Some files in every batch are bad

You generate up to 500 TTS files per batch. A percentage of them have anomalies. The question is: which ones?

The numbers

Every TTS batch has bad files. The only question is whether you find them before or after your users do.

Of TTS batches contain files needing regeneration

Hours to manually listen to a 1K-file batch

Of bad files missed by spot-checking

Cost to re-run an entire batch vs targeted regeneration

These anomalies are common, subtle, and hard to catch without automated analysis.

mic

The TTS voice changes pitch, timbre, or rate across a batch. File 1 sounds different from file 200. You need to find where it drifted.

record_voice_over

The model produces files with inconsistent accent characteristics. Some files sound off compared to the rest of the batch.

volume_off

Unexpected silence gaps, truncated words, or missing content. These files are obviously broken but buried in the batch.

monitoring

Unnatural emphasis, robotic pacing, or inappropriate tone. The file sounds "off" even though the words are correct.

electric_bolt

Clicks, pops, digital artifacts, and noise. Model instability or encoding errors that ruin the file.

replay

Repeated words or phrases caused by model attention failures. The file is clearly broken and needs regenerating.

Manual QA catches 30% of bad files at best. The rest ship to production and your users find them first.

A 500-file batch takes hours to listen through. Nobody has that time, so teams spot-check - and bad files slip through.

Speaker drift is gradual. Each file sounds fine compared to the one before it. You only notice by comparing files far apart in the batch.

Listening to 10% of a batch catches maybe 30% of the bad files. The rest ship to production.

When you can't identify which files are bad, you re-generate everything. 85%+ of those files were fine to begin with.

TTSAudit analyzes your entire batch and tells you exactly which files are bad.