# Automated YouTube Pipeline: Python, edge-tts, and MoviePy

> Source: <https://promptcube3.com/en/threads/3922/>
> Published: 2026-07-27 01:01:08+00:00

# Automated YouTube Pipeline: Python, edge-tts, and MoviePy

Zero cost and one day of coding is all it took to build a faceless YouTube channel for my SaaS, WhaleTrack. Instead of messing with Premiere or hiring a designer, I just scripted the whole thing to turn live site data into videos.

## The Tech Stack

**edge-tts**: Microsoft's neural TTS. I used the 'Andrew' voice—it's free, doesn't need an API key, and sounds surprisingly human.** moviepy 1.0.3**: For the heavy lifting of video assembly.** Pillow**: Handles the image processing.** Puppeteer**: Used to grab high-res screenshots of the live site.** ffmpeg**: The engine under the hood.

## The Workflow

1. **Capturing Visuals**

I didn't want static mockups, so I used Puppeteer to screenshot the actual site.

``` 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' });
```

*Pro tip: If your package.json is set to "type": "module",*`require()`

will crash. Just name your file `.cjs`

to avoid the headache.2. **Generating Audio**

The `edge-tts`

library is a hidden gem for this.

``` python
import edge_tts
comm = edge_tts.Communicate(script, voice='en-US-AndrewNeural', rate='+8%')
await comm.save('audio.mp3')
```

3. **Adding Movement (Ken Burns Effect)**

Static images kill retention. I wrote a helper function to add a slow zoom and pan, making the screenshots feel like dynamic b-roll.

``` python
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)
```

4. **Final Assembly**

I used MoviePy to glue the generated audio and the animated frames together.

``` python
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'))
```

The result is a full 1080p video produced entirely by a Python script. It's a solid AI workflow for anyone wanting to scale content without a production budget.

[Next World-Model-Optimizer: Cutting LLM Costs via Distillation →](/en/threads/3913/)

## All Replies （3）

G

Have you considered how render determinism will affect this? Live screenshots are honest, but a tiny CSS tweak could break tomorrow's video. I prefer using saved screenshot fixtures in the pipeline, then running a separate refresh-assets step only when intentional site changes happen.

0

J

Did something similar with a news bot, but moviepy can be a pain with memory leaks.

0

M

how are you handling the subtitles? moviepy usually makes those a nightmare to align.

0