Initial work for AIDA-X module

Signed-off-by: falkTX <falktx@falktx.com>

.gitmodules

@@ -227,3 +227,6 @@
 [submodule "plugins/Sapphire"]
 	path = plugins/Sapphire
 	url = https://github.com/cosinekitty/sapphire.git
+[submodule "plugins/Cardinal/src/AIDA-X/RTNeural"]
+	path = plugins/Cardinal/src/AIDA-X/RTNeural
+	url = https://github.com/jatinchowdhury18/RTNeural.git

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plugins/Cardinal/plugin.json

@@ -117,6 +117,16 @@
         "Visual"
       ]
     },
+    {
+      "slug": "AIDA-X",
+      "name": "AIDA-X",
+      "description": "Amp Model Player leveraging AI",
+      "manualUrl": "https://github.com/DISTRHO/Cardinal/blob/main/docs/CARDINAL-MODULES.md#aidax",
+      "tags": [
+        "Distortion",
+        "Effect"
+      ]
+    },
     {
       "slug": "Blank",
       "name": "Blank",

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+<path fill="#969696" d="M657.265,161.804c-1.115,2.691-4.089,4.015-6.64,2.959l0,0c-2.551-1.057-3.714-4.097-2.6-6.789l24.824-59.93
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+</svg>

