-------------------------------------------------------------------------- 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 -------------------------------------------------------------------------- 12/14/2022 11:27:46 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True 12/14/2022 11:27:46 - 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=ales/whisper-small-belarusian, 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=0.0001, 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/Dec14_11-27-46_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=12000, 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=64, per_device_train_batch_size=64, 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=, 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=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, ) 12/14/2022 11:27:46 - 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=ales/whisper-small-belarusian, 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=0.0001, 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/Dec14_11-27-46_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=12000, 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=64, per_device_train_batch_size=64, 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=, 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=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, ) 12/14/2022 11:27:46 - INFO - __main__ - output_dir already exists. will try to load last checkpoint. 12/14/2022 11:27:46 - INFO - __main__ - last_checkpoint is None. will try to read from training_args.resume_from_checkpoint 12/14/2022 11:27:46 - 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. 12/14/2022 11:27:46 - INFO - __main__ - dir is not empty, but contains only: ['src', '.gitattributes', '.git', 'train_run_1.log', 'train_20221214-112746.log']. it is OK - will start training 12/14/2022 11:27:46 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f 12/14/2022 11:27:47 - INFO - datasets.info - Loading Dataset Infos from /home/ubuntu/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_11_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%| | 0.00/1.97k [00:00> loading configuration file config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/config.json [INFO|configuration_utils.py:706] 2022-12-14 11:27:47,198 >> Model config WhisperConfig { "_name_or_path": "openai/whisper-small", "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": [ [ 1, 50259 ], [ 2, 50359 ], [ 3, 50363 ] ], "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, "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": "4.26.0.dev0", "use_cache": true, "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--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/preprocessor_config.json [INFO|feature_extraction_utils.py:501] 2022-12-14 11:27:47,277 >> Feature extractor WhisperFeatureExtractor { "chunk_length": 30, "feature_extractor_type": "WhisperFeatureExtractor", "feature_size": 80, "hop_length": 160, "mel_filters": [ [ -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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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 } Downloading: 0%| | 0.00/829 [00:00> loading file vocab.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/vocab.json [INFO|tokenization_utils_base.py:1799] 2022-12-14 11:27:47,688 >> loading file tokenizer.json from cache at None [INFO|tokenization_utils_base.py:1799] 2022-12-14 11:27:47,688 >> loading file merges.txt from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/merges.txt [INFO|tokenization_utils_base.py:1799] 2022-12-14 11:27:47,688 >> loading file normalizer.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/normalizer.json [INFO|tokenization_utils_base.py:1799] 2022-12-14 11:27:47,688 >> loading file added_tokens.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/added_tokens.json [INFO|tokenization_utils_base.py:1799] 2022-12-14 11:27:47,688 >> loading file special_tokens_map.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/special_tokens_map.json [INFO|tokenization_utils_base.py:1799] 2022-12-14 11:27:47,688 >> loading file tokenizer_config.json from cache at /home/ubuntu/.cache/huggingface/hub/models--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/tokenizer_config.json [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|endoftext|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|startoftranscript|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|en|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|zh|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|de|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|es|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ru|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ko|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|fr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ja|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|pt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|tr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|pl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ca|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|nl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ar|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|sv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|it|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|id|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|hi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|fi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|vi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|iw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|uk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|el|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ms|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|cs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ro|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|da|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|hu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,749 >> Adding <|ta|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|no|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|th|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|ur|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|hr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|bg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|lt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|la|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|mi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|ml|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|cy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|sk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|te|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|fa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|lv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|bn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|sr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|az|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|sl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|kn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|et|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|mk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|br|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|eu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|is|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|hy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|ne|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|mn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|bs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|kk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|sq|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|sw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|gl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|mr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,750 >> Adding <|pa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|si|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|km|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|sn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|yo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|so|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|af|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|oc|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|ka|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|be|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|tg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|sd|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|gu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|am|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|yi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|lo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|uz|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|fo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|ht|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|ps|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|tk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|nn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|mt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|sa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|lb|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|my|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|bo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|tl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|mg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|as|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|tt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|haw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|ln|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|ha|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,751 >> Adding <|ba|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> Adding <|jw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> Adding <|su|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> Adding <|translate|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> Adding <|transcribe|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> Adding <|startoflm|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> Adding <|startofprev|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> Adding <|nocaptions|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:27:47,752 >> 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--openai--whisper-small/snapshots/ecd562088fc463cdf07bf4a997944edeae03993e/pytorch_model.bin [INFO|modeling_utils.py:2806] 2022-12-14 11:28:03,276 >> All model checkpoint weights were used when initializing WhisperForConditionalGeneration. [INFO|modeling_utils.py:2814] 2022-12-14 11:28:03,276 >> 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|feature_extraction_utils.py:368] 2022-12-14 11:28:03,414 >> Feature extractor saved in ./preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-14 11:28:03,415 >> tokenizer config file saved in ./tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-14 11:28:03,415 >> Special tokens file saved in ./special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-14 11:28:03,415 >> added tokens file saved in ./added_tokens.json [INFO|configuration_utils.py:447] 2022-12-14 11:28:03,496 >> Configuration saved in ./config.json [INFO|image_processing_utils.py:294] 2022-12-14 11:28:03,497 >> loading configuration file ./preprocessor_config.json [INFO|feature_extraction_utils.py:462] 2022-12-14 11:28:03,502 >> loading configuration file ./preprocessor_config.json [INFO|feature_extraction_utils.py:501] 2022-12-14 11:28:03,517 >> Feature extractor WhisperFeatureExtractor { "chunk_length": 30, "feature_extractor_type": "WhisperFeatureExtractor", 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0.003251380519941449, 0.0027281083166599274, 0.002204835880547762, 0.001681563793681562, 0.001158291706815362, 0.0006350195035338402, 0.00011174729297636077, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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] 2022-12-14 11:28:03,526 >> loading file vocab.json [INFO|tokenization_utils_base.py:1797] 2022-12-14 11:28:03,526 >> loading file tokenizer.json [INFO|tokenization_utils_base.py:1797] 2022-12-14 11:28:03,526 >> loading file merges.txt [INFO|tokenization_utils_base.py:1797] 2022-12-14 11:28:03,526 >> loading file normalizer.json [INFO|tokenization_utils_base.py:1797] 2022-12-14 11:28:03,526 >> loading file added_tokens.json [INFO|tokenization_utils_base.py:1797] 2022-12-14 11:28:03,526 >> loading file special_tokens_map.json [INFO|tokenization_utils_base.py:1797] 2022-12-14 11:28:03,527 >> loading file tokenizer_config.json [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|endoftext|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|startoftranscript|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|en|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|zh|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|de|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|es|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|ru|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|ko|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|fr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|ja|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|pt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|tr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|pl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|ca|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|nl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|ar|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|sv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|it|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|id|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|hi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|fi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|vi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|iw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|uk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|el|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|ms|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|cs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|ro|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,582 >> Adding <|da|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|hu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|ta|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|no|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|th|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|ur|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|hr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|bg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|lt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|la|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|mi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|ml|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|cy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|sk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|te|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|fa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|lv|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|bn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|sr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|az|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|sl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|kn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|et|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|mk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|br|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|eu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|is|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|hy|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|ne|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|mn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|bs|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|kk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|sq|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|sw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|gl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|mr|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,583 >> Adding <|pa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|si|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|km|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|sn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|yo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|so|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|af|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|oc|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|ka|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|be|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|tg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|sd|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|gu|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|am|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|yi|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|lo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|uz|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|fo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|ht|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|ps|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|tk|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|nn|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|mt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|sa|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|lb|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|my|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|bo|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|tl|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|mg|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|as|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|tt|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|haw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|ln|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|ha|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|ba|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,584 >> Adding <|jw|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,585 >> Adding <|su|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,585 >> Adding <|translate|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,585 >> Adding <|transcribe|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,585 >> Adding <|startoflm|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,585 >> Adding <|startofprev|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,585 >> Adding <|nocaptions|> to the vocabulary [INFO|tokenization_utils.py:426] 2022-12-14 11:28:03,585 >> Adding <|notimestamps|> to the vocabulary /home/ubuntu/whisper-small-belarusian/./ is already a clone of https://huggingface.co/ales/whisper-small-belarusian. Make sure you pull the latest changes with `repo.git_pull()`. 12/14/2022 11:28:05 - WARNING - huggingface_hub.repository - /home/ubuntu/whisper-small-belarusian/./ is already a clone of https://huggingface.co/ales/whisper-small-belarusian. Make sure you pull the latest changes with `repo.git_pull()`. [INFO|trainer.py:511] 2022-12-14 11:28:07,794 >> max_steps is given, it will override any value given in num_train_epochs [INFO|trainer.py:565] 2022-12-14 11:28:07,794 >> Using cuda_amp half precision backend /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] 2022-12-14 11:28:07,805 >> ***** Running training ***** [INFO|trainer.py:1642] 2022-12-14 11:28:07,805 >> Num examples = 768000 [INFO|trainer.py:1643] 2022-12-14 11:28:07,805 >> Num Epochs = 9223372036854775807 [INFO|trainer.py:1644] 2022-12-14 11:28:07,805 >> Instantaneous batch size per device = 64 [INFO|trainer.py:1645] 2022-12-14 11:28:07,806 >> Total train batch size (w. parallel, distributed & accumulation) = 64 [INFO|trainer.py:1646] 2022-12-14 11:28:07,806 >> Gradient Accumulation steps = 1 [INFO|trainer.py:1647] 2022-12-14 11:28:07,806 >> Total optimization steps = 12000 [INFO|trainer.py:1648] 2022-12-14 11:28:07,807 >> Number of trainable parameters = 241734912 0%| | 0/12000 [00:00> The following columns in the training set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. 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'epoch': 0.0} {'loss': 1.184, 'learning_rate': 9.4e-06, 'epoch': 0.0} {'loss': 0.4478, 'learning_rate': 1.94e-05, 'epoch': 0.01} {'loss': 0.307, 'learning_rate': 2.94e-05, 'epoch': 0.01} {'loss': 0.2939, 'learning_rate': 3.94e-05, 'epoch': 0.02} {'loss': 0.2464, 'learning_rate': 4.94e-05, 'epoch': 0.02} {'loss': 0.2403, 'learning_rate': 5.94e-05, 'epoch': 0.03} {'loss': 0.2404, 'learning_rate': 6.939999999999999e-05, 'epoch': 0.03} {'loss': 0.2563, 'learning_rate': 7.94e-05, 'epoch': 0.03} {'loss': 0.2366, 'learning_rate': 8.94e-05, 'epoch': 0.04} {'loss': 0.2237, 'learning_rate': 9.94e-05, 'epoch': 0.04} {'loss': 0.234, 'learning_rate': 9.959130434782609e-05, 'epoch': 0.05} {'loss': 0.2301, 'learning_rate': 9.915652173913044e-05, 'epoch': 0.05} {'loss': 0.2404, 'learning_rate': 9.872173913043479e-05, 'epoch': 0.05} {'loss': 0.2155, 'learning_rate': 9.828695652173913e-05, 'epoch': 0.06} {'loss': 0.1606, 'learning_rate': 9.785217391304348e-05, 'epoch': 0.06} {'loss': 0.1744, 'learning_rate': 9.741739130434783e-05, 'epoch': 0.07} {'loss': 0.2005, 'learning_rate': 9.698260869565218e-05, 'epoch': 0.07} {'loss': 0.2168, 'learning_rate': 9.654782608695653e-05, 'epoch': 0.07} {'loss': 0.1448, 'learning_rate': 9.611304347826087e-05, 'epoch': 0.08} {'loss': 0.1907, 'learning_rate': 9.567826086956522e-05, 'epoch': 0.08} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 7.59it/s] Reading metadata...: 13049it [00:00, 67867.94it/s] Reading metadata...: 15872it [00:00, 62714.31it/s] [INFO|trainer_utils.py:689] 2022-12-14 14:12:49,577 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 8%|▊ | 1000/12000 [4:40:13<26:09:13, 8.56s/it][INFO|trainer.py:2700] 2022-12-14 16:08:21,068 >> Saving model checkpoint to ./checkpoint-1000 [INFO|configuration_utils.py:447] 2022-12-14 16:08:21,069 >> Configuration saved in ./checkpoint-1000/config.json [INFO|modeling_utils.py:1680] 2022-12-14 16:08:21,933 >> Model weights saved in ./checkpoint-1000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-14 16:08:21,947 >> Feature extractor saved in ./checkpoint-1000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-14 16:08:21,947 >> tokenizer config file saved in ./checkpoint-1000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-14 16:08:21,947 >> Special tokens file saved in ./checkpoint-1000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-14 16:08:21,948 >> added tokens file saved in ./checkpoint-1000/added_tokens.json [INFO|feature_extraction_utils.py:368] 2022-12-14 16:08:24,166 >> Feature extractor saved in ./preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-14 16:08:24,166 >> tokenizer config file saved in ./tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-14 16:08:24,167 >> Special tokens file saved in ./special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-14 16:08:24,167 >> added tokens file saved in ./added_tokens.json 8%|▊ | 1001/12000 [4:40:41<6405:27:46, 2096.52s/it] 8%|▊ | 1002/12000 [4:40:52<4494:04:26, 1471.06s/it] 8%|▊ | 1003/12000 [4:41:04<3156:43:06, 1033.39s/it] 8%|▊ | 1004/12000 [4:41:15<2219:17:37, 726.58s/it] 8%|▊ | 1005/12000 [4:41:25<1562:21:34, 511.55s/it] 8%|▊ | 1006/12000 [4:41:32<1100:26:26, 360.34s/it] 8%|▊ | 1007/12000 [4:41:44<780:42:21, 255.67s/it] 8%|▊ | 1008/12000 [4:41:53<554:58:47, 181.76s/it] 8%|▊ | 1009/12000 [4:42:02<396:02:55, 129.72s/it] 8%|▊ | 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| 1492/12000 [6:02:06<29:56:52, 10.26s/it] 12%|█▏ | 1493/12000 [6:02:16<29:54:44, 10.25s/it] 12%|█▏ | 1494/12000 [6:02:25<28:41:19, 9.83s/it] 12%|█▏ | 1495/12000 [6:02:33<27:06:49, 9.29s/it] 12%|█▏ | 1496/12000 [6:02:45<29:49:23, 10.22s/it] 12%|█▏ | 1497/12000 [6:02:56<29:56:56, 10.27s/it] 12%|█▏ | 1498/12000 [6:03:04<27:50:04, 9.54s/it] 12%|█▏ | 1499/12000 [6:03:13<27:16:05, 9.35s/it] 12%|█▎ | 1500/12000 [6:03:24<29:00:04, 9.94s/it] 12%|█▎ | 1500/12000 [6:03:24<29:00:04, 9.94s/it] 13%|█▎ | 1501/12000 [6:03:35<29:51:49, 10.24s/it] 13%|█▎ | 1502/12000 [6:03:46<30:24:53, 10.43s/it] 13%|█▎ | 1503/12000 [6:03:54<28:52:52, 9.90s/it] 13%|█▎ | 1504/12000 [6:04:04<28:32:41, 9.79s/it] 13%|█▎ | 1505/12000 [6:04:14<29:10:35, 10.01s/it] 13%|█▎ | 1506/12000 [6:04:22<27:21:01, 9.38s/it] 13%|█▎ | 1507/12000 [6:04:34<29:43:42, 10.20s/it] 13%|█▎ | 1508/12000 [6:04:46<31:12:00, 10.71s/it] 13%|█▎ | 1509/12000 [6:04:58<32:02:32, 11.00s/it] 13%|█▎ | 1510/12000 [6:05:09<32:10:22, 11.04s/it] 13%|█▎ | 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[6:24:46<31:32:26, 10.95s/it] 14%|█▎ | 1630/12000 [6:24:55<29:50:44, 10.36s/it] 14%|█▎ | 1631/12000 [6:25:04<29:11:05, 10.13s/it] 14%|█▎ | 1632/12000 [6:25:16<30:26:56, 10.57s/it] 14%|█▎ | 1633/12000 [6:25:27<30:34:18, 10.62s/it] 14%|█▎ | 1634/12000 [6:25:36<29:01:57, 10.08s/it] 14%|█▎ | 1635/12000 [6:25:43<26:36:55, 9.24s/it] 14%|█▎ | 1636/12000 [6:25:52<26:36:54, 9.24s/it] 14%|█▎ | 1637/12000 [6:26:01<26:37:30, 9.25s/it] 14%|█▎ | 1638/12000 [6:26:13<28:57:26, 10.06s/it] 14%|█▎ | 1639/12000 [6:26:27<32:18:14, 11.22s/it] 14%|█▎ | 1640/12000 [6:26:39<32:45:52, 11.39s/it] 14%|█▎ | 1641/12000 [6:26:50<32:30:56, 11.30s/it] 14%|█▎ | 1642/12000 [6:27:00<30:54:13, 10.74s/it] 14%|█▎ | 1643/12000 [6:27:09<29:36:26, 10.29s/it] 14%|█▎ | 1644/12000 [6:27:21<31:06:19, 10.81s/it] 14%|█▎ | 1645/12000 [6:27:33<32:07:25, 11.17s/it] 14%|█▎ | 1646/12000 [6:27:43<31:32:52, 10.97s/it] 14%|█▎ | 1647/12000 [6:27:52<30:00:46, 10.44s/it] 14%|█▎ | 1648/12000 [6:28:02<28:54:46, 10.05s/it] 14%|█▎ | 1649/12000 [6:28:13<30:19:46, 10.55s/it] 14%|█▍ | 1650/12000 [6:28:24<30:14:22, 10.52s/it] 14%|█▍ | 1650/12000 [6:28:24<30:14:22, 10.52s/it] 14%|█▍ | 1651/12000 [6:28:35<30:37:04, 10.65s/it] 14%|█▍ | 1652/12000 [6:28:45<30:02:28, 10.45s/it] 14%|█▍ | 1653/12000 [6:28:55<30:15:33, 10.53s/it] 14%|█▍ | 1654/12000 [6:29:06<30:39:07, 10.67s/it] 14%|█▍ | 1655/12000 [6:29:16<29:42:11, 10.34s/it] 14%|█▍ | 1656/12000 [6:29:25<28:31:47, 9.93s/it] 14%|█▍ | 1657/12000 [6:29:38<30:46:51, 10.71s/it] 14%|█▍ | 1658/12000 [6:29:46<29:11:59, 10.16s/it] 14%|█▍ | 1659/12000 [6:29:55<28:00:47, 9.75s/it] 14%|█▍ | 1660/12000 [6:30:04<27:16:43, 9.50s/it] 14%|█▍ | 1661/12000 [6:30:14<27:27:05, 9.56s/it] 14%|█▍ | 1662/12000 [6:30:23<27:14:37, 9.49s/it] 14%|█▍ | 1663/12000 [6:30:32<26:17:30, 9.16s/it] 14%|█▍ | 1664/12000 [6:30:43<28:32:32, 9.94s/it] 14%|█▍ | 1665/12000 [6:30:52<27:16:27, 9.50s/it] 14%|█▍ | 1666/12000 [6:31:01<26:58:15, 9.40s/it] 14%|█▍ | 1667/12000 [6:31:10<26:26:59, 9.22s/it] 14%|█▍ | 1668/12000 [6:31:19<26:18:43, 9.17s/it] 14%|█▍ | 1669/12000 [6:31:30<27:41:54, 9.65s/it] 14%|█▍ | 1670/12000 [6:31:39<27:46:12, 9.68s/it] 14%|█▍ | 1671/12000 [6:31:49<27:38:48, 9.64s/it] 14%|█▍ | 1672/12000 [6:32:01<29:58:07, 10.45s/it] 14%|█▍ | 1673/12000 [6:32:13<30:55:31, 10.78s/it] 14%|█▍ | 1674/12000 [6:32:24<31:07:15, 10.85s/it] 14%|█▍ | 1675/12000 [6:32:31<28:21:15, 9.89s/it] 14%|█▍ | 1676/12000 [6:32:41<28:09:18, 9.82s/it] 14%|█▍ | 1677/12000 [6:32:53<29:40:01, 10.35s/it] 14%|█▍ | 1678/12000 [6:33:04<30:10:44, 10.53s/it] 14%|█▍ | 1679/12000 [6:33:15<30:58:02, 10.80s/it] 14%|█▍ | 1680/12000 [6:33:25<30:14:27, 10.55s/it] 14%|█▍ | 1681/12000 [6:33:36<30:47:36, 10.74s/it] 14%|█▍ | 1682/12000 [6:33:46<30:09:21, 10.52s/it] 14%|█▍ | 1683/12000 [6:33:56<29:30:12, 10.29s/it] 14%|█▍ | 1684/12000 [6:34:03<27:05:41, 9.46s/it] 14%|█▍ | 1685/12000 [6:34:11<25:37:59, 8.95s/it] 14%|█▍ | 1686/12000 [6:34:21<26:16:22, 9.17s/it] 14%|█▍ | 1687/12000 [6:34:28<24:51:54, 8.68s/it] 14%|█▍ | 1688/12000 [6:34:40<27:35:46, 9.63s/it] 14%|█▍ | 1689/12000 [6:34:49<27:05:32, 9.46s/it] 14%|█▍ | 1690/12000 [6:34:57<25:40:16, 8.96s/it] 14%|█▍ | 1691/12000 [6:35:08<27:37:10, 9.65s/it] 14%|█▍ | 1692/12000 [6:35:19<28:17:07, 9.88s/it] 14%|█▍ | 1693/12000 [6:35:28<27:58:55, 9.77s/it] 14%|█▍ | 1694/12000 [6:35:38<28:19:00, 9.89s/it] 14%|█▍ | 1695/12000 [6:35:50<29:23:24, 10.27s/it] 14%|█▍ | 1696/12000 [6:35:58<27:47:25, 9.71s/it] 14%|█▍ | 1697/12000 [6:36:09<28:28:01, 9.95s/it] 14%|█▍ | 1698/12000 [6:36:19<28:55:25, 10.11s/it] 14%|█▍ | 1699/12000 [6:36:28<27:31:53, 9.62s/it] 14%|█▍ | 1700/12000 [6:36:35<25:36:50, 8.95s/it] 14%|█▍ | 1700/12000 [6:36:35<25:36:50, 8.95s/it] 14%|█▍ | 1701/12000 [6:36:45<26:40:47, 9.33s/it] 14%|█▍ | 1702/12000 [6:36:55<27:13:11, 9.52s/it] 14%|█▍ | 1703/12000 [6:37:04<26:36:41, 9.30s/it] 14%|█▍ | 1704/12000 [6:37:14<27:16:52, 9.54s/it] 14%|█▍ | 1705/12000 [6:37:24<27:29:13, 9.61s/it] 14%|█▍ | 1706/12000 [6:37:35<28:29:10, 9.96s/it] 14%|█▍ | 1707/12000 [6:37:44<28:10:18, 9.85s/it] 14%|█▍ | 1708/12000 [6:37:55<29:14:13, 10.23s/it] 14%|█▍ | 1709/12000 [6:38:08<31:25:32, 10.99s/it] 14%|█▍ | 1710/12000 [6:38:15<27:46:52, 9.72s/it] 14%|█▍ | 1711/12000 [6:38:25<27:57:34, 9.78s/it] 14%|█▍ | 1712/12000 [6:38:36<29:32:31, 10.34s/it] 14%|█▍ | 1713/12000 [6:38:44<27:31:16, 9.63s/it] 14%|█▍ | 1714/12000 [6:38:53<26:41:11, 9.34s/it] 14%|█▍ | 1715/12000 [6:39:02<26:50:23, 9.39s/it] 14%|█▍ | 1716/12000 [6:39:13<27:32:05, 9.64s/it] 14%|█▍ | 1717/12000 [6:39:25<29:36:50, 10.37s/it] 14%|█▍ | 1718/12000 [6:39:34<28:29:17, 9.97s/it] 14%|█▍ | 1719/12000 [6:39:45<29:08:41, 10.21s/it] 14%|█▍ | 1720/12000 [6:39:53<27:56:08, 9.78s/it] 14%|█▍ | 1721/12000 [6:40:03<27:44:07, 9.71s/it] 14%|█▍ | 1722/12000 [6:40:12<27:25:30, 9.61s/it] 14%|█▍ | 1723/12000 [6:40:21<26:52:33, 9.41s/it] 14%|█▍ | 1724/12000 [6:40:33<29:19:57, 10.28s/it] 14%|█▍ | 1725/12000 [6:40:43<28:58:07, 10.15s/it] 14%|█▍ | 1726/12000 [6:40:55<30:14:20, 10.60s/it] 14%|█▍ | 1727/12000 [6:41:04<29:03:03, 10.18s/it] 14%|█▍ | 1728/12000 [6:41:14<29:01:27, 10.17s/it] 14%|█▍ | 1729/12000 [6:41:24<28:50:35, 10.11s/it] 14%|█▍ | 1730/12000 [6:41:34<28:07:43, 9.86s/it] 14%|█▍ | 1731/12000 [6:41:45<29:23:12, 10.30s/it] 14%|█▍ | 1732/12000 [6:41:55<28:50:31, 10.11s/it] 14%|█▍ | 1733/12000 [6:42:04<28:13:46, 9.90s/it] 14%|█▍ | 1734/12000 [6:42:14<28:03:52, 9.84s/it] 14%|█▍ | 1735/12000 [6:42:24<28:05:41, 9.85s/it] 14%|█▍ | 1736/12000 [6:42:33<27:58:16, 9.81s/it] 14%|█▍ | 1737/12000 [6:42:42<26:52:03, 9.42s/it] 14%|█▍ | 1738/12000 [6:42:52<27:07:35, 9.52s/it] 14%|█▍ | 1739/12000 [6:43:01<26:39:29, 9.35s/it] 14%|█▍ | 1740/12000 [6:43:11<27:40:42, 9.71s/it] 15%|█▍ | 1741/12000 [6:43:19<25:47:41, 9.05s/it] 15%|█▍ | 1742/12000 [6:43:28<25:49:26, 9.06s/it] 15%|█▍ | 1743/12000 [6:43:38<26:53:43, 9.44s/it] 15%|█▍ | 1744/12000 [6:43:49<28:07:36, 9.87s/it] 15%|█▍ | 1745/12000 [6:43:59<28:22:12, 9.96s/it] 15%|█▍ | 1746/12000 [6:44:07<26:36:40, 9.34s/it] 15%|█▍ | 1747/12000 [6:44:17<27:22:18, 9.61s/it] 15%|█▍ | 1748/12000 [6:44:26<26:37:30, 9.35s/it] 15%|█▍ | 1749/12000 [6:44:35<26:21:10, 9.25s/it] 15%|█▍ | 1750/12000 [6:44:45<27:22:30, 9.61s/it] 15%|█▍ | 1750/12000 [6:44:45<27:22:30, 9.61s/it] 15%|█▍ | 1751/12000 [6:44:54<26:25:46, 9.28s/it] 15%|█▍ | 1752/12000 [6:45:03<26:01:37, 9.14s/it] 15%|█▍ | 1753/12000 [6:45:14<28:04:24, 9.86s/it] 15%|█▍ | 1754/12000 [6:45:24<27:44:43, 9.75s/it] 15%|█▍ | 1755/12000 [6:45:35<29:18:33, 10.30s/it] 15%|█▍ | 1756/12000 [6:45:44<27:32:22, 9.68s/it] 15%|█▍ | 1757/12000 [6:45:55<28:42:47, 10.09s/it] 15%|█▍ | 1758/12000 [6:46:06<30:02:38, 10.56s/it] 15%|█▍ | 1759/12000 [6:46:16<29:20:45, 10.32s/it] 15%|█▍ | 1760/12000 [6:46:26<29:14:53, 10.28s/it] 15%|█▍ | 1761/12000 [6:46:38<30:51:01, 10.85s/it] 15%|█▍ | 1762/12000 [6:46:48<30:08:43, 10.60s/it] 15%|█▍ | 1763/12000 [6:46:59<30:33:11, 10.74s/it] 15%|█▍ | 1764/12000 [6:47:12<31:44:48, 11.17s/it] 15%|█▍ | 1765/12000 [6:47:21<30:02:54, 10.57s/it] 15%|█▍ | 1766/12000 [6:47:29<28:15:23, 9.94s/it] 15%|█▍ | 1767/12000 [6:47:38<27:30:44, 9.68s/it] 15%|█▍ | 1768/12000 [6:47:48<27:30:06, 9.68s/it] 15%|█▍ | 1769/12000 [6:47:57<26:50:43, 9.45s/it] 15%|█▍ | 1770/12000 [6:48:07<27:46:51, 9.78s/it] 15%|█▍ | 1771/12000 [6:48:16<26:31:56, 9.34s/it] 15%|█▍ | 1772/12000 [6:48:26<27:29:33, 9.68s/it] 15%|█▍ | 1773/12000 [6:48:35<27:04:43, 9.53s/it] 15%|█▍ | 1774/12000 [6:48:43<25:45:47, 9.07s/it] 15%|█▍ | 1775/12000 [6:48:53<26:03:54, 9.18s/it] 15%|█▍ | 1776/12000 [6:49:02<25:50:58, 9.10s/it] 15%|█▍ | 1777/12000 [6:49:10<25:19:50, 8.92s/it] 15%|█▍ | 1778/12000 [6:49:21<26:45:45, 9.43s/it] 15%|█▍ | 1779/12000 [6:49:31<27:15:38, 9.60s/it] 15%|█▍ | 1780/12000 [6:49:41<27:21:08, 9.63s/it] 15%|█▍ | 1781/12000 [6:49:49<26:32:17, 9.35s/it] 15%|█▍ | 1782/12000 [6:49:57<24:51:04, 8.76s/it] 15%|█▍ | 1783/12000 [6:50:06<25:08:14, 8.86s/it] 15%|█▍ | 1784/12000 [6:50:15<25:08:40, 8.86s/it] 15%|█▍ | 1785/12000 [6:50:25<26:11:27, 9.23s/it] 15%|█▍ | 1786/12000 [6:50:32<24:38:34, 8.69s/it] 15%|█▍ | 1787/12000 [6:50:41<24:33:15, 8.66s/it] 15%|█▍ | 1788/12000 [6:50:51<26:00:06, 9.17s/it] 15%|█▍ | 1789/12000 [6:50:59<24:39:47, 8.70s/it] 15%|█▍ | 1790/12000 [6:51:08<25:16:06, 8.91s/it] 15%|█▍ | 1791/12000 [6:51:17<25:30:41, 9.00s/it] 15%|█▍ | 1792/12000 [6:51:26<25:18:09, 8.92s/it] 15%|█▍ | 1793/12000 [6:51:36<26:10:52, 9.23s/it] 15%|█▍ | 1794/12000 [6:51:44<25:16:36, 8.92s/it] 15%|█▍ | 1795/12000 [6:51:53<25:01:37, 8.83s/it] 15%|█▍ | 1796/12000 [6:52:01<24:53:04, 8.78s/it] 15%|█▍ | 1797/12000 [6:52:14<27:45:50, 9.80s/it] 15%|█▍ | 1798/12000 [6:52:23<27:44:21, 9.79s/it] 15%|█▍ | 1799/12000 [6:52:37<30:58:42, 10.93s/it] 15%|█▌ | 1800/12000 [6:52:46<29:22:46, 10.37s/it] 15%|█▌ | 1800/12000 [6:52:46<29:22:46, 10.37s/it] 15%|█▌ | 1801/12000 [6:52:57<29:52:59, 10.55s/it] 15%|█▌ | 1802/12000 [6:53:07<29:03:07, 10.26s/it] 15%|█▌ | 1803/12000 [6:53:14<27:01:16, 9.54s/it] 15%|█▌ | 1804/12000 [6:53:26<28:42:53, 10.14s/it] 15%|█▌ | 1805/12000 [6:53:35<27:50:59, 9.83s/it] 15%|█▌ | 1806/12000 [6:53:46<28:31:24, 10.07s/it] 15%|█▌ | 1807/12000 [6:53:58<30:24:47, 10.74s/it] 15%|█▌ | 1808/12000 [6:54:08<29:20:20, 10.36s/it] 15%|█▌ | 1809/12000 [6:54:18<29:29:21, 10.42s/it] 15%|█▌ | 1810/12000 [6:54:29<30:04:33, 10.63s/it] 15%|█▌ | 1811/12000 [6:54:40<29:59:34, 10.60s/it] 15%|█▌ | 1812/12000 [6:54:51<30:23:14, 10.74s/it] 15%|█▌ | 1813/12000 [6:55:01<30:08:10, 10.65s/it] 15%|█▌ | 1814/12000 [6:55:11<29:41:04, 10.49s/it] 15%|█▌ | 1815/12000 [6:55:21<29:15:05, 10.34s/it] 15%|█▌ | 1816/12000 [6:55:31<29:03:14, 10.27s/it] 15%|█▌ | 1817/12000 [6:55:40<27:37:39, 9.77s/it] 15%|█▌ | 1818/12000 [6:55:50<27:39:23, 9.78s/it] 15%|█▌ | 1819/12000 [6:55:59<27:02:56, 9.56s/it] 15%|█▌ | 1820/12000 [6:56:07<25:51:17, 