Update app.py

app.py

@@ -1,15 +1,13 @@
 import gradio as gr
 from transformers import pipeline
 import numpy as np
-
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base")
+from ailib import whisper_transcribe
 
 def transcribe(audio):
-    sr, y = audio
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
+    filename = 'audio.wav'
+    open(filename, 'wb').write(audio)
+    return whisper_transcribe(filename)
 
-    return transcriber({"sampling_rate": sr, "raw": y})["text"]
 
 gr.Label("Demo CORFO")
 demo = gr.Interface(