Upload app.py

app.py

@@ -1,5 +1,5 @@
 from transformers import pipeline
-import gradio
+import gradio as gr
 from gradio import Interface, Audio, Label, Number
 
 username = 'bvallegc' ## Complete your username
@@ -20,15 +20,24 @@ def classify_audio(filepath):
     number = classification[0]["score"]
     return label, number
 
-interface_options = {
-    "title": "Spoofing Classification",
-    "description": "The audio classifier for those who are the best and only want and require the best",
-    # Audio from validation file
-    "allow_flagging": "never"
-    
-}
+examples=['TTS_F_LA_E_7682468.wav', 'TTS_M_LA_E_3371601.wav', 'TTS_M_LA_E_7056254.wav']
+examples = [[f"./{f}"] for f in examples]
 
-demo = Interface(
-    fn=classify_audio, inputs= Audio(type="filepath"), outputs=[Label(), Number()], examples=[["TTS_M_LA_E_7056254.wav", "TTS_M_LA_E_3371601.wav", "TTS_M_LA_E_7056254.wav"]], **interface_options
-)
-demo.launch(debug=False)
+gr.Interface(
+    inputs=[
+        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
+        gr.inputs.Audio(source="upload", type='filepath', optional=True),
+        gr.Textbox(label="Paste audio here"),
+    ],
+    
+    outputs=[
+        gr.outputs.Textbox(label="Verification"),
+        gr.Number(label="Probability"),
+    ],
+    
+    verbose=True,
+    examples = examples,
+    title="Spoofing verification classifier",
+    description="Detect machine created audios from human-speech.",
+    theme="huggingface"
+).launch()