aman

Local amanuensis

Python X11 STT daemon that records audio, runs Whisper, applies local AI cleanup, and injects text.

Target User

The canonical Aman user is a desktop professional who wants dictation and rewriting features without learning Python tooling.

• End-user path: native OS package install.
• Developer path: Python/uv workflows.

Persona details and distribution policy are documented in docs/persona-and-distribution.md.

Install (Recommended)

End users do not need uv.

Debian/Ubuntu (.deb)

Download a release artifact and install it:

sudo apt install ./aman_<version>_<arch>.deb

Then enable the user service:

systemctl --user daemon-reload
systemctl --user enable --now aman

Arch Linux

Use the generated packaging inputs (PKGBUILD + source tarball) in dist/arch/ or your own packaging pipeline.

Distribution Matrix

Channel	Audience	Status
Debian package (.deb)	End users on Ubuntu/Debian	Canonical
Arch PKGBUILD + source tarball	Arch maintainers/power users	Supported
Python wheel/sdist	Developers/integrators	Supported

Runtime Dependencies

• X11
• PortAudio runtime (libportaudio2 or distro equivalent)
• GTK3 and AppIndicator runtime (gtk3, libayatana-appindicator3)
• Python GTK and X11 bindings (python3-gi/python-gobject, python-xlib)

Ubuntu/Debian

sudo apt install -y libportaudio2 python3-gi python3-xlib gir1.2-gtk-3.0 libayatana-appindicator3-1

Arch Linux

sudo pacman -S --needed portaudio gtk3 libayatana-appindicator python-gobject python-xlib

Fedora

sudo dnf install -y portaudio gtk3 libayatana-appindicator-gtk3 python3-gobject python3-xlib

openSUSE

sudo zypper install -y portaudio gtk3 libayatana-appindicator3-1 python3-gobject python3-python-xlib

Quickstart

aman run

On first launch, Aman opens a graphical settings window automatically. It includes sections for:

• microphone input
• hotkey
• output backend
• writing profile
• output safety policy
• runtime strategy (managed vs custom Whisper path)
• help/about actions

Config

Create ~/.config/aman/config.json (or let aman create it automatically on first start if missing):

{
  "config_version": 1,
  "daemon": { "hotkey": "Cmd+m" },
  "recording": { "input": "0" },
  "stt": {
    "provider": "local_whisper",
    "model": "base",
    "device": "cpu",
    "language": "auto"
  },
  "models": {
    "allow_custom_models": false,
    "whisper_model_path": ""
  },
  "injection": {
    "backend": "clipboard",
    "remove_transcription_from_clipboard": false
  },
  "safety": {
    "enabled": true,
    "strict": false
  },
  "ux": {
    "profile": "default",
    "show_notifications": true
  },
  "advanced": {
    "strict_startup": true
  },
  "vocabulary": {
    "replacements": [
      { "from": "Martha", "to": "Marta" },
      { "from": "docker", "to": "Docker" }
    ],
    "terms": ["Systemd", "Kubernetes"]
  }
}

config_version is required and currently must be 1. Legacy unversioned configs are migrated automatically on load.

Recording input can be a device index (preferred) or a substring of the device name. If recording.input is explicitly set and cannot be resolved, startup fails instead of falling back to a default device.

Config validation is strict: unknown fields are rejected with a startup error. Validation errors include the exact field and an example fix snippet.

Profile options:

• ux.profile=default: baseline cleanup behavior.
• ux.profile=fast: lower-latency AI generation settings.
• ux.profile=polished: same cleanup depth as default.
• safety.enabled=true: enables fact-preservation checks (names/numbers/IDs/URLs).
• safety.strict=false: fallback to safer draft when fact checks fail.
• safety.strict=true: reject output when fact checks fail.
• advanced.strict_startup=true: keep fail-fast startup validation behavior.

Transcription language:

• stt.language=auto (default) enables Whisper auto-detection.
• You can pin language with Whisper codes (for example en, es, pt, ja, zh) or common names like English/Spanish.
• If a pinned language hint is rejected by the runtime, Aman logs a warning and retries with auto-detect.

Hotkey notes:

• Use one key plus optional modifiers (for example Cmd+m, Super+m, Ctrl+space).
• Super and Cmd are equivalent aliases for the same modifier.

AI cleanup is always enabled and uses the locked local Qwen2.5-1.5B GGUF model downloaded to ~/.cache/aman/models/ during daemon initialization. Prompts are structured with semantic XML tags for both system and user messages to improve instruction adherence and output consistency. Cleanup runs in two local passes:

• pass 1 drafts cleaned text and labels ambiguity decisions (correction/literal/spelling/filler)
• pass 2 audits those decisions conservatively and emits final cleaned_text This keeps Aman in dictation mode: it does not execute editing instructions embedded in transcript text. Before Aman reports ready, local llama runs a tiny warmup completion so the first real transcription is faster. If warmup fails and advanced.strict_startup=true, startup fails fast. With advanced.strict_startup=false, Aman logs a warning and continues. Model downloads use a network timeout and SHA256 verification before activation. Cached models are checksum-verified on startup; mismatches trigger a forced redownload.

Provider policy:

• Aman-managed mode (recommended) is the canonical supported UX: Aman handles model lifecycle and safe defaults for you.
• Expert mode is opt-in and exposes a custom Whisper model path for advanced users.
• Editor model/provider configuration is intentionally not exposed in config.
• Custom Whisper paths are only active with models.allow_custom_models=true.