9.14s/it] 15%|█▌ | 1821/12000 [6:56:17<26:12:19, 9.27s/it] 15%|█▌ | 1822/12000 [6:56:26<26:27:29, 9.36s/it] 15%|█▌ | 1823/12000 [6:56:36<26:34:16, 9.40s/it] 15%|█▌ | 1824/12000 [6:56:45<26:22:35, 9.33s/it] 15%|█▌ | 1825/12000 [6:56:55<26:54:32, 9.52s/it] 15%|█▌ | 1826/12000 [6:57:04<26:30:18, 9.38s/it] 15%|█▌ | 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2022-12-14 18:53:43,434 >> Batch size = 64 {'eval_loss': 0.2546018362045288, 'eval_wer': 25.463875887957045, 'eval_runtime': 6940.5327, 'eval_samples_per_second': 2.287, 'eval_steps_per_second': 0.036, 'epoch': 0.08} {'loss': 0.1314, 'learning_rate': 9.524347826086957e-05, 'epoch': 0.09} {'loss': 0.1387, 'learning_rate': 9.480869565217392e-05, 'epoch': 0.09} {'loss': 0.139, 'learning_rate': 9.437391304347827e-05, 'epoch': 0.1} {'loss': 0.1395, 'learning_rate': 9.393913043478261e-05, 'epoch': 0.1} {'loss': 0.1268, 'learning_rate': 9.350434782608696e-05, 'epoch': 0.1} {'loss': 0.177, 'learning_rate': 9.306956521739131e-05, 'epoch': 0.11} {'loss': 0.1791, 'learning_rate': 9.263478260869566e-05, 'epoch': 0.11} {'loss': 0.0979, 'learning_rate': 9.22e-05, 'epoch': 0.12} {'loss': 0.09, 'learning_rate': 9.176521739130435e-05, 'epoch': 0.12} {'loss': 0.138, 'learning_rate': 9.13304347826087e-05, 'epoch': 0.12} {'loss': 0.1316, 'learning_rate': 9.089565217391305e-05, 'epoch': 0.13} {'loss': 0.104, 'learning_rate': 9.04608695652174e-05, 'epoch': 0.13} {'loss': 0.1265, 'learning_rate': 9.002608695652174e-05, 'epoch': 0.14} {'loss': 0.1421, 'learning_rate': 8.959130434782609e-05, 'epoch': 0.14} {'loss': 0.1799, 'learning_rate': 8.915652173913044e-05, 'epoch': 0.15} {'loss': 0.1733, 'learning_rate': 8.872173913043479e-05, 'epoch': 0.15} {'loss': 0.1525, 'learning_rate': 8.828695652173914e-05, 'epoch': 0.15} {'loss': 0.1638, 'learning_rate': 8.785217391304348e-05, 'epoch': 0.16} {'loss': 0.1642, 'learning_rate': 8.741739130434783e-05, 'epoch': 0.16} {'loss': 0.1482, 'learning_rate': 8.698260869565218e-05, 'epoch': 0.17} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 4.36it/s] Reading metadata...: 13075it [00:00, 50185.45it/s] Reading metadata...: 15872it [00:00, 45292.77it/s] [INFO|trainer_utils.py:689] 2022-12-14 18:53:51,664 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 17%|█▋ | 2000/12000 [9:21:07<27:42:21, 9.97s/it][INFO|trainer.py:2700] 2022-12-14 20:49:15,278 >> Saving model checkpoint to ./checkpoint-2000 [INFO|configuration_utils.py:447] 2022-12-14 20:49:15,279 >> Configuration saved in ./checkpoint-2000/config.json [INFO|modeling_utils.py:1680] 2022-12-14 20:49:16,146 >> Model weights saved in ./checkpoint-2000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-14 20:49:16,161 >> Feature extractor saved in ./checkpoint-2000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-14 20:49:16,162 >> tokenizer config file saved in ./checkpoint-2000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-14 20:49:16,162 >> Special tokens file saved in ./checkpoint-2000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-14 20:49:16,162 >> added tokens file saved in ./checkpoint-2000/added_tokens.json [INFO|feature_extraction_utils.py:368] 2022-12-14 20:49:20,975 >> Feature extractor saved in ./preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-14 20:49:20,976 >> tokenizer config file saved in ./tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-14 20:49:20,976 >> Special tokens file saved in ./special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-14 20:49:20,977 >> added tokens file saved in ./added_tokens.json 17%|█▋ | 2001/12000 [9:22:00<5839:41:37, 2102.50s/it] 17%|█▋ | 2002/12000 [9:22:14<4098:58:21, 1475.93s/it] 17%|█▋ | 2003/12000 [9:22:24<2877:04:41, 1036.06s/it] 17%|█▋ | 2004/12000 [9:22:32<2020:18:40, 727.60s/it] 17%|█▋ | 2005/12000 [9:22:43<1423:11:49, 512.61s/it] 17%|█▋ | 2006/12000 [9:22:53<1005:09:51, 362.08s/it] 17%|█▋ | 2007/12000 [9:23:03<711:48:05, 256.43s/it] 17%|█▋ | 2008/12000 [9:23:14<507:23:50, 182.81s/it] 17%|█▋ | 2009/12000 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2029/12000 [9:26:47<28:48:12, 10.40s/it] 17%|█▋ | 2030/12000 [9:26:59<30:09:45, 10.89s/it] 17%|█▋ | 2031/12000 [9:27:09<29:53:23, 10.79s/it] 17%|█▋ | 2032/12000 [9:27:20<29:36:53, 10.70s/it] 17%|█▋ | 2033/12000 [9:27:30<28:57:35, 10.46s/it] 17%|█▋ | 2034/12000 [9:27:38<27:19:07, 9.87s/it] 17%|█▋ | 2035/12000 [9:27:48<26:46:15, 9.67s/it] 17%|█▋ | 2036/12000 [9:27:58<27:23:15, 9.90s/it] 17%|█▋ | 2037/12000 [9:28:08<27:54:00, 10.08s/it] 17%|█▋ | 2038/12000 [9:28:21<29:51:13, 10.79s/it] 17%|█▋ | 2039/12000 [9:28:31<28:54:38, 10.45s/it] 17%|█▋ | 2040/12000 [9:28:40<28:18:05, 10.23s/it] 17%|█▋ | 2041/12000 [9:28:50<28:05:59, 10.16s/it] 17%|█▋ | 2042/12000 [9:29:00<27:23:34, 9.90s/it] 17%|█▋ | 2043/12000 [9:29:11<29:03:23, 10.51s/it] 17%|█▋ | 2044/12000 [9:29:21<27:56:57, 10.11s/it] 17%|█▋ | 2045/12000 [9:29:29<26:33:43, 9.61s/it] 17%|█▋ | 2046/12000 [9:29:38<26:18:33, 9.52s/it] 17%|█▋ | 2047/12000 [9:29:48<26:01:13, 9.41s/it] 17%|█▋ | 2048/12000 [9:29:57<25:50:10, 9.35s/it] 17%|█▋ | 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2068/12000 [9:33:21<26:33:03, 9.62s/it] 17%|█▋ | 2069/12000 [9:33:30<26:02:23, 9.44s/it] 17%|█▋ | 2070/12000 [9:33:38<25:07:13, 9.11s/it] 17%|█▋ | 2071/12000 [9:33:49<26:16:55, 9.53s/it] 17%|█▋ | 2072/12000 [9:34:00<27:45:58, 10.07s/it] 17%|█▋ | 2073/12000 [9:34:08<26:12:38, 9.51s/it] 17%|█▋ | 2074/12000 [9:34:21<28:50:31, 10.46s/it] 17%|█▋ | 2075/12000 [9:34:29<27:10:34, 9.86s/it] 17%|█▋ | 2076/12000 [9:34:38<26:33:15, 9.63s/it] 17%|█▋ | 2077/12000 [9:34:47<25:19:29, 9.19s/it] 17%|█▋ | 2078/12000 [9:34:56<25:32:45, 9.27s/it] 17%|█▋ | 2079/12000 [9:35:05<25:31:59, 9.27s/it] 17%|█▋ | 2080/12000 [9:35:17<27:10:48, 9.86s/it] 17%|█▋ | 2081/12000 [9:35:25<25:56:43, 9.42s/it] 17%|█▋ | 2082/12000 [9:35:34<25:50:37, 9.38s/it] 17%|█▋ | 2083/12000 [9:35:43<25:27:00, 9.24s/it] 17%|█▋ | 2084/12000 [9:35:54<27:10:27, 9.87s/it] 17%|█▋ | 2085/12000 [9:36:05<27:21:58, 9.94s/it] 17%|█▋ | 2086/12000 [9:36:16<28:19:10, 10.28s/it] 17%|█▋ | 2087/12000 [9:36:25<27:51:32, 10.12s/it] 17%|█▋ | 2088/12000 [9:36:34<26:33:03, 9.64s/it] 17%|█▋ | 2089/12000 [9:36:45<27:51:03, 10.12s/it] 17%|█▋ | 2090/12000 [9:36:54<26:37:08, 9.67s/it] 17%|█▋ | 2091/12000 [9:37:05<27:52:05, 10.12s/it] 17%|█▋ | 2092/12000 [9:37:15<28:03:39, 10.20s/it] 17%|█▋ | 2093/12000 [9:37:27<29:12:24, 10.61s/it] 17%|█▋ | 2094/12000 [9:37:36<27:48:27, 10.11s/it] 17%|█▋ | 2095/12000 [9:37:46<27:49:08, 10.11s/it] 17%|█▋ | 2096/12000 [9:37:56<28:09:56, 10.24s/it] 17%|█▋ | 2097/12000 [9:38:10<30:28:19, 11.08s/it] 17%|█▋ | 2098/12000 [9:38:20<29:49:01, 10.84s/it] 17%|█▋ | 2099/12000 [9:38:31<29:57:43, 10.89s/it] 18%|█▊ | 2100/12000 [9:38:40<28:26:17, 10.34s/it] 18%|█▊ | 2100/12000 [9:38:40<28:26:17, 10.34s/it] 18%|█▊ | 2101/12000 [9:38:48<26:38:12, 9.69s/it] 18%|█▊ | 2102/12000 [9:38:59<27:29:07, 10.00s/it] 18%|█▊ | 2103/12000 [9:39:10<28:12:37, 10.26s/it] 18%|█▊ | 2104/12000 [9:39:20<28:42:00, 10.44s/it] 18%|█▊ | 2105/12000 [9:39:31<29:04:13, 10.58s/it] 18%|█▊ | 2106/12000 [9:39:43<30:00:09, 10.92s/it] 18%|█▊ | 2107/12000 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[9:46:31<27:57:06, 10.21s/it] 18%|█▊ | 2148/12000 [9:46:42<27:51:55, 10.18s/it] 18%|█▊ | 2149/12000 [9:46:51<26:54:11, 9.83s/it] 18%|█▊ | 2150/12000 [9:46:57<24:30:29, 8.96s/it] 18%|█▊ | 2150/12000 [9:46:57<24:30:29, 8.96s/it] 18%|█▊ | 2151/12000 [9:47:07<25:22:50, 9.28s/it] 18%|█▊ | 2152/12000 [9:47:17<25:20:44, 9.27s/it] 18%|█▊ | 2153/12000 [9:47:25<24:21:15, 8.90s/it] 18%|█▊ | 2154/12000 [9:47:34<24:18:36, 8.89s/it] 18%|█▊ | 2155/12000 [9:47:45<26:28:13, 9.68s/it] 18%|█▊ | 2156/12000 [9:47:53<25:20:21, 9.27s/it] 18%|█▊ | 2157/12000 [9:48:05<26:52:19, 9.83s/it] 18%|█▊ | 2158/12000 [9:48:15<27:04:33, 9.90s/it] 18%|█▊ | 2159/12000 [9:48:25<27:19:19, 9.99s/it] 18%|█▊ | 2160/12000 [9:48:33<25:42:30, 9.41s/it] 18%|█▊ | 2161/12000 [9:48:43<26:33:45, 9.72s/it] 18%|█▊ | 2162/12000 [9:48:52<26:01:59, 9.53s/it] 18%|█▊ | 2163/12000 [9:49:02<26:16:13, 9.61s/it] 18%|█▊ | 2164/12000 [9:49:12<26:25:32, 9.67s/it] 18%|█▊ | 2165/12000 [9:49:21<26:01:14, 9.52s/it] 18%|█▊ | 2166/12000 [9:49:32<27:02:21, 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| 2990/12000 [12:06:57<25:25:43, 10.16s/it] 25%|██▍ | 2991/12000 [12:07:08<26:15:46, 10.49s/it] 25%|██▍ | 2992/12000 [12:07:19<26:26:41, 10.57s/it] 25%|██▍ | 2993/12000 [12:07:30<26:52:27, 10.74s/it] 25%|██▍ | 2994/12000 [12:07:41<26:30:08, 10.59s/it] 25%|██▍ | 2995/12000 [12:07:51<26:35:17, 10.63s/it] 25%|██▍ | 2996/12000 [12:08:03<27:23:33, 10.95s/it] 25%|██▍ | 2997/12000 [12:08:12<26:12:43, 10.48s/it] 25%|██▍ | 2998/12000 [12:08:20<24:23:51, 9.76s/it] 25%|██▍ | 2999/12000 [12:08:31<25:09:34, 10.06s/it] 