chore: import upstream snapshot with attribution

fun_text_processing/inverse_text_normalization/id/__init__.py

@@ -0,0 +1,7 @@
+from fun_text_processing.inverse_text_normalization.id.taggers.tokenize_and_classify import (
+    ClassifyFst,
+)
+from fun_text_processing.inverse_text_normalization.id.verbalizers.verbalize import VerbalizeFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.verbalize_final import (
+    VerbalizeFinalFst,
+)

fun_text_processing/inverse_text_normalization/id/clean_eval_data.py

@@ -0,0 +1,384 @@
+from argparse import ArgumentParser
+from typing import List
+
+import regex as re
+from fun_text_processing.text_normalization.data_loader_utils import (
+    EOS_TYPE,
+    Instance,
+    load_files,
+    training_data_to_sentences,
+)
+
+
+"""
+This file is for evaluation purposes.
+filter_loaded_data() cleans data (list of instances) for inverse text normalization. Filters and cleaners can be specified for each semiotic class individually.
+For example, normalized text should only include characters and whitespace characters but no punctuation. 
+            Cardinal unnormalized instances should contain at least one integer and all other characters are removed.
+"""
+
+
+class Filter:
+    """
+    Filter class
+
+    Args:
+        class_type: semiotic class used in dataset
+        process_func: function to transform text
+        filter_func:  function to filter text
+
+    """
+
+    def __init__(self, class_type: str, process_func: object, filter_func: object):
+        self.class_type = class_type
+        self.process_func = process_func
+        self.filter_func = filter_func
+
+    def filter(self, instance: Instance) -> bool:
+        """
+        filter function
+
+        Args:
+            filters given instance with filter function
+
+        Returns: True if given instance fulfills criteria or does not belong to class type
+        """
+        if instance.token_type != self.class_type:
+            return True
+        return self.filter_func(instance)
+
+    def process(self, instance: Instance) -> Instance:
+        """
+        process function
+
+        Args:
+            processes given instance with process function
+
+        Returns: processed instance if instance belongs to expected class type or original instance
+        """
+        if instance.token_type != self.class_type:
+            return instance
+        return self.process_func(instance)
+
+
+def filter_cardinal_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_cardinal_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    un_normalized = re.sub(r"[^0-9]", "", un_normalized)
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_ordinal_1(instance: Instance) -> bool:
+    ok = re.search(r"(st|nd|rd|th)\s*$", instance.un_normalized)
+    return ok
+
+
+def process_ordinal_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    un_normalized = re.sub(r"[,\s]", "", un_normalized)
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_decimal_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_decimal_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    un_normalized = re.sub(r",", "", un_normalized)
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_measure_1(instance: Instance) -> bool:
+    ok = True
+    return ok
+
+
+def process_measure_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    un_normalized = re.sub(r",", "", un_normalized)
+    un_normalized = re.sub(r"m2", "m²", un_normalized)
+    un_normalized = re.sub(r"(\d)([^\d.\s])", r"\1 \2", un_normalized)
+    normalized = re.sub(r"[^a-z\s]", "", normalized)
+    normalized = re.sub(r"per ([a-z\s]*)s$", r"per \1", normalized)
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_money_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_money_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    un_normalized = re.sub(r",", "", un_normalized)
+    un_normalized = re.sub(r"a\$", r"$", un_normalized)
+    un_normalized = re.sub(r"us\$", r"$", un_normalized)
+    un_normalized = re.sub(r"(\d)m\s*$", r"\1 juta", un_normalized)
+    un_normalized = re.sub(r"(\d)bn?\s*$", r"\1 milyar", un_normalized)
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_time_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_time_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    un_normalized = re.sub(r": ", ":", un_normalized)
+    un_normalized = re.sub(r"(\d)\s?a\s?m\s?", r"\1 a.m.", un_normalized)
+    un_normalized = re.sub(r"(\d)\s?p\s?m\s?", r"\1 p.m.", un_normalized)
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_plain_1(instance: Instance) -> bool:
+    ok = True
+    return ok
+
+
+def process_plain_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_punct_1(instance: Instance) -> bool:
+    ok = True
+    return ok
+
+
+def process_punct_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_date_1(instance: Instance) -> bool:
+    ok = True
+    return ok
+
+
+def process_date_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    un_normalized = re.sub(r",", "", un_normalized)
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_letters_1(instance: Instance) -> bool:
+    ok = True
+    return ok
+
+
+def process_letters_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_verbatim_1(instance: Instance) -> bool:
+    ok = True
+    return ok
+
+
+def process_verbatim_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_digit_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_digit_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_telephone_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_telephone_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_electronic_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_electronic_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_fraction_1(instance: Instance) -> bool:
+    ok = re.search(r"[0-9]", instance.un_normalized)
+    return ok
+
+
+def process_fraction_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+def filter_address_1(instance: Instance) -> bool:
+    ok = True
+    return ok
+
+
+def process_address_1(instance: Instance) -> Instance:
+    un_normalized = instance.un_normalized
+    normalized = instance.normalized
+    normalized = re.sub(r"[^a-z ]", "", normalized)
+    return Instance(
+        token_type=instance.token_type, un_normalized=un_normalized, normalized=normalized
+    )
+
+
+filters = []
+filters.append(
+    Filter(class_type="CARDINAL", process_func=process_cardinal_1, filter_func=filter_cardinal_1)
+)
+filters.append(
+    Filter(class_type="ORDINAL", process_func=process_ordinal_1, filter_func=filter_ordinal_1)
+)
+filters.append(
+    Filter(class_type="DECIMAL", process_func=process_decimal_1, filter_func=filter_decimal_1)
+)
+filters.append(
+    Filter(class_type="MEASURE", process_func=process_measure_1, filter_func=filter_measure_1)
+)
+filters.append(Filter(class_type="MONEY", process_func=process_money_1, filter_func=filter_money_1))
+filters.append(Filter(class_type="TIME", process_func=process_time_1, filter_func=filter_time_1))
+
+filters.append(Filter(class_type="DATE", process_func=process_date_1, filter_func=filter_date_1))
+filters.append(Filter(class_type="PLAIN", process_func=process_plain_1, filter_func=filter_plain_1))
+filters.append(Filter(class_type="PUNCT", process_func=process_punct_1, filter_func=filter_punct_1))
+filters.append(
+    Filter(class_type="LETTERS", process_func=process_letters_1, filter_func=filter_letters_1)
+)
+filters.append(
+    Filter(class_type="VERBATIM", process_func=process_verbatim_1, filter_func=filter_verbatim_1)
+)
+filters.append(Filter(class_type="DIGIT", process_func=process_digit_1, filter_func=filter_digit_1))
+filters.append(
+    Filter(class_type="TELEPHONE", process_func=process_telephone_1, filter_func=filter_telephone_1)
+)
+filters.append(
+    Filter(
+        class_type="ELECTRONIC", process_func=process_electronic_1, filter_func=filter_electronic_1
+    )
+)
+filters.append(
+    Filter(class_type="FRACTION", process_func=process_fraction_1, filter_func=filter_fraction_1)
+)
+filters.append(
+    Filter(class_type="ADDRESS", process_func=process_address_1, filter_func=filter_address_1)
+)
+filters.append(Filter(class_type=EOS_TYPE, process_func=lambda x: x, filter_func=lambda x: True))
+
+
+def filter_loaded_data(data: List[Instance], verbose: bool = False) -> List[Instance]:
+    """
+    Filters list of instances
+
+    Args:
+        data: list of instances
+
+    Returns: filtered and transformed list of instances
+    """
+    updates_instances = []
+    for instance in data:
+        updated_instance = False
+        for fil in filters:
+            if fil.class_type == instance.token_type and fil.filter(instance):
+                instance = fil.process(instance)
+                updated_instance = True
+        if updated_instance:
+            if verbose:
+                print(instance)
+            updates_instances.append(instance)
+    return updates_instances
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "--input", help="input file path", type=str, default="./id_with_types/output-00001-of-00100"
+    )
+    parser.add_argument("--verbose", help="print filtered instances", action="store_true")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    file_path = args.input
+
+    print("Loading training data: " + file_path)
+    instance_list = load_files([file_path])  # List of instances
+    filtered_instance_list = filter_loaded_data(instance_list, args.verbose)
+    training_data_to_sentences(filtered_instance_list)

fun_text_processing/inverse_text_normalization/id/data/currency.tsv

@@ -0,0 +1,37 @@
+$	dolar
+$	dolar Amerika
+$	dolar amerika serikat
+Rp	rupiah
+IDR	rupiah
+£	pound inggris
+€	euro
+₩	won
+nzd	dolar selandia baru
+rs	rupee
+chf	swiss franc
+dkk	kroner Denmark
+fim	markka finlandia
+aed	dirham emirat arab
+¥	yen
+czk	koruna ceko
+mro	ouguiya mauritania
+pkr	rupee pakistan
+crc	usus besar kosta rika
+hk$	dollar Hongkong
+npr	rupee nepal
+awg	florin aruba
+nok	kroner norwegia
+tzs	shilling tanzan
+sek	kronor Swedia
+cyp	pound siprus
+r	nyata
+sar	riyal saudi
+cve	escudo tanjung verde
+rsd	dinar serbia
+dm	tanda jerman
+shp	santo helena pound
+php	peso filipina
+cad	dolar Kanada
+ssp	pound sunda selatan
+scr	rupiah seychelles
+mvr	rufiyaa maldivian

fun_text_processing/inverse_text_normalization/id/data/electronic/domain.tsv

@@ -0,0 +1,11 @@
+com
+uk
+fr
+net
+br
+in
+ru
+de
+it
+ai
+id

fun_text_processing/inverse_text_normalization/id/data/electronic/server_name.tsv

@@ -0,0 +1,17 @@
+g mail	gmail
+gmail
+n vidia	nvidia
+nvidia
+outlook
+hotmail
+yahoo
+aol
+gmx
+msn
+live
+yandex
+orange
+wanadoo
+web
+comcast
+google

fun_text_processing/inverse_text_normalization/id/data/electronic/symbols.tsv

@@ -0,0 +1,130 @@
+.	dot
+-	berlari
+-	tanda penghubung
+_	menggarisbawahi
+!	tanda seru
+$	tanda dollar
+&	simbol untuk 'dan
+'	mengutip
+*	asterisk
++	plus
+/	memotong
+?	tanda tanya
+^	sirkomfleks
+`	kutipan tunggal yang tepat
+{	penyangga kiri
+|	batang vertikal
+}	penjepit kanan
+~	pasang surut
+,	koma
+%	persen
+#	tanda pagar
+=	sama dengan
+@	at
+_	garis bawah
+~	tilde
+≥	lebih besar dari atau sama dengan
+≤	lebih kecil dari atau sama dengan
+≠	tidak sama dengan
+≈	mendekati sama dengan
+±	kurang lebih
+×	kali
+A	A
+B	B
+C	C
+D	D
+E	E
+F	F
+G	G
+H	H
+I	I
+J	J
+K	K
+L	L
+M	M
+N	N
+O	O
+P	P
+Q	Q
+R	R
+S	S
+T	T
+U	U
+V	V
+W	W
+X	X
+Y	Y
+Z	Z
+a	A
+b	B
+c	C
+d	D
+e	E
+f	F
+g	G
+h	H
+i	I
+j	J
+k	K
+l	L
+m	M
+n	N
+o	O
+p	P
+q	Q
+r	R
+s	S
+t	T
+u	U
+v	V
+w	W
+x	X
+y	Y
+z	Z
+Α	alfa
+Β	beta
+Γ	gamma
+Δ	delta
+Ε	epsilon
+Ζ	zeta
+Θ	theta
+Ι	iota
+Κ	kappa
+∧	lambda
+Μ	mu
+Ν	nu
+Ξ	ksi
+Ο	omikron
+∏	pi
+Ρ	rho
+∑	sigma
+Τ	tau
+Υ	upsilon
+Φ	phi
+Χ	khi
+Ψ	psi
+Ω	omega
+α	alfa
+β	beta
+γ	gamma
+δ	delta
+ε	epsilon
+ζ	zeta
+η	eta
+θ	theta
+ι	iota
+κ	kappa
+λ	lambda
+μ	mu
+ν	nu
+ξ	ksi
+ο	omikron
+π	pi
+ρ	rho
+σ	sigma
+τ	tau
+υ	upsilon
+φ	phi
+χ	khi
+ψ	psi
+ω	omega

