diff --git "a/log/train_log" "b/log/train_log"
new file mode 100644--- /dev/null
+++ "b/log/train_log"
@@ -0,0 +1,13043 @@
+# Running on vgnj007
+# Started at Thu 10 Nov 16:02:29 CET 2022
+# python3 src/run_speech_recognition_ctc.py --report_to=none --run_name=experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl --preprocessing_num_workers=5 --model_name_or_path=facebook/wav2vec2-large-960h-lv60-self --dataset_name=experiments/data/atcosim_corpus/train --min_duration_in_seconds=1.0 --max_duration_in_seconds=20 --eval_dataset_name=experiments/data/atcosim_corpus/test --train_split_name=train --output_dir=experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl --num_train_epochs=50 --per_device_train_batch_size=24 --per_device_eval_batch_size=24 --gradient_accumulation_steps=4 --learning_rate=5e-4 --weight_decay=0.001 --warmup_steps=500 --evaluation_strategy=steps --text_column_name=text --audio_column_name=audio --length_column_name=input_length '--chars_to_ignore=, ? . ! \; \: " “ % ‘ ” � — ’ … –' --save_steps=500 --eval_steps=500 --logging_steps=500 --layerdrop=0.0 --activation_dropout=0.05 --attention_dropout=0.05 --save_total_limit=1 --feat_proj_dropout=0.0 --mask_time_prob=0.03 --mask_time_length=10 --mask_feature_prob=0.0 --mask_feature_length=10 --gradient_checkpointing --freeze_feature_encoder --fp16 --group_by_length --do_train --do_eval --max_steps 20000 --overwrite_output_dir --freeze_feature_encoder 
+11/10/2022 16:02:41 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True
+11/10/2022 16:02:41 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
+_n_gpu=1,
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_pin_memory=True,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=None,
+disable_tqdm=False,
+do_eval=True,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=500,
+evaluation_strategy=steps,
+fp16=True,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+gradient_accumulation_steps=4,
+gradient_checkpointing=True,
+greater_is_better=None,
+group_by_length=True,
+half_precision_backend=auto,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=0.0005,
+length_column_name=input_length,
+load_best_model_at_end=False,
+local_rank=-1,
+log_level=passive,
+log_level_replica=passive,
+log_on_each_node=True,
+logging_dir=experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/runs/Nov10_16-02-41_vgnj007,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=500,
+logging_strategy=steps,
+lr_scheduler_type=linear,
+max_grad_norm=1.0,
+max_steps=20000,
+metric_for_best_model=None,
+mp_parameters=,
+no_cuda=False,
+num_train_epochs=50.0,
+optim=adamw_hf,
+output_dir=experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=24,
+per_device_train_batch_size=24,
+prediction_loss_only=False,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=[],
+resume_from_checkpoint=None,
+run_name=experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl,
+save_on_each_node=False,
+save_steps=500,
+save_strategy=steps,
+save_total_limit=1,
+seed=42,
+sharded_ddp=[],
+skip_memory_metrics=True,
+tf32=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=500,
+weight_decay=0.001,
+xpu_backend=None,
+)
+11/10/2022 16:02:41 - WARNING - datasets.builder - Using custom data configuration train-982961f3786896dd
+11/10/2022 16:02:41 - WARNING - datasets.builder - Found cached dataset atc_data_loader (/remote/idiap.svm/temp.speech01/jzuluaga/experiments/journal/asr/github/w2v2-air-traffic/experiments/data/atcosim_corpus/train/cache/atc_data_loader/train-982961f3786896dd/0.0.0/3dc36b96f5a9e12dd97c69117ef840e3e971dd43ca1b0750cf000d34695b9d81)
+11/10/2022 16:02:42 - WARNING - datasets.builder - Using custom data configuration test-24d2db3884146b91
+11/10/2022 16:02:42 - WARNING - datasets.builder - Found cached dataset atc_data_loader (/remote/idiap.svm/temp.speech01/jzuluaga/experiments/journal/asr/github/w2v2-air-traffic/experiments/data/atcosim_corpus/test/cache/atc_data_loader/test-24d2db3884146b91/0.0.0/3dc36b96f5a9e12dd97c69117ef840e3e971dd43ca1b0750cf000d34695b9d81)
+loading configuration file config.json from cache at /idiap/temp/jzuluaga/cache/huggingface/models--facebook--wav2vec2-large-960h-lv60-self/snapshots/54074b1c16f4de6a5ad59affb4caa8f2ea03a119/config.json
+Model config Wav2Vec2Config {
+  "_name_or_path": "facebook/wav2vec2-large-960h-lv60-self",
+  "activation_dropout": 0.1,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 256,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "transformers_version": "4.24.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32,
+  "xvector_output_dim": 512
+}
+
+
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+loading file vocab.json
+loading file tokenizer_config.json
+loading file added_tokens.json
+loading file special_tokens_map.json
+Adding <s> to the vocabulary
+Adding </s> to the vocabulary
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+loading configuration file preprocessor_config.json from cache at /idiap/temp/jzuluaga/cache/huggingface/models--facebook--wav2vec2-large-960h-lv60-self/snapshots/54074b1c16f4de6a5ad59affb4caa8f2ea03a119/preprocessor_config.json
+loading configuration file config.json from cache at /idiap/temp/jzuluaga/cache/huggingface/models--facebook--wav2vec2-large-960h-lv60-self/snapshots/54074b1c16f4de6a5ad59affb4caa8f2ea03a119/config.json
+Model config Wav2Vec2Config {
+  "_name_or_path": "facebook/wav2vec2-large-960h-lv60-self",
+  "activation_dropout": 0.1,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 256,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "transformers_version": "4.24.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32,
+  "xvector_output_dim": 512
+}
+
+Feature extractor Wav2Vec2FeatureExtractor {
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}
+
+loading weights file pytorch_model.bin from cache at /idiap/temp/jzuluaga/cache/huggingface/models--facebook--wav2vec2-large-960h-lv60-self/snapshots/54074b1c16f4de6a5ad59affb4caa8f2ea03a119/pytorch_model.bin
+All model checkpoint weights were used when initializing Wav2Vec2ForCTC.
+
+Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-large-960h-lv60-self and are newly initialized: ['wav2vec2.masked_spec_embed']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-large-960h-lv60-self and are newly initialized because the shapes did not match:
+- lm_head.weight: found shape torch.Size([32, 1024]) in the checkpoint and torch.Size([31, 1024]) in the model instantiated
+- lm_head.bias: found shape torch.Size([32]) in the checkpoint and torch.Size([31]) in the model instantiated
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+
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preprocess datasets #4:   2%|▏         | 27/1527 [00:05<01:46, 14.03ex/s]
preprocess datasets #0:   2%|▏         | 25/1528 [00:05<02:04, 12.09ex/s]
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preprocess datasets #0:   2%|▏         | 27/1528 [00:05<01:50, 13.53ex/s]
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preprocess datasets #2:   2%|▏         | 25/1528 [00:05<01:53, 13.19ex/s]
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preprocess datasets #0:   2%|▏         | 29/1528 [00:05<01:49, 13.66ex/s]
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preprocess datasets #2:   2%|▏         | 28/1528 [00:05<01:44, 14.41ex/s]
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preprocess datasets #0:   2%|▏         | 31/1528 [00:05<01:43, 14.46ex/s]
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preprocess datasets #4:   2%|▏         | 33/1527 [00:05<01:43, 14.39ex/s]
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preprocess datasets #1:   2%|▏         | 32/1528 [00:05<02:04, 11.98ex/s]
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preprocess datasets #3:   2%|▏         | 35/1527 [00:05<01:27, 17.12ex/s]
preprocess datasets #0:   2%|▏         | 33/1528 [00:05<01:39, 15.00ex/s]
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preprocess datasets #4:   2%|▏         | 35/1527 [00:05<01:47, 13.88ex/s]
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preprocess datasets #3:   2%|▏         | 37/1527 [00:05<01:23, 17.76ex/s]
preprocess datasets #0:   2%|▏         | 35/1528 [00:06<01:35, 15.68ex/s]
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preprocess datasets #3:   3%|▎         | 39/1527 [00:06<01:26, 17.27ex/s]
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preprocess datasets #0:   2%|▏         | 38/1528 [00:06<01:28, 16.83ex/s]
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preprocess datasets #3:   3%|▎         | 42/1527 [00:06<01:14, 19.91ex/s]
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preprocess datasets #0:   3%|▎         | 40/1528 [00:06<01:30, 16.44ex/s]
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preprocess datasets #3:   3%|▎         | 45/1527 [00:06<01:17, 19.16ex/s]
preprocess datasets #0:   3%|▎         | 42/1528 [00:06<01:28, 16.73ex/s]
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preprocess datasets #2:   3%|▎         | 40/1528 [00:06<01:31, 16.20ex/s]
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preprocess datasets #2:   3%|▎         | 42/1528 [00:06<01:35, 15.52ex/s]
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preprocess datasets #1:   3%|▎         | 45/1528 [00:06<01:38, 15.00ex/s]
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preprocess datasets #3:   3%|▎         | 49/1527 [00:06<01:30, 16.28ex/s]
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preprocess datasets #2:   3%|▎         | 44/1528 [00:06<01:29, 16.49ex/s]
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preprocess datasets #4:   2%|▏         | 37/1527 [00:06<04:06,  6.05ex/s]
preprocess datasets #0:   3%|▎         | 44/1528 [00:06<02:18, 10.72ex/s]
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preprocess datasets #2:   3%|▎         | 46/1528 [00:06<01:32, 15.95ex/s]
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preprocess datasets #4:   3%|▎         | 39/1527 [00:06<03:23,  7.32ex/s]
preprocess datasets #0:   3%|▎         | 47/1528 [00:06<01:50, 13.38ex/s]
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preprocess datasets #3:   3%|▎         | 51/1527 [00:06<01:53, 13.02ex/s]
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preprocess datasets #2:   3%|▎         | 48/1528 [00:06<01:32, 15.94ex/s]
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preprocess datasets #4:   3%|▎         | 41/1527 [00:06<03:00,  8.23ex/s]
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preprocess datasets #3:   3%|▎         | 53/1527 [00:07<01:54, 12.90ex/s]
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preprocess datasets #2:   3%|▎         | 50/1528 [00:07<01:35, 15.52ex/s]
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preprocess datasets #1:   3%|▎         | 53/1528 [00:07<01:32, 15.97ex/s]
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preprocess datasets #2:   3%|▎         | 52/1528 [00:07<01:29, 16.56ex/s]
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preprocess datasets #4:   3%|▎         | 43/1527 [00:07<02:52,  8.61ex/s]
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preprocess datasets #3:   4%|▎         | 56/1527 [00:07<01:40, 14.66ex/s]
preprocess datasets #0:   3%|▎         | 49/1528 [00:07<02:31,  9.75ex/s]
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preprocess datasets #1:   4%|▎         | 55/1528 [00:07<01:32, 15.88ex/s]
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preprocess datasets #2:   4%|▎         | 54/1528 [00:07<01:37, 15.06ex/s]
preprocess datasets #0:   3%|▎         | 51/1528 [00:07<02:13, 11.08ex/s]
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preprocess datasets #4:   3%|▎         | 45/1527 [00:07<02:35,  9.55ex/s]
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preprocess datasets #3:   4%|▍         | 58/1527 [00:07<01:45, 13.89ex/s]
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preprocess datasets #1:   4%|▎         | 57/1528 [00:07<01:39, 14.83ex/s]
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preprocess datasets #2:   4%|▎         | 56/1528 [00:07<01:31, 16.06ex/s]
preprocess datasets #0:   4%|▎         | 54/1528 [00:07<01:50, 13.33ex/s]
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preprocess datasets #4:   3%|▎         | 48/1527 [00:07<02:03, 11.95ex/s]
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preprocess datasets #3:   4%|▍         | 61/1527 [00:07<01:35, 15.39ex/s]
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preprocess datasets #2:   4%|▍         | 58/1528 [00:07<01:35, 15.35ex/s]
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preprocess datasets #1:   4%|▍         | 60/1528 [00:07<01:30, 16.25ex/s]
preprocess datasets #0:   4%|▎         | 56/1528 [00:07<01:46, 13.85ex/s]
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preprocess datasets #4:   3%|▎         | 50/1527 [00:07<02:04, 11.89ex/s]
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preprocess datasets #1:   4%|▍         | 62/1528 [00:07<01:29, 16.34ex/s]
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preprocess datasets #2:   4%|▍         | 60/1528 [00:07<01:37, 14.99ex/s]
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preprocess datasets #3:   4%|▍         | 64/1527 [00:07<01:36, 15.22ex/s]
preprocess datasets #0:   4%|▍         | 58/1528 [00:07<01:41, 14.51ex/s]
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preprocess datasets #0:   4%|▍         | 60/1528 [00:07<01:35, 15.37ex/s]
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preprocess datasets #3:   4%|▍         | 66/1527 [00:07<01:37, 14.91ex/s]
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preprocess datasets #4:   3%|▎         | 52/1527 [00:07<02:19, 10.58ex/s]
preprocess datasets #0:   4%|▍         | 62/1528 [00:08<01:37, 15.01ex/s]
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preprocess datasets #1:   4%|▍         | 67/1528 [00:08<01:24, 17.34ex/s]
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preprocess datasets #3:   4%|▍         | 68/1527 [00:08<01:38, 14.88ex/s]
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preprocess datasets #4:   4%|▎         | 54/1527 [00:08<02:04, 11.86ex/s]
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preprocess datasets #2:   4%|▍         | 64/1528 [00:08<01:51, 13.16ex/s]
preprocess datasets #0:   4%|▍         | 64/1528 [00:08<01:36, 15.18ex/s]
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preprocess datasets #3:   5%|▍         | 70/1527 [00:08<01:32, 15.67ex/s]
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preprocess datasets #1:   5%|▍         | 70/1528 [00:08<01:20, 18.11ex/s]
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preprocess datasets #2:   4%|▍         | 66/1528 [00:08<01:42, 14.32ex/s]
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preprocess datasets #4:   4%|▎         | 56/1527 [00:08<02:04, 11.77ex/s]
preprocess datasets #0:   4%|▍         | 66/1528 [00:08<01:37, 15.03ex/s]
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preprocess datasets #1:   5%|▍         | 72/1528 [00:08<01:22, 17.56ex/s]
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preprocess datasets #3:   5%|▍         | 72/1527 [00:08<01:42, 14.19ex/s]
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preprocess datasets #2:   5%|▍         | 69/1528 [00:08<01:29, 16.23ex/s]
preprocess datasets #0:   4%|▍         | 68/1528 [00:08<01:30, 16.17ex/s]
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preprocess datasets #1:   5%|▍         | 75/1528 [00:08<01:11, 20.39ex/s]
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preprocess datasets #4:   4%|▍         | 58/1527 [00:08<02:07, 11.49ex/s]
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preprocess datasets #3:   5%|▍         | 74/1527 [00:08<01:44, 13.94ex/s]
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preprocess datasets #2:   5%|▍         | 71/1528 [00:08<01:35, 15.21ex/s]
preprocess datasets #0:   5%|▍         | 70/1528 [00:08<01:27, 16.71ex/s]
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preprocess datasets #4:   4%|▍         | 60/1527 [00:08<02:02, 11.98ex/s]
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preprocess datasets #3:   5%|▍         | 76/1527 [00:08<01:43, 14.02ex/s]
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preprocess datasets #2:   5%|▍         | 73/1528 [00:08<01:37, 14.99ex/s]
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preprocess datasets #1:   5%|▌         | 78/1528 [00:08<01:23, 17.46ex/s]
preprocess datasets #0:   5%|▍         | 72/1528 [00:08<01:30, 16.00ex/s]
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preprocess datasets #4:   4%|▍         | 62/1527 [00:08<01:53, 12.92ex/s]
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preprocess datasets #2:   5%|▍         | 75/1528 [00:08<01:31, 15.92ex/s]
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preprocess datasets #1:   5%|▌         | 80/1528 [00:08<01:25, 16.98ex/s]
preprocess datasets #0:   5%|▍         | 75/1528 [00:08<01:21, 17.78ex/s]
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preprocess datasets #1:   5%|▌         | 82/1528 [00:08<01:23, 17.29ex/s]
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preprocess datasets #4:   4%|▍         | 64/1527 [00:08<01:59, 12.29ex/s]
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preprocess datasets #3:   5%|▌         | 82/1527 [00:08<01:33, 15.40ex/s]
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preprocess datasets #4:   4%|▍         | 66/1527 [00:08<01:53, 12.90ex/s]
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preprocess datasets #3:   6%|▌         | 85/1527 [00:09<01:24, 17.12ex/s]
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preprocess datasets #1:   6%|▌         | 86/1528 [00:09<01:31, 15.78ex/s]
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preprocess datasets #4:   4%|▍         | 68/1527 [00:09<01:54, 12.76ex/s]
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preprocess datasets #3:   6%|▌         | 87/1527 [00:09<01:26, 16.63ex/s]
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preprocess datasets #4:   5%|▍         | 70/1527 [00:09<02:00, 12.06ex/s]
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preprocess datasets #3:   6%|▌         | 89/1527 [00:09<01:27, 16.47ex/s]
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preprocess datasets #0:   6%|▌         | 85/1528 [00:09<01:39, 14.47ex/s]
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preprocess datasets #3:   6%|▌         | 91/1527 [00:09<01:23, 17.26ex/s]
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preprocess datasets #4:   5%|▍         | 72/1527 [00:09<01:57, 12.41ex/s]
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preprocess datasets #2:   6%|▌         | 85/1528 [00:09<02:10, 11.03ex/s]
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preprocess datasets #1:   6%|▌         | 92/1528 [00:09<01:41, 14.15ex/s]
preprocess datasets #0:   6%|▌         | 87/1528 [00:09<01:35, 15.13ex/s]
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preprocess datasets #3:   6%|▋         | 96/1527 [00:09<01:19, 17.95ex/s]
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preprocess datasets #2:   6%|▌         | 91/1528 [00:09<01:41, 14.10ex/s]
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preprocess datasets #2:   7%|▋         | 114/1528 [00:11<01:16, 18.39ex/s]
preprocess datasets #0:   7%|▋         | 111/1528 [00:11<01:27, 16.24ex/s]
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preprocess datasets #4:   6%|▌         | 92/1527 [00:11<02:04, 11.55ex/s]
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preprocess datasets #1:   8%|▊         | 119/1528 [00:11<01:20, 17.43ex/s]
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preprocess datasets #3:   7%|▋         | 114/1527 [00:11<01:46, 13.26ex/s]
preprocess datasets #0:   7%|▋         | 113/1528 [00:11<01:29, 15.83ex/s]
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preprocess datasets #2:   8%|▊         | 117/1528 [00:11<01:18, 17.91ex/s]
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preprocess datasets #1:   8%|▊         | 121/1528 [00:11<01:22, 16.97ex/s]
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preprocess datasets #4:   6%|▌         | 94/1527 [00:11<02:04, 11.48ex/s]
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preprocess datasets #3:   8%|▊         | 116/1527 [00:11<01:50, 12.76ex/s]
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preprocess datasets #1:   8%|▊         | 123/1528 [00:11<01:19, 17.74ex/s]
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preprocess datasets #4:   6%|▋         | 96/1527 [00:11<01:53, 12.64ex/s]
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preprocess datasets #2:   8%|▊         | 119/1528 [00:11<01:26, 16.34ex/s]
preprocess datasets #0:   8%|▊         | 115/1528 [00:11<01:49, 12.96ex/s]
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preprocess datasets #1:   8%|▊         | 125/1528 [00:11<01:17, 18.13ex/s]
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preprocess datasets #3:   8%|▊         | 118/1527 [00:11<01:53, 12.42ex/s]
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preprocess datasets #2:   8%|▊         | 121/1528 [00:11<01:24, 16.74ex/s]
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preprocess datasets #4:   6%|▋         | 98/1527 [00:11<01:46, 13.43ex/s]
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preprocess datasets #1:   8%|▊         | 127/1528 [00:11<01:24, 16.61ex/s]
preprocess datasets #0:   8%|▊         | 118/1528 [00:11<01:36, 14.57ex/s]
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preprocess datasets #3:   8%|▊         | 120/1527 [00:11<01:43, 13.56ex/s]
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preprocess datasets #4:   7%|▋         | 100/1527 [00:11<01:39, 14.27ex/s]
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preprocess datasets #2:   8%|▊         | 123/1528 [00:11<01:25, 16.38ex/s]
preprocess datasets #0:   8%|▊         | 120/1528 [00:11<01:33, 15.06ex/s]
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preprocess datasets #1:   8%|▊         | 129/1528 [00:11<01:25, 16.36ex/s]
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preprocess datasets #4:   7%|▋         | 102/1527 [00:11<01:41, 13.99ex/s]
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preprocess datasets #2:   8%|▊         | 125/1528 [00:11<01:30, 15.52ex/s]
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preprocess datasets #3:   8%|▊         | 122/1527 [00:11<01:50, 12.71ex/s]
preprocess datasets #0:   8%|▊         | 123/1528 [00:11<01:27, 16.11ex/s]
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preprocess datasets #1:   9%|▊         | 132/1528 [00:11<01:20, 17.24ex/s]
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preprocess datasets #3:   8%|▊         | 124/1527 [00:11<01:39, 14.09ex/s]
