training.log

05/07/2023 10:33:39 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 2distributed training: True, 16-bits training: True
05/07/2023 10:33:39 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=2,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=1000,
evaluation_strategy=steps,
fp16=True,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=225,
generation_num_beams=None,
gradient_accumulation_steps=2,
gradient_checkpointing=True,
greater_is_better=False,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=1e-05,
length_column_name=input_length,
load_best_model_at_end=True,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./runs/May07_10-33-38_crimv3mgpu025,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=25,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=5000,
metric_for_best_model=wer,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=./,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=32,
per_device_train_batch_size=32,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['wandb'],
resume_from_checkpoint=None,
run_name=./,
save_on_each_node=False,
save_safetensors=False,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=500,
weight_decay=0.0,
xpu_backend=None,
)
05/07/2023 10:33:39 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=2,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=1000,
evaluation_strategy=steps,
fp16=True,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=225,
generation_num_beams=None,
gradient_accumulation_steps=2,
gradient_checkpointing=True,
greater_is_better=False,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=1e-05,
length_column_name=input_length,
load_best_model_at_end=True,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./runs/May07_10-33-38_crimv3mgpu025,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=25,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=5000,
metric_for_best_model=wer,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=./,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=32,
per_device_train_batch_size=32,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['wandb'],
resume_from_checkpoint=None,
run_name=./,
save_on_each_node=False,
save_safetensors=False,
save_steps=1000,
save_strategy=steps,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=500,
weight_decay=0.0,
xpu_backend=None,
)
[INFO|configuration_utils.py:669] 2023-05-07 10:33:51,873 >> loading configuration file config.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/config.json
[INFO|configuration_utils.py:725] 2023-05-07 10:33:51,887 >> Model config WhisperConfig {
  "_name_or_path": "openai/whisper-small",
  "activation_dropout": 0.0,
  "activation_function": "gelu",
  "apply_spec_augment": false,
  "architectures": [
    "WhisperForConditionalGeneration"
  ],
  "attention_dropout": 0.0,
  "begin_suppress_tokens": [
    220,
    50257
  ],
  "bos_token_id": 50257,
  "classifier_proj_size": 256,
  "d_model": 768,
  "decoder_attention_heads": 12,
  "decoder_ffn_dim": 3072,
  "decoder_layerdrop": 0.0,
  "decoder_layers": 12,
  "decoder_start_token_id": 50258,
  "dropout": 0.0,
  "encoder_attention_heads": 12,
  "encoder_ffn_dim": 3072,
  "encoder_layerdrop": 0.0,
  "encoder_layers": 12,
  "eos_token_id": 50257,
  "forced_decoder_ids": [
    [
      1,
      50259
    ],
    [
      2,
      50359
    ],
    [
      3,
      50363
    ]
  ],
  "init_std": 0.02,
  "is_encoder_decoder": true,
  "mask_feature_length": 10,
  "mask_feature_min_masks": 0,
  "mask_feature_prob": 0.0,
  "mask_time_length": 10,
  "mask_time_min_masks": 2,
  "mask_time_prob": 0.05,
  "max_length": 448,
  "max_source_positions": 1500,
  "max_target_positions": 448,
  "model_type": "whisper",
  "num_hidden_layers": 12,
  "num_mel_bins": 80,
  "pad_token_id": 50257,
  "scale_embedding": false,
  "suppress_tokens": [
    1,
    2,
    7,
    8,
    9,
    10,
    14,
    25,
    26,
    27,
    28,
    29,
    31,
    58,
    59,
    60,
    61,
    62,
    63,
    90,
    91,
    92,
    93,
    359,
    503,
    522,
    542,
    873,
    893,
    902,
    918,
    922,
    931,
    1350,
    1853,
    1982,
    2460,
    2627,
    3246,
    3253,
    3268,
    3536,
    3846,
    3961,
    4183,
    4667,
    6585,
    6647,
    7273,
    9061,
    9383,
    10428,
    10929,
    11938,
    12033,
    12331,
    12562,
    13793,
    14157,
    14635,
    15265,
    15618,
    16553,
    16604,
    18362,
    18956,
    20075,
    21675,
    22520,
    26130,
    26161,
    26435,
    28279,
    29464,
    31650,
    32302,
    32470,
    36865,
    42863,
    47425,
    49870,
    50254,
    50258,
    50360,
    50361,
    50362
  ],
  "torch_dtype": "float32",
  "transformers_version": "4.29.0.dev0",
  "use_cache": true,
  "use_weighted_layer_sum": false,
  "vocab_size": 51865
}