25%|██▌ | 3000/12000 [12:08:39<23:38:01, 9.45s/it] 25%|██▌ | 3000/12000 [12:08:39<23:38:01, 9.45s/it][INFO|trainer.py:2955] 2022-12-14 23:36:47,512 >> ***** Running Evaluation ***** [INFO|trainer.py:2959] 2022-12-14 23:36:47,512 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-14 23:36:47,512 >> Batch size = 64 {'eval_loss': 0.1641348898410797, 'eval_wer': 17.16756551281747, 'eval_runtime': 6931.8394, 'eval_samples_per_second': 2.29, 'eval_steps_per_second': 0.036, 'epoch': 0.17} {'loss': 0.1376, 'learning_rate': 8.654782608695653e-05, 'epoch': 0.17} {'loss': 0.1323, 'learning_rate': 8.611304347826088e-05, 'epoch': 0.17} {'loss': 0.1637, 'learning_rate': 8.567826086956522e-05, 'epoch': 0.18} {'loss': 0.1518, 'learning_rate': 8.524347826086957e-05, 'epoch': 0.18} {'loss': 0.1357, 'learning_rate': 8.480869565217392e-05, 'epoch': 0.19} {'loss': 0.1373, 'learning_rate': 8.437391304347827e-05, 'epoch': 0.19} {'loss': 0.1257, 'learning_rate': 8.393913043478261e-05, 'epoch': 0.2} {'loss': 0.1336, 'learning_rate': 8.350434782608696e-05, 'epoch': 0.2} {'loss': 0.1351, 'learning_rate': 8.306956521739131e-05, 'epoch': 0.2} {'loss': 0.1159, 'learning_rate': 8.263478260869566e-05, 'epoch': 0.21} {'loss': 0.1198, 'learning_rate': 8.22e-05, 'epoch': 0.21} {'loss': 0.1179, 'learning_rate': 8.176521739130435e-05, 'epoch': 0.22} {'loss': 0.128, 'learning_rate': 8.13304347826087e-05, 'epoch': 0.22} {'loss': 0.1037, 'learning_rate': 8.089565217391305e-05, 'epoch': 0.23} {'loss': 0.1172, 'learning_rate': 8.04608695652174e-05, 'epoch': 0.23} {'loss': 0.1169, 'learning_rate': 8.002608695652175e-05, 'epoch': 0.23} {'loss': 0.1165, 'learning_rate': 7.959130434782609e-05, 'epoch': 0.24} {'loss': 0.117, 'learning_rate': 7.915652173913044e-05, 'epoch': 0.24} {'loss': 0.1225, 'learning_rate': 7.872173913043479e-05, 'epoch': 0.25} {'loss': 0.1175, 'learning_rate': 7.828695652173914e-05, 'epoch': 0.25} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 3.52it/s] Reading metadata...: 12624it [00:00, 42228.04it/s] Reading metadata...: 15872it [00:00, 38768.48it/s] [INFO|trainer_utils.py:689] 2022-12-14 23:36:55,580 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 25%|██▌ | 3000/12000 [14:05:05<23:38:01, 9.45s/it][INFO|trainer.py:2700] 2022-12-15 01:33:13,625 >> Saving model checkpoint to ./checkpoint-3000 [INFO|configuration_utils.py:447] 2022-12-15 01:33:13,626 >> Configuration saved in ./checkpoint-3000/config.json [INFO|modeling_utils.py:1680] 2022-12-15 01:33:14,474 >> Model weights saved in ./checkpoint-3000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-15 01:33:14,488 >> Feature extractor saved in ./checkpoint-3000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-15 01:33:14,489 >> tokenizer config file saved in ./checkpoint-3000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-15 01:33:14,489 >> Special tokens file saved in ./checkpoint-3000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-15 01:33:14,489 >> 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'eval_samples_per_second': 2.272, 'eval_steps_per_second': 0.035, 'epoch': 0.25} {'loss': 0.1011, 'learning_rate': 7.785217391304348e-05, 'epoch': 0.25} {'loss': 0.1185, 'learning_rate': 7.741739130434783e-05, 'epoch': 0.26} {'loss': 0.1094, 'learning_rate': 7.698260869565218e-05, 'epoch': 0.26} {'loss': 0.1078, 'learning_rate': 7.654782608695651e-05, 'epoch': 0.27} {'loss': 0.1171, 'learning_rate': 7.611304347826088e-05, 'epoch': 0.27} {'loss': 0.1098, 'learning_rate': 7.567826086956522e-05, 'epoch': 0.28} {'loss': 0.0955, 'learning_rate': 7.524347826086957e-05, 'epoch': 0.28} {'loss': 0.1081, 'learning_rate': 7.480869565217392e-05, 'epoch': 0.28} {'loss': 0.124, 'learning_rate': 7.437391304347825e-05, 'epoch': 0.29} {'loss': 0.0987, 'learning_rate': 7.393913043478262e-05, 'epoch': 0.29} {'loss': 0.0937, 'learning_rate': 7.350434782608696e-05, 'epoch': 0.3} {'loss': 0.1307, 'learning_rate': 7.306956521739131e-05, 'epoch': 0.3} {'loss': 0.0974, 'learning_rate': 7.263478260869565e-05, 'epoch': 0.3} {'loss': 0.0971, 'learning_rate': 7.22e-05, 'epoch': 0.31} {'loss': 0.1063, 'learning_rate': 7.176521739130435e-05, 'epoch': 0.31} {'loss': 0.134, 'learning_rate': 7.13304347826087e-05, 'epoch': 0.32} {'loss': 0.1118, 'learning_rate': 7.089565217391305e-05, 'epoch': 0.32} {'loss': 0.109, 'learning_rate': 7.046086956521738e-05, 'epoch': 0.33} {'loss': 0.1063, 'learning_rate': 7.002608695652175e-05, 'epoch': 0.33} {'loss': 0.0958, 'learning_rate': 6.95913043478261e-05, 'epoch': 0.33} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 8690it [00:00, 86892.42it/s] Reading metadata...: 15872it [00:00, 103083.26it/s] [INFO|trainer_utils.py:689] 2022-12-15 04:21:33,815 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 33%|███▎ | 4000/12000 [18:48:01<21:11:00, 9.53s/it][INFO|trainer.py:2700] 2022-12-15 06:16:09,593 >> Saving model checkpoint to ./checkpoint-4000 [INFO|configuration_utils.py:447] 2022-12-15 06:16:09,594 >> Configuration saved in ./checkpoint-4000/config.json [INFO|modeling_utils.py:1680] 2022-12-15 06:16:10,452 >> Model weights saved in ./checkpoint-4000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-15 06:16:10,467 >> Feature extractor saved in ./checkpoint-4000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-15 06:16:10,467 >> tokenizer config file saved in ./checkpoint-4000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-15 06:16:10,467 >> Special tokens file saved in ./checkpoint-4000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-15 06:16:10,468 >> added tokens file saved in ./checkpoint-4000/added_tokens.json [INFO|feature_extraction_utils.py:368] 2022-12-15 06:16:15,437 >> Feature extractor saved in ./preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-15 06:16:15,438 >> tokenizer config file saved in ./tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-15 06:16:15,438 >> Special tokens file saved in ./special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-15 06:16:15,439 >> added tokens file saved in ./added_tokens.json 33%|███▎ | 4001/12000 [18:49:00<4642:16:50, 2089.29s/it] 33%|███▎ | 4002/12000 [18:49:11<3256:13:05, 1465.66s/it] 33%|███▎ | 4003/12000 [18:49:22<2286:50:59, 1029.47s/it] 33%|███▎ | 4004/12000 [18:49:31<1606:23:25, 723.24s/it] 33%|███▎ | 4005/12000 [18:49:43<1132:27:45, 509.93s/it] 33%|███▎ | 4006/12000 [18:49:53<799:22:43, 359.99s/it] 33%|███▎ | 4007/12000 [18:50:01<564:40:27, 254.33s/it] 33%|███▎ | 4008/12000 [18:50:12<402:41:35, 181.39s/it] 33%|███▎ | 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10.45s/it] 42%|████▏ | 4995/12000 [21:35:21<21:14:26, 10.92s/it] 42%|████▏ | 4996/12000 [21:35:30<20:16:26, 10.42s/it] 42%|████▏ | 4997/12000 [21:35:40<19:36:17, 10.08s/it] 42%|████▏ | 4998/12000 [21:35:51<20:39:26, 10.62s/it] 42%|████▏ | 4999/12000 [21:36:01<19:50:44, 10.20s/it] 42%|████▏ | 5000/12000 [21:36:09<18:37:50, 9.58s/it] 42%|████▏ | 5000/12000 [21:36:09<18:37:50, 9.58s/it][INFO|trainer.py:2955] 2022-12-15 09:04:17,161 >> ***** Running Evaluation ***** [INFO|trainer.py:2959] 2022-12-15 09:04:17,161 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-15 09:04:17,161 >> Batch size = 64 {'eval_loss': 0.12609383463859558, 'eval_wer': 13.262534350177791, 'eval_runtime': 6883.3335, 'eval_samples_per_second': 2.306, 'eval_steps_per_second': 0.036, 'epoch': 0.33} {'loss': 0.0891, 'learning_rate': 6.915652173913044e-05, 'epoch': 0.34} {'loss': 0.1144, 'learning_rate': 6.872173913043478e-05, 'epoch': 0.34} {'loss': 0.096, 'learning_rate': 6.828695652173914e-05, 'epoch': 0.35} {'loss': 0.1078, 'learning_rate': 6.785217391304349e-05, 'epoch': 0.35} {'loss': 0.0791, 'learning_rate': 6.741739130434783e-05, 'epoch': 0.35} {'loss': 0.0946, 'learning_rate': 6.698260869565218e-05, 'epoch': 0.36} {'loss': 0.0767, 'learning_rate': 6.654782608695652e-05, 'epoch': 0.36} {'loss': 0.0753, 'learning_rate': 6.611304347826088e-05, 'epoch': 0.37} {'loss': 0.0857, 'learning_rate': 6.567826086956522e-05, 'epoch': 0.37} {'loss': 0.0887, 'learning_rate': 6.524347826086957e-05, 'epoch': 0.38} {'loss': 0.0723, 'learning_rate': 6.480869565217391e-05, 'epoch': 0.38} {'loss': 0.0952, 'learning_rate': 6.437391304347827e-05, 'epoch': 0.38} {'loss': 0.0687, 'learning_rate': 6.393913043478262e-05, 'epoch': 0.39} {'loss': 0.0967, 'learning_rate': 6.350434782608696e-05, 'epoch': 0.39} {'loss': 0.0804, 'learning_rate': 6.30695652173913e-05, 'epoch': 0.4} {'loss': 0.118, 'learning_rate': 6.263478260869565e-05, 'epoch': 0.4} {'loss': 0.1065, 'learning_rate': 6.220000000000001e-05, 'epoch': 0.4} {'loss': 0.1005, 'learning_rate': 6.176521739130436e-05, 'epoch': 0.41} {'loss': 0.1045, 'learning_rate': 6.13304347826087e-05, 'epoch': 0.41} {'loss': 