What to build
A Python example that chains Deepgram's pre-recorded STT with the Instructor library (structured LLM output) to extract typed, validated data from audio recordings. Given an audio file (e.g., a customer support call, a medical consultation, or a meeting), the pipeline transcribes it with Deepgram, then uses an LLM with Instructor to extract structured data into Pydantic models — e.g., extracting order details, appointment information, action items, or complaint summaries as typed JSON.
Why this matters
Developers building data extraction pipelines from audio need more than raw transcripts — they need structured, typed data they can feed into databases, CRMs, or downstream systems. The Instructor library (11K+ GitHub stars) is the standard tool for getting structured output from LLMs, but no example shows how to chain it with Deepgram. This pattern — audio → transcript → structured JSON — is one of the most requested workflows for contact center analytics, medical documentation, and meeting intelligence applications.
Suggested scope
- Language: Python 3.11+
- Deepgram APIs: Pre-recorded STT (Nova-3), optionally Audio Intelligence (sentiment, topics)
- Dependencies:
instructor, openai or anthropic (for LLM), pydantic
- Complexity: Medium — pipeline orchestration + schema design
- Define 2-3 example Pydantic schemas (e.g.,
CustomerComplaint, MeetingActionItems, AppointmentDetails)
- Show how Audio Intelligence features complement LLM extraction
- Include sample audio files or URLs for testing
- Output validated, typed JSON
Acceptance criteria
Raised by the DX intelligence system.
What to build
A Python example that chains Deepgram's pre-recorded STT with the Instructor library (structured LLM output) to extract typed, validated data from audio recordings. Given an audio file (e.g., a customer support call, a medical consultation, or a meeting), the pipeline transcribes it with Deepgram, then uses an LLM with Instructor to extract structured data into Pydantic models — e.g., extracting order details, appointment information, action items, or complaint summaries as typed JSON.
Why this matters
Developers building data extraction pipelines from audio need more than raw transcripts — they need structured, typed data they can feed into databases, CRMs, or downstream systems. The Instructor library (11K+ GitHub stars) is the standard tool for getting structured output from LLMs, but no example shows how to chain it with Deepgram. This pattern — audio → transcript → structured JSON — is one of the most requested workflows for contact center analytics, medical documentation, and meeting intelligence applications.
Suggested scope
instructor,openaioranthropic(for LLM),pydanticCustomerComplaint,MeetingActionItems,AppointmentDetails)Acceptance criteria
Raised by the DX intelligence system.