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Wake Word workflows only download the models the author selected — not every file in the openWakeWord catalog.

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Compile-time provision (cloud)

When you compile or edge-sync a workflow that contains a Wake Word Engine node, WorkflowCodeAssembler calls:
Then verifies load with Model(wakeword_models=[...]) and records prediction_keys from oww_model.models.keys().
The Django compile host must have openwakeword and onnxruntime in cyberwave-backend/requirements/base.txt. Rebuild the Django Docker image after adding them. See Edge workflow dependencies.

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Compile-server packages

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Generated worker (edge)

The compiled wf_<uuid>.py module begins with:
Models are fetched again at worker import (before hooks run) and once more when the engine singleton is created.

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Edge-sync metadata

Each Wake Word node contributes to model_requirements:
Edge operators use this to confirm the worker image and cache contain the required weights.

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Edge worker image

cyberwaveos/edge-ml-worker Dockerfile runs:
at build time (~20 MB all six built-in models). Workflow-specific download_models(model_names=[...]) still runs for clarity and custom images.

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Custom images (air-gapped)

Or download only what you need:

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Threshold vs compile

Detection threshold is a runtime node parameter (default 0.5, one value for all selected wake words). It is embedded in compiled wf_*.py as threshold=... on the engine singleton. Changing threshold only requires save → compile → sync — no extra model download.

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After changing wake words or threshold

  1. Save the Wake Word node in the editor (wake words and/or Detection Threshold).
  2. Compile the workflow (validates models download).
  3. Sync to the edge environment.
  4. Restart the workflow worker container.
  5. Confirm logs: prediction_keys=['alexa'] (not alexa_v0.1) and threshold=0.50 in init / detection lines.