diff --git "a/run_2/train_run_2.log" "b/run_2/train_run_2.log" new file mode 100644--- /dev/null +++ "b/run_2/train_run_2.log" @@ -0,0 +1,34607 @@ +-------------------------------------------------------------------------- +WARNING: No preset parameters were found for the device that Open MPI +detected: + + Local host: 129-213-88-66 + Device name: mlx5_0 + Device vendor ID: 0x02c9 + Device vendor part ID: 4126 + +Default device parameters will be used, which may result in lower +performance. You can edit any of the files specified by the +btl_openib_device_param_files MCA parameter to set values for your +device. + +NOTE: You can turn off this warning by setting the MCA parameter + btl_openib_warn_no_device_params_found to 0. +-------------------------------------------------------------------------- +-------------------------------------------------------------------------- +No OpenFabrics connection schemes reported that they were able to be +used on a specific port. As such, the openib BTL (OpenFabrics +support) will be disabled for this port. + + Local host: 129-213-88-66 + Local device: mlx5_0 + Local port: 1 + CPCs attempted: udcm +-------------------------------------------------------------------------- +12/17/2022 22:46:51 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True +12/17/2022 22:46:51 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments( +_n_gpu=1, +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_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_min_num_params=0, +fsdp_transformer_layer_cls_to_wrap=None, +full_determinism=False, +generation_max_length=225, +generation_num_beams=None, +gradient_accumulation_steps=1, +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=, +ignore_data_skip=True, +include_inputs_for_metrics=False, +jit_mode_eval=False, +label_names=None, +label_smoothing_factor=0.0, +learning_rate=3.5e-05, +length_column_name=length, +load_best_model_at_end=True, +local_rank=-1, +log_level=passive, +log_level_replica=passive, +log_on_each_node=True, +logging_dir=./runs/Dec17_22-46-51_129-213-88-66, +logging_first_step=True, +logging_nan_inf_filter=True, +logging_steps=50, +logging_strategy=steps, +lr_scheduler_type=linear, +max_grad_norm=1.0, +max_steps=6000, +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=False, +past_index=-1, +per_device_eval_batch_size=32, +per_device_train_batch_size=64, +predict_with_generate=True, +prediction_loss_only=False, +push_to_hub=False, +push_to_hub_model_id=None, +push_to_hub_organization=None, +push_to_hub_token=, +ray_scope=last, +remove_unused_columns=True, +report_to=['tensorboard'], +resume_from_checkpoint=None, +run_name=./, +save_on_each_node=False, +save_steps=1000, +save_strategy=steps, +save_total_limit=None, +seed=43, +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=0, +weight_decay=0.0, +xpu_backend=None, +) +12/17/2022 22:46:51 - INFO - __main__ - Data parameters: DataTrainingArguments(dataset_name='mozilla-foundation/common_voice_11_0', dataset_config_name='be', max_train_samples=None, max_eval_samples=None, audio_column_name='audio', text_column_name='sentence', max_duration_in_seconds=30.0, min_duration_in_seconds=0.0, train_split_name='train', eval_split_name='validation', do_lower_case=False, do_remove_punctuation=False, do_normalize_eval=True, language='be', task='transcribe', shuffle_buffer_size=500, streaming_train=True, streaming_eval=False) +12/17/2022 22:46:51 - INFO - __main__ - Model parameters: ModelArguments(model_name_or_path='ales/whisper-small-belarusian', config_name=None, tokenizer_name=None, feature_extractor_name=None, cache_dir=None, use_fast_tokenizer=True, model_revision='main', use_auth_token=True, freeze_feature_encoder=False, freeze_encoder=False, forced_decoder_ids=None, suppress_tokens=None, model_index_name='Whisper Small Belarusian') +12/17/2022 22:46:51 - INFO - __main__ - output_dir already exists. will try to load last checkpoint. +12/17/2022 22:46:51 - INFO - __main__ - last_checkpoint is None. will try to read from training_args.resume_from_checkpoint +12/17/2022 22:46:51 - INFO - __main__ - last_checkpoint is None. resume_from_checkpoint is either None or not existing dir. will try to read from the model saved in the root of output_dir. +12/17/2022 22:46:51 - INFO - __main__ - dir is not empty, but contains only: ['src', 'train_20221217-224651.log', 'train_run_2.log']. it is OK - will start training +12/17/2022 22:46:51 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f +12/17/2022 22:46:51 - INFO - datasets.builder - Overwrite dataset info from restored data version. +12/17/2022 22:46:51 - INFO - datasets.info - Loading Dataset info from /home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/be/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f +12/17/2022 22:46:51 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f +12/17/2022 22:46:51 - INFO - datasets.builder - Overwrite dataset info from restored data version. +12/17/2022 22:46:51 - INFO - datasets.info - Loading Dataset info from /home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/be/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f +12/17/2022 22:46:51 - WARNING - datasets.builder - Found cached dataset common_voice_11_0 (/home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/be/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f) +12/17/2022 22:46:51 - INFO - datasets.info - Loading Dataset info from /home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/be/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f +/home/ubuntu/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed). + from pandas.core.computation.check import NUMEXPR_INSTALLED + Downloading: 0%| | 0.00/1.00k [00:00> loading configuration file config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/config.json +[INFO|configuration_utils.py:706] 2022-12-17 22:46:51,909 >> Model config WhisperConfig { + "_name_or_path": "ales/whisper-small-belarusian", + "activation_dropout": 0.0, + "activation_function": "gelu", + "architectures": [ + "WhisperForConditionalGeneration" + ], + "attention_dropout": 0.0, + "begin_suppress_tokens": [ + 220, + 50257 + ], + "bos_token_id": 50257, + "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": null, + "init_std": 0.02, + "is_encoder_decoder": true, + "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, + "torch_dtype": "float32", + "transformers_version": "4.26.0.dev0", + "use_cache": false, + "vocab_size": 51865 +} + + Downloading: 0%| | 0.00/185k [00:00> loading configuration file preprocessor_config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/preprocessor_config.json +[INFO|feature_extraction_utils.py:501] 2022-12-17 22:46:51,984 >> Feature extractor WhisperFeatureExtractor { + "chunk_length": 30, + "feature_extractor_type": "WhisperFeatureExtractor", + "feature_size": 80, + "hop_length": 160, + "mel_filters": [ + [ + -0.0, + 0.02486259490251541, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 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file tokenizer.json from cache at None +[INFO|tokenization_utils_base.py:1799] 2022-12-17 22:46:52,433 >> loading file merges.txt from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/merges.txt +[INFO|tokenization_utils_base.py:1799] 2022-12-17 22:46:52,433 >> loading file normalizer.json from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/normalizer.json +[INFO|tokenization_utils_base.py:1799] 2022-12-17 22:46:52,433 >> loading file added_tokens.json from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/added_tokens.json +[INFO|tokenization_utils_base.py:1799] 2022-12-17 22:46:52,433 >> loading file special_tokens_map.json from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/special_tokens_map.json +[INFO|tokenization_utils_base.py:1799] 2022-12-17 22:46:52,433 >> loading file tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/tokenizer_config.json +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,495 >> Adding <|endoftext|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|startoftranscript|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|en|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|zh|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|de|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|es|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|ru|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|ko|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|fr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|ja|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|pt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|tr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|pl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|ca|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|nl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|ar|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|sv|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|it|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,496 >> Adding <|id|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|hi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|fi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|vi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|iw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|uk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|el|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|ms|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|cs|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|ro|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|da|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|hu|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|ta|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|no|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|th|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|ur|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|hr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|bg|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|lt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|la|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|mi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|ml|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|cy|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|sk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|te|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,497 >> Adding <|fa|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|lv|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|bn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|sr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|az|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|sl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|kn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|et|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|mk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|br|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|eu|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|is|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|hy|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|ne|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|mn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|bs|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|kk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|sq|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|sw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|gl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|mr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|pa|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|si|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|km|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|sn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|yo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|so|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|af|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|oc|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|ka|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|be|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|tg|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|sd|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|gu|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,498 >> Adding <|am|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|yi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|lo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|uz|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|fo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|ht|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|ps|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|tk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|nn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|mt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|sa|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|lb|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|my|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|bo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|tl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|mg|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|as|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|tt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|haw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|ln|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|ha|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|ba|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,499 >> Adding <|jw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,500 >> Adding <|su|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,500 >> Adding <|translate|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,500 >> Adding <|transcribe|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,500 >> Adding <|startoflm|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,500 >> Adding <|startofprev|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,500 >> Adding <|nocaptions|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 22:46:52,500 >> Adding <|notimestamps|> to the vocabulary + Downloading: 0%| | 0.00/967M [00:00> loading weights file pytorch_model.bin from cache at /home/ubuntu/.cache/huggingface/hub/models--ales--whisper-small-belarusian/snapshots/4f145b9e9f8e539bf2e1c025058cbc5a4847acc6/pytorch_model.bin +[INFO|modeling_utils.py:2806] 2022-12-17 22:47:06,404 >> All model checkpoint weights were used when initializing WhisperForConditionalGeneration. + +[INFO|modeling_utils.py:2814] 2022-12-17 22:47:06,404 >> All the weights of WhisperForConditionalGeneration were initialized from the model checkpoint at ales/whisper-small-belarusian. +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. +12/17/2022 22:47:06 - INFO - __main__ - vectorizing dataset +12/17/2022 22:47:06 - INFO - __main__ - will preprocess data using None processes. + 0%| | 0/15872 [00:00> Feature extractor saved in ./preprocessor_config.json +[INFO|tokenization_utils_base.py:2157] 2022-12-17 23:35:19,791 >> tokenizer config file saved in ./tokenizer_config.json +[INFO|tokenization_utils_base.py:2164] 2022-12-17 23:35:19,791 >> Special tokens file saved in ./special_tokens_map.json +[INFO|tokenization_utils_base.py:2210] 2022-12-17 23:35:19,791 >> added tokens file saved in ./added_tokens.json +[INFO|configuration_utils.py:447] 2022-12-17 23:35:19,877 >> Configuration saved in ./config.json +[INFO|image_processing_utils.py:294] 2022-12-17 23:35:19,879 >> loading configuration file ./preprocessor_config.json +[INFO|feature_extraction_utils.py:462] 2022-12-17 23:35:19,885 >> loading configuration file ./preprocessor_config.json +[INFO|feature_extraction_utils.py:501] 2022-12-17 23:35:19,900 >> Feature extractor WhisperFeatureExtractor { + "chunk_length": 30, + "feature_extractor_type": "WhisperFeatureExtractor", + "feature_size": 80, + "hop_length": 160, + "mel_filters": [ + [ + -0.0, + 0.02486259490251541, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, 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0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.000366741674952209, + 0.0008330700220540166, + 0.0012993983691558242, + 0.0017657268326729536, + 0.0022320549469441175, + 0.002698383294045925, + 0.0031647118739783764, + 0.003141313325613737, + 0.002692554146051407, + 0.0022437951993197203, + 0.00179503601975739, + 0.0013462770730257034, + 0.000897518009878695, + 0.0004487590049393475, + 0.0 + ] + ], + "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:1797] 2022-12-17 23:35:19,909 >> loading file vocab.json +[INFO|tokenization_utils_base.py:1797] 2022-12-17 23:35:19,909 >> loading file tokenizer.json +[INFO|tokenization_utils_base.py:1797] 2022-12-17 23:35:19,909 >> loading file merges.txt +[INFO|tokenization_utils_base.py:1797] 2022-12-17 23:35:19,909 >> loading file normalizer.json +[INFO|tokenization_utils_base.py:1797] 2022-12-17 23:35:19,909 >> loading file added_tokens.json +[INFO|tokenization_utils_base.py:1797] 2022-12-17 23:35:19,909 >> loading file special_tokens_map.json +[INFO|tokenization_utils_base.py:1797] 2022-12-17 23:35:19,909 >> loading file tokenizer_config.json +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|endoftext|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|startoftranscript|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|en|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|zh|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|de|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|es|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|ru|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,025 >> Adding <|ko|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|fr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|ja|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|pt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|tr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|pl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|ca|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|nl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|ar|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|sv|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|it|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|id|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|hi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|fi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|vi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|iw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|uk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|el|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,026 >> Adding <|ms|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|cs|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|ro|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|da|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|hu|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|ta|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|no|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|th|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|ur|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|hr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|bg|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|lt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|la|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|mi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|ml|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|cy|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|sk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|te|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|fa|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,027 >> Adding <|lv|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|bn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|sr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|az|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|sl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|kn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|et|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|mk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|br|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|eu|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|is|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|hy|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|ne|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|mn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|bs|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|kk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,028 >> Adding <|sq|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|sw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|gl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|mr|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|pa|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|si|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|km|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|sn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|yo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|so|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|af|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|oc|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|ka|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|be|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|tg|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|sd|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|gu|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|am|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|yi|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,029 >> Adding <|lo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|uz|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|fo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|ht|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|ps|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|tk|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|nn|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|mt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|sa|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|lb|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|my|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|bo|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|tl|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|mg|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|as|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|tt|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|haw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,030 >> Adding <|ln|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|ha|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|ba|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|jw|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|su|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|translate|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|transcribe|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|startoflm|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|startofprev|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|nocaptions|> to the vocabulary +[INFO|tokenization_utils.py:426] 2022-12-17 23:35:20,031 >> Adding <|notimestamps|> to the vocabulary +[INFO|trainer.py:511] 2022-12-17 23:35:22,055 >> max_steps is given, it will override any value given in num_train_epochs +[INFO|trainer.py:565] 2022-12-17 23:35:22,055 >> Using cuda_amp half