mirror of
https://github.com/Monadical-SAS/reflector.git
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69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
import argparse
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import spacy
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from loguru import logger
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# Define the paths for agenda and transcription files
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def init_argparse() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(
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usage="%(prog)s <AGENDA> <TRANSCRIPTION>",
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description="Compares the transcript of a video or audio file to an agenda using the SpaCy model"
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)
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parser.add_argument("agenda", help="Location of the agenda file")
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parser.add_argument("transcription", help="Location of the transcription file")
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return parser
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args = init_argparse().parse_args()
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agenda_path = args.agenda
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transcription_path = args.transcription
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# Load the spaCy model and add the sentencizer
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spaCy_model = "en_core_web_md"
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nlp = spacy.load(spaCy_model)
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nlp.add_pipe('sentencizer')
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logger.info("Loaded spaCy model " + spaCy_model)
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# Load the agenda
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with open(agenda_path, "r") as f:
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agenda = [line.strip() for line in f.readlines() if line.strip()]
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logger.info("Loaded agenda items")
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# Load the transcription
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with open(transcription_path, "r") as f:
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transcription = f.read()
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logger.info("Loaded transcription")
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# Tokenize the transcription using spaCy
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doc_transcription = nlp(transcription)
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logger.info("Tokenized transcription")
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# Find the items covered in the transcription
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covered_items = {}
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for item in agenda:
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item_doc = nlp(item)
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for sent in doc_transcription.sents:
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if not sent or not all(token.has_vector for token in sent):
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# Skip an empty span or one without any word vectors
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continue
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similarity = sent.similarity(item_doc)
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similarity_threshold = 0.7
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if similarity > similarity_threshold: # Set the threshold to determine what is considered a match
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covered_items[item] = True
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break
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# Count the number of items covered and calculatre the percentage
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num_covered_items = sum(covered_items.values())
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percentage_covered = num_covered_items / len(agenda) * 100
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# Print the results
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print("💬 Agenda items covered in the transcription:")
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for item in agenda:
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if item in covered_items and covered_items[item]:
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print("✅ ", item)
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else:
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print("❌ ", item)
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print("📊 Coverage: {:.2f}%".format(percentage_covered))
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logger.info("Finished comparing agenda to transcription with similarity threshold of " + str(similarity_threshold))
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