WordVoice-base-0.5B

WordVoice: Explicit and Decoupled Multi-Dimensional Word-Level Control for LLM-Based TTS

WordVoice is a novel speech generation framework that transforms traditional implicit end-to-end TTS generation into an explicit, highly controllable paradigm. Built on top of the CosyVoice3 framework, it enables precise and decoupled word-level control over five acoustic dimensions.

• Paper: WordVoice: Explicit and Decoupled Multi-Dimensional Word-Level Control for LLM-Based TTS
• GitHub Repository: XXH333/WordVoice-main
• Project Page & Demo: WordVoice Demo

---

✨ Features

🎯 Explicit Word-Level Control

Supports independent and decoupled control of five acoustic attributes for each input word:

• ⏱️ Duration: Word-level pronunciation duration.
• ⏸️ Boundary: 5-level pause classification (b0–b4).
• 🔊 Energy: Word-level volume/loudness (0–1).
• 🎵 Pitch: Word-level core fundamental frequency (-1–1).
• 📈 Tone: 7 categories of prosodic morphologies (flat, rise, strong rise, fall, strong fall, peak, valley).

🧠 "Acoustic Thinking" Mechanism via Bound-Token

Employs a bound-token (<b>) mechanism within the autoregressive (AR) language model. Before generating the speech tokens for a specific word, the model explicitly predicts its acoustic attributes, realizing an intelligent process of "planning prosody first, then generating sound."

---

🛠️ Quick Start

Installation

We recommend using Conda to manage your Python environment.

conda create -n wordvoice python=3.10 -y
conda activate wordvoice

git clone https://github.com/XXH333/WordVoice-main.git
cd WordVoice-main

pip install -e .
pip install num2words==0.5.14 x_transformers==2.11.24

Download Model Weights

Run the following script to automatically download the pre-trained weights and dependencies (such as CosyVoice3, MMS-FA, etc.):

bash download_models.sh

Inference

You can run the out-of-the-box inference script to experience both the Free Mode and Control Mode of WordVoice:

python wordvoice_infer.py

For custom prompts and detailed control parameters, refer to wordvoice_infer.py and the GitHub repository.

---

📝 Citation

If you find this work or the models useful, please cite:

@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}, 
}