df0: Differentiable Variants of Classical Fundamental Frequency Estimators

This is a Python package containing Pytorch implementations of differentiable variants of classical fundamental frequency estimators (dYIN and dSWIPE). This code accompanies the following paper:

@article{StrahlM25_dYIN_dSWIPE,
  author    = {Sebastian Strahl and Meinard M{\"u}ller},
  title     = {{dYIN} and {dSWIPE}: {D}ifferentiable Variants of Classical Fundamental Frequency Estimators},
  journal   = {},
  volume    = {},
  pages     = {},
  year      = {},
}

For details and references, please check out this paper.

Installation

1. Set up Python environment

We recommend setting up a Python environment including Pytorch before installing df0. You may use the example environment provided as part of this package:

git clone https://github.com/groupmm/df0.git
cd df0
conda env create -f environment.yaml

2. Install df0

Option 1: Installation without cloning this repository:

pip install "git+https://github.com/groupmm/df0.git#egg=df0"

Option 2: Installation by cloning this repository:

git clone https://github.com/groupmm/df0.git
cd df0
pip install -e .

Option 3: Install all packages to run the demo notebook:

pip install "git+https://github.com/groupmm/df0.git#egg=df0[demo]"

or

git clone https://github.com/groupmm/df0.git
cd df0
pip install -e .[demo]

Usage

Python

The usage of dYIN and dSWIPE as a differentiable Pytorch module is demonstrated in demo.ipynb.

Command-line interface

To get the F0 predictions for an audio file, you can use the command-line interface, running one of the following commands in a terminal:

dswipe audio.wav
dyin audio.wav

To get the predictions for all .wav files in dir_audio/, use:

dswipe dir_audio/*.wav

By running these commands, the F0 predictions are stored as .csv files in a folder specified by the command-line argument dir_out (default: f0_csv). Example usage:

dswipe dir_audio/*.wav --dir_out f0_dswipe_csv

Use dswipe -h or dyin -h for an overview of available command-line options.

The output format of our methods is similar to that of CREPE and PESTO, except that there is no confidence:

time,frequency
0.000,63.544
0.010,651.683
0.020,655.458
0.030,651.683
0.040,666.915
0.050,678.573
...

Contribution

Automated code style checks via pre-commit:

pip install pre-commit
pre-commit install

License

The code for this toolbox is published under an MIT license. This does not apply to the data files, which are taken from the FMP notebooks.

Acknowledgements

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Grant No. 500643750 (MU 2686/15-1). The authors are with the International Audio Laboratories Erlangen, a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS.