A tool that records web meeting audio (your microphone and the speaker output) in real time and transcribes it, with live translation and meeting minutes. On top of that it can:
- run an AI assistant (Claude Code or Codex) beside the meeting in the AI Console, which points out open questions as the meeting goes and can write the minutes;
- let you talk a topic through with Claude by voice (VOICEVOX speech), and on Linux with PipeWire send Claude's voice into a meeting app;
- start and end meeting sessions from Google Calendar;
- put browser screenshots on the transcript timeline with a Chrome extension.
It runs on Linux and Windows. Transcription and LibreTranslate translation work fully offline; the rest is optional.
Available on PyPI: https://pypi.org/project/shadow-clerk/
- Audio is processed locally. Transcription runs on-device using Whisper or another local ASR model. Raw audio exists only in memory during processing and is never sent to any external server or written to disk.
- Transcripts are stored locally. Text files are saved to your machine only (
~/.local/share/shadow-clerk/). They are not uploaded anywhere automatically. - LLM calls send transcript text externally. When you use an LLM (Claude, OpenAI, etc.) for translation or meeting minutes, the transcript text is sent to that provider's API. Anthropic and OpenAI do not use API requests for model training by default, but check your plan's data policy. Claude Code Team or Enterprise plans provide contractual guarantees of no data retention.
- Consider consent from meeting participants. Recording a conversation without the knowledge of others may raise legal or ethical concerns depending on your jurisdiction and context. It is generally advisable to inform participants that the conversation is being recorded and to obtain their consent where appropriate.
- Platform support
- Features and requirements
- Setup
- Usage
- Configuration
- File structure
- Troubleshooting
- Building standalone binaries
| 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 |
A fresh Windows install has neither uv nor git. Run every command below in PowerShell:
- Open PowerShell: press the Windows key, type
powershell, and choose Windows PowerShell (or right-click the Start button → Terminal). No administrator rights are needed. - Install uv (pick one):
winget install --id=astral-sh.uv -e # or the official installer: powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
- Install Git (needed for the ReazonSpeech k2 package, which is fetched from a Git repository):
winget install --id Git.Git -e
- Close and reopen PowerShell so the new
PATHtakes effect, then check withuv --versionandgit --version.
If winget is not found, install App Installer from the Microsoft Store, or download uv from https://docs.astral.sh/uv/getting-started/installation/ and Git from https://git-scm.com/download/win.
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]"
# Add the tool directory (%USERPROFILE%\.local\bin) to PATH, then reopen PowerShell:
uv tool update-shellWhy explicit --python and --with:
--python 3.13(uv-managed Python): Microsoft Store Python runs in an AppContainer sandbox that redirects%APPDATA%\shadow-clerkto%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 inpyproject.tomlbut 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 (withonnxruntime.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-clerkpaths in the rest of this README map to that on Windows). Override withSHADOW_CLERK_DATA_DIRif 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 defaultf23is a Linux/xremap convention. On Windows set it tonull(disable PTT) or to one ofmenu/ctrl_r/ctrl_l/alt_r/alt_l/shift_r/shift_linconfig.yaml.- Stopping the daemon:
clerk-util stopworks (Windows path usestaskkill).clerk-util startruns the daemon in the foreground with Ctrl+C handling, mirroring Linux.clerk-daemon --daemondetaches instead: there is nofork(), so it relaunches itself withDETACHED_PROCESSand 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.
- Talk with Claude: sending Claude's voice into a meeting app needs PipeWire, so that option is disabled on Windows.
- Standalone binary:
clerk-daemon.exeandclerk-util.execan be built with PyInstaller. See Building standalone binaries.
To start and stop the daemon without typing commands, create shortcuts once. Run this in PowerShell after installing:
$exe = Join-Path (uv tool dir --bin) "clerk-util.exe"
$shell = New-Object -ComObject WScript.Shell
foreach ($s in @(@("Shadow Clerk", "start -d"), @("Shadow Clerk (stop)", "stop"))) {
$lnk = $shell.CreateShortcut("$([Environment]::GetFolderPath('Desktop'))\$($s[0]).lnk")
$lnk.TargetPath = $exe
$lnk.Arguments = $s[1]
$lnk.WindowStyle = 7 # minimized
$lnk.Save()
}Shadow Clerk starts the daemon in the background; open the dashboard at http://localhost:8765. Shadow Clerk (stop) stops it.
To create one by hand instead: right-click the desktop → New → Shortcut, enter %USERPROFILE%\.local\bin\clerk-util.exe start -d (uv tool dir --bin prints the folder if it differs), and name it.
To start the daemon when you sign in, copy the Shadow Clerk shortcut into the Startup folder (press Win+R and enter shell:startup to open it).
| 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 |
| Transcription (Moonshine Voice) | uv sync --extra moonshine |
4 | 5 | asr_engine: moonshine |
| 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 CLI (claude -p) |
5 | 2 | translation_provider: claude |
| Language detection (pre-translation) | langdetect (included) | — | — | Automatically detects source language to select correct prompt |
| Summary (Claude) | Claude Code CLI (claude -p) |
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 | — | — | llm_provider: api, api_endpoint, api_model |
| Spell check (pre-translation) | transformers (auto-downloaded on first use) | — | — | libretranslate_spell_check: true |
| AI Console (meeting assistant) | Claude Code or Codex CLI, skill from clerk-util install-skill |
— | — | auto_analyze, ai_assistant_command |
| Talk with Claude | Claude Code CLI, a running VOICEVOX engine | — | — | talk_* |
| Send Claude's voice into a meeting app | Linux with PipeWire (pw-dump, pw-link, pw-cat) |
— | — | talk_route_app |
Speaker diarization ([相手A], [相手B]… on the remote side, naming, voiceprints, names during the meeting) |
diarization extra |
— | — | speaker_diarization, speaker_enrollment |
| Google Calendar (auto start/end, expected attendees) | gcal extra, OAuth credentials |
— | — | gcal_integration, gcal_credentials_file |
Pronunciation feedback (phoneme-level, English, for clerk-practice) |
pronounce extra |
— | — | pronounce_enabled |
| Browser screenshots in the transcript | Chrome extension in extension/ |
— | — | — |
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.
sudo apt install libportaudio2 portaudio19-devOn Windows no system package is needed — the sounddevice wheel ships PortAudio. Use the install command in Windows-specific notes instead of step 2.
