Speaking is just easier.

Speak freely, type instantly on Wayland (X11 compatible) — 100% local voice dictation for Linux with 25+ languages, 5 translation backends, speaker diarization, and real-time visual feedback. Text appears right where your cursor is.

What is dictee? • System requirements • Quick start • Features • Installation • Configuration • Usage • Post-processing • Limitations • Roadmap • Wiki

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What is dictee?

dictee is a complete voice dictation system for Linux. Press a shortcut, speak, and the text is typed directly into the active application — any application, any window, any text field.

Transcription is performed 100% locally by default: no audio ever leaves your machine unless you explicitly choose a cloud translation backend.

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Why dictee

• 100% local processing by default — no audio leaves the machine unless you explicitly enable a cloud translation backend. Frozen ONNX models, no training on your data.
• 4 ASR backends to choose from — Parakeet-TDT and Canary run as native Rust binaries (ONNX Runtime, low GPU latency), faster-whisper (99 languages) and Vosk (lightweight CPU) in Python. Transparent switching via Unix socket depending on language, latency or hardware. → 4 ASR backends
• 5 translation backends to choose from — from fully local (Canary, LibreTranslate, Ollama) to cloud (Google, Bing), with an explicit privacy table for each option. → Translation backends
• No duration limit on audio files — the chunked pipeline shipped in v1.3 (dictee-transcribe) diarizes a 54-min keynote in 122 s on an 8 GB GPU, where direct mel loading caps at 10-15 min. Ideal for meeting minutes and long interviews.
• Native Linux integration — KDE Plasma 6 plasmoid + PyQt6 system tray (compatible with GNOME, XFCE, Sway via AppIndicator fallback).

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System requirements

Backend	Min RAM	CPU mode	GPU	Disk
Parakeet-TDT (default)	4 GB	Yes — ~0.8 s per utterance (recent CPU)	NVIDIA 4 GB+ VRAM (~5× faster)	3 GB
Canary-1B v2	6 GB	No — encoder too heavy	NVIDIA 6 GB+ VRAM required	6 GB
faster-whisper	4 GB	Yes — turbo or small	NVIDIA 4 GB+ VRAM (large-v3)	3 GB
Vosk	2 GB	Yes — by design	—	50 MB

Distributions tested: Ubuntu 22.04 / 24.04 · Debian 12 · Fedora 40 / 44 · openSUSE Tumbleweed · Arch Linux · KDE Neon.

Desktop environments: KDE Plasma 6 (full integration via native plasmoid widget) · GNOME, Xfce, Cinnamon (system tray only — GNOME requires the AppIndicator extension).

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Quick start

Three steps to go from zero to dictation in under two minutes:

1. Install

curl -fsSL https://raw.githubusercontent.com/rcspam/dictee/master/install.sh | bash

Prefer to audit before running? install.sh and install.sh.sha256 are published as release assets — download both, verify with sha256sum -c install.sh.sha256, read the script, then run it.

2. Configure

The first-run wizard walks you through backend selection, model download and keyboard shortcut binding. Re-run anytime with dictee --setup.

3. Speak

Press your shortcut (default F9), speak, release. The transcription appears at your cursor.

For detailed install paths (manual .deb/.rpm, GPU prerequisites, AUR, from source), see Installation below or the wiki's Installation and GPU-Setup pages.

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Features

4 ASR backends

Backend	Languages	Model size	Warm latency	Notes
Parakeet-TDT 0.6B v3	25	~2.5 GB	~0.8s CPU · ~0.16s GPU	Default, native punctuation
Canary-1B v2	25	~5 GB	~0.7s GPU	Built-in translation (25 ↔ EN, 48 pairs)
faster-whisper	99	~500 MB–3 GB	~0.3s	Wide language coverage
Vosk	20+	~50 MB	~1.5s	Lightweight, strict offline

Each backend runs as a systemd user service with the same Unix socket protocol — switching is transparent. → ASR-Backends wiki

Accuracy benchmarks

dictee uses Parakeet-TDT 0.6B v3 by default. On the Open ASR Leaderboard, it outperforms Whisper-large-v3 on multilingual transcription while being significantly smaller and faster:

Model	Size	English WER	FLEURS multilingual (avg)	Relative speed
Parakeet-TDT 0.6B v3 (dictee default)	600M	~6.5 %	12.0 %	~10× Whisper-large-v3
Whisper-large-v3	1.55B	7.4 %	12.6 %	baseline
Canary-1B v2 (also bundled)	1B	7.2 %	–	~5× Whisper-large-v3
Whisper-large-v3-turbo	809M	~7.8 %	–	~3-4×
Vosk (CPU fallback)	50 MB	~12-18 %	–	–

Parakeet-TDT v3 wins particularly on French, Greek, Estonian and Maltese. For maximum language coverage (99 languages), switch to faster-whisper; for built-in translation, switch to Canary-1B.

