def test_false_rejection_rate_with_no_occurences_returns_zero(self):
result = evaluator.KWSEvaluator().evaluate(
annotations.LabelList(labels=[]),
annotations.LabelList(
labels=[annotations.Label('four', 2.5, 3.0)]))
assert result.false_rejection_rate() == 0.0
def test_evaluate_with_multiple_labels(self):
ref = evaluator.Outcome(
label_lists={
'a':
annotations.LabelList(labels=[
annotations.Label('a b', start=0, end=3),
annotations.Label('a d', start=3, end=5),
annotations.Label('f a b', start=5, end=6)
])
})
hyp = evaluator.Outcome(label_lists={
'a':
annotations.LabelList(labels=[annotations.Label('a b d f i b')])
})
result = evaluator.ASREvaluator().do_evaluate(ref, hyp)
assert len(result.utt_to_label_pairs) == 1
assert result.utt_to_label_pairs['a'] == [
alignment.LabelPair(annotations.Label('a'),
annotations.Label('a')),
alignment.LabelPair(annotations.Label('b'),
annotations.Label('b')),
alignment.LabelPair(annotations.Label('a'), None),
alignment.LabelPair(annotations.Label('d'),
annotations.Label('d')),
alignment.LabelPair(annotations.Label('f'),
annotations.Label('f')),
alignment.LabelPair(annotations.Label('a'),
annotations.Label('i')),
alignment.LabelPair(annotations.Label('b'),
annotations.Label('b')),
]
def test_validate(self):
ds = resources.create_single_label_corpus()
utt4_ll = annotations.LabelList(idx='default',
labels=[
annotations.Label('a',
start=0.0,
end=1.44),
annotations.Label('a',
start=1.89,
end=10.0),
])
ds.utterances['utt-4'].set_label_list(utt4_ll)
utt6_ll = annotations.LabelList(idx='default',
labels=[
annotations.Label('a',
start=1.33,
end=5.9),
annotations.Label('a',
start=5.9,
end=14.7),
])
ds.utterances['utt-6'].set_label_list(utt6_ll)
val = validation.LabelCoverageValidator('default')
result = val.validate(ds)
assert not result.passed
assert set(result.uncovered_segments.keys()) == {'utt-4', 'utt-6'}
assert result.uncovered_segments['utt-4'] == [(1.44, 1.89)]
assert result.uncovered_segments['utt-6'] == [(0.0, 1.33),
(14.7, 15.0)]
def test_evaluate(self):
ref = evaluator.Outcome(
label_lists={
'a':
annotations.LabelList(
labels=[annotations.Label('a b a d f a b')])
})
hyp = evaluator.Outcome(label_lists={
'a':
annotations.LabelList(labels=[annotations.Label('a b d f i b')])
})
result = evaluator.ASREvaluator().do_evaluate(ref, hyp)
assert len(result.utt_to_label_pairs) == 1
assert result.utt_to_label_pairs['a'] == [
alignment.LabelPair(annotations.Label('a'),
annotations.Label('a')),
alignment.LabelPair(annotations.Label('b'),
annotations.Label('b')),
alignment.LabelPair(annotations.Label('a'), None),
alignment.LabelPair(annotations.Label('d'),
annotations.Label('d')),
alignment.LabelPair(annotations.Label('f'),
annotations.Label('f')),
alignment.LabelPair(annotations.Label('a'),
annotations.Label('i')),
alignment.LabelPair(annotations.Label('b'),
annotations.Label('b')),
]
def test_split(self):
ll_1 = annotations.LabelList('phones', labels=[
annotations.Label('alpha', start=0.0, end=30.0)
])
ll_2 = annotations.LabelList('words', labels=[
annotations.Label('b', start=0.0, end=30.0)
])
utt = tracks.Utterance('utt-1', 'track-x', start=0.0, end=40.0, label_lists=[ll_1, ll_2])
res = utt.split([14.0, 29.5])
assert len(res) == 3
assert res[0].start == 0.0
assert res[0].end == 14.0
assert 'phones' in res[0].label_lists.keys()
assert 'words' in res[0].label_lists.keys()
assert res[1].start == 14.0
assert res[1].end == 29.5
assert 'phones' in res[1].label_lists.keys()
assert 'words' in res[1].label_lists.keys()
assert res[2].start == 29.5
assert res[2].end == 40.0
assert 'phones' in res[2].label_lists.keys()
