diff --git "a/nohup.out" "b/nohup.out"
--- "a/nohup.out"
+++ "b/nohup.out"
@@ -234618,3 +234618,166 @@ If your task is similar to the task the model of the checkpoint was trained on,
 [INFO|modeling_utils.py:1693] 2023-07-23 12:13:17,204 >> Model weights saved in ./checkpoint-4000/pytorch_model.bin
 [INFO|feature_extraction_utils.py:368] 2023-07-23 12:13:17,210 >> Feature extractor saved in ./checkpoint-4000/preprocessor_config.json
 [INFO|feature_extraction_utils.py:368] 2023-07-23 12:13:28,119 >> Feature extractor saved in ./preprocessor_config.json
+
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 71%|███████▏  | 5000/7000 [4:04:20<37:03,  1.11s/it][INFO|trainer.py:2955] 2023-07-23 12:49:28,045 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-23 12:49:28,045 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-23 12:49:28,045 >>   Batch size = 8
+{'eval_loss': 0.2553846836090088, 'eval_wer': 16.353436753209934, 'eval_runtime': 1819.1705, 'eval_samples_per_second': 3.623, 'eval_steps_per_second': 0.453, 'epoch': 1.1}
+{'loss': 0.1, 'learning_rate': 4.583076923076924e-06, 'epoch': 1.1}
+{'loss': 0.0925, 'learning_rate': 4.544615384615385e-06, 'epoch': 1.11}
+{'loss': 0.1064, 'learning_rate': 4.506153846153846e-06, 'epoch': 1.11}
+{'loss': 0.1127, 'learning_rate': 4.467692307692308e-06, 'epoch': 1.11}
+{'loss': 0.1036, 'learning_rate': 4.42923076923077e-06, 'epoch': 1.12}
+{'loss': 0.091, 'learning_rate': 4.392307692307693e-06, 'epoch': 1.12}
+{'loss': 0.105, 'learning_rate': 4.353846153846154e-06, 'epoch': 1.13}
+{'loss': 0.1147, 'learning_rate': 4.315384615384616e-06, 'epoch': 1.13}
+{'loss': 0.0897, 'learning_rate': 4.276923076923078e-06, 'epoch': 1.13}
+{'loss': 0.1268, 'learning_rate': 4.238461538461539e-06, 'epoch': 1.14}
+{'loss': 0.1129, 'learning_rate': 4.2000000000000004e-06, 'epoch': 1.14}
+{'loss': 0.1041, 'learning_rate': 4.161538461538462e-06, 'epoch': 1.14}
+{'loss': 0.1061, 'learning_rate': 4.123076923076924e-06, 'epoch': 1.15}
+{'loss': 0.1059, 'learning_rate': 4.084615384615385e-06, 'epoch': 1.15}
+{'loss': 0.0818, 'learning_rate': 4.0461538461538466e-06, 'epoch': 1.15}
+{'loss': 0.087, 'learning_rate': 4.007692307692308e-06, 'epoch': 1.16}
+{'loss': 0.0835, 'learning_rate': 3.96923076923077e-06, 'epoch': 1.16}
+{'loss': 0.0713, 'learning_rate': 3.930769230769231e-06, 'epoch': 1.16}
+{'loss': 0.0792, 'learning_rate': 3.892307692307693e-06, 'epoch': 1.17}
+{'loss': 0.0799, 'learning_rate': 3.853846153846154e-06, 'epoch': 1.17}
+{'loss': 0.0755, 'learning_rate': 3.815384615384616e-06, 'epoch': 1.18}
+{'loss': 0.0703, 'learning_rate': 3.7769230769230775e-06, 'epoch': 1.18}
+{'loss': 0.11, 'learning_rate': 3.7384615384615384e-06, 'epoch': 1.18}
+{'loss': 0.0891, 'learning_rate': 3.7e-06, 'epoch': 1.19}
+{'loss': 0.0757, 'learning_rate': 3.661538461538462e-06, 'epoch': 1.19}
