From 0029ababe9f838b642ea5f794606147b2bf5a0bb Mon Sep 17 00:00:00 2001 From: dohe0342 Date: Tue, 11 Apr 2023 15:55:55 +0900 Subject: [PATCH] from local --- .../.data2vec_audio.py.swp | Bin 40960 -> 40960 bytes .../.prompt_tuning.py.swp | Bin 86016 -> 86016 bytes .../prompt_tuning.py | 6 +++--- 3 files changed, 3 insertions(+), 3 deletions(-) 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 817e39bf52020fd1c8a20dd9bb136c3053fb741f..2d84528ca79a9f4dd4fa8c8cd27fb7b63a4642d7 100644 GIT binary patch delta 32 mcmZoTz|?SnNi@kI%+puFQqO<^2m}}y%vnrRmTnY%Js$v!NeHL_ delta 32 mcmZoTz|?SnNi@kI%+puFQqO<^2m}}y_*qO-ayE*-o(}+t#0WB_2uII$jiXs%Lg$? zV6tF{ P*5rrzQk%>2yB+`l`g=I= delta 246 zcmZozz}m2Yb;AWg#>&YT1?3qzCVvEy{F?=ZKI`%dFfcF(Fo6gLhRV%?4(AzVm6;hB zc!2mJPNriFy0ddAkrp?=3eYtq``4|}5ff!^G z|75`cbyYEbAP)SI1a3}zQy1F{UWZSs? zlkMV<@!kR|02=}{l4G)9f;?w9&^V9?-)75%n|v$7LQkA1!-rP{4if? Jb6I}Z0{~&xHCO-u diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py index 4ad3dc93a..ed8f12988 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/prompt_tuning.py @@ -1579,6 +1579,7 @@ def run_adapter(rank, world_size, args, wb=None): logging.info("Using DDP") model = DDP(model, device_ids=[rank], find_unused_parameters=True) + ''' adapter_names = [] adapter_param = [] for n, p in model.named_parameters(): @@ -1593,8 +1594,6 @@ def run_adapter(rank, world_size, args, wb=None): for n, p in model.named_parameters(): p.requires_grad = False - prompt = torch.nn.Parameter(torch.randn(50, 512)).to(device) - optimizer_adapter = ScaledAdam( adapter_param, lr=params.adapter_lr, @@ -1602,13 +1601,14 @@ def run_adapter(rank, world_size, args, wb=None): parameters_names=[adapter_names], ) ''' + + prompt = torch.nn.Parameter(torch.randn(50, 512)).to(device) optimizer_adapter = ScaledAdam( [prompt], lr=params.adapter_lr, clipping_scale=5.0, parameters_names=['prompt'], ) - ''' scheduler_adapter = Eden(optimizer_adapter, 10000, 7) #params.lr_batche, params.lr_epochs) optimizer, scheduler = optimizer_adapter, scheduler_adapter