Local Speech Recognition

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Offline speech recognition in Obsidian, powered by a locally deployed sherpa-onnx service. Press a hotkey, speak, and the transcription is inserted at your cursor. No cloud, no API keys.

Features

• Offline transcription via sherpa-onnx-offline-websocket-server over a local WebSocket (default ws://127.0.0.1:6006).
• Hotkey-triggered recording with toggle and push-to-talk modes. The plugin ships with no preset hotkey; assign one yourself in Settings → Hotkeys.
• Microphone selection with device enumeration and one-click refresh.
• 16 kHz 16-bit mono pipeline: AudioWorklet capture with linear downsampling.
• Smart delivery: inserts at the cursor when a Markdown editor is focused (source mode), otherwise copies to the clipboard with a Notice.
• Lexicon (designed for Chinese): add words from the editor context menu; after a transcription, matching words are underlined and can be clicked to replace with a same-pinyin alternative. Optional fuzzy pinyin matching tolerates retroflex/nasal confusions.
• Sidebar view (ribbon icon): a Lexicon tab for managing entries and a Service tab for controlling the service.
• Service management: manual start/stop/restart, auto-start on plugin load, and a connection test.
• Selectable recognition models: SenseVoice, FunASR and Paraformer families with int8/fp16/fp32 variants, each started with its own parameters (see Supported models).
• Trilingual UI: English / 简体中文 / 繁體中文, following the Obsidian interface language or set manually.

Requirements

• Obsidian 1.13.1 or later (uses the declarative settings API).
• Desktop only. The plugin spawns a local subprocess and captures microphone audio, neither of which is available on mobile.
• A sherpa-onnx-offline-websocket-server binary (see Choosing the server binary) plus a model folder holding one of the supported models (see Supported models).

Downloads

• sherpa-onnx releases (server binaries): https://github.com/k2-fsa/sherpa-onnx/releases
• Pre-trained ASR models: https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models

Choosing the server binary

Each sherpa-onnx release ships pre-built archives for desktop platforms. Pick the one matching your OS and CPU, extract it, then point Settings → Service settings → Server binary path at the executable inside its bin/ folder:

• macOS / Linux: <extracted-folder>/bin/sherpa-onnx-offline-websocket-server
• Windows: <extracted-folder>/bin/sherpa-onnx-offline-websocket-server.exe

Notes:

• On Linux, when sherpa-onnx was installed through a package manager (pacman, yay, …) rather than a release archive, locate the installed binary with whereis sherpa-onnx-offline-websocket-server and use that path.
• Prefer the -static archives: their executable is self-contained. The -shared variants additionally need the archive's lib/ folder on the library search path (PATH on Windows, LD_LIBRARY_PATH on Linux, DYLD_LIBRARY_PATH on macOS).
• Append -no-tts where available (e.g. ...-linux-x64-static-no-tts.tar.bz2): the plugin only transcribes speech, and those archives are much smaller.
• macOS may refuse to run the unsigned binary when a browser set the quarantine flag; clear it with xattr -dr com.apple.quarantine <extracted-folder>.

Supported models

Pick a model under Settings → Service settings → Recognition model, then point Model folder path at the folder you extracted. That folder must contain the files listed below; extra files such as test_wavs/ are ignored. Switching models takes effect the next time the service starts.

Qwen3-0.6B/ is a tokenizer folder, not a single file. Archives that match the table, from the ASR models release:

• SenseVoice: sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17 (contains both model.onnx and model.int8.onnx) or sherpa-onnx-sense-voice-zh-en-ja-ko-yue-int8-2024-07-17 (int8 only).
• FunASR: sherpa-onnx-funasr-nano-int8-2025-12-30, sherpa-onnx-funasr-nano-fp16-2025-12-30, and sherpa-onnx-funasr-nano-2025-12-30 for fp32.
• Paraformer: sherpa-onnx-paraformer-zh-2024-03-09 and other archives of the same family — -zh-small-2024-03-09 is the smallest, -trilingual-zh-cantonese-en adds Cantonese, -zh-int8-2025-10-07 targets Sichuanese. Some builds (small, Sichuanese) ship model.int8.onnx only, so pair them with Paraformer (int8).

