| Property | Value |
|---|---|
| Parameter Count | 86.2M |
| License | MIT |
| Base Model | MIT/ast-finetuned-audioset-10-10-0.4593 |
| Performance Metrics | Accuracy: 99.99%, F1: 0.9999, Precision: 1.0 |
What is AST-VoxCelebSpoof-Synthetic-Voice-Detection?
This is a specialized audio transformer model designed to detect synthetic or AI-generated voices with exceptional accuracy. Built upon MIT's Audio Spectrogram Transformer (AST) architecture, it has been fine-tuned on the VoxCelebSpoof dataset to achieve nearly perfect detection capabilities.
Implementation Details
The model utilizes a transformer-based architecture with 86.2M parameters, trained using the Adam optimizer with carefully tuned hyperparameters (learning rate: 5e-05, beta values: 0.9, 0.999). The training process spanned 3 epochs with a batch size of 8, implementing a linear learning rate scheduler.
- F32 tensor precision for optimal performance
- Integrated TensorBoard support for monitoring
- Safetensors implementation for secure model loading
- Inference endpoints available for production deployment
Core Capabilities
- Near-perfect synthetic voice detection (99.99% accuracy)
- Robust performance with 1.0 precision score
- Excellent recall rate of 0.9998
- Specialized in English language audio processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional accuracy in detecting synthetic voices, achieving near-perfect results with 99.99% accuracy and perfect precision scores. It's built on the proven AST architecture and optimized specifically for synthetic voice detection.
Q: What are the recommended use cases?
The model is ideal for: content authenticity verification, digital forensics, media authentication systems, and automated synthetic voice detection in security applications. It's particularly suited for English language audio analysis.