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Added eval test for both backends

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romi1502
2020-07-24 15:02:34 +02:00
parent b03ef93be9
commit 3fcc4ea28f
1 changed files with 69 additions and 32 deletions
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49
tests/test_eval.py
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@@ -25,7 +25,12 @@ from spleeter.commands import evaluate
from spleeter.utils.configuration import load_configuration from spleeter.utils.configuration import load_configuration
res_4stems = { "vocals": { BACKENDS = ["tensorflow", "librosa"]
TEST_CONFIGURATIONS = {el:el for el in BACKENDS}
res_4stems = {
"librosa": {
"vocals": {
"SDR": -0.007, "SDR": -0.007,
"SAR": -19.231, "SAR": -19.231,
"SIR": -4.528, "SIR": -4.528,
@@ -49,8 +54,34 @@ res_4stems = { "vocals": {
"SIR": -4.678, "SIR": -4.678,
"ISR": -0.015 "ISR": -0.015
} }
},
"tensorflow": {
"vocals": {
"SDR": 3.25e-05,
"SAR": -11.153575,
"SIR": -1.3849,
"ISR": 2.75e-05
},
"drums": {
"SDR": -0.079505,
"SAR": -15.7073575,
"SIR": -4.972755,
"ISR": 0.0013575
},
"bass":{
"SDR": 2.5e-06,
"SAR": -10.3520575,
"SIR": -4.272325,
"ISR": 2.5e-06
},
"other":{
"SDR": -1.359175,
"SAR": -14.7076775,
"SIR": -4.761505,
"ISR": -0.01528
}
}
} }
def generate_fake_eval_dataset(path): def generate_fake_eval_dataset(path):
aa = get_default_audio_adapter() aa = get_default_audio_adapter()
@@ -68,12 +99,18 @@ def generate_fake_eval_dataset(path):
aa.save(filename, data, fs) aa.save(filename, data, fs)
def test_evaluate(path="FAKE_MUSDB_DIR"): @pytest.mark.parametrize('backend', TEST_CONFIGURATIONS)
generate_fake_eval_dataset(path) def test_evaluate(backend):
with TemporaryDirectory() as directory:
generate_fake_eval_dataset(directory)
p = create_argument_parser() p = create_argument_parser()
arguments = p.parse_args(["evaluate", "-p", "spleeter:4stems", "--mus_dir", path]) arguments = p.parse_args(["evaluate", "-p", "spleeter:4stems", "--mus_dir", directory, "-B", backend])
params = load_configuration(arguments.configuration) params = load_configuration(arguments.configuration)
metrics = evaluate.entrypoint(arguments, params) metrics = evaluate.entrypoint(arguments, params)
for instrument, metric in metrics.items(): for instrument, metric in metrics.items():
for metric, value in metric.items(): for metric, value in metric.items():
assert np.allclose(np.median(value), res_4stems[instrument][metric], atol=1e-3) assert np.allclose(np.median(value), res_4stems[backend][instrument][metric], atol=1e-3)
# test_evaluate("tensorflow")