Feature Extraction
Transformers
Safetensors
moss-audio-tokenizer
audio
audio-tokenizer
neural-codec
moss-tts-family
MOSS Audio Tokenizer
speech-tokenizer
trust-remote-code
custom_code
Instructions to use OpenMOSS-Team/MOSS-Audio-Tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-Audio-Tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OpenMOSS-Team/MOSS-Audio-Tokenizer", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-Audio-Tokenizer", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Citation
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If you use this code or result in your paper, please cite our work as:
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```tex
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## Citation
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If you use this code or result in your paper, please cite our work as:
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```tex
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@misc{gong2026mossaudiotokenizerscalingaudiotokenizers,
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title={MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models},
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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},
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year={2026},
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eprint={2602.10934},
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archivePrefix={arXiv},
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primaryClass={cs.SD},
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url={https://arxiv.org/abs/2602.10934},
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}
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```
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## License
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<!-- TODO: check and add license -->
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MOSS-Audio-Tokenizer is released under the Apache 2.0 license.
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