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AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head
Rongjie Huang, Mingze Li, Dongchao Yang, Jiatong Shi, Xuankai Chang, Zhenhui Ye, Yuning Wu, Zhiqing Hong, Jiawei Huang, Jinglin Liu, Yi Ren, Zhou Zhao, Shinji Watanabe
TL;DR
Current LLMs have limited ability to process complex audio and conduct spoken conversations, while audio-capable multimodal training faces data and computational constraints. AudioGPT connects ChatGPT with audio foundation models and ASR/TTS interfaces, evaluates consistency, capability, and robustness, and demonstrates multi-round speech, music, sound, and talking-head tasks. Its effectiveness remains dependent on prompt engineering, ChatGPT’s token limit, and the underlying audio models.
Problem
Current LLMs have limited complex-audio processing and spoken-dialogue capabilities, while labeled dialogue data are scarce and multimodal training is computationally intensive.
Method
AudioGPT uses ChatGPT as a general-purpose interface connected to audio foundation models and ASR/TTS modality interfaces for spoken audio tasks.
Results
AudioGPT demonstrates multi-round understanding and generation across speech, music, sound, and talking head tasks while evaluating consistency, capability, and robustness.
Takeaways & Limitations
The system enables users to create rich and diverse audio content through multi-round dialogue.
Takeaways & Limitations
AudioGPT depends on prompt engineering, ChatGPT’s maximum token length, and the accuracy and effectiveness of its audio foundation models.
Abstract
from arXiv · showhide
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}.
1 Introduction
AudioGPT addresses limited audio processing and spoken-dialogue capabilities in current LLMs by connecting ChatGPT with audio foundation models and modality interfaces. It evaluates this system across consistency, capability, and robustness, demonstrating multi-round audio understanding and generation.
- Motivation: Audio modality remains limited in LLMs despite its importance for spoken communication and broader intelligence.The scope includes speech, music, sound, and talking head understanding and generation.
- Motivation: Training audio-capable LLMs is constrained by scarce labeled and multilingual dialogue data and high computational costs.Existing audio foundation models make training a multimodal LLM from scratch unnecessary.
- AudioGPT: AudioGPT equips ChatGPT with audio foundation models and ASR/TTS interfaces instead of training a multimodal or spoken language model from scratch.The system uses ChatGPT as a general-purpose interface for audio understanding and generation in spoken dialogues.
- AudioGPT: AudioGPT processes requests through modality transformation, task analysis, model assignment, and response generation.The stages transform speech and text, infer user intent, select models using structured controls, and return a final response.
- Evaluation: The evaluation framework measures consistency, capability, and robustness for multimodal LLMs coordinating audio foundation models.The experiments cover complex audio processing and tasks involving speech, music, sound, and talking head generation and understanding.
- Evaluation: Demonstrations show multi-round dialogues that support rich and diverse audio content creation.The reported demonstrations emphasize efficiency and conversational interaction across audio tasks.
2 Related Works
Related work establishes LLMs as general-purpose language solvers and highlights audio foundation models for speech processing and generation. AudioGPT differs by coordinating existing audio models through ChatGPT rather than training a complex spoken dialogue model from scratch.
- Large Language Models: Scaling and instruction tuning established strong few-shot and zero-shot capabilities for large language models.The cited work includes scaling laws, GPT-3 few-shot learning, and FLAN zero-shot improvement.
- Audio Foundation Models: Self-supervised and discrete-representation methods became important approaches for speech processing and audio generation.HuBERT, SoundStream, and Encodec use unlabeled speech or hierarchical representations carrying semantic information.
- Spoken Dialogue Models: End-to-end generative spoken dialogue models exist, but data and computational scarcity make training complex audio systems from scratch challenging.AudioGPT instead treats ChatGPT as a general-purpose interface and leverages audio foundation models for audio tasks.
- AudioGPT: AudioGPT coordinates multiple audio foundation models through an LLM interface for audio understanding and generation.This approach connects the strengths of language-model reasoning with specialized audio solvers.
3 AudioGPT
AudioGPT is a prompt-based system that coordinates ChatGPT, modality transformation, task handling, and audio foundation models for multi-modal audio tasks. Its inference pipeline transforms inputs, analyzes tasks, assigns models, executes them, and generates user-facing responses.
- System Formulation: AudioGPT is defined by a modality transformer, dialogue engine, prompt manager, task handler, and a set of audio foundation models.The dialogue engine is an LLM, and the foundation-model set contains P audio models.