[INFO|feature_extraction_utils.py:469] 2023-05-07 10:33:52,076 >> loading configuration file preprocessor_config.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/preprocessor_config.json
[INFO|feature_extraction_utils.py:511] 2023-05-07 10:33:52,082 >> Feature extractor WhisperFeatureExtractor {
  "chunk_length": 30,
  "feature_extractor_type": "WhisperFeatureExtractor",
  "feature_size": 80,
  "hop_length": 160,
  "n_fft": 400,
  "n_samples": 480000,
  "nb_max_frames": 3000,
  "padding_side": "right",
  "padding_value": 0.0,
  "processor_class": "WhisperProcessor",
  "return_attention_mask": false,
  "sampling_rate": 16000
}

[INFO|tokenization_utils_base.py:1810] 2023-05-07 10:33:52,291 >> loading file vocab.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/vocab.json
[INFO|tokenization_utils_base.py:1810] 2023-05-07 10:33:52,291 >> loading file tokenizer.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/tokenizer.json
[INFO|tokenization_utils_base.py:1810] 2023-05-07 10:33:52,291 >> loading file merges.txt from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/merges.txt
[INFO|tokenization_utils_base.py:1810] 2023-05-07 10:33:52,291 >> loading file normalizer.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/normalizer.json
[INFO|tokenization_utils_base.py:1810] 2023-05-07 10:33:52,291 >> loading file added_tokens.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/added_tokens.json
[INFO|tokenization_utils_base.py:1810] 2023-05-07 10:33:52,291 >> loading file special_tokens_map.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/special_tokens_map.json
[INFO|tokenization_utils_base.py:1810] 2023-05-07 10:33:52,291 >> loading file tokenizer_config.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/tokenizer_config.json
[INFO|modeling_utils.py:2542] 2023-05-07 10:33:52,385 >> loading weights file pytorch_model.bin from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/pytorch_model.bin
[INFO|configuration_utils.py:577] 2023-05-07 10:33:52,963 >> Generate config GenerationConfig {
  "_from_model_config": true,
  "begin_suppress_tokens": [
    220,
    50257
  ],
  "bos_token_id": 50257,
  "decoder_start_token_id": 50258,
  "eos_token_id": 50257,
  "max_length": 448,
  "pad_token_id": 50257,
  "transformers_version": "4.29.0.dev0",
  "use_cache": false
}

[INFO|modeling_utils.py:3211] 2023-05-07 10:33:55,474 >> All model checkpoint weights were used when initializing WhisperForConditionalGeneration.

