Commit
·
63865a1
1
Parent(s):
1b2d3ae
training
Browse files- all_results.json +10 -10
- checkpoint-1000/config.json +0 -41
- checkpoint-1000/optimizer.pt +0 -3
- checkpoint-1000/preprocessor_config.json +0 -0
- checkpoint-1000/pytorch_model.bin +0 -3
- checkpoint-1000/rng_state.pth +0 -3
- checkpoint-1000/scaler.pt +0 -3
- checkpoint-1000/scheduler.pt +0 -3
- checkpoint-1000/trainer_state.json +0 -265
- checkpoint-1000/training_args.bin +0 -3
- checkpoint-2000/config.json +0 -41
- checkpoint-2000/optimizer.pt +0 -3
- checkpoint-2000/preprocessor_config.json +0 -0
- checkpoint-2000/pytorch_model.bin +0 -3
- checkpoint-2000/rng_state.pth +0 -3
- checkpoint-2000/scaler.pt +0 -3
- checkpoint-2000/scheduler.pt +0 -3
- checkpoint-2000/trainer_state.json +0 -514
- checkpoint-2000/training_args.bin +0 -3
- eval_results.json +6 -6
- pytorch_model.bin +1 -1
- run.sh +9 -11
- run_speech_recognition_seq2seq_streaming.py +217 -38
- train_results.json +5 -5
- trainer_state.json +941 -314
- training_args.bin +1 -1
all_results.json
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checkpoint-1000/config.json
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|
checkpoint-2000/training_args.bin
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:6e13de8ddcc61da424ec8e8d050cd43e412a6f52aa9be94507835652edf1dedf
|
| 3 |
-
size 3579
|
|
|
|
|
|
|
|
|
|
|
|
eval_results.json
CHANGED
|
@@ -1,8 +1,8 @@
|
|
| 1 |
{
|
| 2 |
-
"epoch":
|
| 3 |
-
"eval_loss": 0.
|
| 4 |
-
"eval_runtime":
|
| 5 |
-
"eval_samples_per_second": 1.
|
| 6 |
-
"eval_steps_per_second": 0.
|
| 7 |
-
"eval_wer":
|
| 8 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"epoch": 1.0,
|
| 3 |
+
"eval_loss": 0.24834245443344116,
|
| 4 |
+
"eval_runtime": 2999.4256,
|
| 5 |
+
"eval_samples_per_second": 1.69,
|
| 6 |
+
"eval_steps_per_second": 0.106,
|
| 7 |
+
"eval_wer": 9.891409525857435
|
| 8 |
}
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3055754841
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ce7f783491edc671deb55f5ce6701ebc753df5b565204547bdda4e4c24df4477
|
| 3 |
size 3055754841
|
run.sh
CHANGED
|
@@ -1,12 +1,14 @@
|
|
| 1 |
python run_speech_recognition_seq2seq_streaming.py \
|
| 2 |
--model_name_or_path="marinone94/whisper-medium-nordic" \
|
| 3 |
-
--
|
| 4 |
-
--
|
| 5 |
--language="swedish" \
|
| 6 |
-
--train_split_name="train+validation" \
|
|
|
|
|
|
|
| 7 |
--eval_split_name="test" \
|
| 8 |
--model_index_name="Whisper Medium Swedish" \
|
| 9 |
-
--max_steps="
|
| 10 |
--output_dir="./" \
|
| 11 |
--per_device_train_batch_size="32" \
|
| 12 |
--per_device_eval_batch_size="16" \
|
|
@@ -20,19 +22,15 @@ python run_speech_recognition_seq2seq_streaming.py \
|
|
| 20 |
--generation_max_length="225" \
|
| 21 |
--length_column_name="input_length" \
|
| 22 |
--max_duration_in_seconds="30" \
|
| 23 |
-
--text_column_name="sentence" \
|
| 24 |
--freeze_feature_encoder="False" \
|
| 25 |
-
--report_to="
|
| 26 |
--metric_for_best_model="wer" \
|
| 27 |
--greater_is_better="False" \
|
| 28 |
--load_best_model_at_end \
|
| 29 |
--gradient_checkpointing \
|
| 30 |
--fp16 \
|
| 31 |
-
--overwrite_output_dir \
|
| 32 |
-
--do_train \
|
| 33 |
-
--do_eval \
|
| 34 |
--predict_with_generate \
|
| 35 |
--do_normalize_eval \
|
| 36 |
--streaming \
|
| 37 |
-
--use_auth_token
|
| 38 |
-
--push_to_hub
|
|
|
|
| 1 |
python run_speech_recognition_seq2seq_streaming.py \
|
| 2 |
--model_name_or_path="marinone94/whisper-medium-nordic" \
|
| 3 |
+
--dataset_train_name="mozilla-foundation/common_voice_11_0,babelbox/babelbox_voice,google/fleurs" \
|
| 4 |
+
--dataset_train_config_name="sv-SE,nst,sv_se" \
|
| 5 |
--language="swedish" \
|
| 6 |
+
--train_split_name="train+validation,train,train+validation+test" \
|
| 7 |
+
--dataset_eval_name="mozilla-foundation/common_voice_11_0" \
|
| 8 |
+
--dataset_eval_config_name="sv-SE" \
|
| 9 |
--eval_split_name="test" \
|
| 10 |
--model_index_name="Whisper Medium Swedish" \
|
| 11 |
+
--max_steps="5000" \
|
| 12 |
--output_dir="./" \
|
| 13 |
--per_device_train_batch_size="32" \
|
| 14 |
--per_device_eval_batch_size="16" \
|
|
|
|
| 22 |
--generation_max_length="225" \
|
| 23 |
--length_column_name="input_length" \
|
| 24 |
--max_duration_in_seconds="30" \
|
| 25 |
+
--text_column_name="sentence,raw_transcription" \
|
| 26 |
--freeze_feature_encoder="False" \
|
| 27 |
+
--report_to="wandb" \
|
| 28 |
--metric_for_best_model="wer" \
|
| 29 |
--greater_is_better="False" \
|
| 30 |
--load_best_model_at_end \
|
| 31 |
--gradient_checkpointing \
|
| 32 |
--fp16 \
|
|
|
|
|
|
|
|
|
|
| 33 |
--predict_with_generate \
|
| 34 |
--do_normalize_eval \
|
| 35 |
--streaming \
|
| 36 |
+
--use_auth_token
|
|
|
run_speech_recognition_seq2seq_streaming.py
CHANGED
|
@@ -20,6 +20,7 @@ with 🤗 Datasets' streaming mode.
