class EsterSpeakerDiarizationProtocol(SpeakerDiarizationProtocol):
"""Base speaker diarization protocol for ESTER database"""
def __init__(self, preprocessors={}, **kwargs):
super(EsterSpeakerDiarizationProtocol,
self).__init__(preprocessors=preprocessors, **kwargs)
self.uem_parser_ = UEMParser()
self.mdtm_parser_ = MDTMParser()
def _subset(self, protocol, subset):
data_dir = op.join(op.dirname(op.realpath(__file__)), 'data')
# load annotated parts
# e.g. /data/{tv|radio|all}.{train|dev|test}.uem
path = op.join(
data_dir, '{protocol}.{subset}.uem'.format(subset=subset,
protocol=protocol))
uems = self.uem_parser_.read(path)
# load annotations
path = op.join(
data_dir, '{protocol}.{subset}.mdtm'.format(subset=subset,
protocol=protocol))
mdtms = self.mdtm_parser_.read(path)
for uri in sorted(uems.uris):
annotated = uems(uri)
annotation = mdtms(uri)
current_file = {
'database': 'Ester',
'uri': uri,
'annotated': annotated,
'annotation': annotation
}
yield current_file
class SwitchBoardSpeakerRecognitionProtocol(SpeakerDiarizationProtocol):
"""My first speaker diarization protocol """
def __init__(self, preprocessors={}, **kwargs):
super(SwitchBoardSpeakerRecognitionProtocol,
self).__init__(preprocessors=preprocessors, **kwargs)
self.mdtm_parser_ = MDTMParser()
def _subset(self, protocol, subset):
data_dir = op.join(op.dirname(op.realpath(__file__)), 'data')
# load annotations
path = op.join(
data_dir,
'switchboard-{protocol}.{subset}.mdtm'.format(subset=subset,
protocol=protocol))
mdtms = self.mdtm_parser_.read(path)
for uri in sorted(mdtms.uris):
annotation = mdtms(uri)
current_file = {
'database': 'SwitchBoard',
'uri': uri,
'annotation': annotation
}
yield current_file
class LibriSpeechSpeakerRecognitionProtocol(SpeakerDiarizationProtocol):
"""My first speaker diarization protocol """
def __init__(self, preprocessors={}, **kwargs):
super(LibriSpeechSpeakerRecognitionProtocol,
self).__init__(preprocessors=preprocessors, **kwargs)
self.mdtm_parser_ = MDTMParser()
def _subset(self, protocol, subset):
data_dir = op.join(op.dirname(op.realpath(__file__)), 'data')
# load annotations
path = op.join(
data_dir,
'librispeech-{protocol}.{subset}.mdtm'.format(subset=subset,
protocol=protocol))
mdtms = self.mdtm_parser_.read(path)
for uri in sorted(mdtms.uris):
annotation = mdtms(uri)
current_file = {
'database':
'LibriSpeech',
'uri':
uri,
'annotation':
annotation,
# annotated part as pyannote.core.Timeline instance
'annotated':
Timeline(uri=uri,
segments=[annotation.get_timeline().extent()])
}
yield current_file
def test_load(sample):
parser = MDTMParser()
annotations = parser.read(sample)
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(sample):
parser = MDTMParser()
annotations = parser.read(sample)
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') ]
class EtapeSpeakerDiarizationProtocol(SpeakerDiarizationProtocol):
"""Base speaker diarization protocol for ETAPE database
This class should be inherited from, not used directly.
Parameters
----------
preprocessors : dict or (key, preprocessor) iterable
When provided, each protocol item (dictionary) are preprocessed, such
that item[key] = preprocessor(**item). In case 'preprocessor' is not
callable, it should be a string containing placeholder for item keys
(e.g. {'wav': '/path/to/{uri}.wav'})
"""
def __init__(self, preprocessors={}, **kwargs):
super(EtapeSpeakerDiarizationProtocol,
self).__init__(preprocessors=preprocessors, **kwargs)
self.uem_parser_ = UEMParser()
self.mdtm_parser_ = MDTMParser()
def _subset(self, protocol, subset):
data_dir = op.join(op.dirname(op.realpath(__file__)), 'data')
# load annotated parts
# e.g. /data/{tv|radio|all}.{train|dev|test}.uem
path = op.join(
data_dir, '{protocol}.{subset}.uem'.format(subset=subset,
protocol=protocol))
uems = self.uem_parser_.read(path)
# load annotations
path = op.join(
data_dir, '{protocol}.{subset}.mdtm'.format(subset=subset,
protocol=protocol))
mdtms = self.mdtm_parser_.read(path)
for uri in sorted(uems.uris):
annotated = uems(uri)
annotation = mdtms(uri)
current_file = {
'database': 'Etape',
'uri': uri,
'annotated': annotated,
'annotation': annotation
}
yield current_file
llss = []
trials = getattr(protocol, '{subset}_trial'.format(subset=subset))()
for current_trial in trials:
reference = current_trial.pop('reference')
hypothesis = speaker_spotting_try_diarization(current_trial)
llss.append(process_trial(current_trial, hypothesis))
import simplejson as json
with open(output_file, 'w') as outfile:
json.dump(llss, outfile)
if arguments['automatic']:
from pyannote.parser import MDTMParser
diarization_mdtm = arguments['<diarization.mdtm>']
parser = MDTMParser()
annotations = parser.read(diarization_mdtm)
REFERENCE = {}
for uri_part in annotations.uris:
uri = uri_part.split('_')[0] + '.Mix-Headset'
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
REFERENCE[uri].update(annotations(uri=uri_part,
modality="speaker"))
llss = []
trials = getattr(protocol, '{subset}_trial'.format(subset=subset))()
