def test_apply_baddim(features_collection):
feats = FeaturesCollection(
{k: v.copy() for k, v in features_collection.items()})
feats['new'] = Features(
np.random.random((2, 1)), np.asarray([0, 1]))
with pytest.raises(ValueError) as err:
apply_cmvn(feats)
assert 'must have consistent dimensions' in str(err.value)
def test_apply_weights(features_collection):
cmvn1 = apply_cmvn(features_collection)
with pytest.raises(ValueError) as err:
apply_cmvn(features_collection, weights={})
assert 'keys differ for ' in str(err.value)
weights = {k: None for k in features_collection.keys()}
cmvn3 = apply_cmvn(features_collection, weights=weights)
assert cmvn3 == cmvn1
def test_apply_cmvn_skipdims(features_collection, skip_dims):
if skip_dims in ([-1], [13]):
with pytest.raises(ValueError) as err:
apply_cmvn(features_collection, skip_dims=skip_dims)
assert 'out of bounds dimensions' in str(err.value)
else:
cmvns = apply_cmvn(
features_collection, skip_dims=skip_dims, by_collection=False)
for feats in cmvns.values():
assert feats.data[:, 2:].mean(axis=0) == pytest.approx(0, abs=1e-5)
assert feats.data[:, 2:].var(axis=0) == pytest.approx(1, abs=1e-5)
assert feats.data[:, :2].mean(axis=0) != pytest.approx(0, abs=1e-5)
assert feats.data[:, :2].var(axis=0) != pytest.approx(1, abs=1e-5)
def test_apply_cmvn_bycollection(features_collection):
cmvns = apply_cmvn(features_collection, by_collection=True)
cmvns = np.concatenate([f.data for f in cmvns.values()], axis=0)
assert cmvns.shape == (
sum(f.nframes for f in features_collection.values()),
features_collection['0'].ndims)
assert cmvns.mean(axis=0) == pytest.approx(0, abs=1e-5)
assert cmvns.var(axis=0) == pytest.approx(1, abs=1e-5)
def test_apply_cmvn_byfeatures(features_collection):
cmvns = apply_cmvn(features_collection, by_collection=False)
for feat in cmvns.values():
assert feat.data.mean(axis=0) == pytest.approx(0, abs=1e-5)
assert feat.data.var(axis=0) == pytest.approx(1, abs=1e-5)