Hey everyone, been lurking for a bit but this is my first post. I'm a DevOps engineer, so my usual world is pipelines and containers, but my marketing team recently pulled me into a project using Resemble AI. They wanted to quickly localize a set of video ads for three new markets: Spanish (LatAm), French, and German.
The goal was to clone our primary English spokesperson's voice and generate natural-sounding audio in those languages, aiming to keep the brand voice consistent. We had the English scripts and professional translations ready. Overall, the voice cloning accuracy was impressive—the tonal quality really did match our spokesperson. However, the localization part had some... interesting results.
For the Spanish and French outputs, the pacing and intonation felt quite natural. The German version, though, had a few words where the emphasis was slightly off, making it sound a bit robotic in sections. We had to do a couple of regeneration cycles with adjusted phoneme hints for those parts.
From a workflow perspective, I ended up treating it a bit like a CI pipeline. I wrote a simple Python script to batch the API calls for each language version, which Resemble's API made pretty straightforward. Here's a basic snippet of how I structured the call for a single script:
```python
import requests
url = "https://app.resemble.ai/api/v1/projects/{project_uuid}/clips"
headers = {"Authorization": "Bearer YOUR_API_KEY"}
data = {
"voice": "cloned_voice_uuid",
"content": "Translated script text here",
"language": "es" # or 'fr', 'de'
}
response = requests.post(url, json=data, headers=headers)
```
A big pitfall we almost missed: the character limits for each API call are stricter than I initially thought. We had to split longer scripts, which added a step to stitch the audio files back together in post. Also, the processing queue time varied—sometimes it was minutes, sometimes longer, so building in some async waiting logic was necessary.
Has anyone else tried this for multi-language voiceovers? I'm curious about how you handled the audio consistency checks across different languages. Did you use any specific monitoring or QA steps in your process? I was thinking of setting up a simple pipeline to flag audio files where the length deviated too much from the source, but maybe there's a better way.
Learning by breaking