formatting

examples/speechcommand_v1/s0/local/prepare_speech_command.py

@@ -2,17 +2,28 @@
 import os
 import sys
 import argparse
-CLASSES = 'unknown, yes, no, up, down, left, right, on, off, stop, go'.split(', ')
+
+CLASSES = 'unknown, yes, no, up, down, left, right, on, off, stop, go'.split(
+    ', ')
 CLASS_TO_IDX = {CLASSES[i]: str(i) for i in range(len(CLASSES))}
 
-if __name__=='__main__':
+if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='prepare kaldi format file for google speech command dataset ')
+    parser = argparse.ArgumentParser(
-    parser.add_argument('--wav_list', required=True, help='wave list is a file containts full path of a wav file in google speech command dataset')
+        description=
-    parser.add_argument('--data_dir', required=True, help='folder to write kaldi format files')
+        'prepare kaldi format file for google speech command dataset ')
+    parser.add_argument(
+        '--wav_list',
+        required=True,
+        help=
+        'wave list is a file containts full path of a wav file in google speech command dataset'
+    )
+    parser.add_argument('--data_dir',
+                        required=True,
+                        help='folder to write kaldi format files')
     args = parser.parse_args()
 
     data_dir = args.data_dir
-    f_wav_scp = open(os.path.join(data_dir,'wav.scp'), 'w')
+    f_wav_scp = open(os.path.join(data_dir, 'wav.scp'), 'w')
     f_text = open(os.path.join(data_dir, 'text'), 'w')
     with open(args.wav_list) as f:
         for line in f.readlines():
@@ -21,9 +32,8 @@ if __name__=='__main__':
             wav_id = '_'.join([keyword, file_name_new])
             file_dir = line.strip()
             f_wav_scp.writelines(wav_id + ' ' + file_dir + '\n')
-            label = CLASS_TO_IDX[keyword] if keyword in CLASS_TO_IDX else CLASS_TO_IDX["unknown"]
+            label = CLASS_TO_IDX[
+                keyword] if keyword in CLASS_TO_IDX else CLASS_TO_IDX["unknown"]
             f_text.writelines(wav_id + ' ' + str(label) + '\n')
     f_wav_scp.close()
     f_text.close()
-
-            

examples/speechcommand_v1/s0/local/split_dataset.py

@@ -4,6 +4,7 @@ import os
 import shutil
 import argparse
 
+
 def move_files(src_folder, to_folder, list_file):
     with open(list_file) as f:
         for line in f.readlines():
@@ -12,14 +13,18 @@ def move_files(src_folder, to_folder, list_file):
             dest = os.path.join(to_folder, dirname)
             if not os.path.exists(dest):
                 os.mkdir(dest)
-            shutil.move(os.path.join(src_folder, line),dest)
+            shutil.move(os.path.join(src_folder, line), dest)
+
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Split google command dataset.')
+    parser = argparse.ArgumentParser(
-    parser.add_argument('root', type=str, help='the path to the root folder of the google commands dataset')
+        description='Split google command dataset.')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='the path to the root folder of the google commands dataset')
     args = parser.parse_args()
 
-
     audio_folder = os.path.join(args.root, 'audio')
     validation_path = os.path.join(audio_folder, 'validation_list.txt')
     test_path = os.path.join(audio_folder, 'testing_list.txt')
@@ -34,4 +39,3 @@ if __name__ == '__main__':
     move_files(audio_folder, test_folder, test_path)
     move_files(audio_folder, valid_folder, validation_path)
     os.rename(audio_folder, train_folder)
-

kws/model/ce.py

@@ -15,15 +15,22 @@
 import torch
 import torch.nn as nn
 
-def acc_frame(logits: torch.Tensor, target: torch.Tensor, ):
+
+def acc_frame(
+    logits: torch.Tensor,
+    target: torch.Tensor,
+):
     if logits is None:
         return 0
     pred = logits.max(1, keepdim=True)[1]
     correct = pred.eq(target.long().view_as(pred)).sum().item()
-    return correct*100.0/logits.size(0)
+    return correct * 100.0 / logits.size(0)
 
