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WavCraft: Audio Editing and Generation with Large Language Models (2403.09527v3)

Published 14 Mar 2024 in eess.AS

Abstract: We introduce WavCraft, a collective system that leverages LLMs to connect diverse task-specific models for audio content creation and editing. Specifically, WavCraft describes the content of raw audio materials in natural language and prompts the LLM conditioned on audio descriptions and user requests. WavCraft leverages the in-context learning ability of the LLM to decomposes users' instructions into several tasks and tackle each task collaboratively with the particular module. Through task decomposition along with a set of task-specific models, WavCraft follows the input instruction to create or edit audio content with more details and rationales, facilitating user control. In addition, WavCraft is able to cooperate with users via dialogue interaction and even produce the audio content without explicit user commands. Experiments demonstrate that WavCraft yields a better performance than existing methods, especially when adjusting the local regions of audio clips. Moreover, WavCraft can follow complex instructions to edit and create audio content on the top of input recordings, facilitating audio producers in a broader range of applications. Our implementation and demos are available at this https://github.com/JinhuaLiang/WavCraft.

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Authors (10)
  1. Jinhua Liang (15 papers)
  2. Huan Zhang (171 papers)
  3. Haohe Liu (59 papers)
  4. Yin Cao (24 papers)
  5. Qiuqiang Kong (86 papers)
  6. Xubo Liu (66 papers)
  7. Wenwu Wang (148 papers)
  8. Mark D. Plumbley (114 papers)
  9. Huy Phan (75 papers)
  10. Emmanouil Benetos (89 papers)
Citations (3)
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Summary

WavCraft: Unveiling a New Horizon in Audio Content Creation and Editing via Natural Language Prompts

Introduction to WavCraft

WavCraft emerges as a cohesive system that ingeniously integrates LLMs with an array of task-specific models tailored for audio content creation and editing. This innovative approach stands out by its ability to interpret and process raw sound materials through natural language descriptions, paving the way for a new paradigm in audio manipulation. By leveraging the intrinsic in-context learning capabilities of LLMs, WavCraft proficiently decomposes complex user instructions into manageable tasks, each addressed collaboratively with specialized audio modules. Such decomposition not only refines the process of creating or editing audio content but also enhances user control through detailed task execution.

Audio Analysis and Task Decomposition

At the heart of WavCraft's operation lies the audio analysis module, which is tasked with translating the essence of input audio clips into natural language descriptors. This process, crucial for understanding the content within audio files, allows the system to respond appropriately to users' commands by generating relevant instructions that are then passed onto an audio programmer module. The module utilizes LLMs to dissect user instructions into basic tasks, each of which is tackled using a suite of expert models designed for specific audio operations. This structured approach to task decomposition sheds light on WavCraft's versatility in audio content manipulation.

Expert Models and Modular Approach

WavCraft's strength lies in its coalition of various audio generation and transformation models, rendering it adept at performing a wide array of audio tasks. From text-to-audio conversion to source separation and beyond, the system employs models such as AudioGen and MusicGen for generating high-fidelity audio content. Additional functionalities such as super-resolution enhancement, audio infilling, and DSP operations further augment WavCraft's capabilities. This modular construction offers substantial flexibility, allowing for the incorporation or substitution of expert models as desired.

Advanced Features and Future Prospects

WavCraft distinguishes itself through several advanced features that underscore its potential to revolutionize audio content creation:

  • Modular Operations: By breaking down complex instructions into elementary tasks, WavCraft can handle intricate editing applications in an explainable manner, enhancing transparency and ease of use.
  • Controllable Editing: The system's profound understanding of user requests enables it to edit targeted audio attributes meticulously while preserving the integrity of the remaining content.
  • Human-AI Co-Creation: WavCraft's design facilitates interactive content creation, allowing for multi-round refinement with users. This co-creative process benefits from the system's ability to maintain consistency throughout the generated audio content.
  • Audio Scriptwriting: Perhaps most intriguingly, WavCraft exhibits the capacity to autonomously generate audio content following high-level outlines, demonstrating a form of creativity hitherto unseen in audio manipulation tools.

Limitations and Areas for Improvement

Despite its impressive capabilities, WavCraft is not without its limitations. The performance of audio analysis models, critical for accurately interpreting audio content, currently restricts the system's effectiveness. Moreover, the inference speed, owing to the need to consult multiple expert models for complex tasks, could benefit from optimization to ensure smoother interaction and usability in practical applications.

Conclusion

WavCraft represents a significant stride forward in the field of artificial intelligence-generated content (AIGC), offering a sophisticated tool for audio content creation and editing through natural language prompts. Its ability to interpret user instructions and raw audio content, decompose tasks, and utilize expert models for specific operations positions it as a versatile and powerful tool in audio production. As research in this field continues to advance, the potential applications and improvements of systems like WavCraft promise to further expand the boundaries of what is possible in audio content creation.

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