plugins/Cardinal/src/AIDA-X.cpp

@@ -0,0 +1,605 @@
+/*
+ * AIDA-X Cardinal plugin
+ * Copyright (C) 2022-2023 Massimo Pennazio <maxipenna@libero.it>
+ * Copyright (C) 2023 Filipe Coelho <falktx@falktx.com>
+ * SPDX-License-Identifier: GPL-3.0-or-later
+ */
+
+#include "plugincontext.hpp"
+#include "ModuleWidgets.hpp"
+
+#include "extra/Sleep.hpp"
+
+#include "AIDA-X/Biquad.cpp"
+#include "AIDA-X/model_variant.hpp"
+
+#ifndef HEADLESS
+# include "ImGuiWidget.hpp"
+# include "ghc/filesystem.hpp"
+#endif
+
+// --------------------------------------------------------------------------------------------------------------------
+
+/* Define a constexpr for converting a gain in dB to a coefficient */
+static constexpr float DB_CO(const float g) { return g > -90.f ? std::pow(10.f, g * 0.05f) : 0.f; }
+
+/* Define a macro to re-maps a number from one range to another  */
+static constexpr float MAP(const float x, const float in_min, const float in_max, const float out_min, const float out_max)
+{
+    return ((x - in_min) * (out_max - out_min) / (in_max - in_min)) + out_min;
+}
+
+/* Defines for tone controls */
+static constexpr const float COMMON_Q = 0.707f;
+
+/* Defines for antialiasing filter */
+static constexpr const float INLPF_MAX_CO = 0.99f * 0.5f; /* coeff * ((samplerate / 2) / samplerate) */
+static constexpr const float INLPF_MIN_CO = 0.25f * 0.5f; /* coeff * ((samplerate / 2) / samplerate) */
+
+// --------------------------------------------------------------------------------------------------------------------
+
+struct DynamicModel {
+    ModelVariantType variant;
+    bool input_skip; /* Means the model has been trained with first input element skipped to the output */
+    float input_gain;
+    float output_gain;
+};
+
+// --------------------------------------------------------------------------------------------------------------------
+// This function carries model calculations
+
+static inline
+void applyModel(DynamicModel* model, float* const out, uint32_t numSamples)
+{
+    const bool input_skip = model->input_skip;
+    const float input_gain = model->input_gain;
+    const float output_gain = model->output_gain;
+
+    std::visit(
+        [&out, numSamples, input_skip, input_gain, output_gain] (auto&& custom_model)
+        {
+            using ModelType = std::decay_t<decltype (custom_model)>;
+
+            if (d_isNotEqual(input_gain, 1.f))
+            {
+                for (uint32_t i=0; i<numSamples; ++i)
+                    out[i] *= input_gain;
+            }
+
+            if constexpr (ModelType::input_size == 1)
+            {
+                if (input_skip)
+                {
+                    for (uint32_t i=0; i<numSamples; ++i)
+                        out[i] += custom_model.forward(out + i);
+                }
+                else
+                {
+                    for (uint32_t i=0; i<numSamples; ++i)
+                        out[i] = custom_model.forward(out + i) * output_gain;
+                }
+            }
+
+            if (input_skip && d_isNotEqual(output_gain, 1.f))
+            {
+                for (uint32_t i=0; i<numSamples; ++i)
+                    out[i] *= output_gain;
+            }
+        },
+        model->variant
+    );
+}
+
+static inline
+float applyModel(DynamicModel* model, float sample)
+{
+    const bool input_skip = model->input_skip;
+    const float input_gain = model->input_gain;
+    const float output_gain = model->output_gain;
+
+    sample *= input_gain;
+
+    std::visit(
+        [&sample, input_skip, output_gain] (auto&& custom_model)
+        {
+            using ModelType = std::decay_t<decltype (custom_model)>;
+            float* out = &sample;
+
+            if constexpr (ModelType::input_size == 1)
+            {
+                if (input_skip)
+                {
+                    sample += custom_model.forward(out);
+                    sample *= output_gain;
+                }
+                else
+                {
+                    sample = custom_model.forward(out) * output_gain;
+                }
+            }
+        },
+        model->variant
+    );
+    
+    return sample;
+}
+
+// --------------------------------------------------------------------------------------------------------------------
+
+struct AidaPluginModule : Module {
+    enum ParamIds {
+        PARAM_INPUT_LEVEL,
+        PARAM_OUTPUT_LEVEL,
+        NUM_PARAMS
+    };
+    enum InputIds {
+        AUDIO_INPUT,
+        NUM_INPUTS
+    };
+    enum OutputIds {
+        AUDIO_OUTPUT,
+        NUM_OUTPUTS
+    };
+    enum LightIds {
+        NUM_LIGHTS
+    };
+
+    enum Parameters {
+        kParameterCount
+    };
+
+    CardinalPluginContext* const pcontext;
+    bool fileChanged = false;
+    std::string currentFile;
+
+    Biquad dc_blocker { bq_type_highpass, 0.5f, COMMON_Q, 0.0f };
+    Biquad in_lpf { bq_type_lowpass, 0.5f, COMMON_Q, 0.0f };
+    dsp::ExponentialFilter inlevel;
+    dsp::ExponentialFilter outlevel;
+    DynamicModel* model = nullptr;
+    std::atomic<bool> activeModel { false };
+
+    AidaPluginModule()
+        : pcontext(static_cast<CardinalPluginContext*>(APP))
+    {
+        config(NUM_PARAMS, NUM_INPUTS, NUM_OUTPUTS, NUM_LIGHTS);
+
+        configInput(AUDIO_INPUT, "Audio");
+        configOutput(AUDIO_OUTPUT, "Audio");
+        configParam(PARAM_INPUT_LEVEL, -12.f, 12.f, 0.f, "Input level", " dB");
+        configParam(PARAM_OUTPUT_LEVEL, -12.f, 12.f, 0.f, "Output level", " dB");
+
+        inlevel.setTau(1 / 30.f);
+        outlevel.setTau(1 / 30.f);
+    }
+
+    ~AidaPluginModule() override
+    {
+        delete model;
+    }
+
+    json_t* dataToJson() override
+    {
+        json_t* const rootJ = json_object();
+        DISTRHO_SAFE_ASSERT_RETURN(rootJ != nullptr, nullptr);
+
+        json_object_set_new(rootJ, "filepath", json_string(currentFile.c_str()));
+
+        return rootJ;
+    }
+
+    void dataFromJson(json_t* const rootJ) override
+    {
+        fileChanged = false;
+
+        if (json_t* const filepathJ = json_object_get(rootJ, "filepath"))
+        {
+            const char* const filepath = json_string_value(filepathJ);
+
+            if (filepath[0] != '\0')
+            {
+                currentFile = filepath;
+                fileChanged = true;
+
+                loadModelFromFile(filepath);
+            }
+        }
+
+        if (! fileChanged)
+        {
+            currentFile.clear();
+            fileChanged = true;
+        }
+    }
+
+    void loadModelFromFile(const char* const filename)
+    {
+        try {
