Spark-TTS is an advanced text-to-speech system that uses the power of large language models (LLM) for highly accurate and natural-sounding voice synthesis. It is designed to be efficient, flexible, and powerful for both research and production use.

Inference Overview of Voice Cloning

Inference Overview of Controlled Generation

Install

Clone and Install

Clone the repo

git clone https://github.com/SparkAudio/Spark-TTS.git
cd Spark-TTS

Install Conda: please see https://docs.conda.io/en/latest/miniconda.html
Create Conda env:

conda create -n sparktts -y python=3.12
conda activate sparktts
pip install -r requirements.txt

Model Download

Download via python:

python

from huggingface_hub import snapshot_download

snapshot_download("MrEzzat/Spark_TTS_Arabic", local_dir="pretrained_models/Spark-TTS-0.5B")

Download via git clone:

mkdir -p pretrained_models

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/MrEzzat/Spark_TTS_Arabic

Basic Usage

You can simply run the demo with the following commands:

cd example
bash infer.sh

Alternatively, you can directly execute the following command in the command line to perform inference：

python -m cli.inference \
    --text "text to synthesis." \
    --device 0 \
    --save_dir "path/to/save/audio" \
    --model_dir pretrained_models/Spark-TTS-0.5B \
    --prompt_text "transcript of the prompt audio" \
    --prompt_speech_path "path/to/prompt_audio"

UI Usage

You can start the UI interface by running python webui.py, which allows you to perform Voice Cloning and Voice Creation. Voice Cloning supports uploading reference audio or directly recording the audio.

Voice Cloning	Voice Creation
Image 1	Image 2

Citation

@misc{wang2025sparktts,
      title={Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens}, 
      author={Xinsheng Wang and Mingqi Jiang and Ziyang Ma and Ziyu Zhang and Songxiang Liu and Linqin Li and Zheng Liang and Qixi Zheng and Rui Wang and Xiaoqin Feng and Weizhen Bian and Zhen Ye and Sitong Cheng and Ruibin Yuan and Zhixian Zhao and Xinfa Zhu and Jiahao Pan and Liumeng Xue and Pengcheng Zhu and Yunlin Chen and Zhifei Li and Xie Chen and Lei Xie and Yike Guo and Wei Xue},
      year={2025},
      eprint={2503.01710},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2503.01710}, 
}