name: local-vosk description: Local speech-to-text using Vosk. Lightweight, fast, fully offline. Perfect for transcribing Telegram voice messages, audio files, or any speech-to-text task without cloud APIs.

Local Vosk STT

Lightweight local speech-to-text using Vosk. Fully offline after model download.

Use Cases

• Telegram voice messages — transcribe .ogg voice notes automatically
• Audio files — any format ffmpeg supports
• Offline transcription — no API keys, no cloud, no costs

Quick Start

# Transcribe Telegram voice message
./skills/local-vosk/scripts/transcribe voice_message.ogg

# Transcribe any audio
./skills/local-vosk/scripts/transcribe audio.mp3

# With language (default: en-us)
./skills/local-vosk/scripts/transcribe audio.wav --lang en-us

Supported Formats

Any format ffmpeg can decode: ogg (Telegram), mp3, wav, m4a, webm, flac, etc.

Models

Default model: vosk-model-small-en-us-0.15 (~40MB)

Other models available at https://alphacephei.com/vosk/models

Setup (if not installed)

pip3 install vosk --user --break-system-packages

# Download 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

Notes

• Quality is good for conversational speech
• For higher accuracy, use larger models or faster-whisper
• Processes audio at ~10x realtime on typical hardware
• Telegram voice messages are .ogg format — works out of the box