update README

README.md

@@ -1,43 +1,42 @@
 # SAP²-ASR Dataset
 
-本数据集用于 **SAP² (Speech-Aware Long Context Pruning and Integration)** 方法的上下文感知自动语音识别（ASR）研究。
+This dataset is designed for **SAP² (Speech-Aware Long Context Pruning and Integration)** research in contextualized automatic speech recognition (ASR).
 
-## 📖 简介
+## 📖 Introduction
 
-SAP² 是一个用于上下文感知自动语音识别的新框架，能够动态剪枝并集成相关的上下文关键词。该方法解决了在特定领域场景（如会议演讲）中利用长上下文信息的挑战，这些场景中大量来自OCR的文本上下文既包含相关信息，也包含大量噪声。
+SAP² is a novel framework for contextualized automatic speech recognition that dynamically prunes and integrates relevant contextual keywords. This method addresses the challenge of leveraging long-context information in domain-specific scenarios (e.g., conference presentations) where extensive OCR-derived textual contexts contain both relevant information and considerable noise.
 
-### 核心特性
+### Key Features
 
-- **语音感知上下文剪枝**：动态过滤来自OCR的文本上下文，仅保留与语音内容直接相关的关键词
-- **跨模态上下文压缩**：使用语音驱动注意力池化（Speech-Driven Attention-based Pooling）将大量文本输入压缩为简洁的、与语音相关的上下文嵌入
-- **最先进的性能**：在 SlideSpeech 数据集上达到 7.71% 的词错误率（WER），在 LibriSpeech 数据集上达到 1.12% 的 WER，相比非上下文基线，在偏向关键词识别方面相对提升了 41.1%
+- **Speech-Aware Context Pruning**: Dynamically filters OCR-derived textual contexts to retain only keywords directly relevant to speech content
+- **Cross-Modal Context Compression**: Uses Speech-Driven Attention-based Pooling to compress extensive textual inputs into concise, speech-relevant context embeddings
+- **State-of-the-Art Performance**: Achieves WER of 7.71% on SlideSpeech and 1.12% on LibriSpeech, with a 41.1% relative improvement in biased keyword recognition over non-contextual baselines
 
-## 📊 数据集结构
+## 📊 Dataset Structure
 
-本数据集包含两个主要子数据集：
+This dataset contains two main sub-datasets:
 
 ### SlideSpeech
 
-- **来源**：SlideSpeech 是一个包含幻灯片的大规模音视频语料库，包含 1,705 个视频，超过 1,000 小时的音频，其中包括 473 小时的高质量转录语音
-- **数据格式**：JSON 格式，包含音频路径和带上下文关键词的对话格式
-- **目录结构**：
-  - `slidespeech_L95/`: 原始数据（对应论文中PC）
-  - `slidespeech_L95_filter/`: 训练TPI第一阶段
-  - `slidespeech_L95_filtered_train`: 训练TPI第二阶段
-  - `slidespeech_L95_5slides/`: 5张幻灯片版本
-  - `slidespeech_L95_multitask/`: 多任务版本（对应论文中的JPI）
+- **Source**: SlideSpeech is a large-scale audio-visual corpus enriched with slides, containing 1,705 videos with 1,000+ hours of audio, including 473 hours of high-quality transcribed speech
+- **Data Format**: JSON format containing audio paths and conversational format with contextual keywords
+- **Directory Structure**:
+  - `slidespeech_L95/`: Original data
+  - `slidespeech_L95_filter/`: Filtered data
+  - `slidespeech_L95_5slides/`: 5-slide version
+  - `slidespeech_L95_multitask/`: Multi-task version
 
 ### LibriSpeech
 
-- **来源**：LibriSpeech 是一个大规模英语朗读语音语料库，源自 LibriVox 项目的有声读物
-- **数据格式**：JSON 格式，包含训练、验证和测试集的不同配置
-- **目录结构**：
-  - `train-clean-460_*.json`: 训练集（clean，460小时）
-  - `train-other-500_*.json`: 训练集（other，500小时）
-  - `dev-clean_*.json`, `dev-other_*.json`: 验证集
-  - `test-clean_*.json`, `test-other_*.json`: 测试集（不同规模：100, 500, 1000, 2000条）
+- **Source**: LibriSpeech is a large-scale corpus of read English speech, derived from audiobooks in the LibriVox project
+- **Data Format**: JSON format with different configurations for training, validation, and test sets
+- **Directory Structure**:
+  - `train-clean-460_*.json`: Training set (clean, 460 hours)
+  - `train-other-500_*.json`: Training set (other, 500 hours)
+  - `dev-clean_*.json`, `dev-other_*.json`: Validation sets
+  - `test-clean_*.json`, `test-other_*.json`: Test sets (various sizes: 100, 500, 1000, 2000 samples)
 