0.099, 'learning_rate': 6.0895652173913044e-05, 'epoch': 0.42} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 4.20it/s] Reading metadata...: 12998it [00:00, 48736.84it/s] Reading metadata...: 15872it [00:00, 44112.30it/s] [INFO|trainer_utils.py:689] 2022-12-15 09:04:25,925 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 42%|████▏ | 5000/12000 [23:30:56<18:37:50, 9.58s/it][INFO|trainer.py:2700] 2022-12-15 10:59:04,578 >> Saving model checkpoint to ./checkpoint-5000 [INFO|configuration_utils.py:447] 2022-12-15 10:59:04,579 >> Configuration saved in ./checkpoint-5000/config.json [INFO|modeling_utils.py:1680] 2022-12-15 10:59:05,584 >> Model weights saved in ./checkpoint-5000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-15 10:59:05,599 >> Feature extractor saved in ./checkpoint-5000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-15 10:59:05,600 >> tokenizer config file saved in ./checkpoint-5000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-15 10:59:05,600 >> Special tokens file saved in ./checkpoint-5000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-15 10:59:05,600 >> 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6.046086956521739e-05, 'epoch': 0.42} {'loss': 0.1015, 'learning_rate': 6.002608695652174e-05, 'epoch': 0.42} {'loss': 0.0909, 'learning_rate': 5.9591304347826095e-05, 'epoch': 0.43} {'loss': 0.1056, 'learning_rate': 5.9156521739130436e-05, 'epoch': 0.43} {'loss': 0.1031, 'learning_rate': 5.8721739130434784e-05, 'epoch': 0.44} {'loss': 0.0842, 'learning_rate': 5.828695652173913e-05, 'epoch': 0.44} {'loss': 0.0866, 'learning_rate': 5.7852173913043486e-05, 'epoch': 0.45} {'loss': 0.0897, 'learning_rate': 5.7417391304347834e-05, 'epoch': 0.45} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 3.60it/s] Reading metadata...: 12820it [00:00, 43508.61it/s] Reading metadata...: 20361it [00:00, 23500.79it/s] Reading metadata...: 33653it [00:00, 41986.17it/s] Reading metadata...: 41800it [00:01, 35733.59it/s] Reading metadata...: 54951it [00:01, 51685.33it/s] Reading metadata...: 63506it [00:01, 41611.69it/s] Reading metadata...: 76609it [00:01, 56286.37it/s] Reading 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[INFO|trainer.py:2960] 2022-12-15 13:43:33,297 >> Batch size = 64 {'loss': 0.067, 'learning_rate': 5.6982608695652175e-05, 'epoch': 1.0} {'loss': 0.0373, 'learning_rate': 5.654782608695652e-05, 'epoch': 1.01} {'loss': 0.0368, 'learning_rate': 5.611304347826087e-05, 'epoch': 1.01} {'loss': 0.0372, 'learning_rate': 5.5678260869565226e-05, 'epoch': 1.01} {'loss': 0.0335, 'learning_rate': 5.524347826086957e-05, 'epoch': 1.02} {'loss': 0.0361, 'learning_rate': 5.4808695652173914e-05, 'epoch': 1.02} {'loss': 0.0345, 'learning_rate': 5.437391304347826e-05, 'epoch': 1.03} {'loss': 0.0357, 'learning_rate': 5.393913043478262e-05, 'epoch': 1.03} {'loss': 0.0353, 'learning_rate': 5.3504347826086965e-05, 'epoch': 1.04} {'loss': 0.0302, 'learning_rate': 5.3069565217391306e-05, 'epoch': 1.04} {'loss': 0.0291, 'learning_rate': 5.2634782608695654e-05, 'epoch': 1.04} {'loss': 0.028, 'learning_rate': 5.22e-05, 'epoch': 1.05} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:01, 1.91s/it] Reading metadata...: 12554it [00:02, 8737.96it/s] Reading metadata...: 15872it [00:02, 7800.90it/s] [INFO|trainer_utils.py:689] 2022-12-15 13:43:44,556 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 50%|█████ | 6000/12000 [28:09:46<15:58:32, 9.59s/it][INFO|trainer.py:2700] 2022-12-15 15:37:54,428 >> Saving model checkpoint to ./checkpoint-6000 [INFO|configuration_utils.py:447] 2022-12-15 15:37:54,429 >> Configuration saved in ./checkpoint-6000/config.json [INFO|modeling_utils.py:1680] 2022-12-15 15:37:55,289 >> Model weights saved in ./checkpoint-6000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-15 15:37:55,304 >> Feature extractor saved in ./checkpoint-6000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-15 15:37:55,304 >> tokenizer config file saved in ./checkpoint-6000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-15 15:37:55,304 >> Special tokens file saved in ./checkpoint-6000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-15 15:37:55,304 >> 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4.785217391304348e-05, 'epoch': 1.09} {'loss': 0.049, 'learning_rate': 4.741739130434783e-05, 'epoch': 1.09} {'loss': 0.0507, 'learning_rate': 4.6982608695652176e-05, 'epoch': 1.1} {'loss': 0.0481, 'learning_rate': 4.6547826086956524e-05, 'epoch': 1.1} {'loss': 0.0708, 'learning_rate': 4.611304347826087e-05, 'epoch': 1.11} {'loss': 0.0758, 'learning_rate': 4.567826086956522e-05, 'epoch': 1.11} {'loss': 0.051, 'learning_rate': 4.524347826086957e-05, 'epoch': 1.11} 12/15/2022 17:58:42 - WARNING - datasets.download.streaming_download_manager - Got disconnected from remote data host. 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in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 58%|█████▊ | 7000/12000 [32:56:40<14:27:06, 10.41s/it][INFO|trainer.py:2700] 2022-12-15 20:24:48,207 >> Saving model checkpoint to ./checkpoint-7000 [INFO|configuration_utils.py:447] 2022-12-15 20:24:48,208 >> Configuration saved in ./checkpoint-7000/config.json [INFO|modeling_utils.py:1680] 2022-12-15 20:24:49,298 >> Model weights saved in ./checkpoint-7000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-15 20:24:49,314 >> Feature extractor saved in ./checkpoint-7000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-15 20:24:49,314 >> tokenizer config file saved in ./checkpoint-7000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-15 20:24:49,314 >> Special tokens file saved in ./checkpoint-7000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-15 20:24:49,314 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10.40s/it][INFO|trainer.py:2955] 2022-12-15 23:10:12,084 >> ***** Running Evaluation ***** [INFO|trainer.py:2959] 2022-12-15 23:10:12,084 >> Num examples: Unknown [INFO|trainer.py:2960] 2022-12-15 23:10:12,084 >> Batch size = 64 {'eval_loss': 0.10288114845752716, 'eval_wer': 10.307825109089038, 'eval_runtime': 6910.216, 'eval_samples_per_second': 2.297, 'eval_steps_per_second': 0.036, 'epoch': 1.13} {'loss': 0.0691, 'learning_rate': 4.306956521739131e-05, 'epoch': 1.14} {'loss': 0.0436, 'learning_rate': 4.2634782608695655e-05, 'epoch': 1.14} {'loss': 0.0457, 'learning_rate': 4.22e-05, 'epoch': 1.14} {'loss': 0.0455, 'learning_rate': 4.176521739130435e-05, 'epoch': 1.15} {'loss': 0.0437, 'learning_rate': 4.13304347826087e-05, 'epoch': 1.15} {'loss': 0.0398, 'learning_rate': 4.0895652173913046e-05, 'epoch': 1.16} {'loss': 0.0602, 'learning_rate': 4.0460869565217394e-05, 'epoch': 1.16} {'loss': 0.0653, 'learning_rate': 4.002608695652174e-05, 'epoch': 1.16} {'loss': 0.0318, 'learning_rate': 3.959130434782609e-05, 'epoch': 1.17} {'loss': 0.0295, 'learning_rate': 3.915652173913044e-05, 'epoch': 1.17} {'loss': 0.0442, 'learning_rate': 3.8721739130434785e-05, 'epoch': 1.18} {'loss': 0.0443, 'learning_rate': 3.828695652173913e-05, 'epoch': 1.18} {'loss': 0.0351, 'learning_rate': 3.785217391304348e-05, 'epoch': 1.19} {'loss': 0.0447, 'learning_rate': 3.741739130434783e-05, 'epoch': 1.19} {'loss': 0.0494, 'learning_rate': 3.698260869565218e-05, 'epoch': 1.19} {'loss': 0.0481, 'learning_rate': 3.6547826086956525e-05, 'epoch': 1.2} {'loss': 0.0404, 'learning_rate': 3.611304347826087e-05, 'epoch': 1.2} {'loss': 0.0482, 'learning_rate': 3.5678260869565214e-05, 'epoch': 1.21} {'loss': 0.0416, 'learning_rate': 3.524347826086957e-05, 'epoch': 1.21} {'loss': 0.0391, 'learning_rate': 3.4808695652173916e-05, 'epoch': 1.21} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 3.91it/s] Reading metadata...: 12252it [00:00, 43938.84it/s] Reading metadata...: 15872it [00:00, 41408.66it/s] [INFO|trainer_utils.py:689] 2022-12-15 23:10:20,459 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 67%|██████▋ | 8000/12000 [37:36:25<11:33:21, 10.40s/it][INFO|trainer.py:2700] 2022-12-16 01:04:33,777 >> Saving model checkpoint to ./checkpoint-8000 [INFO|configuration_utils.py:447] 2022-12-16 01:04:33,778 >> Configuration saved in ./checkpoint-8000/config.json [INFO|modeling_utils.py:1680] 2022-12-16 01:04:34,805 >> Model weights saved in ./checkpoint-8000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-16 01:04:34,820 >> Feature extractor saved in ./checkpoint-8000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-16 01:04:34,820 >> tokenizer config file saved in ./checkpoint-8000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-16 01:04:34,820 >> Special tokens file saved in ./checkpoint-8000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-16 01:04:34,821 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'learning_rate': 