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preprocess datasets #2:   8%|▊         | 128/1528 [00:11<01:22, 17.03ex/s]
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preprocess datasets #4:   7%|▋         | 104/1527 [00:11<01:46, 13.38ex/s]
preprocess datasets #0:   8%|▊         | 125/1528 [00:12<01:25, 16.46ex/s]
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preprocess datasets #1:   9%|▉         | 134/1528 [00:12<01:27, 15.92ex/s]
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preprocess datasets #3:   8%|▊         | 126/1527 [00:12<01:39, 14.04ex/s]
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preprocess datasets #2:   9%|▊         | 130/1528 [00:12<01:30, 15.39ex/s]
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preprocess datasets #4:   7%|▋         | 107/1527 [00:12<01:32, 15.32ex/s]
preprocess datasets #0:   8%|▊         | 127/1528 [00:12<01:23, 16.80ex/s]
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preprocess datasets #1:   9%|▉         | 137/1528 [00:12<01:17, 17.96ex/s]
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preprocess datasets #3:   8%|▊         | 128/1527 [00:12<01:42, 13.66ex/s]
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preprocess datasets #4:   7%|▋         | 109/1527 [00:12<01:27, 16.16ex/s]
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preprocess datasets #2:   9%|▊         | 132/1528 [00:12<01:33, 14.99ex/s]
preprocess datasets #0:   8%|▊         | 129/1528 [00:12<01:26, 16.17ex/s]
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preprocess datasets #1:   9%|▉         | 139/1528 [00:12<01:18, 17.65ex/s]
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preprocess datasets #3:   9%|▊         | 130/1527 [00:12<01:39, 14.06ex/s]
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preprocess datasets #4:   7%|▋         | 112/1527 [00:12<01:20, 17.57ex/s]
preprocess datasets #0:   9%|▊         | 131/1528 [00:12<01:25, 16.30ex/s]
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preprocess datasets #2:   9%|▉         | 134/1528 [00:12<01:36, 14.52ex/s]
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preprocess datasets #1:   9%|▉         | 141/1528 [00:12<01:18, 17.78ex/s]
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preprocess datasets #3:   9%|▊         | 132/1527 [00:12<01:36, 14.47ex/s]
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preprocess datasets #4:   7%|▋         | 114/1527 [00:12<01:21, 17.44ex/s]
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preprocess datasets #2:   9%|▉         | 136/1528 [00:12<01:30, 15.34ex/s]
preprocess datasets #0:   9%|▉         | 134/1528 [00:12<01:20, 17.26ex/s]
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preprocess datasets #1:   9%|▉         | 143/1528 [00:12<01:23, 16.58ex/s]
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preprocess datasets #3:   9%|▉         | 134/1527 [00:12<01:32, 15.01ex/s]
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preprocess datasets #4:   8%|▊         | 116/1527 [00:12<01:25, 16.56ex/s]
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preprocess datasets #2:   9%|▉         | 138/1528 [00:12<01:28, 15.77ex/s]
preprocess datasets #0:   9%|▉         | 137/1528 [00:12<01:15, 18.40ex/s]
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preprocess datasets #3:   9%|▉         | 136/1527 [00:12<01:29, 15.51ex/s]
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preprocess datasets #1:  10%|▉         | 146/1528 [00:12<01:25, 16.15ex/s]
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preprocess datasets #4:   8%|▊         | 118/1527 [00:12<01:34, 14.93ex/s]
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preprocess datasets #2:   9%|▉         | 141/1528 [00:12<01:22, 16.78ex/s]
preprocess datasets #0:   9%|▉         | 139/1528 [00:12<01:18, 17.60ex/s]
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preprocess datasets #3:   9%|▉         | 138/1527 [00:12<01:25, 16.22ex/s]
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preprocess datasets #1:  10%|▉         | 148/1528 [00:12<01:26, 15.97ex/s]
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preprocess datasets #2:   9%|▉         | 143/1528 [00:12<01:19, 17.51ex/s]
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preprocess datasets #4:   8%|▊         | 120/1527 [00:12<01:38, 14.35ex/s]
preprocess datasets #0:   9%|▉         | 141/1528 [00:13<01:23, 16.68ex/s]
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preprocess datasets #3:   9%|▉         | 140/1527 [00:12<01:27, 15.80ex/s]
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preprocess datasets #1:  10%|▉         | 150/1528 [00:13<01:23, 16.56ex/s]
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preprocess datasets #2:  10%|▉         | 146/1528 [00:13<01:12, 19.10ex/s]
preprocess datasets #0:   9%|▉         | 143/1528 [00:13<01:26, 16.02ex/s]
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preprocess datasets #4:   8%|▊         | 122/1527 [00:13<01:39, 14.07ex/s]
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preprocess datasets #3:   9%|▉         | 142/1527 [00:13<01:30, 15.33ex/s]
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preprocess datasets #1:  10%|█         | 153/1528 [00:13<01:17, 17.68ex/s]
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preprocess datasets #2:  10%|▉         | 148/1528 [00:13<01:24, 16.34ex/s]
preprocess datasets #0:   9%|▉         | 145/1528 [00:13<01:27, 15.80ex/s]
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preprocess datasets #4:   8%|▊         | 124/1527 [00:13<01:39, 14.08ex/s]
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preprocess datasets #3:   9%|▉         | 144/1527 [00:13<01:33, 14.73ex/s]
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preprocess datasets #1:  10%|█         | 155/1528 [00:13<01:22, 16.65ex/s]
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preprocess datasets #3:  10%|▉         | 146/1527 [00:13<01:27, 15.69ex/s]
preprocess datasets #0:  10%|▉         | 147/1528 [00:13<01:34, 14.69ex/s]
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preprocess datasets #2:  10%|▉         | 150/1528 [00:13<01:36, 14.21ex/s]
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preprocess datasets #4:   8%|▊         | 126/1527 [00:13<01:44, 13.39ex/s]
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preprocess datasets #1:  10%|█         | 157/1528 [00:13<01:30, 15.10ex/s]
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preprocess datasets #2:  10%|▉         | 152/1528 [00:13<01:30, 15.22ex/s]
preprocess datasets #0:  10%|▉         | 149/1528 [00:13<01:36, 14.25ex/s]
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preprocess datasets #4:   8%|▊         | 128/1527 [00:13<01:42, 13.70ex/s]
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preprocess datasets #1:  10%|█         | 159/1528 [00:13<01:29, 15.35ex/s]
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preprocess datasets #3:  10%|▉         | 148/1527 [00:13<01:49, 12.56ex/s]
preprocess datasets #0:  10%|▉         | 151/1528 [00:13<01:32, 14.91ex/s]
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preprocess datasets #4:   9%|▊         | 130/1527 [00:13<01:35, 14.58ex/s]
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preprocess datasets #2:  10%|█         | 155/1528 [00:13<01:24, 16.29ex/s]
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preprocess datasets #1:  11%|█         | 161/1528 [00:13<01:31, 14.87ex/s]
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preprocess datasets #3:  10%|▉         | 151/1527 [00:13<01:34, 14.52ex/s]
preprocess datasets #0:  10%|█         | 153/1528 [00:13<01:30, 15.27ex/s]
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preprocess datasets #4:   9%|▊         | 132/1527 [00:13<01:35, 14.61ex/s]
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preprocess datasets #3:  10%|█         | 153/1527 [00:13<01:28, 15.53ex/s]
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preprocess datasets #1:  11%|█         | 163/1528 [00:13<01:35, 14.32ex/s]
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preprocess datasets #2:  10%|█         | 158/1528 [00:13<01:28, 15.41ex/s]
preprocess datasets #0:  10%|█         | 155/1528 [00:13<01:27, 15.78ex/s]
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preprocess datasets #4:   9%|▉         | 134/1527 [00:13<01:32, 15.09ex/s]
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preprocess datasets #3:  10%|█         | 156/1527 [00:13<01:14, 18.31ex/s]
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preprocess datasets #1:  11%|█         | 165/1528 [00:14<01:35, 14.20ex/s]
preprocess datasets #0:  10%|█         | 157/1528 [00:14<01:29, 15.33ex/s]
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preprocess datasets #2:  10%|█         | 160/1528 [00:14<01:34, 14.44ex/s]
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preprocess datasets #4:   9%|▉         | 138/1527 [00:14<01:14, 18.58ex/s]
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preprocess datasets #3:  10%|█         | 159/1527 [00:14<01:11, 19.22ex/s]
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preprocess datasets #1:  11%|█         | 167/1528 [00:14<01:31, 14.95ex/s]
preprocess datasets #0:  10%|█         | 159/1528 [00:14<01:28, 15.48ex/s]
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preprocess datasets #2:  11%|█         | 163/1528 [00:14<01:22, 16.62ex/s]
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preprocess datasets #4:   9%|▉         | 141/1527 [00:14<01:16, 18.00ex/s]
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preprocess datasets #1:  11%|█         | 169/1528 [00:14<01:32, 14.72ex/s]
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preprocess datasets #3:  11%|█         | 162/1527 [00:14<01:12, 18.80ex/s]
preprocess datasets #0:  11%|█         | 163/1528 [00:14<01:07, 20.16ex/s]
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preprocess datasets #2:  11%|█         | 166/1528 [00:14<01:22, 16.43ex/s]
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preprocess datasets #4:   9%|▉         | 143/1527 [00:14<01:27, 15.81ex/s]
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preprocess datasets #3:  11%|█         | 164/1527 [00:14<01:20, 16.96ex/s]
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preprocess datasets #1:  11%|█         | 171/1528 [00:14<01:39, 13.58ex/s]
preprocess datasets #0:  11%|█         | 166/1528 [00:14<01:19, 17.20ex/s]
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preprocess datasets #2:  11%|█         | 168/1528 [00:14<01:27, 15.62ex/s]
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preprocess datasets #1:  11%|█▏        | 173/1528 [00:14<01:31, 14.78ex/s]
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preprocess datasets #4:   9%|▉         | 145/1527 [00:14<01:33, 14.74ex/s]
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preprocess datasets #3:  11%|█         | 166/1527 [00:14<01:29, 15.25ex/s]
preprocess datasets #0:  11%|█         | 168/1528 [00:14<01:24, 16.18ex/s]
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preprocess datasets #4:  10%|▉         | 147/1527 [00:14<01:27, 15.71ex/s]
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preprocess datasets #3:  11%|█         | 168/1527 [00:14<01:28, 15.29ex/s]
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preprocess datasets #2:  11%|█         | 170/1528 [00:14<01:44, 13.06ex/s]
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preprocess datasets #1:  12%|█▏        | 176/1528 [00:14<01:38, 13.73ex/s]
preprocess datasets #0:  11%|█         | 170/1528 [00:14<01:27, 15.60ex/s]
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preprocess datasets #4:  10%|▉         | 149/1527 [00:14<01:26, 15.93ex/s]
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preprocess datasets #3:  11%|█         | 170/1527 [00:14<01:23, 16.31ex/s]
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preprocess datasets #2:  11%|█▏        | 172/1528 [00:14<01:35, 14.19ex/s]
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preprocess datasets #1:  12%|█▏        | 178/1528 [00:14<01:35, 14.19ex/s]
preprocess datasets #0:  11%|█▏        | 172/1528 [00:14<01:27, 15.41ex/s]
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preprocess datasets #4:  10%|▉         | 151/1527 [00:14<01:28, 15.46ex/s]
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preprocess datasets #3:  11%|█▏        | 172/1527 [00:14<01:26, 15.67ex/s]
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preprocess datasets #2:  11%|█▏        | 174/1528 [00:15<01:37, 13.89ex/s]
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preprocess datasets #1:  12%|█▏        | 181/1528 [00:15<01:25, 15.69ex/s]
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preprocess datasets #4:  10%|█         | 153/1527 [00:15<01:27, 15.62ex/s]
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preprocess datasets #3:  11%|█▏        | 174/1527 [00:15<01:22, 16.34ex/s]
preprocess datasets #0:  11%|█▏        | 175/1528 [00:15<01:26, 15.56ex/s]
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preprocess datasets #2:  12%|█▏        | 176/1528 [00:15<01:36, 14.06ex/s]
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preprocess datasets #1:  12%|█▏        | 183/1528 [00:15<01:24, 15.91ex/s]
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preprocess datasets #3:  12%|█▏        | 176/1527 [00:15<01:24, 15.98ex/s]
preprocess datasets #0:  12%|█▏        | 177/1528 [00:15<01:23, 16.17ex/s]
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preprocess datasets #4:  10%|█         | 155/1527 [00:15<01:32, 14.77ex/s]
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preprocess datasets #2:  12%|█▏        | 178/1528 [00:15<01:35, 14.10ex/s]
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preprocess datasets #1:  12%|█▏        | 185/1528 [00:15<01:23, 16.10ex/s]
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preprocess datasets #3:  12%|█▏        | 178/1527 [00:15<01:29, 15.15ex/s]
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preprocess datasets #4:  10%|█         | 157/1527 [00:15<01:34, 14.51ex/s]
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preprocess datasets #2:  12%|█▏        | 180/1528 [00:15<01:32, 14.59ex/s]
preprocess datasets #0:  12%|█▏        | 179/1528 [00:15<01:32, 14.56ex/s]
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preprocess datasets #1:  12%|█▏        | 187/1528 [00:15<01:22, 16.24ex/s]
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preprocess datasets #4:  10%|█         | 159/1527 [00:15<01:27, 15.70ex/s]
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preprocess datasets #2:  12%|█▏        | 182/1528 [00:15<01:27, 15.41ex/s]
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preprocess datasets #3:  12%|█▏        | 182/1527 [00:15<01:17, 17.41ex/s]
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preprocess datasets #1:  12%|█▏        | 189/1528 [00:15<01:22, 16.26ex/s]
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preprocess datasets #4:  11%|█         | 161/1527 [00:15<01:26, 15.83ex/s]
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preprocess datasets #2:  12%|█▏        | 184/1528 [00:15<01:21, 16.46ex/s]
preprocess datasets #0:  12%|█▏        | 182/1528 [00:15<01:37, 13.79ex/s]
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preprocess datasets #3:  12%|█▏        | 184/1527 [00:15<01:21, 16.41ex/s]
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preprocess datasets #1:  12%|█▎        | 191/1528 [00:15<01:35, 13.99ex/s]
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preprocess datasets #2:  12%|█▏        | 186/1528 [00:15<01:25, 15.66ex/s]
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preprocess datasets #0:  12%|█▏        | 184/1528 [00:15<01:34, 14.15ex/s]
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preprocess datasets #3:  12%|█▏        | 186/1527 [00:15<01:28, 15.13ex/s]
preprocess datasets #0:  12%|█▏        | 186/1528 [00:15<01:31, 14.67ex/s]
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preprocess datasets #3:  12%|█▏        | 188/1527 [00:16<01:29, 15.00ex/s]
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preprocess datasets #2:  12%|█▏        | 188/1528 [00:16<01:47, 12.42ex/s]
preprocess datasets #0:  12%|█▏        | 188/1528 [00:16<01:29, 14.92ex/s]
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preprocess datasets #1:  13%|█▎        | 195/1528 [00:16<01:41, 13.17ex/s]
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preprocess datasets #2:  12%|█▏        | 190/1528 [00:16<01:42, 13.08ex/s]
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preprocess datasets #3:  12%|█▏        | 190/1527 [00:16<01:37, 13.78ex/s]
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preprocess datasets #4:  11%|█         | 167/1527 [00:16<01:55, 11.76ex/s]
preprocess datasets #0:  12%|█▎        | 191/1528 [00:16<01:21, 16.35ex/s]
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preprocess datasets #1:  13%|█▎        | 197/1528 [00:16<01:37, 13.65ex/s]
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preprocess datasets #2:  13%|█▎        | 192/1528 [00:16<01:34, 14.17ex/s]
preprocess datasets #0:  13%|█▎        | 193/1528 [00:16<01:22, 16.12ex/s]
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preprocess datasets #3:  13%|█▎        | 192/1527 [00:16<01:37, 13.63ex/s]
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preprocess datasets #4:  11%|█         | 169/1527 [00:16<01:50, 12.33ex/s]
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preprocess datasets #1:  13%|█▎        | 200/1528 [00:16<01:23, 15.95ex/s]
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preprocess datasets #2:  13%|█▎        | 194/1528 [00:16<01:31, 14.61ex/s]
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preprocess datasets #3:  13%|█▎        | 194/1527 [00:16<01:34, 14.06ex/s]
preprocess datasets #0:  13%|█▎        | 195/1528 [00:16<01:28, 15.14ex/s]
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preprocess datasets #4:  11%|█         | 171/1527 [00:16<01:48, 12.52ex/s]
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preprocess datasets #1:  13%|█▎        | 202/1528 [00:16<01:23, 15.81ex/s]
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preprocess datasets #2:  13%|█▎        | 196/1528 [00:16<01:27, 15.19ex/s]
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preprocess datasets #3:  13%|█▎        | 196/1527 [00:16<01:35, 13.93ex/s]
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preprocess datasets #0:  14%|█▍        | 221/1528 [00:18<01:17, 16.93ex/s]
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preprocess datasets #0:  15%|█▍        | 223/1528 [00:18<01:27, 14.92ex/s]
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preprocess datasets #3:  15%|█▍        | 225/1527 [00:18<01:17, 16.79ex/s]
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preprocess datasets #0:  15%|█▍        | 225/1528 [00:18<01:48, 11.99ex/s]
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preprocess datasets #0:  15%|█▍        | 227/1528 [00:18<01:41, 12.87ex/s]
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preprocess datasets #0:  15%|█▍        | 229/1528 [00:18<01:36, 13.45ex/s]
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preprocess datasets #0:  15%|█▌        | 231/1528 [00:18<01:29, 14.45ex/s]
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preprocess datasets #0:  15%|█▌        | 233/1528 [00:19<01:32, 14.03ex/s]
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preprocess datasets #0:  15%|█▌        | 235/1528 [00:19<01:35, 13.58ex/s]
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preprocess datasets #1:  16%|█▋        | 252/1528 [00:19<01:18, 16.16ex/s]
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preprocess datasets #3:  16%|█▌        | 238/1527 [00:19<01:34, 13.64ex/s]
preprocess datasets #0:  16%|█▌        | 237/1528 [00:19<01:33, 13.88ex/s]
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preprocess datasets #3:  16%|█▌        | 240/1527 [00:19<01:30, 14.28ex/s]
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preprocess datasets #0:  16%|█▌        | 240/1528 [00:19<01:24, 15.25ex/s]
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preprocess datasets #0:  16%|█▌        | 242/1528 [00:19<01:21, 15.81ex/s]
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preprocess datasets #0:  16%|█▌        | 244/1528 [00:19<01:26, 14.80ex/s]
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preprocess datasets #3:  16%|█▌        | 246/1527 [00:19<01:39, 12.86ex/s]
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preprocess datasets #0:  16%|█▌        | 246/1528 [00:20<01:35, 13.44ex/s]
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preprocess datasets #3:  16%|█▌        | 248/1527 [00:20<01:42, 12.53ex/s]
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preprocess datasets #0:  16%|█▌        | 248/1528 [00:20<01:49, 11.71ex/s]
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preprocess datasets #0:  16%|█▋        | 251/1528 [00:20<01:29, 14.25ex/s]
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preprocess datasets #0:  17%|█▋        | 253/1528 [00:20<01:25, 14.90ex/s]
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preprocess datasets #3:  18%|█▊        | 274/1527 [00:21<01:43, 12.09ex/s]
preprocess datasets #0:  18%|█▊        | 278/1528 [00:22<01:18, 15.86ex/s]
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preprocess datasets #4:  17%|█▋        | 256/1527 [00:22<01:23, 15.22ex/s]
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preprocess datasets #3:  18%|█▊        | 276/1527 [00:22<01:45, 11.89ex/s]
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preprocess datasets #2:  19%|█▉        | 294/1528 [00:22<01:17, 15.95ex/s]
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preprocess datasets #4:  17%|█▋        | 258/1527 [00:22<01:24, 15.04ex/s]
preprocess datasets #0:  18%|█▊        | 281/1528 [00:22<01:30, 13.84ex/s]
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preprocess datasets #3:  18%|█▊        | 279/1527 [00:22<01:29, 14.02ex/s]
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preprocess datasets #0:  19%|█▊        | 283/1528 [00:22<01:28, 14.05ex/s]
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preprocess datasets #2:  20%|█▉        | 298/1528 [00:22<01:19, 15.41ex/s]
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preprocess datasets #0:  19%|█▊        | 285/1528 [00:22<01:30, 13.71ex/s]
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preprocess datasets #0:  19%|█▉        | 287/1528 [00:22<01:27, 14.23ex/s]
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preprocess datasets #2:  20%|█▉        | 303/1528 [00:22<01:12, 16.99ex/s]
preprocess datasets #0:  19%|█▉        | 289/1528 [00:22<01:25, 14.55ex/s]
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preprocess datasets #2:  20%|█▉        | 305/1528 [00:22<01:10, 17.36ex/s]
