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Audio-FLAN dataset unifies audio understanding and generation for LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Audio-FLAN, a new large-scale dataset designed to unify audio understanding and generation tasks for large language models. The dataset comprises over 100 million instances across 80 diverse tasks, covering speech, music, and general sound domains. Audio-FLAN aims to enable zero-shot learning for unified audio-language models, allowing them to handle both comprehension and creation of audio content. AI

IMPACT Enables unified audio-language models for diverse understanding and generation tasks.

RANK_REASON The cluster contains an academic paper detailing a new dataset for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Liumeng Xue, Ziya Zhou, Jiahao Pan, Zixuan Li, Shuai Fan, Yinghao Ma, Sitong Cheng, Dongchao Yang, Haohan Guo, Yujia Xiao, Xinsheng Wang, Zixuan Shen, Chuanbo Zhu, Xinshen Zhang, Tianchi Liu, Ruibin Yuan, Zeyue Tian, Haohe Liu, Xingjian Du, Emmanouil Ben… ·

    Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound

    arXiv:2502.16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs). However, audio understanding and generation are often treated as distinct tasks…

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