Shadow-clerk

A tool that records web meeting audio in real-time and transcribes it. Also supports translation and meeting minutes generation.

Available on PyPI: https://pypi.org/project/shadow-clerk/

Platform support

OS	Status	Notes
Linux (PipeWire/PulseAudio)	Supported	Primary development target
Windows 10/11	Supported	Monitor capture via WASAPI loopback (default playback device)
macOS	Not supported yet	Requires a virtual audio driver (e.g. BlackHole) — not implemented

Windows-specific notes

Recommended install (explicit Windows deps):

uv python install 3.13
uv tool install --python 3.13 --with PyAudioWPatch "shadow-clerk[spell-check,gcal]"
# +ReazonSpeech k2 (Japanese ASR, optional):
uv tool install --python 3.13 --with PyAudioWPatch --with sherpa-onnx --with "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr" "shadow-clerk[spell-check,gcal,reazonspeech]"

Why explicit --python and --with:

• --python 3.13 (uv-managed Python): Microsoft Store Python runs in an AppContainer sandbox that redirects %APPDATA%\shadow-clerk to %LOCALAPPDATA%\Packages\PythonSoftwareFoundation.Python.X.YY_<id>\LocalCache\Roaming\shadow-clerk\. The package id changes when the Python minor version is upgraded, silently moving the data directory and orphaning existing transcripts/config. uv-managed Python avoids the sandbox. Daemon startup also logs a WARNING when Store Python is detected.
• --with PyAudioWPatch: WASAPI loopback monitor capture uses PyAudioWPatch. It's declared as a Windows-only dep in pyproject.toml but some uv versions don't reliably resolve PEP 508 markers from local-editable installs, so passing it explicitly is safer.
• --with sherpa-onnx (ReazonSpeech only): Same reason — ensures uv picks the Windows wheel (with onnxruntime.dll) rather than a stale resolution to the Linux wheel.

Other notes:

• Microphone permission: Allow mic access for the terminal you launch from (Windows Settings → Privacy → Microphone).
• Monitor capture: Uses WASAPI loopback on the system default playback device. Switching the default device in Windows sound settings switches what gets captured.
• Data directory: %APPDATA%\shadow-clerk (the ~/.local/share/shadow-clerk paths in the rest of this README map to that on Windows). Override with SHADOW_CLERK_DATA_DIR if needed.
• Remote Desktop (RDP): When running inside an RDP session, the host's "Remote Audio" virtual device is auto-skipped (it would either segfault or capture nothing useful). The daemon falls back to a non-RDP loopback device if available; otherwise monitor capture is disabled and only the mic is recorded.
• voice_command_key: The default f23 is a Linux/xremap convention. On Windows set it to null (disable PTT) or to one of menu/ctrl_r/ctrl_l/alt_r/alt_l/shift_r/shift_l in config.yaml.
• Stopping the daemon: clerk-util stop works (Windows path uses taskkill). clerk-util start runs the daemon in the foreground with Ctrl+C handling, mirroring Linux. clerk-daemon --daemon detaches instead: there is no fork(), so it relaunches itself with DETACHED_PROCESS and the parent exits.
• AI Console: uses ConPTY through pywinpty, declared as a Windows-only dependency. Without it the daemon still runs; only the console reports the missing package and refuses to start.

Standalone binary

clerk-daemon.exe and clerk-util.exe can be built with PyInstaller. See Building a standalone binary.

Features and requirements

Feature	Requires	Quality	Speed	Related settings
Transcription (default)	faster-whisper (included)	3	4	default_model, default_language
Transcription (Kotoba-Whisper)	Same (auto-downloaded on first use)	5	3	japanese_asr_model: kotoba-whisper
Transcription (ReazonSpeech)	uv sync --extra reazonspeech	5	4	japanese_asr_model: reazonspeech-k2
Interim transcription	Same	2	5	interim_transcription: true, interim_model
Translation (LibreTranslate)	LibreTranslate server	2	4	translation_provider: libretranslate
Translation (OpenAI compatible API)	OpenAI compatible API	3-5	2-5	translation_provider: api, api_endpoint, api_model
Translation (Claude)	Claude Code	5	2	translation_provider: claude
Language detection (pre-translation)	langdetect (included)	—	—	Automatically detects source language to select correct prompt
Summary (Claude)	Claude Code	5	3	llm_provider: claude
Summary (OpenAI compatible API)	OpenAI compatible API	3-5	2-5	llm_provider: api, api_endpoint, api_model
Voice commands (PTT)	None (built-in)	—	—	voice_command_key
Voice commands (LLM matching)	OpenAI compatible API	—	—	api_endpoint, api_model
Spell check (pre-translation)	transformers (auto-downloaded on first use)	—	—	libretranslate_spell_check: true

Minimal setup without LLM: Transcription + LibreTranslate translation requires no external API or Claude Code. Everything runs locally.

