class DragonRule(MappingRule):
mapping = {
"(go-to-sleep | snore | mic-sleep)":
Function(lambda: natlink.setMicState("sleeping")),
"(lock-Dragon|turn-mic-off)":
Function(lambda: natlink.setMicState("off")),
"german profile":
Function(
lambda: natlink.saveUser() + natlink.openUser("Codebold german")),
"englisches Profil":
Function(lambda: natlink.saveUser() + natlink.openUser("Codebold")),
"reload grammar[s]":
Function(lambda: updateAllGrammars()),
"reload <grammar>":
GrammarUpdate(),
"como":
Function(lambda: natlink.recognitionMimic(
["switch", "to", "command", "mode"])),
"diemo":
Function(
lambda: natlink.recognitionMimic(["start", "dictation", "mode"])),
"nomo":
Function(lambda: natlink.recognitionMimic(["normal", "mode", "on"])),
"sleemo":
Function(lambda: natlink.recognitionMimic(["go", "to", "sleep"])),
"dictation":
Function(
lambda: natlink.recognitionMimic(["show", "dictation", "box"])),
"dictox":
Function(lambda: natlink.recognitionMimic(["normal", "mode", "on"])) +
Function(
lambda: natlink.recognitionMimic(["show", "dictation", "box"])),
"transfox":
Function(lambda: natlink.recognitionMimic(["click", "transfer"])) +
Function(lambda: natlink.recognitionMimic(["command", "mode", "on"])),
"blitz NatLink":
Function(blitz_natlink_status),
"show NatLink":
Function(show_natlink_status)
}
extras = [chc_base.grammar]
def doTraining(fileSpecs,userName='',baseModel='',baseTopic=''):
if not userName:
userName = '******'
#
# Expand the file specification list into a list of files without
# extensions
#
allFiles = []
if type(fileSpecs) == type(''):
allFiles = expandFiles(fileSpecs)
else:
for fileSpec in fileSpecs:
allFiles = allFiles + expandFiles(fileSpec)
if not len(allFiles):
print "No files found which match fileSpec:"
print " ",fileSpecs
raise TrainingError,'no files'
#
# Create a new user. Make sure the new user is active.
#
createNewUser(userName,baseModel,baseTopic)
#
# Read the LST files from disk into Python arrays. As we read the LST
# files, we need to convert the format of words from underscores (using
# tilde as an escape character) into spaces.
#
# We build up a master array which contains one entry per input file.
# Each file entry is an array with one entry per utterance. Each
# utterance entry is an array of strings representing the words
# recognized.
#
allWords = []
for fileName in allFiles:
allWords.append(loadWords(fileName))
#
# The NIST wave files have to converted into NatSpeak result objects.
# The easiest way to do this is to build a dictation grammar and recognize
# all of the utterances in the NIST wave files.
#
allResults = []
for fileName in allFiles:
allResults.append(loadResults(fileName))
#
# Produce a single array which contains tuples representing the results
# object and the transcript. Skip over any results which have blank
# transcripts. Print an error if the two arrays sizes do not match
#
combinedResults = []
fileCount = len(allFiles)
for fileNum in range(fileCount):
lstSize = len(allWords[fileNum])
nwvSize = len(allResults[fileNum])
if lstSize != nwvSize:
print 'The number of utterances in',
print allFiles[fileNum]+'.nwv','('+str(nwvSize)+')',
print 'does not match',
print 'the number of lines in',
print allFiles[fileNum]+'.lst','('+str(lstSize)+')'
for resNum in range(lstSize):
words = allWords[fileNum][resNum]
resObj = allResults[fileNum][resNum]
if len(words) and resObj:
combinedResults.append((words,resObj))
#
# Perform calibration on the first N utterances (20 maximum)
#
trainingPass(combinedResults[:20],'calibrate')
#
# Perform batch adaptation on the entire set of utterances.
#
trainingPass(combinedResults,'longtrain')
#
# Perform training on the entire set of utterances.
#
trainingPass(combinedResults,'batchadapt')
#
# Save the user.
#
print 'Saving the user...'
natlink.saveUser()
print 'All done.'
def saveUser():
"""Saves the current user"""
natlink.saveUser()
sr_user_needs_saving = 0
def doTraining(fileSpecs, userName='', baseModel='', baseTopic=''):
if not userName:
userName = '******'
#
# Expand the file specification list into a list of files without
# extensions
#
allFiles = []
if type(fileSpecs) == type(''):
allFiles = expandFiles(fileSpecs)
else:
for fileSpec in fileSpecs:
allFiles = allFiles + expandFiles(fileSpec)
if not len(allFiles):
print "No files found which match fileSpec:"
print " ", fileSpecs
raise TrainingError, 'no files'
#
# Create a new user. Make sure the new user is active.
#
createNewUser(userName, baseModel, baseTopic)
#
# Read the LST files from disk into Python arrays. As we read the LST
# files, we need to convert the format of words from underscores (using
# tilde as an escape character) into spaces.
#
# We build up a master array which contains one entry per input file.
# Each file entry is an array with one entry per utterance. Each
# utterance entry is an array of strings representing the words
# recognized.
#
allWords = []
for fileName in allFiles:
allWords.append(loadWords(fileName))
#
# The NIST wave files have to converted into NatSpeak result objects.
# The easiest way to do this is to build a dictation grammar and recognize
# all of the utterances in the NIST wave files.
#
allResults = []
for fileName in allFiles:
allResults.append(loadResults(fileName))
#
# Produce a single array which contains tuples representing the results
# object and the transcript. Skip over any results which have blank
# transcripts. Print an error if the two arrays sizes do not match
#
combinedResults = []
fileCount = len(allFiles)
for fileNum in range(fileCount):
lstSize = len(allWords[fileNum])
nwvSize = len(allResults[fileNum])
if lstSize != nwvSize:
print 'The number of utterances in',
print allFiles[fileNum] + '.nwv', '(' + str(nwvSize) + ')',
print 'does not match',
print 'the number of lines in',
print allFiles[fileNum] + '.lst', '(' + str(lstSize) + ')'
for resNum in range(lstSize):
words = allWords[fileNum][resNum]
resObj = allResults[fileNum][resNum]
if len(words) and resObj:
combinedResults.append((words, resObj))
#
# Perform calibration on the first N utterances (20 maximum)
#
trainingPass(combinedResults[:20], 'calibrate')
#
# Perform batch adaptation on the entire set of utterances.
#
trainingPass(combinedResults, 'longtrain')
#
# Perform training on the entire set of utterances.
#
trainingPass(combinedResults, 'batchadapt')
#
# Save the user.
#
print 'Saving the user...'
natlink.saveUser()
print 'All done.'
def saveUser():
"""Saves the current user"""
natlink.saveUser()
sr_user_needs_saving = 0