"""ASR Transducer Task."""
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import argparse
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import logging
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from typing import Callable, Collection, Dict, List, Optional, Tuple
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import numpy as np
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import torch
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from typeguard import check_argument_types, check_return_type
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from funasr.models.frontend.abs_frontend import AbsFrontend
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from funasr.models.frontend.default import DefaultFrontend
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from funasr.models.frontend.windowing import SlidingWindow
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from funasr.models.specaug.abs_specaug import AbsSpecAug
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from funasr.models.specaug.specaug import SpecAug
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from funasr.models.decoder.abs_decoder import AbsDecoder as AbsAttDecoder
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from funasr.models.decoder.transformer_decoder import (
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DynamicConvolution2DTransformerDecoder,
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DynamicConvolutionTransformerDecoder,
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LightweightConvolution2DTransformerDecoder,
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LightweightConvolutionTransformerDecoder,
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TransformerDecoder,
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)
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from funasr.models.rnnt_decoder.abs_decoder import AbsDecoder
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from funasr.models.rnnt_decoder.rnn_decoder import RNNDecoder
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from funasr.models.rnnt_decoder.stateless_decoder import StatelessDecoder
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from funasr.models.encoder.chunk_encoder import ChunkEncoder as Encoder
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from funasr.models.e2e_transducer import TransducerModel
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from funasr.models.e2e_transducer_unified import UnifiedTransducerModel
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from funasr.models.joint_network import JointNetwork
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from funasr.layers.abs_normalize import AbsNormalize
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from funasr.layers.global_mvn import GlobalMVN
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from funasr.layers.utterance_mvn import UtteranceMVN
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from funasr.tasks.abs_task import AbsTask
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from funasr.text.phoneme_tokenizer import g2p_choices
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from funasr.train.class_choices import ClassChoices
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from funasr.datasets.collate_fn import CommonCollateFn
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from funasr.datasets.preprocessor import CommonPreprocessor
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from funasr.train.trainer import Trainer
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from funasr.utils.get_default_kwargs import get_default_kwargs
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from funasr.utils.nested_dict_action import NestedDictAction
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from funasr.utils.types import float_or_none, int_or_none, str2bool, str_or_none
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frontend_choices = ClassChoices(
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name="frontend",
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classes=dict(
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default=DefaultFrontend,
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sliding_window=SlidingWindow,
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),
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type_check=AbsFrontend,
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default="default",
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)
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specaug_choices = ClassChoices(
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"specaug",
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classes=dict(
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specaug=SpecAug,
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),
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type_check=AbsSpecAug,
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default=None,
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optional=True,
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)
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normalize_choices = ClassChoices(
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"normalize",
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classes=dict(
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global_mvn=GlobalMVN,
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utterance_mvn=UtteranceMVN,
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),
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type_check=AbsNormalize,
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default="utterance_mvn",
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optional=True,
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)
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encoder_choices = ClassChoices(
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"encoder",
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classes=dict(
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encoder=Encoder,
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),
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default="encoder",
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)
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decoder_choices = ClassChoices(
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"decoder",
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classes=dict(
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rnn=RNNDecoder,
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stateless=StatelessDecoder,
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),
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type_check=AbsDecoder,
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default="rnn",
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)
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att_decoder_choices = ClassChoices(
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"att_decoder",
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classes=dict(
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transformer=TransformerDecoder,
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lightweight_conv=LightweightConvolutionTransformerDecoder,
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lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
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dynamic_conv=DynamicConvolutionTransformerDecoder,
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dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
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),
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type_check=AbsAttDecoder,
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default=None,
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optional=True,
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)
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class ASRTransducerTask(AbsTask):
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"""ASR Transducer Task definition."""
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num_optimizers: int = 1
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class_choices_list = [
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frontend_choices,
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specaug_choices,
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normalize_choices,
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encoder_choices,
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decoder_choices,
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att_decoder_choices,
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]
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trainer = Trainer
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@classmethod
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def add_task_arguments(cls, parser: argparse.ArgumentParser):
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"""Add Transducer task arguments.
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Args:
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cls: ASRTransducerTask object.
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parser: Transducer arguments parser.
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"""
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group = parser.add_argument_group(description="Task related.")
