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

Abstract: 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}.

• The paper introduces AudioGPT, which integrates LLMs with audio models to process diverse audio signals effectively.
• It employs a four-stage pipeline that unifies ASR, TTS, and specialized models to analyze speech, music, and sound.
• Experimental results demonstrate robust multimodal performance, paving the way for more intuitive audio-based human-computer interactions.

AudioGPT: A Multi-Modal AI System for Audio Interaction

The paper "AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head" introduces AudioGPT, a compelling advance in integrating LLMs with audio processing capabilities. It leverages LLMs such as ChatGPT as a versatile interface, complementing them with audio foundation models to enhance capabilities in handling audio-based tasks, thereby extending the potential of AI to engage with spoken dialogue and complex audio environments.

System Overview

AudioGPT is structured around four key stages:

1. Modality Transformation: Converts audio inputs into textual data, allowing LLMs to process spoken language by integrating Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) systems.
2. Task Analysis: Employs LLMs to decipher human intentions embedded in audio inputs, organizing the necessary actions and prompting the system's response.
3. Model Assignment: Utilizes.ChatGPT as a general-purpose interface to delegate tasks among specialized audio models based on the type of audio query received.
4. Response Generation: Synthesizes final outputs by merging processed information into a coherent response, whether in text, audio, or video format.

Technical Contributions

The paper makes several noteworthy contributions:

• Integration with Audio Foundation Models: Incorporates existing audio processing models (e.g., Whisper for speech recognition) rather than developing from scratch, optimizing resource usage.
• Design of an Evaluation Framework: Proposes principles for assessing multi-modal LLMs on consistency, capability, and robustness.
• Demonstration of Multimodal Interaction: Effectively processes diverse tasks across speech, music, sound, and visual domains in dialogue scenarios.

Experimental Findings

The paper demonstrates through a series of experiments that AudioGPT can manage a wide range of audio tasks. By connecting with robust audio models and leveraging ChatGPT's language understanding abilities, the system achieves efficient audio signal processing across multiple tasks such as speech recognition, audio translation, and sound generation.

Implications and Future Directions

Practical Implications

AudioGPT underscores significant advancements in human-computer interaction by enabling more nuanced interactions through audio. This capability is particularly relevant for developing more intuitive AI-driven assistants and tools in domains like content creation, customer service, and accessibility technologies.

Theoretical Implications

From a theoretical standpoint, AudioGPT expands the understanding of multimodal AI systems, providing insights into the synergy between LLMs and domain-specific modules. The work prompts further exploration into optimizing large-scale models' performance across varied modalities.

Future Prospects

Future research could explore the optimization of dialogue length capacities and prompt engineering methodologies to further improve model interaction quality. There is also an opportunity to enhance the system's multi-turn dialogue handling and error resilience in processing unsupported tasks.

Conclusion

AudioGPT exemplifies a substantial leap in multimodal AI systems by effectively equipping LLMs with the capability to handle complex audio tasks. This paper's approach to leveraging existing models for audio processing while harnessing the interactive potential of LLMs marks a significant step forward in developing sophisticated AI interfaces capable of comprehensively understanding and generating audio content.