MOSS-TTS Technical Report
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.
69bcdcb2081b9ad49af57303
Yitian Gong
claimed_verified
2026-03-30T09:10:49.246Z
66c893b2e51ba3009235b1c0
/avatars/5341d40b4b4caca2c145a46eb1754582.svg
yitian gong
fdugyt
user
fdugyt
69bcdcb2081b9ad49af57304
Botian Jiang
69bcdcb2081b9ad49af57305
Yiwei Zhao
69bcdcb2081b9ad49af57306
Yucheng Yuan
69bcdcb2081b9ad49af57307
Kuangwei Chen
69bcdcb2081b9ad49af57308
Yaozhou Jiang
69bcdcb2081b9ad49af57309
Cheng Chang
69bcdcb2081b9ad49af5730a
Dong Hong
69bcdcb2081b9ad49af5730b
Mingshu Chen
69bcdcb2081b9ad49af5730c
Ruixiao Li
69bcdcb2081b9ad49af5730d
Yiyang Zhang
69bcdcb2081b9ad49af5730e
Yang Gao
69bcdcb2081b9ad49af5730f
Hanfu Chen
69bcdcb2081b9ad49af57310
Ke Chen
69bcdcb2081b9ad49af57311
Songlin Wang
69bcdcb2081b9ad49af57312
Xiaogui Yang
69bcdcb2081b9ad49af57313
Yuqian Zhang
69bcdcb2081b9ad49af57314
Kexin Huang
69bcdcb2081b9ad49af57315
ZhengYuan Lin
69bcdcb2081b9ad49af57316
Kang Yu
69bcdcb2081b9ad49af57317
Ziqi Chen
69bcdcb2081b9ad49af57318
Jin Wang
69bcdcb2081b9ad49af57319
Zhaoye Fei
69bcdcb2081b9ad49af5731a
Qinyuan Cheng
claimed_verified
2026-03-23T10:52:19.372Z
63ec4715c81b6a52391c46b8
/avatars/496819b5075a1a834a2b9edeb068c80e.svg
QinyuanCheng
Cqy2019
user
Cqy2019
69bcdcb2081b9ad49af5731b
Shimin Li
69bcdcb2081b9ad49af5731c
Xipeng Qiu
MOSS-TTS Technical Report
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.
MOSS-TTS is a speech generation model using discrete audio tokens and autoregressive modeling with capabilities for voice cloning, pronunciation control, and long-form generation across multiple languages.
speech generation foundation model discrete audio tokens autoregressive modeling large-scale pretraining causal Transformer tokenizer variable-bitrate RVQ unified semantic-acoustic representations frame-local autoregressive module zero-shot voice cloning token-level duration control phoneme-level pronunciation control pinyin-level pronunciation control code-switching long-form generation