Why You Can't Skip TTS QA (And How to Do It in Minutes)

Every team generating TTS audio at scale faces the same problem: some files in every batch come out wrong. Speaker drift, glitches, prosody errors, repetition artifacts - they're unavoidable with current TTS models.

The question isn't whether your batches contain bad files. They do. The question is whether you find them before your users do.

Most teams handle this one of three ways. Some skip QA entirely and hope for the best. Some spot-check a random sample and miss the majority of issues. Some listen to every file - which takes hours and doesn't scale.

None of these work well. Skipping QA means shipping bad audio. Spot-checking catches maybe 30% of issues. Manual listening takes hours even for a 500-file batch.

Automated batch analysis solves this. Submit your files, get a per-file anomaly report in minutes, and only regenerate the ones that are flagged. You go from hours of manual listening to a quick API call.

The economics are straightforward: automated QA costs a fraction of what manual review costs, catches more issues, and takes 95% less time. If you're generating TTS at any meaningful scale, you need automated batch QA.