Bump version -> 0.1.6

.github/workflows/release.yml

@@ -27,7 +27,69 @@ jobs:
           draft: true
           tag_name: ${{github.ref}}
           release_name: Release ${{github.ref}}
-          
+
+  build-linux-x64:
+    name: Build Linux x64
+    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
+        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
+      
+    - 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_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
+        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_content_type: application/tgz
+        
   build-windows:
     name: Build Windows
     needs: create_release
@@ -44,7 +106,7 @@ jobs:
         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.zip
+      run: Compress-Archive -Path ${{github.workspace}}\build\neural_amp_modeler.lv2 -Destination neural_amp_modeler_lv2_win_x64.zip
       
     - name: Upload Plugin Asset
       uses: actions/upload-release-asset@v1
@@ -52,6 +114,34 @@ jobs:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       with:
         upload_url: ${{ needs.create_release.outputs.upload_url }}
-        asset_path: ./neural_amp_modeler.lv2.zip
-        asset_name: neural_amp_modeler.lv2.zip
+        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 -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_content_type: application/zip

CMakeLists.txt

@@ -1,10 +1,10 @@
 cmake_minimum_required(VERSION 3.10)
 
-project(NeuralAmpModelerLv2 VERSION 0.1.5)
+project(NeuralAmpModelerLv2 VERSION 0.1.6)
 
 set(CMAKE_MODULE_PATH "${CMAKE_SOURCE_DIR}/cmake")
 
-set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD 20)
 set(CMAKE_CXX_STANDARD_REQUIRED OFF)
 set(CMAKE_CXX_EXTENSIONS OFF)
 

README.md

@@ -1,8 +1,8 @@
 # neural-amp-modeler-lv2
 
-Bare-bones implementation of [Neural Amp Modeler](https://github.com/sdatkinson/neural-amp-modeler) (NAM) models in an LV2 plugin.
+LV2 plugin for using neural network machine learning amp models.
 
-**There is no 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.
+**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/StompboxUI).
 
 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.
@@ -11,13 +11,21 @@ For amp-only models (the most typical), **you will need to run an impulse repons
 
 ### Models and Performance
 
+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)).
+
 The best source of models is [ToneHunt](https://tonehunt.org/).
 
-NAM 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.
+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.
 
 A Raspberry Pi 4 running a 64bit OS can run "standard" NAM models with a bit of room to spare for a cabinet IR and some lightweight effects.
 
-If you are having trouble running a "standard" model, try looking for "feather" (the least expensive) models. You can find a list of ["feather"-tagged models on ToneHunt](https://tonehunt.org/?tags=feather-mdl). 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.
+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 ToneHunt](https://tonehunt.org/models?tags%5B0%5D=feather-mdl). 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, 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.
 
 ### Building
 
@@ -47,4 +55,6 @@ After building, the plugin will be in **build/neural_amp_modeler.lv2**.
 
 ### Optimization
 
-If you have a relatively modern x64 processor, you can pass "**‑DUSE_NATIVE_ARCH=ON**" on your cmake command line to enable certain processor-specific optimizations.
+If you have a relatively modern x64 processor, you can pass ```-DUSE_NATIVE_ARCH=ON``` on your cmake command line to enable certain processor-specific optimizations.
+
+You can also alter the default model loading behavior with ```-DLSTM_PREFER_NAM=ON``` (use NAM Core instead of RTNeural for NAM LSTM models) and ```-DWAVENET_PREFER_NAM=ON``` (use NAM Core instead of RTNeural or NAM WaveNet models).

resources/neural_amp_modeler.ttl.in

@@ -16,7 +16,7 @@
 
 <@NAM_LV2_ID@#model>
 	a lv2:Parameter;
-	mod:fileTypes "nam,nammodel,json";
+	mod:fileTypes "nam,nammodel,json,aidax";
 	rdfs:label "Neural Model";
 	rdfs:range atom:Path.
 

