VideoTutor is an automated video generation system that transforms text content into educational videos. The system follows a three-stage pipeline: first, script generation processes input data into structured scenes with timing information. Second, animation implementation creates visual elements using libraries like MoviePy. Finally, the rendering process outputs the complete video file using FFmpeg encoding.
The script generation process is the foundation of video creation. It takes raw input data, such as tutorial steps, and transforms them into a structured format with precise timing information. The function iterates through each step, calculates start and end times, and creates scene metadata that will guide the animation and rendering phases.
Animation implementation uses the MoviePy library to create visual video content. The process involves creating background clips, adding text overlays with proper positioning and timing, and compositing multiple layers together. Each scene from the script becomes a video clip with background, text, and potential effects, which are then combined into the final video sequence.
The rendering process is the final stage where composed video clips are encoded into output files. MoviePy uses FFmpeg internally to handle video encoding with H.264 codec for video compression and AAC for audio. The write_videofile method manages the entire encoding pipeline, converting the in-memory video objects into standard MP4 files that can be played on any device.
VideoTutor integrates multiple technologies to create a comprehensive video generation system. Python serves as the core orchestration language, coordinating between MoviePy for video composition, FFmpeg for encoding, text-to-speech APIs for narration, and Pillow for image processing. This technology stack enables automated transformation of educational content into engaging video tutorials, demonstrating the power of combining specialized libraries in a unified workflow.