import argparse
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import logging
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import os
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from pathlib import Path
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from typing import Callable
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from typing import Collection
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Tuple
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from typing import Union
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import numpy as np
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import torch
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import yaml
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from typeguard import check_argument_types
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from typeguard import check_return_type
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from funasr.datasets.collate_fn import CommonCollateFn
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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.models.e2e_vad import E2EVadModel
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from funasr.models.encoder.fsmn_encoder import FSMN
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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.fused import FusedFrontends
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from funasr.models.frontend.s3prl import S3prlFrontend
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from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
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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.specaug.specaug import SpecAugLFR
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from funasr.tasks.abs_task import AbsTask
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from funasr.train.class_choices import ClassChoices
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from funasr.train.trainer import Trainer
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from funasr.utils.types import float_or_none
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from funasr.utils.types import int_or_none
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from funasr.utils.types import 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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s3prl=S3prlFrontend,
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fused=FusedFrontends,
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wav_frontend=WavFrontend,
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wav_frontend_online=WavFrontendOnline,
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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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name="specaug",
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classes=dict(
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specaug=SpecAug,
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specaug_lfr=SpecAugLFR,
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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=None,
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optional=True,
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)
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model_choices = ClassChoices(
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"model",
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classes=dict(
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e2evad=E2EVadModel,
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),
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type_check=object,
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default="e2evad",
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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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fsmn=FSMN,
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),
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type_check=torch.nn.Module,
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default="fsmn",
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)
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class VADTask(AbsTask):
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# If you need more than one optimizers, change this value
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num_optimizers: int = 1
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# Add variable objects configurations
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class_choices_list = [
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# --frontend and --frontend_conf
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frontend_choices,
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# --model and --model_conf
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model_choices,
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]
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# If you need to modify train() or eval() procedures, change Trainer class here
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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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group = parser.add_argument_group(description="Task related")
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# NOTE(kamo): add_arguments(..., required=True) can't be used
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# to provide --print_config mode. Instead of it, do as
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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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"--init",
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type=lambda x: str_or_none(x.lower()),
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default=None,
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help="The initialization method",
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choices=[
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"chainer",
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"xavier_uniform",
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"xavier_normal",
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"kaiming_uniform",
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"kaiming_normal",
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None,
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],
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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 input dimension of the feature",
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)
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group = parser.add_argument_group(description="Preprocess related")
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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="Scale the maximum amplitude to the given value.",
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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 file path of rir scp file.",
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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 for applying RIR convolution.",
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)
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parser.add_argument(
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"--cmvn_file",
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type=str_or_none,
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default=None,
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help="The file path of noise scp file.",
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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 file 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 applying Noise adding.",
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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 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. --encoder and --encoder_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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assert check_argument_types()
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# NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
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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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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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# # NOTE(kamo): Check attribute existence for backward compatibility
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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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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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if not inference:
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retval = ("speech", "text")
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else:
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# Recognition mode
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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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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):
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assert check_argument_types()
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# 4. Encoder
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encoder_class = encoder_choices.get_class(args.encoder)
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encoder = encoder_class(**args.encoder_conf)
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# 5. Build model
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try:
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model_class = model_choices.get_class(args.model)
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except AttributeError:
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model_class = model_choices.get_class("e2evad")
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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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if args.frontend == 'wav_frontend':
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frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
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else:
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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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args.frontend = None
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args.frontend_conf = {}
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frontend = None
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input_size = args.input_size
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model = model_class(encoder=encoder, vad_post_args=args.vad_post_conf, frontend=frontend)
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return model
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# ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
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@classmethod
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def build_model_from_file(
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cls,
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config_file: Union[Path, str] = None,
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model_file: Union[Path, str] = None,
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device: str = "cpu",
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cmvn_file: Union[Path, str] = None,
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):
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"""Build model from the files.
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This method is used for inference or fine-tuning.
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Args:
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config_file: The yaml file saved when training.
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model_file: The model file saved when training.
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device: Device type, "cpu", "cuda", or "cuda:N".
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"""
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assert check_argument_types()
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if config_file is None:
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assert model_file is not None, (
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"The argument 'model_file' must be provided "
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"if the argument 'config_file' is not specified."
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)
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config_file = Path(model_file).parent / "config.yaml"
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else:
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config_file = Path(config_file)
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with config_file.open("r", encoding="utf-8") as f:
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args = yaml.safe_load(f)
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# if cmvn_file is not None:
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args["cmvn_file"] = cmvn_file
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args = argparse.Namespace(**args)
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model = cls.build_model(args)
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model.to(device)
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model_dict = dict()
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model_name_pth = None
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if model_file is not None:
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logging.info("model_file is {}".format(model_file))
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if device == "cuda":
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device = f"cuda:{torch.cuda.current_device()}"
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model_dir = os.path.dirname(model_file)
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model_name = os.path.basename(model_file)
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model_dict = torch.load(model_file, map_location=device)
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model.encoder.load_state_dict(model_dict)
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return model, args
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