Commit
•
43dd921
1
Parent(s):
4123391
End of training
Browse files- all_results.json +15 -15
- eval_results.json +5 -5
- huggingface_training.py +43 -42
- pytorch_model.bin +1 -1
- test_results.json +6 -6
- train_results.json +5 -5
- trainer_state.json +34 -700
- training_args.bin +1 -1
all_results.json
CHANGED
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@@ -1,17 +1,17 @@
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{
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"epoch":
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"eval_loss": 1.
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"eval_runtime":
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"eval_samples_per_second":
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"eval_steps_per_second": 0.
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"eval_wer":
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"test_loss":
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"test_runtime":
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"test_samples_per_second":
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"test_steps_per_second": 0.
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"test_wer":
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"train_loss":
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"train_runtime":
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"train_samples_per_second":
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"train_steps_per_second": 0.
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}
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{
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+
"epoch": 1.0,
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"eval_loss": 1.6191972494125366,
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+
"eval_runtime": 56.3363,
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| 5 |
+
"eval_samples_per_second": 0.071,
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+
"eval_steps_per_second": 0.036,
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+
"eval_wer": 153.2258064516129,
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| 8 |
+
"test_loss": 1.7568330764770508,
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| 9 |
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"test_runtime": 37.8582,
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| 10 |
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"test_samples_per_second": 0.106,
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| 11 |
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"test_steps_per_second": 0.053,
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"test_wer": 138.5964912280702,
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"train_loss": 1.4339025020599365,
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| 14 |
+
"train_runtime": 108.1566,
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"train_samples_per_second": 0.074,
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"train_steps_per_second": 0.018
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}
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eval_results.json
CHANGED
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@@ -1,7 +1,7 @@
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{
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"eval_loss": 1.
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"eval_runtime":
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"eval_samples_per_second":
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"eval_steps_per_second": 0.
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-
"eval_wer":
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}
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{
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"eval_loss": 1.6191972494125366,
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+
"eval_runtime": 56.3363,
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+
"eval_samples_per_second": 0.071,
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+
"eval_steps_per_second": 0.036,
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"eval_wer": 153.2258064516129
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}
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huggingface_training.py
CHANGED
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@@ -1,6 +1,7 @@
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""" Whisper training script using Hugging Face Transformers. """
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import os # used to
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from dataclasses import dataclass # used to define data collator
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from math import ceil # used to round up decimals
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@@ -321,60 +322,60 @@ I hope you haven't left yet. If you have, bad for you, as we are ready for train
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As Whisper is a pretrained model ready to be used off-the-shelf, it is advisable to evaluate it before training on both the validation and test sets. Let's make sure we make no harm to it.
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"""
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eval_metrics = trainer.evaluate(
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)
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trainer.log_metrics("eval", eval_metrics)
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trainer.save_metrics("eval", eval_metrics)
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print(eval_metrics)
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test_metrics = trainer.evaluate(
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)
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trainer.log_metrics("test", test_metrics)
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trainer.save_metrics("test", test_metrics)
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print(test_metrics)
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train_result = trainer.train()
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trainer.save_model()
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metrics = train_result.metrics
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trainer.log_metrics("train", metrics)
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trainer.save_metrics("train", metrics)
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trainer.save_state()
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print(metrics)
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"""ADD SOMETHING ABOUT THE TRAINING.
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Now let's evaluate the
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"""
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final_metrics = trainer.evaluate(
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)
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trainer.log_metrics("test", final_metrics)
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trainer.save_metrics("test", final_metrics)
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print(final_metrics)
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# Pushing to hub during training slows down training
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# so we push it only in the end.
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# Since training is completed and best model has been saved, we first delete the checkpoints
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for filename in os.listdir("."):
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if filename.startswith("checkpoint-"):
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-
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trainer.push_to_hub()
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""" Whisper training script using Hugging Face Transformers. """
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import os # used to find checkpoints
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import shutil
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from dataclasses import dataclass # used to define data collator
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from math import ceil # used to round up decimals
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As Whisper is a pretrained model ready to be used off-the-shelf, it is advisable to evaluate it before training on both the validation and test sets. Let's make sure we make no harm to it.
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"""
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# eval_metrics = trainer.evaluate(
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# eval_dataset=preprocessed_dataset["validation"],
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# metric_key_prefix="eval",
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# max_length=448,
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# num_beams=1,
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# # gen_kwargs={"key": value} to provide additional generation specific arguments by keyword
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# )
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# trainer.log_metrics("eval", eval_metrics)
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# trainer.save_metrics("eval", eval_metrics)
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# print(eval_metrics)
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# test_metrics = trainer.evaluate(
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# eval_dataset=preprocessed_dataset["test"],
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# metric_key_prefix="test",
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# max_length=448,
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# num_beams=1,
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# # gen_kwargs={"key": value} to provide additional generation specific arguments by keyword
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# )
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# trainer.log_metrics("test", test_metrics)
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# trainer.save_metrics("test", test_metrics)
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# print(test_metrics)
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# train_result = trainer.train()
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# trainer.save_model()
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# metrics = train_result.metrics
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# trainer.log_metrics("train", metrics)
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# trainer.save_metrics("train", metrics)
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# trainer.save_state()
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# print(metrics)
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# """ADD SOMETHING ABOUT THE TRAINING.
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# Now let's evaluate the
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# """
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# final_metrics = trainer.evaluate(
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# eval_dataset=preprocessed_dataset["test"],
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# metric_key_prefix="test",
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# max_length=448,
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# num_beams=1,
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# # gen_kwargs={"key": value} to provide additional generation specific arguments by keyword
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# )
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# trainer.log_metrics("test", final_metrics)
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# trainer.save_metrics("test", final_metrics)
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# print(final_metrics)
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# Pushing to hub during training slows down training
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# so we push it only in the end.
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# Since training is completed and best model has been saved, we first delete the checkpoints
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for filename in os.listdir("."):
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if filename.startswith("checkpoint-"):
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shutil.rmtree(f"./{filename}")
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trainer.push_to_hub()
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pytorch_model.bin
CHANGED
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size 151098921
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test_results.json
CHANGED
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{
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train_results.json
CHANGED
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trainer_state.json
CHANGED
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|
| 48 |
}
|
| 49 |
],
|
| 50 |
+
"max_steps": 2,
|
| 51 |
"num_train_epochs": 9223372036854775807,
|
| 52 |
+
"total_flos": 196951080960000.0,
|
| 53 |
"trial_name": null,
|
| 54 |
"trial_params": null
|
| 55 |
}
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3579
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:081bb11364a81f07b31bca0107ccb5d62d955d95e115f206089558cf85595e34
|
| 3 |
size 3579
|