See the Feature Tour for a visual walkthrough with screenshots.

Setup

1. System packages

sudo apt install libportaudio2 portaudio19-dev

2. Install

Install from PyPI:

	Command
Basic	uv tool install shadow-clerk
+ ReazonSpeech	uv tool install "shadow-clerk[reazonspeech]" --with "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr"
+ Spell check	uv tool install "shadow-clerk[spell-check]"
+ Both (ReazonSpeech + Spell check)	uv tool install "shadow-clerk[spell-check,reazonspeech]" --with "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr"
+ Google Calendar	uv tool install "shadow-clerk[gcal]"
All	uv tool install "shadow-clerk[spell-check,gcal,reazonspeech]" --with "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr"

Note: uv tool install maintains a single environment per tool. When reinstalling with different extras, use --force — without it, uv tool install reports "already installed" and does not add the extra. Only the extras specified in the command are included; previously installed extras are removed.

Why reazonspeech-k2-asr is --with'd separately: The reazonspeech-k2-asr package is only distributed via Git (not on PyPI), and PyPI rejects direct URL references in package metadata, so it cannot be declared inside the reazonspeech extra. Pass it via --with whenever you need ReazonSpeech k2.

Alternative: pipx install shadow-clerk (or pip install shadow-clerk inside a venv) also works.

2a. For development

git clone https://github.com/edocode/shadow-clerk.git
cd shadow-clerk

	Command
Basic	uv sync
+ ReazonSpeech	uv sync --extra reazonspeech
+ Spell check	uv sync --extra spell-check
+ Both (ReazonSpeech + Spell check)	uv sync --extra spell-check --extra reazonspeech
+ Google Calendar	uv sync --extra gcal
All	uv sync --extra spell-check --extra gcal --extra reazonspeech

Each uv sync defines the whole environment, and extras do not accumulate. uv sync --extra gcal after uv sync --extra reazonspeech removes ReazonSpeech again — name every extra you want in the same command, as in the last row above. For the same reason, anything installed with uv pip install is undeclared and is removed by the next uv sync, so run it last.

This is all you need for transcription. The following optional extras are available:

Optional: Japanese ASR models

Kotoba-Whisper — No extra install required. The model is auto-downloaded on first use. Just set:

# config.yaml
japanese_asr_model: kotoba-whisper

ReazonSpeech k2 — Requires the reazonspeech extra plus the reazonspeech-k2-asr package, which is only distributed via Git (not on PyPI), so it must be installed separately:

uv tool install "shadow-clerk[reazonspeech]" \
  --with "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr"
# or for development — in this order, and do not run uv sync afterwards:
uv sync --extra reazonspeech
uv pip install "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr"

reazonspeech-k2-asr cannot be declared in pyproject.toml (PyPI rejects direct URL references in metadata), so uv sync does not know about it and removes it. Check what you have with:

uv run python -c "import sherpa_onnx, reazonspeech.k2.asr; print('ok')"

# config.yaml
japanese_asr_model: reazonspeech-k2

Optional: Spell check (pre-translation correction)

Requires the spell-check extra (installs transformers, torch, sentencepiece):

uv tool install "shadow-clerk[spell-check]"
# or for development:
uv sync --extra spell-check

# config.yaml
libretranslate_spell_check: true
spell_check_model: mbyhphat/t5-japanese-typo-correction  # default

The spell check model is auto-downloaded on first use. It corrects Japanese speech recognition typos before sending text to LibreTranslate.

Optional: Google Calendar integration

Automatically starts and ends meeting sessions based on your Google Calendar schedule. Requires the gcal extra:

uv tool install "shadow-clerk[gcal]"
# or for development:
uv sync --extra gcal

Then authenticate and configure:

# One-time OAuth setup (opens browser)
clerk-util gcal-auth ~/credentials.json

# Enable in config
clerk-util write-config-value gcal_integration true
clerk-util write-config-value gcal_credentials_file ~/credentials.json

When enabled, clerk-daemon polls Google Calendar every 60 seconds. Events automatically trigger start_meeting / end_meeting, creating transcript files named transcript-YYYYMMDDHHMM@EventTitle.txt.

