Step-Audio-EditX Technical Report

• URL: http://arxiv.org/abs/2511.03601v1
• Date: Wed, 05 Nov 2025 16:22:19 GMT
• Title: Step-Audio-EditX Technical Report
• Authors: Chao Yan, Boyong Wu, Peng Yang, Pengfei Tan, Guoqiang Hu, Yuxin Zhang, Xiangyu, Zhang, Fei Tian, Xuerui Yang, Xiangyu Zhang, Daxin Jiang, Gang Yu,
• Abstract summary: We present Step-Audio-EditX, the first open-source LLM-based audio model excelling at expressive and iterative audio editing.<n>Our core innovation lies in leveraging only large-margin synthetic data, which circumvents the need for embedding-based priors or auxiliary modules.<n> 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.
• Score: 61.039849674472585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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