|
|
| 20 |
# You can also adapt this script for your own sequence to sequence speech
|
| 21 |
# recognition task. Pointers for this are left as comments.
|
| 22 |
|
|
|
|
| 23 |
import logging
|
| 24 |
import os
|
| 25 |
import sys
|
|
@@ -28,6 +29,7 @@ from typing import Any, Dict, List, Optional, Union
|
|
| 28 |
|
| 29 |
import datasets
|
| 30 |
import torch
|
|
|
|
| 31 |
from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
|
| 32 |
from torch.utils.data import IterableDataset
|
| 33 |
|
|
@@ -60,6 +62,42 @@ require_version("datasets>=1.18.2", "To fix: pip install -r examples/pytorch/spe
|
|
| 60 |
logger = logging.getLogger(__name__)
|
| 61 |
|
| 62 |
|
|
|
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|
| 63 |
@dataclass
|
| 64 |
class ModelArguments:
|
| 65 |
"""
|
|
@@ -127,10 +165,16 @@ class DataTrainingArguments:
|
|
| 127 |
Arguments pertaining to what data we are going to input our model for training and eval.
|
| 128 |
"""
|
| 129 |
|
| 130 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 131 |
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
| 132 |
)
|
| 133 |
-
|
| 134 |
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
| 135 |
)
|
| 136 |
text_column: Optional[str] = field(
|
|
@@ -265,27 +309,131 @@ class DataCollatorSpeechSeq2SeqWithPadding:
|
|
| 265 |
return batch
|
| 266 |
|
| 267 |
|
| 268 |
-
def
|
|
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|
| 269 |
"""
|
| 270 |
Utility function to load a dataset in streaming mode. For datasets with multiple splits,
|
| 271 |
each split is loaded individually and then splits combined by taking alternating examples from
|
| 272 |
each (interleaving).
|
| 273 |
"""
|
| 274 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 275 |
# load multiple splits separated by the `+` symbol with streaming mode
|
| 276 |
-
dataset_splits = [
|
| 277 |
-
|
| 278 |
-
|
| 279 |
-
|
|
|
|
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|
|
|
|
| 280 |
# interleave multiple splits to form one dataset
|
| 281 |
-
interleaved_dataset = interleave_datasets(dataset_splits)
|
| 282 |
return interleaved_dataset
|
| 283 |
else:
|
| 284 |
# load a single split *with* streaming mode
|
| 285 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
| 286 |
return dataset
|
| 287 |
|
| 288 |
|
|
|
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|
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|
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|
|
| 289 |
def main():
|
| 290 |
# 1. Parse input arguments
|
| 291 |
# See all possible arguments in src/transformers/training_args.py
|
|
@@ -349,39 +497,55 @@ def main():
|
|
| 349 |
# Set seed before initializing model.
|
| 350 |
set_seed(training_args.seed)
|
| 351 |
|
|
|
|
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|
|
|
|
|
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|
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|
|
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|
|
| 352 |
# 4. Load dataset
|
| 353 |
raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
|
| 354 |
|
| 355 |
if training_args.do_train:
|
| 356 |
raw_datasets["train"] = load_maybe_streaming_dataset(
|
| 357 |
-
data_args.
|
| 358 |
-
data_args.
|
| 359 |
split=data_args.train_split_name,
|
| 360 |
-
use_auth_token=
|
| 361 |
streaming=data_args.streaming,
|
|
|
|
|
|
|
|
|
|
|
|
|
| 362 |
)
|
| 363 |
|
| 364 |
if training_args.do_eval:
|
| 365 |
raw_datasets["eval"] = load_maybe_streaming_dataset(
|
| 366 |
-
data_args.
|
| 367 |
-
data_args.
|
| 368 |
split=data_args.eval_split_name,
|
| 369 |
-
use_auth_token=
|
| 370 |
streaming=data_args.streaming,
|
|
|
|
|
|
|
|
|
|
|
|
|
| 371 |
)
|
| 372 |
|
| 373 |
raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
|
| 374 |
-
|
| 375 |
if data_args.audio_column_name not in raw_datasets_features:
|
| 376 |
raise ValueError(
|
| 377 |
-
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.
|
| 378 |
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
| 379 |
f"{', '.join(raw_datasets_features)}."
|
| 380 |
)
|
| 381 |
|
| 382 |
-
if
|
| 383 |
raise ValueError(
|
| 384 |
-
f"--text_column_name {
|
| 385 |
"Make sure to set `--text_column_name` to the correct text column - one of "
|
| 386 |
f"{', '.join(raw_datasets_features)}."
