diff --git "a/log/train_log" "b/log/train_log"
new file mode 100644--- /dev/null
+++ "b/log/train_log"
@@ -0,0 +1,21388 @@
+# Running on vgni027
+# Started at Mon 28 Nov 08:09:01 CET 2022
+# python3 src/run_speech_recognition_ctc.py --report_to=none --run_name=experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/ --preprocessing_num_workers=5 --model_name_or_path=facebook/wav2vec2-large-960h-lv60-self --dataset_name=experiments/data/atcosim_uwb_atcc/train --min_duration_in_seconds=0.2 --max_duration_in_seconds=20 --eval_dataset_name=experiments/data/atcosim_uwb_atcc/test --train_split_name=train --output_dir=experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/ --num_train_epochs=50 --per_device_train_batch_size=24 --per_device_eval_batch_size=12 --gradient_accumulation_steps=1 --learning_rate=1e-4 --weight_decay=0.001 --warmup_steps=1000 --evaluation_strategy=steps --text_column_name=text --audio_column_name=audio --length_column_name=input_length '--chars_to_ignore=, ? . ! \; \: " “ % ‘ ” � — ’ … –' --save_steps=1000 --eval_steps=500 --logging_steps=1000 --layerdrop=0.0 --activation_dropout=0.0 --attention_dropout=0.0 --save_total_limit=1 --feat_proj_dropout=0.05 --mask_time_prob=0.075 --mask_time_length=12 --mask_feature_prob=0.0 --mask_feature_length=12 --gradient_checkpointing --freeze_feature_encoder --fp16 --group_by_length --do_train --do_eval --max_steps 10000 --overwrite_output_dir --freeze_feature_encoder 
+11/28/2022 08:09:10 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True
+11/28/2022 08:09:10 - 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=1,
+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=0.0001,
+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/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/runs/Nov28_08-09-10_vgni027,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=1000,
+logging_strategy=steps,
+lr_scheduler_type=linear,
+max_grad_norm=1.0,
+max_steps=10000,
+metric_for_best_model=None,
+mp_parameters=,
+no_cuda=False,
+num_train_epochs=50.0,
+optim=adamw_hf,
+output_dir=experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=12,
+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/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/,
+save_on_each_node=False,
+save_steps=1000,
+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=1000,
+weight_decay=0.001,
+xpu_backend=None,
+)
+11/28/2022 08:09:10 - WARNING - datasets.builder - Using custom data configuration train-229a51d735ef0a2b
+Downloading and preparing dataset atc_data_loader/train to /remote/idiap.svm/temp.speech01/jzuluaga/experiments/journal/asr/github/w2v2-air-traffic/.cache/experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc//train/atc_data_loader/train-229a51d735ef0a2b/0.0.0/f2633cc53c6abe32cddd4152eebde1a4e3c9953e1446e190b8d9a13330cddaa4...
+
Generating train split: 0 examples [00:00, ? examples/s]
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Dataset atc_data_loader downloaded and prepared to /remote/idiap.svm/temp.speech01/jzuluaga/experiments/journal/asr/github/w2v2-air-traffic/.cache/experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc//train/atc_data_loader/train-229a51d735ef0a2b/0.0.0/f2633cc53c6abe32cddd4152eebde1a4e3c9953e1446e190b8d9a13330cddaa4. Subsequent calls will reuse this data.
+11/28/2022 08:10:24 - WARNING - datasets.builder - Using custom data configuration test-b7cef58c6dbd7d6c
+Downloading and preparing dataset atc_data_loader/test to /remote/idiap.svm/temp.speech01/jzuluaga/experiments/journal/asr/github/w2v2-air-traffic/.cache/experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc//test/atc_data_loader/test-b7cef58c6dbd7d6c/0.0.0/f2633cc53c6abe32cddd4152eebde1a4e3c9953e1446e190b8d9a13330cddaa4...
+
Generating test split: 0 examples [00:00, ? examples/s]
Generating test split: 1 examples [00:12, 12.03s/ examples]
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Generating test split: 4591 examples [00:14, 2996.04 examples/s]
                                                                
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
+}
+
+Dataset atc_data_loader downloaded and prepared to /remote/idiap.svm/temp.speech01/jzuluaga/experiments/journal/asr/github/w2v2-air-traffic/.cache/experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc//test/atc_data_loader/test-b7cef58c6dbd7d6c/0.0.0/f2633cc53c6abe32cddd4152eebde1a4e3c9953e1446e190b8d9a13330cddaa4. Subsequent calls will reuse this data.
