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app.py

@@ -102,18 +102,27 @@ def detect_on_gpu(dataset):
     return result
 
 # 修改音频伪造检测主函数
-def audio_deepfake_detection(demonstrations, query_audio_path):
-    demonstration_paths = []
-    demonstration_labels = []
-    for demonstration in demonstrations:
-        if demonstration[0] is not None:
-            demonstration_paths.append(demonstration[0])
-            demonstration_labels.append(demonstration[1])
-    if len(demonstration_paths) != len(demonstration_labels):
-        demonstration_labels = demonstration_labels[:len(demonstration_paths)]
+# def audio_deepfake_detection(demonstrations, query_audio_path):
+#     demonstration_paths = [audio[0] for audio in demonstrations if audio[0] is not None]
+#     demonstration_labels = [audio[1] for audio in demonstrations if audio[1] is not None]
+#     if len(demonstration_paths) != len(demonstration_labels):
+#         demonstration_labels = demonstration_labels[:len(demonstration_paths)]
+    
+#     # 数据集处理
+#     audio_dataset = dataset.DemoDataset(demonstration_paths, demonstration_labels, query_audio_path)
+    
+#     # 调用 GPU 检测函数
+#     result = detect_on_gpu(audio_dataset)
+    
+#     return {
+#         "Is AI Generated": result["is_fake"],
+#         "Confidence": f"{100*result['confidence']:.2f}%"
+#     }
+# 0 demonstrations
+def audio_deepfake_detection(query_audio_path):
     
     # 数据集处理
-    audio_dataset = dataset.DemoDataset(demonstration_paths, demonstration_labels, query_audio_path)
+    audio_dataset = dataset.DemoDataset([], [], query_audio_path)
     
     # 调用 GPU 检测函数
     result = detect_on_gpu(audio_dataset)
@@ -125,28 +134,41 @@ def audio_deepfake_detection(demonstrations, query_audio_path):
 
 # Gradio 界面
 def gradio_ui():
-    def detection_wrapper(demonstration_audio1, label1, demonstration_audio2, label2, demonstration_audio3, label3, query_audio):
-        demonstrations = [
-            (demonstration_audio1, label1),
-            (demonstration_audio2, label2),
-            (demonstration_audio3, label3),
-        ]
-        return audio_deepfake_detection(demonstrations,query_audio)
+    # def detection_wrapper(demonstration_audio1, label1, demonstration_audio2, label2, demonstration_audio3, label3, query_audio):
+    #     demonstrations = [
+    #         (demonstration_audio1, label1),
+    #         (demonstration_audio2, label2),
+    #         (demonstration_audio3, label3),
+    #     ]
+    #     return audio_deepfake_detection(demonstrations,query_audio)
+
+    # interface = gr.Interface(
+    #     fn=detection_wrapper,
+    #     inputs=[
+    #         gr.Audio(sources=["upload"], type="filepath", label="Demonstration Audio 1"),
+    #         gr.Dropdown(choices=["bonafide", "spoof"], value="bonafide", label="Label 1"),
+    #         gr.Audio(sources=["upload"], type="filepath", label="Demonstration Audio 2"),
+    #         gr.Dropdown(choices=["bonafide", "spoof"], value="bonafide", label="Label 2"),
+    #         gr.Audio(sources=["upload"], type="filepath", label="Demonstration Audio 3"),
+    #         gr.Dropdown(choices=["bonafide", "spoof"], value="bonafide", label="Label 3"),
+    #         gr.Audio(sources=["upload"], type="filepath", label="Query Audio (Audio for Detection)")
+    #     ],
+    #     outputs=gr.JSON(label="Detection Results"),
+    #     title="Audio Deepfake Detection System",
+    #     description="Upload demonstration audios and a query audio to detect whether the query is AI-generated.",
+    # )
+    # return interface
+
+    def detection_wrapper(query_audio):
+        return audio_deepfake_detection(query_audio)
 
     interface = gr.Interface(
         fn=detection_wrapper,
         inputs=[
-            # gr.Audio(sources=["upload"], type="filepath", label="Demonstration Audio 1"),
-            # gr.Dropdown(choices=["bonafide", "spoof"], value="bonafide", label="Label 1"),
-            # gr.Audio(sources=["upload"], type="filepath", label="Demonstration Audio 2"),
-            # gr.Dropdown(choices=["bonafide", "spoof"], value="bonafide", label="Label 2"),
-            # gr.Audio(sources=["upload"], type="filepath", label="Demonstration Audio 3"),
-            # gr.Dropdown(choices=["bonafide", "spoof"], value="bonafide", label="Label 3"),
-            gr.Audio(sources=["upload"], type="filepath", label="Query Audio (Audio for Detection)")
+            gr.Audio(sources=["upload"], type="filepath", label="Test Audio")
         ],
         outputs=gr.JSON(label="Detection Results"),
         title="Audio Deepfake Detection System",
-        # description="Upload demonstration audios and a query audio to detect whether the query is AI-generated.",
         description="Upload a test audio to detect whether the audio is AI-generated.",
     )
     return interface

dataset.py

@@ -6,7 +6,7 @@ import librosa
 import numpy as np
 
 class DemoDataset(Dataset):
-    def __init__(self, demonstration_paths=[], demonstration_labels=[], query_path="", sample_rate=16000):
+    def __init__(self, demonstration_paths, demonstration_labels, query_path, sample_rate=16000):
         self.sample_rate = sample_rate
         self.query_path = query_path