See docs/google-calendar-setup.md for full setup instructions including how to obtain credentials.json from Google Cloud Console.

Add the following options if you need translation or summarization.

3. (Optional) LibreTranslate setup

Local translation without LLM. Install via Docker or pip:

# Docker (recommended)
docker run -d -p 5000:5000 libretranslate/libretranslate

# Or pip
pip install libretranslate
libretranslate --host 0.0.0.0 --port 5000

Configuration:

# config.yaml
translation_provider: libretranslate
libretranslate_endpoint: http://localhost:5000

4. (Optional) OpenAI compatible API setup

Used for translation, summarization, and LLM voice command matching:

# config.yaml — OpenAI
llm_provider: api
api_endpoint: https://api.openai.com/v1
api_model: gpt-4o
# Add SHADOW_CLERK_API_KEY=sk-... to ~/.local/share/shadow-clerk/.env

# config.yaml — Ollama (local)
llm_provider: api
api_endpoint: http://localhost:11434/v1
api_model: llama3

5. (Optional) Use Claude CLI as the LLM provider

If you have Claude Code installed (claude on your $PATH), shadow-clerk can shell out to claude -p for translation and summarization. Set in config.yaml:

llm_provider: claude
claude_cli_model: haiku   # or sonnet / opus / a full model id
# claude_cli_path: claude  # full path if not on $PATH

This uses your existing Claude Code OAuth login. No extra setup needed — translation and summarization run inside the daemon as background threads, no Claude Code session required.

Building a standalone binary

packaging/shadow-clerk.spec produces a one-directory PyInstaller bundle containing both clerk-daemon and clerk-util.

PyInstaller does not cross-compile. It only builds for the OS it runs on: the bootloader is a native binary, and the analysis step imports every module to trace dependencies. A Windows .exe therefore has to be built on Windows — a machine, a VM, or a windows-latest GitHub Actions runner. Wine with a Windows Python is the usual workaround, but analysis imports pywinpty (ConPTY), PyAudioWPatch (WASAPI) and ctranslate2, which are exactly the pieces Wine emulates poorly.

# Windows (PowerShell)
uv sync
uv run --with pyinstaller pyinstaller packaging/shadow-clerk.spec
dist\shadow-clerk\clerk-daemon.exe --list-devices   # smoke test

# Linux / macOS
uv sync
uv run --with pyinstaller pyinstaller packaging/shadow-clerk.spec
./dist/shadow-clerk/clerk-daemon --list-devices      # smoke test

--list-devices is a good smoke test: it exercises the bundled PortAudio and the native extensions without recording anything.

To bundle ReazonSpeech and Google Calendar as well, replace the uv sync line — in this order, because reazonspeech-k2-asr is not declared anywhere and a later sync would remove it:

uv sync --extra reazonspeech --extra gcal
uv pip install "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr"
uv run python -c "import sherpa_onnx, reazonspeech.k2.asr; print('ok')"   # check before building
uv run --with pyinstaller pyinstaller packaging/shadow-clerk.spec

The spell-check extra is not bundled even if you install it: the spec drops torch, transformers and sentencepiece in _EXCLUDES. Remove them from that list to include it, and expect the bundle to grow by several GB.

Without a Windows machine, .github/workflows/build-binary.yml runs exactly those steps on windows-latest and ubuntu-latest. Push a v* tag and both binaries are attached to the release, or start it by hand from the Actions tab and download them as artifacts.

Do not uv pip install pyinstaller. uv sync makes the environment match what the project declares and removes everything else, so the next uv sync --extra ... would drop it again. --with puts PyInstaller in a temporary layer over the project environment instead: it can still see the project's packages, and nothing is left behind to be removed.

