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https://github.com/eliasstepanik/strudel.git
synced 2026-01-11 21:58:37 +00:00
disable lint for global worklet variables
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@ -219,7 +219,7 @@ export function applyFM(param, value, begin) {
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if (fmModulationIndex) {
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const ac = getAudioContext();
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const envGain = ac.createGain();
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const { node: modulator } = fm(param, fmHarmonicity, fmModulationIndex, fmWaveform);
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const { node: modulator, stop } = fm(param, fmHarmonicity, fmModulationIndex, fmWaveform);
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if (![fmAttack, fmDecay, fmSustain, fmRelease, fmVelocity].find((v) => v !== undefined)) {
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// no envelope by default
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modulator.connect(param);
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@ -1,23 +1,6 @@
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const processSample = (inputs, outputs, processBlock) => {
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const input = inputs[0];
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const output = outputs[0];
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const blockSize = 128;
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if (input == null || output == null) {
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return false;
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}
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for (let n = 0; n < blockSize; n++) {
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input.forEach((inChannel, i) => {
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const outChannel = output[i % output.length];
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const block = inChannel[n];
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outChannel[n] = processBlock(block, n, inChannel, outChannel);
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});
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}
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return true;
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};
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// coarse, crush, and shape processors adapted from dktr0's webdirt: https://github.com/dktr0/WebDirt/blob/5ce3d698362c54d6e1b68acc47eb2955ac62c793/dist/AudioWorklets.js
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// LICENSE GNU General Public License v3.0 see https://github.com/dktr0/WebDirt/blob/main/LICENSE
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class CoarseProcessor extends AudioWorkletProcessor {
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static get parameterDescriptors() {
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return [{ name: 'coarse', defaultValue: 1 }];
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@ -28,15 +11,24 @@ class CoarseProcessor extends AudioWorkletProcessor {
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}
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process(inputs, outputs, parameters) {
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const input = inputs[0];
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const output = outputs[0];
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const blockSize = 128;
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let coarse = parameters.coarse[0] ?? 0;
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coarse = Math.max(1, coarse);
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return processSample(inputs, outputs, (block, n, inChannel, outChannel) => {
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const value = n % coarse === 0 ? block : outChannel[n - 1];
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return value;
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});
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if (input[0] == null || output[0] == null) {
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return false;
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}
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for (let n = 0; n < blockSize; n++) {
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for (let i = 0; i < input.length; i++) {
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output[i][n] = n % coarse === 0 ? input[i][n] : output[i][n - 1];
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}
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}
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return true;
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}
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}
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registerProcessor('coarse-processor', CoarseProcessor);
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class CrushProcessor extends AudioWorkletProcessor {
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@ -49,13 +41,23 @@ class CrushProcessor extends AudioWorkletProcessor {
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}
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process(inputs, outputs, parameters) {
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const input = inputs[0];
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const output = outputs[0];
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const blockSize = 128;
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let crush = parameters.crush[0] ?? 8;
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crush = Math.max(1, crush);
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return processSample(inputs, outputs, (block) => {
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const x = Math.pow(2, crush - 1);
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return Math.round(block * x) / x;
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});
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if (input[0] == null || output[0] == null) {
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return false;
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}
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for (let n = 0; n < blockSize; n++) {
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for (let i = 0; i < input.length; i++) {
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const x = Math.pow(2, crush - 1);
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output[i][n] = Math.round(input[i][n] * x) / x;
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}
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}
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return true;
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}
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}
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registerProcessor('crush-processor', CrushProcessor);
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@ -73,17 +75,26 @@ class ShapeProcessor extends AudioWorkletProcessor {
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}
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process(inputs, outputs, parameters) {
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const input = inputs[0];
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const output = outputs[0];
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const blockSize = 128;
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let shape = parameters.shape[0];
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const postgain = Math.max(0.001, Math.min(1, parameters.postgain[0]));
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shape = shape < 1 ? shape : 1.0 - 4e-10;
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shape = (2.0 * shape) / (1.0 - shape);
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return processSample(inputs, outputs, (block) => {
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const val = ((1 + shape) * block) / (1 + shape * Math.abs(block));
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return val * postgain;
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});
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const postgain = Math.max(0.001, Math.min(1, parameters.postgain[0]));
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if (input[0] == null || output[0] == null) {
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return false;
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}
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for (let n = 0; n < blockSize; n++) {
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for (let i = 0; i < input.length; i++) {
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output[i][n] = (((1 + shape) * input[i][n]) / (1 + shape * Math.abs(input[i][n]))) * postgain;
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}
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}
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return true;
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}
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}
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registerProcessor('shape-processor', ShapeProcessor);
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class DistortProcessor extends AudioWorkletProcessor {
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@ -99,16 +110,24 @@ class DistortProcessor extends AudioWorkletProcessor {
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}
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process(inputs, outputs, parameters) {
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let shape = parameters.distort[0];
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const input = inputs[0];
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const output = outputs[0];
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const blockSize = 128;
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const shape = Math.expm1(parameters.distort[0]);
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const postgain = Math.max(0.001, Math.min(1, parameters.postgain[0]));
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shape = Math.expm1(shape);
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return processSample(inputs, outputs, (block) => {
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const val = ((1 + shape) * block) / (1 + shape * Math.abs(block));
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return val * postgain;
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});
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if (input[0] == null || output[0] == null) {
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return false;
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}
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for (let n = 0; n < blockSize; n++) {
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for (let i = 0; i < input.length; i++) {
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output[i][n] = (((1 + shape) * input[i][n]) / (1 + shape * Math.abs(input[i][n]))) * postgain;
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}
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}
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return true;
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}
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}
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registerProcessor('distort-processor', DistortProcessor);
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// adjust waveshape to remove frequencies above nyquist to prevent aliasing
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@ -194,9 +213,11 @@ class SuperSawOscillatorProcessor extends AudioWorkletProcessor {
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];
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}
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process(input, outputs, params) {
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// eslint-disable-next-line no-undef
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if (currentTime <= params.begin[0]) {
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return true;
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}
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// eslint-disable-next-line no-undef
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if (currentTime >= params.end[0]) {
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return false;
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}
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@ -219,6 +240,7 @@ class SuperSawOscillatorProcessor extends AudioWorkletProcessor {
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}
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const freq = Math.min(16744, Math.max(1, frequency + adj * 0.01 * frequency));
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const balance = isOdd ? 1 - panspread : panspread;
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// eslint-disable-next-line no-undef
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const dt = freq / sampleRate;
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for (let i = 0; i < output[0].length; i++) {
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