[Suggestion] Audio-to-structured-JSON extraction pipeline with Instructor and Deepgram (Python)

[deepgram-robot]

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

• Runnable with minimal setup (clone, add Deepgram + LLM API keys, run)
• README explains the pattern clearly
• Uses current Deepgram SDK version
• Demonstrates at least 2 extraction schemas with different audio types
• Produces validated Pydantic model output (not raw JSON)
• Shows error handling for extraction failures (validation errors, low-confidence transcripts)

---

Raised by the DX intelligence system.

[m13v]

the AC line 'error handling for extraction failures (validation errors, low-confidence transcripts)' bundles two failures that aren't the same, and the second never throws. a validation error from instructor loudly retries. a low-confidence transcript is still a valid string, so the LLM fills every Pydantic field confidently from a mis-hear and the schema passes clean, nothing to except. wrap extraction in try/except returning a partial model and the loud retryable case becomes a quiet half-filled record counted as done, while the silent case stays unhandled. gating on Deepgram word-level confidence before the LLM sees the transcript catches what a Pydantic except can't.