03/10/2022 15:35:47 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True
03/10/2022 15:35:47 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
bf16=False,
bf16_full_eval=False,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_steps=500,
evaluation_strategy=IntervalStrategy.STEPS,
fp16=True,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
greater_is_better=None,
group_by_length=True,
half_precision_backend=auto,
hub_model_id=None,
hub_strategy=HubStrategy.EVERY_SAVE,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=7.5e-05,
length_column_name=input_length,
load_best_model_at_end=False,
local_rank=-1,
log_level=-1,
log_level_replica=-1,
log_on_each_node=True,
logging_dir=./runs/Mar10_15-35-47_dasuk.dictus.dk,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=100,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=50.0,
optim=OptimizerNames.ADAMW_HF,
output_dir=./,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=./,
save_on_each_node=False,
save_steps=500,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=3,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=2000,
weight_decay=0.0,
xpu_backend=None,
)
03/10/2022 15:35:50 - WARNING - datasets.builder - Reusing dataset common_voice (/home/ling-mun/.cache/huggingface/datasets/mozilla-foundation___common_voice/sv-SE/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)
03/10/2022 15:35:52 - WARNING - datasets.builder - Reusing dataset common_voice (/home/ling-mun/.cache/huggingface/datasets/mozilla-foundation___common_voice/sv-SE/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)
03/10/2022 15:35:52 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/ling-mun/.cache/huggingface/datasets/mozilla-foundation___common_voice/sv-SE/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-8924908ad59e80fc.arrow
03/10/2022 15:35:52 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /home/ling-mun/.cache/huggingface/datasets/mozilla-foundation___common_voice/sv-SE/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-b3928a3c3813bf3a.arrow
loading configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /home/ling-mun/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6
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": "4.18.0.dev0",
  "use_weighted_layer_sum": false,
  "vocab_size": 32,
  "xvector_output_dim": 512
}


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loading file ./vocab.json
loading file ./tokenizer_config.json
loading file ./added_tokens.json
loading file ./special_tokens_map.json
Adding <s> to the vocabulary
Adding </s> to the vocabulary
loading feature extractor configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/preprocessor_config.json from cache at /home/ling-mun/.cache/huggingface/transformers/6fb028b95b394059e7d3b367bbca2382b576c66aebe896f04d2cd34e1b575f5b.d4484dc1c81456a2461485e7168b04347a7b9a4e3b1ef3aba723323b33e12326
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 https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/pytorch_model.bin from cache at /home/ling-mun/.cache/huggingface/transformers/1e6a6507f3b689035cd4b247e2a37c154e27f39143f31357a49b4e38baeccc36.1edb32803799e27ed554eb7dd935f6745b1a0b17b0ea256442fe24db6eb546cd
Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_q.weight', 'quantizer.codevectors', 'project_q.bias', 'quantizer.weight_proj.bias', 'project_hid.bias', 'quantizer.weight_proj.weight', 'project_hid.weight']
- 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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Feature extractor saved in ./preprocessor_config.json
tokenizer config file saved in ./tokenizer_config.json
Special tokens file saved in ./special_tokens_map.json
added tokens file saved in ./added_tokens.json
Configuration saved in ./config.json
loading feature extractor configuration file ./preprocessor_config.json
loading configuration file ./config.json
Model config Wav2Vec2Config {
  "_name_or_path": "./",
  "activation_dropout": 0.1,
  "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": 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": 0.0,
  "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": 64,
  "mask_feature_min_masks": 0,
  "mask_feature_prob": 0.25,
  "mask_time_length": 10,
  "mask_time_min_masks": 2,
  "mask_time_prob": 0.75,
  "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": 34,
  "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": "4.18.0.dev0",
  "use_weighted_layer_sum": false,
  "vocab_size": 37,
  "xvector_output_dim": 512
}

loading feature extractor configuration file ./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
/home/ling-mun/code/DICTUS/asr_hf/xls-r-300m-sv/./ is already a clone of https://huggingface.co/muneson/xls-r-300m-sv. Make sure you pull the latest changes with `repo.git_pull()`.
03/10/2022 15:37:41 - WARNING - huggingface_hub.repository - /home/ling-mun/code/DICTUS/asr_hf/xls-r-300m-sv/./ is already a clone of https://huggingface.co/muneson/xls-r-300m-sv. Make sure you pull the latest changes with `repo.git_pull()`.
Using 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.
/home/ling-mun/code/DICTUS/asr_hf/transformers/src/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 = 11030
  Num Epochs = 50
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 32
  Gradient Accumulation steps = 4
  Total optimization steps = 17200

