strudel/packages/superdough/worklets.mjs

// coarse, crush, and shape processors adapted from dktr0's webdirt: https://github.com/dktr0/WebDirt/blob/5ce3d698362c54d6e1b68acc47eb2955ac62c793/dist/AudioWorklets.js
// LICENSE GNU General Public License v3.0 see https://github.com/dktr0/WebDirt/blob/main/LICENSE

class CoarseProcessor extends AudioWorkletProcessor {
  static get parameterDescriptors() {
    return [{ name: 'coarse', defaultValue: 1 }];
  }

  constructor() {
    super();
  }

  process(inputs, outputs, parameters) {
    const input = inputs[0];
    const output = outputs[0];
    const blockSize = 128;

    let coarse = parameters.coarse[0] ?? 0;
    coarse = Math.max(1, coarse);

    if (input[0] == null || output[0] == null) {
      return false;
    }
    for (let n = 0; n < blockSize; n++) {
      for (let i = 0; i < input.length; i++) {
        output[i][n] = n % coarse === 0 ? input[i][n] : output[i][n - 1];
      }
    }
    return true;
  }
}
registerProcessor('coarse-processor', CoarseProcessor);

class CrushProcessor extends AudioWorkletProcessor {
  static get parameterDescriptors() {
    return [{ name: 'crush', defaultValue: 0 }];
  }

  constructor() {
    super();
  }

  process(inputs, outputs, parameters) {
    const input = inputs[0];
    const output = outputs[0];
    const blockSize = 128;

    let crush = parameters.crush[0] ?? 8;
    crush = Math.max(1, crush);

    if (input[0] == null || output[0] == null) {
      return false;
    }
    for (let n = 0; n < blockSize; n++) {
      for (let i = 0; i < input.length; i++) {
        const x = Math.pow(2, crush - 1);
        output[i][n] = Math.round(input[i][n] * x) / x;
      }
    }
    return true;
  }
}
registerProcessor('crush-processor', CrushProcessor);

class ShapeProcessor extends AudioWorkletProcessor {
  static get parameterDescriptors() {
    return [
      { name: 'shape', defaultValue: 0 },
      { name: 'postgain', defaultValue: 1 },
    ];
  }

  constructor() {
    super();
  }

  process(inputs, outputs, parameters) {
    const input = inputs[0];
    const output = outputs[0];
    const blockSize = 128;

    let shape = parameters.shape[0];
    shape = shape < 1 ? shape : 1.0 - 4e-10;
    shape = (2.0 * shape) / (1.0 - shape);
    const postgain = Math.max(0.001, Math.min(1, parameters.postgain[0]));

    if (input[0] == null || output[0] == null) {
      return false;
    }
    for (let n = 0; n < blockSize; n++) {
      for (let i = 0; i < input.length; i++) {
        output[i][n] = (((1 + shape) * input[i][n]) / (1 + shape * Math.abs(input[i][n]))) * postgain;
      }
    }
    return true;
  }
}
registerProcessor('shape-processor', ShapeProcessor);

class DistortProcessor extends AudioWorkletProcessor {
  static get parameterDescriptors() {
    return [
      { name: 'distort', defaultValue: 0 },
      { name: 'postgain', defaultValue: 1 },
    ];
  }

  constructor() {
    super();
  }

  process(inputs, outputs, parameters) {
    const input = inputs[0];
    const output = outputs[0];
    const blockSize = 128;

    const shape = Math.expm1(parameters.distort[0]);
    const postgain = Math.max(0.001, Math.min(1, parameters.postgain[0]));

    if (input[0] == null || output[0] == null) {
      return false;
    }
    for (let n = 0; n < blockSize; n++) {
      for (let i = 0; i < input.length; i++) {
        output[i][n] = (((1 + shape) * input[i][n]) / (1 + shape * Math.abs(input[i][n]))) * postgain;
      }
    }
    return true;
  }
}
registerProcessor('distort-processor', DistortProcessor);