def __init__(self, lab, args):
#Make a proto utt from the input
if args.intype == "align_mlf":
proto = utterance_load.proto_from_align_lab(lab)
self.txtloaded = False
elif args.intype == "hts_mlf":
proto = utterance_load.proto_from_hts_lab(lab)
self.txtloaded = False
elif args.intype == "txt":
proto = utterance_load.proto_from_txt(lab, args)
self.txtloaded = True
else:
print "Error: Don't know what to do with intype - {0}".format(args.intype)
sys.exit()
#Construct the utt from the proto utt
self.id = proto["id"]
self.phonemes = []
self.syllables = []
self.words = []
#We need to know which phoneme features this utterance is created with.
self.phoneme_features = args.phoneme_features
s_utt_pos = 0
p_utt_pos = 0
for wi, proto_word in enumerate(proto["utt"]):
p_word_pos = 0
word = Word()
word.load_from_proto(proto_word, wi, p_utt_pos, s_utt_pos, len(proto["utt"]), self)
self.words.append(word)
for si, proto_syll in enumerate(proto_word["syllables"]):
syll = Syllable()
syll.load_from_proto(proto_syll, p_utt_pos, si, s_utt_pos, word, self)
self.syllables.append(syll)
for pi, proto_phone in enumerate(proto_syll["phonemes"]):
phoneme = Phoneme()
phoneme.load_from_proto(proto_phone, pi, p_utt_pos, p_word_pos, syll, word, self)
self.phonemes.append(phoneme)
p_word_pos += 1
p_utt_pos += 1
s_utt_pos += 1
syll.add_phonemes()
word.add_phonemes()
word.add_syllables()
#If we should use the stanford parse info
if args.stanfordparse:
if args.intype != "txt":
utterance_load.load_txt(self, os.path.join(args.txtdir, self.id+".txt"))
utterance_load.load_stanford_parse(self, args.parsedict[self.id])
def __init__(self, lab, args):
#Make a proto utt from the input
if args.intype == "align_mlf":
proto = utterance_load.proto_from_align_lab(lab)
self.txtloaded = False
elif args.intype == "state_align_mlf":
proto = utterance_load.proto_from_state_align_lab(lab)
self.txtloaded = False
elif args.intype == "hts_mlf":
proto = utterance_load.proto_from_hts_lab(lab)
self.txtloaded = False
elif args.intype == "sire_lab":
#As we need additional information here we check if args contains it
if not hasattr(args, "context_type"):
raise SiReError("You're trying to create an utterance from a SiRe label but have not told what kind of positional context_type was used!")
if not hasattr(args, "HHEd_fix"):
raise SiReError("You're trying to create an utterance from a SiRe label but have not told if HHEd_fix was used to create the labels!")
proto = utterance_load.proto_from_sire_lab(lab, args.context_type, args.HHEd_fix)
self.txtloaded = False
elif args.intype == "txt":
#Check if args has all the necessary elements and insert defaults if not.
if not hasattr(args, 'pron_reduced') or args.pron_reduced == False:
args.pron_reduced = False
args.lm_score_dir = None
args.reduction_level = 1.0
else:
#If we are we need to check if we know enough to do it and fail if we don't.
if not hasattr(args, 'lm_score_dir'):
raise SiReError("You have asked to produce a reduced phonemisation but no path to a directory containing LM word probabilities to base the reduction on.")
if not hasattr(args, 'reduction_level'):
raise SiReError("You have asked to produce a reduced phonemisation but not specified to which degree the sentence should be reduced.")
if not hasattr(args, 'general_sil_phoneme'):
print "Warning! args does not tell if there is a standard silence phoneme! Using default... (\"sil\")"
args.general_sil_phoneme = "sil"
if not hasattr(args, 'comma_is_pause'):
print "Warning! args does not tell if commas should be used as pauses! Using default... (no)"
args.comma_is_pause = False
if not hasattr(args, 'stanford_pcfg_parse'):
print "Warning! args does not tell if we are using stanford parsing! Using default... (no)"
args.stanford_pcfg_parse = False
if args.stanford_pcfg_parse == False:
args.pcfgdict = False
proto = utterance_load.proto_from_txt(lab, args.dictionary, args.general_sil_phoneme, args.comma_is_pause, args.stanford_pcfg_parse, args.pcfgdict, args.pron_reduced, args.lm_score_dir, args.reduction_level)
self.txtloaded = True
else:
raise SiReError("Don't know what to do with intype - {0}".format(args.intype))
#Construct the utt from the proto utt
self.id = proto["id"]
self.phonemes = []
self.syllables = []
self.words = []
#We need to know which phoneme features this utterance is created with.