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  3%|▎         | 500/17200 [46:50<36:06:46,  7.78s/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 = 4620
  Batch size = 8
{'loss': 14.8954, 'learning_rate': 3.7125e-06, 'epoch': 0.29}
{'loss': 6.9436, 'learning_rate': 7.4625e-06, 'epoch': 0.58}
{'loss': 4.3867, 'learning_rate': 1.1212499999999998e-05, 'epoch': 0.87}
{'loss': 3.7841, 'learning_rate': 1.49625e-05, 'epoch': 1.16}
{'loss': 3.3809, 'learning_rate': 1.8712499999999997e-05, 'epoch': 1.45}


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                                                 Saving model checkpoint to ./checkpoint-500
Configuration saved in ./checkpoint-500/config.json
Model weights saved in ./checkpoint-500/pytorch_model.bin
Feature extractor saved in ./checkpoint-500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json

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  6%|▌         | 1000/17200 [1:41:04<30:37:38,  6.81s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 4620
  Batch size = 8
{'eval_loss': 3.4046430587768555, 'eval_wer': 1.0, 'eval_runtime': 430.4641, 'eval_samples_per_second': 10.733, 'eval_steps_per_second': 1.343, 'epoch': 1.45}
{'loss': 3.1722, 'learning_rate': 2.2462499999999997e-05, 'epoch': 1.74}
{'loss': 3.1355, 'learning_rate': 2.6212499999999997e-05, 'epoch': 2.03}
{'loss': 3.0732, 'learning_rate': 2.99625e-05, 'epoch': 2.32}
{'loss': 2.9905, 'learning_rate': 3.37125e-05, 'epoch': 2.61}
{'loss': 2.931, 'learning_rate': 3.7462499999999996e-05, 'epoch': 2.91}


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                                                 Saving model checkpoint to ./checkpoint-1000
Configuration saved in ./checkpoint-1000/config.json
Model weights saved in ./checkpoint-1000/pytorch_model.bin
Feature extractor saved in ./checkpoint-1000/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json

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  8%|▊         | 1300/17200 [2:17:27<27:19:20,  6.19s/it]
                                                         

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  9%|▊         | 1500/17200 [2:36:11<24:22:28,  5.59s/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 = 4620
  Batch size = 8
{'eval_loss': 2.9344613552093506, 'eval_wer': 0.9944624609924738, 'eval_runtime': 430.2534, 'eval_samples_per_second': 10.738, 'eval_steps_per_second': 1.343, 'epoch': 2.91}
{'loss': 2.9171, 'learning_rate': 4.12125e-05, 'epoch': 3.2}
{'loss': 2.84, 'learning_rate': 4.4962499999999995e-05, 'epoch': 3.49}
{'loss': 2.5535, 'learning_rate': 4.871249999999999e-05, 'epoch': 3.78}
{'loss': 2.0837, 'learning_rate': 5.2462499999999994e-05, 'epoch': 4.07}
{'loss': 1.7673, 'learning_rate': 5.62125e-05, 'epoch': 4.36}


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                                                 Saving model checkpoint to ./checkpoint-1500
Configuration saved in ./checkpoint-1500/config.json
Model weights saved in ./checkpoint-1500/pytorch_model.bin
Feature extractor saved in ./checkpoint-1500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json

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 12%|█▏        | 2000/17200 [3:30:57<21:22:40,  5.06s/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 = 4620
  Batch size = 8
{'eval_loss': 0.8365039825439453, 'eval_wer': 0.7230006730710395, 'eval_runtime': 431.2404, 'eval_samples_per_second': 10.713, 'eval_steps_per_second': 1.34, 'epoch': 4.36}
{'loss': 1.6302, 'learning_rate': 5.9962499999999994e-05, 'epoch': 4.65}
{'loss': 1.5574, 'learning_rate': 6.37125e-05, 'epoch': 4.94}
{'loss': 1.4955, 'learning_rate': 6.746249999999999e-05, 'epoch': 5.23}
{'loss': 1.4532, 'learning_rate': 7.121249999999999e-05, 'epoch': 5.52}
{'loss': 1.4234, 'learning_rate': 7.49625e-05, 'epoch': 5.81}


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                                                 Saving model checkpoint to ./checkpoint-2000
Configuration saved in ./checkpoint-2000/config.json
Model weights saved in ./checkpoint-2000/pytorch_model.bin
Feature extractor saved in ./checkpoint-2000/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-500] due to args.save_total_limit