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" |
| + Moonshine Voice | uv tool install "shadow-clerk[moonshine]" |
| + Google Calendar | uv tool install "shadow-clerk[gcal]" |
| + Speaker diarization | uv tool install "shadow-clerk[diarization]" |
| + Pronunciation feedback | uv tool install "shadow-clerk[pronounce]" |
| All | uv tool install "shadow-clerk[spell-check,gcal,reazonspeech,moonshine,diarization,pronounce]" --with "reazonspeech-k2-asr @ git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/k2-asr" |
Note:
uv tool installmaintains a single environment per tool. When reinstalling with different extras, use--force— without it,uv tool installreports "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-asris--with'd separately: Thereazonspeech-k2-asrpackage is only distributed via Git (not on PyPI), and PyPI rejects direct URL references in package metadata, so it cannot be declared inside thereazonspeechextra. Pass it via--withwhenever you need ReazonSpeech k2.
Alternative: pipx install shadow-clerk (or pip install shadow-clerk inside a venv) also works.
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 |
| + Moonshine Voice | uv sync --extra moonshine |
| + Google Calendar | uv sync --extra gcal |
| + Speaker diarization | uv sync --extra diarization |
| + Pronunciation feedback | uv sync --extra pronounce |
| All | uv sync --extra spell-check --extra gcal --extra reazonspeech --extra moonshine --extra diarization --extra pronounce |
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:
Kotoba-Whisper — No extra install required. The model is auto-downloaded on first use. Just set:
# config.yaml
japanese_asr_model: kotoba-whisperReazonSpeech 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-k2Requires 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 # defaultThe spell check model is auto-downloaded on first use. It corrects Japanese speech recognition typos before sending text to LibreTranslate.
Tells the remote participants apart in the transcript. Requires the diarization extra (installs onnxruntime and kaldi-native-fbank):
uv tool install "shadow-clerk[diarization]"
# or for development:
uv sync --extra diarization# config.yaml (or the dashboard settings modal, Transcription section)
speaker_diarization: true # default false
diarization_threshold: 0.70 # cosine similarity (default 0.70)
diarization_min_sec: 1.0 # shortest utterance to identify, in seconds (default 1.0)The wespeaker ResNet34 ONNX model is downloaded on first use into wespeaker_cache/ in the data directory.
- Utterances from the remote (monitor) side get
[相手A],[相手B]… labels.[相手?]means the utterance was too short (underdiarization_min_sec) or could not be identified. Your own microphone is not labelled. diarization_threshold: lower values avoid splitting one person into several labels but merge different people more often. Real meetings often need about 0.50–0.65.- Labels are unique per transcript file: when the output file changes, the in-meeting clusters are discarded and numbering continues from the file's highest existing label.
# config.yaml (or the dashboard settings modal)
speaker_enrollment: true # default false (requires speaker_diarization: true)
voiceprint_max_samples: 200 # per person (default 200)
voiceprint_min_sec: 3.0 # shorter utterances are never added to voiceprints (default 3.0)
voiceprint_cluster_min_samples: 10 # people with fewer samples are not split into clusters (default 10)
voiceprint_cluster_distance: 0.6 # average-linkage cosine distance for merging clusters (default 0.6)
voiceprint_main_similarity: 0.5 # a cluster this similar to the main one counts as the usual voice (default 0.5)
voiceprint_outlier_max: 2 # largest cluster that can be excluded automatically (default 2)
voiceprint_outlier_ratio: 0.03 # ... and its largest share of the person's samples (default 0.03)- With
speaker_enrollment: true, the diarization embeddings are saved per transcript intranscript-*.voices.jsonl. - After the meeting, the 👥 button in the dashboard lets you name labels such as
相手A(for example太郎). Several labels may get the same name. Expected attendees and already registered people are offered as candidates, and you can also type any name. - Naming rewrites the labels in the transcript and its translations. If
speaker_enrollmentis enabled, that voice is also stored as a voiceprint invoiceprints.json(up tovoiceprint_max_samplesper person). - Your own voice: set
self_name("your name" in the settings modal), turn on diarization and enrollment, then press "Record my voice" under registered people and talk into your mic for one minute. Stretches of at leastvoiceprint_min_secbecome your baseline. When you join from a meeting room through someone else's mic, your voice arriving on the speaker side is then named too.- Once the baseline has 3 samples or more (voiceprints taken by naming or imported count too), a voiceprint is also taken from your mic about once a minute. Your mic also picks up other people (in the room, or when your PC is the room's mic), so a mic utterance is kept only when it scores at least
voiceprint_highagainst your baseline and beats everyone else byvoiceprint_margin. Without a baseline nothing is taken automatically. - The latest 30 of each kind are kept, apart from voiceprints taken by naming. In
voiceprints.json, baseline samples are marked"origin": "enroll"and automatic ones"origin": "mic".
- Once the baseline has 3 samples or more (voiceprints taken by naming or imported count too), a voiceprint is also taken from your mic about once a minute. Your mic also picks up other people (in the room, or when your PC is the room's mic), so a mic utterance is kept only when it scores at least
- Every assigned name is remembered (with its group, person flag and group color, but no voice data, regardless of
speaker_enrollment) inspeaker_names.jsonand offered as a suggestion in every meeting. A row that already has a name shows it as a placeholder; leave the field empty to keep it. Deleting a person from the dashboard removes their name too. - A person's group (a company or organization) is an attribute of the person, set once in the "Registered people" list at the bottom of the naming modal (everyone in the name book plus everyone with voiceprints; edit the group field there). Untick "Person (enroll voiceprint)" in the naming or change-speaker modal to treat a label as a company or role name (for example
acme,先方営業): only then does a group field appear there, and the label gets a group and a color but never enrolls a voiceprint. Group and person status change only when you edit them; otherwise the stored values are kept. - Select transcript lines and press 👤 ("change speaker") to reassign them to another speaker (same name, group and person options). Shift+click a checkbox to select every line from the previously clicked one. The modal asks what to change and shows how many lines that is: the selected lines only (default), everything between the two lines (when exactly two are selected; every remote-side line in that time range, whoever the speaker, plus the
[自分]lines when one of the two is a[自分]line), or all lines of this speaker (the labels of the selected lines across the whole meeting, the same as naming them with 👥: recorded in the history and undoable; selected[相手?]/[相手]/[自分]lines, which cannot be named as a label, are reassigned one by one).[自分]lines can be reassigned too, for when other people talk into your mic (in the same room, or when your PC is the room's mic); no voiceprint is taken from them, since mic lines keep no embedding.[Claude]lines cannot be reassigned. - Speakers are colored by group in the transcript. Set
self_group("your own group" in the settings modal) and[自分]takes that group's color; each group gets an automatic, persistent color. - Minutes (both the LLM method and the AI assistant method) receive a "speakers and groups" block (added only when at least one group or
self_groupexists). With two or more groups they are asked to organize requests, agreements and action items by group. Theclerk-meeting-helperskill (1.1.1) reads it fromGET /api/speaker-roster. - After saving names or groups, if the meeting has minutes the dashboard asks whether to regenerate them. If the meeting has AI-assistant output (advice or analysis files), the assistant rebuilds them (started first if needed, with an LLM fallback); otherwise the LLM does. While a different meeting is in progress the LLM is always used, so the live assistant is not interrupted.
