Mondegreen — v0.1.0

用語集を、お願いではなく制約にする。 A private glossary, compiled into a hard phonetic constraint.

Whisper does not know your colleagues' names, your product names or your team's jargon — and 10,000 of them do not fit in a 244-token prompt. Mondegreen corrects them afterwards, locally, as a span replacement that is structurally unable to leave your glossary.

• Code: https://github.com/NagaYu/mondegreen
• Space: https://huggingface.co/spaces/NagaYu/mondegreen
• Dataset: https://huggingface.co/datasets/NagaYu/mondegreen-asr-errors

What is in this repo

• gate.json — the calibrated conservative gate (3 KB, ships with the package)
• lora/ — LoRA adapter for the candidate re-ranker (Qwen2.5-0.5B base)

Read this before assuming what the LM does

The hard constraint is not learned and not in these weights. The legal replacement set for a span is computed by mondegreen.index.PhoneticIndex as a finite list, under bounds evaluated before any model runs:

bound	what it stops
normalised phonetic distance ≤ τ (0.28)	unrelated words
absolute distance ≤ 0.25 + 0.20·√mora	long terms reached via many cheap edits
mora-count difference ≤ 34%	invented syllables
common dictionary words need near-exact homophony	「稼働率」→「加藤率」
containment guard	「新藤さん」→「新藤」 (deleting an honorific)

The LoRA adapter only re-ranks candidates already inside that set. It cannot add to it, cannot introduce a term that is not in your glossary, and cannot rewrite grammar. On synthetic glossaries only ~1% of spans have more than one legal candidate, so most of the time it is not consulted at all — which is precisely why 4-bit quantisation is safe here, and is asserted by tests/test_quantization.py (removing the LM entirely costs < 2 recall points).

gate.json is the calibrated conservative gate — a logistic regression over 18 interpretable span features (AUC 0.985, ECE 0.053, threshold 0.82). It is 3 KB of JSON and it is the component whose job is to say no.

Results

400 held-out sentences, 10,000-term glossary, evaluation glossary strictly disjoint from training by surface and by reading:

condition	CER	WER	term recall	damage rate
(A) raw Whisper	0.2842	0.2541	25.3%	0.00000
(B) Whisper initial_prompt	0.2796	0.2509	27.2%	0.00009
(C) cloud LLM post-processing	0.0893	0.0944	83.0%	0.00657
(D) Mondegreen	0.1105	0.1217	66.3%	0.00009
(E) Mondegreen, quantised	0.1105	0.1217	66.3%	0.00009

(C) wins on term recall (83.0% vs 66.3%) and does 73× the damage (0.00657 vs 0.00009), needs the transcript to leave the machine, and is therefore unusable on the confidential audio that motivates this project. That trade is the finding, not a footnote.

throughput	464 characters/second (10,000-term glossary)
1 hour of transcription	45 seconds
peak memory	196 MB
machine	Apple M2, 16.0 GB
network	none

Provenance. These numbers are simulated: condition (D) is always the real system, but the error generator and baselines (B)/(C) are explicit models whose parameters are printed in the results file. They are not measured Whisper numbers. See benchmarks/README.md in the repo for how to replace them with measurements.

Use

pip install git+https://github.com/NagaYu/mondegreen

from mondegreen import ConstrainedCorrector, load_glossary
corrector = ConstrainedCorrector(load_glossary("terms.csv"))
print(corrector.correct("進藤さんが両氏誤り訂正の話をしました。").text)
# 新藤さんが量子誤り訂正の話をしました。

With the quantised re-ranker:

from mondegreen.runtime import build_reranker
corrector = ConstrainedCorrector(
    load_glossary("terms.csv"),
    lm=build_reranker("mondegreen-Q4_K_M.gguf"),
)

CLI, with the evidence for every edit:

mondegreen fix transcript.txt --glossary terms.csv
mondegreen explain transcript.txt --glossary terms.csv

Training data

Synthetic. Glossaries are generated by mondegreen.harvest.GlossaryBuilder; carrier sentences by SentenceFactory; errors by the phonetic corruption model in mondegreen.simulate, which perturbs the reading using the same confusion classes the distance function discounts and re-renders it as a homophone kanji spelling. No real audio, no real person's name, and no LLM grading anywhere.

Limitations

• Japanese only. The mora table, the confusion costs and the POS rules are all Japanese-specific.
• Without fugashi/pyopenjtalk the bundled 4,030-kanji fallback table is used. It has no part-of-speech information, so the common-word protection cannot fire and the damage rate rises. Install mondegreen[g2p].
• The n-gram candidate accelerator is not exact (99.67% recall vs exhaustive at 10,000 terms). Misses can only cause a missed correction, never an illegal one — the bound is re-verified on every scored candidate.
• Evaluation is on synthetic glossaries and simulated ASR errors.

⚠️ Intended use

A glossary can contain personal names. Use this only on data under your own control. Do not run it over other people's meeting records, or with a glossary of names you have no relationship to. Mondegreen handles text only, never audio, and makes no network calls.

Citation

@software{mondegreen,
  title  = {Mondegreen: private glossaries as hard phonetic constraints for local ASR correction},
  author = {NagaYu},
  year   = {2026},
  url    = {https://github.com/NagaYu/mondegreen},
  license = {Apache-2.0}
}