Training models
Look [here](https://github.com/dscripka/openWakeWord?tab=readme-ov-file#training-new-models) for information about how to train your own OpenWakeWord models. You can use a [simple Google Colab notebook](https://colab.research.google.com/drive/1q1oe2zOyZp7UsB3jJiQ1IFn8z5YfjwEb?usp=sharing) for a start or use a [more detailed notebook](https://github.com/dscripka/openWakeWord/blob/main/notebooks/automatic_model_training.ipynb) that enables more customization (can produce high quality models, but requires more development experience).
Convert model to ONNX format
You might need to use tf2onnx to convert tensorflow tflite models to onnx format:
bash
pip install -U tf2onnx
python -m tf2onnx.convert --tflite my_model_filename.tflite --output my_model_filename.onnx
Configure RealtimeSTT
Suggested starting parameters for OpenWakeWord usage:
python
with AudioToTextRecorder(
wakeword_backend="oww",
wake_words_sensitivity=0.35,
openwakeword_model_paths="word1.onnx,word2.onnx",
wake_word_buffer_duration=1,
) as recorder:
OpenWakeWord Test
1. Set up the openwakeword test project:
bash
mkdir samantha_wake_word && cd samantha_wake_word
curl -O https://raw.githubusercontent.com/KoljaB/RealtimeSTT/master/tests/openwakeword_test.py
curl -L https://huggingface.co/KoljaB/SamanthaOpenwakeword/resolve/main/suh_mahn_thuh.onnx -o suh_mahn_thuh.onnx
curl -L https://huggingface.co/KoljaB/SamanthaOpenwakeword/resolve/main/suh_man_tuh.onnx -o suh_man_tuh.onnx
Ensure you have `curl` installed for downloading files. If not, you can manually download the files from the provided URLs.
2. Create and activate a virtual environment:
bash
python -m venv venv
- For Windows:
bash
venv\Scripts\activate
- For Unix-like systems (Linux/macOS):
bash
source venv/bin/activate
- For macOS:
Use `python3` instead of `python` and `pip3` instead of `pip` if needed.
3. Install dependencies:
bash
python -m pip install --upgrade pip
python -m pip install RealtimeSTT
python -m pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu121
The PyTorch installation command includes CUDA 12.1 support. Adjust if a different version is required.
4. Run the test script:
bash
python openwakeword_test.py
On the very first start some models for openwakeword are downloaded.