Training in progress, step 3000

checkpoint-3000/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
@@ -748,8 +748,8 @@
     {
       "epoch": 0.6,
       "eval_loss": 0.260960191488266,
-      "eval_runtime": 1803.5499,
-      "eval_samples_per_second": 3.654,
+      "eval_runtime": 1802.8082,
+      "eval_samples_per_second": 3.656,
       "eval_steps_per_second": 0.457,
       "eval_wer": 17.515897768236865,
       "step": 3000

checkpoint-3000/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

@@ -200900,3 +200900,56 @@ If your task is similar to the task the model of the checkpoint was trained on,
 [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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 60%|██████    | 3000/5000 [2:05:25<37:23,  1.12s/it][INFO|trainer.py:2955] 2023-07-23 00:47:40,592 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-07-23 00:47:40,592 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-07-23 00:47:40,592 >>   Batch size = 8
+{'eval_loss': 0.31417879462242126, 'eval_wer': 21.643241929604276, 'eval_runtime': 1807.9177, 'eval_samples_per_second': 3.646, 'eval_steps_per_second': 0.456, 'epoch': 0.4}
+{'loss': 0.212, 'learning_rate': 6.620000000000001e-06, 'epoch': 0.41}
+{'loss': 0.2386, 'learning_rate': 6.564444444444446e-06, 'epoch': 0.41}
+{'loss': 0.2429, 'learning_rate': 6.508888888888889e-06, 'epoch': 0.41}
+{'loss': 0.3079, 'learning_rate': 6.453333333333334e-06, 'epoch': 0.42}
+{'loss': 0.2576, 'learning_rate': 6.397777777777778e-06, 'epoch': 0.42}
+{'loss': 0.2558, 'learning_rate': 6.342222222222223e-06, 'epoch': 0.43}
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+{'loss': 0.222, 'learning_rate': 5.8422222222222226e-06, 'epoch': 0.47}
+{'loss': 0.2453, 'learning_rate': 5.7866666666666674e-06, 'epoch': 0.48}
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+{'loss': 0.2176, 'learning_rate': 5.508888888888889e-06, 'epoch': 0.51}
+{'loss': 0.2565, 'learning_rate': 5.453333333333334e-06, 'epoch': 0.51}
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+{'loss': 0.1891, 'learning_rate': 5.286666666666666e-06, 'epoch': 0.53}
+{'loss': 0.2404, 'learning_rate': 5.231111111111111e-06, 'epoch': 0.53}
+{'loss': 0.2037, 'learning_rate': 5.175555555555556e-06, 'epoch': 0.54}
+{'loss': 0.215, 'learning_rate': 5.12e-06, 'epoch': 0.54}
+{'loss': 0.2115, 'learning_rate': 5.064444444444445e-06, 'epoch': 0.55}
+{'loss': 0.2491, 'learning_rate': 5.00888888888889e-06, 'epoch': 0.55}
+{'loss': 0.1979, 'learning_rate': 4.953333333333334e-06, 'epoch': 0.56}
+{'loss': 0.224, 'learning_rate': 4.897777777777778e-06, 'epoch': 0.56}
+{'loss': 0.2065, 'learning_rate': 4.842222222222223e-06, 'epoch': 0.56}
+{'loss': 0.2144, 'learning_rate': 4.786666666666667e-06, 'epoch': 0.57}
+{'loss': 0.2141, 'learning_rate': 4.731111111111112e-06, 'epoch': 0.57}
+{'loss': 0.1953, 'learning_rate': 4.675555555555556e-06, 'epoch': 0.58}
+{'loss': 0.1907, 'learning_rate': 4.620000000000001e-06, 'epoch': 0.58}
+{'loss': 0.2292, 'learning_rate': 4.564444444444445e-06, 'epoch': 0.59}
+{'loss': 0.2164, 'learning_rate': 4.50888888888889e-06, 'epoch': 0.59}
+{'loss': 0.2145, 'learning_rate': 4.453333333333334e-06, 'epoch': 0.6}
+
+
+
+[INFO|trainer_utils.py:689] 2023-07-23 00:47:42,443 >> 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.
+
                                                     
 60%|██████    | 3000/5000 [2:35:28<37:23,  1.12s/it][INFO|trainer.py:2700] 2023-07-23 01:17:43,401 >> Saving model checkpoint to ./checkpoint-3000
+[INFO|configuration_utils.py:447] 2023-07-23 01:17:43,401 >> Configuration saved in ./checkpoint-3000/config.json
+[INFO|modeling_utils.py:1693] 2023-07-23 01:17:47,574 >> Model weights saved in ./checkpoint-3000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-07-23 01:17:47,584 >> Feature extractor saved in ./checkpoint-3000/preprocessor_config.json
+[INFO|feature_extraction_utils.py:368] 2023-07-23 01:18:00,185 >> Feature extractor saved in ./preprocessor_config.json

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