+            std::ifstream jsonStream(filename, std::ifstream::binary);
+            loadModelFromStream(jsonStream);
+        }
+        catch (const std::exception& e) {
+            d_stderr2("Unable to load json file: %s\nError: %s", filename, e.what());
+        };
+    }
+
+    void loadModelFromStream(std::istream& jsonStream)
+    {
+        int input_size;
+        int input_skip;
+        float input_gain;
+        float output_gain;
+        nlohmann::json model_json;
+
+        try {
+            jsonStream >> model_json;
+
+            /* Understand which model type to load */
+            input_size = model_json["in_shape"].back().get<int>();
+            if (input_size > 1) { // MAX_INPUT_SIZE
+                throw std::invalid_argument("Value for input_size not supported");
+            }
+
+            if (model_json["in_skip"].is_number()) {
+                input_skip = model_json["in_skip"].get<int>();
+                if (input_skip > 1)
+                    throw std::invalid_argument("Values for in_skip > 1 are not supported");
+            }
+            else {
+                input_skip = 0;
+            }
+
+            if (model_json["in_gain"].is_number()) {
+                input_gain = DB_CO(model_json["in_gain"].get<float>());
+            }
+            else {
+                input_gain = 1.0f;
+            }
+
+            if (model_json["out_gain"].is_number()) {
+                output_gain = DB_CO(model_json["out_gain"].get<float>());
+            }
+            else {
+                output_gain = 1.0f;
+            }
+        }
+        catch (const std::exception& e) {
+            d_stderr2("Unable to load json, error: %s", e.what());
+            return;
+        }
+
+        std::unique_ptr<DynamicModel> newmodel = std::make_unique<DynamicModel>();
+
+        try {
+            if (! custom_model_creator (model_json, newmodel->variant))
+                throw std::runtime_error ("Unable to identify a known model architecture!");
+
+            std::visit (
+                [&model_json] (auto&& custom_model)
+                {
+                    using ModelType = std::decay_t<decltype (custom_model)>;
+                    if constexpr (! std::is_same_v<ModelType, NullModel>)
+                    {
+                        custom_model.parseJson (model_json, true);
+                        custom_model.reset();
+                    }
+                },
+                newmodel->variant);
+        }
+        catch (const std::exception& e) {
+            d_stderr2("Error loading model: %s", e.what());
+            return;
+        }
+
+        // save extra info
+        newmodel->input_skip = input_skip != 0;
+        newmodel->input_gain = input_gain;
+        newmodel->output_gain = output_gain;
+
+        // Pre-buffer to avoid "clicks" during initialization
+        float out[2048] = {};
+        applyModel(newmodel.get(), out, ARRAY_SIZE(out));
+
+        // swap active model
+        DynamicModel* const oldmodel = model;
+        model = newmodel.release();
+
+        // if processing, wait for process cycle to complete
+        while (oldmodel != nullptr && activeModel.load())
+            d_msleep(1);
+
+        delete oldmodel;
+    }
+
+    void process(const ProcessArgs& args) override
+    {
+        const float stime = args.sampleTime;
+        const float inlevelv = DB_CO(params[PARAM_INPUT_LEVEL].getValue());
+        const float outlevelv = DB_CO(params[PARAM_OUTPUT_LEVEL].getValue());
+
+        // High frequencies roll-off (lowpass)
+        float sample = in_lpf.process(inputs[AUDIO_INPUT].getVoltage() * 0.1f) * inlevel.process(stime, inlevelv);
+
+        // run model
+        if (model != nullptr)
+        {
+            activeModel.store(true);
+            sample = applyModel(model, sample);
+            activeModel.store(false);
+        }
+
+        // DC blocker filter (highpass)
+        outputs[AUDIO_OUTPUT].setVoltage(dc_blocker.process(sample) * outlevel.process(stime, outlevelv) * 10.f);
+    }
+
+    void onSampleRateChange(const SampleRateChangeEvent& e) override
+    {
+        dc_blocker.setFc(35.0f / e.sampleRate);
+
+        in_lpf.setFc(MAP(66.216f, 0.0f, 100.0f, INLPF_MAX_CO, INLPF_MIN_CO));
+    }
+
+    DISTRHO_DECLARE_NON_COPYABLE_WITH_LEAK_DETECTOR(AidaPluginModule)
+};
+
+// --------------------------------------------------------------------------------------------------------------------
+
+#ifndef HEADLESS
+struct AidaModelListWidget : ImGuiWidget {
+    AidaPluginModule* const module;
+
+    /*
+    bool showError = false;
+    String errorMessage;
+    */
+
+    struct ghcFile {
+        std::string full, base;
+        bool operator<(const ghcFile& other) const noexcept { return base < other.base; }
+    };
+    std::string currentDirectory;
+    std::vector<ghcFile> currentFiles;
+    size_t selectedFile = (size_t)-1;
+
+    AidaModelListWidget(AidaPluginModule* const m)
+        : ImGuiWidget(),
+          module(m)
+    {
+        if (module->fileChanged)
+            reloadDir();
+    }
+
+    void drawImGui() override
+    {
+        const float scaleFactor = getScaleFactor();
+
+        const int flags = ImGuiWindowFlags_NoSavedSettings
+                        | ImGuiWindowFlags_NoTitleBar
+                        | ImGuiWindowFlags_NoResize
+                        | ImGuiWindowFlags_NoCollapse
+                        | ImGuiWindowFlags_NoScrollbar
+                        | ImGuiWindowFlags_NoScrollWithMouse;
+
+        ImGui::SetNextWindowPos(ImVec2(0, 0));
+        ImGui::SetNextWindowSize(ImVec2(box.size.x * scaleFactor, box.size.y * scaleFactor));
+
+        if (ImGui::Begin("Model File List", nullptr, ImGuiWindowFlags_NoTitleBar|ImGuiWindowFlags_NoResize))
+        {
+            /*
+            if (showError)
+            {
+                showError = false;
+                ImGui::OpenPopup("Audio File Error");
+            }
+
+            if (ImGui::BeginPopupModal("Model File Error", nullptr, flags))
+            {
+                ImGui::TextWrapped("Failed to load model file, error was:\n%s", errorMessage.buffer());
+
+                ImGui::Separator();
+
+                if (ImGui::Button("Ok"))
+                    ImGui::CloseCurrentPopup();
+
+                ImGui::EndPopup();
+            }
+            else
+            */
+            if (ImGui::BeginTable("modellist", 1, ImGuiTableFlags_NoSavedSettings))
+            {
+                for (size_t i=0, count=currentFiles.size(); i < count; ++i)
+                {
