datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HearInContextEnglish | 中文
HearInContext
A Benchmark for Implicit Context in Speech Recognition
Illustrative example: the same spoken request is disambiguated as flour or flower by different assistant histories. The dialogue and waveform are illustrative.
Same audio. Different contexts. Different meanings.
HearInContext is a Mandarin–English contextual speech recognition benchmark. It pairs the same audio with dialogue histories supporting different meanings to evaluate… See the full description on the dataset page: https://huggingface.co/datasets/OPPOer/HearInContext.HEAR-HSet
HEAR-HSet — HEAR Hierarchical Evaluation Set
面向零样本语音合成（Zero-shot TTS）的分层评测基准，覆盖基础泛化、副语言可控生成与高难复杂场景三大维度。
共包含 2,702 条 prompt-target 音频对，约 1GB，语言涵盖中文和英文。
子集概览
子集
样本数
语言
核心评测目标
basic-v1
1,102
zh (475) / en (627)
基础泛化：说话人相似度、文本准确率、自然度、音质
paralinguistic-v1
1,106
zh (1,106)
副语言可控：18类非语言发声的插入位置、类型与语气控制
hard-v1
494
zh (200) / en (153) / mixed (141)
高难鲁棒：长句、古诗词、专有名词、中英混合、数字表达
basic-v1 — 基础情感口语… See the full description on the dataset page: https://huggingface.co/datasets/dinosaaaur/HEAR-HSet.HEAR-DS-16k
HEAR-DS Background Audio (16kHz)
Binaural background audio recordings from the HEAR-DS (Hearing Aid Research Database of Sounds) dataset, downsampled to 16kHz and chunked into 10-second segments for speech enhancement and acoustic scene classification research.
Dataset Description
This dataset contains background noise recordings from 7 acoustic environments, captured using in-the-canal (ITC) hearing aid microphones. Each sample includes stereo (left/right ear) audio.… See the full description on the dataset page: https://huggingface.co/datasets/nkdem/HEAR-DS-16k.supreme-court-hearings-asr
Indian Supreme Court Hearings — ASR dataset
Sentence-level, force-aligned audio–text pairs from Indian Supreme Court hearings, prepared
for fine-tuning ASR models (e.g. Whisper). 46.9 hours across 23 hearings / 15 cases.
Load
from datasets import load_dataset
ds = load_dataset("kirandevraj/supreme-court-hearings-asr")
ds["test"][0] # {'audio': {'array', 'sampling_rate': 16000}, 'text': '...', ...}
Splits
split
clips
hours
train
24,422… See the full description on the dataset page: https://huggingface.co/datasets/kirandevraj/supreme-court-hearings-asr.heart-failure-audio