Training a TensorFlow.js model for Speech Commands Using Browser FFT

This directory contains two example notebooks. They demonstrate how to train custom TensorFlow.js audio models and deploy them for inference. The models trained this way expect inputs to be spectrograms in a format consistent with WebAudio's getFloatFrequencyData. Therefore they can be deployed to the browser using the speech-commands library for inference.

Specifically,

• training_custom_audio_model_in_python.ipynb contains steps to preprocess a directory with audio examples stored as .wav files and the steps in which a tf.keras model can be trained on the preprocessed data. It then demonstrates how the trained tf.keras model can be converted to a TensorFlow.js LayersModel that can be loaded with the speech-command library's create() API. In addition, the notebook also shows the steps to convert the trained tf.keras model to a TFLite model for inference on mobile devices.
• tflite_conversion.ipynb illustrates how an audio model trained on Teachable Machine can be converted to TFLite directly.