new attempt

app.py

@@ -5,15 +5,15 @@ import os
 
 # print(os.environ['hf_token'])
 ## Try to load a local model if available
-try:
-    whisper = pipeline(model='/mnt/projects/whisper/WhisperANSP/Models/whisper-large-v2-atco2-asr-atcosim-ANSP-3h1m', task='automatic-speech-recognition')
-    ttl = 'Whisper Large v2 - ATCO2-ATCOSIM-ANSP'
-    dis = 'This demo will transcribe ATC audio files by using the Whisper Large v2 model fine-tuned on the ATCO2, ATCOSIM and ANSP datasets. \n \n Further it uses a Named Entity Recognition model to extract callsigns, commands and values from the transcription. \n This model is based on Google\'s BERT model and fine-tuned on the ATCO2 dataset.'
+# try:
+#     whisper = pipeline(model='/mnt/projects/whisper/WhisperANSP/Models/whisper-large-v2-atco2-asr-atcosim-ANSP-3h1m', task='automatic-speech-recognition')
+#     ttl = 'Whisper Large v2 - ATCO2-ATCOSIM-ANSP'
+#     dis = 'This demo will transcribe ATC audio files by using the Whisper Large v2 model fine-tuned on the ATCO2, ATCOSIM and ANSP datasets. \n \n Further it uses a Named Entity Recognition model to extract callsigns, commands and values from the transcription. \n This model is based on Google\'s BERT model and fine-tuned on the ATCO2 dataset.'
 
-except:
-    whisper = pipeline(model='jlvdoorn/whisper-large-v2-atco2-asr-atcosim', use_auth_token=os.environ['hf_token'], task='automatic-speech-recognition')
-    ttl = 'Whisper Large v2 - ATCO2-ATCOSIM'
-    dis = 'This demo will transcribe ATC audio files by using the Whisper Large v2 model fine-tuned on the ATCO2 and ATCOSIM datasets. \n \n Further it uses a Named Entity Recognition model to extract callsigns, commands and values from the transcription. \n This model is based on Google\'s BERT model and fine-tuned on the ATCO2 dataset.'
+# except:
+whisper = pipeline(model='jlvdoorn/whisper-large-v2-atco2-asr-atcosim', use_auth_token=os.environ['hf_token'], task='automatic-speech-recognition')
+ttl = 'Whisper Large v2 - ATCO2-ATCOSIM'
+dis = 'This demo will transcribe ATC audio files by using the Whisper Large v2 model fine-tuned on the ATCO2 and ATCOSIM datasets. \n \n Further it uses a Named Entity Recognition model to extract callsigns, commands and values from the transcription. \n This model is based on Google\'s BERT model and fine-tuned on the ATCO2 dataset.'
 
 bert_atco_ner = pipeline(model='Jzuluaga/bert-base-ner-atc-en-atco2-1h')