+
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+
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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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+
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+
+
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+
+
+
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+
+
+
+
preprocess datasets #4:   0%|          | 0/3785 [00:00<?, ?ex/s]/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=8000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=8000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
+
+
+
+
preprocess datasets #4:   0%|          | 1/3785 [00:02<2:35:40,  2.47s/ex]/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=8000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
+
+
preprocess datasets #2:   0%|          | 1/3786 [00:02<2:38:06,  2.51s/ex]/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
+
+
+
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
preprocess datasets #0:   0%|          | 1/3786 [00:02<2:41:07,  2.55s/ex]/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
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preprocess datasets #0:   1%|▏         | 53/3786 [00:07<06:28,  9.61ex/s]
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preprocess datasets #3:   4%|▍         | 145/3786 [00:07<02:28, 24.44ex/s]
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preprocess datasets #1:   2%|▏         | 72/3786 [00:07<04:46, 12.95ex/s]
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preprocess datasets #2:   3%|▎         | 112/3786 [00:07<02:16, 26.84ex/s]
preprocess datasets #0:   1%|▏         | 55/3786 [00:07<05:56, 10.45ex/s]
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preprocess datasets #4:   4%|▍         | 149/3785 [00:07<02:01, 29.84ex/s]
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preprocess datasets #1:   2%|▏         | 74/3786 [00:07<04:23, 14.10ex/s]
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preprocess datasets #3:   4%|▍         | 149/3786 [00:07<02:24, 25.20ex/s]
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preprocess datasets #1:   2%|▏         | 76/3786 [00:07<04:17, 14.43ex/s]
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preprocess datasets #0:   2%|▏         | 57/3786 [00:07<05:35, 11.12ex/s]
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preprocess datasets #4:   4%|▍         | 157/3785 [00:07<02:03, 29.44ex/s]
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preprocess datasets #1:   2%|▏         | 78/3786 [00:07<04:24, 14.01ex/s]
preprocess datasets #0:   2%|▏         | 59/3786 [00:07<05:33, 11.18ex/s]
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preprocess datasets #3:   4%|▍         | 158/3786 [00:07<02:28, 24.45ex/s]
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preprocess datasets #1:   2%|▏         | 80/3786 [00:07<04:40, 13.23ex/s]
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preprocess datasets #0:   2%|▏         | 61/3786 [00:07<05:23, 11.51ex/s]
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preprocess datasets #3:   4%|▍         | 167/3786 [00:08<02:05, 28.88ex/s]
preprocess datasets #0:   2%|▏         | 63/3786 [00:08<05:22, 11.56ex/s]
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preprocess datasets #0:   2%|▏         | 65/3786 [00:08<05:35, 11.08ex/s]
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preprocess datasets #3:   5%|▍         | 175/3786 [00:08<02:04, 28.92ex/s]
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preprocess datasets #1:   2%|▏         | 84/3786 [00:08<06:14,  9.89ex/s]
preprocess datasets #0:   2%|▏         | 67/3786 [00:08<05:12, 11.91ex/s]
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preprocess datasets #3:   5%|▍         | 179/3786 [00:08<01:56, 31.01ex/s]
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preprocess datasets #2:   4%|▎         | 136/3786 [00:08<02:46, 21.94ex/s]
preprocess datasets #0:   2%|▏         | 69/3786 [00:08<04:36, 13.47ex/s]
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preprocess datasets #1:   2%|▏         | 86/3786 [00:08<05:34, 11.06ex/s]
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preprocess datasets #4:   5%|▍         | 187/3785 [00:08<01:27, 41.25ex/s]
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preprocess datasets #3:   5%|▍         | 183/3786 [00:08<01:55, 31.08ex/s]
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preprocess datasets #2:   4%|▎         | 139/3786 [00:08<02:35, 23.49ex/s]
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preprocess datasets #2:   4%|▍         | 144/3786 [00:08<02:06, 28.76ex/s]
preprocess datasets #0:   2%|▏         | 71/3786 [00:08<04:48, 12.89ex/s]
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preprocess datasets #1:   2%|▏         | 88/3786 [00:08<05:44, 10.73ex/s]
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preprocess datasets #3:   5%|▍         | 187/3786 [00:08<02:13, 26.89ex/s]
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preprocess datasets #2:   4%|▍         | 147/3786 [00:08<02:08, 28.36ex/s]