fun_text_processing/inverse_text_normalization/id/data/magnitudes.tsv

@@ -0,0 +1,4 @@
+k	ribu
+m	juta
+b	milyar
+t	triliun

fun_text_processing/inverse_text_normalization/id/data/measurements.tsv

@@ -0,0 +1,361 @@
+fahrenheit	f
+celsius	c
+kilometer	km
+meter	m
+sentimeter	cm
+milimeter	mm
+hektar	ha
+mil	mi
+meter persegi	m²
+kilometer persegi	km²
+kaki	ft
+persen	%
+hertz	hz
+kilowat	kw
+daya kuda	hp
+miligram	mg
+kilogram	kg
+gigahertz	ghz
+kilohertz	khz
+megahertz	mhz
+volt	v
+jam	h
+mega coulomb	mc
+kedua	s
+nanometer	nm
+revolusi per menit	rpm
+menit	min
+mili ampere	mA
+persen	%
+kilo watt jam	kwh
+meter kubik	m³
+mil per jam	mph
+tera watt	tw
+mili volt	mv
+megawatt	mw
+mikrometer	μm
+terabyte	tb
+c c	cc
+gram	g
+dalton	da
+suasana	atm
+ohm	ω
+desibel	db
+peta kedua	ps
+ons	oz
+hekto liter	hl
+mikrogram	μg
+petagram	pg
+gigabyte	gb
+kilobit	kb
+elektron volt	ev
+megabita	mb
+kilobyte	kb
+kilobit per detik	kbps
+megabit per detik	mbps
+batu	st
+kilo liter	kl
+tera joule	tj
+kilo volt	kv
+mega volt	mv
+kilonewton	kn
+megameter	mm
+satuan astronomi	au
+halaman	yd
+radian	rad
+lumen	lm
+hekto detik	hs
+tahi lalat	mol
+giga pascal	gpa
+mililiter	ml
+gigawatt	gw
+mega ampere	ma
+simpul	kt
+kekuatan kilogram	kgf
+nano gram	ng
+nanodetik	ns
+mega siemens	ms
+batang	bar
+giga liter	gl
+mikrodetik	μs
+desi ampere	da
+pascal	pa
+desi detik	ds
+mili detik	ms
+meteran desi	dm
+kubik desi meter	dm³
+satuan massa atom	amu
+megabita	mb
+mega farad	mf
+becquerel	bq
+petabit	pb
+milimeter persegi	mm²
+sentimeter persegi	cm²
+mil persegi	sq mi
+kaki persegi	sq ft
+kilopascal	kpa
+candela	cd
+tera liter	tl
+mega detik	ms
+megapascal	mpa
+meteran peta	pm
+peta byte	pb
+giga watt jam	gwh
+kilo kalori	kcal
+abu-abu	gy
+saringan	sv
+kelas seratus	cwt
+c c	cc
+kaki persegi	sq ft
+inci persegi	sq in
+kaki persegi	sqft
+kaki persegi	SqFt
+milidetik	msec
+kilowatt jam	kw·h
+miliampere-jam	mA⋅h
+kilokalori	kcal
+kilokalori	kCal
+kilokalori	Kcal
+milimeter merkuri	mmhg
+milimeter merkuri	mmHg
+milimeter persegi	mm2
+milimeter kubik	mm3
+sentimeter persegi	cm2
+sentimeter kubik	cm3
+kilometer persegi	km2
+kilometer kubik	km3
+desimeter persegi	dm2
+desimeter kubik	dm3
+detik	sec
+jam	hrs
+mil per jam	mph
+kiloherts	khz
+kiloherts	kHz
+megahertz	mhz
+megahertz	mHz
+gigahertz	ghz
+gigahertz	gHz
+kilowatt jam	kwh
+mol	mol
+sendok the	tsp
+kilopascal	kPa
+volume	vol
+volume	Vol
+rotasi per menit	rpm
+detak per menit	bpm
+galon	gal
+pascal	pa
+megapascal	mpa
+miliampere-jam	mah
+menit	min
+detik	SEC
+kalori	cal
+kilokalori	Cal
+meter persegi	m2
+meter kubik	m3
+milimeter	mm
+sentimeter	cm
+sentimeter	CM
+kilometer	km
+kilometer	KM
+miligram	mg
+kilogram	kg
+kilogram	KG
+jam	hr
+hertz	hz
+hertz	Hz
+mililiter	ml
+mililiter	mL
+miliampere	mA
+sentimeter kubik	cc
+piksel	px
+piksel	PX
+volt	v
+volt	V
+kilovolt	kv
+kilovolt	kV
+hektar	ha
+ekar	ac
+karat	ct
+inci	in
+kaki	ft
+yard	yd
+nanometer	nm
+desimeter	dm
+kilobyte	kb
+megabyte	mb
+gigabyte	gb
+terabyte	tb
+kilobyte	KB
+megabyte	MB
+gigabyte	GB
+terabyte	TB
+daya kuda	hp
+desibel	db
+desibel	dB
+kilojoule	kj
+kilojoule	kJ
+ons	oz
+kilowatt	kw
+kilowatt	KW
+joule	j
+joule	J
+gram	g
+gram	G
+liter	l
+liter	L
+meter	m
+ohm-meter	Ω·m
+mikrometer	μm
+mikrogram	μg
+mikroampere	μA
+derajat Celsius	°c
+derajat Celsius	˚c
+derajat Fahrenheit	°f
+derajat Fahrenheit	˚f
+milimeter persegi	mm²
+milimeter kubik	mm³
+sentimeter persegi	cm²
+sentimeter kubik	cm³
+kilometer persegi	km²
+kilometer kubik	km³
+desimeter kubik	dm³
+meter persegi	m²
+meter kubik	m³
+derajat	°
+derajat	˚
+derajat Celsius	℃
+derajat Fahrenheit	℉
+ohm	Ω
+mikrometer	μm
+milimeter persegi	mm2
+milimeter kubik	mm3
+milimeter	mm
+sentimeter persegi	cm2
+sentimeter kubik	cm3
+sentimeter	cm
+sentimeter	CM
+kilometer persegi	km2
+kilometer kubik	km3
+kilometer	km
+kilometer	KM
+desimeter persegi	dm2
+desimeter kubik	dm3
+meter persegi	m2
+meter kubik	m3
+kaki persegi	sq ft
+kaki persegi	sq. ft
+kaki persegi	sq.ft
+kaki persegi	sqft
+kaki persegi	SqFt
+inci persegi	sq in
+mikrogram	μg
+miligram	mg
+kilogram	kg
+kilogram	KG
+milidetik	msec
+detik	sec
+jam	hr
+jam	hrs
+meter per detik	m/s
+kilometer per jam	km/h
+mil per jam	mph
+bit per detik	bit/s
+bit per detik	Bit/s
+byte per detik	byte/s
+byte per detik	Byte/s
+derajat Celsius	°c
+derajat Celsius	˚c
+derajat Fahrenheit	°f
+derajat Fahrenheit	˚f
+kilokalori	kcal
+kilokalori	kCal
+kilokalori	Kcal
+ons cairan	fl.oz
+farad per meter	F/m
+gram per liter	g/l
+gram per mililiter	g/mL
+hertz	hz
+hertz	Hz
+kiloherts	khz
+kiloherts	kHz
+megahertz	mhz
+megahertz	mHz
+gigahertz	ghz
+gigahertz	gHz
+kilometer per jam	km/h
+kilowatt per jam	kw/h
+kilowatt jam	kw·h
+kilowatt jam	kS·h
+kilowatt jam	kwh
+kilowatt jam	kSh
+mililiter	ml
+mililiter	mL
+miligram per mililiter	mg/ml
+miligram per mililiter	mg/mL
+miligram per liter	mg/l
+miligram per liter	mg/L
+miliampere	mA
+miliampere-jam	mA⋅h
+mol	mol
+ohm-meter	Ω·m
+siemens per meter	S/m
+sendok the	tsp
+mikroampere	μA
+kilopascal	kPa
+milimeter merkuri	mmhg
+milimeter merkuri	mmHg
+volume	vol
+volume	Vol
+sentimeter kubik	cc
+rotasi per menit	rpm
+rotasi per menit	r/min
+detak per menit	bpm
+piksel	px
+piksel	PX
+volt	v
+volt	V
+kilovolt	kv
+kilovolt	kV
+hektar	ha
+ekar	ac
+karat	ct
+liter	l
+liter	L
+galon	gal
+mol	mol
+pascal	pa
+megapascal	mpa
+miliampere	ma
+miliampere-jam	mah
+inci	in
+kaki	ft
+yard	yd
+nanometer	nm
+meter	m
+desimeter	dm
+gram	g
+gram	G
+kilobyte	kb
+megabyte	mb
+gigabyte	gb
+terabyte	tb
+kilobyte	KB
+megabyte	MB
+gigabyte	GB
+terabyte	TB
+daya kuda	hp
+desibel	db
+desibel	dB
+joule	j
+joule	J
+kilojoule	kj
+kilojoule	kJ
+ons	oz
+kilowatt	kw
+kilowatt	KW
+menit	min
+detik	SEC
+kalori	cal
+kilokalori	Cal
+inci	"

fun_text_processing/inverse_text_normalization/id/data/months.tsv

@@ -0,0 +1,12 @@
+Januari
+Februari
+Maret
+April
+Mei
+Juni
+Juli
+Agustus
+September
+Oktober
+November
+Desember

fun_text_processing/inverse_text_normalization/id/data/numbers/digit.tsv

@@ -0,0 +1,9 @@
+satu	1
+dua	2
+tiga	3
+empat	4
+lima	5
+enam	6
+tujuh	7
+delapan	8
+sembilan	9

fun_text_processing/inverse_text_normalization/id/data/numbers/dot.tsv

@@ -0,0 +1 @@
+titik	.

fun_text_processing/inverse_text_normalization/id/data/numbers/hundred.tsv

@@ -0,0 +1,2 @@
+ratus
+seratus

fun_text_processing/inverse_text_normalization/id/data/numbers/hundreds.tsv

@@ -0,0 +1,10 @@
+ratus   1
+seratus 1
+dua ratus   2
+tiga ratus  3
+empat ratus 4
+lima ratus  5
+enam ratus  6
+tujuh ratus 7
+delapan ratus   8
+sembilan ratus  9

fun_text_processing/inverse_text_normalization/id/data/numbers/teen.tsv

@@ -0,0 +1,15 @@
+sepuluh	10
+sebelas	11
+duabelas	12
+dua belas	12
+tigabelas	13
+tiga belas	13
+empatbelas	14
+empat belas	14
+limabelas	15
+lima belas	15
+enambelas	16
+enam belas	16
+tujuh belas	17
+delapan belas	18
+sembilan belas	19

fun_text_processing/inverse_text_normalization/id/data/numbers/thousand.tsv

@@ -0,0 +1,10 @@
+ribu   1
+seribu 1
+dua ribu   2
+tiga ribu  3
+empat ribu 4
+lima ribu  5
+enam ribu  6
+tujuh ribu 7
+delapan ribu   8
+sembilan ribu  9

fun_text_processing/inverse_text_normalization/id/data/numbers/thousands.tsv

@@ -0,0 +1,23 @@
+ribu
+seribu
+juta
+miliar
+triliun
+milion lipat empat
+triliun
+sextillion
+septillion
+oktillion
+nonmiliar
+satu juta
+undecillion
+duodecillion
+triliun
+quattuordecillion
+quindecillion
+sexdecillion
+septendeciliun
+octodecillion
+novemdecillion
+vigintillion
+centillion

fun_text_processing/inverse_text_normalization/id/data/numbers/ties.tsv

@@ -0,0 +1,9 @@
+dua puluh	2
+tigapuluh	3
+empat puluh	4
+empat puluh	4
+lima puluh	5
+enam puluh	6
+tujuh puluh	7
+delapan puluh	8
+sembilan puluh	9

fun_text_processing/inverse_text_normalization/id/data/numbers/zero.tsv

@@ -0,0 +1 @@
+nol 0

fun_text_processing/inverse_text_normalization/id/data/ordinals/digit.tsv

@@ -0,0 +1,9 @@
+pertama	satu
+kedua	dua
+ketiga	tiga
+keempat	empat
+kelima	lima
+keenam	enam
+ketujuh	tujuh
+kedelapan	delapan
+kesembilan	sembilan

