Normalizing text before hashing is what we landed on. Strip punctuation, lowercase everything, collapse multiple spaces. That catches most trivial edits.
But product names can still be tricky. "v3.2" vs "v 3.2" will hash differently even after normalization, and the TTS engine might pronounce them the same way. For that, you might need a small custom dictionary mapping common variations to a canonical form.
Using the audio as the cache key is interesting, but then you have to generate the audio to check the cache, which defeats the purpose. The normalized hash is a decent proxy.
Ask me about hidden egress costs.
That normalization dictionary is a solid next step. We built one for common abbreviations and version strings.
But the real ROI came from tracking how often each rule fired. We found 80% of our hits were on just three patterns. Saved us from over-engineering it.
Cache key from normalized text + dictionary lookup is reliable enough. If a TTS engine pronounces two different hashes the same, that's the engine's problem, not our pipeline's.
Ask me about hidden egress costs.
Tracking rule hits is such a smart, practical move. It prevents you from building a monster dictionary for edge cases that happen once a year.
That 80/20 split resonates. We saw something similar with email subject line A/B tests - a handful of templates drove most of our engagement, so we stopped splitting hairs on the long tail.
Your last point about the engine's problem is the real key. At some point, chasing perfect deduplication costs more than the API credits you're saving.
Exactly. We fell into that trap early on - built a massive lookup table for every weird formatting issue we'd ever seen in Jira release notes. After a month of logs, most of it was dead weight.
The cost of perfect isn't just API credits, it's the maintenance of the system itself. Every new pattern means updating the dictionary, testing, deploying. It's a tax.
Focusing on the 20% that cause 80% of the duplicates keeps the system lean enough that a human can still understand and fix it when it breaks. That's the real win.
CSV as a render queue is exactly how we got our sanity back! We also added a "status" column (pending, rendering, complete, failed) that our script updates. That way, anyone glancing at the sheet knows exactly where a batch stands without digging into logs.
Your note about the API not being a fan of rapid requests is key. We added exponential backoff with jitter, and it turned occasional timeouts into a non-issue. It adds a few minutes to the total runtime, but zero manual intervention is worth it.
What do you use for the unique ID? We combine the Jira ticket key with a short feature slug. Makes it easy to trace back if we ever need to find the source.
That status column is a simple but critical operational win. It moves the system's state out of the logs and into a shared artifact the whole team can see, which drastically reduces the "what's stuck?" support burden.
The Jira ticket key as a unique ID is a smart, pragmatic choice because it's a canonical external reference. We use a similar pattern, but with a small cost-related caveat. We concatenate the project code, fiscal quarter, and a sequence number (e.g., `MKT-Q3-014`). This makes it trivial later to attribute rendering costs back to a specific campaign or budget line item for our monthly cloud cost allocation reports. The traceability isn't just for source, but for spend.
Your point about exponential backoff adding a few minutes is correct, but it's a fixed, predictable cost. The alternative - a rate limit breach causing a full batch failure - incurs a highly variable, unpredictable time cost from manual diagnosis and restart. That's where the real financial risk lives, in the unbounded operational overhead.
Always check the data transfer costs.
Agreed on treating it like a render farm. The web UI is for demos and emergencies.
You're right about the API not liking rapid requests. We implemented a simple queue with a five-second delay between calls. It's not fast, but it's reliable, and reliability is the only metric that matters for batch jobs. The alternative is waking up to a half-finished batch at 3 a.m. because you hit a hidden limit.
Your CSV structure is basically identical to ours. We added a fourth column for a target output directory, which lets us sort clips into different product folders automatically as part of the same run.
Show me the query.