Commit 03d0b79e by Anthony Larcher

### merge

parents e120ee8a 3cea5427
 ... @@ -41,7 +41,6 @@ def hac_iv(diar, scores, method="complete", threshold=0.0): ... @@ -41,7 +41,6 @@ def hac_iv(diar, scores, method="complete", threshold=0.0): lscores = copy.deepcopy(scores) lscores = copy.deepcopy(scores) # get the triangular part of the distances # get the triangular part of the distances distances, t = scores2distance(lscores, threshold) distances, t = scores2distance(lscores, threshold) # distance = numpy.copy((scores.scoremat + scores.scoremat.T) / 2.0) * -1.0 # distance = numpy.copy((scores.scoremat + scores.scoremat.T) / 2.0) * -1.0 # numpy.fill_diagonal(distance, numpy.inf) # numpy.fill_diagonal(distance, numpy.inf) # min = numpy.min(distance) # min = numpy.min(distance) ... ...
 ... @@ -984,7 +984,7 @@ class Diar(): ... @@ -984,7 +984,7 @@ class Diar(): channel = 'U' channel = 'U' if diar._attributes.exist('channel'): if diar._attributes.exist('channel'): channel = segment['channel'] channel = segment['channel'] if time_float: if not time_float: lst.append('{:s} 1 {:d} {:d} {:s} {:s} {:s} {:s}\n'.format( lst.append('{:s} 1 {:d} {:d} {:s} {:s} {:s} {:s}\n'.format( segment['show'], segment['start'], segment['stop'] - segment['start'], gender, segment['show'], segment['start'], segment['stop'] - segment['start'], gender, channel, env, segment['cluster'])) channel, env, segment['cluster'])) ... ...
 ... @@ -61,7 +61,7 @@ def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename ... @@ -61,7 +61,7 @@ def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename shutil.copyfile(filename, best_filename) shutil.copyfile(filename, best_filename) class PreNet(nn.Module): class PreNet(nn.Module): def __init(self, def __init__(self, sample_rate=16000, sample_rate=16000, windows_duration=0.2, windows_duration=0.2, frame_shift=0.01): frame_shift=0.01): ... @@ -153,16 +153,11 @@ class SeqToSeq(nn.Module): ... @@ -153,16 +153,11 @@ class SeqToSeq(nn.Module): linear_1, linear_1, linear_2, linear_2, output_size=1): output_size=1): """ :param input_size: super(SeqToSeq, self).__init__() :param lstm_1: self.preprocessor = PreNet(sample_rate=16000, :param lstm_2: windows_duration=0.2, :param linear_1: frame_shift=0.01) :param linear_2: :param output_size: """ super(BLSTM, self).__init__() self.lstm_1 = nn.LSTM(input_size, lstm_1 // 2, bidirectional=True, batch_first=True) self.lstm_1 = nn.LSTM(input_size, lstm_1 // 2, bidirectional=True, batch_first=True) self.lstm_2 = nn.LSTM(lstm_1, lstm_2 // 2, bidirectional=True, batch_first=True) self.lstm_2 = nn.LSTM(lstm_1, lstm_2 // 2, bidirectional=True, batch_first=True) ... ...
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