I Automated YouTube Shorts with Python

Introduction

When scrolling through YouTube Shorts, I stumbled upon videos showcasing random facts about the world. One of them featured the immortal jellyfish, a fascinating creature defying the boundaries of life and death. I realized how easily these videos could be automated with minimal effort. Thus, I embarked on a project using Python, leveraging the MoviePy library. I named this project "Money Printer" as it aimed to generate videos effortlessly. Here's a breakdown of how I automated the process:

Initially, I designed a simple front end where users could input their video ideas, which would then trigger the automatic video generation. Utilizing Python's CGT library, I had it create a video script derived from the user's idea. Next, I relied on the JPT-generated search terms to find suitable stock videos, utilizing the Pexels API for its user-friendliness and cost-effectiveness. However, a hurdle arose with the varying resolutions of the Pexels API videos. To address this, I manually cropped the videos using MoviePy to avoid issues with rendering.

For text-to-speech, I opted for a TikTok TTS unofficial API from GitHub to avoid the robotic sound of other APIs due to budget constraints. Assembly AI facilitated the transcription of the generated speech, offering timestamps and saving the subtitles in SRT format. Leveraging MoviePy once more, I seamlessly burned the subtitles into the video by concatenating the original file with the SRT file.

Upon completion, the application could successfully automate the process, culminating in the creation of an output MP4 video. The final results showed promising engagement on YouTube Shorts, validating the efficiency and effectiveness of the automation bot.

Keywords

• YouTube Shorts
• Python automation
• MoviePy library
• Pexels API
• Text-to-speech
• Assembly AI
• Automation bot

FAQ

• What tools were used to automate the YouTube Shorts creation process?

  • The project utilized Python with the MoviePy library for video editing, CGT for video scripting, Pexels API for stock videos, a TikTok TTS unofficial API for text-to-speech, and Assembly AI for transcription.

• How did the project handle the issue of variable video resolutions from the Pexels API?

  • To overcome the challenge of differing resolutions in Pexels API videos, the creator opted to manually crop the videos using MoviePy instead of resizing them, ensuring a consistent output without rendering errors.

• What was the outcome of testing the automated clips on a YouTube channel?

  • The automated videos garnered a significant number of views on YouTube Shorts, indicating the success and viability of the automation bot for content creation.