README.md

# WordVoice-5A Dataset 🚀

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**A Large-Scale Bilingual Word-level Five-Annotation Dataset for WordVoice**

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## 📖 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, Acoustic 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.

**WordVoice-5A** 是一个约 **4.7k 小时**的大规模中英双语字/词级声学属性标注数据集，专为高精度、细粒度可控语音合成（TTS）设计。该数据集针对当前开源社区缺乏大规模、高质量字/词级对齐与显式声学标注数据的问题，提出了一套结合语言学规则与真实声学统计分布的自动化标注 Pipeline，为每个字/词提供 **时长（Duration）、声学边界（Acoustic Boundary）、能量（Energy）、音高（Pitch）和音调（Tone）** 五维核心属性。WordVoice-5A 致力于打破 LLM-TTS 的"黑盒"特性，推动细粒度语音生成与声学建模研究。

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## 🔗 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。

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## 📊 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：

- **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。

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## ✨ 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**.  
  每个字/词均包含精准的 **时长（Duration）**、**5级声学边界（Acoustic Boundary）**、**能量（Energy）**、**音高（Pitch）** 和 **7类音调（Tone）** 标注。

- **Massive Bilingual Corpus / 超大规模双语语料**  
  The dataset contains **2,546 hours of Mandarin** and **2,138 hours of English**, making it one of the largest publicly available corpora with comprehensive word-level acoustic annotations.  
  数据集包含 **2546 小时中文** 与 **2138 小时英文**，是目前已知规模最大、标注维度最完整的字/词级控制数据集之一。

- **Linguistically Guided Annotation / 语言学专家指导标注**  
  Annotation criteria (e.g., coarticulation-aware truncation and quadratic pitch contour fitting) are carefully designed based on empirical acoustic distributions and linguistic principles.  
  标注标准与阈值（如"掐头去尾"去除协同发音、二次曲线拟合音调等）均基于真实数据分布与语言学规则设计。

- **High-Precision Timestamps / 高精度时间戳**  
  Word boundaries are obtained through dual-model alignment (MFA & Qwen3FA) and refined by loudness-based boundary optimization to ensure high alignment fidelity.  
  采用 **MFA** 与 **Qwen3FA** 双模型交叉验证，并结合基于响度的边界优化策略，保证字/词级时间戳的高精度。

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## 📂 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.

数据集采用 **JSONL** 格式，并配有对应音频文件。每条记录包含音频路径、文本以及对应字/词级五维声学属性标注。

### Data Format Example / 数据格式示例

```json
{
  "utt": "zh_WenetSpeech4TTS_0001681467",
  "audio_path": "test/WenetSpeech4TTS_0001681467.mp3",
  "duration": 1.92,
  "text": "真是巧啊。",
  "mfa_text": "真 是 巧 啊",
  "mfa_words": [
    {"word": "真", "start": 0.69, "end": 0.84},
    {"word": "是", "start": 0.84, "end": 0.95},
    {"word": "巧", "start": 0.95, "end": 1.16},
    {"word": "啊", "start": 1.17, "end": 1.34}],
  "f0": [-0.2686, -0.246, -0.3819, -0.6745],
  "eng": [0.5415, 0.4326, 0.3464, 0.2612],
  "tone": ["flat", "flat", "fall", "fall"],
  "bnd": ["b0", "b0", "b1", "b4"]
}
```

### Annotation Ranges / 标注范围说明

- **Duration（时长）**
  - Float (seconds)
  - 字/词的实际发音时长（单位：秒）。

- **Boundary（声学边界）**
  - Five discrete categories representing the pause level after each word.
  - 表示字/词后的停顿等级，共 5 类：
    - `b0`: No pause / 无停顿
    - `b1`: ≤ 0.05 s (Micro pause / 微停顿)
    - `b2`: ≤ 0.18 s (Word boundary / 词边界)
    - `b3`: ≤ 0.40 s (Comma-level boundary / 逗号级边界)
    - `b4`: > 0.40 s (Sentence boundary / 句号级边界)

- **Energy（能量）**
  - Float normalized to **[0, 1]**
  - 字/词级归一化有效响度。

- **Pitch（音高）**
  - Float normalized to **[-1, 1]**
  - 字/词级核心音高均值。

- **Tone（音调）**
  - Seven discrete pitch contour categories.
  - 字内音高变化轮廓，共 7 类：
    - `flat`
    - `rise`
    - `rrise`
    - `fall`
    - `ffall`
    - `peak`
    - `valley`

---

## 🎯 Use Cases / 使用场景

- Fine-grained controllable LLM-based TTS
- Word-level prosody modeling
- Local prosody editing for audiobook narration and dubbing
- Cross-lingual acoustic feature analysis
- Prosody prediction

- 细粒度可控 LLM-TTS 模型训练
- 字/词级韵律建模
- 有声书与视频配音中的精准局部韵律编辑
- 跨语种字/词级声学特征分析
- 韵律预测

---

## 📝 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}
}
```

## 📄 License / 开源协议
Released under the MIT License.
本数据集基于 MIT 协议开源。