import logging
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import torch
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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.base_model import FunASRModel
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from funasr.models.decoder.abs_decoder import AbsDecoder
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from funasr.models.decoder.sv_decoder import DenseDecoder
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from funasr.models.e2e_sv import ESPnetSVModel
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from funasr.models.encoder.resnet34_encoder import ResNet34, ResNet34_SP_L2Reg
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from funasr.models.encoder.rnn_encoder import RNNEncoder
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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
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from funasr.models.frontend.windowing import SlidingWindow
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from funasr.models.pooling.statistic_pooling import StatisticPooling
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from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
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from funasr.models.postencoder.hugging_face_transformers_postencoder import (
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HuggingFaceTransformersPostEncoder, # noqa: H301
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)
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from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
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from funasr.models.preencoder.linear import LinearProjection
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from funasr.models.preencoder.sinc import LightweightSincConvs
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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.torch_utils.initialize import initialize
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from funasr.train.class_choices import ClassChoices
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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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),
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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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),
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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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espnet=ESPnetSVModel,
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),
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type_check=FunASRModel,
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default="espnet",
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)
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preencoder_choices = ClassChoices(
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name="preencoder",
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classes=dict(
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sinc=LightweightSincConvs,
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linear=LinearProjection,
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),
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type_check=AbsPreEncoder,
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default=None,
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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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resnet34=ResNet34,
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resnet34_sp_l2reg=ResNet34_SP_L2Reg,
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rnn=RNNEncoder,
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),
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type_check=AbsEncoder,
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default="resnet34",
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)
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postencoder_choices = ClassChoices(
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name="postencoder",
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classes=dict(
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hugging_face_transformers=HuggingFaceTransformersPostEncoder,
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),
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type_check=AbsPostEncoder,
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default=None,
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optional=True,
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)
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pooling_choices = ClassChoices(
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name="pooling_type",
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classes=dict(
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statistic=StatisticPooling,
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),
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type_check=torch.nn.Module,
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default="statistic",
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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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dense=DenseDecoder,
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),
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type_check=AbsDecoder,
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default="dense",
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)
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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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# --specaug and --specaug_conf
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specaug_choices,
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# --normalize and --normalize_conf
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normalize_choices,
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# --model and --model_conf
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model_choices,
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# --preencoder and --preencoder_conf
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preencoder_choices,
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# --encoder and --encoder_conf
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encoder_choices,
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# --postencoder and --postencoder_conf
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postencoder_choices,
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# --pooling and --pooling_conf
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pooling_choices,
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# --decoder and --decoder_conf
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decoder_choices,
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]
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def build_sv_model(args):
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# token_list
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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"Speaker number: {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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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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# 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. Pre-encoder input block
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# NOTE(kan-bayashi): Use getattr to keep the compatibility
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if getattr(args, "preencoder", None) is not None:
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preencoder_class = preencoder_choices.get_class(args.preencoder)
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preencoder = preencoder_class(**args.preencoder_conf)
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input_size = preencoder.output_size()
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else:
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preencoder = None
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# 5. Encoder
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encoder_class = encoder_choices.get_class(args.encoder)
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encoder = encoder_class(input_size=input_size, **args.encoder_conf)
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# 6. Post-encoder block
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# NOTE(kan-bayashi): Use getattr to keep the compatibility
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encoder_output_size = encoder.output_size()
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if getattr(args, "postencoder", None) is not None:
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postencoder_class = postencoder_choices.get_class(args.postencoder)
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postencoder = postencoder_class(
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input_size=encoder_output_size, **args.postencoder_conf
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)
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encoder_output_size = postencoder.output_size()
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else:
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postencoder = None
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# 7. Pooling layer
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pooling_class = pooling_choices.get_class(args.pooling_type)
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pooling_dim = (2, 3)
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eps = 1e-12
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if hasattr(args, "pooling_type_conf"):
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if "pooling_dim" in args.pooling_type_conf:
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pooling_dim = args.pooling_type_conf["pooling_dim"]
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if "eps" in args.pooling_type_conf:
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eps = args.pooling_type_conf["eps"]
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pooling_layer = pooling_class(
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pooling_dim=pooling_dim,
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eps=eps,
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)
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if args.pooling_type == "statistic":
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encoder_output_size *= 2
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# 8. 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=vocab_size,
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encoder_output_size=encoder_output_size,
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**args.decoder_conf,
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)
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# 7. 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("espnet")
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model = model_class(
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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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preencoder=preencoder,
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encoder=encoder,
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postencoder=postencoder,
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pooling_layer=pooling_layer,
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decoder=decoder,
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**args.model_conf,
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)
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# FIXME(kamo): Should be done in model?
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# 8. Initialize
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if args.init is not None:
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initialize(model, args.init)
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return model
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