Update app.py

app.py

@@ -13,33 +13,24 @@ pipeline = Pipeline.from_pretrained(
   use_auth_token=os.environ['api'])
 
 def process_audio(audio):
-    # Extract the audio data and sample rate from the tuple
-    audio_data, sample_rate = audio
-    
-    # Ensure the audio data is in the correct format
-    audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
-    
-    # Convert audio_data to a numpy array
-    audio_data = np.array(audio_data)
-    
     # Save the uploaded audio file to a temporary location
-    wavio.write("temp.wav", audio_data[:, np.newaxis], sample_rate, sampwidth=2)
-    
+    with open("temp.wav", "wb") as f:
+        f.write(audio)
+
     # Use the diarization pipeline to process the audio
     diarization = pipeline("temp.wav")
-    
+
     # Remove the temporary file
     os.remove("temp.wav")
-    
+
     # Return the diarization output
     return diarization
 
 with gr.Blocks() as demo:
-    audio_input = gr.Audio(label="Upload Audio")  
+    audio_input = gr.Audio(type="file", label="Upload Audio")  
     process_button = gr.Button("Process")
-    diarization_output = gr.JSON(label="Diarization Output")
+    diarization_output = gr.Textbox(label="Diarization Output")
 
     process_button.click(fn=process_audio, inputs=audio_input, outputs=diarization_output)
 
-demo.launch()
-#
+demo.launch()