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| import math |
| from dataclasses import dataclass, field |
| from typing import Optional |
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| from fairseq.dataclass.configs import FairseqDataclass |
| from fairseq.dataclass.constants import ChoiceEnum |
| from omegaconf import MISSING |
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| DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"]) |
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| @dataclass |
| class DecoderConfig(FairseqDataclass): |
| type: DECODER_CHOICES = field( |
| default="viterbi", |
| metadata={"help": "The type of decoder to use"}, |
| ) |
|
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|
| @dataclass |
| class FlashlightDecoderConfig(FairseqDataclass): |
| nbest: int = field( |
| default=1, |
| metadata={"help": "Number of decodings to return"}, |
| ) |
| unitlm: bool = field( |
| default=False, |
| metadata={"help": "If set, use unit language model"}, |
| ) |
| lmpath: str = field( |
| default=MISSING, |
| metadata={"help": "Language model for KenLM decoder"}, |
| ) |
| lexicon: Optional[str] = field( |
| default=None, |
| metadata={"help": "Lexicon for Flashlight decoder"}, |
| ) |
| beam: int = field( |
| default=50, |
| metadata={"help": "Number of beams to use for decoding"}, |
| ) |
| beamthreshold: float = field( |
| default=50.0, |
| metadata={"help": "Threshold for beam search decoding"}, |
| ) |
| beamsizetoken: Optional[int] = field( |
| default=None, metadata={"help": "Beam size to use"} |
| ) |
| wordscore: float = field( |
| default=-1, |
| metadata={"help": "Word score for KenLM decoder"}, |
| ) |
| unkweight: float = field( |
| default=-math.inf, |
| metadata={"help": "Unknown weight for KenLM decoder"}, |
| ) |
| silweight: float = field( |
| default=0, |
| metadata={"help": "Silence weight for KenLM decoder"}, |
| ) |
| lmweight: float = field( |
| default=2, |
| metadata={"help": "Weight for LM while interpolating score"}, |
| ) |
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