This work presents FreeSVC, a promising multilingual singing voice conversion approach that leverages an enhanced VITS model with Speaker-invariant Clustering (SPIN) for better content representation and the State-of-the-Art (SOTA) speaker encoder ECAPA2. FreeSVC incorporates trainable language embeddings to handle multiple languages and employs an advanced speaker encoder to disentangle speaker characteristics from linguistic content. Designed for zero-shot learning, FreeSVC enables cross-lingual singing voice conversion without extensive language-specific training. We demonstrate that a multilingual content extractor is crucial for optimal cross-language conversion. Our source code and models are publicly available.

翻译：本文提出FreeSVC，一种前景广阔的多语言歌声转换方法。该方法采用增强型VITS模型，结合说话人无关聚类（SPIN）技术以优化内容表征，并集成当前最优（SOTA）说话人编码器ECAPA2。FreeSVC通过引入可训练的语言嵌入向量处理多语言场景，并利用先进的说话人编码器实现说话人特征与语言内容的解耦。该框架专为零样本学习设计，无需大量语言特定训练即可实现跨语言歌声转换。我们论证了多语言内容提取器对于实现最优跨语言转换的关键作用。本研究的源代码与模型均已公开。