def __init__(self, precomputed=None, **kwargs):
super(SpeechActivityDetection, self).__init__()
self.precomputed = precomputed
self.precomputed_ = Precomputed(self.precomputed)
self.has_overlap_ = self.precomputed_.dimension() == 3
self.with_params(**kwargs)
def __init__(self,
feature_extraction,
sad__pre,
scd__pre,
emb__pre,
sad__onset=0.7,
sad__offset=0.7,
sad__dimension=1,
scd__alpha=0.5,
scd__min_duration=1.,
scd__dimension=1,
emb__internal=False,
cls__damping=0.8,
cls__preference=-20,
cls__metric='cosine'):
super(SpeakerDiarizationPreStages, self).__init__()
self.feature_extraction = feature_extraction
# speech activity detection hyper-parameters
self.sad__onset = sad__onset
self.sad__offset = sad__offset
self.sad__dimension = sad__dimension
# speaker change detection hyper-parameters
self.scd__alpha = scd__alpha
self.scd__min_duration = scd__min_duration
self.scd__dimension = scd__dimension
# embedding hyper-parameters
self.emb__internal = emb__internal
# clustering hyper-parameters
self.cls__damping = cls__damping
self.cls__preference = cls__preference
self.cls__metric = cls__metric
step = self.feature_extraction.sliding_window().step
# initialize speech activity detection module
self.sad_ = Precomputed(sad__pre)
self.sad_binarize_ = Binarize(onset=self.sad__onset,
offset=self.sad__offset)
# initialize speaker change detection module
self.scd_ = Precomputed(scd__pre)
self.scd_peak_ = Peak(alpha=self.scd__alpha,
min_duration=self.scd__min_duration,
percentile=False)
# initialize speech turn embedding module
self.emb_ = Precomputed(emb__pre)
# initialize clustering module
self.cls_ = my_cluster.ClusteringAP(metric=self.cls__metric,
damping=self.cls__damping,
preference=self.cls__preference)
def __init__(self,
feature_extraction,
sad__pre,
scd__pre,
emb__pre,
sad__onset=0.7,
sad__offset=0.7,
sad__dimension=1,
scd__alpha=0.5,
scd__min_duration=1.,
scd__dimension=1,
emb__internal=False,
cls__method='average',
cls__threshold=5,
cls__metric='cosine'):
super(SpeakerDiarizationHACPre, self).__init__()
self.feature_extraction = feature_extraction
# speech activity detection hyper-parameters
self.sad__onset = sad__onset
self.sad__offset = sad__offset
self.sad__dimension = sad__dimension
# speaker change detection hyper-parameters
self.scd__alpha = scd__alpha
self.scd__min_duration = scd__min_duration
self.scd__dimension = scd__dimension
# embedding hyper-parameters
self.emb__internal = emb__internal
# clustering hyper-parameters
self.cls__method = cls__method
self.cls__threshold = cls__threshold
self.cls__metric = cls__metric
step = self.feature_extraction.sliding_window().step
# initialize speech activity detection module
self.sad_ = Precomputed(sad__pre)
self.sad_binarize_ = Binarize(onset=self.sad__onset,
offset=self.sad__offset)
# initialize speaker change detection module
self.scd_ = Precomputed(scd__pre)
self.scd_peak_ = Peak(alpha=self.scd__alpha,
min_duration=self.scd__min_duration,
percentile=False)
# initialize speech turn embedding module
self.emb_ = Precomputed(emb__pre)
# initialize clustering module
self.cls_ = my_cluster.ClusteringHAC(metric=self.cls__metric,
method=self.cls__method,
threshold=self.cls__threshold)
def __init__(self, split='', n_support=0, n_query=0, n_way=0, if_cuda=False):
self.precomputed = Precomputed(VCTK_FEATURE_DIR)
self.dataset = self.load_dataset(from_disk=True)
self.split = split
self.n_support = n_support
self.n_query = n_query
self.n_way = n_way
self.dataset = None
self.transforms = None
self.if_cuda = if_cuda
def update_distances(args):
"""Loads user annotation from json path, converts it to pyannote `Annotation`
using regions timings.
From the annotation uri and precomputed embeddings, it computes the
in-cluster distances between every speech turns
Dumps the updated (with correct distances) JSON file to a timestamped file.
