def load_stanford_pcfg_parse(utt, parse, comma_is_pause=False):
if utt.words == None:
raise SiReError(
"No words in utterance! Please load an mlf or txt file first!")
tree = parsetrees.stanfordPcfgTree()
tree.make_tree(parse)
if comma_is_pause == True:
leafs = tree.get_leafs(include_punct=[","])
else:
leafs = tree.get_leafs()
num_w = utt.num_words_no_pau(comma_is_pause)
if len(leafs) != num_w:
#First we try to see if this is due to differences in how words are
#dealt with in parsing and annotation.
#Prime example is using 's in e.g. there's for transcription instead of there is.
#Parsing splits there's into two whereas in e.g. combilex there's is one word.
#If this is the case we split the WORD into two with the 's being a single phoneme
#single syllable word. In other cases the contraction straddles two words and
#we add a "phony" word which affects contexts but adds no phonemes.
utterance_utils.try_split_words(utt)
#Update num_w
num_w = utt.num_words_no_pau(comma_is_pause)
if len(leafs) != num_w:
for w in utt.words:
print w.id
raise SiReError(
"Number of leaves ({0}) not equal to number of words ({1})! In utt ({2})!"
.format(len(leafs), num_w, utt.id))
#Match each word with parse
words = utt.get_words_no_pau(comma_is_pause)
for i, word in enumerate(words):
l = leafs[i].label.split("-")
word.id = l[1]
word.pos = l[0]
#There should always be a parent
word.parent_phrase = leafs[i].parent
#But there might not be more than one
if word.parent_phrase.parent != None:
word.grandparent_phrase = word.parent_phrase.parent
else:
word.grandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
#And certainly we might be done here
if word.grandparent_phrase.parent in [
None, "xx"
] or word.grandparent_phrase.parent.label == "xx":
word.greatgrandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse(
)
else:
word.greatgrandparent_phrase = word.grandparent_phrase.parent
#Now add fake parse for sil, pau and #
for word in utt.words:
if word.id in utt.phoneme_features.get_sil_phonemes():
word.parent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
word.grandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
word.greatgrandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse(
)
word.pos = "sil"
def load_stanford_pcfg_parse(utt, parse, comma_is_pause=False):
if utt.words == None:
raise SiReError("No words in utterance! Please load an mlf or txt file first!")
tree = parsetrees.stanfordPcfgTree()
tree.make_tree(parse)
if comma_is_pause == True:
leafs = tree.get_leafs(include_punct=[","])
else:
leafs = tree.get_leafs()
num_w = utt.num_words_no_pau(comma_is_pause)
if len(leafs) != num_w:
#First we try to see if this is due to differences in how words are
#dealt with in parsing and annotation.
#Prime example is using 's in e.g. there's for transcription instead of there is.
#Parsing splits there's into two whereas in e.g. combilex there's is one word.
#If this is the case we split the WORD into two with the 's being a single phoneme
#single syllable word. In other cases the contraction straddles two words and
#we add a "phony" word which affects contexts but adds no phonemes.
