예제 #1
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 def read(self, path):
     self.mdef = s3mdef.open(os.path.join(path, "mdef"))
     self.mean = s3gau.open(os.path.join(path, "means"))
     self.var = s3gau.open(os.path.join(path, "variances"))
     self.mixw = s3mixw.open(os.path.join(path, "mixture_weights"))
     self.tmat = s3tmat.open(os.path.join(path, "transition_matrices"))
     # Normalize transition matrices and mixture weights
     for t in range(0, len(self.tmat)):
         self.tmat[t] = (self.tmat[t].T / self.tmat[t].sum(1)).T
     for t in range(0, len(self.mixw)):
         self.mixw[t] = (self.mixw[t].T / self.mixw[t].sum(1)).T.clip(self.mwfloor, 1.0)
     # Floor variances and precompute normalizing and inverse variance terms
     self.norm = numpy.empty((len(self.var),
                              len(self.var[0]),
                              len(self.var[0][0])),'d')
     for m,mgau in enumerate(self.var):
         for f,feat in enumerate(mgau):
             fvar = feat.clip(self.varfloor, numpy.inf)
             # log of 1/sqrt((2*pi)^N * det(var))
             det = numpy.log(fvar).sum(1)
             lrd = -0.5 * (det + numpy.log(2 * numpy.pi) * feat.shape[1])
             self.norm[m,f] = lrd
             # "Invert" variances
             feat[:] = (1 / (fvar * 2))
     # Construct senone to codebook mapping
     if os.access(os.path.join(path, "senmgau"), os.F_OK):
         self.senmgau = s3file.S3File(os.path.join(path, "senmgau")).read1d()
     elif len(self.mean) == 1:
         self.senmgau = numpy.ones(len(self.mixw))
     else:
         self.senmgau = numpy.arange(0, len(self.mixw))
     self.senscr = numpy.ones(len(self.mixw)) * WORSTSCORE
예제 #2
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 def read(self, path):
     self.mdef = s3mdef.open(os.path.join(path, "mdef"))
     self.mean = s3gau.open(os.path.join(path, "means"))
     self.var = s3gau.open(os.path.join(path, "variances"))
     self.mixw = s3mixw.open(os.path.join(path, "mixture_weights"))
     self.tmat = s3tmat.open(os.path.join(path, "transition_matrices"))
     # Normalize transition matrices and mixture weights
     for t in range(0, len(self.tmat)):
         self.tmat[t] = (self.tmat[t].T / self.tmat[t].sum(1)).T
     for t in range(0, len(self.mixw)):
         self.mixw[t] = (self.mixw[t].T / self.mixw[t].sum(1)).T.clip(self.mwfloor, 1.0)
     # Floor variances and precompute normalizing and inverse variance terms
     self.norm = numpy.empty((len(self.var),
                              len(self.var[0]),
                              len(self.var[0][0])),'d')
     for m,mgau in enumerate(self.var):
         for f,feat in enumerate(mgau):
             fvar = feat.clip(self.varfloor, numpy.inf)
             # log of 1/sqrt((2*pi)^N * det(var))
             det = numpy.log(fvar).sum(1)
             lrd = -0.5 * (det + numpy.log(2 * numpy.pi) * feat.shape[1])
             self.norm[m,f] = lrd
             # "Invert" variances
             feat[:] = (1 / (fvar * 2))
     # Construct senone to codebook mapping
     if os.access(os.path.join(path, "senmgau"), os.F_OK):
         self.senmgau = s3file.S3File(os.path.join(path, "senmgau")).read1d()
     elif len(self.mean) == 1:
         self.senmgau = numpy.ones(len(self.mixw))
     else:
         self.senmgau = numpy.arange(0, len(self.mixw))
     self.senscr = numpy.ones(len(self.mixw)) * WORSTSCORE
예제 #3
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파일: gmm.py 프로젝트: 10v/cmusphinx
 def write(self, todir):
     """
     Write GMM parameters to files in a directory.
     @param todir: Directory to read parameters from.  The files
     'means', 'variances', and 'mixture_weights' will be created in
     this directory.
     @ptype todir: string
     """
     s3gau.open(os.path.join(todir, "means"),'wb').writeall([[self.means]])
     s3gau.open(os.path.join(todir, "variances"),'wb').writeall([[self.variances]])
     s3mixw.open(os.path.join(todir, "mixture_weights"),'wb').writeall \
         (self.mixw[numpy.newaxis,numpy.newaxis,:])
예제 #4
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파일: gmm.py 프로젝트: lwllovewf2010/jarvis
 def write(self, todir):
     """
     Write GMM parameters to files in a directory.
     @param todir: Directory to read parameters from.  The files
     'means', 'variances', and 'mixture_weights' will be created in
     this directory.
     @ptype todir: string
     """
     s3gau.open(os.path.join(todir, "means"), 'wb').writeall([[self.means]])
     s3gau.open(os.path.join(todir, "variances"),
                'wb').writeall([[self.variances]])
     s3mixw.open(os.path.join(todir, "mixture_weights"),'wb').writeall \
         (self.mixw[numpy.newaxis,numpy.newaxis,:])
예제 #5
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파일: gmm.py 프로젝트: lwllovewf2010/jarvis
 def read(self, fromdir):
     """
     Read GMM parameters from files in a directory.
     @param fromdir: Directory to read parameters from.  The files
     'means', 'variances', and 'mixture_weights' will be read from
     this directory.
     @ptype fromdir: string
     """
     self.means = s3gau.open(os.path.join(fromdir, "means"))
     self.variances = s3gau.open(os.path.join(fromdir, "variances"))
     self.mixw = s3mixw.open(os.path.join(fromdir, "mixture_weights"))
     self.featlen = self.means.veclen[0]
     self.ndensity = self.means.density
예제 #6
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파일: gmm.py 프로젝트: 10v/cmusphinx
 def read(self, fromdir):
     """
     Read GMM parameters from files in a directory.
     @param fromdir: Directory to read parameters from.  The files
     'means', 'variances', and 'mixture_weights' will be read from
     this directory.
     @ptype fromdir: string
     """
     self.means = s3gau.open(os.path.join(fromdir, "means"))
     self.variances = s3gau.open(os.path.join(fromdir, "variances"))
     self.mixw = s3mixw.open(os.path.join(fromdir, "mixture_weights"))
     self.featlen = self.means.veclen[0]
     self.ndensity = self.means.density
예제 #7
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    return Hs


