How I built an automated YouTube video pipeline for my SaaS using Python, edge-tts, and moviepy
To grow it I decided to start a faceless YouTube channel. 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. Andrew Neural is the best voice I found for this style of content.
- ▪To grow it I decided to start a faceless YouTube channel.
- ▪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.
- ▪Andrew Neural is the best voice I found for this style of content.
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| Original publisher | DEV Community |
| Canonical URL | https://dev.to/manpreet_brar_264e408885a/how-i-built-an-automated-youtube-video-pipeline-for-my-saas-using-python-edge-tts-and-moviepy-411j |
| Publication time | Sun, 26 Jul 2026 22:14:20 +0000 |
| Retrieval time | 2026-07-26T22:47:07.234Z |
| Last seen | 2026-07-26T22:47:07.234Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | QPZ0mId3oDDK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 4004749) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Manpreet Brar Posted on Jul 26 How I built an automated YouTube video pipeline for my SaaS using Python, edge-tts, and moviepy #python #buildinpublic #webdev #productivity 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV Community.
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