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 funasr.datasets.collate_fn import CommonCollateFn
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from funasr.datasets.preprocessor import CommonPreprocessor
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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.ctc import CTC
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from funasr.models.decoder.abs_decoder import AbsDecoder
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from funasr.models.decoder.rnn_decoder import RNNDecoder
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from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder, FsmnDecoderSCAMAOpt
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from funasr.models.decoder.transformer_decoder import (
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DynamicConvolution2DTransformerDecoder, # noqa: H301
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)
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from funasr.models.decoder.transformer_decoder import DynamicConvolutionTransformerDecoder
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from funasr.models.decoder.transformer_decoder import (
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LightweightConvolution2DTransformerDecoder, # noqa: H301
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)
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from funasr.models.decoder.transformer_decoder import (
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LightweightConvolutionTransformerDecoder, # noqa: H301
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)
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from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
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from funasr.models.decoder.transformer_decoder import TransformerDecoder
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from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
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from funasr.models.decoder.transformer_decoder import SAAsrTransformerDecoder
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from funasr.models.e2e_asr import ASRModel
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from funasr.models.decoder.rnnt_decoder import RNNTDecoder
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from funasr.models.joint_net.joint_network import JointNetwork
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from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, ContextualParaformer
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from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer
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from funasr.models.e2e_tp import TimestampPredictor
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from funasr.models.e2e_asr_mfcca import MFCCA
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from funasr.models.e2e_sa_asr import SAASRModel
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from funasr.models.e2e_uni_asr import UniASR
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from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
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from funasr.models.e2e_asr_bat import BATModel
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from funasr.models.encoder.abs_encoder import AbsEncoder
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from funasr.models.encoder.conformer_encoder import ConformerEncoder, ConformerChunkEncoder
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from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
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from funasr.models.encoder.rnn_encoder import RNNEncoder
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from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
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from funasr.models.encoder.transformer_encoder import TransformerEncoder
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from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
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from funasr.models.encoder.resnet34_encoder import ResNet34Diar
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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.default import MultiChannelFrontend
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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.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.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3, BATPredictor
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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.models.specaug.specaug import SpecAugLFR
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from funasr.modules.subsampling import Conv1dSubsampling
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from funasr.tasks.abs_task import AbsTask
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from funasr.tokenizer.phoneme_tokenizer import g2p_choices
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from funasr.torch_utils.initialize import initialize
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from funasr.models.base_model import FunASRModel
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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.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
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from funasr.utils.types import int_or_none
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from funasr.utils.types import str2bool
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from funasr.utils.types import str_or_none
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# from funasr.models.paraformer import Paraformer
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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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multichannelfrontend=MultiChannelFrontend,
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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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# specaug_choices = {"specaug":SpecAug}
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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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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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conformer=ConformerEncoder,
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transformer=TransformerEncoder,
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rnn=RNNEncoder,
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sanm=SANMEncoder,
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sanm_chunk_opt=SANMEncoderChunkOpt,
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data2vec_encoder=Data2VecEncoder,
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mfcca_enc=MFCCAEncoder,
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chunk_conformer=ConformerChunkEncoder,
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),
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type_check=AbsEncoder,
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default="rnn",
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)
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encoder_choices2 = ClassChoices(
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"encoder2",
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classes=dict(
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conformer=ConformerEncoder,
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transformer=TransformerEncoder,
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rnn=RNNEncoder,
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sanm=SANMEncoder,
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sanm_chunk_opt=SANMEncoderChunkOpt,
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),
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type_check=AbsEncoder,
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default="rnn",
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)
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asr_encoder_choices = ClassChoices(
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"asr_encoder",
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classes=dict(
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conformer=ConformerEncoder,
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transformer=TransformerEncoder,
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rnn=RNNEncoder,
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sanm=SANMEncoder,
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sanm_chunk_opt=SANMEncoderChunkOpt,
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data2vec_encoder=Data2VecEncoder,
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mfcca_enc=MFCCAEncoder,
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),
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type_check=AbsEncoder,
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default="rnn",
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)
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spk_encoder_choices = ClassChoices(
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"spk_encoder",
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classes=dict(
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resnet34_diar=ResNet34Diar,
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),
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default="resnet34_diar",
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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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decoder_choices = ClassChoices(
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"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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rnn=RNNDecoder,
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fsmn_scama_opt=FsmnDecoderSCAMAOpt,
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paraformer_decoder_sanm=ParaformerSANMDecoder,
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paraformer_decoder_san=ParaformerDecoderSAN,
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contextual_paraformer_decoder=ContextualParaformerDecoder,
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sa_decoder=SAAsrTransformerDecoder,
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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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decoder_choices2 = ClassChoices(
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"decoder2",
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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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rnn=RNNDecoder,
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fsmn_scama_opt=FsmnDecoderSCAMAOpt,
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paraformer_decoder_sanm=ParaformerSANMDecoder,
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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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rnnt_decoder_choices = ClassChoices(
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"rnnt_decoder",
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classes=dict(
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rnnt=RNNTDecoder,
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),
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type_check=RNNTDecoder,
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default="rnnt",
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)
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joint_network_choices = ClassChoices(
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name="joint_network",
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classes=dict(
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joint_network=JointNetwork,
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),
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default="joint_network",
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optional=True,
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)
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predictor_choices = ClassChoices(
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name="predictor",
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classes=dict(
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cif_predictor=CifPredictor,
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ctc_predictor=None,
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cif_predictor_v2=CifPredictorV2,
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cif_predictor_v3=CifPredictorV3,
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bat_predictor=BATPredictor,
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),
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type_check=None,
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default="cif_predictor",
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optional=True,
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)
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predictor_choices2 = ClassChoices(
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name="predictor2",
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classes=dict(
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cif_predictor=CifPredictor,
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ctc_predictor=None,
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cif_predictor_v2=CifPredictorV2,
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),
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type_check=None,
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default="cif_predictor",
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optional=True,
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)
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stride_conv_choices = ClassChoices(
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name="stride_conv",
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classes=dict(
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stride_conv1d=Conv1dSubsampling
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),
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type_check=None,
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default="stride_conv1d",
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optional=True,
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)
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