How I built an automated YouTube video pipeline for my SaaS using Python, edge-tts, and moviepy

A developer built an automated YouTube video pipeline for their SaaS WhaleTrack using Python, edge-tts, and moviepy. The pipeline creates faceless videos with AI voiceover and Ken Burns effects, costing $0 and taking one day to build.

How I built an automated YouTube video pipeline for my SaaS using Python, edge-tts, and moviepy I run a niche tool called WhaleTrack whaletrack.app — it tracks big bets on Polymarket in real time. To grow it I decided to start a faceless YouTube channel. No camera, no editing software, no designer. Just code. Here's the full pipeline I built in one day. edge-tts — Microsoft's neural TTS Andrew voice, free, no API key moviepy 1.0.3 — video assembly Pillow — image processing Puppeteer — screenshot the live website ffmpeg — underlying encoderInstead of fake mockups I screenshot the actual live site using Puppeteer: js js const puppeteer = require 'puppeteer' ; const browser = await puppeteer.launch ; const page = await browser.newPage ; await page.setViewport { width: 1920, height: 1080 } ; await page.goto 'https://whaletrack.app' ; await page.screenshot { path: 'screenshots/home.png' } ; Gotcha: if your package.json has "type": "module", Puppeteer's require breaks. Name the file .cjs not .js. Step 2: AI voiceover import edge tts comm = edge tts.Communicate script, voice='en-US-AndrewNeural', rate='+8%' await comm.save 'audio.mp3' Completely free. Sounds genuinely good. Andrew Neural is the best voice I found for this style of content. Step 3: Ken Burns effect Static screenshots are boring. This adds a slow zoom + pan to make it feel like real video: def apply ken burns img, progress, zoom from, zoom to, pan from, pan to : zoom = zoom from + zoom to - zoom from progress iw, ih = img.size crop w, crop h = int iw / zoom , int ih / zoom ox = int pan from 0 + pan to 0 - pan from 0 progress iw - crop w oy = int pan from 1 + pan to 1 - pan from 1 progress ih - crop h return img.crop ox, oy, ox + crop w, oy + crop h .resize 1920, 1080 , Image.LANCZOS Step 4: Assemble with moviepy from moviepy.editor import VideoClip, AudioFileClip def make frame t : progress = t / duration frame = apply ken burns bg image, progress, 1.0, 1.12, 0,0 , 0.03,0.02 return np.array frame clip = VideoClip make frame, duration=duration clip = clip.set audio AudioFileClip 'audio.mp3' Result A 2.6 minute 1080p YouTube video, fully automated, from a Python script. Total cost: $0. Total time to build the pipeline: ~1 day. The video: https://youtu.be/iwGwUpbhABA The tool: https://whaletrack.app Happy to share the full script if anyone wants it. Building in public — follow along if you're into niche tools + automation.