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import pytest
import torchaudio
from torchaudio.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE
@pytest.mark.parametrize(
"lang,expected",
[
("en", [0.9978380799293518, 4.23893404006958, 24.217193603515625]),
],
)
def test_squim_objective_pretrained_weights(lang, expected, sample_speech):
"""Test that the metric scores estimated by SquimObjective Bundle is identical to the expected result."""
bundle = SQUIM_OBJECTIVE
# Get SquimObjective model
model = bundle.get_model()
# Create a synthetic waveform
waveform, sample_rate = torchaudio.load(sample_speech)
scores = model(waveform)
for i in range(3):
assert abs(scores[i].item() - expected[i]) < 1e-5
@pytest.mark.parametrize(
"task,expected",
[
("speech_separation", [3.9257140159606934, 3.9391300678253174]),
],
)
def test_squim_subjective_pretrained_weights(task, expected, mixture_source, clean_sources):
"""Test that the metric scores estimated by SquimSubjective Bundle is identical to the expected result."""
bundle = SQUIM_SUBJECTIVE
# Get SquimObjective model
model = bundle.get_model()
# Load input mixture audio
waveform, sample_rate = torchaudio.load(mixture_source)
for i, source in enumerate(clean_sources):
# Load clean reference
clean_waveform, sample_rate = torchaudio.load(source)
score = model(waveform, clean_waveform)
assert abs(score.item() - expected[i]) < 1e-5
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