Shadow-clerk

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

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 -e ".[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" -e ".[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.

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

git clone https://gitlab.edocode.co.jp/common/shadow-clerk.git
cd shadow-clerk

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

2a. For development

	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`

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:

uv tool install -e ".[reazonspeech]" \
  --with "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr"
# or for development:
uv sync --extra reazonspeech

# 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 -e ".[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.

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

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
`--monitor`	Monitor device number (sounddevice)	Auto-detect
`--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.

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
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)
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
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.

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

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.

Name		Name	Last commit message	Last commit date
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docs		docs
improvement		improvement
src/shadow_clerk		src/shadow_clerk
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
Makefile		Makefile
README.ja.md		README.ja.md
README.md		README.md
SPEC.md		SPEC.md
THIRD_PARTY_LICENSES.md		THIRD_PARTY_LICENSES.md
pyproject.toml		pyproject.toml

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Shadow-clerk

Platform support

Windows-specific notes

Features and requirements

Setup

1. System packages

2. Install

2a. For development

Optional: Japanese ASR models

Optional: Spell check (pre-translation correction)

Optional: Google Calendar integration

3. (Optional) LibreTranslate setup

4. (Optional) OpenAI compatible API setup

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

Usage

Starting the daemon

Recording & transcription

Voice commands

Push-to-Talk (recommended)

Prefix mode (fallback)

Custom voice commands

LLM fallback

CLI options

Translation & Summary Providers

Claude mode (translation_provider: claude / llm_provider: claude)

API mode (translation_provider: api / llm_provider: api)

LibreTranslate mode (translation_provider: libretranslate)

Recommended configurations

Meeting minutes

Configuration file

Summary source selection

Summary language

File structure

Troubleshooting

Device not found

Monitor source (system audio) not detected

PortAudio error

Slow transcription

Japanese ASR models

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Languages

Claude mode (`translation_provider: claude` / `llm_provider: claude`)

API mode (`translation_provider: api` / `llm_provider: api`)

LibreTranslate mode (`translation_provider: libretranslate`)

Packages