app.py

#%%
from transformers import pipeline
import gradio as gr
import os


#%%
whisper = pipeline(model='jlvdoorn/whisper-large-v2-atco2-asr-atcosim', use_auth_token=os.environ['HUGGINGFACE_TOKEN'])
# bert_atco_ner = pipeline(model='Jzuluaga/bert-base-ner-atc-en-atco2-1h')

#%%
def transcribe(audio_mic, audio_file):
    if audio_file is not None:
        return whisper(audio_file)['text']
    if audio_mic is not None:
        return whisper(audio_mic)['text']
    else:
        return 'There was no audio to transcribe...'

#%%
# def extractCallSignCommand(transcription):
#     if type(transcription) is str:
#         result = bert_atco_ner(transcription)
#         callsigns = []
#         commands = []
#         values = []
#         for item in result:
#             if 'callsign' in item['entity']:
#                 callsigns.append(item['word'])
#             if 'command' in item['entity']:
#                 commands.append(item['word'])
#             if 'value' in item['entity']:
#                 values.append(item['word'])
                
#         return 'Callsigns: ' + ', '.join(callsigns) + '\nCommands: ' + ', '.join(commands) + '\nValues: ' + ', '.join(values)
#     else:
#         return 'There was no transcription to extract a callsign or command from...'

#%%
# def transcribeAndExtract(audio_mic, audio_file, transcribe_only):
#     transcription = transcribe(audio_mic, audio_file)
#     if not transcribe_only:
#         callSignCommandValues = extractCallSignCommand(transcription)
#     else:
#         callSignCommandValues = ''
#     return transcription, callSignCommandValues

#%%
iface = gr.Interface(
        fn=transcribe,
        inputs=[gr.Audio(source='microphone', type='filepath'), gr.Audio(source='upload', type='filepath')],
        outputs=gr.Text(label='Transcription'),
        title='Whisper Large v2 - ATCO2-ASR-ATCOSIM',
        description='This demo will transcribe ATC audio files by using the Whisper Large v2 model fine-tuned on the ATCO2 and ATCOSIM datasets. Further it uses a Named Entity Recognition model to extract callsigns, commands and values from the transcription. This model is based on Google\'s BERT model and fine-tuned on the ATCO2 dataset.',
)

#%%
iface.launch()