test

funasr/models/conformer/encoder.py

@@ -573,7 +573,7 @@ class ConformerEncoder(nn.Module):
             xs_pad, masks = self.embed(xs_pad, masks)
         else:
             xs_pad = self.embed(xs_pad)
-        pdb.set_trace()
+
         intermediate_outs = []
         if len(self.interctc_layer_idx) == 0:
             xs_pad, masks = self.encoders(xs_pad, masks)
@@ -601,12 +601,12 @@ class ConformerEncoder(nn.Module):
                             xs_pad = (x, pos_emb)
                         else:
                             xs_pad = xs_pad + self.conditioning_layer(ctc_out)
-        pdb.set_trace()
+
         if isinstance(xs_pad, tuple):
             xs_pad = xs_pad[0]
         if self.normalize_before:
             xs_pad = self.after_norm(xs_pad)
-        pdb.set_trace()
+
         olens = masks.squeeze(1).sum(1)
         if len(intermediate_outs) > 0:
             return (xs_pad, intermediate_outs), olens, None

funasr/models/lcbnet/model.py

@@ -296,7 +296,6 @@ class LCBNet(nn.Module):
         
         if intermediate_outs is not None:
             return (encoder_out, intermediate_outs), encoder_out_lens
-        pdb.set_trace()
         return encoder_out, encoder_out_lens
     
     def _calc_att_loss(
@@ -444,7 +443,11 @@ class LCBNet(nn.Module):
         encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
         if isinstance(encoder_out, tuple):
             encoder_out = encoder_out[0]
-        
+        pdb.set_trace()
+        ocr = ocr_sample_list[0]
+        ocr_lengths = ocr.new_full([1], dtype=torch.long, fill_value=ocr.size(1))
+        ocr, ocr_lens, _ = self.text_encoder(ocr, ocr_lengths)
+        pdb.set_trace()
         # c. Passed the encoder result and the beam search
         nbest_hyps = self.beam_search(
             x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)