+                    bool wasSelected = selectedFile == i;
+                    bool selected = wasSelected;
+                    ImGui::TableNextRow();
+                    ImGui::TableSetColumnIndex(0);
+                    ImGui::Selectable(currentFiles[i].base.c_str(), &selected);
+
+                    if (selected && ! wasSelected)
+                    {
+                        selectedFile = i;
+                        module->currentFile = currentFiles[i].full;
+                        module->loadModelFromFile(currentFiles[i].full.c_str());
+                    }
+                }
+
+                ImGui::EndTable();
+            }
+        }
+
+        ImGui::End();
+    }
+
+    void step() override
+    {
+        if (module->fileChanged)
+            reloadDir();
+
+        ImGuiWidget::step();
+    }
+
+    void reloadDir()
+    {
+        module->fileChanged = false;
+
+        currentFiles.clear();
+        selectedFile = (size_t)-1;
+
+        static constexpr const char* const supportedExtensions[] = {
+            ".json"
+        };
+
+        using namespace ghc::filesystem;
+        const path currentFile = u8path(module->currentFile);
+        currentDirectory = currentFile.parent_path().generic_u8string();
+
+        directory_iterator it;
+
+        try {
+            it = directory_iterator(u8path(currentDirectory));
+        } DISTRHO_SAFE_EXCEPTION_RETURN("Failed to open current directory",);
+
+        for (directory_iterator itb = begin(it), ite=end(it); itb != ite; ++itb)
+        {
+            if (! itb->is_regular_file())
+                continue;
+            const path filepath = itb->path();
+            const path extension = filepath.extension();
+            for (size_t i=0; i<ARRAY_SIZE(supportedExtensions); ++i)
+            {
+                if (extension.compare(supportedExtensions[i]) == 0)
+                {
+                    currentFiles.push_back({ filepath.generic_u8string(), filepath.filename().generic_u8string() });
+                    break;
+                }
+            }
+        }
+
+        std::sort(currentFiles.begin(), currentFiles.end());
+
+        for (size_t index = 0; index < currentFiles.size(); ++index)
+        {
+            if (currentFiles[index].full.compare(currentFile) == 0)
+            {
+                selectedFile = index;
+                break;
+            }
+        }
+    }
+};
+
+struct AidaKnob : app::SvgKnob {
+    AidaKnob()
+    {
+        minAngle = -0.76 * M_PI;
+        maxAngle = 0.76 * M_PI;
+        shadow->opacity = 0;
+        setSvg(APP->window->loadSvg(asset::plugin(pluginInstance, "res/aida-x-knob.svg")));
+    }
+};
+
+struct AidaWidget : ModuleWidgetWithSideScrews<23> {
+    static constexpr const float previewBoxHeight = 80.0f;
+    static constexpr const float previewBoxBottom = 20.0f;
+    static constexpr const float previewBoxRect[] = {8.0f,
+                                                     380.0f - previewBoxHeight - previewBoxBottom,
+                                                     15.0f * 23 - 16.0f,
+                                                     previewBoxHeight};
+    static constexpr const float startY_list = startY - 2.0f;
+    static constexpr const float fileListHeight = 380.0f - startY_list - previewBoxHeight - previewBoxBottom * 1.5f;
+    static constexpr const float startY_preview = startY_list + fileListHeight;
+
+    AidaPluginModule* const module;
+
+    AidaWidget(AidaPluginModule* const m)
+        : module(m)
+    {
+        setModule(module);
+        setPanel(APP->window->loadSvg(asset::plugin(pluginInstance, "res/AIDA-X.svg")));
+
+        createAndAddScrews();
+
+        addInput(createInput<PJ301MPort>(Vec(startX_In, 25), module, 0));
+        addOutput(createOutput<PJ301MPort>(Vec(startX_Out, 25), module, 0));
+
+        addChild(createParamCentered<AidaKnob>(Vec(box.size.x * 0.5f - 50, box.size.y - 60),
+                                               module, AidaPluginModule::PARAM_INPUT_LEVEL));
+
+        addChild(createParamCentered<AidaKnob>(Vec(box.size.x * 0.5f + 50, box.size.y - 60),
+                                               module, AidaPluginModule::PARAM_OUTPUT_LEVEL));
+
+        if (m != nullptr)
+        {
+            AidaModelListWidget* const listw = new AidaModelListWidget(m);
+            listw->box.pos = Vec(0, startY_list);
+            listw->box.size = Vec(box.size.x, fileListHeight);
+            addChild(listw);
+        }
+    }
+
+    void draw(const DrawArgs& args) override
+    {
+        drawBackground(args.vg);
+        drawOutputJacksArea(args.vg);
+
+        ModuleWidget::draw(args);
+    }
+
+    void drawOutputJacksArea(NVGcontext* const vg)
+    {
+        nvgBeginPath(vg);
+        nvgRoundedRect(vg, startX_Out - 2.5f, startY_list * 0.5f - padding * 0.5f, padding, padding, 4);
+        nvgFillColor(vg, nvgRGB(0xd0, 0xd0, 0xd0));
+        nvgFill(vg);
+    }
+
+    void appendContextMenu(ui::Menu* const menu) override
+    {
+        menu->addChild(new ui::MenuSeparator);
+
+        struct LoadModelFileItem : MenuItem {
+            AidaPluginModule* const module;
+
+            LoadModelFileItem(AidaPluginModule* const m)
+                : module(m)
+            {
+                text = "Load model file...";
+            }
+
+            void onAction(const event::Action&) override
+            {
+                AidaPluginModule* const module = this->module;
+                async_dialog_filebrowser(false, nullptr, nullptr, text.c_str(), [module](char* path)
+                {
+                    if (path == nullptr)
+                        return;
+
+                    module->currentFile = path;
+                    module->fileChanged = true;
+                    module->loadModelFromFile(path);
+                    std::free(path);
+                });
+            }
+        };
+
+        menu->addChild(new LoadModelFileItem(module));
+    }
+
+    DISTRHO_DECLARE_NON_COPYABLE_WITH_LEAK_DETECTOR(AidaWidget)
+};
+#else
+struct AidaWidget : ModuleWidget {
+    AidaWidget(AidaPluginModule* const module) {
+        setModule(module);
+
+        addInput(createInput<PJ301MPort>({}, module, 0));
+        addOutput(createOutput<PJ301MPort>({}, module, 0));
+    }
+};
+#endif
+
+// --------------------------------------------------------------------------------------------------------------------
+
+Model* modelAidaX = createModel<AidaPluginModule, AidaWidget>("AIDA-X");
+
+// --------------------------------------------------------------------------------------------------------------------

