diff --git "a/nohup.out" "b/nohup.out"
--- "a/nohup.out"
+++ "b/nohup.out"
@@ -234455,3 +234455,166 @@ If your task is similar to the task the model of the checkpoint was trained on,
 [INFO|modeling_utils.py:1693] 2023-07-23 09:35:13,485 >> Model weights saved in ./checkpoint-1000/pytorch_model.bin
 [INFO|feature_extraction_utils.py:368] 2023-07-23 09:35:13,495 >> Feature extractor saved in ./checkpoint-1000/preprocessor_config.json
 [INFO|feature_extraction_utils.py:368] 2023-07-23 09:35:23,667 >> Feature extractor saved in ./preprocessor_config.json
+
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 29%|██▊       | 2000/7000 [1:16:59<1:58:58,  1.43s/it][INFO|trainer.py:2955] 2023-07-23 10:02:06,986 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-23 10:02:06,986 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-23 10:02:06,986 >>   Batch size = 8
+{'eval_loss': 0.4127572178840637, 'eval_wer': 28.265624367127064, 'eval_runtime': 1802.5775, 'eval_samples_per_second': 3.656, 'eval_steps_per_second': 0.457, 'epoch': 0.14}
+{'loss': 0.4094, 'learning_rate': 9.196923076923078e-06, 'epoch': 0.15}
+{'loss': 0.3674, 'learning_rate': 9.158461538461539e-06, 'epoch': 0.15}
+{'loss': 0.3613, 'learning_rate': 9.12e-06, 'epoch': 0.15}
+{'loss': 0.3904, 'learning_rate': 9.081538461538463e-06, 'epoch': 0.16}
+{'loss': 0.3865, 'learning_rate': 9.043076923076923e-06, 'epoch': 0.16}
+{'loss': 0.3677, 'learning_rate': 9.004615384615386e-06, 'epoch': 0.16}
+{'loss': 0.3295, 'learning_rate': 8.966153846153846e-06, 'epoch': 0.17}
+{'loss': 0.3207, 'learning_rate': 8.927692307692308e-06, 'epoch': 0.17}
+{'loss': 0.2817, 'learning_rate': 8.88923076923077e-06, 'epoch': 0.17}
+{'loss': 0.3218, 'learning_rate': 8.850769230769231e-06, 'epoch': 0.18}
+{'loss': 0.3502, 'learning_rate': 8.812307692307693e-06, 'epoch': 0.18}
+{'loss': 0.3432, 'learning_rate': 8.773846153846155e-06, 'epoch': 0.19}
+{'loss': 0.3244, 'learning_rate': 8.735384615384616e-06, 'epoch': 0.19}
+{'loss': 0.2607, 'learning_rate': 8.696923076923078e-06, 'epoch': 0.19}
+{'loss': 0.3469, 'learning_rate': 8.658461538461538e-06, 'epoch': 0.2}
+{'loss': 0.2756, 'learning_rate': 8.62e-06, 'epoch': 0.2}
+{'loss': 0.294, 'learning_rate': 8.581538461538463e-06, 'epoch': 0.2}
+{'loss': 0.3915, 'learning_rate': 8.543076923076923e-06, 'epoch': 0.21}
+{'loss': 0.3237, 'learning_rate': 8.504615384615385e-06, 'epoch': 0.21}
+{'loss': 0.2723, 'learning_rate': 8.466153846153847e-06, 'epoch': 0.21}
+{'loss': 0.2917, 'learning_rate': 8.427692307692308e-06, 'epoch': 0.22}
+{'loss': 0.2921, 'learning_rate': 8.38923076923077e-06, 'epoch': 0.22}
+{'loss': 0.3724, 'learning_rate': 8.35076923076923e-06, 'epoch': 0.23}
+{'loss': 0.3364, 'learning_rate': 8.312307692307693e-06, 'epoch': 0.23}