|
| 387 |
)
|
|
@@ -394,7 +558,7 @@ def main():
|
|
| 394 |
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
| 395 |
cache_dir=model_args.cache_dir,
|
| 396 |
revision=model_args.model_revision,
|
| 397 |
-
use_auth_token=
|
| 398 |
)
|
| 399 |
|
| 400 |
config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
|
|
@@ -402,25 +566,19 @@ def main():
|
|
| 402 |
if training_args.gradient_checkpointing:
|
| 403 |
config.update({"use_cache": False})
|
| 404 |
|
| 405 |
-
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
| 406 |
-
model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
|
| 407 |
-
cache_dir=model_args.cache_dir,
|
| 408 |
-
revision=model_args.model_revision,
|
| 409 |
-
use_auth_token=True if model_args.use_auth_token else None,
|
| 410 |
-
)
|
| 411 |
tokenizer = AutoTokenizer.from_pretrained(
|
| 412 |
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
| 413 |
cache_dir=model_args.cache_dir,
|
| 414 |
use_fast=model_args.use_fast_tokenizer,
|
| 415 |
revision=model_args.model_revision,
|
| 416 |
-
use_auth_token=
|
| 417 |
)
|
| 418 |
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 419 |
model_args.model_name_or_path,
|
| 420 |
config=config,
|
| 421 |
cache_dir=model_args.cache_dir,
|
| 422 |
revision=model_args.model_revision,
|
| 423 |
-
use_auth_token=
|
| 424 |
)
|
| 425 |
|
| 426 |
if model.config.decoder_start_token_id is None:
|
|
@@ -448,7 +606,6 @@ def main():
|
|
| 448 |
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
| 449 |
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
| 450 |
audio_column_name = data_args.audio_column_name
|
| 451 |
-
text_column_name = data_args.text_column_name
|
| 452 |
model_input_name = feature_extractor.model_input_names[0]
|
| 453 |
do_lower_case = data_args.do_lower_case
|
| 454 |
do_remove_punctuation = data_args.do_remove_punctuation
|
|
@@ -568,6 +725,9 @@ def main():
|
|
| 568 |
callbacks=[ShuffleCallback()] if data_args.streaming else None,
|
| 569 |
)
|
| 570 |
|
|
|
|
|
|
|
|
|
|
| 571 |
# 12. Training
|
| 572 |
if training_args.do_train:
|
| 573 |
checkpoint = None
|
|
@@ -606,24 +766,43 @@ def main():
|
|
| 606 |
"tasks": "automatic-speech-recognition",
|
| 607 |
"tags": "whisper-event",
|
| 608 |
}
|
| 609 |
-
if data_args.
|
| 610 |
-
kwargs["dataset_tags"] = data_args.
|
| 611 |
-
if data_args.
|
| 612 |
-
kwargs["dataset"] = f"{data_args.
|
| 613 |
else:
|
| 614 |
-
kwargs["dataset"] = data_args.
|
| 615 |
-
if "common_voice" in data_args.
|
| 616 |
-
kwargs["language"] = data_args.
|
| 617 |
if model_args.model_index_name is not None:
|
| 618 |
kwargs["model_name"] = model_args.model_index_name
|
| 619 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 620 |
if training_args.push_to_hub:
|
| 621 |
trainer.push_to_hub(**kwargs)
|
| 622 |
else:
|
| 623 |
trainer.create_model_card(**kwargs)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 624 |
|
| 625 |
return results
|
| 626 |
|
| 627 |
|
| 628 |
if __name__ == "__main__":
|
| 629 |
-
main()
|
|
|
|
| 20 |
# You can also adapt this script for your own sequence to sequence speech
|
| 21 |
# recognition task. Pointers for this are left as comments.
|
| 22 |
|
| 23 |
+
import json
|
| 24 |
import logging
|
| 25 |
import os
|
| 26 |
import sys
|
|
|
|
| 29 |
|
| 30 |
import datasets
|
| 31 |
import torch
|
| 32 |
+
import wandb
|
| 33 |
from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
|
| 34 |
from torch.utils.data import IterableDataset
|
| 35 |
|
|
|
|
| 62 |
logger = logging.getLogger(__name__)
|
| 63 |
|
| 64 |
|
| 65 |
+
SENDING_NOTIFICATION = "*** Sending notification to email ***"
|
| 66 |
+
RECIPIENT_ADDRESS = "[email protected]"
|
| 67 |
+
|
| 68 |
+
wandb_token = os.environ.get("WANDB_TOKEN", "None")
|
| 69 |
+
hf_token = os.environ.get("HF_TOKEN", None)
|
| 70 |
+
if (hf_token is None or wandb_token == "None") and os.path.exists("./creds.txt"):
|
| 71 |
+
with open("./creds.txt", "r") as f:
|
| 72 |
+
lines = f.readlines()
|
| 73 |
+
for line in lines:
|
| 74 |
+
key, value = line.split("=")
|
| 75 |
+
if key == "HF_TOKEN":
|
| 76 |
+
hf_token = value.strip()
|
| 77 |
+
if key == "WANDB_TOKEN":
|
| 78 |
+
wandb_token = value.strip()
|
| 79 |
+
if key == "EMAIL_ADDRESS":
|
| 80 |
+
os.environ["EMAIL_ADDRESS"] = value.strip()
|
| 81 |
+
if key == "EMAIL_PASSWORD":
|
| 82 |
+
os.environ["EMAIL_PASSWORD"] = value.strip()
|
| 83 |
+
|
| 84 |
+
if hf_token is not None:
|
| 85 |
+
try:
|
| 86 |
+
os.makedirs("/root/.huggingface", exist_ok=True)
|
| 87 |
+
with open("/root/.huggingface/token", "w") as f:
|
| 88 |
+
f.write(hf_token)
|
| 89 |
+
logger.info("Huggingface API key set")
|
| 90 |
+
except (PermissionError, OSError):
|
| 91 |
+
logger.warning("Huggingface API key not set, relying on ~/.huggingface/token")
|
| 92 |
+
else:
|
| 93 |
+
logger.warning("Huggingface API key not set, relying on ~/.huggingface/token")
|
| 94 |
+
|
| 95 |
+
wandb.login(key=wandb_token, relogin=True, timeout=5)
|
| 96 |
+
wandb.init(project="whisper", entity="pn-aa")
|
| 97 |
+
|
| 98 |
+
logger.info("Wandb API key set, logging to wandb")
|
| 99 |
+
|
| 100 |
+
|
| 101 |
@dataclass
|
| 102 |
class ModelArguments:
|
| 103 |
"""
|
|
|
|
| 165 |
Arguments pertaining to what data we are going to input our model for training and eval.
