datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MECAT-QAMECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
📖 Paper | 🛠️ GitHub | 🎧 Demo | 🔊 MECAT-Caption (HF)
Dataset Description
MECAT (Multi-Expert Chain for Audio Tasks) is a comprehensive benchmark constructed on large-scale data to evaluate machine understanding of audio content through two core tasks:
Audio Captioning: Generating textual descriptions for given audio
Audio Question Answering: Answering questions about given audio… See the full description on the dataset page: https://huggingface.co/datasets/mispeech/MECAT-QA.MECAT-CaptionMECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
📖 Paper | 🛠️ GitHub | 🎧 Demo | 🔊 MECAT-QA (HF)
Dataset Description
MECAT (Multi-Expert Chain for Audio Tasks) is a comprehensive benchmark constructed on large-scale data to evaluate machine understanding of audio content through two core tasks:
Audio Captioning: Generating textual descriptions for given audio
Audio Question Answering: Answering questions about given audio
Generated via… See the full description on the dataset page: https://huggingface.co/datasets/mispeech/MECAT-Caption.Light-Omni-Training
Light-Omni Training Dataset
This repository contains the training data used by Light-Omni, a multimodal
agent framework for reflexive video understanding with long-term memory.
Light-Omni uses memory-augmented multimodal streams to train adapters for
memory construction, response generation, and reaction/action control.
Links
Project page: https://clare-nie.github.io/Light-Omni/
Code: https://github.com/Clare-Nie/Light-Omni
Dataset:… See the full description on the dataset page: https://huggingface.co/datasets/ClareNie/Light-Omni-Training.