plugins/Cardinal/src/AIDA-X/Biquad.cpp

@@ -0,0 +1,165 @@
+//
+//  Biquad.cpp
+//
+//  Created by Nigel Redmon on 11/24/12
+//  EarLevel Engineering: earlevel.com
+//  Copyright 2012 Nigel Redmon
+//
+//  For a complete explanation of the Biquad code:
+//  http://www.earlevel.com/main/2012/11/26/biquad-c-source-code/
+//
+//  License:
+//
+//  This source code is provided as is, without warranty.
+//  You may copy and distribute verbatim copies of this document.
+//  You may modify and use this source code to create binary code
+//  for your own purposes, free or commercial.
+//
+
+#include <math.h>
+#include "Biquad.h"
+
+Biquad::Biquad() {
+    type = bq_type_lowpass;
+    a0 = 1.0;
+    a1 = a2 = b1 = b2 = 0.0;
+    Fc = 0.50;
+    Q = 0.707;
+    peakGain = 0.0;
+    z1 = z2 = 0.0;
+}
+
+Biquad::Biquad(int type, double Fc, double Q, double peakGainDB) {
+    setBiquad(type, Fc, Q, peakGainDB);
+    z1 = z2 = 0.0;
+}
+
+Biquad::~Biquad() {
+}
+
+void Biquad::setType(int type) {
+    this->type = type;
+    calcBiquad();
+}
+
+void Biquad::setQ(double Q) {
+    this->Q = Q;
+    calcBiquad();
+}
+
+void Biquad::setFc(double Fc) {
+    this->Fc = Fc;
+    calcBiquad();
+}
+
+void Biquad::setPeakGain(double peakGainDB) {
+    this->peakGain = peakGainDB;
+    calcBiquad();
+}
+
+void Biquad::setBiquad(int type, double Fc, double Q, double peakGainDB) {
+    this->type = type;
+    this->Q = Q;
+    this->Fc = Fc;
+    setPeakGain(peakGainDB);
+}
+
+void Biquad::calcBiquad(void) {
+    double norm;
+    double V = pow(10, fabs(peakGain) / 20.0);
+    double K = tan(M_PI * Fc);
+    switch (this->type) {
+        case bq_type_lowpass:
+            norm = 1 / (1 + K / Q + K * K);
+            a0 = K * K * norm;
+            a1 = 2 * a0;
+            a2 = a0;
+            b1 = 2 * (K * K - 1) * norm;
+            b2 = (1 - K / Q + K * K) * norm;
+            break;
+
+        case bq_type_highpass:
+            norm = 1 / (1 + K / Q + K * K);
+            a0 = 1 * norm;
+            a1 = -2 * a0;
+            a2 = a0;
+            b1 = 2 * (K * K - 1) * norm;
+            b2 = (1 - K / Q + K * K) * norm;
+            break;
+
+        case bq_type_bandpass:
+            norm = 1 / (1 + K / Q + K * K);
+            a0 = K / Q * norm;
+            a1 = 0;
+            a2 = -a0;
+            b1 = 2 * (K * K - 1) * norm;
+            b2 = (1 - K / Q + K * K) * norm;
+            break;
+
+        case bq_type_notch:
+            norm = 1 / (1 + K / Q + K * K);
+            a0 = (1 + K * K) * norm;
+            a1 = 2 * (K * K - 1) * norm;
+            a2 = a0;
+            b1 = a1;
+            b2 = (1 - K / Q + K * K) * norm;
+            break;
+
+        case bq_type_peak:
+            if (peakGain >= 0) {    // boost
+                norm = 1 / (1 + 1/Q * K + K * K);
+                a0 = (1 + V/Q * K + K * K) * norm;
+                a1 = 2 * (K * K - 1) * norm;
+                a2 = (1 - V/Q * K + K * K) * norm;
+                b1 = a1;
+                b2 = (1 - 1/Q * K + K * K) * norm;
+            }
+            else {    // cut
+                norm = 1 / (1 + V/Q * K + K * K);
+                a0 = (1 + 1/Q * K + K * K) * norm;
+                a1 = 2 * (K * K - 1) * norm;
+                a2 = (1 - 1/Q * K + K * K) * norm;
+                b1 = a1;
+                b2 = (1 - V/Q * K + K * K) * norm;
+            }
+            break;
+        case bq_type_lowshelf:
+            if (peakGain >= 0) {    // boost
+                norm = 1 / (1 + sqrt(2) * K + K * K);
+                a0 = (1 + sqrt(2*V) * K + V * K * K) * norm;
+                a1 = 2 * (V * K * K - 1) * norm;
+                a2 = (1 - sqrt(2*V) * K + V * K * K) * norm;
+                b1 = 2 * (K * K - 1) * norm;
+                b2 = (1 - sqrt(2) * K + K * K) * norm;
+            }
+            else {    // cut
+                norm = 1 / (1 + sqrt(2*V) * K + V * K * K);
+                a0 = (1 + sqrt(2) * K + K * K) * norm;
+                a1 = 2 * (K * K - 1) * norm;
+                a2 = (1 - sqrt(2) * K + K * K) * norm;
+                b1 = 2 * (V * K * K - 1) * norm;
+                b2 = (1 - sqrt(2*V) * K + V * K * K) * norm;
+            }
+            break;
+        case bq_type_highshelf:
+            if (peakGain >= 0) {    // boost
+                norm = 1 / (1 + sqrt(2) * K + K * K);
+                a0 = (V + sqrt(2*V) * K + K * K) * norm;
+                a1 = 2 * (K * K - V) * norm;
+                a2 = (V - sqrt(2*V) * K + K * K) * norm;
+                b1 = 2 * (K * K - 1) * norm;
+                b2 = (1 - sqrt(2) * K + K * K) * norm;
+            }
+            else {    // cut
+                norm = 1 / (V + sqrt(2*V) * K + K * K);
+                a0 = (1 + sqrt(2) * K + K * K) * norm;
+                a1 = 2 * (K * K - 1) * norm;
+                a2 = (1 - sqrt(2) * K + K * K) * norm;
+                b1 = 2 * (K * K - V) * norm;
+                b2 = (V - sqrt(2*V) * K + K * K) * norm;
+            }
+            break;
+    }
+
+    return;
+}

