Virtual Consistency for Audio Editing

• URL: http://arxiv.org/abs/2509.17219v1
• Date: Sun, 21 Sep 2025 19:54:20 GMT
• Title: Virtual Consistency for Audio Editing
• Abstract summary: We present a virtual-consistency based audio editing system that bypasses inversion by adapting the sampling process of diffusion models.<n>Our pipeline is model-agnostic, requiring no fine-tuning or architectural changes, and achieves substantial speed-ups over recent neural editing baselines.
• Score: 29.848819764769058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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