ownscribe 0.11.0

Fully local meeting transcription and summarization CLI

ownscribe

Local-first meeting transcription and summarization CLI. Record, transcribe, and summarize meetings and system audio entirely on your machine – no cloud, no bots, no data leaving your device.

System audio capture requires macOS 14.2 or later. Other platforms can use the sounddevice backend with an external audio source.

Table of Contents

• Privacy
• Features
• Requirements
• Installation
• Usage
• Configuration
• Summarization Templates
• Speaker Diarization
• Acknowledgments
• Contributing
• License

Privacy

ownscribe does not:

• send audio to external servers
• upload transcripts
• require cloud APIs
• store data outside your machine

All audio, transcripts, and summaries remain local.

Features

• System audio capture — records all system audio natively via Core Audio Taps (macOS 14.2+), no virtual audio drivers needed
• Microphone capture — optionally record system + mic audio simultaneously with --mic
• WhisperX transcription — fast, accurate speech-to-text with word-level timestamps
• Speaker diarization — optional speaker identification via pyannote (requires HuggingFace token)
• Pipeline progress — live checklist showing transcription, diarization sub-steps, and summarization progress
• Local LLM summarization — structured meeting notes with a built-in model (Phi-4-mini); also supports Ollama, LM Studio, or any OpenAI-compatible server
• Summarization templates — built-in presets for meetings, lectures, and quick briefs; define your own in config
• Ask your meetings — ask natural-language questions across all your meeting notes; uses a two-stage LLM pipeline with keyword fallback
  
• Silence auto-stop — automatically stops recording after sustained silence (default: 5 minutes, configurable)
• One command — just run ownscribe, press Ctrl+C when done, get transcript + summary

Requirements

• macOS 14.2+ (for system audio capture)
• Python 3.12+
• uv
• ffmpeg — brew install ffmpeg
• Xcode Command Line Tools (xcode-select --install)

Summarization works out of the box — a local model (Phi-4-mini, ~2.4 GB) downloads automatically on first run. Optionally, you can use Ollama, LM Studio, or any OpenAI-compatible server instead (see Configuration).

Works with any app that outputs audio through Core Audio (Zoom, Teams, Meet, etc.).

Tip: Your terminal app (Terminal, iTerm2, VS Code, etc.) needs Screen Recording permission to capture system audio. Open the settings panel directly with:

open "x-apple.systempreferences:com.apple.preference.security?Privacy_ScreenCapture"

Enable your terminal app, then restart it.

Installation

Quick start with uvx

uvx ownscribe

On macOS, the Swift audio capture helper is downloaded automatically on first run.

Alternative summarization backends

The built-in local model works out of the box. If you'd rather call a hosted backend, install the matching extra:

uv add 'ownscribe[ollama]'   # use Ollama
uv add 'ownscribe[openai]'   # use any OpenAI-compatible server (LM Studio, llama-server, etc.)
uv add 'ownscribe[all]'      # install both

From source

# Clone the repo
git clone https://github.com/paberr/ownscribe.git
cd ownscribe

# Build the Swift audio capture helper (optional - auto-downloads if skipped)
bash swift/build.sh

# Install with all backends
uv sync --extra all

Usage

Record, transcribe, and summarize a meeting

ownscribe                    # records system audio, Ctrl+C to stop

This will:

1. Capture system audio until you press Ctrl+C (or auto-stop after 5 minutes of silence)
2. Transcribe with WhisperX
3. Summarize with your local LLM
4. Save everything to ~/ownscribe/YYYY-MM-DD_HHMMSS/

On first run, WhisperX / pyannote and the summarization model may download model files. ownscribe shows a Preparing models step and best-effort download progress in the TUI while this happens. Use ownscribe warmup to pre-download all models.

Options

ownscribe --mic                               # capture system audio + default mic (press 'm' to mute/unmute)
ownscribe --mic-device "MacBook Pro Microphone" # capture system audio + specific mic
ownscribe --device "MacBook Pro Microphone"   # use mic instead of system audio
ownscribe --no-summarize                      # skip LLM summarization
ownscribe --diarize                           # enable speaker identification
ownscribe --language en                       # set transcription language (default: auto-detect)
ownscribe --model large-v3                    # use a larger Whisper model
ownscribe --format json                       # output as JSON instead of markdown
ownscribe --no-keep-recording                 # auto-delete WAV files after transcription
ownscribe --template lecture                  # use the lecture summarization template
ownscribe --silence-timeout 600               # auto-stop after 10 minutes of silence
ownscribe --silence-timeout 0                 # disable silence auto-stop

