  "vulgarising": "vulgarizing",
+  "waggon": "wagon",
+  "waggons": "wagons",
+  "watercolour": "watercolor",
+  "watercolours": "watercolors",
+  "weaselled": "weaseled",
+  "weaselling": "weaseling",
+  "westernisation": "westernization",
+  "westernise": "westernize",
+  "westernised": "westernized",
+  "westernises": "westernizes",
+  "westernising": "westernizing",
+  "womanise": "womanize",
+  "womanised": "womanized",
+  "womaniser": "womanizer",
+  "womanisers": "womanizers",
+  "womanises": "womanizes",
+  "womanising": "womanizing",
+  "woollen": "woolen",
+  "woollens": "woolens",
+  "woollies": "woolies",
+  "woolly": "wooly",
+  "worshipped": "worshiped",
+  "worshipper": "worshiper",
+  "worshipping": "worshiping",
+  "yodelled": "yodeled",
+  "yodelling": "yodeling",
+  "yoghourt": "yogurt",
+  "yoghourts": "yogurts",
+  "yoghurt": "yogurt",
+  "yoghurts": "yogurts"
+}
+
+
+english_name_normalizer = {
+    # ── Double-letter variants ──────────────────────────────────────────────
+    "alan": "allen",
+    "allan": "allen",
+    "bridgette": "bridget",
+    "charly": "charlie",
+    "charley": "charlie",
+    "garry": "gary",
+    "gregg": "greg",
+    "jacky": "jackie",
+    "joann": "joanne",
+    "joane": "joanne",
+    "kellye": "kelly",
+    "kelli": "kelly",
+    "kelley": "kelly",
+    "lilly": "lily",
+    "micheal": "michael",
+    "michele": "michelle",
+    "mollie": "molly",
+    "phillip": "philip",
+    "sallie": "sally",
+    "stacey": "stacy",
+    "stacie": "stacy",
+    "tracey": "tracy",
+    "tracie": "tracy",
+    "bret": "brett",
+    "carrol": "carol",
+    "carole": "carol",
+    "carroll": "carol",
+    "allison": "alison",
+    "alyson": "alison",
+    "russel": "russell",
+    "douglass": "douglas",
+    "dominick": "dominic",
+    "robb": "rob",
+    # ── Chr/Kr variants ─────────────────────────────────────────────────────
+    "kris": "chris",
+    "kristopher": "christopher",
+    "cristopher": "christopher",
+    "kristina": "christina",
+    "kristen": "kristin",
+    # ── C/K variants ────────────────────────────────────────────────────────
+    "karl": "carl",
+    "kathy": "cathy",
+    "katherine": "catherine",
+    "kathryn": "catherine",
+    "catharine": "catherine",
+    "erik": "eric",
+    "erick": "eric",
+    "caren": "karen",
+    "caryn": "karen",
+    "karin": "karen",
+    "katelyn": "caitlin",
+    "kaitlyn": "caitlin",
+    "kaitlin": "caitlin",
+    "nikole": "nicole",
+    "veronika": "veronica",
+    "viktor": "victor",
+    "viktoria": "victoria",
+    "kevan": "kevin",
+    "patrik": "patrick",
+    "frederik": "frederick",
+    "fredrick": "frederick",
+    "lukas": "lucas",
+    # ── Silent letters / alternate spellings ───────────────────────────────
+    "ann": "anne",
+    "jon": "john",
+    "johnathan": "jonathan",
+    "jonathon": "jonathan",
+    "sara": "sarah",
+    "mathew": "matthew",
+    "nicolas": "nicholas",
+    "rachael": "rachel",
+    "rebekah": "rebecca",
+    "devorah": "deborah",
+    "theresa": "teresa",
+    "suzanne": "susanne",
+    "antony": "anthony",
+    "martyn": "martin",
+    "denis": "dennis",
+    "laurence": "lawrence",
+    "tomas": "thomas",
+    "tobey": "toby",
+    # ── Mac/Mc extensions ───────────────────────────────────────────────────
+    "macarthur": "mcarthur",
+    "macartney": "mccartney",
+    "macarthy": "mccarthy",
+    "maccarthy": "mccarthy",
+    "macdonald": "mcdonald",
+    "mackay": "mckay",
+    "mackenzie": "mckenzie",
+    "macleod": "mcleod",
+    "maclean": "mclean",
+    "macmillan": "mcmillan",
+    "macintosh": "mcintosh",
+    "macintyre": "mcintyre",
+    "macnamara": "mcnamara",
+    "macgowan": "mcgowan",
+    # ── International ─────────────────────────────────
+    "mohamad": "mohammed",
+    "mohamed": "mohammed",
+    "mohammad": "mohammed",
+    "muhammad": "mohammed",
+    "muhamad": "mohammed",
+    "muhammed": "mohammed",
+    "mouhamed": "mohammed",
+    "mouhamad": "mohammed",
+    "mahomet": "mohammed",
+    "fatimah": "fatima",
+    "yusuf": "yousef",
+    "yusef": "yousef",
+    "myriam": "miriam",
+    "rajeev": "rajiv",
+    # ── Miscellaneous homophones ────────────────────────────────────────────
+    "alphonso": "alfonso",
+    "bryan": "brian",
+    "geoffrey": "jeffrey",
+    "jeffery": "jeffrey",
+    "geoff": "jeff",
+    "neal": "neil",
+    "shaun": "sean",
+    "shawn": "sean",
+    "shayne": "shane",
+    "stephen": "steven",
+    "toni": "tony",
+    "leigh": "lee",
+    "lewis": "louis",
+    "marc": "mark",
+    "meghan": "megan",
+    "nathalie": "natalie",
+    "robyn": "robin",
+    "rodger": "roger",
+    "linsey": "lindsay",
+    "lindsey": "lindsay",
+    "zackary": "zachary",
+    "zachery": "zachary",
+    "zak": "zach",
+    "sheri": "sherry",
+    "cheri": "sherry",
+    "sherrie": "sherry",
+    "terri": "terry",
+    "lori": "laurie",
+    "jaime": "jamie",
+    "jayson": "jason",
+    "lesley": "leslie",
+    "lynda": "linda",
+    "lynne": "lynn",
+    "gayle": "gail",
+    "rhonda": "ronda",
+    "yvonne": "ivonne",
+    "stewart": "stuart",
+    "walther": "walter",
+    "symon": "simon",
+    "collin": "colin",
+    "dillon": "dylan",
+    "aron": "aaron",
+    "artur": "arthur",
+    "henri": "henry",
+    "josef": "joseph",
+    "pieter": "peter",
+}
+
+
+# Regex-based multi-word → single-token mappings, plus spellings that vary too
+# freely to enumerate in english_spelling_normalizer (e.g. elongations).
+# Applied after symbol removal, so hyphens/punctuation are already stripped.
+# Keys are used with re.sub; values are the replacement strings.
+english_compound_normalizer = {
+    r"\bet\s+cetera\b": "etc",
+    r"\bal\s+right\b": "alright",
+    r"\ball\s+right\b": "alright",
+    r"\bhow\s+ever\b": "however",
+    r"\bwi\s+fi\b": "wifi",
+    r"\bhi\s+fi\b": "hifi",
+    r"\blo\s+fi\b": "lofi",
+    r"\bsci\s+fi\b": "scifi",
+    r"\be\s+mail\b": "email",
+    r"\be\s+book\b": "ebook",
+    r"\be\s+commerce\b": "ecommerce",
+    r"\bx\s+ray\b": "xray",
+    r"\bt\s+shirt\b": "tshirt",
+    r"\ba\s+m\b": "am",
+    r"\bp\s+m\b": "pm",
+    r"\bo\s+k\b": "okay",
+    r"\bo+h+\b": "oh",
+    r"\booo+\b": "oh",
+}

