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arXiv · 2511.03601

Step-Audio-EditX Technical Report

Chao Yan·Boyong Wu·Peng Yang·Pengfei Tan·Guoqiang Hu·Li Xie·Yuxin Zhang·Xiangyu·Zhang·Fei Tian·Xuerui Yang·Xiangyu Zhang·Daxin Jiang·Shuchang Zhou·Gang Yu
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Abstract

We present Step-Audio-EditX, the first open-source LLM-based audio model excelling at expressive and iterative audio editing encompassing emotion, speaking style, and paralinguistics alongside robust zero-shot text-to-speech (TTS) capabilities. Our core innovation lies in leveraging only large-margin synthetic data, which circumvents the need for embedding-based priors or auxiliary modules. This large-margin learning approach enables both iterative control and high expressivity across voices, and represents a fundamental pivot from the conventional focus on representation-level disentanglement. Evaluation results demonstrate that Step-Audio-EditX surpasses both MiniMax-2.6-hd and Doubao-Seed-TTS-2.0 in emotion editing and other fine-grained control tasks.

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Chao Yan, Boyong Wu, Peng Yang, Pengfei Tan, Guoqiang Hu, Li Xie, Yuxin Zhang, Xiangyu, Zhang, Fei Tian, Xuerui Yang, Xiangyu Zhang, Daxin Jiang, Shuchang Zhou, Gang Yu. 2025-11-05. Step-Audio-EditX Technical Report. https://arxiv.org/abs/2511.03601

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