diff --git "a/log/train_log" "b/log/train_log"
new file mode 100644--- /dev/null
+++ "b/log/train_log"
@@ -0,0 +1,21250 @@
+# Running on vgni005
+# Started at Mon 28 Nov 08:08:31 CET 2022
+# python3 src/run_speech_recognition_ctc.py --report_to=none --run_name=experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m --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-xls-r-300m/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 08:08:41 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True
+11/28/2022 08:08:41 - 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=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-xls-r-300m/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/runs/Nov28_08-08-40_vgni005,
+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-xls-r-300m/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-xls-r-300m/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 08:08:41 - 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-xls-r-300m/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-xls-r-300m/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 08:09:56 - 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-xls-r-300m/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]
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loading configuration file config.json from cache at /idiap/temp/jzuluaga/cache/huggingface/models--facebook--wav2vec2-xls-r-300m/snapshots/1a640f32ac3e39899438a2931f9924c02f080a54/config.json
+Model config Wav2Vec2Config {
+  "_name_or_path": "facebook/wav2vec2-xls-r-300m",
+  "activation_dropout": 0.0,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForPreTraining"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 768,
+  "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": 0.0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 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": 0.075,
+  "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": 0,
+  "proj_codevector_dim": 768,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "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-xls-r-300m/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-xls-r-300m/snapshots/1a640f32ac3e39899438a2931f9924c02f080a54/preprocessor_config.json
+Feature extractor Wav2Vec2FeatureExtractor {
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 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-xls-r-300m/snapshots/1a640f32ac3e39899438a2931f9924c02f080a54/pytorch_model.bin
+Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_q.bias', 'project_q.weight', 'project_hid.weight', 'quantizer.weight_proj.bias', 'quantizer.weight_proj.weight', 'project_hid.bias', 'quantizer.codevectors']
+- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
+- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
+Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.bias', 'lm_head.weight']
+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 #3:   0%|          | 0/3786 [00:00<?, ?ex/s]/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")
+/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 #0:   0%|          | 1/3786 [00:02<2:30:07,  2.38s/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")
+
+
preprocess datasets #1:   0%|          | 1/3786 [00:02<2:29:20,  2.37s/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 #4:   0%|          | 1/3785 [00:02<2:27:49,  2.34s/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")
+
+
+
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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:   2%|▏         | 65/3786 [00:07<05:29, 11.29ex/s]
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preprocess datasets #0:   2%|▏         | 67/3786 [00:08<05:12, 11.90ex/s]
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preprocess datasets #0:   2%|▏         | 69/3786 [00:08<04:39, 13.29ex/s]
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preprocess datasets #0:   2%|▏         | 71/3786 [00:08<04:58, 12.43ex/s]
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preprocess datasets #0:   2%|▏         | 73/3786 [00:08<05:00, 12.36ex/s]
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preprocess datasets #1:   3%|▎         | 112/3786 [00:08<04:40, 13.09ex/s]
preprocess datasets #0:   2%|▏         | 75/3786 [00:08<04:32, 13.63ex/s]
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preprocess datasets #0:   2%|▏         | 77/3786 [00:08<05:02, 12.27ex/s]
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preprocess datasets #3:   6%|▌         | 217/3786 [00:08<01:57, 30.26ex/s]
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preprocess datasets #1:   3%|▎         | 117/3786 [00:08<04:28, 13.68ex/s]
preprocess datasets #0:   2%|▏         | 79/3786 [00:09<04:45, 12.98ex/s]
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preprocess datasets #3:   6%|▌         | 221/3786 [00:09<02:14, 26.42ex/s]
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preprocess datasets #4:   6%|▌         | 209/3785 [00:09<02:02, 29.19ex/s]
preprocess datasets #0:   2%|▏         | 81/3786 [00:09<05:11, 11.90ex/s]
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preprocess datasets #3:   6%|▌         | 224/3786 [00:09<02:11, 27.02ex/s]