evaluation/standard_asr/vendor/multilingual.py

@@ -0,0 +1,94 @@
+# Adapted from Hugging Face text scoring, Apache-2.0; see provenance.json.
+# Only dependency imports and unrelated data-loading/scoring code were removed.
+import re
+from difflib import SequenceMatcher
+import num2words
+from .normalizer import BasicMultilingualTextNormalizer
+FILLER_WORDS = {}
+
+class MultilingualNormalizer(BasicMultilingualTextNormalizer):
+    """BasicMultilingualTextNormalizer with optional number normalization.
+
+    Call with just text for standard normalization (backward-compatible).
+    Pass lang= to also convert digits to words via num2words and remove
+    language-specific filler words (see FILLER_WORDS).
+    """
+
+    def __init__(self, remove_diacritics: bool = True):
+        super().__init__(remove_diacritics)
+        # Pre-compile filler patterns. Each filler word is passed through the
+        # base normalization itself, so the pattern matches the normalized
+        # text exactly (base normalization may strip punctuation such as "…"
+        # or combining marks). Longest-first so that multi-word and longer
+        # variants match before their prefixes. Matched on whitespace
+        # boundaries ((?<!\S) / (?!\S)) rather than \b, which is unreliable
+        # next to combining marks.
+        self._filler_patterns = {}
+        base_normalize = super().__call__
+        for lang, words in FILLER_WORDS.items():
+            normalized_words = {base_normalize(w) for w in words}
+            normalized_words.discard("")
+            self._filler_patterns[lang] = re.compile(
+                r"(?<!\S)(?:"
+                + "|".join(re.escape(w) for w in sorted(normalized_words, key=len, reverse=True))
+                + r")(?!\S)"
+            )
+
+    def _remove_fillers(self, text, lang):
+        pattern = self._filler_patterns.get(lang)
+        if pattern is None:
+            return text
+        text = pattern.sub("", text)
+        return re.sub(r"\s+", " ", text).strip()
+
+    def _normalize_numbers(self, text, lang):
+        # Join space-separated thousand groups (e.g. "10 000" -> "10000")
+        text = re.sub(r"(\d)\s+(\d{3})\b", r"\1\2", text)
+
+        # Convert remaining digit sequences to words
+        def _replace(m):
+            try:
+                return num2words.num2words(int(m.group()), lang=lang)
+            except Exception:
+                return m.group()
+
+        return re.sub(r"\d+", _replace, text)
+
+    def __call__(self, s, lang=None):
+        s = super().__call__(s)
+        if lang is not None:
+            s = self._remove_fillers(s, lang)
+            s = self._normalize_numbers(s, lang)
+        return s
+
+def normalize_compound_pairs(refs, preds):
+    """Align compound word boundaries between ref/pred pairs.
+
+    When a mismatch region has identical characters ignoring whitespace,
+    normalize both sides to the joined form.
+    """
+    new_refs, new_preds = [], []
+    for ref_text, pred_text in zip(refs, preds):
+        ref_words = ref_text.split()
+        pred_words = pred_text.split()
+
+        sm = SequenceMatcher(None, ref_words, pred_words)
+        new_rw, new_pw = [], []
+
+        for tag, i1, i2, j1, j2 in sm.get_opcodes():
+            if tag == "equal":
+                new_rw.extend(ref_words[i1:i2])
+                new_pw.extend(pred_words[j1:j2])
+            else:
+                rc = "".join(ref_words[i1:i2])
+                pc = "".join(pred_words[j1:j2])
+                if rc == pc:
+                    new_rw.append(rc)
+                    new_pw.append(pc)
+                else:
+                    new_rw.extend(ref_words[i1:i2])
+                    new_pw.extend(pred_words[j1:j2])
+
+        new_refs.append(" ".join(new_rw))
+        new_preds.append(" ".join(new_pw))
+    return new_refs, new_preds

