FunASR/fun_text_processing/text_normalization/de/verbalizers/date.py

import pynini
from fun_text_processing.text_normalization.de.utils import get_abs_path, load_labels
from fun_text_processing.text_normalization.en.graph_utils import (
    DAMO_NOT_QUOTE,
    DAMO_SIGMA,
    GraphFst,
    delete_preserve_order,
)
from pynini.lib import pynutil


class DateFst(GraphFst):
    """
    Finite state transducer for verbalizing date, e.g.
        date { day: "vier" month: "april" year: "zwei tausend zwei" } -> "vierter april zwei tausend zwei"
        date { day: "vier" month: "mai" year: "zwei tausend zwei" } -> "vierter mai zwei tausend zwei"

    Args:
        ordinal: ordinal verbalizer GraphFst
        deterministic: if True will provide a single transduction option,
            for False multiple transduction are generated (used for audio-based normalization)
    """

    def __init__(self, ordinal: GraphFst, deterministic: bool = True):
        super().__init__(name="date", kind="verbalize", deterministic=deterministic)

        day_cardinal = pynutil.delete("day: \"") + pynini.closure(DAMO_NOT_QUOTE, 1) + pynutil.delete("\"")
        day = day_cardinal @ pynini.cdrewrite(ordinal.ordinal_stem, "", "[EOS]", DAMO_SIGMA) + pynutil.insert("ter")

        months_names = pynini.union(*[x[1] for x in load_labels(get_abs_path("data/months/abbr_to_name.tsv"))])
        month = pynutil.delete("month: \"") + pynini.closure(DAMO_NOT_QUOTE, 1) + pynutil.delete("\"")
        final_month = month @ months_names
        final_month |= month @ pynini.difference(DAMO_SIGMA, months_names) @ pynini.cdrewrite(
            ordinal.ordinal_stem, "", "[EOS]", DAMO_SIGMA
        ) + pynutil.insert("ter")

        year = pynutil.delete("year: \"") + pynini.closure(DAMO_NOT_QUOTE, 1) + pynutil.delete("\"")

        # day month year
        graph_dmy = day + pynini.accep(" ") + final_month + pynini.closure(pynini.accep(" ") + year, 0, 1)
        graph_dmy |= final_month + pynini.accep(" ") + year

        self.graph = graph_dmy | year
        final_graph = self.graph + delete_preserve_order

        delete_tokens = self.delete_tokens(final_graph)
        self.fst = delete_tokens.optimize()