Problem

The current landscape of audio artificial intelligence is characterized by fragmented tools, which complicates the creation of unified, versatile, and specialized workflows. This fragmentation leads to complex, multi-stage tasks that require frequent tool switching, diminishing user-friendliness, especially for non-professionals. Existing intelligent solutions often lack effective collaborative mechanisms for task decomposition, tool selection, and execution feedback, hindering the integration of diverse audio tools into practical real-world applications. Furthermore, current audio agent technologies suffer from limited functional coverage, burden Large Language Models (LLMs) with excessive contextual information leading to tool hallucination, and are plagued by tightly coupled modules that cause dependency conflicts and impede sustainable development.

Method

AudioFab introduces an open-source, general, and intelligent audio factory framework designed for audio-centered multimodal applications. It utilizes a modular architecture based on the Model Context Protocol (MCP) to standardize interactions between audio tools and LLMs. The framework comprises an MCP Client (user interface), an LLM Planner (core decision-maker), an MCP Server (internal management hub), a Tool Selection module, and a Process Selection module. A key innovation is its specialized audio tool library, managed modularly to support extensibility and prevent dependency conflicts by running each tool in an isolated environment. AudioFab implements an intelligent tool learning process unfolding in four stages: Task Planning, where the LLM interprets user commands and decomposes them into subtasks; Tool Selection, which semantically matches user requests to relevant tools, dynamically adjusts context length, and leverages few-shot learning to ensure accurate tool identification and invocation; Tool Invocation, where structured tool requests are executed through the MCP Server; and Response Generation, where the LLM synthesizes the results from all completed subtasks into a coherent user response.

Results

Due to the absence of standardized benchmarks for evaluating its broad capabilities, AudioFab’s performance is assessed through three illustrative use cases that demonstrate its versatility and effectiveness across different modalities and chained workflows. The Music Creation example showcases its ability to analyze pop song styles, separate vocals, and generate new segments mirroring the original style. The Speech Perception case highlights its capabilities in emotion recognition, content transcription, and regenerating speech with reversed emotions while maintaining original timbre. The Multimodal Processing example demonstrates its power to create audio-driven animations by transforming images based on audio dynamics and synthesizing coherent video. These scenarios collectively confirm AudioFab's effectiveness as a specialized toolset and tool learning solution, indicating its potential as a unified platform for intelligent audio processing across 36 supported audio tasks, ranging from basic operations to advanced applications.

Implications

AudioFab serves as a unified, open-source audio agent framework that integrates 36 key functionalities across speech, sound, and music modalities through an intuitive natural language interface. For non-expert users, it simplifies complex audio tasks by eliminating the need for specialized domain knowledge. For researchers and developers, it significantly reduces the overhead associated with tool deployment, thereby facilitating deeper exploration and more rapid prototyping. Ultimately, this solution lowers the entry barrier for practitioners at all levels, making advanced audio technologies accessible to a broader audience. The project is designed for continuous evolution, with future plans to establish a comprehensive benchmark and leaderboard to assess the framework’s reliability and quality, actively maintained on GitHub.