https://github.com/superkogito/fastft
Implementation of [Librosa](https://github.com/librosa/librosa) like [STFT](https://en.wikipedia.org/wiki/Short-time_Fourier_transform) using [FFTW](https://www.fftw.org/)
https://github.com/superkogito/fastft
audio audio-analysis audio-processing dsp signal-processing speech-processing stft
Last synced: 6 months ago
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Implementation of [Librosa](https://github.com/librosa/librosa) like [STFT](https://en.wikipedia.org/wiki/Short-time_Fourier_transform) using [FFTW](https://www.fftw.org/)
- Host: GitHub
- URL: https://github.com/superkogito/fastft
- Owner: SuperKogito
- License: bsd-3-clause
- Created: 2024-01-31T22:02:40.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-30T19:12:43.000Z (over 1 year ago)
- Last Synced: 2024-05-31T15:13:21.060Z (over 1 year ago)
- Topics: audio, audio-analysis, audio-processing, dsp, signal-processing, speech-processing, stft
- Language: C
- Homepage: https://superkogito.github.io/fastft/
- Size: 46.4 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
**Fastft**: **Fa**st **S**hort **T**ime **F**ourrier **T**ransform implementation based on **F**astest **F**ourier **T**ransform in the **W**est (**FFTW**).
# Motivation
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](https://github.com/deezer/spleeter), [MOSnet](https://github.com/lochenchou/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.
# Project structure
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
```
# Build
To build fastft you will need to install the following two libraries:
- [FFTW](https://www.fftw.org/)
- [Libsndfile](https://libsndfile.github.io/libsndfile/)
Then, simply build the project using:
```
mkdir build
cd build
cmake ..
make
```
## Run the gtests
fastft includes some unit tests based on [gtests](http://google.github.io/googletest/).
These can be run using `build/test/fastft_tests` after the build.
## Comparison
A comparison betweeen librosa's python output and fastft output is present under `comparison/`.
## Demo
A demo / usecase example is available under `example/cMOSNet/`.
## Documentation
The code documentation is available under [https://superkogito.github.io/fastft/](https://superkogito.github.io/fastft/).
## Citation
```
@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).}
}
```