modulate/entity-transcription-benchmark

Entity Transcription Benchmark

Measures whether a speech recognition system transcribes named entities correctly — as distinct from word error rate.

WER weights every token equally. The tokens that matter for redaction, lookup, routing and search are proper nouns, and they are a small fraction of any transcript. A system can improve WER while getting worse at exactly the words a downstream consumer needs, and nothing in the standard evaluation will show it.

2,151 clips, 6.0 hours, 3,245 entity annotations across two acoustically and linguistically different domains.

Built by Chris James and Angel Carrillo-Bermejo at Modulate.

CC-BY-SA-4.0. See Licensing and attribution for upstream sources and terms.

Quick start

from datasets import load_dataset
ds = load_dataset("modulate/entity-transcription-benchmark", split="test")
print(ds[0])

Score with the shipped matcher:

python scripts/matcher_v5.py --selftest        # expect 49/49
python scripts/score_named_entities.py --hypotheses my_transcripts.csv

Use the shipped scorer. Entity accuracy is highly sensitive to normalization — casing, punctuation, spoken numbers — and independently written scorers do not produce comparable numbers. matcher_v5 is the definition of correctness here.

Composition

subset	clips	entities	audio	median clip
MLCommons_TestPortion	1,354	1,632	3.3 h	7.2 s
Belebele_Fleurs_TestPortion	797	1,613	2.6 h	11.3 s
total	2,151	3,245	6.0 h

MLCommons_TestPortion is spontaneous multi-speaker audio from US municipal and legislative meetings. References are verbatim lowercase.

Belebele_Fleurs_TestPortion is volunteers reading encyclopedic prose. References are cased.

Which subset to report

Report MLCommons as your headline number. It is spontaneous, acoustically harder, and independent of the public benchmark suites that models are commonly evaluated on and sometimes trained on.

Belebele and FLEURS are widely used, so training exposure cannot be ruled out for any given model. That subset is provided as a diagnostic: a large gap between a model's score on the two subsets is itself informative.

This is not hypothetical. In our own evaluation, one system led a second by 12.7 points on Belebele/FLEURS and by only 1.9 points on MLCommons — the same two systems, the same scorer, the same run. A large lead on the public-benchmark subset that shrinks to near-nothing on independent audio is the pattern this dataset exists to expose. We found it in our own system first.

No train split

This is an evaluation set. It ships as a single test split and is not partitioned for training. Nothing stops you training on it; doing so simply invalidates it as a measurement, for you and for anyone comparing against you.

Entity types

type	Belebele/FLEURS	MLCommons	total	tier A	tier B
PERSON	396	797	1,193	790	403
GPE	477	380	857	649	208
ORG	220	291	511	508	3
NORP	171	75	246	0	246
LOC	195	28	223	172	51
OTHER_NAMED	154	61	215	215	0

GPE is polities. LOC is physical places that are not polities. NORP is nationality, religious and political groups including adjectives. OTHER_NAMED covers named events, works, products, laws and facilities, merged because two independent annotation passes could not separate those classes reliably — see ANNOTATION_GUIDELINES.md §5.

Metric and difficulty tiers

Per-entity accuracy: the fraction of annotated spans for which the hypothesis contains a match under matcher_v5 normalization.

Report tier A only for the headline figure, and give n.

tier	spans	what it is
A	2,334	discriminative — multi-token names, rare surnames, uncommon places and organizations
B	911	trivial — bare common given names, very high-frequency places, all demonyms

Tier B is 33.5% of MLCommons and 22.6% of Belebele/FLEURS, because meeting speech is full of bare first names. Any English recognizer transcribes david and jim correctly, so including them inflates the score without discriminating between systems. They are retained so users can re-tier or report both; a tier A+B figure is not comparable to a tier A one.

Report as: entity accuracy, MLCommons, tier A, n=1086.

Fields

field	description
audio	clip, redistributed unmodified from the source corpus
transcript	reference transcript from the source corpus
subset	MLCommons_TestPortion or Belebele_Fleurs_TestPortion
entities	JSON array of span strings
entity_types	JSON array of types, parallel to entities
entity_tiers	JSON array of A/B, parallel to entities
entity_offsets	JSON array of {start, end, label, text}
duration_s	clip duration in seconds

Every span is an exact substring of its transcript, verified on every build. 98.8% additionally carry character offsets; the remainder are cases where one string was annotated more times than it occurs in the transcript.

Case follows the source. MLCommons references are verbatim lowercase, so epa, boston red sox and clean air act are correct as written. The scorer normalizes case and punctuation, so a system that capitalizes properly is not penalized.

Annotation quality

The spans and types in this dataset were produced automatically, not written by human annotators. This is the most important thing to know about it, and we would rather state it plainly than have it discovered.

