Remove redundant log.

exp_combined/lid_mms_ecapa_upcon_32_44_it0.4_shared_trainable_raw/lid_inference_test.log

@@ -1,300 +0,0 @@
-# python3 -m espnet2.bin.lid_inference_dist --output_dir exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/inference/valid.accuracy.best/dev_babel_over_10s_lang_cross_train_all_no_filter_lang --dtype float32 --data_path_and_name_and_type dump/raw/dev_babel_over_10s_lang_cross_train_all_no_filter_lang/wav.scp,speech,sound --data_path_and_name_and_type dump/raw/dev_babel_over_10s_lang_cross_train_all_no_filter_lang/utt2spk,lid_labels,text --valid_batch_size 4 --lid_train_config exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/config.yaml --lid_model_file exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/valid.accuracy.best.pth --use_preprocessor true --fix_duration false --num_workers 32 --extract_embd false --save_every 1000 --resume true --save_embd_per_utt true --save_embd_avg_lang true --save_tsne_plot false --ngpu 1 --multiprocessing_distributed True 
-# Started at Mon Jun  2 02:37:15 CDT 2025
-#
-/u/qwang20/miniconda3/envs/espnet2/bin/python3 /work/nvme/bbjs/qwang20/espnet/espnet2/bin/lid_inference_dist.py --output_dir exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/inference/valid.accuracy.best/dev_babel_over_10s_lang_cross_train_all_no_filter_lang --dtype float32 --data_path_and_name_and_type dump/raw/dev_babel_over_10s_lang_cross_train_all_no_filter_lang/wav.scp,speech,sound --data_path_and_name_and_type dump/raw/dev_babel_over_10s_lang_cross_train_all_no_filter_lang/utt2spk,lid_labels,text --valid_batch_size 4 --lid_train_config exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/config.yaml --lid_model_file exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/valid.accuracy.best.pth --use_preprocessor true --fix_duration false --num_workers 32 --extract_embd false --save_every 1000 --resume true --save_embd_per_utt true --save_embd_avg_lang true --save_tsne_plot false --ngpu 1 --multiprocessing_distributed True
-[gpue04] 2025-06-02 02:37:35,038 (abs_task:2406) INFO: config file: exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/config.yaml
-/work/nvme/bbjs/qwang20/s3prl/s3prl/upstream/byol_s/byol_a/common.py:20: UserWarning: torchaudio._backend.set_audio_backend has been deprecated. With dispatcher enabled, this function is no-op. You can remove the function call.
-  torchaudio.set_audio_backend("sox_io")
-/work/nvme/bbjs/qwang20/espnet/espnet2/tasks/abs_task.py:2429: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
-  torch.load(model_file, map_location=device),
-[gpue04] 2025-06-02 02:37:46,607 (lid_inference_dist:86) INFO: Model structure:
-ESPnetLIDUpstreamConditionModel(
-  (frontend): S3prlFrontendCondition(
-    (upstream): S3PRLUpstreamCondition(
-      (upstream): UpstreamExpertCondition(
-        (model): Wav2Vec2ModelCondition(
-          (feature_extractor): Wav2Vec2FeatureEncoder(
-            (conv_layers): ModuleList(
-              (0): Wav2Vec2LayerNormConvLayer(
-                (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))
-                (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
-                (activation): GELUActivation()
-              )
-              (1-4): 4 x Wav2Vec2LayerNormConvLayer(
-                (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))
-                (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
-                (activation): GELUActivation()
-              )
-              (5-6): 2 x Wav2Vec2LayerNormConvLayer(
-                (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))
-                (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
-                (activation): GELUActivation()
-              )
-            )
-          )
-          (feature_projection): Wav2Vec2FeatureProjection(
-            (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)
-            (projection): Linear(in_features=512, out_features=1280, bias=True)
-            (dropout): Dropout(p=0.1, inplace=False)
-          )
-          (encoder): Wav2Vec2EncoderCondition(
-            (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(
-              (conv): ParametrizedConv1d(
-                1280, 1280, kernel_size=(128,), stride=(1,), padding=(64,), groups=16
-                (parametrizations): ModuleDict(
-                  (weight): ParametrizationList(
-                    (0): _WeightNorm()
-                  )
-                )
-              )
-              (padding): Wav2Vec2SamePadLayer()
-              (activation): GELUActivation()
-            )
-            (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
-            (dropout): Dropout(p=0.1, inplace=False)
-            (layers): ModuleList(
-              (0-47): 48 x Wav2Vec2EncoderLayerStableLayerNorm(
-                (attention): Wav2Vec2SdpaAttention(
-                  (k_proj): Linear(in_features=1280, out_features=1280, bias=True)