preprocess datasets #0:  19%|█▉        | 291/1528 [00:22<01:18, 15.74ex/s]
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preprocess datasets #3:  19%|█▉        | 290/1527 [00:22<01:21, 15.26ex/s]
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preprocess datasets #2:  20%|██        | 307/1528 [00:23<01:12, 16.84ex/s]
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preprocess datasets #1:  20%|██        | 313/1528 [00:23<01:13, 16.58ex/s]
preprocess datasets #0:  19%|█▉        | 293/1528 [00:23<01:22, 14.92ex/s]
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preprocess datasets #4:  18%|█▊        | 272/1527 [00:23<01:17, 16.15ex/s]
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preprocess datasets #3:  19%|█▉        | 292/1527 [00:23<01:27, 14.18ex/s]
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preprocess datasets #1:  21%|██        | 315/1528 [00:23<01:15, 16.05ex/s]
preprocess datasets #0:  19%|█▉        | 295/1528 [00:23<01:24, 14.61ex/s]
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preprocess datasets #3:  19%|█▉        | 294/1527 [00:23<01:25, 14.34ex/s]
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preprocess datasets #0:  19%|█▉        | 297/1528 [00:23<01:23, 14.80ex/s]
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preprocess datasets #4:  18%|█▊        | 277/1527 [00:23<01:09, 18.07ex/s]
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preprocess datasets #1:  21%|██        | 319/1528 [00:23<01:10, 17.04ex/s]
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preprocess datasets #3:  19%|█▉        | 296/1527 [00:23<01:25, 14.47ex/s]
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preprocess datasets #4:  18%|█▊        | 279/1527 [00:23<01:09, 17.87ex/s]
preprocess datasets #0:  20%|█▉        | 299/1528 [00:23<01:27, 13.99ex/s]
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preprocess datasets #2:  21%|██        | 315/1528 [00:23<01:17, 15.65ex/s]
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preprocess datasets #3:  20%|█▉        | 298/1527 [00:23<01:21, 15.05ex/s]
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preprocess datasets #4:  18%|█▊        | 281/1527 [00:23<01:11, 17.47ex/s]
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preprocess datasets #2:  21%|██        | 318/1528 [00:23<01:06, 18.31ex/s]
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preprocess datasets #3:  20%|█▉        | 300/1527 [00:23<01:20, 15.18ex/s]
preprocess datasets #0:  20%|█▉        | 301/1528 [00:23<01:36, 12.75ex/s]
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preprocess datasets #1:  21%|██        | 323/1528 [00:23<01:17, 15.57ex/s]
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preprocess datasets #4:  19%|█▊        | 283/1527 [00:23<01:14, 16.81ex/s]
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preprocess datasets #2:  21%|██        | 321/1528 [00:23<01:01, 19.71ex/s]
preprocess datasets #0:  20%|█▉        | 303/1528 [00:23<01:27, 14.05ex/s]
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preprocess datasets #3:  20%|█▉        | 302/1527 [00:23<01:21, 14.99ex/s]
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preprocess datasets #1:  21%|██▏       | 325/1528 [00:23<01:20, 14.99ex/s]
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preprocess datasets #4:  19%|█▊        | 285/1527 [00:23<01:13, 16.92ex/s]
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preprocess datasets #2:  21%|██        | 323/1528 [00:23<01:06, 18.09ex/s]
preprocess datasets #0:  20%|█▉        | 305/1528 [00:23<01:23, 14.65ex/s]
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preprocess datasets #4:  19%|█▉        | 287/1527 [00:23<01:10, 17.65ex/s]
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preprocess datasets #3:  20%|█▉        | 304/1527 [00:23<01:26, 14.16ex/s]
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preprocess datasets #2:  21%|██▏       | 325/1528 [00:24<01:05, 18.24ex/s]
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preprocess datasets #4:  19%|█▉        | 289/1527 [00:24<01:13, 16.94ex/s]
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preprocess datasets #1:  22%|██▏       | 329/1528 [00:24<01:18, 15.33ex/s]
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preprocess datasets #3:  20%|██        | 306/1527 [00:24<01:27, 13.96ex/s]
preprocess datasets #0:  20%|██        | 307/1528 [00:24<01:36, 12.65ex/s]
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preprocess datasets #2:  21%|██▏       | 327/1528 [00:24<01:09, 17.22ex/s]
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preprocess datasets #1:  22%|██▏       | 331/1528 [00:24<01:14, 16.12ex/s]
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preprocess datasets #4:  19%|█▉        | 292/1527 [00:24<01:04, 19.12ex/s]
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preprocess datasets #3:  20%|██        | 308/1527 [00:24<01:21, 14.92ex/s]
preprocess datasets #0:  20%|██        | 309/1528 [00:24<01:29, 13.69ex/s]
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preprocess datasets #2:  22%|██▏       | 330/1528 [00:24<01:03, 18.95ex/s]
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preprocess datasets #1:  22%|██▏       | 333/1528 [00:24<01:12, 16.46ex/s]
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preprocess datasets #4:  19%|█▉        | 294/1527 [00:24<01:07, 18.40ex/s]
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preprocess datasets #3:  20%|██        | 310/1527 [00:24<01:18, 15.60ex/s]
preprocess datasets #0:  20%|██        | 311/1528 [00:24<01:24, 14.46ex/s]
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preprocess datasets #2:  22%|██▏       | 332/1528 [00:24<01:03, 18.85ex/s]
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preprocess datasets #4:  19%|█▉        | 296/1527 [00:24<01:09, 17.63ex/s]
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preprocess datasets #3:  20%|██        | 312/1527 [00:24<01:22, 14.71ex/s]
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preprocess datasets #2:  22%|██▏       | 334/1528 [00:24<01:04, 18.39ex/s]
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preprocess datasets #1:  22%|██▏       | 337/1528 [00:24<01:09, 17.10ex/s]
preprocess datasets #0:  21%|██        | 314/1528 [00:24<01:19, 15.27ex/s]
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preprocess datasets #3:  21%|██        | 314/1527 [00:24<01:20, 15.07ex/s]
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preprocess datasets #4:  20%|█▉        | 298/1527 [00:24<01:24, 14.51ex/s]
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preprocess datasets #2:  22%|██▏       | 337/1528 [00:24<01:04, 18.43ex/s]
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preprocess datasets #1:  22%|██▏       | 339/1528 [00:24<01:12, 16.42ex/s]
preprocess datasets #0:  21%|██        | 316/1528 [00:24<01:29, 13.50ex/s]
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preprocess datasets #3:  21%|██        | 316/1527 [00:24<01:24, 14.40ex/s]
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preprocess datasets #4:  20%|█▉        | 300/1527 [00:24<01:23, 14.69ex/s]
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preprocess datasets #2:  22%|██▏       | 339/1528 [00:24<01:05, 18.22ex/s]
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preprocess datasets #1:  22%|██▏       | 341/1528 [00:24<01:15, 15.62ex/s]
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preprocess datasets #3:  21%|██        | 318/1527 [00:24<01:20, 14.93ex/s]
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preprocess datasets #4:  20%|█▉        | 302/1527 [00:24<01:25, 14.31ex/s]
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preprocess datasets #2:  22%|██▏       | 342/1528 [00:24<01:03, 18.76ex/s]
preprocess datasets #0:  21%|██        | 318/1528 [00:24<01:39, 12.18ex/s]
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preprocess datasets #1:  22%|██▏       | 343/1528 [00:24<01:16, 15.42ex/s]
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preprocess datasets #3:  21%|██        | 320/1527 [00:25<01:18, 15.36ex/s]
preprocess datasets #0:  21%|██        | 320/1528 [00:25<01:31, 13.27ex/s]
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preprocess datasets #2:  23%|██▎       | 344/1528 [00:25<01:06, 17.83ex/s]
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preprocess datasets #1:  23%|██▎       | 345/1528 [00:25<01:18, 15.13ex/s]
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preprocess datasets #4:  20%|█▉        | 305/1527 [00:25<01:17, 15.86ex/s]
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preprocess datasets #4:  20%|██        | 307/1527 [00:25<01:13, 16.67ex/s]
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preprocess datasets #2:  23%|██▎       | 347/1528 [00:25<01:03, 18.62ex/s]
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preprocess datasets #1:  23%|██▎       | 348/1528 [00:25<01:07, 17.51ex/s]
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preprocess datasets #0:  21%|██        | 324/1528 [00:25<01:26, 13.86ex/s]
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preprocess datasets #3:  21%|██        | 324/1527 [00:25<01:27, 13.82ex/s]
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preprocess datasets #2:  23%|██▎       | 349/1528 [00:25<01:07, 17.42ex/s]
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preprocess datasets #2:  23%|██▎       | 351/1528 [00:25<01:07, 17.32ex/s]
preprocess datasets #0:  21%|██▏       | 326/1528 [00:25<01:26, 13.93ex/s]
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preprocess datasets #3:  21%|██▏       | 326/1527 [00:25<01:36, 12.50ex/s]
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preprocess datasets #2:  23%|██▎       | 353/1528 [00:25<01:07, 17.47ex/s]
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preprocess datasets #4:  21%|██        | 315/1527 [00:25<01:10, 17.19ex/s]
preprocess datasets #0:  21%|██▏       | 328/1528 [00:25<01:34, 12.74ex/s]
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preprocess datasets #2:  23%|██▎       | 355/1528 [00:25<01:06, 17.54ex/s]
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preprocess datasets #3:  21%|██▏       | 328/1527 [00:25<01:35, 12.60ex/s]
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preprocess datasets #4:  21%|██        | 317/1527 [00:25<01:12, 16.58ex/s]
preprocess datasets #0:  22%|██▏       | 330/1528 [00:25<01:31, 13.11ex/s]
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preprocess datasets #2:  23%|██▎       | 357/1528 [00:25<01:07, 17.40ex/s]
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preprocess datasets #0:  22%|██▏       | 332/1528 [00:26<01:36, 12.41ex/s]
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preprocess datasets #3:  22%|██▏       | 334/1527 [00:26<01:24, 14.12ex/s]
preprocess datasets #0:  22%|██▏       | 334/1528 [00:26<01:29, 13.40ex/s]
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preprocess datasets #0:  22%|██▏       | 336/1528 [00:26<01:43, 11.49ex/s]
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preprocess datasets #0:  80%|███████▉  | 1218/1528 [01:24<00:21, 14.19ex/s]
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preprocess datasets #0:  80%|████████  | 1228/1528 [01:25<00:19, 15.14ex/s]
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preprocess datasets #0:  80%|████████  | 1230/1528 [01:25<00:21, 14.08ex/s]
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preprocess datasets #0:  83%|████████▎ | 1271/1528 [01:28<00:16, 15.34ex/s]
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preprocess datasets #0:  83%|████████▎ | 1273/1528 [01:28<00:18, 14.02ex/s]
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preprocess datasets #0:  84%|████████▎ | 1278/1528 [01:28<00:17, 13.90ex/s]
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preprocess datasets #0:  84%|████████▍ | 1283/1528 [01:29<00:15, 16.17ex/s]
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preprocess datasets #0:  84%|████████▍ | 1287/1528 [01:29<00:17, 14.08ex/s]
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preprocess datasets #0:  84%|████████▍ | 1289/1528 [01:29<00:16, 14.66ex/s]
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preprocess datasets #0:  85%|████████▍ | 1292/1528 [01:29<00:13, 17.54ex/s]
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preprocess datasets #3:  87%|████████▋ | 1323/1527 [01:29<00:11, 18.30ex/s]
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preprocess datasets #2:  89%|████████▊ | 1353/1528 [01:29<00:17, 10.16ex/s]
preprocess datasets #0:  85%|████████▍ | 1294/1528 [01:29<00:13, 17.47ex/s]
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preprocess datasets #2:  89%|████████▊ | 1355/1528 [01:29<00:15, 11.38ex/s]
preprocess datasets #0:  85%|████████▍ | 1296/1528 [01:29<00:13, 16.72ex/s]
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preprocess datasets #0:  85%|████████▍ | 1298/1528 [01:30<00:14, 15.73ex/s]
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preprocess datasets #3:  87%|████████▋ | 1331/1527 [01:30<00:10, 17.83ex/s]
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preprocess datasets #0:  85%|████████▌ | 1301/1528 [01:30<00:13, 16.66ex/s]
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preprocess datasets #2:  89%|████████▉ | 1361/1528 [01:30<00:13, 12.69ex/s]
preprocess datasets #0:  85%|████████▌ | 1304/1528 [01:30<00:12, 17.56ex/s]
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preprocess datasets #3:  88%|████████▊ | 1337/1527 [01:30<00:11, 17.18ex/s]
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preprocess datasets #0:  85%|████████▌ | 1306/1528 [01:30<00:13, 16.27ex/s]
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preprocess datasets #2:  89%|████████▉ | 1365/1528 [01:30<00:11, 13.67ex/s]
preprocess datasets #0:  86%|████████▌ | 1308/1528 [01:30<00:13, 16.79ex/s]
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preprocess datasets #4:  86%|████████▌ | 1313/1527 [01:30<00:15, 13.90ex/s]
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preprocess datasets #3:  88%|████████▊ | 1341/1527 [01:30<00:10, 17.24ex/s]
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preprocess datasets #2:  89%|████████▉ | 1367/1528 [01:30<00:11, 13.83ex/s]
preprocess datasets #0:  86%|████████▌ | 1310/1528 [01:30<00:12, 16.80ex/s]
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preprocess datasets #1:  90%|█████████ | 1377/1528 [01:30<00:08, 17.11ex/s]
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preprocess datasets #3:  88%|████████▊ | 1343/1527 [01:30<00:10, 17.69ex/s]
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preprocess datasets #4:  86%|████████▌ | 1316/1527 [01:30<00:14, 14.87ex/s]
preprocess datasets #0:  86%|████████▌ | 1312/1528 [01:30<00:12, 16.82ex/s]
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preprocess datasets #1:  90%|█████████ | 1379/1528 [01:30<00:08, 17.01ex/s]
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preprocess datasets #2:  90%|████████▉ | 1369/1528 [01:30<00:12, 13.10ex/s]
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preprocess datasets #3:  88%|████████▊ | 1345/1527 [01:30<00:11, 16.21ex/s]
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preprocess datasets #0:  86%|████████▌ | 1314/1528 [01:31<00:13, 16.25ex/s]
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preprocess datasets #2:  90%|████████▉ | 1371/1528 [01:31<00:11, 13.21ex/s]
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preprocess datasets #3:  88%|████████▊ | 1348/1527 [01:31<00:09, 18.15ex/s]
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preprocess datasets #1:  91%|█████████ | 1383/1528 [01:31<00:09, 15.50ex/s]
preprocess datasets #0:  86%|████████▌ | 1316/1528 [01:31<00:14, 14.50ex/s]
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preprocess datasets #4:  87%|████████▋ | 1322/1527 [01:31<00:13, 15.35ex/s]
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preprocess datasets #2:  90%|████████▉ | 1373/1528 [01:31<00:12, 12.50ex/s]
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preprocess datasets #3:  88%|████████▊ | 1351/1527 [01:31<00:09, 18.18ex/s]
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preprocess datasets #1:  91%|█████████ | 1386/1528 [01:31<00:08, 17.57ex/s]
preprocess datasets #0:  86%|████████▋ | 1318/1528 [01:31<00:14, 14.78ex/s]
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preprocess datasets #1:  91%|█████████ | 1388/1528 [01:31<00:07, 17.86ex/s]
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preprocess datasets #3:  89%|████████▊ | 1353/1527 [01:31<00:11, 15.24ex/s]
preprocess datasets #0:  86%|████████▋ | 1320/1528 [01:31<00:14, 14.75ex/s]
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preprocess datasets #0:  87%|████████▋ | 1322/1528 [01:31<00:14, 14.58ex/s]
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preprocess datasets #2:  90%|█████████ | 1379/1528 [01:31<00:10, 13.82ex/s]
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preprocess datasets #1:  91%|█████████ | 1392/1528 [01:31<00:08, 15.71ex/s]
preprocess datasets #0:  87%|████████▋ | 1324/1528 [01:31<00:14, 14.24ex/s]
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preprocess datasets #2:  90%|█████████ | 1381/1528 [01:31<00:11, 13.05ex/s]
preprocess datasets #0:  87%|████████▋ | 1326/1528 [01:31<00:14, 14.37ex/s]
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preprocess datasets #2:  91%|█████████ | 1383/1528 [01:31<00:10, 14.22ex/s]
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preprocess datasets #3:  89%|████████▉ | 1362/1527 [01:31<00:09, 16.60ex/s]
preprocess datasets #0:  87%|████████▋ | 1328/1528 [01:32<00:13, 15.03ex/s]
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preprocess datasets #0:  87%|████████▋ | 1330/1528 [01:32<00:12, 15.35ex/s]
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preprocess datasets #0:  87%|████████▋ | 1332/1528 [01:32<00:16, 12.08ex/s]
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preprocess datasets #2:   0%|          | 1/380 [00:01<11:26,  1.81s/ex]
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preprocess datasets #1:   0%|          | 1/380 [00:01<11:54,  1.89s/ex]
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preprocess datasets #4:   1%|          | 3/380 [00:01<03:09,  1.99ex/s]
preprocess datasets #0:   1%|          | 4/381 [00:01<02:23,  2.62ex/s]
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preprocess datasets #2:   1%|          | 3/380 [00:01<03:14,  1.94ex/s]
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preprocess datasets #3:   1%|          | 3/380 [00:01<03:13,  1.95ex/s]
preprocess datasets #0:   2%|▏         | 6/381 [00:02<01:29,  4.20ex/s]
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preprocess datasets #1:   1%|          | 3/380 [00:02<03:27,  1.82ex/s]
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preprocess datasets #2:   1%|▏         | 5/380 [00:02<01:44,  3.58ex/s]
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preprocess datasets #4:   1%|▏         | 5/380 [00:02<01:44,  3.59ex/s]
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preprocess datasets #3:   1%|▏         | 5/380 [00:02<01:44,  3.58ex/s]
preprocess datasets #0:   2%|▏         | 8/381 [00:02<01:02,  6.01ex/s]
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preprocess datasets #2:   2%|▏         | 7/380 [00:02<01:09,  5.38ex/s]
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preprocess datasets #3:   2%|▏         | 7/380 [00:02<01:09,  5.36ex/s]
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preprocess datasets #1:   1%|▏         | 5/380 [00:02<01:58,  3.15ex/s]
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preprocess datasets #4:   2%|▏         | 7/380 [00:02<01:16,  4.89ex/s]
preprocess datasets #0:   3%|▎         | 10/381 [00:02<00:48,  7.68ex/s]
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preprocess datasets #3:   2%|▏         | 9/380 [00:02<00:51,  7.24ex/s]
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preprocess datasets #2:   2%|▏         | 9/380 [00:02<00:52,  7.07ex/s]
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preprocess datasets #1:   2%|▏         | 7/380 [00:02<01:18,  4.78ex/s]
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preprocess datasets #4:   2%|▏         | 9/380 [00:02<00:55,  6.71ex/s]
preprocess datasets #0:   3%|▎         | 12/381 [00:02<00:39,  9.31ex/s]
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preprocess datasets #3:   3%|▎         | 11/380 [00:02<00:40,  9.07ex/s]
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preprocess datasets #2:   3%|▎         | 12/380 [00:02<00:35, 10.23ex/s]
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preprocess datasets #1:   2%|▏         | 9/380 [00:02<00:58,  6.38ex/s]
preprocess datasets #0:   4%|▎         | 14/381 [00:02<00:33, 10.98ex/s]
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preprocess datasets #4:   3%|▎         | 11/380 [00:02<00:45,  8.17ex/s]
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preprocess datasets #3:   3%|▎         | 13/380 [00:02<00:35, 10.48ex/s]
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preprocess datasets #2:   4%|▎         | 14/380 [00:02<00:31, 11.47ex/s]
preprocess datasets #0:   4%|▍         | 16/381 [00:02<00:29, 12.44ex/s]
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preprocess datasets #1:   3%|▎         | 11/380 [00:02<00:45,  8.07ex/s]
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preprocess datasets #2:   4%|▍         | 16/380 [00:02<00:28, 12.64ex/s]
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preprocess datasets #3:   4%|▍         | 15/380 [00:02<00:31, 11.42ex/s]
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preprocess datasets #4:   3%|▎         | 13/380 [00:02<00:40,  9.10ex/s]
preprocess datasets #0:   5%|▍         | 18/381 [00:02<00:27, 13.15ex/s]
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preprocess datasets #1:   3%|▎         | 13/380 [00:02<00:39,  9.35ex/s]
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preprocess datasets #3:   4%|▍         | 17/380 [00:02<00:27, 13.20ex/s]
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preprocess datasets #2:   5%|▍         | 18/380 [00:02<00:26, 13.64ex/s]
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preprocess datasets #4:   4%|▍         | 15/380 [00:02<00:33, 10.84ex/s]
preprocess datasets #0:   5%|▌         | 20/381 [00:02<00:25, 14.21ex/s]
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preprocess datasets #1:   4%|▍         | 15/380 [00:02<00:33, 11.04ex/s]
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preprocess datasets #3:   5%|▌         | 19/380 [00:02<00:25, 14.22ex/s]
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preprocess datasets #4:   4%|▍         | 17/380 [00:02<00:29, 12.37ex/s]
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preprocess datasets #2:   5%|▌         | 20/380 [00:02<00:25, 14.39ex/s]
