https://github.com/anira-project/anira
an architecture for neural network inference in real-time audio applications
https://github.com/anira-project/anira
audio audio-processing deep-learning libtorch onnxruntime real-time tensorflow-lite
Last synced: 11 days ago
JSON representation
an architecture for neural network inference in real-time audio applications
- Host: GitHub
- URL: https://github.com/anira-project/anira
- Owner: anira-project
- License: apache-2.0
- Created: 2024-02-04T14:25:31.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2026-06-05T12:03:24.000Z (20 days ago)
- Last Synced: 2026-06-05T14:05:37.654Z (20 days ago)
- Topics: audio, audio-processing, deep-learning, libtorch, onnxruntime, real-time, tensorflow-lite
- Language: C++
- Homepage: https://anira-project.github.io/anira/
- Size: 1.3 MB
- Stars: 217
- Watchers: 5
- Forks: 11
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# 
---
**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](https://github.com/pytorch/pytorch/), [ONNXRuntime](https://github.com/microsoft/onnxruntime/), and [Tensorflow Lite](https://github.com/tensorflow/tensorflow/), anira bridges the gap between advanced neural network architectures and real-time audio processing. In the [paper](https://doi.org/10.1109/IS262782.2024.10704099) 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.
## Documentation
An extensive documentation of anira can be found at [https://anira-project.github.io/anira/](https://anira-project.github.io/anira/).
## 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:
```cpp
#include
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](https://github.com/anira-project/anira/releases/latest), or build from source.
### Option 1: Add as Git Submodule (Recommended)
```bash
# Add anira repo as a submodule
git submodule add https://github.com/anira-project/anira.git modules/anira
```
In your `CMakeLists.txt`:
```cmake
# 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](https://github.com/anira-project/anira/releases/latest).
In your `CMakeLists.txt`:
```cmake
# 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
```bash
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](https://github.com/anira-project/anira/tree/main/examples/juce-audio-plugin/): Demonstrates how to use anira in a real-time audio JUCE / VST3-Plugin.
- [CLAP Plugin Example](https://github.com/anira-project/anira/tree/main/examples/clap-audio-plugin/): Demonstrates how to use anira in a real-time clap plugin.
- [Benchmark](https://github.com/anira-project/anira/tree/main/examples/benchmark/): Demonstrates how to use anira for benchmarking of different neural network models, backends and audio configurations.
- [Minimal Inference](https://github.com/anira-project/anira/tree/main/examples/minimal-inference/): Demonstrates how minimal inference applications can be implemented in all three backends.
### Other examples
- [nn-inference-template](https://github.com/Torsion-Audio/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](https://github.com/anira-project/anira-rt-principle-check) repository with the [rtsan](https://github.com/realtime-sanitizer/rtsan) sanitizer.
## Citation
If you use anira in your research or project, please cite either the [paper](https://doi.org/10.1109/IS262782.2024.10704099) or the software itself:
```bibtex
@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](https://github.com/faressc)
- [Valentin Ackva](https://github.com/vackva)
## Sponsor
## License
This project is licensed under [Apache-2.0](https://github.com/anira-project/anira/tree/main/LICENSE).