Fix smart bypass cmake message

Refactor build and add Raspberry Pi 4/5 builds

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,17 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is. Please do not include AI chat transcripts.
+
+**Environment**
+Please include information on the operating system and plugin host environment where you are experiencing the issue. If you can, please test in multiple different contexts (ie: different DAWs)
+
+**Additional Information**
+Please add any additional detail here.

.github/workflows/build.yml

@@ -20,7 +20,7 @@ jobs:
       env:
         CXX: clang++
       run: |
-        cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DBUILD_UTILS=ON -DBUILD_NAMCORE=ON -DBUILD_STATIC_RTNEURAL=ON 
+        cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DBUILD_UTILS=ON -DBUILD_INTERNAL_STATIC_WAVENET=ON -DBUILD_INTERNAL_STATIC_LSTM=ON -DBUILD_NAMCORE=ON -DBUILD_STATIC_RTNEURAL=ON 
         cmake --build . --config $BUILD_TYPE -j4
         cpack
 
@@ -51,7 +51,7 @@ jobs:
     - name: Build Plugin
       working-directory: ${{github.workspace}}/build
       run: |
-        cmake.exe -G "Visual Studio 17 2022" -A x64 -DBUILD_UTILS=ON -DBUILD_NAMCORE=ON -DBUILD_STATIC_RTNEURAL=ON -T ClangCL ..
+        cmake.exe -G "Visual Studio 17 2022" -A x64 -DBUILD_UTILS=ON -DBUILD_INTERNAL_STATIC_WAVENET=ON -DBUILD_INTERNAL_STATIC_LSTM=ON -DBUILD_NAMCORE=ON -DBUILD_STATIC_RTNEURAL=ON -T ClangCL ..
         cmake --build . --config=release -j4
 
     - name: Run ModelTest

.github/workflows/release.yml

@@ -31,7 +31,12 @@ jobs:
   build-linux-x64:
     name: Build Linux x64
     needs: create_release
+    strategy:
+      matrix:
+        native_arch: [ON, OFF]
     runs-on: ubuntu-latest
+    env:
+      ARCHIVE_NAME: neural_amp_modeler_lv2_linux_x64${{ matrix.native_arch == 'ON' && 'v3' || '' }}.tgz
     steps:
     - uses: actions/checkout@v3.3.0
       with:
@@ -42,12 +47,12 @@ jobs:
       env:
         CXX: clang++
       run: |
-        cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DLSTM_PREFER_NAM=OFF -DWAVENET_PREFER_NAM=OFF
+        cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DUSE_NATIVE_ARCH=${{ matrix.native_arch }} -DBUILD_NAMCORE=ON -DBUILD_INTERNAL_STATIC_WAVENET=ON -DBUILD_INTERNAL_STATIC_LSTM=ON
         cmake --build . --config $BUILD_TYPE -j4
 
     - name: Add LV2 Archive
       working-directory: ${{github.workspace}}/build
-      run: tar -czf neural_amp_modeler_lv2_linux_x64.tgz neural_amp_modeler.lv2
+      run: tar -czf ${{ env.ARCHIVE_NAME }} neural_amp_modeler.lv2
       
     - name: Upload Plugin Asset
       uses: actions/upload-release-asset@v1
@@ -55,30 +60,35 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       with:
         upload_url: ${{ needs.create_release.outputs.upload_url }}
-        asset_path: ./build/neural_amp_modeler_lv2_linux_x64.tgz
-        asset_name: neural_amp_modeler_lv2_linux_x64.tgz
+        asset_path: ./build/${{ env.ARCHIVE_NAME }}
+        asset_name: ${{ env.ARCHIVE_NAME }}
         asset_content_type: application/tgz
 
