mirror of
https://github.com/YuzuZensai/spleeter.git
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88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
#!/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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import filecmp
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import itertools
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from os import makedirs
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from os.path import splitext, basename, exists, 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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import tensorflow as tf
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from spleeter.audio.adapter import get_default_audio_adapter
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from spleeter.commands import create_argument_parser
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from spleeter.commands import evaluate
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from spleeter.utils.configuration import load_configuration
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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 = get_default_audio_adapter()
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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 directory:
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generate_fake_eval_dataset(directory)
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p = create_argument_parser()
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arguments = p.parse_args(["evaluate", "-p", "spleeter:4stems", "--mus_dir", directory, "-B", backend])
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params = load_configuration(arguments.configuration)
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metrics = evaluate.entrypoint(arguments, params)
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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(np.median(value), res_4stems[instrument][m], atol=1e-3) |