add files

funasr_local/models/decoder/sv_decoder.py

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+import torch
+from torch.nn import functional as F
+from funasr_local.models.decoder.abs_decoder import AbsDecoder
+
+
+class DenseDecoder(AbsDecoder):
+    def __init__(
+            self,
+            vocab_size,
+            encoder_output_size,
+            num_nodes_resnet1: int = 256,
+            num_nodes_last_layer: int = 256,
+            batchnorm_momentum: float = 0.5,
+    ):
+        super(DenseDecoder, self).__init__()
+        self.resnet1_dense = torch.nn.Linear(encoder_output_size, num_nodes_resnet1)
+        self.resnet1_bn = torch.nn.BatchNorm1d(num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum)
+
+        self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer)
+        self.resnet2_bn = torch.nn.BatchNorm1d(num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum)
+
+        self.output_dense = torch.nn.Linear(num_nodes_last_layer, vocab_size, bias=False)
+
+    def forward(self, features):
+        embeddings = {}
+        features = self.resnet1_dense(features)
+        embeddings["resnet1_dense"] = features
+        features = F.relu(features)
+        features = self.resnet1_bn(features)
+
+        features = self.resnet2_dense(features)
+        embeddings["resnet2_dense"] = features
+        features = F.relu(features)
+        features = self.resnet2_bn(features)
+
+        features = self.output_dense(features)
+        return features, embeddings