no, no tone.js. you can do reverb by some floating point in convolver as an impulse response. modify the whole code to create a more realistic sound in "Oscillators", and also add simple forest reverb to it. also, the current environment cannot use any gpu accel for ML, so use and focus only in cpu. output awesome artificial intelligent bird singer operetessa model.: <!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Bird Singing Generator with ML</title>
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
  <script>
  async function run() {
// Set the backend to cpu
await tf.setBackend("cpu");
    // Initialize the machine learning model
    const model = tf.sequential();
    model.add(tf.layers.dense({ units: 100, inputShape: [1], activation: 'relu' }));
    model.add(tf.layers.dense({ units: 3 }));

    model.compile({
        loss: "meanSquaredError",
        optimizer: "sgd",
        metrics: ["mse"],
    });

    // Generate random training data for the model
    const inputs = tf.tensor2d(new Array(100).fill().map(() => [Math.random()]));
    const outputs = tf.tensor2d(new Array(100).fill().map(() => [Math.random(), Math.random(), Math.random()]));

    // Train the model
    await model.fit(inputs, outputs, { batchSize: 10, epochs: 50 });

    // Generate new parameters for the bird sound using the trained model
    function generateParameters() {
        const input = tf.tensor2d([Math.random()], [1, 1]);
        const [pitchFactor, rhythmFactor, noteDurationFactor] = model.predict(input).arraySync()[0];
        return { pitchFactor, rhythmFactor, noteDurationFactor };
    }

    const audioContext = new (window.AudioContext || window.webkitAudioContext)();

    function playOscillator(freq, startTime, duration) {
        const oscillator1 = audioContext.createOscillator();
        const oscillator2 = audioContext.createOscillator();
        const gainNode = audioContext.createGain();
        const filterNode = audioContext.createBiquadFilter();

        // Setup oscillators
        oscillator1.type = 'sine';
        oscillator1.frequency.setValueAtTime(freq, startTime);
        oscillator2.type = 'sine';
        oscillator2.frequency.setValueAtTime(freq * 1.01, startTime); // Slight detuning for richer sound

        // Filter settings
        filterNode.type = 'bandpass';
        filterNode.frequency.setValueAtTime(freq, startTime);
        filterNode.Q.setValueAtTime(15, startTime);

        // Setup gain (volume)
        gainNode.gain.setValueAtTime(0.5, startTime);
        gainNode.gain.setValueAtTime(0, startTime + duration);

        // Connect the nodes and start playing
        oscillator1.connect(filterNode);
        oscillator2.connect(filterNode);
        filterNode.connect(gainNode);
        gainNode.connect(audioContext.destination);
        oscillator1.start(startTime);
        oscillator1.stop(startTime + duration);
        oscillator2.start(startTime);
        oscillator2.stop(startTime + duration);
    }

    function playChickadeeCall() {
        const startTime = audioContext.currentTime;
        const { pitchFactor, rhythmFactor, noteDurationFactor } = generateParameters();

        // Play the 'chick' note
        playOscillator(
            4000 * pitchFactor,
            startTime,
            0.1 * rhythmFactor * noteDurationFactor
        );

        // Play the 'dee' notes
        for (let i = 1; i <= 3; i++) {
            playOscillator(
                2000 * pitchFactor,
                startTime + 0.15 * i * rhythmFactor,
                0.1 * noteDurationFactor
            );
        }
    }

    function loopChickadeeCall() {
        playChickadeeCall();
        setTimeout(loopChickadeeCall, Math.random() * 1000 + 500);
    }

    loopChickadeeCall();
}

run();
  </script>
</head>
<body>
  <h1>Bird Singing Generator with Machine Learning</h1>
</body>
</html>