Sources: NVIDIA Parakeet-TDT v3 · Open ASR Leaderboard 2025.

5 translation backends

Backend	Privacy	Speed	Quality	Languages
Canary-1B	🔒 Local	Built-in	Excellent	4
LibreTranslate	🔒 Local	0.1–0.3s	Good	30+
Ollama	🔒 Local	2–3s	Excellent	Any (LLM)
Google Translate	🌐 Cloud	0.2–0.7s	Excellent	130+
Bing Translator	🌐 Cloud	1.7–2.2s	Very good	100+

→ Translation wiki · Ollama-Setup

Post-processing pipeline

A 12-step configurable pipeline transforms raw ASR output before it hits your cursor:

• Regex rules + dictionary — 7 languages, ASR variants, voice commands → Rules-and-Dictionary
• LLM correction — optional fluency polish via local Ollama (first / last / hybrid position) → LLM-Correction
• Numbers & dates — cardinal, ordinal, versions, decimals, French times → Numbers-Dates-Continuation
• Continuation buffer — continue a sentence across dictations with last-word memory
• Short-text keepcaps — per-language exceptions for acronyms and names (new in v1.3)

→ Post-Processing-Overview

Speaker diarization (Meetings)

Answer "who spoke when?" in multi-speaker recordings via NVIDIA's Sortformer model. Up to 4 speakers, ideal for meeting notes and interviews. Triggered via Meeting mode or dictee --meeting. → Diarization wiki

Transcribe audio & video files

Push-to-talk is dictee's main flow, but the bundled dictee-transcribe window also handles offline transcription of any audio or video file you already have. Multi-tab interface, audio player synchronised with the timeline, per-tab translation and LLM analysis, export to PDF / SRT / JSON / Markdown.

• Any input format (mp3, mp4, wav, opus, flac, mkv…) — auto-converted via ffmpeg
• Multi-tab — keep the original transcription side-by-side with translations and LLM analyses (summary, chapters, ASR cleanup…)
• Speaker diarization built-in — toggle on, get up to 4 speakers labelled and renamable
• LLM analysis — 14 providers configurable side by side (Ollama, OpenAI, Claude, Gemini, Mistral, DeepSeek, Groq, Cerebras, OpenRouter…)
• Per-tab translation — Canary / LibreTranslate / Ollama / Google / Bing

→ LLM-Diarization wiki

3 visual interfaces

• KDE Plasma 6 widget — native QML plasmoid, 5 animation styles, live state → Plasmoid-Widget
• System tray icon — PyQt6, works on GNOME/XFCE/Sway (AppIndicator fallback) → Tray-Icon
• animation-speech (external) — fullscreen overlay on wlr-layer-shell compositors

All three share state via a filesystem watcher — any change is reflected instantly across interfaces (multi-user safe with UID suffix).

animation-speech (fullscreen overlay)

animation-speech is a standalone project that provides a fullscreen visual animation during recording, with cancellation via the Escape key. It works on any Wayland compositor supporting wlr-layer-shell (KDE Plasma, Sway, Hyprland…).

sudo dpkg -i animation-speech_1.2.0_all.deb

Download: animation-speech releases

Note: animation-speech is not compatible with GNOME (no wlr-layer-shell support). GNOME users can rely on dictee-tray for visual feedback. Contributions for a GNOME Shell extension are welcome — see the plasmoid source for reference architecture.

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Installation

One-liner (recommended)

Auto-detects distro and GPU, adds the NVIDIA CUDA repo if needed, installs the right package:

curl -fsSL https://raw.githubusercontent.com/rcspam/dictee/master/install.sh | bash

Supported: Ubuntu, Debian, Fedora, openSUSE, Arch Linux. Other distros fall back to the tarball path.

Options (after --):

# Force CPU (skip GPU detection)
curl -fsSL https://raw.githubusercontent.com/rcspam/dictee/master/install.sh | bash -s -- --cpu

# Force GPU (CUDA)
curl -fsSL https://raw.githubusercontent.com/rcspam/dictee/master/install.sh | bash -s -- --gpu

# Pin a specific version
curl -fsSL https://raw.githubusercontent.com/rcspam/dictee/master/install.sh | bash -s -- --version 1.3.4

# Non-interactive
curl -fsSL https://raw.githubusercontent.com/rcspam/dictee/master/install.sh | bash -s -- --non-interactive

Manual install

Download from Releases.

