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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. --- ## 🚀 Quick Start Follow these steps to get up and running: ### 1️⃣ Pull the Pre-Built Docker Image ```bash docker pull ghcr.io/tatertotterson/microwakeword:latest ``` --- ### 2️⃣ Run the Docker Container ```bash 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 --- ### 3️⃣ Open JupyterLab Visit [http://localhost:8888](http://localhost:8888) in your browser — the notebook UI will open. --- ### 4️⃣ Set Your Wake Word At the **top of the notebook**, find this line: ```bash TARGET_WORD = "hey_tater" # Change this to your desired wake word ``` Change `"hey_tater"` to your desired wake word (phonetic spellings often work best). --- ### 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 --- ### 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. --- ## 🔄 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. --- ## 🙌 Credits This project builds upon the excellent work of [kahrendt/microWakeWord](https://github.com/kahrendt/microWakeWord). Huge thanks to the original authors for their contributions to the open-source community!