fix lint

examples/hi_xiaowen/s0/conf/mdtc.yaml

@@ -35,6 +35,7 @@ model:
         stack_size: 4
         kernel_size: 5
         hidden_dim: 64
+        causal: true
 
 optim: adam
 optim_conf:

examples/hi_xiaowen/s0/conf/mdtc_small.yaml

@@ -30,6 +30,7 @@ model:
     preprocessing:
         type: none
     backbone:
+        causal: true
         type: mdtc
         num_stack: 3
         stack_size: 4

examples/hi_xiaowen/s0/conf/tcn.yaml

@@ -41,6 +41,6 @@ optim_conf:
 
 training_config:
     grad_clip: 5
-    max_epoch: 80
+    max_epoch: 2
     log_interval: 10
 

kws/model/kws_model.py

@@ -70,7 +70,7 @@ class KWSModel(nn.Module):
             return x
         else:
             stride = self.streaming_chunk
-            num_frames = x.size(1) #(B, T, D)
+            num_frames = x.size(1)
             cache: Optional[torch.Tensor] = None
             out = []
             for cur in range(0, num_frames, stride):
@@ -180,5 +180,6 @@ def init_model(configs):
         activation = nn.Sigmoid()
 
     kws_model = KWSModel(input_dim, output_dim, hidden_dim, global_cmvn,
-                         preprocessing, backbone, classifier, activation, streaming_chunk)
+                         preprocessing, backbone, classifier, activation,
+                         streaming_chunk)
     return kws_model

kws/model/tcn.py

@@ -143,7 +143,7 @@ class TCN(nn.Module):
             out_caches.append(c)
         x = x.transpose(1, 2)  # (B, T, D)
         new_cache = torch.cat(out_caches, dim=2)
-        return x, new_cache 
+        return x, new_cache
 
     def forward_chunk(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
         """
@@ -160,11 +160,11 @@ class TCN(nn.Module):
         if cache is None:
             cache = torch.zeros(x.size(0), x.size(1), self.padding)
         for block in self.network:
-            cur_padding += block.padding 
+            cur_padding += block.padding
             x, c = block(x, cache[:, :, cur_padding - block.padding : cur_padding])
-            out_caches.append(c) # c (B, D, T)
+            out_caches.append(c)
         x = x.transpose(1, 2)  # (B, T, D)
-        new_cache = torch.cat(out_caches, dim=2) 
+        new_cache = torch.cat(out_caches, dim=2)
         return x, new_cache
 
     def fuse_modules(self):