earnings22
flax-wav2vec2-ctc-earnings22-baselinewav2vec2-ctc-earnings22-baselineflax-wav2vec2-2-bart-large-earnings22-robustflax-wav2vec2-2-bart-large-earnings22-baselineflax-wav2vec2-2-bart-large-earnings22-cased-hidden-activation-featproj-dropout-0.2flax-wav2vec2-2-bart-large-earnings22-hidden-activation-featproj-dropout-0.2flax-wav2vec2-ctc-earnings22-cased-hidden-activation-featproj-dropout-0.2flax-wav2vec2-ctc-earnings22-robust
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.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.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.