[INFO|modeling_utils.py:3219] 2023-05-07 10:33:55,474 >> All the weights of WhisperForConditionalGeneration were initialized from the model checkpoint at openai/whisper-small.
If your task is similar to the task the model of the checkpoint was trained on, you can already use WhisperForConditionalGeneration for predictions without further training.
[INFO|configuration_utils.py:539] 2023-05-07 10:33:55,680 >> loading configuration file generation_config.json from cache at /home/local/QCRI/dizham/.cache/huggingface/hub/models--openai--whisper-small/snapshots/f6744499d1eba717bcf4d6be735e3d386ffb60ad/generation_config.json
[INFO|configuration_utils.py:577] 2023-05-07 10:33:55,681 >> Generate config GenerationConfig {
  "begin_suppress_tokens": [
    220,
    50257
  ],
  "bos_token_id": 50257,
  "decoder_start_token_id": 50258,
  "eos_token_id": 50257,
  "forced_decoder_ids": [
    [
      1,
      null
    ],
    [
      2,
      50359
    ]
  ],
  "is_multilingual": true,
  "lang_to_id": {
    "<|af|>": 50327,
    "<|am|>": 50334,
    "<|ar|>": 50272,
    "<|as|>": 50350,
    "<|az|>": 50304,
    "<|ba|>": 50355,
    "<|be|>": 50330,
    "<|bg|>": 50292,
    "<|bn|>": 50302,
    "<|bo|>": 50347,
    "<|br|>": 50309,
    "<|bs|>": 50315,
    "<|ca|>": 50270,
    "<|cs|>": 50283,
    "<|cy|>": 50297,
    "<|da|>": 50285,
    "<|de|>": 50261,
    "<|el|>": 50281,
    "<|en|>": 50259,
    "<|es|>": 50262,
    "<|et|>": 50307,
    "<|eu|>": 50310,
    "<|fa|>": 50300,
    "<|fi|>": 50277,
    "<|fo|>": 50338,
    "<|fr|>": 50265,
    "<|gl|>": 50319,
    "<|gu|>": 50333,
    "<|haw|>": 50352,
    "<|ha|>": 50354,
    "<|he|>": 50279,
    "<|hi|>": 50276,
    "<|hr|>": 50291,
    "<|ht|>": 50339,
    "<|hu|>": 50286,
    "<|hy|>": 50312,
    "<|id|>": 50275,
    "<|is|>": 50311,
    "<|it|>": 50274,
    "<|ja|>": 50266,
    "<|jw|>": 50356,
    "<|ka|>": 50329,
    "<|kk|>": 50316,
    "<|km|>": 50323,
    "<|kn|>": 50306,
    "<|ko|>": 50264,
    "<|la|>": 50294,
    "<|lb|>": 50345,
    "<|ln|>": 50353,
    "<|lo|>": 50336,
    "<|lt|>": 50293,
    "<|lv|>": 50301,
    "<|mg|>": 50349,
    "<|mi|>": 50295,
    "<|mk|>": 50308,
    "<|ml|>": 50296,
    "<|mn|>": 50314,
    "<|mr|>": 50320,
    "<|ms|>": 50282,
    "<|mt|>": 50343,
    "<|my|>": 50346,
    "<|ne|>": 50313,
    "<|nl|>": 50271,
    "<|nn|>": 50342,
    "<|no|>": 50288,
    "<|oc|>": 50328,
    "<|pa|>": 50321,
    "<|pl|>": 50269,
    "<|ps|>": 50340,
    "<|pt|>": 50267,
    "<|ro|>": 50284,
    "<|ru|>": 50263,
    "<|sa|>": 50344,
    "<|sd|>": 50332,
    "<|si|>": 50322,
    "<|sk|>": 50298,
    "<|sl|>": 50305,
    "<|sn|>": 50324,
    "<|so|>": 50326,
    "<|sq|>": 50317,
    "<|sr|>": 50303,
    "<|su|>": 50357,
    "<|sv|>": 50273,
    "<|sw|>": 50318,
    "<|ta|>": 50287,
    "<|te|>": 50299,
    "<|tg|>": 50331,
    "<|th|>": 50289,
    "<|tk|>": 50341,
    "<|tl|>": 50348,
    "<|tr|>": 50268,
    "<|tt|>": 50351,
    "<|uk|>": 50280,
    "<|ur|>": 50290,
    "<|uz|>": 50337,
    "<|vi|>": 50278,
    "<|yi|>": 50335,
    "<|yo|>": 50325,
    "<|zh|>": 50260
  },
  "max_initial_timestamp_index": 1,
  "max_length": 448,
  "no_timestamps_token_id": 50363,
  "pad_token_id": 50257,
  "return_timestamps": false,
  "suppress_tokens": [
    1,
    2,
    7,
    8,
    9,
    10,
    14,
    25,
    26,
    27,
    28,
    29,
    31,
    58,
    59,
    60,
    61,
    62,
    63,
    90,
    91,
    92,
    93,
    359,
    503,
    522,
    542,
    873,
    893,
    902,
    918,
    922,
    931,
    1350,
    1853,
    1982,
    2460,
    2627,
    3246,
    3253,
    3268,
    3536,
    3846,
    3961,
    4183,
    4667,
    6585,
    6647,
    7273,
    9061,
    9383,
    10428,
    10929,
    11938,
    12033,
    12331,
    12562,
    13793,
    14157,
    14635,
    15265,
    15618,
    16553,
    16604,
    18362,
    18956,
    20075,
    21675,
    22520,
    26130,
    26161,
    26435,
    28279,
    29464,
    31650,
    32302,
    32470,
    36865,
    42863,
    47425,
    49870,
    50254,
    50258,
    50358,
    50359,
    50360,
    50361,
    50362
  ],
  "task_to_id": {
    "transcribe": 50359,
    "translate": 50358
  },
  "transformers_version": "4.29.0.dev0"
}

