pitch-detectors

collection of pitch (f0, fundamental frequency) detection algorithms with unified interface

list of algorithms

algorithm	cpu	gpu	MDBStemSynth accuracy	Mir1K accuracy
Crepe	✓	✓	0.886 ± 0.059	0.759 ± 0.073
Penn	✓	✓	0.699 ± 0.263	0.660 ± 0.090
PipTrack	✓		0.524 ± 0.286	0.864 ± 0.071
PraatAC	✓		0.777 ± 0.302	0.859 ± 0.074
PraatCC	✓		0.776 ± 0.300	0.872 ± 0.068
PraatSHS	✓		0.534 ± 0.238	0.578 ± 0.169
Pyin	✓		0.722 ± 0.252	0.888 ± 0.047
Rapt	✓		0.791 ± 0.282	0.827 ± 0.078
Reaper	✓		0.792 ± 0.261	0.795 ± 0.083
Spice	✓	✓	0.024 ± 0.034	0.889 ± 0.046
Swipe	✓		0.784 ± 0.276	0.796 ± 0.062
TorchCrepe	✓	✓	0.764 ± 0.273	0.774 ± 0.084
TorchYin	✓		0.735 ± 0.268	0.866 ± 0.052
World	✓		0.869 ± 0.135	0.842 ± 0.075
Yaapt	✓		0.740 ± 0.271	0.744 ± 0.107
Yin	✓		0.749 ± 0.269	0.884 ± 0.043

accuracy is mean raw pitch accuracy

install

all agorithms tested on python3.10, this is recommended python version to use

pip install pitch-detectors

usage

from scipy.io import wavfile
from pitch_detectors import algorithms
import matplotlib.pyplot as plt

fs, a = wavfile.read('data/b1a5da49d564a7341e7e1327aa3f229a.wav')
pitch = algorithms.Crepe(a, fs)
plt.plot(pitch.t, pitch.f0)
plt.show()

additional features

• robust (vote + median) ensemble algorithm using all models
• json import/export