Commit
•
309997b
1
Parent(s):
c97f56c
End of training
Browse files- all_results.json +17 -0
- checkpoint-360/config.json +142 -0
- checkpoint-360/optimizer.pt +3 -0
- checkpoint-360/preprocessor_config.json +0 -0
- checkpoint-360/pytorch_model.bin +3 -0
- checkpoint-360/rng_state.pth +3 -0
- checkpoint-360/scaler.pt +3 -0
- checkpoint-360/scheduler.pt +3 -0
- checkpoint-360/trainer_state.json +637 -0
- checkpoint-360/training_args.bin +3 -0
- checkpoint-400/config.json +142 -0
- checkpoint-400/optimizer.pt +3 -0
- checkpoint-400/preprocessor_config.json +0 -0
- checkpoint-400/pytorch_model.bin +3 -0
- checkpoint-400/rng_state.pth +3 -0
- checkpoint-400/scaler.pt +3 -0
- checkpoint-400/scheduler.pt +3 -0
- checkpoint-400/trainer_state.json +706 -0
- checkpoint-400/training_args.bin +3 -0
- eval_results.json +7 -0
- huggingface_training.py +45 -45
- pytorch_model.bin +1 -1
- test_results.json +8 -0
- train_results.json +7 -0
- trainer_state.json +721 -0
- training_args.bin +1 -1
all_results.json
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{
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"epoch": 9.09,
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"eval_loss": 1.8916987180709839,
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"eval_runtime": 96.9796,
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"eval_samples_per_second": 4.063,
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"eval_steps_per_second": 0.134,
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"eval_wer": 15.494331342191881,
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"test_loss": 0.5623113512992859,
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"test_runtime": 121.6703,
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"test_samples_per_second": 5.318,
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"test_steps_per_second": 0.173,
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"test_wer": 20.965372507869883,
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"train_loss": 0.35074408769753995,
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"train_runtime": 2707.3827,
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"train_samples_per_second": 9.621,
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"train_steps_per_second": 0.15
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}
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checkpoint-360/config.json
ADDED
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@@ -0,0 +1,142 @@
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{
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"_name_or_path": "openai/whisper-tiny",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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"WhisperForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"begin_suppress_tokens": [
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],
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"bos_token_id": 50257,
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"d_model": 384,
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"decoder_attention_heads": 6,
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"decoder_ffn_dim": 1536,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 4,
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"decoder_start_token_id": 50258,
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"dropout": 0.0,
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"encoder_attention_heads": 6,
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"encoder_ffn_dim": 1536,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 4,
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"eos_token_id": 50257,
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"forced_decoder_ids": [
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[
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],
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"max_length": 448,
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"max_source_positions": 1500,
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"max_target_positions": 448,
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"model_type": "whisper",
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"num_hidden_layers": 4,
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"num_mel_bins": 80,
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"pad_token_id": 50257,
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"scale_embedding": false,
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"suppress_tokens": [
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}
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checkpoint-360/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:513fbc5d03bd07f32d77cf2f5dcc0d8298575b96fbda2ed1de30f1cb859889ae
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size 302183173
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checkpoint-360/preprocessor_config.json
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The diff for this file is too large to render.
See raw diff
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checkpoint-360/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:48879b1ce776151b602f3a1bdf10683d776d3f0765214b322443dddb1d951006
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size 151098921
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checkpoint-360/rng_state.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:89f9781ff6e5ab617d91036a7029d39a2832fa624ae853afb0f238fb19535016
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size 14575
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checkpoint-360/scaler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:12d681e2b2a56f2134611cbb1679a9f32470e4cf3a48f4a2243741f0852b30ae
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size 557
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checkpoint-360/scheduler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:922e864e56c484925ddcd495d1c992405fc4f95d13329256b422ef0f40cc0891
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size 627
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checkpoint-360/trainer_state.json
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"epoch": 9.08,
|
| 687 |
+
"learning_rate": 3.6923076923076927e-07,
|
| 688 |
+
"loss": 0.0876,
|
| 689 |
+
"step": 400
|
| 690 |
+
},
|
| 691 |
+
{
|
| 692 |
+
"epoch": 9.08,
|
| 693 |
+
"eval_loss": 0.5568162202835083,
|
| 694 |
+
"eval_runtime": 89.7223,
|
| 695 |
+
"eval_samples_per_second": 4.391,
|
| 696 |
+
"eval_steps_per_second": 0.145,
|
| 697 |
+
"eval_wer": 19.3465805193222,
|
| 698 |
+
"step": 400
|
| 699 |
+
}
|
| 700 |
+
],
|
| 701 |
+
"max_steps": 407,
|
| 702 |
+
"num_train_epochs": 9223372036854775807,
|
| 703 |
+
"total_flos": 6.2536891981824e+17,
|
| 704 |
+
"trial_name": null,
|
| 705 |
+
"trial_params": null
|
| 706 |
+
}
|
checkpoint-400/training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fcbca0d141969bcb1c3cd0ef5a009221139334753b899d88e4d5003bd23f4b5f
|
| 3 |
+
size 3579
|
eval_results.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"eval_loss": 1.8916987180709839,
|
| 3 |
+
"eval_runtime": 96.9796,
|
| 4 |
+
"eval_samples_per_second": 4.063,
|
| 5 |
+
"eval_steps_per_second": 0.134,
|
| 6 |
+
"eval_wer": 15.494331342191881
|
| 7 |
+
}
|
huggingface_training.py
CHANGED
|
@@ -32,8 +32,8 @@ dataset = load_dataset(dataset_id, dataset_language_code, streaming=True)
|
|
| 32 |
|
| 33 |
"""The first time you run this code, make sure everything works fine using a small sample and low number of training steps. Just uncomment the next cell and run it. One note: since the dataset is loaded in streaming mode, the instruction will not be executed immediately. Instead, the dataset will be subsampled only when data will be needed during training."""
