from fun_text_processing.text_normalization.en.graph_utils import GraphFst
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from fun_text_processing.text_normalization.en.verbalizers.whitelist import WhiteListFst
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from fun_text_processing.text_normalization.ru.verbalizers.cardinal import CardinalFst
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from fun_text_processing.text_normalization.ru.verbalizers.date import DateFst
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from fun_text_processing.text_normalization.ru.verbalizers.decimal import DecimalFst
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from fun_text_processing.text_normalization.ru.verbalizers.electronic import ElectronicFst
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from fun_text_processing.text_normalization.ru.verbalizers.measure import MeasureFst
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from fun_text_processing.text_normalization.ru.verbalizers.money import MoneyFst
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from fun_text_processing.text_normalization.ru.verbalizers.ordinal import OrdinalFst
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from fun_text_processing.text_normalization.ru.verbalizers.telephone import TelephoneFst
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from fun_text_processing.text_normalization.ru.verbalizers.time import TimeFst
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class VerbalizeFst(GraphFst):
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"""
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Composes other verbalizer grammars.
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For deployment, this grammar will be compiled and exported to OpenFst Finate State Archiv (FAR) File.
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More details to deployment at NeMo/tools/text_processing_deployment.
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Args:
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deterministic: if True will provide a single transduction option,
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for False multiple options (used for audio-based normalization)
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"""
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def __init__(self, deterministic: bool = True):
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super().__init__(name="verbalize", kind="verbalize", deterministic=deterministic)
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cardinal = CardinalFst()
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cardinal_graph = cardinal.fst
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ordinal_graph = OrdinalFst().fst
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decimal = DecimalFst()
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decimal_graph = decimal.fst
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date = DateFst()
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date_graph = date.fst
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measure = MeasureFst()
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measure_graph = measure.fst
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electronic = ElectronicFst()
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electronic_graph = electronic.fst
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whitelist_graph = WhiteListFst().fst
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money_graph = MoneyFst().fst
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telephone_graph = TelephoneFst().fst
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time_graph = TimeFst().fst
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graph = (
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measure_graph
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| cardinal_graph
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| decimal_graph
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| ordinal_graph
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| date_graph
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| electronic_graph
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| money_graph
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| whitelist_graph
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| telephone_graph
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| time_graph
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)
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self.fst = graph
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