"""
json_path = Path(args['<json_path>'])
uri = args['<uri>']
with open(json_path, 'r') as file:
gecko_json = json.load(file)
hypothesis, _, _, _ = gecko_JSON_to_Annotation(gecko_json, uri, 'speaker')
colors = get_colors(uri)
precomputed = Precomputed(embeddings)
protocol = args['<database.task.protocol>']
protocol = get_protocol(protocol)
for reference in getattr(protocol, 'test')():
if reference['uri'] == uri:
features = precomputed(reference)
break
distances_per_speaker = get_distances_per_speaker(features, hypothesis)
gecko_json = annotation_to_GeckoJSON(hypothesis, distances_per_speaker,
colors)
name = f"{json_path.stem}.{TIMESTAMP}.json"
updated_path = Path(json_path.parent, name)
with open(updated_path, 'w') as file:
json.dump(gecko_json, file)
print(f"succefully dumped {updated_path}")
def __init__(self,
feature_extraction,
emb__pre,
emb__internal=False,
cls__damping=0.8,
cls__preference=-20,
cls__metric='cosine'):
super(SpeakerDiarizationOracleSegAP, self).__init__()
self.feature_extraction = feature_extraction
# embedding hyper-parameters
self.emb__internal = emb__internal
# clustering hyper-parameters
self.cls__damping = cls__damping
self.cls__preference = cls__preference
self.cls__metric = cls__metric
step = self.feature_extraction.sliding_window().step
# initialize speech turn embedding module
self.emb_ = Precomputed(emb__pre)
# initialize clustering module
self.cls_ = my_cluster.ClusteringAP(metric=self.cls__metric,
damping=self.cls__damping,
preference=self.cls__preference)
def __init__(self, precomputed=None, **kwargs):
super(SpeechActivityDetection, self).__init__()
self.precomputed = precomputed
self.precomputed_ = Precomputed(self.precomputed)
self.has_overlap_ = self.precomputed_.dimension() == 3
self.with_params(**kwargs)
def extract(protocol_name, file_finder, experiment_dir, robust=False, parallel=False):
protocol = get_protocol(protocol_name)
# load configuration file
config_yml = experiment_dir + "/config.yml"
with open(config_yml, "r") as fp:
config = yaml.load(fp, Loader=yaml.SafeLoader)
FeatureExtraction = get_class_by_name(
config["feature_extraction"]["name"],
default_module_name="pyannote.audio.features",
)
feature_extraction = FeatureExtraction(
**config["feature_extraction"].get("params", {})
)
sliding_window = feature_extraction.sliding_window
dimension = feature_extraction.dimension
# create metadata file at root that contains
# sliding window and dimension information
precomputed = Precomputed(
root_dir=experiment_dir, sliding_window=sliding_window, dimension=dimension
)
if parallel:
extract_one = functools.partial(
helper_extract,
file_finder=file_finder,
experiment_dir=experiment_dir,
config_yml=config_yml,
robust=robust,
)
n_jobs = cpu_count()
pool = Pool(n_jobs)
imap = pool.imap
else:
feature_extraction = init_feature_extraction(experiment_dir)
extract_one = functools.partial(
helper_extract,
file_finder=file_finder,
experiment_dir=experiment_dir,
feature_extraction=feature_extraction,
robust=robust,
)
imap = map
for result in imap(extract_one, protocol.files()):
if result is None:
continue
print(result)
def get_file(protocol, uri, embeddings=None):
for reference in protocol.files():
if reference['uri'] == uri:
if embeddings:
precomputed = Precomputed(embeddings)
features = precomputed(reference)
return reference, features
return reference
raise ValueError(f'{uri} is not in {protocol}')
def with_params(self,
sad_onset=0.7,
sad_offset=0.7,
scd_alpha=0.5,
scd_min_duration=1.):
# initialize speech activity detection
self.sad_ = Precomputed(self.sad)
self.sad_onset = sad_onset
self.sad_offset = sad_offset
self.sad_binarize_ = Binarize(onset=sad_onset, offset=sad_offset)
# initialize speaker change detection
self.scd_ = Precomputed(self.scd)
self.scd_alpha = scd_alpha
self.scd_min_duration = scd_min_duration
self.scd_peak_ = Peak(alpha=scd_alpha, min_duration=scd_min_duration)
return self
def main():
usage = "%prog [options] database, raw_score_path"
desc = "Write the output of the binary overlap detector into test based on a threshold"
version = "%prog 0.1"
parser = OptionParser(usage=usage, description=desc, version=version)
parser.add_option("-t", "--onset", action="store", type="float", help="Onset Threshold", default=0.70)
parser.add_option("-f", "--offset", action="store", type="float", help="Offset Threshold", default=0.70)
parser.add_option("-d", "--dev", action="store_true", help="Print output based on development set", default=False)
parser.add_option("-o", "--outputfile", action="store", type="string", help="Output file", default="./overlap.txt")
(opt, args) = parser.parse_args()
if(len(args)!=2):
parser.error("Incorrect number of arguments")
database, raw_score_path = args
# get test file of protocol
protocol = get_protocol(database)
# load precomputed overlap scores as pyannote.core.SlidingWindowFeature
precomputed = Precomputed(raw_score_path)
# StackedRNN model
# initialize binarizer
# onset / offset are tunable parameters (and should be tuned for better
# performance). we use log_scale=True because of the final log-softmax in the
binarize = Binarize(onset=opt.onset, offset=opt.offset, log_scale=True)
fw = open(opt.outputfile, 'wt')
if opt.dev:
for test_file in protocol.development():
ovl_scores = precomputed(test_file)
# binarize overlap scores to obtain overlap regions as pyannote.core.Timeline
ovl_regions = binarize.apply(ovl_scores, dimension=1)
ovl_regions.uri = test_file['uri']
# write the output into text
write_txt(fw, ovl_regions)
else:
for test_file in protocol.test():
ovl_scores = precomputed(test_file)
# binarize overlap scores to obtain overlap regions as pyannote.core.Timeline
ovl_regions = binarize.apply(ovl_scores, dimension=1)
ovl_regions.uri = test_file['uri']
# write the output into text
write_txt(fw, ovl_regions)
fw.close()
def helper_extract(current_file, file_finder=None, experiment_dir=None,
config_yml=None, feature_extraction=None,
robust=False):
if feature_extraction is None:
feature_extraction = init_feature_extraction(experiment_dir)
precomputed = Precomputed(root_dir=experiment_dir)
return process_current_file(current_file, file_finder=file_finder,
precomputed=precomputed,
feature_extraction=feature_extraction,
robust=robust)
def with_params(self,
sad_onset=0.7,
sad_offset=0.7,
scd_alpha=0.5,
scd_min_duration=1.,
cls_preference=-7.0,
cls_damping=0.8):
# initialize speech activity detection and speaker change detection
super().with_params(sad_onset=sad_onset,
sad_offset=sad_offset,
scd_alpha=scd_alpha,
scd_min_duration=scd_min_duration)
# initialize speech turn embedding
self.emb_ = Precomputed(self.emb)
# initialize clustering module
self.cls_damping = cls_damping
self.cls_preference = cls_preference
# NOTE cls_preference could be a multiplicative factor of a default
# affinity value (e.g. median affinity value)
self.cls_ = sklearn.cluster.AffinityPropagation(
damping=cls_damping,
preference=cls_preference,
affinity='precomputed',
max_iter=200,
convergence_iter=15)
