🎤 meetings — local audio → transcript → summary + action items

A single-file shell script CLI that transcribes meeting recordings (using GGUF Whisper or Parakeet models), then generates a summary and extracts action items using Ollama. Everything runs 100% locally.

audio file ──▶ ffmpeg ──▶ whisper.cpp / parakeet.cpp ──▶ Ollama ──▶ report.md
                    │                    │                      │
              16kHz mono WAV      GGUF transcription      summary + actions

Quick start

# 1. Clone/download
git clone <this-repo> meetings-cli && cd meetings-cli

# 2. One-time setup (installs whisper.cpp, downloads model, pulls Ollama LLM)
./meetings setup

# 3. Process a meeting recording
./meetings recording.mp3

# 4. Check everything is healthy
./meetings doctor

What it does

Step	Tool	What happens
1. Convert	ffmpeg	Any audio → 16kHz mono WAV
2. Transcribe	whisper.cpp or parakeet.cpp	GGUF/GGML model → text transcript
3. Summarize	Ollama	Transcript → structured summary (topic, key points, decisions, open questions)
4. Extract	Ollama	Transcript → numbered action items (who, what, when, priority)

Output

For each audio file, a directory is created containing:

2026-06-07_1402_team_standup/
├── report.md          # Combined: summary + actions + transcript
├── transcript.txt     # Raw transcription
├── summary.md         # LLM-generated summary
└── action_items.md    # Extracted action items

Requirements

Dependency	Install	Purpose
ffmpeg	`brew install ffmpeg`	Audio format conversion
whisper.cpp	`brew install whisper-cpp`	Speech-to-text (GGML models)
Ollama	ollama.com	LLM for summarization
jq	`brew install jq`	JSON parsing for Ollama API

./meetings setup handles all of this automatically.

STT engines

whisper.cpp (default, recommended)

Battle-tested, many languages, large model ecosystem
Models from ggerganov/whisper.cpp
Install: brew install whisper-cpp

Model	Size	Best for
tiny.en	75 MB	Quick tests, English
base.en	142 MB	Good balance, English
small.en	466 MB	Recommended for English
medium.en	1.5 GB	High accuracy, English
large-v3-turbo	809 MB	Best multilingual, fast
large-v3	2.9 GB	Best accuracy, any language

parakeet.cpp (alternative, faster)

NVIDIA Parakeet models, excellent English, smaller footprint
Models from mudler/parakeet-cpp-gguf
Install: Build from source or use Docker

Model	Size	Best for
tdt_ctc-110m-q8_0	178 MB	Fast, good English
tdt_ctc-110m-f16	268 MB	Fast, lossless English
tdt-0.6b-v3-f16	1.4 GB	Multilingual

Configuration

Environment variables

MEETINGS_DIR       # Config & models directory (default: ~/.meetings)
MEETINGS_STT       # STT engine: whisper | parakeet
MEETINGS_STT_MODEL # Path to GGUF/GGML model file
MEETINGS_LLM       # Ollama model for summarization (default: llama3.1:8b)
MEETINGS_THREADS   # Thread count for STT (default: 4)
MEETINGS_LANG      # Language code (default: en; use "auto" for multilingual)
MEETINGS_OUTPUT    # Output directory (default: .)

CLI flags

./meetings recording.mp3 --stt whisper --llm llama3.1:8b --lang en --output ./reports

Config file

Saved at ~/.meetings/config after running ./meetings setup:

STT_ENGINE=whisper
OLLAMA_MODEL=llama3.1:8b
THREADS=4
STT_MODEL=/home/user/.meetings/models/ggml-small.en.bin

Commands

./meetings <audio_file>              # Run the full pipeline
./meetings setup                     # Install deps + download model (interactive)
./meetings doctor                    # Check all dependencies
./meetings config                    # Show current configuration
./meetings help                      # Show help

How Ollama fits in

Ollama does NOT run the whisper/parakeet models — those use their own inference engines (whisper.cpp / parakeet.cpp). Ollama is only used for the LLM steps:

Summary generation — sends the transcript to an Ollama model with a structured summarization prompt
Action item extraction — sends the transcript to an Ollama model with an action-item extraction prompt

You can use any Ollama model. Smaller models (llama3.2:1b, gemma3:1b) are faster; larger models (llama3.1:8b, qwen2.5-coder:7b) produce better summaries.

Example

$ ./meetings team_standup.m4a

  ┌─────────────────────────────────────────────┐
  │          🎤  M E E T I N G S               │
  │   audio → transcript → summary + actions   │
  │       whisper.cpp · parakeet.cpp · ollama   │
  └─────────────────────────────────────────────┘

  Input:       team_standup.m4a
  STT engine:  whisper
  STT model:   ggml-small.en.bin
  LLM model:   llama3.1:8b
  Language:    en
  Output:      ./2026-06-07_1402_team_standup/

  ── Step 1/4 — Converting audio ──
  ▸ Converting audio to 16kHz mono WAV...
  ✓ Audio converted: 1.2M

  ── Step 2/4 — Transcribing with whisper ──
  ▸ Transcribing with whisper.cpp...
  ✓ Transcript: 847 words

  ── Step 3/4 — Summarizing (llama3.1:8b) ──
  ✓ Summary saved

  ── Step 4/4 — Extracting action items (llama3.1:8b) ──
  ✓ Action items saved

  ✓ All done! Files saved to: ./2026-06-07_1402_team_standup/

    📄 Report:        ./2026-06-07_1402_team_standup/report.md
    📝 Transcript:    ./2026-06-07_1402_team_standup/transcript.txt
    📋 Summary:      ./2026-06-07_1402_team_standup/summary.md
    ✅ Action Items:  ./2026-06-07_1402_item_standup/action_items.md

License

MIT