Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

游雁

2023-09-13 33d3d2084403fd34b79c835d2f2fe04f6cd8f738

 funasr/models/e2e_uni_asr.py

@@ -8,7 +8,6 @@
from typing import Union

import torch
from typeguard import check_argument_types

from funasr.models.e2e_asr_common import ErrorCalculator
from funasr.modules.nets_utils import th_accuracy
@@ -18,9 +17,12 @@
)
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.base_model import FunASRModel
from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.layers.abs_normalize import AbsNormalize
from funasr.torch_utils.device_funcs import force_gatherable
from funasr.models.base_model import FunASRModel
from funasr.modules.streaming_utils.chunk_utilis import sequence_mask
@@ -37,19 +39,17 @@

class UniASR(FunASRModel):
    """
    Author: Speech Lab, Alibaba Group, China
    Author: Speech Lab of DAMO Academy, Alibaba Group
    """

    def __init__(
        self,
        vocab_size: int,
        token_list: Union[Tuple[str, ...], List[str]],
        frontend: Optional[torch.nn.Module],
        specaug: Optional[torch.nn.Module],
        normalize: Optional[torch.nn.Module],
        preencoder: Optional[AbsPreEncoder],
        encoder: torch.nn.Module,
        postencoder: Optional[AbsPostEncoder],
        frontend: Optional[AbsFrontend],
        specaug: Optional[AbsSpecAug],
        normalize: Optional[AbsNormalize],
        encoder: AbsEncoder,
        decoder: AbsDecoder,
        ctc: CTC,
        ctc_weight: float = 0.5,
@@ -77,9 +77,10 @@
        loss_weight_model1: float = 0.5,
        enable_maas_finetune: bool = False,
        freeze_encoder2: bool = False,
        preencoder: Optional[AbsPreEncoder] = None,
        postencoder: Optional[AbsPostEncoder] = None,
        encoder1_encoder2_joint_training: bool = True,
    ):
        assert check_argument_types()
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
        assert 0.0 <= interctc_weight < 1.0, interctc_weight

@@ -176,7 +177,6 @@
        decoding_ind: int = None,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Frontend + Encoder + Decoder + Calc loss

        Args:
                        speech: (Batch, Length, ...)
                        speech_lengths: (Batch, )
@@ -466,7 +466,6 @@
        self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Frontend + Encoder. Note that this method is used by asr_inference.py

        Args:
                        speech: (Batch, Length, ...)
                        speech_lengths: (Batch, )
@@ -530,7 +529,6 @@
        ind: int = 0,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Frontend + Encoder. Note that this method is used by asr_inference.py

        Args:
                        speech: (Batch, Length, ...)
                        speech_lengths: (Batch, )
@@ -624,9 +622,7 @@
        ys_pad_lens: torch.Tensor,
    ) -> torch.Tensor:
        """Compute negative log likelihood(nll) from transformer-decoder

        Normally, this function is called in batchify_nll.

        Args:
                        encoder_out: (Batch, Length, Dim)
                        encoder_out_lens: (Batch,)
@@ -663,7 +659,6 @@
        batch_size: int = 100,
    ):
        """Compute negative log likelihood(nll) from transformer-decoder

        To avoid OOM, this fuction seperate the input into batches.
        Then call nll for each batch and combine and return results.
        Args:
@@ -1069,4 +1064,3 @@
            ys_hat = self.ctc2.argmax(encoder_out).data
            cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
        return loss_ctc, cer_ctc