Update README.md

README.md

@@ -1,25 +1,4 @@
----
-license: apache-2.0
----
----
-language:
-- zh
-- en
-task_categories:
-- text-to-speech
-- audio-to-audio
-tags:
-- speech
-- prosody
-- controllable-tts
-- alignment
-- word-level
-size_categories:
-- 10M<n<100M
-license: cc-by-nc-4.0
----
-
-# Dataset Card for WordVoice-5A
+# WordVoice-5A: A Large-Scale Bilingual Dataset for Fine-Grained Controllable TTS
 
 ## Dataset Description / 数据集简介
 
@@ -35,13 +14,11 @@ WordVoice-5A 数据集包含约 4,684 小时的高质量语音数据（中文 2,
 
 - **可控语音合成 (Controllable TTS)**：训练支持字级声学属性显式控制的 TTS 模型。
 - **韵律建模 (Prosody Modeling)**：研究中英双语在连续语流中的微观韵律与协同发音规律。
-- **细粒度声学对齐 (Fine-grained Alignment)**：为多模态对齐（如数字人唇形同步）提供高精度的字级时间戳。
 
 The WordVoice-5A dataset contains approximately 4,684 hours of high-quality speech data (2,546 hours of Mandarin, 2,138 hours of English), encompassing over 52 million character/word-level annotations. Derived from the open-source LEMAS dataset and refined through rigorous dual-model cross-validation and cleansing, it is specifically designed for:
 
 - **Controllable TTS**: Training TTS models that support explicit word-level acoustic control.
 - **Prosody Modeling**: Researching micro-prosody and coarticulation patterns in continuous bilingual speech.
-- **Fine-grained Alignment**: Providing high-precision word-level timestamps for cross-modal alignment (e.g., lip-sync for digital avatars).
 
 ---
 
@@ -60,35 +37,22 @@ The WordVoice-5A dataset contains approximately 4,684 hours of high-quality spee
 
 ---
 
-## Data Structure / 数据结构
-
-每条数据样本包含音频文件以及对应的字级多维属性列表。数据格式示例如下：
-Each data instance contains the audio file and a list of word-level multi-dimensional attributes. An example of the data structure is as follows:
-
-```json
-{
-  "audio_id": "zh_00001",
-  "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"
-    }
-    // ...
-  ]
+## 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}
 }