Notes:

• Output: dist/shadow-clerk/, roughly 460 MB with the default dependencies and no extras.
• Extras are collected only if installed in the environment you build from, so install them before building — every extra you want named in one uv sync, and any uv pip install last. See Setup for why the order matters. The spec collects sherpa_onnx (which carries its own onnxruntime DLL in lib/) and reazonspeech.k2.asr when they are there; the ASR weights themselves are fetched on first use, like the Whisper models.
• Whisper models are not bundled. small is around 500 MB and is fetched from Hugging Face on first run, then cached (%USERPROFILE%\.cache\huggingface on Windows, ~/.cache/huggingface elsewhere). For an offline bundle, add that cache to datas in the spec, or ship a converted CT2 model and point --model at it.
• packaging/hooks/ overrides PyInstaller's bundled hooks. There is one today: the bundled hook-webrtcvad.py calls copy_metadata('webrtcvad'), but this project depends on webrtcvad-wheels, so without the override the build aborts with ImportErrorWhenRunningHook.
• Add a dependency that ships DLLs or data files? Add it to _PACKAGES in the spec. PyInstaller only follows import statements, so anything else is silently left out and fails at runtime rather than at build time.

Usage

Starting the daemon

If you installed via uv tool install:

clerk-daemon

For development (uv sync):

uv run clerk-daemon

Note: uv run uses the project .venv, while uv tool install uses its own isolated environment. Make sure extras (e.g. spell-check, reazonspeech) are installed in the matching environment.

Recording & transcription

# Basic (record mic + system audio, auto-transcribe)
clerk-daemon

# List available devices
clerk-daemon --list-devices

# With options
clerk-daemon \
  --language ja \
  --model small \
  --output ~/my-transcript.txt \
  --verbose

Press Ctrl+C to stop recording.

Voice commands

Push-to-Talk (recommended)

Hold down the Menu key (next to Right Alt) while speaking a command — no wake word needed:

[Hold Menu key] "start translation" → Translation starts
[Hold Menu key] "start meeting"     → Meeting session starts

The trigger key can be changed via voice_command_key in config.yaml (ctrl_r, ctrl_l, alt_r, alt_l, shift_r, shift_l). Set to null to disable.

Prefix mode (fallback)

During recording, say the wake word (default: "sheruku" / "シェルク") followed by a command for hands-free control:

Voice command	Action
"sheruku, start meeting"	Start a new meeting session
"sheruku, end meeting"	End the meeting session
"sheruku, language ja"	Switch transcription language to Japanese
"sheruku, language en"	Switch transcription language to English
"sheruku, unset language"	Reset to auto-detect
"sheruku, start translation"	Start the translation loop
"sheruku, stop translation"	Stop the translation loop

The separator (comma, space) between the wake word and command is optional. The wake word can be changed via wake_word in config.yaml.

Custom voice commands

You can register custom voice commands in config.yaml under custom_commands. They are evaluated after built-in commands:

custom_commands:
  - pattern: "youtube"
    action: "xdg-open https://www.youtube.com"
  - pattern: "gmail|mail"
    action: "xdg-open https://mail.google.com"

• pattern: Regular expression (case-insensitive)
• action: Shell command to execute

Start the meeting analysis by voice — glossary already has クラーク as a wake word, so this pattern fires when you say it while holding the PTT key:

custom_commands:
  - pattern: (クラーク|クラーク、)?分析(開始|して)
    action: curl -sX POST localhost:8765/api/console/start

LLM fallback

If a voice command doesn't match any built-in or custom command and api_endpoint is configured, the utterance is sent to the LLM as a query. The response is printed to stdout and saved to .clerk_response.

"sheruku, what is 1+1?" → LLM returns the answer

CLI options

Option	Description	Default
--output, -o	Output file path	~/.local/share/shadow-clerk/transcript-YYYYMMDD.txt
--model, -m	Whisper model size (tiny, base, small, medium, large-v3)	small
--language, -l	Language code (ja, en, etc.). Auto-detect if omitted	Auto
--mic	Microphone device number	Auto-detect (or mic_device config)
--monitor	Monitor device number (sounddevice)	Auto-detect (or monitor_device config)
--backend	Audio backend (auto, pipewire, pulseaudio, sounddevice)	auto
--list-devices	List devices and exit	-
--verbose, -v	Verbose logging	-
--dashboard / --no-dashboard	Enable/disable dashboard	Enabled
--dashboard-port	Dashboard port number	8765
--beam-size	Whisper beam size (1=fast, 5=accurate)	5
--compute-type	Whisper compute precision (int8, float16, float32)	int8
--device	Whisper device (cpu, cuda)	cpu

Translation & Summary Providers

Translation and summary each support multiple providers with different operation modes:

Claude mode (translation_provider: claude / llm_provider: claude)

clerk-daemon shells out to claude -p per request, reusing your existing Claude Code OAuth login.