assert 'words' in res[2].label_lists.keys()
def test_align_different_start(self):
ref = annotations.LabelList(labels=[
annotations.Label('b', 4, 9)
])
hyp = annotations.LabelList(labels=[
annotations.Label('b', 2, 8)
])
result = alignment.InvariantSegmentAligner().align(ref, hyp)
assert len(result) == 3
segment = result[0]
assert segment.start == 2
assert segment.end == 4
assert segment.ref == []
assert segment.hyp == [annotations.Label('b', 2, 8)]
segment = result[1]
assert segment.start == 4
assert segment.end == 8
assert segment.ref == [annotations.Label('b', 4, 9)]
assert segment.hyp == [annotations.Label('b', 2, 8)]
segment = result[2]
assert segment.start == 8
assert segment.end == 9
assert segment.ref == [annotations.Label('b', 4, 9)]
assert segment.hyp == []
def test_create_event_list(self):
ll_ref = annotations.LabelList(labels=[
annotations.Label('a', 0.89, 13.73),
annotations.Label('a', 13.73, 17.49),
annotations.Label('b', 17.49, 22.75)
])
ll_hyp = annotations.LabelList(labels=[
annotations.Label('b', 0.1, 1.656),
annotations.Label('a', 1.656, 1.976),
annotations.Label('b', 1.976, 3.896),
annotations.Label('a', 3.896, 3.957)
])
events = alignment.InvariantSegmentAligner.create_event_list(ll_ref, ll_hyp, time_threshold=0.01)
assert events[0] == (0.1, [(0.1, 'S', 1, annotations.Label('b', 0.1, 1.656))])
assert events[1] == (0.89, [(0.89, 'S', 0, annotations.Label('a', 0.89, 13.73))])
assert events[2] == (1.656, [(1.656, 'E', 1, annotations.Label('b', 0.1, 1.656)),
(1.656, 'S', 1, annotations.Label('a', 1.656, 1.976))])
assert events[3] == (1.976, [(1.976, 'E', 1, annotations.Label('a', 1.656, 1.976)),
(1.976, 'S', 1, annotations.Label('b', 1.976, 3.896))])
assert events[4] == (3.896, [(3.896, 'E', 1, annotations.Label('b', 1.976, 3.896)),
(3.896, 'S', 1, annotations.Label('a', 3.896, 3.957))])
assert events[5] == (3.957, [(3.957, 'E', 1, annotations.Label('a', 3.896, 3.957))])
assert events[6] == (13.73, [(13.73, 'E', 0, annotations.Label('a', 0.89, 13.73)),
(13.73, 'S', 0, annotations.Label('a', 13.73, 17.49))])
assert events[7] == (17.49, [(17.49, 'E', 0, annotations.Label('a', 13.73, 17.49)),
(17.49, 'S', 0, annotations.Label('b', 17.49, 22.75))])
assert events[8] == (22.75, [(22.75, 'E', 0, annotations.Label('b', 17.49, 22.75))])
def test_validate_returns_part_of_overlapping_label(self):
ds = resources.create_single_label_corpus()
utt4_ll = annotations.LabelList(idx='default',
labels=[
annotations.Label('a',
start=0.0,
end=9.0),
annotations.Label('b',
start=9.0,
end=13.0),
])
ds.utterances['utt-4'].set_label_list(utt4_ll)
utt6_ll = annotations.LabelList(idx='default',
labels=[
annotations.Label('a',
start=-2.0,
end=5.9),
annotations.Label('b',
start=5.9,
end=14.7),
])
ds.utterances['utt-6'].set_label_list(utt6_ll)
val = validation.LabelOverflowValidator('default')
result = val.validate(ds)
assert not result.passed
assert set(result.overflow_segments.keys()) == {'utt-4', 'utt-6'}
assert result.overflow_segments['utt-4'] == [(10.0, 13.0, 'b')]
assert result.overflow_segments['utt-6'] == [(-2.0, 0.0, 'a')]
def test_eq_ignores_label_list_relation(self):
a = annotations.Label('some label A', 1.0, 2.0)
b = annotations.Label('some label a', 1.0, 2.0)
al = annotations.LabelList(idx='one', labels=[a])
bl = annotations.LabelList(idx='another', labels=[b])
assert a.label_list == al
assert b.label_list == bl
assert a == b
def test_align(self):
ref = annotations.LabelList(labels=[
annotations.Label('a', 0, 3),
annotations.Label('b', 3, 6),
annotations.Label('c', 7, 10)
])
hyp = annotations.LabelList(labels=[
annotations.Label('a', 0, 3),
