fix typo (#445)

egs/aishell/ASR/conformer_ctc/conformer.py

@@ -364,7 +364,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/aishell/ASR/conformer_mmi/conformer.py

@@ -364,7 +364,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/aishell/ASR/transducer_stateless/conformer.py

@@ -362,7 +362,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/aishell4/ASR/pruned_transducer_stateless5/conformer.py

@@ -384,7 +384,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/gigaspeech/ASR/conformer_ctc/conformer.py

@@ -369,7 +369,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/librispeech/ASR/conformer_ctc/conformer.py

@@ -369,7 +369,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/librispeech/ASR/conformer_mmi/conformer.py

@@ -363,7 +363,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -359,7 +359,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/librispeech/ASR/pruned_transducer_stateless5/conformer.py

@@ -383,7 +383,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/librispeech/ASR/pruned_transducer_stateless6/conformer.py

@@ -382,7 +382,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)

egs/librispeech/ASR/streaming_conformer_ctc/conformer.py

@@ -760,7 +760,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x_size_1, self.d_model)

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -363,7 +363,7 @@ class RelPositionalEncoding(torch.nn.Module):
                 ):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
-        # Suppose `i` means to the position of query vecotr and `j` means the
+        # Suppose `i` means to the position of query vector and `j` means the
         # position of key vector. We use position relative positions when keys
         # are to the left (i>j) and negative relative positions otherwise (i<j).
         pe_positive = torch.zeros(x.size(1), self.d_model)