Datasets:

modulate
/

entity-transcription-benchmark

---
license: cc-by-sa-4.0
task_categories:
  - automatic-speech-recognition
language:
  - en
tags:
  - named-entity-recognition
  - speech
  - benchmark
  - entity-transcription
  - asr-evaluation
pretty_name: Entity Transcription Benchmark
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet
---
# 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](#licensing-and-attribution) for upstream sources and terms.

## Quick start

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

Or benchmark a system end to end. The harness transcribes every clip through
your endpoint and scores the result — no transcript file needed up front:

```bash
huggingface-cli download modulate/entity-transcription-benchmark \
  --repo-type dataset --include "scripts/*" --local-dir .
cd scripts && pip install -r requirements.txt
export MY_STT_KEY=...
python run_benchmark.py --endpoint https://api.example.com/v1/transcribe \
  --api-key-env MY_STT_KEY --response-path result.transcript \
  --system-name my-system --out runs/mine.jsonl
python score_benchmark.py --hyps runs/mine.jsonl
```

Full instructions, vendor shortcuts and the scoring protocol are in
[Reproducing our numbers](#reproducing-our-numbers) below.

**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`](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`](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](https://creativecommons.org/licenses/by-sa/4.0/) — [dataset](https://huggingface.co/datasets/MLCommons/peoples_speech)
- **Belebele** — [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/) — [dataset](https://huggingface.co/datasets/facebook/belebele)
- **FLEURS** — [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — [dataset](https://huggingface.co/datasets/google/fleurs)

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.

## Reproducing our numbers

This repository ships the scoring harness, not just the data. It is
vendor-neutral: point it at any speech-to-text endpoint, name the environment
variable holding your key, and say where the transcript sits in the JSON
response.

```bash
huggingface-cli download modulate/entity-transcription-benchmark \
  --repo-type dataset --include "scripts/*" --local-dir .
cd scripts && pip install -r requirements.txt
export MY_STT_KEY=...
python run_benchmark.py \
  --endpoint https://api.example.com/v1/transcribe \
  --api-key-env MY_STT_KEY \
  --response-path result.transcript \
  --system-name my-system \
  --out runs/mine.jsonl
python score_benchmark.py --hyps runs/mine.jsonl
```

Convenience adapters for Deepgram, OpenAI and ElevenLabs are available through
`--provider`, so you do not have to look up their response shapes. They are
shortcuts; the generic path above is the supported one. Endpoints requiring no
authentication work by omitting `--api-key-env`.

Collection is resumable, so an interrupted run continues where it stopped. API
keys are read from the environment and never from the command line, so they stay
out of shell history and out of the run manifest.

### The scoring protocol

Five things determine the number. Change any of them and the result is no longer
comparable to ours:

| | Default |
|---|---|
| Entity spans scored | tier A only |
| Clips transcribed | all 2,151 |
| Metric | span-level entity accuracy |
| Matcher | `scripts/matcher_v5.py`, self-test must pass 49/49 |
| WER | computed over every transcribed clip |

`entity_tiers` is an array parallel to `entities`, so a single clip can hold both
tier A and tier B spans; the scorer filters spans, not clips. See
[Metric and difficulty tiers](#metric-and-difficulty-tiers) for what each tier
contains and why the headline uses tier A.

`matcher_v5` does considerably more than substring matching: Unicode
canonicalization, abbreviation aliases, spoken-form reconstruction for numbers,
years and dates, and contiguous token matching. A scorer written from scratch
will not produce comparable figures, which is why `score_benchmark.py` refuses to
run when the matcher is missing or its self-test fails.

Hypotheses are scored exactly as the endpoint returns them. If your system has a
formatting layer, benchmark it on and off and state which one your headline used.

Two conventions apply to the figures we publish, which the scorer does not apply
for you: clips where both systems returned nothing are excluded, and a category
is not reported within a subset unless it holds enough examples to be meaningful.
Run the scorer straight and it counts every span.

### Reporting

Scoring writes a per-span hits CSV, a summary JSON and a run manifest. Publish
the summary and the manifest together — a percentage without the manifest is not
reproducible by anyone, including us. Pin the annotations you scored against with
`--revision <commit-sha>`, and record the vendor model string and run date, since
hosted models change without version bumps.

If your number disagrees with ours, diff at the span level rather than arguing
about the percentage:

```bash
python score_benchmark.py --hyps runs/mine.jsonl --compare reference/<run>.hits.csv
```

The full protocol, every option and the known limits are in
[scripts/BENCHMARK.md](scripts/BENCHMARK.md).