|
| 34 |
|
| 35 |
-
test_script = True
|
| 36 |
-
|
| 37 |
|
| 38 |
## Sample dataset for testing
|
| 39 |
if test_script is True:
|
|
@@ -236,14 +236,14 @@ Last, we can track our training using several experiment tracking tools. I use W
|
|
| 236 |
"""
|
| 237 |
|
| 238 |
## If you don't want to track your experiment with WandB, run this!
|
| 239 |
-
os.environ["WANDB_DISABLED"] = "true"
|
| 240 |
-
report_to = "none"
|
| 241 |
|
| 242 |
# If you have a wandb account, login!
|
| 243 |
# Otherwise, edit this cell to loging with your favourite experiment tracker(s)
|
| 244 |
-
|
| 245 |
-
|
| 246 |
-
|
| 247 |
|
| 248 |
# Define (and create, if missing) output directory
|
| 249 |
output_dir = "."
|
|
@@ -264,12 +264,12 @@ eval_bs = 2 if test_script is True else 32
|
|
| 264 |
# Then we infer the number of steps
|
| 265 |
# TODO: how did I find it?
|
| 266 |
num_training_samples = 2602
|
| 267 |
-
num_epochs =
|
| 268 |
max_steps_full_training = ceil(num_training_samples * num_epochs / train_bs)
|
| 269 |
max_steps = 2 if test_script is True else max_steps_full_training
|
| 270 |
|
| 271 |
# We don't want to evaluate too often since it slows down training a lot
|
| 272 |
-
eval_steps = 1 if test_script is True else int(max_steps /
|
| 273 |
logging_steps = 1 if test_script is True else int(max_steps / 100)
|
| 274 |
|
| 275 |
training_args = Seq2SeqTrainingArguments(
|
|
@@ -319,54 +319,54 @@ I hope you haven't left yet. If you have, bad for you, as we are ready for train
|
|
| 319 |
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.
|
| 320 |
"""
|
| 321 |
|
| 322 |
-
|
| 323 |
-
|
| 324 |
-
|
| 325 |
-
|
| 326 |
-
|
| 327 |
-
#
|
| 328 |
-
|
| 329 |
|
| 330 |
-
|
| 331 |
-
|
| 332 |
-
|
| 333 |
|
| 334 |
-
|
| 335 |
-
|
| 336 |
-
|
| 337 |
-
|
| 338 |
-
|
| 339 |
-
#
|
| 340 |
-
|
| 341 |
|
| 342 |
-
|
| 343 |
-
|
| 344 |
-
|
| 345 |
|
| 346 |
-
|
| 347 |
trainer.save_model()
|
| 348 |
|
| 349 |
-
|
| 350 |
-
|
| 351 |
-
|
| 352 |
-
|
| 353 |
-
|
| 354 |
|
| 355 |
"""ADD SOMETHING ABOUT THE TRAINING.
|
| 356 |
|
| 357 |
Now let's evaluate the
|
| 358 |
"""
|
| 359 |
|
| 360 |
-
|
| 361 |
-
|
| 362 |
-
|
| 363 |
-
|
| 364 |
-
|
| 365 |
-
#
|
| 366 |
-
|
| 367 |
|
| 368 |
-
|
| 369 |
-
|
| 370 |
-
|
| 371 |
|
| 372 |
trainer.push_to_hub()
|
|
|
|
| 32 |
|
| 33 |
"""The first time you run this code, make sure everything works fine using a small sample and low number of training steps. Just uncomment the next cell and run it. One note: since the dataset is loaded in streaming mode, the instruction will not be executed immediately. Instead, the dataset will be subsampled only when data will be needed during training."""
