Update README.md

README.md

@@ -1,70 +1,135 @@
-# WordVoice-5A: A Large-Scale Bilingual Dataset for Word-level Controllable TTS
+# WordVoice-5A: A Large-Scale Bilingual Dataset for Word-Level Controllable TTS
 
 ## 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 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.
 
-**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.
+**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）、边界（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。
 
 ---
 
 ## 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：
+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:
 
-- **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。
+- **Controllable TTS:** Training TTS models with explicit word-level acoustic control.
+- **Prosody Modeling:** Studying bilingual micro-prosody and coarticulation in continuous speech.
 
-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:
+**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��。该数据集主要面向以下研究方向
 
-- **Controllable TTS**: Training TTS models that support explicit word-level acoustic control.
-- **Prosody Modeling**: Researching micro-prosody and coarticulation patterns in continuous bilingual speech.
+- **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
 
-- **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。
+- **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（Boundary）**、**能量（Energy）**、**音高（Pitch）** 和 **7类音调（Tone）** 标注。
 
-*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.
+- **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。
 
 ---
 
 ## Data Structure & Annotation Details / 数据结构与标注说明
 
-数据集以 `JSONL` 格式提供，并配有对应的 `WAV` 音频文件。每条记录包含音频路径、文本以及每个字/词的五维属性标注列表。
-The dataset is provided in `JSONL` format paired with `WAV` audio files. Each record contains the audio path, text transcript, and a list of 5-dimensional annotations for each character/word.
+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
 {
-  "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"
-    }
-  ]
-}
+  "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}
+}
+```