• Highest quality — especially for Japanese homophone correction (ja→ja)
• Requires claude on PATH — found automatically if Claude Code is installed
• No Claude Code session required — the daemon spawns claude -p per job, so you don't need to keep a Claude Code terminal open
• Translation and summary both run inside daemon threads — same plumbing as api / libretranslate
• Cost tracking: claude -p --output-format json returns total_cost_usd, which is logged by the daemon

# config.yaml
translation_provider: claude   # Translation by Claude
llm_provider: claude           # Summary by Claude (default)
claude_cli_path: claude        # full path if not on PATH
claude_cli_model: haiku        # haiku / sonnet / opus or a full model id

API mode (translation_provider: api / llm_provider: api)

clerk-daemon calls an external API (OpenAI-compatible) internally. Claude Code is not required.

• Works without Claude Code — clerk-daemon handles translation and summary on its own
• Quality depends on model — high-end models (GPT-4o) produce good results; smaller models may struggle with Japanese correction
• How translation works: An internal thread in clerk-daemon processes translation. Started/stopped via voice commands or dashboard
• Summary works similarly: clerk-util summarize generates minutes via the external API

# config.yaml
translation_provider: api     # Translation via external API
llm_provider: api             # Summary via external API
api_endpoint: https://api.openai.com/v1
api_model: gpt-4o

LibreTranslate mode (translation_provider: libretranslate)

Translation only. Runs locally without any external API or Claude Code (summary still needs llm_provider).

Recommended configurations

Use case	Translation	Summary	Notes
Best quality (Claude CLI)	translation_provider: claude	llm_provider: claude	Highest quality, needs claude CLI
Autonomous (external API)	translation_provider: api	llm_provider: api	OpenAI-compatible, quality varies by model
Fully local	translation_provider: libretranslate	—	No LLM needed, lower quality
Hybrid	translation_provider: api	llm_provider: claude	Auto translation + high-quality summary

Meeting minutes

Three ways to generate minutes: automatically at meeting end, on demand from the dashboard, or via clerk-util from the command line:

clerk-util start                                   # Start daemon (background)
clerk-util stop                                    # Stop daemon
clerk-util recorder-status                         # Show running state
clerk-util summarize                               # Update minutes from transcript diff
clerk-util summarize --mode full                   # Regenerate from full transcript
clerk-util summarize 20260425 --mode full          # Specify date
clerk-util command start_meeting                   # Start meeting session
clerk-util command end_meeting                     # End meeting session (auto_summary linked)
clerk-util command translate_start                 # Start translation loop
clerk-util command translate_stop                  # Stop translation loop

Meeting start/end is also available via voice commands ("clerk, start meeting" / "clerk, end meeting") or dashboard buttons. The dashboard's "Generate Summary" button can trigger minutes generation at any time.

Generated meeting minutes are saved to ~/.local/share/shadow-clerk/summary-YYYYMMDD.md.

AI Console

• The assistant reaches these APIs at $SHADOW_CLERK_URL, injected into the console's environment, so it never has to guess the port.

An AI assistant (claude or codex) can run inside a PTY under the dashboard's AI Console tab (next to Logs), watching the meeting transcript and writing generated documents back for the dashboard to display.

• Starting: automatically when a meeting starts (auto_analyze: true auto_summary_via_console: true # let the console write the minutes when it is running), or manually via the "Start Analysis" button. Either way, ai_assistant_init_prompt (default /clerk-meeting-helper {transcript} {lang}) is sent to the PTY once the assistant's TUI is ready — {transcript} and {meeting} are substituted with the active file paths, and {lang} with translate_language, so the skill writes in the language you read.

• Generated documents: shown in the Summary panel's [Summary][Advice][Analysis] tabs:

  • advice-<stem>.md — open questions and suggestions (overwritten each time)
  • analysis-<stem>.md — confirmed facts (appended)

  Both are Markdown and are rendered to HTML server-side. Raw HTML inside them is escaped, never rendered.

• Session lifecycle: one PTY session is reused for the whole run; it is not stopped when the meeting ends, so it stays available for writing up minutes afterward. Stop it from the Console tab's stop button.