- Inference Pipeline: During inference, AudioGPT transforms input modalities into a consistent query, extracts structured arguments, assigns an audio processor, and generates the final response.The final response combines the transformed query, context, and executed processor output.
- Modality Transformation: The modality transformer converts an audio query description into text while leaving a text query in textual form.This creates a consistent query representation before task analysis.
- Task Analysis: Task analysis uses dialogue context, input/output modalities, and query descriptions to select a task family and generate model-specific arguments.For audio- or image-input families, arguments may include resources from previous context.
- Supported Tasks: Supported task families include speech recognition, speech translation, audio captioning, speech enhancement, speech separation, and audio generation tasks.The listed capabilities also include sound extraction and detection, talking-head synthesis, text-to-speech, text-to-audio, image-to-audio, and singing synthesis.
- Model Assignment and Response Generation: After selecting a model and assigning its resources, AudioGPT executes the processor and returns outputs suited to the task, such as audio, text, video, or classification visualizations.Audio outputs can include waveform images and downloadable or playable audio files.
4 Evaluating Multi-Modal LLMs
AudioGPT is evaluated as a multi-modal LLM on consistency, capability, and robustness, focusing on intention understanding, audio-task execution, and special cases.
- Evaluation dimensions: The evaluation examines whether AudioGPT understands user intentions and coordinates audio foundation models for complex audio tasks.Consistency concerns intention understanding and model assignment, while capability concerns downstream audio processing.
- Consistency: Zero-shot consistency testing assesses whether models can reason and solve task questions without prior examples.Models receive no explicit task-specific demonstrations.
- Consistency: Consistency evaluation uses human task prompts, semantically varied LLM-generated descriptions, and crowd-sourced ratings of response alignment with human intention.The process tests comprehension across varied expressions and collects judgments from human raters.
- Capability: Capability evaluation reports metrics and downstream datasets for speech, music, sound, and talking-head understanding and generation.Audio foundation models serve as task executors for complex audio information.
- Robustness: Robustness evaluation covers long task chains and unsupported tasks through prompts, generated interaction sessions, and subjective user ratings.The rating process follows three steps and uses the same 20-100 scale described for consistency evaluation.
5 Experiments
The experiments use GPT-3.5-turbo with LangChain and demonstrate AudioGPT in multi-round dialogue and a broad range of simple audio tasks.
- Experimental setup: The experiments use gpt-3.5-turbo with LangChain, greedy decoding, and a maximum generation length of 2048 tokens.Audio foundation models require only a flexible NVIDIA T4 GPU on Hugging Face Spaces.
- Multiple-round dialogue: A 12-round dialogue demonstrates AudioGPT processing speech, music, sound, and talking-head understanding and generation.The case includes follow-up questions, context maintenance, and active interaction with users.
6 Limitation
AudioGPT has limitations in prompt engineering, dialogue length, and dependence on the accuracy and effectiveness of audio foundation models.
- Prompt Engineering: AudioGPT requires time-consuming, expertise-dependent prompt engineering to describe many audio foundation models in natural language.This constraint follows from using ChatGPT to connect the foundation models.
- Length Limitation: ChatGPT’s maximum token length can limit multi-turn dialogue and affect users’ contextual instructions.The limitation concerns the system’s ability to sustain extended conversational context.
- Capability Limitation: AudioGPT’s audio-processing capability is heavily influenced by the accuracy and effectiveness of its audio foundation models.The system relies heavily on those models for processing audio information.
7 Conclusion
The paper presents AudioGPT as a system connecting ChatGPT with audio foundation models and modality transformation for spoken dialogue. It evaluates the system across consistency, capability, and robustness, demonstrating broad audio-task performance in multi-round dialogues.
- Conclusion: AudioGPT connects ChatGPT with audio foundation models and a modality transformation interface for spoken dialogue.The system is organized around modality transformation, task analysis, model assignment, and response generation.
- Conclusion: The evaluation addresses human-intention understanding and cooperation between a multi-modal LLM and foundation models.The reported evaluation dimensions are consistency, capability, and robustness.
- Conclusion: AudioGPT demonstrates speech, music, sound, and talking-head understanding and generation in multi-round dialogues.The conclusion frames these results as enabling creation of rich and diverse audio content.
- Conclusion: The experiments are designed more for demonstration because the manuscript mainly covers system description.This scope qualification accompanies the reported experimental conclusions.