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| language: |
| - zh |
| - en |
| license: cc-by-4.0 |
| --- |
| # WordVoice-5A Dataset 🚀 |
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| <div align="center"> |
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| [](https://arxiv.org/abs/2607.06461) |
| [](https://xxh333.github.io/wordvoice-demo/) |
| [](https://github.com/XXH333/WordVoice-5A-Pipeline) |
| [](https://github.com/XXH333/WordVoice-main) |
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| **A Large-Scale Bilingual Word-level Five-Annotation Dataset for WordVoice** |
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| </div> |
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| --- |
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| ## 📖 Dataset Description / 数据集简介 |
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| **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. |
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| **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 |
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| 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： |
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| - 🛠️ **[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 |
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| 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](https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train)** 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: |
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| - **Controllable TTS:** Training TTS models with explicit word-level acoustic control. |
| - **Prosody Modeling:** Studying bilingual micro-prosody and coarticulation in continuous speech. |
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| **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](https://huggingface.co/datasets/LEMAS-Project/LEMAS-Dataset-train)** 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： |
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| - **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 |
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| - **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。 |
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| - **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。 |
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| - **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。 |
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| - **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 |
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| The dataset is provided in **JSONL** format paired with corresponding **audio files**. Each record contains the audio path, transcript, and word-level acoustic annotations. |
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| 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。 |
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| ### Data Format Example / 数据格式示例 |
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| ```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"] |
| } |
| ``` |
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| ### Annotation Ranges / 标注范围说明 |
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| - **Duration（时长）** |
| - Float (seconds) |
| - 字/词的实际发音时长（单位：秒）。 |
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| - **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) |
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| - **Energy（能量）** |
| - Float normalized to **[0, 1]** |
| - 字/词级归一化有效响度。 |
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| - **Pitch（音高）** |
| - Float normalized to **[-1, 1]** |
| - 字/词级核心音高均值。 |
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| - **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` |
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| --- |
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| ## 🎯 Use Cases / 使用场景 |
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| - 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 |
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| - 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 |
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| --- |
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| ## 📝 Citation / 引用 |
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| If you find this dataset useful in your research, please cite our paper: |
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| ```bibtex |
| @misc{nie2026wordvoice, |
| title={WordVoice: Explicit and Decoupled Multi-Dimensional Word-Level Control for LLM-Based TTS}, |
| author={Sihang Nie and Jinxin Ji and Xiaofen Xing and Deyi Tuo and Chengbin Jin and Jialong Mai and Xiangmin Xu}, |
| year={2026}, |
| eprint={2607.06461}, |
| archivePrefix={arXiv}, |
| primaryClass={eess.AS}, |
| url={https://arxiv.org/abs/2607.06461}, |
| } |
| ``` |