preprocess datasets #0:   6%|▌         | 22/381 [00:03<00:23, 15.46ex/s]
+
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preprocess datasets #3:   6%|▌         | 21/380 [00:02<00:23, 15.58ex/s]
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preprocess datasets #1:   4%|▍         | 17/380 [00:03<00:31, 11.69ex/s]
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preprocess datasets #4:   5%|▌         | 19/380 [00:03<00:27, 13.29ex/s]
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preprocess datasets #2:   6%|▌         | 22/380 [00:03<00:24, 14.91ex/s]
preprocess datasets #0:   6%|▋         | 24/381 [00:03<00:22, 15.72ex/s]
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preprocess datasets #3:   6%|▌         | 23/380 [00:03<00:23, 15.31ex/s]
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preprocess datasets #1:   5%|▌         | 19/380 [00:03<00:27, 12.96ex/s]
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preprocess datasets #2:   6%|▋         | 24/380 [00:03<00:22, 15.56ex/s]
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preprocess datasets #4:   6%|▌         | 22/380 [00:03<00:22, 15.82ex/s]
preprocess datasets #0:   7%|▋         | 26/381 [00:03<00:23, 15.30ex/s]
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preprocess datasets #3:   7%|▋         | 25/380 [00:03<00:21, 16.26ex/s]
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preprocess datasets #1:   6%|▌         | 21/380 [00:03<00:27, 13.24ex/s]
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preprocess datasets #2:   7%|▋         | 26/380 [00:03<00:23, 14.82ex/s]
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preprocess datasets #4:   6%|▋         | 24/380 [00:03<00:23, 15.46ex/s]
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preprocess datasets #3:   7%|▋         | 27/380 [00:03<00:21, 16.35ex/s]
preprocess datasets #0:   7%|▋         | 28/381 [00:03<00:23, 14.90ex/s]
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preprocess datasets #1:   6%|▌         | 23/380 [00:03<00:26, 13.62ex/s]
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preprocess datasets #4:   7%|▋         | 26/380 [00:03<00:24, 14.68ex/s]
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preprocess datasets #2:   8%|▊         | 29/380 [00:03<00:21, 16.00ex/s]
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preprocess datasets #3:   8%|▊         | 29/380 [00:03<00:21, 16.50ex/s]
preprocess datasets #0:   8%|▊         | 30/381 [00:03<00:22, 15.59ex/s]
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preprocess datasets #4:   7%|▋         | 28/380 [00:03<00:22, 15.57ex/s]
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preprocess datasets #2:   8%|▊         | 31/380 [00:03<00:21, 16.54ex/s]
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preprocess datasets #3:   8%|▊         | 31/380 [00:03<00:21, 16.18ex/s]
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preprocess datasets #1:   7%|▋         | 25/380 [00:03<00:28, 12.44ex/s]
preprocess datasets #0:   8%|▊         | 32/381 [00:03<00:22, 15.38ex/s]
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preprocess datasets #2:   9%|▊         | 33/380 [00:03<00:20, 16.86ex/s]
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preprocess datasets #3:   9%|▊         | 33/380 [00:03<00:21, 16.38ex/s]
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preprocess datasets #4:   8%|▊         | 30/380 [00:03<00:24, 14.47ex/s]
preprocess datasets #0:   9%|▉         | 35/381 [00:03<00:19, 17.34ex/s]
+
preprocess datasets #1:   7%|▋         | 27/380 [00:03<00:29, 12.02ex/s]
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preprocess datasets #2:   9%|▉         | 35/380 [00:03<00:20, 16.55ex/s]
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preprocess datasets #3:   9%|▉         | 35/380 [00:03<00:20, 16.92ex/s]
preprocess datasets #0:  10%|▉         | 37/381 [00:03<00:20, 16.75ex/s]
+
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preprocess datasets #4:   8%|▊         | 32/380 [00:03<00:25, 13.45ex/s]
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preprocess datasets #1:   8%|▊         | 29/380 [00:03<00:27, 12.61ex/s]
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preprocess datasets #2:  10%|▉         | 37/380 [00:03<00:21, 16.30ex/s]
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preprocess datasets #3:  10%|▉         | 37/380 [00:03<00:20, 16.97ex/s]
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preprocess datasets #4:   9%|▉         | 34/380 [00:03<00:23, 14.65ex/s]
preprocess datasets #0:  10%|█         | 40/381 [00:04<00:19, 17.19ex/s]
+
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preprocess datasets #2:  10%|█         | 39/380 [00:04<00:20, 16.63ex/s]
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preprocess datasets #1:   8%|▊         | 31/380 [00:04<00:27, 12.91ex/s]
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preprocess datasets #3:  11%|█         | 40/380 [00:04<00:18, 18.57ex/s]
preprocess datasets #0:  11%|█         | 42/381 [00:04<00:19, 17.61ex/s]
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preprocess datasets #4:   9%|▉         | 36/380 [00:04<00:24, 14.13ex/s]
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preprocess datasets #2:  11%|█         | 41/380 [00:04<00:20, 16.28ex/s]
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preprocess datasets #3:  11%|█         | 42/380 [00:04<00:18, 18.35ex/s]
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preprocess datasets #1:   9%|▊         | 33/380 [00:04<00:26, 12.95ex/s]
preprocess datasets #0:  12%|█▏        | 44/381 [00:04<00:18, 17.94ex/s]
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preprocess datasets #4:  10%|█         | 38/380 [00:04<00:23, 14.76ex/s]
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preprocess datasets #3:  12%|█▏        | 45/380 [00:04<00:16, 20.79ex/s]
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preprocess datasets #2:  12%|█▏        | 44/380 [00:04<00:18, 17.86ex/s]
preprocess datasets #0:  12%|█▏        | 46/381 [00:04<00:19, 17.38ex/s]
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preprocess datasets #1:   9%|▉         | 35/380 [00:04<00:26, 12.87ex/s]
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preprocess datasets #4:  11%|█         | 40/380 [00:04<00:23, 14.77ex/s]
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preprocess datasets #3:  13%|█▎        | 48/380 [00:04<00:15, 20.77ex/s]
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preprocess datasets #2:  12%|█▏        | 46/380 [00:04<00:19, 16.97ex/s]
preprocess datasets #0:  13%|█▎        | 48/381 [00:04<00:19, 16.83ex/s]
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preprocess datasets #4:  11%|█         | 42/380 [00:04<00:21, 15.87ex/s]
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preprocess datasets #1:  10%|▉         | 37/380 [00:04<00:26, 13.04ex/s]
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preprocess datasets #2:  13%|█▎        | 48/380 [00:04<00:19, 16.76ex/s]
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preprocess datasets #3:  13%|█▎        | 51/380 [00:04<00:16, 19.62ex/s]
preprocess datasets #0:  13%|█▎        | 51/381 [00:04<00:17, 19.35ex/s]
+
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preprocess datasets #4:  12%|█▏        | 44/380 [00:04<00:20, 16.31ex/s]
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preprocess datasets #1:  10%|█         | 39/380 [00:04<00:25, 13.27ex/s]
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preprocess datasets #2:  13%|█▎        | 50/380 [00:04<00:20, 15.97ex/s]
preprocess datasets #0:  14%|█▍        | 53/381 [00:04<00:16, 19.34ex/s]
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preprocess datasets #3:  14%|█▍        | 53/380 [00:04<00:17, 18.63ex/s]
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preprocess datasets #4:  12%|█▏        | 46/380 [00:04<00:22, 15.13ex/s]
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preprocess datasets #1:  11%|█         | 41/380 [00:04<00:24, 13.60ex/s]
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preprocess datasets #2:  14%|█▎        | 52/380 [00:04<00:20, 16.14ex/s]
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preprocess datasets #3:  14%|█▍        | 55/380 [00:04<00:17, 18.77ex/s]
preprocess datasets #0:  15%|█▍        | 56/381 [00:04<00:16, 19.22ex/s]
+
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preprocess datasets #4:  13%|█▎        | 48/380 [00:04<00:20, 15.92ex/s]
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preprocess datasets #2:  14%|█▍        | 54/380 [00:04<00:19, 16.66ex/s]
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preprocess datasets #3:  15%|█▌        | 57/380 [00:04<00:17, 18.37ex/s]
preprocess datasets #0:  15%|█▌        | 58/381 [00:05<00:16, 19.15ex/s]
+
preprocess datasets #1:  11%|█▏        | 43/380 [00:05<00:27, 12.26ex/s]
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preprocess datasets #4:  13%|█▎        | 50/380 [00:05<00:22, 14.71ex/s]
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preprocess datasets #2:  15%|█▍        | 56/380 [00:05<00:19, 16.84ex/s]
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preprocess datasets #3:  16%|█▌        | 59/380 [00:05<00:17, 18.25ex/s]
preprocess datasets #0:  16%|█▌        | 61/381 [00:05<00:15, 20.22ex/s]
+
preprocess datasets #1:  12%|█▏        | 45/380 [00:05<00:26, 12.72ex/s]
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preprocess datasets #3:  16%|█▌        | 61/380 [00:05<00:17, 18.19ex/s]
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preprocess datasets #2:  16%|█▌        | 59/380 [00:05<00:17, 17.95ex/s]
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preprocess datasets #4:  14%|█▎        | 52/380 [00:05<00:24, 13.45ex/s]
preprocess datasets #0:  17%|█▋        | 64/381 [00:05<00:15, 20.71ex/s]
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preprocess datasets #3:  17%|█▋        | 63/380 [00:05<00:17, 18.26ex/s]
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preprocess datasets #1:  12%|█▏        | 47/380 [00:05<00:25, 13.01ex/s]
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preprocess datasets #2:  16%|█▌        | 61/380 [00:05<00:17, 17.92ex/s]
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preprocess datasets #4:  14%|█▍        | 54/380 [00:05<00:22, 14.48ex/s]
preprocess datasets #0:  18%|█▊        | 67/381 [00:05<00:16, 19.42ex/s]
+
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preprocess datasets #2:  17%|█▋        | 63/380 [00:05<00:17, 17.61ex/s]
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preprocess datasets #3:  17%|█▋        | 65/380 [00:05<00:20, 15.40ex/s]
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preprocess datasets #4:  15%|█▍        | 56/380 [00:05<00:21, 14.97ex/s]
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preprocess datasets #1:  13%|█▎        | 49/380 [00:05<00:26, 12.53ex/s]
+
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preprocess datasets #2:  17%|█▋        | 66/380 [00:05<00:15, 20.62ex/s]
preprocess datasets #0:  18%|█▊        | 69/381 [00:05<00:16, 18.55ex/s]
+
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preprocess datasets #4:  15%|█▌        | 58/380 [00:05<00:20, 15.44ex/s]
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preprocess datasets #3:  18%|█▊        | 68/380 [00:05<00:18, 17.22ex/s]
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preprocess datasets #1:  13%|█▎        | 51/380 [00:05<00:25, 12.90ex/s]
preprocess datasets #0:  19%|█▊        | 71/381 [00:05<00:18, 17.21ex/s]
+
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preprocess datasets #4:  16%|█▌        | 60/380 [00:05<00:20, 15.97ex/s]
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preprocess datasets #2:  18%|█▊        | 69/380 [00:05<00:16, 19.03ex/s]
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preprocess datasets #3:  18%|█▊        | 70/380 [00:05<00:18, 17.18ex/s]
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preprocess datasets #1:  14%|█▍        | 53/380 [00:05<00:24, 13.16ex/s]
preprocess datasets #0:  19%|█▉        | 73/381 [00:05<00:17, 17.32ex/s]
+
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preprocess datasets #4:  16%|█▋        | 62/380 [00:05<00:20, 15.81ex/s]
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preprocess datasets #2:  19%|█▊        | 71/380 [00:05<00:16, 18.85ex/s]
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preprocess datasets #3:  19%|█▉        | 72/380 [00:05<00:18, 16.93ex/s]
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preprocess datasets #1:  14%|█▍        | 55/380 [00:05<00:24, 13.09ex/s]
preprocess datasets #0:  20%|█▉        | 75/381 [00:05<00:17, 17.12ex/s]
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preprocess datasets #4:  17%|█▋        | 64/380 [00:05<00:19, 16.07ex/s]
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preprocess datasets #3:  19%|█▉        | 74/380 [00:05<00:17, 17.04ex/s]
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preprocess datasets #2:  19%|█▉        | 74/380 [00:06<00:16, 18.75ex/s]
preprocess datasets #0:  20%|██        | 77/381 [00:06<00:17, 17.53ex/s]
+
preprocess datasets #1:  15%|█▌        | 57/380 [00:06<00:23, 13.54ex/s]
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preprocess datasets #3:  20%|██        | 76/380 [00:06<00:18, 16.73ex/s]
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preprocess datasets #2:  20%|██        | 76/380 [00:06<00:16, 18.90ex/s]
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preprocess datasets #4:  17%|█▋        | 66/380 [00:06<00:21, 14.85ex/s]
preprocess datasets #0:  21%|██        | 80/381 [00:06<00:14, 20.65ex/s]
+
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preprocess datasets #3:  21%|██        | 78/380 [00:06<00:17, 16.98ex/s]
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preprocess datasets #2:  21%|██        | 78/380 [00:06<00:16, 18.71ex/s]
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preprocess datasets #4:  18%|█▊        | 68/380 [00:06<00:20, 15.44ex/s]
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preprocess datasets #3:  21%|██        | 80/380 [00:06<00:17, 17.22ex/s]
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preprocess datasets #1:  16%|█▌        | 59/380 [00:06<00:29, 10.85ex/s]
preprocess datasets #0:  22%|██▏       | 83/381 [00:06<00:15, 18.98ex/s]
+
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preprocess datasets #2:  21%|██        | 80/380 [00:06<00:16, 17.97ex/s]
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preprocess datasets #4:  18%|█▊        | 70/380 [00:06<00:21, 14.36ex/s]
preprocess datasets #0:  22%|██▏       | 85/381 [00:06<00:15, 19.10ex/s]
+
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preprocess datasets #2:  22%|██▏       | 82/380 [00:06<00:17, 17.14ex/s]
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preprocess datasets #3:  22%|██▏       | 82/380 [00:06<00:18, 15.83ex/s]
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preprocess datasets #1:  16%|█▌        | 61/380 [00:06<00:27, 11.46ex/s]
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preprocess datasets #4:  19%|█▉        | 72/380 [00:06<00:21, 14.23ex/s]
preprocess datasets #0:  23%|██▎       | 87/381 [00:06<00:15, 18.86ex/s]
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preprocess datasets #2:  22%|██▏       | 84/380 [00:06<00:17, 16.74ex/s]
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preprocess datasets #3:  22%|██▏       | 84/380 [00:06<00:18, 15.77ex/s]
preprocess datasets #0:  23%|██▎       | 89/381 [00:06<00:15, 18.76ex/s]
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preprocess datasets #4:  19%|█▉        | 74/380 [00:06<00:21, 14.32ex/s]
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preprocess datasets #1:  17%|█▋        | 63/380 [00:06<00:28, 11.02ex/s]
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preprocess datasets #2:  23%|██▎       | 86/380 [00:06<00:17, 16.79ex/s]
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preprocess datasets #3:  23%|██▎       | 86/380 [00:06<00:18, 15.78ex/s]
preprocess datasets #0:  24%|██▍       | 91/381 [00:06<00:16, 17.67ex/s]
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preprocess datasets #1:  17%|█▋        | 65/380 [00:06<00:26, 11.81ex/s]
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preprocess datasets #2:  23%|██▎       | 88/380 [00:06<00:17, 16.29ex/s]
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preprocess datasets #4:  20%|██        | 76/380 [00:06<00:22, 13.38ex/s]
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preprocess datasets #3:  23%|██▎       | 89/380 [00:06<00:17, 17.08ex/s]
preprocess datasets #0:  24%|██▍       | 93/381 [00:06<00:15, 18.01ex/s]
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preprocess datasets #4:  21%|██        | 78/380 [00:06<00:20, 14.64ex/s]
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preprocess datasets #2:  24%|██▎       | 90/380 [00:06<00:17, 16.37ex/s]
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preprocess datasets #1:  18%|█▊        | 68/380 [00:06<00:22, 13.99ex/s]
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preprocess datasets #3:  24%|██▍       | 91/380 [00:07<00:17, 16.30ex/s]
preprocess datasets #0:  25%|██▍       | 95/381 [00:07<00:16, 16.93ex/s]
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preprocess datasets #4:  21%|██▏       | 81/380 [00:07<00:17, 16.71ex/s]
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preprocess datasets #2:  24%|██▍       | 93/380 [00:07<00:16, 17.80ex/s]
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preprocess datasets #1:  18%|█▊        | 70/380 [00:07<00:23, 13.42ex/s]
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preprocess datasets #3:  24%|██▍       | 93/380 [00:07<00:17, 16.31ex/s]
preprocess datasets #0:  25%|██▌       | 97/381 [00:07<00:16, 16.80ex/s]
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preprocess datasets #4:  22%|██▏       | 83/380 [00:07<00:17, 17.21ex/s]
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preprocess datasets #2:  25%|██▌       | 95/380 [00:07<00:16, 17.69ex/s]
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preprocess datasets #3:  25%|██▌       | 95/380 [00:07<00:16, 16.94ex/s]
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preprocess datasets #1:  19%|█▉        | 72/380 [00:07<00:21, 14.55ex/s]
preprocess datasets #0:  26%|██▌       | 99/381 [00:07<00:17, 16.25ex/s]
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preprocess datasets #4:  22%|██▏       | 85/380 [00:07<00:16, 17.73ex/s]
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preprocess datasets #2:  26%|██▌       | 97/380 [00:07<00:15, 18.24ex/s]
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preprocess datasets #3:  26%|██▌       | 97/380 [00:07<00:16, 17.07ex/s]
preprocess datasets #0:  27%|██▋       | 101/381 [00:07<00:17, 16.46ex/s]
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preprocess datasets #4:  23%|██▎       | 87/380 [00:07<00:16, 17.72ex/s]
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preprocess datasets #2:  26%|██▌       | 99/380 [00:07<00:15, 18.38ex/s]
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preprocess datasets #1:  19%|█▉        | 74/380 [00:07<00:23, 13.12ex/s]
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preprocess datasets #3:  26%|██▌       | 99/380 [00:07<00:15, 17.73ex/s]
preprocess datasets #0:  27%|██▋       | 103/381 [00:07<00:16, 16.41ex/s]
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preprocess datasets #4:  23%|██▎       | 89/380 [00:07<00:16, 17.27ex/s]
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preprocess datasets #2:  27%|██▋       | 101/380 [00:07<00:16, 17.24ex/s]
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preprocess datasets #3:  27%|██▋       | 101/380 [00:07<00:15, 17.92ex/s]
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preprocess datasets #1:  20%|██        | 77/380 [00:07<00:20, 15.09ex/s]
preprocess datasets #0:  28%|██▊       | 105/381 [00:07<00:17, 15.83ex/s]
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preprocess datasets #4:  24%|██▍       | 91/380 [00:07<00:18, 15.90ex/s]
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preprocess datasets #3:  27%|██▋       | 103/380 [00:07<00:16, 17.21ex/s]
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preprocess datasets #2:  27%|██▋       | 104/380 [00:07<00:14, 18.40ex/s]
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preprocess datasets #1:  21%|██        | 79/380 [00:07<00:20, 14.90ex/s]
preprocess datasets #0:  28%|██▊       | 107/381 [00:07<00:16, 16.41ex/s]
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preprocess datasets #4:  24%|██▍       | 93/380 [00:07<00:17, 16.07ex/s]
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preprocess datasets #3:  28%|██▊       | 105/380 [00:07<00:16, 16.54ex/s]
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preprocess datasets #2:  28%|██▊       | 106/380 [00:07<00:16, 16.90ex/s]
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preprocess datasets #1:  22%|██▏       | 82/380 [00:07<00:17, 16.71ex/s]
preprocess datasets #0:  29%|██▊       | 109/381 [00:07<00:16, 16.29ex/s]
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preprocess datasets #4:  25%|██▌       | 95/380 [00:07<00:16, 16.83ex/s]
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preprocess datasets #3:  28%|██▊       | 107/380 [00:07<00:15, 17.37ex/s]
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preprocess datasets #1:  22%|██▏       | 84/380 [00:08<00:17, 16.84ex/s]
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preprocess datasets #2:  28%|██▊       | 108/380 [00:07<00:16, 16.51ex/s]
preprocess datasets #0:  29%|██▉       | 111/381 [00:08<00:16, 16.22ex/s]
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preprocess datasets #4:  26%|██▌       | 97/380 [00:08<00:17, 16.27ex/s]
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preprocess datasets #3:  29%|██▉       | 110/380 [00:08<00:14, 18.36ex/s]
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preprocess datasets #1:  23%|██▎       | 86/380 [00:08<00:17, 16.47ex/s]
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preprocess datasets #2:  29%|██▉       | 110/380 [00:08<00:16, 16.30ex/s]
preprocess datasets #0:  30%|██▉       | 113/381 [00:08<00:17, 15.35ex/s]
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preprocess datasets #4:  26%|██▌       | 99/380 [00:08<00:19, 14.65ex/s]
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preprocess datasets #3:  29%|██▉       | 112/380 [00:08<00:15, 16.89ex/s]
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preprocess datasets #2:  29%|██▉       | 112/380 [00:08<00:16, 16.47ex/s]
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preprocess datasets #1:  23%|██▎       | 88/380 [00:08<00:18, 15.89ex/s]
