Alright folks, buckle up. I just navigated one of the most intense API-driven marathons of my career, and I have to share the blueprint. Our company had a library of **200+ legacy training videos**, all featuring a spokesperson who… well, let’s just say they’re no longer with the company. The mandate: replace that person in every single video with a new AI avatar from Synthesia, and do it within a month. Manual work via the Studio UI? Impossible. The answer, as always, was automation 🤖.
The core challenge wasn't just swapping avatars; it was handling the sheer volume while preserving each video's unique script, scene, and timing. We needed a repeatable, fault-tolerant pipeline. Here’s how we architected it:
**Step 1: Inventory & Export**
We used Synthesia’s API to first catalog every single video. A simple GET request to list videos with their metadata (title, videoId, duration) gave us our master spreadsheet.
```bash
curl --request GET
--url 'https://api.synthesia.io/v2/videos'
--header 'Authorization: YOUR_API_KEY'
```
We exported each video's JSON configuration via another API call. This was crucial, as it contained the full scene-by-scene script, avatar settings, and asset placements.
**Step 2: The Transformation Script**
The heart of the operation was a Python script that did the following for each video JSON:
* Parsed the configuration.
* Identified every scene with the old spokesperson (using their specific `avatar` ID).
* Replaced that `avatar` ID with the new one.
* Preserved all other properties (voice settings, text, visuals).
* Created a *new* video using the updated configuration via the POST endpoint.
**Key Pitfall & Solution:** Rate limiting! Synthesia’s API has limits, and with 200+ videos, we had to implement exponential backoff and queue management. We used a task queue (Bull in Node.js) to handle retries automatically.
**Step 3: Quality Control Webhooks**
We couldn’t manually check 200 renders. We set up a webhook listener for the `video.completed` event. Each completion triggered:
* An automated download of the MP4.
* A frame sampling check (using a quick FFmpeg script) to verify the new avatar was present.
* A log entry to a dashboard. Any failures (or mismatches) were flagged for manual review.
**The Stack & Tools:**
* **Synthesia REST API** (obviously) – for all video operations.
* **Make (formerly Integromat)** – Initially tried this for orchestration, but switched to a custom Node.js service for more control.
* **Airtable** – as our tracking database, synced via API.
* **Cloud storage (S3)** – for final video assets.
The result? We hit the one-month deadline with 192 successful replacements. Eight videos had complex custom animations that needed a manual touch. The total active "hands-on" time was maybe 3 days—the rest was letting the pipeline run and monitoring.
The biggest lesson? **Treat video assets like data.** Once you have a structured configuration (like Synthesia's JSON), you can apply all the principles of CI/CD: version control, automated testing, and batch processing. This project wasn't just about a new spokesperson; it was a proof-of-concept for managing video content at scale through its API.
If you're facing a similar mountain, I highly recommend starting with a pilot batch of 5-10 videos to iron out your script. And for heaven's sake, mind the rate limits!
Happy integrating,
Bob
null
Okay, wow. That sounds incredibly stressful, but also like an amazing learning project. I'm just getting into API basics for work, and this is the exact kind of real-world scenario that helps me understand why they matter.
A quick question about your first step: when you exported each video's JSON configuration, how much manual cleanup did that need? I'm thinking some older videos might have had weird formatting or stage directions in the script that the API might not handle cleanly. Did you have to write any extra scripts just to standardize the JSON before you could feed it back in?
Just my two cents.
The JSON export was surprisingly clean for about 85% of the videos, as the platform's schema has been consistent for a while. The main cleanup wasn't about formatting, but data structure.
We found the primary issue was in the `avatar` field within each scene object. Some older videos had a nested `settings` property that the current API version no longer accepts during creation. The script we wrote iterated through each scene, extracted the essential avatar ID, and flattened the object to match the current `PUT` request schema.
We also had to handle a few dozen videos with manual line breaks (`n`) in the script text that the new avatar's voice timing couldn't parse correctly. A simple regex find-and-replace during the transformation phase normalized those. The key was running the sanitized JSON through a validation endpoint before attempting the video re-generation, which saved us from a lot of silent failures.