| --- |
| license: apache-2.0 |
| task_categories: |
| - translation |
| - automatic-speech-recognition |
| language: |
| - zh |
| - en |
| size_categories: |
| - 100K<n<1M |
| --- |
| # Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System |
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| <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> |
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| 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. |
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| ## 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}, |
| } |
| ``` |
