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

TL;DR

AudioGPT is a multi-modal AI system that enhances large language models with audio processing capabilities, enabling complex understanding and generation of speech, music, and sounds in multi-turn dialogues, advancing human-AI audio interactions.

Contribution

The paper introduces AudioGPT, a novel multi-modal system that integrates foundation models and speech interfaces with LLMs for comprehensive audio understanding and generation.

Findings

Demonstrates effective multi-turn dialogue capabilities with audio content

Shows robustness and consistency in audio understanding tasks

Enables creation of diverse audio content with ease

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}.