Automate Viral Shorts with Python - Pytube, LangChain, & FFmpeg Tutorial!

Automate Viral Shorts with Python - Pytube, LangChain, & FFmpeg Tutorial!

AI Nuggets

Based on the provided transcript, here are the detailed instructions, CLI commands, website URLs, and tips extracted and formatted into an easy-to-follow outline:

Requirements

Create a requirements.txt file with the following libraries:

Lang chain

Lang chain community

P tube

Lang chain openai

EnV (python-dotenv)

Environment Setup
Create a new Python virtual environment:
python -m venv <environment_name>  
Use Command + Shift + P on a Mac to open the command palette and type “virtual environment” to create it through Visual Studio Code.
Install dependencies from requirements.txt when prompted by Visual Studio Code.
Environment Variables

Create a .env file to store environment variables such as OPENAI_KEY.

Paste your OpenAI API key into the .env file (do not expose your key publicly).

Python Script (main.py)
Import necessary libraries:
import os  
import json  
import subprocess  
from typing import List  
from pytube import YouTube  
from youtube_transcript_api import YouTubeTranscriptApi  
from langchain.core.pantic import BaseModel, Field  
from langchain.core.openai import ChatOpenAI  
from dotenv import load_dotenv  
Load environment variables using load_dotenv().
Download YouTube Video and Transcript

Set the YouTube URL variable with the video URL.

Create a directory for downloaded videos if it doesn’t exist.

Use pytube to download the video and save it to the specified directory.

Use YouTubeTranscriptApi to download the transcript using the video ID.

LLM Setup

Define the LLM (Language Learning Model) using ChatOpenAI with the desired model (e.g., GPT-4.0), temperature, max tokens, timeout, and max retries.

Build Prompt for LLM

Construct a prompt to send to the LLM, asking it to identify segments that can be extracted as subtopics from the video transcript.

Define Output Structure

Create BaseModel classes to define the expected output structure from the LLM, including segments with start time, end time, YouTube title, description, and duration.

Invoke LLM

Send the transcript and prompt to the LLM and receive structured output based on the defined classes.

Generate Clips with FFMpeg
Create a directory for generated clips if it doesn’t exist.
Loop through the segments provided by the LLM and use ffmpeg to extract clips from the downloaded video based on the start and end times.
Example FFMpeg command:
ffmpeg -i <input_file> -ss <start_time> -to <end_time> -c:v libx264 -c:a aac -b:a 192k <output_file>  
Save segment information to a .txt and .json file for further use.
Tips

Ensure that the LLM provides accurate timestamps for the video segments.

Adjust the temperature and max tokens of the LLM as needed for your specific use case.

Use the subprocess module to call external commands like FFMpeg from within the Python script.

Store sensitive information like API keys in environment variables and load them with load_dotenv().

Additional Resources

Visit the YouTube video URL provided in the transcript for a visual guide and additional context: http://youtube.com/watch?v=XNU0Cg1DqYo

Please note that the actual CLI commands, URLs, and specific variable values were not provided in the transcript and are represented here as placeholders where necessary.