y-ren16/MCLP-RPTTS

<h1 align="center"> MCLP-RPTTS: Expressive Role-Play TTS Model </h1>

<p align="center"> Yong Ren^,1,2, Jingbei Li^,1, Haiyang Sun¹, Yujie Chen³, Cheng Yi¹, Yechang Huang¹, Hao Gu², Ye Bai², Xuerui Yang¹ </p>

<p align="center"> ¹StepFun &nbsp; ²University of Chinese Academy of Sciences &nbsp; ³Beihang University </p>

<p align="center"> ^*Equal contribution </p>

<p align="center"> 📑 <a href="https://arxiv.org/abs/2601.22661">Paper</a> &nbsp;|&nbsp; 💻 <a href="https://github.com/y-ren16/MCLP">Code</a> &nbsp;|&nbsp; 📊 <a href="https://huggingface.co/datasets/y-ren16/WenetSpeech-RP">Dataset</a> &nbsp;|&nbsp; 🔢 <a href="https://huggingface.co/y-ren16/MCLP-Score">MCLP-Score Model</a> </p>

Model Description

MCLP-RPTTS is a Role-Play Text-to-Speech model fine-tuned from Step-Audio-2-mini using SFT + GRPO with the MCLP (Mean Continuation Log-Probability) reward. It generates expressive speech that is stylistically consistent with role-play instructions including scene descriptions, character profiles, and dialogue history.

This model is presented in:

Evaluating and Rewarding LALMs for Expressive Role-Play TTS via Mean Continuation Log-Probability Yong Ren\, Jingbei Li\, Haiyang Sun, Yujie Chen, Cheng Yi, Yechang Huang, Hao Gu, Ye Bai, Xuerui Yang ICML 2026

Key Results

Usage

# Clone the inference code
git clone https://github.com/y-ren16/MCLP.git
cd MCLP

# Run role-play TTS inference
python generate_roleplay_stepaudio2_multigpu.py \
    --model-path /path/to/MCLP-RPTTS \
    --input-jsonl /path/to/WenetSpeech-RP/eval/eval_w_history.jsonl \
    --output-dir ./outputs/roleplay_tts \
    --audio-base /path/to/extracted_test_audio \
    --prompt-base /path/to/WenetSpeech-RP/eval/audio \
    --gpus 1

For detailed usage instructions, please refer to the code repository.

Requirements

• —Python >= 3.10
• —PyTorch >= 2.3 with CUDA
• —GPU: at least 1x A100/H100 (80GB) for inference

pip install transformers==4.49.0 torchaudio librosa onnxruntime s3tokenizer diffusers hyperpyyaml numpy

Related Resources

Citation

@inproceedings{ren2026mclp,
  title={Evaluating and Rewarding LALMs for Expressive Role-Play TTS via Mean Continuation Log-Probability},
  author={Ren, Yong and Li, Jingbei and Sun, Haiyang and Chen, Yujie and Yi, Cheng and Huang, Yechang and Gu, Hao and Bai, Ye and Yang, Xuerui},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year={2026}
}

License

This model is released under the Apache 2.0 License.

Acknowledgements

This project builds upon:

• —Step-Audio 2
• —CosyVoice
• —FlashCosyVoice