def get_full_config(self):
ans = []
# note: each value of self.descriptors is (descriptor, dim,
# normalized-string, output-string).
# by 'descriptor_final_string' we mean a string that can appear in
# config-files, i.e. it contains the 'final' names of nodes.
descriptor_final_string = self.descriptors['input']['final-string']
input_dim = self.descriptors['input']['dim']
output_dim = self.output_dim()
transform_file = self.config['affine-transform-file']
idct_mat = common_lib.compute_idct_matrix(
input_dim, output_dim, self.config['cepstral-lifter'])
# append a zero column to the matrix, this is the bias of the fixed
# affine component
for n in range(0, output_dim):
idct_mat[n].append(0)
common_lib.write_kaldi_matrix(transform_file, idct_mat)
# write the 'real' component to final.config
line = 'component name={0} type=FixedAffineComponent matrix={1}'.format(
self.name, transform_file)
ans.append(('final', line))
# write a random version of the component, with the same dims, to ref.config
line = 'component name={0} type=FixedAffineComponent input-dim={1} output-dim={2}'.format(
self.name, input_dim, output_dim)
ans.append(('ref', line))
# the component-node gets written to final.config and ref.config.
line = 'component-node name={0} component={0} input={1}'.format(
self.name, descriptor_final_string)
ans.append(('final', line))
ans.append(('ref', line))
return ans
def get_full_config(self):
ans = []
# note: each value of self.descriptors is (descriptor, dim,
# normalized-string, output-string).
# by 'descriptor_final_string' we mean a string that can appear in
# config-files, i.e. it contains the 'final' names of nodes.
descriptor_final_string = self.descriptors['input']['final-string']
input_dim = self.descriptors['input']['dim']
output_dim = self.output_dim()
transform_file = self.config['affine-transform-file']
idct_mat = common_lib.compute_idct_matrix(
input_dim, output_dim, self.config['cepstral-lifter'])
# append a zero column to the matrix, this is the bias of the fixed
# affine component
for n in range(0, output_dim):
idct_mat[n].append(0)
common_lib.write_kaldi_matrix(transform_file, idct_mat)
# write the 'real' component to final.config
line = 'component name={0} type=FixedAffineComponent matrix={1}'.format(
self.name, transform_file)
ans.append(('final', line))
# write a random version of the component, with the same dims, to ref.config
line = 'component name={0} type=FixedAffineComponent input-dim={1} output-dim={2}'.format(
self.name, input_dim, output_dim)
ans.append(('ref', line))
# the component-node gets written to final.config and ref.config.
line = 'component-node name={0} component={0} input={1}'.format(
self.name, descriptor_final_string)
ans.append(('final', line))
ans.append(('ref', line))
return ans
def convert_mfcc_to_fbank(feats):
num_mel = np.shape(feats)[-1]
idct_mat = common_lib.compute_idct_matrix(num_mel, num_mel, 22.0)
idct_mat2 = np.transpose(idct_mat)
fbank = feats.dot(idct_mat2)
return fbank