- Already-named speakers stay in the list and can be renamed (for a typo, say); their voiceprints from that meeting move to the new name.
- Utterances shorter than
voiceprint_min_secstill get the name in the transcript, but are not added to the voiceprint (short replies such as "yes" make unreliable voiceprints).[相手?]lines (too short to identify) can be named with 👤 the same way; they never had an embedding. The utterance length is stored asdurin*.voices.jsonl; older rows without it are treated as long enough. - Undoing a naming: each label naming is recorded in the meeting's
transcript-*.naming.jsonl, listed newest first under "Naming history" in the naming modal (when,label → name, lines and voiceprints). "Undo" puts the original label back on the lines that still carry that name (lines moved to someone else later are left alone), removes the voiceprints that naming added and gives back the voiceprints it took from a previously named person (unless that person was deleted, or the voiceprint was deleted with "Delete" in the clusters view). Voiceprints that naming added but that are already gone (dropped byvoiceprint_max_samples, deleted) or moved to someone else are left alone; the result says how many. The history is kept only for namings made with this version; it follows the meeting when it is renamed or deleted. - Reviewing a person's voiceprints: "Details" next to a registered person splits their voiceprints into clusters (people with fewer than
voiceprint_cluster_min_samplessamples are shown as one cluster). Each cluster shows its kind, number of samples, the meetings (and original labels when the history knows them) and an example utterance:- Usual voice — the main cluster. No action.
- Different voice — far from the main cluster: another person mixed in, or the same person on a different line or device. It is still used; you decide. "Delete" removes those samples from the voiceprint store only (the transcript keeps the name; the sidecar rows get
noenrollso they are not enrolled again). "Move to…" moves them to another person (with name suggestions) and renames the matching transcript lines that still carry the old name; meetings being recorded or translated are not rewritten and are counted in the result. - Excluded (laughter etc.) — a tiny, distant cluster (at most
voiceprint_outlier_maxsamples andvoiceprint_outlier_ratioof the person's total) is excluded from matching automatically. "Restore" brings it back, and it is never excluded automatically again. The exclusion is stored per sample asstatusinvoiceprints.jsonand is recomputed whenever samples are added to or removed from that person (naming, undo, delete, move). A restored sample is shown under the usual voice.
- Naming and line reassignment are refused for a file being translated, and when the name book or voiceprint store is unreadable. The file currently being recorded is never rewritten (that would break the live AI-assistant stream's read offsets):
- During a meeting, 👥 and 👤 save the names provisionally in
transcript-*.provisional.json. The transcript, translations and voiceprints are left alone; the dashboard only shows the provisional names (dotted underline). Reopening the modal shows them for editing; clearing a name (👥) or saving 👤 with an empty name cancels them. When the meeting ends — by the button, the voice command, the calendar or a skill — they are applied with the same processing as a normal naming (line reassignments first, then labels), before the automatic minutes. Whatever cannot be applied stays in the file and is logged. - The daily file cannot be named while it is recorded; wait until the date changes or extract it as a meeting with ⏱ first.
- During a meeting, 👥 and 👤 save the names provisionally in
- A file whose translation is running (one-shot or the continuous loop) cannot be named until it finishes; provisional names wait for it (up to 2 minutes) when the meeting ends.
- Merging lines into an existing meeting can mix two different speakers under the same label; name such a file carefully (or extract into a new meeting instead).
# config.yaml (or the dashboard settings modal)
voiceprint_high: 0.60 # score for a confident name (default 0.60)
voiceprint_low: 0.50 # score for a tentative "name?" (default 0.50)
voiceprint_margin: 0.05 # a confident name must beat the runner-up by this much (default 0.05)
voiceprint_non_attendee_penalty: 0.05 # added to both scores for people not in the meeting's attendees (default 0.05)
voiceprint_min_utterances: 2 # a label needs this many utterances before any name is shown (default 2)- With both
speaker_diarizationandspeaker_enrollmenton, each remote-side label (相手A…) is matched against the registered voiceprints while recording (samples excluded as outliers are not used). The mic side is never matched; it stays[自分]. - The result has three levels, and a wrong name is avoided rather than a right one maximized:
- Confident — score ≥
voiceprint_highand ahead of the runner-up byvoiceprint_margin: the line is written as[alice]. - Tentative — score ≥
voiceprint_low:[alice?]. - None — the meeting label stays,
[相手A]. People not in the meeting's attendee list (or every candidate, when the meeting has none) needvoiceprint_non_attendee_penaltymore on both scores. No name is shown until the label hasvoiceprint_min_utterancesutterances.
- Confident — score ≥
- The match is redone each time the label gets a new utterance and applies to the following lines; lines already written are not rewritten. If the voiceprint store cannot be read, no name is shown and recording goes on.
- In the transcript,
[alice?]takesalice's group color. Minutes andGET /api/speaker-rostertreatalice?as unconfirmed (tentative: true, no group); the LLM is told not to state it is that person. - Identified names never enter the voiceprints by themselves. They are added only when you confirm them in the naming modal after the meeting:
- An
alice?label is listed as "Unconfirmed", withaliceshown greyed out in the name field and an unticked "Confirm as alice" box. The row is saved only when you tick the box or type a name; saving other rows leaves it alone. Then it is named like any other label (alice?→alice), its voiceprints are added and it is recorded in the history, so it can be undone. - A confident
[alice]shares the label ofalicenamed by hand. When some of its lines were identified during the meeting, the row shows "Confirm N identified lines and add to voiceprints". Tick it and save to add those lines toalice's voiceprints. The transcript is not changed; the history entry has no lines and only the added voiceprints, so undo removes just those and marks the lines as identified-but-unconfirmed again. Saving only the group or person flag of such a label does not add the identified lines. - Giving the label another name works as usual: all its lines, identified ones included, move to that name and its voiceprints. Undoing it marks the identified lines that went back to the label as unconfirmed again.
- Names cannot end in
?, so a real name is never mistaken for a tentative one.