preprocess datasets #0:   2%|▏         | 73/3786 [00:08<04:50, 12.77ex/s]
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preprocess datasets #3:   5%|▌         | 190/3786 [00:08<02:28, 24.14ex/s]
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preprocess datasets #1:   2%|▏         | 90/3786 [00:09<06:04, 10.14ex/s]
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preprocess datasets #4:   5%|▌         | 196/3785 [00:08<02:07, 28.23ex/s]
preprocess datasets #0:   2%|▏         | 75/3786 [00:09<04:21, 14.17ex/s]
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preprocess datasets #2:   4%|▍         | 150/3786 [00:09<02:29, 24.37ex/s]
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preprocess datasets #3:   5%|▌         | 193/3786 [00:09<02:32, 23.56ex/s]
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preprocess datasets #1:   2%|▏         | 92/3786 [00:09<05:45, 10.70ex/s]
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preprocess datasets #2:   4%|▍         | 153/3786 [00:09<02:41, 22.52ex/s]
preprocess datasets #0:   2%|▏         | 77/3786 [00:09<04:58, 12.42ex/s]
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preprocess datasets #4:   5%|▌         | 205/3785 [00:09<01:54, 31.20ex/s]
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preprocess datasets #3:   5%|▌         | 198/3786 [00:09<02:24, 24.76ex/s]
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preprocess datasets #2:   4%|▍         | 158/3786 [00:09<02:19, 25.92ex/s]
preprocess datasets #0:   2%|▏         | 79/3786 [00:09<04:44, 13.04ex/s]
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preprocess datasets #1:   2%|▏         | 94/3786 [00:09<06:00, 10.23ex/s]
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preprocess datasets #2:   4%|▍         | 162/3786 [00:09<02:11, 27.63ex/s]
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preprocess datasets #3:   5%|▌         | 201/3786 [00:09<02:37, 22.77ex/s]
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preprocess datasets #4:   6%|▌         | 209/3785 [00:09<02:15, 26.44ex/s]
preprocess datasets #0:   2%|▏         | 81/3786 [00:09<05:08, 12.00ex/s]
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preprocess datasets #1:   3%|▎         | 96/3786 [00:09<05:51, 10.51ex/s]
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preprocess datasets #2:   4%|▍         | 165/3786 [00:09<02:16, 26.59ex/s]
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preprocess datasets #4:   6%|▌         | 212/3785 [00:09<02:14, 26.58ex/s]
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preprocess datasets #3:   5%|▌         | 205/3786 [00:09<02:34, 23.19ex/s]
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preprocess datasets #2:   4%|▍         | 168/3786 [00:09<02:17, 26.32ex/s]
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preprocess datasets #1:   3%|▎         | 98/3786 [00:09<05:39, 10.87ex/s]
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preprocess datasets #4:   6%|▌         | 215/3785 [00:09<02:23, 24.85ex/s]
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preprocess datasets #3:   6%|▌         | 209/3786 [00:09<02:17, 26.08ex/s]
preprocess datasets #0:   2%|▏         | 83/3786 [00:09<06:01, 10.24ex/s]
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preprocess datasets #4:   6%|▌         | 220/3785 [00:09<02:02, 29.13ex/s]
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preprocess datasets #3:   6%|▌         | 212/3786 [00:09<02:27, 24.26ex/s]
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preprocess datasets #1:   3%|▎         | 100/3786 [00:09<05:33, 11.06ex/s]
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preprocess datasets #2:   5%|▍         | 177/3786 [00:09<01:50, 32.72ex/s]
preprocess datasets #0:   2%|▏         | 85/3786 [00:10<05:44, 10.74ex/s]
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preprocess datasets #4:   6%|▌         | 224/3785 [00:10<02:11, 27.12ex/s]
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preprocess datasets #3:   6%|▌         | 215/3786 [00:10<02:31, 23.56ex/s]
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preprocess datasets #2:   5%|▍         | 182/3786 [00:10<01:47, 33.52ex/s]
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preprocess datasets #1:   3%|▎         | 102/3786 [00:10<05:36, 10.94ex/s]
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preprocess datasets #4:   6%|▌         | 227/3785 [00:10<02:09, 27.51ex/s]
preprocess datasets #0:   2%|▏         | 87/3786 [00:10<05:28, 11.25ex/s]
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preprocess datasets #3:   6%|▌         | 218/3786 [00:10<02:30, 23.71ex/s]
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preprocess datasets #2:   5%|▍         | 186/3786 [00:10<01:48, 33.12ex/s]
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preprocess datasets #1:   3%|▎         | 104/3786 [00:10<05:20, 11.49ex/s]
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preprocess datasets #4:   6%|▌         | 231/3785 [00:10<02:00, 29.54ex/s]
