import logging
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import torch
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from funasr.layers.global_mvn import GlobalMVN
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from funasr.layers.label_aggregation import LabelAggregate
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from funasr.layers.utterance_mvn import UtteranceMVN
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from funasr.models.e2e_diar_eend_ola import DiarEENDOLAModel
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from funasr.models.e2e_diar_sond import DiarSondModel
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from funasr.models.encoder.conformer_encoder import ConformerEncoder
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from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
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from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
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from funasr.models.encoder.opennmt_encoders.ci_scorers import DotScorer, CosScorer
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from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
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from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
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from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
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from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
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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.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.wav_frontend import WavFrontendMel23
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from funasr.models.frontend.windowing import SlidingWindow
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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.eend_ola.encoder import EENDOLATransformerEncoder
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from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
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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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wav_frontend_mel23=WavFrontendMel23,
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),
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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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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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default=None,
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optional=True,
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)
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label_aggregator_choices = ClassChoices(
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"label_aggregator",
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classes=dict(
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label_aggregator=LabelAggregate
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),
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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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sond=DiarSondModel,
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eend_ola=DiarEENDOLAModel,
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),
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default="sond",
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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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san=SelfAttentionEncoder,
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fsmn=FsmnEncoder,
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conv=ConvEncoder,
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resnet34=ResNet34Diar,
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resnet34_sp_l2reg=ResNet34SpL2RegDiar,
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sanm_chunk_opt=SANMEncoderChunkOpt,
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data2vec_encoder=Data2VecEncoder,
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ecapa_tdnn=ECAPA_TDNN,
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eend_ola_transformer=EENDOLATransformerEncoder,
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),
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default="resnet34",
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)
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speaker_encoder_choices = ClassChoices(
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"speaker_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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san=SelfAttentionEncoder,
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fsmn=FsmnEncoder,
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conv=ConvEncoder,
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sanm_chunk_opt=SANMEncoderChunkOpt,
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data2vec_encoder=Data2VecEncoder,
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),
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default=None,
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optional=True
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)
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cd_scorer_choices = ClassChoices(
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"cd_scorer",
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classes=dict(
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san=SelfAttentionEncoder,
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),
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default=None,
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optional=True,
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)
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ci_scorer_choices = ClassChoices(
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"ci_scorer",
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classes=dict(
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dot=DotScorer,
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cosine=CosScorer,
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conv=ConvEncoder,
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),
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type_check=torch.nn.Module,
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default=None,
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optional=True,
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)
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# decoder is used for output (e.g. post_net in SOND)
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decoder_choices = ClassChoices(
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"decoder",
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classes=dict(
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rnn=RNNEncoder,
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fsmn=FsmnEncoder,
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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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# encoder_decoder_attractor is used for EEND-OLA
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encoder_decoder_attractor_choices = ClassChoices(
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"encoder_decoder_attractor",
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classes=dict(
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eda=EncoderDecoderAttractor,
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),
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type_check=torch.nn.Module,
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default="eda",
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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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# --label_aggregator and --label_aggregator_conf
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label_aggregator_choices,
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# --model and --model_conf
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model_choices,
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# --encoder and --encoder_conf
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encoder_choices,
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# --speaker_encoder and --speaker_encoder_conf
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speaker_encoder_choices,
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# --cd_scorer and cd_scorer_conf
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cd_scorer_choices,
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# --ci_scorer and ci_scorer_conf
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ci_scorer_choices,
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# --decoder and --decoder_conf
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decoder_choices,
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# --eda and --eda_conf
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encoder_decoder_attractor_choices,
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]
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def build_diar_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"Vocabulary size: {vocab_size}")
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# frontend
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if args.input_size is None:
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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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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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# 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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if args.model == "sond":
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# 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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# 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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# speaker encoder
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if getattr(args, "speaker_encoder", None) is not None:
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speaker_encoder_class = speaker_encoder_choices.get_class(args.speaker_encoder)
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speaker_encoder = speaker_encoder_class(**args.speaker_encoder_conf)
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else:
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speaker_encoder = None
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# ci scorer
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if getattr(args, "ci_scorer", None) is not None:
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ci_scorer_class = ci_scorer_choices.get_class(args.ci_scorer)
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ci_scorer = ci_scorer_class(**args.ci_scorer_conf)
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else:
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ci_scorer = None
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# cd scorer
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if getattr(args, "cd_scorer", None) is not None:
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cd_scorer_class = cd_scorer_choices.get_class(args.cd_scorer)
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cd_scorer = cd_scorer_class(**args.cd_scorer_conf)
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else:
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cd_scorer = None
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# decoder
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decoder_class = decoder_choices.get_class(args.decoder)
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decoder = decoder_class(**args.decoder_conf)
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# logger aggregator
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if getattr(args, "label_aggregator", None) is not None:
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label_aggregator_class = label_aggregator_choices.get_class(args.label_aggregator)
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label_aggregator = label_aggregator_class(**args.label_aggregator_conf)
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else:
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label_aggregator = None
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model_class = model_choices.get_class(args.model)
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model = model_class(
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vocab_size=vocab_size,
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frontend=frontend,
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specaug=specaug,
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normalize=normalize,
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label_aggregator=label_aggregator,
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encoder=encoder,
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speaker_encoder=speaker_encoder,
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ci_scorer=ci_scorer,
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cd_scorer=cd_scorer,
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decoder=decoder,
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token_list=token_list,
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**args.model_conf,
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)
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elif args.model_name == "eend_ola":
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# encoder-decoder attractor
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encoder_decoder_attractor_class = encoder_decoder_attractor_choices.get_class(args.encoder_decoder_attractor)
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encoder_decoder_attractor = encoder_decoder_attractor_class(**args.encoder_decoder_attractor_conf)
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# 9. Build model
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model_class = model_choices.get_class(args.model)
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model = model_class(
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frontend=frontend,
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encoder=encoder,
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encoder_decoder_attractor=encoder_decoder_attractor,
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**args.model_conf,
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)
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else:
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raise NotImplementedError("Not supported model: {}".format(args.model))
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# 10. 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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