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

Certainly! To create a `merge()` function that calls `parse_transcript()` and merges the transcript segments into sentences ending with a period (`.`), we can follow these steps: 1. Parse the input content to get the list of transcript segments. 2. Iterate through the segments and concatenate the text until a period (`.`) is encountered. 3. Collect these concatenated sentences into a list and return the list. Here's how you can achieve this: 

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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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    merged_sentences = []
    current_sentence = []
    
    for start_seconds, end_seconds, text in transcript_segments:
        current_sentence.append(text)
        if text.strip().endswith('.'):
            merged_sentences.append(' '.join(current_sentence).strip())
            current_sentence = []
    
    # To handle any leftover words that might not end with a period
    if current_sentence:
        merged_sentences.append(' '.join(current_sentence).strip())
    
    return merged_sentences

# Example content
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"""

# Use the merge function
sentences = merge(content)
for sentence in sentences:
    print(sentence)
Output: 
1
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the robust ness of neural networks .
And this is joint
This `merge` function will properly call the `parse_transcript` function, process the parsed segments, and combine them into sentences based on the presence of a period.

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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    merged_sentences = []
    current_sentence = []
    
    for start_seconds, end_seconds, text in transcript_segments:
        current_sentence.append(text)
        if text.strip().endswith('.'):
            merged_sentences.append(' '.join(current_sentence).strip())
            current_sentence = []
    
    # To handle any leftover words that might not end with a period
    if current_sentence:
        merged_sentences.append(' '.join(current_sentence).strip())
    
    return merged_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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    merged_sentences = []
    current_sentence = []
    
    for start_seconds, end_seconds, text in transcript_segments:
        current_sentence.append(text)
        if text.strip().endswith('.'):
            merged_sentences.append(' '.join(current_sentence).strip())
            current_sentence = []
    
    # To handle any leftover words that might not end with a period
    if current_sentence:
        merged_sentences.append(' '.join(current_sentence).strip())
    
    return merged_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 52, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got ['the robust ness of neural networks .', 'And this is joint'] 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

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 achieve the merging of words into sentences that end with a period, we can create a `merge()` function that first calls the `parse_transcript()` function to get the list of transcript segments. Then, we can iterate over these segments, accumulating words until we encounter a period, which indicates the end of a sentence. Here's how you can implement the `merge()` function: 

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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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for start_seconds, end_seconds, word in transcript_segments:
        current_sentence.append(word)
        if word.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # Handle any remaining words that do not form a complete sentence (optional)
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    return sentences

# 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
'''

print(merge(content))
### Explanation: 1. **Parsing the Transcript**: - `parse_transcript(content)` uses a regular expression to extract timestamps and words from the transcript content. - The timestamps are converted to seconds, but for merging words into sentences, only the words are relevant. 2. **Merging Words into Sentences**: - The `merge(content)` function calls `parse_transcript(content)` to get the parsed segments. - It initializes an empty list `sentences` to store the final sentences and another list `current_sentence` to accumulate words. - It iterates through the segments, appending each word to `current_sentence`. - When it encounters a word ending with a period (`.`), it joins all accumulated words in `current_sentence` into a single string, adds that string to `sentences`, and then resets `current_sentence` for the next sentence. - Optionally, any remaining words that do not form a complete sentence can be added as a final sentence. This implementation should handle the provided transcript content and group the words into sentences correctly.

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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for start_seconds, end_seconds, word in transcript_segments:
        current_sentence.append(word)
        if word.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # Handle any remaining words that do not form a complete sentence (optional)
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    return sentences

# 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
'''

print(merge(content))

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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for start_seconds, end_seconds, word in transcript_segments:
        current_sentence.append(word)
        if word.endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # Handle any remaining words that do not form a complete sentence (optional)
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    return sentences

# 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
'''

print(merge(content))




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: [' the robust ness of neural networks .', ' And this is joint'] Traceback (most recent call last): File "/usr/src/app/main.py", line 69, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got [' the robust ness of neural networks .', ' And this is joint'] 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

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 achieve the merging of words into sentences that end with a period, you can create a `merge` function that first calls the `parse_transcript` function to get the list of segments. Then, it will iterate through these segments, accumulating words into sentences until it encounters a period (`.`), at which point it will finalize the current sentence and start a new one. Here's the complete implementation: 

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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))

    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for _, _, text in segments:
        current_sentence.append(text.strip())
        if text.strip().endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # In case there's any leftover words that don't end in a period, join them into a final sentence
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    return sentences

# 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 = merge(content)
print(sentences)
This will output: 
1
['the robust ness of neural networks .', 'And this is joint']
The `merge` function calls `parse_transcript` to get the list of segments. It then iterates through these segments, constructing sentences by accumulating words into a list (`current_sentence`). Whenever it encounters a word that ends with a period (`.`), it joins the accumulated words into a single sentence and appends it to the `sentences` list. After the loop, it checks if there are any leftover words that didn't end in a period and forms a final sentence if necessary.

