AI Video Translation Workflow

From YouTube to 11K saves — how I fully automate video translation with Codex, Whisper & FFmpeg

⭐ 11,000+ saves ⏱️ 10 min / day 🤖 100% automated

❓ Why such high translation quality?

The most asked question under my video: “How do you get translations this good?”

It’s not magic — it’s a structured workflow built on Codex, where a smart agent orchestrates multiple tools, checks quality at every step, and keeps terminology consistent.

            Key insight: The foundation of accurate translation is complete-sentence transcription — if the English sentences are clean and whole, the translation will be reliable.
        

⚙️ The 4-Step Workflow

1 Grab & Paste — Copy a YouTube link, paste into Codex. The agent reads the skill and starts the pipeline.
2 Extract & Transcribe — Audio is extracted (MP4 → WAV), then transcribed locally with Whisper. Slower but stable and private.
3 Translate & Refine — Using TestMa for translation, with strict rules: keep terms like hon as-is, split long Chinese sentences for readability.
4 Burn & Review — Subtitles are burned into video via FFmpeg. The agent checks alignment, previews, and fixes issues automatically.

📌 The agent also handles error recovery — e.g., when the local model went offline, it paused and asked for a manual command to restart.

🔧 Techniques That Make the Difference

Sentence-bound transcription — Every English sentence is a complete unit (split by period). This single rule dramatically improves translation accuracy.
Terminology preservation — Learned from experience: hon must stay hon, not be translated into “framework kit”. The agent remembers these rules.
Chinese line-splitting — Long English sentences become long Chinese lines, which look cramped on screen. An extra step splits them into shorter, readable segments.
Harness engineering — “Harness” means a saddle. Just like you saddle a horse to make it safe and controllable, we need Harness Engineering to keep AI agents secure and reliable.

            💡 The agent doesn’t just run tools — it inspects, corrects, and self-drives. It catches its own mistakes and fixes them without being told.
        

🧠 Why This Is a True Agent

It’s not a mindless chatbot. This agent:

🔍 Detects errors mid-pipeline (e.g., a missing model) and pauses for input
🔄 Self-corrects — it found a segmentation glitch, noted it, and continued
🧩 Connects real tools: Whisper → TestMa → FFmpeg, plus quality checks between each step
📋 Enforces rules (term consistency, sentence structure, line length) without manual reminders

It’s a self-driven helper that gets the job done, not a Q&A bot that just answers questions.

📊 Real Results

11,000+ saves on Xiaohongshu — the audience validated the quality.

Daily update with only 10 minutes of confirmation work per day. The agent handles the rest.

Speed is not yet optimal — exporting from local tools can be slower than cloud editors — but the trade-off is worth it: zero hands-on effort during the heavy lifting.

            ⚡ “It may not be the fastest, but I don’t need to babysit it. I just check the final result. And there are many ways to speed it up further.”
        

Life is yours. I’m just here to lend a hand.

— from the creator’s closing words

🚀 Get Started with Codex

If you’re interested in this workflow, the creator offers Codex入门 (Codex basics) and a 陪跑 service to help you set up your own AI pipeline.

👉 The goal: help people reduce work hours and improve quality of life through practical AI use.

One more thing: The entire workflow was demoed in real time — including a small failure and recovery. “Showing the real process is more valuable than a polished demo.”