annotations.Label('b', 4, 8),
annotations.Label('c', 8, 10)
])
result = alignment.InvariantSegmentAligner().align(ref, hyp)
assert len(result) == 6
segment = result[0]
assert segment.start == 0
assert segment.end == 3
assert segment.ref == [annotations.Label('a', 0, 3)]
assert segment.hyp == [annotations.Label('a', 0, 3)]
segment = result[1]
assert segment.start == 3
assert segment.end == 4
assert segment.ref == [annotations.Label('b', 3, 6)]
assert segment.hyp == []
segment = result[2]
assert segment.start == 4
assert segment.end == 6
assert segment.ref == [annotations.Label('b', 3, 6)]
assert segment.hyp == [annotations.Label('b', 4, 8)]
segment = result[3]
assert segment.start == 6
assert segment.end == 7
assert segment.ref == []
assert segment.hyp == [annotations.Label('b', 4, 8)]
segment = result[4]
assert segment.start == 7
assert segment.end == 8
assert segment.ref == [annotations.Label('c', 7, 10)]
assert segment.hyp == [annotations.Label('b', 4, 8)]
segment = result[5]
assert segment.start == 8
assert segment.end == 10
assert segment.ref == [annotations.Label('c', 7, 10)]
assert segment.hyp == [annotations.Label('c', 8, 10)]
def corpus_with_more_labels():
""" Corpus with an extra label-list. """
corpus = resources.create_single_label_corpus()
corpus.utterances['utt-1'].set_label_list(
annotations.LabelList(idx='radio',
labels=[annotations.Label('alpha')]))
corpus.utterances['utt-2'].set_label_list(
annotations.LabelList(idx='radio',
labels=[annotations.Label('alpha')]))
corpus.utterances['utt-3'].set_label_list(
annotations.LabelList(idx='radio', labels=[annotations.Label('beta')]))
corpus.utterances['utt-4'].set_label_list(
annotations.LabelList(idx='radio', labels=[annotations.Label('beta')]))
corpus.utterances['utt-5'].set_label_list(
annotations.LabelList(idx='radio', labels=[annotations.Label('beta')]))
corpus.utterances['utt-6'].set_label_list(
annotations.LabelList(idx='radio', labels=[annotations.Label('beta')]))
corpus.utterances['utt-7'].set_label_list(
annotations.LabelList(idx='radio', labels=[annotations.Label('beta')]))
corpus.utterances['utt-8'].set_label_list(
annotations.LabelList(idx='radio', labels=[annotations.Label('beta')]))
return corpus
def read_labels(path, corpus):
for label_file in glob.glob(
os.path.join(path, '{}_*.txt'.format(LABEL_FILE_PREFIX))):
file_name = os.path.basename(label_file)
key = file_name[len('{}_'.format(LABEL_FILE_PREFIX)
):len(file_name) - len('.txt')]
utterance_labels = collections.defaultdict(list)
labels = textfile.read_separated_lines_generator(label_file,
separator=' ',
max_columns=4)
for record in labels:
label = record[3]
start = float(record[1])
end = float(record[2])
meta = None
meta_match = META_PATTERN.match(label)
if end == -1:
end = float('inf')
if meta_match is not None:
meta_json = meta_match.group(2)
meta = json.loads(meta_json)
label = meta_match.group(1)
utterance_labels[record[0]].append(
annotations.Label(label, start, end, meta=meta))
for utterance_idx, labels in utterance_labels.items():
ll = annotations.LabelList(idx=key, labels=labels)
corpus.utterances[utterance_idx].set_label_list(ll)
def test_encode_label_ends_at_utterance_end(self):
track = tracks.FileTrack('file1',
resources.sample_wav_file('med_len.wav'))
utt = tracks.Utterance('utt1', track, start=3, end=14)
ll = annotations.LabelList(labels=[
annotations.Label('speech', 0, 4),
annotations.Label('music', 4, 9),
annotations.Label('speech', 9, float('inf')),
])
utt.set_label_list(ll)
enc = encoding.FrameHotEncoder(['music', 'speech', 'noise'],
'default',
frame_settings=units.FrameSettings(
32000, 16000),
sr=16000)
