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--- |
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license: apache-2.0 |
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task_categories: |
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- translation |
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- automatic-speech-recognition |
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language: |
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- zh |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System |
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<p align="center"> |
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<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> |
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<a href="https://huggingface.co/ByteDance/Attention2Probability" alt="Model"><img src="https://img.shields.io/badge/Model-A2P-yellow?logo=huggingface"/></a> |
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<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 |
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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. |
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## Training step |
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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. |
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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. |
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## Citation |
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If you find A2P useful, please cite the paper: |
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``` |
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@misc{du2025attention2probabilityattentiondriventerminologyprobability, |
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title={Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System}, |
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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}, |
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year={2025}, |
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eprint={2508.18701}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2508.18701}, |
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} |
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``` |
