From fdba1cb6cac06be3296ef06e4f06084d4dc9c482 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 3 Jan 2023 22:27:36 +0900
Subject: [PATCH] from local

---
 .../.data2vec_audio.py.swp                    | Bin 45056 -> 45056 bytes
 .../.train_adapter.py.swp                     | Bin 77824 -> 77824 bytes
 .../data2vec_audio.py                         |   4 ++--
 3 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp
index c167e442b410b681c5c60ed889bb5bca8ec48f88..28d79e7bfad094a147c06a6ef8fe39c7255788b6 100644
GIT binary patch
delta 408
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zgQy5<X=-R`X>+O+`U8x>zCQTC<1Me)@Dv-Kx>50+dZn;qR$_%#P<lfd%SfSPNzXV(
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z<dMND;s~Q@xIM<6*J&Qlx}mXz>6}$Z9lN?c5{(c$v6<V?MJDGW$*F|8OE*#HH8$=J
IU3`cB0mcbMvj6}9

delta 393
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zM52q?VzG2=Q;9#IBAz$-CC~Pj>hV%Nes2WHi-hEaR*)&SdZiBpSiv%4sP#yf$ist;
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diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp
index 89e814e59427dbb5a9d6d019f9291870ab373955..4582ad0fdbddbda797f5c84c0a2ce72a788f3de3 100644
GIT binary patch
delta 36
qcmZp8z|!!5MJ&l6%+puFQqO<^2m}}yn02=#bM!QdZ53mDr4InMu?c$s

delta 36
qcmZp8z|!!5MJ&l6%+puFQqO<^2m}}yF6nGZp4;6hwpEPrl|BH*T?*X*

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
index ad7ba0400..7844bd64d 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
@@ -41,8 +41,8 @@ class TransformerEncoderAdapter(TransformerEncoder):
         super().__init__(args)
         self.adapters = ResidualAdapterModule()
 
-        for p in self.adapters.parameters():
-            p.data /= 10.
+        #for p in self.adapters.parameters():
+        #    p.data /= 10.
         #p.data = nn.Parameter(torch.zeros(p.size()).to('cuda'))
         #p.data = nn.Parameter(torch.randn(p.size()).to('cuda')/20.)