SoulX-Singer: Towards High-Quality Zero-Shot Singing Voice Synthesis
Authors: name: Jiale Qian, name: Hao Meng, name: Tian Zheng, name: Pengcheng Zhu, name: Haopeng Lin, name: Yuhang Dai, name: Hanke Xie, name: Wenxiao Cao, name: Ruixuan Shang, name: Jun Wu, name: Hongmei Liu, name: Hanlin Wen, name: Jian Zhao, name: Zhonglin Jiang, name: Yong Chen, name: Shunshun Yin, name: Ming Tao, name: Jianguo Wei, name: Lei Xie, name: Xinsheng Wang
Organization: Soul-AILab

While recent years have witnessed rapid progress in speech synthesis, open-source singing voice synthesis (SVS) systems still face significant barriers to industrial deployment, particularly in terms of robustness and zero-shot generalization. In this report, we introduce SoulX-Singer, a high-quality open-source SVS system designed with practical deployment considerations in mind. SoulX-Singer supports controllable singing generation conditioned on either symbolic musical scores (MIDI) or melodic representations, enabling flexible and expressive control in real-world production workflows. Trained on more than 42,000 hours of vocal data, the system supports Mandarin Chinese, English, and Cantonese and consistently achieves state-of-the-art synthesis quality across languages under diverse musical conditions. Furthermore, to enable reliable evaluation of zero-shot SVS performance in practical scenarios, we construct SoulX-Singer-Eval, a dedicated benchmark with strict training-test disentanglement, facilitating systematic assessment in zero-shot settings.