Class: Diarize::SuperVector

Inherits:
Object
  • Object
show all
Defined in:
lib/diarize/super_vector.rb

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(vector) ⇒ SuperVector

Returns a new instance of SuperVector.



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# File 'lib/diarize/super_vector.rb', line 5

def initialize(vector)
  @vector = vector
end

Instance Attribute Details

#vector ⇒ Object (readonly)

Returns the value of attribute vector.



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# File 'lib/diarize/super_vector.rb', line 3

def vector
  @vector
end

Class Method Details

.divergence(sv1, sv2) ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 57

def self.divergence(sv1, sv2)
  # ubm_gaussian_weights.mul(((sv1.vector - sv2.vector) ** 2) / ubm_covariance).sum
  diff   = sv1.vector - sv2.vector
  square = diff.map {|el| el ** 2}
  codiv  = Vector.elements(square.each.with_index.inject([]) {|a,(el,ix)| a << el / ubm_covariance[ix]})
  mult   = ubm_gaussian_weights.each.with_index.inject([]) {|a,(el,ix)| a << el * codiv[ix]}
  mult.inject(0, :+)
end

.generate_from_model(model) ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 9

def self.generate_from_model(model)
  # Generates a supervector from a LIUM GMM
  dim = model.nb_of_components * model.components.get(0).dim
  # vector = DoubleMatrix.new(1, dim)
  # vector = Vector.elements(Array.new(dim, 0))
  vector = Array.new(dim, 0)
  model.nb_of_components.times do |k|
    gaussian = model.components.get(k)
    gaussian.dim.times do |i|
      vector[k * gaussian.dim + i] = gaussian.mean(i)
    end
  end
  # SuperVector.new(vector)
  SuperVector.new(Vector.elements(vector))
end

.ubm_covariance ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 41

def self.ubm_covariance
  # Returns a vector of diagonal covariances, same dimension as speaker's super vectors
  @@ubm_covariance ||= begin
    ubm = Speaker.ubm
    # cov = DoubleMatrix.new(1, ubm.supervector.dim)
    cov = Array.new(ubm.supervector.dim)
    ubm.model.nb_of_components.times do |k|
      gaussian = ubm.model.components.get(k)
      gaussian.dim.times do |i|
        cov[k * gaussian.dim + i] = gaussian.getCovariance(i, i)
      end
    end
    Vector.elements(cov)
  end
end

.ubm_gaussian_weights ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 25

def self.ubm_gaussian_weights
  # Returns a vector of gaussian weights, same dimension as speaker's super vectors
  @@ubm_gaussian_weights ||= begin
    ubm = Speaker.ubm
    # weights = DoubleMatrix.new(1, ubm.supervector.dim)
    weights = Array.new(ubm.supervector.dim, 0)
    ubm.model.nb_of_components.times do |k|
      gaussian = ubm.model.components.get(k)
      gaussian.dim.times do |i|
        weights[k * gaussian.dim + i] = gaussian.weight
      end
    end
    Vector.elements(weights)
  end
end

Instance Method Details

#dim ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 66

def dim
  @vector.size
end

#hash ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 70

def hash
  @vector.hash
end

#sha ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 74

def sha
  Digest::SHA256.hexdigest(hash.to_s)
end

#to_a ⇒ Object 



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# File 'lib/diarize/super_vector.rb', line 78

def to_a
  @vector.to_a
end