DDSP-SVC Project Overview
The DDSP-SVC project is a remarkable open-source initiative focused on singing voice conversion. Its primary goal is to develop easily accessible AI voice changer software that can function efficiently on average personal computers. The project offers various models and methods to achieve high-quality voice conversion with minimal resource consumption, making it an appealing alternative to existing solutions in the field.
Key Features of DDSP-SVC
1. Efficient Performance
Compared to other well-known projects like SO-VITS-SVC, DDSP-SVC demands less from computer hardware during both training and synthesis stages. It offers faster training speeds akin to the RVC project, making it a practical choice for users with limited computing resources.
2. High-Quality Output
Although the initial audio output from a DDSP model may not be optimal, the inclusion of a pre-trained vocoder-based enhancer or a shallow diffusion model vastly improves sound quality. In some cases, the result can match or even exceed the quality produced by other popular solutions.
3. Compatibility and Flexibility
The project maintains compatibility with previous model versions, ensuring flexibility and ease of use. Its infrastructure supports multi-speaker training and can configure models for specific speakers, making it adaptable to various use cases.
Detailed Workflow
New Rectified-Flow Based Model (6.0 - Experimental)
- Preprocessing: Initiated using a configuration file specific to this model. It prepares data for training.
- Training: Model training is conducted with specified configuration settings.
- Non-Real-Time Inference: Offers tools for processing audio files to apply voice conversion in non-real-time settings. Various parameters like key changes and speaker ID can be adjusted during this process.
Improved DDSP Cascade Diffusion Model (5.0 - Update)
- Preprocessing, Training, and Inference: Similar processes are followed as in the experimental model, with a focus on incorporating a pre-trained sub-model, leveraging improved sound modeling techniques.
Shallow Diffusion Model (3.0 - Update)
- Training: Focuses on both diffusion and DDSP models, allowing a more comprehensive training routine.
- Inference and Real-Time GUI: Provides flexible tools for inference, and a real-time graphical user interface for instant voice conversion.
Technical Requirements
- Dependencies: The setup requires specific Python, PyTorch, and torchaudio versions for seamless operation.
- Pretrained Models: Utilizing pre-trained models like ContentVec and HubertSoft is crucial. These models are responsible for feature encoding and pitch extraction.
- Hardware Considerations: While high-performance inferences reduce the need for high-powered hardware, users should follow recommended configurations for optimal performance.
How to Get Started
- Install Dependencies: Follow the project’s guide to install necessary packages using PyTorch and additional Python libraries.
- Set Up Pre-trained Models: Download and configure pre-trained encoders and vocoders as specified.
- Data Preparation: Organize your dataset according to the guidelines and perform preprocessing to make your data training-ready.
- Model Training and Evaluation: Conduct model training using the available configurations and visualize the training process using TensorBoard.
Legal and Ethical Considerations
The DDSP-SVC encourages the legal and ethical use of its tools. Users should only train models using data they are authorized to use and avoid any misuse of the synthesized audio content.
Conclusion
DDSP-SVC is a pioneering project providing efficient and high-quality voice conversion solutions that are easy to implement on personal computing devices. Whether training a model from scratch or using pre-trained assets, this project offers the essential tools needed for seamless and high-fidelity voice transformation.