actual = enc.encode_utterance(utt)
expected = np.array([
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[1, 1, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 0, 0],
[1, 1, 0],
[0, 1, 0],
]).astype(np.float32)
assert np.array_equal(expected, actual)
def read_labels(path, corpus):
label_reference_file = os.path.join(path, LABEL_FILE)
label_references = textfile.read_separated_lines(label_reference_file,
separator=' ',
max_columns=3)
for record in label_references:
utt_idx = record[0]
label_path = os.path.join(path, record[1])
label_idx = None
if len(record) > 2:
label_idx = record[2]
ll = annotations.LabelList(idx=label_idx)
for label in audacity.read_label_file(label_path):
start = label[0]
end = label[1]
value = label[2]
if end < 0:
end = float('inf')
ll.addl(value, start, end)
ll.apply(extract_meta_from_label_value)
corpus.utterances[utt_idx].set_label_list(ll)
def test_read_samples(self):
path = resources.sample_wav_file('wav_1.wav')
track = tracks.FileTrack('wav', path)
issuer = issuers.Issuer('toni')
utt = tracks.Utterance('t', track, issuer=issuer, start=1.0, end=2.30)
l1 = annotations.Label('a', 0.15, 0.448)
l2 = annotations.Label('a', 0.5, 0.73)
ll = annotations.LabelList(labels=[l1, l2])
utt.set_label_list(ll)
expected, __ = librosa.core.load(path,
sr=None,
offset=1.15,
duration=0.298)
assert np.array_equal(l1.read_samples(), expected)
expected, __ = librosa.core.load(path,
sr=None,
offset=1.5,
duration=1.73 - 1.5)
print(expected.shape)
print(l2.read_samples().shape)
assert np.array_equal(l2.read_samples(), expected)
def test_read_label_list_de(self):
path = os.path.join(os.path.dirname(__file__), 'audacity_labels_de.txt')
ll = audacity.read_label_list(path)
assert ll == annotations.LabelList(labels=[
annotations.Label('music', 43352.824046, 43525.837661),
annotations.Label('speech_male', 43512.446969, 43531.343483),
])
def test_align_empty_hypothesis(self):
ref = annotations.LabelList(labels=[
annotations.Label('b', 4, 8)
])
hyp = annotations.LabelList(labels=[
])
result = alignment.InvariantSegmentAligner().align(ref, hyp)
assert len(result) == 1
segment = result[0]
assert segment.start == 4
assert segment.end == 8
assert segment.ref == [annotations.Label('b', 4, 8)]
assert segment.hyp == []
def setup_method(self):
self.ll_1 = annotations.LabelList(idx='alpha',
labels=[
annotations.Label('a', 3.2, 4.5),
annotations.Label('b', 5.1, 8.9),
annotations.Label(
'c', 7.2, 10.5),
annotations.Label('d', 10.5, 14),
annotations.Label('d', 15, 18)
])
self.ll_2 = annotations.LabelList(idx='bravo',
labels=[
annotations.Label('a', 1.0, 4.2),
annotations.Label('e', 4.2, 7.9),
annotations.Label(
'c', 7.2, 10.5),
annotations.Label('f', 10.5, 14),
annotations.Label('d', 15, 17.3)
])
self.ll_duplicate_idx = annotations.LabelList(
idx='charlie',
labels=[
annotations.Label('t', 1.0, 4.2),
annotations.Label('h', 4.2, 7.9)
])
self.ll_3 = annotations.LabelList(idx='charlie',
labels=[
annotations.Label('a', 1.0, 4.2),
annotations.Label('g', 4.2, 7.9)
])
self.track = tracks.FileTrack('wav',
resources.sample_wav_file('wav_1.wav'))
self.issuer = issuers.Issuer('toni')
self.utt = tracks.Utterance('test',
self.track,
issuer=self.issuer,
start=1.25,
end=1.30,
label_lists=[
self.ll_1, self.ll_2,
self.ll_duplicate_idx, self.ll_3
])
def test_encode_utterance_with_single_label(self):
ll = annotations.LabelList(idx='go',
labels=[annotations.Label('a c b')])
utt = tracks.Utterance('utt-1', None, label_lists=ll)
encoder = encoding.TokenOrdinalEncoder('go', ['a', 'b', 'c'])
encoded = encoder.encode_utterance(utt)
assert np.array_equal(encoded, [0, 2, 1])
