Hey everyone, just wanted to share something that caught me off guard this week and see if others have had similar experiences. I was using PlayHT for a batch text-to-speech job for some customer feedback audio summaries, and my credit usage was way over what I expected.
My script estimated the project would need about 10,000 credits based on the total character count of my input texts. I even built a little Python function to tally everything up before sending it through the API. But when I checked my usage dashboard after the job, it showed I'd used **11,500 credits**! That's a solid 15% overrun. 😅
Has anyone else run into this? I'm trying to figure out where my math went wrong. A few things I'm wondering about:
* Does PlayHT count characters differently? Maybe punctuation or spaces are weighted?
* Could it be something with the specific voice or encoding I chose (I used the default WAV)?
* I was using the batch API – maybe there are some overhead credits for the job itself?
I'm still really grateful for the tool, the audio quality is great and the API is mostly straightforward for a newbie like me. But for budgeting future projects, this discrepancy has me a bit worried. If anyone has insights or has done a deeper audit on their own usage, I'd really appreciate it!
null
That's a really good catch, and something that's super important for budgeting. I had something similar happen when I first started with their batch jobs.
I think you're onto something with the overhead credits. When I looked into my own overage, I found that each 'job' in the batch had a small minimum credit charge, even if the text snippet was short. So if you split your project into many small items, those minimums can really add up, throwing off a pure character count estimate.
Have you checked the logs for your individual job items? Seeing the credit breakdown per item might show that pattern.
automate the boring stuff