plugins/Cardinal/src/AIDA-X/Biquad.h

@@ -0,0 +1,60 @@
+//
+//  Biquad.h
+//
+//  Created by Nigel Redmon on 11/24/12
+//  EarLevel Engineering: earlevel.com
+//  Copyright 2012 Nigel Redmon
+//
+//  For a complete explanation of the Biquad code:
+//  http://www.earlevel.com/main/2012/11/26/biquad-c-source-code/
+//
+//  License:
+//
+//  This source code is provided as is, without warranty.
+//  You may copy and distribute verbatim copies of this document.
+//  You may modify and use this source code to create binary code
+//  for your own purposes, free or commercial.
+//
+
+#ifndef Biquad_h
+#define Biquad_h
+
+enum {
+    bq_type_lowpass = 0,
+    bq_type_highpass,
+    bq_type_bandpass,
+    bq_type_notch,
+    bq_type_peak,
+    bq_type_lowshelf,
+    bq_type_highshelf
+};
+
+class Biquad {
+public:
+    Biquad();
+    Biquad(int type, double Fc, double Q, double peakGainDB);
+    ~Biquad();
+    void setType(int type);
+    void setQ(double Q);
+    void setFc(double Fc);
+    void setPeakGain(double peakGainDB);
+    void setBiquad(int type, double Fc, double Q, double peakGainDB);
+    float process(float in);
+
+protected:
+    void calcBiquad(void);
+
+    int type;
+    double a0, a1, a2, b1, b2;
+    double Fc, Q, peakGain;
+    double z1, z2;
+};
+
+inline float Biquad::process(float in) {
+    double out = in * a0 + z1;
+    z1 = in * a1 + z2 - b1 * out;
+    z2 = in * a2 - b2 * out;
+    return out;
+}
+
+#endif // Biquad_h

