SoulX-Singer：实现高质量零样本歌声合成的探索之路

摘要

近年来，虽然语音合成技术发展迅猛，但开源歌唱合成系统在工业级部署中仍面临重大挑战，尤其在鲁棒性与零样本泛化能力方面。本报告推出SoulX-Singer——一款基于实际部署考量设计的高质量开源歌唱合成系统。该系统支持基于符号乐谱（MIDI）或旋律表征的可控歌声生成，可在真实生产流程中实现灵活且富有表现力的控制。经过超过4.2万小时人声数据训练，该系统支持中文普通话、英语及粤语，并在多样化音乐场景下持续实现跨语言的顶尖合成质量。此外，为在实际场景中实现零样本歌唱合成性能的可靠评估，我们构建了具有严格训练-测试数据分离特性的专用基准数据集SoulX-Singer-Eval，为零样本场景下的系统化评估提供支持。

English

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.

SoulX-Singer：实现高质量零样本歌声合成的探索之路

SoulX-Singer: Towards High-Quality Zero-Shot Singing Voice Synthesis

摘要

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