Note: a model of the same architecture with different file names can in theory be forced to run by renaming its files to the names listed above, but this is not guaranteed to work.

Installation

1. In Obsidian, open Settings → Community plugins → Browse, search for "Local Speech Recognition", and install it. After installation, enable the plugin.
2. If you prefer a manual install, download main.js, manifest.json, and styles.css from the GitHub release, copy them to <vault>/.obsidian/plugins/local-speech-recognition/, reload Obsidian, then enable the plugin in Settings → Community plugins.

Quick start

1. Download the server binary and the model from the links above, and unpack the model folder somewhere local.
2. Open plugin settings → Service settings: fill in the server binary path and model folder path, check host/port/threads, then click Test connection or Start service (or enable auto-start).
3. Open Speech input: pick a microphone. If the list is empty or unlabeled, allow microphone access once, then click Refresh microphones. Choose an input mode.
4. Open Obsidian Settings → Hotkeys, find "Toggle speech recognition", and assign a hotkey.
5. Focus a note editor, press the hotkey, speak, then press it again. The text is inserted at the cursor. If no editor is focused, the result goes to the clipboard with a Notice instead.
6. Optional: build a lexicon of names or jargon. Select a word in an editor and pick Add to lexicon from the right-click menu; later transcriptions will underline it and offer one-click corrections. See Lexicon below.

Settings reference

General:

Service settings:

Speech input:

Lexicon settings:

Usage notes

• Toggle mode: press once to start, press again to stop and transcribe.
• Push-to-talk mode (transitional): press to start; releasing the hotkey or pressing again stops and transcribes. Obsidian hotkeys only deliver key-down events, so exact hold-to-talk semantics are approximated for now.
• Recording is capped at 280 seconds (the server rejects utterances over 300 seconds) and then transcribed automatically.
• While a transcription is in flight, new triggers are rejected with a Notice.
• Lexicon: select text in an editor and choose Add to lexicon from the right-click menu. After that, every transcription is scanned for lexicon words; matches get a dotted underline, and clicking one opens a menu of same-pinyin replacements.
• Manage entries in the sidebar (ribbon icon) → Lexicon: search, filter by enabled state, add/edit/delete, and batch enable/disable/delete.

Lexicon

The lexicon is designed for Chinese. Each entry pairs a word with its pinyin, and matching compares the pinyin of transcribed text against those entries. That means a word can still be flagged when the speech model returns a homophone — e.g. the lexicon knows 知识, so a transcription of 资石 (identical pinyin zhi shi) is underlined and can be corrected with one click. Because matching is pinyin-based, the lexicon has no effect on non-Chinese text: Latin-script segments are ignored, and words without a pinyin are skipped.

Enable Fuzzy pinyin matching to also treat words as equivalent when they differ only in retroflex initials (zh/z, ch/c, sh/s) or front/back nasals (an/ang, en/eng, in/ing, ian/iang, uan/uang). This helps with accents and model confusions, but it trades precision for recall — with it on, 张 and 脏 become mutually interchangeable — so it is off by default.

Notes:

• Pinyin is generated automatically when you add a word; you can edit it, or regenerate it from the word, in the entry dialog.
• An entry can be disabled instead of deleted; disabled entries are kept in the list but excluded from matching.
• Assigning a weight ranks entries that share a pinyin, so higher-weight words appear first among the replacements.
• Adding a word that already exists (same word and pinyin) is rejected with a Notice; homophones with different characters are allowed.

Troubleshooting

Privacy

All recognition runs locally. Microphone audio is sent only to the WebSocket address you configure (default localhost) and never to any third party.

Development

bun install
bun run dev      # watch + rebuild into dist
bun run build    # typecheck + production build
bun run lint
bun run format

License

GPL-3.0-only. See LICENSE.