fun_text_processing/inverse_text_normalization/id/data/ordinals/teen.tsv

@@ -0,0 +1 @@
+keduabelas	dua belas

fun_text_processing/inverse_text_normalization/id/data/suppletive.tsv

@@ -0,0 +1,83 @@
+deer
+fish
+sheep
+foot	feet
+goose	geese
+man	men
+mouse	mice
+tooth	teeth
+woman	women
+won
+child	children
+ox	oxen
+wife	wives
+wolf	wolves
+analysis	analyses
+criterion	criteria
+lbs
+focus	foci
+percent
+hertz
+kroner	krone
+inch	inches
+calory	calories
+yen
+megahertz
+gigahertz
+kilohertz
+hertz
+CC
+c c
+horsepower
+hundredweight
+kilogram force	kilograms force
+mega siemens
+revolution per minute	revolutions per minute
+mile per hour	miles per hour
+megabit per second	megabits per second
+square foot	square feet
+kilobit per second	kilobits per second
+degree Celsius	degrees Celsius
+degree Fahrenheit	degrees Fahrenheit
+ATM
+AU
+BQ
+CC
+CD
+DA
+EB
+EV
+F
+GB
+G
+GL
+GPA
+GY
+HA
+H
+HL
+GP
+HS
+KB
+KL
+KN
+KT
+KV
+LM
+MA
+MA
+MB
+MC
+MF
+M
+MM
+MS
+MV
+MW
+PB
+PG
+PS
+S
+TB
+YB
+ZB

fun_text_processing/inverse_text_normalization/id/data/time/minute_to.tsv

@@ -0,0 +1,59 @@
+1	59
+2	58
+3	57
+4	56
+5	55
+6	54
+7	53
+8	52
+9	51
+10	50
+11	49
+12	48
+13	47
+14	46
+15	45
+16	44
+17	43
+18	42
+19	41
+20	40
+21	39
+22	38
+23	37
+24	36
+25	35
+26	34
+27	33
+28	32
+29	31
+30	30
+31	29
+32	28
+33	27
+34	26
+35	25
+36	24
+37	23
+38	22
+39	21
+40	20
+41	19
+42	18
+43	17
+44	16
+45	15
+46	14
+47	13
+48	12
+49	11
+50	10
+51	9
+52	8
+53	7
+54	6
+55	5
+56	4
+57	3
+58	2
+59	1

fun_text_processing/inverse_text_normalization/id/data/time/time_suffix.tsv

@@ -0,0 +1,8 @@
+p m	p.m.
+pm	p.m.
+p.m.
+p.m	p.m.
+am	a.m.
+a.m.
+a.m	a.m.
+a m	a.m.

fun_text_processing/inverse_text_normalization/id/data/time/time_zone.tsv

@@ -0,0 +1,7 @@
+cst	c s t
+cet	c e t
+pst	p s t
+est	e s t
+pt	p t
+et	e t
+gmt	g m t

fun_text_processing/inverse_text_normalization/id/data/time/to_hour.tsv

@@ -0,0 +1,24 @@
+satu	1
+dua	2
+tiga	3
+empat	4
+lima	5
+enam	6
+tujuh	7
+delapan	8
+sembilan	9
+sepuluh	10
+sebelas	11
+dua belas	12
+satu	13
+dua	14
+tiga	15
+empat	16
+lima	17
+enam	18
+tujuh	19
+delapan	20
+sembilan	21
+sepuluh	22
+sebelas	23
+dua belas	24

fun_text_processing/inverse_text_normalization/id/data/whitelist.tsv

@@ -0,0 +1,12 @@
+e.g.	misalnya
+dr.	dokter
+mr.	tuan
+mrs.	rindu
+st.	santo
+7-eleven	tujuhsebelas
+es3	e s tiga
+s&p	s dan p
+ASAP	a s a p
+AT&T	a t dan t
+LLP	l l p
+ATM	a t m

fun_text_processing/inverse_text_normalization/id/graph_utils.py

@@ -0,0 +1,209 @@
+import os
+import string
+from pathlib import Path
+from typing import Dict
+
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from pynini import Far
+from pynini.examples import plurals
+from pynini.export import export
+from pynini.lib import byte, pynutil, utf8
+
+DAMO_CHAR = utf8.VALID_UTF8_CHAR
+
+DAMO_DIGIT = byte.DIGIT
+DAMO_LOWER = pynini.union(*string.ascii_lowercase).optimize()
+DAMO_UPPER = pynini.union(*string.ascii_uppercase).optimize()
+DAMO_ALPHA = pynini.union(DAMO_LOWER, DAMO_UPPER).optimize()
+DAMO_ALNUM = pynini.union(DAMO_DIGIT, DAMO_ALPHA).optimize()
+DAMO_HEX = pynini.union(*string.hexdigits).optimize()
+DAMO_NON_BREAKING_SPACE = "\u00A0"
+DAMO_SPACE = " "
+DAMO_WHITE_SPACE = pynini.union(" ", "\t", "\n", "\r", "\u00A0").optimize()
+DAMO_NOT_SPACE = pynini.difference(DAMO_CHAR, DAMO_WHITE_SPACE).optimize()
+DAMO_NOT_QUOTE = pynini.difference(DAMO_CHAR, r'"').optimize()
+
+DAMO_PUNCT = pynini.union(*map(pynini.escape, string.punctuation)).optimize()
+DAMO_GRAPH = pynini.union(DAMO_ALNUM, DAMO_PUNCT).optimize()
+
+DAMO_SIGMA = pynini.closure(DAMO_CHAR)
+
+delete_space = pynutil.delete(pynini.closure(DAMO_WHITE_SPACE))
+delete_zero_or_one_space = pynutil.delete(pynini.closure(DAMO_WHITE_SPACE, 0, 1))
+insert_space = pynutil.insert(" ")
+delete_extra_space = pynini.cross(pynini.closure(DAMO_WHITE_SPACE, 1), " ")
+delete_preserve_order = pynini.closure(
+    pynutil.delete(" preserve_order: true")
+    | (pynutil.delete(' field_order: "') + DAMO_NOT_QUOTE + pynutil.delete('"'))
+)
+
+suppletive = pynini.string_file(get_abs_path("data/suppletive.tsv"))
+# _v = pynini.union("a", "e", "i", "o", "u")
+_c = pynini.union(
+    "b",
+    "c",
+    "d",
+    "f",
+    "g",
+    "h",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "p",
+    "q",
+    "r",
+    "s",
+    "t",
+    "v",
+    "w",
+    "x",
+    "y",
+    "z",
+)
+_ies = DAMO_SIGMA + _c + pynini.cross("y", "ies")
+_es = DAMO_SIGMA + pynini.union("s", "sh", "ch", "x", "z") + pynutil.insert("es")
+_s = DAMO_SIGMA + pynutil.insert("s")
+
+graph_plural = plurals._priority_union(
+    suppletive,
+    plurals._priority_union(_ies, plurals._priority_union(_es, _s, DAMO_SIGMA), DAMO_SIGMA),
+    DAMO_SIGMA,
+).optimize()
+
+SINGULAR_TO_PLURAL = graph_plural
+PLURAL_TO_SINGULAR = pynini.invert(graph_plural)
+TO_LOWER = pynini.union(
+    *[pynini.cross(x, y) for x, y in zip(string.ascii_uppercase, string.ascii_lowercase)]
+)
+TO_UPPER = pynini.invert(TO_LOWER)
+MIN_NEG_WEIGHT = -0.0001
+MIN_POS_WEIGHT = 0.0001
+
+
+def generator_main(file_name: str, graphs: Dict[str, "pynini.FstLike"]):
+    """
+    Exports graph as OpenFst finite state archive (FAR) file with given file name and rule name.
+
+    Args:
+        file_name: exported file name
+        graphs: Mapping of a rule name and Pynini WFST graph to be exported
+    """
+    exporter = export.Exporter(file_name)
+    for rule, graph in graphs.items():
+        exporter[rule] = graph.optimize()
+    exporter.close()
+    print(f"Created {file_name}")
+
+
+def get_plurals(fst):
+    """
+    Given singular returns plurals
+
+    Args:
+        fst: Fst
+
+    Returns plurals to given singular forms
+    """
+    return SINGULAR_TO_PLURAL @ fst
+
+
+def get_singulars(fst):
+    """
+    Given plural returns singulars
+
+    Args:
+        fst: Fst
+
+    Returns singulars to given plural forms
+    """
+    return PLURAL_TO_SINGULAR @ fst
+
+
+def convert_space(fst) -> "pynini.FstLike":
+    """
+    Converts space to nonbreaking space.
+    Used only in tagger grammars for transducing token values within quotes, e.g. name: "hello kitty"
+    This is making transducer significantly slower, so only use when there could be potential spaces within quotes, otherwise leave it.
+
+    Args:
+        fst: input fst
+
+    Returns output fst where breaking spaces are converted to non breaking spaces
+    """
+    return fst @ pynini.cdrewrite(
+        pynini.cross(DAMO_SPACE, DAMO_NON_BREAKING_SPACE), "", "", DAMO_SIGMA
+    )
+
+
+class GraphFst:
+    """
+    Base class for all grammar fsts.
+
+    Args:
+        name: name of grammar class
+        kind: either 'classify' or 'verbalize'
+        deterministic: if True will provide a single transduction option,
+            for False multiple transduction are generated (used for audio-based normalization)
+    """
+
+    def __init__(self, name: str, kind: str, deterministic: bool = True):
+        self.name = name
+        self.kind = kind
+        self._fst = None
+        self.deterministic = deterministic
+
+        self.far_path = Path(os.path.dirname(__file__) + "/grammars/" + kind + "/" + name + ".far")
+        if self.far_exist():
+            self._fst = Far(
+                self.far_path, mode="r", arc_type="standard", far_type="default"
+            ).get_fst()
+
+    def far_exist(self) -> bool:
+        """
+        Returns true if FAR can be loaded
+        """
+        return self.far_path.exists()
+
+    @property
+    def fst(self) -> "pynini.FstLike":
+        return self._fst
+
+    @fst.setter
+    def fst(self, fst):
+        self._fst = fst
+
+    def add_tokens(self, fst) -> "pynini.FstLike":
+        """
+        Wraps class name around to given fst
+
+        Args:
+            fst: input fst
+
+        Returns:
+            Fst: fst
+        """
+        return pynutil.insert(f"{self.name} {{ ") + fst + pynutil.insert(" }")
+
+    def delete_tokens(self, fst) -> "pynini.FstLike":
+        """
+        Deletes class name wrap around output of given fst
+
+        Args:
+            fst: input fst
+
+        Returns:
+            Fst: fst
+        """
+        res = (
+            pynutil.delete(f"{self.name}")
+            + delete_space
+            + pynutil.delete("{")
+            + delete_space
+            + fst
+            + delete_space
+            + pynutil.delete("}")
+        )
+        return res @ pynini.cdrewrite(pynini.cross("\u00A0", " "), "", "", DAMO_SIGMA)

fun_text_processing/inverse_text_normalization/id/id_itn_test_expected.txt

@@ -0,0 +1,29 @@
+2022
+300
+9999
+100001
+100
+1000
+10289
+1289
+01 2345-6789
+14
+15
+16
+17
+18
+19
+20
+106
+600
+100
+100
+1 miliar
+123
+123
+24 maret
+10076
+100076
+10076 rupiah
+76
++62 21 6539-0605

fun_text_processing/inverse_text_normalization/id/id_itn_test_input.txt

@@ -0,0 +1,29 @@
+dua ribu dua puluh dua
+tiga ribu
+sembilan ribu sembilan ratus sembilan puluh sembilan
+seribu satu
+ribu
+seribu
+seribu dua ratus delapan puluh sembilan
+ribu dua ratus delapan puluh sembilan
+nol satu dua tiga empat lima enam tujuh delapan sembilan
+empat belas
+limabelas
+enambelas
+tujuh belas
+delapan belas
+sembilan belas
+dua puluh
+seratus enam
+enam ratus
+ratus
+seratus
+satu miliar
+seratus dua puluh tiga
+ratus dua puluh tiga
+dua puluh empat maret
+ribu tujuh puluh enam
+seribu tujuh puluh enam
+ribu tujuh puluh enam rupiah
+tujuh puluh enam
+ditambah enam dua dua satu enam lima tiga sembilan nol enam nol lima

