This is great, thank you for sharing. I work on these APIs at OpenAI, it's a surprise to me that it still works reasonably well at 2/3x speed, but on the other hand for phone channels we get 8khz audio that is upsampled to 24khz for the model and it still works well. Note there's probably a measurable decrease in transcription accuracy that worsens as you deviate from 1x speed. Also we really need to support bigger/longer file uploads :)
I kind of want to take a more proper poke at this but focus more one summarization accuracy over word-for-word accuracy, though I see the value in both.
I'm actually curious, if I run transcriptions back-to-back-to-back on the exact same audio, how much variance should I expect?
Maybe I'll try three approaches:
- A straight diff comparison (I know a lot of people are calling for this, but I really think this is less useful than it sounds)
- A "variance within the modal" test running it multiple times against the same audio, tracking how much it varies between runs
- An LLM analysis assessing if the primary points from a talk were captured and summarized at 1x, 2x, 3x, 4x runs (I think this is far more useful and interesting)
Quick Feedback: Would it be cool to research this internally and maybe find a sweet spot in speed multiplier where the loss is minimal. This pre-processing is quite cheap and could bring down the API price eventually.
