earning
Datasets
All datasets matching “earning”esb-datasets-earnings22-validation-tiny-filteredA filtered (<=30s duration) slice (512 samples) of the Earnings22 dataset.
def add_duration(sample):
y, sr = sample['audio']["array"], sample['audio']["sampling_rate"]
sample['duration_ms']=librosa.get_duration(y=y, sr=sr) * 1000
return sample
tedlium = load_dataset("esb/datasets", "earnings22", split='validation', trust_remote_code=True)
# compute duration to filter
tedlium = tedlium.map(add_duration)
tedlium = tedlium.select(range(512))
# Whisper max supported duration
tedlium… See the full description on the dataset page: https://huggingface.co/datasets/D4nt3/esb-datasets-earnings22-validation-tiny-filtered.Earnings22-Cleaned-AA
Earnings22-Cleaned-AA
Quick links: AA Speech-to-Text Leaderboard | AA-WER v2.0 article
Earnings22-Cleaned-AA is a cleaned subset of the English Earnings-22 test data from esb/datasets, a corpus of corporate earnings calls from global companies with speakers of many different nationalities and accents. This cleaned subset is the Earnings-22 portion included in AA-WER v2. We manually reviewed and corrected errors in the original ground-truth transcriptions to ensure fairer evaluation… See the full description on the dataset page: https://huggingface.co/datasets/ArtificialAnalysis/Earnings22-Cleaned-AA.earnings22
Dataset Card for Earnings 22
Dataset Summary
Earnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.
This dataset contains 125 files totalling roughly 119 hours of English language earnings calls from global countries.
This dataset provides the full audios, transcripts, and accompanying metadata such as ticker symbol, headquarters country,
and our defined "Language Region".
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/distil-whisper/earnings22.sp500_earnings_transcripts
S&P 500 Earnings Transcripts Dataset
This comprehensive dataset contains earnings call transcripts for S&P 500 companies and US large-caps, spanning from 2005 to 2025. Earnings calls provide valuable insights into company performance, strategic initiatives, and management perspectives that are essential for financial analysis, natural language processing research, and market sentiment studies.
Dataset Description
This collection includes:
Complete transcripts: Full… See the full description on the dataset page: https://huggingface.co/datasets/Bose345/sp500_earnings_transcripts.earnings-call-transcriptslanguage:
en
tags:
finance
earnings-calls
transcripts
nlp
llm
rag
financial-analysis
license: other
pretty_name: Earnings Call Transcripts
size_categories:
- 10K<n<100K
Earnings Call Transcripts Dataset
A cleaned financial NLP dataset containing earnings call transcripts collected from publicly available earnings call pages.
Dataset Overview
This dataset contains:
Company earnings call transcripts
Ticker symbols
Earnings quarters
Earnings years
Call dates… See the full description on the dataset page: https://huggingface.co/datasets/Rogersurf/earnings-call-transcripts.earnings25
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., &… See the full description on the dataset page: https://huggingface.co/datasets/florencejiang/earnings25.