src/CMakeLists.txt

@@ -1,8 +1,29 @@
-add_subdirectory(../deps/NeuralAudio NeuralAudio)
+if (MSVC)
+	add_compile_options(
+		"$<$<CONFIG:DEBUG>:/W4>"
+		"$<$<CONFIG:RELEASE>:/O2>"
+	)
+else()
+	add_compile_options(
+		-Wall 
+		# -Wpedantic -Wextra -Wstrict-aliasing -Wunreachable-code -Weffc++ -Wno-unused-parameter
+		"$<$<CONFIG:DEBUG>:-Og;-ggdb>"
+		"$<$<CONFIG:RELWITHDEBINFO>:-Ofast>"
+		"$<$<CONFIG:RELEASE>:-Ofast>"
+	)
+endif()
 
-include_directories(SYSTEM ../deps/NeuralAudio)
-include_directories(SYSTEM ../deps/lv2/include)
-include_directories(SYSTEM ../deps/denormal)
+
+if (CMAKE_SYSTEM_PROCESSOR MATCHES "(amd64)|(AMD64)|(x86_64)")
+	option(USE_NATIVE_ARCH "Enable architecture-specific optimizations" OFF)
+
+	if (USE_NATIVE_ARCH)
+		add_compile_options(-march=x86-64-v3)
+		message("Enabling -march=x86-64-v3")
+	endif (USE_NATIVE_ARCH)
+endif ()
+
+add_subdirectory(../deps/NeuralAudio NeuralAudio)
 
 set(SOURCES nam_lv2.cpp
 	nam_plugin.cpp
@@ -12,6 +33,10 @@ set(NA_SOURCES ../deps/NeuralAudio/NeuralAudio/NeuralModel.h)
 
 add_library(neural_amp_modeler SHARED ${SOURCES} ${NA_SOURCES})
 
+target_include_directories(neural_amp_modeler PUBLIC ../deps/NeuralAudio)
+target_include_directories(neural_amp_modeler PUBLIC ../deps/lv2/include)
+target_include_directories(neural_amp_modeler PUBLIC ../deps/denormal)
+
 target_link_libraries(neural_amp_modeler PRIVATE NeuralAudio)
 
 source_group(TREE ${CMAKE_CURRENT_SOURCE_DIR} FILES ${SOURCES})
@@ -23,14 +48,23 @@ if (DISABLE_DENORMALS)
 	add_definitions(-DDISABLE_DENORMALS)
 endif (DISABLE_DENORMALS)
 
-if (CMAKE_SYSTEM_PROCESSOR MATCHES "(amd64)|(AMD64)|(x86_64)")
-	option(USE_NATIVE_ARCH "Enable architecture-specific optimizations" OFF)
+option(LSTM_PREFER_NAM "Always use NAM Core for NAM LSTM models" ON)
 
-	if (USE_NATIVE_ARCH)
-		set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=x86-64-v3")
-		message("Enabling -march=x86-64-v3")
-	endif (USE_NATIVE_ARCH)
-endif ()
+if (LSTM_PREFER_NAM)
+	add_definitions(-DLSTM_PREFER_NAM)
+	message("Using NAM Core for LSTM models")
+else()
+	message("Using RTNeural for LSTM models")
+endif (LSTM_PREFER_NAM)
+
+option(WAVENET_PREFER_NAM "Always use NAM Core for NAM WaveNet models" OFF)
+
+if (WAVENET_PREFER_NAM)
+	add_definitions(-DWAVENET_PREFER_NAM)
+	message("Using NAM Core for WaveNet models")
+else()
+	message("Using RTNeural for WaveNet models")
+endif (WAVENET_PREFER_NAM)
 
 set_target_properties(neural_amp_modeler
 	PROPERTIES
@@ -44,18 +78,3 @@ set_target_properties(neural_amp_modeler
 if (CMAKE_SYSTEM_NAME STREQUAL "Windows")
 	target_compile_definitions(neural_amp_modeler PRIVATE NOMINMAX WIN32_LEAN_AND_MEAN)
 endif()
-
-if (MSVC)
-	target_compile_options(neural_amp_modeler PRIVATE
-		"$<$<CONFIG:DEBUG>:/W4>"
-		"$<$<CONFIG:RELEASE>:/O2>"
-	)
-else()
-	target_compile_options(neural_amp_modeler PRIVATE
-		-Wall 
-		# -Wpedantic -Wextra -Wstrict-aliasing -Wunreachable-code -Weffc++ -Wno-unused-parameter
-		"$<$<CONFIG:DEBUG>:-Og;-ggdb>"
-		"$<$<CONFIG:RELWITHDEBINFO>:-Ofast>"
-		"$<$<CONFIG:RELEASE>:-Ofast>"
-	)
-endif()

src/nam_lv2.cpp

@@ -14,8 +14,8 @@
 #include "nam_plugin.h"
 