-  build-linux-x64v3:
-    name: Build Linux x64v3
+  build-rpi:
+    name: Build Raspberry Pi
     needs: create_release
+    strategy:
+      matrix:
+        native_arch: [rpi4, rpi5]
     runs-on: ubuntu-latest
+    container: 
+      image: ghcr.io/tttapa/docker-arm-cross-toolchain:aarch64-rpi3-linux-gnu-gcc12
+    env:
+      ARCHIVE_NAME: neural_amp_modeler_lv2_${{ matrix.native_arch }}.tgz
     steps:
     - uses: actions/checkout@v3.3.0
       with:
         submodules: recursive
-        
+    
     - name: Build Plugin
       working-directory: ${{github.workspace}}/build
-      env:
-        CXX: clang++
       run: |
-        cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DUSE_NATIVE_ARCH=ON -DLSTM_PREFER_NAM=OFF -DWAVENET_PREFER_NAM=OFF
+        cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE -DBUILD_NAMCORE=ON -DBUILD_INTERNAL_STATIC_WAVENET=ON -DBUILD_INTERNAL_STATIC_LSTM=ON -DCMAKE_TOOLCHAIN_FILE=/home/develop/opt/x-tools/aarch64-rpi3-linux-gnu/aarch64-${{ matrix.native_arch }}-linux-gnu.toolchain.cmake
         cmake --build . --config $BUILD_TYPE -j4
 
     - name: Add LV2 Archive
       working-directory: ${{github.workspace}}/build
-      run: tar -czf neural_amp_modeler_lv2_linux_x64v3.tgz neural_amp_modeler.lv2
+      run: tar -czf ${{ env.ARCHIVE_NAME }} neural_amp_modeler.lv2
       
     - name: Upload Plugin Asset
       uses: actions/upload-release-asset@v1
@@ -86,14 +96,19 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       with:
         upload_url: ${{ needs.create_release.outputs.upload_url }}
-        asset_path: ./build/neural_amp_modeler_lv2_linux_x64v3.tgz
-        asset_name: neural_amp_modeler_lv2_linux_x64v3.tgz
+        asset_path: ./build/${{ env.ARCHIVE_NAME }}
+        asset_name: ${{ env.ARCHIVE_NAME }}
         asset_content_type: application/tgz
         
   build-windows:
     name: Build Windows
     needs: create_release
+    strategy:
+      matrix:
+        native_arch: [ON, OFF]
     runs-on: windows-latest
+    env:
+      ARCHIVE_NAME: neural_amp_modeler_lv2_win_x64${{ matrix.native_arch == 'ON' && 'v3' || '' }}.zip
     steps:
     - uses: actions/checkout@v3.3.0
       with:
@@ -102,11 +117,11 @@ jobs:
     - name: Build Plugin
       working-directory: ${{github.workspace}}/build
       run: |
-        cmake.exe -G "Visual Studio 17 2022" -A x64  -T ClangCL -DLSTM_PREFER_NAM=OFF -DWAVENET_PREFER_NAM=OFF ..
+        cmake.exe -G "Visual Studio 17 2022" -A x64  -T ClangCL -DUSE_NATIVE_ARCH=${{ matrix.native_arch }} -DBUILD_NAMCORE=ON -DBUILD_INTERNAL_STATIC_WAVENET=ON -DBUILD_INTERNAL_STATIC_LSTM=ON ..
         cmake --build . --config=release -j4
 
     - name: Add LV2 Archive
-      run: Compress-Archive -Path ${{github.workspace}}\build\neural_amp_modeler.lv2 -Destination neural_amp_modeler_lv2_win_x64.zip
+      run: Compress-Archive -Path ${{github.workspace}}\build\neural_amp_modeler.lv2 -Destination ${{ env.ARCHIVE_NAME }}
       
