README.md

---
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

## Dataset Description / 数据集简介

**WordVoice-5A** 是一个约 4.7k 小时的大规模中英双语字级声学属性标注数据集，专为高精度、细粒度可控的语音合成（TTS）设计。该数据集解决了当前开源社区缺乏大规模、高质量字级对齐与声学标注数据的问题。通过融合语言学规则与声学特征统计分布的自动化标注 Pipeline，我们提取了五维核心属性（时长、边界、能量、音高、音调）。该数据集旨在打破 LLM-TTS 的“黑盒”特性，推动精细化语音生成与声学建模的未来研究。

**WordVoice-5A** is a large-scale, ~4.7k-hour bilingual (Mandarin and English) dataset with fine-grained word-level acoustic annotations, designed for high-precision, controllable Text-to-Speech (TTS). It addresses the critical scarcity of massive, high-quality word-aligned and acoustically annotated data in the open-source community. Processed through a linguistically-guided automated pipeline based on empirical acoustic distributions, it provides five-dimensional core attributes (Duration, Boundary, Energy, Pitch, and Tone). This dataset aims to break the "black-box" nature of LLM-TTS and facilitate future research in fine-grained speech generation and acoustic modeling.

---

## Dataset Summary / 数据集概述

WordVoice-5A 数据集包含约 4,684 小时的高质量语音数据（中文 2,546 小时，英文 2,138 小时），涵盖超过 5,200 万个字/词级标注。数据源自开源 LEMAS 数据集，经过严格的双模型交叉验证与清洗。数据集专门设计用于：

- **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).

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## Key Features / 主要特点

- **五维字级标注 (5-Dimensional Annotations)**：每个字/词均包含精准的时长 (Duration)、5级声学边界 (Boundary)、能量 (Energy)、音高 (Pitch) 与 7类音调 (Tone) 标注。
- **超大规模双语语料 (Massive Bilingual Corpus)**：包含中文 2546h 与英文 2138h，是目前已知规模最大、标注维度最全的字级控制数据集。
- **语言学专家指导 (Linguistically-Guided)**：分类标准与阈值（如“掐头去尾”去除协同发音、二次曲线拟合音调）均基于真实数据分布与语言学规则严格论证。
- **高精度时间戳 (High-Precision Timestamps)**：采用 MFA 与 Qwen3FA 双模型交叉验证，结合基于响度的边界优化，严格剔除不良对齐数据。

*English:*
- **5-Dimensional Word-Level Annotations**: Every character/word is annotated with precise Duration, 5-level Acoustic Boundary, Energy, Pitch, and 7-category Tone.
- **Massive Bilingual Corpus**: Comprising 2,546h of Mandarin and 2,138h of English, making it the largest and most comprehensively annotated dataset for word-level control.
- **Linguistically-Guided**: Classification standards and thresholds (e.g., truncation for coarticulation mitigation, quadratic curve fitting for Tone) are strictly driven by empirical data distributions and linguistic rules.
- **High-Precision Timestamps**: Utilizes dual-model (MFA & Qwen3FA) cross-validation combined with loudness-based boundary optimization to ensure alignment fidelity.

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## 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"
    }
    // ...
  ]
}