Wake Word Detection (also known as Hot word detection) is a technique mainly used in ChatBots to wake them. 'Okay Google', 'Siri' and 'Alexa' are the wake words used by Google assistant, Apple and Amazon's Alexa respectively.
The main function of any chatbot is to take a audio input by user and answer what it is designed for! Let's take an example of Google Assistant, as soon as it listen the wake words it starts to take the audio input by user (and sends it to cloud for speech synthesis) and then gives the answer. That's why the Wake word prevents us to send every thing on clound for speech synthesis. As speech synthesis takes too much computational power, wake word save this by only sending the audio fragment which user wants it to synthesize. In short, Wake word is a point in time from when we have to start doing speech synthesis.
In this project, we have created a Wake word detection using TensorFlow. Which has an awesome accuracy of 98% on the test data. You can even train this Deep learning Model on whatever Wake Word you like. So, let us see how you can do it...
First, go to the file PreparingData.py. It contains two function record_audio_and_save() and record_background_sound().
record_audio_and_save():- It records a audio of 2 seconds of you saying the Wake Word. It takes two parameters namely n_times and save_path. n_times is 'How many times it
should record you saying the Wake Word?'. Default is set to 50. In save_path you have to provide the path to the directory where it can store generated
.wav files.
record_background_sound():- It records a audio of 2 seconds of the background sounds. It takes two parameters namely n_times and save_path. n_times is 'How many times it
should record the backgound sounds?'. Default is set to 50. In save_path you have to provide the path to the directory where it can store generated
.wav files.
Note:- Don't provide the same directory to both the functions or it will overwrite the previous!
In this file you don't have to do anything it will take all your audio files, pre-process it, make a pandas dataframe and save it as csv from where we will load it for training.
Note:- If you have changed the directory of audio files from default audio_data/ and background_sound/ to some other. Don't forget it to change in this file too.
Sit and relax! Let the model train...
Okay... What is does it takes the csv file and convert the data to numpy array.
The Model architecture which is as follows:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 256) 10496
_________________________________________________________________
activation (Activation) (None, 256) 0
_________________________________________________________________
dropout (Dropout) (None, 256) 0
_________________________________________________________________
dense_1 (Dense) (None, 256) 65792
_________________________________________________________________
activation_1 (Activation) (None, 256) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 2) 514
=================================================================
Total params: 76,802
Trainable params: 76,802
Non-trainable params: 0
_________________________________________________________________
After that it will save the model for future use...
It will load the saved model and run the prediction every time it prints Say Now: it records the sound live and give it to the model for prediction which it then decides
whether it has wake word or not!
My advice is to go through to the each file and see what it does rather than blindly running them.