preprocess datasets #0:  30%|███       | 115/381 [00:08<00:16, 16.14ex/s]
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preprocess datasets #3:  30%|███       | 114/380 [00:08<00:16, 16.05ex/s]
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preprocess datasets #2:  30%|███       | 114/380 [00:08<00:16, 15.88ex/s]
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preprocess datasets #4:  27%|██▋       | 101/380 [00:08<00:19, 14.03ex/s]
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preprocess datasets #1:  24%|██▎       | 90/380 [00:08<00:18, 15.67ex/s]
preprocess datasets #0:  31%|███       | 117/381 [00:08<00:16, 15.92ex/s]
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preprocess datasets #3:  31%|███       | 116/380 [00:08<00:16, 16.09ex/s]
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preprocess datasets #4:  27%|██▋       | 103/380 [00:08<00:18, 14.84ex/s]
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preprocess datasets #2:  31%|███       | 116/380 [00:08<00:17, 15.43ex/s]
preprocess datasets #0:  31%|███       | 119/381 [00:08<00:16, 16.24ex/s]
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preprocess datasets #1:  24%|██▍       | 93/380 [00:08<00:17, 16.67ex/s]
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preprocess datasets #3:  31%|███       | 118/380 [00:08<00:16, 16.04ex/s]
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preprocess datasets #4:  28%|██▊       | 105/380 [00:08<00:18, 14.98ex/s]
preprocess datasets #0:  32%|███▏      | 121/381 [00:08<00:16, 16.19ex/s]
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preprocess datasets #2:  31%|███▏      | 119/380 [00:08<00:15, 17.17ex/s]
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preprocess datasets #1:  25%|██▌       | 95/380 [00:08<00:17, 16.55ex/s]
preprocess datasets #0:  32%|███▏      | 123/381 [00:08<00:15, 17.06ex/s]
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preprocess datasets #3:  32%|███▏      | 120/380 [00:08<00:16, 15.54ex/s]
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preprocess datasets #4:  28%|██▊       | 107/380 [00:08<00:18, 14.56ex/s]
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preprocess datasets #2:  32%|███▏      | 121/380 [00:08<00:15, 16.79ex/s]
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preprocess datasets #1:  26%|██▌       | 97/380 [00:08<00:17, 16.16ex/s]
preprocess datasets #0:  33%|███▎      | 125/381 [00:08<00:16, 15.99ex/s]
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preprocess datasets #3:  32%|███▏      | 122/380 [00:08<00:16, 15.26ex/s]
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preprocess datasets #4:  29%|██▊       | 109/380 [00:08<00:18, 14.87ex/s]
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preprocess datasets #1:  26%|██▌       | 99/380 [00:08<00:16, 16.54ex/s]
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preprocess datasets #2:  32%|███▏      | 123/380 [00:08<00:15, 16.19ex/s]
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preprocess datasets #1:  27%|██▋       | 101/380 [00:09<00:16, 16.68ex/s]
preprocess datasets #0:  33%|███▎      | 127/381 [00:09<00:16, 15.26ex/s]
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preprocess datasets #3:  33%|███▎      | 125/380 [00:09<00:15, 16.56ex/s]
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preprocess datasets #4:  29%|██▉       | 111/380 [00:09<00:18, 14.39ex/s]
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preprocess datasets #2:  33%|███▎      | 126/380 [00:09<00:15, 15.92ex/s]
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preprocess datasets #3:  33%|███▎      | 127/380 [00:09<00:15, 16.78ex/s]
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preprocess datasets #1:  27%|██▋       | 103/380 [00:09<00:17, 15.93ex/s]
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preprocess datasets #4:  30%|██▉       | 113/380 [00:09<00:17, 15.10ex/s]
preprocess datasets #0:  34%|███▍      | 129/381 [00:09<00:18, 13.52ex/s]
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preprocess datasets #3:  34%|███▍      | 129/380 [00:09<00:14, 17.03ex/s]
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preprocess datasets #4:  30%|███       | 115/380 [00:09<00:17, 15.45ex/s]
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preprocess datasets #2:  34%|███▍      | 129/380 [00:09<00:15, 15.90ex/s]
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preprocess datasets #1:  28%|██▊       | 105/380 [00:09<00:18, 15.14ex/s]
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preprocess datasets #3:  34%|███▍      | 131/380 [00:09<00:14, 17.54ex/s]
preprocess datasets #0:  35%|███▍      | 132/381 [00:09<00:16, 14.91ex/s]
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preprocess datasets #4:  31%|███       | 117/380 [00:09<00:16, 15.90ex/s]
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preprocess datasets #1:  28%|██▊       | 108/380 [00:09<00:16, 16.99ex/s]
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preprocess datasets #2:  34%|███▍      | 131/380 [00:09<00:16, 14.66ex/s]
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preprocess datasets #3:  35%|███▌      | 133/380 [00:09<00:13, 17.95ex/s]
preprocess datasets #0:  35%|███▌      | 134/381 [00:09<00:16, 15.29ex/s]
+
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preprocess datasets #4:  31%|███▏      | 119/380 [00:09<00:16, 16.12ex/s]
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preprocess datasets #1:  29%|██▉       | 110/380 [00:09<00:16, 16.81ex/s]
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preprocess datasets #3:  36%|███▌      | 135/380 [00:09<00:13, 18.28ex/s]
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preprocess datasets #2:  35%|███▌      | 134/380 [00:09<00:14, 16.76ex/s]
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preprocess datasets #4:  32%|███▏      | 121/380 [00:09<00:16, 15.86ex/s]
preprocess datasets #0:  36%|███▌      | 136/381 [00:09<00:17, 14.22ex/s]
+
preprocess datasets #1:  30%|██▉       | 113/380 [00:09<00:13, 19.78ex/s]
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preprocess datasets #3:  36%|███▌      | 137/380 [00:09<00:12, 18.71ex/s]
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preprocess datasets #2:  36%|███▌      | 136/380 [00:09<00:14, 16.59ex/s]
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preprocess datasets #4:  32%|███▏      | 123/380 [00:09<00:16, 15.18ex/s]
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preprocess datasets #3:  37%|███▋      | 139/380 [00:09<00:13, 18.41ex/s]
preprocess datasets #0:  36%|███▌      | 138/381 [00:09<00:16, 14.42ex/s]
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preprocess datasets #2:  36%|███▋      | 138/380 [00:09<00:14, 16.78ex/s]
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preprocess datasets #4:  33%|███▎      | 125/380 [00:09<00:16, 15.70ex/s]
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preprocess datasets #2:  37%|███▋      | 140/380 [00:09<00:13, 17.49ex/s]
preprocess datasets #0:  37%|███▋      | 140/381 [00:09<00:16, 14.71ex/s]
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preprocess datasets #3:  37%|███▋      | 142/380 [00:09<00:13, 17.41ex/s]
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preprocess datasets #1:  31%|███       | 116/380 [00:10<00:18, 14.63ex/s]
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preprocess datasets #2:  37%|███▋      | 142/380 [00:10<00:13, 17.43ex/s]
preprocess datasets #0:  37%|███▋      | 142/381 [00:10<00:15, 15.38ex/s]
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preprocess datasets #4:  33%|███▎      | 127/380 [00:10<00:17, 14.43ex/s]
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preprocess datasets #1:  31%|███       | 118/380 [00:10<00:17, 15.10ex/s]
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preprocess datasets #3:  38%|███▊      | 144/380 [00:10<00:13, 17.09ex/s]
preprocess datasets #0:  38%|███▊      | 144/381 [00:10<00:15, 14.97ex/s]
+
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preprocess datasets #2:  38%|███▊      | 144/380 [00:10<00:14, 16.05ex/s]
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preprocess datasets #1:  32%|███▏      | 120/380 [00:10<00:16, 15.88ex/s]
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preprocess datasets #3:  38%|███▊      | 146/380 [00:10<00:13, 17.26ex/s]
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preprocess datasets #4:  34%|███▍      | 129/380 [00:10<00:18, 13.80ex/s]
preprocess datasets #0:  38%|███▊      | 146/381 [00:10<00:15, 15.21ex/s]
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preprocess datasets #2:  38%|███▊      | 146/380 [00:10<00:15, 15.32ex/s]
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preprocess datasets #4:  34%|███▍      | 131/380 [00:10<00:17, 14.24ex/s]
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preprocess datasets #1:  32%|███▏      | 122/380 [00:10<00:17, 14.75ex/s]
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preprocess datasets #3:  39%|███▉      | 148/380 [00:10<00:14, 15.72ex/s]
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preprocess datasets #2:  39%|███▉      | 148/380 [00:10<00:14, 16.30ex/s]
preprocess datasets #0:  39%|███▉      | 148/381 [00:10<00:15, 14.88ex/s]
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preprocess datasets #4:  35%|███▌      | 133/380 [00:10<00:16, 14.97ex/s]
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preprocess datasets #1:  33%|███▎      | 124/380 [00:10<00:17, 14.90ex/s]
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preprocess datasets #3:  39%|███▉      | 150/380 [00:10<00:15, 15.28ex/s]
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preprocess datasets #2:  39%|███▉      | 150/380 [00:10<00:15, 15.01ex/s]
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preprocess datasets #3:  40%|████      | 152/380 [00:10<00:14, 16.05ex/s]
preprocess datasets #0:  39%|███▉      | 150/381 [00:10<00:16, 13.83ex/s]
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preprocess datasets #1:  33%|███▎      | 126/380 [00:10<00:17, 14.94ex/s]
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preprocess datasets #4:  36%|███▌      | 136/380 [00:10<00:14, 16.38ex/s]
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preprocess datasets #2:  40%|████      | 152/380 [00:10<00:14, 15.25ex/s]
preprocess datasets #0:  40%|███▉      | 152/381 [00:10<00:15, 14.32ex/s]
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preprocess datasets #1:  34%|███▎      | 128/380 [00:10<00:16, 15.15ex/s]
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preprocess datasets #3:  41%|████      | 155/380 [00:10<00:12, 17.43ex/s]
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preprocess datasets #4:  37%|███▋      | 139/380 [00:10<00:14, 16.26ex/s]
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preprocess datasets #2:  41%|████      | 154/380 [00:10<00:15, 14.63ex/s]
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preprocess datasets #1:  34%|███▍      | 130/380 [00:10<00:16, 15.21ex/s]
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preprocess datasets #3:  41%|████▏     | 157/380 [00:10<00:14, 15.90ex/s]
preprocess datasets #0:  40%|████      | 154/381 [00:10<00:17, 12.84ex/s]
+
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preprocess datasets #4:  37%|███▋      | 141/380 [00:10<00:14, 16.22ex/s]
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preprocess datasets #2:  41%|████      | 156/380 [00:11<00:14, 15.05ex/s]
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preprocess datasets #3:  42%|████▏     | 160/380 [00:11<00:12, 18.18ex/s]
preprocess datasets #0:  41%|████      | 156/381 [00:11<00:16, 13.63ex/s]
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preprocess datasets #2:  42%|████▏     | 158/380 [00:11<00:13, 16.13ex/s]
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preprocess datasets #4:  38%|███▊      | 143/380 [00:11<00:15, 15.28ex/s]
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preprocess datasets #1:  35%|███▍      | 132/380 [00:11<00:19, 12.88ex/s]
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preprocess datasets #3:  43%|████▎     | 162/380 [00:11<00:12, 17.04ex/s]
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preprocess datasets #2:  42%|████▏     | 160/380 [00:11<00:13, 16.58ex/s]
preprocess datasets #0:  41%|████▏     | 158/381 [00:11<00:16, 13.65ex/s]
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preprocess datasets #4:  38%|███▊      | 145/380 [00:11<00:15, 15.28ex/s]
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preprocess datasets #1:  35%|███▌      | 134/380 [00:11<00:18, 13.01ex/s]
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preprocess datasets #3:  43%|████▎     | 164/380 [00:11<00:12, 17.19ex/s]
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preprocess datasets #2:  43%|████▎     | 162/380 [00:11<00:13, 16.67ex/s]
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preprocess datasets #4:  39%|███▊      | 147/380 [00:11<00:14, 16.17ex/s]
preprocess datasets #0:  42%|████▏     | 160/381 [00:11<00:15, 13.83ex/s]
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preprocess datasets #1:  36%|███▌      | 136/380 [00:11<00:18, 13.18ex/s]
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preprocess datasets #3:  44%|████▎     | 166/380 [00:11<00:12, 16.95ex/s]
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preprocess datasets #4:  39%|███▉      | 149/380 [00:11<00:13, 17.06ex/s]
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preprocess datasets #2:  43%|████▎     | 164/380 [00:11<00:13, 16.06ex/s]
preprocess datasets #0:  43%|████▎     | 162/381 [00:11<00:15, 14.48ex/s]
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preprocess datasets #1:  36%|███▋      | 138/380 [00:11<00:16, 14.63ex/s]
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preprocess datasets #3:  44%|████▍     | 168/380 [00:11<00:12, 17.21ex/s]
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preprocess datasets #4:  40%|███▉      | 151/380 [00:11<00:13, 16.72ex/s]
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preprocess datasets #2:  44%|████▎     | 166/380 [00:11<00:13, 15.47ex/s]
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preprocess datasets #1:  37%|███▋      | 140/380 [00:11<00:16, 14.80ex/s]
preprocess datasets #0:  43%|████▎     | 165/381 [00:11<00:13, 15.88ex/s]
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preprocess datasets #3:  45%|████▍     | 170/380 [00:11<00:12, 16.90ex/s]
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preprocess datasets #4:  40%|████      | 153/380 [00:11<00:13, 16.44ex/s]
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preprocess datasets #2:  44%|████▍     | 168/380 [00:11<00:14, 14.64ex/s]
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preprocess datasets #1:  37%|███▋      | 142/380 [00:11<00:16, 14.76ex/s]
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preprocess datasets #3:  45%|████▌     | 172/380 [00:11<00:12, 17.01ex/s]
preprocess datasets #0:  44%|████▍     | 167/381 [00:11<00:13, 15.34ex/s]
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preprocess datasets #4:  41%|████      | 155/380 [00:11<00:13, 17.17ex/s]
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preprocess datasets #1:  38%|███▊      | 145/380 [00:11<00:13, 17.23ex/s]
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preprocess datasets #4:  41%|████▏     | 157/380 [00:11<00:12, 17.56ex/s]
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preprocess datasets #2:  45%|████▌     | 171/380 [00:11<00:12, 16.38ex/s]
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preprocess datasets #3:  46%|████▌     | 175/380 [00:11<00:11, 17.34ex/s]
preprocess datasets #0:  44%|████▍     | 169/381 [00:12<00:15, 13.68ex/s]
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preprocess datasets #2:  46%|████▌     | 173/380 [00:12<00:13, 15.86ex/s]
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preprocess datasets #1:  39%|███▊      | 147/380 [00:12<00:14, 16.02ex/s]
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preprocess datasets #3:  47%|████▋     | 177/380 [00:12<00:11, 17.44ex/s]
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preprocess datasets #4:  42%|████▏     | 160/380 [00:12<00:12, 17.77ex/s]
preprocess datasets #0:  45%|████▍     | 171/381 [00:12<00:15, 13.19ex/s]
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preprocess datasets #0:  45%|████▌     | 173/381 [00:12<00:14, 13.89ex/s]
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preprocess datasets #0:  46%|████▋     | 177/381 [00:12<00:14, 13.70ex/s]
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preprocess datasets #1:  41%|████▏     | 157/380 [00:12<00:12, 17.39ex/s]
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preprocess datasets #0:  47%|████▋     | 179/381 [00:12<00:14, 13.58ex/s]
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preprocess datasets #0:  48%|████▊     | 181/381 [00:12<00:13, 14.44ex/s]
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preprocess datasets #2:  49%|████▊     | 185/380 [00:13<00:14, 13.90ex/s]
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preprocess datasets #0:  48%|████▊     | 183/381 [00:13<00:15, 12.42ex/s]
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preprocess datasets #3:  51%|█████     | 192/380 [00:13<00:11, 16.20ex/s]
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preprocess datasets #2:  49%|████▉     | 187/380 [00:13<00:13, 14.82ex/s]
preprocess datasets #0:  49%|████▊     | 185/381 [00:13<00:14, 13.79ex/s]
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preprocess datasets #0:  49%|████▉     | 187/381 [00:13<00:15, 12.42ex/s]
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preprocess datasets #0:  50%|████▉     | 189/381 [00:13<00:13, 13.76ex/s]
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preprocess datasets #2:  51%|█████     | 193/380 [00:13<00:12, 14.74ex/s]
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preprocess datasets #3:  52%|█████▏    | 199/380 [00:13<00:11, 15.30ex/s]
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preprocess datasets #4:  47%|████▋     | 179/380 [00:13<00:15, 12.68ex/s]
preprocess datasets #0:  50%|█████     | 191/381 [00:13<00:15, 12.45ex/s]
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preprocess datasets #3:  53%|█████▎    | 201/380 [00:13<00:12, 14.08ex/s]
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preprocess datasets #4:  48%|████▊     | 181/380 [00:13<00:15, 13.11ex/s]
preprocess datasets #0:  51%|█████     | 193/381 [00:13<00:13, 13.47ex/s]
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preprocess datasets #4:  48%|████▊     | 184/380 [00:13<00:12, 16.19ex/s]
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preprocess datasets #3:  53%|█████▎    | 203/380 [00:13<00:13, 13.54ex/s]
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preprocess datasets #2:  52%|█████▏    | 197/380 [00:13<00:14, 12.53ex/s]
preprocess datasets #0:  51%|█████▏    | 196/381 [00:13<00:12, 14.78ex/s]
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preprocess datasets #4:  49%|████▉     | 186/380 [00:13<00:12, 16.10ex/s]
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preprocess datasets #2:  53%|█████▎    | 200/380 [00:14<00:12, 14.80ex/s]
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preprocess datasets #3:  54%|█████▍    | 206/380 [00:14<00:11, 15.10ex/s]
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preprocess datasets #4:  49%|████▉     | 188/380 [00:14<00:11, 16.59ex/s]
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preprocess datasets #1:  48%|████▊     | 182/380 [00:14<00:12, 15.66ex/s]
preprocess datasets #0:  52%|█████▏    | 198/381 [00:14<00:13, 13.21ex/s]
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preprocess datasets #3:  55%|█████▍    | 208/380 [00:14<00:11, 15.34ex/s]
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preprocess datasets #4:  50%|█████     | 190/380 [00:14<00:11, 16.19ex/s]
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preprocess datasets #1:  48%|████▊     | 184/380 [00:14<00:12, 15.71ex/s]
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preprocess datasets #2:  53%|█████▎    | 203/380 [00:14<00:12, 14.35ex/s]
preprocess datasets #0:  52%|█████▏    | 200/381 [00:14<00:13, 13.32ex/s]
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preprocess datasets #3:  55%|█████▌    | 210/380 [00:14<00:11, 14.79ex/s]
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preprocess datasets #4:  51%|█████     | 193/380 [00:14<00:10, 18.21ex/s]
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preprocess datasets #2:  54%|█████▍    | 205/380 [00:14<00:11, 15.28ex/s]
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preprocess datasets #1:  49%|████▉     | 186/380 [00:14<00:13, 13.90ex/s]
preprocess datasets #0:  53%|█████▎    | 202/381 [00:14<00:12, 13.84ex/s]
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preprocess datasets #3:  56%|█████▌    | 212/380 [00:14<00:11, 15.22ex/s]
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preprocess datasets #4:  51%|█████▏    | 195/380 [00:14<00:10, 17.98ex/s]
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preprocess datasets #2:  55%|█████▍    | 208/380 [00:14<00:10, 16.75ex/s]
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preprocess datasets #1:  49%|████▉     | 188/380 [00:14<00:13, 14.34ex/s]
preprocess datasets #0:  54%|█████▎    | 204/381 [00:14<00:12, 14.38ex/s]
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preprocess datasets #4:  52%|█████▏    | 197/380 [00:14<00:10, 17.42ex/s]
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preprocess datasets #3:  56%|█████▋    | 214/380 [00:14<00:11, 14.14ex/s]
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preprocess datasets #2:  56%|█████▌    | 211/380 [00:14<00:09, 17.97ex/s]
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preprocess datasets #1:  50%|█████     | 190/380 [00:14<00:13, 14.26ex/s]
preprocess datasets #0:  54%|█████▍    | 206/381 [00:14<00:12, 14.58ex/s]
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preprocess datasets #4:  52%|█████▏    | 199/380 [00:14<00:10, 16.95ex/s]
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preprocess datasets #3:  57%|█████▋    | 216/380 [00:14<00:11, 14.44ex/s]
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preprocess datasets #2:  56%|█████▌    | 213/380 [00:14<00:10, 16.70ex/s]
preprocess datasets #0:  55%|█████▍    | 208/381 [00:14<00:11, 14.94ex/s]
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preprocess datasets #1:  51%|█████     | 192/380 [00:14<00:13, 14.10ex/s]
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preprocess datasets #4:  53%|█████▎    | 202/380 [00:14<00:09, 19.35ex/s]
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preprocess datasets #3:  57%|█████▋    | 218/380 [00:14<00:10, 15.15ex/s]
