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WSC-Eval-ASR/Easy/duration.txt

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+====== Summary ======
+files: 6981
+Total duration: 8h 32min 51.02s (8.55h)
+Total seconds: 30771.02s

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WSC-Eval-ASR/process_textgrid.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import re
+from pathlib import Path
+
+def extract_first_item_text(textgrid_file):
+    """提取第一个item的文本（直接拼接，不留空格）"""
+    with open(textgrid_file, 'r', encoding='utf-8') as f:
+        content = f.read()
+    
+    # 提取item [1]的全部内容
+    first_item_match = re.search(r'item\s*\[1\]:.*?intervals\s*:\s*size\s*=\s*\d+(.*?)(?=item\s*\[2\]|\Z)', 
+                                content, re.DOTALL)
+    if not first_item_match:
+        return ""
+    
+    # 提取所有非空text字段
+    texts = re.findall(r'text\s*=\s*"(.*?)"', first_item_match.group(1))
+    return "".join([t for t in texts if t.strip()])  # 直接拼接，不留空格
+
+def process_textgrids(textgrid_dir, wav_dir, output_dir):
+    """处理目录下所有TextGrid文件"""
+    textgrid_dir = Path(textgrid_dir)
+    wav_dir = Path(wav_dir)
+    output_dir = Path(output_dir)
+    
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    with open(output_dir/'wav.scp', 'w', encoding='utf-8') as f_wav, \
+         open(output_dir/'text', 'w', encoding='utf-8') as f_text:
+        
+        for textgrid_file in textgrid_dir.glob('*.TextGrid'):
+            audio_id = textgrid_file.stem
+            transcript = extract_first_item_text(textgrid_file)
+            
+            # 写入wav.scp
+            wav_path = wav_dir/f"{audio_id}.wav"
+            f_wav.write(f"{audio_id} {wav_path}\n")
+            
+            # 写入text（直接拼接的文本）
+            f_text.write(f"{audio_id} {transcript}\n")
+
+if __name__ == "__main__":
+    textgrid_dir = ""
+    wav_dir = ""
+    output_dir = ""
+    
+    process_textgrids(textgrid_dir, wav_dir, output_dir)
+    print(f"处理完成！输出目录: {output_dir}")

WSC-Eval-ASR/readme.md

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+## 标注规则：
+- 标注文本、性别、年龄、情感；
+- 针对多说话人，能区分的，按照时间顺序标注文本，非主说话人的文本用（）区分，副语言信息以主说话人为主；
+- 关于英文部分，拼读大写字母，字母之间空格，单词小写；英文和中文之间无空格；
+- （）代表有非主说话人说话，但是听不清；
+- （文本内容）代表非主说话人说话，能听清内容；
+- *代表部分字词听不清，不确定具体内容；
+## 子集划分：
+- short/long：0~10s音频为short，10~30s音频为long，两个合并为WenetSpeech-Chuan-Eval-ASR的完整版；
+- Easy/Hard：以识别场景难易程度划分，Easy场景多为安静场景下（有声书、朗读），Hard多为带噪场景（直播、短视频等），两部分合并为WenetSpeech-Chuan-Eval-ASR的完整版，该部分和论文中的子集划分一致；
+- 需要注意：Long部分给的是原始长音频，Easy和Hard部分用到的是Long部分切分成短音频后的结果
+## 论文中测试集文本标准化处理
+- 去标点；
+- *号直接去掉；
+- （）内的内容保留，（）去掉；