Anira Documentation

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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

C++ Library

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

C++ Build Options

By default, LibTorch, ONNXRuntime and LiteRT are enabled. You can disable specific backends as needed:

• LibTorch: -DANIRA_WITH_LIBTORCH=OFF

• OnnxRuntime: -DANIRA_WITH_ONNXRUNTIME=OFF

• LiteRT (LiteRt* C API): -DANIRA_WITH_LITERT=OFF — runs .tflite models through LiteRT’s native CompiledModel runtime. Enabled by default; it is the modern TensorFlow-Lite-family backend.

• TensorFlow Lite (legacy TfLite* C API): -DANIRA_WITH_TFLITE=ON — the same runtime as LiteRT exposed through the older C API, so the two are mutually exclusive. To use it, disable LiteRT: -DANIRA_WITH_LITERT=OFF -DANIRA_WITH_TFLITE=ON.

Platform / backend support

anira builds on the targets below; the pre-built backends it downloads ship per target as shared and/or static (anira’s linkage follows BUILD_SHARED_LIBS):

Target	LibTorch	ONNXRuntime	LiteRT	TFLite (legacy)
macOS x86_64	shared	shared · static	shared · static	shared · static
macOS arm64	shared	shared · static	shared · static	shared · static
macOS universal	shared	shared · static	shared · static	shared · static
Linux x86_64	shared	shared · static	shared · static	shared · static
Linux aarch64	shared	shared · static	shared · static	shared · static
Windows x86_64	shared	shared · static	shared · static	shared · static
Windows arm64	shared	shared · static	shared · static	shared · static
WASM (Emscripten)	—	static	—	—

LibTorch is shared-only (auto-disabled for fully static anira builds). LiteRT and TFLite are the same runtime via two C APIs and are mutually exclusive (LiteRT is the default). On WebAssembly only ONNX Runtime is supported. Backends for Android and iOS are also published in the anira-project/backends release for cross-builds. — = not provided.

Pre-built backend binaries are downloaded at configure time from the anira-project/backends release pinned by ANIRA_BACKENDS_VERSION. Integrity is checked live: when GitHub is reachable, anira fetches each asset’s published SHA256 and re-downloads any backend whose archive changed upstream or downloaded incompletely (the download is verified against that hash). Nothing is pinned in-repo. Linkage and source are configurable:

• Linkage follows BUILD_SHARED_LIBS (shared anira → shared backends, static → static). Decouple a single engine with -DANIRA_<ENGINE>_LINKAGE=shared|static where <ENGINE> is LIBTORCH|ONNXRUNTIME|TFLITE|LITERT. LibTorch is shared-only.

• Backends release tag: -DANIRA_BACKENDS_VERSION=v2.1.1.

• Offline / reproducible builds: -DANIRA_BACKENDS_SKIP_REMOTE_CHECK=ON skips the GitHub query and reuses whatever is already in modules/.

• Bring your own backend (no fork): -DANIRA_<ENGINE>_ROOTDIR=/path/to/prebuilt (a tree with include/ + lib/), or a custom source via -DANIRA_<ENGINE>_URL=... -DANIRA_<ENGINE>_SHA256=....

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

Anira Web (Web / JavaScript)

Anira is available as the @anira-project/anira package for use in web applications:

# npm
npm install @anira-project/anira

# pnpm
pnpm add @anira-project/anira

# yarn
yarn add @anira-project/anira

Building @anira-project/anira from source

If you want to build the WASM module and JavaScript bindings yourself, you need to provide your own Emscripten SDK. The CMake presets expect the EMSDK environment variable to be set to the root of your emsdk installation.

git clone https://github.com/anira-project/anira.git
cd anira

export EMSDK=/path/to/your/emsdk

# Configure and build the WASM module (release)
cmake --preset web-prod
cmake --build --preset web-prod

# Build the JavaScript package
cd web
npm install
npm run build

For packaging it locally, use

npm pack

in the web folder, which will create a .tgz file that can be installed with npm or yarn.

Then install the package in your project:

npm install path/to/anira/web/anira-project-anira-x.x.x.tgz

A debug preset is also available via cmake --preset web / cmake --build --preset web.

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.

Citation

If you use anira in your research or project, please cite either the paper or the software itself:

@inproceedings{ackvaschulz2024anira,
    author={Ackva, Valentin and Schulz, Fares},
    booktitle={2024 IEEE 5th International Symposium on the Internet of Sounds (IS2)},
    title={ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications},
    year={2024},
    volume={},
    number={},
    pages={1-10},
    publisher={IEEE},
    doi={10.1109/IS262782.2024.10704099}
}

@software{ackvaschulz2024anira,
    author = {Valentin Ackva and Fares Schulz},
    title = {anira: an architecture for neural network inference in real-time audio application},
    url = {https://github.com/anira-project/anira},
    version = {x.x.x},
    year = {2024},
}

Contributors

• Fares Schulz

• Valentin Ackva

Sponsor

License

This project is licensed under Apache-2.0.

Next Steps

New to Anira? Start with Getting Started for installation and basic usage.

Ready to dive deeper? Check out the Usage Guide and explore Custom Pre/Post Processing or Custom Backend Definition for advanced use cases.

Having issues? See Troubleshooting & FAQ or visit the GitHub repository for support.