evaluation/standard_asr/vendor/normalizer.py

@@ -0,0 +1,753 @@
+# Copyright 2022 The OpenAI team and The HuggingFace Team. All rights reserved.
+# Most of the code is copy pasted from the original whisper repository
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+import unicodedata
+from fractions import Fraction
+from typing import Iterator, List, Match, Optional, Union
+from .english_abbreviations import english_name_normalizer, english_spelling_normalizer, english_compound_normalizer
+
+import regex
+
+
+# non-ASCII letters that are not separated by "NFKD" normalization
+ADDITIONAL_DIACRITICS = {
+    "œ": "oe",
+    "Œ": "OE",
+    "ø": "o",
+    "Ø": "O",
+    "æ": "ae",
+    "Æ": "AE",
+    "ß": "ss",
+    "ẞ": "SS",
+    "đ": "d",
+    "Đ": "D",
+    "ð": "d",
+    "Ð": "D",
+    "þ": "th",
+    "Þ": "th",
+    "ł": "l",
+    "Ł": "L",
+}
+
+
+def remove_symbols_and_diacritics(s: str, keep=""):
+    """
+    Replace any other markers, symbols, and punctuations with a space, and drop any diacritics (category 'Mn' and some
+    manual mappings)
+    """
+
+    def replace_character(char):
+        if char in keep:
+            return char
+        elif char in ADDITIONAL_DIACRITICS:
+            return ADDITIONAL_DIACRITICS[char]
+
+        elif unicodedata.category(char) == "Mn":
+            return ""
+
+        elif unicodedata.category(char)[0] in "MSP":
+            return " "
+
+        return char
+
+    return "".join(replace_character(c) for c in unicodedata.normalize("NFKD", s))
+
+
+def remove_symbols(s: str):
+    """
+    Replace any other markers, symbols, punctuations with a space, keeping diacritics
+    """
+    return "".join(" " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s))
+
+
+def remove_symbols_keep_marks(s: str):
+    """
+    Replace symbols and punctuation with a space, keeping combining marks.
+
+    Unlike `remove_symbols`, combining marks (category 'M') are preserved. This
+    is required for scripts like Devanagari, where vowel signs (matras) and the
+    virama are combining marks that are integral to words.
+    """
+    return "".join(" " if unicodedata.category(c)[0] in "SP" else c for c in unicodedata.normalize("NFKC", s))
+
+
+class BasicTextNormalizer:
+    def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
+        self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
+        self.split_letters = split_letters
+
+    def __call__(self, s: str):
+        s = s.lower()
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = self.clean(s).lower()
+
+        if self.split_letters:
+            s = " ".join(regex.findall(r"\X", s, regex.U))
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s
+
+
+class BasicMultilingualTextNormalizer:
+    def __init__(self, remove_diacritics: bool = True):
+        # When keeping diacritics, also keep combining marks (category 'M'):
+        # scripts like Devanagari encode vowel signs and the virama as
+        # combining marks, so stripping them mangles words.
+        self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols_keep_marks
+
+    def __call__(self, s: str):
+        s = s.lower()
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = self.clean(s).lower()
+
+        # Remove punctuation and extra spaces
+        s = regex.sub(r"[^\w\s]", "", s)
+        s = re.sub(r"\s+", " ", s).strip()
+
+        return s
+
+
+class EnglishNumberNormalizer:
+    """
+    Convert any spelled-out numbers into arabic numbers, while handling:
+
+    - remove any commas
+    - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
+    - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
+    - spell out `one` and `ones`
+    - interpret successive single-digit numbers as nominal: `one oh one` -> `101`
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        self.zeros = {"o", "oh", "zero"}
+        # fmt: off
+        self.ones = {
+            name: i
+            for i, name in enumerate(
+                ["one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen"],
+                start=1,
+            )
+        }
+        # fmt: on
+        self.ones_plural = {
+            "sixes" if name == "six" else name + "s": (value, "s") for name, value in self.ones.items()
+        }
+        self.ones_ordinal = {
+            "zeroth": (0, "th"),
+            "first": (1, "st"),
+            "second": (2, "nd"),
+            "third": (3, "rd"),
+            "fifth": (5, "th"),
+            "twelfth": (12, "th"),
+            **{
+                name + ("h" if name.endswith("t") else "th"): (value, "th")
+                for name, value in self.ones.items()
+                if value > 3 and value != 5 and value != 12
+            },
+        }
+        self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal}
+
+        self.tens = {
+            "twenty": 20,
+            "thirty": 30,
+            "forty": 40,
+            "fifty": 50,
+            "sixty": 60,
+            "seventy": 70,
+            "eighty": 80,
+            "ninety": 90,
+        }
+        self.tens_plural = {name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()}
+        self.tens_ordinal = {name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()}
+        self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal}
+
+        self.multipliers = {
+            "hundred": 100,
+            "thousand": 1_000,
+            "million": 1_000_000,
+            "billion": 1_000_000_000,
+            "trillion": 1_000_000_000_000,
+            "quadrillion": 1_000_000_000_000_000,
+            "quintillion": 1_000_000_000_000_000_000,
+            "sextillion": 1_000_000_000_000_000_000_000,
+            "septillion": 1_000_000_000_000_000_000_000_000,
+            "octillion": 1_000_000_000_000_000_000_000_000_000,
+            "nonillion": 1_000_000_000_000_000_000_000_000_000_000,
+            "decillion": 1_000_000_000_000_000_000_000_000_000_000_000,
+        }
+        self.multipliers_plural = {name + "s": (value, "s") for name, value in self.multipliers.items()}
+        self.multipliers_ordinal = {name + "th": (value, "th") for name, value in self.multipliers.items()}
+        self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal}
+        self.decimals = {*self.ones, *self.tens, *self.zeros}
+
+        self.preceding_prefixers = {
+            "minus": "-",
+            "negative": "-",
+            "plus": "+",
+            "positive": "+",
+        }
+        self.following_prefixers = {
+            "pound": "£",
+            "pounds": "£",
+            "euro": "€",
+            "euros": "€",
+            "dollar": "$",
+            "dollars": "$",
+            "cent": "¢",
+            "cents": "¢",
+        }
+        self.prefixes = set(list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()))
+        self.suffixers = {
+            "per": {"cent": "%"},
+            "percent": "%",
+        }
+        self.specials = {"and", "double", "triple", "point"}
