Upload app.py

app.py

@@ -0,0 +1,93 @@
+import joblib
+from transformers import AutoFeatureExtractor, WavLMModel
+import torch
+import soundfile as sf
+import numpy as np
+import gradio as gr
+
+
+class HuggingFaceFeatureExtractor:
+    def __init__(self, model_class, name):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.feature_extractor = AutoFeatureExtractor.from_pretrained(name)
+        self.model = model_class.from_pretrained(name)
+        self.model.eval()
+        self.model.to(self.device)
+
+    def __call__(self, audio, sr):
+        inputs = self.feature_extractor(
+            audio,
+            sampling_rate=sr,
+            return_tensors="pt",
+            padding=True,
+        )
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        return outputs.last_hidden_state
+
+
+FEATURE_EXTRACTORS = {
+    "wavlm-base": lambda: HuggingFaceFeatureExtractor(WavLMModel, "microsoft/wavlm-base"),
+    "wavLM-V1": lambda: HuggingFaceFeatureExtractor(WavLMModel, "DavidCombei/wavLM-base-DeepFake_UTCN"),
+    "wavLM-V2": lambda: HuggingFaceFeatureExtractor(WavLMModel, "DavidCombei/wavLM-base-UTCN"),
+    "wavLM-V3": lambda: HuggingFaceFeatureExtractor(WavLMModel, "DavidCombei/wavLM-base-UTCN_114k"),
+}
+
+
+model1 = joblib.load('model1.joblib')
+model2 = joblib.load('model2.joblib')
+model3 = joblib.load('model3.joblib')
+model4 = joblib.load('model4.joblib')
+final_model = joblib.load('final_model.joblib')
+
+
+def process_audio(file_audio):
+    audio, sr = sf.read(file_audio)
+
+    extractor_1 = FEATURE_EXTRACTORS['wavlm-base']()
+    extractor_2 = FEATURE_EXTRACTORS['wavLM-V1']()
+    extractor_3 = FEATURE_EXTRACTORS['wavLM-V2']()
+    extractor_4 = FEATURE_EXTRACTORS['wavLM-V3']()
+
+    eval1 = extractor_1(audio, sr)
+    eval1 = torch.mean(eval1, dim=1).cpu().numpy()
+
+    eval2 = extractor_2(audio, sr)
+    eval2 = torch.mean(eval2, dim=1).cpu().numpy()
+
+    eval3 = extractor_3(audio, sr)
+    eval3 = torch.mean(eval3, dim=1).cpu().numpy()
+
+    eval4 = extractor_4(audio, sr)
+    eval4 = torch.mean(eval4, dim=1).cpu().numpy()
+
+    eval1 = eval1.reshape(1, -1)
+    eval2 = eval2.reshape(1, -1)
+    eval3 = eval3.reshape(1, -1)
+    eval4 = eval4.reshape(1, -1)
+
+    eval_prob1 = model1.predict_proba(eval1)[:, 1].reshape(-1, 1)
+    eval_prob2 = model2.predict_proba(eval2)[:, 1].reshape(-1, 1)
+    eval_prob3 = model3.predict_proba(eval3)[:, 1].reshape(-1, 1)
+    eval_prob4 = model4.predict_proba(eval4)[:, 1].reshape(-1, 1)
+
+    eval_combined_probs = np.hstack((eval_prob1, eval_prob2, eval_prob3, eval_prob4))
+
+    final_prob = final_model.predict_proba(eval_combined_probs)[:, 1]
+
+    if final_prob < 0.5:
+        return f"Fake with a confidence of: {final_prob[0]:.4f}"
+    else:
+        return f"Real with a confidence of: {final_prob[0]:.4f}"
+
+
+interface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs="text",
+    title="Audio Deepfake Detection",
+    description="Upload an audio file to detect whether it is fake or real.",
+)
+
+interface.launch()