fun_text_processing/inverse_text_normalization/id/id_unit_test.tsv

@@ -0,0 +1,29 @@
+dua ribu dua puluh dua	2022
+tiga ribu	3000
+sembilan ribu sembilan ratus sembilan puluh sembilan	9999
+seribu satu	1001
+ribu	1000
+seribu	1000
+seribu dua ratus delapan puluh sembilan	1289
+ribu dua ratus delapan puluh sembilan	1289
+nol satu dua tiga empat lima enam tujuh delapan sembilan	01 2345-6789
+empat belas	14
+limabelas	15
+enambelas	16
+tujuh belas	17
+delapan belas	18
+sembilan belas	19
+dua puluh	20
+seratus enam	106
+enam ratus	600
+ratus	100
+seratus	100
+satu miliar	1 miliar
+seratus dua puluh tiga	123
+ratus dua puluh tiga	123
+dua puluh empat maret 	24 maret
+ribu tujuh puluh enam	1076
+seribu tujuh puluh enam	1076
+ribu tujuh puluh enam rupiah	1076 rupiah
+tujuh puluh enam	76
+ditambah enam dua dua satu enam lima tiga sembilan nol enam nol lima	+62 21 6539-0605

fun_text_processing/inverse_text_normalization/id/taggers/cardinal.py

@@ -0,0 +1,161 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path, num_to_word
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_ALPHA,
+    DAMO_DIGIT,
+    DAMO_SIGMA,
+    DAMO_SPACE,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class CardinalFst(GraphFst):
+    """
+    Finite state transducer for classifying cardinals
+        e.g. minus twenty three -> cardinal { integer: "23" negative: "-" } }
+    Numbers below thirteen are not converted.
+    """
+
+    def __init__(self, deterministic: bool = True):
+        super().__init__(name="cardinal", kind="classify", deterministic=deterministic)
+        graph_zero = pynini.string_file(get_abs_path("data/numbers/zero.tsv"))
+        graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
+        graph_ties = pynini.string_file(get_abs_path("data/numbers/ties.tsv"))
+        graph_teen = pynini.string_file(get_abs_path("data/numbers/teen.tsv"))
+        graph_hundreds = pynini.string_file(get_abs_path("data/numbers/hundreds.tsv"))
+        graph_thousand = pynini.string_file(get_abs_path("data/numbers/thousand.tsv"))
+
+        graph_hundred = pynini.cross("ratus", "") | pynini.cross("seratus", "")
+
+        graph_hundred_component = pynini.union(
+            graph_digit + delete_space + graph_hundred, pynutil.insert("0")
+        )
+        graph_hundred_component += delete_space
+        graph_hundred_component += pynini.union(
+            graph_teen | pynutil.insert("00"),
+            (graph_ties | pynutil.insert("0")) + delete_space + (graph_digit | pynutil.insert("0")),
+        )
+
+        graph_one_hundred_component = pynini.union(
+            pynini.cross("ratus", "1") | pynini.cross("seratus", "1")
+        )
+        graph_one_hundred_component += delete_space
+        graph_one_hundred_component += pynini.union(
+            graph_teen | pynutil.insert("00"),
+            (graph_ties | pynutil.insert("0")) + delete_space + (graph_digit | pynutil.insert("0")),
+        )
+        graph_hundred_component = graph_hundred_component | graph_one_hundred_component
+
+        graph_hundred_component_at_least_one_none_zero_digit = graph_hundred_component @ (
+            pynini.closure(DAMO_DIGIT) + (DAMO_DIGIT - "0") + pynini.closure(DAMO_DIGIT)
+        )
+        self.graph_hundred_component_at_least_one_none_zero_digit = (
+            graph_hundred_component_at_least_one_none_zero_digit
+        )
+        graph_thousand = pynini.cross("ribu", "") | pynini.cross("seribu", "")
+        graph_one_thousand_component = pynini.union(
+            pynini.cross("ribu", "1") | pynini.cross("seribu", "1")
+        )
+
+        graph_thousands = pynini.union(
+            graph_hundred_component_at_least_one_none_zero_digit
+            + delete_space
+            + (pynutil.delete("ribu") | pynutil.delete("seribu")),
+            pynutil.insert("000", weight=0.1),
+        )
+        graph_thousands = graph_thousands | (pynutil.insert("00") + graph_one_thousand_component)
+
+        graph_million = pynini.union(
+            graph_hundred_component_at_least_one_none_zero_digit
+            + delete_space
+            + (pynutil.delete("juta") | pynutil.delete("sejuta")),
+            pynutil.insert("000", weight=0.1),
+        )
+        graph_billion = pynini.union(
+            graph_hundred_component_at_least_one_none_zero_digit
+            + delete_space
+            + (
+                pynutil.delete("miliar")
+                | pynutil.delete("semiliar")
+                | pynutil.delete("milyar")
+                | pynutil.delete("semilyar")
+            ),
+            pynutil.insert("000", weight=0.1),
+        )
+        graph_trillion = pynini.union(
+            graph_hundred_component_at_least_one_none_zero_digit
+            + delete_space
+            + (pynutil.delete("triliun") | pynutil.delete("setriliun")),
+            pynutil.insert("000", weight=0.1),
+        )
+        graph_quadrillion = pynini.union(
+            graph_hundred_component_at_least_one_none_zero_digit
+            + delete_space
+            + pynutil.delete("milion lipat empat"),
+            pynutil.insert("000", weight=0.1),
+        )
+        graph_quintillion = pynini.union(
+            graph_hundred_component_at_least_one_none_zero_digit
+            + delete_space
+            + pynutil.delete("triliun"),
+            pynutil.insert("000", weight=0.1),
+        )
+        graph_sextillion = pynini.union(
+            graph_hundred_component_at_least_one_none_zero_digit
+            + delete_space
+            + pynutil.delete("sextillion"),
+            pynutil.insert("000", weight=0.1),
+        )
+
+        graph = pynini.union(
+            graph_sextillion
+            + delete_space
+            + graph_quintillion
+            + delete_space
+            + graph_quadrillion
+            + delete_space
+            + graph_trillion
+            + delete_space
+            + graph_billion
+            + delete_space
+            + graph_million
+            + delete_space
+            + graph_thousands
+            + delete_space
+            + graph_hundred_component,
+            # graph_digit,
+            graph_zero,
+        )
+
+        graph = graph @ pynini.union(
+            pynutil.delete(pynini.closure("0"))
+            + pynini.difference(DAMO_DIGIT, "0")
+            + pynini.closure(DAMO_DIGIT),
+            "0",
+        )
+
+        labels_exception = ["nol"]
+        graph_exception = pynini.union(*labels_exception)
+
+        graph = (
+            pynini.cdrewrite(pynutil.delete("dan"), DAMO_SPACE, DAMO_SPACE, DAMO_SIGMA)
+            @ (DAMO_ALPHA + DAMO_SIGMA)
+            @ graph
+        )
+
+        self.graph_no_exception = graph
+
+        self.graph = (pynini.project(graph, "input") - graph_exception.arcsort()) @ graph
+
+        optional_minus_graph = pynini.closure(
+            pynutil.insert("negative: ") + pynini.cross("kurang", '"-"') + DAMO_SPACE, 0, 1
+        )
+
+        final_graph = (
+            optional_minus_graph + pynutil.insert('integer: "') + self.graph + pynutil.insert('"')
+        )
+
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/date.py

@@ -0,0 +1,150 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_ALPHA,
+    DAMO_DIGIT,
+    GraphFst,
+    delete_extra_space,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+graph_teen = pynini.string_file(get_abs_path("data/numbers/teen.tsv")).optimize()
+graph_digit = pynini.string_file(get_abs_path("data/numbers/digit.tsv")).optimize()
+ties_graph = pynini.string_file(get_abs_path("data/numbers/ties.tsv")).optimize()
+
+
+def _get_month_graph():
+    """
+    Transducer for month, e.g. march -> march
+    """
+    month_graph = pynini.string_file(get_abs_path("data/months.tsv"))
+    return month_graph
+
+
+def _get_ties_graph():
+    """
+    Transducer for 20-99 e.g
+    twenty three -> 23
+    """
+    graph = ties_graph + (delete_space + graph_digit | pynutil.insert("0"))
+    return graph
+
+
+def _get_range_graph():
+    """
+    Transducer for decades (1**0s, 2**0s), centuries (2*00s, 1*00s), millennia (2000s)
+    """
+    graph_ties = _get_ties_graph()
+    graph = (graph_ties | graph_teen) + delete_space + pynini.cross("ratusan", "00s")
+    graph |= pynini.cross("dua", "2") + delete_space + pynini.cross("ribuan", "000s")
+    graph |= (
+        (graph_ties | graph_teen)
+        + delete_space
+        + (pynini.closure(DAMO_ALPHA, 1) + (pynini.cross("ies", "y") | pynutil.delete("s")))
+        @ (graph_ties | pynini.cross("sepuluh", "10"))
+        + pynutil.insert("s")
+    )
+    graph @= pynini.union("1", "2") + DAMO_DIGIT + DAMO_DIGIT + DAMO_DIGIT + "s"
+    return graph
+
+
+def _get_year_graph():
+    """
+    Transducer for year, e.g. twenty twenty -> 2020
+    """
+
+    def _get_digits_graph():
+        zero = pynini.cross((pynini.accep("oh") | pynini.accep("o")), "0")
+        graph = zero + delete_space + graph_digit
+        graph.optimize()
+        return graph
+
+    def _get_thousands_graph():
+        graph_ties = _get_ties_graph()
+        graph_hundred_component = (
+            graph_digit + delete_space + pynutil.delete("ratus")
+        ) | pynutil.insert("0")
+        graph = (
+            graph_digit
+            + delete_space
+            + pynutil.delete("ribu")
+            + delete_space
+            + graph_hundred_component
+            + delete_space
+            + (graph_teen | graph_ties)
+        )
+        return graph
+
+    graph_ties = _get_ties_graph()
+    graph_digits = _get_digits_graph()
+    graph_thousands = _get_thousands_graph()
+    year_graph = (
+        # 20 19, 40 12, 2012 - assuming no limit on the year
+        (graph_teen + delete_space + (graph_ties | graph_digits | graph_teen))
+        | (graph_ties + delete_space + (graph_ties | graph_digits | graph_teen))
+        | graph_thousands
+    )
+    year_graph.optimize()
+    return year_graph
+
+
+class DateFst(GraphFst):
+    """
+    Finite state transducer for classifying date,
+        e.g. january fifth twenty twelve -> date { month: "january" day: "5" year: "2012" preserve_order: true }
+        e.g. the fifth of january twenty twelve -> date { day: "5" month: "january" year: "2012" preserve_order: true }
+        e.g. twenty twenty -> date { year: "2012" preserve_order: true }
+
+    Args:
+        ordinal: OrdinalFst
+    """
+
+    def __init__(self, ordinal: GraphFst):
+        super().__init__(name="date", kind="classify")
+
+        ordinal_graph = ordinal.graph
+        year_graph = _get_year_graph()
+        YEAR_WEIGHT = 0.001
+        year_graph = pynutil.add_weight(year_graph, YEAR_WEIGHT)
+        month_graph = _get_month_graph()
+
+        month_graph = pynutil.insert('month: "') + month_graph + pynutil.insert('"')
+
+        day_graph = (
+            pynutil.insert('day: "') + pynutil.add_weight(ordinal_graph, -0.7) + pynutil.insert('"')
+        )
+        graph_year = (
+            delete_extra_space
+            + pynutil.insert('year: "')
+            + pynutil.add_weight(year_graph, -YEAR_WEIGHT)
+            + pynutil.insert('"')
+        )
+        optional_graph_year = pynini.closure(
+            graph_year,
+            0,
+            1,
+        )
+        graph_mdy = month_graph + (
+            (delete_extra_space + day_graph)
+            | graph_year
+            | (delete_extra_space + day_graph + graph_year)
+        )
+        graph_dmy = (
+            pynutil.delete("the")
+            + delete_space
+            + day_graph
+            + delete_space
+            + pynutil.delete("of")
+            + delete_extra_space
+            + month_graph
+            + optional_graph_year
+        )
+        graph_year = (
+            pynutil.insert('year: "') + (year_graph | _get_range_graph()) + pynutil.insert('"')
+        )
+
+        final_graph = graph_mdy | graph_dmy | graph_year
+        final_graph += pynutil.insert(" preserve_order: true")
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/decimal.py

