Local voice-to-text transcription for OpenClaw using Parakeet TDT 0.6b v3

Local transcription setup for OpenClaw

A community developer has adapted NVIDIA's Parakeet TDT 0.6b v3 model for local voice-to-text transcription within OpenClaw. The model runs via ONNX inference on CPU, eliminating API costs and supporting 25 European languages.

Technical implementation

The solution uses a GitHub repository (groxaxo/parakeet-tdt-0.6b-v3-fastapi-openai) that provides a Docker container for CPU deployment. The container exposes an OpenAI-compatible API endpoint at http://127.0.0.1:5092/v1.

Supported languages include: Bulgarian (bg), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), German (de), Greek (el), Hungarian (hu), Italian (it), Latvian (lv), Lithuanian (lt), Maltese (mt), Polish (pl), Portuguese (pt), Romanian (ro), Slovak (sk), Slovenian (sl), Spanish (es), Swedish (sv), Russian (ru), and Ukrainian (uk).

Integration with OpenClaw

The developer provides a Python script for transcription:

#!/home/openclaw/.local/share/pipx/venvs/openai/bin/python
import sys
from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:5092/v1",
    api_key="sk-no-key-required"
)
audio_file = open(sys.argv[1], "rb")
transcript = client.audio.transcriptions.create(
    model="parakeet-tdt-0.6b-v3",
    file=audio_file,
    response_format="text"
)
print(transcript)

This script can be configured in OpenClaw's openclaw.json file:

"tools": {
    "media": {
        "audio": {
            "enabled": true,
            "models": [
                {
                    "type": "cli",
                    "command": "/home/openclaw/.local/bin/transcribe",
                    "args": ["{{MediaPath}}"],
                    "timeoutSeconds": 60
                }
            ]
        }
    }
}

Alternatively, OpenClaw can be configured to directly use the OpenAI-compatible API endpoint with the model name and dummy API key from the script.

Deployment notes

The developer tested this on an ARM64 Ubuntu Linux VM on a Mac Mini with M4 Pro, noting it should run reasonably fast on any decent Intel-compatible CPU. The Docker container is built following the README instructions in the GitHub repository.