# fast_bss_eval

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 helpyou!

`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.

- seamlessly works with
**both**numpy arrays and pytorch tensors - very fast
- can be even faster by using an iterative solver (add
`use_cg_iter=10`

option to the function call) - differentiable via pytorch
- can run on GPU via pytorch

## Author

## Quick Start

### Install

```
# 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 .
```

### Use

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)
```

## Benchmark

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).

## License

2021 (c) Robin Scheibler, LINE Corporation

This code is released under MIT License.