precision backend +12/17/2022 23:35:22 - INFO - __main__ - will launch training and pass resume_from_checkpoint=None +/home/ubuntu/.local/lib/python3.8/site-packages/transformers/optimization.py:306: 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:1641] 2022-12-17 23:35:22,065 >> ***** Running training ***** +[INFO|trainer.py:1642] 2022-12-17 23:35:22,065 >> Num examples = 384000 +[INFO|trainer.py:1643] 2022-12-17 23:35:22,065 >> Num Epochs = 9223372036854775807 +[INFO|trainer.py:1644] 2022-12-17 23:35:22,065 >> Instantaneous batch size per device = 64 +[INFO|trainer.py:1645] 2022-12-17 23:35:22,065 >> Total train batch size (w. parallel, distributed & accumulation) = 64 +[INFO|trainer.py:1646] 2022-12-17 23:35:22,065 >> Gradient Accumulation steps = 1 +[INFO|trainer.py:1647] 2022-12-17 23:35:22,065 >> Total optimization steps = 6000 +[INFO|trainer.py:1648] 2022-12-17 23:35:22,066 >> Number of trainable parameters = 241734912 + 0%| | 0/6000 [00:00> The following columns in the training set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, labels_truncated, input_length. If labels_length, labels_truncated, input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. + 0%| | 1/6000 [01:18<130:01:19, 78.03s/it] 0%| | 1/6000 [01:18<130:01:19, 78.03s/it] 0%| | 2/6000 [01:28<63:39:27, 38.21s/it] 0%| | 3/6000 [01:35<40:09:55, 24.11s/it] 0%| | 4/6000 [01:45<30:52:06, 18.53s/it] 0%| | 5/6000 [01:54<25:04:26, 15.06s/it] 0%| | 6/6000 [02:05<22:38:27, 13.60s/it] 0%| | 7/6000 [02:13<19:51:39, 11.93s/it] 0%| | 8/6000 [02:23<18:31:04, 11.13s/it] 0%| | 9/6000 [02:32<17:17:12, 10.39s/it] 0%| | 10/6000 [02:42<17:22:36, 10.44s/it] 0%| | 11/6000 [02:54<18:16:28, 10.98s/it] 0%| | 12/6000 [03:06<18:37:20, 11.20s/it] 0%| | 13/6000 [03:16<18:06:24, 10.89s/it] 0%| | 14/6000 [03:27<17:56:35, 10.79s/it] 0%| | 15/6000 [03:35<16:39:48, 10.02s/it] 0%| | 16/6000 [03:44<16:15:51, 9.78s/it] 0%| | 17/6000 [03:55<16:33:31, 9.96s/it] 0%| | 18/6000 [04:03<16:01:46, 9.65s/it] 0%| | 19/6000 [04:12<15:18:50, 9.22s/it] 0%| | 20/6000 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If sentence, input_length, labels_truncated, labels_length, audio are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. +[INFO|trainer.py:2955] 2022-12-18 02:22:45,885 >> ***** Running Evaluation ***** +[INFO|trainer.py:2957] 2022-12-18 02:22:45,885 >> Num examples = 15872 +[INFO|trainer.py:2960] 2022-12-18 02:22:45,885 >> Batch size = 32 +{'loss': 0.0175, 'learning_rate': 3.4994166666666664e-05, 'epoch': 0.0} +{'loss': 0.0183, 'learning_rate': 3.4708333333333334e-05, 'epoch': 0.01} +{'loss': 0.0204, 'learning_rate': 3.441666666666666e-05, 'epoch': 0.02} +{'loss': 0.0201, 'learning_rate': 3.4124999999999996e-05, 'epoch': 0.03} +{'loss': 0.0203, 'learning_rate': 3.3833333333333334e-05, 'epoch': 0.03} +{'loss': 0.0218, 'learning_rate': 3.3541666666666664e-05, 'epoch': 0.04} +{'loss': 0.0207, 'learning_rate': 3.3249999999999995e-05, 'epoch': 0.05} +{'loss': 0.0185, 'learning_rate': 3.295833333333333e-05, 'epoch': 0.06} +{'loss': 0.0223, 'learning_rate': 3.2672499999999996e-05, 'epoch': 0.07} +{'loss': 0.0194, 'learning_rate': 3.2380833333333333e-05, 'epoch': 0.07} +{'loss': 0.0267, 'learning_rate': 3.2089166666666664e-05, 'epoch': 0.08} +{'loss': 0.0194, 'learning_rate': 3.1797499999999995e-05, 'epoch': 0.09} +{'loss': 0.0206, 'learning_rate': 3.150583333333333e-05, 'epoch': 0.1} +{'loss': 0.0286, 'learning_rate': 3.121416666666666e-05, 'epoch': 0.11} +{'loss': 0.0368, 'learning_rate': 3.0922499999999994e-05, 'epoch': 0.12} +{'loss': 0.0351, 'learning_rate': 3.063083333333333e-05, 'epoch': 0.12} +{'loss': 0.0337, 'learning_rate': 3.0339166666666666e-05, 'epoch': 0.13} +{'loss': 0.0345, 'learning_rate': 3.00475e-05, 'epoch': 0.14} +{'loss': 0.0338, 'learning_rate': 2.975583333333333e-05, 'epoch': 0.15} +{'loss': 0.0324, 'learning_rate': 2.9464166666666665e-05, 'epoch': 0.16} +{'loss': 0.0292, 'learning_rate': 2.91725e-05, 'epoch': 0.17} + + 0%| | 0/496 [00:00> Saving model checkpoint to ./checkpoint-1000 +[INFO|configuration_utils.py:447] 2022-12-18 03:47:56,732 >> Configuration saved in ./checkpoint-1000/config.json +[INFO|modeling_utils.py:1680] 2022-12-18 03:47:57,581 >> Model weights saved in ./checkpoint-1000/pytorch_model.bin +[INFO|feature_extraction_utils.py:368] 2022-12-18 03:47:57,595 >> Feature extractor saved in ./checkpoint-1000/preprocessor_config.json +[INFO|tokenization_utils_base.py:2157] 2022-12-18 03:47:57,595 >> tokenizer config file saved in ./checkpoint-1000/tokenizer_config.json +[INFO|tokenization_utils_base.py:2164] 2022-12-18 03:47:57,595 >> Special tokens file saved in ./checkpoint-1000/special_tokens_map.json +[INFO|tokenization_utils_base.py:2210] 2022-12-18 03:47:57,595 >> added tokens file saved in ./checkpoint-1000/added_tokens.json + 17%|█▋ | 1001/6000 [4:12:46<2142:46:50, 1543.11s/it] 17%|█▋ | 1002/6000 [4:12:55<1503:29:18, 1082.94s/it] 17%|█▋ | 1003/6000 [4:13:06<1056:34:31, 761.19s/it] 17%|█▋ | 1004/6000 [4:13:17<744:04:37, 536.16s/it] 17%|█▋ | 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[4:39:51<12:25:55, 9.26s/it] 19%|█▉ | 1167/6000 [4:40:00<12:33:36, 9.36s/it] 19%|█▉ | 1168/6000 [4:40:10<12:38:53, 9.42s/it] 19%|█▉ | 1169/6000 [4:40:18<11:52:45, 8.85s/it] 20%|█▉ | 1170/6000 [4:40:27<12:09:36, 9.06s/it] 20%|█▉ | 1171/6000 [4:40:39<13:10:43, 9.82s/it] 20%|█▉ | 1172/6000 [4:40:48<12:45:20, 9.51s/it] 20%|█▉ | 1173/6000 [4:40:58<12:58:32, 9.68s/it] 20%|█▉ | 1174/6000 [4:41:06<12:37:12, 9.41s/it] 20%|█▉ | 1175/6000 [4:41:15<12:20:51, 9.21s/it] 20%|█▉ | 1176/6000 [4:41:25<12:35:02, 9.39s/it] 20%|█▉ | 1177/6000 [4:41:34<12:29:19, 9.32s/it] 20%|█▉ | 1178/6000 [4:41:45<13:15:01, 9.89s/it] 20%|█▉ | 1179/6000 [4:41:55<12:58:19, 9.69s/it] 20%|█▉ | 1180/6000 [4:42:06<13:40:14, 10.21s/it] 20%|█▉ | 1181/6000 [4:42:15<13:22:01, 9.99s/it] 20%|█▉ | 1182/6000 [4:42:24<12:40:45, 9.47s/it] 20%|█▉ | 1183/6000 [4:42:34<13:04:58, 9.78s/it] 20%|█▉ | 1184/6000 [4:42:45<13:30:55, 