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AST-AMVD-SAD-v2 – AI Model by AnodHuang | AlphaNeural AI
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safetensors
audio-spectrogram-transformer
AnodHuang/AMVD_AS
MattyB95/AST-ASVspoof2019-Synthetic-Voice-Detection
finetune
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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
