This project allows to detect a specific utterance on a live audio stream, to do so it generates a set of features from some audio samples to later compare them with the features generated from the stream, to calculate the probability of a match.
The features can be loaded from a previous generated model file or extracted from the samples before start the live streaming.
A CLI for Rustpotter is available here.
rust
let mut detector_builder = detector::FeatureDetectorBuilder::new();
let mut word_detector = detector_builder.build();
let name = String::from("hey home");
let path = String::from("./hey_home.rpw");
word_detector.add_keyword(
name.clone(),
false,
true,
None,
vec!["./<audio sample path>.wav", "./<audio sample path>2.wav", ...],
);
match word_detector.create_wakeword_model(name.clone(), path) {
Ok(_) => {
println!("{} created!", name);
}
Err(message) => {
panic!(message);
}
};
```rust
let mut detectorbuilder = detector::FeatureDetectorBuilder::new();
detectorbuilder.setthreshold(0.4);
detectorbuilder.setsamplerate(16000);
let mut worddetector = detectorbuilder.build();
let result = worddetector.addkeywordfrommodel(command.modelpath, command.averagetemplates, true, None);
if result.iserr() {
panic!("Unable to load keyword model");
}
while true {
let mut framebuffer: Vec
```
This project started as a port of the project node-personal-wakeword and uses the method described in this medium article.
The motivation behind this project is to learn about audio analysis and Rust, also to have access to an open source personal wakeword spotter to use in other open projects. Feel free to suggest any improvements or fixes.