Update README.md

README.md

@@ -1,6 +1,21 @@
-# WordVoice-5A: A Large-Scale Bilingual Dataset for Word-Level Controllable TTS
+# WordVoice-5A Dataset 🚀
 
-## Dataset Description / 数据集简介
+<div align="center">
+
+[![Paper](https://img.shields.io/badge/Paper-arxiv_2025-blue.svg)](#)
+[![DemoPage](https://img.shields.io/badge/DemoPage-WordVoice-yellow.svg)](https://xxh333.github.io/wordvoice-demo/)
+[![Pipeline](https://img.shields.io/badge/Pipeline-WordVoice--5A--Pipeline-blue.svg)](https://github.com/XXH333/WordVoice-5A-Pipeline)
+[![Model](https://img.shields.io/badge/Model-WordVoice-red.svg)](https://github.com/XXH333/WordVoice-main)
+[![Dataset](https://img.shields.io/badge/Dataset-WordVoice--5A-green.svg)](https://huggingface.co/datasets/XXH333/WordVoice-5A)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+
+**A Large-Scale Bilingual Dataset for Word-Level Controllable TTS**
+
+</div>
+
+---
+
+## 📖 Dataset Description / 数据集简介
 
 **WordVoice-5A** is a large-scale bilingual (Mandarin and English) dataset containing approximately **4.7k hours** of speech with fine-grained **word-level acoustic annotations**, designed for high-precision controllable Text-to-Speech (TTS). It addresses the scarcity of large-scale, high-quality word-aligned datasets with explicit acoustic annotations in the open-source community. Through a linguistically guided automatic annotation pipeline based on empirical acoustic distributions, the dataset provides five core acoustic attributes for each word: **Duration, Boundary, Energy, Pitch, and Tone**. It aims to break the "black-box" nature of LLM-based TTS and facilitate future research in fine-grained speech generation and acoustic modeling.
 
@@ -8,21 +23,31 @@
 
 ---
 
-## Dataset Summary / 数据集概述
+## 🔗 WordVoice Ecosystem / WordVoice 生态系统
+
+This dataset is a core component of the WordVoice project. We also provide the official data processing pipeline and the pre-trained TTS models:
+本数据集是 WordVoice 项目的核心部分。我们同时开源了官方的数据处理流水线与预训练 TTS 模型：
+
+- 🛠️ **[WordVoice Data Pipeline](https://github.com/XXH333/WordVoice-5A-Pipeline)**: The linguistically-guided automated annotation toolkit used to build this dataset. / 用于构建本数据集的语言学指导自动化标注工具包。
+- 🧠 **[WordVoice Model](https://github.com/XXH333/WordVoice-main)**: The official implementation of the WordVoice TTS framework, supporting explicit multi-dimensional word-level control. / WordVoice TTS 框架的官方实现，支持显式的多维字级控制。
+
+---
+
+## 📊 Dataset Summary / 数据集概述
 
 The **WordVoice-5A** dataset contains approximately **4,684 hours** of high-quality speech, including **2,546 hours of Mandarin** and **2,138 hours of English**, with over **52 million word-level annotations**. The raw speech and text data are sourced from the open-source **LEMAS** corpus. Our primary contribution lies in the comprehensive **data annotation** rather than data cleansing. By applying our rigorous dual-model alignment and linguistically guided pipeline to the raw data, we successfully extracted and annotated five-dimensional acoustic attributes for every single word. It is specifically designed for:
 
 - **Controllable TTS:** Training TTS models with explicit word-level acoustic control.
 - **Prosody Modeling:** Studying bilingual micro-prosody and coarticulation in continuous speech.
 
-**WordVoice-5A** 数据集包含约 **4,684 小时**高质量语音，其中中文 **2,546 小时**、英文 **2,138 小时**，共包含超过 **5,200 万**字/词级标注。本数据集的原始语音与文本数据来源于开源的 **LEMAS** 语料库。我们的核心工作是对原始数据进行了深度的**数据标注**（而非单纯的数据清洗）。通过严格的双模型交叉对齐与语言学指导的自动化 Pipeline，我们成功为原始语料中的每一个字/词提取并标注了五维声学属性。该数据集主要面向以下研究方向
+**WordVoice-5A** 数据集包含约 **4,684 小时**高质量语音，其中中文 **2,546 小时**、英文 **2,138 小时**，共包含超过 **5,200 万**字/词级标注。本数据集的原始语音与文本数据来源于开源的 **LEMAS** 语料库。我们的核心工作是对原始数据进行了深度的**数据标注**（而非单纯的数据清洗）。通过严格的双模型交叉对齐与语言学指导的自动化 Pipeline，我们成功为原始语料中的每一个字/词提取并标注了五维声学属性。该数据集主要面向以下研究方向：
 
 - **可控语音合成（Controllable TTS）**：训练支持字/词级声学属性显式控制的 TTS 模型。
 - **韵律建模（Prosody Modeling）**：研究中英双语连续语流中的微观韵律及协同发音规律。
 
 ---
 
-## Key Features / 主要特点
+## ✨ Key Features / 主要特点
 
 - **5-Dimensional Word-Level Annotations / 五维字级标注**  
   Each character/word is annotated with **Duration, Boundary, Energy, Pitch, and Tone**.  
@@ -42,7 +67,7 @@ The **WordVoice-5A** dataset contains approximately **4,684 hours** of high-qual
 
 ---
 
-## Data Structure & Annotation Details / 数据结构与标注说明
+## 📂 Data Structure & Annotation Details / 数据结构与标注说明
 
 The dataset is provided in **JSONL** format paired with corresponding **audio files**. Each record contains the audio path, transcript, and word-level acoustic annotations.
 
@@ -105,7 +130,7 @@ The dataset is provided in **JSONL** format paired with corresponding **audio fi
 
 ---
 
-## Use Cases / 使用场景
+## 🎯 Use Cases / 使用场景
 
 - Fine-grained controllable LLM-based TTS
 - Word-level prosody modeling
@@ -121,7 +146,7 @@ The dataset is provided in **JSONL** format paired with corresponding **audio fi
 
 ---
 
-## Citation / 引用
+## 📝 Citation / 引用
 
 If you find this dataset useful in your research, please cite our paper:
 
@@ -132,4 +157,8 @@ If you find this dataset useful in your research, please cite our paper:
   booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
   year={2027}
 }
-```
+```
+
+## 📄 License / 开源协议
+Released under the MIT License.
+本数据集基于 MIT 协议开源。