def reduce_ppg_dim(ppgs: Matrix, transform: SparseMatrix) -> Matrix:
"""Reduce full PPGs to monophone PPGs.
Args:
ppgs: A T*D PPG matrix.
transform: A d*D sparse matrix.
Returns:
monophone_ppgs: A T*d matrix. Containing PPGs reduced into monophones.
"""
num_frames = ppgs.num_rows
num_phones = transform.num_rows
# Convert the sparse matrix to a full matrix to avoid having to keep the
# matrix type consistent
full_transform = Matrix(num_phones, transform.num_cols)
transform.copy_to_mat(full_transform)
monophone_ppg = Matrix(num_frames, num_phones)
monophone_ppg.add_mat_mat_(ppgs, full_transform,
MatrixTransposeType.NO_TRANS,
MatrixTransposeType.TRANS, 1.0, 0.0)
return monophone_ppg
def apply_feat_transform(feats: Matrix, transform: Matrix) -> Matrix:
"""Apply an LDA/fMLLR transform on the input features.
The transform is a simple matrix multiplication: F = FT' (' is transpose) in
the case of LDA. For fMLLR, please see
http://kaldi-asr.org/doc/transform.html#transform_cmllr_global
This function is an extremely simplified version of
https://github.com/kaldi-asr/kaldi/blob/5.3/src/featbin/transform-feats.cc
Args:
feats: A T*D feature matrix.
transform: A D'*D matrix, where D' is the output feature dim.
Returns:
feats_out: A T*D' matrix.
"""
feat_dim = feats.num_cols
transform_rows = transform.num_rows
transform_cols = transform.num_cols
feats_out = Matrix(feats.num_rows, transform_rows)
if transform_cols == feat_dim:
feats_out.add_mat_mat_(feats, transform, MatrixTransposeType.NO_TRANS,
MatrixTransposeType.TRANS, 1.0, 0.0)
elif transform_cols == feat_dim + 1:
# Append the implicit 1.0 to the input feature.
linear_part = SubMatrix(transform, 0, transform_rows, 0, feat_dim)
feats_out.add_mat_mat_(feats, linear_part,
MatrixTransposeType.NO_TRANS,
MatrixTransposeType.TRANS, 1.0, 0.0)
offset = Vector(transform_rows)
offset.copy_col_from_mat_(transform, feat_dim)
feats_out.add_vec_to_rows_(1.0, offset)
else:
logging.error(("Transform matrix has bad dimension %dx%d versus feat "
"dim %d") % (transform_rows, transform_cols, feat_dim))
return feats_out