update

funasr/build_utils/build_diar_model.py

@@ -0,0 +1,296 @@
+import logging
+
+import torch
+
+from funasr.layers.global_mvn import GlobalMVN
+from funasr.layers.label_aggregation import LabelAggregate
+from funasr.layers.utterance_mvn import UtteranceMVN
+from funasr.models.e2e_diar_eend_ola import DiarEENDOLAModel
+from funasr.models.e2e_diar_sond import DiarSondModel
+from funasr.models.encoder.conformer_encoder import ConformerEncoder
+from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
+from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
+from funasr.models.encoder.opennmt_encoders.ci_scorers import DotScorer, CosScorer
+from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
+from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
+from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
+from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
+from funasr.models.encoder.rnn_encoder import RNNEncoder
+from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
+from funasr.models.encoder.transformer_encoder import TransformerEncoder
+from funasr.models.frontend.default import DefaultFrontend
+from funasr.models.frontend.fused import FusedFrontends
+from funasr.models.frontend.s3prl import S3prlFrontend
+from funasr.models.frontend.wav_frontend import WavFrontend
+from funasr.models.frontend.wav_frontend import WavFrontendMel23
+from funasr.models.frontend.windowing import SlidingWindow
+from funasr.models.specaug.specaug import SpecAug
+from funasr.models.specaug.specaug import SpecAugLFR
+from funasr.modules.eend_ola.encoder import EENDOLATransformerEncoder
+from funasr.modules.eend_ola.encoder_decoder_attractor import EncoderDecoderAttractor
+from funasr.torch_utils.initialize import initialize
+from funasr.train.class_choices import ClassChoices
+
+frontend_choices = ClassChoices(
+    name="frontend",
+    classes=dict(
+        default=DefaultFrontend,
+        sliding_window=SlidingWindow,
+        s3prl=S3prlFrontend,
+        fused=FusedFrontends,
+        wav_frontend=WavFrontend,
+        wav_frontend_mel23=WavFrontendMel23,
+    ),
+    default="default",
+)
+specaug_choices = ClassChoices(
+    name="specaug",
+    classes=dict(
+        specaug=SpecAug,
+        specaug_lfr=SpecAugLFR,
+    ),
+    default=None,
+    optional=True,
+)
+normalize_choices = ClassChoices(
+    "normalize",
+    classes=dict(
+        global_mvn=GlobalMVN,
+        utterance_mvn=UtteranceMVN,
+    ),
+    default=None,
+    optional=True,
+)
+label_aggregator_choices = ClassChoices(
+    "label_aggregator",
+    classes=dict(
+        label_aggregator=LabelAggregate
+    ),
+    default=None,
+    optional=True,
+)
+model_choices = ClassChoices(
+    "model",
+    classes=dict(
+        sond=DiarSondModel,
+        eend_ola=DiarEENDOLAModel,
+    ),
+    default="sond",
+)
+encoder_choices = ClassChoices(
+    "encoder",
+    classes=dict(
+        conformer=ConformerEncoder,
+        transformer=TransformerEncoder,
+        rnn=RNNEncoder,
+        sanm=SANMEncoder,
+        san=SelfAttentionEncoder,
+        fsmn=FsmnEncoder,
+        conv=ConvEncoder,
+        resnet34=ResNet34Diar,
+        resnet34_sp_l2reg=ResNet34SpL2RegDiar,
+        sanm_chunk_opt=SANMEncoderChunkOpt,
+        data2vec_encoder=Data2VecEncoder,
+        ecapa_tdnn=ECAPA_TDNN,
+        eend_ola_transformer=EENDOLATransformerEncoder,
+    ),
+    default="resnet34",
+)
+speaker_encoder_choices = ClassChoices(
+    "speaker_encoder",
+    classes=dict(
+        conformer=ConformerEncoder,
+        transformer=TransformerEncoder,
+        rnn=RNNEncoder,
+        sanm=SANMEncoder,
+        san=SelfAttentionEncoder,
+        fsmn=FsmnEncoder,
+        conv=ConvEncoder,
+        sanm_chunk_opt=SANMEncoderChunkOpt,
+        data2vec_encoder=Data2VecEncoder,
+    ),
+    default=None,
+    optional=True
+)
+cd_scorer_choices = ClassChoices(
+    "cd_scorer",
+    classes=dict(
+        san=SelfAttentionEncoder,
+    ),
+    default=None,
+    optional=True,
+)
+ci_scorer_choices = ClassChoices(
+    "ci_scorer",
+    classes=dict(
+        dot=DotScorer,
+        cosine=CosScorer,
+        conv=ConvEncoder,
+    ),
+    type_check=torch.nn.Module,
+    default=None,
+    optional=True,
+)
+# decoder is used for output (e.g. post_net in SOND)
+decoder_choices = ClassChoices(
+    "decoder",
+    classes=dict(
+        rnn=RNNEncoder,
+        fsmn=FsmnEncoder,
+    ),
+    type_check=torch.nn.Module,
+    default="fsmn",
+)
+# encoder_decoder_attractor is used for EEND-OLA
+encoder_decoder_attractor_choices = ClassChoices(
+    "encoder_decoder_attractor",
+    classes=dict(
+        eda=EncoderDecoderAttractor,
+    ),
+    type_check=torch.nn.Module,
+    default="eda",
+)
+class_choices_list = [
+    # --frontend and --frontend_conf
+    frontend_choices,
+    # --specaug and --specaug_conf
+    specaug_choices,
+    # --normalize and --normalize_conf
+    normalize_choices,
+    # --label_aggregator and --label_aggregator_conf
+    label_aggregator_choices,
+    # --model and --model_conf
+    model_choices,
+    # --encoder and --encoder_conf
+    encoder_choices,
+    # --speaker_encoder and --speaker_encoder_conf
+    speaker_encoder_choices,
+    # --cd_scorer and cd_scorer_conf
