How to beginner · 4 min read

How to summarize YouTube video transcript

Q: How to summarize YouTube video transcript

Use the OpenAI Python SDK to send the YouTube transcript text as input to a chat model like gpt-4o with a prompt to summarize it. Extract the transcript (via YouTube API or third-party tools), then call client.chat.completions.create with the transcript in the messages array and parse the summary from the response.

Quick answer

Use the OpenAI Python SDK to send the YouTube transcript text as input to a chat model like gpt-4o with a prompt to summarize it. Extract the transcript (via YouTube API or third-party tools), then call client.chat.completions.create with the transcript in the messages array and parse the summary from the response.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the openai Python package and set your OpenAI API key as an environment variable.

Install package: pip install openai
Set environment variable in your shell: export OPENAI_API_KEY='your_api_key' (Linux/macOS) or setx OPENAI_API_KEY "your_api_key" (Windows)

bash

pip install openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl (xx kB)
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

Extract the YouTube transcript text (using YouTube API or third-party libraries), then send it to the OpenAI gpt-4o chat model with a prompt to summarize. The example below assumes you have the transcript as a string.

python

import os
from openai import OpenAI

# Initialize OpenAI client
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Example YouTube transcript text (replace with actual transcript)
youtube_transcript = "In this video, we explore the basics of AI summarization techniques..."

# Prepare messages for chat completion
messages = [
    {"role": "user", "content": f"Summarize the following YouTube video transcript:\n\n{youtube_transcript}"}
]

# Call the chat completion endpoint
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages
)

# Extract summary text
summary = response.choices[0].message.content
print("Summary:", summary)

output

Summary: This video explains the fundamentals of AI summarization techniques, covering key concepts and practical approaches to effectively condense information.

Common variations

Async usage: Use async with await client.chat.completions.create(...) for asynchronous calls.
Streaming: Enable stream=True to receive partial summary tokens as they generate.
Different models: Use lighter models like gpt-4o-mini for faster, cheaper summaries.
Chunking: For long transcripts, split text into chunks and summarize each, then combine summaries.

python

import asyncio
import os
from openai import OpenAI

async def async_summarize(transcript: str):
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    messages = [{"role": "user", "content": f"Summarize the following YouTube video transcript:\n\n{transcript}"}]
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages
    )
    return response.choices[0].message.content

# Example async call
if __name__ == "__main__":
    transcript = "This is a sample transcript for async summarization."
    summary = asyncio.run(async_summarize(transcript))
    print("Async summary:", summary)

output

Async summary: This sample transcript demonstrates how to perform asynchronous summarization using the OpenAI API.

Troubleshooting

If you get 401 Unauthorized, verify your OPENAI_API_KEY environment variable is set correctly.
If the transcript is too long, split it into smaller chunks to avoid token limits.
For incomplete summaries, increase max_tokens parameter in chat.completions.create.
Check network connectivity if requests time out or fail.

✅

Key Takeaways

Use the OpenAI Python SDK with gpt-4o to summarize YouTube transcripts efficiently.
Extract transcripts externally, then send them as user messages for summarization.
Handle long transcripts by chunking to respect token limits.
Async and streaming calls improve responsiveness for large inputs.
Always set your API key securely via environment variables.

Verified 2026-04 · gpt-4o, gpt-4o-mini

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