Types. 2,813 of 3,245 spans are a documented one-to-one rename of the source corpus's own labels, with no model judgement involved. The remaining 522 carried source labels that were not entity types at all — a class containing real entities alongside things that were not names. Those were classified by two independent model passes over a closed inventory:

• raw agreement 82.5%, Cohen's κ = 0.785
• 351 auto-decided where both passes agreed
• 69 disagreements adjudicated by hand
• 90 spans judged not to be entities and removed from the release

Spans. Recovered by LLM extraction after a capitalization heuristic returned zero spans on the lowercase MLCommons references, then corrected by a deterministic boundary sweep implementing the guideline's §4 rules. Every edit was re-validated as an exact substring and reverted if it broke the contract.

Review. A stratified sample of 100 spans was reviewed by hand against ANNOTATION_GUIDELINES.md §8. 100 were judged correct, giving a span accuracy of 100% (95% Wilson CI 96.3–100.0).

Caveat on that figure: the review was conducted by one reviewer, who is also an author of the dataset. Single-reviewer self-assessment is the weakest form of validation and the interval above does not account for reviewer bias. The full review sheet is published as span_review.csv so the judgements can be audited or disputed, and we would welcome an independent pass. Treat the true error rate as somewhere at or above zero and below the upper bound of the interval.

Substring validation proves a span exists in its reference. It does not prove the span is correct. Those are different claims and are reported separately above.

Working papers included

Published in the repository so the decisions can be checked rather than trusted:

file	what it holds
span_review.csv	the 100 reviewed spans and their verdicts
type_decisions_final.csv	every type decision, model-proposed or adjudicated
removed_duplicates.csv	the 683 repeated readings removed, with what was kept instead

Duplicates

FLEURS has multiple speakers read the same sentence. 683 clips repeated a transcript that already appeared elsewhere, which would have silently weighted those entities. They were collapsed to one clip per unique transcript, keeping the best member by alignment status, then annotation count, then duration. Nearly all removals were from the Belebele/FLEURS subset (1,469 → 797); MLCommons lost 11.

The published set contains no duplicate transcripts, exact or near (Jaccard ≥ 0.8 over word 5-grams).

Known limitations

• Annotation quality is machine-generated with single-reviewer validation, as described above.
• 47 clips carry zero entity annotations after non-entity removal. They are retained deliberately as negative examples: a system should predict nothing on them. Exclude them if your metric cannot handle empty references.
• Six entity types. No dates, money, times or quantities.
• English only.
• Two domains: read encyclopedic prose, and US municipal and legislative meetings. Do not generalize to conversational, telephony, broadcast, or accented-L2 speech.
• MLCommons references are verbatim transcripts and carry disfluencies and transcription noise from the source corpus.
• Boundary artifacts remain in an unmeasured portion of unreviewed spans.

Authors

• Chris James, Modulate — corpus construction and data collection
• Angel Carrillo-Bermejo, Modulate — annotation pipeline, scoring, release

Licensing and attribution

Derived from three openly licensed corpora, audio redistributed unmodified:

• MLCommons People's Speech — CC-BY-SA-4.0 — dataset
• Belebele — CC-BY-SA-4.0 — dataset
• FLEURS — CC-BY-4.0 — dataset

Share-alike propagates; this dataset is CC-BY-SA-4.0. Original contributions are the spans, types, tiers, offsets, guidelines and scorer.

Personal and sensitive information

The audio contains identifiable speakers. The MLCommons subset consists of recordings of public municipal and legislative meetings in which named individuals speak and are named by others; those names appear in the reference transcripts and are annotated as entities.

All audio is redistributed from openly licensed corpora under their original terms. Requests concerning a specific recording should be directed to the upstream corpus maintainer as well as to us.

Removal requests. If you appear in a recording in this dataset and want it removed, email ml.modulate@modulate.ai with the clip filename or a description of the recording. We will acknowledge within five working days and, where the request is valid, remove the clip and its annotations in the next version, listing the removal in the changelog without identifying the requester. Because the underlying audio is redistributed from upstream corpora, we will also point you to the relevant maintainer, since removal here does not remove it there.

We have added no information that makes any speaker more identifiable than they already are in the source corpora. No speaker identifiers, demographics or linkage data are published.

Versioning

Current version: 1.0.

Corrections are expected and ship as subsequent versions. Always report the version you evaluated against — an entity accuracy figure is not comparable across versions of the ground truth.

version	change
1.0	initial release

Feedback

If you find a wrong span, a wrong type, or a tier assignment you disagree with, open a discussion on this repository. Disputed annotations are more useful to us than silence, and corrections are credited in the changelog.

Direct contact: ml.modulate@modulate.ai — reaches the Modulate ML team.

Citation

@misc{entity_transcription_benchmark_2026,
  title        = {Entity Transcription Benchmark},
  author       = {James, Chris and Carrillo-Bermejo, Angel},
  year         = {2026},
  publisher    = {Modulate},
  howpublished = {\url{https://huggingface.co/datasets/modulate/entity-transcription-benchmark}}
}

Please also cite the upstream corpora: MLCommons People's Speech, Belebele, and FLEURS.