+{'loss': 0.0699, 'learning_rate': 3.6230769230769236e-06, 'epoch': 1.19}
+{'loss': 0.0839, 'learning_rate': 3.5846153846153845e-06, 'epoch': 1.2}
+{'loss': 0.1029, 'learning_rate': 3.5461538461538463e-06, 'epoch': 1.2}
+{'loss': 0.0806, 'learning_rate': 3.507692307692308e-06, 'epoch': 1.2}
+{'loss': 0.0808, 'learning_rate': 3.4692307692307698e-06, 'epoch': 1.21}
+{'loss': 0.0801, 'learning_rate': 3.4307692307692307e-06, 'epoch': 1.21}
+{'loss': 0.0705, 'learning_rate': 3.3923076923076924e-06, 'epoch': 1.21}
+{'loss': 0.0711, 'learning_rate': 3.353846153846154e-06, 'epoch': 1.22}
+{'loss': 0.0721, 'learning_rate': 3.315384615384616e-06, 'epoch': 1.22}
+{'loss': 0.0806, 'learning_rate': 3.276923076923077e-06, 'epoch': 1.23}
+{'loss': 0.0816, 'learning_rate': 3.2384615384615385e-06, 'epoch': 1.23}
+{'loss': 0.078, 'learning_rate': 3.2000000000000003e-06, 'epoch': 1.23}
+{'loss': 0.0858, 'learning_rate': 3.161538461538462e-06, 'epoch': 1.24}
+{'loss': 0.0948, 'learning_rate': 3.123076923076923e-06, 'epoch': 1.24}
+{'loss': 0.0863, 'learning_rate': 3.0846153846153847e-06, 'epoch': 1.24}
+
+
Reading metadata...: 0it [00:00, ?it/s]
+
Reading metadata...: 1it [00:00,  2.76it/s]
Reading metadata...: 6591it [00:00, 17131.72it/s]
+[INFO|trainer_utils.py:689] 2023-07-23 12:49:29,974 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`,  you can safely ignore this message.
+
                                                     

 71%|███████▏  | 5000/7000 [4:35:26<37:03,  1.11s/it][INFO|trainer.py:2700] 2023-07-23 13:20:33,178 >> Saving model checkpoint to ./checkpoint-5000
+[INFO|configuration_utils.py:447] 2023-07-23 13:20:33,178 >> Configuration saved in ./checkpoint-5000/config.json
+[INFO|modeling_utils.py:1693] 2023-07-23 13:20:40,382 >> Model weights saved in ./checkpoint-5000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-07-23 13:20:40,395 >> Feature extractor saved in ./checkpoint-5000/preprocessor_config.json
+
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 86%|████████▌ | 6000/7000 [4:55:21<18:40,  1.12s/it][INFO|trainer.py:2955] 2023-07-23 13:40:28,336 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-23 13:40:28,336 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-23 13:40:28,336 >>   Batch size = 8
+{'eval_loss': 0.23512449860572815, 'eval_wer': 14.787962250394912, 'eval_runtime': 1865.1318, 'eval_samples_per_second': 3.534, 'eval_steps_per_second': 0.442, 'epoch': 1.24}
+{'loss': 0.0899, 'learning_rate': 3.0461538461538464e-06, 'epoch': 1.25}
+{'loss': 0.064, 'learning_rate': 3.007692307692308e-06, 'epoch': 1.25}
+{'loss': 0.0698, 'learning_rate': 2.969230769230769e-06, 'epoch': 1.25}
+{'loss': 0.0794, 'learning_rate': 2.930769230769231e-06, 'epoch': 1.26}
+{'loss': 0.0758, 'learning_rate': 2.8923076923076925e-06, 'epoch': 1.26}