def test_encode_utterance_with_non_existing_label_list_raises_error(self):
ll = annotations.LabelList(idx='go',
labels=[annotations.Label('a c b unknown')])
utt = tracks.Utterance('utt-1', None, label_lists=ll)
encoder = encoding.TokenOrdinalEncoder('not_existing', ['a', 'b', 'c'])
with pytest.raises(ValueError):
encoder.encode_utterance(utt)
def test_read_label_list_with_empty_value(self):
path = os.path.join(os.path.dirname(__file__), 'audacity_labels_empty_value.txt')
ll = audacity.read_label_list(path)
assert ll == annotations.LabelList(labels=[
annotations.Label('music', 1, 4),
annotations.Label('', 4, 7),
annotations.Label('speech_male', 7, 9),
])
def ll_ref():
return annotations.LabelList(labels=[
annotations.Label('up', start=5.28, end=5.99),
annotations.Label('down', start=10.35, end=11.12),
annotations.Label('right', start=20.87, end=22.01),
annotations.Label('up', start=33.00, end=33.4),
annotations.Label('up', start=33.4, end=33.8),
annotations.Label('down', start=39.28, end=40.0)
])
def ll_hyp():
return annotations.LabelList(labels=[
annotations.Label('up', start=5.20, end=5.88),
annotations.Label('right', start=10.30, end=11.08),
annotations.Label('up', start=32.00, end=32.5),
annotations.Label('up', start=34.2, end=34.8),
annotations.Label('left', start=39.3, end=39.9),
annotations.Label('down', start=39.27, end=40.01)
])
def test_align_with_empty_segments(self):
ref = annotations.LabelList(labels=[
annotations.Label('a', 0, 3),
annotations.Label('b', 4, 6),
])
hyp = annotations.LabelList(labels=[
annotations.Label('a', 0, 3),
annotations.Label('c', 5, 8),
])
result = alignment.InvariantSegmentAligner().align(ref, hyp)
assert len(result) == 5
segment = result[0]
assert segment.start == 0
assert segment.end == 3
assert segment.ref == [annotations.Label('a', 0, 3)]
assert segment.hyp == [annotations.Label('a', 0, 3)]
segment = result[1]
assert segment.start == 3
assert segment.end == 4
assert segment.ref == []
assert segment.hyp == []
segment = result[2]
assert segment.start == 4
assert segment.end == 5
assert segment.ref == [annotations.Label('b', 4, 6)]
assert segment.hyp == []
segment = result[3]
assert segment.start == 5
assert segment.end == 6
assert segment.ref == [annotations.Label('b', 4, 6)]
assert segment.hyp == [annotations.Label('c', 5, 8)]
segment = result[4]
assert segment.start == 6
assert segment.end == 8
assert segment.ref == []
assert segment.hyp == [annotations.Label('c', 5, 8)]
def relabel(label_list, projections):
"""
Relabel an entire :py:class:`~audiomate.annotations.LabelList` using user-defined projections.
Labels can be renamed, removed or overlapping labels can be flattened to a single label per segment.
Each entry in the dictionary of projections represents a single projection that maps a combination of labels (key)
to a single new label (value). The combination of labels to be mapped is a tuple of naturally sorted labels that
apply to one or more segments simultaneously. By defining a special wildcard projection using `('**',)` is is not
required to specify a projection for every single combination of labels.
This method raises a :py:class:`~audiomate.corpus.utils.labellist.UnmappedLabelsException` if a projection for one
or more combinations of labels is not defined.
Args:
label_list (audiomate.annotations.LabelList): The label list to relabel
projections (dict): A dictionary that maps tuples of label combinations to string
labels.
Returns:
audiomate.annotations.LabelList: New label list with remapped labels
Raises:
UnmappedLabelsException: If a projection for one or more combinations of labels is not defined.