+
+        self.words = {
+            key
+            for mapping in [
+                self.zeros,
+                self.ones,
+                self.ones_suffixed,
+                self.tens,
+                self.tens_suffixed,
+                self.multipliers,
+                self.multipliers_suffixed,
+                self.preceding_prefixers,
+                self.following_prefixers,
+                self.suffixers,
+                self.specials,
+            ]
+            for key in mapping
+        }
+        self.literal_words = {"one", "ones"}
+
+    def process_words(self, words: List[str]) -> Iterator[str]:
+        prefix: Optional[str] = None
+        value: Optional[Union[str, int]] = None
+        skip = False
+
+        def to_fraction(s: str):
+            try:
+                return Fraction(s)
+            except ValueError:
+                return None
+
+        def is_digit_token(token: Optional[str]) -> bool:
+            """True for tokens that continue a digit sequence ("four", "oh", "20")."""
+            return token is not None and bool(
+                re.match(r"^\d+$", token)
+                or token in self.zeros
+                or token in self.ones
+                or token in self.tens
+            )
+
+        def output(result: Union[str, int]):
+            nonlocal prefix, value
+            result = str(result)
+            if prefix is not None:
+                result = prefix + result
+            value = None
+            prefix = None
+            return result
+
+        if len(words) == 0:
+            return
+
+        for i, current in enumerate(words):
+            prev = words[i - 1] if i != 0 else None
+            next = words[i + 1] if i != len(words) - 1 else None
+            if skip:
+                skip = False
+                continue
+
+            next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
+            has_prefix = current[0] in self.prefixes
+            current_without_prefix = current[1:] if has_prefix else current
+            if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
+                # arabic numbers (potentially with signs and fractions)
+                f = to_fraction(current_without_prefix)
+                if f is None:
+                    raise ValueError("Converting the fraction failed")
+
+                if value is not None:
+                    if isinstance(value, str) and value.endswith("."):
+                        # concatenate decimals / ip address components
+                        value = str(value) + str(current)
+                        continue
+                    else:
+                        yield output(value)
+
+                prefix = current[0] if has_prefix else prefix
+                if f.denominator == 1:
+                    value = f.numerator  # store integers as int
+                else:
+                    value = current_without_prefix
+            elif current not in self.words:
+                # non-numeric words
+                if value is not None:
+                    yield output(value)
+                yield output(current)
+            elif current in self.zeros:
+                # "oh" is far more often the interjection than a spoken zero, so
+                # read it as a digit only inside a digit sequence: the sequence
+                # has to continue on the right ("four oh one", "nineteen oh
+                # five"), and with nothing pending on the left it takes two more
+                # digit tokens, i.e. a serial/phone-style reading ("oh seven nine
+                # eight"). On its own — including the doubled "oh oh" — it stays
+                # a word. "o" and "zero" are unchanged.
+                next2 = words[i + 2] if i + 2 < len(words) else None
+                in_number = is_digit_token(next) and (
+                    value is not None or is_digit_token(next2)
+                )
+                if current == "oh" and not in_number:
+                    if value is not None:
+                        yield output(value)  # don't drop a pending number
+                    yield output(current)
+                else:
+                    value = str(value or "") + "0"
+            elif current in self.ones:
+                ones = self.ones[current]
+
+                if value is None:
+                    value = ones
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:  # replace the last zero with the digit
+                        value = value[:-1] + str(ones)
+                    else:
+                        value = str(value) + str(ones)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+            elif current in self.ones_suffixed:
+                # ordinal or cardinal; yield the number right away
+                ones, suffix = self.ones_suffixed[current]
+                if value is None:
+                    yield output(str(ones) + suffix)
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:
+                        yield output(value[:-1] + str(ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                value = None
+            elif current in self.tens:
+                tens = self.tens[current]
+                if value is None:
+                    value = tens
+                elif isinstance(value, str):
+                    value = str(value) + str(tens)
+                else:
+                    if value % 100 == 0:
+                        value += tens
+                    else:
+                        value = str(value) + str(tens)
+            elif current in self.tens_suffixed:
+                # ordinal or cardinal; yield the number right away
+                tens, suffix = self.tens_suffixed[current]
+                if value is None:
+                    yield output(str(tens) + suffix)
+                elif isinstance(value, str):
+                    yield output(str(value) + str(tens) + suffix)
+                else:
+                    if value % 100 == 0:
+                        yield output(str(value + tens) + suffix)
+                    else:
+                        yield output(str(value) + str(tens) + suffix)
+            elif current in self.multipliers:
+                multiplier = self.multipliers[current]
+                if value is None:
+                    value = multiplier
+                elif isinstance(value, str) or value == 0:
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        value = p.numerator
+                    else:
+                        yield output(value)
+                        value = multiplier
+                else:
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+            elif current in self.multipliers_suffixed:
+                multiplier, suffix = self.multipliers_suffixed[current]
+                if value is None:
+                    yield output(str(multiplier) + suffix)
+                elif isinstance(value, str):