• Working directory: ai_assistant_workdir sets the default launch directory. Per-meeting overrides live in DATA_DIR/meeting.yaml (meetings[].workdir), editable from the gear icon (⚙) on each meeting's row in the meeting list. The first time shadow-clerk writes to a config.yaml it didn't create itself, it saves a one-time <path>.bak copy alongside it, since the YAML writer preserves keys but not your hand-written comments.

• Permissions: the assistant runs shell scripts from the meeting skill, so allow them in that working directory's .claude/settings.json — otherwise it stops mid-meeting at a permission prompt.

• Installing the skill: the assistant runs a bundled skill that has to be copied into the agent's own skills directory. The dashboard offers this on a first run, and the command does the same:

clerk-util install-skill                        # ~/.claude/skills/   (Claude Code)
clerk-util install-skill --target agents        # ~/.agents/skills/   (Codex and others)
clerk-util install-skill --target /path/to/dir  # anywhere else; remembered for updates
clerk-util install-skill --link                 # symlink instead of copy (POSIX only)

A destination holding a skill shadow-clerk did not write is left alone unless you pass --force. Per-meeting working directories live in DATA_DIR/meeting.yaml.

• Orphaned process on SIGKILL: the daemon stops the assistant on normal shutdown, but if the daemon itself is killed with SIGKILL (kill -9), the assistant process can be left running (it is detached from any terminal). Find and kill it manually with pgrep -af claude (or codex).
• Security note if you expose the dashboard: the AI Console's terminal content (including whatever the assistant reads or prints from your files) is streamed over the same /api/events SSE used by the rest of the dashboard, and that SSE fan-out has no per-client filtering. If you bind the dashboard beyond localhost, anyone who can reach it can watch the assistant's terminal live. Console input (/api/console/input etc.) itself stays localhost-only and additionally checks the Origin header to reject cross-origin requests from your own browser.

Configuration file

Customize defaults and auto-features in ~/.local/share/shadow-clerk/config.yaml:

# shadow-clerk config
translate_language: en        # Translation target language (ja/en/etc)
auto_translate: false         # Auto-start translation on start meeting
auto_summary: false           # Auto-generate summary on end meeting
auto_analyze: false           # Launch the AI assistant and run the meeting skill when a meeting starts
ai_assistant_command: claude  # Command to run in the AI Console (claude, codex, ...)
ai_assistant_args: ''         # Arguments for that command, split with shlex
ai_assistant_init_prompt: /clerk-meeting-helper {transcript} {lang}  # Sent to the PTY once the TUI is ready. {transcript}, {meeting} and {lang} are substituted ({lang} = translate_language)
ai_assistant_workdir: ''      # Default working directory. Per-meeting overrides live in `DATA_DIR/meeting.yaml` の meetings[].workdir
default_language: null        # Default language for clerk-daemon (null=auto-detect)
default_model: small          # Default Whisper model for clerk-daemon
output_directory: null        # Transcript output directory (null=data directory)
mic_device: null              # Microphone device name (null=OS default; not an index — indices shift between runs)
monitor_device: null          # Monitor/speaker device name (null=OS default). Selectable from the dashboard settings panel
llm_provider: claude          # LLM for summary ("claude" or "api")
translation_provider: null    # Translation provider (null=use llm_provider, "claude", "api", "libretranslate")
api_endpoint: null            # OpenAI Compatible API base URL
api_model: null               # API model name (gpt-4o, etc.)
api_key_env: SHADOW_CLERK_API_KEY  # Environment variable name for API key
api_disable_thinking: false   # Disable reasoning-model thinking for translation/interim (Qwen3 etc.; sends enable_thinking=false). Summary always keeps thinking.
summary_source: null          # Summary source (null=auto: prefer translation if exists / "transcript" / "translate")
summary_language: null        # Summary output language (null=fallback to ui_language / ja, en, zh, ...)
libretranslate_endpoint: null     # LibreTranslate API URL (e.g. http://localhost:5000)
libretranslate_api_key: null      # LibreTranslate API key (null if not required)
libretranslate_spell_check: false # Spell check before LibreTranslate translation
spell_check_model: mbyhphat/t5-japanese-typo-correction  # Spell check model
custom_commands: []               # Custom voice commands (list of pattern + action)
initial_prompt: null              # Whisper initial_prompt (vocabulary hints for recognition)
voice_command_key: f23         # Push-to-Talk key (null=disabled)
wake_word: シェルク              # Wake word (trigger word for voice commands)
whisper_beam_size: 5           # Whisper beam size (1=fast, 5=accurate)
whisper_compute_type: int8     # Compute precision (int8/float16/float32)
whisper_device: cpu            # Device (cpu/cuda)
interim_transcription: false   # Interim transcription (real-time display while speaking)
interim_model: base            # Model for interim transcription
interim_translation: true      # Translate interim transcription to dashboard's interim panel
interim_translation_provider: null  # null=auto, "api", "libretranslate", or "claude"
japanese_asr_model: default    # Japanese ASR model (default/kotoba-whisper/reazonspeech-k2)
kotoba_whisper_model: kotoba-tech/kotoba-whisper-v2.0-faster  # Kotoba-Whisper model
interim_japanese_asr_model: default  # Japanese ASR for interim transcription
reazonspeech_precision: fp32   # ReazonSpeech k2: fp32 / int8 / int8-fp32 (fp16 is invalid)
ui_language: ja                # UI language (ja/en) — dashboard, terminal output, LLM prompts

