json stamp_sents for websocket-server

雾聪

2024-01-08 2acef4bdaea588adee3098a057a395937dff4e6a

 funasr/models/e2e_asr_contextual_paraformer.py

@@ -9,7 +9,6 @@
import numpy as np

import torch
from typeguard import check_argument_types

from funasr.layers.abs_normalize import AbsNormalize
from funasr.models.ctc import CTC
@@ -73,7 +72,6 @@
        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

@@ -127,6 +125,7 @@
        if self.crit_attn_weight > 0:
            self.attn_loss = torch.nn.L1Loss()
        self.crit_attn_smooth = crit_attn_smooth
        self.length_normalized_loss = length_normalized_loss

    def forward(
            self,
@@ -136,7 +135,7 @@
            text_lengths: torch.Tensor,
            hotword_pad: torch.Tensor,
            hotword_lengths: torch.Tensor,
            ideal_attn: torch.Tensor,
            dha_pad: torch.Tensor,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Frontend + Encoder + Decoder + Calc loss

@@ -209,7 +208,7 @@
        # 2b. Attention decoder branch
        if self.ctc_weight != 1.0:
            loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
                encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths, ideal_attn
                encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
            )

        # 3. CTC-Att loss definition
@@ -233,6 +232,8 @@

        stats["loss"] = torch.clone(loss.detach())
        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        if self.length_normalized_loss:
            batch_size = int((text_lengths + self.predictor_bias).sum())
        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
        return loss, stats, weight
    
@@ -244,7 +245,6 @@
            ys_pad_lens: torch.Tensor,
            hotword_pad: torch.Tensor,
            hotword_lengths: torch.Tensor,
            ideal_attn: torch.Tensor,
    ):
        encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
            encoder_out.device)
@@ -352,6 +352,7 @@
            else:
                hw_embed = self.bias_embed(hw_list_pad)
            hw_embed, (h_n, _) = self.bias_encoder(hw_embed)
            hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
        else:
            hw_lengths = [len(i) for i in hw_list]
            hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)