  Audio editing is applicable for various purposes, such as adding background
sound effects, replacing a musical instrument, and repairing damaged audio.
Recently, some diffusion-based methods achieved zero-shot audio editing by
using a diffusion and denoising process conditioned on the text description of
the output audio. However, these methods still have some problems: 1) they have
not been trained on editing tasks and cannot ensure good editing effects; 2)
they can erroneously modify audio segments that do not require editing; 3) they
need a complete description of the output audio, which is not always available
or necessary in practical scenarios. In this work, we propose AUDIT, an
instruction-guided audio editing model based on latent diffusion models.
Specifically, AUDIT has three main design features: 1) we construct triplet
training data (instruction, input audio, output audio) for different audio
editing tasks and train a diffusion model using instruction and input (to be
edited) audio as conditions and generating output (edited) audio; 2) it can
automatically learn to only modify segments that need to be edited by comparing
the difference between the input and output audio; 3) it only needs edit
instructions instead of full target audio descriptions as text input. AUDIT
achieves state-of-the-art results in both objective and subjective metrics for
several audio editing tasks (e.g., adding, dropping, replacement, inpainting,
super-resolution). Demo samples are available at https://audit-demo.github.io/.