+{'loss': 0.0645, 'learning_rate': 2.8538461538461543e-06, 'epoch': 1.26}
+{'loss': 0.071, 'learning_rate': 2.815384615384615e-06, 'epoch': 1.27}
+{'loss': 0.0555, 'learning_rate': 2.776923076923077e-06, 'epoch': 1.27}
+{'loss': 0.0727, 'learning_rate': 2.7384615384615387e-06, 'epoch': 1.28}
+{'loss': 0.0714, 'learning_rate': 2.7000000000000004e-06, 'epoch': 1.28}
+{'loss': 0.0631, 'learning_rate': 2.6615384615384613e-06, 'epoch': 1.28}
+{'loss': 0.067, 'learning_rate': 2.623076923076923e-06, 'epoch': 1.29}
+{'loss': 0.0519, 'learning_rate': 2.584615384615385e-06, 'epoch': 1.29}
+{'loss': 0.0629, 'learning_rate': 2.5461538461538465e-06, 'epoch': 1.29}
+{'loss': 0.0787, 'learning_rate': 2.507692307692308e-06, 'epoch': 1.3}
+{'loss': 0.0524, 'learning_rate': 2.469230769230769e-06, 'epoch': 1.3}
+{'loss': 0.0618, 'learning_rate': 2.430769230769231e-06, 'epoch': 1.3}
+{'loss': 0.0693, 'learning_rate': 2.3923076923076922e-06, 'epoch': 1.31}
+{'loss': 0.0658, 'learning_rate': 2.353846153846154e-06, 'epoch': 1.31}
+{'loss': 0.0764, 'learning_rate': 2.3153846153846153e-06, 'epoch': 1.31}
+{'loss': 0.0664, 'learning_rate': 2.276923076923077e-06, 'epoch': 1.32}
+{'loss': 0.045, 'learning_rate': 2.2384615384615384e-06, 'epoch': 1.32}
+{'loss': 0.065, 'learning_rate': 2.2e-06, 'epoch': 1.33}
+{'loss': 0.0683, 'learning_rate': 2.1615384615384614e-06, 'epoch': 1.33}
+{'loss': 0.0452, 'learning_rate': 2.123076923076923e-06, 'epoch': 1.33}
+{'loss': 0.0574, 'learning_rate': 2.0846153846153845e-06, 'epoch': 1.34}
+{'loss': 0.0629, 'learning_rate': 2.0461538461538462e-06, 'epoch': 1.34}
+{'loss': 0.0622, 'learning_rate': 2.0076923076923076e-06, 'epoch': 1.34}
+{'loss': 0.0717, 'learning_rate': 1.9692307692307693e-06, 'epoch': 1.35}
+{'loss': 0.0792, 'learning_rate': 1.930769230769231e-06, 'epoch': 1.35}
+{'loss': 0.078, 'learning_rate': 1.8923076923076924e-06, 'epoch': 1.35}
+{'loss': 0.0534, 'learning_rate': 1.8538461538461541e-06, 'epoch': 1.36}
+{'loss': 0.0583, 'learning_rate': 1.8153846153846154e-06, 'epoch': 1.36}
+{'loss': 0.0493, 'learning_rate': 1.7769230769230772e-06, 'epoch': 1.36}
+{'loss': 0.0566, 'learning_rate': 1.7384615384615385e-06, 'epoch': 1.37}
+{'loss': 0.0478, 'learning_rate': 1.7000000000000002e-06, 'epoch': 1.37}
+{'loss': 0.0477, 'learning_rate': 1.6615384615384616e-06, 'epoch': 1.38}
+{'loss': 0.0889, 'learning_rate': 1.6230769230769233e-06, 'epoch': 1.38}
+{'loss': 0.0553, 'learning_rate': 1.5846153846153846e-06, 'epoch': 1.38}
+{'loss': 0.0636, 'learning_rate': 1.5461538461538464e-06, 'epoch': 1.39}
+
+
Reading metadata...: 0it [00:00, ?it/s]
+
Reading metadata...: 1it [00:00,  2.66it/s]
Reading metadata...: 6591it [00:00, 16513.03it/s]
+[INFO|trainer_utils.py:689] 2023-07-23 13:40:30,291 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`,  you can safely ignore this message.