Manage configuration from Claude Code:

clerk-util read-config                                # Show current config
clerk-util write-config-value default_model tiny      # Change a setting
clerk-util write-config-value auto_translate true     # Enable auto-translation

With auto_translate: true, translation starts automatically when a meeting session begins. With auto_summary: true, meeting minutes are generated automatically when a meeting session ends. With auto_analyze: true, the AI assistant is launched in the AI Console when a meeting session begins.

Summary source selection

When summary_source is unset (null/auto), the summary is generated from the translation file if one exists (falling back to the transcript if not). To pin the behavior explicitly:

clerk-util write-config-value summary_source transcript   # always use transcript
clerk-util write-config-value summary_source translate    # always use translation (fallback to transcript if missing)

Summary language

summary_language controls the output language of the summary. When unset (null), it falls back to ui_language:

clerk-util write-config-value summary_language en   # summarize in English
clerk-util write-config-value summary_language ja   # summarize in Japanese

Audio device selection

mic_device / monitor_device pin capture to a device by name, not device number — device numbers shift between daemon runs and can even change while the daemon is running. null (the default) follows the OS default device. Pick a device from the dashboard settings panel.

If the configured device disappears (unplugged, sink removed), the daemon falls back to the automatic device and keeps recording, then switches back on its own once the device reappears. The configured value is never overwritten by the fallback. If the device never actually disappeared — another application just grabbed it exclusively — the daemon won't retry on its own; use "Refresh list" to force a retry once it's free.

The --mic / --monitor CLI flags (device numbers, see CLI options above) take priority over mic_device / monitor_device. While a flag is in effect, the corresponding dropdown is disabled on the dashboard.

A device connected after the daemon started won't appear in the dropdown until the list is refreshed ("Refresh list" in the settings panel); refreshing briefly interrupts both capture streams.

Audio level meters

The dashboard header shows a level bar for each capture path (mic/speaker), next to the mute buttons. The bars use the crest factor — peak level divided by RMS — to tell real speech from noise: speech runs 3–10 or higher, while steady electrical noise (a dead built-in mic emitting only hum, say) sits at 1–2.

A bar highlights yellow after 10 seconds of loud-but-flat audio (steady noise, no speech dynamics) — this applies to both mic and monitor. The mic bar additionally turns red after 30 seconds of exact silence: a live microphone always has some noise floor, so true zero means nothing is reaching it at all (e.g. a powered-off headset whose dongle still shows up as a device). The monitor skips this check — a sink monitor reads back exactly zero whenever nothing is playing, which is its normal idle state, not a fault. Both bars also get a fallback outline when the configured device for that path is unavailable and the automatic one is in use, matching the fallback described above.

