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88 lines
2.3 KiB
88 lines
2.3 KiB
#!/usr/bin/env python
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# coding: utf8
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""" Unit testing for Separator class. """
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__email__ = 'spleeter@deezer.com'
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__author__ = 'Deezer Research'
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__license__ = 'MIT License'
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from os import makedirs
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from os.path import join
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from tempfile import TemporaryDirectory
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import pytest
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import numpy as np
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from spleeter.__main__ import evaluate
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from spleeter.audio.adapter import AudioAdapter
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BACKENDS = ['tensorflow', 'librosa']
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TEST_CONFIGURATIONS = {el: el for el in BACKENDS}
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res_4stems = {
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'vocals': {
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'SDR': 3.25e-05,
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'SAR': -11.153575,
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'SIR': -1.3849,
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'ISR': 2.75e-05
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},
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'drums': {
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'SDR': -0.079505,
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'SAR': -15.7073575,
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'SIR': -4.972755,
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'ISR': 0.0013575
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},
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'bass': {
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'SDR': 2.5e-06,
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'SAR': -10.3520575,
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'SIR': -4.272325,
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'ISR': 2.5e-06
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},
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'other': {
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'SDR': -1.359175,
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'SAR': -14.7076775,
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'SIR': -4.761505,
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'ISR': -0.01528
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}
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}
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def generate_fake_eval_dataset(path):
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"""
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generate fake evaluation dataset
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"""
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aa = AudioAdapter.default()
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n_songs = 2
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fs = 44100
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duration = 3
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n_channels = 2
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rng = np.random.RandomState(seed=0)
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for song in range(n_songs):
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song_path = join(path, 'test', f'song{song}')
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makedirs(song_path, exist_ok=True)
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for instr in ['mixture', 'vocals', 'bass', 'drums', 'other']:
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filename = join(song_path, f'{instr}.wav')
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data = rng.rand(duration*fs, n_channels)-0.5
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aa.save(filename, data, fs)
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@pytest.mark.parametrize('backend', TEST_CONFIGURATIONS)
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def test_evaluate(backend):
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with TemporaryDirectory() as dataset:
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with TemporaryDirectory() as evaluation:
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generate_fake_eval_dataset(dataset)
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metrics = evaluate(
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adapter='spleeter.audio.ffmpeg.FFMPEGProcessAudioAdapter',
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output_path=evaluation,
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stft_backend=backend,
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params_filename='spleeter:4stems',
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mus_dir=dataset,
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mwf=False,
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verbose=False)
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for instrument, metric in metrics.items():
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for m, value in metric.items():
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assert np.allclose(
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np.median(value),
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res_4stems[instrument][m],
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atol=1e-3)
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