changed to one example

app.py

@@ -1,16 +1,22 @@
 from transformers import pipeline
 from datasets import load_dataset
 import gradio as gr
+import numpy as np
 import os
 
 atco2 = load_dataset('jlvdoorn/atco2-asr', split='validation')
 atcosim = load_dataset('jlvdoorn/atcosim', split='validation')
 
 num_examples = 3
-examples_atco2   = [ [{'sampling_rate': atco2[i]['audio']['sampling_rate'], 'raw': atco2[i]['audio']['array']}, False, 'large-v3'] for i in range(num_examples)]
+examples_atco2   = [[atco2[i]['audio']['array'].astype(np.float32)] for i in range(num_examples)]
 #examples_atcosim = [ [{'sampling_rate': atcosim[i]['audio']['sampling_rate'], 'raw': atcosim[i]['audio']['array']}, False, 'large-v3'] for i in range(num_examples)]
 examples = examples_atco2 #+ examples_atcosim
 
+sr, y = atco2[0]['audio']['sampling_rate'], atco2[0]['audio']['array']
+y = y.astype(np.float32)
+y /= np.max(np.abs(y))
+examples = [[y]]
+
 whisper = pipeline(model='jlvdoorn/whisper-large-v3-atco2-asr-atcosim')
 
 def transcribe(audio):