Ubuntu / Debian (CPU):

sudo apt install ./dictee-cpu_1.3.4_amd64.deb

Ubuntu / Debian (GPU): requires the NVIDIA CUDA APT repo — see GPU-Setup for the one-time setup, then:

sudo apt install ./dictee-cuda_1.3.4_amd64.deb

Fedora / openSUSE (CPU):

sudo dnf install ./dictee-cpu-1.3.4-1.x86_64.rpm

Fedora / openSUSE (GPU): add the CUDA repo first (see GPU-Setup), then dictee-cuda-1.3.4-1.x86_64.rpm.

Arch Linux (AUR): PKGBUILD in the repo root (x86_64 + aarch64). Clone + makepkg -si.

aarch64 / Jetson: no pre-built package — build from source. CUDA limited to NVIDIA Jetson boards.

Other distros (tarball):

tar xzf dictee-1.3.4_amd64.tar.gz
cd dictee-1.3.4
sudo ./install.sh

The tarball ships binaries only — system dependencies must be installed beforehand via your distro's package manager. Names vary by distro; pick the equivalents in yours:

• python3 (≥3.10), python3-pip, python3-venv
• python3-evdev, python3-pyqt6 (+ qtmultimedia + qtsvg), python3-numpy
• pulseaudio-utils, pipewire (or alsa-utils), libnotify(-bin), sox
• wl-clipboard (Wayland), xclip (X11)
• translate-shell, curl

Example for Debian-derived distros (Mint, MX, Pop!_OS…):

sudo apt install python3 python3-pip python3-venv python3-evdev \
    python3-pyqt6 python3-pyqt6.qtmultimedia python3-pyqt6.qtsvg python3-numpy \
    pulseaudio-utils pipewire libnotify-bin sox wl-clipboard xclip \
    translate-shell curl

From source: cargo build --release --features sortformer then sudo ./install.sh. See Developer-Guide for full Cargo features and package build scripts.

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Configuration

First launch triggers a setup wizard (backend, model, shortcuts).

Reconfigure anytime from the application menu, tray icon, Plasma widget, or by running:

dictee --setup

Backend switching (one-liner)

# Show current backends
dictee-switch-backend status

# Switch ASR (parakeet · canary · whisper · vosk)
dictee-switch-backend asr canary

# Switch translation (canary · libretranslate · ollama · google · bing)
dictee-switch-backend translate ollama

The tray and plasmoid include backend sub-menus — no terminal required.

For detailed configuration (all ASR backends, translation matrix, plasmoid settings, keyboard shortcuts on tiling WMs), see the wiki:

• ASR-Backends · Translation
• Plasmoid-Widget · Tray-Icon
• Keyboard-Shortcuts (KDE/GNOME/Sway/i3/Hyprland)

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Usage

# Simple dictation — transcribe and type
dictee

# Dictate + translate (default: system language → English)
dictee --translate
dictee --translate --ollama            # 100% local via Ollama

# Change target language
DICTEE_LANG_TARGET=es dictee --translate   # → Spanish

# Meeting mode (diarization, up to 4 speakers)
dictee --meeting

# Cancel ongoing dictation
dictee --cancel

# Test post-processing rules live
dictee-test-rules                       # interactive
dictee-test-rules --loop                # continuous loop
dictee-test-rules --wav file.wav        # from audio file

→ Full command reference: CLI-Reference wiki

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Post-processing

dictee runs a configurable 12-step pipeline after transcription and before paste:

1. ASR variants normalization
2. Dictionary substitution
3. Numbers & dates conversion
4. Continuation buffer merge
5. Regex rules (pre-LLM)
6. LLM correction (optional, first position)
7. Regex rules (post-LLM)
8. Short-text exceptions (keepcaps)
9. Extended match mode
10. Final capitalization
11. Translation (optional)
12. Paste / inject

Configure via dictee --setup → Post-processing tab, or test rules live with dictee-test-rules.

