datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
voice-acting-edge-top3
Edge-case reward-Top-3 — voice annotations
1,494,503 synthetic expressive-speech utterances (the reward-Top-3 selection of the
edge-case corpus), annotated with:
a CrisperWhisper-format transcript with corrected vocal bursts — each surviving burst
carries a class name from laion/vocal-burst-detector-v2
at its original timestamp;
raw voice scores at two granularities — whole utterance and per sentence — from
laion/Empathic-Insight-Voice-Plus
(all 40 emotions), the 57 VoiceNet… See the full description on the dataset page: https://huggingface.co/datasets/laion/voice-acting-edge-top3.ark-asr-3b-open-asr-leaderboard-results
ARK-ASR-3B Open ASR Leaderboard Results
Raw JSONL manifests for AutoArk-AI/ARK-ASR-3B on the public English
short-form hf-audio/open-asr-leaderboard splits.
These manifests were generated on a local 8x RTX 4090 machine and scored with
the shared Open ASR Leaderboard scorer:
PYTHONPATH=. python - <<'PY'
from normalizer.eval_utils import score_results
score_results(
'ark_asr/results.AutoArk-AI-ARK-ASR-3B_20260622_official',
'AutoArk-AI/ARK-ASR-3B',
)
PY
Important:… See the full description on the dataset page: https://huggingface.co/datasets/Edge0/ark-asr-3b-open-asr-leaderboard-results.EdgeMMEval
EdgeMMEval
Minimal multimodal evaluation dataset for on-device inference testing.
Covers functional correctness, accuracy, latency stress, and memory
pressure across image, audio, text, multi-turn, combination, structured
output, and tool-calling cases.
Dataset summary
The test split is defined in data/test/metadata.jsonl (200 rows). Each
row has a test_id (for example IMG-001, STO-020) and a modality.
Modality
Samples
Focus
Image
34
VQA, OCR, description… See the full description on the dataset page: https://huggingface.co/datasets/CortexSwarm/EdgeMMEval.