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preprocess datasets #3:   6%|▌         | 221/3786 [00:10<02:21, 25.12ex/s]
preprocess datasets #0:   2%|▏         | 89/3786 [00:10<05:25, 11.37ex/s]
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preprocess datasets #2:   5%|▌         | 190/3786 [00:10<02:00, 29.82ex/s]
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preprocess datasets #4:   6%|▌         | 235/3785 [00:10<01:54, 30.94ex/s]
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preprocess datasets #3:   6%|▌         | 225/3786 [00:10<02:14, 26.57ex/s]
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preprocess datasets #1:   3%|▎         | 106/3786 [00:10<05:13, 11.73ex/s]
preprocess datasets #0:   2%|▏         | 91/3786 [00:10<05:17, 11.63ex/s]
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preprocess datasets #1:   3%|▎         | 108/3786 [00:10<04:37, 13.27ex/s]
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preprocess datasets #3:   6%|▌         | 230/3786 [00:10<01:55, 30.82ex/s]
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preprocess datasets #2:   5%|▌         | 194/3786 [00:10<02:06, 28.33ex/s]
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preprocess datasets #4:   6%|▋         | 239/3785 [00:10<02:05, 28.37ex/s]
preprocess datasets #0:   2%|▏         | 93/3786 [00:10<05:10, 11.90ex/s]
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preprocess datasets #3:   6%|▌         | 235/3786 [00:10<01:47, 32.90ex/s]
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preprocess datasets #4:   6%|▋         | 243/3785 [00:10<02:00, 29.29ex/s]
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preprocess datasets #4:   7%|▋         | 248/3785 [00:10<01:46, 33.33ex/s]
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preprocess datasets #3:   6%|▋         | 239/3786 [00:10<02:02, 28.92ex/s]
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preprocess datasets #0:   3%|▎         | 95/3786 [00:10<05:46, 10.66ex/s]
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preprocess datasets #4:   7%|▋         | 254/3785 [00:10<01:29, 39.48ex/s]
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preprocess datasets #3:   6%|▋         | 242/3786 [00:10<02:08, 27.65ex/s]
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preprocess datasets #0:   3%|▎         | 97/3786 [00:11<05:32, 11.09ex/s]
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preprocess datasets #3:   6%|▋         | 245/3786 [00:11<02:14, 26.31ex/s]
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preprocess datasets #0:   3%|▎         | 99/3786 [00:11<05:09, 11.92ex/s]
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preprocess datasets #3:   7%|▋         | 248/3786 [00:11<02:11, 26.82ex/s]
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preprocess datasets #0:   3%|▎         | 101/3786 [00:11<05:17, 11.59ex/s]
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preprocess datasets #0:   3%|▎         | 103/3786 [00:11<05:05, 12.06ex/s]
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preprocess datasets #0:   3%|▎         | 124/3786 [00:13<04:48, 12.71ex/s]
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preprocess datasets #0:   3%|▎         | 126/3786 [00:13<04:45, 12.80ex/s]
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preprocess datasets #0:   3%|▎         | 128/3786 [00:13<04:52, 12.49ex/s]
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preprocess datasets #0:   3%|▎         | 130/3786 [00:13<04:56, 12.31ex/s]
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preprocess datasets #0:   3%|▎         | 132/3786 [00:13<04:34, 13.32ex/s]
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preprocess datasets #0:   4%|▎         | 134/3786 [00:13<04:26, 13.70ex/s]
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preprocess datasets #0:   4%|▎         | 137/3786 [00:14<03:59, 15.22ex/s]
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preprocess datasets #2:   8%|▊         | 300/3786 [00:14<02:01, 28.70ex/s]
preprocess datasets #0:   4%|▎         | 139/3786 [00:14<04:20, 13.98ex/s]
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preprocess datasets #3:   9%|▉         | 348/3786 [00:14<01:27, 39.24ex/s]
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preprocess datasets #0:   4%|▎         | 141/3786 [00:14<04:46, 12.73ex/s]
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preprocess datasets #3:  10%|▉         | 360/3786 [00:14<01:16, 44.88ex/s]
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preprocess datasets #0:   4%|▍         | 143/3786 [00:14<05:08, 11.82ex/s]
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preprocess datasets #0:   4%|▍         | 145/3786 [00:14<05:21, 11.31ex/s]
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preprocess datasets #1:   4%|▍         | 156/3786 [00:14<06:24,  9.43ex/s]
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preprocess datasets #2:   9%|▊         | 323/3786 [00:15<01:59, 28.86ex/s]
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preprocess datasets #1:   4%|▍         | 158/3786 [00:15<05:52, 10.30ex/s]