→ Deep dives: Post-Processing-Overview · Rules-and-Dictionary · LLM-Correction · Numbers-Dates-Continuation

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Known limitations

• Long-file diarization: the chunked pipeline shipped in v1.3 (used by dictee-transcribe) lifts the VRAM cap (54-min keynote diarized in 122 s on 8 GB). In continuous live dictation (push-to-talk held without releasing), a single utterance > 10-15 min on an 8 GB GPU may still OOM — rare in practice, split the file or switch to the CPU backend. → Diarization wiki
• AMD / Intel GPUs are not currently supported — dictee falls back to CPU.
• No real-time streaming — Parakeet-TDT and Canary require the full utterance; only Nemotron (EN-only, via Rust binary) streams natively.

For bug reports and workarounds, see Troubleshooting.

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Roadmap

v1.3.4 (current) — Universal chunked transcription + dictee-transcribe UX hardening:

• Universal chunked transcription in dictee-transcribe — files of any length now split automatically into 180 s chunks on every host (CPU and GPU). New per-backend cap on live-dictation recording duration (Canary 2:30, Parakeet 4:30; Whisper / Vosk uncapped) to prevent silent crashes.
• Five-site target-tab UI hardening in dictee-transcribe — text editor, rename panel, timeline markers, audio player swap, and transcription render now only update the global UI when the target tab is visible. No more cross-tab corruption when transcribing one file while reviewing another.
• Translate skip surfacing — silent translate-skip cases now show a colored status message (i18n in 6 languages: fr / de / es / it / pt / uk).
• Diarize falls back to standalone Parakeet + Sortformer when the PTT daemon is Canary — avoids silent mistranscription on files whose language ≠ DICTEE_LANG_SOURCE (Canary daemon is locked at startup). Standalone binary costs ~5–10 s extra model load.
• Socket read timeout bumped 30 → 120 s for large files.
• GPU-fallback warning suppressed when stderr is piped.
• Default cheatsheet shortcut now "Same key + Shift" (was Disabled).
• PTT / dictee stale-state cleanup on next F9 keypress (recovers from a daemon killed mid-flight, OOM, signal).

v1.3.0 → v1.3.3 — The v1.3 series. Major additions over v1.2:

• dictee-transcribe — dedicated window for offline transcription of audio/video files (timeline player, multi-tab, per-tab translation and LLM analysis, export to PDF / SRT / JSON / Markdown).
• Speaker diarization up to 4 speakers via NVIDIA Sortformer, plus a chunked pipeline that lifts the VRAM cap on long files (54-min keynote diarized in 122 s).
• LLM analysis on diarized transcripts — synthesis, chapters, ASR cleanup; 14 providers configurable side by side (Ollama, OpenAI, Claude, Gemini, Mistral, DeepSeek, Groq, Cerebras, OpenRouter…).
• Canary-1B v2 ASR backend (NVIDIA AED) with built-in translation on 12 native pairs.
• Portable CUDA libs via pip venv at postinst — no NVIDIA repo required.
• CUDA → CPU runtime fallback + DICTEE_FORCE_CPU=1 override (v1.3.2).
• Cross-distro packaging consistency — Arch .install group hooks, python-evdev hard depends, sg docker / sg input wrappers, udev 0660 direct (v1.3.3, closes #5 + #6).
• Short-text keepcaps exceptions (7 languages), extended match mode, version-number dictation, multi-user safe state, plasmoid cross-process toggles, 682 postprocess + 148 pipeline tests (v1.3.0).

v1.4+ (planned)

• Hotword boosting — bias ASR decoding toward custom names (shallow fusion on TDT logits, Parakeet only)
• Whisper translate — multi-target translation via task="translate" (EN-only, offline)
• Moonshine CPU backend
• CLI speech-to-text — pipe audio, get text
• VAD — hands-free dictation without push-to-talk
• Streaming transcription with live text display
• Built-in overlay — replace external animation-speech
• AppImage / Flatpak packaging
• COSMIC / GNOME Shell applets (contributions welcome!)

→ Full history: Changelog wiki

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Credits

The transcription engine builds on parakeet-rs by Enes Altun — Rust library for NVIDIA Parakeet inference via ONNX Runtime. The Rust Canary implementation was originally ported from onnx-asr by Ivan Stupakov and is now fully self-contained. Parakeet and Canary ONNX models are provided by NVIDIA (downloaded separately from HuggingFace, not redistributed by this project).

Keyboard input simulation uses dotool by geb (GPL-3.0).

License

This project is distributed under the GPL-3.0-or-later license (see LICENSE).

The original parakeet-rs code by Enes Altun is under the MIT license (see LICENSE-MIT). dotool is bundled under GPL-3.0.