def _get_tagged_text(self, text, n_tagged):
"""
Returns text after tokenize and classify
Args;
text: input text
n_tagged: number of tagged options to consider, -1 - return all possible tagged options
"""
if n_tagged == -1:
if self.lang == "en":
# this to keep arpabet phonemes in the list of options
if "[" in text and "]" in text:
tagged_texts = rewrite.rewrites(text, self.tagger.fst)
else:
try:
tagged_texts = rewrite.rewrites(
text, self.tagger.fst_no_digits)
except pynini.lib.rewrite.Error:
tagged_texts = rewrite.rewrites(text, self.tagger.fst)
else:
tagged_texts = rewrite.rewrites(text, self.tagger.fst)
else:
if self.lang == "en":
# this to keep arpabet phonemes in the list of options
if "[" in text and "]" in text:
tagged_texts = rewrite.top_rewrites(text,
self.tagger.fst,
nshortest=n_tagged)
else:
try:
# try self.tagger graph that produces output without digits
tagged_texts = rewrite.top_rewrites(
text,
self.tagger.fst_no_digits,
nshortest=n_tagged)
except pynini.lib.rewrite.Error:
tagged_texts = rewrite.top_rewrites(text,
self.tagger.fst,
nshortest=n_tagged)
else:
tagged_texts = rewrite.top_rewrites(text,
self.tagger.fst,
nshortest=n_tagged)
return tagged_texts
def normalize(
self,
text: str,
n_tagged: int,
punct_post_process: bool = True,
verbose: bool = False,
) -> str:
"""
Main function. Normalizes tokens from written to spoken form
e.g. 12 kg -> twelve kilograms
Args:
text: string that may include semiotic classes
n_tagged: number of tagged options to consider, -1 - to get all possible tagged options
punct_post_process: whether to normalize punctuation
verbose: whether to print intermediate meta information
Returns:
normalized text options (usually there are multiple ways of normalizing a given semiotic class)
"""
assert (
len(text.split()) < 500
), "Your input is too long. Please split up the input into sentences, or strings with fewer than 500 words"
original_text = text
text = pre_process(text) # to handle []
text = text.strip()
if not text:
if verbose:
print(text)
return text
text = pynini.escape(text)
if self.lm:
if self.lang not in ["en"]:
raise ValueError(f"{self.lang} is not supported in LM mode")
if self.lang == "en":
try:
lattice = rewrite.rewrite_lattice(
text, self.tagger.fst_no_digits)
except pynini.lib.rewrite.Error:
lattice = rewrite.rewrite_lattice(text, self.tagger.fst)
lattice = rewrite.lattice_to_nshortest(lattice, n_tagged)
tagged_texts = [(x[1], float(x[2]))
for x in lattice.paths().items()]
tagged_texts.sort(key=lambda x: x[1])
tagged_texts, weights = list(zip(*tagged_texts))
else:
if n_tagged == -1:
if self.lang == "en":
try:
tagged_texts = rewrite.rewrites(
text, self.tagger.fst_no_digits)
except pynini.lib.rewrite.Error:
tagged_texts = rewrite.rewrites(text, self.tagger.fst)
else:
tagged_texts = rewrite.rewrites(text, self.tagger.fst)
else:
if self.lang == "en":
try:
tagged_texts = rewrite.top_rewrites(
text,
self.tagger.fst_no_digits,
nshortest=n_tagged)
except pynini.lib.rewrite.Error:
tagged_texts = rewrite.top_rewrites(text,
self.tagger.fst,
nshortest=n_tagged)
else:
tagged_texts = rewrite.top_rewrites(text,
self.tagger.fst,
nshortest=n_tagged)
# non-deterministic Eng normalization uses tagger composed with verbalizer, no permutation in between
if self.lang == "en":
normalized_texts = tagged_texts
else:
normalized_texts = []
for tagged_text in tagged_texts:
self._verbalize(tagged_text, normalized_texts, verbose=verbose)
if len(normalized_texts) == 0:
raise ValueError()
if punct_post_process:
# do post-processing based on Moses detokenizer
if self.processor:
normalized_texts = [
self.processor.detokenize([t]) for t in normalized_texts
]
normalized_texts = [
post_process_punct(input=original_text, normalized_text=t)
for t in normalized_texts
]
if self.lm:
return normalized_texts, weights
normalized_texts = set(normalized_texts)
return normalized_texts
def normalize(self, text: str, n_tagged: int, punct_post_process: bool = True, verbose: bool = False,) -> str:
"""
Main function. Normalizes tokens from written to spoken form
e.g. 12 kg -> twelve kilograms
Args:
text: string that may include semiotic classes
n_tagged: number of tagged options to consider, -1 - to get all possible tagged options
punct_post_process: whether to normalize punctuation
verbose: whether to print intermediate meta information
Returns:
normalized text options (usually there are multiple ways of normalizing a given semiotic class)
"""
original_text = text
if self.lang == "en":
text = pre_process(text)
text = text.strip()
if not text:
if verbose:
print(text)
return text
text = pynini.escape(text)
if n_tagged == -1:
if self.lang == "en":
try:
tagged_texts = rewrite.rewrites(text, self.tagger.fst_no_digits)
except pynini.lib.rewrite.Error:
tagged_texts = rewrite.rewrites(text, self.tagger.fst)
else:
tagged_texts = rewrite.rewrites(text, self.tagger.fst)
else:
if self.lang == "en":
try:
tagged_texts = rewrite.top_rewrites(text, self.tagger.fst_no_digits, nshortest=n_tagged)
except pynini.lib.rewrite.Error:
tagged_texts = rewrite.top_rewrites(text, self.tagger.fst, nshortest=n_tagged)
else:
tagged_texts = rewrite.top_rewrites(text, self.tagger.fst, nshortest=n_tagged)
# non-deterministic Eng normalization uses tagger composed with verbalizer, no permutation in between
if self.lang == "en":
normalized_texts = tagged_texts
else:
normalized_texts = []
for tagged_text in tagged_texts:
self._verbalize(tagged_text, normalized_texts, verbose=verbose)
if len(normalized_texts) == 0:
raise ValueError()
if punct_post_process:
# do post-processing based on Moses detokenizer
if self.processor:
normalized_texts = [self.processor.detokenize([t]) for t in normalized_texts]
normalized_texts = [
post_process_punct(input=original_text, normalized_text=t) for t in normalized_texts
]
else:
print("NEMO_NLP collection is not available: skipping punctuation post_processing")
normalized_texts = set(normalized_texts)
return normalized_texts