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# Copyright (c) 2008 Carnegie Mellon University
#
# You may copy and modify this freely under the same terms as
# Sphinx-III
"""
Train generic Gaussian Mixture Models from speech data.
This module defines a GMM class which can be used to train generic
models of speech for use in speaker identification or VTLN.
"""
__author__ = "David Huggins-Daines <dhdaines@gmail.com>"
__version__ = "$Revision$"
import sys
import os
from . import s3gau
from . import s3mixw
import numpy
from functools import reduce
def logadd(x, y):
"""Log-add two numbers."""
return x + numpy.log(1 + numpy.exp(y - x))
class GMM(object):
"""
Class representing a Gaussian Mixture Model.
"""
def __init__(self,
fromdir=None,
featlen=13,
ndensity=256,
mixwfloor=0.001,
varfloor=0.001):
"""
Constructor for GMM class.
@param fromdir: Directory to read initial parameters from.
@ptype fromdir: string
@param featlen: Dimensionality of input features.
@ptype featlen: int
@param ndensity: Number of Gaussian components.
@ptype ndensity: int
@param varfloor: Floor value to apply to variances before evaluation.
@ptype varfloor: float
@param mixwfloor: Floor value to apply to mixture weights before evaluation.
@ptype mixwfloor: float
"""
if fromdir is not None:
self.read(fromdir)
else:
self.random_init(featlen, ndensity)
self.varfloor = varfloor
self.mixwfloor = mixwfloor
self.precompute()
self.reset()
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
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, :])
def random_init(self, featlen=13, ndensity=256):
"""
Initialize parameters with arbitrary initial values.
"""
self.means = numpy.random.random((ndensity, featlen)) * 10 - 5
self.variances = numpy.ones((ndensity, featlen))
self.mixw = numpy.random.random(ndensity)
self.mixw /= self.mixw.sum()
self.featlen = featlen
self.ndensity = ndensity
def precompute(self):
"""
Precompute Gaussian invariants for density calculation.
"""
variances = self.variances.clip(self.varfloor, numpy.inf)
mixw = self.mixw.clip(self.mixwfloor, numpy.inf)
self.inv_var = 0.5 / variances
self.log_det_var = (
numpy.log(mixw) - # mixw * 1 /
0.5 * # sqrt
(
self.featlen * numpy.log(2 * numpy.pi) # 2pi ** featlen
+ numpy.log(variances).sum(1))) # prod(v for v in variances)
def reset(self):
"""
Reset internal accumulators.
"""
self.mixwacc = numpy.zeros(self.ndensity, 'd')
self.meanacc = numpy.zeros((self.ndensity, self.featlen), 'd')
self.varacc = numpy.zeros((self.ndensity, self.featlen), 'd')
self.nfr = 0
self.avgll = 0.0
def evaluate(self, frames, accumulate=True):
"""
Evaluate one or more frames of data according to the model.
@param frames: Array of frames of data.
@ptype frames: numpy.ndarray
@param accumulate: Whether to accumulate counts for training from this data.
@ptype accumulate: boolean
@return: Average log-likelihood of data per frame.
@rtype: float
"""
diff = numpy.zeros((self.ndensity, self.featlen), 'd')
post = numpy.zeros(self.ndensity, 'd')
avgll = 0.0
for frame in frames:
self.nfr += 1
diff = frame - self.means
post = self.log_det_var - (diff * self.inv_var * diff).sum(1)
# Likelihood = sum of Gaussian densities
ll = reduce(logadd, post)
self.avgll += ll
avgll += ll
if accumulate:
# Normalize them to get posterior probabilities for each mixture
post = numpy.exp(post - ll)
# Mixture weight counts are just sums of posteriors
self.mixwacc += post
# Accumulate mean and variance counts
self.meanacc += post[:, numpy.newaxis] * frame
self.varacc += post[:, numpy.newaxis] * diff * diff
return avgll / len(frames)
def normalize(self):
"""
Normalize accumulation counts to obtain updated parameters.
"""
for i in range(0, self.ndensity):
if self.mixwacc[i] == 0:
sys.stderr.write("Warning: mixture %d never observed\n" % i)
# Copy from previous density if possible
idx = max(i - 1, 0)
self.means[i] = self.means[idx]
self.variances[i] = self.variances[idx]
self.mixwacc[i] = self.mixwacc[idx]
else:
# Mixture weight counts conveniently serve as occupation counts
self.means[i] = self.meanacc[i] / self.mixwacc[i]
self.variances[i] = self.varacc[i] / self.mixwacc[i]
self.mixw = self.mixwacc / self.nfr
# Recompute things for evaluation
self.precompute()
return self.avgll / self.nfr
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