Faster Whisper Local Service

OpenClaw local speech-to-text backend using faster-whisper over HTTP on 127.0.0.1:18790. Use when you want voice transcription without external APIs, without...

Provision a local STT backend used by voice skills.

What this sets up

• Python venv for faster-whisper
• transcribe-server.py HTTP endpoint at http://127.0.0.1:18790/transcribe
• systemd user service: openclaw-transcribe.service

Important: Model download on first run

On first startup, faster-whisper downloads model weights from Hugging Face (~1.5 GB for medium). This requires internet access and disk space. After the initial download, models are cached locally and the service runs fully offline.

Model	Download size	RAM usage
tiny	~75 MB	~400 MB
base	~150 MB	~500 MB
small	~500 MB	~800 MB
medium	~1.5 GB	~1.4 GB
large-v3	~3.0 GB	~3.5 GB

To pre-download models in an air-gapped environment, see faster-whisper docs.

Security notes

Network isolation

• Binds to 127.0.0.1 only — not reachable from the network.
• CORS restricted to a single origin (https://127.0.0.1:8443 by default).
• No credentials, API keys, or secrets are used or stored.

Input validation

• Upload size limit: Requests exceeding the configured limit are rejected before processing (HTTP 413). Default: 50 MB, configurable via MAX_UPLOAD_MB.
• Magic-byte check: Only files with recognized audio signatures (WAV, OGG, FLAC, MP3, WebM, M4A) are accepted. Unrecognized formats are rejected (HTTP 415) before reaching GStreamer.
• Subprocess safety: All arguments to gst-launch-1.0 are passed as a list — no shell expansion or injection is possible.

GStreamer dependency

The service uses GStreamer's decodebin for audio format conversion. Like any media library, GStreamer's parsers process binary data and should be kept up to date. Mitigation: install gst-launch-1.0 from your OS vendor's trusted packages and apply security updates regularly. The magic-byte pre-filter above reduces the attack surface by rejecting non-audio payloads before they reach GStreamer.

No data exfiltration

• No outbound network calls (after initial model download).
• No telemetry, analytics, or phone-home behavior.
• Temporary files are created in a per-request TemporaryDirectory and cleaned up immediately.

Reproducibility defaults

• Pinned package: faster-whisper==1.1.1 (override via env)
• Explicit dependency check for gst-launch-1.0
• CORS restricted to one origin by default
• Configurable workspace/service paths (no hardcoded user path)

Deploy

bash scripts/deploy.sh

With custom settings:

WORKSPACE=~/.openclaw/workspace \
TRANSCRIBE_PORT=18790 \
WHISPER_MODEL_SIZE=medium \
WHISPER_LANGUAGE=auto \
TRANSCRIBE_ALLOWED_ORIGIN=https://10.0.0.42:8443 \
bash scripts/deploy.sh

Language setting

Default: auto (auto-detect language). Set WHISPER_LANGUAGE=de for German-only, en for English-only, etc. Fixed language is faster and more accurate if you only use one language.

Idempotent: safe to run repeatedly.

What this skill modifies

What	Path	Action
Python venv	$WORKSPACE/.venv-faster-whisper/	Creates venv, installs faster-whisper via pip
Transcribe server	$WORKSPACE/voice-input/transcribe-server.py	Writes server script
Systemd service	~/.config/systemd/user/openclaw-transcribe.service	Creates + enables persistent service
Model cache	~/.cache/huggingface/	Downloads model weights on first run

Uninstall

systemctl --user stop openclaw-transcribe.service
systemctl --user disable openclaw-transcribe.service
rm -f ~/.config/systemd/user/openclaw-transcribe.service
systemctl --user daemon-reload

Optional full cleanup:

rm -rf ~/.openclaw/workspace/.venv-faster-whisper
rm -f ~/.openclaw/workspace/voice-input/transcribe-server.py

Verify

bash scripts/status.sh

Expected:

• service active
• endpoint responds (HTTP 200/500 acceptable for invalid sample payload)

Notes

• This skill provides backend transcription only.
• Pair with webchat-voice-proxy for browser mic + HTTPS/WSS integration.
• For one-step install, use webchat-voice-full-stack (deploys backend + proxy in order).