for current_trial in trials:
reference = current_trial.pop('reference')
hypothesis = speaker_spotting_try_diarization(current_trial)
from pyannote.database import get_protocol, FileFinder
protocol = get_protocol('AMI.SpeakerSpotting.MixHeadset', progress=True)
from pyannote.core import Annotation, Segment, Timeline
REFERENCE = {}
for current_file in protocol.development():
uri = current_file['uri']
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
REFERENCE[uri].update(current_file['annotation'])
from pyannote.parser import MDTMParser
sad_dev = '/people/yin/projects/online_clustering/spotting/AMI.SpeakerSpotting.MixHeadset.development.mdtm'
parser_dev = MDTMParser()
annotations_dev = parser_dev.read(sad_dev)
SAD = {}
for item in protocol.development():
uri = item['uri']
SAD[uri] = annotations_dev(uri=uri,
modality="speaker").get_timeline().support()
class PyannoteFeatureExtractionError(Exception):
pass
class Precomputed(object):
"""Load precomputed features from HDF5 file
Parameters
----------
# enrolment consists in summing all relevant embeddings
def speaker_spotting_enrol(current_enrolment):
enrol_with = current_enrolment['enrol_with']
embeddings = precomputed(current_enrolment)
return np.sum(embeddings.crop(enrol_with), axis=0, keepdims=True)
models = {}
for current_enrolment in protocol.development_enrolment():
model_id = current_enrolment.pop('model_id')
models[model_id] = speaker_spotting_enrol(current_enrolment)
from pyannote.parser import MDTMParser
cluster_mdtm = '/people/yin/projects/online_clustering/spotting/EURECOM-online-diarization-pyannote-VAD.dev.WithOffset.mdtm'
parser_dev = MDTMParser()
annotations_dev = parser_dev.read(cluster_mdtm)
REFERENCE = {}
for uri_part in annotations_dev.uris:
uri = uri_part.split('_')[0] + '.Mix-Headset'
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
REFERENCE[uri].update(annotations_dev(uri=uri_part, modality="speaker"))
# Trials
from pyannote.core import SlidingWindow, SlidingWindowFeature
from pyannote.audio.embedding.utils import cdist
# trial consists in comparing each embedding to the target embedding
from pyannote.database import get_protocol, FileFinder
protocol = get_protocol('AMI.SpeakerSpotting.MixHeadset', progress=True)
from pyannote.core import Annotation,Segment, Timeline
REFERENCE = {}
for current_file in protocol.test():
uri = current_file['uri']
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
REFERENCE[uri].update(current_file['annotation'])
from pyannote.parser import MDTMParser
sad_tst = '/people/yin/projects/online_clustering/spotting_test/AMI.SpeakerSpotting.MixHeadset.test.mdtm'
parser_tst = MDTMParser()
annotations_tst = parser_tst.read(sad_tst)
SAD = {}
for item in protocol.test():
uri = item['uri']
SAD[uri] = annotations_tst(uri=uri, modality="speaker").get_timeline().support()
class PyannoteFeatureExtractionError(Exception):
pass
class Precomputed(object):
"""Load precomputed features from HDF5 file
Parameters
----------
features_h5 : str
Path to HDF5 file generated by script 'feature_extraction.py'.
"""
def speaker_spotting_enrol(current_enrolment):
enrol_with = current_enrolment['enrol_with']
embeddings = precomputed(current_enrolment)
return np.sum(embeddings.crop(enrol_with), axis=0, keepdims=True)
models = {}
for current_enrolment in protocol.test_enrolment():
model_id = current_enrolment.pop('model_id')
models[model_id] = speaker_spotting_enrol(current_enrolment)
from pyannote.parser import MDTMParser
cluster_mdtm = 'OD_AVAD_tst.mdtm'
parser_tst = MDTMParser()
annotations_tst = parser_tst.read(cluster_mdtm)
REFERENCE = {}
for uri_part in annotations_tst.uris:
uri = uri_part.split('_')[0] + '.Mix-Headset'
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
REFERENCE[uri].update(annotations_tst(uri=uri_part, modality="speaker"))
# Trials
from pyannote.core import SlidingWindow, SlidingWindowFeature
from pyannote.audio.embedding.utils import cdist
# trial consists in comparing each embedding to the target embedding
llss = []
trials = getattr(protocol, '{subset}_trial'.format(subset=subset))()
for current_trial in trials:
reference = current_trial.pop('reference')
hypothesis = speaker_spotting_try_diarization(current_trial)
llss.append(process_trial(current_trial, hypothesis))
import simplejson as json
with open(output_file, 'w') as outfile:
json.dump(llss, outfile)
if arguments['automatic']:
from pyannote.parser import MDTMParser
diarization_mdtm = arguments['<diarization.mdtm>']
parser = MDTMParser()
annotations = parser.read(diarization_mdtm)
REFERENCE = {}
for uri_part in annotations.uris:
uri = uri_part.split('_')[0] + '.Mix-Headset'
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
REFERENCE[uri].update(annotations(uri=uri_part, modality="speaker"))
llss = []
trials = getattr(protocol, '{subset}_trial'.format(subset=subset))()
for current_trial in trials:
reference = current_trial.pop('reference')
hypothesis = speaker_spotting_try_diarization(current_trial)
llss.append(process_trial(current_trial, hypothesis))
