datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.Stocks-Quarterly-Earnings
Stocks Quarterly Earnings
This dataset includes quarterly earnings report data for various stocks.
355,371 rows over 6,406 symbols, 8 columns, covering 1996-01-31 to 2026-07-31. Refreshed monthly.
Strategies Built on This Data
490 papers in the Papers With Backtest catalogue declare this dataset as an input. 456 of them have been coded and run over their own full history. The median replicated Sharpe ratio is +0.20, and 32% clear a t-statistic of 1.96 on their own… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Stocks-Quarterly-Earnings.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.sp500-earnings-transcripts
S&P 500 Earnings Call Transcripts
Dataset Description
This dataset provides earnings call transcripts for S&P 500 companies, primarily covering 2014-2024, along with quarterly financial metrics and company fundamentals.
📄 Paper: This dataset was prepared for and used in Ca'Zorzi, Manu, Lopardo. Verba Volant, Transcripta Manent: What Corporate Earnings Calls Reveal About the AI Stock Rally. No. 3093. European Central Bank, 2025.
Coverage Statistics
Time… See the full description on the dataset page: https://huggingface.co/datasets/glopardo/sp500-earnings-transcripts.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/kurry/sp500_earnings_transcripts.earnings_callThe dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. Furthermore, the data collection was made possible by Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance enabled the search for stock values and Thomson Reuters Eikon provided the earnings call transcripts. Lastly, the dataset can be used as a benchmark for the evaluation of several NLP techniques to understand their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure.Earnings22-Cleaned-AA-chunked
Earnings22-Cleaned-AA-chunked
Quick links: AA Streaming Speech to Text Leaderboard | Speech to Text methodology
Earnings22-Cleaned-AA-chunked is a chunked version of Earnings22-Cleaned-AA, the cleaned Earnings-22 subset used by Artificial Analysis for streaming Speech to Text evaluation.
The original Earnings-22 data comes from esb/datasets, a corpus of corporate earnings calls. Artificial Analysis manually reviewed and corrected the reference transcripts in the cleaned subset… See the full description on the dataset page: https://huggingface.co/datasets/ArtificialAnalysis/Earnings22-Cleaned-AA-chunked.Fin_Corpus_EarningCall
Dataset Card for "Fin_Corpus_EarningCall"
More Information needed
earnings-calls-qa
Lamini Earning Calls QA Dataset
Description
This dataset contains transcripts of earning calls for various companies, along with questions and answers related to the companies' financial performance and other relevant topics.
Format
The transcripts, questions, and answers are in the form of jsonlines files, with each json object in the file containing the transcript of an earning call for a single company.
Data Pipeline Code
The entire data pipeline… See the full description on the dataset page: https://huggingface.co/datasets/lamini/earnings-calls-qa.earnings-call-data
S&P 500 earnings episodes (2005–2025)
Augmented release built on Bose345/sp500_earnings_transcripts (same transcript calendar span as that collection: 2005–2025). Static tabular data for supervised learning or RL-style experiments on earnings-call episodes. Each row is one company–quarter call, keyed by a stable episode_id, with long-form text (full earnings transcript, SEC press materials), pre-earnings price context, OHLCV anchors, SEC XBRL fundamentals (xbrl_* columns), and… See the full description on the dataset page: https://huggingface.co/datasets/RudrakshNanavaty/earnings-call-data.earnings21
Earnings 21
The Earnings 21 dataset ( also referred to as earnings21 ) is a 39-hour corpus of earnings calls containing entity dense speech from nine different financial sectors. This corpus is intended to benchmark automatic speech recognition (ASR) systems in the wild with special attention towards named entity recognition (NER).