3.263478260869565e-05, 'epoch': 1.24} {'loss': 0.0365, 'learning_rate': 3.2200000000000003e-05, 'epoch': 1.24} {'loss': 0.035, 'learning_rate': 3.1765217391304345e-05, 'epoch': 1.24} {'loss': 0.0523, 'learning_rate': 3.13304347826087e-05, 'epoch': 1.25} {'loss': 0.0602, 'learning_rate': 3.089565217391304e-05, 'epoch': 1.25} {'loss': 0.0504, 'learning_rate': 3.046086956521739e-05, 'epoch': 1.26} {'loss': 0.05, 'learning_rate': 3.0026086956521743e-05, 'epoch': 1.26} {'loss': 0.0579, 'learning_rate': 2.9591304347826087e-05, 'epoch': 1.26} {'loss': 0.0509, 'learning_rate': 2.915652173913044e-05, 'epoch': 1.27} {'loss': 0.0439, 'learning_rate': 2.8721739130434783e-05, 'epoch': 1.27} {'loss': 0.0435, 'learning_rate': 2.8286956521739134e-05, 'epoch': 1.28} {'loss': 0.0419, 'learning_rate': 2.785217391304348e-05, 'epoch': 1.28} {'loss': 0.0615, 'learning_rate': 2.7417391304347827e-05, 'epoch': 1.29} {'loss': 0.0404, 'learning_rate': 2.6982608695652174e-05, 'epoch': 1.29} {'loss': 0.045, 'learning_rate': 2.6547826086956522e-05, 'epoch': 1.29} {'loss': 0.0423, 'learning_rate': 2.6113043478260874e-05, 'epoch': 1.3} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 2.07it/s] Reading metadata...: 12970it [00:00, 29597.07it/s] Reading metadata...: 15872it [00:00, 26229.41it/s] [INFO|trainer_utils.py:689] 2022-12-16 03:53:21,868 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 75%|███████▌ | 9000/12000 [42:19:21<9:04:09, 10.88s/it][INFO|trainer.py:2700] 2022-12-16 05:47:28,883 >> Saving model checkpoint to ./checkpoint-9000 [INFO|configuration_utils.py:447] 2022-12-16 05:47:28,884 >> Configuration saved in ./checkpoint-9000/config.json [INFO|modeling_utils.py:1680] 2022-12-16 05:47:29,989 >> Model weights saved in ./checkpoint-9000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-16 05:47:30,005 >> Feature extractor saved in ./checkpoint-9000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-16 05:47:30,005 >> tokenizer config file saved in ./checkpoint-9000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-16 05:47:30,005 >> Special tokens file saved in ./checkpoint-9000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-16 05:47:30,005 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2.315, 'eval_steps_per_second': 0.036, 'epoch': 1.3} {'loss': 0.031, 'learning_rate': 2.5678260869565218e-05, 'epoch': 1.3} {'loss': 0.0355, 'learning_rate': 2.524347826086957e-05, 'epoch': 1.31} {'loss': 0.0278, 'learning_rate': 2.4808695652173914e-05, 'epoch': 1.31} {'loss': 0.0295, 'learning_rate': 2.437391304347826e-05, 'epoch': 1.31} {'loss': 0.0323, 'learning_rate': 2.3939130434782613e-05, 'epoch': 1.32} {'loss': 0.0313, 'learning_rate': 2.3504347826086957e-05, 'epoch': 1.32} {'loss': 0.029, 'learning_rate': 2.3069565217391305e-05, 'epoch': 1.33} {'loss': 0.0327, 'learning_rate': 2.2634782608695653e-05, 'epoch': 1.33} {'loss': 0.0246, 'learning_rate': 2.22e-05, 'epoch': 1.34} {'loss': 0.0333, 'learning_rate': 2.176521739130435e-05, 'epoch': 1.34} {'loss': 0.031, 'learning_rate': 2.1330434782608697e-05, 'epoch': 1.34} {'loss': 0.0471, 'learning_rate': 2.0895652173913044e-05, 'epoch': 1.35} {'loss': 0.0725, 'learning_rate': 2.0460869565217392e-05, 'epoch': 1.35} {'loss': 0.0773, 'learning_rate': 2.002608695652174e-05, 'epoch': 1.36} {'loss': 0.0616, 'learning_rate': 1.9591304347826088e-05, 'epoch': 1.36} {'loss': 0.0698, 'learning_rate': 1.9156521739130436e-05, 'epoch': 1.36} {'loss': 0.0631, 'learning_rate': 1.8721739130434784e-05, 'epoch': 1.37} {'loss': 0.0592, 'learning_rate': 1.828695652173913e-05, 'epoch': 1.37} {'loss': 0.0583, 'learning_rate': 1.785217391304348e-05, 'epoch': 1.38} {'loss': 0.0604, 'learning_rate': 1.7417391304347827e-05, 'epoch': 1.38} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 3.98it/s] Reading metadata...: 13106it [00:00, 47503.71it/s] Reading metadata...: 15872it [00:00, 42664.10it/s] [INFO|trainer_utils.py:689] 2022-12-16 08:34:31,094 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 83%|████████▎ | 10000/12000 [47:00:11<5:26:48, 9.80s/it][INFO|trainer.py:2700] 2022-12-16 10:28:19,090 >> Saving model checkpoint to ./checkpoint-10000 [INFO|configuration_utils.py:447] 2022-12-16 10:28:19,091 >> Configuration saved in ./checkpoint-10000/config.json [INFO|modeling_utils.py:1680] 2022-12-16 10:28:19,937 >> Model weights saved in ./checkpoint-10000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-16 10:28:19,951 >> Feature extractor saved in ./checkpoint-10000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-16 10:28:19,952 >> tokenizer config file saved in ./checkpoint-10000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-16 10:28:19,952 >> Special tokens file saved in ./checkpoint-10000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-16 10:28:19,952 >> added tokens file saved in ./checkpoint-10000/added_tokens.json [INFO|feature_extraction_utils.py:368] 2022-12-16 10:28:24,748 >> Feature extractor saved in ./preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-16 10:28:24,749 >> tokenizer config file saved in ./tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-16 10:28:24,749 >> Special tokens file saved in ./special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-16 10:28:24,750 >> added tokens file saved in ./added_tokens.json 83%|████████▎ | 10001/12000 [47:01:23<1154:38:16, 2079.39s/it] 83%|████████▎ | 10002/12000 [47:01:35<809:55:16, 1459.32s/it] 83%|████████▎ | 10003/12000 [47:01:44<568:02:11, 1024.00s/it] 83%|████████▎ | 10004/12000 [47:01:51<398:41:39, 719.09s/it] 83%|████████▎ | 10005/12000 [47:01:59<280:19:36, 505.85s/it] 83%|████████▎ | 10006/12000 [47:02:09<197:42:51, 356.96s/it] 83%|████████▎ | 10007/12000 [47:02:19<139:56:39, 252.78s/it] 83%|████████▎ 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{'loss': 0.0326, 'learning_rate': 1.0043478260869566e-05, 'epoch': 1.45} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1461it [00:00, 14609.25it/s] Reading metadata...: 14687it [00:00, 83812.51it/s] Reading metadata...: 23069it [00:00, 38080.43it/s] Reading metadata...: 36524it [00:00, 60134.05it/s] Reading metadata...: 45231it [00:00, 47094.37it/s] Reading metadata...: 58519it [00:01, 50158.15it/s] Reading metadata...: 71820it [00:01, 65211.44it/s] Reading metadata...: 80424it [00:01, 54793.82it/s] Reading metadata...: 93614it [00:01, 69316.80it/s] Reading metadata...: 102587it [00:01, 62467.39it/s] Reading metadata...: 115911it [00:01, 76933.16it/s] Reading metadata...: 125336it [00:02, 49476.76it/s] Reading metadata...: 137685it [00:02, 50872.39it/s] Reading metadata...: 150895it [00:02, 64090.80it/s] Reading metadata...: 159583it [00:02, 57041.18it/s] Reading metadata...: 172585it [00:02, 70431.60it/s] Reading metadata...: 181631it [00:03, 65641.33it/s] Reading 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8.73913043478261e-06, 'epoch': 2.01} Reading metadata...: 0it [00:00, ?it/s] Reading metadata...: 1it [00:00, 4.71it/s] Reading metadata...: 12858it [00:00, 51743.75it/s] Reading metadata...: 15872it [00:00, 47364.33it/s] [INFO|trainer_utils.py:689] 2022-12-16 13:13:26,583 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: labels_length, input_length, labels_truncated. If labels_length, input_length, labels_truncated are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message. 92%|█████████▏| 11000/12000 [51:39:10<3:02:58, 10.98s/it][INFO|trainer.py:2700] 2022-12-16 15:07:17,863 >> Saving model checkpoint to ./checkpoint-11000 [INFO|configuration_utils.py:447] 2022-12-16 15:07:17,864 >> Configuration saved in ./checkpoint-11000/config.json [INFO|modeling_utils.py:1680] 2022-12-16 15:07:18,718 >> Model weights saved in ./checkpoint-11000/pytorch_model.bin [INFO|feature_extraction_utils.py:368] 2022-12-16 15:07:18,732 >> Feature extractor saved in ./checkpoint-11000/preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-16 15:07:18,733 >> tokenizer config file saved in ./checkpoint-11000/tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-16 15:07:18,733 >> Special tokens file saved in ./checkpoint-11000/special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-16 15:07:18,733 >> added tokens file saved in ./checkpoint-11000/added_tokens.json [INFO|feature_extraction_utils.py:368] 2022-12-16 15:07:23,375 >> Feature extractor saved in ./preprocessor_config.json [INFO|tokenization_utils_base.py:2157] 2022-12-16 15:07:23,376 >> tokenizer config file saved in ./tokenizer_config.json [INFO|tokenization_utils_base.py:2164] 2022-12-16 15:07:23,376 >> Special tokens file saved in ./special_tokens_map.json [INFO|tokenization_utils_base.py:2210] 2022-12-16 15:07:23,377 >> added tokens file saved in ./added_tokens.json