[firered] simplified code (#2686)

wenet/firered/model.py

@@ -46,17 +46,6 @@ def __init__(
         self.eos = special_tokens["eos"]
         self.decode_maxlen = self.decoder.embed[1].max_len
 
-        # fix subsampling
-        odim = 32
-        idim = 80
-        self.encoder.embed.conv = torch.nn.Sequential(
-            torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU(),
-            torch.nn.Conv2d(odim, odim, 3, 2), torch.nn.ReLU())
-        self.encoder.embed.out = torch.nn.Sequential(
-            torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2),
-                            self.encoder.output_size()))
-
-        # fix final norm in conformer
         del self.encoder.after_norm
 
     @torch.jit.unused

wenet/firered/subsampling.py

@@ -36,7 +36,6 @@ def __init__(self,
                  pos_enc_class: torch.nn.Module,
                  odim: int = 32):
         """Construct an Conv2dSubsampling4 object."""
-
         super().__init__(idim, d_model, dropout_rate, pos_enc_class)
         del self.conv, self.out
         self.conv = torch.nn.Sequential(
@@ -46,7 +45,7 @@ def __init__(self,
             torch.nn.ReLU(),
         )
         self.out = torch.nn.Sequential(
-            torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim))
+            torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), d_model))
         self.pos_enc = pos_enc_class
         # The right context for every conv layer is computed by:
         # (kernel_size - 1) * frame_rate_of_this_layer

wenet/utils/class_utils.py

@@ -76,7 +76,7 @@
     "crossattn": MultiHeadedCrossAttention,
     'shaw_rel_selfattn': ShawRelPositionMultiHeadedAttention,
     'rope_abs_selfattn': RopeMultiHeadedAttention,
-    'firered_rel_selfattn': FiredRelPositionMultiHeadedAttention,
+    'firered_rel_selfattn': FiredRelPositionMultiHeadedAttention
 }
 
 WENET_MLP_CLASSES = {