Training a TensorFlow.js model for Speech Commands Using Browser FFT
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’screate()
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.