MOSS Transcribe Diarize: Accurate Transcription with Speaker Diarization
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.
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MOSI. AI
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Donghua Yu
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2026-01-09T08:36:37.004Z
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Donghua Yu
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Zhengyuan Lin
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Chen Yang
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Chen Yang
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Yiyang Zhang
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Hanfu Chen
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Jingqi Chen
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Ke Chen
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Liwei Fan
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Yi Jiang
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Jie Zhu
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Muchen Li
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Wenxuan Wang
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Yang Wang
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Zhe Xu
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Xu Zhe
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Yitian Gong
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Yuqian Zhang
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Wenbo Zhang
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Zhaoye Fei
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Qinyuan Cheng
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Shimin Li
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Xipeng Qiu
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2026-01-07T13:16:50.004Z
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Xipeng Qiu
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MOSS Transcribe Diarize: Accurate Transcription with Speaker Diarization
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.
A unified multimodal large language model for end-to-end speaker-attributed, time-stamped transcription with extended context window and strong generalization across benchmarks.
multimodal large language model end-to-end paradigm speaker diarization time-stamped transcription context window robust generalization