Use gcc15 for linux builds

Oversampling support

.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

@@ -14,13 +14,19 @@ jobs:
     - uses: actions/checkout@v3.3.0
       with:
         submodules: recursive
+
+    - name: Setup GCC 15
+      uses: egor-tensin/setup-gcc@v2
+      with:
+        version: '15'
     
     - name: Build Plugin
       working-directory: ${{github.workspace}}/build
       env:
-        CXX: clang++
+        CXX: g++-15
+        CXXFLAGS: "-static-libstdc++ -static-libgcc"              
       run: |
-        cmake .. -DCMAKE_BUILD_TYPE=$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 +57,7 @@ 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 18 2026" -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 +31,56 @@ 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:
+        submodules: recursive
+
+    - name: Setup GCC 15
+      uses: egor-tensin/setup-gcc@v2
+      with:
+        version: '15'
+    
+    - name: Build Plugin
+      working-directory: ${{github.workspace}}/build
+      env:
+        CXX: g++-15
+        CXXFLAGS: "-static-libstdc++ -static-libgcc"
+      run: |
+        cmake .. -DCMAKE_BUILD_TYPE=$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 ${{ env.ARCHIVE_NAME }} neural_amp_modeler.lv2
+      
+    - name: Upload Plugin Asset
+      uses: actions/upload-release-asset@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      with:
+        upload_url: ${{ needs.create_release.outputs.upload_url }}
+        asset_path: ./build/${{ env.ARCHIVE_NAME }}
+        asset_name: ${{ env.ARCHIVE_NAME }}
+        asset_content_type: application/tgz
+
+  build-rpi:
+    name: Build Raspberry Pi
+    needs: create_release
+    strategy:
+      matrix:
+        native_arch: [rpi4, rpi5]
+    runs-on: ubuntu-latest
+    container: 
+      image: ghcr.io/tttapa/docker-arm-cross-toolchain:aarch64-rpi3-linux-gnu-gcc15
+    env:
+      ARCHIVE_NAME: neural_amp_modeler_lv2_${{ matrix.native_arch }}.tgz
     steps:
     - uses: actions/checkout@v3.3.0
       with:
@@ -40,14 +89,14 @@ jobs:
     - name: Build Plugin
       working-directory: ${{github.workspace}}/build
       env:
-        CXX: clang++
+        CXXFLAGS: "-static-libstdc++ -static-libgcc"
       run: |
-        cmake .. -DCMAKE_BUILD_TYPE=$BUILD_TYPE -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_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
@@ -55,45 +104,19 @@ 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_content_type: application/tgz
-
-  build-linux-x64v3:
-    name: Build Linux x64v3
-    needs: create_release
-    runs-on: ubuntu-latest
-    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 --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
-      
-    - name: Upload Plugin Asset
-      uses: actions/upload-release-asset@v1
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      with:
-        upload_url: ${{ needs.create_release.outputs.upload_url }}
-        asset_path: ./build/neural_amp_modeler_lv2_linux_x64v3.tgz
-        asset_name: neural_amp_modeler_lv2_linux_x64v3.tgz
+        asset_path: ./build/${{ 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 +125,11 @@ jobs:
     - name: Build Plugin
       working-directory: ${{github.workspace}}/build
       run: |
-        cmake.exe -G "Visual Studio 17 2022" -A x64  -T ClangCL -DLSTM_PREFER_NAM=OFF -DWAVENET_PREFER_NAM=OFF ..
+        cmake.exe -G "Visual Studio 18 2026" -A x64  -T ClangCL -DUSE_NATIVE_ARCH=${{ 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 +137,6 @@ 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 0.2.1)
 
 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

@@ -5,29 +5,38 @@ LV2 plugin for neural network machine learning amp model playback using the [Neu
 **There is no custom plugin user interface**. Setting the model to use requires that your LV2 host supports atom:Path parameters. Reaper does as of v6.82. Carla and Ardour do. If your favorite LV2 host does not support atom:Path, let them know you want it.
 If you are looking for a GUI version, @brummer10 [has one here](https://github.com/brummer10/neural-amp-modeler-ui) that works for Linux and Windows. You may also be interested in the the version shipped with the [MOD Desktop App](https://github.com/moddevices/mod-desktop-app), or my digital pedalboard app [Stompbox](https://github.com/mikeoliphant/Stompbox).
 
-To get the intended behavior, **you must run your audio host at the same sample rate the model was trained at** (usually 48kHz) - no resampling is done by the plugin.
+To get the intended behavior, **you must run your audio host at the same sample rate the model was trained at** (usually 48kHz) - no resampling is done by the plugin. An exception to this is oversampling (running at an even multiple of the model sample rate). See the "[Oversampling](https://github.com/mikeoliphant/neural-amp-modeler-lv2/blob/main/README.md#oversampling)" section below.
 
 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
 
 The expected input level to the plugin is 12dBu. For models that include input level information, they will be calibrated against this level. If you know the input level of your audio interface, you should adjust the input level relative to the expected 12dBu to provide the appropriate signal level to the model.
 
+## Oversampling
+
+If you run at a sample rate that is an even multiple of the model sample rate, the model will be correctly oversampled to behave as expected. So, if the model sample rate is 48kHz (which they typically are), and you are running at 96kHz, the model will be 2x oversampled. This also means that if your DAW supports oversampling, you can safely use it. Running oversampled comes with a signficant performance cost, but can be useful for reducing aliasing.
+
 ## Building
 
 First clone the repository:

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

@@ -45,6 +45,8 @@ namespace NAM {
 	{
 		this->sampleRate = sampleRate;
 
+		loader.SetExternalSampleRate((int)sampleRate);
+
 		// for fetching initial options, can be null
 		LV2_Options_Option* options = nullptr;
 
@@ -128,7 +130,7 @@ 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 +214,7 @@ namespace NAM {
 	{
 		maxBufferSize = size;
 
-		NeuralAudio::NeuralModel::SetDefaultMaxAudioBufferSize(size);
+		loader.SetDefaultMaxAudioBufferSize(size);
 	}
 
 	void Plugin::process(uint32_t n_samples) noexcept
@@ -256,7 +258,7 @@ 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;