def meanStdNorm(fileScps, fileDims, meanStdOutPath):
""" Calculate the mean and std from a list of file lists
fileScpsString: 'file_list_1,file_list_2,...'
fileDimsString: 'feat_dim_1,feat_dim_2,...'
meanStdOutPat: path to save the mean_std vector [mean, std]
"""
stats.getMeanStd_merge(fileScps, fileDims, meanStdOutPath)
def meanStdNorm(fileScps, fileDims, meanStdOutPath):
""" Calculate the mean and std from a list of file lists
fileScpsString: 'file_list_1,file_list_2,...'
fileDimsString: 'feat_dim_1,feat_dim_2,...'
meanStdOutPat: path to save the mean_std vector [mean, std]
"""
stats.getMeanStd_merge(fileScps, fileDims, meanStdOutPath)
def meanStdNormMask(fileScps,
fileDims,
fileNormMask,
meanStdOutPath,
f0Dim=-1):
"""
"""
assert len(fileDims) == len(fileNormMask), \
"Unequal length feature dim & norm mask"
# calcualte the mean/std
stats.getMeanStd_merge(fileScps, fileDims, meanStdOutPath + '.unmasked',
f0Dim)
meanStdData = py_rw.read_raw_mat(meanStdOutPath + '.unmasked', 1)
assert meanStdData.shape[0] == sum(fileDims) * 2, \
"%s dimension not %d" % (meanStdOutPath + '.unmasked', sum(fileDims) * 2)
featDims = []
startDim = 0
for dim in fileDims:
featDims.append([startDim, startDim + dim])
startDim = startDim + dim
for dimRange, normMask in zip(featDims, fileNormMask):
if len(normMask) == 0:
pass
elif len(normMask) == 1 and (normMask[0] == 0
or normMask[0] == 'not_norm'):
meanStdData[dimRange[0]:dimRange[1]] = 0.0
meanStdData[dimRange[0] + sum(fileDims):dimRange[1] +
sum(fileDims)] = 1.0
elif len(normMask) == 2:
assert dimRange[0] <= normMask[0], 'normMask range error' % (
str(normMask))
assert dimRange[1] >= normMask[1], 'normMask range error' % (
str(normMask))
meanStdData[normMask[0]:normMask[1]] = 0.0
meanStdData[normMask[0] + sum(fileDims):normMask[1] +
sum(fileDims)] = 1.0
else:
print("Wrong format of NormMask %s" % (str(normMask)))
print('normmask %s' % (str(normMask)))
py_rw.write_raw_mat(meanStdData, meanStdOutPath)
def meanStdNormMask(fileScps, fileDims, fileNormMask, meanStdOutPath, f0Dim=-1):
"""
"""
assert len(fileDims) == len(fileNormMask), \
"Unequal length feature dim & norm mask"
# calcualte the mean/std
stats.getMeanStd_merge(fileScps, fileDims, meanStdOutPath + '.unmasked', f0Dim)
meanStdData = py_rw.read_raw_mat(meanStdOutPath + '.unmasked', 1)
assert meanStdData.shape[0] == sum(fileDims) * 2, \
"%s dimension not %d" % (meanStdOutPath + '.unmasked', sum(fileDims) * 2)
featDims = []
startDim = 0
for dim in fileDims:
featDims.append([startDim, startDim + dim])
startDim = startDim + dim
for dimRange, normMask in zip(featDims, fileNormMask):
if len(normMask) == 0:
pass
elif len(normMask) == 1 and normMask[0] == 0:
meanStdData[dimRange[0]:dimRange[1]] = 0.0
meanStdData[dimRange[0]+sum(fileDims):dimRange[1]+sum(fileDims)] = 1.0
elif len(normMask) == 2:
assert dimRange[0] <= normMask[0], 'normMask range error' % (str(normMask))
assert dimRange[1] >= normMask[1], 'normMask range error' % (str(normMask))
meanStdData[normMask[0]:normMask[1]] = 0.0
meanStdData[normMask[0]+sum(fileDims):normMask[1]+sum(fileDims)] = 1.0
else:
print "Wrong format of NormMask %s" % (str(normMask))
print 'normmask %s' % (str(normMask))
py_rw.write_raw_mat(meanStdData, meanStdOutPath)