Subcommands

ownscribe devices                  # list audio devices (uses native CoreAudio when available)
ownscribe apps                     # list running apps with PIDs for use with --pid
ownscribe warmup                   # prefetch WhisperX/pyannote models before a meeting
ownscribe transcribe recording.wav # transcribe an audio file (saves alongside the input)
ownscribe summarize transcript.md  # summarize a transcript (saves alongside the input)
ownscribe resume ./2026-02-20_1736 # resume a failed/partial pipeline in a directory
ownscribe ask "question"           # search your meetings with a natural-language question
ownscribe config                   # open config file in $EDITOR
ownscribe cleanup                  # remove ownscribe data from disk

Use warmup ahead of time to avoid first-run model download delays while recording:

ownscribe warmup                    # prefetch Whisper model (+ diarization if enabled in config)
ownscribe warmup --language en      # also prefetch alignment model for English
ownscribe warmup --with-diarization # force diarization warmup for this run

Searching Meeting Notes

Use ask to search across all your meeting notes with natural-language questions:

ownscribe ask "What did Anna say about the deadline?"
ownscribe ask "budget decisions" --since 2026-01-01
ownscribe ask "action items from last week" --limit 5

This runs a two-stage pipeline:

1. Find — sends meeting summaries to the LLM to identify which meetings are relevant
2. Answer — sends the full transcripts of relevant meetings to the LLM to produce an answer with quotes

If the LLM finds no relevant meetings, a keyword fallback searches summaries and transcripts directly.

Configuration

Config is stored at ~/.config/ownscribe/config.toml. Run ownscribe config to create and edit it.

[audio]
backend = "coreaudio"     # "coreaudio" or "sounddevice"
device = ""               # empty = system audio
mic = false               # also capture microphone input
mic_device = ""           # specific mic device name (empty = default)
silence_timeout = 300     # seconds of silence before auto-stop; 0 = disabled

[transcription]
model = "base"            # tiny, base, small, medium, large-v3
language = ""             # empty = auto-detect

[diarization]
enabled = false
hf_token = ""             # HuggingFace token for pyannote
telemetry = false         # allow HuggingFace Hub + pyannote metrics telemetry
device = "auto"           # "auto" (mps if available), "mps", or "cpu"

[summarization]
enabled = true
backend = "local"         # "local" (built-in, no server needed), "ollama", or "openai"
model = "phi-4-mini"      # local: "phi-4-mini", path to GGUF, or hf:owner/repo/file.gguf; ollama/openai: model name
# host = "http://localhost:11434"  # only for ollama/openai backends
# template = "meeting"    # "meeting", "lecture", "brief", or a custom name
# context_size = 0        # 0 = auto-detect from model; set manually for OpenAI-compatible backends

# Custom templates (optional):
# [templates.my-standup]
# system_prompt = "You summarize daily standups."
# prompt = "List each person's update:\n{transcript}"

[output]
dir = "~/ownscribe"
format = "markdown"       # "markdown" or "json"
keep_recording = true     # false = auto-delete WAV after transcription

Precedence: CLI flags > environment variables (HF_TOKEN, OLLAMA_HOST) > config file > defaults.

Summarization Templates

Built-in templates control how transcripts are summarized:

Template	Best for	Output style
meeting	Meetings, standups, 1:1s	Summary, Key Points, Action Items, Decisions
lecture	Lectures, seminars, talks	Summary, Key Concepts, Key Takeaways
brief	Quick overviews	3-5 bullet points

Use --template on the CLI or set template in [summarization] config. Default is meeting.

Define custom templates in config:

[templates.my-standup]
system_prompt = "You summarize daily standups."
prompt = "List each person's update:\n{transcript}"

Then use with --template my-standup or template = "my-standup" in config.

Speaker Diarization

Speaker identification requires a HuggingFace token with access to the pyannote diarization model:

1. Accept the terms for pyannote/speaker-diarization-community-1 on HuggingFace
2. Create a token at https://huggingface.co/settings/tokens
3. Set HF_TOKEN env var or add hf_token to config
4. Run with --diarize

On Apple Silicon Macs, diarization automatically uses the Metal Performance Shaders (MPS) GPU backend for ~10x faster processing. Set device = "cpu" in the [diarization] config section to disable this.

Acknowledgments

ownscribe builds on some excellent open-source projects:

• WhisperX — fast speech recognition with word-level timestamps and speaker diarization
• faster-whisper — CTranslate2-based Whisper inference
• pyannote.audio — speaker diarization
• llama.cpp / llama-cpp-python — local LLM inference
• Ollama — local LLM serving
• Click — CLI framework

Contributing

See CONTRIBUTING.md for development setup, tests, and open contribution areas.

License

MIT