Ast Fakeaudio Detector

🚀 AST Fine-tuned for Fake Audio Detection

This model is fine - tuned for detecting fake/synthetic audio, offering high - accuracy binary classification.

🚀 Quick Start

This model is a binary classification head fine - tuned version of [MIT/ast - finetuned - audioset - 10 - 10 - 0.4593](https://huggingface.co/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.

✨ Features

• 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: Probabilities [fake_prob, real_prob]
• Training Hardware: 2x NVIDIA T4 GPUs

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
import torchaudio
import soundfile as sf
import numpy as np
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification

# Load model and move to available device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "WpythonW/ast-fakeaudio-detector"

extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForAudioClassification.from_pretrained(model_name).to(device)
model.eval()

# Process multiple audio files
audio_files = ["audio1.wav", "audio2.mp3", "audio3.ogg"]
processed_batch = []

for audio_path in audio_files:
    # Load audio file
    audio_data, sr = sf.read(audio_path)
    
    # Convert stereo to mono if needed
    if len(audio_data.shape) > 1 and audio_data.shape[1] > 1:
        audio_data = np.mean(audio_data, axis=1)
    
    # Resample to 16kHz if needed
    if sr != 16000:
        waveform = torch.from_numpy(audio_data).float()
        if len(waveform.shape) == 1:
            waveform = waveform.unsqueeze(0)
        
        resample = torchaudio.transforms.Resample(
            orig_freq=sr, 
            new_freq=16000
        )
        waveform = resample(waveform)
        audio_data = waveform.squeeze().numpy()
    
    processed_batch.append(audio_data)

# Prepare batch input
inputs = extractor(
    processed_batch,
    sampling_rate=16000,
    padding=True,
    return_tensors="pt"
)
inputs = {k: v.to(device) for k, v in inputs.items()}

# Get predictions
with torch.no_grad():
    logits = model(**inputs).logits
    probabilities = torch.nn.functional.softmax(logits, dim=-1)

# Process results
for filename, probs in zip(audio_files, probabilities):
    fake_prob = float(probs[0].cpu())
    real_prob = float(probs[1].cpu())
    prediction = "FAKE" if fake_prob > real_prob else "REAL"
    
    print(f"\nFile: {filename}")
    print(f"Fake probability: {fake_prob:.2%}")
    print(f"Real probability: {real_prob:.2%}")
    print(f"Verdict: {prediction}")

📚 Documentation

Limitations

⚠️ Important Note

Important considerations when using this model:

1. The model works with 16kHz audio input
2. Performance may vary with different types of audio manipulation not present in training data
3. The model was trained on audio samples ranging from 4 to 10 seconds in duration.

📄 License

The model is licensed under the Apache - 2.0 license.

📊 Model Information

📈 Model Results