# sklearn documentation: Preferences for each point - points with
# larger values of preferences are more likely to be chosen as
# exemplars. The number of exemplars, ie of clusters, is influenced by
# the input preferences value. If the preferences are not passed as
# arguments, they will be set to the median of the input similarities.
# NOTE one could set the preference value of each speech turn
# according to their duration. longer speech turns are expected to
# have more accurate embeddings, therefore should be prefered for
# exemplars
return self
def __init__(self,
emb__pre,
cls__method='average',
cls__threshold=5,
cls__metric='cosine'):
super(SpeakerDiarizationOnSceneHAC, self).__init__()
# clustering hyper-parameters
self.cls__method = cls__method
self.cls__threshold = cls__threshold
self.cls__metric = cls__metric
# initialize speech turn embedding module
self.emb_ = Precomputed(emb__pre)
# initialize clustering module
self.cls_ = my_cluster.ClusteringHAC(metric=self.cls__metric,
method=self.cls__method,
threshold=self.cls__threshold)
def with_params(self,
sad_onset=0.7,
sad_offset=0.7,
scd_alpha=0.5,
scd_min_duration=1.,
cls_threshold=0.8):
# initialize speech activity detection and speaker change detection
super().with_params(sad_onset=sad_onset,
sad_offset=sad_offset,
scd_alpha=scd_alpha,
scd_min_duration=scd_min_duration)
# initialize speech turn embedding
self.emb_ = Precomputed(self.emb)
# initialize clustering module
self.cls_threshold = cls_threshold
self.cls_ = HierarchicalPoolingClustering(metric=self.metric)
return self
def check(protocol_name, file_finder, experiment_dir):
protocol = get_protocol(protocol_name)
precomputed = Precomputed(experiment_dir)
for subset in ['development', 'test', 'train']:
try:
file_generator = getattr(protocol, subset)()
first_item = next(file_generator)
except NotImplementedError as e:
continue
for current_file in getattr(protocol, subset)():
try:
audio = file_finder(current_file)
current_file['audio'] = audio
except ValueError as e:
print(e)
continue
duration = get_audio_duration(current_file)
try:
features = precomputed(current_file)
except PyannoteFeatureExtractionError as e:
print(e)
continue
if not np.isclose(duration,
features.getExtent().duration,
atol=1.):
uri = get_unique_identifier(current_file)
print('Duration mismatch for "{uri}"'.format(uri=uri))
if np.any(np.isnan(features.data)):
uri = get_unique_identifier(current_file)
print('NaN for "{uri}"'.format(uri=uri))
def xp_objective(args, **kwargs):
import sys
sys.path.append("/people/yin/projects/")
from pyannote.database import get_protocol, get_annotated, FileFinder
protocol = get_protocol('Etape.SpeakerDiarization.TV',
preprocessors={'audio': FileFinder()})
from pyannote.metrics.diarization import GreedyDiarizationErrorRate
metric = GreedyDiarizationErrorRate()
from optimize_cluster import speaker_diarization
from pyannote.audio.features import Precomputed
feature_extraction = Precomputed(
'/vol/work1/bredin/feature_extraction/mfcc')
sad_pre = '/vol/work1/yin/speech_activity_detection/shallow/train/REPERE.SpeakerDiarization.All.train/tune/Etape.SpeakerDiarization.TV.development/apply'
scd_pre = '/vol/work1/yin/speaker_change_detection/paper/train/REPERE.SpeakerDiarization.All.train/tune/Etape.SpeakerDiarization.Debug.development/apply'
emb_pre = '/vol/work1/yin/embedding/20180124'
args['cls__damping'] = float(args['cls__damping'])
args['cls__preference'] = float(args['cls__preference'])
pipeline = speaker_diarization.SpeakerDiarizationPre(
feature_extraction, sad_pre, scd_pre, emb_pre, **args)
try:
for current_file in protocol.train():
hypothesis = pipeline(current_file, annotated=True)
if hypothesis is None:
return 100
reference = current_file['annotation']
uem = get_annotated(current_file)
metric(reference, hypothesis, uem=uem)
except MemoryError as error:
return 100
return abs(metric)
from pyannote.core import Segment
from pyannote.audio.features import Precomputed, utils
import pandas as pd
import numpy as np
from glob import glob
# REPERE evaluation protocol
# cf. https://github.com/pyannote/pyannote-database#pyannote-database
from pyannote.database import get_protocol
#different from the files I use
protocol = get_protocol('REPERE.SpeakerDiarization.Plumcot')
precomputed = Precomputed('/vol/work1/dyab/training_set/mfcc')
train_dir = "/vol/work1/dyab/training_set/residual_local/"
output_dir_train = "/vol/work1/dyab/training_set/numpy_arrays_local_audio/"
y_labels_dir_train = "/vol/work1/dyab/training_set/numpy_arrays_local_landmarks/"
dev_dir = "/vol/work1/dyab/development_set/residual_cluster_old/"
output_dir_dev = "/vol/work1/dyab/development_set/numpy_arrays_cluster_old_audio/"
y_labels_dir_dev = "/vol/work1/dyab/development_set/numpy_arrays_cluster_old_landmarks/"
test_dir = "/vol/work1/dyab/test_set/residual/"
output_dir_test = "/vol/work1/dyab/test_set/numpy_arrays_audio/"
y_labels_dir_test = "/vol/work1/dyab/test_set/numpy_arrays_landmarks/"
def generate_audio_features(dir, output_dir, y_labels_dir):
for current_file in protocol.test(
): # iterate on all files of Phase2 training set
print(current_file['uri'])
import numpy as np
import matplotlib.pyplot as plt
# AMI protocol
from pyannote.database import get_protocol
protocol = get_protocol('Test.SpeakerDiarization.MixHeadset')
from pyannote.database import get_annotated
# precomputed scores
from pyannote.audio.features import Precomputed
precomputed = Precomputed('./precomputed/scd')
from pyannote.metrics.diarization import DiarizationPurityCoverageFMeasure
metric = DiarizationPurityCoverageFMeasure()
from pyannote.metrics.segmentation import SegmentationPurityCoverageFMeasure
metric = SegmentationPurityCoverageFMeasure()
# peak detection
min_duration = 1.0
from pyannote.audio.signal import Peak
# alpha / min_duration are tunable parameters (and should be tuned for better performance)
# we use log_scale = True because of the final log-softmax in the StackedRNN model
alphas = np.linspace(0, 1, 20)
purity_list = []
coverage_list = []
for alpha in alphas:
def apply_pretrained(validate_dir: Path,
protocol_name: str,
subset: Optional[str] = "test",
duration: Optional[float] = None,
step: float = 0.25,
device: Optional[torch.device] = None,
batch_size: int = 32,
pretrained: Optional[str] = None,
Pipeline: type = None,
**kwargs):
"""Apply pre-trained model
Parameters
----------
validate_dir : Path
protocol_name : `str`
subset : 'train' | 'development' | 'test', optional
Defaults to 'test'.
