datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
avqa-processedMusic-AVQAAVQA-videos
AVQA — Audio-Visual Question Answering (videos + annotations)
A drop-in package of the AVQA dataset (Yang et al., ACM MM 2022): real-life
audio-visual question answering over short in-the-wild clips. The original release
ships only the QA annotations and expects users to collect the source videos from
VGGSound themselves. This repository bundles the source video clips together
with the official train/val annotations, so the dataset is usable without any
YouTube scraping.… See the full description on the dataset page: https://huggingface.co/datasets/juyil/AVQA-videos.AVQA
Summary | 摘要
This dataset is collected from the AVQA training subset (train_qa.json). We converted the data to the R1-AQA format, where each line in the text file represents a JSON object with specific keys.
The AVQA training set originally consists of approximately 40k samples. However, we use only about 38k samples because some data sources have become invalid (e.g. link failure, or less than 10 seconds).
Given that there is no quick link to the audio mentioned in the above two… See the full description on the dataset page: https://huggingface.co/datasets/Joysw909/AVQA.MUSIC-AVQA_cls-preprocessedvalor32k-avqa-v2
Valor32k-AVQA v2.0
Valor32k-AVQA v2.0 is an open-ended audio-visual question answering dataset and benchmark with 28,861 videos and 225,487 question-answer pairs in this Hugging Face release. Each question is annotated with a modality label (visual, audio, or audio-visual) and one of six categories: description, action, count, temporal, location, and relative-position.
Links
Paper: ACM Digital Library
Project page: inesriahi.github.io/valor32k-avqa-2
Code and… See the full description on the dataset page: https://huggingface.co/datasets/inesriahi/valor32k-avqa-v2.AVQA-R1-6KThis repository contains data presented in EchoInk-R1: Exploring Audio-Visual Reasoning in Multimodal LLMs via Reinforcement Learning.
For training and inference, please refer to the Code: https://github.com/HarryHsing/EchoInk
Data Format in AVQA-R1-6K:
{
"problem_id": 0,
"problem": "What is the source of the sound in the video?",
"data_type": "image_audio",
"problem_type": "multiple choice",
"options": [
"A. motorcycle",
"B. automobile"… See the full description on the dataset page: https://huggingface.co/datasets/harryhsing/AVQA-R1-6K.AVQA-Audio-Rubrics
AVQA Audio-Reasoning Rubrics
Project Page | Paper | Code
Audio-grounded, binary-evaluable evaluation rubrics for the full
AVQA training set, generated for
process-level reward modeling in audio reasoning RL (e.g. GRPO / RLHF with
rubric-as-reward).
Each training question is annotated with 5 rubrics, one per evaluation
facet, that judge the quality of an audio-reasoning response — not just final
answer correctness. The rubrics are designed to be scored Yes/No by an
LLM judge that… See the full description on the dataset page: https://huggingface.co/datasets/umd-zhou-lab/AVQA-Audio-Rubrics.AVQA_valavqa_hardavqamusic_avqa_hardmusic_avqaavqa_2025