How to Build a Speech-to-Text (STT) Note Taking App in Python

A tutorial explains how to build a low-cost, scalable speech-to-text note-taking application in Python that records microphone audio or accepts existing files, transcribes them through Deepgram’s API, and formats the resulting transcript with timestamps, paragraphs, and speaker diarization. It then uses an LLM such as Google Gemini or a local Ollama model to generate structured meeting notes containing a summary, topic-based chapters with concise key points, and any action items or assignees. The implementation covers recording with PyAudio and Wave, safely extracting relevant fields from Deepgram’s JSON response, enforcing reliable LLM output through Pydantic schemas and structured JSON generation, and assembling the workflow into a command-line tool that exports transcript and Markdown note files. It also discusses trade-offs in audio chunk size, the role of finite-state constraints in structured LLM outputs, available free API usage, and possible extensions such as Streamlit interfaces, integrations with Obsidian or Notion, and timestamp-linked navigation within transcripts.

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