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AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

Authors :
Huang, Rongjie
Li, Mingze
Yang, Dongchao
Shi, Jiatong
Chang, Xuankai
Ye, Zhenhui
Wu, Yuning
Hong, Zhiqing
Huang, Jiawei
Liu, Jinglin
Ren, Yi
Zhao, Zhou
Watanabe, Shinji
Publication Year :
2023

Abstract

Large language models (LLMs) have exhibited remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Despite the recent success, current LLMs are not capable of processing complex audio information or conducting spoken conversations (like Siri or Alexa). In this work, we propose a multi-modal AI system named AudioGPT, which complements LLMs (i.e., ChatGPT) with 1) foundation models to process complex audio information and solve numerous understanding and generation tasks; and 2) the input/output interface (ASR, TTS) to support spoken dialogue. With an increasing demand to evaluate multi-modal LLMs of human intention understanding and cooperation with foundation models, we outline the principles and processes and test AudioGPT in terms of consistency, capability, and robustness. Experimental results demonstrate the capabilities of AudioGPT in solving AI tasks with speech, music, sound, and talking head understanding and generation in multi-round dialogues, which empower humans to create rich and diverse audio content with unprecedented ease. Our system is publicly available at \url{https://github.com/AIGC-Audio/AudioGPT}.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2304.12995
Document Type :
Working Paper