grpc client remove VAD

huangmingming

2023-03-13 49c00a7d6cb9c05d4bd0bb0fc8b59a2eed4b8950

 funasr/tasks/diar.py

@@ -23,7 +23,8 @@
from funasr.layers.utterance_mvn import UtteranceMVN
from funasr.layers.label_aggregation import LabelAggregate
from funasr.models.ctc import CTC
from funasr.models.encoder.resnet34_encoder import ResNet34Diar
from funasr.models.encoder.resnet34_encoder import ResNet34Diar, ResNet34SpL2RegDiar
from funasr.models.encoder.ecapa_tdnn_encoder import ECAPA_TDNN
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
@@ -121,10 +122,12 @@
        fsmn=FsmnEncoder,
        conv=ConvEncoder,
        resnet34=ResNet34Diar,
        resnet34_sp_l2reg=ResNet34SpL2RegDiar,
        sanm_chunk_opt=SANMEncoderChunkOpt,
        data2vec_encoder=Data2VecEncoder,
        ecapa_tdnn=ECAPA_TDNN,
    ),
    type_check=AbsEncoder,
    type_check=torch.nn.Module,
    default="resnet34",
)
speaker_encoder_choices = ClassChoices(
@@ -158,6 +161,7 @@
    classes=dict(
        dot=DotScorer,
        cosine=CosScorer,
        conv=ConvEncoder,
    ),
    type_check=torch.nn.Module,
    default=None,
@@ -187,6 +191,8 @@
        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
@@ -567,19 +573,24 @@
        var_dict_torch = model.state_dict()
        var_dict_torch_update = dict()
        # speech encoder
        var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.encoder is not None:
            var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # speaker encoder
        var_dict_torch_update_local = model.speaker_encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.speaker_encoder is not None:
            var_dict_torch_update_local = model.speaker_encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # cd scorer
        var_dict_torch_update_local = model.cd_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.cd_scorer is not None:
            var_dict_torch_update_local = model.cd_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # ci scorer
        var_dict_torch_update_local = model.ci_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.ci_scorer is not None:
            var_dict_torch_update_local = model.ci_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)
        # decoder
        var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
        var_dict_torch_update.update(var_dict_torch_update_local)
        if model.decoder is not None:
            var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
            var_dict_torch_update.update(var_dict_torch_update_local)

        return var_dict_torch_update