Initial Query

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.

LLM Generation

Query

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.

Output

To solve this problem, we need to merge words from a parsed transcript into sentences that end with a period. The solution involves parsing the transcript using a given function and then processing the parsed segments to form complete sentences. ### Approach 1. **Parsing the Transcript**: The provided `parse_transcript` function converts the transcript content into a list of segments, each containing start time, end time, and text (words or punctuation). 2. **Merging Segments into Sentences**: We iterate through each parsed segment, collecting words until we encounter a segment with a period. When a period is found, we join the collected words into a sentence, append the period, and reset the collection for the next sentence. This ensures that each sentence ends with a period and starts from the first word after the previous period. ### Solution Code 

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import re
from datetime import timedelta

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.strip()))
    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []
    for start, end, text in segments:
        stripped_text = text.strip()
        if stripped_text == '.':
            if current_sentence:
                sentence = ' '.join(current_sentence) + '.'
                sentences.append(sentence)
                current_sentence = []
        else:
            current_sentence.append(stripped_text)
    return sentences
### Explanation 1. **Parsing the Transcript**: The `parse_transcript` function uses a regular expression to extract segments from the transcript content. Each segment includes start and end times converted to seconds and the corresponding text, which is stripped of leading/trailing whitespace. 2. **Building Sentences**: The `merge` function processes each segment from the parsed transcript. Words are collected into `current_sentence` until a period is encountered. When a period is found, the collected words are joined into a sentence, the period is appended, and the sentence is added to the result list. Any trailing words without a period are ignored, ensuring only complete sentences are returned. This approach efficiently groups words into sentences based on the presence of periods, ensuring correct sentence termination and handling potential whitespace issues in the input data.

Extract Code

I extracted the following code from that output: 

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import re
from datetime import timedelta

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.strip()))
    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []
    for start, end, text in segments:
        stripped_text = text.strip()
        if stripped_text == '.':
            if current_sentence:
                sentence = ' '.join(current_sentence) + '.'
                sentences.append(sentence)
                current_sentence = []
        else:
            current_sentence.append(stripped_text)
    return sentences

Run Code Interpreter

Running the following program: 

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import re
from datetime import timedelta

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.strip()))
    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []
    for start, end, text in segments:
        stripped_text = text.strip()
        if stripped_text == '.':
            if current_sentence:
                sentence = ' '.join(current_sentence) + '.'
                sentences.append(sentence)
                current_sentence = []
        else:
            current_sentence.append(stripped_text)
    return sentences




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')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 58, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' ^^^^^^^^^^^^^^^^^^ AssertionError: Wrong answer; got ['the robust ness of neural networks.'] instead of [' the robustness of neural networks.', ' And this is joint']

Substring Evaluation

Testing if the previous output contains the string All tests passed: False