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--- |
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datasets: |
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- LEMAS-Project/LEMAS-Dataset-train |
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- LEMAS-Project/LEMAS-Dataset-eval |
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language: |
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- it |
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- pt |
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- es |
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- fr |
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- de |
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- vi |
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- id |
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- ru |
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- en |
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- zh |
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license: cc-by-nc-4.0 |
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pipeline_tag: text-to-speech |
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tags: |
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- zero-shot |
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- multilingual |
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--- |
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# LEMAS-TTS |
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LEMAS-TTS is a multilingual zero-shot text-to-speech system, presented in the paper [LEMAS: A 150K-Hour Large-scale Extensible Multilingual Audio Suite with Generative Speech Models](https://huggingface.co/papers/2601.04233). |
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- **Project Page:** [https://lemas-project.github.io/LEMAS-Project](https://lemas-project.github.io/LEMAS-Project) |
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- **Paper:** [https://arxiv.org/abs/2601.04233](https://arxiv.org/abs/2601.04233) |
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- **GitHub Repository:** [https://github.com/LEMAS-Project/LEMAS-TTS](https://github.com/LEMAS-Project/LEMAS-TTS) |
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- **Hugging Face Demo:** [https://huggingface.co/spaces/LEMAS-Project/LEMAS-TTS](https://huggingface.co/spaces/LEMAS-Project/LEMAS-TTS) |
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## Model Description |
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LEMAS-TTS is built upon a non-autoregressive flow-matching framework. It leverages the massive scale and linguistic diversity of the LEMAS-Dataset to achieve robust zero-shot multilingual synthesis. The model incorporates accent-adversarial training and CTC loss to mitigate cross-lingual accent issues, enhancing synthesis stability and quality across diverse languages. |
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## Supported Languages |
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The model supports 10 major languages for zero-shot synthesis: |
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- Chinese (zh) |
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- English (en) |
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- Spanish (es) |
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- Russian (ru) |
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- French (fr) |
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- German (de) |
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- Italian (it) |
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- Portuguese (pt) |
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- Indonesian (id) |
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- Vietnamese (vi) |
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## Training Data |
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LEMAS-TTS was trained on the [LEMAS-Dataset](https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train), which is, to our knowledge, currently the largest open-source multilingual speech corpus with word-level timestamps. It covers over 150,000 hours across 10 major languages. |
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## Citation |
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```bibtex |
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@article{zhao2026lemas, |
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title={LEMAS: A 150K-Hour Large-scale Extensible Multilingual Audio Suite with Generative Speech Models}, |
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author={Zhao, Zhiyuan and Lin, Lijian and Zhu, Ye and Xie, Kai and Liu, Yunfei and Li, Yu}, |
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journal={arXiv preprint arXiv:2601.04233}, |
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year={2026} |
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} |
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``` |
