Create a script to summarize transcripts

To summarize transcripts effectively, here’s a Python script that uses the transformers library (from Hugging Face) to summarize text. The script will work by loading a pre-trained model for summarization, taking the transcript as input, and providing a concise summary of the content.

python
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from transformers import pipeline

# Initialize the summarizer model from Hugging Face
summarizer = pipeline("summarization")

def summarize_transcript(transcript_text, max_length=150, min_length=50):
    """
    Summarizes the transcript text using a pre-trained transformer model.
    
    Args:
    - transcript_text (str): The full transcript to be summarized.
    - max_length (int): Maximum length of the summary (default is 150 tokens).
    - min_length (int): Minimum length of the summary (default is 50 tokens).
    
    Returns:
    - str: The summarized transcript.
    """
    # Generate summary
    summary = summarizer(
        transcript_text,
        max_length=max_length,
        min_length=min_length,
        do_sample=False
    )
    
    return summary[0]['summary_text']

# Example usage:
transcript = """
Your full transcript text goes here. This is a sample placeholder to simulate 
what might be a long transcript of dialogue, lecture, or meeting notes.
It could be several paragraphs of text, and the script will summarize it.
"""

summary = summarize_transcript(transcript)
print("Summary:", summary)

How this works:

1. Load Summarization Pipeline: We use Hugging Face’s pipeline() with the summarization task to load a pre-trained model.

2. Summarizing Function: The summarize_transcript() function processes the input transcript and outputs a summarized version, where you can adjust the max_length and min_length parameters to control the size of the summary.

3. Example Usage: The example transcript is a placeholder for your actual data. Replace it with your transcript text, and it will produce a summary.

Requirements:

To run the script, make sure you have the transformers and torch libraries installed. If not, install them using pip:

bash
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pip install transformers torch

This script can be used for various transcript types, from meetings to video captions or interviews.