def apply(self, wav):
"""Computes distance between sliding windows embeddings
Parameter
---------
wav : str
Path to wav audio file
Returns
-------
predictions : SlidingWindowFeature
"""
from pyannote.algorithms.stats.gaussian import Gaussian
current_file = {'uri': wav, 'medium': {'wav': wav}}
t, left, right = next(self.from_file(current_file))
y = []
for xL, xR in zip(left, right):
gL = Gaussian(covariance_type=self.covariance_type).fit(xL)
gR = Gaussian(covariance_type=self.covariance_type).fit(xR)
y.append(gL.bic(gR, penalty_coef=0)[0])
y = np.array(y)
window = SlidingWindow(duration=2 * self.duration,
step=self.step,
start=0.)
return SlidingWindowFeature(y, window)
def apply(self, current_file):
"""Computes BIC distance between sliding windows
Parameter
---------
current_file : dict
Returns
-------
predictions : SlidingWindowFeature
"""
from pyannote.algorithms.stats.gaussian import Gaussian
t, left, right = next(self.from_file(current_file))
y = []
for xL, xR in zip(left, right):
gL = Gaussian(covariance_type=self.covariance_type).fit(xL)
gR = Gaussian(covariance_type=self.covariance_type).fit(xR)
y.append(gL.bic(gR, penalty_coef=0)[0])
y = np.array(y)
window = SlidingWindow(duration=2 * self.duration,
step=self.step,
start=0.)
return SlidingWindowFeature(y, window)