duration : `float`, optional
step : `float`, optional
device : `torch.device`, optional
batch_size : `int`, optional
pretrained : `str`, optional
Pipeline : `type`
"""
if pretrained is None:
pretrained = Pretrained(validate_dir=validate_dir,
duration=duration,
step=step,
batch_size=batch_size,
device=device)
output_dir = validate_dir / 'apply' / f'{pretrained.epoch_:04d}'
else:
if pretrained in torch.hub.list('pyannote/pyannote-audio'):
output_dir = validate_dir / pretrained
else:
output_dir = validate_dir
pretrained = Wrapper(pretrained,
duration=duration,
step=step,
batch_size=batch_size,
device=device)
params = {}
try:
params['classes'] = pretrained.classes
except AttributeError as e:
pass
try:
params['dimension'] = pretrained.dimension
except AttributeError as e:
pass
# create metadata file at root that contains
# sliding window and dimension information
precomputed = Precomputed(root_dir=output_dir,
sliding_window=pretrained.sliding_window,
**params)
# file generator
protocol = get_protocol(protocol_name,
progress=True,
preprocessors=pretrained.preprocessors_)
for current_file in getattr(protocol, subset)():
fX = pretrained(current_file)
precomputed.dump(current_file, fX)
# do not proceed with the full pipeline
# when there is no such thing for current task
if Pipeline is None:
return
# do not proceed with the full pipeline when its parameters cannot be loaded.
# this might happen when applying a model that has not been validated yet
try:
pipeline_params = pretrained.pipeline_params_
except AttributeError as e:
return
# instantiate pipeline
pipeline = Pipeline(scores=output_dir)
pipeline.instantiate(pipeline_params)
# load pipeline metric (when available)
try:
metric = pipeline.get_metric()
except NotImplementedError as e:
metric = None
# apply pipeline and dump output to RTTM files
output_rttm = output_dir / f'{protocol_name}.{subset}.rttm'
with open(output_rttm, 'w') as fp:
for current_file in getattr(protocol, subset)():
hypothesis = pipeline(current_file)
pipeline.write_rttm(fp, hypothesis)
# compute evaluation metric (when possible)
if 'annotation' not in current_file:
metric = None
# compute evaluation metric (when available)
if metric is None:
continue
reference = current_file['annotation']
uem = get_annotated(current_file)
_ = metric(reference, hypothesis, uem=uem)
# print pipeline metric (when available)
if metric is None:
return
output_eval = output_dir / f'{protocol_name}.{subset}.eval'
with open(output_eval, 'w') as fp:
fp.write(str(metric))
import pickle
import random
from itertools import cycle, islice
dirname = os.path.dirname(os.path.realpath(__file__))
#VCTK_DATA_DIR = os.path.join(dirname, '../../data/vctk')
VCTK_DATA_DIR = '/w/148/spoclab/data3/jixuan/SpeakerEmbedding/few_shot_learning/data/vctk'
VCTK_AUDIO_DIR = '/p/spoclab/data3/jixuan/VCTK-Corpus/wav48'
VCTK_FEATURE_DIR = '/p/spoclab/data3/jixuan/VCTK-Corpus/playground/feature-extraction'
#VCTK_AUDIO_DIR = '/h/jixuan/Documents/data/VCTK-Corpus/wav48'
#VCTK_FEATURE_DIR = '/h/jixuan/Documents/data/VCTK-Corpus/playground/feature-extraction'
OMNIGLOT_CACHE = {}
DATASET_CACHE = {}
precomputed = Precomputed(VCTK_FEATURE_DIR)
def get_feature(cfile, seg):
return precomputed.crop(cfile, seg, mode='center', fixed=2.0)
def convert_tensor(key, d):
d[key] = torch.from_numpy(np.array(d[key], np.float32, copy=False))
return d
def convert_cuda(key, d):
if hasattr(d[key], 'cuda'):
d[key] = d[key].cuda()
return d
class SpeechActivityDetection(Pipeline):
"""Speech activity detection pipeline
Parameters
----------
precomputed : str
Path to precomputed SAD scores.
"""
def __init__(self, precomputed=None, **kwargs):
super(SpeechActivityDetection, self).__init__()
self.precomputed = precomputed
self.precomputed_ = Precomputed(self.precomputed)
self.has_overlap_ = self.precomputed_.dimension() == 3
self.with_params(**kwargs)
def get_tune_space(self):
space = {
'speech_onset': chocolate.uniform(0., 1.),
'speech_offset': chocolate.uniform(0., 1.),
'speech_min_duration_on': chocolate.uniform(0., 2.),
'speech_min_duration_off': chocolate.uniform(0., 2.),
'speech_pad_onset': chocolate.uniform(-1., 1.),
'speech_pad_offset': chocolate.uniform(-1., 1.)