-                  (v_proj): Linear(in_features=1280, out_features=1280, bias=True)
-                  (q_proj): Linear(in_features=1280, out_features=1280, bias=True)
-                  (out_proj): Linear(in_features=1280, out_features=1280, bias=True)
-                )
-                (dropout): Dropout(p=0.1, inplace=False)
-                (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
-                (feed_forward): Wav2Vec2FeedForward(
-                  (intermediate_dropout): Dropout(p=0.0, inplace=False)
-                  (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)
-                  (intermediate_act_fn): GELUActivation()
-                  (output_dense): Linear(in_features=5120, out_features=1280, bias=True)
-                  (output_dropout): Dropout(p=0.1, inplace=False)
-                )
-                (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
-              )
-            )
-            (ecapa_encoder): ModuleDict(
-              (32): IdentityEncoder()
-              (36): IdentityEncoder()
-              (40): IdentityEncoder()
-              (44): IdentityEncoder()
-            )
-            (pooling): ModuleDict(
-              (32): ChnAttnStatPooling(
-                (attention): Sequential(
-                  (0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
-                  (1): ReLU()
-                  (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                  (3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
-                )
-                (softmax): Softmax(dim=2)
-              )
-              (36): ChnAttnStatPooling(
-                (attention): Sequential(
-                  (0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
-                  (1): ReLU()
-                  (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                  (3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
-                )
-                (softmax): Softmax(dim=2)
-              )
-              (40): ChnAttnStatPooling(
-                (attention): Sequential(
-                  (0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
-                  (1): ReLU()
-                  (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                  (3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
-                )
-                (softmax): Softmax(dim=2)
-              )
-              (44): ChnAttnStatPooling(
-                (attention): Sequential(
-                  (0): Conv1d(3840, 128, kernel_size=(1,), stride=(1,))
-                  (1): ReLU()
-                  (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                  (3): Conv1d(128, 1280, kernel_size=(1,), stride=(1,))
-                )
-                (softmax): Softmax(dim=2)
-              )
-            )
-            (projector): ModuleDict(
-              (32): RawNet3Projector(
-                (bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                (fc): Linear(in_features=2560, out_features=192, bias=True)
-              )
-              (36): RawNet3Projector(
-                (bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                (fc): Linear(in_features=2560, out_features=192, bias=True)
-              )
-              (40): RawNet3Projector(
-                (bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                (fc): Linear(in_features=2560, out_features=192, bias=True)
-              )
-              (44): RawNet3Projector(
-                (bn): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                (fc): Linear(in_features=2560, out_features=192, bias=True)
-              )
-            )
-            (lang2vec_head): ModuleDict(
-              (32): Sequential(
-                (0): Linear(in_features=192, out_features=299, bias=True)
-              )
-              (36): Sequential(
-                (0): Linear(in_features=192, out_features=299, bias=True)
-              )
-              (40): Sequential(
-                (0): Linear(in_features=192, out_features=299, bias=True)
-              )
-              (44): Sequential(
-                (0): Linear(in_features=192, out_features=299, bias=True)
-              )
-            )
-            (aamsoftmax_weight): ParameterDict()
-            (lang2vec_conditioning_projs): Linear(in_features=299, out_features=1280, bias=True)
-            (aamsoftmax_loss): AAMSoftmaxSCTopKLang2Vec(
-              (ce): CrossEntropyLoss()
-              (lang2vec_head): Sequential(
-                (0): Linear(in_features=192, out_features=299, bias=True)
-              )
-              (lang2vec_loss): MSELoss()
-            )
-          )
-        )
-      )
-    )
-    (featurizer): Featurizer()
-  )
-  (normalize): UtteranceMVN(norm_means=True, norm_vars=False)
-  (encoder): EcapaTdnnEncoder(
-    (conv): Conv1d(1280, 512, kernel_size=(5,), stride=(1,), padding=(2,))
-    (relu): ReLU()
-    (bn): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    (layer1): EcapaBlock(
