mirror of
https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git
synced 2026-06-12 20:10:19 -06:00
83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
<div align="center">
|
|
<img src="https://raw.githubusercontent.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker/refs/heads/main/mmw.png" alt="MicroWakeWord Trainer Logo" width="100" />
|
|
<h1>microWakeWord Trainer Docker</h1>
|
|
</div>
|
|
|
|
Easily train microWakeWord detection models with this pre-built Docker image.
|
|
|
|
## Prerequisites
|
|
|
|
- Advanced notebook requires Nvidia RTX xx90, you need more then 20GB vram.
|
|
|
|
## Quick Start
|
|
|
|
Follow these steps to get started with the microWakeWord Trainer:
|
|
|
|
### 1. Pull the Pre-Built Docker Image
|
|
|
|
Pull the Docker image from Docker Hub:
|
|
```bash
|
|
docker pull ghcr.io/tatertotterson/microwakeword:latest
|
|
```
|
|
|
|
### 2. Run the Docker Container
|
|
|
|
Start the container with a mapped volume for saving your data and expose the Jupyter Notebook:
|
|
```bash
|
|
docker run --rm -it \
|
|
--gpus all \
|
|
-p 8888:8888 \
|
|
-v $(pwd):/data \
|
|
ghcr.io/tatertotterson/microwakeword:latest
|
|
```
|
|
--gpus all: Enables GPU acceleration.
|
|
-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:
|
|
```bash
|
|
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:
|
|
```bash
|
|
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](https://github.com/kahrendt/microWakeWord). A huge thank you to the original authors for their contributions to the open-source community!
|
|
|
|
|
|
|
|
|