|
| 166 |
"""
|
| 167 |
|
| 168 |
+
dataset_train_name: str = field(
|
| 169 |
+
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
| 170 |
+
)
|
| 171 |
+
dataset_train_config_name: Optional[str] = field(
|
| 172 |
+
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
| 173 |
+
)
|
| 174 |
+
dataset_eval_name: str = field(
|
| 175 |
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
| 176 |
)
|
| 177 |
+
dataset_eval_config_name: Optional[str] = field(
|
| 178 |
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
| 179 |
)
|
| 180 |
text_column: Optional[str] = field(
|
|
|
|
| 309 |
return batch
|
| 310 |
|
| 311 |
|
| 312 |
+
def rename_col_and_resample(dataset, dataset_name, text_column_names, text_col_name_ref, audio_column_name, sampling_rate):
|
| 313 |
+
raw_datasets_features = list(dataset.features.keys())
|
| 314 |
+
logger.info(f"Dataset {dataset_name} - Features: {raw_datasets_features}")
|
| 315 |
+
|
| 316 |
+
if text_col_name_ref not in raw_datasets_features:
|
| 317 |
+
if len(text_column_names) == 1:
|
| 318 |
+
raise ValueError("None of the text column names provided found in dataset."
|
| 319 |
+
f"Text columns: {text_column_names}"
|
| 320 |
+
f"Dataset columns: {raw_datasets_features}")
|
| 321 |
+
flag = False
|
| 322 |
+
for text_column_name in text_column_names:
|
| 323 |
+
if text_column_name in raw_datasets_features:
|
| 324 |
+
logger.info(f"Renaming text column {text_column_name} to {text_col_name_ref}")
|
| 325 |
+
dataset = dataset.rename_column(text_column_name, text_col_name_ref)
|
| 326 |
+
flag = True
|
| 327 |
+
break
|
| 328 |
+
if flag is False:
|
| 329 |
+
raise ValueError("None of the text column names provided found in dataset."
|
| 330 |
+
f"Text columns: {text_column_names}"
|
| 331 |
+
f"Dataset columns: {raw_datasets_features}")
|
| 332 |
+
if audio_column_name is not None and sampling_rate is not None:
|
| 333 |
+
ds_sr = int(dataset.features[audio_column_name].sampling_rate)
|
| 334 |
+
if ds_sr != sampling_rate:
|
| 335 |
+
dataset = dataset.cast_column(
|
| 336 |
+
audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)
|
| 337 |
+
)
|
| 338 |
+
|
| 339 |
+
raw_datasets_features = list(dataset.features.keys())
|
| 340 |
+
raw_datasets_features.remove(audio_column_name)
|
| 341 |
+
raw_datasets_features.remove(text_col_name_ref)
|
| 342 |
+
# Keep only audio and sentence
|
| 343 |
+
dataset = dataset.remove_columns(column_names=raw_datasets_features)
|
| 344 |
+
return dataset
|
| 345 |
+
|
| 346 |
+
|
| 347 |
+
def load_maybe_streaming_dataset(
|
| 348 |
+
dataset_names,
|
| 349 |
+
dataset_config_names,
|
| 350 |
+
split="train",
|
| 351 |
+
streaming=True,
|
| 352 |
+
audio_column_name=None,
|
| 353 |
+
sampling_rate=None,
|
| 354 |
+
**kwargs
|
| 355 |
+
):
|
| 356 |
"""
|
| 357 |
Utility function to load a dataset in streaming mode. For datasets with multiple splits,
|
| 358 |
each split is loaded individually and then splits combined by taking alternating examples from
|
| 359 |
each (interleaving).
|
| 360 |
"""
|
| 361 |
+
text_column_names = None
|
| 362 |
+
if "text_column_name" in kwargs:
|
| 363 |
+
text_column_names = kwargs.pop("text_column_name").split(",")
|
| 364 |
+
text_col_name_ref = text_column_names[0]
|
| 365 |
+
|
| 366 |
+
if "," in dataset_names or "+" in split:
|
| 367 |
# load multiple splits separated by the `+` symbol with streaming mode
|
| 368 |
+
dataset_splits = []
|
| 369 |
+
for dataset_name, dataset_config_name, split_names in zip(
|
| 370 |
+
dataset_names.split(","), dataset_config_names.split(","), split.split(",")
|
| 371 |
+
):
|
| 372 |
+
for split_name in split_names.split("+"):
|
| 373 |
+
if dataset_config_name:
|
| 374 |
+
dataset = load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, **kwargs)
|
| 375 |
+
else:
|
| 376 |
+
dataset = load_dataset(dataset_name, split=split_name, streaming=streaming, **kwargs)
|
| 377 |
+
|
| 378 |
+
dataset = rename_col_and_resample(
|
| 379 |
+
dataset,
|
| 380 |
+
dataset_name,
|
| 381 |
+
text_column_names,
|
| 382 |
+
text_col_name_ref,
|
| 383 |
+
audio_column_name,
|
| 384 |
+
sampling_rate
|
| 385 |
+
)
|
| 386 |
+
|
| 387 |
+
dataset_splits.append(dataset)
|
| 388 |
+
|
| 389 |
# interleave multiple splits to form one dataset
|
| 390 |
+
interleaved_dataset = interleave_datasets(dataset_splits, stopping_strategy="all_exhausted")
|
| 391 |
return interleaved_dataset
|
| 392 |
else:
|
| 393 |
# load a single split *with* streaming mode
|
| 394 |
+
|
| 395 |
+
dataset = load_dataset(dataset_names, dataset_config_names, split=split, streaming=streaming, **kwargs)
|
| 396 |
+
dataset = rename_col_and_resample(
|
| 397 |
+
dataset,
|
| 398 |
+
dataset_names,
|
| 399 |
+
text_column_names,
|
| 400 |
+
text_col_name_ref,
|
| 401 |
+
audio_column_name,
|
| 402 |
+
sampling_rate
|
| 403 |
+
)
|
| 404 |
return dataset
|
| 405 |
|
| 406 |
|
| 407 |
+
def notify_me(recipient, message=None):
|
| 408 |
+
"""
|
| 409 |
+
Send an email to the specified address with the specified message
|
| 410 |
+
"""
|
| 411 |
+
sender = os.environ.get("EMAIL_ADDRESS", None)
|
| 412 |
+
password = os.environ.get("EMAIL_PASSWORD", None)
|
| 413 |
+
if sender is None:
|
| 414 |
+
logging.warning("No email address specified, not sending notification")
|
| 415 |
+
if password is None:
|
| 416 |
+
logging.warning("No email password specified, not sending notification")
|
| 417 |
+
if message is None:
|
| 418 |
+
message = "Training is finished!"