In this repo, we provided the transcoded files to 16KHz with the formatted text. This limits the utility of the original dataset due to the restrictions… See the full description on the dataset page: https://huggingface.co/datasets/Revai/earnings21.earnings22_test@article{del2022earnings,
title={Earnings-22: A practical benchmark for accents in the wild},
author={Del Rio, Miguel and Ha, Peter and McNamara, Quinten and Miller, Corey and Chandra, Shipra},
journal={arXiv preprint arXiv:2203.15591},
year={2022}
}
@article{wang2024audiobench,
title={AudioBench: A Universal Benchmark for Audio Large Language Models},
author={Wang, Bin and Zou, Xunlong and Lin, Geyu and Sun, Shuo and Liu, Zhuohan and Zhang, Wenyu and Liu, Zhengyuan and Aw, AiTi… See the full description on the dataset page: https://huggingface.co/datasets/AudioLLMs/earnings22_test.earnings22_robust_splitfrom datasets import load_dataset, DatasetDict
ds = load_dataset("anton-l/earnings22_robust", split="test")
print(ds)
print("\n", "Split to ==>", "\n")
# split train 90%/ dev 5% / test 5%
# split twice and combine
train_devtest = ds.train_test_split(shuffle=True, seed=1, test_size=0.1)
dev_test = train_devtest['test'].train_test_split(shuffle=True, seed=1, test_size=0.5)
ds_train_dev_test = DatasetDict({'train': train_devtest['train'], 'validation': dev_test['train'], 'test':… See the full description on the dataset page: https://huggingface.co/datasets/sanchit-gandhi/earnings22_robust_split.EarningsCallVoice
EarningsCallVoice: Core-100
EarningsCallVoice is a benchmark family for studying executive vocal delivery
in earnings-call question answering. Core-100 contains 100 manually
verified units. Each unit provides:
an authentic reference clip from the executive's prepared remarks;
the text of an analyst question;
an authentic answer clip from the same executive in the Q&A;
exact reference and answer transcripts;
cryptographic hashes and technical metadata.
The question is text… See the full description on the dataset page: https://huggingface.co/datasets/gmarti/EarningsCallVoice.earnings22_splitWe partition the earnings22 dataset at https://huggingface.co/datasets/anton-l/earnings22_baseline_5_gram by source_id:
Validation: 4420696 4448760 4461799 4469836 4473238 4482110
Test: 4432298 4450488 4470290 4479741 4483338 4485244
Train: remainder
Official script for processing these splits will be released shortly.
earnings22The Earnings 22 dataset ( also referred to as earnings22 ) is a 119-hour corpus of English-language earnings calls collected from global companies.
The primary purpose is to serve as a benchmark for industrial and academic automatic speech recognition (ASR) models on real-world accented speech.earnings22_baseline_5_gram\nThe Earnings 22 dataset ( also referred to as earnings22 ) is a 119-hour corpus of English-language earnings calls collected from global companies.
The primary purpose is to serve as a benchmark for industrial and academic automatic speech recognition (ASR) models on real-world accented speech.earnings-call-llama4-maverick-summary
Earnings Call Summary Dataset (Llama-4-Maverick-17B-128E-Instruct-FP8)
Dataset Description
This dataset contains comprehensive summaries of corporate earnings call transcripts generated using the Llama-4-Maverick-17B-128E-Instruct-FP8 model. Each summary provides structured insights into company performance, strategic initiatives, market conditions, and forward-looking guidance.
Dataset Features
High-quality summaries: Generated using… See the full description on the dataset page: https://huggingface.co/datasets/PursuitOfDataScience/earnings-call-llama4-maverick-summary.EarningsCall-Benchsp500_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/churchill1254/sp500_earnings_transcripts.earnings-calendar
US Earnings Calendar
When a company announced its results, to the second, what those results
were, and which session could act on them.
454 611 earnings releases · 258 482 paired with reported figures ·
4 382 182 corporate events · 2003-04-25 to 2026-09-04
The pipeline lives in recipe/ at the same revision as the data.
See PIPELINE.md for the method.
This is not a calendar of future releases
Nothing here schedules an announcement. A row appears when EDGAR accepted… See the full description on the dataset page: https://huggingface.co/datasets/ZipLime/earnings-calendar.dia-earning21-all
Earnings 21
The Earnings 21 dataset ( also referred to as earnings21 ) is a 39-hour corpus of earnings calls containing entity dense speech from nine different financial sectors. This corpus is intended to benchmark automatic speech recognition (ASR) systems in the wild with special attention towards named entity recognition (NER).
This work has been recently accepted to Interspeech 2021!
File Format Overview
In the following section, we provide an overview of the file… See the full description on the dataset page: https://huggingface.co/datasets/ggfox00000/dia-earning21-all.earnings22_split_resampledWe partition the earnings22 dataset at https://huggingface.co/datasets/anton-l/earnings22_baseline_5_gram by source_id:
Validation: 4420696 4448760 4461799 4469836 4473238 4482110
Test: 4432298 4450488 4470290 4479741 4483338 4485244
Train: remainder
Official script for processing these splits will be released shortly.
earnings22-longform-with-ids
Earnings 22 longform test dataset with an index
Exact same content as the dataset used in the open ASR leaderboard here (split earnings22), but with the additional column "id".
We used the index of each sample as an id.
contextual-earnings22earnings22-keywords
Annotation of this dataset is still in progress. Argmax will publish a conference paper on the annotation process later in 2025.
The license for this dataset is the same as that of the original dataset.
stocks-earnings-income_statement