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import pynini
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from fun_text_processing.text_normalization.de.utils import get_abs_path, load_labels
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from fun_text_processing.text_normalization.en.graph_utils import (
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DAMO_DIGIT,
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DAMO_SIGMA,
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GraphFst,
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convert_space,
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delete_preserve_order,
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)
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from pynini.lib import pynutil
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class TimeFst(GraphFst):
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"""
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Finite state transducer for verbalizing electronic, e.g.
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time { hours: "2" minutes: "15"} -> "zwei uhr fünfzehn"
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time { minutes: "15" hours: "2" } -> "viertel nach zwei"
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time { minutes: "15" hours: "2" } -> "fünfzehn nach zwei"
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time { hours: "14" minutes: "15"} -> "vierzehn uhr fünfzehn"
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time { minutes: "15" hours: "14" } -> "viertel nach zwei"
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time { minutes: "15" hours: "14" } -> "fünfzehn nach drei"
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time { minutes: "45" hours: "14" } -> "viertel vor drei"
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Args:
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cardinal_tagger: cardinal_tagger tagger GraphFst
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deterministic: if True will provide a single transduction option,
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for False multiple transduction are generated (used for audio-based normalization)
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"""
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def __init__(self, cardinal_tagger: GraphFst, deterministic: bool = True):
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super().__init__(name="time", kind="verbalize", deterministic=deterministic)
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# add weight so when using inverse text normalization this conversion is depriotized
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night_to_early = pynutil.add_weight(
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pynini.invert(pynini.string_file(get_abs_path("data/time/hour_to_night.tsv"))).optimize(), weight=0.0001
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)
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hour_to = pynini.invert(pynini.string_file(get_abs_path("data/time/hour_to.tsv"))).optimize()
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minute_to = pynini.invert(pynini.string_file(get_abs_path("data/time/minute_to.tsv"))).optimize()
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time_zone_graph = pynini.invert(
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convert_space(pynini.union(*[x[1] for x in load_labels(get_abs_path("data/time/time_zone.tsv"))]))
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)
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graph_zero = pynini.invert(pynini.string_file(get_abs_path("data/numbers/zero.tsv"))).optimize()
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number_verbalization = graph_zero | cardinal_tagger.two_digit_non_zero
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hour = pynutil.delete("hours: \"") + pynini.closure(DAMO_DIGIT, 1) + pynutil.delete("\"")
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hour_verbalized = hour @ number_verbalization @ pynini.cdrewrite(
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pynini.cross("eins", "ein"), "[BOS]", "[EOS]", DAMO_SIGMA
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) + pynutil.insert(" uhr")
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minute = pynutil.delete("minutes: \"") + pynini.closure(DAMO_DIGIT, 1) + pynutil.delete("\"")
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zone = pynutil.delete("zone: \"") + time_zone_graph + pynutil.delete("\"")
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optional_zone = pynini.closure(pynini.accep(" ") + zone, 0, 1)
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second = pynutil.delete("seconds: \"") + pynini.closure(DAMO_DIGIT, 1) + pynutil.delete("\"")
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graph_hms = (
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hour_verbalized
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+ pynini.accep(" ")
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+ minute @ number_verbalization
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+ pynutil.insert(" minuten")
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+ pynini.accep(" ")
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+ second @ number_verbalization
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+ pynutil.insert(" sekunden")
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+ optional_zone
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)
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graph_hms @= pynini.cdrewrite(
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pynini.cross("eins minuten", "eine minute") | pynini.cross("eins sekunden", "eine sekunde"),
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pynini.union(" ", "[BOS]"),
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"",
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DAMO_SIGMA,
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)
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min_30 = [str(x) for x in range(1, 31)]
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min_30 = pynini.union(*min_30)
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min_29 = [str(x) for x in range(1, 30)]
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min_29 = pynini.union(*min_29)
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graph_h = hour_verbalized
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graph_hm = hour_verbalized + pynini.accep(" ") + minute @ number_verbalization
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graph_m_past_h = (
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minute @ min_30 @ (number_verbalization | pynini.cross("15", "viertel"))
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+ pynini.accep(" ")
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+ pynutil.insert("nach ")
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# + hour @ number_verbalization
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+ hour @ pynini.cdrewrite(night_to_early, "[BOS]", "[EOS]", DAMO_SIGMA) @ number_verbalization
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)
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graph_m30_h = (
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minute @ pynini.cross("30", "halb")
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+ pynini.accep(" ")
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+ hour @ pynini.cdrewrite(night_to_early, "[BOS]", "[EOS]", DAMO_SIGMA) @ hour_to @ number_verbalization
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)
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graph_m_to_h = (
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minute @ minute_to @ min_29 @ (number_verbalization | pynini.cross("15", "viertel"))
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+ pynini.accep(" ")
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+ pynutil.insert("vor ")
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+ hour @ pynini.cdrewrite(night_to_early, "[BOS]", "[EOS]", DAMO_SIGMA) @ hour_to @ number_verbalization
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)
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self.graph = (
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graph_hms
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| graph_h
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| graph_hm
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| pynutil.add_weight(graph_m_past_h, weight=0.0001)
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| pynutil.add_weight(graph_m30_h, weight=0.0001)
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| pynutil.add_weight(graph_m_to_h, weight=0.0001)
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) + optional_zone
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delete_tokens = self.delete_tokens(self.graph + delete_preserve_order)
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self.fst = delete_tokens.optimize()
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