def test_load_annotation(sample_annotation):
parser = REPEREParser()
annotations = parser.read(sample_annotation)
speech1 = annotations(uri="uri1", modality="speech")
assert list(speech1.itertracks(label=True)) == [
(Segment(1, 3.5), 0, 'alice'), (Segment(3, 7.5), 1, 'barbara'),
(Segment(6, 9), 2, 'chris')
]
def test_load_annotation(sample_annotation):
parser = REPEREParser()
annotations = parser.read(sample_annotation)
speech1 = annotations(uri="uri1", modality="speech")
assert list(speech1.itertracks(label=True)) == [
(Segment(1, 3.5), 0, 'alice'),
(Segment(3, 7.5), 1, 'barbara'),
(Segment(6, 9), 2, 'chris')]
# manual speaker identification
manual_speaker_identification = MDTMParser("data/manual_speaker.mdtm", \
multitrack=True)
# --------------------------------------------------
# LOAD MONOMODAL COMPONENTS OUTPUT ON TEST SET
# as described in Section "2. Monomodal Components"
# --------------------------------------------------
# automatic speaker diarization
auto_speaker_diarization = MDTMParser("data/auto_speaker_diarization.mdtm", \
multitrack=True)
# automatic speaker identification
auto_speaker_identification = \
REPEREParser("data/auto_speaker_identification.repere", \
multitrack=True, confidence=False)
# overlaid name detection output
auto_overlaid_names = REPEREParser("data/auto_overlaid_names.repere", \
multitrack=True, confidence=False)
# ----------------------------------------------
# INITIALIZE NAME PROPAGATION ALGORITHMS
# as described in Section "3. Name Propagation"
# ----------------------------------------------
# 'on' stands for overlaid name detection
# 'sd' stands for (unsupervised) speaker diarization
# 'sid' stands for (supervised) speaker identification