Edge Impulse Inc. / Tutorial: Responding to your voice
This is the finished Edge Impulse project for the tutorial 'Responding to your voice'. From here you acquire new training data, design impulses and train models.
About this project
Hello, World!
Have you ever wanted to make your own "Ok, Google" or "Alexa" keyword spotting model? Clone this project to build an embedded ML project detecting the "Hello World" keyword phrase.
You can also follow our tutorial to guide you through building your keyword spotting model, from data collection to deployment on embedded devices.
Sensor & Block Information
- Microphone audio data (.wav files) @ 16000 Hz
- MFCC DSP block for human voice audio
- Neural Network Classifier with prediction outputs: "helloworld", "noise", "unknown"
Creating your first impulse (100% complete)
Acquire data
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Deploy
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| MFCC training data | NPY file | 1649 windows | |
| MFCC training labels | NPY file | 1649 windows | |
| MFCC testing data | NPY file | 413 windows | |
| MFCC testing labels | NPY file | 413 windows | |
| NN Classifier model | TensorFlow Lite (float32) | 18 KB | |
| NN Classifier model | TensorFlow Lite (int8 quantized) | 9 KB | |
| NN Classifier model | TensorFlow Lite (int8 quantized with float32 input and output) | 9 KB | |
| NN Classifier model | TensorFlow SavedModel | 28 KB |
Summary
Data collected
34m 22sProject info
| Project ID | 69619 |
| Project version | 8 |
|
|