+{'loss': 0.3661, 'learning_rate': 8.273846153846155e-06, 'epoch': 0.23}
+{'loss': 0.3307, 'learning_rate': 8.235384615384615e-06, 'epoch': 0.24}
+{'loss': 0.3201, 'learning_rate': 8.196923076923078e-06, 'epoch': 0.24}
+{'loss': 0.2973, 'learning_rate': 8.15846153846154e-06, 'epoch': 0.24}
+{'loss': 0.3013, 'learning_rate': 8.120000000000002e-06, 'epoch': 0.25}
+{'loss': 0.3349, 'learning_rate': 8.081538461538462e-06, 'epoch': 0.25}
+{'loss': 0.3244, 'learning_rate': 8.043076923076923e-06, 'epoch': 0.25}
+{'loss': 0.3331, 'learning_rate': 8.004615384615385e-06, 'epoch': 0.26}
+{'loss': 0.2723, 'learning_rate': 7.966153846153847e-06, 'epoch': 0.26}
+{'loss': 0.316, 'learning_rate': 7.927692307692308e-06, 'epoch': 0.26}
+{'loss': 0.2648, 'learning_rate': 7.88923076923077e-06, 'epoch': 0.27}
+{'loss': 0.2965, 'learning_rate': 7.850769230769232e-06, 'epoch': 0.27}
+{'loss': 0.256, 'learning_rate': 7.812307692307694e-06, 'epoch': 0.28}
+{'loss': 0.2156, 'learning_rate': 7.773846153846155e-06, 'epoch': 0.28}
+{'loss': 0.2418, 'learning_rate': 7.735384615384615e-06, 'epoch': 0.28}
+{'loss': 0.2693, 'learning_rate': 7.696923076923077e-06, 'epoch': 0.29}
+
+
Reading metadata...: 0it [00:00, ?it/s]
+
Reading metadata...: 1it [00:00,  2.92it/s]
Reading metadata...: 6591it [00:00, 18029.02it/s]
+[INFO|trainer_utils.py:689] 2023-07-23 10:02:08,821 >> 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.
+
                                                       

 29%|██▊       | 2000/7000 [1:47:17<1:58:58,  1.43s/it][INFO|trainer.py:2700] 2023-07-23 10:32:24,762 >> Saving model checkpoint to ./checkpoint-2000
+[INFO|configuration_utils.py:447] 2023-07-23 10:32:24,762 >> Configuration saved in ./checkpoint-2000/config.json
+[INFO|modeling_utils.py:1693] 2023-07-23 10:32:35,392 >> Model weights saved in ./checkpoint-2000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-07-23 10:32:35,405 >> Feature extractor saved in ./checkpoint-2000/preprocessor_config.json
+
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 43%|████▎     | 3000/7000 [2:07:40<1:14:24,  1.12s/it][INFO|trainer.py:2955] 2023-07-23 10:52:47,456 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-23 10:52:47,456 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-23 10:52:47,456 >>   Batch size = 8
+{'eval_loss': 0.3239877223968506, 'eval_wer': 22.052330997610273, 'eval_runtime': 1817.7748, 'eval_samples_per_second': 3.626, 'eval_steps_per_second': 0.453, 'epoch': 0.29}
+{'loss': 0.2191, 'learning_rate': 7.65846153846154e-06, 'epoch': 0.29}
+{'loss': 0.2437, 'learning_rate': 7.620000000000001e-06, 'epoch': 0.29}
+{'loss': 0.2519, 'learning_rate': 7.581538461538462e-06, 'epoch': 0.3}
+{'loss': 0.3198, 'learning_rate': 7.544615384615386e-06, 'epoch': 0.3}
+{'loss': 0.2708, 'learning_rate': 7.5061538461538465e-06, 'epoch': 0.3}