- An
- In
*.voices.jsonl,labelis the label shown in the transcript (alice,alice?or相手A),clusteris the meeting label it was grouped under, andauto: truemarks a name that came from identification and has not been confirmed yet. Naming or confirming clearsauto; undoing that naming sets it again. After a restart on the same file, numbering also countscluster, so a相手Ashown asaliceis not reused for someone else. Older rows without these fields work as before.
Sharing voiceprints: someone who uses diarization a lot can hand their voiceprints to someone who has few. In "Registered people", check people (or pick a group with "Select by group…") and press "Export" to save one JSON file; the receiver presses "Import", chooses the file, and for each person checks whether to import and edits the name to import as (an existing name adds to that person, a new name creates one). The file holds the voiceprints used for matching (excluded samples are left out), each person's group and color, and the embedding model; it holds no meeting names. Imported samples are marked "origin": "import" / "source": "import", importing the same file twice adds nothing, and a file made with a different model is refused. People new to the receiver get the file's group and color; existing people keep theirs. The same is available as clerk-util voiceprints-export / voiceprints-import. Get each person's consent before passing their voiceprints on.
Privacy: voiceprints are biometric data. They stay inside the data directory unless you export them, and are never passed to an LLM. Audio is not stored. Deleting a person from the dashboard removes their stored voiceprints and every embedding named after them in the per-meeting *.voices.jsonl files; deleting voiceprints.json and all *.voices.jsonl files erases everything. *.naming.jsonl holds only labels, names and timestamps, no voice data.
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 gcalThen authenticate and configure:
# One-time OAuth setup (opens browser)
clerk-util gcal-auth ~/credentials.json
# Enable in config (gcal-auth already does this when it succeeds)
clerk-util write-config-value gcal_integration true
clerk-util write-config-value gcal_credentials_file ~/credentials.jsonWhen enabled, clerk-daemon polls Google Calendar every 60 seconds. Events automatically trigger start_meeting / end_meeting, creating transcript files named transcript-YYYYMMDDHHMM@EventTitle.txt. The event's invitees are saved as the meeting's expected attendees and shown above its summary, and the 📅 button in the dashboard header lists today's events.
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.
Phoneme-level pronunciation feedback for clerk-practice (English only).
uv sync --extra pronounceDownloads a 1.2GB phoneme recognition model on first use. espeak-ng ships as a
wheel (espeakng-loader), so no system package is needed. Note that
phonemizer-fork installs under the phonemizer import name and conflicts with
the original phonemizer package.
Enable it in Settings → Pronunciation feedback.
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 5000Configuration:
# config.yaml
translation_provider: libretranslate
libretranslate_endpoint: http://localhost:5000Used 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: llama3If 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
# translation_claude_model: haiku # translation only; unset = claude_cli_model
# claude_cli_path: claude # full path if not on $PATHThis 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.
If you installed via uv tool install:
clerk-daemonFor development (uv sync):
uv run clerk-daemonNote:
uv runuses the project.venv, whileuv tool installuses its own isolated environment. Make sure extras (e.g.spell-check,reazonspeech) are installed in the matching environment.
The dashboard is served at http://localhost:8765.
# 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
# In the background (logs go to daemon.log in the data directory)
clerk-daemon -dPress Ctrl+C to stop recording (or clerk-util stop for a background daemon).
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) 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.
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.
The dashboard (http://localhost:8765, --dashboard-port to change) shows the transcript and translation live and is where most features are driven from. It listens on 127.0.0.1 only; pass --dashboard-host 0.0.0.0 to reach it from another machine. Write APIs stay restricted to same-origin requests from localhost either way.
| Area | What it holds |
|---|---|
| Header | Meeting and translation toggles, Summary (generate minutes), Talk with Claude, font size, transcript/translation layout, Glossary, PTT, custom Commands, 📅 today's Google Calendar events (only when the integration is on), ⚙ settings, ❓ help |
| Left pane: Dates / Meetings / Search | Daily transcripts by date; meeting files grouped by name (sort by name or newest, rename a meeting group, ⚙ per-meeting working directory); search by year/month/day/hour and text across transcripts, translations and summaries |
| Transcript / Translation | Live text with mute buttons and level meters. ⏱ turns part of a daily transcript into meetings: split it all (or a selection) at silences of a chosen length, or extract the selected lines. 📂 assigns a meeting file to an existing or new meeting name, 🗑 deletes the file or merges a meeting back into the daily transcript |
| Right pane: Summary / AI Analysis | The minutes (copy, reload, regenerate; expected attendees from the calendar on top). AI Analysis shows the AI Console's Advice and Analysis documents and its Start analysis button |
| Bottom pane: AI Console / Talk with Claude / Logs | The two AI Console terminals (meeting assistant and talk) with the transcript beside them, and the daemon log |
A Welcome dialog appears on first run: it offers to install the skill for an AI agent and points to the settings worth checking first ("Do not show this again" hides it). When the bundled skill is newer than the installed copy, a dialog offers to update it.
Hold down the push-to-talk key while speaking a command — no wake word needed:
[Hold PTT key] "start translation" → Translation starts
[Hold PTT key] "start meeting" → Meeting session starts
The key is voice_command_key in config.yaml. The default f23 suits a keyboard remapper (e.g. xremap mapping the Menu key to F23); other values are menu (the Menu key next to Right Alt), ctrl_r, ctrl_l, alt_r, alt_l, shift_r, shift_l. Set to null to disable. On Windows, see Windows-specific notes.
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.
You can register custom voice commands in config.yaml under custom_commands (or with the Commands button on the dashboard). 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. This pattern fires when you say it while
holding the PTT key, with or without a leading クラーク:
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/startIf a voice command doesn't match any built-in or custom command and an LLM is configured (api_endpoint, or llm_provider: claude), 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
clerk-daemon 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, wasapi) |
auto |
--list-devices |
List devices and exit | - |
--verbose, -v |
Verbose logging | - |
--dashboard / --no-dashboard |
Enable/disable dashboard | Enabled |
--dashboard-port |
Dashboard port number (falls back to dashboard_port in config.yaml, which clerk-util also reads) |
8765 |
--dashboard-host |
Dashboard bind address. Only loopback by default; pass 0.0.0.0 to expose it on the LAN (write APIs still accept localhost only) |
127.0.0.1 |
--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 |
--daemon, -d |
Run in the background; logs go to daemon.log in the data directory |
- |
clerk-util manages the daemon and the data directory. Run clerk-util help for the same list.
