mirror of
https://github.com/cmusphinx/sphinxtrain.git
synced 2026-05-17 13:10:52 +00:00
David Huggins-Daines
79 lines
2.1 KiB
Python
Executable File
79 lines
2.1 KiB
Python
Executable File
#!/usr/bin/env python
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"""
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Calculate Fisher's linear discriminant for acoustic models.
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This module implements Linear Discriminant Analysis for single
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stream Sphinx-III acoustic models.
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"""
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# Copyright (c) 2006 Carnegie Mellon University
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#
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# You may copy and modify this freely under the same terms as
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# Sphinx-III
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__author__ = "David Huggins-Daines <dhdaines@gmail.com>"
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__version__ = "$Revision$"
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import sys
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try:
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import numpy
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except ImportError:
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print("FATAL: Failed to import numpy modules. "
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"Check that numpy and scipy are installed")
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sys.exit(1)
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from cmusphinx import s3lda, s3gaucnt
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def makelda(gauden_counts):
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"""
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Calculate an LDA matrix from a set of mean/full-covariance
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counts as output by the 'bw' program from SphinxTrain.
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@param gauden_counts: Set of full covariance occupation counts.
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@type gauden_counts: cmusphinx.s3gaucnt.S3FullGauCntFile
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"""
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if not gauden_counts.pass2var:
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raise Exception("Please re-run bw with '-2passvar yes'")
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mean = numpy.concatenate([x[0] for x in gauden_counts.mean])
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var = numpy.concatenate([x[0] for x in gauden_counts.var])
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dnom = gauden_counts.dnom.ravel()
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# If CMN was used, this should actually be very close to zero
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globalmean = mean.sum(0) / dnom.sum()
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sw = var.sum(0)
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sb = numpy.zeros(var[0].shape, 'd')
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for d, m in zip(dnom, mean):
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diff = m / d - globalmean
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sb += d * numpy.outer(diff, diff)
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print("Sw:\n", sw)
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print("Sb:\n", sb)
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BinvA = numpy.dot(numpy.linalg.inv(sw), sb)
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u, v = numpy.linalg.eig(BinvA)
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top = list(u.argsort())
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top.reverse()
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u = u.take(top)
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# Remember, the eigenvalues are in the columns, but Sphinx expects
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# them to be in the rows.
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v = v.T.take(top, 0)
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print("u:\n", u)
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print("v:\n", v)
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return v
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if __name__ == '__main__':
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if len(sys.argv) < 3:
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sys.stderr.write("Usage: %s OUTFILE ACCUMDIRS...\n" % (sys.argv[0]))
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sys.exit(1)
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ldafn = sys.argv[1]
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accumdirs = sys.argv[2:]
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gauden = s3gaucnt.accumdirs_full(accumdirs)
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lda = makelda(gauden)
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s3lda.open(ldafn, 'w').writeall(lda[numpy.newaxis, :])
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