preprocess datasets #0:   4%|▍         | 147/3786 [00:15<05:55, 10.24ex/s]
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preprocess datasets #0:   4%|▍         | 149/3786 [00:15<06:08,  9.86ex/s]
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preprocess datasets #1:   4%|▍         | 160/3786 [00:15<06:45,  8.95ex/s]
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preprocess datasets #0:   4%|▍         | 151/3786 [00:15<05:48, 10.43ex/s]
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preprocess datasets #2:   9%|▉         | 344/3786 [00:15<01:48, 31.76ex/s]
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preprocess datasets #0:   4%|▍         | 153/3786 [00:15<05:39, 10.71ex/s]
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preprocess datasets #2:   9%|▉         | 348/3786 [00:15<01:46, 32.39ex/s]
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preprocess datasets #0:   4%|▍         | 155/3786 [00:15<05:31, 10.96ex/s]
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preprocess datasets #2:   9%|▉         | 352/3786 [00:15<01:42, 33.63ex/s]
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preprocess datasets #1:   4%|▍         | 165/3786 [00:15<06:18,  9.57ex/s]
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preprocess datasets #4:  11%|█         | 423/3785 [00:15<01:34, 35.62ex/s]
preprocess datasets #0:   4%|▍         | 157/3786 [00:16<05:24, 11.18ex/s]
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preprocess datasets #2:   9%|▉         | 356/3786 [00:16<01:57, 29.31ex/s]
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preprocess datasets #3:  11%|█         | 411/3786 [00:16<01:50, 30.44ex/s]
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preprocess datasets #1:   4%|▍         | 167/3786 [00:16<06:05,  9.89ex/s]
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preprocess datasets #4:  11%|█▏        | 427/3785 [00:16<01:46, 31.54ex/s]
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preprocess datasets #2:  10%|▉         | 360/3786 [00:16<01:57, 29.13ex/s]
preprocess datasets #0:   4%|▍         | 159/3786 [00:16<05:24, 11.19ex/s]
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preprocess datasets #1:   4%|▍         | 169/3786 [00:16<05:53, 10.23ex/s]
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preprocess datasets #2:  10%|▉         | 365/3786 [00:16<01:40, 33.88ex/s]
preprocess datasets #0:   4%|▍         | 162/3786 [00:16<04:12, 14.33ex/s]
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preprocess datasets #2:  10%|▉         | 375/3786 [00:16<01:26, 39.64ex/s]
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preprocess datasets #0:   4%|▍         | 166/3786 [00:16<04:38, 12.98ex/s]
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preprocess datasets #3:  11%|█▏        | 431/3786 [00:16<01:44, 32.23ex/s]
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preprocess datasets #3:  12%|█▏        | 441/3786 [00:17<01:35, 34.89ex/s]
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preprocess datasets #3:  12%|█▏        | 445/3786 [00:17<01:33, 35.79ex/s]
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preprocess datasets #2:  18%|█▊        | 690/3786 [00:26<01:32, 33.63ex/s]
preprocess datasets #0:   7%|▋         | 283/3786 [00:26<04:58, 11.74ex/s]
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preprocess datasets #0:  53%|█████▎    | 2021/3786 [02:57<03:09,  9.29ex/s]
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=8000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+/idiap/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/site-packages/datasets/features/audio.py:279: FutureWarning: Pass orig_sr=32000, target_sr=16000 as keyword args. From version 0.10 passing these as positional arguments will result in an error
+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
+
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
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+  array = librosa.resample(array, sampling_rate, self.sampling_rate, res_type="kaiser_best")
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preprocess datasets #0:   6%|▌         | 53/945 [00:04<00:50, 17.76ex/s]
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preprocess datasets #0:   6%|▌         | 58/945 [00:04<00:52, 16.88ex/s]
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preprocess datasets #0:   6%|▋         | 61/945 [00:04<00:51, 17.28ex/s]
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preprocess datasets #0:   7%|▋         | 64/945 [00:04<00:49, 17.79ex/s]
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preprocess datasets #0:   7%|▋         | 66/945 [00:05<00:52, 16.77ex/s]
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preprocess datasets #0:   8%|▊         | 72/945 [00:05<00:57, 15.26ex/s]
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preprocess datasets #0:   8%|▊         | 74/945 [00:05<00:58, 14.77ex/s]
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preprocess datasets #0:   9%|▊         | 81/945 [00:06<00:48, 17.74ex/s]