10.10s/it] 20%|█▉ | 1185/6000 [4:42:55<13:16:58, 9.93s/it] 20%|█▉ | 1186/6000 [4:43:05<13:24:25, 10.03s/it] 20%|█▉ | 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[4:49:51<13:37:23, 10.28s/it] 20%|██ | 1228/6000 [4:50:00<13:19:17, 10.05s/it] 20%|██ | 1229/6000 [4:50:08<12:19:24, 9.30s/it] 20%|██ | 1230/6000 [4:50:19<12:52:55, 9.72s/it] 21%|██ | 1231/6000 [4:50:29<12:55:01, 9.75s/it] 21%|██ | 1232/6000 [4:50:40<13:41:08, 10.33s/it] 21%|██ | 1233/6000 [4:50:50<13:19:37, 10.06s/it] 21%|██ | 1234/6000 [4:50:58<12:45:55, 9.64s/it] 21%|██ | 1235/6000 [4:51:10<13:31:51, 10.22s/it] 21%|██ | 1236/6000 [4:51:19<13:00:29, 9.83s/it] 21%|██ | 1237/6000 [4:51:26<12:06:54, 9.16s/it] 21%|██ | 1238/6000 [4:51:36<12:06:41, 9.16s/it] 21%|██ | 1239/6000 [4:51:44<11:50:05, 8.95s/it] 21%|██ | 1240/6000 [4:51:56<13:11:13, 9.97s/it] 21%|██ | 1241/6000 [4:52:06<13:03:42, 9.88s/it] 21%|██ | 1242/6000 [4:52:14<12:25:34, 9.40s/it] 21%|██ | 1243/6000 [4:52:24<12:36:10, 9.54s/it] 21%|██ | 1244/6000 [4:52:33<12:25:24, 9.40s/it] 21%|██ | 1245/6000 [4:52:44<13:05:03, 9.91s/it] 21%|██ | 1246/6000 [4:52:53<12:45:03, 9.66s/it] 21%|██ | 1247/6000 [4:53:02<12:20:02, 9.34s/it] 21%|██ | 1248/6000 [4:53:11<12:13:36, 9.26s/it] 21%|██ | 1249/6000 [4:53:22<12:48:09, 9.70s/it] 21%|██ | 1250/6000 [4:53:31<12:28:45, 9.46s/it] 21%|██ | 1250/6000 [4:53:31<12:28:45, 9.46s/it] 21%|██ | 1251/6000 [4:53:41<12:49:49, 9.73s/it] 21%|██ | 1252/6000 [4:53:51<12:47:31, 9.70s/it] 21%|██ | 1253/6000 [4:54:00<12:42:06, 9.63s/it] 21%|██ | 1254/6000 [4:54:08<11:52:28, 9.01s/it] 21%|██ | 1255/6000 [4:54:18<12:13:03, 9.27s/it] 21%|██ | 1256/6000 [4:54:27<12:18:16, 9.34s/it] 21%|██ | 1257/6000 [4:54:37<12:38:27, 9.59s/it] 21%|██ | 1258/6000 [4:54:48<13:03:28, 9.91s/it] 21%|██ | 1259/6000 [4:55:00<13:59:47, 10.63s/it] 21%|██ | 1260/6000 [4:55:11<14:08:18, 10.74s/it] 21%|██ | 1261/6000 [4:55:21<13:41:04, 10.40s/it] 21%|██ | 1262/6000 [4:55:31<13:35:54, 10.33s/it] 21%|██ | 1263/6000 [4:55:41<13:32:28, 10.29s/it] 21%|██ | 1264/6000 [4:55:51<13:11:33, 10.03s/it] 21%|██ | 1265/6000 [4:55:59<12:24:49, 9.44s/it] 21%|██ | 1266/6000 [4:56:08<12:31:46, 9.53s/it] 21%|██ | 1267/6000 [4:56:18<12:27:46, 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[5:38:19<11:45:40, 9.46s/it] 25%|██▌ | 1524/6000 [5:38:28<11:55:16, 9.59s/it] 25%|██▌ | 1525/6000 [5:38:37<11:30:42, 9.26s/it] 25%|██▌ | 1526/6000 [5:38:47<11:49:40, 9.52s/it] 25%|██▌ | 1527/6000 [5:38:57<12:01:50, 9.68s/it] 25%|██▌ | 1528/6000 [5:39:07<11:57:48, 9.63s/it] 25%|██▌ | 1529/6000 [5:39:17<12:09:42, 9.79s/it] 26%|██▌ | 1530/6000 [5:39:28<12:34:38, 10.13s/it] 26%|██▌ | 1531/6000 [5:39:37<12:24:53, 10.00s/it] 26%|██▌ | 1532/6000 [5:39:46<12:03:25, 9.71s/it] 26%|██▌ | 1533/6000 [5:39:56<11:57:04, 9.63s/it] 26%|██▌ | 1534/6000 [5:40:06<12:13:45, 9.86s/it] 26%|██▌ | 1535/6000 [5:40:15<11:47:34, 9.51s/it] 26%|██▌ | 1536/6000 [5:40:25<11:50:20, 9.55s/it] 26%|██▌ | 1537/6000 [5:40:35<12:03:33, 9.73s/it] 26%|██▌ | 1538/6000 [5:40:43<11:28:27, 9.26s/it] 26%|██▌ | 1539/6000 [5:40:54<12:08:33, 9.80s/it] 26%|██▌ | 1540/6000 [5:41:03<11:45:24, 9.49s/it] 26%|██▌ | 1541/6000 [5:41:15<12:44:12, 10.28s/it] 26%|██▌ | 1542/6000 [5:41:26<12:58:09, 10.47s/it] 26%|██▌ | 1543/6000 [5:41:36<12:46:30, 10.32s/it] 26%|██▌ | 1544/6000 [5:41:44<11:49:44, 9.56s/it] 26%|██▌ | 1545/6000 [5:41:52<11:25:53, 9.24s/it] 26%|██▌ | 1546/6000 [5:41:59<10:44:39, 8.68s/it] 26%|██▌ | 1547/6000 [5:42:08<10:39:56, 8.62s/it] 26%|██▌ | 1548/6000 [5:42:20<11:56:25, 9.66s/it] 26%|██▌ | 1549/6000 [5:42:30<12:03:55, 9.76s/it] 26%|██▌ | 1550/6000 [5:42:38<11:31:43, 9.33s/it] 26%|██▌ | 1550/6000 [5:42:38<11:31:43, 9.33s/it] 26%|██▌ | 1551/6000 [5:42:47<11:25:18, 9.24s/it] 26%|██▌ | 1552/6000 [5:42:58<11:58:24, 9.69s/it] 26%|██▌ | 1553/6000 [5:43:08<11:54:07, 9.64s/it] 26%|██▌ | 1554/6000 [5:43:18<12:17:15, 9.95s/it] 26%|██▌ | 1555/6000 [5:43:25<11:16:45, 9.14s/it] 26%|██▌ | 1556/6000 [5:43:37<12:09:13, 9.85s/it] 26%|██▌ | 1557/6000 [5:43:48<12:43:25, 10.31s/it] 26%|██▌ | 1558/6000 [5:44:00<13:19:32, 10.80s/it] 26%|██▌ | 1559/6000 [5:44:13<14:04:00, 11.40s/it] 26%|██▌ | 1560/6000 [5:44:25<14:03:56, 11.40s/it] 26%|██▌ | 1561/6000 [5:44:35<13:52:16, 11.25s/it] 26%|██▌ | 1562/6000 [5:44:47<14:05:04, 11.43s/it] 26%|██▌ | 1563/6000 [5:44:59<14:12:07, 11.52s/it] 26%|██▌ | 1564/6000 [5:45:09<13:43:30, 11.14s/it] 26%|██▌ | 1565/6000 [5:45:17<12:38:45, 10.27s/it] 26%|██▌ | 1566/6000 [5:45:29<13:02:56, 10.59s/it] 26%|██▌ | 1567/6000 [5:45:38<12:37:57, 10.26s/it] 26%|██▌ | 1568/6000 [5:45:48<12:19:15, 10.01s/it] 26%|██▌ | 1569/6000 [5:45:58<12:22:07, 10.05s/it] 26%|██▌ | 1570/6000 [5:46:07<11:52:48, 9.65s/it] 26%|██▌ | 1571/6000 [5:46:15<11:27:37, 9.32s/it] 26%|██▌ | 1572/6000 [5:46:27<12:15:16, 9.96s/it] 26%|██▌ | 1573/6000 [5:46:34<11:21:36, 9.24s/it] 26%|██▌ | 1574/6000 [5:46:47<12:34:11, 10.22s/it] 26%|██▋ | 1575/6000 [5:46:57<12:33:23, 10.22s/it] 26%|██▋ | 1576/6000 [5:47:06<12:05:38, 9.84s/it] 26%|██▋ | 1577/6000 [5:47:15<11:57:14, 9.73s/it] 26%|██▋ | 1578/6000 [5:47:25<12:03:07, 9.81s/it] 26%|██▋ | 1579/6000 [5:47:35<11:53:02, 9.68s/it] 26%|██▋ | 1580/6000 [5:47:43<11:24:25, 9.29s/it] 26%|██▋ | 1581/6000 [5:47:54<11:55:10, 9.71s/it] 26%|██▋ | 1582/6000 [5:48:03<11:47:56, 9.61s/it] 26%|██▋ | 1583/6000 [5:48:11<11:09:00, 9.09s/it] 26%|██▋ | 1584/6000 [5:48:21<11:32:50, 9.41s/it] 26%|██▋ | 1585/6000 [5:48:33<12:21:16, 10.07s/it] 26%|██▋ | 1586/6000 [5:48:42<12:10:38, 9.93s/it] 26%|██▋ | 1587/6000 [5:48:51<11:43:40, 9.57s/it] 26%|██▋ | 1588/6000 [5:49:02<12:22:10, 10.09s/it] 26%|██▋ | 1589/6000 [5:49:13<12:25:39, 10.14s/it] 26%|██▋ | 1590/6000 [5:49:22<12:11:04, 9.95s/it] 27%|██▋ | 1591/6000 [5:49:34<12:43:49, 10.39s/it] 27%|██▋ | 1592/6000 [5:49:44<12:35:36, 10.29s/it] 27%|██▋ | 1593/6000 [5:49:56<13:24:11, 10.95s/it] 27%|██▋ | 1594/6000 [5:50:07<13:14:04, 10.81s/it] 27%|██▋ | 1595/6000 [5:50:19<13:46:35, 11.26s/it] 27%|██▋ | 1596/6000 [5:50:28<12:51:44, 10.51s/it] 27%|██▋ | 1597/6000 [5:50:38<12:52:45, 10.53s/it] 