[INFO|feature_extraction_utils.py:369] 2023-05-07 10:33:56,907 >> Feature extractor saved in ./preprocessor_config.json
[INFO|tokenization_utils_base.py:2181] 2023-05-07 10:33:56,915 >> tokenizer config file saved in ./tokenizer_config.json
[INFO|tokenization_utils_base.py:2188] 2023-05-07 10:33:56,922 >> Special tokens file saved in ./special_tokens_map.json
[INFO|configuration_utils.py:458] 2023-05-07 10:33:57,075 >> Configuration saved in ./config.json
[INFO|image_processing_utils.py:307] 2023-05-07 10:33:57,075 >> loading configuration file ./preprocessor_config.json
[INFO|feature_extraction_utils.py:467] 2023-05-07 10:33:57,084 >> loading configuration file ./preprocessor_config.json
[INFO|feature_extraction_utils.py:511] 2023-05-07 10:33:57,085 >> Feature extractor WhisperFeatureExtractor {
  "chunk_length": 30,
  "feature_extractor_type": "WhisperFeatureExtractor",
  "feature_size": 80,
  "hop_length": 160,
  "n_fft": 400,
  "n_samples": 480000,
  "nb_max_frames": 3000,
  "padding_side": "right",
  "padding_value": 0.0,
  "processor_class": "WhisperProcessor",
  "return_attention_mask": false,
  "sampling_rate": 16000
}

[INFO|tokenization_utils_base.py:1808] 2023-05-07 10:33:57,086 >> loading file vocab.json
[INFO|tokenization_utils_base.py:1808] 2023-05-07 10:33:57,086 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:1808] 2023-05-07 10:33:57,086 >> loading file merges.txt
[INFO|tokenization_utils_base.py:1808] 2023-05-07 10:33:57,086 >> loading file normalizer.json
[INFO|tokenization_utils_base.py:1808] 2023-05-07 10:33:57,086 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1808] 2023-05-07 10:33:57,086 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1808] 2023-05-07 10:33:57,086 >> loading file tokenizer_config.json
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|startoftranscript|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|en|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|zh|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|de|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|es|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|ru|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|ko|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|fr|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|ja|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|pt|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|tr|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|pl|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|ca|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|nl|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|ar|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|sv|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|it|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|id|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|hi|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|fi|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,149 >> Adding <|vi|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|he|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|uk|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|el|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|ms|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|cs|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|ro|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|da|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|hu|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|ta|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|no|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|th|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|ur|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|hr|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|bg|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|lt|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|la|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|mi|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|ml|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|cy|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|sk|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|te|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|fa|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|lv|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|bn|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|sr|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|az|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|sl|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|kn|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|et|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|mk|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|br|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|eu|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|is|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|hy|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|ne|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|mn|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|bs|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|kk|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|sq|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|sw|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|gl|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,150 >> Adding <|mr|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|pa|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|si|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|km|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|sn|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|yo|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|so|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|af|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|oc|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|ka|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|be|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|tg|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|sd|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|gu|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|am|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|yi|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|lo|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|uz|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|fo|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|ht|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|ps|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|tk|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|nn|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|mt|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|sa|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|lb|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|my|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|bo|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|tl|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|mg|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|as|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|tt|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|haw|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|ln|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|ha|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|ba|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|jw|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|su|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|translate|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|transcribe|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|startoflm|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,151 >> Adding <|startofprev|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,152 >> Adding <|nocaptions|> to the vocabulary
[INFO|tokenization_utils.py:426] 2023-05-07 10:33:57,152 >> Adding <|notimestamps|> to the vocabulary
/home/local/QCRI/dizham/kanari/whisper/whisper-small-ar/./ is already a clone of https://huggingface.co/danielizham/whisper-small-ar. Make sure you pull the latest changes with `repo.git_pull()`.
05/07/2023 10:34:00 - WARNING - huggingface_hub.repository - /home/local/QCRI/dizham/kanari/whisper/whisper-small-ar/./ is already a clone of https://huggingface.co/danielizham/whisper-small-ar. Make sure you pull the latest changes with `repo.git_pull()`.
[INFO|trainer.py:565] 2023-05-07 10:34:02,856 >> max_steps is given, it will override any value given in num_train_epochs
[INFO|trainer.py:622] 2023-05-07 10:34:02,856 >> Using cuda_amp half precision backend
/home/local/QCRI/dizham/miniconda3/envs/whisper/lib/python3.9/site-packages/transformers/optimization.py:407: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
[INFO|trainer.py:1771] 2023-05-07 10:34:02,869 >> ***** Running training *****
[INFO|trainer.py:1772] 2023-05-07 10:34:02,869 >>   Num examples = 640,000
[INFO|trainer.py:1773] 2023-05-07 10:34:02,869 >>   Num Epochs = 9,223,372,036,854,775,807
[INFO|trainer.py:1774] 2023-05-07 10:34:02,870 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:1775] 2023-05-07 10:34:02,870 >>   Total train batch size (w. parallel, distributed & accumulation) = 128
[INFO|trainer.py:1776] 2023-05-07 10:34:02,870 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:1777] 2023-05-07 10:34:02,870 >>   Total optimization steps = 5,000
[INFO|trainer.py:1778] 2023-05-07 10:34:02,871 >>   Number of trainable parameters = 241,734,912
[INFO|integrations.py:720] 2023-05-07 10:34:02,872 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
wandb: Currently logged in as: danielizham. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.15.2
wandb: Run data is saved locally in /home/local/QCRI/dizham/kanari/whisper/whisper-small-ar/wandb/run-20230507_103405-9zf5xxpu
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run fast-feather-2
wandb: ⭐️ View project at https://wandb.ai/danielizham/huggingface
wandb: 🚀 View run at https://wandb.ai/danielizham/huggingface/runs/9zf5xxpu