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# required = parser.get_default("required")
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# required += ["token_list"]
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group.add_argument(
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"--token_list",
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type=str_or_none,
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default=None,
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help="Integer-string mapper for tokens.",
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)
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group.add_argument(
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"--split_with_space",
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type=str2bool,
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default=True,
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help="whether to split text using <space>",
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)
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group.add_argument(
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"--input_size",
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type=int_or_none,
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default=None,
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help="The number of dimensions for input features.",
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)
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group.add_argument(
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"--init",
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type=str_or_none,
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default=None,
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help="Type of model initialization to use.",
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)
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group.add_argument(
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"--model_conf",
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action=NestedDictAction,
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default=get_default_kwargs(TransducerModel),
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help="The keyword arguments for the model class.",
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)
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# group.add_argument(
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# "--encoder_conf",
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# action=NestedDictAction,
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# default={},
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# help="The keyword arguments for the encoder class.",
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# )
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group.add_argument(
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"--joint_network_conf",
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action=NestedDictAction,
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default={},
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help="The keyword arguments for the joint network class.",
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)
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group = parser.add_argument_group(description="Preprocess related.")
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group.add_argument(
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"--use_preprocessor",
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type=str2bool,
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default=True,
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help="Whether to apply preprocessing to input data.",
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)
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group.add_argument(
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"--token_type",
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type=str,
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default="bpe",
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choices=["bpe", "char", "word", "phn"],
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help="The type of tokens to use during tokenization.",
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)
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group.add_argument(
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"--bpemodel",
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type=str_or_none,
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default=None,
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help="The path of the sentencepiece model.",
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)
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parser.add_argument(
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"--non_linguistic_symbols",
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type=str_or_none,
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help="The 'non_linguistic_symbols' file path.",
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)
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parser.add_argument(
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"--cleaner",
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type=str_or_none,
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choices=[None, "tacotron", "jaconv", "vietnamese"],
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default=None,
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help="Text cleaner to use.",
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)
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parser.add_argument(
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"--g2p",
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type=str_or_none,
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choices=g2p_choices,
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default=None,
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help="g2p method to use if --token_type=phn.",
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)
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parser.add_argument(
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"--speech_volume_normalize",
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type=float_or_none,
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default=None,
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help="Normalization value for maximum amplitude scaling.",
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)
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parser.add_argument(
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"--rir_scp",
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type=str_or_none,
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default=None,
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help="The RIR SCP file path.",
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)
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parser.add_argument(
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"--rir_apply_prob",
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type=float,
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default=1.0,
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help="The probability of the applied RIR convolution.",
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)
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parser.add_argument(
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"--noise_scp",
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type=str_or_none,
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default=None,
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help="The path of noise SCP file.",
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)
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parser.add_argument(
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"--noise_apply_prob",
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type=float,
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default=1.0,
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help="The probability of the applied noise addition.",
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)
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parser.add_argument(
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"--noise_db_range",
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type=str,
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default="13_15",
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help="The range of the noise decibel level.",
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)
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for class_choices in cls.class_choices_list:
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# Append --<name> and --<name>_conf.
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# e.g. --decoder and --decoder_conf
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class_choices.add_arguments(group)
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@classmethod
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def build_collate_fn(
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cls, args: argparse.Namespace, train: bool
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) -> Callable[
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[Collection[Tuple[str, Dict[str, np.ndarray]]]],
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Tuple[List[str], Dict[str, torch.Tensor]],
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]:
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"""Build collate function.
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Args:
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cls: ASRTransducerTask object.
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args: Task arguments.
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train: Training mode.
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Return:
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: Callable collate function.
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"""
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assert check_argument_types()
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return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
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@classmethod
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def build_preprocess_fn(
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cls, args: argparse.Namespace, train: bool
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) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
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"""Build pre-processing function.
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Args:
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cls: ASRTransducerTask object.
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args: Task arguments.
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train: Training mode.
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Return:
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: Callable pre-processing function.
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"""
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assert check_argument_types()
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if args.use_preprocessor:
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retval = CommonPreprocessor(
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train=train,
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token_type=args.token_type,
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token_list=args.token_list,
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bpemodel=args.bpemodel,
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non_linguistic_symbols=args.non_linguistic_symbols,
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text_cleaner=args.cleaner,
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g2p_type=args.g2p,
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split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
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rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
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rir_apply_prob=args.rir_apply_prob
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if hasattr(args, "rir_apply_prob")
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else 1.0,
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noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
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noise_apply_prob=args.noise_apply_prob
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if hasattr(args, "noise_apply_prob")
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else 1.0,
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noise_db_range=args.noise_db_range
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if hasattr(args, "noise_db_range")
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else "13_15",
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speech_volume_normalize=args.speech_volume_normalize
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if hasattr(args, "rir_scp")
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else None,
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)
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else:
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retval = None
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assert check_return_type(retval)
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return retval
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@classmethod
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def required_data_names(
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cls, train: bool = True, inference: bool = False
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) -> Tuple[str, ...]:
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"""Required data depending on task mode.