Use -v/--verbose to enable DEBUG logs, including recognized/processed transcript text and llama.cpp logs (llama:: prefix). Without -v, logs are INFO level.

Vocabulary correction:

• vocabulary.replacements is deterministic correction (from -> to).
• vocabulary.terms is a preferred spelling list used as hinting context.
• Wildcards are intentionally rejected (*, ?, [, ], {, }) to avoid ambiguous rules.
• Rules are deduplicated case-insensitively; conflicting replacements are rejected.

STT hinting:

• Vocabulary is passed to Whisper as compact hotwords only when that argument is supported by the installed faster-whisper runtime.
• Aman enables word_timestamps when supported and runs a conservative alignment heuristic pass (self-correction/restart detection) before the editor stage.

Fact guard:

• Aman runs a deterministic fact-preservation verifier after editor output.
• If facts are changed/invented and safety.strict=false, Aman falls back to the safer aligned draft.
• If facts are changed/invented and safety.strict=true, processing fails and output is not injected.

systemd user service

make install-service

Service notes:

• The user unit launches aman from PATH.
• Package installs should provide the aman command automatically.
• Inspect failures with systemctl --user status aman and journalctl --user -u aman -f.

Usage

• Press the hotkey once to start recording.
• Press it again to stop and run STT.
• Press Esc while recording to cancel without processing.
• Esc is only captured during active recording.
• Recording start is aborted if the cancel listener cannot be armed.
• Transcript contents are logged only when -v/--verbose is used.
• Tray menu includes: Settings..., Help, About, Pause/Resume Aman, Reload Config, Run Diagnostics, Open Config Path, and Quit.
• If required settings are not saved, Aman enters a Settings Required tray mode and does not capture audio.

Wayland note:

• Running under Wayland currently exits with a message explaining that it is not supported yet.

Injection backends:

• clipboard: copy to clipboard and inject via Ctrl+Shift+V (GTK clipboard + XTest)
• injection: type the text with simulated keypresses (XTest)
• injection.remove_transcription_from_clipboard: when true and backend is clipboard, restores/clears the clipboard after paste so the transcript is not kept there

Editor stage:

• Canonical local llama.cpp editor model (managed by Aman).
• Runtime flow is explicit: ASR -> Alignment Heuristics -> Editor -> Fact Guard -> Vocabulary -> Injection.

Build and packaging (maintainers):

make build
make package
make package-deb
make package-arch
make release-check

make package-deb installs Python dependencies while creating the package. For offline packaging, set AMAN_WHEELHOUSE_DIR to a directory containing the required wheels.

Benchmarking (STT bypass, always dry):

aman bench --text "draft a short email to Marta confirming lunch" --repeat 10 --warmup 2
aman bench --text-file ./bench-input.txt --repeat 20 --json

bench does not capture audio and never injects text to desktop apps. It runs the processing path from input transcript text through alignment/editor/fact-guard/vocabulary cleanup and prints timing summaries.

Model evaluation lab (dataset + matrix sweep):

aman build-heuristic-dataset --input benchmarks/heuristics_dataset.raw.jsonl --output benchmarks/heuristics_dataset.jsonl
aman eval-models --dataset benchmarks/cleanup_dataset.jsonl --matrix benchmarks/model_matrix.small_first.json --heuristic-dataset benchmarks/heuristics_dataset.jsonl --heuristic-weight 0.25 --output benchmarks/results/latest.json
aman sync-default-model --report benchmarks/results/latest.json --artifacts benchmarks/model_artifacts.json --constants src/constants.py

eval-models runs a structured model/parameter sweep over a JSONL dataset and outputs latency + quality metrics (including hybrid score, pass-1/pass-2 latency breakdown, and correction safety metrics for I mean and spelling-disambiguation cases). When --heuristic-dataset is provided, the report also includes alignment-heuristic quality metrics (exact match, token-F1, rule precision/recall, per-tag breakdown). sync-default-model promotes the report winner to the managed default model constants using the artifact registry and can be run in --check mode for CI/release gates.

Control:

make run
make run config.example.json
make doctor
make self-check
make eval-models
make sync-default-model
make check-default-model
make check

Developer setup (optional, uv workflow):

uv sync --extra x11
uv run aman run --config ~/.config/aman/config.json

Developer setup (optional, pip workflow):

make install-local
aman run --config ~/.config/aman/config.json

CLI (internal/support fallback):

aman run --config ~/.config/aman/config.json
aman doctor --config ~/.config/aman/config.json --json
aman self-check --config ~/.config/aman/config.json --json
aman bench --text "example transcript" --repeat 5 --warmup 1
aman build-heuristic-dataset --input benchmarks/heuristics_dataset.raw.jsonl --output benchmarks/heuristics_dataset.jsonl --json
aman eval-models --dataset benchmarks/cleanup_dataset.jsonl --matrix benchmarks/model_matrix.small_first.json --heuristic-dataset benchmarks/heuristics_dataset.jsonl --heuristic-weight 0.25 --json
aman sync-default-model --check --report benchmarks/results/latest.json --artifacts benchmarks/model_artifacts.json --constants src/constants.py
aman version
aman init --config ~/.config/aman/config.json --force