}
if self.has_overlap_:
space.update({
'overlap_onset': chocolate.uniform(0., 1.),
'overlap_offset': chocolate.uniform(0., 1.),
'overlap_min_duration_on': chocolate.uniform(0., 2.),
'overlap_min_duration_off': chocolate.uniform(0., 2.),
'overlap_pad_onset': chocolate.uniform(-1., 1.),
'overlap_pad_offset': chocolate.uniform(-1., 1.)
})
return space
def get_tune_metric(self):
return DetectionErrorRate()
def with_params(self, **params):
# initialize speech/non-speech binarizer
speech_params = {
'_'.join(param.split('_')[1:]): value
for param, value in params.items() if param.startswith('speech_')
}
self.speech_binarize_ = Binarize(**speech_params)
# initialize overlap binarizer
if self.has_overlap_:
overlap_params = {
'_'.join(param.split('_')[1:]): value
for param, value in params.items()
if param.startswith('overlap_')
}
self.overlap_binarize_ = Binarize(**overlap_params)
return self
def apply(self, current_file):
# extract precomputed scores
precomputed = self.precomputed_(current_file)
# if this check has not been done yet, do it once and for all
if not hasattr(self, "log_scale_"):
# heuristic to determine whether scores are log-scaled
if np.nanmean(precomputed.data) < 0:
self.log_scale_ = True
else:
self.log_scale_ = False
data = np.exp(precomputed.data) if self.log_scale_ \
else precomputed.data
# speech vs. non-speech
speech_prob = SlidingWindowFeature(1. - data[:, 0],
precomputed.sliding_window)
speech = self.speech_binarize_.apply(speech_prob)
if self.has_overlap_:
# overlap vs. non-overlap
overlap_prob = SlidingWindowFeature(data[:, 2],
precomputed.sliding_window)
overlap = self.overlap_binarize_.apply(overlap_prob)
# overlap speech can only happen in speech regions
overlap = overlap.crop(speech)
else:
# empty timeline
overlap = Timeline()
speech = speech.to_annotation(generator='string')
overlap = overlap.to_annotation(generator='int')
hypothesis = speech.update(overlap)
return hypothesis
def apply(self, protocol_name, output_dir, step=None, internal=False):
# load best performing model
with open(self.validate_txt_, 'r') as fp:
eers = SortedDict(np.loadtxt(fp))
best_epoch = int(eers.iloc[np.argmin(eers.values())])
embedding = SequenceEmbeddingAutograd.load(self.train_dir_, best_epoch)
# guess sequence duration from path (.../3.2+0.8/...)
directory = basename(dirname(self.experiment_dir))
duration, _, _, _ = self._directory_to_params(directory)
if step is None:
step = 0.5 * duration
# initialize embedding extraction
batch_size = self.approach_.batch_size
extraction = Extraction(embedding,
self.feature_extraction_,
duration,
step=step,
batch_size=batch_size,
internal=internal)
sliding_window = extraction.sliding_window
dimension = extraction.dimension
# create metadata file at root that contains
# sliding window and dimension information
path = Precomputed.get_config_path(output_dir)
mkdir_p(dirname(path))
f = h5py.File(path)
f.attrs['start'] = sliding_window.start
f.attrs['duration'] = sliding_window.duration
f.attrs['step'] = sliding_window.step
f.attrs['dimension'] = dimension
f.close()
# file generator
protocol = get_protocol(protocol_name,
progress=True,
preprocessors=self.preprocessors_)
for subset in ['development', 'test', 'train']:
try:
file_generator = getattr(protocol, subset)()
first_item = next(file_generator)
except NotImplementedError as e:
continue
file_generator = getattr(protocol, subset)()
for current_file in file_generator:
fX = extraction.apply(current_file)
path = Precomputed.get_path(output_dir, current_file)
mkdir_p(dirname(path))
f = h5py.File(path)
f.attrs['start'] = sliding_window.start
f.attrs['duration'] = sliding_window.duration
f.attrs['step'] = sliding_window.step
f.attrs['dimension'] = dimension
f.create_dataset('features', data=fX.data)
f.close()
def __init__(self, wrappable: Wrappable, **params):
super().__init__()
from pyannote.audio.features import Pretrained
from pyannote.audio.features import Precomputed
from pyannote.audio.features import FeatureExtraction
from pyannote.audio.features import RawAudio
scorer = None
msg = ""
# corner
if isinstance(wrappable, dict):
wrappable, custom_params = dict(wrappable).popitem()
params.update(**custom_params)
# If `wrappable` already complies with the `FeatureExtraction` API , it
# is kept unchanged. This includes instances of any `FeatureExtraction`
# subclass,`RawAudio` instances, `Precomputed` instances, and
# `Pretrained` instances.
if isinstance(wrappable,
(FeatureExtraction, RawAudio, Pretrained, Precomputed)):
scorer = wrappable
elif Path(wrappable).is_dir():
directory = Path(wrappable)
# If `wrappable` is a `Path` to a directory containing precomputed
# features or scores, wrap the corresponding `Precomputed` instance
try:
scorer = Precomputed(root_dir=directory)
except Exception as e:
scorer = None
# If `wrappable` is a `Path` to a validation directory,
# wrap the corresponding `Pretrained` instance
if scorer is None:
try:
scorer = Pretrained(validate_dir=directory, **params)
except Exception as e:
scorer = None
if scorer is None:
msg = (f'"{wrappable}" directory does not seem to be the path '
f"to precomputed features nor the path to a model "
f"validation step.")
