README.md

metadata

license: cc-by-4.0
language:
  - en
task_categories:
  - automatic-speech-recognition
tags:
  - speech
  - asr
  - benchmark
  - finance
  - earnings-calls
  - long-form
  - speaker-diarization
pretty_name: Earnings25
size_categories:
  - n<1K
configs:
  - config_name: segmented
    data_files:
      - split: test
        path: segmented/test-*.parquet
    default: true
  - config_name: full
    data_files:
      - split: test
        path: full/test-*.parquet

Earnings25

A 500-hour speech benchmark for finance — S&P 500 earnings calls with reference transcripts, industry labels, and named-speaker attribution.

Citation

Earnings25 is introduced in our Interspeech 2026 paper, which sets out the sampling design, the evaluation protocol, and reference baselines for Whisper and Parakeet-TDT. Start there for the full picture.

Jiang, D., Zhou, H., Wadhawan, A., Fahy, B., Ramesh, V., Weisberg, D., Derkachevskiy, D., Sheehan, H., Prasad, S., & Franceschini, M. (2026). Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance. Proc. Interspeech 2026, Sydney, Australia. arXiv:2607.23813. https://arxiv.org/abs/2607.23813

@inproceedings{jiang2026earnings25,
  title     = {Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance},
  author    = {Jiang, Denglin and Zhou, Haoran and Wadhawan, Anshul and Fahy, Brendan and
               Ramesh, Vinay and Weisberg, David and Derkachevskiy, Dmitriy and Sheehan, Helen and
               Prasad, Srivas and Franceschini, Michele},
  booktitle = {Proc. Interspeech 2026},
  year      = {2026},
  address   = {Sydney, Australia},
  note      = {Dataset: \url{https://doi.org/10.5281/zenodo.18762167}}
}

📄 Paper (arXiv:2607.23813) · 💾 Zenodo DOI 10.5281/zenodo.18762167

Denglin Jiang, Haoran Zhou, Anshul Wadhawan, Brendan Fahy, Vinay Ramesh, David Weisberg, Dmitriy Derkachevskiy, Helen Sheehan, Srivas Prasad, Michele Franceschini — created at Bloomberg, Interspeech 2026 (Sydney).

About the paper

Existing ASR benchmarks are dominated by read speech, broadcast news, and short utterances. None of them stress what production financial speech systems actually face: hour-long calls, telephony-grade audio, dense domain jargon, and rapid turn-taking among operators, executives, and analysts.

Earnings25 was built to close that gap. It pairs 498 hours of complete S&P 500 earnings calls from Q4 2025 with a 46-hour industry-balanced set covering 290 distinct industries — one segment each — so error can be attributed to domain rather than averaged away. Every item carries company, industry, country, and market-capitalization metadata, and the long-form split adds speaker turns with named attribution.

The paper reports reproducible baselines for Whisper (base / medium / large-v2) and Parakeet-TDT under four scoring conditions, and shows that the ranking of systems is stable across them while absolute error varies sharply by industry. Parakeet-TDT leads at 10.8% WER on the long-form split; Whisper-large-v2 trails it by roughly three points despite being the more widely deployed model.

Quick start

from datasets import load_dataset

# 46 h, one segment per industry, 16 kHz — start here
ds = load_dataset("florencejiang/earnings25", "segmented", split="test")

# 498 h of complete calls, ~58 min each — long-form, with speaker turns
full = load_dataset("florencejiang/earnings25", "full", split="test", streaming=True)

print(ds[0]["text"][:300])
print(ds[0]["industry"], ds[0]["company"], ds[0]["duration_s"])

Why this benchmark

Earnings calls break general-purpose ASR in specific, measurable ways: telephony compression and variable recording quality, dense financial jargon and acronyms, rapid speaker transitions between operators, executives, and analysts, accented English from international executives, and hour-long durations that expose long-form decoding failures. Aggregate WER hides all of it. Earnings25 ships industry labels on every item and speaker turns on the long-form split, so the number can be broken apart.

The two configs

	segmented	full
Hours	45.9	~498
Items	290 segments	514 complete calls
Mean duration	9.5 min	~58 min
Audio	MP3, 16 kHz mono	MP3, original sample rate
Industries	290 — exactly one segment each	284
Countries	US only	12 (93.8% US)
Period	2025	2025 Q4
Speaker turns	none (segments is empty)	~1,000 per call
Use it for	industry-stratified WER, fast iteration	long-form decoding, diarization

Both are evaluation-only. There is no train split by design — treat any training on this data as contamination of the benchmark.

Fields

Field	Type	Notes
id	string	item identifier; segmented ids encode the source call and offsets
audio	Audio	MP3 bytes, decoded on access
text	string	reference transcript for the whole item
company	string	issuer name
industry	string	industry label (290 distinct in segmented)
country	string	issuer domicile
release_date	string	ISO 8601 call timestamp
market_cap	float64	issuer market capitalization at release
duration_s	float32
sample_rate, channels	int32	as stored
num_speakers	int32	count of attributed speakers
speakers	list	{speaker_id, name} — e.g. {"1", "Operator"}, {"2", "Vicente Reynal"}
segments	list	{start, end, text, speaker_id} — populated in full only

On speaker labels

Speakers are attributed by name, not by role. speaker_id in segments joins to the speakers list, where "Operator" is identifiable by name but executives and analysts are not distinguished from one another. Deriving a role taxonomy requires joining the participant names against an external source. Segment text is phrase-level ("Thank you for standing by,"), not word-level.

Baselines

Numbers below are from the paper. WER-N = text-normalized; nc/np = case- and punctuation-insensitive. Score against the text field; for full, decode long-form rather than truncating to the first 30 s.

full (498 h)

Model	WER	WER-N	WER-nc-np	WER-N-nc-np
whisper-base	0.1785	0.1707	0.1157	0.1104
whisper-medium	0.1440	0.1366	0.0859	0.0815
whisper-large-v2	0.1404	0.1341	0.0842	0.0804
parakeet-tdt-0.6b-v2	0.1084	0.1033	0.0641	0.0611

segmented (46 h)

Model	WER	WER-N	WER-nc-np	WER-N-nc-np
whisper-base	0.1937	0.1860	0.1202	0.1161
whisper-medium	0.1554	0.1493	0.0868	0.0837
whisper-large-v2	0.1533	0.1472	0.0846	0.0817
parakeet-tdt-0.6b-v2	0.1111	0.1050	0.0647	0.0606

Submit a result: open a Discussion with your model, config, the four WER figures, and enough detail to reproduce (decoding settings, normalization). Verified runs are added to the table above.

Limitations

• English only; segmented is entirely US-domiciled and full is 93.8% US. Not a test of multilingual or non-US-market ASR.
• segments is empty for every item in segmented. Diarization and speaker-conditioned evaluation are possible on full only.
• Speaker turns are phrase-level. There are no word-level timestamps in this release.
• Speakers carry names, not roles — see On speaker labels.
• full retains original recording quality, so audio conditions vary by call. That is deliberate, but it means cross-call WER variance is high.
• One segment per industry in segmented means per-industry WER rests on a single sample and is indicative, not significant.

Relationship to the paper

This repository mirrors the Zenodo release of record (v1.0.0) and describes the files as shipped. Two details differ from the paper: the paper lists segmented audio as 16 kHz WAV where the release ships 16 kHz MP3, and describes word-level forced alignment that the released manifests do not include. Cite the paper, not this mirror.

License and provenance

Transcripts, annotations, and metadata are released under CC BY 4.0. Audio is redistributed from publicly disclosed corporate earnings calls. The dataset was created at Bloomberg; attribution belongs to all ten authors.