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.
@article{arxiv.2509.17219,
title = {Virtual Consistency for Audio Editing},
author = {Matthieu Cervera and Francesco Paissan and Mirco Ravanelli and Cem Subakan},
journal= {arXiv preprint arXiv:2509.17219},
year = {2025}
}