Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.
Given a parse function like def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments for data like [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint write a merge() function that (1) calls parse and (2) merges words into sentences that end in period. Start with the first word and end with the period. Return a list of strings.
Here's how you can implement the `merge` function to merge words into sentences:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import re from datetime import timedelta def parse_transcript(content): """ Parse the transcript content and return a list of time-stamped words. Args: content (str): The content of the transcript. Returns: list: A list of tuples containing the start time, end time, and text. """ transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments def merge(transcript_segments): """ Merge the time-stamped words into sentences. Args: transcript_segments (list): A list of tuples containing the start time, end time, and text. Returns: list: A list of strings representing the merged sentences. """ sentences = [] current_sentence = [] for _, _, text in transcript_segments: current_sentence.append(text) if text.strip() == '.': sentences.append(' '.join(current_sentence)) current_sentence = [] return sentences def main(content): """ Parse the transcript content and merge the words into sentences. Args: content (str): The content of the transcript. Returns: list: A list of strings representing the merged sentences. """ transcript_segments = parse_transcript(content) return merge(transcript_segments) # Example usage content = """ [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint """ sentences = main(content) for sentence in sentences: print(sentence) |
I extracted the following code from that output:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import re from datetime import timedelta def parse_transcript(content): """ Parse the transcript content and return a list of time-stamped words. Args: content (str): The content of the transcript. Returns: list: A list of tuples containing the start time, end time, and text. """ transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments def merge(transcript_segments): """ Merge the time-stamped words into sentences. Args: transcript_segments (list): A list of tuples containing the start time, end time, and text. Returns: list: A list of strings representing the merged sentences. """ sentences = [] current_sentence = [] for _, _, text in transcript_segments: current_sentence.append(text) if text.strip() == '.': sentences.append(' '.join(current_sentence)) current_sentence = [] return sentences def main(content): """ Parse the transcript content and merge the words into sentences. Args: content (str): The content of the transcript. Returns: list: A list of strings representing the merged sentences. """ transcript_segments = parse_transcript(content) return merge(transcript_segments) # Example usage content = """ [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint """ sentences = main(content) for sentence in sentences: print(sentence) |
Running the following program:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import re from datetime import timedelta def parse_transcript(content): """ Parse the transcript content and return a list of time-stamped words. Args: content (str): The content of the transcript. Returns: list: A list of tuples containing the start time, end time, and text. """ transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments def merge(transcript_segments): """ Merge the time-stamped words into sentences. Args: transcript_segments (list): A list of tuples containing the start time, end time, and text. Returns: list: A list of strings representing the merged sentences. """ sentences = [] current_sentence = [] for _, _, text in transcript_segments: current_sentence.append(text) if text.strip() == '.': sentences.append(' '.join(current_sentence)) current_sentence = [] return sentences def main(content): """ Parse the transcript content and merge the words into sentences. Args: content (str): The content of the transcript. Returns: list: A list of strings representing the merged sentences. """ transcript_segments = parse_transcript(content) return merge(transcript_segments) # Example usage content = """ [00:00:14.240 --> 00:00:14.420] the [00:00:14.420 --> 00:00:14.860] robust [00:00:14.860 --> 00:00:15.020] ness [00:00:15.020 --> 00:00:15.140] of [00:00:15.140 --> 00:00:15.500] neural [00:00:15.500 --> 00:00:15.870] networks [00:00:15.870 --> 00:00:16.200] . [00:00:16.200 --> 00:00:16.410] And [00:00:16.410 --> 00:00:16.700] this [00:00:16.700 --> 00:00:16.840] is [00:00:16.840 --> 00:00:17.200] joint """ sentences = main(content) for sentence in sentences: print(sentence) def parse_transcript(content): transcript_segments = [] pattern = r'\[(\d{2}:\d{2}:\d{2}\.\d{3}) --> (\d{2}:\d{2}:\d{2}\.\d{3})\] (.*)' matches = re.findall(pattern, content) for start_time, end_time, text in matches: start_seconds = timedelta(hours=int(start_time[:2]), minutes=int(start_time[3:5]), seconds=int(start_time[6:8]), milliseconds=int(start_time[9:])).total_seconds() end_seconds = timedelta(hours=int(end_time[:2]), minutes=int(end_time[3:5]), seconds=int(end_time[6:8]), milliseconds=int(end_time[9:])).total_seconds() transcript_segments.append((start_seconds, end_seconds, text)) return transcript_segments answer = merge("[00:00:14.240 --> 00:00:14.420] the\n[00:00:14.420 --> 00:00:14.860] robust\n[00:00:14.860 --> 00:00:15.020] ness\n[00:00:15.020 --> 00:00:15.140] of\n[00:00:15.140 --> 00:00:15.500] neural\n[00:00:15.500 --> 00:00:15.870] networks\n[00:00:15.870 --> 00:00:16.200] .\n[00:00:16.200 --> 00:00:16.410] And\n[00:00:16.410 --> 00:00:16.700] this\n[00:00:16.700 --> 00:00:16.840] is\n[00:00:16.840 --> 00:00:17.200] joint\n") expected = [' the robustness of neural networks.', ' And this is joint'] assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' print('All tests passed') |
the robust ness of neural networks .
Traceback (most recent call last):
File "/usr/src/app/main.py", line 98, in <module>
answer = merge("[00:00:14.240 --> 00:00:14.420] the\n[00:00:14.420 --> 00:00:14.860] robust\n[00:00:14.860 --> 00:00:15.020] ness\n[00:00:15.020 --> 00:00:15.140] of\n[00:00:15.140 --> 00:00:15.500] neural\n[00:00:15.500 --> 00:00:15.870] networks\n[00:00:15.870 --> 00:00:16.200] .\n[00:00:16.200 --> 00:00:16.410] And\n[00:00:16.410 --> 00:00:16.700] this\n[00:00:16.700 --> 00:00:16.840] is\n[00:00:16.840 --> 00:00:17.200] joint\n")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/src/app/main.py", line 40, in merge
for _, _, text in transcript_segments:
^^^^^^^^^^
ValueError: not enough values to unpack (expected 3, got 1)
Testing if the previous output contains the string All tests passed
: False