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README.md

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-license: apache-2.0
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+# Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2508.18701" alt="paper"><img src="https://img.shields.io/badge/Paper-A2P-blue?logo=arxiv&logoColor=white"/></a>
+  <a href="https://huggingface.co/ByteDance/Attention2Probability" alt="Model"><img src="https://img.shields.io/badge/Model-A2P-yellow?logo=huggingface"/></a>
+  <a href="https://huggingface.co/datasets/ByteDance/Attention2Probability" alt="Dataset"><img src="https://img.shields.io/badge/Dataset-A2P-yellow?logo=huggingface"/></a>
+
+
+Attention2Probability (A2P) is a lightweight intervention scheme for speech terminology. The core approach is to use the cross-attention mechanism to retrieve the terms that may appear in the audio and add these terms to the prompt of the llm to complete the term intervention.
+
+## Data description
+This project does not provide audio data for librispeech and aishell2. Please download them from other addresses. All the training data is provided in the data_json folder. The prefix path needs to be modified before use.
+
+## Training step
+For English, the LibriSpeech dataset should first be utilized for pre-training. Subsequently, the second-stage training on LibriSpeech can be conducted by modifying the settings in the dataset configuration. 
+
+For Chinese, retrieving a single character in isolation lacks practical significance; thus, the Retriever can be directly trained using the Aishell-2 dataset. Finally, the models for both languages are fine-tuned on real-world data.
+
+
+## Citation
+If you find A2P useful, please cite the paper:
+```
+@misc{du2025attention2probabilityattentiondriventerminologyprobability,
+      title={Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System}, 
+      author={Yanfan Du and Jun Zhang and Bin Wang and Jin Qiu and Lu Huang and Yuan Ge and Xiaoqian Liu and Tong Xiao and Jingbo Zhu},
+      year={2025},
+      eprint={2508.18701},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.18701}, 
+}
+```