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        yield output(str(p.numerator) + suffix)
+                    else:
+                        yield output(value)
+                        yield output(str(multiplier) + suffix)
+                else:  # int
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+                    yield output(str(value) + suffix)
+                value = None
+            elif current in self.preceding_prefixers:
+                # apply prefix (positive, minus, etc.) if it precedes a number
+                if value is not None:
+                    yield output(value)
+
+                if next in self.words or next_is_numeric:
+                    prefix = self.preceding_prefixers[current]
+                else:
+                    yield output(current)
+            elif current in self.following_prefixers:
+                # apply prefix (dollars, cents, etc.) only after a number
+                if value is not None:
+                    prefix = self.following_prefixers[current]
+                    yield output(value)
+                else:
+                    yield output(current)
+            elif current in self.suffixers:
+                # apply suffix symbols (percent -> '%')
+                if value is not None:
+                    suffix = self.suffixers[current]
+                    if isinstance(suffix, dict):
+                        if next in suffix:
+                            yield output(str(value) + suffix[next])
+                            skip = True
+                        else:
+                            yield output(value)
+                            yield output(current)
+                    else:
+                        yield output(str(value) + suffix)
+                else:
+                    yield output(current)
+            elif current in self.specials:
+                if next not in self.words and not next_is_numeric:
+                    # apply special handling only if the next word can be numeric
+                    if value is not None:
+                        yield output(value)
+                    yield output(current)
+                elif current == "and":
+                    # ignore "and" after hundreds, thousands, etc.
+                    if prev not in self.multipliers:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "double" or current == "triple":
+                    if next in self.ones or next in self.zeros:
+                        repeats = 2 if current == "double" else 3
+                        ones = self.ones.get(next, 0)
+                        value = str(value or "") + str(ones) * repeats
+                        skip = True
+                    else:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "point":
+                    if next in self.decimals or next_is_numeric:
+                        value = str(value or "") + "."
+                else:
+                    # should all have been covered at this point
+                    raise ValueError(f"Unexpected token: {current}")
+            else:
+                # all should have been covered at this point
+                raise ValueError(f"Unexpected token: {current}")
+
+        if value is not None:
+            yield output(value)
+
+    def preprocess(self, s: str):
+        # replace "<number> and a half" with "<number> point five"
+        results = []
+
+        segments = re.split(r"\band\s+a\s+half\b", s)
+        for i, segment in enumerate(segments):
+            if len(segment.strip()) == 0:
+                continue
+            if i == len(segments) - 1:
+                results.append(segment)
+            else:
+                results.append(segment)
+                last_word = segment.rsplit(maxsplit=2)[-1]
+                if last_word in self.decimals or last_word in self.multipliers:
+                    results.append("point five")
+                else:
+                    results.append("and a half")
+
+        s = " ".join(results)
+
+        # put a space at number/letter boundary
+        s = re.sub(r"([a-z])([0-9])", r"\1 \2", s)
+        s = re.sub(r"([0-9])([a-z])", r"\1 \2", s)
+
+        # but remove spaces which could be a suffix
+        s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
+
+        return s
+
+    def postprocess(self, s: str):
+        def combine_cents(m: Match):
+            try:
+                currency = m.group(1)
+                integer = m.group(2)
+                cents = int(m.group(3))
+                return f"{currency}{integer}.{cents:02d}"
+            except ValueError:
+                return m.string
+
+        def extract_cents(m: Match):
+            try:
+                return f"¢{int(m.group(1))}"
+            except ValueError:
+                return m.string
+
+        # apply currency postprocessing; "$2 and ¢7" -> "$2.07"
+        s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s)
+        s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s)
+
+        # write "one(s)" instead of "1(s)", just for the readability
+        s = re.sub(r"\b1(s?)\b", r"one\1", s)
+
+        return s
+
+    def __call__(self, s: str):
+        s = self.preprocess(s)
+        s = " ".join(word for word in self.process_words(s.split()) if word is not None)
+        s = self.postprocess(s)
+
+        return s
+
+
+class EnglishSpellingNormalizer:
+    """
+    Applies British-American spelling mappings as listed in [1].
+
+    [1] https://www.tysto.com/uk-us-spelling-list.html
+    """
+
+    def __init__(self, english_spelling_mapping):
+        self.mapping = english_spelling_mapping
+
+    def __call__(self, s: str):
+        return " ".join(self.mapping.get(word, word) for word in s.split())
+
+
+class EnglishAcronymNormalizer:
+    """
+    Collapse sequences of single-character tokens (letters or digits) into single words.
+
+    This normalizes acronym spacing so that both spaced-out and joined forms match:
+        - "b b c" -> "bbc"
+        - "5 g" -> "5g"
+
+    Lone single-character words surrounded by multi-character words are left untouched
+    (e.g. "a big cat" stays "a big cat").
+    """
+
+    def __call__(self, s: str) -> str:
+        words = s.split()
+        result = []
+        i = 0
+        while i < len(words):
+            if len(words[i]) == 1 and words[i].isalnum():
+                # Start of a potential acronym run
+                run = [words[i]]
+                j = i + 1
+                while j < len(words) and len(words[j]) == 1 and words[j].isalnum():
+                    run.append(words[j])
+                    j += 1
+                # Require 3+ tokens if the run contains common words "a" or "i",
+                # otherwise 2+ is enough (e.g. "5 g" -> "5g")
+                has_common_word = any(c in ("a", "i") for c in run)
+                min_run = 3 if has_common_word else 2
+                if len(run) >= min_run:
+                    result.append("".join(run))