+{'loss': 0.265, 'learning_rate': 7.467692307692308e-06, 'epoch': 0.31}
+{'loss': 0.2935, 'learning_rate': 7.42923076923077e-06, 'epoch': 0.31}
+{'loss': 0.2595, 'learning_rate': 7.390769230769231e-06, 'epoch': 0.31}
+{'loss': 0.2613, 'learning_rate': 7.3523076923076935e-06, 'epoch': 0.32}
+{'loss': 0.2229, 'learning_rate': 7.313846153846155e-06, 'epoch': 0.32}
+{'loss': 0.2814, 'learning_rate': 7.275384615384616e-06, 'epoch': 0.33}
+{'loss': 0.2681, 'learning_rate': 7.236923076923078e-06, 'epoch': 0.33}
+{'loss': 0.2531, 'learning_rate': 7.198461538461539e-06, 'epoch': 0.33}
+{'loss': 0.2104, 'learning_rate': 7.16e-06, 'epoch': 0.34}
+{'loss': 0.2294, 'learning_rate': 7.121538461538462e-06, 'epoch': 0.34}
+{'loss': 0.2556, 'learning_rate': 7.0830769230769235e-06, 'epoch': 0.34}
+{'loss': 0.2185, 'learning_rate': 7.044615384615386e-06, 'epoch': 0.35}
+{'loss': 0.2585, 'learning_rate': 7.006153846153847e-06, 'epoch': 0.35}
+{'loss': 0.2325, 'learning_rate': 6.967692307692308e-06, 'epoch': 0.35}
+{'loss': 0.2068, 'learning_rate': 6.9292307692307705e-06, 'epoch': 0.36}
+{'loss': 0.2233, 'learning_rate': 6.890769230769231e-06, 'epoch': 0.36}
+{'loss': 0.2649, 'learning_rate': 6.852307692307692e-06, 'epoch': 0.36}
+{'loss': 0.2524, 'learning_rate': 6.8138461538461545e-06, 'epoch': 0.37}
+{'loss': 0.2847, 'learning_rate': 6.775384615384616e-06, 'epoch': 0.37}
+{'loss': 0.1954, 'learning_rate': 6.736923076923078e-06, 'epoch': 0.38}
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+{'loss': 0.2138, 'learning_rate': 6.660000000000001e-06, 'epoch': 0.38}
+{'loss': 0.2245, 'learning_rate': 6.621538461538463e-06, 'epoch': 0.39}
+{'loss': 0.2137, 'learning_rate': 6.583076923076923e-06, 'epoch': 0.39}
+{'loss': 0.2558, 'learning_rate': 6.5446153846153846e-06, 'epoch': 0.39}
+{'loss': 0.1981, 'learning_rate': 6.506153846153847e-06, 'epoch': 0.4}
+{'loss': 0.2327, 'learning_rate': 6.467692307692308e-06, 'epoch': 0.4}
+{'loss': 0.219, 'learning_rate': 6.42923076923077e-06, 'epoch': 0.4}
+{'loss': 0.226, 'learning_rate': 6.3907692307692315e-06, 'epoch': 0.41}
+{'loss': 0.2192, 'learning_rate': 6.352307692307693e-06, 'epoch': 0.41}
+{'loss': 0.2146, 'learning_rate': 6.313846153846155e-06, 'epoch': 0.41}
+{'loss': 0.2058, 'learning_rate': 6.2753846153846155e-06, 'epoch': 0.42}
+{'loss': 0.2369, 'learning_rate': 6.236923076923077e-06, 'epoch': 0.42}
+{'loss': 0.2202, 'learning_rate': 6.198461538461539e-06, 'epoch': 0.42}
+{'loss': 0.2228, 'learning_rate': 6.16e-06, 'epoch': 0.43}
+
+
Reading metadata...: 0it [00:00, ?it/s]
+
Reading metadata...: 1it [00:00,  2.81it/s]
Reading metadata...: 6591it [00:00, 17446.97it/s]
+[INFO|trainer_utils.py:689] 2023-07-23 10:52:49,603 >> 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.
