# Bagpiper-Edit: Zero-Shot Open-Ended Audio Editing via Rich-Caption

arXiv: https://arxiv.org/abs/2606.21227 · HTML: https://mlsanity.com/paper/2606.21227/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [SEmoEdit: Probing and Harnessing the Editability of Pre-trained Speech Flows](https://mlsanity.com/paper/2609.34648/) (2026)

## Abstract (arXiv)

Current text-guided audio editing methods rely on paired training data, predefined operation templates, and separate processing pipelines across speech, music, and sound. We present Bagpiper-Edit to enable open-ended audio editing via free-form natural language instructions. We reformulate audio editing as a rich-caption rewriting task by treating a rich caption as the semantic representation of an audio clip. The user request is translated into an edited caption, which then guides Bagpiper-Edit to generate the target edited audio with the original audio as contextual acoustic anchor. This unlocks the potential of free-form editing, and circumvents the need for paired audio-editing training data, enabling powerful zero-shot editing capabilities. Evaluations across speech, audio, and free-form editing show Bagpiper-Edit maintains good consistency to the original audio and achieves similar performance to other expert models in most cases. Demo: https://bagpiper-edit.github.io, Codes: https://github.com/espnet/espnet/pull/6417 & https://github.com/HsunGong/espnet