if hasattr(args, 'dictionary'):
self.phoneme_features = args.dictionary.phoneme_feats
elif hasattr(args, 'phoneme_features'):
self.phoneme_features = args.phoneme_features
else:
raise SiReError("args does not contain either a dictionary or a phoneme featureset!")
s_utt_pos = 0
p_utt_pos = 0
for wi, proto_word in enumerate(proto["utt"]):
p_word_pos = 0
word = Word()
word.load_from_proto(proto_word, wi, p_utt_pos, s_utt_pos, len(proto["utt"]), self)
self.words.append(word)
for si, proto_syll in enumerate(proto_word["syllables"]):
syll = Syllable()
syll.load_from_proto(proto_syll, p_utt_pos, si, s_utt_pos, word, self)
self.syllables.append(syll)
for pi, proto_phone in enumerate(proto_syll["phonemes"]):
phoneme = Phoneme()
phoneme.load_from_proto(proto_phone, pi, p_utt_pos, p_word_pos, syll, word, self)
self.phonemes.append(phoneme)
p_word_pos += 1
p_utt_pos += 1
s_utt_pos += 1
syll.add_phonemes()
word.add_phonemes()
word.add_syllables()
#If we should use the stanford pcfg parse info
if hasattr(args, 'stanford_pcfg_parse') and args.stanford_pcfg_parse:
print "Loading stanford pcfg parse info to utt..."
if args.intype != "txt":
utterance_load.load_txt(self, os.path.join(args.txtdir, self.id+".txt"))
utterance_load.load_stanford_pcfg_parse(self, args.pcfgdict[self.id], args.comma_is_pause)
#If we should use the stanford dependency parse info
if hasattr(args, 'stanford_dependency_parse') and args.stanford_dependency_parse:
print "Loading stanford dependency parse info to utt..."
if args.intype != "txt" and self.txtloaded == False:
utterance_load.load_txt(self, os.path.join(args.txtdir, self.id+".txt"))
utterance_load.load_stanford_dependency_parse(self, args.dependencydict[self.id])
#If we output a Festival context set we should modify the UTT a bit further.
#Right now we use the full festival features as standard, but some operations, like corpus analysis, does not rely on this and it is a nuisance to have the text a requirement so this is still just an option.
if args.festival_features == True:
#We need to know the words
if args.intype != "txt" and self.txtloaded == False:
utterance_load.load_txt(self, os.path.join(args.txtdir, self.id+".txt"))
#If we have a pcfg parse we have a proper POS tag mechanism and they have already been added
if not args.stanford_pcfg_parse:
pos.simple_festival_pos_predict(self)
prosody.simple_festival_accent_predict(self)
def __init__(self, lab, args):
#Make a proto utt from the input
if args.intype == "align_mlf":
proto = utterance_load.proto_from_align_lab(lab)
self.txtloaded = False
elif args.intype == "state_align_mlf":
proto = utterance_load.proto_from_state_align_lab(lab)
self.txtloaded = False
elif args.intype == "hts_mlf":
proto = utterance_load.proto_from_hts_lab(lab, args.state_level)
self.txtloaded = False
elif args.intype == "sire_lab":
#As we need additional information here we check if args contains it
if not hasattr(args, "context_type"):
raise SiReError(
"You're trying to create an utterance from a SiRe label but have not told what kind of positional context_type was used!"
)
if not hasattr(args, "HHEd_fix"):
raise SiReError(
"You're trying to create an utterance from a SiRe label but have not told if HHEd_fix was used to create the labels!"
)
proto = utterance_load.proto_from_sire_lab(lab, args.context_type,
args.HHEd_fix)
self.txtloaded = False
elif args.intype == "txt":
#Check if args has all the necessary elements and insert defaults if not.
if not hasattr(args, 'pron_reduced') or args.pron_reduced == False:
args.pron_reduced = False
args.lm_score_dir = None
args.reduction_level = 1.0
else:
#If we are we need to check if we know enough to do it and fail if we don't.
if not hasattr(args, 'lm_score_dir'):
raise SiReError(
"You have asked to produce a reduced phonemisation but no path to a directory containing LM word probabilities to base the reduction on."
