Step-Audio-EditX Technical Report

chao yan, Boyong Wu, Peng Yang, Pengfei Tan, Guoqiang Hu, Yuxin Zhang, Xiangyu, Zhang, FeiTian, Yang, Xiangyu, Daxin Jiang, Gang Yu

Step-Audio-EditX Technical Report: 30 upvotes on Hugging Face Daily Papers, #4 of 25 papers on 2025-11-05. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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