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preprocess datasets #1:   3%|▎         | 121/3786 [00:09<04:57, 12.32ex/s]
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preprocess datasets #3:   6%|▌         | 227/3786 [00:09<02:27, 24.10ex/s]
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preprocess datasets #2:   5%|▌         | 193/3786 [00:09<02:14, 26.69ex/s]
preprocess datasets #0:   2%|▏         | 83/3786 [00:09<05:59, 10.30ex/s]
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preprocess datasets #1:   3%|▎         | 123/3786 [00:09<05:00, 12.20ex/s]
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preprocess datasets #4:   6%|▌         | 221/3785 [00:09<01:51, 31.91ex/s]
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preprocess datasets #3:   6%|▌         | 231/3786 [00:09<02:14, 26.39ex/s]
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preprocess datasets #2:   5%|▌         | 196/3786 [00:09<02:16, 26.27ex/s]
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preprocess datasets #1:   3%|▎         | 125/3786 [00:09<04:45, 12.84ex/s]
preprocess datasets #0:   2%|▏         | 85/3786 [00:09<05:43, 10.77ex/s]
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preprocess datasets #3:   6%|▌         | 235/3786 [00:09<02:13, 26.62ex/s]
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preprocess datasets #4:   6%|▌         | 225/3785 [00:09<02:01, 29.32ex/s]
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preprocess datasets #2:   5%|▌         | 204/3786 [00:09<02:00, 29.85ex/s]
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preprocess datasets #1:   3%|▎         | 127/3786 [00:09<04:52, 12.51ex/s]
preprocess datasets #0:   2%|▏         | 87/3786 [00:09<05:32, 11.13ex/s]
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preprocess datasets #3:   6%|▋         | 238/3786 [00:09<02:22, 24.88ex/s]
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preprocess datasets #3:   6%|▋         | 241/3786 [00:09<02:19, 25.39ex/s]
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preprocess datasets #2:   5%|▌         | 208/3786 [00:09<02:05, 28.44ex/s]
preprocess datasets #0:   2%|▏         | 89/3786 [00:10<05:27, 11.29ex/s]
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preprocess datasets #3:   6%|▋         | 244/3786 [00:10<02:30, 23.55ex/s]
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preprocess datasets #1:   3%|▎         | 129/3786 [00:10<05:59, 10.18ex/s]
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preprocess datasets #2:   6%|▌         | 211/3786 [00:10<02:18, 25.81ex/s]
preprocess datasets #0:   2%|▏         | 91/3786 [00:10<05:21, 11.51ex/s]
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preprocess datasets #3:   7%|▋         | 247/3786 [00:10<02:33, 23.03ex/s]
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preprocess datasets #4:   6%|▋         | 237/3785 [00:10<02:30, 23.53ex/s]
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preprocess datasets #1:   3%|▎         | 131/3786 [00:10<05:53, 10.33ex/s]
preprocess datasets #0:   2%|▏         | 93/3786 [00:10<05:10, 11.89ex/s]
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preprocess datasets #2:   6%|▌         | 221/3786 [00:10<01:38, 36.24ex/s]
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preprocess datasets #3:   7%|▋         | 250/3786 [00:10<02:40, 22.02ex/s]
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preprocess datasets #4:   6%|▋         | 241/3785 [00:10<02:25, 24.29ex/s]
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preprocess datasets #2:   6%|▌         | 225/3786 [00:10<01:38, 36.11ex/s]
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preprocess datasets #1:   4%|▎         | 133/3786 [00:10<05:49, 10.44ex/s]
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preprocess datasets #3:   7%|▋         | 253/3786 [00:10<02:33, 23.04ex/s]
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preprocess datasets #4:   6%|▋         | 244/3785 [00:10<02:33, 23.07ex/s]
preprocess datasets #0:   3%|▎         | 95/3786 [00:10<05:52, 10.46ex/s]
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preprocess datasets #3:   7%|▋         | 256/3786 [00:10<02:36, 22.55ex/s]
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preprocess datasets #1:   4%|▎         | 135/3786 [00:10<05:47, 10.51ex/s]
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preprocess datasets #2:   6%|▌         | 235/3786 [00:10<01:41, 34.90ex/s]
preprocess datasets #0:   3%|▎         | 97/3786 [00:10<05:41, 10.80ex/s]
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preprocess datasets #3:   7%|▋         | 261/3786 [00:10<02:14, 26.22ex/s]
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preprocess datasets #4:   7%|▋         | 253/3785 [00:10<02:02, 28.82ex/s]
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preprocess datasets #1:   4%|▎         | 137/3786 [00:10<05:32, 10.99ex/s]
preprocess datasets #0:   3%|▎         | 99/3786 [00:10<05:18, 11.57ex/s]
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preprocess datasets #2:   6%|▋         | 240/3786 [00:10<01:47, 32.85ex/s]