-      (conv1): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
-      (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (convs): ModuleList(
-        (0-6): 7 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(2,), dilation=(2,))
-      )
-      (bns): ModuleList(
-        (0-6): 7 x BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      )
-      (conv3): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
-      (bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (relu): ReLU()
-      (se): SEModule(
-        (se): Sequential(
-          (0): AdaptiveAvgPool1d(output_size=1)
-          (1): Conv1d(512, 128, kernel_size=(1,), stride=(1,))
-          (2): ReLU()
-          (3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (4): Conv1d(128, 512, kernel_size=(1,), stride=(1,))
-          (5): Sigmoid()
-        )
-      )
-    )
-    (layer2): EcapaBlock(
-      (conv1): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
-      (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (convs): ModuleList(
-        (0-6): 7 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
-      )
-      (bns): ModuleList(
-        (0-6): 7 x BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      )
-      (conv3): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
-      (bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (relu): ReLU()
-      (se): SEModule(
-        (se): Sequential(
-          (0): AdaptiveAvgPool1d(output_size=1)
-          (1): Conv1d(512, 128, kernel_size=(1,), stride=(1,))
-          (2): ReLU()
-          (3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (4): Conv1d(128, 512, kernel_size=(1,), stride=(1,))
-          (5): Sigmoid()
-        )
-      )
-    )
-    (layer3): EcapaBlock(
-      (conv1): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
-      (bn1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (convs): ModuleList(
-        (0-6): 7 x Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(4,), dilation=(4,))
-      )
-      (bns): ModuleList(
-        (0-6): 7 x BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      )
-      (conv3): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
-      (bn3): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (relu): ReLU()
-      (se): SEModule(
-        (se): Sequential(
-          (0): AdaptiveAvgPool1d(output_size=1)
-          (1): Conv1d(512, 128, kernel_size=(1,), stride=(1,))
-          (2): ReLU()
-          (3): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-          (4): Conv1d(128, 512, kernel_size=(1,), stride=(1,))
-          (5): Sigmoid()
-        )
-      )
-    )
-    (layer4): Conv1d(1536, 1536, kernel_size=(1,), stride=(1,))
-    (mp3): MaxPool1d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
-  )
-  (pooling): ChnAttnStatPooling(
-    (attention): Sequential(
-      (0): Conv1d(4608, 128, kernel_size=(1,), stride=(1,))
-      (1): ReLU()
-      (2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (3): Conv1d(128, 1536, kernel_size=(1,), stride=(1,))
-    )
-    (softmax): Softmax(dim=2)
-  )
-  (projector): RawNet3Projector(
-    (bn): BatchNorm1d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    (fc): Linear(in_features=3072, out_features=192, bias=True)
-  )
-  (loss): AAMSoftmaxSCTopKLang2Vec(
-    (ce): CrossEntropyLoss()
-    (lang2vec_head): Sequential(
-      (0): Linear(in_features=192, out_features=299, bias=True)
-    )
-    (lang2vec_loss): MSELoss()
-  )
-)
-
-Model summary:
-    Class Name: ESPnetLIDUpstreamConditionModel
-    Total Number of model parameters: 977.14 M
-    Number of trainable parameters: 977.14 M (100.0%)
-    Size: 3.91 GB
-    Type: torch.float32
-/u/qwang20/miniconda3/envs/espnet2/lib/python3.11/site-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 32 worker processes in total. Our suggested max number of worker in current system is 16, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
-  warnings.warn(_create_warning_msg(
-/work/nvme/bbjs/qwang20/espnet/espnet2/train/reporter.py:321: UserWarning: The stats of the previous epoch=-1doesn't exist.
-  warnings.warn(
-[gpue04] 2025-06-02 02:37:47,156 (lid_trainer:102) INFO: [Rank 0] Resume: 0 utterances found in exp_all_no_filter_raw/spk_mms_ecapa_upcon_32_44_it0.4_sharedCondProj_butUpdate_50k_lr1e-5_datasetup0.3_raw/inference/valid.accuracy.best/dev_babel_over_10s_lang_cross_train_all_no_filter_lang/lids0
-[gpue04] 2025-06-02 02:38:41,828 (lid_trainer:207) INFO: [Rank 0] Saved 1000 utts at step 0
-[gpue04] 2025-06-02 02:39:27,483 (lid_trainer:207) INFO: [Rank 0] Saved 1000 utts at step 1
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-[gpue04] 2025-06-02 02:55:19,223 (lid_inference_dist:200) INFO: args.save_embd_per_utt: True
-[gpue04] 2025-06-02 02:55:19,224 (lid_inference_dist:215) INFO: args.save_tsne_plot: False
-# Accounting: time=1085 threads=1
-# Ended (code 0) at Mon Jun  2 02:55:20 CDT 2025, elapsed time 1085 seconds