| Subcommand | Description |
|---|---|
start [opts] |
Run clerk-daemon with these options, in the foreground (add -d to run it in the background) |
stop |
Stop clerk-daemon (SIGTERM on Linux, taskkill on Windows) |
restart [opts] |
Stop clerk-daemon, wait for it to exit, then start it with these options |
recorder-status |
Print running or stopped |
gui [opts] |
Open a small window showing the daemon's state and the tail of daemon.log, with start / stop / restart buttons. clerk-util --gui works too |
command <cmd> |
Send a command to the running daemon: start_meeting, end_meeting, translate_start, translate_stop, ... |
summarize [DATE|FILE] [--mode full|update] |
Generate minutes (full by default). DATE is YYYYMMDD or YYYYMMDDHHMM[@name], FILE a transcript-*.txt; without either, the current meeting or today |
ls |
List the data directory (and the output directory if it differs) |
read-config |
Print config.yaml (writes one with the defaults if missing) |
write-config-value <key> <value> |
Change one key in config.yaml |
gcal-auth <credentials.json> [token_file] |
Google Calendar OAuth; on success it also enables gcal_integration |
voiceprints-export <out.json> [name ...] |
Export voiceprints to pass to someone else (everyone with voiceprints when no name is given) |
voiceprints-import <file.json> [name[=to] ...] |
Import exported voiceprints, optionally only some people or under another name |
install-skill [--target claude|agents|<path>] [--link] [--force] |
Install the bundled skills for an AI agent (see AI Console) |
run-llm <args...> |
Run the LLM client directly (translate, query, match-command, summarize, spell-check) |
help |
Show usage |
Translation and summary each support multiple providers with different operation modes:
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
claudeon PATH — found automatically if Claude Code is installed - No Claude Code session required — the daemon spawns
claude -pper 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 jsonreturnstotal_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
translation_claude_model: null # Model for translation only (null = claude_cli_model)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 summarizegenerates 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-4oTranslation only. Runs locally without any external API or Claude Code (summary still needs llm_provider).
| 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 |
Talk mode lets you discuss a topic with Claude by voice. Claude asks questions and replies
through text-to-speech; your answers are transcribed as usual. Both sides are written to the
transcript ([Claude] lines are Claude's), so you can read the discussion back later.
Requirements:
-
Claude Code CLI (
claude_cli_path) -
A running VOICEVOX engine (default
http://localhost:50021). The engine runs as a separate process and is not bundled. To start it, either:- Download and launch the VOICEVOX app from the official site (GUI + engine).
- Or download the engine-only package (no GUI) from the GitHub releases and run
./run(Linux) /run.exe(Windows). - Or run via Docker:
docker run --rm -p 50021:50021 voicevox/voicevox_engine:cpu-ubuntu20.04-latest
Follow the terms of the voice you use; the dashboard shows the required credit (
VOICEVOX:<name>) while talk mode is on. When talk mode starts, words VOICEVOX is known to misread (bundledvoicevox_dict.tsv) are added to the engine's user dictionary, unless the same word is already registered there. -
Headphones. While talk mode is on, the monitor channel is not transcribed, so Claude's own voice does not come back as
[Others]lines.
Click Talk with Claude in the dashboard header, enter a topic (or leave it empty), pick a
persona, and start. Voice settings in the same dialog picks the VOICEVOX speaker and adjusts speed, pitch, intonation and volume, with a preview button; changes apply from the next sentence. Unticking Use the glossary turns off the glossary replacements in the transcript for that conversation, and Claude does not read the glossary either (for conversations that do not need it, such as language practice; saved with presets). By default Claude runs in a second AI Console (Talk with Claude tab) with the clerk-talk skill,
so it can edit files and run commands with the usual permission prompts, and the meeting helper keeps
running in the AI Console tab. Set talk_engine: headless to use a background claude -p process
instead: it answers a little faster but cannot ask for permission, so it only gets the tools in
talk_allowed_tools. Install the skills with clerk-util install-skill (all of them are installed together).
The clerk-talk skill pre-approves only its own curl calls to http://localhost (and Monitor and Agent); if
Claude Code still asks, allow them once, or add the same rules to your Claude Code settings.
Screen captures taken with the browser extension during talk mode are looked at by a background subagent, so Claude can follow "this box here".
Sending Claude's voice to a meeting (Linux, PipeWire). In the start dialog, pick the meeting app under
Send Claude's voice to. shadow-clerk plays the speech through a named PipeWire stream and links it into that
app's microphone input with pw-link, so the other participants hear Claude mixed with your microphone, and you
still hear it in your headset. If the app reopens its microphone, the link is restored automatically. Muting
yourself in the meeting app mutes Claude too, because the voice goes into the same input. Needs pw-dump,
pw-link and pw-cat; on other platforms the option is disabled. With a route set, speech plays to PipeWire's
default output and talk_output_devices is not used.
While a meeting app is chosen, the other participants are transcribed as [Others] again, so Claude can follow
them. Claude's own voice also reaches your headset and therefore the monitor channel; monitor lines that overlap
Claude's speech in time (or start within talk_echo_tail_sec after it) are dropped, whatever they say, because short
replies and fillers can't be told apart from Claude's voice by text. If someone talks over Claude, their words in
that overlap are dropped too. Without a meeting app chosen, the monitor stays muted during talk mode as before.
Interim (live) monitor text is not shown while Claude is speaking.
With a meeting app chosen, Claude also waits for the floor: while the interim (live) transcription of the monitor
has text, someone else is talking, so a new reply waits until they finish (at most talk_floor_wait_sec) before it is
spoken. This is much earlier than waiting for the finished transcript line. If anyone's line is transcribed while it
waits, the reply is not spoken, because the topic may have moved on: /api/say answers
{"status": "held", "heard": [...lines...]} and Claude rethinks it (the headless engine drops the rest of that turn and
gets the lines with a note). Interim transcription runs for this even
when interim_transcription is off (it is then not shown on the dashboard). GET /api/speaking returns the flag
({"speaking": true, "sources": ["monitor"]}); the talk skill also uses it before raising a point on its own.
Your own voice counts too: while the microphone's voice detection hears you speaking (muted mic and push-to-talk excluded), a reply waits for you to finish, even without a meeting app chosen. Use earphones, since Claude's own voice leaking into the microphone would count as you speaking.
You can also make Claude speak any text:
curl -s -X POST localhost:8765/api/say -d '{"text":"Hello"}'Claude never stops listening on its own: when the conversation seems to be over, it asks first, and only on a
clear yes does it end talk mode with POST /api/talk-end. That call lets the sentence being spoken finish first.
If you want to speak another language (for example to practise English while the recognition language is ja,
which would turn English into katakana), just tell Claude: it switches the recognition language with
POST /api/language ({"language": "en"}, or "auto") and switches back before the conversation ends.