|
| 419 |
+
|
| 420 |
+
if sender is not None:
|
| 421 |
+
import smtplib
|
| 422 |
+
from email.mime.text import MIMEText
|
| 423 |
+
|
| 424 |
+
msg = MIMEText(message)
|
| 425 |
+
msg["Subject"] = "Training updates..."
|
| 426 |
+
msg["From"] = "[email protected]"
|
| 427 |
+
msg["To"] = recipient
|
| 428 |
+
|
| 429 |
+
# send the email
|
| 430 |
+
smtp_obj = smtplib.SMTP("smtp.gmail.com", 587)
|
| 431 |
+
smtp_obj.starttls()
|
| 432 |
+
smtp_obj.login(sender, password)
|
| 433 |
+
smtp_obj.sendmail(sender, recipient, msg.as_string())
|
| 434 |
+
smtp_obj.quit()
|
| 435 |
+
|
| 436 |
+
|
| 437 |
def main():
|
| 438 |
# 1. Parse input arguments
|
| 439 |
# See all possible arguments in src/transformers/training_args.py
|
|
|
|
| 497 |
# Set seed before initializing model.
|
| 498 |
set_seed(training_args.seed)
|
| 499 |
|
| 500 |
+
# Load feature extractor
|
| 501 |
+
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
| 502 |
+
model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
|
| 503 |
+
cache_dir=model_args.cache_dir,
|
| 504 |
+
revision=model_args.model_revision,
|
| 505 |
+
use_auth_token=hf_token if model_args.use_auth_token else None,
|
| 506 |
+
)
|
| 507 |
+
|
| 508 |
# 4. Load dataset
|
| 509 |
raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
|
| 510 |
|
| 511 |
if training_args.do_train:
|
| 512 |
raw_datasets["train"] = load_maybe_streaming_dataset(
|
| 513 |
+
data_args.dataset_train_name,
|
| 514 |
+
data_args.dataset_train_config_name,
|
| 515 |
split=data_args.train_split_name,
|
| 516 |
+
use_auth_token=hf_token if model_args.use_auth_token else None,
|
| 517 |
streaming=data_args.streaming,
|
| 518 |
+
text_column_name=data_args.text_column_name,
|
| 519 |
+
audio_column_name=data_args.audio_column_name,
|
| 520 |
+
sampling_rate=int(feature_extractor.sampling_rate),
|
| 521 |
+
# language=data_args.language_train
|
| 522 |
)
|
| 523 |
|
| 524 |
if training_args.do_eval:
|
| 525 |
raw_datasets["eval"] = load_maybe_streaming_dataset(
|
| 526 |
+
data_args.dataset_eval_name,
|
| 527 |
+
data_args.dataset_eval_config_name,
|
| 528 |
split=data_args.eval_split_name,
|
| 529 |
+
use_auth_token=hf_token if model_args.use_auth_token else None,
|
| 530 |
streaming=data_args.streaming,
|
| 531 |
+
text_column_name=data_args.text_column_name,
|
| 532 |
+
audio_column_name=data_args.audio_column_name,
|
| 533 |
+
sampling_rate=int(feature_extractor.sampling_rate),
|
| 534 |
+
# language=data_args.language_eval
|
| 535 |
)
|
| 536 |
|
| 537 |
raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
|
| 538 |
+
text_column_name = data_args.text_column_name.split(",")[0]
|
| 539 |
if data_args.audio_column_name not in raw_datasets_features:
|
| 540 |
raise ValueError(
|
| 541 |
+
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_train_name}'. "
|
| 542 |
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
| 543 |
f"{', '.join(raw_datasets_features)}."
|
| 544 |
)
|
| 545 |
|
| 546 |
+
if text_column_name not in raw_datasets_features:
|
| 547 |
raise ValueError(
|
| 548 |
+
f"--text_column_name {text_column_name} not found in dataset '{data_args.dataset_train_name}'. "
|
| 549 |
"Make sure to set `--text_column_name` to the correct text column - one of "
|
| 550 |
f"{', '.join(raw_datasets_features)}."
