![]() ![]() The output of the model gives us a score (essentially, the probability) of how close the model thinks the features match the wake word, “stop.” If that score is over some threshold (we’ll set it at 50%), we can have the Raspberry Pi perform some action. These MFCCs are our features which will be sent to an inference engine running our tflite model file. ![]() Then, we’ll have a microphone always listening, which converts every second of audio to the mel frequency cepstral coefficients (MFCCs). To do that, we need to copy the tflite model file to the Raspberry Pi. ![]() We want to perform real-time inference on the Raspberry Pi so that it will respond to spoken words as they occur. “Inference” (in the machine learning vocabulary) is the process of inferring meaning from a new set of unseen data. When you run this code, it should convert the Keras model into a TensorFlow Lite model file. Open(tflite_filename, 'wb').write(tflite_model) Model = models.load_model(keras_model_filename)Ĭonverter = _keras_model(model) Tflite_filename = 'wake_word_stop_lite.tflite' If you prefer video, this tutorial can be viewed on YouTube here: We can do this to develop our own voice assistant hardware, like the Amazon Echo, or create a new type of hardware interface. In the rest of this tutorial, we will develop a Python program for a Raspberry Pi that reads the TensorFlow Lite model file and uses it to perform wake word recognition in real time. Additionally, TensorFlow Lite model files are optimized for storage, which means they are perfect for use in embedded systems, like single board computers and microcontrollers. This allows processors to stream the data without needing to load it all into memory first. ![]() A FlatBuffer is a special type of storage container that allows large amounts of data to be read in chunks from flash storage. The TensorFlow Lite model file differs from a regular TensorFlow model file in that the weights and operations are stored as a FlatBuffer in the TensorFlow Lite file. In the previous tutorial, we trained a convolutional neural network (CNN) using TensorFlow and Keras to respond to the spoken word “stop.” We saved that model into a file that we will read and convert to a TensorFlow Lite model file in this tutorial. ![]()
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January 2023
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