Local Vosk STT provides a lightweight and fully offline speech-to-text solution for transcribing audio files and voice messages without relying on cloud-based APIs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install local-vosk
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install local-vosk using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Local Vosk Speech-to-Text?
The Local Vosk STT skill brings high-performance, private transcription capabilities to your local environment. Built on the Vosk framework, it allows users to process audio files, including Telegram voice notes, in real-time or faster. By leveraging Openclaw Skills, developers can integrate voice processing into their workflows while maintaining 100% data privacy and zero API costs.
This skill is designed for those who require efficiency and reliability. Unlike cloud-based solutions, this tool remains functional without an internet connection, ensuring that sensitive audio data never leaves your local machine. It serves as a foundational component for building privacy-first AI assistants and automation tools.
Local Vosk Speech-to-Text Use Cases
- Automatically transcribing Telegram voice messages in .ogg format.
- Processing various audio formats like mp3, wav, and flac using ffmpeg.
- Implementing speech-to-text in environments with restricted or no internet access.
- Reducing latency and costs by avoiding third-party cloud transcription services.
How Local Vosk Speech-to-Text Works
- The user provides an audio file path to the transcription script via the command line.
- The system utilizes ffmpeg to decode the audio into a format compatible with the Vosk engine.
- The Vosk engine loads the pre-downloaded language model to analyze the audio stream.
- The skill performs the transcription locally, outputting the text content directly to the console or integrated workflow.
Local Vosk Speech-to-Text Setup
To get started with this skill, install the Vosk library and download a language model:
pip3 install vosk --user --break-system-packages
# Download and setup the default small model
mkdir -p ~/vosk-models && cd ~/vosk-models
wget https://alphacephei.com/vosk/models/vosk-model-small-en-us-0.15.zip
unzip vosk-model-small-en-us-0.15.zip
Local Vosk Speech-to-Text Data Schema & Taxonomy
| Data Component | Description |
|---|---|
| Local Models | Stored in ~/vosk-models/; identifies linguistic patterns for recognition. |
| Input Support | Processes any audio container supported by ffmpeg (ogg, mp3, wav, etc.). |
| Execution Scripts | Transcription logic housed in ./skills/local-vosk/scripts/transcribe. |
Local Vosk Speech-to-Text Advanced Features
- Support for multiple languages by downloading additional Vosk models for specific locales.
- High-speed processing reaching approximately 10x real-time on standard consumer hardware.
- Direct compatibility with Telegram's .ogg voice message format for seamless bot integrations.
- Minimal resource footprint (~40MB for the small model) ideal for edge deployments.
SKILL.md
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