microWakeWord Trainer Docker

🥔 MicroWakeWord Trainer – Tater Approved

✅ Tater Totterson tested & working on an NVIDIA RTX 3070 Laptop GPU (8 GB VRAM).
Easily train microWakeWord detection models with this pre-built Docker image and JupyterLab notebook.

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🚀 Quick Start

Follow these steps to get up and running:

1️⃣ Pull the Pre-Built Docker Image

docker pull ghcr.io/tatertotterson/microwakeword:latest

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2️⃣ Run the Docker Container

docker run --rm -it \
    --gpus all \
    -p 8888:8888 \
    -v $(pwd):/data \
    ghcr.io/tatertotterson/microwakeword:latest

What these flags do:

• --gpus all → Enables GPU acceleration
• -p 8888:8888 → Exposes JupyterLab on port 8888
• -v $(pwd):/data → Saves your work in the current folder

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3️⃣ Open JupyterLab

Visit http://localhost:8888 in your browser — the notebook UI will open.

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4️⃣ Set Your Wake Word

At the top of the notebook, find this line:

TARGET_WORD = "hey_tater"  # Change this to your desired wake word

Change "hey_tater" to your desired wake word (phonetic spellings often work best).

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5️⃣ Run the Notebook

Run all cells in the notebook. This process will:

• Generate wake word samples
• Train a detection model
• Output a quantized .tflite model ready for on-device use

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6️⃣ Retrieve the Trained Model & JSON

When training finishes, download links for both the .tflite model and its .json manifest will be displayed in the last cell.

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🔄 Resetting to a Clean State

If you need to start fresh:

1. Delete the data folder that was mapped to your Docker container.
2. Restart the container using the steps above.
3. A fresh copy of the notebook will be placed into the data directory.

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🙌 Credits

This project builds upon the excellent work of kahrendt/microWakeWord.
Huge thanks to the original authors for their contributions to the open-source community!