Language practice. In talk mode, say that you want to practise a language ("I want to practise English").
Claude switches to the bundled clerk-practice skill: it starts a practice meeting (英語練習 for English) without
the meeting assistant, reads the end of the last few practice sessions to suggest what to do today, asks how many minutes to
practice (and, a minute before time is up, whether to stop or keep going), and runs
pronunciation, conversation, listening or immersion practice (practice language only; corrections are written to the AI analysis tab instead of spoken). Corrections go to Advice and the practice log with example
sentences to Analysis in the AI analysis tab; lines starting with 🔊 are read aloud when you click them, in the
language tagged after it (🔊[en] …); 🔊[en|sentence] Question 1 shows only "Question 1" and reads the hidden sentence (listening practice). Claude speaks practice-language sentences with the browser's voice (Web Speech API)
instead of VOICEVOX, so keep the dashboard open and click it once (browsers only allow speech after a user action);
without such a tab those sentences fall back to VOICEVOX. The speaker (monitor) is muted while practising, and
Claude ends with a "今日の練習のまとめ: …" line that the next session builds on.
The skill uses these localhost-only endpoints, which you can also call yourself:
| Endpoint | Body / query | Effect |
|---|---|---|
POST /api/meeting |
{"action": "start", "name": "…", "analyze": false} or {"action": "end"} |
Start or end a meeting; analyze: false skips the AI assistant even with auto_analyze |
POST /api/mute |
{"source": "mic" or "monitor", "muted": true} |
Same as the mute buttons; returns previous |
POST /api/generated |
{"kind": "advice" or "analysis", "mode": "replace" or "append", "text": "…"} |
Write the current transcript's advice/analysis (up to 20,000 characters) |
GET /api/meeting-history |
?meeting=…&count=3&tail=15 |
tail (0–50) adds the last lines of each past transcript |
POST /api/say |
{"text": "…", "lang": "en", "display": "…"} |
A lang other than the VOICEVOX language is read by the dashboard's browser voice (ignored when Claude's voice is sent to a meeting app). display, if given, is written to the transcript instead of text (which is what is spoken), e.g. to hide the spelling in pronunciation drills |
GET /api/watch without file follows the current transcript: when the day changes or a meeting starts or ends it
sends <notice>…</notice> and continues with the new file, so Claude keeps hearing you past midnight.
| Key | Default | Description |
|---|---|---|
talk_voicevox_url |
http://localhost:50021 |
VOICEVOX engine |
talk_speaker_id |
3 |
VOICEVOX style ID |
talk_speed / talk_pitch / talk_intonation / talk_volume |
1.0 / 0.0 / 1.0 / 1.0 |
Speed (0.5–2.0), pitch (-0.15–0.15), intonation (0–2), volume (0–2) |
talk_output_devices |
[] |
Output device names (empty = default output). Not used when a route is set; pw-cat plays to the default output |
talk_engine |
console |
console (AI Console + clerk-talk skill) or headless (claude -p) |
talk_route_app |
"" |
Last app chosen under "Send Claude's voice to" (start-dialog default) |
talk_echo_tail_sec |
0.3 |
A monitor line that starts within this many seconds after Claude stops speaking is treated as Claude's own voice (covers output and capture latency). Lines that start later are kept. If the other side starts talking right after Claude with no silence gap, the VAD merges the two into one segment and it is dropped |
talk_workdir |
"" |
Working directory for the talk session (empty = ai_assistant_workdir, then home). The start dialog can override it per session |
talk_model |
"" |
Model for the talk session (empty = claude default); applies to both engines, picked in the start dialog |
talk_allowed_tools |
WebSearch,WebFetch,Read,Grep,Glob |
Tools Claude may use while talking ("" = none) (headless only) |
talk_language |
"" |
Conversation language (empty = translate_language). Falls back to the TTS default when unsupported (VOICEVOX: Japanese only) |
talk_personas |
{} |
Name → personality / how to respond. Edit from the start dialog |
talk_default_persona |
"" |
Persona selected by default |
talk_filler_sec |
8 |
If Claude has said nothing for this many seconds after you spoke, say a short "hmm" (not written to the transcript). At most once while waiting for a reply, and at least 30 s apart. 0 disables it. Also in Settings |
talk_floor_wait_sec |
10 |
With a meeting app chosen, how long a reply waits for the others to stop talking (interim text present) before it is spoken anyway. 0 disables waiting |
talk_stop_words |
["待って", "ストップ", "止めて", "やめて", "stop", "wait", "hold on"] |
Saying one of these stops Claude mid-sentence. Substring match, so avoid short kana that appear inside other words; ASCII words match whole words |
Claude speaks each part of a reply as soon as it is written, in short sentences, so you can cut in between them. Say a stop word ("wait", "ちょっと待って") and playback stops, the rest of that turn is dropped, and Claude is told where it was cut off.
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 -d # Start daemon (background)
clerk-util stop # Stop daemon
clerk-util recorder-status # Show running state
clerk-util gui # Open the status window (start/stop/restart + log)
clerk-util summarize # Generate minutes for the current meeting (or today)
clerk-util summarize --mode update # Update minutes from the transcript diff
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 ("sheruku, start meeting" / "sheruku, end meeting") or dashboard buttons. The dashboard's Summary button can trigger minutes generation at any time.
When auto_summary is on and the AI Console is running at meeting end, the minutes are written by the assistant in the console instead (auto_summary_via_console, on by default); otherwise the configured LLM writes them.
Generated meeting minutes are saved to ~/.local/share/shadow-clerk/summary-YYYYMMDD.md (summary-YYYYMMDDHHMM@Title.md for a meeting). Put a summary_template.md in the data directory to use your own minutes format.
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.
-
Installing the skill: the assistant runs a bundled skill (
clerk-meeting-helper;clerk-talk,clerk-practiceandvoicevox-singfor talk mode are installed with it) that has to be copied into the agent's own skills directory. The Welcome dialog 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. -
Starting: automatically when a meeting starts (
auto_analyze: true), or manually with the Start analysis button (AI Analysis tab) or the console's Launch 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}withtranslate_language, so the skill writes in the language you read. -
Reaching the dashboard: the assistant calls the dashboard's HTTP API at
$SHADOW_CLERK_URL, which the console puts in its environment, so it never has to guess the port. -
Generated documents: shown in the right pane's AI Analysis tab:
advice-<stem>.md— open questions and suggestions (overwritten each time)analysis-<stem>.md— confirmed facts (appended)
Both are Markdown and are rendered to HTML server-side. Raw HTML inside them is escaped, never rendered.
