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arXiv · 2606.21227

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

Xun Gong·Jinchuan Tian·Haoran Wang·William Chen·Shinji Watanabe·Yanmin Qian
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Abstract

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

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Xun Gong, Jinchuan Tian, Haoran Wang, William Chen, Shinji Watanabe, Yanmin Qian. 2026-06-19. Bagpiper-Edit: Zero-Shot Open-Ended Audio Editing via Rich-Caption. https://arxiv.org/abs/2606.21227

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