# If `wrappable` is a `Path` to a pretrined model checkpoint,
# wrap the corresponding `Pretrained` instance
elif Path(wrappable).is_file():
checkpoint = Path(wrappable)
try:
validate_dir = checkpoint.parents[1] / "validate" / "fake"
epoch = int(checkpoint.stem)
scorer = Pretrained(validate_dir=validate_dir,
epoch=epoch,
**params)
except Exception as e:
msg = (f'"{wrappable}" directory does not seem to be the path '
f"to a pretrained model checkpoint.")
scorer = None
elif isinstance(wrappable, Text):
# If `wrappable` is a `Text` starting with '@' such as '@key',
# it means that one should read the "key" key of protocol files
if wrappable.startswith("@"):
key = wrappable[1:]
scorer = partial(_use_existing_key, key)
# scorer = lambda current_file: current_file[key]
# If `wrappable` is a `Text` containing the name of an existing
# `torch.hub` model, wrap the corresponding `Pretrained`.
else:
try:
import torch
scorer = torch.hub.load("pyannote/pyannote-audio",
wrappable, **params)
if not isinstance(scorer, Pretrained):
msg = (
f'"{wrappable}" exists on torch.hub but does not '
f"return a `Pretrained` model instance.")
scorer = None
except Exception as e:
msg = (f"Could not load {wrappable} model from torch.hub. "
f"The following exception was raised:\n{e}")
scorer = None
# warn the user the something went wrong
if scorer is None:
raise ValueError(msg)
self.scorer_ = scorer
res['scores'] = pscores
return res
if __name__ == '__main__':
arguments = docopt(__doc__, version='Speaker-spotting')
# protocol
protocol_name = arguments['<database.task.protocol>']
embedding_path = arguments['<embedding_path>']
protocol = get_protocol(protocol_name, progress=True)
# subset (train, development, or test)
subset = arguments['--subset']
output_file = arguments['<output_file>']
from pyannote.audio.features import Precomputed
precomputed = Precomputed(embedding_path)
models = {}
enrolments = getattr(protocol,
'{subset}_enrolment'.format(subset=subset))()
for current_enrolment in enrolments:
model_id = current_enrolment.pop('model_id')
models[model_id] = speaker_spotting_enrol(current_enrolment)
if arguments['oracle']:
REFERENCE = {}
for current_file in getattr(protocol, subset)():
uri = current_file['uri']
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
REFERENCE[uri].update(current_file['annotation'])
class SpeechActivityDetection(Pipeline):
"""Speech activity detection pipeline
Parameters
----------
precomputed : str
Path to precomputed SAD scores.
"""
def __init__(self, precomputed=None, **kwargs):
super(SpeechActivityDetection, self).__init__()
self.precomputed = precomputed
self.precomputed_ = Precomputed(self.precomputed)
self.has_overlap_ = self.precomputed_.dimension() == 3
self.with_params(**kwargs)
def get_tune_space(self):
space = {
'speech_onset': chocolate.uniform(0., 1.),
'speech_offset': chocolate.uniform(0., 1.),
'speech_min_duration_on': chocolate.uniform(0., 2.),
'speech_min_duration_off': chocolate.uniform(0., 2.),
'speech_pad_onset': chocolate.uniform(-1., 1.),
'speech_pad_offset': chocolate.uniform(-1., 1.)
}
if self.has_overlap_:
space.update({
'overlap_onset': chocolate.uniform(0., 1.),
'overlap_offset': chocolate.uniform(0., 1.),
'overlap_min_duration_on': chocolate.uniform(0., 2.),
'overlap_min_duration_off': chocolate.uniform(0., 2.),
'overlap_pad_onset': chocolate.uniform(-1., 1.),
'overlap_pad_offset': chocolate.uniform(-1., 1.)
})
return space
def get_tune_metric(self):
return DetectionErrorRate()
def with_params(self, **params):
# initialize speech/non-speech binarizer
speech_params = {
'_'.join(param.split('_')[1:]): value
for param, value in params.items()
if param.startswith('speech_')}
self.speech_binarize_ = Binarize(**speech_params)
# initialize overlap binarizer
if self.has_overlap_:
overlap_params = {
'_'.join(param.split('_')[1:]): value
for param, value in params.items()
if param.startswith('overlap_')}
self.overlap_binarize_ = Binarize(**overlap_params)
return self
def apply(self, current_file):
# extract precomputed scores
precomputed = self.precomputed_(current_file)
# if this check has not been done yet, do it once and for all
if not hasattr(self, "log_scale_"):
# heuristic to determine whether scores are log-scaled
if np.nanmean(precomputed.data) < 0:
self.log_scale_ = True
else:
self.log_scale_ = False
data = np.exp(precomputed.data) if self.log_scale_ \
else precomputed.data
# speech vs. non-speech
speech_prob = SlidingWindowFeature(
1. - data[:, 0],
precomputed.sliding_window)
speech = self.speech_binarize_.apply(speech_prob)
if self.has_overlap_:
# overlap vs. non-overlap
overlap_prob = SlidingWindowFeature(
data[:, 2], precomputed.sliding_window)
overlap = self.overlap_binarize_.apply(overlap_prob)
# overlap speech can only happen in speech regions
overlap = overlap.crop(speech)
else:
# empty timeline
overlap = Timeline()
speech = speech.to_annotation(generator='string')
overlap = overlap.to_annotation(generator='int')
hypothesis = speech.update(overlap)
return hypothesis
# coding: utf-8
import sys
sys.path.append("../")
import clustering
import numpy as np
from pyannote.audio.features import Precomputed
precomputed = Precomputed('/vol/work1/bredin/speaker_spotting/embeddings')
from pyannote.database import get_protocol, FileFinder