)
if not hasattr(args, 'reduction_level'):
raise SiReError(
"You have asked to produce a reduced phonemisation but not specified to which degree the sentence should be reduced."
)
if not hasattr(args, 'general_sil_phoneme'):
print "Warning! args does not tell if there is a standard silence phoneme! Using default... (\"sil\")"
args.general_sil_phoneme = "sil"
if not hasattr(args, 'comma_is_pause'):
print "Warning! args does not tell if commas should be used as pauses! Using default... (no)"
args.comma_is_pause = False
if not hasattr(args, 'stanford_pcfg_parse'):
print "Warning! args does not tell if we are using stanford parsing! Using default... (no)"
args.stanford_pcfg_parse = False
if args.stanford_pcfg_parse == False:
args.pcfgdict = False
proto = utterance_load.proto_from_txt(
lab, args.dictionary, args.general_sil_phoneme,
args.comma_is_pause, args.stanford_pcfg_parse, args.pcfgdict,
args.pron_reduced, args.lm_score_dir, args.reduction_level)
self.txtloaded = True
else:
raise SiReError("Don't know what to do with intype - {0}".format(
args.intype))
#Construct the utt from the proto utt
self.id = proto["id"]
self.phonemes = []
self.syllables = []
self.words = []
#We need to know which phoneme features this utterance is created with.
if hasattr(args, 'dictionary'):
self.phoneme_features = args.dictionary.phoneme_feats
elif hasattr(args, 'phoneme_features'):
self.phoneme_features = args.phoneme_features
else:
raise SiReError(
"args does not contain either a dictionary or a phoneme featureset!"
)
s_utt_pos = 0
p_utt_pos = 0
for wi, proto_word in enumerate(proto["utt"]):
p_word_pos = 0
word = Word()
word.load_from_proto(proto_word, wi, p_utt_pos, s_utt_pos,
len(proto["utt"]), self)
self.words.append(word)
for si, proto_syll in enumerate(proto_word["syllables"]):
syll = Syllable()
syll.load_from_proto(proto_syll, p_utt_pos, si, s_utt_pos,
word, self)
self.syllables.append(syll)
for pi, proto_phone in enumerate(proto_syll["phonemes"]):
phoneme = Phoneme()
phoneme.load_from_proto(proto_phone, pi, p_utt_pos,
p_word_pos, syll, word, self)
self.phonemes.append(phoneme)
p_word_pos += 1
p_utt_pos += 1
s_utt_pos += 1
syll.add_phonemes()
word.add_phonemes()
word.add_syllables()
#If we should use the stanford pcfg parse info
if hasattr(args, 'stanford_pcfg_parse') and args.stanford_pcfg_parse:
print "Loading stanford pcfg parse info to utt..."
if args.intype != "txt":
utterance_load.load_txt(
self, os.path.join(args.txtdir, self.id + ".txt"))
utterance_load.load_stanford_pcfg_parse(self,
args.pcfgdict[self.id],
args.comma_is_pause)
#If we should use the stanford dependency parse info
if hasattr(args, 'stanford_dependency_parse'
) and args.stanford_dependency_parse:
print "Loading stanford dependency parse info to utt..."
if args.intype != "txt" and self.txtloaded == False:
utterance_load.load_txt(
self, os.path.join(args.txtdir, self.id + ".txt"))
utterance_load.load_stanford_dependency_parse(
self, args.dependencydict[self.id])
#If we output a Festival context set we should modify the UTT a bit further.
#Right now we use the full festival features as standard, but some operations, like corpus analysis, does not rely on this and it is a nuisance to have the text a requirement so this is still just an option.
if args.festival_features == True:
#We need to know the words
if args.intype != "txt" and self.txtloaded == False:
# print "ID", self.id
utterance_load.load_txt(
self, os.path.join(args.txtdir, self.id + ".txt"),
args.emphasis)
#If we have a pcfg parse we have a proper POS tag mechanism and they have already been added
if not args.stanford_pcfg_parse:
pos.simple_festival_pos_predict(self)
prosody.simple_festival_accent_predict(self)