27%|██▋ | 1598/6000 [5:50:49<12:52:39, 10.53s/it] 27%|██▋ | 1599/6000 [5:50:56<11:35:06, 9.48s/it] 27%|██▋ | 1600/6000 [5:51:07<12:02:12, 9.85s/it] 27%|██▋ | 1600/6000 [5:51:07<12:02:12, 9.85s/it] 27%|██▋ | 1601/6000 [5:51:15<11:24:10, 9.33s/it] 27%|██▋ | 1602/6000 [5:51:25<11:44:32, 9.61s/it] 27%|██▋ | 1603/6000 [5:51:33<11:09:03, 9.13s/it] 27%|██▋ | 1604/6000 [5:51:42<11:02:04, 9.04s/it] 27%|██▋ | 1605/6000 [5:51:52<11:33:45, 9.47s/it] 27%|██▋ | 1606/6000 [5:52:02<11:30:56, 9.43s/it] 27%|██▋ | 1607/6000 [5:52:11<11:33:35, 9.47s/it] 27%|██▋ | 1608/6000 [5:52:22<11:57:00, 9.80s/it] 27%|██▋ | 1609/6000 [5:52:33<12:27:59, 10.22s/it] 27%|██▋ | 1610/6000 [5:52:43<12:26:08, 10.20s/it] 27%|██▋ | 1611/6000 [5:52:54<12:44:03, 10.45s/it] 27%|██▋ | 1612/6000 [5:53:05<12:56:04, 10.61s/it] 27%|██▋ | 1613/6000 [5:53:15<12:49:26, 10.52s/it] 27%|██▋ | 1614/6000 [5:53:25<12:25:12, 10.19s/it] 27%|██▋ | 1615/6000 [5:53:36<12:46:36, 10.49s/it] 27%|██▋ | 1616/6000 [5:53:46<12:35:02, 10.33s/it] 27%|██▋ | 1617/6000 [5:53:56<12:24:40, 10.19s/it] 27%|██▋ | 1618/6000 [5:54:06<12:26:05, 10.22s/it] 27%|██▋ | 1619/6000 [5:54:16<12:14:59, 10.07s/it] 27%|██▋ | 1620/6000 [5:54:26<12:18:49, 10.12s/it] 27%|██▋ | 1621/6000 [5:54:35<11:46:58, 9.69s/it] 27%|██▋ | 1622/6000 [5:54:47<12:45:17, 10.49s/it] 27%|██▋ | 1623/6000 [5:54:57<12:39:01, 10.40s/it] 27%|██▋ | 1624/6000 [5:55:07<12:25:19, 10.22s/it] 27%|██▋ | 1625/6000 [5:55:19<12:54:24, 10.62s/it] 27%|██▋ | 1626/6000 [5:55:29<12:43:50, 10.48s/it] 27%|██▋ | 1627/6000 [5:55:38<12:09:24, 10.01s/it] 27%|██▋ | 1628/6000 [5:55:48<12:10:20, 10.02s/it] 27%|██▋ | 1629/6000 [5:55:59<12:40:08, 10.43s/it] 27%|██▋ | 1630/6000 [5:56:09<12:24:14, 10.22s/it] 27%|██▋ | 1631/6000 [5:56:18<12:09:32, 10.02s/it] 27%|██▋ | 1632/6000 [5:56:27<11:28:22, 9.46s/it] 27%|██▋ | 1633/6000 [5:56:37<11:49:48, 9.75s/it] 27%|██▋ | 1634/6000 [5:56:48<12:10:04, 10.03s/it] 27%|██▋ | 1635/6000 [5:56:56<11:24:48, 9.41s/it] 27%|██▋ | 1636/6000 [5:57:05<11:13:05, 9.25s/it] 27%|██▋ | 1637/6000 [5:57:12<10:32:51, 8.70s/it] 27%|██▋ | 1638/6000 [5:57:23<11:19:24, 9.35s/it] 27%|██▋ | 1639/6000 [5:57:32<11:24:29, 9.42s/it] 27%|██▋ | 1640/6000 [5:57:45<12:35:32, 10.40s/it] 27%|██▋ | 1641/6000 [5:57:57<13:06:55, 10.83s/it] 27%|██▋ | 1642/6000 [5:58:09<13:38:41, 11.27s/it] 27%|██▋ | 1643/6000 [5:58:17<12:32:01, 10.36s/it] 27%|██▋ | 1644/6000 [5:58:27<12:03:50, 9.97s/it] 27%|██▋ | 1645/6000 [5:58:35<11:36:47, 9.60s/it] 27%|██▋ | 1646/6000 [5:58:45<11:32:45, 9.55s/it] 27%|██▋ | 1647/6000 [5:58:54<11:19:44, 9.37s/it] 27%|██▋ | 1648/6000 [5:59:03<11:18:47, 9.36s/it] 27%|██▋ | 1649/6000 [5:59:12<11:03:17, 9.15s/it] 28%|██▊ | 1650/6000 [5:59:19<10:35:11, 8.76s/it] 28%|██▊ | 1650/6000 [5:59:19<10:35:11, 8.76s/it] 28%|██▊ | 1651/6000 [5:59:29<10:44:09, 8.89s/it] 28%|██▊ | 1652/6000 [5:59:38<10:52:56, 9.01s/it] 28%|██▊ | 1653/6000 [5:59:51<12:19:29, 10.21s/it] 28%|██▊ | 1654/6000 [6:00:01<12:18:34, 10.20s/it] 28%|██▊ | 1655/6000 [6:00:11<12:05:43, 10.02s/it] 28%|██▊ | 1656/6000 [6:00:20<11:55:26, 9.88s/it] 28%|██▊ | 1657/6000 [6:00:31<12:21:44, 10.25s/it] 28%|██▊ | 1658/6000 [6:00:42<12:30:47, 10.37s/it] 28%|██▊ | 1659/6000 [6:00:51<12:01:05, 9.97s/it] 28%|██▊ | 1660/6000 [6:01:01<12:00:12, 9.96s/it] 28%|██▊ | 1661/6000 [6:01:10<11:42:17, 9.71s/it] 28%|██▊ | 1662/6000 [6:01:20<11:34:08, 9.60s/it] 28%|██▊ | 1663/6000 [6:01:31<12:20:10, 10.24s/it] 28%|██▊ | 1664/6000 [6:01:40<11:47:57, 9.80s/it] 28%|██▊ | 1665/6000 [6:01:51<12:21:12, 10.26s/it] 28%|██▊ | 1666/6000 [6:02:02<12:29:47, 10.38s/it] 28%|██▊ | 1667/6000 [6:02:11<12:02:32, 10.01s/it] 28%|██▊ | 1668/6000 [6:02:20<11:44:30, 9.76s/it] 28%|██▊ | 1669/6000 [6:02:29<11:17:55, 9.39s/it] 28%|██▊ | 1670/6000 [6:02:38<11:02:27, 9.18s/it] 28%|██▊ | 1671/6000 [6:02:48<11:36:44, 9.66s/it] 28%|██▊ | 1672/6000 [6:02:59<11:59:36, 9.98s/it] 28%|██▊ | 1673/6000 [6:03:09<12:05:46, 10.06s/it] 28%|██▊ | 1674/6000 [6:03:20<12:12:37, 10.16s/it] 28%|██▊ | 1675/6000 [6:03:28<11:25:38, 9.51s/it] 28%|██▊ | 1676/6000 [6:03:38<11:45:20, 9.79s/it] 28%|██▊ | 1677/6000 [6:03:48<11:43:19, 9.76s/it] 28%|██▊ | 1678/6000 [6:03:57<11:40:35, 9.73s/it] 28%|██▊ | 1679/6000 [6:04:08<12:04:15, 10.06s/it] 28%|██▊ | 1680/6000 [6:04:19<12:08:48, 10.12s/it] 28%|██▊ | 1681/6000 [6:04:27<11:30:25, 9.59s/it] 28%|██▊ | 1682/6000 [6:04:36<11:10:35, 9.32s/it] 28%|██▊ | 1683/6000 [6:04:46<11:26:35, 9.54s/it] 28%|██▊ | 1684/6000 [6:04:57<12:12:40, 10.19s/it] 28%|██▊ | 1685/6000 [6:05:09<12:49:28, 10.70s/it] 28%|██▊ | 1686/6000 [6:05:19<12:23:38, 10.34s/it] 28%|██▊ | 1687/6000 [6:05:27<11:36:42, 9.69s/it] 28%|██▊ | 1688/6000 [6:05:38<12:11:38, 10.18s/it] 28%|██▊ | 1689/6000 [6:05:49<12:33:55, 10.49s/it] 28%|██▊ | 1690/6000 [6:05:57<11:25:41, 9.55s/it] 28%|██▊ | 1691/6000 [6:06:06<11:20:23, 9.47s/it] 28%|██▊ | 1692/6000 [6:06:16<11:34:44, 9.68s/it] 28%|██▊ | 1693/6000 [6:06:24<11:03:00, 9.24s/it] 28%|██▊ | 1694/6000 [6:06:33<10:45:04, 8.99s/it] 28%|██▊ | 1695/6000 [6:06:41<10:19:16, 8.63s/it] 28%|██▊ | 1696/6000 [6:06:51<10:50:11, 9.06s/it] 28%|██▊ | 1697/6000 [6:07:01<11:07:27, 9.31s/it] 28%|██▊ | 1698/6000 [6:07:09<10:50:58, 9.08s/it] 28%|██▊ | 1699/6000 [6:07:19<11:04:34, 9.27s/it] 28%|██▊ | 1700/6000 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| 1994/6000 [6:55:51<12:24:33, 11.15s/it] 33%|███▎ | 1995/6000 [6:56:02<12:39:43, 11.38s/it] 33%|███▎ | 1996/6000 [6:56:15<13:04:33, 11.76s/it] 33%|███▎ | 1997/6000 [6:56:25<12:18:39, 11.07s/it] 33%|███▎ | 1998/6000 [6:56:36<12:31:04, 11.26s/it] 33%|███▎ | 1999/6000 [6:56:47<12:15:38, 11.03s/it] 33%|███▎ | 2000/6000 [6:56:58<12:16:46, 11.05s/it] 33%|███▎ | 2000/6000 [6:56:58<12:16:46, 11.05s/it][INFO|trainer.py:710] 2022-12-18 06:32:20,407 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: sentence, input_length, labels_truncated, labels_length, audio. If sentence, input_length, labels_truncated, labels_length, audio are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. +[INFO|trainer.py:2955] 2022-12-18 06:32:20,410 >> ***** Running Evaluation ***** +[INFO|trainer.py:2957] 2022-12-18 06:32:20,410 >> Num examples = 15872 +[INFO|trainer.py:2960] 2022-12-18 06:32:20,410 >> Batch size = 32 +{'eval_loss': 0.0846627876162529, 'eval_wer': 7.858370356291528, 'eval_runtime': 5110.8454, 'eval_samples_per_second': 3.106, 'eval_steps_per_second': 0.097, 'epoch': 0.17} +{'loss': 0.0331, 'learning_rate': 2.8880833333333333e-05, 'epoch': 0.17} +{'loss': 0.0288, 'learning_rate': 2.8589166666666664e-05, 'epoch': 0.18} +{'loss': 0.0284, 'learning_rate': 2.8297499999999998e-05, 'epoch': 0.19} +{'loss': 0.0294, 'learning_rate': 2.8005833333333332e-05, 'epoch': 0.2} +{'loss': 0.0295, 'learning_rate': 2.7714166666666663e-05, 'epoch': 0.21} +{'loss': 0.0281, 'learning_rate': 2.7422499999999997e-05, 'epoch': 0.22} +{'loss': 0.0255, 'learning_rate': 2.713083333333333e-05, 'epoch': 0.23} +{'loss': 0.0276, 'learning_rate': 2.6839166666666665e-05, 'epoch': 0.23} +{'loss': 0.0228, 'learning_rate': 2.6547499999999996e-05, 'epoch': 0.24} +{'loss': 0.0268, 'learning_rate': 2.625583333333333e-05, 'epoch': 0.25} +{'loss': 0.0259, 'learning_rate': 2.5964166666666664e-05, 'epoch': 0.26} +{'loss': 0.0218, 'learning_rate': 2.56725e-05, 'epoch': 0.27} +{'loss': 0.0253, 'learning_rate': 2.538083333333333e-05, 'epoch': 0.28} +{'loss': 0.0236, 'learning_rate': 2.5089166666666663e-05, 'epoch': 0.28} +{'loss': 0.0186, 'learning_rate': 2.4797499999999998e-05, 'epoch': 0.29} +{'loss': 0.0196, 'learning_rate': 2.4505833333333332e-05, 'epoch': 0.3} +{'loss': 0.0202, 'learning_rate': 2.4214166666666663e-05, 'epoch': 0.31} +{'loss': 0.0201, 'learning_rate': 2.3922499999999997e-05, 'epoch': 0.32} +{'loss': 0.0193, 'learning_rate': 2.363083333333333e-05, 'epoch': 0.33} +{'loss': 0.0165, 'learning_rate': 2.333916666666666e-05, 'epoch': 0.33} + + 0%| | 0/496 [00:00> Saving model checkpoint to ./checkpoint-2000 +[INFO|configuration_utils.py:447] 2022-12-18 07:57:26,616 >> Configuration saved in ./checkpoint-2000/config.json +[INFO|modeling_utils.py:1680] 2022-12-18 07:57:27,555 >> Model weights saved in ./checkpoint-2000/pytorch_model.bin +[INFO|feature_extraction_utils.py:368] 2022-12-18 07:57:27,570 >> Feature extractor saved in ./checkpoint-2000/preprocessor_config.json +[INFO|tokenization_utils_base.py:2157] 2022-12-18 07:57:27,570 >> tokenizer config file saved in ./checkpoint-2000/tokenizer_config.json +[INFO|tokenization_utils_base.py:2164] 2022-12-18 07:57:27,570 >> Special tokens file saved in ./checkpoint-2000/special_tokens_map.json +[INFO|tokenization_utils_base.py:2210] 2022-12-18 07:57:27,571 >> added tokens file saved in ./checkpoint-2000/added_tokens.json + 33%|███▎ | 2001/6000 [8:22:16<1714:15:17, 1543.22s/it] 33%|��██▎ | 2002/6000 [8:22:27<1203:16:16, 1083.49s/it] 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10:43:41,463 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: sentence, input_length, labels_truncated, labels_length, audio. If sentence, input_length, labels_truncated, labels_length, audio are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. +[INFO|trainer.py:2955] 2022-12-18 10:43:41,468 >> ***** Running Evaluation ***** +[INFO|trainer.py:2957] 2022-12-18 10:43:41,469 >> Num examples = 15872 +[INFO|trainer.py:2960] 2022-12-18 10:43:41,469 >> Batch size = 32 +{'eval_loss': 0.08362394571304321, 'eval_wer': 7.3166868610075, 'eval_runtime': 5106.204, 'eval_samples_per_second': 3.108, 'eval_steps_per_second': 0.097, 'epoch': 0.33} +{'loss': 0.02, 'learning_rate': 2.3047499999999996e-05, 'epoch': 0.34} +{'loss': 0.0174, 'learning_rate': 2.275583333333333e-05, 'epoch': 0.35} +{'loss': 0.0156, 'learning_rate': 2.2464166666666667e-05, 'epoch': 0.36} +{'loss': 0.0181, 'learning_rate': 2.2172499999999995e-05, 'epoch': 0.37} +{'loss': 0.019, 'learning_rate': 2.1880833333333332e-05, 'epoch': 0.38} +{'loss': 0.0156, 'learning_rate': 2.1589166666666666e-05, 'epoch': 0.38} +{'loss': 0.0183, 'learning_rate': 2.12975e-05, 'epoch': 0.39} +{'loss': 0.0208, 'learning_rate': 2.100583333333333e-05, 'epoch': 0.4} +{'loss': 0.017, 'learning_rate': 2.0714166666666666e-05, 'epoch': 0.41} +{'loss': 0.0179, 'learning_rate': 2.04225e-05, 'epoch': 0.42} +{'loss': 0.0242, 'learning_rate': 2.0130833333333334e-05, 'epoch': 0.42} +{'loss': 0.0171, 'learning_rate': 1.9839166666666665e-05, 'epoch': 0.43} +{'loss': 0.0174, 'learning_rate': 1.95475e-05, 'epoch': 0.44} +{'loss': 0.0179, 'learning_rate': 1.9255833333333333e-05, 'epoch': 0.45} +{'loss': 0.0233, 'learning_rate': 1.8964166666666664e-05, 'epoch': 0.46} +{'loss': 0.0203, 'learning_rate': 1.8672499999999998e-05, 'epoch': 0.47} +{'loss': 0.0189, 'learning_rate': 1.8380833333333332e-05, 'epoch': 0.47} +{'loss': 0.0198, 'learning_rate': 1.8089166666666666e-05, 'epoch': 0.48} +{'loss': 0.0199, 'learning_rate': 1.7797499999999997e-05, 'epoch': 0.49} +{'loss': 0.0165, 'learning_rate': 1.750583333333333e-05, 'epoch': 0.5} + + 0%| | 0/496 [00:00> Saving model checkpoint to ./checkpoint-3000 +[INFO|configuration_utils.py:447] 2022-12-18 12:08:50,522 >> Configuration saved in ./checkpoint-3000/config.json +[INFO|modeling_utils.py:1680] 2022-12-18 12:08:51,515 >> Model weights saved in ./checkpoint-3000/pytorch_model.bin +[INFO|feature_extraction_utils.py:368] 2022-12-18 12:08:51,530 >> Feature extractor saved in ./checkpoint-3000/preprocessor_config.json +[INFO|tokenization_utils_base.py:2157] 2022-12-18 12:08:51,530 >> tokenizer config file saved in ./checkpoint-3000/tokenizer_config.json +[INFO|tokenization_utils_base.py:2164] 2022-12-18 12:08:51,531 >> Special tokens file saved in ./checkpoint-3000/special_tokens_map.json +[INFO|tokenization_utils_base.py:2210] 2022-12-18 12:08:51,531 >> added tokens file saved in ./checkpoint-3000/added_tokens.json + 50%|█████ | 3001/6000 [12:33:40<1285:45:15, 1543.42s/it] 50%|█████ | 3002/6000 [12:33:51<902:37:49, 1083.88s/it] 50%|█████ | 3003/6000 [12:34:02<634:25:33, 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