| 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 | |