datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
vocal-affect-bench
VocalAffectBench
VocalAffectBench is a test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw audio.
Paper: VocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio Models
The benchmark targets the expressed emotion — what the speaker conveys through vocal tone, prosody, pace, intensity, and pauses — not inferred internal state.
Contents
280 human-recorded English WAV clips, totalling 2.32 hours.
7… See the full description on the dataset page: https://huggingface.co/datasets/besimple-ai/vocal-affect-bench.vocalcoachbench-review
VocalCoachBench
VocalCoachBench is a singing-audio benchmark for evaluating vocal coaching
judgments. This release contains expert annotations for 515 singing recordings:
free-form coaching feedback, atomic diagnosis/correction claims, Top-3 issue
labels, same-song triplet rankings, and segment-conditioned issue labels.
Subsets:
same_song / Dataset A: 207 Amazing Grace performances from DAMP-S-AG.
Audio is not redistributed; use audio_filename to match the official release.… See the full description on the dataset page: https://huggingface.co/datasets/vocalcoachbench/vocalcoachbench-review.speech2speech_vocalnetVocalset-Breath
Dataset Card — VocalSet-Breath
Dataset summary
VocalSet-Breath is a hand-labeled annotation layer on top of VocalSet (Wilkins et al., 2018) that adds time-aligned audible-breath-event labels. To our knowledge it is the first openly released dedicated breath-event layer for singing voice — onset/offset times with per-event confidence and explicit hard negatives. (Singing corpora such as GTSinger, Opencpop, and M4Singer carry breath only as phoneme tokens or… See the full description on the dataset page: https://huggingface.co/datasets/Ewakaa/Vocalset-Breath.VOCALOID_songscarnatic-raga-vocalsAlpaca-Vocaloid-charShargpt-Vocaloid-char