RVC-Project/Retrieval-based-Voice-Conversion-WebUI
Retrieval Based Voice Conversion WebUI
This project is a comprehensive software suite for voice synthesis and model management, providing a framework for training custom acoustic models and performing voice conversion. It utilizes deep-learning-based acoustic modeling to map source audio characteristics to target voice identities, enabling the transformation of input audio into specific vocal profiles.
The system distinguishes itself through a feature-retrieval-based inference mechanism, which employs vector index files to perform nearest-neighbor searches on acoustic features for high-fidelity timbre matching. Users can manage these processes through a browser-based orchestration layer or via command-line interface scripts, allowing for both graphical interaction and automated workflow execution. The platform also supports voice model hybridization, enabling the merging of distinct model checkpoints to create blended vocal identities.
The software includes a modular audio processing pipeline that integrates pitch extraction, vocal track isolation, and timbre fidelity adjustment. These tools facilitate the preparation of high-quality training data and the refinement of conversion results. The project supports both offline and real-time voice conversion, with persistent checkpoint management to allow for incremental model training and the resumption of interrupted sessions.