Update README.md

README.md

@@ -37,22 +37,34 @@ The WordVoice-5A dataset contains approximately 4,684 hours of high-quality spee
 
 ---
 
-## Use Cases / 使用场景
-
-- 细粒度可控 LLM-TTS 模型训练 (Training fine-grained controllable LLM-TTS models)
-- 有声书演播与视频配音中的精准局部韵律编辑 (Precise local prosody editing for audiobook narration and video dubbing)
-- 跨语种字级声学特征分析与韵律预测 (Cross-lingual word-level acoustic feature analysis and prosody prediction)
-
----
-
-## Citation / 引用
-
-If you find this dataset useful in your research, please cite our paper:
-
-```bibtex
-@inproceedings{wordvoice2027,
-  title={WordVoice: Explicit and Decoupled Multi-Dimensional Word-Level Control for LLM-Based TTS},
-  author={Your Name and Co-authors},
-  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
-  year={2027}
+## Data Structure & Annotation Details / 数据结构与标注说明
+
+数据集以 `JSONL` 格式提供，并配有对应的 `WAV` 音频文件。每条记录包含音频路径、文本以及每个字/词的五维属性标注列表。
+The dataset is provided in `JSONL` format paired with `WAV` audio files. Each record contains the audio path, text transcript, and a list of 5-dimensional annotations for each character/word.
+
+### Data Format Example / 数据格式示例
+```json
+{
+  "audio_path": "wavs/zh_00001.wav",
+  "text": "你好世界",
+  "word_level_annotations": [
+    {
+      "word": "你",
+      "timestamp": [0.12, 0.35],
+      "duration": 0.23,
+      "boundary": "b1",
+      "energy": 0.75,
+      "pitch": -0.12,
+      "tone": "fall"
+    },
+    {
+      "word": "好",
+      "timestamp": [0.35, 0.60],
+      "duration": 0.25,
+      "boundary": "b3",
+      "energy": 0.82,
+      "pitch": 0.45,
+      "tone": "rise"
+    }
+  ]
 }