-### 数据格式示例
+### Data Format Example
 
 ```json
 {
@@ -55,42 +54,42 @@ SAP² 是一个用于上下文感知自动语音识别的新框架，能够动
 }
 ```
 
-**关键标记**：
-- `<|startofcontext|>` 和 `<|endofcontext|>`: 用于标记上下文关键词的特殊标记
-- `<audio>...</audio>`: 音频文件路径标记
+**Key Tokens**:
+- `<|startofcontext|>` and `<|endofcontext|>`: Special tokens for marking contextual keywords
+- `<audio>...</audio>`: Audio file path token
 
-## 🚀 使用方法
+## 🚀 Usage
 
-### 加载数据集
+### Loading the Dataset
 
 ```python
 import json
 
-# 加载 SlideSpeech 数据集
+# Load SlideSpeech dataset
 with open('slidespeech/slidespeech_L95_filter/train.json', 'r') as f:
     slidespeech_train = json.load(f)
 
-# 加载 LibriSpeech 数据集
+# Load LibriSpeech dataset
 with open('librispeech/train-clean-460_filter.json', 'r') as f:
     librispeech_train = json.load(f)
 ```
 
-### 与 SAP² 模型一起使用
+### Using with SAP² Model
 
-详细的使用说明、训练和推理代码请参考：
+For detailed usage instructions, training and inference code, please refer to:
 
 **🔗 [SAP²-ASR GitHub Repository](https://github.com/jymh/SAP2-ASR.git)**
 
-该仓库包含：
-- 完整的模型实现代码
-- 训练和推理脚本
-- 数据预处理工具
-- 评估脚本
-- 详细的使用文档
+The repository contains:
+- Complete model implementation code
+- Training and inference scripts
+- Data preprocessing tools
+- Evaluation scripts
+- Detailed documentation
 
-## 📎 引用
+## 📎 Citation
 
-如果您在研究中使用了本数据集，请引用以下论文：
+If you use this dataset in your research, please cite the following paper:
 
 ```bibtex
 @article{rong2025speechaware,
@@ -101,29 +100,25 @@ with open('librispeech/train-clean-460_filter.json', 'r') as f:
 }
 ```
 
-**论文链接**：[https://www.arxiv.org/abs/2511.11139](https://www.arxiv.org/abs/2511.11139)
+**Paper Link**: [https://www.arxiv.org/abs/2511.11139](https://www.arxiv.org/abs/2511.11139)
 
-## 📚 相关资源
+## 📚 Related Resources
 
-- **代码仓库**：[https://github.com/jymh/SAP2-ASR.git](https://github.com/jymh/SAP2-ASR.git)
-- **论文**：[arXiv:2511.11139](https://www.arxiv.org/abs/2511.11139)
-- **SlideSpeech 原始数据集**：[https://slidespeech.github.io/](https://slidespeech.github.io/)
-- **LibriSpeech 原始数据集**：[OpenSLR](https://www.openslr.org/12/)
+- **Code Repository**: [https://github.com/jymh/SAP2-ASR.git](https://github.com/jymh/SAP2-ASR.git)
+- **Paper**: [arXiv:2511.11139](https://www.arxiv.org/abs/2511.11139)
+- **SlideSpeech Original Dataset**: [https://slidespeech.github.io/](https://slidespeech.github.io/)
+- **LibriSpeech Original Dataset**: [OpenSLR](https://www.openslr.org/12/)
 
-## 🏛 许可证
+## 🏛 License
 
-本数据集的使用请遵循原始数据集的许可证要求。对于 SlideSpeech 和 LibriSpeech，请参考其原始资源页面的许可证说明。
+The use of this dataset should follow the license requirements of the original datasets. For SlideSpeech and LibriSpeech, please refer to the license information on their original resource pages.
 
-## ⚠️ 注意事项
+## ⚠️ Notes
 
-1. 音频文件路径可能需要根据实际环境进行调整
-2. 数据集文件较大，请确保有足够的存储空间
-3. 使用前请仔细阅读 [GitHub 仓库](https://github.com/jymh/SAP2-ASR.git) 中的详细文档
+1. Audio file paths may need to be adjusted according to your actual environment
+2. The dataset files are large, please ensure you have sufficient storage space
+3. Please carefully read the detailed documentation in the [GitHub repository](https://github.com/jymh/SAP2-ASR.git) before use
 
 ---
 
-**更多信息和使用示例，请访问 [SAP²-ASR GitHub Repository](https://github.com/jymh/SAP2-ASR.git)**
-
----
-license: apache-2.0
----
+**For more information and usage examples, please visit [SAP²-ASR GitHub Repository](https://github.com/jymh/SAP2-ASR.git)**