minor debug

s4d/clustering/hac_iv.py

@@ -41,7 +41,6 @@ def hac_iv(diar, scores, method="complete", threshold=0.0):
     lscores = copy.deepcopy(scores)
     # get the triangular part of the distances
     distances, t = scores2distance(lscores, threshold)
-
     # distance = numpy.copy((scores.scoremat + scores.scoremat.T) / 2.0) * -1.0
     # numpy.fill_diagonal(distance, numpy.inf)
     # min = numpy.min(distance)

s4d/diar.py

@@ -984,7 +984,7 @@ class Diar():
             channel = 'U'
             if diar._attributes.exist('channel'):
                 channel = segment['channel']
-            if time_float:
+            if not time_float:
                 lst.append('{:s} 1 {:d} {:d} {:s} {:s} {:s} {:s}\n'.format(
                     segment['show'], segment['start'], segment['stop'] - segment['start'], gender,
                     channel, env, segment['cluster']))

s4d/nnet/seqtoseq.py

@@ -34,7 +34,6 @@ from torch import optim
 from torch.utils.data import Dataset
 import logging
 
-from sidekit.nnet.vad_rnn import BLSTM
 
 __license__ = "LGPL"
 __author__ = "Anthony Larcher"
@@ -48,7 +47,7 @@ __docformat__ = 'reS'
 
 
 class PreNet(nn.Module):
-    def __init(self,
+    def __init__(self,
                sample_rate=16000,
                windows_duration=0.2,
                frame_shift=0.01):
@@ -132,6 +131,7 @@ class BLSTM(nn.Module):
 class SeqToSeq(nn.Module):
 
     def __init__(self):
+        super(SeqToSeq, self).__init__()
         self.preprocessor = PreNet(sample_rate=16000,
                                    windows_duration=0.2,
                                    frame_shift=0.01)