-def cross_entropy(logits: torch.Tensor, target: torch.Tensor, 
+
-                  lengths: torch.Tensor, min_duration: int = 0):
+def cross_entropy(logits: torch.Tensor,
+                  target: torch.Tensor,
+                  lengths: torch.Tensor,
+                  min_duration: int = 0):
     """ Cross Entropy Loss
     Attributes:
         logits: (B, D), D is the number of keywords plus 1 (non-keyword)
@@ -37,4 +44,4 @@ def cross_entropy(logits: torch.Tensor, target: torch.Tensor,
     cross_entropy = nn.CrossEntropyLoss()
     loss = cross_entropy(logits, target)
     acc = acc_frame(logits, target)
-    return loss, acc
+    return loss, acc

kws/model/classifier.py

@@ -1,12 +1,10 @@
 import torch
 import torch.nn as nn
 
+
 class GlobalClassifier(nn.Module):
     """Add a global average pooling before the classifier"""
-    def __init__(
+    def __init__(self, classifier: nn.Module):
-        self,
-        classifier: nn.Module
-    ):
         super(GlobalClassifier, self).__init__()
         self.classifier = classifier
 
@@ -14,15 +12,13 @@ class GlobalClassifier(nn.Module):
         x = torch.mean(x, dim=1)
         return self.classifier(x)
 
+
 class LastClassifier(nn.Module):
     """Select last frame to do the classification"""
-    def __init__(
+    def __init__(self, classifier: nn.Module):
-        self,
-        classifier: nn.Module
-    ):
         super(LastClassifier, self).__init__()
         self.classifier = classifier
 
     def forward(self, x: torch.Tensor):
         x = x[:, -1, :]
-        return self.classifier(x)
+        return self.classifier(x)

kws/model/kws_model.py

@@ -24,6 +24,7 @@ from kws.model.mdtc import MDTC
 from kws.model.classifier import GlobalClassifier, LastClassifier
 from kws.utils.cmvn import load_cmvn
 
+
 class KWSModel(torch.nn.Module):
     """Our model consists of four parts:
     1. global_cmvn: Optional, (idim, idim)
@@ -31,16 +32,10 @@ class KWSModel(torch.nn.Module):
     3. backbone: backbone or feature extractor of the whole network, (hdim, hdim)
     4. classifier: output layer or classifier of KWS model, (hdim, odim)
     """
-    def __init__(
+    def __init__(self, idim: int, odim: int, hdim: int,
-        self,
+                 global_cmvn: Optional[torch.nn.Module],
-        idim: int,
+                 preprocessing: Optional[torch.nn.Module],
-        odim: int,
+                 backbone: torch.nn.Module, classifier: torch.nn.Module):
-        hdim: int,
-        global_cmvn: Optional[torch.nn.Module],
-        preprocessing: Optional[torch.nn.Module],
-        backbone: torch.nn.Module,
-        classifier: torch.nn.Module
-    ):
         super().__init__()
         self.idim = idim
         self.odim = odim

kws/model/max_pooling_RHE.py

@@ -39,7 +39,7 @@ def RHE(indice: torch.Tensor, k: int):
             reserve.append(indice[i])
             rm_s = max(indice[i] - k, 0)
             rm_e = min(indice[i] + k, lenght)
-            available_indice[rm_s : rm_e + 1] = 0
+            available_indice[rm_s:rm_e + 1] = 0
         else:
             continue
 
@@ -48,7 +48,9 @@ def RHE(indice: torch.Tensor, k: int):
     return torch.tensor(reserve).long()
 
 
-def downsample_training_sample_and_calculate_loss(logits, targets, ratio: float = 10):
+def downsample_training_sample_and_calculate_loss(logits,
+                                                  targets,
+                                                  ratio: float = 10):
     num_training = 0
     loss = 0
     for i in range(len(logits)):
@@ -69,8 +71,11 @@ def downsample_training_sample_and_calculate_loss(logits, targets, ratio: float
     return loss / num_training
 
 
-def max_pooling_RHE_binary_CE(logits, targets, lengths, RHE_thr=10000, max_ratio=1):
+def max_pooling_RHE_binary_CE(logits,
-
+                              targets,
+                              lengths,
+                              RHE_thr=10000,
+                              max_ratio=1):
     """Max-pooling loss with regional hard example mining
         For each keyword utterance, select the frame with the highest posterior.
             The keyword is triggered when any of the frames is triggered.
@@ -108,7 +113,8 @@ def max_pooling_RHE_binary_CE(logits, targets, lengths, RHE_thr=10000, max_ratio
                 if logits[i, max_idx, j] >= 0.5:
                     num_hit += 1
             else:
-                sorted_logits, sorted_index = torch.sort(logits[i, :end_idx], dim=0)
+                sorted_logits, sorted_index = torch.sort(logits[i, :end_idx],
+                                                         dim=0)
                 reversed_index = torch.flip(sorted_index, dims=[0])
                 selected_indexes = RHE(reversed_index[:, j], RHE_thr)
                 new_logits[j].append(logits[i, selected_indexes, j])