Create model.py

model.py

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+"""
+🔥 TEST YOUR AUDIO MODEL FROM HUGGING FACE
+Simma7/audio_model
+"""
+
+import torch
+import librosa
+from transformers import AutoProcessor, AutoModel
+from huggingface_hub import snapshot_download
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# ================= DOWNLOAD MODEL =================
+print("⬇️ Downloading model from Hugging Face...")
+
+model_dir = snapshot_download("Simma7/audio_model")
+
+print("✅ Download complete")
+
+# ================= LOAD MODEL =================
+print("🧠 Loading model...")
+
+processor = AutoProcessor.from_pretrained(model_dir)
+model = AutoModel.from_pretrained(model_dir).to(DEVICE)
+
+print("✅ Model loaded")
+
+# ================= PREDICT =================
+def predict(audio_path):
+    audio, sr = librosa.load(audio_path, sr=16000)
+
+    inputs = processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
+
+    with torch.no_grad():
+        outputs = model(**inputs.to(DEVICE))
+
+    # embedding-based score
+    embedding = outputs.last_hidden_state.mean(dim=1)
+    prob = torch.sigmoid(embedding.mean()).item()
+
+    return prob
+
+# ================= MAIN =================
+if __name__ == "__main__":
+    audio_path = "test.wav"   # 🔥 put your audio file
+
+    print("\n🔍 Analyzing audio...")
+
+    prob = predict(audio_path)
+
+    if prob > 0.5:
+        print("\n🔴 FAKE AUDIO")
+    else:
+        print("\n🟢 REAL AUDIO")
+
+    print(f"📊 Confidence: {prob:.4f}")