| --- |
| 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: |
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|
