Your own 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.
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
mv ~/Downloads/hey_parlor.onnx ~/Library/Caches/parlour/models/openwakeword/hey_parlour.onnxThe file name, without the .onnx, is what goes in the config:
{ "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:
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.