From 23e9d7edf0949f161b20fdaf37cfc7a6d659a0ba Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Fri, 9 Dec 2022 17:23:11 +0900
Subject: [PATCH] from local

---
 .../.train.py.swp                             | Bin 57344 -> 61440 bytes
 .../train.py                                  |  10 +++++++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp
index 7676e6c3dcfea508ccf8e0c619169e9df0b58165..0d95a93be4ce750d5069223b97cbd6d323094f14 100644
GIT binary patch
delta 668
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z9GrxLZ9s4k9Q*`2IS3*M4kC`~qW`9?C<q?>a=AQrPws8~Qe3|m1GABdp%L0IMF@(I
zkg3-LrR%H`c=igOlISZ7N~z-O^tac-C>`~RoY?2Sptnk0)Kd@$=@#)FqZQjfpbN9d
zE<$!;8eDLW@D3DV0czc=unnWo2LWh>+ZIA>*oQ6HgcJne0l%V7vG3x;f~)pgDkNfS
zEURW{hRIf*Gf7LUIU0-6-e|KoZN_G@OfzYWS~^$eB?}FmhZ^IYSeQ8J8xOXuFvE)e
z8ZF6&!L)S9%o~g^sivxH(i&%q&eN(kCu#ZIj49`gEHi)SviQpXi7YNFi)J#Cl9aq9
z{gXALb6TIpxHAo^YK^wjG?PIj38tdoOZvG`Y(g^SME=;ho~V717(85%(}X;^aVb3Y
z<87Y)r<agxsK6-{VG_a+f)8AmD=5Prtil+CL4<eYdxIO;gDH=_;eT<L?&UW7F@DtY
E1uv(o<NyEw

delta 181
zcmZp8z}#?vSuDvQ%+puFQqO<^2m}}yHolvb%(`Wx*c*OEp2^GtYZ(hBZxj&U{6av2
zf8qm$O$sauyo^AVcR>VDdBJ8uhkVAURu%?^79b7>VlyB%1!8U>zQfGGunvgV0&zVM
zdjhc$5DNk^0}x+f0?Gq%2N2f*aV-!>0I?ttgRJ<-xH<IgYNpAC&bFHmx!BDH0CTt{
A>;M1&

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
index 376fa999d..40a3f3183 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
@@ -676,7 +676,15 @@ def compute_loss(
     feature = feature.to(device)
 
     supervisions = batch["supervisions"]
-    feature_lens = supervisions["num_frames"].to(device)
+    if feature.ndim == 2:
+        feature_lens = []
+        for supervision in supervisions['cut']:
+            try: feature_lens.append(supervision.tracks[0].cut.recording.num_samples)
+            except: feature_lens.append(supervision.recording.num_samples)
+        feature_lens = torch.tensor(feature_lens)
+
+    elif feature.ndim == 3:
+        feature_lens = supervisions["num_frames"].to(device)
 
     batch_idx_train = params.batch_idx_train
     warm_step = params.warm_step