---
license: mit
datasets:
- AnodHuang/AMVD_AS
base_model:
- MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection
---
# AST-AMVD-SAD-v1
## Description
A fine-tuned audio classification model for detecting AI-generated audio content.
## Author
- Kunyang Huang (huangku@kean.edu)
- Bin Hu (binhu.philip@gmail.com)
## Model Details
### Model Description
- Architecture: Based on the Audio Spectrogram Transformer (AST) architecture from MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection
- Input: Audio waveforms converted to mel-spectrogram representations
- Output: Four-class classification for audio authenticity detection
### Intended Use
**This model is designed to:**
- Detect AI-generated audio content
- Identify different types of synthetic audio:
  - Class 0 (H): Real Human Audio
  - Class 1 (C): AI Cloned Audio
  - Class 2 (A): AI Generated Audio
  - Class 3 (Combined): Mixed Human/AI Audio
- Primary use cases include:
  - Content authenticity verification
  - AI-generated content detection systems
  - Audio forensics applications
### Training Data
- Dataset: AMVD_AS Dataset
- Data Composition:
  - Balanced samples across four categories
  - Contains both synthetic and genuine human audio samples
## Training Procedure
### Fine-tuning Parameters
- Base Model: MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection
- Initial Learning Rate: 4e-5 → 1e-5 (linear decay)	
- Total Training Steps:	25,000
- Batch Size: 32
- Warmup Steps:	5,000
- Weight Decay: 0.01
- Gradient Clip Norm: 1.0
- Training Duration: ~4.5 hours (A100 GPU)
## Evaluation
### Validation Performance
- Training Loss: 0.0874  
- Eval Loss: 0.07367  
- Eval Accuracy: 0.98109  
- Final Steps per Second: 2.566  
- Final Samples per Second: 10.264  
- Runtime at 25k Steps: 824.1802  
- Gradient Norm: 0.000075778  
- LR Stability: 1e-5