|
| 34 |
|
| 35 |
+
# test_script = True
|
| 36 |
+
test_script = False
|
| 37 |
|
| 38 |
## Sample dataset for testing
|
| 39 |
if test_script is True:
|
|
|
|
| 236 |
"""
|
| 237 |
|
| 238 |
## If you don't want to track your experiment with WandB, run this!
|
| 239 |
+
# os.environ["WANDB_DISABLED"] = "true"
|
| 240 |
+
# report_to = "none"
|
| 241 |
|
| 242 |
# If you have a wandb account, login!
|
| 243 |
# Otherwise, edit this cell to loging with your favourite experiment tracker(s)
|
| 244 |
+
wandb.login()
|
| 245 |
+
wandb.init(project="whisper-training-post")
|
| 246 |
+
report_to = "wandb"
|
| 247 |
|
| 248 |
# Define (and create, if missing) output directory
|
| 249 |
output_dir = "."
|
|
|
|
| 264 |
# Then we infer the number of steps
|
| 265 |
# TODO: how did I find it?
|
| 266 |
num_training_samples = 2602
|
| 267 |
+
num_epochs = 10
|
| 268 |
max_steps_full_training = ceil(num_training_samples * num_epochs / train_bs)
|
| 269 |
max_steps = 2 if test_script is True else max_steps_full_training
|
| 270 |
|
| 271 |
# We don't want to evaluate too often since it slows down training a lot
|
| 272 |
+
eval_steps = 1 if test_script is True else int(max_steps / 10)
|
| 273 |
logging_steps = 1 if test_script is True else int(max_steps / 100)
|
| 274 |
|
| 275 |
training_args = Seq2SeqTrainingArguments(
|
|
|
|
| 319 |
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.
|
| 320 |
"""
|
| 321 |
|
| 322 |
+
eval_metrics = trainer.evaluate(
|
| 323 |
+
eval_dataset=preprocessed_dataset["validation"],
|
| 324 |
+
metric_key_prefix="eval",
|
| 325 |
+
max_length=448,
|
| 326 |
+
num_beams=1,
|
| 327 |
+
# gen_kwargs={"key": value} to provide additional generation specific arguments by keyword
|
| 328 |
+
)
|
| 329 |
|
| 330 |
+
trainer.log_metrics("eval", eval_metrics)
|
| 331 |
+
trainer.save_metrics("eval", eval_metrics)
|
| 332 |
+
print(eval_metrics)
|
| 333 |
|
| 334 |
+
test_metrics = trainer.evaluate(
|
| 335 |
+
eval_dataset=preprocessed_dataset["test"],
|
| 336 |
+
metric_key_prefix="test",
|
| 337 |
+
max_length=448,
|
| 338 |
+
num_beams=1,
|
| 339 |
+
# gen_kwargs={"key": value} to provide additional generation specific arguments by keyword
|
| 340 |
+
)
|
| 341 |
|
| 342 |
+
trainer.log_metrics("test", test_metrics)
|
| 343 |
+
trainer.save_metrics("test", test_metrics)
|
| 344 |
+
print(test_metrics)
|
| 345 |
|
| 346 |
+
train_result = trainer.train()
|
| 347 |
trainer.save_model()
|
| 348 |
|
| 349 |
+
metrics = train_result.metrics
|
| 350 |
+
trainer.log_metrics("train", metrics)
|
| 351 |
+
trainer.save_metrics("train", metrics)
|
| 352 |
+
trainer.save_state()
|
| 353 |
+
print(metrics)
|
| 354 |
|
| 355 |
"""ADD SOMETHING ABOUT THE TRAINING.
|
| 356 |
|
| 357 |
Now let's evaluate the
|
| 358 |
"""
|
| 359 |
|
| 360 |
+
final_metrics = trainer.evaluate(
|
| 361 |
+
eval_dataset=preprocessed_dataset["test"],
|
| 362 |
+
metric_key_prefix="test",
|
| 363 |
+
max_length=448,
|
| 364 |
+
num_beams=1,
|
| 365 |
+
# gen_kwargs={"key": value} to provide additional generation specific arguments by keyword
|
| 366 |
+
)
|
| 367 |
|
| 368 |
+
trainer.log_metrics("test", final_metrics)
|
| 369 |
+
trainer.save_metrics("test", final_metrics)
|
| 370 |
+
print(final_metrics)
|
| 371 |
|
| 372 |
trainer.push_to_hub()
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 151098921
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:21343063174657acd721a023a2780da91e0bede1cc15233f17e5468d93d0ae51
|
| 3 |
size 151098921
|
test_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 9.09,
|
| 3 |
+
"test_loss": 0.5623113512992859,
|
| 4 |
+
"test_runtime": 121.6703,
|
| 5 |
+
"test_samples_per_second": 5.318,
|
| 6 |
+
"test_steps_per_second": 0.173,
|
| 7 |
+
"test_wer": 20.965372507869883
|
| 8 |
+
}
|
train_results.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 9.09,
|
| 3 |
+
"train_loss": 0.35074408769753995,
|
| 4 |
+
"train_runtime": 2707.3827,
|
| 5 |
+
"train_samples_per_second": 9.621,
|
| 6 |
+
"train_steps_per_second": 0.15
|
| 7 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,721 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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