# `micro_speech` — Micro Speech Audio Module Example

The `micro_speech` module runs Google’s TensorFlow Lite for Microcontrollers Micro Speech framework for voice recognition.

Please see this guide for training a new model.

## Constructors

class micro_speech.MicroSpeech

  Creates a MicroSpeech voice recognition class.

  audio_callback(buf_in)

    Pass this method to `audio.start_streaming()` to fill the `MicroSpeech` class with audio samples.

    `MicroSpeech` will compute the FFT of the audio samples and keep a sliding window internally of the FFT the last 100ms or so of audio samples received as features for voice recognition.

  listen(tf_model[, threshold=0.9[, timeout=1000[, filter=None]]])

    Executes the tensor flow lite model `tf_model`, which should be a path to a tensor flow lite model on disk, on the audio stream.

    This method will continue to execute the model until it classifies a result that has a confidence ratio above `threshold` and that’s within the range specified by `filter`.

    For example, if the model is designed to classify sounds into the four labels [‘Silence’, ‘Unknown’, ‘Yes’, ‘No’], then a `threshold` of 0.7 mean that listen() only returns when the confidence score for one of those classes goes above 0.7. `filter` can then be `[2, 3]` to specify that we only care about ‘Yes’ or ‘No’ going above 0.7.

    `timeout` is the amount of time to run the model on audio data. If zero then listen will run forever until a result passes the threshold and filter criteria.

    Returns the index of the the label with the highest confidence score. E.g. for the example above 0, 1, 2, or 3 for [‘Silence’, ‘Unknown’, ‘Yes’, ‘No’] respectively.

Language and External Links

Language
  English
  中文

External links
  openmv.io
  forums.openmv.io
  github.com/openmv/openmv
  micropython.org
  forum.micropython.org
  github.com/micropython/micropython