Comment by 2dvisio
12 hours ago
Still find the Copilot transcripts orders of magnitude worse than something like Wispr Flow and they tend to allucinate constantly and do not adapt to a company's context (that Copilot has access too...). I am talking about acronyms of products / teams, names of people (even when they are in the call), etc.
Can anyone familiar with the technical details shed light on why this is so.
Is it because of a globally trained model (as opposed to trained[tweaked on] on context specific data) or because of using different classes of models.
Neither copilot nor flow can natively handle audio to my understanding, so there is already a transcription model converting it to text that then GPT tries to summarise.
It could be they simply use a mediocre transcription model. Wispr is amazing but would hurt their pride to use a competitor.
But i feel it's more likley the experience is; GPT didn't actually improve on the raw transcription, just made it worse. Especially as any miss-transcipted words may trip it up and make it misunderstand while making the summary.
if i can choose between a potentially confused and misunderstood summary, and a badly spellchecked (flipped words) raw transcription, i would trust the latter.
Ye i didn't even think about advanced meetings summary bots. Just raw word for word transcription please. Wispr is pretty great.