Speaker-Attributed, Time-Stamped Transcription (SATS) aims to transcribe what is said and to precisely determine the timing of each speaker, which is particularly valuable for meeting transcription. Existing SATS systems rarely adopt an end-to-end formulation and are further constrained by limited context windows, weak long-range speaker memory, and the inability to output timestamps. To address these limitations, we present MOSS Transcribe Diarize, a unified multimodal large language model that jointly performs Speaker-Attributed, Time-Stamped Transcription in an end-to-end paradigm. Trained on extensive real wild data and equipped with a 128k context window for up to 90-minute inputs, MOSS Transcribe Diarize scales well and generalizes robustly. Across comprehensive evaluations, it outperforms state-of-the-art commercial systems on multiple public and in-house benchmarks.

翻译：说话人归属的时间戳转录旨在转录说话内容并精确确定每位说话者的时间信息，这对于会议转录尤为有价值。现有的说话人归属时间戳转录系统很少采用端到端架构，并进一步受限于有限的上下文窗口、较弱的长程说话人记忆以及无法输出时间戳。为应对这些局限，我们提出了MOSS Transcribe Diarize，这是一个统一的多模态大语言模型，能以端到端范式联合执行说话人归属的时间戳转录。该模型基于大量真实野外数据进行训练，并配备128k上下文窗口以支持长达90分钟的输入，具备良好的可扩展性和鲁棒的泛化能力。在全面评估中，其在多个公开及内部基准测试上均优于最先进的商业系统。