whisper-input 0.6.0a3

跨平台语音输入工具 —— 按住快捷键说话,松开自动输入(SenseVoice ONNX 本地推理,支持中英日韩粤混合)

English | 中文

Whisper Input

Cross-platform voice input tool — hold a hotkey, speak, release to have speech transcribed and typed into the focused window.

Uses the official DAMO Academy SenseVoice-Small ONNX quantized model (direct inference via Microsoft onnxruntime), fully offline after first download. Supports Chinese, English, Japanese, Korean, and Cantonese with built-in punctuation, inverse text normalization, and casing. The model is downloaded from ModelScope CDN (~231 MB) on first launch, then works permanently offline.

Supports Linux (X11) and macOS.

Features

• Local speech recognition, works offline
• Multi-language mixed input (Chinese, English, etc.)
• Configurable hotkey (distinguishes left/right modifier keys)
• Browser-based settings UI + system tray
• Auto-start on login
• Automatic platform detection with matching backend

System Requirements

Common

• Python 3.12 + uv (recommended) or pipx:

  curl -LsSf https://astral.sh/uv/install.sh | sh
  

Linux

• Ubuntu 24.04+ / Debian 13+ (X11 desktop environment)
• Any x86_64 CPU (onnxruntime CPU inference, RTF ~ 0.1, latency < 1s for short utterances)

macOS

• macOS 12+ (Monterey or later)
• Apple Silicon (recommended) or Intel Mac, both use CPU ONNX inference

Installation

macOS

# Install system dependency
brew install portaudio

# Install the tool
uv tool install whisper-input
# or: pipx install whisper-input

# Run
whisper-input

First-run permissions required in System Settings > Privacy & Security:

1. Accessibility and Input Monitoring (for global hotkey listening and text input)
2. Microphone (for voice recording; the system will prompt on first recording)

Note: The PyPI-installed whisper-input runs as ~/.local/share/uv/tools/whisper-input/bin/python (or ~/.local/pipx/venvs/whisper-input/bin/python for pipx). macOS permission dialogs will show this Python binary, not "Whisper Input.app". Add the corresponding Python path to Accessibility / Input Monitoring.

Linux

# Install system dependencies (see table below for details)
sudo apt install xdotool xclip pulseaudio-utils libportaudio2 \
                 libgirepository-2.0-dev libcairo2-dev gir1.2-gtk-3.0 \
                 gir1.2-ayatanaappindicator3-0.1

# Add yourself to the input group (evdev needs /dev/input/* access)
sudo usermod -aG input $USER && newgrp input

# Install the tool
uv tool install whisper-input
# or: pipx install whisper-input

# Run
whisper-input

System dependency reference:

Package	Purpose	Notes
xdotool, xclip	Text input	xclip for X11 clipboard, xdotool to simulate Shift+Insert paste
libportaudio2	Audio recording	PortAudio library, runtime dependency of Python sounddevice
pulseaudio-utils	Sound notifications	Provides paplay for start/stop recording sounds
libgirepository-2.0-dev, libcairo2-dev	Build dependencies	Headers for compiling pygobject and pycairo C extensions
gir1.2-gtk-3.0	Recording overlay	GTK 3 typelib for the recording status overlay
gir1.2-ayatanaappindicator3-0.1	System tray icon	AppIndicator typelib, runtime dependency of pystray on Linux

On first run, whisper-input downloads the SenseVoice ONNX model (~231 MB) via modelscope.snapshot_download to ~/.cache/modelscope/hub/. After one successful download, the app is fully offline.

From Source (Contributors)

git clone https://github.com/pkulijing/whisper-input
cd whisper-input
bash scripts/setup_macos.sh   # or setup_linux.sh
uv run whisper-input

Usage

# Specify hotkey
whisper-input -k KEY_FN          # macOS: Fn/Globe key
whisper-input -k KEY_RIGHTALT    # Linux: Right Alt key

# More options
whisper-input --help

A browser settings page opens automatically on startup; you can also access it via the system tray icon.

How to use

1. Start the app, then hold the hotkey to begin recording
   • macOS default: Right Command key
   • Linux default: Right Ctrl key
2. Speak into the microphone
3. Release the hotkey, wait for recognition
4. The recognized text is automatically typed at the cursor position

Release Flow (Maintainers)

PyPI distribution via GitHub Actions tag trigger + Trusted Publishing (OIDC):

1. Bump version in pyproject.toml
2. git commit -am "release: v0.5.1" and push to master
3. git tag v0.5.1 && git push --tags
4. .github/workflows/release.yml triggers automatically: verify tag matches version -> uv build -> publish to PyPI via pypa/gh-action-pypi-publish -> create GitHub Release

Configuration

Config file config.yaml, also editable via the browser settings UI:

Setting	Description	macOS Default	Linux Default
hotkey	Trigger hotkey	KEY_RIGHTMETA	KEY_RIGHTCTRL
sensevoice.use_itn	Inverse text normalization	true	true
sound.enabled	Recording sound notification	true	true
ui.language	Interface language (zh/en/fr)	zh	zh

Known Limitations

• Linux supports X11 only; Wayland is not yet supported
• Super/Win key is intercepted by GNOME desktop, not recommended as hotkey
• macOS requires Accessibility permission for global hotkey monitoring
• First run downloads the SenseVoice ONNX model (~231 MB from DAMO Academy ModelScope)

Technical Architecture

The project uses src layout with all Python code under src/whisper_input/, installable as a standard package. The entry point is the whisper-input console script (equivalent to python -m whisper_input).

Hold hotkey -> HotkeyListener (whisper_input.backends) -> AudioRecorder (sounddevice)
Release     -> stt.SenseVoiceSTT (onnxruntime) -> InputMethod -> Text typed into focused window

Platform backends (whisper_input.backends) auto-select at runtime via sys.platform:

• Linux: evdev for keyboard events + xclip/xdotool clipboard paste
• macOS: pynput global keyboard listener + pbcopy/pbpaste + Cmd+V paste

STT inference (whisper_input.stt):

• Model: DAMO Academy official iic/SenseVoiceSmall-onnx (quantized), downloaded via modelscope.snapshot_download to ~/.cache/modelscope/hub/
• Runtime: Microsoft official onnxruntime, no torch dependency
• Feature extraction, BPE decoding, meta-tag post-processing: ported from DAMO's funasr_onnx (MIT license, ~250 lines pure Python), bit-aligned with FunASR
• Dependency tree: onnxruntime + kaldi-native-fbank + sentencepiece + numpy + modelscope (modelscope base is only 36 MB, no torch/transformers)

Common features:

• 300ms delay on modifier key press to distinguish combos (e.g., Ctrl+C) from single triggers
• Clipboard paste instead of key simulation, avoiding CJK encoding issues
• Unified CPU inference path, zero code difference between macOS/Linux

License

MIT