onnx

funasr/export/models/encoder/sanm_encoder.py

@@ -158,13 +158,14 @@ class SANMVadEncoder(nn.Module):
     def forward(self,
                 speech: torch.Tensor,
                 speech_lengths: torch.Tensor,
-                vad_mask: torch.Tensor,
+                vad_masks: torch.Tensor,
                 sub_masks: torch.Tensor,
                 ):
         speech = speech * self._output_size ** 0.5
         mask = self.make_pad_mask(speech_lengths)
+        vad_masks = self.prepare_mask(mask, vad_masks)
         mask = self.prepare_mask(mask, sub_masks)
-        vad_mask = self.prepare_mask(mask, vad_mask)
+        
         if self.embed is None:
             xs_pad = speech
         else:
@@ -176,7 +177,7 @@ class SANMVadEncoder(nn.Module):
         # encoder_outs = self.model.encoders(xs_pad, mask)
         for layer_idx, encoder_layer in enumerate(self.model.encoders):
             if layer_idx == len(self.model.encoders) - 1:
-                mask = vad_mask
+                mask = vad_masks
             encoder_outs = encoder_layer(xs_pad, mask)
             xs_pad, masks = encoder_outs[0], encoder_outs[1]
         
@@ -187,26 +188,26 @@ class SANMVadEncoder(nn.Module):
     def get_output_size(self):
         return self.model.encoders[0].size
     
-    def get_dummy_inputs(self):
-        feats = torch.randn(1, 100, self.feats_dim)
-        return (feats)
-    
-    def get_input_names(self):
-        return ['feats']
-    
-    def get_output_names(self):
-        return ['encoder_out', 'encoder_out_lens', 'predictor_weight']
-    
-    def get_dynamic_axes(self):
-        return {
-            'feats': {
-                1: 'feats_length'
-            },
-            'encoder_out': {
-                1: 'enc_out_length'
-            },
-            'predictor_weight': {
-                1: 'pre_out_length'
-            }
-            
-        }
+    # def get_dummy_inputs(self):
+    #     feats = torch.randn(1, 100, self.feats_dim)
+    #     return (feats)
+    #
+    # def get_input_names(self):
+    #     return ['feats']
+    #
+    # def get_output_names(self):
+    #     return ['encoder_out', 'encoder_out_lens', 'predictor_weight']
+    #
+    # def get_dynamic_axes(self):
+    #     return {
+    #         'feats': {
+    #             1: 'feats_length'
+    #         },
+    #         'encoder_out': {
+    #             1: 'enc_out_length'
+    #         },
+    #         'predictor_weight': {
+    #             1: 'pre_out_length'
+    #         }
+    #
+    #     }

funasr/export/models/vad_realtime_transformer.py

@@ -66,13 +66,13 @@ class VadRealtimeTransformer(nn.Module):
         length = 10
         text_indexes = torch.tensor([[266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757, 266757]], dtype=torch.int32)
         text_lengths = torch.tensor([length], dtype=torch.int32)
-        vad_mask = vad_mask(10, 3, dtype=torch.float32)[None, None, :, :]
+        vad_masks = vad_mask(10, 3, dtype=torch.float32)[None, None, :, :]
         sub_masks = torch.ones(length, length, dtype=torch.float32)
         sub_masks = torch.tril(sub_masks).type(torch.float32)
-        return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
+        return (text_indexes, text_lengths, vad_masks, sub_masks[None, None, :, :])
 
     def get_input_names(self):
-        return ['input', 'text_lengths', 'vad_mask', 'sub_masks']
+        return ['input', 'text_lengths', 'vad_masks', 'sub_masks']
 
     def get_output_names(self):
         return ['logits']
@@ -82,7 +82,7 @@ class VadRealtimeTransformer(nn.Module):
             'input': {
                 1: 'feats_length'
             },
-            'vad_mask': {
+            'vad_masks': {
                 2: 'feats_length1',
                 3: 'feats_length2'
             },