Training in progress, step 2000

checkpoint-2000/trainer_state.json

@@ -250,9 +250,9 @@
     {
       "epoch": 0.2,
       "eval_loss": 0.41016528010368347,
-      "eval_runtime": 1788.2777,
-      "eval_samples_per_second": 3.686,
-      "eval_steps_per_second": 0.461,
+      "eval_runtime": 1814.0427,
+      "eval_samples_per_second": 3.633,
+      "eval_steps_per_second": 0.454,
       "eval_wer": 28.34865729677184,
       "step": 1000
     },
@@ -499,8 +499,8 @@
     {
       "epoch": 0.4,
       "eval_loss": 0.31417879462242126,
-      "eval_runtime": 1808.8984,
-      "eval_samples_per_second": 3.644,
+      "eval_runtime": 1807.9177,
+      "eval_samples_per_second": 3.646,
       "eval_steps_per_second": 0.456,
       "eval_wer": 21.643241929604276,
       "step": 2000

checkpoint-2000/training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9d66007a736788880333f143a38bacc2f66a8cab53d5a9aba13249e3048d3a20
+oid sha256:a13c314f78e95fbdb83f90ffb2bc7a882d83957078ec2bb7e6c5baa43309eafa
 size 3643

nohup.out

@@ -200847,3 +200847,56 @@ If your task is similar to the task the model of the checkpoint was trained on,
 [INFO|modeling_utils.py:1693] 2023-07-22 23:32:36,546 >> Model weights saved in ./checkpoint-1000/pytorch_model.bin
 [INFO|feature_extraction_utils.py:368] 2023-07-22 23:32:36,559 >> Feature extractor saved in ./checkpoint-1000/preprocessor_config.json
 [INFO|feature_extraction_utils.py:368] 2023-07-22 23:32:47,637 >> Feature extractor saved in ./preprocessor_config.json
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 40%|████      | 2000/5000 [1:12:51<1:08:07,  1.36s/it][INFO|trainer.py:2955] 2023-07-22 23:55:06,928 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-22 23:55:06,928 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-22 23:55:06,928 >>   Batch size = 8
+{'eval_loss': 0.41016528010368347, 'eval_wer': 28.34865729677184, 'eval_runtime': 1814.0427, 'eval_samples_per_second': 3.633, 'eval_steps_per_second': 0.454, 'epoch': 0.2}
+{'loss': 0.409, 'learning_rate': 8.842222222222223e-06, 'epoch': 0.2}
+{'loss': 0.3674, 'learning_rate': 8.786666666666668e-06, 'epoch': 0.21}
+{'loss': 0.3591, 'learning_rate': 8.73111111111111e-06, 'epoch': 0.21}
+{'loss': 0.3892, 'learning_rate': 8.675555555555556e-06, 'epoch': 0.22}
+{'loss': 0.3843, 'learning_rate': 8.62e-06, 'epoch': 0.23}
+{'loss': 0.3605, 'learning_rate': 8.564444444444445e-06, 'epoch': 0.23}
+{'loss': 0.326, 'learning_rate': 8.50888888888889e-06, 'epoch': 0.23}
+{'loss': 0.3103, 'learning_rate': 8.453333333333334e-06, 'epoch': 0.24}
+{'loss': 0.2766, 'learning_rate': 8.397777777777778e-06, 'epoch': 0.24}
+{'loss': 0.3204, 'learning_rate': 8.342222222222222e-06, 'epoch': 0.25}
+{'loss': 0.3426, 'learning_rate': 8.286666666666668e-06, 'epoch': 0.26}
+{'loss': 0.3417, 'learning_rate': 8.231111111111112e-06, 'epoch': 0.26}
+{'loss': 0.3179, 'learning_rate': 8.175555555555556e-06, 'epoch': 0.27}
+{'loss': 0.2598, 'learning_rate': 8.120000000000002e-06, 'epoch': 0.27}
+{'loss': 0.3453, 'learning_rate': 8.064444444444444e-06, 'epoch': 0.28}
+{'loss': 0.2752, 'learning_rate': 8.00888888888889e-06, 'epoch': 0.28}
+{'loss': 0.2927, 'learning_rate': 7.953333333333334e-06, 'epoch': 0.28}
+{'loss': 0.3859, 'learning_rate': 7.897777777777778e-06, 'epoch': 0.29}
+{'loss': 0.3137, 'learning_rate': 7.842222222222224e-06, 'epoch': 0.29}
+{'loss': 0.2678, 'learning_rate': 7.786666666666666e-06, 'epoch': 0.3}
+{'loss': 0.2803, 'learning_rate': 7.731111111111112e-06, 'epoch': 0.3}
+{'loss': 0.2828, 'learning_rate': 7.675555555555556e-06, 'epoch': 0.31}
+{'loss': 0.3655, 'learning_rate': 7.620000000000001e-06, 'epoch': 0.32}
+{'loss': 0.3321, 'learning_rate': 7.564444444444446e-06, 'epoch': 0.32}
+{'loss': 0.3649, 'learning_rate': 7.50888888888889e-06, 'epoch': 0.33}
+{'loss': 0.3229, 'learning_rate': 7.453333333333334e-06, 'epoch': 0.33}
+{'loss': 0.3115, 'learning_rate': 7.3977777777777786e-06, 'epoch': 0.34}
+{'loss': 0.2925, 'learning_rate': 7.342222222222223e-06, 'epoch': 0.34}
+{'loss': 0.3014, 'learning_rate': 7.2866666666666675e-06, 'epoch': 0.34}
+{'loss': 0.3303, 'learning_rate': 7.231111111111112e-06, 'epoch': 0.35}
+{'loss': 0.3174, 'learning_rate': 7.1755555555555556e-06, 'epoch': 0.35}
+{'loss': 0.3249, 'learning_rate': 7.1200000000000004e-06, 'epoch': 0.36}
+{'loss': 0.2678, 'learning_rate': 7.0644444444444445e-06, 'epoch': 0.36}
+{'loss': 0.3088, 'learning_rate': 7.008888888888889e-06, 'epoch': 0.37}
+{'loss': 0.2515, 'learning_rate': 6.953333333333334e-06, 'epoch': 0.38}
+{'loss': 0.2838, 'learning_rate': 6.897777777777779e-06, 'epoch': 0.38}
+{'loss': 0.2494, 'learning_rate': 6.842222222222222e-06, 'epoch': 0.39}
+{'loss': 0.205, 'learning_rate': 6.786666666666667e-06, 'epoch': 0.39}
+{'loss': 0.2439, 'learning_rate': 6.731111111111111e-06, 'epoch': 0.4}
+{'loss': 0.2547, 'learning_rate': 6.675555555555556e-06, 'epoch': 0.4}
+
+
+
+[INFO|trainer_utils.py:689] 2023-07-22 23:55:09,297 >> 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.
+
                                                       
 40%|████      | 2000/5000 [1:42:59<1:08:07,  1.36s/it][INFO|trainer.py:2700] 2023-07-23 00:25:14,846 >> Saving model checkpoint to ./checkpoint-2000
+[INFO|configuration_utils.py:447] 2023-07-23 00:25:14,847 >> Configuration saved in ./checkpoint-2000/config.json
+[INFO|modeling_utils.py:1693] 2023-07-23 00:25:18,835 >> Model weights saved in ./checkpoint-2000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-07-23 00:25:18,852 >> Feature extractor saved in ./checkpoint-2000/preprocessor_config.json
+[INFO|feature_extraction_utils.py:368] 2023-07-23 00:25:31,974 >> Feature extractor saved in ./preprocessor_config.json

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