Can AI replace Avoma?

Build a closest honest personal substitute for Avoma in an empty repository.
Use Python 3.12, FastAPI, SQLite, whisper.cpp, and a minimal HTMX interface; do not offer alternative stacks.
The core loop is: record or import a meeting, transcribe it locally, identify speakers with manual correction, generate structured notes, and maintain a lightweight coaching notebook from user-owned recordings.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Add explicit start, pause, resume, and stop controls with a visible recording indicator.
Support importing WAV, MP3, M4A, and MP4 files through ffmpeg.
Run transcription locally and display timestamped editable segments.
Let the user rename speakers and propagate corrections through the transcript.
Generate decisions, action items, questions, and a concise summary from approved text.
Export Markdown, plain text, and WebVTT beside the original recording.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out silent background capture.
Deliberately leave out automatic bot attendance in video meetings.
Deliberately leave out team workspaces and enterprise retention controls.
Finish by running the tests and listing the exact commands used.