utterance_utils.try_split_words(utt)
#Update num_w
num_w = utt.num_words_no_pau(comma_is_pause)
if len(leafs) != num_w:
for w in utt.words:
print w.id
raise SiReError("Number of leaves ({0}) not equal to number of words ({1})! In utt ({2})!".format(len(leafs), num_w, utt.id))
#Match each word with parse
words = utt.get_words_no_pau(comma_is_pause)
for i, word in enumerate(words):
l = leafs[i].label.split("-")
word.id = l[1]
word.pos = l[0]
#There should always be a parent
word.parent_phrase = leafs[i].parent
#But there might not be more than one
if word.parent_phrase.parent != None:
word.grandparent_phrase = word.parent_phrase.parent
else:
word.grandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
#And certainly we might be done here
if word.grandparent_phrase.parent in [None, "xx"] or word.grandparent_phrase.parent.label == "xx":
word.greatgrandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
else:
word.greatgrandparent_phrase = word.grandparent_phrase.parent
#Now add fake parse for sil, pau and #
for word in utt.words:
if word.id in utt.phoneme_features.get_sil_phonemes():
word.parent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
word.grandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
word.greatgrandparent_phrase = parsetrees.get_fake_stanford_pcfg_parse()
word.pos = "sil"
def proto_from_txt(lab, dictionary, general_sil_phoneme="sil", comma_is_pause=False, stanfordparse=False, pcfgdict=None, pron_reduced=False, lm_score_dir=None, reduction_level=1.0, phoneme_lm_prons=False):
#Create words
proto = {"utt":[]}
proto["id"] = lab[0].split("/")[-1]
#First we check if we need to reduce some words, and which
if pron_reduced == True and phoneme_lm_prons == True:
raise SiReError("Cannot produce reduced pronunciations in combination with phoneme LM based pronunciation choice.")
elif pron_reduced == True:
if os.path.isdir(lm_score_dir):
words = reduce_word_tuples(lab[1:], os.path.join(lm_score_dir, proto["id"]+".scored"), reduction_level)
else:
raise SiReError("The directory with reduction scores does not exist!")
elif phoneme_lm_prons == True:
if os.path.isdir(lm_score_dir):
#As we do not keep stress information for the phoneme LM to score we may have a few potential versions of each word.
words = find_potential_words(lab[1:], os.path.join(lm_score_dir, proto["id"]+".path"))
raise SiReError("Not implemented yet! Phoneme_lm_scoring. ")
else:
raise SiReError("The directory with reduction scores does not exist!")
else:
words = [(x, False) for x in lab[1:]]
#Make words and look up in dictionary
#If no parse exists (i.e. no pos tags) we will simply grab the first pronunciation we can find that is not reduced (if one exist).
#We also forget the pos tag of that in the process.
#We start with silence.
proto["utt"].append({"id":"sil", "syllables":dictionary.make_entry(general_sil_phoneme, general_sil_phoneme+" 0", False)["syllables"]})
if not stanfordparse:
for word in words:
#If we need to keep some punctuation
if comma_is_pause == True:
proto["utt"].append({"id":word[0], "syllables":dictionary.get_single_entry(word[0], reduced=word[1], punct_as_sil=([","], "sil"))["syllables"]})
else:
proto["utt"].append({"id":word[0], "syllables":dictionary.get_single_entry(word[0], reduced=word[1])["syllables"]})
else: #Else a pcfg parse should exist and we can get the pos tags from that.
tree = parsetrees.stanfordPcfgTree()
tree.make_tree(pcfgdict[proto["id"]])
#Do we need some punctuation?
if comma_is_pause == True:
leafs = tree.get_leafs(include_punct=[","])
else:
leafs = tree.get_leafs()
#In this case we need to do some merging
if len(leafs) != len(words):
leafs = merge(leafs, words, proto["id"])
for i, leaf in enumerate(leafs):
pos, word = leaf.label.lower().split("-")
if word != words[i][0]:
raise SiReError("Word ({0}) from parse does not match word ({1}) from txt! In {2}.".format(word, words[i][0], proto["id"]))
else:
word = words[i]
if comma_is_pause:
c_best = dictionary.get_single_entry(word[0], pos, word[1], punct_as_sil=([","], "sil"))
else:
c_best = dictionary.get_single_entry(word[0], pos, word[1])
proto["utt"].append({"id":word[0], "syllables":c_best["syllables"]})
#We end with silence.