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preprocess datasets #1:  51%|█████     | 194/380 [00:14<00:12, 14.75ex/s]
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preprocess datasets #4:  54%|█████▎    | 204/380 [00:14<00:09, 18.49ex/s]
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preprocess datasets #2:  57%|█████▋    | 215/380 [00:14<00:10, 15.11ex/s]
preprocess datasets #0:  55%|█████▌    | 210/381 [00:15<00:12, 13.89ex/s]
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preprocess datasets #3:  58%|█████▊    | 220/380 [00:14<00:10, 15.42ex/s]
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preprocess datasets #1:  52%|█████▏    | 196/380 [00:15<00:12, 15.06ex/s]
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preprocess datasets #4:  54%|█████▍    | 206/380 [00:15<00:10, 17.19ex/s]
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preprocess datasets #2:  57%|█████▋    | 217/380 [00:15<00:10, 15.13ex/s]
preprocess datasets #0:  56%|█████▌    | 212/381 [00:15<00:12, 13.75ex/s]
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preprocess datasets #3:  58%|█████▊    | 222/380 [00:15<00:10, 15.04ex/s]
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preprocess datasets #1:  52%|█████▏    | 198/380 [00:15<00:12, 15.16ex/s]
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preprocess datasets #4:  55%|█████▍    | 208/380 [00:15<00:09, 17.57ex/s]
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preprocess datasets #2:  58%|█████▊    | 220/380 [00:15<00:09, 17.00ex/s]
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preprocess datasets #3:  59%|█████▉    | 224/380 [00:15<00:10, 14.53ex/s]
preprocess datasets #0:  56%|█████▌    | 214/381 [00:15<00:13, 11.98ex/s]
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preprocess datasets #2:  58%|█████▊    | 222/380 [00:15<00:09, 16.68ex/s]
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preprocess datasets #4:  56%|█████▌    | 211/380 [00:15<00:09, 17.25ex/s]
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preprocess datasets #3:  59%|█████▉    | 226/380 [00:15<00:09, 15.41ex/s]
preprocess datasets #0:  57%|█████▋    | 216/381 [00:15<00:13, 12.19ex/s]
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preprocess datasets #3:  60%|██████    | 229/380 [00:15<00:08, 17.80ex/s]
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preprocess datasets #2:  59%|█████▉    | 224/380 [00:15<00:09, 15.68ex/s]
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preprocess datasets #4:  57%|█████▋    | 215/380 [00:15<00:08, 19.96ex/s]
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preprocess datasets #1:  53%|█████▎    | 202/380 [00:15<00:14, 12.62ex/s]
preprocess datasets #0:  57%|█████▋    | 218/381 [00:15<00:12, 12.76ex/s]
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preprocess datasets #1:  54%|█████▎    | 204/380 [00:15<00:12, 13.61ex/s]
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preprocess datasets #4:  57%|█████▋    | 218/380 [00:15<00:08, 20.14ex/s]
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preprocess datasets #3:  61%|██████    | 231/380 [00:15<00:09, 15.25ex/s]
preprocess datasets #0:  58%|█████▊    | 220/381 [00:15<00:11, 14.04ex/s]
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preprocess datasets #2:  60%|██████    | 228/380 [00:15<00:10, 14.67ex/s]
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preprocess datasets #4:  58%|█████▊    | 221/380 [00:15<00:07, 20.34ex/s]
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preprocess datasets #3:  61%|██████▏   | 233/380 [00:15<00:10, 13.94ex/s]
preprocess datasets #0:  59%|█████▊    | 223/381 [00:15<00:10, 15.15ex/s]
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preprocess datasets #2:  61%|██████    | 230/380 [00:15<00:10, 14.67ex/s]
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preprocess datasets #3:  62%|██████▏   | 235/380 [00:16<00:10, 13.58ex/s]
preprocess datasets #0:  59%|█████▉    | 226/381 [00:16<00:09, 16.82ex/s]
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preprocess datasets #4:  59%|█████▉    | 224/380 [00:16<00:09, 16.60ex/s]
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preprocess datasets #2:  61%|██████▏   | 233/380 [00:16<00:09, 15.63ex/s]
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preprocess datasets #1:  55%|█████▌    | 210/380 [00:16<00:12, 13.12ex/s]
preprocess datasets #0:  60%|█████▉    | 228/381 [00:16<00:09, 16.55ex/s]
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preprocess datasets #2:  62%|██████▏   | 235/380 [00:16<00:09, 15.19ex/s]
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preprocess datasets #3:  62%|██████▏   | 237/380 [00:16<00:14, 10.11ex/s]
preprocess datasets #0:  60%|██████    | 230/381 [00:16<00:09, 15.23ex/s]
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preprocess datasets #4:  60%|██████    | 229/380 [00:16<00:08, 17.07ex/s]
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preprocess datasets #2:  62%|██████▏   | 237/380 [00:16<00:10, 13.31ex/s]
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preprocess datasets #3:  63%|██████▎   | 239/380 [00:16<00:12, 10.97ex/s]
preprocess datasets #0:  61%|██████    | 232/381 [00:16<00:10, 14.69ex/s]
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preprocess datasets #4:  61%|██████    | 231/380 [00:16<00:09, 16.19ex/s]
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preprocess datasets #1:  57%|█████▋    | 217/380 [00:16<00:10, 16.13ex/s]
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preprocess datasets #2:  63%|██████▎   | 239/380 [00:16<00:10, 13.53ex/s]
preprocess datasets #0:  61%|██████▏   | 234/381 [00:16<00:09, 15.22ex/s]
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preprocess datasets #3:  63%|██████▎   | 241/380 [00:16<00:12, 11.43ex/s]
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preprocess datasets #1:  58%|█████▊    | 219/380 [00:16<00:10, 15.23ex/s]
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preprocess datasets #2:  63%|██████▎   | 241/380 [00:16<00:09, 14.32ex/s]
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preprocess datasets #3:  64%|██████▍   | 243/380 [00:16<00:11, 12.36ex/s]
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preprocess datasets #4:  62%|██████▏   | 236/380 [00:16<00:08, 17.22ex/s]
preprocess datasets #0:  62%|██████▏   | 236/381 [00:16<00:10, 13.57ex/s]
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preprocess datasets #4:  63%|██████▎   | 238/380 [00:16<00:08, 17.07ex/s]
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preprocess datasets #1:  59%|█████▊    | 223/380 [00:16<00:09, 16.19ex/s]
preprocess datasets #0:  62%|██████▏   | 238/381 [00:16<00:10, 13.76ex/s]
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preprocess datasets #3:  64%|██████▍   | 245/380 [00:16<00:11, 11.91ex/s]
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preprocess datasets #2:  64%|██████▍   | 243/380 [00:17<00:13, 10.01ex/s]
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preprocess datasets #4:  63%|██████▎   | 241/380 [00:17<00:07, 17.47ex/s]
preprocess datasets #0:  63%|██████▎   | 240/381 [00:17<00:10, 13.74ex/s]
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preprocess datasets #4:  64%|██████▍   | 243/380 [00:17<00:07, 17.64ex/s]
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preprocess datasets #0:  64%|██████▎   | 242/381 [00:17<00:09, 14.38ex/s]
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preprocess datasets #1:  60%|██████    | 229/380 [00:17<00:09, 15.32ex/s]
preprocess datasets #0:  64%|██████▍   | 244/381 [00:17<00:09, 14.85ex/s]
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preprocess datasets #3:  66%|██████▌   | 251/380 [00:17<00:09, 13.22ex/s]
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preprocess datasets #1:  61%|██████    | 231/380 [00:17<00:09, 15.24ex/s]
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preprocess datasets #0:  65%|██████▌   | 248/381 [00:17<00:08, 16.45ex/s]
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preprocess datasets #3:  67%|██████▋   | 253/380 [00:17<00:10, 12.69ex/s]
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preprocess datasets #2:  66%|██████▌   | 251/380 [00:17<00:09, 13.15ex/s]
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preprocess datasets #0:  66%|██████▌   | 252/381 [00:17<00:08, 16.04ex/s]
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preprocess datasets #0:  67%|██████▋   | 254/381 [00:17<00:08, 15.37ex/s]
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preprocess datasets #0:  67%|██████▋   | 257/381 [00:18<00:07, 16.50ex/s]
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preprocess datasets #0:  68%|██████▊   | 259/381 [00:18<00:07, 15.56ex/s]
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preprocess datasets #0:  70%|███████   | 268/381 [00:18<00:07, 15.02ex/s]
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preprocess datasets #0:  71%|███████   | 270/381 [00:19<00:07, 15.66ex/s]
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preprocess datasets #3:  72%|███████▏  | 275/380 [00:19<00:08, 12.95ex/s]
preprocess datasets #0:  72%|███████▏  | 273/381 [00:19<00:06, 16.49ex/s]
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preprocess datasets #4:  72%|███████▏  | 274/380 [00:19<00:07, 13.64ex/s]
preprocess datasets #0:  72%|███████▏  | 275/381 [00:19<00:06, 16.75ex/s]
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preprocess datasets #3:  73%|███████▎  | 278/380 [00:19<00:07, 14.50ex/s]
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preprocess datasets #0:  73%|███████▎  | 277/381 [00:19<00:06, 16.85ex/s]
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preprocess datasets #2:  73%|███████▎  | 277/380 [00:19<00:07, 14.29ex/s]
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preprocess datasets #3:  74%|███████▎  | 280/380 [00:19<00:07, 13.88ex/s]
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preprocess datasets #1:  69%|██████▉   | 262/380 [00:19<00:07, 15.35ex/s]
preprocess datasets #0:  73%|███████▎  | 279/381 [00:19<00:06, 16.53ex/s]
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preprocess datasets #4:  73%|███████▎  | 279/380 [00:19<00:07, 14.37ex/s]
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preprocess datasets #2:  74%|███████▎  | 280/380 [00:19<00:06, 15.07ex/s]
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preprocess datasets #1:  69%|██████▉   | 264/380 [00:19<00:07, 15.16ex/s]
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preprocess datasets #3:  74%|███████▍  | 282/380 [00:19<00:07, 13.32ex/s]
preprocess datasets #0:  74%|███████▍  | 281/381 [00:19<00:06, 16.01ex/s]
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preprocess datasets #0:  74%|███████▍  | 283/381 [00:19<00:05, 16.80ex/s]
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preprocess datasets #3:  75%|███████▍  | 284/380 [00:19<00:07, 12.06ex/s]
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preprocess datasets #1:  71%|███████   | 268/380 [00:19<00:06, 16.11ex/s]
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preprocess datasets #2:  75%|███████▌  | 285/380 [00:19<00:05, 16.85ex/s]
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preprocess datasets #4:  75%|███████▌  | 285/380 [00:19<00:05, 16.63ex/s]
preprocess datasets #0:  75%|███████▍  | 285/381 [00:19<00:06, 15.56ex/s]
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preprocess datasets #2:  76%|███████▌  | 287/380 [00:19<00:05, 16.79ex/s]
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preprocess datasets #1:  71%|███████   | 270/380 [00:20<00:07, 15.01ex/s]
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preprocess datasets #4:  76%|███████▌  | 287/380 [00:20<00:05, 15.84ex/s]
preprocess datasets #0:  76%|███████▌  | 288/381 [00:20<00:05, 17.01ex/s]
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preprocess datasets #3:  76%|███████▌  | 288/380 [00:20<00:06, 13.92ex/s]
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preprocess datasets #2:  76%|███████▌  | 289/380 [00:20<00:05, 17.09ex/s]
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preprocess datasets #4:  76%|███████▌  | 289/380 [00:20<00:05, 16.14ex/s]
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preprocess datasets #1:  72%|███████▏  | 272/380 [00:20<00:07, 14.07ex/s]
preprocess datasets #0:  76%|███████▌  | 290/381 [00:20<00:05, 17.03ex/s]
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preprocess datasets #3:  76%|███████▋  | 290/380 [00:20<00:05, 15.14ex/s]
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preprocess datasets #4:  77%|███████▋  | 291/380 [00:20<00:05, 16.71ex/s]
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preprocess datasets #2:  77%|███████▋  | 291/380 [00:20<00:05, 14.97ex/s]
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preprocess datasets #1:  72%|███████▏  | 274/380 [00:20<00:07, 14.84ex/s]
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preprocess datasets #3:  77%|███████▋  | 292/380 [00:20<00:05, 15.14ex/s]
preprocess datasets #0:  77%|███████▋  | 292/381 [00:20<00:05, 16.06ex/s]
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preprocess datasets #4:  77%|███████▋  | 293/380 [00:20<00:04, 17.49ex/s]
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preprocess datasets #3:  77%|███████▋  | 294/380 [00:20<00:05, 16.18ex/s]
preprocess datasets #0:  77%|███████▋  | 294/381 [00:20<00:05, 16.92ex/s]
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preprocess datasets #2:  77%|███████▋  | 293/380 [00:20<00:05, 14.93ex/s]
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preprocess datasets #1:  73%|███████▎  | 277/380 [00:20<00:06, 16.89ex/s]
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preprocess datasets #4:  78%|███████▊  | 295/380 [00:20<00:05, 16.43ex/s]
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preprocess datasets #2:  78%|███████▊  | 295/380 [00:20<00:05, 15.74ex/s]
preprocess datasets #0:  78%|███████▊  | 297/381 [00:20<00:04, 19.17ex/s]
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preprocess datasets #3:  78%|███████▊  | 297/380 [00:20<00:04, 16.79ex/s]
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preprocess datasets #4:  78%|███████▊  | 297/380 [00:20<00:05, 15.20ex/s]
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preprocess datasets #2:  78%|███████▊  | 297/380 [00:20<00:05, 15.63ex/s]
preprocess datasets #0:  79%|███████▊  | 300/381 [00:20<00:04, 20.04ex/s]
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preprocess datasets #3:  79%|███████▊  | 299/380 [00:20<00:05, 15.00ex/s]
preprocess datasets #0:  80%|███████▉  | 303/381 [00:20<00:03, 21.79ex/s]
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preprocess datasets #2:  79%|███████▊  | 299/380 [00:20<00:05, 14.19ex/s]
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preprocess datasets #3:  79%|███████▉  | 301/380 [00:20<00:05, 14.15ex/s]
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preprocess datasets #4:  80%|███████▉  | 303/380 [00:20<00:04, 17.97ex/s]
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preprocess datasets #2:  79%|███████▉  | 301/380 [00:20<00:05, 14.46ex/s]
preprocess datasets #0:  80%|████████  | 306/381 [00:21<00:03, 19.83ex/s]
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preprocess datasets #2:  80%|███████▉  | 303/380 [00:21<00:05, 14.79ex/s]
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preprocess datasets #4:  80%|████████  | 305/380 [00:21<00:04, 16.08ex/s]
preprocess datasets #0:  81%|████████  | 309/381 [00:21<00:03, 19.16ex/s]
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preprocess datasets #3:  80%|███████▉  | 303/380 [00:21<00:06, 11.03ex/s]
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preprocess datasets #2:  80%|████████  | 305/380 [00:21<00:04, 15.33ex/s]
preprocess datasets #0:  82%|████████▏ | 311/381 [00:21<00:03, 18.59ex/s]
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preprocess datasets #2:  81%|████████  | 307/380 [00:21<00:04, 14.73ex/s]
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preprocess datasets #3:  80%|████████  | 305/380 [00:21<00:06, 11.34ex/s]
preprocess datasets #0:  82%|████████▏ | 314/381 [00:21<00:03, 20.82ex/s]
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preprocess datasets #3:  81%|████████  | 307/380 [00:21<00:05, 12.22ex/s]
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preprocess datasets #2:  81%|████████▏ | 309/380 [00:21<00:04, 14.48ex/s]
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preprocess datasets #4:  82%|████████▏ | 312/380 [00:21<00:04, 16.38ex/s]
preprocess datasets #0:  83%|████████▎ | 317/381 [00:21<00:03, 18.39ex/s]
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preprocess datasets #3:  81%|████████▏ | 309/380 [00:21<00:05, 12.16ex/s]
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preprocess datasets #4:  83%|████████▎ | 314/380 [00:21<00:04, 15.78ex/s]
preprocess datasets #0:  84%|████████▎ | 319/381 [00:21<00:03, 18.06ex/s]
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preprocess datasets #3:  82%|████████▏ | 311/380 [00:21<00:05, 13.72ex/s]
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preprocess datasets #4:  83%|████████▎ | 317/380 [00:21<00:03, 17.34ex/s]
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preprocess datasets #0:  85%|████████▍ | 322/381 [00:21<00:03, 18.30ex/s]
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preprocess datasets #3:  82%|████████▏ | 313/380 [00:21<00:04, 14.45ex/s]
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preprocess datasets #4:  84%|████████▍ | 319/380 [00:21<00:03, 17.63ex/s]
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preprocess datasets #2:  83%|████████▎ | 315/380 [00:21<00:04, 13.91ex/s]
preprocess datasets #0:  85%|████████▌ | 324/381 [00:22<00:03, 17.46ex/s]
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preprocess datasets #3:  83%|████████▎ | 315/380 [00:21<00:04, 14.79ex/s]
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preprocess datasets #4:  84%|████████▍ | 321/380 [00:22<00:03, 17.21ex/s]
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preprocess datasets #2:  83%|████████▎ | 317/380 [00:22<00:04, 14.27ex/s]
preprocess datasets #0:  86%|████████▌ | 326/381 [00:22<00:03, 16.53ex/s]
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preprocess datasets #3:  83%|████████▎ | 317/380 [00:22<00:04, 13.64ex/s]
preprocess datasets #0:  86%|████████▌ | 328/381 [00:22<00:03, 17.21ex/s]
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preprocess datasets #3:  84%|████████▍ | 319/380 [00:22<00:04, 14.44ex/s]
preprocess datasets #0:  87%|████████▋ | 331/381 [00:22<00:02, 19.00ex/s]
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preprocess datasets #3:  84%|████████▍ | 321/380 [00:22<00:04, 13.94ex/s]
preprocess datasets #0:  87%|████████▋ | 333/381 [00:22<00:02, 18.46ex/s]
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preprocess datasets #3:  85%|████████▌ | 323/380 [00:22<00:03, 14.87ex/s]
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preprocess datasets #0:  88%|████████▊ | 335/381 [00:22<00:02, 17.12ex/s]
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preprocess datasets #3:  86%|████████▌ | 325/380 [00:22<00:03, 15.06ex/s]
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preprocess datasets #4:  87%|████████▋ | 332/380 [00:22<00:02, 16.74ex/s]
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+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/preprocessor_config.json
+tokenizer config file saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/tokenizer_config.json
+Special tokens file saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/special_tokens_map.json
+added tokens file saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/added_tokens.json
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/config.json
+loading configuration file experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/preprocessor_config.json
+loading configuration file experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/config.json
+Model config Wav2Vec2Config {
+  "_name_or_path": "experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl",
+  "activation_dropout": 0.05,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.05,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": true,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.0,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.0,
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.03,
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 28,
+  "proj_codevector_dim": 256,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "transformers_version": "4.24.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 31,
+  "xvector_output_dim": 512
+}
+
+loading configuration file experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/preprocessor_config.json
+Feature extractor Wav2Vec2FeatureExtractor {
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}
+
+loading file vocab.json
+loading file tokenizer_config.json
+loading file added_tokens.json
+loading file special_tokens_map.json
+Adding <s> to the vocabulary
+Adding </s> to the vocabulary
+max_steps is given, it will override any value given in num_train_epochs
+Using cuda_amp half precision backend
+The following columns in the training set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+/idiap/home/jzuluaga/.local/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
+  warnings.warn(
+***** Running training *****
+  Num examples = 7480
+  Num Epochs = 257
+  Instantaneous batch size per device = 24
+  Total train batch size (w. parallel, distributed & accumulation) = 96
+  Gradient Accumulation steps = 4
+  Total optimization steps = 20000
+  Number of trainable parameters = 311260319
+
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  1%|▏         | 250/20000 [18:14<18:59:11,  3.46s/it]
  1%|▏         | 251/20000 [18:17<18:01:49,  3.29s/it]
  1%|▏         | 252/20000 [18:19<16:50:14,  3.07s/it]
  1%|▏         | 253/20000 [18:21<15:00:22,  2.74s/it]
  1%|▏         | 254/20000 [18:30<24:18:54,  4.43s/it]
  1%|▏         | 255/20000 [18:36<27:31:53,  5.02s/it]
  1%|▏         | 256/20000 [18:42<28:38:32,  5.22s/it]
  1%|▏         | 257/20000 [18:47<28:41:42,  5.23s/it]
  1%|▏         | 258/20000 [18:52<28:17:14,  5.16s/it]
  1%|▏         | 259/20000 [18:57<27:41:56,  5.05s/it]
  1%|▏         | 260/20000 [19:01<26:48:19,  4.89s/it]
  1%|▏         | 261/20000 [19:06<25:58:58,  4.74s/it]
  1%|▏         | 262/20000 [19:10<25:07:23,  4.58s/it]
  1%|▏         | 263/20000 [19:14<24:15:01,  4.42s/it]
  1%|▏         | 264/20000 [19:18<23:20:10,  4.26s/it]
  1%|▏         | 265/20000 [19:22<22:28:02,  4.10s/it]
  1%|▏         | 266/20000 [19:25<21:39:41,  3.95s/it]
  1%|▏         | 267/20000 [19:29<20:52:20,  3.81s/it]
  1%|▏         | 268/20000 [19:32<19:59:21,  3.65s/it]