plugins/Cardinal/src/AIDA-X/RTNeural

@@ -0,0 +1 @@
+Subproject commit 74e9d354937346f31858e976a2eefc1c25cdcccd

plugins/Cardinal/src/AIDA-X/model_variant.hpp

@@ -0,0 +1,683 @@
+#include <variant>
+#include <RTNeural/RTNeural.h>
+
+#define MAX_INPUT_SIZE 3
+struct NullModel { static constexpr int input_size = 0; static constexpr int output_size = 0; };
+using ModelType_GRU_8_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 8>, RTNeural::DenseT<float, 8, 1>>;
+using ModelType_GRU_8_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 8>, RTNeural::DenseT<float, 8, 1>>;
+using ModelType_GRU_8_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 8>, RTNeural::DenseT<float, 8, 1>>;
+using ModelType_GRU_12_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 12>, RTNeural::DenseT<float, 12, 1>>;
+using ModelType_GRU_12_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 12>, RTNeural::DenseT<float, 12, 1>>;
+using ModelType_GRU_12_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 12>, RTNeural::DenseT<float, 12, 1>>;
+using ModelType_GRU_16_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 16>, RTNeural::DenseT<float, 16, 1>>;
+using ModelType_GRU_16_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 16>, RTNeural::DenseT<float, 16, 1>>;
+using ModelType_GRU_16_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 16>, RTNeural::DenseT<float, 16, 1>>;
+using ModelType_GRU_20_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 20>, RTNeural::DenseT<float, 20, 1>>;
+using ModelType_GRU_20_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 20>, RTNeural::DenseT<float, 20, 1>>;
+using ModelType_GRU_20_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 20>, RTNeural::DenseT<float, 20, 1>>;
+using ModelType_GRU_32_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 32>, RTNeural::DenseT<float, 32, 1>>;
+using ModelType_GRU_32_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 32>, RTNeural::DenseT<float, 32, 1>>;
+using ModelType_GRU_32_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 32>, RTNeural::DenseT<float, 32, 1>>;
+using ModelType_GRU_40_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 40>, RTNeural::DenseT<float, 40, 1>>;
+using ModelType_GRU_40_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 40>, RTNeural::DenseT<float, 40, 1>>;
+using ModelType_GRU_40_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 40>, RTNeural::DenseT<float, 40, 1>>;
+using ModelType_GRU_64_1 = RTNeural::ModelT<float, 1, 1, RTNeural::GRULayerT<float, 1, 64>, RTNeural::DenseT<float, 64, 1>>;
+using ModelType_GRU_64_2 = RTNeural::ModelT<float, 2, 1, RTNeural::GRULayerT<float, 2, 64>, RTNeural::DenseT<float, 64, 1>>;
+using ModelType_GRU_64_3 = RTNeural::ModelT<float, 3, 1, RTNeural::GRULayerT<float, 3, 64>, RTNeural::DenseT<float, 64, 1>>;
+using ModelType_LSTM_8_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 8>, RTNeural::DenseT<float, 8, 1>>;
+using ModelType_LSTM_8_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 8>, RTNeural::DenseT<float, 8, 1>>;
+using ModelType_LSTM_8_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 8>, RTNeural::DenseT<float, 8, 1>>;
+using ModelType_LSTM_12_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 12>, RTNeural::DenseT<float, 12, 1>>;
+using ModelType_LSTM_12_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 12>, RTNeural::DenseT<float, 12, 1>>;
+using ModelType_LSTM_12_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 12>, RTNeural::DenseT<float, 12, 1>>;
+using ModelType_LSTM_16_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 16>, RTNeural::DenseT<float, 16, 1>>;
+using ModelType_LSTM_16_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 16>, RTNeural::DenseT<float, 16, 1>>;
+using ModelType_LSTM_16_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 16>, RTNeural::DenseT<float, 16, 1>>;
+using ModelType_LSTM_20_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 20>, RTNeural::DenseT<float, 20, 1>>;
+using ModelType_LSTM_20_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 20>, RTNeural::DenseT<float, 20, 1>>;
+using ModelType_LSTM_20_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 20>, RTNeural::DenseT<float, 20, 1>>;
+using ModelType_LSTM_32_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 32>, RTNeural::DenseT<float, 32, 1>>;
+using ModelType_LSTM_32_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 32>, RTNeural::DenseT<float, 32, 1>>;
+using ModelType_LSTM_32_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 32>, RTNeural::DenseT<float, 32, 1>>;
+using ModelType_LSTM_40_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 40>, RTNeural::DenseT<float, 40, 1>>;
+using ModelType_LSTM_40_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 40>, RTNeural::DenseT<float, 40, 1>>;
+using ModelType_LSTM_40_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 40>, RTNeural::DenseT<float, 40, 1>>;
+using ModelType_LSTM_64_1 = RTNeural::ModelT<float, 1, 1, RTNeural::LSTMLayerT<float, 1, 64>, RTNeural::DenseT<float, 64, 1>>;
+using ModelType_LSTM_64_2 = RTNeural::ModelT<float, 2, 1, RTNeural::LSTMLayerT<float, 2, 64>, RTNeural::DenseT<float, 64, 1>>;
+using ModelType_LSTM_64_3 = RTNeural::ModelT<float, 3, 1, RTNeural::LSTMLayerT<float, 3, 64>, RTNeural::DenseT<float, 64, 1>>;
+using ModelVariantType = std::variant<NullModel,ModelType_GRU_8_1,ModelType_GRU_8_2,ModelType_GRU_8_3,ModelType_GRU_12_1,ModelType_GRU_12_2,ModelType_GRU_12_3,ModelType_GRU_16_1,ModelType_GRU_16_2,ModelType_GRU_16_3,ModelType_GRU_20_1,ModelType_GRU_20_2,ModelType_GRU_20_3,ModelType_GRU_32_1,ModelType_GRU_32_2,ModelType_GRU_32_3,ModelType_GRU_40_1,ModelType_GRU_40_2,ModelType_GRU_40_3,ModelType_GRU_64_1,ModelType_GRU_64_2,ModelType_GRU_64_3,ModelType_LSTM_8_1,ModelType_LSTM_8_2,ModelType_LSTM_8_3,ModelType_LSTM_12_1,ModelType_LSTM_12_2,ModelType_LSTM_12_3,ModelType_LSTM_16_1,ModelType_LSTM_16_2,ModelType_LSTM_16_3,ModelType_LSTM_20_1,ModelType_LSTM_20_2,ModelType_LSTM_20_3,ModelType_LSTM_32_1,ModelType_LSTM_32_2,ModelType_LSTM_32_3,ModelType_LSTM_40_1,ModelType_LSTM_40_2,ModelType_LSTM_40_3,ModelType_LSTM_64_1,ModelType_LSTM_64_2,ModelType_LSTM_64_3>;
+
+inline bool is_model_type_ModelType_GRU_8_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 8;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_8_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 8;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_8_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 8;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_12_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 12;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_12_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 12;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_12_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 12;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_16_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 16;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_16_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 16;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_16_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 16;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_20_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 20;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_20_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 20;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_20_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 20;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_32_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 32;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_32_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 32;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_32_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 32;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_40_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 40;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_40_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 40;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_40_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 40;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_64_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 64;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_64_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 64;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_GRU_64_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "gru";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 64;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_8_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 8;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_8_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 8;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_8_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 8;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_12_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 12;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_12_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 12;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_12_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 12;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_16_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 16;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_16_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 16;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_16_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 16;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_20_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 20;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_20_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 20;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_20_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 20;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_32_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 32;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_32_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 32;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_32_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 32;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_40_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 40;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_40_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 40;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_40_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 40;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_64_1 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 64;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 1;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_64_2 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 64;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 2;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool is_model_type_ModelType_LSTM_64_3 (const nlohmann::json& model_json) {
+    const auto json_layers = model_json.at ("layers");
+    const auto rnn_layer_type = json_layers.at (0).at ("type").get<std::string>();
+    const auto is_layer_type_correct = rnn_layer_type == "lstm";
+    const auto hidden_size = json_layers.at (0).at ("shape").back().get<int>();
+    const auto is_hidden_size_correct = hidden_size == 64;
+    const auto input_size = model_json.at ("in_shape").back().get<int>();
+    const auto is_input_size_correct = input_size == 3;
+    return is_layer_type_correct && is_hidden_size_correct && is_input_size_correct;
+}
+
+inline bool custom_model_creator (const nlohmann::json& model_json, ModelVariantType& model) {
+    if (is_model_type_ModelType_GRU_8_1 (model_json)) {
+        model.emplace<ModelType_GRU_8_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_8_2 (model_json)) {
+        model.emplace<ModelType_GRU_8_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_8_3 (model_json)) {
+        model.emplace<ModelType_GRU_8_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_12_1 (model_json)) {
+        model.emplace<ModelType_GRU_12_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_12_2 (model_json)) {
+        model.emplace<ModelType_GRU_12_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_12_3 (model_json)) {
+        model.emplace<ModelType_GRU_12_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_16_1 (model_json)) {
+        model.emplace<ModelType_GRU_16_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_16_2 (model_json)) {
+        model.emplace<ModelType_GRU_16_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_16_3 (model_json)) {
+        model.emplace<ModelType_GRU_16_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_20_1 (model_json)) {
+        model.emplace<ModelType_GRU_20_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_20_2 (model_json)) {
+        model.emplace<ModelType_GRU_20_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_20_3 (model_json)) {
+        model.emplace<ModelType_GRU_20_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_32_1 (model_json)) {
+        model.emplace<ModelType_GRU_32_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_32_2 (model_json)) {
+        model.emplace<ModelType_GRU_32_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_32_3 (model_json)) {
+        model.emplace<ModelType_GRU_32_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_40_1 (model_json)) {
+        model.emplace<ModelType_GRU_40_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_40_2 (model_json)) {
+        model.emplace<ModelType_GRU_40_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_40_3 (model_json)) {
+        model.emplace<ModelType_GRU_40_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_64_1 (model_json)) {
+        model.emplace<ModelType_GRU_64_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_64_2 (model_json)) {
+        model.emplace<ModelType_GRU_64_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_GRU_64_3 (model_json)) {
+        model.emplace<ModelType_GRU_64_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_8_1 (model_json)) {
+        model.emplace<ModelType_LSTM_8_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_8_2 (model_json)) {
+        model.emplace<ModelType_LSTM_8_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_8_3 (model_json)) {
+        model.emplace<ModelType_LSTM_8_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_12_1 (model_json)) {
+        model.emplace<ModelType_LSTM_12_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_12_2 (model_json)) {
+        model.emplace<ModelType_LSTM_12_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_12_3 (model_json)) {
+        model.emplace<ModelType_LSTM_12_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_16_1 (model_json)) {
+        model.emplace<ModelType_LSTM_16_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_16_2 (model_json)) {
+        model.emplace<ModelType_LSTM_16_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_16_3 (model_json)) {
+        model.emplace<ModelType_LSTM_16_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_20_1 (model_json)) {
+        model.emplace<ModelType_LSTM_20_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_20_2 (model_json)) {
+        model.emplace<ModelType_LSTM_20_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_20_3 (model_json)) {
+        model.emplace<ModelType_LSTM_20_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_32_1 (model_json)) {
+        model.emplace<ModelType_LSTM_32_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_32_2 (model_json)) {
+        model.emplace<ModelType_LSTM_32_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_32_3 (model_json)) {
+        model.emplace<ModelType_LSTM_32_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_40_1 (model_json)) {
+        model.emplace<ModelType_LSTM_40_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_40_2 (model_json)) {
+        model.emplace<ModelType_LSTM_40_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_40_3 (model_json)) {
+        model.emplace<ModelType_LSTM_40_3>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_64_1 (model_json)) {
+        model.emplace<ModelType_LSTM_64_1>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_64_2 (model_json)) {
+        model.emplace<ModelType_LSTM_64_2>();
+        return true;
+    }
+    else if (is_model_type_ModelType_LSTM_64_3 (model_json)) {
+        model.emplace<ModelType_LSTM_64_3>();
+        return true;
+    }
+    model.emplace<NullModel>();
+    return false;
+}

