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import re
from thefuzz import fuzz
import numpy as np
def match_mask_and_transcript(split_punct, transcript, classification):
"""
Input:
split_punct: the punctuated text, split on ?/!/.\s,
transcript: original transcript with timestamps
classification: classification object (list of numbers 0,1)
Output: times
"""
# Get the sponsored part
sponsored_segment = []
for i, val in enumerate(classification):
if val == 1:
sponsored_segment.append(split_punct[i])
segment = " ".join(sponsored_segment)
sim_scores = list()
# Check the similarity scores between the sponsored part and the transcript parts
for elem in transcript:
sim_scores.append(fuzz.partial_ratio(segment, elem["text"]))
# Get the scores and check if they are above mean + 2*stdev
scores = np.array(sim_scores)
timestamp_mask = (scores > np.mean(scores) + np.std(scores) * 2).astype(int)
timestamps = [
(transcript[i]["start"], transcript[i]["duration"])
for i, elem in enumerate(timestamp_mask)
if elem == 1
]
# Get the timestamp segments
times = []
current = -1
current_time = 0
for elem in timestamps:
# Threshold of 5 to see if it is a jump to another segment (also to make sure smaller segments are added together
if elem[0] > (current_time + 15):
current += 1
times.append((elem[0], elem[0] + elem[1]))
current_time = elem[0] + elem[1]
else:
times[current] = (times[current][0], elem[0] + elem[1])
current_time = elem[0] + elem[1]
return_times = [x for x in times if (x[1] - x[0]) > 10]
return return_times, timestamps
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