Virtual Consistency for Audio Editing

2025-09-21

AI Summary

Free-form text-guided audio editing remains hindered by the slow reverse diffusion process. This paper introduces the Virtual Consistency Framework—a model-agnostic approach that enables efficient, text-driven audio editing without model fine-tuning, architectural modification, or explicit inversion. Instead, it integrates text-conditioned embeddings directly into the diffusion sampling iterations and enforces virtual consistency constraints to preserve the original audio’s structural integrity, balancing fidelity and edit controllability. The method is compatible with any pre-trained diffusion-based audio model. Experiments demonstrate a several-fold speedup over existing neural editing methods. It achieves superior performance in both subjective evaluation (MOS: 4.21) and objective metrics (STOI: 0.93, ESTOI: 0.87), significantly outperforming baseline approaches. These results are further validated through a user study involving 16 participants.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Responsible Web: Consent frameworks and practices on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Free-form, text-based audio editing remains a persistent challenge, despite progress in inversion-based neural methods. Current approaches rely on slow inversion procedures, limiting their practicality. We present a virtual-consistency based audio editing system that bypasses inversion by adapting the sampling process of diffusion models. Our pipeline is model-agnostic, requiring no fine-tuning or architectural changes, and achieves substantial speed-ups over recent neural editing baselines. Crucially, it achieves this efficiency without compromising quality, as demonstrated by quantitative benchmarks and a user study involving 16 participants.

Problem

• Enabling free-form text-based audio editing
• Overcoming slow inversion procedures in neural methods
• Achieving efficient editing without quality compromise

Innovation

• Bypasses inversion via diffusion model sampling
• Model-agnostic pipeline requiring no fine-tuning
• Achieves speed-ups without compromising audio quality