@@ -0,0 +1,100 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_DIGIT,
+    GraphFst,
+    delete_extra_space,
+    delete_space,
+)
+from pynini.lib import pynutil
+import pdb
+
+
+def get_quantity(
+    decimal: "pynini.FstLike", cardinal_up_to_hundred: "pynini.FstLike"
+) -> "pynini.FstLike":
+    """
+    Returns FST that transforms either a cardinal or decimal followed by a quantity into a numeral,
+    e.g. one million -> integer_part: "1" quantity: "million"
+    e.g. one point five million -> integer_part: "1" fractional_part: "5" quantity: "million"
+
+    Args:
+        decimal: decimal FST
+        cardinal_up_to_hundred: cardinal FST
+    """
+    numbers = cardinal_up_to_hundred @ (
+        pynutil.delete(pynini.closure("0"))
+        + pynini.difference(DAMO_DIGIT, "0")
+        + pynini.closure(DAMO_DIGIT)
+    )
+    suffix = pynini.union("juta", "miliar", "triliun", "kuadriliun", "triliun", "sextillion")
+    res = (
+        pynutil.insert('integer_part: "')
+        + numbers
+        + pynutil.insert('"')
+        + delete_extra_space
+        + pynutil.insert('quantity: "')
+        + suffix
+        + pynutil.insert('"')
+    )
+    res |= (
+        decimal
+        + delete_extra_space
+        + pynutil.insert('quantity: "')
+        + (suffix | "ribu")
+        + pynutil.insert('"')
+    )
+    return res
+
+
+class DecimalFst(GraphFst):
+    """
+    Finite state transducer for classifying decimal
+        e.g. minus twelve point five o o six billion -> decimal { negative: "true" integer_part: "12"  fractional_part: "5006" quantity: "billion" }
+        e.g. one billion -> decimal { integer_part: "1" quantity: "billion" }
+    Args:
+        cardinal: CardinalFst
+    """
+
+    def __init__(self, cardinal: GraphFst):
+        super().__init__(name="decimal", kind="classify")
+
+        cardinal_graph = cardinal.graph_no_exception
+
+        graph_decimal = pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
+
+        graph_decimal |= pynini.string_file(get_abs_path("data/numbers/zero.tsv")) | pynini.cross(
+            "o", "0"
+        )
+
+        graph_decimal = pynini.closure(graph_decimal + delete_space) + graph_decimal
+        self.graph = graph_decimal
+
+        point = pynutil.delete("point")  # titik
+
+        optional_graph_negative = pynini.closure(
+            pynutil.insert("negative: ") + pynini.cross("kurang", '"true"') + delete_extra_space,
+            0,
+            1,
+        )
+
+        graph_fractional = (
+            pynutil.insert('fractional_part: "') + graph_decimal + pynutil.insert('"')
+        )
+        graph_integer = pynutil.insert('integer_part: "') + cardinal_graph + pynutil.insert('"')
+        final_graph_wo_sign = (
+            pynini.closure(graph_integer + delete_extra_space, 0, 1)
+            + point
+            + delete_extra_space
+            + graph_fractional
+        )
+        final_graph = optional_graph_negative + final_graph_wo_sign
+
+        self.final_graph_wo_negative = final_graph_wo_sign | get_quantity(
+            final_graph_wo_sign, cardinal.graph_hundred_component_at_least_one_none_zero_digit
+        )
+        final_graph |= optional_graph_negative + get_quantity(
+            final_graph_wo_sign, cardinal.graph_hundred_component_at_least_one_none_zero_digit
+        )
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/electronic.py

@@ -0,0 +1,100 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_ALPHA,
+    GraphFst,
+    insert_space,
+)
+from pynini.lib import pynutil
+
+
+class ElectronicFst(GraphFst):
+    """
+    Finite state transducer for classifying electronic: as URLs, email addresses, etc.
+        e.g. c d f one at a b c dot e d u -> tokens { electronic { username: "cdf1" domain: "abc.edu" } }
+    """
+
+    def __init__(self):
+        super().__init__(name="electronic", kind="classify")
+
+        delete_extra_space = pynutil.delete(" ")
+        alpha_num = (
+            DAMO_ALPHA
+            | pynini.string_file(get_abs_path("data/numbers/digit.tsv"))
+            | pynini.string_file(get_abs_path("data/numbers/zero.tsv"))
+        )
+
+        symbols = pynini.string_file(get_abs_path("data/electronic/symbols.tsv")).invert()
+
+        accepted_username = alpha_num | symbols
+        process_dot = pynini.cross("dot", ".")
+        username = (
+            alpha_num + pynini.closure(delete_extra_space + accepted_username)
+        ) | pynutil.add_weight(pynini.closure(DAMO_ALPHA, 1), weight=0.0001)
+        username = pynutil.insert('username: "') + username + pynutil.insert('"')
+        single_alphanum = pynini.closure(alpha_num + delete_extra_space) + alpha_num
+        server = single_alphanum | pynini.string_file(
+            get_abs_path("data/electronic/server_name.tsv")
+        )
+        domain = single_alphanum | pynini.string_file(get_abs_path("data/electronic/domain.tsv"))
+        domain_graph = (
+            pynutil.insert('domain: "')
+            + server
+            + delete_extra_space
+            + process_dot
+            + delete_extra_space
+            + domain
+            + pynutil.insert('"')
+        )
+        graph = (
+            username
+            + delete_extra_space
+            + pynutil.delete("at")
+            + insert_space
+            + delete_extra_space
+            + domain_graph
+        )
+
+        ############# url ###
+        protocol_end = pynini.cross(pynini.union("w w w", "www"), "www")
+        protocol_start = (
+            pynini.cross("h t t p", "http") | pynini.cross("h t t p s", "https")
+        ) + pynini.cross(" colon slash slash ", "://")
+        # .com,
+        ending = (
+            delete_extra_space
+            + symbols
+            + delete_extra_space
+            + (
+                domain
+                | pynini.closure(
+                    accepted_username + delete_extra_space,
+                )
+                + accepted_username
+            )
+        )
+
+        protocol_default = (
+            (
+                (pynini.closure(delete_extra_space + accepted_username, 1) | server)
+                | pynutil.add_weight(pynini.closure(DAMO_ALPHA, 1), weight=0.0001)
+            )
+            + pynini.closure(ending, 1)
+        ).optimize()
+        protocol = (
+            pynini.closure(protocol_start, 0, 1)
+            + protocol_end
+            + delete_extra_space
+            + process_dot
+            + protocol_default
+        ).optimize()
+
+        protocol |= (
+            pynini.closure(protocol_end + delete_extra_space + process_dot, 0, 1) + protocol_default
+        )
+
+        protocol = pynutil.insert('protocol: "') + protocol.optimize() + pynutil.insert('"')
+        graph |= protocol
+
+        final_graph = self.add_tokens(graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/fraction.py

@@ -0,0 +1,11 @@
+from fun_text_processing.inverse_text_normalization.id.graph_utils import GraphFst
+
+
+class FractionFst(GraphFst):
+    """
+    Finite state transducer for classifying fraction
+    """
+
+    def __init__(self):
+        super().__init__(name="fraction", kind="classify")
+        # integer_part # numerator # denominator

fun_text_processing/inverse_text_normalization/id/taggers/measure.py

@@ -0,0 +1,97 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_SIGMA,
+    GraphFst,
+    convert_space,
+    delete_extra_space,
+    delete_space,
+    get_singulars,
+)
+from pynini.lib import pynutil
+
+
+class MeasureFst(GraphFst):
+    """
+    Finite state transducer for classifying measure
+        e.g. minus twelve kilograms -> measure { negative: "true" cardinal { integer: "12" } units: "kg" }
+
+    Args:
+        cardinal: CardinalFst
+        decimal: DecimalFst
+    """
+
+    def __init__(self, cardinal: GraphFst, decimal: GraphFst):
+        super().__init__(name="measure", kind="classify")
+
+        cardinal_graph = cardinal.graph_no_exception
+
+        graph_unit = pynini.string_file(get_abs_path("data/measurements.tsv"))
+        graph_unit_singular = pynini.invert(graph_unit)  # singular -> abbr
+        graph_unit_plural = get_singulars(graph_unit_singular)  # plural -> abbr
+
+        optional_graph_negative = pynini.closure(
+            pynutil.insert("negative: ") + pynini.cross("kurang", '"true"') + delete_extra_space,
+            0,
+            1,
+        )
+
+        unit_singular = convert_space(graph_unit_singular)
+        unit_plural = convert_space(graph_unit_plural)
+        unit_misc = (
+            pynutil.insert("/")
+            + pynutil.delete("per")
+            + delete_space
+            + convert_space(graph_unit_singular)
+        )
+
+        unit_singular = (
+            pynutil.insert('units: "')
+            + (
+                unit_singular
+                | unit_misc
+                | pynutil.add_weight(unit_singular + delete_space + unit_misc, 0.01)
+            )
+            + pynutil.insert('"')
+        )
+        unit_plural = (
+            pynutil.insert('units: "')
+            + (
+                unit_plural
+                | unit_misc
+                | pynutil.add_weight(unit_plural + delete_space + unit_misc, 0.01)
+            )
+            + pynutil.insert('"')
+        )
+
+        subgraph_decimal = (
+            pynutil.insert("decimal { ")
+            + optional_graph_negative
+            + decimal.final_graph_wo_negative
+            + pynutil.insert(" }")
+            + delete_extra_space
+            + unit_plural
+        )
+        subgraph_cardinal = (
+            pynutil.insert("cardinal { ")
+            + optional_graph_negative
+            + pynutil.insert('integer: "')
+            + ((DAMO_SIGMA - "satu") @ cardinal_graph)
+            + pynutil.insert('"')
+            + pynutil.insert(" }")
+            + delete_extra_space
+            + unit_plural
+        )
+        subgraph_cardinal |= (
+            pynutil.insert("cardinal { ")
+            + optional_graph_negative
+            + pynutil.insert('integer: "')
+            + pynini.cross("satu", "1")
+            + pynutil.insert('"')
+            + pynutil.insert(" }")
+            + delete_extra_space
+            + unit_singular
+        )
+        final_graph = subgraph_decimal | subgraph_cardinal
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/money.py

@@ -0,0 +1,110 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_DIGIT,
+    DAMO_NOT_SPACE,
+    DAMO_SIGMA,
+    GraphFst,
+    convert_space,
+    delete_extra_space,
+    delete_space,
+    get_singulars,
+    insert_space,
+)
+from pynini.lib import pynutil
+
+
+class MoneyFst(GraphFst):
+    """
+    Finite state transducer for classifying money
+        e.g. twelve dollars and five cents -> money { integer_part: "12" fractional_part: 05 currency: "$" }
+
+    Args:
+        cardinal: CardinalFst
+        decimal: DecimalFst
+    """
+
+    def __init__(self, cardinal: GraphFst, decimal: GraphFst):
+        super().__init__(name="money", kind="classify")
+        # quantity, integer_part, fractional_part, currency
+
+        cardinal_graph = cardinal.graph_no_exception
+        # add support for missing hundred (only for 3 digit numbers)
+        # "one fifty" -> "one hundred fifty"
+        with_hundred = pynini.compose(
+            pynini.closure(DAMO_NOT_SPACE)
+            + pynini.accep(" ")
+            + pynutil.insert("ratus ")
+            + DAMO_SIGMA,
+            pynini.compose(cardinal_graph, DAMO_DIGIT**3),
+        )
+        cardinal_graph |= with_hundred
+        graph_decimal_final = decimal.final_graph_wo_negative
+
+        unit = pynini.string_file(get_abs_path("data/currency.tsv"))
+        unit_singular = pynini.invert(unit)
+        unit_plural = get_singulars(unit_singular)
+
+        graph_unit_singular = (
+            pynutil.insert('currency: "') + convert_space(unit_singular) + pynutil.insert('"')
+        )
+        graph_unit_plural = (
+            pynutil.insert('currency: "') + convert_space(unit_plural) + pynutil.insert('"')
+        )
+
+        add_leading_zero_to_double_digit = (DAMO_DIGIT + DAMO_DIGIT) | (
+            pynutil.insert("0") + DAMO_DIGIT
+        )
+        # twelve dollars (and) fifty cents, zero cents
+        cents_standalone = (
+            pynutil.insert('fractional_part: "')
+            + pynini.union(
+                pynutil.add_weight(((DAMO_SIGMA - "satu") @ cardinal_graph), -0.7)
+                @ add_leading_zero_to_double_digit
+                + delete_space
+                + pynutil.delete("sen"),
+                pynini.cross("satu", "01") + delete_space + pynutil.delete("sen"),
+            )
+            + pynutil.insert('"')
+        )
+
+        optional_cents_standalone = pynini.closure(
+            delete_space
+            + pynini.closure(pynutil.delete("dan") + delete_space, 0, 1)
+            + insert_space
+            + cents_standalone,
+            0,
+            1,
+        )
+        # twelve dollars fifty, only after integer
+        optional_cents_suffix = pynini.closure(
+            delete_extra_space
+            + pynutil.insert('fractional_part: "')
+            + pynutil.add_weight(cardinal_graph @ add_leading_zero_to_double_digit, -0.7)
+            + pynutil.insert('"'),
+            0,
+            1,
+        )
+
+        graph_integer = (
+            pynutil.insert('integer_part: "')
+            + ((DAMO_SIGMA - "satu") @ cardinal_graph)
+            + pynutil.insert('"')
+            + delete_extra_space
+            + graph_unit_plural
+            + (optional_cents_standalone | optional_cents_suffix)
+        )
+        graph_integer |= (
+            pynutil.insert('integer_part: "')
+            + pynini.cross("satu", "1")
+            + pynutil.insert('"')
+            + delete_extra_space
+            + graph_unit_singular
+            + (optional_cents_standalone | optional_cents_suffix)
+        )
+        graph_decimal = graph_decimal_final + delete_extra_space + graph_unit_plural
+        graph_decimal |= pynutil.insert('currency: "$" integer_part: "0" ') + cents_standalone
+        final_graph = graph_integer | graph_decimal
+
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/ordinal.py

