From bac2820eb8b1dd22fc6892ffb50efa0d201b0f31 Mon Sep 17 00:00:00 2001 From: dohe0342 Date: Mon, 26 Dec 2022 15:00:40 +0900 Subject: [PATCH] from local --- .../.train.py.swp | Bin 98304 -> 98304 bytes .../train.py | 4 ++-- 2 files changed, 2 insertions(+), 2 deletions(-) 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 540bfab81804f316b63b8e8e9c1a45ac11e2cdd3..d43475eacc0b7692750d28570aabd84281a0c026 100644 GIT binary patch delta 248 zcmZo@U~6b#6HPJ*^Ym4))H7fJ0s#gFL5r2i?>CCR-_NQC6nM6o^MEvC{dqP9h6zCI z55#;x{Fs%2VFwV;0OC9#b^+q|EDQ`AfVdcleSla8h}nRc6^P$5Gcc?L;@Lnv3y70| z*dK`ffY=d;wSkxci1#otFl+?kJ|NBqVm~0(WMbfBkOnfOfS4DEpH1F)&~)CNs(H!)%nmOcJ>2LScuH(&q& delta 220 zcmW;AzY76z9LDj_-MQ-&m()#C!b&GUVz#)!L}zC+S_}r?e?cdciOF~_bxLtXV)X}5 zitQ_dte!mes&@}#3S)}iHY~HAsALVoB1EC`qH}We*H$j!;BIgi6NVRsXbcUA!c!*N zz!Z8=gKvPyg8?+502+khBN44&4m0RN3oI}p4=U_MqBTsQ4;JJ^(kO*Uf)3nv{Nm`Q Y+Sj-0az7*4k8|3s9+tvC;>Wx910@|WQUCw| 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 5f51cc601..b6d3135de 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py @@ -1022,7 +1022,7 @@ def train_one_epoch( # in the batch and there is no normalization to it so far. scaler.scale(loss).backward() - if params.multi_optim and batch_idx % params.accum_grads == 0: + if params.multi_optim and (batch_idx+1) % params.accum_grads == 0: set_batch_count(model, params.batch_idx_train) scheduler_enc.step_batch(params.batch_idx_train) scheduler_dec.step_batch(params.batch_idx_train) @@ -1031,7 +1031,7 @@ def train_one_epoch( scaler.update() optimizer_enc.zero_grad() optimizer_dec.zero_grad() - elif not params.multi_optim and batch_idx % params.accum_grads == 0: + elif not params.multi_optim and (batch_idx+1) % params.accum_grads == 0: set_batch_count(model, params.batch_idx_train) scheduler.step_batch(params.batch_idx_train) scaler.step(optimizer)