Speaking Speed
Catch files that are noticeably faster or slower than the rest of your batch - before they reach your users.
The problem
TTS engines don't always speak at a consistent pace. Across a batch, some files may come out significantly faster or slower than the rest - making the final product feel uneven and unprofessional.
Two files generated with the same voice and settings can end up at noticeably different speeds. Chapter 3 at 120 WPM next to Chapter 4 at 160 WPM creates a jarring listening experience.
Longer audio files are especially prone to random pauses - sometimes a few seconds, sometimes over a minute. These gaps throw off the pacing of the entire piece.
Some text patterns cause the TTS engine to speed up unexpectedly - lists, numbers, or technical terms can trigger rapid-fire speech that sounds unnatural and is hard to follow.
Pace issues are subtle. You won't notice a file is 15% faster than average unless you're comparing it side-by-side with the rest of the batch - and nobody has time for that at scale.
What people are saying
"For audios longer than 1.5-2mins, gpt-4o-mini-tts works really unstable: random pauses from few seconds to more than a minute, random volume and tone changes, repeating last few sentences in random order."
"The OpenAI voices are excellent in terms of realism but they sometimes skip phrases and on occasion, entire paragraphs. Sometimes when submitting a single word, the API will return silence."
"The only issue I have had is that I have to create pause blocks to stretch the time frame out instead of moving a block over in the timeline."
"I'm having the exact same issue with having very long silences in the audio. It's quite weird."
How we detect it
We measure how fast each file is spoken and compare it to the rest of the batch to find the outliers.
We transcribe each file
Every audio file is transcribed to determine the word count and identify the spoken content. This gives us an accurate foundation for measuring pace.
We calculate the speaking rate
Using the word count and the audio duration, we calculate the words-per-minute for each file. This gives a precise, objective measure of pace.
We compare against the batch
Each file's speed is compared to the batch median. Files that deviate significantly - whether too fast or too slow - are flagged as outliers.
What you get
Precise speed data for every file, with clear outlier flags
Per-file WPM
The exact words-per-minute for every file in your batch.
Deviation scores
How far each file's speed deviates from the batch median.
Outlier flags
Files that are too fast or too slow are clearly marked for regeneration.
Other audit checks
Keep your audio pacing consistent
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