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 15%|█▍        | 2500/17200 [4:25:38<17:12:15,  4.21s/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 = 4620
  Batch size = 8
{'eval_loss': 0.5055595636367798, 'eval_wer': 0.46295049868445204, 'eval_runtime': 430.883, 'eval_samples_per_second': 10.722, 'eval_steps_per_second': 1.341, 'epoch': 5.81}
{'loss': 1.409, 'learning_rate': 7.451151315789474e-05, 'epoch': 6.1}
{'loss': 1.3522, 'learning_rate': 7.401809210526315e-05, 'epoch': 6.39}
{'loss': 1.353, 'learning_rate': 7.352467105263158e-05, 'epoch': 6.68}
{'loss': 1.3337, 'learning_rate': 7.30361842105263e-05, 'epoch': 6.97}
{'loss': 1.3118, 'learning_rate': 7.254276315789473e-05, 'epoch': 7.27}


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                                                 Saving model checkpoint to ./checkpoint-2500
Configuration saved in ./checkpoint-2500/config.json
Model weights saved in ./checkpoint-2500/pytorch_model.bin
Feature extractor saved in ./checkpoint-2500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-1000] due to args.save_total_limit

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 17%|█▋        | 3000/17200 [5:20:22<14:13:48,  3.61s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 4620
  Batch size = 8
{'eval_loss': 0.42015719413757324, 'eval_wer': 0.4671113014746375, 'eval_runtime': 430.532, 'eval_samples_per_second': 10.731, 'eval_steps_per_second': 1.343, 'epoch': 7.27}
{'loss': 1.2949, 'learning_rate': 7.204934210526316e-05, 'epoch': 7.56}
{'loss': 1.2507, 'learning_rate': 7.155592105263157e-05, 'epoch': 7.85}
{'loss': 1.2766, 'learning_rate': 7.10625e-05, 'epoch': 8.14}
{'loss': 1.2661, 'learning_rate': 7.057401315789473e-05, 'epoch': 8.43}
{'loss': 1.2411, 'learning_rate': 7.008059210526316e-05, 'epoch': 8.72}


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                                                 Saving model checkpoint to ./checkpoint-3000
Configuration saved in ./checkpoint-3000/config.json
Model weights saved in ./checkpoint-3000/pytorch_model.bin
Feature extractor saved in ./checkpoint-3000/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-1500] due to args.save_total_limit

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 20%|██        | 3500/17200 [6:15:42<28:17:32,  7.43s/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 = 4620
  Batch size = 8
{'eval_loss': 0.38536974787712097, 'eval_wer': 0.421801382855045, 'eval_runtime': 430.7248, 'eval_samples_per_second': 10.726, 'eval_steps_per_second': 1.342, 'epoch': 8.72}
{'loss': 1.2475, 'learning_rate': 6.958717105263157e-05, 'epoch': 9.01}
{'loss': 1.211, 'learning_rate': 6.909375e-05, 'epoch': 9.3}
{'loss': 1.1935, 'learning_rate': 6.860032894736842e-05, 'epoch': 9.59}
{'loss': 1.2139, 'learning_rate': 6.810690789473684e-05, 'epoch': 9.88}
{'loss': 1.2054, 'learning_rate': 6.761348684210526e-05, 'epoch': 10.17}


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                                                 Saving model checkpoint to ./checkpoint-3500
Configuration saved in ./checkpoint-3500/config.json
Model weights saved in ./checkpoint-3500/pytorch_model.bin
Feature extractor saved in ./checkpoint-3500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-2000] due to args.save_total_limit

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 23%|██▎       | 4000/17200 [7:10:41<23:30:56,  6.41s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.
***** Running Evaluation *****
  Num examples = 4620
  Batch size = 8
{'eval_loss': 0.3583095967769623, 'eval_wer': 0.3834057394603194, 'eval_runtime': 430.7535, 'eval_samples_per_second': 10.725, 'eval_steps_per_second': 1.342, 'epoch': 10.17}
{'loss': 1.1956, 'learning_rate': 6.712006578947368e-05, 'epoch': 10.46}
{'loss': 1.1758, 'learning_rate': 6.66266447368421e-05, 'epoch': 10.75}
{'loss': 1.1809, 'learning_rate': 6.613322368421052e-05, 'epoch': 11.05}
{'loss': 1.1758, 'learning_rate': 6.564473684210526e-05, 'epoch': 11.34}
{'loss': 1.1637, 'learning_rate': 6.515131578947369e-05, 'epoch': 11.63}


