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61 lines
1.5 KiB
Markdown
61 lines
1.5 KiB
Markdown
# microWakeWord Trainer Docker
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Easily train wake word detection models with this pre-built Docker image.
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## Prerequisites
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- Docker installed on your system.
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- An NVIDIA GPU with CUDA support (optional but recommended for faster training).
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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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### 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
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Once the training is complete, the quantized .tflite model will be available for download. Follow the instructions in the last cell of the notebook to download the model.
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