StepFun
On April 16th, StepStar announced the official release of its new generation speech generation model, StepAudio 2.5 TTS.
Model details
StepAudio 2.5 TTS is a proprietary speech generation system positioned as part of the StepAudio family of models designed to move beyond rigid tag-based interfaces toward more intuitive control. Unlike conventional TTS pipelines that rely on markup tags, it accepts plain natural language instructions to steer emotion, pacing, pauses, and overall delivery. The model is also documented as supporting zero-shot voice cloning with full timbre and emotional control, and it operates across Chinese and English, making it useful for multilingual expressive synthesis workflows where natural language cues are preferable to structured tags.
The system's emphasis on three core capabilities—global context control, in-text context control, and zero-sample replication with full-tone control—reflects a design direction aimed at flexible, context-aware speech generation rather than one-off utterance synthesis. Built as a specialized text-to-speech component within StepFun's unified StepAudio 2.5 architecture (described in arXiv:2605.23463), it is offered as a commercial API endpoint, fits teams that want fine-grained expressive control via conversational prompts, and is less suited to strict structured-output or tool-calling scenarios where deterministic formatting is required.
StepFun
On April 16th, StepStar announced the official release of its new generation speech generation model, StepAudio 2.5 TTS.
StepFun (China)
StepAudio 2.5 TTS is a contextual speech synthesis model developed by StepFun, released in August 2026 as a 3B-parameter open-weights release. According to the catalog entry, the model shifts from traditional narration toward expressive delivery by integrating contextual understanding throughout its generation pipeline The model's core innovation is a Dual-level Context Control framework: Global Context lets users set a consistent emotional register or persona for an entire text block via natural language descriptions, while Inline Context enables sentence-specific adjustments by placing instructions in parentheses within the input t