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preprocess datasets #0:   9%|▉         | 85/945 [00:06<00:47, 18.25ex/s]
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preprocess datasets #0:   9%|▉         | 87/945 [00:06<00:48, 17.75ex/s]
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preprocess datasets #0:   9%|▉         | 89/945 [00:06<00:49, 17.40ex/s]
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preprocess datasets #0:  10%|▉         | 91/945 [00:06<00:54, 15.69ex/s]
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preprocess datasets #0:  10%|▉         | 93/945 [00:06<00:52, 16.34ex/s]
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preprocess datasets #0:  10%|█         | 97/945 [00:07<00:57, 14.64ex/s]
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preprocess datasets #0:  10%|█         | 99/945 [00:07<00:59, 14.22ex/s]
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preprocess datasets #0:  11%|█▏        | 107/945 [00:07<01:06, 12.61ex/s]
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preprocess datasets #0:  12%|█▏        | 111/945 [00:08<01:06, 12.46ex/s]
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preprocess datasets #0:  13%|█▎        | 119/945 [00:08<01:05, 12.64ex/s]
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+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/preprocessor_config.json
+tokenizer config file saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/tokenizer_config.json
+Special tokens file saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/special_tokens_map.json
+added tokens file saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/added_tokens.json
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/config.json
+loading configuration file experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/preprocessor_config.json
+loading configuration file experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/config.json
+Model config Wav2Vec2Config {
+  "_name_or_path": "experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/",
+  "activation_dropout": 0.0,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.0,
+  "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": 0.05,
+  "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": 12,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 12,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.075,
+  "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/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/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/user/jzuluaga/miniconda3/envs/w2v2/lib/python3.10/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 = 18925
+  Num Epochs = 13
+  Instantaneous batch size per device = 24
+  Total train batch size (w. parallel, distributed & accumulation) = 24
+  Gradient Accumulation steps = 1
+  Total optimization steps = 10000
+  Number of trainable parameters = 311260319
+
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  5%|▌         | 500/10000 [08:04<54:08,  2.92it/s]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 = 4723
+  Batch size = 12
+
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 10%|█         | 1000/10000 [18:43<3:22:07,  1.35s/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 = 4723
+  Batch size = 12
+{'eval_loss': 2.2638113498687744, 'eval_wer': 0.9359337678636492, 'eval_runtime': 149.9992, 'eval_samples_per_second': 31.487, 'eval_steps_per_second': 2.627, 'epoch': 0.63}
+{'loss': 2.6089, 'learning_rate': 9.970000000000001e-05, 'epoch': 1.27}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-1000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-1000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-1000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-1000/preprocessor_config.json
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 15%|█▌        | 1500/10000 [29:11<3:04:31,  1.30s/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 = 4723
+  Batch size = 12
+{'eval_loss': 0.7277476787567139, 'eval_wer': 0.24067851503987692, 'eval_runtime': 148.226, 'eval_samples_per_second': 31.864, 'eval_steps_per_second': 2.658, 'epoch': 1.27}
+
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 20%|██        | 2000/10000 [39:35<2:15:40,  1.02s/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 = 4723
+  Batch size = 12
+{'eval_loss': 0.5800275802612305, 'eval_wer': 0.1745475891664305, 'eval_runtime': 148.0487, 'eval_samples_per_second': 31.902, 'eval_steps_per_second': 2.661, 'epoch': 1.9}
+{'loss': 0.6019, 'learning_rate': 8.892222222222223e-05, 'epoch': 2.53}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-2000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-2000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-2000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-2000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-1000] due to args.save_total_limit
+
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 25%|██▌       | 2500/10000 [50:03<1:42:47,  1.22it/s]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 = 4723
+  Batch size = 12
