Endpointing VAPI vs Deepgram
Is vapi's endpointing model for determining 'end of turn' just a configuration of deepgram's endpointing or does vapi have its own secret sauce under the hood? On my VAPI app, the end of turn recognition works great, but I'm also realizing this is probably because end-speech is based on reaching the end of the text returned from the llm. I have a "suggestions" feature that suggests what a user should say when practicing speaking to an AI agent using VAPI's speech-end events. But now I'm trying to offer this same feature for real life conversations that are streaming real customer call audio instead of talking to AI customers and I have to figure out how to do the endpointing as nicely as vapi does it.
