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