# Your own wake word

Source: https://heyparlour.app/docs/wake-word

> Train a wake word of your own and drop it in.

The stock words are `hey_jarvis`, `alexa` and `hey_mycroft`. If you would
rather Parlour answered to its own name, train one. openWakeWord makes this
an hour's work on a free Colab GPU, and Parlour runs any model the notebook
produces. The provider in `packages/parlour/src/providers/wake/openwakeword.ts`
uses the same three stage pipeline for every word. A new word is just a new
`.onnx` file and one line of config.

## Train it

Open the simple notebook and set the runtime to a GPU (Runtime > Change
runtime type > T4):

https://colab.research.google.com/drive/1q1oe2zOyZp7UsB3jJiQ1IFn8z5YfjwEb

Fill in the form cell and run everything.

| Field | Try | Why |
| --- | --- | --- |
| `target_word` | `hey parlor` | Piper synthesises the training clips and reads spellings literally. The US spelling comes out right, while "parlour" is sometimes read as "par-loor". The model learns the sound, not the spelling, so spell it however you say it. |
| `number_of_examples` | `2000` | The default is 1000. More examples cost minutes and noticeably tighten the model. |
| `number_of_training_steps` | `20000` | The default is 10000. |
| `false_activation_penalty` | `1500` | Leave it for the first model. Raise it towards 3000 if the result wakes for the television. |

The notebook downloads a couple of gigabytes of speech and noise without your
word, generates clips with it, trains, and writes
`my_custom_model/hey_parlor.onnx`. Download that file from the file browser
on the left of the notebook.

If the first model fires too easily or not at all, try the
[full notebook](https://github.com/dscripka/openWakeWord/blob/main/notebooks/automatic_model_training.ipynb).
It runs the same process with two extra levers that matter more than any of
the numbers above. `target_phrase` takes a list of spellings
(`["hey parlour", "hey parlor", "hey par lur"]`). `custom_negative_phrases`
takes things that sound like your word but should not fire
(`["hey harlow", "hey carla", "parlay", "hey pilot"]`).

## Install it

```bash
mv ~/Downloads/hey_parlor.onnx ~/Library/Caches/parlour/models/openwakeword/hey_parlour.onnx
```

The file name, without the `.onnx`, is what goes in the config:

```json
{ "wake": { "provider": "openwakeword", "words": ["hey_parlour", "hey_jarvis"], "threshold": 0.5, "refractoryMs": 1500 } }
```

`parlour restart` picks it up. `parlour doctor` confirms it, or names the
file it cannot find. Keep `hey_jarvis` in the list for the first day, as above. It
costs a little CPU and gives you a word you know works while you judge the new
one.

The provider feeds each word the last 16 speech embeddings. Models the
notebooks train for a phrase of a few syllables expect exactly that. If yours
stays silent, check its inputs first, from a checkout of this repository:

```bash
cd packages/parlour && node -e '
const ort = require("onnxruntime-node");
ort.InferenceSession.create(process.env.HOME + "/Library/Caches/parlour/models/openwakeword/hey_parlour.onnx")
  .then((s) => console.log(s.inputNames, s.outputNames))'
```

## Tune it

Every detection is logged with its score, such as `"hey_parlour" fired at
0.83 on local`, so leave it running and talk to it. Custom models tend to
score a little lower than the stock ones, so you can bring `threshold` down
to 0.4. If it wakes for the room, raise `false_activation_penalty` and
retrain. Pushing the threshold much past 0.7 mostly stops it hearing you too.
The other settings are in [Tuning](/docs/tuning).