+{'eval_loss': 0.48867326974868774, 'eval_wer': 0.15135014776729688, 'eval_runtime': 148.4526, 'eval_samples_per_second': 31.815, 'eval_steps_per_second': 2.654, 'epoch': 2.53}
+
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 30%|███       | 3000/10000 [1:00:23<1:31:27,  1.28it/s]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 = 4723
+  Batch size = 12
+{'eval_loss': 0.466572642326355, 'eval_wer': 0.14213999433221328, 'eval_runtime': 148.0238, 'eval_samples_per_second': 31.907, 'eval_steps_per_second': 2.662, 'epoch': 3.17}
+{'loss': 0.4722, 'learning_rate': 7.78111111111111e-05, 'epoch': 3.8}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-3000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-3000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-3000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-3000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-2000] due to args.save_total_limit
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 35%|███▌      | 3500/10000 [1:10:52<58:08,  1.86it/s]  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 = 4723
+  Batch size = 12
+{'eval_loss': 0.44257038831710815, 'eval_wer': 0.14505485607870128, 'eval_runtime': 149.7003, 'eval_samples_per_second': 31.55, 'eval_steps_per_second': 2.632, 'epoch': 3.8}
+
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 40%|████      | 4000/10000 [1:21:16<2:22:55,  1.43s/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 = 4723
+  Batch size = 12
+{'eval_loss': 0.41759932041168213, 'eval_wer': 0.12481276061697907, 'eval_runtime': 149.0588, 'eval_samples_per_second': 31.685, 'eval_steps_per_second': 2.643, 'epoch': 4.44}
+{'loss': 0.4278, 'learning_rate': 6.671111111111111e-05, 'epoch': 5.07}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-4000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-4000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-4000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-4000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-3000] due to args.save_total_limit
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+***** Running Evaluation *****
+  Num examples = 4723
+  Batch size = 12
+{'eval_loss': 0.4364745318889618, 'eval_wer': 0.12388162422573985, 'eval_runtime': 148.5505, 'eval_samples_per_second': 31.794, 'eval_steps_per_second': 2.652, 'epoch': 5.07}
+
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 50%|█████     | 5000/10000 [1:42:39<1:45:19,  1.26s/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 = 4723
+  Batch size = 12
+{'eval_loss': 0.3815610408782959, 'eval_wer': 0.1177280272053763, 'eval_runtime': 149.3382, 'eval_samples_per_second': 31.626, 'eval_steps_per_second': 2.638, 'epoch': 5.7}
+{'loss': 0.369, 'learning_rate': 5.560000000000001e-05, 'epoch': 6.34}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-5000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-5000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-5000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-5000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-4000] due to args.save_total_limit
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+***** Running Evaluation *****
+  Num examples = 4723
+  Batch size = 12
+{'eval_loss': 0.4113306403160095, 'eval_wer': 0.11716124853244808, 'eval_runtime': 159.7075, 'eval_samples_per_second': 29.573, 'eval_steps_per_second': 2.467, 'epoch': 6.34}
+
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 60%|██████    | 6000/10000 [2:04:28<1:01:58,  1.08it/s]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 = 4723
+  Batch size = 12
+{'eval_loss': 0.3862614035606384, 'eval_wer': 0.1230112141208858, 'eval_runtime': 152.4609, 'eval_samples_per_second': 30.978, 'eval_steps_per_second': 2.584, 'epoch': 6.97}
+{'loss': 0.341, 'learning_rate': 4.448888888888889e-05, 'epoch': 7.6}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-6000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-6000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-6000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-6000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-5000] due to args.save_total_limit
+
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+***** Running Evaluation *****
+  Num examples = 4723
+  Batch size = 12
+{'eval_loss': 0.384976863861084, 'eval_wer': 0.1116149143759362, 'eval_runtime': 159.7493, 'eval_samples_per_second': 29.565, 'eval_steps_per_second': 2.466, 'epoch': 7.6}
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 70%|███████   | 7000/10000 [2:26:56<41:32,  1.20it/s]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 = 4723
+  Batch size = 12
+{'eval_loss': 0.401400089263916, 'eval_wer': 0.11406420792680458, 'eval_runtime': 155.917, 'eval_samples_per_second': 30.292, 'eval_steps_per_second': 2.527, 'epoch': 8.24}
+{'loss': 0.3119, 'learning_rate': 3.337777777777778e-05, 'epoch': 8.87}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-7000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-7000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-7000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-7000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-6000] due to args.save_total_limit
