add streaming inference at tcn and ds_tcn model

examples/hi_xiaowen/s0/run.sh

@@ -101,7 +101,8 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     --batch_size 256 \
     --checkpoint $score_checkpoint \
     --score_file $result_dir/score.txt  \
-    --num_workers 8
+    --num_workers 8 \
+    --streaming_chunk 20
 
   for keyword in 0 1; do
     python kws/bin/compute_det.py \
@@ -136,7 +137,8 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     --jit_model \
     --checkpoint $dir/$quantize_score_checkpoint \
     --score_file $result_dir/score.txt \
-    --num_workers 8
+    --num_workers 8 \
+    --streaming_chunk 20
   for keyword in 0 1; do
     python kws/bin/compute_det.py \
       --keyword $keyword \
@@ -158,4 +160,4 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     --config $dir/config.yaml \
     --jit_model $dir/$jit_model  \
     --onnx_model $dir/$onnx_model
-fi
+fi

kws/bin/score.py

@@ -55,6 +55,10 @@ def get_args():
                         default=100,
                         type=int,
                         help='prefetch number')
+    parser.add_argument('--streaming_chunk',
+                        default=-1,
+                        type=int,
+                        help='streaming chunk size for inference')
     parser.add_argument('--score_file',
                         required=True,
                         help='output score file')
@@ -84,6 +88,8 @@ def main():
     test_conf['feature_extraction_conf']['dither'] = 0.0
     test_conf['batch_conf']['batch_size'] = args.batch_size
 
+    configs['model']['streaming_chunk'] = args.streaming_chunk
+
     test_dataset = Dataset(args.test_data, test_conf)
     test_data_loader = DataLoader(test_dataset,
                                   batch_size=None,

kws/model/kws_model.py

@@ -46,6 +46,7 @@ class KWSModel(nn.Module):
         backbone: nn.Module,
         classifier: nn.Module,
         activation: nn.Module,
+        streaming_chunk: int,
     ):
         super().__init__()
         self.idim = idim
@@ -56,15 +57,33 @@ class KWSModel(nn.Module):
         self.backbone = backbone
         self.classifier = classifier
         self.activation = activation
+        self.streaming_chunk = streaming_chunk
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         if self.global_cmvn is not None:
             x = self.global_cmvn(x)
-        x = self.preprocessing(x)
+        if self.streaming_chunk <= 0:
-        x, _ = self.backbone(x)
+            x = self.preprocessing(x)
-        x = self.classifier(x)
+            x, _ = self.backbone(x)
-        x = self.activation(x)
+            x = self.classifier(x)
-        return x
+            x = self.activation(x)
+            return x
+        else:
+            stride = self.streaming_chunk
+            num_frames = x.size(1) #(B, T, D)
+            cache: Optional[torch.Tensor] = None
+            out = []
+            for cur in range(0, num_frames, stride):
+                end = min(cur + stride, num_frames)
+                x_s = x[:, cur:end, :]
+                x_s = self.preprocessing(x_s)
+                x_s, cache = self.backbone.forward_chunk(x_s, cache)
+                x_s = self.classifier(x_s)
+                x_s = self.activation(x_s)
+                out.append(x_s)
+            out_ = torch.cat(out, dim=1)
+            # wait all num_frames out, then return
+            return out_
 
     def fuse_modules(self):
         self.preprocessing.fuse_modules()
@@ -87,6 +106,10 @@ def init_model(configs):
     output_dim = configs['output_dim']
     hidden_dim = configs['hidden_dim']
 
+    streaming_chunk = -1
+    if ('streaming_chunk' in configs and configs['streaming_chunk'] > 0):
+        streaming_chunk = configs['streaming_chunk']
+
     prep_type = configs['preprocessing']['type']
     if prep_type == 'linear':
         preprocessing = LinearSubsampling1(input_dim, hidden_dim)
@@ -157,5 +180,5 @@ def init_model(configs):
         activation = nn.Sigmoid()
 
     kws_model = KWSModel(input_dim, output_dim, hidden_dim, global_cmvn,
-                         preprocessing, backbone, classifier, activation)
+                         preprocessing, backbone, classifier, activation, streaming_chunk)
     return kws_model

kws/model/tcn.py

@@ -143,6 +143,28 @@ 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 
+
+    def forward_chunk(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        """
+        Args:
+            x (torch.Tensor): Input tensor (B, T, D)
+
+        Returns:
+            torch.Tensor(B, T, D)
+            torch.Tensor(B, D, C): C is the accumulated cache size
+        """
+        x = x.transpose(1, 2)  # (B, D, T)
+        out_caches = []
+        cur_padding = 0
+        if cache is None:
+            cache = torch.zeros(x.size(0), x.size(1), self.padding)
+        for block in self.network:
+            cur_padding += block.padding 
+            x, c = block(x, cache[:, :, cur_padding - block.padding : cur_padding])
+            out_caches.append(c) # c (B, D, T)
+        x = x.transpose(1, 2)  # (B, T, D)
+        new_cache = torch.cat(out_caches, dim=2) 
         return x, new_cache
 
     def fuse_modules(self):