mirror of
https://github.com/YuzuZensai/spleeter.git
synced 2026-01-06 04:32:43 +00:00
79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf8
|
|
|
|
""" Unit testing for Separator class. """
|
|
|
|
__email__ = 'spleeter@deezer.com'
|
|
__author__ = 'Deezer Research'
|
|
__license__ = 'MIT License'
|
|
|
|
import filecmp
|
|
import itertools
|
|
from os import makedirs
|
|
from os.path import splitext, basename, exists, join
|
|
from tempfile import TemporaryDirectory
|
|
|
|
import pytest
|
|
import numpy as np
|
|
|
|
import tensorflow as tf
|
|
|
|
from spleeter.audio.adapter import get_default_audio_adapter
|
|
from spleeter.commands import create_argument_parser
|
|
|
|
from spleeter.commands import evaluate
|
|
|
|
from spleeter.utils.configuration import load_configuration
|
|
|
|
res_4stems = { "vocals": {
|
|
"SDR": -0.007,
|
|
"SAR": -19.231,
|
|
"SIR": -4.528,
|
|
"ISR": 0.000
|
|
},
|
|
"drums": {
|
|
"SDR": -0.071,
|
|
"SAR": -14.496,
|
|
"SIR": -4.987,
|
|
"ISR": 0.001
|
|
},
|
|
"bass":{
|
|
"SDR": -0.001,
|
|
"SAR": -12.426,
|
|
"SIR": -7.198,
|
|
"ISR": -0.001
|
|
},
|
|
"other":{
|
|
"SDR": -1.453,
|
|
"SAR": -14.899,
|
|
"SIR": -4.678,
|
|
"ISR": -0.015
|
|
}
|
|
}
|
|
|
|
|
|
def generate_fake_eval_dataset(path):
|
|
aa = get_default_audio_adapter()
|
|
n_songs = 2
|
|
fs = 44100
|
|
duration = 3
|
|
n_channels = 2
|
|
rng = np.random.RandomState(seed=0)
|
|
for song in range(n_songs):
|
|
song_path = join(path, "test", f"song{song}")
|
|
makedirs(song_path, exist_ok=True)
|
|
for instr in ["mixture", "vocals", "bass", "drums", "other"]:
|
|
filename = join(song_path, f"{instr}.wav")
|
|
data = rng.rand(duration*fs, n_channels)-0.5
|
|
aa.save(filename, data, fs)
|
|
|
|
|
|
def test_evaluate(path="FAKE_MUSDB_DIR"):
|
|
generate_fake_eval_dataset(path)
|
|
p = create_argument_parser()
|
|
arguments = p.parse_args(["evaluate", "-p", "spleeter:4stems", "--mus_dir", path])
|
|
params = load_configuration(arguments.configuration)
|
|
metrics = evaluate.entrypoint(arguments, params)
|
|
for instrument, metric in metrics.items():
|
|
for metric, value in metric.items():
|
|
assert np.allclose(np.median(value), res_4stems[instrument][metric], atol=1e-3) |