diff --git a/.gitattributes b/.gitattributes index 1ef325f1b111266a6b26e0196871bd78baa8c2f3..f2adc91801740874fee8ba6b677733f4960d5cb9 100644 --- a/.gitattributes +++ b/.gitattributes @@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +*.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 7b95401dc46245ac339fc25059d4a56d90b4cde5..205007843e9385e2392bbb25dd4d6e4df74b4209 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,129 @@ +# 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。 + +## 📖 简介 + +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。 + +### 核心特性 + +- **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% + +## 📊 数据集结构 + +本数据集包含两个主要子数据集： + +### 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） + +### 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） + +### 数据格式示例 + +```json +{ + "messages": [ + { + "role": "user", + "content": "Transcribe speech to text according to keywords may appear in the utterance. Possible keywords are: <|startofcontext|>keyword1 keyword2 keyword3<|endofcontext|>" + }, + { + "role": "assistant", + "content": "transcribed text" + } + ], + "audios": "/path/to/audio.wav" +} +``` + +**关键标记**： +- `<|startofcontext|>` 和 `<|endofcontext|>`: 用于标记上下文关键词的特殊标记 +- ``: 音频文件路径标记 + +## 🚀 使用方法 + +### 加载数据集 + +```python +import json + +# 加载 SlideSpeech 数据集 +with open('slidespeech/slidespeech_L95_filter/train.json', 'r') as f: + slidespeech_train = json.load(f) + +# 加载 LibriSpeech 数据集 +with open('librispeech/train-clean-460_filter.json', 'r') as f: + librispeech_train = json.load(f) +``` + +### 与 SAP² 模型一起使用 + +详细的使用说明、训练和推理代码请参考： + +**🔗 [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 + +## 📎 引用 + +如果您在研究中使用了本数据集，请引用以下论文： + +```bibtex +@article{rong2025speechaware, + title={Speech-Aware Long Context Pruning and Integration for Contextualized Automatic Speech Recognition}, + author={Rong, Yiming and Zhang, Yixin and Wang, Ziyi and Jiang, Deyang and Zhao, Yunlong and Wu, Haoran and Zhou, Shiyu and Xu, Bo}, + journal={arXiv preprint arXiv:2511.11139}, + year={2025} +} +``` + +**论文链接**：[https://www.arxiv.org/abs/2511.11139](https://www.arxiv.org/abs/2511.11139) + +## 📚 相关资源 + +- **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/) + +## 🏛 许可证 + +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。 + +## ⚠️ 注意事项 + +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 + +--- + +**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 --- diff --git a/librispeech/dev-clean_filter.json 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统计文本中<|startofcontext|>和<|endofcontext|>之间的词数 + + Args: + text: 输入文本 + + Returns: + context区域内的词数 + """ + # 使用正则表达式匹配<|startofcontext|>和<|endofcontext|>之间的内容 + pattern = r'<\|startofcontext\|>(.*?)<\|endofcontext\|>' + matches = re.findall(pattern, text, re.DOTALL) + + total_words = 0 + for match in matches: + # 去除首尾空白字符，然后按空白字符分割计算词数 + words = match.strip().split(", ") + + words = [] if words == [''] else words + print(words) + total_words += len(words) + + return total_words + + +def analyze_json_file(file_path: str) -> Dict[str, Any]: + """ + 分析JSON文件中的context词数统计 + + Args: + file_path: JSON文件路径 + + Returns: + 包含统计结果的字典 + """ + print(f"正在读取文件: {file_path}") + + with open(file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + print(f"总样本数: {len(data)}") + + word_counts = [] + samples_with_context = 0 + samples_without_context = 0 + + for i, sample in enumerate(data): + if i % 1000 == 0: + print(f"处理进度: {i}/{len(data)}") + + # 检查每条消息中的content + sample_word_count = 0 + has_context = False + + for message in sample.get('messages', []): + content = message.get('content', '') + word_count = count_words_in_context(content) + sample_word_count += word_count + + if '<|startofcontext|>' in content and '<|endofcontext|>' in content: + has_context = True + + if has_context: + samples_with_context += 1 + word_counts.append(sample_word_count) + else: + samples_without_context += 1 + + # 计算统计信息 + if word_counts: + avg_words = sum(word_counts) / len(word_counts) + max_words = max(word_counts) + min_words = min(word_counts) + + # 计算中位数 + sorted_counts = sorted(word_counts) + n = len(sorted_counts) + median_words = (sorted_counts[n//2 - 1] + sorted_counts[n//2]) / 2 if n % 2 == 0 else sorted_counts[n//2] + else: + avg_words = max_words = min_words = median_words = 0 + + results = { + 'total_samples': len(data), + 'samples_with_context': samples_with_context, + 'samples_without_context': samples_without_context, + 'average_words_per_sample': avg_words, + 'max_words_per_sample': max_words, + 'min_words_per_sample': min_words, + 'median_words_per_sample': median_words, + 'word_counts_distribution': word_counts[:10] if len(word_counts) > 10 else word_counts # 显示前10个样本的词数 + } + + return results + + +def main(): + """主函数""" + file_path = "/data8/rym/Projects/ms-swift/data/add_context_token/slidespeech_L95_5slides_filtered_train/test_from_window_size2_no_param.json" + + try: + results = analyze_json_file(file_path) + + print("\n" + "="*60) + print("统计结果:") + print("="*60) + print(f"总样本数: {results['total_samples']}") + print(f"包含context的样本数: {results['samples_with_context']}") + print(f"不包含context的样本数: {results['samples_without_context']}") + print(f"平均每条样本context区域词数: {results['average_words_per_sample']:.2f}") + print(f"最大词数: {results['max_words_per_sample']}") + print(f"最小词数: {results['min_words_per_sample']}") + print(f"中位数词数: {results['median_words_per_sample']:.2f}") + + if results['word_counts_distribution']: + 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