def test_split_by_label_length_issuer_separated_only_uses_given_label_list(self, split_mock):
corpus = resources.create_multi_label_corpus()
splitter = splitting.Splitter(corpus, random_seed=INITIAL_SEED)
for utt in corpus.utterances.values():
utt.set_label_list(annotations.LabelList.create_single(
'another label', idx='some-idx'
))
split_mock.return_value = {
'train': ['spk-1', 'spk-2'],
'test': ['spk-3']
}
res = splitter.split_by_label_length(
{'train': 0.5, 'test': 0.5},
label_list_idx='default',
separate_issuers=True
)
assert res['train'].utterances.keys() == {
'utt-1', 'utt-2', 'utt-3', 'utt-4', 'utt-5'
}
assert res['test'].utterances.keys() == {
'utt-6', 'utt-7', 'utt-8'
}
split_mock.assert_called_with(
{
'spk-1': {'length': 32},
'spk-2': {'length': 33},
'spk-3': {'length': 32},
},
{'train': 0.5, 'test': 0.5},
seed=mock.ANY
)
def test_encode_full_utterance(self):
ds = resources.create_multi_label_corpus()
enc = encoding.FrameHotEncoder(['music', 'speech', 'noise'],
'default',
frame_settings=units.FrameSettings(
32000, 16000),
sr=16000)
actual = enc.encode_utterance(ds.utterances['utt-6'])
expected = np.array([
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[1, 0, 0],
[1, 0, 0],
]).astype(np.float32)
assert np.array_equal(expected, actual)
def test_split_by_number_of_utterances_seed(self):
corpus = resources.create_multi_label_corpus()
res1 = splitting.Splitter(
corpus, random_seed=15).split_by_number_of_utterances({
'train': 0.6,
'test': 0.2
})
corpus = resources.create_multi_label_corpus()
res2 = splitting.Splitter(
corpus, random_seed=15).split_by_number_of_utterances({
'train': 0.6,
'test': 0.2
})
assert set(res1['train'].utterances.keys()) == set(
res2['train'].utterances.keys())
assert set(res1['test'].utterances.keys()) == set(
res2['test'].utterances.keys())
def test_split_by_proportionally_distribute_labels_by_number_seed(self):
corpus = resources.create_multi_label_corpus()
splitter = splitting.Splitter(corpus, random_seed=15)
res1 = splitter.split_by_proportionally_distribute_labels(
{
'train': 0.6,
'test': 0.2
}, use_lengths=False)
corpus = resources.create_multi_label_corpus()
splitter = splitting.Splitter(corpus, random_seed=15)
res2 = splitter.split_by_proportionally_distribute_labels(
{
'train': 0.6,
'test': 0.2
}, use_lengths=False)
assert set(res1['train'].utterances.keys()) == set(
res2['train'].utterances.keys())
assert set(res1['test'].utterances.keys()) == set(
res2['test'].utterances.keys())
def test_encode_utterance(self):
ds = resources.create_multi_label_corpus()
enc = label_encoding.FrameOrdinalEncoder(
['music', 'speech', 'noise'],
frame_settings=units.FrameSettings(32000, 16000),
sr=16000)
actual = enc.encode(ds.utterances['utt-6'])
expected = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,
0]).astype(np.int)
assert np.array_equal(expected, actual)
def test_split_by_label_length_only_uses_given_label_list(self, split_mock):
corpus = resources.create_multi_label_corpus()
splitter = splitting.Splitter(corpus, random_seed=INITIAL_SEED)
for utt in corpus.utterances.values():
utt.set_label_list(annotations.LabelList.create_single(
'another label', idx='some-idx'
))
split_mock.return_value = {
'train': ['utt-1', 'utt-3'],
'test': ['utt-3', 'utt-4'],
'dev': ['utt-5', 'utt-6'],
}
res = splitter.split_by_label_length(
{'train': 0.6, 'test': 0.2, 'dev': 0.2},
label_list_idx='default'
)
assert res['train'].utterances.keys() == {'utt-1', 'utt-3'}
assert res['test'].utterances.keys() == {'utt-3', 'utt-4'}
assert res['dev'].utterances.keys() == {'utt-5', 'utt-6'}
split_mock.assert_called_with(
{
'utt-1': {'length': 16},
'utt-2': {'length': 16},
'utt-3': {'length': 11},
'utt-4': {'length': 16},
'utt-5': {'length': 6},
'utt-6': {'length': 16},
'utt-7': {'length': 11},
'utt-8': {'length': 5},
},
{'train': 0.6, 'test': 0.2, 'dev': 0.2},
seed=mock.ANY
)
def corpus():
return resources.create_multi_label_corpus()
def splitter():
corpus = resources.create_multi_label_corpus()
return splitting.Splitter(corpus)
def setUp(self):
self.corpus = resources.create_multi_label_corpus()
self.splitter = splitting.Splitter(self.corpus)
def setUp(self):
self.ds = resources.create_multi_label_corpus()
def splitter():
corpus = resources.create_multi_label_corpus()
return splitting.Splitter(corpus, random_seed=INITIAL_SEED)