  1%|▏         | 269/20000 [19:35<19:05:33,  3.48s/it]
  1%|▏         | 270/20000 [19:38<18:05:34,  3.30s/it]
  1%|▏         | 271/20000 [19:41<16:55:09,  3.09s/it]
  1%|▏         | 272/20000 [19:42<14:58:15,  2.73s/it]
  1%|▏         | 273/20000 [19:51<25:12:05,  4.60s/it]
  1%|▏         | 274/20000 [19:58<28:35:14,  5.22s/it]
  1%|▏         | 275/20000 [20:04<29:48:39,  5.44s/it]
  1%|▏         | 276/20000 [20:09<29:48:01,  5.44s/it]
  1%|▏         | 277/20000 [20:15<29:16:39,  5.34s/it]
  1%|▏         | 278/20000 [20:19<28:29:10,  5.20s/it]
  1%|▏         | 279/20000 [20:24<27:25:05,  5.01s/it]
  1%|▏         | 280/20000 [20:28<26:22:02,  4.81s/it]
  1%|▏         | 281/20000 [20:33<25:21:59,  4.63s/it]
  1%|▏         | 282/20000 [20:37<24:26:48,  4.46s/it]
  1%|▏         | 283/20000 [20:41<23:28:48,  4.29s/it]
  1%|▏         | 284/20000 [20:44<22:40:53,  4.14s/it]
  1%|▏         | 285/20000 [20:48<21:54:03,  4.00s/it]
  1%|▏         | 286/20000 [20:52<21:07:37,  3.86s/it]
  1%|▏         | 287/20000 [20:55<20:11:11,  3.69s/it]
  1%|▏         | 288/20000 [20:58<19:13:15,  3.51s/it]
  1%|▏         | 289/20000 [21:01<18:12:15,  3.32s/it]
  1%|▏         | 290/20000 [21:03<17:06:59,  3.13s/it]
  1%|▏         | 291/20000 [21:05<15:12:05,  2.78s/it]
  1%|▏         | 292/20000 [21:14<24:28:34,  4.47s/it]
  1%|▏         | 293/20000 [21:20<27:26:12,  5.01s/it]
  1%|▏         | 294/20000 [21:26<28:31:28,  5.21s/it]
  1%|▏         | 295/20000 [21:31<28:34:29,  5.22s/it]
  1%|▏         | 296/20000 [21:36<28:14:57,  5.16s/it]
  1%|▏         | 297/20000 [21:41<27:43:42,  5.07s/it]
  1%|▏         | 298/20000 [21:45<26:50:16,  4.90s/it]
  1%|▏         | 299/20000 [21:50<25:56:26,  4.74s/it]
  2%|▏         | 300/20000 [21:54<25:04:07,  4.58s/it]
  2%|▏         | 301/20000 [21:58<24:10:02,  4.42s/it]
  2%|▏         | 302/20000 [22:02<23:20:57,  4.27s/it]
  2%|▏         | 303/20000 [22:06<22:31:38,  4.12s/it]
  2%|▏         | 304/20000 [22:09<21:45:58,  3.98s/it]
  2%|▏         | 305/20000 [22:13<21:03:04,  3.85s/it]
  2%|▏         | 306/20000 [22:16<20:08:18,  3.68s/it]
  2%|▏         | 307/20000 [22:19<19:07:43,  3.50s/it]
  2%|▏         | 308/20000 [22:22<18:03:25,  3.30s/it]
  2%|▏         | 309/20000 [22:25<16:50:04,  3.08s/it]
  2%|▏         | 310/20000 [22:27<15:00:34,  2.74s/it]
  2%|▏         | 311/20000 [22:32<19:38:32,  3.59s/it]
  2%|▏         | 312/20000 [22:35<18:48:28,  3.44s/it]
  2%|▏         | 313/20000 [22:45<29:39:15,  5.42s/it]
  2%|▏         | 314/20000 [22:52<31:21:09,  5.73s/it]
  2%|▏         | 315/20000 [22:58<31:19:32,  5.73s/it]
  2%|▏         | 316/20000 [23:03<30:35:54,  5.60s/it]
  2%|▏         | 317/20000 [23:08<29:39:05,  5.42s/it]
  2%|▏         | 318/20000 [23:13<28:41:29,  5.25s/it]
  2%|▏         | 319/20000 [23:17<27:35:35,  5.05s/it]
  2%|▏         | 320/20000 [23:22<26:33:18,  4.86s/it]
  2%|▏         | 321/20000 [23:26<25:29:53,  4.66s/it]
  2%|▏         | 322/20000 [23:30<24:27:17,  4.47s/it]
  2%|▏         | 323/20000 [23:34<23:31:32,  4.30s/it]
  2%|▏         | 324/20000 [23:38<22:39:27,  4.15s/it]
  2%|▏         | 325/20000 [23:41<21:49:36,  3.99s/it]
  2%|▏         | 326/20000 [23:45<21:11:50,  3.88s/it]
  2%|▏         | 327/20000 [23:48<20:39:12,  3.78s/it]
  2%|▏         | 328/20000 [23:52<19:53:50,  3.64s/it]
  2%|▏         | 329/20000 [23:55<18:47:19,  3.44s/it]
  2%|▏         | 330/20000 [23:57<17:24:11,  3.19s/it]
  2%|▏         | 331/20000 [23:59<15:22:08,  2.81s/it]
  2%|▏         | 332/20000 [24:08<25:00:13,  4.58s/it]
  2%|▏         | 333/20000 [24:14<28:05:14,  5.14s/it]
  2%|▏         | 334/20000 [24:20<28:55:41,  5.30s/it]
  2%|▏         | 335/20000 [24:25<28:49:50,  5.28s/it]
  2%|▏         | 336/20000 [24:30<28:15:06,  5.17s/it]
  2%|▏         | 337/20000 [24:35<27:31:18,  5.04s/it]
  2%|▏         | 338/20000 [24:39<26:35:52,  4.87s/it]
  2%|▏         | 339/20000 [24:44<25:37:36,  4.69s/it]
  2%|▏         | 340/20000 [24:48<24:41:22,  4.52s/it]
  2%|▏         | 341/20000 [24:52<23:47:15,  4.36s/it]
  2%|▏         | 342/20000 [24:56<22:53:59,  4.19s/it]
  2%|▏         | 343/20000 [24:59<22:03:25,  4.04s/it]
  2%|▏         | 344/20000 [25:03<21:21:34,  3.91s/it]
  2%|▏         | 345/20000 [25:06<20:39:48,  3.78s/it]
  2%|▏         | 346/20000 [25:10<19:51:34,  3.64s/it]
  2%|▏         | 347/20000 [25:13<18:59:09,  3.48s/it]
  2%|▏         | 348/20000 [25:16<17:58:58,  3.29s/it]
  2%|▏         | 349/20000 [25:18<16:45:43,  3.07s/it]
  2%|▏         | 350/20000 [25:20<14:44:47,  2.70s/it]
  2%|▏         | 351/20000 [25:29<24:57:42,  4.57s/it]
  2%|▏         | 352/20000 [25:35<27:44:18,  5.08s/it]
  2%|▏         | 353/20000 [25:41<28:30:40,  5.22s/it]
  2%|▏         | 354/20000 [25:46<28:25:39,  5.21s/it]
  2%|▏         | 355/20000 [25:51<27:59:51,  5.13s/it]
  2%|▏         | 356/20000 [25:56<27:20:45,  5.01s/it]
  2%|▏         | 357/20000 [26:00<26:25:40,  4.84s/it]
  2%|▏         | 358/20000 [26:04<25:36:18,  4.69s/it]
  2%|▏         | 359/20000 [26:09<24:50:35,  4.55s/it]
  2%|▏         | 360/20000 [26:13<24:01:51,  4.40s/it]
  2%|▏         | 361/20000 [26:17<23:08:40,  4.24s/it]
  2%|▏         | 362/20000 [26:20<22:18:17,  4.09s/it]
  2%|▏         | 363/20000 [26:24<21:32:36,  3.95s/it]
  2%|▏         | 364/20000 [26:27<20:49:19,  3.82s/it]
  2%|▏         | 365/20000 [26:31<19:56:36,  3.66s/it]
  2%|▏         | 366/20000 [26:34<19:02:18,  3.49s/it]
  2%|▏         | 367/20000 [26:37<18:03:04,  3.31s/it]
  2%|▏         | 368/20000 [26:39<16:55:12,  3.10s/it]
  2%|▏         | 369/20000 [26:41<14:57:54,  2.74s/it]
  2%|▏         | 370/20000 [26:50<24:22:38,  4.47s/it]
  2%|▏         | 371/20000 [26:56<27:25:30,  5.03s/it]
  2%|▏         | 372/20000 [27:02<28:33:19,  5.24s/it]
  2%|▏         | 373/20000 [27:07<28:42:26,  5.27s/it]
  2%|▏         | 374/20000 [27:12<28:15:47,  5.18s/it]
  2%|▏         | 375/20000 [27:17<27:36:28,  5.06s/it]
  2%|▏         | 376/20000 [27:21<26:39:41,  4.89s/it]
  2%|▏         | 377/20000 [27:26<25:46:24,  4.73s/it]
  2%|▏         | 378/20000 [27:30<24:54:24,  4.57s/it]
  2%|▏         | 379/20000 [27:34<24:03:27,  4.41s/it]
  2%|▏         | 380/20000 [27:38<23:12:30,  4.26s/it]
  2%|▏         | 381/20000 [27:42<22:21:11,  4.10s/it]
  2%|▏         | 382/20000 [27:45<21:30:48,  3.95s/it]
  2%|▏         | 383/20000 [27:49<20:45:06,  3.81s/it]
  2%|▏         | 384/20000 [27:52<19:50:57,  3.64s/it]
  2%|▏         | 385/20000 [27:55<18:53:02,  3.47s/it]
  2%|▏         | 386/20000 [27:58<17:53:12,  3.28s/it]
  2%|▏         | 387/20000 [28:00<16:45:51,  3.08s/it]
  2%|▏         | 388/20000 [28:02<14:55:59,  2.74s/it]
  2%|▏         | 389/20000 [28:08<19:13:24,  3.53s/it]
  2%|▏         | 390/20000 [28:11<18:14:35,  3.35s/it]
  2%|▏         | 391/20000 [28:21<28:48:05,  5.29s/it]
  2%|▏         | 392/20000 [28:27<30:31:23,  5.60s/it]
  2%|▏         | 393/20000 [28:33<30:43:21,  5.64s/it]
  2%|▏         | 394/20000 [28:38<30:05:05,  5.52s/it]
  2%|▏         | 395/20000 [28:43<29:09:30,  5.35s/it]
  2%|▏         | 396/20000 [28:48<28:14:13,  5.19s/it]
  2%|▏         | 397/20000 [28:52<27:06:17,  4.98s/it]
  2%|▏         | 398/20000 [28:56<26:03:10,  4.78s/it]
  2%|▏         | 399/20000 [29:01<25:03:09,  4.60s/it]
  2%|▏         | 400/20000 [29:05<24:02:01,  4.41s/it]
  2%|▏         | 401/20000 [29:08<23:05:27,  4.24s/it]
  2%|▏         | 402/20000 [29:12<22:15:45,  4.09s/it]
  2%|▏         | 403/20000 [29:16<21:27:32,  3.94s/it]
  2%|▏         | 404/20000 [29:19<20:41:08,  3.80s/it]
  2%|▏         | 405/20000 [29:22<19:46:06,  3.63s/it]
  2%|▏         | 406/20000 [29:26<18:51:20,  3.46s/it]
  2%|▏         | 407/20000 [29:28<17:52:52,  3.29s/it]
  2%|▏         | 408/20000 [29:31<16:44:37,  3.08s/it]
  2%|▏         | 409/20000 [29:33<15:02:36,  2.76s/it]
  2%|▏         | 410/20000 [29:42<25:03:49,  4.61s/it]
  2%|▏         | 411/20000 [29:48<27:55:16,  5.13s/it]
  2%|▏         | 412/20000 [29:54<28:51:17,  5.30s/it]
  2%|▏         | 413/20000 [29:59<28:43:23,  5.28s/it]
  2%|▏         | 414/20000 [30:04<28:09:23,  5.18s/it]
  2%|▏         | 415/20000 [30:09<27:24:49,  5.04s/it]
  2%|▏         | 416/20000 [30:13<26:30:12,  4.87s/it]
  2%|▏         | 417/20000 [30:18<25:36:32,  4.71s/it]
  2%|▏         | 418/20000 [30:22<24:43:23,  4.55s/it]
  2%|▏         | 419/20000 [30:26<23:51:15,  4.39s/it]
  2%|▏         | 420/20000 [30:30<22:56:43,  4.22s/it]
  2%|▏         | 421/20000 [30:33<22:03:16,  4.06s/it]
  2%|▏         | 422/20000 [30:37<21:18:17,  3.92s/it]
  2%|▏         | 423/20000 [30:40<20:33:20,  3.78s/it]
  2%|▏         | 424/20000 [30:44<19:41:52,  3.62s/it]
  2%|▏         | 425/20000 [30:47<18:47:32,  3.46s/it]
  2%|▏         | 426/20000 [30:50<17:48:20,  3.27s/it]
  2%|▏         | 427/20000 [30:52<16:35:01,  3.05s/it]
  2%|▏         | 428/20000 [30:54<14:31:13,  2.67s/it]
  2%|▏         | 429/20000 [31:03<24:37:53,  4.53s/it]
  2%|▏         | 430/20000 [31:09<28:01:00,  5.15s/it]
  2%|▏         | 431/20000 [31:15<29:07:46,  5.36s/it]
  2%|▏         | 432/20000 [31:21<29:08:34,  5.36s/it]
  2%|▏         | 433/20000 [31:26<28:35:07,  5.26s/it]
  2%|▏         | 434/20000 [31:30<27:47:23,  5.11s/it]
  2%|▏         | 435/20000 [31:35<26:49:53,  4.94s/it]
  2%|▏         | 436/20000 [31:39<25:55:18,  4.77s/it]
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  2%|▏         | 439/20000 [31:51<23:08:22,  4.26s/it]
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  2%|▏         | 442/20000 [32:02<20:44:41,  3.82s/it]
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  2%|▏         | 448/20000 [32:24<24:08:55,  4.45s/it]
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  2%|▏         | 450/20000 [32:36<28:04:56,  5.17s/it]
  2%|▏         | 451/20000 [32:42<28:10:51,  5.19s/it]
  2%|▏         | 452/20000 [32:46<27:47:34,  5.12s/it]
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  2%|▏         | 454/20000 [32:56<26:08:28,  4.81s/it]
  2%|▏         | 455/20000 [33:00<25:18:39,  4.66s/it]
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  2%|▏         | 464/20000 [33:32<17:47:40,  3.28s/it]
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  2%|▏         | 468/20000 [33:45<18:51:16,  3.48s/it]
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  2%|▏         | 477/20000 [34:35<24:54:44,  4.59s/it]
  2%|▏         | 478/20000 [34:39<23:58:16,  4.42s/it]
  2%|▏         | 479/20000 [34:43<23:00:41,  4.24s/it]
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  2%|▏         | 481/20000 [34:50<21:24:04,  3.95s/it]
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  2%|▏         | 489/20000 [35:22<26:59:55,  4.98s/it]
  2%|▏         | 490/20000 [35:28<28:00:10,  5.17s/it]
  2%|▏         | 491/20000 [35:33<28:05:02,  5.18s/it]
  2%|▏         | 492/20000 [35:38<27:41:08,  5.11s/it]
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  2%|▎         | 500/20000 [36:11<21:26:44,  3.96s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'loss': 1.4757, 'learning_rate': 0.000499, 'epoch': 6.41}
+
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+
                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-500/preprocessor_config.json
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  3%|▎         | 642/20000 [47:32<16:51:51,  3.14s/it]
  3%|▎         | 643/20000 [47:34<15:03:20,  2.80s/it]
  3%|▎         | 644/20000 [47:42<24:10:42,  4.50s/it]
  3%|▎         | 645/20000 [47:49<27:15:30,  5.07s/it]
  3%|▎         | 646/20000 [47:54<28:20:06,  5.27s/it]
  3%|▎         | 647/20000 [48:00<28:22:11,  5.28s/it]
  3%|▎         | 648/20000 [48:05<27:50:51,  5.18s/it]
  3%|▎         | 649/20000 [48:09<27:10:38,  5.06s/it]
  3%|▎         | 650/20000 [48:14<26:18:30,  4.89s/it]
  3%|▎         | 651/20000 [48:18<25:26:19,  4.73s/it]
  3%|▎         | 652/20000 [48:22<24:30:44,  4.56s/it]
  3%|▎         | 653/20000 [48:26<23:36:41,  4.39s/it]
  3%|▎         | 654/20000 [48:30<22:39:41,  4.22s/it]
  3%|▎         | 655/20000 [48:34<21:48:51,  4.06s/it]
  3%|▎         | 656/20000 [48:37<21:01:26,  3.91s/it]
  3%|▎         | 657/20000 [48:41<20:17:05,  3.78s/it]
  3%|▎         | 658/20000 [48:44<19:23:58,  3.61s/it]
  3%|▎         | 659/20000 [48:47<18:29:05,  3.44s/it]
  3%|▎         | 660/20000 [48:50<17:27:19,  3.25s/it]
  3%|▎         | 661/20000 [48:52<16:18:27,  3.04s/it]
  3%|▎         | 662/20000 [48:54<14:19:31,  2.67s/it]
  3%|▎         | 663/20000 [49:03<24:01:18,  4.47s/it]
  3%|▎         | 664/20000 [49:09<27:12:39,  5.07s/it]
  3%|▎         | 665/20000 [49:15<28:09:19,  5.24s/it]
  3%|▎         | 666/20000 [49:20<28:06:20,  5.23s/it]
  3%|▎         | 667/20000 [49:25<27:41:06,  5.16s/it]
  3%|▎         | 668/20000 [49:30<27:05:07,  5.04s/it]
  3%|▎         | 669/20000 [49:35<26:16:53,  4.89s/it]
  3%|▎         | 670/20000 [49:39<25:24:13,  4.73s/it]
  3%|▎         | 671/20000 [49:43<24:35:53,  4.58s/it]
  3%|▎         | 672/20000 [49:47<23:55:21,  4.46s/it]
  3%|▎         | 673/20000 [49:51<23:16:54,  4.34s/it]
  3%|▎         | 674/20000 [49:55<22:31:22,  4.20s/it]
  3%|▎         | 675/20000 [49:59<21:47:29,  4.06s/it]
  3%|▎         | 676/20000 [50:03<21:03:45,  3.92s/it]
  3%|▎         | 677/20000 [50:06<20:14:12,  3.77s/it]
  3%|▎         | 678/20000 [50:09<19:22:14,  3.61s/it]
  3%|▎         | 679/20000 [50:12<18:17:57,  3.41s/it]
  3%|▎         | 680/20000 [50:15<17:04:28,  3.18s/it]
  3%|▎         | 681/20000 [50:17<14:57:51,  2.79s/it]
  3%|▎         | 682/20000 [50:25<24:15:56,  4.52s/it]
  3%|▎         | 683/20000 [50:32<27:27:42,  5.12s/it]
  3%|▎         | 684/20000 [50:38<28:36:36,  5.33s/it]
  3%|▎         | 685/20000 [50:43<28:29:28,  5.31s/it]
  3%|▎         | 686/20000 [50:48<27:57:49,  5.21s/it]
  3%|▎         | 687/20000 [50:53<27:14:57,  5.08s/it]
  3%|▎         | 688/20000 [50:57<26:18:44,  4.90s/it]
  3%|▎         | 689/20000 [51:01<25:26:40,  4.74s/it]
  3%|▎         | 690/20000 [51:06<24:37:00,  4.59s/it]
  3%|▎         | 691/20000 [51:10<23:43:07,  4.42s/it]
  3%|▎         | 692/20000 [51:14<22:48:42,  4.25s/it]
  3%|▎         | 693/20000 [51:17<21:54:19,  4.08s/it]
  3%|▎         | 694/20000 [51:21<21:05:59,  3.93s/it]
  3%|▎         | 695/20000 [51:24<20:20:05,  3.79s/it]
  3%|▎         | 696/20000 [51:28<19:23:38,  3.62s/it]
  3%|▎         | 697/20000 [51:31<18:27:53,  3.44s/it]
  3%|▎         | 698/20000 [51:33<17:27:36,  3.26s/it]
  3%|▎         | 699/20000 [51:36<16:19:05,  3.04s/it]
  4%|▎         | 700/20000 [51:38<14:34:12,  2.72s/it]
  4%|▎         | 701/20000 [51:43<19:09:33,  3.57s/it]
  4%|▎         | 702/20000 [51:46<18:01:36,  3.36s/it]
  4%|▎         | 703/20000 [51:56<28:13:10,  5.26s/it]
  4%|▎         | 704/20000 [52:02<29:48:56,  5.56s/it]
  4%|▎         | 705/20000 [52:08<29:46:57,  5.56s/it]
  4%|▎         | 706/20000 [52:13<29:10:33,  5.44s/it]
  4%|▎         | 707/20000 [52:18<28:20:13,  5.29s/it]
  4%|▎         | 708/20000 [52:23<27:22:42,  5.11s/it]
  4%|▎         | 709/20000 [52:27<26:14:56,  4.90s/it]
  4%|▎         | 710/20000 [52:31<25:18:40,  4.72s/it]
  4%|▎         | 711/20000 [52:36<24:21:21,  4.55s/it]
  4%|▎         | 712/20000 [52:39<23:28:05,  4.38s/it]
  4%|▎         | 713/20000 [52:43<22:35:28,  4.22s/it]
  4%|▎         | 714/20000 [52:47<21:48:38,  4.07s/it]
  4%|▎         | 715/20000 [52:51<20:59:18,  3.92s/it]
  4%|▎         | 716/20000 [52:54<20:11:27,  3.77s/it]
  4%|▎         | 717/20000 [52:57<19:18:16,  3.60s/it]
  4%|▎         | 718/20000 [53:00<18:24:36,  3.44s/it]
  4%|▎         | 719/20000 [53:03<17:25:21,  3.25s/it]
  4%|▎         | 720/20000 [53:06<16:19:17,  3.05s/it]
  4%|▎         | 721/20000 [53:08<14:30:39,  2.71s/it]
  4%|▎         | 722/20000 [53:16<23:54:52,  4.47s/it]
  4%|▎         | 723/20000 [53:22<26:50:57,  5.01s/it]
  4%|▎         | 724/20000 [53:28<27:55:22,  5.21s/it]
  4%|▎         | 725/20000 [53:33<27:56:47,  5.22s/it]
  4%|▎         | 726/20000 [53:38<27:29:11,  5.13s/it]
  4%|▎         | 727/20000 [53:43<26:46:49,  5.00s/it]
  4%|▎         | 728/20000 [53:47<25:50:19,  4.83s/it]
  4%|▎         | 729/20000 [53:52<24:58:56,  4.67s/it]
  4%|▎         | 730/20000 [53:56<24:13:20,  4.53s/it]
  4%|▎         | 731/20000 [54:00<23:22:14,  4.37s/it]
  4%|▎         | 732/20000 [54:04<22:32:50,  4.21s/it]
  4%|▎         | 733/20000 [54:07<21:40:36,  4.05s/it]
  4%|▎         | 734/20000 [54:11<20:51:20,  3.90s/it]
  4%|▎         | 735/20000 [54:14<20:10:43,  3.77s/it]
  4%|▎         | 736/20000 [54:18<19:20:09,  3.61s/it]
  4%|▎         | 737/20000 [54:21<18:27:20,  3.45s/it]
  4%|▎         | 738/20000 [54:24<17:27:49,  3.26s/it]
  4%|▎         | 739/20000 [54:26<16:14:21,  3.04s/it]
  4%|▎         | 740/20000 [54:28<14:10:58,  2.65s/it]
  4%|▎         | 741/20000 [54:36<23:33:29,  4.40s/it]
  4%|▎         | 742/20000 [54:43<26:46:31,  5.01s/it]
  4%|▎         | 743/20000 [54:49<27:59:52,  5.23s/it]
  4%|▎         | 744/20000 [54:54<28:02:38,  5.24s/it]
  4%|▎         | 745/20000 [54:59<27:35:53,  5.16s/it]
  4%|▎         | 746/20000 [55:04<27:02:59,  5.06s/it]
  4%|▎         | 747/20000 [55:08<26:22:26,  4.93s/it]
  4%|▎         | 748/20000 [55:13<25:28:10,  4.76s/it]
  4%|▎         | 749/20000 [55:17<24:31:58,  4.59s/it]
  4%|▍         | 750/20000 [55:21<23:34:18,  4.41s/it]
  4%|▍         | 751/20000 [55:25<22:38:41,  4.24s/it]
  4%|▍         | 752/20000 [55:28<21:45:15,  4.07s/it]
  4%|▍         | 753/20000 [55:32<20:58:27,  3.92s/it]
  4%|▍         | 754/20000 [55:35<20:12:39,  3.78s/it]
  4%|▍         | 755/20000 [55:39<19:18:14,  3.61s/it]
  4%|▍         | 756/20000 [55:42<18:22:45,  3.44s/it]
  4%|▍         | 757/20000 [55:44<17:25:11,  3.26s/it]
  4%|▍         | 758/20000 [55:47<16:15:53,  3.04s/it]
  4%|▍         | 759/20000 [55:49<14:26:08,  2.70s/it]
  4%|▍         | 760/20000 [55:58<24:35:50,  4.60s/it]
  4%|▍         | 761/20000 [56:04<27:35:09,  5.16s/it]
  4%|▍         | 762/20000 [56:10<28:25:22,  5.32s/it]
  4%|▍         | 763/20000 [56:15<28:20:28,  5.30s/it]
  4%|▍         | 764/20000 [56:20<27:49:05,  5.21s/it]
  4%|▍         | 765/20000 [56:25<27:04:28,  5.07s/it]
  4%|▍         | 766/20000 [56:30<26:10:43,  4.90s/it]
  4%|▍         | 767/20000 [56:34<25:16:00,  4.73s/it]
  4%|▍         | 768/20000 [56:38<24:28:23,  4.58s/it]
  4%|▍         | 769/20000 [56:42<23:39:32,  4.43s/it]
  4%|▍         | 770/20000 [56:46<22:49:09,  4.27s/it]
  4%|▍         | 771/20000 [56:50<21:59:09,  4.12s/it]
  4%|▍         | 772/20000 [56:53<21:11:21,  3.97s/it]
  4%|▍         | 773/20000 [56:57<20:25:00,  3.82s/it]
  4%|▍         | 774/20000 [57:00<19:28:44,  3.65s/it]
  4%|▍         | 775/20000 [57:03<18:34:09,  3.48s/it]
  4%|▍         | 776/20000 [57:06<17:37:42,  3.30s/it]
  4%|▍         | 777/20000 [57:09<16:32:19,  3.10s/it]
  4%|▍         | 778/20000 [57:11<14:43:35,  2.76s/it]
  4%|▍         | 779/20000 [57:16<19:09:10,  3.59s/it]
  4%|▍         | 780/20000 [57:19<18:16:32,  3.42s/it]
  4%|▍         | 781/20000 [57:29<28:40:44,  5.37s/it]
  4%|▍         | 782/20000 [57:36<30:13:47,  5.66s/it]
  4%|▍         | 783/20000 [57:41<30:13:38,  5.66s/it]
  4%|▍         | 784/20000 [57:46<29:35:16,  5.54s/it]
  4%|▍         | 785/20000 [57:51<28:36:20,  5.36s/it]
  4%|▍         | 786/20000 [57:56<27:36:28,  5.17s/it]
  4%|▍         | 787/20000 [58:01<26:30:46,  4.97s/it]
  4%|▍         | 788/20000 [58:05<25:27:57,  4.77s/it]
  4%|▍         | 789/20000 [58:09<24:32:11,  4.60s/it]
  4%|▍         | 790/20000 [58:13<23:38:08,  4.43s/it]
  4%|▍         | 791/20000 [58:17<22:45:25,  4.26s/it]
  4%|▍         | 792/20000 [58:21<21:55:04,  4.11s/it]
  4%|▍         | 793/20000 [58:24<21:03:54,  3.95s/it]
  4%|▍         | 794/20000 [58:28<20:15:17,  3.80s/it]
  4%|▍         | 795/20000 [58:31<19:21:56,  3.63s/it]
  4%|▍         | 796/20000 [58:34<18:27:28,  3.46s/it]
  4%|▍         | 797/20000 [58:37<17:26:16,  3.27s/it]
  4%|▍         | 798/20000 [58:39<16:14:55,  3.05s/it]
  4%|▍         | 799/20000 [58:41<14:23:55,  2.70s/it]
  4%|▍         | 800/20000 [58:50<23:46:28,  4.46s/it]
  4%|▍         | 801/20000 [58:56<26:45:47,  5.02s/it]
  4%|▍         | 802/20000 [59:02<27:37:31,  5.18s/it]
  4%|▍         | 803/20000 [59:07<27:33:59,  5.17s/it]
  4%|▍         | 804/20000 [59:12<27:05:57,  5.08s/it]
  4%|▍         | 805/20000 [59:16<26:25:36,  4.96s/it]
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  4%|▍         | 807/20000 [59:25<24:40:23,  4.63s/it]
  4%|▍         | 808/20000 [59:29<23:52:43,  4.48s/it]
  4%|▍         | 809/20000 [59:33<23:04:48,  4.33s/it]
  4%|▍         | 810/20000 [59:37<22:18:08,  4.18s/it]
  4%|▍         | 811/20000 [59:41<21:31:12,  4.04s/it]
  4%|▍         | 812/20000 [59:44<20:46:11,  3.90s/it]
  4%|▍         | 813/20000 [59:48<20:04:36,  3.77s/it]
  4%|▍         | 814/20000 [59:51<19:13:01,  3.61s/it]
  4%|▍         | 815/20000 [59:54<18:18:36,  3.44s/it]
  4%|▍         | 816/20000 [59:57<17:26:14,  3.27s/it]
  4%|▍         | 817/20000 [1:00:00<16:21:54,  3.07s/it]
  4%|▍         | 818/20000 [1:00:02<14:35:10,  2.74s/it]
  4%|▍         | 819/20000 [1:00:10<24:01:07,  4.51s/it]
  4%|▍         | 820/20000 [1:00:17<27:03:23,  5.08s/it]
  4%|▍         | 821/20000 [1:00:22<27:58:41,  5.25s/it]
  4%|▍         | 822/20000 [1:00:27<27:53:07,  5.23s/it]
  4%|▍         | 823/20000 [1:00:32<27:24:14,  5.14s/it]
  4%|▍         | 824/20000 [1:00:37<26:43:10,  5.02s/it]
  4%|▍         | 825/20000 [1:00:42<25:46:01,  4.84s/it]
  4%|▍         | 826/20000 [1:00:46<24:53:39,  4.67s/it]
  4%|▍         | 827/20000 [1:00:50<24:00:23,  4.51s/it]
  4%|▍         | 828/20000 [1:00:54<23:06:47,  4.34s/it]
  4%|▍         | 829/20000 [1:00:58<22:28:28,  4.22s/it]
  4%|▍         | 830/20000 [1:01:02<21:42:28,  4.08s/it]
  4%|▍         | 831/20000 [1:01:05<20:57:52,  3.94s/it]
  4%|▍         | 832/20000 [1:01:09<20:11:55,  3.79s/it]
  4%|▍         | 833/20000 [1:01:12<19:25:37,  3.65s/it]
  4%|▍         | 834/20000 [1:01:15<18:28:43,  3.47s/it]
  4%|▍         | 835/20000 [1:01:18<17:28:44,  3.28s/it]
  4%|▍         | 836/20000 [1:01:20<16:21:36,  3.07s/it]
  4%|▍         | 837/20000 [1:01:22<14:29:41,  2.72s/it]
  4%|▍         | 838/20000 [1:01:31<24:45:36,  4.65s/it]
  4%|▍         | 839/20000 [1:01:38<27:44:03,  5.21s/it]
  4%|▍         | 840/20000 [1:01:44<28:50:06,  5.42s/it]
  4%|▍         | 841/20000 [1:01:49<28:51:09,  5.42s/it]
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  4%|▍         | 843/20000 [1:01:59<27:30:46,  5.17s/it]
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  4%|▍         | 854/20000 [1:02:41<17:37:09,  3.31s/it]
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  4%|▍         | 860/20000 [1:03:10<30:27:00,  5.73s/it]
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  4%|▍         | 870/20000 [1:03:56<21:50:31,  4.11s/it]
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  5%|▌         | 1000/20000 [1:13:11<25:23:11,  4.81s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.061434268951416016, 'eval_wer': 0.03469206911829863, 'eval_runtime': 68.9385, 'eval_samples_per_second': 26.937, 'eval_steps_per_second': 1.131, 'epoch': 6.41}
+{'loss': 0.0624, 'learning_rate': 0.0004872051282051282, 'epoch': 12.82}
