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import pynini
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from fun_text_processing.text_normalization.zh.graph_utils import GraphFst
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from fun_text_processing.text_normalization.zh.verbalizers.cardinal import Cardinal
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from fun_text_processing.text_normalization.zh.verbalizers.char import Char
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from fun_text_processing.text_normalization.zh.verbalizers.date import Date
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from fun_text_processing.text_normalization.zh.verbalizers.fraction import Fraction
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from fun_text_processing.text_normalization.zh.verbalizers.math_symbol import MathSymbol
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from fun_text_processing.text_normalization.zh.verbalizers.measure import Measure
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from fun_text_processing.text_normalization.zh.verbalizers.money import Money
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from fun_text_processing.text_normalization.zh.verbalizers.time import Time
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from fun_text_processing.text_normalization.zh.verbalizers.whitelist import Whitelist
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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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date = Date(deterministic=deterministic)
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cardinal = Cardinal(deterministic=deterministic)
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char = Char(deterministic=deterministic)
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fraction = Fraction(deterministic=deterministic)
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math_symbol = MathSymbol(deterministic=deterministic)
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money = Money(deterministic=deterministic)
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measure = Measure(deterministic=deterministic)
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time = Time(deterministic=deterministic)
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whitelist = Whitelist(deterministic=deterministic)
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graph = pynini.union(
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date.fst,
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cardinal.fst,
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fraction.fst,
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char.fst,
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math_symbol.fst,
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money.fst,
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measure.fst,
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time.fst,
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whitelist.fst,
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)
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self.fst = graph.optimize()
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