protocol = get_protocol('AMI.SpeakerSpotting.MixHeadset', progress=True)
from pyannote.core import Annotation,Segment, Timeline
# 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.test_enrolment():
model_id = current_enrolment.pop('model_id')
models[model_id] = speaker_spotting_enrol(current_enrolment)
REFERENCE = {}
for current_file in protocol.test():
uri = current_file['uri']
if uri not in REFERENCE:
REFERENCE[uri] = Annotation(uri=uri)
class VCTKLoader:
def __init__(self, split='', n_support=0, n_query=0, n_way=0, if_cuda=False):
self.precomputed = Precomputed(VCTK_FEATURE_DIR)
self.dataset = self.load_dataset(from_disk=True)
self.split = split
self.n_support = n_support
self.n_query = n_query
self.n_way = n_way
self.dataset = None
self.transforms = None
self.if_cuda = if_cuda
def shuffle_dataset(self):
num_label = len(self.dataset['class'])
num_data = len(self.dataset['data'][0]) # x: num_labels * num_samples_per_label
# num_label * num_data
data_index = np.tile(np.arange(num_data), (num_label,1))
label_index = np.tile(np.arange(num_label).reshape(-1, 1), (1,num_data))
label_index = np.expand_dims(label_index, axis=2)
data_index = np.expand_dims(data_index, axis=2)
data_label_idx = np.concatenate((data_index, label_index), axis=2)
# shuffle rows (labels)
np.random.shuffle(data_label_idx)
# shuffle data index for each row (for each label)
for dl in data_label_idx:
np.random.shuffle(dl)
index_batches = sub_matrix(data_label_idx, self.n_support+self.n_query, self.n_way)
# optional: shuffle batches
np.random.shuffle(index_batches)
return index_batches
def __iter__(self):
if self.dataset is None:
self.dataset = self.load_dataset(from_disk=True)[self.split]
transforms = [partial(batch_from_index, self.dataset['data']), partial(convert_tensor, 'data')]
if self.if_cuda:
transforms.append(CudaTransform())
self.transforms = compose(transforms)
index_batches = self.shuffle_dataset()
batches = TransformDataset(ListDataset(index_batches), self.transforms)
print(f"\nSize of batches: {len(batches)}")
for batch in batches:
batch['n_way'] = self.n_way
batch['n_support'] = self.n_support
batch['n_query'] = self.n_query
yield batch
def get_feature(self, cfile, seg):
return self.precomputed.crop(cfile, seg, mode='center', fixed=2.0)
# return precomputed(cfile).crop(seg, mode='center', fixed=2.0)
def load_speaker_file(self, protocol_name='SpeakerEmbedding.All', from_disk=False):
database = VCTK()
protocol = database.get_protocol(protocol_name.split('.')[0], protocol_name.split('.')[1])
speaker_file = { 'train': {}, 'val': {}, 'test':{}, 'unseen':{}}
if from_disk:
print('Loading speaker_file from disk...')
vctk_file_name = os.path.join(VCTK_DATA_DIR, 'vctk_speaker_file')
if not os.path.isfile(vctk_file_name):
raise ValueError(f'{vctk_file_name} not found')
else:
with open(vctk_file_name, 'rb') as vctk_file:
speaker_file = pickle.load(vctk_file)
return speaker_file
print('Loading unseen set...')
for current_file in protocol.unseen_iter():
speaker = current_file['uri'].split('_')[0]
if not speaker in speaker_file['unseen']:
speaker_file['unseen'][speaker] = []
speaker_file['unseen'][speaker].append(current_file)
print('Loading training set...')
for current_file in protocol.train():
speaker = current_file['uri'].split('_')[0]
if not speaker in speaker_file['train']:
speaker_file['train'][speaker] = []
speaker_file['train'][speaker].append(current_file)
print('Loading test set...')
for current_file in protocol.test():
speaker = current_file['uri'].split('_')[0]
if not speaker in speaker_file['test']:
speaker_file['test'][speaker] = []
speaker_file['test'][speaker].append(current_file)
print('Loading development set...')
for current_file in protocol.development():
speaker = current_file['uri'].split('_')[0]
if not speaker in speaker_file['val']:
speaker_file['val'][speaker] = []
speaker_file['val'][speaker].append(current_file)
with open(os.path.join(VCTK_DATA_DIR, 'vctk_speaker_file'), 'wb') as vctk_file:
pickle.dump(speaker_file, vctk_file, -1)
return speaker_file
def load_speaker_segments(self, seg_dur=2.0, overlap_ratio=0.25, from_disk=False):
'''
2 seconds segments, with overlapping ratio = 0.25
|----||----|
|----|
'''
spk_seg = { 'train': {}, 'val': {}, 'test':{}, 'unseen':{}}
if from_disk:
print('Loading speaker_segments from disk...')
vctk_file_name = os.path.join(VCTK_DATA_DIR, 'vctk_speaker_segments')
if os.path.isfile(vctk_file_name):
with open(vctk_file_name, 'rb') as vctk_file:
spk_seg = pickle.load(vctk_file)
return spk_seg
else:
raise ValueError(f'{vctk_file_name} not found')
def fetch_spk_seg(speaker_file):
speaker_seg = {}
for spk, sfiles in speaker_file.items():
speaker_seg[spk] = []
for sfile in sfiles:
duration = sfile['annotated'].duration()
if duration < seg_dur:
continue
half_seg = seg_dur / 2
for mid in np.arange(half_seg, duration-half_seg, seg_dur*(1-overlap_ratio)):
speaker_seg[spk].append(
(Segment(mid-half_seg, mid+half_seg), sfile)
)
return speaker_seg
spk_file = self.load_speaker_file(from_disk=from_disk)
for sub in ['train', 'val', 'test', 'unseen']:
spk_file[sub] = fetch_spk_seg(spk_file[sub])
with open(os.path.join(VCTK_DATA_DIR, 'vctk_speaker_segments'), 'wb') as vctk_file:
pickle.dump(spk_file, vctk_file, -1)
return spk_file
def load_dataset(self, from_disk=False):
print('Loading dataset...')
dataset = { 'train': {}, 'val': {}, 'test':{}, 'unseen':{}}
if from_disk:
vctk_file_name = os.path.join(VCTK_DATA_DIR, 'vctk_datasets')
if os.path.isfile(vctk_file_name):
with open(vctk_file_name, 'rb') as vctk_file:
dataset = pickle.load(vctk_file)
return dataset
else:
raise ValueError(f'{vctk_file_name} not found')
spk_seg = self.load_speaker_segments(from_disk=from_disk)
with open(os.path.join(VCTK_DATA_DIR, 'vctk_datasets'), 'wb') as vctk_file:
for sub in dataset.keys():
speaker_segments = spk_seg[sub]
seg_count = []
for spk, seg in speaker_segments.items():
seg_count.append(len(seg))
max_count = max(seg_count)
for spk in speaker_segments.keys():
speaker_segments[spk] = list(islice(cycle(speaker_segments[spk]), max_count))
y_labels = speaker_segments.keys()
dataset[sub] = {
'class': list(y_labels),
'data': [speaker_segments[label] for label in y_labels]
}
pickle.dump(dataset, vctk_file, -1)
return dataset
# load MFCC features into memory
# take too much memory, not recommand
def load_features(self, from_disk=False):
feature_dataset = {'train': {}, 'val': {}, 'test': {}, 'unseen':{}}
if from_disk:
vctk_file_name = os.path.join(VCTK_DATA_DIR, 'vctk_feature_datasets')
if os.path.isfile(vctk_file_name):
with open(vctk_file_name, 'rb') as vctk_file:
feature_dataset = pickle.load(vctk_file)
return feature_dataset
else:
raise ValueError(f'{vctk_file_name} not found')
dataset = self.load_dataset(from_disk=True)
with open(os.path.join(VCTK_DATA_DIR, 'vctk_feature_datasets'), 'wb') as vctk_file:
for sub in feature_dataset.items():
print(f'Loading feature: {sub}')
subset = dataset[sub]
for spk, seg_list in subset.items():
print(f'Speaker: {spk}')
feature_list = []
for seg, cfile in seg_list:
feature_list.append(self.get_feature(cfile, seg))
feature_dataset[sub][spk] = np.array(feature_list)
pickle.dump(feature_dataset, vctk_file, -1)
return feature_dataset
# load data for same/different experiments
def load_same_diff_data(self, from_disk=False):
exp_dataset = {'train': {},'val': {}, 'test': {}, 'unseen': {}}
n_pair = 40
n_pair_unseen = 100
print(f'Loading same/diff data, #pair: {n_pair}, #pair_unseen: {n_pair_unseen}')
file_name = os.path.join(VCTK_DATA_DIR, f'same_diff_exp_norepeat_{n_pair}_{n_pair_unseen}')
if from_disk:
if os.path.isfile(file_name):
with open(file_name, 'rb') as dfile:
exp_dataset = pickle.load(dfile)
return exp_dataset
else:
raise ValueError(f'{file_name} not found, generate first?')
def gen_same_diff(data, labels, first_n=-1, n_same_pair=20):
same_pairs = []
diff_pairs = []
for spk, spk_data in zip(labels, data):
first_n = len(spk_data) # if first_n == -1 or first_n > len(spk_data) else first_n
# get same pairs
ind = list(range(first_n))
n = 0
same_pair_ind = []
ind_his = set()
while n < n_same_pair:
i1 = random.choice(ind)
ind.remove(i1)
i2 = random.choice(ind)
if (not (i1, i2) in ind_his) and (not (i2, i1) in ind_his):
ind_his.add((i1, i2))
same_pair_ind.append((i1, i2))
n += 1
else:
print('skip repeated pair')
ind = list(range(first_n))
spk_same_pairs = [(spk_data[ind[0]], spk_data[ind[1]]) for ind in same_pair_ind]
same_pairs.extend(spk_same_pairs)
# get different pairs
labels_ind = list(range(len(labels)))
n = 0
pair_his = set()
while n < n_same_pair * len(labels):
s1 = random.choice(labels_ind)
ind1 = random.choice(list(range(len(data[s1]))))
labels_ind.remove(s1)
s2 = random.choice(labels_ind)
ind2 = random.choice(list(range(len(data[s2]))))
pair1 = (s1, ind1); pair2 = (s2, ind2)
if (not (pair1, pair2) in pair_his ) and (not (pair2, pair1) in pair_his):
pair_his.add((pair1, pair2))
diff_pairs.append((data[s1][ind1], data[s2][ind2]))
n += 1
else:
print('skip repeated pair')
labels_ind = list(range(len(labels)))
print(len(same_pairs), len(diff_pairs))
return same_pairs, diff_pairs
print("Loading dataset from disk, instead of generating from scratch")
dataset = self.load_dataset(from_disk=True)
for subset in ['train', 'val', 'test', 'unseen']:
first_n = -1 # if subset == 'unseen' else 200
npair = n_pair_unseen if subset == 'unseen' else n_pair
data = dataset[subset]['data']
labels = dataset[subset]['class']
same_pairs, diff_pairs = gen_same_diff(data, labels, first_n=first_n, n_same_pair=npair)
assert len(same_pairs) == len(diff_pairs)
assert len(same_pairs) == len(labels) * npair
exp_dataset[subset] = {
'same': same_pairs,
'diff': diff_pairs
}
with open(os.path.join(VCTK_DATA_DIR, file_name), 'wb') as dfile:
pickle.dump(exp_dataset, dfile, -1)
return exp_dataset
