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83 lines
2.5 KiB
Markdown
83 lines
2.5 KiB
Markdown
<div align="center">
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<img src="https://raw.githubusercontent.com/MasterPhooey/MicroWakeWord-Trainer-Docker/refs/heads/main/mmw.png" alt="MicroWakeWord Trainer Logo" width="100" />
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<h1>microWakeWord Trainer Docker</h1>
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</div>
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Easily train MicroWakeWord detection models with this pre-built Docker image.
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## Prerequisites
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- Advanced notebook requires Nvidia RTX xx90, you need more then 20GB vram.
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## Quick Start
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Follow these steps to get started with the microWakeWord Trainer:
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### 1. Pull the Pre-Built Docker Image
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Pull the Docker image from Docker Hub:
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```bash
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docker pull masterphooey/microwakeword-trainer
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```
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### 2. Run the Docker Container
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Start the container with a mapped volume for saving your data and expose the Jupyter Notebook:
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```bash
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docker run --rm -it \
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--gpus all \
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-p 8888:8888 \
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-v $(pwd):/data \
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masterphooey/microwakeword-trainer
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```
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--gpus all: Enables GPU acceleration.
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-p 8888:8888: Exposes the Jupyter Notebook on port 8888.
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-v $(pwd):/data: Maps the current directory to the container's /data directory for saving your files.
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### 3. Access Jupyter Notebook
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Open your web browser and navigate to:
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```bash
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http://localhost:8888
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```
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The notebook interface should appear.
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### 4. Edit the Wake Word
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Locate and edit the second cell in the notebook to specify your desired wake word:
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```bash
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target_word = 'khum_puter' # Phonetic spellings may produce better samples
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```
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Change 'khum_puter' to your desired wake word.
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### 5. Run the Notebook
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Run all cells in the notebook. The process will:
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Generate wake word samples.
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Train a detection model.
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Output a quantized .tflite model for on-device use.
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### 6. Retrieve the Trained Model and JSON
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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.
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## Resetting to a Clean State
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If you need to start fresh:
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### Delete your data folder:
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Locate and delete the data folder that was mapped to your Docker container.
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### Restart the Docker container:
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Run the container again using the steps provided above.
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### Fresh notebook generated:
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Upon restarting, a clean version of the training notebook will be placed in the newly created data directory.
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This will reset your MicroWakeWord-Training-Docker environment to its initial state.
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## Credits
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This project builds upon the excellent work of [kahrendt/microWakeWord](https://github.com/kahrendt/microWakeWord). A huge thank you to the original authors for their contributions to the open-source community!
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