README.md

---
license: apache-2.0
language:
  - zh
  - en
tags:
  - tts
  - speech-evaluation
  - continuation-score
  - role-play
  - reward-model
pipeline_tag: text-to-speech
---

<h1 align="center">
  MCLP-Score: Continuation Score Model for MCLP Metric
</h1>

<p align="center">
  Yong Ren<sup>*,1,2</sup>, Jingbei Li<sup>*,1</sup>, Haiyang Sun<sup>1</sup>, Yujie Chen<sup>3</sup>, Cheng Yi<sup>1</sup>, Yechang Huang<sup>1</sup>, Hao Gu<sup>2</sup>, Ye Bai<sup>2</sup>, Xuerui Yang<sup>1</sup>
</p>

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

<p align="center">
  <sup>*</sup>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-RPTTS">MCLP-RPTTS Model</a>
</p>

## Model Description

**MCLP-Score** is the Continuation Score model used to compute the **MCLP (Mean Continuation Log-Probability)** metric. Given a ground-truth audio prefix, this model evaluates how well a generated audio segment continues the stylistic pattern of the ground-truth, producing a log-probability score that measures expressive consistency.

The MCLP metric serves as both:
1. **An evaluation metric** for role-play TTS quality (correlation with human MOS: Spearman ρ = 0.94)
2. **A reward signal** for GRPO-based reinforcement learning to improve TTS expressiveness

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

## How MCLP Works

The MCLP metric computes the mean log-probability of audio tokens in the **generated segment**, conditioned on a **ground-truth audio prefix**:

```
MCLP = (1/N) * Σ log P(token_i | gt_prefix, token_1, ..., token_{i-1})
```

Higher MCLP scores indicate better stylistic consistency with the ground-truth speaking style.

## Usage

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

# Compute MCLP scores
python compute_contination_score.py \
    --model-path /path/to/MCLP-Score \
    --audio-dir ./outputs/roleplay_tts \
    --gt-jsonl /path/to/WenetSpeech-RP/eval/eval_w_history.jsonl \
    --gt-dir /path/to/WenetSpeech-RP/eval/audio \
    --save-json mclp_results.json
```

**Output:**
```
MCLP (Mean avg_log_prob): -4.636xxx
Mean avg_prob: 0.xxxxx
Mean avg_rank: xx.xx
```

For detailed usage instructions, please refer to the [code repository](https://github.com/y-ren16/MCLP).

## Requirements

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

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

## Related Resources

| Resource | Link |
|----------|------|
| 📑 Paper | [arXiv:2601.22661](https://arxiv.org/abs/2601.22661) |
| 💻 Inference Code | [github.com/y-ren16/MCLP](https://github.com/y-ren16/MCLP) |
| 📊 WenetSpeech-RP Dataset | [huggingface.co/datasets/y-ren16/WenetSpeech-RP](https://huggingface.co/datasets/y-ren16/WenetSpeech-RP) |
| 🗣️ MCLP-RPTTS Model | [huggingface.co/y-ren16/MCLP-RPTTS](https://huggingface.co/y-ren16/MCLP-RPTTS) |

## Citation

```bibtex
@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](LICENSE).

## Acknowledgements

This project builds upon:
- [Step-Audio 2](https://github.com/stepfun-ai/Step-Audio2)
- [CosyVoice](https://github.com/FunAudioLLM/CosyVoice)
- [FlashCosyVoice](https://github.com/xingchensong/FlashCosyVoice)