From f057f0376baf8a495dd678baf0d1579e7ed0d311 Mon Sep 17 00:00:00 2001 From: dohe0342 Date: Thu, 26 Jan 2023 14:37:36 +0900 Subject: [PATCH] from local --- .../.train_adapter.py.swp | Bin 81920 -> 81920 bytes .../train_adapter.py | 8 ++++---- 2 files changed, 4 insertions(+), 4 deletions(-) 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 e1e94f4e60aacbb451ad4ebd2e81f356b068fb63..047d94a8f27498648e75e9bfff0f04b293e95988 100644 GIT binary patch delta 387 zcmXZYKS;ws6bA5jHc1O2NtHSj#REkYi$rnpADu)?Q4}N~MT)i&3N@7)!9i$Hgt|3m zh=U;3L2=Uz4uZRgLdDU+(NzZ>T>KLK;CJxuyWw$`C0dpkTUE8xEFT?I2~mh9qR+X$ ztJtl=45y)VF~R##+A1x(Xtk!lCx#cTHM7coglGkppc6Wv6$E$)6Wu`>bcn-`MDz}) zpusqNibR)ChZGEe1TO-S2iI@_RWM)?df+KU!?YJ7T7Y>Nfp6^h1@~|P=dc6okO9qk zw5tj3hx}u$ROVZznJwgb!3b_lecRmRlRUE=jNRVxVMJ=*C}x9VjOPnE6ZKrl&=cOt sk?!`+$V4XiUx&SdG%%k*{l81gH$y6m%l2E?O($8u^J-7IwIrMV1;6Y}00000 delta 399 zcmXBQF-yZx5C`y!F>RrgBuX80XpAV$phTglL&+vcP`553Xp^8tYik>^l|tjB4uUm9 zy69p-SIOX@E{bCXUBt~n1Sjzu_)mNAd)#|>ymvgu;T(sHXVuL7f;2m!5<(zKiEjpb z6TcIfy!$F77|#C_`s=YPRQGziEqPx&jWYFGgvf>&n1*pkz;~GF15Ut!6bt|h_aUMk z$UqF9Iid$JVGf4jx0mPz+R%baIEDgf@WT>aurx(mNH$>!`apy)%=iS4(1b&%K`?vN zZ8whG^rAc}At+d^O3qFyowQiWZQDuPs@Uc3fq!ro3Hhz&sy8;o6X_6q5GL%zJNDPG z4^`0l+ewufod?3!(CEX(U9%QCA{v25*{1t}=XEA$WF C{!QTk diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py index bd9eaa656..acc0a5e82 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py @@ -792,6 +792,7 @@ def compute_loss( batch: dict, is_training: bool, decode: bool = False, + pl_texts: dict = None, ) -> Tuple[Tensor, MetricsTracker]: """ Compute transducer loss given the model and its inputs. @@ -832,11 +833,10 @@ def compute_loss( batch_idx_train = params.batch_idx_train warm_step = params.warm_step - texts = batch["supervisions"]["text"] - print(texts) - exit() + #texts = batch["supervisions"]["text"] + texts = [] + for utt_id in - #texts = batch["greedy pseudo text"] token_ids = sp.encode(texts, out_type=int) y = k2.RaggedTensor(token_ids).to(device)