HVC-Audio-Convert Base Models
Overview
These models serve as the foundational components for HVC-Audio-Convert (Soft-VC Voice Conversion), an advanced voice conversion framework that combines SoftVC feature extraction with the VITS (Conditional Variational Autoencoder with Adversarial Learning) architecture.
Key Features
- High-quality voice conversion capabilities
- Pre-trained on diverse vocal datasets
- Supports cross-lingual voice conversion
- Compatible with HVC-Audio-Convert v4.0 and newer
Technical Details
- Architecture: Based on VITS (Conditional Variational Autoencoder)
- Feature Extraction: Hibernates content encoder
- Training Data: Curated multi-speaker datasets
- Model Format: PyTorch checkpoints
Usage
- Download the desired base model
- Use with HVC-Audio-Convert framework
- Fine-tune on target voice data
- Perform voice conversion
Requirements
- HVC-Audio-Convert framework
- Python 3.8+
- PyTorch 1.13.0+
- CUDA compatible GPU (recommended)
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Citation
If you use these models in your research, please cite:
- Downloads last month
- 0
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
HF Inference API was unable to determine this model's library.