Example:
>>> projections = {
... ('a',): 'a',
... ('b',): 'b',
... ('c',): 'c',
... ('a', 'b',): 'a_b',
... ('a', 'b', 'c',): 'a_b_c',
... ('**',): 'b_c',
... }
>>> label_list = annotations.LabelList(labels=[
... annotations.Label('a', 3.2, 4.5),
... annotations.Label('b', 4.0, 4.9),
... annotations.Label('c', 4.2, 5.1)
... ])
>>> ll = relabel(label_list, projections)
>>> [l.value for l in ll]
['a', 'a_b', 'a_b_c', 'b_c', 'c']
"""
unmapped_combinations = find_missing_projections(label_list, projections)
if len(unmapped_combinations) > 0:
raise UnmappedLabelsException('Unmapped combinations: {}'.format(unmapped_combinations))
new_labels = []
for labeled_segment in label_list.ranges():
combination = tuple(sorted([label.value for label in labeled_segment[2]]))
label_mapping = projections[combination] if combination in projections else projections[WILDCARD_COMBINATION]
if label_mapping == '':
continue
new_labels.append(annotations.Label(label_mapping, labeled_segment[0], labeled_segment[1]))
return annotations.LabelList(idx=label_list.idx, labels=new_labels)
def test_all_tokens_with_custom_delimiter(self):
corpus = resources.create_dataset()
ll = annotations.LabelList(idx='test', labels=[
annotations.Label('a, b, a, c')
])
corpus.utterances['utt-1'].set_label_list(ll)
target_lls = ['test']
expected_tokens = {'a', 'b', 'c'}
assert corpus.all_tokens(delimiter=',', label_list_ids=target_lls) == expected_tokens
def test_all_tokens_returns_only_from_selected_label_lists(self):
corpus = resources.create_dataset()
ll = annotations.LabelList(idx='test', labels=[
annotations.Label('what can he do')
])
corpus.utterances['utt-1'].set_label_list(ll)
target_lls = [audiomate.corpus.LL_WORD_TRANSCRIPT]
expected_tokens = {'who', 'am', 'i', 'are', 'is', 'he', 'you', 'she', 'they'}
assert corpus.all_tokens(label_list_ids=target_lls) == expected_tokens
def utt_with_noise():
utt = tracks.Utterance('utt-2', 'file-2')
utt.set_label_list(
annotations.LabelList(idx='alpha',
labels=[
annotations.Label('music', 0, 5),
annotations.Label('speech', 5, 12),
annotations.Label('noise', 13, 15)
]))
utt.set_label_list(
annotations.LabelList(idx='bravo',
labels=[
annotations.Label('music', 0, 1),
annotations.Label('speech', 2, 6)
]))
return utt
def test_split_with_overlap(self):
ll_1 = annotations.LabelList('phones',
labels=[
annotations.Label('alpha',
start=0.0,
end=30.0),
annotations.Label('bravo',
start=20.0,
end=42.0)
])
ll_2 = annotations.LabelList(
'words', labels=[annotations.Label('b', start=8.0, end=30.0)])
utt = tracks.Utterance('utt-1',
'file-x',
start=10.0,
end=55.0,
label_lists=[ll_1, ll_2])
res = utt.split([12.0, 24.0], track_relative=False, overlap=2.0)
assert len(res) == 3
assert res[0].start == 10.0
assert res[0].end == 24.0
assert res[0].label_lists['phones'] == annotations.LabelList(
idx='phones', labels=[annotations.Label('alpha', 0.0, 14.0)])
assert res[0].label_lists['words'] == annotations.LabelList(
idx='words', labels=[annotations.Label('b', 8.0, 14.0)])
print(res[1].label_lists['phones'].labels)
assert res[1].start == 20.0
assert res[1].end == 36.0
assert res[1].label_lists['phones'] == annotations.LabelList(
idx='phones',
labels=[
annotations.Label('alpha', 0.0, 16.0),
annotations.Label('bravo', 10.0, 16.0),
])
assert res[1].label_lists['words'] == annotations.LabelList(
idx='words', labels=[annotations.Label('b', 0.0, 16.0)])
assert res[2].start == 32.0
assert res[2].end == 55.0
assert res[2].label_lists['phones'] == annotations.LabelList(
idx='phones',
labels=[
annotations.Label('alpha', 0.0, 8.0),
annotations.Label('bravo', 0.0, 20.0),
])
assert res[2].label_lists['words'] == annotations.LabelList(
idx='words', labels=[annotations.Label('b', 0.0, 8.0)])
def classification_ref_and_hyp_label_list():
"""
Sample output of a classification system, consisting of a reference and hypothesis label-list.
"""
ll_ref = annotations.LabelList(labels=[
annotations.Label('music', start=0, end=5),
annotations.Label('speech', start=5, end=11),
annotations.Label('mix', start=11, end=14),
annotations.Label('speech', start=14, end=19)
])
ll_hyp = annotations.LabelList(labels=[
annotations.Label('music', start=0, end=4),
annotations.Label('speech', start=4, end=6),
annotations.Label('mix', start=8, end=16),
annotations.Label('speech', start=16, end=21)
])
return ll_ref, ll_hyp
