deepgram-python-audio-intelligence

You add sentiment analysis and summarization to Deepgram speech-to-text by enabling analytics overlay flags on the listen endpoints using the Python SDK, surfacing these insights directly alongside your transcripts.

Yes, diarization and topic detection work on live audio streams by using the WebSocket connection path in the Deepgram Python SDK, supporting real-time ingestion for live analytics overlays.

Available audio intelligence overlays include diarization, summarization, topic detection, intents, sentiment analysis, language detection, and redaction, providing structured insights alongside the audio transcription process.

Yes, the Deepgram Python SDK supports both REST and WebSocket paths for audio intelligence, processing files and URLs via REST while handling live streams through WebSocket connections.

Use Deepgram audio intelligence overlays instead of plain transcription when you need structured analytics like summaries, topics, and sentiment directly from the audio input, rather than just raw text output.

Yes, analyzing sentiment on already transcribed text requires a separate text intelligence pathway, while this Skill applies analytics overlays directly during the audio speech-to-text ingestion process.