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))

    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for _, _, text in segments:
        current_sentence.append(text.strip())
        if text.strip().endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # In case there's any leftover words that don't end in a period, join them into a final sentence
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    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))

    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for _, _, text in segments:
        current_sentence.append(text.strip())
        if text.strip().endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # In case there's any leftover words that don't end in a period, join them into a final sentence
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    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 52, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got ['the robust ness of neural networks .', 'And this is joint'] 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

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 implement the `merge` function that calls `parse_transcript` and merges words into sentences that end with a period, we can follow these steps: 1. Parse the content to get the list of transcript segments. 2. Iterate through the transcript segments and merge words until we find a period. 3. Collect the merged sentences into a list and return it. Here’s how you can do it: 

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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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for start_seconds, end_seconds, text in transcript_segments:
        current_sentence.append(text.strip())
        if text.strip().endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # In case there are any remaining words that didn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    return sentences

# 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
"""

merged_sentences = merge(content)
for sentence in merged_sentences:
    print(sentence)
### Explanation: 1. **Parsing the Transcript**: - The `parse_transcript` function processes the given content and extracts timestamps and text segments. - Each segment is converted to a tuple containing the start time in seconds, end time in seconds, and the text. 2. **Merging Words into Sentences**: - The `merge` function calls `parse_transcript` to get the list of transcript segments. - It iterates through the segments, appending each word to the `current_sentence` list. - When a word ending with a period (`.`) is encountered, it joins the `current_sentence` list into a single string, adds it to the `sentences` list, and then clears `current_sentence` to start collecting the next sentence. - After the loop, it checks if there are any remaining words in `current_sentence` that didn't form a complete sentence and adds them as well. This approach ensures that words are merged into complete sentences ending with a period, as specified.

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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for start_seconds, end_seconds, text in transcript_segments:
        current_sentence.append(text.strip())
        if text.strip().endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # In case there are any remaining words that didn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    return sentences

# 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
"""

merged_sentences = merge(content)
for sentence in merged_sentences:
    print(sentence)

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))

    return transcript_segments

def merge(content):
    transcript_segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for start_seconds, end_seconds, text in transcript_segments:
        current_sentence.append(text.strip())
        if text.strip().endswith('.'):
            sentences.append(' '.join(current_sentence))
            current_sentence = []

    # In case there are any remaining words that didn't end with a period
    if current_sentence:
        sentences.append(' '.join(current_sentence))

    return sentences

# 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
"""

merged_sentences = merge(content)
for sentence in merged_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')
And got the output: the robust ness of neural networks . And this is joint Traceback (most recent call last): File "/usr/src/app/main.py", line 71, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got ['the robust ness of neural networks .', 'And this is joint'] 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

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

Certainly! To achieve this, you'll want to create a `merge` function that: 1. Uses the `parse_transcript` function to get the parsed segments. 2. Iterates through these segments, merging words until it encounters a period (`.`). 3. Collects the merged sentences into a list and returns them. Here's a sample implementation: 

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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))

    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for _, _, word in segments:
        current_sentence.append(word)
        if word.endswith('.'):
            sentences.append(' '.join(current_sentence).strip())
            current_sentence = []

    if current_sentence:  # If there are remaining words after the loop
        sentences.append(' '.join(current_sentence).strip())

    return sentences

# 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
"""

merged_sentences = merge(content)
for sentence in merged_sentences:
    print(sentence)
The `merge` function processes the transcript by calling `parse_transcript` to get the segments, then iterates through them to build sentences. It uses a period (`.`) to identify the end of a sentence and collects the full sentence in the `sentences` list. When you run this script with the provided `content`, it will output the merged sentences: 
1
2
the robust ness of neural networks .
And this is joint
Note: Ensure the `parse_transcript` function and `merge` function are both within the same script or module so they can be called correctly.

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))

    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for _, _, word in segments:
        current_sentence.append(word)
        if word.endswith('.'):
            sentences.append(' '.join(current_sentence).strip())
            current_sentence = []

    if current_sentence:  # If there are remaining words after the loop
        sentences.append(' '.join(current_sentence).strip())

    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))

    return transcript_segments

def merge(content):
    segments = parse_transcript(content)
    sentences = []
    current_sentence = []

    for _, _, word in segments:
        current_sentence.append(word)
        if word.endswith('.'):
            sentences.append(' '.join(current_sentence).strip())
            current_sentence = []

    if current_sentence:  # If there are remaining words after the loop
        sentences.append(' '.join(current_sentence).strip())

    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 51, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got ['the robust ness of neural networks .', 'And this is joint'] 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