if __name__ == '__main__':

    def usage():
        sys.stderr.write("Usage: %s INMEAN INVAR ACCUMDIRS...\n" % sys.argv[0])

    try:
        opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
    except getopt.GetoptError:
        usage()
        sys.exit(2)
    if len(args) < 3:
        usage()
        sys.exit(2)
    ldafn = None
    for o, a in opts:
        if o in ('-h', '--help'):
            usage()
            sys.exit()

    inmean = s3gau.open(args[0])
    invar = s3gau.open(args[1])
    accumdirs = args[2:]
    stats = s3gaucnt.accumdirs(accumdirs)

    Ws = estimate_mllr_mean(stats, inmean, invar)
    Hs = estimate_mllr_variance(stats, inmean, invar, Ws)
    write_mllr(sys.stdout, Ws, Hs)
예제 #8
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                         sys.argv[0])

    try:
        opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
    except getopt.GetoptError:
        usage()
        sys.exit(2)
    if len(args) < 3:
        usage()
        sys.exit(2)
    ldafn = None
    for o, a in opts:
        if o in ('-h', '--help'):
            usage()
            sys.exit()
    outmean = 'means.cmllr'
    outvar = 'variances.cmllr'
    inmean = s3gau.open(args[0])
    invar = s3gau.open(args[1])
    mdef = s3mdef.open(args[2])
    accumdirs = args[3:]
    stats = s3gaucnt.accumdirs_full(accumdirs)
    Ws = estimate_cmllr(stats, inmean, invar, mdef)
    write_mllr(open("cmllr_matrix", "w"), Ws)
    Wp = solve_transform(Ws)
    param = solve_mllr(Wp, inmean, invar, mdef)
    om = s3gau.open(outmean, "wb")
    om.writeall(param[0])
    om = s3gau.open(outvar, "wb")
    om.writeall(param[1])
예제 #9
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    try:
        opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
    except getopt.GetoptError:
        usage()
        sys.exit(2)
    if len(args) < 3:
        usage()
        sys.exit(2)
    ldafn = None
    for o, a in opts:
        if o in ('-h', '--help'):
            usage()
            sys.exit()
    outmean = 'means.cmllr'
    outvar  = 'variances.cmllr'
    print 'begin'
    inmean = s3gau.open(args[0])
    invar = s3gau.open(args[1]) 
    mdef = s3mdef.open(args[2])
    accumdirs = args[3:]
    stats = s3gaucnt.accumdirs(accumdirs)
    Ws = estimate_cmllr(stats, inmean, invar, mdef)
    Wp = solve_transform(Ws)
    write_mllr(sys.stdout, Wp)
    param = solve_mllr(Wp, inmean, invar, mdef)
    om = s3gau.open(outmean,"wb")
    om.writeall(param[0])
    om = s3gau.open(outvar,"wb")
    om.writeall(param[1])
    print 'end'
예제 #10
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                norm += stats.dnom[j][i][k]
        Hs.append(H / norm)
    return Hs

if __name__ == '__main__':
    def usage():
        sys.stderr.write("Usage: %s INMEAN INVAR ACCUMDIRS...\n" % sys.argv[0])

    try:
        opts, args = getopt.getopt(sys.argv[1:], "h", ["help"])
    except getopt.GetoptError:
        usage()
        sys.exit(2)
    if len(args) < 3:
        usage()
        sys.exit(2)
    ldafn = None
    for o, a in opts:
        if o in ('-h', '--help'):
            usage()
            sys.exit()

    inmean = s3gau.open(args[0])
    invar = s3gau.open(args[1])
    accumdirs = args[2:]
    stats = s3gaucnt.accumdirs(accumdirs)

    Ws = estimate_mllr_mean(stats, inmean, invar)
    Hs = estimate_mllr_variance(stats, inmean, invar, Ws)
    write_mllr(sys.stdout, Ws, Hs)