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| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Magenta.js Model Loader and Player</title> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.11.0"></script> | |
| <script src="https://cdn.jsdelivr.net/npm/@magenta/music"></script> | |
| </head> | |
| <body> | |
| <button onclick="generateAndPlayMusic()">Generate and Play Music</button> | |
| <script> | |
| class MSEWithPositivePressure extends tf.layers.Layer { | |
| constructor() { | |
| super({}); | |
| } | |
| call(inputs) { | |
| let y_true = inputs[0]; | |
| let y_pred = inputs[1]; | |
| let mse = tf.mean(tf.square(tf.sub(y_true, y_pred))); | |
| let positive_pressure = tf.mean(tf.maximum(tf.scalar(0), tf.neg(y_pred))); | |
| return tf.add(mse, positive_pressure); | |
| } | |
| static get className() { | |
| return 'MSEWithPositivePressure'; | |
| } | |
| } | |
| // tf.serialization.registerClass(MSEWithPositivePressure); | |
| let model; | |
| async function loadModel() { | |
| tf.serialization.registerClass(MSEWithPositivePressure); | |
| // model = await tf.loadLayersModel('/js_model/model.json'); | |
| model = await tf.loadGraphModel('/js_model/model.json'); | |
| console.log("Model loaded successfully!"); | |
| } | |
| async function generateAndPlayMusic() { | |
| if (!model) { | |
| await loadModel(); | |
| } | |
| let inputValues = [60, 0.5, 0.5, 62, 0.5, 0.5, 64, 0.5, 0.5]; | |
| let numNotes = inputValues.length / 3; | |
| let inputSequence; | |
| if (numNotes > 25) { | |
| let inputData = new Array(numNotes * 3).fill(0).concat(inputValues); | |
| inputSequence = tf.tensor3d(inputData, [1, numNotes, 3]); | |
| } else { | |
| const padding = new Array((25 - numNotes) * 3).fill(0); | |
| let inputData = padding.concat(inputValues); | |
| inputSequence = tf.tensor3d(inputData, [1, 25, 3]); | |
| } | |
| // inputSequence = inputSequence.bufferSync(); | |
| // for (let i = 0; i < inputValues.length; i++) { | |
| // inputSequence.set(inputValues[i], 0, 24 - numNotes + Math.floor(i / 3), i % 3); | |
| // } | |
| // inputSequence = inputSequence.toTensor(); | |
| const temperature = 2.0 // 0.5 // 2.0; | |
| const numPredictions = 40; // 120; | |
| let generatedNotes = []; | |
| for (let i = 0; i < numPredictions; i++) { | |
| const predictions = await model.executeAsync(inputSequence); | |
| // const pitchProbs = tf.softmax(predictions[2]); | |
| // const pitch = tf.multinomial(pitchProbs, 1).dataSync()[0]; | |
| // const pitchProbs = tf.softmax(predictions[2].dataSync()).div(temperature); | |
| // const pitch = tf.multinomial(pitchProbs, 1).dataSync()[0]; | |
| // const pitchLogitsArray = predictions[2].dataSync(); | |
| const pitchLogitsArray = predictions[2].dataSync().map(value => value / temperature); | |
| // const pitchLogitsTensor = tf.tensor(pitchLogitsArray).div(temperature); | |
| // const pitchProbs = tf.softmax(pitchLogitsTensor); | |
| const pitchProbs = tf.softmax(pitchLogitsArray); | |
| const pitch = tf.multinomial(pitchProbs, 1).dataSync()[0]; | |
| const clippedPitch = Math.min(Math.max(pitch, 21), 108); | |
| const step = Math.max(0, predictions[1].dataSync()[0]); | |
| const duration = Math.max(0, predictions[0].dataSync()[0]); | |
| // console.log('///////////////////') | |
| // console.log(predictions[0].dataSync(),predictions[1].dataSync(),predictions[2].dataSync()) | |
| // console.log('/////////////////// //') | |
| // console.log({predictions, pitch, step, duration}) | |
| console.log('pitch:', pitch, {pitchLogitsArray, pitchProbs}) | |
| generatedNotes.push([clippedPitch, step, duration]); | |
| // ζ°γγγγΌγγηζ | |
| const newNote = tf.tensor3d([[[clippedPitch * 1.0, step, duration]]], [1, 1, 3]); | |
| // ε ₯εγ·γΌγ±γ³γΉγ«ζ°γγγγΌγγθΏ½ε | |
| inputSequence = inputSequence.slice([0, 1, 0], [-1, -1, -1]).concat(newNote, 1); | |
| } | |
| // ηζγγγγγΌγγNoteSequenceγ«ε€ζ | |
| const noteSequence = { | |
| ticksPerQuarter: 220, | |
| totalTime: generatedNotes.length / 2, | |
| timeSignatures: [{ time: 0, numerator: 4, denominator: 4 }], | |
| tempos: [{ time: 0, qpm: 120 }], | |
| notes: generatedNotes.map((note, index) => ({ | |
| startTime: index / 2, | |
| endTime: (index + 1) / 2, | |
| pitch: note[0], | |
| velocity: 80 | |
| })) | |
| }; | |
| // // NoteSequenceγεηγγ | |
| // const player = new mm.Player(); | |
| // player.start(noteSequence); | |
| // Play the note sequence using SoundFontPlayer | |
| const soundfontURL = 'https://storage.googleapis.com/magentadata/js/soundfonts/sgm_plus'; | |
| const player = new mm.SoundFontPlayer(soundfontURL); | |
| player.start(noteSequence); | |
| } | |
| </script> | |
| </body> | |
| </html> | |