@@ -0,0 +1,29 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import DAMO_CHAR, GraphFst
+from pynini.lib import pynutil
+
+
+class OrdinalFst(GraphFst):
+    """
+    Finite state transducer for classifying ordinal
+        e.g. thirteenth -> ordinal { integer: "13" }
+
+    Args:
+        cardinal: CardinalFst
+    """
+
+    def __init__(self, cardinal: GraphFst):
+        super().__init__(name="ordinal", kind="classify")
+
+        cardinal_graph = cardinal.graph_no_exception
+        graph_digit = pynini.string_file(get_abs_path("data/ordinals/digit.tsv"))
+        graph_teens = pynini.string_file(get_abs_path("data/ordinals/teen.tsv"))
+        graph = pynini.closure(DAMO_CHAR) + pynini.union(
+            graph_digit, graph_teens, pynini.cross("tieth", "ty"), pynini.cross("th", "")
+        )  # TODO
+
+        self.graph = graph @ cardinal_graph
+        final_graph = pynutil.insert('integer: "') + self.graph + pynutil.insert('"')
+        final_graph = self.add_tokens(final_graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/punctuation.py

@@ -0,0 +1,20 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import GraphFst
+from pynini.lib import pynutil
+
+
+class PunctuationFst(GraphFst):
+    """
+    Finite state transducer for classifying punctuation
+        e.g. a, -> tokens { name: "a" } tokens { name: "," }
+    """
+
+    def __init__(self):
+        super().__init__(name="punctuation", kind="classify")
+
+        s = "!#$%&'()*+,-./:;<=>?@^_`{|}~"
+        punct = pynini.union(*s)
+
+        graph = pynutil.insert('name: "') + punct + pynutil.insert('"')
+
+        self.fst = graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/telephone.py

@@ -0,0 +1,149 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_ALNUM,
+    DAMO_ALPHA,
+    DAMO_DIGIT,
+    GraphFst,
+    insert_space,
+)
+from pynini.lib import pynutil
+
+
+def get_serial_number(cardinal):
+    """
+    any alphanumerical character sequence with at least one number with length greater equal to 3
+    """
+    digit = pynini.compose(cardinal.graph_no_exception, DAMO_DIGIT)
+    character = digit | DAMO_ALPHA
+    sequence = character + pynini.closure(pynutil.delete(" ") + character, 2)
+    sequence = sequence @ (pynini.closure(DAMO_ALNUM) + DAMO_DIGIT + pynini.closure(DAMO_ALNUM))
+    return sequence.optimize()
+
+
+class TelephoneFst(GraphFst):
+    """
+    Finite state transducer for classifying telephone numbers, e.g.
+        one two three one two three five six seven eight -> { number_part: "123-123-5678" }
+
+    This class also support card number and IP format.
+        "one two three dot one double three dot o dot four o" -> { number_part: "123.133.0.40"}
+
+        "three two double seven three two one four three two one four three double zero five" ->
+            { number_part: 3277 3214 3214 3005}
+
+    Args:
+        cardinal: CardinalFst
+    """
+
+    def __init__(self, cardinal: GraphFst):
+        super().__init__(name="telephone", kind="classify")
+        # country code, number_part, extension
+        digit_to_str = (
+            pynini.invert(pynini.string_file(get_abs_path("data/numbers/digit.tsv")).optimize())
+            | pynini.cross("0", pynini.union("o", "oh", "nol")).optimize()
+        )
+
+        str_to_digit = pynini.invert(digit_to_str)
+
+        double_digit = pynini.union(
+            *[
+                pynini.cross(
+                    pynini.project(str(i) @ digit_to_str, "output")
+                    + pynini.accep(" ")
+                    + pynini.project(str(i) @ digit_to_str, "output"),
+                    pynutil.insert("dobel ") + pynini.project(str(i) @ digit_to_str, "output"),
+                )
+                for i in range(10)
+            ]
+        )
+        double_digit.invert()
+
+        # to handle cases like "one twenty three"
+        two_digit_cardinal = pynini.compose(cardinal.graph_no_exception, DAMO_DIGIT**2)
+        double_digit_to_digit = (
+            pynini.compose(double_digit, str_to_digit + pynutil.delete(" ") + str_to_digit)
+            | two_digit_cardinal
+        )
+
+        single_or_double_digit = (
+            pynutil.add_weight(double_digit_to_digit, -0.0001) | str_to_digit
+        ).optimize()
+        single_or_double_digit |= (
+            single_or_double_digit
+            + pynini.closure(
+                pynutil.add_weight(pynutil.delete(" ") + single_or_double_digit, 0.0001)
+            )
+        ).optimize()
+
+        number_part = pynini.compose(
+            single_or_double_digit,
+            DAMO_DIGIT**2
+            + pynutil.insert(" ")
+            + DAMO_DIGIT**4
+            + pynutil.insert("-")
+            + DAMO_DIGIT**4,
+        ).optimize()
+        number_part = (
+            pynutil.insert('number_part: "') + number_part.optimize() + pynutil.insert('"')
+        )
+
+        cardinal_option = pynini.compose(single_or_double_digit, DAMO_DIGIT ** (2, 3))
+
+        country_code = (
+            pynutil.insert('country_code: "')
+            + pynini.closure(pynini.cross("ditambah ", "+"), 0, 1)
+            + (
+                (pynini.closure(str_to_digit + pynutil.delete(" "), 0, 2) + str_to_digit)
+                | cardinal_option
+            )
+            + pynutil.insert('"')
+        )
+
+        optional_country_code = pynini.closure(
+            country_code + pynutil.delete(" ") + insert_space, 0, 1
+        ).optimize()
+        graph = optional_country_code + number_part
+
+        # credit card number
+        space_four_digits = insert_space + DAMO_DIGIT**4
+        credit_card_graph = pynini.compose(
+            single_or_double_digit, DAMO_DIGIT**4 + space_four_digits**3
+        ).optimize()
+        graph |= (
+            pynutil.insert('number_part: "') + credit_card_graph.optimize() + pynutil.insert('"')
+        )
+
+        # SSN
+        ssn_graph = pynini.compose(
+            single_or_double_digit,
+            DAMO_DIGIT**3
+            + pynutil.insert("-")
+            + DAMO_DIGIT**2
+            + pynutil.insert("-")
+            + DAMO_DIGIT**4,
+        ).optimize()
+        graph |= pynutil.insert('number_part: "') + ssn_graph.optimize() + pynutil.insert('"')
+
+        # ip
+        digit_or_double = (
+            pynini.closure(str_to_digit + pynutil.delete(" "), 0, 1) + double_digit_to_digit
+        )
+        digit_or_double |= double_digit_to_digit + pynini.closure(
+            pynutil.delete(" ") + str_to_digit, 0, 1
+        )
+        digit_or_double |= str_to_digit + (pynutil.delete(" ") + str_to_digit) ** (0, 2)
+        digit_or_double |= cardinal_option
+        digit_or_double = digit_or_double.optimize()
+
+        ip_graph = digit_or_double + (pynini.cross(" dot ", ".") + digit_or_double) ** 3
+
+        graph |= pynutil.insert('number_part: "') + ip_graph.optimize() + pynutil.insert('"')
+        graph |= (
+            pynutil.insert('number_part: "')
+            + pynutil.add_weight(get_serial_number(cardinal=cardinal), weight=0.0001)
+            + pynutil.insert('"')
+        )
+
+        final_graph = self.add_tokens(graph)
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/time.py

@@ -0,0 +1,151 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.taggers.cardinal import CardinalFst
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path, num_to_word
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    GraphFst,
+    convert_space,
+    delete_extra_space,
+    delete_space,
+    insert_space,
+)
+from pynini.lib import pynutil
+
+
+class TimeFst(GraphFst):
+    """
+    Finite state transducer for classifying time
+        e.g. twelve thirty -> time { hours: "12" minutes: "30" }
+        e.g. twelve past one -> time { minutes: "12" hours: "1" }
+        e.g. two o clock a m -> time { hours: "2" suffix: "a.m." }
+        e.g. quarter to two -> time { hours: "1" minutes: "45" }
+        e.g. quarter past two -> time { hours: "2" minutes: "15" }
+        e.g. half past two -> time { hours: "2" minutes: "30" }
+    """
+
+    def __init__(self):
+        super().__init__(name="time", kind="classify")
+        # hours, minutes, seconds, suffix, zone, style, speak_period
+
+        suffix_graph = pynini.string_file(get_abs_path("data/time/time_suffix.tsv"))
+        time_zone_graph = pynini.invert(pynini.string_file(get_abs_path("data/time/time_zone.tsv")))
+        to_hour_graph = pynini.string_file(get_abs_path("data/time/to_hour.tsv"))
+        minute_to_graph = pynini.string_file(get_abs_path("data/time/minute_to.tsv"))
+
+        cardinal = pynutil.add_weight(CardinalFst().graph_no_exception, weight=-0.7)
+
+        labels_hour = [
+            "nol",
+            "satu",
+            "dua",
+            "tiga",
+            "empat",
+            "lima",
+            "enam",
+            "tujuh",
+            "delapan",
+            "sembilan",
+            "sepuluh",
+            "sebelas",
+            "duabelas",
+            "tigabelas",
+        ]
+        labels_minute_single = [num_to_word(x) for x in range(1, 10)]
+        labels_minute_double = [num_to_word(x) for x in range(10, 60)]
+
+        graph_hour = pynini.union(*labels_hour) @ cardinal
+
+        graph_minute_single = pynini.union(*labels_minute_single) @ cardinal
+        graph_minute_double = pynini.union(*labels_minute_double) @ cardinal
+        graph_minute_verbose = pynini.cross("setengah", "30") | pynini.cross("seperempat", "15")
+        oclock = pynini.cross(pynini.union("jam", "pukul"), "")
+
+        final_graph_hour = pynutil.insert('hours: "') + graph_hour + pynutil.insert('"')
+        graph_minute = (
+            oclock + pynutil.insert("00")
+            | pynutil.delete("o") + delete_space + graph_minute_single
+            | graph_minute_double
+        )
+        final_suffix = (
+            pynutil.insert('suffix: "') + convert_space(suffix_graph) + pynutil.insert('"')
+        )
+        final_suffix = delete_space + insert_space + final_suffix
+        final_suffix_optional = pynini.closure(final_suffix, 0, 1)
+        final_time_zone_optional = pynini.closure(
+            delete_space
+            + insert_space
+            + pynutil.insert('zone: "')
+            + convert_space(time_zone_graph)
+            + pynutil.insert('"'),
+            0,
+            1,
+        )
+
+        # five o' clock
+        # two o eight, two thirty five (am/pm)
+        # two pm/am
+        graph_hm = (
+            final_graph_hour
+            + delete_extra_space
+            + pynutil.insert('minutes: "')
+            + graph_minute
+            + pynutil.insert('"')
+        )
+        # 10 past four, quarter past four, half past four
+        graph_m_past_h = (
+            pynutil.insert('minutes: "')
+            + pynini.union(graph_minute_single, graph_minute_double, graph_minute_verbose)
+            + pynutil.insert('"')
+            + delete_space
+            + pynutil.delete("setengah")
+            + delete_extra_space
+            + final_graph_hour
+        )
+
+        graph_quarter_time = (
+            pynutil.insert('minutes: "')
+            + pynini.cross("seperempat", "45")
+            + pynutil.insert('"')
+            + delete_space
+            + pynutil.delete(pynini.union("to", "till"))
+            + delete_extra_space
+            + pynutil.insert('hours: "')
+            + to_hour_graph
+            + pynutil.insert('"')
+        )
+
+        graph_m_to_h_suffix_time = (
+            pynutil.insert('minutes: "')
+            + ((graph_minute_single | graph_minute_double).optimize() @ minute_to_graph)
+            + pynutil.insert('"')
+            + pynini.closure(
+                delete_space + pynutil.delete(pynini.union("min", "mins", "menit")), 0, 1
+            )
+            + delete_space
+            + pynutil.delete(pynini.union("sampai"))
+            + delete_extra_space
+            + pynutil.insert('hours: "')
+            + to_hour_graph
+            + pynutil.insert('"')
+            + final_suffix
+        )
+
+        graph_h = (
+            final_graph_hour
+            + delete_extra_space
+            + pynutil.insert('minutes: "')
+            + (pynutil.insert("00") | graph_minute)
+            + pynutil.insert('"')
+            + final_suffix
+            + final_time_zone_optional
+        )
+        final_graph = (
+            (graph_hm | graph_m_past_h | graph_quarter_time)
+            + final_suffix_optional
+            + final_time_zone_optional
+        )
+        final_graph |= graph_h
+        final_graph |= graph_m_to_h_suffix_time
+
+        final_graph = self.add_tokens(final_graph.optimize())
+
+        self.fst = final_graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/tokenize_and_classify.py

