A crazy idea or it's feasible: Technique that saves 30% on Transcribe Costs

[N2U]

@vb

can you share an update on the WER using different speeds of the input files because that seems to be the heart of the matter right now.

I have decided to throw my hat into the game, and have started to benchmark this.

I’ve posted the the results and code over here:

How Audio Speed Affects Transcription Accuracy: Benchmark Insights

There has recently been a few discussions about speeding up audio files to save on transcription costs, so I have written a benchmark to show how this affects the WER (word error rate). Before we jump into the results, here’s a disclaimer: Disclaimer: All benchmarks on speech-to-text model’s are done at normal playback speed, there is no guarantee that the advertised WER is accurate at other playback speeds. I’d like to give a shout-out to @sps who originally confirmed that this does save on A…

but the short answer is that OpenAI cannot guarantee that the WER stays the same as you increase the speed.