Building a Conversational AI Flow with Deepgram

The article discusses the challenge of determining when a speaker has finished talking in virtual conversations and how it can be addressed using Deepgram's real-time speech-to-text service. Two main mechanisms are provided by Deepgram to help build conversational flows: interim results and endpointing. Interim results are sent back every few seconds, indicating that more audio is being gathered and the transcription may change as additional context is given. Once enough audio has been collected for the best possible prediction, a finalized transcript is sent back. Endpointing detects the end of speech and sends an immediate message when silence is detected. The article provides code examples in Python that demonstrate how to use these mechanisms to determine when someone is done talking and respond accordingly.