+
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+
                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-500] due to args.save_total_limit
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  5%|▌         | 1008/20000 [1:14:58<29:01:24,  5.50s/it]
  5%|▌         | 1009/20000 [1:15:01<25:10:28,  4.77s/it]
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  5%|▌         | 1011/20000 [1:15:07<19:31:19,  3.70s/it]
  5%|▌         | 1012/20000 [1:15:09<16:33:32,  3.14s/it]
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  5%|▌         | 1020/20000 [1:15:55<27:56:50,  5.30s/it]
  5%|▌         | 1021/20000 [1:15:59<26:44:42,  5.07s/it]
  5%|▌         | 1022/20000 [1:16:04<25:36:54,  4.86s/it]
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  5%|▌         | 1024/20000 [1:16:12<23:42:13,  4.50s/it]
  5%|▌         | 1025/20000 [1:16:16<22:46:59,  4.32s/it]
  5%|▌         | 1026/20000 [1:16:20<21:56:16,  4.16s/it]
  5%|▌         | 1027/20000 [1:16:23<21:04:20,  4.00s/it]
  5%|▌         | 1028/20000 [1:16:27<20:18:48,  3.85s/it]
  5%|▌         | 1029/20000 [1:16:30<19:25:08,  3.69s/it]
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  5%|▌         | 1080/20000 [1:20:10<23:39:37,  4.50s/it]
  5%|▌         | 1081/20000 [1:20:14<22:52:04,  4.35s/it]
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  5%|▌         | 1084/20000 [1:20:25<20:32:41,  3.91s/it]
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  5%|▌         | 1087/20000 [1:20:35<18:04:10,  3.44s/it]
  5%|▌         | 1088/20000 [1:20:38<17:09:33,  3.27s/it]
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  8%|▊         | 1500/20000 [1:49:57<15:40:24,  3.05s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.05250529572367668, 'eval_wer': 0.027735932653965442, 'eval_runtime': 68.4693, 'eval_samples_per_second': 27.122, 'eval_steps_per_second': 1.139, 'epoch': 12.82}
+{'loss': 0.0388, 'learning_rate': 0.0004743846153846154, 'epoch': 19.23}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1000] due to args.save_total_limit
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  8%|▊         | 1602/20000 [1:58:32<26:25:23,  5.17s/it]
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 10%|█         | 2000/20000 [2:26:44<20:24:44,  4.08s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06932416558265686, 'eval_wer': 0.024147097917589722, 'eval_runtime': 67.8311, 'eval_samples_per_second': 27.377, 'eval_steps_per_second': 1.15, 'epoch': 19.23}
+{'loss': 0.03, 'learning_rate': 0.0004615641025641026, 'epoch': 25.64}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-1500] due to args.save_total_limit
+
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 12%|█▎        | 2500/20000 [3:03:39<26:39:11,  5.48s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.0665554478764534, 'eval_wer': 0.024412937527691626, 'eval_runtime': 69.0525, 'eval_samples_per_second': 26.893, 'eval_steps_per_second': 1.13, 'epoch': 25.64}
+{'loss': 0.0235, 'learning_rate': 0.0004487435897435898, 'epoch': 32.05}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2000] due to args.save_total_limit
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 15%|█▌        | 3000/20000 [3:40:18<15:16:17,  3.23s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06039978191256523, 'eval_wer': 0.026007975188303056, 'eval_runtime': 67.8702, 'eval_samples_per_second': 27.361, 'eval_steps_per_second': 1.149, 'epoch': 32.05}
+{'loss': 0.0226, 'learning_rate': 0.0004359230769230769, 'epoch': 38.46}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-2500] due to args.save_total_limit
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 18%|█▊        | 3500/20000 [4:16:59<19:10:22,  4.18s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06249115616083145, 'eval_wer': 0.022950819672131147, 'eval_runtime': 67.3401, 'eval_samples_per_second': 27.576, 'eval_steps_per_second': 1.158, 'epoch': 38.46}
+{'loss': 0.0163, 'learning_rate': 0.0004231025641025641, 'epoch': 44.87}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3000] due to args.save_total_limit
+
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 20%|██        | 4000/20000 [4:53:34<23:01:35,  5.18s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06026700139045715, 'eval_wer': 0.01953921134249003, 'eval_runtime': 67.7207, 'eval_samples_per_second': 27.421, 'eval_steps_per_second': 1.152, 'epoch': 44.87}
+{'loss': 0.0157, 'learning_rate': 0.0004102820512820513, 'epoch': 51.28}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-3500] due to args.save_total_limit
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 22%|██▎       | 4500/20000 [5:30:17<14:52:37,  3.46s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.0627710148692131, 'eval_wer': 0.02091271599468321, 'eval_runtime': 67.8528, 'eval_samples_per_second': 27.368, 'eval_steps_per_second': 1.15, 'epoch': 51.28}
+{'loss': 0.0152, 'learning_rate': 0.0003974615384615385, 'epoch': 57.69}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4000] due to args.save_total_limit
+
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 25%|██▌       | 5000/20000 [6:06:59<19:35:02,  4.70s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06921559572219849, 'eval_wer': 0.023836951705804164, 'eval_runtime': 67.9506, 'eval_samples_per_second': 27.329, 'eval_steps_per_second': 1.148, 'epoch': 57.69}
+{'loss': 0.0122, 'learning_rate': 0.00038464102564102567, 'epoch': 64.1}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-4500] due to args.save_total_limit
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 28%|██▊       | 5500/20000 [6:43:38<20:14:01,  5.02s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06069288030266762, 'eval_wer': 0.02100132919805051, 'eval_runtime': 67.6564, 'eval_samples_per_second': 27.448, 'eval_steps_per_second': 1.153, 'epoch': 64.1}
+{'loss': 0.011, 'learning_rate': 0.0003718205128205128, 'epoch': 70.51}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5000] due to args.save_total_limit
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 30%|███       | 6000/20000 [7:20:32<14:03:19,  3.61s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06082445755600929, 'eval_wer': 0.021311475409836064, 'eval_runtime': 68.2551, 'eval_samples_per_second': 27.207, 'eval_steps_per_second': 1.143, 'epoch': 70.51}
+{'loss': 0.0114, 'learning_rate': 0.000359, 'epoch': 76.92}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-5500] due to args.save_total_limit
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 32%|███▎      | 6500/20000 [7:57:17<18:15:56,  4.87s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06809388101100922, 'eval_wer': 0.021089942401417813, 'eval_runtime': 68.8171, 'eval_samples_per_second': 26.985, 'eval_steps_per_second': 1.133, 'epoch': 76.92}
+{'loss': 0.0106, 'learning_rate': 0.00034620512820512825, 'epoch': 83.33}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6000] due to args.save_total_limit
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 35%|███▌      | 7000/20000 [8:33:44<15:51:55,  4.39s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.061287231743335724, 'eval_wer': 0.02100132919805051, 'eval_runtime': 67.6622, 'eval_samples_per_second': 27.445, 'eval_steps_per_second': 1.153, 'epoch': 83.33}
+{'loss': 0.0081, 'learning_rate': 0.0003333846153846154, 'epoch': 89.74}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-6500] due to args.save_total_limit
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 38%|███▊      | 7500/20000 [9:10:35<13:51:12,  3.99s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06544984877109528, 'eval_wer': 0.019627824545857334, 'eval_runtime': 68.1647, 'eval_samples_per_second': 27.243, 'eval_steps_per_second': 1.144, 'epoch': 89.74}
+{'loss': 0.0078, 'learning_rate': 0.00032056410256410257, 'epoch': 96.15}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7000] due to args.save_total_limit
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 40%|████      | 8000/20000 [9:47:10<16:37:59,  4.99s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06121820956468582, 'eval_wer': 0.01905183872396987, 'eval_runtime': 68.399, 'eval_samples_per_second': 27.15, 'eval_steps_per_second': 1.14, 'epoch': 96.15}
+{'loss': 0.0082, 'learning_rate': 0.00030774358974358976, 'epoch': 102.56}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-7500] due to args.save_total_limit
+
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 42%|████▎     | 8500/20000 [10:23:18<8:37:44,  2.70s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07580074667930603, 'eval_wer': 0.02365972529906956, 'eval_runtime': 67.4807, 'eval_samples_per_second': 27.519, 'eval_steps_per_second': 1.156, 'epoch': 102.56}
+{'loss': 0.0078, 'learning_rate': 0.0002949230769230769, 'epoch': 108.97}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8000] due to args.save_total_limit
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 45%|████▌     | 9000/20000 [10:59:57<12:32:20,  4.10s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06641749292612076, 'eval_wer': 0.02060256978289765, 'eval_runtime': 67.4225, 'eval_samples_per_second': 27.543, 'eval_steps_per_second': 1.157, 'epoch': 108.97}
+{'loss': 0.0075, 'learning_rate': 0.00028210256410256414, 'epoch': 115.38}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-8500] due to args.save_total_limit
+
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 48%|████▊     | 9500/20000 [11:36:19<14:56:20,  5.12s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06580852717161179, 'eval_wer': 0.019716437749224634, 'eval_runtime': 67.9347, 'eval_samples_per_second': 27.335, 'eval_steps_per_second': 1.148, 'epoch': 115.38}
+{'loss': 0.0052, 'learning_rate': 0.0002692820512820513, 'epoch': 121.79}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9000] due to args.save_total_limit
+
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 50%|█████     | 10000/20000 [12:12:40<9:26:06,  3.40s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06690431386232376, 'eval_wer': 0.021843154630039874, 'eval_runtime': 66.8582, 'eval_samples_per_second': 27.775, 'eval_steps_per_second': 1.167, 'epoch': 121.79}
+{'loss': 0.0054, 'learning_rate': 0.00025646153846153847, 'epoch': 128.21}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-9500] due to args.save_total_limit
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 52%|█████▎    | 10500/20000 [12:49:03<11:18:00,  4.28s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.06948971748352051, 'eval_wer': 0.021089942401417813, 'eval_runtime': 67.1148, 'eval_samples_per_second': 27.669, 'eval_steps_per_second': 1.162, 'epoch': 128.21}
+{'loss': 0.0053, 'learning_rate': 0.00024364102564102563, 'epoch': 134.62}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10000] due to args.save_total_limit
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 55%|█████▌    | 11000/20000 [13:25:28<14:01:30,  5.61s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07259159535169601, 'eval_wer': 0.022684980062029243, 'eval_runtime': 66.7066, 'eval_samples_per_second': 27.838, 'eval_steps_per_second': 1.169, 'epoch': 134.62}
+{'loss': 0.0046, 'learning_rate': 0.00023082051282051282, 'epoch': 141.03}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-10500] due to args.save_total_limit
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 57%|█████▊    | 11500/20000 [14:01:48<8:27:27,  3.58s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07016939669847488, 'eval_wer': 0.021178555604785113, 'eval_runtime': 68.2063, 'eval_samples_per_second': 27.226, 'eval_steps_per_second': 1.144, 'epoch': 141.03}
+{'loss': 0.0043, 'learning_rate': 0.000218, 'epoch': 147.44}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11000] due to args.save_total_limit
+
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 60%|██████    | 12000/20000 [14:38:08<9:52:07,  4.44s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.08461024612188339, 'eval_wer': 0.020026583961010192, 'eval_runtime': 66.3999, 'eval_samples_per_second': 27.967, 'eval_steps_per_second': 1.175, 'epoch': 147.44}
+{'loss': 0.0041, 'learning_rate': 0.00020517948717948718, 'epoch': 153.85}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-11500] due to args.save_total_limit
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 62%|██████▎   | 12500/20000 [15:14:21<9:40:51,  4.65s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07643292099237442, 'eval_wer': 0.020026583961010192, 'eval_runtime': 66.6168, 'eval_samples_per_second': 27.876, 'eval_steps_per_second': 1.171, 'epoch': 153.85}
+{'loss': 0.0032, 'learning_rate': 0.00019235897435897437, 'epoch': 160.26}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12000] due to args.save_total_limit
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 65%|██████▌   | 13000/20000 [15:50:50<7:12:11,  3.70s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.0785411074757576, 'eval_wer': 0.02007089056269384, 'eval_runtime': 67.7953, 'eval_samples_per_second': 27.391, 'eval_steps_per_second': 1.151, 'epoch': 160.26}
+{'loss': 0.0028, 'learning_rate': 0.0001795641025641026, 'epoch': 166.67}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-12500] due to args.save_total_limit
+
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 68%|██████▊   | 13500/20000 [16:27:09<9:10:12,  5.08s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.08392436057329178, 'eval_wer': 0.019672131147540985, 'eval_runtime': 67.1052, 'eval_samples_per_second': 27.673, 'eval_steps_per_second': 1.162, 'epoch': 166.67}
+{'loss': 0.0035, 'learning_rate': 0.00016674358974358975, 'epoch': 173.08}
+
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+
                                               

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+
                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13000] due to args.save_total_limit
+
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 70%|███████   | 14000/20000 [17:03:10<4:28:34,  2.69s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07846853882074356, 'eval_wer': 0.02100132919805051, 'eval_runtime': 66.3462, 'eval_samples_per_second': 27.99, 'eval_steps_per_second': 1.176, 'epoch': 173.08}
+{'loss': 0.0027, 'learning_rate': 0.00015394871794871794, 'epoch': 179.49}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-13500] due to args.save_total_limit
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 72%|███████▎  | 14500/20000 [17:39:28<5:54:14,  3.86s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07303400337696075, 'eval_wer': 0.018785999113867965, 'eval_runtime': 66.6716, 'eval_samples_per_second': 27.853, 'eval_steps_per_second': 1.17, 'epoch': 179.49}
+{'loss': 0.002, 'learning_rate': 0.00014112820512820513, 'epoch': 185.9}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14000] due to args.save_total_limit
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 75%|███████▌  | 15000/20000 [18:15:43<7:00:35,  5.05s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07940459251403809, 'eval_wer': 0.019317678334071775, 'eval_runtime': 66.4899, 'eval_samples_per_second': 27.929, 'eval_steps_per_second': 1.173, 'epoch': 185.9}
+{'loss': 0.002, 'learning_rate': 0.00012830769230769232, 'epoch': 192.31}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-14500] due to args.save_total_limit
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 78%|███████▊  | 15500/20000 [18:51:50<3:45:28,  3.01s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.08587377518415451, 'eval_wer': 0.02113424900310146, 'eval_runtime': 67.8686, 'eval_samples_per_second': 27.362, 'eval_steps_per_second': 1.149, 'epoch': 192.31}
+{'loss': 0.0019, 'learning_rate': 0.00011548717948717949, 'epoch': 198.72}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15000] due to args.save_total_limit
+
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 80%|████████  | 16000/20000 [19:28:18<4:54:16,  4.41s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07269652187824249, 'eval_wer': 0.018342933097031458, 'eval_runtime': 66.1737, 'eval_samples_per_second': 28.063, 'eval_steps_per_second': 1.179, 'epoch': 198.72}
+{'loss': 0.0017, 'learning_rate': 0.00010266666666666668, 'epoch': 205.13}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-15500] due to args.save_total_limit
+
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 82%|████████▎ | 16500/20000 [20:04:43<5:01:51,  5.17s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07843895256519318, 'eval_wer': 0.018653079308817013, 'eval_runtime': 67.2939, 'eval_samples_per_second': 27.595, 'eval_steps_per_second': 1.159, 'epoch': 205.13}
+{'loss': 0.0016, 'learning_rate': 8.984615384615384e-05, 'epoch': 211.54}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16000] due to args.save_total_limit
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 85%|████████▌ | 17000/20000 [20:41:01<2:43:27,  3.27s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.08008446544408798, 'eval_wer': 0.019627824545857334, 'eval_runtime': 67.2442, 'eval_samples_per_second': 27.616, 'eval_steps_per_second': 1.16, 'epoch': 211.54}
+{'loss': 0.0014, 'learning_rate': 7.702564102564103e-05, 'epoch': 217.95}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-16500] due to args.save_total_limit
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 88%|████████▊ | 17500/20000 [21:17:36<3:05:54,  4.46s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.0820729061961174, 'eval_wer': 0.01847585290208241, 'eval_runtime': 67.2612, 'eval_samples_per_second': 27.609, 'eval_steps_per_second': 1.16, 'epoch': 217.95}
+{'loss': 0.0011, 'learning_rate': 6.420512820512821e-05, 'epoch': 224.36}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17000] due to args.save_total_limit
+
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 90%|█████████ | 18000/20000 [21:54:01<2:57:01,  5.31s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.08215450495481491, 'eval_wer': 0.017634027470093044, 'eval_runtime': 67.1514, 'eval_samples_per_second': 27.654, 'eval_steps_per_second': 1.162, 'epoch': 224.36}
+{'loss': 0.001, 'learning_rate': 5.1384615384615385e-05, 'epoch': 230.77}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-17500] due to args.save_total_limit
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 92%|█████████▎| 18500/20000 [22:30:30<1:33:57,  3.76s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.0855555310845375, 'eval_wer': 0.017058041648205582, 'eval_runtime': 67.2619, 'eval_samples_per_second': 27.609, 'eval_steps_per_second': 1.16, 'epoch': 230.77}
+{'loss': 0.001, 'learning_rate': 3.856410256410257e-05, 'epoch': 237.18}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18000] due to args.save_total_limit
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 95%|█████████▌| 19000/20000 [23:06:53<1:17:40,  4.66s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.07919255644083023, 'eval_wer': 0.017589720868409393, 'eval_runtime': 66.733, 'eval_samples_per_second': 27.827, 'eval_steps_per_second': 1.169, 'epoch': 237.18}
+{'loss': 0.001, 'learning_rate': 2.574358974358974e-05, 'epoch': 243.59}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-18500] due to args.save_total_limit
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 98%|█████████▊| 19500/20000 [23:42:54<27:25,  3.29s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.08259344846010208, 'eval_wer': 0.017279574656623838, 'eval_runtime': 67.3537, 'eval_samples_per_second': 27.571, 'eval_steps_per_second': 1.158, 'epoch': 243.59}
+{'loss': 0.0006, 'learning_rate': 1.2923076923076924e-05, 'epoch': 250.0}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19500
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19500/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19500/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19500/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19000] due to args.save_total_limit
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100%|██████████| 20000/20000 [24:19:14<00:00,  3.85s/it]The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+{'eval_loss': 0.08542538434267044, 'eval_wer': 0.016969428444838282, 'eval_runtime': 66.6818, 'eval_samples_per_second': 27.849, 'eval_steps_per_second': 1.17, 'epoch': 250.0}
+{'loss': 0.0007, 'learning_rate': 1.282051282051282e-07, 'epoch': 256.41}
+
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+
                                               

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                                               Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-20000
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-20000/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-20000/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-20000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/checkpoint-19500] due to args.save_total_limit
+
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
                                                        

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+Saving model checkpoint to experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl
+Configuration saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/config.json
+Model weights saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/pytorch_model.bin
+Feature extractor saved in experiments/results/robust_model/wav2vec2-large-960h-lv60-self/atcosim_corpus/0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl/preprocessor_config.json
+{'eval_loss': 0.08499366790056229, 'eval_wer': 0.016747895436420027, 'eval_runtime': 66.4242, 'eval_samples_per_second': 27.957, 'eval_steps_per_second': 1.174, 'epoch': 256.41}
+{'train_runtime': 87629.7749, 'train_samples_per_second': 21.91, 'train_steps_per_second': 0.228, 'train_loss': 0.04605116495639086, 'epoch': 256.41}
+***** train metrics *****
+  epoch                    =            256.41
+  train_loss               =            0.0461
+  train_runtime            = 1 day, 0:20:29.77
+  train_samples            =              7480
+  train_samples_per_second =             21.91
+  train_steps_per_second   =             0.228
+11/11/2022 16:27:23 - INFO - __main__ - *** Evaluate ***
+The following columns in the evaluation set don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
+***** Running Evaluation *****
+  Num examples = 1857
+  Batch size = 24
+
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+Dropping the following result as it does not have all the necessary fields:
+{'metrics': [{'name': 'Wer', 'type': 'wer', 'value': 0.016747895436420027}]}
+***** eval metrics *****
+  epoch                   =     256.41
+  eval_loss               =      0.085
+  eval_runtime            = 0:01:08.05
+  eval_samples            =       1857
+  eval_samples_per_second =     27.286
+  eval_steps_per_second   =      1.146
+  eval_wer                =     0.0167
+# Accounting: time=87964 threads=1
+# Finished at Fri 11 Nov 16:28:33 CET 2022 with status 0