How to summarize YouTube Videos in Minutes (I)

Hey there, readers! Today, I’m thrilled to introduce you to an incredible tool that will completely transform the way you summarize YouTube videos. Get ready to dive into the captivating world of video content summarization using the powerful GPT4All. Trust me, this is an opportunity you don’t want to miss!

Setting up the Magic

Before we embark on this exciting journey, let’s ensure we have everything we need to get started. Install the necessary dependencies by running the following command:

pip install youtube-transcript-api transformers

Once you’re all set, let’s move on to loading the transcripts of a fascinating YouTube video titled LangChain Explained in 13 Minutes. Here’s how you can achieve that using Python:

from langchain.document_loaders import YoutubeLoader
url = 'https://www.youtube.com/watch?v=aywZrzNaKjs'

loader = YoutubeLoader.from_youtube_url(url)
transcript = loader.load()

print(transcript)

Breaking it Down: Chunking the Transcripts

To overcome the limitations of the LLM (Language Model), we need to divide the transcript into manageable chunks. If you’re unfamiliar with this process, take a look at my previous post on setting up GPT4All. For a more detailed explanation, explore the Recursive Character and Summarization examples.

Here’s how you can split the transcript using Python:

from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size = 2000, chunk_overlap  = 200)

texts = text_splitter.split_documents(transcript)
print(len(texts))
print(texts)

Enter GPT4All: Your Summarization Superpower

Now that we have our transcript chunks ready, it’s time to unleash the power of GPT4All for summarization. Brace yourself for amazement! Here’s how you can set it up:

from langchain.llms import GPT4All
from langchain.prompts import PromptTemplate

llm = GPT4All(model="X:/ggml-gpt4all-j-v1.3-groovy.bin", n_ctx=2048, n_threads=8)

Let the Magic Unfold: Executing the Chain

Now, it’s time to witness the magic in action. Run the chain and watch as GPT4All generates a summary of the video:

chain = load_summarize_chain(llm, chain_type="map_reduce", verbose=True)
summary = chain.run(texts)

Prepare to be amazed as GPT4All works its wonders!

import textwrap

wrapped_text = textwrap.fill(summary, width=80)
print(wrapped_text)

In this guide, we’ve utilized the powerful Map Reduce chain type. If you’re curious, you can explore other available options here.

Summarizing with OpenAI (Optional)

As a comparison, let’s explore an alternative approach using OpenAI for summarization. Follow the code snippet below to make the switch:

import os

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())

from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0.9)
chain = load_summarize_chain(llm, chain_type="map_reduce", verbose=True)
summary = chain.run(texts)

And here’s the summary generated by OpenAI:

Stay Tuned: The Future of Video Content Summarization

Exciting things lie ahead! In my next blog post, we’ll explore the thrilling world of video contents without embedded transcripts. Stay tuned for more exciting updates!