+
                                                       

 43%|████▎     | 3000/7000 [2:37:46<1:14:24,  1.12s/it][INFO|trainer.py:2700] 2023-07-23 11:22:53,229 >> Saving model checkpoint to ./checkpoint-3000
+[INFO|configuration_utils.py:447] 2023-07-23 11:22:53,230 >> Configuration saved in ./checkpoint-3000/config.json
+[INFO|modeling_utils.py:1693] 2023-07-23 11:22:57,269 >> Model weights saved in ./checkpoint-3000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-07-23 11:22:57,281 >> Feature extractor saved in ./checkpoint-3000/preprocessor_config.json
+
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+
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 57%|█████▋    | 4000/7000 [2:57:46<56:22,  1.13s/it][INFO|trainer.py:2955] 2023-07-23 11:42:53,940 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-23 11:42:53,940 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-23 11:42:53,940 >>   Batch size = 8
+{'loss': 0.2355, 'learning_rate': 5.6984615384615395e-06, 'epoch': 1.0}
+{'loss': 0.2297, 'learning_rate': 5.66e-06, 'epoch': 1.0}
+{'loss': 0.2265, 'learning_rate': 5.621538461538461e-06, 'epoch': 1.01}
+{'loss': 0.21, 'learning_rate': 5.5830769230769235e-06, 'epoch': 1.01}
+{'loss': 0.1944, 'learning_rate': 5.544615384615385e-06, 'epoch': 1.01}
+{'loss': 0.1844, 'learning_rate': 5.506153846153847e-06, 'epoch': 1.02}
+{'loss': 0.1774, 'learning_rate': 5.467692307692308e-06, 'epoch': 1.02}
+{'loss': 0.1688, 'learning_rate': 5.42923076923077e-06, 'epoch': 1.03}
+{'loss': 0.1473, 'learning_rate': 5.390769230769232e-06, 'epoch': 1.03}
+{'loss': 0.1743, 'learning_rate': 5.352307692307692e-06, 'epoch': 1.03}
+{'loss': 0.1759, 'learning_rate': 5.313846153846154e-06, 'epoch': 1.04}
+{'loss': 0.1507, 'learning_rate': 5.275384615384616e-06, 'epoch': 1.04}
+{'loss': 0.1556, 'learning_rate': 5.236923076923077e-06, 'epoch': 1.04}
+{'loss': 0.1545, 'learning_rate': 5.198461538461539e-06, 'epoch': 1.05}
+{'loss': 0.1143, 'learning_rate': 5.1600000000000006e-06, 'epoch': 1.05}
+{'loss': 0.1168, 'learning_rate': 5.121538461538462e-06, 'epoch': 1.05}
+{'loss': 0.1095, 'learning_rate': 5.083076923076924e-06, 'epoch': 1.06}
+{'loss': 0.1098, 'learning_rate': 5.044615384615385e-06, 'epoch': 1.06}
+{'loss': 0.102, 'learning_rate': 5.006153846153846e-06, 'epoch': 1.06}
+{'loss': 0.1263, 'learning_rate': 4.967692307692308e-06, 'epoch': 1.07}
+{'loss': 0.093, 'learning_rate': 4.929230769230769e-06, 'epoch': 1.07}
+{'loss': 0.1163, 'learning_rate': 4.8907692307692315e-06, 'epoch': 1.08}
+{'loss': 0.1005, 'learning_rate': 4.852307692307693e-06, 'epoch': 1.08}
+{'loss': 0.1036, 'learning_rate': 4.813846153846154e-06, 'epoch': 1.08}
+{'loss': 0.1289, 'learning_rate': 4.7753846153846155e-06, 'epoch': 1.09}
+{'loss': 0.1238, 'learning_rate': 4.736923076923078e-06, 'epoch': 1.09}
+{'loss': 0.1218, 'learning_rate': 4.698461538461539e-06, 'epoch': 1.09}
+{'loss': 0.1223, 'learning_rate': 4.66e-06, 'epoch': 1.1}
+{'loss': 0.1002, 'learning_rate': 4.621538461538462e-06, 'epoch': 1.1}
+
+
Reading metadata...: 0it [00:00, ?it/s]
+
Reading metadata...: 1it [00:00,  2.00it/s]
Reading metadata...: 6591it [00:00, 12589.33it/s]
+[INFO|trainer_utils.py:689] 2023-07-23 11:42:56,176 >> 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.
+
                                                     

 57%|█████▋    | 4000/7000 [3:28:06<56:22,  1.13s/it][INFO|trainer.py:2700] 2023-07-23 12:13:13,111 >> Saving model checkpoint to ./checkpoint-4000
+[INFO|configuration_utils.py:447] 2023-07-23 12:13:13,112 >> Configuration saved in ./checkpoint-4000/config.json
+[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