+    cd_scorer_choices,
+    # --ci_scorer and ci_scorer_conf
+    ci_scorer_choices,
+    # --decoder and --decoder_conf
+    decoder_choices,
+    # --eda and --eda_conf
+    encoder_decoder_attractor_choices,
+]
+
+
+def build_diar_model(args):
+    # token_list
+    if args.token_list is not None:
+        with open(args.token_list) as f:
+            token_list = [line.rstrip() for line in f]
+        args.token_list = list(token_list)
+        vocab_size = len(token_list)
+        logging.info(f"Vocabulary size: {vocab_size}")
+    else:
+        vocab_size = None
+
+    # frontend
+    if args.input_size is None:
+        frontend_class = frontend_choices.get_class(args.frontend)
+        if args.frontend == 'wav_frontend':
+            frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
+        else:
+            frontend = frontend_class(**args.frontend_conf)
+        input_size = frontend.output_size()
+    else:
+        args.frontend = None
+        args.frontend_conf = {}
+        frontend = None
+        input_size = args.input_size
+
+    # encoder
+    encoder_class = encoder_choices.get_class(args.encoder)
+    encoder = encoder_class(input_size=input_size, **args.encoder_conf)
+
+    if args.model_name == "sond":
+        # data augmentation for spectrogram
+        if args.specaug is not None:
+            specaug_class = specaug_choices.get_class(args.specaug)
+            specaug = specaug_class(**args.specaug_conf)
+        else:
+            specaug = None
+
+        # normalization layer
+        if args.normalize is not None:
+            normalize_class = normalize_choices.get_class(args.normalize)
+            normalize = normalize_class(**args.normalize_conf)
+        else:
+            normalize = None
+
+        # speaker encoder
+        if getattr(args, "speaker_encoder", None) is not None:
+            speaker_encoder_class = speaker_encoder_choices.get_class(args.speaker_encoder)
+            speaker_encoder = speaker_encoder_class(**args.speaker_encoder_conf)
+        else:
+            speaker_encoder = None
+
+        # ci scorer
+        if getattr(args, "ci_scorer", None) is not None:
+            ci_scorer_class = ci_scorer_choices.get_class(args.ci_scorer)
+            ci_scorer = ci_scorer_class(**args.ci_scorer_conf)
+        else:
+            ci_scorer = None
+
+        # cd scorer
+        if getattr(args, "cd_scorer", None) is not None:
+            cd_scorer_class = cd_scorer_choices.get_class(args.cd_scorer)
+            cd_scorer = cd_scorer_class(**args.cd_scorer_conf)
+        else:
+            cd_scorer = None
+
+        # decoder
+        decoder_class = decoder_choices.get_class(args.decoder)
+        decoder = decoder_class(
+            vocab_size=vocab_size,
+            encoder_output_size=encoder.output_size(),
+            **args.decoder_conf,
+        )
+
+        # logger aggregator
+        if getattr(args, "label_aggregator", None) is not None:
+            label_aggregator_class = label_aggregator_choices.get_class(args.label_aggregator)
+            label_aggregator = label_aggregator_class(**args.label_aggregator_conf)
+        else:
+            label_aggregator = None
+
+        model_class = model_choices.get_class(args.model)
+        model = model_class(
+            vocab_size=vocab_size,
+            frontend=frontend,
+            specaug=specaug,
+            normalize=normalize,
+            label_aggregator=label_aggregator,
+            encoder=encoder,
+            speaker_encoder=speaker_encoder,
+            ci_scorer=ci_scorer,
+            cd_scorer=cd_scorer,
+            decoder=decoder,
+            token_list=token_list,
+            **args.model_conf,
+        )
+
+    elif args.model_name == "eend_ola":
+        # encoder-decoder attractor
+        encoder_decoder_attractor_class = encoder_decoder_attractor_choices.get_class(args.encoder_decoder_attractor)
+        encoder_decoder_attractor = encoder_decoder_attractor_class(**args.encoder_decoder_attractor_conf)
+
+        # 9. Build model
+        model_class = model_choices.get_class(args.model)
+        model = model_class(
+            frontend=frontend,
+            encoder=encoder,
+            encoder_decoder_attractor=encoder_decoder_attractor,
+            **args.model_conf,
+        )
+
+    else:
+        raise NotImplementedError("Not supported model: {}".format(args.model))
+
+    # 10. Initialize
+    if args.init is not None:
+        initialize(model, args.init)
+
+    return model

funasr/build_utils/build_model.py

@@ -3,6 +3,7 @@ from funasr.build_utils.build_lm_model import build_lm_model
 from funasr.build_utils.build_pretrain_model import build_pretrain_model
 from funasr.build_utils.build_punc_model import build_punc_model
 from funasr.build_utils.build_vad_model import build_vad_model
+from funasr.build_utils.build_diar_model import build_diar_model
 
 
 def build_model(args):
@@ -16,6 +17,8 @@ def build_model(args):
         model = build_punc_model(args)
     elif args.task_name == "vad":
         model = build_vad_model(args)
+    elif args.task_name == "diar":
+        model = build_diar_model(args)
     else:
         raise NotImplementedError("Not supported task: {}".format(args.task_name))