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                                                 Saving model checkpoint to ./checkpoint-4000
Configuration saved in ./checkpoint-4000/config.json
Model weights saved in ./checkpoint-4000/pytorch_model.bin
Feature extractor saved in ./checkpoint-4000/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-2500] due to args.save_total_limit

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 26%|██▌       | 4500/17200 [8:05:34<18:20:13,  5.20s/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 = 4620
  Batch size = 8
{'eval_loss': 0.33186060190200806, 'eval_wer': 0.3366578963470599, 'eval_runtime': 430.8575, 'eval_samples_per_second': 10.723, 'eval_steps_per_second': 1.342, 'epoch': 11.63}
{'loss': 1.1641, 'learning_rate': 6.46578947368421e-05, 'epoch': 11.92}
{'loss': 1.1499, 'learning_rate': 6.416447368421053e-05, 'epoch': 12.21}
{'loss': 1.1344, 'learning_rate': 6.367105263157894e-05, 'epoch': 12.5}
{'loss': 1.1382, 'learning_rate': 6.317763157894737e-05, 'epoch': 12.79}
{'loss': 1.1655, 'learning_rate': 6.268421052631578e-05, 'epoch': 13.08}


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                                                 Saving model checkpoint to ./checkpoint-4500
Configuration saved in ./checkpoint-4500/config.json
Model weights saved in ./checkpoint-4500/pytorch_model.bin
Feature extractor saved in ./checkpoint-4500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-3000] due to args.save_total_limit

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 29%|██▉       | 5000/17200 [9:00:21<16:09:57,  4.77s/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 = 4620
  Batch size = 8
{'eval_loss': 0.3261863589286804, 'eval_wer': 0.3206265679495809, 'eval_runtime': 431.0686, 'eval_samples_per_second': 10.718, 'eval_steps_per_second': 1.341, 'epoch': 13.08}
{'loss': 1.1345, 'learning_rate': 6.21907894736842e-05, 'epoch': 13.37}
{'loss': 1.1284, 'learning_rate': 6.169736842105262e-05, 'epoch': 13.66}
{'loss': 1.1354, 'learning_rate': 6.120394736842105e-05, 'epoch': 13.95}
{'loss': 1.1135, 'learning_rate': 6.0710526315789474e-05, 'epoch': 14.24}
{'loss': 1.1251, 'learning_rate': 6.021710526315789e-05, 'epoch': 14.53}


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                                                 Saving model checkpoint to ./checkpoint-5000
Configuration saved in ./checkpoint-5000/config.json
Model weights saved in ./checkpoint-5000/pytorch_model.bin
Feature extractor saved in ./checkpoint-5000/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-3500] due to args.save_total_limit

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 32%|███▏      | 5500/17200 [9:55:04<12:50:50,  3.95s/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 = 4620
  Batch size = 8
{'eval_loss': 0.3292250335216522, 'eval_wer': 0.3205041913969284, 'eval_runtime': 431.5464, 'eval_samples_per_second': 10.706, 'eval_steps_per_second': 1.339, 'epoch': 14.53}
{'loss': 1.1123, 'learning_rate': 5.972368421052631e-05, 'epoch': 14.82}
{'loss': 1.1174, 'learning_rate': 5.9230263157894734e-05, 'epoch': 15.12}
{'loss': 1.1133, 'learning_rate': 5.873684210526315e-05, 'epoch': 15.41}
{'loss': 1.0926, 'learning_rate': 5.8243421052631574e-05, 'epoch': 15.7}
{'loss': 1.0875, 'learning_rate': 5.7749999999999994e-05, 'epoch': 15.99}


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                                                 Saving model checkpoint to ./checkpoint-5500
Configuration saved in ./checkpoint-5500/config.json
Model weights saved in ./checkpoint-5500/pytorch_model.bin
Feature extractor saved in ./checkpoint-5500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-4000] due to args.save_total_limit

