Spaces:
Sleeping
Sleeping
# Voice Cloning Model | |
This is a few-shot voice cloning model based on meta-learning approach. The model can clone a voice using just a few seconds of audio samples. | |
## Model Description | |
- **Model Type:** Speaker Encoder (Voice Cloning) | |
- **Language(s):** Language Independent | |
- **License:** MIT | |
- **Parent Model:** None | |
- **Resources for more information:** | |
- [GitHub Repository](https://github.com/yourusername/voice_clone_app) | |
## Uses | |
This model is designed for: | |
- Voice cloning with few samples | |
- Speaker verification | |
- Voice similarity analysis | |
### Training Data | |
The model was trained on: | |
- VCTK Dataset (109 speakers) | |
- Each speaker has approximately 400 utterances | |
- High-quality audio recordings at 48kHz | |
### Training Procedure | |
The model was trained using: | |
- Meta-learning approach (few-shot learning) | |
- Contrastive loss function | |
- Data augmentation techniques | |
## Performance and Limitations | |
### Performance Factors | |
The model's performance depends on: | |
- Quality of input audio | |
- Length of reference audio | |
- Similarity between source and target voices | |
### Out-of-Scope Use | |
This model should not be used for: | |
- Generating fake or misleading content | |
- Impersonating without consent | |
- Commercial use without proper licensing | |
## Ethical Considerations | |
Please use this model responsibly: | |
- Obtain proper consent before cloning someone's voice | |
- Be transparent about AI-generated content | |
- Consider privacy implications | |
## Technical Specifications | |
- Input: Mel-spectrogram of audio | |
- Output: Speaker embedding vector (512-dim) | |
- Framework: PyTorch | |
- Model Size: ~10MB |