HVC Audio Convert

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

1. Download the desired base model
2. Use with HVC-Audio-Convert framework
3. Fine-tune on target voice data
4. 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:

pt · 211 MB · SHA-256 409452a27ab3…cdf9 · Hugging Face

--- license: apache-2.0 tags: - Hibernates - HVC-Audio-Convert pipeline_tag: audio-to-audio --- # 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 1. Download the desired base model 2. Use with HVC-Audio-Convert framework 3. Fine-tune on target voice data 4. 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: