def get_num_speakers(self, ivecs, min_speakers=2, max_speakers=6):
""" Obtain number of speakers from pretrained model.
:param ivecs: input i-vectors
:type ivecs: numpy.array
:param min_speakers: minimal number of speakers from model
:type min_speakers: int
:param max_speakers: maximal number of speakers from model
:type max_speakers: int
:returns: estimated number of speakers and KMeans centroid
:rtype: tuple
"""
avg, centroids_list = [], []
features = []
for num_speakers in range(min_speakers, max_speakers + 1):
sklearnkmeans = sklearnKMeans(n_clusters=num_speakers).fit(ivecs)
centroids = KMeans(sklearnkmeans.cluster_centers_, num_speakers,
self.plda).fit(ivecs)
centroids_list.append(centroids)
scores = self.s_norm(centroids,
centroids)[np.tril_indices(num_speakers, -1)]
features.append(Normalization.get_features(scores))
num_speakers = np.argmax(
np.sum(self.model.test(features, prob=True), axis=0))
# raw_input('ENTER')
return num_speakers + min_speakers, centroids_list[num_speakers]
def get_num_speakers(self, ivecs, min_speakers=2, max_speakers=6):
avg, centroids_list = [], []
features = []
for num_speakers in range(min_speakers, max_speakers + 1):
sklearnkmeans = sklearnKMeans(n_clusters=num_speakers).fit(ivecs)
centroids = KMeans(sklearnkmeans.cluster_centers_, num_speakers,
self.plda).fit(ivecs)
centroids_list.append(centroids)
scores = self.s_norm(centroids,
centroids)[np.tril_indices(num_speakers, -1)]
features.append(Normalization.get_features(scores))
num_speakers = np.argmax(
np.sum(self.model.test(features, prob=True), axis=0))
# raw_input('ENTER')
return num_speakers + min_speakers, centroids_list[num_speakers]