-
Minutes: with
auto_summary: trueandauto_summary_via_console: true(the default), the minutes at meeting end are written by the assistant while it is running. -
Session lifecycle: one PTY session is reused for the whole run; it is not stopped when the meeting ends, so it stays available for writing up minutes afterward. Stop it from the Console tab's stop button.
-
Working directory:
ai_assistant_workdirsets the default launch directory. Per-meeting overrides live inDATA_DIR/meeting.yaml(meetings[].workdir), editable from the gear icon (⚙) on each meeting's row in the meeting list. -
Meeting settings: the ⚙ next to the "Meetings" heading opens a list of meetings. Filter by name, tick meetings, and change whether the AI assistant analyzes them (with
auto_analyze, a meeting set to Off doesn't launch the assistant), their publish flag, working directory, destination (Artifact or a Notion URL) and whether the AI analysis Advice is published, all at once. The ⚙ on a row edits that one meeting from its current values. With Advice published, clerk-meeting-helper keeps the published copy up to date during the meeting so everyone can follow it. Values go tomeeting.yaml; a meeting that matched a rule shared with other meetings gets its own^name$rule, copied from that rule and placed just before it. The first time shadow-clerk writes to aconfig.yamlit didn't create itself, it saves a one-time<path>.bakcopy 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. -
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 withpgrep -af claude(orcodex). -
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/eventsSSE 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/inputetc.) itself stays localhost-only and additionally checks theOriginheader to reject cross-origin requests from your own browser.
A small Chrome extension in extension/ captures the visible tab — a shared screen in a browser meeting, say — saves the image in the data directory, and adds a [画面] line to the transcript being recorded, so the image sits on the same timeline as the speech. Load it unpacked from chrome://extensions; see extension/README.md for installation, settings and what gets written.
Customize defaults and auto-features in ~/.local/share/shadow-clerk/config.yaml. The main keys, with their defaults:
# --- Meeting automation ---
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_summary_via_console: true # With auto_summary: let the AI Console write the minutes when it is running
auto_analyze: false # Launch the AI assistant and run the meeting skill when a meeting starts
# --- AI Console ---
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 meetings[].workdir of DATA_DIR/meeting.yaml
# --- Transcription ---
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)
initial_prompt: null # Whisper initial_prompt (vocabulary hints for recognition)
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)
whisper_vad_filter: true # Silero VAD: drop non-speech inside each segment
whisper_vad_threshold: 0.35 # Silero VAD speech threshold (0.2 / 0.35 / 0.5)
interim_transcription: false # Interim transcription (real-time display while speaking)
interim_model: base # Model for interim transcription
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
english_asr_model: default # English ASR (default/moonshine/a faster-whisper model name such as medium.en)
asr_engine: whisper # ASR engine for all languages (whisper/moonshine)
interim_asr_engine: whisper # ASR engine for interim transcription
reazonspeech_model: ja # ReazonSpeech k2: ja / ja-en (Japanese-English bilingual)
reazonspeech_precision: fp32 # ReazonSpeech k2: fp32 / int8 / int8-fp32 (fp16 is invalid)
# --- Audio devices ---
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, translation and summary ---
llm_provider: claude # LLM for summary ("claude" or "api")
translation_provider: null # Translation provider (null=use llm_provider, "claude", "api", "libretranslate")
claude_cli_path: claude # claude command (full path if not on PATH)
claude_cli_model: haiku # Model for claude -p (haiku / sonnet / opus / full model id)
translation_claude_model: null # claude -p model for translation only (null = claude_cli_model)
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.
interim_translation: true # Translate interim transcription to dashboard's interim panel
interim_translation_provider: null # null=auto, "api", "libretranslate", or "claude"
translation_hiragana_step: true # Have the LLM reread Japanese as kana before translating, to catch misrecognized homophones
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
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, ...)
summary_length: half # Minimum length of the minutes (half / 1page / 2pages ... 5pages, in A4 pages)
summary_hiragana_step: true # Same kana rereading step before summarizing
# --- Voice commands ---
voice_command_key: f23 # Push-to-Talk key (null=disabled)
wake_word: シェルク # Wake word (trigger word for voice commands)
custom_commands: [] # Custom voice commands (list of pattern + action)
# --- Google Calendar ---
gcal_integration: false # Start/end meetings from calendar events
gcal_credentials_file: null # OAuth credentials.json
gcal_token_file: null # Saved token (null=DATA_DIR/gcal_token.json)
gcal_calendar_id: primary # Calendar to watch
gcal_buffer_minutes: 2 # Send start_meeting this many minutes before an event starts
gcal_end_buffer_minutes: 1 # Send end_meeting this many minutes after it ends
# --- Dashboard ---
dashboard_port: 8765 # Dashboard port. --dashboard-port overrides it; clerk-util reads this key to reach the daemon
gui_scale: 0 # Display scale for `clerk-util gui`. 0 asks the compositor; set 0.5-4.0 if the window comes out too small or too large
# --- UI ---
ui_language: ja # UI language (ja/en) — dashboard, terminal output, LLM promptsTalk mode keys (talk_*) are listed under Talk with Claude. The dashboard also keeps a few keys of its own in this file (welcome_dismissed, skill_update_dismissed_version, skill_install_targets); you don't need to edit them.
Manage configuration from the command line (or from ⚙ on the dashboard):
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.