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[INFO|trainer_utils.py:693] 2023-05-07 10:35:32,760 >> The following columns in the training 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.
/home/local/QCRI/dizham/miniconda3/envs/whisper/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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  3%|▎         | 163/5000 [1:15:43<20:26:07, 15.21s/it]{'loss': 0.9879, 'learning_rate': 4.6000000000000004e-07, 'epoch': 0.01}
{'loss': 0.8962, 'learning_rate': 9.600000000000001e-07, 'epoch': 0.01}
{'loss': 0.6006, 'learning_rate': 1.46e-06, 'epoch': 0.01}
{'loss': 0.4218, 'learning_rate': 1.9600000000000003e-06, 'epoch': 0.02}
{'loss': 0.4419, 'learning_rate': 2.46e-06, 'epoch': 0.03}
{'loss': 0.4007, 'learning_rate': 2.96e-06, 'epoch': 0.03}


Reading metadata...: 0it [00:00, ?it/s]

Reading metadata...: 1it [00:00,  2.54it/s]

Reading metadata...: 15060it [00:00, 40107.89it/s]

Reading metadata...: 23919it [00:01, 14960.32it/s]
Reading metadata...: 28043it [00:01, 18033.45it/s]


Reading metadata...: 0it [00:00, ?it/s]

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Reading metadata...: 10438it [00:01, 8535.33it/s]

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  7%|▋         | 327/5000 [2:32:23<17:43:56, 13.66s/it]{'loss': 0.3592, 'learning_rate': 3.46e-06, 'epoch': 1.0}
{'loss': 0.3448, 'learning_rate': 3.96e-06, 'epoch': 1.01}
{'loss': 0.3673, 'learning_rate': 4.4600000000000005e-06, 'epoch': 1.01}
{'loss': 0.273, 'learning_rate': 4.960000000000001e-06, 'epoch': 1.02}
{'loss': 0.3088, 'learning_rate': 5.460000000000001e-06, 'epoch': 1.02}
{'loss': 0.302, 'learning_rate': 5.9600000000000005e-06, 'epoch': 1.03}
{'loss': 0.2583, 'learning_rate': 6.460000000000001e-06, 'epoch': 1.03}


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Reading metadata...: 23979it [00:02, 8389.85it/s] 
Reading metadata...: 28043it [00:02, 9574.58it/s]


Reading metadata...: 0it [00:00, ?it/s]

Reading metadata...: 1it [00:03,  3.94s/it]
Reading metadata...: 10438it [00:04, 2601.71it/s]

