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# Copyright (c) 2006 Carnegie Mellon University
#
# You may copy and modify this freely under the same terms as
# Sphinx-III
"""Read/write Sphinx-III Gaussian parameter count files.
This module reads and writes the expected Gaussian mixture occupancy
count files created by SphinxTrain's implementation of the
Forward-Backward algorithm for training (semi-)continuous HMMs.
"""
__author__ = "David Huggins-Daines <dhdaines@gmail.com>"
__version__ = "$Revision$"
from struct import unpack
from numpy import reshape, frombuffer
from .s3file import S3File
import os
def open(filename, mode="rb", attr={"version": 1.0}):
if mode in ("r", "rb"):
return S3GauCntFile(filename)
else:
raise Exception("mode must be 'r' or 'rb'")
def accumdirs(accumdirs):
"Read and accumulate counts from several directories"
gauden = None
for d in accumdirs:
try:
subgau = S3GauCntFile(os.path.join(d, "gauden_counts"), "rb")
except OSError:
subgau = None
continue
if gauden is None:
gauden = subgau
else:
for m, mgau in enumerate(gauden.mean):
for f, feat in enumerate(mgau):
gauden.mean[m][f] += subgau.mean[m][f]
gauden.var[m][f] += subgau.var[m][f]
gauden.dnom[m][f] += subgau.dnom[m][f]
return gauden
def accumdirs_full(accumdirs):
"Read and accumulate full-covariance counts from several directories"
gauden = None
for d in accumdirs:
try:
subgau = S3FullGauCntFile(os.path.join(d, "gauden_counts"), "rb")
except OSError:
subgau = None
continue
if gauden is None:
gauden = subgau
else:
for m, mgau in enumerate(gauden.mean):
for f, feat in enumerate(mgau):
gauden.mean[m][f] += subgau.mean[m][f]
gauden.var[m][f] += subgau.var[m][f]
gauden.dnom[m][f] += subgau.dnom[m][f]
return gauden
def open_full(filename, mode="rb", attr={"version": 1.0}):
if mode in ("r", "rb"):
return S3FullGauCntFile(filename, mode)
else:
raise Exception("mode must be 'r', 'rb'")
class S3GauCntFile(S3File):
"Read Sphinx-III format Gaussian count files"
def __init__(self, file, mode="rb"):
S3File.__init__(self, file, mode)
self._load()
def readgauheader(self):
if self.fileattr["version"] != "1.0":
raise Exception("Version mismatch: must be 1.0 but is " +
self.fileattr["version"])
self.fh.seek(self.data_start, 0)
self.has_mean = unpack(self.swap + "I", self.fh.read(4))[0]
self.has_var = unpack(self.swap + "I", self.fh.read(4))[0]
self.pass2var = unpack(self.swap + "I", self.fh.read(4))[0]
self.n_mgau = unpack(self.swap + "I", self.fh.read(4))[0]
self.density = unpack(self.swap + "I", self.fh.read(4))[0]
self.n_feat = unpack(self.swap + "I", self.fh.read(4))[0]
self.veclen = unpack(self.swap + "I" * self.n_feat,
self.fh.read(4 * self.n_feat))
self.blk = sum(self.veclen)
def _load(self):
self.readgauheader()
if self.has_mean:
self.mean = self._loadgau()
if self.has_var:
self.var = self._loadgau()
self.dnom = self.read3d()
def _loadgau(self):
self._nfloats = unpack(self.swap + "I", self.fh.read(4))[0]
if self._nfloats != self.n_mgau * self.density * self.blk:
raise Exception(
("Number of data points %d doesn't match " +
"total %d = %d*%d*%d") %
(self._nfloats, self.n_mgau * self.density * self.blk,
self.n_mgau, self.density, self.blk))
spam = self.fh.read(self._nfloats * 4)
data = frombuffer(spam, 'f').copy()
if self.otherend:
data = data.byteswap()
params = []
r = 0
for i in range(0, self.n_mgau):
mgau = []
params.append(mgau)
for j in range(0, self.n_feat):
rnext = r + self.density * self.veclen[j]
gmm = reshape(data[r:rnext], (self.density, self.veclen[j]))
mgau.append(gmm)
r = rnext
return params
class S3FullGauCntFile(S3GauCntFile):
"Read Sphinx-III format Gaussian full covariance matrix files"
def _load(self):
self.readgauheader()
if self.has_mean:
self.mean = self._loadgau()
if self.has_var:
self.var = self._loadfullgau()
self.dnom = self.read3d()
def _loadfullgau(self):
self._nfloats = unpack(self.swap + "I", self.fh.read(4))[0]
if self._nfloats != self.n_mgau * self.density * self.blk * self.blk:
raise Exception(
("Number of data points %d doesn't match " +
"total %d = %d*%d*%d*%d") %
(self._nfloats, self.n_mgau * self.density * self.blk *
self.blk, self.n_mgau, self.density, self.blk, self.blk))
spam = self.fh.read(self._nfloats * 4)
data = frombuffer(spam, 'f').copy()
if self.otherend:
data = data.byteswap()
params = []
r = 0
for i in range(0, self.n_mgau):
mgau = []
params.append(mgau)
for j in range(0, self.n_feat):
rnext = r + self.density * self.veclen[j] * self.veclen[j]
gmm = reshape(data[r:rnext],
(self.density, self.veclen[j], self.veclen[j]))
mgau.append(gmm)
r = rnext
return params
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