modifications to get the mdtc model torch-scriptable (#14)

* modifying some implmentations of mdtc to get the model torch-scripting through

* modifications to get the mdtc model torch-scriptable

Co-authored-by: lxiao336 <shawl336@163.com>

examples/hi_xiaowen/s0/run.sh

@@ -102,7 +102,8 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     --test_data data/test/data.list \
     --batch_size 256 \
     --checkpoint $score_checkpoint \
-    --score_file $result_dir/score.txt
+    --score_file $result_dir/score.txt \
+    --num_workers 8
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then

kws/model/kws_model.py

@@ -18,7 +18,7 @@ from typing import Optional
 import torch
 
 from kws.model.cmvn import GlobalCMVN
-from kws.model.subsampling import LinearSubsampling1, Conv1dSubsampling1
+from kws.model.subsampling import LinearSubsampling1, Conv1dSubsampling1, NoSubsampling
 from kws.model.tcn import TCN, CnnBlock, DsCnnBlock
 from kws.model.mdtc import MDTC
 from kws.utils.cmvn import load_cmvn
@@ -52,8 +52,7 @@ class KWSModel(torch.nn.Module):
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         if self.global_cmvn is not None:
             x = self.global_cmvn(x)
-        if self.preprocessing:
-            x = self.preprocessing(x)
+        x = self.preprocessing(x)
         x, _ = self.backbone(x)
         x = self.classifier(x)
         x = torch.sigmoid(x)
@@ -82,7 +81,7 @@ def init_model(configs):
     elif prep_type == 'cnn1d_s1':
         preprocessing = Conv1dSubsampling1(input_dim, hidden_dim)
     elif prep_type == 'none':
-        preprocessing = None
+        preprocessing = NoSubsampling()
     else:
         print('Unknown preprocessing type {}'.format(prep_type))
         sys.exit(1)

kws/model/mdtc.py

@@ -241,16 +241,27 @@ class MDTC(nn.Module):
         outputs = outputs.transpose(1, 2)
         outputs_list = []
         outputs = self.relu(self.preprocessor(outputs))
-        for i in range(len(self.blocks)):
-            outputs = self.blocks[i](outputs)
+        for block in self.blocks:
+            outputs = block(outputs)
             outputs_list.append(outputs)
 
-        if self.causal:
-            outputs_list = self.normalize_length_causal(outputs_list)
-        else:
-            outputs_list = self.normalize_length(outputs_list)
+        normalized_outputs = []
+        output_size = outputs_list[-1].shape[-1]
+        for x in outputs_list:
+            remove_length = x.shape[-1] - output_size
+            if remove_length != 0:
+                if self.causal:
+                    normalized_outputs.append(x[:, :, remove_length:])
+                else:
+                    remove_length = remove_length // 2
+                    normalized_outputs.append(x[:, :,
+                                              remove_length:-remove_length])
+            else:
+                normalized_outputs.append(x)
 
-        outputs = sum(outputs_list)
+        outputs = torch.zeros_like(outputs_list[-1], dtype=outputs_list[-1].dtype)
+        for x in normalized_outputs:
+            outputs += x
         outputs = outputs.transpose(1, 2)
         return outputs, None
 

kws/model/subsampling.py

@@ -23,6 +23,14 @@ class SubsamplingBase(torch.nn.Module):
         super().__init__()
         self.subsampling_rate = 1
 
+class NoSubsampling(SubsamplingBase):
+    """No subsampling in accordance to the 'none' preprocessing
+    """
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x
 
 class LinearSubsampling1(SubsamplingBase):
     """Linear transform the input without subsampling