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 10%|▉         | 490/5000 [3:51:29<19:35:34, 15.64s/it]{'loss': 0.2545, 'learning_rate': 6.96e-06, 'epoch': 2.0}
{'loss': 0.2599, 'learning_rate': 7.4600000000000006e-06, 'epoch': 2.01}
{'loss': 0.2464, 'learning_rate': 7.960000000000002e-06, 'epoch': 2.01}
{'loss': 0.1981, 'learning_rate': 8.46e-06, 'epoch': 2.02}
{'loss': 0.2316, 'learning_rate': 8.96e-06, 'epoch': 2.02}
{'loss': 0.2077, 'learning_rate': 9.460000000000001e-06, 'epoch': 2.03}


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Reading metadata...: 23848it [00:02, 13351.04it/s]
Reading metadata...: 28043it [00:02, 10627.78it/s]


Reading metadata...: 0it [00:00, ?it/s]

Reading metadata...: 1it [00:01,  1.89s/it]
Reading metadata...: 10438it [00:01, 5308.23it/s]

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{'loss': 0.2001, 'learning_rate': 9.94888888888889e-06, 'epoch': 3.01}
{'loss': 0.1875, 'learning_rate': 9.893333333333334e-06, 'epoch': 3.01}
{'loss': 0.15, 'learning_rate': 9.837777777777778e-06, 'epoch': 3.02}
{'loss': 0.1534, 'learning_rate': 9.782222222222222e-06, 'epoch': 3.02}
{'loss': 0.1565, 'learning_rate': 9.726666666666668e-06, 'epoch': 3.03}
{'loss': 0.1266, 'learning_rate': 9.671111111111112e-06, 'epoch': 3.03}


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{'loss': 0.1291, 'learning_rate': 9.56e-06, 'epoch': 4.01}
{'loss': 0.1197, 'learning_rate': 9.504444444444446e-06, 'epoch': 4.01}
{'loss': 0.0921, 'learning_rate': 9.44888888888889e-06, 'epoch': 4.02}
{'loss': 0.1023, 'learning_rate': 9.393333333333334e-06, 'epoch': 4.02}
{'loss': 0.0962, 'learning_rate': 9.33777777777778e-06, 'epoch': 4.03}


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 20%|██        | 1000/5000 [7:48:38<31:29:41, 28.35s/it][INFO|trainer.py:3138] 2023-05-07 18:22:49,172 >> ***** Running Evaluation *****
[INFO|trainer.py:3142] 2023-05-07 18:22:49,172 >>   Num examples: Unknown
[INFO|trainer.py:3143] 2023-05-07 18:22:49,172 >>   Batch size = 64
{'loss': 0.0519, 'learning_rate': 8.893333333333333e-06, 'epoch': 6.0}


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[INFO|trainer_utils.py:693] 2023-05-07 18:23:04,305 >> 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.

                                                        

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[INFO|configuration_utils.py:458] 2023-05-07 19:00:17,393 >> Configuration saved in ./checkpoint-1000/config.json
[INFO|configuration_utils.py:364] 2023-05-07 19:00:17,398 >> Configuration saved in ./checkpoint-1000/generation_config.json
[INFO|modeling_utils.py:1855] 2023-05-07 19:00:20,753 >> Model weights saved in ./checkpoint-1000/pytorch_model.bin
[INFO|feature_extraction_utils.py:369] 2023-05-07 19:00:20,758 >> Feature extractor saved in ./checkpoint-1000/preprocessor_config.json
[INFO|feature_extraction_utils.py:369] 2023-05-07 19:00:30,115 >> Feature extractor saved in ./preprocessor_config.json
Adding files tracked by Git LFS: ['wandb/run-20230506_113337-ysywp688/run-ysywp688.wandb', 'wandb/run-20230507_103405-9zf5xxpu/run-9zf5xxpu.wandb']. This may take a bit of time if the files are large.
{'eval_loss': 0.43405279517173767, 'eval_wer': 54.25600000000001, 'eval_runtime': 2248.2056, 'eval_samples_per_second': 4.644, 'eval_steps_per_second': 0.073, 'epoch': 6.0}
05/07/2023 19:00:40 - WARNING - huggingface_hub.repository - Adding files tracked by Git LFS: ['wandb/run-20230506_113337-ysywp688/run-ysywp688.wandb', 'wandb/run-20230507_103405-9zf5xxpu/run-9zf5xxpu.wandb']. This may take a bit of time if the files are large.
/home/local/QCRI/dizham/miniconda3/envs/whisper/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.034, 'learning_rate': 8.726666666666667e-06, 'epoch': 6.02}
{'loss': 0.0415, 'learning_rate': 8.671111111111113e-06, 'epoch': 6.02}
{'loss': 0.0402, 'learning_rate': 8.615555555555555e-06, 'epoch': 6.03}


