---
license: apache-2.0
datasets:
- WpythonW/real-fake-voices-dataset2
- mozilla-foundation/common_voice_17_0
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
widget:
- text: Upload an audio file to check if it's real or synthetic
inference:
  parameters:
    sampling_rate: 16000
    audio_channel: mono
model-index:
- name: ast-fakeaudio-detector
  results:
  - task:
      type: audio-classification
      name: Audio Classification
    dataset:
      name: real-fake-voices-dataset2
      type: WpythonW/real-fake-voices-dataset2
    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](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.

## 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**: Probabilities [fake_prob, real_prob]
- **Training Hardware**: 2x NVIDIA T4 GPUs

# Usage Guide

## Model Usage
```python
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}")
```

## Limitations

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.