Do you have a zillion BSS audio files to process and it is taking days ? Is your simulation never ending ?
Fear no more!
fast_bss_eval
is here to help you!
fast_bss_eval
is a fast implementation of the bss_eval metrics for the
evaluation of blind source separation. Our implementation of the bss_eval
metrics has the following advantages compared to other existing ones.
use_cg_iter=10
option to the function call)# from pypi
pip install fast-bss-eval
# or from source
git clone https://github.com/fakufaku/fast_bss_eval
cd fast_bss_eval
pip install -e .
Assuming you have multichannel signals for the estmated and reference sources
stored in wav format files names my_estimate_file.wav
and
my_reference_file.wav
, respectively, you can quickly evaluate the bss_eval
metrics as follows.
from scipy.io import wavfile
import fast_bss_eval
# open the files, we assume the sampling rate is known
# to be the same
fs, ref = wavfile.read("my_reference_file.wav")
_, est = wavfile.read("my_estimate_file.wav")
# compute the metrics
sdr, sir, sar, perm = fast_bss_eval.bss_eval_sources(ref.T, est.T)
This package is significantly faster than other packages that also allow to compute bss_eval metrics such as mir_eval or sigsep/bsseval. We did a benchmark using numpy/torch, single/double precision floating point arithmetic (fp32/fp64), and using either Gaussian elimination or a conjugate gradient descent (solve/CGD10).
If you use this package in your own research, please cite our paper describing it.
@misc{scheibler_sdr_2021,
title={SDR --- Medium Rare with Fast Computations},
author={Robin Scheibler},
year={2021},
eprint={2110.06440},
archivePrefix={arXiv},
primaryClass={eess.AS}
}
2021 (c) Robin Scheibler, LINE Corporation
All of this code is released under MIT License with the exception of fast_bss_eval/torch/hungarian.py
which is under 3-clause BSD License.