     - name: Upload Plugin Asset
       uses: actions/upload-release-asset@v1
@@ -114,34 +129,6 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       with:
         upload_url: ${{ needs.create_release.outputs.upload_url }}
-        asset_path: ./neural_amp_modeler_lv2_win_x64.zip
-        asset_name: neural_amp_modeler_lv2_win_x64.zip
-        asset_content_type: application/zip
-
-  build-windows-x64v3:
-    name: Build Windows x64v3
-    needs: create_release
-    runs-on: windows-latest
-    steps:
-    - uses: actions/checkout@v3.3.0
-      with:
-        submodules: recursive
-    
-    - name: Build Plugin
-      working-directory: ${{github.workspace}}/build
-      run: |
-        cmake.exe -G "Visual Studio 17 2022" -A x64 -DUSE_NATIVE_ARCH=ON -DLSTM_PREFER_NAM=OFF -DWAVENET_PREFER_NAM=OFF -T ClangCL ..
-        cmake --build . --config=release -j4
-
-    - name: Add LV2 Archive
-      run: Compress-Archive -Path ${{github.workspace}}\build\neural_amp_modeler.lv2 -Destination neural_amp_modeler_lv2_win_x64v3.zip
-      
-    - name: Upload Plugin Asset
-      uses: actions/upload-release-asset@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      with:
-        upload_url: ${{ needs.create_release.outputs.upload_url }}
-        asset_path: ./neural_amp_modeler_lv2_win_x64v3.zip
-        asset_name: neural_amp_modeler_lv2_win_x64v3.zip
+        asset_path: ./${{ env.ARCHIVE_NAME }}
+        asset_name: ${{ env.ARCHIVE_NAME }}
         asset_content_type: application/zip

CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required(VERSION 3.10)
 
-project(NeuralAmpModelerLv2 VERSION 0.1.9)
+project(NeuralAmpModelerLv2 VERSION 0.2.0)
 
 set(CMAKE_MODULE_PATH "${CMAKE_SOURCE_DIR}/cmake")
 set(LIB_INSTALL_DIR ${CMAKE_INSTALL_PREFIX}/lib CACHE PATH "The library install dir (default: lib)")

README.md

@@ -9,20 +9,25 @@ To get the intended behavior, **you must run your audio host at the same sample
 
 For amp-only models (the most typical), **you will need to run an impulse reponse after this plugin** to model the cabinet.
 
-## Models Supported
+## Usage
 
-The plugin supports both [Neural Amp Modeler (NAM)](https://github.com/sdatkinson/neural-amp-modeler) models and [RTNeural keras json models](https://github.com/jatinchowdhury18/RTNeural) (like those used by [Aida-X](https://github.com/AidaDSP/AIDA-X)).
+Your DAW should expose the following input controls:
+
+**Input:** - Input (pre-model) gain in dB.
+
+**Output:** - Output (post-model) volume in dB.
+
+**Quality:** - Model quality (if applicable). For NAM A2 models, a value below 0.5 will give you a "lite" model and a value above 0.5 will give you a "full" model.
+
+**Model:** - The model file (ie: xxx.nam) to use.
+
+## Models Supported and Performance
+
+The plugin supports both [Neural Amp Modeler (NAM)](https://github.com/sdatkinson/neural-amp-modeler) models (both A1 and A2) and [RTNeural keras json models](https://github.com/jatinchowdhury18/RTNeural) (like those used by [Aida-X](https://github.com/AidaDSP/AIDA-X)).
 
 The best source of models is [Tone3000](https://www.tone3000.com/).
 
-For more information on model type support, see the [NeuralAudio](https://github.com/mikeoliphant/NeuralAudio) repository, which is where the model handling code lives.
-
-## Performance
-
-NAM WaveNet models are generally quite expensive to run. This isn't (much of) an issue on modern PCs, but you may have trouble running on less powerful hardware.
-
-If you are having trouble running a "standard" model, try looking for "feather", or even "nano" (the least expensive) models. You can find a list of ["feather"-tagged models on Tone3000](https://www.tone3000.com/search?sizes=feather). Note that tagging models is up to the submitter, so not all "feather" models are tagged as such - you should be able to find more if you dig around.
-
+For more information on model type support and performance, see the [NeuralAudio](https://github.com/mikeoliphant/NeuralAudio) repository, which is where the model handling code lives.
 
