MOSS-TTS Technical Report
Authors: name: Yitian Gong, name: Botian Jiang, name: Yiwei Zhao, name: Yucheng Yuan, name: Kuangwei Chen, name: Yaozhou Jiang, name: Cheng Chang, name: Dong Hong, name: Mingshu Chen, name: Ruixiao Li, name: Yiyang Zhang, name: Yang Gao, name: Hanfu Chen, name: Ke Chen, name: Songlin Wang, name: Xiaogui Yang, name: Yuqian Zhang, name: Kexin Huang, name: ZhengYuan Lin, name: Kang Yu, name: Ziqi Chen, name: Jin Wang, name: Zhaoye Fei, name: Qinyuan Cheng, name: Shimin Li, name: Xipeng Qiu
Organization: OpenMOSS

This technical report presents MOSS-TTS, a speech generation foundation model built on a scalable recipe: discrete audio tokens, autoregressive modeling, and large-scale pretraining. Built on MOSS-Audio-Tokenizer, a causal Transformer tokenizer that compresses 24 kHz audio to 12.5 fps with variable-bitrate RVQ and unified semantic-acoustic representations, we release two complementary generators: MOSS-TTS, which emphasizes structural simplicity, scalability, and long-context/control-oriented deployment, and MOSS-TTS-Local-Transformer, which introduces a frame-local autoregressive module for higher modeling efficiency, stronger speaker preservation, and a shorter time to first audio. Across multilingual and open-domain settings, MOSS-TTS supports zero-shot voice cloning, token-level duration control, phoneme-/pinyin-level pronunciation control, smooth code-switching, and stable long-form generation. This report summarizes the design, training recipe, and empirical characteristics of the released models.