+                else:
+                    result.extend(run)
+                i = j
+            else:
+                result.append(words[i])
+                i += 1
+        return " ".join(result)
+
+
+class EnglishNameNormalizer:
+    """
+    Collapse common name spelling variants to a single canonical form.
+
+    This is intentionally conservative and token-based so it can be extended
+    with project-specific aliases when needed.
+    """
+
+    def __init__(self, english_name_mapping=english_name_normalizer):
+        self.mapping = english_name_mapping
+
+    def __call__(self, s: str):
+        return " ".join(self.mapping.get(word, word) for word in s.split())
+
+
+class EnglishTextNormalizer:
+    def __init__(self, english_spelling_mapping=english_spelling_normalizer):
+        # Filler words / hesitations to remove. Written as regexes so that
+        # arbitrary elongation is covered without enumerating every spelling
+        # ("uh", "uhh", "uuuh", "uhhhh", ...). Each alternative is wrapped in
+        # \b...\b below, so a shorter alternative cannot match a prefix of a
+        # longer token and the order of the single-token patterns is irrelevant.
+        # Hyphens are word boundaries too, which is why most hyphenated forms
+        # need no entry ("um-hmm" is matched as "um" + "hmm") — but any whose
+        # halves are not both fillers must be listed *before* the patterns,
+        # otherwise only the first half is matched ("ah-ha" -> "ha").
+        filler_words = [
+            "ah-ha",         # "ha" alone is not a filler, so match the pair first
+            r"a+h+m*",       # ah, aah, ahh, ahhh, aaah, ahm, ahmm
+            r"a+h+a+",       # aha, ahaa, ahaaa
+            r"e+h+m*",       # eh, ehh, eeeh, ehhh, ehm, ehmm
+            r"e+m+",         # em, emm
+            r"e+r+m*",       # er, err, errr, erm
+            r"h+a+h+",       # hah, hahh
+            r"h+e+h+",       # heh, hehh
+            r"h+m+",         # hm, hmm, hmmm, hhm
+            r"h+u+h+",       # huh, huhh
+            r"m{2,}",        # mm, mmm, mmmm
+            r"m+h+m*",       # mh, mhm, mhmm, mmhm
+            r"t+s+k+",       # tsk
+            r"u+g+h+",       # ugh, uuugh
+            r"u+h+m*",       # uh, uuh, uhh, uhhh, uuuh, uhm, uuuhm
+            r"u+h+u+[hm]*",  # uhuh, uhum
+            r"u+m+h*",       # um, umm, ummm, uuum, umh
+            # Irregular forms, not worth a pattern of their own.
+            "ahem", "eheh", "ehehe", "ehr", "hmmph", "hum", "hunh", "mhum", "mmkay",
+        ]
+        self.ignore_patterns = r"\b(" + "|".join(filler_words) + r")\b"
+        self.replacers = {
+            # Bare o'clock times: the ":00" is not spoken as words, so drop it
+            # ("2:00 AM" -> "2 am"). Applied here, while the colon is still
+            # present, so that a time is distinguishable from an unrelated
+            # digit sequence — by the time symbols are stripped "3:00" and
+            # "3 00" look alike. Without this the minutes are absorbed into the
+            # hour ("3:00" -> "30") or left as a stray token ("11:00" -> "11 0").
+            r"\b(\d{1,2}):00\b": r"\1",
+            # common contractions
+            r"\bwon't\b": "will not",
+            r"\bcan't\b": "can not",
+            r"\blet's\b": "let us",
+            r"\bain't\b": "aint",
+            r"\by'all\b": "you all",
+            r"\bwanna\b": "want to",
+            r"\bgotta\b": "got to",
+            r"\bgonna\b": "going to",
+            r"\bi'ma\b": "i am going to",
+            r"\bimma\b": "i am going to",
+            r"\bwoulda\b": "would have",
+            r"\bcoulda\b": "could have",
+            r"\bshoulda\b": "should have",
+            r"\bma'am\b": "madam",
+            # contractions in titles/prefixes
+            r"\bmr\b": "mister ",
+            r"\bmrs\b": "missus ",
+            r"\bst\b": "saint ",
+            r"\bdr\b": "doctor ",
+            r"\bprof\b": "professor ",
+            r"\bcapt\b": "captain ",
+            r"\bgov\b": "governor ",
+            r"\bald\b": "alderman ",
+            r"\bgen\b": "general ",
+            r"\bsen\b": "senator ",
+            r"\brep\b": "representative ",
+            r"\bpres\b": "president ",
+            r"\brev\b": "reverend ",
+            r"\bhon\b": "honorable ",
+            r"\basst\b": "assistant ",
+            r"\bassoc\b": "associate ",
+            r"\blt\b": "lieutenant ",
+            r"\bcol\b": "colonel ",
+            r"\bjr\b": "junior ",
+            r"\bsr\b": "senior ",
+            r"\besq\b": "esquire ",
+            # prefect tenses, ideally it should be any past participles, but it's harder..
+            r"'d been\b": " had been",
+            r"'s been\b": " has been",
+            r"'d gone\b": " had gone",
+            r"'s gone\b": " has gone",
+            r"'d done\b": " had done",  # "'s done" is ambiguous
+            r"'s got\b": " has got",
+            # general contractions
+            r"n't\b": " not",
+            r"'re\b": " are",
+            r"\b(it|he|she|what|that|who|here|there|how|when|where|why|this)'s\b": r"\1 is",
+            r"'d\b": " would",
+            r"'ll\b": " will",
+            r"'t\b": " not",
+            r"'ve\b": " have",
+            r"'m\b": " am",
+        }
+        self.standardize_numbers = EnglishNumberNormalizer()
+        self.standardize_spellings = EnglishSpellingNormalizer(english_spelling_mapping)
+        self.standardize_names = EnglishNameNormalizer()
+        self.standardize_acronyms = EnglishAcronymNormalizer()
+        # Multi-word compound mappings — defined in english_abbreviations.py
+        self.compound_words = english_compound_normalizer
+
+    def __call__(self, s: str):
+        s = s.lower()
+
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = re.sub(self.ignore_patterns, "", s)
+        s = re.sub(r"\s+'", "'", s)  # standardize when there's a space before an apostrophe
+
+        for pattern, replacement in self.replacers.items():
+            s = re.sub(pattern, replacement, s)
+
+        s = re.sub(r"(\d),(\d)", r"\1\2", s)  # remove commas between digits
+        s = re.sub(r"\.([^0-9]|$)", r" \1", s)  # remove periods not followed by numbers
+        s = remove_symbols_and_diacritics(s, keep=".%$¢€£")  # keep some symbols for numerics
+
+        # Normalize hardcoded compound words (e.g. "wi fi" -> "wifi" after hyphen removal)
+        for pattern, replacement in self.compound_words.items():
+            s = re.sub(pattern, replacement, s)
+
+        s = self.standardize_numbers(s)
+        s = self.standardize_spellings(s)
+        s = self.standardize_names(s)
+        s = self.standardize_acronyms(s)
+
+        # now remove prefix/suffix symbols that are not preceded/followed by numbers
+        s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s)
+        s = re.sub(r"([^0-9])%", r"\1 ", s)
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s

