df0-pitch 1.0.1

Python package with Pytorch implementations of differentiable variants of classical fundamental frequency estimators (dYIN and dSWIPE)

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_df0_TASLPRO,
  author  = {Sebastian Strahl and Meinard M{\"u}ller},
  title   = {{dYIN} and {dSWIPE}: {D}ifferentiable Variants of Classical Fundamental Frequency Estimators},
  journal = {{IEEE/ACM} Transactions on Audio, Speech, and Language Processing},
  volume  = {33},
  pages   = {2622--2633},
  year    = {2025},
  doi     = {10.1109/TASLPRO.2025.3581119}
}

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
conda activate df0

2. Install df0

Option 1: Install from PyPI

pip install df0-pitch

To also install the dependencies required to run the demo notebook:

pip install "df0-pitch[demo]"

Option 2: Install by cloning this repository (for development)

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

Usage

Python

import torch
import librosa
from df0.dyin import dYIN
from df0.dswipe import dSWIPE

x, fs = librosa.load("audio.wav", sr=16000)
x = torch.from_numpy(x)

dyin = dYIN(fs=fs, hop_size=160)
f0_hz = dyin(x)["f0_hz"]  # shape: (n_frames,)

dswipe = dSWIPE(fs=fs, hop_size=160)
f0_hz = dswipe(x)["f0_hz"]  # shape: (n_frames,)

For more details, see 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
...

Tests

We provide automated tests for each algorithm. To run them:

pip install "df0-pitch[test]"
pytest

Contribution

Automated code style checks via pre-commit:

pip install pre-commit
pre-commit install
pre-commit run --all-files

License

The code for this toolbox is published under an MIT license.

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.