|
| 551 |
)
|
|
|
|
| 558 |
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
| 559 |
cache_dir=model_args.cache_dir,
|
| 560 |
revision=model_args.model_revision,
|
| 561 |
+
use_auth_token=hf_token if model_args.use_auth_token else None,
|
| 562 |
)
|
| 563 |
|
| 564 |
config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
|
|
|
|
| 566 |
if training_args.gradient_checkpointing:
|
| 567 |
config.update({"use_cache": False})
|
| 568 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 569 |
tokenizer = AutoTokenizer.from_pretrained(
|
| 570 |
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
| 571 |
cache_dir=model_args.cache_dir,
|
| 572 |
use_fast=model_args.use_fast_tokenizer,
|
| 573 |
revision=model_args.model_revision,
|
| 574 |
+
use_auth_token=hf_token if model_args.use_auth_token else None,
|
| 575 |
)
|
| 576 |
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 577 |
model_args.model_name_or_path,
|
| 578 |
config=config,
|
| 579 |
cache_dir=model_args.cache_dir,
|
| 580 |
revision=model_args.model_revision,
|
| 581 |
+
use_auth_token=hf_token if model_args.use_auth_token else None,
|
| 582 |
)
|
| 583 |
|
| 584 |
if model.config.decoder_start_token_id is None:
|
|
|
|
| 606 |
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
| 607 |
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
| 608 |
audio_column_name = data_args.audio_column_name
|
|
|
|
| 609 |
model_input_name = feature_extractor.model_input_names[0]
|
| 610 |
do_lower_case = data_args.do_lower_case
|
| 611 |
do_remove_punctuation = data_args.do_remove_punctuation
|
|
|
|
| 725 |
callbacks=[ShuffleCallback()] if data_args.streaming else None,
|
| 726 |
)
|
| 727 |
|
| 728 |
+
orig_push_to_hub = trainer.args.push_to_hub
|
| 729 |
+
trainer.args.push_to_hub = False
|
| 730 |
+
|
| 731 |
# 12. Training
|
| 732 |
if training_args.do_train:
|
| 733 |
checkpoint = None
|
|
|
|
| 766 |
"tasks": "automatic-speech-recognition",
|
| 767 |
"tags": "whisper-event",
|
| 768 |
}
|
| 769 |
+
if data_args.dataset_train_name is not None:
|
| 770 |
+
kwargs["dataset_tags"] = data_args.dataset_train_name
|
| 771 |
+
if data_args.dataset_train_config_name is not None:
|
| 772 |
+
kwargs["dataset"] = f"{data_args.dataset_train_name} {data_args.dataset_train_config_name}"
|
| 773 |
else:
|
| 774 |
+
kwargs["dataset"] = data_args.dataset_train_name
|
| 775 |
+
if "common_voice" in data_args.dataset_train_name:
|
| 776 |
+
kwargs["language"] = data_args.dataset_train_config_name[:2]
|
| 777 |
if model_args.model_index_name is not None:
|
| 778 |
kwargs["model_name"] = model_args.model_index_name
|
| 779 |
|
| 780 |
+
logger.info("*** Training stats written ***")
|
| 781 |
+
logger.info(json.dumps(kwargs, indent=4))
|
| 782 |
+
|
| 783 |
+
# Training complete notification
|
| 784 |
+
logger.info("*** Training and eval complete ***")
|
| 785 |
+
logger.info(SENDING_NOTIFICATION)
|
| 786 |
+
with open(os.path.join(training_args.output_dir, "train_results.json"), "r") as f:
|
| 787 |
+
train_results = json.load(f)
|
| 788 |
+
with open(os.path.join(training_args.output_dir, "eval_results.json"), "r") as f:
|
| 789 |
+
eval_results = json.load(f)
|
| 790 |
+
notify_me(recipient=RECIPIENT_ADDRESS,
|
| 791 |
+
message=f"Training complete! {train_results = } {eval_results = }")
|
| 792 |
+
|
| 793 |
+
trainer.args.push_to_hub = orig_push_to_hub
|
| 794 |
if training_args.push_to_hub:
|
| 795 |
trainer.push_to_hub(**kwargs)
|
| 796 |
else:
|
| 797 |
trainer.create_model_card(**kwargs)
|
| 798 |
+
|
| 799 |
+
with open(os.path.join(training_args.output_dir, "README.md"), "r") as f:
|
| 800 |
+
readme = f.read()
|
| 801 |
+
notify_me(recipient=RECIPIENT_ADDRESS,
|
| 802 |
+
message=f"Model pushed to hub! {readme = }")
|
| 803 |
|
| 804 |
return results
|
| 805 |
|
| 806 |
|
| 807 |
if __name__ == "__main__":
|
| 808 |
+
main()
|
train_results.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
-
"epoch":
|
| 3 |
-
"train_loss": 0.
|
| 4 |
-
"train_runtime":
|
| 5 |
-
"train_samples_per_second": 3.
|
| 6 |
-
"train_steps_per_second": 0.
|
| 7 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"epoch": 1.0,
|
| 3 |
+
"train_loss": 0.025400285175442697,
|
| 4 |
+
"train_runtime": 51804.3597,
|
| 5 |
+
"train_samples_per_second": 3.089,
|
| 6 |
+
"train_steps_per_second": 0.097
|
| 7 |
}
|
trainer_state.json
CHANGED
|
@@ -1,8 +1,8 @@
|
|
| 1 |
{
|
| 2 |
-
"best_metric":
|
| 3 |
-
"best_model_checkpoint": "./checkpoint-
|
| 4 |
-
"epoch":
|
| 5 |
-
"global_step":
|
| 6 |
"is_hyper_param_search": false,
|
| 7 |
"is_local_process_zero": true,
|
| 8 |
"is_world_process_zero": true,
|
|
@@ -10,634 +10,1261 @@
|
|
| 10 |
{
|
| 11 |
"epoch": 0.01,
|
| 12 |
"learning_rate": 9.200000000000001e-07,
|
| 13 |
-
"loss": 0.
|
| 14 |
"step": 25
|
| 15 |
},
|
| 16 |
{
|
| 17 |
-
"epoch": 0.
|
| 18 |
"learning_rate": 1.9200000000000003e-06,
|
| 19 |
-
"loss": 0.
|
| 20 |
"step": 50
|
| 21 |
},
|
| 22 |
{
|
| 23 |
-
"epoch": 0.
|
| 24 |
"learning_rate": 2.92e-06,
|
| 25 |
-
"loss": 0.
|
| 26 |
"step": 75
|
| 27 |
},
|
| 28 |
{
|
| 29 |
-
"epoch": 0.
|
| 30 |
"learning_rate": 3.920000000000001e-06,
|
| 31 |
-
"loss": 0.
|
| 32 |
"step": 100
|
| 33 |
},
|
| 34 |
{
|
| 35 |
-
"epoch": 0.
|
| 36 |
"learning_rate": 4.92e-06,
|
| 37 |
-
"loss": 0.
|
| 38 |
"step": 125
|
| 39 |
},
|
| 40 |
{
|
| 41 |
-
"epoch": 0.
|
| 42 |
"learning_rate": 5.92e-06,
|
| 43 |
-
"loss": 0.
|
| 44 |
"step": 150
|
| 45 |
},
|
| 46 |
{
|
| 47 |
-
"epoch": 0.
|
| 48 |
"learning_rate": 6.92e-06,
|
| 49 |
-
"loss": 0.
|
| 50 |
"step": 175
|
| 51 |
},
|
| 52 |
{
|
| 53 |
-
"epoch": 0.
|
| 54 |
"learning_rate": 7.92e-06,
|
| 55 |
-
"loss": 0.