evaluation/standard_asr/vendor/provenance.json

@@ -0,0 +1,23 @@
+{
+  "repository": "https://github.com/huggingface/open_asr_leaderboard",
+  "revision": "48219c6028db0517d704600d92f31edfc96e8c23",
+  "license": "Apache-2.0",
+  "upstream_sha256": {
+    "normalizer/normalizer.py": "490b56393484ef386b486f3679cc2261264af280b3512154431e0bdcf2778295",
+    "normalizer/english_abbreviations.py": "52997cc963e0bd6568d15554ad8d5fb0759f20f361483632a22f90d2d0e07db1",
+    "normalizer/data_utils.py": "738f362b477ee357f743a567b8bbd69fac49e56dce0a477cbdd52dec863842b8",
+    "normalizer/eval_utils.py": "27138d5884e39f5e6422e1dc48ce60dc2f5c15a56e63117115d7e6088d29e449"
+  },
+  "vendored_sha256": {
+    "LICENSE": "5ee13882fce0975f0ad3c3d5c2042af4896c2a669be2d6df5b3d256811267c85",
+    "english_abbreviations.py": "52997cc963e0bd6568d15554ad8d5fb0759f20f361483632a22f90d2d0e07db1",
+    "__init__.py": "8c39e42af3204c41c564c970e24525da160663836b54acc1b629e4342f862334",
+    "multilingual.py": "4c8db6c4171c8c2f5567d2ed625a3a1f0d45156af45ca42d9555c0c9b61a29d5",
+    "normalizer.py": "490b56393484ef386b486f3679cc2261264af280b3512154431e0bdcf2778295"
+  },
+  "modifications": {
+    "normalizer.py": "None",
+    "english_abbreviations.py": "None",
+    "multilingual.py": "Exact MultilingualNormalizer and normalize_compound_pairs definitions extracted; imports minimized, empty FILLER_WORDS preserved."
+  }
+}

evidence/domains-r3-20260908/README.md

@@ -0,0 +1,18 @@
+# Orukeet r3: accent and domain comparison
+
+Orukeet scores **15.25% pooled WER versus Parakeet's 16.72%** on 12,006 recordings across 47 partitions and 25 languages, an 8.8% relative reduction. It improves 36 of the 47 partitions. On all 20 English partitions, comprising 5,120 recordings, WER is **8.84% versus 9.51%**, a 7.0% relative reduction; every English partition improves.
+
+| Pooled comparison | Parakeet errors / words | Parakeet WER | Orukeet errors / words | Orukeet WER |
+|:--|--:|--:|--:|--:|
+| All 47 partitions | 43,939 / 262,747 | 16.72% | 40,068 / 262,698 | 15.25% |
+| All 20 English partitions | 9,032 / 94,993 | 9.51% | 8,399 / 94,993 | 8.84% |
+
+The sample retains its existing 256 recordings per partition and all 230 Lesbos recordings. Of these, 6,118 recordings were included in the earlier FT-4035 adaptation. Greek and Italian EuroSpeech retain their audited human transcript spans. The generic manifest adds three evaluator field aliases while preserving all original fields, references, waveforms and membership.
+
+Both models were decoded afresh, using FP32 NeMo weights, BF16 CUDA autocast, greedy-batch TDT and identical duration-sorted 16 kHz audio. Orukeet is the r3 checkpoint, SHA-256 `031c8ddab4845aeced904a7cde8e8aa57993b2e344716cf83a545b079c473b56`. All 12,006 recordings are scored; 70 empty Parakeet hypotheses and 61 empty Orukeet hypotheses remain.
+
+The final scores use the same pinned English/multilingual normalization and compound-aware WER as the current complete LibriSpeech/FLEURS comparison. This protocol differs from the older domain report's diagnostic scoring, so the current report uses only these freshly paired scores. `inference-comparison.json` retains the earlier diagnostic normalization; `comparison.json` records the final manuscript scores.
+
+`hypotheses-audit.json` independently re-scores all 24,012 predictions and verifies every numerator and denominator against the per-record numeric evidence. Full-partition scoring with unmodified upstream normalizer definitions reproduces all 94 model/partition WERs. `scores.csv` retains full-precision WER/CER and edit counts. The [complete table](../../docs/current-checkpoint-benchmarks.md) includes all 47 comparisons.
+
+All weights, manifests, predictions and report materials remain private for review.

evidence/domains-r3-20260908/comparison.json

@@ -0,0 +1,1535 @@
+{
+  "status": "complete",
+  "publication_authorized": false,
+  "rows": 12006,
+  "sets": {
+    "eurospeech_bg": {
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+      "hours": 1.072353802083333,
+      "language": "bg",
+      "models": {
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+          "words": 7448,
+          "errors": 1059,
+          "substitutions": 631,
+          "deletions": 122,
+          "insertions": 306,
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+          "char_errors": 3352,
+          "utterance_error": 230,
+          "wer": 14.21858216970999,
+          "cer": 7.198539675722109
+        },
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+          "errors": 971,
+          "substitutions": 545,
+          "deletions": 104,
+          "insertions": 322,
+          "chars": 46565,
+          "char_errors": 3128,
+          "utterance_error": 224,
+          "wer": 13.03705692803437,
+          "cer": 6.717491678299152
+        }
+      }
+    },
+    "eurospeech_de": {
+      "rows": 256,
+      "hours": 1.0912282638888895,
+      "language": "de",
+      "models": {
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+          "errors": 1131,
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+          "wer": 13.40047393364929,
+          "cer": 8.533264834318008
+        },
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+          "wer": 11.142687981053879,
+          "cer": 7.147872249336416
+        }
+      }
+    },
+    "eurospeech_el": {
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+      }
+    },
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+      "models": {
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+    "eurospeech_et": {
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+      "models": {
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+          "errors": 2355,
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+    "eurospeech_fi": {
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evidence/domains-r3-20260908/hypotheses-audit.json

@@ -0,0 +1,35 @@
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evidence/domains-r3-20260908/inference-comparison.json

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evidence/domains-r3-20260908/transfer.json

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evidence/r3-promotion-20260908/ci/smoke-results-ci-darwin-arm64.json

