import torch
from transformers import pipeline
model_id = 'distil-whisper/distil-large-v2'

pipe = pipeline(
    "automatic-speech-recognition",
    model=model_id,
    chunk_length_s=15
)

# os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'
# os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = '1'
# os.environ['TRANSFORMERS_VERBOSITY'] = 'error'


def score_audio(audio_path, true_result):
    true_result = true_result.split('/')
    transcription = pipe(audio_path)['text'].lower()
 

    result = {'transcription': transcription,
              'score': int(any([x.lower() in transcription for x in true_result])),
              }
    return result