update _hw_repre

shixian.shi

2024-02-22 66d3b5c21277ebe10b0697b368f200cc66458441

 funasr/models/seaco_paraformer/model.py

@@ -19,11 +19,9 @@

from funasr.register import tables
from funasr.utils import postprocess_utils
from funasr.metrics.compute_acc import th_accuracy
from funasr.models.paraformer.model import Paraformer
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.paraformer.search import Hypothesis
from funasr.models.paraformer.cif_predictor import mae_loss
from funasr.train_utils.device_funcs import force_gatherable
from funasr.models.bicif_paraformer.model import BiCifParaformer
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
@@ -76,7 +74,7 @@
                self.lstm_proj = torch.nn.Linear(self.inner_dim*2, self.inner_dim)
            else:
                self.lstm_proj = None
            self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
            # self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
        elif self.bias_encoder_type == 'mean':
            self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
        else:
@@ -274,6 +272,8 @@
                                hotword_lengths):
        if self.bias_encoder_type != 'lstm':
            logging.error("Unsupported bias encoder type")
            
        '''
        hw_embed = self.decoder.embed(hotword_pad)
        hw_embed, (_, _) = self.bias_encoder(hw_embed)
        if self.lstm_proj is not None:
@@ -281,26 +281,20 @@
        _ind = np.arange(0, hw_embed.shape[0]).tolist()
        selected = hw_embed[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
        return selected
        '''

    '''
     def calc_predictor(self, encoder_out, encoder_out_lens):
        encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
            encoder_out.device)
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = self.predictor(encoder_out,
                                                                                                          None,
                                                                                                          encoder_out_mask,
                                                                                                          ignore_id=self.ignore_id)
        return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index


    def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
        encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
            encoder_out.device)
        ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
                                                                                            encoder_out_mask,
                                                                                            token_num)
        return ds_alphas, ds_cif_peak, us_alphas, us_peaks
    '''        
        # hw_embed = self.sac_embedding(hotword_pad)
        hw_embed = self.decoder.embed(hotword_pad)
        hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hotword_lengths.cpu().type(torch.int64), batch_first=True, enforce_sorted=False)
        packed_rnn_output, _ = self.bias_encoder(hw_embed)
        rnn_output = torch.nn.utils.rnn.pad_packed_sequence(packed_rnn_output, batch_first=True)[0]
        if self.lstm_proj is not None:
            hw_hidden = self.lstm_proj(rnn_output)
        else:
            hw_hidden = rnn_output
        _ind = np.arange(0, hw_hidden.shape[0]).tolist()
        selected = hw_hidden[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
        return selected      
  
    def inference(self,
                 data_in,