README.md

metadata

license: apache-2.0
datasets:
  - 012shin/fake-audio-detection-augmented
language:
  - en
metrics:
  - accuracy
  - f1
  - recall
  - precision
base_model:
  - MIT/ast-finetuned-audioset-10-10-0.4593
pipeline_tag: audio-classification
library_name: transformers
tags:
  - audio
  - audio-classification
  - fake-audio-detection
  - ast
model-index:
  - name: ast-fakeaudio-detector
    results:
      - task:
          type: audio-classification
          name: Audio Classification
        dataset:
          name: fake-audio-detection-augmented
          type: 012shin/fake-audio-detection-augmented
        metrics:
          - type: accuracy
            value: 0.9662
          - type: f1
            value: 0.971
          - type: precision
            value: 0.9692
          - type: recall
            value: 0.9728

AST Fine-tuned for Fake Audio Detection

This model is a binary classification head fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 for detecting fake/synthetic audio. The original AST (Audio Spectrogram Transformer) classification head was replaced with a binary classification layer optimized for fake audio detection.

Model Description

• Base Model: MIT/ast-finetuned-audioset-10-10-0.4593 (AST pretrained on AudioSet)
• Task: Binary classification (fake/real audio detection)
• Input: Audio converted to Mel spectrogram (128 mel bins, 1024 time frames)
• Output: Binary prediction (0: real audio, 1: fake audio)
• Training Hardware: 2x NVIDIA T4 GPUs

Training Configuration

{
    'learning_rate': 1e-5,
    'weight_decay': 0.01,
    'n_iterations': 1500,
    'batch_size': 16,
    'gradient_accumulation_steps': 8,
    'validate_every': 500,
    'val_samples': 5000
}

Dataset Distribution

The model was trained on a filtered dataset with the following class distribution:

Training Set:
- Fake Audio (0): 29,089 samples (53.97%)
- Real Audio (1): 24,813 samples (46.03%)

Test Set:
- Fake Audio (0): 7,229 samples (53.64%)
- Real Audio (1): 6,247 samples (46.36%)

Model Performance

Final metrics on validation set:

• Accuracy: 0.9662 (96.62%)
• F1 Score: 0.9710 (97.10%)
• Precision: 0.9692 (96.92%)
• Recall: 0.9728 (97.28%)

Usage Guide

Model Usage

from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
import torchaudio
import torch

# Load audio file
waveform, sample_rate = torchaudio.load("path_to_audio.ogg")

# Initialize model and feature extractor
model_name = "WpythonW/ast-fakeaudio-detector"
extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForAudioClassification.from_pretrained(model_name)

# Process audio and get predictions
inputs = extractor(waveform.squeeze(), sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    probabilities = torch.nn.functional.softmax(logits, dim=-1)

print(f"Probability of fake audio: {probabilities[0][0]:.2%}")

Limitations

Important considerations when using this model:

1. The model works best with 16kHz audio input
2. Performance may vary with different types of audio manipulation not present in training data
3. Very short audio clips (<1 second) might not provide reliable results
4. The model should not be used as the sole determiner for real/fake audio detection

Training Details

The training process involved:

1. Loading the base AST model pretrained on AudioSet
2. Replacing the classification head with a binary classifier
3. Fine-tuning on the fake audio detection dataset for 1500 iterations
4. Using gradient accumulation (8 steps) with batch size 16
5. Implementing validation checks every 500 steps