From 6427c834dfd97b1f05c6659cdc7ccf010bf82fe1 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 24 四月 2023 19:50:07 +0800
Subject: [PATCH] update

---
 funasr/export/models/encoder/sanm_encoder.py |   69 ++++++++++++++++++----------------
 1 files changed, 37 insertions(+), 32 deletions(-)

diff --git a/funasr/export/models/encoder/sanm_encoder.py b/funasr/export/models/encoder/sanm_encoder.py
index 3b7b414..f583f56 100644
--- a/funasr/export/models/encoder/sanm_encoder.py
+++ b/funasr/export/models/encoder/sanm_encoder.py
@@ -9,6 +9,7 @@
 from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward
 from funasr.export.models.modules.feedforward import PositionwiseFeedForward as PositionwiseFeedForward_export
 
+
 class SANMEncoder(nn.Module):
     def __init__(
         self,
@@ -148,23 +149,23 @@
         self.num_heads = model.encoders[0].self_attn.h
         self.hidden_size = model.encoders[0].self_attn.linear_out.out_features
     
-    def prepare_mask(self, mask):
+    def prepare_mask(self, mask, sub_masks):
         mask_3d_btd = mask[:, :, None]
-        if len(mask.shape) == 2:
-            mask_4d_bhlt = 1 - mask[:, None, None, :]
-        elif len(mask.shape) == 3:
-            mask_4d_bhlt = 1 - mask[:, None, :]
-        mask_4d_bhlt = mask_4d_bhlt * -10000.0
+        mask_4d_bhlt = (1 - sub_masks) * -10000.0
         
         return mask_3d_btd, mask_4d_bhlt
     
     def forward(self,
                 speech: torch.Tensor,
                 speech_lengths: torch.Tensor,
+                vad_masks: torch.Tensor,
+                sub_masks: torch.Tensor,
                 ):
         speech = speech * self._output_size ** 0.5
         mask = self.make_pad_mask(speech_lengths)
-        mask = self.prepare_mask(mask)
+        vad_masks = self.prepare_mask(mask, vad_masks)
+        mask = self.prepare_mask(mask, sub_masks)
+        
         if self.embed is None:
             xs_pad = speech
         else:
@@ -173,8 +174,12 @@
         encoder_outs = self.model.encoders0(xs_pad, mask)
         xs_pad, masks = encoder_outs[0], encoder_outs[1]
         
-        encoder_outs = self.model.encoders(xs_pad, mask)
-        xs_pad, masks = encoder_outs[0], encoder_outs[1]
+        # 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_masks
+            encoder_outs = encoder_layer(xs_pad, mask)
+            xs_pad, masks = encoder_outs[0], encoder_outs[1]
         
         xs_pad = self.model.after_norm(xs_pad)
         
@@ -183,26 +188,26 @@
     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'
+    #         }
+    #
+    #     }

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