update

haoneng.lhn

2023-09-21 8ae9fa8365eba7d33c8d8f5fa51d12903ca6a409

 funasr/models/e2e_asr_paraformer.py

@@ -10,7 +10,6 @@
import torch
import random
import numpy as np
from typeguard import check_argument_types

from funasr.layers.abs_normalize import AbsNormalize
from funasr.losses.label_smoothing_loss import (
@@ -80,7 +79,6 @@
            postencoder: Optional[AbsPostEncoder] = None,
            use_1st_decoder_loss: bool = False,
    ):
        assert check_argument_types()
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
        assert 0.0 <= interctc_weight < 1.0, interctc_weight

@@ -242,7 +240,7 @@
            loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight

        if self.use_1st_decoder_loss and pre_loss_att is not None:
            loss = loss + pre_loss_att
            loss = loss + (1 - self.ctc_weight) * pre_loss_att

        # Collect Attn branch stats
        stats["loss_att"] = loss_att.detach() if loss_att is not None else None
@@ -645,7 +643,6 @@
            postencoder: Optional[AbsPostEncoder] = None,
            use_1st_decoder_loss: bool = False,
    ):
        assert check_argument_types()
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
        assert 0.0 <= interctc_weight < 1.0, interctc_weight

@@ -1160,8 +1157,8 @@
                                                                                           mask_chunk_predictor=mask_chunk_predictor,
                                                                                           target_label_length=None,
                                                                                           )
        predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas[:, :-1],
                                                                                             encoder_out_lens)
        predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
                                                                                             encoder_out_lens+1 if self.predictor.tail_threshold > 0.0 else encoder_out_lens)

        scama_mask = None
        if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
@@ -1255,7 +1252,6 @@
            preencoder: Optional[AbsPreEncoder] = None,
            postencoder: Optional[AbsPostEncoder] = None,
    ):
        assert check_argument_types()
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
        assert 0.0 <= interctc_weight < 1.0, interctc_weight

@@ -1528,7 +1524,6 @@
            preencoder: Optional[AbsPreEncoder] = None,
            postencoder: Optional[AbsPostEncoder] = None,
    ):
        assert check_argument_types()
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
        assert 0.0 <= interctc_weight < 1.0, interctc_weight

@@ -1806,7 +1801,6 @@
            preencoder: Optional[AbsPreEncoder] = None,
            postencoder: Optional[AbsPostEncoder] = None,
    ):
        assert check_argument_types()
        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
        assert 0.0 <= interctc_weight < 1.0, interctc_weight

@@ -2113,7 +2107,7 @@

        return loss_att, acc_att, cer_att, wer_att, loss_pre

    def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None):
    def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None, clas_scale=1.0):
        if hw_list is None:
            # default hotword list
            hw_list = [torch.Tensor([self.sos]).long().to(encoder_out.device)]  # empty hotword list