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{'loss': 0.0225, 'learning_rate': 8.337777777777777e-06, 'epoch': 7.02}
{'loss': 0.0255, 'learning_rate': 8.282222222222223e-06, 'epoch': 7.03}
{'loss': 0.0221, 'learning_rate': 8.226666666666667e-06, 'epoch': 7.03}


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{'loss': 0.0201, 'learning_rate': 8.115555555555557e-06, 'epoch': 8.01}
{'loss': 0.0182, 'learning_rate': 8.06e-06, 'epoch': 8.01}
{'loss': 0.013, 'learning_rate': 8.004444444444445e-06, 'epoch': 8.02}
{'loss': 0.0148, 'learning_rate': 7.948888888888889e-06, 'epoch': 8.02}
{'loss': 0.016, 'learning_rate': 7.893333333333335e-06, 'epoch': 8.03}


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{'loss': 0.0132, 'learning_rate': 7.782222222222223e-06, 'epoch': 9.01}
{'loss': 0.0128, 'learning_rate': 7.726666666666667e-06, 'epoch': 9.01}
{'loss': 0.0096, 'learning_rate': 7.67111111111111e-06, 'epoch': 9.02}
{'loss': 0.0085, 'learning_rate': 7.6155555555555564e-06, 'epoch': 9.02}
{'loss': 0.0093, 'learning_rate': 7.5600000000000005e-06, 'epoch': 9.03}
{'loss': 0.0108, 'learning_rate': 7.504444444444445e-06, 'epoch': 9.03}


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{'loss': 0.0085, 'learning_rate': 7.393333333333333e-06, 'epoch': 10.01}
{'loss': 0.0087, 'learning_rate': 7.337777777777778e-06, 'epoch': 10.01}
{'loss': 0.006, 'learning_rate': 7.282222222222222e-06, 'epoch': 10.02}
{'loss': 0.0064, 'learning_rate': 7.226666666666667e-06, 'epoch': 10.02}
{'loss': 0.0072, 'learning_rate': 7.171111111111112e-06, 'epoch': 10.03}


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{'loss': 0.006, 'learning_rate': 7.06e-06, 'epoch': 11.01}
{'loss': 0.006, 'learning_rate': 7.004444444444445e-06, 'epoch': 11.01}
{'loss': 0.0053, 'learning_rate': 6.948888888888889e-06, 'epoch': 11.02}
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{'loss': 0.0051, 'learning_rate': 6.782222222222222e-06, 'epoch': 11.03}


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 40%|████      | 2000/5000 [16:12:55<22:45:51, 27.32s/it][INFO|trainer.py:3138] 2023-05-08 02:47:05,817 >> ***** Running Evaluation *****
[INFO|trainer.py:3142] 2023-05-08 02:47:05,817 >>   Num examples: Unknown
[INFO|trainer.py:3143] 2023-05-08 02:47:05,817 >>   Batch size = 64
{'loss': 0.005, 'learning_rate': 6.726666666666667e-06, 'epoch': 12.0}
{'loss': 0.0042, 'learning_rate': 6.671111111111112e-06, 'epoch': 12.01}


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[INFO|trainer_utils.py:693] 2023-05-08 02:47:16,424 >> 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 [16:48:47<22:45:51, 27.32s/it][INFO|trainer.py:2877] 2023-05-08 03:22:58,551 >> Saving model checkpoint to ./checkpoint-2000
[INFO|configuration_utils.py:458] 2023-05-08 03:22:58,556 >> Configuration saved in ./checkpoint-2000/config.json
[INFO|configuration_utils.py:364] 2023-05-08 03:22:58,560 >> Configuration saved in ./checkpoint-2000/generation_config.json
[INFO|modeling_utils.py:1855] 2023-05-08 03:23:01,997 >> Model weights saved in ./checkpoint-2000/pytorch_model.bin
[INFO|feature_extraction_utils.py:369] 2023-05-08 03:23:02,003 >> Feature extractor saved in ./checkpoint-2000/preprocessor_config.json
[INFO|feature_extraction_utils.py:369] 2023-05-08 03:23:12,574 >> Feature extractor saved in ./preprocessor_config.json