@@ -0,0 +1,659 @@
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+      "error": "PCM exceeds the 30 second request limit"
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+  "measurement": "Sequential smoke-call wall time includes file read and JSON IPC; no comparative speed claim. Startup includes loading, without weight hashing."
+}

evidence/r3-promotion-20260908/ci/smoke-results-ci-linux-x64.json

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+}

evidence/r3-promotion-20260908/ci/smoke-results-ci-win32-x64.json

@@ -0,0 +1,659 @@
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+}

evidence/r3-promotion-20260908/export-environment.json

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evidence/r3-promotion-20260908/f16-cpu.json

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+      "name": "silence_returns_empty_text",
+      "passed": true
+    },
+    {
+      "name": "reject_bad-length",
+      "passed": true,
+      "error": "PCM byte count must be divisible by four"
+    },
+    {
+      "name": "reject_nan",
+      "passed": true,
+      "error": "PCM samples must be finite and between -1 and 1"
+    },
+    {
+      "name": "reject_out-of-range",
+      "passed": true,
+      "error": "PCM samples must be finite and between -1 and 1"
+    },
+    {
+      "name": "reject_too-long",
+      "passed": true,
+      "error": "PCM exceeds the 30 second request limit"
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+      "passed": true
+    },
+    {
+      "name": "invalid_operation_path_and_rate_recover",
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+      "name": "real_transcription_after_all_request_errors",
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+      "name": "shutdown_exit_zero_and_json_only_stdout",
+      "passed": true
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+  "measurement": "Sequential smoke-call wall time includes file read and JSON IPC; no comparative speed claim. Startup includes loading, without weight hashing."
+}

evidence/r3-promotion-20260908/f16-lineage.json

@@ -0,0 +1,19 @@
+{
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+}

evidence/r3-promotion-20260908/f16-metal.json

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+            "text": "boissons",
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+          {
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+            "text": "tabac.",
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+      "scope": "Fixed qualitative fixture regression against the recorded Metal output; not a language benchmark.",
+      "normalization": "NFC, lowercase, Unicode punctuation/symbols replaced by spaces, whitespace tokenization"
+    },
+    {
+      "name": "multilingual_es_419_repeats_without_added_word_errors",
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+      "reference": "Esto parece tener sentido, ya que en la Tierra no se percibe su movimiento, ¿cierto?",
+      "prior_fixture_text": "Esto parece tener sentido ya que en la Tierra no se percibe su movimiento, ¿cierto?",
+      "result": {
+        "audio_seconds": 7.68,
+        "text": "Esto parece tener sentido, ya que en la Tierra no se percibe su movimiento, ¿cierto?",
+        "words": [
+          {
+            "text": "Esto",
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+          },
+          {
+            "text": "parece",
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+          {
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+          {
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+          {
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+          {
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+          {
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+          {
+            "text": "movimiento,",
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+          },
+          {
+            "text": "¿cierto?",
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+        ],
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+      "scope": "Fixed qualitative fixture regression against the recorded Metal output; not a language benchmark.",
+      "normalization": "NFC, lowercase, Unicode punctuation/symbols replaced by spaces, whitespace tokenization"
+    },
+    {
+      "name": "multilingual_lv_lv_repeats_without_added_word_errors",
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+      "fixture_sha256": "0046336e59aca17a93029fcd1a6352751b118dab6ffb4fd1c215c7f048674e26",
+      "reference": "Tas mums ir devis vilcienus, automašīnas un daudzus citus transportlīdzekļus.",
+      "prior_fixture_text": "Tas mums ir devas vilcienus, automašīnas un daudzus citas transportlīdzekļus.",
+      "result": {
+        "audio_seconds": 7.14,
+        "text": "Tas mums ir devis vilcienus, automašīnas un daudzus citas transportlīdzekļus.",
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+          {
+            "text": "Tas",
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+          {
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+          {
+            "text": "ir",
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+          },
+          {
+            "text": "devis",
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+          },
+          {
+            "text": "vilcienus,",
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+          },
+          {
+            "text": "automašīnas",
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+          },
+          {
+            "text": "un",
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+          },
+          {
+            "text": "daudzus",
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+            "end": 4.8
+          },
+          {
+            "text": "citas",
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+            "end": 5.04
+          },
+          {
+            "text": "transportlīdzekļus.",
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+            "end": 6.96
+          }
+        ],
+        "final": true
+      },
+      "reference_words": 10,
+      "prior_fixture_errors": 2,
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+      "reference_model_sha256": "8967e04bd73fd8e88bccbfe97d0ec952beac73b1b3b3cba4b5c03316fcd8baa0",
+      "scope": "Fixed qualitative fixture regression against the recorded Metal output; not a language benchmark.",
+      "normalization": "NFC, lowercase, Unicode punctuation/symbols replaced by spaces, whitespace tokenization"
+    },
+    {
+      "name": "empty_returns_empty_text",
+      "passed": true
+    },
+    {
+      "name": "silence_returns_empty_text",
+      "passed": true
+    },
+    {
+      "name": "reject_bad-length",
+      "passed": true,
+      "error": "PCM byte count must be divisible by four"
+    },
+    {
+      "name": "reject_nan",
+      "passed": true,
+      "error": "PCM samples must be finite and between -1 and 1"
+    },
+    {
+      "name": "reject_out-of-range",
+      "passed": true,
+      "error": "PCM samples must be finite and between -1 and 1"
+    },
+    {
+      "name": "reject_too-long",
+      "passed": true,
+      "error": "PCM exceeds the 30 second request limit"
+    },
+    {
+      "name": "malformed_duplicate_oversized_and_invalid_id_requests_recover",
+      "passed": true
+    },
+    {
+      "name": "invalid_operation_path_and_rate_recover",
+      "passed": true
+    },
+    {
+      "name": "real_transcription_after_all_request_errors",
+      "passed": true
+    },
+    {
+      "name": "shutdown_exit_zero_and_json_only_stdout",
+      "passed": true
+    },
+    {
+      "name": "native_log_confirms_metal_gpu_backend",
+      "passed": true
+    }
+  ],
+  "measurement": "Sequential smoke-call wall time includes file read and JSON IPC; no comparative speed claim. Startup includes loading, without weight hashing."
+}