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preprocess datasets #3:   7%|▋         | 264/3786 [00:10<02:16, 25.79ex/s]
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preprocess datasets #4:   7%|▋         | 257/3785 [00:10<01:56, 30.30ex/s]
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preprocess datasets #1:   4%|▎         | 139/3786 [00:10<05:27, 11.15ex/s]
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preprocess datasets #3:   7%|▋         | 269/3786 [00:10<01:58, 29.74ex/s]
preprocess datasets #0:   3%|▎         | 101/3786 [00:11<05:30, 11.14ex/s]
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preprocess datasets #4:   7%|▋         | 261/3785 [00:11<02:17, 25.59ex/s]
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preprocess datasets #3:   7%|▋         | 273/3786 [00:11<01:55, 30.34ex/s]
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preprocess datasets #0:   3%|▎         | 103/3786 [00:11<05:21, 11.45ex/s]
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preprocess datasets #0:   3%|▎         | 105/3786 [00:11<04:43, 13.00ex/s]
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preprocess datasets #0:   3%|▎         | 107/3786 [00:11<04:41, 13.07ex/s]
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preprocess datasets #3:   8%|▊         | 285/3786 [00:11<01:49, 32.00ex/s]
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preprocess datasets #3:   8%|▊         | 295/3786 [00:11<02:17, 25.36ex/s]
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preprocess datasets #0:   4%|▎         | 134/3786 [00:13<04:41, 12.97ex/s]
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preprocess datasets #0:   4%|▎         | 136/3786 [00:13<04:17, 14.17ex/s]
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preprocess datasets #0:   4%|▎         | 140/3786 [00:14<04:48, 12.64ex/s]
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preprocess datasets #0:   4%|▍         | 146/3786 [00:14<05:56, 10.20ex/s]
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preprocess datasets #0:   4%|▍         | 148/3786 [00:15<05:43, 10.59ex/s]
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preprocess datasets #0:   4%|▍         | 160/3786 [00:16<04:50, 12.49ex/s]
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preprocess datasets #0:   7%|▋         | 257/3786 [00:25<04:29, 13.09ex/s]
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preprocess datasets #0:   7%|▋         | 259/3786 [00:25<05:03, 11.62ex/s]
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preprocess datasets #2:  17%|█▋        | 657/3786 [00:26<01:42, 30.64ex/s]
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preprocess datasets #1:   8%|▊         | 318/3786 [00:26<04:41, 12.34ex/s]
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preprocess datasets #3:  20%|█▉        | 756/3786 [00:26<01:12, 42.05ex/s]
preprocess datasets #0:   7%|▋         | 273/3786 [00:26<04:36, 12.70ex/s]
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preprocess datasets #0:   7%|▋         | 275/3786 [00:26<05:08, 11.40ex/s]
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preprocess datasets #1:   9%|▊         | 322/3786 [00:26<04:59, 11.57ex/s]
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preprocess datasets #3:  20%|██        | 772/3786 [00:26<01:15, 39.82ex/s]
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preprocess datasets #1:   9%|▊         | 324/3786 [00:26<04:51, 11.86ex/s]
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preprocess datasets #2:  18%|█▊        | 676/3786 [00:26<01:42, 30.21ex/s]
preprocess datasets #0:   7%|▋         | 279/3786 [00:26<04:39, 12.55ex/s]
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preprocess datasets #3:  21%|██        | 777/3786 [00:26<01:24, 35.48ex/s]
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preprocess datasets #2:  18%|█▊        | 684/3786 [00:27<01:51, 27.75ex/s]
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preprocess datasets #0:   7%|▋         | 283/3786 [00:27<05:18, 11.00ex/s]
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preprocess datasets #1:   9%|▉         | 337/3786 [00:27<04:05, 14.08ex/s]
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preprocess datasets #3:  21%|██▏       | 805/3786 [00:27<01:36, 30.97ex/s]
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preprocess datasets #4:  21%|██        | 784/3785 [00:27<01:30, 33.16ex/s]
preprocess datasets #0:   8%|▊         | 289/3786 [00:27<05:24, 10.77ex/s]
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preprocess datasets #2:  19%|█▊        | 705/3786 [00:27<01:58, 26.08ex/s]
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preprocess datasets #3:  21%|██▏       | 809/3786 [00:27<01:40, 29.64ex/s]
preprocess datasets #0:   8%|▊         | 291/3786 [00:28<04:58, 11.70ex/s]
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preprocess datasets #0:   8%|▊         | 301/3786 [00:29<06:12,  9.37ex/s]
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preprocess datasets #3:   0%|          | 0/944 [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=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")
+
+
+
+
+