+
                                                     

 86%|████████▌ | 6000/7000 [5:25:36<18:40,  1.12s/it][INFO|trainer.py:2700] 2023-07-23 14:10:43,495 >> Saving model checkpoint to ./checkpoint-6000
+[INFO|configuration_utils.py:447] 2023-07-23 14:10:43,496 >> Configuration saved in ./checkpoint-6000/config.json
+[INFO|modeling_utils.py:1693] 2023-07-23 14:10:45,744 >> Model weights saved in ./checkpoint-6000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-07-23 14:10:45,750 >> Feature extractor saved in ./checkpoint-6000/preprocessor_config.json
+
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Reading metadata...: 1it [00:00,  2.01it/s]
Reading metadata...: 10918it [00:00, 20339.47it/s]
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Reading metadata...: 0it [00:00, ?it/s]
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Reading metadata...: 1it [00:00,  3.22it/s]
Reading metadata...: 6591it [00:00, 19144.17it/s]
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100%|██████████| 7000/7000 [5:45:34<00:00,  1.31s/it][INFO|trainer.py:2955] 2023-07-23 14:30:41,439 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-23 14:30:41,439 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-23 14:30:41,439 >>   Batch size = 8
+{'loss': 0.0577, 'learning_rate': 6.261538461538462e-07, 'epoch': 2.0}
+{'loss': 0.0713, 'learning_rate': 5.876923076923077e-07, 'epoch': 2.0}
+{'loss': 0.0697, 'learning_rate': 5.492307692307692e-07, 'epoch': 2.01}
+{'loss': 0.0452, 'learning_rate': 5.107692307692308e-07, 'epoch': 2.01}
+{'loss': 0.0579, 'learning_rate': 4.723076923076923e-07, 'epoch': 2.02}
+{'loss': 0.05, 'learning_rate': 4.3384615384615384e-07, 'epoch': 2.02}
+{'loss': 0.0397, 'learning_rate': 3.9538461538461537e-07, 'epoch': 2.02}
+{'loss': 0.0411, 'learning_rate': 3.569230769230769e-07, 'epoch': 2.03}
+{'loss': 0.0379, 'learning_rate': 3.184615384615385e-07, 'epoch': 2.03}
+{'loss': 0.04, 'learning_rate': 2.8e-07, 'epoch': 2.03}
+{'loss': 0.0393, 'learning_rate': 2.4153846153846155e-07, 'epoch': 2.04}
+{'loss': 0.0412, 'learning_rate': 2.0307692307692308e-07, 'epoch': 2.04}
+{'loss': 0.0479, 'learning_rate': 1.6461538461538462e-07, 'epoch': 2.04}
+{'loss': 0.0387, 'learning_rate': 1.2615384615384617e-07, 'epoch': 2.05}
+{'loss': 0.0453, 'learning_rate': 8.769230769230769e-08, 'epoch': 2.05}
+{'loss': 0.0255, 'learning_rate': 4.923076923076924e-08, 'epoch': 2.05}
+{'loss': 0.0271, 'learning_rate': 1.076923076923077e-08, 'epoch': 2.06}
+
+
Reading metadata...: 0it [00:00, ?it/s]
+
Reading metadata...: 1it [00:00,  3.33it/s]
Reading metadata...: 6591it [00:00, 20383.95it/s]
+[INFO|trainer_utils.py:689] 2023-07-23 14:30:43,859 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`,  you can safely ignore this message.
+
                                                     

100%|██████████| 7000/7000 [6:15:41<00:00,  1.31s/it][INFO|trainer.py:2700] 2023-07-23 15:00:49,055 >> Saving model checkpoint to ./checkpoint-7000
+[INFO|configuration_utils.py:447] 2023-07-23 15:00:49,055 >> Configuration saved in ./checkpoint-7000/config.json
+[INFO|modeling_utils.py:1693] 2023-07-23 15:00:51,263 >> Model weights saved in ./checkpoint-7000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-07-23 15:00:51,269 >> Feature extractor saved in ./checkpoint-7000/preprocessor_config.json
+[INFO|feature_extraction_utils.py:368] 2023-07-23 15:01:50,286 >> Feature extractor saved in ./preprocessor_config.json