Bagpiper-Edit: Zero-Shot Open-Ended Audio Editing via Rich-Caption
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
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
Bagpiper-Edit introduces a novel self-supervised framework for zero-shot open-ended audio editing by reformulating the task as rich-caption rewriting, effectively eliminating the need for paired editing datasets while maintaining high acoustic consistency.
Methodology
The paper proposes Bagpiper-Edit, a framework that reformulates open-ended audio editing as a rich-caption rewriting task. The core innovation lies in bypassing the need for paired audio-editing training data by introducing a self-supervised training paradigm. This involves two strategies: audio repetition (to teach timbre/background preservation) and audio segmentation (to teach continuity across adjacent clips). The method leverages a pre-trained audio foundation model (Bagpiper-Base) and uses an external LLM to rewrite the rich caption based on user instructions, which then guides the audio generation conditioned on the original audio as an "acoustic anchor." The approach is theoretically sound and addresses a significant bottleneck in the field: the scarcity of high-quality paired editing datasets. The use of contiguous audio segments for self-supervision is a clever and resource-efficient technique for learning acoustic consistency.
Results & evaluation
The evaluation covers three domains: speech editing, audio-event editing, and free-form editing. The authors compare Bagpiper-Edit against specialized expert models (CosyVoice-3, Ming-UniAudio-Edit, Step-Audio-EditX) and generative models (AudioLDM2). Results show that Bagpiper-Edit (specifically the Multi-Turn variant) achieves competitive performance, often surpassing expert models in speaker similarity preservation and matching them in naturalness and editing accuracy. The metrics used (WER, SpkSIM, DNSMOS, FAD, CLAP scores) are standard and appropriate. The inclusion of LLM-based human preference scoring adds a layer of subjective evaluation, though reliance on LLMs for judgment can be noisy. The ablation between Single-Turn and Multi-Turn patterns provides useful insight into the training dynamics.
Reproducibility
The paper provides links to a demo and a GitHub PR for the code. The training data sources (YODAS, LAION-Audio, etc.) are publicly available. The self-supervised training strategy is clearly described, allowing for potential reproduction. However, the reliance on specific large LLMs (Qwen3-235B, Qwen3-8B) for caption extraction and rewriting introduces external dependencies that might vary in performance depending on the version or API access. The exact hyperparameters for the self-supervised phase are partially detailed, but the full pipeline complexity might make exact replication challenging without the complete codebase.
Limitations
The paper acknowledges that as a zero-shot model, it may exhibit less stability than expert models trained on massive paired data. It also notes limitations in handling highly complex acoustic environments like multi-speaker separation. The reliance on an external LLM for caption rewriting means that errors in the LLM's understanding or rewriting can propagate to the audio generation. Additionally, the "rich caption" representation, while powerful, may lose some fine-grained acoustic details present in the original audio if the captioning process is not perfectly lossless.
Broader impact
This work significantly lowers the barrier for open-ended audio editing by removing the dependency on expensive paired datasets. It enables a more natural, free-form interface for audio manipulation, which has broad applications in content creation, accessibility, and audio post-production. The self-supervised approach could inspire similar techniques in other modalities where paired data is scarce. However, the ease of editing audio also raises concerns about deepfakes and misinformation, necessitating robust watermarking or detection mechanisms, which are not discussed in the paper. Bagpiper-Edit introduces a novel self-supervised framework for zero-shot open-ended audio editing by reformulating the task as rich-caption rewriting, effectively eliminating the need for paired editing datasets while maintaining high acoustic consistency.
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