preprocess datasets #4:   0%|          | 1/944 [00:01<23:29,  1.49s/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 #1:   0%|          | 1/945 [00:01<24:28,  1.56s/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")
+
preprocess datasets #0:   0%|          | 1/945 [00:01<24:37,  1.57s/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")
+
+
+
+
preprocess datasets #3:   0%|          | 1/944 [00:01<24:37,  1.57s/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%|          | 1/945 [00:01<25:19,  1.61s/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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preprocess datasets #2:   1%|          | 11/945 [00:02<01:47,  8.66ex/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")
+
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+Feature extractor saved in experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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": [
+    "Wav2Vec2ForPreTraining"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 768,
+  "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_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": 768,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "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-xls-r-300m/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,
+  "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 [07:53<53:12,  2.98it/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 [18:23<3:16:21,  1.31s/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 = 4723
+  Batch size = 12
+{'eval_loss': 3.0458309650421143, 'eval_wer': 1.0, 'eval_runtime': 143.841, 'eval_samples_per_second': 32.835, 'eval_steps_per_second': 2.739, 'epoch': 0.63}
+{'loss': 2.9181, 'learning_rate': 9.98e-05, 'epoch': 1.27}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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 [28:41<2:57:57,  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': 1.1502786874771118, 'eval_wer': 0.4722885713129023, 'eval_runtime': 143.5317, 'eval_samples_per_second': 32.906, 'eval_steps_per_second': 2.745, 'epoch': 1.27}
+
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 20%|██        | 2000/10000 [38:55<2:10:30,  1.02it/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': 0.8274601101875305, 'eval_wer': 0.3499858305331768, 'eval_runtime': 142.4484, 'eval_samples_per_second': 33.156, 'eval_steps_per_second': 2.766, 'epoch': 1.9}
+{'loss': 0.7646, 'learning_rate': 8.89111111111111e-05, 'epoch': 2.53}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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 [49:26<1:43:37,  1.21it/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': 0.6990081667900085, 'eval_wer': 0.2845228938099672, 'eval_runtime': 145.8955, 'eval_samples_per_second': 32.372, 'eval_steps_per_second': 2.701, 'epoch': 2.53}
+
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 30%|███       | 3000/10000 [59:52<1:31:10,  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': 0.5828294157981873, 'eval_wer': 0.2508602890571232, 'eval_runtime': 147.7617, 'eval_samples_per_second': 31.964, 'eval_steps_per_second': 2.666, 'epoch': 3.17}
+{'loss': 0.5394, 'learning_rate': 7.780000000000001e-05, 'epoch': 3.8}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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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+***** Running Evaluation *****
+  Num examples = 4723
+  Batch size = 12
+{'eval_loss': 0.5363064408302307, 'eval_wer': 0.24873486903364236, 'eval_runtime': 147.8939, 'eval_samples_per_second': 31.935, 'eval_steps_per_second': 2.664, 'epoch': 3.8}
+
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 40%|████      | 4000/10000 [1:21:03<2:27:26,  1.47s/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': 0.5467007756233215, 'eval_wer': 0.21711671592243229, 'eval_runtime': 151.8112, 'eval_samples_per_second': 31.111, 'eval_steps_per_second': 2.595, 'epoch': 4.44}
+{'loss': 0.4558, 'learning_rate': 6.66888888888889e-05, 'epoch': 5.07}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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': 0.5289521813392639, 'eval_wer': 0.20901987773774341, 'eval_runtime': 152.3052, 'eval_samples_per_second': 31.01, 'eval_steps_per_second': 2.587, 'epoch': 5.07}
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 50%|█████     | 5000/10000 [1:42:37<1:36:30,  1.16s/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': 0.4992470443248749, 'eval_wer': 0.20464758511801143, 'eval_runtime': 148.0991, 'eval_samples_per_second': 31.891, 'eval_steps_per_second': 2.66, 'epoch': 5.7}
+{'loss': 0.3773, 'learning_rate': 5.558888888888889e-05, 'epoch': 6.34}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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': 0.49340707063674927, 'eval_wer': 0.2051738796000162, 'eval_runtime': 155.8909, 'eval_samples_per_second': 30.297, 'eval_steps_per_second': 2.527, 'epoch': 6.34}
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 60%|██████    | 6000/10000 [2:04:03<59:54,  1.11it/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': 0.47003018856048584, 'eval_wer': 0.19827132504756892, 'eval_runtime': 146.5312, 'eval_samples_per_second': 32.232, 'eval_steps_per_second': 2.689, 'epoch': 6.97}