## 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

```bibtex
@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.

	---
	license: cc-by-sa-4.0
	task_categories:
	- automatic-speech-recognition
	language:
	- en
	tags:
	- named-entity-recognition
	- speech
	- benchmark
	- entity-transcription
	- asr-evaluation
	pretty_name: Entity Transcription Benchmark
	size_categories:
	- 1K<n<10K
	configs:
	- config_name: default
	data_files:
	- split: test
	path: data/test-*.parquet
	---

	# 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](#licensing-and-attribution) for upstream sources and terms.

	## Quick start

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

	Or benchmark a system end to end. The harness transcribes every clip through
	your endpoint and scores the result — no transcript file needed up front:

	```bash
	huggingface-cli download modulate/entity-transcription-benchmark \
	--repo-type dataset --include "scripts/*" --local-dir .
	cd scripts && pip install -r requirements.txt

	export MY_STT_KEY=...
	python run_benchmark.py --endpoint https://api.example.com/v1/transcribe \
	--api-key-env MY_STT_KEY --response-path result.transcript \
	--system-name my-system --out runs/mine.jsonl

	python score_benchmark.py --hyps runs/mine.jsonl
	```

	Full instructions, vendor shortcuts and the scoring protocol are in
	[Reproducing our numbers](#reproducing-our-numbers) below.

	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`](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`](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](https://creativecommons.org/licenses/by-sa/4.0/) — [dataset](https://huggingface.co/datasets/MLCommons/peoples_speech)
	- Belebele — [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/) — [dataset](https://huggingface.co/datasets/facebook/belebele)
	- FLEURS — [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — [dataset](https://huggingface.co/datasets/google/fleurs)

	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.

	## Reproducing our numbers

	This repository ships the scoring harness, not just the data. It is
	vendor-neutral: point it at any speech-to-text endpoint, name the environment
	variable holding your key, and say where the transcript sits in the JSON
	response.

	```bash
	huggingface-cli download modulate/entity-transcription-benchmark \
	--repo-type dataset --include "scripts/*" --local-dir .
	cd scripts && pip install -r requirements.txt

	export MY_STT_KEY=...
	python run_benchmark.py \
	--endpoint https://api.example.com/v1/transcribe \
	--api-key-env MY_STT_KEY \
	--response-path result.transcript \
	--system-name my-system \
	--out runs/mine.jsonl

	python score_benchmark.py --hyps runs/mine.jsonl
	```

	Convenience adapters for Deepgram, OpenAI and ElevenLabs are available through
	`--provider`, so you do not have to look up their response shapes. They are
	shortcuts; the generic path above is the supported one. Endpoints requiring no
	authentication work by omitting `--api-key-env`.

	Collection is resumable, so an interrupted run continues where it stopped. API
	keys are read from the environment and never from the command line, so they stay
	out of shell history and out of the run manifest.

	### The scoring protocol

	Five things determine the number. Change any of them and the result is no longer
	comparable to ours:

	\| \| Default \|
	\|---\|---\|
	\| Entity spans scored \| tier A only \|
	\| Clips transcribed \| all 2,151 \|
	\| Metric \| span-level entity accuracy \|
	\| Matcher \| `scripts/matcher_v5.py`, self-test must pass 49/49 \|
	\| WER \| computed over every transcribed clip \|

	`entity_tiers` is an array parallel to `entities`, so a single clip can hold both
	tier A and tier B spans; the scorer filters spans, not clips. See
	[Metric and difficulty tiers](#metric-and-difficulty-tiers) for what each tier
	contains and why the headline uses tier A.

	`matcher_v5` does considerably more than substring matching: Unicode
	canonicalization, abbreviation aliases, spoken-form reconstruction for numbers,
	years and dates, and contiguous token matching. A scorer written from scratch
	will not produce comparable figures, which is why `score_benchmark.py` refuses to
	run when the matcher is missing or its self-test fails.

	Hypotheses are scored exactly as the endpoint returns them. If your system has a
	formatting layer, benchmark it on and off and state which one your headline used.

	Two conventions apply to the figures we publish, which the scorer does not apply
	for you: clips where both systems returned nothing are excluded, and a category
	is not reported within a subset unless it holds enough examples to be meaningful.
	Run the scorer straight and it counts every span.

	### Reporting

	Scoring writes a per-span hits CSV, a summary JSON and a run manifest. Publish
	the summary and the manifest together — a percentage without the manifest is not
	reproducible by anyone, including us. Pin the annotations you scored against with
	`--revision <commit-sha>`, and record the vendor model string and run date, since
	hosted models change without version bumps.

	If your number disagrees with ours, diff at the span level rather than arguing
	about the percentage:

	```bash
	python score_benchmark.py --hyps runs/mine.jsonl --compare reference/<run>.hits.csv
	```

	The full protocol, every option and the known limits are in
	[scripts/BENCHMARK.md](scripts/BENCHMARK.md).

	## 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

	```bibtex
	@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.