@@ -0,0 +1,102 @@
+import os
+
+import pynini
+from fun_text_processing.inverse_text_normalization.id.taggers.cardinal import CardinalFst
+from fun_text_processing.inverse_text_normalization.id.taggers.date import DateFst
+from fun_text_processing.inverse_text_normalization.id.taggers.decimal import DecimalFst
+from fun_text_processing.inverse_text_normalization.id.taggers.electronic import ElectronicFst
+from fun_text_processing.inverse_text_normalization.id.taggers.measure import MeasureFst
+from fun_text_processing.inverse_text_normalization.id.taggers.money import MoneyFst
+from fun_text_processing.inverse_text_normalization.id.taggers.ordinal import OrdinalFst
+from fun_text_processing.inverse_text_normalization.id.taggers.punctuation import PunctuationFst
+from fun_text_processing.inverse_text_normalization.id.taggers.telephone import TelephoneFst
+from fun_text_processing.inverse_text_normalization.id.taggers.time import TimeFst
+from fun_text_processing.inverse_text_normalization.id.taggers.whitelist import WhiteListFst
+from fun_text_processing.inverse_text_normalization.id.taggers.word import WordFst
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    GraphFst,
+    delete_extra_space,
+    delete_space,
+    generator_main,
+)
+from pynini.lib import pynutil
+
+import logging
+
+
+class ClassifyFst(GraphFst):
+    """
+    Final class that composes all other classification grammars. This class can process an entire sentence, that is lower cased.
+    For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File.
+    More details to deployment at NeMo/tools/text_processing_deployment.
+
+    Args:
+        cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache.
+        overwrite_cache: set to True to overwrite .far files
+    """
+
+    def __init__(self, cache_dir: str = None, overwrite_cache: bool = False):
+        super().__init__(name="tokenize_and_classify", kind="classify")
+
+        far_file = None
+        if cache_dir is not None and cache_dir != "None":
+            os.makedirs(cache_dir, exist_ok=True)
+            far_file = os.path.join(cache_dir, "_id_itn.far")
+        if not overwrite_cache and far_file and os.path.exists(far_file):
+            self.fst = pynini.Far(far_file, mode="r")["tokenize_and_classify"]
+            logging.info(f"ClassifyFst.fst was restored from {far_file}.")
+        else:
+            logging.info(f"Creating ClassifyFst grammars.")
+            cardinal = CardinalFst()
+            cardinal_graph = cardinal.fst
+
+            ordinal = OrdinalFst(cardinal)
+            ordinal_graph = ordinal.fst
+
+            decimal = DecimalFst(cardinal)
+            decimal_graph = decimal.fst
+
+            measure_graph = MeasureFst(cardinal=cardinal, decimal=decimal).fst
+            date_graph = DateFst(ordinal=ordinal).fst
+            word_graph = WordFst().fst
+            time_graph = TimeFst().fst
+            money_graph = MoneyFst(cardinal=cardinal, decimal=decimal).fst
+            whitelist_graph = WhiteListFst().fst
+            punct_graph = PunctuationFst().fst
+            electronic_graph = ElectronicFst().fst
+            telephone_graph = TelephoneFst(cardinal).fst
+
+            classify = (
+                pynutil.add_weight(whitelist_graph, 1.01)
+                | pynutil.add_weight(time_graph, 1.1)
+                | pynutil.add_weight(date_graph, 1.09)
+                | pynutil.add_weight(decimal_graph, 1.1)
+                | pynutil.add_weight(measure_graph, 1.1)
+                | pynutil.add_weight(cardinal_graph, 1.1)
+                | pynutil.add_weight(ordinal_graph, 1.1)
+                | pynutil.add_weight(money_graph, 1.1)
+                | pynutil.add_weight(telephone_graph, 1.1)
+                | pynutil.add_weight(electronic_graph, 1.1)
+                | pynutil.add_weight(word_graph, 100)
+            )
+
+            punct = (
+                pynutil.insert("tokens { ")
+                + pynutil.add_weight(punct_graph, weight=1.1)
+                + pynutil.insert(" }")
+            )
+            token = pynutil.insert("tokens { ") + classify + pynutil.insert(" }")
+            token_plus_punct = (
+                pynini.closure(punct + pynutil.insert(" "))
+                + token
+                + pynini.closure(pynutil.insert(" ") + punct)
+            )
+
+            graph = token_plus_punct + pynini.closure(delete_extra_space + token_plus_punct)
+            graph = delete_space + graph + delete_space
+
+            self.fst = graph.optimize()
+
+            if far_file:
+                generator_main(far_file, {"tokenize_and_classify": self.fst})
+                logging.info(f"ClassifyFst grammars are saved to {far_file}.")

fun_text_processing/inverse_text_normalization/id/taggers/whitelist.py

@@ -0,0 +1,19 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.utils import get_abs_path
+from fun_text_processing.inverse_text_normalization.id.graph_utils import GraphFst, convert_space
+from pynini.lib import pynutil
+
+
+class WhiteListFst(GraphFst):
+    """
+    Finite state transducer for classifying whitelisted tokens
+        e.g. misses -> tokens { name: "mrs." }
+    This class has highest priority among all classifier grammars. Whitelisted tokens are defined and loaded from "data/whitelist.tsv".
+    """
+
+    def __init__(self):
+        super().__init__(name="whitelist", kind="classify")
+
+        whitelist = pynini.string_file(get_abs_path("data/whitelist.tsv")).invert()
+        graph = pynutil.insert('name: "') + convert_space(whitelist) + pynutil.insert('"')
+        self.fst = graph.optimize()

fun_text_processing/inverse_text_normalization/id/taggers/word.py

@@ -0,0 +1,15 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import DAMO_NOT_SPACE, GraphFst
+from pynini.lib import pynutil
+
+
+class WordFst(GraphFst):
+    """
+    Finite state transducer for classifying plain tokens, that do not belong to any special class. This can be considered as the default class.
+        e.g. sleep -> tokens { name: "sleep" }
+    """
+
+    def __init__(self):
+        super().__init__(name="word", kind="classify")
+        word = pynutil.insert('name: "') + pynini.closure(DAMO_NOT_SPACE, 1) + pynutil.insert('"')
+        self.fst = word.optimize()

fun_text_processing/inverse_text_normalization/id/text_cases.tsv

@@ -0,0 +1,40 @@
+dua ribu dua puluh dua  2022
+nol satu dua tiga empat lima enam tujuh delapan sembilan    0123456789
+empat belas 14
+limabelas   15
+enambelas   16
+tujuh belas 17
+delapan belas   18
+sembilan belas  19
+dua puluh   20
+seratus enam    106
+enam ratus  600
+ratus   100
+seratus 100
+satu juta   1,000,000
+satu miliar 1 miliar
+seratus dua puluh tiga  123
+ratus dua puluh tiga  123
+dua puluh empat maret 24th March
+seribu dua ratus delapan puluh sembilan 1289
+lima juta tiga ribu tujuh puluh enam rupiah Rp5003076
+ribu tujuh puluh enam rupiah    Rp1076
+tujuh puluh enam rupiah dollar   $1076
+ditambah enam dua dua satu enam lima tiga sembilan nol enam nol lima +62 21 6539-0605
+tiga ribu   3000
+sembilan ribu sembilan ratus sembilan puluh sembilan    9999
+seribu satu 1001
+nol 0
+satu    1
+dua 2
+tiga    3
+empat   4
+lima    5
+enam    6
+tujuh   7
+delapan 8
+sembilan    9
+sepuluh 10
+sebelas 11
+dua belas   12
+tigabelas   13

fun_text_processing/inverse_text_normalization/id/utils.py

@@ -0,0 +1,33 @@
+import os
+from typing import Union
+
+import inflect
+
+_inflect = inflect.engine()
+
+
+def num_to_word(x: Union[str, int]):
+    """
+    converts integer to spoken representation
+
+    Args
+        x: integer
+
+    Returns: spoken representation
+    """
+    if isinstance(x, int):
+        x = str(x)
+        x = _inflect.number_to_words(str(x)).replace("-", " ").replace(",", "")
+    return x
+
+
+def get_abs_path(rel_path):
+    """
+    Get absolute path
+
+    Args:
+        rel_path: relative path to this file
+
+    Returns absolute path
+    """
+    return os.path.dirname(os.path.abspath(__file__)) + "/" + rel_path

fun_text_processing/inverse_text_normalization/id/verbalizers/cardinal.py

@@ -0,0 +1,38 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_NOT_QUOTE,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class CardinalFst(GraphFst):
+    """
+    Finite state transducer for verbalizing cardinal
+        e.g. cardinal { integer: "23" negative: "-" } -> -23
+    """
+
+    def __init__(self):
+        super().__init__(name="cardinal", kind="verbalize")
+        optional_sign = pynini.closure(
+            pynutil.delete("negative:")
+            + delete_space
+            + pynutil.delete('"')
+            + DAMO_NOT_QUOTE
+            + pynutil.delete('"')
+            + delete_space,
+            0,
+            1,
+        )
+        graph = (
+            pynutil.delete("integer:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        self.numbers = graph
+        graph = optional_sign + graph
+        delete_tokens = self.delete_tokens(graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/date.py

@@ -0,0 +1,70 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_NOT_QUOTE,
+    GraphFst,
+    delete_extra_space,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class DateFst(GraphFst):
+    """
+    Finite state transducer for verbalizing date, e.g.
+        date { month: "january" day: "5" year: "2012" preserve_order: true } -> february 5 2012
+        date { day: "5" month: "january" year: "2012" preserve_order: true } -> 5 february 2012
+    """
+
+    def __init__(self):
+        super().__init__(name="date", kind="verbalize")
+        month = (
+            pynutil.delete("month:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        day = (
+            pynutil.delete("day:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        year = (
+            pynutil.delete("year:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + delete_space
+            + pynutil.delete('"')
+        )
+
+        # month (day) year
+        graph_mdy = (
+            month
+            + pynini.closure(delete_extra_space + day, 0, 1)
+            + pynini.closure(delete_extra_space + year, 0, 1)
+        )
+
+        # (day) month year
+        graph_dmy = (
+            pynini.closure(day + delete_extra_space, 0, 1)
+            + month
+            + pynini.closure(delete_extra_space + year, 0, 1)
+        )
+
+        optional_preserve_order = pynini.closure(
+            pynutil.delete("preserve_order:") + delete_space + pynutil.delete("true") + delete_space
+            | pynutil.delete("field_order:")
+            + delete_space
+            + pynutil.delete('"')
+            + DAMO_NOT_QUOTE
+            + pynutil.delete('"')
+            + delete_space
+        )
+
+        final_graph = (graph_mdy | year | graph_dmy) + delete_space + optional_preserve_order
+
+        delete_tokens = self.delete_tokens(final_graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/decimal.py