|
| 56 |
"step": 200
|
| 57 |
},
|
| 58 |
{
|
| 59 |
-
"epoch": 0.
|
| 60 |
"learning_rate": 8.920000000000001e-06,
|
| 61 |
-
"loss": 0.
|
| 62 |
"step": 225
|
| 63 |
},
|
| 64 |
{
|
| 65 |
-
"epoch": 0.
|
| 66 |
"learning_rate": 9.920000000000002e-06,
|
| 67 |
-
"loss": 0.
|
| 68 |
"step": 250
|
| 69 |
},
|
| 70 |
{
|
| 71 |
-
"epoch": 0.
|
| 72 |
-
"learning_rate": 9.
|
| 73 |
-
"loss": 0.
|
| 74 |
"step": 275
|
| 75 |
},
|
| 76 |
{
|
| 77 |
-
"epoch": 0.
|
| 78 |
-
"learning_rate": 9.
|
| 79 |
-
"loss": 0.
|
| 80 |
"step": 300
|
| 81 |
},
|
| 82 |
{
|
| 83 |
-
"epoch":
|
| 84 |
-
"learning_rate": 9.
|
| 85 |
-
"loss": 0.
|
| 86 |
"step": 325
|
| 87 |
},
|
| 88 |
{
|
| 89 |
-
"epoch":
|
| 90 |
-
"learning_rate": 9.
|
| 91 |
-
"loss": 0.
|
| 92 |
"step": 350
|
| 93 |
},
|
| 94 |
{
|
| 95 |
-
"epoch":
|
| 96 |
-
"learning_rate": 9.
|
| 97 |
-
"loss": 0.
|
| 98 |
"step": 375
|
| 99 |
},
|
| 100 |
{
|
| 101 |
-
"epoch":
|
| 102 |
-
"learning_rate": 9.
|
| 103 |
-
"loss": 0.
|
| 104 |
"step": 400
|
| 105 |
},
|
| 106 |
{
|
| 107 |
-
"epoch":
|
| 108 |
-
"learning_rate": 9.
|
| 109 |
-
"loss": 0.
|
| 110 |
"step": 425
|
| 111 |
},
|
| 112 |
{
|
| 113 |
-
"epoch":
|
| 114 |
-
"learning_rate": 9.
|
| 115 |
-
"loss": 0.
|
| 116 |
"step": 450
|
| 117 |
},
|
| 118 |
{
|
| 119 |
-
"epoch": 1
|
| 120 |
-
"learning_rate": 9.
|
| 121 |
-
"loss": 0.
|
| 122 |
"step": 475
|
| 123 |
},
|
| 124 |
{
|
| 125 |
-
"epoch": 1
|
| 126 |
-
"learning_rate":
|
| 127 |
-
"loss": 0.
|
| 128 |
"step": 500
|
| 129 |
},
|
| 130 |
{
|
| 131 |
-
"epoch": 1
|
| 132 |
-
"learning_rate":
|
| 133 |
-
"loss": 0.
|
| 134 |
"step": 525
|
| 135 |
},
|
| 136 |
{
|
| 137 |
-
"epoch":
|
| 138 |
-
"learning_rate":
|
| 139 |
-
"loss": 0.
|
| 140 |
"step": 550
|
| 141 |
},
|
| 142 |
{
|
| 143 |
-
"epoch":
|
| 144 |
-
"learning_rate":
|
| 145 |
-
"loss": 0.
|
| 146 |
"step": 575
|
| 147 |
},
|
| 148 |
{
|
| 149 |
-
"epoch":
|
| 150 |
-
"learning_rate":
|
| 151 |
-
"loss": 0.
|
| 152 |
"step": 600
|
| 153 |
},
|
| 154 |
{
|
| 155 |
-
"epoch":
|
| 156 |
-
"learning_rate":
|
| 157 |
-
"loss": 0.
|
| 158 |
"step": 625
|
| 159 |
},
|
| 160 |
{
|
| 161 |
-
"epoch":
|
| 162 |
-
"learning_rate":
|
| 163 |
-
"loss": 0.
|
| 164 |
"step": 650
|
| 165 |
},
|
| 166 |
{
|
| 167 |
-
"epoch":
|
| 168 |
-
"learning_rate":
|
| 169 |
-
"loss": 0.
|
| 170 |
"step": 675
|
| 171 |
},
|
| 172 |
{
|
| 173 |
-
"epoch":
|
| 174 |
-
"learning_rate":
|
| 175 |
-
"loss": 0.
|
| 176 |
"step": 700
|
| 177 |
},
|
| 178 |
{
|
| 179 |
-
"epoch":
|
| 180 |
-
"learning_rate":
|
| 181 |
-
"loss": 0.
|
| 182 |
"step": 725
|
| 183 |
},
|
| 184 |
{
|
| 185 |
-
"epoch":
|
| 186 |
-
"learning_rate":
|
| 187 |
-
"loss": 0.
|
| 188 |
"step": 750
|
| 189 |
},
|
| 190 |
{
|
| 191 |
-
"epoch":
|
| 192 |
-
"learning_rate":
|
| 193 |
-
"loss": 0.
|
| 194 |
"step": 775
|
| 195 |
},
|
| 196 |
{
|
| 197 |
-
"epoch":
|
| 198 |
-
"learning_rate":
|
| 199 |
-
"loss": 0.
|
| 200 |
"step": 800
|
| 201 |
},
|
| 202 |
{
|
| 203 |
-
"epoch":
|
| 204 |
-
"learning_rate":
|
| 205 |
-
"loss": 0.
|
| 206 |
"step": 825
|
| 207 |
},
|
| 208 |
{
|
| 209 |
-
"epoch":
|
| 210 |
-
"learning_rate":
|
| 211 |
-
"loss": 0.
|
| 212 |
"step": 850
|
| 213 |
},
|
| 214 |
{
|
| 215 |
-
"epoch":
|
| 216 |
-
"learning_rate":
|
| 217 |
-
"loss": 0.
|
| 218 |
"step": 875
|
| 219 |
},
|
| 220 |
{
|
| 221 |
-
"epoch":
|
| 222 |
-
"learning_rate":
|
| 223 |
-
"loss": 0.
