Welcome to ssspy’s documentation!#
ssspy is a Python toolkit for sound source separation.
Installation#
You can install ssspy by pip.
pip install git+https://github.com/tky823/ssspy.git
or clone the repository.
git clone https://github.com/tky823/ssspy.git
cd ssspy
pip install -e .
Note
If you fail to install ssspy, please update setuptools by
python -m pip install --upgrade setuptools
Build Documentation Locally (optional)#
To build the documentation locally, you have to include docs when installing ssspy.
pip install -e ".[docs]"
When you build the documentation, run the following command.
cd docs/
make html
Or, you can build the documentation automatically using sphinx-autobuild.
# in ssspy/
sphinx-autobuild docs docs/_build/html
Quick Example of Blind Source Separation#
import numpy as np
import scipy.signal as ss
import IPython.display as ipd
import matplotlib.pyplot as plt
from ssspy.utils.dataset import download_sample_speech_data
from ssspy.bss.iva import AuxLaplaceIVA
n_fft, hop_length = 4096, 2048
window = "hann"
waveform_src_img, sample_rate = download_sample_speech_data(n_sources=3)
waveform_mix = np.sum(waveform_src_img, axis=1)
_, _, spectrogram_mix = ss.stft(
waveform_mix,
window=window,
nperseg=n_fft,
noverlap=n_fft-hop_length
)
iva = AuxLaplaceIVA()
spectrogram_est = iva(spectrogram_mix)
_, waveform_est = ss.istft(
spectrogram_est,
window=window,
nperseg=n_fft,
noverlap=n_fft-hop_length
)
for idx, waveform in enumerate(waveform_est):
print("Estimated source: {}".format(idx + 1))
ipd.display(ipd.Audio(waveform, rate=sample_rate))
print()
plt.figure()
plt.plot(iva.loss)
plt.show()
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