 ## Input Calibration
 

models/README.md

@@ -1,12 +0,0 @@
-These are some sample NAM models designed to be used for performance testing on CPU-limited devices. They are all based on a [LiveSpice model of a Boss SD-1 pedal](https://blog.nostatic.org/2023/04/this-boss-sd-1-pedal-does-not-exist.html).
-
-All models were trained for 300 epochs using the NAM "v1_1_1.wav" capture signal.
-
-| Model | ESR | CPU%<br>(RPi4 64bit) | Notes |
-| --- |--- | :-: | --- |
-| [BossWN-feather.nam](https://github.com/mikeoliphant/neural-amp-modeler-lv2/blob/main/models/BossWN-feather.nam) | .0001 | 37% | WaveNet "feather" preset |
-| [BossWN-4x2x1.nam](https://github.com/mikeoliphant/neural-amp-modeler-lv2/blob/main/models/BossWN-4x2x1.nam) | .0003 | 28% | WaveNet 4x2 channel |
-| [BossLSTM-2x16.nam](https://github.com/mikeoliphant/neural-amp-modeler-lv2/blob/main/models/BossLSTM-2x16.nam) | .0013 | 28% | LSTM 2x16 layers |
-| [BossLSTM-1x24.nam](https://github.com/mikeoliphant/neural-amp-modeler-lv2/blob/main/models/BossLSTM-1x24.nam) | .0017 | 22% | LSTM 1x24 layer |
-| [BossLSTM-2x8.nam](https://github.com/mikeoliphant/neural-amp-modeler-lv2/blob/main/models/BossLSTM-2x8.nam) | .0019 | 17% | LSTM 2x8 layers |
-| [BossLSTM-1x16.nam](https://github.com/mikeoliphant/neural-amp-modeler-lv2/blob/main/models/BossLSTM-1x16.nam) | .0041 | 15% | LSTM 1x16 layer |

resources/neural_amp_modeler.ttl.in

@@ -45,7 +45,7 @@ Models supported:
   Neural Amp Modeler (NAM): https://github.com/sdatkinson/neural-amp-modeler
   RTNeural keras/Aida-x models: https://github.com/jatinchowdhury18/RTNeural
 
-A large collection of models is available at https://tonehunt.org
+A large collection of models is available at https://www.tone3000.com
 """;
 
 	patch:writable <@NAM_LV2_ID@#model>;

src/CMakeLists.txt

@@ -58,6 +58,9 @@ option(SMART_BYPASS_ENABLED "Enable auto-bypass on silence" OFF)
 
 if (SMART_BYPASS_ENABLED)
 	add_definitions(-DSMART_BYPASS_ENABLED)
+	message(STATUS "Smart Bypass enabled")
+else()
+	message(STATUS "Smart Bypass NOT enabled")
 endif (SMART_BYPASS_ENABLED)
 
 set_target_properties(neural_amp_modeler

src/nam_plugin.cpp

@@ -128,7 +128,7 @@ namespace NAM {
 					{
 						lv2_log_trace(&nam->logger, "Staging model change: `%s`\n", msg->path);
 
-						model = NeuralAudio::NeuralModel::CreateFromFile(msg->path);
+						model = nam->loader.CreateFromFile(msg->path);
 					}
 
 					if (model != nullptr)
@@ -212,7 +212,7 @@ namespace NAM {
 	{
 		maxBufferSize = size;
 
-		NeuralAudio::NeuralModel::SetDefaultMaxAudioBufferSize(size);
+		loader.SetDefaultMaxAudioBufferSize(size);
 	}
 
 	void Plugin::process(uint32_t n_samples) noexcept
@@ -256,7 +256,7 @@ namespace NAM {
 		{
 			qualityScale = *(ports.quality_scale);
 
-			NeuralAudio::NeuralModel::SetDefaultQualityScaleFactor(qualityScale);
+			loader.SetDefaultQualityScaleFactor(qualityScale);
 
 			if (currentModel != nullptr)
 			{

src/nam_plugin.h

@@ -71,6 +71,7 @@ namespace NAM {
 		LV2_Log_Logger logger = {};
 		LV2_Worker_Schedule* schedule = nullptr;
 
+		NeuralAudio::NeuralModelLoader loader;
 		NeuralAudio::NeuralModel* currentModel = nullptr;
 		std::string currentModelPath;
 		float prevDCInput = 0;