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| import gradio as gr | |
| import torch | |
| import torchaudio | |
| import numpy as np | |
| from transformers import AutoFeatureExtractor, AutoModelForAudioClassification | |
| import torch.nn.functional as F | |
| import torchaudio.transforms as T | |
| MODEL_ID = "Zeyadd-Mostaffa/Deepfake-Audio-Detection-v1" | |
| # 1) Load model & feature extractor | |
| feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID) | |
| model = AutoModelForAudioClassification.from_pretrained(MODEL_ID) | |
| model.eval() | |
| # Optionally use GPU if available | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| label_names = ["fake", "real"] # According to your label2id = {"fake": 0, "real": 1} | |
| def classify_audio(audio_file): | |
| """ | |
| audio_file: path to the uploaded file (WAV, MP3, etc.) | |
| Returns: predicted label and confidence score | |
| """ | |
| # 2) Load the audio file | |
| waveform, sr = torchaudio.load(audio_file) | |
| # If stereo, pick one channel or average | |
| if waveform.shape[0] > 1: | |
| waveform = torch.mean(waveform, dim=0, keepdim=True) | |
| waveform = waveform.squeeze() # (samples,) | |
| # 3) Resample if needed | |
| if sr != 16000: | |
| resampler = T.Resample(sr, 16000) | |
| waveform = resampler(waveform) | |
| sr = 16000 | |
| # 3) Preprocess with feature_extractor | |
| inputs = feature_extractor( | |
| waveform.numpy(), | |
| sampling_rate=sr, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=int(16000 * 6.0), # 6 second max | |
| ) | |
| # Move everything to device | |
| input_values = inputs["input_values"].to(device) | |
| with torch.no_grad(): | |
| logits = model(input_values).logits | |
| # 4) Calculate probabilities using softmax | |
| probabilities = F.softmax(logits, dim=-1) | |
| # Get predicted label and confidence | |
| confidence, pred_id = torch.max(probabilities, dim=-1) | |
| predicted_label = label_names[pred_id.item()] | |
| # 5) Return label and confidence percentage | |
| return f"Prediction: {predicted_label}, Confidence: {confidence.item() * 100:.2f}%" | |
| # 6) Build Gradio interface | |
| demo = gr.Interface( | |
| fn=classify_audio, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs="text", | |
| title="Wav2Vec2 Deepfake Detection", | |
| description="Upload an audio sample to check if it is fake or real, along with confidence." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |