ml.apps — ML Apps

The ml.apps module contains various ML application classes.

class MicroSpeech – Speech Recognition

The MicroSpeech object is used to recognize simple spoken words using the MicroSpeech model from TensorFlow Lite for Microcontrollers. The model recognizes the words “yes” and “no” by default.

You can customize the model to recognize other words by training a new model. See the Micro Speech guide.

Constructors

class ml.apps.MicroSpeech(preprocessor: str = None, micro_speech: str = None, labels: list[str, ...] = None) → MicroSpeech

Creates a MicroSpeech object. If no preprocessor is provided, the default preprocessor is used. If no micro_speech model is provided, the default model is used. If no labels are provided, the default labels are used from the default model.

Methods

listen(timeout: int = 0, callback=None, threshold: float = 0.65, filter: list[str, ...] = ['Yes', 'No']) → tuple[str, float]

Listens for a spoken word and returns the word and confidence level as a tuple if the confidence level is above the threshold and the word is in the filter list.

timeout is the maximum time in milliseconds to listen for a word. If zero, the method will listen indefinitely until a word is recognized. If -1 is passed, the method will not block and will return immediately with the result tuple which may contain None if no word is recognized. If a positive value is passed, the method will listen for that amount of time in milliseconds and then return the result tuple.

callback is a function that will be called with the word and confidence level instead of returning the result. When combined with a timeout of zero, this allows you to listen for words indefinitely and process them as they are recognized.

threshold is the minimum confidence level required to return a result.

filter is a list of words that the model should recognize. If the recognized word is not in the filter list, the result is ignored.