Update app.py

app.py

@@ -25,27 +25,26 @@ def transcribe(file_upload, microphone):
     torchaudio.save('enhanced.wav', out, sr)
     enhanced = AudioSegment.from_wav('enhanced.wav') #只有去完噪的需要降bitrate再做語音識別
     enhanced.export('enhanced.wav', format="wav", bitrate="256k")
-    return  "enhanced.wav"
+    return "enhanced.wav"
 
 demo = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="語音質檢麥克風實時錄音"),        
-        gr.inputs.Audio(source="upload", type="filepath", optional=True, label="語音質檢原始音檔"),
+        gr.Audio(source="microphone", type="filepath", optional=True, label="語音質檢麥克風實時錄音"),        
+        gr.Audio(source="upload", type="filepath", optional=True, label="語音質檢原始音檔"),
     ],
-    #outputs=gr.outputs.Textbox(label="語音質檢音檔噪音去除結果"),
-    outputs=gr.outputs.Audio(type="filepath", label="Output"),
+    outputs=gr.Audio(type="filepath", label="Output"),
     title="<p style='text-align: center'><a href='https://www.twman.org/AI' target='_blank'>語音質檢噪音去除 (語音增強)：Meta Denoiser</a>",
     description=(
         "為了提升語音識別的效果，可以在識別前先進行噪音去除"
     ),
     allow_flagging="never",
-examples=[
-    ["exampleAudio/15s_2020-03-27_sep1.wav"],
-    ["exampleAudio/13s_2020-03-27_sep2.wav"],
-    ["exampleAudio/30s_2020-04-23_sep1.wav"],
-    ["exampleAudio/15s_2020-04-23_sep2.wav"],
-],    
+    examples=[
+        ["exampleAudio/15s_2020-03-27_sep1.wav"],
+        ["exampleAudio/13s_2020-03-27_sep2.wav"],
+        ["exampleAudio/30s_2020-04-23_sep1.wav"],
+        ["exampleAudio/15s_2020-04-23_sep2.wav"],
+    ],
 )
 
 demo.launch(enable_queue=True)