microWakeWord Custom Trainer

Easily train microWakeWord detection models with this pre-built Docker image.

Prerequisites

• Docker installed on your system.
• An NVIDIA GPU with CUDA support (optional but recommended for faster training).

Quick Start

Follow these steps to get started with the microWakeWord Custom Trainer:

1. Build the Docker Imagte

If you don't have the directory already on your machine, clone the GitHub repo:

git clone https://github.com/stujenn/microWakeWord-Custom-Trainer
cd microWakeWord-Trainer-Docker

Build the docker image from the provided dockerfile:

docker build -t microwakeword-custom-trainer .

2. Run the Docker Container

Start the container with a mapped volume for saving your data and expose the Jupyter Notebook:

docker run --rm -it \
    --gpus all \
    -p 8888:8888 \
    -v $(pwd):/data \
    microwakeword-custom-trainer

--gpus all: Enables GPU acceleration (optional, remove if not using a GPU). -p 8888:8888: Exposes the Jupyter Notebook on port 8888. -v $(pwd):/data: Maps the current directory to the container's /data directory for saving your files.

3. Access Jupyter Notebook

Open your web browser and navigate to:

http://localhost:8888

The notebook interface should appear.

4. Edit the Wake Word

Locate and edit the second cell in the notebook to specify your desired wake word:

target_word = 'khum_puter'  # Phonetic spellings may produce better samples

Change 'khum_puter' to your desired wake word.

5. Run the Notebook

Run all cells in the notebook. The process will:

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

6. Retrieve the Trained Model and JSON

Once the training is complete, the quantized .tflite model and .json will be available for download. Follow the instructions in the last cell of the notebook to download the model.

Resetting to a Clean State

If you need to start fresh:

Delete your data folder:

Locate and delete the data folder that was mapped to your Docker container.

Restart the Docker container:

Run the container again using the steps provided above.

Fresh notebook generated:

Upon restarting, a clean version of the training notebook will be placed in the newly created data directory. This will reset your MicroWakeWord-Training-Docker environment to its initial state.

Credits

This project builds upon the excellent work of kahrendt/microWakeWord. A huge thank you to the original authors for their contributions to the open-source community!