From 237708d1c3efed75300f32808ee15824588125cf Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 3 Jan 2023 17:04:58 +0900
Subject: [PATCH] from local

---
 egs/librispeech/ASR/.run_adapter.sh.swp       | Bin 12288 -> 12288 bytes
 .../.data2vec_audio.py.swp                    | Bin 45056 -> 45056 bytes
 .../data2vec_audio.py                         |   2 +-
 3 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/egs/librispeech/ASR/.run_adapter.sh.swp b/egs/librispeech/ASR/.run_adapter.sh.swp
index b83ad4d92cda5a0606ef1389e938aa02f6d6ba88..06138da613719961d698caa49299d75271bfe9c0 100644
GIT binary patch
delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE1_rB#o0GW}Hj2H{2LOR32v`6B

delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE1_pzNo0IR#Zxnl_4*-Rz2_FCe

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 5c0e5dbe472f6eb66ed883c9e6d89559d45edbe5..62292f92a5af0cd836e9102fc8811dba1adc5b10 100644
GIT binary patch
delta 352
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z9dwC`pq3`r7XLtzy~6{KH#NMe(O;|w`vJ*rnDtDhU`dCF<8wuNKp75FNMaV1o-sxj
zx46J5irB_F5?F<W?`3I#N7QhHUF;x<c|`h)sG>L0!wK?8<HM9XXu?Gt21a3Nh&C?a
z!NorIkiz?tR7DXn%tO*EUT}vp&T)WjXj57vHZ1%Gr5}9Z2?N}q1{Wnbu>G^ENuZIt
Q5Bpo!$L7L@-)R+M|Kx`_ivR!s

delta 349
zcmWN@KT84u0EgjUXKJUNC@!rY!MO-VgPXWS&DGG5C3FNWf#@jQ4T7uTF8FU~O1LQ-
zbclwi2y$t2Z0QRqN$>E$<1Y+<VT?E1;fIjqbj<d?S58RhNMmkEW7JWCjSPObrB^&-
zhyfb7MFj`QVFw9V_=!ssJfek59N`cdtYgw&OrAc-2-i5nF1}2uhYnn%v4Po^G({Ia
zJh(W)F>HKBr6wv!VjYp*@rHZUQNt;Uk%E*Z4lJy~(h}cz!UTP^;Nl7;I6?L9B@`s>
N8`Id!2mS6v>L2>oHb(#e

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 dd0507e05..aba350f42 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
@@ -43,7 +43,7 @@ class TransformerEncoderAdapter(TransformerEncoder):
 
         for p in self.adapters.parameters():
         #    p.data = nn.Parameter(torch.zeros(p.size()).to('cuda'))
-            p.data = nn.Parameter(torch.randn(p.size()).to('cuda')/100.)
+            p.data = nn.Parameter(torch.randn(p.size()).to('cuda')/20.)
 
     def forward(self, x, padding_mask=None, layer=None, tgt_layer=None):
         x, layer_results = self.extract_features_with_adapter(