Add application file

app.py

@@ -0,0 +1,67 @@
+import gradio as gr
+import torch
+import torchaudio
+import numpy as np
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+
+MODEL_ID = "Zeyadd-Mostaffa/wav2vec_checkpoints"
+
+# 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: "fake" or "real"
+    """
+
+    # 2) Load the audio file
+    # torchaudio returns (waveform, sample_rate)
+    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) Preprocess with feature_extractor
+    inputs = feature_extractor(
+        waveform.numpy(),
+        sampling_rate=sr,
+        return_tensors="pt",
+        truncation=True,
+        max_length=int(feature_extractor.sampling_rate * 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
+        pred_id = torch.argmax(logits, dim=-1).item()
+
+    # 4) Return label text
+    predicted_label = label_names[pred_id]
+    return predicted_label
+
+
+# 5) Build Gradio interface
+demo = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs="text",
+    title="Wav2Vec2 Deepfake Detection",
+    description="Upload an audio sample to check if it is fake or real."
+)
+
+if __name__ == "__main__":
+    demo.launch()