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 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
shadow-clerk/ # Repository
pyproject.toml # Project definition & dependencies
src/shadow_clerk/ # Main package
clerk_daemon.py # clerk-daemon entry point (recording, transcription, dashboard: _daemon_*.py)
clerk_util.py # clerk-util: data directory operations & process management
llm_client.py # Translation, summary & LLM queries (_llm_*.py)
gcal_monitor.py # Google Calendar polling
skill_install.py # install-skill
i18n.py # Internationalization (ja/en)
domain/ # Domain value objects
skills/ # Bundled skills: clerk-meeting-helper, clerk-talk, clerk-practice, voicevox-sing
talk_prompts/ # Talk mode system prompts
extension/ # Chrome screenshot extension
packaging/ # PyInstaller spec and hooks
docs/ # Feature tour, Google Calendar setup
tests/ # Tests
SPEC.md # Architecture and module design (Japanese)
~/.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
transcript-YYYYMMDDHHMM@Title.attendees.json # Expected attendees from the calendar event
summary-YYYYMMDD.md # Meeting minutes (corresponds to transcript)
summary-YYYYMMDDHHMM@Title.md # Meeting minutes (named session)
advice-YYYYMMDDHHMM@Title.md # AI Console: open questions and suggestions
analysis-YYYYMMDDHHMM@Title.md # AI Console: confirmed facts
shot-YYYYMMDDHHMM@Title-HHMMSS.png # Browser screenshots
meeting.yaml # Per-meeting working directories
glossary.txt # Glossary (TSV: translation terms & reading-based text replacement)
misheard.tsv # Misheard word pairs collected by the skills
config.yaml # Configuration file
.env # API key (SHADOW_CLERK_API_KEY)
daemon.log / daemon.pid # Daemon log and PID
gcal_token.json # Google Calendar OAuth token (created by gcal-auth)
gcal_processed.json # Calendar events already started/ended (survives daemon restarts)
wespeaker_cache/ # Speaker diarization ONNX model (downloaded on first use)
speaker_names.json # Names, groups and colors of speakers (no voice data)
voiceprints.json # Voiceprints (speaker_enrollment, after naming)
transcript-YYYYMMDDHHMM@Title.voices.jsonl # Speaker diarization embeddings (speaker_enrollment)
transcript-YYYYMMDDHHMM@Title.naming.jsonl # Naming history of the meeting (for undo)
transcript-YYYYMMDDHHMM@Title.provisional.json # Speaker names given during the meeting (applied when it ends)
# List available devices
clerk-daemon --list-devices
# PipeWire: check status
wpctl status
# PulseAudio: list sources
pactl list short sourcesOn 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 5Make sure libportaudio2 is installed:
dpkg -l | grep portaudioIf 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-pulseUse a lighter model with --model tiny:
clerk-daemon --model tinyasr_engine: moonshine (and interim_asr_engine for interim transcription) switches
recognition to Moonshine Voice for every
language it supports (ar, de, en, es, ja, ko, tl, uk, vi, zh). It needs the moonshine
extra, and the model for the current language is downloaded on first use. Languages it
does not support, and default_language: auto, fall back to Whisper. For ja, a
japanese_asr_model other than default still takes precedence.
On CPU it is several times faster than Whisper small with comparable or better Japanese
accuracy. initial_prompt is passed as Moonshine's context (term biasing); the Whisper
settings (default_model, whisper_beam_size, whisper_compute_type) are not used.
License: only the English models are MIT. Models for other languages, including Japanese, are under the non-commercial Moonshine Community License.
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.
reazonspeech_model: ja-en switches to the Japanese-English bilingual Zipformer. Japanese
accuracy matches ja, and English speech is transcribed instead of collapsing into
はい; it is also slightly faster. The upstream repository has been made private, so it is
downloaded from the mirror kept by the sherpa-onnx author (csukuangfj/reazonspeech-k2-v2-ja-en).
reazonspeech_precision: int8 is about twice as fast as fp32 with near-identical output.
Selection guide:
| Use case | Settings |
|---|---|
| Japanese meetings on CPU (recommended) | japanese_asr_model: reazonspeech-k2, reazonspeech_model: ja-en, reazonspeech_precision: int8 |
| Japanese-focused, accuracy priority (GPU) | japanese_asr_model: kotoba-whisper, whisper_beam_size: 5 |
| Japanese-focused, speed priority (CPU, no extras) | japanese_asr_model: default, default_model: small, whisper_beam_size: 3 |
| Non-Japanese languages, fewer hallucinations | asr_engine: moonshine (English models are MIT; others non-commercial) |
| Multilingual | japanese_asr_model: reazonspeech-k2, reazonspeech_model: ja-en, default_model: small |
Measured on CPU (int8) with real conversational clips split by the daemon's own VAD:
ReazonSpeech k2 ja-en was the fastest (~0.2 s per utterance) with Japanese accuracy equal
to ja, and it also transcribes English, which ja collapses into はい. Whisper small was
about 20× slower and Kotoba-Whisper about twice as slow again, without being more accurate. Moonshine's
Japanese model is close in Japanese but loops on English speech.
Whisper: Silero VAD (whisper_vad_filter, on by default)
Segments are cut by webrtcvad, which also lets keyboard noise and breathing through.
Without a second VAD, Whisper answers such noise-only segments with its own
initial_prompt — the wake word — and only the no_speech_prob filter stands between
that and a false voice command. whisper_vad_filter runs faster-whisper's built-in Silero
VAD inside each segment: noise-only segments produce nothing and cost almost no CPU, and
normal-volume speech transcribes the same. It applies to Whisper and Kotoba-Whisper, not
to ReazonSpeech or Moonshine.
The trade-off is very quiet speech. With the speech at 1/10–1/20 volume plus noise, the
wake word survived 9/12 times without VAD, 4/12 with threshold 0.35 and 3/12 with the
upstream default 0.5 — hence the default of whisper_vad_threshold: 0.35. If voice
commands are missed on a quiet microphone, turn whisper_vad_filter off.
condition_on_previous_text: false, often recommended alongside it, is deliberately not
used: it only matters across 30-second windows inside one call, and every segment here is
transcribed on its own, so it changed nothing on short segments and caused a repetition
loop on a long mixed-language one.
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: 1Interim 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.nullfalls back totranslation_provider; if that isclaudeit is auto-routed toapithenlibretranslate(claude is too slow for interim, ~5-10s per call). Set toclaudeonly 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.
packaging/shadow-clerk.spec produces a one-directory PyInstaller bundle containing both clerk-daemon and clerk-util.
Without a Windows machine, .github/workflows/build-binary.yml builds the bundle on windows-latest and ubuntu-latest with ReazonSpeech and Google Calendar included. 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.
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.
To build locally:
# 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.specThe 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.
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 anyuv pip installlast. See Setup for why the order matters. The spec collectssherpa_onnx(which carries its ownonnxruntimeDLL inlib/) andreazonspeech.k2.asrwhen they are there; the ASR weights themselves are fetched on first use, like the Whisper models. - Whisper models are not bundled.
smallis around 500 MB and is fetched from Hugging Face on first run, then cached (%USERPROFILE%\.cache\huggingfaceon Windows,~/.cache/huggingfaceelsewhere). For an offline bundle, add that cache todatasin the spec, or ship a converted CT2 model and point--modelat it. packaging/hooks/overrides PyInstaller's bundled hooks. There is one today: the bundledhook-webrtcvad.pycallscopy_metadata('webrtcvad'), but this project depends onwebrtcvad-wheels, so without the override the build aborts withImportErrorWhenRunningHook.- Add a dependency that ships DLLs or data files? Add it to
_PACKAGESin the spec. PyInstaller only followsimportstatements, so anything else is silently left out and fails at runtime rather than at build time.