#Singing Voice Conversion

This project utilizes the SoftVC content encoder paired with the VITS model for singing voice conversion, maintaining original pitch and intonations without text conversion. Key features include a visible f0 editor and speaker mix timeline editor, ensuring uninterrupted sound with NSF HiFiGAN vocoder integration. Tailored for offline purposes, it is intended for converting fictional character voices without real-time application support. Its academic focus emphasizes the user's responsibility for dataset authorization. The 4.1-Stable update offers enhanced sound quality and dynamic fusion capabilities.

This project offers an end-to-end method for converting singing voices through variational inference and adversarial learning, leveraging the VITS model. Designed for deep learning beginners, it focuses on hands-on practice with essential Python and PyTorch knowledge. It supports training with multiple speakers, creating distinctive voices by mixing, and handling light accompaniment. While needing at least 6GB VRAM for training, it delivers strong performance with features like noise immunity and advanced sound quality enhancement. Real-time voice conversion is not supported, but the project provides comprehensive instruction for training and inference, aiding learners in optimizing model operations.

DDSP-SVC provides a novel method for singing voice conversion utilizing advanced models such as rectified-flow and cascade diffusion, designed to function effectively on typical computer hardware. It significantly reduces training time, similar to RVC, and allows real-time voice conversion with lower resource usage compared to other projects, while ensuring high synthesis quality with vocoder enhancements. The platform supports multi-speaker models with detailed configuration, emphasizing legal data use for training, and includes a comprehensive guide and visualization tools to improve user interaction.