+{'loss': 0.3301, 'learning_rate': 4.447777777777778e-05, 'epoch': 7.6}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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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 65%|██████▌   | 6500/10000 [2:15:24<44:20,  1.32it/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': 0.4938233494758606, 'eval_wer': 0.18738107768916237, 'eval_runtime': 154.4839, 'eval_samples_per_second': 30.573, 'eval_steps_per_second': 2.55, 'epoch': 7.6}
+
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 70%|███████   | 7000/10000 [2:26:42<39:37,  1.26it/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': 0.5363861322402954, 'eval_wer': 0.18934456094894944, 'eval_runtime': 163.224, 'eval_samples_per_second': 28.936, 'eval_steps_per_second': 2.414, 'epoch': 8.24}
+{'loss': 0.2938, 'learning_rate': 3.336666666666667e-05, 'epoch': 8.87}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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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 75%|███████▌  | 7500/10000 [2:38:12<14:56,  2.79it/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': 0.5169841051101685, 'eval_wer': 0.18300878506943039, 'eval_runtime': 164.5878, 'eval_samples_per_second': 28.696, 'eval_steps_per_second': 2.394, 'epoch': 8.87}
+
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 80%|████████  | 8000/10000 [2:49:47<49:26,  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': 0.5407580733299255, 'eval_wer': 0.18149062790980122, 'eval_runtime': 163.5687, 'eval_samples_per_second': 28.875, 'eval_steps_per_second': 2.409, 'epoch': 9.51}
+{'loss': 0.2674, 'learning_rate': 2.225555555555556e-05, 'epoch': 10.14}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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 [3:01:20<37:26,  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': 0.5580623149871826, 'eval_wer': 0.173272337152342, 'eval_runtime': 162.7994, 'eval_samples_per_second': 29.011, 'eval_steps_per_second': 2.42, 'epoch': 10.14}
+
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 90%|█████████ | 9000/10000 [3:12:34<19:18,  1.16s/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': 0.538887083530426, 'eval_wer': 0.1719161167564066, 'eval_runtime': 163.5851, 'eval_samples_per_second': 28.872, 'eval_steps_per_second': 2.409, 'epoch': 10.77}
+{'loss': 0.24, 'learning_rate': 1.1144444444444445e-05, 'epoch': 11.41}
+
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+
                                                 

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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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': 0.5344488024711609, 'eval_wer': 0.17141006436986356, 'eval_runtime': 163.7329, 'eval_samples_per_second': 28.846, 'eval_steps_per_second': 2.406, 'epoch': 11.41}
+
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100%|██████████| 10000/10000 [3:34:43<00:00,  1.14it/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': 0.5502966046333313, 'eval_wer': 0.16863689729160763, 'eval_runtime': 154.8226, 'eval_samples_per_second': 30.506, 'eval_steps_per_second': 2.545, 'epoch': 12.04}
+{'loss': 0.211, 'learning_rate': 3.3333333333333334e-08, 'epoch': 12.67}
+
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                                                 Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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 =)
+
+
+
                                                       

100%|██████████| 10000/10000 [3:37:34<00:00,  1.14it/s]
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+Saving model checkpoint to experiments/results/bs_exp/linear/wav2vec2-xls-r-300m/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-xls-r-300m/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-xls-r-300m/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-xls-r-300m/atcosim_uwb_atcc/0.0ld_0.0ad_0.0attd_0.05fpd_0.075mtp_12mtl_0.0mfp_12mfl_1acc/preprocessor_config.json
+{'eval_loss': 0.5595183372497559, 'eval_wer': 0.16865713938706936, 'eval_runtime': 157.1471, 'eval_samples_per_second': 30.055, 'eval_steps_per_second': 2.507, 'epoch': 12.67}
+{'train_runtime': 13054.0758, 'train_samples_per_second': 18.385, 'train_steps_per_second': 0.766, 'train_loss': 0.6397389862060547, 'epoch': 12.67}
+***** train metrics *****
+  epoch                    =      12.67
+  train_loss               =     0.6397
+  train_runtime            = 3:37:34.07
+  train_samples            =      18925
+  train_samples_per_second =     18.385
+  train_steps_per_second   =      0.766
+11/28/2022 11:57:50 - 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': 0.16865713938706936}]}
+***** eval metrics *****
+  epoch                   =      12.67
+  eval_loss               =     0.5595
+  eval_runtime            = 0:02:41.95
+  eval_samples            =       4723
+  eval_samples_per_second =     29.162
+  eval_steps_per_second   =      2.433
+  eval_wer                =     0.1687
+# Accounting: time=13924 threads=1
+# Finished at Mon 28 Nov 12:00:35 CET 2022 with status 0