From b7e95dc5e1f19944f99a1805f16e844341f5f7db Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Wed, 12 Apr 2023 14:43:16 +0900
Subject: [PATCH] from local

---
 egs/librispeech/ASR/.decode.sh.swp            | Bin 12288 -> 12288 bytes
 .../.data2vec_audio.py.swp                    | Bin 40960 -> 40960 bytes
 .../data2vec_audio.py                         |   1 +
 3 files changed, 1 insertion(+)

diff --git a/egs/librispeech/ASR/.decode.sh.swp b/egs/librispeech/ASR/.decode.sh.swp
index f5b7e9c4942ff94d7977106f916fd01fe9add481..797abcbb2f7a1deb99d072060a48de85bf3bf70f 100644
GIT binary patch
delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE28O+kW+@s6Hj2H{2LOPO2z&ql

delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE28LY@W+{UEH;TQ|2LONC2w(sJ

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 3ad50f7aa6d8e5b0a17af23d7e9ddfc7de933622..09073357542def03d5ccbf48e33cab7fb2ab398b 100644
GIT binary patch
delta 91
zcmZoTz|?SnX~P5oMwiVK1&Z}~ce5}sJZEKKU=UznaG5OV@L4gKoq-_;h*g1D1&A4d
ncn=!`Ln#pJ0rB(As*Z<PHuJevGEO#hmzr#(Cb4;vdtL+p-h~xc

delta 111
zcmZoTz|?SnX~P5oM(52F1&Z}~pRq77@Uby4FbFU(I8PRI_^epT&cNUU#NI%x4#bav
zvU7kq6o^48w{2E+Jj60N-c4w-mYe8gJvZ6O`L0}(7r3%dj&T#+tmS6LIN4r}d-Ejs
Gya)ifR~wc9

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 383a98a00..7f0c5cf79 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
@@ -495,6 +495,7 @@ class Data2VecAudioModel(BaseFairseqModel):
             features = torch.cat([prompt, features], dim=1)
             prompt_padding_mask = torch.zeros(prompt.size()[0], prompt.size()[1]).type(torch.BoolTensor).to(features.device)
             padding_mask = torch.cat([prompt_padding_mask, padding_mask], dim=1)
+            print(padding_mask.size())
 
         features = self.layer_norm(features)