|
| 224 |
"step": 900
|
| 225 |
},
|
| 226 |
{
|
| 227 |
-
"epoch":
|
| 228 |
-
"learning_rate":
|
| 229 |
-
"loss": 0.
|
| 230 |
"step": 925
|
| 231 |
},
|
| 232 |
{
|
| 233 |
-
"epoch":
|
| 234 |
-
"learning_rate":
|
| 235 |
-
"loss": 0.
|
| 236 |
"step": 950
|
| 237 |
},
|
| 238 |
{
|
| 239 |
-
"epoch":
|
| 240 |
-
"learning_rate":
|
| 241 |
-
"loss": 0.
|
| 242 |
"step": 975
|
| 243 |
},
|
| 244 |
{
|
| 245 |
-
"epoch":
|
| 246 |
-
"learning_rate":
|
| 247 |
-
"loss": 0.
|
| 248 |
"step": 1000
|
| 249 |
},
|
| 250 |
{
|
| 251 |
-
"epoch":
|
| 252 |
-
"eval_loss": 0.
|
| 253 |
-
"eval_runtime":
|
| 254 |
-
"eval_samples_per_second": 1.
|
| 255 |
-
"eval_steps_per_second": 0.
|
| 256 |
-
"eval_wer":
|
| 257 |
"step": 1000
|
| 258 |
},
|
| 259 |
{
|
| 260 |
-
"epoch":
|
| 261 |
-
"learning_rate":
|
| 262 |
-
"loss": 0.
|
| 263 |
"step": 1025
|
| 264 |
},
|
| 265 |
{
|
| 266 |
-
"epoch":
|
| 267 |
-
"learning_rate":
|
| 268 |
-
"loss": 0.
|
| 269 |
"step": 1050
|
| 270 |
},
|
| 271 |
{
|
| 272 |
-
"epoch":
|
| 273 |
-
"learning_rate":
|
| 274 |
-
"loss": 0.
|
| 275 |
"step": 1075
|
| 276 |
},
|
| 277 |
{
|
| 278 |
-
"epoch":
|
| 279 |
-
"learning_rate":
|
| 280 |
-
"loss": 0.
|
| 281 |
"step": 1100
|
| 282 |
},
|
| 283 |
{
|
| 284 |
-
"epoch":
|
| 285 |
-
"learning_rate":
|
| 286 |
-
"loss": 0.
|
| 287 |
"step": 1125
|
| 288 |
},
|
| 289 |
{
|
| 290 |
-
"epoch":
|
| 291 |
-
"learning_rate":
|
| 292 |
-
"loss": 0.
|
| 293 |
"step": 1150
|
| 294 |
},
|
| 295 |
{
|
| 296 |
-
"epoch":
|
| 297 |
-
"learning_rate":
|
| 298 |
-
"loss": 0.
|
| 299 |
"step": 1175
|
| 300 |
},
|
| 301 |
{
|
| 302 |
-
"epoch":
|
| 303 |
-
"learning_rate":
|
| 304 |
-
"loss": 0.
|
| 305 |
"step": 1200
|
| 306 |
},
|
| 307 |
{
|
| 308 |
-
"epoch":
|
| 309 |
-
"learning_rate":
|
| 310 |
-
"loss": 0.
|
| 311 |
"step": 1225
|
| 312 |
},
|
| 313 |
{
|
| 314 |
-
"epoch":
|
| 315 |
-
"learning_rate":
|
| 316 |
-
"loss": 0.
|
| 317 |
"step": 1250
|
| 318 |
},
|
| 319 |
{
|
| 320 |
-
"epoch":
|
| 321 |
-
"learning_rate":
|
| 322 |
-
"loss": 0.
|
| 323 |
"step": 1275
|
| 324 |
},
|
| 325 |
{
|
| 326 |
-
"epoch":
|
| 327 |
-
"learning_rate":
|
| 328 |
-
"loss": 0.
|
| 329 |
"step": 1300
|
| 330 |
},
|
| 331 |
{
|
| 332 |
-
"epoch":
|
| 333 |
-
"learning_rate":
|
| 334 |
-
"loss": 0.
|
| 335 |
"step": 1325
|
| 336 |
},
|
| 337 |
{
|
| 338 |
-
"epoch":
|
| 339 |
-
"learning_rate":
|
| 340 |
-
"loss": 0.
|
| 341 |
"step": 1350
|
| 342 |
},
|
| 343 |
{
|
| 344 |
-
"epoch":
|
| 345 |
-
"learning_rate":
|
| 346 |
-
"loss": 0.
|
| 347 |
"step": 1375
|
| 348 |
},
|
| 349 |
{
|
| 350 |
-
"epoch":
|
| 351 |
-
"learning_rate":
|
| 352 |
-
"loss": 0.
|
| 353 |
"step": 1400
|
| 354 |
},
|
| 355 |
{
|
| 356 |
-
"epoch":
|
| 357 |
-
"learning_rate":
|
| 358 |
-
"loss": 0.
|
| 359 |
"step": 1425
|
| 360 |
},
|
| 361 |
{
|
| 362 |
-
"epoch":
|
| 363 |
-
"learning_rate":
|
| 364 |
-
"loss": 0.
|
| 365 |
"step": 1450
|
| 366 |
},
|
| 367 |
{
|
| 368 |
-
"epoch":
|
| 369 |
-
"learning_rate":
|
| 370 |
-
"loss": 0.
|
| 371 |
"step": 1475
|
| 372 |
},
|
| 373 |
{
|
| 374 |
-
"epoch":
|
| 375 |
-
"learning_rate":
|
| 376 |
-
"loss": 0.
|
| 377 |
"step": 1500
|
| 378 |
},
|
| 379 |
{
|
| 380 |
-
"epoch":
|
| 381 |
-
"learning_rate":
|
| 382 |
-
"loss": 0.
|
| 383 |
"step": 1525
|
| 384 |
},
|
| 385 |
{
|
| 386 |
-
"epoch":
|
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