Update app.py

app.py

@@ -33,7 +33,7 @@ model = WhisperForConditionalGeneration.from_pretrained("mskov/whisper-small-esc
 
 # Remove brackets and extra spaces
 
-    
+'''    
 def map_to_pred(batch):
     cleaned_transcription = re.sub(r'\[[^\]]+\]', '', batch['category']).strip()
     print("cleaned transcript", cleaned_transcription)
@@ -69,11 +69,12 @@ labels = dataset["audio"]  # Replace "labels" with the appropriate key in your d
 print("labels are ", labels)
 
 # Compute WER
+wer = load("wer")
 wer_score = wer(labels, predicted_text)
 
 # Print or return WER score
 print(f"Word Error Rate (WER): {wer_score}")
-'''
+
     
 def transcribe(audio):
     text = pipe(audio)["text"]