MOSS-TTS-Nano is an open-source multilingual tiny speech generation model from MOSI.AI and the OpenMOSS team. With only 0.1B parameters, it is designed for realtime speech generation, can run directly on CPU without a GPU, and keeps the deployment stack simple enough for local demos, web serving, and lightweight product integration.
News
2026.4.10: We release MOSS-TTS-Nano. A demo Space is available at OpenMOSS-Team/MOSS-TTS-Nano. You can also view the demo and more details at openmoss.github.io/MOSS-TTS-Nano-Demo/.
MOSS-TTS-Nano focuses on the part of TTS deployment that matters most in practice: small footprint, low latency, good enough quality for realtime products, and simple local setup. It uses a pure autoregressive Audio Tokenizer + LLM pipeline and keeps the inference workflow friendly for both terminal users and web-demo users.
Main Features
Tiny model size: only 0.1B parameters
Native audio format: 48 kHz, 2-channel output
Multilingual: supports Chinese, English, and more
Pure autoregressive architecture: built on Audio Tokenizer + LLM
Streaming inference: low realtime latency and fast first audio
CPU friendly: streaming generation can run on a 4-core CPU
Long-text capable: supports long input with automatic chunked voice cloning
Open-source deployment: direct python infer.py, python app.py, and packaged CLI support
Supported Languages
MOSS-TTS-Nano currently supports 20 languages:
Language
Code
Flag
Language
Code
Flag
Language
Code
Flag
Chinese
zh
🇨🇳
English
en
🇺🇸
German
de
🇩🇪
Spanish
es
🇪🇸
French
fr
🇫🇷
Japanese
ja
🇯🇵
Italian
it
🇮🇹
Hungarian
hu
🇭🇺
Korean
ko
🇰🇷
Russian
ru
🇷🇺
Persian (Farsi)
fa
🇮🇷
Arabic
ar
🇸🇦
Polish
pl
🇵🇱
Portuguese
pt
🇵🇹
Czech
cs
🇨🇿
Danish
da
🇩🇰
Swedish
sv
🇸🇪
Greek
el
🇬🇷
Turkish
tr
🇹🇷
Quickstart
Environment Setup
We recommend a clean Python environment first, then installing the project in editable mode so the moss-tts-nano command becomes available locally.
The examples below intentionally keep arguments minimal and rely on the repository defaults.
By default, the code loads OpenMOSS-Team/MOSS-TTS-Nano and OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano.
This repository keeps the direct Python entrypoint for local inference. The example below uses voice clone mode, which is the main recommended workflow for MOSS-TTS-Nano.
moss-tts-nano generate writes to generated_audio/moss_tts_nano_output.wav by default.
--prompt-speech is the friendly alias for the reference audio path used by voice cloning.
--text-file is supported for long-form synthesis.
CLI Command: moss-tts-nano serve
You can also launch the web demo through the packaged CLI:
moss-tts-nano serve
This command forwards to app.py, keeps the model loaded in memory, and serves the local browser demo plus HTTP generation endpoints.
MOSS-Audio-Tokenizer-Nano
Introduction
MOSS-Audio-Tokenizer is the unified discrete audio interface for the entire MOSS-TTS family. It is built on the Cat (Causal Audio Tokenizer with Transformer) architecture, a CNN-free audio tokenizer composed entirely of causal Transformer blocks. It serves as the shared audio backbone for MOSS-TTS, MOSS-TTS-Nano, MOSS-TTSD, MOSS-VoiceGenerator, MOSS-SoundEffect, and MOSS-TTS-Realtime, providing a consistent audio representation across the full product family.
To further improve perceptual quality while reducing inference cost, we trained MOSS-Audio-Tokenizer-Nano, a lightweight tokenizer with approximately 20 million parameters designed for high-fidelity audio compression. It supports 48 kHz input and output as well as stereo audio, which helps reduce compression loss and improve listening quality. It can compress 48 kHz stereo audio into a 12.5 Hz token stream and uses RVQ with 16 codebooks, enabling high-fidelity reconstruction across variable bitrates from 0.125 kbps to 4 kbps.
This repository will follow the license specified in the root LICENSE file. If you are reading this before that file is published, please treat the repository as not yet licensed for redistribution.
Citation
If you use the MOSS-TTS work in your research or product, please cite:
1@misc{gong2026mossttstechnicalreport,
2 title={MOSS-TTS Technical Report},
3 author={Yitian Gong and Botian Jiang and Yiwei Zhao and Yucheng Yuan and Kuangwei Chen and Yaozhou Jiang and Cheng Chang and Dong Hong and Mingshu Chen and Ruixiao Li and Yiyang Zhang and Yang Gao and Hanfu Chen and Ke Chen and Songlin Wang and Xiaogui Yang and Yuqian Zhang and Kexin Huang and ZhengYuan Lin and Kang Yu and Ziqi Chen and Jin Wang and Zhaoye Fei and Qinyuan Cheng and Shimin Li and Xipeng Qiu},
4 year={2026},
5 eprint={2603.18090},
6 archivePrefix={arXiv},
7 primaryClass={cs.SD},
8 url={https://arxiv.org/abs/2603.18090}
9}
bibtex
1@misc{gong2026mossaudiotokenizerscalingaudiotokenizers,
2 title={MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models},
3 author={Yitian Gong and Kuangwei Chen and Zhaoye Fei and Xiaogui Yang and Ke Chen and Yang Wang and Kexin Huang and Mingshu Chen and Ruixiao Li and Qingyuan Cheng and Shimin Li and Xipeng Qiu},
4 year={2026},
5 eprint={2602.10934},
6 archivePrefix={arXiv},
7 primaryClass={cs.SD},
8 url={https://arxiv.org/abs/2602.10934},
9}
