The project utilizes a SoftVC content encoder to extract speech features from the source audio. These feature vectors are then directly fed into a VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) model, bypassing the need for text-based intermediate representations. This approach allows the system to maintain the original pitch and intonations of the source audio while changing the voice characteristics to match the target speaker.
So-vits-svc incorporates several advanced technologies to improve the quality and efficiency of voice conversion. It uses a NSF HiFiGAN vocoder, which helps solve issues related to sound interruption that can occur in other voice conversion systems. The project also supports various sampling rates, with 44.1kHz being the standard, allowing for high-quality audio output.
The system is designed to be versatile and can handle a wide range of voice types and singing styles. It can be used for converting both male and female voices and is capable of handling various musical genres. This makes it a powerful tool for content creators, musicians, and anyone interested in voice transformation technology.
So-vits-svc requires training on voice samples to create a model for a specific target voice. Users need to provide a dataset of the target voice, which the system then uses to learn the unique characteristics of that voice. The training process can be computationally intensive, often requiring a GPU for efficient processing.
The project is continuously evolving, with updates and improvements being made by the development team and the open-source community. It offers both a command-line interface for advanced users and a graphical user interface for those who prefer a more visual approach to operating the software.
Key features of so-vits-svc:
- Singing voice conversion while preserving pitch and intonation
- SoftVC content encoder for speech feature extraction
- VITS model for voice transformation
- NSF HiFiGAN vocoder for improved sound quality
- Support for 44.1kHz sampling rate
- Ability to handle both male and female voices
- Customizable voice models through training
- Open-source project with active community development
- Command-line and graphical user interfaces
- Compatibility with various operating systems
- Automatic pitch prediction for voice conversion (optional feature)
- K-means clustering to reduce timbre leakage
- NSF-HIFIGAN Enhancer for potential sound quality improvement
- Support for multiple languages in voice conversion