From 1a91afa118fad52c46542bbbcec5f6a10bfb7838 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Sat, 21 Jan 2023 15:33:21 +0900
Subject: [PATCH] from local

---
 .../ASR/conformer_ctc2/.conformer.py.swp      | Bin 61440 -> 65536 bytes
 .../ASR/conformer_ctc2/conformer.py           |  16 ++++++++++------
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/egs/librispeech/ASR/conformer_ctc2/.conformer.py.swp b/egs/librispeech/ASR/conformer_ctc2/.conformer.py.swp
index f9f11eb4d9867a411eb7b411e93f5365a66c6e96..aa55848ac7774e514b7f11df469c5de3e4022ebb 100644
GIT binary patch
delta 1125
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delta 807
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D2bY$c

diff --git a/egs/librispeech/ASR/conformer_ctc2/conformer.py b/egs/librispeech/ASR/conformer_ctc2/conformer.py
index 6a3739841..49b45ec7f 100644
--- a/egs/librispeech/ASR/conformer_ctc2/conformer.py
+++ b/egs/librispeech/ASR/conformer_ctc2/conformer.py
@@ -64,6 +64,7 @@ class Conformer(Transformer):
         dropout: float = 0.1,
         layer_dropout: float = 0.075,
         cnn_module_kernel: int = 31,
+        group_num: int = 0,
     ) -> None:
         super(Conformer, self).__init__(
             num_features=num_features,
@@ -101,12 +102,13 @@ class Conformer(Transformer):
             cnn_module_kernel,
         )
         self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers)
-
+        
         self.group_num = group_num
-        self.group_layer_num = int(self.encoder_layers // self.group_num)
-        self.alpha = nn.Parameter(torch.rand(self.group_num))
-        self.sigmoid = nn.Sigmoid()
-        self.layer_norm = nn.LayerNorm(d_model)
+        if self.group_num != 0:
+            self.group_layer_num = int(self.encoder_layers // self.group_num)
+            self.alpha = nn.Parameter(torch.rand(self.group_num))
+            self.sigmoid = nn.Sigmoid()
+            self.layer_norm = nn.LayerNorm(d_model)
 
     def run_encoder(
         self,
@@ -146,8 +148,10 @@ class Conformer(Transformer):
         x, layer_outputs = self.encoder(
             x, pos_emb, src_key_padding_mask=mask, warmup=warmup
         )  # (T, N, C)
-
+        
+        if self.group_num != 0:
         # x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
+        
 
         # return x, lengths
         return x, mask