//

// This software is released under the MIT License.

// https://opensource.org/licenses/MIT

​

/* ===

ml5 Example

Sound classification using pre-trained custom SpeechCommands18w and p5.js

This example uses a callback pattern to create the classifier

=== */

​

const modelJson = 'https://teachablemachine.withgoogle.com/models/-WRDWUT2c/';

// Two variable to hold the label and confidence of the result

let label;

let confidence;

// Initialize a sound classifier method.

let classifier;

​

function preload() {

  // Load the pre-trianed custom SpeechCommands18w sound classifier model

  classifier = ml5.soundClassifier(modelJson + 'model.json');

}

​

function setup() {

  noCanvas();

  // ml5 also supports using callback pattern to create the classifier

  // classifier = ml5.soundClassifier(modelJson, modelReady);

​

  // Create 'label' and 'confidence' div to hold results

  label = createDiv('Label: ...');

  confidence = createDiv('Confidence: ...');

  // Classify the sound from microphone in real time

  classifier.classify(gotResult);

}

​

// If you use callback pattern to create the classifier, you can use the following callback function

// function modelReady() {

//   classifier.classify(gotResult);

// }

​

// A function to run when we get any errors and the results

function gotResult(error, results) {

  // Display error in the console

  if (error) {

    console.error(error);

  }

  // The results are in an array ordered by confidence.

  console.log(results);

  // Show the first label and confidence

  label.html('Label: ' + results[0].label);

  confidence.html('Confidence: ' + nf(results[0].confidence, 0, 2)); // Round the confidence to 0.01

}

​