Fastft: Fast Short Time Fourrier Transform implementation based on Fastest Fourier Transform in the West (FFTW).
The Short Time Fourier Transform (STFT) is a common tool in audio AI tasks. However, there is currently no standard implementation in C that facilitates fast and efficient features extraction for the purpose of inference. Fastft aims to address this gap by offering an implementation based on the Fastest Fourier Transform in the West (FFTW). This implementation is suitable for Spectrogram/STFT-based inference (e.g., models like Spleeter, MOSnet, etc.), and it can also be extended to cover feature extraction algorithms such as MFCC. The implementation aims to be as efficient as possible in order to target CPU-based low-latency inference solutions.
While some deep learning libraries offer the option of incorporating STFT into the model, these implementations often differ and may restrict developer flexibility; two critical considerations when targeting embedded hardware.
The following is an illustration of the different components of the project.
├── CMakeLists.txt
├── comparison # compare Librosa.stft to Fastft
│ ├── benchmark.py
│ ├── CMakeLists.txt
│ ├── fastft_librosa_mse.png
│ ├── fastft_vs_librosa.png
│ ├── main.cpp
│ ├── README.md
│ └── requirements.txt
├── docs # project documentation
│ ├── Doxygen
│ │ ├── Doxyfile.cfg
│ │ └── xml
│ └── Sphinx
│ ├── make.bat
│ ├── Makefile
│ └── source
├── example # usecase/ example
│ └── cMOSNet
│ ├── CMakeLists.txt
│ ├── main.c
│ ├── mosnet_cnn.onnx
│ ├── README.md
│ ├── reference
│ └── test.wav
├── extras
│ ├── logo.png
│ └── small_logo.png
├── include
│ ├── pad.h
│ ├── signal.h
│ ├── spectral.h
│ ├── trafo_istft.h
│ ├── trafo_stft.h
│ └── window.h
├── README.md
├── resources
│ ├── chirp.wav
│ └── test.wav
├── src
│ ├── pad.c
│ ├── signal.c
│ ├── spectral.c
│ ├── trafo_istft.c
│ ├── trafo_stft.c
│ └── window.c
└── test # gtests folder
├── CMakeLists.txt
├── main.cpp
└── test_stft.cpp
To build fastft you will need to install the following two libraries:
Then, simply build the project using:
mkdir build
cd build
cmake ..
make
fastft includes some unit tests based on gtests.
These can be run using build/test/fastft_tests after the build.
A comparison betweeen librosa's python output and fastft output is present under comparison/.
A demo / usecase example is available under example/cMOSNet/.
The code documentation is available under https://superkogito.github.io/fastft/.
@misc{Malek2024,
url = {https://github.com/SuperKogito/fastft},
year = {2024},
author = {Ayoub Malek},
title = {Fastft: Fast Short Time Fourrier Transform implementation based on Fastest Fourier Transform in the West (FFTW).}
}