Wake word detection is a popular technology used in voice-activated devices to listen for a specific word or phrase to wake up the device and start processing further commands. In this tutorial, we will show you how to implement wake word detection using TensorFlow Lite Micro, a lightweight machine learning library tailored for microcontrollers.
Step 1: Set up your project
To begin, create a new HTML file and add the necessary imports and scripts to use TensorFlow Lite Micro. You can download the TensorFlow Lite Micro library from the official GitHub repository.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<title>Wake Word Detection Tutorial</title>
<script src="https://cdn.jsdelivr.net/npm/tfjs@3.9.0/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter@3.9.0/dist/tfjs-converter.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter@3.9.0/dist/tfjs-converter.js"></script>
</head>
<body>
</body>
</html>Step 2: Load the TensorFlow Lite Micro model
Now, we need to load the pre-trained TensorFlow Lite Micro model for wake word detection. You can use models like "Hey Google" or "Alexa" or train your custom wake word model.
<script>
async function loadModel() {
const modelPath = 'models/wake_word_model.tflite';
const model = await tf.lite.loadTFLiteModel(modelPath);
console.log('Model loaded successfully.');
}
loadModel();
</script>Step 3: Gather audio input
To perform wake word detection, we need to capture audio input from the user’s microphone. You can use the Web Audio API to access the user’s microphone and process audio data.
<script>
async function captureAudio() {
const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
const audioContext = new AudioContext();
const microphone = audioContext.createMediaStreamSource(stream);
// Process audio data here...
}
captureAudio();
</script>Step 4: Preprocess audio data
Before feeding the audio data into the TensorFlow Lite model, we need to preprocess the data by converting it into a format compatible with the model’s input layer.
<script>
function preprocessAudioData(audioData) {
// Preprocess audio data here...
return preprocessedData;
}
</script>Step 5: Perform wake word detection
Finally, we can feed the preprocessed audio data into the TensorFlow Lite model and perform wake word detection to determine if the input matches the wake word.
<script>
async function detectWakeWord(audioData) {
const inputTensor = tf.tensor(preprocessAudioData(audioData));
const output = model.predict(inputTensor);
// Check if the output indicates the presence of the wake word
if (output > 0.5) {
console.log('Wake word detected!');
} else {
console.log('No wake word detected.');
}
}
</script>That’s it! You have successfully implemented wake word detection using TensorFlow Lite Micro in an HTML document. Remember to customize the model and preprocessing steps according to your requirements.
hi, any idea on how to use our own custom dataset?
Thank you