plugins/Cardinal/src/plugin.hpp

@@ -28,6 +28,7 @@ using namespace rack;
 
 extern Plugin* pluginInstance;
 
+extern Model* modelAidaX;
 extern Model* modelAudioFile;
 extern Model* modelAudioToCVPitch;
 extern Model* modelCarla;

plugins/Makefile

@@ -223,6 +223,7 @@ MINIPLUGIN_FILES = plugins-mini.cpp
 # --------------------------------------------------------------
 # Cardinal (built-in)
 
+PLUGIN_FILES += Cardinal/src/AIDA-X.cpp
 PLUGIN_FILES += Cardinal/src/Blank.cpp
 PLUGIN_FILES += Cardinal/src/ExpanderInputMIDI.cpp
 PLUGIN_FILES += Cardinal/src/ExpanderOutputMIDI.cpp
@@ -1306,6 +1307,20 @@ ifeq ($(WASM),true)
 CARLA_FLAGS += -DDISTRHO_RUNNER_INDIRECT_WASM_CALLS
 endif
 
+# --------------------------------------------------------------
+# RTNeural flags, used in AIDA-X
+
+RTNEURAL_FLAGS  = -std=gnu++17
+RTNEURAL_FLAGS += -DRTNEURAL_DEFAULT_ALIGNMENT=16
+RTNEURAL_FLAGS += -DRTNEURAL_USE_EIGEN=1
+
+ifeq ($(WASM),true)
+RTNEURAL_FLAGS += -DEIGEN_DONT_VECTORIZE=1
+endif
+
+RTNEURAL_FLAGS += -ICardinal/src/AIDA-X/RTNeural
+RTNEURAL_FLAGS += -ICardinal/src/AIDA-X/RTNeural/modules/Eigen
+
 # --------------------------------------------------------------
 # Build targets
 
@@ -1975,6 +1990,19 @@ $(BUILD_DIR)/Cardinal/%.cpp.o: Cardinal/%.cpp
 		-Dstbrp_rect=stbrp_rect_cardinal \
 		$(CARLA_FLAGS)
 
+$(BUILD_DIR)/Cardinal/src/AIDA-X.cpp.o: Cardinal/src/AIDA-X.cpp
+	-@mkdir -p "$(shell dirname $(BUILD_DIR)/$<)"
+	@echo "Compiling $<"
+	$(SILENT)$(CXX) $< $(BUILD_CXX_FLAGS) -c -o $@ \
+		-DpluginInstance=pluginInstance__Cardinal \
+		-Dstbrp_context=stbrp_context_cardinal \
+		-Dstbrp_coord=stbrp_coord_cardinal \
+		-Dstbtt_fontinfo=stbtt_fontinfo_cardinal \
+		-Dstbrp_node=stbrp_node_cardinal \
+		-Dstbrp_rect=stbrp_rect_cardinal \
+		$(RTNEURAL_FLAGS) \
+		$(CARLA_FLAGS)
+
 $(BUILD_DIR)/21kHz/%.cpp.o: 21kHz/%.cpp
 	-@mkdir -p "$(shell dirname $(BUILD_DIR)/$<)"
 	@echo "Compiling $<"

plugins/plugins-mini.cpp

@@ -212,6 +212,7 @@ static void initStatic__Cardinal()
         /*
        #endif
         */
+        spl.removeModule("AIDA-X");
         spl.removeModule("AudioFile");
         spl.removeModule("Blank");
         spl.removeModule("Carla");

plugins/plugins.cpp

@@ -972,6 +972,7 @@ static void initStatic__Cardinal()
     const StaticPluginLoader spl(p, "Cardinal");
     if (spl.ok())
     {
+        p->addModel(modelAidaX);
         p->addModel(modelCardinalBlank);
         p->addModel(modelExpanderInputMIDI);
         p->addModel(modelExpanderOutputMIDI);