File structure

shadow-clerk/                          # Repository
  pyproject.toml                       # Project definition & dependencies
  src/shadow_clerk/                    # Main package
    __init__.py                        # Data directory configuration
    clerk_daemon.py                    # Recording, VAD, transcription & dashboard
    llm_client.py                      # External API translation & summary
    i18n.py                            # Internationalization (ja/en)
    clerk_util.py                      # Data directory operations & process management

~/.local/share/shadow-clerk/           # Runtime data
  transcript-YYYYMMDD.txt              # Transcription output (date-based)
  transcript-YYYYMMDDHHMM.txt          # Meeting session transcript
  transcript-YYYYMMDDHHMM@Title.txt    # Meeting session transcript (with event title)
  transcript-YYYYMMDD-<lang>.txt       # Translation output
  summary-YYYYMMDD.md                  # Meeting minutes (corresponds to transcript)
  summary-YYYYMMDDHHMM@Title.md        # Meeting minutes (named session)
  glossary.txt                         # Glossary (TSV: translation terms & reading-based text replacement)
  config.yaml                          # Configuration file
  gcal_token.json                      # Google Calendar OAuth token (created by gcal-auth)

Troubleshooting

Device not found

# List available devices
clerk-daemon --list-devices

# PipeWire: check status
wpctl status

# PulseAudio: list sources
pactl list short sources

Monitor source (system audio) not detected

On PipeWire, check sink (output) devices with wpctl status. On PulseAudio, look for sources containing .monitor with pactl list short sources.

You can also specify the device number manually:

clerk-daemon --monitor 5

PortAudio error

Make sure libportaudio2 is installed:

dpkg -l | grep portaudio

If you see PortAudioError: Error initializing PortAudio: ... PulseAudio_Initialize: Can't connect to server, the PulseAudio-compatible service may have crashed. On PipeWire systems, restart pipewire-pulse:

systemctl --user restart pipewire-pulse

Slow transcription

Use a lighter model with --model tiny:

clerk-daemon --model tiny

Japanese ASR models

The japanese_asr_model setting selects the ASR backend used when language=ja. When the language changes to something other than ja, it automatically reverts to standard Whisper.

Value	Model	Requires	Japanese accuracy	CPU speed
default	Standard Whisper	—	Depends on model size	Depends on model size
kotoba-whisper	Kotoba-Whisper	Auto-downloaded on first use	High (rivals large-v3)	~medium
reazonspeech-k2	ReazonSpeech k2	uv sync --extra reazonspeech	High	Fast

Kotoba-Whisper retains the full large-v3 encoder (32 layers) while distilling the decoder down to just 2 layers. Since it has only 2 decoder layers, beam=5 has almost no speed penalty.

ReazonSpeech k2 uses sherpa-onnx for inference. When selected, Whisper-specific settings (default_model, whisper_beam_size, whisper_compute_type, initial_prompt) are not used.

Selection guide:

Use case	Settings
Japanese-focused, accuracy priority	japanese_asr_model: kotoba-whisper, whisper_beam_size: 5
Japanese-focused, fast & accurate	japanese_asr_model: reazonspeech-k2
Japanese-focused, speed priority (CPU)	japanese_asr_model: default, default_model: small, whisper_beam_size: 3
Multilingual	japanese_asr_model: kotoba-whisper, default_model: small (Kotoba for ja, small for others)

Interim transcription:

interim_japanese_asr_model controls which Japanese ASR model is used for interim transcription (real-time display while speaking). On CPU, keeping the default (default with a lightweight model like tiny/base) is recommended.

# Japanese accuracy priority (GPU recommended)
japanese_asr_model: kotoba-whisper
interim_japanese_asr_model: kotoba-whisper
whisper_beam_size: 5

# Japanese accuracy + fast interim (CPU recommended)
japanese_asr_model: kotoba-whisper
interim_japanese_asr_model: default
interim_model: base
whisper_beam_size: 5        # Kotoba has only 2 decoder layers, beam=5 is fine

# ReazonSpeech (fast & accurate, CPU friendly)
japanese_asr_model: reazonspeech-k2
interim_japanese_asr_model: default
interim_model: base

# Maximum speed (CPU)
japanese_asr_model: default
default_model: small
interim_model: base
whisper_beam_size: 1

Interim translation:

When interim_transcription is on, the daemon also emits a translation of each pre-confirmed line to the dashboard's interim panel. Two knobs control this:

• interim_translation: true — toggle the translation panel without disabling interim ASR.
• interim_translation_provider: null | "api" | "libretranslate" | "claude" — pick the backend explicitly. null falls back to translation_provider; if that is claude it is auto-routed to api then libretranslate (claude is too slow for interim, ~5-10s per call). Set to claude only if you accept the latency.

The interim panel needs sub-second responses to be useful, so libretranslate (local) is recommended; api is OK with a fast model. Confirmed-transcript translation is unaffected — it always uses translation_provider.