From 31eed1834f9ff17d6246008f64d3e061f58ef80a Mon Sep 17 00:00:00 2001
From: 凌匀 <ailsa.zly@alibaba-inc.com>
Date: 星期一, 27 二月 2023 13:33:55 +0800
Subject: [PATCH] in_cache & support soundfile read

---
 funasr/models/encoder/fsmn_encoder.py |   44 +++++++++++++++++---------------------------
 1 files changed, 17 insertions(+), 27 deletions(-)

diff --git a/funasr/models/encoder/fsmn_encoder.py b/funasr/models/encoder/fsmn_encoder.py
index 54a113d..c749dc4 100755
--- a/funasr/models/encoder/fsmn_encoder.py
+++ b/funasr/models/encoder/fsmn_encoder.py
@@ -79,14 +79,12 @@
         else:
             self.conv_right = None
 
-    def forward(self, input: torch.Tensor, in_cache=None):
+    def forward(self, input: torch.Tensor, cache: torch.Tensor):
         x = torch.unsqueeze(input, 1)
         x_per = x.permute(0, 3, 2, 1)  # B D T C
-        if in_cache is None:  # offline
-            y_left = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride, 0])
-        else:
-            y_left = torch.cat((in_cache, x_per), dim=2)
-            in_cache = y_left[:, :, -(self.lorder - 1) * self.lstride:, :]
+
+        y_left = torch.cat((cache, x_per), dim=2)
+        cache = y_left[:, :, -(self.lorder - 1) * self.lstride:, :]
         y_left = self.conv_left(y_left)
         out = x_per + y_left
 
@@ -100,7 +98,7 @@
         out_per = out.permute(0, 3, 2, 1)
         output = out_per.squeeze(1)
 
-        return output, in_cache
+        return output, cache
 
 
 class BasicBlock(nn.Sequential):
@@ -124,28 +122,25 @@
         self.affine = AffineTransform(proj_dim, linear_dim)
         self.relu = RectifiedLinear(linear_dim, linear_dim)
 
-    def forward(self, input: torch.Tensor, in_cache=None):
+    def forward(self, input: torch.Tensor, in_cache: Dict[str, torch.Tensor]):
         x1 = self.linear(input)  # B T D
-        if in_cache is not None:  # Dict[str, tensor.Tensor]
-            cache_layer_name = 'cache_layer_{}'.format(self.stack_layer)
-            if cache_layer_name not in in_cache:
-                in_cache[cache_layer_name] = torch.zeros(x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1)
-            x2, in_cache[cache_layer_name] = self.fsmn_block(x1, in_cache[cache_layer_name])
-        else:
-            x2, _ = self.fsmn_block(x1)
+        cache_layer_name = 'cache_layer_{}'.format(self.stack_layer)
+        if cache_layer_name not in in_cache:
+            in_cache[cache_layer_name] = torch.zeros(x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1)
+        x2, in_cache[cache_layer_name] = self.fsmn_block(x1, in_cache[cache_layer_name])
         x3 = self.affine(x2)
         x4 = self.relu(x3)
-        return x4, in_cache
+        return x4
 
 
 class FsmnStack(nn.Sequential):
     def __init__(self, *args):
         super(FsmnStack, self).__init__(*args)
 
-    def forward(self, input: torch.Tensor, in_cache=None):
+    def forward(self, input: torch.Tensor, in_cache: Dict[str, torch.Tensor]):
         x = input
         for module in self._modules.values():
-            x, in_cache = module(x, in_cache)
+            x = module(x, in_cache)
         return x
 
 
@@ -174,8 +169,7 @@
             lstride: int,
             rstride: int,
             output_affine_dim: int,
-            output_dim: int,
-            streaming=False
+            output_dim: int
     ):
         super(FSMN, self).__init__()
 
@@ -186,8 +180,6 @@
         self.proj_dim = proj_dim
         self.output_affine_dim = output_affine_dim
         self.output_dim = output_dim
-        self.in_cache_original = dict() if streaming else None
-        self.in_cache = copy.deepcopy(self.in_cache_original)
 
         self.in_linear1 = AffineTransform(input_dim, input_affine_dim)
         self.in_linear2 = AffineTransform(input_affine_dim, linear_dim)
@@ -201,12 +193,10 @@
     def fuse_modules(self):
         pass
 
-    def cache_reset(self):
-        self.in_cache = copy.deepcopy(self.in_cache_original)
-
     def forward(
             self,
             input: torch.Tensor,
+            in_cache: Dict[str, torch.Tensor]
     ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
         """
         Args:
@@ -218,7 +208,7 @@
         x1 = self.in_linear1(input)
         x2 = self.in_linear2(x1)
         x3 = self.relu(x2)
-        x4 = self.fsmn(x3, self.in_cache)  # if in_cache is not None, self.fsmn is streaming's format, it will update automatically in self.fsmn
+        x4 = self.fsmn(x3, in_cache)  # self.in_cache will update automatically in self.fsmn
         x5 = self.out_linear1(x4)
         x6 = self.out_linear2(x5)
         x7 = self.softmax(x6)
@@ -307,4 +297,4 @@
     print('input shape: {}'.format(x.shape))
     print('output shape: {}'.format(y.shape))
 
-    print(fsmn.to_kaldi_net())
+    print(fsmn.to_kaldi_net())
\ No newline at end of file

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