update

嘉渊

2023-04-24 6427c834dfd97b1f05c6659cdc7ccf010bf82fe1

 funasr/models/e2e_sv.py

@@ -10,22 +10,11 @@
import torch
from typeguard import check_argument_types

from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
    LabelSmoothingLoss,  # noqa: H301
)
from funasr.models.ctc import CTC
from funasr.models.decoder.abs_decoder import AbsDecoder
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.modules.add_sos_eos import add_sos_eos
from funasr.modules.e2e_asr_common import ErrorCalculator
from funasr.modules.nets_utils import th_accuracy
from funasr.models.base_model import FunASRModel
from funasr.torch_utils.device_funcs import force_gatherable
from funasr.train.abs_espnet_model import AbsESPnetModel

if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
    from torch.cuda.amp import autocast
@@ -36,18 +25,18 @@
        yield


class ESPnetSVModel(AbsESPnetModel):
class ESPnetSVModel(FunASRModel):
    """CTC-attention hybrid Encoder-Decoder model"""

    def __init__(
            self,
            vocab_size: int,
            token_list: Union[Tuple[str, ...], List[str]],
            frontend: Optional[AbsFrontend],
            specaug: Optional[AbsSpecAug],
            normalize: Optional[AbsNormalize],
            frontend: Optional[torch.nn.Module],
            specaug: Optional[torch.nn.Module],
            normalize: Optional[torch.nn.Module],
            preencoder: Optional[AbsPreEncoder],
            encoder: AbsEncoder,
            encoder: torch.nn.Module,
            postencoder: Optional[AbsPostEncoder],
            pooling_layer: torch.nn.Module,
            decoder: AbsDecoder,