 // LV2 Functions
-static LV2_Handle instantiate(const LV2_Descriptor*, double rate, const char*, const LV2_Feature* const* features
-) {
+static LV2_Handle instantiate(const LV2_Descriptor*, double rate, const char*, const LV2_Feature* const* features)
+{
 	try
 	{
 		auto nam = std::make_unique<NAM::Plugin>();

src/nam_plugin.cpp

@@ -11,7 +11,23 @@ namespace NAM {
 	Plugin::Plugin()
 	{
 		// prevent allocations on the audio thread
-		currentModelPath.reserve(MAX_FILE_NAME+1);
+		currentModelPath.reserve(MAX_FILE_NAME + 1);
+
+		NeuralAudio::NeuralModel::SetLSTMLoadMode(
+#ifdef LSTM_PREFER_NAM
+			NeuralAudio::PreferNAMCore
+#else
+			NeuralAudio::PreferRTNeural
+#endif
+		);
+
+		NeuralAudio::NeuralModel::SetWaveNetLoadMode(
+#ifdef WAVENET_PREFER_NAM
+			NeuralAudio::PreferNAMCore
+#else
+			NeuralAudio::PreferRTNeural
+#endif
+		);
 	}
 
 	Plugin::~Plugin()
@@ -107,31 +123,24 @@ namespace NAM {
 						lv2_log_trace(&nam->logger, "Staging model change: `%s`\n", msg->path);
 
 						model = NeuralAudio::NeuralModel::CreateFromFile(msg->path);
-
-						// Pre-run model to ensure all needed buffers are allocated in advance
-						//if (const int32_t numSamples = nam->maxBufferSize)
-						//{
-						//	float* buffer = new float[numSamples];
-						//	memset(buffer, 0, numSamples * sizeof(float));
-
-						//	model->Process(buffer, buffer, numSamples);
-						//	//model->finalize_(numSamples);
-
-						//	delete[] buffer;
-						//}
 					}
 
-					response.model = model;
+					if (model != nullptr)
+					{
+						response.model = model;
 
-					memcpy(response.path, msg->path, pathlen);
+						memcpy(response.path, msg->path, pathlen);
+					}
 				}
 				catch (const std::exception&)
+				{
+				}
+
+				if (model == nullptr)
 				{
 					response.path[0] = '\0';
 
 					lv2_log_error(&nam->logger, "Unable to load model from: '%s'\n", msg->path);
-					
-					//result = LV2_WORKER_ERR_UNKNOWN;
 				}
 
 				respond(handle, sizeof(response), &response);
@@ -181,6 +190,13 @@ namespace NAM {
 		return LV2_WORKER_SUCCESS;
 	}
 
+	void Plugin::set_max_buffer_size(int size) noexcept
+	{
+		maxBufferSize = size;
+
+		NeuralAudio::NeuralModel::SetDefaultMaxAudioBufferSize(size);
+	}
+
 	void Plugin::process(uint32_t n_samples) noexcept
 	{
 		lv2_atom_forge_set_buffer(&atom_forge, (uint8_t*)ports.notify, ports.notify->atom.size);
@@ -317,7 +333,7 @@ namespace NAM {
 		{
 			if (options[i].key == nam->uris.bufSize_maxBlockLength && options[i].type == nam->uris.atom_Int)
 			{
-				nam->maxBufferSize = *(const int32_t*)options[i].value;
+				nam->set_max_buffer_size(*(const int32_t*)options[i].value);
 				break;
 			}
 		}

src/nam_plugin.h

@@ -79,6 +79,7 @@ namespace NAM {
 		~Plugin();
 
 		bool initialize(double rate, const LV2_Feature* const* features) noexcept;
+		void set_max_buffer_size(int size) noexcept;
 		void process(uint32_t n_samples) noexcept;
 
 		void write_current_path();
@@ -118,6 +119,6 @@ namespace NAM {
 
 		float inputLevel = 0;
 		float outputLevel = 0;
-		int32_t maxBufferSize = 0;
+		int32_t maxBufferSize = 512;
 	};
 }