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 35%|███▍      | 6000/17200 [10:50:03<21:26:15,  6.89s/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 = 4620
  Batch size = 8
{'eval_loss': 0.31460341811180115, 'eval_wer': 0.3206265679495809, 'eval_runtime': 430.6582, 'eval_samples_per_second': 10.728, 'eval_steps_per_second': 1.342, 'epoch': 15.99}
{'loss': 1.0691, 'learning_rate': 5.725657894736842e-05, 'epoch': 16.28}
{'loss': 1.0672, 'learning_rate': 5.6763157894736834e-05, 'epoch': 16.57}
{'loss': 1.0824, 'learning_rate': 5.626973684210526e-05, 'epoch': 16.86}
{'loss': 1.0776, 'learning_rate': 5.577631578947368e-05, 'epoch': 17.15}
{'loss': 1.071, 'learning_rate': 5.52828947368421e-05, 'epoch': 17.44}


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                                                 Saving model checkpoint to ./checkpoint-6000
Configuration saved in ./checkpoint-6000/config.json
Model weights saved in ./checkpoint-6000/pytorch_model.bin
Feature extractor saved in ./checkpoint-6000/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-4500] due to args.save_total_limit

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 38%|███▊      | 6500/17200 [11:45:08<22:19:46,  7.51s/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 = 4620
  Batch size = 8
{'eval_loss': 0.296974778175354, 'eval_wer': 0.2854739032001469, 'eval_runtime': 430.7853, 'eval_samples_per_second': 10.725, 'eval_steps_per_second': 1.342, 'epoch': 17.44}
{'loss': 1.0677, 'learning_rate': 5.478947368421052e-05, 'epoch': 17.73}
{'loss': 1.0683, 'learning_rate': 5.429605263157895e-05, 'epoch': 18.02}
{'loss': 1.058, 'learning_rate': 5.380263157894736e-05, 'epoch': 18.31}
{'loss': 1.0517, 'learning_rate': 5.330921052631578e-05, 'epoch': 18.6}
{'loss': 1.0494, 'learning_rate': 5.281578947368421e-05, 'epoch': 18.89}


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                                                 Saving model checkpoint to ./checkpoint-6500
Configuration saved in ./checkpoint-6500/config.json
Model weights saved in ./checkpoint-6500/pytorch_model.bin
Feature extractor saved in ./checkpoint-6500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-5000] due to args.save_total_limit

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 41%|████      | 7000/17200 [12:40:16<17:04:29,  6.03s/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 = 4620
  Batch size = 8
{'eval_loss': 0.30185556411743164, 'eval_wer': 0.28975708254298477, 'eval_runtime': 430.6894, 'eval_samples_per_second': 10.727, 'eval_steps_per_second': 1.342, 'epoch': 18.89}
{'loss': 1.0512, 'learning_rate': 5.232236842105262e-05, 'epoch': 19.19}
{'loss': 1.0347, 'learning_rate': 5.182894736842105e-05, 'epoch': 19.48}
{'loss': 1.0379, 'learning_rate': 5.133552631578947e-05, 'epoch': 19.77}
{'loss': 1.0391, 'learning_rate': 5.084210526315789e-05, 'epoch': 20.06}
{'loss': 1.0462, 'learning_rate': 5.034868421052631e-05, 'epoch': 20.35}


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                                                 Saving model checkpoint to ./checkpoint-7000
Configuration saved in ./checkpoint-7000/config.json
Model weights saved in ./checkpoint-7000/pytorch_model.bin
Feature extractor saved in ./checkpoint-7000/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json
Deleting older checkpoint [checkpoint-5500] due to args.save_total_limit

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 44%|████▎     | 7500/17200 [13:35:04<14:50:42,  5.51s/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 = 4620
  Batch size = 8
{'eval_loss': 0.299389123916626, 'eval_wer': 0.29150094841828306, 'eval_runtime': 431.1497, 'eval_samples_per_second': 10.716, 'eval_steps_per_second': 1.341, 'epoch': 20.35}
{'loss': 1.0366, 'learning_rate': 4.9860197368421045e-05, 'epoch': 20.64}
{'loss': 1.0303, 'learning_rate': 4.936677631578947e-05, 'epoch': 20.93}
{'loss': 1.0302, 'learning_rate': 4.8873355263157886e-05, 'epoch': 21.22}
{'loss': 1.0257, 'learning_rate': 4.837993421052631e-05, 'epoch': 21.51}
{'loss': 1.0322, 'learning_rate': 4.788651315789473e-05, 'epoch': 21.8}


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                                                 Saving model checkpoint to ./checkpoint-7500
Configuration saved in ./checkpoint-7500/config.json
Model weights saved in ./checkpoint-7500/pytorch_model.bin
Feature extractor saved in ./checkpoint-7500/preprocessor_config.json
Feature extractor saved in ./preprocessor_config.json