anira

an architecture for neural network inference in real-time audio applications

Anira is a high-performance library designed to enable easy real-time safe integration of neural network inference within audio applications. Compatible with multiple inference backends, LibTorch, ONNXRuntime, and Tensorflow Lite, anira bridges the gap between advanced neural network architectures and real-time audio processing. In the paper you can find more information about the architecture and the design decisions of anira, as well as extensive performance evaluations with the built-in benchmarking capabilities.

Features

Real-time Safe Execution: Ensures deterministic runtimes suitable for real-time audio applications
Thread Pool Management: Utilizes a static thread pool to avoid oversubscription and enables efficient parallel inference
Minimal Latency: Designed to minimize latency while maintaining real-time safety
Built-in Benchmarking: Includes tools for evaluating the real-time performance of neural networks
Comprehensive Inference Engine Support: Integrates common inference engines, LibTorch, ONNXRuntime, and TensorFlow Lite
Flexible Neural Network Integration: Supports a variety of neural network models, including stateful and stateless models
Cross-Platform Compatibility: Works seamlessly on macOS, Linux, and Windows

Usage

The basic usage of anira is as follows:

#include <anira/anira.h>

anira::InferenceConfig inference_config(
        {{"path/to/your/model.onnx", anira::InferenceBackend::ONNX}}, // Model path
        {{{256, 1, 1}}, {{256, 1}}},  // Input, Output shape
        5.33f // Maximum inference time in ms
);

// Create a pre- and post-processor instance
anira::PrePostProcessor pp_processor(inference_config);

// Create an InferenceHandler instance
anira::InferenceHandler inference_handler(pp_processor, inference_config);

// Pass the host configuration and allocate memory for audio processing
inference_handler.prepare({buffer_size, sample_rate});

// Select the inference backend
inference_handler.set_inference_backend(anira::InferenceBackend::ONNX);

// Optionally get the latency of the inference process in samples
unsigned int latency_in_samples = inference_handler.get_latency();

// Real-time safe audio processing in process callback of your application
process(float** audio_data, int num_samples) {
    inference_handler.process(audio_data, num_samples);
}
// audio_data now contains the processed audio samples

Installation

Anira can be easily integrated into your CMake project. You can either add anira as a submodule, download the pre-built binaries from the releases page, or build from source.

Option 1: Add as Git Submodule (Recommended)

# Add anira repo as a submodule
git submodule add https://github.com/anira-project/anira.git modules/anira

In your CMakeLists.txt:

# Setup your project and target
project(your_project)
add_executable(your_target main.cpp ...)

# Add anira as a subdirectory
add_subdirectory(modules/anira)

# Link your target to the anira library
target_link_libraries(your_target anira::anira)

Option 2: Use Pre-built Binaries

Download pre-built binaries from the releases page.

In your CMakeLists.txt:

# Setup your project and target
project(your_project)
add_executable(your_target main.cpp ...)

# Add the path to the anira library as cmake prefix path and find the package
list(APPEND CMAKE_PREFIX_PATH "path/to/anira")
find_package(anira REQUIRED)

# Link your target to the anira library
target_link_libraries(your_target anira::anira)

Option 3: Build from Source

git clone https://github.com/anira-project/anira.git
cd anira
cmake . -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release --target anira
cmake --install build --prefix /path/to/install/directory

Build options

By default, all three inference engines are installed. You can disable specific backends as needed:

LibTorch: -DANIRA_WITH_LIBTORCH=OFF
OnnxRuntime: -DANIRA_WITH_ONNXRUNTIME=OFF
Tensorflow Lite: -DANIRA_WITH_TFLITE=OFF

Moreover, the following options are available:

Build anira with benchmark capabilities: -DANIRA_WITH_BENCHMARK=ON
Build example applications, plugins and populate example neural models: -DANIRA_WITH_EXAMPLES=ON
Build anira with tests: -DANIRA_WITH_TESTS=ON
Build anira with documentation: -DANIRA_WITH_DOCS=ON
Disable the logging system: -DANIRA_WITH_LOGGING=OFF

Examples

Build in examples

Simple JUCE Audio Plugin: Demonstrates how to use anira in a real-time audio JUCE / VST3-Plugin.
CLAP Plugin Example: Demonstrates how to use anira in a real-time clap plugin.
Benchmark: Demonstrates how to use anira for benchmarking of different neural network models, backends and audio configurations.
Minimal Inference: Demonstrates how minimal inference applications can be implemented in all three backends.

Other examples

nn-inference-template: Another more JUCE / VST3-Plugin that uses anira for real-time safe neural network inference. This plugin is more complex than the simple JUCE Audio Plugin example as it has a more appealing GUI.

Real-time safety

anira's real-time safety is checked in this repository with the rtsan sanitizer.

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