+
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+***** Running Evaluation *****
+  Num examples = 4723
+  Batch size = 12
+{'eval_loss': 0.39530250430107117, 'eval_wer': 0.10782964252459415, 'eval_runtime': 165.7417, 'eval_samples_per_second': 28.496, 'eval_steps_per_second': 2.377, 'epoch': 8.87}
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 80%|████████  | 8000/10000 [2:50:01<49:21,  1.48s/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 = 4723
+  Batch size = 12
+{'eval_loss': 0.4018384516239166, 'eval_wer': 0.10801182138374965, 'eval_runtime': 163.4263, 'eval_samples_per_second': 28.9, 'eval_steps_per_second': 2.411, 'epoch': 9.51}
+{'loss': 0.3008, 'learning_rate': 2.2277777777777778e-05, 'epoch': 10.14}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-8000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-8000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-8000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-8000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-7000] due to args.save_total_limit
+
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 85%|████████▌ | 8500/10000 [3:01:48<37:24,  1.50s/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 = 4723
+  Batch size = 12
+{'eval_loss': 0.3963571786880493, 'eval_wer': 0.10744504271082142, 'eval_runtime': 172.5171, 'eval_samples_per_second': 27.377, 'eval_steps_per_second': 2.284, 'epoch': 10.14}
+
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 90%|█████████ | 9000/10000 [3:13:17<19:08,  1.15s/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 = 4723
+  Batch size = 12
+{'eval_loss': 0.39167362451553345, 'eval_wer': 0.10780940042913242, 'eval_runtime': 165.6389, 'eval_samples_per_second': 28.514, 'eval_steps_per_second': 2.379, 'epoch': 10.77}
+{'loss': 0.2741, 'learning_rate': 1.1166666666666668e-05, 'epoch': 11.41}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-9000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-9000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-9000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-9000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-8000] due to args.save_total_limit
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+***** Running Evaluation *****
+  Num examples = 4723
+  Batch size = 12
+{'eval_loss': 0.3961273431777954, 'eval_wer': 0.10568398040565159, 'eval_runtime': 164.9191, 'eval_samples_per_second': 28.638, 'eval_steps_per_second': 2.389, 'epoch': 11.41}
+
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100%|██████████| 10000/10000 [3:36:20<00:00,  1.06it/s]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 = 4723
+  Batch size = 12
+{'eval_loss': 0.39744970202445984, 'eval_wer': 0.10529938059187888, 'eval_runtime': 164.8733, 'eval_samples_per_second': 28.646, 'eval_steps_per_second': 2.39, 'epoch': 12.04}
+{'loss': 0.2531, 'learning_rate': 5.555555555555556e-08, 'epoch': 12.67}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-10000
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-10000/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-10000/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-10000/preprocessor_config.json
+Deleting older checkpoint [experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/checkpoint-9000] 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/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/
+Configuration saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/config.json
+Model weights saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/pytorch_model.bin
+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-large-960h-lv60-self/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/preprocessor_config.json
+{'eval_loss': 0.40417608618736267, 'eval_wer': 0.10485405449172099, 'eval_runtime': 164.6908, 'eval_samples_per_second': 28.678, 'eval_steps_per_second': 2.392, 'epoch': 12.67}
+{'train_runtime': 13155.4001, 'train_samples_per_second': 18.243, 'train_steps_per_second': 0.76, 'train_loss': 0.5960800704956055, 'epoch': 12.67}
+***** train metrics *****
+  epoch                    =      12.67
+  train_loss               =     0.5961
+  train_runtime            = 3:39:15.40
+  train_samples            =      18925
+  train_samples_per_second =     18.243
+  train_steps_per_second   =       0.76
+11/28/2022 11:59:48 - 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 = 4723
+  Batch size = 12
+
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+Dropping the following result as it does not have all the necessary fields:
+{'metrics': [{'name': 'Wer', 'type': 'wer', 'value': 0.10485405449172099}]}
+***** eval metrics *****
+  epoch                   =      12.67
+  eval_loss               =     0.4042
+  eval_runtime            = 0:02:43.54
+  eval_samples            =       4723
+  eval_samples_per_second =     28.879
+  eval_steps_per_second   =      2.409
+  eval_wer                =     0.1049
+# Accounting: time=14013 threads=1
+# Finished at Mon 28 Nov 12:02:34 CET 2022 with status 0