Audio Editing with Non-Rigid Text Prompts
Francesco Paissan Zhepei Wang Mirco Ravanelli Paris Smaragdis Cem Subakan
In this paper, we explore audio-editing with non-rigid text edits. We show
that the proposed editing pipeline is able to create audio edits that remain
faithful to the input audio. We explore text prompts that perform addition,
style transfer, and in-painting. We quantitatively and qualitatively show that
the edits are able to obtain results which outperform Audio-LDM, a recently
released text-prompted audio generation model. Qualitative inspection of the
results points out that the edits given by our approach remain more faithful to
the input audio in terms of keeping the original onsets and offsets of the
audio events.