proto["utt"].append({"id":"sil", "syllables":dictionary.make_entry(general_sil_phoneme, general_sil_phoneme+" 0", False)["syllables"]})
#Add phony times to phonemes
#Phony phoneme duration counter
cur_dur = 0
for word in proto["utt"]:
for syll in word["syllables"]:
#Add phony time to phonemes
for phon in syll["phonemes"]:
phon["start"] = cur_dur
#Add 100ms in HTK lab format
cur_dur += 1000000
phon["end"] = cur_dur
return proto
def proto_from_txt(lab,
dictionary,
general_sil_phoneme="sil",
comma_is_pause=False,
stanfordparse=False,
pcfgdict=None,
pron_reduced=False,
lm_score_dir=None,
reduction_level=1.0,
phoneme_lm_prons=False):
#Create words
proto = {"utt": []}
proto["id"] = lab[0].split("/")[-1]
#First we check if we need to reduce some words, and which
if pron_reduced == True and phoneme_lm_prons == True:
raise SiReError(
"Cannot produce reduced pronunciations in combination with phoneme LM based pronunciation choice."
)
elif pron_reduced == True:
if os.path.isdir(lm_score_dir):
words = reduce_word_tuples(
lab[1:], os.path.join(lm_score_dir, proto["id"] + ".scored"),
reduction_level)
else:
raise SiReError(
"The directory with reduction scores does not exist!")
elif phoneme_lm_prons == True:
if os.path.isdir(lm_score_dir):
#As we do not keep stress information for the phoneme LM to score we may have a few potential versions of each word.
words = find_potential_words(
lab[1:], os.path.join(lm_score_dir, proto["id"] + ".path"))
raise SiReError("Not implemented yet! Phoneme_lm_scoring. ")
else:
raise SiReError(
"The directory with reduction scores does not exist!")
else:
words = [(x, False) for x in lab[1:]]
#Make words and look up in dictionary
#If no parse exists (i.e. no pos tags) we will simply grab the first pronunciation we can find that is not reduced (if one exist).
#We also forget the pos tag of that in the process.
#We start with silence.
proto["utt"].append({
"id":
"sil",
"syllables":
dictionary.make_entry(general_sil_phoneme, general_sil_phoneme + " 0",
False)["syllables"]
})
if not stanfordparse:
for word in words:
#If we need to keep some punctuation
if comma_is_pause == True:
proto["utt"].append({
"id":
word[0],
"syllables":
dictionary.get_single_entry(
word[0], reduced=word[1],
punct_as_sil=([","], "sil"))["syllables"]
})
else:
proto["utt"].append({
"id":
word[0],
"syllables":
dictionary.get_single_entry(word[0],
reduced=word[1])["syllables"]
})
else: #Else a pcfg parse should exist and we can get the pos tags from that.
tree = parsetrees.stanfordPcfgTree()
tree.make_tree(pcfgdict[proto["id"]])
#Do we need some punctuation?
if comma_is_pause == True:
leafs = tree.get_leafs(include_punct=[","])
else:
leafs = tree.get_leafs()
#In this case we need to do some merging
if len(leafs) != len(words):
leafs = merge(leafs, words, proto["id"])
for i, leaf in enumerate(leafs):
pos, word = leaf.label.lower().split("-")
if word != words[i][0]:
raise SiReError(
"Word ({0}) from parse does not match word ({1}) from txt! In {2}."
.format(word, words[i][0], proto["id"]))
else:
word = words[i]
if comma_is_pause:
c_best = dictionary.get_single_entry(word[0],
pos,
word[1],
punct_as_sil=([","],
"sil"))
else:
c_best = dictionary.get_single_entry(word[0], pos, word[1])
proto["utt"].append({
"id": word[0],
"syllables": c_best["syllables"]
})
#We end with silence.
proto["utt"].append({
"id":
"sil",
"syllables":
dictionary.make_entry(general_sil_phoneme, general_sil_phoneme + " 0",
False)["syllables"]
})
#Add phony times to phonemes
#Phony phoneme duration counter
cur_dur = 0
for word in proto["utt"]:
for syll in word["syllables"]:
#Add phony time to phonemes
for phon in syll["phonemes"]:
phon["start"] = cur_dur
#Add 100ms in HTK lab format
cur_dur += 1000000
phon["end"] = cur_dur
return proto