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Args:
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cls: ASRTransducerTask object.
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train: Training mode.
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inference: Inference mode.
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Return:
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retval: Required task data.
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"""
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if not inference:
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retval = ("speech", "text")
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else:
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retval = ("speech",)
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return retval
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@classmethod
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def optional_data_names(
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cls, train: bool = True, inference: bool = False
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) -> Tuple[str, ...]:
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"""Optional data depending on task mode.
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Args:
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cls: ASRTransducerTask object.
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train: Training mode.
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inference: Inference mode.
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Return:
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retval: Optional task data.
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"""
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retval = ()
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assert check_return_type(retval)
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return retval
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@classmethod
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def build_model(cls, args: argparse.Namespace) -> TransducerModel:
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"""Required data depending on task mode.
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Args:
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cls: ASRTransducerTask object.
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args: Task arguments.
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Return:
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model: ASR Transducer model.
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"""
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assert check_argument_types()
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if isinstance(args.token_list, str):
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with open(args.token_list, encoding="utf-8") as f:
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token_list = [line.rstrip() for line in f]
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# Overwriting token_list to keep it as "portable".
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args.token_list = list(token_list)
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elif isinstance(args.token_list, (tuple, list)):
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token_list = list(args.token_list)
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else:
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raise RuntimeError("token_list must be str or list")
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vocab_size = len(token_list)
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logging.info(f"Vocabulary size: {vocab_size }")
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# 1. frontend
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if args.input_size is None:
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# Extract features in the model
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frontend_class = frontend_choices.get_class(args.frontend)
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frontend = frontend_class(**args.frontend_conf)
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input_size = frontend.output_size()
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else:
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# Give features from data-loader
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frontend = None
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input_size = args.input_size
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# 2. Data augmentation for spectrogram
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if args.specaug is not None:
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specaug_class = specaug_choices.get_class(args.specaug)
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specaug = specaug_class(**args.specaug_conf)
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else:
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specaug = None
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# 3. Normalization layer
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if args.normalize is not None:
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normalize_class = normalize_choices.get_class(args.normalize)
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normalize = normalize_class(**args.normalize_conf)
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else:
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normalize = None
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# 4. Encoder
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if getattr(args, "encoder", None) is not None:
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encoder_class = encoder_choices.get_class(args.encoder)
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encoder = encoder_class(input_size, **args.encoder_conf)
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else:
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encoder = Encoder(input_size, **args.encoder_conf)
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encoder_output_size = encoder.output_size
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# 5. Decoder
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decoder_class = decoder_choices.get_class(args.decoder)
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decoder = decoder_class(
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vocab_size,
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**args.decoder_conf,
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)
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decoder_output_size = decoder.output_size
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if getattr(args, "att_decoder", None) is not None:
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att_decoder_class = att_decoder_choices.get_class(args.att_decoder)
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att_decoder = att_decoder_class(
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vocab_size=vocab_size,
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encoder_output_size=encoder_output_size,
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**args.att_decoder_conf,
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)
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else:
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att_decoder = None
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# 6. Joint Network
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joint_network = JointNetwork(
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vocab_size,
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encoder_output_size,
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decoder_output_size,
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**args.joint_network_conf,
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)
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# 7. Build model
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if encoder.unified_model_training:
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model = UnifiedTransducerModel(
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vocab_size=vocab_size,
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token_list=token_list,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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encoder=encoder,
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decoder=decoder,
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att_decoder=att_decoder,
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joint_network=joint_network,
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**args.model_conf,
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)
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else:
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model = TransducerModel(
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vocab_size=vocab_size,
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token_list=token_list,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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encoder=encoder,
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decoder=decoder,
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att_decoder=att_decoder,
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joint_network=joint_network,
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**args.model_conf,
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)
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# 8. Initialize model
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if args.init is not None:
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raise NotImplementedError(
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"Currently not supported.",
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"Initialization part will be reworked in a short future.",
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)
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#assert check_return_type(model)
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return model
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