@@ -0,0 +1,48 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_NOT_QUOTE,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class DecimalFst(GraphFst):
+    """
+    Finite state transducer for verbalizing decimal, e.g.
+        decimal { negative: "true" integer_part: "12"  fractional_part: "5006" quantity: "billion" } -> -12.5006 billion
+    """
+
+    def __init__(self):
+        super().__init__(name="decimal", kind="verbalize")
+        optionl_sign = pynini.closure(pynini.cross('negative: "true"', "-") + delete_space, 0, 1)
+        integer = (
+            pynutil.delete("integer_part:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        optional_integer = pynini.closure(integer + delete_space, 0, 1)
+        fractional = (
+            pynutil.insert(".")
+            + pynutil.delete("fractional_part:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        optional_fractional = pynini.closure(fractional + delete_space, 0, 1)
+        quantity = (
+            pynutil.delete("quantity:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        optional_quantity = pynini.closure(pynutil.insert(" ") + quantity + delete_space, 0, 1)
+        graph = optional_integer + optional_fractional + optional_quantity
+        self.numbers = graph
+        graph = optionl_sign + graph
+        delete_tokens = self.delete_tokens(graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/electronic.py

@@ -0,0 +1,45 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_NOT_QUOTE,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class ElectronicFst(GraphFst):
+    """
+    Finite state transducer for verbalizing electronic
+        e.g. tokens { electronic { username: "cdf1" domain: "abc.edu" } } -> cdf1@abc.edu
+    """
+
+    def __init__(self):
+        super().__init__(name="electronic", kind="verbalize")
+        user_name = (
+            pynutil.delete("username:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        domain = (
+            pynutil.delete("domain:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+
+        protocol = (
+            pynutil.delete("protocol:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+
+        graph = user_name + delete_space + pynutil.insert("@") + domain
+        graph |= protocol
+
+        delete_tokens = self.delete_tokens(graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/fraction.py

@@ -0,0 +1,10 @@
+from fun_text_processing.inverse_text_normalization.id.graph_utils import GraphFst
+
+
+class FractionFst(GraphFst):
+    """
+    Finite state transducer for verbalizing fraction,
+    """
+
+    def __init__(self):
+        super().__init__(name="fraction", kind="verbalize")

fun_text_processing/inverse_text_normalization/id/verbalizers/measure.py

@@ -0,0 +1,51 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_CHAR,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class MeasureFst(GraphFst):
+    """
+    Finite state transducer for verbalizing measure, e.g.
+        measure { negative: "true" cardinal { integer: "12" } units: "kg" } -> -12 kg
+
+    Args:
+        decimal: DecimalFst
+        cardinal: CardinalFst
+    """
+
+    def __init__(self, decimal: GraphFst, cardinal: GraphFst):
+        super().__init__(name="measure", kind="verbalize")
+        optional_sign = pynini.closure(pynini.cross('negative: "true"', "-"), 0, 1)
+        unit = (
+            pynutil.delete("units:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_CHAR - " ", 1)
+            + pynutil.delete('"')
+            + delete_space
+        )
+        graph_decimal = (
+            pynutil.delete("decimal {")
+            + delete_space
+            + optional_sign
+            + delete_space
+            + decimal.numbers
+            + delete_space
+            + pynutil.delete("}")
+        )
+        graph_cardinal = (
+            pynutil.delete("cardinal {")
+            + delete_space
+            + optional_sign
+            + delete_space
+            + cardinal.numbers
+            + delete_space
+            + pynutil.delete("}")
+        )
+        graph = (graph_cardinal | graph_decimal) + delete_space + pynutil.insert(" ") + unit
+        delete_tokens = self.delete_tokens(graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/money.py

@@ -0,0 +1,30 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_CHAR,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class MoneyFst(GraphFst):
+    """
+    Finite state transducer for verbalizing money, e.g.
+        money { integer_part: "12" fractional_part: "05" currency: "$" } -> $12.05
+
+    Args:
+        decimal: DecimalFst
+    """
+
+    def __init__(self, decimal: GraphFst):
+        super().__init__(name="money", kind="verbalize")
+        unit = (
+            pynutil.delete("currency:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_CHAR - " ", 1)
+            + pynutil.delete('"')
+        )
+        graph = unit + delete_space + decimal.numbers
+        delete_tokens = self.delete_tokens(graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/ordinal.py

@@ -0,0 +1,48 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_NOT_QUOTE,
+    DAMO_SIGMA,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class OrdinalFst(GraphFst):
+    """
+    Finite state transducer for verbalizing ordinal, e.g.
+       ordinal { integer: "13" } -> 13th
+    """
+
+    def __init__(self):
+        super().__init__(name="ordinal", kind="verbalize")
+        graph = (
+            pynutil.delete("integer:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        convert_eleven = pynini.cross("11", "11th")
+        convert_twelve = pynini.cross("12", "12th")
+        convert_thirteen = pynini.cross("13", "13th")
+        convert_one = pynini.cross("1", "1st")
+        convert_two = pynini.cross("2", "2nd")
+        convert_three = pynini.cross("3", "3rd")
+        convert_rest = pynutil.insert("th", weight=0.01)
+
+        suffix = pynini.cdrewrite(
+            convert_eleven
+            | convert_twelve
+            | convert_thirteen
+            | convert_one
+            | convert_two
+            | convert_three
+            | convert_rest,
+            "",
+            "[EOS]",
+            DAMO_SIGMA,
+        )
+        graph = graph @ suffix
+        delete_tokens = self.delete_tokens(graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/telephone.py

@@ -0,0 +1,30 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import DAMO_NOT_QUOTE, GraphFst
+from pynini.lib import pynutil
+
+
+class TelephoneFst(GraphFst):
+    """
+    Finite state transducer for verbalizing telephone, e.g.
+        telephone { number_part: "123-123-5678" }
+        -> 123-123-5678
+    """
+
+    def __init__(self):
+        super().__init__(name="telephone", kind="verbalize")
+
+        number_part = (
+            pynutil.delete('number_part: "')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+        )
+        optional_country_code = pynini.closure(
+            pynutil.delete('country_code: "')
+            + pynini.closure(DAMO_NOT_QUOTE, 1)
+            + pynutil.delete('"')
+            + pynini.accep(" "),
+            0,
+            1,
+        )
+        delete_tokens = self.delete_tokens(optional_country_code + number_part)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/time.py

@@ -0,0 +1,68 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_CHAR,
+    DAMO_DIGIT,
+    GraphFst,
+    delete_space,
+    insert_space,
+)
+from pynini.lib import pynutil
+
+
+class TimeFst(GraphFst):
+    """
+    Finite state transducer for verbalizing time, e.g.
+        time { hours: "12" minutes: "30" } -> 12:30
+        time { hours: "1" minutes: "12" } -> 01:12
+        time { hours: "2" suffix: "a.m." } -> 02:00 a.m.
+    """
+
+    def __init__(self):
+        super().__init__(name="time", kind="verbalize")
+        add_leading_zero_to_double_digit = (DAMO_DIGIT + DAMO_DIGIT) | (
+            pynutil.insert("0") + DAMO_DIGIT
+        )
+        hour = (
+            pynutil.delete("hours:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_DIGIT, 1)
+            + pynutil.delete('"')
+        )
+        minute = (
+            pynutil.delete("minutes:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_DIGIT, 1)
+            + pynutil.delete('"')
+        )
+        suffix = (
+            delete_space
+            + insert_space
+            + pynutil.delete("suffix:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_CHAR - " ", 1)
+            + pynutil.delete('"')
+        )
+        optional_suffix = pynini.closure(suffix, 0, 1)
+        zone = (
+            delete_space
+            + insert_space
+            + pynutil.delete("zone:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_CHAR - " ", 1)
+            + pynutil.delete('"')
+        )
+        optional_zone = pynini.closure(zone, 0, 1)
+        graph = (
+            hour @ add_leading_zero_to_double_digit
+            + delete_space
+            + pynutil.insert(":")
+            + (minute @ add_leading_zero_to_double_digit)
+            + optional_suffix
+            + optional_zone
+        )
+        delete_tokens = self.delete_tokens(graph)
+        self.fst = delete_tokens.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/verbalize.py

@@ -0,0 +1,47 @@
+from fun_text_processing.inverse_text_normalization.id.verbalizers.cardinal import CardinalFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.date import DateFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.decimal import DecimalFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.electronic import ElectronicFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.measure import MeasureFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.money import MoneyFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.ordinal import OrdinalFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.telephone import TelephoneFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.time import TimeFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.whitelist import WhiteListFst
+from fun_text_processing.inverse_text_normalization.id.graph_utils import GraphFst
+
+
+class VerbalizeFst(GraphFst):
+    """
+    Composes other verbalizer grammars.
+    For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File.
+    More details to deployment at NeMo/tools/text_processing_deployment.
+    """
+
+    def __init__(self):
+        super().__init__(name="verbalize", kind="verbalize")
+        cardinal = CardinalFst()
+        cardinal_graph = cardinal.fst
+        ordinal_graph = OrdinalFst().fst
+        decimal = DecimalFst()
+        decimal_graph = decimal.fst
+        measure_graph = MeasureFst(decimal=decimal, cardinal=cardinal).fst
+        money_graph = MoneyFst(decimal=decimal).fst
+        time_graph = TimeFst().fst
+        date_graph = DateFst().fst
+        whitelist_graph = WhiteListFst().fst
+        telephone_graph = TelephoneFst().fst
+        electronic_graph = ElectronicFst().fst
+        graph = (
+            time_graph
+            | date_graph
+            | money_graph
+            | measure_graph
+            | ordinal_graph
+            | decimal_graph
+            | cardinal_graph
+            | whitelist_graph
+            | telephone_graph
+            | electronic_graph
+        )
+        self.fst = graph

fun_text_processing/inverse_text_normalization/id/verbalizers/verbalize_final.py

@@ -0,0 +1,33 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.verbalizers.verbalize import VerbalizeFst
+from fun_text_processing.inverse_text_normalization.id.verbalizers.word import WordFst
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    GraphFst,
+    delete_extra_space,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class VerbalizeFinalFst(GraphFst):
+    """
+    Finite state transducer that verbalizes an entire sentence, e.g.
+    tokens { name: "its" } tokens { time { hours: "12" minutes: "30" } } tokens { name: "now" } -> its 12:30 now
+    """
+
+    def __init__(self):
+        super().__init__(name="verbalize_final", kind="verbalize")
+        verbalize = VerbalizeFst().fst
+        word = WordFst().fst
+        types = verbalize | word
+        graph = (
+            pynutil.delete("tokens")
+            + delete_space
+            + pynutil.delete("{")
+            + delete_space
+            + types
+            + delete_space
+            + pynutil.delete("}")
+        )
+        graph = delete_space + pynini.closure(graph + delete_extra_space) + graph + delete_space
+        self.fst = graph

fun_text_processing/inverse_text_normalization/id/verbalizers/whitelist.py

@@ -0,0 +1,27 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_CHAR,
+    DAMO_SIGMA,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class WhiteListFst(GraphFst):
+    """
+    Finite state transducer for verbalizing whitelist
+        e.g. tokens { name: "mrs." } -> mrs.
+    """
+
+    def __init__(self):
+        super().__init__(name="whitelist", kind="verbalize")
+        graph = (
+            pynutil.delete("name:")
+            + delete_space
+            + pynutil.delete('"')
+            + pynini.closure(DAMO_CHAR - " ", 1)
+            + pynutil.delete('"')
+        )
+        graph = graph @ pynini.cdrewrite(pynini.cross("\u00A0", " "), "", "", DAMO_SIGMA)
+        self.fst = graph.optimize()

fun_text_processing/inverse_text_normalization/id/verbalizers/word.py

@@ -0,0 +1,29 @@
+import pynini
+from fun_text_processing.inverse_text_normalization.id.graph_utils import (
+    DAMO_CHAR,
+    DAMO_SIGMA,
+    GraphFst,
+    delete_space,
+)
+from pynini.lib import pynutil
+
+
+class WordFst(GraphFst):
+    """
+    Finite state transducer for verbalizing plain tokens
+        e.g. tokens { name: "sleep" } -> sleep
+    """
+
+    def __init__(self):
+        super().__init__(name="word", kind="verbalize")
+        chars = pynini.closure(DAMO_CHAR - " ", 1)
+        char = (
+            pynutil.delete("name:")
+            + delete_space
+            + pynutil.delete('"')
+            + chars
+            + pynutil.delete('"')
+        )
+        graph = char @ pynini.cdrewrite(pynini.cross("\u00A0", " "), "", "", DAMO_SIGMA)
+
+        self.fst = graph.optimize()